From 2b7fbc5f61116b7431f4f4241501b5e73fd38444 Mon Sep 17 00:00:00 2001
From: kshitij12345 <kshitijkalambarkar@gmail.com>
Date: Thu, 20 Jun 2019 11:34:19 +0530
Subject: [PATCH 001/813] [bug] fix higher grad log  (#15120)

* fix bug with higher order log implementation.

* bug: the head_grads were not preserved in higher order.
* add test to validate the fix of the same.

* fix grad for head_grads and update relevant test

* address comments

* remove assertion for y_grad gradient.
* rename variables.
* fix and update computation.

* address comments

* explicitly pass arguments with name.

* fix mistyped comment.

Co-Authored-By: Lin Yuan <apeforest@gmail.com>
---
 .../tensor/elemwise_unary_op_basic.cc         | 68 ++++++++++++-------
 .../python/unittest/test_higher_order_grad.py | 21 ++++--
 2 files changed, 59 insertions(+), 30 deletions(-)

diff --git a/src/operator/tensor/elemwise_unary_op_basic.cc b/src/operator/tensor/elemwise_unary_op_basic.cc
index f2b8dd6b1314..98dc8dad825f 100644
--- a/src/operator/tensor/elemwise_unary_op_basic.cc
+++ b/src/operator/tensor/elemwise_unary_op_basic.cc
@@ -1090,20 +1090,26 @@ MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_log,
                                                   unary_bwd<mshadow_op::log_grad>)
 .set_attr<nnvm::FGradient>("FGradient",
   [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
-    // For f(x) -> f = log
+    // ograds[0]: dL/dxgrad
+    // inputs[0]: dL/dy
+    // inputs[1]: x
+    // f(x) = y = log(x)
+    // f'(x) = 1/x
     // f''(x) = -1 * (f'(x) * f'(x))
-    auto gx = nnvm::NodeEntry{n};
-    auto ggx_mid = MakeNode("elemwise_mul", n->attrs.name + "_backward_mid_grad_grad",
-                            {gx, gx}, nullptr, &n);
-    auto ggx = MakeNode("negative", n->attrs.name + "_backward_grad_grad",
-                        {nnvm::NodeEntry{ggx_mid}}, nullptr, &n);
+    auto dydx_mul_dldy = nnvm::NodeEntry{n};  // f'(x) * head_grads
+    auto dlogx = MakeNode("reciprocal", n->attrs.name + "_dlogx",
+                            {n->inputs[1]}, nullptr, &n);
+    auto d2ydx2_mid = MakeNode("elemwise_mul", n->attrs.name + "_d2ydx2_mid",
+                            {dydx_mul_dldy, nnvm::NodeEntry{dlogx}}, nullptr, &n);
+    auto d2ydx2 = MakeNode("negative", n->attrs.name + "_d2ydx2",
+                        {nnvm::NodeEntry{d2ydx2_mid}}, nullptr, &n);
 
     std::vector<nnvm::NodeEntry> ret;
 
     ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "_backward_grad_grad",
-                             {ograds[0], gx}, nullptr, &n));
+                             {ograds[0], nnvm::NodeEntry{dlogx}}, nullptr, &n));
     ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "_backward_grad_grad_inp",
-                             {ograds[0], nnvm::NodeEntry{ggx}}, nullptr, &n));
+                             {ograds[0], nnvm::NodeEntry{d2ydx2}}, nullptr, &n));
     return ret;
   });
 
@@ -1111,23 +1117,28 @@ MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_log10,
                                                   unary_bwd<mshadow_op::log10_grad>)
 .set_attr<nnvm::FGradient>("FGradient",
   [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
-    // For f(x) -> f = log10
+    // ograds[0]: dL/dxgrad
+    // inputs[0]: dL/dy
+    // inputs[1]: x
+    // f(x) = y = log10(x)
     // f'(x) = 1 / (log(10) * x)
     // f''(x) = -1 * (f'(x) * 1/x)
-    auto gx = nnvm::NodeEntry{n, 0, 0};
-    auto g_lx = MakeNode("reciprocal", n->attrs.name + "_backward_log_grad",
+    auto dydx_mul_dldy = nnvm::NodeEntry{n};  // f'(x) * head_grads
+    auto dydx = MakeNode("elemwise_div", n->attrs.name + "_dydx",
+                            {n->inputs[0]}, nullptr, &n);
+    auto dlogx = MakeNode("reciprocal", n->attrs.name + "_dlogx",
                             {n->inputs[1]}, nullptr, &n);
-    auto ggx_mid = MakeNode("elemwise_mul", n->attrs.name + "_backward_mid_grad_grad",
-                            {gx, nnvm::NodeEntry{g_lx}}, nullptr, &n);
-    auto ggx = MakeNode("negative", n->attrs.name + "_backward_grad_grad",
-                        {nnvm::NodeEntry{ggx_mid}}, nullptr, &n);
+    auto d2ydx2_mid = MakeNode("elemwise_mul", n->attrs.name + "_d2ydx2_mid",
+                            {dydx_mul_dldy, nnvm::NodeEntry{dlogx}}, nullptr, &n);
+    auto d2ydx2 = MakeNode("negative", n->attrs.name + "_d2ydx2",
+                        {nnvm::NodeEntry{d2ydx2_mid}}, nullptr, &n);
 
     std::vector<nnvm::NodeEntry> ret;
 
     ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "_backward_grad_grad",
-                             {ograds[0], gx}, nullptr, &n));
+                             {ograds[0], nnvm::NodeEntry{dydx}}, nullptr, &n));
     ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "_backward_grad_grad_inp",
-                             {ograds[0], nnvm::NodeEntry{ggx}}, nullptr, &n));
+                             {ograds[0], nnvm::NodeEntry{d2ydx2}}, nullptr, &n));
     return ret;
   });
 
@@ -1135,23 +1146,28 @@ MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_log2,
                                                   unary_bwd<mshadow_op::log2_grad>)
 .set_attr<nnvm::FGradient>("FGradient",
   [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
-    // For f(x) -> f = log2
+    // ograds[0]: dL/dxgrad
+    // inputs[0]: dL/dy
+    // inputs[1]: x
+    // f(x) = y = log2(x)
     // f'(x) = 1 / (log(2) * x)
     // f''(x) = -1 * (f'(x) * 1/x)
-    auto gx = nnvm::NodeEntry{n};
-    auto g_lx = MakeNode("reciprocal", n->attrs.name + "_backward_log_grad",
+    auto dydx_mul_dldy = nnvm::NodeEntry{n};  // f'(x) * head_grads
+    auto dydx = MakeNode("elemwise_div", n->attrs.name + "_dydx",
+                            {n->inputs[0]}, nullptr, &n);
+    auto dlogx = MakeNode("reciprocal", n->attrs.name + "_dlogx",
                             {n->inputs[1]}, nullptr, &n);
-    auto ggx_mid = MakeNode("elemwise_mul", n->attrs.name + "_backward_mid_grad_grad",
-                            {gx, nnvm::NodeEntry{g_lx}}, nullptr, &n);
-    auto ggx = MakeNode("negative", n->attrs.name + "_backward_grad_grad",
-                        {nnvm::NodeEntry{ggx_mid}}, nullptr, &n);
+    auto d2ydx2_mid = MakeNode("elemwise_mul", n->attrs.name + "_d2ydx2_mid",
+                            {dydx_mul_dldy, nnvm::NodeEntry{dlogx}}, nullptr, &n);
+    auto d2ydx2 = MakeNode("negative", n->attrs.name + "_d2ydx2",
+                        {nnvm::NodeEntry{d2ydx2_mid}}, nullptr, &n);
 
     std::vector<nnvm::NodeEntry> ret;
 
     ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "_backward_grad_grad",
-                             {ograds[0], gx}, nullptr, &n));
+                             {ograds[0], nnvm::NodeEntry{dydx}}, nullptr, &n));
     ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "_backward_grad_grad_inp",
-                             {ograds[0], nnvm::NodeEntry{ggx}}, nullptr, &n));
+                             {ograds[0], nnvm::NodeEntry{d2ydx2}}, nullptr, &n));
     return ret;
   });
 
diff --git a/tests/python/unittest/test_higher_order_grad.py b/tests/python/unittest/test_higher_order_grad.py
index 77bfa68157aa..4f1ea9a6c7b8 100644
--- a/tests/python/unittest/test_higher_order_grad.py
+++ b/tests/python/unittest/test_higher_order_grad.py
@@ -108,13 +108,26 @@ def grad_grad_op(x):
 
 def check_second_order_unary(x, op, grad_grad_op):
     x = nd.array(x)
-    expect_grad_grad = grad_grad_op(x)
+    grad_grad_x = grad_grad_op(x)
     x.attach_grad()
+
+    # Manual head_grads.
+    y_grad = nd.random.normal(shape=x.shape)
+    head_grad_grads = nd.random.normal(shape=x.shape)
+
+    # Perform compute.
     with autograd.record():
         y = op(x)
-        y_grad = autograd.grad(y, x, create_graph=True, retain_graph=True)[0]
-    y_grad.backward()
-    assert_almost_equal(expect_grad_grad.asnumpy(), x.grad.asnumpy())
+        x_grad = autograd.grad(heads=y, variables=x, head_grads=y_grad,
+                               create_graph=True, retain_graph=True)[0]
+    x_grad.backward(head_grad_grads)
+
+    # Compute expected values.
+    expected_grad_grad = grad_grad_x.asnumpy() * head_grad_grads.asnumpy() * \
+        y_grad.asnumpy()
+
+    # Validate the gradients.
+    assert_almost_equal(expected_grad_grad, x.grad.asnumpy())
 
 
 if __name__ == '__main__':

From 12c422697413cfd69d846cdb4d1d487c487220e2 Mon Sep 17 00:00:00 2001
From: Lin Yuan <apeforest@gmail.com>
Date: Thu, 20 Jun 2019 02:27:45 -0700
Subject: [PATCH 002/813] update committer info (#15289)

---
 CONTRIBUTORS.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md
index c76f8c6edbc8..43d988037de9 100644
--- a/CONTRIBUTORS.md
+++ b/CONTRIBUTORS.md
@@ -71,6 +71,8 @@ The committers are the granted write access to the project.
   - Tao is a major contributor to the MXNet MKL-DNN backend and performance on CPU.
 * [Zach Kimberg](https://github.com/zachgk)
   - Zach is one of the major maintainers of the MXNet Scala package.
+* [Lin Yuan](https://github.com/apeforest)
+  - Lin supports MXNet distributed training using Horovod and is also a major contributor to higher order gradients.
 
 
 ### Become a Committer
@@ -199,7 +201,6 @@ List of Contributors
 * [Thomas Delteil](https://github.com/ThomasDelteil)
 * [Jesse Brizzi](https://github.com/jessebrizzi)
 * [Hang Zhang](http://hangzh.com)
-* [Lin Yuan](https://github.com/apeforest)
 * [Kou Ding](https://github.com/chinakook)
 * [Istvan Fehervari](https://github.com/ifeherva)
 * [Aaron Markham](https://github.com/aaronmarkham)

From 2de0db0911f2e71728fa85ab342bd99a10974fc9 Mon Sep 17 00:00:00 2001
From: Piyush Ghai <ghai.8@osu.edu>
Date: Thu, 20 Jun 2019 13:03:48 -0700
Subject: [PATCH 003/813] Showing proper error when csr array is not 2D in
 shape. (#15242)

* Showing proper error when csr array is not 2D in shape.

* Fixed failing CI

* Nudge to CI
---
 python/mxnet/ndarray/ndarray.py              | 4 ++++
 tests/python/unittest/test_sparse_ndarray.py | 5 +++++
 2 files changed, 9 insertions(+)

diff --git a/python/mxnet/ndarray/ndarray.py b/python/mxnet/ndarray/ndarray.py
index 7e21daedcde1..3fb1af6a7336 100644
--- a/python/mxnet/ndarray/ndarray.py
+++ b/python/mxnet/ndarray/ndarray.py
@@ -2227,6 +2227,10 @@ def tostype(self, stype):
         NDArray, CSRNDArray or RowSparseNDArray
             A copy of the array with the chosen storage stype
         """
+        if stype == 'csr' and len(self.shape) != 2:
+            raise ValueError("To convert to a CSR, the NDArray should be 2 Dimensional. Current "
+                             "shape is %s" % str(self.shape))
+
         return op.cast_storage(self, stype=stype)
 
     def to_dlpack_for_read(self):
diff --git a/tests/python/unittest/test_sparse_ndarray.py b/tests/python/unittest/test_sparse_ndarray.py
index 3b4c684e8696..9a1fce4ff197 100644
--- a/tests/python/unittest/test_sparse_ndarray.py
+++ b/tests/python/unittest/test_sparse_ndarray.py
@@ -963,6 +963,11 @@ def test_sparse_nd_check_format():
     indptr_list = [0, -2, 2, 3]
     a = mx.nd.sparse.csr_matrix((data_list, indices_list, indptr_list), shape=shape)
     assertRaises(mx.base.MXNetError, a.check_format)
+    # CSR format should be 2 Dimensional.
+    a = mx.nd.array([1, 2, 3])
+    assertRaises(ValueError, a.tostype, 'csr')
+    a = mx.nd.array([[[1, 2, 3]]])
+    assertRaises(ValueError, a.tostype, 'csr')
     # Row Sparse format indices should be less than the number of rows
     shape = (3, 2)
     data_list = [[1, 2], [3, 4]]

From c45d23b5e89344ecd4151097f94e3478803933cc Mon Sep 17 00:00:00 2001
From: Przemyslaw Tredak <ptredak@nvidia.com>
Date: Thu, 20 Jun 2019 16:50:54 -0700
Subject: [PATCH 004/813] Proper bulking of ops not using FCompute (#15272)

* Less syncs

* Trigger CI
---
 src/imperative/imperative_utils.h | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/imperative/imperative_utils.h b/src/imperative/imperative_utils.h
index 4e63e4d2b3be..b867162abc9b 100644
--- a/src/imperative/imperative_utils.h
+++ b/src/imperative/imperative_utils.h
@@ -464,7 +464,7 @@ inline void PushFComputeEx(const FComputeEx& fn,
       InvalidateOutputs(outputs, req);
 #endif
       fn(attrs, opctx, inputs, req, outputs);
-      if (ctx.dev_mask() == gpu::kDevMask && exec_type == ExecType::kSync) {
+      if (ctx.dev_mask() == gpu::kDevMask && exec_type == ExecType::kSync && !rctx.is_bulk) {
         rctx.get_stream<gpu>()->Wait();
       }
     };
@@ -512,7 +512,7 @@ inline void PushOperator(const OpStatePtr& state,
 #endif
       fcompute_ex(state, opctx, inputs, req, outputs);
       if (ctx.dev_mask() == gpu::kDevMask && exec_type == ExecType::kSync
-          && rctx.get_stream<gpu>()) {
+          && rctx.get_stream<gpu>() && !rctx.is_bulk) {
         rctx.get_stream<gpu>()->Wait();
       }
     };
@@ -562,7 +562,7 @@ inline void PushOperator(const OpStatePtr& state,
         // post-fcompute fallback, cast to original storage type, if necessary
         CastNonDefaultStorage(post_temp_src, post_temp_dst, opctx, is_gpu);
         if (is_gpu && exec_type == ExecType::kSync
-            && rctx.get_stream<gpu>()) {
+            && rctx.get_stream<gpu>() && !rctx.is_bulk) {
           rctx.get_stream<gpu>()->Wait();
         }
       };

From 4a9e9f67a5e7e5ddf2e394c6381f4f6449626af2 Mon Sep 17 00:00:00 2001
From: Disi A <adis@live.cn>
Date: Fri, 21 Jun 2019 04:02:38 -0400
Subject: [PATCH 005/813] Typo fix in plan_memory relase -> release. (#15299)

---
 src/nnvm/plan_memory.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/nnvm/plan_memory.cc b/src/nnvm/plan_memory.cc
index ce442ed1a0cf..427d1c751cba 100644
--- a/src/nnvm/plan_memory.cc
+++ b/src/nnvm/plan_memory.cc
@@ -301,7 +301,7 @@ size_t AllocMemory(const Graph& ret, const IndexedGraph& idx,
       auto sid = storage[eid];
       // storage_ref_count == 0 means it is taken by inplace op
       if (sid < 0) continue;
-      // if we decrease it to zero, means we are ready to relase
+      // if we decrease it to zero, means we are ready to release
       --storage_ref_count[sid];
       if (storage_ref_count[sid] == 0) {
         allocator->Release(sid, nid);

From 3f8fd005402968e77ecb05eb3c73eb1c5e35b332 Mon Sep 17 00:00:00 2001
From: Zhaoqi Zhu <zhaoqizh@usc.edu>
Date: Fri, 21 Jun 2019 13:18:28 -0700
Subject: [PATCH 006/813] Fixing duplication in operator profiling (#15240)

* initial

* adding a bool in ProfileStat to control if add to aggregate stats

* add test case

* fix style

* stylefix

* testcases

* fix type

* fix comment

* Update profiler.h
---
 src/profiler/aggregate_stats.cc        |  4 +++-
 src/profiler/profiler.h                | 15 +++++++++++++++
 tests/python/unittest/test_profiler.py | 22 ++++++++++++++++++++++
 3 files changed, 40 insertions(+), 1 deletion(-)

diff --git a/src/profiler/aggregate_stats.cc b/src/profiler/aggregate_stats.cc
index ff3894650545..98ac5e96b761 100644
--- a/src/profiler/aggregate_stats.cc
+++ b/src/profiler/aggregate_stats.cc
@@ -85,7 +85,9 @@ inline std::priority_queue<pi>
 
 void AggregateStats::OnProfileStat(const ProfileStat& stat) {
   std::unique_lock<std::mutex> lk(m_);
-  stat.SaveAggregate(&stats_[stat.categories_.c_str()][stat.name_.c_str()]);
+  if (stat.enable_aggregate_) {
+    stat.SaveAggregate(&stats_[stat.categories_.c_str()][stat.name_.c_str()]);
+  }
 }
 
 void AggregateStats::DumpTable(std::ostream& os, int sort_by, int ascending) {
diff --git a/src/profiler/profiler.h b/src/profiler/profiler.h
index f9eb0af9acc1..a6d9ecf06fee 100644
--- a/src/profiler/profiler.h
+++ b/src/profiler/profiler.h
@@ -128,6 +128,9 @@ struct ProfileStat {
   /*! \brief operation categories (comma-delimited) */
   profile_stat_string categories_;
 
+  /*! \brief whether to add this stat to AggregateStats */
+  bool enable_aggregate_ = true;
+
   /* !\brief Process id */
   size_t process_id_ = current_process_id();
 
@@ -807,6 +810,13 @@ struct ProfileTask : public ProfileDuration {
 
   ProfileObjectType type() const override { return kTask; }
 
+  /*!
+   * \brief Whether to add stat to AggregateStats
+   */
+  void enableAggregateStats(bool enabled = true) {
+    enable_aggregate_ = enabled;
+  }
+
  protected:
   /*!
    * \brief Task statistic object
@@ -831,6 +841,7 @@ struct ProfileTask : public ProfileDuration {
   inline void SendStat() {
     Profiler::Get()->AddNewProfileStat<ProfileTaskStat>([this](ProfileTaskStat *stat) {
       stat->categories_.set(domain_->name());
+      stat->enable_aggregate_ = enable_aggregate_;
     }, name_.c_str(), start_time_, ProfileStat::NowInMicrosec());
   }
   /*! \brief Task name */
@@ -843,6 +854,8 @@ struct ProfileTask : public ProfileDuration {
   VTUNE_ONLY_CODE(std::unique_ptr<vtune::VTuneTask> vtune_task_);
   /*! \brief NVTX duration object */
   NVTX_ONLY_CODE(std::unique_ptr<nvtx::NVTXDuration> nvtx_duration_);
+  /*! \brief whether to add this stat to AggregateStats */
+  bool enable_aggregate_ = true;
 
  protected:
   /*! \brief Task's start tick */
@@ -1150,6 +1163,8 @@ struct ProfileOperator : public ProfileEvent {
       , as_task_(name, &domain_)
       , name_(name)
       , attributes_(attributes) {
+    // make as_task_ not to add stat to AggregateStats; otherwise we will add twice
+    as_task_.enableAggregateStats(false);
     SetCategories(domain_.name());
   }
   /*!
diff --git a/tests/python/unittest/test_profiler.py b/tests/python/unittest/test_profiler.py
index 09e571be0088..b76bfbc82d12 100644
--- a/tests/python/unittest/test_profiler.py
+++ b/tests/python/unittest/test_profiler.py
@@ -269,6 +269,28 @@ def check_sorting(debug_str, sort_by, ascending):
             check_sorting(debug_str, sb, asc)
     profiler.set_state('stop')
 
+def test_aggregate_duplication():
+    file_name = 'test_aggregate_duplication.json'
+    enable_profiler(profile_filename = file_name, run=True, continuous_dump=True, \
+                    aggregate_stats=True)
+    inp = mx.nd.zeros(shape=(100, 100))
+    y = mx.nd.sqrt(inp)
+    inp = inp + 1
+    inp = inp + 1
+    mx.nd.waitall()
+    profiler.dump(False)
+    debug_str = profiler.dumps(format = 'json')
+    target_dict = json.loads(debug_str)
+    assert 'Time' in target_dict and 'operator' in target_dict['Time'] \
+        and 'sqrt' in target_dict['Time']['operator'] \
+        and 'Count' in target_dict['Time']['operator']['sqrt'] \
+        and '_plus_scalar' in target_dict['Time']['operator'] \
+        and 'Count' in target_dict['Time']['operator']['_plus_scalar']
+    # they are called once and twice respectively
+    assert target_dict['Time']['operator']['sqrt']['Count'] == 1
+    assert target_dict['Time']['operator']['_plus_scalar']['Count'] == 2
+    profiler.set_state('stop')
+    
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 8b5f376d17b644385706016caf8b1e58e95d96df Mon Sep 17 00:00:00 2001
From: Rohit Kumar Srivastava <srivastava.141@osu.edu>
Date: Fri, 21 Jun 2019 13:21:20 -0700
Subject: [PATCH 007/813] [MXNET-1413] Adding Large Tensor support for sort
 operators (#15170)

---
 src/operator/tensor/init_op.h         |   2 +-
 src/operator/tensor/ordering_op-inl.h | 190 +++++++++++++++-----------
 tests/nightly/test_large_array.py     |  26 ++++
 tests/python/unittest/test_ndarray.py |  13 +-
 4 files changed, 149 insertions(+), 82 deletions(-)

diff --git a/src/operator/tensor/init_op.h b/src/operator/tensor/init_op.h
index fd491534f83a..c7a10541adbd 100644
--- a/src/operator/tensor/init_op.h
+++ b/src/operator/tensor/init_op.h
@@ -487,7 +487,7 @@ void EyeFill(const nnvm::NodeAttrs& attrs,
 
 struct range_fwd {
   template<typename DType>
-  MSHADOW_XINLINE static void Map(index_t i, int repeat, DType start, DType step,
+  MSHADOW_XINLINE static void Map(index_t i, index_t repeat, DType start, DType step,
                                   int req, DType* out) {
     KERNEL_ASSIGN(out[i], req, start + (i/repeat) * step);
   }
diff --git a/src/operator/tensor/ordering_op-inl.h b/src/operator/tensor/ordering_op-inl.h
index 1dda90104205..98bca3a43c60 100644
--- a/src/operator/tensor/ordering_op-inl.h
+++ b/src/operator/tensor/ordering_op-inl.h
@@ -81,12 +81,18 @@ struct TopKParam : public dmlc::Parameter<TopKParam> {
       .describe("Whether to choose k largest or k smallest elements."
                 " Top K largest elements will be chosen if set to false.");
     DMLC_DECLARE_FIELD(dtype)
+    // TODO(srivrohi): remove support for real data type in mxnet-2.0
     .add_enum("uint8", mshadow::kUint8)
     .add_enum("int32", mshadow::kInt32)
+    .add_enum("int64", mshadow::kInt64)
     .add_enum("float16", mshadow::kFloat16)
     .add_enum("float32", mshadow::kFloat32)
     .add_enum("float64", mshadow::kFloat64)
-    .set_default(mshadow::kFloat32)
+#if MXNET_USE_INT64_TENSOR_SIZE == 1
+    .set_default(mshadow::kInt64)
+#else
+    .set_default(mshadow::kInt32)
+#endif
     .describe("DType of the output indices when ret_typ is \"indices\" or \"both\". "
               "An error will be raised if the selected data type cannot precisely represent the "
               "indices.");
@@ -116,21 +122,33 @@ struct ArgSortParam : public dmlc::Parameter<ArgSortParam> {
     DMLC_DECLARE_FIELD(is_ascend).set_default(true)
       .describe("Whether to sort in ascending or descending order.");
     DMLC_DECLARE_FIELD(dtype)
+    // TODO(srivrohi): remove support for real data type in mxnet-2.0
     .add_enum("uint8", mshadow::kUint8)
     .add_enum("int32", mshadow::kInt32)
+    .add_enum("int64", mshadow::kInt64)
     .add_enum("float16", mshadow::kFloat16)
     .add_enum("float32", mshadow::kFloat32)
     .add_enum("float64", mshadow::kFloat64)
-    .set_default(mshadow::kFloat32)
+#if USE_INT64_TENSOR_SIZE == 1
+    .set_default(mshadow::kInt64)
+#else
+    .set_default(mshadow::kInt32)
+#endif
     .describe("DType of the output indices. It is only valid when ret_typ is \"indices\" or"
               " \"both\". An error will be raised if the selected data type cannot precisely "
               "represent the indices.");
   }
 };
 
-inline void ParseTopKParam(const mxnet::TShape& src_shape, const TopKParam& param,
-                           mxnet::TShape *target_shape, int *batch_size, int *element_num,
-                           int *axis, int *k, bool *do_transpose, bool *is_ascend) {
+inline void ParseTopKParam(const TShape& src_shape,
+                           const TopKParam& param,
+                           TShape *target_shape,
+                           size_t *batch_size,
+                           index_t *element_num,
+                           int *axis,
+                           index_t *k,
+                           bool *do_transpose,
+                           bool *is_ascend) {
   *do_transpose = false;
   *k = param.k;
   *is_ascend = param.is_ascend;
@@ -179,14 +197,14 @@ using namespace mshadow;
 
 struct fill_ind_to_one {
   template<typename DType>
-  MSHADOW_XINLINE static void Map(int i, const int* indices, DType* out) {
+  MSHADOW_XINLINE static void Map(int i, const index_t* indices, DType* out) {
     out[indices[i]] = static_cast<DType>(1);
   }
 };
 
 struct fill_ind {
   template<typename DType>
-  MSHADOW_XINLINE static void Map(int i, const int* indices, const DType* val,
+  MSHADOW_XINLINE static void Map(int i, const index_t* indices, const DType* val,
                                   int req, DType* out) {
     KERNEL_ASSIGN(out[indices[i]], req, val[i]);
   }
@@ -194,39 +212,43 @@ struct fill_ind {
 
 template<typename DType>
 MSHADOW_FORCE_INLINE void TopKSort(const Tensor<cpu, 1, DType>& dat,
-                                   const Tensor<cpu, 1, int>& ind,
+                                   const Tensor<cpu, 1, index_t>& ind,
                                    const Tensor<cpu, 1, char>& work,
-                                   int K, int N, bool is_ascend,
+                                   index_t K, index_t N, bool is_ascend,
                                    Stream<cpu> *s) {
   // Use full sort when K is relatively large.
   const bool full_sort(K*8 > N);
   // Batch size.
-  const int M(work.size(0)/(sizeof(DType)*N));
+  const index_t M(work.size(0)/(sizeof(DType)*N));
   const int omp_threads(engine::OpenMP::Get()->GetRecommendedOMPThreadCount());
   #pragma omp parallel for num_threads(omp_threads)
-  for (int i = 0; i < M; ++i) {
+  for (index_t i = 0; i < M; ++i) {
     // Tensor `work` stores the flattened source data, while `dat` stores the sorted result.
     DType *vals = reinterpret_cast<DType*>(work.dptr_);
     DType *sorted_vals = dat.dptr_+i*N;
-    int *indices = ind.dptr_+i*N;
+    index_t *indices = ind.dptr_+i*N;
     if (is_ascend) {
       if (full_sort) {
         std::sort(indices, indices+N,
-                  [&](const int& i1, const int& i2){ return vals[i1] < vals[i2]; });
+                  [&](const index_t& i1, const index_t& i2){
+          return vals[i1] < vals[i2]; });
       } else {
         std::partial_sort(indices, indices+K, indices+N,
-                          [&](const int& i1, const int& i2){ return vals[i1] < vals[i2]; });
+                          [&](const index_t& i1, const index_t& i2){
+          return vals[i1] < vals[i2]; });
       }
     } else {
       if (full_sort) {
         std::sort(indices, indices+N,
-                  [&](const int& i1, const int& i2){ return vals[i1] > vals[i2]; });
+                  [&](const index_t& i1, const index_t& i2){
+          return vals[i1] > vals[i2]; });
       } else {
         std::partial_sort(indices, indices+K, indices+N,
-                          [&](const int& i1, const int& i2){ return vals[i1] > vals[i2]; });
+                          [&](const index_t& i1, const index_t& i2){
+          return vals[i1] > vals[i2]; });
       }
     }
-    for (int j = 0; j < K; ++j) {
+    for (index_t j = 0; j < K; ++j) {
       sorted_vals[j] = vals[indices[j]];
     }
   }
@@ -235,18 +257,19 @@ MSHADOW_FORCE_INLINE void TopKSort(const Tensor<cpu, 1, DType>& dat,
 #ifdef __CUDACC__
 
 template<typename DType>
-MSHADOW_XINLINE bool TopKCompare(DType val1, int ind1, DType val2, int ind2, bool is_ascend) {
+MSHADOW_XINLINE bool TopKCompare(DType val1, index_t ind1, DType val2, index_t ind2,
+                                 bool is_ascend) {
   // Negative indices denote undefined values which are considered arbitrary small resp. large.
   return (ind2 < 0) || (ind1 >= 0 && ((is_ascend && val1 < val2) || (!is_ascend && val1 > val2)));
 }
 
 template<typename DType>
-MSHADOW_XINLINE void MergeTopK(int K, DType *val1, int *ind1, DType *val2, int *ind2,
+MSHADOW_XINLINE void MergeTopK(index_t K, DType *val1, index_t *ind1, DType *val2, index_t *ind2,
                                bool is_ascend) {
   // In-place merge of two sorted top-K lists into val1/ind1. First determine the intervals
   // [0,..,i1], [0,..i2] of the two lists that will be part of the merged list.
-  int i1(K-1), i2(K-1);
-  for (int i = 0; i < K; ++i) {
+  index_t i1(K-1), i2(K-1);
+  for (index_t i = 0; i < K; ++i) {
     if (TopKCompare(val1[i1], ind1[i1], val2[i2], ind2[i2], is_ascend)) {
       --i2;
     } else {
@@ -254,7 +277,7 @@ MSHADOW_XINLINE void MergeTopK(int K, DType *val1, int *ind1, DType *val2, int *
     }
   }
   // Now merge the lists from back to front.
-  for (int i = K; i--;) {
+  for (index_t i = K; i--;) {
     if (i2 < 0 || i1 >= 0 && TopKCompare(val2[i2], ind2[i2], val1[i1], ind1[i1], is_ascend)) {
       val1[i] = val1[i1];
       ind1[i] = ind1[i1];
@@ -268,28 +291,29 @@ MSHADOW_XINLINE void MergeTopK(int K, DType *val1, int *ind1, DType *val2, int *
 }
 
 template<typename DType>
-__global__ void PartialSortSmallK(int K, int N, DType *val, int *ind, bool is_ascend) {
+__global__ void PartialSortSmallK(index_t K, index_t N, DType *val, index_t *ind, bool is_ascend) {
   // Buffer for pairwise reduction.
-  extern __shared__ int buff[];
+  extern __shared__ index_t buff[];
   // Start of buffer sections associated with this thread.
-  const int offset(threadIdx.x*K);
-  int *ind_buff = &buff[offset];
+  const index_t offset(threadIdx.x*K);
+  index_t *ind_buff = &buff[offset];
   DType *val_buff = reinterpret_cast<DType*>(&buff[blockDim.x*K])+offset;
   // Initialize top-K values for this thread.
-  for (int i = 0; i < K; ++i) {
+  for (index_t i = 0; i < K; ++i) {
     ind_buff[i] = -1;
   }
   // Range of values this thread cares about. Each thread block processes
   // a different batch item (i.e. a different set of ind/val where we
   // have to select the top-K elements). All threads within the same
   // block work on the same batch item.
-  const int first(blockIdx.x*N+threadIdx.x), last((blockIdx.x+1)*N);
+  const index_t first(blockIdx.x*N+threadIdx.x), last((blockIdx.x+1)*N);
   // Select top-K from this range and store it sorted in the buffer.
   // We assume a small K, so linear insertion is o.k.
-  for (int i = first; i < last; i += blockDim.x) {
+  for (index_t i = first; i < last; i += blockDim.x) {
     DType cur_val(val[i]);
-    int cur_ind(ind[i]);
-    for (int j = K; j-- && TopKCompare(cur_val, cur_ind, val_buff[j], ind_buff[j], is_ascend); ) {
+    index_t cur_ind(ind[i]);
+    for (index_t j = K; j-- && TopKCompare(cur_val, cur_ind, val_buff[j],
+                                           ind_buff[j], is_ascend); ) {
       if (j+1 < K) {
         val_buff[j+1] = val_buff[j];
         ind_buff[j+1] = ind_buff[j];
@@ -300,7 +324,7 @@ __global__ void PartialSortSmallK(int K, int N, DType *val, int *ind, bool is_as
   }
   // Recursive merge of sorted lists for this thread block. Note that blockDim.x is not
   // necessary a power of two, therefore the additional checks for last_s.
-  for (unsigned int s = (blockDim.x+1)/2, last_s = blockDim.x;
+  for (index_t s = (blockDim.x+1)/2, last_s = blockDim.x;
        last_s > 1; last_s = s, s = (s+1)/2) {
     __syncthreads();
     if (threadIdx.x < s && threadIdx.x+s < last_s) {
@@ -309,7 +333,7 @@ __global__ void PartialSortSmallK(int K, int N, DType *val, int *ind, bool is_as
   }
   // Final updates on master thread.
   if (threadIdx.x == 0) {
-    for (int i = 0; i < K; ++i) {
+    for (index_t i = 0; i < K; ++i) {
       ind[blockIdx.x*N+i] = ind_buff[i];
       val[blockIdx.x*N+i] = val_buff[i];
     }
@@ -318,20 +342,21 @@ __global__ void PartialSortSmallK(int K, int N, DType *val, int *ind, bool is_as
 
 template<typename DType>
 MSHADOW_FORCE_INLINE void TopKSort(const Tensor<gpu, 1, DType>& dat,
-                                   const Tensor<gpu, 1, int>& ind,
+                                   const Tensor<gpu, 1, index_t>& ind,
                                    const Tensor<gpu, 1, char>& work,
-                                   int K, int N, bool is_ascend,
+                                   index_t K, index_t N, bool is_ascend,
                                    Stream<gpu> *s) {
   // Use full sort for all but very small K for which we
   // can do a partial sort entirely within shared memory.
   const bool full_sort(K > 5);
   // Batch size.
-  const int M(dat.size(0)/N);
+  const index_t M(dat.size(0)/N);
   if (full_sort) {
     // Divide workspace into two parts. The first one is needed to store batch ids.
-    size_t alignment = std::max(sizeof(DType), sizeof(int));
-    size_t id_size = PadBytes(sizeof(int) * ind.size(0), alignment);
-    Tensor<gpu, 1, int> batch_id(reinterpret_cast<int*>(work.dptr_), Shape1(ind.size(0)), s);
+    size_t alignment = std::max(sizeof(DType), sizeof(index_t));
+    size_t id_size = PadBytes(sizeof(index_t) * ind.size(0), alignment);
+    Tensor<gpu, 1, index_t> batch_id(reinterpret_cast<index_t*>(work.dptr_),
+                                     Shape1(ind.size(0)), s);
     Tensor<gpu, 1, char> sort_work(work.dptr_+id_size, Shape1(work.size(0)-id_size), s);
     mxnet::op::SortByKey(dat, ind, is_ascend, &sort_work);
     if (M > 1) {
@@ -380,20 +405,22 @@ void TopKImpl(const RunContext &ctx,
   Tensor<xpu, 1, char> workspace;
   Tensor<xpu, 1, char> temp_workspace;
   Tensor<xpu, 1, DType> sorted_dat;
-  Tensor<xpu, 1, int> indices, sel_indices;
-  int batch_size, element_num;  // number of batches + the size of each batch
+  Tensor<xpu, 1, index_t> indices, sel_indices;
+  size_t batch_size = 0;
+  index_t element_num = 0;  // number of batches + the size of each batch
   int axis = 0;
   bool do_transpose = false;
   bool is_ascend = false;
-  int k = 0;
+  index_t k = 0;
   size_t alignment = std::max(sizeof(DType), sizeof(int));
   mxnet::TShape target_shape;
   ParseTopKParam(src.shape_, param,
                  &target_shape, &batch_size, &element_num, &axis, &k, &do_transpose, &is_ascend);
-  CHECK_LE(element_num, mxnet::common::MaxIntegerValue<IDType>())
-    << "'IDType' does not have a sufficient precision to represent the indices of the input array. "
-    << "The total element_num is " << element_num << ", but the selected IDType can only represent "
-    << mxnet::common::MaxIntegerValue<IDType>() << " elements";
+  CHECK_LE(element_num, mxnet::common::MaxIntegerValue<index_t>())
+    << "'index_t' does not have a sufficient precision to represent "
+    << "the indices of the input array. The total element_num is "
+    << element_num << ", but the selected index_t can only represent "
+    << mxnet::common::MaxIntegerValue<index_t>() << " elements";
   Tensor<xpu, 3, DType> dat = src.FlatTo3D<xpu, DType>(axis, axis, s);
   size_t temp_size = 0;
   // Temp space needed by the gpu-based full sorts.
@@ -404,11 +431,11 @@ void TopKImpl(const RunContext &ctx,
   temp_size = std::max(temp_size,
     mxnet::op::SortByKeyWorkspaceSize<DType, int, xpu>(src.Size()));
   // Additional temp space for gpu full sorts for batch ids.
-  temp_size += PadBytes(sizeof(int) * src.Size(), alignment);
+  temp_size += PadBytes(sizeof(index_t) * src.Size(), alignment);
   // Temp space for cpu sorts.
   temp_size = std::max(temp_size, static_cast<size_t>(sizeof(DType) * src.Size()));
   size_t workspace_size = temp_size + PadBytes(sizeof(DType) * src.Size(), alignment)
-                                    + PadBytes(sizeof(int) * src.Size(), alignment);
+                                    + PadBytes(sizeof(index_t) * src.Size(), alignment);
   if (param.ret_typ == topk_enum::kReturnMask) {
     workspace_size += PadBytes(sizeof(int) * batch_size * k, alignment);
   }
@@ -417,14 +444,14 @@ void TopKImpl(const RunContext &ctx,
   sorted_dat = Tensor<xpu, 1, DType>(reinterpret_cast<DType*>(workspace_curr_ptr),
                                       Shape1(src.Size()), s);  // contain sorted dat
   workspace_curr_ptr += PadBytes(sizeof(DType) * src.Size(), alignment);
-  indices = Tensor<xpu, 1, int>(reinterpret_cast<int*>(workspace_curr_ptr),
+  indices = Tensor<xpu, 1, index_t>(reinterpret_cast<index_t*>(workspace_curr_ptr),
                                 Shape1(src.Size()), s);  // indices in the original matrix
-  workspace_curr_ptr += PadBytes(sizeof(int) * src.Size(), alignment);
+  workspace_curr_ptr += PadBytes(sizeof(index_t) * src.Size(), alignment);
 
   if (param.ret_typ == topk_enum::kReturnMask) {
-    sel_indices = Tensor<xpu, 1, int>(reinterpret_cast<int*>(workspace_curr_ptr),
+    sel_indices = Tensor<xpu, 1, index_t>(reinterpret_cast<index_t*>(workspace_curr_ptr),
                                       Shape1(batch_size * k), s);
-    workspace_curr_ptr += PadBytes(sizeof(int) * batch_size * k, alignment);
+    workspace_curr_ptr += PadBytes(sizeof(index_t) * batch_size * k, alignment);
     CHECK_EQ(sel_indices.CheckContiguous(), true);
   }
 
@@ -454,7 +481,7 @@ void TopKImpl(const RunContext &ctx,
     workspace_curr_ptr += temp_size;
   }
 
-  mxnet_op::Kernel<range_fwd, xpu>::Launch(s, batch_size * element_num, 1, 0, 1,
+  mxnet_op::Kernel<range_fwd, xpu>::Launch(s, batch_size * element_num, 1, index_t{0}, index_t{1},
     kWriteTo, indices.dptr_);
   CHECK_EQ(indices.CheckContiguous(), true);
 
@@ -551,7 +578,7 @@ void TopK(const nnvm::NodeAttrs& attrs,
     });
   } else {
     MXNET_NO_FLOAT16_TYPE_SWITCH(inputs[0].type_flag_, DType, {
-      TopKImpl<xpu, DType, int>(ctx.run_ctx, ctx.requested[0], req, inputs[0], outputs, param);
+      TopKImpl<xpu, DType, index_t>(ctx.run_ctx, ctx.requested[0], req, inputs[0], outputs, param);
     });
   }
 }
@@ -569,7 +596,8 @@ void Sort(const nnvm::NodeAttrs& attrs,
   topk_param.k = 0;
   topk_param.ret_typ = topk_enum::kReturnValue;
   MXNET_NO_FLOAT16_TYPE_SWITCH(inputs[0].type_flag_, DType, {
-    TopKImpl<xpu, DType, int>(ctx.run_ctx, ctx.requested[0], req, inputs[0], outputs, topk_param);
+    TopKImpl<xpu, DType, index_t>(ctx.run_ctx, ctx.requested[0], req, inputs[0],
+                                  outputs, topk_param);
   });
 }
 
@@ -605,30 +633,32 @@ void TopKBackwardImpl(const OpContext &ctx,
   using namespace mshadow::expr;
   Stream<xpu> *s = ctx.run_ctx.get_stream<xpu>();
   CHECK(param.ret_typ == topk_enum::kReturnValue || param.ret_typ == topk_enum::kReturnBoth);
-  int batch_size, element_num;  // number of batches + the size of each batch
+  size_t batch_size = 0;
+  index_t element_num = 0;  // number of batches + the size of each batch
   int axis = 0;
   bool do_transpose = false;
   bool is_ascend = false;
-  int k = 0;
+  index_t k = 0;
   mxnet::TShape target_shape;
   ParseTopKParam(outputs[0].shape_, param,
                  &target_shape, &batch_size, &element_num, &axis, &k, &do_transpose, &is_ascend);
   CHECK_LE(element_num, mxnet::common::MaxIntegerValue<IDType>())
-    << "'IDType' does not have a sufficient precision to represent the indices of the input array. "
-    << "The total element_num is " << element_num << ", but the selected IDType can only represent "
+    << "'IDType' does not have a sufficient precision to represent "
+    << "the indices of the input array. The total element_num is " << element_num
+    << ", but the selected index_t can only represent "
     << mxnet::common::MaxIntegerValue<IDType>() << " elements";
-  Tensor<xpu, 1, int> workspace =
-    ctx.requested[0].get_space_typed<xpu, 1, int>(Shape1(batch_size * k + batch_size), s);
-  Tensor<xpu, 1, int> sel_indices =
-    Tensor<xpu, 1, int>(workspace.dptr_, Shape1(batch_size * k), s);
-  Tensor<xpu, 1, int> batch_shift =
-    Tensor<xpu, 1, int>(workspace.dptr_ + batch_size * k, Shape1(batch_size), s);
+  Tensor<xpu, 1, index_t> workspace =
+    ctx.requested[0].get_space_typed<xpu, 1, index_t>(Shape1(batch_size * k + batch_size), s);
+  Tensor<xpu, 1, index_t> sel_indices =
+    Tensor<xpu, 1, index_t>(workspace.dptr_, Shape1(batch_size * k), s);
+  Tensor<xpu, 1, index_t> batch_shift =
+    Tensor<xpu, 1, index_t>(workspace.dptr_ + batch_size * k, Shape1(batch_size), s);
 
   Tensor<xpu, 2, DType> out_grad =
     inputs[0].get_with_shape<xpu, 2, DType>(Shape2(inputs[0].shape_.Size(), 1), s);
   Tensor<xpu, 2, DType> in_grad =
     outputs[0].get_with_shape<xpu, 2, DType>(Shape2(outputs[0].shape_.Size(), 1), s);
-  mxnet_op::Kernel<range_fwd, xpu>::Launch(s, batch_size, 1, 0, element_num, kWriteTo,
+  mxnet_op::Kernel<range_fwd, xpu>::Launch(s, batch_size, 1, index_t{0}, element_num, kWriteTo,
                                            batch_shift.dptr_);
   if (do_transpose) {
     Tensor<xpu, 1, IDType> indices = inputs[2].FlatTo1D<xpu, IDType>(s);
@@ -639,13 +669,13 @@ void TopKBackwardImpl(const OpContext &ctx,
                                          mxnet::TShape(Shape3(src_shape[0], src_shape[2], k))),
                             Shape3(0, 2, 1)),
                           Shape1(batch_size * k));
-    sel_indices += tcast<int>(indices);
+    sel_indices += tcast<index_t>(indices);
     sel_indices = transpose_indices(sel_indices, Shape3(src_shape[0], src_shape[2], src_shape[1]),
                                     Shape3(0, 2, 1));
   } else {
     Tensor<xpu, 2, IDType> indices =
       inputs[2].get_with_shape<xpu, 2, IDType>(Shape2(batch_size, k), s);
-    sel_indices = reshape(tcast<int>(indices) +
+    sel_indices = reshape(tcast<index_t>(indices) +
                           broadcast_to(inplace_reshape(batch_shift, Shape2(batch_size, 1)),
                                        mxnet::TShape(Shape2(batch_size, k))),
                           Shape1(batch_size * k));
@@ -680,7 +710,7 @@ void TopKBackward_(const nnvm::NodeAttrs& attrs,
     });
   } else if (param.ret_typ == topk_enum::kReturnValue) {
     MXNET_NO_FLOAT16_TYPE_SWITCH(inputs[0].type_flag_, DType, {
-      TopKBackwardImpl<xpu, DType, int>(ctx, inputs, req, outputs, param);
+      TopKBackwardImpl<xpu, DType, index_t>(ctx, inputs, req, outputs, param);
     });
   } else {
     LOG(FATAL) << "Not Implemented";
@@ -715,14 +745,11 @@ inline bool TopKType(const nnvm::NodeAttrs& attrs,
   size_t out_size = out_attrs->size();
   CHECK_EQ(in_size, 1);
   CHECK(out_size == 1 || out_size == 2);
+  //  out_attr[0] -> stores value
+  //  out_attr[1] -> stores indices
   if (out_size > 1) {
-    if (param.ret_typ == topk_enum::kReturnValue) {
-      CHECK(type_assign(&(*out_attrs)[1], mshadow::kInt32))
+    CHECK(type_assign(&(*out_attrs)[1], param.dtype))
         << "Failed to set the type of ret_indices.";
-    } else {
-      CHECK(type_assign(&(*out_attrs)[1], param.dtype))
-        << "Failed to set the type of ret_indices.";
-    }
   }
   if (param.ret_typ == topk_enum::kReturnIndices) {
     CHECK(type_assign(&(*out_attrs)[0], param.dtype))
@@ -752,11 +779,12 @@ inline bool TopKShapeImpl(const TopKParam& param,
     CHECK_EQ(out_attrs->size(), 2U);
   }
   mxnet::TShape& in_shape = (*in_attrs)[0];
-  int batch_size, element_num;  // number of batches + the size of each batch
+  size_t batch_size = 0;
+  index_t element_num = 0;  // number of batches + the size of each batch
   int axis = 0;
   bool do_transpose = false;
   bool is_ascend = false;
-  int k = 0;
+  index_t k = 0;
   mxnet::TShape target_shape;
   ParseTopKParam(in_shape, param,
     &target_shape, &batch_size, &element_num, &axis, &k, &do_transpose, &is_ascend);
@@ -785,8 +813,12 @@ inline bool SortType(const nnvm::NodeAttrs& attrs,
   size_t out_size = out_attrs->size();
   CHECK_EQ(in_size, 1);
   CHECK_EQ(out_size, 2);
+#if MXNET_USE_INT64_TENSOR_SIZE == 1
+  CHECK(type_assign(&(*out_attrs)[1], mshadow::kInt64))
+#else
   CHECK(type_assign(&(*out_attrs)[1], mshadow::kInt32))
-          << "Failed to set the type of ret_indices to int32.";
+#endif
+      << "Failed to set the type of ret_indices";
   CHECK(type_assign(&data_type, (*in_attrs)[0])) << "Incompatible dtype of input, in_attrs[0]="
                                                  << (*in_attrs)[0];
   CHECK(type_assign(&data_type, (*out_attrs)[0])) << "Incompatible dtype of output, out_attrs[0]="
@@ -816,7 +848,7 @@ inline bool ArgSortType(const nnvm::NodeAttrs& attrs,
                         std::vector<int> *out_attrs) {
   const ArgSortParam& param = nnvm::get<ArgSortParam>(attrs.parsed);
   CHECK(type_assign(&(*out_attrs)[0], param.dtype))
-          << "Failed to set the type of ret_indices to int32.";
+      << "Failed to set the type of ret_indices.";
   return true;
 }
 
diff --git a/tests/nightly/test_large_array.py b/tests/nightly/test_large_array.py
index cbba608d5d2f..0df481a01987 100644
--- a/tests/nightly/test_large_array.py
+++ b/tests/nightly/test_large_array.py
@@ -326,6 +326,32 @@ def test_softmax():
     assert_almost_equal(output.asnumpy(), true_output, rtol=1e-5, atol=1e-5)
 
 
+def test_argsort():
+    b = create_2d_tensor(rows=LARGE_X, columns=SMALL_Y)
+    s = nd.argsort(b, axis=0, is_ascend=False, dtype=np.int64)
+    mx.nd.waitall()
+    assert (s[0].asnumpy() == (LARGE_X - 1)).all()
+
+
+def test_sort():
+    b = create_2d_tensor(rows=LARGE_X, columns=SMALL_Y)
+    s = nd.sort(b, axis=0, is_ascend=False)
+    assert np.sum(s[-1][SMALL_Y//2:SMALL_Y].asnumpy() == 0).all()
+    s = nd.sort(b, is_ascend=False)
+    assert np.sum(s[0].asnumpy() == 0).all()
+
+
+def test_topk():
+    b = create_2d_tensor(rows=LARGE_X, columns=SMALL_Y)
+    k = nd.topk(b, k=10, axis=0, dtype=np.int64)
+    assert np.sum(k.asnumpy() == (LARGE_X - 1)) == SMALL_Y
+    ind, val = mx.nd.topk(b, k=3, axis=0, dtype=np.int64, ret_typ="both", is_ascend=False)
+    assert np.all(ind == val)
+    b = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
+    l = nd.topk(b, k=1, axis=-1, dtype=np.int64, ret_typ="value")
+    assert l.sum() == np.sum(np.arange(0, SMALL_Y))
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()
diff --git a/tests/python/unittest/test_ndarray.py b/tests/python/unittest/test_ndarray.py
index e5315900c725..d84b4f082b63 100644
--- a/tests/python/unittest/test_ndarray.py
+++ b/tests/python/unittest/test_ndarray.py
@@ -29,6 +29,7 @@
 from mxnet.test_utils import np_reduce
 from mxnet.test_utils import same
 from mxnet.test_utils import random_sample, rand_shape_nd
+from mxnet import runtime
 from numpy.testing import assert_allclose
 import mxnet.autograd
 
@@ -747,6 +748,7 @@ def test_linspace():
 def test_order():
     ctx = default_context()
     dat_size = 5
+    is_large_tensor_enabled = runtime.Features().is_enabled('INT64_TENSOR_SIZE')
     def gt_topk(dat, axis, ret_typ, k, is_ascend):
         if ret_typ == "indices":
             if is_ascend:
@@ -819,7 +821,11 @@ def get_large_matrix():
 
         # test for ret_typ=indices
         nd_ret_topk = mx.nd.topk(a_nd, axis=1, ret_typ="indices", k=3, is_ascend=True).asnumpy()
-        assert nd_ret_topk.dtype == np.float32  # Test the default dtype
+        # Test the default dtype
+        if is_large_tensor_enabled:
+            assert nd_ret_topk.dtype == np.int64
+        else:
+            assert nd_ret_topk.dtype == np.int32
         gt = gt_topk(a_npy, axis=1, ret_typ="indices", k=3, is_ascend=True)
         assert_almost_equal(nd_ret_topk, gt)
         nd_ret_topk = mx.nd.topk(a_nd, axis=3, ret_typ="indices", k=2, is_ascend=False, dtype=np.float64).asnumpy()
@@ -860,7 +866,10 @@ def get_large_matrix():
         nd_ret_topk_val = nd_ret_topk_val.asnumpy()
         nd_ret_topk_ind = nd_ret_topk_ind.asnumpy()
         assert nd_ret_topk_val.dtype == dtype
-        assert nd_ret_topk_ind.dtype == np.float32
+        if is_large_tensor_enabled:
+            assert nd_ret_topk_ind.dtype == np.int64
+        else:
+            assert nd_ret_topk_ind.dtype == np.int32
         gt_val = gt_topk(a_npy, axis=1, ret_typ="value", k=3, is_ascend=True)
         gt_ind = gt_topk(a_npy, axis=1, ret_typ="indices", k=3, is_ascend=True)
         assert_almost_equal(nd_ret_topk_val, gt_val)

From b4ce4e714a59f9f34d3db751e102609a34a9ce4e Mon Sep 17 00:00:00 2001
From: pengxin99 <yc316ypx@126.com>
Date: Sat, 22 Jun 2019 07:30:33 +0800
Subject: [PATCH 008/813] improve layernorm CPU performance (#15313)

---
 src/operator/mkl_functions-inl.h | 15 ++++++++-------
 1 file changed, 8 insertions(+), 7 deletions(-)

diff --git a/src/operator/mkl_functions-inl.h b/src/operator/mkl_functions-inl.h
index 608034732e0e..ae23b1e1d874 100644
--- a/src/operator/mkl_functions-inl.h
+++ b/src/operator/mkl_functions-inl.h
@@ -137,23 +137,24 @@ MSHADOW_XINLINE static void LayerNormLastDim(index_t m,
   for (index_t i = 0; i < m; i++) {
     DType* in_offset = a + i * n;
     DType* out_offset = b + i * n;
+    DType x_sum = 0.0f;
+    DType x_square_sum = 0.0f;
 
-    sum_(n, in_offset, &(mean[i]));
-    mean[i] /= n;
-    var[i] = 0.0f;
 #if !defined(_MSC_VER)
 #pragma omp simd
 #endif
     for (index_t j = 0; j < n; j++) {
-      out_offset[j] = in_offset[j] - mean[i];
-      var[i] += out_offset[j] * out_offset[j];
+      x_sum += in_offset[j];
+      x_square_sum += in_offset[j] * in_offset[j];
     }
-    var[i] = math::sqrt(var[i] / n + eps);
+    mean[i] = x_sum / n;
+    var[i] = math::sqrt(x_square_sum / n - mean[i] * mean[i] + eps);
+
 #if !defined(_MSC_VER)
 #pragma omp simd
 #endif
     for (index_t j = 0; j < n; j++) {
-      out_offset[j] = out_offset[j] * gamma[j] / var[i] + beta[j];
+      out_offset[j] = (in_offset[j] - mean[i]) * gamma[j] / var[i] + beta[j];
     }
   }
 }

From e6fad30e45e6ec0ddef5c18093e8163cd2a7c62c Mon Sep 17 00:00:00 2001
From: Zixuan Wei <9527221+zixuanweeei@users.noreply.github.com>
Date: Sat, 22 Jun 2019 14:26:27 +0800
Subject: [PATCH 009/813] Efficient MXNet sampling in the multinomial
 distribution (#15311)

* Effective multinomial

* Meaningful uniform data pointer as input

* Remove beginning Zeros from CDFs

* Double precision for accumulated var
---
 src/operator/random/sample_multinomial_op.h | 42 ++++++++++++---------
 1 file changed, 24 insertions(+), 18 deletions(-)

diff --git a/src/operator/random/sample_multinomial_op.h b/src/operator/random/sample_multinomial_op.h
index 377df4f313da..5a0b9bb21acb 100644
--- a/src/operator/random/sample_multinomial_op.h
+++ b/src/operator/random/sample_multinomial_op.h
@@ -122,25 +122,29 @@ inline bool SampleMultinomialOpType(const nnvm::NodeAttrs& attrs,
 struct SampleMultinomialKernel {
   template<typename DType, typename IType>
   MSHADOW_XINLINE static void Map(int i, index_t K, index_t M,
-                                  DType* dist, float* uniform, IType* out,
-                                  DType* prob) {
+                                  DType* dist, float* uniform, float* cum_table,
+                                  IType* out, DType* prob) {
+    double acc = 0.0;
+    // CDF table
+    for (index_t c = 0; c < K; ++c) {
+      acc += dist[i*K + c];
+      cum_table[i*K + c] = static_cast<float>(acc);
+    }
     for (index_t j = 0; j < M; ++j) {
+      index_t left = 0, right = K;
+      index_t middle = left + (right - left) / 2;
       DType loc = static_cast<DType>(uniform[i*M + j]);
-      DType acc = 0;
-      bool found = false;
-      for (index_t k = 0; k < K; ++k) {
-        acc += dist[i*K + k];
-        if (acc > loc) {
-          found = true;
-          out[i*M + j] = static_cast<IType>(k);
-          if (prob != nullptr) prob[i*M + j] = logf(dist[i*K + k]);
-          break;
+      while (right - left > 0) {
+        middle = left + (right - left) / 2;
+        DType cum_prob = cum_table[i*K + middle];
+        if (cum_prob < loc) {
+          left = middle + 1;
+        } else {
+          right = middle;
         }
       }
-      if (!found) {
-        out[i*M + j] = static_cast<IType>(K-1);
-        if (prob != nullptr) prob[i*M + j] = logf(dist[i*K + K - 1]);
-      }
+      out[i*M + j] = static_cast<IType>(left);
+      if (prob != nullptr) prob[i*M + j] = logf(dist[i*K + left]);
     }
   }
 };
@@ -163,12 +167,14 @@ void SampleMultinomialForward(const nnvm::NodeAttrs& attrs,
   Stream<xpu> *s = ctx.get_stream<xpu>();
   MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
     Random<xpu, float> *prnd = ctx.requested[0].get_random<xpu, float>(s);
-    Tensor<xpu, 1, float> uniform =
-      ctx.requested[1].get_space_typed<xpu, 1, float>(Shape1(N*M), s);
+    Tensor<xpu, 1, float> workspace =
+      ctx.requested[1].get_space_typed<xpu, 1, float>(Shape1(N*M + N*K), s);
+    Tensor<xpu, 1, float> uniform(workspace.dptr_, Shape1(N*M));
     prnd->SampleUniform(&uniform, 0, 1);
     MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, IType, {
       Kernel<SampleMultinomialKernel, xpu>::Launch(
-        s, N, K, M, inputs[0].dptr<DType>(), uniform.dptr_, outputs[0].dptr<IType>(),
+        s, N, K, M, inputs[0].dptr<DType>(), uniform.dptr_, workspace.dptr_ + N*M,
+        outputs[0].dptr<IType>(),
         param.get_prob ? outputs[1].dptr<DType>() : nullptr);
     });
   });

From 0340536394bf8205b5394bc9f60649b28d0a2e4f Mon Sep 17 00:00:00 2001
From: nihui <shuizhuyuanluo@126.com>
Date: Sat, 22 Jun 2019 16:44:29 +0800
Subject: [PATCH 010/813] indent changes (#15321)

---
 src/io/image_aug_default.cc | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/io/image_aug_default.cc b/src/io/image_aug_default.cc
index a1975ef487b7..0e84b7c9dee9 100644
--- a/src/io/image_aug_default.cc
+++ b/src/io/image_aug_default.cc
@@ -163,8 +163,8 @@ struct DefaultImageAugmentParam : public dmlc::Parameter<DefaultImageAugmentPara
     DMLC_DECLARE_FIELD(saturation).set_default(0.0f)
         .describe("Add a random value in ``[-saturation, saturation]`` to "
                   "the saturation of image.");
-        DMLC_DECLARE_FIELD(pca_noise).set_default(0.0f)
-                .describe("Add PCA based noise to the image.");
+    DMLC_DECLARE_FIELD(pca_noise).set_default(0.0f)
+        .describe("Add PCA based noise to the image.");
     DMLC_DECLARE_FIELD(random_h).set_default(0)
         .describe("Add a random value in ``[-random_h, random_h]`` to "
                   "the H channel in HSL color space.");

From f44f6cfbe752fd8b8036307cecf6a30a30ad8557 Mon Sep 17 00:00:00 2001
From: Dave Liepmann <dave.liepmann@gmail.com>
Date: Sat, 22 Jun 2019 19:35:45 +0200
Subject: [PATCH 011/813] Extend Clojure BERT example (#15023)

* Clojure predictor example: add rich comment

This provides an entry point for folks working on this example in
their REPL rather than the command line.

* Clojure BERT example: refactor prepare-data fn for purity

* Clojure BERT example: test fitted model on samples

* Clojure BERT example: namespace docstring & comment

* Clojure BERT example: format intro, add references

* Clojure BERT example: minor refactor

* Clojure BERT example: trim sentence pair explorations

* Clojure BERT example: port experiment to iPynb

* Clojure BERT example: fix test

Underlying fn was refactored

* Clojure BERT example: add sentence-pair prediction test
---
 .../examples/bert/fine-tune-bert.ipynb        | 145 ++++++++++++++++--
 .../src/bert/bert_sentence_classification.clj | 113 +++++++++++---
 .../bert_sentence_classification_test.clj     |  24 ++-
 .../predictor/src/infer/predictor_example.clj |   6 +
 4 files changed, 248 insertions(+), 40 deletions(-)

diff --git a/contrib/clojure-package/examples/bert/fine-tune-bert.ipynb b/contrib/clojure-package/examples/bert/fine-tune-bert.ipynb
index 425a9993ad93..5934477ea338 100644
--- a/contrib/clojure-package/examples/bert/fine-tune-bert.ipynb
+++ b/contrib/clojure-package/examples/bert/fine-tune-bert.ipynb
@@ -10,15 +10,16 @@
     "\n",
     "Pre-trained language representations have been shown to improve many downstream NLP tasks such as question answering, and natural language inference. To apply pre-trained representations to these tasks, there are two strategies:\n",
     "\n",
-    "feature-based approach, which uses the pre-trained representations as additional features to the downstream task.\n",
-    "fine-tuning based approach, which trains the downstream tasks by fine-tuning pre-trained parameters.\n",
-    "While feature-based approaches such as ELMo [3] (introduced in the previous tutorial) are effective in improving many downstream tasks, they require task-specific architectures. Devlin, Jacob, et al proposed BERT [1] (Bidirectional Encoder Representations from Transformers), which fine-tunes deep bidirectional representations on a wide range of tasks with minimal task-specific parameters, and obtained state- of-the-art results.\n",
+    " - **feature-based approach**, which uses the pre-trained representations as additional features to the downstream task.\n",
+    " - **fine-tuning based approach**, which trains the downstream tasks by fine-tuning pre-trained parameters.\n",
+    " \n",
+    "While feature-based approaches such as ELMo [1] are effective in improving many downstream tasks, they require task-specific architectures. Devlin, Jacob, et al proposed BERT [2] (Bidirectional Encoder Representations from Transformers), which fine-tunes deep bidirectional representations on a wide range of tasks with minimal task-specific parameters, and obtained state- of-the-art results.\n",
     "\n",
     "In this tutorial, we will focus on fine-tuning with the pre-trained BERT model to classify semantically equivalent sentence pairs. Specifically, we will:\n",
     "\n",
-    "load the state-of-the-art pre-trained BERT model and attach an additional layer for classification,\n",
-    "process and transform sentence pair data for the task at hand, and\n",
-    "fine-tune BERT model for sentence classification.\n",
+    " 1. load the state-of-the-art pre-trained BERT model and attach an additional layer for classification\n",
+    " 2. process and transform sentence pair data for the task at hand, and \n",
+    " 3. fine-tune BERT model for sentence classification.\n",
     "\n"
    ]
   },
@@ -59,6 +60,7 @@
     "            [org.apache.clojure-mxnet.callback :as callback]\n",
     "            [org.apache.clojure-mxnet.context :as context]\n",
     "            [org.apache.clojure-mxnet.dtype :as dtype]\n",
+    "            [org.apache.clojure-mxnet.infer :as infer]\n",
     "            [org.apache.clojure-mxnet.eval-metric :as eval-metric]\n",
     "            [org.apache.clojure-mxnet.io :as mx-io]\n",
     "            [org.apache.clojure-mxnet.layout :as layout]\n",
@@ -89,7 +91,7 @@
     "\n",
     "![bert](https://gluon-nlp.mxnet.io/_images/bert-sentence-pair.png)\n",
     "\n",
-    "where the model takes a pair of sequences and pools the representation of the first token in the sequence. Note that the original BERT model was trained for masked language model and next sentence prediction tasks, which includes layers for language model decoding and classification. These layers will not be used for fine-tuning sentence pair classification.\n",
+    "where the model takes a pair of sequences and *pools* the representation of the first token in the sequence. Note that the original BERT model was trained for masked language model and next sentence prediction tasks, which includes layers for language model decoding and classification. These layers will not be used for fine-tuning sentence pair classification.\n",
     "\n",
     "Let's load the pre-trained BERT using the module API in MXNet."
    ]
@@ -114,12 +116,15 @@
    ],
    "source": [
     "(def model-path-prefix \"data/static_bert_base_net\")\n",
+    "\n",
     ";; the vocabulary used in the model\n",
     "(def vocab (bert-util/get-vocab))\n",
-    ";; the input question\n",
+    "\n",
     ";; the maximum length of the sequence\n",
     "(def seq-length 128)\n",
     "\n",
+    "(def batch-size 32)\n",
+    "\n",
     "(def bert-base (m/load-checkpoint {:prefix model-path-prefix :epoch 0}))"
    ]
   },
@@ -291,7 +296,7 @@
    "source": [
     "(defn pre-processing\n",
     "  \"Preprocesses the sentences in the format that BERT is expecting\"\n",
-    "  [ctx idx->token token->idx train-item]\n",
+    "  [idx->token token->idx train-item]\n",
     "    (let [[sentence-a sentence-b label] train-item\n",
     "       ;;; pre-processing tokenize sentence\n",
     "          token-1 (bert-util/tokenize (string/lower-case sentence-a))\n",
@@ -319,7 +324,7 @@
     "(def idx->token (:idx->token vocab))\n",
     "(def token->idx (:token->idx vocab))\n",
     "(def dev (context/default-context))\n",
-    "(def processed-datas (mapv #(pre-processing dev idx->token token->idx %) data-train-raw))\n",
+    "(def processed-datas (mapv #(pre-processing idx->token token->idx %) data-train-raw))\n",
     "(def train-count (count processed-datas))\n",
     "(println \"Train Count is = \" train-count)\n",
     "(println \"[PAD] token id = \" (get token->idx \"[PAD]\"))\n",
@@ -375,8 +380,6 @@
     "                                 (into []))\n",
     "                    :train-num (count processed-datas)})\n",
     "\n",
-    "(def batch-size 32)\n",
-    "\n",
     "(def train-data\n",
     "  (let [{:keys [data0s data1s data2s labels train-num]} prepared-data\n",
     "        data-desc0 (mx-io/data-desc {:name \"data0\"\n",
@@ -480,7 +483,7 @@
     "(def num-epoch 3)\n",
     "\n",
     "(def fine-tune-model (m/module model-sym {:contexts [dev]\n",
-    "                                         :data-names [\"data0\" \"data1\" \"data2\"]}))\n",
+    "                                          :data-names [\"data0\" \"data1\" \"data2\"]}))\n",
     "\n",
     "(m/fit fine-tune-model {:train-data train-data  :num-epoch num-epoch\n",
     "                        :fit-params (m/fit-params {:allow-missing true\n",
@@ -489,6 +492,122 @@
     "                                                   :optimizer (optimizer/adam {:learning-rate 5e-6 :episilon 1e-9})\n",
     "                                                   :batch-end-callback (callback/speedometer batch-size 1)})})\n"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Explore results from the fine-tuned model\n",
+    "\n",
+    "Now that our model is fitted, we can use it to infer semantic equivalence of arbitrary sentence pairs. Note that for demonstration purpose we skipped the warmup learning rate schedule and validation on dev dataset used in the original implementation. This means that our model's performance will be significantly less than optimal. Please visit [here](https://gluon-nlp.mxnet.io/model_zoo/bert/index.html) for the complete fine-tuning scripts (using Python and GluonNLP).\n",
+    "\n",
+    "To do inference with our model we need a predictor. It must have a batch size of 1 so we can feed the model a single sentence pair."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "#'bert.bert-sentence-classification/fine-tuned-predictor"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(def fine-tuned-prefix \"fine-tune-sentence-bert\")\n",
+    "\n",
+    "(m/save-checkpoint fine-tune-model {:prefix fine-tuned-prefix :epoch 3})\n",
+    "\n",
+    "(def fine-tuned-predictor\n",
+    "    (infer/create-predictor (infer/model-factory fine-tuned-prefix\n",
+    "                                                 [{:name \"data0\" :shape [1 seq-length] :dtype dtype/FLOAT32 :layout layout/NT}\n",
+    "                                                  {:name \"data1\" :shape [1 seq-length] :dtype dtype/FLOAT32 :layout layout/NT}\n",
+    "                                                  {:name \"data2\" :shape [1]            :dtype dtype/FLOAT32 :layout layout/N}])\n",
+    "                            {:epoch 3}))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can write a function that feeds a sentence pair to the fine-tuned model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "#'bert.bert-sentence-classification/predict-equivalence"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(defn predict-equivalence\n",
+    "    [predictor sentence1 sentence2]\n",
+    "    (let [vocab (bert.util/get-vocab)\n",
+    "          processed-test-data (mapv #(pre-processing (:idx->token vocab)\n",
+    "                                                     (:token->idx vocab) %)\n",
+    "                                    [[sentence1 sentence2]])\n",
+    "          prediction (infer/predict-with-ndarray predictor\n",
+    "                                                 [(ndarray/array (slice-inputs-data processed-test-data 0) [1 seq-length])\n",
+    "                                                  (ndarray/array (slice-inputs-data processed-test-data 1) [1 seq-length])\n",
+    "                                                  (ndarray/array (slice-inputs-data processed-test-data 2) [1])])]\n",
+    "      (ndarray/->vec (first prediction))))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0.2633881 0.7366119]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    ";; Modify an existing sentence pair to test:\n",
+    ";; [\"1\"\n",
+    ";;  \"69773\"\n",
+    ";;  \"69792\"\n",
+    ";;  \"Cisco pared spending to compensate for sluggish sales .\"\n",
+    ";;  \"In response to sluggish sales , Cisco pared spending .\"]\n",
+    "(predict-equivalence fine-tuned-predictor\n",
+    "                     \"The company cut spending to compensate for weak sales .\"\n",
+    "                     \"In response to poor sales results, the company cut spending .\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "\n",
+    "[1] Peters, Matthew E., et al. “Deep contextualized word representations.” arXiv preprint arXiv:1802.05365 (2018).\n",
+    "\n",
+    "[2] Devlin, Jacob, et al. “Bert: Pre-training of deep bidirectional transformers for language understanding.” arXiv preprint arXiv:1810.04805 (2018)."
+   ]
   }
  ],
  "metadata": {
diff --git a/contrib/clojure-package/examples/bert/src/bert/bert_sentence_classification.clj b/contrib/clojure-package/examples/bert/src/bert/bert_sentence_classification.clj
index 8c056b719feb..6ec4d586ad17 100644
--- a/contrib/clojure-package/examples/bert/src/bert/bert_sentence_classification.clj
+++ b/contrib/clojure-package/examples/bert/src/bert/bert_sentence_classification.clj
@@ -16,12 +16,21 @@
 ;;
 
 (ns bert.bert-sentence-classification
+  "Fine-tuning Sentence Pair Classification with BERT
+  This tutorial focuses on fine-tuning with the pre-trained BERT model to classify semantically equivalent sentence pairs.
+
+  Specifically, we will:
+    1. load the state-of-the-art pre-trained BERT model
+    2. attach an additional layer for classification
+    3. process and transform sentence pair data for the task at hand
+    4. fine-tune BERT model for sentence classification"
   (:require [bert.util :as bert-util]
             [clojure-csv.core :as csv]
             [clojure.string :as string]
             [org.apache.clojure-mxnet.callback :as callback]
             [org.apache.clojure-mxnet.context :as context]
             [org.apache.clojure-mxnet.dtype :as dtype]
+            [org.apache.clojure-mxnet.infer :as infer]
             [org.apache.clojure-mxnet.io :as mx-io]
             [org.apache.clojure-mxnet.layout :as layout]
             [org.apache.clojure-mxnet.module :as m]
@@ -29,8 +38,25 @@
             [org.apache.clojure-mxnet.optimizer :as optimizer]
             [org.apache.clojure-mxnet.symbol :as sym]))
 
+;; Pre-trained language representations have been shown to improve
+;; many downstream NLP tasks such as question answering, and natural
+;; language inference. To apply pre-trained representations to these
+;; tasks, there are two strategies:
+
+;;  *  feature-based approach, which uses the pre-trained representations as additional features to the downstream task.
+;;  * fine-tuning based approach, which trains the downstream tasks by fine-tuning pre-trained parameters.
+
+;; While feature-based approaches such as ELMo are effective in
+;; improving many downstream tasks, they require task-specific
+;; architectures. Devlin, Jacob, et al proposed BERT (Bidirectional
+;; Encoder Representations from Transformers), which fine-tunes deep
+;; bidirectional representations on a wide range of tasks with minimal
+;; task-specific parameters, and obtained state-of-the-art results.
+
 (def model-path-prefix "data/static_bert_base_net")
-;; epoch number of the model
+
+(def fine-tuned-prefix "fine-tune-sentence-bert")
+
 ;; the maximum length of the sequence
 (def seq-length 128)
 
@@ -38,20 +64,19 @@
   "Preprocesses the sentences in the format that BERT is expecting"
   [idx->token token->idx train-item]
   (let [[sentence-a sentence-b label] train-item
-       ;;; pre-processing tokenize sentence
+        ;; pre-processing tokenize sentence
         token-1 (bert-util/tokenize (string/lower-case sentence-a))
         token-2 (bert-util/tokenize (string/lower-case sentence-b))
         valid-length (+ (count token-1) (count token-2))
-        ;;; generate token types [0000...1111...0000]
+        ;; generate token types [0000...1111...0000]
         qa-embedded (into (bert-util/pad [] 0 (count token-1))
-
                           (bert-util/pad [] 1 (count token-2)))
         token-types (bert-util/pad qa-embedded 0 seq-length)
-        ;;; make BERT pre-processing standard
+        ;; make BERT pre-processing standard
         token-2 (conj token-2 "[SEP]")
         token-1 (into [] (concat ["[CLS]"] token-1 ["[SEP]"] token-2))
         tokens (bert-util/pad token-1 "[PAD]" seq-length)
-        ;;; pre-processing - token to index translation
+        ;; pre-processing - token to index translation
         indexes (bert-util/tokens->idxs token->idx tokens)]
     {:input-batch [indexes
                    token-types
@@ -83,19 +108,18 @@
 
 (defn get-raw-data []
   (csv/parse-csv (string/replace (slurp "data/dev.tsv") "\"" "")
-               :delimiter \tab
-               :strict true))
+                 :delimiter \tab
+                 :strict true))
 
 (defn prepare-data
-  "This prepares the senetence pairs into NDArrays for use in NDArrayIterator"
-  []
-  (let [raw-file (get-raw-data)
-        vocab (bert-util/get-vocab)
+  "This prepares the sentence pairs into NDArrays for use in NDArrayIterator"
+  [raw-data]
+  (let [vocab (bert-util/get-vocab)
         idx->token (:idx->token vocab)
         token->idx (:token->idx vocab)
-        data-train-raw (->> raw-file
+        data-train-raw (->> raw-data
                             (mapv #(vals (select-keys % [3 4 0])))
-                            (rest) ;;drop header
+                            (rest) ; drop header
                             (into []))
         processed-datas (mapv #(pre-processing idx->token token->idx %) data-train-raw)]
     {:data0s (slice-inputs-data processed-datas 0)
@@ -111,7 +135,7 @@
   [dev num-epoch]
   (let [bert-base (m/load-checkpoint {:prefix model-path-prefix :epoch 0})
         model-sym (fine-tune-model (m/symbol bert-base) {:num-classes 2 :dropout 0.1})
-        {:keys [data0s data1s data2s labels train-num]} (prepare-data)
+        {:keys [data0s data1s data2s labels train-num]} (prepare-data (get-raw-data))
         batch-size 32
         data-desc0 (mx-io/data-desc {:name "data0"
                                      :shape [train-num seq-length]
@@ -138,14 +162,16 @@
                                         {:label {label-desc (ndarray/array labels [train-num]
                                                                            {:ctx dev})}
                                          :data-batch-size batch-size})
-        model (m/module model-sym {:contexts [dev]
-                                   :data-names ["data0" "data1" "data2"]})]
-    (m/fit model {:train-data train-data  :num-epoch num-epoch
-                  :fit-params (m/fit-params {:allow-missing true
-                                             :arg-params (m/arg-params bert-base)
-                                             :aux-params (m/aux-params bert-base)
-                                             :optimizer (optimizer/adam {:learning-rate 5e-6 :episilon 1e-9})
-                                             :batch-end-callback (callback/speedometer batch-size 1)})})))
+        fitted-model (m/fit (m/module model-sym {:contexts [dev]
+                                                 :data-names ["data0" "data1" "data2"]})
+                            {:train-data train-data  :num-epoch num-epoch
+                             :fit-params (m/fit-params {:allow-missing true
+                                                        :arg-params (m/arg-params bert-base)
+                                                        :aux-params (m/aux-params bert-base)
+                                                        :optimizer (optimizer/adam {:learning-rate 5e-6 :epsilon 1e-9})
+                                                        :batch-end-callback (callback/speedometer batch-size 1)})})]
+    (m/save-checkpoint fitted-model {:prefix fine-tuned-prefix :epoch num-epoch})
+    fitted-model))
 
 (defn -main [& args]
   (let [[dev-arg num-epoch-arg] args
@@ -154,7 +180,46 @@
     (println "Running example with " dev " and " num-epoch " epochs ")
     (train dev num-epoch)))
 
+;; For evaluating the model
+(defn predict-equivalence
+  "Get the fine-tuned model's opinion on whether two sentences are equivalent:"
+  [predictor sentence1 sentence2]
+  (let [vocab (bert.util/get-vocab)
+        processed-test-data (mapv #(pre-processing (:idx->token vocab)
+                                                   (:token->idx vocab) %)
+                                  [[sentence1 sentence2]])
+        prediction (infer/predict-with-ndarray predictor
+                                               [(ndarray/array (slice-inputs-data processed-test-data 0) [1 seq-length])
+                                                (ndarray/array (slice-inputs-data processed-test-data 1) [1 seq-length])
+                                                (ndarray/array (slice-inputs-data processed-test-data 2) [1])])]
+    (ndarray/->vec (first prediction))))
+
 (comment
 
   (train (context/cpu 0) 3)
-  (m/save-checkpoint model {:prefix "fine-tune-sentence-bert" :epoch 3}))
+
+  (m/save-checkpoint model {:prefix fine-tuned-prefix :epoch 3})
+
+  
+  ;;;; Explore results from the fine-tuned model
+
+  ;; We need a predictor with a batch size of 1, so we can feed the
+  ;; model a single sentence pair.
+  (def fine-tuned-predictor
+    (infer/create-predictor (infer/model-factory fine-tuned-prefix
+                                                 [{:name "data0" :shape [1 seq-length] :dtype dtype/FLOAT32 :layout layout/NT}
+                                                  {:name "data1" :shape [1 seq-length] :dtype dtype/FLOAT32 :layout layout/NT}
+                                                  {:name "data2" :shape [1]            :dtype dtype/FLOAT32 :layout layout/N}])
+                            {:epoch 3}))
+
+  ;; Modify an existing sentence pair to test:
+  ;; ["1"
+  ;;  "69773"
+  ;;  "69792"
+  ;;  "Cisco pared spending to compensate for sluggish sales ."
+  ;;  "In response to sluggish sales , Cisco pared spending ."]
+  (predict-equivalence fine-tuned-predictor
+                       "The company cut spending to compensate for weak sales ."
+                       "In response to poor sales results, the company cut spending .")  
+
+  )
diff --git a/contrib/clojure-package/examples/bert/test/bert/bert_sentence_classification_test.clj b/contrib/clojure-package/examples/bert/test/bert/bert_sentence_classification_test.clj
index 355f23ea3cfd..c26301e34fe6 100644
--- a/contrib/clojure-package/examples/bert/test/bert/bert_sentence_classification_test.clj
+++ b/contrib/clojure-package/examples/bert/test/bert/bert_sentence_classification_test.clj
@@ -26,6 +26,7 @@
             [org.apache.clojure-mxnet.context :as context]
             [org.apache.clojure-mxnet.dtype :as dtype]
             [org.apache.clojure-mxnet.eval-metric :as eval-metric]
+            [org.apache.clojure-mxnet.infer :as infer]
             [org.apache.clojure-mxnet.io :as mx-io]
             [org.apache.clojure-mxnet.layout :as layout]
             [org.apache.clojure-mxnet.ndarray :as ndarray]
@@ -34,6 +35,8 @@
 
 (def model-dir "data/")
 
+(def test-prefix "test-fine-tuning-bert-sentence-pairs")
+
 (when-not (.exists (io/file (str model-dir "static_bert_qa-0002.params")))
   (println "Downloading bert qa data")
   (sh "./get_bert_data.sh"))
@@ -47,7 +50,7 @@
           num-epoch 1
           bert-base (m/load-checkpoint {:prefix model-path-prefix :epoch 0})
           model-sym (fine-tune-model (m/symbol bert-base) {:num-classes 2 :dropout 0.1})
-          {:keys [data0s data1s data2s labels train-num]} (prepare-data)
+          {:keys [data0s data1s data2s labels train-num]} (prepare-data (get-raw-data))
           batch-size 32
           data-desc0 (mx-io/data-desc {:name "data0"
                                        :shape [train-num seq-length]
@@ -82,5 +85,20 @@
                                                :aux-params (m/aux-params bert-base)
                                                :optimizer (optimizer/adam {:learning-rate 5e-6 :episilon 1e-9})
                                                :batch-end-callback (callback/speedometer batch-size 1)})})
-      (is (< 0.5 (-> (m/score model {:eval-data train-data :eval-metric (eval-metric/accuracy) })
-                     (last)))))))
+      (m/save-checkpoint model {:prefix test-prefix :epoch num-epoch})
+      (testing "accuracy"
+        (is (< 0.5 (last (m/score model {:eval-data train-data :eval-metric (eval-metric/accuracy)})))))
+      (testing "prediction"
+        (let [test-predictor (infer/create-predictor (infer/model-factory test-prefix
+                                                                          [{:name "data0" :shape [1 seq-length] :dtype dtype/FLOAT32 :layout layout/NT}
+                                                                           {:name "data1" :shape [1 seq-length] :dtype dtype/FLOAT32 :layout layout/NT}
+                                                                           {:name "data2" :shape [1]            :dtype dtype/FLOAT32 :layout layout/N}])
+                                                     {:epoch num-epoch})
+              prediction (predict-equivalence test-predictor
+                                              "The company cut spending to compensate for weak sales ."
+                                              "In response to poor sales results, the company cut spending .")]
+          ;; We can't say much about how the model will find this prediction, so we test only the prediction's shape.
+          (is (vector? prediction))
+          (is (number? (first prediction)))
+          (is (number? (second prediction)))
+          (is (= 2 (count prediction))))))))
diff --git a/contrib/clojure-package/examples/infer/predictor/src/infer/predictor_example.clj b/contrib/clojure-package/examples/infer/predictor/src/infer/predictor_example.clj
index 05eb0add3138..41a003a86ce0 100644
--- a/contrib/clojure-package/examples/infer/predictor/src/infer/predictor_example.clj
+++ b/contrib/clojure-package/examples/infer/predictor/src/infer/predictor_example.clj
@@ -99,3 +99,9 @@
       (:help options) (println summary)
       (some? errors) (println (join "\n" errors))
       :else (run-predictor options))))
+
+(comment
+  (run-predictor {:model-path-prefix "models/resnet-18/resnet-18"
+                  :input-image "images/kitten.jpg"})
+
+  )

From 7fe478afc4f8bed58b9816ddce57a3c75997c9f2 Mon Sep 17 00:00:00 2001
From: Pedro Larroy <pedro.larroy.lists@gmail.com>
Date: Mon, 24 Jun 2019 01:20:33 -0700
Subject: [PATCH 012/813] Fix build_ccache_wrappers: (#14631)

* Fix broken links
* Make it idempotent
fixes https://github.com/apache/incubator-mxnet/pull/13456
fixes https://github.com/apache/incubator-mxnet/issues/14117
fixes https://github.com/apache/incubator-mxnet/issues/11516
---
 ci/docker/runtime_functions.sh | 49 ++++++++++++++--------------------
 1 file changed, 20 insertions(+), 29 deletions(-)

diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index 1ad67280617d..0fef2a049125 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -86,37 +86,29 @@ build_ccache_wrappers() {
     # But in the beginning, we'll make this opt-in. In future, loads of processes like
     # the scala make step or numpy compilation and other pip package generations
     # could be heavily sped up by using ccache as well.
-    mkdir /tmp/ccache-redirects
+    mkdir -p /tmp/ccache-redirects
     export PATH=/tmp/ccache-redirects:$PATH
-    ln -s ccache /tmp/ccache-redirects/gcc
-    ln -s ccache /tmp/ccache-redirects/gcc-8
-    ln -s ccache /tmp/ccache-redirects/g++
-    ln -s ccache /tmp/ccache-redirects/g++-8
-    ln -s ccache /tmp/ccache-redirects/nvcc
-    ln -s ccache /tmp/ccache-redirects/clang++-3.9
-    ln -s ccache /tmp/ccache-redirects/clang-3.9
-    ln -s ccache /tmp/ccache-redirects/clang++-5.0
-    ln -s ccache /tmp/ccache-redirects/clang-5.0
-    ln -s ccache /tmp/ccache-redirects/clang++-6.0
-    ln -s ccache /tmp/ccache-redirects/clang-6.0
-    ln -s ccache /usr/local/bin/gcc
-    ln -s ccache /usr/local/bin/gcc-8
-    ln -s ccache /usr/local/bin/g++
-    ln -s ccache /usr/local/bin/g++-8
-    ln -s ccache /usr/local/bin/nvcc
-    ln -s ccache /usr/local/bin/clang++-3.9
-    ln -s ccache /usr/local/bin/clang-3.9
-    ln -s ccache /usr/local/bin/clang++-5.0
-    ln -s ccache /usr/local/bin/clang-5.0
-    ln -s ccache /usr/local/bin/clang++-6.0
-    ln -s ccache /usr/local/bin/clang-6.0
-
-    export NVCC=ccache
+    CCACHE=`which ccache`
+    ln -sf $CCACHE /tmp/ccache-redirects/gcc
+    ln -sf $CCACHE /tmp/ccache-redirects/gcc-8
+    ln -sf $CCACHE /tmp/ccache-redirects/g++
+    ln -sf $CCACHE /tmp/ccache-redirects/g++-8
+    ln -sf $CCACHE /tmp/ccache-redirects/clang++-3.9
+    ln -sf $CCACHE /tmp/ccache-redirects/clang-3.9
+    ln -sf $CCACHE /tmp/ccache-redirects/clang++-5.0
+    ln -sf $CCACHE /tmp/ccache-redirects/clang-5.0
+    ln -sf $CCACHE /tmp/ccache-redirects/clang++-6.0
+    ln -sf $CCACHE /tmp/ccache-redirects/clang-6.0
+    #Doesn't work: https://github.com/ccache/ccache/issues/373
+    # ln -sf $CCACHE /tmp/ccache-redirects/nvcc
+    # ln -sf $CCACHE /tmp/ccache-redirects/nvcc
+    # export NVCC="/tmp/ccache-redirects/nvcc"
 
     # Uncomment if you would like to debug CCache hit rates.
     # You can monitor using tail -f ccache-log
-    # export CCACHE_LOGFILE=/work/mxnet/ccache-log
-    # export CCACHE_DEBUG=1
+    #export CCACHE_LOGFILE=/work/mxnet/ccache-log
+    #export CCACHE_LOGFILE=/tmp/ccache-log
+    #export CCACHE_DEBUG=1
 }
 
 build_wheel() {
@@ -689,8 +681,7 @@ build_ubuntu_gpu_mkldnn_nocudnn() {
 
 build_ubuntu_gpu_cuda100_cudnn7() {
     set -ex
-    # unfortunately this build has problems in 3rdparty dependencies with ccache and make
-    # build_ccache_wrappers
+    build_ccache_wrappers
     make \
         DEV=1                                     \
         ENABLE_TESTCOVERAGE=1                     \

From 51acd4d328c6da475c058806565d93bcddee7402 Mon Sep 17 00:00:00 2001
From: Maxim Berman <bermanmaxim@gmail.com>
Date: Tue, 25 Jun 2019 16:11:13 -0700
Subject: [PATCH 013/813] [MXNET-1086] added sub and mul to ONNX->TensorRT
 conversion (#15344)

* added sub and mul to ONNX->TensorRT conversion

* add test for elementwise ops in TRT
---
 CMakeLists.txt                                |  2 +-
 .../subgraph/tensorrt/nnvm_to_onnx-inl.h      | 12 ++++
 .../subgraph/tensorrt/nnvm_to_onnx.cc         | 12 ++++
 tests/python/tensorrt/test_ops.py             | 68 +++++++++++++++++++
 4 files changed, 93 insertions(+), 1 deletion(-)
 create mode 100644 tests/python/tensorrt/test_ops.py

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 2142a09d6d2e..0148ac302d54 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -47,7 +47,7 @@ mxnet_option(ENABLE_CUDA_RTC      "Build with CUDA runtime compilation support"
 mxnet_option(BUILD_CPP_EXAMPLES   "Build cpp examples" ON)
 mxnet_option(INSTALL_EXAMPLES     "Install the example source files." OFF)
 mxnet_option(USE_SIGNAL_HANDLER   "Print stack traces on segfaults." ON)
-mxnet_option(USE_TENSORRT         "Enable infeference optimization with TensorRT." OFF)
+mxnet_option(USE_TENSORRT         "Enable inference optimization with TensorRT." OFF)
 mxnet_option(USE_ASAN             "Enable Clang/GCC ASAN sanitizers." OFF)
 mxnet_option(ENABLE_TESTCOVERAGE  "Enable compilation with test coverage metric output" OFF)
 mxnet_option(USE_INT64_TENSOR_SIZE "Use int64_t to represent the total number of elements in a tensor" OFF)
diff --git a/src/operator/subgraph/tensorrt/nnvm_to_onnx-inl.h b/src/operator/subgraph/tensorrt/nnvm_to_onnx-inl.h
index 4a88aee886db..edf4d357e922 100644
--- a/src/operator/subgraph/tensorrt/nnvm_to_onnx-inl.h
+++ b/src/operator/subgraph/tensorrt/nnvm_to_onnx-inl.h
@@ -126,6 +126,16 @@ void ConvertElementwiseAdd(NodeProto *node_proto,
                     const nnvm::IndexedGraph &ig,
                     const array_view<IndexedGraph::NodeEntry> &inputs);
 
+void ConvertElementwiseSub(NodeProto *node_proto,
+                    const NodeAttrs &attrs,
+                    const nnvm::IndexedGraph &ig,
+                    const array_view<IndexedGraph::NodeEntry> &inputs);
+
+void ConvertElementwiseMul(NodeProto *node_proto,
+                    const NodeAttrs &attrs,
+                    const nnvm::IndexedGraph &ig,
+                    const array_view<IndexedGraph::NodeEntry> &inputs);
+
 void ConvertConcatenate(NodeProto *node_proto,
                     const NodeAttrs &attrs,
                     const nnvm::IndexedGraph &ig,
@@ -152,6 +162,8 @@ static const std::unordered_map<std::string, ConverterFunction> converter_map =
   {"Concat", ConvertConcatenate},
   {"Dropout", ConvertDropout},
   {"elemwise_add", ConvertElementwiseAdd},
+  {"elemwise_sub", ConvertElementwiseSub},
+  {"elemwise_mul", ConvertElementwiseMul},
   {"Flatten", ConvertFlatten},
   {"FullyConnected", ConvertFullyConnected},
   {"Pad", ConvertPad},
diff --git a/src/operator/subgraph/tensorrt/nnvm_to_onnx.cc b/src/operator/subgraph/tensorrt/nnvm_to_onnx.cc
index da89c2b476ee..9d98a48c2ec2 100644
--- a/src/operator/subgraph/tensorrt/nnvm_to_onnx.cc
+++ b/src/operator/subgraph/tensorrt/nnvm_to_onnx.cc
@@ -393,6 +393,18 @@ void ConvertElementwiseAdd(NodeProto* node_proto, const NodeAttrs& /*attrs*/,
   node_proto->set_op_type("Add");
 }
 
+void ConvertElementwiseSub(NodeProto* node_proto, const NodeAttrs& /*attrs*/,
+                           const nnvm::IndexedGraph& /*ig*/,
+                           const array_view<IndexedGraph::NodeEntry>& /*inputs*/) {
+  node_proto->set_op_type("Sub");
+}
+
+void ConvertElementwiseMul(NodeProto* node_proto, const NodeAttrs& /*attrs*/,
+                           const nnvm::IndexedGraph& /*ig*/,
+                           const array_view<IndexedGraph::NodeEntry>& /*inputs*/) {
+  node_proto->set_op_type("Mul");
+}
+
 void ConvertConcatenate(NodeProto* node_proto, const NodeAttrs& attrs,
                         const nnvm::IndexedGraph& /*ig*/,
                         const array_view<IndexedGraph::NodeEntry>& /*inputs*/) {
diff --git a/tests/python/tensorrt/test_ops.py b/tests/python/tensorrt/test_ops.py
new file mode 100644
index 000000000000..2df9104aa06c
--- /dev/null
+++ b/tests/python/tensorrt/test_ops.py
@@ -0,0 +1,68 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+from mxnet.test_utils import assert_almost_equal
+import mxnet as mx
+import numpy as np
+import os
+
+def check_elementwise_random(op='sum', shape=(1, 3, 224, 224)):
+    """
+    Check elementwise operators with vanilla/TensorRT executors with uniform random tensors
+    """
+    a = mx.sym.Variable('a')
+    b = mx.sym.Variable('b')
+    if op == 'sum':
+        sym = a + b
+    elif op == 'sub':
+        sym = a - b
+    elif op == 'mul':
+        sym = a * b
+
+    a_data = mx.ndarray.random.uniform(shape=shape, ctx=mx.gpu())
+    b_data = mx.ndarray.random.uniform(shape=shape, ctx=mx.gpu())
+
+    executor = sym.simple_bind(ctx=mx.gpu(), a=shape, b=shape,
+                               grad_req='null', force_rebind=True)
+    y = executor.forward(is_train=False, a=a_data, b=b_data)
+    trt_sym = sym.get_backend_symbol('TensorRT')
+    original_precision_value = mx.contrib.tensorrt.get_use_fp16()
+    try:
+        mx.contrib.tensorrt.set_use_fp16(True)
+        executor = trt_sym.simple_bind(ctx=mx.gpu(), a=shape, b=shape,
+                                       grad_req='null', force_rebind=True)
+        y_trt = executor.forward(is_train=False, a=a_data, b=b_data)
+        mx.contrib.tensorrt.set_use_fp16(False)
+        executor = trt_sym.simple_bind(ctx=mx.gpu(), a=shape, b=shape,
+                                       grad_req='null', force_rebind=True)
+        y_trt_fp32 = executor.forward(is_train=False, a=a_data, b=b_data)
+        assert_almost_equal(y[0].asnumpy(), y_trt[0].asnumpy(), 1e-1, 1e-2)
+        assert_almost_equal(y[0].asnumpy(), y_trt_fp32[0].asnumpy(), 1e-4, 1e-4)
+    finally:
+        mx.contrib.tensorrt.set_use_fp16(original_precision_value)
+
+
+def test_elementwise():
+    for op in ['sum', 'sub', 'mul']:
+        for shape in [(20, 25), (3, 4, 20), (1, 3, 20, 25), (10, 10, 100, 100)]:
+            for itry in range(10):
+                check_elementwise_random(op, shape)
+
+
+if __name__ == '__main__':
+    import nose
+    nose.runmodule()

From 009907a8edde855c81078ac24fdedf188fa870d4 Mon Sep 17 00:00:00 2001
From: Wuxun Zhang <wuxun.zhang@intel.com>
Date: Thu, 27 Jun 2019 06:50:05 +0800
Subject: [PATCH 014/813] [C++] Improve inference script to support benchmark
 on Imagenet (#15164)

* refactor C++ inference script to support benchmark on Imagenet

* improve README, add INT8 test and some fixes

* fix cpplint

* some changes and code rebase

* fix CI

* refresh performance numbers in README

* address comments

* fix lint

* address comments

* improve README

* separate timing function for Windows and Linux

* retrigger CI
---
 cpp-package/example/inference/README.md       | 108 +++-
 .../example/inference/imagenet_inference.cpp  | 610 ++++++++++++++++++
 .../example/inference/inception_inference.cpp | 444 -------------
 .../inference/unit_test_imagenet_inference.sh |  63 ++
 .../unit_test_inception_inference.sh          |  42 --
 cpp-package/example/mlp_csv.cpp               |   1 +
 cpp-package/include/mxnet-cpp/initializer.h   |  50 ++
 cpp-package/include/mxnet-cpp/io.h            |   2 +
 cpp-package/tests/ci_test.sh                  |   4 +-
 example/quantization/README.md                |  18 +-
 10 files changed, 830 insertions(+), 512 deletions(-)
 create mode 100644 cpp-package/example/inference/imagenet_inference.cpp
 delete mode 100644 cpp-package/example/inference/inception_inference.cpp
 create mode 100755 cpp-package/example/inference/unit_test_imagenet_inference.sh
 delete mode 100755 cpp-package/example/inference/unit_test_inception_inference.sh

diff --git a/cpp-package/example/inference/README.md b/cpp-package/example/inference/README.md
index 865388b549db..272586da5da9 100644
--- a/cpp-package/example/inference/README.md
+++ b/cpp-package/example/inference/README.md
@@ -30,34 +30,112 @@ To build examples use following commands:
 
 This directory contains following examples. In order to run the examples, ensure that the path to the MXNet shared library is added to the OS specific environment variable viz. **LD\_LIBRARY\_PATH** for Linux, Mac and Ubuntu OS and **PATH** for Windows OS.
 
-### [inception_inference.cpp](<https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/inference/inception_inference.cpp>)
+## [imagenet_inference.cpp](<https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/inference/imagenet_inference.cpp>)
 
-This example demonstrates image classification workflow with pre-trained models using MXNet C++ API. The command line parameters the example can accept are as shown below:
+This example demonstrates image classification workflow with pre-trained models using MXNet C++ API. Now this script also supports inference with quantized CNN models generated by Intel® MKL-DNN (see this [quantization flow](https://github.com/apache/incubator-mxnet/blob/master/example/quantization/README.md)). By using C++ API, the latency of most models will be reduced to some extent compared with current Python implementation.
 
+Most of CNN models have been tested on Linux systems. And 50000 images are used to collect accuracy numbers. Please refer to this [README](https://github.com/apache/incubator-mxnet/blob/master/example/quantization/README.md) for  more details about accuracy.
+
+The following performance numbers are collected via using C++ inference API on AWS EC2 C5.12xlarge. The environment variables are set like below:
+
+```
+export KMP_AFFINITY=granularity=fine,noduplicates,compact,1,0
+export OMP_NUM_THREADS=$(vCPUs/2)
+export MXNET_SUBGRAPH_BACKEND=MKLDNN
+export MXNET_ENGINE_TYPE=NaiveEngine
+```
+Also users are recommended to use ```numactl``` or ```taskset``` to bind a running process to the specified cores.
+
+| Model | Dataset |BS=1 (imgs/sec) |BS=64 (imgs/sec) |
+|:---|:---|:---:|:---:|
+| |  |FP32 / INT8 | FP32 / INT8 |
+| ResNet18-V1  | [Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)  |369.00 / 778.82|799.7 / 2598.04|
+| ResNet50-V1  | [Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)  |160.72 / 405.84|349.73 / 1297.65 |
+| ResNet101-V1 | [Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)  | 89.56 / 197.55| 193.25 / 740.47|
+|Squeezenet 1.0|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)	  | 294.46 /  899.28| 857.70 / 3065.13|
+|MobileNet 1.0|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)    |554.94 / 676.59|1279.44 / 3393.43|
+|MobileNetV2 1.0|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)  |303.40 / 776.40|994.25 / 4227.77|
+|Inception V3|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)     |108.20 /  219.20 | 232.22 / 870.09 |
+|ResNet152-V2|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)     |52.28 / 64.62|107.03 / 134.04 |
+|Inception-BN|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)     | 211.86 / 306.37| 632.79 / 2115.28|
+
+The command line to launch inference by this script can accept are as shown below:
 ```
-./inception_inference --help
+./imagenet_inference --help
 Usage:
-inception_inference --symbol <model symbol file in json format>
-                    --params <model params file>
-					--image <path to the image used for prediction
-					--synset file containing labels for prediction
-					[--input_shape <dimensions of input image e.g "3 224 224"]
-					[--mean file containing mean image for normalizing the input image
-					[--gpu] Specify this option if workflow needs to be run in gpu context
+imagenet_inference  --symbol_file <model symbol file in json format>
+                    --params_file <model params file>
+		    --dataset <dataset used to benchmark>
+		    --data_nthreads <number of threads for data decoding, default: 60>
+		    --input_shape <shape of input image e.g "3 224 224">]
+		    --rgb_mean <mean value to be subtracted on R/G/B channel e.g "0 0 0">
+		    --rgb_std <standard deviation on R/G/B channel. e.g "1 1 1">
+		    --batch_size <number of images per batch>
+		    --num_skipped_batches <skip the number of batches for inference>
+		    --num_inference_batches <number of batches used for inference>
+		    --data_layer_type <default: "float32", choices: ["float32", "int8", "uint8"]>
+		    --gpu <whether to run inference on GPU, default: false>
+		    --benchmark <whether to use dummy data to run inference, default: false>
 ```
-The model json and param file and synset files are required to run this example.  The sample command line is as follows:
 
+Follow the below steps to do inference with more models.
+
+- Download the pre-trained FP32 models into ```./model``` directory.
+- Refer this [README](https://github.com/apache/incubator-mxnet/blob/master/example/quantization/README.md) to generate the corresponding quantized models and also put them into ```./model``` directory.
+- Prepare [validation dataset](http://data.mxnet.io/data/val_256_q90.rec) and put it into ```./data``` directory.
+
+The below command lines show how to run inference with FP32/INT8 resnet50_v1 model. Because the C++ inference script provides the almost same command line as this [Python script](https://github.com/apache/incubator-mxnet/blob/master/example/quantization/imagenet_inference.py) and then users can easily go from Python to C++.
 ```
+# set MKLDNN as subgraph backend
+export MXNET_SUBGRAPH_BACKEND=MKLDNN
+
+# FP32 inference
+./imagenet_inference --symbol_file "./model/resnet50_v1-symbol.json" --params_file "./model/resnet50_v1-0000.params" --dataset "./data/val_256_q90.rec" --rgb_mean "123.68 116.779 103.939" --rgb_std "58.393 57.12 57.375" --batch_size 64 --num_skipped_batches 50 --num_inference_batches 500
+
+# INT8 inference
+./imagenet_inference --symbol_file "./model/resnet50_v1-quantized-5batches-naive-symbol.json" --params_file "./model/resnet50_v1-quantized-0000.params" --dataset "./data/val_256_q90.rec" --rgb_mean "123.68 116.779 103.939" --rgb_std "58.393 57.12 57.375" --batch_size 64 --num_skipped_batches 50 --num_inference_batches 500
+
+# FP32 dummy data
+./imagenet_inference --symbol_file "./model/resnet50_v1-symbol.json" --batch_size 64 --num_inference_batches 500 --benchmark
+
+# INT8 dummy data
+./imagenet_inference --symbol_file "./model/resnet50_v1-quantized-5batches-naive-symbol.json" --batch_size 64 --num_inference_batches 500 --benchmark
 
-./inception_inference --symbol "./model/Inception-BN-symbol.json" --params "./model/Inception-BN-0126.params" --synset "./model/synset.txt" --mean "./model/mean_224.nd" --image "./model/dog.jpg"
 ```
-Alternatively, The script [unit_test_inception_inference.sh](<https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/inference/unit_test_inception_inference.sh>) downloads the pre-trained **Inception** model and a test image. The users can invoke this script as follows:
+For a quick inference test, users can directly run [unit_test_imagenet_inference.sh](<https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/inference/unit_test_imagenet_inference.sh>) by using the below command. This script will automatically download the pre-trained **Inception-Bn** and **resnet50_v1_int8** model and **validation dataset** which are required for inference.
 
 ```
-./unit_test_inception_inference.sh
+./unit_test_imagenet_inference.sh
+```
+And you may get the similiar outputs like below:
+```
+>>> INFO: FP32 real data
+imagenet_inference.cpp:282: Loading the model from ./model/Inception-BN-symbol.json
+imagenet_inference.cpp:295: Loading the model parameters from ./model/Inception-BN-0126.params
+imagenet_inference.cpp:443: INFO:Dataset for inference: ./data/val_256_q90.rec
+imagenet_inference.cpp:444: INFO:label_name = softmax_label
+imagenet_inference.cpp:445: INFO:rgb_mean: (123.68, 116.779, 103.939)
+imagenet_inference.cpp:447: INFO:rgb_std: (1, 1, 1)
+imagenet_inference.cpp:449: INFO:Image shape: (3, 224, 224)
+imagenet_inference.cpp:451: INFO:Finished inference with: 500 images
+imagenet_inference.cpp:453: INFO:Batch size = 1 for inference
+imagenet_inference.cpp:454: INFO:Accuracy: 0.744
+imagenet_inference.cpp:455: INFO:Throughput: xxxx images per second
+
+>>> INFO: FP32 dummy data
+imagenet_inference.cpp:282: Loading the model from ./model/Inception-BN-symbol.json
+imagenet_inference.cpp:372: Running the forward pass on model to evaluate the performance..
+imagenet_inference.cpp:387: benchmark completed!
+imagenet_inference.cpp:388: batch size: 1 num batch: 500 throughput: xxxx imgs/s latency:xxxx ms
+
+>>> INFO: INT8 dummy data
+imagenet_inference.cpp:282: Loading the model from ./model/resnet50_v1_int8-symbol.json
+imagenet_inference.cpp:372: Running the forward pass on model to evaluate the performance..
+imagenet_inference.cpp:387: benchmark completed!
+imagenet_inference.cpp:388: batch size: 1 num batch: 500 throughput: xxxx imgs/s latency:xxxx ms
 ```
 
-### [sentiment_analysis_rnn.cpp](<https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/inference/sentiment_analysis_rnn.cpp>)
+## [sentiment_analysis_rnn.cpp](<https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/inference/sentiment_analysis_rnn.cpp>)
 This example demonstrates how you can load a pre-trained RNN model and use it to predict the sentiment expressed in the given movie review with the MXNet C++ API. The example is capable of processing variable legnth inputs. It performs the following tasks
 - Loads the pre-trained RNN model.
 - Loads the dictionary file containing the word to index mapping.
diff --git a/cpp-package/example/inference/imagenet_inference.cpp b/cpp-package/example/inference/imagenet_inference.cpp
new file mode 100644
index 000000000000..7eaf991ada4e
--- /dev/null
+++ b/cpp-package/example/inference/imagenet_inference.cpp
@@ -0,0 +1,610 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*
+ * This example demonstrates image classification workflow with pre-trained models using MXNet C++ API.
+ * The example performs following tasks.
+ * 1. Load the pre-trained model.
+ * 2. Load the parameters of pre-trained model.
+ * 3. Load the inference dataset and create a new ImageRecordIter.
+ * 4. Run the forward pass and obtain throughput & accuracy.
+ */
+#ifndef _WIN32
+#include <sys/time.h>
+#endif
+#include <fstream>
+#include <iostream>
+#include <map>
+#include <chrono>
+#include <string>
+#include <vector>
+#include <random>
+#include <type_traits>
+#include <opencv2/opencv.hpp>
+#include "mxnet/c_api.h"
+#include "mxnet/tuple.h"
+#include "mxnet-cpp/MxNetCpp.h"
+#include "mxnet-cpp/initializer.h"
+
+using namespace mxnet::cpp;
+
+double ms_now() {
+  double ret;
+#ifdef _WIN32
+  auto timePoint = std::chrono::high_resolution_clock::now().time_since_epoch();
+  ret = std::chrono::duration<double, std::milli>(timePoint).count();
+#else
+  struct timeval time;
+  gettimeofday(&time, NULL);
+  ret = 1e+3 * time.tv_sec + 1e-3 * time.tv_usec;
+#endif
+  return ret;
+}
+
+
+// define the data type for NDArray, aliged with the definition in mshadow/base.h
+enum TypeFlag {
+  kFloat32 = 0,
+  kFloat64 = 1,
+  kFloat16 = 2,
+  kUint8 = 3,
+  kInt32 = 4,
+  kInt8  = 5,
+  kInt64 = 6,
+};
+
+/*
+ * class Predictor
+ *
+ * This class encapsulates the functionality to load the model, prepare dataset and run the forward pass.
+ */
+
+class Predictor {
+ public:
+    Predictor() {}
+    Predictor(const std::string& model_json_file,
+              const std::string& model_params_file,
+              const Shape& input_shape,
+              bool use_gpu,
+              const std::string& dataset,
+              const int data_nthreads,
+              const std::string& data_layer_type,
+              const std::vector<float>& rgb_mean,
+              const std::vector<float>& rgb_std,
+              int shuffle_chunk_seed,
+              int seed, bool benchmark);
+    void BenchmarkScore(int num_inference_batches);
+    void Score(int num_skipped_batches, int num_inference_batches);
+    ~Predictor();
+
+ private:
+    bool CreateImageRecordIter();
+    bool AdvanceDataIter(int skipped_batches);
+    void LoadModel(const std::string& model_json_file);
+    void LoadParameters(const std::string& model_parameters_file);
+    void InitParameters();
+
+    inline bool FileExists(const std::string &name) {
+      std::ifstream fhandle(name.c_str());
+      return fhandle.good();
+    }
+    int GetDataLayerType();
+
+    std::map<std::string, NDArray> args_map_;
+    std::map<std::string, NDArray> aux_map_;
+    Symbol net_;
+    Executor *executor_;
+    Shape input_shape_;
+    Context global_ctx_ = Context::cpu();
+
+    MXDataIter *val_iter_;
+    bool use_gpu_;
+    std::string dataset_;
+    int data_nthreads_;
+    std::string data_layer_type_;
+    std::vector<float> rgb_mean_;
+    std::vector<float> rgb_std_;
+    int shuffle_chunk_seed_;
+    int seed_;
+    bool benchmark_;
+};
+
+
+/*
+ * The constructor takes following parameters as input:
+ * 1. model_json_file:  The model in json formatted file.
+ * 2. model_params_file: File containing model parameters
+ * 3. input_shape: Shape of input data to the model. Since this class will be running one inference at a time,
+ *                 the input shape is required to be in format Shape(1, number_of_channels, height, width)
+ *                 The input image will be resized to (height x width) size before running the inference.
+ * 4. use_gpu: determine if run inference on GPU
+ * 5. dataset: data file (.rec) to be used for inference
+ * 6. data_nthreads: number of threads for data loading
+ * 7. data_layer_type: data type for data layer
+ * 8. rgb_mean: mean value to be subtracted on R/G/B channel
+ * 9. rgb_std: standard deviation on R/G/B channel
+ * 10. shuffle_chunk_seed: shuffling chunk seed
+ * 11. seed: shuffling seed
+ * 12. benchmark: use dummy data for inference
+ *
+ * The constructor will:
+ *  1. Create ImageRecordIter based on the given dataset file.
+ *  2. Load the model and parameter files.
+ *  3. Infer and construct NDArrays according to the input argument and create an executor.
+ */
+Predictor::Predictor(const std::string& model_json_file,
+                     const std::string& model_params_file,
+                     const Shape& input_shape,
+                     bool use_gpu,
+                     const std::string& dataset,
+                     const int data_nthreads,
+                     const std::string& data_layer_type,
+                     const std::vector<float>& rgb_mean,
+                     const std::vector<float>& rgb_std,
+                     int shuffle_chunk_seed,
+                     int seed, bool benchmark)
+    : input_shape_(input_shape),
+      use_gpu_(use_gpu),
+      dataset_(dataset),
+      data_nthreads_(data_nthreads),
+      data_layer_type_(data_layer_type),
+      rgb_mean_(rgb_mean),
+      rgb_std_(rgb_std),
+      shuffle_chunk_seed_(shuffle_chunk_seed),
+      seed_(seed),
+      benchmark_(benchmark) {
+  if (use_gpu) {
+    global_ctx_ = Context::gpu();
+  }
+
+  // initilize data iterator
+  if (!benchmark_ && !CreateImageRecordIter()) {
+    LG << "Error: failed to create ImageRecordIter";
+    throw std::runtime_error("ImageRecordIter cannot be created");
+  }
+
+  // Load the model
+  LoadModel(model_json_file);
+  // Initilize the parameters
+  // benchmark=false, load parameters from file
+  // benchmark=true, randomly initialize parameters
+  if (!benchmark_) {
+    LoadParameters(model_params_file);
+  } else {
+    InitParameters();
+  }
+
+  int dtype = GetDataLayerType();
+  if (dtype == -1) {
+    throw std::runtime_error("Unsupported data layer type...");
+  }
+  args_map_["data"] = NDArray(input_shape_, global_ctx_, false, dtype);
+  Shape label_shape(input_shape_[0]);
+  args_map_["softmax_label"] = NDArray(label_shape, global_ctx_, false);
+  std::vector<NDArray> arg_arrays;
+  std::vector<NDArray> grad_arrays;
+  std::vector<OpReqType> grad_reqs;
+  std::vector<NDArray> aux_arrays;
+
+  // infer and create ndarrays according to the given input ndarrays.
+  net_.InferExecutorArrays(global_ctx_, &arg_arrays, &grad_arrays, &grad_reqs,
+                           &aux_arrays, args_map_, std::map<std::string, NDArray>(),
+                           std::map<std::string, OpReqType>(), aux_map_);
+  for (auto& i : grad_reqs) i = OpReqType::kNullOp;
+
+  // Create an executor after binding the model to input parameters.
+  executor_ = new Executor(net_, global_ctx_, arg_arrays, grad_arrays, grad_reqs, aux_arrays);
+}
+
+/*
+ * The following function is used to get the data layer type for input data
+ */
+int Predictor::GetDataLayerType() {
+  int ret_type = -1;
+  if (data_layer_type_ == "float32") {
+    ret_type = kFloat32;
+  } else if (data_layer_type_ == "int8") {
+    ret_type = kInt8;
+  } else if (data_layer_type_ == "uint8") {
+    ret_type = kUint8;
+  } else {
+    LG << "Unsupported data layer type " << data_layer_type_ << "..."
+       << "Please use one of {float32, int8, uint8}";
+  }
+  return ret_type;
+}
+
+/*
+ * create a new ImageRecordIter according to the given parameters
+ */
+bool Predictor::CreateImageRecordIter() {
+  val_iter_ = new MXDataIter("ImageRecordIter");
+  if (!FileExists(dataset_)) {
+    LG << "Error: " << dataset_ << " must be provided";
+    return false;
+  }
+
+  std::vector<index_t> shape_vec;
+  for (index_t i = 1; i < input_shape_.ndim(); i++)
+    shape_vec.push_back(input_shape_[i]);
+  mxnet::TShape data_shape(shape_vec.begin(), shape_vec.end());
+
+  // set image record parser parameters
+  val_iter_->SetParam("path_imgrec", dataset_);
+  val_iter_->SetParam("label_width", 1);
+  val_iter_->SetParam("data_shape", data_shape);
+  val_iter_->SetParam("preprocess_threads", data_nthreads_);
+  val_iter_->SetParam("shuffle_chunk_seed", shuffle_chunk_seed_);
+
+  // set Batch parameters
+  val_iter_->SetParam("batch_size", input_shape_[0]);
+
+  // image record parameters
+  val_iter_->SetParam("shuffle", true);
+  val_iter_->SetParam("seed", seed_);
+
+  // set normalize parameters
+  val_iter_->SetParam("mean_r", rgb_mean_[0]);
+  val_iter_->SetParam("mean_g", rgb_mean_[1]);
+  val_iter_->SetParam("mean_b", rgb_mean_[2]);
+  val_iter_->SetParam("std_r", rgb_std_[0]);
+  val_iter_->SetParam("std_g", rgb_std_[1]);
+  val_iter_->SetParam("std_b", rgb_std_[2]);
+
+  // set prefetcher parameters
+  if (use_gpu_) {
+    val_iter_->SetParam("ctx", "gpu");
+  } else {
+    val_iter_->SetParam("ctx", "cpu");
+  }
+  val_iter_->SetParam("dtype", data_layer_type_);
+
+  val_iter_->CreateDataIter();
+  return true;
+}
+
+/*
+ * The following function loads the model from json file.
+ */
+void Predictor::LoadModel(const std::string& model_json_file) {
+  if (!FileExists(model_json_file)) {
+    LG << "Model file " << model_json_file << " does not exist";
+    throw std::runtime_error("Model file does not exist");
+  }
+  LG << "Loading the model from " << model_json_file << std::endl;
+  net_ = Symbol::Load(model_json_file);
+}
+
+
+/*
+ * The following function loads the model parameters.
+ */
+void Predictor::LoadParameters(const std::string& model_parameters_file) {
+  if (!FileExists(model_parameters_file)) {
+    LG << "Parameter file " << model_parameters_file << " does not exist";
+    throw std::runtime_error("Model parameters does not exist");
+  }
+  LG << "Loading the model parameters from " << model_parameters_file << std::endl;
+  std::map<std::string, NDArray> parameters;
+  NDArray::Load(model_parameters_file, 0, &parameters);
+  for (const auto &k : parameters) {
+    if (k.first.substr(0, 4) == "aux:") {
+      auto name = k.first.substr(4, k.first.size() - 4);
+      aux_map_[name] = k.second.Copy(global_ctx_);
+    }
+    if (k.first.substr(0, 4) == "arg:") {
+      auto name = k.first.substr(4, k.first.size() - 4);
+      args_map_[name] = k.second.Copy(global_ctx_);
+    }
+  }
+  /*WaitAll is need when we copy data between GPU and the main memory*/
+  NDArray::WaitAll();
+}
+
+/*
+ * The following function randomly initializes the parameters when benchmark_ is true.
+ */
+void Predictor::InitParameters() {
+  std::vector<mx_uint> data_shape;
+  for (index_t i = 0; i < input_shape_.ndim(); i++) {
+    data_shape.push_back(input_shape_[i]);
+  }
+
+  std::map<std::string, std::vector<mx_uint> > arg_shapes;
+  std::vector<std::vector<mx_uint> > aux_shapes, in_shapes, out_shapes;
+  arg_shapes["data"] = data_shape;
+  net_.InferShape(arg_shapes, &in_shapes, &aux_shapes, &out_shapes);
+
+  // initializer to call
+  Xavier xavier(Xavier::uniform, Xavier::avg, 2.0f);
+
+  auto arg_name_list = net_.ListArguments();
+  for (index_t i = 0; i < in_shapes.size(); i++) {
+    const auto &shape = in_shapes[i];
+    const auto &arg_name = arg_name_list[i];
+    int paramType = kFloat32;
+    if (Initializer::StringEndWith(arg_name, "weight_quantize") ||
+        Initializer::StringEndWith(arg_name, "bias_quantize")) {
+      paramType = kInt8;
+    }
+    NDArray tmp_arr(shape, global_ctx_, false, paramType);
+    xavier(arg_name, &tmp_arr);
+    args_map_[arg_name] = tmp_arr.Copy(global_ctx_);
+  }
+
+  auto aux_name_list = net_.ListAuxiliaryStates();
+  for (index_t i = 0; i < aux_shapes.size(); i++) {
+    const auto &shape = aux_shapes[i];
+    const auto &aux_name = aux_name_list[i];
+    NDArray tmp_arr(shape, global_ctx_, false);
+    xavier(aux_name, &tmp_arr);
+    aux_map_[aux_name] = tmp_arr.Copy(global_ctx_);
+  }
+  /*WaitAll is need when we copy data between GPU and the main memory*/
+  NDArray::WaitAll();
+}
+
+/*
+ * The following function runs the forward pass on the model
+ * and use dummy data for benchmark.
+ */
+void Predictor::BenchmarkScore(int num_inference_batches) {
+  // Create dummy data
+  std::vector<float> dummy_data(input_shape_.Size());
+  std::default_random_engine generator;
+  std::uniform_real_distribution<float> val(0.0f, 1.0f);
+  for (size_t i = 0; i < static_cast<size_t>(input_shape_.Size()); ++i) {
+    dummy_data[i] = static_cast<float>(val(generator));
+  }
+  executor_->arg_dict()["data"].SyncCopyFromCPU(
+        dummy_data.data(),
+        input_shape_.Size());
+  NDArray::WaitAll();
+
+  LG << "Running the forward pass on model to evaluate the performance..";
+
+  // warm up.
+  for (int i = 0; i < 5; i++) {
+    executor_->Forward(false);
+    NDArray::WaitAll();
+  }
+
+  // Run the forward pass.
+  double ms = ms_now();
+  for (int i = 0; i < num_inference_batches; i++) {
+    executor_->Forward(false);
+    NDArray::WaitAll();
+  }
+  ms = ms_now() - ms;
+  LG << " benchmark completed!";
+  LG << " batch size: " << input_shape_[0] << " num batch: " << num_inference_batches
+     << " throughput: " << 1000.0 * input_shape_[0] * num_inference_batches / ms
+     << " imgs/s latency:" << ms / input_shape_[0] / num_inference_batches << " ms";
+}
+
+/*
+ * \param skipped_batches skip the first number of batches
+ *
+ */
+bool Predictor::AdvanceDataIter(int skipped_batches) {
+  assert(skipped_batches >= 0);
+  if (skipped_batches == 0) return true;
+  int skipped_count = 0;
+  while (val_iter_->Next()) {
+    if (++skipped_count >= skipped_batches) break;
+  }
+  if (skipped_count != skipped_batches) return false;
+  return true;
+}
+
+/*
+ * The following function runs the forward pass on the model
+ * and use real data for testing accuracy and performance.
+ */
+void Predictor::Score(int num_skipped_batches, int num_inference_batches) {
+  // Create metrics
+  Accuracy val_acc;
+
+  val_iter_->Reset();
+  val_acc.Reset();
+  int nBatch = 0;
+
+  if (!AdvanceDataIter(num_skipped_batches)) {
+    LG << "skipped batches should less than total batches!";
+    return;
+  }
+
+  double ms = ms_now();
+  while (val_iter_->Next()) {
+    auto data_batch = val_iter_->GetDataBatch();
+    data_batch.data.CopyTo(&args_map_["data"]);
+    data_batch.label.CopyTo(&args_map_["softmax_label"]);
+    NDArray::WaitAll();
+
+    // running on forward pass
+    executor_->Forward(false);
+    NDArray::WaitAll();
+    val_acc.Update(data_batch.label, executor_->outputs[0]);
+
+    if (++nBatch >= num_inference_batches) {
+      break;
+    }
+  }
+  ms = ms_now() - ms;
+  auto args_name = net_.ListArguments();
+  LG << "INFO:" << "Dataset for inference: " << dataset_;
+  LG << "INFO:" << "label_name = " << args_name[args_name.size()-1];
+  LG << "INFO:" << "rgb_mean: " << "(" << rgb_mean_[0] << ", " << rgb_mean_[1]
+     << ", " << rgb_mean_[2] << ")";
+  LG << "INFO:" << "rgb_std: " << "(" << rgb_std_[0] << ", " << rgb_std_[1]
+     << ", " << rgb_std_[2] << ")";
+  LG << "INFO:" << "Image shape: " << "(" << input_shape_[1] << ", "
+     << input_shape_[2] << ", " << input_shape_[3] << ")";
+  LG << "INFO:" << "Finished inference with: " << nBatch * input_shape_[0]
+     << " images ";
+  LG << "INFO:" << "Batch size = " << input_shape_[0] << " for inference";
+  LG << "INFO:" << "Accuracy: " << val_acc.Get();
+  LG << "INFO:" << "Throughput: " << (1000.0 * nBatch * input_shape_[0] / ms)
+     << " images per second";
+}
+
+Predictor::~Predictor() {
+  if (executor_) {
+    delete executor_;
+  }
+  if (!benchmark_ && val_iter_) {
+    delete val_iter_;
+  }
+  MXNotifyShutdown();
+}
+
+/*
+ * Convert the input string of number into the vector.
+ */
+template<typename T>
+std::vector<T> createVectorFromString(const std::string& input_string) {
+  std::vector<T> dst_vec;
+  char *p_next;
+  T elem;
+  bool bFloat = std::is_same<T, float>::value;
+  if (!bFloat) {
+    elem = strtol(input_string.c_str(), &p_next, 10);
+  } else {
+    elem = strtof(input_string.c_str(), &p_next);
+  }
+
+  dst_vec.push_back(elem);
+  while (*p_next) {
+    if (!bFloat) {
+      elem = strtol(p_next, &p_next, 10);
+    } else {
+      elem = strtof(p_next, &p_next);
+    }
+    dst_vec.push_back(elem);
+  }
+  return dst_vec;
+}
+
+void printUsage() {
+    std::cout << "Usage:" << std::endl;
+    std::cout << "imagenet_inference --symbol_file <model symbol file in json format>" << std::endl
+              << "--params_file <model params file> " << std::endl
+              << "--dataset <dataset used to run inference> " << std::endl
+              << "--data_nthreads <default: 60> " << std::endl
+              << "--input_shape <shape of input image e.g \"3 224 224\">] " << std::endl
+              << "--rgb_mean <mean value to be subtracted on RGB channel e.g \"0 0 0\">"
+              << std::endl
+              << "--rgb_std <standard deviation on R/G/B channel. e.g \"1 1 1\"> " << std::endl
+              << "--batch_size <number of images per batch> " << std::endl
+              << "--num_skipped_batches <skip the number of batches for inference> " << std::endl
+              << "--num_inference_batches <number of batches used for inference> " << std::endl
+              << "--data_layer_type <default: \"float32\" "
+              << "choices: [\"float32\",\"int8\",\"uint8\"]>" << std::endl
+              << "--gpu  <whether to run inference on GPU, default: false>" << std::endl
+              << "--benchmark <whether to use dummy data to run inference, default: false>"
+              << std::endl;
+}
+
+int main(int argc, char** argv) {
+  std::string model_file_json;
+  std::string model_file_params;
+  std::string dataset("");
+  std::string input_rgb_mean("0 0 0");
+  std::string input_rgb_std("1 1 1");
+  bool use_gpu = false;
+  bool benchmark = false;
+  int batch_size = 64;
+  int num_skipped_batches = 0;
+  int num_inference_batches = 100;
+  std::string data_layer_type("float32");
+  std::string input_shape("3 224 224");
+  int seed = 48564309;
+  int shuffle_chunk_seed = 3982304;
+  int data_nthreads = 60;
+
+  int index = 1;
+  while (index < argc) {
+    if (strcmp("--symbol_file", argv[index]) == 0) {
+      index++;
+      model_file_json = (index < argc ? argv[index]:"");
+    } else if (strcmp("--params_file", argv[index]) == 0) {
+      index++;
+      model_file_params = (index < argc ? argv[index]:"");
+    } else if (strcmp("--dataset", argv[index]) == 0) {
+      index++;
+      dataset = (index < argc ? argv[index]:dataset);
+    } else if (strcmp("--data_nthreads", argv[index]) == 0) {
+      index++;
+      data_nthreads = strtol(argv[index], nullptr, 10);
+    } else if (strcmp("--input_shape", argv[index]) == 0) {
+      index++;
+      input_shape = (index < argc ? argv[index]:input_shape);
+    } else if (strcmp("--rgb_mean", argv[index]) == 0) {
+      index++;
+      input_rgb_mean = (index < argc ? argv[index]:input_rgb_mean);
+    } else if (strcmp("--rgb_std", argv[index]) == 0) {
+      index++;
+      input_rgb_std = (index < argc ? argv[index]:input_rgb_std);
+    } else if (strcmp("--batch_size", argv[index]) == 0) {
+      index++;
+      batch_size = strtol(argv[index], nullptr, 10);
+    }  else if (strcmp("--num_skipped_batches", argv[index]) == 0) {
+      index++;
+      num_skipped_batches = strtol(argv[index], nullptr, 10);
+    }  else if (strcmp("--num_inference_batches", argv[index]) == 0) {
+      index++;
+      num_inference_batches = strtol(argv[index], nullptr, 10);
+    } else if (strcmp("--data_layer_type", argv[index]) == 0) {
+      index++;
+      data_layer_type = (index < argc ? argv[index]:data_layer_type);
+    } else if (strcmp("--gpu", argv[index]) == 0) {
+      use_gpu = true;
+    } else if (strcmp("--benchmark", argv[index]) == 0) {
+      benchmark = true;
+    } else if (strcmp("--help", argv[index]) == 0) {
+      printUsage();
+      return 0;
+    }
+    index++;
+  }
+
+  if (model_file_json.empty() || (!benchmark && model_file_params.empty())) {
+    LG << "ERROR: Model details such as symbol, param files are not specified";
+    printUsage();
+    return 1;
+  }
+  std::vector<index_t> input_dimensions = createVectorFromString<index_t>(input_shape);
+  input_dimensions.insert(input_dimensions.begin(), batch_size);
+  Shape input_data_shape(input_dimensions);
+
+  std::vector<float> rgb_mean = createVectorFromString<float>(input_rgb_mean);
+  std::vector<float> rgb_std = createVectorFromString<float>(input_rgb_std);
+
+  // Initialize the predictor object
+  Predictor predict(model_file_json, model_file_params, input_data_shape, use_gpu, dataset,
+                    data_nthreads, data_layer_type, rgb_mean, rgb_std, shuffle_chunk_seed,
+                    seed, benchmark);
+
+  if (benchmark) {
+    predict.BenchmarkScore(num_inference_batches);
+  } else {
+    predict.Score(num_skipped_batches, num_inference_batches);
+  }
+  return 0;
+}
diff --git a/cpp-package/example/inference/inception_inference.cpp b/cpp-package/example/inference/inception_inference.cpp
deleted file mode 100644
index cb952aa69f54..000000000000
--- a/cpp-package/example/inference/inception_inference.cpp
+++ /dev/null
@@ -1,444 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- */
-
-/*
- * This example demonstrates image classification workflow with pre-trained models using MXNet C++ API.
- * The example performs following tasks.
- * 1. Load the pre-trained model.
- * 2. Load the parameters of pre-trained model.
- * 3. Load the image to be classified  in to NDArray.
- * 4. Normalize the image using the mean of images that were used for training.
- * 5. Run the forward pass and predict the input image.
- */
-
-#include <sys/stat.h>
-#include <iostream>
-#include <fstream>
-#include <map>
-#include <string>
-#include <vector>
-#include "mxnet-cpp/MxNetCpp.h"
-#include <opencv2/opencv.hpp>
-
-using namespace mxnet::cpp;
-
-static mx_float DEFAULT_MEAN_R = 123.675;
-static mx_float DEFAULT_MEAN_G = 116.28;
-static mx_float DEFAULT_MEAN_B = 103.53;
-/*
- * class Predictor
- *
- * This class encapsulates the functionality to load the model, process input image and run the forward pass.
- */
-
-class Predictor {
- public:
-    Predictor() {}
-    Predictor(const std::string& model_json_file,
-              const std::string& model_params_file,
-              const Shape& input_shape,
-              bool gpu_context_type = false,
-              const std::string& synset_file = "",
-              const std::string& mean_image_file = "");
-    void PredictImage(const std::string& image_file);
-    ~Predictor();
-
- private:
-    void LoadModel(const std::string& model_json_file);
-    void LoadParameters(const std::string& model_parameters_file);
-    void LoadSynset(const std::string& synset_file);
-    NDArray LoadInputImage(const std::string& image_file);
-    void LoadMeanImageData();
-    void LoadDefaultMeanImageData();
-    void NormalizeInput(const std::string& mean_image_file);
-    inline bool FileExists(const std::string& name) {
-        struct stat buffer;
-        return (stat(name.c_str(), &buffer) == 0);
-    }
-    NDArray mean_img;
-    std::map<std::string, NDArray> args_map;
-    std::map<std::string, NDArray> aux_map;
-    std::vector<std::string> output_labels;
-    Symbol net;
-    Executor *executor;
-    Shape input_shape;
-    NDArray mean_image_data;
-    NDArray std_dev_image_data;
-    Context global_ctx = Context::cpu();
-    std::string mean_image_file;
-};
-
-
-/*
- * The constructor takes following parameters as input:
- * 1. model_json_file:  The model in json formatted file.
- * 2. model_params_file: File containing model parameters
- * 3. synset_file: File containing the list of image labels
- * 4. input_shape: Shape of input data to the model. Since this class will be running one inference at a time,
- *                 the input shape is required to be in format Shape(1, number_of_channels, height, width)
- * The input image will be resized to (height x width) size before running the inference.
- * The constructor will:
- *  1. Load the model and parameter files.
- *  2. Load the synset file.
- *  3. Invoke the SimpleBind to bind the input argument to the model and create an executor.
- *
- *  The SimpleBind is expected to be invoked only once.
- */
-Predictor::Predictor(const std::string& model_json_file,
-                     const std::string& model_params_file,
-                     const Shape& input_shape,
-                     bool gpu_context_type,
-                     const std::string& synset_file,
-                     const std::string& mean_image_file):
-                     input_shape(input_shape),
-                     mean_image_file(mean_image_file) {
-  if (gpu_context_type) {
-    global_ctx = Context::gpu();
-  }
-  // Load the model
-  LoadModel(model_json_file);
-
-  // Load the model parameters.
-  LoadParameters(model_params_file);
-
-  /*
-   * The data will be used to output the exact label that matches highest output of the model.
-   */
-  LoadSynset(synset_file);
-
-  /*
-   * Load the mean image data if specified.
-   */
-  if (!mean_image_file.empty()) {
-    LoadMeanImageData();
-  } else {
-    LG << "Mean image file for normalizing the input is not provide."
-       << " We will use the default mean values for R,G and B channels.";
-    LoadDefaultMeanImageData();
-  }
-
-  // Create an executor after binding the model to input parameters.
-  args_map["data"] = NDArray(input_shape, global_ctx, false);
-  executor = net.SimpleBind(global_ctx, args_map, std::map<std::string, NDArray>(),
-                              std::map<std::string, OpReqType>(), aux_map);
-}
-
-/*
- * The following function loads the model from json file.
- */
-void Predictor::LoadModel(const std::string& model_json_file) {
-  if (!FileExists(model_json_file)) {
-    LG << "Model file " << model_json_file << " does not exist";
-    throw std::runtime_error("Model file does not exist");
-  }
-  LG << "Loading the model from " << model_json_file << std::endl;
-  net = Symbol::Load(model_json_file);
-}
-
-
-/*
- * The following function loads the model parameters.
- */
-void Predictor::LoadParameters(const std::string& model_parameters_file) {
-  if (!FileExists(model_parameters_file)) {
-    LG << "Parameter file " << model_parameters_file << " does not exist";
-    throw std::runtime_error("Model parameters does not exist");
-  }
-  LG << "Loading the model parameters from " << model_parameters_file << std::endl;
-  std::map<std::string, NDArray> parameters;
-  NDArray::Load(model_parameters_file, 0, &parameters);
-  for (const auto &k : parameters) {
-    if (k.first.substr(0, 4) == "aux:") {
-      auto name = k.first.substr(4, k.first.size() - 4);
-      aux_map[name] = k.second.Copy(global_ctx);
-    }
-    if (k.first.substr(0, 4) == "arg:") {
-      auto name = k.first.substr(4, k.first.size() - 4);
-      args_map[name] = k.second.Copy(global_ctx);
-    }
-  }
-  /*WaitAll is need when we copy data between GPU and the main memory*/
-  NDArray::WaitAll();
-}
-
-
-/*
- * The following function loads the synset file.
- * This information will be used later to report the label of input image.
- */
-void Predictor::LoadSynset(const std::string& synset_file) {
-  if (!FileExists(synset_file)) {
-    LG << "Synset file " << synset_file << " does not exist";
-    throw std::runtime_error("Synset file does not exist");
-  }
-  LG << "Loading the synset file.";
-  std::ifstream fi(synset_file.c_str());
-  if (!fi.is_open()) {
-    std::cerr << "Error opening synset file " << synset_file << std::endl;
-    throw std::runtime_error("Error in opening the synset file.");
-  }
-  std::string synset, lemma;
-  while (fi >> synset) {
-    getline(fi, lemma);
-    output_labels.push_back(lemma);
-  }
-  fi.close();
-}
-
-
-/*
- * The following function loads the mean data from mean image file.
- * This data will be used for normalizing the image before running the forward
- * pass.
- * The output data has the same shape as that of the input image data.
- */
-void Predictor::LoadMeanImageData() {
-  LG << "Load the mean image data that will be used to normalize "
-     << "the image before running forward pass.";
-  mean_image_data = NDArray(input_shape, global_ctx, false);
-  mean_image_data.SyncCopyFromCPU(
-        NDArray::LoadToMap(mean_image_file)["mean_img"].GetData(),
-        input_shape.Size());
-}
-
-
-/*
- * The following function loads the default mean values for
- * R, G and B channels into NDArray that has the same shape as that of
- * input image.
- */
-void Predictor::LoadDefaultMeanImageData() {
-  LG << "Loading the default mean image data";
-  std::vector<float> array;
-  /*resize pictures to (224, 224) according to the pretrained model*/
-  int height = input_shape[2];
-  int width = input_shape[3];
-  int channels = input_shape[1];
-  std::vector<mx_float> default_means;
-  default_means.push_back(DEFAULT_MEAN_R);
-  default_means.push_back(DEFAULT_MEAN_G);
-  default_means.push_back(DEFAULT_MEAN_B);
-  for (int c = 0; c < channels; ++c) {
-    for (int i = 0; i < height; ++i) {
-      for (int j = 0; j < width; ++j) {
-        array.push_back(default_means[c]);
-      }
-    }
-  }
-  mean_image_data = NDArray(input_shape, global_ctx, false);
-  mean_image_data.SyncCopyFromCPU(array.data(), input_shape.Size());
-}
-
-
-/*
- * The following function loads the input image into NDArray.
- */
-NDArray Predictor::LoadInputImage(const std::string& image_file) {
-  if (!FileExists(image_file)) {
-    LG << "Image file " << image_file << " does not exist";
-    throw std::runtime_error("Image file does not exist");
-  }
-  LG << "Loading the image " << image_file << std::endl;
-  std::vector<float> array;
-  cv::Mat mat = cv::imread(image_file);
-  /*resize pictures to (224, 224) according to the pretrained model*/
-  int height = input_shape[2];
-  int width = input_shape[3];
-  int channels = input_shape[1];
-  cv::resize(mat, mat, cv::Size(height, width));
-  for (int c = 0; c < channels; ++c) {
-    for (int i = 0; i < height; ++i) {
-      for (int j = 0; j < width; ++j) {
-        array.push_back(static_cast<float>(mat.data[(i * height + j) * 3 + c]));
-      }
-    }
-  }
-  NDArray image_data = NDArray(input_shape, global_ctx, false);
-  image_data.SyncCopyFromCPU(array.data(), input_shape.Size());
-  return image_data;
-}
-
-
-/*
- * The following function runs the forward pass on the model.
- * The executor is created in the constructor.
- *
- */
-void Predictor::PredictImage(const std::string& image_file) {
-  // Load the input image
-  NDArray image_data = LoadInputImage(image_file);
-
-  // Normalize the image
-  image_data.Slice(0, 1) -= mean_image_data;
-
-  LG << "Running the forward pass on model to predict the image";
-  /*
-   * The executor->arg_arrays represent the arguments to the model.
-   *
-   * Copying the image_data that contains the NDArray of input image
-   * to the arg map of the executor. The input is stored with the key "data" in the map.
-   *
-   */
-  image_data.CopyTo(&(executor->arg_dict()["data"]));
-
-  // Run the forward pass.
-  executor->Forward(false);
-
-  // The output is available in executor->outputs.
-  auto array = executor->outputs[0].Copy(Context::cpu());
-  /*
-   * Find out the maximum accuracy and the index associated with that accuracy.
-   * This is done by using the argmax operator on NDArray.
-   */
-  auto predicted = array.ArgmaxChannel();
-  /*
-   * Wait until all the previous write operations on the 'predicted'
-   * NDArray to be complete before we read it.
-   * This method guarantees that all previous write operations that pushed into the backend engine
-   * for execution are actually finished.
-   */
-  predicted.WaitToRead();
-
-  int best_idx = predicted.At(0);
-  float best_accuracy = array.At(0, best_idx);
-
-  if (output_labels.empty()) {
-    LG << "The model predicts the highest accuracy of " << best_accuracy << " at index "
-       << best_idx;
-  } else {
-    LG << "The model predicts the input image to be a [" << output_labels[best_idx]
-       << " ] with Accuracy = " << best_accuracy << std::endl;
-  }
-}
-
-
-Predictor::~Predictor() {
-  delete executor;
-  MXNotifyShutdown();
-}
-
-
-/*
- * Convert the input string of number of hidden units into the vector of integers.
- */
-std::vector<index_t> getShapeDimensions(const std::string& hidden_units_string) {
-    std::vector<index_t> dimensions;
-    char *p_next;
-    int num_unit = strtol(hidden_units_string.c_str(), &p_next, 10);
-    dimensions.push_back(num_unit);
-    while (*p_next) {
-        num_unit = strtol(p_next, &p_next, 10);
-        dimensions.push_back(num_unit);
-    }
-    return dimensions;
-}
-
-void printUsage() {
-    std::cout << "Usage:" << std::endl;
-    std::cout << "inception_inference --symbol <model symbol file in json format>  " << std::endl
-              << "--params <model params file> " << std::endl
-              << "--image <path to the image used for prediction> " << std::endl
-              << "--synset <file containing labels for prediction> " << std::endl
-              << "[--input_shape <dimensions of input image e.g \"3 224 224\">] " << std::endl
-              << "[--mean <file containing mean image for normalizing the input image>] "
-              << std::endl
-              << "[--gpu  <Specify this option if workflow needs to be run in gpu context>]"
-              << std::endl;
-}
-
-int main(int argc, char** argv) {
-  std::string model_file_json;
-  std::string model_file_params;
-  std::string synset_file = "";
-  std::string mean_image = "";
-  std::string input_image = "";
-  bool gpu_context_type = false;
-
-  std::string input_shape = "3 224 224";
-    int index = 1;
-    while (index < argc) {
-        if (strcmp("--symbol", argv[index]) == 0) {
-            index++;
-            model_file_json = (index < argc ? argv[index]:"");
-        } else if (strcmp("--params", argv[index]) == 0) {
-            index++;
-            model_file_params = (index < argc ? argv[index]:"");
-        } else if (strcmp("--synset", argv[index]) == 0) {
-            index++;
-            synset_file = (index < argc ? argv[index]:"");
-        } else if (strcmp("--mean", argv[index]) == 0) {
-            index++;
-            mean_image = (index < argc ? argv[index]:"");
-        } else if (strcmp("--image", argv[index]) == 0) {
-            index++;
-            input_image = (index < argc ? argv[index]:"");
-        } else if (strcmp("--input_shape", argv[index]) == 0) {
-            index++;
-            input_shape = (index < argc ? argv[index]:input_shape);
-        } else if (strcmp("--gpu", argv[index]) == 0) {
-            gpu_context_type = true;
-        } else if (strcmp("--help", argv[index]) == 0) {
-            printUsage();
-            return 0;
-        }
-        index++;
-    }
-
-  if (model_file_json.empty() || model_file_params.empty() || synset_file.empty()) {
-    LG << "ERROR: Model details such as symbol, param and/or synset files are not specified";
-    printUsage();
-    return 1;
-  }
-
-  if (input_image.empty()) {
-    LG << "ERROR: Path to the input image is not specified.";
-    printUsage();
-    return 1;
-  }
-
-  std::vector<index_t> input_dimensions = getShapeDimensions(input_shape);
-
-  /*
-   * Since we are running inference for 1 image, add 1 to the input_dimensions so that
-   * the shape of input data for the model will be
-   * {no. of images, channels, height, width}
-   */
-  input_dimensions.insert(input_dimensions.begin(), 1);
-
-  Shape input_data_shape(input_dimensions);
-
-  try {
-    // Initialize the predictor object
-    Predictor predict(model_file_json, model_file_params, input_data_shape, gpu_context_type,
-                      synset_file, mean_image);
-
-    // Run the forward pass to predict the image.
-    predict.PredictImage(input_image);
-  } catch (std::runtime_error &error) {
-    LG << "Execution failed with ERROR: " << error.what();
-  } catch (...) {
-    /*
-     * If underlying MXNet code has thrown an exception the error message is
-     * accessible through MXGetLastError() function.
-     */
-    LG << "Execution failed with following MXNet error";
-    LG << MXGetLastError();
-  }
-  return 0;
-}
diff --git a/cpp-package/example/inference/unit_test_imagenet_inference.sh b/cpp-package/example/inference/unit_test_imagenet_inference.sh
new file mode 100755
index 000000000000..c645388cd419
--- /dev/null
+++ b/cpp-package/example/inference/unit_test_imagenet_inference.sh
@@ -0,0 +1,63 @@
+#!/usr/bin/env bash
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+set -ex
+# create ./model directory if not existed
+if [ ! -d model ]; then
+    mkdir -p model
+fi
+# create ./data directory if not existed
+if [ ! -d data ]; then
+    mkdir -p data
+fi
+# Downloading the data and model if not existed
+model_file=./model/Inception-BN-symbol.json
+params_file=./model/Inception-BN-0126.params
+if [ ! -f ${model_file} ] || [ ! -f ${params_file} ]; then
+    wget -nc http://data.mxnet.io/models/imagenet/inception-bn.tar.gz
+    tar -xvzf inception-bn.tar.gz -C model
+fi
+cd model
+wget -nc https://raw.githubusercontent.com/dmlc/gluon-cv/master/gluoncv/model_zoo/quantized/resnet50_v1_int8-symbol.json
+cd ../data
+wget -nc http://data.mxnet.io/data/val_256_q90.rec
+cd ..
+
+# Running inference on imagenet.
+if [ "$(uname)" == "Darwin" ]; then
+    echo ">>> INFO: FP32 real data"
+    DYLD_LIBRARY_PATH=${DYLD_LIBRARY_PATH}:../../../lib ./imagenet_inference --symbol_file "./model/Inception-BN-symbol.json" --params_file "./model/Inception-BN-0126.params" --dataset "./data/val_256_q90.rec" --rgb_mean "123.68 116.779 103.939" --batch_size 1 --num_skipped_batches 50 --num_inference_batches 500
+
+    echo ">>> INFO: FP32 dummy data"
+    DYLD_LIBRARY_PATH=${DYLD_LIBRARY_PATH}:../../../lib ./imagenet_inference --symbol_file "./model/Inception-BN-symbol.json" --batch_size 1 --num_inference_batches 500 --benchmark
+else
+    echo ">>> INFO: FP32 real data"
+    LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:../../../lib ./imagenet_inference --symbol_file "./model/Inception-BN-symbol.json" --params_file "./model/Inception-BN-0126.params" --dataset "./data/val_256_q90.rec" --rgb_mean "123.68 116.779 103.939" --batch_size 1 --num_skipped_batches 50 --num_inference_batches 500
+
+    echo ">>> INFO: FP32 dummy data"
+    LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:../../../lib ./imagenet_inference --symbol_file "./model/Inception-BN-symbol.json" --batch_size 1 --num_inference_batches 500 --benchmark
+
+    lib_name=$(ls -a ../../../lib | grep -oE 'mkldnn' | tail -1)
+    if [[ -n ${lib_name} ]] && [[ 'mkldnn' =~ ${lib_name} ]]; then
+        echo ">>> INFO: INT8 dummy data"
+        LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:../../../lib ./imagenet_inference --symbol_file "./model/resnet50_v1_int8-symbol.json" --batch_size 1 --num_inference_batches 500 --benchmark
+    else
+        echo "Skipped INT8 test because mkldnn was not found which is required for running inference with quantized models."
+    fi
+fi
diff --git a/cpp-package/example/inference/unit_test_inception_inference.sh b/cpp-package/example/inference/unit_test_inception_inference.sh
deleted file mode 100755
index c3c4630f6e4a..000000000000
--- a/cpp-package/example/inference/unit_test_inception_inference.sh
+++ /dev/null
@@ -1,42 +0,0 @@
-#!/usr/bin/env bash
-
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-# Downloading the data and model
-mkdir -p model
-wget -nc http://data.mxnet.io/models/imagenet/inception-bn.tar.gz
-wget -nc -O model/dog.jpg https://github.com/dmlc/web-data/blob/master/mxnet/doc/tutorials/python/predict_image/dog.jpg?raw=true
-wget -nc -O model/mean_224.nd https://github.com/dmlc/web-data/raw/master/mxnet/example/feature_extract/mean_224.nd
-tar -xvzf inception-bn.tar.gz -C model
-
-
-# Running the example with dog image.
-if [ "$(uname)" == "Darwin" ]; then
-    DYLD_LIBRARY_PATH=${DYLD_LIBRARY_PATH}:../../../lib ./inception_inference --symbol "./model/Inception-BN-symbol.json" --params "./model/Inception-BN-0126.params" --synset "./model/synset.txt" --mean "./model/mean_224.nd" --image "./model/dog.jpg" 2&> inception_inference.log
-else
-    LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:../../../lib ./inception_inference --symbol "./model/Inception-BN-symbol.json" --params "./model/Inception-BN-0126.params" --synset "./model/synset.txt" --mean "./model/mean_224.nd" --image "./model/dog.jpg" 2&> inception_inference.log
-fi
-result=`grep -c "pug-dog" inception_inference.log`
-if [ $result == 1 ];
-then
-    echo "PASS: inception_inference correctly identified the image."
-    exit 0
-else
-    echo "FAIL: inception_inference FAILED to identify the image."
-    exit 1
-fi
diff --git a/cpp-package/example/mlp_csv.cpp b/cpp-package/example/mlp_csv.cpp
index 0d9103783a79..09842e3b402b 100644
--- a/cpp-package/example/mlp_csv.cpp
+++ b/cpp-package/example/mlp_csv.cpp
@@ -25,6 +25,7 @@
  * CSV format.
  */
 #include <chrono>
+#include <string>
 #include "utils.h"
 #include "mxnet-cpp/MxNetCpp.h"
 
diff --git a/cpp-package/include/mxnet-cpp/initializer.h b/cpp-package/include/mxnet-cpp/initializer.h
index 021808b38e34..34725b9dfa81 100644
--- a/cpp-package/include/mxnet-cpp/initializer.h
+++ b/cpp-package/include/mxnet-cpp/initializer.h
@@ -30,6 +30,7 @@
 #include <cmath>
 #include <string>
 #include <vector>
+#include <random>
 #include "mxnet-cpp/ndarray.h"
 
 namespace mxnet {
@@ -67,6 +68,14 @@ class Initializer {
       InitZero(arr);
     } else if (StringEndWith(name, "moving_avg")) {
       InitZero(arr);
+    } else if (StringEndWith(name, "min")) {
+      InitZero(arr);
+    } else if (StringEndWith(name, "max")) {
+      InitOne(arr);
+    } else if (StringEndWith(name, "weight_quantize")) {
+      InitQuantizedWeight(arr);
+    } else if (StringEndWith(name, "bias_quantize")) {
+      InitQuantizedBias(arr);
     } else {
       InitDefault(arr);
     }
@@ -91,6 +100,14 @@ class Initializer {
   virtual void InitGamma(NDArray* arr) { (*arr) = 1.0f; }
   virtual void InitBeta(NDArray* arr) { (*arr) = 0.0f; }
   virtual void InitWeight(NDArray* arr) {}
+  virtual void InitQuantizedWeight(NDArray* arr) {
+    std::default_random_engine generator;
+    std::uniform_int_distribution<int32_t> _val(-127, 127);
+    (*arr) = _val(generator);
+  }
+  virtual void InitQuantizedBias(NDArray* arr) {
+    (*arr) = 0;
+  }
   virtual void InitDefault(NDArray* arr) {}
 };
 
@@ -122,6 +139,14 @@ class Uniform : public Initializer {
   Uniform(float begin, float end)
     : begin(begin), end(end) {}
   void operator()(const std::string &name, NDArray *arr) override {
+    if (StringEndWith(name, "weight_quantize")) {
+      InitQuantizedWeight(arr);
+      return;
+    }
+    if (StringEndWith(name, "bias_quantize")) {
+      InitQuantizedBias(arr);
+      return;
+    }
     NDArray::SampleUniform(begin, end, arr);
   }
  protected:
@@ -133,6 +158,14 @@ class Normal : public Initializer {
   Normal(float mu, float sigma)
     : mu(mu), sigma(sigma) {}
   void operator()(const std::string &name, NDArray *arr) override {
+    if (StringEndWith(name, "weight_quantize")) {
+      InitQuantizedWeight(arr);
+      return;
+    }
+    if (StringEndWith(name, "bias_quantize")) {
+      InitQuantizedBias(arr);
+      return;
+    }
     NDArray::SampleGaussian(mu, sigma, arr);
   }
  protected:
@@ -143,6 +176,14 @@ class Bilinear : public Initializer {
  public:
   Bilinear() {}
   void operator()(const std::string &name, NDArray *arr) override {
+    if (StringEndWith(name, "weight_quantize")) {
+      InitQuantizedWeight(arr);
+      return;
+    }
+    if (StringEndWith(name, "bias_quantize")) {
+      InitQuantizedBias(arr);
+      return;
+    }
     InitBilinear(arr);
   }
 };
@@ -164,6 +205,15 @@ class Xavier : public Initializer {
       : rand_type(rand_type), factor_type(factor_type), magnitude(magnitude) {}
 
   void operator()(const std::string &name, NDArray* arr) override {
+    if (StringEndWith(name, "weight_quantize")) {
+      InitQuantizedWeight(arr);
+      return;
+    }
+    if (StringEndWith(name, "bias_quantize")) {
+      InitQuantizedBias(arr);
+      return;
+    }
+
     Shape shape(arr->GetShape());
     float hw_scale = 1.0f;
     if (shape.ndim() > 2) {
diff --git a/cpp-package/include/mxnet-cpp/io.h b/cpp-package/include/mxnet-cpp/io.h
index 7099d7d46fee..7d2d620bd886 100644
--- a/cpp-package/include/mxnet-cpp/io.h
+++ b/cpp-package/include/mxnet-cpp/io.h
@@ -60,6 +60,8 @@ class DataIter {
     return DataBatch{GetData(), GetLabel(), GetPadNum(), GetIndex()};
   }
   void Reset() { BeforeFirst(); }
+
+  virtual ~DataIter() = default;
 };
 
 class MXDataIterMap {
diff --git a/cpp-package/tests/ci_test.sh b/cpp-package/tests/ci_test.sh
index ef7fceacfd6e..e90493442246 100755
--- a/cpp-package/tests/ci_test.sh
+++ b/cpp-package/tests/ci_test.sh
@@ -63,8 +63,8 @@ cp ../../build/cpp-package/example/test_ndarray_copy .
 sh unittests/unit_test_mlp_csv.sh
 
 cd inference
-cp ../../../build/cpp-package/example/inception_inference .
-./unit_test_inception_inference.sh
+cp ../../../build/cpp-package/example/imagenet_inference .
+./unit_test_imagenet_inference.sh
 
 cp ../../../build/cpp-package/example/sentiment_analysis_rnn .
 ./unit_test_sentiment_analysis_rnn.sh
diff --git a/example/quantization/README.md b/example/quantization/README.md
index 93a14cf473ad..40f1371c33cc 100644
--- a/example/quantization/README.md
+++ b/example/quantization/README.md
@@ -27,15 +27,15 @@ The following models have been tested on Linux systems.
 
 | Model | Source | Dataset | FP32 Accuracy (top-1/top-5)| INT8 Accuracy (top-1/top-5)|
 |:---|:---|---|:---:|:---:|
-| [ResNet18-V1](#3)  | [Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)  | [Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)  |70.07%/89.30%|69.85%/89.23%|
-| [ResNet50-V1](#3)  | [Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)  | [Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)  | 75.87%/92.72%  |  75.71%/92.65% |
-| [ResNet101-V1](#3)  | [Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)  | [Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)  | 77.3%/93.58%  | 77.09%/93.41%  |
-|[Squeezenet 1.0](#4)|[Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|57.01%/79.71%|56.62%/79.55%|
-|[MobileNet 1.0](#5)|[Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|69.76%/89.32%|69.61%/89.09%|
-|[MobileNetV2 1.0](#6)|[Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|70.14%/89.60%|69.53%/89.24%|
-|[Inception V3](#7)|[Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|76.49%/93.10% |76.38%/93% |
-|[ResNet152-V2](#8)|[MXNet ModelZoo](http://data.mxnet.io/models/imagenet/resnet/152-layers/)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|76.76%/93.03%|76.48%/92.96%|
-|[Inception-BN](#9)|[MXNet ModelZoo](http://data.mxnet.io/models/imagenet/inception-bn/)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|72.09%/90.60%|72.00%/90.53%|
+| [ResNet18-V1](#3)  | [Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)  | [Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)  |70.15%/89.38%|69.92%/89.26%|
+| [ResNet50-V1](#3)  | [Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)  | [Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)  | 76.34%/93.13%  |  75.91%/92.95% |
+| [ResNet101-V1](#3)  | [Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)  | [Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)  | 77.33%/93.59%  | 77.05%/93.43%  |
+|[Squeezenet 1.0](#4)|[Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|56.98%/79.20%|52.98%/77.21%|
+|[MobileNet 1.0](#5)|[Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|72.23%/90.64%|72.03%/90.42%|
+|[MobileNetV2 1.0](#6)|[Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|70.27%/89.62%|69.70%/89.26%|
+|[Inception V3](#7)|[Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|77.76%/93.83% |77.87%/93.78% |
+|[ResNet152-V2](#8)|[MXNet ModelZoo](http://data.mxnet.io/models/imagenet/resnet/152-layers/)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|76.65%/93.07%|76.36%/92.89%|
+|[Inception-BN](#9)|[MXNet ModelZoo](http://data.mxnet.io/models/imagenet/inception-bn/)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|72.28%/90.63%|72.20%/90.56%|
 | [SSD-VGG16](#10) | [example/ssd](https://github.com/apache/incubator-mxnet/tree/master/example/ssd)  | VOC2007/2012  | 0.8366 mAP  | 0.8364 mAP  |
 | [SSD-VGG16](#10) | [example/ssd](https://github.com/apache/incubator-mxnet/tree/master/example/ssd)  | COCO2014  | 0.2552 mAP  | 0.253 mAP  |
 

From ba30644612357930fd4543f01800d89be7963f8e Mon Sep 17 00:00:00 2001
From: Pedro Larroy <pedro.larroy.lists@gmail.com>
Date: Wed, 26 Jun 2019 17:13:53 -0700
Subject: [PATCH 015/813] [DOC] Clarify that global pooling is going to reset
 padding (#15269)

This behaviour changed from older MXNet versions in which global pooling
would consider padding. This clarifies the user documentation.

See also #14421
---
 src/operator/nn/pooling.cc | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/operator/nn/pooling.cc b/src/operator/nn/pooling.cc
index 870557756128..41a486e141d1 100644
--- a/src/operator/nn/pooling.cc
+++ b/src/operator/nn/pooling.cc
@@ -389,8 +389,8 @@ The definition of *f* depends on ``pooling_convention``, which has two options:
 
     f(x, k, p, s) = ceil((x+2*p-k)/s)+1
 
-But ``global_pool`` is set to be true, then do a global pooling, namely reset
-``kernel=(height, width)``.
+When ``global_pool`` is set to be true, then global pooling is performed. It will reset
+``kernel=(height, width)`` and set the appropiate padding to 0.
 
 Three pooling options are supported by ``pool_type``:
 

From 582489cebc16a8af21738281dcaee5eee54e478d Mon Sep 17 00:00:00 2001
From: Zhennan Qin <zhennan.qin@intel.com>
Date: Thu, 27 Jun 2019 11:08:26 +0800
Subject: [PATCH 016/813] Fix Cached_op with static_shape=true (#15298)

* Fix

* run ci
---
 src/imperative/cached_op.cc |  7 ++++--
 src/nnvm/legacy_op_util.cc  | 47 +++++++++++++++----------------------
 2 files changed, 24 insertions(+), 30 deletions(-)

diff --git a/src/imperative/cached_op.cc b/src/imperative/cached_op.cc
index d7e1543ec781..efe38019cfda 100644
--- a/src/imperative/cached_op.cc
+++ b/src/imperative/cached_op.cc
@@ -81,6 +81,7 @@ struct CachedOp::CachedOpState {
 
   std::vector<NDArray> buff;
   std::vector<NDArray*> arrays;
+  std::vector<NDArray*> arrays_with_in_out;
   std::vector<OpReqType> array_reqs;
 
   std::vector<OpStatePtr> op_states;
@@ -762,7 +763,8 @@ OpStatePtr CachedOp::StaticForward(
   // We are going to add input and output arrays to the array list.
   // The input and output arrays should only be valid for this run,
   // so we shouldn't modify the state's array list.
-  auto arrays = state.arrays;
+  state.arrays_with_in_out = state.arrays;
+  auto& arrays = state.arrays_with_in_out;
   if (config_.static_shape) {
     for (auto i : config_.param_indices) {
       auto nid = idx.input_nodes()[i];
@@ -1063,7 +1065,8 @@ void CachedOp::StaticBackward(
   // We are going to add input and output arrays to the array list.
   // The input and output arrays should only be valid for this run,
   // so we shouldn't modify the state's array list.
-  auto arrays = state.arrays;
+  state.arrays_with_in_out = state.arrays;
+  auto& arrays = state.arrays_with_in_out;
   for (size_t i = 0; i < state.info.bwd_input_eid.size(); ++i) {
     auto eid = state.info.bwd_input_eid[i];
     if (eid == kEidNotExist) {
diff --git a/src/nnvm/legacy_op_util.cc b/src/nnvm/legacy_op_util.cc
index 698666f94d90..3e03b6bd2d16 100644
--- a/src/nnvm/legacy_op_util.cc
+++ b/src/nnvm/legacy_op_util.cc
@@ -79,7 +79,6 @@ class OperatorState {
  public:
   OperatorState(Operator *opr, const OperatorProperty *prop) {
     opr_ = opr;
-    fwd_init_ = bwd_init_ = false;
 
     in_data_fwd_.resize(prop->ListArguments().size());
     in_data_bwd_.resize(prop->ListArguments().size());
@@ -110,19 +109,16 @@ class OperatorState {
                const std::vector<TBlob>& inputs,
                const std::vector<OpReqType>& req,
                const std::vector<TBlob>& outputs) {
-    if (!fwd_init_) {
-      CHECK_EQ(inputs.size(), in_data_fwd_.size() + aux_data_.size());
-      CHECK_EQ(outputs.size(), out_data_.size());
-      // in_data_bwd_ has the same tblobs as the ones in in_data_fwd_, except that the ones
-      // referred by arg_data_ptr_ will be overriden
-      for (size_t i = 0; i < in_data_fwd_.size(); ++i) in_data_fwd_[i] = inputs[i];
-      for (size_t i = 0; i < in_data_fwd_.size(); ++i) in_data_bwd_[i] = inputs[i];
-      for (size_t i = 0; i < aux_data_.size(); ++i) {
-        aux_data_[i] = inputs[i + in_data_fwd_.size()];
-      }
-      for (size_t i = 0; i < out_data_.size(); ++i) out_data_[i] = outputs[i];
-      fwd_init_ = true;
+    CHECK_EQ(inputs.size(), in_data_fwd_.size() + aux_data_.size());
+    CHECK_EQ(outputs.size(), out_data_.size());
+    // in_data_bwd_ has the same tblobs as the ones in in_data_fwd_, except that the ones
+    // referred by arg_data_ptr_ will be overriden
+    for (size_t i = 0; i < in_data_fwd_.size(); ++i) in_data_fwd_[i] = inputs[i];
+    for (size_t i = 0; i < in_data_fwd_.size(); ++i) in_data_bwd_[i] = inputs[i];
+    for (size_t i = 0; i < aux_data_.size(); ++i) {
+      aux_data_[i] = inputs[i + in_data_fwd_.size()];
     }
+    for (size_t i = 0; i < out_data_.size(); ++i) out_data_[i] = outputs[i];
     opr_->Forward(ctx, in_data_fwd_, req, out_data_, aux_data_);
   }
 
@@ -130,27 +126,22 @@ class OperatorState {
                 const std::vector<TBlob>& inputs,
                 const std::vector<OpReqType>& req,
                 const std::vector<TBlob>& outputs) {
-    if (!bwd_init_) {
-      CHECK(fwd_init_);
-      CHECK_EQ(arg_data_ptr_.size() + aux_data_.size(), inputs.size());
-      // override tblobs pointed by arg_data_ptr_ since they might not contain
-      // initialized data during forward pass.
-      for (size_t i = 0; i < arg_data_ptr_.size(); ++i) {
-        *arg_data_ptr_[i] = inputs[i];
-      }
-      for (size_t i = 0; i < aux_data_.size(); ++i) {
-        aux_data_[i] = inputs[inputs.size() - aux_data_.size() + i];
-      }
-      CHECK_EQ(outputs.size(), in_grad_.size());
-      for (size_t i = 0; i < outputs.size(); ++i) in_grad_[i] = outputs[i];
-      bwd_init_ = true;
+    CHECK_EQ(arg_data_ptr_.size() + aux_data_.size(), inputs.size());
+    // override tblobs pointed by arg_data_ptr_ since they might not contain
+    // initialized data during forward pass.
+    for (size_t i = 0; i < arg_data_ptr_.size(); ++i) {
+      *arg_data_ptr_[i] = inputs[i];
+    }
+    for (size_t i = 0; i < aux_data_.size(); ++i) {
+      aux_data_[i] = inputs[inputs.size() - aux_data_.size() + i];
     }
+    CHECK_EQ(outputs.size(), in_grad_.size());
+    for (size_t i = 0; i < outputs.size(); ++i) in_grad_[i] = outputs[i];
     opr_->Backward(ctx, out_grad_, in_data_bwd_, out_data_, req, in_grad_, aux_data_);
   }
 
  private:
   Operator *opr_;
-  bool fwd_init_, bwd_init_;
   // input data blobs for forward and backward
   // in_data_fwd_ and in_data_bwd_ will hold different tblobs when StorageFallbackOpExecutor
   // performs storage fallback on a non-default input NDArray. The one in in_data_fwd_ is

From cd19367dcbd7fb022c0ffc13face6c45c7e2aacb Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Fri, 28 Jun 2019 11:23:46 +0800
Subject: [PATCH 017/813] add 'asnumpy' dtype option to check_symbolic_backward
 (#15186)

---
 python/mxnet/test_utils.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/python/mxnet/test_utils.py b/python/mxnet/test_utils.py
index bd102412c6e2..d247c0fcde95 100644
--- a/python/mxnet/test_utils.py
+++ b/python/mxnet/test_utils.py
@@ -1133,7 +1133,7 @@ def check_symbolic_backward(sym, location, out_grads, expected, rtol=1e-5, atol=
     >>> grad_expected = ograd.copy().asnumpy()
     >>> check_symbolic_backward(sym_add, [mat1, mat2], [ograd], [grad_expected, grad_expected])
     """
-    assert dtype in (np.float16, np.float32, np.float64)
+    assert dtype == 'asnumpy' or dtype in (np.float16, np.float32, np.float64)
     if ctx is None:
         ctx = default_context()
 
@@ -1146,7 +1146,7 @@ def check_symbolic_backward(sym, location, out_grads, expected, rtol=1e-5, atol=
     args_grad_npy = {k:np.random.normal(size=v.shape) for k, v in expected.items()}
     args_grad_data = {}
     for k, v in args_grad_npy.items():
-        nd = mx.nd.array(v, ctx=ctx, dtype=dtype)
+        nd = mx.nd.array(v, ctx=ctx, dtype=expected[k].dtype if dtype == "asnumpy" else dtype)
         if grad_stypes is not None and k in grad_stypes:
             stype = grad_stypes[k]
             if stype is not None and stype != 'default':
@@ -1170,7 +1170,7 @@ def check_symbolic_backward(sym, location, out_grads, expected, rtol=1e-5, atol=
         outg = list()
         for arr in out_grads:
             if isinstance(arr, np.ndarray):
-                outg.append(mx.nd.array(arr, ctx=ctx, dtype=dtype))
+                outg.append(mx.nd.array(arr, ctx=ctx, dtype=arr.dtype if dtype == "asnumpy" else dtype))
             else:
                 outg.append(arr)
         out_grads = outg
@@ -1178,7 +1178,7 @@ def check_symbolic_backward(sym, location, out_grads, expected, rtol=1e-5, atol=
         outg = dict()
         for k, v in out_grads.items():
             if isinstance(v, np.ndarray):
-                outg[k] = mx.nd.array(v, ctx=ctx, dtype=dtype)
+                outg[k] = mx.nd.array(v, ctx=ctx, dtype=v.dtype if dtype == "asnumpy" else dtype)
             else:
                 outg[k] = v
         out_grads = outg

From 92fce90671b6665fed9e4b92e76a9cefda288fc5 Mon Sep 17 00:00:00 2001
From: Zhaoqi Zhu <zhaoqizh@usc.edu>
Date: Fri, 28 Jun 2019 08:39:06 -0700
Subject: [PATCH 018/813] Custom Operator Profiling Enhancement (#15210)

* working version

* style fix

* several fixes

* resolve issues in the comments

* revert to using thread-safe Get() for singleton class CustomOpProfiler

* indentation

* Now supports Naive Engine

* style fix

* tidiness

* tests added

* style fix

* add a new test case which has multiple custom ops

* testcases fix

* fix

* fix style

* minor naive engine fix

* simplify some branching logic

* better desing style

* fix

* fix

* fix

* fix

* fix

* fix

* add isprofiling check to onCustomStart

* fix

* rename dummy_wait

* fix conflict

* improve test

* fix

* fix test cases

* fix test cases

* fix testcases

* revert back to reduce overhead

* fix style

* Re-Trigger build

* rename var

* Re-Trigger build
---
 src/engine/naive_engine.cc             |   9 +-
 src/engine/threaded_engine.cc          |  10 +-
 src/engine/threaded_engine.h           |   1 +
 src/operator/custom/custom-inl.h       |  23 +++-
 src/operator/custom/custom.cc          |   4 +-
 src/profiler/custom_op_profiler.h      | 125 +++++++++++++++++++
 src/profiler/profiler.h                |  53 +++++++--
 tests/python/unittest/test_profiler.py | 159 +++++++++++++++++++++++++
 8 files changed, 365 insertions(+), 19 deletions(-)
 create mode 100644 src/profiler/custom_op_profiler.h

diff --git a/src/engine/naive_engine.cc b/src/engine/naive_engine.cc
index c321e48d562d..9f44b55cfcaf 100644
--- a/src/engine/naive_engine.cc
+++ b/src/engine/naive_engine.cc
@@ -29,6 +29,7 @@
 #include "../profiler/profiler.h"
 #include "./openmp.h"
 #include "../common/object_pool.h"
+#include "../profiler/custom_op_profiler.h"
 
 namespace mxnet {
 namespace engine {
@@ -160,15 +161,19 @@ class NaiveEngine final : public Engine {
     profiler::Profiler *profiler = profiler::Profiler::Get();
     NaiveOpr *opr = nullptr;
     const bool profiling = opr_name && profiler->IsProfiling(profiler::Profiler::kImperative);
+    // GenerateDisplayName() will return a pointer to the correct name of the operator
+    const char* display_name = profiling ?
+                               profiler::CustomOpProfiler::Get()->GenerateDisplayName(opr_name) :
+                               opr_name;
     if (profiling) {
       opr = NewOperator(exec_fun, const_vars, mutable_vars,
-                        prop, opr_name)->Cast<NaiveOpr>();
+                        prop, display_name)->Cast<NaiveOpr>();
       opr->profiling = profiling;
       std::unique_ptr<profiler::ProfileOperator::Attributes> attrs;
       if (profiler->AggregateEnabled()) {
         attrs.reset(new profiler::ProfileOperator::Attributes());
       }
-      opr->opr_profile.reset(new profiler::ProfileOperator(opr->opr_name, attrs.release()));
+      opr->opr_profile.reset(new profiler::ProfileOperator(display_name, attrs.release()));
       opr->opr_profile->start(exec_ctx.dev_type, exec_ctx.dev_id);
     }
     if (exec_ctx.dev_mask() == gpu::kDevMask) {
diff --git a/src/engine/threaded_engine.cc b/src/engine/threaded_engine.cc
index 38311908bdcd..4375149c729c 100644
--- a/src/engine/threaded_engine.cc
+++ b/src/engine/threaded_engine.cc
@@ -287,8 +287,11 @@ void ThreadedEngine::DeleteOperator(OprHandle op) {
 
 void ThreadedEngine::Push(OprHandle op, Context exec_ctx, int priority, bool profiling) {
   BulkFlush();
-
   ThreadedOpr* threaded_opr = ThreadedOpr::CastFromBase(op);
+  if (profiling) {
+    threaded_opr->opr_name =
+        profiler::CustomOpProfiler::Get()->GenerateDisplayName(threaded_opr->opr_name);
+  }
   OprBlock* opr_block = OprBlock::New();
   opr_block->opr = threaded_opr;
 
@@ -333,9 +336,10 @@ void ThreadedEngine::PushAsync(AsyncFn fn, Context exec_ctx,
         << device_count_;
   }
 #endif
-  ThreadedOpr *opr = NewOperator(std::move(fn), const_vars, mutable_vars, prop, opr_name, wait);
-  opr->temporary = true;
   const bool profiling = profiler_->IsProfiling(profiler::Profiler::kImperative);
+  ThreadedOpr *opr = NewOperator(std::move(fn), const_vars, mutable_vars,
+                                 prop, opr_name, wait);
+  opr->temporary = true;
   Push(opr, exec_ctx, priority, profiling);
 }
 
diff --git a/src/engine/threaded_engine.h b/src/engine/threaded_engine.h
index c39f322596ae..3d56b7c79b90 100644
--- a/src/engine/threaded_engine.h
+++ b/src/engine/threaded_engine.h
@@ -43,6 +43,7 @@
 #include "../profiler/profiler.h"
 #include "./openmp.h"
 #include "../common/object_pool.h"
+#include "../profiler/custom_op_profiler.h"
 
 namespace mxnet {
 namespace engine {
diff --git a/src/operator/custom/custom-inl.h b/src/operator/custom/custom-inl.h
index 3bf63b75cfdb..1173b14eb2fe 100644
--- a/src/operator/custom/custom-inl.h
+++ b/src/operator/custom/custom-inl.h
@@ -43,6 +43,7 @@
 #include <condition_variable>
 #include <queue>
 #include "../operator_common.h"
+#include "../../profiler/custom_op_profiler.h"
 
 namespace mxnet {
 namespace op {
@@ -76,9 +77,16 @@ class CustomOperator {
             bool training, const std::vector<NDArray>& arrs,
             const std::vector<int>& tags,
             const std::unordered_set<int>& output_tags,
-            const std::vector<NDArray>& outputs) {
+            const std::vector<NDArray>& outputs,
+            const std::string op_type = "") {
     if (naive_engine_) {
-      func();
+      if (profiler::Profiler::Get()->IsProfiling(profiler::Profiler::kImperative)) {
+        profiler::CustomOpProfiler::Get()->OnCustomBegin(op_type);
+        func();
+        profiler::CustomOpProfiler::Get()->OnCustomEnd();
+      } else {
+        func();
+      }
       for (size_t i = 0, out_idx = 0; i < arrs.size(); i++) {
         if (arrs[i].storage_type() == kDefaultStorage ||
             arrs[i].storage_type() == kUndefinedStorage)
@@ -97,7 +105,13 @@ class CustomOperator {
       bool prev_training = Imperative::Get()->set_is_training(training);
 
       try {
-        func();
+        if (profiler::Profiler::Get()->IsProfiling(profiler::Profiler::kImperative)) {
+          profiler::CustomOpProfiler::Get()->OnCustomBegin(op_type);
+          func();
+          profiler::CustomOpProfiler::Get()->OnCustomEnd();
+        } else {
+          func();
+        }
       } catch (dmlc::Error& e) {
         exception_ =
             std::make_shared<std::exception_ptr>(std::current_exception());
@@ -143,8 +157,7 @@ class CustomOperator {
 
             ctx.async_on_complete();
           },
-          ctx.run_ctx.ctx, vars, vars2, FnProperty::kNoSkip, 0,
-          "CustomOperator");
+          ctx.run_ctx.ctx, vars, vars2, FnProperty::kNoSkip, 0, "CustomOperatorWait");
     });
     // increase num_threads if there is not enough threads to execute custom operator
     if (q_.size() > num_free_threads_)
diff --git a/src/operator/custom/custom.cc b/src/operator/custom/custom.cc
index 77fe2e6e4b1c..5d2c284be46f 100644
--- a/src/operator/custom/custom.cc
+++ b/src/operator/custom/custom.cc
@@ -345,7 +345,7 @@ void ForwardEx(const OpStatePtr& state, const OpContext& ctx,
             static_cast<int>(ctx.is_train),
             params.info->contexts[kCustomOpForward]));
       },
-      ctx, false, ctx.is_train, cpys, tags, output_tags, outputs);
+      ctx, false, ctx.is_train, cpys, tags, output_tags, outputs, params.op_type);
 }
 
 void BackwardEx(const OpStatePtr& state, const OpContext& ctx,
@@ -415,7 +415,7 @@ void BackwardEx(const OpStatePtr& state, const OpContext& ctx,
         ptrs.size(), const_cast<void**>(ptrs.data()), const_cast<int*>(tags.data()),
         reinterpret_cast<const int*>(req.data()), static_cast<int>(ctx.is_train),
         params.info->contexts[kCustomOpBackward]));
-    }, ctx, false, ctx.is_train, cpys, tags, output_tags, outputs);
+    }, ctx, false, ctx.is_train, cpys, tags, output_tags, outputs, "_backward_" + params.op_type);
 }
 
 // infer storage backward function for custom op which assigns kDefaultStorage for
diff --git a/src/profiler/custom_op_profiler.h b/src/profiler/custom_op_profiler.h
new file mode 100644
index 000000000000..9449e5b84a3a
--- /dev/null
+++ b/src/profiler/custom_op_profiler.h
@@ -0,0 +1,125 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one
+* or more contributor license agreements.  See the NOTICE file
+* distributed with this work for additional information
+* regarding copyright ownership.  The ASF licenses this file
+* to you under the Apache License, Version 2.0 (the
+* "License"); you may not use this file except in compliance
+* with the License.  You may obtain a copy of the License at
+*
+*   http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing,
+* software distributed under the License is distributed on an
+* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+* KIND, either express or implied.  See the License for the
+* specific language governing permissions and limitations
+* under the License.
+*/
+#ifndef MXNET_PROFILER_CUSTOM_OP_PROFILER_H_
+#define MXNET_PROFILER_CUSTOM_OP_PROFILER_H_
+
+#include <string>
+#include <unordered_set>
+#include <unordered_map>
+#include <thread>
+#include "./profiler.h"
+
+namespace mxnet {
+namespace profiler {
+
+using Tid = std::thread::id;
+using TaskPtr = std::unique_ptr<ProfileTask>;
+
+  /*!
+   * \brief Singleton class to assist profiling python callback of custom operators
+   * and to assist linking sub-operators to custom operators
+   */
+class CustomOpProfiler {
+ public:
+  static CustomOpProfiler* Get() {
+    static std::mutex mtx;
+    static std::unique_ptr<CustomOpProfiler> prof = nullptr;
+    if (!prof) {
+      std::unique_lock<std::mutex> lk(mtx);
+      if (!prof)
+        prof = std::make_unique<CustomOpProfiler>();
+    }
+    return prof.get();
+  }
+  /*!
+   * \brief Called before the callback of custom operators to start a profile task for python 
+   * code execution time
+   * \param op_type The registed name of the custom operator
+   */
+  void OnCustomBegin(const std::string& op_type) {
+    const Tid tid = std::this_thread::get_id();
+    const std::string task_name = MakePythonCodeName(op_type);
+    std::lock_guard<std::mutex> lock(mutex_);
+    tid_to_op_type_[tid] = op_type;
+    tasks_[tid] = std::make_unique<ProfileTask>(task_name.c_str(), &custom_op_domain);
+    tasks_[tid]->start();
+  }
+
+  /*!
+   * \brief Called after the callback of custom operators to stop the profile task for python 
+   * code execution time
+   */
+  void OnCustomEnd() {
+    const Tid tid = std::this_thread::get_id();
+    std::lock_guard<std::mutex> lock(mutex_);
+    tid_to_op_type_.erase(tid);
+    // this should never fail
+    CHECK(tasks_.find(tid) != tasks_.end()) << "thread_id not found. " <<
+        "Please use OnCustomBegin() and OnCustomEnd() in pairs.";
+    tasks_[tid]->stop();
+    tasks_.erase(tid);
+  }
+
+  /*!
+   * \brief Generate a display name for sub-operators, which is the name used for OprBlock
+   * and later by profiler, and store it in a unordered_set so that it can be referenced 
+   * in the future.
+   * Notice if the operator is not a sub-operator, just return the char pointer back.
+   * \param op_type The registed name of the operator
+   * \return Returns a pointer to the display name generated
+   */
+  const char* GenerateDisplayName(const char* op_type) {
+    if (!op_type) {
+      return nullptr;
+    }
+    Tid tid = std::this_thread::get_id();
+    std::lock_guard<std::mutex> lock(mutex_);
+    if (tid_to_op_type_.find(tid) == tid_to_op_type_.end()) {
+      return op_type;
+    }
+    std::string name = MakeSubOperatorName(tid, op_type);
+    return display_names_.insert(name).first->c_str();
+  }
+
+ private:
+  /* !\brief make the display name for sub-operators */
+  inline std::string MakeSubOperatorName(const Tid& tid, const char* op_type) {
+    return tid_to_op_type_[tid] + "::" + std::string(op_type);
+  }
+  /* !\brief make the display name for the pure python call back function i.e.
+   * forward() or backward() in the custom operator definition
+   */
+  inline std::string MakePythonCodeName(const std::string& op_type) {
+    return op_type + "::pure_python";
+  }
+  /*! \brief class mutex */
+  std::mutex mutex_;
+  /* !\brief display names for sub-operators in custom ops */
+  std::unordered_set<std::string> display_names_;
+  /* !\brief profiling tasks for pure python code in custom operators */
+  std::unordered_map<Tid, TaskPtr> tasks_;
+  /* !\brief the maping from thread id to the registered name op the custom operator
+   * that is runnin on that thread
+   */
+  std::unordered_map<Tid, std::string> tid_to_op_type_;
+};
+}  // namespace profiler
+}  // namespace mxnet
+
+#endif  // MXNET_PROFILER_CUSTOM_OP_PROFILER_H_
diff --git a/src/profiler/profiler.h b/src/profiler/profiler.h
index a6d9ecf06fee..49ef99da5bba 100644
--- a/src/profiler/profiler.h
+++ b/src/profiler/profiler.h
@@ -790,6 +790,13 @@ struct ProfileTask : public ProfileDuration {
     NVTX_ONLY_CODE(nvtx_duration_.reset(new nvtx::NVTXDuration(name)));
   }
 
+  /*!
+   * \brief Set the domain
+   */
+  void setDomain(ProfileDomain* domain) {
+    domain_ = domain;
+  }
+
   /*!
    * \brief Start the profiling scope
    */
@@ -1111,6 +1118,8 @@ struct ProfileMarker {
   VTUNE_ONLY_CODE(std::unique_ptr<vtune::VTuneInstantMarker> vtune_instant_marker_);
 };
 
+static ProfileDomain custom_op_domain("Custom Operator");
+
 /*!
  * \brief Operator profiler object. Logs as both an independent event and a task in
  * the operator domain
@@ -1162,10 +1171,16 @@ struct ProfileOperator : public ProfileEvent {
     : ProfileEvent(name)
       , as_task_(name, &domain_)
       , name_(name)
-      , attributes_(attributes) {
+      , attributes_(attributes)
+      , profiling_(!IsDeprecatedOperator(name)) {
+    if (IsSubOperatorOfCustom(name)) {
+      as_task_.setDomain(&custom_op_domain);
+      SetCategories(custom_op_domain.name());
+    } else {
+      SetCategories(domain_.name());
+    }
     // make as_task_ not to add stat to AggregateStats; otherwise we will add twice
     as_task_.enableAggregateStats(false);
-    SetCategories(domain_.name());
   }
   /*!
    * \brief Start the profiling scope
@@ -1175,15 +1190,19 @@ struct ProfileOperator : public ProfileEvent {
   void start(mxnet::Context::DeviceType dev_type, uint32_t dev_id) {
     dev_type_ = dev_type;
     dev_id_ = dev_id;
-    ProfileEvent::start();
-    as_task_.start();
+    if (profiling_) {
+      ProfileEvent::start();
+      as_task_.start();
+    }
   }
   /*!
    * \brief Stop the profiling scope
    */
   void stop() override {
-    as_task_.stop();
-    ProfileEvent::stop();
+    if (profiling_) {
+      as_task_.stop();
+      ProfileEvent::stop();
+    }
   }
 
   /*!
@@ -1208,7 +1227,11 @@ struct ProfileOperator : public ProfileEvent {
       if (attributes) {
         name_.append(attributes->to_string().c_str());
       }
-      categories_.set("operator");
+      if (IsSubOperatorOfCustom(name)) {
+        categories_.set(custom_op_domain.name());
+      } else {
+        categories_.set("operator");
+      }
       items_[kStart].timestamp_ = start_time;
       items_[kStop].timestamp_ = stop_time;
     }
@@ -1228,6 +1251,20 @@ struct ProfileOperator : public ProfileEvent {
       start_time_, ProfileStat::NowInMicrosec(),
       attributes_.get());
   }
+  /*!
+   * \brief Check if this operator is no longer profiled
+   * Notice that this operator may still be used for e.g synchronization
+   */
+  inline static bool IsDeprecatedOperator(const char* name) {
+    return strcmp(name, "CustomOperatorWait") == 0 ||
+           strcmp(name, "Custom") == 0 || strcmp(name, "_backward_Custom") == 0;
+  }
+  /*!
+   * \brief Check if this operator a sub-operator of a custom operator
+   */
+  inline static bool IsSubOperatorOfCustom(const char* name) {
+    return strstr(name, "::");
+  }
   /*! \brief Also log the operator as a task in the operator domain */
   ProfileTask as_task_;
   /* !\brief Operator name */
@@ -1240,6 +1277,8 @@ struct ProfileOperator : public ProfileEvent {
   static ProfileDomain domain_;
   /*! \brief Optional operator attributes */
   std::unique_ptr<Attributes> attributes_;
+  /*! \brief Whether to profile or not */
+  const bool profiling_;
 };
 
 /*
diff --git a/tests/python/unittest/test_profiler.py b/tests/python/unittest/test_profiler.py
index b76bfbc82d12..ab7d29f104ca 100644
--- a/tests/python/unittest/test_profiler.py
+++ b/tests/python/unittest/test_profiler.py
@@ -23,6 +23,7 @@
 import os
 import json
 from collections import OrderedDict
+from common import run_in_spawned_process
 
 def enable_profiler(profile_filename, run=True, continuous_dump=False, aggregate_stats=False):
     profiler.set_config(profile_symbolic=True,
@@ -291,6 +292,164 @@ def test_aggregate_duplication():
     assert target_dict['Time']['operator']['_plus_scalar']['Count'] == 2
     profiler.set_state('stop')
     
+def test_custom_operator_profiling(seed = None, file_name = None):
+    class Sigmoid(mx.operator.CustomOp):
+        def forward(self, is_train, req, in_data, out_data, aux):
+            x = in_data[0].asnumpy()
+            import numpy as np
+            y = 1.0 / (1.0 + np.exp(-x))
+            self.assign(out_data[0], req[0], mx.nd.array(y))
+            # Create a dummy matrix using nd.zeros. Test if 'MySigmoid::_zeros' is in dump file
+            dummy = mx.nd.zeros(shape=(100, 100))
+
+        def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
+            y = out_data[0].asnumpy()
+            dy = out_grad[0].asnumpy()
+            dx = dy*(1.0 - y)*y
+            self.assign(in_grad[0], req[0], mx.nd.array(dx))
+
+    @mx.operator.register('MySigmoid')
+    class SigmoidProp(mx.operator.CustomOpProp):
+        def __init__(self):
+            super(SigmoidProp, self).__init__(True)
+
+        def list_arguments(self):
+            return ['data']
+
+        def list_outputs(self):
+            return ['output']
+
+        def infer_shape(self, in_shapes):
+            data_shape = in_shapes[0]
+            output_shape = data_shape
+            return (data_shape,), (output_shape,), ()
+
+        def create_operator(self, ctx, in_shapes, in_dtypes):
+            return Sigmoid()
+
+    if file_name is None:
+        file_name = 'test_custom_operator_profiling.json'
+    enable_profiler(profile_filename = file_name, run=True, continuous_dump=True,\
+                    aggregate_stats=True)
+    x = mx.nd.array([0, 1, 2, 3])
+    x.attach_grad()
+    with mx.autograd.record():
+        y = mx.nd.Custom(x, op_type='MySigmoid')
+    y.backward()
+    mx.nd.waitall()
+    profiler.dump(False)
+    debug_str = profiler.dumps(format = 'json')
+    target_dict = json.loads(debug_str)
+    assert 'Time' in target_dict and 'Custom Operator' in target_dict['Time'] \
+        and 'MySigmoid::pure_python' in target_dict['Time']['Custom Operator'] \
+        and '_backward_MySigmoid::pure_python' in target_dict['Time']['Custom Operator'] \
+        and 'MySigmoid::_zeros' in target_dict['Time']['Custom Operator']
+    profiler.set_state('stop')
+
+def test_custom_operator_profiling_multiple_custom_ops_imperative(seed = None, \
+        mode = 'imperative', file_name = None):
+    class MyAdd(mx.operator.CustomOp):
+        def forward(self, is_train, req, in_data, out_data, aux):        
+            self.assign(out_data[0], req[0], in_data[0] + 1)
+
+        def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
+            self.assign(in_grad[0], req[0], out_grad[0])
+
+    @mx.operator.register('MyAdd1')
+    class MyAdd1Prop(mx.operator.CustomOpProp):
+        def __init__(self):
+            super(MyAdd1Prop, self).__init__(need_top_grad=True)
+
+        def list_arguments(self):
+            return ['data']
+
+        def list_outputs(self):
+            return ['output']
+
+        def infer_shape(self, in_shape):
+            # inputs, outputs, aux
+            return [in_shape[0]], [in_shape[0]], []
+
+        def create_operator(self, ctx, shapes, dtypes):
+            return MyAdd()
+
+    @mx.operator.register('MyAdd2')
+    class MyAdd2Prop(mx.operator.CustomOpProp):
+        def __init__(self):
+            super(MyAdd2Prop, self).__init__(need_top_grad=True)
+
+        def list_arguments(self):
+            return ['data']
+
+        def list_outputs(self):
+            return ['output']
+
+        def infer_shape(self, in_shape):
+            # inputs, outputs, aux
+            return [in_shape[0]], [in_shape[0]], []
+
+        def create_operator(self, ctx, shapes, dtypes):
+            return MyAdd()
+
+    if file_name is None:
+        file_name = 'test_custom_operator_profiling_multiple_custom_ops_imperative.json'
+    enable_profiler(profile_filename = file_name, run=True, continuous_dump=True,\
+                    aggregate_stats=True)
+    inp = mx.nd.zeros(shape=(100, 100))
+    if mode == 'imperative':
+        x = inp + 1
+        y = mx.nd.Custom(inp, op_type='MyAdd1')
+        z = mx.nd.Custom(inp, op_type='MyAdd2')
+    elif mode == 'symbolic':
+        a = mx.symbol.Variable('a')
+        b = a + 1
+        c = mx.symbol.Custom(data=a, op_type='MyAdd1')
+        d = mx.symbol.Custom(data=a, op_type='MyAdd2')
+        b.bind(mx.cpu(), {'a': inp}).forward()
+        c.bind(mx.cpu(), {'a': inp}).forward()
+        d.bind(mx.cpu(), {'a': inp}).forward()
+    mx.nd.waitall()
+    profiler.dump(False)
+    debug_str = profiler.dumps(format = 'json')
+    target_dict = json.loads(debug_str)
+    '''
+    We are calling _plus_scalar within MyAdd1 and MyAdd2 and outside both the custom 
+    operators, so in aggregate stats we should have three different kinds of 
+    _plus_scalar under domains "Custom Operator" and "operator"
+    '''
+    assert 'Time' in target_dict and 'Custom Operator' in target_dict['Time'] \
+        and 'MyAdd1::pure_python' in target_dict['Time']['Custom Operator'] \
+        and 'MyAdd2::pure_python' in target_dict['Time']['Custom Operator'] \
+        and 'MyAdd1::_plus_scalar' in target_dict['Time']['Custom Operator'] \
+        and 'MyAdd2::_plus_scalar' in target_dict['Time']['Custom Operator'] \
+        and '_plus_scalar' not in target_dict['Time']['Custom Operator'] \
+        and 'operator' in target_dict['Time'] \
+        and '_plus_scalar' in target_dict['Time']['operator']
+    profiler.set_state('stop')
+
+def test_custom_operator_profiling_multiple_custom_ops_symbolic():
+    run_in_spawned_process(test_custom_operator_profiling_multiple_custom_ops_imperative, \
+            {'MXNET_EXEC_BULK_EXEC_INFERENCE' : 0, \
+            'MXNET_EXEC_BULK_EXEC_TRAIN' : 0}, \
+            'symbolic', \
+            'test_custom_operator_profiling_multiple_custom_ops_symbolic.json')
+
+def test_custom_operator_profiling_naive_engine():
+    # run the three tests above using Naive Engine
+    run_in_spawned_process(test_custom_operator_profiling, \
+            {'MXNET_ENGINE_TYPE' : "NaiveEngine"}, \
+            'test_custom_operator_profiling_naive.json')
+    run_in_spawned_process(test_custom_operator_profiling_multiple_custom_ops_imperative, \
+            {'MXNET_ENGINE_TYPE' : "NaiveEngine"}, \
+            'imperative', \
+            'test_custom_operator_profiling_multiple_custom_ops_imperative_naive.json')
+    run_in_spawned_process(test_custom_operator_profiling_multiple_custom_ops_imperative, \
+            {'MXNET_ENGINE_TYPE' : "NaiveEngine", \
+            'MXNET_EXEC_BULK_EXEC_INFERENCE' : 0, \
+            'MXNET_EXEC_BULK_EXEC_TRAIN' : 0}, \
+            'symbolic', \
+            'test_custom_operator_profiling_multiple_custom_ops_symbolic_naive.json')
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From e8f3e91a713909d633c457ceae8affcf8214ff84 Mon Sep 17 00:00:00 2001
From: Sandeep Krishnamurthy <sandeep.krishna98@gmail.com>
Date: Fri, 28 Jun 2019 11:02:32 -0700
Subject: [PATCH 019/813] [Opperf] Make module/namespace of the operator
 parameterized (#15226)

* Make module/namespace parameterized to choose between mx.nd or mx.np

* Fix comments

* Add automated way to fetch compile/runtime flags for MXNet

* Fix warmup and runs count

* Fix Pooling operator benchmarks
---
 benchmark/opperf/README.md                    |   5 +-
 .../opperf/nd_operations/binary_operators.py  |   8 +-
 .../opperf/nd_operations/gemm_operators.py    |  13 +-
 .../nd_operations/nn_activation_operators.py  |  15 +-
 .../nd_operations/nn_basic_operators.py       |  10 +-
 .../opperf/nd_operations/nn_conv_operators.py |  20 +-
 .../random_sampling_operators.py              |   6 +-
 .../nd_operations/reduction_operators.py      |   6 +-
 .../opperf/nd_operations/unary_operators.py   |   6 +-
 benchmark/opperf/opperf.py                    |  17 +-
 .../mxnet_operator_benchmark_results_gpu.md   | 322 ++++++++++++++++++
 benchmark/opperf/rules/default_params.py      |  10 +-
 benchmark/opperf/utils/common_utils.py        |  22 +-
 benchmark/opperf/utils/op_registry_utils.py   |  26 +-
 14 files changed, 421 insertions(+), 65 deletions(-)
 create mode 100644 benchmark/opperf/results/mxnet_operator_benchmark_results_gpu.md

diff --git a/benchmark/opperf/README.md b/benchmark/opperf/README.md
index 99c75be2bf7b..132eb84bf650 100644
--- a/benchmark/opperf/README.md
+++ b/benchmark/opperf/README.md
@@ -24,10 +24,11 @@ With this utility, for each MXNet operator you can get the following details:
 **Timing**
 1. Forward execution time
 2. Backward execution time
-3. Time spent for memory management
 
 **Memory**
-1. Total memory allocated
+1. Average and Max memory allocated
+
+NOTE: This is the `pool memory`. It does not reflect the exact memory requested by the operator.
 
 # Motivation
 
diff --git a/benchmark/opperf/nd_operations/binary_operators.py b/benchmark/opperf/nd_operations/binary_operators.py
index 7f93621eb2ec..cca8f9d1f2ad 100644
--- a/benchmark/opperf/nd_operations/binary_operators.py
+++ b/benchmark/opperf/nd_operations/binary_operators.py
@@ -38,7 +38,7 @@
     get_all_elemen_wise_binary_operators
 
 
-def run_mx_binary_broadcast_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=10, runs=50):
+def run_mx_binary_broadcast_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, runs=100):
     """Runs benchmarks with the given context and precision (dtype)for all the binary
     broadcast operators in MXNet.
 
@@ -48,9 +48,9 @@ def run_mx_binary_broadcast_operators_benchmarks(ctx=mx.cpu(), dtype='float32',
         Context to run benchmarks
     dtype: str, default 'float32'
         Precision to use for benchmarks
-    warmup: int, default 10
+    warmup: int, default 25
         Number of times to run for warmup
-    runs: int, default 50
+    runs: int, default 100
         Number of runs to capture benchmark results
 
     Returns
@@ -65,7 +65,7 @@ def run_mx_binary_broadcast_operators_benchmarks(ctx=mx.cpu(), dtype='float32',
     return mx_binary_op_results
 
 
-def run_mx_binary_element_wise_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=10, runs=50):
+def run_mx_binary_element_wise_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, runs=100):
     """Runs benchmarks with the given context and precision (dtype)for all the binary
     element_wise operators in MXNet.
 
diff --git a/benchmark/opperf/nd_operations/gemm_operators.py b/benchmark/opperf/nd_operations/gemm_operators.py
index 69a0f4c23121..e1eaabc78954 100644
--- a/benchmark/opperf/nd_operations/gemm_operators.py
+++ b/benchmark/opperf/nd_operations/gemm_operators.py
@@ -16,10 +16,9 @@
 # under the License.
 
 import mxnet as mx
-from mxnet import nd
 from benchmark.opperf.utils.benchmark_utils import run_performance_test
 from benchmark.opperf.utils.common_utils import merge_map_list
-
+from benchmark.opperf.rules.default_params import MX_OP_MODULE
 """Performance benchmark tests for MXNet NDArray GEMM Operators.
 
 1. dot
@@ -35,7 +34,7 @@
 """
 
 
-def run_gemm_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=10, runs=50):
+def run_gemm_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, runs=100):
     """Runs benchmarks with the given context and precision (dtype)for all the GEMM
     operators (dot, batch_dot) in MXNet.
 
@@ -45,9 +44,9 @@ def run_gemm_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=10, runs
         Context to run benchmarks
     dtype: str, default 'float32'
         Precision to use for benchmarks
-    warmup: int, default 10
+    warmup: int, default 25
         Number of times to run for warmup
-    runs: int, default 50
+    runs: int, default 100
         Number of runs to capture benchmark results
 
     Returns
@@ -57,7 +56,7 @@ def run_gemm_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=10, runs
     """
     # Benchmark tests for dot and batch_dot operators
     dot_benchmark_res = run_performance_test(
-        [nd.dot], run_backward=True,
+        [getattr(MX_OP_MODULE, "dot")], run_backward=True,
         dtype=dtype, ctx=ctx,
         inputs=[{"lhs": (1024, 1024),
                  "rhs": (1024, 1024)},
@@ -71,7 +70,7 @@ def run_gemm_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=10, runs
         warmup=warmup, runs=runs)
 
     batch_dot_benchmark_res = run_performance_test(
-        [nd.batch_dot], run_backward=True,
+        [getattr(MX_OP_MODULE, "batch_dot")], run_backward=True,
         dtype=dtype, ctx=ctx,
         inputs=[{"lhs": (32, 1024, 1024),
                  "rhs": (32, 1024, 1024)},
diff --git a/benchmark/opperf/nd_operations/nn_activation_operators.py b/benchmark/opperf/nd_operations/nn_activation_operators.py
index 16ea2c6f64f4..ae5ec90f367e 100644
--- a/benchmark/opperf/nd_operations/nn_activation_operators.py
+++ b/benchmark/opperf/nd_operations/nn_activation_operators.py
@@ -16,9 +16,9 @@
 # under the License.
 
 import mxnet as mx
-from mxnet import nd
 from benchmark.opperf.utils.benchmark_utils import run_performance_test
 from benchmark.opperf.utils.common_utils import merge_map_list
+from benchmark.opperf.rules.default_params import MX_OP_MODULE
 
 """Performance benchmark tests for MXNet NDArray Activation Operators.
 
@@ -35,7 +35,7 @@
 """
 
 
-def run_activation_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=10, runs=50):
+def run_activation_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, runs=100):
     """Runs benchmarks with the given context and precision (dtype)for all the activation
     operators (relu, sigmoid, softmax) in MXNet.
 
@@ -45,9 +45,9 @@ def run_activation_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=10
         Context to run benchmarks
     dtype: str, default 'float32'
         Precision to use for benchmarks
-    warmup: int, default 10
+    warmup: int, default 25
         Number of times to run for warmup
-    runs: int, default 50
+    runs: int, default 100
         Number of runs to capture benchmark results
 
     Returns
@@ -56,7 +56,7 @@ def run_activation_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=10
 
     """
     # Relu and its variation
-    relu_benchmark_res = run_performance_test([nd.LeakyReLU],
+    relu_benchmark_res = run_performance_test([getattr(MX_OP_MODULE, "LeakyReLU")],
                                               run_backward=True,
                                               dtype=dtype,
                                               ctx=ctx,
@@ -78,7 +78,7 @@ def run_activation_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=10
 
     # Sigmoid => Covered as part of Unary ops
     # Hard_Sigmoid
-    hard_sigmoid_benchmark_res = run_performance_test([nd.hard_sigmoid],
+    hard_sigmoid_benchmark_res = run_performance_test([getattr(MX_OP_MODULE, "hard_sigmoid")],
                                                       run_backward=True,
                                                       dtype=dtype,
                                                       ctx=ctx,
@@ -90,7 +90,8 @@ def run_activation_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=10
                                                       runs=runs)
 
     # Softmax, LogSoftmax
-    softmax_benchmark_res = run_performance_test([nd.softmax, nd.log_softmax],
+    softmax_benchmark_res = run_performance_test([getattr(MX_OP_MODULE, "softmax"),
+                                                  getattr(MX_OP_MODULE, "log_softmax")],
                                                  run_backward=True,
                                                  dtype=dtype,
                                                  ctx=ctx,
diff --git a/benchmark/opperf/nd_operations/nn_basic_operators.py b/benchmark/opperf/nd_operations/nn_basic_operators.py
index d91b285f41aa..597f7719d2cb 100644
--- a/benchmark/opperf/nd_operations/nn_basic_operators.py
+++ b/benchmark/opperf/nd_operations/nn_basic_operators.py
@@ -16,9 +16,9 @@
 # under the License.
 
 import mxnet as mx
-from mxnet import nd
 from benchmark.opperf.utils.benchmark_utils import run_performance_test
 from benchmark.opperf.utils.common_utils import merge_map_list
+from benchmark.opperf.rules.default_params import MX_OP_MODULE
 
 """Performance benchmark tests for MXNet NDArray basic NN Operators.
 
@@ -29,9 +29,9 @@
 """
 
 
-def run_nn_basic_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=10, runs=50):
+def run_nn_basic_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, runs=100):
     # FullyConnnected operator benchmarks
-    fc_benchmark_res = run_performance_test([nd.FullyConnected],
+    fc_benchmark_res = run_performance_test([getattr(MX_OP_MODULE, "FullyConnected")],
                                             run_backward=True,
                                             dtype=dtype,
                                             ctx=ctx,
@@ -49,7 +49,7 @@ def run_nn_basic_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=10,
                                             runs=runs)
 
     # Dropout benchmarks
-    dropout_benchmark_res = run_performance_test([nd.Dropout],
+    dropout_benchmark_res = run_performance_test([getattr(MX_OP_MODULE, "Dropout")],
                                                  run_backward=True,
                                                  dtype=dtype,
                                                  ctx=ctx,
@@ -62,7 +62,7 @@ def run_nn_basic_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=10,
                                                  warmup=warmup,
                                                  runs=runs)
     # BatchNorm benchmarks
-    batchnorm_benchmark_res = run_performance_test([nd.BatchNorm],
+    batchnorm_benchmark_res = run_performance_test([getattr(MX_OP_MODULE, "BatchNorm")],
                                                    run_backward=True,
                                                    dtype=dtype,
                                                    ctx=ctx,
diff --git a/benchmark/opperf/nd_operations/nn_conv_operators.py b/benchmark/opperf/nd_operations/nn_conv_operators.py
index e4749ec90de4..e42205f8f56c 100644
--- a/benchmark/opperf/nd_operations/nn_conv_operators.py
+++ b/benchmark/opperf/nd_operations/nn_conv_operators.py
@@ -16,9 +16,9 @@
 # under the License.
 
 import mxnet as mx
-from mxnet import nd
 from benchmark.opperf.utils.benchmark_utils import run_performance_test
 from benchmark.opperf.utils.common_utils import merge_map_list
+from benchmark.opperf.rules.default_params import MX_OP_MODULE
 
 """Performance benchmark tests for MXNet NDArray Convolution and Pooling Operators.
 
@@ -51,7 +51,7 @@
 """
 
 
-def run_pooling_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=10, runs=50):
+def run_pooling_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, runs=100):
     pool_types = ['avg', 'max', 'sum']
     global_pool_types = [0, 1]
 
@@ -61,7 +61,7 @@ def run_pooling_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=10, r
     for pool_type in pool_types:
         for global_pool in global_pool_types:
             for pool1d_data in [(32, 3, 256), (32, 3, 64)]:
-                pool1d_benchmark_res += run_performance_test([nd.Pooling],
+                pool1d_benchmark_res += run_performance_test([getattr(MX_OP_MODULE, "Pooling")],
                                                              run_backward=True,
                                                              dtype=dtype,
                                                              ctx=ctx,
@@ -70,13 +70,12 @@ def run_pooling_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=10, r
                                                                       "pool_type": pool_type,
                                                                       "global_pool": global_pool,
                                                                       "stride": 1,
-                                                                      "pad": 1,
-                                                                      "layout": 'NCW'}
+                                                                      "pad": 1}
                                                                      ],
                                                              warmup=warmup,
                                                              runs=runs)
             for pool2d_data in [(32, 3, 256, 256), (32, 3, 64, 64)]:
-                pool2d_benchmark_res += run_performance_test([nd.Pooling],
+                pool2d_benchmark_res += run_performance_test([getattr(MX_OP_MODULE, "Pooling")],
                                                              run_backward=True,
                                                              dtype=dtype,
                                                              ctx=ctx,
@@ -85,8 +84,7 @@ def run_pooling_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=10, r
                                                                       "pool_type": pool_type,
                                                                       "global_pool": global_pool,
                                                                       "stride": (1, 1),
-                                                                      "pad": (0, 0),
-                                                                      "layout": 'NCHW'}
+                                                                      "pad": (0, 0)}
                                                                      ],
                                                              warmup=warmup,
                                                              runs=runs)
@@ -95,11 +93,11 @@ def run_pooling_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=10, r
     return mx_pooling_op_results
 
 
-def run_convolution_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=10, runs=50):
+def run_convolution_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, runs=100):
     # Conv1D Benchmarks
     conv1d_benchmark_res = []
     for conv_data in [(32, 3, 256), (32, 3, 64)]:
-        conv1d_benchmark_res += run_performance_test([nd.Convolution],
+        conv1d_benchmark_res += run_performance_test([getattr(MX_OP_MODULE, "Convolution")],
                                                      run_backward=True,
                                                      dtype=dtype,
                                                      ctx=ctx,
@@ -118,7 +116,7 @@ def run_convolution_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=1
     # Conv2D Benchmarks
     conv2d_benchmark_res = []
     for conv_data in [(32, 3, 256, 256), (32, 3, 64, 64)]:
-        conv2d_benchmark_res += run_performance_test([nd.Convolution],
+        conv2d_benchmark_res += run_performance_test([getattr(MX_OP_MODULE, "Convolution")],
                                                      run_backward=True,
                                                      dtype=dtype,
                                                      ctx=ctx,
diff --git a/benchmark/opperf/nd_operations/random_sampling_operators.py b/benchmark/opperf/nd_operations/random_sampling_operators.py
index bad8c8e4c040..d0cf2c2fea4a 100644
--- a/benchmark/opperf/nd_operations/random_sampling_operators.py
+++ b/benchmark/opperf/nd_operations/random_sampling_operators.py
@@ -34,7 +34,7 @@
 from benchmark.opperf.utils.op_registry_utils import get_all_random_sampling_operators
 
 
-def run_mx_random_sampling_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=10, runs=50):
+def run_mx_random_sampling_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, runs=100):
     """Runs benchmarks with the given context and precision (dtype)for all the random sampling
     operators in MXNet.
 
@@ -44,9 +44,9 @@ def run_mx_random_sampling_operators_benchmarks(ctx=mx.cpu(), dtype='float32', w
         Context to run benchmarks
     dtype: str, default 'float32'
         Precision to use for benchmarks
-    warmup: int, default 10
+    warmup: int, default 25
         Number of times to run for warmup
-    runs: int, default 50
+    runs: int, default 100
         Number of runs to capture benchmark results
 
     Returns
diff --git a/benchmark/opperf/nd_operations/reduction_operators.py b/benchmark/opperf/nd_operations/reduction_operators.py
index 5bfe06621136..e34debc95810 100644
--- a/benchmark/opperf/nd_operations/reduction_operators.py
+++ b/benchmark/opperf/nd_operations/reduction_operators.py
@@ -31,7 +31,7 @@
 from benchmark.opperf.utils.benchmark_utils import run_op_benchmarks
 
 
-def run_mx_reduction_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=10, runs=50):
+def run_mx_reduction_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, runs=100):
     """Runs benchmarks with the given context and precision (dtype)for all the reduction
     operators in MXNet.
 
@@ -41,9 +41,9 @@ def run_mx_reduction_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=
         Context to run benchmarks
     dtype: str, default 'float32'
         Precision to use for benchmarks
-    warmup: int, default 10
+    warmup: int, default 25
         Number of times to run for warmup
-    runs: int, default 50
+    runs: int, default 100
         Number of runs to capture benchmark results
 
     Returns
diff --git a/benchmark/opperf/nd_operations/unary_operators.py b/benchmark/opperf/nd_operations/unary_operators.py
index a562eebf2a92..790a4c12b64b 100644
--- a/benchmark/opperf/nd_operations/unary_operators.py
+++ b/benchmark/opperf/nd_operations/unary_operators.py
@@ -35,7 +35,7 @@
 from benchmark.opperf.utils.benchmark_utils import run_op_benchmarks
 
 
-def run_mx_unary_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=10, runs=50):
+def run_mx_unary_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, runs=100):
     """Runs benchmarks with the given context and precision (dtype)for all the unary
     operators in MXNet.
 
@@ -45,9 +45,9 @@ def run_mx_unary_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=10,
         Context to run benchmarks
     dtype: str, default 'float32'
         Precision to use for benchmarks
-    warmup: int, default 10
+    warmup: int, default 25
         Number of times to run for warmup
-    runs: int, default 50
+    runs: int, default 100
         Number of runs to capture benchmark results
 
     Returns
diff --git a/benchmark/opperf/opperf.py b/benchmark/opperf/opperf.py
index 34c6cf96b723..68b404185bf8 100755
--- a/benchmark/opperf/opperf.py
+++ b/benchmark/opperf/opperf.py
@@ -40,7 +40,8 @@
 from benchmark.opperf.nd_operations.nn_basic_operators import run_nn_basic_operators_benchmarks
 
 from benchmark.opperf.utils.common_utils import merge_map_list, save_to_file
-from benchmark.opperf.utils.op_registry_utils import get_operators_with_no_benchmark
+from benchmark.opperf.utils.op_registry_utils import get_operators_with_no_benchmark,\
+    get_current_runtime_features
 
 
 def run_all_mxnet_operator_benchmarks(ctx=mx.cpu(), dtype='float32'):
@@ -102,17 +103,18 @@ def _parse_mxnet_context(ctx):
         device_id = int(ctx[4:-1])
         return mx.gpu(device_id)
 
+
 def main():
     # 1. GET USER INPUTS
-    parser = argparse.ArgumentParser(
-        description='Run all the MXNet operators (NDArray) benchmarks')
+    parser = argparse.ArgumentParser(description='Run all the MXNet operator benchmarks')
 
     parser.add_argument('--ctx', type=str, default='cpu',
                         help='Global context to run all benchmarks. By default, cpu on a '
                              'CPU machine, gpu(0) on a GPU machine. '
                              'Valid Inputs - cpu, gpu, gpu(0), gpu(1)...')
     parser.add_argument('--dtype', type=str, default='float32', help='DType (Precision) to run benchmarks. By default, '
-                                                                     'float32. Valid Inputs - float32, float64.')
+                                                                     'float32. Valid Inputs - float32, float64, int32, '
+                                                                     'int64')
     parser.add_argument('-f', '--output-format', type=str, default='json',
                         choices=['json', 'md'],
                         help='Benchmark result output format. By default, json. '
@@ -129,17 +131,20 @@ def main():
     # 2. RUN BENCHMARKS
     ctx = _parse_mxnet_context(args.ctx)
     dtype = args.dtype
-    final_benchmark_results = run_all_mxnet_operator_benchmarks(ctx=ctx, dtype=args.dtype)
+    final_benchmark_results = run_all_mxnet_operator_benchmarks(ctx=ctx, dtype=dtype)
 
     # 3. PREPARE OUTPUTS
-    save_to_file(final_benchmark_results, args.output_file, args.output_format)
+    run_time_features = get_current_runtime_features()
+    save_to_file(final_benchmark_results, args.output_file, args.output_format, run_time_features)
 
     # 4. Generate list of MXNet operators not covered in benchmarks
     ops_not_covered = get_operators_with_no_benchmark(final_benchmark_results.keys())
     for idx, op in enumerate(ops_not_covered):
         print(f"{idx}. {op}")
+
     return 0
 
+
 if __name__ == '__main__':
     sys.exit(main())
 
diff --git a/benchmark/opperf/results/mxnet_operator_benchmark_results_gpu.md b/benchmark/opperf/results/mxnet_operator_benchmark_results_gpu.md
new file mode 100644
index 000000000000..284eb7cc78e3
--- /dev/null
+++ b/benchmark/opperf/results/mxnet_operator_benchmark_results_gpu.md
@@ -0,0 +1,322 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# MXNet GPU Operator Benchmarks
+
+## Settings
+
+1. MXNet - v1.5.0 with CUDA 10.1
+2. Instance - P3.2x
+ 
+| Operator | Avg Forward Time (ms) | Avg. Backward Time (ms) | Max Mem Usage (Storage) (Bytes) | Inputs |
+| :---: | :---: | :---: | :---:| :--- |
+| rcbrt | 0.0384 | 0.0393 | 2097.1521 | {'data': (1024, 1024)} |
+| rcbrt | 0.0268 | 0.0252 | 20.0 | {'data': (10000, 1)} |
+| rcbrt | 0.0378 | 0.039 | 2000.0 | {'data': (10000, 100)} |
+| min | 0.0679 | 0.0955 | 0.002 | {'axis': (), 'data': (1024, 1024)} |
+| min | 0.0337 | 0.022 | 0.002 | {'axis': 0, 'data': (10000, 1)} |
+| min | 0.0669 | 0.0921 | 0.002 | {'axis': (0, 1), 'data': (10000, 100)} |
+| broadcast_maximum | 0.0284 | 0.0308 | 0.012 | {'lhs': [(1024, 1024), (10000, 10), (10000, 1)], 'rhs': [(1024, 1024), (10000, 10), (10000, 1)]} |
+| Flatten | 0.0416 | --- | 2097.1521 | {'data': (1024, 1024)} |
+| Flatten | 0.03 | --- | 20.0 | {'data': (10000, 1)} |
+| Flatten | 0.0437 | --- | 2000.0 | {'data': (10000, 100)} |
+| stop_gradient | --- | --- | 2097.1521 | {'data': (1024, 1024)} |
+| stop_gradient | --- | --- | 20.0 | {'data': (10000, 1)} |
+| stop_gradient | --- | --- | 2000.0 | {'data': (10000, 100)} |
+| broadcast_minimum | 0.0284 | 0.0294 | 0.012 | {'lhs': [(1024, 1024), (10000, 10), (10000, 1)], 'rhs': [(1024, 1024), (10000, 10), (10000, 1)]} |
+| random_generalized_negative_binomial | 63.299 | --- | 2097.1521 | {'shape': (1024, 1024)} |
+| random_generalized_negative_binomial | 0.6491 | --- | 20.0 | {'shape': (10000, 1)} |
+| random_generalized_negative_binomial | 60.3705 | --- | 2000.0 | {'shape': (10000, 100)} |
+| broadcast_sub | 0.0198 | 0.0208 | 0.012 | {'lhs': [(1024, 1024), (10000, 10), (10000, 1)], 'rhs': [(1024, 1024), (10000, 10), (10000, 1)]} |
+| round | 0.0363 | --- | 2097.1521 | {'data': (1024, 1024)} |
+| round | 0.0248 | --- | 20.0 | {'data': (10000, 1)} |
+| round | 0.0358 | --- | 2000.0 | {'data': (10000, 100)} |
+| cosh | 0.0384 | 0.0389 | 2097.1521 | {'data': (1024, 1024)} |
+| cosh | 0.0271 | 0.0255 | 20.0 | {'data': (10000, 1)} |
+| cosh | 0.0378 | 0.0387 | 2000.0 | {'data': (10000, 100)} |
+| max | 0.068 | 0.095 | 0.002 | {'axis': (), 'data': (1024, 1024)} |
+| max | 0.0336 | 0.0219 | 0.002 | {'axis': 0, 'data': (10000, 1)} |
+| max | 0.0665 | 0.0918 | 0.002 | {'axis': (0, 1), 'data': (10000, 100)} |
+| tanh | 0.0391 | 0.038 | 2097.1521 | {'data': (1024, 1024)} |
+| tanh | 0.0271 | 0.025 | 20.0 | {'data': (10000, 1)} |
+| tanh | 0.0373 | 0.0376 | 2000.0 | {'data': (10000, 100)} |
+| relu | 0.0382 | 0.0383 | 2097.1521 | {'data': (1024, 1024)} |
+| relu | 0.0272 | 0.0251 | 20.0 | {'data': (10000, 1)} |
+| relu | 0.0383 | 0.038 | 2000.0 | {'data': (10000, 100)} |
+| negative | 0.0367 | --- | 2097.1521 | {'data': (1024, 1024)} |
+| negative | 0.025 | --- | 20.0 | {'data': (10000, 1)} |
+| negative | 0.0361 | --- | 2000.0 | {'data': (10000, 100)} |
+| random_randint | 9.0025 | --- | 2097.1521 | {'shape': (1024, 1024), 'high': 5, 'low': 0} |
+| random_randint | 0.0976 | --- | 20.0 | {'shape': (10000, 1), 'high': 5, 'low': 0} |
+| random_randint | 8.589 | --- | 2000.0 | {'shape': (10000, 100), 'high': 5, 'low': 0} |
+| trunc | 0.0367 | --- | 2097.1521 | {'data': (1024, 1024)} |
+| trunc | 0.0251 | --- | 20.0 | {'data': (10000, 1)} |
+| trunc | 0.0359 | --- | 2000.0 | {'data': (10000, 100)} |
+| log_softmax | 0.0433 | 0.0387 | 2097.1521 | {'axis': -1, 'data': (1024, 1024), 'temperature': 0.5} |
+| log_softmax | 0.055 | 0.0386 | 20.0 | {'axis': -1, 'data': (10000, 1), 'temperature': 0.5} |
+| log_softmax | 0.08 | 0.0553 | 2000.0 | {'axis': -1, 'data': (10000, 100), 'temperature': 0.5} |
+| broadcast_greater_equal | 0.0191 | --- | 0.012 | {'lhs': [(1024, 1024), (10000, 10), (10000, 1)], 'rhs': [(1024, 1024), (10000, 10), (10000, 1)]} |
+| arctan | 0.0386 | 0.04 | 2097.1521 | {'data': (1024, 1024)} |
+| arctan | 0.0266 | 0.0248 | 20.0 | {'data': (10000, 1)} |
+| arctan | 0.0378 | 0.0394 | 2000.0 | {'data': (10000, 100)} |
+| sqrt | 0.0385 | 0.041 | 2097.1521 | {'data': (1024, 1024)} |
+| sqrt | 0.0269 | 0.025 | 20.0 | {'data': (10000, 1)} |
+| sqrt | 0.0378 | 0.0397 | 2000.0 | {'data': (10000, 100)} |
+| dot | 0.215 | 0.4045 | 2097.1521 | {'lhs': (1024, 1024), 'rhs': (1024, 1024)} |
+| dot | 0.031 | 0.0633 | 2000.0 | {'lhs': (1000, 10), 'transpose_b': True, 'rhs': (1000, 10)} |
+| dot | 0.042 | 0.0388 | 0.2 | {'lhs': (1000, 1), 'transpose_b': True, 'transpose_a': True, 'rhs': (100, 1000)} |
+| floor | 0.0366 | --- | 2097.1521 | {'data': (1024, 1024)} |
+| floor | 0.0249 | --- | 20.0 | {'data': (10000, 1)} |
+| floor | 0.0366 | --- | 2000.0 | {'data': (10000, 100)} |
+| broadcast_logical_xor | 0.0264 | --- | 0.012 | {'lhs': [(1024, 1024), (10000, 10), (10000, 1)], 'rhs': [(1024, 1024), (10000, 10), (10000, 1)]} |
+| shuffle | 0.1016 | --- | 2097.1521 | {'data': (1024, 1024)} |
+| shuffle | 0.2845 | --- | 60.0 | {'data': (10000, 1)} |
+| shuffle | 0.2798 | --- | 2000.0 | {'data': (10000, 100)} |
+| nansum | 0.0741 | 0.0948 | 0.002 | {'axis': (), 'data': (1024, 1024)} |
+| nansum | 0.0368 | 0.0221 | 0.002 | {'axis': 0, 'data': (10000, 1)} |
+| nansum | 0.0731 | 0.0918 | 0.002 | {'axis': (0, 1), 'data': (10000, 100)} |
+| nanprod | 0.0717 | 0.0955 | 0.002 | {'axis': (), 'data': (1024, 1024)} |
+| nanprod | 0.0344 | 0.0223 | 0.002 | {'axis': 0, 'data': (10000, 1)} |
+| nanprod | 0.0711 | 0.0921 | 0.002 | {'axis': (0, 1), 'data': (10000, 100)} |
+| broadcast_div | 0.02 | 0.0227 | 0.012 | {'lhs': [(1024, 1024), (10000, 10), (10000, 1)], 'rhs': [(1024, 1024), (10000, 10), (10000, 1)]} |
+| LeakyReLU | 0.0326 | 0.0333 | 2097.1521 | {'slope': 0.1, 'data': (1024, 1024), 'act_type': 'leaky'} |
+| LeakyReLU | 0.0347 | 0.0199 | 20.0 | {'slope': 0.1, 'data': (10000, 1), 'act_type': 'leaky'} |
+| LeakyReLU | 0.0316 | 0.0327 | 2000.0 | {'slope': 0.1, 'data': (10000, 100), 'act_type': 'leaky'} |
+| LeakyReLU | 0.0328 | 0.0334 | 2097.1521 | {'slope': 0.1, 'data': (1024, 1024), 'act_type': 'elu'} |
+| LeakyReLU | 0.0208 | 0.0194 | 20.0 | {'slope': 0.1, 'data': (10000, 1), 'act_type': 'elu'} |
+| LeakyReLU | 0.0315 | 0.0326 | 2000.0 | {'slope': 0.1, 'data': (10000, 100), 'act_type': 'elu'} |
+| LeakyReLU | 0.0323 | 0.033 | 2097.1521 | {'data': (1024, 1024), 'act_type': 'selu'} |
+| LeakyReLU | 0.0209 | 0.0194 | 20.0 | {'data': (10000, 1), 'act_type': 'selu'} |
+| LeakyReLU | 0.0315 | 0.0325 | 2000.0 | {'data': (10000, 100), 'act_type': 'selu'} |
+| LeakyReLU | 0.0349 | 0.0917 | 2097.1521 | {'gamma': (1, 1024), 'data': (1024, 1024), 'act_type': 'prelu'} |
+| LeakyReLU | 0.0244 | 0.0465 | 20.0 | {'gamma': (1, 1), 'data': (10000, 1), 'act_type': 'prelu'} |
+| LeakyReLU | 0.034 | 0.1003 | 2000.0 | {'gamma': (1, 100), 'data': (10000, 100), 'act_type': 'prelu'} |
+| FullyConnected | 0.3141 | 0.3486 | 4.096 | {'weight': (64, 196608), 'num_hidden': 64, 'data': (32, 3, 256, 256), 'bias': (64,), 'flatten': True} |
+| FullyConnected | 0.1381 | 0.5142 | 3145.728 | {'weight': (64, 256), 'num_hidden': 64, 'data': (32, 3, 256, 256), 'bias': (64,), 'flatten': False} |
+| square | 0.038 | 0.038 | 2097.1521 | {'data': (1024, 1024)} |
+| square | 0.0281 | 0.0262 | 20.0 | {'data': (10000, 1)} |
+| square | 0.0424 | 0.0422 | 2000.0 | {'data': (10000, 100)} |
+| elemwise_mul | 0.0186 | --- | 0.012 | {'lhs': [(1024, 1024), (10000, 10), (10000, 1)], 'rhs': [(1024, 1024), (10000, 10), (10000, 1)]} |
+| broadcast_not_equal | 0.0262 | --- | 0.012 | {'lhs': [(1024, 1024), (10000, 10), (10000, 1)], 'rhs': [(1024, 1024), (10000, 10), (10000, 1)]} |
+| broadcast_logical_or | 0.019 | --- | 0.012 | {'lhs': [(1024, 1024), (10000, 10), (10000, 1)], 'rhs': [(1024, 1024), (10000, 10), (10000, 1)]} |
+| logical_not | 0.0366 | --- | 2097.1521 | {'data': (1024, 1024)} |
+| logical_not | 0.0247 | --- | 20.0 | {'data': (10000, 1)} |
+| logical_not | 0.0357 | --- | 2000.0 | {'data': (10000, 100)} |
+| broadcast_add | 0.0284 | 0.0289 | 0.012 | {'lhs': [(1024, 1024), (10000, 10), (10000, 1)], 'rhs': [(1024, 1024), (10000, 10), (10000, 1)]} |
+| broadcast_hypot | 0.0202 | 0.0233 | 0.012 | {'lhs': [(1024, 1024), (10000, 10), (10000, 1)], 'rhs': [(1024, 1024), (10000, 10), (10000, 1)]} |
+| sign | 0.038 | 0.0333 | 2097.1521 | {'data': (1024, 1024)} |
+| sign | 0.0269 | 0.0252 | 20.0 | {'data': (10000, 1)} |
+| sign | 0.0379 | 0.0326 | 2000.0 | {'data': (10000, 100)} |
+| arccos | 0.0385 | 0.0424 | 2097.1521 | {'data': (1024, 1024)} |
+| arccos | 0.0268 | 0.0253 | 20.0 | {'data': (10000, 1)} |
+| arccos | 0.0374 | 0.0413 | 2000.0 | {'data': (10000, 100)} |
+| erf | 0.0383 | 0.0384 | 2097.1521 | {'data': (1024, 1024)} |
+| erf | 0.0266 | 0.0247 | 20.0 | {'data': (10000, 1)} |
+| erf | 0.0374 | 0.0384 | 2000.0 | {'data': (10000, 100)} |
+| degrees | 0.0375 | 0.0326 | 2097.1521 | {'data': (1024, 1024)} |
+| degrees | 0.0265 | 0.0247 | 20.0 | {'data': (10000, 1)} |
+| degrees | 0.0376 | 0.0317 | 2000.0 | {'data': (10000, 100)} |
+| fix | 0.0366 | --- | 2097.1521 | {'data': (1024, 1024)} |
+| fix | 0.0251 | --- | 20.0 | {'data': (10000, 1)} |
+| fix | 0.0362 | --- | 2000.0 | {'data': (10000, 100)} |
+| cos | 0.0386 | 0.0389 | 2097.1521 | {'data': (1024, 1024)} |
+| cos | 0.0276 | 0.0255 | 20.0 | {'data': (10000, 1)} |
+| cos | 0.0385 | 0.0388 | 2000.0 | {'data': (10000, 100)} |
+| broadcast_plus | --- | --- | 0.012 | {'lhs': [(1024, 1024), (10000, 10), (10000, 1)], 'rhs': [(1024, 1024), (10000, 10), (10000, 1)]} |
+| sample_exponential | 0.1359 | --- | 4194.3042 | {'lam': [1.0, 8.5], 'shape': (1024, 1024)} |
+| sample_exponential | 0.0734 | --- | 40.0 | {'lam': [1.0, 8.5], 'shape': (10000, 1)} |
+| sample_exponential | 0.1323 | --- | 4000.0 | {'lam': [1.0, 8.5], 'shape': (10000, 100)} |
+| make_loss | 0.0415 | --- | 2097.1521 | {'data': (1024, 1024)} |
+| make_loss | 0.0301 | --- | 20.0 | {'data': (10000, 1)} |
+| make_loss | 0.0432 | --- | 2000.0 | {'data': (10000, 100)} |
+| argmax_channel | 0.288 | --- | 2.048 | {'data': (1024, 1024)} |
+| argmax_channel | 0.0264 | --- | 20.0 | {'data': (10000, 1)} |
+| argmax_channel | 0.0439 | --- | 20.0 | {'data': (10000, 100)} |
+| BlockGrad | 0.042 | --- | 2097.1521 | {'data': (1024, 1024)} |
+| BlockGrad | 0.0295 | --- | 20.0 | {'data': (10000, 1)} |
+| BlockGrad | 0.0438 | --- | 2000.0 | {'data': (10000, 100)} |
+| BatchNorm | 1.1379 | 2.3158 | 12582.9238 | {'gamma': (3,), 'moving_var': (3,), 'data': (32, 3, 256, 256), 'beta': (3,), 'moving_mean': (3,)} |
+| BatchNorm | 1.7465 | 3.5398 | 19200.0117 | {'gamma': (3,), 'moving_var': (3,), 'data': (32, 3, 10000, 10), 'beta': (3,), 'moving_mean': (3,)} |
+| sample_negative_binomial | 1.5065 | --- | 4194.3042 | {'shape': (1024, 1024), 'p': [0.4, 0.77], 'k': [20, 49]} |
+| sample_negative_binomial | 0.8542 | --- | 40.0 | {'shape': (10000, 1), 'p': [0.4, 0.77], 'k': [20, 49]} |
+| sample_negative_binomial | 1.476 | --- | 4000.0 | {'shape': (10000, 100), 'p': [0.4, 0.77], 'k': [20, 49]} |
+| batch_dot | 4.7282 | 9.3181 | 67108.8672 | {'lhs': (32, 1024, 1024), 'rhs': (32, 1024, 1024)} |
+| batch_dot | 0.3021 | 1.229 | 64000.0 | {'lhs': (32, 1000, 10), 'transpose_b': True, 'rhs': (32, 1000, 10)} |
+| batch_dot | 0.0523 | 0.0636 | 6.4 | {'lhs': (32, 1000, 1), 'transpose_b': True, 'transpose_a': True, 'rhs': (32, 100, 1000)} |
+| rsqrt | 0.0386 | 0.0422 | 2097.1521 | {'data': (1024, 1024)} |
+| rsqrt | 0.0268 | 0.0252 | 20.0 | {'data': (10000, 1)} |
+| rsqrt | 0.0377 | 0.0408 | 2000.0 | {'data': (10000, 100)} |
+| broadcast_logical_and | 0.0191 | --- | 0.012 | {'lhs': [(1024, 1024), (10000, 10), (10000, 1)], 'rhs': [(1024, 1024), (10000, 10), (10000, 1)]} |
+| mean | 0.0818 | 0.043 | 0.002 | {'axis': (), 'data': (1024, 1024)} |
+| mean | 0.0384 | 0.026 | 0.002 | {'axis': 0, 'data': (10000, 1)} |
+| mean | 0.0801 | 0.0421 | 0.002 | {'axis': (0, 1), 'data': (10000, 100)} |
+| ceil | 0.0363 | --- | 2097.1521 | {'data': (1024, 1024)} |
+| ceil | 0.0249 | --- | 20.0 | {'data': (10000, 1)} |
+| ceil | 0.0357 | --- | 2000.0 | {'data': (10000, 100)} |
+| min_axis | 0.0569 | --- | 0.002 | {'axis': (), 'data': (1024, 1024)} |
+| min_axis | 0.0319 | --- | 0.002 | {'axis': 0, 'data': (10000, 1)} |
+| min_axis | 0.0563 | --- | 0.002 | {'axis': (0, 1), 'data': (10000, 100)} |
+| sigmoid | 0.0395 | 0.0392 | 2097.1521 | {'data': (1024, 1024)} |
+| sigmoid | 0.0412 | 0.0256 | 20.0 | {'data': (10000, 1)} |
+| sigmoid | 0.0378 | 0.0384 | 2000.0 | {'data': (10000, 100)} |
+| broadcast_power | 0.0209 | 0.023 | 0.012 | {'lhs': [(1024, 1024), (10000, 10), (10000, 1)], 'rhs': [(1024, 1024), (10000, 10), (10000, 1)]} |
+| gamma | 0.0415 | 0.0761 | 2097.1521 | {'data': (1024, 1024)} |
+| gamma | 0.0279 | 0.0279 | 20.0 | {'data': (10000, 1)} |
+| gamma | 0.041 | 0.0736 | 2000.0 | {'data': (10000, 100)} |
+| radians | 0.0378 | 0.0327 | 2097.1521 | {'data': (1024, 1024)} |
+| radians | 0.0268 | 0.0248 | 20.0 | {'data': (10000, 1)} |
+| radians | 0.0373 | 0.032 | 2000.0 | {'data': (10000, 100)} |
+| prod | 0.0671 | 0.0949 | 0.002 | {'axis': (), 'data': (1024, 1024)} |
+| prod | 0.0334 | 0.0218 | 0.002 | {'axis': 0, 'data': (10000, 1)} |
+| prod | 0.0663 | 0.091 | 0.002 | {'axis': (0, 1), 'data': (10000, 100)} |
+| abs | 0.0382 | 0.039 | 2097.1521 | {'data': (1024, 1024)} |
+| abs | 0.0271 | 0.0254 | 20.0 | {'data': (10000, 1)} |
+| abs | 0.0369 | 0.0381 | 2000.0 | {'data': (10000, 100)} |
+| reciprocal | 0.0378 | 0.0399 | 2097.1521 | {'data': (1024, 1024)} |
+| reciprocal | 0.0267 | 0.0248 | 20.0 | {'data': (10000, 1)} |
+| reciprocal | 0.0374 | 0.0391 | 2000.0 | {'data': (10000, 100)} |
+| sample_generalized_negative_binomial | 1.0903 | --- | 4194.3042 | {'mu': [2.0, 2.5], 'alpha': [0.0, 2.5], 'shape': (1024, 1024)} |
+| sample_generalized_negative_binomial | 0.6051 | --- | 40.0 | {'mu': [2.0, 2.5], 'alpha': [0.0, 2.5], 'shape': (10000, 1)} |
+| sample_generalized_negative_binomial | 1.0655 | --- | 4000.0 | {'mu': [2.0, 2.5], 'alpha': [0.0, 2.5], 'shape': (10000, 100)} |
+| rint | 0.0369 | --- | 2097.1521 | {'data': (1024, 1024)} |
+| rint | 0.025 | --- | 20.0 | {'data': (10000, 1)} |
+| rint | 0.0357 | --- | 2000.0 | {'data': (10000, 100)} |
+| arcsin | 0.0383 | 0.0412 | 2097.1521 | {'data': (1024, 1024)} |
+| arcsin | 0.0269 | 0.0253 | 20.0 | {'data': (10000, 1)} |
+| arcsin | 0.038 | 0.0402 | 2000.0 | {'data': (10000, 100)} |
+| sample_poisson | 0.4035 | --- | 4194.3042 | {'lam': [1.0, 8.5], 'shape': (1024, 1024)} |
+| sample_poisson | 0.3288 | --- | 40.0 | {'lam': [1.0, 8.5], 'shape': (10000, 1)} |
+| sample_poisson | 0.4029 | --- | 4000.0 | {'lam': [1.0, 8.5], 'shape': (10000, 100)} |
+| Pooling | 0.0225 | 0.0262 | 49.152 | {'stride': 1, 'pool_type': 'avg', 'data': (32, 3, 256), 'layout': 'NCW', 'global_pool': 0, 'kernel': 3, 'pad': 1} |
+| sample_uniform | 0.1386 | --- | 4194.3042 | {'shape': (1024, 1024), 'high': [1.0, 3.7], 'low': [0.0, 2.5]} |
+| sample_uniform | 0.0776 | --- | 40.0 | {'shape': (10000, 1), 'high': [1.0, 3.7], 'low': [0.0, 2.5]} |
+| sample_uniform | 0.1358 | --- | 4000.0 | {'shape': (10000, 100), 'high': [1.0, 3.7], 'low': [0.0, 2.5]} |
+| ones_like | 0.0326 | --- | 2097.1521 | {'data': (1024, 1024)} |
+| ones_like | 0.0251 | --- | 40.0 | {'data': (10000, 1)} |
+| ones_like | 0.032 | --- | 2000.0 | {'data': (10000, 100)} |
+| identity | --- | --- | 2097.1521 | {'data': (1024, 1024)} |
+| identity | --- | --- | 40.0 | {'data': (10000, 1)} |
+| identity | --- | --- | 2000.0 | {'data': (10000, 100)} |
+| broadcast_lesser_equal | 0.0189 | --- | 0.012 | {'lhs': [(1024, 1024), (10000, 10), (10000, 1)], 'rhs': [(1024, 1024), (10000, 10), (10000, 1)]} |
+| broadcast_lesser | 0.0189 | --- | 0.012 | {'lhs': [(1024, 1024), (10000, 10), (10000, 1)], 'rhs': [(1024, 1024), (10000, 10), (10000, 1)]} |
+| zeros_like | 0.0326 | --- | 2097.1521 | {'data': (1024, 1024)} |
+| zeros_like | 0.0252 | --- | 40.0 | {'data': (10000, 1)} |
+| zeros_like | 0.0321 | --- | 2000.0 | {'data': (10000, 100)} |
+| random_uniform | 3.1027 | --- | 2097.1521 | {'shape': (1024, 1024), 'high': 5, 'low': 0} |
+| random_uniform | 0.0588 | --- | 20.0 | {'shape': (10000, 1), 'high': 5, 'low': 0} |
+| random_uniform | 2.9599 | --- | 2000.0 | {'shape': (10000, 100), 'high': 5, 'low': 0} |
+| broadcast_mod | 0.0196 | 0.0227 | 0.012 | {'lhs': [(1024, 1024), (10000, 10), (10000, 1)], 'rhs': [(1024, 1024), (10000, 10), (10000, 1)]} |
+| cbrt | 0.0383 | 0.0405 | 2097.1521 | {'data': (1024, 1024)} |
+| cbrt | 0.0272 | 0.0251 | 20.0 | {'data': (10000, 1)} |
+| cbrt | 0.0377 | 0.0394 | 2000.0 | {'data': (10000, 100)} |
+| broadcast_minus | --- | --- | 0.012 | {'lhs': [(1024, 1024), (10000, 10), (10000, 1)], 'rhs': [(1024, 1024), (10000, 10), (10000, 1)]} |
+| random_negative_binomial | 52.4089 | --- | 2097.1521 | {'shape': (1024, 1024), 'p': 1, 'k': 1} |
+| random_negative_binomial | 0.5224 | --- | 20.0 | {'shape': (10000, 1), 'p': 1, 'k': 1} |
+| random_negative_binomial | 49.9987 | --- | 2000.0 | {'shape': (10000, 100), 'p': 1, 'k': 1} |
+| log2 | 0.0382 | 0.0405 | 2097.1521 | {'data': (1024, 1024)} |
+| log2 | 0.0269 | 0.0253 | 20.0 | {'data': (10000, 1)} |
+| log2 | 0.0376 | 0.0403 | 2000.0 | {'data': (10000, 100)} |
+| broadcast_greater | 0.0191 | --- | 0.012 | {'lhs': [(1024, 1024), (10000, 10), (10000, 1)], 'rhs': [(1024, 1024), (10000, 10), (10000, 1)]} |
+| max_axis | 0.0564 | --- | 0.002 | {'axis': (), 'data': (1024, 1024)} |
+| max_axis | 0.0315 | --- | 0.002 | {'axis': 0, 'data': (10000, 1)} |
+| max_axis | 0.0561 | --- | 0.002 | {'axis': (0, 1), 'data': (10000, 100)} |
+| sample_gamma | 0.3435 | --- | 4194.3042 | {'alpha': [0.0, 2.5], 'beta': [1.0, 0.7], 'shape': (1024, 1024)} |
+| sample_gamma | 0.2938 | --- | 40.0 | {'alpha': [0.0, 2.5], 'beta': [1.0, 0.7], 'shape': (10000, 1)} |
+| sample_gamma | 0.341 | --- | 4000.0 | {'alpha': [0.0, 2.5], 'beta': [1.0, 0.7], 'shape': (10000, 100)} |
+| sin | 0.0382 | 0.0386 | 2097.1521 | {'data': (1024, 1024)} |
+| sin | 0.0268 | 0.0244 | 20.0 | {'data': (10000, 1)} |
+| sin | 0.0374 | 0.0386 | 2000.0 | {'data': (10000, 100)} |
+| sum | 0.0797 | 0.0341 | 0.002 | {'axis': (), 'data': (1024, 1024)} |
+| sum | 0.0362 | 0.021 | 0.002 | {'axis': 0, 'data': (10000, 1)} |
+| sum | 0.0771 | 0.0335 | 0.002 | {'axis': (0, 1), 'data': (10000, 100)} |
+| erfinv | 0.1069 | 0.1112 | 2097.1521 | {'data': (1024, 1024)} |
+| erfinv | 0.029 | 0.0271 | 20.0 | {'data': (10000, 1)} |
+| erfinv | 0.1043 | 0.1055 | 2000.0 | {'data': (10000, 100)} |
+| random_gamma | 46.4529 | --- | 2097.1521 | {'shape': (1024, 1024)} |
+| random_gamma | 0.466 | --- | 20.0 | {'shape': (10000, 1)} |
+| random_gamma | 44.4208 | --- | 2000.0 | {'shape': (10000, 100)} |
+| broadcast_mul | 0.019 | 0.0222 | 0.012 | {'lhs': [(1024, 1024), (10000, 10), (10000, 1)], 'rhs': [(1024, 1024), (10000, 10), (10000, 1)]} |
+| arccosh | 0.0405 | 0.0414 | 2097.1521 | {'data': (1024, 1024)} |
+| arccosh | 0.0269 | 0.0253 | 20.0 | {'data': (10000, 1)} |
+| arccosh | 0.0396 | 0.0402 | 2000.0 | {'data': (10000, 100)} |
+| log1p | 0.0383 | 0.0399 | 2097.1521 | {'data': (1024, 1024)} |
+| log1p | 0.0269 | 0.0248 | 20.0 | {'data': (10000, 1)} |
+| log1p | 0.0376 | 0.0394 | 2000.0 | {'data': (10000, 100)} |
+| size_array | 0.0229 | --- | 0.004 | {'data': (1024, 1024)} |
+| size_array | 0.0367 | --- | 0.004 | {'data': (10000, 1)} |
+| size_array | 0.022 | --- | 0.004 | {'data': (10000, 100)} |
+| arcsinh | 0.0396 | 0.0406 | 2097.1521 | {'data': (1024, 1024)} |
+| arcsinh | 0.0269 | 0.0249 | 20.0 | {'data': (10000, 1)} |
+| arcsinh | 0.0388 | 0.0396 | 2000.0 | {'data': (10000, 100)} |
+| elemwise_div | 0.0188 | --- | 0.012 | {'lhs': [(1024, 1024), (10000, 10), (10000, 1)], 'rhs': [(1024, 1024), (10000, 10), (10000, 1)]} |
+| elemwise_add | 0.019 | --- | 0.012 | {'lhs': [(1024, 1024), (10000, 10), (10000, 1)], 'rhs': [(1024, 1024), (10000, 10), (10000, 1)]} |
+| elemwise_sub | 0.0186 | --- | 0.012 | {'lhs': [(1024, 1024), (10000, 10), (10000, 1)], 'rhs': [(1024, 1024), (10000, 10), (10000, 1)]} |
+| arctanh | 0.0385 | 0.0397 | 2097.1521 | {'data': (1024, 1024)} |
+| arctanh | 0.0266 | 0.0245 | 20.0 | {'data': (10000, 1)} |
+| arctanh | 0.0376 | 0.0391 | 2000.0 | {'data': (10000, 100)} |
+| log | 0.0381 | 0.0399 | 2097.1521 | {'data': (1024, 1024)} |
+| log | 0.0268 | 0.025 | 20.0 | {'data': (10000, 1)} |
+| log | 0.0377 | 0.0394 | 2000.0 | {'data': (10000, 100)} |
+| gammaln | 0.0461 | 0.0664 | 2097.1521 | {'data': (1024, 1024)} |
+| gammaln | 0.0273 | 0.0266 | 20.0 | {'data': (10000, 1)} |
+| gammaln | 0.0455 | 0.0645 | 2000.0 | {'data': (10000, 100)} |
+| Dropout | 0.122 | 0.0957 | 25165.8242 | {'mode': 'always', 'data': (32, 3, 256, 256), 'p': 0.5} |
+| Dropout | 0.0739 | 0.0241 | 400.0 | {'mode': 'always', 'data': (10000, 10), 'p': 0.5} |
+| softmax | 0.0454 | 0.0438 | 2097.1521 | {'axis': -1, 'data': (1024, 1024), 'temperature': 0.5} |
+| softmax | 0.056 | 0.0377 | 20.0 | {'axis': -1, 'data': (10000, 1), 'temperature': 0.5} |
+| softmax | 0.0805 | 0.0609 | 2000.0 | {'axis': -1, 'data': (10000, 100), 'temperature': 0.5} |
+| expm1 | 0.0386 | 0.0387 | 2097.1521 | {'data': (1024, 1024)} |
+| expm1 | 0.0275 | 0.025 | 20.0 | {'data': (10000, 1)} |
+| expm1 | 0.0375 | 0.0381 | 2000.0 | {'data': (10000, 100)} |
+| log10 | 0.0382 | 0.0403 | 2097.1521 | {'data': (1024, 1024)} |
+| log10 | 0.0274 | 0.0251 | 20.0 | {'data': (10000, 1)} |
+| log10 | 0.0369 | 0.0386 | 2000.0 | {'data': (10000, 100)} |
+| random_poisson | 15.4201 | --- | 2097.1521 | {'shape': (1024, 1024)} |
+| random_poisson | 0.176 | --- | 20.0 | {'shape': (10000, 1)} |
+| random_poisson | 14.9428 | --- | 2000.0 | {'shape': (10000, 100)} |
+| sinh | 0.0381 | 0.0381 | 2097.1521 | {'data': (1024, 1024)} |
+| sinh | 0.0269 | 0.0246 | 20.0 | {'data': (10000, 1)} |
+| sinh | 0.0376 | 0.038 | 2000.0 | {'data': (10000, 100)} |
+| random_normal | 16.4453 | --- | 2097.1521 | {'shape': (1024, 1024)} |
+| random_normal | 0.1864 | --- | 20.0 | {'shape': (10000, 1)} |
+| random_normal | 15.553 | --- | 2000.0 | {'shape': (10000, 100)} |
+| hard_sigmoid | 0.031 | 0.0336 | 2097.1521 | {'alpha': 0.25, 'data': (1024, 1024), 'beta': 0.5} |
+| hard_sigmoid | 0.0197 | 0.0182 | 20.0 | {'alpha': 0.25, 'data': (10000, 1), 'beta': 0.5} |
+| hard_sigmoid | 0.0303 | 0.0322 | 2000.0 | {'alpha': 0.25, 'data': (10000, 100), 'beta': 0.5} |
+| flatten | --- | --- | 2097.1521 | {'data': (1024, 1024)} |
+| flatten | --- | --- | 20.0 | {'data': (10000, 1)} |
+| flatten | --- | --- | 2000.0 | {'data': (10000, 100)} |
+| random_exponential | 15.9188 | --- | 2097.1521 | {'shape': (1024, 1024)} |
+| random_exponential | 0.1663 | --- | 40.0 | {'shape': (10000, 1)} |
+| random_exponential | 15.1982 | --- | 2000.0 | {'shape': (10000, 100)} |
+| tan | 0.0395 | 0.0377 | 2097.1521 | {'data': (1024, 1024)} |
+| tan | 0.027 | 0.025 | 20.0 | {'data': (10000, 1)} |
+| tan | 0.0384 | 0.037 | 2000.0 | {'data': (10000, 100)} |
+| broadcast_equal | 0.019 | --- | 0.012 | {'lhs': [(1024, 1024), (10000, 10), (10000, 1)], 'rhs': [(1024, 1024), (10000, 10), (10000, 1)]} |
+| softsign | 0.0384 | 0.0402 | 2097.1521 | {'data': (1024, 1024)} |
+| softsign | 0.0269 | 0.025 | 20.0 | {'data': (10000, 1)} |
+| softsign | 0.0375 | 0.04 | 2000.0 | {'data': (10000, 100)} |
+| Convolution | 0.0578 | 0.1121 | 1040.384 | {'stride': (1,), 'data': (32, 3, 256), 'bias': (64,), 'layout': 'NCW', 'dilate': (1,), 'kernel': (3,), 'weight': (64, 3, 3), 'pad': (0,), 'num_filter': 64} |
+| sample_normal | 0.1549 | --- | 4194.3042 | {'mu': [2.0, 2.5], 'sigma': [1.0, 3.7], 'shape': (1024, 1024)} |
+| sample_normal | 0.1027 | --- | 40.0 | {'mu': [2.0, 2.5], 'sigma': [1.0, 3.7], 'shape': (10000, 1)} |
+| sample_normal | 0.1522 | --- | 4000.0 | {'mu': [2.0, 2.5], 'sigma': [1.0, 3.7], 'shape': (10000, 100)} |
+| exp | 0.0365 | --- | 2097.1521 | {'data': (1024, 1024)} |
+| exp | 0.0246 | --- | 20.0 | {'data': (10000, 1)} |
+| exp | 0.0356 | --- | 2000.0 | {'data': (10000, 100)} |
+| sum_axis | 0.0637 | --- | 0.004 | {'axis': (), 'data': (1024, 1024)} |
+| sum_axis | 0.0351 | --- | 0.002 | {'axis': 0, 'data': (10000, 1)} |
+| sum_axis | 0.0631 | --- | 0.002 | {'axis': (0, 1), 'data': (10000, 100)} |
\ No newline at end of file
diff --git a/benchmark/opperf/rules/default_params.py b/benchmark/opperf/rules/default_params.py
index 59b2aff53570..df6cdae0c48d 100644
--- a/benchmark/opperf/rules/default_params.py
+++ b/benchmark/opperf/rules/default_params.py
@@ -14,10 +14,16 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
+import sys
+
+# We will use all operators inside NDArray Module
+# If you want to run benchmark for all operators in different namespace,
+# for example mxnet.numpy.op, update here. All operators for benchmarks
+# will be picked up from this module
+MX_OP_MODULE = sys.modules["mxnet.ndarray.op"]
 
-"""Default Input Tensor shapes to use for benchmarking"""
 
-""""""
+"""Default Input Tensor shapes to use for benchmarking"""
 
 # For Unary operators like abs, arccos, arcsin etc..
 DEFAULT_DATA = [(1024, 1024), (10000, 1), (10000, 100)]
diff --git a/benchmark/opperf/utils/common_utils.py b/benchmark/opperf/utils/common_utils.py
index 9fe2e19b13b3..0b4fab97811a 100644
--- a/benchmark/opperf/utils/common_utils.py
+++ b/benchmark/opperf/utils/common_utils.py
@@ -44,7 +44,7 @@ def merge_map_list(map_list):
     return dict(ChainMap(*map_list))
 
 
-def save_to_file(inp_dict, out_filepath, out_format='json'):
+def save_to_file(inp_dict, out_filepath, out_format='json', runtime_features=None):
     """Saves the given input dictionary to the given output file.
 
     By default, saves the input dictionary as JSON file. Other supported formats include:
@@ -58,6 +58,8 @@ def save_to_file(inp_dict, out_filepath, out_format='json'):
         Output file path
     out_format: str, default 'json'
         Format of the output file. Supported options - 'json', 'md'. Default - json.
+    runtime_features: map
+        Dictionary of runtime_features.
 
     """
     if out_format == 'json':
@@ -67,7 +69,7 @@ def save_to_file(inp_dict, out_filepath, out_format='json'):
     elif out_format == 'md':
         # Save as md
         with open(out_filepath, "w") as result_file:
-            result_file.write(_prepare_markdown(inp_dict))
+            result_file.write(_prepare_markdown(inp_dict, runtime_features))
     else:
         raise ValueError("Invalid output file format provided - '{}'. Supported - json, md".format(format))
 
@@ -107,11 +109,19 @@ def _prepare_op_benchmark_result(op, op_bench_result):
                                                max_mem_usage, inputs)
 
 
-def _prepare_markdown(results):
-    results_markdown = [
+def _prepare_markdown(results, runtime_features=None):
+    results_markdown = []
+    if runtime_features and 'runtime_features' in runtime_features:
+        results_markdown.append("# Runtime Features")
+        idx = 0
+        for key, value in runtime_features['runtime_features'].items():
+            results_markdown.append('{}. {} : {}'.format(idx, key, value))
+
+    results_markdown.append("# Benchmark Results")
+    results_markdown.append(
         "| Operator | Avg Forward Time (ms) | Avg. Backward Time (ms) | Max Mem Usage (Storage) (Bytes)"
-        " | Inputs |",
-        "| :---: | :---: | :---: | :---:| :--- |"]
+        " | Inputs |")
+    results_markdown.append("| :---: | :---: | :---: | :---:| :--- |")
 
     for op, op_bench_results in sorted(results.items(), key=itemgetter(0)):
         for op_bench_result in op_bench_results:
diff --git a/benchmark/opperf/utils/op_registry_utils.py b/benchmark/opperf/utils/op_registry_utils.py
index f5b47cb7a9e4..5da0e322eccc 100644
--- a/benchmark/opperf/utils/op_registry_utils.py
+++ b/benchmark/opperf/utils/op_registry_utils.py
@@ -17,13 +17,11 @@
 
 """Utilities to interact with MXNet operator registry."""
 import ctypes
-import sys
+from operator import itemgetter
+from mxnet import runtime
 from mxnet.base import _LIB, check_call, py_str, OpHandle, c_str, mx_uint
 
-from benchmark.opperf.rules.default_params import DEFAULTS_INPUTS
-
-# We will use all operators inside NDArray Module
-mx_nd_module = sys.modules["mxnet.ndarray.op"]
+from benchmark.opperf.rules.default_params import DEFAULTS_INPUTS, MX_OP_MODULE
 
 # Operators where parameter have special criteria that cannot be cleanly automated.
 # Example: sample_multinomial operator has a parameter 'data'. It expects values to sum up to 1.
@@ -62,7 +60,7 @@ def _select_ops(operator_names, filters=("_contrib", "_"), merge_op_forward_back
     for cur_op_name in operator_names:
         if not cur_op_name.startswith(filters):
             mx_operators[cur_op_name] = {"has_backward": False,
-                                         "nd_op_handle": getattr(mx_nd_module, cur_op_name)}
+                                         "nd_op_handle": getattr(MX_OP_MODULE, cur_op_name)}
 
         if cur_op_name.startswith("_backward_"):
             operators_with_backward.append(cur_op_name)
@@ -329,3 +327,19 @@ def get_operators_with_no_benchmark(operators_with_benchmark):
     """
     all_mxnet_operators = _get_all_mxnet_operators().keys()
     return list(set(all_mxnet_operators) - set(operators_with_benchmark))
+
+
+def get_current_runtime_features():
+    """Get all current runtime time flags/configuration for MXNet.
+
+    Returns
+    -------
+    Map of current runtime features such as compile flags used by MXNet.
+        Example: {'runtime_features': {'OPENCV' : '✔ OPENCV', 'CUDA': '✖ CUDA'}}
+    """
+    features = runtime.Features()
+    runtime_features = {}
+    for feature, config in sorted(features.items(), key=itemgetter(0)):
+        runtime_features[feature] = config
+
+    return {'runtime_features': runtime_features}

From 8aaacde92987bb984b100eeec9557ec204ff0610 Mon Sep 17 00:00:00 2001
From: Haibin Lin <linhaibin.eric@gmail.com>
Date: Fri, 28 Jun 2019 11:42:01 -0700
Subject: [PATCH 020/813] Update sparse_retain Documentation (#15394)

* Update sparse_retain.cc

* Update sparse_retain.cc
---
 src/operator/tensor/sparse_retain.cc | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/operator/tensor/sparse_retain.cc b/src/operator/tensor/sparse_retain.cc
index 007541b00b37..b1f38c6d13b1 100644
--- a/src/operator/tensor/sparse_retain.cc
+++ b/src/operator/tensor/sparse_retain.cc
@@ -31,7 +31,7 @@ namespace op {
 // accepts row-sparse format ndarrays. It will be registered
 // under mxnet.ndarray.sparse with name retain.
 NNVM_REGISTER_OP(_sparse_retain)
-.describe(R"code(pick rows specified by user input index array from a row sparse matrix
+.describe(R"code(Pick rows specified by user input index array from a row sparse matrix
 and save them in the output sparse matrix.
 
 Example::
@@ -39,10 +39,10 @@ Example::
   data = [[1, 2], [3, 4], [5, 6]]
   indices = [0, 1, 3]
   shape = (4, 2)
-  rsp_in = row_sparse(data, indices)
+  rsp_in = row_sparse_array(data, indices)
   to_retain = [0, 3]
   rsp_out = retain(rsp_in, to_retain)
-  rsp_out.values = [[1, 2], [5, 6]]
+  rsp_out.data = [[1, 2], [5, 6]]
   rsp_out.indices = [0, 3]
 
 The storage type of ``retain`` output depends on storage types of inputs

From 11e6d4519a3c7d8b64ab2e1b4c05bb5626fa9ae8 Mon Sep 17 00:00:00 2001
From: Haibin Lin <linhaibin.eric@gmail.com>
Date: Fri, 28 Jun 2019 14:21:32 -0700
Subject: [PATCH 021/813] [AMP] Move topk from FP16_FP32_FUNCS to FP32_FUNCS
 (#15342)

---
 python/mxnet/contrib/amp/lists/symbol.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/mxnet/contrib/amp/lists/symbol.py b/python/mxnet/contrib/amp/lists/symbol.py
index 9c99340ab75d..066618b4073d 100644
--- a/python/mxnet/contrib/amp/lists/symbol.py
+++ b/python/mxnet/contrib/amp/lists/symbol.py
@@ -340,7 +340,6 @@
     'take',
     'tanh',
     'tile',
-    'topk',
     'transpose',
     'trunc',
     'uniform',
@@ -463,6 +462,7 @@
     '_sparse_norm',
     '_sparse_rsqrt',
     'argsort',
+    'topk',
 
     # Neural network
     'SoftmaxOutput',

From ca565a00285d4fb0ca77ba9dc651a07ce1f01b24 Mon Sep 17 00:00:00 2001
From: Anirudh Subramanian <anirudh2290@apache.org>
Date: Fri, 28 Jun 2019 14:22:04 -0700
Subject: [PATCH 022/813] Conversion from FP32 model to Mixed Precision model
 (#15118)

* Initial AMP commit

* Fix

* Merge AMP Changes

* AMP Changes to support conditional op names switch

* Add example and fix issues with AMP conversion

* Remove amp convert symbol test

* Fix comment for inference use case

* Remove input_names for convert_hybrid_block

* Check all conditions

* Fix lint

* Fix error_str for load_dict

* Fix lint, Add tests, fix bugs, add examples

* Fix warnings

* Add license for example script

* Remove gpu test and move tests to test_contrib_amp

* Clean up AMP tests

* Add additional comments, add tutorial

* Move the test to gpu dir

* Make the code python3 compatible

* Upgrade archive utility, fixes: #15084

* Allow AR path to be chosen by user

* Use current_context in tutorial

* Update __all__

* Merge with load params API changes

* Revert "Allow AR path to be chosen by user"

This reverts commit 94156b631102e8a1086b3f8b693f2994932d4035.

* Revert "Upgrade archive utility, fixes: #15084"

This reverts commit ea7dd3298cb71f5ce096fe407d7a8f0cc42d43dd.

* Set numpy dtype to float32

* Address review comments

* Add range based for

* Change quantized to low precision

* Fix lint

* Fix pylint

* Forward args for Node::Create

* Fixes

* Add dtype casting wherever needed

* Fix lint in source

* Add cast_optional_params to example

* Tweak example

* Add README

* Add README

* Add cast_optional_params test for convert_model and convert_hybrid_bloc
---
 docs/tutorials/amp/amp_tutorial.md            |  42 ++
 example/automatic-mixed-precision/README.md   |  35 ++
 .../amp_model_conversion.py                   | 119 +++++
 example/automatic-mixed-precision/common      |   1 +
 include/mxnet/c_api.h                         |  49 ++
 python/mxnet/contrib/amp/amp.py               | 353 ++++++++++++++-
 python/mxnet/gluon/parameter.py               |  44 +-
 python/mxnet/module/executor_group.py         |   8 +
 python/mxnet/test_utils.py                    |  83 ++++
 src/c_api/c_api_symbolic.cc                   | 204 +++++++++
 src/nnvm/amp_infer_unknown.cc                 | 148 ++++++
 src/nnvm/low_precision_pass.cc                | 261 +++++++++++
 tests/python/gpu/test_contrib_amp.py          | 428 ++++++++++++++++++
 tests/python/tensorrt/test_tensorrt_lenet5.py |   2 +-
 tests/python/unittest/test_contrib_amp.py     |  85 ----
 15 files changed, 1766 insertions(+), 96 deletions(-)
 create mode 100644 example/automatic-mixed-precision/README.md
 create mode 100644 example/automatic-mixed-precision/amp_model_conversion.py
 create mode 120000 example/automatic-mixed-precision/common
 create mode 100644 src/nnvm/amp_infer_unknown.cc
 create mode 100644 src/nnvm/low_precision_pass.cc
 create mode 100644 tests/python/gpu/test_contrib_amp.py
 delete mode 100644 tests/python/unittest/test_contrib_amp.py

diff --git a/docs/tutorials/amp/amp_tutorial.md b/docs/tutorials/amp/amp_tutorial.md
index be18929e23a9..31cd23b19ae7 100644
--- a/docs/tutorials/amp/amp_tutorial.md
+++ b/docs/tutorials/amp/amp_tutorial.md
@@ -31,12 +31,14 @@ For demonstration purposes we will use synthetic data loader.
 
 
 ```python
+import os
 import logging
 import warnings
 import time
 import mxnet as mx
 import mxnet.gluon as gluon
 from mxnet import autograd
+from mxnet.test_utils import download_model
 import gluoncv as gcv
 from gluoncv.model_zoo import get_model
 
@@ -249,6 +251,46 @@ for epoch in range(1):
 
 We got 60% speed increase from 3 additional lines of code!
 
+## Inference with AMP
+
+To do inference with mixed precision for a trained model in FP32, you can use the conversion APIs: `amp.convert_model` for symbolic model and `amp.convert_hybrid_block` for gluon models. The conversion APIs will take the FP32 model as input and will return a mixed precision model, which can be used to run inference. Below, we demonstrate for a gluon model and a symbolic model: 1. Conversion from FP32 model to mixed precision model 2. Run inference on the mixed precision model.
+
+```python
+with mx.Context(mx.gpu(0)):
+    # Below is an example of converting a gluon hybrid block to a mixed precision block
+    model = get_model("resnet50_v1")
+    model.collect_params().initialize(ctx=mx.current_context())
+    model.hybridize()
+    model(mx.nd.zeros((1, 3, 224, 224)))
+    converted_model = amp.convert_hybrid_block(model)
+
+    # Run dummy inference with the converted gluon model
+    result = converted_model.forward(mx.nd.random.uniform(shape=(1, 3, 224, 224),
+                                                          dtype=np.float32))
+
+    # Below is an example of converting a symbolic model to a mixed precision model
+    dir_path = os.path.dirname(os.path.realpath(__file__))
+    model_path = os.path.join(dir_path, 'model')
+    if not os.path.isdir(model_path):
+        os.mkdir(model_path)
+    prefix, epoch = mx.test_utils.download_model("imagenet1k-resnet-18", dst_dir=model_path)
+    sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)
+    result_sym, result_arg_params, result_aux_params = amp.convert_model(sym,
+                                                                         arg_params,
+                                                                         aux_params)
+
+    # Run dummy inference with the converted symbolic model
+    mod = mx.mod.Module(result_sym, data_names=["data"], label_names=["softmax_label"], context=mx.current_context())
+    mod.bind(data_shapes=[['data', (1, 3, 224, 224)]], label_shapes=[['softmax_label', (1,)]])
+    mod.set_params(result_arg_params, result_aux_params)
+    mod.forward(mx.io.DataBatch(data=[mx.nd.ones((1, 3, 224, 224))],
+                                label=[mx.nd.ones((1,))]))
+    mod.get_outputs()[0].wait_to_read()
+    print("Conversion and Inference completed successfully")
+```
+
+
+
 ## Current limitations of AMP
 
 - AMP's dynamic loss scaling currently supports only Gluon trainer with `update_on_kvstore=False` option set
diff --git a/example/automatic-mixed-precision/README.md b/example/automatic-mixed-precision/README.md
new file mode 100644
index 000000000000..49147cd87242
--- /dev/null
+++ b/example/automatic-mixed-precision/README.md
@@ -0,0 +1,35 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Conversion of FP32 models to Mixed Precision Models
+
+
+This folder contains examples for converting FP32 models to mixed precision models. The script allows for converting FP32 symbolic models or gluon models to mixed precision model.
+
+## Basic Usages
+
+1. AMP Model Conversion for a gluon model, casting the params wherever possible to FP16. The below script will convert the `resnet101_v1` model to Mixed Precision Model and cast params to FP16 wherever possible, load this converted model and run inference on it.
+
+```bash
+python amp_model_conversion.py --model resnet101_v1 --use-gluon-model  --run-dummy-inference --cast-optional-params
+```
+
+2. AMP Model Conversion for a symbolic model, keeping the params in FP32 wherever possible (--cast-optional-params not used).
+
+```bash
+python amp_model_conversion.py --model imagenet1k-resnet-152  --run-dummy-inference
+```
diff --git a/example/automatic-mixed-precision/amp_model_conversion.py b/example/automatic-mixed-precision/amp_model_conversion.py
new file mode 100644
index 000000000000..fcc2ad69dd62
--- /dev/null
+++ b/example/automatic-mixed-precision/amp_model_conversion.py
@@ -0,0 +1,119 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import os
+import logging
+import argparse
+import mxnet as mx
+from common import modelzoo
+import gluoncv
+from gluoncv.model_zoo import get_model
+from mxnet.contrib.amp import amp
+import numpy as np
+
+def download_model(model_name, logger=None):
+    dir_path = os.path.dirname(os.path.realpath(__file__))
+    model_path = os.path.join(dir_path, 'model')
+    if logger is not None:
+        logger.info('Downloading model {}... into path {}'.format(model_name, model_path))
+    return modelzoo.download_model(args.model, os.path.join(dir_path, 'model'))
+
+
+def save_symbol(fname, sym, logger=None):
+    if logger is not None:
+        logger.info('Saving symbol into file at {}'.format(fname))
+    sym.save(fname, remove_amp_cast=False)
+
+
+def save_params(fname, arg_params, aux_params, logger=None):
+    if logger is not None:
+        logger.info('Saving params into file at {}'.format(fname))
+    save_dict = {('arg:%s' % k): v.as_in_context(mx.cpu()) for k, v in arg_params.items()}
+    save_dict.update({('aux:%s' % k): v.as_in_context(mx.cpu()) for k, v in aux_params.items()})
+    mx.nd.save(fname, save_dict)
+
+
+if __name__ == '__main__':
+    symbolic_models = ['imagenet1k-resnet-152',
+                       'imagenet1k-resnet-18',
+                       'imagenet1k-resnet-34',
+                       'imagenet1k-resnet-50',
+                       'imagenet1k-resnet-101',
+                       'imagenet1k-resnext-50',
+                       'imagenet1k-resnext-101',
+                       'imagenet1k-resnext-101-64x4d',
+                       'imagenet11k-place365ch-resnet-152',
+                       'imagenet11k-place365ch-resnet-50']
+    gluon_models = ['resnet18_v1',
+                    'resnet50_v1',
+                    'resnet101_v1',
+                    'squeezenet1.0',
+                    'mobilenet1.0',
+                    'mobilenetv2_1.0',
+                    'inceptionv3']
+    models = symbolic_models + gluon_models
+
+    parser = argparse.ArgumentParser(description='Convert a provided FP32 model to a mixed precision model')
+    parser.add_argument('--model', type=str, choices=models)
+    parser.add_argument('--run-dummy-inference', action='store_true', default=False,
+                        help='Will generate random input of shape (1, 3, 224, 224) '
+                             'and run a dummy inference forward pass')
+    parser.add_argument('--use-gluon-model', action='store_true', default=False,
+                        help='If enabled, will download pretrained model from Gluon-CV '
+                             'and convert to mixed precision model ')
+    parser.add_argument('--cast-optional-params', action='store_true', default=False,
+                        help='If enabled, will try to cast params to target dtype wherever possible')
+    args = parser.parse_args()
+    logging.basicConfig()
+    logger = logging.getLogger('logger')
+    logger.setLevel(logging.INFO)
+
+    if not args.use_gluon_model:
+        assert args.model in symbolic_models, "Please choose one of the available symbolic models: {} \
+                                               If you want to use gluon use the script with --use-gluon-model".format(symbolic_models)
+
+        prefix, epoch = download_model(model_name=args.model, logger=logger)
+        sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)
+        result_sym, result_arg_params, result_aux_params = amp.convert_model(sym, arg_params, aux_params,
+                                                                             cast_optional_params=args.cast_optional_params)
+        sym_name = "%s-amp-symbol.json" % (prefix)
+        save_symbol(sym_name, result_sym, logger)
+        param_name = '%s-%04d.params' % (prefix + '-amp', epoch)
+        save_params(param_name, result_arg_params, result_aux_params, logger)
+        if args.run_dummy_inference:
+            logger.info("Running inference on the mixed precision model with dummy input, batch size: 1")
+            mod = mx.mod.Module(result_sym, data_names=['data'], label_names=['softmax_label'], context=mx.gpu(0))
+            mod.bind(data_shapes=[['data', (1, 3, 224, 224)]], label_shapes=[['softmax_label', (1,)]])
+            mod.set_params(arg_params, aux_params)
+            mod.forward(mx.io.DataBatch(data=[mx.nd.ones((1, 3, 224, 224))],
+                                        label=[mx.nd.ones((1,))]))
+            result = mod.get_outputs()[0].asnumpy()
+            logger.info("Inference run successfully")
+    else:
+        assert args.model in gluon_models, "Please choose one of the available gluon models: {} \
+                                            If you want to use symbolic model instead, remove --use-gluon-model when running the script".format(gluon_models)
+        net = gluoncv.model_zoo.get_model(args.model, pretrained=True)
+        net.hybridize()
+        result_before1 = net.forward(mx.nd.zeros((1, 3, 224, 224)))
+        net.export("{}".format(args.model))
+        net = amp.convert_hybrid_block(net, cast_optional_params=args.cast_optional_params)
+        net.export("{}-amp".format(args.model), remove_amp_cast=False)
+        if args.run_dummy_inference:
+            logger.info("Running inference on the mixed precision model with dummy inputs, batch size: 1")
+            result_after = net.forward(mx.nd.zeros((1, 3, 224, 224), dtype=np.float32, ctx=mx.gpu(0)))
+            result_after = net.forward(mx.nd.zeros((1, 3, 224, 224), dtype=np.float32, ctx=mx.gpu(0)))
+            logger.info("Inference run successfully")
diff --git a/example/automatic-mixed-precision/common b/example/automatic-mixed-precision/common
new file mode 120000
index 000000000000..cafb9140ab6a
--- /dev/null
+++ b/example/automatic-mixed-precision/common
@@ -0,0 +1 @@
+../image-classification/common
\ No newline at end of file
diff --git a/include/mxnet/c_api.h b/include/mxnet/c_api.h
index a2da6db978cb..bd30e44f910c 100644
--- a/include/mxnet/c_api.h
+++ b/include/mxnet/c_api.h
@@ -1764,6 +1764,55 @@ MXNET_DLL int MXQuantizeSymbol(SymbolHandle sym_handle, SymbolHandle *ret_sym_ha
                                const mx_uint num_offline, const char **offline_params,
                                const char *quantized_dtype, const bool calib_quantize);
 
+/*!
+ * \brief Convert a symbol into a mixed precision symbol with cast operators for target dtype casting
+ * \param sym_handle symbol to be converted
+ * \param ret_sym_handle mixed precision symbol result
+ * \param num_args number of arguments for known dtypes
+ * \param arg_type_data arg types of the arguments
+ * \param target_dtype target_dtype for mixed precision symbol
+ * \param cast_optional_params whether to cast optional params to target_dtype
+ * \param num_target_dtype_op_names number of ops to be casted to target_dtype
+ * \param num_fp32_op_names number of ops to be casted to FP32
+ * \param num_widest_dtype_op_names number of ops to be casted to widest dtype
+ * \param num_conditional_fp32_op_names number of ops to be casted to FP32 based on a condition
+ * \param num_excluded_symbols number of symbols to be excluded from casting
+ * \param num_model_params number of model parameters
+ * \param num_widest_dtype_op_names number of ops to be casted to the widest dtype
+ * \param num_conditional_fp32_op_names number of ops to be cast to fp32 based on precision
+ * \param target_dtype_op_names op names to be casted to target_dtype
+ * \param fp32_op_names op names to be casted to fp32
+ * \param widest_dtype_op_names names to be casted to widest dtype
+ * \param conditional_fp32_op_names names to be casted to FP32 conditionally
+ * \param excluded_symbols symbol names to be excluded from casting
+ * \param param_names param names for conditional FP32 casting
+ * \param param_values param values for conditional FP32 casting
+ * \param arg_names argument names for which type information is provided
+ * \param model_param_names names for model parameters
+ */
+MXNET_DLL int MXReducePrecisionSymbol(SymbolHandle sym_handle,
+                                      SymbolHandle *ret_sym_handle,
+                                      mx_uint num_args,
+                                      const int* arg_type_data,
+                                      mx_uint num_ind_ptr,
+                                      const int* ind_ptr,
+                                      const int* target_dtype,
+                                      const int cast_optional_params,
+                                      const mx_uint num_target_dtype_op_names,
+                                      const mx_uint num_fp32_op_names,
+                                      const mx_uint num_widest_dtype_op_names,
+                                      const mx_uint num_conditional_fp32_op_names,
+                                      const mx_uint num_excluded_symbols,
+                                      const mx_uint num_model_params,
+                                      const char **target_dtype_op_names,
+                                      const char **fp32_op_names,
+                                      const char **widest_dtype_op_names,
+                                      const char **conditional_fp32_op_names,
+                                      const char **excluded_symbols,
+                                      const char **conditional_param_names,
+                                      const char **conditional_param_vals,
+                                      const char **model_param_names,
+                                      const char **arg_names);
 /*!
  * \brief Set calibration table to node attributes in the sym
  * \param sym_handle symbol whose node attributes are to be set by calibration table
diff --git a/python/mxnet/contrib/amp/amp.py b/python/mxnet/contrib/amp/amp.py
index bb3972092139..ef2f7209d946 100755
--- a/python/mxnet/contrib/amp/amp.py
+++ b/python/mxnet/contrib/amp/amp.py
@@ -17,21 +17,28 @@
 
 # coding: utf-8
 """Functions for enabling AMP (automatic mixed precision)."""
-__all__ = ['init', 'init_trainer', 'scale_loss', 'unscale']
+__all__ = ['init', 'init_trainer', 'scale_loss', 'unscale', 'convert_model',
+           'convert_hybrid_block', 'list_fp16_ops', 'list_fp32_ops',
+           'list_fp16_fp32_ops', 'list_conditional_fp32_ops',
+           'convert_symbol']
 
 from types import MethodType
+from array import array
+import ctypes
 import logging
 import contextlib
 import numpy as np
 
 from ... import symbol
+from ...context import gpu
 from ...symbol import Symbol
 from ...symbol import contrib as symbol_contrib
 from ... import ndarray
-from ...ndarray import NDArray
+from ...ndarray import NDArray, _DTYPE_NP_TO_MX, _DTYPE_MX_TO_NP
 from . import lists
 from ...gluon import trainer
 from ... import base
+from ...base import c_str_array, SymbolHandle, check_call, _LIB, mx_uint, c_array_buf
 from ... import optimizer as opt
 from .loss_scaler import LossScaler
 
@@ -342,3 +349,345 @@ def unscale(optimizer_or_trainer):
     else:
         raise TypeError("optimizer_or_trainer should be a Gluon Trainer or "
                         "an optimizer, instead is %s" % type(optimizer_or_trainer))
+
+def convert_symbol(sym, target_dtype="float16", target_dtype_ops=None,
+                   fp32_ops=None, conditional_fp32_ops=None,
+                   excluded_sym_names=None, data_names=None,
+                   cast_optional_params=False):
+    """Given a symbol object representing a neural network of data type FP32 and target_dtype,
+    add cast layers according to the op lists (target_dtype_ops, fp32_ops,
+    conditional_fp32_ops) if provided, otherwise use the default
+    lists provided by the framework.
+
+    Parameters
+    ----------
+    sym : Symbol
+        FP32 neural network symbol
+    target_dtype : str or numpy, optional defaults to float16
+        currently only supports float16. The target dtype indicates to add cast layers
+        when possible so that lower precision computation can be leveraged.
+    target_dtype_ops : list of strs, optional
+        Override the list of operator names casted to the target_dtype.
+        If None, uses the framework's default list to be casted to target_dtype.
+    fp32_ops : list of strs, optional
+        Override the list of operator names casted to FP32.
+        If None, uses the framework's default list to be casted to FP32.
+    conditional_fp32_ops : list of (string, string, list of string), optional
+        Override the list of functions to be casted to FP32.
+        The format of the list is
+        (name of the function, name of the parameter,
+         list of values of the parameter that make the operator to be casted to FP32)
+    excluded_sym_names : list of strs, optional
+        A list of strings that represent the names of symbols that users want to exclude
+        from being casted to FP16 or FP32.
+    data_names : list of strs, optional
+        A list of strings that represent input data tensor names to the model
+    cast_optional_params : bool, default False
+        Whether to cast the arg_params and aux_params that don't require to be in FP16
+        because of a cast layer following it, but will reduce the computation and memory
+        overhead of the model if casted.
+    """
+    assert isinstance(sym, Symbol), "First argument to convert_symbol should be Symbol"
+
+    if target_dtype != "float16":
+        raise ValueError("Only target_dtype float16 is supported currently")
+
+    if target_dtype_ops is not None:
+        assert isinstance(target_dtype_ops, list), "target_dtype_ops should be a list of strs"
+    else:
+        target_dtype_ops = lists.symbol.FP16_FUNCS
+
+    if fp32_ops is not None:
+        assert isinstance(fp32_ops, list), "fp32_ops should be a list of strs"
+    else:
+        fp32_ops = lists.symbol.FP32_FUNCS
+
+    if conditional_fp32_ops is not None:
+        assert isinstance(conditional_fp32_ops, list), "conditional_fp32_ops should be a list"
+    else:
+        conditional_fp32_ops = lists.symbol.CONDITIONAL_FP32_FUNCS
+
+    original_conditional_op_names = []
+    conditional_op_names = []
+    param_names = []
+    param_vals = []
+    indptr = [0]
+    for conditional_fp32_op in conditional_fp32_ops:
+        assert isinstance(conditional_fp32_op[0], str) and isinstance(conditional_fp32_op[1], str) \
+            and isinstance(conditional_fp32_op[2], list), "conditional_fp32_ops should be a list of " \
+                                                          "(str, str, list of str)"
+        param_vals += conditional_fp32_op[2]
+        indptr.append(len(param_vals))
+        param_names.append(conditional_fp32_op[1])
+        conditional_op_names.append(conditional_fp32_op[0])
+
+    if excluded_sym_names is not None:
+        assert isinstance(excluded_sym_names, list), "excluded_sym_names should be a list of strs"
+    else:
+        excluded_sym_names = []
+
+    for original_conditional_fp32_op in lists.symbol.CONDITIONAL_FP32_FUNCS:
+        original_conditional_op_names.append(original_conditional_fp32_op[0])
+
+    # Op lists should not have intersection
+    common_ops = set(target_dtype_ops) & set(fp32_ops)
+    assert len(common_ops) == 0, "Ops cannot be in two or more lists. " \
+                                 "Common ops in target_dtype_ops and fp32_ops {}".format(common_ops)
+    common_ops = set(target_dtype_ops) & set(conditional_op_names)
+    assert len(common_ops) == 0, "Ops cannot be in two or more lists. " \
+                                 "Common ops in target_dtype_ops and conditional_fp32_ops {}".format(common_ops)
+    common_ops = set(conditional_op_names) & set(fp32_ops)
+    assert len(common_ops) == 0, "Ops cannot be in two or more lists. " \
+                                 "Common ops in fp32_ops and conditional_fp32_ops {}".format(common_ops)
+
+    combined_ops = set(target_dtype_ops + fp32_ops + conditional_op_names)
+    all_fp16_fp32_ops = set(lists.symbol.FP16_FUNCS + lists.symbol.FP32_FUNCS
+                            + lists.symbol.FP16_FP32_FUNCS + original_conditional_op_names)
+
+    illegal_ops = combined_ops - all_fp16_fp32_ops
+    assert not illegal_ops, '''Can only choose ops from one of the three lists
+                            for fp16_ops and fp32_ops
+                            1. amp.list_fp16_ops()
+                            2. amp.list_fp32_ops()
+                            3. amp.list_fp16_fp32_ops()
+                            4. amp.list_conditional_fp32_ops()
+                            Op %s not in any of them''' % (illegal_ops)
+
+    widest_dtype_ops = lists.symbol.WIDEST_TYPE_CASTS
+    target_dtype = _DTYPE_NP_TO_MX[np.dtype(target_dtype).type]
+
+    # Prepare a data_names list based on list_inputs if its not provided
+    # Add all names in list for the nodes in the symbol which don't have
+    # __dtype__ set
+    attr_dict = sym.attr_dict()
+    if data_names is None:
+        data_names = []
+        for sym_name in sym.list_inputs():
+            if not sym_name in attr_dict:
+                data_names.append(sym_name)
+                continue
+            if not "__dtype__" in attr_dict[sym_name]:
+                data_names.append(sym_name)
+    model_param_names = list(set(sym.list_inputs()) - set(data_names))
+
+    # Since assumption is that it is a FP32 model, set dtypes for all
+    # data_names to float32
+    str_keys = []
+    sdata = []
+    for k in data_names:
+        str_keys.append(k)
+        sdata.append(0)
+    keys = c_str_array(str_keys)
+
+    out = SymbolHandle()
+    check_call(_LIB.MXReducePrecisionSymbol(sym.handle,
+                                            ctypes.byref(out),
+                                            mx_uint(len(sdata)),
+                                            c_array_buf(ctypes.c_int, array('i', sdata)),
+                                            mx_uint(len(indptr)),
+                                            c_array_buf(ctypes.c_int, array('i', indptr)),
+                                            ctypes.byref(ctypes.c_int(target_dtype)),
+                                            ctypes.c_int(cast_optional_params),
+                                            mx_uint(len(target_dtype_ops)),
+                                            mx_uint(len(fp32_ops)),
+                                            mx_uint(len(widest_dtype_ops)),
+                                            mx_uint(len(conditional_op_names)),
+                                            mx_uint(len(excluded_sym_names)),
+                                            mx_uint(len(model_param_names)),
+                                            c_str_array(target_dtype_ops),
+                                            c_str_array(fp32_ops),
+                                            c_str_array(widest_dtype_ops),
+                                            c_str_array(conditional_op_names),
+                                            c_str_array(excluded_sym_names),
+                                            c_str_array(param_names),
+                                            c_str_array(param_vals),
+                                            c_str_array(model_param_names),
+                                            keys))
+    return Symbol(out)
+
+def convert_model(sym, arg_params, aux_params, target_dtype="float16", target_dtype_ops=None,
+                  fp32_ops=None, conditional_fp32_ops=None, excluded_sym_names=None,
+                  cast_optional_params=False):
+    """API for converting a model from FP32 model to a mixed precision model.
+    MXNet tries to convert the FP32 model to mixed precision model by adding
+    cast layers using amp_cast and amp_multicast operators which can be used for inference use cases.
+    The decision on which cast layer to add is based on hardcoded lists for Automatic Mixed Precision
+    in MXNet. These lists can be overridden by the user by providing their own lists
+    using : targe_precision_ops, fp32_ops, widest_precision_ops, conditional_fp32_ops
+
+    arg_params : dict
+        Dictionary of name to `NDArray`.
+    aux_params : dict
+        Dictionary of name to `NDArray`.
+    target_dtype : str
+        Currently only supports float16. The target dtype indicates to add cast layers
+        when possible so that lower precision computation can be leveraged.
+    target_dtype_ops : list of strs
+        Override the list of operator names casted to target_dtype.
+        If None, uses the framework's default list to be casted to target dtype.
+    fp32_ops : list of strs
+        Override the lists of operator names casted to FP32.
+        If None, uses the framework's default list to be casted to FP32.
+    widest_dtype_ops : list of strs
+        A list of op names provided by user which should run in widest precision among its inputs.
+        If None, uses the framework's default list of widest_precision_ops.
+    conditional_fp32_ops : list of (string, string, list of string)
+        Override the list of operators to be casted to FP32.
+        The format of the list is
+        (name of the function, name of the parameter,
+         list of values of the parameter that make the operator to be casted to
+        fp32)
+    excluded_sym_names : list of strs
+        A list of strings that represent the names of symbols that users want to exclude
+        from being executed in lower precision.
+    cast_optional_params : bool, default False
+        Whether to cast the arg_params and aux_params that don't require to be in FP16
+        because of a cast layer following it, but will reduce the computation and memory
+        overhead of the model if casted.
+    """
+    if excluded_sym_names is None:
+        excluded_sym_names = []
+        if not isinstance(excluded_sym_names, list):
+            raise ValueError('excluded_sym_names must be a list of strings representing'
+                             ' the names of the symbols that should not be casted,'
+                             ' while received type %s' % str(type(excluded_sym_names)))
+
+    if target_dtype != "float16":
+        raise ValueError("Only target_dtype float16 is supported currently")
+
+    assert isinstance(sym, Symbol), "First argument to convert_model should be Symbol"
+    assert isinstance(arg_params, dict), "Second argument to convert_model should be a dict of name to ndarray"
+    assert isinstance(aux_params, dict), "Third argument to convert_model should be a dict of name to ndarray"
+
+    param_names = list(arg_params.keys()) + list(aux_params.keys())
+
+    # Only pass non params as data_names, param types can be inferred
+    data_names = list(set(sym.list_inputs()) - set(param_names))
+
+    sym = convert_symbol(sym, target_dtype, target_dtype_ops,
+                         fp32_ops, conditional_fp32_ops,
+                         excluded_sym_names, data_names,
+                         cast_optional_params)
+
+    # If dtype is set for params, cast the param to that dtype
+    attr_dict = sym.attr_dict()
+    for sym_name in sym.list_arguments():
+        if sym_name in attr_dict and "__dtype__" in attr_dict[sym_name]:
+            if attr_dict[sym_name]["__dtype__"] != "-1":
+                typ = _DTYPE_MX_TO_NP[int(attr_dict[sym_name]["__dtype__"])]
+                arg_params[sym_name] = arg_params[sym_name].astype(typ)
+
+    for sym_name in sym.list_auxiliary_states():
+        if sym_name in attr_dict and "__dtype__" in attr_dict[sym_name]:
+            if attr_dict[sym_name]["__dtype__"] != "-1":
+                typ = _DTYPE_MX_TO_NP[int(attr_dict[sym_name]["__dtype__"])]
+                aux_params[sym_name] = aux_params[sym_name].astype(typ)
+
+    # Return the converted symbol and casted params
+    return sym, arg_params, aux_params
+
+def convert_hybrid_block(block, target_dtype="float16", target_dtype_ops=None,
+                         fp32_ops=None, conditional_fp32_ops=None,
+                         excluded_sym_names=None, ctx=gpu(0),
+                         cast_optional_params=False):
+    """Given a hybrid block/symbol block representing a FP32 model and a target_dtype,
+    return a block with mixed precision support which can be used for inference use cases.
+
+    Parameters
+    ----------
+    block : HybridBlock or SymbolBlock object
+        FP32 HybridBlock or SymbolBlock object
+    target_dtype : str or numpy
+        currently only supports fp16. The target dtype indicates to add cast layers
+        when possible so that lower precision computation can be leveraged.
+    target_precision_ops : list of strs
+        Override the list of operator names casted to target_dtype.
+        If None, uses the framework's default list to be casted to FP32.
+    conditional_fp32_ops : list of (str, str, list of str)
+        Override the list of functions to be casted to FP32.
+        The format of the list is
+        (name of the function, name of the parameter,
+         list of values of the parameter that make the operator to be casted to FP32
+    excluded_sym_names : list of strs
+        A list of strings that represent the names of symbols that users want to exclude
+        from being quantized
+    ctx : Context
+        Context on which model parameters should live
+    cast_optional_params : bool, default False
+        Whether to cast the arg_params and aux_params that don't require to be in FP16
+        because of a cast layer following it, but will reduce the computation and memory
+        overhead of the model if casted.
+    """
+    from ...gluon import HybridBlock, SymbolBlock
+    assert isinstance(block, HybridBlock), "block input should be a HybridBlock"
+    if not block._cached_graph:
+        raise RuntimeError(
+            "Please first call block.hybridize() and then run forward with "
+            "this block at least once before calling export.")
+
+    # Prepare inputs to pass to the convert_symbol API
+    inputs, sym = block._cached_graph
+    input_names = []
+    for inp in inputs:
+        input_names.append(inp.name)
+    converted_sym = convert_symbol(sym, target_dtype, target_dtype_ops,
+                                   fp32_ops, conditional_fp32_ops,
+                                   excluded_sym_names, data_names=input_names,
+                                   cast_optional_params=cast_optional_params)
+
+    arg_names = set(converted_sym.list_arguments())
+    aux_names = set(converted_sym.list_auxiliary_states())
+    arg_dict = {}
+
+    # If dtype for the param was set in the json, cast the
+    # param to this dtype
+    attr_dict = converted_sym.attr_dict()
+    for name, param in block.collect_params().items():
+        if name in arg_names:
+            arg_dict['arg:%s'%name] = param._reduce()
+            if name in attr_dict and "__dtype__" in attr_dict[name]:
+                if attr_dict[name]["__dtype__"] != "-1":
+                    typ = _DTYPE_MX_TO_NP[int(attr_dict[name]["__dtype__"])]
+                    arg_dict['arg:%s'%name] = arg_dict['arg:%s'%name].astype(typ)
+        else:
+            assert name in aux_names
+            arg_dict['aux:%s'%name] = param._reduce()
+            if name in attr_dict and "__dtype__" in attr_dict[name]:
+                if attr_dict[name]["__dtype__"] != "-1":
+                    typ = _DTYPE_MX_TO_NP[int(attr_dict[name]["__dtype__"])]
+                    arg_dict['aux:%s'%name] = arg_dict['aux:%s'%name].astype(typ)
+
+    # Create a symbolblock and cast the params to the dtypes based
+    # on the dtype information from the converted_symbol
+    ret = SymbolBlock(converted_sym, inputs)
+    for key, param in ret.collect_params().items():
+        arg_param_name = "arg:%s" % key
+        if arg_param_name in arg_dict and param.dtype != arg_dict[arg_param_name].dtype:
+            param.cast(arg_dict[arg_param_name].dtype)
+
+        aux_param_name = "aux:%s" % key
+        if aux_param_name in arg_dict and param.dtype != arg_dict[aux_param_name].dtype:
+            param.cast(arg_dict[aux_param_name].dtype)
+
+    ret.collect_params().load_dict(arg_dict, ctx=ctx)
+    return ret
+
+def list_fp16_ops():
+    """Get the default list of FP16 ops for AMP
+    """
+    return lists.symbol.FP16_FUNCS
+
+def list_fp32_ops():
+    """Get the default list of FP32 ops for AMP
+    """
+    return lists.symbol.FP32_FUNCS
+
+def list_fp16_fp32_ops():
+    """Get the default list of ops which run in both FP16 and FP32
+    """
+    return lists.symbol.FP16_FP32_FUNCS
+
+def list_conditional_fp32_ops():
+    """Get the conditional fp32 ops list
+    """
+    return lists.symbol.CONDITIONAL_FP32_FUNCS
diff --git a/python/mxnet/gluon/parameter.py b/python/mxnet/gluon/parameter.py
index 548407584715..a174d82341af 100644
--- a/python/mxnet/gluon/parameter.py
+++ b/python/mxnet/gluon/parameter.py
@@ -16,7 +16,7 @@
 # under the License.
 
 # coding: utf-8
-# pylint: disable=unnecessary-pass
+# pylint: disable=unnecessary-pass, too-many-lines
 """Neural network parameter."""
 __all__ = ['DeferredInitializationError', 'Parameter', 'Constant',
            'ParameterDict', 'tensor_types']
@@ -955,23 +955,51 @@ def load(self, filename, ctx=None, allow_missing=False,
                 assert name.startswith(restore_prefix), \
                     "restore_prefix is '%s' but Parameters name '%s' does not start " \
                     "with '%s'"%(restore_prefix, name, restore_prefix)
-        lprefix = len(restore_prefix)
         ndarray_load = ndarray.load(filename)
+        self.load_dict(ndarray_load, ctx, allow_missing,
+                       ignore_extra, restore_prefix, filename, cast_dtype, dtype_source)
+
+    def load_dict(self, param_dict, ctx=None, allow_missing=False,
+                  ignore_extra=False, restore_prefix='', filename=None, cast_dtype=False,
+                  dtype_source="current"):
+        """Load parameters from dict
+
+        Parameters
+        ----------
+        param_dict : dict
+            Dictionary containing model parameters, preprended with arg: and aux: names
+        ctx : Context or list of Context
+            Context(s) initialize loaded parameters on.
+        allow_missing : bool, default False
+            Whether to silently skip loading parameters not represented in the file.
+        ignore_extra : bool, default False
+            Whether to silently ignore parameters from the file that are not
+            present in this ParameterDict.
+        restore_prefix : str, default ''
+            prepend prefix to names of stored parameters before loading
+        filename : str, default None
+        cast_dtype : bool, default False
+            Cast the data type of the NDArray loaded from the checkpoint to the dtype
+            provided by the Parameter if any
+        """
+        lprefix = len(restore_prefix)
         loaded = [(k[4:] if k.startswith('arg:') or k.startswith('aux:') else k, v) \
-                  for k, v in ndarray_load.items()] if isinstance(ndarray_load, dict) else ndarray_load
+                  for k, v in param_dict.items()] if isinstance(param_dict, dict) else param_dict
         arg_dict = {restore_prefix+k: v for k, v in loaded}
+        error_str = "file: %s" % (filename) if filename else "param_dict"
         if not allow_missing:
             for name in self.keys():
                 assert name in arg_dict, \
-                    "Parameter '%s' is missing in file '%s', which contains parameters: %s. " \
+                    "Parameter '%s' is missing in %s, which contains parameters: %s. " \
                     "Please make sure source and target networks have the same prefix."%(
-                        name[lprefix:], filename, _brief_print_list(arg_dict.keys()))
+                        name[lprefix:], error_str, _brief_print_list(arg_dict.keys()))
         for name in arg_dict:
             if name not in self._params:
                 assert ignore_extra, \
-                    "Parameter '%s' loaded from file '%s' is not present in ParameterDict, " \
+                    "Parameter '%s' loaded from %s is not present in ParameterDict, " \
                     "choices are: %s. Set ignore_extra to True to ignore. " \
                     "Please make sure source and target networks have the same prefix."%(
-                        name[lprefix:], filename, _brief_print_list(self._params.keys()))
+                        name[lprefix:], error_str, _brief_print_list(self._params.keys()))
                 continue
-            self[name]._load_init(arg_dict[name], ctx, cast_dtype=cast_dtype, dtype_source=dtype_source)
+            self[name]._load_init(arg_dict[name], ctx, cast_dtype=cast_dtype,
+                                  dtype_source=dtype_source)
diff --git a/python/mxnet/module/executor_group.py b/python/mxnet/module/executor_group.py
index c4050699bd52..637acce317cc 100755
--- a/python/mxnet/module/executor_group.py
+++ b/python/mxnet/module/executor_group.py
@@ -26,6 +26,7 @@
 from .. import ndarray as nd
 from ..io import DataDesc
 from ..executor_manager import _split_input_slice
+from ..ndarray import _DTYPE_MX_TO_NP
 
 
 def _load_general(data, targets, major_axis):
@@ -651,6 +652,13 @@ def _bind_ith_exec(self, i, data_shapes, label_shapes, shared_group):
             input_shapes.update(dict(label_shapes))
 
         input_types = {x.name: x.dtype for x in data_shapes}
+        attr_dict = self.symbol.attr_dict()
+
+        for sym_name in self.symbol.list_inputs():
+            if sym_name in input_types and sym_name in attr_dict \
+            and "__dtype__" in attr_dict[sym_name] and attr_dict[sym_name]["__dtype__"] != "-1":
+                input_types[sym_name] = _DTYPE_MX_TO_NP[int(attr_dict[sym_name]["__dtype__"])]
+
         if label_shapes is not None:
             input_types.update({x.name: x.dtype for x in label_shapes})
 
diff --git a/python/mxnet/test_utils.py b/python/mxnet/test_utils.py
index d247c0fcde95..aa46a9628b81 100644
--- a/python/mxnet/test_utils.py
+++ b/python/mxnet/test_utils.py
@@ -29,6 +29,7 @@
 import logging
 import bz2
 import zipfile
+import json
 from contextlib import contextmanager
 import numpy as np
 import numpy.testing as npt
@@ -1521,6 +1522,72 @@ def download(url, fname=None, dirname=None, overwrite=False, retries=5):
     logging.info("downloaded %s into %s successfully", url, fname)
     return fname
 
+def download_model(model_name, dst_dir='./', meta_info=None):
+    """Download a model from data.mxnet.io
+
+    Parameters
+    ----------
+    model_name : str
+        Model name to download
+    dst_dir : str
+        Destination Directory to download the model
+    meta_info : dict of dict
+        Mapping from model_name to dict of the following structure:
+        {'symbol': url, 'params': url}
+
+    Returns
+    -------
+    Two element tuple containing model_name and epoch for the params saved
+    """
+    _base_model_url = 'http://data.mxnet.io/models/'
+    _default_model_info = {
+        'imagenet1k-inception-bn': {'symbol':_base_model_url+'imagenet/inception-bn/Inception-BN-symbol.json',
+                                    'params':_base_model_url+'imagenet/inception-bn/Inception-BN-0126.params'},
+        'imagenet1k-resnet-18': {'symbol':_base_model_url+'imagenet/resnet/18-layers/resnet-18-symbol.json',
+                                 'params':_base_model_url+'imagenet/resnet/18-layers/resnet-18-0000.params'},
+        'imagenet1k-resnet-34': {'symbol':_base_model_url+'imagenet/resnet/34-layers/resnet-34-symbol.json',
+                                 'params':_base_model_url+'imagenet/resnet/34-layers/resnet-34-0000.params'},
+        'imagenet1k-resnet-50': {'symbol':_base_model_url+'imagenet/resnet/50-layers/resnet-50-symbol.json',
+                                 'params':_base_model_url+'imagenet/resnet/50-layers/resnet-50-0000.params'},
+        'imagenet1k-resnet-101': {'symbol':_base_model_url+'imagenet/resnet/101-layers/resnet-101-symbol.json',
+                                  'params':_base_model_url+'imagenet/resnet/101-layers/resnet-101-0000.params'},
+        'imagenet1k-resnet-152': {'symbol':_base_model_url+'imagenet/resnet/152-layers/resnet-152-symbol.json',
+                                  'params':_base_model_url+'imagenet/resnet/152-layers/resnet-152-0000.params'},
+        'imagenet1k-resnext-50': {'symbol':_base_model_url+'imagenet/resnext/50-layers/resnext-50-symbol.json',
+                                  'params':_base_model_url+'imagenet/resnext/50-layers/resnext-50-0000.params'},
+        'imagenet1k-resnext-101': {'symbol':_base_model_url+'imagenet/resnext/101-layers/resnext-101-symbol.json',
+                                   'params':_base_model_url+'imagenet/resnext/101-layers/resnext-101-0000.params'},
+        'imagenet1k-resnext-101-64x4d':
+            {'symbol':_base_model_url+'imagenet/resnext/101-layers/resnext-101-64x4d-symbol.json',
+             'params':_base_model_url+'imagenet/resnext/101-layers/resnext-101-64x4d-0000.params'},
+        'imagenet11k-resnet-152':
+            {'symbol':_base_model_url+'imagenet-11k/resnet-152/resnet-152-symbol.json',
+             'params':_base_model_url+'imagenet-11k/resnet-152/resnet-152-0000.params'},
+        'imagenet11k-place365ch-resnet-152':
+            {'symbol':_base_model_url+'imagenet-11k-place365-ch/resnet-152-symbol.json',
+             'params':_base_model_url+'imagenet-11k-place365-ch/resnet-152-0000.params'},
+        'imagenet11k-place365ch-resnet-50':
+            {'symbol':_base_model_url+'imagenet-11k-place365-ch/resnet-50-symbol.json',
+             'params':_base_model_url+'imagenet-11k-place365-ch/resnet-50-0000.params'},
+    }
+
+
+    if meta_info is None:
+        meta_info = _default_model_info
+    meta_info = dict(meta_info)
+    if model_name not in meta_info:
+        return (None, 0)
+    if not os.path.isdir(dst_dir):
+        os.mkdir(dst_dir)
+    meta = dict(meta_info[model_name])
+    assert 'symbol' in meta, "missing symbol url"
+    model_name = os.path.join(dst_dir, model_name)
+    mx.test_utils.download(meta['symbol'], model_name+'-symbol.json')
+    assert 'params' in meta, "mssing parameter file url"
+    mx.test_utils.download(meta['params'], model_name+'-0000.params')
+    return (model_name, 0)
+
+
 def get_mnist():
     """Download and load the MNIST dataset
 
@@ -2073,6 +2140,22 @@ def compare_optimizer(opt1, opt2, shape, dtype, w_stype='default', g_stype='defa
         compare_ndarray_tuple(state1, state2, rtol=rtol, atol=atol)
     assert_almost_equal(w1.asnumpy(), w2.asnumpy(), rtol=rtol, atol=atol)
 
+
+def same_symbol_structure(sym1, sym2):
+    """Compare two symbols to check if they have the same computation graph structure.
+    Returns true if operator corresponding to a particular node id is same in both
+    symbols for all nodes
+    """
+    conf = json.loads(sym1.tojson())
+    nodes = conf["nodes"]
+    conf2 = json.loads(sym2.tojson())
+    nodes2 = conf2["nodes"]
+    for node1, node2 in zip(nodes, nodes2):
+        if node1["op"] != node2["op"]:
+            return False
+    return True
+
+
 class EnvManager(object):
     """Environment variable setter and unsetter via with idiom"""
     def __init__(self, key, val):
diff --git a/src/c_api/c_api_symbolic.cc b/src/c_api/c_api_symbolic.cc
index 4c6229ee29b0..80ae5438c20d 100644
--- a/src/c_api/c_api_symbolic.cc
+++ b/src/c_api/c_api_symbolic.cc
@@ -810,6 +810,210 @@ int MXQuantizeSymbol(SymbolHandle sym_handle,
   API_END_HANDLE_ERROR(delete s);
 }
 
+// helper function to add mapping of node_name -> dtype map
+// for the given indexed graph and inferred_dtypes
+static void _SetInputDTypes(
+    const nnvm::IndexedGraph& idx,
+    const nnvm::DTypeVector& inferred_dtypes,
+    std::unordered_map<std::string, int>* node_name_dtype_map,
+    std::unordered_map<std::string, int>* node_without_dtype_map) {
+  const std::string dtype_keyword = "__dtype__";
+  for (uint32_t nid : idx.input_nodes()) {
+    const auto& node = idx[nid].source;
+    const auto& node_with_dtype = node->attrs.dict.find(dtype_keyword);
+    // input nodes classified into nodes_with_dtype, nodes_without_dtype
+    // This classification required because if param_names not provided
+    // we want to update dtypes of only those nodes which have dtypes set
+    // inferred_dtypes are obtained for the nodes, if unknown
+    // dtype is set to fp32
+    if (node_with_dtype != node->attrs.dict.end()) {
+      if (inferred_dtypes[idx.entry_id(nid, 0)] == -1) {
+        (*node_name_dtype_map)[node->attrs.name] = 0;
+      } else {
+        (*node_name_dtype_map)[node->attrs.name] =
+            inferred_dtypes[idx.entry_id(nid, 0)];
+      }
+    } else {
+      if (inferred_dtypes[idx.entry_id(nid, 0)] == -1) {
+        (*node_without_dtype_map)[node->attrs.name] = 0;
+      } else {
+        (*node_without_dtype_map)[node->attrs.name] =
+            inferred_dtypes[idx.entry_id(nid, 0)];
+      }
+    }
+  }
+}
+
+// helper function update the node dtype attrs for a vector of nodeptrs
+// given the node name to dtype information and the names of model_params
+// if model_params is provided the function will dtype of only model params.
+// if model_params is empty, the function will dtype of all nodes which had
+// a prior dtype set.
+// args is a const_reference vector of NodePtrs. NodePtrs are immutable but
+// the Nodes they are pointing will be mutated in this function
+static void _UpdateSymDTypeAttrs(
+    const std::unordered_map<std::string, int>& node_name_dtype_map,
+    const std::unordered_map<std::string, int>& node_without_dtype_map,
+    const std::unordered_set<std::string>& model_params,
+    const std::vector<nnvm::NodePtr>& args) {
+  const std::string dtype_keyword = "__dtype__";
+
+  // Update args to have the right dtype attrs
+  if (model_params.size() > 0) {
+    // if model params provided, set dtype only for model params
+    for (size_t i = 0; i < args.size(); ++i) {
+      const std::string& node_name = args[i]->attrs.name;
+      auto it_model_params = model_params.find(node_name);
+      auto it_with_dtype = node_name_dtype_map.find(node_name);
+      auto it_without_dtype = node_without_dtype_map.find(node_name);
+      if (it_model_params != model_params.end()) {
+        // need to update __dtype__ attribute if already set, else set it
+        if (it_with_dtype != node_name_dtype_map.end()) {
+          args[i]->attrs.dict[dtype_keyword] =
+              std::to_string(it_with_dtype->second);
+        } else {
+          CHECK(it_without_dtype != node_without_dtype_map.end())
+              << "make sure all nodes without dtype have properly been added "
+                 "in node_without_dtype_map";
+          args[i]->attrs.dict[dtype_keyword] =
+              std::to_string(it_without_dtype->second);
+        }
+      }
+    }
+  } else {
+    // if model params not provided, update __dtype__ for all inputs,
+    // which already had it set, don't touch the rest
+    for (size_t i = 0; i < args.size(); ++i) {
+      auto it = node_name_dtype_map.find(args[i]->attrs.name);
+      if (it != node_name_dtype_map.end()) {
+        if (args[i]->attrs.dict.find(dtype_keyword) !=
+            args[i]->attrs.dict.end()) {
+          args[i]->attrs.dict[dtype_keyword] = std::to_string(it->second);
+        }
+      }
+    }
+  }
+}
+
+int MXReducePrecisionSymbol(SymbolHandle sym_handle,
+                            SymbolHandle *ret_sym_handle,
+                            mx_uint num_args,
+                            const int *arg_type_data,
+                            mx_uint num_ind_ptr,
+                            const int* ind_ptr,
+                            const int* target_dtype,
+                            const int cast_optional_params,
+                            const mx_uint num_target_dtype_op_names,
+                            const mx_uint num_fp32_op_names,
+                            const mx_uint num_widest_dtype_op_names,
+                            const mx_uint num_conditional_fp32_op_names,
+                            const mx_uint num_excluded_symbols,
+                            const mx_uint num_model_params,
+                            const char **target_dtype_op_names,
+                            const char **fp32_op_names,
+                            const char **widest_dtype_op_names,
+                            const char **conditional_fp32_op_names,
+                            const char **excluded_symbols,
+                            const char **param_names,
+                            const char **param_vals,
+                            const char **model_param_names,
+                            const char **arg_names) {
+  nnvm::Symbol *result_sym = new nnvm::Symbol();
+  API_BEGIN();
+  nnvm::Symbol *sym = static_cast<nnvm::Symbol *>(sym_handle);
+  nnvm::Graph g = Symbol2Graph(*sym);
+  std::unordered_set<std::string> target_dtype_ops;
+  std::unordered_set<std::string> fp32_ops;
+  std::unordered_set<std::string> widest_dtype_ops;
+  std::unordered_set<std::string> excluded_syms;
+  std::unordered_set<std::string> model_params;
+
+  // conditional_fp32_ops contains the mapping of op_name -> (map of param_name -> param_values)
+  // which need to be conditionally selected to be casted to FP32
+  std::unordered_map<std::string,
+                     std::unordered_map<std::string,
+                                        std::vector<std::string>>> conditional_fp32_ops;
+  int target_dt = *target_dtype;
+
+  for (size_t i = 0; i < num_target_dtype_op_names; ++i) {
+    target_dtype_ops.emplace(target_dtype_op_names[i]);
+  }
+  for (size_t i = 0; i < num_fp32_op_names; ++i) {
+    fp32_ops.emplace(fp32_op_names[i]);
+  }
+  for (size_t i = 0; i < num_widest_dtype_op_names; ++i) {
+    widest_dtype_ops.emplace(widest_dtype_op_names[i]);
+  }
+  for (size_t i = 0; i < num_excluded_symbols; ++i) {
+    excluded_syms.emplace(excluded_symbols[i]);
+  }
+  for (size_t i = 0; i < num_model_params; ++i) {
+    model_params.emplace(model_param_names[i]);
+  }
+
+  for (size_t i = 0; i < num_ind_ptr - 1; ++i) {
+    for (int j = ind_ptr[i]; j < ind_ptr[i + 1]; ++j) {
+      conditional_fp32_ops[conditional_fp32_op_names[i]][param_names[i]]
+          .emplace_back(std::string(param_vals[j]));
+    }
+  }
+
+  std::unordered_map<std::string, int> kwargs;
+  std::unordered_map<std::string, int> node_name_dtype_map, node_without_dtype_map;
+  nnvm::DTypeVector arg_types(g.indexed_graph().input_nodes().size(), -1);
+  for (mx_uint i = 0; i < num_args; ++i) {
+    kwargs[arg_names[i]] = arg_type_data[i];
+    node_name_dtype_map[arg_names[i]] = arg_type_data[i];
+  }
+  mxnet::MatchArguments(g.indexed_graph(), kwargs, &arg_types, "InferType");
+
+  g.attrs["target_dtype_ops"] =
+      std::make_shared<nnvm::any>(std::move(target_dtype_ops));
+  g.attrs["fp32_ops"] = std::make_shared<nnvm::any>(std::move(fp32_ops));
+  g.attrs["widest_dtype_ops"] =
+      std::make_shared<nnvm::any>(std::move(widest_dtype_ops));
+  g.attrs["conditional_fp32_ops"] =
+      std::make_shared<nnvm::any>(std::move(conditional_fp32_ops));
+  g.attrs["excluded_syms"] =
+      std::make_shared<nnvm::any>(std::move(excluded_syms));
+  g.attrs["target_dtype"] = std::make_shared<nnvm::any>(target_dt);
+
+  g = ApplyPass(std::move(g), "ReducePrecision");
+  // Need to run type inference since it is possible that inferred
+  // type of some inputs has changed
+  g = mxnet::exec::InferType(std::move(g), std::move(arg_types), "");
+  const nnvm::DTypeVector &inferred_dtypes =
+      g.GetAttr<nnvm::DTypeVector>("dtype");
+
+  g.attrs["inferred_dtypes"] = std::make_shared<dmlc::any>(std::move(inferred_dtypes));
+  g.attrs["target_dtype"] = std::make_shared<nnvm::any>(target_dt);
+
+  if (cast_optional_params) {
+    g = ApplyPass(std::move(g), "AMPInferUnknown");
+    const nnvm::DTypeVector &inferred_dtype_result =
+        g.GetAttr<nnvm::DTypeVector>("inferred_dtype_result");
+    const nnvm::IndexedGraph &idx = g.indexed_graph();
+    // set node name -> input dtype mapping using infer dtype
+    _SetInputDTypes(idx, inferred_dtype_result, &node_name_dtype_map, &node_without_dtype_map);
+  } else {
+    const nnvm::IndexedGraph &idx = g.indexed_graph();
+    // set node name -> input dtype mapping using infer dtype
+    _SetInputDTypes(idx, inferred_dtypes, &node_name_dtype_map, &node_without_dtype_map);
+  }
+
+
+  result_sym->outputs = g.outputs;
+  *ret_sym_handle = result_sym;
+  nnvm::Symbol *ret_sym = static_cast<nnvm::Symbol *>(*ret_sym_handle);
+  const std::vector<nnvm::NodePtr>& args = ret_sym->ListInputs(nnvm::Symbol::kAll);
+
+  // update symbol dtype attrs using the node name -> dtype mapping, if dtype is already set
+  // in the symbol, else set dtype for the model_params
+  _UpdateSymDTypeAttrs(node_name_dtype_map, node_without_dtype_map, model_params, args);
+
+  API_END_HANDLE_ERROR(delete result_sym);
+}
+
 int MXSetCalibTableToQuantizedSymbol(SymbolHandle qsym_handle,
                                      const mx_uint num_layers,
                                      const char** layer_names,
diff --git a/src/nnvm/amp_infer_unknown.cc b/src/nnvm/amp_infer_unknown.cc
new file mode 100644
index 000000000000..1de3104d054f
--- /dev/null
+++ b/src/nnvm/amp_infer_unknown.cc
@@ -0,0 +1,148 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2016 by Contributors
+ * \file low_precision_pass.cc
+ * \brief Use the Mixed Precision Model to infer the dtypes of
+ * unknown input nodes
+ */
+
+#include <nnvm/node.h>
+#include <nnvm/graph.h>
+#include <nnvm/pass.h>
+#include <nnvm/op_attr_types.h>
+#include <nnvm/graph_attr_types.h>
+#include <mxnet/base.h>
+#include <algorithm>
+#include <functional>
+#include "../common/utils.h"
+#include "../operator/tensor/amp_cast.h"
+
+namespace mxnet {
+using nnvm::Graph;
+using nnvm::NodePtr;
+using nnvm::NodeEntry;
+using dmlc::any;
+using mxnet::op::AMPCastParam;
+
+// If a var node is not visited, visit it and set inferred_dtype_result as result_dtype,
+// If already visited compare the result_dtype with existing inferred_dtype_result
+static void CheckAndUpdateInferredDtypes(
+    const nnvm::DTypeVector &inferred_dtypes, const nnvm::IndexedGraph &idx,
+    const NodeEntry &node_entry,
+    mshadow::TypeFlag result_dtype,
+    std::unordered_map<std::string, mshadow::TypeFlag> *visited_vars,
+    nnvm::DTypeVector *inferred_dtype_result) {
+  const NodePtr &input_node = node_entry.node;
+  if (!visited_vars->count(input_node->attrs.name)) {
+    if ((*inferred_dtype_result)[idx.entry_id(node_entry)] == -1) {
+      (*visited_vars)[input_node->attrs.name] = result_dtype;
+      (*inferred_dtype_result)[idx.entry_id(node_entry)] = result_dtype;
+    }
+  } else {
+    auto it = visited_vars->find(input_node->attrs.name);
+    CHECK(it != visited_vars->end());
+    if (it->second != result_dtype) {
+      (*inferred_dtype_result)[idx.entry_id(node_entry)] =
+          inferred_dtypes[idx.entry_id(node_entry)];
+    }
+  }
+}
+
+// Graph pass to infer unknown nodes which are input nodes
+// as FP16 if possible
+Graph AMPInferUnknown(Graph &&src) {
+  const nnvm::DTypeVector &inferred_dtypes =
+      src.GetAttr<nnvm::DTypeVector>("inferred_dtypes");
+  const int target_dtype = src.GetAttr<int>("target_dtype");
+  CHECK(target_dtype == mshadow::kFloat16)
+      << "Only float16 target_dtype is supported yet";
+
+  nnvm::DTypeVector inferred_dtype_result(inferred_dtypes);
+  const nnvm::IndexedGraph &idx = src.indexed_graph();
+
+  std::unordered_map<std::string, mshadow::TypeFlag> visited_vars;
+
+  // Visits all nodes which are amp_cast and amp_multicast,
+  // and check if inputs to these nodes are variables.
+  // If input nodes are variables, set dtype for these inputs
+  // and check for conflicts if an input node goes to two cast nodes
+  DFSVisit(src.outputs, [&](const NodePtr &node) {
+    if (!node->is_variable()) {
+      std::string op_name = node->op()->name;
+
+      if (op_name == "amp_cast") {
+        // for amp_cast set inferred_dtypes for input_nodes and add
+        // to visited_vars, if a var is being visited second time
+        // and already has dtype set, make sure the dtype inferred again
+        // is same, otherwise reset dtype to original dtype
+        for (const NodeEntry &node_entry : node->inputs) {
+          const NodePtr &input_node = node_entry.node;
+          if (input_node->is_variable() &&
+              (node->attrs.dict.find("dtype") != node->attrs.dict.end())) {
+            const AMPCastParam &param =
+                nnvm::get<AMPCastParam>(node->attrs.parsed);
+            CHECK(param.dtype != -1)
+                << "amp_cast node shouldn't have unknown dtype";
+            CheckAndUpdateInferredDtypes(inferred_dtypes, idx, node_entry,
+                                         static_cast<mshadow::TypeFlag>(param.dtype),
+                                         &visited_vars, &inferred_dtype_result);
+          }
+        }
+      } else if (op_name == "amp_multicast") {
+        // for amp_multicast, for non var input nodes, keep track of biggest dtype.
+        // If the biggest dtype is same as target_dtype, set this for the input_var nodes
+        // if it is not already set
+        mshadow::TypeFlag max_dtype = static_cast<mshadow::TypeFlag>(target_dtype);
+        for (const NodeEntry& node_entry : node->inputs) {
+          const NodePtr& input_node = node_entry.node;
+          if (!input_node->is_variable()) {
+            // if one input is not a variable then don't infer the dtype of other
+            // input node dtypes
+            max_dtype = mshadow::kFloat32;
+          }
+        }
+        if (max_dtype == target_dtype) {
+          for (const NodeEntry &node_entry : node->inputs) {
+            const NodePtr &input_node = node_entry.node;
+            if (input_node->is_variable()) {
+              CheckAndUpdateInferredDtypes(inferred_dtypes, idx, node_entry,
+                                           max_dtype, &visited_vars,
+                                           &inferred_dtype_result);
+            }
+          }
+        }
+      }
+    }
+  });
+
+  Graph ret;
+  ret.attrs["inferred_dtype_result"] =
+      std::make_shared<dmlc::any>(std::move(inferred_dtype_result));
+  ret.outputs = std::move(src.outputs);
+  return ret;
+}
+
+NNVM_REGISTER_PASS(AMPInferUnknown)
+    .describe("Infer dtypes of different nodes for the mixed precision model")
+    .set_body(AMPInferUnknown)
+    .set_change_graph(true)
+    .provide_graph_attr("inferred_dtypes");
+}  // namespace mxnet
diff --git a/src/nnvm/low_precision_pass.cc b/src/nnvm/low_precision_pass.cc
new file mode 100644
index 000000000000..7cd0178108f4
--- /dev/null
+++ b/src/nnvm/low_precision_pass.cc
@@ -0,0 +1,261 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2016 by Contributors
+ * \file low_precision_pass.cc
+ * \brief Return new graph with amp_cast and amp_multicast operators added wherever required
+ */
+
+#include <nnvm/node.h>
+#include <nnvm/graph.h>
+#include <nnvm/pass.h>
+#include <nnvm/op_attr_types.h>
+#include <mxnet/base.h>
+#include <algorithm>
+#include <functional>
+
+namespace mxnet {
+using nnvm::Symbol;
+using nnvm::Node;
+using nnvm::NodePtr;
+using nnvm::NodeEntry;
+using nnvm::Graph;
+
+// create a node for operator : op_name with name : node_name
+static NodePtr CreateNode(std::string op_name, std::string node_name) {
+  NodePtr node = Node::Create();
+  node->attrs.name = node_name;
+  if (op_name == "nullptr") {
+    node->attrs.op = nullptr;
+    // ugly workaround because VariableParam is not exposed
+    node->attrs.parsed = nnvm::Symbol::CreateVariable(node->attrs.name)
+                             .outputs[0]
+                             .node->attrs.parsed;
+  } else {
+    node->attrs.op = Op::Get(op_name);
+  }
+  return node;
+}
+
+static NodePtr InsertNode(std::string op_name, std::string node_name, NodePtr current,
+                          NodeEntry previous) {
+    NodePtr node = CreateNode(op_name, node_name);
+    node->inputs.emplace_back(previous);
+    current->inputs.emplace_back(NodeEntry{node, 0, 0});
+    return node;
+}
+
+// get suffix for a node entry so that it can be used for amp_cast/amp_multicast node name
+static std::string GetSuffix(const nnvm::NodeEntry &node_entry,
+                             const std::unordered_map<Node*, NodePtr> &mirror_map) {
+  static const auto &flist_outputs =
+      nnvm::Op::GetAttr<nnvm::FListOutputNames>("FListOutputNames");
+  std::string suffix = "";
+  NodePtr mirror_node = mirror_map.at(node_entry.node.get());
+  if (mirror_node->op() != nullptr) {
+      auto list_output_names_func = flist_outputs.get(node_entry.node->op(), nullptr);
+      if (list_output_names_func != nullptr) {
+          std::vector<std::string> names = list_output_names_func(node_entry.node->attrs);
+          suffix = "_" + names[node_entry.index];
+      } else {
+          suffix = "_" + std::to_string(node_entry.index);
+      }
+  }
+  return suffix;
+}
+
+// add amp_cast node between curr_node and input
+static void AddCastNode(const nnvm::NodeEntry &e, const std::string &suffix,
+                        const nnvm::NodeEntry &input, const std::string dtype,
+                        nnvm::NodeEntryMap<NodeEntry> *mirror_entry_map,
+                        NodePtr curr_node) {
+  NodePtr cast_node =
+      InsertNode("amp_cast", e.node->attrs.name + suffix + "_amp_cast_" + dtype,
+                 curr_node, input);
+  cast_node->attrs.dict["dtype"] = dtype;
+  cast_node->op()->attr_parser(&(cast_node->attrs));
+  (*mirror_entry_map)[e] = NodeEntry{std::move(cast_node), 0, e.version};
+  return;
+}
+
+// add amp_multicast node between curr_node and inputs
+static void AddMultiCastNode(const std::vector<NodeEntry> &inputs,
+                             const std::string &node_name,
+                             const std::unordered_map<Node *, NodePtr> &mirror_map,
+                             NodePtr curr_node) {
+  NodePtr node =
+      CreateNode("amp_multicast",
+                 inputs[0].node->attrs.name + node_name + "_amp_multicast");
+  for (const auto &node_entry : inputs) {
+    NodePtr mirror_node = mirror_map.at(node_entry.node.get());
+    NodeEntry mirror_entry = NodeEntry{std::move(mirror_node), node_entry.index,
+                                       node_entry.version};
+    node->inputs.emplace_back(mirror_entry);
+  }
+  node->attrs.dict["num_outputs"] = std::to_string(inputs.size());
+  node->op()->attr_parser(&(node->attrs));
+  for (uint32_t i = 0; i < inputs.size(); ++i) {
+    const auto &e = inputs[i];
+    curr_node->inputs.emplace_back(
+        NodeEntry{node, static_cast<uint32_t>(i), e.version});
+  }
+  return;
+}
+
+static bool CheckConditionalFP32(
+    const std::unordered_map<
+        std::string, std::unordered_map<std::string, std::vector<std::string>>>
+        &conditional_fp32_ops,
+    const std::unordered_set<std::string> &excluded_syms, NodePtr node) {
+  if (node->is_variable() || (excluded_syms.count(node->attrs.name) > 0) ||
+      conditional_fp32_ops.count(node->op()->name) == 0) {
+    return false;
+  } else {
+    // Iterate through all conditional ops
+    auto it = conditional_fp32_ops.find(node->op()->name);
+    if (it != conditional_fp32_ops.end()) {
+      auto it_params = it->second;
+      // For each param name, iterate through param values to check
+      // if the provided param name is equal to any of the values
+      for (auto it_param = it_params.begin(); it_param != it_params.end();
+           it_param++) {
+        auto param_key = node->attrs.dict.find(it_param->first);
+        if (param_key != node->attrs.dict.end()) {
+          auto it_param_vals = it_param->second;
+          if (std::find(it_param_vals.begin(), it_param_vals.end(),
+                        param_key->second) != it_param_vals.end()) {
+            return true;
+          }
+        }
+      }
+    }
+    return false;
+  }
+}
+
+Graph ReducePrecision(Graph &&src) {
+  const auto target_dtype_ops =
+      src.GetAttr<std::unordered_set<std::string>>("target_dtype_ops");
+  const auto fp32_ops =
+      src.GetAttr<std::unordered_set<std::string>>("fp32_ops");
+  const auto widest_dtype_ops =
+      src.GetAttr<std::unordered_set<std::string>>("widest_dtype_ops");
+  const auto target_dtype = src.GetAttr<int>("target_dtype");
+  const auto excluded_syms = src.GetAttr<std::unordered_set<std::string>>("excluded_syms");
+  const auto conditional_fp32_ops = src.GetAttr<std::unordered_map<
+      std::string, std::unordered_map<std::string, std::vector<std::string>>>>(
+      "conditional_fp32_ops");
+
+  CHECK(target_dtype == mshadow::kFloat16)
+      << "Only float16 target_dtype is supported yet";
+
+  // Additional data structures to share common cast node inputs among different nodes
+  std::unordered_map<Node *, NodePtr> mirror_map;
+  nnvm::NodeEntryMap<NodeEntry> mirror_fp32_map;
+  nnvm::NodeEntryMap<NodeEntry> mirror_target_dtype_map;
+
+  // Visit nodes in a topologically sorted order
+  DFSVisit(src.outputs, [&](const NodePtr &node) {
+    NodePtr new_node = Node::Create(*node);
+    new_node->inputs.clear();
+
+    /* 1. for node which needs to run in FP32 mode, add amp_cast operators
+     * (to fp32) after its inputs
+     * 2. for node which needs to run in FP16 mode, add amp_cast operators
+     * (to target_dtype) after its inputs
+     * 3. for nodes which need to run in widest dtype among its inputs, add
+     * amp_multicast operators between op and its inputs
+     * 4. for nodes which need to run in FP32 mode, based on a specific condition,
+     * check the condition, and if true add amp_cast (to fp32) after its inputs
+     * 4. for other nodes, create copy node and add it to the mirror_map
+     */
+    if (!node->is_variable() && fp32_ops.count(node->op()->name) > 0 &&
+        excluded_syms.count(node->attrs.name) == 0) {
+      for (const auto& node_entry : node->inputs) {
+        if (mirror_fp32_map.count(node_entry)) {
+          new_node->inputs.emplace_back(mirror_fp32_map[node_entry]);
+        } else {
+          NodePtr mirror_node = mirror_map.at(node_entry.node.get());
+          NodeEntry mirror_entry = NodeEntry{mirror_node, node_entry.index, node_entry.version};
+          std::string suffix = GetSuffix(node_entry, mirror_map);
+          AddCastNode(node_entry, suffix, mirror_entry, "float32", &mirror_fp32_map,
+                      new_node);
+        }
+      }
+    } else if (!node->is_variable() &&
+               target_dtype_ops.count(node->op()->name) > 0 &&
+               excluded_syms.count(node->attrs.name) == 0) {
+      for (const auto& node_entry : node->inputs) {
+        if (mirror_target_dtype_map.count(node_entry)) {
+          new_node->inputs.emplace_back(mirror_target_dtype_map[node_entry]);
+        } else {
+          NodePtr mirror_node = mirror_map.at(node_entry.node.get());
+          NodeEntry mirror_entry = NodeEntry{mirror_node, node_entry.index, node_entry.version};
+          std::string suffix = GetSuffix(node_entry, mirror_map);
+          AddCastNode(node_entry, suffix, mirror_entry, "float16",
+                      &mirror_target_dtype_map, new_node);
+        }
+      }
+    } else if (!node->is_variable() &&
+               widest_dtype_ops.count(node->op()->name) > 0 &&
+               excluded_syms.count(node->attrs.name) == 0) {
+      CHECK(node->inputs.size() > 0)
+          << "Please check the symbol. node name: " << node->attrs.name
+          << "op name " << node->op()->name << " has no inputs."
+          << "It is likely that something went wrong during symbolic construction.";
+      const auto &e = node->inputs[0];
+      std::string suffix = GetSuffix(e, mirror_map);
+      AddMultiCastNode(node->inputs, suffix, mirror_map, new_node);
+    } else if (CheckConditionalFP32(conditional_fp32_ops, excluded_syms, node)) {
+      for (const auto& node_entry : node->inputs) {
+        if (mirror_fp32_map.count(node_entry)) {
+          new_node->inputs.emplace_back(mirror_fp32_map[node_entry]);
+        } else {
+          NodePtr mirror_node = mirror_map.at(node_entry.node.get());
+          NodeEntry mirror_entry = NodeEntry{mirror_node, node_entry.index, node_entry.version};
+          std::string suffix = GetSuffix(node_entry, mirror_map);
+          AddCastNode(node_entry, suffix, mirror_entry, "float32", &mirror_fp32_map,
+                      new_node);
+        }
+      }
+    } else {
+      for (const auto& node_entry : node->inputs) {
+        NodePtr mirror_node = mirror_map.at(node_entry.node.get());
+        new_node->inputs.emplace_back(mirror_node, node_entry.index, node_entry.version);
+      }
+    }
+    mirror_map[node.get()] = std::move(new_node);
+  });
+
+  std::vector<NodeEntry> outputs;
+  for (const auto& e : src.outputs) {
+      outputs.emplace_back(mirror_map.at(e.node.get()), e.index, e.version);
+  }
+
+  Graph ret;
+  ret.outputs = std::move(outputs);
+  return ret;
+}
+
+NNVM_REGISTER_PASS(ReducePrecision)
+    .describe("add cast layers for low precision inference")
+    .set_body(ReducePrecision)
+    .set_change_graph(true);
+}  // namespace mxnet
diff --git a/tests/python/gpu/test_contrib_amp.py b/tests/python/gpu/test_contrib_amp.py
new file mode 100644
index 000000000000..7927cc99160b
--- /dev/null
+++ b/tests/python/gpu/test_contrib_amp.py
@@ -0,0 +1,428 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import os
+import sys
+import mxnet as mx
+import numpy as np
+import warnings
+import collections
+import ctypes
+import mxnet.contrib.amp as amp
+from nose.tools import assert_raises
+from mxnet.test_utils import set_default_context, download_model, same_symbol_structure
+from mxnet.gluon.model_zoo.vision import get_model
+from mxnet.gluon import SymbolBlock
+from mxnet.contrib.amp import amp
+curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
+sys.path.insert(0, os.path.join(curr_path, '../unittest'))
+from common import with_seed, teardown
+
+def test_amp_coverage():
+    conditional = [item[0] for item in amp.lists.symbol.CONDITIONAL_FP32_FUNCS]
+
+    # Check for duplicates
+    for a in [amp.lists.symbol.FP16_FUNCS,
+          amp.lists.symbol.FP16_FP32_FUNCS,
+          amp.lists.symbol.FP32_FUNCS,
+          amp.lists.symbol.WIDEST_TYPE_CASTS,
+          conditional]:
+        ret = [item for item, count in collections.Counter(a).items() if count > 1]
+        assert ret == [], "Elements " + str(ret) + " are duplicated in the AMP lists."
+
+    t = []
+    for a in [amp.lists.symbol.FP16_FUNCS,
+              amp.lists.symbol.FP16_FP32_FUNCS,
+              amp.lists.symbol.FP32_FUNCS,
+              amp.lists.symbol.WIDEST_TYPE_CASTS,
+              conditional]:
+        t += a
+    ret = [item for item, count in collections.Counter(t).items() if count > 1]
+    assert ret == [], "Elements " + str(ret) + " exist in more than 1 AMP list."
+
+    # Check the coverage
+    py_str = lambda x: x.decode('utf-8')
+
+    plist = ctypes.POINTER(ctypes.c_char_p)()
+    size = ctypes.c_uint()
+
+    mx.base._LIB.MXListAllOpNames(ctypes.byref(size),
+                                     ctypes.byref(plist))
+    op_names = []
+    for i in range(size.value):
+        s = py_str(plist[i])
+        if not s.startswith("_backward") \
+           and not s.startswith("_contrib_backward_"):
+            op_names.append(s)
+
+    ret1 = set(op_names) - set(t)
+
+    if ret1 != set():
+        warnings.warn("Operators " + str(ret1) + " do not exist in AMP lists (in "
+                       "python/mxnet/contrib/amp/lists/symbol.py) - please add them. "
+                       """Please follow these guidelines for choosing a proper list:
+                       - if your operator is not to be used in a computational graph
+                         (e.g. image manipulation operators, optimizers) or does not have
+                         inputs, put it in FP16_FP32_FUNCS list,
+                       - if your operator requires FP32 inputs or is not safe to use with lower
+                         precision, put it in FP32_FUNCS list,
+                       - if your operator supports both FP32 and lower precision, has
+                         multiple inputs and expects all inputs to be of the same
+                         type, put it in WIDEST_TYPE_CASTS list,
+                       - if your operator supports both FP32 and lower precision and has
+                         either a single input or supports inputs of different type,
+                         put it in FP16_FP32_FUNCS list,
+                       - if your operator is both safe to use in lower precision and
+                         it is highly beneficial to use it in lower precision, then
+                         put it in FP16_FUNCS (this is unlikely for new operators)
+                       - If you are not sure which list to choose, FP32_FUNCS is the
+                         safest option""")
+
+@with_seed()
+def test_amp_conversion():
+    def check_amp_convert_symbol():
+        x = mx.sym.var("x")
+        y = mx.sym.var("y")
+        z = mx.sym.FullyConnected(x, y, num_hidden=10, no_bias=True)
+        siny = mx.sym.sin(y)
+        res = z + siny
+        # Compare symbols with similar computation graphs created using convert_symbol and manually.
+        res_converted = amp.convert_symbol(res, target_dtype="float16",
+                                           target_dtype_ops=["FullyConnected"],
+                                           fp32_ops=["sin"])
+
+        x_fp16 = mx.sym.amp_cast(x, dtype="float16")
+        y_fp16 = mx.sym.amp_cast(y, dtype="float16")
+        amp_casted_siny = mx.sym.sin(mx.sym.amp_cast(y, dtype="float32"))
+        z = mx.sym.FullyConnected(x_fp16, y_fp16, num_hidden=10, no_bias=True)
+        outs = mx.sym.amp_multicast(z, amp_casted_siny, num_outputs=2)
+        res_expected = outs[0] + outs[1]
+        assert same_symbol_structure(res_converted, res_expected), \
+            "convert_symbol generating wrong computation graph"
+
+        # convert_symbol called with incorrect inputs
+        assert_raises(AssertionError, amp.convert_symbol, res,
+                      target_dtype="float16", target_dtype_ops=["FullyConnected"],
+                      fp32_ops=["elemwise_add"])
+        assert_raises(AssertionError, amp.convert_symbol, res,
+                      target_dtype="float16", target_dtype_ops=["FullyConnected"],
+                      fp32_ops=["Activation"],
+                      conditional_fp32_ops=[('Activation', 'act_type', ['selu'])])
+        assert_raises(AssertionError, amp.convert_symbol, res,
+                      target_dtype="float16", target_dtype_ops=["Activation"],
+                      fp32_ops=["Activation"],
+                      conditional_fp32_ops=[('Activation', 'act_type', ['selu'])])
+        assert_raises(AssertionError, amp.convert_symbol, res,
+                      target_dtype="float16", target_dtype_ops=["FullyConnected"],
+                      fp32_ops=["FullyConnected"])
+
+        # Test for op in conditional ops with condition not satisfied
+        x = mx.sym.var("x")
+        y = mx.sym.var("y")
+        fc_cond = mx.sym.FullyConnected(x, y, num_hidden=10, no_bias=True)
+        res_converted = amp.convert_symbol(fc_cond, target_dtype="float16",
+                                           target_dtype_ops=[],
+                                           fp32_ops=["sin"],
+                                           conditional_fp32_ops=[("FullyConnected", "no_bias", ["False"])])
+
+        res_expected = mx.sym.FullyConnected(x, y, num_hidden=10, no_bias=True)
+        assert same_symbol_structure(res_converted, res_expected), \
+            "convert_symbol generating wrong computation graph when conditional ops is used"
+
+        # Test for op in conditional ops with condition satisfied
+        res_converted = amp.convert_symbol(fc_cond, target_dtype="float16", target_dtype_ops=[],
+                                           fp32_ops=["sin"],
+                                           conditional_fp32_ops=[("FullyConnected", "no_bias", ["True"])])
+        x_fp32 = mx.sym.amp_cast(x, dtype="float32")
+        y_fp32 = mx.sym.amp_cast(y, dtype="float32")
+        res_expected = mx.sym.FullyConnected(x_fp32, y_fp32, num_hidden=10, no_bias=True)
+        assert same_symbol_structure(res_converted, res_expected), \
+            "convert_symbol generating wrong computation graph when conditional ops used with satisfying condition"
+
+        # Test with a real world model, default inputs for convert_symbol
+        dir_path = os.path.dirname(os.path.realpath(__file__))
+        model_path = os.path.join(dir_path, 'model')
+        if not os.path.isdir(model_path):
+            os.mkdir(model_path)
+
+        prefix, epoch = download_model("imagenet1k-resnet-18", dst_dir=model_path)
+        sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)
+        inputs = {}
+        inputs['data'] = mx.nd.ones((1, 3, 224, 224))
+        inputs.update(arg_params)
+        converted_sym = amp.convert_symbol(sym)
+        exe = converted_sym.simple_bind(mx.gpu(0), data=(1, 3, 224, 224), grad_req='null')
+        exe.forward(is_train=False, **inputs)
+        exe.outputs[0].asnumpy()
+
+        inputs2 = {}
+        inputs2['data'] = mx.nd.ones((1, 3, 224, 224))
+        inputs2['fc1_weight'] = inputs['fc1_weight'].astype(np.float16)
+        inputs2['fc1_bias'] = inputs['fc1_bias'].astype(np.float16)
+
+        # Test with a real world model, tweak inputs for convert_symbol
+        converted_sym = amp.convert_symbol(sym, target_dtype="float16",
+                                           target_dtype_ops=["Convolution"], data_names=["data"],
+                                           cast_optional_params=True)
+        converted_sym2 = amp.convert_symbol(sym, target_dtype="float16",
+                                            target_dtype_ops=["Convolution"], data_names=["data"],
+                                            cast_optional_params=False)
+
+        exe = converted_sym.simple_bind(mx.gpu(0), data=(1, 3, 224, 224), grad_req='null')
+        exe2 = converted_sym2.simple_bind(mx.gpu(), data=(1, 3, 224, 224), grad_req='null')
+
+        converted_args = converted_sym.list_arguments()
+        converted_auxs = converted_sym.list_auxiliary_states()
+        for i, key in enumerate(exe.arg_arrays):
+            if converted_args[i] in arg_params:
+                arg_params[converted_args[i]] = arg_params[converted_args[i]].astype(exe.arg_arrays[i].dtype)
+        for i, key in enumerate(exe.aux_arrays):
+            if converted_auxs[i] in aux_params:
+                aux_params[converted_auxs[i]] = aux_params[converted_auxs[i]].astype(exe.aux_arrays[i].dtype)
+
+        inputs2.update(arg_params)
+        exe.forward(is_train=False, **inputs2)
+        exe.outputs[0].wait_to_read()
+
+        inputs['fc1_weight'] = inputs['fc1_weight'].astype(np.float16)
+        inputs['fc1_bias'] = inputs['fc1_bias'].astype(np.float16)
+        exe2.forward(is_train=False, **inputs)
+        exe2.outputs[0].wait_to_read()
+
+
+    def check_amp_convert_model():
+        # Test with real world model, default inputs for convert_model
+        dir_path = os.path.dirname(os.path.realpath(__file__))
+        model_path = os.path.join(dir_path, 'model')
+        if not os.path.isdir(model_path):
+            os.mkdir(model_path)
+        prefix, epoch = download_model("imagenet1k-resnet-18", dst_dir=model_path)
+
+        sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)
+
+        # Test with real world model, tweak inputs for convert_model
+        result_sym, result_arg_params, result_aux_params = amp.convert_model(sym,
+                                                                             arg_params,
+                                                                             aux_params,
+                                                                             target_dtype="float16",
+                                                                             target_dtype_ops=["Convolution"])
+        mod = mx.mod.Module(result_sym, data_names=["data"], label_names=["softmax_label"], context=mx.gpu())
+        mod.bind(data_shapes=[['data', (1, 3, 224, 224)]], label_shapes=[['softmax_label', (1,)]])
+
+        mod.set_params(result_arg_params, result_aux_params)
+        mod.forward(mx.io.DataBatch(data=[mx.nd.ones((1, 3, 224, 224))],
+                                    label=[mx.nd.ones((1,))]))
+        mod.get_outputs()[0].asnumpy()
+        assert mod._arg_params["stage2_unit1_conv2_weight"].dtype == np.float32
+
+        # Call convert_model with cast_optional_params set to True
+        result_sym, result_arg_params, result_aux_params = amp.convert_model(sym,
+                                                                             arg_params,
+                                                                             aux_params,
+                                                                             target_dtype="float16",
+                                                                             target_dtype_ops=["Convolution"], cast_optional_params=True)
+        mod = mx.mod.Module(result_sym, data_names=["data"], label_names=["softmax_label"], context=mx.gpu())
+        mod.bind(data_shapes=[['data', (1, 3, 224, 224)]], label_shapes=[['softmax_label', (1,)]])
+        mod.set_params(result_arg_params, result_aux_params)
+        mod.forward(mx.io.DataBatch(data=[mx.nd.ones((1, 3, 224, 224))],
+                                    label=[mx.nd.ones((1,))]))
+        mod.get_outputs()[0].asnumpy()
+        assert mod._arg_params["stage2_unit1_conv2_weight"].dtype == np.float16
+
+
+    def check_amp_convert_hybrid_block():
+        # Test conversion for hybrid block on CPU
+        model_cpu = get_model("resnet50_v1")
+        model_cpu.collect_params().initialize(ctx=mx.cpu())
+        model_cpu.hybridize()
+        model_cpu(mx.nd.random.uniform(0, 1, shape=(1, 3, 224, 224), ctx=mx.cpu()))
+        converted_model_cpu = amp.convert_hybrid_block(model_cpu)
+
+        # Test with real world model, default inputs for convert_hybrid_block
+        model = get_model("resnet50_v1")
+        model.collect_params().initialize(ctx=mx.gpu())
+        model.hybridize()
+        model(mx.nd.zeros((1, 3, 224, 224)))
+        converted_model = amp.convert_hybrid_block(model)
+        result = converted_model.forward(mx.nd.zeros((1, 3, 224, 224),
+                                                     dtype=np.float32))
+        result = converted_model.forward(mx.nd.zeros((1, 3, 224, 224),
+                                                     dtype=np.float32))
+
+        # Test with real world model, tweak inputs for convert_hybrid_block
+        converted_model = amp.convert_hybrid_block(model, target_dtype="float16",
+                                                   target_dtype_ops=["Convolution"])
+        result = converted_model.forward(mx.nd.zeros((1, 3, 224, 224),
+                                                      dtype=np.float32))
+        result = converted_model.forward(mx.nd.zeros((1, 3, 224, 224),
+                                                     dtype=np.float32))
+
+        # Check symbolic block
+        dir_path = os.path.dirname(os.path.realpath(__file__))
+        model_path = os.path.join(dir_path, 'model')
+        if not os.path.isdir(model_path):
+            os.mkdir(model_path)
+        prefix, epoch = download_model("imagenet1k-resnet-18", dst_dir=model_path)
+        net = SymbolBlock.imports(os.path.join(model_path, "imagenet1k-resnet-18-symbol.json"),
+                                  input_names=["data", "softmax_label"],
+                                  param_file=os.path.join(model_path, "imagenet1k-resnet-18-0000.params"))
+        net.collect_params().reset_ctx(ctx=mx.gpu())
+        net.hybridize()
+        net(mx.nd.zeros((1, 3, 224, 224)), mx.nd.zeros((1,)))
+        converted_model = amp.convert_hybrid_block(net)
+        result = converted_model.forward(mx.nd.zeros((1, 3, 224, 224)), mx.nd.zeros((1,)))
+        result = converted_model.forward(mx.nd.zeros((1, 3, 224, 224)), mx.nd.zeros((1,)))
+
+        # Check symbolic block, tweaked inputs
+        converted_model = amp.convert_hybrid_block(net, target_dtype="float16", target_dtype_ops=["Convolution"])
+        result = converted_model.forward(mx.nd.zeros((1, 3, 224, 224)), mx.nd.zeros((1, )))
+        result = converted_model.forward(mx.nd.zeros((1, 3, 224, 224)), mx.nd.zeros((1, )))
+        params = converted_model.collect_params()
+        assert params["stage2_unit1_conv2_weight"].dtype == np.float32
+
+        # Pass cast_optional_params as True to convert_hybrid_block
+        converted_model = amp.convert_hybrid_block(net, target_dtype="float16", target_dtype_ops=["Convolution"],
+                                                   cast_optional_params=True)
+        params = converted_model.collect_params()
+        assert params["stage2_unit1_conv2_weight"].dtype == np.float16
+
+    with mx.Context(mx.gpu(0)):
+        check_amp_convert_symbol()
+        check_amp_convert_model()
+        check_amp_convert_hybrid_block()
+
+
+@with_seed()
+def test_module_backward_compatibility():
+    channel_num = 10
+    conv_layer_filter_dims = [2, 3]
+    conv_layer_strides = [1, 1]
+    dimension = 5
+    data_len = 10
+
+    data = mx.sym.var("data")
+    conv = mx.sym.Convolution(data,
+                              num_filter=channel_num,
+                              kernel=tuple(conv_layer_filter_dims),
+                              stride=tuple(conv_layer_strides))
+
+    bn = mx.sym.BatchNorm(conv,
+                          eps=0.001,
+                          momentum=0.9,
+                          fix_gamma=False,
+                          use_global_stats=False,
+                          output_mean_var=False,
+                          name="conv0_batchnorm")
+    fc = mx.sym.FullyConnected(bn, num_hidden=10, name="fullyconnected")
+    mod = mx.mod.Module(fc, data_names=["data"], context=mx.gpu(0))
+    mod.bind(data_shapes=[['data', (1, 3, 224, 224)]])
+    mod.init_params()
+
+    arg_params, aux_params = mod.get_params()
+    for param_key, param_val in arg_params.items():
+        assert param_val.dtype == np.float32, "Incorrect inference type for arg_params," \
+                                               "please check simple_bind for module executor"
+    for param_key, param_val in aux_params.items():
+        assert param_val.dtype == np.float32, "Incorrect inference type for aux_params," \
+                                               "please check simple_bind for module executor"
+
+
+    sym, arg_params, aux_params = amp.convert_model(mod._symbol, mod._arg_params, mod._aux_params, target_dtype_ops=["Convolution"])
+    mod = mx.mod.Module(sym, data_names=["data"], context=mx.gpu(0))
+    mod.bind(data_shapes=[['data', (1, 3, 224, 224)]])
+    mod.set_params(arg_params, aux_params)
+    assert arg_params["fullyconnected_weight"].dtype == np.float16, \
+        "Module API is overwriting the inferred dtype for a mixed precision model"
+
+
+@with_seed()
+def test_fp16_casting():
+    data = mx.sym.var("data")
+    out1 = mx.sym.amp_cast(data, dtype="float16")
+    out2 = mx.sym.amp_cast(data, dtype="float32")
+    out3 = mx.sym.amp_cast(data, dtype="float16")
+    # When two ops from data, with different dtypes,
+    # data should be float32
+    res = mx.sym.Group([out1, out2])
+    final_res = amp.convert_symbol(res, data_names=[], cast_optional_params=True)
+    exe = final_res.simple_bind(ctx=mx.gpu(), data=(1, 2))
+    assert exe.arg_arrays[0].dtype == np.float32
+
+    # When two ops from data, both casted to float16,
+    # data should be float16
+    res = mx.sym.Group([out1, out3])
+    final_res = amp.convert_symbol(res, data_names=[], cast_optional_params=True)
+    exe = final_res.simple_bind(ctx=mx.gpu(), data=(1, 2))
+    assert exe.arg_arrays[0].dtype == np.float16
+
+    # AMP Multicast test where one node is float32, another is float16
+    data = mx.sym.var("data", dtype=np.float32)
+    data2 = mx.sym.var("data2", dtype=np.float16)
+    out4 = mx.sym.amp_multicast(data, data2, num_outputs=2)
+    final_res = amp.convert_symbol(out4, cast_optional_params=True)
+    exe = final_res.simple_bind(ctx=mx.gpu(), data2=(1, 2), data=(1, 2))
+    assert exe.arg_arrays[0].dtype == np.float16
+
+    # AMP Multicast test where two non input nodes are float16,
+    # and one input node is float32
+    data = mx.sym.var("data", dtype=np.float32)
+    data2 = mx.sym.var("data2", dtype=np.float16)
+    data3 = mx.sym.var("data3", dtype=np.float16)
+    out5 = mx.sym.amp_multicast(data,
+                                mx.sym.elemwise_add(data2, data3),
+                                num_outputs=2)
+    final_res = amp.convert_symbol(out5, target_dtype_ops=[],
+                                   fp32_ops=[], cast_optional_params=True)
+    exe = final_res.simple_bind(ctx=mx.gpu(), data=(1, 2), data2=(1, 2), data3=(1, 2))
+    assert exe.arg_arrays[0].dtype == np.float32
+
+    # AMP Multicast test where three input nodes one fp16, one fp32
+    # one unknown
+    data = mx.sym.var("data", dtype=np.float16)
+    data2 = mx.sym.var("data2", dtype=np.float32)
+    data3 = mx.sym.var("data3")
+    out6 = mx.sym.amp_multicast(data, data2, data3, num_outputs=3)
+    final_res = amp.convert_symbol(out6, target_dtype_ops=[],
+                                   fp32_ops=[], cast_optional_params=True)
+    exe = final_res.simple_bind(ctx=mx.gpu(), data=(1, 2), data2=(1, 2),
+                                data3=(1, 2))
+    assert exe.arg_arrays[2].dtype == np.float32
+
+    # Input node to amp_multicast and amp_cast, if dtypes conflict
+    # and input node is already fp16, it should still be fp16
+    data = mx.sym.var("data", dtype=np.float16)
+    data2 = mx.sym.var("data2", dtype=np.float32)
+    out7 = mx.sym.Group([mx.sym.amp_multicast(data, data2, num_outputs=2), mx.sym.amp_cast(data, dtype="float16")])
+    final_res = amp.convert_symbol(out7, target_dtype_ops=[],
+                                   fp32_ops=[], cast_optional_params=True)
+    exe = final_res.simple_bind(ctx=mx.gpu(), data=(1, 2), data2=(1, 2))
+    assert exe.arg_arrays[0].dtype == np.float16
+
+    # Input node to amp_multicast and amp_cast, if dtypes conflict
+    # and input node is already fp32, it should be changed to fp16
+    data = mx.sym.var("data", dtype=np.float32)
+    data2 = mx.sym.var("data2", dtype=np.float16)
+    out8 = mx.sym.Group([mx.sym.amp_multicast(data, data2, num_outputs=2), mx.sym.amp_cast(data, dtype="float16")])
+    final_res = amp.convert_symbol(out8, target_dtype_ops=[],
+                                   fp32_ops=[], cast_optional_params=True)
+    exe = final_res.simple_bind(ctx=mx.gpu(), data=(1, 2), data2=(1, 2))
+    assert exe.arg_arrays[0].dtype == np.float16
+
+
+if __name__ == '__main__':
+    import nose
+    nose.runmodule()
diff --git a/tests/python/tensorrt/test_tensorrt_lenet5.py b/tests/python/tensorrt/test_tensorrt_lenet5.py
index ce88b9de3f5c..d105d6517887 100644
--- a/tests/python/tensorrt/test_tensorrt_lenet5.py
+++ b/tests/python/tensorrt/test_tensorrt_lenet5.py
@@ -46,7 +46,7 @@ def run_inference(sym, arg_params, aux_params, mnist, all_test_labels, batch_siz
     # Get this value from all_test_labels
     # Also get classes from the dataset
     num_ex = 10000
-    all_preds = np.zeros([num_ex, 10])
+    all_preds = np.zeros([num_ex, 10], dtype=np.float32)
     test_iter = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], batch_size)
 
     example_ct = 0
diff --git a/tests/python/unittest/test_contrib_amp.py b/tests/python/unittest/test_contrib_amp.py
deleted file mode 100644
index 13048c35371e..000000000000
--- a/tests/python/unittest/test_contrib_amp.py
+++ /dev/null
@@ -1,85 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-import mxnet as mx
-import warnings
-import collections
-import ctypes
-import mxnet.contrib.amp as amp
-
-def test_amp_coverage():
-    conditional = [item[0] for item in amp.lists.symbol.CONDITIONAL_FP32_FUNCS]
-
-    # Check for duplicates
-    for a in [amp.lists.symbol.FP16_FUNCS,
-          amp.lists.symbol.FP16_FP32_FUNCS,
-          amp.lists.symbol.FP32_FUNCS,
-          amp.lists.symbol.WIDEST_TYPE_CASTS,
-          conditional]:
-        ret = [item for item, count in collections.Counter(a).items() if count > 1]
-        assert ret == [], "Elements " + str(ret) + " are duplicated in the AMP lists."
-
-    t = []
-    for a in [amp.lists.symbol.FP16_FUNCS,
-              amp.lists.symbol.FP16_FP32_FUNCS,
-              amp.lists.symbol.FP32_FUNCS,
-              amp.lists.symbol.WIDEST_TYPE_CASTS,
-              conditional]:
-        t += a
-    ret = [item for item, count in collections.Counter(t).items() if count > 1]
-    assert ret == [], "Elements " + str(ret) + " exist in more than 1 AMP list."
-
-    # Check the coverage
-    py_str = lambda x: x.decode('utf-8')
-
-    plist = ctypes.POINTER(ctypes.c_char_p)()
-    size = ctypes.c_uint()
-
-    mx.base._LIB.MXListAllOpNames(ctypes.byref(size),
-                                     ctypes.byref(plist))
-    op_names = []
-    for i in range(size.value):
-        s = py_str(plist[i])
-        if not s.startswith("_backward") \
-           and not s.startswith("_contrib_backward_"):
-            op_names.append(s)
-
-    ret1 = set(op_names) - set(t)
-
-    if ret1 != set():
-        warnings.warn("Operators " + str(ret1) + " do not exist in AMP lists (in "
-                       "python/mxnet/contrib/amp/lists/symbol.py) - please add them. "
-                       """Please follow these guidelines for choosing a proper list:
-                       - if your operator is not to be used in a computational graph
-                         (e.g. image manipulation operators, optimizers) or does not have
-                         inputs, put it in FP16_FP32_FUNCS list,
-                       - if your operator requires FP32 inputs or is not safe to use with lower
-                         precision, put it in FP32_FUNCS list,
-                       - if your operator supports both FP32 and lower precision, has
-                         multiple inputs and expects all inputs to be of the same
-                         type, put it in WIDEST_TYPE_CASTS list,
-                       - if your operator supports both FP32 and lower precision and has
-                         either a single input or supports inputs of different type,
-                         put it in FP16_FP32_FUNCS list,
-                       - if your operator is both safe to use in lower precision and
-                         it is highly beneficial to use it in lower precision, then
-                         put it in FP16_FUNCS (this is unlikely for new operators)
-                       - If you are not sure which list to choose, FP32_FUNCS is the
-                         safest option""")
-
-if __name__ == '__main__':
-    test_amp_coverage()

From 06df38c5e6582be119879b0988b3c90d5e2b76b2 Mon Sep 17 00:00:00 2001
From: Zhennan Qin <zhennan.qin@intel.com>
Date: Sat, 29 Jun 2019 15:06:04 +0800
Subject: [PATCH 023/813] point fix the vector declaration in MultiBoxDetection
 (#15300)

* point fix the vector declaration in MultiBoxDetection

* Rerun ci

* run ci

* CI
---
 src/operator/contrib/multibox_detection.cc | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/operator/contrib/multibox_detection.cc b/src/operator/contrib/multibox_detection.cc
index 65fe5f1208bb..37bb5a500d71 100644
--- a/src/operator/contrib/multibox_detection.cc
+++ b/src/operator/contrib/multibox_detection.cc
@@ -98,8 +98,7 @@ inline void MultiBoxDetectionForward(const Tensor<cpu, 3, DType> &out,
   const DType *p_anchor = anchors.dptr_;
 
   const int omp_threads = mxnet::engine::OpenMP::Get()->GetRecommendedOMPThreadCount();
-  std::vector<DType> outputs;
-  outputs.reserve(num_anchors * 6);
+  std::vector<DType> outputs(num_anchors * 6);
   for (int nbatch = 0; nbatch < num_batches; ++nbatch) {
     const DType *p_cls_prob = cls_prob.dptr_ + nbatch * num_classes * num_anchors;
     const DType *p_loc_pred = loc_pred.dptr_ + nbatch * num_anchors * 4;

From b869ecd65fe4779f31845993c8d5744e5343ec49 Mon Sep 17 00:00:00 2001
From: Alexander Chalk <contact@alexchalk.net>
Date: Sun, 30 Jun 2019 15:58:35 -0400
Subject: [PATCH 024/813] [Clojure] Add fastText example (#15340)

* Add fastText to CNN text classification examples

* Update repl running instructions

* Complete solution with OOM workaround

* Complete solution with smaller fastText dataset

* Add approx validation accuracy to readme

* Add threading macro

* Use consistent fasttext casing

* Add bangs to io reader functions

* Reference default context setting in readme

* Change fasttext references in readme

* Add data fetching shellscript for fasttext
---
 .../cnn-text-classification/README.md         | 22 +++++++++--
 .../get_fasttext_data.sh                      | 24 ++++++++++++
 .../cnn_text_classification/data_helper.clj   | 37 ++++++++++++++-----
 3 files changed, 69 insertions(+), 14 deletions(-)
 create mode 100755 contrib/clojure-package/examples/cnn-text-classification/get_fasttext_data.sh

diff --git a/contrib/clojure-package/examples/cnn-text-classification/README.md b/contrib/clojure-package/examples/cnn-text-classification/README.md
index f2ed939bee16..8f8e6200ec7c 100644
--- a/contrib/clojure-package/examples/cnn-text-classification/README.md
+++ b/contrib/clojure-package/examples/cnn-text-classification/README.md
@@ -29,8 +29,7 @@ You also must download the glove word embeddings. The suggested one to use is th
 ## Usage
 
 You can run through the repl with
-`(train-convnet {:embedding-size 50 :batch-size 100 :test-size 100 :num-epoch 10 :max-examples 1000 :pretrained-embedding :glove})`
-
+`(train-convnet {:devs [(context/default-context)] :embedding-size 50 :batch-size 100 :test-size 100 :num-epoch 10 :max-examples 1000 :pretrained-embedding :glove})`
 or
 `JVM_OPTS="-Xmx1g" lein run` (cpu)
 
@@ -49,6 +48,21 @@ and then run
 - `lein uberjar`
 - `java -Xms1024m -Xmx2048m -jar target/cnn-text-classification-0.1.0-SNAPSHOT-standalone.jar`
 
+## Usage with fastText
+
+Using fastText instead of glove is fairly straightforward, as the pretrained embedding format is very similar.
+
+Download the 'Simple English' pretrained wiki word vectors (text) from the fastText
+[site](https://fasttext.cc/docs/en/pretrained-vectors.html) and place them in the
+`data/fasttext` directory. Alternatively just run `./get_fasttext_data.sh`.
+
+Then you can run training on a subset of examples through the repl using:
+```
+(train-convnet {:devs [(context/default-context)] :embedding-size 300 :batch-size 100 :test-size 100 :num-epoch 10 :max-examples 1000 :pretrained-embedding :fasttext})
+```
+
+Expect a validation accuracy of `~0.67` with the above parameters.
+
 ## Usage with word2vec
 
 You can also use word2vec embeddings in order to train the text classification model.
@@ -58,7 +72,7 @@ you'll need to unzip them and place them in the `contrib/clojure-package/data` d
 
 Then you can run training on a subset of examples through the repl using:
 ```
-(train-convnet {:embedding-size 300 :batch-size 100 :test-size 100 :num-epoch 10 :max-examples 1000 :pretrained-embedding :word2vec})
+(train-convnet {:devs [(context/default-context)] :embedding-size 300 :batch-size 100 :test-size 100 :num-epoch 10 :max-examples 1000 :pretrained-embedding :word2vec})
 ```
 Note that loading word2vec embeddings consumes memory and takes some time.
 
@@ -66,7 +80,7 @@ You can also train them using `JVM_OPTS="-Xmx8g" lein run` once you've modified
 the parameters to `train-convnet` (see above) in `src/cnn_text_classification/classifier.clj`.
 In order to run training with word2vec on the complete data set, you will need to run:
 ```
-(train-convnet {:embedding-size 300 :batch-size 100 :test-size 1000 :num-epoch 10 :pretrained-embedding :word2vec})
+(train-convnet {:devs [(context/default-context)] :embedding-size 300 :batch-size 100 :test-size 1000 :num-epoch 10 :pretrained-embedding :word2vec})
 ```
 You should be able to achieve an accuracy of `~0.78` using the parameters above.
 
diff --git a/contrib/clojure-package/examples/cnn-text-classification/get_fasttext_data.sh b/contrib/clojure-package/examples/cnn-text-classification/get_fasttext_data.sh
new file mode 100755
index 000000000000..2bfe96659402
--- /dev/null
+++ b/contrib/clojure-package/examples/cnn-text-classification/get_fasttext_data.sh
@@ -0,0 +1,24 @@
+#!/bin/bash
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+set -evx
+
+mkdir -p data/fasttext
+cd data/fasttext
+wget https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.simple.vec
diff --git a/contrib/clojure-package/examples/cnn-text-classification/src/cnn_text_classification/data_helper.clj b/contrib/clojure-package/examples/cnn-text-classification/src/cnn_text_classification/data_helper.clj
index 82ba13087a37..df132c3167cd 100644
--- a/contrib/clojure-package/examples/cnn-text-classification/src/cnn_text_classification/data_helper.clj
+++ b/contrib/clojure-package/examples/cnn-text-classification/src/cnn_text_classification/data_helper.clj
@@ -33,6 +33,8 @@
   [embedding-size]
   (format "data/glove/glove.6B.%dd.txt" embedding-size))
 
+(def fasttext-file-path "data/fasttext/wiki.simple.vec")
+
 (defn r-string
   "Reads a string from the given DataInputStream `dis` until a space or newline is reached."
   [dis]
@@ -62,7 +64,7 @@
           vect (mapv (fn [_] (read-float dis)) (range embedding-size))]
       (cons [word vect] (lazy-seq (load-w2v-vectors dis embedding-size (dec num-vectors)))))))
 
-(defn load-word2vec-model
+(defn load-word2vec-model!
   "Loads the word2vec model stored in a binary format from the given `path`.
   By default only the first 100 embeddings are loaded."
   ([path embedding-size opts]
@@ -75,8 +77,8 @@
            _  (println "Processing with " {:dim dim :word-size word-size} " loading max vectors " max-vectors)
            _ (if (not= embedding-size dim)
                (throw (ex-info "Mismatch in embedding size"
-                      {:input-embedding-size embedding-size
-                       :word2vec-embedding-size dim})))
+                       {:input-embedding-size embedding-size
+                        :word2vec-embedding-size dim})))
            vectors (load-w2v-vectors dis dim max-vectors)
            word2vec (if vocab
                       (->> vectors
@@ -88,17 +90,30 @@
        (println "Finished")
        {:num-embed dim :word2vec word2vec})))
   ([path embedding-size]
-   (load-word2vec-model path embedding-size {:max-vectors 100})))
+   (load-word2vec-model! path embedding-size {:max-vectors 100})))
 
-(defn read-text-embedding-pairs [rdr]
-  (for [^String line (line-seq rdr)
+(defn read-text-embedding-pairs [pairs]
+  (for [^String line pairs
         :let [fields (.split line " ")]]
     [(aget fields 0)
      (mapv #(Float/parseFloat ^String %) (rest fields))]))
 
-(defn load-glove [glove-file-path]
+(defn load-glove! [glove-file-path]
   (println "Loading the glove pre-trained word embeddings from " glove-file-path)
-  (into {} (read-text-embedding-pairs (io/reader glove-file-path))))
+  (->> (io/reader glove-file-path)
+       line-seq
+       read-text-embedding-pairs
+       (into {})))
+
+(def remove-fasttext-metadata rest)
+
+(defn load-fasttext! [fasttext-file-path]
+  (println "Loading the fastText pre-trained word embeddings from " fasttext-file-path)
+  (->> (io/reader fasttext-file-path)
+       line-seq
+       remove-fasttext-metadata
+       read-text-embedding-pairs
+       (into {})))
 
 (defn clean-str [s]
   (-> s
@@ -188,9 +203,11 @@
         sentences-padded  (pad-sentences sentences)
         vocab (build-vocab sentences-padded)
         vocab-embeddings (case pretrained-embedding
-                           :glove (->> (load-glove (glove-file-path embedding-size))
+                           :glove (->> (load-glove! (glove-file-path embedding-size))
                                        (build-vocab-embeddings vocab embedding-size))
-                           :word2vec (->> (load-word2vec-model w2v-file-path embedding-size {:vocab vocab})
+                           :fasttext (->> (load-fasttext! fasttext-file-path)
+                                          (build-vocab-embeddings vocab embedding-size))
+                           :word2vec (->> (load-word2vec-model! w2v-file-path embedding-size {:vocab vocab})
                                           (:word2vec)
                                           (build-vocab-embeddings vocab embedding-size))
                            vocab)

From d74b9930ede1ff814c8f06fc5d63bee309f8e551 Mon Sep 17 00:00:00 2001
From: Rohit Kumar Srivastava <srivastava.141@osu.edu>
Date: Mon, 1 Jul 2019 09:46:57 -0700
Subject: [PATCH 025/813] Revert default return type for indices in argsort()
 and topk() back to float32 (#15360)

---
 src/operator/tensor/ordering_op-inl.h | 25 +++++++++++++------------
 tests/python/unittest/test_ndarray.py | 10 ++--------
 2 files changed, 15 insertions(+), 20 deletions(-)

diff --git a/src/operator/tensor/ordering_op-inl.h b/src/operator/tensor/ordering_op-inl.h
index 98bca3a43c60..880acf1f4cae 100644
--- a/src/operator/tensor/ordering_op-inl.h
+++ b/src/operator/tensor/ordering_op-inl.h
@@ -88,11 +88,7 @@ struct TopKParam : public dmlc::Parameter<TopKParam> {
     .add_enum("float16", mshadow::kFloat16)
     .add_enum("float32", mshadow::kFloat32)
     .add_enum("float64", mshadow::kFloat64)
-#if MXNET_USE_INT64_TENSOR_SIZE == 1
-    .set_default(mshadow::kInt64)
-#else
-    .set_default(mshadow::kInt32)
-#endif
+    .set_default(mshadow::kFloat32)
     .describe("DType of the output indices when ret_typ is \"indices\" or \"both\". "
               "An error will be raised if the selected data type cannot precisely represent the "
               "indices.");
@@ -129,11 +125,7 @@ struct ArgSortParam : public dmlc::Parameter<ArgSortParam> {
     .add_enum("float16", mshadow::kFloat16)
     .add_enum("float32", mshadow::kFloat32)
     .add_enum("float64", mshadow::kFloat64)
-#if USE_INT64_TENSOR_SIZE == 1
-    .set_default(mshadow::kInt64)
-#else
-    .set_default(mshadow::kInt32)
-#endif
+    .set_default(mshadow::kFloat32)
     .describe("DType of the output indices. It is only valid when ret_typ is \"indices\" or"
               " \"both\". An error will be raised if the selected data type cannot precisely "
               "represent the indices.");
@@ -748,8 +740,17 @@ inline bool TopKType(const nnvm::NodeAttrs& attrs,
   //  out_attr[0] -> stores value
   //  out_attr[1] -> stores indices
   if (out_size > 1) {
-    CHECK(type_assign(&(*out_attrs)[1], param.dtype))
-        << "Failed to set the type of ret_indices.";
+    if (param.ret_typ == topk_enum::kReturnValue) {
+#if MXNET_USE_INT64_TENSOR_SIZE == 1
+      CHECK(type_assign(&(*out_attrs)[1], mshadow::kInt64))
+#else
+      CHECK(type_assign(&(*out_attrs)[1], mshadow::kInt32))
+#endif
+          << "Failed to set the type of ret_indices.";
+    } else {
+      CHECK(type_assign(&(*out_attrs)[1], param.dtype))
+          << "Failed to set the type of ret_indices.";
+    }
   }
   if (param.ret_typ == topk_enum::kReturnIndices) {
     CHECK(type_assign(&(*out_attrs)[0], param.dtype))
diff --git a/tests/python/unittest/test_ndarray.py b/tests/python/unittest/test_ndarray.py
index d84b4f082b63..f40bb3053358 100644
--- a/tests/python/unittest/test_ndarray.py
+++ b/tests/python/unittest/test_ndarray.py
@@ -822,10 +822,7 @@ def get_large_matrix():
         # test for ret_typ=indices
         nd_ret_topk = mx.nd.topk(a_nd, axis=1, ret_typ="indices", k=3, is_ascend=True).asnumpy()
         # Test the default dtype
-        if is_large_tensor_enabled:
-            assert nd_ret_topk.dtype == np.int64
-        else:
-            assert nd_ret_topk.dtype == np.int32
+        assert nd_ret_topk.dtype == np.float32
         gt = gt_topk(a_npy, axis=1, ret_typ="indices", k=3, is_ascend=True)
         assert_almost_equal(nd_ret_topk, gt)
         nd_ret_topk = mx.nd.topk(a_nd, axis=3, ret_typ="indices", k=2, is_ascend=False, dtype=np.float64).asnumpy()
@@ -866,10 +863,7 @@ def get_large_matrix():
         nd_ret_topk_val = nd_ret_topk_val.asnumpy()
         nd_ret_topk_ind = nd_ret_topk_ind.asnumpy()
         assert nd_ret_topk_val.dtype == dtype
-        if is_large_tensor_enabled:
-            assert nd_ret_topk_ind.dtype == np.int64
-        else:
-            assert nd_ret_topk_ind.dtype == np.int32
+        assert nd_ret_topk_ind.dtype == np.float32
         gt_val = gt_topk(a_npy, axis=1, ret_typ="value", k=3, is_ascend=True)
         gt_ind = gt_topk(a_npy, axis=1, ret_typ="indices", k=3, is_ascend=True)
         assert_almost_equal(nd_ret_topk_val, gt_val)

From 7210cc459d3b9e9780998f9a9ec4ebc2f6a9d749 Mon Sep 17 00:00:00 2001
From: Aaron Markham <markhama@amazon.com>
Date: Mon, 1 Jul 2019 16:20:39 -0700
Subject: [PATCH 026/813] nano instructions (#15117)

* nano instructions

* bumping CI

* update to have working whl files for python 3 and 2
---
 docs/install/index.md          |  85 +---------
 docs/install/install-jetson.md | 289 +++++++++++++++++++++++++++++++++
 2 files changed, 292 insertions(+), 82 deletions(-)
 create mode 100644 docs/install/install-jetson.md

diff --git a/docs/install/index.md b/docs/install/index.md
index 5fef5ca47e57..ac7bd048a588 100644
--- a/docs/install/index.md
+++ b/docs/install/index.md
@@ -26,6 +26,7 @@
    c_plus_plus.md
    centos_setup.md
    download.md
+   install-jetson.md
    java_setup.md
    osx_setup.md
    scala_setup.md
@@ -1447,89 +1448,9 @@ You are now ready to run MXNet on your Raspberry Pi device. You can get started
 
 <div class="nvidia-jetson">
 
-# Nvidia Jetson TX family
+# NVIDIA Jetson Devices
 
-MXNet supports the Ubuntu Arch64 based operating system so you can run MXNet on NVIDIA Jetson Devices.
-
-These instructions will walk through how to build MXNet for the Pascal based [NVIDIA Jetson TX2](http://www.nvidia.com/object/embedded-systems-dev-kits-modules.html) and install the corresponding python language bindings.
-
-For the purposes of this install guide we will assume that CUDA is already installed on your Jetson device.
-
-**Install MXNet**
-
-Installing MXNet is a two-step process:
-
-1. Build the shared library from the MXNet C++ source code.
-2. Install the supported language-specific packages for MXNet.
-
-**Step 1** Build the Shared Library
-
-You need the following additional dependencies:
-
-- Git (to pull code from GitHub)
-
-- libatlas (for linear algebraic operations)
-
-- libopencv (for computer vision operations)
-
-- python pip (to load relevant python packages for our language bindings)
-
-Install these dependencies using the following commands in any directory:
-
-```
-    sudo apt-get update
-    sudo apt-get -y install git build-essential libatlas-base-dev libopencv-dev graphviz python-pip
-    sudo pip install pip --upgrade
-    sudo pip install setuptools numpy --upgrade
-    sudo pip install graphviz==0.8.4 \
-                     jupyter
-```
-
-Clone the MXNet source code repository using the following `git` command in your home directory:
-```
-    git clone https://github.com/apache/incubator-mxnet.git --recursive
-    cd incubator-mxnet
-```
-
-Edit the Makefile to install the MXNet with CUDA bindings to leverage the GPU on the Jetson:
-```
-    cp make/crosscompile.jetson.mk config.mk
-```
-
-Edit the Mshadow Makefile to ensure MXNet builds with Pascal's hardware level low precision acceleration by editing 3rdparty/mshadow/make/mshadow.mk and adding the following after line 122:
-```
-MSHADOW_CFLAGS += -DMSHADOW_USE_PASCAL=1
-```
-
-Now you can build the complete MXNet library with the following command:
-```
-    make -j $(nproc)
-```
-
-Executing this command creates a file called `libmxnet.so` in the mxnet/lib directory.
-
-**Step 2** Install MXNet Python Bindings
-
-To install Python bindings run the following commands in the MXNet directory:
-
-```
-    cd python
-    pip install --upgrade pip
-    pip install -e .
-```
-
-Note that the `-e` flag is optional. It is equivalent to `--editable` and means that if you edit the source files, these changes will be reflected in the package installed.
-
-Add the mxnet folder to the path:
-
-```
-    cd ..
-    export MXNET_HOME=$(pwd)
-    echo "export PYTHONPATH=$MXNET_HOME/python:$PYTHONPATH" >> ~/.rc
-    source ~/.rc
-```
-
-You are now ready to run MXNet on your NVIDIA Jetson TX2 device.
+To install MXNet on a Jetson TX or Nano, please refer to the [Jetson installation guide](install-jetson.html).
 
 </div> <!-- End of jetson -->
 </div> <!-- End of devices -->
diff --git a/docs/install/install-jetson.md b/docs/install/install-jetson.md
new file mode 100644
index 000000000000..a93f930a8182
--- /dev/null
+++ b/docs/install/install-jetson.md
@@ -0,0 +1,289 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Install MXNet on a Jetson
+
+MXNet supports the Ubuntu Arch64 based operating system so you can run MXNet on NVIDIA Jetson Devices, such as the [TX2](http://www.nvidia.com/object/embedded-systems-dev-kits-modules.html) or [Nano](https://developer.nvidia.com/embedded/learn/get-started-jetson-nano-devkit).
+
+These instructions will walk through how to build MXNet and install the corresponding language bindings. Python is the default binding, but you may also try out one of the many language bindings MXNet has to offer. These instructions also cover how to setup MXNet's Java Inference API.
+
+For the purposes of this install guide we will assume that CUDA is already installed on your Jetson device. The disk image provided by NVIDIA's getting started guides will have the Jetson toolkit preinstalled, and this also includes CUDA. You should double check what versions are installed and which version you plan to use.
+
+You have several options for installing MXNet:
+1. Use a Jetson MXNet pip wheel for Python development.
+2. Use precompiled Jetson MXNet binaries.
+3. Build MXNet from source
+   * On a faster Linux computer using cross-compilation
+   * On the Jetson itself (very slow and not recommended)
+
+
+## Prerequisites
+To build from source or to use the Python wheel, you must install the following dependencies on your Jetson.
+Cross-compiling will require dependencies installed on that machine as well.
+
+### Python API
+
+To use the Python API you need the following dependencies:
+
+```bash
+sudo apt update
+sudo apt -y install \
+                        build-essential \
+                        git \
+                        graphviz \
+                        libatlas-base-dev \
+                        libopencv-dev \
+                        python-pip
+
+sudo pip install --upgrade \
+                        pip \
+                        setuptools
+
+sudo pip install \
+                        graphviz==0.8.4 \
+                        jupyter \
+                        numpy==1.15.2
+```
+
+If you plan to cross-compile you will need to install these dependencies on that computer as well.
+
+<!--
+### Java API
+
+To use the Java inference API you only need the following dependencies:
+
+```
+sudo apt install \
+                        maven \
+                        openjdk-8-java
+```
+
+You may try to build the Java API .jar file yourself. If so, you will need these dependencies on that computer as well.
+
+**Note:** The `mvn install` option for building the Java API .jar after compiling the MXNet binary files is only available on MXNet >= v1.5.0.
+-->
+
+### Configure CUDA
+
+You can check to see what version of CUDA is running with `nvcc`.
+
+```bash
+nvcc --version
+```
+
+To switch CUDA versions on a device or computer that has more than one version installed, use the following and replace the version as appropriate.
+
+```bash
+sudo rm /usr/local/cuda
+sudo ln -s /usr/local/cuda-10.0 /usr/local/cuda
+```
+
+**Note:** When cross-compiling, change the CUDA version on the host computer you're using to match the version you're running on your Jetson device.
+**Note:** CUDA 10.1 is recommended but doesn't ship with the Nano's SD card image. You may want to go through CUDA upgrade steps first.
+
+### Download the source & setup some environment variables:
+
+These steps are optional, but some of the following instructions expect MXNet source files and the `MXNET_HOME` environment variable.
+
+Clone the MXNet source code repository using the following `git` command in your home directory:
+
+```bash
+git clone --recursive https://github.com/apache/incubator-mxnet.git mxnet
+cd mxnet
+```
+
+Setup your environment variables for MXNet.
+
+```bash
+cd ..
+export MXNET_HOME=$(pwd)
+echo "export PYTHONPATH=$MXNET_HOME/python:$PYTHONPATH" >> ~/.rc
+source ~/.rc
+```
+
+**Note:** Change the `~/.rc` steps according to how you prefer to use your shell. Otherwise, your environment variables will be gone after you logout.
+
+
+## Install MXNet for Python
+
+To use a prepared Python wheel, download it to your Jetson, and run it.
+* [MXNet 1.4.0 - Python 3](https://s3.us-east-2.amazonaws.com/mxnet-public/install/jetson/1.4.0/mxnet-1.4.0-cp36-cp36m-linux_aarch64.whl)
+* [MXNet 1.4.0 - Python 2](https://s3.us-east-2.amazonaws.com/mxnet-public/install/jetson/1.4.0/mxnet-1.4.0-cp27-cp27mu-linux_aarch64.whl)
+
+
+It should download the required dependencies, but if you have issues,
+install the dependencies in the prerequisites section, then run the pip wheel.
+
+```bash
+sudo pip install mxnet-1.4.0-cp36-cp36m-linux_aarch64.whl
+```
+
+<!--
+## Install MXNet for Java
+
+The MXNet Java API for Jetson devices is still experimental. For best results try with the master branch.
+
+### Use a pre-built jar
+
+The package for Jetson is not currently on Maven, but you can download it from S3 instead.
+The following jar was cross-compiled for Jetson using MXNet v1.4.1.
+* https://s3.us-east-2.amazonaws.com/mxnet-public/install/jetson/1.4.1/mxnet-full_2.11-INTERNAL.jar
+
+Place the file on your Jetson where your project can find it.
+
+### Build your own jar
+
+You can build a jar from any pre-compiled MXNet binary.
+Follow the build from source instructions first. Once you have build the `libmxnet.so` file you are ready to build your jar files.
+
+```bash
+cd $MXNET_HOME/scala-package
+mvn package
+```
+
+This will generate a file named similar to `src/lib/mxnet-full_2.11-INTERNAL.jar`.
+
+### Use the MXNet-Java jar in your project
+
+MXNet-Java can be easily included in your Maven managed project.
+The following is an example entry for your project's `.pom` file.
+
+```
+<dependency>
+  <groupId>org.apache.mxnet</groupId>
+  <artifactId>mxnet-full_2.11-INTERNAL</artifactId>
+  <version>1.4.1</version>
+  <systemPath>${basedir}/src/lib/mxnet-full_2.11-INTERNAL.jar</systemPath>
+</dependency>
+```
+
+Refer to the [Java setup](https://mxnet.incubator.apache.org/versions/master/install/java_setup.html) page for further information.
+-->
+
+## Use a Pre-compiled MXNet Binary
+
+If you want to just use a pre-compiled binary you can download it from S3:
+* https://s3.us-east-2.amazonaws.com/mxnet-public/install/jetson/1.4.1/libmxnet.so
+
+Place this file in `$MXNET_HOME/lib`.
+
+To use this with the MXNet Python binding, you must match the source directory's checked out version with the binary's source version, then install it with pip.
+
+```bash
+cd $MXNET_HOME
+git checkout v1.4.x
+git submodule update --init
+cd python
+sudo pip install -e .
+```
+
+## Build MXNet from Source
+
+Installing MXNet from source is a two-step process:
+
+1. Build the shared library from the MXNet C++ source code.
+2. Install the supported language-specific packages for MXNet.
+
+You can use a Docker method or you can build from source manually.
+
+### Docker
+
+You must have installed Docker and be able to run `docker` without `sudo`.
+Follow these [setup instructions to get to this point](https://docs.docker.com/install/linux/#manage-docker-as-a-non-root-user).
+Then run the following to execute cross-compilation via Docker.
+
+```bash
+$MXNET_HOME/ci/build.py -p jetson
+```
+
+### Manual
+
+**Step 1** Build the Shared Library
+
+(Skip this sub-step for compiling on the Jetson device directly.)
+Edit the Makefile to install the MXNet with CUDA bindings to leverage the GPU on the Jetson:
+
+```bash
+cp $MXNET_HOME/make/crosscompile.jetson.mk config.mk
+```
+
+Now edit `config.mk` to make some additional changes for the Nano. Update the following settings:
+
+1. Update the CUDA path. `USE_CUDA_PATH = /usr/local/cuda`
+2. Add `-gencode arch=compute-63, code=sm_62` to the `CUDA_ARCH` setting.
+3. Update the NVCC settings. `NVCCFLAGS := -m64`
+4. (optional, but recommended) Turn on OpenCV. `USE_OPENCV = 1`
+
+Now edit the Mshadow Makefile to ensure MXNet builds with Pascal's hardware level low precision acceleration by editing `3rdparty/mshadow/make/mshadow.mk`.
+The last line has `MSHADOW_USE_PASCAL` set to `0`. Change this to `1` to enable it.
+
+```bash
+MSHADOW_CFLAGS += -DMSHADOW_USE_PASCAL=1
+```
+
+Now you can build the complete MXNet library with the following command:
+
+```bash
+cd $MXNET_HOME
+make -j $(nproc)
+```
+
+Executing this command creates a file called `libmxnet.so` in the `mxnet/lib` directory.
+
+**Step 2** Install MXNet Python Bindings (optional)
+
+To install Python bindings run the following commands in the MXNet directory:
+
+```bash
+cd $MXNET_HOME/python
+sudo pip install -e .
+```
+
+Note that the `-e` flag is optional. It is equivalent to `--editable` and means that if you edit the source files, these changes will be reflected in the package installed.
+
+**Step 3** Install the MXNet Java & Scala Bindings (optional)
+
+Change directories to `scala-package` and run `mvn install`.
+
+```bash
+cd $MXNET_HOME/scala-package
+mvn install
+```
+
+This creates the required `.jar` file to use in your Java or Scala projects.
+
+## Conclusion and Next Steps
+
+You are now ready to run MXNet on your NVIDIA Jetson TX2 or Nano device.
+You can verify your MXNet Python installation with the following:
+
+```python
+import mxnet
+mxnet.__version__
+```
+
+You can also verify MXNet can use your GPU with the following test:
+
+```python
+import mxnet as mx
+a = mx.nd.ones((2, 3), mx.gpu())
+b = a * 2 + 1
+b.asnumpy()
+```
+
+If everything is working, it will report the version number.
+For assistance, head over to the [MXNet Forum](https://discuss.mxnet.io/).

From c6bb2cec919851afa04dbeda96bc1cab588c0fba Mon Sep 17 00:00:00 2001
From: Zhennan Qin <zhennan.qin@intel.com>
Date: Wed, 3 Jul 2019 01:56:52 +0800
Subject: [PATCH 027/813] Use omp threads for cpu data loader (#15379)

* Use omp threads for cpu data loader

* fix lint

* CI
---
 src/io/iter_image_recordio_2.cc | 27 +++++++++++++++++----------
 1 file changed, 17 insertions(+), 10 deletions(-)

diff --git a/src/io/iter_image_recordio_2.cc b/src/io/iter_image_recordio_2.cc
index 5d9e81d3f6b4..565b67104149 100644
--- a/src/io/iter_image_recordio_2.cc
+++ b/src/io/iter_image_recordio_2.cc
@@ -134,19 +134,25 @@ inline void ImageRecordIOParser2<DType>::Init(
   record_param_.InitAllowUnknown(kwargs);
   batch_param_.InitAllowUnknown(kwargs);
   normalize_param_.InitAllowUnknown(kwargs);
+  PrefetcherParam prefetch_param;
+  prefetch_param.InitAllowUnknown(kwargs);
   n_parsed_ = 0;
   overflow = false;
   rnd_.seed(kRandMagic + record_param_.seed);
   int maxthread, threadget;
-  #pragma omp parallel
-  {
-    // be conservative, set number of real cores
-    maxthread = std::max(omp_get_num_procs(), 1);
-  }
-  param_.preprocess_threads = std::min(maxthread, param_.preprocess_threads);
-  #pragma omp parallel num_threads(param_.preprocess_threads)
-  {
-    threadget = omp_get_num_threads();
+  if (prefetch_param.ctx == PrefetcherParam::CtxType::kCPU) {
+    threadget = engine::OpenMP::Get()->GetRecommendedOMPThreadCount();
+  } else {
+    #pragma omp parallel
+    {
+      // be conservative, set number of real cores
+      maxthread = std::max(omp_get_num_procs() / 2, 1);
+    }
+    param_.preprocess_threads = std::min(maxthread, param_.preprocess_threads);
+    #pragma omp parallel num_threads(param_.preprocess_threads)
+    {
+      threadget = omp_get_num_threads();
+    }
   }
   param_.preprocess_threads = threadget;
 
@@ -822,7 +828,8 @@ class ImageRecordIter2Wrapper : public IIterator<DataBatch> {
       dtype = prefetch_param.dtype.value();
     }
     if (prefetch_param.ctx == PrefetcherParam::CtxType::kCPU) {
-      LOG(INFO) << "Create ImageRecordIter2 optimized for CPU backend.";
+      LOG(INFO) << "Create ImageRecordIter2 optimized for CPU backend."
+                << "Use omp threads instead of preprocess_threads.";
       switch (dtype) {
         case mshadow::kFloat32:
           record_iter_ = new ImageRecordIter2CPU<float>();

From 512a491547c17f4caffc4cc962d95a6467186a3d Mon Sep 17 00:00:00 2001
From: Zhaoqi Zhu <zhaoqizh@usc.edu>
Date: Tue, 2 Jul 2019 20:55:18 -0700
Subject: [PATCH 028/813] Temporarily Commenting out Flaky Test (#15436)

* comment out flaky test

* Update test_profiler.py

* Update test_profiler.py

* Update test_profiler.py
---
 tests/python/unittest/test_profiler.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/tests/python/unittest/test_profiler.py b/tests/python/unittest/test_profiler.py
index ab7d29f104ca..b9447f951764 100644
--- a/tests/python/unittest/test_profiler.py
+++ b/tests/python/unittest/test_profiler.py
@@ -24,6 +24,7 @@
 import json
 from collections import OrderedDict
 from common import run_in_spawned_process
+import unittest
 
 def enable_profiler(profile_filename, run=True, continuous_dump=False, aggregate_stats=False):
     profiler.set_config(profile_symbolic=True,
@@ -427,6 +428,7 @@ def create_operator(self, ctx, shapes, dtypes):
         and '_plus_scalar' in target_dict['Time']['operator']
     profiler.set_state('stop')
 
+@unittest.skip("Flaky test https://github.com/apache/incubator-mxnet/issues/15406")
 def test_custom_operator_profiling_multiple_custom_ops_symbolic():
     run_in_spawned_process(test_custom_operator_profiling_multiple_custom_ops_imperative, \
             {'MXNET_EXEC_BULK_EXEC_INFERENCE' : 0, \
@@ -434,6 +436,7 @@ def test_custom_operator_profiling_multiple_custom_ops_symbolic():
             'symbolic', \
             'test_custom_operator_profiling_multiple_custom_ops_symbolic.json')
 
+@unittest.skip("Flaky test https://github.com/apache/incubator-mxnet/issues/15406")
 def test_custom_operator_profiling_naive_engine():
     # run the three tests above using Naive Engine
     run_in_spawned_process(test_custom_operator_profiling, \

From 15475788cee87eb6c6b08ddd0af245af7c05536f Mon Sep 17 00:00:00 2001
From: Disi A <adis@live.cn>
Date: Wed, 3 Jul 2019 00:05:24 -0400
Subject: [PATCH 029/813] Remove mhard-float option. This is already deprecated
 by Google. (#15435)

---
 amalgamation/Makefile | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/amalgamation/Makefile b/amalgamation/Makefile
index d4b2ee0ef228..701c1f155e47 100644
--- a/amalgamation/Makefile
+++ b/amalgamation/Makefile
@@ -114,8 +114,8 @@ jni_libmxnet_predict.so: jni_libmxnet_predict.o
 ifneq ($(ANDROID), 1)
         android:
 else
-        CFLAGS+=  -mhard-float -D_NDK_MATH_NO_SOFTFP=1 -O3
-        LDFLAGS+=  -Wl,--no-warn-mismatch -lm_hard
+        CFLAGS+= -O3
+        LDFLAGS+= -Wl,--no-warn-mismatch -lm_hard
         android: jni_libmxnet_predict.so
 endif
 

From 6a8d9eb5fd4f7133c094149dc80a3a236534f223 Mon Sep 17 00:00:00 2001
From: Lin Yuan <apeforest@gmail.com>
Date: Tue, 2 Jul 2019 22:53:39 -0700
Subject: [PATCH 030/813] [MXNET-978] Higher order gradient for sigmoid
 (#15288)

* try to add support some ops

* add unit test for second order grad

* implement grad for relu and add unit test

* fix lint

* register FGradient attribute for backward relu

* resolve conflict

* remove unused imports

* change gradient using set_attr

* remove higher order grad test for negative(x)

* fix lint

* reverse indent

* remove unused backward operator

* refactor backward for sin(x) and cos(x)

* change value init to list init

* change to list initialization

* generate random shape in test

* fix a bug in second order backward

* fix lint

* fix lint

* address reviewer comment and renaming

* test 2nd order gradient for sigmoid

* higher order grads for sigmoid

* add unit test

* remove blank lines

* update test

* fix lint

* fix third order gradient for sigmoid
---
 src/common/exec_utils.h                       |  5 ++--
 src/imperative/imperative.cc                  |  4 +++
 .../tensor/elemwise_unary_op_basic.cc         | 30 ++++++++++++++++++-
 src/operator/tensor/elemwise_unary_op_trig.cc |  4 +--
 .../python/unittest/test_higher_order_grad.py | 17 +++++++++++
 5 files changed, 54 insertions(+), 6 deletions(-)

diff --git a/src/common/exec_utils.h b/src/common/exec_utils.h
index 0551b429f17e..d8b7a33bf22b 100644
--- a/src/common/exec_utils.h
+++ b/src/common/exec_utils.h
@@ -286,7 +286,6 @@ inline void LogMemoryPlan(const nnvm::Graph& g) {
   const auto &idx = g.indexed_graph();
   const auto& vshape = g.GetAttr<mxnet::ShapeVector>("shape");
   const auto& vtype = g.GetAttr<nnvm::DTypeVector>("dtype");
-  const auto& vstorage = g.GetAttr<nnvm::StorageVector>("storage_id");
   // find node range
   uint32_t node_start = 0, node_end = idx.num_nodes();
   if (g.attrs.count("node_range")) {
@@ -304,13 +303,13 @@ inline void LogMemoryPlan(const nnvm::Graph& g) {
         auto eid = idx.entry_id(e);
         size_t kilo_bytes = vshape[eid].Size() * mshadow::mshadow_sizeof(vtype[eid]) / 1024;
         LOG(INFO) << "\t\tinput " << eid << ": " << vshape[eid] << " ("
-                  << kilo_bytes << " KB) -> " << storage_str(vstorage[eid]);
+                  << kilo_bytes << " KB)";
       }
       for (uint32_t index = 0; index < inode.source->num_outputs(); ++index) {
         uint32_t eid = idx.entry_id(nid, index);
         size_t kilo_bytes = vshape[eid].Size() * mshadow::mshadow_sizeof(vtype[eid]) / 1024;
         LOG(INFO) << "\t\toutput " << eid << ": " << vshape[eid] << " ("
-                  << kilo_bytes << " KB) -> " << storage_str(vstorage[eid]);
+                  << kilo_bytes << " KB)";
       }
     }
   }
diff --git a/src/imperative/imperative.cc b/src/imperative/imperative.cc
index d8fba1c169ec..e2c0c9d4c9d4 100644
--- a/src/imperative/imperative.cc
+++ b/src/imperative/imperative.cc
@@ -501,6 +501,10 @@ std::vector<NDArray*> Imperative::Backward(
     }
   }
 
+  if (dmlc::GetEnv("MXNET_MEM_PLAN_VERBOSE_LOGGING", false)) {
+    common::LogMemoryPlan(graph);
+  }
+
   // Execution
 
   bool prev_recording = set_is_recording(create_graph);
diff --git a/src/operator/tensor/elemwise_unary_op_basic.cc b/src/operator/tensor/elemwise_unary_op_basic.cc
index 98dc8dad825f..26c74085dbe6 100644
--- a/src/operator/tensor/elemwise_unary_op_basic.cc
+++ b/src/operator/tensor/elemwise_unary_op_basic.cc
@@ -121,7 +121,35 @@ The storage type of ``sigmoid`` output is always dense
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{"_backward_sigmoid"});
 
 MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU(_backward_sigmoid,
-                                               unary_bwd<mshadow_op::sigmoid_grad>);
+                                               unary_bwd<mshadow_op::sigmoid_grad>)
+.set_attr<nnvm::FGradient>("FGradient",
+    [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
+      // n->inputs[0] : y_grad
+      // n->inputs[1] : f(x) = sigmoid(x)
+      // ograds[0] : head_grads
+      // f''(x) = f'(x) * (1 - 2*f(x))
+      // NodeEntry{n} : y_grad * f'(x)
+      auto ones = MakeNode("ones_like", n->attrs.name + "_grad_ones", {n->inputs[1]}, nullptr, &n);
+      const std::unordered_map<std::string, std::string> args = {{"scalar", "2.0"}};
+      auto two_y = MakeNode("_mul_scalar", n->attrs.name + "_mul_two", {n->inputs[1]}, &args, &n);
+      auto one_minus_two_y = MakeNode("elemwise_sub", n->attrs.name + "_grad_sub",
+                                    {nnvm::NodeEntry{ones}, nnvm::NodeEntry{two_y}}, nullptr, &n);
+      auto grad_grad_mid = MakeNode("elemwise_mul", n->attrs.name + "_grad_mul",
+                                    {n->inputs[0], nnvm::NodeEntry{one_minus_two_y}}, nullptr, &n);
+      auto dydx = MakeNode("elemwise_div", n->attrs.name + "_grad_div",
+                           {nnvm::NodeEntry{n}, n->inputs[0]}, nullptr, &n);
+
+      // when building gradient graph, the backward node of n->inputs[1] will be
+      // added to the graph again, therefore f`(x) will be multiplied
+      std::vector<nnvm::NodeEntry> ret;
+      ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "backward_grad_grad",
+                                {ograds[0], nnvm::NodeEntry{dydx}}, nullptr, &n));
+      ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "backward_grad_grad_in",
+                                {ograds[0], nnvm::NodeEntry{grad_grad_mid}}, nullptr, &n));
+      return ret;
+    });
+
+
 
 DMLC_REGISTER_PARAMETER(HardSigmoidParam);
 MXNET_OPERATOR_REGISTER_UNARY(hard_sigmoid)
diff --git a/src/operator/tensor/elemwise_unary_op_trig.cc b/src/operator/tensor/elemwise_unary_op_trig.cc
index b7cf76e4eb2d..13410e9422a5 100644
--- a/src/operator/tensor/elemwise_unary_op_trig.cc
+++ b/src/operator/tensor/elemwise_unary_op_trig.cc
@@ -49,7 +49,7 @@ The storage type of ``sin`` output depends upon the input storage type:
 MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_sin, unary_bwd<mshadow_op::sin_grad>)
 .set_attr<nnvm::FGradient>("FGradient",
     [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
-      // ograds[0]: d^2L/dx^2
+      // ograds[0]: head_grad_grads (dL/dxgrad)
       // inputs[0]: dL/dy
       // inputs[1]: x (ElemwiseUseIn)
       // f(x) = sin(x)
@@ -92,7 +92,7 @@ The storage type of ``cos`` output is always dense
 MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU(_backward_cos, unary_bwd<mshadow_op::cos_grad>)
 .set_attr<nnvm::FGradient>("FGradient",
     [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
-      // ograds[0]: d^2L/dx^2
+      // ograds[0]: head_grad_grads (dL/dx_grad)
       // inputs[0]: dL/dy
       // inputs[1]: x (ElemwiseUseIn)
       // f(x) = cos(x)
diff --git a/tests/python/unittest/test_higher_order_grad.py b/tests/python/unittest/test_higher_order_grad.py
index 4f1ea9a6c7b8..ad14c5050c1b 100644
--- a/tests/python/unittest/test_higher_order_grad.py
+++ b/tests/python/unittest/test_higher_order_grad.py
@@ -106,6 +106,23 @@ def grad_grad_op(x):
         check_second_order_unary(array, log10, grad_grad_op)
 
 
+@with_seed()
+def test_sigmoid():
+    def sigmoid(x):
+        return nd.sigmoid(x)
+
+    def grad_op(x):
+        return sigmoid(x) * (1 - sigmoid(x))
+
+    def grad_grad_op(x):
+        return grad_op(x) * (1 - 2 * sigmoid(x))
+
+    for dim in range(1, 5):
+        shape = rand_shape_nd(dim)
+        array = random_arrays(shape)
+        check_second_order_unary(array, sigmoid, grad_grad_op)
+
+
 def check_second_order_unary(x, op, grad_grad_op):
     x = nd.array(x)
     grad_grad_x = grad_grad_op(x)

From 3df3e2c6160b63b39510483bcdf8111398c21f2a Mon Sep 17 00:00:00 2001
From: Thom Lane <thom.e.lane@gmail.com>
Date: Wed, 3 Jul 2019 09:31:49 -0700
Subject: [PATCH 031/813] [TUTORIAL] Gluon performance tips and tricks (#15427)

* Adding Gluon performance tips and tricks tutorial. Summary of a number of performance features provided in MXNet. Useful for beginner audience.

* Added licence and download buttons.

* Updates based on feedback.

* Updates based on feedback.

* Update test_tutorials.py

* Update index.md

* Update performance.md

* Update performance.md
---
 docs/tutorials/gluon/performance.md | 483 ++++++++++++++++++++++++++++
 docs/tutorials/index.md             |   1 +
 tests/tutorials/test_tutorials.py   |   3 +
 3 files changed, 487 insertions(+)
 create mode 100644 docs/tutorials/gluon/performance.md

diff --git a/docs/tutorials/gluon/performance.md b/docs/tutorials/gluon/performance.md
new file mode 100644
index 000000000000..27a7e9ee6517
--- /dev/null
+++ b/docs/tutorials/gluon/performance.md
@@ -0,0 +1,483 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Gluon Performance Tips & Tricks
+
+Compared to traditional machine learning methods, the field of deep-learning has increased model accuracy across a wide range of tasks, but it has also increased the amount of computation required for model training and inference. Specialised hardware chips, such as GPUs and FPGAs, can speed up the execution of networks, but it can sometimes be hard to write code that uses the hardware to its full potential. We will be looking at a few simple tips and trick in this tutorial that you can use to speed up training and ultimately save on training costs. You'll find most of these tips and tricks useful for inference too.
+
+We'll start by writing some code to train an image classification network for the CIFAR-10 dataset, and then benchmark the throughput of the network in terms of samples processed per second. After some performance analysis, we'll identify the bottlenecks (i.e. the components limiting throughput) and improve the training speed step-by-step. We'll bring together all the tips and tricks at the end and evaluate our performance gains.
+
+
+```python
+from __future__ import print_function
+import multiprocessing
+import time
+import mxnet as mx
+import numpy as np
+```
+
+An Amazon EC2 p3.2xlarge instance was used to benchmark the code in this tutorial. You are likely to get different results and find different bottlenecks on other hardware, but these tips and tricks should still help improve training speed for bottleneck components. A GPU is recommended for this tutorial.
+
+
+```python
+ctx = mx.gpu() if mx.test_utils.list_gpus() else mx.cpu()
+print("Using {} context.".format(ctx))
+```
+
+    Using gpu(0) context.
+
+
+We'll use the `CIFAR10` dataset provided out-of-the-box with Gluon.
+
+
+```python
+dataset = mx.gluon.data.vision.CIFAR10(train=True)
+print('{} samples'.format(len(dataset)))
+```
+
+    50000 samples
+
+
+So we can learn how to identify training bottlenecks, let's intentionally introduce a bottleneck by adding a short `sleep` into the data loading pipeline. We transform each 32x32 CIFAR-10 image to 224x224 so we can use it with the ResNet-50 network designed for ImageNet. [CIFAR-10 specific ResNet networks](https://gluon-cv.mxnet.io/api/model_zoo.html#gluoncv.model_zoo.get_cifar_resnet) exist but we use the more standard ImageNet variants in this example.
+
+
+```python
+def transform_fn(x):
+    time.sleep(0.01)  # artificial slow-down
+    image = mx.image.imresize(x, w=224, h=224)
+    return image.astype('float32').transpose((2, 0, 1))
+
+dataset = dataset.transform_first(transform_fn)
+```
+
+Setting our batch size to 16, we can create the `DataLoader`.
+
+
+```python
+batch_size = 16
+dataloader = mx.gluon.data.DataLoader(dataset,
+                                      batch_size=batch_size,
+                                      shuffle=True,
+                                      last_batch="discard")
+print('{} batches'.format(len(dataloader)))
+```
+
+    3125 batches
+
+
+Up next, we create all of the other components required for training, such as the network, the loss function, the evaluation metric and parameter trainer.
+
+
+```python
+net = mx.gluon.model_zoo.vision.resnet50_v2(pretrained=False, ctx=ctx)
+net.initialize(mx.init.Xavier(magnitude=2.3), ctx=ctx)
+loss_fn = mx.gluon.loss.SoftmaxCrossEntropyLoss()
+metric = mx.metric.Accuracy()
+learning_rate = 0.001
+trainer = mx.gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': learning_rate})
+```
+
+## Initial Benchmark
+
+As a starting point, let's benchmark the throughput of our training loop: calculating the average samples per second across 25 iterations, where each iteration is a batch of 16 samples. We'll run a single forward pass through the network before starting our benchmark timer to avoid including shape inference and lazy initialization in the throughput calculations.
+
+
+```python
+def single_forward(net, dataloader, dtype='float32'):
+    data, label = next(iter(dataloader))
+    data = data.astype(dtype)
+    data = data.as_in_context(ctx)
+    pred = net(data)
+    pred.wait_to_read()
+```
+
+
+```python
+single_forward(net, dataloader)
+iters = 25
+num_samples = 0
+num_iters = 0
+start_time = time.time()
+for iter_idx, (data, label) in enumerate(dataloader):
+    num_samples += data.shape[0]
+    num_iters += 1
+    data = data.as_in_context(ctx)
+    label = label.as_in_context(ctx)
+    with mx.autograd.record():
+        pred = net(data)
+        loss = loss_fn(pred, label)
+    loss.backward()
+    trainer.step(data.shape[0])
+    metric.update(label, pred)
+    print('.', end='')
+    if num_iters >= iters:
+        break
+mx.nd.waitall()
+end_time = time.time()
+total_time = end_time - start_time
+print('\n')
+print('average iterations/sec: {:.4f}'.format(num_iters/total_time))
+print('average samples/sec: {:.4f}'.format(num_samples/total_time))
+```
+
+    .........................
+    
+    average iterations/sec: 4.4936
+    average samples/sec: 71.8975
+
+
+Although ~70 samples per second might sound respectable, let's see if we can do any better by identifying the bottleneck in the training loop and optimizing that component. A significant amount of time can be wasted by optimizing components that aren't bottlenecks.
+
+## Identifying the bottleneck
+
+Monitoring the CPU (with `top`) and GPU utilization (with `nvidia-smi`) provide clues as to where potential bottlenecks lie. With the example above, when simultaneously running these monitoring tool, you might spot a single process on the CPU fixed at ~100% utilization while the GPU utilization behaves erratically and often falls to ~0%. Seeing behaviour like can indicate the CPU is struggling to process data and the GPU is being starved of data.
+
+MXNet's Profiler is another highly recommended tool for identifying bottlenecks, since it gives timing data for individual MXNet operations. Check out [this comprehensive tutorial](https://mxnet.incubator.apache.org/versions/master/tutorials/python/profiler.html) for more details. As a simpler form of analysis, we will split our training loop into two common components:
+
+1. Data Loading
+2. Network Execution (forward and backward passes)
+
+We define two function to independently benchmark these components: `benchmark_dataloader` and `benchmark_network`.
+
+
+```python
+def benchmark_dataloader(dataloader, iters=25):
+    num_samples = 0
+    num_iters = 0
+    start_time = time.time()
+    startup_time = None
+    for iter_idx, sample in enumerate(dataloader):
+        if startup_time is None:
+            startup_time = time.time()
+        num_samples += sample[0].shape[0]
+        num_iters += 1
+        if num_iters >= iters:
+            break
+        print('.', end='')
+    end_time = time.time()
+    total_time = end_time - start_time
+    total_startup_time = startup_time - start_time
+    total_iter_time = end_time - startup_time
+    print('\n')
+    print('total startup time: {:.4f}'.format(total_startup_time))
+    print('average iterations/sec: {:.4f}'.format(num_iters/total_iter_time))
+    print('average samples/sec: {:.4f}'.format(num_samples/total_iter_time))
+    
+    
+def benchmark_network(data, label, net, loss_fn, trainer, iters=25):
+    num_samples = 0
+    num_iters = 0
+    mx.nd.waitall()
+    start_time = time.time()
+    for iter_idx in range(iters):
+        num_samples += data.shape[0]
+        num_iters += 1
+        with mx.autograd.record():
+            pred = net(data)
+            loss = loss_fn(pred, label)
+        loss.backward()
+        trainer.step(data.shape[0])
+        mx.nd.waitall()
+        if num_iters >= iters:
+            break
+        print('.', end='')
+    end_time = time.time()
+    total_time = end_time - start_time
+    print('\n')
+    print('average iterations/sec: {:.4f}'.format(num_iters/total_time))
+    print('average samples/sec: {:.4f}'.format(num_samples/total_time))
+```
+
+Our `benchmark_dataloader` function just loops through the `DataLoader` for a given number of iterations: it doesn't transfer the data to the correct context or pass it to the network. Our `benchmark_network` function just performs a forward and backward pass on an identical (and pre-transferred) batch of data: it doesn't require new data to be loaded. We'll run both of these functions now.
+
+
+```python
+print('\n', '### benchmark_dataloader', '\n')
+benchmark_dataloader(dataloader)
+print('\n', '### benchmark_network', '\n')
+data, label = next(iter(dataloader))
+data = data.as_in_context(ctx)
+label = label.as_in_context(ctx)
+benchmark_network(data, label, net, loss_fn, trainer)
+```
+
+    
+     ### benchmark_dataloader 
+    
+    ........................
+    
+    total startup time: 0.1697
+    average iterations/sec: 6.2201
+    average samples/sec: 99.5217
+    
+     ### benchmark_network 
+    
+    ........................
+    
+    average iterations/sec: 15.1908
+    average samples/sec: 243.0525
+
+
+Our data loading pipeline appears to be the bottleneck for training: ~100 samples/second compared with ~250 samples/second for network execution. One limiting factor could be disk throughput when reading samples (using a SSD instead of HDD can help with this), but in this case we intentionally added a delay in data transformation. Augmentation can often be a bottleneck in training if the following trick isn't applied.
+
+## Tips & Tricks #1: Use multiple workers on `DataLoader`
+
+In the previous section, we established that the data loading component of the training loop was the bottleneck. Instead of simply removing the artificial delay, let's assume it was some pre-processing or augmentation step that couldn't be removed. We found that the CPU utilization was fixed at 100%, but this was just for a single core. Usually machines have multiple cores and with one easy trick we can leverage more CPU cores to pre-process the data. Setting `num_workers` on the `DataLoader` will result in multiple workers being used to preprocess the data. We can use `multiprocessing.cpu_count()` to find the number of CPU cores available on the machine, and we save 1 core for the main thread.
+
+
+```python
+num_workers = multiprocessing.cpu_count() - 1
+dataloader = mx.gluon.data.DataLoader(dataset,
+                                      batch_size=batch_size,
+                                      shuffle=True,
+                                      last_batch="discard",
+                                      num_workers=num_workers)
+print('Using {} workers for DataLoader.'.format(num_workers))
+```
+
+    Using 7 workers for DataLoader.
+
+
+We benchmark the two main components once again:
+
+
+```python
+print('\n', '### benchmark_dataloader', '\n')
+benchmark_dataloader(dataloader)
+print('\n', '### benchmark_network', '\n')
+data, label = next(iter(dataloader))
+data = data.as_in_context(ctx)
+label = label.as_in_context(ctx)
+benchmark_network(data, label, net, loss_fn, trainer, iters=10)
+```
+
+    
+     ### benchmark_dataloader 
+    
+    ........................
+    
+    total startup time: 0.1935
+    average iterations/sec: 46.2375
+    average samples/sec: 739.7994
+    
+     ### benchmark_network 
+    
+    .........
+    
+    average iterations/sec: 14.8614
+    average samples/sec: 237.7819
+
+
+Our data loading pipeline is no longer the bottleneck for training throughput: ~700 samples per second versus ~250 samples per second for network execution as before. Check out [NVIDIA DALI](https://docs.nvidia.com/deeplearning/sdk/dali-developer-guide/docs/examples/mxnet/gluon.html) if you need to futher optimize your data pipeline. We can now focus our attention on improving the network throughput.
+
+## Tips & Tricks #2: Hybridize the network
+
+Gluon networks run in imperative mode by default, executing `NDArray` operations as the lines of code are stepped through one by one. While in imperative mode, debugging is often simplified and more flexible networks can be defined (using Python control flow). But this comes at a slight cost in terms of throughput. Since the network doesn't know what line of code will be run next, the network operations cannot be optimized and additional memory allocations are required (which all takes time). Most networks though can be written as `HybridBlocks` and, with the `hybridize` method, can be converted to symbolic mode execution. We can expect throughput to increase slightly in this mode. Watch out though: debugging can get more complicated. Setting `static_alloc=True` and `static_shape=True` reduce the number of memory allocations required while training. Once again, we run `single_forward` to force the hybridization process to occur before benchmarking.
+
+
+```python
+net.hybridize(static_alloc=True, static_shape=True)
+single_forward(net, dataloader)
+```
+
+
+```python
+print('\n', '### benchmark_dataloader', '\n')
+benchmark_dataloader(dataloader)
+print('\n', '### benchmark_network', '\n')
+data, label = next(iter(dataloader))
+data = data.as_in_context(ctx)
+label = label.as_in_context(ctx)
+benchmark_network(data, label, net, loss_fn, trainer)
+```
+
+    
+     ### benchmark_dataloader 
+    
+    ........................
+    
+    total startup time: 0.1847
+    average iterations/sec: 46.3004
+    average samples/sec: 740.8072
+    
+     ### benchmark_network 
+    
+    ........................
+    
+    average iterations/sec: 16.5738
+    average samples/sec: 265.1812
+
+
+We can see quite a modest ~10% increase in throughput after hybridization. Gains can depend on a number of factors including the network architecture and the batch size used (a larger increase expected for smaller batch size). Our network execution is still the bottleneck in training so let's focus on that again.
+
+## Tips & Tricks #3: Increase the batch size
+
+GPUs are optimized for high throughput and they do this by performing many operations in parallel. Our NVIDIA Tesla V100 GPU utilization peaks at ~85% while running the last example. Although this is already quite high, there's still room improvement given this metric shows the percentage of time *at least one* kernel is running (over the last 1 second by default). Given we have enough memory available, the throughput of the network can be improved by increasing the batch size since more samples are processed in parallel. At this stage we're using approximately 1/4 of the available GPU memory, so let's increase out batch size by a factor of 4, from 16 to 64. Changing the batch size does have some side effects though. Using the same optimizer with same hyperparameters often leads to slower convergence. More gradients, from more samples, are averaged which leads to a smaller variance in the batch gradient overall. One simple trick to mitigate this is to increase the learning rate by the same factor: so in this case, from 0.001 to 0.004.
+
+
+```python
+batch_size = batch_size * 4
+print('batch_size: {}'.format(batch_size))
+learning_rate = learning_rate * 4
+print('learning_rate: {}'.format(learning_rate))
+dataloader = mx.gluon.data.DataLoader(dataset,
+                                            batch_size=batch_size,
+                                            shuffle=True,
+                                            last_batch="discard",
+                                            num_workers=num_workers)
+trainer = mx.gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': learning_rate})
+single_forward(net, dataloader)
+```
+
+    batch_size: 64
+    learning_rate: 0.004
+
+
+
+```python
+print('\n', '### benchmark_dataloader', '\n')
+benchmark_dataloader(dataloader)
+print('\n', '### benchmark_network', '\n')
+data, label = next(iter(dataloader))
+data = data.as_in_context(ctx)
+label = label.as_in_context(ctx)
+benchmark_network(data, label, net, loss_fn, trainer)
+```
+
+    
+     ### benchmark_dataloader 
+    
+    ........................
+    
+    total startup time: 0.7195
+    average iterations/sec: 11.7149
+    average samples/sec: 749.7521
+    
+     ### benchmark_network 
+    
+    ........................
+    
+    average iterations/sec: 5.5370
+    average samples/sec: 354.3710
+
+
+Once again, we see improvements in throughput. ~30% higher this time. Checking GPU memory usage, we still have room to increase the batch size higher than 64 (on NVIDIA Tesla V100). When the batch size starts to reach very large numbers (>512), simple tricks such as linear scaling of the learning rate might be insufficient for maintaining good convergence. Consider using a [warm-up learning rate schedule](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/learning_rate_schedules_advanced.html) and changing to specialized optimizers such as [LBSGD](https://mxnet.incubator.apache.org/api/python/optimization/optimization.html#mxnet.optimizer.LBSGD). 
+
+## Tips & Tricks #4: Using Mixed-Precision (`float32` and `float16`)
+
+Model execution is still our bottleneck, so let's try out a new trick called [mixed precision](https://mxnet.incubator.apache.org/versions/master/faq/float16.html) training. Some recent GPUs have cores that are optimized for 'half-precision' (i.e. `float16`) operations and they can be much faster than their 'full-precision' (i.e. `float32`) counterparts. Given all of the randomness already in neural network training, this reduction in precision doesn't significantly impact the model accuracy in many cases. Convergence is slightly better when you keep the network parameters at full-precision but forward and backward passes can be performed at half-precision: hence the term 'mixed-precision'. Also check out [Automatic Mixed Precision](https://mxnet.incubator.apache.org/versions/master/tutorials/amp/amp_tutorial.html) (AMP) for a more automated way of optimizing your network.
+
+We need to `cast` the network to `'float16'`, configure our optimizer to use `multi_precision` and convert our input data types to `'float16'` too.
+
+
+```python
+net.cast('float16')
+trainer = mx.gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': learning_rate,
+                                                         'multi_precision': True})
+single_forward(net, dataloader, dtype='float16')
+```
+
+
+```python
+print('\n', '### benchmark_dataloader', '\n')
+benchmark_dataloader(dataloader)
+print('\n', '### benchmark_network', '\n')
+data, label = next(iter(dataloader))
+data = data.astype('float16').as_in_context(ctx)
+label = label.astype('float16').as_in_context(ctx)
+benchmark_network(data, label, net, loss_fn, trainer)
+```
+
+    
+     ### benchmark_dataloader 
+    
+    ........................
+    
+    total startup time: 0.7006
+    average iterations/sec: 11.6537
+    average samples/sec: 745.8338
+    
+     ### benchmark_network 
+    
+    ........................
+    
+    average iterations/sec: 11.3614
+    average samples/sec: 727.1298
+
+
+Overall we see a substantial increase in training throughput: double compared to full-precision training.
+
+## Tips & Tricks #5: Others
+
+Many other tips and tricks exist for optimizing the throughput of training.
+
+One area we didn't explicitly benchmark in this tutorial is data transfer from CPU to GPU memory. Usually this isn't an issue, but for very large arrays this can become a bottleneck too. You might be able to compress your data significantly before transferring if your data is sparse (i.e. mostly zero values). Check out the [sparse array tutorial](https://mxnet.incubator.apache.org/versions/master/tutorials/index.html) for more details and an example of how this can impact training speed.
+
+Another useful trick if data pre-processing or data transfer is the bottleneck is pre-fetching batches. You can write your training loop to transfer the next batch of data to GPU before processing the current batch. Once again, this trick is memory permitting.
+
+And finally, if you are an advanced user, check out the various [environment variables](https://mxnet.incubator.apache.org/faq/env_var.html) that can be configured to change the behaviour of the MXNet backend.
+
+## Final Benchmark
+
+We will now combine all of the above tricks and tips in the complete training loop and compare to the initial benchmark.
+
+
+```python
+iters = 25
+num_samples = 0
+num_iters = 0
+start_time = time.time()
+for iter_idx, (data, label) in enumerate(dataloader):
+    num_samples += data.shape[0]
+    num_iters += 1
+    data = data.as_in_context(ctx).astype('float16')
+    label = label.as_in_context(ctx).astype('float16')
+    with mx.autograd.record():
+        pred = net(data)
+        loss = loss_fn(pred, label)
+    loss.backward()
+    trainer.step(data.shape[0])
+    metric.update(label, pred)
+    print('.', end='')
+    if num_iters >= iters:
+        break
+end_time = time.time()
+total_time = end_time - start_time
+print('\n')
+print('average iterations/sec: {:.4f}'.format(num_iters/total_time))
+print('average samples/sec: {:.4f}'.format(num_samples/total_time))
+```
+
+    .........................
+    
+    average iterations/sec: 7.9435
+    average samples/sec: 508.3821
+
+
+Using the above tips and tricks we managed to increase the throughput of training by ~600% from the initial benchmark! Our training throughput is less than the throughput of the individual components we tested, but there are additional overheads that we didn't previously measure (such as data transfer to GPU).
+
+## Conclusion
+
+We learned a number of tips and tricks to optimize the throughput of training, and they lead to a considerable increase compared to our initial baseline. As general rules, set `num_workers` on the `DataLoader` to >0, and hybridize your network if you're not debugging. You should increase `batch_size` where possible, but do this with care because of its potential effects on convergence. And finally, consider mixed precision training for substantial speed-ups.
+
+## Recommended Next Steps
+
+* Use the [MXNet Profiler](https://mxnet.incubator.apache.org/versions/master/tutorials/python/profiler.html) to identify additional bottlenecks and other areas for optimization.
+* Check out the [hybridization tutorial](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/hybrid.html) for more details on how to write custom `HybridBlock`s.
+* Consider using [multi-GPU](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/multi_gpu.html) and [multi-host](https://github.com/apache/incubator-mxnet/tree/master/example/distributed_training) training if you reach the limits of single GPU training.
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md
index 32bdf8aba4cc..d66e6c8d9a25 100644
--- a/docs/tutorials/index.md
+++ b/docs/tutorials/index.md
@@ -91,6 +91,7 @@ Select API:&nbsp;
    * [Image similiarity search with InfoGAN](/tutorials/gluon/info_gan.html)
 * Practitioner Guides
     * [Gotchas using NumPy](/tutorials/gluon/gotchas_numpy_in_mxnet.html)
+    * [Gluon Performance Tips & Tricks](/tutorials/gluon/performance.html)
     * [Multi-GPU training](/tutorials/gluon/multi_gpu.html)<span style="color:red"> (new!) </span>([Alternative](http://gluon.mxnet.io/chapter07_distributed-learning/multiple-gpus-gluon.html) <img src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg" alt="External link" height="15px" style="margin: 0px 0px 3px 3px;"/>)
     * [Checkpointing and Model Serialization (a.k.a. saving and loading)](/tutorials/gluon/save_load_params.html) <img src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg" alt="External link" height="15px" style="margin: 0px 0px 3px 3px;"/> ([Alternative](http://gluon.mxnet.io/chapter03_deep-neural-networks/serialization.html))
     * [Distributed Training](https://github.com/apache/incubator-mxnet/tree/master/example/distributed_training)
diff --git a/tests/tutorials/test_tutorials.py b/tests/tutorials/test_tutorials.py
index b5f84d550636..829d56f419af 100644
--- a/tests/tutorials/test_tutorials.py
+++ b/tests/tutorials/test_tutorials.py
@@ -114,6 +114,9 @@ def test_gluon_save_load_params():
 
 def test_gluon_hybrid():
     assert _test_tutorial_nb('gluon/hybrid')
+
+def test_gluon_performance():
+    assert _test_tutorial_nb('gluon/performance')
     
 def test_gluon_pretrained_models():
     assert _test_tutorial_nb('gluon/pretrained_models')    

From d49445fcdc01102bea04e5dd83272f928d557bb6 Mon Sep 17 00:00:00 2001
From: Zhaoqi Zhu <zhaoqizh@usc.edu>
Date: Wed, 3 Jul 2019 10:26:04 -0700
Subject: [PATCH 032/813] Updating profiler tutorial to include new custom
 operator profiling (#15403)

* update profiler tutorial

* Update profiler.md

* Update profiler.md

* Update profiler.md

* Update docs/tutorials/python/profiler.md

Co-Authored-By: Aaron Markham <markhama@amazon.com>

* Update docs/tutorials/python/profiler.md

Co-Authored-By: Aaron Markham <markhama@amazon.com>

* Update docs/tutorials/python/profiler.md

Co-Authored-By: Aaron Markham <markhama@amazon.com>

* Update profiler.md

change image url to dmlc and add a code example

* Update profiler.md

* Update profiler.md

* Update profiler.md

* Update profiler.md

* Re-trigger build

* Update profiler.md
---
 docs/tutorials/python/profiler.md | 75 +++++++++++++++++++++++++++++++
 1 file changed, 75 insertions(+)

diff --git a/docs/tutorials/python/profiler.md b/docs/tutorials/python/profiler.md
index 9eed452c2e27..f2da62833fcf 100644
--- a/docs/tutorials/python/profiler.md
+++ b/docs/tutorials/python/profiler.md
@@ -206,6 +206,81 @@ Let's zoom in to check the time taken by operators
 
 The above picture visualizes the sequence in which the operators were executed and the time taken by each operator.
 
+### Profiling Custom Operators
+Should the existing NDArray operators fail to meet all your model's needs, MXNet supports [Custom Operators](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/customop.html) that you can define in Python. In `forward()` and `backward()` of a custom operator, there are two kinds of code: "pure Python" code (NumPy operators included) and "sub-operators" (NDArray operators called within `forward()` and `backward()`). With that said, MXNet can profile the execution time of both kinds without additional setup. Specifically, the MXNet profiler will break a single custom operator call into a pure Python event and several sub-operator events if there are any. Furthermore, all of those events will have a prefix in their names, which is, conveniently, the name of the custom operator you called.
+
+Let's try profiling custom operators with the following code example:
+
+```python
+
+import mxnet as mx
+from mxnet import nd
+from mxnet import profiler
+
+class MyAddOne(mx.operator.CustomOp):
+    def forward(self, is_train, req, in_data, out_data, aux):  
+        self.assign(out_data[0], req[0], in_data[0]+1)
+
+    def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
+        self.assign(in_grad[0], req[0], out_grad[0])
+
+@mx.operator.register('MyAddOne')
+class CustomAddOneProp(mx.operator.CustomOpProp):
+    def __init__(self):
+        super(CustomAddOneProp, self).__init__(need_top_grad=True)
+
+    def list_arguments(self):
+        return ['data']
+
+    def list_outputs(self):
+        return ['output']
+
+    def infer_shape(self, in_shape):
+        return [in_shape[0]], [in_shape[0]], []
+
+    def create_operator(self, ctx, shapes, dtypes):
+        return MyAddOne()
+
+
+inp = mx.nd.zeros(shape=(500, 500))
+
+profiler.set_config(profile_all=True, continuous_dump = True)
+profiler.set_state('run')
+
+w = nd.Custom(inp, op_type="MyAddOne")
+
+mx.nd.waitall()
+
+profiler.set_state('stop')
+profiler.dump()
+```
+
+Here, we have created a custom operator called `MyAddOne`, and within its `forward()` function, we simply add one to the input. We can visualize the dump file in `chrome://tracing/`:
+
+![Custom Operator Profiling Screenshot](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tutorials/python/profiler/profiler_output_custom_operator_chrome.png)
+
+As shown by the screenshot, in the **Custom Operator** domain where all the custom operator-related events fall into, we can easily visualize the execution time of each segment of `MyAddOne`. We can tell that `MyAddOne::pure_python` is executed first. We also know that `CopyCPU2CPU` and `_plus_scalr` are two "sub-operators" of `MyAddOne` and the sequence in which they are executed.
+
+Please note that: to be able to see the previously described information, you need to set `profile_imperative` to `True` even when you are using custom operators in [symbolic mode](https://mxnet.incubator.apache.org/versions/master/tutorials/basic/symbol.html) (refer to the code snippet below, which is the symbolic-mode equivelent of the code example above). The reason is that within custom operators, pure python code and sub-operators are still called imperatively. 
+
+```python 
+# Set profile_all to True
+profiler.set_config(profile_all=True, aggregate_stats=True, continuous_dump = True)
+# OR, Explicitly Set profile_symbolic and profile_imperative to True
+profiler.set_config(profile_symbolic = True, profile_imperative = True, \
+    aggregate_stats=True, continuous_dump = True)
+
+profiler.set_state('run')
+# Use Symbolic Mode
+a = mx.symbol.Variable('a')
+b = mx.symbol.Custom(data=a, op_type='MyAddOne')
+c = b.bind(mx.cpu(), {'a': inp})
+y = c.forward()
+mx.nd.waitall()
+profiler.set_state('stop')
+profiler.dump()
+```
+
 ## Advanced: Using NVIDIA Profiling Tools
 
 MXNet's Profiler is the recommended starting point for profiling MXNet code, but NVIDIA also provides a couple of tools for low-level profiling of CUDA code: [NVProf](https://devblogs.nvidia.com/cuda-pro-tip-nvprof-your-handy-universal-gpu-profiler/), [Visual Profiler](https://developer.nvidia.com/nvidia-visual-profiler) and [Nsight Compute](https://developer.nvidia.com/nsight-compute). You can use these tools to profile all kinds of executables, so they can be used for profiling Python scripts running MXNet. And you can use these in conjunction with the MXNet Profiler to see high-level information from MXNet alongside the low-level CUDA kernel information.

From 5078853650cfe45869b808f3198abc4735b96f25 Mon Sep 17 00:00:00 2001
From: Aaron Markham <markhama@amazon.com>
Date: Wed, 3 Jul 2019 12:40:52 -0700
Subject: [PATCH 033/813] remove comments from nano instructions (#15433)

* remove comments

* remove java references
---
 docs/install/install-jetson.md | 60 +---------------------------------
 1 file changed, 1 insertion(+), 59 deletions(-)

diff --git a/docs/install/install-jetson.md b/docs/install/install-jetson.md
index a93f930a8182..6b0d65060342 100644
--- a/docs/install/install-jetson.md
+++ b/docs/install/install-jetson.md
@@ -19,7 +19,7 @@
 
 MXNet supports the Ubuntu Arch64 based operating system so you can run MXNet on NVIDIA Jetson Devices, such as the [TX2](http://www.nvidia.com/object/embedded-systems-dev-kits-modules.html) or [Nano](https://developer.nvidia.com/embedded/learn/get-started-jetson-nano-devkit).
 
-These instructions will walk through how to build MXNet and install the corresponding language bindings. Python is the default binding, but you may also try out one of the many language bindings MXNet has to offer. These instructions also cover how to setup MXNet's Java Inference API.
+These instructions will walk through how to build MXNet and install MXNet's Python language binding. 
 
 For the purposes of this install guide we will assume that CUDA is already installed on your Jetson device. The disk image provided by NVIDIA's getting started guides will have the Jetson toolkit preinstalled, and this also includes CUDA. You should double check what versions are installed and which version you plan to use.
 
@@ -61,22 +61,6 @@ sudo pip install \
 
 If you plan to cross-compile you will need to install these dependencies on that computer as well.
 
-<!--
-### Java API
-
-To use the Java inference API you only need the following dependencies:
-
-```
-sudo apt install \
-                        maven \
-                        openjdk-8-java
-```
-
-You may try to build the Java API .jar file yourself. If so, you will need these dependencies on that computer as well.
-
-**Note:** The `mvn install` option for building the Java API .jar after compiling the MXNet binary files is only available on MXNet >= v1.5.0.
--->
-
 ### Configure CUDA
 
 You can check to see what version of CUDA is running with `nvcc`.
@@ -132,48 +116,6 @@ install the dependencies in the prerequisites section, then run the pip wheel.
 sudo pip install mxnet-1.4.0-cp36-cp36m-linux_aarch64.whl
 ```
 
-<!--
-## Install MXNet for Java
-
-The MXNet Java API for Jetson devices is still experimental. For best results try with the master branch.
-
-### Use a pre-built jar
-
-The package for Jetson is not currently on Maven, but you can download it from S3 instead.
-The following jar was cross-compiled for Jetson using MXNet v1.4.1.
-* https://s3.us-east-2.amazonaws.com/mxnet-public/install/jetson/1.4.1/mxnet-full_2.11-INTERNAL.jar
-
-Place the file on your Jetson where your project can find it.
-
-### Build your own jar
-
-You can build a jar from any pre-compiled MXNet binary.
-Follow the build from source instructions first. Once you have build the `libmxnet.so` file you are ready to build your jar files.
-
-```bash
-cd $MXNET_HOME/scala-package
-mvn package
-```
-
-This will generate a file named similar to `src/lib/mxnet-full_2.11-INTERNAL.jar`.
-
-### Use the MXNet-Java jar in your project
-
-MXNet-Java can be easily included in your Maven managed project.
-The following is an example entry for your project's `.pom` file.
-
-```
-<dependency>
-  <groupId>org.apache.mxnet</groupId>
-  <artifactId>mxnet-full_2.11-INTERNAL</artifactId>
-  <version>1.4.1</version>
-  <systemPath>${basedir}/src/lib/mxnet-full_2.11-INTERNAL.jar</systemPath>
-</dependency>
-```
-
-Refer to the [Java setup](https://mxnet.incubator.apache.org/versions/master/install/java_setup.html) page for further information.
--->
-
 ## Use a Pre-compiled MXNet Binary
 
 If you want to just use a pre-compiled binary you can download it from S3:

From fc54781e08c3d20c184fdf108ee993d349542384 Mon Sep 17 00:00:00 2001
From: Haohuan Wang <haohuanw@umich.edu>
Date: Wed, 3 Jul 2019 17:27:45 -0700
Subject: [PATCH 034/813] enable TensorRT integration with cpp api (#15335)

---
 cpp-package/example/inference/README.md       |  14 +++
 .../example/inference/imagenet_inference.cpp  | 104 ++++++++++++----
 cpp-package/include/mxnet-cpp/MxNetCpp.h      |   1 +
 cpp-package/include/mxnet-cpp/contrib.h       | 115 ++++++++++++++++++
 cpp-package/include/mxnet-cpp/symbol.h        |  17 +++
 cpp-package/include/mxnet-cpp/symbol.hpp      |  35 ++++++
 6 files changed, 260 insertions(+), 26 deletions(-)
 create mode 100644 cpp-package/include/mxnet-cpp/contrib.h

diff --git a/cpp-package/example/inference/README.md b/cpp-package/example/inference/README.md
index 272586da5da9..81cf9d856c23 100644
--- a/cpp-package/example/inference/README.md
+++ b/cpp-package/example/inference/README.md
@@ -75,6 +75,7 @@ imagenet_inference  --symbol_file <model symbol file in json format>
 		    --num_inference_batches <number of batches used for inference>
 		    --data_layer_type <default: "float32", choices: ["float32", "int8", "uint8"]>
 		    --gpu <whether to run inference on GPU, default: false>
+		    --enableTRT  <whether to run inference with TensorRT, default: false>"
 		    --benchmark <whether to use dummy data to run inference, default: false>
 ```
 
@@ -134,6 +135,19 @@ imagenet_inference.cpp:372: Running the forward pass on model to evaluate the pe
 imagenet_inference.cpp:387: benchmark completed!
 imagenet_inference.cpp:388: batch size: 1 num batch: 500 throughput: xxxx imgs/s latency:xxxx ms
 ```
+For running this example with TensorRT, you can quickly try the following example to run a benchmark test for testing Inception BN:
+```
+./imagenet_inference --symbol_file "./model/Inception-BN-symbol.json" --params_file "./model/Inception-BN-0126.params" --batch_size 16 --num_inference_batches 500 --benchmark --enableTRT
+```
+Sample output will looks like this (the example is running on a AWS P3.2xl machine):
+```
+imagenet_inference.cpp:302: Loading the model from ./model/Inception-BN-symbol.json
+build_subgraph.cc:686: start to execute partition graph.
+imagenet_inference.cpp:317: Loading the model parameters from ./model/Inception-BN-0126.params
+imagenet_inference.cpp:424: Running the forward pass on model to evaluate the performance..
+imagenet_inference.cpp:439:  benchmark completed!
+imagenet_inference.cpp:440:  batch size: 16 num batch: 500 throughput: 6284.78 imgs/s latency:0.159115 ms
+```
 
 ## [sentiment_analysis_rnn.cpp](<https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/inference/sentiment_analysis_rnn.cpp>)
 This example demonstrates how you can load a pre-trained RNN model and use it to predict the sentiment expressed in the given movie review with the MXNet C++ API. The example is capable of processing variable legnth inputs. It performs the following tasks
diff --git a/cpp-package/example/inference/imagenet_inference.cpp b/cpp-package/example/inference/imagenet_inference.cpp
index 7eaf991ada4e..4f5a3bb8bbe6 100644
--- a/cpp-package/example/inference/imagenet_inference.cpp
+++ b/cpp-package/example/inference/imagenet_inference.cpp
@@ -82,6 +82,7 @@ class Predictor {
               const std::string& model_params_file,
               const Shape& input_shape,
               bool use_gpu,
+              bool enable_tensorrt,
               const std::string& dataset,
               const int data_nthreads,
               const std::string& data_layer_type,
@@ -98,6 +99,13 @@ class Predictor {
     bool AdvanceDataIter(int skipped_batches);
     void LoadModel(const std::string& model_json_file);
     void LoadParameters(const std::string& model_parameters_file);
+    void SplitParamMap(const std::map<std::string, NDArray> &paramMap,
+        std::map<std::string, NDArray> *argParamInTargetContext,
+        std::map<std::string, NDArray> *auxParamInTargetContext,
+        Context targetContext);
+    void ConvertParamMapToTargetContext(const std::map<std::string, NDArray> &paramMap,
+        std::map<std::string, NDArray> *paramMapInTargetContext,
+        Context targetContext);
     void InitParameters();
 
     inline bool FileExists(const std::string &name) {
@@ -115,6 +123,7 @@ class Predictor {
 
     MXDataIter *val_iter_;
     bool use_gpu_;
+    bool enable_tensorrt_;
     std::string dataset_;
     int data_nthreads_;
     std::string data_layer_type_;
@@ -134,14 +143,15 @@ class Predictor {
  *                 the input shape is required to be in format Shape(1, number_of_channels, height, width)
  *                 The input image will be resized to (height x width) size before running the inference.
  * 4. use_gpu: determine if run inference on GPU
- * 5. dataset: data file (.rec) to be used for inference
- * 6. data_nthreads: number of threads for data loading
- * 7. data_layer_type: data type for data layer
- * 8. rgb_mean: mean value to be subtracted on R/G/B channel
- * 9. rgb_std: standard deviation on R/G/B channel
- * 10. shuffle_chunk_seed: shuffling chunk seed
- * 11. seed: shuffling seed
- * 12. benchmark: use dummy data for inference
+ * 5. enable_tensorrt: determine if enable TensorRT
+ * 6. dataset: data file (.rec) to be used for inference
+ * 7. data_nthreads: number of threads for data loading
+ * 8. data_layer_type: data type for data layer
+ * 9. rgb_mean: mean value to be subtracted on R/G/B channel
+ * 10. rgb_std: standard deviation on R/G/B channel
+ * 11. shuffle_chunk_seed: shuffling chunk seed
+ * 12. seed: shuffling seed
+ * 13. benchmark: use dummy data for inference
  *
  * The constructor will:
  *  1. Create ImageRecordIter based on the given dataset file.
@@ -152,6 +162,7 @@ Predictor::Predictor(const std::string& model_json_file,
                      const std::string& model_params_file,
                      const Shape& input_shape,
                      bool use_gpu,
+                     bool enable_tensorrt,
                      const std::string& dataset,
                      const int data_nthreads,
                      const std::string& data_layer_type,
@@ -161,6 +172,7 @@ Predictor::Predictor(const std::string& model_json_file,
                      int seed, bool benchmark)
     : input_shape_(input_shape),
       use_gpu_(use_gpu),
+      enable_tensorrt_(enable_tensorrt),
       dataset_(dataset),
       data_nthreads_(data_nthreads),
       data_layer_type_(data_layer_type),
@@ -182,12 +194,12 @@ Predictor::Predictor(const std::string& model_json_file,
   // Load the model
   LoadModel(model_json_file);
   // Initilize the parameters
-  // benchmark=false, load parameters from file
-  // benchmark=true, randomly initialize parameters
-  if (!benchmark_) {
-    LoadParameters(model_params_file);
-  } else {
+  // benchmark=true && model_params_file.empty(), randomly initialize parameters
+  // else, load parameters
+  if (benchmark_ && model_params_file.empty()) {
     InitParameters();
+  } else {
+    LoadParameters(model_params_file);
   }
 
   int dtype = GetDataLayerType();
@@ -289,9 +301,11 @@ void Predictor::LoadModel(const std::string& model_json_file) {
   }
   LG << "Loading the model from " << model_json_file << std::endl;
   net_ = Symbol::Load(model_json_file);
+  if (enable_tensorrt_) {
+    net_ = net_.GetBackendSymbol("TensorRT");
+  }
 }
 
-
 /*
  * The following function loads the model parameters.
  */
@@ -303,20 +317,50 @@ void Predictor::LoadParameters(const std::string& model_parameters_file) {
   LG << "Loading the model parameters from " << model_parameters_file << std::endl;
   std::map<std::string, NDArray> parameters;
   NDArray::Load(model_parameters_file, 0, &parameters);
-  for (const auto &k : parameters) {
-    if (k.first.substr(0, 4) == "aux:") {
-      auto name = k.first.substr(4, k.first.size() - 4);
-      aux_map_[name] = k.second.Copy(global_ctx_);
-    }
-    if (k.first.substr(0, 4) == "arg:") {
-      auto name = k.first.substr(4, k.first.size() - 4);
-      args_map_[name] = k.second.Copy(global_ctx_);
-    }
+  if (enable_tensorrt_) {
+    std::map<std::string, NDArray> intermediate_args_map;
+    std::map<std::string, NDArray> intermediate_aux_map;
+    SplitParamMap(parameters, &intermediate_args_map, &intermediate_aux_map, Context::cpu());
+    contrib::InitTensorRTParams(net_, &intermediate_args_map, &intermediate_aux_map);
+    ConvertParamMapToTargetContext(intermediate_args_map, &args_map_, global_ctx_);
+    ConvertParamMapToTargetContext(intermediate_aux_map, &aux_map_, global_ctx_);
+  } else {
+    SplitParamMap(parameters, &args_map_, &aux_map_, global_ctx_);
   }
   /*WaitAll is need when we copy data between GPU and the main memory*/
   NDArray::WaitAll();
 }
 
+/*
+ * The following function split loaded param map into arg parm
+ *   and aux param with target context
+ */
+void Predictor::SplitParamMap(const std::map<std::string, NDArray> &paramMap,
+    std::map<std::string, NDArray> *argParamInTargetContext,
+    std::map<std::string, NDArray> *auxParamInTargetContext,
+    Context targetContext) {
+  for (const auto& pair : paramMap) {
+    std::string type = pair.first.substr(0, 4);
+    std::string name = pair.first.substr(4);
+    if (type == "arg:") {
+      (*argParamInTargetContext)[name] = pair.second.Copy(targetContext);
+    } else if (type == "aux:") {
+      (*auxParamInTargetContext)[name] = pair.second.Copy(targetContext);
+    }
+  }
+}
+
+/*
+ * The following function copy the param map into the target context
+ */
+void Predictor::ConvertParamMapToTargetContext(const std::map<std::string, NDArray> &paramMap,
+    std::map<std::string, NDArray> *paramMapInTargetContext,
+    Context targetContext) {
+  for (const auto& pair : paramMap) {
+    (*paramMapInTargetContext)[pair.first] = pair.second.Copy(targetContext);
+  }
+}
+
 /*
  * The following function randomly initializes the parameters when benchmark_ is true.
  */
@@ -517,6 +561,8 @@ void printUsage() {
               << "--data_layer_type <default: \"float32\" "
               << "choices: [\"float32\",\"int8\",\"uint8\"]>" << std::endl
               << "--gpu  <whether to run inference on GPU, default: false>" << std::endl
+              << "--enableTRT  <whether to run inference with TensorRT, "
+              << "default: false>" << std::endl
               << "--benchmark <whether to use dummy data to run inference, default: false>"
               << std::endl;
 }
@@ -528,6 +574,7 @@ int main(int argc, char** argv) {
   std::string input_rgb_mean("0 0 0");
   std::string input_rgb_std("1 1 1");
   bool use_gpu = false;
+  bool enable_tensorrt = false;
   bool benchmark = false;
   int batch_size = 64;
   int num_skipped_batches = 0;
@@ -575,6 +622,9 @@ int main(int argc, char** argv) {
       data_layer_type = (index < argc ? argv[index]:data_layer_type);
     } else if (strcmp("--gpu", argv[index]) == 0) {
       use_gpu = true;
+    } else if (strcmp("--enableTRT", argv[index]) == 0) {
+      use_gpu = true;
+      enable_tensorrt = true;
     } else if (strcmp("--benchmark", argv[index]) == 0) {
       benchmark = true;
     } else if (strcmp("--help", argv[index]) == 0) {
@@ -584,7 +634,9 @@ int main(int argc, char** argv) {
     index++;
   }
 
-  if (model_file_json.empty() || (!benchmark && model_file_params.empty())) {
+  if (model_file_json.empty()
+      || (!benchmark && model_file_params.empty())
+      || (enable_tensorrt && model_file_params.empty())) {
     LG << "ERROR: Model details such as symbol, param files are not specified";
     printUsage();
     return 1;
@@ -597,8 +649,8 @@ int main(int argc, char** argv) {
   std::vector<float> rgb_std = createVectorFromString<float>(input_rgb_std);
 
   // Initialize the predictor object
-  Predictor predict(model_file_json, model_file_params, input_data_shape, use_gpu, dataset,
-                    data_nthreads, data_layer_type, rgb_mean, rgb_std, shuffle_chunk_seed,
+  Predictor predict(model_file_json, model_file_params, input_data_shape, use_gpu, enable_tensorrt,
+                    dataset, data_nthreads, data_layer_type, rgb_mean, rgb_std, shuffle_chunk_seed,
                     seed, benchmark);
 
   if (benchmark) {
diff --git a/cpp-package/include/mxnet-cpp/MxNetCpp.h b/cpp-package/include/mxnet-cpp/MxNetCpp.h
index 7ac039dd8816..a513565377fd 100644
--- a/cpp-package/include/mxnet-cpp/MxNetCpp.h
+++ b/cpp-package/include/mxnet-cpp/MxNetCpp.h
@@ -39,5 +39,6 @@
 #include "mxnet-cpp/io.hpp"
 #include "mxnet-cpp/metric.h"
 #include "mxnet-cpp/initializer.h"
+#include "mxnet-cpp/contrib.h"
 
 #endif  // MXNET_CPP_MXNETCPP_H_
diff --git a/cpp-package/include/mxnet-cpp/contrib.h b/cpp-package/include/mxnet-cpp/contrib.h
new file mode 100644
index 000000000000..890ab2bf0062
--- /dev/null
+++ b/cpp-package/include/mxnet-cpp/contrib.h
@@ -0,0 +1,115 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+*  Copyright (c) 2019 by Contributors
+* \file contrib.h
+* \brief utility function to enable some contrib features
+* \author Haohuan Wang
+*/
+#ifndef MXNET_CPP_CONTRIB_H_
+#define MXNET_CPP_CONTRIB_H_
+
+#include <iostream>
+#include <string>
+#include <map>
+#include <vector>
+#include "mxnet-cpp/symbol.h"
+
+namespace mxnet {
+namespace cpp {
+namespace details {
+
+  /*!
+   * split a string with the given delimiter
+   * @param str string to be parsed
+   * @param delimiter delimiter
+   * @return delimited list of string
+   */
+  inline std::vector<std::string> split(const std::string& str, const std::string& delimiter) {
+    std::vector<std::string> splitted;
+    size_t last = 0;
+    size_t next = 0;
+    while ((next = str.find(delimiter, last)) != std::string::npos) {
+      splitted.push_back(str.substr(last, next - last));
+      last = next + 1;
+    }
+    splitted.push_back(str.substr(last));
+    return splitted;
+  }
+
+}  // namespace details
+
+namespace contrib {
+
+  // needs to be same with
+  //   https://github.com/apache/incubator-mxnet/blob/1c874cfc807cee755c38f6486e8e0f4d94416cd8/src/operator/subgraph/tensorrt/tensorrt-inl.h#L190
+  static const std::string TENSORRT_SUBGRAPH_PARAM_IDENTIFIER = "subgraph_params_names";
+  // needs to be same with
+  //   https://github.com/apache/incubator-mxnet/blob/master/src/operator/subgraph/tensorrt/tensorrt.cc#L244
+  static const std::string TENSORRT_SUBGRAPH_PARAM_PREFIX = "subgraph_param_";
+  /*!
+   * this is a mimic to https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/contrib/tensorrt.py#L37
+   * @param symbol symbol that already called subgraph api
+   * @param argParams original arg params, params needed by tensorrt will be removed after calling this function
+   * @param auxParams original aux params, params needed by tensorrt will be removed after calling this function
+   */
+  inline void InitTensorRTParams(const mxnet::cpp::Symbol& symbol,
+      std::map<std::string, mxnet::cpp::NDArray> *argParams,
+      std::map<std::string, mxnet::cpp::NDArray> *auxParams) {
+    mxnet::cpp::Symbol internals = symbol.GetInternals();
+    mx_uint numSymbol = internals.GetNumOutputs();
+    for (mx_uint i = 0; i < numSymbol; ++i) {
+        std::map<std::string, std::string> attrs = internals[i].ListAttributes();
+        if (attrs.find(TENSORRT_SUBGRAPH_PARAM_IDENTIFIER) != attrs.end()) {
+            std::string new_params_names;
+            std::map<std::string, mxnet::cpp::NDArray> tensorrtParams;
+            std::vector<std::string> keys = details::split(
+                attrs[TENSORRT_SUBGRAPH_PARAM_IDENTIFIER], ";");
+            for (const auto& key : keys) {
+                if (argParams->find(key) != argParams->end()) {
+                    new_params_names += key + ";";
+                    tensorrtParams[TENSORRT_SUBGRAPH_PARAM_PREFIX + key] = (*argParams)[key];
+                    argParams->erase(key);
+                } else if (auxParams->find(key) != auxParams->end()) {
+                    new_params_names += key + ";";
+                    tensorrtParams[TENSORRT_SUBGRAPH_PARAM_PREFIX + key] = (*auxParams)[key];
+                    auxParams->erase(key);
+                }
+            }
+            std::map<std::string, std::string> new_attrs = {};
+            for (const auto& kv : tensorrtParams) {
+                // passing the ndarray address into TRT node attributes to get the weight
+                uint64_t address = reinterpret_cast<uint64_t>(kv.second.GetHandle());
+                new_attrs[kv.first] = std::to_string(address);
+            }
+            if (!new_attrs.empty()) {
+                internals[i].SetAttributes(new_attrs);
+                internals[i].SetAttribute(TENSORRT_SUBGRAPH_PARAM_IDENTIFIER,
+                    new_params_names.substr(0, new_params_names.length() - 1));
+            }
+        }
+    }
+}
+
+}  // namespace contrib
+}  // namespace cpp
+}  // namespace mxnet
+
+#endif  // MXNET_CPP_CONTRIB_H_
diff --git a/cpp-package/include/mxnet-cpp/symbol.h b/cpp-package/include/mxnet-cpp/symbol.h
index a25824cad602..d72eeaad1a5a 100644
--- a/cpp-package/include/mxnet-cpp/symbol.h
+++ b/cpp-package/include/mxnet-cpp/symbol.h
@@ -178,6 +178,23 @@ class Symbol {
   std::vector<std::string> ListOutputs() const;
   /*! \return get the descriptions of auxiliary data for this symbol */
   std::vector<std::string> ListAuxiliaryStates() const;
+  /*! \return get all attributes for this symbol */
+  std::map<std::string, std::string> ListAttributes() const;
+  /*!
+   * \brief set key-value attribute to the symbol
+   * @param key string represent the key for the attribute
+   * @param value string represent the value for the attribute
+   */
+  void SetAttribute(const std::string& key, const std::string& value);
+  /*!
+   * \brief set a series of key-value attribute to the symbol
+   * @param attrs string:string map represent the key value attributes
+   */
+  void SetAttributes(const std::map<std::string, std::string>& attrs);
+  /*! \return get number of outputs for this symbol */
+  mx_uint GetNumOutputs() const;
+  /*! \return get the new symbol through subgraph API for this symbol */
+  mxnet::cpp::Symbol GetBackendSymbol(const std::string& backendName) const;
   /*! \return get the name of the symbol */
   std::string GetName() const;
   /*!
diff --git a/cpp-package/include/mxnet-cpp/symbol.hpp b/cpp-package/include/mxnet-cpp/symbol.hpp
index 2e3fb7a2d5de..811d894e0ffa 100644
--- a/cpp-package/include/mxnet-cpp/symbol.hpp
+++ b/cpp-package/include/mxnet-cpp/symbol.hpp
@@ -172,6 +172,41 @@ inline std::vector<std::string> Symbol::ListAuxiliaryStates() const {
   return ret;
 }
 
+inline std::map<std::string, std::string> Symbol::ListAttributes() const {
+    mx_uint size;
+    const char** pairs;
+    CHECK_EQ(MXSymbolListAttrShallow(GetHandle(), &size, &pairs), 0);
+    std::map<std::string, std::string> attributes;
+    for (mx_uint i = 0; i < size; ++i) {
+        // pairs is 2 * size with key, value pairs according to
+        //   https://github.com/apache/incubator-mxnet/blob/master/include/mxnet/c_api.h#L1428
+        attributes[pairs[2 * i]] = pairs[2 * i + 1];
+    }
+    return attributes;
+}
+
+inline void Symbol::SetAttribute(const std::string &key, const std::string &value) {
+    CHECK_EQ(MXSymbolSetAttr(GetHandle(), key.c_str(), value.c_str()), 0);
+}
+
+inline void Symbol::SetAttributes(const std::map<std::string, std::string> &attrs) {
+    for (const auto& kv : attrs) {
+        SetAttribute(kv.first, kv.second);
+    }
+}
+
+inline mx_uint Symbol::GetNumOutputs() const {
+    mx_uint numOutputs;
+    CHECK_EQ(MXSymbolGetNumOutputs(GetHandle(), &numOutputs), 0);
+    return numOutputs;
+}
+
+inline mxnet::cpp::Symbol Symbol::GetBackendSymbol(const std::string &backendName) const {
+    SymbolHandle symbolHandle;
+    CHECK_EQ(MXGenBackendSubgraph(GetHandle(), backendName.c_str(), &symbolHandle), 0);
+    return mxnet::cpp::Symbol(symbolHandle);
+}
+
 inline std::string Symbol::GetName() const {
   int success;
   const char* out_name;

From faccc59bc0ed7e22933c1f86f3aabac6f13fe1a9 Mon Sep 17 00:00:00 2001
From: Pedro Larroy <pedro.larroy.lists@gmail.com>
Date: Wed, 3 Jul 2019 17:36:56 -0700
Subject: [PATCH 035/813] Fix memory leak in NaiveEngine (#15405)

* Fix memory leak in NaiveEngine
Fixes #15375

* Fix lint
---
 src/engine/naive_engine.cc | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/src/engine/naive_engine.cc b/src/engine/naive_engine.cc
index 9f44b55cfcaf..9cfe9b28f862 100644
--- a/src/engine/naive_engine.cc
+++ b/src/engine/naive_engine.cc
@@ -159,15 +159,18 @@ class NaiveEngine final : public Engine {
         NaiveEngine::OnComplete, nullptr);
     this->req_completed_ = false;
     profiler::Profiler *profiler = profiler::Profiler::Get();
-    NaiveOpr *opr = nullptr;
+    auto opr_deleter = [this](NaiveOpr* p) {
+      this->DeleteOperator(p);
+    };
+    std::unique_ptr<NaiveOpr, decltype(opr_deleter)> opr(nullptr, opr_deleter);
     const bool profiling = opr_name && profiler->IsProfiling(profiler::Profiler::kImperative);
     // GenerateDisplayName() will return a pointer to the correct name of the operator
     const char* display_name = profiling ?
                                profiler::CustomOpProfiler::Get()->GenerateDisplayName(opr_name) :
                                opr_name;
     if (profiling) {
-      opr = NewOperator(exec_fun, const_vars, mutable_vars,
-                        prop, display_name)->Cast<NaiveOpr>();
+      opr.reset(NewOperator(exec_fun, const_vars, mutable_vars,
+                        prop, display_name)->Cast<NaiveOpr>());
       opr->profiling = profiling;
       std::unique_ptr<profiler::ProfileOperator::Attributes> attrs;
       if (profiler->AggregateEnabled()) {

From d7c542af3b5c6ab35acad93d3b1114cd492f98cd Mon Sep 17 00:00:00 2001
From: Thom Lane <thom.e.lane@gmail.com>
Date: Wed, 3 Jul 2019 20:13:32 -0700
Subject: [PATCH 036/813] [TUTORIAL] Gluon and Sparse NDArray (#15396)

* Added Gluon Sparse NDArray tutorial.

* Updates to fix ASCII issue and feedback changes.
---
 docs/tutorials/index.md              |   2 +-
 docs/tutorials/sparse/train_gluon.md | 470 +++++++++++++++++++++++++++
 tests/tutorials/test_tutorials.py    |   3 +
 3 files changed, 474 insertions(+), 1 deletion(-)
 create mode 100644 docs/tutorials/sparse/train_gluon.md

diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md
index d66e6c8d9a25..6e31e825e2ca 100644
--- a/docs/tutorials/index.md
+++ b/docs/tutorials/index.md
@@ -113,9 +113,9 @@ Select API:&nbsp;
             * [Difference between reshape and transpose](/tutorials/basic/reshape_transpose.html)
             * [NDArray Indexing](https://mxnet.incubator.apache.org/tutorials/basic/ndarray_indexing.html)
             * Sparse NDArray
+                * [Introduction to Sparse NDArray](/tutorials/sparse/train_gluon.html)
                 * [Sparse Gradient Updates (RowSparseNDArray)](/tutorials/sparse/row_sparse.html)
                 * [Compressed Sparse Row Storage Format (CSRNDArray)](/tutorials/sparse/csr.html)
-                * [Linear Regression with Sparse Symbols](/tutorials/sparse/train.html)
         * Symbol
             * [Symbol API](/tutorials/basic/symbol.html) (Caution: written before Gluon existed)
         * KVStore
diff --git a/docs/tutorials/sparse/train_gluon.md b/docs/tutorials/sparse/train_gluon.md
new file mode 100644
index 000000000000..e9c5301aed84
--- /dev/null
+++ b/docs/tutorials/sparse/train_gluon.md
@@ -0,0 +1,470 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+
+# Sparse NDArrays with Gluon
+
+When working on machine learning problems, you may encounter situations where the input data is sparse (i.e. the majority of values are zero). One example of this is in recommendation systems. You could have millions of user and product features, but only a few of these features are present for each sample. Without special treatment, the sheer magnitude of the feature space can lead to out-of-memory situations and cause significant slowdowns when training and making predictions.
+
+MXNet supports a number of sparse storage types (often called 'stype' for short) for these situations. In this tutorial, we'll start by generating some sparse data, write it to disk in the LibSVM format and then read back using the [`LibSVMIter`](https://mxnet.incubator.apache.org/api/python/io/io.html) for training. We use the Gluon API to train the model and leverage sparse storage types such as [`CSRNDArray`](https://mxnet.incubator.apache.org/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray) and [`RowSparseNDArray`](https://mxnet.incubator.apache.org/api/python/ndarray/sparse.html?highlight=rowsparsendarray#mxnet.ndarray.sparse.RowSparseNDArray) to maximise performance and memory efficiency.
+
+
+```python
+import mxnet as mx
+import numpy as np
+import time
+```
+
+### Generating Sparse Data
+
+You will most likely have a sparse dataset in mind already if you're reading this tutorial, but let's create a dummy dataset to use in the examples that follow. Using `rand_ndarray` we will generate 1000 samples, each with 1,000,000 features of which 99.999% of values will be zero (i.e. 10 non-zero features for each sample). We take this as our input data for training and calculate a label based on an arbitrary rule: whether the feature sum is higher than average.
+
+
+```python
+num_samples = 1000
+num_features = 1000000
+data = mx.test_utils.rand_ndarray((num_samples, num_features), stype='csr', density=0.00001)
+# generate label: 1 if row sum above average, 0 otherwise.
+label = data.sum(axis=1) > data.sum(axis=1).mean()
+```
+
+
+```python
+print(type(data))
+print(data[:10].asnumpy())
+print('{:,.0f} elements'.format(np.product(data.shape)))
+print('{:,.0f} non-zero elements'.format(data.data.size))
+```
+
+    <class 'mxnet.ndarray.sparse.CSRNDArray'>
+    [[0. 0. 0. ... 0. 0. 0.]
+     [0. 0. 0. ... 0. 0. 0.]
+     [0. 0. 0. ... 0. 0. 0.]
+     ...
+     [0. 0. 0. ... 0. 0. 0.]
+     [0. 0. 0. ... 0. 0. 0.]
+     [0. 0. 0. ... 0. 0. 0.]]
+    1,000,000,000 elements
+    10,000 non-zero elements
+
+
+Our storage type is CSR (Compressed Sparse Row) which is the ideal type for sparse data along multiple axes. See [this in-depth tutorial](https://mxnet.incubator.apache.org/versions/master/tutorials/sparse/csr.html) for more information. Just to confirm the generation process ran correctly, we can see that the vast majority of values are indeed zero. One of the first questions to ask would be how much memory is saved by storing this data in a [`CSRNDArray`](https://mxnet.incubator.apache.org/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray) versus a standard [`NDArray`](https://mxnet.incubator.apache.org/versions/master/api/python/ndarray/sparse.html?highlight=ndarray#module-mxnet.ndarray). Since sparse arrays are constructed from many components (e.g. `data`, `indices` and `indptr`) we define a function called `get_nbytes` to calculate the number of bytes taken in memory to store an array. We compare the same data stored in a standard [`NDArray`](https://mxnet.incubator.apache.org/versions/master/api/python/ndarray/sparse.html?highlight=ndarray#module-mxnet.ndarray) (with `data.tostype('default')`) to the [`CSRNDArray`](https://mxnet.incubator.apache.org/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray).
+
+
+```python
+def get_nbytes(array):
+    fn = lambda a: a.size * np.dtype(a).itemsize
+    if isinstance(array, mx.ndarray.sparse.CSRNDArray):
+        return fn(array.data) + fn(array.indices) + fn(array.indptr)
+    elif isinstance(array, mx.ndarray.sparse.RowSparseNDArray):
+        return fn(array.data) + fn(array.indices)
+    elif isinstance(array, mx.ndarray.NDArray):
+        return fn(array)
+    else:
+        TypeError('{} not supported'.format(type(array)))
+```
+
+
+```python
+print('NDarray:', get_nbytes(data.tostype('default'))/1000000, 'MBs')
+print('CSRNDArray', get_nbytes(data)/1000000, 'MBs')
+```
+
+    NDarray: 4000.0 MBs
+    CSRNDArray 0.128008 MBs
+
+
+Given the extremely high sparsity of the data, we observe a huge memory saving here! 0.13 MBs versus 4 GBs: ~30,000 times smaller. You can experiment with the amount of sparsity and see how these two storage types compare. When the number of non-zero values increases, this difference will reduce. And when the number of non-zero values exceeds ~1/3 you will find that this sparse storage type take more memory than dense! So use wisely.
+
+### Writing Sparse Data
+
+Since there is such a large size difference between dense and sparse storage formats here, we ideally want to store the data on disk in a sparse storage format too. MXNet supports a format called LibSVM and has a data iterator called [`LibSVMIter`](https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=libsvmiter) specifically for data formatted this way.
+
+A LibSVM file has a row for each sample, and each row starts with the label: in this case `0.0` or `1.0` since we have a classification task. After this we have a variable number of `key:value` pairs separated by spaces, where the key is column/feature index and the value is the value of that feature. When working with your own sparse data in a custom format you should try to convert your data into this format. We define a `save_as_libsvm` function to save the `data` ([`CSRNDArray`](https://mxnet.incubator.apache.org/versions/master/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray)) and `label` (`NDArray`) to disk in LibSVM format.
+
+
+```python
+def save_as_libsvm(filepath, data, label):
+    with open(filepath, 'w') as openfile:
+        for row_idx in range(data.shape[0]):
+            data_sample = data[row_idx]
+            label_sample = label[row_idx]
+            col_idxs = data_sample.indices.asnumpy().tolist()
+            values = data_sample.data.asnumpy().tolist()
+            label_str = str(label_sample.asscalar())
+            value_strs = ['{}:{}'.format(idx, value) for idx, value in zip(col_idxs, values)]
+            value_str = " ".join(value_strs)
+            sample_str = '{} {}\n'.format(label_str, value_str)
+            openfile.write(sample_str)
+```
+
+
+```python
+filepath = 'dataset.libsvm'
+save_as_libsvm(filepath, data, label)
+```
+
+We have now written the `data` and `label` to disk, and can inspect the first 10 lines of the file:
+
+
+```python
+with open(filepath, 'r') as openfile:
+    lines = [openfile.readline() for _ in range(10)]
+for line in lines:
+    print(line[:80] + '...' if len(line) > 80 else line)
+```
+
+    0.0 35454:0.22486156225204468 80954:0.39130592346191406 81941:0.1988530308008194...
+    1.0 37029:0.5980494618415833 52916:0.15797750651836395 71623:0.32251599431037903...
+    1.0 89962:0.47770974040031433 216426:0.21326342225074768 271027:0.18589609861373...
+    1.0 7071:0.9432336688041687 81664:0.7788773775100708 117459:0.8166475296020508 4...
+    0.0 380966:0.16906292736530304 394363:0.7987179756164551 458442:0.56873309612274...
+    0.0 89361:0.9099966287612915 141813:0.5927085280418396 282489:0.293381005525589 ...
+    0.0 150427:0.4747847020626068 169376:0.2603490948677063 179377:0.237988427281379...
+    0.0 49774:0.2822582423686981 91245:0.5794865489006042 102970:0.7004560232162476 ...
+    1.0 97133:0.0024336236529052258 109855:0.9895315766334534 116765:0.2465638816356...
+    0.0 803440:0.4020800292491913
+    
+
+
+Some storage overhead is introduced by serializing the data as characters (with spaces and colons). `dataset.libsvm` is 250 KBs but the original `data` and `label` were 132 KBs combined. Compared with the 4GB dense `NDArray` though, this isn't a huge issue.
+
+### Reading Sparse Data
+
+Using [`LibSVMIter`](https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=libsvmiter), we can quickly and easily load data into batches ready for training. Although Gluon [`Dataset`](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset)s can be written to return sparse arrays, Gluon [`DataLoader`](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader)s currently convert each sample to dense before stacking up to create the batch. As a result, [`LibSVMIter`](https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=libsvmiter) is the recommended method of loading sparse data in batches.
+
+Similar to using a [`DataLoader`](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader), you must specify the required `batch_size`. Since we're dealing with sparse data and the column shape isn't explicitly stored in the LibSVM file, we additionally need to provide the shape of the data and label. Our [`LibSVMIter`](https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=libsvmiter) returns batches in a slightly different form to a [`DataLoader`](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader). We get `DataBatch` objects instead of `tuple`. See the [appendix of this tutorial](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/datasets.html) for more information.
+
+
+```python
+data_iter = mx.io.LibSVMIter(data_libsvm='test.libsvm', data_shape=(num_features,), label_shape=(1,), batch_size=10)
+for batch in data_iter:
+    data = batch.data[0]
+    print('data.stype: {}'.format(data.stype))
+    label = batch.label[0]
+    print('label.stype: {}'.format(label.stype))
+    break
+```
+
+    data.stype: csr
+    label.stype: default
+
+
+We can see that `data` and `label` are in the appropriate storage formats, given their sparse and dense values respectively. We can avoid out-of-memory issues that might have occurred if `data` was in dense storage format. Another benefit of storing the data efficiently is the reduced data transfer time when using GPUs. Although the transfer time for a single batch is small, we transfer `data` and `label` to the GPU every iteration so this time can become significant. We will time the transfer of the sparse `data` to GPU (if available) and compare to the time for its dense counterpart.
+
+
+```python
+ctx = mx.gpu() if mx.test_utils.list_gpus() else mx.cpu()
+```
+
+
+```python
+%%timeit
+data_on_ctx = data.as_in_context(ctx)
+data_on_ctx.wait_to_read()
+```
+
+    192 microseconds +- 51.1 microseconds per loop (mean +- std. dev. of 7 runs, 1 loop each)
+
+
+
+```python
+print('sparse batch: {} MBs'.format(get_nbytes(data)/1000000))
+data = data.tostype('default')  # avoid timing this sparse to dense conversion
+print('dense batch: {} MBs'.format(get_nbytes(data)/1000000))
+```
+
+    sparse batch: 0.001348 MBs
+    dense batch: 40.0 MBs
+
+
+
+```python
+%%timeit
+data_on_ctx = data.as_in_context(ctx)
+data_on_ctx.wait_to_read()
+```
+
+    4 ms +- 36.8 microseconds per loop (mean +- std. dev. of 7 runs, 100 loops each)
+
+
+Although results will change depending on system specifications and degree of sparsity, the sparse array can be transferred from CPU to GPU significantly faster than the dense array. We see a ~25x speed up for sparse vs dense for this specific batch of data.
+
+## Gluon Models for Sparse Data
+
+Our next step is to define a network. We have an input of 1,000,000 features and we want to make a binary prediction. We don't have any spatial or temporal relationships between features, so we'll use a 3 layer fully-connected network where the last layer has 1 output unit (with sigmoid activation). Since we're working with sparse data, we'd ideally like to use network operators that can exploit this sparsity for improved performance and memory efficiency.
+
+Gluon's [`nn.Dense`](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/nn.html?highlight=dense#mxnet.gluon.nn.Dense) block can used with [`CSRNDArray`](https://mxnet.incubator.apache.org/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray) input arrays but it doesn't exploit the sparsity. Under the hood, [`Dense`](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/nn.html?highlight=dense#mxnet.gluon.nn.Dense) uses the [`FullyConnected`](https://mxnet.incubator.apache.org/versions/master/api/python/ndarray/ndarray.html?highlight=fullyconnected#mxnet.ndarray.FullyConnected) operator which isn't optimized for [`CSRNDArray`](https://mxnet.incubator.apache.org/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray) arrays. We'll implement a `Block` that does exploit this sparsity, *but first*, let's just remind ourselves of the [`Dense`](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/nn.html?highlight=dense#mxnet.gluon.nn.Dense) implementation by creating an equivalent `Block` called `FullyConnected`.
+
+
+```python
+class FullyConnected(mx.gluon.HybridBlock):
+    def __init__(self, in_units, units):
+        super(FullyConnected, self).__init__()
+        with self.name_scope():
+            self._units = units
+            self.weight = self.params.get('weight', shape=(units, in_units),
+                                          init=None, allow_deferred_init=True,
+                                          dtype='float32', stype='default', grad_stype='default')
+            self.bias = self.params.get('bias', shape=(units),
+                                          init='zeros', allow_deferred_init=True,
+                                          dtype='float32', stype='default', grad_stype='default')
+
+    def hybrid_forward(self, F, x, weight, bias):
+        return F.FullyConnected(x, weight, bias, num_hidden=self._units)
+```
+
+Our `weight` and `bias` parameters are dense (see `stype='default'`) and so are their gradients (see `grad_stype='default'`). Our `weight` parameter has shape `(units, in_units)` because the [`FullyConnected`](https://mxnet.incubator.apache.org/versions/master/api/python/ndarray/ndarray.html?highlight=fullyconnected#mxnet.ndarray.FullyConnected) operator performs the following calculation:
+
+$$Y = XW^T + b$$
+
+We could instead have created our parameter with shape `(in_units, units)` and avoid the transpose of the weight matrix. We'll see why this is so important later on. And instead of [`FullyConnected`](https://mxnet.incubator.apache.org/versions/master/api/python/ndarray/ndarray.html?highlight=fullyconnected#mxnet.ndarray.FullyConnected) we could have used [`mx.sparse.dot`](https://mxnet.incubator.apache.org/versions/master/api/python/ndarray/sparse.html?highlight=sparse.dot#mxnet.ndarray.sparse.dot) to fully exploit the sparsity of the [`CSRNDArray`](https://mxnet.incubator.apache.org/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray) input arrays. We'll now implement an alternative `Block` called `FullyConnectedSparse` using these ideas. We take `grad_stype` of the `weight` as an argument (called `weight_grad_stype`), since we're going to change this later on.
+
+
+```python
+class FullyConnectedSparse(mx.gluon.HybridBlock):
+    def __init__(self, in_units, units, weight_grad_stype='default'):
+        super(FullyConnectedSparse, self).__init__()
+        with self.name_scope():
+            self._units = units
+            self.weight = self.params.get('weight', shape=(in_units, units),
+                                          init=None, allow_deferred_init=True,
+                                          dtype='float32', stype='default', grad_stype=weight_grad_stype)
+            self.bias = self.params.get('bias', shape=(units),
+                                          init='zeros', allow_deferred_init=True,
+                                          dtype='float32', stype='default', grad_stype='default')
+
+    def hybrid_forward(self, F, x, weight, bias):
+        return F.sparse.dot(x, weight) + bias
+```
+
+Once again, we're using a dense `weight`, so both `FullyConnected` and `FullyConnectedSparse` will return dense array outputs. When constructing a multi-layer network therefore, only the first layer needs to be optimized for sparse inputs. Our first layer is often responsible for reducing the feature dimension dramatically (e.g. 1,000,000 features down to 128 features). We'll set the number of units in our 3 layers to be 128, 8 and 1.
+
+We will use [`timeit`](https://docs.python.org/2/library/timeit.html) to check the performance of these two variants, and analyse some [MXNet Profiler](https://mxnet.incubator.apache.org/versions/master/tutorials/python/profiler.html) traces that have been created from these benchmarks. Additionally, we will inspect the memory usage of the weights (and gradients) using the `print_memory_allocation` function defined below:
+
+
+```python
+def print_memory_allocation(net, block_idxs):
+    blocks = [net[block_idx] for block_idx in block_idxs]
+    weight_nbytes = [get_nbytes(b.weight.data()) for b in blocks]
+    weight_nbytes_pct = [b/sum(weight_nbytes) for b in weight_nbytes]
+    weight_grad_nbytes = [get_nbytes(b.weight.grad()) for b in blocks]
+    weight_grad_nbytes_pct = [b/sum(weight_grad_nbytes) for b in weight_grad_nbytes]
+    print("Memory Allocation for Weight:")
+    for i in range(len(block_idxs)):
+        print('{:7.3f} MBs ({:7.3f}%) for {:<40}'.format(weight_nbytes[i]/1000000,
+                                                              weight_nbytes_pct[i]*100,
+                                                              blocks[i].name))
+    print("Memory Allocation for Weight Gradient:")
+    for i in range(len(block_idxs)):
+        print('{:7.3f} MBs ({:7.3f}%) for {:<40}'.format(weight_grad_nbytes[i]/1000000,
+                                                              weight_grad_nbytes_pct[i]*100,
+                                                              blocks[i].name))
+```
+
+### Benchmark: `FullyConnected`
+
+We'll create a network using `nn.Dense` and benchmark the training.
+
+```python
+net = mx.gluon.nn.Sequential()
+net.add(
+    mx.gluon.nn.Dense(in_units=num_features, units=128),
+    mx.gluon.nn.Activation('sigmoid'),
+    mx.gluon.nn.Dense(in_units=128, units=8),
+    mx.gluon.nn.Activation('sigmoid'),
+    mx.gluon.nn.Dense(in_units=8, units=1),
+    mx.gluon.nn.Activation('sigmoid'),
+)
+net.initialize(ctx=ctx)
+trainer = mx.gluon.Trainer(net.collect_params(), 'sgd')
+loss_fn = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
+```
+
+
+```python
+%%timeit
+data_iter.reset()
+for batch in data_iter:
+    data = batch.data[0]
+    data = data.as_in_context(ctx)
+    label = batch.label[0].as_in_context(ctx)
+    with mx.autograd.record():
+        pred = net(data)
+        loss = loss_fn(pred, label)
+    loss.backward()
+    trainer.step(data.shape[0])
+    loss.wait_to_read()
+```
+
+    532 ms +- 3.47 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
+
+
+![fully connected](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/ndarray/sparse/fully_connected.png)
+
+We can see the first [`FullyConnected`](https://mxnet.incubator.apache.org/versions/master/api/python/ndarray/ndarray.html?highlight=fullyconnected#mxnet.ndarray.FullyConnected) operator takes a significant proportion of time to execute (~25% of the iteration) because there are 1,000,000 input features (to 128). After this, the other [`FullyConnected`](https://mxnet.incubator.apache.org/versions/master/api/python/ndarray/ndarray.html?highlight=fullyconnected#mxnet.ndarray.FullyConnected) operators are much faster because they have input features of 128 (to 8) and 8 (to 1). On the backward pass, we see the same pattern (but in reverse). And finally, the parameter update step takes a large amount of time on the weight matrix of the first `FullyConnected` `Block`. When checking the memory allocations below, we can see the weight matrix of the first `FullyConnected` `Block` is responsible for 99.999% of the memory compared to other [`FullyConnected`](https://mxnet.incubator.apache.org/versions/master/api/python/ndarray/ndarray.html?highlight=fullyconnected#mxnet.ndarray.FullyConnected) weight matrices.
+
+
+```python
+print_memory_allocation(net, block_idxs=[0, 2, 4])
+```
+
+    Memory Allocation for Weight:
+    512.000 MBs ( 99.999%) for dense0                                  
+      0.004 MBs (  0.001%) for dense1                                  
+      0.000 MBs (  0.000%) for dense2                                  
+    Memory Allocation for Weight Gradient:
+    512.000 MBs ( 99.999%) for dense0                                  
+      0.004 MBs (  0.001%) for dense1                                  
+      0.000 MBs (  0.000%) for dense2                                  
+
+
+### Benchmark: `FullyConnectedSparse`
+
+We will now switch the first layer from `FullyConnected` to `FullyConnectedSparse`.
+
+
+```python
+net = mx.gluon.nn.Sequential()
+net.add(
+    FullyConnectedSparse(in_units=num_features, units=128),
+    mx.gluon.nn.Activation('sigmoid'),
+    FullyConnected(in_units=128, units=8),
+    mx.gluon.nn.Activation('sigmoid'),
+    FullyConnected(in_units=8, units=1),
+    mx.gluon.nn.Activation('sigmoid'),
+)
+net.initialize(ctx=ctx)
+trainer = mx.gluon.Trainer(net.collect_params(), 'sgd')
+loss_fn = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
+```
+
+
+```python
+%%timeit
+data_iter.reset()
+for batch in data_iter:
+    data = batch.data[0]
+    data = data.as_in_context(ctx)
+    label = batch.label[0].as_in_context(ctx)
+    with mx.autograd.record():
+        pred = net(data)
+        loss = loss_fn(pred, label)
+    loss.backward()
+    trainer.step(data.shape[0])
+    loss.wait_to_read()
+```
+
+    528 ms +- 22.7 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
+
+
+![fully connected sparse](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/ndarray/sparse/fully_connected_sparse.png)
+
+We see the forward pass of `dot` and `add` (equivalent to [`FullyConnected`](https://mxnet.incubator.apache.org/versions/master/api/python/ndarray/ndarray.html?highlight=fullyconnected#mxnet.ndarray.FullyConnected) operator) is much faster now: 1.54ms vs 0.26ms. And this explains the reduction in overall time for the epoch. We didn't gain any benefit on the backward pass or parameter updates though.
+
+![fully connected sparse backward](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/ndarray/sparse/fully_connected_sparse_backward.png)
+
+Our first weight matrix and its gradients still take up the same amount of memory as before.
+
+
+```python
+print_memory_allocation(net, block_idxs=[0, 2, 4])
+```
+
+    Memory Allocation for Weight:
+    512.000 MBs ( 99.999%) for fullyconnectedsparse0                   
+      0.004 MBs (  0.001%) for fullyconnected0                         
+      0.000 MBs (  0.000%) for fullyconnected1                         
+    Memory Allocation for Weight Gradient:
+    512.000 MBs ( 99.999%) for fullyconnectedsparse0                   
+      0.004 MBs (  0.001%) for fullyconnected0                         
+      0.000 MBs (  0.000%) for fullyconnected1                         
+
+
+### Benchmark: `FullyConnectedSparse` with `grad_stype=row_sparse` 
+
+One useful outcome of sparsity in our [`CSRNDArray`](https://mxnet.incubator.apache.org/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray) input is that our gradients will be row sparse. We can exploit this fact to give us potentially huge memory savings and speed improvements. Creating our `weight` parameter with shape `(units, in_units)` and not transposing in the forward pass are important pre-requisite for obtaining row sparse gradients. Using [`nn.Dense`](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/nn.html?highlight=dense#mxnet.gluon.nn.Dense) would have led to column sparse gradients which are not supported in MXNet. We previously had `grad_stype` of the `weight` parameter in the first layer set to `'default'` so we were handling the gradient as a dense array. Switching this to `'row_sparse'` can give us these potential improvements.
+
+
+```python
+net = mx.gluon.nn.Sequential()
+net.add(
+    FullyConnectedSparse(in_units=num_features, units=128, weight_grad_stype='row_sparse'),
+    mx.gluon.nn.Activation('sigmoid'),
+    FullyConnected(in_units=128, units=8),
+    mx.gluon.nn.Activation('sigmoid'),
+    FullyConnected(in_units=8, units=1),
+    mx.gluon.nn.Activation('sigmoid'),
+)
+net.initialize(ctx=ctx)
+trainer = mx.gluon.Trainer(net.collect_params(), 'sgd')
+loss_fn = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
+```
+
+
+```python
+%%timeit
+data_iter.reset()
+for batch in data_iter:
+    data = batch.data[0]
+    data = data.as_in_context(ctx)
+    label = batch.label[0].as_in_context(ctx)
+    with mx.autograd.record():
+        pred = net(data)
+        loss = loss_fn(pred, label)
+    loss.backward()
+    trainer.step(data.shape[0])
+    loss.wait_to_read()
+```
+
+    334 ms +- 16.9 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
+
+
+![fully connected sparse grad backward](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/ndarray/sparse/fully_connected_sparse_grad_backward.png)
+
+We can see a huge reduction in the time taken for the backward pass and parameter update step: 3.99ms vs 0.18ms. And this reduces the overall time of the epoch significantly. Our gradient consumes a much smaller amount of memory and means only a subset of parameters need updating as part of the `sgd_update` step. Some optimizers don't support sparse gradients however, so reference the specific optimizer's documentation for more details.
+
+
+```python
+print_memory_allocation(net, block_idxs=[0, 2, 4])
+```
+
+    Memory Allocation for Weight:
+    512.000 MBs ( 99.999%) for fullyconnectedsparse1                   
+      0.004 MBs (  0.001%) for fullyconnected2                         
+      0.000 MBs (  0.000%) for fullyconnected3                         
+    Memory Allocation for Weight Gradient:
+      0.059 MBs ( 93.490%) for fullyconnectedsparse1                   
+      0.004 MBs (  6.460%) for fullyconnected2                         
+      0.000 MBs (  0.050%) for fullyconnected3                         
+
+
+### Advanced: Sparse `weight`
+
+You can optimize this example further by setting the weight's `stype` to `'row_sparse'`, but whether `'row_sparse'` weights make sense or not will depends on your specific task. See [`contrib.SparseEmbedding `](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/contrib/nn/basic_layers.py#L118) for an example of this.
+
+# Conclusion
+
+As part of this tutorial, we learned how to write sparse data to disk in LibSVM format and load it back in sparse batches with the [`LibSVMIter`](https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=libsvmiter). We learned how to improve the performance of Gluon's [`nn.Dense`](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/nn.html?highlight=dense#mxnet.gluon.nn.Dense) on sparse arrays using `mx.nd.sparse`. And lastly, we set `grad_stype` to `'row_sparse'` to reduce the size of the gradient and speed up the parameter update step.
+
+## Recommended Next Steps
+
+* More detail on the [`CSRNDArray`](https://mxnet.incubator.apache.org/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray) sparse array format can be found in [this tutorial](https://mxnet.incubator.apache.org/versions/master/tutorials/sparse/csr.html).
+* More detail on the [`RowSparseNDArray`](https://mxnet.incubator.apache.org/api/python/ndarray/sparse.html?highlight=rowsparsendarray#mxnet.ndarray.sparse.RowSparseNDArray) sparse array format can be found in [this tutorial](https://mxnet.incubator.apache.org/versions/master/tutorials/sparse/row_sparse.html).
+* Users of the Module API can see a symbolic only example in [this tutorial](https://mxnet.incubator.apache.org/versions/master/tutorials/sparse/train.html).
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/tests/tutorials/test_tutorials.py b/tests/tutorials/test_tutorials.py
index 829d56f419af..0c4954acbd8b 100644
--- a/tests/tutorials/test_tutorials.py
+++ b/tests/tutorials/test_tutorials.py
@@ -193,6 +193,9 @@ def test_sparse_csr():
 def test_sparse_train():
     assert _test_tutorial_nb('sparse/train')
 
+def test_sparse_train_gluon():
+    assert _test_tutorial_nb('sparse/train_gluon')
+
 def test_speech_recognition_ctc():
     assert _test_tutorial_nb('speech_recognition/ctc')
 

From 8ebaa5c0384ecbef244150859b3e24ea2f02095d Mon Sep 17 00:00:00 2001
From: sunrongda <38203631+sunrongda@users.noreply.github.com>
Date: Thu, 4 Jul 2019 20:02:17 +0800
Subject: [PATCH 037/813] REAME   MTCNN   Link URL Error in original website
 (#15020)

* eval_metric.py

* Update eval_metric.py

* Update eval_metric.py

* faster cnn

* use_global_stats=False

use_global_stats=False

* readme

* Delete symbol_resnet.py

* resnet

* Update README.md

* Update README.md

* update the link of MTCNN
---
 example/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/example/README.md b/example/README.md
index 16b556dcda08..be9903099e19 100644
--- a/example/README.md
+++ b/example/README.md
@@ -171,7 +171,7 @@ If your tutorial depends on specific packages, simply add them to this provision
 * [MultiGPU enabled image generative models (GAN and DCGAN)](https://github.com/tqchen/mxnet-gan) by [Tianqi Chen](https://github.com/tqchen)
 * [Deep reinforcement learning for playing flappybird by mxnet](https://github.com/li-haoran/DRL-FlappyBird) by LIHaoran
 * [Neural Style in Markov Random Field (MRF) and Perceptual Losses Realtime transfer](https://github.com/zhaw/neural_style) by [zhaw](https://github.com/zhaw)
-* [MTCNN Face keypoints detection and alignment](https://pangyupo.github.io/2016/10/22/mxnet-mtcnn/) ([github](https://github.com/pangyupo/mxnet_mtcnn_face_detection)) in Chinese by [pangyupo](https://github.com/pangyupo)
+* [MTCNN Face keypoints detection and alignment](https://github.com/YYuanAnyVision/mxnet_mtcnn_face_detection) by [yuanyang](https://github.com/YYuanAnyVision), source code for [paper](https://kpzhang93.github.io/papers/spl.pdf) "Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Neural Networks", [Kaipeng Zhang](https://github.com/kpzhang93), Zhanpeng Zhang, Zhifeng Li and Yu Qiao, IEEE Signal Processing Letters, 23(10), 2016
 * [SSD: Single Shot MultiBox Object Detector](https://github.com/zhreshold/mxnet-ssd) by [zhreshold](https://github.com/zhreshold)
 * [Fast Neural Style in Scala](https://github.com/Ldpe2G/DeepLearningForFun/tree/master/Mxnet-Scala/FastNeuralStyle) by [Ldpe2G](https://github.com/Ldpe2G)
 * [LSTM Human Activity Recognition](https://github.com/Ldpe2G/DeepLearningForFun/tree/master/Mxnet-Scala/HumanActivityRecognition) by [Ldpe2G](https://github.com/Ldpe2G)

From 0ec48864f6edc2b57c0002ff75c94ddfe472e405 Mon Sep 17 00:00:00 2001
From: Pedro Larroy <pedro.larroy.lists@gmail.com>
Date: Thu, 4 Jul 2019 14:53:51 -0700
Subject: [PATCH 038/813] =?UTF-8?q?Expose=20get=5Fall=5Fregistered=5Fopera?=
 =?UTF-8?q?tors=20and=20get=5Foperator=5Farguments=20in=20the=E2=80=A6=20(?=
 =?UTF-8?q?#15364)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Expose get_all_registered_operators and get_operator_arguments in the Python API.

* Code review

* CR
---
 benchmark/opperf/utils/op_registry_utils.py | 81 ++-------------------
 python/mxnet/operator.py                    | 61 +++++++++++++++-
 tests/python/unittest/test_operator.py      | 19 ++++-
 3 files changed, 84 insertions(+), 77 deletions(-)

diff --git a/benchmark/opperf/utils/op_registry_utils.py b/benchmark/opperf/utils/op_registry_utils.py
index 5da0e322eccc..6509be37f39d 100644
--- a/benchmark/opperf/utils/op_registry_utils.py
+++ b/benchmark/opperf/utils/op_registry_utils.py
@@ -20,6 +20,7 @@
 from operator import itemgetter
 from mxnet import runtime
 from mxnet.base import _LIB, check_call, py_str, OpHandle, c_str, mx_uint
+import mxnet as mx
 
 from benchmark.opperf.rules.default_params import DEFAULTS_INPUTS, MX_OP_MODULE
 
@@ -75,89 +76,19 @@ def _select_ops(operator_names, filters=("_contrib", "_"), merge_op_forward_back
     return mx_operators
 
 
-def _get_all_registered_ops():
-    """Get all registered MXNet operator names.
-
-
-    Returns
-    -------
-    ["operator_name"]
-    """
-    plist = ctypes.POINTER(ctypes.c_char_p)()
-    size = ctypes.c_uint()
-
-    check_call(_LIB.MXListAllOpNames(ctypes.byref(size),
-                                     ctypes.byref(plist)))
-
-    mx_registered_operator_names = [py_str(plist[i]) for i in range(size.value)]
-    return mx_registered_operator_names
-
-
-def _get_op_handles(op_name):
-    """Get handle for an operator with given name - op_name.
-
-    Parameters
-    ----------
-    op_name: str
-        Name of operator to get handle for.
-    """
-    op_handle = OpHandle()
-    check_call(_LIB.NNGetOpHandle(c_str(op_name), ctypes.byref(op_handle)))
-    return op_handle
-
-
-def _get_op_arguments(op_handle):
-    """Given operator name and handle, fetch operator arguments - number of arguments,
-    argument names, argument types.
-
-    Parameters
-    ----------
-    op_handle: OpHandle
-        Handle for the operator
-
-    Returns
-    -------
-    (narg, arg_names, arg_types)
-    """
-    real_name = ctypes.c_char_p()
-    desc = ctypes.c_char_p()
-    num_args = mx_uint()
-    arg_names = ctypes.POINTER(ctypes.c_char_p)()
-    arg_types = ctypes.POINTER(ctypes.c_char_p)()
-    arg_descs = ctypes.POINTER(ctypes.c_char_p)()
-    key_var_num_args = ctypes.c_char_p()
-    ret_type = ctypes.c_char_p()
-
-    check_call(_LIB.MXSymbolGetAtomicSymbolInfo(
-        op_handle, ctypes.byref(real_name), ctypes.byref(desc),
-        ctypes.byref(num_args),
-        ctypes.byref(arg_names),
-        ctypes.byref(arg_types),
-        ctypes.byref(arg_descs),
-        ctypes.byref(key_var_num_args),
-        ctypes.byref(ret_type)))
-
-    narg = int(num_args.value)
-    arg_names = [py_str(arg_names[i]) for i in range(narg)]
-    arg_types = [py_str(arg_types[i]) for i in range(narg)]
-
-    return narg, arg_names, arg_types
-
-
 def _set_op_arguments(mx_operators):
     """Fetch and set operator arguments - nargs, arg_names, arg_types
     """
     for op_name in mx_operators:
-        op_handle = _get_op_handles(op_name)
-        narg, arg_names, arg_types = _get_op_arguments(op_handle)
-        mx_operators[op_name]["params"] = {"narg": narg,
-                                           "arg_names": arg_names,
-                                           "arg_types": arg_types}
+        operator_arguments = mx.operator.get_operator_arguments(op_name)
+        mx_operators[op_name]["params"] = {"narg": operator_arguments.narg,
+                                           "arg_names": operator_arguments.names,
+                                           "arg_types": operator_arguments.types}
 
 
 def _get_all_mxnet_operators():
     # Step 1 - Get all registered op names and filter it
-    operator_names = _get_all_registered_ops()
+    operator_names = mx.operator.get_all_registered_operators()
     mx_operators = _select_ops(operator_names)
 
     # Step 2 - Get all parameters for the operators
diff --git a/python/mxnet/operator.py b/python/mxnet/operator.py
index 33e9b89a032c..8d14a805ef56 100644
--- a/python/mxnet/operator.py
+++ b/python/mxnet/operator.py
@@ -22,13 +22,15 @@
 
 import traceback
 import warnings
+import collections
 
 from array import array
 from threading import Lock
+import ctypes
 from ctypes import CFUNCTYPE, POINTER, Structure, pointer
 from ctypes import c_void_p, c_int, c_char, c_char_p, cast, c_bool
 
-from .base import _LIB, check_call, MXCallbackList, c_array, c_array_buf, mx_int
+from .base import _LIB, check_call, MXCallbackList, c_array, c_array_buf, mx_int, OpHandle
 from .base import c_str, mx_uint, mx_float, ctypes2numpy_shared, NDArrayHandle, py_str
 from . import symbol, context
 from .ndarray import NDArray, _DTYPE_NP_TO_MX, _DTYPE_MX_TO_NP
@@ -1099,3 +1101,60 @@ def delete_entry(_):
     return do_register
 
 register("custom_op")(CustomOpProp)
+
+
+def get_all_registered_operators():
+    """Get all registered MXNet operator names.
+
+    Returns
+    -------
+    operator_names : list of string
+    """
+    plist = ctypes.POINTER(ctypes.c_char_p)()
+    size = ctypes.c_uint()
+
+    check_call(_LIB.MXListAllOpNames(ctypes.byref(size),
+                                     ctypes.byref(plist)))
+
+    mx_registered_operator_names = [py_str(plist[i]) for i in range(size.value)]
+    return mx_registered_operator_names
+
+OperatorArguments = collections.namedtuple('OperatorArguments', ['narg', 'names', 'types'])
+
+def get_operator_arguments(op_name):
+    """Given operator name, fetch operator arguments - number of arguments,
+    argument names, argument types.
+
+    Parameters
+    ----------
+    op_name: str
+        Handle for the operator
+
+    Returns
+    -------
+    operator_arguments : OperatorArguments, namedtuple with number of arguments, names and types
+    """
+    op_handle = OpHandle()
+    check_call(_LIB.NNGetOpHandle(c_str(op_name), ctypes.byref(op_handle)))
+    real_name = ctypes.c_char_p()
+    desc = ctypes.c_char_p()
+    num_args = mx_uint()
+    arg_names = ctypes.POINTER(ctypes.c_char_p)()
+    arg_types = ctypes.POINTER(ctypes.c_char_p)()
+    arg_descs = ctypes.POINTER(ctypes.c_char_p)()
+    key_var_num_args = ctypes.c_char_p()
+    ret_type = ctypes.c_char_p()
+
+    check_call(_LIB.MXSymbolGetAtomicSymbolInfo(
+        op_handle, ctypes.byref(real_name), ctypes.byref(desc),
+        ctypes.byref(num_args),
+        ctypes.byref(arg_names),
+        ctypes.byref(arg_types),
+        ctypes.byref(arg_descs),
+        ctypes.byref(key_var_num_args),
+        ctypes.byref(ret_type)))
+
+    narg = int(num_args.value)
+    arg_names = [py_str(arg_names[i]) for i in range(narg)]
+    arg_types = [py_str(arg_types[i]) for i in range(narg)]
+    return OperatorArguments(narg, arg_names, arg_types)
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index d50304712196..ca9ecc45621b 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -27,10 +27,11 @@
 from distutils.version import LooseVersion
 from numpy.testing import assert_allclose, assert_array_equal
 from mxnet.test_utils import *
+from mxnet.operator import *
 from mxnet.base import py_str, MXNetError, _as_list
 from common import setup_module, with_seed, teardown, assert_raises_cudnn_not_satisfied, assertRaises
 from common import run_in_spawned_process
-from nose.tools import assert_raises
+from nose.tools import assert_raises, ok_
 import unittest
 import os
 
@@ -8655,6 +8656,22 @@ def test_add_n():
     assert_almost_equal(rslt.asnumpy(), add_n_rslt.asnumpy(), atol=1e-5)
 
 
+def test_get_all_registered_operators():
+    ops = get_all_registered_operators()
+    ok_(isinstance(ops, list))
+    ok_(len(ops) > 0)
+    ok_('Activation' in ops)
+
+
+def test_get_operator_arguments():
+    operator_arguments = get_operator_arguments('Activation')
+    ok_(isinstance(operator_arguments, OperatorArguments))
+    ok_(operator_arguments.names == ['data', 'act_type'])
+    ok_(operator_arguments.types
+        == ['NDArray-or-Symbol', "{'relu', 'sigmoid', 'softrelu', 'softsign', 'tanh'}, required"])
+    ok_(operator_arguments.narg == 2)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 612b9d1ed441e5af239cab6064b648beba3b99bb Mon Sep 17 00:00:00 2001
From: Serge Panev <spanev@nvidia.com>
Date: Fri, 5 Jul 2019 10:09:31 +0900
Subject: [PATCH 039/813] Update Horovod docs links in README (#15366)

* Update Horovod docs links in README

Signed-off-by: Serge Panev <spanev@nvidia.com>

* retrigger CI

* retrigger ci

* Retrigger

* Update README.md

* Retrigger CI
---
 example/distributed_training-horovod/README.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/example/distributed_training-horovod/README.md b/example/distributed_training-horovod/README.md
index bfaf9d97dbd9..8c939eadfe55 100644
--- a/example/distributed_training-horovod/README.md
+++ b/example/distributed_training-horovod/README.md
@@ -43,8 +43,8 @@ $ pip install horovod
 ```
 
 This basic installation is good for laptops and for getting to know Horovod.
-If you're installing Horovod on a server with GPUs, read the [Horovod on GPU](https://github.com/horovod/horovod/blob/master/docs/gpus.md) page.
-If you want to use Docker, read the [Horovod in Docker](https://github.com/horovod/horovod/blob/master/docs/docker.md) page.
+If you're installing Horovod on a server with GPUs, read the [Horovod on GPU](https://github.com/horovod/horovod/blob/master/docs/gpus.rst) page.
+If you want to use Docker, read the [Horovod in Docker](https://github.com/horovod/horovod/blob/master/docs/docker.rst) page.
 
 ## Install MPI
 MPI is required to run distributed training with Horovod. Install [Open MPI](https://www.open-mpi.org/) or another MPI implementation.
@@ -177,7 +177,7 @@ model.fit(train_data,
 # Running Horovod
 
 The example commands below show how to run distributed training. See the 
-[Running Horovod](https://github.com/horovod/horovod/blob/master/docs/running.md)
+[Running Horovod](https://github.com/horovod/horovod/blob/master/docs/running.rst)
 page for more instructions.
 
 1. To run on a machine with 4 CPUs:
@@ -198,4 +198,4 @@ $ mpirun -np 8 \
     -x NCCL_DEBUG=INFO \
     -mca pml ob1 -mca btl ^openib \
     python train.py
-```
\ No newline at end of file
+```

From 74dbadbc3fc56e8a9e91f9724d92c32afea1bb54 Mon Sep 17 00:00:00 2001
From: Sergey Sokolov <Sergei.Sokolov@gmail.com>
Date: Fri, 5 Jul 2019 03:43:16 -0700
Subject: [PATCH 040/813] [TUTORIAL] Revise Naming tutorial (#15365)

* Revise Naming tutorial

* Code review fixes

* Force build
---
 docs/tutorials/gluon/naming.md | 148 ++++++++++-----------------------
 1 file changed, 43 insertions(+), 105 deletions(-)

diff --git a/docs/tutorials/gluon/naming.md b/docs/tutorials/gluon/naming.md
index e667ad3cb79e..c2293a9644e1 100644
--- a/docs/tutorials/gluon/naming.md
+++ b/docs/tutorials/gluon/naming.md
@@ -15,14 +15,12 @@
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
 
-
 # Naming of Gluon Parameter and Blocks
 
-In gluon, each Parameter or Block has a name (and prefix). Parameter names are specified by users and Block names can be either specified by users or automatically created.
+In Gluon, each [`Parameter`](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/gluon.html#mxnet.gluon.Parameter) has a name and each [`Block`](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/gluon.html#mxnet.gluon.Block) has a prefix. `Parameter` names are specified by users, and `Block` names can be either specified by users or automatically created.
 
 In this tutorial we talk about the best practices on naming. First, let's import MXNet and Gluon:
 
-
 ```python
 from __future__ import print_function
 import mxnet as mx
@@ -31,60 +29,49 @@ from mxnet import gluon
 
 ## Naming Blocks
 
-When creating a block, you can assign a prefix to it:
-
+When you create a `Block`, you can assign it a prefix:
 
 ```python
 mydense = gluon.nn.Dense(100, prefix='mydense_')
 print(mydense.prefix)
+print(mydense.name)
 ```
 
-    mydense_
-
-
-When no prefix is given, Gluon will automatically generate one:
+The prefix of the `Block` acts like its name. As you can see from the example above, the only difference is that the last `_` is removed from the `Block`'s name.
 
+When no prefix is given, `Gluon` will automatically generate one:
 
 ```python
 dense0 = gluon.nn.Dense(100)
-print(dense0.prefix)
+dense0.prefix
 ```
 
-    dense0_
-
-
-When you create more Blocks of the same kind, they will be named with incrementing suffixes to avoid collision:
-
+When you create more Blocks of the same kind, they will be named with an incrementing indices to avoid naming collision. To illustrate that, let's create a new `Dense` layer without specifying the prefix:
 
 ```python
 dense1 = gluon.nn.Dense(100)
-print(dense1.prefix)
+dense1.prefix
 ```
 
-    dense1_
-
+As we can see prefixes of `dense0` and `dense1` blocks are different.
 
 ## Naming Parameters
 
-Parameters within a Block will be named by prepending the prefix of the Block to the name of the Parameter:
-
+Parameters within a `Block` will be named by prepending the prefix of the `Block` to the name of the `Parameter`:
 
 ```python
-print(dense0.collect_params())
+dense0.collect_params()
 ```
 
-    dense0_ (
-      Parameter dense0_weight (shape=(100, 0), dtype=<type 'numpy.float32'>)
-      Parameter dense0_bias (shape=(100,), dtype=<type 'numpy.float32'>)
-    )
+As we can see, both `weight` and `bias` parameters of the `Dense` block have the same prefix `dense0_`.
 
+If you create a new `Parameter`, for example, when you [create a custom block](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/custom_layer.html#parameters-of-a-custom-layer), you have to specify its name as Gluon cannot set a default one.
 
 ## Name scopes
 
-To manage the names of nested Blocks, each Block has a `name_scope` attached to it. All Blocks created within a name scope will have its parent Block's prefix prepended to its name.
-
-Let's demonstrate this by first defining a simple neural net:
+To manage the names of nested Blocks, each `Block` has a `name_scope` attached to it. All Blocks created within a name scope will have its parent `Block`'s prefix prepended to its name. We can retrieve the `name_scope` by using [`.name_scope()`](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/gluon.html#mxnet.gluon.Block.name_scope) method of the `Block`.
 
+Let's demonstrate this by first defining a simple neural network:
 
 ```python
 class Model(gluon.Block):
@@ -101,12 +88,7 @@ class Model(gluon.Block):
         return mx.nd.relu(self.mydense(x))
 ```
 
-Now let's instantiate our neural net.
-
-- Note that `model0.dense0` is named as `model0_dense0_` instead of `dense0_`.
-
-- Also note that although we specified `mydense_` as prefix for `model.mydense`, its parent's prefix is automatically prepended to generate the prefix `model0_mydense_`.
-
+Here we defined three `Dense` layers, out of which only the last one has a prefix. Now, let's create and initialize our network.
 
 ```python
 model0 = Model()
@@ -118,16 +100,13 @@ print(model0.dense1.prefix)
 print(model0.mydense.prefix)
 ```
 
-    model0_
-    model0_dense0_
-    model0_dense1_
-    model0_mydense_
-
+- Note that `model0.dense0` is named as `model0_dense0_` instead of `dense0_`.
 
-If we instantiate `Model` again, it will be given a different name like shown before for `Dense`.
+- As before, to avoid naming collision, the next `Dense` layer got automatic prefix `dense1_`, and its full prefix is `model0_dense1_`.
 
-- Note that `model1.dense0` is still named as `dense0_` instead of `dense2_`, following dense layers in previously created `model0`. This is because each instance of model's name scope is independent of each other.
+- Also note that although we specified `mydense_` as prefix for `model.mydense`, its parent's prefix is automatically prepended to generate the prefix `model0_mydense_`.
 
+If we create another instance of `Model`, it will be given a different name like shown before in the example with the `Dense` layer.
 
 ```python
 model1 = Model()
@@ -137,16 +116,11 @@ print(model1.dense1.prefix)
 print(model1.mydense.prefix)
 ```
 
-    model1_
-    model1_dense0_
-    model1_dense1_
-    model1_mydense_
-
-
-**It is recommended that you manually specify a prefix for the top level Block, i.e. `model = Model(prefix='mymodel_')`, to avoid potential confusions in naming.**
+Note that `model1.dense0` is still named as `dense0_` instead of `dense2_`, following `Dense` layers in previously created `model0`. This is because each instance of model's name scope is independent of each other.
 
-The same principle also applies to container blocks like Sequential. `name_scope` can be used inside `__init__` as well as out side of `__init__`:
+**It is recommended that you manually specify a prefix for the top level `Block`, i.e. `model = Model(prefix='mymodel_')`, to avoid potential confusion in naming.**
 
+The same principle applies to container blocks like [`Sequential`](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/gluon.html#mxnet.gluon.nn.Sequential). `name_scope` can be used inside as well as outside of `__init__`. It is recommended to use `name_scope` with container blocks as the naming of the parameters inside of it tends to be more clear.
 
 ```python
 net = gluon.nn.Sequential()
@@ -158,13 +132,7 @@ print(net[0].prefix)
 print(net[1].prefix)
 ```
 
-    sequential0_
-    sequential0_dense0_
-    sequential0_dense1_
-
-
-`gluon.model_zoo` also behaves similarly:
-
+Models loaded from [`gluon.model_zoo`](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/model_zoo.html#module-mxnet.gluon.model_zoo) behave in a similar way:
 
 ```python
 net = gluon.nn.Sequential()
@@ -174,40 +142,16 @@ with net.name_scope():
 print(net.prefix, net[0].prefix, net[1].prefix)
 ```
 
-    sequential1_ sequential1_alexnet0_ sequential1_alexnet1_
-
-
-## Saving and loading
-
-Because model0 and model1 have different prefixes, their parameters also have different names:
+## Saving and loading of parameters
 
+Because `model0` and `model1` have different prefixes, their parameters also have different names:
 
 ```python
 print(model0.collect_params(), '\n')
 print(model1.collect_params())
 ```
 
-    model0_ (
-      Parameter model0_dense0_weight (shape=(20L, 20L), dtype=<type 'numpy.float32'>)
-      Parameter model0_dense0_bias (shape=(20L,), dtype=<type 'numpy.float32'>)
-      Parameter model0_dense1_weight (shape=(20L, 20L), dtype=<type 'numpy.float32'>)
-      Parameter model0_dense1_bias (shape=(20L,), dtype=<type 'numpy.float32'>)
-      Parameter model0_mydense_weight (shape=(20L, 20L), dtype=<type 'numpy.float32'>)
-      Parameter model0_mydense_bias (shape=(20L,), dtype=<type 'numpy.float32'>)
-    ) 
-    
-    model1_ (
-      Parameter model1_dense0_weight (shape=(20, 0), dtype=<type 'numpy.float32'>)
-      Parameter model1_dense0_bias (shape=(20,), dtype=<type 'numpy.float32'>)
-      Parameter model1_dense1_weight (shape=(20, 0), dtype=<type 'numpy.float32'>)
-      Parameter model1_dense1_bias (shape=(20,), dtype=<type 'numpy.float32'>)
-      Parameter model1_mydense_weight (shape=(20, 0), dtype=<type 'numpy.float32'>)
-      Parameter model1_mydense_bias (shape=(20,), dtype=<type 'numpy.float32'>)
-    )
-
-
-As a result, if you try to save parameters from model0 and load it with model1, you'll get an error due to unmatching names:
-
+As a result, if you try to save parameters of `model0` and then load it into `model1`, you'll get an error due to unmatching names:
 
 ```python
 model0.collect_params().save('model.params')
@@ -217,11 +161,7 @@ except Exception as e:
     print(e)
 ```
 
-    Parameter 'model1_dense0_weight' is missing in file 'model.params', which contains parameters: 'model0_mydense_weight', 'model0_dense1_bias', 'model0_dense1_weight', 'model0_dense0_weight', 'model0_dense0_bias', 'model0_mydense_bias'. Please make sure source and target networks have the same prefix.
-
-
-To solve this problem, we use `save_parameters`/`load_parameters` instead of `collect_params` and `save`/`load`. `save_parameters` uses model structure, instead of parameter name, to match parameters.
-
+To solve this problem, we use `save_parameters`/`load_parameters` instead of `collect_params` and `save`/`load`. The `save_parameters` method uses model structure instead of parameter names to match parameters.
 
 ```python
 model0.save_parameters('model.params')
@@ -229,20 +169,11 @@ model1.load_parameters('model.params')
 print(mx.nd.load('model.params').keys())
 ```
 
-    ['dense0.bias', 'mydense.bias', 'dense1.bias', 'dense1.weight', 'dense0.weight', 'mydense.weight']
-
-
-## Replacing Blocks from networks and fine-tuning
-
-Sometimes you may want to load a pretrained model, and replace certain Blocks in it for fine-tuning.
+## Replacing Blocks in networks and fine-tuning
 
-For example, the alexnet in model zoo has 1000 output dimensions, but maybe you only have 100 classes in your application.
-
-To see how to do this, we first load a pretrained AlexNet.
-
-- In Gluon model zoo, all image classification models follow the format where the feature extraction layers are named `features` while the output layer is named `output`.
-- Note that the output layer is a dense block with 1000 dimension outputs.
+Sometimes you may want to load a pretrained model, and replace certain Blocks in it for fine-tuning. For example, the [`AlexNet`](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/model_zoo.html#vision) model in the model zoo has 1000 output dimensions, but maybe you have only 100 classes in your application. Let's see how to change the number of output dimensions from 1000 to 100. 
 
+The first step is to load a pretrained `AlexNet`:
 
 ```python
 alexnet = gluon.model_zoo.vision.alexnet(pretrained=True)
@@ -250,12 +181,13 @@ print(alexnet.output)
 print(alexnet.output.prefix)
 ```
 
-    Dense(4096 -> 1000, linear)
-    alexnet0_dense2_
+- In Gluon Model Zoo, all image classification models follow the format where the feature extraction layers are named `features` while the output layer is named `output`.
 
+- Note that the output layer is a `Dense` block with 1000 dimension outputs.
 
-To change the output to 100 dimension, we replace it with a new block.
+- Some networks, including `AlexNet`, provide a way to directly specify number of output classes by using `classes` argument. We will still show how to do it in a replacing block manner for demonstration purposes.
 
+To change the output to 100 dimension, we replace it with a new `Block` which has only 100 units.
 
 ```python
 with alexnet.name_scope():
@@ -265,8 +197,14 @@ print(alexnet.output)
 print(alexnet.output.prefix)
 ```
 
-    Dense(None -> 100, linear)
-    alexnet0_dense3_
+## Conclusion
+
+Each `Block` has a `prefix` and each `Parameter` has a `name`. Gluon can automatically generate unique prefixes for blocks, but it is up to you to specify Parameter's name if you create a `Parameter` yourself. Prefixes act like name spaces and create a hierarchy following the nesting of blocks in your model. To save and load model parameters, it is better to use `save_parameters`/`load_parameters` methods on the parent `Block`.
+
+## Recommended Next Steps
+
+- Learn more about [model serialization](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/save_load_params.html) in Gluon.
 
+- [Create custom blocks](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/custom_layer.html) with your own parameters.
 
 <!-- INSERT SOURCE DOWNLOAD BUTTONS -->

From 728b8db572e3652ea76e89a0d8f91be8dc895e41 Mon Sep 17 00:00:00 2001
From: Sergey Sokolov <Sergei.Sokolov@gmail.com>
Date: Fri, 5 Jul 2019 03:44:30 -0700
Subject: [PATCH 041/813] Revise Symbol tutorial (#15343)

* Revise Symbol tutorial

* Force flaky build

* Address code review comments

* Split tie weight example into blocks

* Force build
---
 docs/tutorials/basic/symbol.md | 370 +++++++++++++++++----------------
 1 file changed, 192 insertions(+), 178 deletions(-)

diff --git a/docs/tutorials/basic/symbol.md b/docs/tutorials/basic/symbol.md
index cff12aca3c95..57e014f36f54 100644
--- a/docs/tutorials/basic/symbol.md
+++ b/docs/tutorials/basic/symbol.md
@@ -17,50 +17,21 @@
 
 # Symbol - Neural network graphs
 
-In a [previous tutorial](http://mxnet.io/tutorials/basic/ndarray.html), we introduced `NDArray`,
-the basic data structure for manipulating data in MXNet.
-And just using NDArray by itself, we can execute a wide range of mathematical operations.
-In fact, we could define and update a full neural network just by using `NDArray`.
-`NDArray` allows you to write programs for scientific computation
-in an imperative fashion, making full use of the native control of any front-end language. Gluon uses this approach under the hood (before hybridization) to allow for flexible and debugable networks.
-So you might wonder, why don't we just use `NDArray` for all computation?
-
-MXNet also provides the Symbol API, an interface for symbolic programming.
-With symbolic programming, rather than executing operations step by step,
-we first define a *computation graph*.
-This graph contains placeholders for inputs and designated outputs.
-We can then compile the graph, yielding a function
-that can be bound to `NDArray`s and run.
-MXNet's Symbol API is similar to the network configurations
-used by [Caffe](http://caffe.berkeleyvision.org/)
-and the symbolic programming in [Theano](http://deeplearning.net/software/theano/). And Gluon takes advantage of this approach under the hood after the network has been hybridized.
-
-Another advantage conferred by symbolic approach is that
-we can optimize our functions before using them.
-For example, when we execute mathematical computations in imperative fashion,
-we don't know at the time that we run each operation,
-which values will be needed later on.
-But with symbolic programming, we declare the required outputs in advance.
-This means that we can recycle memory allocated in intermediate steps,
-as by performing operations in place. Symbolic API also uses less memory for the
-same network. Refer to [How To](http://mxnet.io/faq/index.html) and
-[Architecture](http://mxnet.io/architecture/index.html) section to know more.
-
-In our design notes, we present [a more thorough discussion on the comparative strengths
-of imperative and symbolic programing](http://mxnet.io/architecture/program_model.html).
-But in this document, we'll focus on teaching you how to use MXNet's Symbol API.
-In MXNet, we can compose Symbols from other Symbols, using operators,
-such as simple matrix operations (e.g. "+"),
-or whole neural network layers (e.g. convolution layer).
-Operator can take multiple input variables,
-can produce multiple output symbols
-and can maintain internal state symbols.
-
-For a visual explanation of these concepts, see
-[Symbolic Configuration and Execution in Pictures](http://mxnet.io/api/python/symbol_in_pictures/symbol_in_pictures.html).
-
-To make things concrete, let's take a hands-on look at the Symbol API.
-There are a few different ways to compose a `Symbol`.
+In the [previous tutorial](http://mxnet.io/tutorials/basic/ndarray.html), we introduced [`NDArray`](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html), the basic data structure for manipulating data in MXNet.
+Just using `NDArray` by itself, we can execute a wide range of mathematical operations. In fact, we could define and update a full neural network just by using `NDArray`.
+
+`NDArray` allows you to write programs for scientific computation in an imperative fashion, making full use of the native control of any front-end language. Gluon API uses this approach under the hood (before hybridization) to allow for flexible and debugable networks. So you might wonder, why don't we just use `NDArray` for all computation?
+
+MXNet also provides the `Symbol API`, an interface for symbolic programming. With symbolic programming, rather than executing operations step by step, we first define a [computation graph](https://mxnet.incubator.apache.org/versions/master/faq/visualize_graph.html). This graph contains placeholders for inputs and designated outputs. We can then compile the graph, yielding a function that can be bound to `NDArray`s and run. MXNet's `Symbol API` is similar to the network configurations used by [Caffe](http://caffe.berkeleyvision.org/) and the symbolic programming in [Theano](http://deeplearning.net/software/theano/). Gluon API takes advantage of this approach under the hood after the network has been hybridized.
+
+Another advantage conferred by symbolic approach is that we can optimize our functions before using them. For example, when we execute mathematical computations in imperative fashion, we don't know at the time that we run each operation, which values will be needed later on. But with symbolic programming, we declare the required outputs in advance. This means that we can recycle memory allocated in intermediate steps, as by performing operations in place. Symbolic API also uses less memory for the
+same network. Refer to [How To](http://mxnet.io/faq/index.html) and [Architecture](http://mxnet.io/architecture/index.html) section to know more.
+
+In our design notes, we present [a more thorough discussion on the comparative strengths of imperative and symbolic programing](http://mxnet.io/architecture/program_model.html). In this document, however, we'll focus on explaining how to use MXNet's `Symbol API`.
+
+In MXNet, we can compose Symbols from other Symbols, using operators, such as simple matrix operations (e.g. `+`), or whole neural network layers (e.g. convolution layer). Operator can take multiple input variables, can produce multiple output symbols and can maintain internal state symbols. For a visual explanation of these concepts, see [Symbolic Configuration and Execution in Pictures](http://mxnet.io/api/python/symbol_in_pictures/symbol_in_pictures.html).
+
+To make things concrete, let's take a hands-on look at the `Symbol API`. There are a few different ways to compose a [`Symbol`](http://mxnet.incubator.apache.org/api/python/symbol/symbol.html).
 
 ## Prerequisites
 
@@ -71,20 +42,14 @@ To complete this tutorial, we need:
     ```
     pip install jupyter
     ```
-- GPUs - A section of this tutorial uses GPUs. If you don't have GPUs on your machine, simply
-set the variable gpu_device to mx.cpu().
+- GPUs (optional). A section of this tutorial uses GPUs, if one is available. If not, the code will automatically switch to CPU.
 
 ## Basic Symbol Composition
 
 ### Basic Operators
 
-The following example builds a simple expression: `a + b`.
-First, we create two placeholders with  `mx.sym.Variable`,
-giving them the names `a` and `b`.
-We then construct the desired symbol by using the operator `+`.
-We don't need to name our variables while creating them,
-MXNet will automatically generate a unique name for each.
-In the example below, `c` is assigned a unique name automatically.
+The following example builds a simple expression: `a + b`. First, we create two placeholders with `mx.sym.Variable`,
+giving them the names `a` and `b`. We then construct the desired symbol by using the operator `+`. We don't need to name our variables while creating them, MXNet will automatically generate a unique name for each. In the example below, `c` is assigned a unique name automatically.
 
 ```python
 import mxnet as mx
@@ -97,7 +62,7 @@ c = a + b
 Most operators supported by `NDArray` are also supported by `Symbol`, for example:
 
 ```python
-# elemental wise multiplication
+# element-wise multiplication
 d = a * b
 # matrix multiplication
 e = mx.sym.dot(a, b)
@@ -106,12 +71,11 @@ f = mx.sym.reshape(d+e, shape=(1,4))
 # broadcast
 g = mx.sym.broadcast_to(f, shape=(2,4))
 # plot
-mx.viz.plot_network(symbol=g, node_attrs={"shape":"oval","fixedsize":"false"})
+mx.viz.plot_network(symbol=g,
+                    node_attrs={"shape": "oval", "fixedsize": "false"})
 ```
 
-The computations declared in the above examples can be bound to the input data
-for evaluation by using `bind` method. We discuss this further in the
-[Symbol Manipulation](#symbol-manipulation) section.
+The computations declared in the above examples can be bound to the input data for evaluation by using `bind` method. We discuss this further in the [Symbol Manipulation](#symbol-manipulation) section.
 
 ### Basic Neural Networks
 
@@ -125,23 +89,20 @@ net = mx.sym.FullyConnected(data=net, name='fc1', num_hidden=128)
 net = mx.sym.Activation(data=net, name='relu1', act_type="relu")
 net = mx.sym.FullyConnected(data=net, name='fc2', num_hidden=10)
 net = mx.sym.SoftmaxOutput(data=net, name='out')
-mx.viz.plot_network(net, shape={'data':(100,200)}, node_attrs={"shape":"oval","fixedsize":"false"})
+mx.viz.plot_network(net,
+                    shape={'data':(100, 200)},
+                    node_attrs={"shape": "oval", "fixedsize": "false"})
 ```
 
-Each symbol takes a (unique) string name. NDArray and Symbol both represent
-a single tensor. *Operators* represent the computation between tensors.
-Operators take symbol (or NDArray) as inputs and might also additionally accept
-other hyperparameters such as the number of hidden neurons (*num_hidden*) or the
-activation type (*act_type*) and produce the output.
+Each `Symbol` takes a unique string name. `NDArray` and `Symbol` both represent a single tensor. *Operators* represent the computation between tensors. Operators take `Symbol` or `NDArray` as inputs and might also additionally accept other hyperparameters such as the number of hidden neurons (`num_hidden`) or the activation type (`act_type`) and produce the output.
 
-We can view a symbol simply as a function taking several arguments.
-And we can retrieve those arguments with the following method call:
+We can view a `Symbol` simply as a function taking several arguments. And we can retrieve those arguments with the following method call:
 
 ```python
 net.list_arguments()
 ```
 
-These arguments are the parameters and inputs needed by each symbol:
+These arguments are the parameters and inputs needed by each `Symbol`:
 
 - *data*: Input data needed by the variable *data*.
 - *fc1_weight* and *fc1_bias*: The weight and bias for the first fully connected layer *fc1*.
@@ -157,16 +118,12 @@ net = mx.symbol.FullyConnected(data=net, weight=w, name='fc1', num_hidden=128)
 net.list_arguments()
 ```
 
-In the above example, `FullyConnected` layer has 3 inputs: data, weight, bias.
-When any input is not specified, a variable will be automatically generated for it.
+In the above example, `FullyConnected` layer has 3 inputs: data, weight, bias. When any input is not specified, a variable will be automatically generated for it.
 
 ## More Complicated Composition
 
-MXNet provides well-optimized symbols for layers commonly used in deep learning
-(see [src/operator](https://github.com/dmlc/mxnet/tree/master/src/operator)).
-We can also define new operators in Python. The following example first
-performs an element-wise add between two symbols, then feeds them to the fully
-connected operator:
+MXNet provides well-optimized Symbols for layers commonly used in deep learning (see [src/operator](https://github.com/dmlc/mxnet/tree/master/src/operator)). We can also define new operators in Python. The following example first
+performs an element-wise add between two Symbols, then feeds them to the fully connected operator:
 
 ```python
 lhs = mx.symbol.Variable('data1')
@@ -175,8 +132,7 @@ net = mx.symbol.FullyConnected(data=lhs + rhs, name='fc1', num_hidden=128)
 net.list_arguments()
 ```
 
-We can also construct a symbol in a more flexible way than the single forward
-composition depicted in the preceding example:
+We can also construct a `Symbol` in a more flexible way than the single forward composition depicted in the preceding example:
 
 ```python
 data = mx.symbol.Variable('data')
@@ -188,14 +144,9 @@ composed = net2(data2=net1, name='composed')
 composed.list_arguments()
 ```
 
-In this example, *net2* is used as a function to apply to an existing symbol *net1*,
-and the resulting *composed* symbol will have all the attributes of *net1* and *net2*.
+In this example, `net2` is used as a function to apply to an existing `Symbol` `net1`, and the resulting *composed* `Symbol` will have all the attributes of `net1` and `net2`.
 
-Once you start building some bigger networks, you might want to name some
-symbols with a common prefix to outline the structure of your network.
-You can use the
-[Prefix](https://github.com/dmlc/mxnet/blob/master/python/mxnet/name.py)
-NameManager as follows:
+Once you start building some bigger networks, you might want to name some symbols with a common prefix to outline the structure of your network. You can use the [Prefix class](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/name.py#L93) as follows:
 
 ```python
 data = mx.sym.Variable("data")
@@ -209,63 +160,76 @@ net.list_arguments()
 
 ### Modularized Construction for Deep Networks
 
-Constructing a *deep* network layer by layer, (like the Google Inception network),
-can be tedious owing to the large number of layers.
-So, for such networks, we often modularize the construction.
+Constructing a *deep* network layer by layer, (like the Google Inception network), can be tedious owing to the large number of layers. So, for such networks, we often modularize the construction.
 
-For example, in Google Inception network,
-we can first define a factory function which chains the convolution,
-batch normalization and rectified linear unit (ReLU) activation layers together.
+For example, in Google Inception network, we can first define a factory function which chains the convolution, batch normalization and rectified linear unit (ReLU) activation layers together.
 
 ```python
-def ConvFactory(data, num_filter, kernel, stride=(1,1), pad=(0, 0),name=None, suffix=''):
-    conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=kernel,
-                  stride=stride, pad=pad, name='conv_%s%s' %(name, suffix))
-    bn = mx.sym.BatchNorm(data=conv, name='bn_%s%s' %(name, suffix))
-    act = mx.sym.Activation(data=bn, act_type='relu', name='relu_%s%s'
-                  %(name, suffix))
+def ConvFactory(data, num_filter, kernel,
+                stride=(1, 1), pad=(0, 0), name=None, suffix=''):
+    conv = mx.sym.Convolution(data=data, num_filter=num_filter,
+                              kernel=kernel, stride=stride, pad=pad,
+                              name='conv_%s%s' % (name, suffix))
+
+    bn = mx.sym.BatchNorm(data=conv, name='bn_%s%s' % (name, suffix))
+
+    act = mx.sym.Activation(data=bn, act_type='relu',
+                            name='relu_%s%s' % (name, suffix))
     return act
+
 prev = mx.sym.Variable(name="Previous Output")
-conv_comp = ConvFactory(data=prev, num_filter=64, kernel=(7,7), stride=(2, 2))
+conv_comp = ConvFactory(data=prev, num_filter=64, kernel=(7, 7), stride=(2, 2))
 shape = {"Previous Output" : (128, 3, 28, 28)}
-mx.viz.plot_network(symbol=conv_comp, shape=shape, node_attrs={"shape":"oval","fixedsize":"false"})
+mx.viz.plot_network(symbol=conv_comp, shape=shape,
+                    node_attrs={"shape": "oval", "fixedsize": "false"})
 ```
 
-Then we can define a function that constructs an inception module based on
-factory function `ConvFactory`.
+Then we can define a function that constructs an inception module based on factory function `ConvFactory`.
 
 ```python
-def InceptionFactoryA(data, num_1x1, num_3x3red, num_3x3, num_d3x3red, num_d3x3,
-                      pool, proj, name):
+def InceptionFactoryA(data, num_1x1, num_3x3red, num_3x3, num_d3x3red,
+                      num_d3x3, pool, proj, name):
     # 1x1
-    c1x1 = ConvFactory(data=data, num_filter=num_1x1, kernel=(1, 1), name=('%s_1x1' % name))
+    c1x1 = ConvFactory(data=data, num_filter=num_1x1, kernel=(1, 1),
+                       name=('%s_1x1' % name))
+
     # 3x3 reduce + 3x3
-    c3x3r = ConvFactory(data=data, num_filter=num_3x3red, kernel=(1, 1), name=('%s_3x3' % name), suffix='_reduce')
-    c3x3 = ConvFactory(data=c3x3r, num_filter=num_3x3, kernel=(3, 3), pad=(1, 1), name=('%s_3x3' % name))
+    c3x3r = ConvFactory(data=data, num_filter=num_3x3red, kernel=(1, 1),
+                        name=('%s_3x3' % name), suffix='_reduce')
+    c3x3 = ConvFactory(data=c3x3r, num_filter=num_3x3, kernel=(3, 3),
+                       pad=(1, 1), name=('%s_3x3' % name))
+
     # double 3x3 reduce + double 3x3
-    cd3x3r = ConvFactory(data=data, num_filter=num_d3x3red, kernel=(1, 1), name=('%s_double_3x3' % name), suffix='_reduce')
-    cd3x3 = ConvFactory(data=cd3x3r, num_filter=num_d3x3, kernel=(3, 3), pad=(1, 1), name=('%s_double_3x3_0' % name))
-    cd3x3 = ConvFactory(data=cd3x3, num_filter=num_d3x3, kernel=(3, 3), pad=(1, 1), name=('%s_double_3x3_1' % name))
+    cd3x3r = ConvFactory(data=data, num_filter=num_d3x3red, kernel=(1, 1),
+                         name=('%s_double_3x3' % name), suffix='_reduce')
+    cd3x3 = ConvFactory(data=cd3x3r, num_filter=num_d3x3, kernel=(3, 3),
+                        pad=(1, 1), name=('%s_double_3x3_0' % name))
+    cd3x3 = ConvFactory(data=cd3x3, num_filter=num_d3x3, kernel=(3, 3),
+                        pad=(1, 1), name=('%s_double_3x3_1' % name))
+
     # pool + proj
-    pooling = mx.sym.Pooling(data=data, kernel=(3, 3), stride=(1, 1), pad=(1, 1), pool_type=pool, name=('%s_pool_%s_pool' % (pool, name)))
-    cproj = ConvFactory(data=pooling, num_filter=proj, kernel=(1, 1), name=('%s_proj' %  name))
+    pooling = mx.sym.Pooling(data=data, kernel=(3, 3), stride=(1, 1),
+                             pad=(1, 1), pool_type=pool,
+                             name=('%s_pool_%s_pool' % (pool, name)))
+    cproj = ConvFactory(data=pooling, num_filter=proj, kernel=(1, 1),
+                        name=('%s_proj' %  name))
+
     # concat
-    concat = mx.sym.Concat(*[c1x1, c3x3, cd3x3, cproj], name='ch_concat_%s_chconcat' % name)
+    concat = mx.sym.Concat(*[c1x1, c3x3, cd3x3, cproj],
+                           name='ch_concat_%s_chconcat' % name)
     return concat
+
 prev = mx.sym.Variable(name="Previous Output")
 in3a = InceptionFactoryA(prev, 64, 64, 64, 64, 96, "avg", 32, name="in3a")
-mx.viz.plot_network(symbol=in3a, shape=shape, node_attrs={"shape":"oval","fixedsize":"false"})
+mx.viz.plot_network(symbol=in3a, shape=shape,
+                    node_attrs={"shape": "oval", "fixedsize": "false"})
 ```
 
-Finally, we can obtain the whole network by chaining multiple inception
-modules. See a complete example
-[here](https://github.com/dmlc/mxnet/blob/master/example/image-classification/symbols/inception-bn.py).
+Finally, we can obtain the whole network by chaining multiple inception modules. See a complete example [here](https://github.com/dmlc/mxnet/blob/master/example/image-classification/symbols/inception-bn.py).
 
 ### Group Multiple Symbols
 
-To construct neural networks with multiple loss layers, we can use
-`mxnet.sym.Group` to group multiple symbols together. The following example
-groups two outputs:
+To construct neural networks with multiple loss layers, we can use `mxnet.sym.Group` to group multiple Symbols together. The following example groups two outputs:
 
 ```python
 net = mx.sym.Variable('data')
@@ -279,48 +243,35 @@ group.list_outputs()
 
 ## Relations to NDArray
 
-As you can see now, both `Symbol` and `NDArray` provide multi-dimensional array
-operations, such as `c = a + b` in MXNet. We briefly clarify the differences here.
+As you can see now, both `Symbol` and `NDArray` provide multi-dimensional array operations, such as `c = a + b` in MXNet. We briefly clarify the differences here.
 
-The `NDArray` provides an imperative programming alike interface, in which the
-computations are evaluated sentence by sentence. While `Symbol` is closer to
-declarative programming, in which we first declare the computation and then
-evaluate with data. Examples in this category include regular expressions and
-SQL.
+The `NDArray` provides an imperative programming alike interface, in which the computations are evaluated sentence by sentence. While `Symbol` is closer to declarative programming, in which we first declare the computation and then evaluate with data. Examples in this category include regular expressions and SQL.
 
 The pros for `NDArray`:
 
 - Straightforward.
-- Easy to work with native language features (for loop, if-else condition, ..)
-  and libraries (numpy, ..).
+- Easy to work with native language features (for loop, if-else condition, ..) and libraries (numpy, ..).
 - Easy step-by-step code debugging.
 
 The pros for `Symbol`:
 
-- Provides almost all functionalities of NDArray, such as `+`, `*`, `sin`,
-  `reshape` etc.
+- Provides almost all functionalities of NDArray, such as `+`, `*`, `sin`, `reshape` etc.
 - Easy to save, load and visualize.
 - Easy for the backend to optimize the computation and memory usage.
 
 ## Symbol Manipulation
 
-One important difference of `Symbol` compared to `NDArray` is that we first
-declare the computation and then bind the computation with data to run.
-
-In this section, we introduce the functions to manipulate a symbol directly. But
-note that, most of them are wrapped by the high-level packages: `Module` and `Gluon`.
+One important difference of `Symbol` compared to `NDArray` is that we first declare the computation and then bind the computation with data to run. In this section, we introduce the functions to manipulate a `Symbol` directly. But note that, most of them are wrapped by the high-level packages: [`Module`](https://mxnet.incubator.apache.org/api/python/module/module.html) and [`Gluon`](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html).
 
 ### Shape and Type Inference
 
-For each symbol, we can query its arguments, auxiliary states and outputs.
-We can also infer the output shape and type of the symbol given the known input
-shape or type of some arguments, which facilitates memory allocation.
+For each `Symbol`, we can query its arguments, auxiliary states and outputs. We can also infer the output shape and type of the `Symbol` given the known input shape or type of some arguments, which facilitates memory allocation.
 
 ```python
 arg_name = c.list_arguments()  # get the names of the inputs
 out_name = c.list_outputs()    # get the names of the outputs
 # infers output shape given the shape of input arguments
-arg_shape, out_shape, _ = c.infer_shape(a=(2,3), b=(2,3))
+arg_shape, out_shape, _ = c.infer_shape(a=(2, 3), b=(2, 3))
 # infers output type given the type of input arguments
 arg_type, out_type, _ = c.infer_type(a='float32', b='float32')
 {'input' : dict(zip(arg_name, arg_shape)),
@@ -331,13 +282,9 @@ arg_type, out_type, _ = c.infer_type(a='float32', b='float32')
 
 ### Bind with Data and Evaluate
 
-The symbol `c` constructed above declares what computation should be run. To
-evaluate it, we first need to feed the arguments, namely free variables, with data.
+The `Symbol` `c` constructed above declares what computation should be run. To evaluate it, we first need to feed the arguments, namely free variables, with data.
 
-We can do it by using the `bind` method, which accepts device context and
-a `dict` mapping free variable names to `NDArray`s as arguments and returns an
-executor. The executor provides `forward` method for evaluation and an attribute
-`outputs` to get all the results.
+We can do it by using the [`bind`](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.Symbol.bind) method, which accepts device context and a `dict` mapping free variable names to `NDArray`s as arguments and returns an [`Executor`](https://mxnet.incubator.apache.org/api/python/executor/executor.html#mxnet.executor.Executor). The `Executor` provides [`forward`](https://mxnet.incubator.apache.org/api/python/executor/executor.html#mxnet.executor.Executor.forward) method for evaluation and an attribute `outputs` to get all the results.
 
 ```python
 ex = c.bind(ctx=mx.cpu(), args={'a' : mx.nd.ones([2,3]),
@@ -347,11 +294,10 @@ print('number of outputs = %d\nthe first output = \n%s' % (
            len(ex.outputs), ex.outputs[0].asnumpy()))
 ```
 
-We can evaluate the same symbol on GPU with different data.
+We can evaluate the same `Symbol` on GPU with different data.
 
-**Note** In order to execute the following section on a cpu set gpu_device to mx.cpu().
 ```python
-gpu_device=mx.gpu() # Change this to mx.cpu() in absence of GPUs.
+gpu_device = mx.gpu() if mx.test_utils.list_gpus() else mx.cpu()
 
 ex_gpu = c.bind(ctx=gpu_device, args={'a' : mx.nd.ones([3,4], gpu_device)*2,
                                       'b' : mx.nd.ones([3,4], gpu_device)*3})
@@ -359,8 +305,7 @@ ex_gpu.forward()
 ex_gpu.outputs[0].asnumpy()
 ```
 
-We can also use `eval` method to evaluate the symbol. It combines calls to `bind`
-and `forward` methods.
+We can also use [eval](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.Symbol.eval) method to evaluate the `Symbol`. It combines calls to `bind` and `forward` methods.
 
 ```python
 ex = c.eval(ctx = mx.cpu(), a = mx.nd.ones([2,3]), b = mx.nd.ones([2,3]))
@@ -368,24 +313,13 @@ print('number of outputs = %d\nthe first output = \n%s' % (
             len(ex), ex[0].asnumpy()))
 ```
 
-For neural nets, a more commonly used pattern is ```simple_bind```, which
-creates all of the argument arrays for you. Then you can call ```forward```,
-and ```backward``` (if the gradient is needed) to get the gradient.
+For neural nets, a more commonly used pattern is [simple_bind](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.Symbol.simple_bind), which creates all of the argument arrays for you. Then you can call `forward`, and [`backward`](https://mxnet.incubator.apache.org/api/python/executor/executor.html#mxnet.executor.Executor.backward) to get gradients if needed.
 
 ### Load and Save
 
-Logically symbols correspond to ndarrays. They both represent a tensor. They both
-are inputs/outputs of operators. We can either serialize a `Symbol` object by
-using `pickle`, or by using `save` and `load` methods directly as we discussed in
-[NDArray tutorial](http://mxnet.io/tutorials/basic/ndarray.html#serialize-from-to-distributed-filesystems).
+Logically Symbols correspond to NDArrays. They both represent a tensor. They both are inputs/outputs of operators. We can either serialize a `Symbol` object by using `pickle`, or by using `save` and `load` methods directly as it is explained in [this NDArray tutorial](http://mxnet.io/tutorials/basic/ndarray.html#serialize-from-to-distributed-filesystems).
 
-When serializing `NDArray`, we serialize the tensor data in it and directly dump to
-disk in binary format.
-But symbol uses a concept of graph. Graphs are composed by chaining operators. They are
-implicitly represented by output symbols. So, when serializing a `Symbol`, we
-serialize the graph of which the symbol is an output. While serialization, Symbol
-uses more readable `json` format for serialization. To convert symbol to `json`
-string, use `tojson` method.
+When serializing `NDArray`, we serialize the tensor data in it and directly dump to disk in binary format. But `Symbol` uses a concept of graph. Graphs are composed by chaining operators. They are implicitly represented by output Symbols. So, when serializing a `Symbol`, we serialize the graph of which the `Symbol` is an output. While serialization, `Symbol` uses more readable `json` format for serialization. To convert `Symbol` to `json` string, use [`tojson`](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.Symbol.tojson) method.
 
 ```python
 print(c.tojson())
@@ -396,25 +330,16 @@ c.tojson() == c2.tojson()
 
 ## Customized Symbol
 
-Most operators such as `mx.sym.Convolution` and `mx.sym.Reshape` are implemented
-in C++ for better performance. MXNet also allows users to write new operators
-using any front-end language such as Python. It often makes the developing and
-debugging much easier. To implement an operator in Python, refer to
-[How to create new operators](http://mxnet.io/faq/new_op.html).
+Most operators such as [`mx.sym.Convolution`](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.Convolution) and [`mx.sym.Reshape`](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.reshape) are implemented in C++ for better performance. MXNet also allows users to write new operators using any front-end language such as Python. It often makes the developing and debugging much easier. To implement an operator in Python, refer to [How to create new operators](http://mxnet.io/faq/new_op.html).
 
 ## Advanced Usages
 
 ### Type Cast
 
-By default, MXNet uses 32-bit floats.
-But for better accuracy-performance,
-we can also use a lower precision data type.
-For example, The Nvidia Tesla Pascal GPUs
-(e.g. P100) have improved 16-bit float performance,
-while GTX Pascal GPUs (e.g. GTX 1080) are fast on 8-bit integers.
+By default, MXNet uses 32-bit floats. But for better accuracy-performance, we can also use a lower precision data type.
+For example, The NVIDIA Tesla Pascal GPUs (e.g. P100) have improved 16-bit float performance, while GTX Pascal GPUs (e.g. GTX 1080) are fast on 8-bit integers.
 
-To convert the data type as per the requirements,
-we can use `mx.sym.cast` operator as follows:
+To convert the data type as per the requirements, we can use [`mx.sym.cast`](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.cast) operator as follows:
 
 ```python
 a = mx.sym.Variable('data')
@@ -429,18 +354,107 @@ print({'input':arg, 'output':out})
 
 ### Variable Sharing
 
-To share the contents between several symbols,
-we can bind these symbols with the same array as follows:
+To share the contents between several Symbols, we can bind these Symbols with the same array as follows:
 
 ```python
 a = mx.sym.Variable('a')
 b = mx.sym.Variable('b')
 b = a + a * a
 
-data = mx.nd.ones((2,3))*2
+data = mx.nd.ones((2,3)) * 2
 ex = b.bind(ctx=mx.cpu(), args={'a':data, 'b':data})
 ex.forward()
 ex.outputs[0].asnumpy()
 ```
 
+### Weight tying
+
+You can use same principle to tie weights of different layers. In the example below two `FullyConnected` layers share same weights and biases, but process different data. Let's demonstrate how we can do it.
+
+In this example we first create training and evaluation datasets. Both of them consist of two individual `NDArray`s. We are using `NDArrayIter` to iterate over all of them.
+
+```python
+import numpy as np
+import mxnet as mx
+
+# Training data
+train_data_size = 4
+train_data1 = mx.random.uniform(shape=(train_data_size, 2))
+train_data2 = mx.random.uniform(shape=(train_data_size, 2))
+train_label = mx.nd.array([i % 2 for i in range(train_data_size)])
+batch_size = 3
+
+# Evaluation Data
+eval_data_size = 3
+eval_data1 = mx.random.uniform(shape=(eval_data_size, 2))
+eval_data2 = mx.random.uniform(shape=(eval_data_size, 2))
+eval_label = np.array([i % 2 for i in range(eval_data_size)])
+
+train_iter = mx.io.NDArrayIter({'inputs_left': train_data1,
+                                'inputs_right': train_data2},
+                               train_label, batch_size, shuffle=True,
+                               label_name='labels', last_batch_handle='pad')
+
+eval_iter = mx.io.NDArrayIter({'inputs_left': eval_data1,
+                               'inputs_right': eval_data2},
+                              eval_label, batch_size, shuffle=False, 
+                              label_name='labels',
+                              last_batch_handle='pad')
+```
+
+We define a `Symbol` for both `inputs_left` and `inputs_right` variables, and separate symbols for `shared_weight` and `shared_bias`. We use `shared_weight` and `shared_bias` symbols in both `FullyConnected` layers, making sure that they are using the same data underlying the symbols. This is where weight tying is happening.
+
+```python
+num_hidden_nodes = 2
+
+# Assume the left and right inputs have the same shape as each other
+inputs_left = mx.sym.var('inputs_left')
+inputs_right = mx.sym.var('inputs_right')
+labels = mx.symbol.Variable('labels')
+
+shared_weight = mx.symbol.Variable('shared_weight')
+shared_bias = mx.symbol.Variable('shared_bias')
+
+fc_left_sym = mx.sym.FullyConnected(data=inputs_left, weight=shared_weight,
+                                    bias=shared_bias,
+                                    num_hidden=num_hidden_nodes, name='fc_left')
+
+fc_right_sym = mx.sym.FullyConnected(data=inputs_right, weight=shared_weight,
+                                     bias=shared_bias,
+                                     num_hidden=num_hidden_nodes, name='fc_right')
+
+combined = mx.sym.concat(fc_left_sym, fc_right_sym)
+output = mx.sym.SoftmaxOutput(data=combined, label=labels, name='softmax')
+```
+
+In the next lines of the code, we use `Module API` to start the training. We first create a `Module` object and then call `fit` providing data iterators. To use trained model for prediction, we use `predict` method, providing evaluation data iterator.  
+
+```python
+model = mx.mod.Module(
+    symbol=output,
+    data_names=['inputs_left', 'inputs_right'],
+    label_names=['labels']
+)
+
+model.fit(train_iter, eval_iter,
+          optimizer_params={'learning_rate': 0.01, 'momentum': 0.9},
+          num_epoch=1,
+          eval_metric='acc')
+
+result = model.predict(eval_iter).asnumpy()
+print(result)
+```
+
+## Recommended Next Steps
+
+- Learn how to [use Module API to train neural network](https://mxnet.incubator.apache.org/versions/master/tutorials/basic/module.html).
+
+- Explore ways you can [load data using Data Iterators](https://mxnet.incubator.apache.org/versions/master/tutorials/basic/data.html).
+
+- [Use pretrained models](https://mxnet.incubator.apache.org/versions/master/tutorials/python/predict_image.html) for image object detection.
+
+- [Hybridize your models](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/hybrid.html) to get the best from both `Gluon` and `Symbol API`.
+
+- Convert your existing `Module API` code to `Gluon` as it is explained [here](https://mxnet.incubator.apache.org/versions/master/tutorials/python/module_to_gluon.html). 
+
 <!-- INSERT SOURCE DOWNLOAD BUTTONS -->

From dfe923ac254be93ae0a580bfd26b52fb6ea065e8 Mon Sep 17 00:00:00 2001
From: Serge Panev <spanev@nvidia.com>
Date: Sat, 6 Jul 2019 03:08:33 +0900
Subject: [PATCH 042/813] Update fp16 docs: Block.cast is inplace (#15458)

Signed-off-by: Serge Panev <spanev@nvidia.com>
---
 docs/faq/float16.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/faq/float16.md b/docs/faq/float16.md
index 465668610413..22d4ef48c2f6 100644
--- a/docs/faq/float16.md
+++ b/docs/faq/float16.md
@@ -42,7 +42,7 @@ With Gluon API, you need to take care of three things to convert a model to supp
 1. Cast Gluon `Block`'s parameters and expected input type to float16 by calling the [cast](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.Block.cast) method of the `Block` representing the network.
 
 ```python
-net = net.cast('float16')
+net.cast('float16')
 ```
 
 2. Ensure the data input to the network is of float16 type. If your `DataLoader` or `Iterator` produces output in another datatype, then you would have to cast your data. There are different ways you can do this. The easiest would be to use the [astype](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.astype) method of NDArrays.

From bc570dbd948f23873388bbe9fb84de50679dbef9 Mon Sep 17 00:00:00 2001
From: Tao Lv <tao.a.lv@intel.com>
Date: Sat, 6 Jul 2019 14:14:04 +0800
Subject: [PATCH 043/813] [OP] Add a new arange_like operator to contrib
 (#15400)

* add contrib.arange_like operator

* add parameter to template

* fix lint complains

* inum -> num_in

* add unit tests
---
 src/operator/tensor/init_op.cc         | 38 ++++++++++++++-
 src/operator/tensor/init_op.cu         |  5 +-
 src/operator/tensor/init_op.h          | 64 ++++++++++++++++++++++++--
 tests/python/unittest/test_operator.py | 22 +++++++++
 4 files changed, 123 insertions(+), 6 deletions(-)

diff --git a/src/operator/tensor/init_op.cc b/src/operator/tensor/init_op.cc
index a58498b85aa5..0cbdaa43c198 100644
--- a/src/operator/tensor/init_op.cc
+++ b/src/operator/tensor/init_op.cc
@@ -32,6 +32,7 @@ DMLC_REGISTER_PARAMETER(InitOpParam);
 DMLC_REGISTER_PARAMETER(InitOpWithScalarParam);
 DMLC_REGISTER_PARAMETER(InitOpWithoutDTypeParam);
 DMLC_REGISTER_PARAMETER(RangeParam);
+DMLC_REGISTER_PARAMETER(RangeLikeParam);
 DMLC_REGISTER_PARAMETER(EyeParam);
 DMLC_REGISTER_PARAMETER(LinspaceParam);
 
@@ -97,9 +98,44 @@ NNVM_REGISTER_OP(_arange)
 .set_attr_parser(RangeParamParser)
 .set_attr<mxnet::FInferShape>("FInferShape", RangeShape)
 .set_attr<nnvm::FInferType>("FInferType", InitType<RangeParam>)
-.set_attr<FCompute>("FCompute<cpu>", RangeCompute<cpu>)
+.set_attr<FCompute>("FCompute<cpu>", RangeCompute<cpu, RangeParam>)
 .add_arguments(RangeParam::__FIELDS__());
 
+NNVM_REGISTER_OP(_contrib_arange_like)
+.describe(R"code(Return an array with evenly spaced values. If axis is not given, the output will 
+have the same shape as the input array. Otherwise, the output will be a 1-D array with size of 
+the specified axis in input shape.
+
+Examples::
+
+  x = [[0.14883883 0.7772398  0.94865847 0.7225052 ]
+       [0.23729339 0.6112595  0.66538996 0.5132841 ]
+       [0.30822644 0.9912457  0.15502319 0.7043658 ]]
+       <NDArray 3x4 @cpu(0)>
+
+  out = mx.nd.contrib.arange_like(x, start=0)
+
+    [[ 0.  1.  2.  3.]
+     [ 4.  5.  6.  7.]
+     [ 8.  9. 10. 11.]]
+     <NDArray 3x4 @cpu(0)>
+
+  out = mx.nd.contrib.arange_like(x, start=0, axis=-1)
+
+    [0. 1. 2. 3.]
+    <NDArray 4 @cpu(0)>
+)code")
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<RangeLikeParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", RangeLikeShape)
+.set_attr<nnvm::FInferType>("FInferType", InitType<RangeLikeParam, 1>)
+.set_attr<nnvm::FIgnoreInputs>("FIgnoreInputs",
+    [](const NodeAttrs& attrs) { return std::vector<uint32_t>(1, 0); })
+.set_attr<FCompute>("FCompute<cpu>", RangeCompute<cpu, RangeLikeParam>)
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.add_argument("data", "NDArray-or-Symbol", "The input");
+
 NNVM_REGISTER_OP(_linspace)
 .describe("Return evenly spaced numbers over a specified interval. Similar to Numpy")
 .set_num_inputs(0)
diff --git a/src/operator/tensor/init_op.cu b/src/operator/tensor/init_op.cu
index 5829ff2237e8..ee6963bd87c0 100644
--- a/src/operator/tensor/init_op.cu
+++ b/src/operator/tensor/init_op.cu
@@ -62,7 +62,10 @@ NNVM_REGISTER_OP(_full)
 .set_attr<FCompute>("FCompute<gpu>", InitFillWithScalarCompute<gpu>);
 
 NNVM_REGISTER_OP(_arange)
-.set_attr<FCompute>("FCompute<gpu>", RangeCompute<gpu>);
+.set_attr<FCompute>("FCompute<gpu>", RangeCompute<gpu, RangeParam>);
+
+NNVM_REGISTER_OP(_contrib_arange_like)
+.set_attr<FCompute>("FCompute<gpu>", RangeCompute<gpu, RangeLikeParam>);
 
 NNVM_REGISTER_OP(_linspace)
 .set_attr<FCompute>("FCompute<gpu>", LinspaceCompute<gpu>);
diff --git a/src/operator/tensor/init_op.h b/src/operator/tensor/init_op.h
index c7a10541adbd..51c84363489a 100644
--- a/src/operator/tensor/init_op.h
+++ b/src/operator/tensor/init_op.h
@@ -174,6 +174,40 @@ struct RangeParam : public dmlc::Parameter<RangeParam> {
   }
 };
 
+struct RangeLikeParam : public dmlc::Parameter<RangeLikeParam> {
+  double start;
+  double step;
+  int repeat;
+  std::string ctx;
+  int dtype;
+  dmlc::optional<int> axis;
+
+  DMLC_DECLARE_PARAMETER(RangeLikeParam) {
+    DMLC_DECLARE_FIELD(start)
+    .set_default(0)
+    .describe("Start of interval. The interval includes this value. The default start value is 0.");
+    DMLC_DECLARE_FIELD(step)
+    .set_default(1)
+    .describe("Spacing between values.");
+    DMLC_DECLARE_FIELD(repeat)
+    .set_default(1)
+    .describe("The repeating time of all elements."
+              " E.g repeat=3, the element a will be repeated three times --> a, a, a.");
+    DMLC_DECLARE_FIELD(ctx)
+    .set_default("")
+    .describe("Context of output, in format [cpu|gpu|cpu_pinned](n)."
+              "Only used for imperative calls.");
+    DMLC_DECLARE_FIELD(dtype).set_default(mshadow::kFloat32)
+    MXNET_ADD_ALL_TYPES
+    .describe("Target data type.");
+    DMLC_DECLARE_FIELD(axis)
+    .set_default(dmlc::optional<int>())
+    .describe("Arange elements according to the size of a certain axis of input array."
+              " The negative numbers are interpreted counting from the backward."
+              " If not provided, will arange elements according to the input shape.");
+  }
+};
+
 /*! \brief Initialize and fill output with an arbitrary value */
 struct InitOpWithScalarParam : dmlc::Parameter<InitOpWithScalarParam> {
   mxnet::TShape shape;
@@ -250,12 +284,12 @@ inline bool InitShape(const nnvm::NodeAttrs& attrs,
   return shape_is_known(out_attrs->at(0));
 }
 
-template<typename ParamType>
+template<typename ParamType, int num_in = 0U>
 inline bool InitType(const nnvm::NodeAttrs& attrs,
                        std::vector<int> *in_attrs,
                        std::vector<int> *out_attrs) {
   const ParamType& param = nnvm::get<ParamType>(attrs.parsed);
-  CHECK_EQ(in_attrs->size(), 0U);
+  CHECK_EQ(in_attrs->size(), num_in);
   CHECK_EQ(out_attrs->size(), 1U);
   TYPE_ASSIGN_CHECK(*out_attrs, 0, param.dtype);
   return true;
@@ -493,7 +527,7 @@ struct range_fwd {
   }
 };
 
-template<typename xpu>
+template<typename xpu, typename ParamType>
 void RangeCompute(const nnvm::NodeAttrs& attrs,
                   const OpContext& ctx,
                   const std::vector<TBlob>& inputs,
@@ -501,7 +535,7 @@ void RangeCompute(const nnvm::NodeAttrs& attrs,
                   const std::vector<TBlob>& outputs) {
   using namespace mxnet_op;
   Stream<xpu> *s = ctx.get_stream<xpu>();
-  const RangeParam& param = nnvm::get<RangeParam>(attrs.parsed);
+  const ParamType& param = nnvm::get<ParamType>(attrs.parsed);
   MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
       // Force unsigned params to take two's complement form on ARM to ensure consistency with x86
       // results.  Casting negative floats to unsigned types is undefined in the CPP standard.
@@ -588,6 +622,28 @@ inline bool LinspaceShape(const nnvm::NodeAttrs& attrs,
   return true;
 }
 
+inline bool RangeLikeShape(const nnvm::NodeAttrs& attrs,
+                           mxnet::ShapeVector *in_attrs,
+                           mxnet::ShapeVector *out_attrs) {
+  const RangeLikeParam& param = nnvm::get<RangeLikeParam>(attrs.parsed);
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  int real_axis = -1;
+  if (param.axis.has_value()) {
+    real_axis = param.axis.value() < 0 ?
+        (param.axis.value() + (*in_attrs)[0].ndim()) : param.axis.value();
+    CHECK(real_axis >=0 && real_axis < (*in_attrs)[0].ndim())
+        << "cannot handle param.axis " << param.axis.value() << ".";
+  }
+  if (real_axis == -1) {
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, (*in_attrs)[0]);
+  } else {
+    const index_t out_size = (*in_attrs)[0][real_axis];
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, mxnet::TShape({static_cast<nnvm::dim_t>(out_size)}));
+  }
+  return true;
+}
+
 }  // namespace op
 }  // namespace mxnet
 
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index ca9ecc45621b..b550139d341b 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -4042,11 +4042,33 @@ def test_arange_inferstop():
         exe.forward()
         assert_almost_equal(exe.outputs[0].asnumpy(), np.array([0,1,2,3,4]))
 
+    def test_arange_like():
+        shape_list = [(10,), (10, 20), (10, 20, 30), (10, 20, 30, 40)]
+        axis_list = [0, -1]
+        for sh in shape_list:
+            for axis in axis_list:
+                val = np.random.rand(*sh)
+                data = mx.nd.array(val)
+                nd_out = mx.nd.contrib.arange_like(data, start=0, axis=axis)
+                np_out = np.arange(start=0, stop=sh[axis])
+                assert_almost_equal(nd_out.asnumpy(), np_out)
+
+    def test_arange_like_without_axis():
+        shape_list = [(10,), (10, 20), (10, 20, 30), (10, 20, 30, 40)]
+        for sh in shape_list:
+            val = np.random.rand(*sh)
+            data = mx.nd.array(val)
+            nd_out = mx.nd.contrib.arange_like(data, start=0)
+            np_out = np.arange(start=0, stop=val.size)
+            assert_almost_equal(nd_out.asnumpy(), np_out.reshape(sh))
+
     test_basic_val_init(mx.sym.zeros, np.zeros, (3, 4), np.float32)
     test_basic_val_init(mx.sym.ones, np.ones, 3, np.int32)
     test_basic_val_init(mx.sym.ones, np.ones, (2, 2, 3), np.float16)
     test_arange()
     test_arange_inferstop()
+    test_arange_like()
+    test_arange_like_without_axis()
 
 
 @with_seed()

From a6ed12fe4f49ffceb12b25244a16fde42cfa11b6 Mon Sep 17 00:00:00 2001
From: Disi A <adis@live.cn>
Date: Sun, 7 Jul 2019 07:07:20 -0400
Subject: [PATCH 044/813] Had a few PRs merged. Hope to become an official
 contributor and potentially a commiter. (#15451)

---
 CONTRIBUTORS.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md
index 43d988037de9..750a22a806d1 100644
--- a/CONTRIBUTORS.md
+++ b/CONTRIBUTORS.md
@@ -244,6 +244,7 @@ List of Contributors
 * [Shoubhik Bhattacharya](https://github.com/shoubhik)
 * [Rohit Srivastava](https://github.com/access2rohit)
 * [Caner Turkmen](https://github.com/canerturkmen)
+* [Disi A](https://github.com/adis300)
 
 Label Bot
 ---------

From a3ae30979989f488cd933c2fbb6416a4e187de9d Mon Sep 17 00:00:00 2001
From: kshitij12345 <kshitijkalambarkar@gmail.com>
Date: Mon, 8 Jul 2019 02:35:09 +0530
Subject: [PATCH 045/813] [MXNET-978] Higher Order Gradient Support
 `reciprocal`, `abs`. (#15413)

* add higher order support for reciprocal and abs

* add relevant tests

* address comments

* fix extra line in tests.
* fix missing space.
* fix incorrect comment.
---
 .../tensor/elemwise_unary_op_basic.cc         | 54 ++++++++++++++++++-
 .../python/unittest/test_higher_order_grad.py | 27 ++++++++++
 2 files changed, 79 insertions(+), 2 deletions(-)

diff --git a/src/operator/tensor/elemwise_unary_op_basic.cc b/src/operator/tensor/elemwise_unary_op_basic.cc
index 26c74085dbe6..6da384d3679b 100644
--- a/src/operator/tensor/elemwise_unary_op_basic.cc
+++ b/src/operator/tensor/elemwise_unary_op_basic.cc
@@ -717,7 +717,38 @@ Example::
 
 MXNET_OPERATOR_REGISTER_BINARY(_backward_reciprocal)
 .set_attr<FCompute>("FCompute<cpu>",
-  ElemwiseBinaryOp::Compute<cpu, unary_bwd<mshadow_op::reciprocal_grad> >);
+  ElemwiseBinaryOp::Compute<cpu, unary_bwd<mshadow_op::reciprocal_grad> >)
+.set_attr<nnvm::FGradient>("FGradient",
+  [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
+    // ograds[0]: dL/dxgrad
+    // inputs[0]: dL/dy
+    // inputs[1]: x
+    // f(x) = y = 1/x
+    // f'(x) = -1/x^2
+    // f''(x) = 2/x^3 = -2 * (f'(x) * f(x))
+
+    const std::unordered_map<std::string, std::string> args = {{"scalar", "-2.0"}};
+
+    auto dydx_mul_dldy = nnvm::NodeEntry{n};  // f'(x) * head_grads
+    auto dydx = MakeNode("elemwise_div", n->attrs.name + "_dydx",
+                         {dydx_mul_dldy, n->inputs[0]}, nullptr, &n);
+    auto fx = MakeNode("reciprocal", n->attrs.name + "_fx",
+                       {n->inputs[1]}, nullptr, &n);
+
+    auto d2ydx2_mid = MakeNode("elemwise_mul", n->attrs.name + "_d2ydx2_mid",
+                               {dydx_mul_dldy, nnvm::NodeEntry{fx}}, nullptr, &n);
+
+    auto d2ydx2 = MakeNode("_mul_scalar", n->attrs.name + "_d2ydx2",
+                           {nnvm::NodeEntry{d2ydx2_mid}}, &args, &n);
+
+    std::vector<nnvm::NodeEntry> ret;
+
+    ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "_backward_grad_grad",
+                             {ograds[0], nnvm::NodeEntry{dydx}}, nullptr, &n));
+    ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "_backward_grad_grad_inp",
+                             {ograds[0], nnvm::NodeEntry{d2ydx2}}, nullptr, &n));
+    return ret;
+});
 
 // abs
 MXNET_OPERATOR_REGISTER_UNARY_WITH_RSP_CSR(abs, cpu, mshadow_op::abs)
@@ -736,7 +767,26 @@ The storage type of ``abs`` output depends upon the input storage type:
 )code" ADD_FILELINE)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_abs"});
 
-MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU(_backward_abs, unary_bwd<mshadow_op::sign>);
+MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU(_backward_abs, unary_bwd<mshadow_op::sign>)
+.set_attr<nnvm::FGradient>("FGradient",
+    [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
+      // ograds[0]: dL/dxgrad
+      // inputs[0]: dL/dy
+      // inputs[1]: x
+      // f(x) -> abs(x)
+      // f'(x) = 1 if x > 0 else -1
+      // f''(x) = 0
+      auto dydx = MakeNode("elemwise_div", n->attrs.name + "_dydx",
+                           {nnvm::NodeEntry{n}, n->inputs[0]}, nullptr, &n);
+
+      std::vector<nnvm::NodeEntry> ret;
+      ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "_backward_grad_grad",
+                                {ograds[0], nnvm::NodeEntry(dydx)}, nullptr, &n));
+      ret.emplace_back(MakeNode("zeros_like", n->attrs.name + "_backward_grad_grad_in",
+                                {n->inputs[1]}, nullptr, &n));
+      return ret;
+    });
+
 
 // sign
 MXNET_OPERATOR_REGISTER_UNARY_WITH_RSP_CSR(sign, cpu, mshadow_op::sign)
diff --git a/tests/python/unittest/test_higher_order_grad.py b/tests/python/unittest/test_higher_order_grad.py
index ad14c5050c1b..0f07d014d435 100644
--- a/tests/python/unittest/test_higher_order_grad.py
+++ b/tests/python/unittest/test_higher_order_grad.py
@@ -107,6 +107,33 @@ def grad_grad_op(x):
 
 
 @with_seed()
+def test_reciprocal():
+    def reciprocal(x):
+        return nd.reciprocal(x)
+
+    def grad_grad_op(x):
+        return 2 / x**3
+
+    for dim in range(1, 5):
+        shape = rand_shape_nd(dim)
+        array = random_arrays(shape)
+        check_second_order_unary(array, reciprocal, grad_grad_op)
+
+
+@with_seed()
+def test_abs():
+    def abs(x):
+        return nd.abs(x)
+
+    def grad_grad_op(x):
+        return nd.zeros_like(x)
+
+    for dim in range(1, 5):
+        shape = rand_shape_nd(dim)
+        array = random_arrays(shape)
+        check_second_order_unary(array, abs, grad_grad_op)
+
+
 def test_sigmoid():
     def sigmoid(x):
         return nd.sigmoid(x)

From 091fece5431b43c931568d279c1dd9d664318c36 Mon Sep 17 00:00:00 2001
From: Wuxun Zhang <wuxun.zhang@intel.com>
Date: Mon, 8 Jul 2019 10:07:37 +0800
Subject: [PATCH 046/813] fix fp32 flatten issue (#15351)

* Fix flatten issue before slice op

* fix cpplint

* address comments

* retrigger CI

* trigger CI

* retrigger CI

* use SupportMKLDNNReshape and update operator list
---
 docs/tutorials/mkldnn/operator_list.md      |   2 +
 src/operator/nn/mkldnn/mkldnn_base-inl.h    |   2 +
 src/operator/nn/mkldnn/mkldnn_flatten.cc    |  87 +++++++++
 src/operator/nn/mkldnn/mkldnn_ops-inl.h     |   9 +-
 src/operator/nn/mkldnn/mkldnn_reshape-inl.h |  68 +++++++
 src/operator/nn/mkldnn/mkldnn_reshape.cc    | 185 +++++++++-----------
 src/operator/tensor/matrix_op.cc            |  20 +--
 tests/python/gpu/test_operator_gpu.py       |  14 ++
 tests/python/mkl/test_mkldnn.py             |  20 +++
 9 files changed, 296 insertions(+), 111 deletions(-)
 create mode 100644 src/operator/nn/mkldnn/mkldnn_flatten.cc
 create mode 100644 src/operator/nn/mkldnn/mkldnn_reshape-inl.h

diff --git a/docs/tutorials/mkldnn/operator_list.md b/docs/tutorials/mkldnn/operator_list.md
index 4958f8d9b602..0ef0f29f4cdc 100644
--- a/docs/tutorials/mkldnn/operator_list.md
+++ b/docs/tutorials/mkldnn/operator_list.md
@@ -44,6 +44,8 @@ To help users understanding MKL-DNN backend better, the following table summariz
 | **elemwise_add**   | 1D-4D input                | Y                        | Y              | Y              |
 | **Concat**         | 1D-4D input                | Y                        | Y              | Y              |
 | **slice**          | 1D-4D input                | N                        | Y              | N              |
+| **Reshape**        | 1D-4D input                | N                        | Y              | N              |
+| **Flatten**        | 1D-4D input                | N                        | Y              | N              |
 | **Quantization**   | 1D-4D input                | N                        | N              | Y              |
 | **Dequantization** | 1D-4D input                | N                        | N              | Y              |
 | **Requantization** | 1D-4D input                | N                        | N              | Y              |
diff --git a/src/operator/nn/mkldnn/mkldnn_base-inl.h b/src/operator/nn/mkldnn/mkldnn_base-inl.h
index 5670983e6aa3..e01b7b14082b 100644
--- a/src/operator/nn/mkldnn/mkldnn_base-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_base-inl.h
@@ -176,6 +176,7 @@ struct DeconvolutionParam;
 struct SoftmaxParam;
 struct SoftmaxOutputParam;
 struct TransposeParam;
+struct ReshapeParam;
 bool SupportMKLDNNAct(const ActivationParam& param);
 bool SupportMKLDNNAct(const ActivationParam& param, const NDArray &input);
 bool SupportQuantizedMKLDNNAct(const ActivationParam &param);
@@ -184,6 +185,7 @@ bool SupportMKLDNNDeconv(const DeconvolutionParam& params, const NDArray &input)
 bool SupportMKLDNNSoftmax(const SoftmaxParam& param, const NDArray &input, const NDArray &output);
 bool SupportMKLDNNSoftmaxOutput(const SoftmaxOutputParam &param);
 bool SupportMKLDNNTranspose(const TransposeParam& param, const NDArray &data);
+bool SupportMKLDNNReshape(const ReshapeParam &param, const NDArray &data);
 }  // namespace op
 
 static int GetTypeSize(int dtype) {
diff --git a/src/operator/nn/mkldnn/mkldnn_flatten.cc b/src/operator/nn/mkldnn/mkldnn_flatten.cc
new file mode 100644
index 000000000000..fdc02f960009
--- /dev/null
+++ b/src/operator/nn/mkldnn/mkldnn_flatten.cc
@@ -0,0 +1,87 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file mkldnn_flatten.cc
+ * \brief Implement flatten operator by using mkldnn reorder primitive
+ * \author Wuxun Zhang
+*/
+
+#if MXNET_USE_MKLDNN == 1
+
+#include "mkldnn_reshape-inl.h"
+
+namespace mxnet {
+namespace op {
+
+class MKLDNNFlattenFwd : public MKLDNNReshapeFwd {
+ public:
+  explicit MKLDNNFlattenFwd(const OpReqType &req,
+                            const NDArray &input,
+                            const NDArray &output)
+    : MKLDNNReshapeFwd(req, input, output) {}
+};
+
+static MKLDNNFlattenFwd &GetFlattenForward(const OpReqType &req,
+                                           const NDArray &input,
+                                           const NDArray &output) {
+#if DMLC_CXX11_THREAD_LOCAL
+  static thread_local std::unordered_map<OpSignature,
+                                         MKLDNNFlattenFwd, OpHash> fwds;
+#else
+  static MX_THREAD_LOCAL std::unordered_map<OpSignature,
+                                            MKLDNNFlattenFwd, OpHash> fwds;
+#endif
+  OpSignature key;
+  key.AddSign(req);
+  key.AddSign(input);
+
+  auto it = fwds.find(key);
+  if (it == fwds.end()) {
+    MKLDNNFlattenFwd fwd(req, input, output);
+    it = AddToCache(&fwds, key, fwd);
+  }
+  return it->second;
+}
+
+void MKLDNNFlattenForward(const nnvm::NodeAttrs &attrs,
+                          const OpContext &ctx,
+                          const NDArray &input,
+                          const OpReqType &req,
+                          const NDArray &output) {
+  if (req == kNullOp) return;
+  CHECK_NE(req, kAddTo) << "kAddTo is not supported yet";
+
+  auto fwd = GetFlattenForward(req, input, output);
+  auto ws_size = fwd.GetWorkspaceSize();
+  void* ws_ptr = nullptr;
+  if (ws_size) {
+    mshadow::Stream<cpu> *s = ctx.get_stream<cpu>();
+    mshadow::Tensor<cpu, 1, char> ws = ctx.requested[0]
+      .get_space_typed<cpu, 1, char>(mshadow::Shape1(ws_size), s);
+    ws_ptr = reinterpret_cast<void*>(ws.dptr_);
+  }
+
+  fwd.Execute(input, output, ws_ptr);
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif
diff --git a/src/operator/nn/mkldnn/mkldnn_ops-inl.h b/src/operator/nn/mkldnn/mkldnn_ops-inl.h
index 2699a02f9b9b..502abff6231b 100644
--- a/src/operator/nn/mkldnn/mkldnn_ops-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_ops-inl.h
@@ -119,12 +119,17 @@ void MKLDNNTransposeForward(const nnvm::NodeAttrs& attrs,
                             const OpReqType &req,
                             const NDArray &output);
 
-void MKLDNNReshapeForward(const nnvm::NodeAttrs &attrs,
+void MKLDNNReshapeForward(const nnvm::NodeAttrs& attrs,
                           const OpContext &ctx,
-                          const NDArray &data,
+                          const NDArray &input,
                           const OpReqType &req,
                           const NDArray &output);
 
+void MKLDNNFlattenForward(const nnvm::NodeAttrs &attrs,
+                          const OpContext &ctx,
+                          const NDArray &input,
+                          const OpReqType &req,
+                          const NDArray &output);
 }  // namespace op
 }  // namespace mxnet
 #endif  // MXNET_USE_MKLDNN == 1
diff --git a/src/operator/nn/mkldnn/mkldnn_reshape-inl.h b/src/operator/nn/mkldnn/mkldnn_reshape-inl.h
new file mode 100644
index 000000000000..63e367b4dc7f
--- /dev/null
+++ b/src/operator/nn/mkldnn/mkldnn_reshape-inl.h
@@ -0,0 +1,68 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file mkldnn_reshape-inl.h
+ * \brief Function definition of mkldnn reshape operator
+ */
+
+#ifndef MXNET_OPERATOR_NN_MKLDNN_MKLDNN_RESHAPE_INL_H_
+#define MXNET_OPERATOR_NN_MKLDNN_MKLDNN_RESHAPE_INL_H_
+
+#if MXNET_USE_MKLDNN == 1
+#include <vector>
+#include "mkldnn_base-inl.h"
+#include "../../tensor/matrix_op-inl.h"
+
+namespace mxnet {
+namespace op {
+
+class MKLDNNReshapeFwd {
+ protected:
+  std::shared_ptr<mkldnn::memory> data_;
+  std::shared_ptr<mkldnn::memory> out_;
+  std::shared_ptr<mkldnn::memory> temp_;
+  std::vector<mkldnn::primitive> prims_;
+  bool needInvalidateInput = false;
+
+ public:
+  MKLDNNReshapeFwd(const OpReqType &req,
+                   const NDArray &input,
+                   const NDArray &output);
+  int GetWorkspaceSize();
+  void SetNewMem(const NDArray &input,
+                 const NDArray &output,
+                 void* workspace = nullptr);
+  void Execute(const NDArray &input,
+               const NDArray &output,
+               void* workspace = nullptr);
+};
+
+typedef ParamOpSign<ReshapeParam> MKLDNNReshapeSignature;
+MKLDNNReshapeFwd &GetReshapeForward(const ReshapeParam& param,
+                                    const OpReqType &req,
+                                    const NDArray &input,
+                                    const NDArray &output);
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_USE_MKLDNN == 1
+#endif  // MXNET_OPERATOR_NN_MKLDNN_MKLDNN_RESHAPE_INL_H_
diff --git a/src/operator/nn/mkldnn/mkldnn_reshape.cc b/src/operator/nn/mkldnn/mkldnn_reshape.cc
index 4f1d67a8ff9e..063c85dae39a 100644
--- a/src/operator/nn/mkldnn/mkldnn_reshape.cc
+++ b/src/operator/nn/mkldnn/mkldnn_reshape.cc
@@ -26,7 +26,7 @@
 #if MXNET_USE_MKLDNN == 1
 
 #include <mkldnn.hpp>
-#include "../../tensor/matrix_op-inl.h"
+#include "mkldnn_reshape-inl.h"
 
 namespace mxnet {
 namespace op {
@@ -43,117 +43,106 @@ bool SupportMKLDNNReshape(const ReshapeParam &param,
   return true;
 }
 
-typedef ParamOpSign<ReshapeParam> MKLDNNReshapeSignature;
-
-class MKLDNNReshapeForward {
-  std::shared_ptr<mkldnn::memory> data_;
-  std::shared_ptr<mkldnn::memory> out_;
-  std::shared_ptr<mkldnn::memory> temp_;
-  std::vector<mkldnn::primitive> prims_;
-
-  bool needInvalidateInput = false;
-
- public:
-  MKLDNNReshapeForward(const ReshapeParam &param,
-                       const OpReqType &req,
-                       const NDArray &input,
-                       const NDArray &output) {
-    auto engine = CpuEngine::Get()->get_engine();
-
-    // data_
-    auto in_mem = input.GetMKLDNNData();
-    auto in_pd = in_mem->get_primitive_desc();
-    data_ = std::make_shared<mkldnn::memory>(in_pd, nullptr);
-
-    // temp_
-    auto temp_dims = mkldnn::memory::dims(input.shape().begin(), input.shape().end());
-    auto temp_type = static_cast<mkldnn::memory::data_type>(in_pd.desc().data.data_type);
-    auto temp_fmt = static_cast<mkldnn::memory::format>(GetDefaultFormat(in_pd.desc()));
-    auto temp_desc = mkldnn::memory::desc(temp_dims, temp_type, temp_fmt);
-    auto temp_pd = mkldnn::memory::primitive_desc(temp_desc, engine);
-    temp_ = std::make_shared<mkldnn::memory>(temp_pd, nullptr);
-
-    // destination
-    out_ = std::make_shared<mkldnn::memory>(temp_pd, nullptr);
-
-    if (req == kWriteInplace) {
-      // If the input has MKL-DNN internal layout, we need reorder it to a temporal buffer with
-      // default layout and copy from the temporal buffer back to output buffer which has the same
-      // address with input buffer.
-      // If the input has default layout, then nothing need to do.
-      if (input.IsMKLDNNData()) {
-        prims_.push_back(mkldnn::reorder(*data_, *temp_));   // reorder to default
-        prims_.push_back(mkldnn::reorder(*temp_, *out_));    // copy back
-        needInvalidateInput = true;
-      }
-    } else if (req == kWriteTo) {
-      if (input.IsMKLDNNData()) {
-        prims_.push_back(mkldnn::reorder(*data_, *temp_));   // reorder to default
-        prims_.push_back(mkldnn::reorder(*temp_, *out_));    // copy to the output buffer
-        needInvalidateInput = false;
-      } else {
-        prims_.push_back(mkldnn::reorder(*data_, *out_));    // copy directly from input to output
-        needInvalidateInput = false;
-      }
+MKLDNNReshapeFwd::MKLDNNReshapeFwd(const OpReqType &req,
+                                   const NDArray &input,
+                                   const NDArray &output) {
+  auto engine = CpuEngine::Get()->get_engine();
+
+  // data_
+  auto in_mem = input.GetMKLDNNData();
+  auto in_pd = in_mem->get_primitive_desc();
+  data_ = std::make_shared<mkldnn::memory>(in_pd, nullptr);
+
+  // temp_
+  auto temp_dims = mkldnn::memory::dims(input.shape().begin(), input.shape().end());
+  auto temp_type = static_cast<mkldnn::memory::data_type>(in_pd.desc().data.data_type);
+  auto temp_fmt = static_cast<mkldnn::memory::format>(GetDefaultFormat(in_pd.desc()));
+  auto temp_desc = mkldnn::memory::desc(temp_dims, temp_type, temp_fmt);
+  auto temp_pd = mkldnn::memory::primitive_desc(temp_desc, engine);
+  temp_ = std::make_shared<mkldnn::memory>(temp_pd, nullptr);
+
+  // destination
+  out_ = std::make_shared<mkldnn::memory>(temp_pd, nullptr);
+
+  if (req == kWriteInplace) {
+    // If the input has MKL-DNN internal layout, we need reorder it to a temporal buffer with
+    // default layout and copy from the temporal buffer back to output buffer which has the same
+    // address with input buffer.
+    // If the input has default layout, then nothing need to do.
+    if (input.IsMKLDNNData()) {
+      prims_.push_back(mkldnn::reorder(*data_, *temp_));   // reorder to default
+      prims_.push_back(mkldnn::reorder(*temp_, *out_));    // copy back
+      needInvalidateInput = true;
+    }
+  } else if (req == kWriteTo) {
+    if (input.IsMKLDNNData()) {
+      prims_.push_back(mkldnn::reorder(*data_, *temp_));   // reorder to default
+      prims_.push_back(mkldnn::reorder(*temp_, *out_));    // copy to the output buffer
+      needInvalidateInput = false;
     } else {
-      LOG(FATAL) << "not supported req type: " << req;
+      prims_.push_back(mkldnn::reorder(*data_, *out_));    // copy directly from input to output
+      needInvalidateInput = false;
     }
+  } else {
+    LOG(FATAL) << "not supported req type: " << req;
   }
+}
 
-  int GetWorkspaceSize() {
-    return temp_ ? temp_->get_primitive_desc().get_size() : 0;
-  }
+int MKLDNNReshapeFwd::GetWorkspaceSize() {
+  return temp_ ? temp_->get_primitive_desc().get_size() : 0;
+}
 
-  void SetNewMem(const NDArray &input, const NDArray &output, void* workspace = nullptr) {
-    if (input.IsMKLDNNData()) {
-      this->data_->set_data_handle(input.GetMKLDNNData()->get_data_handle());
-    } else {
-      MSHADOW_TYPE_SWITCH(input.dtype(), DTYPE, {
-        this->data_->set_data_handle(input.data().dptr<DTYPE>());
-      })
-    }
+void MKLDNNReshapeFwd::SetNewMem(const NDArray &input,
+                                 const NDArray &output,
+                                 void* workspace) {
+  if (input.IsMKLDNNData()) {
+    this->data_->set_data_handle(input.GetMKLDNNData()->get_data_handle());
+  } else {
+    MSHADOW_TYPE_SWITCH(input.dtype(), DTYPE, {
+      this->data_->set_data_handle(input.data().dptr<DTYPE>());
+    })
+  }
 
-    if (output.IsMKLDNNData()) {
-      this->out_->set_data_handle(output.GetMKLDNNData()->get_data_handle());
-    } else {
-      MSHADOW_TYPE_SWITCH(output.dtype(), DTYPE, {
-        this->out_->set_data_handle(output.data().dptr<DTYPE>());
-      })
-    }
+  if (output.IsMKLDNNData()) {
+    this->out_->set_data_handle(output.GetMKLDNNData()->get_data_handle());
+  } else {
+    MSHADOW_TYPE_SWITCH(output.dtype(), DTYPE, {
+      this->out_->set_data_handle(output.data().dptr<DTYPE>());
+    })
+  }
 
-    if (workspace) {
-      this->temp_->set_data_handle(workspace);
-    }
+  if (workspace) {
+    this->temp_->set_data_handle(workspace);
   }
+}
 
-  void Execute(const NDArray &input,
-               const NDArray &output,
-               void* workspace = nullptr) {
-    // set memory handles
-    SetNewMem(input, output, workspace);
-    // register primitives
-    auto stream = MKLDNNStream::Get();
-    for (auto &v : this->prims_) {
-      stream->RegisterPrim(v);
-    }
-    stream->Submit();
-    // invalidate mkldnn memory in input
-    if (needInvalidateInput) {
-      const_cast<NDArray &>(input).InvalidateMKLDNNData();
-    }
+void MKLDNNReshapeFwd::Execute(const NDArray &input,
+                               const NDArray &output,
+                               void* workspace) {
+  // set memory handles
+  SetNewMem(input, output, workspace);
+  // register primitives
+  auto stream = MKLDNNStream::Get();
+  for (auto &v : this->prims_) {
+    stream->RegisterPrim(v);
   }
-};
+  stream->Submit();
+  // invalidate mkldnn memory in input
+  if (needInvalidateInput) {
+    const_cast<NDArray &>(input).InvalidateMKLDNNData();
+  }
+}
 
-static MKLDNNReshapeForward &GetReshapeForward(const ReshapeParam& param,
-                                               const OpReqType &req,
-                                               const NDArray &input,
-                                               const NDArray &output) {
+MKLDNNReshapeFwd &GetReshapeForward(const ReshapeParam& param,
+                                    const OpReqType &req,
+                                    const NDArray &input,
+                                    const NDArray &output) {
 #if DMLC_CXX11_THREAD_LOCAL
   static thread_local std::unordered_map<MKLDNNReshapeSignature,
-                                         MKLDNNReshapeForward, OpHash> fwds;
+                                         MKLDNNReshapeFwd, OpHash> fwds;
 #else
   static MX_THREAD_LOCAL std::unordered_map<MKLDNNReshapeSignature,
-                                            MKLDNNReshapeForward, OpHash> fwds;
+                                            MKLDNNReshapeFwd, OpHash> fwds;
 #endif
   MKLDNNReshapeSignature key(param);
   key.AddSign(req);
@@ -162,7 +151,7 @@ static MKLDNNReshapeForward &GetReshapeForward(const ReshapeParam& param,
 
   auto it = fwds.find(key);
   if (it == fwds.end()) {
-    MKLDNNReshapeForward fwd(param, req, input, output);
+    MKLDNNReshapeFwd fwd(req, input, output);
     it = AddToCache(&fwds, key, fwd);
   }
   return it->second;
diff --git a/src/operator/tensor/matrix_op.cc b/src/operator/tensor/matrix_op.cc
index b4abc9f5974a..c2bcb29193a7 100644
--- a/src/operator/tensor/matrix_op.cc
+++ b/src/operator/tensor/matrix_op.cc
@@ -111,12 +111,13 @@ static void ReshapeComputeExCPU(const nnvm::NodeAttrs& attrs,
                                 const std::vector<NDArray>& inputs,
                                 const std::vector<OpReqType>& req,
                                 const std::vector<NDArray>& outputs) {
+  const ReshapeParam& param = nnvm::get<ReshapeParam>(attrs.parsed);
   CHECK_EQ(inputs.size(), 1U);
   CHECK_EQ(outputs.size(), 1U);
   // If inputs are supposed to be in MKLDNN format and
   // MKLDNNsupport the data type or the shape. Then convert
   // it to the output format and shape
-  if (SupportMKLDNNArray(inputs[0].dtype(), inputs[0].shape())) {
+  if (SupportMKLDNNReshape(param, inputs[0])) {
     MKLDNNReshapeForward(attrs, ctx, inputs[0], req[0], outputs[0]);
     return;
   }
@@ -233,12 +234,9 @@ static void FlattenEx(const nnvm::NodeAttrs& attrs,
   CHECK_EQ(inputs.size(), 1U);
   CHECK_EQ(outputs.size(), 1U);
 #if MXNET_USE_MKLDNN == 1
-  if (inputs[0].IsMKLDNNData()) {
-    MKLDNNCopy(attrs, ctx, inputs[0], req[0], outputs[0]);
-    // If the output is a special MKLDNN layout and the number of dimensions
-    // is larger than 2, we should use the default layout.
-    if (outputs[0].IsMKLDNNData() && inputs[0].shape().ndim() > 2)
-      const_cast<NDArray &>(outputs[0]).Reorder2Default();
+  auto data_ndim = inputs[0].shape().ndim();
+  if (data_ndim <= 4 && inputs[0].dtype() == mshadow::kFloat32) {
+    MKLDNNFlattenForward(attrs, ctx, inputs[0], req[0], outputs[0]);
     return;
   } else {
     // This happens if inputs are supposed to be in MKLDNN format
@@ -252,10 +250,10 @@ static void FlattenEx(const nnvm::NodeAttrs& attrs,
 
 #if MXNET_USE_MKLDNN == 1
 static inline bool FlattenStorageType(const nnvm::NodeAttrs& attrs,
-                                   const int dev_mask,
-                                   DispatchMode* dispatch_mode,
-                                   std::vector<int> *in_attrs,
-                                   std::vector<int> *out_attrs) {
+                                      const int dev_mask,
+                                      DispatchMode* dispatch_mode,
+                                      std::vector<int> *in_attrs,
+                                      std::vector<int> *out_attrs) {
   CHECK_EQ(in_attrs->size(), 1);
   CHECK_EQ(out_attrs->size(), 1);
   return MKLDNNStorageType(attrs, dev_mask, true, dispatch_mode, in_attrs,
diff --git a/tests/python/gpu/test_operator_gpu.py b/tests/python/gpu/test_operator_gpu.py
index 064f783ec6c8..5b4f81d96065 100644
--- a/tests/python/gpu/test_operator_gpu.py
+++ b/tests/python/gpu/test_operator_gpu.py
@@ -1129,6 +1129,20 @@ def test_pooling_full_2d_type(pool_type):
     test_pooling_full_2d_type('sum')
 
 
+@with_seed()
+def test_flatten_slice_after_conv():
+    ctx_list = []
+
+    data = mx.sym.Variable('conv_data')
+    conv = mx.symbol.Convolution(data=data, name='conv', num_filter=16, kernel=(3,3), stride=(1,1))
+    flatten = mx.symbol.flatten(data=conv)
+    slice_sym = mx.symbol.slice(data=flatten, begin=0, end=1)
+
+    ctx_list = [{'ctx': mx.gpu(0), 'conv_data': (2, 16, 16, 16), 'type_dict': {'conv_data': np.float32}},
+                {'ctx': mx.cpu(0), 'conv_data': (2, 16, 16, 16), 'type_dict': {'conv_data': np.float32}}]
+    check_consistency(slice_sym, ctx_list)
+
+
 @with_seed()
 def test_global_pooling():
     def test_1d_pooling(pool_type, p_value=2):
diff --git a/tests/python/mkl/test_mkldnn.py b/tests/python/mkl/test_mkldnn.py
index 662edcfeb739..3e623b59977d 100644
--- a/tests/python/mkl/test_mkldnn.py
+++ b/tests/python/mkl/test_mkldnn.py
@@ -233,6 +233,26 @@ def hybrid_forward(self, F, x, *args, **kwargs):
     mx.test_utils.assert_almost_equal(out1.asnumpy(), out2.asnumpy(), rtol=1e-5, atol=1e-6)
 
 
+@with_seed()
+def test_flatten_slice_after_conv():
+    data = mx.symbol.Variable('data')
+    weight = mx.symbol.Variable('weight')
+    bias = mx.symbol.Variable('bias')
+    conv1= mx.symbol.Convolution(data = data, weight=weight, bias=bias, name='conv1', num_filter=64, kernel=(3,3), stride=(1,1))
+    flatten1 = mx.symbol.flatten(data = conv1)
+    slice1 = mx.symbol.slice(data = flatten1, begin=0, end=1)
+
+    shape = (2, 16, 16, 16)
+    val = np.random.rand(2, 16, 16, 16).astype(np.float32)
+    exe = slice1.simple_bind(Context.default_ctx, data=shape)
+    exe.arg_arrays[0][:] = val
+    exe.arg_arrays[1][:] = np.random.normal(size=exe.arg_arrays[1].shape)
+    exe.arg_arrays[2][:] = np.random.normal(size=exe.arg_arrays[2].shape)
+    p = exe.forward(is_train=False)
+    p[0].wait_to_read()
+    print(p[0])
+
+
 def test_mkldnn_sum_inplace_with_cpu_layout():
 
     x_shape = (32, 3, 224, 224)

From 1ae73de6915119c2d453d1e0fe163aa6b353f30f Mon Sep 17 00:00:00 2001
From: suyz526 <35927539+suyz526@users.noreply.github.com>
Date: Mon, 8 Jul 2019 20:30:34 +0200
Subject: [PATCH 047/813] fix doc for sort and argsort (#15317)

---
 src/operator/tensor/ordering_op.cc | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/operator/tensor/ordering_op.cc b/src/operator/tensor/ordering_op.cc
index e2f014d1ad41..b0ade2076a2c 100644
--- a/src/operator/tensor/ordering_op.cc
+++ b/src/operator/tensor/ordering_op.cc
@@ -114,7 +114,7 @@ Examples::
              [ 1.,  3.]]
 
   // flattens and then sorts
-  sort(x) = [ 1.,  1.,  3.,  4.]
+  sort(x, axis=None) = [ 1.,  1.,  3.,  4.]
 
   // sorts along the first axis
   sort(x, axis=0) = [[ 1.,  1.],
@@ -173,7 +173,7 @@ Examples::
                         [ 0.,  1.,  0.]]
 
   // flatten and then sort
-  argsort(x) = [ 3.,  1.,  5.,  0.,  4.,  2.]
+  argsort(x, axis=None) = [ 3.,  1.,  5.,  0.,  4.,  2.]
 )code" ADD_FILELINE)
 .set_num_inputs(1)
 .set_num_outputs(1)

From 09c71bf3144b09a28b9d09d33703a3dcbf4ca9a5 Mon Sep 17 00:00:00 2001
From: Lanking <lanking520@live.com>
Date: Mon, 8 Jul 2019 11:48:30 -0700
Subject: [PATCH 048/813] Add Sparse NDArray support for Scala (#15378)

* add Sparse Support

* add imperative invoke sparse support

* add retain method and comments

* add getData method

* add Sparse NDIter test

* remove debug line
---
 .../main/scala/org/apache/mxnet/DType.scala   |  17 +-
 .../scala/org/apache/mxnet/Executor.scala     |   9 +-
 .../main/scala/org/apache/mxnet/LibInfo.scala |  27 ++-
 .../main/scala/org/apache/mxnet/NDArray.scala |  65 +++++-
 .../scala/org/apache/mxnet/SparseFormat.scala |  25 +++
 .../org/apache/mxnet/SparseNDArray.scala      | 196 ++++++++++++++++++
 .../scala/org/apache/mxnet/NDArraySuite.scala |  16 ++
 .../org/apache/mxnet/SparseNDArraySuite.scala |  93 +++++++++
 .../native/org_apache_mxnet_native_c_api.cc   |  75 ++++++-
 .../native/org_apache_mxnet_native_c_api.h    |  48 ++++-
 10 files changed, 543 insertions(+), 28 deletions(-)
 create mode 100644 scala-package/core/src/main/scala/org/apache/mxnet/SparseFormat.scala
 create mode 100644 scala-package/core/src/main/scala/org/apache/mxnet/SparseNDArray.scala
 create mode 100644 scala-package/core/src/test/scala/org/apache/mxnet/SparseNDArraySuite.scala

diff --git a/scala-package/core/src/main/scala/org/apache/mxnet/DType.scala b/scala-package/core/src/main/scala/org/apache/mxnet/DType.scala
index f3a8e8e9a4a5..1d5cc2847ac0 100644
--- a/scala-package/core/src/main/scala/org/apache/mxnet/DType.scala
+++ b/scala-package/core/src/main/scala/org/apache/mxnet/DType.scala
@@ -24,26 +24,17 @@ object DType extends Enumeration {
   val Float16 = Value(2, "float16")
   val UInt8 = Value(3, "uint8")
   val Int32 = Value(4, "int32")
+  val Int8 = Value(5, "int8")
+  val Int64 = Value(6, "int64")
   val Unknown = Value(-1, "unknown")
   private[mxnet] def numOfBytes(dtype: DType): Int = {
     dtype match {
-      case DType.UInt8 => 1
+      case DType.UInt8 | DType.Int8 => 1
       case DType.Int32 => 4
       case DType.Float16 => 2
       case DType.Float32 => 4
-      case DType.Float64 => 8
+      case DType.Float64 | DType.Int64 => 8
       case DType.Unknown => 0
     }
   }
-  private[mxnet] def getType(dtypeStr: String): DType = {
-    dtypeStr match {
-      case "UInt8" => DType.UInt8
-      case "Int32" => DType.Int32
-      case "Float16" => DType.Float16
-      case "Float32" => DType.Float32
-      case "Float64" => DType.Float64
-      case _ => throw new IllegalArgumentException(
-        s"DType: $dtypeStr not found! please set it in DType.scala")
-    }
-  }
 }
diff --git a/scala-package/core/src/main/scala/org/apache/mxnet/Executor.scala b/scala-package/core/src/main/scala/org/apache/mxnet/Executor.scala
index b0fae0f9d58d..6365f9cb4645 100644
--- a/scala-package/core/src/main/scala/org/apache/mxnet/Executor.scala
+++ b/scala-package/core/src/main/scala/org/apache/mxnet/Executor.scala
@@ -159,7 +159,14 @@ class Executor private[mxnet](private[mxnet] val handle: ExecutorHandle,
   private def getOutputs: Array[NDArray] = {
     val ndHandles = ArrayBuffer[NDArrayHandle]()
     checkCall(_LIB.mxExecutorOutputs(handle, ndHandles))
-    ndHandles.toArray.map(new NDArray(_, addToCollector = false))
+    ndHandles.toArray.map(ele => {
+        val nd = new NDArray(ele, addToCollector = false)
+        if (nd.isSparse) {
+          nd.asInstanceOf[SparseNDArray]
+        }
+        nd
+      }
+    )
   }
 
   /**
diff --git a/scala-package/core/src/main/scala/org/apache/mxnet/LibInfo.scala b/scala-package/core/src/main/scala/org/apache/mxnet/LibInfo.scala
index 640ecf5d5978..0ee6476be365 100644
--- a/scala-package/core/src/main/scala/org/apache/mxnet/LibInfo.scala
+++ b/scala-package/core/src/main/scala/org/apache/mxnet/LibInfo.scala
@@ -31,13 +31,14 @@ private[mxnet] class LibInfo {
   @native def mxListAllOpNames(names: ListBuffer[String]): Int
   @native def nnGetOpHandle(opName: String, opHandle: RefLong): Int
   // NDArray
-  @native def mxImperativeInvoke(creator: FunctionHandle,
+  @native def mxImperativeInvokeEx(creator: FunctionHandle,
                                  inputs: Array[NDArrayHandle],
                                  outputsGiven: Array[NDArrayHandle],
                                  outputs: ArrayBuffer[NDArrayHandle],
                                  numParams: Int,
                                  paramKeys: Array[String],
-                                 paramVals: Array[String]): Int
+                                 paramVals: Array[String],
+                                 outStype: ArrayBuffer[Int]): Int
   @native def mxNDArrayFree(handle: NDArrayHandle): Int
   @native def mxNDArrayCreateNone(out: NDArrayHandleRef): Int
   @native def mxNDArrayCreateEx(shape: Array[Int],
@@ -47,6 +48,20 @@ private[mxnet] class LibInfo {
                                 delayAlloc: Int,
                                 dtype: Int,
                                 out: NDArrayHandleRef): Int
+  // scalastyle:off parameterNum
+  @native def mxNDArrayCreateSparseEx(storageType: Int,
+                                      shape: Array[Int],
+                                      ndim: Int,
+                                      devType: Int,
+                                      devId: Int,
+                                      delayAlloc: Int,
+                                      dtype: Int,
+                                      numAux: Int,
+                                      auxTypes: Array[Int],
+                                      auxNdims: Array[Int],
+                                      auxShapes: Array[Int],
+                                      out: NDArrayHandleRef): Int
+  // scalastyle:on parameterNum
   @native def mxNDArrayWaitAll(): Int
   @native def mxNDArrayWaitToRead(handle: NDArrayHandle): Int
   @native def mxListFunctions(functions: ListBuffer[FunctionHandle]): Int
@@ -76,6 +91,9 @@ private[mxnet] class LibInfo {
   @native def mxNDArrayGetShape(handle: NDArrayHandle,
                                 ndim: MXUintRef,
                                 data: ArrayBuffer[Int]): Int
+  @native def mxNDArraySyncCopyFromNDArray(handleDst: NDArrayHandle,
+                                           handleSrc: NDArrayHandle,
+                                           locator: Int): Int
   @native def mxNDArraySyncCopyToCPU(handle: NDArrayHandle,
                                      data: Array[Byte],
                                      size: Int): Int
@@ -105,10 +123,15 @@ private[mxnet] class LibInfo {
   @native def mxNDArraySave(fname: String,
                             handles: Array[NDArrayHandle],
                             keys: Array[String]): Int
+  @native def mxNDArrayGetDataNDArray(handle: NDArrayHandle, out: NDArrayHandleRef): Int
+  @native def mxNDArrayGetAuxNDArray(handle: NDArrayHandle,
+                                     location: Int,
+                                     out: NDArrayHandleRef): Int
   @native def mxNDArrayGetContext(handle: NDArrayHandle, devTypeId: RefInt, devId: RefInt): Int
   @native def mxNDArraySaveRawBytes(handle: NDArrayHandle, buf: ArrayBuffer[Byte]): Int
   @native def mxNDArrayLoadFromRawBytes(bytes: Array[Byte], handle: NDArrayHandleRef): Int
   @native def mxNDArrayGetDType(handle: NDArrayHandle, dtype: RefInt): Int
+  @native def mxNDArrayGetStorageType(handle: NDArrayHandle, stype: RefInt): Int
 
   // KVStore Server
   @native def mxInitPSEnv(keys: Array[String], values: Array[String]): Int
diff --git a/scala-package/core/src/main/scala/org/apache/mxnet/NDArray.scala b/scala-package/core/src/main/scala/org/apache/mxnet/NDArray.scala
index 40888012fc5a..1b7b31b32cce 100644
--- a/scala-package/core/src/main/scala/org/apache/mxnet/NDArray.scala
+++ b/scala-package/core/src/main/scala/org/apache/mxnet/NDArray.scala
@@ -21,7 +21,8 @@ import java.nio.{ByteBuffer, ByteOrder}
 
 import org.apache.mxnet.Base._
 import org.apache.mxnet.DType.DType
-import org.apache.mxnet.MX_PRIMITIVES.{MX_PRIMITIVE_TYPE}
+import org.apache.mxnet.MX_PRIMITIVES.MX_PRIMITIVE_TYPE
+import org.apache.mxnet.SparseFormat.SparseFormat
 import org.slf4j.LoggerFactory
 
 import scala.collection.mutable
@@ -113,10 +114,22 @@ object NDArray extends NDArrayBase {
       }
 
     val outputs = ArrayBuffer.empty[NDArrayHandle]
-    checkCall(_LIB.mxImperativeInvoke(function.handle, ndArgs.map(_.handle).toArray, outputVars,
-      outputs, updatedKwargs.size, updatedKwargs.keys.toArray, updatedKwargs.values.toArray))
+    val outStypes = ArrayBuffer.empty[Int]
+    checkCall(_LIB.mxImperativeInvokeEx(function.handle,
+      ndArgs.map(_.handle).toArray,
+      outputVars,
+      outputs,
+      updatedKwargs.size,
+      updatedKwargs.keys.toArray,
+      updatedKwargs.values.toArray,
+      outStypes))
     new NDArrayFuncReturn(Option(oriOutputs).getOrElse {
-      val outputArrs = outputs.map(new NDArray(_)).toArray
+      val outputArrs = (outputs zip outStypes).map(
+        ele => ele._2 match {
+          case 0 => new NDArray(ele._1)
+          case _ => new SparseNDArray(ele._1)
+        }
+        ).toArray
       addDependency(ndArgs.toArray, outputArrs)
       outputArrs
     })
@@ -943,6 +956,12 @@ class NDArray private[mxnet](private[mxnet] val handle: NDArrayHandle,
     DType(mxDtype.value)
   }
 
+  val sparseFormat: SparseFormat = {
+    val mxSF = new RefInt
+    checkCall(_LIB.mxNDArrayGetStorageType(handle, mxSF))
+    SparseFormat(mxSF.value)
+  }
+
   /**
    * Return a copied numpy array of current array with specified type.
    * @param dtype Desired type of result array.
@@ -1309,6 +1328,30 @@ class NDArray private[mxnet](private[mxnet] val handle: NDArrayHandle,
     if (this.context == context) this else this.copyTo(context)
   }
 
+  /**
+    * check if NDArray is SparseNDArray
+    * @return Boolean
+    */
+  def isSparse: Boolean = {
+      this.sparseFormat.id != 0
+  }
+
+  /**
+    * Convert a NDArray to SparseNDArray
+    *
+    * @param sfOption the target sparse type
+    * @return SparseNDArray
+    */
+  def toSparse(sfOption : Option[SparseFormat] = None): SparseNDArray = {
+    val sf = sfOption.getOrElse(SparseFormat.ROW_SPARSE)
+    if (sf.id == 0) throw new IllegalArgumentException("Require Sparse")
+    if (isSparse && sfOption.isEmpty) {
+        this.asInstanceOf[SparseNDArray]
+    } else {
+      NDArray.api.cast_storage(this, sf.toString).head.asInstanceOf[SparseNDArray]
+    }
+  }
+
   override def equals(o: Any): Boolean = o match {
     case that: NDArray =>
       that != null && that.shape == this.shape && that.toArray.sameElements(this.toArray)
@@ -1479,6 +1522,7 @@ private[mxnet] class NDArrayInternal (private val internal: Array[Byte], private
       case DType.Float32 => units.map(wrapBytes(_).getFloat.toDouble)
       case DType.Float64 => units.map(wrapBytes(_).getDouble)
       case DType.Int32 => units.map(wrapBytes(_).getInt.toDouble)
+      case DType.Int64 => units.map(wrapBytes(_).getLong.toDouble)
       case DType.UInt8 => internal.map(_.toDouble)
     }
   }
@@ -1488,6 +1532,7 @@ private[mxnet] class NDArrayInternal (private val internal: Array[Byte], private
       case DType.Float32 => units.map(wrapBytes(_).getFloat)
       case DType.Float64 => units.map(wrapBytes(_).getDouble.toFloat)
       case DType.Int32 => units.map(wrapBytes(_).getInt.toFloat)
+      case DType.Int64 => units.map(wrapBytes(_).getLong.toFloat)
       case DType.UInt8 => internal.map(_.toFloat)
     }
   }
@@ -1497,15 +1542,27 @@ private[mxnet] class NDArrayInternal (private val internal: Array[Byte], private
       case DType.Float32 => units.map(wrapBytes(_).getFloat.toInt)
       case DType.Float64 => units.map(wrapBytes(_).getDouble.toInt)
       case DType.Int32 => units.map(wrapBytes(_).getInt)
+      case DType.Int64 => units.map(wrapBytes(_).getLong.toInt)
       case DType.UInt8 => internal.map(_.toInt)
     }
   }
+  def toLongArray: Array[Long] = {
+    require(dtype != DType.Float16, "Currently cannot convert float16 to native numerical types")
+    dtype match {
+      case DType.Float32 => units.map(wrapBytes(_).getFloat.toLong)
+      case DType.Float64 => units.map(wrapBytes(_).getDouble.toLong)
+      case DType.Int32 => units.map(wrapBytes(_).getInt.toLong)
+      case DType.Int64 => units.map(wrapBytes(_).getLong)
+      case DType.UInt8 => internal.map(_.toLong)
+    }
+  }
   def toByteArray: Array[Byte] = {
     require(dtype != DType.Float16, "Currently cannot convert float16 to native numerical types")
     dtype match {
       case DType.Float16 | DType.Float32 => units.map(wrapBytes(_).getFloat.toByte)
       case DType.Float64 => units.map(wrapBytes(_).getDouble.toByte)
       case DType.Int32 => units.map(wrapBytes(_).getInt.toByte)
+      case DType.Int64 => units.map(wrapBytes(_).getLong.toByte)
       case DType.UInt8 => internal.clone()
     }
   }
diff --git a/scala-package/core/src/main/scala/org/apache/mxnet/SparseFormat.scala b/scala-package/core/src/main/scala/org/apache/mxnet/SparseFormat.scala
new file mode 100644
index 000000000000..acb0c0f24070
--- /dev/null
+++ b/scala-package/core/src/main/scala/org/apache/mxnet/SparseFormat.scala
@@ -0,0 +1,25 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.mxnet
+
+object SparseFormat extends Enumeration {
+  type SparseFormat = Value
+  val DEFAULT = Value(0, "default")
+  val ROW_SPARSE = Value(1, "row_sparse")
+  val CSR = Value(2, "csr")
+}
diff --git a/scala-package/core/src/main/scala/org/apache/mxnet/SparseNDArray.scala b/scala-package/core/src/main/scala/org/apache/mxnet/SparseNDArray.scala
new file mode 100644
index 000000000000..f3fe638e41ed
--- /dev/null
+++ b/scala-package/core/src/main/scala/org/apache/mxnet/SparseNDArray.scala
@@ -0,0 +1,196 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.mxnet
+
+import org.apache.mxnet.Base.{NDArrayHandle, NDArrayHandleRef, checkCall, _LIB}
+import org.apache.mxnet.DType.DType
+import org.apache.mxnet.SparseFormat.SparseFormat
+
+object SparseNDArray {
+  /**
+    * Create a Compressed Sparse Row Storage (CSR) Format Matrix
+    * @param data the data to feed
+    * @param indices The indices array stores the column index for each non-zero element in data
+    * @param indptr The indptr array is what will help identify the rows where the data appears
+    * @param shape the shape of CSR NDArray to be created
+    * @param ctx the context of this NDArray
+    * @return SparseNDArray
+    */
+  def csrMatrix(data: Array[Float], indices: Array[Float],
+                indptr: Array[Float], shape: Shape, ctx: Context): SparseNDArray = {
+    val fmt = SparseFormat.CSR
+    val dataND = NDArray.array(data, Shape(data.length), ctx)
+    val indicesND = NDArray.array(indices, Shape(indices.length), ctx).asType(DType.Int64)
+    val indptrND = NDArray.array(indptr, Shape(indptr.length), ctx).asType(DType.Int64)
+    val dTypes = Array(indptrND.dtype, indicesND.dtype)
+    val shapes = Array(indptrND.shape, indicesND.shape)
+    val handle =
+      newAllocHandle(fmt, shape, ctx, false, DType.Float32, dTypes, shapes)
+    checkCall(_LIB.mxNDArraySyncCopyFromNDArray(handle, dataND.handle, -1))
+    checkCall(_LIB.mxNDArraySyncCopyFromNDArray(handle, indptrND.handle, 0))
+    checkCall(_LIB.mxNDArraySyncCopyFromNDArray(handle, indicesND.handle, 1))
+    new SparseNDArray(handle)
+  }
+
+  /**
+    * RowSparseNDArray stores the matrix in row sparse format,
+    * which is designed for arrays of which most row slices are all zeros
+    * @param data Any Array(Array(... Array(Float)))
+    * @param indices the indices to store the data
+    * @param shape shape of the NDArray
+    * @param ctx Context
+    * @return SparseNDArray
+    */
+  def rowSparseArray(data: Array[_], indices: Array[Float],
+                     shape: Shape, ctx: Context): SparseNDArray = {
+    val dataND = NDArray.toNDArray(data)
+    val indicesND = NDArray.array(indices, Shape(indices.length), ctx).asType(DType.Int64)
+    rowSparseArray(dataND, indicesND, shape, ctx)
+  }
+
+  /**
+    * RowSparseNDArray stores the matrix in row sparse format,
+    * which is designed for arrays of which most row slices are all zeros
+    * @param data NDArray input
+    * @param indices in NDArray. Only DType.Int64 supported
+    * @param shape shape of the NDArray
+    * @param ctx Context
+    * @return
+    */
+  def rowSparseArray(data: NDArray, indices: NDArray,
+                     shape: Shape, ctx: Context): SparseNDArray = {
+    val fmt = SparseFormat.ROW_SPARSE
+    val handle = newAllocHandle(fmt, shape, ctx, false,
+      DType.Float32, Array(indices.dtype), Array(indices.shape))
+    checkCall(_LIB.mxNDArraySyncCopyFromNDArray(handle, data.handle, -1))
+    checkCall(_LIB.mxNDArraySyncCopyFromNDArray(handle, indices.handle, 0))
+    new SparseNDArray(handle)
+  }
+
+  def retain(sparseNDArray: SparseNDArray, indices: Array[Float]): SparseNDArray = {
+    if (sparseNDArray.sparseFormat == SparseFormat.CSR) {
+      throw new IllegalArgumentException("CSR not supported")
+    }
+    NDArray.genericNDArrayFunctionInvoke("_sparse_retain",
+      Seq(sparseNDArray, NDArray.toNDArray(indices))).head.toSparse()
+  }
+
+  private def newAllocHandle(stype : SparseFormat,
+                             shape: Shape,
+                             ctx: Context,
+                             delayAlloc: Boolean,
+                             dtype: DType = DType.Float32,
+                             auxDTypes: Array[DType],
+                             auxShapes: Array[Shape]) : NDArrayHandle = {
+    val hdl = new NDArrayHandleRef
+    checkCall(_LIB.mxNDArrayCreateSparseEx(
+      stype.id,
+      shape.toArray,
+      shape.length,
+      ctx.deviceTypeid,
+      ctx.deviceId,
+      if (delayAlloc) 1 else 0,
+      dtype.id,
+      auxDTypes.length,
+      auxDTypes.map(_.id),
+      auxShapes.map(_.length),
+      auxShapes.map(_.get(0)),
+      hdl)
+    )
+    hdl.value
+  }
+}
+
+/**
+  * Sparse NDArray is the child class of NDArray designed to hold the Sparse format
+  *
+  * <p> Currently, Rowsparse and CSR typed NDArray is supported. Most of the Operators
+  * will convert Sparse NDArray to dense. Basic operators like <code>add</code> will
+  * have optimization for sparse operattions</p>
+  * @param handle The pointer that SparseNDArray holds
+  * @param writable whether the NDArray is writable
+  */
+class SparseNDArray private[mxnet] (override private[mxnet] val handle: NDArrayHandle,
+                                    override val writable: Boolean = true)
+  extends NDArray(handle, writable) {
+
+  private lazy val dense: NDArray = toDense
+
+  override def toString: String = {
+    dense.toString
+  }
+
+  /**
+    * Convert a SparseNDArray to dense NDArray
+    * @return NDArray
+    */
+  def toDense: NDArray = {
+      NDArray.api.cast_storage(this, SparseFormat.DEFAULT.toString).head
+  }
+
+  override def toArray: Array[Float] = {
+    dense.toArray
+  }
+
+  override def at(idx: Int): NDArray = {
+    dense.at(idx)
+  }
+
+  override def slice(start: Int, end: Int): NDArray = {
+    NDArray.api.slice(this, Shape(start), Shape(end))
+  }
+
+  /**
+    * Get the Data portion from a Row Sparse NDArray
+    * @return NDArray
+    */
+  def getData: NDArray = {
+    require(this.sparseFormat == SparseFormat.ROW_SPARSE, "Not Supported for CSR")
+    val handle = new NDArrayHandleRef
+    _LIB.mxNDArrayGetDataNDArray(this.handle, handle)
+    new NDArray(handle.value, false)
+  }
+
+  /**
+    * Get the indptr Array from a CSR NDArray
+    * @return NDArray
+    */
+  def getIndptr: NDArray = {
+    require(this.sparseFormat == SparseFormat.CSR, "Not Supported for row sparse")
+    getAuxNDArray(0)
+  }
+
+  /**
+    * Get the indice Array
+    * @return NDArray
+    */
+  def getIndices: NDArray = {
+    if (this.sparseFormat == SparseFormat.ROW_SPARSE) {
+      getAuxNDArray(0)
+    } else {
+      getAuxNDArray(1)
+    }
+  }
+
+  private def getAuxNDArray(idx: Int): NDArray = {
+    val handle = new NDArrayHandleRef
+    checkCall(_LIB.mxNDArrayGetAuxNDArray(this.handle, idx, handle))
+    new NDArray(handle.value, false)
+  }
+
+}
diff --git a/scala-package/core/src/test/scala/org/apache/mxnet/NDArraySuite.scala b/scala-package/core/src/test/scala/org/apache/mxnet/NDArraySuite.scala
index c2ef641f9c9a..82b9edc8f4bb 100644
--- a/scala-package/core/src/test/scala/org/apache/mxnet/NDArraySuite.scala
+++ b/scala-package/core/src/test/scala/org/apache/mxnet/NDArraySuite.scala
@@ -45,6 +45,22 @@ class NDArraySuite extends FunSuite with BeforeAndAfterAll with Matchers {
     assert(ndones.toScalar === 1f)
   }
 
+  test("to sparse") {
+    val arr = Array(
+      Array(1f, 0f, 0f),
+      Array(0f, 3f, 0f),
+      Array(0f, 0f, 1f)
+    )
+    val nd = NDArray.toNDArray(arr)
+    assert(!nd.isSparse)
+    // row sparse
+    var ndSparse = nd.toSparse()
+    assert(ndSparse.getIndices.toArray sameElements Array(0f, 1f, 2f))
+    // csr
+    ndSparse = nd.toSparse(Some(SparseFormat.CSR))
+    assert(ndSparse.getIndptr.toArray sameElements Array(0f, 1f, 2f, 3f))
+  }
+
   test("to float 64 scalar") {
     val ndzeros = NDArray.zeros(Shape(1), dtype = DType.Float64)
     assert(ndzeros.toFloat64Scalar === 0d)
diff --git a/scala-package/core/src/test/scala/org/apache/mxnet/SparseNDArraySuite.scala b/scala-package/core/src/test/scala/org/apache/mxnet/SparseNDArraySuite.scala
new file mode 100644
index 000000000000..f9968efd80c5
--- /dev/null
+++ b/scala-package/core/src/test/scala/org/apache/mxnet/SparseNDArraySuite.scala
@@ -0,0 +1,93 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.mxnet
+
+import org.apache.mxnet.io.NDArrayIter
+import org.scalatest.FunSuite
+import org.slf4j.LoggerFactory
+
+class SparseNDArraySuite  extends FunSuite {
+
+  private val logger = LoggerFactory.getLogger(classOf[SparseNDArraySuite])
+
+  test("create CSR NDArray") {
+    val data = Array(7f, 8f, 9f)
+    val indices = Array(0f, 2f, 1f)
+    val indptr = Array(0f, 2f, 2f, 3f)
+    val shape = Shape(3, 4)
+    val sparseND = SparseNDArray.csrMatrix(data, indices, indptr, shape, Context.cpu())
+    assert(sparseND.shape == Shape(3, 4))
+    assert(sparseND.toArray
+      sameElements Array(7.0f, 0.0f, 8.0f, 0.0f,
+                         0.0f, 0.0f, 0.0f, 0.0f,
+                         0.0f, 9.0f, 0.0f, 0.0f))
+    assert(sparseND.sparseFormat == SparseFormat.CSR)
+    assert(sparseND.getIndptr.toArray sameElements indptr)
+    assert(sparseND.getIndices.toArray sameElements indices)
+  }
+
+  test("create Row Sparse NDArray") {
+    val data = Array(
+      Array(1f, 2f),
+      Array(3f, 4f)
+    )
+    val indices = Array(1f, 4f)
+    val shape = Shape(6, 2)
+    val sparseND = SparseNDArray.rowSparseArray(data, indices, shape, Context.cpu())
+    assert(sparseND.sparseFormat == SparseFormat.ROW_SPARSE)
+    assert(sparseND.shape == Shape(6, 2))
+    assert(sparseND.at(1).toArray sameElements Array(1f, 2f))
+    assert(sparseND.getIndices.toArray sameElements indices)
+  }
+
+  test("Test retain") {
+    val arr = Array(
+      Array(1f, 2f),
+      Array(3f, 4f),
+      Array(5f, 6f)
+    )
+    val indices = Array(0f, 1f, 3f)
+    val rspIn = SparseNDArray.rowSparseArray(arr, indices, Shape(4, 2), Context.cpu())
+    val toRetain = Array(0f, 3f)
+    val rspOut = SparseNDArray.retain(rspIn, toRetain)
+    assert(rspOut.getData.toArray sameElements Array(1f, 2f, 5f, 6f))
+    assert(rspOut.getIndices.toArray sameElements Array(0f, 3f))
+  }
+
+  test("Test add") {
+    val nd = NDArray.array(Array(1f, 2f, 3f), Shape(3)).toSparse(Some(SparseFormat.ROW_SPARSE))
+    val nd2 = nd + nd
+    assert(nd2.isInstanceOf[SparseNDArray])
+    assert(nd2.toArray sameElements Array(2f, 4f, 6f))
+  }
+
+  test("Test DataIter") {
+    val nd = NDArray.array(Array(1f, 2f, 3f), Shape(1, 3)).toSparse(Some(SparseFormat.CSR))
+    val arr = IndexedSeq(nd, nd, nd, nd)
+    val iter = new NDArrayIter(arr)
+    while (iter.hasNext) {
+      val tempArr = iter.next().data
+      tempArr.foreach(ele => {
+        assert(ele.sparseFormat == SparseFormat.CSR)
+        assert(ele.shape == Shape(1, 3))
+      })
+    }
+  }
+
+
+}
diff --git a/scala-package/native/src/main/native/org_apache_mxnet_native_c_api.cc b/scala-package/native/src/main/native/org_apache_mxnet_native_c_api.cc
index 9b19fd360fc4..387a0b17e252 100644
--- a/scala-package/native/src/main/native/org_apache_mxnet_native_c_api.cc
+++ b/scala-package/native/src/main/native/org_apache_mxnet_native_c_api.cc
@@ -93,6 +93,31 @@ JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxNDArrayCreateEx
   return ret;
 }
 
+JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxNDArrayCreateSparseEx
+  (JNIEnv *env, jobject obj, jint storageType, jintArray shape, jint ndim, jint devType,
+    jint devId, jint delayAlloc, jint dtype, jint numAux, jintArray auxTypes,
+    jintArray auxNdims, jintArray auxShapes, jobject ndArrayHandle) {
+    jint *shapeArr = env->GetIntArrayElements(shape, NULL);
+    jint *auxTypesArr = env->GetIntArrayElements(auxTypes, NULL);
+    jint *auxNdimsArr = env->GetIntArrayElements(auxNdims, NULL);
+    jint *auxShapesArr = env->GetIntArrayElements(auxShapes, NULL);
+    NDArrayHandle out;
+    int ret = MXNDArrayCreateSparseEx(storageType,
+     reinterpret_cast<const mx_uint *>(shapeArr),
+     static_cast<mx_uint>(ndim),
+     devType, devId, delayAlloc, dtype,
+     static_cast<mx_uint>(numAux),
+     reinterpret_cast<int *>(auxTypesArr),
+     reinterpret_cast<mx_uint *>(auxNdimsArr),
+     reinterpret_cast<const mx_uint *>(auxShapesArr),  &out);
+    env->ReleaseIntArrayElements(shape, shapeArr, 0);
+    env->ReleaseIntArrayElements(auxTypes, auxTypesArr, 0);
+    env->ReleaseIntArrayElements(auxNdims, auxNdimsArr, 0);
+    env->ReleaseIntArrayElements(auxShapes, auxShapesArr, 0);
+    SetLongField(env, ndArrayHandle, reinterpret_cast<jlong>(out));
+    return ret;
+}
+
 JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxNDArrayWaitAll(JNIEnv *env, jobject obj) {
   return MXNDArrayWaitAll();
 }
@@ -179,10 +204,10 @@ JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxFuncGetInfo
   return ret;
 }
 
-JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxImperativeInvoke
+JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxImperativeInvokeEx
   (JNIEnv *env, jobject obj, jlong funcPtr, jlongArray inputs,
     jlongArray outputsGiven, jobject outputs, jint numParams,
-    jobjectArray paramKeys, jobjectArray paramVals) {
+    jobjectArray paramKeys, jobjectArray paramVals, jobject outStypes) {
 
   const char **cParamKeys = NULL;
   const char **cParamVals = NULL;
@@ -204,6 +229,7 @@ JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxImperativeInvoke
   int numOutputs = 0;
   jlong *cOutputsGiven = NULL;
   NDArrayHandle *cOutputs = NULL;
+  const int *cOutStypes;
   if (outputsGiven) {
     cOutputsGiven = env->GetLongArrayElements(outputsGiven, NULL);
     cOutputs = reinterpret_cast<NDArrayHandle *>(cOutputsGiven);
@@ -211,14 +237,15 @@ JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxImperativeInvoke
   }
   jlong *cInputs = env->GetLongArrayElements(inputs, NULL);
   jsize numInputs = env->GetArrayLength(inputs);
-  int ret = MXImperativeInvoke(reinterpret_cast<AtomicSymbolCreator>(funcPtr),
+  int ret = MXImperativeInvokeEx(reinterpret_cast<AtomicSymbolCreator>(funcPtr),
                                static_cast<int>(numInputs),
                                reinterpret_cast<NDArrayHandle *>(cInputs),
                                &numOutputs,
                                &cOutputs,
                                static_cast<int>(numParams),
                                cParamKeys,
-                               cParamVals);
+                               cParamVals,
+                               &cOutStypes);
   env->ReleaseLongArrayElements(inputs, cInputs, 0);
   if (cOutputsGiven) {
     env->ReleaseLongArrayElements(outputsGiven, cOutputsGiven, 0);
@@ -240,7 +267,9 @@ JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxImperativeInvoke
 
   if (cOutputs) {
     jclass longCls = env->FindClass("java/lang/Long");
+    jclass intCls = env->FindClass("java/lang/Integer");
     jmethodID longConst = env->GetMethodID(longCls, "<init>", "(J)V");
+    jmethodID intConst = env->GetMethodID(intCls, "<init>", "(I)V");
     // scala.collection.mutable.ListBuffer append method
     jclass listClass = env->FindClass("scala/collection/mutable/ArrayBuffer");
     jmethodID listAppend = env->GetMethodID(listClass, "$plus$eq",
@@ -249,6 +278,9 @@ JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxImperativeInvoke
       env->CallObjectMethod(outputs, listAppend,
                             env->NewObject(longCls, longConst,
                             reinterpret_cast<uint64_t>(cOutputs[i])));
+      env->CallObjectMethod(outStypes, listAppend,
+                            env->NewObject(intCls, intConst,
+                            cOutStypes[i]));
     }
   }
 
@@ -379,6 +411,14 @@ JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxNDArrayGetShape
   return ret;
 }
 
+JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxNDArraySyncCopyFromNDArray
+  (JNIEnv *env, jobject obj, jlong dstPtr, jlong srcPtr, jint locator) {
+  int ret = MXNDArraySyncCopyFromNDArray(reinterpret_cast<NDArrayHandle>(dstPtr),
+                                   reinterpret_cast<NDArrayHandle>(srcPtr),
+                                   locator);
+  return ret;
+}
+
 JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxNDArraySyncCopyToCPU
   (JNIEnv *env, jobject obj, jlong ndArrayPtr, jbyteArray data, jint size) {
   jbyte *pdata = env->GetByteArrayElements(data, NULL);
@@ -434,6 +474,25 @@ JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxFloat64NDArraySyncCopyFro
   return ret;
 }
 
+JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxNDArrayGetDataNDArray
+  (JNIEnv *env, jobject obj, jlong arrayPtr, jobject ndArrayHandle) {
+  NDArrayHandle out;
+  int ret = MXNDArrayGetDataNDArray(reinterpret_cast<NDArrayHandle>(arrayPtr),
+                                     &out);
+  SetLongField(env, ndArrayHandle, reinterpret_cast<jlong>(out));
+  return ret;
+}
+
+JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxNDArrayGetAuxNDArray
+  (JNIEnv *env, jobject obj, jlong arrayPtr, jint location, jobject ndArrayHandle) {
+  NDArrayHandle out;
+  int ret = MXNDArrayGetAuxNDArray(reinterpret_cast<NDArrayHandle>(arrayPtr),
+                                   static_cast<mx_uint>(location),
+                                   &out);
+  SetLongField(env, ndArrayHandle, reinterpret_cast<jlong>(out));
+  return ret;
+}
+
 JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxNDArrayGetContext
   (JNIEnv *env, jobject obj, jlong arrayPtr, jobject devTypeId, jobject devId) {
   int outDevType;
@@ -540,6 +599,14 @@ JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxNDArrayGetDType
   return ret;
 }
 
+JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxNDArrayGetStorageType
+  (JNIEnv * env, jobject obj, jlong jhandle, jobject jstype) {
+  int stype;
+  int ret = MXNDArrayGetStorageType(reinterpret_cast<NDArrayHandle>(jhandle), &stype);
+  SetIntField(env, jstype, stype);
+  return ret;
+}
+
 JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxInitPSEnv
   (JNIEnv *env, jobject obj, jobjectArray jkeys, jobjectArray jvals) {
   // keys and values
diff --git a/scala-package/native/src/main/native/org_apache_mxnet_native_c_api.h b/scala-package/native/src/main/native/org_apache_mxnet_native_c_api.h
index fac32bb0a410..c8ee0ce8d22b 100644
--- a/scala-package/native/src/main/native/org_apache_mxnet_native_c_api.h
+++ b/scala-package/native/src/main/native/org_apache_mxnet_native_c_api.h
@@ -41,11 +41,11 @@ JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_nnGetOpHandle
 
 /*
  * Class:     org_apache_mxnet_LibInfo
- * Method:    mxImperativeInvoke
- * Signature: (J[J[JLscala/collection/mutable/ArrayBuffer;I[Ljava/lang/String;[Ljava/lang/String;)I
+ * Method:    mxImperativeInvokeEx
+ * Signature: (J[J[JLscala/collection/mutable/ArrayBuffer;I[Ljava/lang/String;[Ljava/lang/String;Lscala/collection/mutable/ArrayBuffer;)I
  */
-JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxImperativeInvoke
-  (JNIEnv *, jobject, jlong, jlongArray, jlongArray, jobject, jint, jobjectArray, jobjectArray);
+JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxImperativeInvokeEx
+  (JNIEnv *, jobject, jlong, jlongArray, jlongArray, jobject, jint, jobjectArray, jobjectArray, jobject);
 
 /*
  * Class:     org_apache_mxnet_LibInfo
@@ -71,6 +71,14 @@ JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxNDArrayCreateNone
 JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxNDArrayCreateEx
   (JNIEnv *, jobject, jintArray, jint, jint, jint, jint, jint, jobject);
 
+/*
+ * Class:     org_apache_mxnet_LibInfo
+ * Method:    mxNDArrayCreateSparseEx
+ * Signature: (I[IIIIIII[I[I[ILorg/apache/mxnet/Base/RefLong;)I
+ */
+JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxNDArrayCreateSparseEx
+  (JNIEnv *, jobject, jint, jintArray, jint, jint, jint, jint, jint, jint, jintArray, jintArray, jintArray, jobject);
+
 /*
  * Class:     org_apache_mxnet_LibInfo
  * Method:    mxNDArrayWaitAll
@@ -135,6 +143,14 @@ JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxFuncInvokeEx
 JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxNDArrayGetShape
   (JNIEnv *, jobject, jlong, jobject, jobject);
 
+/*
+ * Class:     org_apache_mxnet_LibInfo
+ * Method:    mxNDArraySyncCopyFromNDArray
+ * Signature: (JJI)I
+ */
+JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxNDArraySyncCopyFromNDArray
+  (JNIEnv *, jobject, jlong, jlong, jint);
+
 /*
  * Class:     org_apache_mxnet_LibInfo
  * Method:    mxNDArraySyncCopyToCPU
@@ -199,6 +215,22 @@ JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxNDArrayLoad
 JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxNDArraySave
   (JNIEnv *, jobject, jstring, jlongArray, jobjectArray);
 
+/*
+ * Class:     org_apache_mxnet_LibInfo
+ * Method:    mxNDArrayGetDataNDArray
+ * Signature: (JLorg/apache/mxnet/Base/RefLong;)I
+ */
+JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxNDArrayGetDataNDArray
+  (JNIEnv *, jobject, jlong, jobject);
+
+/*
+ * Class:     org_apache_mxnet_LibInfo
+ * Method:    mxNDArrayGetAuxNDArray
+ * Signature: (JILorg/apache/mxnet/Base/RefLong;)I
+ */
+JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxNDArrayGetAuxNDArray
+  (JNIEnv *, jobject, jlong, jint, jobject);
+
 /*
  * Class:     org_apache_mxnet_LibInfo
  * Method:    mxNDArrayGetContext
@@ -231,6 +263,14 @@ JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxNDArrayLoadFromRawBytes
 JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxNDArrayGetDType
   (JNIEnv *, jobject, jlong, jobject);
 
+/*
+ * Class:     org_apache_mxnet_LibInfo
+ * Method:    mxNDArrayGetStorageType
+ * Signature: (JLorg/apache/mxnet/Base/RefInt;)I
+ */
+JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxNDArrayGetStorageType
+  (JNIEnv *, jobject, jlong, jobject);
+
 /*
  * Class:     org_apache_mxnet_LibInfo
  * Method:    mxInitPSEnv

From b62aaf3d984c647a2fbab4a292ad1f11e6147abe Mon Sep 17 00:00:00 2001
From: kshitij12345 <kshitijkalambarkar@gmail.com>
Date: Tue, 9 Jul 2019 02:48:25 +0530
Subject: [PATCH 049/813] fix comment (#15481)

---
 src/operator/tensor/elemwise_binary_op_extended.cu | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/operator/tensor/elemwise_binary_op_extended.cu b/src/operator/tensor/elemwise_binary_op_extended.cu
index 585d379b0bef..0ae6ac966a2b 100644
--- a/src/operator/tensor/elemwise_binary_op_extended.cu
+++ b/src/operator/tensor/elemwise_binary_op_extended.cu
@@ -20,7 +20,7 @@
 /*!
  *  Copyright (c) 2016 by Contributors
  * \file elemwise_binary_op_extended.cu
- * \brief GPU Implementation of unary function.
+ * \brief GPU Implementation of binary function.
  */
 #include "./elemwise_unary_op.h"
 #include "./elemwise_binary_op.h"

From e02c0a290eeb761d6c0690420ab420ac00c98f26 Mon Sep 17 00:00:00 2001
From: Tao Lv <tao.a.lv@intel.com>
Date: Tue, 9 Jul 2019 10:07:25 +0800
Subject: [PATCH 050/813] Upgrade MKL-DNN submodule to v0.20 release (#15422)

* update mkldnn to v0.20 release

* update mkldnn format numbers

* retrigger ci
---
 3rdparty/mkldnn                   | 2 +-
 tests/cpp/operator/mkldnn_test.cc | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/3rdparty/mkldnn b/3rdparty/mkldnn
index 41bee20d7eb4..d89bf4babd7c 160000
--- a/3rdparty/mkldnn
+++ b/3rdparty/mkldnn
@@ -1 +1 @@
-Subproject commit 41bee20d7eb4a67feeeeb8d597b3598994eb1959
+Subproject commit d89bf4babd7cce7efa6613387dca79c123164084
diff --git a/tests/cpp/operator/mkldnn_test.cc b/tests/cpp/operator/mkldnn_test.cc
index 3e7b62d8b33f..ab624e3a3c44 100644
--- a/tests/cpp/operator/mkldnn_test.cc
+++ b/tests/cpp/operator/mkldnn_test.cc
@@ -100,9 +100,9 @@ static void VerifyDefMem(const mkldnn::memory &mem) {
 
 TEST(MKLDNN_UTIL_FUNC, MemFormat) {
   // Check whether the number of format is correct.
-  CHECK_EQ(mkldnn_format_last, 145);
+  CHECK_EQ(mkldnn_format_last, 158);
   CHECK_EQ(mkldnn_nchw, 7);
-  CHECK_EQ(mkldnn_oihw, 16);
+  CHECK_EQ(mkldnn_oihw, 17);
 }
 
 static void VerifyMem(const mkldnn::memory &mem) {

From 6b2b927871acaba1d7bd124437e94da661118d6d Mon Sep 17 00:00:00 2001
From: Robert Stone <talby@trap.mtview.ca.us>
Date: Tue, 9 Jul 2019 17:38:04 -0700
Subject: [PATCH 051/813] [Perl] - simplify aliasing strategy (#15395)

* alternate support for emulation of Python "import mxnet.someclass as sc"
   moving responsibility to the imported class instead of the importer.
 * simpler dispatch for constructor aliases to have fewer anonymous
   subroutines in stack traces on errors.
---
 perl-package/AI-MXNet/lib/AI/MXNet.pm         | 116 +++++-------------
 .../AI-MXNet/lib/AI/MXNet/AutoGrad.pm         |  19 +--
 perl-package/AI-MXNet/lib/AI/MXNet/Base.pm    |  16 +--
 .../AI-MXNet/lib/AI/MXNet/Callback.pm         |   1 +
 perl-package/AI-MXNet/lib/AI/MXNet/Context.pm |   8 +-
 perl-package/AI-MXNet/lib/AI/MXNet/Contrib.pm |  10 +-
 .../AI-MXNet/lib/AI/MXNet/Contrib/NDArray.pm  |   1 +
 .../AI-MXNet/lib/AI/MXNet/Contrib/Symbol.pm   |   1 +
 .../AI-MXNet/lib/AI/MXNet/CudaModule.pm       |   5 +-
 perl-package/AI-MXNet/lib/AI/MXNet/Engine.pm  |   3 +-
 perl-package/AI-MXNet/lib/AI/MXNet/Gluon.pm   |  38 +-----
 .../AI-MXNet/lib/AI/MXNet/Gluon/Block.pm      |   7 +-
 .../AI-MXNet/lib/AI/MXNet/Gluon/Data.pm       |   4 +-
 .../lib/AI/MXNet/Gluon/Data/Vision.pm         |   3 +
 .../AI-MXNet/lib/AI/MXNet/Gluon/Loss.pm       |   3 +-
 .../AI-MXNet/lib/AI/MXNet/Gluon/NN.pm         |  21 +---
 .../lib/AI/MXNet/Gluon/NN/Activation.pm       |   2 +
 .../lib/AI/MXNet/Gluon/NN/BasicLayers.pm      |   4 +-
 .../lib/AI/MXNet/Gluon/NN/ConvLayers.pm       |   4 +-
 .../AI-MXNet/lib/AI/MXNet/Gluon/Parameter.pm  |   5 +
 .../AI-MXNet/lib/AI/MXNet/Gluon/RNN.pm        |  21 +---
 .../AI-MXNet/lib/AI/MXNet/Gluon/Trainer.pm    |   3 +
 .../AI-MXNet/lib/AI/MXNet/Gluon/Utils.pm      |   2 +-
 perl-package/AI-MXNet/lib/AI/MXNet/IO.pm      |   1 +
 perl-package/AI-MXNet/lib/AI/MXNet/Image.pm   |   1 +
 .../AI-MXNet/lib/AI/MXNet/Initializer.pm      |   1 +
 perl-package/AI-MXNet/lib/AI/MXNet/KVStore.pm |   1 +
 perl-package/AI-MXNet/lib/AI/MXNet/LinAlg.pm  |   1 +
 perl-package/AI-MXNet/lib/AI/MXNet/Metric.pm  |   1 +
 perl-package/AI-MXNet/lib/AI/MXNet/Module.pm  |   1 +
 perl-package/AI-MXNet/lib/AI/MXNet/Monitor.pm |   1 +
 perl-package/AI-MXNet/lib/AI/MXNet/NDArray.pm |   1 +
 perl-package/AI-MXNet/lib/AI/MXNet/NS.pm      |  78 ++++++++++++
 .../AI-MXNet/lib/AI/MXNet/Optimizer.pm        |   1 +
 perl-package/AI-MXNet/lib/AI/MXNet/RNN.pm     |   1 +
 perl-package/AI-MXNet/lib/AI/MXNet/Random.pm  |   1 +
 .../AI-MXNet/lib/AI/MXNet/RecordIO.pm         |   1 +
 perl-package/AI-MXNet/lib/AI/MXNet/Symbol.pm  |   1 +
 .../AI-MXNet/lib/AI/MXNet/Symbol/AttrScope.pm |  12 +-
 .../lib/AI/MXNet/Symbol/NameManager.pm        |   6 +-
 .../AI-MXNet/lib/AI/MXNet/TestUtils.pm        |   9 +-
 .../AI-MXNet/lib/AI/MXNet/Visualization.pm    |   1 +
 perl-package/AI-MXNet/t/AI-MXNet.t            |   5 +-
 .../AI-MXNet/t/test_multi_device_exec.t       |   4 +-
 44 files changed, 210 insertions(+), 216 deletions(-)
 create mode 100644 perl-package/AI-MXNet/lib/AI/MXNet/NS.pm

diff --git a/perl-package/AI-MXNet/lib/AI/MXNet.pm b/perl-package/AI-MXNet/lib/AI/MXNet.pm
index ffc72f9513d3..b5665c88eddf 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet.pm
@@ -19,105 +19,51 @@ package AI::MXNet;
 use v5.14.0;
 use strict;
 use warnings;
+use AI::MXNet::NS 'global';
 use AI::MXNet::Base;
-use AI::MXNet::Callback;
-use AI::MXNet::NDArray;
-use AI::MXNet::Symbol;
+use AI::MXNet::Callback 'callback';
+use AI::MXNet::NDArray qw(nd ndarray);
+use AI::MXNet::Context 'context';
+use AI::MXNet::Symbol qw(sym symbol);
 use AI::MXNet::Executor;
 use AI::MXNet::Executor::Group;
 use AI::MXNet::CudaModule;
-use AI::MXNet::Random;
-use AI::MXNet::Initializer;
-use AI::MXNet::Optimizer;
-use AI::MXNet::KVStore;
+use AI::MXNet::Random qw(rnd random);
+use AI::MXNet::Initializer qw(init initializer);
+use AI::MXNet::Optimizer qw(optimizer opt);
+use AI::MXNet::KVStore 'kv';
 use AI::MXNet::KVStoreServer;
-use AI::MXNet::IO;
-use AI::MXNet::Metric;
+use AI::MXNet::IO 'io';
+use AI::MXNet::Metric 'metric';
 use AI::MXNet::LRScheduler;
-use AI::MXNet::Monitor;
+use AI::MXNet::Monitor 'mon';
 use AI::MXNet::Profiler;
 use AI::MXNet::Module::Base;
-use AI::MXNet::Module;
+use AI::MXNet::Module qw(mod module);
 use AI::MXNet::Module::Bucketing;
-use AI::MXNet::RNN;
-use AI::MXNet::Visualization;
-use AI::MXNet::RecordIO;
-use AI::MXNet::Image;
-use AI::MXNet::Contrib;
-use AI::MXNet::LinAlg;
+use AI::MXNet::RNN 'rnn';
+use AI::MXNet::Visualization 'viz';
+use AI::MXNet::RecordIO 'recordio';
+use AI::MXNet::Image qw(img image);
+use AI::MXNet::Contrib 'contrib';
+use AI::MXNet::LinAlg 'linalg';
 use AI::MXNet::CachedOp;
-use AI::MXNet::AutoGrad;
-use AI::MXNet::Gluon;
+use AI::MXNet::AutoGrad 'autograd';
+use AI::MXNet::Gluon 'gluon';
 use AI::MXNet::NDArray::Sparse;
 use AI::MXNet::Symbol::Sparse;
-use AI::MXNet::Engine;
+use AI::MXNet::Engine 'engine';
 our $VERSION = '1.4';
 
-sub import
-{
-    my ($class, $short_name) = @_;
-    if($short_name)
-    {
-        $short_name =~ s/[^\w:]//g;
-        if(length $short_name)
-        {
-            my $short_name_package =<<"EOP";
-            package $short_name;
-            no warnings 'redefine';
-            sub nd { 'AI::MXNet::NDArray' }
-            sub ndarray { 'AI::MXNet::NDArray' }
-            sub sym { 'AI::MXNet::Symbol' }
-            sub symbol { 'AI::MXNet::Symbol' }
-            sub init { 'AI::MXNet::Initializer' }
-            sub initializer { 'AI::MXNet::Initializer' }
-            sub optimizer { 'AI::MXNet::Optimizer' }
-            sub opt { 'AI::MXNet::Optimizer' }
-            sub rnd { 'AI::MXNet::Random' }
-            sub random { 'AI::MXNet::Random' }
-            sub Context { shift; AI::MXNet::Context->new(\@_) }
-            sub context { 'AI::MXNet::Context' }
-            sub cpu { AI::MXNet::Context->cpu(\$_[1]//0) }
-            sub cpu_pinned { AI::MXNet::Context->cpu_pinned(\$_[1]//0) }
-            sub gpu { AI::MXNet::Context->gpu(\$_[1]//0) }
-            sub kv { 'AI::MXNet::KVStore' }
-            sub recordio { 'AI::MXNet::RecordIO' }
-            sub io { 'AI::MXNet::IO' }
-            sub metric { 'AI::MXNet::Metric' }
-            sub mod { 'AI::MXNet::Module' }
-            sub module { 'AI::MXNet::Module' }
-            sub mon { 'AI::MXNet::Monitor' }
-            sub viz { 'AI::MXNet::Visualization' }
-            sub rnn { 'AI::MXNet::RNN' }
-            sub callback { 'AI::MXNet::Callback' }
-            sub img { 'AI::MXNet::Image' }
-            sub image { 'AI::MXNet::Image' }
-            sub contrib { 'AI::MXNet::Contrib' }
-            sub linalg { 'AI::MXNet::LinAlg' }
-            sub autograd { 'AI::MXNet::AutoGrad' }
-            sub engine { 'AI::MXNet::Engine' }
-            sub name { '$short_name' }
-            sub rtc { '$short_name' }
-            sub gluon { 'AI::MXNet::Gluon' }
-            sub CudaModule { shift; AI::MXNet::CudaModule->new(\@_) }
-            sub AttrScope { shift; AI::MXNet::Symbol::AttrScope->new(\@_) }
-            *AI::MXNet::Symbol::AttrScope::current = sub { \$${short_name}::AttrScope; };
-            \$${short_name}::AttrScope = AI::MXNet::Symbol::AttrScope->new;
-            sub Prefix { AI::MXNet::Symbol::Prefix->new(prefix => \$_[1]) }
-            *AI::MXNet::Symbol::NameManager::current = sub { \$${short_name}::NameManager; };
-            *AI::MXNet::Symbol::NameManager::set_current = sub { \$${short_name}::NameManager = \$_[1]; };
-            \$${short_name}::NameManager = AI::MXNet::Symbol::NameManager->new;
-            *AI::MXNet::Context::current_ctx = sub { \$${short_name}::Context; };
-            *AI::MXNet::Context::current_context = sub { \$${short_name}::Context; };
-            *AI::MXNet::Context::set_current = sub { \$${short_name}::Context = \$_[1]; };
-            \$${short_name}::Context = AI::MXNet::Context->new(device_type => 'cpu', device_id => 0);
-            package nd;
-            \@nd::ISA = ('AI::MXNet::NDArray');
-            1;
-EOP
-            eval $short_name_package;
-        }
-    }
-}
+sub cpu { AI::MXNet::Context->cpu($_[1]//0) }
+sub cpu_pinned { AI::MXNet::Context->cpu_pinned($_[1]//0) }
+sub gpu { AI::MXNet::Context->gpu($_[1]//0) }
+sub name { __PACKAGE__ }
+sub rtc { __PACKAGE__ }
+sub Prefix { AI::MXNet::Symbol::Prefix->new(prefix => $_[1]) }
+our $AttrScope = AI::MXNet::Symbol::AttrScope->new;
+our $NameManager = AI::MXNet::Symbol::NameManager->new;
+our $Context = AI::MXNet::Context->new(device_type => 'cpu', device_id => 0);
 
 1;
 __END__
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/AutoGrad.pm b/perl-package/AI-MXNet/lib/AI/MXNet/AutoGrad.pm
index c1e5f06e12bd..d6272b5a1def 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/AutoGrad.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/AutoGrad.pm
@@ -18,29 +18,12 @@
 package AI::MXNet::AutoGrad;
 use strict;
 use warnings;
+use AI::MXNet::NS 'global';
 use AI::MXNet::Base;
 use AI::MXNet::Function::Parameters;
 use Scalar::Util qw(blessed);
 use Carp qw(confess);
 
-sub import
-{
-    my ($class, $short_name) = @_;
-    if($short_name)
-    {
-        $short_name =~ s/[^\w:]//g;
-        if(length $short_name)
-        {
-            my $short_name_package =<<"EOP";
-            package $short_name;
-            use parent 'AI::MXNet::AutoGrad';
-            1;
-EOP
-            eval $short_name_package;
-        }
-    }
-}
-
 =head1 NAME
 
     AI::MXNet::AutoGrad - Autograd for NDArray.
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Base.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Base.pm
index 3f6bd8341325..13513c577175 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Base.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Base.pm
@@ -32,14 +32,14 @@ use base qw(Exporter);
 use List::Util qw(shuffle);
 use Data::Dumper;
 
-@AI::MXNet::Base::EXPORT = qw(product enumerate assert zip check_call build_param_doc
-                              pdl cat dog svd bisect_left pdl_shuffle as_array ascsr rand_sparse
-                              DTYPE_STR_TO_MX DTYPE_MX_TO_STR DTYPE_MX_TO_PDL
-                              DTYPE_PDL_TO_MX DTYPE_MX_TO_PERL GRAD_REQ_MAP
-                              STORAGE_TYPE_UNDEFINED STORAGE_TYPE_DEFAULT
-                              STORAGE_TYPE_ROW_SPARSE STORAGE_TYPE_CSR
-                              STORAGE_TYPE_STR_TO_ID STORAGE_TYPE_ID_TO_STR STORAGE_AUX_TYPES);
-@AI::MXNet::Base::EXPORT_OK = qw(pzeros pceil pones digitize hash array_index range);
+our @EXPORT = qw(product enumerate assert zip check_call build_param_doc
+                 pdl cat dog svd bisect_left pdl_shuffle as_array ascsr rand_sparse
+                 DTYPE_STR_TO_MX DTYPE_MX_TO_STR DTYPE_MX_TO_PDL
+                 DTYPE_PDL_TO_MX DTYPE_MX_TO_PERL GRAD_REQ_MAP
+                 STORAGE_TYPE_UNDEFINED STORAGE_TYPE_DEFAULT
+                 STORAGE_TYPE_ROW_SPARSE STORAGE_TYPE_CSR
+                 STORAGE_TYPE_STR_TO_ID STORAGE_TYPE_ID_TO_STR STORAGE_AUX_TYPES);
+our @EXPORT_OK = qw(pzeros pceil pones digitize hash array_index range);
 
 use constant DTYPE_STR_TO_MX => {
     float32 => 0,
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Callback.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Callback.pm
index b2a0b2948154..72ea1bd4502c 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Callback.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Callback.pm
@@ -19,6 +19,7 @@ package AI::MXNet::Callback;
 use strict;
 use warnings;
 use List::Util qw/max/;
+use AI::MXNet::NS;
 use AI::MXNet::Function::Parameters;
 use Mouse;
 use overload "&{}" => sub { my $self = shift; sub { $self->call(@_) } };
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Context.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Context.pm
index 7ae99be7b99e..2cca47f9ab4d 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Context.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Context.pm
@@ -19,6 +19,7 @@ package AI::MXNet::Context;
 use strict;
 use warnings;
 use Mouse;
+use AI::MXNet::NS;
 use AI::MXNet::Base;
 use AI::MXNet::Types;
 use AI::MXNet::Function::Parameters;
@@ -216,12 +217,12 @@ method gpu_memory_info($device_id=0)
 
 method current_ctx()
 {
-    return $AI::MXNet::current_ctx;
+    return $AI::MXNet::Context;
 }
 
 method set_current(AI::MXNet::Context $current)
 {
-    $AI::MXNet::current_ctx = $current;
+    $AI::MXNet::Context = $current;
 }
 
 *current_context = \&current_ctx;
@@ -234,5 +235,6 @@ method deepcopy()
     );
 }
 
-$AI::MXNet::current_ctx = __PACKAGE__->new(device_type => 'cpu', device_id => 0);
+__PACKAGE__->AI::MXNet::NS::register('AI::MXNet');
 
+1;
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Contrib.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Contrib.pm
index c470acab60e7..f9a99f706302 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Contrib.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Contrib.pm
@@ -18,8 +18,9 @@
 package AI::MXNet::Contrib;
 use strict;
 use warnings;
-use AI::MXNet::Contrib::Symbol;
-use AI::MXNet::Contrib::NDArray;
+use AI::MXNet::NS;
+use AI::MXNet::Contrib::Symbol qw(sym symbol);
+use AI::MXNet::Contrib::NDArray qw(nd ndarray);
 
 =head1 NAME
 
@@ -47,9 +48,4 @@ use AI::MXNet::Contrib::NDArray;
     }
 =cut
 
-sub sym    { 'AI::MXNet::Contrib::Symbol'  }
-sub symbol { 'AI::MXNet::Contrib::Symbol'  }
-sub nd     { 'AI::MXNet::Contrib::NDArray' }
-sub ndarray { 'AI::MXNet::Contrib::NDArray' }
-
 1;
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Contrib/NDArray.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Contrib/NDArray.pm
index 574ecc443f79..83e7cbabce5a 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Contrib/NDArray.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Contrib/NDArray.pm
@@ -18,6 +18,7 @@
 package AI::MXNet::Contrib::NDArray;
 use strict;
 use warnings;
+use AI::MXNet::NS;
 use parent 'AI::MXNet::AutoLoad';
 sub config { ('contrib', 'AI::MXNet::NDArray') }
 
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Contrib/Symbol.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Contrib/Symbol.pm
index d5a041a085fe..03f4b90e3373 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Contrib/Symbol.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Contrib/Symbol.pm
@@ -18,6 +18,7 @@
 package AI::MXNet::Contrib::Symbol;
 use strict;
 use warnings;
+use AI::MXNet::NS;
 use parent 'AI::MXNet::AutoLoad';
 sub config { ('contrib', 'AI::MXNet::Symbol') }
 
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/CudaModule.pm b/perl-package/AI-MXNet/lib/AI/MXNet/CudaModule.pm
index b3272fe8b048..67e6b60a0190 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/CudaModule.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/CudaModule.pm
@@ -18,6 +18,7 @@
 package AI::MXNet::CudaModule;
 use strict;
 use warnings;
+use AI::MXNet::NS;
 use AI::MXNet::Base;
 use Mouse;
 use AI::MXNet::Function::Parameters;
@@ -195,6 +196,8 @@ method get_kernel(Str $name, Str $signature)
     return AI::MXNet::CudaKernel->new($handle, $name, \@is_ndarray, \@dtypes);
 }
 
+__PACKAGE__->AI::MXNet::NS::register('AI::MXNet');
+
 package AI::MXNet::CudaKernel;
 use Mouse;
 use AI::MXNet::Base;
@@ -296,4 +299,4 @@ method launch(
     );
 }
 
-1;
\ No newline at end of file
+1;
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Engine.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Engine.pm
index 1d73e5584268..e74be0230d7b 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Engine.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Engine.pm
@@ -20,6 +20,7 @@ use strict;
 use warnings;
 use AI::MXNet::Function::Parameters;
 use AI::MXNet::Base;
+use AI::MXNet::NS;
 
 =head1 NAME
 
@@ -100,4 +101,4 @@ method bulk(Int $size, CodeRef $sub)
     Carp::confess($err) if $err;
 }
 
-1;
\ No newline at end of file
+1;
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon.pm
index fde2f6ac5a63..657be74c5a6d 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon.pm
@@ -18,42 +18,14 @@
 package AI::MXNet::Gluon;
 use strict;
 use warnings;
-use AI::MXNet::Gluon::Loss;
+use AI::MXNet::NS 'global';
+use AI::MXNet::Gluon::Loss 'loss';
 use AI::MXNet::Gluon::Trainer;
 use AI::MXNet::Gluon::Utils;
-use AI::MXNet::Gluon::Data;
-use AI::MXNet::Gluon::NN;
-use AI::MXNet::Gluon::RNN;
-
-sub import
-{
-    my ($class, $short_name) = @_;
-    if($short_name)
-    {
-        $short_name =~ s/[^\w:]//g;
-        if(length $short_name)
-        {
-            my $short_name_package =<<"EOP";
-            package $short_name;
-            no warnings 'redefine';
-            sub data { 'AI::MXNet::Gluon::Data' }
-            sub nn { 'AI::MXNet::Gluon::NN_' }
-            sub rnn { 'AI::MXNet::Gluon::RNN_' }
-            sub loss { 'AI::MXNet::Gluon::Loss_' }
-            sub utils { 'AI::MXNet::Gluon::Utils' }
-            sub model_zoo { require AI::MXNet::Gluon::ModelZoo; 'AI::MXNet::Gluon::ModelZoo' }
-            sub Trainer { shift; AI::MXNet::Gluon::Trainer->new(\@_); }
-            sub Parameter { shift; AI::MXNet::Gluon::Parameter->new(\@_); }
-            sub ParameterDict { shift; AI::MXNet::Gluon::ParameterDict->new(\@_); }
-            \@${short_name}::ISA = ('AI::MXNet::Gluon_');
-            1;
-EOP
-            eval $short_name_package;
-        }
-    }
-}
+use AI::MXNet::Gluon::Data 'data';
+use AI::MXNet::Gluon::NN 'nn';
+use AI::MXNet::Gluon::RNN 'rnn';
 
-sub data { 'AI::MXNet::Gluon::Data' }
 sub utils { 'AI::MXNet::Gluon::Utils' }
 sub model_zoo { require AI::MXNet::Gluon::ModelZoo; 'AI::MXNet::Gluon::ModelZoo' }
 
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Block.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Block.pm
index 599c3c3bef6e..b58704b5d8a1 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Block.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Block.pm
@@ -731,8 +731,13 @@ method forward(@args)
 method register(Str $container)
 {
     my $sub_name = $self->_class_name;
+    my $dest = $self->can('new');
+    my $func = sub {
+        splice @_, 0, 1, $self;
+        goto $dest;
+    };
     no strict 'refs';
-    *{$container.'_::'.$sub_name} = sub { shift; $self->new(@_) };
+    *{"$container\::$sub_name"} = $func;
 }
 
 =head2 summary
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Data.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Data.pm
index e2287c23dafe..95612d9d8cf3 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Data.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Data.pm
@@ -18,11 +18,11 @@
 package AI::MXNet::Gluon::Data;
 use strict;
 use warnings;
+use AI::MXNet::NS;
 use AI::MXNet::Gluon::Data::Set;
 use AI::MXNet::Gluon::Data::Sampler;
 use AI::MXNet::Gluon::Data::Loader;
-use AI::MXNet::Gluon::Data::Vision;
-sub vision { 'AI::MXNet::Gluon::Data::Vision' }
+use AI::MXNet::Gluon::Data::Vision 'vision';
 
 1;
 
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Data/Vision.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Data/Vision.pm
index 5711af350e5f..1c9d551b3d1e 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Data/Vision.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Data/Vision.pm
@@ -15,6 +15,9 @@
 # specific language governing permissions and limitations
 # under the License.
 
+package AI::MXNet::Gluon::Data::Vision;
+use AI::MXNet::NS;
+
 package AI::MXNet::Gluon::Data::Vision::DownloadedDataSet;
 use strict;
 use warnings;
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Loss.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Loss.pm
index 3eb62eb5a2ef..a5938595df6f 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Loss.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Loss.pm
@@ -18,6 +18,7 @@
 use strict;
 use warnings;
 package AI::MXNet::Gluon::Loss;
+use AI::MXNet::NS;
 use AI::MXNet::Gluon::Block;
 use AI::MXNet::Function::Parameters;
 
@@ -995,4 +996,4 @@ method _cosine_similarity($F, $x, $y, $axis=-1)
 
 __PACKAGE__->register('AI::MXNet::Gluon::Loss');
 
-1;
\ No newline at end of file
+1;
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/NN.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/NN.pm
index 673ee5de3686..c8b4c7f4ef60 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/NN.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/NN.pm
@@ -18,27 +18,10 @@
 package AI::MXNet::Gluon::NN;
 use strict;
 use warnings;
+use AI::MXNet::NS 'global';
 use AI::MXNet::Gluon::Block;
 use AI::MXNet::Gluon::NN::Activation;
 use AI::MXNet::Gluon::NN::BasicLayers;
 use AI::MXNet::Gluon::NN::ConvLayers;
 
-sub import
-{
-    my ($class, $short_name) = @_;
-    if($short_name)
-    {
-        $short_name =~ s/[^\w:]//g;
-        if(length $short_name)
-        {
-            my $short_name_package =<<"EOP";
-            package $short_name;
-            \@${short_name}::ISA = ('AI::MXNet::Gluon::NN_');
-            1;
-EOP
-            eval $short_name_package;
-        }
-    }
-}
-
-1;
\ No newline at end of file
+1;
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/NN/Activation.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/NN/Activation.pm
index 1d6342f4955b..63fd80d705b8 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/NN/Activation.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/NN/Activation.pm
@@ -240,3 +240,5 @@ method hybrid_forward(GluonClass $F, GluonInput $x)
 }
 
 __PACKAGE__->register('AI::MXNet::Gluon::NN');
+
+1;
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/NN/BasicLayers.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/NN/BasicLayers.pm
index 954179095cdf..6c554bfd0626 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/NN/BasicLayers.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/NN/BasicLayers.pm
@@ -272,7 +272,7 @@ sub BUILD
         if(defined $self->activation)
         {
             $self->act(
-                AI::MXNet::Gluon::NN::Activation->new(
+                AI::MXNet::Gluon::NN->Activation(
                     activation => $self->activation,
                     prefix => $self->activation.'_'
                 )
@@ -920,4 +920,4 @@ use overload '""' => sub {
 
 __PACKAGE__->register('AI::MXNet::Gluon::NN');
 
-1;
\ No newline at end of file
+1;
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/NN/ConvLayers.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/NN/ConvLayers.pm
index a4bb89b2b2a2..0e11714d4a41 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/NN/ConvLayers.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/NN/ConvLayers.pm
@@ -156,7 +156,7 @@ sub BUILD
         if(defined $self->activation)
         {
             $self->act(
-                AI::MXNet::Gluon::NN::Activation->new(
+                AI::MXNet::Gluon::NN->Activation(
                     activation => $self->activation,
                     prefix     => $self->activation.'_'
                 )
@@ -1415,4 +1415,4 @@ method hybrid_forward(GluonClass $F, GluonInput $x)
 
 __PACKAGE__->register('AI::MXNet::Gluon::NN');
 
-1;
\ No newline at end of file
+1;
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Parameter.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Parameter.pm
index 89cd0cac6229..a914c1ff68ca 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Parameter.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Parameter.pm
@@ -19,6 +19,7 @@ use strict;
 use warnings;
 use Hash::Ordered;
 package AI::MXNet::Gluon::Parameter;
+use AI::MXNet::NS;
 use AI::MXNet::Function::Parameters;
 
 =head1 NAME 
@@ -799,6 +800,8 @@ method cast(Dtype $dtype)
     });
 }
 
+__PACKAGE__->AI::MXNet::NS::register('AI::MXNet::Gluon');
+
 package AI::MXNet::Gluon::Constant;
 use strict;
 use warnings;
@@ -1323,4 +1326,6 @@ method load(
     }
 }
 
+__PACKAGE__->AI::MXNet::NS::register('AI::MXNet::Gluon');
+
 1;
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/RNN.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/RNN.pm
index cdd9468f228c..bf5736ccbb9e 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/RNN.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/RNN.pm
@@ -18,25 +18,8 @@
 package AI::MXNet::Gluon::RNN;
 use strict;
 use warnings;
+use AI::MXNet::NS 'global';
 use AI::MXNet::Gluon::RNN::Layer;
 use AI::MXNet::Gluon::RNN::Cell;
 
-sub import
-{
-    my ($class, $short_name) = @_;
-    if($short_name)
-    {
-        $short_name =~ s/[^\w:]//g;
-        if(length $short_name)
-        {
-            my $short_name_package =<<"EOP";
-            package $short_name;
-            \@${short_name}::ISA = ('AI::MXNet::Gluon::RNN_');
-            1;
-EOP
-            eval $short_name_package;
-        }
-    }
-}
-
-1;
\ No newline at end of file
+1;
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Trainer.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Trainer.pm
index 6117777eed8f..12cd7c2391c3 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Trainer.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Trainer.pm
@@ -18,6 +18,7 @@
 use strict;
 use warnings;
 package AI::MXNet::Gluon::Trainer;
+use AI::MXNet::NS;
 use AI::MXNet::Base;
 use AI::MXNet::Function::Parameters;
 use IO::File;
@@ -554,4 +555,6 @@ method load_states(Str $fname)
     }
 }
 
+__PACKAGE__->AI::MXNet::NS::register('AI::MXNet::Gluon');
+
 1;
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Utils.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Utils.pm
index 497356da89a8..66d8acc6c5b1 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Utils.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Utils.pm
@@ -25,7 +25,7 @@ use File::Path qw(make_path);
 use HTTP::Tiny;
 use Exporter;
 use base qw(Exporter);
-@AI::MXNet::Gluon::Utils::EXPORT_OK = qw(download check_sha1);
+our @EXPORT_OK = qw(download check_sha1);
 
 =head1 NAME
 
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/IO.pm b/perl-package/AI-MXNet/lib/AI/MXNet/IO.pm
index 19e7cfdb8fe3..5dd3955b2438 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/IO.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/IO.pm
@@ -18,6 +18,7 @@
 package AI::MXNet::IO;
 use strict;
 use warnings;
+use AI::MXNet::NS;
 use AI::MXNet::Base;
 use AI::MXNet::Function::Parameters;
 use Scalar::Util qw/blessed/;
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Image.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Image.pm
index 9c7fa120f343..ab4e7964918b 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Image.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Image.pm
@@ -19,6 +19,7 @@ package AI::MXNet::Image;
 use strict;
 use warnings;
 use Scalar::Util qw(blessed);
+use AI::MXNet::NS;
 use AI::MXNet::Base;
 use AI::MXNet::Function::Parameters;
 use AI::MXNet::Image::NDArray;
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Initializer.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Initializer.pm
index 75c8b1e3dad1..089731f16eee 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Initializer.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Initializer.pm
@@ -45,6 +45,7 @@ around BUILDARGS => sub {
 # Base class for Initializers
 package AI::MXNet::Initializer;
 use Mouse;
+use AI::MXNet::NS;
 use AI::MXNet::Base qw(:DEFAULT pzeros pceil);
 use AI::MXNet::NDArray;
 use JSON::PP;
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/KVStore.pm b/perl-package/AI-MXNet/lib/AI/MXNet/KVStore.pm
index 15aad76c7b4a..259ad0df5191 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/KVStore.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/KVStore.pm
@@ -18,6 +18,7 @@
 package AI::MXNet::KVStore;
 use strict;
 use warnings;
+use AI::MXNet::NS;
 use AI::MXNet::Base;
 use AI::MXNet::NDArray;
 use AI::MXNet::Optimizer;
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/LinAlg.pm b/perl-package/AI-MXNet/lib/AI/MXNet/LinAlg.pm
index be1262fb6a87..3fef1b873cab 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/LinAlg.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/LinAlg.pm
@@ -18,6 +18,7 @@
 package AI::MXNet::LinAlg;
 use strict;
 use warnings;
+use AI::MXNet::NS;
 use AI::MXNet::LinAlg::Symbol;
 use AI::MXNet::LinAlg::NDArray;
 
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Metric.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Metric.pm
index 0941316960a9..4c2baa43d1fd 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Metric.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Metric.pm
@@ -18,6 +18,7 @@
 package AI::MXNet::Metric;
 use strict;
 use warnings;
+use AI::MXNet::NS;
 use AI::MXNet::Function::Parameters;
 use Scalar::Util qw/blessed/;
 use JSON::PP;
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Module.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Module.pm
index 38c2ae645969..097f038ed11a 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Module.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Module.pm
@@ -32,6 +32,7 @@ has [qw/_param_names _fixed_param_names
 ] => (is => 'rw', init_arg => undef);
 
 package AI::MXNet::Module;
+use AI::MXNet::NS;
 use AI::MXNet::Base;
 use AI::MXNet::Function::Parameters;
 use List::Util qw(max);
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Monitor.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Monitor.pm
index 76fdfd24e7e8..9e4a96849a00 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Monitor.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Monitor.pm
@@ -17,6 +17,7 @@
 
 package AI::MXNet::Monitor;
 use Mouse;
+use AI::MXNet::NS;
 use AI::MXNet::Function::Parameters;
 use AI::MXNet::Base;
 
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/NDArray.pm b/perl-package/AI-MXNet/lib/AI/MXNet/NDArray.pm
index f466aaa11a3d..f75cc84b2a8f 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/NDArray.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/NDArray.pm
@@ -59,6 +59,7 @@ package AI::MXNet::NDArray;
 
 use strict;
 use warnings;
+use AI::MXNet::NS;
 use AI::MXNet::Base;
 use AI::MXNet::NDArray::Slice;
 use AI::MXNet::Context;
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/NS.pm b/perl-package/AI-MXNet/lib/AI/MXNet/NS.pm
new file mode 100644
index 000000000000..03cd5f195182
--- /dev/null
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/NS.pm
@@ -0,0 +1,78 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+package AI::MXNet::NS;
+# this class is similar to Exporter, in that it will add an "import"
+# method to the calling package.  It is to allow a package to emulate
+# the python "import mxnet as mx" style aliasing as "use AI::MXNet 'mx'"
+use strict;
+use warnings;
+
+sub _sym : lvalue
+{
+    my ($pkg, $name) = @_;
+    no strict 'refs';
+    *{"$pkg\::$name"};
+}
+
+sub import
+{
+    my (undef, $opt) = @_;
+    my $class = caller();
+    my $func = sub { $class };
+    _sym($class, 'import') = sub {
+        my (undef, @names) = @_;
+        @names = map { s/[^\w:]//sgr } @names;
+        my $target = caller();
+
+        _sym($names[0], '') = _sym($class, '') if
+            @names == 1 and $opt and $opt eq 'global';
+
+        _sym($target, $_) = $func for @names;
+    };
+}
+
+my $autoload_template = q(
+    sub AUTOLOAD
+    {
+        our ($AUTOLOAD, %AUTOLOAD);
+        my $name = $AUTOLOAD =~ s/.*:://sr;
+        my $func = $AUTOLOAD{$name};
+        Carp::carp(qq(Can't locate object method "$name" via package "${\ __PACKAGE__ }"))
+            unless $func;
+        goto $func;
+    }
+);
+
+# using AUTOLOAD here allows for the addition of an AI::MXNet::SomeClass
+# class to coexist with an AI::MXNet->SomeClass() shorthand constructor.
+sub register
+{
+    my ($class, $target) = @_;
+    my $name = $class =~ s/.*:://sr;
+    my $dest = $class->can('new');
+    ${_sym($target, 'AUTOLOAD')}{$name} = sub {
+        splice @_, 0, 1, $class;
+        goto $dest;
+    };
+    return if $target->can('AUTOLOAD');
+    eval sprintf 'package %s { %s }', $target, $autoload_template;
+    die if $@;
+    return;
+}
+
+1;
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Optimizer.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Optimizer.pm
index ad0e45503220..7e78cd384220 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Optimizer.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Optimizer.pm
@@ -18,6 +18,7 @@
 package AI::MXNet::Optimizer;
 use strict;
 use warnings;
+use AI::MXNet::NS;
 use AI::MXNet::Base;
 use AI::MXNet::NDArray;
 use AI::MXNet::Random;
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/RNN.pm b/perl-package/AI-MXNet/lib/AI/MXNet/RNN.pm
index 07e72a755723..6ba9bf431e08 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/RNN.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/RNN.pm
@@ -18,6 +18,7 @@
 package AI::MXNet::RNN;
 use strict;
 use warnings;
+use AI::MXNet::NS;
 use AI::MXNet::Function::Parameters;
 use AI::MXNet::RNN::IO;
 use AI::MXNet::RNN::Cell;
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Random.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Random.pm
index 7a99b1dc7ea9..ddc0edca1ba8 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Random.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Random.pm
@@ -19,6 +19,7 @@ package AI::MXNet::Random;
 use strict;
 use warnings;
 use Scalar::Util qw/blessed/;
+use AI::MXNet::NS;
 use AI::MXNet::Base;
 use AI::MXNet::NDArray::Base;
 use AI::MXNet::Function::Parameters;
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/RecordIO.pm b/perl-package/AI-MXNet/lib/AI/MXNet/RecordIO.pm
index f22e2ce92789..5637260a6b08 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/RecordIO.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/RecordIO.pm
@@ -18,6 +18,7 @@
 package AI::MXNet::RecordIO;
 use strict;
 use warnings;
+use AI::MXNet::NS;
 use AI::MXNet::Function::Parameters;
 use AI::MXNet::Types;
 use AI::MXNet::Base;
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Symbol.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Symbol.pm
index e4953f17031a..32e1105210d5 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Symbol.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Symbol.pm
@@ -24,6 +24,7 @@ package AI::MXNet::Symbol;
 
 use strict;
 use warnings;
+use AI::MXNet::NS;
 use AI::MXNet::Base;
 use AI::MXNet::Symbol::Base;
 use AI::MXNet::Symbol::Random;
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Symbol/AttrScope.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Symbol/AttrScope.pm
index c728ed1b6ce8..549939f006aa 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Symbol/AttrScope.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Symbol/AttrScope.pm
@@ -19,6 +19,7 @@ package AI::MXNet::Symbol::AttrScope;
 use strict;
 use warnings;
 use Mouse;
+use AI::MXNet::NS;
 use AI::MXNet::Function::Parameters;
 around BUILDARGS => sub {
     my $orig  = shift;
@@ -58,7 +59,12 @@ has 'attr' => (
 
 method current()
 {
-    $AI::MXNet::curr_attr_scope;
+    $AI::MXNet::AttrScope;
+}
+
+method set_current(AI::MXNet::Symbol::AttrScope $new)
+{
+    $AI::MXNet::AttrScope = $new;
 }
 
 =head2 get
@@ -83,4 +89,6 @@ method get(Maybe[HashRef[Str]] $attr=)
     return bless (\%ret, 'AI::MXNet::Util::Printable');
 }
 
-$AI::MXNet::curr_attr_scope = __PACKAGE__->new;
+__PACKAGE__->AI::MXNet::NS::register('AI::MXNet');
+
+1;
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Symbol/NameManager.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Symbol/NameManager.pm
index 0126655186fd..2238a4366789 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Symbol/NameManager.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Symbol/NameManager.pm
@@ -79,16 +79,14 @@ method get(Maybe[Str] $name, Str $hint)
 
 method current()
 {
-    $AI::MXNet::Symbol::NameManager;
+    $AI::MXNet::NameManager;
 }
 
 method set_current(AI::MXNet::Symbol::NameManager $new)
 {
-    $AI::MXNet::Symbol::NameManager = $new;
+    $AI::MXNet::NameManager = $new;
 }
 
-$AI::MXNet::Symbol::NameManager = __PACKAGE__->new;
-
 package AI::MXNet::Symbol::Prefix;
 use Mouse;
 
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/TestUtils.pm b/perl-package/AI-MXNet/lib/AI/MXNet/TestUtils.pm
index c04b79c5e4d0..3131692fc981 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/TestUtils.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/TestUtils.pm
@@ -18,6 +18,7 @@
 package AI::MXNet::TestUtils;
 use strict;
 use warnings;
+use AI::MXNet 'mx';
 use PDL;
 use Carp qw(confess);
 use Scalar::Util qw(blessed);
@@ -26,10 +27,10 @@ use AI::MXNet::Function::Parameters;
 use AI::MXNet::Base;
 use Exporter;
 use base qw(Exporter);
-@AI::MXNet::TestUtils::EXPORT_OK = qw(same reldiff almost_equal GetMNIST_ubyte
-                                      GetCifar10 pdl_maximum pdl_minimum mlp2 conv dies_ok
-                                      check_consistency zip assert enumerate same_array dies_like allclose rand_shape_2d
-                                      rand_shape_3d rand_sparse_ndarray random_arrays rand_ndarray randint pdl);
+our @EXPORT_OK = qw(same reldiff almost_equal GetMNIST_ubyte
+                    GetCifar10 pdl_maximum pdl_minimum mlp2 conv dies_ok
+                    check_consistency zip assert enumerate same_array dies_like allclose rand_shape_2d
+                    rand_shape_3d rand_sparse_ndarray random_arrays rand_ndarray randint pdl);
 use constant default_numerical_threshold => 1e-6;
 =head1 NAME
 
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Visualization.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Visualization.pm
index 1574ea58307f..90ec1da4e289 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Visualization.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Visualization.pm
@@ -18,6 +18,7 @@
 package AI::MXNet::Visualization;
 use strict;
 use warnings;
+use AI::MXNet::NS;
 use AI::MXNet::Base;
 use AI::MXNet::Function::Parameters;
 use JSON::PP;
diff --git a/perl-package/AI-MXNet/t/AI-MXNet.t b/perl-package/AI-MXNet/t/AI-MXNet.t
index d876fe3ee12b..ad5a0266e06f 100644
--- a/perl-package/AI-MXNet/t/AI-MXNet.t
+++ b/perl-package/AI-MXNet/t/AI-MXNet.t
@@ -17,5 +17,8 @@
 
 use strict;
 use warnings;
-use Test::More tests => 1;
+use Test::More tests => 3;
 BEGIN { use_ok('AI::MXNet') };
+
+isa_ok(AI::MXNet->Context(), 'AI::MXNet::Context');
+isa_ok(AI::MXNet::Context->new(), 'AI::MXNet::Context');
diff --git a/perl-package/AI-MXNet/t/test_multi_device_exec.t b/perl-package/AI-MXNet/t/test_multi_device_exec.t
index 6a33a2375b7d..1b37e6ee9981 100644
--- a/perl-package/AI-MXNet/t/test_multi_device_exec.t
+++ b/perl-package/AI-MXNet/t/test_multi_device_exec.t
@@ -62,11 +62,11 @@ sub test_ctx_group
         my ($arr, $name) = @$_;
         if(exists $set_stage1{ $name })
         {
-            ok($arr->context == $group2ctx->{stage1});
+            cmp_ok($arr->context, '==', $group2ctx->{stage1});
         }
         else
         {
-            ok($arr->context == $group2ctx->{stage2});
+            cmp_ok($arr->context, '==', $group2ctx->{stage2});
         }
     }
 }

From d82c89a9df74ba7c8f0e42da187ceb7c62bcb355 Mon Sep 17 00:00:00 2001
From: kshitij12345 <kshitijkalambarkar@gmail.com>
Date: Wed, 10 Jul 2019 07:58:04 +0530
Subject: [PATCH 052/813] Opperf: Support Python<3.6 (#15487)

* support python<3.6

* fix: use kwargs

Co-Authored-By: Lin Yuan <apeforest@gmail.com>
---
 benchmark/opperf/opperf.py                | 7 ++++---
 benchmark/opperf/utils/benchmark_utils.py | 4 ++--
 2 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/benchmark/opperf/opperf.py b/benchmark/opperf/opperf.py
index 68b404185bf8..a73db4fdae89 100755
--- a/benchmark/opperf/opperf.py
+++ b/benchmark/opperf/opperf.py
@@ -125,8 +125,9 @@ def main():
                              'output file.')
 
     args = parser.parse_args()
-    logging.info(f"Running MXNet operator benchmarks with the following options: {args}")
-    assert not os.path.isfile(args.output_file), f"Output file {args.output_file} already exists."
+    logging.info("Running MXNet operator benchmarks with the following options: {args}".format(args=args))
+    assert not os.path.isfile(args.output_file),\
+        "Output file {output_file} already exists.".format(output_file=args.output_file)
 
     # 2. RUN BENCHMARKS
     ctx = _parse_mxnet_context(args.ctx)
@@ -140,7 +141,7 @@ def main():
     # 4. Generate list of MXNet operators not covered in benchmarks
     ops_not_covered = get_operators_with_no_benchmark(final_benchmark_results.keys())
     for idx, op in enumerate(ops_not_covered):
-        print(f"{idx}. {op}")
+        print("{idx}. {op}".format(idx=idx, op=op))
 
     return 0
 
diff --git a/benchmark/opperf/utils/benchmark_utils.py b/benchmark/opperf/utils/benchmark_utils.py
index dc4890b3df0f..adf5d533ff52 100644
--- a/benchmark/opperf/utils/benchmark_utils.py
+++ b/benchmark/opperf/utils/benchmark_utils.py
@@ -55,14 +55,14 @@ def _run_nd_operator_performance_test(op, inputs, run_backward, warmup, runs, kw
 
     # Run Benchmarks
     op_benchmark_result = {op.__name__: []}
-    logging.info(f"Begin Benchmark - {op.__name__}")
+    logging.info("Begin Benchmark - {name}".format(name=op.__name__))
     for idx, kwargs in enumerate(kwargs_list):
         _, profiler_output = benchmark_helper_func(op, runs, **kwargs)
 
         # Add inputs used for profiling this operator into result
         profiler_output["inputs"] = inputs[idx]
         op_benchmark_result[op.__name__].append(profiler_output)
-    logging.info(f"Complete Benchmark - {op.__name__}")
+    logging.info("Complete Benchmark - {name}".format(name=op.__name__))
     return op_benchmark_result
 
 

From 7d4d1bc26d52cd87eb97536ff154c7f127b68a55 Mon Sep 17 00:00:00 2001
From: Konrad Heidler <k.heidler@tum.de>
Date: Wed, 10 Jul 2019 04:32:39 +0200
Subject: [PATCH 053/813] Two fixes for info_gan.md example Code (#15323)

* Two fixes for info_gan.md example Code

* retrigger CI
---
 docs/tutorials/gluon/info_gan.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/docs/tutorials/gluon/info_gan.md b/docs/tutorials/gluon/info_gan.md
index 91adf6c75108..c35df69fb6d3 100644
--- a/docs/tutorials/gluon/info_gan.md
+++ b/docs/tutorials/gluon/info_gan.md
@@ -339,9 +339,11 @@ with SummaryWriter(logdir='./logs/') as sw:
                 fake_image = generator(g_input)
 
                 sw.add_scalar(tag='Loss_D', value={'test':d_error_epoch.asscalar()/count}, global_step=counter)
-                sw.add_scalar(tag='Loss_G', value={'test':d_error_epoch.asscalar()/count}, global_step=counter)
+                sw.add_scalar(tag='Loss_G', value={'test':g_error_epoch.asscalar()/count}, global_step=counter)
                 sw.add_image(tag='data_image', image=((fake_image[0]+ 1.0) * 127.5).astype(np.uint8)  , global_step=counter)
                 sw.flush()
+            
+            counter += 1
         
         discriminator.save_parameters("infogan_d_latest.params")
         generator.save_parameters("infogan_g_latest.params")

From 6191dd7083cac7574f8172a4a062bb9408cc7621 Mon Sep 17 00:00:00 2001
From: Lanking <lanking520@live.com>
Date: Wed, 10 Jul 2019 15:22:07 -0700
Subject: [PATCH 054/813] fix the bug on Scala Sparse (#15500)

* fix the bug on sparse tensor

* update jenkins
---
 ci/docker/runtime_functions.sh                   | 16 ++++++++--------
 ci/jenkins/Jenkins_steps.groovy                  |  4 ++--
 .../main/scala/org/apache/mxnet/NDArray.scala    |  4 +++-
 3 files changed, 13 insertions(+), 11 deletions(-)

diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index 0fef2a049125..424ab2cf7e92 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -952,13 +952,6 @@ unittest_ubuntu_python3_quantization_gpu() {
     nosetests-3.4 $NOSE_COVERAGE_ARGUMENTS $NOSE_TIMER_ARGUMENTS --with-xunit --xunit-file nosetests_quantization_gpu.xml --verbose tests/python/quantization_gpu
 }
 
-unittest_ubuntu_cpu_scala() {
-    set -ex
-    scala_prepare
-    cd scala-package
-    mvn -B integration-test
-}
-
 unittest_centos7_cpu_scala() {
     set -ex
     cd /work/mxnet
@@ -1156,12 +1149,19 @@ integrationtest_ubuntu_cpu_dist_kvstore() {
     ../../tools/launch.py -n 3 --launcher local python test_server_profiling.py
 }
 
+integrationtest_ubuntu_cpu_scala() {
+    set -ex
+    scala_prepare
+    cd scala-package
+    mvn -B verify -DskipTests=false
+}
+
 integrationtest_ubuntu_gpu_scala() {
     set -ex
     scala_prepare
     cd scala-package
     export SCALA_TEST_ON_GPU=1
-    mvn -B integration-test -DskipTests=false
+    mvn -B verify -DskipTests=false
 }
 
 integrationtest_ubuntu_gpu_dist_kvstore() {
diff --git a/ci/jenkins/Jenkins_steps.groovy b/ci/jenkins/Jenkins_steps.groovy
index 668d2f7c7dca..4eb84b64fa91 100644
--- a/ci/jenkins/Jenkins_steps.groovy
+++ b/ci/jenkins/Jenkins_steps.groovy
@@ -941,7 +941,7 @@ def test_unix_scala_cpu() {
         ws('workspace/ut-scala-cpu') {
           timeout(time: max_time, unit: 'MINUTES') {
             utils.unpack_and_init('cpu', mx_lib, true)
-            utils.docker_run('ubuntu_cpu', 'unittest_ubuntu_cpu_scala', false)
+            utils.docker_run('ubuntu_cpu', 'integrationtest_ubuntu_cpu_scala', false)
             utils.publish_test_coverage()
           }
         }
@@ -955,7 +955,7 @@ def test_unix_scala_mkldnn_cpu(){
         ws('workspace/ut-scala-mkldnn-cpu') {
           timeout(time: max_time, unit: 'MINUTES') {
             utils.unpack_and_init('mkldnn_cpu', mx_mkldnn_lib, true)
-            utils.docker_run('ubuntu_cpu', 'unittest_ubuntu_cpu_scala', false)
+            utils.docker_run('ubuntu_cpu', 'integrationtest_ubuntu_cpu_scala', false)
             utils.publish_test_coverage()
           }
         }
diff --git a/scala-package/core/src/main/scala/org/apache/mxnet/NDArray.scala b/scala-package/core/src/main/scala/org/apache/mxnet/NDArray.scala
index 1b7b31b32cce..717120bcf984 100644
--- a/scala-package/core/src/main/scala/org/apache/mxnet/NDArray.scala
+++ b/scala-package/core/src/main/scala/org/apache/mxnet/NDArray.scala
@@ -956,7 +956,9 @@ class NDArray private[mxnet](private[mxnet] val handle: NDArrayHandle,
     DType(mxDtype.value)
   }
 
-  val sparseFormat: SparseFormat = {
+  // This is a optimization on the SparseFormat checking
+  // TODO: In some cases, the checking on Sparse is invalid (-1)
+  lazy val sparseFormat: SparseFormat = {
     val mxSF = new RefInt
     checkCall(_LIB.mxNDArrayGetStorageType(handle, mxSF))
     SparseFormat(mxSF.value)

From 7a83883ec3aa2fe8b84dcf26f5f96ee83b4592e2 Mon Sep 17 00:00:00 2001
From: Anirudh Subramanian <anirudh2290@ufl.edu>
Date: Wed, 10 Jul 2019 20:15:29 -0700
Subject: [PATCH 055/813] Improve docs for AMP (#15455)

---
 docs/tutorials/amp/amp_tutorial.md | 49 ++++++++++++++++++++++++++++--
 1 file changed, 47 insertions(+), 2 deletions(-)

diff --git a/docs/tutorials/amp/amp_tutorial.md b/docs/tutorials/amp/amp_tutorial.md
index 31cd23b19ae7..b75ac97b9cc8 100644
--- a/docs/tutorials/amp/amp_tutorial.md
+++ b/docs/tutorials/amp/amp_tutorial.md
@@ -17,7 +17,7 @@
 
 # Using AMP (Automatic Mixed Precision) in MXNet
 
-Training Deep Learning networks is a very computationally intensive task. Novel model architectures tend to have increasing number of layers and parameters, which slows down training. Fortunately, new generations of training hardware as well as software optimizations, make it a feasible task. 
+Training Deep Learning networks is a very computationally intensive task. Novel model architectures tend to have increasing number of layers and parameters, which slows down training. Fortunately, new generations of training hardware as well as software optimizations, make it a feasible task.
 
 However, where most of the (both hardware and software) optimization opportunities exists is in exploiting lower precision (like FP16) to, for example, utilize Tensor Cores available on new Volta and Turing GPUs. While training in FP16 showed great success in image classification tasks, other more complicated neural networks typically stayed in FP32 due to difficulties in applying the FP16 training guidelines.
 
@@ -253,7 +253,10 @@ We got 60% speed increase from 3 additional lines of code!
 
 ## Inference with AMP
 
-To do inference with mixed precision for a trained model in FP32, you can use the conversion APIs: `amp.convert_model` for symbolic model and `amp.convert_hybrid_block` for gluon models. The conversion APIs will take the FP32 model as input and will return a mixed precision model, which can be used to run inference. Below, we demonstrate for a gluon model and a symbolic model: 1. Conversion from FP32 model to mixed precision model 2. Run inference on the mixed precision model.
+To do inference with mixed precision for a trained model in FP32, you can use the conversion APIs: `amp.convert_model` for symbolic model and `amp.convert_hybrid_block` for gluon models. The conversion APIs will take the FP32 model as input and will return a mixed precision model, which can be used to run inference.
+Below, we demonstrate for a gluon model and a symbolic model:
+- Conversion from FP32 model to mixed precision model.
+- Run inference on the mixed precision model.
 
 ```python
 with mx.Context(mx.gpu(0)):
@@ -289,6 +292,48 @@ with mx.Context(mx.gpu(0)):
     print("Conversion and Inference completed successfully")
 ```
 
+You can also customize the operators to run in FP16 versus the operator to run in FP32 or to conditionally run in FP32.
+Also, you can force cast the params wherever possible to FP16. Below is an example which demonstrates both these use cases
+for symbolic model. You can do the same for gluon hybrid block with `amp.convert_hybrid_block` API, `cast_optional_params` flag.
+
+```python
+with mx.Context(mx.gpu(0)):
+    # Below is an example of converting a symbolic model to a mixed precision model
+    # with only Convolution op being force casted to FP16.
+    dir_path = os.path.dirname(os.path.realpath(__file__))
+    model_path = os.path.join(dir_path, 'model')
+    if not os.path.isdir(model_path):
+        os.mkdir(model_path)
+    prefix, epoch = mx.test_utils.download_model("imagenet1k-resnet-18", dst_dir=model_path)
+    sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)
+
+    # All Convolution ops should run in FP16, SoftmaxOutput and FullyConnected should run in FP32
+    # cast_optional_params=True: Force cast params to FP16 wherever possible
+    result_sym, result_arg_params, result_aux_params = amp.convert_model(sym,
+                                                                         arg_params,
+                                                                         aux_params,
+                                                                         target_dtype_ops=["Convolution"],
+                                                                         fp32_ops=["SoftmaxOutput", "FullyConnected"],
+                                                                         cast_optional_params=True)
+
+    # Run dummy inference with the converted symbolic model
+    mod = mx.mod.Module(result_sym, data_names=["data"], label_names=["softmax_label"], context=mx.current_context())
+    mod.bind(data_shapes=[['data', (1, 3, 224, 224)]], label_shapes=[['softmax_label', (1,)]])
+    mod.set_params(result_arg_params, result_aux_params)
+    mod.forward(mx.io.DataBatch(data=[mx.nd.ones((1, 3, 224, 224))],
+                                label=[mx.nd.ones((1,))]))
+    mod.get_outputs()[0].wait_to_read()
+
+    # Assert that the params for conv are in FP16, this is because cast_optional_params is set to True
+    assert mod._arg_params["conv0_weight"].dtype == np.float16
+    # FullyConnected params stay in FP32
+    assert mod._arg_params["fc1_bias"].dtype == np.float32
+
+    print("Conversion and Inference completed successfully")
+
+    # Serialize AMP model and save to disk
+    mod.save_checkpoint("amp_tutorial_model", 0, remove_amp_cast=False)
+```
 
 
 ## Current limitations of AMP

From 68460a927dceef5dfbd5d8db0550df1530ef57fd Mon Sep 17 00:00:00 2001
From: Xinyu Chen <xinyu1.chen@intel.com>
Date: Thu, 11 Jul 2019 11:29:36 +0800
Subject: [PATCH 056/813] [Doc] Add MKL install method apt/yum into tutorial
 (#15491)

* add mkl install method

* update in build instruction

* trigger
---
 docs/install/build_from_source.md      | 5 +++--
 docs/tutorials/mkldnn/MKLDNN_README.md | 2 +-
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/docs/install/build_from_source.md b/docs/install/build_from_source.md
index b47267db8ef5..6fbfa247c037 100644
--- a/docs/install/build_from_source.md
+++ b/docs/install/build_from_source.md
@@ -123,8 +123,7 @@ You can set the BLAS library explicitly by setting the BLAS variable to:
 
 See the [cmake/ChooseBLAS.cmake](https://github.com/apache/incubator-mxnet/blob/master/cmake/ChooseBlas.cmake) file for the options.
 
-Intel's MKL (Math Kernel Library) is one of the most powerful math libraries
-https://software.intel.com/en-us/mkl
+[Intel's MKL (Math Kernel Library)](https://software.intel.com/en-us/mkl) is one of the most powerful math libraries
 
 It has following flavors:
 
@@ -144,6 +143,8 @@ shipped as a subrepo with MXNet source code (see 3rdparty/mkldnn or the [MKL-DNN
 Since the full MKL library is almost always faster than any other BLAS library it's turned on by default,
 however it needs to be downloaded and installed manually before doing `cmake` configuration.
 Register and download on the [Intel performance libraries website](https://software.intel.com/en-us/performance-libraries).
+You can also install MKL through [YUM](https://software.intel.com/en-us/articles/installing-intel-free-libs-and-python-yum-repo)
+or [APT](https://software.intel.com/en-us/articles/installing-intel-free-libs-and-python-apt-repo) Repository.
 
 Note: MKL is supported only for desktop builds and the framework itself supports the following
 hardware:
diff --git a/docs/tutorials/mkldnn/MKLDNN_README.md b/docs/tutorials/mkldnn/MKLDNN_README.md
index 460fa200cc46..516b2b3e796a 100644
--- a/docs/tutorials/mkldnn/MKLDNN_README.md
+++ b/docs/tutorials/mkldnn/MKLDNN_README.md
@@ -214,7 +214,7 @@ With MKL BLAS, the performace is expected to furtherly improved with variable ra
 You can redistribute not only dynamic libraries but also headers, examples and static libraries on accepting the license [Intel Simplified license](https://software.intel.com/en-us/license/intel-simplified-software-license).
 Installing the full MKL installation enables MKL support for all operators under the linalg namespace.
 
-  1. Download and install the latest full MKL version following instructions on the [intel website.](https://software.intel.com/en-us/mkl)
+  1. Download and install the latest full MKL version following instructions on the [intel website.](https://software.intel.com/en-us/mkl) You can also install MKL through [YUM](https://software.intel.com/en-us/articles/installing-intel-free-libs-and-python-yum-repo) or [APT](https://software.intel.com/en-us/articles/installing-intel-free-libs-and-python-apt-repo) Repository.
 
   2. Run `make -j ${nproc} USE_BLAS=mkl`
 

From 1f3195f2bdcc972d236fdd40a958b9f7d6052635 Mon Sep 17 00:00:00 2001
From: Aaron Markham <markhama@amazon.com>
Date: Thu, 11 Jul 2019 14:23:20 -0700
Subject: [PATCH 057/813] Julia docs (#15454)

* add julia docs generation option

* add julia docs to website build

* update links to point to local julia site

* fix mxnet build setting bug

* fix link for site

* update ubuntu guide for julia

* turn building mxnet on by default

* readd env var since CI uses it

* fold julia docs into main website jenkins routine

* fix ubuntu julia setup steps

* cleanup mentions of the old julia docs pipeline
---
 ci/docker/runtime_functions.sh  | 20 +++-----
 ci/jenkins/Jenkins_steps.groovy | 17 +------
 ci/jenkins/Jenkinsfile_website  |  1 -
 docs/api/julia/index.md         | 11 +++--
 docs/install/ubuntu_setup.md    | 84 +++++++++++++++++++++++++++++----
 docs/install/windows_setup.md   |  2 +-
 docs/mxdoc.py                   | 15 +++++-
 docs/settings.ini               | 19 +++++++-
 8 files changed, 123 insertions(+), 46 deletions(-)

diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index 424ab2cf7e92..b00fa3220d17 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -58,7 +58,7 @@ EOF
     else
         echo "NOTE: cython is used."
         return 0
-    fi 
+    fi
 }
 
 build_ccache_wrappers() {
@@ -1415,15 +1415,7 @@ deploy_docs() {
     set -ex
     pushd .
 
-    export CC="ccache gcc"
-    export CXX="ccache g++"
-    make docs SPHINXOPTS=-W USE_MKLDNN=0
-
-    popd
-}
-
-deploy_jl_docs() {
-    set -ex
+    # Setup for Julia docs
     export PATH="/work/julia10/bin:$PATH"
     export MXNET_HOME='/work/mxnet'
     export JULIA_DEPOT_PATH='/work/julia-depot'
@@ -1433,11 +1425,13 @@ deploy_jl_docs() {
     # FIXME
     export LD_PRELOAD='/usr/lib/x86_64-linux-gnu/libjemalloc.so'
     export LD_LIBRARY_PATH=/work/mxnet/lib:$LD_LIBRARY_PATH
+    # End Julia setup
 
-    make -C julia/docs
+    export CC="ccache gcc"
+    export CXX="ccache g++"
+    make docs SPHINXOPTS=-W USE_MKLDNN=0
 
-    # TODO: make Jenkins worker push to MXNet.jl ph-pages branch if master build
-    # ...
+    popd
 }
 
 build_static_scala_mkl() {
diff --git a/ci/jenkins/Jenkins_steps.groovy b/ci/jenkins/Jenkins_steps.groovy
index 4eb84b64fa91..40700ad22bb1 100644
--- a/ci/jenkins/Jenkins_steps.groovy
+++ b/ci/jenkins/Jenkins_steps.groovy
@@ -17,7 +17,7 @@
 // specific language governing permissions and limitations
 // under the License.
 //
-// This file contains the steps that will be used in the 
+// This file contains the steps that will be used in the
 // Jenkins pipelines
 
 utils = load('ci/Jenkinsfile_utils.groovy')
@@ -1381,7 +1381,7 @@ def docs_website() {
             if ( master_url == 'jenkins.mxnet-ci.amazon-ml.com') {
                 sh "ci/other/ci_deploy_doc.sh ${env.BRANCH_NAME} ${env.BUILD_NUMBER}"
             } else {
-                print "Skipping staging documentation publishing since we are not running in prod. Host: {$master_url}" 
+                print "Skipping staging documentation publishing since we are not running in prod. Host: {$master_url}"
             }
           }
         }
@@ -1389,19 +1389,6 @@ def docs_website() {
     }]
 }
 
-def docs_julia() {
-    return ['Julia docs': {
-      node(NODE_LINUX_CPU) {
-        ws('workspace/julia-docs') {
-          timeout(time: max_time, unit: 'MINUTES') {
-            utils.unpack_and_init('cpu', mx_lib)
-            utils.docker_run('ubuntu_cpu', 'deploy_jl_docs', false)
-          }
-        }
-      }
-    }]
-}
-
 def misc_asan_cpu() {
     return ['CPU ASAN': {
       node(NODE_LINUX_CPU) {
diff --git a/ci/jenkins/Jenkinsfile_website b/ci/jenkins/Jenkinsfile_website
index acdd2be4d00e..5e97696a2953 100644
--- a/ci/jenkins/Jenkinsfile_website
+++ b/ci/jenkins/Jenkinsfile_website
@@ -39,7 +39,6 @@ core_logic: {
 
   utils.parallel_stage('Deploy', [
     custom_steps.docs_website(),
-    custom_steps.docs_julia()
   ])
 }
 ,
diff --git a/docs/api/julia/index.md b/docs/api/julia/index.md
index 8aa884eb2772..3eb7df68b4e1 100644
--- a/docs/api/julia/index.md
+++ b/docs/api/julia/index.md
@@ -17,7 +17,7 @@
 
 # MXNet - Julia API
 
-See the [MXNet Julia Reference Manual](https://media.readthedocs.org/pdf/mxnet-test/latest/mxnet-test.pdf).
+See the [MXNet Julia Site](site/index.html) for examples and API reference docs.
 
 MXNet supports the Julia programming language. The MXNet Julia package brings flexible and efficient GPU
 computing and the state-of-art deep learning to Julia.
@@ -26,8 +26,9 @@ computing and the state-of-art deep learning to Julia.
 - It also enables you to construct and customize the state-of-art deep learning models in Julia,
   and apply them to tasks such as image classification and data science challenges.
 
+## Installation
+* [Ubuntu installation guide](../../install/ubuntu_setup.html)
+* Mac / Windows guides are not available (contributions welcome!)
 
-&nbsp;
-
-## Julia API Reference
-Julia documents are available at [http://dmlc.ml/MXNet.jl/latest/](http://dmlc.ml/MXNet.jl/latest/).
+## Docs
+To build your own copy of the [MXNet Julia Site](site/index.html), run `make -C julia/docs` from the MXNet source root directory. You can also generate it with Docker by using `dev_menu.py` from the root directory and choosing to build the entire website. The Julia site will be located in `api/julia/site/`.
diff --git a/docs/install/ubuntu_setup.md b/docs/install/ubuntu_setup.md
index ef700b4f353b..db8a0bb48067 100644
--- a/docs/install/ubuntu_setup.md
+++ b/docs/install/ubuntu_setup.md
@@ -310,25 +310,93 @@ Refer to the [Clojure setup guide](https://github.com/apache/incubator-mxnet/tre
 
 ### Install the MXNet Package for Julia
 
-The MXNet package for Julia is hosted in a separate repository, MXNet.jl, which is available on [GitHub](https://github.com/dmlc/MXNet.jl). To use Julia binding it with an existing libmxnet installation, set the ```MXNET_HOME``` environment variable by running the following command:
+#### Install Julia
+The package available through `apt-get` is old and not compatible with the latest version of MXNet.
+Fetch the latest version (1.0.3 at the time of this writing).
 
 ```bash
-    export MXNET_HOME=/<path to>/libmxnet
+wget -qO julia-10.tar.gz https://julialang-s3.julialang.org/bin/linux/x64/1.0/julia-1.0.3-linux-x86_64.tar.gz
 ```
 
-The path to the existing libmxnet installation should be the root directory of libmxnet. In other words, you should be able to find the ```libmxnet.so``` file at ```$MXNET_HOME/lib```. For example, if the root directory of libmxnet is ```~```, you would run the following command:
+Place the extracted files somewhere like a julia folder in your home dir.
 
 ```bash
-    export MXNET_HOME=/~/libmxnet
+mkdir ~/julia
+mv julia-10.tar.gz ~/julia
+cd ~/julia
+tar xvf julia-10.tar.gz
 ```
 
-You might want to add this command to your ```~/.bashrc``` file. If you do, you can install the Julia package in the Julia console using the following command:
+Test Julia.
+```bash
+cd julia-1.0.3/bin
+julia -e 'using InteractiveUtils; versioninfo()'
+```
+
+If you're still getting the old version, remove it.
+```bash
+sudo apt remove julia
+```
+
+Update your PATH to have Julia's new location. Add this to your `.zshrc`, `.bashrc`, `.profile` or `.bash_profile`.
+```bash
+export PATH=~/julia/julia-1.0.3/bin:$PATH
+```
+
+Validate your PATH.
+```bash
+echo $PATH
+```
+
+Validate Julia works and is the expected version.
+```bash
+julia -e 'using InteractiveUtils; versioninfo()'
+```
+
+#### Setup Your MXNet-Julia Environment
+
+**For each of the following environment variables, add the commands to your `.zshrc`, `.bashrc`, `.profile` or `.bash_profile` to make them persist.**
+
+Create a `julia-depot` folder and environment variable.
+```bash
+mkdir julia-depot
+export JULIA_DEPOT_PATH=$HOME/julia/julia-depot
+```
+
+To use the Julia binding with an existing `libmxnet` installation, set the `MXNET_HOME` environment variable to the MXNet source root. For example:
+```bash
+export MXNET_HOME=$HOME/incubator-mxnet
+```
 
-```julia
-    Pkg.add("MXNet")
+Now set the `LD_LIBRARY_PATH` environment variable to where `libmxnet.so` is found. If you can't find it, you might have skipped the building MXNet step. Go back and [build MXNet](#build-the-shared-library) first. For example:
+```bash
+export LD_LIBRARY_PATH=$HOME/incubator-mxnet/lib:$LD_LIBRARY_PATH
+```
+
+Verify the location of `libjemalloc.so` and set the `LD_PRELOAD` environment variable.
+```bash
+export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libjemalloc.so:$LD_PRELOAD
+```
+
+With all of these updates, here's an example of what you might want to have in your `.zshrc`, `.bashrc`, `.profile` or `.bash_profile`.
+
+```
+export PATH=$HOME/bin:$HOME/.local/bin:$HOME/julia/julia-1.0.3/bin:$PATH
+export JULIA_DEPOT_PATH=$HOME/julia/julia-depot
+export MXNET_HOME=$HOME/incubator-mxnet
+export LD_LIBRARY_PATH=$HOME/incubator-mxnet/lib:$LD_LIBRARY_PATH
+export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libjemalloc.so:$LD_PRELOAD
+```
+
+Install MXNet with Julia:
+
+```bash
+julia --color=yes --project=./ -e \
+	  'using Pkg; \
+	   Pkg.develop(PackageSpec(name="MXNet", path = joinpath(ENV["MXNET_HOME"], "julia")))'
 ```
 
-For more details about installing and using MXNet with Julia, see the [MXNet Julia documentation](http://dmlc.ml/MXNet.jl/latest/user-guide/install/).
+For more details about installing and using MXNet with Julia, see the [MXNet Julia documentation](../api/julia/site/).
 <hr>
 
 
diff --git a/docs/install/windows_setup.md b/docs/install/windows_setup.md
index f2561488be4d..4fb4e565bf8c 100644
--- a/docs/install/windows_setup.md
+++ b/docs/install/windows_setup.md
@@ -494,7 +494,7 @@ You might want to add this command to your ```~/.bashrc``` file. If you do, you
 	Pkg.add("MXNet")
 ```
 
-For more details about installing and using MXNet with Julia, see the [MXNet Julia documentation](http://dmlc.ml/MXNet.jl/latest/user-guide/install/).
+For more details about installing and using MXNet with Julia, see the [MXNet Julia documentation](/api/julia/site/).
 
 
 ## Installing the MXNet Package for Scala
diff --git a/docs/mxdoc.py b/docs/mxdoc.py
index 280f2a6ce282..f70dcfad1d0e 100644
--- a/docs/mxdoc.py
+++ b/docs/mxdoc.py
@@ -41,7 +41,7 @@
 
 for section in [ _DOC_SET ]:
     print("Document sets to generate:")
-    for candidate in [ 'scala_docs', 'java_docs', 'clojure_docs', 'doxygen_docs', 'r_docs' ]:
+    for candidate in [ 'scala_docs', 'java_docs', 'clojure_docs', 'doxygen_docs', 'julia_docs', 'r_docs' ]:
         print('%-12s  : %s' % (candidate, parser.get(section, candidate)))
 
 _MXNET_DOCS_BUILD_MXNET = parser.getboolean('mxnet', 'build_mxnet')
@@ -50,6 +50,7 @@
 _CLOJURE_DOCS = parser.getboolean(_DOC_SET, 'clojure_docs')
 _DOXYGEN_DOCS = parser.getboolean(_DOC_SET,  'doxygen_docs')
 _R_DOCS = parser.getboolean(_DOC_SET, 'r_docs')
+_JULIA_DOCS = parser.getboolean(_DOC_SET, 'julia_docs')
 _ARTIFACTS = parser.getboolean(_DOC_SET, 'artifacts')
 
 # white list to evaluate the code block output, such as ['tutorials/gluon']
@@ -95,6 +96,13 @@ def build_mxnet(app):
         _run_cmd("cd %s/.. && make -j$(nproc) USE_MKLDNN=0 USE_CPP_PACKAGE=1 " %
                 app.builder.srcdir)
 
+def build_julia_docs(app):
+    """build Julia docs"""
+    dest_path = app.builder.outdir + '/api/julia/site'
+    _run_cmd('cd {}/.. && make -C julia/docs'.format(app.builder.srcdir))
+    _run_cmd('mkdir -p {}'.format(dest_path))
+    _run_cmd('cd {}/.. && cp -a julia/docs/site/* {}'.format(app.builder.srcdir, dest_path))
+
 def build_r_docs(app):
     """build r pdf"""
     r_root = app.builder.srcdir + '/../R-package'
@@ -458,7 +466,7 @@ def copy_artifacts(app):
 def setup(app):
     # If MXNET_DOCS_BUILD_MXNET is set something different than 1
     # Skip the build step
-    if os.getenv('MXNET_DOCS_BUILD_MXNET', '1') == '1' or _MXNET_DOCS_BUILD_MXNET:
+    if os.getenv('MXNET_DOCS_BUILD_MXNET') == '1'or _MXNET_DOCS_BUILD_MXNET:
         print("Building MXNet!")
         app.connect("builder-inited", build_mxnet)
     if _DOXYGEN_DOCS:
@@ -476,6 +484,9 @@ def setup(app):
     if _CLOJURE_DOCS:
         print("Building Clojure Docs!")
         app.connect("builder-inited", build_clojure_docs)
+    if _JULIA_DOCS:
+        print("Building Julia Docs!")
+        app.connect("builder-inited", build_julia_docs)
     if _R_DOCS:
         print("Building R Docs!")
         app.connect("builder-inited", build_r_docs)
diff --git a/docs/settings.ini b/docs/settings.ini
index ec64dd784cdb..22887f7a576b 100644
--- a/docs/settings.ini
+++ b/docs/settings.ini
@@ -16,7 +16,7 @@
 # under the License.
 
 [mxnet]
-build_mxnet = 0
+build_mxnet = 1
 
 [document_sets_tutorial]
 artifacts = 0
@@ -24,6 +24,7 @@ clojure_docs = 0
 doxygen_docs = 1
 java_docs = 0
 r_docs = 0
+julia_docs = 0
 scala_docs = 0
 
 [document_sets_default]
@@ -32,6 +33,7 @@ clojure_docs = 1
 doxygen_docs = 1
 java_docs = 1
 r_docs = 0
+julia_docs = 1
 scala_docs = 1
 
 [document_sets_1.4.0]
@@ -40,6 +42,7 @@ clojure_docs = 1
 doxygen_docs = 1
 java_docs = 1
 r_docs = 0
+julia_docs = 0
 scala_docs = 1
 
 [document_sets_v1.4.x]
@@ -48,6 +51,7 @@ clojure_docs = 1
 doxygen_docs = 1
 java_docs = 1
 r_docs = 0
+julia_docs = 0
 scala_docs = 1
 
 [document_sets_1.3.1]
@@ -56,6 +60,7 @@ clojure_docs = 1
 doxygen_docs = 1
 java_docs = 0
 r_docs = 0
+julia_docs = 0
 scala_docs = 1
 
 [document_sets_1.3.0]
@@ -64,6 +69,7 @@ clojure_docs = 1
 doxygen_docs = 1
 java_docs = 0
 r_docs = 0
+julia_docs = 0
 scala_docs = 1
 
 [document_sets_v1.3.x]
@@ -72,6 +78,7 @@ clojure_docs = 0
 doxygen_docs = 1
 java_docs = 0
 r_docs = 0
+julia_docs = 0
 scala_docs = 0
 
 [document_sets_1.2.0]
@@ -80,6 +87,7 @@ clojure_docs = 0
 doxygen_docs = 1
 java_docs = 0
 r_docs = 0
+julia_docs = 0
 scala_docs = 1
 
 [document_sets_v1.2.0]
@@ -88,6 +96,7 @@ clojure_docs = 0
 doxygen_docs = 1
 java_docs = 0
 r_docs = 0
+julia_docs = 0
 scala_docs = 1
 
 [document_sets_1.1.0]
@@ -96,6 +105,7 @@ clojure_docs = 0
 doxygen_docs = 1
 java_docs = 0
 r_docs = 0
+julia_docs = 0
 scala_docs = 0
 
 [document_sets_v1.1.0]
@@ -104,6 +114,7 @@ clojure_docs = 0
 doxygen_docs = 1
 java_docs = 0
 r_docs = 0
+julia_docs = 0
 scala_docs = 0
 
 [document_sets_1.0.0]
@@ -112,6 +123,7 @@ clojure_docs = 0
 doxygen_docs = 1
 java_docs = 0
 r_docs = 0
+julia_docs = 0
 scala_docs = 0
 
 [document_sets_v1.0.0]
@@ -120,6 +132,7 @@ clojure_docs = 0
 doxygen_docs = 1
 java_docs = 0
 r_docs = 0
+julia_docs = 0
 scala_docs = 0
 
 [document_sets_0.12.0]
@@ -128,6 +141,7 @@ clojure_docs = 0
 doxygen_docs = 1
 java_docs = 0
 r_docs = 0
+julia_docs = 0
 scala_docs = 0
 
 [document_sets_v0.12.0]
@@ -136,6 +150,7 @@ clojure_docs = 0
 doxygen_docs = 1
 java_docs = 0
 r_docs = 0
+julia_docs = 0
 scala_docs = 0
 
 [document_sets_0.11.0]
@@ -144,6 +159,7 @@ clojure_docs = 0
 doxygen_docs = 1
 java_docs = 0
 r_docs = 0
+julia_docs = 0
 scala_docs = 0
 
 [document_sets_v0.11.0]
@@ -152,4 +168,5 @@ clojure_docs = 0
 doxygen_docs = 1
 java_docs = 0
 r_docs = 0
+julia_docs = 0
 scala_docs = 0

From 5ffd598499ee23372b446a4303504c72cace57f4 Mon Sep 17 00:00:00 2001
From: Iblis Lin <iblis@hs.ntnu.edu.tw>
Date: Fri, 12 Jul 2019 05:23:35 +0800
Subject: [PATCH 058/813] CI: upgrade Julia version from 1.0.3 to 1.0.4
 (#15502)

This is a bugfix release. No breaking changes.
see: https://julialang.org/downloads/
---
 ci/docker/install/ubuntu_julia.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ci/docker/install/ubuntu_julia.sh b/ci/docker/install/ubuntu_julia.sh
index 6849fe228502..435ec46db6c7 100755
--- a/ci/docker/install/ubuntu_julia.sh
+++ b/ci/docker/install/ubuntu_julia.sh
@@ -40,4 +40,4 @@ function install_julia() {
 }
 
 install_julia 0.7 0.7.0
-install_julia 1.0 1.0.3
+install_julia 1.0 1.0.4

From 554b1965595fbac10052ce23987773c185ef5e04 Mon Sep 17 00:00:00 2001
From: Yimin Jiang <jymthu@gmail.com>
Date: Fri, 12 Jul 2019 06:18:35 +0800
Subject: [PATCH 059/813] Rebase #13757 to master (#15189)

* Update .gitmodules

* Set ImageNet data augmentation by default

https://github.com/apache/incubator-mxnet/blob/a38278ddebfcc9459d64237086cd7977ec20c70e/example/image-classification/train_imagenet.py#L42

When I try to train imagenet with this line commented, the train-accuracy reaches 99% while the validation-accuracy is only less than 50% (single machine, 8 GPUs, global batchsize=2048, Resnet50). Absolutely this is overfitting.

Then I uncomment this line and try again with the same experiment settings. This time both train and validation accuracy converge to about 70%.

Thus, it seems that this data augmentation is pretty important for ImageNet training. Perhaps it will be better to uncomment this as default, so that future developers won't get confused by the over-fit issue.

* Add argument for imagenet data augmentation

* Enable data-aug with argument

* Update .gitmodules
---
 example/image-classification/common/fit.py     | 4 +++-
 example/image-classification/train_imagenet.py | 4 ++--
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/example/image-classification/common/fit.py b/example/image-classification/common/fit.py
index 5775f30bd885..8e8b0197960a 100755
--- a/example/image-classification/common/fit.py
+++ b/example/image-classification/common/fit.py
@@ -142,6 +142,8 @@ def add_fit_args(parser):
     train.add_argument('--profile-server-suffix', type=str, default='',
                        help='profile server actions into a file with name like rank1_ followed by this suffix \
                              during distributed training')
+    train.add_argument('--use-imagenet-data-augmentation', type=int, default=0,
+                       help='enable data augmentation of ImageNet data, default disabled')
     return train
 
 
@@ -335,4 +337,4 @@ def fit(args, network, data_loader, **kwargs):
     if args.profile_server_suffix:
         mx.profiler.set_state(state='run', profile_process='server')
     if args.profile_worker_suffix:
-        mx.profiler.set_state(state='run', profile_process='worker')
\ No newline at end of file
+        mx.profiler.set_state(state='run', profile_process='worker')
diff --git a/example/image-classification/train_imagenet.py b/example/image-classification/train_imagenet.py
index 0835f5d3ba9b..421c15db73ad 100644
--- a/example/image-classification/train_imagenet.py
+++ b/example/image-classification/train_imagenet.py
@@ -38,8 +38,6 @@ def set_imagenet_aug(aug):
     fit.add_fit_args(parser)
     data.add_data_args(parser)
     data.add_data_aug_args(parser)
-    # uncomment to set standard augmentations for imagenet training
-    # set_imagenet_aug(parser)
     parser.set_defaults(
         # network
         network          = 'resnet',
@@ -56,6 +54,8 @@ def set_imagenet_aug(aug):
         dtype            = 'float32'
     )
     args = parser.parse_args()
+    if args.use_imagenet_data_augmentation:
+        set_imagenet_aug(parser)
 
     # load network
     from importlib import import_module

From 6a564bec3e2c0d8388da1e89d39851e54f055a31 Mon Sep 17 00:00:00 2001
From: Dick Carter <dcarter@nvidia.com>
Date: Fri, 12 Jul 2019 09:53:58 -0700
Subject: [PATCH 060/813] cuda/cuDNN lib version checking.  Force cuDNN v7
 usage. (#15449)

* Add STATIC_ASSERT_{CUDA,CUDNN}_VERSION_GE macros.  Protect rnn.cc against CUDNN_VERSION < 7000.

* Omit cuda/cudnn lib version checks when no visible gpu devices.

* Move STATIC_ASSERT_... to resource.cc.

* Remove function names in cuda/cudnn version check impl.

* Remove runtime cuda lib check- major.minor already needed for program load.
---
 docs/faq/env_var.md      |  10 ++++
 include/mxnet/resource.h |   8 +--
 src/common/cuda_utils.cc | 105 +++++++++++++++++++++++++++++++++++++++
 src/common/cuda_utils.h  |  27 ++++++++++
 src/resource.cc          |  22 ++++----
 5 files changed, 158 insertions(+), 14 deletions(-)
 create mode 100644 src/common/cuda_utils.cc

diff --git a/docs/faq/env_var.md b/docs/faq/env_var.md
index cdd528cd8c8f..a5ba07098005 100644
--- a/docs/faq/env_var.md
+++ b/docs/faq/env_var.md
@@ -242,6 +242,16 @@ If ctypes is used, it must be `mxnet._ctypes.ndarray.NDArrayBase`.
 	- If set to '0', disallows implicit type conversions to Float16 to use Tensor Cores
 	- If set to '1', allows CUDA ops like RNN and Convolution to use TensorCores even with Float32 input data by using implicit type casting to Float16. Only has an effect if `MXNET_CUDA_ALLOW_TENSOR_CORE` is `1`.
 
+* MXNET_CUDA_VERSION_CHECKING
+  - 0(false) or 1(true) ```(default=1)```
+  - If set to '0', disallows various runtime checks of the cuda library version and associated warning messages.
+  - If set to '1', permits these checks (e.g. compile vs. link mismatch, old version no longer CI-tested)
+
+* MXNET_CUDNN_VERSION_CHECKING
+  - 0(false) or 1(true) ```(default=1)```
+  - If set to '0', disallows various runtime checks of the cuDNN library version and associated warning messages.
+  - If set to '1', permits these checks (e.g. compile vs. link mismatch, old version no longer CI-tested)
+
 * MXNET_GLUON_REPO
   - Values: String ```(default='https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/'```
   - The repository url to be used for Gluon datasets and pre-trained models.
diff --git a/include/mxnet/resource.h b/include/mxnet/resource.h
index 34c8f88d1ca9..f8ee6364807c 100644
--- a/include/mxnet/resource.h
+++ b/include/mxnet/resource.h
@@ -44,11 +44,11 @@ struct ResourceRequest {
     kTempSpace,
     /*! \brief common::RandGenerator<xpu> object, which can be used in GPU kernel functions */
     kParallelRandom
-#if MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 7
+#if MXNET_USE_CUDNN == 1
     ,
     /*! \brief cudnnDropoutDescriptor_t object for GPU dropout kernel functions */
     kCuDNNDropoutDesc
-#endif  // MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 7
+#endif  // MXNET_USE_CUDNN == 1
   };
   /*! \brief type of resources */
   Type type;
@@ -162,7 +162,7 @@ struct Resource {
         reinterpret_cast<DType*>(get_space_internal(shape.Size() * sizeof(DType))),
         shape, shape[ndim - 1], stream);
   }
-#if MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 7
+#if MXNET_USE_CUDNN == 1
   /*!
    * \brief Get cudnn dropout descriptor from shared state space.
    *
@@ -175,7 +175,7 @@ struct Resource {
       mshadow::Stream<gpu> *stream,
       const float dropout,
       uint64_t seed) const;
-#endif  // MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 7
+#endif  // MXNET_USE_CUDNN == 1
 
   /*!
    * \brief Get CPU space as mshadow Tensor in specified type.
diff --git a/src/common/cuda_utils.cc b/src/common/cuda_utils.cc
new file mode 100644
index 000000000000..728d1e6681e6
--- /dev/null
+++ b/src/common/cuda_utils.cc
@@ -0,0 +1,105 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file cuda_utils.cc
+ * \brief CUDA debugging utilities.
+ */
+
+#include <mxnet/base.h>
+#include "cuda_utils.h"
+
+#if MXNET_USE_CUDA == 1
+
+namespace mxnet {
+namespace common {
+namespace cuda {
+
+// The oldest version of cuda used in upstream MXNet CI testing, both for unix and windows.
+// Users that have rebuilt MXNet against older versions will we advised with a warning to upgrade
+// their systems to match the CI level.  Minimally, users should rerun the CI locally.
+#if defined(_MSC_VER)
+#define MXNET_CI_OLDEST_CUDA_VERSION  9020
+#else
+#define MXNET_CI_OLDEST_CUDA_VERSION 10000
+#endif
+
+// Dynamic init here will emit a warning if runtime and compile-time cuda lib versions mismatch.
+// Also if the user has recompiled their source to a version no longer tested by upstream CI.
+bool cuda_version_check_performed = []() {
+  // Don't bother with checks if there are no GPUs visible (e.g. with CUDA_VISIBLE_DEVICES="")
+  if (dmlc::GetEnv("MXNET_CUDA_VERSION_CHECKING", true) && Context::GetGPUCount() > 0) {
+    // Not currently performing a runtime check of linked-against vs. compiled-against
+    // cuda runtime library, as major.minor must match for libmxnet.so to even load, per:
+    // https://docs.nvidia.com/deploy/cuda-compatibility/#binary-compatibility
+    if (CUDA_VERSION < MXNET_CI_OLDEST_CUDA_VERSION)
+      LOG(WARNING) << "Upgrade advisory: this mxnet has been built against cuda library version "
+                   << CUDA_VERSION << ", which is older than the oldest version tested by CI ("
+                   << MXNET_CI_OLDEST_CUDA_VERSION << ").  "
+                   << "Set MXNET_CUDA_VERSION_CHECKING=0 to quiet this warning.";
+  }
+  return true;
+}();
+
+}  // namespace cuda
+}  // namespace common
+}  // namespace mxnet
+
+#endif  // MXNET_USE_CUDA
+
+#if MXNET_USE_CUDNN == 1
+
+namespace mxnet {
+namespace common {
+namespace cudnn {
+
+// The oldest version of CUDNN used in upstream MXNet CI testing, both for unix and windows.
+// Users that have rebuilt MXNet against older versions will we advised with a warning to upgrade
+// their systems to match the CI level.  Minimally, users should rerun the CI locally.
+#if defined(_MSC_VER)
+#define MXNET_CI_OLDEST_CUDNN_VERSION 7600
+#else
+#define MXNET_CI_OLDEST_CUDNN_VERSION 7600
+#endif
+
+// Dynamic init here will emit a warning if runtime and compile-time cudnn lib versions mismatch.
+// Also if the user has recompiled their source to a version no longer tested by upstream CI.
+bool cudnn_version_check_performed = []() {
+  // Don't bother with checks if there are no GPUs visible (e.g. with CUDA_VISIBLE_DEVICES="")
+  if (dmlc::GetEnv("MXNET_CUDNN_VERSION_CHECKING", true) && Context::GetGPUCount() > 0) {
+    size_t linkedAgainstCudnnVersion = cudnnGetVersion();
+    if (linkedAgainstCudnnVersion != CUDNN_VERSION)
+      LOG(WARNING) << "cuDNN library mismatch: linked-against version " << linkedAgainstCudnnVersion
+                   << " != compiled-against version " << CUDNN_VERSION << ".  "
+                   << "Set MXNET_CUDNN_VERSION_CHECKING=0 to quiet this warning.";
+    if (CUDNN_VERSION < MXNET_CI_OLDEST_CUDNN_VERSION)
+      LOG(WARNING) << "Upgrade advisory: this mxnet has been built against cuDNN library version "
+                   <<  CUDNN_VERSION << ", which is older than the oldest version tested by CI ("
+                   << MXNET_CI_OLDEST_CUDNN_VERSION << ").  "
+                   << "Set MXNET_CUDNN_VERSION_CHECKING=0 to quiet this warning.";
+  }
+  return true;
+}();
+
+}  // namespace cudnn
+}  // namespace common
+}  // namespace mxnet
+
+#endif  // MXNET_USE_CUDNN
diff --git a/src/common/cuda_utils.h b/src/common/cuda_utils.h
index 0dd9d2db3722..acc8d5fac6df 100644
--- a/src/common/cuda_utils.h
+++ b/src/common/cuda_utils.h
@@ -47,12 +47,20 @@ extern __cuda_fake_struct threadIdx;
 extern __cuda_fake_struct blockIdx;
 #endif
 
+#define QUOTE(x) #x
+#define QUOTEVALUE(x) QUOTE(x)
+
 #if MXNET_USE_CUDA
 
 #include <cuda_runtime.h>
 #include <cublas_v2.h>
 #include <curand.h>
 
+#define STATIC_ASSERT_CUDA_VERSION_GE(min_version) \
+  static_assert(CUDA_VERSION >= min_version, "Compiled-against CUDA version " \
+      QUOTEVALUE(CUDA_VERSION) " is too old, please upgrade system to version " \
+      QUOTEVALUE(min_version) " or later.")
+
 /*!
  * \brief When compiling a __device__ function, check that the architecture is >= Kepler (3.0)
  *        Note that __CUDA_ARCH__ is not defined outside of a __device__ function
@@ -441,6 +449,25 @@ inline cublasMath_t SetCublasMathMode(cublasHandle_t blas_handle, cublasMath_t n
 
 #include <cudnn.h>
 
+// Creating CUDNN_VERSION_AS_STRING as follows avoids a static_assert error message that shows
+// the formula for CUDNN_VERSION, i.e. "1000 * 7 + 100 * 6 + 0" rather than number "7600".
+static_assert(CUDNN_PATCHLEVEL < 100 && CUDNN_MINOR < 10,
+              "CUDNN_VERSION_AS_STRING macro assumptions violated.");
+#if CUDNN_PATCHLEVEL >= 10
+#define CUDNN_VERSION_AS_STRING QUOTEVALUE(CUDNN_MAJOR) \
+                                QUOTEVALUE(CUDNN_MINOR) \
+                                QUOTEVALUE(CUDNN_PATCHLEVEL)
+#else
+#define CUDNN_VERSION_AS_STRING QUOTEVALUE(CUDNN_MAJOR) \
+                                QUOTEVALUE(CUDNN_MINOR) \
+                                "0" QUOTEVALUE(CUDNN_PATCHLEVEL)
+#endif
+
+#define STATIC_ASSERT_CUDNN_VERSION_GE(min_version) \
+  static_assert(CUDNN_VERSION >= min_version, "Compiled-against cuDNN version " \
+      CUDNN_VERSION_AS_STRING " is too old, please upgrade system to version " \
+      QUOTEVALUE(min_version) " or later.")
+
 #define CUDNN_CALL(func)                                                      \
   {                                                                           \
     cudnnStatus_t e = (func);                                                 \
diff --git a/src/resource.cc b/src/resource.cc
index cd6320d393b1..3f461243e499 100644
--- a/src/resource.cc
+++ b/src/resource.cc
@@ -92,9 +92,9 @@ class ResourceManagerImpl : public ResourceManager {
     gpu_temp_space_copy_ = dmlc::GetEnv("MXNET_GPU_TEMP_COPY", 1);
     cpu_native_rand_copy_ = dmlc::GetEnv("MXNET_CPU_PARALLEL_RAND_COPY", 1);
     gpu_native_rand_copy_ = dmlc::GetEnv("MXNET_GPU_PARALLEL_RAND_COPY", 4);
-#if MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 7
+#if MXNET_USE_CUDNN == 1
     gpu_cudnn_dropout_state_copy_ = dmlc::GetEnv("MXNET_GPU_CUDNN_DROPOUT_STATE_COPY", 4);
-#endif  // MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 7
+#endif  // MXNET_USE_CUDNN == 1
     engine_ref_ = Engine::_GetSharedRef();
     storage_ref_ = Storage::_GetSharedRef();
     cpu_rand_.reset(new ResourceRandom<cpu>(
@@ -113,9 +113,9 @@ class ResourceManagerImpl : public ResourceManager {
     gpu_rand_.Clear();
     gpu_space_.Clear();
     gpu_parallel_rand_.Clear();
-#if MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 7
+#if MXNET_USE_CUDNN == 1
     gpu_cudnn_dropout_state_.Clear();
-#endif  // MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 7
+#endif  // MXNET_USE_CUDNN == 1
 #endif
     if (engine_ref_ != nullptr) {
       engine_ref_ = nullptr;
@@ -153,14 +153,14 @@ class ResourceManagerImpl : public ResourceManager {
             return new ResourceParallelRandom<gpu>(ctx, gpu_native_rand_copy_, global_seed_);
           })->GetNext();
         }
-#if MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 7
+#if MXNET_USE_CUDNN == 1
         case ResourceRequest::kCuDNNDropoutDesc: {
           return gpu_cudnn_dropout_state_.Get(ctx.dev_id, [ctx, this]() {
             return new ResourceTempSpace<ResourceRequest::kCuDNNDropoutDesc>(
                 ctx, gpu_cudnn_dropout_state_copy_);
           })->GetNext();
         }
-#endif  // MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 7
+#endif  // MXNET_USE_CUDNN == 1
         default: LOG(FATAL) << "Unknown supported type " << req.type;
       }
 #else
@@ -399,13 +399,13 @@ class ResourceManagerImpl : public ResourceManager {
   common::LazyAllocArray<ResourceTempSpace<ResourceRequest::kTempSpace>> gpu_space_;
   /*! \brief GPU parallel (on device) random number resources */
   common::LazyAllocArray<ResourceParallelRandom<gpu> > gpu_parallel_rand_;
-#if MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 7
+#if MXNET_USE_CUDNN == 1
   /*! \brief number of copies in GPU cudnn dropout descriptor resources */
   int gpu_cudnn_dropout_state_copy_;
   /*! \brief GPU parallel (on device) random number resources */
   common::LazyAllocArray<ResourceTempSpace<ResourceRequest::kCuDNNDropoutDesc>>
     gpu_cudnn_dropout_state_;
-#endif  // MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 7
+#endif  // MXNET_USE_CUDNN == 1
 #endif
 };
 }  // namespace resource
@@ -418,7 +418,7 @@ void* Resource::get_host_space_internal(size_t size) const {
   return static_cast<resource::SpaceAllocator*>(ptr_)->GetHostSpace(size);
 }
 
-#if MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 7
+#if MXNET_USE_CUDNN == 1
 void Resource::get_cudnn_dropout_desc(
     cudnnDropoutDescriptor_t* dropout_desc,
     mshadow::Stream<gpu> *stream,
@@ -442,6 +442,8 @@ void Resource::get_cudnn_dropout_desc(
                                          dropout_state_size,
                                          seed));
   } else {
+    // cudnnRestoreDropoutDescriptor() introduced with cuDNN v7
+    STATIC_ASSERT_CUDNN_VERSION_GE(7000);
     CUDNN_CALL(cudnnRestoreDropoutDescriptor(*dropout_desc, stream->dnn_handle_,
                                              dropout,
                                              state_space->handle.dptr,
@@ -449,7 +451,7 @@ void Resource::get_cudnn_dropout_desc(
                                              seed));
   }
 }
-#endif  // MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 7
+#endif  // MXNET_USE_CUDNN == 1
 
 ResourceManager* ResourceManager::Get() {
   typedef dmlc::ThreadLocalStore<resource::ResourceManagerImpl> inst;

From 9ff6c46edaa09cb468f9083ef9644dace5cce7b8 Mon Sep 17 00:00:00 2001
From: Pedro Larroy <pedro.larroy.lists@gmail.com>
Date: Fri, 12 Jul 2019 11:04:47 -0700
Subject: [PATCH 061/813] Add -R option to ci/build.py to avoid rebuilding
 containers (#15426)

There are a couple of problems which trigger continuous rebuild of the containers which have difficult solutions, this option enables a workaround.
https://github.com/docker/docker.github.io/issues/8886
---
 ci/build.py | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/ci/build.py b/ci/build.py
index 3d6fb9b4d893..e6a183fefa34 100755
--- a/ci/build.py
+++ b/ci/build.py
@@ -416,6 +416,10 @@ def main() -> int:
                         help="Only build the container, don't build the project",
                         action='store_true')
 
+    parser.add_argument("-R", "--run-only",
+                        help="Only run the container, don't rebuild the container",
+                        action='store_true')
+
     parser.add_argument("-a", "--all",
                         help="build for all platforms",
                         action='store_true')
@@ -492,8 +496,12 @@ def signal_handler(signum, _):
         tag = get_docker_tag(platform=platform, registry=args.docker_registry)
         if args.docker_registry:
             load_docker_cache(tag=tag, docker_registry=args.docker_registry)
-        build_docker(platform=platform, docker_binary=docker_binary, registry=args.docker_registry,
+        if not args.run_only:
+            build_docker(platform=platform, docker_binary=docker_binary, registry=args.docker_registry,
                      num_retries=args.docker_build_retries, no_cache=args.no_cache)
+        else:
+            logging.info("Skipping docker build step.")
+
         if args.build_only:
             logging.warning("Container was just built. Exiting due to build-only.")
             return 0

From 2565fa2460495b0524b62460f27eb2d0c2f70648 Mon Sep 17 00:00:00 2001
From: Lai Wei <royweilai@gmail.com>
Date: Fri, 12 Jul 2019 13:17:59 -0700
Subject: [PATCH 062/813] fix nightly CI failure (#15452)

* fix nightly

* update with nosetest and fixed seed

* remove line
---
 ci/docker/runtime_functions.sh                |  6 +-
 docs/tutorials/amp/amp_tutorial.md            |  1 +
 tests/nightly/estimator/test_estimator_cnn.py | 32 ++++++---
 tests/nightly/estimator/test_sentiment_rnn.py | 69 +++++++++----------
 4 files changed, 55 insertions(+), 53 deletions(-)

diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index b00fa3220d17..596b956efc84 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -1403,10 +1403,8 @@ nightly_estimator() {
     set -ex
     cd /work/mxnet/tests/nightly/estimator
     export PYTHONPATH=/work/mxnet/python/
-    python test_estimator_cnn.py --type gpu
-    python test_sentiment_rnn.py --type gpu
-    python test_estimator_cnn.py --type cpu
-    python test_sentiment_rnn.py --type cpu
+    nosetests test_estimator_cnn.py
+    nosetests test_sentiment_rnn.py
 }
 
 # Deploy
diff --git a/docs/tutorials/amp/amp_tutorial.md b/docs/tutorials/amp/amp_tutorial.md
index b75ac97b9cc8..5f9bd1195a1c 100644
--- a/docs/tutorials/amp/amp_tutorial.md
+++ b/docs/tutorials/amp/amp_tutorial.md
@@ -35,6 +35,7 @@ import os
 import logging
 import warnings
 import time
+import numpy as np
 import mxnet as mx
 import mxnet.gluon as gluon
 from mxnet import autograd
diff --git a/tests/nightly/estimator/test_estimator_cnn.py b/tests/nightly/estimator/test_estimator_cnn.py
index c60dc544b347..4a3bb2076524 100644
--- a/tests/nightly/estimator/test_estimator_cnn.py
+++ b/tests/nightly/estimator/test_estimator_cnn.py
@@ -17,14 +17,22 @@
 
 # Test gluon estimator on CNN models
 
-import argparse
-import numpy as np
+import os
+import sys
+
 import mxnet as mx
+import numpy as np
 from mxnet import gluon, init, nd
 from mxnet.gluon import data
 from mxnet.gluon.contrib.estimator import estimator
 from mxnet.gluon.model_zoo import vision
 
+# use with_seed decorator in python/unittest/common.py
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', '..', 'python', 'unittest'))
+from common import with_seed
+import unittest
+
+
 def load_data_mnist(batch_size, resize=None, num_workers=4):
     '''
     Load MNIST dataset
@@ -44,6 +52,7 @@ def load_data_mnist(batch_size, resize=None, num_workers=4):
         num_workers=num_workers)
     return train_iter, test_iter
 
+
 def bilinear_kernel(in_channels, out_channels, kernel_size):
     '''
     Bilinear interpolation using transposed convolution
@@ -60,6 +69,7 @@ def bilinear_kernel(in_channels, out_channels, kernel_size):
     weight[range(in_channels), range(out_channels), :, :] = filt
     return nd.array(weight)
 
+
 def get_net(model_name, context):
     if model_name == 'FCN':
         num_classes = 21
@@ -82,6 +92,8 @@ def get_net(model_name, context):
         loss_axis = -1
     return net, input_shape, label_shape, loss_axis
 
+
+@with_seed()
 def test_estimator_cpu():
     '''
     Test estimator by doing one pass over each model with synthetic data
@@ -112,6 +124,10 @@ def test_estimator_cpu():
                 val_data=val_data,
                 epochs=1)
 
+
+# using fixed seed to reduce flakiness in accuracy assertion
+@with_seed(7)
+@unittest.skipIf(mx.context.num_gpus() < 1, "skip if no GPU")
 def test_estimator_gpu():
     '''
     Test estimator by training resnet18_v1 for 5 epochs on MNIST and verify accuracy
@@ -139,13 +155,7 @@ def test_estimator_gpu():
 
     assert acc.get()[1] > 0.80
 
+
 if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='test gluon estimator')
-    parser.add_argument('--type', type=str, default='cpu')
-    opt = parser.parse_args()
-    if opt.type == 'cpu':
-        test_estimator_cpu()
-    elif opt.type == 'gpu':
-        test_estimator_gpu()
-    else:
-        raise RuntimeError("Unknown test type")
+    import nose
+    nose.runmodule()
diff --git a/tests/nightly/estimator/test_sentiment_rnn.py b/tests/nightly/estimator/test_sentiment_rnn.py
index d54af491f325..d2eef9b6733c 100644
--- a/tests/nightly/estimator/test_sentiment_rnn.py
+++ b/tests/nightly/estimator/test_sentiment_rnn.py
@@ -20,17 +20,23 @@
 https://github.com/d2l-ai/d2l-en/blob/master/chapter_natural-language-processing/sentiment-analysis-rnn.md
 https://github.com/d2l-ai/d2l-en/blob/master/chapter_natural-language-processing/sentiment-analysis-cnn.md"""
 
-import argparse
+import collections
 import os
-import tarfile
 import random
-import collections
+import sys
+import tarfile
+
 import mxnet as mx
 from mxnet import nd, gluon
 from mxnet.contrib import text
 from mxnet.gluon import nn, rnn
 from mxnet.gluon.contrib.estimator import estimator
 
+# use with_seed decorator in python/unittest/common.py
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', '..', 'python', 'unittest'))
+from common import with_seed
+import unittest
+
 
 class TextCNN(nn.Block):
     def __init__(self, vocab, embed_size, kernel_sizes, num_channels,
@@ -175,14 +181,10 @@ def pad(x):
     return features, labels
 
 
-def run(net, train_dataloader, test_dataloader, **kwargs):
+def run(net, train_dataloader, test_dataloader, num_epochs, ctx, lr):
     '''
     Train a test sentiment model
     '''
-    num_epochs = kwargs['epochs']
-    ctx = kwargs['ctx']
-    batch_size = kwargs['batch_size']
-    lr = kwargs['lr']
 
     # Define trainer
     trainer = mx.gluon.Trainer(net.collect_params(), 'adam', {'learning_rate': lr})
@@ -199,14 +201,17 @@ def run(net, train_dataloader, test_dataloader, **kwargs):
     return acc
 
 
-def test_estimator_cpu(**kwargs):
+@with_seed()
+def test_estimator_cpu():
     '''
     Test estimator by doing one pass over each model with synthetic data
     '''
     models = ['TextCNN', 'BiRNN']
-    ctx = kwargs['ctx']
-    batch_size = kwargs['batch_size']
-    embed_size = kwargs['embed_size']
+    ctx = mx.cpu()
+    batch_size = 64
+    embed_size = 100
+    lr = 1
+    num_epochs = 1
 
     train_data = mx.nd.random.randint(low=0, high=100, shape=(2 * batch_size, 500))
     train_label = mx.nd.random.randint(low=0, high=2, shape=(2 * batch_size,))
@@ -229,18 +234,22 @@ def test_estimator_cpu(**kwargs):
             net = BiRNN(vocab_list, embed_size, num_hiddens, num_layers)
         net.initialize(mx.init.Xavier(), ctx=ctx)
 
-        run(net, train_dataloader, val_dataloader, **kwargs)
+        run(net, train_dataloader, val_dataloader, num_epochs=num_epochs, ctx=ctx, lr=lr)
 
 
-def test_estimator_gpu(**kwargs):
+# using fixed seed to reduce flakiness in accuracy assertion
+@with_seed(7)
+@unittest.skipIf(mx.context.num_gpus() < 1, "skip if no GPU")
+def test_estimator_gpu():
     '''
     Test estimator by training Bidirectional RNN for 5 epochs on the IMDB dataset
     and verify accuracy
     '''
-    ctx = kwargs['ctx']
-    batch_size = kwargs['batch_size']
-    num_epochs = kwargs['epochs']
-    embed_size = kwargs['embed_size']
+    ctx = mx.gpu(0)
+    batch_size = 64
+    num_epochs = 5
+    embed_size = 100
+    lr = 0.01
 
     # data
     download_imdb()
@@ -263,27 +272,11 @@ def test_estimator_gpu(**kwargs):
     net.embedding.weight.set_data(glove_embedding.idx_to_vec)
     net.embedding.collect_params().setattr('grad_req', 'null')
 
-    acc = run(net, train_dataloader, test_dataloader, **kwargs)
+    acc = run(net, train_dataloader, test_dataloader, num_epochs=num_epochs, ctx=ctx, lr=lr)
 
     assert acc.get()[1] > 0.70
 
 
-parser = argparse.ArgumentParser(description='test gluon estimator')
-parser.add_argument('--type', type=str, default='cpu')
-opt = parser.parse_args()
-kwargs = {
-    'batch_size': 64,
-    'lr': 0.01,
-    'embed_size': 100
-}
-
-if opt.type == 'cpu':
-    kwargs['ctx'] = mx.cpu()
-    kwargs['epochs'] = 1
-    test_estimator_cpu(**kwargs)
-elif opt.type == 'gpu':
-    kwargs['ctx'] = mx.gpu()
-    kwargs['epochs'] = 5
-    test_estimator_gpu(**kwargs)
-else:
-    raise RuntimeError("Unknown test type")
+if __name__ == '__main__':
+    import nose
+    nose.runmodule()

From 9c5acb452c35cbf7eb8259bd18be5bd88e01bc30 Mon Sep 17 00:00:00 2001
From: Wang Jiajun <wangjiajun.phy@gmail.com>
Date: Fri, 12 Jul 2019 18:17:28 -0500
Subject: [PATCH 063/813] Accelerate ROIPooling layer (#14894)

* refactor roi_pooling backward

* update max_idx

* refactor gpu part

* update

* update max_idx as int

* remove unused arguments

* trigger CI

* fix index type as index_t

* remove assert, fix invalid box

* update

* fix

* trigger CI

* retrigger CI
---
 src/operator/roi_pooling-inl.h |  11 ++-
 src/operator/roi_pooling.cc    | 128 ++++++++-------------------------
 src/operator/roi_pooling.cu    | 127 ++++++++------------------------
 3 files changed, 66 insertions(+), 200 deletions(-)

diff --git a/src/operator/roi_pooling-inl.h b/src/operator/roi_pooling-inl.h
index ce0efe9b07c9..a189fe231826 100644
--- a/src/operator/roi_pooling-inl.h
+++ b/src/operator/roi_pooling-inl.h
@@ -83,7 +83,7 @@ class ROIPoolingOp : public Operator {
     Tensor<xpu, 4, DType> data = in_data[roipool::kData].get<xpu, 4, DType>(s);
     Tensor<xpu, 2, DType> bbox = in_data[roipool::kBox].get<xpu, 2, DType>(s);
     Tensor<xpu, 4, DType> out = out_data[roipool::kOut].get<xpu, 4, DType>(s);
-    Tensor<xpu, 4, DType> max_idx = out_data[roipool::kMaxIdx].get<xpu, 4, DType>(s);
+    Tensor<xpu, 4, index_t> max_idx = out_data[roipool::kMaxIdx].get<xpu, 4, index_t>(s);
     CHECK_EQ(data.CheckContiguous(), true);
     CHECK_EQ(bbox.CheckContiguous(), true);
     CHECK_EQ(out.CheckContiguous(), true);
@@ -114,7 +114,7 @@ class ROIPoolingOp : public Operator {
 
     Tensor<xpu, 4, DType> grad_out = out_grad[roipool::kOut].get<xpu, 4, DType>(s);
     Tensor<xpu, 2, DType> bbox = in_data[roipool::kBox].get<xpu, 2, DType>(s);
-    Tensor<xpu, 4, DType> max_idx = out_data[roipool::kMaxIdx].get<xpu, 4, DType>(s);
+    Tensor<xpu, 4, index_t> max_idx = out_data[roipool::kMaxIdx].get<xpu, 4, index_t>(s);
     Tensor<xpu, 4, DType> grad_in = in_grad[roipool::kData].get<xpu, 4, DType>(s);
     Tensor<xpu, 2, DType> grad_roi = in_grad[roipool::kBox].get<xpu, 2, DType>(s);
     CHECK_EQ(grad_out.CheckContiguous(), true);
@@ -195,6 +195,7 @@ class ROIPoolingProp : public OperatorProperty {
   bool InferType(std::vector<int> *in_type,
                  std::vector<int> *out_type,
                  std::vector<int> *aux_type) const override {
+    using namespace mshadow;
     CHECK_EQ(in_type->size(), 2U);
     int dtype = (*in_type)[0];
     CHECK_EQ(dtype, (*in_type)[1]);
@@ -202,7 +203,11 @@ class ROIPoolingProp : public OperatorProperty {
 
     out_type->clear();
     out_type->push_back(dtype);
-    out_type->push_back(dtype);
+# if MXNET_USE_INT64_TENSOR_SIZE == 1
+    out_type->push_back(kInt64);
+# else
+    out_type->push_back(kInt32);
+# endif
     return true;
   }
 
diff --git a/src/operator/roi_pooling.cc b/src/operator/roi_pooling.cc
index 8862d0db1401..bba3bea5ce6a 100644
--- a/src/operator/roi_pooling.cc
+++ b/src/operator/roi_pooling.cc
@@ -40,12 +40,13 @@ template<typename Dtype>
 inline void ROIPoolForward(const Tensor<cpu, 4, Dtype> &out,
                            const Tensor<cpu, 4, Dtype> &data,
                            const Tensor<cpu, 2, Dtype> &bbox,
-                           const Tensor<cpu, 4, Dtype> &max_idx,
+                           const Tensor<cpu, 4, index_t> &max_idx,
                            const float spatial_scale_) {
   const Dtype *bottom_data = data.dptr_;
   const Dtype *bottom_rois = bbox.dptr_;
   Dtype *top_data = out.dptr_;
-  Dtype *argmax_data = max_idx.dptr_;
+  index_t *argmax_data = max_idx.dptr_;
+  const int batch_size = data.size(0);
   const int channels_ = data.size(1);
   const int height_ = data.size(2);
   const int width_ = data.size(3);
@@ -53,25 +54,25 @@ inline void ROIPoolForward(const Tensor<cpu, 4, Dtype> &out,
   const int pooled_width_ = out.size(3);
 
   const int num_rois = bbox.size(0);
-  const int data_size = data.size(1) * data.size(2) * data.size(3);
-  const int data_size_c = data.size(2) * data.size(3);
-  const int out_size_c = out.size(2) * out.size(3);
-  const int out_size = channels_ * out_size_c;
-  const int max_idx_size_c = max_idx.size(2) * max_idx.size(3);
-  const int max_idx_size = channels_ * max_idx_size_c;
+  const index_t data_size = data.size(1) * data.size(2) * data.size(3);
+  const index_t data_size_c = data.size(2) * data.size(3);
+  const index_t out_size_c = out.size(2) * out.size(3);
+  const index_t out_size = channels_ * out_size_c;
+  const index_t max_idx_size_c = max_idx.size(2) * max_idx.size(3);
+  const index_t max_idx_size = channels_ * max_idx_size_c;
   // For each ROI R = [batch_index x1 y1 x2 y2]: max pool over R
   for (int n = 0; n < num_rois; ++n) {
     // Increment ROI data pointer
     const Dtype *bottom_rois_n = bottom_rois + n * bbox.size(1);
     Dtype *top_data_n = top_data + n * out_size;
-    Dtype *argmax_data_n = argmax_data + n * max_idx_size;
-    int roi_batch_ind = bottom_rois_n[0];
+    index_t *argmax_data_n = argmax_data + n * max_idx_size;
     int roi_start_w = std::round(bottom_rois_n[1] * spatial_scale_);
     int roi_start_h = std::round(bottom_rois_n[2] * spatial_scale_);
     int roi_end_w = std::round(bottom_rois_n[3] * spatial_scale_);
     int roi_end_h = std::round(bottom_rois_n[4] * spatial_scale_);
-    assert(roi_batch_ind >= 0);
-    assert(static_cast<index_t>(roi_batch_ind) < data.size(0) /* batch size */);
+
+    int roi_batch_ind = static_cast<int>(bottom_rois_n[0]);
+    bool is_ind_invalid = (roi_batch_ind < 0) || (roi_batch_ind >= batch_size);
 
     // force malformed ROIs to be 1 * 1
     int roi_height = max(roi_end_h - roi_start_h + 1, 1);
@@ -81,14 +82,15 @@ inline void ROIPoolForward(const Tensor<cpu, 4, Dtype> &out,
     const Dtype bin_size_w = static_cast<Dtype>(roi_width)
                              / static_cast<Dtype>(pooled_width_);
 
-    const Dtype* batch_data = bottom_data + data_size * roi_batch_ind;
+    index_t offset_batch_data = data_size * roi_batch_ind;
 
     #pragma omp parallel for
     for (int c = 0; c < channels_; ++c) {
       // Increment all data pointers
-      const Dtype* batch_data_c = batch_data + c * data_size_c;
+      index_t offset_batch_data_c = offset_batch_data + c * data_size_c;
+      const Dtype* batch_data_c = bottom_data + offset_batch_data_c;
       Dtype* top_data_c = top_data_n + c * out_size_c;
-      Dtype* argmax_data_c = argmax_data_n + c * max_idx_size_c;
+      index_t* argmax_data_c = argmax_data_n + c * max_idx_size_c;
 
       for (int ph = 0; ph < pooled_height_; ++ph) {
         for (int pw = 0; pw < pooled_width_; ++pw) {
@@ -111,18 +113,19 @@ inline void ROIPoolForward(const Tensor<cpu, 4, Dtype> &out,
 
           bool is_empty = (hend <= hstart) || (wend <= wstart);
 
-          const int pool_index = ph * pooled_width_ + pw;
-          if (is_empty) {
+          const index_t pool_index = ph * pooled_width_ + pw;
+          if (is_empty || is_ind_invalid) {
             top_data_c[pool_index] = 0;
             argmax_data_c[pool_index] = -1;
+            continue;
           }
 
           for (int h = hstart; h < hend; ++h) {
             for (int w = wstart; w < wend; ++w) {
-              const int index = h * width_ + w;
+              const index_t index = h * width_ + w;
               if (batch_data_c[index] > top_data_c[pool_index]) {
                 top_data_c[pool_index] = batch_data_c[index];
-                argmax_data_c[pool_index] = index;
+                argmax_data_c[pool_index] = offset_batch_data_c + index;
               }
             }
           }
@@ -137,91 +140,18 @@ template<typename Dtype>
 inline void ROIPoolBackwardAcc(const Tensor<cpu, 4, Dtype> &in_grad,
                                const Tensor<cpu, 4, Dtype> &out_grad,
                                const Tensor<cpu, 2, Dtype> &bbox,
-                               const Tensor<cpu, 4, Dtype> &max_idx,
+                               const Tensor<cpu, 4, index_t> &max_idx,
                                const float spatial_scale_) {
   const Dtype *top_diff = out_grad.dptr_;
-  const Dtype *bottom_rois = bbox.dptr_;
   Dtype *bottom_diff = in_grad.dptr_;
-  Dtype *argmax_data = max_idx.dptr_;
-
-  const int batch_size_ = in_grad.size(0);
-  const int channels_ = in_grad.size(1);
-  const int height_ = in_grad.size(2);
-  const int width_ = in_grad.size(3);
-  const int pooled_height_ = out_grad.size(2);
-  const int pooled_width_ = out_grad.size(3);
-
-  const int num_rois = bbox.size(0);
-
-  for (int b = 0; b < batch_size_; ++b) {
-    for (int c = 0; c < channels_; ++c) {
-      for (int h = 0; h < height_; ++h) {
-        for (int w = 0; w < width_; ++w) {
-          int offset_bottom_diff = (b * channels_ + c) * height_ * width_;
-          offset_bottom_diff += h * width_ + w;
-
-          Dtype gradient = 0;
-          // Accumulate gradient over all ROIs that pooled this element
-          for (int roi_n = 0; roi_n < num_rois; ++roi_n) {
-            const Dtype* offset_bottom_rois = bottom_rois + roi_n * 5;
-            int roi_batch_ind = offset_bottom_rois[0];
-            assert(roi_batch_ind >= 0);
-            assert(roi_batch_ind < batch_size_);
-            if (b != roi_batch_ind) {
-              continue;
-            }
+  index_t *argmax_data = max_idx.dptr_;
 
-            int roi_start_w = std::round(offset_bottom_rois[1] * spatial_scale_);
-            int roi_start_h = std::round(offset_bottom_rois[2] * spatial_scale_);
-            int roi_end_w = std::round(offset_bottom_rois[3] * spatial_scale_);
-            int roi_end_h = std::round(offset_bottom_rois[4] * spatial_scale_);
+  const index_t count = out_grad.shape_.Size();
 
-            bool in_roi = (w >= roi_start_w && w <= roi_end_w &&
-                           h >= roi_start_h && h <= roi_end_h);
-            if (!in_roi) {
-              continue;
-            }
-
-            // force malformed ROIs to be 1 * 1
-            int roi_height = max(roi_end_h - roi_start_h + 1, 1);
-            int roi_width = max(roi_end_w - roi_start_w + 1, 1);
-            const Dtype bin_size_h = static_cast<Dtype>(roi_height)
-                                     / static_cast<Dtype>(pooled_height_);
-            const Dtype bin_size_w = static_cast<Dtype>(roi_width)
-                                     / static_cast<Dtype>(pooled_width_);
-
-            // compute pooled regions correspond to original (h, w) point
-            int phstart = static_cast<int>(floor(static_cast<Dtype>(h - roi_start_h)
-                                                 / bin_size_h));
-            int pwstart = static_cast<int>(floor(static_cast<Dtype>(w - roi_start_w)
-                                                 / bin_size_w));
-            int phend = static_cast<int>(ceil(static_cast<Dtype>(h - roi_start_h + 1)
-                                              / bin_size_h));
-            int pwend = static_cast<int>(ceil(static_cast<Dtype>(w - roi_start_w + 1)
-                                              / bin_size_w));
-
-            // clip to boundaries of pooled region
-            phstart = min(max(phstart, 0), pooled_height_);
-            phend = min(max(phend, 0), pooled_height_);
-            pwstart = min(max(pwstart, 0), pooled_width_);
-            pwend = min(max(pwend, 0), pooled_width_);
-
-            // accumulate over gradients in pooled regions
-            int offset = (roi_n * channels_ + c) * pooled_height_ * pooled_width_;
-            const Dtype* offset_top_diff = top_diff + offset;
-            const Dtype* offset_argmax_data = argmax_data + offset;
-            for (int ph = phstart; ph < phend; ++ph) {
-              for (int pw = pwstart; pw < pwend; ++pw) {
-                const int pooled_index = ph * pooled_width_ + pw;
-                if (static_cast<int>(offset_argmax_data[pooled_index]) == h * width_ + w) {
-                  gradient += offset_top_diff[pooled_index];
-                }
-              }
-            }
-          }
-          bottom_diff[offset_bottom_diff] += gradient;
-        }
-      }
+  for (int index = 0; index < count; ++index) {
+    index_t max_idx = argmax_data[index];
+    if (max_idx >= 0) {
+      bottom_diff[max_idx] += top_diff[index];
     }
   }
 
diff --git a/src/operator/roi_pooling.cu b/src/operator/roi_pooling.cu
index 9ea99b309aaf..b4013a672711 100644
--- a/src/operator/roi_pooling.cu
+++ b/src/operator/roi_pooling.cu
@@ -34,12 +34,12 @@ namespace cuda {
 
 template<typename Dtype>
 __global__ void ROIPoolForwardKernel(const int count, const Dtype* bottom_data,
-                                     const float spatial_scale, const int channels,
-                                     const int height, const int width,
+                                     const float spatial_scale, const int batch_size,
+                                     const int channels, const int height, const int width,
                                      const int pooled_height, const int pooled_width,
                                      const Dtype* bottom_rois, Dtype* top_data,
-                                     Dtype* argmax_data) {
-  for (int index = (blockIdx.x + blockIdx.y * gridDim.x) * blockDim.x + threadIdx.x;
+                                     index_t* argmax_data) {
+  for (index_t index = (blockIdx.x + blockIdx.y * gridDim.x) * blockDim.x + threadIdx.x;
        index < count;
        index += blockDim.x * gridDim.x * gridDim.y) {
     // (n, c, ph, pw) is an element in the pooled output
@@ -49,11 +49,11 @@ __global__ void ROIPoolForwardKernel(const int count, const Dtype* bottom_data,
     int n = index / pooled_width / pooled_height / channels;
 
     bottom_rois += n * 5;
-    int roi_batch_ind = bottom_rois[0];
+    int roi_batch_ind = static_cast<int>(bottom_rois[0]);
 
-    if (roi_batch_ind < 0) {
+    if (roi_batch_ind < 0 || roi_batch_ind >= batch_size) {
       top_data[index] = 0;
-      argmax_data[index] = 0;
+      argmax_data[index] = -1;
       continue;
     }
 
@@ -89,19 +89,20 @@ __global__ void ROIPoolForwardKernel(const int count, const Dtype* bottom_data,
     // Define an empty pooling region to be zero
     Dtype maxval = is_empty ? 0 : -FLT_MAX;
     // If nothing is pooled, argmax = -1 causes nothing to be backprop'd
-    int maxidx = -1;
-    bottom_data += (roi_batch_ind * channels + c) * height * width;
+    index_t maxidx = -1;
+    index_t offset_bottom_data = (roi_batch_ind * channels + c) * height * width;
+    bottom_data += offset_bottom_data;
     for (int h = hstart; h < hend; ++h) {
       for (int w = wstart; w < wend; ++w) {
-        int bottom_index = h * width + w;
+        index_t bottom_index = h * width + w;
         if (bottom_data[bottom_index] > maxval) {
           maxval = bottom_data[bottom_index];
-          maxidx = bottom_index;
+          maxidx = offset_bottom_data + bottom_index;
         }
       }
     }
     top_data[index] = maxval;
-    argmax_data[index] = (Dtype)maxidx;
+    argmax_data[index] = maxidx;
   }
 }
 
@@ -109,13 +110,14 @@ template<typename Dtype>
 inline void ROIPoolForward(const Tensor<gpu, 4, Dtype> &out,
                            const Tensor<gpu, 4, Dtype> &data,
                            const Tensor<gpu, 2, Dtype> &bbox,
-                           const Tensor<gpu, 4, Dtype> &max_idx,
+                           const Tensor<gpu, 4, index_t> &max_idx,
                            const float spatial_scale) {
   const Dtype *bottom_data = data.dptr_;
   const Dtype *bottom_rois = bbox.dptr_;
   Dtype *top_data = out.dptr_;
-  Dtype *argmax_data = max_idx.dptr_;
-  const int count = out.shape_.Size();
+  index_t *argmax_data = max_idx.dptr_;
+  const index_t count = out.shape_.Size();
+  const int batch_size = data.size(0);
   const int channels = data.size(1);
   const int height = data.size(2);
   const int width = data.size(3);
@@ -127,84 +129,21 @@ inline void ROIPoolForward(const Tensor<gpu, 4, Dtype> &out,
   CheckLaunchParam(dimGrid, dimBlock, "ROIPooling Forward");
   cudaStream_t stream = Stream<gpu>::GetStream(out.stream_);
   ROIPoolForwardKernel<Dtype><<<dimGrid, dimBlock, 0, stream>>>(
-      count, bottom_data, spatial_scale, channels, height, width,
+      count, bottom_data, spatial_scale, batch_size, channels, height, width,
       pooled_height, pooled_width, bottom_rois, top_data, argmax_data);
   MSHADOW_CUDA_POST_KERNEL_CHECK(ROIPoolForwardKernel);
 }
 
 template<typename Dtype>
 __global__ void ROIPoolBackwardAccKernel(const int count, const Dtype* top_diff,
-                                         const Dtype* argmax_data, const int num_rois,
-                                         const float spatial_scale, const int channels,
-                                         const int height, const int width,
-                                         const int pooled_height, const int pooled_width,
-                                         Dtype* bottom_diff, const Dtype* bottom_rois) {
-  for (int index = (blockIdx.x + blockIdx.y * gridDim.x) * blockDim.x + threadIdx.x;
+                                         const index_t* argmax_data, Dtype* bottom_diff) {
+  for (index_t index = (blockIdx.x + blockIdx.y * gridDim.x) * blockDim.x + threadIdx.x;
        index < count;
        index += blockDim.x * gridDim.x * gridDim.y) {
-    // (n, c, h, w) coords in bottom data
-    int w = index % width;
-    int h = (index / width) % height;
-    int c = (index / width / height) % channels;
-    int n = index / width / height / channels;
-
-    Dtype gradient = 0;
-    // Accumulate gradient over all ROIs that pooled this element
-    for (int roi_n = 0; roi_n < num_rois; ++roi_n) {
-      const Dtype* offset_bottom_rois = bottom_rois + roi_n * 5;
-      int roi_batch_ind = offset_bottom_rois[0];
-      // Skip if ROI's batch index doesn't match n
-      if (n != roi_batch_ind) {
-        continue;
-      }
-
-      int roi_start_w = round(offset_bottom_rois[1] * spatial_scale);
-      int roi_start_h = round(offset_bottom_rois[2] * spatial_scale);
-      int roi_end_w = round(offset_bottom_rois[3] * spatial_scale);
-      int roi_end_h = round(offset_bottom_rois[4] * spatial_scale);
-
-      // Skip if ROI doesn't include (h, w)
-      const bool in_roi = (w >= roi_start_w && w <= roi_end_w &&
-                           h >= roi_start_h && h <= roi_end_h);
-      if (!in_roi) {
-        continue;
-      }
-
-      int offset = (roi_n * channels + c) * pooled_height * pooled_width;
-      const Dtype* offset_top_diff = top_diff + offset;
-      const Dtype* offset_argmax_data = argmax_data + offset;
-
-      // Compute feasible set of pooled units that could have pooled
-      // this bottom unit
-
-      // Force malformed ROIs to be 1x1
-      int roi_width = max(roi_end_w - roi_start_w + 1, 1);
-      int roi_height = max(roi_end_h - roi_start_h + 1, 1);
-
-      Dtype bin_size_h = static_cast<Dtype>(roi_height)
-                         / static_cast<Dtype>(pooled_height);
-      Dtype bin_size_w = static_cast<Dtype>(roi_width)
-                         / static_cast<Dtype>(pooled_width);
-
-      int phstart = floor(static_cast<Dtype>(h - roi_start_h) / bin_size_h);
-      int phend = ceil(static_cast<Dtype>(h - roi_start_h + 1) / bin_size_h);
-      int pwstart = floor(static_cast<Dtype>(w - roi_start_w) / bin_size_w);
-      int pwend = ceil(static_cast<Dtype>(w - roi_start_w + 1) / bin_size_w);
-
-      phstart = min(max(phstart, 0), pooled_height);
-      phend = min(max(phend, 0), pooled_height);
-      pwstart = min(max(pwstart, 0), pooled_width);
-      pwend = min(max(pwend, 0), pooled_width);
-
-      for (int ph = phstart; ph < phend; ++ph) {
-        for (int pw = pwstart; pw < pwend; ++pw) {
-          if (static_cast<int>(offset_argmax_data[ph * pooled_width + pw]) == (h * width + w)) {
-            gradient += offset_top_diff[ph * pooled_width + pw];
-          }
-        }
-      }
+    index_t max_idx = argmax_data[index];
+    if (max_idx >= 0) {
+      atomicAdd(&bottom_diff[max_idx], top_diff[index]);
     }
-    bottom_diff[index] += gradient;
   }
 }
 
@@ -212,27 +151,19 @@ template<typename Dtype>
 inline void ROIPoolBackwardAcc(const Tensor<gpu, 4, Dtype> &in_grad,
                                const Tensor<gpu, 4, Dtype> &out_grad,
                                const Tensor<gpu, 2, Dtype> &bbox,
-                               const Tensor<gpu, 4, Dtype> &max_idx,
+                               const Tensor<gpu, 4, index_t> &max_idx,
                                const float spatial_scale) {
   const Dtype *top_diff = out_grad.dptr_;
-  const Dtype *bottom_rois = bbox.dptr_;
   Dtype *bottom_diff = in_grad.dptr_;
-  Dtype *argmax_data = max_idx.dptr_;
-  const int count = in_grad.shape_.Size();
-  const int num_rois = bbox.size(0);
-  const int channels = in_grad.size(1);
-  const int height = in_grad.size(2);
-  const int width = in_grad.size(3);
-  const int pooled_height = out_grad.size(2);
-  const int pooled_width = out_grad.size(3);
+  index_t *argmax_data = max_idx.dptr_;
+  const index_t count = out_grad.shape_.Size();
   const int gridSize = (count + kMaxThreadsPerBlock - 1) / kMaxThreadsPerBlock;
   dim3 dimGrid(kMaxGridDim, (gridSize + kMaxGridDim - 1) / kMaxGridDim);
   dim3 dimBlock(kMaxThreadsPerBlock);
   CheckLaunchParam(dimGrid, dimBlock, "ROIPooling Backward");
   cudaStream_t stream = Stream<gpu>::GetStream(in_grad.stream_);
   ROIPoolBackwardAccKernel<Dtype><<<dimGrid, dimBlock, 0, stream>>>(
-      count, top_diff, argmax_data, num_rois, spatial_scale, channels, height, width,
-      pooled_height, pooled_width, bottom_diff, bottom_rois);
+      count, top_diff, argmax_data, bottom_diff);
   MSHADOW_CUDA_POST_KERNEL_CHECK(ROIPoolBackwardAccKernel);
 }
 
@@ -242,7 +173,7 @@ template<typename Dtype>
 inline void ROIPoolForward(const Tensor<gpu, 4, Dtype> &out,
                            const Tensor<gpu, 4, Dtype> &data,
                            const Tensor<gpu, 2, Dtype> &bbox,
-                           const Tensor<gpu, 4, Dtype> &max_idx,
+                           const Tensor<gpu, 4, index_t> &max_idx,
                            const float spatial_scale) {
   cuda::ROIPoolForward(out, data, bbox, max_idx, spatial_scale);
 }
@@ -251,7 +182,7 @@ template<typename Dtype>
 inline void ROIPoolBackwardAcc(const Tensor<gpu, 4, Dtype> &in_grad,
                                const Tensor<gpu, 4, Dtype> &out_grad,
                                const Tensor<gpu, 2, Dtype> &bbox,
-                               const Tensor<gpu, 4, Dtype> &max_idx,
+                               const Tensor<gpu, 4, index_t> &max_idx,
                                const float spatial_scale) {
   cuda::ROIPoolBackwardAcc(in_grad, out_grad, bbox, max_idx, spatial_scale);
 }

From cbb6f7fd6e297c17fd267b29174a4ed29100c757 Mon Sep 17 00:00:00 2001
From: Ruslan Baratov <ruslan_baratov@yahoo.com>
Date: Sat, 13 Jul 2019 03:26:01 +0300
Subject: [PATCH 064/813] Docs: Fix misprints (#15505)

* Docs: Fix 'bahavior' -> 'behavior'

* Docs: Fix 'the the' -> 'the'

* retrigger CI

* retrigger CI
---
 NEWS.md                                                       | 4 ++--
 R-package/R/viz.graph.R                                       | 4 ++--
 contrib/clojure-package/README.md                             | 2 +-
 docs/api/python/gluon/gluon.md                                | 2 +-
 docs/install/windows_setup.md                                 | 2 +-
 docs/tutorials/mkldnn/MKLDNN_README.md                        | 2 +-
 example/gan/CGAN_mnist_R/README.md                            | 2 +-
 include/mxnet/ndarray.h                                       | 2 +-
 perl-package/AI-MXNet/lib/AI/MXNet/Gluon.pm                   | 2 +-
 perl-package/AI-MXNet/lib/AI/MXNet/Module/Base.pm             | 2 +-
 python/mxnet/contrib/onnx/onnx2mx/_op_translations.py         | 2 +-
 python/mxnet/gluon/data/dataloader.py                         | 2 +-
 python/mxnet/module/base_module.py                            | 2 +-
 python/mxnet/module/python_module.py                          | 2 +-
 .../src/main/scala/org/apache/mxnet/module/BaseModule.scala   | 2 +-
 .../mxnetexamples/javaapi/infer/objectdetector/README.md      | 2 +-
 .../apache/mxnetexamples/javaapi/infer/predictor/README.md    | 2 +-
 .../org/apache/mxnetexamples/infer/objectdetector/README.md   | 2 +-
 src/operator/tensor/diag_op-inl.h                             | 2 +-
 src/operator/tensor/matrix_op.cc                              | 2 +-
 tools/staticbuild/README.md                                   | 4 ++--
 21 files changed, 24 insertions(+), 24 deletions(-)

diff --git a/NEWS.md b/NEWS.md
index 59f8de831c50..ee8a73cc4bc8 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -678,8 +678,8 @@ This fixes an buffer overflow detected by ASAN.
   This PR adds or updates the docs for the infer_range feature.
 
   Clarifies the param in the C op docs
-  Clarifies the param in the the Scala symbol docs
-  Adds the param for the the Scala ndarray docs
+  Clarifies the param in the Scala symbol docs
+  Adds the param for the Scala ndarray docs
   Adds the param for the Python symbol docs
   Adds the param for the Python ndarray docs
 
diff --git a/R-package/R/viz.graph.R b/R-package/R/viz.graph.R
index 58043721feb6..ab876afdfa1e 100644
--- a/R-package/R/viz.graph.R
+++ b/R-package/R/viz.graph.R
@@ -34,7 +34,7 @@
 #' @param symbol a \code{string} representing the symbol of a model.
 #' @param shape a \code{numeric} representing the input dimensions to the symbol.
 #' @param direction a \code{string} representing the direction of the graph, either TD or LR.
-#' @param type a \code{string} representing the rendering engine of the the graph, either graph or vis.
+#' @param type a \code{string} representing the rendering engine of the graph, either graph or vis.
 #' @param graph.width.px a \code{numeric} representing the size (width) of the graph. In pixels
 #' @param graph.height.px a \code{numeric} representing the size (height) of the graph. In pixels
 #'
@@ -169,4 +169,4 @@ graph.viz <- function(symbol, shape=NULL, direction="TD", type="graph", graph.wi
   return(graph_render)
 }
 
-globalVariables(c("color", "shape", "label", "id", ".", "op"))
\ No newline at end of file
+globalVariables(c("color", "shape", "label", "id", ".", "op"))
diff --git a/contrib/clojure-package/README.md b/contrib/clojure-package/README.md
index 7566ade66ce8..7bb417edf3d3 100644
--- a/contrib/clojure-package/README.md
+++ b/contrib/clojure-package/README.md
@@ -237,7 +237,7 @@ If you are having trouble getting started or have a question, feel free to reach
 There are quite a few examples in the examples directory. To use.
 
 `lein install` in the main project
-`cd` in the the example project of interest
+`cd` in the example project of interest
 
 There are README is every directory outlining instructions.
 
diff --git a/docs/api/python/gluon/gluon.md b/docs/api/python/gluon/gluon.md
index c063a71d43ea..19e462e8a773 100644
--- a/docs/api/python/gluon/gluon.md
+++ b/docs/api/python/gluon/gluon.md
@@ -28,7 +28,7 @@
 
 The Gluon package is a high-level interface for MXNet designed to be easy to use, while keeping most of the flexibility of a low level API. Gluon supports both imperative and symbolic programming, making it easy to train complex models imperatively in Python and then deploy with a symbolic graph in C++ and Scala.
 
-Based on the the [Gluon API specification](https://github.com/gluon-api/gluon-api), the Gluon API in Apache MXNet provides a clear, concise, and simple API for deep learning. It makes it easy to prototype, build, and train deep learning models without sacrificing training speed.
+Based on the [Gluon API specification](https://github.com/gluon-api/gluon-api), the Gluon API in Apache MXNet provides a clear, concise, and simple API for deep learning. It makes it easy to prototype, build, and train deep learning models without sacrificing training speed.
 
 **Advantages**
 
diff --git a/docs/install/windows_setup.md b/docs/install/windows_setup.md
index 4fb4e565bf8c..8a3b1f3b099e 100644
--- a/docs/install/windows_setup.md
+++ b/docs/install/windows_setup.md
@@ -183,7 +183,7 @@ cd C:\incubator-mxnet\build
 cmake -G "Visual Studio 15 2017 Win64" -T cuda=9.2,host=x64 -DUSE_CUDA=1 -DUSE_CUDNN=1 -DUSE_NVRTC=1 -DUSE_OPENCV=1 -DUSE_OPENMP=1 -DUSE_BLAS=open -DUSE_LAPACK=1 -DUSE_DIST_KVSTORE=0 -DCUDA_ARCH_LIST=Common -DCUDA_TOOLSET=9.2 -DCUDNN_INCLUDE=C:\cuda\include -DCUDNN_LIBRARY=C:\cuda\lib\x64\cudnn.lib "C:\incubator-mxnet"
 ```
 * Make sure you set the environment variables correctly (OpenBLAS_HOME, OpenCV_DIR) and change the version of the Visual studio 2017 to v14.11 before enter above command.
-6. After the CMake successfully completed, compile the the MXNet source code by using following command:
+6. After the CMake successfully completed, compile the MXNet source code by using following command:
 ```
 msbuild mxnet.sln /p:Configuration=Release;Platform=x64 /maxcpucount
 ```
diff --git a/docs/tutorials/mkldnn/MKLDNN_README.md b/docs/tutorials/mkldnn/MKLDNN_README.md
index 516b2b3e796a..c9e940fdeb3e 100644
--- a/docs/tutorials/mkldnn/MKLDNN_README.md
+++ b/docs/tutorials/mkldnn/MKLDNN_README.md
@@ -135,7 +135,7 @@ command:
 >"C:\Program Files (x86)\IntelSWTools\compilers_and_libraries\windows\mkl\bin\mklvars.bat" intel64
 >cmake -G "Visual Studio 14 Win64" .. -DUSE_CUDA=0 -DUSE_CUDNN=0 -DUSE_NVRTC=0 -DUSE_OPENCV=1 -DUSE_OPENMP=1 -DUSE_PROFILER=1 -DUSE_BLAS=mkl -DUSE_LAPACK=1 -DUSE_DIST_KVSTORE=0 -DCUDA_ARCH_NAME=All -DUSE_MKLDNN=1 -DCMAKE_BUILD_TYPE=Release -DMKL_ROOT="C:\Program Files (x86)\IntelSWTools\compilers_and_libraries\windows\mkl" 
 ```
-4. After the CMake successfully completed, in Visual Studio, open the solution file ```.sln``` and compile it, or compile the the MXNet source code by using following command:
+4. After the CMake successfully completed, in Visual Studio, open the solution file ```.sln``` and compile it, or compile the MXNet source code by using following command:
 ```r
 msbuild mxnet.sln /p:Configuration=Release;Platform=x64 /maxcpucount
 ```
diff --git a/example/gan/CGAN_mnist_R/README.md b/example/gan/CGAN_mnist_R/README.md
index bf0bb08b1147..99d2e1c1f63b 100644
--- a/example/gan/CGAN_mnist_R/README.md
+++ b/example/gan/CGAN_mnist_R/README.md
@@ -94,7 +94,7 @@ update_args_D<- updater_D(weight = exec_D$ref.arg.arrays, grad = exec_D$ref.grad
 mx.exec.update.arg.arrays(exec_D, update_args_D, skip.null=TRUE)
 ```
 
-The generator loss comes from the backpropagation of the the discriminator loss into its generated output. By faking the generator labels to be real samples into the discriminator, the discriminator back-propagated loss provides the generator with the information on how to best adapt its parameters to trick the discriminator into believing the fake samples are real.
+The generator loss comes from the backpropagation of the discriminator loss into its generated output. By faking the generator labels to be real samples into the discriminator, the discriminator back-propagated loss provides the generator with the information on how to best adapt its parameters to trick the discriminator into believing the fake samples are real.
 
 This requires to backpropagate the gradients up to the input data of the discriminator (whereas this input gradient is typically ignored in vanilla feedforward network).
 
diff --git a/include/mxnet/ndarray.h b/include/mxnet/ndarray.h
index 34e891e0f336..428245b56d0e 100644
--- a/include/mxnet/ndarray.h
+++ b/include/mxnet/ndarray.h
@@ -197,7 +197,7 @@ class NDArray {
   }
   /*
    * This indicates whether an array is a view of another array (created by
-   * reshape or slice). If an array is a view and the the data is stored in
+   * reshape or slice). If an array is a view and the data is stored in
    * MKLDNN format, we need to convert the data to the default format when
    * data in the view is accessed.
    */
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon.pm
index 657be74c5a6d..1badacb7ece1 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon.pm
@@ -41,7 +41,7 @@ sub model_zoo { require AI::MXNet::Gluon::ModelZoo; 'AI::MXNet::Gluon::ModelZoo'
     AI::MXNet::Gluon supports both imperative and symbolic programming,
     making it easy to train complex models imperatively in Perl.
 
-    Based on the the Gluon API specification,
+    Based on the Gluon API specification,
     the Gluon API in Apache MXNet provides a clear, concise, and simple API for deep learning.
     It makes it easy to prototype, build, and train deep learning models without sacrificing training speed.
 
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Module/Base.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Module/Base.pm
index 542cf498f495..6b572f4cceb5 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Module/Base.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Module/Base.pm
@@ -684,7 +684,7 @@ method output_shapes() { confess("NotImplemented") }
 
 =head2 get_params
 
-    The parameters, these are potentially a copies of the the actual parameters used
+    The parameters, these are potentially a copies of the actual parameters used
     to do computation on the device.
 
     Returns
diff --git a/python/mxnet/contrib/onnx/onnx2mx/_op_translations.py b/python/mxnet/contrib/onnx/onnx2mx/_op_translations.py
index 24bb727e6401..734b438581a5 100644
--- a/python/mxnet/contrib/onnx/onnx2mx/_op_translations.py
+++ b/python/mxnet/contrib/onnx/onnx2mx/_op_translations.py
@@ -24,7 +24,7 @@
 # Method definitions for the callable objects mapped in the import_helper module
 
 def identity(attrs, inputs, proto_obj):
-    """Returns the identity function of the the input."""
+    """Returns the identity function of the input."""
     return 'identity', attrs, inputs
 
 def random_uniform(attrs, inputs, proto_obj):
diff --git a/python/mxnet/gluon/data/dataloader.py b/python/mxnet/gluon/data/dataloader.py
index accd968cc9df..65fd7d84b5bb 100644
--- a/python/mxnet/gluon/data/dataloader.py
+++ b/python/mxnet/gluon/data/dataloader.py
@@ -268,7 +268,7 @@ def shutdown(self):
         if not self._shutdown:
             # send shutdown signal to the fetcher and join data queue first
             # Remark:   loop_fetcher need to be joined prior to the workers.
-            #           otherwise, the the fetcher may fail at getting data
+            #           otherwise, the fetcher may fail at getting data
             self._data_queue.put((None, None))
             self._fetcher.join()
             # send shutdown signal to all worker processes
diff --git a/python/mxnet/module/base_module.py b/python/mxnet/module/base_module.py
index 754e369b4e63..0d5515f172b8 100644
--- a/python/mxnet/module/base_module.py
+++ b/python/mxnet/module/base_module.py
@@ -619,7 +619,7 @@ def output_shapes(self):
     # Parameters of a module
     ################################################################################
     def get_params(self):
-        """Gets parameters, those are potentially copies of the the actual parameters used
+        """Gets parameters, those are potentially copies of the actual parameters used
         to do computation on the device.
 
         Returns
diff --git a/python/mxnet/module/python_module.py b/python/mxnet/module/python_module.py
index df1648e82694..a5d6f157e6a5 100644
--- a/python/mxnet/module/python_module.py
+++ b/python/mxnet/module/python_module.py
@@ -94,7 +94,7 @@ def output_shapes(self):
     # Parameters of a module
     ################################################################################
     def get_params(self):
-        """Gets parameters, those are potentially copies of the the actual parameters used
+        """Gets parameters, those are potentially copies of the actual parameters used
         to do computation on the device. Subclass should override this method if contains
         parameters.
 
diff --git a/scala-package/core/src/main/scala/org/apache/mxnet/module/BaseModule.scala b/scala-package/core/src/main/scala/org/apache/mxnet/module/BaseModule.scala
index 7fbdae5b3e21..f2f4c20b8833 100644
--- a/scala-package/core/src/main/scala/org/apache/mxnet/module/BaseModule.scala
+++ b/scala-package/core/src/main/scala/org/apache/mxnet/module/BaseModule.scala
@@ -306,7 +306,7 @@ abstract class BaseModule {
 
   // Parameters of a module
   /**
-   * Get parameters, those are potentially copies of the the actual parameters used
+   * Get parameters, those are potentially copies of the actual parameters used
    * to do computation on the device.
    * @return `(argParams, auxParams)`, a pair of dictionary of name to value mapping.
    */
diff --git a/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/objectdetector/README.md b/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/objectdetector/README.md
index 4c4512f152c8..f6f8b674c08e 100644
--- a/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/objectdetector/README.md
+++ b/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/objectdetector/README.md
@@ -103,7 +103,7 @@ Class: dog
 Probabilties: 0.8226818
 (Coord:,83.82353,179.13998,206.63783,476.7875)
 ```
-the outputs come from the the input image, with top3 predictions picked.
+the outputs come from the input image, with top3 predictions picked.
 
 
 ## Infer API Details
diff --git a/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/predictor/README.md b/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/predictor/README.md
index 09189cb83268..02e09f39cdcf 100644
--- a/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/predictor/README.md
+++ b/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/predictor/README.md
@@ -75,4 +75,4 @@ Probability : 0.30337515 Class : n02123159 tiger cat
 Predict with NDArray
 Probability : 0.30337515 Class : n02123159 tiger cat
 ```
-the outputs come from the the input image, with top1 predictions picked.
\ No newline at end of file
+the outputs come from the input image, with top1 predictions picked.
diff --git a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/objectdetector/README.md b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/objectdetector/README.md
index e5d3bbee0490..25f040e36e37 100644
--- a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/objectdetector/README.md
+++ b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/objectdetector/README.md
@@ -103,7 +103,7 @@ Class: dog
 Probabilties: 0.8226818
 (Coord:,83.82353,179.13998,206.63783,476.7875)
 ```
-the outputs come from the the input image, with top3 predictions picked.
+the outputs come from the input image, with top3 predictions picked.
 
 
 ## Infer API Details
diff --git a/src/operator/tensor/diag_op-inl.h b/src/operator/tensor/diag_op-inl.h
index c95c1ce414f2..73eb4e1daf54 100644
--- a/src/operator/tensor/diag_op-inl.h
+++ b/src/operator/tensor/diag_op-inl.h
@@ -71,7 +71,7 @@ inline mxnet::TShape DiagShapeImpl(const mxnet::TShape& ishape, const int k,
   int32_t x1 = CheckAxis(axis1, ishape.ndim());
   int32_t x2 = CheckAxis(axis2, ishape.ndim());
 
-  CHECK_NE(x1, x2) << "axis1 and axis2 cannot refer to the the same axis " << x1;
+  CHECK_NE(x1, x2) << "axis1 and axis2 cannot refer to the same axis " << x1;
 
   auto h = ishape[x1];
   auto w = ishape[x2];
diff --git a/src/operator/tensor/matrix_op.cc b/src/operator/tensor/matrix_op.cc
index c2bcb29193a7..e78050aec009 100644
--- a/src/operator/tensor/matrix_op.cc
+++ b/src/operator/tensor/matrix_op.cc
@@ -270,7 +270,7 @@ NNVM_REGISTER_OP(Flatten)
 For an input array with shape ``(d1, d2, ..., dk)``, `flatten` operation reshapes
 the input array into an output array of shape ``(d1, d2*...*dk)``.
 
-Note that the bahavior of this function is different from numpy.ndarray.flatten,
+Note that the behavior of this function is different from numpy.ndarray.flatten,
 which behaves similar to mxnet.ndarray.reshape((-1,)).
 
 Example::
diff --git a/tools/staticbuild/README.md b/tools/staticbuild/README.md
index bfccbab184bd..b861cc3308c6 100644
--- a/tools/staticbuild/README.md
+++ b/tools/staticbuild/README.md
@@ -34,7 +34,7 @@ This would build the mxnet package based on MKLDNN and and pypi configuration se
 As the result, users would have a complete static dependencies in `/staticdeps` in the root folder as well as a static-linked `libmxnet.so` file lives in `lib`. You can build your language binding by using the `libmxnet.so`.
 
 ## `build_lib.sh`
-This script clones the most up-to-date master and builds the MXNet backend with a static library. In order to run the static library, you must set the the following environment variables:
+This script clones the most up-to-date master and builds the MXNet backend with a static library. In order to run the static library, you must set the following environment variables:
 
 - `DEPS_PATH` Path to your static dependencies
 - `STATIC_BUILD_TARGET` Either `pip` or `maven` as your publish platform
@@ -46,4 +46,4 @@ It is not recommended to run this file alone since there are a bunch of variable
 After running this script, you would have everything you need ready in the `/lib` folder.
 
 ## `build_wheel.sh`
-This script builds the python package. It also runs a sanity test.
\ No newline at end of file
+This script builds the python package. It also runs a sanity test.

From d677d1aefd30e58ff9265abd6f66617d10e2771b Mon Sep 17 00:00:00 2001
From: Anirudh Subramanian <anirudh2290@apache.org>
Date: Fri, 12 Jul 2019 19:21:42 -0700
Subject: [PATCH 065/813] FP16 Support for C Predict API (#15245)

* Upgrade archive utility and add back FC improvement

This reverts commit 65434886f6caa7210ed3ff39cd4e950c023d8328.

* Change permissions for Ubuntu AR

* Extract and cd into binutils dir

* Allow AR path to be chosen by user

* Add AR path to build

* Fix AR paths

* Revert AR flag in makefile

* Build from source doc updated

* Commit for C Predict API

* Add FP16 predict support

* Add Test Predictor fixes

* Add test for predictor

* Cleanup fixes

* Fixes

* Add support for forward pass only for gpu

* Fix Reshape, move test to gpu

* Add monitor callback for C Predict API

* Add tests, default dtype and set_monitor_callback

* Improve error

* Fix c_str_array
---
 amalgamation/python/mxnet_predict.py | 131 ++++++++++++++++++++++++-
 include/mxnet/c_predict_api.h        |  65 +++++++++++++
 src/c_api/c_predict_api.cc           | 140 ++++++++++++++++++++++++++-
 tests/python/gpu/test_predictor.py   | 128 ++++++++++++++++++++++++
 4 files changed, 454 insertions(+), 10 deletions(-)
 create mode 100644 tests/python/gpu/test_predictor.py

diff --git a/amalgamation/python/mxnet_predict.py b/amalgamation/python/mxnet_predict.py
index a91d3849b0d2..48e3cd4a5145 100644
--- a/amalgamation/python/mxnet_predict.py
+++ b/amalgamation/python/mxnet_predict.py
@@ -25,17 +25,77 @@
 
 import os
 import sys
+from array import array
 import ctypes
 import logging
 import numpy as np
 
+# pylint: disable= no-member
+_DTYPE_NP_TO_MX = {
+    None: -1,
+    np.float32: 0,
+    np.float64: 1,
+    np.float16: 2,
+    np.uint8: 3,
+    np.int32: 4,
+    np.int8: 5,
+    np.int64: 6,
+}
+
+_DTYPE_MX_TO_NP = {
+    -1: None,
+    0: np.float32,
+    1: np.float64,
+    2: np.float16,
+    3: np.uint8,
+    4: np.int32,
+    5: np.int8,
+    6: np.int64,
+}
+
 __all__ = ["Predictor", "load_ndarray_file"]
 
 if sys.version_info[0] == 3:
     py_str = lambda x: x.decode('utf-8')
+
+    def c_str_array(strings):
+        """Create ctypes const char ** from a list of Python strings.
+
+        Parameters
+        ----------
+        strings : list of string
+            Python strings.
+
+        Returns
+        -------
+        (ctypes.c_char_p * len(strings))
+            A const char ** pointer that can be passed to C API.
+        """
+        arr = (ctypes.c_char_p * len(strings))()
+        arr[:] = [s.encode('utf-8') for s in strings]
+        return arr
+
+
 else:
     py_str = lambda x: x
 
+    def c_str_array(strings):
+        """Create ctypes const char ** from a list of Python strings.
+
+        Parameters
+        ----------
+        strings : list of strings
+            Python strings.
+
+        Returns
+        -------
+        (ctypes.c_char_p * len(strings))
+            A const char ** pointer that can be passed to C API.
+        """
+        arr = (ctypes.c_char_p * len(strings))()
+        arr[:] = strings
+        return arr
+
 
 def c_str(string):
     """"Convert a python string to C string."""
@@ -48,6 +108,11 @@ def c_array(ctype, values):
     """Create ctypes array from a python array."""
     return (ctype * len(values))(*values)
 
+def c_array_buf(ctype, buf):
+    """Create ctypes array from a Python buffer."""
+    return (ctype * len(buf)).from_buffer(buf)
+
+
 
 def _find_lib_path():
     """Find mxnet library."""
@@ -87,9 +152,18 @@ def _check_call(ret):
     if ret != 0:
         raise RuntimeError(py_str(_LIB.MXGetLastError()))
 
+
+def _monitor_callback_wrapper(callback):
+    """A wrapper for the user-defined handle."""
+    def callback_handle(name, array, _):
+        """ ctypes function """
+        callback(name, array)
+    return callback_handle
+
 _LIB = _load_lib()
 # type definitions
 mx_uint = ctypes.c_uint
+mx_int = ctypes.c_int
 mx_float = ctypes.c_float
 mx_float_p = ctypes.POINTER(mx_float)
 PredictorHandle = ctypes.c_void_p
@@ -116,10 +190,13 @@ class Predictor(object):
 
     dev_id : int, optional
         The device id of the predictor.
+
+    type_dict : Dict of str->numpy.dtype
+        Input type dictionary, name->dtype
     """
     def __init__(self, symbol_file,
                  param_raw_bytes, input_shapes,
-                 dev_type="cpu", dev_id=0):
+                 dev_type="cpu", dev_id=0, type_dict=None):
         dev_type = devstr2type[dev_type]
         indptr = [0]
         sdata = []
@@ -133,7 +210,26 @@ def __init__(self, symbol_file,
         handle = PredictorHandle()
         param_raw_bytes = bytearray(param_raw_bytes)
         ptr = (ctypes.c_char * len(param_raw_bytes)).from_buffer(param_raw_bytes)
-        _check_call(_LIB.MXPredCreate(
+
+        # data types
+        num_provided_arg_types = 0
+        # provided type argument names
+        provided_arg_type_names = ctypes.POINTER(ctypes.c_char_p)()
+        # provided types
+        provided_arg_type_data = ctypes.POINTER(mx_uint)()
+        if type_dict is not None:
+            provided_arg_type_names = []
+            provided_arg_type_data = []
+            for k, v in type_dict.items():
+                v = np.dtype(v).type
+                if v in _DTYPE_NP_TO_MX:
+                    provided_arg_type_names.append(k)
+                    provided_arg_type_data.append(_DTYPE_NP_TO_MX[v])
+            num_provided_arg_types = mx_uint(len(provided_arg_type_names))
+            provided_arg_type_names = c_str_array(provided_arg_type_names)
+            provided_arg_type_data = c_array_buf(ctypes.c_int, array('i', provided_arg_type_data))
+
+        _check_call(_LIB.MXPredCreateEx(
             c_str(symbol_file),
             ptr, len(param_raw_bytes),
             ctypes.c_int(dev_type), ctypes.c_int(dev_id),
@@ -141,7 +237,11 @@ def __init__(self, symbol_file,
             c_array(ctypes.c_char_p, keys),
             c_array(mx_uint, indptr),
             c_array(mx_uint, sdata),
+            num_provided_arg_types,
+            provided_arg_type_names,
+            provided_arg_type_data,
             ctypes.byref(handle)))
+        self.type_dict = type_dict
         self.handle = handle
 
     def __del__(self):
@@ -160,10 +260,18 @@ def forward(self, **kwargs):
         >>> predictor.forward(data=mydata)
         >>> out = predictor.get_output(0)
         """
+        if self.type_dict and len(self.type_dict) != len(kwargs.items()):
+            raise ValueError("number of kwargs should be same as len of type_dict" \
+                             "Please check your forward pass inputs" \
+                             "or type_dict passed to Predictor instantiation")
+
         for k, v in kwargs.items():
             if not isinstance(v, np.ndarray):
                 raise ValueError("Expect numpy ndarray as input")
-            v = np.asarray(v, dtype=np.float32, order='C')
+            if self.type_dict and k in self.type_dict:
+                v = np.asarray(v, dtype=self.type_dict[k], order='C')
+            else:
+                v = np.asarray(v, dtype=np.float32, order='C')
             _check_call(_LIB.MXPredSetInput(
                 self.handle, c_str(k),
                 v.ctypes.data_as(mx_float_p),
@@ -218,18 +326,30 @@ def get_output(self, index):
         """
         pdata = ctypes.POINTER(mx_uint)()
         ndim = mx_uint()
+        out_type = mx_int()
         _check_call(_LIB.MXPredGetOutputShape(
             self.handle, index,
             ctypes.byref(pdata),
             ctypes.byref(ndim)))
+        _check_call(_LIB.MXPredGetOutputType(
+            self.handle, index,
+            ctypes.byref(out_type)))
         shape = tuple(pdata[:ndim.value])
-        data = np.empty(shape, dtype=np.float32)
+        data = np.empty(shape, dtype=_DTYPE_MX_TO_NP[out_type.value])
         _check_call(_LIB.MXPredGetOutput(
             self.handle, mx_uint(index),
             data.ctypes.data_as(mx_float_p),
             mx_uint(data.size)))
         return data
 
+    def set_monitor_callback(self, callback, monitor_all=False):
+        cb_type = ctypes.CFUNCTYPE(None, ctypes.c_char_p, ctypes.c_void_p, ctypes.c_void_p)
+        self._monitor_callback = cb_type(_monitor_callback_wrapper(callback))
+        _check_call(_LIB.MXPredSetMonitorCallback(self.handle,
+                                                  self._monitor_callback,
+                                                  None,
+                                                  ctypes.c_int(monitor_all)))
+
 
 def load_ndarray_file(nd_bytes):
     """Load ndarray file and return as list of numpy array.
@@ -273,4 +393,5 @@ def load_ndarray_file(nd_bytes):
     if len(keys) == 0 or len(keys[0]) == 0:
         return arrs
     else:
-        return {keys[i] : arrs[i] for i in range(len(keys))}
+        return {keys[i] : arrs[i] for i in range(len(keys))
+  }
diff --git a/include/mxnet/c_predict_api.h b/include/mxnet/c_predict_api.h
index ecbbf8dfc819..18bec625f05f 100644
--- a/include/mxnet/c_predict_api.h
+++ b/include/mxnet/c_predict_api.h
@@ -49,6 +49,12 @@ typedef float mx_float;
 typedef void *PredictorHandle;
 /*! \brief handle to NDArray list */
 typedef void *NDListHandle;
+/*! \brief handle to NDArray */
+typedef void *NDArrayHandle;
+/*! \brief callback used for add monitoring to nodes in the graph */
+typedef void (*PredMonitorCallback)(const char*,
+                                    NDArrayHandle,
+                                    void*);
 
 /*!
  * \brief Get the last error happeneed.
@@ -85,6 +91,44 @@ MXNET_DLL int MXPredCreate(const char* symbol_json_str,
                            const mx_uint* input_shape_data,
                            PredictorHandle* out);
 
+/*!
+ * \brief create a predictor
+ * \param symbol_json_str The JSON string of the symbol.
+ * \param param_bytes The in-memory raw bytes of parameter ndarray file.
+ * \param param_size The size of parameter ndarray file.
+ * \param dev_type The device type, 1: cpu, 2: gpu
+ * \param dev_id The device id of the predictor.
+ * \param num_input_nodes Number of input nodes to the net.
+ *    For feedforward net, this is 1.
+ * \param input_keys The name of the input argument.
+ *    For feedforward net, this is {"data"}
+ * \param input_shape_indptr Index pointer of shapes of each input node.
+ *    The length of this array = num_input_nodes + 1.
+ *    For feedforward net that takes 4 dimensional input, this is {0, 4}.
+ * \param input_shape_data A flattened data of shapes of each input node.
+ *    For feedforward net that takes 4 dimensional input, this is the shape data.
+ * \param num_provided_arg_dtypes
+ *    The length of provided_arg_dtypes.
+ * \param provided_arg_dtype_names
+ *    The provided_arg_dtype_names the names of args for which dtypes are provided.
+ * \param provided_arg_dtypes
+ *    The provided_arg_dtypes the dtype provided
+ * \param out The created predictor handle.
+ * \return 0 when success, -1 when failure.
+ */
+MXNET_DLL int MXPredCreateEx(const char* symbol_json_str,
+                             const void* param_bytes,
+                             int param_size,
+                             int dev_type, int dev_id,
+                             const mx_uint num_input_nodes,
+                             const char** input_keys,
+                             const mx_uint* input_shape_indptr,
+                             const mx_uint* input_shape_data,
+                             const mx_uint num_provided_arg_dtypes,
+                             const char** provided_arg_dtype_names,
+                             const int* provided_arg_dtypes,
+                             PredictorHandle* out);
+
 /*!
  * \brief create a predictor wich customized outputs
  * \param symbol_json_str The JSON string of the symbol.
@@ -186,6 +230,18 @@ MXNET_DLL int MXPredGetOutputShape(PredictorHandle handle,
                                    mx_uint index,
                                    mx_uint** shape_data,
                                    mx_uint* shape_ndim);
+
+/*!
+ * \brief Get the dtype of output node.
+ * The returned data type is only valid before next call to MXPred function.
+ * \param handle The handle of the predictor.
+ * \param out_index The index of the output node, set to 0 if there is only one output.
+ * \param out_dtype The dtype of the output node
+ */
+MXNET_DLL int MXPredGetOutputType(PredictorHandle handle,
+                                  mx_uint out_index,
+                                  int* out_dtype);
+
 /*!
  * \brief Set the input data of predictor.
  * \param handle The predictor handle.
@@ -269,6 +325,15 @@ MXNET_DLL int MXNDListGet(NDListHandle handle,
                           const mx_float** out_data,
                           const mx_uint** out_shape,
                           mx_uint* out_ndim);
+
+/*!
+ * \brief set a call back to notify the completion of operation and allow for
+ * additional monitoring
+ */
+MXNET_DLL int MXPredSetMonitorCallback(PredictorHandle handle,
+                                       PredMonitorCallback callback,
+                                       void* callback_handle,
+                                       bool monitor_all);
 /*!
  * \brief Free a MXAPINDList
  * \param handle The handle of the MXAPINDList.
diff --git a/src/c_api/c_predict_api.cc b/src/c_api/c_predict_api.cc
index 7de23ef935ef..b371fd044dc5 100644
--- a/src/c_api/c_predict_api.cc
+++ b/src/c_api/c_predict_api.cc
@@ -47,6 +47,9 @@ struct MXAPIPredictor {
   std::vector<NDArray> aux_arrays;
   // output shapes
   mxnet::ShapeVector out_shapes;
+  // output types
+  nnvm::DTypeVector out_dtypes;
+
   // uint32_t buffer for output shapes
   std::vector<uint32_t> out_shapes_buffer;
   // key to arguments
@@ -88,7 +91,7 @@ int _CreatePartialOut(const char* symbol_json_str,
                       const void* param_bytes,
                       int param_size,
                       int dev_type, int dev_id,
-                      mx_uint num_input_nodes,
+                      const mx_uint num_input_nodes,
                       const char** input_keys,
                       const mx_uint* input_shape_indptr,
                       const mx_uint* input_shape_data,
@@ -97,6 +100,9 @@ int _CreatePartialOut(const char* symbol_json_str,
                       // This is used for parallel inference.
                       int num_threads,
                       bool lazy,
+                      const mx_uint num_provided_arg_dtypes,
+                      const char** provided_arg_dtype_names,
+                      const int* provided_arg_dtypes,
                       PredictorHandle* out) {
   using nnvm::Symbol;
 
@@ -135,6 +141,7 @@ int _CreatePartialOut(const char* symbol_json_str,
 
   // load the parameters
   std::unordered_map<std::string, NDArray> arg_params, aux_params;
+  std::unordered_map<std::string, int> arg_types, aux_types;
   {
     std::unordered_set<std::string> arg_names, aux_names;
     std::vector<std::string> arg_names_vec = sym.ListInputNames(Symbol::kReadOnlyArgs);
@@ -156,12 +163,23 @@ int _CreatePartialOut(const char* symbol_json_str,
         std::string name(names[i].c_str() + 4);
         if (aux_names.count(name) != 0) {
           aux_params[name] = data[i];
+          aux_types[name] = data[i].dtype();
         }
       }
       if (!strncmp(names[i].c_str(), "arg:", 4)) {
         std::string name(names[i].c_str() + 4);
         if (arg_names.count(name) != 0) {
           arg_params[name] = data[i];
+          arg_types[name] = data[i].dtype();
+        }
+      }
+    }
+
+    if (num_provided_arg_dtypes > 0) {
+      for (mx_uint i = 0; i < num_provided_arg_dtypes; ++i) {
+        if (aux_types.count(provided_arg_dtype_names[i]) == 0 &&
+            arg_types.count(provided_arg_dtype_names[i]) == 0) {
+          arg_types[provided_arg_dtype_names[i]] = provided_arg_dtypes[i];
         }
       }
     }
@@ -179,6 +197,7 @@ int _CreatePartialOut(const char* symbol_json_str,
   mxnet::ShapeVector out_shapes(sym.ListOutputNames().size());
   mxnet::ShapeVector aux_shapes(aux_names.size());
   mxnet::ShapeVector arg_shapes;
+  nnvm::DTypeVector result_arg_types, result_out_types, result_aux_types;
   std::unordered_map<std::string, size_t> key2arg;
   for (size_t i = 0; i < arg_names.size(); ++i) {
     std::string key = arg_names[i];
@@ -187,6 +206,7 @@ int _CreatePartialOut(const char* symbol_json_str,
 
   try {
     mxnet::ShapeVector in_shapes;
+    nnvm::DTypeVector in_types;
     for (std::string key : sym.ListInputNames(Symbol::kAll)) {
       if (known_shape.count(key) != 0) {
         in_shapes.push_back(known_shape[key]);
@@ -194,14 +214,38 @@ int _CreatePartialOut(const char* symbol_json_str,
         in_shapes.emplace_back();
       }
     }
+
+    for (std::string key : sym.ListInputNames(Symbol::kAll)) {
+      if (arg_types.count(key) != 0) {
+        in_types.push_back(arg_types[key]);
+      } else if (aux_types.count(key) != 0) {
+        in_types.push_back(aux_types[key]);
+      } else {
+        // if key not in arg_types or aux_types set to FP32
+        in_types.push_back(0);
+      }
+    }
     nnvm::Graph g; g.outputs = sym.outputs;
     g = mxnet::exec::InferShape(std::move(g), std::move(in_shapes), "__shape__");
+    g = mxnet::exec::InferType(std::move(g), std::move(in_types), "__dtype__");
     bool infer_complete = (g.GetAttr<size_t>("shape_num_unknown_nodes") == 0);
+    // This is tricky for AMP Use case, for example, with only weights input types
+    // cannot be inferred in AMP. Thus for AMP converted model type_dict will be
+    // required
+    bool infer_type_complete = (g.GetAttr<size_t>("dtype_num_unknown_nodes") == 0);
     CHECK(infer_complete)
       << "The shape information of is not enough to get the shapes";
+    CHECK(infer_type_complete)
+        << "The type information is not enough, please provide input arg_types "
+           "with provided_arg_dtype_names and provided_arg_dtypes."
+           "If using amalgamation python frontend you can use type_dict in Predictor API"
+           "to provide this information";
     CopyAttr(g.indexed_graph(),
              g.GetAttr<mxnet::ShapeVector>("shape"),
              &arg_shapes, &out_shapes, &aux_shapes);
+    CopyAttr(g.indexed_graph(),
+             g.GetAttr<nnvm::DTypeVector>("dtype"),
+             &result_arg_types, &result_out_types, &result_aux_types);
   } catch (const mxnet::op::InferShapeError &err) {
     throw dmlc::Error(err.msg);
   }
@@ -210,19 +254,31 @@ int _CreatePartialOut(const char* symbol_json_str,
 
   std::vector<NDArray> arg_arrays, aux_arrays;
   for (size_t i = 0; i < arg_shapes.size(); ++i) {
-    NDArray nd = NDArray(arg_shapes[i], ctx);
+    NDArray nd;
+    if (result_arg_types[i] != -1) {
+      nd = NDArray(arg_shapes[i], ctx, false, result_arg_types[i]);
+    } else {
+      nd = NDArray(arg_shapes[i], ctx);
+    }
     if (arg_params.count(arg_names[i]) != 0) {
       CopyFromTo(arg_params[arg_names[i]], &nd);
     }
     arg_arrays.push_back(nd);
   }
+
   for (size_t i = 0; i < aux_shapes.size(); ++i) {
-    NDArray nd = NDArray(aux_shapes[i], ctx);
+    NDArray nd;
+    if (result_aux_types[i] != -1) {
+      nd = NDArray(aux_shapes[i], ctx, false, result_aux_types[i]);
+    } else {
+      nd = NDArray(aux_shapes[i], ctx);
+    }
     if (aux_params.count(aux_names[i]) != 0) {
       CopyFromTo(aux_params[aux_names[i]], &nd);
     }
     aux_arrays.push_back(nd);
   }
+
   // bind
   for (int i = 0; i < num_threads; i++) {
     std::unique_ptr<MXAPIPredictor> ret(new MXAPIPredictor());
@@ -232,6 +288,7 @@ int _CreatePartialOut(const char* symbol_json_str,
     ret->arg_arrays = arg_arrays;
     ret->aux_arrays = aux_arrays;
     ret->out_shapes = out_shapes;
+    ret->out_dtypes = result_out_types;
 
     if (!lazy) {
       std::map<std::string, Context> ctx_map;
@@ -272,6 +329,9 @@ int MXPredCreatePartialOut(const char* symbol_json_str,
       output_keys,
       1,
       false,
+      0,
+      nullptr,
+      nullptr,
       out);
 }
 
@@ -295,9 +355,44 @@ int MXPredCreate(const char* symbol_json_str,
       input_shape_indptr,
       input_shape_data,
       0,
-      NULL,
+      nullptr,
+      1,
+      false,
+      0,
+      nullptr,
+      nullptr,
+      out);
+}
+
+int MXPredCreateEx(const char* symbol_json_str,
+                   const void* param_bytes,
+                   int param_size,
+                   int dev_type, int dev_id,
+                   mx_uint num_input_nodes,
+                   const char** input_keys,
+                   const mx_uint* input_shape_indptr,
+                   const mx_uint* input_shape_data,
+                   const mx_uint num_provided_arg_dtypes,
+                   const char** provided_arg_dtype_names,
+                   const int* provided_arg_dtypes,
+                   PredictorHandle* out) {
+  return _CreatePartialOut(
+      symbol_json_str,
+      param_bytes,
+      param_size,
+      dev_type,
+      dev_id,
+      num_input_nodes,
+      input_keys,
+      input_shape_indptr,
+      input_shape_data,
+      0,
+      nullptr,
       1,
       false,
+      num_provided_arg_dtypes,
+      provided_arg_dtype_names,
+      provided_arg_dtypes,
       out);
 }
 
@@ -330,9 +425,12 @@ int MXPredCreateMultiThread(const char* symbol_json_str,
       input_shape_indptr,
       input_shape_data,
       0,
-      NULL,
+      nullptr,
       num_threads,
       true,
+      0,
+      nullptr,
+      nullptr,
       out);
 }
 
@@ -421,6 +519,7 @@ int MXPredReshape(mx_uint num_input_nodes,
                                    p->exec.get()));
     ret->out_shapes = out_shapes;
     ret->out_arrays = ret->exec->outputs();
+    ret->out_dtypes = p->out_dtypes;
   }
   *out = ret.release();
   API_END();
@@ -444,6 +543,21 @@ int MXPredGetOutputShape(PredictorHandle handle,
   API_END();
 }
 
+int MXPredGetOutputType(PredictorHandle handle,
+                        mx_uint out_index,
+                        int* out_dtype) {
+  MXAPIPredictor* p = static_cast<MXAPIPredictor*>(handle);
+  API_BEGIN();
+  CHECK_LT(out_index, p->out_arrays.size())
+      << "Index exceed number of outputs, provided out_index should be less than "
+      << p->out_arrays.size();
+
+  const int s = p->out_dtypes[out_index];
+  CHECK_GE(s, 0);
+  out_dtype[out_index] = s;
+  API_END();
+}
+
 int MXPredSetInput(PredictorHandle handle,
                    const char* key,
                    const mx_float* data,
@@ -543,6 +657,22 @@ int MXNDListGet(NDListHandle handle,
   API_END();
 }
 
+int MXPredSetMonitorCallback(PredictorHandle handle,
+                             PredMonitorCallback callback,
+                             void* callback_handle,
+                             bool monitor_all) {
+  MXAPIPredictor* p = static_cast<MXAPIPredictor*>(handle);
+  API_BEGIN();
+  PredMonitorCallback callback_temp = callback;
+  void* callback_handle_temp = callback_handle;
+  std::function<void(const char*, void*)> clbk
+  = [callback_temp, callback_handle_temp](const char* name, void* handle) {
+    callback_temp(name, handle, callback_handle_temp);
+  };
+  p->exec->SetMonitorCallback(clbk, monitor_all);
+  API_END();
+}
+
 int MXNDListFree(NDListHandle handle) {
   API_BEGIN();
   delete static_cast<MXAPINDList*>(handle);
diff --git a/tests/python/gpu/test_predictor.py b/tests/python/gpu/test_predictor.py
new file mode 100644
index 000000000000..4838a76c7cb1
--- /dev/null
+++ b/tests/python/gpu/test_predictor.py
@@ -0,0 +1,128 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+from __future__ import print_function
+import sys, os
+curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
+sys.path.append(os.path.join(curr_path, "../../../amalgamation/python/"))
+from mxnet_predict import Predictor, load_ndarray_file
+
+import ctypes
+import numpy as np
+import mxnet as mx
+import mxnet.ndarray as nd
+from mxnet.ndarray import NDArray
+from mxnet import gluon
+from mxnet.test_utils import assert_almost_equal, download_model
+from mxnet.contrib.amp import amp
+from mxnet.base import NDArrayHandle, py_str
+sys.path.insert(0, os.path.join(curr_path, '../unittest'))
+from common import setup_module, with_seed, teardown
+
+@with_seed()
+def test_predictor_with_dtype():
+    prefix = 'test_predictor_simple_dense'
+    symbol_file = "%s-symbol.json" % prefix
+    param_file = "%s-0000.params" % prefix
+
+    input1 = np.random.uniform(size=(1, 3))
+    input1 = input1.astype(np.float16)
+
+    block = mx.gluon.nn.HybridSequential()
+    block.add(mx.gluon.nn.Dense(7))
+    block.add(mx.gluon.nn.Dense(3))
+    block.cast(np.float16)
+    block.hybridize()
+    block.initialize(ctx=mx.gpu(0))
+    tmp = mx.nd.array(input1, dtype=np.float16, ctx=mx.gpu(0))
+    out1 = block.forward(tmp)
+    block.export(prefix)
+
+    predictor = Predictor(open(symbol_file, "r").read(),
+                          open(param_file, "rb").read(),
+                          {"data": input1.shape},
+                          dev_type="gpu",
+                          dev_id=0,
+                          type_dict={"data": input1.dtype})
+    predictor.forward(data=input1)
+    predictor_out1 = predictor.get_output(0)
+
+    assert_almost_equal(out1.asnumpy(), predictor_out1, rtol=1e-5, atol=1e-6)
+
+def compare_module_cpredict(result_sym, result_arg_params, result_aux_params, monitor_callback=False):
+    # Dummmy inputs
+    input1 = np.ones((1, 3, 224, 224))
+    input1 = input1.astype(np.float32)
+    nd_dict = {}
+    def pred_mon_callback(name, arr):
+        nd_dict[name] = arr
+    mod = mx.mod.Module(result_sym, data_names=["data"], label_names=["softmax_label"], context=mx.gpu())
+    mod.bind(data_shapes=[['data', (1, 3, 224, 224)]], label_shapes=[['softmax_label', (1,)]], for_training=False)
+    mod.set_params(result_arg_params, result_aux_params)
+    mod.forward(mx.io.DataBatch(data=[mx.nd.array(input1, ctx=mx.gpu())],
+                                label=[mx.nd.ones((1,), ctx=mx.gpu())]))
+    prefix = "test_predictor_amp"
+    mod.save_checkpoint(prefix, 0, remove_amp_cast=False)
+    sym_file = "{}-symbol.json".format(prefix)
+    params_file = "{}-0000.params".format(prefix)
+    predictor = Predictor(open(sym_file, "r").read(),
+                          open(params_file, "rb").read(),
+                          {'data': (1, 3, 224, 224),
+                           'softmax_label': (1,)},
+                          dev_type="gpu",
+                          dev_id=0)
+    if monitor_callback:
+        predictor.set_monitor_callback(pred_mon_callback, monitor_all=True)
+    predictor.forward(data=input1, softmax_label=mx.nd.ones((1,)).asnumpy())
+    predictor_out1 = predictor.get_output(0)
+    if monitor_callback:
+        assert len(nd_dict) > 0, "Callback not called"
+    assert_almost_equal(mod.get_outputs()[0].asnumpy(), predictor_out1, atol=1e-1, rtol=1e-1)
+
+
+@with_seed()
+def test_predictor_amp():
+    dir_path = os.path.dirname(os.path.realpath(__file__))
+    model_path = os.path.join(dir_path, 'model')
+    if not os.path.isdir(model_path):
+        os.mkdir(model_path)
+    prefix, epoch = download_model("imagenet1k-resnet-18", dst_dir=model_path)
+
+    sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)
+
+
+    # Convert model to mixed precision model, params in FP32
+    result_sym, result_arg_params, result_aux_params = amp.convert_model(sym,
+                                                                         arg_params,
+                                                                         aux_params,
+                                                                         target_dtype="float16",
+                                                                         target_dtype_ops=["Convolution"])
+    compare_module_cpredict(result_sym, result_arg_params, result_aux_params)
+
+    # Convert model to mixed precision model, params in FP16
+    result_sym, result_arg_params, result_aux_params = amp.convert_model(sym,
+                                                                         arg_params,
+                                                                         aux_params,
+                                                                         target_dtype="float16",
+                                                                         target_dtype_ops=["Convolution"],
+                                                                         cast_optional_params=True)
+    compare_module_cpredict(result_sym, result_arg_params, result_aux_params, monitor_callback=True)
+
+
+if __name__ == '__main__':
+    import nose
+    nose.runmodule()

From b25ec8efb77462bb7b033ff98dc6558c74b68790 Mon Sep 17 00:00:00 2001
From: Pedro Larroy <pedro.larroy.lists@gmail.com>
Date: Sat, 13 Jul 2019 22:23:50 -0700
Subject: [PATCH 066/813] Improve diagnose.py, adding build features info and
 binary library path. (#15499)

---
 tools/diagnose.py | 22 +++++++++++++++++-----
 1 file changed, 17 insertions(+), 5 deletions(-)
 mode change 100644 => 100755 tools/diagnose.py

diff --git a/tools/diagnose.py b/tools/diagnose.py
old mode 100644
new mode 100755
index 0f878f918e3c..f3026e635844
--- a/tools/diagnose.py
+++ b/tools/diagnose.py
@@ -97,6 +97,11 @@ def check_pip():
     except ImportError:
         print('No corresponding pip install for current python.')
 
+def get_build_features_str():
+    import mxnet.runtime
+    features = mxnet.runtime.Features()
+    return '\n'.join(map(str, list(features.values())))
+
 def check_mxnet():
     print('----------MXNet Info-----------')
     try:
@@ -105,13 +110,20 @@ def check_mxnet():
         mx_dir = os.path.dirname(mxnet.__file__)
         print('Directory    :', mx_dir)
         commit_hash = os.path.join(mx_dir, 'COMMIT_HASH')
-        with open(commit_hash, 'r') as f:
-            ch = f.read().strip()
-            print('Commit Hash   :', ch)
+        if os.path.exists(commit_hash):
+            with open(commit_hash, 'r') as f:
+                ch = f.read().strip()
+                print('Commit Hash   :', ch)
+        else:
+            print('Commit hash file "{}" not found. Not installed from pre-built package or built from source.'.format(commit_hash))
+        print('Library      :', mxnet.libinfo.find_lib_path())
+        try:
+            print('Build features:')
+            print(get_build_features_str())
+        except Exception:
+            print('No runtime build feature info available')
     except ImportError:
         print('No MXNet installed.')
-    except IOError:
-        print('Hashtag not found. Not installed from pre-built package.')
     except Exception as e:
         import traceback
         if not isinstance(e, IOError):

From 38a44db4d752c6dade436438fc53d374f66c49b2 Mon Sep 17 00:00:00 2001
From: Sheng Zha <szha@users.noreply.github.com>
Date: Sat, 13 Jul 2019 23:31:45 -0700
Subject: [PATCH 067/813] update ratcheck for apache-rat 0.13 release (#15417)

* update ratcheck for apache-rat 0.13 release

* remove minpy folder
---
 Makefile               |  8 ++++----
 python/minpy/README.md | 21 ---------------------
 2 files changed, 4 insertions(+), 25 deletions(-)
 delete mode 100644 python/minpy/README.md

diff --git a/Makefile b/Makefile
index 02a74a067031..a7cf8a56a433 100644
--- a/Makefile
+++ b/Makefile
@@ -699,15 +699,15 @@ rclean:
 	$(RM) -r R-package/src/image_recordio.h R-package/NAMESPACE R-package/man R-package/R/mxnet_generated.R \
 		R-package/inst R-package/src/*.o R-package/src/*.so mxnet_*.tar.gz
 
-build/rat/apache-rat/target/apache-rat-0.13-SNAPSHOT.jar:
+build/rat/apache-rat/target/apache-rat-0.13.jar:
 	mkdir -p build
-	svn co http://svn.apache.org/repos/asf/creadur/rat/branches/0.12-release/ build/rat; \
+	svn co http://svn.apache.org/repos/asf/creadur/rat/tags/apache-rat-project-0.13/ build/rat; \
 	cd build/rat; \
 	mvn -Dmaven.test.skip=true install;
 
-ratcheck: build/rat/apache-rat/target/apache-rat-0.13-SNAPSHOT.jar
+ratcheck: build/rat/apache-rat/target/apache-rat-0.13.jar
 	exec 5>&1; \
-	RAT_JAR=build/rat/apache-rat/target/apache-rat-0.13-SNAPSHOT.jar; \
+	RAT_JAR=build/rat/apache-rat/target/apache-rat-0.13.jar; \
 	OUTPUT=$(java -jar $(RAT_JAR) -E tests/nightly/apache_rat_license_check/rat-excludes -d .|tee >(cat - >&5)); \
     ERROR_MESSAGE="Printing headers for text files without a valid license header"; \
     echo "-------Process The Output-------"; \
diff --git a/python/minpy/README.md b/python/minpy/README.md
deleted file mode 100644
index 0278ca1c00fb..000000000000
--- a/python/minpy/README.md
+++ /dev/null
@@ -1,21 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-MXNet Python Package
-====================
-
-This is the WIP directory for MinPy project.

From 6acf7e6a051e75d9f1cca0ec3c198c38c0f6a3fe Mon Sep 17 00:00:00 2001
From: Kellen Sunderland <kellen.sunderland@gmail.com>
Date: Sun, 14 Jul 2019 20:02:54 -0700
Subject: [PATCH 068/813] Small typo fixes in batch_norm-inl.h (#15527)

* Small typo fixes in batch_norm-inl.h
---
 src/operator/nn/batch_norm-inl.h | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/src/operator/nn/batch_norm-inl.h b/src/operator/nn/batch_norm-inl.h
index 70e91c164090..a82771cd4401 100644
--- a/src/operator/nn/batch_norm-inl.h
+++ b/src/operator/nn/batch_norm-inl.h
@@ -57,7 +57,7 @@ enum BatchNormOpAuxiliary {kMovingMean, kMovingVar};  // aux_states
 constexpr int DEFAULT_AXIS = 1;
 }  // namespace batchnorm
 
-/*! \brief Parameters for BatchNoram operator */
+/*! \brief Parameters for BatchNorm operator */
 struct BatchNormParam : public dmlc::Parameter<BatchNormParam> {
   double eps;
   float momentum;
@@ -167,7 +167,7 @@ void BatchNormBackwardImpl(mshadow::Stream<gpu> *stream,
  * \param out_data array of output data, pointer is used to indicate that this is holder
  *        the space of TBlob in out_data must be pre-allocated with InferShape
  * \param aux_states Auxiliary states of operator. Normally operator doesn't
- *        need, epecial case like Batch Norm requires.
+ *        need, special case like Batch Norm requires.
  * \sa OpReqType, OpContext
  */
 template <typename xpu, typename DType, typename AccReal>
@@ -413,4 +413,3 @@ extern volatile bool disable_mkl;
 #endif
 
 #endif  // MXNET_OPERATOR_NN_BATCH_NORM_INL_H_
-

From b88705e7744be1cc22122eb2c5b492e495ab45bb Mon Sep 17 00:00:00 2001
From: Wang Jiajun <wangjiajun.phy@gmail.com>
Date: Mon, 15 Jul 2019 13:21:25 -0500
Subject: [PATCH 069/813] fix heap-use-after-free in scala (#15503)

* fix heap-use-after-free

* trigger CI
---
 .../src/main/native/org_apache_mxnet_native_c_api.cc       | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/scala-package/native/src/main/native/org_apache_mxnet_native_c_api.cc b/scala-package/native/src/main/native/org_apache_mxnet_native_c_api.cc
index 387a0b17e252..5c704c9646a2 100644
--- a/scala-package/native/src/main/native/org_apache_mxnet_native_c_api.cc
+++ b/scala-package/native/src/main/native/org_apache_mxnet_native_c_api.cc
@@ -247,9 +247,6 @@ JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxImperativeInvokeEx
                                cParamVals,
                                &cOutStypes);
   env->ReleaseLongArrayElements(inputs, cInputs, 0);
-  if (cOutputsGiven) {
-    env->ReleaseLongArrayElements(outputsGiven, cOutputsGiven, 0);
-  }
 
   // release allocated memory
   if (numParams > 0) {
@@ -284,6 +281,10 @@ JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxImperativeInvokeEx
     }
   }
 
+  if (cOutputsGiven) {
+    env->ReleaseLongArrayElements(outputsGiven, cOutputsGiven, 0);
+  }
+
   return ret;
 }
 

From 9724ce6821075ff90dc5cca8edc596601dd667db Mon Sep 17 00:00:00 2001
From: Tao Lv <tao.a.lv@intel.com>
Date: Tue, 16 Jul 2019 06:50:44 +0800
Subject: [PATCH 070/813] Avoid memory copy for dropout inference (#15521)

* fix dropout for inference
---
 src/operator/nn/dropout-inl.h | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/operator/nn/dropout-inl.h b/src/operator/nn/dropout-inl.h
index 2a643a266b2b..cb02be6e0338 100644
--- a/src/operator/nn/dropout-inl.h
+++ b/src/operator/nn/dropout-inl.h
@@ -398,6 +398,8 @@ class DropoutOp {
           }
         }
       } else {
+        if (req[dropout::kOut] == kWriteInplace) return;
+
         MXNET_ASSIGN_REQ_SWITCH(req[dropout::kOut], Req, {
           mxnet_op::Kernel<mxnet_op::op_with_req<mshadow_op::identity, Req>, xpu>::Launch(
             s, out.Size(), out.dptr<DType>(), in.dptr<DType>());

From 41ecf583bb551c2b43670c0b6db2ef87560b9f5c Mon Sep 17 00:00:00 2001
From: Aaron Markham <markhama@amazon.com>
Date: Mon, 15 Jul 2019 16:04:19 -0700
Subject: [PATCH 071/813] add julia env settings (#15523)

---
 docs/build_version_doc/build_all_version.sh | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/docs/build_version_doc/build_all_version.sh b/docs/build_version_doc/build_all_version.sh
index 3c432bbedfc9..ee467f99e0a4 100755
--- a/docs/build_version_doc/build_all_version.sh
+++ b/docs/build_version_doc/build_all_version.sh
@@ -46,6 +46,17 @@ set -x
 # Set OPTS to any Sphinx build options, like -W for "warnings as errors"
 OPTS=
 
+# Setup environment for Julia docs
+export PATH="$1/bin:$PATH"
+export MXNET_HOME='/work/mxnet'
+export JULIA_DEPOT_PATH='/work/julia-depot'
+export INTEGRATION_TEST=1
+
+julia -e 'using InteractiveUtils; versioninfo()'
+export LD_PRELOAD='/usr/lib/x86_64-linux-gnu/libjemalloc.so'
+export LD_LIBRARY_PATH=/work/mxnet/lib:$LD_LIBRARY_PATH
+
+
 # $1 is the list of branches/tags to build
 if [ -z "$1" ]
   then

From 1b725c3c91246f916ec0e626d94b78dda451e631 Mon Sep 17 00:00:00 2001
From: Zixuan Wei <zixuan.wei@intel.com>
Date: Tue, 16 Jul 2019 10:51:17 +0800
Subject: [PATCH 072/813] [MKLDNN] Independent gradients requests check with
 respect to weights and bias of convolution (#15497)

* Independent req[kBias] and req[kWeight] check

* Add UT for independent conv gradient requests

* Update conv independent grad UT with no_bias enabled

* Check req[kWeight] for avoiding unnecessary prim registration

* Check `OpReqTpye` in CommitOutput automatically

* Lock cudnn autotune for accurate conv output

* Ignore independent gradients test on GPU

* Trigger CI

* Sets a low bar for autotuned cudnn convolution
---
 src/operator/nn/mkldnn/mkldnn_convolution.cc | 15 ++--
 tests/python/unittest/test_operator.py       | 82 ++++++++++++++++++++
 2 files changed, 89 insertions(+), 8 deletions(-)

diff --git a/src/operator/nn/mkldnn/mkldnn_convolution.cc b/src/operator/nn/mkldnn/mkldnn_convolution.cc
index 6a91ae0d92a1..9cab2dd0e2b3 100644
--- a/src/operator/nn/mkldnn/mkldnn_convolution.cc
+++ b/src/operator/nn/mkldnn/mkldnn_convolution.cc
@@ -507,9 +507,9 @@ class MKLDNNConvBackward {
           mkldnn::primitive::at(*this->weight), *this->in_grad));
   }
 
-void SetWeightNewMem(const mkldnn::memory &data,
-                     const mkldnn::memory &out_grad,
-                     const mkldnn::memory &in_grad_weight) {
+  void SetWeightNewMem(const mkldnn::memory &data,
+                       const mkldnn::memory &out_grad,
+                       const mkldnn::memory &in_grad_weight) {
     if (this->data == nullptr)
       this->data = std::shared_ptr<mkldnn::memory>(new mkldnn::memory(
           bwdWeights_pd.src_primitive_desc(), data.get_data_handle()));
@@ -649,7 +649,7 @@ void MKLDNNConvolutionBackward(const nnvm::NodeAttrs& attrs, const OpContext &ct
     MKLDNNStream::Get()->RegisterPrim(convBwd.GetBwdData());
     CommitOutput(in_grad[conv::kData], in_grad_mem);
   }
-  if (req[conv::kWeight]) {
+  if (req[conv::kWeight] || req[conv::kBias]) {
     MKLDNNConvBackward &convBwdWeight = GetConvBwd(attrs, data,
         weight, bias, out_grad, fwd_pd);
     if (convBwdWeight.bwdData_pd.diff_dst_primitive_desc() !=
@@ -662,17 +662,16 @@ void MKLDNNConvolutionBackward(const nnvm::NodeAttrs& attrs, const OpContext &ct
         in_grad[conv::kWeight],
         convBwdWeight.bwdWeights_pd.diff_weights_primitive_desc(),
         req[conv::kWeight]);
-    mkldnn_output_t in_grad_bias;
     if (param.no_bias) {
       convBwdWeight.SetWeightNewMem(*data_mem, *out_grad_mem,
-                              *in_grad_weight.second);
+          *in_grad_weight.second);
       MKLDNNStream::Get()->RegisterPrim(convBwdWeight.GetBwdWeights());
     } else {
-      in_grad_bias = CreateMKLDNNMem(
+      auto in_grad_bias = CreateMKLDNNMem(
           in_grad[conv::kBias],
           convBwdWeight.bwdWeights_pd.diff_bias_primitive_desc(), req[conv::kBias]);
       convBwdWeight.SetWeightNewMem(*data_mem, *out_grad_mem,
-                              *in_grad_weight.second, *in_grad_bias.second);
+          *in_grad_weight.second, *in_grad_bias.second);
       MKLDNNStream::Get()->RegisterPrim(convBwdWeight.GetBwdWeights());
       CommitOutput(in_grad[conv::kBias], in_grad_bias);
     }
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index b550139d341b..aeddc7a893df 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -1907,6 +1907,88 @@ def test_depthwise_convolution():
                             for arr1, arr2 in zip(exe1.outputs + exe1.grad_arrays, exe2.outputs + exe2.grad_arrays):
                                 np.testing.assert_allclose(arr1.asnumpy(), arr2.asnumpy(), rtol=1e-3, atol=1e-3)
 
+
+@with_seed()
+def test_convolution_independent_gradients():
+    ctx = default_context()
+    # set a low bar for autotuned cudnn conv
+    atol = 1.0e-1 if ctx.device_type == "gpu" else 1.0e-3
+    rtol = 1.0e-2 if ctx.device_type == "gpu" else 1.0e-3
+    reqs = ["null", "write", "add"]
+    var_names = ["x", "w", "b"]
+    dims = [1, 2]
+    num_bases = [1, 16, 64]
+    kernel_xs = [3, 5]
+    stride_xs = [1, 2]
+    pad_xs = [0, 1]
+    in_sizes = [7, 32]
+    no_biases = [True, False]
+    for dim, num_base, kernel_x, stride_x, pad_x , in_size, no_bias in \
+            itertools.product(dims, num_bases, kernel_xs, stride_xs, pad_xs, in_sizes, no_biases):
+        # Prepare params shape
+        kernel = (kernel_x,) * dim
+        stride = (stride_x,) * dim
+        pad = (pad_x,) * dim
+        num_filter = num_base
+        x_shape = (2, num_base) + (in_size,) * dim
+        w_shape = (num_filter, num_base) + kernel
+
+        # Symbols definition
+        x = mx.sym.Variable('x')
+        w = mx.sym.Variable('w')
+        b = mx.sym.Variable('b') if not no_bias else None
+        conv = mx.sym.Convolution(x, w, b, num_filter=num_filter, 
+            kernel=kernel, stride=stride, pad=pad, no_bias=no_bias)
+        
+        for req_kind in reqs:
+            # Binding args for conv with possible dependent gradients
+            base_args = {
+                'x': mx.nd.random.normal(shape=x_shape),
+                'w': mx.nd.random.normal(shape=w_shape),
+                'b': mx.nd.random.normal(shape=(num_filter, )) if not no_bias else None}
+            args1 = copy.deepcopy(base_args)
+            grad1 = {
+                'x': mx.nd.zeros(shape=x_shape),
+                'w': mx.nd.zeros(shape=w_shape),
+                'b': mx.nd.zeros(shape=(num_filter, )) if not no_bias else None}
+
+            grad_req1 = [req_kind] * 3
+            grad_req1 = dict(zip(var_names, grad_req1))
+
+            exe1 = conv.bind(ctx, args1, args_grad=grad1, grad_req=grad_req1)
+            exe1.forward(is_train=True)
+            exe1.backward(exe1.outputs[0])
+            
+            for x_req, w_req, b_req in itertools.product(reqs, repeat=3):
+                # Binding args for conv with independent gradients
+                args2 = copy.deepcopy(base_args)    # Deepcopy the same params of `exe1`
+                grad2 = {
+                    'x': mx.nd.zeros(shape=x_shape),
+                    'w': mx.nd.zeros(shape=w_shape),
+                    'b': mx.nd.zeros(shape=(num_filter, )) if not no_bias else None}
+                grad_req2 = {"x": x_req, "w": w_req, "b": b_req}
+                exe2 = conv.bind(ctx, args2, args_grad=grad2, grad_req=grad_req2)
+                    
+                exe2.forward(is_train=True)
+                np.testing.assert_allclose(exe1.outputs[0].asnumpy(),
+                    exe2.outputs[0].asnumpy(), rtol=rtol, atol=atol)
+                
+                exe2.backward(exe2.outputs[0])
+                for var_name in var_names:
+                    if var_name == "b" and no_bias:
+                        continue
+                    if grad_req2[var_name] == "null":
+                        exe2_var_grad = grad2[var_name].asnumpy()
+                        np.testing.assert_allclose(exe2_var_grad,
+                            np.zeros_like(exe2_var_grad), rtol=rtol, atol=atol)
+                    if grad_req2[var_name] != grad_req1[var_name]:
+                        continue
+                    np.testing.assert_allclose(args1[var_name].asnumpy(),
+                        args2[var_name].asnumpy(), rtol=rtol, atol=atol)
+                    np.testing.assert_allclose(grad1[var_name].asnumpy(),
+                        grad2[var_name].asnumpy(), rtol=rtol, atol=atol)
+
+
 def gen_broadcast_data(idx):
     # Manually set test cases
     binary_op_data_shape = np.array(

From 9777717ec0e55d63d6306df21fc92c1635281733 Mon Sep 17 00:00:00 2001
From: Aaron Markham <markhama@amazon.com>
Date: Tue, 16 Jul 2019 06:08:52 -0700
Subject: [PATCH 073/813] website build for julia: fix path to be static
 (#15554)

---
 ci/docker/runtime_functions.sh              | 10 ++++++++++
 docs/build_version_doc/build_all_version.sh | 11 -----------
 2 files changed, 10 insertions(+), 11 deletions(-)

diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index 596b956efc84..d2b0d6b0e274 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -1210,6 +1210,16 @@ test_ubuntu_cpu_python3() {
 build_docs() {
     set -ex
     pushd .
+
+    # Setup environment for Julia docs
+    export PATH="/work/julia10/bin:$PATH"
+    export MXNET_HOME='/work/mxnet'
+    export JULIA_DEPOT_PATH='/work/julia-depot'
+
+    julia -e 'using InteractiveUtils; versioninfo()'
+    export LD_PRELOAD='/usr/lib/x86_64-linux-gnu/libjemalloc.so'
+    export LD_LIBRARY_PATH=/work/mxnet/lib:$LD_LIBRARY_PATH
+
     cd /work/mxnet/docs/build_version_doc
     # Parameters are set in the Jenkins pipeline: restricted-website-build
     # $1: the list of branches/tags to build
diff --git a/docs/build_version_doc/build_all_version.sh b/docs/build_version_doc/build_all_version.sh
index ee467f99e0a4..3c432bbedfc9 100755
--- a/docs/build_version_doc/build_all_version.sh
+++ b/docs/build_version_doc/build_all_version.sh
@@ -46,17 +46,6 @@ set -x
 # Set OPTS to any Sphinx build options, like -W for "warnings as errors"
 OPTS=
 
-# Setup environment for Julia docs
-export PATH="$1/bin:$PATH"
-export MXNET_HOME='/work/mxnet'
-export JULIA_DEPOT_PATH='/work/julia-depot'
-export INTEGRATION_TEST=1
-
-julia -e 'using InteractiveUtils; versioninfo()'
-export LD_PRELOAD='/usr/lib/x86_64-linux-gnu/libjemalloc.so'
-export LD_LIBRARY_PATH=/work/mxnet/lib:$LD_LIBRARY_PATH
-
-
 # $1 is the list of branches/tags to build
 if [ -z "$1" ]
   then

From 300cb69fc9dac5e69636fe280f340967d30ed53d Mon Sep 17 00:00:00 2001
From: Paul King <paulk@asert.com.au>
Date: Wed, 17 Jul 2019 06:14:45 +1000
Subject: [PATCH 074/813] some minor typos/clarifications (#15538)

---
 scala-package/README.md | 28 ++++++++++++++--------------
 1 file changed, 14 insertions(+), 14 deletions(-)

diff --git a/scala-package/README.md b/scala-package/README.md
index c7d0cecf15ac..c0f32e47f080 100644
--- a/scala-package/README.md
+++ b/scala-package/README.md
@@ -18,24 +18,24 @@
 MXNet Package for Scala/Java
 =====
 
-The MXNet Scala/Java Package brings flexible and efficient GPU/CPU computing and state-of-art deep learning to JVM.
+The MXNet Scala/Java Package brings flexible and efficient GPU/CPU computing and state-of-art deep learning to the JVM.
 
 - It enables you to write seamless tensor/matrix computation with multiple GPUs
-  in Scala, Java and other languages built on JVM.
+  in Scala, Java and other JVM languages.
 - It also enables you to construct and customize the state-of-art deep learning models in JVM languages,
   and apply them to tasks such as image classification and data science challenges.
-- The Scala/Java Inferece APIs provides an easy out of the box solution for loading pre-trained MXNet models and running inference on them.
-  
+- The Scala/Java _Inference API_ provides an easy out of the box solution for performing inference tasks using pre-trained MXNet models.
+
 Pre-Built Maven Packages
 ------------------------
 
 ### Stable ###
 
 The MXNet Scala/Java packages can be easily included in your Maven managed project.
-The stable jar files for the packages are available on the [MXNet Maven Package Repository](https://search.maven.org/search?q=g:org.apache.mxnet)
+The stable jar files for the packages are available on the [MXNet Maven Package Repository](https://search.maven.org/search?q=g:org.apache.mxnet).
 Currently we provide packages for Linux (Ubuntu 16.04) (CPU and GPU) and macOS (CPU only). Stable packages for Windows and CentOS will come soon. For now, if you have a CentOS machine, follow the ```Build From Source``` section below. 
 
-To add MXNet Scala/Java package to your project, add the dependency as shown below corresponding to your platform, under the ```dependencies``` tag in your project's ```pom.xml``` :
+To add the MXNet Scala/Java packages to your project, add the dependency as shown below corresponding to your platform, under the ```dependencies``` tag in your project's ```pom.xml``` :
 
 **Linux GPU**
 
@@ -77,7 +77,7 @@ To add MXNet Scala/Java package to your project, add the dependency as shown bel
 
 ### Nightly ###
 
-Apart from these, the nightly builds representing the bleeding edge development  on Scala/Java packages are also available on the [MXNet Maven Nexus Package Repository](https://repository.apache.org/#nexus-search;gav~org.apache.mxnet~~~~). 
+Apart from these, the nightly builds representing the bleeding edge development on Scala/Java packages are also available on the [MXNet Maven Nexus Package Repository](https://repository.apache.org/#nexus-search;gav~org.apache.mxnet~~~~). 
 Currently we provide nightly packages for Linux (CPU and GPU) and MacOS (CPU only). The Linux nightly jar files also work on CentOS. Nightly packages for Windows will come soon.
 
 Add the following ```repository``` to your project's ```pom.xml``` file : 
@@ -133,8 +133,8 @@ Also, add the dependency which corresponds to your platform to the ```dependenci
 Build From Source
 -----------------
 
-Checkout the [Installation Guide](http://mxnet.incubator.apache.org/install/index.html) contains instructions to install mxnet package and build it from source. Scala maven build assume you already have a ``lib/libmxnet.so`` file.
-If you have built MXNet from source and are looking to setup Scala from that point, you may simply run the following from the MXNet source root, Scala build will detect your platform (OSX/Linux) and libmxnet.so flavor (CPU/GPU):
+The [Installation Guide](http://mxnet.incubator.apache.org/install/index.html) contains instructions to install mxnet or build it from source. The Scala/Java package is built from source using Maven. The maven build assumes you already have a ``lib/libmxnet.so`` file.
+If you have built MXNet from source and are looking to set up Scala\Java from that point, you may simply run the following from the MXNet source root, the build will detect your platform (OSX/Linux) and libmxnet.so flavor (CPU/GPU):
 
 ```bash
 cd scala-package
@@ -156,21 +156,21 @@ mvn -Dsuites=org.apache.mxnet.NDArraySuite integration-test
 ```
 
 If everything goes well, you will find jars for `assembly`, `core` and `example` modules.
-Also it produces the native library in `native/target`, which you can use to cooperate with the `core` module.
+Also it produces the native library in `native/target`, which you can use in conjunction with the `core` module.
 
 Deploy to repository
 --------------------
 
-By default, `maven deploy` will deploy artifacts to local file system, you can file then in: ``scala-package/deploy/target/repo`` folder.
+By default, `maven deploy` will deploy artifacts to local file system, you can find them in the ``scala-package/deploy/target/repo`` folder.
 
-For nightly build in CI, a snapshot build will be uploaded to apache repository with follow command:
+For nightly builds (typically done by CI), a snapshot build will be uploaded to an apache snapshot repository with the following command:
 
 ```bash
 cd scala-package
 mvn deploy -Pnightly
 ```
 
-Use following command to deploy release build (push artifacts to apache staging repository):
+Use the following command when performing a release (pushes artifacts to an apache staging repository):
 
 ```bash
 cd scala-package
@@ -208,7 +208,7 @@ Caused by: java.lang.ClassNotFoundException: org.apache.mxnet.NDArray
         at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:331)
         at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
 ```
-Please make sure your $CLASSPATH is able to find `mxnet-full_scala_version-INTERNAL.jar`.
+Please make sure your $CLASSPATH contains `mxnet-full_scala_version-INTERNAL.jar`.
 
 - To set up the Scala Project using IntelliJ IDE on macOS follow the instructions [here](https://mxnet.incubator.apache.org/tutorials/scala/mxnet_scala_on_intellij.html).
 - Several examples on using the Scala APIs are provided in the [Scala Examples Folder](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/)

From 4d07d78e1aff36f603231568bfff88242d073756 Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Tue, 16 Jul 2019 15:54:54 -0700
Subject: [PATCH 075/813] broadcast axis is alias to broadcast axes; doc fix
 (#15546)

---
 src/operator/tensor/broadcast_reduce_op_value.cc | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/operator/tensor/broadcast_reduce_op_value.cc b/src/operator/tensor/broadcast_reduce_op_value.cc
index 861060514181..7e8a7f05659b 100644
--- a/src/operator/tensor/broadcast_reduce_op_value.cc
+++ b/src/operator/tensor/broadcast_reduce_op_value.cc
@@ -221,6 +221,8 @@ MXNET_OPERATOR_REGISTER_BROADCAST(broadcast_axis)
 Broadcasting is allowed on axes with size 1, such as from `(2,1,3,1)` to
 `(2,8,3,9)`. Elements will be duplicated on the broadcasted axes.
 
+`broadcast_axes` is an alias to the function `broadcast_axis`.
+
 Example::
 
    // given x of shape (1,2,1)

From 57d097b18e0c117e2f0be8287bb94858266a9798 Mon Sep 17 00:00:00 2001
From: Sandeep Krishnamurthy <sandeep.krishna98@gmail.com>
Date: Wed, 17 Jul 2019 12:14:29 -0700
Subject: [PATCH 076/813] Add transpose_conv, sorting and searching operator
 benchmarks to Opperf (#15475)

* Add Transpose Convolution op benchmarks

* Add sorting and searching ops for opperf

* Remove redudant logic, make it uniform

* Dummy commit for CI retrigger

* Address code review comments
---
 benchmark/opperf/nd_operations/README.md      |  6 --
 .../opperf/nd_operations/nn_conv_operators.py | 49 +++++++++++++++-
 .../sorting_searching_operators.py            | 56 +++++++++++++++++++
 benchmark/opperf/opperf.py                    | 12 +++-
 benchmark/opperf/rules/default_params.py      |  7 ++-
 benchmark/opperf/utils/op_registry_utils.py   | 26 ++++++++-
 6 files changed, 143 insertions(+), 13 deletions(-)
 create mode 100644 benchmark/opperf/nd_operations/sorting_searching_operators.py

diff --git a/benchmark/opperf/nd_operations/README.md b/benchmark/opperf/nd_operations/README.md
index 7aa220c4368a..b98a0d3d0c48 100644
--- a/benchmark/opperf/nd_operations/README.md
+++ b/benchmark/opperf/nd_operations/README.md
@@ -62,12 +62,10 @@
 40. crop
 41. rmsprop_update
 43. RNN
-44. argmin
 45. SoftmaxOutput
 46. linalg_extractdiag
 47. sgd_mom_update
 48. SequenceLast
-49. Deconvolution
 50. flip
 51. SequenceReverse
 52. swapaxes
@@ -86,18 +84,15 @@
 65. tile
 66. space_to_depth
 67. gather_nd
-68. argsort
 69. SequenceMask
 70. reshape_like
 71. slice_axis
 72. stack
-73. topk
 74. khatri_rao
 75. multi_mp_sgd_update
 76. linalg_sumlogdiag
 77. broadcast_to
 78. IdentityAttachKLSparseReg
-79. sort
 80. SpatialTransformer
 81. Concat
 82. uniform
@@ -129,7 +124,6 @@
 110. split
 111. MAERegressionOutput
 112. Correlation
-113. argmax
 114. batch_take
 115. L2Normalization
 116. broadcast_axis
diff --git a/benchmark/opperf/nd_operations/nn_conv_operators.py b/benchmark/opperf/nd_operations/nn_conv_operators.py
index e42205f8f56c..973058929b2f 100644
--- a/benchmark/opperf/nd_operations/nn_conv_operators.py
+++ b/benchmark/opperf/nd_operations/nn_conv_operators.py
@@ -102,7 +102,7 @@ def run_convolution_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=2
                                                      dtype=dtype,
                                                      ctx=ctx,
                                                      inputs=[{"data": conv_data,
-                                                              "weight": (64, 3, 3,),
+                                                              "weight": (64, 3, 3),
                                                               "bias": (64,),
                                                               "kernel": (3,),
                                                               "stride": (1,),
@@ -135,3 +135,50 @@ def run_convolution_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=2
     # Prepare combined results
     mx_conv_op_results = merge_map_list(conv1d_benchmark_res + conv2d_benchmark_res)
     return mx_conv_op_results
+
+
+def run_transpose_convolution_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=10, runs=50):
+    # Conv1DTranspose Benchmarks
+    conv1d_transpose_benchmark_res = []
+    for conv_data in [(32, 3, 256), (32, 3, 64)]:
+        conv1d_transpose_benchmark_res += run_performance_test([getattr(MX_OP_MODULE, "Deconvolution")],
+                                                               run_backward=True,
+                                                               dtype=dtype,
+                                                               ctx=ctx,
+                                                               inputs=[{"data": conv_data,
+                                                                        "weight": (3, 64, 3),
+                                                                        "bias": (64,),
+                                                                        "kernel": (3,),
+                                                                        "stride": (1,),
+                                                                        "dilate": (1,),
+                                                                        "pad": (0,),
+                                                                        "adj": (0,),
+                                                                        "num_filter": 64,
+                                                                        "no_bias": False,
+                                                                        "layout": 'NCW'}
+                                                                       ],
+                                                               warmup=warmup,
+                                                               runs=runs)
+    # Conv2DTranspose Benchmarks
+    conv2d_transpose_benchmark_res = []
+    for conv_data in [(32, 3, 256, 256), (32, 3, 64, 64)]:
+        conv2d_transpose_benchmark_res += run_performance_test([getattr(MX_OP_MODULE, "Deconvolution")],
+                                                               run_backward=True,
+                                                               dtype=dtype,
+                                                               ctx=ctx,
+                                                               inputs=[{"data": conv_data,
+                                                                        "weight": (3, 64, 3, 3),
+                                                                        "bias": (64,),
+                                                                        "kernel": (3, 3),
+                                                                        "stride": (1, 1),
+                                                                        "dilate": (1, 1),
+                                                                        "pad": (0, 0),
+                                                                        "num_filter": 64,
+                                                                        "no_bias": False,
+                                                                        "layout": 'NCHW'}
+                                                                       ],
+                                                               warmup=warmup,
+                                                               runs=runs)
+    # Prepare combined results
+    mx_transpose_conv_op_results = merge_map_list(conv1d_transpose_benchmark_res + conv2d_transpose_benchmark_res)
+    return mx_transpose_conv_op_results
diff --git a/benchmark/opperf/nd_operations/sorting_searching_operators.py b/benchmark/opperf/nd_operations/sorting_searching_operators.py
new file mode 100644
index 000000000000..ab98b3feee06
--- /dev/null
+++ b/benchmark/opperf/nd_operations/sorting_searching_operators.py
@@ -0,0 +1,56 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import mxnet as mx
+from benchmark.opperf.utils.benchmark_utils import run_op_benchmarks
+from benchmark.opperf.utils.op_registry_utils import get_all_sorting_searching_operators
+
+
+""" Performance benchmark tests for MXNet NDArray Sorting and Searching Operations
+1. sort
+2. argsort
+3. topk
+4. argmax
+5. argmin
+"""
+
+
+def run_sorting_searching_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, runs=100):
+    """Runs benchmarks with the given context and precision (dtype)for all the sorting and searching
+    operators in MXNet.
+
+    Parameters
+    ----------
+    ctx: mx.ctx
+        Context to run benchmarks
+    dtype: str, default 'float32'
+        Precision to use for benchmarks
+    warmup: int, default 25
+        Number of times to run for warmup
+    runs: int, default 100
+        Number of runs to capture benchmark results
+
+    Returns
+    -------
+    Dictionary of results. Key -> Name of the operator, Value -> Benchmark results.
+
+    """
+    # Fetch all Random Sampling Operators
+    mx_sort_search_ops = get_all_sorting_searching_operators()
+    # Run benchmarks
+    mx_sort_search_op_results = run_op_benchmarks(mx_sort_search_ops, dtype, ctx, warmup, runs)
+    return mx_sort_search_op_results
diff --git a/benchmark/opperf/opperf.py b/benchmark/opperf/opperf.py
index a73db4fdae89..b2258afaaaf7 100755
--- a/benchmark/opperf/opperf.py
+++ b/benchmark/opperf/opperf.py
@@ -34,13 +34,14 @@
 from benchmark.opperf.nd_operations.gemm_operators import run_gemm_operators_benchmarks
 from benchmark.opperf.nd_operations.random_sampling_operators import run_mx_random_sampling_operators_benchmarks
 from benchmark.opperf.nd_operations.reduction_operators import run_mx_reduction_operators_benchmarks
+from benchmark.opperf.nd_operations.sorting_searching_operators import run_sorting_searching_operators_benchmarks
 from benchmark.opperf.nd_operations.nn_activation_operators import run_activation_operators_benchmarks
 from benchmark.opperf.nd_operations.nn_conv_operators import run_pooling_operators_benchmarks, \
-    run_convolution_operators_benchmarks
+    run_convolution_operators_benchmarks, run_transpose_convolution_operators_benchmarks
 from benchmark.opperf.nd_operations.nn_basic_operators import run_nn_basic_operators_benchmarks
 
 from benchmark.opperf.utils.common_utils import merge_map_list, save_to_file
-from benchmark.opperf.utils.op_registry_utils import get_operators_with_no_benchmark,\
+from benchmark.opperf.utils.op_registry_utils import get_operators_with_no_benchmark, \
     get_current_runtime_features
 
 
@@ -74,6 +75,9 @@ def run_all_mxnet_operator_benchmarks(ctx=mx.cpu(), dtype='float32'):
     # Run all Reduction operations benchmarks with default input values
     mxnet_operator_benchmark_results.append(run_mx_reduction_operators_benchmarks(ctx=ctx, dtype=dtype))
 
+    # Run all Sorting and Searching operations benchmarks with default input values
+    mxnet_operator_benchmark_results.append(run_sorting_searching_operators_benchmarks(ctx=ctx, dtype=dtype))
+
     # ************************ MXNET NN OPERATOR BENCHMARKS ****************************
 
     # Run all basic NN operations benchmarks with default input values
@@ -88,6 +92,9 @@ def run_all_mxnet_operator_benchmarks(ctx=mx.cpu(), dtype='float32'):
     # Run all Convolution operations benchmarks with default input values
     mxnet_operator_benchmark_results.append(run_convolution_operators_benchmarks(ctx=ctx, dtype=dtype))
 
+    # Run all Transpose Convolution operations benchmarks with default input values
+    mxnet_operator_benchmark_results.append(run_transpose_convolution_operators_benchmarks(ctx=ctx, dtype=dtype))
+
     # ****************************** PREPARE FINAL RESULTS ********************************
     final_benchmark_result_map = merge_map_list(mxnet_operator_benchmark_results)
     return final_benchmark_result_map
@@ -148,4 +155,3 @@ def main():
 
 if __name__ == '__main__':
     sys.exit(main())
-
diff --git a/benchmark/opperf/rules/default_params.py b/benchmark/opperf/rules/default_params.py
index df6cdae0c48d..2c8f3d436e0d 100644
--- a/benchmark/opperf/rules/default_params.py
+++ b/benchmark/opperf/rules/default_params.py
@@ -56,7 +56,11 @@
 
 # For reduction operators
 # NOTE: Data used is DEFAULT_DATA
-DEFAULT_AXIS = [(), 0, (0, 1)]
+DEFAULT_AXIS_SHAPE = [(), 0, (0, 1)]
+
+# For sorting and searching operators
+# NOTE: Data used is DEFAULT_DATA
+DEFAULT_AXIS = [0]
 
 # Default Inputs. MXNet Op Param Name to Default Input mapping
 DEFAULTS_INPUTS = {"data": DEFAULT_DATA,
@@ -76,6 +80,7 @@
                    "p": DEFAULT_P,
                    "k_nd": DEFAULT_K_ND,
                    "p_nd": DEFAULT_P_ND,
+                   "axis_shape": DEFAULT_AXIS_SHAPE,
                    "axis": DEFAULT_AXIS}
 
 # These are names of MXNet operator parameters that is of type NDArray.
diff --git a/benchmark/opperf/utils/op_registry_utils.py b/benchmark/opperf/utils/op_registry_utils.py
index 6509be37f39d..88ea7a16cfe6 100644
--- a/benchmark/opperf/utils/op_registry_utils.py
+++ b/benchmark/opperf/utils/op_registry_utils.py
@@ -16,10 +16,8 @@
 # under the License.
 
 """Utilities to interact with MXNet operator registry."""
-import ctypes
 from operator import itemgetter
 from mxnet import runtime
-from mxnet.base import _LIB, check_call, py_str, OpHandle, c_str, mx_uint
 import mxnet as mx
 
 from benchmark.opperf.rules.default_params import DEFAULTS_INPUTS, MX_OP_MODULE
@@ -121,6 +119,10 @@ def prepare_op_inputs(arg_params):
             arg_values[arg_name] = DEFAULTS_INPUTS[arg_name]
         elif "float" in arg_type and arg_name + "_float" in DEFAULTS_INPUTS:
             arg_values[arg_name] = DEFAULTS_INPUTS[arg_name + "_float"]
+        elif "Shape" in arg_type and arg_name + "_shape" in DEFAULTS_INPUTS:
+            # This is for cases where in some ops 'axis' is Int in some ops a shape tuple.
+            # Ex: axis in sum is shape, axis in sort is int.
+            arg_values[arg_name] = DEFAULTS_INPUTS[arg_name + "_shape"]
 
     # Number of different inputs we want to use to test
     # the operator
@@ -240,6 +242,26 @@ def get_all_reduction_operators():
     return reduction_mx_operators
 
 
+def get_all_sorting_searching_operators():
+    """Gets all Sorting and Searching operators registered with MXNet.
+
+    Returns
+    -------
+    {"operator_name": {"has_backward", "nd_op_handle", "params"}}
+    """
+    sort_search_ops = ['sort', 'argsort', 'argmax', 'argmin', 'topk']
+
+    # Get all mxnet operators
+    mx_operators = _get_all_mxnet_operators()
+
+    # Filter for Sort and search operators
+    sort_search_mx_operators = {}
+    for op_name, op_params in mx_operators.items():
+        if op_name in sort_search_ops and op_name not in unique_ops:
+            sort_search_mx_operators[op_name] = mx_operators[op_name]
+    return sort_search_mx_operators
+
+
 def get_operators_with_no_benchmark(operators_with_benchmark):
     """Gets all MXNet operators with not benchmark.
 

From cb0697fc35abeaac43b0089baa5f0fe6044f8d26 Mon Sep 17 00:00:00 2001
From: Dick Carter <dcarter@nvidia.com>
Date: Wed, 17 Jul 2019 13:41:58 -0700
Subject: [PATCH 077/813] Bypass cuda/cudnn checks if no driver. (#15551)

* Bypass cuda/cudnn checks if no driver.

* Perform cuda/cudnn checks only when a gpu context is created.

* Fix cpplint.
---
 docs/faq/env_var.md      |   4 +-
 include/mxnet/base.h     |  40 +++++++++++++--
 src/base.cc              |  95 +++++++++++++++++++++++++++++++++++
 src/common/cuda_utils.cc | 105 ---------------------------------------
 4 files changed, 132 insertions(+), 112 deletions(-)
 create mode 100644 src/base.cc
 delete mode 100644 src/common/cuda_utils.cc

diff --git a/docs/faq/env_var.md b/docs/faq/env_var.md
index a5ba07098005..f6d48af70089 100644
--- a/docs/faq/env_var.md
+++ b/docs/faq/env_var.md
@@ -242,12 +242,12 @@ If ctypes is used, it must be `mxnet._ctypes.ndarray.NDArrayBase`.
 	- If set to '0', disallows implicit type conversions to Float16 to use Tensor Cores
 	- If set to '1', allows CUDA ops like RNN and Convolution to use TensorCores even with Float32 input data by using implicit type casting to Float16. Only has an effect if `MXNET_CUDA_ALLOW_TENSOR_CORE` is `1`.
 
-* MXNET_CUDA_VERSION_CHECKING
+* MXNET_CUDA_LIB_CHECKING
   - 0(false) or 1(true) ```(default=1)```
   - If set to '0', disallows various runtime checks of the cuda library version and associated warning messages.
   - If set to '1', permits these checks (e.g. compile vs. link mismatch, old version no longer CI-tested)
 
-* MXNET_CUDNN_VERSION_CHECKING
+* MXNET_CUDNN_LIB_CHECKING
   - 0(false) or 1(true) ```(default=1)```
   - If set to '0', disallows various runtime checks of the cuDNN library version and associated warning messages.
   - If set to '1', permits these checks (e.g. compile vs. link mismatch, old version no longer CI-tested)
diff --git a/include/mxnet/base.h b/include/mxnet/base.h
index b239cb1f7302..c1e2da7a9db3 100644
--- a/include/mxnet/base.h
+++ b/include/mxnet/base.h
@@ -191,6 +191,11 @@ struct Context {
    * \return The number of GPUs that are available.
    */
   inline static int32_t GetGPUCount();
+  /*!
+   * Is the cuda driver installed and visible to the system.
+   * \return Whether the driver is present.
+   */
+  inline static bool GPUDriverPresent();
   /*!
    * Get the number of streams that a GPU Worker has available to operations.
    * \return The number of streams that are available.
@@ -222,6 +227,14 @@ struct Context {
    * \return Context
    */
   inline static Context FromString(const std::string& str);
+
+ private:
+#if MXNET_USE_CUDA
+    static void CudaLibChecks();
+#endif
+#if MXNET_USE_CUDNN
+    static void CuDNNLibChecks();
+#endif
 };
 
 #if MXNET_USE_CUDA
@@ -387,17 +400,21 @@ inline bool Context::operator<(const Context &b) const {
 inline Context Context::Create(DeviceType dev_type, int32_t dev_id) {
   Context ctx;
   ctx.dev_type = dev_type;
-  if (dev_id < 0) {
-    ctx.dev_id = 0;
-    if (dev_type & kGPU) {
+  ctx.dev_id = dev_id < 0 ? 0 : dev_id;
+  if (dev_type & kGPU) {
+#if MXNET_USE_CUDA
+    CudaLibChecks();
+#endif
+#if MXNET_USE_CUDNN
+    CuDNNLibChecks();
+#endif
+    if (dev_id < 0) {
 #if MXNET_USE_CUDA
       CHECK_EQ(cudaGetDevice(&ctx.dev_id), cudaSuccess);
 #else
       LOG(FATAL) << "Please compile with CUDA enabled for cuda features";
 #endif
     }
-  } else {
-    ctx.dev_id = dev_id;
   }
   return ctx;
 }
@@ -417,8 +434,21 @@ inline Context Context::GPU(int32_t dev_id) {
   return Create(kGPU, dev_id);
 }
 
+inline bool Context::GPUDriverPresent() {
+#if MXNET_USE_CUDA
+  int cuda_driver_version = 0;
+  CHECK_EQ(cudaDriverGetVersion(&cuda_driver_version), cudaSuccess);
+  return cuda_driver_version > 0;
+#else
+  return false;
+#endif
+}
+
 inline int32_t Context::GetGPUCount() {
 #if MXNET_USE_CUDA
+  if (!GPUDriverPresent()) {
+    return 0;
+  }
   int32_t count;
   cudaError_t e = cudaGetDeviceCount(&count);
   if (e == cudaErrorNoDevice) {
diff --git a/src/base.cc b/src/base.cc
new file mode 100644
index 000000000000..96d66ad1d379
--- /dev/null
+++ b/src/base.cc
@@ -0,0 +1,95 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file base.cc
+ * \brief Implementation of base declarations, e.g. context
+ */
+#include <mxnet/base.h>
+
+namespace mxnet {
+
+#define UNUSED(x) (void)(x)
+
+#if MXNET_USE_CUDA == 1
+// The oldest version of cuda used in upstream MXNet CI testing, both for unix and windows.
+// Users that have rebuilt MXNet against older versions will we advised with a warning to upgrade
+// their systems to match the CI level.  Minimally, users should rerun the CI locally.
+#if defined(_MSC_VER)
+#define MXNET_CI_OLDEST_CUDA_VERSION  9020
+#else
+#define MXNET_CI_OLDEST_CUDA_VERSION 10000
+#endif
+
+void Context::CudaLibChecks() {
+  // One-time init here will emit a warning if no gpus or gpu driver is seen.
+  // Also if the user has recompiled their source to a version no longer tested by upstream CI.
+  static bool cuda_lib_checks_performed = []() {
+    if (dmlc::GetEnv("MXNET_CUDA_LIB_CHECKING", true)) {
+      if (!GPUDriverPresent())
+        LOG(WARNING) << "Please install cuda driver for GPU use.  No cuda driver detected.";
+      else if (GetGPUCount() == 0)
+        LOG(WARNING) << "GPU context requested, but no GPUs found.";
+      else if (CUDA_VERSION < MXNET_CI_OLDEST_CUDA_VERSION)
+        LOG(WARNING) << "Upgrade advisory: this mxnet has been built against cuda library version "
+                     << CUDA_VERSION << ", which is older than the oldest version tested by CI ("
+                     << MXNET_CI_OLDEST_CUDA_VERSION << ").  "
+                     << "Set MXNET_CUDA_LIB_CHECKING=0 to quiet this warning.";
+    }
+    return true;
+  }();
+  UNUSED(cuda_lib_checks_performed);
+}
+#endif  // MXNET_USE_CUDA
+
+#if MXNET_USE_CUDNN == 1
+// The oldest version of CUDNN used in upstream MXNet CI testing, both for unix and windows.
+// Users that have rebuilt MXNet against older versions will we advised with a warning to upgrade
+// their systems to match the CI level.  Minimally, users should rerun the CI locally.
+#if defined(_MSC_VER)
+#define MXNET_CI_OLDEST_CUDNN_VERSION 7600
+#else
+#define MXNET_CI_OLDEST_CUDNN_VERSION 7600
+#endif
+
+void Context::CuDNNLibChecks() {
+  // One-time init here will emit a warning if runtime and compile-time cudnn lib versions mismatch.
+  // Also if the user has recompiled their source to a version no longer tested by upstream CI.
+  static bool cudnn_lib_checks_performed = []() {
+    // Don't bother with checks if there are no GPUs visible (e.g. with CUDA_VISIBLE_DEVICES="")
+    if (dmlc::GetEnv("MXNET_CUDNN_LIB_CHECKING", true) && GetGPUCount() > 0) {
+      size_t linkedAgainstCudnnVersion = cudnnGetVersion();
+      if (linkedAgainstCudnnVersion != CUDNN_VERSION)
+        LOG(WARNING) << "cuDNN lib mismatch: linked-against version " << linkedAgainstCudnnVersion
+                     << " != compiled-against version " << CUDNN_VERSION << ".  "
+                     << "Set MXNET_CUDNN_LIB_CHECKING=0 to quiet this warning.";
+      if (CUDNN_VERSION < MXNET_CI_OLDEST_CUDNN_VERSION)
+        LOG(WARNING) << "Upgrade advisory: this mxnet has been built against cuDNN lib version "
+                     <<  CUDNN_VERSION << ", which is older than the oldest version tested by CI ("
+                     << MXNET_CI_OLDEST_CUDNN_VERSION << ").  "
+                     << "Set MXNET_CUDNN_LIB_CHECKING=0 to quiet this warning.";
+    }
+    return true;
+  }();
+  UNUSED(cudnn_lib_checks_performed);
+}
+#endif  // MXNET_USE_CUDNN
+
+}  // namespace mxnet
diff --git a/src/common/cuda_utils.cc b/src/common/cuda_utils.cc
deleted file mode 100644
index 728d1e6681e6..000000000000
--- a/src/common/cuda_utils.cc
+++ /dev/null
@@ -1,105 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- */
-
-/*!
- * Copyright (c) 2019 by Contributors
- * \file cuda_utils.cc
- * \brief CUDA debugging utilities.
- */
-
-#include <mxnet/base.h>
-#include "cuda_utils.h"
-
-#if MXNET_USE_CUDA == 1
-
-namespace mxnet {
-namespace common {
-namespace cuda {
-
-// The oldest version of cuda used in upstream MXNet CI testing, both for unix and windows.
-// Users that have rebuilt MXNet against older versions will we advised with a warning to upgrade
-// their systems to match the CI level.  Minimally, users should rerun the CI locally.
-#if defined(_MSC_VER)
-#define MXNET_CI_OLDEST_CUDA_VERSION  9020
-#else
-#define MXNET_CI_OLDEST_CUDA_VERSION 10000
-#endif
-
-// Dynamic init here will emit a warning if runtime and compile-time cuda lib versions mismatch.
-// Also if the user has recompiled their source to a version no longer tested by upstream CI.
-bool cuda_version_check_performed = []() {
-  // Don't bother with checks if there are no GPUs visible (e.g. with CUDA_VISIBLE_DEVICES="")
-  if (dmlc::GetEnv("MXNET_CUDA_VERSION_CHECKING", true) && Context::GetGPUCount() > 0) {
-    // Not currently performing a runtime check of linked-against vs. compiled-against
-    // cuda runtime library, as major.minor must match for libmxnet.so to even load, per:
-    // https://docs.nvidia.com/deploy/cuda-compatibility/#binary-compatibility
-    if (CUDA_VERSION < MXNET_CI_OLDEST_CUDA_VERSION)
-      LOG(WARNING) << "Upgrade advisory: this mxnet has been built against cuda library version "
-                   << CUDA_VERSION << ", which is older than the oldest version tested by CI ("
-                   << MXNET_CI_OLDEST_CUDA_VERSION << ").  "
-                   << "Set MXNET_CUDA_VERSION_CHECKING=0 to quiet this warning.";
-  }
-  return true;
-}();
-
-}  // namespace cuda
-}  // namespace common
-}  // namespace mxnet
-
-#endif  // MXNET_USE_CUDA
-
-#if MXNET_USE_CUDNN == 1
-
-namespace mxnet {
-namespace common {
-namespace cudnn {
-
-// The oldest version of CUDNN used in upstream MXNet CI testing, both for unix and windows.
-// Users that have rebuilt MXNet against older versions will we advised with a warning to upgrade
-// their systems to match the CI level.  Minimally, users should rerun the CI locally.
-#if defined(_MSC_VER)
-#define MXNET_CI_OLDEST_CUDNN_VERSION 7600
-#else
-#define MXNET_CI_OLDEST_CUDNN_VERSION 7600
-#endif
-
-// Dynamic init here will emit a warning if runtime and compile-time cudnn lib versions mismatch.
-// Also if the user has recompiled their source to a version no longer tested by upstream CI.
-bool cudnn_version_check_performed = []() {
-  // Don't bother with checks if there are no GPUs visible (e.g. with CUDA_VISIBLE_DEVICES="")
-  if (dmlc::GetEnv("MXNET_CUDNN_VERSION_CHECKING", true) && Context::GetGPUCount() > 0) {
-    size_t linkedAgainstCudnnVersion = cudnnGetVersion();
-    if (linkedAgainstCudnnVersion != CUDNN_VERSION)
-      LOG(WARNING) << "cuDNN library mismatch: linked-against version " << linkedAgainstCudnnVersion
-                   << " != compiled-against version " << CUDNN_VERSION << ".  "
-                   << "Set MXNET_CUDNN_VERSION_CHECKING=0 to quiet this warning.";
-    if (CUDNN_VERSION < MXNET_CI_OLDEST_CUDNN_VERSION)
-      LOG(WARNING) << "Upgrade advisory: this mxnet has been built against cuDNN library version "
-                   <<  CUDNN_VERSION << ", which is older than the oldest version tested by CI ("
-                   << MXNET_CI_OLDEST_CUDNN_VERSION << ").  "
-                   << "Set MXNET_CUDNN_VERSION_CHECKING=0 to quiet this warning.";
-  }
-  return true;
-}();
-
-}  // namespace cudnn
-}  // namespace common
-}  // namespace mxnet
-
-#endif  // MXNET_USE_CUDNN

From 35b54804413624115cec854e14fc35ecf3034412 Mon Sep 17 00:00:00 2001
From: Zhaoqi Zhu <zhaoqizh@usc.edu>
Date: Wed, 17 Jul 2019 15:14:57 -0700
Subject: [PATCH 078/813] Utility to help developers debug operators: Tensor
 Inspector (#15490)

* initial. Supports Tensor, TBlob, NDArray

* add GPU tensor support

* move run context reference to constructors

* sanity fix

* sanity fix

* fix checker bug & add new checker type

* add more checker types

* fix gpu tensor constructor call

* add value dumping funtionality

* sanity fix

* sanity fix

* add dumping support to interactive print

* sanity fix

* Re-Trigger build

* add namespace before Tensor

* add more checker types

* bug fix

* fix comments

* change int to size_t

* miscellaneous

* sanity fix

* remove unnecessary inlines

* change size_t to index_t

* bug fixes and add print value options in value_check()

* fix warnings

* add negative check

* Re-Trigger build

* Re-Trigger build

* change int to size_t
---
 src/common/tensor_inspector.h | 815 ++++++++++++++++++++++++++++++++++
 1 file changed, 815 insertions(+)
 create mode 100644 src/common/tensor_inspector.h

diff --git a/src/common/tensor_inspector.h b/src/common/tensor_inspector.h
new file mode 100644
index 000000000000..2df94b7fc04f
--- /dev/null
+++ b/src/common/tensor_inspector.h
@@ -0,0 +1,815 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file tensor_inspector.h
+ * \brief utility to inspect tensor objects
+ * \author Zhaoqi Zhu
+ */
+
+#ifndef MXNET_COMMON_TENSOR_INSPECTOR_H_
+#define MXNET_COMMON_TENSOR_INSPECTOR_H_
+
+#include <algorithm>
+#include <cmath>
+#include <string>
+#include <vector>
+#include <fstream>
+#include "../../3rdparty/mshadow/mshadow/base.h"
+#include "../../tests/cpp/include/test_util.h"
+
+namespace mxnet {
+
+/*!
+ * \brief this singleton struct mediates individual TensorInspector objects
+ * so that we can control the global behavior from each of them
+ */
+struct InspectorManager {
+  static InspectorManager* get() {
+    static std::mutex mtx;
+    static std::unique_ptr<InspectorManager> im = nullptr;
+    if (!im) {
+      std::unique_lock<std::mutex> lk(mtx);
+      if (!im)
+        im = std::make_unique<InspectorManager>();
+    }
+    return im.get();
+  }
+  /* !\brief mutex used to lock interactive_print() and check_value() */
+  std::mutex mutex_;
+  /* !\brief skip all interactive prints */
+  bool interactive_print_skip_all_ = false;
+  /* !\brief skip all value checks */
+  bool check_value_skip_all_ = false;
+  /* !\brief visit count for interactive print tags */
+  std::unordered_map<std::string, int> interactive_print_tag_counter_;
+  /* !\brief visit count for check value tags */
+  std::unordered_map<std::string, int> check_value_tag_counter_;
+  /* !\brief visit count for dump value tags */
+  std::unordered_map<std::string, int> dump_to_file_tag_counter_;
+};
+
+/*!
+ * \brief Enum for building value checkers for TensorInspector::check_value()
+ */
+enum CheckerType {
+  NegativeChecker,  // check if is negative
+  PositiveChecker,  // check if is positive
+  ZeroChecker,  // check if is zero
+  NaNChecker,  // check if is NaN, will always return false if DType is not a float type
+  InfChecker,  // check if is infinity, will always return false if DType is not a float type
+  PositiveInfChecker,  // check if is positive infinity,
+                       // will always return false if DType is not a float type
+  NegativeInfChecker,  // check if is nagative infinity,
+                       // will always return false if DType is not a float type
+  FiniteChecker,  // check if is finite, will always return false if DType is not a float type
+  NormalChecker,  // check if is neither infinity nor NaN
+  AbnormalChecker,  // chekck if is infinity or nan
+};
+
+/**
+ *  _______                      _____                           _             
+ * |__   __|                    |_   _|                         | |            
+ *    | | ___ _ __  ___  ___  _ __| |  _ __  ___ _ __   ___  ___| |_ ___  _ __ 
+ *    | |/ _ \ '_ \/ __|/ _ \| '__| | | '_ \/ __| '_ \ / _ \/ __| __/ _ \| '__|
+ *    | |  __/ | | \__ \ (_) | | _| |_| | | \__ \ |_) |  __/ (__| || (_) | |   
+ *    |_|\___|_| |_|___/\___/|_||_____|_| |_|___/ .__/ \___|\___|\__\___/|_|   
+ *                                              | |                            
+ *                                              |_|   
+ */
+
+/*!
+ * \brief This class provides a unified interface to inspect the value of all data types
+ * including Tensor, TBlob, and NDArray. If the tensor resides on GPU, then it will be
+ * copied from GPU memory back to CPU memory to be operated on. Internally, all data types
+ * are stored as a TBlob object tb_.
+ */
+class TensorInspector {
+ private:
+  /*!
+   * \brief generate the tensor info, including data type and shape 
+   * \tparam DType the data type
+   * \tparam StreamType the type of the stream object
+   * \param os stream object to output to
+   */
+  template<typename DType, typename StreamType>
+  void tensor_info_to_string(StreamType* os) {
+    const int dimension = tb_.ndim();
+    *os << "<" << infer_type_string(typeid(DType)) << " Tensor ";
+    *os << tb_.shape_[0];
+    for (int i = 1; i < dimension; ++i) {
+      *os << 'x' << tb_.shape_[i];
+    }
+    *os << ">" << std::endl;
+  }
+
+  /*!
+   * \brief output the tensor info, including data type and shape 
+   * \tparam DType the data type
+   * \tparam StreamType the type of the stream object
+   * \param os stream object to output to
+   * \param shape the shape of the tensor
+   */
+  template<typename DType, typename StreamType>
+  void tensor_info_to_string(StreamType* os, const std::vector<index_t>& shape) {
+    const int dimension = shape.size();
+    *os << "<" << infer_type_string(typeid(DType)) << " Tensor ";
+    *os << shape[0];
+    for (int i = 1; i < dimension; ++i) {
+      *os << 'x' << shape[i];
+    }
+    *os << ">" << std::endl;
+  }
+
+  /*!
+   * \brief output the tensor in a structured format 
+   * \tparam DType the data type
+   * \tparam StreamType the type of the stream object
+   * \param os stream object to output to
+   */
+  template<typename DType, typename StreamType>
+  void to_string_helper(StreamType* os) {
+#if MXNET_USE_CUDA
+    if (tb_.dev_mask() == gpu::kDevMask) {
+      TensorInspector(test::CAccessAsCPU(ctx_, tb_, false)(), ctx_)
+          .to_string_helper<DType>(os);
+      return;
+    }
+#endif  // MXNET_USE_CUDA
+    const int dimension = tb_.ndim();
+    std::vector<index_t> offsets;
+    index_t multiple = 1;
+    for (int i = dimension - 1; i >= 0; --i) {
+      multiple *= tb_.shape_[i];
+      offsets.push_back(multiple);
+    }
+    *os << std::string(dimension, '[');
+    *os << tb_.dptr<DType>()[0];
+    for (index_t i = 1; i < static_cast<index_t>(tb_.shape_.Size()); ++i) {
+      int n = 0;
+      for (auto off : offsets) {
+        n += (i % off == 0);
+      }
+      if (n) {
+        *os << std::string(n, ']') << ", " <<  std::string(n, '[');
+      } else  {
+        *os << ", ";
+      }
+      *os << tb_.dptr<DType>()[i];
+    }
+    *os << std::string(dimension, ']') << std::endl;
+    tensor_info_to_string<DType>(os);
+  }
+
+  /*!
+   * \brief output the tensor in a structured format 
+   * \tparam DType the data type
+   * \tparam StreamType the type of the stream object
+   * \param os stream object to output to
+   * \param dptr the data pointer
+   */
+  template<typename DType, typename StreamType>
+  void to_string_helper(StreamType* os, const DType* dptr) {
+#if MXNET_USE_CUDA
+    if (tb_.dev_mask() == gpu::kDevMask) {
+      TensorInspector(test::CAccessAsCPU(ctx_, tb_, false)(), ctx_)
+          .to_string_helper<DType>(os, dptr);
+      return;
+    }
+#endif  // MXNET_USE_CUDA
+    *os << *dptr << std::endl;
+    *os << "<" << typeid(*dptr).name() << ">" << std::endl;
+  }
+
+  /*!
+   * \brief output a part of the tensor in a structed format 
+   * \tparam DType the data type
+   * \tparam StreamType the type of the stream object
+   * \param os stream object to output to
+   * \param sub_shape the sub-shape of the desired part of the tensor
+   * \param offset the position of the first value of the desired part of the tensor
+   */
+  template<typename DType, typename StreamType>
+  void to_string_helper(StreamType* os, const std::vector<index_t>& sub_shape, index_t offset) {
+#if MXNET_USE_CUDA
+    if (tb_.dev_mask() == gpu::kDevMask) {
+      TensorInspector(test::CAccessAsCPU(ctx_, tb_, false)(), ctx_)
+          .to_string_helper<DType>(os, sub_shape, offset);
+      return;
+    }
+#endif  // MXNET_USE_CUDA
+    DType* dptr = tb_.dptr<DType>() + offset;
+    if (sub_shape.size() == 0) {
+      to_string_helper<DType>(os, dptr);
+      return;
+    }
+    const int dimension = sub_shape.size();
+    std::vector<index_t> offsets;
+    index_t multiple = 1;
+    for (int i = dimension - 1; i >= 0; --i) {
+      multiple *= sub_shape[i];
+      offsets.push_back(multiple);
+    }
+    std::stringstream ss;
+    *os << std::string(dimension, '[');
+    *os << dptr[0];
+    for (index_t i = 1; i < multiple; ++i) {
+      int n = 0;
+      for (auto off : offsets) {
+        n += (i % off == 0);
+      }
+      if (n) {
+        *os << std::string(n, ']') << ", " <<  std::string(n, '[');
+      } else  {
+        *os << ", ";
+      }
+      *os << dptr[i];
+    }
+    *os << std::string(dimension, ']') << std::endl;
+    tensor_info_to_string<DType>(os, sub_shape);
+  }
+
+  /*!
+   * \brief helper function to calculate the sub_shape and offset for the desired part of the tensor,
+   * given its coordinates in the original tensor
+   * \param pos the coordinates of the desired part of the tensor
+   * \param sub_shape the sub-shape of the desired part of the tensor; calculated here
+   * \param offset the position of the first value of the desired part of the tensor; calculated here
+   */
+  void print_locator(const std::vector<index_t>& pos, std::vector<index_t>* sub_shape,
+      index_t* offset) {
+    const int dimension = tb_.ndim();
+    const int sub_dim = dimension - pos.size();
+    sub_shape->resize(sub_dim);
+    index_t multiple = 1;
+    for (size_t i = pos.size(), j = 0; i < static_cast<size_t>(dimension); ++i, ++j) {
+      (*sub_shape)[j] = tb_.shape_[i];
+      multiple *= tb_.shape_[i];
+    }
+    index_t sum = 0;
+    index_t m = 1;
+    for (index_t i = pos.size() - 1; i >= 0; --i) {
+      sum += pos[i] * m;
+      m *= tb_.shape_[i];
+    }
+    *offset = sum * multiple;
+  }
+
+  /*!
+   * \brief parse the coordinate of the desired part of the tensor, given a string that represents that
+   * coordinate
+   * \param pos the coordinates of the desired part of the tensor, calculated here
+   * \param str the string that represents the coordinate
+   */
+  bool parse_position(std::vector<index_t>* pos, const std::string& str) {
+    const int dimension = tb_.ndim();
+    std::istringstream ss(str);
+    index_t n;
+    while (ss >> n) {
+      pos->push_back(n);
+      if (ss.peek() == ',') {
+        ss.ignore();
+      }
+    }
+    if (pos->size() > static_cast<size_t>(dimension)) {
+      return false;
+    }
+    for (size_t i = 0; i < pos->size(); ++i) {
+      if ((*pos)[i] > (tb_.shape_[i] - 1) || (*pos)[i] < 0) {
+        return false;
+      }
+    }
+    return !pos->empty();
+  }
+
+  /*!
+   * \brief interactive print the tensor value
+   * \tparam DType the data type
+   * \param tag the name given to this call
+   */
+  template<typename DType>
+  void interactive_print_helper(std::string tag) {
+#if MXNET_USE_CUDA
+    if (tb_.dev_mask() == gpu::kDevMask) {
+      TensorInspector(test::CAccessAsCPU(ctx_, tb_, false)(), ctx_)
+          .interactive_print_helper<DType>(tag);
+      return;
+    }
+#endif  // MXNET_USE_CUDA
+    std::lock_guard<std::mutex> lock(InspectorManager::get()->mutex_);
+    InspectorManager::get()->interactive_print_tag_counter_[tag] += 1;
+    while (!InspectorManager::get()->interactive_print_skip_all_) {
+      std::cout << "----------Interactive Print----------" << std::endl;
+      if (tag != "") {
+        std::cout << "Tag: " << tag << "  Visit: " <<
+            InspectorManager::get()->interactive_print_tag_counter_[tag] <<  std::endl;
+      }
+      tensor_info_to_string<DType>(&std::cout);
+      std::cout << "To print a part of the tensor, " <<
+          "please specify a position, seperated by \",\"" << std::endl;
+      std::cout << "\"e\" for the entire tensor, " <<
+          "\"d\" to dump value to file, " <<
+          "\"b\" to break, " <<
+          "\"s\" to skip all: ";
+      std::string str;
+      std::cin >> str;
+      if (str == "b") {
+        break;
+      } else if (str == "e") {
+        to_string_helper<DType>(&std::cout);
+        continue;
+      } else if (str == "s") {
+        InspectorManager::get()->interactive_print_skip_all_ = true;
+        break;
+      } else if (str == "d") {
+        while (true) {
+          std::cout << "Please enter a tag: ";
+          std::cin >> str;
+          if (str.find(' ') != std::string::npos) {
+            std::cout << "Invalid tag name. No space allowed.";
+            continue;
+          }
+          dump_to_file_helper<DType>(str);
+          break;
+        }
+        continue;
+      }
+      std::vector<index_t> pos;
+      if (parse_position(&pos, str)) {
+        std::vector<index_t> sub_shape;
+        index_t offset;
+        print_locator(pos, &sub_shape, &offset);
+        to_string_helper<DType>(&std::cout, sub_shape, offset);
+      } else {
+        std::cout << "invalid command/indices" << std::endl;
+      }
+    }
+  }
+
+  /*!
+   * \brief build the lambda function, aka the checker, given its type
+   * \tparam DType the data type
+   * \param ct the type of the checker
+   */
+  template<typename DType>
+  std::function<bool(DType)> get_checker(CheckerType ct) {
+    switch (ct) {
+      case NegativeChecker:
+        return [] (DType x) {
+              return x < 0;
+            };
+      case PositiveChecker:
+        return [] (DType x) {
+              return x > 0;
+            };
+      case ZeroChecker:
+        return [] (DType x) {
+              return x == 0;
+            };
+      case NaNChecker:
+        if (std::is_same<DType, float>::value || std::is_same<DType, double>::value ||
+            std::is_same<DType, mshadow::half::half_t>::value) {
+          return [] (DType x) {
+                return x != x;
+              };
+        } else {
+          LOG(WARNING) << "NaNChecker only applies to float types. " <<
+              "Lambda will always return false.";
+        }
+        break;
+      case InfChecker:
+        if (std::is_same<DType, float>::value || std::is_same<DType, double>::value ||
+            std::is_same<DType, mshadow::half::half_t>::value) {
+          return [] (DType x) {
+                return x == (DType)1.0 / 0.0f || x == -(DType)1.0 / 0.0f;
+              };
+        } else {
+          LOG(WARNING) << "InfChecker only applies to float types. " <<
+              "Lambda will always return false.";
+        }
+        break;
+      case PositiveInfChecker:
+        if (std::is_same<DType, float>::value || std::is_same<DType, double>::value ||
+            std::is_same<DType, mshadow::half::half_t>::value) {
+          return [] (DType x) {
+                return x == (DType)1.0 /  0.0f;
+              };
+        } else {
+          LOG(WARNING) << "PositiveInfChecker only applies to float types. " <<
+              "Lambda will always return false.";
+        }
+        break;
+      case NegativeInfChecker:
+        if (std::is_same<DType, float>::value || std::is_same<DType, double>::value ||
+            std::is_same<DType, mshadow::half::half_t>::value) {
+          return [] (DType x) {
+                return x == -(DType)1.0 /  0.0f;
+              };
+        } else {
+          LOG(WARNING) << "NegativeInfChecker only applies to float types. " <<
+              "Lambda will always return false.";
+        }
+        break;
+      case FiniteChecker:
+        if (std::is_same<DType, float>::value || std::is_same<DType, double>::value ||
+            std::is_same<DType, mshadow::half::half_t>::value) {
+          return [] (DType x) {
+                return x != (DType)1.0 /  0.0f && x != -(DType)1.0 /  0.0f;
+              };
+        } else {
+          LOG(WARNING) << "FiniteChecker only applies to float types. " <<
+              "Lambda will always return false.";
+        }
+        break;
+      case NormalChecker:
+        if (std::is_same<DType, float>::value || std::is_same<DType, double>::value ||
+            std::is_same<DType, mshadow::half::half_t>::value) {
+          return [] (DType x) {
+                return x != (DType)1.0 /  0.0f && x != -(DType)1.0 /  0.0f &&
+                    x == x;
+              };
+        } else {
+          LOG(WARNING) << "NormalChecker only applies to float types. " <<
+              "Lambda will always return false.";
+        }
+        break;
+      case AbnormalChecker:
+        if (std::is_same<DType, float>::value || std::is_same<DType, double>::value ||
+            std::is_same<DType, mshadow::half::half_t>::value) {
+          return [] (DType x) {
+                return x == (DType)1.0 /  0.0f || x == -(DType)1.0 /  0.0f ||
+                    x != x;
+              };
+        } else {
+          LOG(WARNING) << "AbnormalChecker only applies to float types. " <<
+              "Lambda will always return false.";
+        }
+        break;
+      default:
+        return [] (DType x) {
+              return false;
+            };
+    }
+    return [] (DType x) {return false;};
+  }
+
+  /*!
+   * \brief calculate the coordinate of a value in the tensor, given its index
+   * \param idx the index of the value in the tensor
+   */
+  std::vector<index_t> index_to_coordinates(index_t idx) {
+    const int dimension = tb_.ndim();
+    std::vector<index_t> ret;
+    for (int i = dimension - 1; i >= 0; --i) {
+      ret.push_back(idx % tb_.shape_[i]);
+      idx /= tb_.shape_[i];
+    }
+    std::reverse(ret.begin(), ret.end());
+    return ret;
+  }
+
+  /*!
+   * \brief check/validate the values within the tensor, find the coordinates
+   * where the value checker evaluates to true
+   * \tparam DType the data type
+   * \param ret a vector of coordinates which itself is a vector of int; calculated here
+   * \param checker the lambda function to check each value of within the tensor
+   * \param interactive wherether to allow the user to interactively check the coordinates
+   * \param tag the name given to this call
+   */
+  template<typename DType>
+  void check_value_helper(std::vector<std::vector<index_t>>* ret,
+      const std::function<bool(DType)>& checker, bool interactive, std::string tag) {
+#if MXNET_USE_CUDA
+    if (tb_.dev_mask() == gpu::kDevMask) {
+      return TensorInspector(test::CAccessAsCPU(ctx_, tb_, false)(), ctx_)
+          .check_value_helper<DType>(ret, checker, interactive, tag);
+    }
+#endif  // MXNET_USE_CUDA
+    index_t count = 0;
+    std::stringstream ss;
+    ss << "[";
+    bool first_pass = true;
+    for (index_t i = 0; i <static_cast<index_t>(tb_.shape_.Size()); ++i) {
+      if (checker(tb_.dptr<DType>()[i])) {
+        ++count;
+        if (!first_pass) {
+          ss  << ", ";
+        }
+        first_pass = false;
+        std::vector<index_t> coords = index_to_coordinates(i);
+        ss << "(" << coords[0];
+        for (size_t i = 1; i < coords.size(); ++i) {
+          ss << ", " << coords[i];
+        }
+        ss << ")";
+        ret->push_back(coords);
+      }
+    }
+    ss << "]" << std::endl;
+    if (interactive) {
+      std::lock_guard<std::mutex> lock(InspectorManager::get()->mutex_);
+       InspectorManager::get()->check_value_tag_counter_[tag] += 1;
+      while (!InspectorManager::get()->check_value_skip_all_) {
+        std::cout << "----------Value Check----------" << std::endl;
+        tensor_info_to_string<DType>(&std::cout);
+        if (tag != "") {
+          std::cout << "Tag: " << tag << "  Visit: " <<
+              InspectorManager::get()->check_value_tag_counter_[tag] <<  std::endl;
+        }
+        std::cout << count << " value(s) found." << std::endl;
+        std::cout << "To print a part of the tensor," <<
+            " please specify a position, seperated by \",\"" << std::endl;
+        std::cout << "\"e\" for the entire tensor, " <<
+            "\"p\" to print the coordinates of the values found, " <<
+            "\"b\" to break, " <<
+            "\"s\" to skip all: ";
+        std::string str;
+        std::cin >> str;
+        if (str == "b") {
+          break;
+        } else if (str == "e") {
+          to_string_helper<DType>(&std::cout);
+          continue;
+        } else if (str == "p") {
+          std::cout << ss.str() << std::endl;
+          continue;
+        } else if (str == "s") {
+          InspectorManager::get()->check_value_skip_all_ = true;
+          break;
+        }
+        std::vector<index_t> pos;
+        if (parse_position(&pos, str)) {
+          std::vector<index_t> sub_shape;
+          index_t offset;
+          print_locator(pos, &sub_shape, &offset);
+          to_string_helper<DType>(&std::cout, sub_shape, offset);
+        } else {
+          std::cout << "invalid command/indices" << std::endl;
+        }
+      }
+    }
+  }
+
+  /*!
+   * \brief infer the python type, given the c++ type
+   * \tparam ti the type info 
+   */
+  inline char infer_type(const std::type_info& ti) {
+    if (ti == typeid(float)) return 'f';
+    else if (ti == typeid(double)) return 'f';
+    else if (ti == typeid(mshadow::half::half_t) ) return 'f';
+    else if (ti == typeid(uint8_t)) return 'u';
+    else if (ti == typeid(int32_t)) return 'i';
+    else if (ti == typeid(int64_t)) return 'i';
+    else
+      return '?';
+  }
+
+  /*!
+   * \brief infer the python type, given the c++ type
+   * \tparam ti the type info 
+   */
+  inline std::string infer_type_string(const std::type_info& ti) {
+    if (ti == typeid(float)) return "float";
+    else if (ti == typeid(double)) return "double";
+    else if (ti == typeid(mshadow::half::half_t) ) return "mshasow::half::half_t";
+    else if (ti == typeid(uint8_t)) return "uint8_t";
+    else if (ti == typeid(int32_t)) return "int32_t";
+    else if (ti == typeid(int64_t)) return "int64_t";
+    else
+      return "unknown tyoe";
+  }
+
+  /*!
+   * \brief check if the host machine is big or small endian
+   */
+  inline char endian_test() {
+    int x = 1;
+    return (reinterpret_cast<char*>(&x)[0]) ? '<' : '>';
+  }
+
+  /*!
+   * \brief generate the header following npy 1.0 format
+   * \tparam DType the data type
+   */
+  template<typename DType>
+  std::string get_header() {
+    const int dimension = tb_.ndim();
+    std::string dict;
+    dict += "{'descr':'";
+    dict += endian_test();
+    dict += infer_type(typeid(DType));
+    dict += std::to_string(sizeof(DType));
+    dict += "','fortran_order':False,'shape':(";
+    dict += std::to_string(tb_.shape_[0]);
+    for (int i = 1; i < dimension; ++i) {
+      dict += ',';
+      dict += std::to_string(tb_.shape_[i]);
+    }
+    if (dimension == 1) {
+       dict += ",";
+    }
+    dict += ")} ";
+    int padding_size = 64 - ((10 + dict.size()) % 64);
+    dict += std::string(padding_size, ' ');
+    dict.back() = '\n';
+    std::string header;
+    header += static_cast<char>(0x93);
+    header += "NUMPY";
+    header += static_cast<char>(0x01);
+    header += static_cast<char>(0x00);
+    header += static_cast<char>((uint16_t)dict.size() & 0x00ff);
+    header += static_cast<char>(((uint16_t)dict.size() >> 8) & 0x00ff);
+    header += dict;
+    return header;
+  }
+
+  /*!
+   * \brief write the header and the date to an npy file
+   * \tparam DType the data type
+   * \param header the header of the file
+   * \param filename the file name
+   */
+  template<typename DType>
+  void write_npy(const std::string& header, const std::string& filename) {
+    std::ofstream file;
+    file.exceptions(std::ofstream::failbit | std::ofstream::badbit);
+    try {
+      file.open(filename, std::ios::out | std::ios::binary);
+      file.write(header.c_str(), header.size());
+      file.write(reinterpret_cast<char*>(tb_.dptr<DType>()), sizeof(DType) * tb_.shape_.Size());
+      file.close();
+      std::cout << "Tensor dumped to file: " << filename << std::endl;
+    } catch (std::ofstream::failure e) {
+      std::cerr << "Exception opening/writing/closing file " << filename << std::endl;
+    }
+  }
+
+  /*!
+   * \brief dump the value of the tensor to a file with name "[tag]_[visit count].npy" in npy format
+   * the dump file follows npy 1.0 stantand
+   * \tparam DType the data type
+   * \param tag the name given to this call
+   */
+  template<typename DType>
+  void dump_to_file_helper(const std::string& tag) {
+#if MXNET_USE_CUDA
+    if (tb_.dev_mask() == gpu::kDevMask) {
+      TensorInspector(test::CAccessAsCPU(ctx_, tb_, false)(), ctx_)
+          .dump_to_file_helper<DType>(tag);
+      return;
+    }
+#endif  // MXNET_USE_CUDA
+    std::string header = get_header<DType>();
+    InspectorManager::get()->dump_to_file_tag_counter_[tag] += 1;
+    const int visit = InspectorManager::get()->dump_to_file_tag_counter_[tag];
+    std::string filename = tag + "_" + std::to_string(visit) + ".npy";
+    write_npy<DType>(header, filename);
+  }
+
+   /*!
+   * \brief validate that the shape
+   */
+  inline void validate_shape() {
+    const int dimension = tb_.ndim();
+    CHECK(dimension > 0) << "Tensor Inspector does not support empty tensors " <<
+        "or tensors of unknow shape.";
+    for (int i = 0; i < dimension; ++i) {
+      CHECK(tb_.shape_[i] != 0) << "Invalid tensor shape: shape_[" << i << "] is 0";
+    }
+  }
+
+  /* !\brief the tensor blob */
+  const TBlob tb_;
+  /* !\brief the run context of the tensor */
+  const RunContext& ctx_;
+
+ public:
+   /*!
+   * \brief construct from Tensor object
+   * \tparam Device the device the tensor resides in
+   * \tparam dimension the dimension of the tensor
+   * \tparam DType the data type
+   * \param ts the source tensor object
+   * \param ctx the run context of the tensor
+   */
+  template<typename Device, int dimension, typename DType>
+  TensorInspector(const mshadow::Tensor<Device, dimension, DType>& ts, const RunContext& ctx):
+      tb_(ts), ctx_(ctx) {
+    validate_shape();
+  }
+
+  /*!
+   * \brief construct from TBlob object
+   * \param tb the source tblob object
+   * \param ctx the run context of the tensor
+   */
+  TensorInspector(const TBlob& tb, const RunContext& ctx):
+      tb_(tb), ctx_(ctx) {
+    validate_shape();
+  }
+
+  /*!
+   * \brief construct from NDArray object. Currently this only works with kDefaultStorage
+   * \param arr the source ndarray object
+   * \param ctx the run context of the tensor
+   */
+  TensorInspector(const NDArray& arr, const RunContext& ctx):
+      tb_(arr.data()), ctx_(ctx) {
+    validate_shape();
+  }
+
+  /*!
+   * \brief print the tensor to std::cout
+   */
+  void print_string() {
+    std::cout << to_string() << std::endl;
+  }
+
+  /*!
+   * \brief return a string which contains the values and other info of the tensor
+   */
+  std::string to_string() {
+    std::stringstream ss;
+    MSHADOW_TYPE_SWITCH(tb_.type_flag_, DType, {
+      to_string_helper<DType>(&ss);
+    });
+    return ss.str();
+  }
+
+  /*!
+   * \brief interactively print the tensor value
+   * \param tag the name given to this call
+   */
+  void interactive_print(std::string tag = "") {
+    MSHADOW_TYPE_SWITCH(tb_.type_flag_, DType, {
+      interactive_print_helper<DType>(tag);
+    });
+  }
+
+  /*!
+   * \brief check/validate the values within the tensor, return the coordinates
+   * where the value checker evaluates to true
+   * \tparam ValueChecker the type of the lambda
+   * \param checker the lambda function to check each value of within the tensor
+   * \param interactive wherether to allow the user to interactively check the coordinates
+   * \param tag the name given to this call
+   */
+  template<typename ValueChecker>
+  std::vector<std::vector<index_t>> check_value(const ValueChecker& checker,
+      bool interactive = false, std::string tag = "") {
+    std::vector<std::vector<index_t>> ret;
+    MSHADOW_TYPE_SWITCH(tb_.type_flag_, DType, {
+      check_value_helper<DType>(&ret, checker, ret, interactive, tag);
+    });
+    return ret;
+  }
+
+  /*!
+   * \brief check/validate the values within the tensor, return the coordinates
+   * where the lambda evaluates to true
+   * \param ct the type of the checker
+   * \param interactive wherether to allow the user to interactively check the coordinates
+   * \param tag the name given to this call
+   */
+  std::vector<std::vector<index_t>> check_value(CheckerType ct, bool interactive = false,
+      std::string tag = "") {
+    std::vector<std::vector<index_t>> ret;
+    MSHADOW_TYPE_SWITCH(tb_.type_flag_, DType, {
+      check_value_helper<DType>(&ret, get_checker<DType>(ct), interactive, tag);
+    });
+    return ret;
+  }
+
+  /*!
+   * \brief dump the value of the tensor to a file with name "tag_[visit count].npy" in npy format
+   * \param tag the name given to this call
+   */
+  void dump_to_file(std::string tag) {
+    MSHADOW_TYPE_SWITCH(tb_.type_flag_, DType, {
+      dump_to_file_helper<DType>(tag);
+    });
+  }
+};
+
+}  // namespace mxnet
+
+#endif  // MXNET_COMMON_TENSOR_INSPECTOR_H_

From 9983adbaa8993c038acb65d7d6195d012c418967 Mon Sep 17 00:00:00 2001
From: Carin Meier <cmeier@gigasquidsoftware.com>
Date: Wed, 17 Jul 2019 21:45:52 -0400
Subject: [PATCH 079/813] make clojure api generator tests less brittle
 (#15579)

---
 .../test/dev/generator_test.clj               | 41 +++++++++++--------
 1 file changed, 24 insertions(+), 17 deletions(-)

diff --git a/contrib/clojure-package/test/dev/generator_test.clj b/contrib/clojure-package/test/dev/generator_test.clj
index acc81afcbcd5..3d7d41f79d18 100644
--- a/contrib/clojure-package/test/dev/generator_test.clj
+++ b/contrib/clojure-package/test/dev/generator_test.clj
@@ -17,7 +17,14 @@
 
 (ns dev.generator-test
   (:require [clojure.test :refer :all]
-            [dev.generator :as gen]))
+            [dev.generator :as gen]
+            [clojure.string :as string]))
+
+(defn file-function-name [f]
+  (->> (string/split (slurp f) #"\n")
+       (take 33)
+       last
+       (string/trim)))
 
 (deftest test-clojure-case
   (is (= "foo-bar" (gen/clojure-case "FooBar")))
@@ -334,20 +341,20 @@
           fns (gen/all-symbol-api-functions gen/op-names)
           _ (gen/write-to-file [(first fns) (second fns)]
                                (gen/symbol-api-gen-ns false)
-                               fname)
-          good-contents (slurp "test/good-test-symbol-api.clj")
-          contents (slurp fname)]
-      (is (= good-contents contents))))
+                               fname)]
+      (is (= "activation"
+             (file-function-name "test/good-test-symbol-api.clj")
+             (file-function-name fname)))))
 
   (testing "symbol-random-api"
     (let [fname "test/test-symbol-random-api.clj"
           fns (gen/all-symbol-random-api-functions gen/op-names)
           _ (gen/write-to-file [(first fns) (second fns)]
                                (gen/symbol-api-gen-ns true)
-                               fname)
-          good-contents (slurp "test/good-test-symbol-random-api.clj")
-          contents (slurp fname)]
-      (is (= good-contents contents))))
+                               fname)]
+      (is (= "exponential"
+             (file-function-name "test/good-test-symbol-random-api.clj")
+             (file-function-name fname)))))
 
 
  (testing "symbol"
@@ -364,20 +371,20 @@
           fns (gen/all-ndarray-api-functions gen/op-names)
           _ (gen/write-to-file [(first fns) (second fns)]
                                (gen/ndarray-api-gen-ns false)
-                               fname)
-          good-contents (slurp "test/good-test-ndarray-api.clj")
-          contents (slurp fname)]
-      (is (= good-contents contents))))
+                               fname)]
+      (is (= "activation"
+             (file-function-name "test/good-test-ndarray-api.clj")
+             (file-function-name fname)))))
 
   (testing "ndarray-random-api"
     (let [fname "test/test-ndarray-random-api.clj"
           fns (gen/all-ndarray-random-api-functions gen/op-names)
           _ (gen/write-to-file [(first fns) (second fns)]
                                (gen/ndarray-api-gen-ns true)
-                               fname)
-          good-contents (slurp "test/good-test-ndarray-random-api.clj")
-          contents (slurp fname)]
-      (is (= good-contents contents))))
+                               fname)]
+      (is (= "exponential"
+             (file-function-name "test/good-test-ndarray-random-api.clj")
+             (file-function-name fname)))))
 
   (testing "ndarray"
     (let [fname "test/test-ndarray.clj"

From dd14c813d095abe7195076b7460975ccec62a388 Mon Sep 17 00:00:00 2001
From: Aaron Markham <markhama@amazon.com>
Date: Wed, 17 Jul 2019 21:15:06 -0700
Subject: [PATCH 080/813] refine Nano setup directions (#15524)

* refine directions

* resolving feedback on instructions
---
 docs/install/install-jetson.md | 66 ++++++++++++++++++----------------
 1 file changed, 35 insertions(+), 31 deletions(-)

diff --git a/docs/install/install-jetson.md b/docs/install/install-jetson.md
index 6b0d65060342..0b50ceb3fcc5 100644
--- a/docs/install/install-jetson.md
+++ b/docs/install/install-jetson.md
@@ -19,13 +19,12 @@
 
 MXNet supports the Ubuntu Arch64 based operating system so you can run MXNet on NVIDIA Jetson Devices, such as the [TX2](http://www.nvidia.com/object/embedded-systems-dev-kits-modules.html) or [Nano](https://developer.nvidia.com/embedded/learn/get-started-jetson-nano-devkit).
 
-These instructions will walk through how to build MXNet and install MXNet's Python language binding. 
+These instructions will walk through how to build MXNet and install MXNet's Python language binding.
 
 For the purposes of this install guide we will assume that CUDA is already installed on your Jetson device. The disk image provided by NVIDIA's getting started guides will have the Jetson toolkit preinstalled, and this also includes CUDA. You should double check what versions are installed and which version you plan to use.
 
-You have several options for installing MXNet:
-1. Use a Jetson MXNet pip wheel for Python development.
-2. Use precompiled Jetson MXNet binaries.
+After installing the prerequisites, you have several options for installing MXNet:
+1. Use a Jetson MXNet pip wheel for Python development and use a precompiled Jetson MXNet binary.
 3. Build MXNet from source
    * On a faster Linux computer using cross-compilation
    * On the Jetson itself (very slow and not recommended)
@@ -35,7 +34,7 @@ You have several options for installing MXNet:
 To build from source or to use the Python wheel, you must install the following dependencies on your Jetson.
 Cross-compiling will require dependencies installed on that machine as well.
 
-### Python API
+### Python Dependencies
 
 To use the Python API you need the following dependencies:
 
@@ -60,49 +59,54 @@ sudo pip install \
 ```
 
 If you plan to cross-compile you will need to install these dependencies on that computer as well.
+If you get an error about something being busy, you can restart the Nano and this error will go away. You can then continue installation of the prerequisites.
 
-### Configure CUDA
+### Download the source & setup some environment variables:
 
-You can check to see what version of CUDA is running with `nvcc`.
+These steps are optional, but some of the following instructions expect MXNet source files and the `MXNET_HOME` environment variable. Also, CUDA commands will not work out of the box without updating your path.
+
+Clone the MXNet source code repository using the following `git` command in your home directory:
 
 ```bash
-nvcc --version
+git clone --recursive https://github.com/apache/incubator-mxnet.git mxnet
 ```
 
-To switch CUDA versions on a device or computer that has more than one version installed, use the following and replace the version as appropriate.
+Setup your environment variables for MXNet and CUDA in your `.profile` file in your home directory.
+Add the following to the file.
 
 ```bash
-sudo rm /usr/local/cuda
-sudo ln -s /usr/local/cuda-10.0 /usr/local/cuda
+export PATH=/usr/local/cuda/bin:$PATH
+export MXNET_HOME=$HOME/mxnet/
+export PYTHONPATH=$MXNET_HOME/python:$PYTHONPATH
 ```
 
-**Note:** When cross-compiling, change the CUDA version on the host computer you're using to match the version you're running on your Jetson device.
-**Note:** CUDA 10.1 is recommended but doesn't ship with the Nano's SD card image. You may want to go through CUDA upgrade steps first.
+You can then apply this change immediately with the following:
+```bash
+source .profile
+```
 
-### Download the source & setup some environment variables:
+**Note:** Change the `~/.profile` steps according to how you prefer to use your shell. Otherwise, your environment variables will be gone after you logout.
 
-These steps are optional, but some of the following instructions expect MXNet source files and the `MXNET_HOME` environment variable.
+### Configure CUDA
 
-Clone the MXNet source code repository using the following `git` command in your home directory:
+You can check to see what version of CUDA is running with `nvcc`.
 
 ```bash
-git clone --recursive https://github.com/apache/incubator-mxnet.git mxnet
-cd mxnet
+nvcc --version
 ```
 
-Setup your environment variables for MXNet.
+To switch CUDA versions on a device or computer that has more than one version installed, use the following and replace the symbolic link to the version you want. This one uses CUDA 10.0, which is preinstalled on the Nano.
 
 ```bash
-cd ..
-export MXNET_HOME=$(pwd)
-echo "export PYTHONPATH=$MXNET_HOME/python:$PYTHONPATH" >> ~/.rc
-source ~/.rc
+sudo rm /usr/local/cuda
+sudo ln -s /usr/local/cuda-10.0 /usr/local/cuda
 ```
 
-**Note:** Change the `~/.rc` steps according to how you prefer to use your shell. Otherwise, your environment variables will be gone after you logout.
+**Note:** When cross-compiling, change the CUDA version on the host computer you're using to match the version you're running on your Jetson device.
+**Note:** CUDA 10.1 is recommended but doesn't ship with the Nano's SD card image. You may want to go through CUDA upgrade steps first.
 
 
-## Install MXNet for Python
+## Option 1. Install MXNet for Python
 
 To use a prepared Python wheel, download it to your Jetson, and run it.
 * [MXNet 1.4.0 - Python 3](https://s3.us-east-2.amazonaws.com/mxnet-public/install/jetson/1.4.0/mxnet-1.4.0-cp36-cp36m-linux_aarch64.whl)
@@ -113,12 +117,10 @@ It should download the required dependencies, but if you have issues,
 install the dependencies in the prerequisites section, then run the pip wheel.
 
 ```bash
-sudo pip install mxnet-1.4.0-cp36-cp36m-linux_aarch64.whl
+sudo pip install mxnet-1.4.0-cp27-cp27mu-linux_aarch64.whl
 ```
 
-## Use a Pre-compiled MXNet Binary
-
-If you want to just use a pre-compiled binary you can download it from S3:
+Now use a pre-compiled binary you can download it from S3 which is a patch v1.4.1:
 * https://s3.us-east-2.amazonaws.com/mxnet-public/install/jetson/1.4.1/libmxnet.so
 
 Place this file in `$MXNET_HOME/lib`.
@@ -128,12 +130,14 @@ To use this with the MXNet Python binding, you must match the source directory's
 ```bash
 cd $MXNET_HOME
 git checkout v1.4.x
-git submodule update --init
+git submodule update --init --recursive
 cd python
 sudo pip install -e .
 ```
 
-## Build MXNet from Source
+Refer to the following Conclusion and Next Steps section to test your installation. 
+
+## Option 2. Build MXNet from Source
 
 Installing MXNet from source is a two-step process:
 

From c8a1de429b4ba81c14348db0c04f6d3ea3ec4a17 Mon Sep 17 00:00:00 2001
From: Aaron Markham <markhama@amazon.com>
Date: Thu, 18 Jul 2019 00:12:45 -0700
Subject: [PATCH 081/813] Julia path patch (#15561)

* fix path to be static

* add MXNET_HOME setting for Julia docs

* Update docs/mxdoc.py

Co-Authored-By: Iblis Lin <iblis@hs.ntnu.edu.tw>
---
 docs/mxdoc.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/docs/mxdoc.py b/docs/mxdoc.py
index f70dcfad1d0e..f2bb9714de0c 100644
--- a/docs/mxdoc.py
+++ b/docs/mxdoc.py
@@ -98,6 +98,8 @@ def build_mxnet(app):
 
 def build_julia_docs(app):
     """build Julia docs"""
+    os.environ['MXNET_HOME'] = os.path.abspath(os.path.join(app.builder.srcdir, '..'))
+    print("Julia will check for MXNet in {}/lib".format(os.environ.get('MXNET_HOME')))
     dest_path = app.builder.outdir + '/api/julia/site'
     _run_cmd('cd {}/.. && make -C julia/docs'.format(app.builder.srcdir))
     _run_cmd('mkdir -p {}'.format(dest_path))

From f44fdd683112d593d239847b1fa6932f5ab5c6c0 Mon Sep 17 00:00:00 2001
From: Anirudh Subramanian <anirudh2290@apache.org>
Date: Thu, 18 Jul 2019 13:54:11 -0700
Subject: [PATCH 082/813] Fix AMP Tutorial failures (#15526)

---
 docs/tutorials/amp/amp_tutorial.md | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/docs/tutorials/amp/amp_tutorial.md b/docs/tutorials/amp/amp_tutorial.md
index 5f9bd1195a1c..9da0505e9ff6 100644
--- a/docs/tutorials/amp/amp_tutorial.md
+++ b/docs/tutorials/amp/amp_tutorial.md
@@ -262,19 +262,20 @@ Below, we demonstrate for a gluon model and a symbolic model:
 ```python
 with mx.Context(mx.gpu(0)):
     # Below is an example of converting a gluon hybrid block to a mixed precision block
-    model = get_model("resnet50_v1")
-    model.collect_params().initialize(ctx=mx.current_context())
-    model.hybridize()
-    model(mx.nd.zeros((1, 3, 224, 224)))
-    converted_model = amp.convert_hybrid_block(model)
+    with warnings.catch_warnings(record=True) as w:
+        warnings.simplefilter("ignore")
+        model = get_model("resnet50_v1")
+        model.collect_params().initialize(ctx=mx.current_context())
+        model.hybridize()
+        model(mx.nd.zeros((1, 3, 224, 224)))
+        converted_model = amp.convert_hybrid_block(model)
 
     # Run dummy inference with the converted gluon model
     result = converted_model.forward(mx.nd.random.uniform(shape=(1, 3, 224, 224),
                                                           dtype=np.float32))
 
     # Below is an example of converting a symbolic model to a mixed precision model
-    dir_path = os.path.dirname(os.path.realpath(__file__))
-    model_path = os.path.join(dir_path, 'model')
+    model_path = "model"
     if not os.path.isdir(model_path):
         os.mkdir(model_path)
     prefix, epoch = mx.test_utils.download_model("imagenet1k-resnet-18", dst_dir=model_path)
@@ -301,8 +302,7 @@ for symbolic model. You can do the same for gluon hybrid block with `amp.convert
 with mx.Context(mx.gpu(0)):
     # Below is an example of converting a symbolic model to a mixed precision model
     # with only Convolution op being force casted to FP16.
-    dir_path = os.path.dirname(os.path.realpath(__file__))
-    model_path = os.path.join(dir_path, 'model')
+    model_path = "model"
     if not os.path.isdir(model_path):
         os.mkdir(model_path)
     prefix, epoch = mx.test_utils.download_model("imagenet1k-resnet-18", dst_dir=model_path)

From 67bbf5a7b35a6e6e57061b81fee9899a9ddde87b Mon Sep 17 00:00:00 2001
From: Jake Lee <gstu1130@gmail.com>
Date: Thu, 18 Jul 2019 17:29:12 -0700
Subject: [PATCH 083/813] [Dependency Update] Bump up the CI Nvidia docker to
 CUDA 10.1 (#14986)

* bump up the nvidia docker to CUDA 10.1

* change the func name

* change the groovy file

* clean up code & create cu101 docker file

* add cuda 10.1

* update the miss groovy

* change description

* move up the cudnn installation

* update label & func for nightly build

* update to cuda 10.1

* upgrade to build cu101mkl

* do not use nvidia docker in build static test

* fix cuda_patch_typo

* use nvidia docker

* fix the right config

* use nvidia docker and install latest cuDNN

* fix the typo

* use default cudnn
---
 ci/docker/Dockerfile.build.centos7_gpu        |  2 +-
 ci/docker/Dockerfile.build.ubuntu_base_gpu    |  2 +-
 ci/docker/Dockerfile.build.ubuntu_build_cuda  |  2 +-
 ci/docker/Dockerfile.build.ubuntu_gpu_cu101   | 82 +++++++++++++++++++
 ci/docker/Dockerfile.build.ubuntu_nightly_gpu |  2 +-
 ci/docker/Dockerfile.publish.ubuntu1404_gpu   |  2 +-
 ci/docker/install/ubuntu_cudnn.sh             |  4 +
 ci/docker/install/ubuntu_nvidia.sh            |  2 +-
 ci/docker/runtime_functions.sh                |  8 +-
 ci/jenkins/Jenkins_steps.groovy               | 44 +++++-----
 tests/nightly/JenkinsfileForBinaries          |  4 +-
 tools/setup_gpu_build_tools.sh                |  2 +-
 12 files changed, 121 insertions(+), 35 deletions(-)
 create mode 100644 ci/docker/Dockerfile.build.ubuntu_gpu_cu101

diff --git a/ci/docker/Dockerfile.build.centos7_gpu b/ci/docker/Dockerfile.build.centos7_gpu
index 1a927c4d5832..7e49e88b3a52 100644
--- a/ci/docker/Dockerfile.build.centos7_gpu
+++ b/ci/docker/Dockerfile.build.centos7_gpu
@@ -18,7 +18,7 @@
 #
 # Dockerfile to build and run MXNet on CentOS 7 for GPU
 
-FROM nvidia/cuda:10.0-devel-centos7
+FROM nvidia/cuda:10.1-devel-centos7
 
 WORKDIR /work/deps
 
diff --git a/ci/docker/Dockerfile.build.ubuntu_base_gpu b/ci/docker/Dockerfile.build.ubuntu_base_gpu
index 40e1da657203..94e6437e578b 100644
--- a/ci/docker/Dockerfile.build.ubuntu_base_gpu
+++ b/ci/docker/Dockerfile.build.ubuntu_base_gpu
@@ -19,7 +19,7 @@
 # Dockerfile to run the MXNet Installation Tests on Ubuntu 16.04
 # This should run in an empty docker with ubuntu and cuda.
 
-FROM nvidia/cuda:10.0-devel-ubuntu16.04
+FROM nvidia/cuda:10.1-devel-ubuntu16.04
 
 WORKDIR /work/deps
 
diff --git a/ci/docker/Dockerfile.build.ubuntu_build_cuda b/ci/docker/Dockerfile.build.ubuntu_build_cuda
index f568fbc386eb..47f1d1f9ca58 100644
--- a/ci/docker/Dockerfile.build.ubuntu_build_cuda
+++ b/ci/docker/Dockerfile.build.ubuntu_build_cuda
@@ -21,7 +21,7 @@
 # package generation, requiring the actual CUDA library to be
 # present
 
-FROM nvidia/cuda:10.0-devel-ubuntu16.04
+FROM nvidia/cuda:10.1-devel-ubuntu16.04
 
 WORKDIR /work/deps
 
diff --git a/ci/docker/Dockerfile.build.ubuntu_gpu_cu101 b/ci/docker/Dockerfile.build.ubuntu_gpu_cu101
new file mode 100644
index 000000000000..32f0a0a8d862
--- /dev/null
+++ b/ci/docker/Dockerfile.build.ubuntu_gpu_cu101
@@ -0,0 +1,82 @@
+# -*- mode: dockerfile -*-
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+# Dockerfile to run MXNet on Ubuntu 16.04 for GPU
+
+FROM nvidia/cuda:10.1-devel-ubuntu16.04
+
+WORKDIR /work/deps
+
+COPY install/ubuntu_core.sh /work/
+RUN /work/ubuntu_core.sh
+
+COPY install/deb_ubuntu_ccache.sh /work/
+RUN /work/deb_ubuntu_ccache.sh
+
+COPY install/ubuntu_python.sh /work/
+RUN /work/ubuntu_python.sh
+
+COPY install/ubuntu_scala.sh /work/
+COPY install/sbt.gpg /work/
+RUN /work/ubuntu_scala.sh
+
+COPY install/ubuntu_r.sh /work/
+COPY install/r.gpg /work/
+RUN /work/ubuntu_r.sh
+
+COPY install/ubuntu_perl.sh /work/
+RUN /work/ubuntu_perl.sh
+
+COPY install/ubuntu_clang.sh /work/
+RUN /work/ubuntu_clang.sh
+
+COPY install/ubuntu_mklml.sh /work/
+RUN /work/ubuntu_mklml.sh
+
+COPY install/ubuntu_tvm.sh /work/
+RUN /work/ubuntu_tvm.sh
+
+COPY install/ubuntu_llvm.sh /work/
+RUN /work/ubuntu_llvm.sh
+
+COPY install/ubuntu_caffe.sh /work/
+RUN /work/ubuntu_caffe.sh
+
+COPY install/ubuntu_onnx.sh /work/
+RUN /work/ubuntu_onnx.sh
+
+COPY install/ubuntu_docs.sh /work/
+COPY install/docs_requirements /work/
+RUN /work/ubuntu_docs.sh
+
+COPY install/ubuntu_tutorials.sh /work/
+RUN /work/ubuntu_tutorials.sh
+
+ENV CUDNN_VERSION=7.5.1.10
+COPY install/ubuntu_cudnn.sh /work/
+RUN /work/ubuntu_cudnn.sh
+
+# Always last
+ARG USER_ID=0
+ARG GROUP_ID=0
+COPY install/ubuntu_adduser.sh /work/
+RUN /work/ubuntu_adduser.sh
+
+COPY runtime_functions.sh /work/
+
+WORKDIR /work/mxnet
diff --git a/ci/docker/Dockerfile.build.ubuntu_nightly_gpu b/ci/docker/Dockerfile.build.ubuntu_nightly_gpu
index a667f7b7a94f..275a5a54fc66 100644
--- a/ci/docker/Dockerfile.build.ubuntu_nightly_gpu
+++ b/ci/docker/Dockerfile.build.ubuntu_nightly_gpu
@@ -18,7 +18,7 @@
 #
 # Dockerfile to run MXNet on Ubuntu 16.04 for CPU
 
-FROM nvidia/cuda:10.0-devel-ubuntu16.04
+FROM nvidia/cuda:10.1-devel-ubuntu16.04
 
 WORKDIR /work/deps
 
diff --git a/ci/docker/Dockerfile.publish.ubuntu1404_gpu b/ci/docker/Dockerfile.publish.ubuntu1404_gpu
index 9855986a2891..4d9fa819a39e 100644
--- a/ci/docker/Dockerfile.publish.ubuntu1404_gpu
+++ b/ci/docker/Dockerfile.publish.ubuntu1404_gpu
@@ -18,7 +18,7 @@
 #
 # Dockerfile to run MXNet on Ubuntu 14.04 for GPU
 
-FROM ubuntu:14.04
+FROM nvidia/cuda:10.1-cudnn7-devel-ubuntu14.04
 
 WORKDIR /work/deps
 
diff --git a/ci/docker/install/ubuntu_cudnn.sh b/ci/docker/install/ubuntu_cudnn.sh
index 3d260046b5e7..b773fea0f82f 100755
--- a/ci/docker/install/ubuntu_cudnn.sh
+++ b/ci/docker/install/ubuntu_cudnn.sh
@@ -32,6 +32,10 @@ fi
 apt-get update || true
 
 case ${CUDA_VERSION} in
+	10\.1*)
+		export libcudnn7_version="${CUDNN_VERSION}-1+cuda10.1"
+		export libcudnn7_dev_version="${CUDNN_VERSION}-1+cuda10.1"
+		;;
 	10\.0*)
 		export libcudnn7_version="${CUDNN_VERSION}-1+cuda10.0"
 		export libcudnn7_dev_version="${CUDNN_VERSION}-1+cuda10.0"
diff --git a/ci/docker/install/ubuntu_nvidia.sh b/ci/docker/install/ubuntu_nvidia.sh
index 36eb21b8a03e..41f68a21858b 100755
--- a/ci/docker/install/ubuntu_nvidia.sh
+++ b/ci/docker/install/ubuntu_nvidia.sh
@@ -22,4 +22,4 @@ set -ex
 # Retrieve ppa:graphics-drivers and install nvidia-drivers.
 # Note: DEBIAN_FRONTEND required to skip the interactive setup steps
 apt update
-DEBIAN_FRONTEND=noninteractive apt install -y --no-install-recommends cuda-10-0
+DEBIAN_FRONTEND=noninteractive apt install -y --no-install-recommends cuda-10-1
diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index d2b0d6b0e274..2518f4c7c64f 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -579,7 +579,7 @@ build_ubuntu_cpu_mkldnn_mkl() {
 }
 
 build_ubuntu_gpu() {
-    build_ubuntu_gpu_cuda100_cudnn7
+    build_ubuntu_gpu_cuda101_cudnn7
 }
 
 build_ubuntu_gpu_tensorrt() {
@@ -679,7 +679,7 @@ build_ubuntu_gpu_mkldnn_nocudnn() {
         -j$(nproc)
 }
 
-build_ubuntu_gpu_cuda100_cudnn7() {
+build_ubuntu_gpu_cuda101_cudnn7() {
     set -ex
     build_ccache_wrappers
     make \
@@ -1460,10 +1460,10 @@ build_static_python_mkl() {
     popd
 }
 
-build_static_python_cu100mkl() {
+build_static_python_cu101mkl() {
     set -ex
     pushd .
-    export mxnet_variant=cu100mkl
+    export mxnet_variant=cu101mkl
     ./ci/publish/python/build.sh
     popd
 }
diff --git a/ci/jenkins/Jenkins_steps.groovy b/ci/jenkins/Jenkins_steps.groovy
index 40700ad22bb1..c27a61383e46 100644
--- a/ci/jenkins/Jenkins_steps.groovy
+++ b/ci/jenkins/Jenkins_steps.groovy
@@ -153,7 +153,7 @@ def compile_unix_int64_gpu() {
         ws('workspace/build-gpu-int64') {
           timeout(time: max_time, unit: 'MINUTES') {
             utils.init_git()
-            utils.docker_run('ubuntu_gpu_cu100', 'build_ubuntu_gpu_large_tensor', false)
+            utils.docker_run('ubuntu_gpu_cu101', 'build_ubuntu_gpu_large_tensor', false)
             utils.pack_lib('ubuntu_gpu_int64', mx_cmake_lib, true)
           }
         }
@@ -232,12 +232,12 @@ def compile_unix_mkldnn_nocudnn_gpu() {
 }
 
 def compile_unix_full_gpu() {
-    return ['GPU: CUDA10.0+cuDNN7': {
+    return ['GPU: CUDA10.1+cuDNN7': {
       node(NODE_LINUX_CPU) {
         ws('workspace/build-gpu') {
           timeout(time: max_time, unit: 'MINUTES') {
             utils.init_git()
-            utils.docker_run('ubuntu_build_cuda', 'build_ubuntu_gpu_cuda100_cudnn7', false)
+            utils.docker_run('ubuntu_build_cuda', 'build_ubuntu_gpu_cuda101_cudnn7', false)
             utils.pack_lib('gpu', mx_lib_cpp_examples, true)
           }
         }
@@ -251,7 +251,7 @@ def compile_unix_cmake_mkldnn_gpu() {
         ws('workspace/build-cmake-mkldnn-gpu') {
           timeout(time: max_time, unit: 'MINUTES') {
             utils.init_git()
-            utils.docker_run('ubuntu_gpu_cu100', 'build_ubuntu_gpu_cmake_mkldnn', false)
+            utils.docker_run('ubuntu_gpu_cu101', 'build_ubuntu_gpu_cmake_mkldnn', false)
             utils.pack_lib('cmake_mkldnn_gpu', mx_cmake_mkldnn_lib, true)
           }
         }
@@ -265,7 +265,7 @@ def compile_unix_cmake_gpu() {
         ws('workspace/build-cmake-gpu') {
           timeout(time: max_time, unit: 'MINUTES') {
             utils.init_git()
-            utils.docker_run('ubuntu_gpu_cu100', 'build_ubuntu_gpu_cmake', false)
+            utils.docker_run('ubuntu_gpu_cu101', 'build_ubuntu_gpu_cmake', false)
             utils.pack_lib('cmake_gpu', mx_cmake_lib_cython, true)
           }
         }
@@ -631,7 +631,7 @@ def test_static_python_gpu() {
         ws('workspace/ut-publish-python-gpu') {
           timeout(time: max_time, unit: 'MINUTES') {
             utils.init_git()
-            utils.docker_run("publish.ubuntu1404_gpu", 'build_static_python_cu100mkl', true)
+            utils.docker_run("publish.ubuntu1404_gpu", 'build_static_python_cu101mkl', true)
           }
         }
     }
@@ -662,7 +662,7 @@ def test_unix_python2_gpu() {
         ws('workspace/ut-python2-gpu') {
           try {
             utils.unpack_and_init('gpu', mx_lib, true)
-            python2_gpu_ut('ubuntu_gpu_cu100')
+            python2_gpu_ut('ubuntu_gpu_cu101')
             utils.publish_test_coverage()
           } finally {
             utils.collect_test_results_unix('nosetests_gpu.xml', 'nosetests_python2_gpu.xml')
@@ -679,7 +679,7 @@ def test_unix_python2_quantize_gpu() {
           timeout(time: max_time, unit: 'MINUTES') {
             try {
               utils.unpack_and_init('gpu', mx_lib, true)
-              utils.docker_run('ubuntu_gpu_cu100', 'unittest_ubuntu_python2_quantization_gpu', true)
+              utils.docker_run('ubuntu_gpu_cu101', 'unittest_ubuntu_python2_quantization_gpu', true)
               utils.publish_test_coverage()
             } finally {
               utils.collect_test_results_unix('nosetests_quantization_gpu.xml', 'nosetests_python2_quantize_gpu.xml')
@@ -696,7 +696,7 @@ def test_unix_python2_mkldnn_gpu() {
         ws('workspace/ut-python2-mkldnn-gpu') {
           try {
             utils.unpack_and_init('mkldnn_gpu', mx_mkldnn_lib, true)
-            python2_gpu_ut('ubuntu_gpu_cu100')
+            python2_gpu_ut('ubuntu_gpu_cu101')
             utils.publish_test_coverage()
           } finally {
             utils.collect_test_results_unix('nosetests_gpu.xml', 'nosetests_python2_mkldnn_gpu.xml')
@@ -746,7 +746,7 @@ def test_unix_python3_gpu() {
         ws('workspace/ut-python3-gpu') {
           try {
             utils.unpack_and_init('gpu', mx_lib_cython, true)
-            python3_gpu_ut_cython('ubuntu_gpu_cu100')
+            python3_gpu_ut_cython('ubuntu_gpu_cu101')
             utils.publish_test_coverage()
           } finally {
             utils.collect_test_results_unix('nosetests_gpu.xml', 'nosetests_python3_gpu.xml')
@@ -763,7 +763,7 @@ def test_unix_python3_quantize_gpu() {
           timeout(time: max_time, unit: 'MINUTES') {
             try {
               utils.unpack_and_init('gpu', mx_lib, true)
-              utils.docker_run('ubuntu_gpu_cu100', 'unittest_ubuntu_python3_quantization_gpu', true)
+              utils.docker_run('ubuntu_gpu_cu101', 'unittest_ubuntu_python3_quantization_gpu', true)
               utils.publish_test_coverage()
             } finally {
               utils.collect_test_results_unix('nosetests_quantization_gpu.xml', 'nosetests_python3_quantize_gpu.xml')
@@ -848,7 +848,7 @@ def test_unix_python3_mkldnn_gpu() {
         ws('workspace/ut-python3-mkldnn-gpu') {
           try {
             utils.unpack_and_init('mkldnn_gpu', mx_mkldnn_lib, true)
-            python3_gpu_ut('ubuntu_gpu_cu100')
+            python3_gpu_ut('ubuntu_gpu_cu101')
             utils.publish_test_coverage()
           } finally {
             utils.collect_test_results_unix('nosetests_gpu.xml', 'nosetests_python3_mkldnn_gpu.xml')
@@ -864,7 +864,7 @@ def test_unix_python3_mkldnn_nocudnn_gpu() {
         ws('workspace/ut-python3-mkldnn-gpu-nocudnn') {
           try {
             utils.unpack_and_init('mkldnn_gpu_nocudnn', mx_mkldnn_lib, true)
-            python3_gpu_ut_nocudnn('ubuntu_gpu_cu100')
+            python3_gpu_ut_nocudnn('ubuntu_gpu_cu101')
             utils.publish_test_coverage()
           } finally {
             utils.collect_test_results_unix('nosetests_gpu.xml', 'nosetests_python3_mkldnn_gpu_nocudnn.xml')
@@ -898,7 +898,7 @@ def test_unix_python3_integration_gpu() {
         ws('workspace/it-python-gpu') {
           timeout(time: max_time, unit: 'MINUTES') {
             utils.unpack_and_init('gpu', mx_lib, true)
-            utils.docker_run('ubuntu_gpu_cu100', 'integrationtest_ubuntu_gpu_python', true)
+            utils.docker_run('ubuntu_gpu_cu101', 'integrationtest_ubuntu_gpu_python', true)
             utils.publish_test_coverage()
           }
         }
@@ -913,7 +913,7 @@ def test_unix_caffe_gpu() {
             timeout(time: max_time, unit: 'MINUTES') {
                 utils.init_git()
                 utils.unpack_lib('gpu', mx_lib)
-                utils.docker_run('ubuntu_gpu_cu100', 'integrationtest_ubuntu_gpu_caffe', true)
+                utils.docker_run('ubuntu_gpu_cu101', 'integrationtest_ubuntu_gpu_caffe', true)
                 utils.publish_test_coverage()
             }
             }
@@ -927,7 +927,7 @@ def test_unix_cpp_package_gpu() {
         ws('workspace/it-cpp-package') {
           timeout(time: max_time, unit: 'MINUTES') {
             utils.unpack_and_init('gpu', mx_lib_cpp_examples, true)
-            utils.docker_run('ubuntu_gpu_cu100', 'integrationtest_ubuntu_gpu_cpp_package', true)
+            utils.docker_run('ubuntu_gpu_cu101', 'integrationtest_ubuntu_gpu_cpp_package', true)
             utils.publish_test_coverage()
           }
         }
@@ -969,7 +969,7 @@ def test_unix_scala_gpu() {
         ws('workspace/ut-scala-gpu') {
           timeout(time: max_time, unit: 'MINUTES') {
             utils.unpack_and_init('gpu', mx_lib, true)
-            utils.docker_run('ubuntu_gpu_cu100', 'integrationtest_ubuntu_gpu_scala', true)
+            utils.docker_run('ubuntu_gpu_cu101', 'integrationtest_ubuntu_gpu_scala', true)
             utils.publish_test_coverage()
           }
         }
@@ -1052,7 +1052,7 @@ def test_unix_cpp_gpu() {
         ws('workspace/ut-cpp-gpu') {
           timeout(time: max_time, unit: 'MINUTES') {
             utils.unpack_and_init('cmake_gpu', mx_cmake_lib, true)
-            utils.docker_run('ubuntu_gpu_cu100', 'unittest_cpp', true)
+            utils.docker_run('ubuntu_gpu_cu101', 'unittest_cpp', true)
             utils.publish_test_coverage()
           }
         }
@@ -1066,7 +1066,7 @@ def test_unix_cpp_mkldnn_gpu() {
         ws('workspace/ut-cpp-mkldnn-gpu') {
           timeout(time: max_time, unit: 'MINUTES') {
             utils.unpack_and_init('cmake_mkldnn_gpu', mx_cmake_mkldnn_lib, true)
-            utils.docker_run('ubuntu_gpu_cu100', 'unittest_cpp', true)
+            utils.docker_run('ubuntu_gpu_cu101', 'unittest_cpp', true)
             utils.publish_test_coverage()
           }
         }
@@ -1094,7 +1094,7 @@ def test_unix_perl_gpu() {
         ws('workspace/ut-perl-gpu') {
           timeout(time: max_time, unit: 'MINUTES') {
             utils.unpack_and_init('gpu', mx_lib, true)
-            utils.docker_run('ubuntu_gpu_cu100', 'unittest_ubuntu_cpugpu_perl', true)
+            utils.docker_run('ubuntu_gpu_cu101', 'unittest_ubuntu_cpugpu_perl', true)
             utils.publish_test_coverage()
           }
         }
@@ -1108,7 +1108,7 @@ def test_unix_r_gpu() {
         ws('workspace/ut-r-gpu') {
           timeout(time: max_time, unit: 'MINUTES') {
             utils.unpack_and_init('gpu', mx_lib, true)
-            utils.docker_run('ubuntu_gpu_cu100', 'unittest_ubuntu_gpu_R', true)
+            utils.docker_run('ubuntu_gpu_cu101', 'unittest_ubuntu_gpu_R', true)
             utils.publish_test_coverage()
           }
         }
@@ -1176,7 +1176,7 @@ def test_unix_distributed_kvstore_gpu() {
         ws('workspace/it-dist-kvstore') {
           timeout(time: max_time, unit: 'MINUTES') {
             utils.unpack_and_init('gpu', mx_lib, true)
-            utils.docker_run('ubuntu_gpu_cu100', 'integrationtest_ubuntu_gpu_dist_kvstore', true)
+            utils.docker_run('ubuntu_gpu_cu101', 'integrationtest_ubuntu_gpu_dist_kvstore', true)
             utils.publish_test_coverage()
           }
         }
diff --git a/tests/nightly/JenkinsfileForBinaries b/tests/nightly/JenkinsfileForBinaries
index d5f1ebdd6fef..725c76969e23 100755
--- a/tests/nightly/JenkinsfileForBinaries
+++ b/tests/nightly/JenkinsfileForBinaries
@@ -31,11 +31,11 @@ utils.assign_node_labels(utility: 'utility', linux_cpu: 'mxnetlinux-cpu', linux_
 utils.main_wrapper(
 core_logic: {
   stage('Build') {
-    parallel 'GPU: CUDA9.1+cuDNN7': {
+    parallel 'GPU: CUDA10.1+cuDNN7': {
       node(NODE_LINUX_CPU) {
         ws('workspace/build-gpu') {
           utils.init_git()
-          utils.docker_run('ubuntu_build_cuda', 'build_ubuntu_gpu_cuda100_cudnn7', false)
+          utils.docker_run('ubuntu_build_cuda', 'build_ubuntu_gpu_cuda101_cudnn7', false)
           utils.pack_lib('gpu', mx_lib)
         }
       }
diff --git a/tools/setup_gpu_build_tools.sh b/tools/setup_gpu_build_tools.sh
index 724af3c90ec1..b25be6b697e1 100755
--- a/tools/setup_gpu_build_tools.sh
+++ b/tools/setup_gpu_build_tools.sh
@@ -29,7 +29,7 @@ DEPS_PATH=$2
 >&2 echo "Setting CUDA versions for $VARIANT"
 if [[ $VARIANT == cu101* ]]; then
     CUDA_VERSION='10.1.105-1'
-    CUDA_PATCH_VERSION='10.1.105-1'
+    CUDA_PATCH_VERSION='10.1.0.105-1'
     LIBCUDA_VERSION='418.39-0ubuntu1'
     LIBCUDNN_VERSION='7.6.0.64-1+cuda10.1'
     LIBNCCL_VERSION='2.4.7-1+cuda10.1'

From 45c360cee9ac9d1c7008652fcabb0dceac1df38f Mon Sep 17 00:00:00 2001
From: nuslq <46871447+nuslq@users.noreply.github.com>
Date: Thu, 18 Jul 2019 21:37:56 -0700
Subject: [PATCH 084/813] fixed config.mk and Makefile bugs for installing mkl
 (#15424)

* fixed config.mk and Makefile bugs for installing mkl

* remove comments from the previous changes
---
 Makefile | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/Makefile b/Makefile
index a7cf8a56a433..c5ac4ebe01fa 100644
--- a/Makefile
+++ b/Makefile
@@ -244,13 +244,13 @@ ifeq ($(USE_CUDNN), 1)
 	LDFLAGS += -lcudnn
 endif
 
-ifeq ($(use_blas), open)
+ifeq ($(USE_BLAS), open)
 	CFLAGS += -DMXNET_USE_BLAS_OPEN=1
-else ifeq ($(use_blas), atlas)
+else ifeq ($(USE_BLAS), atlas)
 	CFLAGS += -DMXNET_USE_BLAS_ATLAS=1
-else ifeq ($(use_blas), mkl)
+else ifeq ($(USE_BLAS), mkl)
 	CFLAGS += -DMXNET_USE_BLAS_MKL=1
-else ifeq ($(use_blas), apple)
+else ifeq ($(USE_BLAS), apple)
 	CFLAGS += -DMXNET_USE_BLAS_APPLE=1
 endif
 

From b887c06f6c74e64fca668dbf2a69b67ba2a197d3 Mon Sep 17 00:00:00 2001
From: david-seiler <1927983+david-seiler@users.noreply.github.com>
Date: Fri, 19 Jul 2019 07:41:58 +0200
Subject: [PATCH 085/813] PDF operators for each distribution for which we have
 a random sampler (plus also the PDF of the Dirichlet).  Supports
 probabilities and log-probabilities, as well as gradients. (#14617)

---
 python/mxnet/contrib/amp/lists/symbol.py |  17 +
 src/operator/random/pdf_op.cc            | 319 ++++++++++++
 src/operator/random/pdf_op.cu            |  48 ++
 src/operator/random/pdf_op.h             | 622 +++++++++++++++++++++++
 tests/python/unittest/test_random.py     | 111 +++-
 5 files changed, 1110 insertions(+), 7 deletions(-)
 create mode 100644 src/operator/random/pdf_op.cc
 create mode 100644 src/operator/random/pdf_op.cu
 create mode 100644 src/operator/random/pdf_op.h

diff --git a/python/mxnet/contrib/amp/lists/symbol.py b/python/mxnet/contrib/amp/lists/symbol.py
index 066618b4073d..9a587dfa73c1 100644
--- a/python/mxnet/contrib/amp/lists/symbol.py
+++ b/python/mxnet/contrib/amp/lists/symbol.py
@@ -600,6 +600,23 @@
     '_sparse_elemwise_mul',
     '_sparse_elemwise_sub',
     '_sparse_sum',
+
+    'random_pdf_gamma',
+    'random_pdf_exponential',
+    'random_pdf_uniform',
+    'random_pdf_negative_binomial',
+    'random_pdf_generalized_negative_binomial',
+    'random_pdf_dirichlet',
+    'random_pdf_normal',
+    'random_pdf_poisson',
+    '_random_pdf_gamma',
+    '_random_pdf_exponential',
+    '_random_pdf_uniform',
+    '_random_pdf_negative_binomial',
+    '_random_pdf_generalized_negative_binomial',
+    '_random_pdf_dirichlet',
+    '_random_pdf_normal',
+    '_random_pdf_poisson',
     ]
 
 LOSS_OUTPUT_FUNCTIONS = [
diff --git a/src/operator/random/pdf_op.cc b/src/operator/random/pdf_op.cc
new file mode 100644
index 000000000000..070ca81d61ff
--- /dev/null
+++ b/src/operator/random/pdf_op.cc
@@ -0,0 +1,319 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2018 by Contributors
+ * \file pdf_op.cc
+ * \brief CPU-operators for computing the pdf of random distributions. 
+ */
+
+#include "./pdf_op.h"
+
+namespace mxnet {
+namespace op {
+
+DMLC_REGISTER_PARAMETER(PdfParam);
+
+#define MXNET_OPERATOR_REGISTER_PDF(distr, pdffunc, num_parms, \
+                                    parm_name_1, parm_name_2, \
+                                    parm_desc_1, parm_desc_2, \
+                                    description, vectorparms) \
+  NNVM_REGISTER_OP(_random_pdf_##distr) \
+  .add_alias("random_pdf_" #distr) \
+  .describe(description()+std::string(ADD_FILELINE)) \
+  .set_num_inputs(num_parms+1) \
+  .set_num_outputs(1) \
+  .set_attr_parser(ParamParser<PdfParam>) \
+  .set_attr<nnvm::FListInputNames>("FListInputNames", \
+    [](const NodeAttrs& attrs) { \
+      std::vector<std::string> v = {"sample", parm_name_1, parm_name_2}; \
+      v.resize(num_parms+1); \
+      return v; \
+    }) \
+  .set_attr<mxnet::FInferShape>("FInferShape", PdfOpShape<vectorparms>) \
+  .set_attr<nnvm::FInferType>("FInferType", ElemwiseType<num_parms+1, 1>) \
+  .set_attr<FCompute>("FCompute<cpu>", PdfOpForward<cpu, pdffunc, num_parms, vectorparms>) \
+  .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseInOut{"_backward_pdf_" #distr}) \
+  .add_argument("sample", "NDArray-or-Symbol", "Samples from the distributions.") \
+  .add_argument(parm_name_1, "NDArray-or-Symbol", parm_desc_1) \
+  .add_arguments(PdfParam::__FIELDS__())
+
+#define MXNET_OPERATOR_REGISTER_PDF_GRAD(distr, pdffunc, num_parms, vectorparms) \
+  NNVM_REGISTER_OP(_backward_pdf_##distr) \
+  .set_num_inputs(num_parms+3) \
+  .set_num_outputs(num_parms+1) \
+  .set_attr_parser(ParamParser<PdfParam>) \
+  .set_attr<nnvm::FInplaceOption>("FInplaceOption", [](const NodeAttrs& attrs) \
+    { std::vector<std::pair<int, int> > v = {{1, 0}, {2, 1}, {3, 2}}; \
+    v.resize(num_parms+1); \
+    return v; }) \
+  .set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& attrs) \
+    { return std::vector<ResourceRequest>{ResourceRequest::kTempSpace}; }) \
+  .set_attr<nnvm::TIsBackward>("TIsBackward", true) \
+  .set_attr<FCompute>("FCompute<cpu>", PdfOpBackward<cpu, pdffunc##_Grad, num_parms, vectorparms>);
+
+
+#define MXNET_OPERATOR_REGISTER_PDF1(distr, pdffunc, parm_name, parm_desc, \
+                                     description, vectorparms) \
+    MXNET_OPERATOR_REGISTER_PDF(distr, pdffunc, 1, parm_name, parm_name, \
+                                parm_desc, parm_desc, description, vectorparms); \
+    MXNET_OPERATOR_REGISTER_PDF_GRAD(distr, pdffunc, 1, vectorparms)
+
+#define MXNET_OPERATOR_REGISTER_PDF2(distr, pdffunc, parm_name_1, parm_name_2, \
+                                     parm_desc_1, parm_desc_2, description) \
+  MXNET_OPERATOR_REGISTER_PDF(distr, pdffunc, 2, parm_name_1, parm_name_2, \
+                                   parm_desc_1, parm_desc_2, description, false) \
+  .add_argument(parm_name_2, "NDArray-or-Symbol", parm_desc_2); \
+  MXNET_OPERATOR_REGISTER_PDF_GRAD(distr, pdffunc, 2, false)
+
+inline std::string uniform_desc() {
+  return std::string(R"code(Computes the value of the PDF of *sample* of
+uniform distributions on the intervals given by *[low,high)*.
+
+*low* and *high* must have the same shape, which must match the leftmost subshape
+of *sample*.  That is, *sample* can have the same shape as *low* and *high*, in which
+case the output contains one density per distribution, or *sample* can be a tensor
+of tensors with that shape, in which case the output is a tensor of densities such that
+the densities at index *i* in the output are given by the samples at index *i* in *sample*
+parameterized by the values of *low* and *high* at index *i*.
+
+Examples::
+
+    random_pdf_uniform(sample=[[1,2,3,4]], low=[0], high=[10]) = [0.1, 0.1, 0.1, 0.1]
+
+    sample = [[[1, 2, 3],
+               [1, 2, 3]],
+              [[1, 2, 3],
+               [1, 2, 3]]]
+    low  = [[0, 0],
+            [0, 0]]
+    high = [[ 5, 10],
+            [15, 20]]
+    random_pdf_uniform(sample=sample, low=low, high=high) =
+        [[[0.2,        0.2,        0.2    ],
+          [0.1,        0.1,        0.1    ]],
+         [[0.06667,    0.06667,    0.06667],
+          [0.05,       0.05,       0.05   ]]]
+
+)code");
+}
+
+inline std::string normal_desc() {
+  return std::string(R"code(Computes the value of the PDF of *sample* of
+normal distributions with parameters *mu* (mean) and *sigma* (standard deviation).
+
+*mu* and *sigma* must have the same shape, which must match the leftmost subshape
+of *sample*.  That is, *sample* can have the same shape as *mu* and *sigma*, in which
+case the output contains one density per distribution, or *sample* can be a tensor
+of tensors with that shape, in which case the output is a tensor of densities such that
+the densities at index *i* in the output are given by the samples at index *i* in *sample*
+parameterized by the values of *mu* and *sigma* at index *i*.
+
+Examples::
+
+    sample = [[-2, -1, 0, 1, 2]]
+    random_pdf_normal(sample=sample, mu=[0], sigma=[1]) =
+        [[0.05399097, 0.24197073, 0.3989423, 0.24197073, 0.05399097]]
+
+    random_pdf_normal(sample=sample*2, mu=[0,0], sigma=[1,2]) =
+        [[0.05399097, 0.24197073, 0.3989423,  0.24197073, 0.05399097],
+         [0.12098537, 0.17603266, 0.19947115, 0.17603266, 0.12098537]]
+)code");
+}
+
+inline std::string gamma_desc() {
+  return std::string(R"code(Computes the value of the PDF of *sample* of
+gamma distributions with parameters *alpha* (shape) and *beta* (rate).
+
+*alpha* and *beta* must have the same shape, which must match the leftmost subshape
+of *sample*.  That is, *sample* can have the same shape as *alpha* and *beta*, in which
+case the output contains one density per distribution, or *sample* can be a tensor
+of tensors with that shape, in which case the output is a tensor of densities such that
+the densities at index *i* in the output are given by the samples at index *i* in *sample*
+parameterized by the values of *alpha* and *beta* at index *i*.
+
+Examples::
+
+  random_pdf_gamma(sample=[[1,2,3,4,5]], alpha=[5], beta=[1]) =
+      [[0.01532831, 0.09022352, 0.16803136, 0.19536681, 0.17546739]]
+
+  sample = [[1, 2, 3, 4, 5],
+            [2, 3, 4, 5, 6],
+            [3, 4, 5, 6, 7]]
+
+  random_pdf_gamma(sample=sample, alpha=[5,6,7], beta=[1,1,1]) =
+      [[0.01532831, 0.09022352, 0.16803136, 0.19536681, 0.17546739],
+       [0.03608941, 0.10081882, 0.15629345, 0.17546739, 0.16062315],
+       [0.05040941, 0.10419563, 0.14622283, 0.16062315, 0.14900276]]
+)code");
+}
+
+inline std::string exponential_desc() {
+  return std::string(R"code(Computes the value of the PDF of *sample* of
+exponential distributions with parameters *lam* (rate).
+
+The shape of *lam* must match the leftmost subshape of *sample*.  That is, *sample*
+can have the same shape as *lam*, in which case the output contains one density per
+distribution, or *sample* can be a tensor of tensors with that shape, in which case
+the output is a tensor of densities such that the densities at index *i* in the output
+are given by the samples at index *i* in *sample* parameterized by the value of *lam*
+at index *i*.
+
+Examples::
+
+  random_pdf_exponential(sample=[[1, 2, 3]], lam=[1]) =
+      [[0.36787945, 0.13533528, 0.04978707]]
+
+  sample = [[1,2,3],
+            [1,2,3],
+            [1,2,3]]
+
+  random_pdf_exponential(sample=sample, lam=[1,0.5,0.25]) =
+      [[0.36787945, 0.13533528, 0.04978707],
+       [0.30326533, 0.18393973, 0.11156508],
+       [0.1947002,  0.15163267, 0.11809164]]
+)code");
+}
+
+inline std::string poisson_desc() {
+  return std::string(R"code(Computes the value of the PDF of *sample* of
+Poisson distributions with parameters *lam* (rate).
+
+The shape of *lam* must match the leftmost subshape of *sample*.  That is, *sample*
+can have the same shape as *lam*, in which case the output contains one density per
+distribution, or *sample* can be a tensor of tensors with that shape, in which case
+the output is a tensor of densities such that the densities at index *i* in the output
+are given by the samples at index *i* in *sample* parameterized by the value of *lam*
+at index *i*.
+
+Examples::
+
+    random_pdf_poisson(sample=[[0,1,2,3]], lam=[1]) =
+        [[0.36787945, 0.36787945, 0.18393973, 0.06131324]]
+
+    sample = [[0,1,2,3],
+              [0,1,2,3],
+              [0,1,2,3]]
+
+    random_pdf_poisson(sample=sample, lam=[1,2,3]) =
+        [[0.36787945, 0.36787945, 0.18393973, 0.06131324],
+         [0.13533528, 0.27067056, 0.27067056, 0.18044704],
+         [0.04978707, 0.14936121, 0.22404182, 0.22404182]]
+)code");
+}
+
+inline std::string negative_binomial_desc() {
+  return std::string(R"code(Computes the value of the PDF of samples of
+negative binomial distributions with parameters *k* (failure limit) and *p* (failure probability).
+
+*k* and *p* must have the same shape, which must match the leftmost subshape
+of *sample*.  That is, *sample* can have the same shape as *k* and *p*, in which
+case the output contains one density per distribution, or *sample* can be a tensor
+of tensors with that shape, in which case the output is a tensor of densities such that
+the densities at index *i* in the output are given by the samples at index *i* in *sample*
+parameterized by the values of *k* and *p* at index *i*.
+
+Examples::
+
+    random_pdf_negative_binomial(sample=[[1,2,3,4]], k=[1], p=a[0.5]) =
+        [[0.25, 0.125, 0.0625, 0.03125]]
+
+    # Note that k may be real-valued
+    sample = [[1,2,3,4],
+              [1,2,3,4]]
+    random_pdf_negative_binomial(sample=sample, k=[1, 1.5], p=[0.5, 0.5]) =
+        [[0.25,       0.125,      0.0625,     0.03125   ],
+         [0.26516506, 0.16572815, 0.09667476, 0.05437956]]
+)code");
+}
+
+inline std::string generalized_negative_binomial_desc() {
+  return std::string(R"code(Computes the value of the PDF of *sample* of
+generalized negative binomial distributions with parameters *mu* (mean)
+and *alpha* (dispersion).  This can be understood as a reparameterization of
+the negative binomial, where *k* = *1 / alpha* and *p* = *1 / (mu \* alpha + 1)*.
+
+*mu* and *alpha* must have the same shape, which must match the leftmost subshape
+of *sample*.  That is, *sample* can have the same shape as *mu* and *alpha*, in which
+case the output contains one density per distribution, or *sample* can be a tensor
+of tensors with that shape, in which case the output is a tensor of densities such that
+the densities at index *i* in the output are given by the samples at index *i* in *sample*
+parameterized by the values of *mu* and *alpha* at index *i*.
+
+Examples::
+
+    random_pdf_generalized_negative_binomial(sample=[[1, 2, 3, 4]], alpha=[1], mu=[1]) =
+        [[0.25, 0.125, 0.0625, 0.03125]]
+
+    sample = [[1,2,3,4],
+              [1,2,3,4]]
+    random_pdf_generalized_negative_binomial(sample=sample, alpha=[1, 0.6666], mu=[1, 1.5]) =
+        [[0.25,       0.125,      0.0625,     0.03125   ],
+         [0.26517063, 0.16573331, 0.09667706, 0.05437994]]
+)code");
+}
+
+inline std::string dirichlet_desc() {
+  return std::string(R"code(Computes the value of the PDF of *sample* of
+Dirichlet distributions with parameter *alpha*.
+
+The shape of *alpha* must match the leftmost subshape of *sample*.  That is, *sample*
+can have the same shape as *alpha*, in which case the output contains one density per
+distribution, or *sample* can be a tensor of tensors with that shape, in which case
+the output is a tensor of densities such that the densities at index *i* in the output
+are given by the samples at index *i* in *sample* parameterized by the value of *alpha*
+at index *i*.
+
+Examples::
+
+    random_pdf_dirichlet(sample=[[1,2],[2,3],[3,4]], alpha=[2.5, 2.5]) =
+        [38.413498, 199.60245, 564.56085]
+
+    sample = [[[1, 2, 3], [10, 20, 30], [100, 200, 300]],
+              [[0.1, 0.2, 0.3], [0.01, 0.02, 0.03], [0.001, 0.002, 0.003]]]
+
+    random_pdf_dirichlet(sample=sample, alpha=[0.1, 0.4, 0.9]) =
+        [[2.3257459e-02, 5.8420084e-04, 1.4674458e-05],
+         [9.2589635e-01, 3.6860607e+01, 1.4674468e+03]]
+)code");
+}
+
+MXNET_OPERATOR_REGISTER_PDF2(uniform, PDF_Uniform, "low", "high",
+  "Lower bounds of the distributions.", "Upper bounds of the distributions.", uniform_desc)
+MXNET_OPERATOR_REGISTER_PDF2(normal, PDF_Normal, "mu", "sigma",
+  "Means of the distributions.", "Standard deviations of the distributions.", normal_desc)
+MXNET_OPERATOR_REGISTER_PDF2(gamma, PDF_Gamma, "alpha", "beta",
+  "Alpha (shape) parameters of the distributions.", "Beta (scale) parameters of the distributions.",
+  gamma_desc)
+MXNET_OPERATOR_REGISTER_PDF1(exponential, PDF_Exponential, "lam",
+  "Lambda (rate) parameters of the distributions.", exponential_desc, false)
+MXNET_OPERATOR_REGISTER_PDF1(poisson, PDF_Poisson, "lam",
+  "Lambda (rate) parameters of the distributions.", poisson_desc, false)
+MXNET_OPERATOR_REGISTER_PDF2(negative_binomial, PDF_NegativeBinomial, "k", "p",
+  "Limits of unsuccessful experiments.", "Failure probabilities in each experiment.",
+  negative_binomial_desc)
+MXNET_OPERATOR_REGISTER_PDF2(generalized_negative_binomial,
+  PDF_GeneralizedNegativeBinomial, "mu", "alpha",
+  "Means of the distributions.", "Alpha (dispersion) parameters of the distributions.",
+  generalized_negative_binomial_desc)
+MXNET_OPERATOR_REGISTER_PDF1(dirichlet, PDF_Dirichlet, "alpha",
+  "Concentration parameters of the distributions.", dirichlet_desc, true)
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/random/pdf_op.cu b/src/operator/random/pdf_op.cu
new file mode 100644
index 000000000000..e77720b2c329
--- /dev/null
+++ b/src/operator/random/pdf_op.cu
@@ -0,0 +1,48 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2018 by Contributors
+ * \file pdf_op.cu
+ * \brief GPU-operators for computing the pdf of random distributions. 
+ */
+
+#include "./pdf_op.h"
+
+namespace mxnet {
+namespace op {
+
+#define MXNET_OPERATOR_REGISTER_PDF(distr, pdffunc, num_parms, vector_parms) \
+  NNVM_REGISTER_OP(_random_pdf_##distr) \
+  .set_attr<FCompute>("FCompute<gpu>", PdfOpForward<gpu, pdffunc, num_parms, vector_parms>); \
+  NNVM_REGISTER_OP(_backward_pdf_##distr) \
+  .set_attr<FCompute>("FCompute<gpu>", PdfOpBackward<gpu, pdffunc##_Grad, num_parms, vector_parms>);
+
+MXNET_OPERATOR_REGISTER_PDF(uniform, PDF_Uniform, 2, false)
+MXNET_OPERATOR_REGISTER_PDF(normal, PDF_Normal, 2, false)
+MXNET_OPERATOR_REGISTER_PDF(gamma, PDF_Gamma, 2, false)
+MXNET_OPERATOR_REGISTER_PDF(exponential, PDF_Exponential, 1, false)
+MXNET_OPERATOR_REGISTER_PDF(poisson, PDF_Poisson, 1, false)
+MXNET_OPERATOR_REGISTER_PDF(negative_binomial, PDF_NegativeBinomial, 2, false)
+MXNET_OPERATOR_REGISTER_PDF(generalized_negative_binomial,
+                            PDF_GeneralizedNegativeBinomial, 2, false)
+MXNET_OPERATOR_REGISTER_PDF(dirichlet, PDF_Dirichlet, 1, true)
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/random/pdf_op.h b/src/operator/random/pdf_op.h
new file mode 100644
index 000000000000..62d473927fb0
--- /dev/null
+++ b/src/operator/random/pdf_op.h
@@ -0,0 +1,622 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2018 by Contributors
+ * \file pdf_op.h
+ * \brief Operators for computing the pdf of random distributions.
+ */
+#ifndef MXNET_OPERATOR_RANDOM_PDF_OP_H_
+#define MXNET_OPERATOR_RANDOM_PDF_OP_H_
+
+#include <mxnet/operator_util.h>
+#include <vector>
+#include <algorithm>
+#include "../mshadow_op.h"
+#include "../mxnet_op.h"
+#include "../operator_common.h"
+#include "../elemwise_op_common.h"
+#include "../special_functions-inl.h"
+#include "../tensor/broadcast_reduce_op.h"
+
+namespace mxnet {
+namespace op {
+
+template<typename DType>
+MSHADOW_XINLINE static DType ceph_psi(DType val) { return special_functions::cephes::psi(val); }
+template<>
+MSHADOW_XINLINE mshadow::half::half_t ceph_psi(mshadow::half::half_t val) {
+    return special_functions::cephes::psi<float>(val);
+}
+
+template<bool logpdf>
+struct PDF_Uniform {
+  template<typename DType, typename IType1, typename IType2>
+  MSHADOW_XINLINE static void Map(int start, int length, int sample_size,
+                                  DType *out, IType1 *sample, IType2 *lower, IType2 *upper) {
+    const int index(start / sample_size);
+    const DType l(lower[index]), h(upper[index]);
+    const int end = start + length;
+    for (int i = start; i < end; ++i) {
+        // No check whether sample is in the support.
+        out[i] = logpdf ? -DType(log(h - l)) : DType(1.0) / (h - l);
+    }
+  }
+};
+
+template<bool logpdf>
+struct PDF_Uniform_Grad {
+  template<typename DType, typename IType1, typename IType2>
+  MSHADOW_XINLINE static void Map(int start, int length, int sample_size, OpReqType req,
+                  DType *out, IType1 *sample, IType2 *lower, IType2 *upper,
+                  DType *grad_out, IType1 *grad_sample, IType2 *grad_lower, IType2 *grad_upper) {
+    const int index(start / sample_size);
+    const DType l(lower[index]), h(upper[index]);
+
+    const int end = start + length;
+    for (int i = start; i < end; ++i) {
+        const DType scaling(grad_out[i]*(logpdf ? DType(1) : out[i]));
+        grad_lower[i]  = scaling / (h - l);
+        grad_upper[i]  = scaling / (l - h);
+        KERNEL_ASSIGN(grad_sample[i], req, 0);
+    }
+  }
+};
+
+template<bool logpdf>
+struct PDF_Normal {
+  template<typename DType, typename IType1, typename IType2>
+  MSHADOW_XINLINE static void Map(int start, int length, int sample_size,
+                                  DType *out, IType1 *sample, IType2 *loc, IType2 *scale) {
+    const int index(start / sample_size);
+    const DType u(loc[index]), s(scale[index]), sq(s * s);
+    const DType normalizer(sqrt(2.0 * mxnet_op::PI) * s);
+
+    const int end = start + length;
+    for (int i = start; i < end; ++i) {
+        const DType x(sample[i]);
+        const DType exponent((DType(-0.5) * (x - u) * (x - u)) / (sq));
+        out[i] = logpdf ? exponent - log(normalizer) : exp(exponent) / normalizer;
+    }
+  }
+};
+
+template<bool logpdf>
+struct PDF_Normal_Grad {
+  template<typename DType, typename IType1, typename IType2>
+  MSHADOW_XINLINE static void Map(int start, int length, int sample_size, OpReqType req,
+                  DType *out, IType1 *sample, IType2 *loc, IType2 *scale,
+                  DType *grad_out, IType1 *grad_sample, IType2 *grad_loc, IType2 *grad_scale) {
+    const int index(start / sample_size);
+    const DType u(loc[index]), s(scale[index]), s_squared(s * s), s_cubed(s_squared * s);
+
+    const int end = start + length;
+    for (int i = start; i < end; ++i) {
+        const DType x(sample[i]);
+        const DType scaling(grad_out[i]*(logpdf ? DType(1) : out[i]));
+        grad_loc[i]    = scaling * (x - u) / s_squared;
+        grad_scale[i]  = scaling * ((x - u) * (x - u) - s_squared) / s_cubed;
+        KERNEL_ASSIGN(grad_sample[i], req, scaling * (u - x) / s_squared);
+    }
+  }
+};
+
+template<bool logpdf>
+struct PDF_Gamma {
+  template<typename DType, typename IType1, typename IType2>
+  MSHADOW_XINLINE static void Map(int start, int length, int sample_size,
+                                  DType *out, IType1 *sample, IType2 *alpha, IType2 *beta) {
+    const int index(start / sample_size);
+    const DType a(alpha[index]), b(beta[index]), lgamma_a(lgamma(a)), a_log_b(a * log(b));
+
+    const int end = start + length;
+    for (int i = start; i < end; ++i) {
+        const DType x(sample[i]);
+        const DType lpdf(a_log_b + (a - 1) * log(x) - b * x - lgamma_a);
+        out[i] = logpdf ? lpdf : DType(exp(lpdf));
+    }
+  }
+};
+
+template<bool logpdf>
+struct PDF_Gamma_Grad {
+  template<typename DType, typename IType1, typename IType2>
+  MSHADOW_XINLINE static void Map(int start, int length, int sample_size, OpReqType req,
+                  DType *out, IType1 *sample, IType2 *alpha, IType2 *beta,
+                  DType *grad_out, IType1 *grad_sample, IType2 *grad_alpha, IType2 *grad_beta) {
+    const int index(start / sample_size);
+    const DType a(alpha[index]), b(beta[index]), log_b(log(b)), ceph_psi_a(ceph_psi(a));
+
+    const int end = start + length;
+    for (int i = start; i < end; ++i) {
+        const DType x(sample[i]);
+        const DType scaling(grad_out[i]*(logpdf ? DType(1) : out[i]));
+        grad_alpha[i]  = scaling * (log_b + log(x) - ceph_psi_a);
+        grad_beta[i]   = scaling * (a / b - x);
+        KERNEL_ASSIGN(grad_sample[i], req, scaling * ((a - 1) / x - b));
+    }
+  }
+};
+
+template<bool logpdf>
+struct PDF_Exponential {
+  template<typename DType, typename IType1, typename IType2>
+  MSHADOW_XINLINE static void Map(int start, int length, int sample_size,
+                                  DType *out, IType1 *sample, IType2 *lambda) {
+    const int index(start / sample_size);
+    const DType l(lambda[index]), log_l(log(l));
+
+    const int end = start + length;
+    for (int i = start; i < end; ++i) {
+        const DType x(sample[i]);
+        out[i] = logpdf ? log_l - l * x : l * exp(-l * x);
+    }
+  }
+};
+
+template<bool logpdf>
+struct PDF_Exponential_Grad {
+  template<typename DType, typename IType1, typename IType2>
+  MSHADOW_XINLINE static void Map(int start, int length, int sample_size, OpReqType req,
+                  DType *out, IType1 *sample, IType2 *lambda,
+                  DType *grad_out, IType1 *grad_sample, IType2 *grad_lambda) {
+    const int index(start / sample_size);
+    const DType l(lambda[index]);
+
+    const int end = start + length;
+    for (int i = start; i < end; ++i) {
+        const DType x(sample[i]);
+        const DType scaling(grad_out[i]*(logpdf ? DType(1) : out[i]));
+        grad_lambda[i] = scaling * (DType(1) / l - x);
+        KERNEL_ASSIGN(grad_sample[i], req, -scaling * l);
+    }
+  }
+};
+
+template<bool logpdf>
+struct PDF_Poisson {
+  template<typename DType, typename IType1, typename IType2>
+  MSHADOW_XINLINE static void Map(int start, int length, int sample_size,
+                                  DType *out, IType1 *sample, IType2 *lambda) {
+    const int index(start / sample_size);
+    const DType l(lambda[index]), log_l(log(l));
+
+    const int end = start + length;
+    for (int i = start; i < end; ++i) {
+        const DType x(sample[i]);
+        const DType lpdf((x * log_l - lgamma(x + 1)) - l);
+        out[i] = logpdf ? lpdf  : DType(exp(lpdf));
+    }
+  }
+};
+
+template<bool logpdf>
+struct PDF_Poisson_Grad {
+  template<typename DType, typename IType1, typename IType2>
+  MSHADOW_XINLINE static void Map(int start, int length, int sample_size, OpReqType req,
+                  DType *out, IType1 *sample, IType2 *lambda,
+                  DType *grad_out, IType1 *grad_sample, IType2 *grad_lambda) {
+    const int index(start / sample_size);
+    const DType l(lambda[index]);
+
+    const int end = start + length;
+    for (int i = start; i < end; ++i) {
+        const DType x(sample[i]);
+        const DType scaling(grad_out[i]*(logpdf ? DType(1) : out[i]));
+        grad_lambda[i] = scaling * (x / l - DType(1));
+        KERNEL_ASSIGN(grad_sample[i], req, 0);
+    }
+  }
+};
+
+
+template<bool logpdf>
+struct PDF_NegativeBinomial {
+  template<typename DType, typename IType1, typename IType2>
+  MSHADOW_XINLINE static void Map(int start, int length, int sample_size,
+                                  DType *out, IType1 *sample, IType2 *limit, IType2 *prob) {
+    const int index(start / sample_size);
+    const DType l(limit[index]), p(prob[index]), lgamma_l(lgamma(l));
+
+    const int end = start + length;
+    for (int i = start; i < end; ++i) {
+        const DType x(sample[i]);
+        const DType lpdf((lgamma(x + l) - lgamma(x + 1) - lgamma_l) + l * log(p) + x * log(1 - p));
+        out[i] = logpdf ? lpdf : DType(exp(lpdf));
+    }
+  }
+
+  template<typename DType>
+  MSHADOW_XINLINE static DType LPDF(DType l, DType p, DType x) {
+    // Note that "p" is the failure and not the success probability.
+    return (lgamma(x + l) - lgamma(x + 1) - lgamma(l)) + l * log(p) + x * log(1 - p);
+  }
+};
+
+template<bool logpdf>
+struct PDF_NegativeBinomial_Grad {
+  template<typename DType, typename IType1, typename IType2>
+  MSHADOW_XINLINE static void Map(int start, int length, int sample_size, OpReqType req,
+                  DType *out, IType1 *sample, IType2 *limit, IType2 *prob,
+                  DType *grad_out, IType1 *grad_sample, IType2 *grad_limit, IType2 *grad_prob) {
+    const int index(start / sample_size);
+    const int end = start + length;
+    for (int i = start; i < end; ++i) {
+        DType grad_l(0), grad_p(0);
+        LPDF_GRAD(DType(limit[index]), DType(prob[index]),
+                  DType(sample[i]), out[i],
+                  grad_out[i], &grad_l, &grad_p);
+        grad_limit[i]  = grad_l;
+        grad_prob[i]   = grad_p;
+        KERNEL_ASSIGN(grad_sample[i], req, 0);
+    }
+  }
+
+  template<typename DType>
+  MSHADOW_XINLINE static void LPDF_GRAD(DType l, DType p, DType x, DType o, DType grad_o,
+                                        DType* grad_l, DType* grad_p) {
+    const DType scaling(grad_o * (logpdf ? DType(1) : o));
+    *grad_l = scaling * ((ceph_psi(x + l) - ceph_psi(l)) + log(p));
+    *grad_p = scaling * (l / p - x / (1 - p));
+  }
+};
+
+template<bool logpdf>
+struct PDF_GeneralizedNegativeBinomial {
+  template<typename DType, typename IType1, typename IType2>
+  MSHADOW_XINLINE static void Map(int start, int length, int sample_size,
+                                  DType *out, IType1 *sample, IType2 *mu, IType2 *alpha) {
+    const int index(start / sample_size);
+
+    // Reparameterize with limit = 1 / alpha, prob = 1 / (mu * alpha + 1)
+    const DType limit(1.0 / alpha[index]), prob(1.0 / (mu[index]*alpha[index]+1.0));
+
+    const int end = start + length;
+    for (int i = start; i < end; ++i) {
+        const DType lpdf(PDF_NegativeBinomial<logpdf>::LPDF(limit, prob, DType(sample[i])));
+        out[i] = logpdf ? lpdf : DType(exp(lpdf));
+    }
+  }
+};
+
+template<bool logpdf>
+struct PDF_GeneralizedNegativeBinomial_Grad {
+  template<typename DType, typename IType1, typename IType2>
+  MSHADOW_XINLINE static void Map(int start, int length, int sample_size, OpReqType req,
+                  DType *out, IType1 *sample, IType2 *mu, IType2 *alpha,
+                  DType *grad_out, IType1 *grad_sample, IType2 *grad_mu, IType2 *grad_alpha) {
+    const int index(start / sample_size);
+    const DType fmu(mu[index]), falpha(alpha[index]), den(fmu * falpha + 1.0);
+
+    // Reparameterize with limit = 1 / alpha, prob = 1 / (mu * alpha + 1)
+    const DType limit(1.0 / falpha), prob(1.0 / (fmu * falpha + 1.0));
+
+    const int end = start + length;
+    for (int i = start; i < end; ++i) {
+        // Grad returned as d_limit, d_prob
+        DType grad_l(0), grad_p(0);
+        PDF_NegativeBinomial_Grad<logpdf>::LPDF_GRAD(limit, prob,
+            DType(sample[i]), out[i],
+            grad_out[i], &grad_l, &grad_p);
+        grad_mu[i]     = -grad_p * falpha / (den * den);
+        grad_alpha[i]  = -grad_l / (falpha * falpha) - grad_p * fmu / (den * den);
+        KERNEL_ASSIGN(grad_sample[i], req, 0);
+    }
+  }
+};
+
+template<bool logpdf>
+struct PDF_Dirichlet {
+  template<typename DType, typename IType1, typename IType2>
+  MSHADOW_XINLINE static void Map(int start, int length, int sample_size, int k,
+                                  DType *out, IType1 *sample, IType2 *alpha) {
+    const int index(start / sample_size);
+    const int end = start + length;
+    for (int i = start; i < end; ++i) {
+        const IType1 *cur_sample = sample + i * k;
+        const IType2 *cur_alpha  = alpha + index * k;
+        DType sum_alpha(0), sum_lgamma(0), sum_sample(0);
+        for (int j = 0; j < k; ++j) {
+          sum_alpha  += cur_alpha[j];
+          sum_lgamma += lgamma(cur_alpha[j]);
+          sum_sample += (cur_alpha[j]-1) * log(cur_sample[j]);
+        }
+        DType lpdf(sum_sample + (lgamma(sum_alpha) - sum_lgamma));
+        out[i] = logpdf ? lpdf : DType(exp(lpdf));
+    }
+  }
+};
+
+
+template<bool logpdf>
+struct PDF_Dirichlet_Grad {
+  template<typename DType, typename IType1, typename IType2>
+  MSHADOW_XINLINE static void Map(int start, int length, int sample_size,
+                  OpReqType req, int k,
+                  DType *out, IType1 *sample, IType2 *alpha,
+                  DType *grad_out, IType1 *grad_sample, IType2 *grad_alpha
+                  ) {
+    const int index(start / sample_size);
+    const int end = start + length;
+
+    for (int i = start; i < end; ++i) {
+        // Digamma function
+        const IType1 *cur_sample = sample + i * k;
+        const IType2 *cur_alpha = alpha + index * k;
+
+        const DType scaling(grad_out[i]*(logpdf ? DType(1) : out[i]));
+        DType sum_alpha(0);
+        for (int j = 0; j < k; ++j) {
+          sum_alpha += cur_alpha[j];
+        }
+        const DType psi_sum(ceph_psi(sum_alpha));
+
+        for (int j = 0; j < k; ++j) {
+          size_t grad_alpha_index = i%sample_size + sample_size * (j + k * index);
+          size_t grad_sample_index = i * k + j;
+
+          // order grad_alpha differently to allow efficient reduction at the end.
+          grad_alpha[grad_alpha_index] =
+            scaling * (log(cur_sample[j]) + (psi_sum - ceph_psi(cur_alpha[j])));
+          KERNEL_ASSIGN(grad_sample[grad_sample_index],
+            req, scaling * (cur_alpha[j]-1) / cur_sample[j]);
+        }
+    }
+  }
+};
+
+struct PdfParam : public dmlc::Parameter<PdfParam> {
+  bool is_log;
+  DMLC_DECLARE_PARAMETER(PdfParam) {
+    DMLC_DECLARE_FIELD(is_log).set_default(false)
+    .describe("If set, compute the density of the log-probability instead of the probability.");
+  }
+};
+
+template<bool vparm = false>
+inline bool PdfOpShape(const nnvm::NodeAttrs& attrs,
+                       std::vector<TShape>* in_attrs,
+                       std::vector<TShape>* out_attrs) {
+  CHECK_GT(in_attrs->size(), 1)
+    << "pdf operator takes at least 2 arguments (" << in_attrs->size() << " given)";
+  CHECK_EQ(out_attrs->size(), 1);
+  // All inputs must be defined in order to infer output shape.
+  if ( std::all_of((*in_attrs).begin(),
+                   (*in_attrs).end(),
+                   [](const TShape& s){ return s.ndim() > 0; }) ) {
+    // Tensors of distribution parameters must have same shape.
+    for (size_t i = 2; i < in_attrs->size(); ++i) {
+      SHAPE_ASSIGN_CHECK(*in_attrs, i, (*in_attrs)[i - 1]);
+    }
+    // Tensors of distribution parameters must match leftmost subshape of samples.
+    CHECK_LE((*in_attrs)[1].ndim(), (*in_attrs)[0].ndim())
+      << "dimension of input samples (" << (*in_attrs)[0].ndim()
+      << ") must be at least dimension of distribution parameters ("<< (*in_attrs)[1].ndim() << ")";
+    TShape tshape((*in_attrs)[0].begin(), (*in_attrs)[0].begin() + (*in_attrs)[1].ndim());
+    if (vparm) {
+      *(tshape.end() - 1) = *((*in_attrs)[0].end() - 1);
+    }
+    for (size_t i = 1; i < in_attrs->size(); ++i) {
+      SHAPE_ASSIGN_CHECK(*in_attrs, i, tshape);
+    }
+    // Output shape must equal input tensor of samples except for last dimension if we are
+    // dealing with samples that are itself vectors. Be aware of the special case where we
+    // are dealing with a single vector sample.
+    if (vparm && ((*in_attrs)[0].ndim() == 1)) {
+      // Special case where we are dealing with a single vector sample.
+      SHAPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::Shape1(1));
+    } else {
+      TShape oshape((*in_attrs)[0].begin(), (*in_attrs)[0].end() - (vparm ? 1 : 0));
+      SHAPE_ASSIGN_CHECK(*out_attrs, 0, oshape);
+    }
+    return true;
+  }
+  return false;
+}
+
+template<typename OP>
+struct LaunchExWrapper {
+  template<typename ...Args>
+  MSHADOW_XINLINE static void Map(const int start, const int length, const int sample_size,
+        Args... args) {
+    // Apply the operator to the sample in strides of sample_size, so that
+    // the operators can assume that their distribution parameters are constant.
+    int i = start;
+
+    // Get aligned
+    const int align_step = sample_size - (i % sample_size);
+    const int first_stride = length > align_step ? align_step : length;
+    OP::Map(i, first_stride, sample_size, args...);
+    i += first_stride;
+
+    const int end = start + length - sample_size;
+    for (; i < end; i += sample_size) {
+      OP::Map(i, sample_size, sample_size, args...);
+    }
+
+    // Last stride might not be aligned either
+    const int last_stride = start + length - i;
+    if (last_stride > 0) {  // Don't overstep even if length <= sample_size
+      OP::Map(i, last_stride, sample_size, args...);
+    }
+  }
+};
+
+template<typename xpu, typename DType, typename pdf, int pnum, bool vparm = false>
+struct PdfCaller;
+
+template<typename xpu, typename DType, typename pdf>
+struct PdfCaller<xpu, DType, pdf, 1, false> {
+  static void op(const std::vector<TBlob>& inputs,
+                 const std::vector<TBlob>& outputs,
+                 mshadow::Stream<xpu> *s) {
+    CHECK_EQ(inputs[0].Size()%inputs[1].Size(), 0);
+    CHECK_EQ(inputs[0].Size()%outputs[0].Size(), 0);
+    index_t num_samples(inputs[0].Size() / inputs[1].Size());
+    mxnet_op::Kernel<LaunchExWrapper<pdf>, xpu>::LaunchEx(s, outputs[0].Size(), num_samples,
+                outputs[0].dptr<DType>(), inputs[0].dptr<DType>(), inputs[1].dptr<DType>());
+  }
+};
+
+template<typename xpu, typename DType, typename pdf>
+struct PdfCaller<xpu, DType, pdf, 1, true> {
+  static void op(const std::vector<TBlob>& inputs,
+                 const std::vector<TBlob>& outputs,
+                 mshadow::Stream<xpu> *s) {
+    CHECK_EQ(inputs[0].Size()%inputs[1].Size(), 0);
+    CHECK_EQ(inputs[0].Size()%outputs[0].Size(), 0);
+    index_t num_samples(inputs[0].Size() / inputs[1].Size());
+    index_t sample_size(inputs[0].Size() / outputs[0].Size());
+
+    // Covers distributions parametrized by a vector of parameters (Dirichlet distribution).
+    mxnet_op::Kernel<LaunchExWrapper<pdf>, xpu>::LaunchEx(s, outputs[0].Size(),
+                num_samples, sample_size,
+                outputs[0].dptr<DType>(), inputs[0].dptr<DType>(), inputs[1].dptr<DType>());
+  }
+};
+
+template<typename xpu, typename DType, typename pdf>
+struct PdfCaller<xpu, DType, pdf, 2, false> {
+  static void op(const std::vector<TBlob>& inputs,
+                 const std::vector<TBlob>& outputs,
+                 mshadow::Stream<xpu> *s) {
+    CHECK_EQ(inputs[0].Size()%inputs[1].Size(), 0);
+    CHECK_EQ(inputs[0].Size(), outputs[0].Size());
+    index_t num_samples(inputs[0].Size() / inputs[1].Size());
+    mxnet_op::Kernel<LaunchExWrapper<pdf>, xpu>::LaunchEx(s, outputs[0].Size(), num_samples,
+                outputs[0].dptr<DType>(), inputs[0].dptr<DType>(),
+                inputs[1].dptr<DType>(), inputs[2].dptr<DType>());
+  }
+};
+
+template<typename xpu, template<bool> class pdf, int pnum, bool vparm>
+void PdfOpForward(const nnvm::NodeAttrs& attrs,
+                  const OpContext& ctx,
+                  const std::vector<TBlob>& inputs,
+                  const std::vector<OpReqType>& req,
+                  const std::vector<TBlob>& outputs) {
+  CHECK_NE(req[0], kAddTo);
+  CHECK_EQ(inputs.size(), pnum + 1);
+  CHECK_EQ(outputs.size(), 1);
+  mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+  const PdfParam& param = nnvm::get<PdfParam>(attrs.parsed);
+  MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+    if ( param.is_log ) {
+      PdfCaller<xpu, DType, pdf<true>, pnum, vparm>::op(inputs, outputs, s);
+    } else {
+      PdfCaller<xpu, DType, pdf<false>, pnum, vparm>::op(inputs, outputs, s);
+    }
+  });
+}
+
+
+template<typename xpu, typename DType, typename pdfgrad, int pnum, int vparm = false>
+struct PdfGradCaller;
+
+template<typename xpu, typename DType, typename pdfgrad>
+struct PdfGradCaller<xpu, DType, pdfgrad, 1, false> {
+  static void op(const std::vector<TBlob>& inputs,
+                 const std::vector<OpReqType>& req,
+                 const std::vector<TBlob>& grads,
+                 mshadow::Stream<xpu> *s) {
+    index_t num_samples(inputs[1].Size() / inputs[2].Size());
+    mxnet_op::Kernel<LaunchExWrapper<pdfgrad>, xpu>::LaunchEx(s, inputs[0].Size(),
+                num_samples, req[0],
+                inputs[3].dptr<DType>(), inputs[1].dptr<DType>(), inputs[2].dptr<DType>(),
+                inputs[0].dptr<DType>(), grads[0].dptr<DType>(), grads[1].dptr<DType>());
+  }
+};
+
+template<typename xpu, typename DType, typename pdfgrad>
+struct PdfGradCaller<xpu, DType, pdfgrad, 1, true> {
+  static void op(const std::vector<TBlob>& inputs,
+                 const std::vector<OpReqType>& req,
+                 const std::vector<TBlob>& grads,
+                 mshadow::Stream<xpu> *s) {
+    index_t num_samples(inputs[1].Size() / inputs[2].Size());
+    index_t sample_size(inputs[1].Size() / inputs[0].Size());
+    mxnet_op::Kernel<LaunchExWrapper<pdfgrad>, xpu>::LaunchEx(s, inputs[0].Size(), num_samples,
+                req[0], sample_size,
+                inputs[3].dptr<DType>(), inputs[1].dptr<DType>(), inputs[2].dptr<DType>(),
+                inputs[0].dptr<DType>(), grads[0].dptr<DType>(), grads[1].dptr<DType>());
+  }
+};
+
+template<typename xpu, typename DType, typename pdfgrad>
+struct PdfGradCaller<xpu, DType, pdfgrad, 2, false> {
+  static void op(const std::vector<TBlob>& inputs,
+                 const std::vector<OpReqType>& req,
+                 const std::vector<TBlob>& grads,
+                 mshadow::Stream<xpu> *s) {
+    index_t num_samples(inputs[1].Size() / inputs[2].Size());
+    mxnet_op::Kernel<LaunchExWrapper<pdfgrad>, xpu>::LaunchEx(s, inputs[0].Size(),
+                num_samples, req[0],
+                inputs[4].dptr<DType>(), inputs[1].dptr<DType>(), inputs[2].dptr<DType>(),
+                inputs[3].dptr<DType>(), inputs[0].dptr<DType>(),
+                grads[0].dptr<DType>(), grads[1].dptr<DType>(), grads[2].dptr<DType>());
+  }
+};
+
+template<typename xpu, template<bool> class pdfgrad, int pnum, bool vparm>
+void PdfOpBackward(const nnvm::NodeAttrs& attrs,
+                   const OpContext& ctx,
+                   const std::vector<TBlob>& inputs,
+                   const std::vector<OpReqType>& req,
+                   const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  CHECK_EQ(inputs.size(), pnum + 3);
+  CHECK_EQ(outputs.size(), pnum + 1);
+  mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+  const PdfParam& param = nnvm::get<PdfParam>(attrs.parsed);
+  const size_t N(outputs[1].Size());
+  const TShape src_shape(Shape2(N, outputs[0].Size() / N)), dst_shape(Shape2(N, 1));
+  // Inputs to PdfOpBackward: grad, samples, parm1, parm2, pdf.
+  MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+    const size_t red_work_size(broadcast::ReduceWorkspaceSize<2, DType>(
+            s, dst_shape, kAddTo, src_shape));
+    const size_t tmp_size(outputs[0].Size() * pnum * sizeof(DType) + red_work_size);
+    Tensor<xpu, 1, char> tmp_space =
+            ctx.requested[0].get_space_typed<xpu, 1, char>(Shape1(tmp_size), s);
+    std::vector<TBlob> grads = {outputs[0]};
+    grads.push_back(TBlob(tmp_space.dptr_, outputs[0].shape_,
+                          outputs[1].dev_mask(), outputs[1].type_flag_, outputs[1].dev_id()));
+    if (pnum == 2) {
+      grads.push_back(TBlob(tmp_space.dptr_ + outputs[0].Size() * sizeof(DType), outputs[0].shape_,
+                            outputs[2].dev_mask(), outputs[2].type_flag_, outputs[2].dev_id()));
+    }
+    if (param.is_log) {
+      PdfGradCaller<xpu, DType, pdfgrad<true>, pnum, vparm>::op(inputs, req, grads, s);
+    } else {
+      PdfGradCaller<xpu, DType, pdfgrad<false>, pnum, vparm>::op(inputs, req, grads, s);
+    }
+    Tensor<xpu, 1, char> red_work(
+            tmp_space.dptr_ + pnum * outputs[0].Size() * sizeof(DType), Shape1(red_work_size), s);
+    broadcast::Reduce<red::sum, 2, DType, op::mshadow_op::identity>(
+       s, outputs[1].reshape(dst_shape), req[1], red_work, grads[1].reshape(src_shape));
+    if (pnum == 2) {
+      broadcast::Reduce<red::sum, 2, DType, op::mshadow_op::identity>(
+       s, outputs[2].reshape(dst_shape), req[2], red_work, grads[2].reshape(src_shape));
+    }
+  });
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_RANDOM_PDF_OP_H_
diff --git a/tests/python/unittest/test_random.py b/tests/python/unittest/test_random.py
index 4d147197a150..8fdca619a5c8 100644
--- a/tests/python/unittest/test_random.py
+++ b/tests/python/unittest/test_random.py
@@ -25,6 +25,7 @@
 from common import setup_module, with_seed, random_seed, teardown
 import scipy.stats as ss
 import unittest
+from mxnet.test_utils import *
 
 def same(a, b):
     return np.sum(a != b) == 0
@@ -38,6 +39,9 @@ def check_with_device(device, dtype):
             'name': 'normal',
             'symbol': mx.sym.random.normal,
             'ndop': mx.nd.random.normal,
+            'pdfsymbol': mx.sym.random_pdf_normal,
+            'pdffunc': ss.norm.pdf,
+            'discrete': False,
             'params': { 'loc': 10.0, 'scale': 0.5 },
             'inputs': [ ('loc',[ [ 0.0, 2.5 ], [ -9.75, -7.0 ] ]) , ('scale',[ [ 1.0, 3.7 ], [ 4.2, 1.5 ] ]) ],
             'checks': [
@@ -68,6 +72,9 @@ def check_with_device(device, dtype):
             'name': 'uniform',
             'symbol': mx.sym.random.uniform,
             'ndop': mx.nd.random.uniform,
+            'pdfsymbol': mx.sym.random_pdf_uniform,
+            'pdffunc': lambda x, low, high: ss.uniform.pdf(x, low, high-low),
+            'discrete': False,
             'params': { 'low': -1.5, 'high': 3.0 },
             'inputs': [ ('low', [ [ 0.0, 2.5 ], [ -9.75, -1.0 ] ]) , ('high', [ [ 1.0, 3.7 ], [ 4.2, 10.5 ] ]) ],
             'checks': [
@@ -89,8 +96,11 @@ def check_with_device(device, dtype):
             'name': 'gamma',
             'symbol': mx.sym.random.gamma,
             'ndop': mx.nd.random.gamma,
+            'pdfsymbol': mx.sym.random_pdf_gamma,
+            'pdffunc': lambda x, alpha, beta: ss.gamma.pdf(x, alpha, 0, 1/beta),
+            'discrete': False,
             'params': { 'alpha': 9.0, 'beta': 0.5 },
-            'inputs': [ ('alpha', [ [ 0.0, 2.5 ], [ 9.75, 11.0 ] ]) , ('beta', [ [ 1.0, 0.7 ], [ 0.5, 0.3 ] ]) ],
+            'inputs': [ ('alpha', [ [ 0.1, 2.5 ], [ 9.75, 11.0 ] ]) , ('beta', [ [ 1.0, 0.7 ], [ 0.5, 0.3 ] ]) ],
             'checks': [
                 ('mean', lambda x, params: np.mean(x.astype(np.float64)) - params['alpha'] * params['beta'], tol),
                 ('std', lambda x, params: np.std(x.astype(np.float64)) - np.sqrt(params['alpha'] * params['beta'] ** 2), tol)
@@ -110,6 +120,9 @@ def check_with_device(device, dtype):
             'name': 'exponential',
             'symbol': mx.sym.random.exponential,
             'ndop': mx.nd.random.exponential,
+            'pdfsymbol': mx.sym.random_pdf_exponential,
+            'pdffunc': lambda x, lam: ss.expon.pdf(x, 0, 1/lam),
+            'discrete': False,
             'params': { 'scale': 1.0/4.0 },
             'inputs': [ ('scale', [ [ 1.0/1.0, 1.0/8.5 ], [ 1.0/2.7 , 1.0/0.5 ] ]) ],
             'checks': [
@@ -131,6 +144,9 @@ def check_with_device(device, dtype):
             'name': 'poisson',
             'symbol': mx.sym.random.poisson,
             'ndop': mx.nd.random.poisson,
+            'pdfsymbol': mx.sym.random_pdf_poisson,
+            'pdffunc': ss.poisson.pmf,
+            'discrete': True,
             'params': { 'lam': 4.0 },
             'inputs': [ ('lam', [ [ 25.0, 8.5 ], [ 2.7 , 0.5 ] ]) ],
             'checks': [
@@ -152,6 +168,9 @@ def check_with_device(device, dtype):
             'name': 'neg_binomial',
             'symbol': mx.sym.random.negative_binomial,
             'ndop': mx.nd.random.negative_binomial,
+            'pdfsymbol': mx.sym.random_pdf_negative_binomial,
+            'pdffunc': ss.nbinom.pmf,
+            'discrete': True,
             'params': { 'k': 3, 'p': 0.4 },
             'inputs': [ ('k', [ [ 3, 4 ], [ 5 , 6 ] ]) , ('p', [ [ 0.4 , 0.77 ], [ 0.5, 0.84 ] ]) ],
             'checks': [
@@ -173,6 +192,9 @@ def check_with_device(device, dtype):
             'name': 'gen_neg_binomial',
             'symbol': mx.sym.random.generalized_negative_binomial,
             'ndop': mx.nd.random.generalized_negative_binomial,
+            'pdfsymbol': mx.sym.random_pdf_generalized_negative_binomial,
+            'pdffunc': lambda x, mu, alpha: ss.nbinom.pmf(x, 1.0/alpha, 1.0/(mu*alpha+1.0)),
+            'discrete': True,
             'params': { 'mu': 2.0, 'alpha': 0.3 },
             'inputs': [ ('mu', [ [ 2.0, 2.5 ], [ 1.3, 1.9 ] ]) , ('alpha', [ [ 1.0, 0.1 ], [ 0.2, 0.5 ] ]) ],
             'checks': [
@@ -195,6 +217,9 @@ def check_with_device(device, dtype):
 
     # Create enough samples such that we get a meaningful distribution.
     shape = (500, 500)
+    # Test pdf on smaller shapes as backward checks will take too long otherwise.
+    # This must be a subshape of the former one.
+    pdfshape = (30, 30)
     for symbdic in symbols:
         name = symbdic['name']
         ndop = symbdic['ndop']
@@ -270,7 +295,7 @@ def check_with_device(device, dtype):
         # check multi-distribution sampling
         symbol = symbdic['symbol']
         params = { 'shape' : shape, 'dtype' : dtype }
-        single_param = len(symbdic['inputs']) == 1;
+        single_param = len(symbdic['inputs']) == 1
         v1 = mx.sym.Variable('v1')
         v2 = mx.sym.Variable('v2')
         Y = symbol(v1,**params) if single_param else symbol(v1,v2,**params)
@@ -290,12 +315,84 @@ def check_with_device(device, dtype):
                 for check_name, check_func, tol in symbdic['checks']:
                     assert np.abs(check_func(samples, params)) < tol, "symbolic test: %s check for `%s` did not pass" % (check_name, name)
 
-@with_seed()
-def test_random():
-    check_with_device(mx.context.current_context(), 'float16')
-    check_with_device(mx.context.current_context(), 'float32')
-    check_with_device(mx.context.current_context(), 'float64')
+        # check pdfs with only a subset of the generated samples
+        un1 = np.resize(un1, (un1.shape[0], un1.shape[1], pdfshape[0], pdfshape[1]))
+        print(name)
+        symbol  = symbdic['pdfsymbol']
+        pdffunc = symbdic['pdffunc']
+        v0 = mx.sym.Variable('v0')
+        v1 = mx.sym.Variable('v1')
+        v2 = mx.sym.Variable('v2')
+        p1 = np.array(symbdic['inputs'][0][1])
+        p2 = None if single_param else np.array(symbdic['inputs'][1][1])
+        # Move samples away from boundaries of support
+        if name == 'gamma' or name == 'exponential':
+           un1 = np.maximum(un1, 1e-1)
+        if name == 'uniform':
+           un1 = np.minimum(np.maximum(un1.reshape((un1.shape[0],un1.shape[1],-1)), p1.reshape((p1.shape[0],p1.shape[1],-1))+1e-4),
+                            p2.reshape((p2.shape[0],p2.shape[1],-1))-1e-4).reshape(un1.shape) 
+        for use_log in [False, True]:
+            test_pdf = symbol(v0, v1, is_log=use_log) if single_param else symbol(v0, v1, v2, is_log=use_log)
+            forw_atol  = 1e-7 if dtype != np.float16 else 1e-3
+            forw_rtol  = 1e-4 if dtype != np.float16 else 5e-2
+            backw_atol = 1e-3
+            backw_rtol = 5e-2
+            if single_param:
+                res = pdffunc(un1.reshape((un1.shape[0],un1.shape[1],-1)),
+                    p1.reshape((p1.shape[0],p1.shape[1],-1))).reshape(un1.shape)
+                if use_log: 
+                    res = np.log(res)
+                check_symbolic_forward(test_pdf, [un1, p1], [res], atol=forw_atol, rtol=forw_rtol, dtype=dtype)
+                if dtype == np.float64:
+                  grad_nodes = ['v1'] if symbdic['discrete'] else ['v0', 'v1']
+                  check_numeric_gradient(test_pdf, [un1, p1], grad_nodes=grad_nodes, atol=backw_atol, rtol=backw_rtol, dtype=dtype)
+            else:
+                res = pdffunc(un1.reshape((un1.shape[0],un1.shape[1],-1)),
+                    p1.reshape((p1.shape[0],p1.shape[1],-1)),
+                    p2.reshape((p2.shape[0],p2.shape[1],-1))).reshape(un1.shape)
+                if use_log:
+                    res = np.log(res)
+                check_symbolic_forward(test_pdf, [un1, p1, p2], [res], atol=forw_atol, rtol=forw_rtol, dtype=dtype)
+                if dtype == np.float64:
+                  grad_nodes = ['v1', 'v2'] if symbdic['discrete'] else ['v0', 'v1', 'v2']
+                  print(backw_rtol)
+                  check_numeric_gradient(test_pdf, [un1, p1, p2], grad_nodes=grad_nodes, atol=backw_atol, rtol=backw_rtol, dtype=dtype)
+
+@with_seed(1000)
+def test_dirichlet():
+    num_classes = 2
+    num = 100
+    alpha = np.random.uniform(low=0.5, high=2, size=(4, num_classes))
+
+    samples = []
+    results = []
+    for a in alpha:
+        v = ss.dirichlet.rvs(a, size=num)
+        samples.append(v)
+        results.append(ss.dirichlet.logpdf(v.transpose(), a))
+    samples = np.concatenate(samples, axis=0).reshape((2, 2, num, num_classes))
+    results = np.concatenate(results, axis=0).reshape((2, 2, num))
+
+    alpha = alpha.reshape((2, 2, num_classes))
+
+    for dtype in [np.float32, np.float64]:
+        forw_atol  = 1e-5
+        forw_rtol  = 1e-4
+        for use_log in [False, True]:
+            v0 = mx.sym.Variable('v0')
+            v1 = mx.sym.Variable('v1')
+            test_pdf = mx.sym.random_pdf_dirichlet(v0, v1, is_log=use_log)
+            res = results if use_log else np.exp(results)
+            check_symbolic_forward(test_pdf, [samples, alpha], [res], atol=forw_atol, rtol=forw_rtol, dtype=dtype)
+            if dtype == np.float64:
+                backw_atol = 1e-2
+                backw_rtol = 1e-2
+                eps = 1e-5
+                check_numeric_gradient(test_pdf, [samples, alpha], numeric_eps=eps, atol=backw_atol, rtol=backw_rtol, dtype=dtype)
 
+def test_random():
+    for dtype in [np.float16, np.float32, np.float64]:
+        check_with_device(mx.context.current_context(), dtype)
 
 # Set seed variously based on `start_seed` and `num_init_seeds`, then set seed finally to `final_seed`
 def set_seed_variously(init_seed, num_init_seeds, final_seed):

From eec0fb4eda40f4fb8222a8d93d8face454aead09 Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Thu, 18 Jul 2019 22:44:34 -0700
Subject: [PATCH 086/813] Group Normalization (#14959)

* GroupNorm

* add to amp list

* re-write forward
---
 python/mxnet/contrib/amp/lists/symbol.py |   1 +
 python/mxnet/gluon/nn/basic_layers.py    |  91 +++++-
 src/operator/nn/group_norm-inl.h         | 347 +++++++++++++++++++++++
 src/operator/nn/group_norm.cc            | 131 +++++++++
 src/operator/nn/group_norm.cu            |  37 +++
 tests/python/unittest/test_gluon.py      |   9 +
 tests/python/unittest/test_operator.py   |  91 ++++++
 7 files changed, 706 insertions(+), 1 deletion(-)
 create mode 100644 src/operator/nn/group_norm-inl.h
 create mode 100644 src/operator/nn/group_norm.cc
 create mode 100644 src/operator/nn/group_norm.cu

diff --git a/python/mxnet/contrib/amp/lists/symbol.py b/python/mxnet/contrib/amp/lists/symbol.py
index 9a587dfa73c1..c6cc3d1b1f00 100644
--- a/python/mxnet/contrib/amp/lists/symbol.py
+++ b/python/mxnet/contrib/amp/lists/symbol.py
@@ -471,6 +471,7 @@
     'log_softmax',
     'InstanceNorm',
     'LayerNorm',
+    'GroupNorm',
     'L2Normalization',
     'LRN',
     'SoftmaxActivation',
diff --git a/python/mxnet/gluon/nn/basic_layers.py b/python/mxnet/gluon/nn/basic_layers.py
index 3d6976c32740..b1482ce6dd82 100644
--- a/python/mxnet/gluon/nn/basic_layers.py
+++ b/python/mxnet/gluon/nn/basic_layers.py
@@ -19,7 +19,8 @@
 # pylint: disable= arguments-differ
 """Basic neural network layers."""
 __all__ = ['Sequential', 'HybridSequential', 'Dense', 'Dropout', 'Embedding',
-           'BatchNorm', 'InstanceNorm', 'LayerNorm', 'Flatten', 'Lambda', 'HybridLambda']
+           'BatchNorm', 'InstanceNorm', 'LayerNorm', 'GroupNorm',
+           'Flatten', 'Lambda', 'HybridLambda']
 import warnings
 import numpy as np
 
@@ -616,6 +617,94 @@ def __repr__(self):
                                            for k, v in self._kwargs.items()]))
 
 
+class GroupNorm(HybridBlock):
+    r"""
+    Applies group normalization to the n-dimensional input array.
+    This operator takes an n-dimensional input array where the leftmost 2 axis are
+    `batch` and `channel` respectively:
+
+    .. math::
+
+      x = x.reshape((N, num_groups, C // num_groups, ...))
+      axis = (2, ...)
+      out = \frac{x - mean[x, axis]}{ \sqrt{Var[x, axis] + \epsilon}} * gamma + beta
+
+    Parameters
+    ----------
+    num_groups: int, default 1
+        Number of groups to separate the channel axis into.
+    epsilon: float, default 1e-5
+        Small float added to variance to avoid dividing by zero.
+    center: bool, default True
+        If True, add offset of `beta` to normalized tensor.
+        If False, `beta` is ignored.
+    scale: bool, default True
+        If True, multiply by `gamma`. If False, `gamma` is not used.
+    beta_initializer: str or `Initializer`, default 'zeros'
+        Initializer for the beta weight.
+    gamma_initializer: str or `Initializer`, default 'ones'
+        Initializer for the gamma weight.
+
+
+    Inputs:
+        - **data**: input tensor with shape (N, C, ...).
+
+    Outputs:
+        - **out**: output tensor with the same shape as `data`.
+
+    References
+    ----------
+        `Group Normalization
+        <https://arxiv.org/pdf/1803.08494.pdf>`_
+
+    Examples
+    --------
+    >>> # Input of shape (2, 3, 4)
+    >>> x = mx.nd.array([[[ 0,  1,  2,  3],
+                          [ 4,  5,  6,  7],
+                          [ 8,  9, 10, 11]],
+                         [[12, 13, 14, 15],
+                          [16, 17, 18, 19],
+                          [20, 21, 22, 23]]])
+    >>> # Group normalization is calculated with the above formula
+    >>> layer = GroupNorm()
+    >>> layer.initialize(ctx=mx.cpu(0))
+    >>> layer(x)
+    [[[-1.5932543 -1.3035717 -1.0138891 -0.7242065]
+      [-0.4345239 -0.1448413  0.1448413  0.4345239]
+      [ 0.7242065  1.0138891  1.3035717  1.5932543]]
+     [[-1.5932543 -1.3035717 -1.0138891 -0.7242065]
+      [-0.4345239 -0.1448413  0.1448413  0.4345239]
+      [ 0.7242065  1.0138891  1.3035717  1.5932543]]]
+    <NDArray 2x3x4 @cpu(0)>
+    """
+    def __init__(self, num_groups=1, epsilon=1e-5, center=True, scale=True,
+                 beta_initializer='zeros', gamma_initializer='ones',
+                 prefix=None, params=None):
+        super(GroupNorm, self).__init__(prefix=prefix, params=params)
+        self._kwargs = {'eps': epsilon, 'num_groups': num_groups, 'center': center, 'scale': scale}
+        self._num_groups = num_groups
+        self._epsilon = epsilon
+        self._center = center
+        self._scale = scale
+        self.gamma = self.params.get('gamma', grad_req='write' if scale else 'null',
+                                     shape=(num_groups,), init=gamma_initializer,
+                                     allow_deferred_init=True)
+        self.beta = self.params.get('beta', grad_req='write' if center else 'null',
+                                    shape=(num_groups,), init=beta_initializer,
+                                    allow_deferred_init=True)
+
+    def hybrid_forward(self, F, data, gamma, beta):
+        norm_data = F.GroupNorm(data, gamma=gamma, beta=beta, num_groups=self._num_groups, eps=self._epsilon)
+        return norm_data
+
+    def __repr__(self):
+        s = '{name}({content})'
+        return s.format(name=self.__class__.__name__,
+                        content=', '.join(['='.join([k, v.__repr__()])
+                                           for k, v in self._kwargs.items()]))
+
+
 class Lambda(Block):
     r"""Wraps an operator or an expression as a Block object.
 
diff --git a/src/operator/nn/group_norm-inl.h b/src/operator/nn/group_norm-inl.h
new file mode 100644
index 000000000000..69d5a304dc2c
--- /dev/null
+++ b/src/operator/nn/group_norm-inl.h
@@ -0,0 +1,347 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file group_norm-inl.h
+ * \brief Implements Group Normalization (https://arxiv.org/abs/1803.08494).
+ * \author Hao Jin
+*/
+
+#ifndef MXNET_OPERATOR_NN_GROUP_NORM_INL_H_
+#define MXNET_OPERATOR_NN_GROUP_NORM_INL_H_
+
+#include <dmlc/logging.h>
+#include <dmlc/parameter.h>
+#include <mxnet/operator.h>
+#include <mshadow/base.h>
+#include <map>
+#include <algorithm>
+#include <vector>
+#include <string>
+#include <utility>
+#include "./moments-inl.h"
+#include "../mshadow_op.h"
+#include "../operator_common.h"
+#include "../mxnet_op.h"
+#include "../tensor/broadcast_reduce_op.h"
+
+namespace mxnet {
+namespace op {
+
+namespace groupnorm {
+enum GroupNormOpInputs {kData, kGamma, kBeta};  // kGamma: scaling parameters, kBeta: shift biases
+enum GroupNormOpOutputs {kOut, kMean, kStd};  // req, out_data
+}  // namespace groupnorm
+
+struct GroupNormParam : public dmlc::Parameter<GroupNormParam> {
+  int num_groups;
+  float eps;
+  bool output_mean_var;
+  DMLC_DECLARE_PARAMETER(GroupNormParam) {
+    DMLC_DECLARE_FIELD(num_groups).set_default(1)
+      .describe("Total number of groups.");
+    DMLC_DECLARE_FIELD(eps).set_default(1e-5f)
+      .describe("An `epsilon` parameter to prevent division by 0.");
+    DMLC_DECLARE_FIELD(output_mean_var).set_default(false)
+      .describe("Output the mean and std calculated along the given axis.");
+  }
+};
+
+
+template<typename xpu>
+void GroupNormCompute(const nnvm::NodeAttrs& attrs,
+                      const OpContext& ctx,
+                      const std::vector<TBlob>& inputs,
+                      const std::vector<OpReqType>& req,
+                      const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mshadow::expr;
+  using namespace mxnet_op;
+  const GroupNormParam& param = nnvm::get<GroupNormParam>(attrs.parsed);
+  const int num_groups = param.num_groups;
+  if (req[0] == kNullOp) return;
+  CHECK_NE(req[0], kAddTo);
+
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  const TBlob& data = inputs[groupnorm::kData];
+  const TBlob& mean = outputs[groupnorm::kMean];
+  const TBlob& std = outputs[groupnorm::kStd];
+  const mxnet::TShape& data_shape = data.shape_;
+  CHECK_GE(data_shape.ndim(), 3U)
+    << "input should have at least 3 dims and "
+    << "the first 2 dims should be batch and channel respectively";
+  CHECK_EQ(data_shape[1] % num_groups, 0)
+    << "number of channel should be divisible by num_groups.";
+
+  mxnet::TShape temp_data_shape(data_shape.ndim() + 1, 1);
+  temp_data_shape[0] = data_shape[0];
+  temp_data_shape[1] = num_groups;
+  temp_data_shape[2] = data_shape[1] / num_groups;
+  for (int i = 2; i < data_shape.ndim(); ++i) {
+    temp_data_shape[i+1] = data_shape[i];
+  }
+
+  mxnet::TShape moments_shape(temp_data_shape.ndim(), 1);
+  for (int i = 0; i < data.shape_.ndim(); ++i) {
+    moments_shape[i] = (i < mean.shape_.ndim()) ? mean.shape_[i] : 1;
+  }
+
+  mxnet::TShape red_src_shape, red_dst_shape;
+  BroadcastReduceShapeCompact(temp_data_shape, moments_shape, &red_src_shape, &red_dst_shape);
+  int channel_size = red_src_shape.Size() / red_dst_shape.Size();
+
+  TBlob data_ = data.reshape(red_src_shape);
+  const TBlob& mean_ = mean.reshape(red_dst_shape);
+  const TBlob& std_ = std.reshape(red_dst_shape);
+
+  Tensor<xpu, 1, char> workspace;
+
+  size_t workspace_size = 0;
+  MSHADOW_REAL_TYPE_SWITCH(data.type_flag_, DType, {
+    BROADCAST_NDIM_SWITCH(red_dst_shape.ndim(), NDim, {
+      workspace_size =
+        broadcast::ReduceWorkspaceSize<NDim, DType>(s, red_dst_shape, req[0], red_src_shape);
+    });
+  });
+
+  workspace = ctx.requested[0].get_space_typed<xpu, 1, char>(Shape1(workspace_size), s);
+
+  // Calculate mean
+  MSHADOW_REAL_TYPE_SWITCH(data.type_flag_, DType, {
+    BROADCAST_NDIM_SWITCH(red_dst_shape.ndim(), NDim, {
+      broadcast::Reduce<mshadow_op::sum, NDim, DType, mshadow_op::identity, true>(
+        s, mean_, req[0], workspace, data_);
+      Tensor<xpu, 1, DType> mean_data_tensor = mean_.FlatTo1D<xpu, DType>(s);
+      mean_data_tensor /= scalar<DType>(channel_size);
+    });
+  });
+
+  TBlob data_grp = data.reshape(temp_data_shape);
+  const TBlob& mean_grp = mean.reshape(moments_shape);
+  const TBlob& std_grp = std.reshape(moments_shape);
+  const TBlob& output = outputs[groupnorm::kOut].reshape(temp_data_shape);
+
+  // Calculate data = data - mean
+  BinaryBroadcastCompute<xpu, op::mshadow_op::minus>(attrs, ctx,
+                                                     {data_grp, mean_grp},
+                                                     {kWriteTo}, {output});
+
+  // Calculate std
+  const TBlob centered_out = outputs[groupnorm::kOut].reshape(red_src_shape);
+  MSHADOW_REAL_TYPE_SWITCH(output.type_flag_, DType, {
+    BROADCAST_NDIM_SWITCH(red_dst_shape.ndim(), NDim, {
+      broadcast::Reduce<mshadow_op::sum, NDim, DType, mshadow_op::square, true>(
+        s, std_, req[0], workspace, centered_out);
+      Tensor<xpu, 1, DType> std_data_tensor = std_.FlatTo1D<xpu, DType>(s);
+      std_data_tensor = F<mshadow_op::square_root>(std_data_tensor / scalar<DType>(channel_size)
+                        + scalar<DType>(param.eps));
+    });
+  });
+
+  // Calculate data = data / std
+  BinaryBroadcastCompute<xpu, mshadow_op::div>(attrs, ctx,
+                                               {output, std_grp},
+                                               {kWriteTo}, {output});
+
+  mxnet::TShape new_param_shape(data_shape.ndim() + 1, 1);
+  new_param_shape[1] = num_groups;
+
+  const TBlob& gamma = inputs[groupnorm::kGamma].reshape(new_param_shape);
+  const TBlob& beta = inputs[groupnorm::kBeta].reshape(new_param_shape);
+
+  // Calculate data = data * gamma
+  BinaryBroadcastCompute<xpu, op::mshadow_op::mul>(attrs, ctx,
+                                                   {output, gamma},
+                                                   {kWriteTo}, {output});
+  // Calculate data = data + beta
+  BinaryBroadcastCompute<xpu, op::mshadow_op::plus>(attrs, ctx,
+                                                   {output, beta},
+                                                   {kWriteTo}, {output});
+}
+
+/*
+Calculate the gradient of group normalization.
+We have the following gradient for gamma, beta and x:
+
+\bar{x} = (x - mean) / std
+w = og * r / std
+grad_gamma = sum(\bar{x} og, exclude_axis)
+grad_beta = sum(og, exclude_axis)
+grad_x = w - mean(w, axis) - \bar{x} * mean(w * \bar{x}, axis)
+*/
+template<typename xpu>
+void GroupNormGradCompute(const nnvm::NodeAttrs& attrs,
+                          const OpContext& ctx,
+                          const std::vector<TBlob>& inputs,
+                          const std::vector<OpReqType>& req,
+                          const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mshadow::expr;
+  using namespace mxnet_op;
+  CHECK_EQ(inputs.size(), 5U);
+  CHECK_EQ(outputs.size(), 3U);
+  const GroupNormParam& param = nnvm::get<GroupNormParam>(attrs.parsed);
+  const int num_groups = param.num_groups;
+
+  const TBlob& data = inputs[1];
+  const mxnet::TShape& dshape = data.shape_;
+
+  mxnet::TShape temp_dshape(dshape.ndim() + 1, 1);
+  temp_dshape[0] = dshape[0];
+  temp_dshape[1] = num_groups;
+  temp_dshape[2] = dshape[1] / num_groups;
+  for (int i = 2; i < dshape.ndim(); ++i) {
+    temp_dshape[i+1] = dshape[i];
+  }
+  const TBlob& data_ = data.reshape(temp_dshape);
+  const TBlob& ograd = inputs[0].reshape(temp_dshape);
+
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  // Reshape gamma to be broadcastable
+  mxnet::TShape new_param_shape(dshape.ndim() + 1, 1);
+  new_param_shape[1] = num_groups;
+
+  const TBlob& gamma = inputs[2].reshape(new_param_shape);
+
+  const TBlob& mean = inputs[3];
+  const TBlob& std = inputs[4];
+
+  mxnet::TShape moments_shape(temp_dshape.ndim(), 1);
+  for (int i = 0; i < dshape.ndim(); ++i) {
+    moments_shape[i] = (i < mean.shape_.ndim()) ? mean.shape_[i] : 1;
+  }
+  const TBlob& mean_ = mean.reshape(moments_shape);
+  const TBlob& std_ = std.reshape(moments_shape);
+
+  // Prepare the necessary shapes for reduction
+  mxnet::TShape red_src_shape, red_dst_shape, red_exclude_src_shape, red_exclude_dst_shape;
+  BroadcastReduceShapeCompact(temp_dshape, mean_.shape_, &red_src_shape, &red_dst_shape);
+  BroadcastReduceShapeCompact(temp_dshape, gamma.shape_,
+                              &red_exclude_src_shape, &red_exclude_dst_shape);
+
+  int N = red_src_shape.Size() / red_dst_shape.Size();
+
+  // Initialize the workspace + Construct the temporary TBlobs
+  Tensor<xpu, 1, char> workspace;
+  size_t reduce_workspace_size = 0;
+  size_t data_size = 0;
+  size_t red_out_size = 0;
+  MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+    data_size = sizeof(DType) * data.Size();
+    red_out_size = sizeof(DType) * mean.Size();
+    // There are two types of reduction workloads: reduce over axis and reduce exclude axis
+    // We take the maximum of the workspace sizes required by these workloads.
+    // Also, we explicitly set the req_type=kAddto in case we want to use it.
+    BROADCAST_NDIM_SWITCH(red_dst_shape.ndim(), NDim, {
+      reduce_workspace_size =
+        std::max(reduce_workspace_size,
+                 broadcast::ReduceWorkspaceSize<NDim, DType>(s, red_dst_shape,
+                                                             kAddTo, red_src_shape));
+    });
+    BROADCAST_NDIM_SWITCH(red_exclude_dst_shape.ndim(), NDim, {
+      reduce_workspace_size =
+        std::max(reduce_workspace_size,
+                 broadcast::ReduceWorkspaceSize<NDim, DType>(s, red_exclude_dst_shape, kAddTo,
+                                                             red_exclude_src_shape));
+    });
+  });
+  workspace = ctx.requested[0].get_space_typed<xpu, 1, char>(
+    Shape1(reduce_workspace_size + data_size * 2 + red_out_size), s);
+  const TBlob normalized_data =
+    TBlob(workspace.dptr_ + reduce_workspace_size,
+          data_.shape_, data.dev_mask(), data.type_flag_, data.dev_id());
+  const TBlob ograd_mult = TBlob(workspace.dptr_ + reduce_workspace_size + data_size,
+                                 data_.shape_, ograd.dev_mask(), ograd.type_flag_, ograd.dev_id());
+  const TBlob red_out = TBlob(workspace.dptr_ + reduce_workspace_size + data_size * 2,
+                              mean_.shape_, mean.dev_mask(), mean.type_flag_, mean.dev_id());
+  // Compute normalized_data = (data - mean) / std
+  BinaryBroadcastCompute<xpu, op::mshadow_op::minus>(attrs, ctx,
+                                                    {data_, mean_},
+                                                    {kWriteTo}, {normalized_data});
+  BinaryBroadcastCompute<xpu, op::mshadow_op::div>(attrs, ctx,
+                                                   {normalized_data, std_},
+                                                   {kWriteTo}, {normalized_data});
+  // Calculate grad_beta
+  if (req[2] != kNullOp) {
+    MSHADOW_REAL_TYPE_SWITCH(outputs[2].type_flag_, DType, {
+      BROADCAST_NDIM_SWITCH(red_exclude_dst_shape.ndim(), NDim, {
+        broadcast::Reduce<red::sum, NDim, DType, op::mshadow_op::identity, true>(
+          s, outputs[2].reshape(red_exclude_dst_shape), req[2], workspace,
+          ograd.reshape(red_exclude_src_shape));
+      });
+    });
+  }
+  // Calculate grad_gamma, it will be sum(ograd * normalized_data, exclude_axis)
+  ElemwiseBinaryOp::Compute<xpu, op::mshadow_op::mul>(attrs, ctx, {normalized_data, ograd},
+                                                      {kWriteTo}, {ograd_mult});
+  if (req[1] != kNullOp) {
+    MSHADOW_REAL_TYPE_SWITCH(outputs[1].type_flag_, DType, {
+      BROADCAST_NDIM_SWITCH(red_exclude_dst_shape.ndim(), NDim, {
+        broadcast::Reduce<mshadow_op::sum, NDim, DType, op::mshadow_op::identity, true>(
+          s, outputs[1].reshape(red_exclude_dst_shape), req[1], workspace,
+          ograd_mult.reshape(red_exclude_src_shape));
+      });
+    });
+  }
+
+  // Calculate grad_data:
+  //   ograd_mult = ograd * gamma / std
+  //   grad_data = ograd_mult - mean(ograd_mult, axis)
+  //               + normalized_data * (-mean(normalized_data * ograd_mult, axis))
+  if (req[0] != kNullOp) {
+    const TBlob output_ = outputs[0].reshape(data_.shape_);
+    BinaryBroadcastCompute<xpu, op::mshadow_op::mul>(attrs, ctx,
+                                                    {ograd, gamma},
+                                                    {kWriteTo}, {ograd_mult});
+    BinaryBroadcastCompute<xpu, op::mshadow_op::div>(attrs, ctx,
+                                                    {ograd_mult, std_},
+                                                    {kWriteTo}, {ograd_mult});
+    MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+      BROADCAST_NDIM_SWITCH(red_dst_shape.ndim(), NDim, {
+        broadcast::Reduce<mshadow_op::sum, NDim, DType, op::mshadow_op::identity, true>(
+          s, red_out.reshape(red_dst_shape), kWriteTo, workspace,
+          ograd_mult.reshape(red_src_shape));
+      });
+      Tensor<xpu, 1, DType> red_out_tensor = red_out.FlatTo1D<xpu, DType>(s);
+      red_out_tensor /= scalar<DType>(N);
+    });
+    BinaryBroadcastCompute<xpu, op::mshadow_op::minus>(attrs, ctx,
+                                                      {ograd_mult, red_out},
+                                                      {req[0]}, {output_});
+    ElemwiseBinaryOp::Compute<xpu, op::mshadow_op::mul>(attrs, ctx, {ograd_mult, normalized_data},
+                                                        {kWriteTo}, {ograd_mult});
+    MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+      BROADCAST_NDIM_SWITCH(red_dst_shape.ndim(), NDim, {
+        broadcast::Reduce<mshadow_op::sum, NDim, DType, op::mshadow_op::identity, true>(
+          s, red_out.reshape(red_dst_shape), kWriteTo, workspace,
+          ograd_mult.reshape(red_src_shape));
+      });
+      Tensor<xpu, 1, DType> red_out_tensor = red_out.FlatTo1D<xpu, DType>(s);
+      red_out_tensor /= scalar<DType>(-N);
+    });
+    BinaryBroadcastCompute<xpu, op::mshadow_op::mul>(attrs, ctx,
+                                                     {normalized_data, red_out},
+                                                     {kAddTo}, {output_});
+  }
+}
+
+}  // namespace op
+}  // namespace mxnet
+#endif  // MXNET_OPERATOR_NN_GROUP_NORM_INL_H_
diff --git a/src/operator/nn/group_norm.cc b/src/operator/nn/group_norm.cc
new file mode 100644
index 000000000000..b4698abeff83
--- /dev/null
+++ b/src/operator/nn/group_norm.cc
@@ -0,0 +1,131 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file group_norm.cc
+ * \brief Implements Group Normalization (https://arxiv.org/abs/1803.08494).
+*/
+
+#include "group_norm-inl.h"
+#include <nnvm/op_attr_types.h>
+#include "../elemwise_op_common.h"
+
+namespace mxnet {
+namespace op {
+
+DMLC_REGISTER_PARAMETER(GroupNormParam);
+
+static bool GroupNormShape(const nnvm::NodeAttrs& attrs,
+                           mxnet::ShapeVector *in_shape,
+                           mxnet::ShapeVector *out_shape) {
+  const GroupNormParam& param = nnvm::get<GroupNormParam>(attrs.parsed);
+  using namespace mshadow;
+  CHECK_EQ(in_shape->size(), 3U) << "Input:[data, gamma, beta]";
+  const mxnet::TShape &dshape = in_shape->at(groupnorm::kData);
+  CHECK_GE(dshape.ndim(), 3U);
+  const int num_groups = param.num_groups;
+  CHECK_EQ(dshape[1] % num_groups, 0) << "# of channels must be divisible by # of groups";
+
+  if (!mxnet::ndim_is_known(dshape)) {
+    return false;
+  }
+
+  in_shape->at(groupnorm::kGamma) = mxnet::TShape(Shape1(num_groups));
+  in_shape->at(groupnorm::kBeta) = mxnet::TShape(Shape1(num_groups));
+
+  out_shape->clear();
+  out_shape->push_back(dshape);
+
+  mxnet::TShape moments_shape(2, 1);
+  moments_shape[0] = dshape[0];
+  moments_shape[1] = num_groups;
+  out_shape->push_back(moments_shape);
+  out_shape->push_back(moments_shape);
+  return true;
+}
+
+NNVM_REGISTER_OP(GroupNorm)
+.describe(R"code(Group normalization.
+
+The input channels are separated into ``num_groups`` groups, each containing ``num_channels / num_groups`` channels.
+The mean and standard-deviation are calculated separately over the each group.
+
+.. math::
+
+  data = data.reshape((N, num_groups, C // num_groups, ...))
+  out = \frac{data - mean(data, axis)}{\sqrt{var(data, axis) + \epsilon}} * gamma + beta
+
+Both ``gamma`` and ``beta`` are learnable parameters.
+
+)code" ADD_FILELINE)
+.set_num_inputs(3)
+.set_num_outputs(3)
+.set_attr_parser(ParamParser<GroupNormParam>)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+    [](const NodeAttrs& attrs) {
+  return std::vector<std::string>{"data", "gamma", "beta"};
+})
+.set_attr<nnvm::FListOutputNames>("FListOutputNames",
+    [](const NodeAttrs& attrs) {
+  return std::vector<std::string>{"output", "mean", "std"};
+})
+.set_attr<nnvm::FNumVisibleOutputs>("FNumVisibleOutputs",
+    [](const NodeAttrs& attrs) {
+  const GroupNormParam& param = nnvm::get<GroupNormParam>(attrs.parsed);
+  return param.output_mean_var ? 3 : 1;
+})
+.set_attr<mxnet::FInferShape>("FInferShape", GroupNormShape)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<3, 3>)
+.set_attr<FCompute>("FCompute<cpu>", GroupNormCompute<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", [](const nnvm::NodePtr& n,
+                                           const std::vector<nnvm::NodeEntry>& ograds) {
+  std::vector<nnvm::NodeEntry> heads;
+  heads.push_back(ograds[0]);  // ograd
+  heads.push_back(n->inputs[0]);  // data
+  heads.push_back(n->inputs[1]);  // gamma
+  heads.emplace_back(nnvm::NodeEntry{n, 1, 0});  // mean
+  heads.emplace_back(nnvm::NodeEntry{ n, 2, 0 });  // std
+  return MakeGradNode("_backward_GroupNorm", n, heads, n->attrs.dict);
+})
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs) {
+  return std::vector<std::pair<int, int> >{{0, 0}};
+})
+.set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& n) {
+  return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+})
+.add_argument("data", "NDArray-or-Symbol", "Input data")
+.add_argument("gamma", "NDArray-or-Symbol", "gamma array")
+.add_argument("beta", "NDArray-or-Symbol", "beta array")
+.add_arguments(GroupNormParam::__FIELDS__());
+
+
+NNVM_REGISTER_OP(_backward_GroupNorm)
+.set_num_inputs(5)
+.set_num_outputs(3)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr_parser(ParamParser<GroupNormParam>)
+.set_attr<FCompute>("FCompute<cpu>", GroupNormGradCompute<cpu>)
+.set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& n) {
+  return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+});
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/nn/group_norm.cu b/src/operator/nn/group_norm.cu
new file mode 100644
index 000000000000..136c3337468c
--- /dev/null
+++ b/src/operator/nn/group_norm.cu
@@ -0,0 +1,37 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file group_norm.cu
+ * \brief Implements Group Normalization (https://arxiv.org/abs/1803.08494).
+*/
+#include "./group_norm-inl.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(GroupNorm)
+.set_attr<FCompute>("FCompute<gpu>", GroupNormCompute<gpu>);
+
+NNVM_REGISTER_OP(_backward_GroupNorm)
+.set_attr<FCompute>("FCompute<gpu>", GroupNormGradCompute<gpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/tests/python/unittest/test_gluon.py b/tests/python/unittest/test_gluon.py
index d52e7f8bc832..b59ce2d0864c 100644
--- a/tests/python/unittest/test_gluon.py
+++ b/tests/python/unittest/test_gluon.py
@@ -743,6 +743,15 @@ def test_layernorm():
     check_layer_forward(layer, (2, 10, 10, 10))
 
 
+@with_seed()
+def test_groupnorm():
+    layer = nn.GroupNorm()
+    check_layer_forward(layer, (2, 10, 10, 10))
+    layer = nn.GroupNorm(num_groups=2)
+    check_layer_forward(layer, (2, 10, 10, 10))
+    layer = nn.GroupNorm(num_groups=5)
+    check_layer_forward(layer, (2, 10, 10, 10))
+
 @with_seed()
 def test_reflectionpad():
     layer = nn.ReflectionPad2D(3)
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index aeddc7a893df..749f0f2bed23 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -1830,6 +1830,97 @@ def _test_batchnorm_impl(op, shape, axis, cudnn_off, output_mean_var):
                                              cudnn_off, output_mean_var)
 
 
+@with_seed()
+def test_groupnorm():
+    acc_types = {'float16': 'float32', 'float32': 'float64', 'float64': 'float64'}
+    def x_hat_helper(x, num_groups, eps):
+        dtype = x.dtype
+        dshape = x.shape
+        assert len(dshape) == 4
+        acc_type = acc_types[str(dtype)]
+        new_shape = (dshape[0], num_groups, int(dshape[1] / num_groups), dshape[2], dshape[3])
+        new_moments_shape = (dshape[0], num_groups, 1, 1, 1)
+        data = x.reshape(new_shape)
+        mean = np.mean(data, axis=(2, 3, 4), keepdims=False, dtype=acc_type).astype(dtype)
+        std = np.sqrt(np.var(data, axis=(2, 3, 4), dtype=acc_type, keepdims=False).astype(dtype) + eps)
+        x_hat = (data - mean.reshape(new_moments_shape)) / std.reshape(new_moments_shape)
+        return x_hat, mean, std
+
+    def np_groupnorm(data, gamma, beta, num_groups, eps):
+        new_param_shape = (1, num_groups, 1, 1, 1)
+        x_hat, mean, std = x_hat_helper(data, num_groups, eps)
+        out = x_hat * gamma.reshape(new_param_shape) + beta.reshape(new_param_shape)
+        return out.reshape(dshape), mean, std
+
+    def np_groupnorm_grad(ograd, data, gamma, beta, mean, std, num_groups, eps):
+        x_hat, mean, std = x_hat_helper(data, num_groups, eps)
+        new_shape = x_hat.shape
+        dshape = data.shape
+        dtype = data.dtype
+        new_moments_shape = (new_shape[0], num_groups, 1, 1, 1)
+        new_param_shape = (1, num_groups, 1, 1, 1)
+        acc_type = acc_types[str(dtype)]
+        ograd = ograd.reshape(new_shape)
+        data = data.reshape(new_shape)
+        gamma = gamma.reshape(new_param_shape)
+        beta = beta.reshape(new_param_shape)
+        mean = mean.reshape(new_moments_shape)
+        std = std.reshape(new_moments_shape)
+        beta_grad = np.sum(ograd, axis=(0, 2, 3, 4), dtype=acc_type, keepdims=False).astype(dtype)
+        gamma_grad = np.sum(x_hat * ograd, axis=(0, 2, 3, 4), dtype=acc_type, keepdims=False).astype(dtype)
+        x_hat_grad = ograd * gamma
+        ograd_mult = x_hat_grad / std
+        red_out = np.mean(ograd_mult, axis=(2, 3, 4), dtype=acc_type, keepdims=True).astype(dtype)
+        data_grad = ograd_mult - red_out
+        red_out = np.mean(ograd_mult * x_hat, axis=(2, 3, 4), dtype=acc_type, keepdims=True).astype(dtype)
+        data_grad = data_grad - x_hat * red_out
+        return data_grad.reshape(dshape), gamma_grad, beta_grad
+
+
+    batch_size = random.randint(1, 8)
+    num_groups = random.randint(2, 3)
+    num_channels = random.randint(2, 3) * num_groups
+    height = random.randint(1, 5)
+    width = random.randint(1, 5)
+    dshape = (batch_size, num_channels, height, width)
+    param_shape = (num_groups,)
+    temp_shape = (batch_size, num_groups, int(num_channels / num_groups), height, width)
+    np_data = np.random.uniform(0.2, 1.0, dshape)
+    np_gamma = np.random.uniform(-1.0, 1.0, param_shape)
+    np_beta = np.random.uniform(-1.0, 1.0, param_shape)
+    data_sym = mx.sym.Variable("data")
+    gamma_sym = mx.sym.Variable("gamma")
+    beta_sym = mx.sym.Variable("beta")
+    for dtype in [np.float16, np.float32, np.float64]:
+        eps = 1e-2 if dtype == np.float16 else 1e-5
+        mx_data = mx.nd.array(np_data, dtype=dtype)
+        mx_gamma = mx.nd.array(np_gamma, dtype=dtype)
+        mx_beta = mx.nd.array(np_beta, dtype=dtype)
+        np_out, np_mean, np_std = np_groupnorm(np_data.astype(dtype),
+                                               np_gamma.astype(dtype),
+                                               np_beta.astype(dtype),
+                                               num_groups=num_groups,
+                                               eps=eps)
+        mx_sym = mx.sym.GroupNorm(data=data_sym, gamma=gamma_sym, beta=beta_sym,
+                                  num_groups=num_groups, eps=eps, output_mean_var=True)
+        check_symbolic_forward(mx_sym, [mx_data, mx_gamma, mx_beta], [np_out, np_mean, np_std],
+                               rtol=1e-2 if dtype == np.float16 else 1e-3,
+                               atol=5e-3 if dtype == np.float16 else 1e-5, dtype=dtype)
+        mx_sym = mx.sym.GroupNorm(data=data_sym, gamma=gamma_sym, beta=beta_sym,
+                                  num_groups=num_groups, eps=eps, output_mean_var=False)
+        np_ograd = np.random.uniform(-1.0, 1.0, dshape).astype(dtype)
+        np_data_grad, np_gamma_grad, np_beta_grad = np_groupnorm_grad(np_ograd,
+                                                                      np_data.astype(dtype),
+                                                                      np_gamma.astype(dtype),
+                                                                      np_beta.astype(dtype),
+                                                                      np_mean, np_std,
+                                                                      num_groups, eps)
+        check_symbolic_backward(mx_sym, [mx_data, mx_gamma, mx_beta], [mx.nd.array(np_ograd)],
+                                [np_data_grad, np_gamma_grad, np_beta_grad],
+                                rtol=1e-2 if dtype == np.float16 else 1e-3,
+                                atol=5e-2 if dtype == np.float16 else 1e-5, dtype=dtype)
+
+
 @with_seed()
 def test_convolution_grouping():
     for dim in [1, 2, 3]:

From 6bdcef25b18f572ad528c54cc993a434c9932610 Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Fri, 19 Jul 2019 10:03:11 -0700
Subject: [PATCH 087/813] Deprecate USE_PROFILER flag (#15595)

* deprecate USE_PROFILER

* Trigger notification
---
 CMakeLists.txt                         | 1 -
 benchmark/opperf/README.md             | 2 --
 docs/faq/env_var.md                    | 2 +-
 docs/faq/perf.md                       | 4 +---
 docs/install/osx_setup.md              | 1 -
 docs/tutorials/mkldnn/MKLDNN_README.md | 2 +-
 example/profiler/README.md             | 3 +--
 make/maven/maven_darwin_mkl.mk         | 2 --
 make/maven/maven_linux_cu90mkl.mk      | 3 ---
 make/maven/maven_linux_cu92mkl.mk      | 3 ---
 make/maven/maven_linux_mkl.mk          | 3 ---
 make/pip/pip_darwin_cpu.mk             | 3 ---
 make/pip/pip_darwin_mkl.mk             | 3 ---
 make/pip/pip_linux_cpu.mk              | 3 ---
 make/pip/pip_linux_cu100.mk            | 3 ---
 make/pip/pip_linux_cu100mkl.mk         | 3 ---
 make/pip/pip_linux_cu101.mk            | 3 ---
 make/pip/pip_linux_cu101mkl.mk         | 3 ---
 make/pip/pip_linux_cu75.mk             | 3 ---
 make/pip/pip_linux_cu75mkl.mk          | 3 ---
 make/pip/pip_linux_cu80.mk             | 3 ---
 make/pip/pip_linux_cu80mkl.mk          | 3 ---
 make/pip/pip_linux_cu90.mk             | 3 ---
 make/pip/pip_linux_cu90mkl.mk          | 3 ---
 make/pip/pip_linux_cu91.mk             | 3 ---
 make/pip/pip_linux_cu91mkl.mk          | 3 ---
 make/pip/pip_linux_cu92.mk             | 3 ---
 make/pip/pip_linux_cu92mkl.mk          | 3 ---
 make/pip/pip_linux_mkl.mk              | 3 ---
 tests/jenkins/run_test.sh              | 1 -
 tests/jenkins/run_test_ubuntu.sh       | 1 -
 31 files changed, 4 insertions(+), 78 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 0148ac302d54..19a93c731e00 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -34,7 +34,6 @@ mxnet_option(USE_MKLDNN           "Use MKLDNN variant of MKL (if MKL found)" ON
 mxnet_option(USE_OPERATOR_TUNING  "Enable auto-tuning of operators" ON IF NOT MSVC)
 mxnet_option(USE_GPERFTOOLS       "Build with GPerfTools support" OFF)
 mxnet_option(USE_JEMALLOC         "Build with Jemalloc support"   ON)
-mxnet_option(USE_PROFILER         "Build with Profiler support"   ON)
 mxnet_option(USE_DIST_KVSTORE     "Build with DIST_KVSTORE support" OFF)
 mxnet_option(USE_PLUGINS_WARPCTC  "Use WARPCTC Plugins" OFF)
 mxnet_option(USE_PLUGIN_CAFFE     "Use Caffe Plugin" OFF)
diff --git a/benchmark/opperf/README.md b/benchmark/opperf/README.md
index 132eb84bf650..e6a367d38f28 100644
--- a/benchmark/opperf/README.md
+++ b/benchmark/opperf/README.md
@@ -47,8 +47,6 @@ Hence, in this utility, we will build the functionality to allow users and devel
 
 ## Prerequisites
 
-This utility uses MXNet profiler under the hood to fetch compute and memory metrics. Hence, you need to build MXNet with `USE_PROFILER=1` flag.
-
 Make sure to build the flavor of MXNet, for example - with/without MKL, with CUDA 9 or 10.1 etc., on which you would like to measure operator performance. Finally, you need to add path to your cloned MXNet repository to the PYTHONPATH.
 
 ```
diff --git a/docs/faq/env_var.md b/docs/faq/env_var.md
index f6d48af70089..05f70b01a69b 100644
--- a/docs/faq/env_var.md
+++ b/docs/faq/env_var.md
@@ -188,7 +188,7 @@ $env:MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
 
 ## Control the profiler
 
-When USE_PROFILER is enabled in Makefile or CMake, the following environments can be used to profile the application without changing code. Execution options may affect the granularity of profiling result. If you need profiling result of every operator, please set `MXNET_EXEC_BULK_EXEC_INFERENCE`, `MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN` and `MXNET_EXEC_BULK_EXEC_TRAIN` to 0.
+The following environments can be used to profile the application without changing code. Execution options may affect the granularity of profiling result. If you need profiling result of every operator, please set `MXNET_EXEC_BULK_EXEC_INFERENCE`, `MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN` and `MXNET_EXEC_BULK_EXEC_TRAIN` to 0.
 
 * MXNET_PROFILER_AUTOSTART
   - Values: 0(false) or 1(true) ```(default=0)```
diff --git a/docs/faq/perf.md b/docs/faq/perf.md
index 62b40247081c..03a8dbbb20a1 100644
--- a/docs/faq/perf.md
+++ b/docs/faq/perf.md
@@ -276,9 +276,7 @@ that gives detailed information about execution time at the symbol level.
 This feature complements general profiling tools like _nvprof_ and _gprof_
 by summarizing at the operator level, instead of a function, kernel, or instruction level.
 
-In order to be able to use the profiler, you must compile _MXNet_ with the `USE_PROFILER=1` flag in `config.mk`.
-
-The profiler can then be turned on with an [environment variable](http://mxnet.io/faq/env_var.html#control-the-profiler)
+The profiler can be turned on with an [environment variable](http://mxnet.io/faq/env_var.html#control-the-profiler)
 for an entire program run, or programmatically for just part of a run.
 See [example/profiler](https://github.com/dmlc/mxnet/tree/master/example/profiler)
 for complete examples of how to use the profiler in code, but briefly, the Python code looks like:
diff --git a/docs/install/osx_setup.md b/docs/install/osx_setup.md
index 6d38c466001b..525b61bc2488 100644
--- a/docs/install/osx_setup.md
+++ b/docs/install/osx_setup.md
@@ -119,7 +119,6 @@ echo "USE_OPENCV=1" >> ./config.mk
 echo "USE_OPENMP=1" >> ./config.mk
 echo "USE_MKLDNN=1" >> ./config.mk
 echo "USE_BLAS=apple" >> ./config.mk
-echo "USE_PROFILER=1" >> ./config.mk
 LIBRARY_PATH=$(brew --prefix llvm)/lib/ make -j $(sysctl -n hw.ncpu)
 ```
 
diff --git a/docs/tutorials/mkldnn/MKLDNN_README.md b/docs/tutorials/mkldnn/MKLDNN_README.md
index c9e940fdeb3e..0b8c6b04517b 100644
--- a/docs/tutorials/mkldnn/MKLDNN_README.md
+++ b/docs/tutorials/mkldnn/MKLDNN_README.md
@@ -97,7 +97,7 @@ cd incubator-mxnet
 ### Build MXNet with MKL-DNN
 
 ```
-LIBRARY_PATH=$(brew --prefix llvm)/lib/ make -j $(sysctl -n hw.ncpu) CC=$(brew --prefix llvm)/bin/clang CXX=$(brew --prefix llvm)/bin/clang++ USE_OPENCV=1 USE_OPENMP=1 USE_MKLDNN=1 USE_BLAS=apple USE_PROFILER=1
+LIBRARY_PATH=$(brew --prefix llvm)/lib/ make -j $(sysctl -n hw.ncpu) CC=$(brew --prefix llvm)/bin/clang CXX=$(brew --prefix llvm)/bin/clang++ USE_OPENCV=1 USE_OPENMP=1 USE_MKLDNN=1 USE_BLAS=apple
 ```
 
 <h2 id="3">Windows</h2>
diff --git a/example/profiler/README.md b/example/profiler/README.md
index 255ad8f65579..933e04db67d3 100644
--- a/example/profiler/README.md
+++ b/example/profiler/README.md
@@ -19,8 +19,7 @@
 
 This folder contains examples of using MXNet profiler to generate profiling results in json files.
 Please refer to [this link](http://mxnet.incubator.apache.org/faq/perf.html?highlight=profiler#profiler)
-for visualizing profiling results and make sure that you have installed a version of MXNet compiled
-with `USE_PROFILER=1`.
+for visualizing profiling results.
 
 - profiler_executor.py. To run this example,
     - clone mxnet-memonger (git clone https://github.com/dmlc/mxnet-memonger.git).
diff --git a/make/maven/maven_darwin_mkl.mk b/make/maven/maven_darwin_mkl.mk
index f68f1565f990..a7f2bdb027d4 100644
--- a/make/maven/maven_darwin_mkl.mk
+++ b/make/maven/maven_darwin_mkl.mk
@@ -33,8 +33,6 @@ DEV = 0
 # whether compile with debug
 DEBUG = 0
 
-# whether compiler with profiler
-USE_PROFILER = 1
 
 # whether to turn on signal handler (e.g. segfault logger)
 USE_SIGNAL_HANDLER = 1
diff --git a/make/maven/maven_linux_cu90mkl.mk b/make/maven/maven_linux_cu90mkl.mk
index 3d3b2c48c58b..8da56d803973 100644
--- a/make/maven/maven_linux_cu90mkl.mk
+++ b/make/maven/maven_linux_cu90mkl.mk
@@ -33,9 +33,6 @@ DEV = 0
 # whether compile with debug
 DEBUG = 0
 
-# whether compiler with profiler
-USE_PROFILER = 1
-
 # whether to turn on signal handler (e.g. segfault logger)
 USE_SIGNAL_HANDLER = 1
 
diff --git a/make/maven/maven_linux_cu92mkl.mk b/make/maven/maven_linux_cu92mkl.mk
index ab93801b0b56..dd3e86b8a64f 100644
--- a/make/maven/maven_linux_cu92mkl.mk
+++ b/make/maven/maven_linux_cu92mkl.mk
@@ -33,9 +33,6 @@ DEV = 0
 # whether compile with debug
 DEBUG = 0
 
-# whether compiler with profiler
-USE_PROFILER = 1
-
 # whether to turn on signal handler (e.g. segfault logger)
 USE_SIGNAL_HANDLER = 1
 
diff --git a/make/maven/maven_linux_mkl.mk b/make/maven/maven_linux_mkl.mk
index dfe5065b9a9c..3c8534a7e2aa 100644
--- a/make/maven/maven_linux_mkl.mk
+++ b/make/maven/maven_linux_mkl.mk
@@ -33,9 +33,6 @@ DEV = 0
 # whether compile with debug
 DEBUG = 0
 
-# whether compiler with profiler
-USE_PROFILER = 1
-
 # whether to turn on signal handler (e.g. segfault logger)
 USE_SIGNAL_HANDLER = 1
 
diff --git a/make/pip/pip_darwin_cpu.mk b/make/pip/pip_darwin_cpu.mk
index 2c80c42387ad..2497aadc9d3d 100644
--- a/make/pip/pip_darwin_cpu.mk
+++ b/make/pip/pip_darwin_cpu.mk
@@ -33,9 +33,6 @@ DEV = 0
 # whether compile with debug
 DEBUG = 0
 
-# whether compiler with profiler
-USE_PROFILER = 1
-
 # whether to turn on signal handler (e.g. segfault logger)
 USE_SIGNAL_HANDLER = 1
 
diff --git a/make/pip/pip_darwin_mkl.mk b/make/pip/pip_darwin_mkl.mk
index 2fac512140d0..1859936f180e 100644
--- a/make/pip/pip_darwin_mkl.mk
+++ b/make/pip/pip_darwin_mkl.mk
@@ -33,9 +33,6 @@ DEV = 0
 # whether compile with debug
 DEBUG = 0
 
-# whether compiler with profiler
-USE_PROFILER = 1
-
 # whether to turn on signal handler (e.g. segfault logger)
 USE_SIGNAL_HANDLER = 1
 
diff --git a/make/pip/pip_linux_cpu.mk b/make/pip/pip_linux_cpu.mk
index 9ed88bb5bd1b..4d3b95f4dbd8 100644
--- a/make/pip/pip_linux_cpu.mk
+++ b/make/pip/pip_linux_cpu.mk
@@ -33,9 +33,6 @@ DEV = 0
 # whether compile with debug
 DEBUG = 0
 
-# whether compiler with profiler
-USE_PROFILER = 1
-
 # whether to turn on signal handler (e.g. segfault logger)
 USE_SIGNAL_HANDLER = 1
 
diff --git a/make/pip/pip_linux_cu100.mk b/make/pip/pip_linux_cu100.mk
index f3bea65a7260..a972c538e512 100644
--- a/make/pip/pip_linux_cu100.mk
+++ b/make/pip/pip_linux_cu100.mk
@@ -33,9 +33,6 @@ DEV = 0
 # whether compile with debug
 DEBUG = 0
 
-# whether compiler with profiler
-USE_PROFILER = 1
-
 # whether to turn on signal handler (e.g. segfault logger)
 USE_SIGNAL_HANDLER = 1
 
diff --git a/make/pip/pip_linux_cu100mkl.mk b/make/pip/pip_linux_cu100mkl.mk
index 4dfcb213feb1..1f5d99642f91 100644
--- a/make/pip/pip_linux_cu100mkl.mk
+++ b/make/pip/pip_linux_cu100mkl.mk
@@ -33,9 +33,6 @@ DEV = 0
 # whether compile with debug
 DEBUG = 0
 
-# whether compiler with profiler
-USE_PROFILER = 1
-
 # whether to turn on signal handler (e.g. segfault logger)
 USE_SIGNAL_HANDLER = 1
 
diff --git a/make/pip/pip_linux_cu101.mk b/make/pip/pip_linux_cu101.mk
index 6aef44237f29..b180d048557f 100644
--- a/make/pip/pip_linux_cu101.mk
+++ b/make/pip/pip_linux_cu101.mk
@@ -33,9 +33,6 @@ DEV = 0
 # whether compile with debug
 DEBUG = 0
 
-# whether compiler with profiler
-USE_PROFILER = 1
-
 # whether to turn on signal handler (e.g. segfault logger)
 USE_SIGNAL_HANDLER = 1
 
diff --git a/make/pip/pip_linux_cu101mkl.mk b/make/pip/pip_linux_cu101mkl.mk
index 1bb939d0a04c..d893fd611bad 100644
--- a/make/pip/pip_linux_cu101mkl.mk
+++ b/make/pip/pip_linux_cu101mkl.mk
@@ -33,9 +33,6 @@ DEV = 0
 # whether compile with debug
 DEBUG = 0
 
-# whether compiler with profiler
-USE_PROFILER = 1
-
 # whether to turn on signal handler (e.g. segfault logger)
 USE_SIGNAL_HANDLER = 1
 
diff --git a/make/pip/pip_linux_cu75.mk b/make/pip/pip_linux_cu75.mk
index c686c20db456..3070a907825a 100644
--- a/make/pip/pip_linux_cu75.mk
+++ b/make/pip/pip_linux_cu75.mk
@@ -33,9 +33,6 @@ DEV = 0
 # whether compile with debug
 DEBUG = 0
 
-# whether compiler with profiler
-USE_PROFILER = 1
-
 # whether to turn on signal handler (e.g. segfault logger)
 USE_SIGNAL_HANDLER = 1
 
diff --git a/make/pip/pip_linux_cu75mkl.mk b/make/pip/pip_linux_cu75mkl.mk
index ec87676c6c96..e263794600df 100644
--- a/make/pip/pip_linux_cu75mkl.mk
+++ b/make/pip/pip_linux_cu75mkl.mk
@@ -33,9 +33,6 @@ DEV = 0
 # whether compile with debug
 DEBUG = 0
 
-# whether compiler with profiler
-USE_PROFILER = 1
-
 # whether to turn on signal handler (e.g. segfault logger)
 USE_SIGNAL_HANDLER = 1
 
diff --git a/make/pip/pip_linux_cu80.mk b/make/pip/pip_linux_cu80.mk
index 72a04deeb400..969229e22608 100644
--- a/make/pip/pip_linux_cu80.mk
+++ b/make/pip/pip_linux_cu80.mk
@@ -33,9 +33,6 @@ DEV = 0
 # whether compile with debug
 DEBUG = 0
 
-# whether compiler with profiler
-USE_PROFILER = 1
-
 # whether to turn on signal handler (e.g. segfault logger)
 USE_SIGNAL_HANDLER = 1
 
diff --git a/make/pip/pip_linux_cu80mkl.mk b/make/pip/pip_linux_cu80mkl.mk
index 1bd295138936..a42220d3d467 100644
--- a/make/pip/pip_linux_cu80mkl.mk
+++ b/make/pip/pip_linux_cu80mkl.mk
@@ -33,9 +33,6 @@ DEV = 0
 # whether compile with debug
 DEBUG = 0
 
-# whether compiler with profiler
-USE_PROFILER = 1
-
 # whether to turn on signal handler (e.g. segfault logger)
 USE_SIGNAL_HANDLER = 1
 
diff --git a/make/pip/pip_linux_cu90.mk b/make/pip/pip_linux_cu90.mk
index b831d6586795..1bca27b2b80c 100644
--- a/make/pip/pip_linux_cu90.mk
+++ b/make/pip/pip_linux_cu90.mk
@@ -33,9 +33,6 @@ DEV = 0
 # whether compile with debug
 DEBUG = 0
 
-# whether compiler with profiler
-USE_PROFILER = 1
-
 # whether to turn on signal handler (e.g. segfault logger)
 USE_SIGNAL_HANDLER = 1
 
diff --git a/make/pip/pip_linux_cu90mkl.mk b/make/pip/pip_linux_cu90mkl.mk
index 8ab13075b3d0..8a8c273f3a09 100644
--- a/make/pip/pip_linux_cu90mkl.mk
+++ b/make/pip/pip_linux_cu90mkl.mk
@@ -33,9 +33,6 @@ DEV = 0
 # whether compile with debug
 DEBUG = 0
 
-# whether compiler with profiler
-USE_PROFILER = 1
-
 # whether to turn on signal handler (e.g. segfault logger)
 USE_SIGNAL_HANDLER = 1
 
diff --git a/make/pip/pip_linux_cu91.mk b/make/pip/pip_linux_cu91.mk
index cb66394683a9..fb6be493cf15 100644
--- a/make/pip/pip_linux_cu91.mk
+++ b/make/pip/pip_linux_cu91.mk
@@ -33,9 +33,6 @@ DEV = 0
 # whether compile with debug
 DEBUG = 0
 
-# whether compiler with profiler
-USE_PROFILER = 1
-
 # whether to turn on signal handler (e.g. segfault logger)
 USE_SIGNAL_HANDLER = 1
 
diff --git a/make/pip/pip_linux_cu91mkl.mk b/make/pip/pip_linux_cu91mkl.mk
index a94874503c92..c29fcef0ce0b 100644
--- a/make/pip/pip_linux_cu91mkl.mk
+++ b/make/pip/pip_linux_cu91mkl.mk
@@ -33,9 +33,6 @@ DEV = 0
 # whether compile with debug
 DEBUG = 0
 
-# whether compiler with profiler
-USE_PROFILER = 1
-
 # whether to turn on signal handler (e.g. segfault logger)
 USE_SIGNAL_HANDLER = 1
 
diff --git a/make/pip/pip_linux_cu92.mk b/make/pip/pip_linux_cu92.mk
index 8796a3bbdaa7..616ee14ccf9c 100644
--- a/make/pip/pip_linux_cu92.mk
+++ b/make/pip/pip_linux_cu92.mk
@@ -33,9 +33,6 @@ DEV = 0
 # whether compile with debug
 DEBUG = 0
 
-# whether compiler with profiler
-USE_PROFILER = 1
-
 # whether to turn on signal handler (e.g. segfault logger)
 USE_SIGNAL_HANDLER = 1
 
diff --git a/make/pip/pip_linux_cu92mkl.mk b/make/pip/pip_linux_cu92mkl.mk
index b9766ec36dc8..b36f97d67fe7 100644
--- a/make/pip/pip_linux_cu92mkl.mk
+++ b/make/pip/pip_linux_cu92mkl.mk
@@ -33,9 +33,6 @@ DEV = 0
 # whether compile with debug
 DEBUG = 0
 
-# whether compiler with profiler
-USE_PROFILER = 1
-
 # whether to turn on signal handler (e.g. segfault logger)
 USE_SIGNAL_HANDLER = 1
 
diff --git a/make/pip/pip_linux_mkl.mk b/make/pip/pip_linux_mkl.mk
index 95146ee225ba..1cf389ae4a57 100644
--- a/make/pip/pip_linux_mkl.mk
+++ b/make/pip/pip_linux_mkl.mk
@@ -33,9 +33,6 @@ DEV = 0
 # whether compile with debug
 DEBUG = 0
 
-# whether compiler with profiler
-USE_PROFILER = 1
-
 # whether to turn on signal handler (e.g. segfault logger)
 USE_SIGNAL_HANDLER = 1
 
diff --git a/tests/jenkins/run_test.sh b/tests/jenkins/run_test.sh
index 5ef8c1ac01ef..5ded74291f25 100755
--- a/tests/jenkins/run_test.sh
+++ b/tests/jenkins/run_test.sh
@@ -25,7 +25,6 @@ cp make/config.mk .
 echo "USE_CUDA=1" >> config.mk
 echo "USE_CUDA_PATH=/usr/local/cuda" >> config.mk
 echo "USE_CUDNN=1" >> config.mk
-echo "USE_PROFILER=1" >> config.mk
 echo "DEV=1" >> config.mk
 echo "EXTRA_OPERATORS=example/ssd/operator" >> config.mk
 echo "USE_CPP_PACKAGE=1" >> config.mk
diff --git a/tests/jenkins/run_test_ubuntu.sh b/tests/jenkins/run_test_ubuntu.sh
index 28e00331b47e..0459d2cc8ec5 100755
--- a/tests/jenkins/run_test_ubuntu.sh
+++ b/tests/jenkins/run_test_ubuntu.sh
@@ -38,7 +38,6 @@ cp make/config.mk .
 echo "USE_CUDA=1" >> config.mk
 echo "USE_CUDA_PATH=/usr/local/cuda" >> config.mk
 echo "USE_CUDNN=1" >> config.mk
-echo "USE_PROFILER=1" >> config.mk
 echo "DEV=1" >> config.mk
 echo "EXTRA_OPERATORS=example/ssd/operator" >> config.mk
 echo "USE_CPP_PACKAGE=1" >> config.mk

From 076b2f330c60f05cb939beea28dd04cd571a34c0 Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Fri, 19 Jul 2019 10:43:10 -0700
Subject: [PATCH 088/813] Softmax with length (#15169)

* softmax with length forward

* softmax with length backward

* new macro to reduce compile-time heap usage and limit length to integers only

* address comments
---
 src/operator/mxnet_op.h                |  51 +++
 src/operator/nn/softmax-inl.h          | 428 +++++++++++++++++++++----
 src/operator/nn/softmax.cc             |  33 +-
 tests/python/unittest/test_operator.py |  39 ++-
 4 files changed, 487 insertions(+), 64 deletions(-)

diff --git a/src/operator/mxnet_op.h b/src/operator/mxnet_op.h
index f17b708a7687..52788f697f11 100644
--- a/src/operator/mxnet_op.h
+++ b/src/operator/mxnet_op.h
@@ -363,6 +363,57 @@ inline int get_num_threads<cpu>(const int N) {
     LOG(FATAL) << "Unknown type enum " << type;            \
   }
 
+#define MXNET_INT_TYPE_SWITCH(type, DType, ...)\
+  switch (type) {                                          \
+  case mshadow::kFloat32:                                  \
+    {                                                      \
+      typedef float DType;                                 \
+      LOG(FATAL) << "This operation only support "         \
+                    "integer types, not float32";          \
+    }                                                      \
+    break;                                                 \
+  case mshadow::kFloat64:                                  \
+    {                                                      \
+      typedef double DType;                                \
+      LOG(FATAL) << "This operation only support "         \
+                    "integer types, not float64";          \
+    }                                                      \
+    break;                                                 \
+  case mshadow::kFloat16:                                  \
+    {                                                      \
+      typedef mshadow::half::half_t DType;                 \
+      LOG(FATAL) << "This operation only support "         \
+                    "integer types, not float16";          \
+    }                                                      \
+    break;                                                 \
+  case mshadow::kUint8:                                    \
+    {                                                      \
+      typedef uint8_t DType;                               \
+      {__VA_ARGS__}                                        \
+    }                                                      \
+    break;                                                 \
+  case mshadow::kInt8:                                     \
+    {                                                      \
+      typedef int8_t DType;                                \
+      {__VA_ARGS__}                                        \
+    }                                                      \
+    break;                                                 \
+  case mshadow::kInt32:                                    \
+    {                                                      \
+      typedef int32_t DType;                               \
+      {__VA_ARGS__}                                        \
+    }                                                      \
+    break;                                                 \
+  case mshadow::kInt64:                                    \
+    {                                                      \
+      typedef int64_t DType;                               \
+      {__VA_ARGS__}                                        \
+    }                                                      \
+    break;                                                 \
+  default:                                                 \
+    LOG(FATAL) << "Unknown type enum " << type;            \
+  }
+
 /*!
  * \brief assign the val to out according
  * to request in Kernel::Launch
diff --git a/src/operator/nn/softmax-inl.h b/src/operator/nn/softmax-inl.h
index d6113b05dbb9..2c82d839e5ed 100644
--- a/src/operator/nn/softmax-inl.h
+++ b/src/operator/nn/softmax-inl.h
@@ -75,7 +75,7 @@ inline void Softmax(Stream<cpu> *s, DType *in, OType *out,
   index_t sa = stride[axis];
 
   #pragma omp parallel for
-  for (int i = 0; i < static_cast<int>(N); ++i) {
+  for (index_t i = 0; i < N; ++i) {
     index_t base = unravel_dot(i, sshape, stride);
 
     DType mmax = negate ? -in[base] : in[base];
@@ -113,6 +113,60 @@ inline void Softmax(Stream<cpu> *s, DType *in, OType *out,
   }
 }
 
+template<typename OP, bool negate, typename AType, typename DType, typename OType,
+         typename IType, int ndim>
+inline void SoftmaxWithLength(Stream<cpu> *s, DType *in, OType *out, IType *length,
+                              Shape<ndim> shape, int axis, const DType temperature) {
+  index_t M = shape[axis];
+  index_t N = shape.Size()/M;
+  Shape<ndim> stride = calc_stride(shape);
+  Shape<ndim> sshape = shape;
+  sshape[axis] = 1;
+  index_t sa = stride[axis];
+
+  #pragma omp parallel for
+  for (index_t i = 0; i < N; ++i) {
+    index_t len = static_cast<index_t>(length[i]);
+    index_t base = unravel_dot(i, sshape, stride);
+
+    DType mmax = negate ? -in[base] : in[base];
+    DType val;
+    for (index_t j = 1; j < len; ++j) {
+      val = negate ? -in[base + j*sa] : in[base + j*sa];
+      if (mmax < val) mmax = val;
+    }
+    for (index_t j = len; j < M; ++j) {
+      out[base + j*sa] = OType(0.0f);
+    }
+
+    AType sum = AType(0);
+    DType in_val;
+    // By default temperature is 1.0.
+    // Adding a branch here to save the CPU 'divide-by-1' computation at runtime
+    if (temperature == 1.0) {
+      for (index_t j = 0; j < len; ++j) {
+        in_val = negate ? -in[base + j*sa] : in[base + j*sa];
+        sum += std::exp(in_val - mmax);
+      }
+
+      for (index_t j = 0; j < len; ++j) {
+        in_val = negate ? -in[base + j*sa] : in[base + j*sa];
+        out[base + j*sa] = OP::Map(in_val - mmax, sum);
+      }
+    } else {
+      for (index_t j = 0; j < len; ++j) {
+        in_val = negate ? -in[base + j*sa] : in[base + j*sa];
+        sum += std::exp((in_val - mmax)/temperature);
+      }
+
+      for (index_t j = 0; j < len; ++j) {
+        in_val = negate ? -in[base + j*sa] : in[base + j*sa];
+        out[base + j*sa] = OP::Map((in_val - mmax)/temperature, sum);
+      }
+    }
+  }
+}
+
 
 struct softmax_bwd {
   template<typename DType, typename AType>
@@ -136,7 +190,7 @@ struct log_softmax_bwd {
 
 
 template<typename OP1, typename OP2, int Req, bool negate,
-  typename AType, typename DType, typename OType, int ndim>
+         typename AType, typename DType, typename OType, int ndim>
 inline void SoftmaxGrad(Stream<cpu> *s, OType *out, OType *ograd,
                         DType *igrad, Shape<ndim> shape, int axis,
                         const DType temperature) {
@@ -148,7 +202,7 @@ inline void SoftmaxGrad(Stream<cpu> *s, OType *out, OType *ograd,
   index_t sa = stride[axis];
 
   #pragma omp parallel for
-  for (int i = 0; i < static_cast<int>(N); ++i) {
+  for (index_t i = 0; i < N; ++i) {
     index_t base = unravel_dot(i, sshape, stride);
 
     AType sum = AType(0);
@@ -177,10 +231,55 @@ inline void SoftmaxGrad(Stream<cpu> *s, OType *out, OType *ograd,
   }
 }
 
+template<typename OP1, typename OP2, int Req, bool negate,
+         typename AType, typename DType, typename OType, typename IType, int ndim>
+inline void SoftmaxWithLengthGrad(Stream<cpu> *s, OType *out, OType *ograd,
+                                  DType *igrad, IType *length, Shape<ndim> shape,
+                                  int axis, const DType temperature) {
+  index_t M = shape[axis];
+  index_t N = shape.Size()/M;
+  Shape<ndim> stride = calc_stride(shape);
+  Shape<ndim> sshape = shape;
+  sshape[axis] = 1;
+  index_t sa = stride[axis];
+
+  #pragma omp parallel for
+  for (index_t i = 0; i < N; ++i) {
+    index_t base = unravel_dot(i, sshape, stride);
+    index_t len = static_cast<index_t>(length[i]);
+
+    AType sum = AType(0);
+    for (index_t j = 0; j < len; ++j) {
+      sum += OP1::Map(ograd[base + j*sa], out[base + j*sa]);
+    }
+
+    // By default temperature is 1.0.
+    // Adding a branch here to save the CPU 'divide-by-1' computation at runtime
+    DType final_result;
+    if (temperature == 1.0) {
+      for (index_t j = 0; j < M; ++j) {
+        final_result = negate ?
+                       -OP2::Map(ograd[base + j*sa], out[base + j*sa], sum) :
+                       OP2::Map(ograd[base + j*sa], out[base + j*sa], sum);
+        final_result = (j < len) ? final_result : DType(0.0f);
+        KERNEL_ASSIGN(igrad[base + j*sa], Req, final_result);
+      }
+    } else {
+      for (index_t j = 0; j < M; ++j) {
+        final_result = negate ?
+                       -OP2::Map(ograd[base + j*sa], out[base + j*sa], sum) / temperature :
+                       OP2::Map(ograd[base + j*sa], out[base + j*sa], sum) / temperature;
+        final_result = (j < len) ? final_result : DType(0.0f);
+        KERNEL_ASSIGN(igrad[base + j*sa], Req, final_result);
+      }
+    }
+  }
+}
+
 
 #ifdef __CUDACC__
 template<int x_bits, typename OP, bool negate, typename AType, int ndim,
-  typename DType, typename OType>
+         typename DType, typename OType>
 __global__ void softmax_compute_kernel(DType *in, OType *out, index_t M, int axis,
                                        Shape<ndim> sshape, Shape<ndim> stride,
                                        const double temperature) {
@@ -235,9 +334,68 @@ inline void Softmax(Stream<gpu> *s, DType *in, OType *out,
   MSHADOW_CUDA_POST_KERNEL_CHECK(softmax_compute_kernel);
 }
 
+template<int x_bits, typename OP, bool negate, typename AType, int ndim,
+         typename DType, typename OType, typename IType>
+__global__ void softmax_with_length_kernel(DType *in, OType *out, IType *length,
+                                           index_t M, int axis, Shape<ndim> sshape,
+                                           Shape<ndim> stride, const double temperature) {
+  const unsigned x_size = 1 << x_bits;
+  __shared__ AType smem[x_size];
+  index_t sa = stride[axis];
+  index_t base = unravel_dot(blockIdx.x, sshape, stride);
+  index_t x = threadIdx.x;
+  index_t len = static_cast<index_t>(length[blockIdx.x]);
+
+  red::maximum::SetInitValue(smem[x]);
+  for (index_t i = x; i < len; i += x_size) {
+    smem[x] = ::max(smem[x], negate ? -in[base + i*sa] : in[base + i*sa]);
+  }
+  __syncthreads();
+  cuda::Reduce1D<red::maximum, x_bits>(smem);
+  __syncthreads();
+  DType smax = smem[0];
+  __syncthreads();
+
+  red::sum::SetInitValue(smem[x]);
+  DType val;
+  for (index_t i = x; i < len; i += x_size) {
+    val = negate ? -in[base + i*sa]:in[base + i*sa];
+    smem[x] += static_cast<AType>(expf((val - smax) / static_cast<AType>(temperature)));
+  }
+  __syncthreads();
+  cuda::Reduce1D<red::sum, x_bits>(smem);
+  __syncthreads();
+  AType ssum = smem[0];
+  __syncthreads();
+
+  for (index_t i = x; i < M; i += x_size) {
+    val = negate ? -in[base + i*sa] : in[base + i*sa];
+    out[base + i*sa] =
+      (i < len) ? OType(OP::Map((val - smax)/static_cast<DType>(temperature), ssum)) : OType(0.0f);
+  }
+}
+
+template<typename OP, bool negate, typename AType, typename DType, typename OType,
+         typename IType, int ndim>
+inline void SoftmaxWithLength(Stream<gpu> *s, DType *in, OType *out, IType *length,
+                    Shape<ndim> shape, int axis, const double temperature) {
+  const int x_bits = 7;
+  const int x_size = 1 << x_bits;
+  index_t M = shape[axis];
+  index_t N = shape.Size()/M;
+  Shape<ndim> stride = calc_stride(shape);
+  Shape<ndim> sshape = shape;
+  sshape[axis] = 1;
+
+  softmax_with_length_kernel<x_bits, OP, negate, AType, ndim>
+    <<<N, x_size, 0, mshadow::Stream<gpu>::GetStream(s)>>>(
+      in, out, length, M, axis, sshape, stride, temperature);
+  MSHADOW_CUDA_POST_KERNEL_CHECK(softmax_compute_kernel);
+}
+
 
 template<int x_bits, typename OP1, typename OP2, int Req, bool negate, typename AType, int ndim,
-  typename DType, typename OType>
+         typename DType, typename OType>
 __global__ void softmax_gradient_kernel(OType *out, OType *ograd, DType *igrad,
                                         index_t M, int axis, Shape<ndim> sshape,
                                         Shape<ndim> stride, const double temperature) {
@@ -269,7 +427,7 @@ __global__ void softmax_gradient_kernel(OType *out, OType *ograd, DType *igrad,
 
 
 template<typename OP1, typename OP2, int Req, bool negate, typename AType, int ndim,
-  typename DType, typename OType>
+         typename DType, typename OType>
 inline void SoftmaxGrad(Stream<gpu> *s, OType *out, OType *ograd,
                         DType *igrad, Shape<ndim> shape, int axis,
                         const double temperature) {
@@ -286,6 +444,60 @@ inline void SoftmaxGrad(Stream<gpu> *s, OType *out, OType *ograd,
       out, ograd, igrad, M, axis, sshape, stride, temperature);
   MSHADOW_CUDA_POST_KERNEL_CHECK(softmax_gradient_kernel);
 }
+
+template<int x_bits, typename OP1, typename OP2, int Req, bool negate, typename AType, int ndim,
+         typename DType, typename OType, typename IType>
+__global__ void softmax_with_length_grad_kernel(OType *out, OType *ograd, DType *igrad,
+                                                IType *length, index_t M, int axis,
+                                                Shape<ndim> sshape, Shape<ndim> stride,
+                                                const double temperature) {
+  const unsigned x_size = 1 << x_bits;
+  __shared__ AType smem[x_size];
+  index_t sa = stride[axis];
+  index_t base = unravel_dot(blockIdx.x, sshape, stride);
+  index_t x = threadIdx.x;
+  index_t len = static_cast<index_t>(length[blockIdx.x]);
+
+  red::sum::SetInitValue(smem[x]);
+  for (index_t i = x; i < len; i += x_size) {
+    smem[x] += OP1::Map(ograd[base + i*sa], out[base + i*sa]);
+  }
+  __syncthreads();
+  cuda::Reduce1D<red::sum, x_bits>(smem);
+  __syncthreads();
+  AType ssum = smem[0];
+  __syncthreads();
+
+  DType final_result;
+  for (index_t i = x; i < M; i += x_size) {
+    final_result =
+      negate ?
+      -OP2::Map(ograd[base + i*sa], out[base + i*sa], ssum) :
+      OP2::Map(ograd[base + i*sa], out[base + i*sa], ssum);
+    final_result = (i < len) ? final_result : DType(0.0f);
+    KERNEL_ASSIGN(igrad[base + i*sa], Req, final_result / static_cast<DType>(temperature));
+  }
+}
+
+
+template<typename OP1, typename OP2, int Req, bool negate, typename AType, int ndim,
+         typename DType, typename OType, typename IType>
+inline void SoftmaxWithLengthGrad(Stream<gpu> *s, OType *out, OType *ograd,
+                                  DType *igrad, IType *length, Shape<ndim> shape, int axis,
+                                  const double temperature) {
+  const int x_bits = 7;
+  const int x_size = 1 << x_bits;
+  index_t M = shape[axis];
+  index_t N = shape.Size()/M;
+  Shape<ndim> stride = calc_stride(shape);
+  Shape<ndim> sshape = shape;
+  sshape[axis] = 1;
+
+  softmax_with_length_grad_kernel<x_bits, OP1, OP2, Req, negate, AType, ndim>
+    <<<N, x_size, 0, mshadow::Stream<gpu>::GetStream(s)>>>(
+      out, ograd, igrad, length, M, axis, sshape, stride, temperature);
+  MSHADOW_CUDA_POST_KERNEL_CHECK(softmax_with_length_grad_kernel);
+}
 #endif
 
 }  // namespace mxnet_op
@@ -295,6 +507,7 @@ struct SoftmaxParam : public dmlc::Parameter<SoftmaxParam> {
   int axis;
   dmlc::optional<double> temperature;
   dmlc::optional<int> dtype;
+  dmlc::optional<bool> use_length;
   DMLC_DECLARE_PARAMETER(SoftmaxParam) {
     DMLC_DECLARE_FIELD(axis).set_default(-1)
     .describe("The axis along which to compute softmax.");
@@ -307,6 +520,9 @@ struct SoftmaxParam : public dmlc::Parameter<SoftmaxParam> {
     .set_default(dmlc::optional<int>())
     .describe("DType of the output in case this can't be inferred. "
               "Defaults to the same as input's dtype if not defined (dtype=None).");
+    DMLC_DECLARE_FIELD(use_length)
+    .set_default(dmlc::optional<bool>(false))
+    .describe("Whether to use the length input as a mask over the data input.");
   }
 };
 
@@ -315,27 +531,71 @@ static inline bool softmax_has_dtype_override(const nnvm::NodeAttrs& attrs) {
   return param.dtype.has_value() && param.dtype.value() != -1;
 }
 
+static inline bool softmax_use_length(const nnvm::NodeAttrs& attrs) {
+  const SoftmaxParam& param = nnvm::get<SoftmaxParam>(attrs.parsed);
+  return param.use_length.value();
+}
+
 static inline bool SoftmaxOpType(const nnvm::NodeAttrs& attrs,
                                  std::vector<int>* in_attrs,
                                  std::vector<int>* out_attrs) {
-  CHECK_EQ(in_attrs->size(), 1);
   CHECK_EQ(out_attrs->size(), 1);
   const SoftmaxParam& param = nnvm::get<SoftmaxParam>(attrs.parsed);
+  CHECK_EQ(in_attrs->size(), softmax_use_length(attrs) ? 2U : 1U);
 
   if (softmax_has_dtype_override(attrs)) {
     TYPE_ASSIGN_CHECK(*out_attrs, 0, param.dtype.value());
     type_assign(&(*in_attrs)[0], (*out_attrs)[0]);
     return true;
   } else {
-    return ElemwiseType<1, 1>(attrs, in_attrs, out_attrs);
+    std::vector<int> tmp = {in_attrs->at(0)};
+    return ElemwiseType<1, 1>(attrs, &tmp, out_attrs);
+  }
+}
+
+static inline bool SoftmaxOpShape(const nnvm::NodeAttrs& attrs,
+                                  mxnet::ShapeVector *in_attrs,
+                                  mxnet::ShapeVector *out_attrs) {
+  CHECK_EQ(out_attrs->size(), 1U);
+  const SoftmaxParam& param = nnvm::get<SoftmaxParam>(attrs.parsed);
+  CHECK_EQ(in_attrs->size(), param.use_length.value() ? 2U : 1U);
+
+  if (param.use_length.value()) {
+    mxnet::TShape& dshape = in_attrs->at(0);
+    mxnet::TShape tmp_shape((dshape.ndim() == 1) ? 1U : dshape.ndim() - 1, 1);
+    int j = 0;
+    for (int i = 0; i < dshape.ndim(); ++i) {
+      if (i != param.axis) {
+        tmp_shape[j++] = dshape[i];
+      }
+    }
+    SHAPE_ASSIGN_CHECK(*in_attrs, 1, tmp_shape);
   }
+  mxnet::ShapeVector tmp = {in_attrs->at(0)};
+  return ElemwiseShape<1, 1>(attrs, &tmp, out_attrs);
 }
 
 static inline bool SoftmaxGradOpShape(const nnvm::NodeAttrs& attrs,
                                       mxnet::ShapeVector *in_attrs,
                                       mxnet::ShapeVector *out_attrs) {
-  if (softmax_has_dtype_override(attrs)) {
-    return ElemwiseShape<3, 1>(attrs, in_attrs, out_attrs);
+  if (softmax_has_dtype_override(attrs) || softmax_use_length(attrs)) {
+    if (softmax_use_length(attrs)) {
+      mxnet::ShapeVector ins = {in_attrs->at(0), in_attrs->at(1), in_attrs->at(3)};
+      mxnet::ShapeVector dgrad = {out_attrs->at(0)};
+      bool res = ElemwiseShape<3, 1>(attrs, &ins, &dgrad);
+      SHAPE_ASSIGN_CHECK(*in_attrs, 0, ins[0]);
+      SHAPE_ASSIGN_CHECK(*in_attrs, 1, ins[1]);
+      SHAPE_ASSIGN_CHECK(*in_attrs, 3, ins[2]);
+      SHAPE_ASSIGN_CHECK(*out_attrs, 0, dgrad[0]);
+      mxnet::ShapeVector length = {in_attrs->at(2)};
+      mxnet::ShapeVector lgrad = {out_attrs->at(1)};
+      res = (res && ElemwiseShape<1, 1>(attrs, &length, &lgrad));
+      SHAPE_ASSIGN_CHECK(*in_attrs, 2, length[0]);
+      SHAPE_ASSIGN_CHECK(*out_attrs, 1, lgrad[0]);
+      return res;
+    } else {
+      return ElemwiseShape<3, 1>(attrs, in_attrs, out_attrs);
+    }
   } else {
     return ElemwiseShape<2, 1>(attrs, in_attrs, out_attrs);
   }
@@ -344,17 +604,21 @@ static inline bool SoftmaxGradOpShape(const nnvm::NodeAttrs& attrs,
 static inline bool SoftmaxGradOpType(const nnvm::NodeAttrs& attrs,
                                      std::vector<int>* in_attrs,
                                      std::vector<int>* out_attrs) {
-  CHECK_EQ(out_attrs->size(), 1);
-  if (softmax_has_dtype_override(attrs)) {
-    CHECK_EQ(in_attrs->size(), 3);
+  CHECK_EQ(out_attrs->size(), softmax_use_length(attrs) ? 2U : 1U);
+  if (softmax_has_dtype_override(attrs) || softmax_use_length(attrs)) {
+    CHECK_EQ(in_attrs->size(), softmax_use_length(attrs) ? 4U : 3U);
     int in_dtype = (*in_attrs)[1];
-    int out_dtype = (*in_attrs)[2];
+    int out_dtype = (*in_attrs)[softmax_use_length(attrs) ? 3 : 2];
     TYPE_ASSIGN_CHECK(*in_attrs, 0, out_dtype);
     TYPE_ASSIGN_CHECK(*out_attrs, 0, in_dtype);
+    if (softmax_use_length(attrs)) {
+      TYPE_ASSIGN_CHECK(*out_attrs, 1, in_attrs->at(2));
+    }
 
-    return (*out_attrs)[0] != -1 && (*in_attrs)[0] != -1;
+    return (*out_attrs)[0] != -1 && (*in_attrs)[0] != -1 &&
+           (*out_attrs)[1] != -1 && (*in_attrs)[1] != -1;
   } else {
-    CHECK_EQ(in_attrs->size(), 2);
+    CHECK_EQ(in_attrs->size(), 2U);
     int out_dtype = (*in_attrs)[1];
     TYPE_ASSIGN_CHECK(*out_attrs, 0, out_dtype);
     TYPE_ASSIGN_CHECK(*in_attrs, 0, out_dtype);
@@ -365,20 +629,31 @@ static inline bool SoftmaxGradOpType(const nnvm::NodeAttrs& attrs,
 
 static inline std::vector<std::pair<int, int> >
 SoftmaxGradOpInplaceOption(const nnvm::NodeAttrs& attrs) {
-  if (softmax_has_dtype_override(attrs)) {
-    return std::vector<std::pair<int, int> >{{0, 0}, {1, 0}, {2, 0}};
+  if (softmax_has_dtype_override(attrs) || softmax_use_length(attrs)) {
+    if (softmax_use_length(attrs)) {
+      return std::vector<std::pair<int, int> >{{0, 0}, {1, 0}, {2, 1}, {3, 0}};
+    } else {
+      return std::vector<std::pair<int, int> >{{0, 0}, {1, 0}, {2, 0}};
+    }
   } else {
     return std::vector<std::pair<int, int> >{{0, 0}, {1, 0}};
   }
 }
 
 static inline uint32_t SoftmaxGradOpNumInputs(const nnvm::NodeAttrs& attrs) {
-  return softmax_has_dtype_override(attrs) ? 3 : 2;
+  if (softmax_has_dtype_override(attrs) || softmax_use_length(attrs)) {
+    return softmax_use_length(attrs) ? 4 : 3;
+  }
+  return 2;
 }
 
 static inline std::vector<std::string> SoftmaxGradOpInputNames(const nnvm::NodeAttrs& attrs) {
-  if (softmax_has_dtype_override(attrs)) {
-    return std::vector<std::string>{"ograd", "data", "output"};
+  if (softmax_has_dtype_override(attrs) || softmax_use_length(attrs)) {
+    if (softmax_use_length(attrs)) {
+      return std::vector<std::string>{"ograd", "data", "length", "output"};
+    } else {
+      return std::vector<std::string>{"ograd", "data", "output"};
+    }
   } else {
     return std::vector<std::string>{"ograd", "output"};
   }
@@ -388,7 +663,7 @@ struct SoftmaxFGradient {
   const char *op_name;
   std::vector<nnvm::NodeEntry> operator()(const nnvm::NodePtr& n,
                                           const std::vector<nnvm::NodeEntry>& ograds) const {
-    if (softmax_has_dtype_override(n->attrs)) {
+    if (softmax_has_dtype_override(n->attrs) || softmax_use_length(n->attrs)) {
       return ElemwiseGradUseInOut {op_name}(n, ograds);
     } else {
       return ElemwiseGradUseOut {op_name}(n, ograds);
@@ -419,30 +694,46 @@ void SoftmaxCompute(const nnvm::NodeAttrs& attrs,
 
   MXNET_REAL_ACC_TYPE_SWITCH(inputs[0].type_flag_, DType, AType, {
     MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, OType, {
-      if (safe_acc) {
-        if (shape.ndim() == 2) {
-          Softmax<OP, negate, AType>(
-              ctx.get_stream<xpu>(), inputs[0].dptr<DType>(),
-              outputs[0].dptr<OType>(), shape.get<2>(), axis,
-              static_cast<DType>(temperature));
+      if (!param.use_length.value()) {
+        if (safe_acc) {
+          if (shape.ndim() == 2) {
+            Softmax<OP, negate, AType>(
+                ctx.get_stream<xpu>(), inputs[0].dptr<DType>(),
+                outputs[0].dptr<OType>(), shape.get<2>(), axis,
+                static_cast<DType>(temperature));
+          } else {
+            Softmax<OP, negate, AType>(
+                ctx.get_stream<xpu>(), inputs[0].dptr<DType>(),
+                outputs[0].dptr<OType>(), shape.get<3>(), axis,
+                static_cast<DType>(temperature));
+          }
         } else {
-          Softmax<OP, negate, AType>(
-              ctx.get_stream<xpu>(), inputs[0].dptr<DType>(),
-              outputs[0].dptr<OType>(), shape.get<3>(), axis,
-              static_cast<DType>(temperature));
+          if (shape.ndim() == 2) {
+            Softmax<OP, negate, DType>(
+                ctx.get_stream<xpu>(), inputs[0].dptr<DType>(),
+                outputs[0].dptr<OType>(), shape.get<2>(), axis,
+                static_cast<DType>(temperature));
+          } else {
+            Softmax<OP, negate, DType>(
+                ctx.get_stream<xpu>(), inputs[0].dptr<DType>(),
+                outputs[0].dptr<OType>(), shape.get<3>(), axis,
+                static_cast<DType>(temperature));
+          }
         }
       } else {
-        if (shape.ndim() == 2) {
-          Softmax<OP, negate, DType>(
+        MXNET_INT_TYPE_SWITCH(inputs[1].type_flag_, IType, {
+          if (shape.ndim() == 2) {
+            SoftmaxWithLength<OP, negate, AType>(
               ctx.get_stream<xpu>(), inputs[0].dptr<DType>(),
-              outputs[0].dptr<OType>(), shape.get<2>(), axis,
-              static_cast<DType>(temperature));
-        } else {
-          Softmax<OP, negate, DType>(
+              outputs[0].dptr<OType>(), inputs[1].dptr<IType>(),
+              shape.get<2>(), axis, static_cast<DType>(temperature));
+          } else {
+            SoftmaxWithLength<OP, negate, AType>(
               ctx.get_stream<xpu>(), inputs[0].dptr<DType>(),
-              outputs[0].dptr<OType>(), shape.get<3>(), axis,
-              static_cast<DType>(temperature));
-        }
+              outputs[0].dptr<OType>(), inputs[1].dptr<IType>(),
+              shape.get<3>(), axis, static_cast<DType>(temperature));
+          }
+        });
       }
     });
   });
@@ -464,35 +755,56 @@ void SoftmaxGradCompute(const nnvm::NodeAttrs& attrs,
   mxnet::TShape shape = AxisShapeCompact(inputs[0].shape_, &axis, true);
 
   int out_idx = softmax_has_dtype_override(attrs) ? 2 : 1;
+  out_idx = softmax_use_length(attrs) ? 3 : out_idx;
   bool safe_acc = dmlc::GetEnv("MXNET_SAFE_ACCUMULATION", false);
 
   MXNET_REAL_ACC_TYPE_SWITCH(inputs[0].type_flag_, OType, AType, {
     MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, DType, {
       MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
-        if (safe_acc) {
-          if (shape.ndim() == 2) {
-            SoftmaxGrad<OP1, OP2, Req, negate, AType>(
-                ctx.get_stream<xpu>(), inputs[out_idx].dptr<OType>(),
-                inputs[0].dptr<OType>(), outputs[0].dptr<DType>(),
-                shape.get<2>(), axis, static_cast<DType>(temperature));
+        if (!softmax_use_length(attrs)) {
+          if (safe_acc) {
+            if (shape.ndim() == 2) {
+              SoftmaxGrad<OP1, OP2, Req, negate, AType>(
+                  ctx.get_stream<xpu>(), inputs[out_idx].dptr<OType>(),
+                  inputs[0].dptr<OType>(), outputs[0].dptr<DType>(),
+                  shape.get<2>(), axis, static_cast<DType>(temperature));
+            } else {
+              SoftmaxGrad<OP1, OP2, Req, negate, AType>(
+                  ctx.get_stream<xpu>(), inputs[out_idx].dptr<OType>(),
+                  inputs[0].dptr<OType>(), outputs[0].dptr<DType>(),
+                  shape.get<3>(), axis, static_cast<DType>(temperature));
+            }
           } else {
-            SoftmaxGrad<OP1, OP2, Req, negate, AType>(
-                ctx.get_stream<xpu>(), inputs[out_idx].dptr<OType>(),
-                inputs[0].dptr<OType>(), outputs[0].dptr<DType>(),
-                shape.get<3>(), axis, static_cast<DType>(temperature));
+            if (shape.ndim() == 2) {
+              SoftmaxGrad<OP1, OP2, Req, negate, DType>(
+                  ctx.get_stream<xpu>(), inputs[out_idx].dptr<OType>(),
+                  inputs[0].dptr<OType>(), outputs[0].dptr<DType>(),
+                  shape.get<2>(), axis, static_cast<DType>(temperature));
+            } else {
+              SoftmaxGrad<OP1, OP2, Req, negate, DType>(
+                  ctx.get_stream<xpu>(), inputs[out_idx].dptr<OType>(),
+                  inputs[0].dptr<OType>(), outputs[0].dptr<DType>(),
+                  shape.get<3>(), axis, static_cast<DType>(temperature));
+            }
           }
         } else {
-          if (shape.ndim() == 2) {
-            SoftmaxGrad<OP1, OP2, Req, negate, DType>(
+          MXNET_INT_TYPE_SWITCH(inputs[2].type_flag_, IType, {
+            if (req[1] != kNullOp) {
+              mxnet_op::Kernel<mxnet_op::set_zero, xpu>::Launch(
+                ctx.get_stream<xpu>(), outputs[1].Size(), outputs[1].dptr<IType>());
+            }
+            if (shape.ndim() == 2) {
+              SoftmaxWithLengthGrad<OP1, OP2, Req, negate, AType>(
                 ctx.get_stream<xpu>(), inputs[out_idx].dptr<OType>(),
                 inputs[0].dptr<OType>(), outputs[0].dptr<DType>(),
-                shape.get<2>(), axis, static_cast<DType>(temperature));
-          } else {
-            SoftmaxGrad<OP1, OP2, Req, negate, DType>(
+                inputs[2].dptr<IType>(), shape.get<2>(), axis, static_cast<DType>(temperature));
+            } else {
+              SoftmaxWithLengthGrad<OP1, OP2, Req, negate, AType>(
                 ctx.get_stream<xpu>(), inputs[out_idx].dptr<OType>(),
                 inputs[0].dptr<OType>(), outputs[0].dptr<DType>(),
-                shape.get<3>(), axis, static_cast<DType>(temperature));
-          }
+                inputs[2].dptr<IType>(), shape.get<3>(), axis, static_cast<DType>(temperature));
+            }
+          });
         }
       });
     });
diff --git a/src/operator/nn/softmax.cc b/src/operator/nn/softmax.cc
index e44bbbb6b8f6..5a581e4ea5ef 100644
--- a/src/operator/nn/softmax.cc
+++ b/src/operator/nn/softmax.cc
@@ -59,14 +59,23 @@ inline static bool SoftmaxStorageType(const nnvm::NodeAttrs& attrs,
                                       DispatchMode* dispatch_mode,
                                       std::vector<int> *in_attrs,
                                       std::vector<int> *out_attrs) {
-  CHECK_EQ(in_attrs->size(), 1);
-  CHECK_EQ(out_attrs->size(), 1);
+  const SoftmaxParam& param = nnvm::get<SoftmaxParam>(attrs.parsed);
+  CHECK_EQ(in_attrs->size(), (param.use_length.value()) ? 2U : 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+
+  if (param.use_length.value()) {
+    auto& out_stype = out_attrs->at(0);
+    return storage_type_assign(&out_stype, kDefaultStorage,
+                               dispatch_mode, DispatchMode::kFCompute);
+  }
 
   return MKLDNNStorageType(attrs, dev_mask, true, dispatch_mode, in_attrs,
                            out_attrs);
 }
 #endif
 
+
+
 NNVM_REGISTER_OP(softmax)
 .describe(R"code(Applies the softmax function.
 
@@ -92,6 +101,13 @@ Example::
 
 )code" ADD_FILELINE)
 .set_attr_parser(ParamParser<SoftmaxParam>)
+.set_attr<nnvm::FListOutputNames>("FListInputNames",
+    [](const NodeAttrs& attrs){
+    const SoftmaxParam& param = nnvm::get<SoftmaxParam>(attrs.parsed);
+    return (param.use_length.value()) ?
+           std::vector<std::string>{"data", "length"} :
+           std::vector<std::string>{"data"};
+})
 .set_attr<nnvm::FListOutputNames>("FListOutputNames",
     [](const NodeAttrs& attrs) {
     return std::vector<std::string>{"output"};
@@ -103,20 +119,27 @@ Example::
 .set_attr<FInferStorageType>("FInferStorageType", SoftmaxStorageType)
 #endif
 .set_attr<nnvm::FGradient>("FGradient", SoftmaxFGradient{"_backward_softmax"})
+// .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
 .set_attr<nnvm::FInferType>("FInferType", SoftmaxOpType)
-.set_num_inputs(1)
+.set_num_inputs([](const nnvm::NodeAttrs& attrs) {
+    const SoftmaxParam& param = nnvm::get<SoftmaxParam>(attrs.parsed);
+    return (param.use_length.value()) ? 2 : 1;
+  })
 .set_num_outputs(1)
-.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
+.set_attr<mxnet::FInferShape>("FInferShape", SoftmaxOpShape)
 .set_attr<nnvm::FInplaceOption>("FInplaceOption",
   [](const NodeAttrs& attrs){
     return std::vector<std::pair<int, int> >{{0, 0}};
   })
 .add_argument("data", "NDArray-or-Symbol", "The input array.")
+.add_argument("length", "NDArray-or-Symbol", "The length array.")
 .add_arguments(SoftmaxParam::__FIELDS__());
 
 NNVM_REGISTER_OP(_backward_softmax)
 .set_num_inputs(SoftmaxGradOpNumInputs)
-.set_num_outputs(1)
+.set_num_outputs([](const nnvm::NodeAttrs& attrs) {
+    return (softmax_use_length(attrs) ? 2 : 1);
+  })
 .set_attr<nnvm::FListInputNames>("FListInputNames", SoftmaxGradOpInputNames)
 .set_attr<mxnet::FInferShape>("FInferShape", SoftmaxGradOpShape)
 .set_attr<nnvm::FInferType>("FInferType", SoftmaxGradOpType)
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 749f0f2bed23..fea07f540624 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -5196,6 +5196,39 @@ def check_dtypes_almost_equal(op_name,
         check_dtypes_almost_equal('log_softmax', 1e-3, 1e-3, 1e-3, 1e-3,
                                   'float32', 'float64', 'float64')
 
+
+@with_seed()
+def test_softmax_with_length():
+    def np_softmax_with_length(data, length):
+        res = np.zeros(data.shape)
+        for i in range(length.shape[0]):
+            for j in range(length.shape[1]):
+                leng = int(length[i, j])
+                res[i, 0:leng, j] = np_softmax(data[i, 0:leng, j])
+        return res
+
+    ndim = 3
+    shape = rand_shape_nd(ndim, dim=10)
+    len_shape = list(shape)
+    del len_shape[1]
+    len_shape = tuple(len_shape)
+    for dtype in [np.float16, np.float32, np.float64]:
+        mx_data = rand_ndarray(shape, dtype=dtype)
+        np_data = mx_data.asnumpy()
+        np_length = np.random.randint(1, shape[1] + 1, len_shape)
+        mx_length = mx.nd.array(np_length, dtype=np.int32)
+        np_out = np_softmax_with_length(np_data, np_length)
+        data = mx.sym.Variable("data")
+        length = mx.sym.Variable("length")
+        mx_sym = mx.sym.softmax(data=data, length=length, use_length=True, axis=1)
+        location = {"data": mx_data, "length": mx_length}
+        rtol = 1e-2 if dtype == np.float16 else 1e-3
+        atol = 1e-4 if dtype == np.float16 else 1e-5
+        check_symbolic_forward(mx_sym, location, [np_out], rtol=rtol, atol=atol, dtype="asnumpy")
+        check_symbolic_backward(mx_sym, location, [np.ones(shape, dtype=dtype)],
+                                [np.zeros(shape), np.zeros(len_shape, dtype=np.int32)], rtol=1e-2, atol=1e-3, dtype="asnumpy")
+
+
 @with_seed()
 def test_pick():
     def test_pick_helper(index_type=np.int32):
@@ -8034,7 +8067,11 @@ def get_output_names_callback(name, arr):
     check_name(cc_sym, ['data', 'concat_arg0', 'data', 'concat_arg1', 'concat_output'])
 
     sm_sym = mx.sym.softmax(data, name='softmax')
-    check_name(sm_sym, ['data', 'softmax_input0', 'softmax_output'])
+    check_name(sm_sym, ['data', 'softmax_data', 'softmax_output'])
+
+    length = mx.sym.Variable("length", shape=(10, 10, 10))
+    sm_sym = mx.sym.softmax(data, length, axis=1, use_length=True, name='softmax')
+    check_name(sm_sym, ['data', 'softmax_data', 'length', 'softmax_length', 'softmax_output'])
 
     sa_sym = mx.sym.SoftmaxActivation(data, name='softmax')
     check_name(sa_sym, ['data', 'softmax_input0', 'softmax_output'])

From eab6da6208fdc7b68437dadc6d7d58ee11c2f528 Mon Sep 17 00:00:00 2001
From: Pedro Larroy <pedro.larroy.lists@gmail.com>
Date: Fri, 19 Jul 2019 15:14:21 -0700
Subject: [PATCH 089/813] Fix warnings in CLang: (#15270)

In file included from ../src/kvstore/kvstore.cc:28:
../src/kvstore/./kvstore_local.h:281:23: warning: lambda capture 'this' is not used [-Wunused-lambda-capture]
    auto validator = [this](const int key, const NDArray& nd, bool ignore_sparse) -> bool {
                      ^
../src/kvstore/./kvstore_local.h:326:23: warning: lambda capture 'this' is not used [-Wunused-lambda-capture]
    auto validator = [this](const int key, const RSPVal& val_rowid, bool ignore_sparse) -> bool {
                      ^
In file included from ../src/c_api/c_api_profile.cc:35:
../src/c_api/../profiler/./profiler.h:1160:8: warning: 'mxnet::profiler::ProfileOperator::start' hides overloaded virtual function [-Woverloaded-virtual]
  void start(mxnet::Context::DeviceType dev_type, uint32_t dev_id) {
       ^
../src/c_api/../profiler/./profiler.h:870:8: note: hidden overloaded virtual function 'mxnet::profiler::ProfileEvent::start' declared here: different number of parameters (0 vs 2)
  void start() override {
       ^
../src/c_api/../profiler/./profiler.h:1212:8: warning: lambda capture 'this' is not used [-Wunused-lambda-capture]
      [this](OprExecStat *stat) {}, name_.c_str(), dev_type_, dev_id_,
       ^

See also #14940
---
 src/engine/naive_engine.cc   | 6 +++---
 src/engine/threaded_engine.h | 2 +-
 src/kvstore/kvstore_local.h  | 4 ++--
 src/profiler/profiler.h      | 4 ++--
 4 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/src/engine/naive_engine.cc b/src/engine/naive_engine.cc
index 9cfe9b28f862..92b38a8ea368 100644
--- a/src/engine/naive_engine.cc
+++ b/src/engine/naive_engine.cc
@@ -128,7 +128,7 @@ class NaiveEngine final : public Engine {
             attrs.reset(new profiler::ProfileOperator::Attributes());
           }
           opr->opr_profile.reset(new profiler::ProfileOperator(opr->opr_name, attrs.release()));
-          opr->opr_profile->start(exec_ctx.dev_type, exec_ctx.dev_id);
+          opr->opr_profile->startForDevice(exec_ctx.dev_type, exec_ctx.dev_id);
         }
         opr->fn(ctx, on_complete);
         if (opr->profiling) {
@@ -176,8 +176,8 @@ class NaiveEngine final : public Engine {
       if (profiler->AggregateEnabled()) {
         attrs.reset(new profiler::ProfileOperator::Attributes());
       }
-      opr->opr_profile.reset(new profiler::ProfileOperator(display_name, attrs.release()));
-      opr->opr_profile->start(exec_ctx.dev_type, exec_ctx.dev_id);
+      opr->opr_profile.reset(new profiler::ProfileOperator(opr->opr_name, attrs.release()));
+      opr->opr_profile->startForDevice(exec_ctx.dev_type, exec_ctx.dev_id);
     }
     if (exec_ctx.dev_mask() == gpu::kDevMask) {
 #if MXNET_USE_CUDA
diff --git a/src/engine/threaded_engine.h b/src/engine/threaded_engine.h
index 3d56b7c79b90..bf74485ba442 100644
--- a/src/engine/threaded_engine.h
+++ b/src/engine/threaded_engine.h
@@ -359,7 +359,7 @@ class ThreadedEngine : public Engine {
       const Context& ctx = opr_block->ctx;
       opr_block->opr_profile.reset(new profiler::ProfileOperator(threaded_opr->opr_name,
                                                                  attrs.release()));
-      opr_block->opr_profile->start(ctx.dev_type, ctx.dev_id);
+      opr_block->opr_profile->startForDevice(ctx.dev_type, ctx.dev_id);
     }
     CallbackOnComplete callback =
         this->CreateCallback(ThreadedEngine::OnCompleteStatic, opr_block);
diff --git a/src/kvstore/kvstore_local.h b/src/kvstore/kvstore_local.h
index 4e004a3a3008..5c3c80838a36 100644
--- a/src/kvstore/kvstore_local.h
+++ b/src/kvstore/kvstore_local.h
@@ -278,7 +278,7 @@ class KVStoreLocal : public KVStore {
                                 std::vector<std::vector<NDArray>> *grouped_vals,
                                 bool ignore_sparse) {
     // check if the storage type of a value is valid
-    auto validator = [this](const int key, const NDArray& nd, bool ignore_sparse) -> bool {
+    auto validator = [](const int key, const NDArray& nd, bool ignore_sparse) -> bool {
       CHECK(!ignore_sparse) << "Cannot ignore sparse arrays for push";
       auto stype = nd.storage_type();
       // valid NDArray
@@ -323,7 +323,7 @@ class KVStoreLocal : public KVStore {
                                    std::vector<std::vector<RSPVal>> *grouped_vals,
                                    bool ignore_sparse) {
     // check if the storage type of a value is valid
-    auto validator = [this](const int key, const RSPVal& val_rowid, bool ignore_sparse) -> bool {
+    auto validator = [](const int key, const RSPVal& val_rowid, bool ignore_sparse) -> bool {
       CHECK(!ignore_sparse) << "Cannot ignore sparse arrays in row_sparse_pull";
       auto val_stype = val_rowid.first->storage_type();
       auto rowid_stype = val_rowid.second.storage_type();
diff --git a/src/profiler/profiler.h b/src/profiler/profiler.h
index 49ef99da5bba..6df7ce4339da 100644
--- a/src/profiler/profiler.h
+++ b/src/profiler/profiler.h
@@ -1187,7 +1187,7 @@ struct ProfileOperator : public ProfileEvent {
    * \param dev_type Device type that the profiling will occur on
    * \param dev_id Device id associated with this opr
    */
-  void start(mxnet::Context::DeviceType dev_type, uint32_t dev_id) {
+  void startForDevice(mxnet::Context::DeviceType dev_type, uint32_t dev_id) {
     dev_type_ = dev_type;
     dev_id_ = dev_id;
     if (profiling_) {
@@ -1247,7 +1247,7 @@ struct ProfileOperator : public ProfileEvent {
    */
   void SendStat() override {
     Profiler::Get()->AddNewProfileStat<OprExecStat>(
-      [this](OprExecStat *stat) {}, name_.c_str(), dev_type_, dev_id_,
+      [](OprExecStat *stat) {}, name_.c_str(), dev_type_, dev_id_,
       start_time_, ProfileStat::NowInMicrosec(),
       attributes_.get());
   }

From da71324882808f6e6b1bc520076f65113636bc54 Mon Sep 17 00:00:00 2001
From: Abhinav Sharma <abhinav0301@gmail.com>
Date: Fri, 19 Jul 2019 15:33:11 -0700
Subject: [PATCH 090/813] Fix dumps for Constant initializer (#15150)

* update dumps for const init

* add test

* fix for numpy input

* randomize test array shape and dim

* fix test

* replace type with isinstance
---
 python/mxnet/initializer.py        |  6 ++++++
 tests/python/unittest/test_init.py | 19 ++++++++++++++++++-
 2 files changed, 24 insertions(+), 1 deletion(-)

diff --git a/python/mxnet/initializer.py b/python/mxnet/initializer.py
index aca7c58707e2..277bfd0f4fa5 100755
--- a/python/mxnet/initializer.py
+++ b/python/mxnet/initializer.py
@@ -464,6 +464,12 @@ def __init__(self, value):
     def _init_weight(self, _, arr):
         arr[:] = self.value
 
+    def dumps(self):
+        val = self._kwargs['value']
+        if not np.isscalar(val):
+            self._kwargs['value'] = val.tolist() if isinstance(val, np.ndarray) else val.asnumpy().tolist()
+        return json.dumps([self.__class__.__name__.lower(), self._kwargs])
+
 @register
 class Uniform(Initializer):
     """Initializes weights with random values uniformly sampled from a given range.
diff --git a/tests/python/unittest/test_init.py b/tests/python/unittest/test_init.py
index c8bf01f48ca3..6d8830c1d089 100644
--- a/tests/python/unittest/test_init.py
+++ b/tests/python/unittest/test_init.py
@@ -17,6 +17,7 @@
 
 import mxnet as mx
 import numpy as np
+import json
 
 def test_default_init():
     data = mx.sym.Variable('data')
@@ -67,10 +68,26 @@ def test_bilinear_init():
     bili_1d = np.array([[1/float(4), 3/float(4), 3/float(4), 1/float(4)]])
     bili_2d = bili_1d * np.transpose(bili_1d)
     assert (bili_2d == bili_weight.asnumpy()).all()
-    
+
+def test_const_init_dumps():
+    shape = tuple(np.random.randint(1, 10, size=np.random.randint(1, 5)))
+    # test NDArray input
+    init = mx.init.Constant(mx.nd.ones(shape))
+    val = init.dumps()
+    assert val == json.dumps([init.__class__.__name__.lower(), init._kwargs])
+    # test scalar input
+    init = mx.init.Constant(1)
+    assert init.dumps() == '["constant", {"value": 1}]'
+    # test numpy input
+    init = mx.init.Constant(np.ones(shape))
+    val = init.dumps()
+    assert val == json.dumps([init.__class__.__name__.lower(), init._kwargs])
+
+
 if __name__ == '__main__':
     test_variable_init()
     test_default_init()
     test_aux_init()
     test_rsp_const_init()
     test_bilinear_init()
+    test_const_init_dumps()

From 7e632c961ef7d94aaff6c5ddbd023b343082f70d Mon Sep 17 00:00:00 2001
From: Lin Yuan <apeforest@gmail.com>
Date: Fri, 19 Jul 2019 15:34:23 -0700
Subject: [PATCH 091/813] add myself to interested modules (#15590)

---
 CODEOWNERS | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/CODEOWNERS b/CODEOWNERS
index 9d4145c6b0f2..7007e80b2035 100644
--- a/CODEOWNERS
+++ b/CODEOWNERS
@@ -23,17 +23,17 @@
 /julia/                           @iblis17
 
 # C++ base
-/src/kvstore/     @rahul003 @anirudh2290 @eric-haibin-lin
+/src/kvstore/     @rahul003 @anirudh2290 @eric-haibin-lin @apeforest
 /include/         @anirudh2290 @pllarroy @eric-haibin-lin
-/src/c_api/       @anirudh2290 @eric-haibin-lin
+/src/c_api/       @anirudh2290 @eric-haibin-lin @apeforest
 /src/common/      @anirudh2290
-/src/engine/      @anirudh2290 @eric-haibin-lin
-/src/executor/    @anirudh2290 @eric-haibin-lin
-/src/imperative/  @anirudh2290 @eric-haibin-lin
-/src/io/          @anirudh2290 @eric-haibin-lin
-/src/ndarray/     @anirudh2290 @eric-haibin-lin
-/src/nnvm/        @anirudh2290 @eric-haibin-lin
-/src/operator/    @anirudh2290 @eric-haibin-lin
+/src/engine/      @anirudh2290 @eric-haibin-lin @apeforest
+/src/executor/    @anirudh2290 @eric-haibin-lin @apeforest
+/src/imperative/  @anirudh2290 @eric-haibin-lin @apeforest
+/src/io/          @anirudh2290 @eric-haibin-lin @apeforest
+/src/ndarray/     @anirudh2290 @eric-haibin-lin @apeforest
+/src/nnvm/        @anirudh2290 @eric-haibin-lin @apeforest
+/src/operator/    @anirudh2290 @eric-haibin-lin @apeforest
 /src/profiler/    @anirudh2290 @eric-haibin-lin
 /src/storage/     @anirudh2290 @eric-haibin-lin
 /tests/cpp/       @anirudh2290

From d14fa692b075bc70c58d8e5ad2bd8d442f7449b9 Mon Sep 17 00:00:00 2001
From: yifeim <yma@yma.io>
Date: Fri, 19 Jul 2019 15:57:16 -0700
Subject: [PATCH 092/813] Update profiler.md (#15477)

Add one line "dump all results to log file before download" to the main example. Otherwise, it is likely that a user may download partially-written and broken files.
---
 docs/tutorials/python/profiler.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/docs/tutorials/python/profiler.md b/docs/tutorials/python/profiler.md
index f2da62833fcf..5be6bf83ac5d 100644
--- a/docs/tutorials/python/profiler.md
+++ b/docs/tutorials/python/profiler.md
@@ -154,6 +154,8 @@ run_training_iteration(*next(itr))
 mx.nd.waitall()
 # Ask the profiler to stop recording
 profiler.set_state('stop')
+# Dump all results to log file before download
+profiler.dump()
 ```
 
 Between running and stopping the profiler, you can also pause and resume the profiler using `profiler.pause()` and `profiler.resume()` respectively to profile only parts of the code you want to profile.

From cc861f0a3fecea8fb94c9687396e27966e5ddcde Mon Sep 17 00:00:00 2001
From: Zhaoqi Zhu <zhaoqizh@usc.edu>
Date: Fri, 19 Jul 2019 22:41:09 -0700
Subject: [PATCH 093/813] Tensor Inspector Tutorial (#15517)

* add tensor inspector tutorial

* link docs

* link docs

* add license

* Revert "add license"

This reverts commit 32881e5acff6a0dc833e52f9c82c4967df40f006.

* Revert "link docs"

This reverts commit f93ae219262513e8ce52c0d68abd8eb3f40b2ed5.

* Revert "link docs"

This reverts commit 160b8912e14746f2de1b660c6b24a24197ffcb46.

* Revert "add tensor inspector tutorial"

This reverts commit 3b53981a4fe932e8ae80e4ea1ab5cd0260a12574.

* add tensor inspector doc

* fix api name

* add new test and limitations section

* fix

* update urls and other fixes

* fix urls

* fix
---
 docs/faq/add_op_in_backend.md         |   3 +
 docs/faq/develop_and_hack.md          |   1 +
 docs/faq/tensor_inspector_tutorial.md | 168 ++++++++++++++++++++++++++
 3 files changed, 172 insertions(+)
 create mode 100644 docs/faq/tensor_inspector_tutorial.md

diff --git a/docs/faq/add_op_in_backend.md b/docs/faq/add_op_in_backend.md
index 15f4ed9fbab4..248a0b058370 100644
--- a/docs/faq/add_op_in_backend.md
+++ b/docs/faq/add_op_in_backend.md
@@ -674,3 +674,6 @@ We welcome your contributions to MXNet.
 [quadratic_op.cu](https://github.com/apache/incubator-mxnet/blob/master/src/operator/contrib/quadratic_op.cu),
 and
 [test_operator.py](https://github.com/apache/incubator-mxnet/blob/master/tests/python/unittest/test_operator.py#L6514).
+
+## Additional Resources
+- [Use TensorInspector to Help Debug Operators](tensor_inspector_tutorial.md)
diff --git a/docs/faq/develop_and_hack.md b/docs/faq/develop_and_hack.md
index 0e7d221f7dc3..d2710e923be4 100644
--- a/docs/faq/develop_and_hack.md
+++ b/docs/faq/develop_and_hack.md
@@ -19,6 +19,7 @@
 - [Create new operators](new_op.md)
 - [Use Torch from MXNet](torch.md)
 - [Set environment variables of MXNet](env_var.md)
+- [Use TensorInspector to Help Debug Operators](tensor_inspector_tutorial.md)
 
 # Other Resources
 - [MXNet System Architecture Overview](/architecture/overview.html)
diff --git a/docs/faq/tensor_inspector_tutorial.md b/docs/faq/tensor_inspector_tutorial.md
new file mode 100644
index 000000000000..e77c7447fd17
--- /dev/null
+++ b/docs/faq/tensor_inspector_tutorial.md
@@ -0,0 +1,168 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Use TensorInspector to Help Debug Operators
+
+## Introduction
+
+When developing new operators, developers need to deal with tensor objects extensively. This new utility, Tensor Inspector, mainly aims to help developers debug by providing unified interfaces to print, check, and dump the tensor value. To developers' convenience, this utility works for all the three data types: Tensors, TBlobs, and NDArrays. Also, it supports both CPU and GPU tensors.
+
+
+## Usage 
+
+This utility is located in `src/common/tensor_inspector.h`. To use it in any operator code, just include it using `#include "{path}/tensor_inspector.h"`, construct an `TensorInspector` object, and call the APIs on that object. You can run any script that uses the operator you just modified then.
+
+The screenshot below shows a sample usage in `src/operator/nn/convolution-inl.h`.
+
+![tensor_inspector_example_usage](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/faq/tensor_inspector_tutorial/tensor_inspector_example_usage.png)
+
+
+## Functionalities/APIs
+
+### Create a TensorInspector Object from Tensor, TBlob, and NDArray Objects
+
+You can create a `TensorInspector` object by passing in two things: 1) an object of type `Tensor`, `Tbob`, or `NDArray`, and 2) an `RunContext` object.
+
+Essentially, `TensorInspector` can be understood as a wrapper class around `TBlob`. Internally, the `Tensor`, `Tbob`, or `NDArray` object that you passed in will be converted to a `TBlob` object. The `RunContext` object is used when the tensor is a GPU tensor; in such a case, we need to use the context information to copy the data from GPU memory to CPU/main memory.
+
+Following are the three constructors:
+
+```c++
+// Construct from Tensor object
+template<typename Device, int dimension, typename DType MSHADOW_DEFAULT_DTYPE>
+TensorInspector(const mshadow::Tensor<Device, dimension, DType>& ts, const RunContext& ctx);
+
+// Construct from TBlob object
+TensorInspector(const TBlob& tb, const RunContext& ctx);
+
+// Construct from NDArray object
+TensorInspector(const NDArray& arr, const RunContext& ctx):
+```
+
+### Print Tensor Value (Static) 
+
+To print out the tensor value in a nicely structured way, you can use this API:
+
+```c++
+void print_string();
+```
+
+This API will print the entire tensor to `std::cout` and preserve the shape (it supports all dimensions from 1 and up). You can copy the output and interpret it with any `JSON` loader. You can find some useful information about the tensor on the last line of the output. Refer to the case below, we are able to know that this is a float-typed tensor with shape 20x1x5x5.
+
+![tensor_inspector_to_string](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/faq/tensor_inspector_tutorial/tensor_inspector_to_string.png)
+
+If instead of printing the tensor to `std::cout`, you just need a `string`, you can use this API:
+```c++
+std::string void to_string();
+```
+
+### Interactively Print Tensor Value (Dynamic) 
+
+Sometimes at compilation time, you may not know which part of a tensor to inspect. Also, it may be nice to pause the operator control flow to “zoom into” a specific, erroneous part of a tensor multiple times until you are satisfied. In this regard, you can use this API to interactively inspect the tensor:
+
+```c++
+void  interactive_print(std::string tag =  "") {
+```
+
+This API will set a "break point" in your code. When that "break point" is reached, you will enter a loop that will keep asking you for further command input. In the API call, `tag` is an optional parameter to give the call a name, so that you can identify it when you have multiple `interactive_print()` calls in different parts of your code. A visit count will tell you how many times you stepped into this particular "break point", should this operator be called more than once. Note that all `interactive_print()` calls are properly locked, so you can use it in many different places without issues.
+
+![tensor_inspector_interactive_print](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/faq/tensor_inspector_tutorial/tensor_inspector_interactive_print.png)
+
+There are many useful commands available, as described in the previous screenshot: you can type "e" to print out the entire tensor, "d" to dump the tensor to file (see below), "b" to break from this command loop, and "s" to skip all future `interactive_print()`. Most importantly, in this screen, you can specify a part of the tensor that you are particularly interested in and want to print out. For example, for this 64x20x24x24 tensor, you can type in "0, 0" and presss enter to check the sub-tensor with shape 24x24 at coordinate (0, 0). 
+
+### Check Tensor Value
+
+Sometimes, developers might want to check if the tensor contains unexpected values which could be negative values, NaNs, infinities or others. To facilitate that, you can use these APIs:
+
+```c++
+template<typename ValueChecker>
+std::vector<std::vector<int>> check_value(const ValueChecker& checker,
+		bool interactive = false, std::string tag = "");
+// OR
+std::vector<std::vector<int>> check_value(CheckerType ct,
+		bool interactive = false, std::string tag =  "");
+```
+
+In the first API, `ValueChecker checker` is a bool lambda function that takes in a single parameter which is of the same data type as the tensor.  For example:
+
+```c++
+// use the same DType as in the tensor object
+[] (DType x) {return x == 0};
+```
+
+This checker is called on every value within the tensor. The return of the API is a `vector` of all the coordinates where the checker evaluates to `true`. The coordinates are themselves represented by `vector<int>`. If you set `interactive` to true, you will set a "break point" and enter a loop that asks for commands. This is similar to `interactive_print()`. You can type "p" to print the coordinates, "b" to break from the loop, and "s" to skip all future "break points" in `interactive_print()`. You can also specify a coordinate to print only a part of the tensor or type "e" to print out the entire tensor.  Just like `interactive_print()`, this this interactive screen is also properly locked.
+
+![tensor_inspector_value_check](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/faq/tensor_inspector_tutorial/tensor_inspector_value_check.png)
+
+Also, there are a bunch of built-int value checkers. Refer to the Enum below:
+
+```c++
+enum  CheckerType {
+	NegativeChecker, // check if negative
+	PositiveChecker, // check if positive
+	ZeroChecker, // check for zero
+	NaNChecker, // check if for NaN, will always return false if DType is not a float type
+	InfChecker, // check for infinity, will always return false if DType is not a float type
+	PositiveInfChecker, // check for positive infinity,
+						// will always return false if DType is not a float type
+	NegativeInfChecker, // check for nagative infinity,
+						// will always return false if DType is not a float type
+	FiniteChecker, // check if finite, will always return false if DType is not a float type
+	NormalChecker, // check if it is neither infinity nor NaN
+	AbnormalChecker, // chekck if it is infinity or nan
+};
+```
+
+Remember the second API?
+
+```c++
+std::vector<std::vector<int>> check_value(CheckerType ct,
+		bool interactive = false, std::string tag =  "");
+```
+
+You can simply pass in a value from `CheckerType` where you would have passed in your own lambda if you were using the first API. Note that it's the developer's responsibility to pass in a valid value checker.
+
+### Dump Tensor Value
+
+Sometimes, you might want to dump the tensor to a file in binary mode. Then, you might want to use a python script to further analyze the tensor value. Or, you might do that simply because a binary dump has better precision and is faster to load than the output copy-pasted from `print_string()` and loaded as a `JSON` string. Either way, you can use this API:
+
+```c++
+void dump_to_file(std::string tag);
+```
+
+This API will create a file with name  "{tag}_{visit_count}.npy", where tag is the name that we give to the call, and visit is the visit count, should the operated be called more than once.
+
+The output format is `.npy`, version 1.0. This is the Numpy format and we can easily load it with the following code:
+
+```
+import numpy as np
+a = np.load('abc_1.npy')
+print(a)
+```
+
+Let's see how it runs:
+
+![tensor_inspector_dump_to_file](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/faq/tensor_inspector_tutorial/tensor_inspector_dump_to_file.png)
+
+Notice: in `interactive_print()`, you could also do value dumping with command "d". You will be prompted to enter the `tag` value:
+
+![tensor_inspector_interactive_print](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/faq/tensor_inspector_tutorial/tensor_inspector_interactive_print.png)
+
+### Test Coverage and Limitations
+
+This utility has been tested on Mac and Ubuntu with and without CUDNN and MKLDNN. Supports for `Tensor`, `TBlob`, and `NDArray`, as well as for CPU and GPU have been manually tested. 
+
+Currently, this utility only supports non-empty tensors and tensors with known shapes i.e. `tb_.ndim() > 0`. Also, this utility only supports dense `NDArray` objects, i.e. when the type is `kDefaultStorage`. 
+

From d5df2473563b69cdd9ddb0b47966e6d66637f1d2 Mon Sep 17 00:00:00 2001
From: Iblis Lin <iblis@hs.ntnu.edu.tw>
Date: Sat, 20 Jul 2019 22:17:43 +0800
Subject: [PATCH 094/813] julia: bump window prebult binary version to v1.5.0
 (#15608)

- remove file REQUIRE, it's stale for Pkg3.jl
---
 julia/REQUIRE       |  6 ------
 julia/deps/build.jl | 16 ++++++++++------
 2 files changed, 10 insertions(+), 12 deletions(-)
 delete mode 100644 julia/REQUIRE

diff --git a/julia/REQUIRE b/julia/REQUIRE
deleted file mode 100644
index 8008da3d2aaa..000000000000
--- a/julia/REQUIRE
+++ /dev/null
@@ -1,6 +0,0 @@
-julia 0.7
-Formatting
-BinDeps
-JSON
-MacroTools
-Reexport
diff --git a/julia/deps/build.jl b/julia/deps/build.jl
index badca65be577..a87343d9dab5 100644
--- a/julia/deps/build.jl
+++ b/julia/deps/build.jl
@@ -24,7 +24,7 @@ using LinearAlgebra
 ################################################################################
 libmxnet_detected = false
 libmxnet_curr_ver = get(ENV, "MXNET_COMMIT", "master")
-curr_win = "20180211"  # v1.1.0
+curr_win = "20190608"  # v1.5.0
 
 if haskey(ENV, "MXNET_HOME")
   MXNET_HOME = ENV["MXNET_HOME"]
@@ -119,21 +119,25 @@ if !libmxnet_detected
     base_url = "https://github.com/yajiedesign/mxnet/releases/download/weekly_binary_build_v2/prebuildbase_win10_x64_vc14_v2.7z"
 
     if libmxnet_curr_ver == "master"
+      _cmd = "{
+        [System.Net.ServicePointManager]::SecurityProtocol='tls12';
+        Invoke-WebRequest -Uri 'https://api.github.com/repos/yajiedesign/mxnet/releases/latest'
+        -OutFile 'mxnet.json'}"
       # download_cmd uses powershell 2, but we need powershell 3 to do this
-      run(`powershell -NoProfile -Command Invoke-WebRequest -Uri "https://api.github.com/repos/yajiedesign/mxnet/releases/latest" -OutFile "mxnet.json"`)
+      run(`powershell -NoProfile -Command $_cmd`)
       curr_win = JSON.parsefile("mxnet.json")["tag_name"]
       @info("Can't use MXNet master on Windows, using latest binaries from $curr_win.")
     end
     # TODO: Get url from JSON.
-    name = "mxnet_x64_vc14_$(HAS_CUDA ? "gpu" : "cpu").7z"
+    # TODO: detect cuda version and select corresponding url.
+    name = "mxnet_x64_$(HAS_CUDA ? "vc141_gpu_cu101" : "vc14_cpu").7z"
     package_url = "https://github.com/yajiedesign/mxnet/releases/download/$(curr_win)/$(curr_win)_$(name)"
 
-    exe7z = joinpath(JULIA_HOME, "7z.exe")
+    exe7z = joinpath(Sys.BINDIR, "7z.exe")
 
     run(download_cmd(package_url, "mxnet.7z"))
     # this command will create the dir "usr\\lib"
-    run(`$exe7z x mxnet.7z build lib -y -ousr`)
-    run(`cmd /c copy "usr\\build\\*.dll" "usr\\lib"`)
+    run(`$exe7z e mxnet.7z *\\build\\* *\\lib\\* -y -ousr\\lib`)
 
     run(download_cmd(base_url, "mxnet_base.7z"))
     run(`$exe7z x mxnet_base.7z -y -ousr`)

From 8f5930b2c95a6b7594ff6535a097e35b3315bc6d Mon Sep 17 00:00:00 2001
From: nicklhy <nicklhy@gmail.com>
Date: Sun, 21 Jul 2019 06:10:36 +0800
Subject: [PATCH 095/813] fix normalize mean error bug (#15539)

* fix normalize mean error bug

* add scalar mean/std tests for image_normalize
---
 src/operator/image/image_random-inl.h  |  2 +-
 tests/python/unittest/test_operator.py | 53 +++++++++++++++++++++++++-
 2 files changed, 52 insertions(+), 3 deletions(-)

diff --git a/src/operator/image/image_random-inl.h b/src/operator/image/image_random-inl.h
index aeb189f35b78..e00b255bfd30 100644
--- a/src/operator/image/image_random-inl.h
+++ b/src/operator/image/image_random-inl.h
@@ -339,7 +339,7 @@ void NormalizeOpForward(const nnvm::NodeAttrs &attrs,
   std::vector<float> mean(3);
   std::vector<float> std(3);
   if (param.mean.ndim() == 1) {
-    mean[0] = mean[1] = mean[3] = param.mean[0];
+    mean[0] = mean[1] = mean[2] = param.mean[0];
   } else {
     mean[0] = param.mean[0];
     mean[1] = param.mean[1];
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index fea07f540624..915a83f2f342 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -8678,7 +8678,7 @@ def test_invalid_max_pooling_pad_type_same():
 
 @with_seed()
 def test_image_normalize():
-    # Part 1 - Test 3D Input
+    # Part 1 - Test 3D input with 3D mean/std
     shape_3d = (3, 28, 28)
     mean = (0, 1, 2)
     std = (3, 2, 1)
@@ -8709,7 +8709,7 @@ def test_image_normalize():
     # check backward using finite difference
     check_numeric_gradient(img_norm_sym, [data_in_3d], atol=0.001)
 
-    # Part 2 - Test 4D Input
+    # Part 2 - Test 4D input with 3D mean/std
     shape_4d = (2, 3, 28, 28)
 
     data_in_4d = mx.nd.random.uniform(0, 1, shape_4d)
@@ -8741,6 +8741,55 @@ def test_image_normalize():
     # check backward using finite difference
     check_numeric_gradient(img_norm_sym, [data_in_4d], atol=0.001)
 
+    # Part 3 - Test 3D input with scalar mean/std
+    shape_3d = (3, 28, 28)
+    mean = 1.0
+    std = 2.0
+
+    data_in_3d = mx.nd.random.uniform(0, 1, shape_3d)
+    data_expected_3d = data_in_3d.asnumpy()
+    data_expected_3d[:][:][:] = (data_expected_3d[:][:][:] - 1.0) / 2.0
+
+    data = mx.symbol.Variable('data')
+    img_norm_sym = mx.sym.image.normalize(data=data, mean=mean, std=std)
+
+    # check forward
+    check_symbolic_forward(img_norm_sym, [data_in_3d], [data_expected_3d],
+                           rtol=1e-5, atol=1e-5)
+
+    # Gradient is 1/std_dev
+    grad_expected_3d = np.ones(shape_3d)
+    grad_expected_3d[:][:][:] = 1 / 2.0
+
+    # check backward
+    check_symbolic_backward(img_norm_sym, location=[data_in_3d], out_grads=[mx.nd.ones(shape_3d)],
+                            expected=[grad_expected_3d], rtol=1e-5, atol=1e-5)
+
+    # check backward using finite difference
+    check_numeric_gradient(img_norm_sym, [data_in_3d], atol=0.001)
+
+    # Part 4 - Test 4D input with scalar mean/std
+    shape_4d = (2, 3, 28, 28)
+
+    data_in_4d = mx.nd.random.uniform(0, 1, shape_4d)
+    data_expected_4d = data_in_4d.asnumpy()
+    data_expected_4d[:][:][:][:] = (data_expected_4d[:][:][:][:] - 1.0) / 2.0
+
+    # check forward
+    check_symbolic_forward(img_norm_sym, [data_in_4d], [data_expected_4d],
+                           rtol=1e-5, atol=1e-5)
+
+    # Gradient is 1/std_dev
+    grad_expected_4d = np.ones(shape_4d)
+    grad_expected_4d[:][:][:][:] = 1 / 2.0
+
+    # check backward
+    check_symbolic_backward(img_norm_sym, location=[data_in_4d], out_grads=[mx.nd.ones(shape_4d)],
+                            expected=[grad_expected_4d], rtol=1e-5, atol=1e-5)
+
+    # check backward using finite difference
+    check_numeric_gradient(img_norm_sym, [data_in_4d], atol=0.001)
+
 @with_seed()
 def test_index_array():
     def test_index_array_default():

From f553b3134b6601c3588b2a0461bc6d4eb3d2dbf8 Mon Sep 17 00:00:00 2001
From: Iblis Lin <iblis@hs.ntnu.edu.tw>
Date: Sun, 21 Jul 2019 06:22:36 +0800
Subject: [PATCH 096/813] julia: remove Travis CI related files (#15616)

We do not use travis since Julia package got merged into main repo.
---
 julia/.travis.yml                 | 59 -------------------------------
 julia/test/travis/run_coverage.sh | 18 ----------
 julia/test/travis/run_test.sh     | 28 ---------------
 julia/test/travis/setup_env.sh    | 38 --------------------
 4 files changed, 143 deletions(-)
 delete mode 100644 julia/.travis.yml
 delete mode 100644 julia/test/travis/run_coverage.sh
 delete mode 100755 julia/test/travis/run_test.sh
 delete mode 100755 julia/test/travis/setup_env.sh

diff --git a/julia/.travis.yml b/julia/.travis.yml
deleted file mode 100644
index 680df7af481e..000000000000
--- a/julia/.travis.yml
+++ /dev/null
@@ -1,59 +0,0 @@
-# Documentation: http://docs.travis-ci.com/user/languages/julia/
-sudo: false
-
-language: julia
-
-os:
-  - linux
-  - osx
-osx_image: xcode8
-
-julia:
-  - 0.6
-#  - nightly 0.6 supports depends on #170
-
-branches:
-  only:
-    - master
-    - stable
-    - /^v\d+\.\d+(\.\d+)?(-\S*)?$/  # for tagging
-
-cache:
-  directories:
-    - $TRAVIS_BUILD_DIR/deps/src
-
-# dependent apt packages
-addons:
-  apt:
-    sources:
-      - ubuntu-toolchain-r-test
-    packages:
-      - doxygen
-      - wget
-      - git
-      - libcurl4-openssl-dev
-      - unzip
-      - libatlas-base-dev
-      - libatlas-dev
-      - libopencv-dev
-      - gcc-4.8
-      - g++-4.8
-
-before_install:
-  - export TRAVIS_DIR=test/travis
-  - source ${TRAVIS_DIR}/setup_env.sh
-
-notifications:
-  email: false
-
-script:
-  - ${TRAVIS_DIR}/run_test.sh
-
-after_success:
-  # See https://github.com/dmlc/MXNet.jl/pull/303#issuecomment-341171774
-  - julia -e 'using MXNet; mx._sig_checker()'
-
-  - source ${TRAVIS_DIR}/run_coverage.sh
-  - echo $TRAVIS_JULIA_VERSION
-  - julia -e 'Pkg.add("Documenter")'
-  - julia -e 'cd(Pkg.dir("MXNet")); include(joinpath("docs", "make.jl"))'
diff --git a/julia/test/travis/run_coverage.sh b/julia/test/travis/run_coverage.sh
deleted file mode 100644
index ee22b258b549..000000000000
--- a/julia/test/travis/run_coverage.sh
+++ /dev/null
@@ -1,18 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-julia -e 'cd(Pkg.dir("MXNet")); Pkg.add("Coverage"); using Coverage; Codecov.submit(process_folder())'
diff --git a/julia/test/travis/run_test.sh b/julia/test/travis/run_test.sh
deleted file mode 100755
index 414b1450b554..000000000000
--- a/julia/test/travis/run_test.sh
+++ /dev/null
@@ -1,28 +0,0 @@
-#!/bin/bash
-
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-set -e
-
-if [[ -a .git/shallow ]]; then git fetch --unshallow; fi
-julia -e 'Pkg.clone(pwd())'
-(
-    cd `julia -e 'println(Pkg.dir("MXNet", "deps"))'` &&
-    ln -fs $TRAVIS_BUILD_DIR/deps/src
-)
-julia -e 'Pkg.build("MXNet"); Pkg.test("MXNet"; coverage=true)'
diff --git a/julia/test/travis/setup_env.sh b/julia/test/travis/setup_env.sh
deleted file mode 100755
index 9d2f7341998d..000000000000
--- a/julia/test/travis/setup_env.sh
+++ /dev/null
@@ -1,38 +0,0 @@
-#!/bin/bash
-
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-
-echo "##########################"
-echo $TRAVIS_OS_NAME
-
-if [ ${TRAVIS_OS_NAME} == "osx" ]; then
-  brew update >/dev/null 2>&1
-  brew tap homebrew/science
-  brew info opencv
-  brew install graphviz
-  brew install opencv
-fi
-
-if [ ${TRAVIS_OS_NAME} == "linux" ]; then
-  mkdir shadow_bin
-  ln -s `which gcc-4.8` shadow_bin/gcc
-  ln -s `which g++-4.8` shadow_bin/g++
-
-  export PATH=$PWD/shadow_bin:$PATH
-fi

From 5086ff0b3eb68b55b7b92e1232a79ffeadbe752d Mon Sep 17 00:00:00 2001
From: Sam Skalicky <samskalicky@gmail.com>
Date: Sat, 20 Jul 2019 16:53:42 -0700
Subject: [PATCH 097/813] changed constructor args (#15601)

---
 plugin/warpctc/warpctc-inl.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/plugin/warpctc/warpctc-inl.h b/plugin/warpctc/warpctc-inl.h
index 37677d21fd14..9fcbedce74f1 100644
--- a/plugin/warpctc/warpctc-inl.h
+++ b/plugin/warpctc/warpctc-inl.h
@@ -254,7 +254,7 @@ class WarpCTCProp : public OperatorProperty {
     CHECK_EQ(in_shape->size(), 2) << "Input:[data, label]";
     const mxnet::TShape &dshape = in_shape->at(0);
     if (dshape.ndim() == 0) return false;
-    mxnet::TShape label_shape(dshape.ndim() - 1);
+    mxnet::TShape label_shape(dshape.ndim() - 1, 1);
     label_shape[0] = param_.label_length * (dshape[0] / param_.input_length);
     SHAPE_ASSIGN_CHECK(*in_shape, warpctc_enum::kLabel, label_shape);
 

From 2b4c9c07c5f0039b93c51f7d9fb7123a0847c679 Mon Sep 17 00:00:00 2001
From: kshitij12345 <kshitijkalambarkar@gmail.com>
Date: Sun, 21 Jul 2019 07:48:29 +0530
Subject: [PATCH 098/813] [fix] print `self` in warning. (#15614)

* use format

* make pylint happy

* Update block.py
---
 python/mxnet/gluon/block.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/python/mxnet/gluon/block.py b/python/mxnet/gluon/block.py
index 3bac3c023bf8..bd22cf85d561 100644
--- a/python/mxnet/gluon/block.py
+++ b/python/mxnet/gluon/block.py
@@ -842,8 +842,9 @@ def hybridize(self, active=True, **kwargs):
         self._flags = list(kwargs.items())
         self._clear_cached_op()
         if active and self._forward_hooks or self._forward_pre_hooks:
-            warnings.warn('"{}" is being hybridized while still having forward hook/pre-hook. '
-                          'If "{}" is a child of HybridBlock, the hooks will not take effect.')
+            warnings.warn('"{block}" is being hybridized while still having forward hook/pre-hook. '
+                          'If "{block}" is a child of HybridBlock, the hooks will not take effect.'
+                          .format(block=self))
         super(HybridBlock, self).hybridize(active, **kwargs)
 
     def cast(self, dtype):

From 399d3a0dc64386e106527257721a51c6152aff4e Mon Sep 17 00:00:00 2001
From: Iblis Lin <iblis@hs.ntnu.edu.tw>
Date: Sun, 21 Jul 2019 13:45:26 +0800
Subject: [PATCH 099/813] julia: bump binding version to v1.6.0 (#15607)

---
 julia/Project.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/julia/Project.toml b/julia/Project.toml
index 82a94c5a0767..994a696c1399 100644
--- a/julia/Project.toml
+++ b/julia/Project.toml
@@ -1,7 +1,7 @@
 name = "MXNet"
 uuid = "a7949054-b901-59c6-b8e3-7238c29bf7f0"
 authors = ["Chiyuan Zhang <pluskid@gmail.com>", "Valentin Churavy <vchuravy@mit.edu>", "Iblis Lin <iblis@hs.ntnu.edu.tw>"]
-version = "1.5.0"
+version = "1.6.0"
 
 [deps]
 BinDeps = "9e28174c-4ba2-5203-b857-d8d62c4213ee"

From d599bc3c1356b3471d27026711f67467122992f9 Mon Sep 17 00:00:00 2001
From: Neo Chien <cchung100m@cs.ccu.edu.tw>
Date: Sun, 21 Jul 2019 16:07:39 +0800
Subject: [PATCH 100/813] [MXNET-1411] solve pylint error issue#14851 (#15113)

* fix pylint error: no-else-raise in _export_helper.py

* fix pylint error: no-else-raise in _translation_utils.py

* fix pylint error: Bad option value 'no-else-raise' (bad-option-value) in vocab.py

* fix pylint error: Bad option value 'no-else-raise' (bad-option-value) in trainer.py

* fix pylint error: Bad option value 'no-else-raise' (bad-option-value) in utils.py

* fix pylint error: Bad option value 'no-else-raise' (bad-option-value) in detection.py

* fix pylint error: Bad option value 'no-else-raise' (bad-option-value) in image.py

* fix pylint error: Bad option value 'no-else-raise' (bad-option-value) in model.py

* fix pylint error: Bad option value 'no-else-raise' (bad-option-value) in sparse.py

* fix pylint error: Bad option value 'no-else-raise' (bad-option-value) in test_utils.py

* fix pylint error: R1720: Unnecessary else after raise (no-else-raise) for vocab.py

* fix pylint error: R1720: Unnecessary else after raise (no-else-raise) for model.py

* fix pylint error: R1720: Unnecessary else after raise (no-else-raise) for _translation_utils.py

* fix pylint error: R1720: Unnecessary else after raise (no-else-raise) for _export_helper.py

* fix pylint error: R1720: Unnecessary else after raise (no-else-raise) for test_utils.py

* fix pylint error: R1720: Unnecessary else after raise (no-else-raise) for image.py

* fix pylint error: R1720: Unnecessary else after raise (no-else-raise) for trainer.py

* fix pylint error: R1720: Unnecessary else after raise (no-else-raise) for detection.py

* fix pylint error: R1720: Unnecessary else after raise (no-else-raise) for utils.py

* fix pylint error: R1720: Unnecessary else after raise (no-else-raise) for sparse.py

* fix pylint error:R1719: The if expression can be replaced with 'bool(test)' (simplifiable-if-expression)
---
 .../contrib/onnx/mx2onnx/_export_helper.py    | 38 +++++++++----------
 .../onnx/onnx2mx/_translation_utils.py        | 30 +++++++--------
 python/mxnet/contrib/text/vocab.py            |  5 +--
 python/mxnet/gluon/trainer.py                 | 12 +++---
 python/mxnet/gluon/utils.py                   |  8 ++--
 python/mxnet/image/detection.py               | 18 ++++-----
 python/mxnet/image/image.py                   | 24 ++++++------
 python/mxnet/model.py                         |  5 +--
 python/mxnet/ndarray/sparse.py                | 10 ++---
 python/mxnet/test_utils.py                    | 28 +++++++-------
 10 files changed, 88 insertions(+), 90 deletions(-)

diff --git a/python/mxnet/contrib/onnx/mx2onnx/_export_helper.py b/python/mxnet/contrib/onnx/mx2onnx/_export_helper.py
index e73ff70fa5b0..fdb6689579be 100644
--- a/python/mxnet/contrib/onnx/mx2onnx/_export_helper.py
+++ b/python/mxnet/contrib/onnx/mx2onnx/_export_helper.py
@@ -40,26 +40,26 @@ def load_module(sym_filepath, params_filepath):
     params : params object
         Model weights including both arg and aux params.
     """
-    if not (os.path.isfile(sym_filepath) and os.path.isfile(params_filepath)): # pylint: disable=no-else-raise
+    if not (os.path.isfile(sym_filepath) and os.path.isfile(params_filepath)):
         raise ValueError("Symbol and params files provided are invalid")
-    else:
-        try:
-            # reads symbol.json file from given path and
-            # retrieves model prefix and number of epochs
-            model_name = sym_filepath.rsplit('.', 1)[0].rsplit('-', 1)[0]
-            params_file_list = params_filepath.rsplit('.', 1)[0].rsplit('-', 1)
-            # Setting num_epochs to 0 if not present in filename
-            num_epochs = 0 if len(params_file_list) == 1 else int(params_file_list[1])
-        except IndexError:
-            logging.info("Model and params name should be in format: "
-                         "prefix-symbol.json, prefix-epoch.params")
-            raise
 
-        sym, arg_params, aux_params = mx.model.load_checkpoint(model_name, num_epochs)
+    try:
+        # reads symbol.json file from given path and
+        # retrieves model prefix and number of epochs
+        model_name = sym_filepath.rsplit('.', 1)[0].rsplit('-', 1)[0]
+        params_file_list = params_filepath.rsplit('.', 1)[0].rsplit('-', 1)
+        # Setting num_epochs to 0 if not present in filename
+        num_epochs = 0 if len(params_file_list) == 1 else int(params_file_list[1])
+    except IndexError:
+        logging.info("Model and params name should be in format: "
+                     "prefix-symbol.json, prefix-epoch.params")
+        raise
 
-        # Merging arg and aux parameters
-        params = {}
-        params.update(arg_params)
-        params.update(aux_params)
+    sym, arg_params, aux_params = mx.model.load_checkpoint(model_name, num_epochs)
 
-        return sym, params
+    # Merging arg and aux parameters
+    params = {}
+    params.update(arg_params)
+    params.update(aux_params)
+
+    return sym, params
diff --git a/python/mxnet/contrib/onnx/onnx2mx/_translation_utils.py b/python/mxnet/contrib/onnx/onnx2mx/_translation_utils.py
index 48ede28ab022..0160c5c61974 100644
--- a/python/mxnet/contrib/onnx/onnx2mx/_translation_utils.py
+++ b/python/mxnet/contrib/onnx/onnx2mx/_translation_utils.py
@@ -178,23 +178,23 @@ def _fix_channels(op_name, attrs, inputs, proto_obj):
     these attributes. We check the shape of weights provided to get the number.
     """
     weight_name = inputs[1].name
-    if not weight_name in proto_obj._params: # pylint: disable=no-else-raise
+    if not weight_name in proto_obj._params:
         raise ValueError("Unable to get channels/units attr from onnx graph.")
-    else:
-        wshape = proto_obj._params[weight_name].shape
-        assert len(wshape) >= 2, "Weights shape is invalid: {}".format(wshape)
 
-        if op_name == 'FullyConnected':
-            attrs['num_hidden'] = wshape[0]
-        else:
-            if op_name == 'Convolution':
-                # Weight shape for Conv and FC: (M x C x kH x kW) : M is number of
-                # feature maps/hidden  and C is number of channels
-                attrs['num_filter'] = wshape[0]
-            elif op_name == 'Deconvolution':
-                # Weight shape for DeConv : (C x M x kH x kW) : M is number of
-                # feature maps/filters and C is number of channels
-                attrs['num_filter'] = wshape[1]
+    wshape = proto_obj._params[weight_name].shape
+    assert len(wshape) >= 2, "Weights shape is invalid: {}".format(wshape)
+
+    if op_name == 'FullyConnected':
+        attrs['num_hidden'] = wshape[0]
+    else:
+        if op_name == 'Convolution':
+            # Weight shape for Conv and FC: (M x C x kH x kW) : M is number of
+            # feature maps/hidden  and C is number of channels
+            attrs['num_filter'] = wshape[0]
+        elif op_name == 'Deconvolution':
+            # Weight shape for DeConv : (C x M x kH x kW) : M is number of
+            # feature maps/filters and C is number of channels
+            attrs['num_filter'] = wshape[1]
     return attrs
 
 
diff --git a/python/mxnet/contrib/text/vocab.py b/python/mxnet/contrib/text/vocab.py
index 6e9920d601b6..7db3ee844bb5 100644
--- a/python/mxnet/contrib/text/vocab.py
+++ b/python/mxnet/contrib/text/vocab.py
@@ -210,9 +210,8 @@ def to_tokens(self, indices):
 
         tokens = []
         for idx in indices:
-            if not isinstance(idx, int) or idx > max_idx: # pylint: disable=no-else-raise
+            if not isinstance(idx, int) or idx > max_idx:
                 raise ValueError('Token index %d in the provided `indices` is invalid.' % idx)
-            else:
-                tokens.append(self.idx_to_token[idx])
+            tokens.append(self.idx_to_token[idx])
 
         return tokens[0] if to_reduce else tokens
diff --git a/python/mxnet/gluon/trainer.py b/python/mxnet/gluon/trainer.py
index 0939490a8307..c8aeb614160a 100644
--- a/python/mxnet/gluon/trainer.py
+++ b/python/mxnet/gluon/trainer.py
@@ -249,11 +249,11 @@ def _init_kvstore(self):
 
     @property
     def learning_rate(self):
-        if not isinstance(self._optimizer, opt.Optimizer): # pylint: disable=no-else-raise
+        if not isinstance(self._optimizer, opt.Optimizer):
             raise UserWarning("Optimizer has to be defined before its learning "
                               "rate can be accessed.")
-        else:
-            return self._optimizer.learning_rate
+
+        return self._optimizer.learning_rate
 
     @property
     def optimizer(self):
@@ -270,11 +270,11 @@ def set_learning_rate(self, lr):
         lr : float
             The new learning rate of the optimizer.
         """
-        if not isinstance(self._optimizer, opt.Optimizer): # pylint: disable=no-else-raise
+        if not isinstance(self._optimizer, opt.Optimizer):
             raise UserWarning("Optimizer has to be defined before its learning "
                               "rate is mutated.")
-        else:
-            self._optimizer.set_learning_rate(lr)
+
+        self._optimizer.set_learning_rate(lr)
 
     def _row_sparse_pull(self, parameter, out, row_id, full_idx=False):
         """Internal method to invoke pull operations on KVStore. If `full_idx` is set to True,
diff --git a/python/mxnet/gluon/utils.py b/python/mxnet/gluon/utils.py
index 3957b7402688..2060f61a0212 100644
--- a/python/mxnet/gluon/utils.py
+++ b/python/mxnet/gluon/utils.py
@@ -341,11 +341,11 @@ def download(url, path=None, overwrite=False, sha1_hash=None, retries=5, verify_
                 break
             except Exception as e:
                 retries -= 1
-                if retries <= 0: # pylint: disable=no-else-raise
+                if retries <= 0:
                     raise e
-                else:
-                    print('download failed due to {}, retrying, {} attempt{} left'
-                          .format(repr(e), retries, 's' if retries > 1 else ''))
+
+                print('download failed due to {}, retrying, {} attempt{} left'
+                      .format(repr(e), retries, 's' if retries > 1 else ''))
 
     return fname
 
diff --git a/python/mxnet/image/detection.py b/python/mxnet/image/detection.py
index a70e5723072f..48cf5bc1cf67 100644
--- a/python/mxnet/image/detection.py
+++ b/python/mxnet/image/detection.py
@@ -809,23 +809,23 @@ def next(self):
         pad = batch_size - i
         # handle padding for the last batch
         if pad != 0:
-            if self.last_batch_handle == 'discard': # pylint: disable=no-else-raise
+            if self.last_batch_handle == 'discard':
                 raise StopIteration
             # if the option is 'roll_over', throw StopIteration and cache the data
-            elif self.last_batch_handle == 'roll_over' and \
+            if self.last_batch_handle == 'roll_over' and \
                 self._cache_data is None:
                 self._cache_data = batch_data
                 self._cache_label = batch_label
                 self._cache_idx = i
                 raise StopIteration
+
+            _ = self._batchify(batch_data, batch_label, i)
+            if self.last_batch_handle == 'pad':
+                self._allow_read = False
             else:
-                _ = self._batchify(batch_data, batch_label, i)
-                if self.last_batch_handle == 'pad':
-                    self._allow_read = False
-                else:
-                    self._cache_data = None
-                    self._cache_label = None
-                    self._cache_idx = None
+                self._cache_data = None
+                self._cache_label = None
+                self._cache_idx = None
 
         return io.DataBatch([batch_data], [batch_label], pad=pad)
 
diff --git a/python/mxnet/image/image.py b/python/mxnet/image/image.py
index a142282c83a6..bffd286cb538 100644
--- a/python/mxnet/image/image.py
+++ b/python/mxnet/image/image.py
@@ -1198,10 +1198,10 @@ def __init__(self, batch_size, data_shape, label_width=1,
             logging.info('%s: loading recordio %s...',
                          class_name, path_imgrec)
             if path_imgidx:
-                self.imgrec = recordio.MXIndexedRecordIO(path_imgidx, path_imgrec, 'r')  # pylint: disable=redefined-variable-type
+                self.imgrec = recordio.MXIndexedRecordIO(path_imgidx, path_imgrec, 'r')
                 self.imgidx = list(self.imgrec.keys)
             else:
-                self.imgrec = recordio.MXRecordIO(path_imgrec, 'r')  # pylint: disable=redefined-variable-type
+                self.imgrec = recordio.MXRecordIO(path_imgrec, 'r')
                 self.imgidx = None
         else:
             self.imgrec = None
@@ -1224,7 +1224,7 @@ def __init__(self, batch_size, data_shape, label_width=1,
             imgkeys = []
             index = 1
             for img in imglist:
-                key = str(index)  # pylint: disable=redefined-variable-type
+                key = str(index)
                 index += 1
                 if len(img) > 2:
                     label = nd.array(img[:-1], dtype=dtype)
@@ -1374,23 +1374,23 @@ def next(self):
         pad = batch_size - i
         # handle padding for the last batch
         if pad != 0:
-            if self.last_batch_handle == 'discard': # pylint: disable=no-else-raise
+            if self.last_batch_handle == 'discard':
                 raise StopIteration
             # if the option is 'roll_over', throw StopIteration and cache the data
-            elif self.last_batch_handle == 'roll_over' and \
+            if self.last_batch_handle == 'roll_over' and \
                 self._cache_data is None:
                 self._cache_data = batch_data
                 self._cache_label = batch_label
                 self._cache_idx = i
                 raise StopIteration
+
+            _ = self._batchify(batch_data, batch_label, i)
+            if self.last_batch_handle == 'pad':
+                self._allow_read = False
             else:
-                _ = self._batchify(batch_data, batch_label, i)
-                if self.last_batch_handle == 'pad':
-                    self._allow_read = False
-                else:
-                    self._cache_data = None
-                    self._cache_label = None
-                    self._cache_idx = None
+                self._cache_data = None
+                self._cache_label = None
+                self._cache_idx = None
 
         return io.DataBatch([batch_data], [batch_label], pad=pad)
 
diff --git a/python/mxnet/model.py b/python/mxnet/model.py
index 7e324a1d6b7e..aee4a8ce2b45 100644
--- a/python/mxnet/model.py
+++ b/python/mxnet/model.py
@@ -642,10 +642,9 @@ def _init_iter(self, X, y, is_train):
         """Initialize the iterator given input."""
         if isinstance(X, (np.ndarray, nd.NDArray)):
             if y is None:
-                if is_train: # pylint: disable=no-else-raise
+                if is_train:
                     raise ValueError('y must be specified when X is numpy.ndarray')
-                else:
-                    y = np.zeros(X.shape[0])
+                y = np.zeros(X.shape[0])
             if not isinstance(y, (np.ndarray, nd.NDArray)):
                 raise TypeError('y must be ndarray when X is numpy.ndarray')
             if X.shape[0] != y.shape[0]:
diff --git a/python/mxnet/ndarray/sparse.py b/python/mxnet/ndarray/sparse.py
index 4987cb57b6ea..52c5de78ddd4 100644
--- a/python/mxnet/ndarray/sparse.py
+++ b/python/mxnet/ndarray/sparse.py
@@ -639,10 +639,10 @@ def __getitem__(self, key):
         if isinstance(key, int):
             raise Exception("__getitem__ with int key is not implemented for RowSparseNDArray yet")
         if isinstance(key, py_slice):
-            if key.step is not None or key.start is not None or key.stop is not None: # pylint: disable=no-else-raise
+            if key.step is not None or key.start is not None or key.stop is not None:
                 raise Exception('RowSparseNDArray only supports [:] for __getitem__')
-            else:
-                return self
+
+            return self
         if isinstance(key, tuple):
             raise ValueError('Multi-dimension indexing is not supported')
         raise ValueError('Undefined behaviour for {}'.format(key))
@@ -1102,9 +1102,9 @@ def row_sparse_array(arg1, shape=None, ctx=None, dtype=None):
     # construct a row sparse array from (D0, D1 ..) or (data, indices)
     if isinstance(arg1, tuple):
         arg_len = len(arg1)
-        if arg_len < 2: # pylint: disable=no-else-raise
+        if arg_len < 2:
             raise ValueError("Unexpected length of input tuple: " + str(arg_len))
-        elif arg_len > 2:
+        if arg_len > 2:
             # empty ndarray with shape
             _check_shape(arg1, shape)
             return empty('row_sparse', arg1, ctx=ctx, dtype=dtype)
diff --git a/python/mxnet/test_utils.py b/python/mxnet/test_utils.py
index aa46a9628b81..0e260ceb7676 100644
--- a/python/mxnet/test_utils.py
+++ b/python/mxnet/test_utils.py
@@ -212,11 +212,11 @@ def _get_powerlaw_dataset_csr(num_rows, num_cols, density=0.1, dtype=None):
                 return mx.nd.array(output_arr).tostype("csr")
         col_max = col_max * 2
 
-    if unused_nnz > 0: # pylint: disable=no-else-raise
+    if unused_nnz > 0:
         raise ValueError("not supported for this density: %s"
                          " for this shape (%s,%s)" % (density, num_rows, num_cols))
-    else:
-        return mx.nd.array(output_arr).tostype("csr")
+
+    return mx.nd.array(output_arr).tostype("csr")
 
 
 def assign_each(the_input, function):
@@ -1407,10 +1407,10 @@ def check_consistency(sym, ctx_list, scale=1.0, grad_req='write',
             except AssertionError as e:
                 print('Predict Err: ctx %d vs ctx %d at %s'%(i, max_idx, name))
                 traceback.print_exc()
-                if raise_on_err: # pylint: disable=no-else-raise
+                if raise_on_err:
                     raise e
-                else:
-                    print(str(e))
+
+                print(str(e))
 
     # train
     if grad_req != 'null':
@@ -1434,10 +1434,10 @@ def check_consistency(sym, ctx_list, scale=1.0, grad_req='write',
                 except AssertionError as e:
                     print('Train Err: ctx %d vs ctx %d at %s'%(i, max_idx, name))
                     traceback.print_exc()
-                    if raise_on_err: # pylint: disable=no-else-raise
+                    if raise_on_err:
                         raise e
-                    else:
-                        print(str(e))
+
+                    print(str(e))
 
     return gt
 
@@ -1514,11 +1514,11 @@ def download(url, fname=None, dirname=None, overwrite=False, retries=5):
                 break
         except Exception as e:
             retries -= 1
-            if retries <= 0: # pylint: disable=no-else-raise
+            if retries <= 0:
                 raise e
-            else:
-                print("download failed, retrying, {} attempt{} left"
-                      .format(retries, 's' if retries > 1 else ''))
+
+            print("download failed, retrying, {} attempt{} left"
+                  .format(retries, 's' if retries > 1 else ''))
     logging.info("downloaded %s into %s successfully", url, fname)
     return fname
 
@@ -1661,7 +1661,7 @@ def get_mnist_iterator(batch_size, input_shape, num_parts=1, part_index=0):
     """
 
     get_mnist_ubyte()
-    flat = False if len(input_shape) == 3 else True # pylint: disable=simplifiable-if-expression
+    flat = not bool(len(input_shape) == 3)
 
     train_dataiter = mx.io.MNISTIter(
         image="data/train-images-idx3-ubyte",

From 9d859c8585fe7924bf904938b844a721b5cf98cf Mon Sep 17 00:00:00 2001
From: William Tambellini <william.tambellini@gmail.com>
Date: Sun, 21 Jul 2019 11:59:22 -0700
Subject: [PATCH 101/813] logging (#15106)

---
 example/named_entity_recognition/src/metrics.py    | 13 ++++++++-----
 example/named_entity_recognition/src/ner.py        |  8 ++++++++
 example/named_entity_recognition/src/preprocess.py |  7 +++++++
 3 files changed, 23 insertions(+), 5 deletions(-)

diff --git a/example/named_entity_recognition/src/metrics.py b/example/named_entity_recognition/src/metrics.py
index ef5f64fb1af3..a1d270af6863 100644
--- a/example/named_entity_recognition/src/metrics.py
+++ b/example/named_entity_recognition/src/metrics.py
@@ -19,6 +19,7 @@
 
 # -*- coding: utf-8 -*-
 
+import logging
 import mxnet as mx
 import numpy as np
 import pickle
@@ -43,17 +44,17 @@ def classifer_metrics(label, pred):
     corr_pred = (prediction == label) == (pred_is_entity == True)
 
     #how many entities are there?
-    num_entities = np.sum(label_is_entity)
-    entity_preds = np.sum(pred_is_entity)
-
+    # better to cast to float for safer further ratio computations
+    num_entities = float(np.sum(label_is_entity))
+    entity_preds = float(np.sum(pred_is_entity)) 
     #how many times did we correctly predict an entity?
-    correct_entitites = np.sum(corr_pred[pred_is_entity])
+    correct_entitites = float(np.sum(corr_pred[pred_is_entity]))
 
     #precision: when we predict entity, how often are we right?
     if entity_preds == 0:
         precision = np.nan
     else:
-        precision = correct_entitites/entity_preds
+        precision = correct_entitites / entity_preds
 
     #recall: of the things that were an entity, how many did we catch?
     recall = correct_entitites / num_entities
@@ -64,6 +65,8 @@ def classifer_metrics(label, pred):
         f1 = 0
     else:
         f1 = 2 * precision * recall / (precision + recall)
+
+    logging.debug("Metrics results: precision=%f recall=%f f1=%f", precision, recall, f1)
     return precision, recall, f1
 
 def entity_precision(label, pred):
diff --git a/example/named_entity_recognition/src/ner.py b/example/named_entity_recognition/src/ner.py
index 7f5dd84527cc..6accb2826f2d 100644
--- a/example/named_entity_recognition/src/ner.py
+++ b/example/named_entity_recognition/src/ner.py
@@ -93,6 +93,7 @@ def build_vocab(nested_list):
     """
     # Build vocabulary
     word_counts = Counter(itertools.chain(*nested_list))
+    logging.info("build_vocab: word_counts=%d" % (len(word_counts)))
 
     # Mapping from index to label
     vocabulary_inv = [x[0] for x in word_counts.most_common()]
@@ -114,6 +115,7 @@ def build_iters(data_dir, max_records, train_fraction, batch_size, buckets=None)
     :param buckets: size of each bucket in the iterators
     :return: train_iter, val_iter, word_to_index, index_to_word, pos_to_index, index_to_pos
     """
+
     # Read in data as numpy array
     df = pd.read_pickle(os.path.join(data_dir, "ner_data.pkl"))[:max_records]
 
@@ -135,12 +137,14 @@ def build_iters(data_dir, max_records, train_fraction, batch_size, buckets=None)
 
     # Split into training and testing data
     idx=int(len(indexed_tokens)*train_fraction)
+    logging.info("Preparing train/test datasets splitting at idx %d on total %d sentences using a batchsize of %d", idx, len(indexed_tokens), batch_size)
     X_token_train, X_char_train, Y_train = indexed_tokens[:idx], indexed_chars[:idx], indexed_entities[:idx]
     X_token_test, X_char_test, Y_test = indexed_tokens[idx:], indexed_chars[idx:], indexed_entities[idx:]
 
     # build iterators to feed batches to network
     train_iter = iterators.BucketNerIter(sentences=X_token_train, characters=X_char_train, label=Y_train,
                                          max_token_chars=5, batch_size=batch_size, buckets=buckets)
+    logging.info("Creating the val_iter using %d sentences", len(X_token_test))
     val_iter = iterators.BucketNerIter(sentences=X_token_test, characters=X_char_test, label=Y_test,
                                          max_token_chars=train_iter.max_token_chars, batch_size=batch_size, buckets=train_iter.buckets)
     return train_iter, val_iter, word_to_index, char_to_index, entity_to_index
@@ -205,6 +209,8 @@ def sym_gen(seq_len):
 def train(train_iter, val_iter):
     import metrics
     devs = mx.cpu() if args.gpus is None or args.gpus is '' else [mx.gpu(int(i)) for i in args.gpus.split(',')]
+    logging.info("train on device %s using optimizer %s at learningrate %f for %d epochs using %d records: lstm_state_size=%d ...",
+          devs, args.optimizer, args.lr, args.num_epochs, args.max_records, args.lstm_state_size)
     module = mx.mod.BucketingModule(sym_gen, train_iter.default_bucket_key, context=devs)
     module.fit(train_data=train_iter,
                eval_data=val_iter,
@@ -225,6 +231,8 @@ def train(train_iter, val_iter):
     train_iter, val_iter, word_to_index, char_to_index, entity_to_index = build_iters(args.data_dir, args.max_records,
                                                                      args.train_fraction, args.batch_size, args.buckets)
 
+    logging.info("validation iterator: %s", val_iter)
+
     # Define the recurrent layer
     bi_cell = mx.rnn.SequentialRNNCell()
     for layer_num in range(args.lstm_layers):
diff --git a/example/named_entity_recognition/src/preprocess.py b/example/named_entity_recognition/src/preprocess.py
index 6ae348ad8bae..22c1c360c62d 100644
--- a/example/named_entity_recognition/src/preprocess.py
+++ b/example/named_entity_recognition/src/preprocess.py
@@ -19,6 +19,7 @@
 
 # -*- coding: utf-8 -*-
 
+import logging
 import pandas as pd
 import numpy as np
 
@@ -45,6 +46,12 @@
 
 #join the results on utterance id
 df = df1.merge(df2.merge(df3, how = "left", on = "utterance_id"), how = "left", on = "utterance_id")
+pd.option_context('display.max_colwidth', None)
+pd.option_context('display.max_rowwidth', None)
+
+logging.info("preprocess: 1st sentence:")
+logging.info(df['token'].iloc[0].tolist())
+logging.info(df['BILOU_tag'].iloc[0].tolist())
 
 #save the dataframe to a csv file
 df.to_pickle("../data/ner_data.pkl")
\ No newline at end of file

From 9a1a1028529c91c83f275edb74b272f8ad3dcc3d Mon Sep 17 00:00:00 2001
From: Wuxun Zhang <wuxun.zhang@intel.com>
Date: Mon, 22 Jul 2019 07:25:30 +0800
Subject: [PATCH 102/813] Add omp parallel optimization for
 _contrib_BilinearReisze2D (#15584)

* Add omp parallel optimization for bilinear_resize op

* retrigger CI

* retrigger CI

* trigger CI
---
 src/operator/contrib/bilinear_resize.cc | 146 ++++++++++++++----------
 tests/python/gpu/test_operator_gpu.py   |  16 +++
 tests/python/unittest/test_operator.py  |   2 +-
 3 files changed, 101 insertions(+), 63 deletions(-)

diff --git a/src/operator/contrib/bilinear_resize.cc b/src/operator/contrib/bilinear_resize.cc
index 441ea53ad9c6..346324753a26 100644
--- a/src/operator/contrib/bilinear_resize.cc
+++ b/src/operator/contrib/bilinear_resize.cc
@@ -23,7 +23,6 @@
  * \author Hang Zhang
 */
 #include "bilinear_resize-inl.h"
-// #include "elemwise_op_common.h"
 #include "../elemwise_op_common.h"
 
 namespace mxnet {
@@ -44,56 +43,66 @@ void SpatialUpSamplingBilinearUpdateOutput(mshadow::Stream<cpu> *s,
   int inputHeight = itensor.size(2);
   int inputWidth = itensor.size(3);
 
+  const auto nthreads = engine::OpenMP::Get()->GetRecommendedOMPThreadCount();
+
   DType *idata = itensor.dptr_;
   DType *odata = otensor.dptr_;
   channels = nbatch * channels;
+  const int input_elems_per_channel = inputWidth * inputHeight;
+  const int output_elems_per_channel = outputWidth * outputHeight;
+
   // special case: just copy
   if (inputHeight == outputHeight && inputWidth == outputWidth) {
-    for (int h2 = 0; h2 < outputHeight; ++h2) {
+#pragma omp parallel for num_threads(nthreads)
+    for (int index = 0; index < output_elems_per_channel; index++) {
+      const int h2 = index / outputWidth;
       const int h1 = h2;
-      for (int w2 = 0; w2 < outputWidth; ++w2) {
-        const int w1 = w2;
-        const DType* pos1 = &idata[h1 * inputWidth + w1];
-        DType* pos2 = &odata[h2 * outputWidth + w2];
-        for (int c = 0; c < channels; ++c) {
-          pos2[0] = pos1[0];
-          pos1 += inputWidth * inputHeight;
-          pos2 += outputWidth * outputHeight;
-        }
+      const int w2 = index % outputWidth;
+      const int w1 = w2;
+      const DType* pos1 = &idata[h1 * inputWidth + w1];
+      DType* pos2 = &odata[index];
+      for (int c = 0; c < channels; ++c) {
+        *pos2 = *pos1;
+        pos1 += input_elems_per_channel;
+        pos2 += output_elems_per_channel;
       }
     }
     return;
   }
+
   const float rheight =(outputHeight > 1) ? static_cast<float>(inputHeight - 1)/
                        (outputHeight - 1) : 0.f;
   const float rwidth = (outputWidth > 1) ? static_cast<float>(inputWidth - 1) /
                        (outputWidth - 1) : 0.f;
-  for (int h2 = 0; h2 < outputHeight; ++h2) {
+#pragma omp parallel for num_threads(nthreads)
+  for (int index = 0; index < output_elems_per_channel; index++) {
+    const int h2 = index / outputWidth;
+    const int w2 = index % outputWidth;
+
     const float h1r = rheight * h2;
     const int h1 = h1r;
     const int h1p = (h1 < inputHeight - 1) ? 1 : 0;
     const DType h1lambda = h1r - h1;
     const DType h0lambda = (DType)1. - h1lambda;
-    for (int w2 = 0; w2 < outputWidth; ++w2) {
-      const float w1r = rwidth * w2;
-      const int w1 = w1r;
-      const int w1p = (w1 < inputWidth - 1) ? 1 : 0;
-      const DType w1lambda = w1r - w1;
-      const DType w0lambda = (DType)1. - w1lambda;
-      const DType* pos1 = &idata[h1 * inputWidth + w1];
-      DType* pos2 = &odata[h2 * outputWidth + w2];
-      for (int c = 0; c < channels; ++c) {
-        pos2[0] = h0lambda * (w0lambda * pos1[0]+ w1lambda * pos1[w1p])
-                  + h1lambda * (w0lambda * pos1[h1p * inputWidth]
-                  + w1lambda * pos1[h1p * inputWidth + w1p]);
-        pos1 += inputWidth * inputHeight;
-        pos2 += outputWidth * outputHeight;
-      }
+
+    const float w1r = rwidth * w2;
+    const int w1 = w1r;
+    const int w1p = (w1 < inputWidth - 1) ? 1 : 0;
+    const DType w1lambda = w1r - w1;
+    const DType w0lambda = (DType)1. - w1lambda;
+    const DType* pos1 = &idata[h1 * inputWidth + w1];
+    DType* pos2 = &odata[index];
+
+    for (int c = 0; c < channels; ++c) {
+      *pos2 = h0lambda * (w0lambda * (*pos1) + w1lambda * *(pos1 + w1p))
+                  + h1lambda * (w0lambda * *(pos1 + h1p * inputWidth)
+                  + w1lambda * *(pos1 + h1p * inputWidth + w1p));
+      pos1 += input_elems_per_channel;
+      pos2 += output_elems_per_channel;
     }
   }
 }
 
-
 template<typename xpu, typename DType, typename AccReal>
 void SpatialUpSamplingBilinearUpdateGradInput(mshadow::Stream<cpu> *s,
                                               const std::vector<TBlob> &input,
@@ -109,23 +118,28 @@ void SpatialUpSamplingBilinearUpdateGradInput(mshadow::Stream<cpu> *s,
   int inputHeight = gradInput.size(2);
   int inputWidth = gradInput.size(3);
 
+  const auto nthreads = engine::OpenMP::Get()->GetRecommendedOMPThreadCount();
+
   DType *dataInput = gradInput.dptr_;
   DType *dataOutput = gradOutput.dptr_;
   channels = nbatch * channels;
+  const int input_elems_per_channel = inputWidth * inputHeight;
+  const int output_elems_per_channel = outputWidth * outputHeight;
 
   // special case: same-size matching grids
   if (inputHeight == outputHeight && inputWidth == outputWidth) {
-    for (int h2 = 0; h2 < outputHeight; ++h2) {
+#pragma omp parallel for num_threads(nthreads)
+    for (int index = 0; index < output_elems_per_channel; index++) {
+      const int h2 = index / outputWidth;
       const int h1 = h2;
-      for (int w2 = 0; w2 < outputWidth; ++w2) {
-        const int w1 = w2;
-        DType* pos1 = &dataInput[h1 * inputWidth + w1];
-        const DType* pos2 = &dataOutput[h2 * outputWidth + w2];
-        for (int c = 0; c < channels; ++c) {
-          pos1[0] += pos2[0];
-          pos1 += inputWidth * inputHeight;
-          pos2 += outputWidth * outputHeight;
-        }
+      const int w2 = index % outputWidth;
+      const int w1 = w2;
+      DType* pos1 = &dataInput[h1 * inputWidth + w1];
+      const DType* pos2 = &dataOutput[index];
+      for (int c = 0; c < channels; ++c) {
+        *pos1 += *pos2;
+        pos1 += input_elems_per_channel;
+        pos2 += output_elems_per_channel;
       }
     }
     return;
@@ -134,28 +148,36 @@ void SpatialUpSamplingBilinearUpdateGradInput(mshadow::Stream<cpu> *s,
                        (outputHeight - 1) : 0.f;
   const float rwidth = (outputWidth > 1) ? static_cast<float>(inputWidth - 1)/
                        (outputWidth - 1) : 0.f;
-  for (int h2 = 0; h2 < outputHeight; ++h2) {
+
+#pragma omp parallel for num_threads(nthreads)
+  for (int index = 0; index < output_elems_per_channel; index++) {
+    const int h2 = index / outputWidth;
+    const int w2 = index % outputWidth;
+
     const float h1r = rheight * h2;
     const int h1 = h1r;
     const int h1p = (h1 < inputHeight - 1) ? 1 : 0;
     const DType h1lambda = h1r - h1;
     const DType h0lambda = (DType)1. - h1lambda;
-    for (int w2 = 0; w2 < outputWidth; ++w2) {
-      const float w1r = rwidth * w2;
-      const int w1 = w1r;
-      const int w1p = (w1 < inputWidth - 1) ? 1 : 0;
-      const DType w1lambda = w1r - w1;
-      const DType w0lambda = (DType)1. - w1lambda;
-      DType* posInput = &dataInput[h1 * inputWidth + w1];
-      const DType* posOutput = &dataOutput[h2 * outputWidth + w2];
-      for (int c = 0; c < channels; ++c) {
-        posInput[0] += h0lambda * w0lambda * posOutput[0];
-        posInput[w1p] += h0lambda * w1lambda * posOutput[0];
-        posInput[h1p * inputWidth] += h1lambda * w0lambda * posOutput[0];
-        posInput[h1p * inputWidth + w1p] += h1lambda * w1lambda * posOutput[0];
-        posInput += inputWidth * inputHeight;
-        posOutput += outputWidth * outputHeight;
+
+    const float w1r = rwidth * w2;
+    const int w1 = w1r;
+    const int w1p = (w1 < inputWidth - 1) ? 1 : 0;
+    const DType w1lambda = w1r - w1;
+    const DType w0lambda = (DType)1. - w1lambda;
+
+    DType* posInput = &dataInput[h1 * inputWidth + w1];
+    const DType* posOutput = &dataOutput[index];
+    for (int c = 0; c < channels; ++c) {
+      #pragma omp critical
+      {
+        *posInput += h0lambda * w0lambda * (*posOutput);
+        *(posInput + w1p) += h0lambda * w1lambda * (*posOutput);
+        *(posInput + h1p * inputWidth) += h1lambda * w0lambda * (*posOutput);
+        *(posInput + h1p * inputWidth + w1p) += h1lambda * w1lambda * (*posOutput);
       }
+      posInput += input_elems_per_channel;
+      posOutput += output_elems_per_channel;
     }
   }
 
@@ -165,19 +187,19 @@ void SpatialUpSamplingBilinearUpdateGradInput(mshadow::Stream<cpu> *s,
     int inputWidthLike = gradInputLike.size(3);
     DType *dataInputLike = gradInputLike.dptr_;
     int channelsLike = nbatch * gradInputLike.size(1);
-    for (int h_like = 0; h_like < inputHeightLike; ++h_like) {
-      for (int w_like = 0; w_like < inputWidthLike; ++w_like) {
-        DType *posInput = &dataInputLike[h_like * inputWidthLike + w_like];
-        for (int c = 0; c < channelsLike; ++c) {
-          posInput[0] = 0;
-          posInput += inputWidthLike * inputHeightLike;
-        }
+
+    const int inputLike_elems_per_channel = inputHeightLike * inputWidthLike;
+#pragma omp parallel for num_threads(nthreads)
+    for (int index = 0; index < inputLike_elems_per_channel; index++) {
+      DType *posInput = &dataInputLike[index];
+      for (int c = 0; c < channelsLike; ++c) {
+        *posInput = 0;
+        posInput += inputLike_elems_per_channel;
       }
     }
   }
 }
 
-
 DMLC_REGISTER_PARAMETER(BilinearSampleParam);
 
 NNVM_REGISTER_OP(_contrib_BilinearResize2D)
diff --git a/tests/python/gpu/test_operator_gpu.py b/tests/python/gpu/test_operator_gpu.py
index 5b4f81d96065..f9814abc9c0b 100644
--- a/tests/python/gpu/test_operator_gpu.py
+++ b/tests/python/gpu/test_operator_gpu.py
@@ -1143,6 +1143,22 @@ def test_flatten_slice_after_conv():
     check_consistency(slice_sym, ctx_list)
 
 
+@with_seed()
+def test_bilinear_resize_op():
+    ctx_list = [{'ctx': mx.cpu(0), 'data': (2, 2, 20, 20), 'type_dict': {'data': np.float32}},
+                {'ctx': mx.gpu(0), 'data': (2, 2, 20, 20), 'type_dict': {'data': np.float32}}]
+
+    data = mx.sym.Variable('data')
+    sym = mx.sym.contrib.BilinearResize2D(data, height=10, width=5)
+    check_consistency(sym, ctx_list)
+
+    sym = mx.sym.contrib.BilinearResize2D(data, None, scale_height=2, scale_width=0.5, mode='odd_scale')
+    check_consistency(sym, ctx_list)
+
+    sym = mx.sym.contrib.BilinearResize2D(data, None, scale_height=0.5, scale_width=2, mode='to_even_up')
+    check_consistency(sym, ctx_list)
+
+
 @with_seed()
 def test_global_pooling():
     def test_1d_pooling(pool_type, p_value=2):
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 915a83f2f342..d195ea9ef2f3 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -7649,7 +7649,7 @@ def py_bilinear_resize(x, outputHeight, outputWidth):
                 w1r = 1.0 * w2 * rwidth
                 w1 = int(np.floor(w1r))
                 w1lambda = w1r - w1
-                w1p = 1 if w1 < (inputHeight - 1) else 0
+                w1p = 1 if w1 < (inputWidth - 1) else 0
                 for b in range(batch):
                     for c in range(channel):
                         y[b][c][h2][w2] = (1-h1lambda)*((1-w1lambda)*x[b][c][h1][w1] + \

From 77254f2a829d481d61e015e900923f1edb4af39f Mon Sep 17 00:00:00 2001
From: Yizhi Liu <liuyizhi@apache.org>
Date: Sun, 21 Jul 2019 20:58:28 -0700
Subject: [PATCH 103/813] Infra to use tvm write op kernels (#15550)

* intra to use tvm write op kernels

* add cmake support for tvm op

* fix header lint

* cleanup USE_TVM_OP logic in Makefile

* add doc, cmake def, etc.

* fix doc

* test rand shape

* add with_seed to test

* improve err msg. add #if
---
 3rdparty/dlpack                       |   2 +-
 3rdparty/dmlc-core                    |   2 +-
 3rdparty/tvm                          |   2 +-
 CMakeLists.txt                        |  23 +++++
 Makefile                              |  38 ++++++++
 cmake/BuildTVM.cmake                  | 135 ++++++++++++++++++++++++++
 contrib/tvmop/__init__.py             |  22 +++++
 contrib/tvmop/basic/__init__.py       |  19 ++++
 contrib/tvmop/basic/ufunc.py          |  50 ++++++++++
 contrib/tvmop/compile.py              |  59 +++++++++++
 contrib/tvmop/opdef.py                | 111 +++++++++++++++++++++
 contrib/tvmop/prepare_tvm.sh          |  63 ++++++++++++
 contrib/tvmop/utils.py                |  20 ++++
 include/mxnet/c_api.h                 |   9 ++
 include/mxnet/libinfo.h               |   7 ++
 make/config.mk                        |   3 +
 make/osx.mk                           |   3 +
 python/mxnet/base.py                  |   7 +-
 python/mxnet/libinfo.py               |  10 +-
 src/c_api/c_api.cc                    |   9 ++
 src/libinfo.cc                        |   4 +
 src/operator/contrib/tvmop/ufunc.cc   |  66 +++++++++++++
 src/operator/tvmop/op_module.cc       | 117 ++++++++++++++++++++++
 src/operator/tvmop/op_module.h        |  63 ++++++++++++
 tests/python/gpu/test_operator_gpu.py |   1 +
 tests/python/unittest/test_tvm_op.py  |  38 ++++++++
 26 files changed, 874 insertions(+), 9 deletions(-)
 create mode 100644 cmake/BuildTVM.cmake
 create mode 100644 contrib/tvmop/__init__.py
 create mode 100644 contrib/tvmop/basic/__init__.py
 create mode 100644 contrib/tvmop/basic/ufunc.py
 create mode 100644 contrib/tvmop/compile.py
 create mode 100644 contrib/tvmop/opdef.py
 create mode 100644 contrib/tvmop/prepare_tvm.sh
 create mode 100644 contrib/tvmop/utils.py
 create mode 100644 src/operator/contrib/tvmop/ufunc.cc
 create mode 100644 src/operator/tvmop/op_module.cc
 create mode 100644 src/operator/tvmop/op_module.h
 create mode 100644 tests/python/unittest/test_tvm_op.py

diff --git a/3rdparty/dlpack b/3rdparty/dlpack
index 10892ac964f1..b90e93907206 160000
--- a/3rdparty/dlpack
+++ b/3rdparty/dlpack
@@ -1 +1 @@
-Subproject commit 10892ac964f1af7c81aae145cd3fab78bbccd297
+Subproject commit b90e939072066c160b18ea1e7156537b8d3710f6
diff --git a/3rdparty/dmlc-core b/3rdparty/dmlc-core
index 3943914eed66..f1ff6cc117f4 160000
--- a/3rdparty/dmlc-core
+++ b/3rdparty/dmlc-core
@@ -1 +1 @@
-Subproject commit 3943914eed66470bd010df581e29e4dca4f7df6f
+Subproject commit f1ff6cc117f4e95169a9f62be549c8fe3e15c20f
diff --git a/3rdparty/tvm b/3rdparty/tvm
index 21935dcbf56a..afd4b3e44509 160000
--- a/3rdparty/tvm
+++ b/3rdparty/tvm
@@ -1 +1 @@
-Subproject commit 21935dcbf56ad3bd66ebff9891a6bc3865b8106d
+Subproject commit afd4b3e4450984358e9d79a7e8e578483cb7b017
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 19a93c731e00..7c479f756295 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -42,6 +42,7 @@ mxnet_option(USE_MXNET_LIB_NAMING "Use MXNet library naming conventions." ON)
 mxnet_option(USE_GPROF            "Compile with gprof (profiling) flag" OFF)
 mxnet_option(USE_CXX14_IF_AVAILABLE "Build with C++14 if the compiler supports it" OFF)
 mxnet_option(USE_VTUNE            "Enable use of Intel Amplifier XE (VTune)" OFF) # one could set VTUNE_ROOT for search path
+mxnet_option(USE_TVM_OP           "Enable use of TVM operator build system." OFF)
 mxnet_option(ENABLE_CUDA_RTC      "Build with CUDA runtime compilation support" ON)
 mxnet_option(BUILD_CPP_EXAMPLES   "Build cpp examples" ON)
 mxnet_option(INSTALL_EXAMPLES     "Install the example source files." OFF)
@@ -733,6 +734,28 @@ if(USE_DIST_KVSTORE)
   list(APPEND mxnet_LINKER_LIBS ${pslite_LINKER_LIBS})
 endif()
 
+if(USE_TVM_OP)
+  add_definitions(-DMXNET_USE_TVM_OP=1)
+  list(APPEND mxnet_LINKER_LIBS ${CMAKE_CURRENT_BINARY_DIR}/3rdparty/tvm/libtvm_runtime.so)
+  include(cmake/BuildTVM.cmake)
+  add_subdirectory("3rdparty/tvm")
+
+  if(NOT Python3_EXECUTABLE)
+    find_package(PythonInterp 3 REQUIRED)
+    set(Python3_EXECUTABLE ${PYTHON_EXECUTABLE} CACHE FILEPATH "Path to the python3 executable")
+    if(NOT Python3_EXECUTABLE)
+      message(FATAL_ERROR "No python3 interpreter found to build TVM operators")
+    endif()
+  endif()
+
+  add_custom_command(TARGET mxnet POST_BUILD
+    COMMAND ${CMAKE_COMMAND} -E env
+          PYTHONPATH="${CMAKE_CURRENT_SOURCE_DIR}/3rdparty/tvm/python:${CMAKE_CURRENT_SOURCE_DIR}/3rdparty/tvm/topi/python:${CMAKE_CURRENT_SOURCE_DIR}/contrib"
+          LD_LIBRARY_PATH="${CMAKE_CURRENT_BINARY_DIR}/3rdparty/tvm/build"
+          ${Python3_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/contrib/tvmop/compile.py -o${CMAKE_CURRENT_BINARY_DIR}/libtvmop.so
+    )
+endif()
+
 target_link_libraries(mxnet PUBLIC ${mxnet_LINKER_LIBS})
 
 if(USE_PLUGINS_WARPCTC)
diff --git a/Makefile b/Makefile
index c5ac4ebe01fa..ce840a3d30aa 100644
--- a/Makefile
+++ b/Makefile
@@ -52,6 +52,14 @@ ifndef AMALGAMATION_PATH
 	AMALGAMATION_PATH = $(ROOTDIR)/amalgamation
 endif
 
+ifndef TVM_PATH
+	TVM_PATH = $(TPARTYDIR)/tvm
+endif
+
+ifndef LLVM_PATH
+	LLVM_PATH = $(TVM_PATH)/build/llvm
+endif
+
 ifneq ($(USE_OPENMP), 1)
 	export NO_OPENMP = 1
 endif
@@ -589,6 +597,35 @@ $(DMLC_CORE)/libdmlc.a: DMLCCORE
 DMLCCORE:
 	+ cd $(DMLC_CORE); $(MAKE) libdmlc.a USE_SSE=$(USE_SSE) config=$(ROOTDIR)/$(config); cd $(ROOTDIR)
 
+ifeq ($(USE_TVM_OP), 1)
+LIB_DEP += lib/libtvm_runtime.so lib/libtvmop.so
+CFLAGS += -I$(TVM_PATH)/include -DMXNET_USE_TVM_OP=1
+LDFLAGS += -L$(TVM_PATH)/build -ltvm_runtime
+
+TVM_USE_CUDA := OFF
+ifeq ($(USE_CUDA), 1)
+	TVM_USE_CUDA := ON
+	ifneq ($(USE_CUDA_PATH), NONE)
+		TVM_USE_CUDA := $(USE_CUDA_PATH)
+	endif
+endif
+lib/libtvm_runtime.so:
+	echo "Compile TVM"
+	[ -e $(LLVM_PATH)/bin/llvm-config ] || sh $(ROOTDIR)/contrib/tvmop/prepare_tvm.sh; \
+	cd $(TVM_PATH)/build; \
+	cmake -DUSE_LLVM="$(LLVM_PATH)/bin/llvm-config" \
+		  -DUSE_SORT=OFF -DUSE_CUDA=$(TVM_USE_CUDA) -DUSE_CUDNN=OFF ..; \
+	$(MAKE) VERBOSE=1; \
+	cp $(TVM_PATH)/build/libtvm_runtime.so $(ROOTDIR)/lib/libtvm_runtime.so; \
+	cd $(ROOTDIR)
+
+lib/libtvmop.so: lib/libtvm_runtime.so $(wildcard contrib/tvmop/*/*.py contrib/tvmop/*.py)
+	echo "Compile TVM operators"
+	PYTHONPATH=$(TVM_PATH)/python:$(TVM_PATH)/topi/python:$(ROOTDIR)/contrib:$PYTHONPATH \
+		LD_LIBRARY_PATH=lib \
+	    python3 $(ROOTDIR)/contrib/tvmop/compile.py -o $(ROOTDIR)/lib/libtvmop.so
+endif
+
 NNVM_INC = $(wildcard $(NNVM_PATH)/include/*/*.h)
 NNVM_SRC = $(wildcard $(NNVM_PATH)/src/*/*/*.cc $(NNVM_PATH)/src/*/*.cc $(NNVM_PATH)/src/*.cc)
 $(NNVM_PATH)/lib/libnnvm.a: $(NNVM_INC) $(NNVM_SRC)
@@ -726,6 +763,7 @@ clean: rclean cyclean $(EXTRA_PACKAGES_CLEAN)
 	cd $(DMLC_CORE); $(MAKE) clean; cd -
 	cd $(PS_PATH); $(MAKE) clean; cd -
 	cd $(NNVM_PATH); $(MAKE) clean; cd -
+	cd $(TVM_PATH); $(MAKE) clean; cd -
 	cd $(AMALGAMATION_PATH); $(MAKE) clean; cd -
 	$(RM) -r  $(patsubst %, %/*.d, $(EXTRA_OPERATORS)) $(patsubst %, %/*/*.d, $(EXTRA_OPERATORS))
 	$(RM) -r  $(patsubst %, %/*.o, $(EXTRA_OPERATORS)) $(patsubst %, %/*/*.o, $(EXTRA_OPERATORS))
diff --git a/cmake/BuildTVM.cmake b/cmake/BuildTVM.cmake
new file mode 100644
index 000000000000..ad8517cafe26
--- /dev/null
+++ b/cmake/BuildTVM.cmake
@@ -0,0 +1,135 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+message(STATUS "Prepare external packages for TVM...")
+execute_process(COMMAND "${CMAKE_CURRENT_SOURCE_DIR}/contrib/tvmop/prepare_tvm.sh")
+
+# Whether enable ROCM runtime
+#
+# Possible values:
+# - ON: enable ROCM with cmake's auto search
+# - OFF: disable ROCM
+# - /path/to/rocm: use specific path to rocm
+set(USE_ROCM OFF)
+
+# Whether enable SDAccel runtime
+set(USE_SDACCEL OFF)
+
+# Whether enable Intel FPGA SDK for OpenCL (AOCL) runtime
+set(USE_AOCL OFF)
+
+# Whether enable OpenCL runtime
+set(USE_OPENCL OFF)
+
+# Whether enable Metal runtime
+set(USE_METAL OFF)
+
+# Whether enable Vulkan runtime
+#
+# Possible values:
+# - ON: enable Vulkan with cmake's auto search
+# - OFF: disable vulkan
+# - /path/to/vulkan-sdk: use specific path to vulkan-sdk
+set(USE_VULKAN OFF)
+
+# Whether enable OpenGL runtime
+set(USE_OPENGL OFF)
+
+# Whether to enable SGX runtime
+#
+# Possible values for USE_SGX:
+# - /path/to/sgxsdk: path to Intel SGX SDK
+# - OFF: disable SGX
+#
+# SGX_MODE := HW|SIM
+set(USE_SGX OFF)
+set(SGX_MODE "SIM")
+set(RUST_SGX_SDK "/path/to/rust-sgx-sdk")
+
+# Whether enable RPC runtime
+set(USE_RPC ON)
+
+# Whether embed stackvm into the runtime
+set(USE_STACKVM_RUNTIME OFF)
+
+# Whether enable tiny embedded graph runtime.
+set(USE_GRAPH_RUNTIME ON)
+
+# Whether enable additional graph debug functions
+set(USE_GRAPH_RUNTIME_DEBUG OFF)
+
+# Whether build with LLVM support
+# Requires LLVM version >= 4.0
+#
+# Possible values:
+# - ON: enable llvm with cmake's find search
+# - OFF: disable llvm
+# - /path/to/llvm-config: enable specific LLVM when multiple llvm-dev is available.
+set(USE_LLVM "${CMAKE_CURRENT_SOURCE_DIR}/3rdparty/tvm/build/llvm/bin/llvm-config")
+
+#---------------------------------------------
+# Contrib libraries
+#---------------------------------------------
+# Whether use BLAS, choices: openblas, mkl, atlas, apple
+set(USE_BLAS none)
+
+# /path/to/mkl: mkl root path when use mkl blas library
+# set(USE_MKL_PATH /opt/intel/mkl) for UNIX
+# set(USE_MKL_PATH ../IntelSWTools/compilers_and_libraries_2018/windows/mkl) for WIN32
+set(USE_MKL_PATH none)
+
+# Whether use contrib.random in runtime
+set(USE_RANDOM OFF)
+
+# Whether use NNPack
+set(USE_NNPACK OFF)
+
+# Whether use CuDNN
+if(USE_CUDNN AND USE_CUDA)
+    detect_cuDNN()
+    if(HAVE_CUDNN)
+        set(USE_CUDNN ON)
+    else()
+        set(USE_CUDNN OFF)
+    endif()
+else()
+    set(USE_CUDNN OFF)
+endif()
+
+# Whether use cuBLAS
+set(USE_CUBLAS OFF)
+
+# Whether use MIOpen
+set(USE_MIOPEN OFF)
+
+# Whether use MPS
+set(USE_MPS OFF)
+
+# Whether use rocBlas
+set(USE_ROCBLAS OFF)
+
+# Whether use contrib sort
+set(USE_SORT OFF)
+
+# Build ANTLR parser for Relay text format
+set(USE_ANTLR OFF)
+
+# Build TSIM for VTA
+set(USE_VTA_TSIM OFF)
+
+# Whether use Relay debug mode
+set(USE_RELAY_DEBUG OFF)
diff --git a/contrib/tvmop/__init__.py b/contrib/tvmop/__init__.py
new file mode 100644
index 000000000000..31189d499b5a
--- /dev/null
+++ b/contrib/tvmop/__init__.py
@@ -0,0 +1,22 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# coding: utf-8
+from .opdef import defop
+from .utils import AllTypes, RealTypes
+
+from . import basic
diff --git a/contrib/tvmop/basic/__init__.py b/contrib/tvmop/basic/__init__.py
new file mode 100644
index 000000000000..fc0fa720009f
--- /dev/null
+++ b/contrib/tvmop/basic/__init__.py
@@ -0,0 +1,19 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# coding: utf-8
+from . import ufunc
diff --git a/contrib/tvmop/basic/ufunc.py b/contrib/tvmop/basic/ufunc.py
new file mode 100644
index 000000000000..0419e5fd2ca9
--- /dev/null
+++ b/contrib/tvmop/basic/ufunc.py
@@ -0,0 +1,50 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# coding: utf-8
+import tvm
+from .. import defop, AllTypes
+
+def compute_add(dtype, ndim):
+    A = tvm.placeholder([tvm.var() for _ in range(ndim)], name='A', dtype=dtype)
+    B = tvm.placeholder([tvm.var() for _ in range(ndim)], name='B', dtype=dtype)
+    C = tvm.compute([tvm.var() for _ in range(ndim)],
+                    lambda *index: A[index] + B[index], name='C')
+    s = tvm.create_schedule(C.op)
+    return s, A, B, C
+
+@defop(name="vadd", target="cpu", auto_broadcast=True,
+       dtype=AllTypes, ndim=list(range(1, 6)))
+def vadd(dtype, ndim):
+    s, A, B, C = compute_add(dtype, ndim)
+    axes = [axis for axis in C.op.axis]
+    fused = s[C].fuse(*axes)
+    s[C].parallel(fused)
+
+    return s, [A, B, C]
+
+@defop(name="cuda_vadd", target="cuda", auto_broadcast=True,
+       dtype=["float32", "float64"], ndim=list(range(1, 6)))
+def vadd_gpu(dtype, ndim):
+    s, A, B, C = compute_add(dtype, ndim)
+    s = tvm.create_schedule(C.op)
+    axes = [axis for axis in C.op.axis]
+    fused = s[C].fuse(*axes)
+    bx, tx = s[C].split(fused, factor=64)
+    s[C].bind(bx, tvm.thread_axis("blockIdx.x"))
+    s[C].bind(tx, tvm.thread_axis("threadIdx.x"))
+    return s, [A, B, C]
diff --git a/contrib/tvmop/compile.py b/contrib/tvmop/compile.py
new file mode 100644
index 000000000000..94274fe4142a
--- /dev/null
+++ b/contrib/tvmop/compile.py
@@ -0,0 +1,59 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# coding: utf-8
+"""TVM Operator compile entry point"""
+import tvm
+
+import os
+import argparse
+from tvmop.opdef import __OP_DEF__
+
+def get_target(device):
+    if device == "cpu":
+        return "llvm"
+    elif device == "cuda" or device == "gpu":
+        return "cuda"
+    assert False, "Unknown device " + device
+
+
+if __name__ == "__main__":
+    import sys
+    sys.path.append(os.path.dirname(sys.path[0]))
+    parser = argparse.ArgumentParser(description="Generate tvm operators")
+    parser.add_argument("-o", action="store", required=True, dest="target_path",
+                        help="Target path which stores compiled library")
+    arguments = parser.parse_args()
+
+    func_list_llvm = []
+    func_list_cuda = []
+
+    # TODO: attach instruction features to the library, e.g., avx-512, etc.
+    for operator_def in __OP_DEF__:
+        for sch, args in operator_def.invoke_all():
+            if tvm.module.enabled(get_target(operator_def.target)):
+                func_list = func_list_llvm if operator_def.target == "cpu" else func_list_cuda
+                func_lower = tvm.lower(sch, args,
+                                       name=operator_def.get_op_name(args),
+                                       binds=operator_def.get_binds(args))
+                func_list.append(func_lower)
+
+    lowered_funcs = {get_target("cpu") : func_list_llvm}
+    if len(func_list_cuda) > 0:
+        lowered_funcs[get_target("cuda")] = func_list_cuda
+    func_binary = tvm.build(lowered_funcs, name="tvmop")
+    func_binary.export_library(arguments.target_path)
diff --git a/contrib/tvmop/opdef.py b/contrib/tvmop/opdef.py
new file mode 100644
index 000000000000..c65824588047
--- /dev/null
+++ b/contrib/tvmop/opdef.py
@@ -0,0 +1,111 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# coding: utf-8
+import tvm
+from itertools import product
+
+__OP_DEF__ = []
+
+class OpDef:
+    """Specify the properties of an operator and
+    construct the value combination of the arguments
+    e.g., ldtype=["float32", "int32"], rdtype=["float16", "int16"],
+    then the argument combination is
+    [
+        {"ldtype": "float32", "rdtype": "float16"},
+        {"ldtype": "float32", "rdtype": "int16"},
+        {"ldtype": "int32", "rdtype": "float16"},
+        {"ldtype": "int32", "rdtype": "int16"},
+    ]
+
+    Parameters
+    ----------
+    func : function
+         The function to define the operator (in tvm compute and schedule).
+         It will get the argument combination extracted by this class.
+    name : str
+         function name.
+    target : str
+         {"cpu", "gpu", "cuda"}
+    auto_broadcast : bool
+         auto_broadcast=True allows one to implement broadcast computation
+         without considering whether dimension size equals to one.
+         TVM maps buffer[i][j][k] -> buffer[i][0][k] if dimension i's shape equals 1.
+    """
+    def __init__(self, func, name, target, auto_broadcast, **kwargs):
+        # construct the value combination of the arguments
+        # e.g., ldtype=["float32", "int32"], rdtype=["float16", "int16"]
+        # arg_combination = [
+        #   {"ldtype": "float32", "rdtype": "float16"},
+        #   {"ldtype": "float32", "rdtype": "int16"},
+        #   {"ldtype": "int32", "rdtype": "float16"},
+        #   {"ldtype": "int32", "rdtype": "int16"},
+        # ]
+        args = [k for k in kwargs]
+        values = [kwargs[k] if isinstance(kwargs[k], (list, tuple)) else [kwargs[k]]
+                  for k in args]
+        cart_product = product(*values)
+        self.arg_combination = [{k: v for k, v in zip(args, comb_values)}
+                                for comb_values in cart_product]
+        self.func = func
+        self.name = name
+        self.target = target
+        self.auto_broadcast = auto_broadcast
+
+    def __call__(self, *args, **kwargs):
+        return self.func(*args, **kwargs)
+
+    def invoke_all(self):
+        for each_kwargs in self.arg_combination:
+            yield self.func(**each_kwargs)
+
+    def get_op_name(self, args):
+        return self.name + ''.join(["%s_%d" % (arg.dtype, len(arg.shape)) for arg in args])
+
+    def get_binds(self, args):
+        if self.auto_broadcast:
+            return {arg: tvm.decl_buffer(arg.shape, arg.dtype, buffer_type="auto_broadcast")
+                    for arg in args}
+        return None
+
+
+def defop(name, target=None, auto_broadcast=False, **kwargs):
+    """Decorator to define a tvm operator.
+    Parameters
+    ----------
+    name : str
+        function name
+    target : str
+        {"cpu", "gpu", "cuda"}
+    auto_broadcast : bool
+        auto_broadcast=True allows one to implement broadcast computation
+        without considering whether dimension size equals to one.
+        TVM maps buffer[i][j][k] -> buffer[i][0][k] if dimension i's shape equals 1.
+    Returns
+    -------
+    fdef : function
+        A wrapped operator definition function, which returns (schedule, [tensors])
+    """
+    assert name is not None and len(name) > 0
+    target = "cpu" if target is None else target
+    def _defop(func):
+        opdef = OpDef(func, name, target, auto_broadcast, **kwargs)
+        __OP_DEF__.append(opdef)
+        return opdef
+    return _defop
+
diff --git a/contrib/tvmop/prepare_tvm.sh b/contrib/tvmop/prepare_tvm.sh
new file mode 100644
index 000000000000..7ebe256446a4
--- /dev/null
+++ b/contrib/tvmop/prepare_tvm.sh
@@ -0,0 +1,63 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+#!/bin/sh
+
+LLVM_VERSION="8.0.0"
+LLVM_ROOT="http://releases.llvm.org/${LLVM_VERSION}/"
+LLVM_PKG="clang+llvm-${LLVM_VERSION}-x86_64-linux-gnu"
+
+os=`uname`
+if [ "$os" = "Linux" ] && [ "$(arch)" = "x86_64" ]; then
+    DISTRIB_ID=$(cat /etc/*-release | grep DISTRIB_ID | sed 's/DISTRIB_ID=//g' | tr '[:upper:]' '[:lower:]')
+    DISTRIB_RELEASE=$(cat /etc/*-release | grep DISTRIB_RELEASE | sed 's/DISTRIB_RELEASE=//g' | tr '[:upper:]' '[:lower:]')
+    if [ "$DISTRIB_ID" = "ubuntu" ]; then
+        LLVM_PKG=${LLVM_PKG}-${DISTRIB_ID}-${DISTRIB_RELEASE}
+    else
+        echo "Downloading LLVM only supports Ubuntu. Please download manually."
+        exit 1
+    fi
+else
+    echo "Cannot identify operating system. Try downloading LLVM manually."
+    exit 1
+fi
+
+LLVM_URL=${LLVM_ROOT}${LLVM_PKG}.tar.xz
+
+TVM_PATH=`dirname $0`/../../3rdparty/tvm
+DST=${TVM_PATH}/build
+rm -rf $DST
+mkdir -p $DST
+DST=`cd $DST; pwd`
+
+if [ -x "$(command -v curl)" ]; then
+    curl -L -o "${DST}/${LLVM_PKG}.tar.xz" "$LLVM_URL"
+elif [ -x "$(command -v wget)" ]; then
+    wget -O "${DST}/${LLVM_PKG}.tar.xz" "$LLVM_URL"
+else
+    echo "curl or wget not available"
+    exit 1
+fi
+
+if [ \! $? ]; then
+    echo "Download from $LLVM_URL to $DST failed"
+    exit 1
+fi
+
+tar -xf "$DST/${LLVM_PKG}.tar.xz" -C $DST
+mv $DST/${LLVM_PKG} $DST/llvm
+echo "Downloaded and unpacked LLVM libraries to $DST"
diff --git a/contrib/tvmop/utils.py b/contrib/tvmop/utils.py
new file mode 100644
index 000000000000..0b2416b4f3ae
--- /dev/null
+++ b/contrib/tvmop/utils.py
@@ -0,0 +1,20 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# coding: utf-8
+AllTypes = ["float32", "float64", "float16", "uint8", "int8", "int32", "int64"]
+RealTypes = ["float32", "float64", "float16"]
diff --git a/include/mxnet/c_api.h b/include/mxnet/c_api.h
index bd30e44f910c..058f859ae7bc 100644
--- a/include/mxnet/c_api.h
+++ b/include/mxnet/c_api.h
@@ -506,6 +506,15 @@ MXNET_DLL int MXGetGPUMemoryInformation64(int dev, uint64_t *free_mem, uint64_t
  */
 MXNET_DLL int MXGetVersion(int *out);
 
+/*!
+ * \brief Load TVM operator from the binary library
+ * \param libpath TVM operators lib file
+ * \return 0 when success, -1 when failure happens
+ */
+#if MXNET_USE_TVM_OP
+MXNET_DLL int MXLoadTVMOp(const char *libpath);
+#endif  // MXNET_USE_TVM_OP
+
 
 //-------------------------------------
 // Part 1: NDArray creation and deletion
diff --git a/include/mxnet/libinfo.h b/include/mxnet/libinfo.h
index 8b58a398c673..1972688c7739 100644
--- a/include/mxnet/libinfo.h
+++ b/include/mxnet/libinfo.h
@@ -127,6 +127,10 @@
 #define MXNET_USE_INT64_TENSOR_SIZE MSHADOW_INT64_TENSOR_SIZE
 #endif
 
+#ifndef MXNET_USE_TVM_OP
+#define MXNET_USE_TVM_OP 0
+#endif
+
 namespace mxnet {
 namespace features {
 // Check compile flags such as CMakeLists.txt
@@ -185,6 +189,9 @@ enum : unsigned {
   SIGNAL_HANDLER,
   DEBUG,
 
+  // TVM operator
+  TVM_OP,
+
   // size indicator
   MAX_FEATURES
 };
diff --git a/make/config.mk b/make/config.mk
index 4bddb8ba461d..982d15b19656 100644
--- a/make/config.mk
+++ b/make/config.mk
@@ -62,6 +62,9 @@ ADD_LDFLAGS =
 # the additional compile flags you want to add
 ADD_CFLAGS =
 
+# whether to build operators written in TVM
+USE_TVM_OP = 0
+
 #---------------------------------------------
 # matrix computation libraries for CPU/GPU
 #---------------------------------------------
diff --git a/make/osx.mk b/make/osx.mk
index 0b5842e59524..25f3ba6df55b 100644
--- a/make/osx.mk
+++ b/make/osx.mk
@@ -53,6 +53,9 @@ ADD_LDFLAGS =
 # the additional compile flags you want to add
 ADD_CFLAGS =
 
+# whether to build operators written in TVM
+USE_TVM_OP = 0
+
 #---------------------------------------------
 # matrix computation libraries for CPU/GPU
 #---------------------------------------------
diff --git a/python/mxnet/base.py b/python/mxnet/base.py
index 73fae4876873..bf8026359d02 100644
--- a/python/mxnet/base.py
+++ b/python/mxnet/base.py
@@ -16,7 +16,7 @@
 # under the License.
 
 # coding: utf-8
-# pylint: disable=invalid-name, no-member, trailing-comma-tuple, bad-mcs-classmethod-argument, unnecessary-pass
+# pylint: disable=invalid-name, no-member, trailing-comma-tuple, bad-mcs-classmethod-argument, unnecessary-pass, wrong-import-position
 """ctypes library of mxnet and helper functions."""
 from __future__ import absolute_import
 
@@ -734,3 +734,8 @@ def write_all_str(module_file, module_all_list):
 
 ctypes.pythonapi.PyCapsule_New.restype = ctypes.py_object
 ctypes.pythonapi.PyCapsule_GetPointer.restype = ctypes.c_void_p
+
+from .runtime import Features
+if Features().is_enabled("TVM_OP"):
+    _LIB_TVM_OP = libinfo.find_lib_path("libtvmop")
+    check_call(_LIB.MXLoadTVMOp(c_str(_LIB_TVM_OP[0])))
diff --git a/python/mxnet/libinfo.py b/python/mxnet/libinfo.py
index ff795f914a4b..fb0859b9954a 100644
--- a/python/mxnet/libinfo.py
+++ b/python/mxnet/libinfo.py
@@ -23,7 +23,7 @@
 import logging
 
 
-def find_lib_path():
+def find_lib_path(prefix='libmxnet'):
     """Find MXNet dynamic library files.
 
     Returns
@@ -61,13 +61,13 @@ def find_lib_path():
         dll_path[0:0] = [p.strip() for p in os.environ['LD_LIBRARY_PATH'].split(":")]
     if os.name == 'nt':
         os.environ['PATH'] = os.path.dirname(__file__) + ';' + os.environ['PATH']
-        dll_path = [os.path.join(p, 'libmxnet.dll') for p in dll_path]
+        dll_path = [os.path.join(p, prefix + '.dll') for p in dll_path]
     elif platform.system() == 'Darwin':
-        dll_path = [os.path.join(p, 'libmxnet.dylib') for p in dll_path] + \
-                   [os.path.join(p, 'libmxnet.so') for p in dll_path]
+        dll_path = [os.path.join(p, prefix + '.dylib') for p in dll_path] + \
+                   [os.path.join(p, prefix + '.so') for p in dll_path]
     else:
         dll_path.append('../../../')
-        dll_path = [os.path.join(p, 'libmxnet.so') for p in dll_path]
+        dll_path = [os.path.join(p, prefix + '.so') for p in dll_path]
     lib_path = [p for p in dll_path if os.path.exists(p) and os.path.isfile(p)]
     if len(lib_path) == 0:
         raise RuntimeError('Cannot find the MXNet library.\n' +
diff --git a/src/c_api/c_api.cc b/src/c_api/c_api.cc
index 35bd3eeb477a..5207bdfa444b 100644
--- a/src/c_api/c_api.cc
+++ b/src/c_api/c_api.cc
@@ -48,6 +48,7 @@
 #include "./c_api_common.h"
 #include "../operator/custom/custom-inl.h"
 #include "../operator/tensor/matrix_op-inl.h"
+#include "../operator/tvmop/op_module.h"
 #include "../common/utils.h"
 
 using namespace mxnet;
@@ -159,6 +160,14 @@ int MXGetVersion(int *out) {
   API_END();
 }
 
+#if MXNET_USE_TVM_OP
+int MXLoadTVMOp(const char *libpath) {
+  API_BEGIN();
+  tvm::runtime::TVMOpModule::Get()->Load(libpath);
+  API_END();
+}
+#endif  // MXNET_USE_TVM_OP
+
 int MXNDArrayCreateNone(NDArrayHandle *out) {
   API_BEGIN();
   *out = new NDArray();
diff --git a/src/libinfo.cc b/src/libinfo.cc
index f67b45ed1c14..b31d7e4301e0 100644
--- a/src/libinfo.cc
+++ b/src/libinfo.cc
@@ -89,6 +89,9 @@ class FeatureSet {
     feature_bits.set(INT64_TENSOR_SIZE, MXNET_USE_INT64_TENSOR_SIZE);
     feature_bits.set(SIGNAL_HANDLER, MXNET_USE_SIGNAL_HANDLER);
 
+    // TVM operators
+    feature_bits.set(TVM_OP, MXNET_USE_TVM_OP);
+
 #ifndef NDEBUG
     feature_bits.set(DEBUG);
 #endif
@@ -159,6 +162,7 @@ const std::vector<std::string> EnumNames::names = {
   "INT64_TENSOR_SIZE",
   "SIGNAL_HANDLER",
   "DEBUG",
+  "TVM_OP",
 };
 
 }  // namespace features
diff --git a/src/operator/contrib/tvmop/ufunc.cc b/src/operator/contrib/tvmop/ufunc.cc
new file mode 100644
index 000000000000..faba671322e2
--- /dev/null
+++ b/src/operator/contrib/tvmop/ufunc.cc
@@ -0,0 +1,66 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file ufunc.cc
+ * \brief
+ * \author Yizhi Liu
+ */
+#ifdef MXNET_USE_TVM_OP
+#include <tvm/runtime/packed_func.h>
+#include <tvm/runtime/registry.h>
+#include <tvm/runtime/c_runtime_api.h>
+#include <mxnet/base.h>
+#include "../../tensor/elemwise_binary_broadcast_op.h"
+#include "../../tvmop/op_module.h"
+#include "../../tensor/elemwise_binary_op.h"
+
+namespace mxnet {
+namespace op {
+
+static constexpr char func_vadd_cpu[] = "vadd";
+static constexpr char func_vadd_gpu[] = "cuda_vadd";
+
+template<const char* func>
+void TVMBroadcastCompute(const nnvm::NodeAttrs& attrs,
+                         const mxnet::OpContext& ctx,
+                         const std::vector<TBlob>& inputs,
+                         const std::vector<OpReqType>& req,
+                         const std::vector<TBlob>& outputs) {
+  CHECK_EQ(inputs.size(), 2U);
+  CHECK_EQ(outputs.size(), 1U);
+  tvm::runtime::TVMOpModule::Get()->Call(func, ctx, {inputs[0], inputs[1], outputs[0]});
+}
+
+NNVM_REGISTER_OP(_contrib_tvm_vadd)
+    .set_num_inputs(2)
+    .set_num_outputs(1)
+    .add_argument("a", "NDArray-or-Symbol", "first input")
+    .add_argument("b", "NDArray-or-Symbol", "second input")
+    .set_attr<mxnet::FInferShape>("FInferShape", BinaryBroadcastShape)
+    .set_attr<nnvm::FInferType>("FInferType", mxnet::op::ElemwiseType<2, 1>)
+    .set_attr<mxnet::FCompute>("FCompute<cpu>", mxnet::op::TVMBroadcastCompute<func_vadd_cpu>)
+#if MXNET_USE_CUDA
+    .set_attr<mxnet::FCompute>("FCompute<gpu>", mxnet::op::TVMBroadcastCompute<func_vadd_gpu>);
+#endif  // MXNET_USE_CUDA
+
+}  // namespace op
+}  // namespace mxnet
+#endif  // MXNET_USE_TVM_OP
diff --git a/src/operator/tvmop/op_module.cc b/src/operator/tvmop/op_module.cc
new file mode 100644
index 000000000000..d1d1c1d45d53
--- /dev/null
+++ b/src/operator/tvmop/op_module.cc
@@ -0,0 +1,117 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file op_module.cc
+ * \brief Invoke registered TVM operators.
+ * \author Yizhi Liu
+ */
+#if MXNET_USE_TVM_OP
+#include <tvm/runtime/packed_func.h>
+#include <tvm/runtime/registry.h>
+#include <tvm/runtime/c_runtime_api.h>
+#include <string>
+#include <vector>
+#include "op_module.h"
+
+using namespace tvm::runtime;
+
+namespace tvm {
+namespace runtime {
+
+void TVMOpModule::Load(const std::string &filepath) {
+  static const PackedFunc *f_load = Registry::Get("module._LoadFromFile");
+  std::lock_guard<std::mutex> lock(mutex_);
+  Module module = (*f_load)(filepath, "");
+  module_ptr_ = std::make_shared<Module>();
+  *module_ptr_ = module;
+}
+
+PackedFunc GetFunction(const std::shared_ptr<Module> &module,
+                       const std::string &op_name,
+                       const std::vector<mxnet::TBlob> &args) {
+  std::ostringstream func_name;
+  func_name << op_name;
+  for (const auto &arg : args) {
+    switch (arg.type_flag_) {
+      case mshadow::kFloat32:
+        func_name << "float32";
+        break;
+      case mshadow::kFloat64:
+        func_name << "float64";
+        break;
+      case mshadow::kFloat16:
+        func_name << "float16";
+        break;
+      case mshadow::kUint8:
+        func_name << "uint8";
+        break;
+      case mshadow::kInt32:
+        func_name << "int32";
+        break;
+      case mshadow::kInt8:
+        func_name << "int8";
+        break;
+      case mshadow::kInt64:
+        func_name << "int64";
+        break;
+      default:
+        LOG(FATAL) << "Unknown dtype " << arg.type_flag_;
+    }
+    func_name << "_" << arg.shape_.ndim();
+  }
+  return module->GetFunction(func_name.str(), false);
+}
+
+void TVMOpModule::Call(const std::string &func_name,
+                       const mxnet::OpContext& ctx,
+                       const std::vector<mxnet::TBlob> &args) {
+  std::vector<int> type_codes;
+  std::vector<TVMValue> values;
+
+  type_codes.resize(args.size());
+  values.resize(args.size());
+  for (size_t i = 0; i < args.size(); ++i) {
+    type_codes[i] = kArrayHandle;
+    values[i].v_handle = const_cast<DLTensor *>(&(args[i].dltensor()));
+  }
+
+  TVMArgs tvm_args(&values[0], &type_codes[0], args.size());
+  TVMRetValue rv;
+
+#if MXNET_USE_CUDA
+  int dev_type = (ctx.run_ctx.ctx.dev_type == mxnet::Context::DeviceType::kGPU) ? kDLGPU : kDLCPU;
+  int dev_id = ctx.run_ctx.ctx.dev_id;
+  if (dev_type == kDLGPU) {
+    void *stream = static_cast<void *>(ctx.run_ctx.get_stream<mxnet::gpu>()->stream_);
+    TVMSetStream(dev_type, dev_id, stream);
+  }
+#endif
+  GetFunction(module_ptr_, func_name, args).CallPacked(tvm_args, &rv);
+#if MXNET_USE_CUDA
+  if (dev_type == kDLGPU) {
+    TVMSetStream(dev_type, dev_id, nullptr);
+  }
+#endif
+}
+
+}  // namespace runtime
+}  // namespace tvm
+#endif  // MXNET_USE_TVM_OP
diff --git a/src/operator/tvmop/op_module.h b/src/operator/tvmop/op_module.h
new file mode 100644
index 000000000000..04e97ef51f4e
--- /dev/null
+++ b/src/operator/tvmop/op_module.h
@@ -0,0 +1,63 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file op_module.h
+ * \brief Invoke registered TVM operators.
+ * \author Yizhi Liu
+ */
+#ifndef MXNET_OPERATOR_TVMOP_OP_MODULE_H_
+#define MXNET_OPERATOR_TVMOP_OP_MODULE_H_
+
+#if MXNET_USE_TVM_OP
+#include <mxnet/base.h>
+#include <mxnet/op_attr_types.h>
+#include <mutex>
+#include <string>
+#include <vector>
+
+namespace tvm {
+namespace runtime {
+
+class Module;
+class TVMOpModule {
+ public:
+  // Load TVM operators binary
+  void Load(const std::string& filepath);
+
+  void Call(const std::string& func_name,
+            const mxnet::OpContext& ctx,
+            const std::vector<mxnet::TBlob>& args);
+
+  static TVMOpModule *Get() {
+    static TVMOpModule inst;
+    return &inst;
+  }
+
+ private:
+  std::mutex mutex_;
+  std::shared_ptr<Module> module_ptr_;
+};
+
+}  // namespace runtime
+}  // namespace tvm
+
+#endif  // MXNET_USE_TVM_OP
+#endif  // MXNET_OPERATOR_TVMOP_OP_MODULE_H_
diff --git a/tests/python/gpu/test_operator_gpu.py b/tests/python/gpu/test_operator_gpu.py
index f9814abc9c0b..91ba9fb68750 100644
--- a/tests/python/gpu/test_operator_gpu.py
+++ b/tests/python/gpu/test_operator_gpu.py
@@ -44,6 +44,7 @@
 from test_ndarray import *
 from test_subgraph_op import *
 from test_contrib_operator import test_multibox_target_op
+from test_tvm_op import *
 
 set_default_context(mx.gpu(0))
 del test_support_vector_machine_l1_svm  # noqa
diff --git a/tests/python/unittest/test_tvm_op.py b/tests/python/unittest/test_tvm_op.py
new file mode 100644
index 000000000000..3ab2a25bc20e
--- /dev/null
+++ b/tests/python/unittest/test_tvm_op.py
@@ -0,0 +1,38 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import mxnet as mx
+from mxnet.test_utils import same, rand_shape_nd
+from mxnet.runtime import Features
+from common import with_seed
+
+_features = Features()
+
+@with_seed()
+def test_tvm_broadcast_add():
+    if _features.is_enabled("TVM_OP"):
+        a_shape = rand_shape_nd(4)
+        b_shape = (1,) + a_shape[1:2] + (1, 1)
+        a = mx.nd.normal(shape=a_shape)
+        b = mx.nd.normal(shape=b_shape)
+        c = mx.nd.contrib.tvm_vadd(a, b)
+        c_np = a.asnumpy() + b.asnumpy()
+        assert same(c.asnumpy(), c_np)
+
+if __name__ == '__main__':
+    import nose
+    nose.runmodule()

From 858e17115e5240553f5e7fe932643c3850433b72 Mon Sep 17 00:00:00 2001
From: Lai Wei <royweilai@gmail.com>
Date: Tue, 23 Jul 2019 09:05:31 -0700
Subject: [PATCH 104/813] 1.5.0 news (#15137)

* 1.5.0 news

* update news

* add news

* update news

* trigger ci:

* fix

* address comments

* Update NEWS.md

Co-Authored-By: Aaron Markham <markhama@amazon.com>

* Update NEWS.md

Co-Authored-By: Aaron Markham <markhama@amazon.com>

* Update NEWS.md

Co-Authored-By: Aaron Markham <markhama@amazon.com>

* Update NEWS.md

Co-Authored-By: Aaron Markham <markhama@amazon.com>

* Update NEWS.md

Co-Authored-By: Aaron Markham <markhama@amazon.com>
---
 NEWS.md | 1031 +++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 1031 insertions(+)

diff --git a/NEWS.md b/NEWS.md
index ee8a73cc4bc8..8d8ef2c4ffd5 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -17,6 +17,1037 @@
 
 MXNet Change Log
 ================
+- [MXNet Change Log](#mxnet-change-log)
+  * [1.5.0](#150)
+    + [New Features](#new-features)
+      - [Automatic Mixed Precision(experimental)](#automatic-mixed-precision-experimental-)
+      - [MKL-DNN Reduced precision inference and RNN API support](#mkl-dnn-reduced-precision-inference-and-rnn-api-support)
+      - [Dynamic Shape(experimental)](#dynamic-shape-experimental-)
+      - [Large Tensor Support](#large-tensor-support)
+      - [Dependency Update](#dependency-update)
+      - [Gluon Fit API(experimental)](#gluon-fit-api-experimental-)
+      - [New Operators](#new-operators)
+    + [Feature Improvements](#feature-improvements)
+      - [Operators](#operators)
+      - [MKLDNN](#mkldnn)
+      - [ONNX](#onnx)
+      - [TensorRT](#tensorrt)
+      - [FP16 Support](#fp16-support)
+      - [Deep Graph Library(DGL) support](#deep-graph-library-dgl--support)
+      - [Horovod Integration](#horovod-integration)
+      - [Dynamic Shape](#dynamic-shape)
+      - [Backend Engine](#backend-engine)
+      - [Large Tensor Support](#large-tensor-support-1)
+      - [Quantization](#quantization)
+      - [Profiler](#profiler)
+      - [CoreML](#coreml)
+    + [Front End API](#front-end-api)
+      - [Gluon](#gluon)
+      - [Python](#python)
+    + [Language Bindings](#language-bindings)
+      - [Scala](#scala)
+      - [Java](#java)
+      - [C++](#c--)
+      - [Clojure](#clojure)
+      - [Julia](#julia)
+      - [Perl:](#perl-)
+      - [R](#r)
+    + [Performance Improvements](#performance-improvements)
+    + [Example and Tutorials](#example-and-tutorials)
+    + [Website](#website)
+    + [Documentation](#documentation)
+    + [Build and Test](#build-and-test)
+    + [Bug-fixes](#bug-fixes)
+    + [License](#license)
+    + [Depreciations](#depreciations)
+    + [Known Issues](#known-issues)
+  * [1.4.1](#141)
+    + [Bug-fixes](#bug-fixes-1)
+  * [1.4.0](#140)
+    + [New Features](#new-features-1)
+      - [Java Inference API](#java-inference-api)
+      - [Julia API](#julia-api)
+      - [Control Flow Operators (experimental)](#control-flow-operators--experimental-)
+      - [SVRG Optimization](#svrg-optimization)
+      - [Subgraph API (experimental)](#subgraph-api--experimental-)
+      - [JVM Memory Management](#jvm-memory-management)
+      - [Topology-aware AllReduce (experimental)](#topology-aware-allreduce--experimental-)
+      - [MKLDNN backend: Graph optimization and Quantization (experimental)](#mkldnn-backend--graph-optimization-and-quantization--experimental-)
+        * [Graph Optimization](#graph-optimization)
+        * [Quantization](#quantization-1)
+    + [New Operators](#new-operators-1)
+    + [Feature improvements](#feature-improvements)
+      - [Operator](#operator)
+      - [Optimizer](#optimizer)
+      - [Sparse](#sparse)
+      - [ONNX](#onnx-1)
+      - [MKLDNN](#mkldnn-1)
+      - [Inference](#inference)
+      - [Other](#other)
+    + [Frontend API updates](#frontend-api-updates)
+      - [Gluon](#gluon-1)
+      - [Symbol](#symbol)
+    + [Language API updates](#language-api-updates)
+      - [Java](#java-1)
+      - [R](#r-1)
+      - [Scala](#scala-1)
+      - [Clojure](#clojure-1)
+      - [Perl](#perl)
+      - [Julia](#julia-1)
+    + [Performance benchmarks and improvements](#performance-benchmarks-and-improvements)
+    + [Bug fixes](#bug-fixes)
+    + [Licensing updates](#licensing-updates)
+    + [Improvements](#improvements)
+      - [Tutorial](#tutorial)
+      - [Example](#example)
+      - [Documentation](#documentation-1)
+      - [Website](#website-1)
+      - [MXNet Distributions](#mxnet-distributions)
+      - [Installation](#installation)
+      - [Build and CI](#build-and-ci)
+      - [3rd party](#3rd-party)
+        * [TVM:](#tvm-)
+        * [CUDNN:](#cudnn-)
+        * [Horovod:](#horovod-)
+    + [Deprications](#deprications)
+    + [Other](#other-1)
+    + [How to build MXNet](#how-to-build-mxnet)
+    + [List of submodules used by Apache MXNet (Incubating) and when they were updated last](#list-of-submodules-used-by-apache-mxnet--incubating--and-when-they-were-updated-last)
+  * [1.3.1](#131)
+    + [Bug fixes](#bug-fixes-1)
+    + [Documentation fixes](#documentation-fixes)
+    + [Other Improvements](#other-improvements)
+    + [Submodule updates](#submodule-updates)
+    + [Known issues](#known-issues)
+  * [1.3.0](#130)
+    + [New Features - Gluon RNN layers are now HybridBlocks](#new-features---gluon-rnn-layers-are-now-hybridblocks)
+    + [MKL-DNN improvements](#mkl-dnn-improvements)
+    + [New Features - Gluon Model Zoo Pre-trained Models](#new-features---gluon-model-zoo-pre-trained-models)
+    + [New Features - Clojure package (experimental)](#new-features---clojure-package--experimental-)
+    + [New Features - Synchronized Cross-GPU Batch Norm (experimental)](#new-features---synchronized-cross-gpu-batch-norm--experimental-)
+    + [New Features - Sparse Tensor Support for Gluon (experimental)](#new-features---sparse-tensor-support-for-gluon--experimental-)
+    + [New Features - Control flow operators (experimental)](#new-features---control-flow-operators--experimental-)
+    + [New Features - Scala API Improvements (experimental)](#new-features---scala-api-improvements--experimental-)
+    + [New Features - Rounding GPU Memory Pool for dynamic networks with variable-length inputs and outputs (experimental)](#new-features---rounding-gpu-memory-pool-for-dynamic-networks-with-variable-length-inputs-and-outputs--experimental-)
+    + [New Features - Topology-aware AllReduce (experimental)](#new-features---topology-aware-allreduce--experimental-)
+    + [New Features - Export MXNet models to ONNX format (experimental)](#new-features---export-mxnet-models-to-onnx-format--experimental-)
+    + [New Features - TensorRT Runtime Integration (experimental)](#new-features---tensorrt-runtime-integration--experimental-)
+    + [New Examples - Scala](#new-examples---scala)
+    + [Maintenance - Flaky Tests improvement effort](#maintenance---flaky-tests-improvement-effort)
+    + [Maintenance - MXNet Model Backwards Compatibility Checker](#maintenance---mxnet-model-backwards-compatibility-checker)
+    + [Maintenance - Integrated testing for "the Straight Dope"](#maintenance---integrated-testing-for--the-straight-dope-)
+    + [Bug-fixes](#bug-fixes-2)
+    + [Performance Improvements](#performance-improvements-1)
+    + [API Changes](#api-changes)
+    + [Other features](#other-features)
+    + [Usability Improvements](#usability-improvements)
+  * [1.2.0](#120)
+    + [New Features - Added Scala Inference APIs](#new-features---added-scala-inference-apis)
+    + [New Features - Added a Module to Import ONNX models into MXNet](#new-features---added-a-module-to-import-onnx-models-into-mxnet)
+    + [New Features - Added Support for Model Quantization with Calibration](#new-features---added-support-for-model-quantization-with-calibration)
+    + [New Features - MKL-DNN Integration](#new-features---mkl-dnn-integration)
+    + [New Features - Added Exception Handling Support for Operators](#new-features---added-exception-handling-support-for-operators)
+    + [New Features - Enhanced FP16 support](#new-features---enhanced-fp16-support)
+    + [New Features - Added Profiling Enhancements](#new-features---added-profiling-enhancements)
+    + [Breaking Changes](#breaking-changes)
+    + [Bug Fixes](#bug-fixes)
+    + [Performance Improvements](#performance-improvements-2)
+    + [API Changes](#api-changes-1)
+    + [Sparse Support](#sparse-support)
+    + [Deprecations](#deprecations)
+    + [Other Features](#other-features)
+    + [Usability Improvements](#usability-improvements-1)
+    + [Known Issues](#known-issues-1)
+  * [1.1.0](#110)
+    + [Usability Improvements](#usability-improvements-2)
+    + [Bug-fixes](#bug-fixes-3)
+    + [New Features](#new-features-2)
+    + [API Changes](#api-changes-2)
+    + [Deprecations](#deprecations-1)
+    + [Performance Improvements](#performance-improvements-3)
+    + [Known Issues](#known-issues-2)
+  * [1.0.0](#100)
+    + [Performance](#performance)
+    + [New Features - Gradient Compression [Experimental]](#new-features---gradient-compression--experimental-)
+    + [New Features - Support of NVIDIA Collective Communication Library (NCCL) [Experimental]](#new-features---support-of-nvidia-collective-communication-library--nccl---experimental-)
+    + [New Features - Advanced Indexing [General Availability]](#new-features---advanced-indexing--general-availability-)
+    + [New Features - Gluon [General Availability]](#new-features---gluon--general-availability-)
+    + [New Features - ARM / Raspberry Pi support [Experimental]](#new-features---arm---raspberry-pi-support--experimental-)
+    + [New Features - NVIDIA Jetson support [Experimental]](#new-features---nvidia-jetson-support--experimental-)
+    + [New Features - Sparse Tensor Support [General Availability]](#new-features---sparse-tensor-support--general-availability-)
+    + [Bug-fixes](#bug-fixes-4)
+    + [Doc Updates](#doc-updates)
+  * [0.12.1](#0121)
+    + [Bug-fixes](#bug-fixes-5)
+  * [0.12.0](#0120)
+    + [Performance](#performance-1)
+    + [New Features - Gluon](#new-features---gluon)
+    + [New Features - Autograd](#new-features---autograd)
+    + [New Features - Sparse Tensor Support](#new-features---sparse-tensor-support)
+    + [Other New Features](#other-new-features)
+    + [API Changes](#api-changes-3)
+    + [Bug-fixes](#bug-fixes-6)
+  * [0.11.0](#0110)
+    + [Major Features](#major-features)
+    + [API Changes](#api-changes-4)
+    + [Performance Improvements](#performance-improvements-4)
+    + [Bugfixes](#bugfixes)
+    + [Refactors](#refactors)
+  * [0.10.0](#0100)
+  * [0.9.3](#093)
+  * [v0.8](#v08)
+  * [v0.7](#v07)
+  * [v0.5 (initial release)](#v05--initial-release-)
+
+## 1.5.0
+
+### New Features
+
+#### Automatic Mixed Precision(experimental)
+Training Deep Learning networks is a very computationally intensive task. Novel model architectures tend to have increasing numbers of layers and parameters, which slow down training. Fortunately, software optimizations and new generations of training hardware make it a feasible task. 
+However, most of the hardware and software optimization opportunities exist in exploiting lower precision (e.g. FP16) to, for example, utilize Tensor Cores available on new Volta and Turing GPUs. While training in FP16 showed great success in image classification tasks, other more complicated neural networks typically stayed in FP32 due to difficulties in applying the FP16 training guidelines.
+That is where AMP (Automatic Mixed Precision) comes into play. It automatically applies the guidelines of FP16 training, using FP16 precision where it provides the most benefit, while conservatively keeping in full FP32 precision operations unsafe to do in FP16. To learn more about AMP, check out this [tutorial](https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/amp/amp_tutorial.md). 
+
+#### MKL-DNN Reduced precision inference and RNN API support
+Two advanced features, fused computation and reduced-precision kernels, are introduced by MKL-DNN in the recent version. These features can significantly speed up the inference performance on CPU for a broad range of deep learning topologies. MXNet MKL-DNN backend provides optimized implementations for various operators covering a broad range of applications including image classification, object detection, and natural language processing. Refer to the [MKL-DNN operator documentation](https://github.com/apache/incubator-mxnet/blob/v1.5.x/docs/tutorials/mkldnn/operator_list.md) for more information.
+
+#### Dynamic Shape(experimental)
+MXNet now supports Dynamic Shape in both imperative and symbolic mode. MXNet used to require that operators statically infer the output shapes from the input shapes. However, there exist some operators that don't meet this requirement. Examples are:
+* while_loop: its output size depends on the number of iterations in the loop.
+* boolean indexing: its output size depends on the value of the input data.
+* many operators can be extended to take a shape symbol as input and the shape symbol can determine the output shape of these operators (with this extension, the symbol interface of MXNet can fully support shape).
+To support dynamic shape and such operators, we have modified MXNet backend. Now MXNet supports operators with dynamic shape such as [`contrib.while_loop`](https://mxnet.incubator.apache.org/api/python/ndarray/contrib.html#mxnet.ndarray.contrib.while_loop), [`contrib.cond`](https://mxnet.incubator.apache.org/api/python/ndarray/contrib.html#mxnet.ndarray.contrib.cond), and [`mxnet.ndarray.contrib.boolean_mask`](https://mxnet.incubator.apache.org/api/python/ndarray/contrib.html#contrib)
+Note: Currently dynamic shape does not work with Gluon deferred initialization.
+
+#### Large Tensor Support
+Currently, MXNet supports maximal tensor size of around 4 billon (2^32). This is due to uint32_t being used as the default data type for tensor size, as well as variable indexing.
+This limitation has created many problems when larger tensors are used in the model. 
+A naive solution to this problem is to replace all uint32_t in the MXNet backend source code to int64_t.
+This solution is not viable, however, because many data structures use uint32_t as the data type for its members.
+Unnecessarily replacing these variables to int64_t will increase the memory consumption causing another limitation. Second, MXNet has many submodule dependencies. 
+Updating the variable types in the MXNet repository is not enough. We also need to make sure different libraries, such as MKLDNN, MShadow etc. supports the int64_t integer data type. 
+Third, many front end APIs assume unsigned 32-bit integer interface. Only updating the interface in C/C++ will cause all the language bindings to fail.
+Therefore, we need a systematic approach to enhance MXNet to support large tensors. 
+Now you can enable large tensor support by changing the following build flag to 1: `USE_INT64_TENSOR_SIZE = 1`. Note this is set to 0 by default.
+For more details please refer to the [design document](https://cwiki.apache.org/confluence/display/MXNET/Large+Tensor+Support).
+
+#### Dependency Update
+MXNet has added support for CUDA 10, CUDA 10.1, cudnn7.5, NCCL 2.4.2, and numpy 1.16.0.
+These updates are available through PyPI packages and build from source, refer to [installation guid](https://mxnet.incubator.apache.org/versions/master/install/index.html) for more details.
+
+#### Gluon Fit API(experimental)
+Training a model in Gluon requires users to write the training loop. This is useful because of its imperative nature, however repeating the same code across multiple models can become tedious and repetitive with boilerplate code. 
+The training loop can also be overwhelming to some users new to deep learning. We have introduced an Estimator and Fit API to help facilitate training loop.
+Note: this feature is still experimental, for more details, refer to [design document](https://cwiki.apache.org/confluence/display/MXNET/Gluon+Fit+API+-+Tech+Design).
+
+#### New Operators
+* split_v2 (#13687)
+* Gradient multiplier (contrib) operator (#13632)
+* Image normalize operator - GPU support, 3D/4D inputs (#13802)
+* Image ToTensor operator - GPU support, 3D/4D inputs (#13837)
+* Add Gluon Transformer Crop (#14259)
+* GELU (#14449)
+* AdamW operator (Fixing Weight Decay Regularization in Adam) (#13728)
+* [MXNET-1382] Add the index_array operator (#14638)
+* add an operator for computing the likelihood of a Hawkes self-exciting process (#14683)
+* Add numpy linspace (#14927)
+
+
+### Feature Improvements
+
+#### Operators
+* make ROIAlign support position-sensitive pooling (#13088)
+* Add erfinv operator for calculating inverse error function (#13811)
+* Added optional parameters to BilinearResize2D to do relative scaling (#13985)
+* MXNET-1295 Adding integer index support to Sequence* family of operators. (#13880)
+* Export resize and support batch size (#14014)
+* CUDNN dropout (#13896)
+* Relaxing type requirements for slice_like op (#14097)
+* Relaxing type requirements for reshape_like op (#14325)
+* Parallelize CPU version and add GPU version of boolean_mask op (#14090)
+* Add NHWC layout support to Pooling (cpu, gpu cuda, gpu cuDNN) (#13749)
+* Multi-precision AdamW update op (#14171)
+* [op] add back support for scalar type rescale_grad argument for adamw_update/mp_adamw_update (#14221)
+* move choose_element_0index to operator (#14273)
+* Optimize NMS (#14290)
+* Optimize NMS part 2 (#14352)
+* add background class in box_nms (#14058)
+* Use cudnn for dropout by default (#14278)
+* In-place updates for Nadam, Adadelta, Adamax and SGLD (#13960)
+* Aggregate SGD (#13346)
+* Add proper exception message for negative shape in array creation routines (#14362)
+* Support multi-threading for Custom Operator (#14363)
+* moveaxis operator now accepts negative indices and sequence of ints as well. (#14321)
+* Support SyncBatchNorm5D (#14542)
+* Add nd.power and sym.pow (#14606)
+* Change RNN OP to stateful (#14476)
+* Add imresize and copyMakeBorder to mx.image (#13357)
+* add ctx for rand_ndarray and rand_sparse_ndarray (#14966)
+* Add cpu implementation for Deformable PSROIPooling (#14886)
+* Add warning for fp16 inputs with MXNET_SAFE_ACCUMULATION=0 (#15046)
+* Safe LayerNorm (#15002)
+* use MXNET_SAFE_ACCUMULATION for softmax accumulator (#15037)
+* LayerNorm acceleration on GPU  (#14935)
+* Add matrix inversion operator in linalg (#14963)
+* implementation for equivalence of tf.moments (#14842)
+* Use env var to enforce safe accumulation in ReduceAxesCompute (#14830)
+* [MXNet-1211] Factor and "Like" modes in BilinearResize2D operator (#13226)
+* added extraction/generation of diagonal and triangonal matrices to linalg (#14501)
+* [Mxnet-1397] Support symbolic api for requantize and dequantize (#14749)
+* [MXNET-978] Support higher order gradient for `log`. (#14992)
+* Add cpu implementation for Deformable Convolution (#14879)
+
+#### MKLDNN
+* Feature/mkldnn static (#13628)
+* Feature/mkldnn static 2 (#13503)
+* support mkl log when dtype is fp32 or fp64 (#13150)
+* Add reshape op supported by MKL-DNN (#12980)
+* Move the debug output message into MXNET_MKLDNN_DEBUG (#13662)
+* Integrate MKLDNN Conv1d and support 3d layout (#13530)
+* Making MKL-DNN default on MXNet master (#13681)
+* Add mkldnn OP for slice (#13730)
+* mkldnn s8 conv API change for master (#13903)
+* [MKLDNN] Enable signed int8 support for convolution. (#13697)
+* add mkldnn softmax_output (#13699)
+* MKLDNN based Quantized FullyConnected Operator and its fusion (#14128)
+* Fix entropy for uint8 (#14150)
+* Update MKL-DNN to v0.18 release (was: fix the Dense layer issue) (#13668)
+* [MKL-DNN] Enable s8 support for inner product and 3d input with flatten=false (#14466)
+* Optimize transpose operator with MKL-DNN (#14545)
+* [MKLDNN] Remove repeat parts in MKLDNN.md (#14995)
+* [MKLDNN] Enable more convolution + activation fusion (#14819)
+* Update MKL-DNN submodule to v0.19 (#14783)
+* Add mkldnn_version.h to pip package (#14899)
+* [MKLDNN] add quantized sum (#14614)
+* [MKLDNN]Refactor requantize to speed up execution (#14608)
+* [MKLDNN]Add quantized relu (#14604)
+* Add MKLDNN headers to pip package (#14339)
+* add symbolic link to mkldnn header files in include (#14300)
+* disable default MKLDNN for cross compilation (#13893)
+* Update MKLDNN_README.md (#13653)
+* [Quantization] Support zero-size tensor input for quantization flow (#15031)
+* Support 3D input for MKL-DNN softmax operator (#14818)
+* Add primitive cache for MKL-DNN sum(elemwise_add operator (#14914)
+* Fix reshape to add in-place back (#14903)
+* [int8] Add MobileNetV2_1.0 & ResNet18 Quantization (#14823)
+* [MKLDNN]Improve quantizeV2 and dequantize latency (#14641)
+* added mkldnn dependency for plugin compile target (#14274)
+* Support Quantized Fully Connected by INT8 GEMM (#12922)
+
+#### ONNX
+* ONNX export: Instance normalization, Shape (#12920)
+* ONNX export: Logical operators (#12852)
+* ONNX import/export: Size (#13112)
+* ONNX export: Add Flatten before Gemm (#13356)
+* ONNX import/export: Add missing tests, ONNX export: LogSoftMax (#13654)
+* ONNX import: Hardmax (#13717)
+* [MXNET-898] ONNX import/export: Sample_multinomial, ONNX export: GlobalLpPool, LpPool (#13500)
+* ONNX ops: norm exported and lpnormalization imported (#13806)
+* [MXNET-880] ONNX export: Random uniform, Random normal, MaxRoiPool (#13676)
+* ONNX export: Add Crop, Deconvolution and fix the default stride of Pooling to 1 (#12399)
+* onnx export ops (#13821)
+* ONNX export: broadcast_to, tile ops (#13981)
+* ONNX export: Support equal length splits (#14121)
+
+#### TensorRT
+* [MXNET-1252][1 of 2] Decouple NNVM to ONNX from NNVM to TenosrRT conversion (#13659)
+* [MXNET-703] Update to TensorRT 5, ONNX IR 3. Fix inference bugs. (#13310)
+* [MXNET-703] Minor refactor of TensorRT code (#13311)
+* reformat trt to use subgraph API, add fp16 support (#14040)
+
+#### FP16 Support
+* Update mshadow to support batch_dot with fp16. (#13716)
+* float32 → float16 cast consistency across implementations (#13857)
+* modifying SyncBN doc for FP16 use case (#14041)
+* support dot(vector, vector) for fp16 inputs on GPU (#14102)
+* softmax for fp16 with fp32 accumulator (#14098)
+* [MXNET-1327] Allow RNN Layers to be initialized to fp16 (#14219)
+* fp16 safe norm operator (#14616)
+* NAG Optimizer with multi-precision support (#14568)
+
+#### Deep Graph Library(DGL) support
+* Add graph_compact operator. (#13436)
+* Accelerate DGL csr neighbor sampling (#13588)
+
+#### Horovod Integration
+* Add extra header file to export for error checking (#13795)
+* whitelist symbols for using MXNet error handling externally (#13812)
+* Use CPUPinned context in ImageRecordIOParser2 (#13980)
+* Add pin_device_id option to Gluon DataLoader (#14136)
+
+#### Dynamic Shape
+* [MXNET-1315] Add checks for dynamic-shaped operators in CachedOp (#14018)
+* [MXNET-1325] Make InferShapeAttr a standalone pass (#14193)
+* [MXNET-1324] Add NaiveRunGraph to imperative utils (#14192)
+* [MXNET-1352] Allow dynamic shape in while_loop and if conditionals (#14393)
+
+#### Backend Engine
+* Add infer_type_partial (#14214)
+* Tidy up storage allocation and deallocation (#14480)
+* Add MXEnginePushAsync and MXEnginePushSync C APIs (#14615)
+* Enhance subgraph API (#14113)
+* Enhance PartitionGraph (#14277)
+* Allow clearing gpu cache (#14252)
+* Fix warning / static function in header. (#14900)
+* Simplify creation of NodeEntry instances and use emplace_back (#14095)
+* Add unpooled gpu memory type (#14716)
+* [MXNET-1398] Enable zero-copy from numpy to MXNet NDArray (#14733)
+* Use DEFAULT macro in C APIs (#14767)
+* Avoid unnecessary vector copies in imperative_utils.cc (#14665)
+* Support populating errors back to MXNet engine in callback (#13922)
+* Restore save/load ndarray to 1.4.1 (#15073)
+* Enable serializing/deserializing ndarrays in np_shape semantics (#15090)
+* [numpy] Support zero-dim and zero-size tensors in MXNet (#14661)
+* Rename np_compat to np_shape (#15063)
+* [MXNET-1330] Bring nnvm::Tuple to mxnet::Tuple (#14270)
+
+#### Large Tensor Support
+* Large array support for randint (#14242)
+* [MXNET-1185] Support large array in several operators (part 1) (#13418)
+* [MXNET-1401] adding more operators to test support for Large Tensor (#14944)
+* [MXNET-1410]Adding Large Tensor Support for tensor transpose (#15059)
+
+#### Quantization
+* Exclude concat layer for gpu quantization (#14060)
+* Enhance gpu quantization (#14094)
+* Register fake grad to subgraph and quantized operators (#14275)
+* Add int8 data loader (#14123)
+
+#### Profiler
+* [MXNET-857] Add initial NVTX profiler implementation (#12328)
+
+#### CoreML
+* Add more support for mxnet_to_coreml (#14222)
+
+
+### Front End API
+
+#### Gluon
+* Add pixelshuffle layers (#13571)
+* [MXNET-766] add dynamic_unroll RNN for HybridBlock (#11948)
+* add pos_weight for SigmoidBinaryCrossEntropyLoss (#13612)
+* Rewrite dataloader with process pool, improves responsiveness and reliability (#13447)
+* Complimentary gluon DataLoader improvements (#13606)
+* [Fit-API] Adress PR comments (#14885)
+* [Fit API] update estimator (#14849)
+* [MXNET-1396][Fit-API] Update default handler logic (#14765)
+* [Fit API] improve event handlers (#14685)
+* move to gluon contrib (#14635)
+* move estimator to contrib (#14633)
+* [MXNet-1340][Fit API]Update train stats (#14494)
+* [MXNet-1334][Fit API]base class for estimator and eventhandler (#14346)
+* [MXNET-1333] Estimator and Fit API (#14629)
+* Add support for fast variable-length LSTM (#14208)
+* Add the Gluon Implementation of Deformable Convolution (#14810)
+* hybridize rnn and add model graph (#13244)
+
+#### Python
+* Python BucketingModule bind() with grad_req = 'add' (#13984)
+* Refine runtime feature discovery python API and add documentation to … (#14130)
+* Runtime feature detection (#13549)
+* Add dtype visualization to plot_network (#14066)
+* [MXNET-1359] Adds a multiclass-MCC metric derived from Pearson (#14461)
+* support long for mx.random.seed (#14314)
+* Optimization of metric evaluation (#13471)
+* [MXNET-1403] Disable numpy's writability of NDArray once it is zero-copied to MXNet (#14948)
+* Refactor ImageRecordIter (#14824)
+
+
+### Language Bindings
+
+#### Scala
+* [MXNET-1260] Float64 DType computation support in Scala/Java (#13678)
+* [MXNET-1000] get Ndarray real value and form it from a NDArray (#12690)
+* Now passing DType of Label downstream to Label's DataDesc object (#14038)
+* Scala interpreter instructions (#14169)
+* Add default parameters for Scala NDArray.arange (#13816)
+* [MXNET-1287] Up scala comp (#14667)
+* [MXNET-1385] Improved Scala Init and Macros warning messages (#14656)
+* Remove all usages of makefile for scala (#14013)
+* Update scala-package gitignore configuration. (#13962)
+* [MXNET-1177]Adding Scala Demo to be run as a part of Nightly CI (#13823)
+* [MXNET-1287] Miscellaneous Scala warning fixes (#14658)
+* Fix jar path and add missing ones for spark jobs (#14020)
+* [MXNET-1155] Add scala packageTest utility (#13046)
+* [MXNET-1195] Cleanup Scala README file (#13582)
+* Add scalaclean to make clean (#14322)
+* Add maven wraper to scala project. (#13702)
+* Add new Maven build for Scala package (#13819)
+* [MXNET-1287] Feat dep (#14668)
+* add Apache header on all XML (#14138)
+* update the version name (#14076)
+* change to compile time (#13835)
+* [MXNET-918] Random module (#13039)
+* Avoid secondary deployment of package to local (#14647)
+
+#### Java
+* [MXNET-1180] Java Image API (#13807)
+* [MXNET-1285] Draw bounding box with Scala/Java Image API (#14474)
+* Add BERT QA Scala/Java example (#14592)
+* [MXNET-1232] fix demo and add Eclipse support (#13979)
+* [MXNET-1331] Removal of non-MXNET classes from JAR (#14303)
+* Java install info update (#13912)
+* [MXNET-1226] add Docs update for MXNet Java (#14395)
+* [MXNET-1383] Java new use of ParamObject (#14645)
+* MXNET-1302 Exclude commons-codec and commons-io from assembled JAR (#14000)
+
+#### C++
+* print error message for mxnet::cpp::Operator::Invoke when failed (#14318)
+* build docs with CPP package (#13983)
+* Update inception_inference.cpp (#14674)
+* Optimize C++ API (#13496)
+
+#### Clojure 
+* [Clojure] - Add Spec Validations to the Optimizer namespace (#13499)
+* [Clojure] Add Spec Validations for the Random namespace (#13523)
+* [Clojure] Correct the versions in the README so they correspond to the latest maven.org release ([#13507)
+* Port of scala infer package to clojure (#13595)
+* Clojure example for fixed label-width captcha recognition (#13769)
+* Update project.clj file to use the snapshots repo to be able to pull (#13935)
+* [Clojure] Add resource scope to clojure package (#13993)
+* [clojure-package] improve docstrings in image.clj (#14307)
+* [Clojure] Helper function for n-dim vector to ndarray (#14305)
+* [clojure]: add comp-metric based on CompositeEvalMetric (#14553)
+* [Clojure] enhance draw bounding box (#14567)
+* [Clojure] Add methods based on NDArrayAPI/SymbolAPI (#14195)
+* [Clojure] Clojure BERT QA example (#14691)
+* [clojure-package][wip] add ->nd-vec function in ndarray.clj (#14308)
+* [Clojure] Correct the versions in the README so they correspond to the latest maven.org release (#13507)
+* Update version to v1.5.0 including clojure package (#13566)
+* [clojure][generator] ndarray/symbol api random merged (#14800)
+* upgrade codox to work with lein 2.9.0 (#14133)
+* [clojure] fix: image test does not rely on s3 to run (#15122)
+
+#### Julia
+* Julia v0.7/1.0 support and drop v0.6 support (#12845)
+* Julia: split ndarray.jl into several snippets (#14001)
+* Julia: split symbolic-node.jl into several snippets (#14024)
+* Julia: rename mx.clip to clamp for NDArray (#14027)
+* Julia: add binding for runtime feature detection (#13992)
+
+#### Perl:
+* Two more gluon loss classes. (#14194)
+
+#### R
+* add NAG optimizer to r api (#14023)
+* R-Package Makefile (#14068)
+
+
+### Performance Improvements
+
+* Less cudaGet/SetDevice calls in Gluon execution (#13764)
+* Improve bulking in Gluon (#13890)
+* Increase perfomance of BulkAppend and BulkFlush (#14067)
+* Performance improvement in ToTensor GPU Kernel (#14099)
+* Performance improvement in Normalize GPU Kernel (#14139)
+* Bulked op segments to allow Variable nodes (#14200)
+* Performance improving for MKL-DNN Quantized FullyConnected (#14528)
+* speedup SequenceMask on GPU (#14445)
+* Dual stream cudnn Convolution backward() with MXNET_GPU_WORKER_NSTREAMS=2. (#14006)
+* Speedup `_contrib_index_copy` (#14359)
+* use mkl sparse matrix to improve performance (#14492)
+* Re-enable static cached_op optimization (#14931)
+* Speed up SequenceReverse (#14627)
+* Improve FC perf when no_bias=False (#15033)
+* Improve cached_op performance for static mode (#14785)
+
+
+### Example and Tutorials
+
+* [MXNET-949] Module API to Gluon API tutorial (#12542)
+* Support SSD f32/int8 evaluation on COCO dataset (#14646)
+* [MXNET-1209] Tutorial transpose reshape  (#13208)
+* [Clojure] Add Fine Tuning Sentence Pair Classification BERT Example (#14769)
+* example/ssd/evaluate/eval_metric.py (#14561)
+* Add examples of running MXNet with Horovod (#14286)
+* Added link to landing page for Java examples (#14481)
+* Update lip reading example (#13647)
+* [MXNET-1121] Example to demonstrate the inference workflow using RNN (#13680)
+* [MXNET-1301] Remove the unnecessary WaitAll statements from inception_inference example (#13972)
+* Modifying clojure CNN text classification example (#13865)
+* [MXNET-1210 ] Gluon Audio - Example (#13325)
+* add examples and fix the dependency problem (#13620)
+* add quantization example to readme (#14186)
+* Add an inference script providing both accuracy and benchmark result for original wide_n_deep example (#13895)
+* Update autoencoder example (#12933)
+*  #13813 examples with opencv4/origami (#13813)
+* [MXNET-1083] Add the example to demonstrate the inference workflow using C++ API (#13294)
+* Add tutorial on how to use build from source jar (#14197)
+* Gluon end to end tutorial (#13411)
+* Update MXNetTutorialTemplate.ipynb (#13568)
+* Simplifications and some fun stuff for the MNIST Gluon tutorial (#13094)
+* Clarify dependency on OpenCV in CNN Visualization tutorial. (#13495)
+* Update row_sparse tutorial (#13414)
+* add clojure tutorials to index (#14814)
+* Update lstm_crf.py (#14865)
+
+
+### Website
+
+* Version switching user experience improvements (#13921)
+* fix toctree Sphinx errors (#13489)
+* fix link (#15036)
+* fix website build (#14148)
+* Fixed mailing list addresses (#13766)
+* website publish updates (#14015)
+* use relative links; update links (#13741)
+* update social media section (#13705)
+* [MXNET] Updated http://data.dmlc.ml/ links to http://data.mxnet.io/ (#15065)
+
+### Documentation
+* [MXNET-1402] MXNet docs change for 1.4.1 release (#14949)
+* Add API documentation for upsampling operator with examples (#14919)
+* Make docblocks for Gluon BatchNorm and SyncBatchNorm consistent with the code (#14840)
+* [DOC] Update ubuntu install instructions from source (#14534)
+* [Clojure] Better api docstrings by replacing newlines (#14752)
+* Fix documentation for bilinear upsampling and add unit test (#14035)
+* Updated docs for R-package installation (#14269)
+* [docstring] improve docstring and indentation in `module.clj` (#14705)
+* The folder python-howto was removed in an earlier commit. The reference to that folder was not removed. Making a PR to remove the reference to this folder to keep documents consistent (#14573)
+* Updated documentation about nightly tests (#14493)
+* [Doc] Start the tutorials for MKL-DNN backend (#14202)
+* [DOC] fix sym.arange doc (#14237)
+* fix render issue in NDArray linalg docs (#14258)
+* [clojure-package] fix docstrings in `normal.clj` (#14295)
+* [DOC] Refine documentation of runtime feature detection (#14238)
+* [MXNET-1178] updating scala docs (#14070)
+* Fix website scala doc (#14065)
+*  Return value docs for nd.random.* and sym.random.* (#13994)
+* Fixing the doc for symbolic version of rand_zipfian (#13978)
+* fix doc of take operator (#13947)
+* beta doc fixes (#13860)
+* [MXNET-1255] update hybridize documentation (#13597)
+* Update Adam optimizer documentation (#13754)
+* local docs build feature (#13682)
+* gluon docfix (#13631)
+* Added javadocs and improved example instructions (#13711)
+* [MXNET-1164] Generate the document for cpp-package using Doxygen (#12977)
+* Fix warning in waitall doc (#13618)
+* Updated docs for randint operator (#13541)
+* Update java setup docs for 1.4.0 (#13536)
+* clarify ops faq regarding docs strings (#13492)
+* [MXNET-1158] JVM Memory Management Documentation (#13105)
+* Fixing a 404 in the ubuntu setup doc (#13542)
+* Fix READMEs for examples (#14179)
+* [Doc] Add MKL-DNN operator list (#14891)
+* Fixed some typos in AvgPooling Docs (#14324)
+* doc fix (#13465)
+* Change Straight Dope to Dive into Deep Learning (#14465)
+* [DEV] update code owner (#14862)
+* Add notes about debug with libstdc++ symbols (#13533)
+* Mention additional language bindings and add links (#14798)
+* add contributors from intel (#14455)
+* what's new - add 1.4.0 release (#14435)
+* added note about cuda9.2 requirement (#14140)
+* Remove unnecessary "also" in README.md (#14543)
+* Updated news.md with the latest mkldnn submodule version (#14298)
+* add new cloud providers to install page (#14039)
+* Update NOTICE (#14043)
+* Update README.md (#13973)
+* Update profiler doc (#13901)
+* Add CODEOWNERS for Julia package (#13872)
+* update code owner (#13737)
+* Update git clone location to apache github (#13706)
+* NEWS.md backport from v1.4.x to master (#13693)
+* Update CODEOWNERS, add Pedro Larroy. (#13579)
+* [MXNET-1225] Always use config.mk in make install instructions (#13364)
+* Docs & website sphinx errors squished 🌦  (#13488)
+* add Qing's Key to master (#14180)
+* add KEY for zachgk (#14965)
+* corrected a spellign (#14247)
+* 1.4 release (#14297)
+
+
+### Build and Test
+
+* Fix scala doc build break for v1.3.1 (#13820)
+* Adds additional CUDA build environments (#14909)
+* Pins version of scikit-learn for python2 due to drop in support (#14928)
+* upgrade the libpng to 1.6.35 (#14620)
+* Updates to cudnn package installation (#14923)
+* Improve order of execution of install scripts. (#14867)
+* Installs qemu pip requirements from qemu requirements file (#14355)
+* update raspberry pi install instructions (#14172)
+* update the scala installation tutorial on intellij (#14033)
+* Removes unneeded nvidia driver ppa installation (#13814)
+* script for installing gpu libraries and build tools (#13646)
+* Set install path for libmxnet.so dynamic lib on Mac OS (#13629)
+* compatibility with opencv4 (#14313)
+* Flaky test #14189 (#14190)
+* Enforce determinism for backwards compatibility checker (#14463)
+* Change CUB submodule to track Nvidia CUB project. (#13322)
+* Updates gpu tests to use CUDNN_VERSION supplied by the environment but default to 7.0.3 if not set (#14595)
+* upgrade the version to 2.0.2 (#14621)
+* [Dependency Update] Upgrade the libtiff to 4.0.10 (#14623)
+* [Dependency Update] Upgrade cuDNN & NCCL (#14884)
+* [Dependency Update] Upgrade openssl to 1.1.1b (#14837)
+* [Dependency Update] Upgrade CI to use latest cuDNN (#14950)
+* GPU RNN to use TempSpace resource for workspace. (#15056)
+* Add vim-nox to ci/docker/install/ubuntu_core.sh (#14632)
+* Fix dockerized GPU builds in dev_menu (#14603)
+* [MXNET-1093] Add python3 Docker images for each MXNet release (#12791)
+* increased docker shared memory (#14119)
+* Fix permissions of ci/docker/install/ubuntu_publish.sh (#13840)
+* Dockerfiles for Publish Testing (#13707)
+* Fix test randint (#14990)
+* Silence excessive mkldnn logging output on tests. (#14947)
+* Fix test memory with ResourceScope (#14666)
+* Sync Horovod distributed training examples with latest changes (#14748)
+* use mx.context.num_gpus instead of mx.test_utils.list_gpus in MF recommender example (#14926)
+* [MXNET-1400] adding tests cases to verify large tensor support for depth_to_space and space_to_depth (#14797)
+* rewrite test_custom_op_exc (#14878)
+* [Clojure] Remove unneeded test files (#14813)
+* Use correct stash name when running nightly tests (#14809)
+* julia/ndarray: fix flaky test cases for `clamp` (#14776)
+* Updates tolerances for test_layer_bidirectional (#14682)
+* Adds context parameter to check_rnn_layer_forward calls in test_lstmp (#14529)
+* reenable the test (#14483)
+* temporarily disable integ tests with a dependency on origami repo (#14448)
+* Bypass ThreadedEngine in test_operator_gpu.py:test_convolution_multiple_streams. (#14338)
+* Updated the MLP test to accept the number of epochs. Reduced the epochs in ci_test.sh to shorten the CI build time (#14149)
+* follow up on fix nightly test (#14134)
+* Julia: enable integration test (#14025)
+* fix test_depthwise_convoltuion for occasional CI failures (#14016)
+* fix test_stn (#14063)
+* Add a test for SGLD optimizer with comparisons for set noise seeds. (#13762)
+* Code modification for  testcases of various network models in directory example (#12498)
+* Remove MXNET_STORAGE_FALLBACK_LOG_VERBOSE from test_autograd.py (#13830)
+* [MXNET-1263] Unit Tests for Java Predictor and Object Detector APIs (#13794)
+* ONNX test code cleanup (#13553)
+*  #13385 [Clojure] - Turn examples into integration tests (#13554)
+* add cpp example inception to nightly test (#13534)
+* Fix flaky test test_random:test_randint_generator (#13498)
+* Adding test for softmaxoutput (#13116)
+* [MXNET-1235] Add a test for AdaMax optimizer (#13467)
+* [MXNET-545] Fix broken cython build (#10951)
+* Update mkldnn window build instructions in MKLDNN_README.md (#14952)
+* Added USE_SIGNAL_HANDLER to other Linux builds which didn't had it (#14122)
+* Static build for Python (#13916)
+* Julia: add windows-cpu build (#13937)
+* Static build instruction for MXNet in general (#13914)
+* Jenkins nightly maven with static build script and gpu (#13767)
+* Re-organize Scala maven build (#13626)
+* disable error checking when building old versions (#13725)
+* scripts for building libmxnet binary and wheel (#13648)
+* Improve dev_menu usability, local build and virtualenv (#13529)
+* Scripts for building dependency libraries of MXNet (#13282)
+* [MXNET-1224]: improve scala maven jni build and packing. (#13493)
+* fix compile error in debug mode (#13873)
+* add ccache to docs build (#13832)
+* Decreases test sensitivity (#15014)
+* bump up atol for test_bilinear_resize_op (#15011)
+* Add STL checks via -D_GLIBCXX_ASSERTIONS in debug mode (#14896)
+* clean up duplicate cudnn installation (#14996)
+* fix custom op fork test (#14753)
+* fix pi instructions (#14746)
+* Reenable TensorRT step (#14654)
+* Fixes for CI downloads (#14504)
+* Fixed tutorial warnings (#14472)
+* Fixes static build script for cub directory rename (#14578)
+* add a compiler flag to use int64 as tensor size (#14570)
+* Upgrade Pylint version to 2.3.1 (#14807)
+* Fixes installation nightly test by filtering out the git commands (#14144)
+* fix nightly test on tutorials (#14036)
+* Fix MXNet R package build (#13952)
+* re-enable test after issue fixed https://github.com/apache/incubator-mxnet/issues/10973 (#14032)
+* Add back R tests and fix typo around R and perl tests (#13940)
+* Fix document build (#13927)
+* Temporarily disables windows pipeline to unblock PRs (#14261)
+* Fix USE_MKLDNN check in Makefile (#13775)
+* Fix spelling in threaded_engine_test (#14709)
+* Fix cmake options parsing in dev_menu (#13458)
+* Add Local test stage and option to jump directly to menu item from commandline (#13809)
+* Add CPU test coverage and refine cmake builds (#13338)
+* ONNX test code cleanup - part 2 (#13738)
+* Rearrange tests written only for update_on_kvstore = True (#13514)
+* add batch norm test (#13625)
+* Adadelta optimizer test (#13443)
+* Skip flaky test https://github.com/apache/incubator-mxnet/issues/13446 (#13480)
+* Comment out test_unix_python3_tensorrt_gpu step (#14642)
+* Enable bulking test on windows (#14392)
+* rewrote the concat test to avoid flaky failures (#14049)
+* #13624 clojure nightly tests (#13624)
+* Temporarily disable website testing (#13887)
+* adding tolerance to flaky test (#13850)
+* Add publish test of PyPi cu100mkl (#14637)
+* CMake: Enable installation of cpp-package headers (#13339)
+* Use USE_SIGNAL_HANDLER by default set to ON in CMakeLists.txt (#14599)
+* Improve CMake handling of sse2 and sse3 (#14757)
+* Update base CUDA image for CI to v10.0 cuDNN 7.3.1 (#14513)
+* Updates build_lib.sh to copy the cub library license (#14347)
+* Add license check to dev_menu, docs build with docker (#14166)
+* Print reproduction command on CI failure (#14815)
+* change mxnet_option behavior (#14743)
+* [DEP] upgrade dmlc-core (#14510)
+* Use ubuntu_rat container for rat check (#14678)
+* Added repeats for github status updates (#14530)
+* add filter to warnings (#14532)
+* CI Changes for Codified Windows AMIs (#14336)
+* Refactors USE_NVRTC setting to ENABLE_CUDA_RTC in pip make config files (#14250)
+* pypi package description. manifest/setup.py update (#14255)
+* make rat-excludes compliant with apache release policy (#14142)
+* Add libhdf5-dev to ubuntu_core.sh (#14079)
+* Added logging to GitHub commit status publishing (#13615)
+* [CI] Prevent timeouts when rebuilding containers with docker. (#13818)
+* [MXNET-862] Basic maven jenkins pipeline (#13450)
+* Scope requests so it's not needed for dev_menu (#13771)
+* Add timeout/retry logic to docker cache download (#13573)
+* turn on Sphinx warnings as errors (#13544)
+* [MXNET-1251] Basic configuration to do static-linking (#13621)
+* Improve CCache handling (#13456)
+* build config for maven and pip (#13556)
+* Add Intel MKL blas to Jenkins (#13607)
+* Add workspace cleaning after job finished (#13490)
+* Add a retry to qemu_provision (#13551)
+* Deprecate Jenkinsfile (#13474)
+* [MXNET-1408] Adding test to verify Large Tensor Support for ravel and unravel (#15048)
+* move amp test and change op support to warning (#15085)
+* Fixes call to build ubuntu gpu in nightly tests (#14964)
+* rat check make target (#15127)
+* add epsilon for tolerance level (#15098)
+* Change mx.test_utils.list_gpus to mx.context.num_gpus where possible (#14946)
+* bump up cudnn to 7.5.1 & nccl 2.4.2 (#14988)
+* Disables TensorRT build step (#14958)
+* disable flaky integration test (#14151)
+* Disables large tensor size cpu test step (#14982)
+* Disable Flaky Test test_poisson_generator (#14540)
+* Disabled flaky test test_negative_binomial_generator (#13784)
+* Disabled flaky test test_gluon_data.test_recordimage_dataset_with_data_loader_multiworker (#13527)
+
+
+### Bug-fixes
+
+* Improve dev_menu virtualenv handling (#14788)
+* Fallback to dense version for grad(reshape), grad(expand_dims) (#13599)
+* Fix the bug of BidirectionalCell (#13575)
+* set _scale in Trainer using optimizer rescale_grad (#14593)
+* [MXNET-1379] update reshape operator (#14600)
+* Add repr for SymbolBlock (#14423)
+* Cudnn conv dgrad algo filtering (#14310)
+* Fix memory leak for size-zero ndarray (#14365)
+* Fixes the test_sgld (#14473)
+* Revert "Fix memory leak for size-zero ndarray (#14365)" (#14477)
+* fix custom operation in fork (#14451)
+* Fixes test_operator_gpu.test_multinomial_generator (#14475)
+* support leading dimension of -1 in ravel/unravel (#14356)
+* begin=end not a valid input (#14403)
+* Fix NaN value comparisons in relu, max and min ops (#14262)
+* fix engine crash in shutdown phase (#14382)
+* fix OOM error during resource allocation (#14444)
+* Fix relative difference scala (#14417)
+* Correct update count with Gluon trainer and update_on_kvstore=False (#14377)
+* Fix crashes on visualization (#14425)
+* Reorder module import orders for dist-kvstore (#13742)
+* Fixes for trainer with update_on_kvstore=False (#13721)
+* Fix errors in docstrings for subgraph op; use code directive (#13463)
+* Add resiliency to onnx export code (#13426)
+* update github location for sampled_block.py (#13508)
+* Revert "Manually track num_max_thread (#12380)" (#13501)
+* Revert "Feature/mkldnn static 2 (#13503)" (#13540)
+* [MXNET-1110] Add header files required by horovod (#13062)
+* [MXAPPS-1020] Clean up some Sphinx warnings. (#13539)
+* [MXNET-1249] Fix Object Detector Performance with GPU (#13522)
+* [MXNET-769] Use MXNET_HOME in a tempdir in windows to prevent access denied due t… (#13531)
+* Chi_square_check for discrete distribution fix (#13543)
+* Fix use-before-assignment in convert_dot (#13511)
+* fix the situation where idx didn't align with rec (#13550)
+* fix link for gluon model zoo (#13583)
+* Fix exception handling api doc (#13519)
+* [MXNET-1253] fix control_flow_op (#13555)
+* fix the Float not showing correctly problem (#13617)
+* fix quantize pass error when the quantization supported Op are excluded in the model (#13596)
+* Fix for import mxnet taking long time if multiple process launched (#13602)
+* Revert "Feature/mkldnn static (#13628)" (#13638)
+* updated reference to Apache MXNet (#13645)
+* Fix incorrect delete in MXExecutorReshape exception handling (#13376)
+* add build fix for Scala/Java build (#13655)
+* remove omp which can cause ssd accuracy variance (#13622)
+* Fix Jetson compilation (#13532)
+* Revert "Fix Jetson compilation" (#13665)
+* Fix Jetson compilation (#13666)
+* Revert "Revert "[MXNET-43] Fix Jetson compilation" (#13665)" (#13672)
+* fix unpicklable transform_first on windows (#13686)
+* Fix NDArray ToDLPack Bug (#13698)
+* Fix the quantization script to support Python2 (#13700)
+* Update basic_layers.py (#13732)
+* [MXNET-1231] Allow not using Some in the Scala operators (#13619)
+* [MXNET-244] Work around likely compiler bug on nested inlines and temporary acces… (#13535)
+* Use curl to download sample data instead of wget. (#13761)
+* fix bipartite match memory corruption (#13727)
+* remove attributes clear on TRT nodes for GetOptimizedSymbol (#13703)
+* fix redirection issues; set default version to master (#13796)
+* fix for params with no dims in onnx (#13413)
+* Remove semicolon in libmxnet.sym file (#13822)
+* remove useless code (#13777)
+* Fixing a symlink issue with R install (#13708)
+* fix minor indentation (#13827)
+* Fix Tree Reduction on new instance type p3dn.24xlarge (#13852)
+* [Clojure] package infer tweaks (#13864)
+* Fix cpp examples build on Mac. (#13826)
+* Fix launch bounds in spatial transformer (#13188)
+* Update example scripts classpath. (#13849)
+* fix ssd quantization script error (#13843)
+* Avoid adding SegfaultLogger if process already has sig handler. (#13842)
+* fix the fetching GPU problem (#13889)
+* Fix SN-GAN example doc (#13877)
+* update Spectral Normalization Code (#13868)
+* Fixed java benchmark failing error by fixing the classpath (#13891)
+* Fix the order of error term's operands (#13745)
+* fix bug in nag optimizer (#13683)
+* Fix BatchNorm converter for CoreML when fix_gamma=True (#13557)
+* Fix for test always returning true (#13911)
+* Add error checking for cpp examples. (#13828)
+* julia: fix `argmax` for NDArray (#13871)
+* test_ImageRecordIter_seed_augmentation flaky test fix (#12485)
+* Julia: fix filename quoting in docstring (#13894)
+* Flaky maven binary download (#13974)
+* [MXNET-1293] Adding Iterables instead of List to method signature for infer APIs in Java (#13977)
+* Sample python bilinear initializer at integral points in y-direction (#12983)
+* Fix inconsistent handling for FResourceRequestEx for imperative and symbolic executor (#14007)
+* [MXNET-1258] fix unittest for ROIAlign Operator (#13609)
+* Fix performance regression in normalize operator (#14055)
+* Remove inplace support for ToTensor operator (#14083)
+* Addresses comments in runtime feature discovery API (#13964)
+* The latest version of leiningen has a dependency problem with codox (#14132)
+* Fix quote on LBSGD docs (#13975)
+* Fixes spelling (#14168)
+* Fix broken amalgamation (#12792)
+* Fix nd.pick large array issue (#14082)
+* Fix req=null in SliceLikeBackward (#14209)
+* onnx broadcast ops fixes (#13604)
+* fix update params (#14218)
+* MXNet Java bug fixes and experience improvement (#14213)
+* reverting broadcasting fixes (#14299)
+* fix memory-related issues to enable ASAN tests (#14223)
+* FIX: flaky test exponential generator (#14287)
+* fix SoftmaxOutput resource bug (#14302)
+* Fix shape inference pass (#14153)
+* Limit workspace for cudnnGet results (#14326)
+* #14199: catch subprocess.CalledProcessError in get_gpus() (#14212)
+* Fixes #14181, validate model output shape for ObjectDetector. (#14215)
+* Optimizer MXKVStoreUpdater bug fix in serializeState method (#14337)
+* Add proper exception message for negative shape in array creation routines (#14362)
+* Fix NaN value comparisons in relu, max and min ops (#14262)
+* fix engine crash in shutdown phase (#14382)
+* Flaky test #14189 (#14190)
+* Correct update count with Gluon trainer and update_on_kvstore=False (#14377)
+* Fix relative difference scala (#14417)
+* fix OOM error during resource allocation (#14444)
+* Fix crashes on visualization (#14425)
+* begin=end not a valid input (#14403)
+* Fix memory leak for size-zero ndarray (#14365)
+* Fixes the test_sgld (#14473)
+* Revert "Fix memory leak for size-zero ndarray (#14365)" (#14477)
+* fix custom operation in fork (#14451)
+* Fixes test_operator_gpu.test_multinomial_generator (#14475)
+* Fix script retrieval (#14519)
+* Memory fixes. Resolves #10867, and resolves #14080 (#14372)
+* Chouffe/clojure fix tests (#14531)
+* [clojure][image] add draw-bounding-box interop (#14533)
+* fix tests (#14565)
+* Do not touch GPU 0 during ReleaseAll (#14550)
+* [MXNET-1357] Fix the cpp-examples to add exception handling (#14441)
+* fix build cpp examples option (#14562)
+* Fix flaky test poisson generator & test_negative_binomial_generator (#14571)
+* Fixing unintentional variable overloading (#14438)
+* fix quantize graph pass (#14605)
+* replace std::random_shuffle to std::shuffle (#14523)
+* Add exception handling support for waitall (#14397)
+* split_and_load can now handle num_ctx > num_data. Issue #13909 (#14607)
+* Fix aspect ratio sampling for RandomResizedCrop (#14585)
+* [MXNET-400] support string type for kvstore key in cpp-package (#10792)
+* Fix warning on macro expansion using defined. (#14598)
+* Fix scaladoc scalastyle violations in Infer package (#14671)
+* Fix profiler check (#14677)
+* Tweak the copy for the cudnn autotuning warning. (#14680)
+* Properly handling custom op exception by modify engine (#14693)
+* Disable USE_GPERFTOOLS (#14711)
+* Reference engine from chunk via weak pointer (#14591)
+* [C++] fix type inconsistent issue when loading quantized parameters (#15038)
+* Fix crash in random.shuffle operator (#15041)
+* [MXNET-1406] [BUG] Fix DLManagedTensor deleter (#15016)
+* Fixes lint issue in AMP (#15015)
+* Fixed issue where the estimator was printing beyond the dataset size … (#14464)
+* Fixes cuDNN version for CUDA 9.0 build environment (#15001)
+* Fix the incorrect MKLDNN/MKL logic in cmake  (#14877)
+* Fixed and re-enables TensorRT steps (#14960)
+* Fix the return type of sparse.clip operator (#14856)
+* Fix sample_multinomial number of outputs bug (#14873)
+* [MXNET-13578] Fix cmake installation failed (#14692)
+* Fix iterator over symbol when multiple children have the same name (#14597)
+* Fixes for wine detection tutorial (#13886)
+* Scala/Java Predict API fix #14756 (#14804)
+* Fix GELU backward possible NaN (#14782)
+* fix shape index bug (#14518)
+* [BUGFIX] fix ELU function will appear nan when calculating the gradient (#14673)
+* Change size_t to int within for loop to fix windows build error (#14740)
+* [contrib][op] fix MultiBoxPrior confusing results if first ratio is not 1.0 (#13763)
+* Fix scalastyle (#14669)
+* fix Makefile (#14424)
+* [v1.4.x] Update MKL-DNN to fix the OSX build issue (#14141) (#14182)
+* add preprocessed data and pretrained model info; minor format/spelling fixes (#14170)
+* Fixes libjpeg-turbo dependency under Ubuntu 16.04 (#14127)
+* Fix website error pages (#13963)
+* fix Makefile for rpkg (#13590)
+* fix c complier to clang (#13778)
+* Fix #13521 (#13537)
+* [MXNET-1234] Fix shape inference problems in Activation backward (#13409)
+* Revert the change broadcast_to param shape (#14998)
+* Fix infer shape partial after unknown shape changed to -1 (#14869)
+* fix add_n bug: when input mem overlap with output mem, results is wrong (#14889)
+* [Bugfix] Fix layer norm for large input shape (#14870)
+* Fix Clojure BERT example's context argument (#14843)
+* fix min max on zero-sized ndarray (#14745)
+* fix acc_type_switch macro with extra tests (#14773)
+* fix bug in profiler tutorial when using cpu (#13695)
+* [MXNET-1291] solve pylint errors in examples with issue no.12205 (#13815)
+* data preparation file moved in example (#14781)
+* [MXNET-1291] solve pylint errors in examples with issue no.12205 (#13848)
+*  Prevent crashes for opencv exception and std::exception (#14433)
+* Set idx2name for Optimizer object (#14703)
+* Revert "Bumped minor version from 1.4.0 to 1.5.0 on master, updated License file" (#13558)
+* [BUGFIX] fix unknown parameter shapes when np_shape is turned on. (#15097)
+* Add gluonCV to fix AMP Tutorial (#15039)
+* fix the if condition for LayerNorm (#15094)
+* [MKLDNN]Fix mkldnn deconvolution forward with bias (#15088)
+* NER example: fix divisions by zero (#15068)
+* remove warning in tutorial: (#15135)
+* [MXNET-1291] solve pylint errors in examples with issue no.12205 (#13938)
+* Revert "Improve cached_op performance for static mode (#14785)" (#14868)
+* Fix mkldnn backend when using naive engine (#15089)
+* fix gluon rnn cell single step unroll (#15081)
+* Revert "Improve FC perf when no_bias=False (#15033)" (#15099)
+
+
+### License
+
+* Updates python setup.py for recent license changes (#14778)
+* [MXNET-1377] Add static-dependencies licenses (#14726)
+* add license (#13793)
+* License update  (#13565)
+* Bumped minor version from 1.4.0 to 1.5.0 on master, updated License file (#13478)
+* License Googletest and Appendix (#14687)
+* Add copyrights for third party licenses to license file (#13851)
+* Improve license_header tool by only traversing files under revision c… (#13803)
+* Update LICENSE File with subcomponents (#13808)
+
+### Depreciations
+
+* Julia: deprecate `mx.empty`, replace it with `UndefInitializer` (#13934)
+ * Deprecate NDArrayCollector and instead use ResourceScope (#14780)
+
+### Known Issues
+* Amalgamation compile problems(#14808)
+* Dynamic Shape does not support reverse shape inference and deferred initialization. (#14983)
+* Disables flaky test_random_size_crop (#15019)
+* Disables flaky test_l2_normalization (#15006)
+* Disables flaky TestStochasticTiming_2D test (#14412)
+* Disables flaky test_operator.test_sgld test (#14410)
+* Disables test_bulking due to flakyness (#14971)
+* Disabled flaky test (#13758)
+* Disables flaky test_droupout (#15003)
+* Disables flaky test_operator_gpu.test_activation (#14969)
+
 
 ## 1.4.1
 

From 0e0c7f394e5686ea4b3fa9ab4c41e3cd12cb214a Mon Sep 17 00:00:00 2001
From: Haohuan Wang <haohuanw@umich.edu>
Date: Tue, 23 Jul 2019 22:17:42 -0700
Subject: [PATCH 105/813] fix LinearRegressionOutput with empty label (#15620)

---
 cpp-package/example/CMakeLists.txt         |  4 ++
 cpp-package/example/test_regress_label.cpp | 56 ++++++++++++++++++++++
 cpp-package/tests/ci_test.sh               |  3 ++
 src/operator/regression_output-inl.h       |  3 +-
 4 files changed, 65 insertions(+), 1 deletion(-)
 create mode 100644 cpp-package/example/test_regress_label.cpp

diff --git a/cpp-package/example/CMakeLists.txt b/cpp-package/example/CMakeLists.txt
index b4cea68fbd05..643a92d9a3bc 100644
--- a/cpp-package/example/CMakeLists.txt
+++ b/cpp-package/example/CMakeLists.txt
@@ -27,6 +27,10 @@ endif()
 
 include_directories(${CMAKE_CURRENT_SOURCE_DIR}/../include)
 
+add_executable(test_regress_label test_regress_label.cpp ${CPP_PACKAGE_HAEDERS})
+target_link_libraries(test_regress_label ${CPP_EXAMPLE_LIBS})
+add_dependencies(test_regress_label ${CPPEX_DEPS})
+
 add_executable(lenet lenet.cpp ${CPP_PACKAGE_HEADERS})
 target_link_libraries(lenet ${CPP_EXAMPLE_LIBS})
 add_dependencies(lenet ${CPPEX_DEPS})
diff --git a/cpp-package/example/test_regress_label.cpp b/cpp-package/example/test_regress_label.cpp
new file mode 100644
index 000000000000..8d1d6444b138
--- /dev/null
+++ b/cpp-package/example/test_regress_label.cpp
@@ -0,0 +1,56 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ * 
+ * This file is used for testing LinearRegressionOutput can
+ *   still bind if label is not provided
+ */
+
+#include <iostream>
+#include <vector>
+#include <string>
+#include "dmlc/logging.h"
+#include "mxnet-cpp/MxNetCpp.h"
+
+using namespace mxnet::cpp;
+
+int main() {
+    LOG(INFO) << "Running LinearRegressionOutput symbol testing, "
+                 "executor should be able to bind without label.";
+    Symbol data = Symbol::Variable("data");
+    Symbol label = Symbol::Variable("regress_label");
+    Symbol symbol = LinearRegressionOutput(data, label);
+    std::map<std::string, mxnet::cpp::OpReqType> opReqMap;
+    for (const auto& iter : symbol.ListArguments()) {
+        opReqMap[iter] = mxnet::cpp::OpReqType::kNullOp;
+    }
+    std::map<std::string, mxnet::cpp::NDArray> argMap({
+        {"data", NDArray(Shape{1, 3}, Context::cpu(), true)}
+    });
+
+    try {
+        symbol.SimpleBind(Context::cpu(),
+                argMap,
+                std::map<std::string, mxnet::cpp::NDArray>(),
+                opReqMap,
+                std::map<std::string, mxnet::cpp::NDArray>());
+    } catch (const std::exception& e) {
+        LOG(ERROR) << "Error binding the symbol: " << MXGetLastError() << " " << e.what();
+        throw;
+    }
+    return 0;
+}
diff --git a/cpp-package/tests/ci_test.sh b/cpp-package/tests/ci_test.sh
index e90493442246..2ac9d6fcd741 100755
--- a/cpp-package/tests/ci_test.sh
+++ b/cpp-package/tests/ci_test.sh
@@ -60,6 +60,9 @@ cp ../../build/cpp-package/example/test_score .
 cp ../../build/cpp-package/example/test_ndarray_copy .
 ./test_ndarray_copy
 
+cp ../../build/cpp-package/example/test_regress_label .
+./test_regress_label
+
 sh unittests/unit_test_mlp_csv.sh
 
 cd inference
diff --git a/src/operator/regression_output-inl.h b/src/operator/regression_output-inl.h
index ba59937a7152..dcee8027dff0 100644
--- a/src/operator/regression_output-inl.h
+++ b/src/operator/regression_output-inl.h
@@ -59,7 +59,8 @@ inline bool RegressionOpShape(const nnvm::NodeAttrs& attrs,
   const mxnet::TShape &dshape = in_attrs->at(0);
   if (!shape_is_known(dshape)) return false;
   auto &lshape = (*in_attrs)[1];
-  if (lshape.ndim() == 0) {
+  // if label is not defined, manually build the shape based on dshape
+  if (lshape.ndim() == -1) {
     // special treatment for 1D output, to allow 1D label by default.
     // Think about change convention later
     if (dshape.ndim() == 2 && dshape[1] == 1) {

From 01fae12e58d73d5989a23cfc8b7519f4772fdf1f Mon Sep 17 00:00:00 2001
From: Lai Wei <royweilai@gmail.com>
Date: Wed, 24 Jul 2019 13:38:42 -0700
Subject: [PATCH 106/813] bump up version from 1.5.0 to 1.6.0 on master
 (#15072)

* bump up version

* address comments

* trigger

* trigger
---
 R-package/DESCRIPTION                          |  2 +-
 contrib/clojure-package/README.md              |  4 ++--
 .../clojure-package/examples/bert/project.clj  |  2 +-
 .../examples/captcha/project.clj               |  2 +-
 .../cnn-text-classification/project.clj        |  2 +-
 .../clojure-package/examples/gan/project.clj   |  2 +-
 .../examples/imclassification/project.clj      |  2 +-
 .../examples/infer/imageclassifier/project.clj |  2 +-
 .../examples/infer/objectdetector/project.clj  |  2 +-
 .../examples/infer/predictor/project.clj       |  2 +-
 .../examples/module/project.clj                |  2 +-
 .../examples/multi-label/project.clj           |  2 +-
 .../examples/neural-style/project.clj          |  2 +-
 .../examples/pre-trained-models/project.clj    |  2 +-
 .../examples/profiler/project.clj              |  2 +-
 .../clojure-package/examples/rnn/project.clj   |  2 +-
 .../examples/tutorial/project.clj              |  2 +-
 .../examples/visualization/project.clj         |  2 +-
 contrib/clojure-package/project.clj            |  2 +-
 .../clojure-package/scripts/update_versions.sh |  2 +-
 .../tutorials/scala/mxnet_scala_on_intellij.md |  6 +++---
 julia/NEWS.md                                  |  5 ++++-
 python/mxnet/libinfo.py                        |  2 +-
 scala-package/README.md                        | 18 +++++++++---------
 .../main/scala/org/apache/mxnet/NDArray.scala  |  2 +-
 .../org/apache/mxnet/NDArrayCollector.scala    | 12 ++++++------
 scala-package/mxnet-demo/java-demo/README.md   |  4 ++--
 scala-package/mxnet-demo/java-demo/pom.xml     |  4 ++--
 scala-package/mxnet-demo/scala-demo/pom.xml    |  4 ++--
 scala-package/packageTest/README.md            |  2 +-
 snapcraft.yaml                                 |  2 +-
 31 files changed, 53 insertions(+), 50 deletions(-)

diff --git a/R-package/DESCRIPTION b/R-package/DESCRIPTION
index 70aa66e36b7e..b124877c8d3c 100644
--- a/R-package/DESCRIPTION
+++ b/R-package/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: mxnet
 Type: Package
 Title: MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
-Version: 1.5.0
+Version: 1.6.0
 Date: 2017-06-27
 Author: Tianqi Chen, Qiang Kou, Tong He, Anirudh Acharya <https://github.com/anirudhacharya>
 Maintainer: Qiang Kou <qkou@qkou.info>
diff --git a/contrib/clojure-package/README.md b/contrib/clojure-package/README.md
index 7bb417edf3d3..c8971d327805 100644
--- a/contrib/clojure-package/README.md
+++ b/contrib/clojure-package/README.md
@@ -137,7 +137,7 @@ With this option, you will install a Git revision of the Clojure package source
 
 - Install additional dependencies as described in [the corresponding section for Option 1](#installing-additional-dependencies),
 
-- Recursively clone the MXNet repository and checkout the desired version, (example 1.3.1). You should use the latest [version](https://search.maven.org/search?q=clojure-mxnet)), and a clone into the `~/mxnet` directory:
+- Recursively clone the MXNet repository and checkout the desired version, (example 1.4.1). You should use the latest [version](https://search.maven.org/search?q=clojure-mxnet)), and a clone into the `~/mxnet` directory:
 
   ```bash
   git clone --recursive https://github.com/apache/incubator-mxnet.git ~/mxnet
@@ -174,7 +174,7 @@ In that case, however, breakage can happen at any point, for instance when the S
 
 With this option, you will compile the core MXNet C++ package and jars for both Scala and Clojure language bindings from source. If you intend to make changes to the code in any of the parts, or if you simply want the latest and greatest features, this choice is for you.
 
-The first step is to recursively clone the MXNet repository and checkout the desired version, (example 1.3.1). You should use the latest [version](https://search.maven.org/search?q=clojure-mxnet)), and clone into the `~/mxnet` directory:
+The first step is to recursively clone the MXNet repository and checkout the desired version, (example 1.4.1). You should use the latest [version](https://search.maven.org/search?q=clojure-mxnet)), and clone into the `~/mxnet` directory:
 
   ```bash
   git clone --recursive https://github.com/apache/incubator-mxnet.git ~/mxnet
diff --git a/contrib/clojure-package/examples/bert/project.clj b/contrib/clojure-package/examples/bert/project.clj
index 05061b476241..44ae4d536d26 100644
--- a/contrib/clojure-package/examples/bert/project.clj
+++ b/contrib/clojure-package/examples/bert/project.clj
@@ -23,7 +23,7 @@
             ;;; so if you run into trouble please delete the `lein-juptyter` plugin
             [lein-jupyter "0.1.16" :exclusions [org.clojure/tools.nrepl org.clojure/clojure org.codehaus.plexus/plexus-utils org.clojure/tools.reader]]]
   :dependencies [[org.clojure/clojure "1.9.0"]
-                 [org.apache.mxnet.contrib.clojure/clojure-mxnet "1.5.0-SNAPSHOT"]
+                 [org.apache.mxnet.contrib.clojure/clojure-mxnet "1.6.0-SNAPSHOT"]
                  [cheshire "5.8.1"]
                  [clojure-csv/clojure-csv "2.0.1"]]
   :pedantic? :skip
diff --git a/contrib/clojure-package/examples/captcha/project.clj b/contrib/clojure-package/examples/captcha/project.clj
index fa37fecbe035..795f3fea875a 100644
--- a/contrib/clojure-package/examples/captcha/project.clj
+++ b/contrib/clojure-package/examples/captcha/project.clj
@@ -19,7 +19,7 @@
   :description "Captcha recognition via multi-label classification"
   :plugins [[lein-cljfmt "0.5.7"]]
   :dependencies [[org.clojure/clojure "1.9.0"]
-                 [org.apache.mxnet.contrib.clojure/clojure-mxnet "1.5.0-SNAPSHOT"]]
+                 [org.apache.mxnet.contrib.clojure/clojure-mxnet "1.6.0-SNAPSHOT"]]
   :main ^:skip-aot captcha.train-ocr
   :profiles {:train {:main captcha.train-ocr}
              :infer {:main captcha.infer-ocr}
diff --git a/contrib/clojure-package/examples/cnn-text-classification/project.clj b/contrib/clojure-package/examples/cnn-text-classification/project.clj
index 29ebefe5d200..1b8859fd732c 100644
--- a/contrib/clojure-package/examples/cnn-text-classification/project.clj
+++ b/contrib/clojure-package/examples/cnn-text-classification/project.clj
@@ -19,6 +19,6 @@
   :description "CNN text classification with MXNet"
   :plugins [[lein-cljfmt "0.5.7"]]
   :dependencies [[org.clojure/clojure "1.9.0"]
-                 [org.apache.mxnet.contrib.clojure/clojure-mxnet "1.5.0-SNAPSHOT"]]
+                 [org.apache.mxnet.contrib.clojure/clojure-mxnet "1.6.0-SNAPSHOT"]]
   :pedantic? :skip
   :main cnn-text-classification.classifier)
diff --git a/contrib/clojure-package/examples/gan/project.clj b/contrib/clojure-package/examples/gan/project.clj
index 439398783312..4048b0c1cb2e 100644
--- a/contrib/clojure-package/examples/gan/project.clj
+++ b/contrib/clojure-package/examples/gan/project.clj
@@ -20,7 +20,7 @@
   :plugins [[lein-cljfmt "0.5.7"]]
   :repositories [["vendredi" {:url "https://repository.hellonico.info/repository/hellonico/"}]]
   :dependencies [[org.clojure/clojure "1.9.0"]
-                 [org.apache.mxnet.contrib.clojure/clojure-mxnet "1.5.0-SNAPSHOT"]
+                 [org.apache.mxnet.contrib.clojure/clojure-mxnet "1.6.0-SNAPSHOT"]
                  [origami "4.0.0-3"]
                  ]
   :main gan.gan-mnist)
\ No newline at end of file
diff --git a/contrib/clojure-package/examples/imclassification/project.clj b/contrib/clojure-package/examples/imclassification/project.clj
index 5f77cf55cf35..702a33d67ee8 100644
--- a/contrib/clojure-package/examples/imclassification/project.clj
+++ b/contrib/clojure-package/examples/imclassification/project.clj
@@ -19,6 +19,6 @@
   :description "Clojure examples for image classification"
   :plugins [[lein-cljfmt "0.5.7"]]
   :dependencies [[org.clojure/clojure "1.9.0"]
-                 [org.apache.mxnet.contrib.clojure/clojure-mxnet "1.5.0-SNAPSHOT"]]
+                 [org.apache.mxnet.contrib.clojure/clojure-mxnet "1.6.0-SNAPSHOT"]]
   :pedantic? :skip
   :main imclassification.train-mnist)
diff --git a/contrib/clojure-package/examples/infer/imageclassifier/project.clj b/contrib/clojure-package/examples/infer/imageclassifier/project.clj
index 2d5b171d9ab7..dcca5982fd28 100644
--- a/contrib/clojure-package/examples/infer/imageclassifier/project.clj
+++ b/contrib/clojure-package/examples/infer/imageclassifier/project.clj
@@ -20,6 +20,6 @@
   :plugins [[lein-cljfmt "0.5.7"]]
   :dependencies [[org.clojure/clojure "1.9.0"]
                  [org.clojure/tools.cli "0.4.1"]
-                 [org.apache.mxnet.contrib.clojure/clojure-mxnet "1.5.0-SNAPSHOT"]]
+                 [org.apache.mxnet.contrib.clojure/clojure-mxnet "1.6.0-SNAPSHOT"]]
   :main ^:skip-aot infer.imageclassifier-example
   :profiles {:uberjar {:aot :all}})
diff --git a/contrib/clojure-package/examples/infer/objectdetector/project.clj b/contrib/clojure-package/examples/infer/objectdetector/project.clj
index da01797f5a21..1d29be2d8e94 100644
--- a/contrib/clojure-package/examples/infer/objectdetector/project.clj
+++ b/contrib/clojure-package/examples/infer/objectdetector/project.clj
@@ -22,6 +22,6 @@
   :aliases {"run-detector" ["run" "--" "-m" "models/resnet50_ssd/resnet50_ssd_model" "-i" "images/dog.jpg" "-d" "images/"]}
   :dependencies [[org.clojure/clojure "1.9.0"]
                  [org.clojure/tools.cli "0.4.1"]
-                 [org.apache.mxnet.contrib.clojure/clojure-mxnet "1.5.0-SNAPSHOT"]]
+                 [org.apache.mxnet.contrib.clojure/clojure-mxnet "1.6.0-SNAPSHOT"]]
   :main ^:skip-aot infer.objectdetector-example
   :profiles {:uberjar {:aot :all}})
diff --git a/contrib/clojure-package/examples/infer/predictor/project.clj b/contrib/clojure-package/examples/infer/predictor/project.clj
index 0bd1eaee671d..936d9179b76e 100644
--- a/contrib/clojure-package/examples/infer/predictor/project.clj
+++ b/contrib/clojure-package/examples/infer/predictor/project.clj
@@ -20,6 +20,6 @@
   :plugins [[lein-cljfmt "0.5.7"]]
   :dependencies [[org.clojure/clojure "1.9.0"]
                  [org.clojure/tools.cli "0.4.1"]
-                 [org.apache.mxnet.contrib.clojure/clojure-mxnet "1.5.0-SNAPSHOT"]]
+                 [org.apache.mxnet.contrib.clojure/clojure-mxnet "1.6.0-SNAPSHOT"]]
   :main ^:skip-aot infer.predictor-example
   :profiles {:uberjar {:aot :all}})
diff --git a/contrib/clojure-package/examples/module/project.clj b/contrib/clojure-package/examples/module/project.clj
index b667a2a4e122..83519e8e6886 100644
--- a/contrib/clojure-package/examples/module/project.clj
+++ b/contrib/clojure-package/examples/module/project.clj
@@ -19,7 +19,7 @@
   :description "Clojure examples for module"
   :plugins [[lein-cljfmt "0.5.7"]]
   :dependencies [[org.clojure/clojure "1.9.0"]
-                 [org.apache.mxnet.contrib.clojure/clojure-mxnet "1.5.0-SNAPSHOT"]]
+                 [org.apache.mxnet.contrib.clojure/clojure-mxnet "1.6.0-SNAPSHOT"]]
   :pedantic? :skip
   :main mnist-mlp)
 
diff --git a/contrib/clojure-package/examples/multi-label/project.clj b/contrib/clojure-package/examples/multi-label/project.clj
index 6e6a14340d36..d9ec86eb84f2 100644
--- a/contrib/clojure-package/examples/multi-label/project.clj
+++ b/contrib/clojure-package/examples/multi-label/project.clj
@@ -19,5 +19,5 @@
   :description "Example of multi-label classification"
   :plugins [[lein-cljfmt "0.5.7"]]
   :dependencies [[org.clojure/clojure "1.9.0"]
-                 [org.apache.mxnet.contrib.clojure/clojure-mxnet "1.5.0-SNAPSHOT"]]
+                 [org.apache.mxnet.contrib.clojure/clojure-mxnet "1.6.0-SNAPSHOT"]]
   :main multi-label.core)
diff --git a/contrib/clojure-package/examples/neural-style/project.clj b/contrib/clojure-package/examples/neural-style/project.clj
index 2614a6987dc9..32a640be1490 100644
--- a/contrib/clojure-package/examples/neural-style/project.clj
+++ b/contrib/clojure-package/examples/neural-style/project.clj
@@ -20,6 +20,6 @@
   :plugins [[lein-cljfmt "0.5.7"]]
   :repositories [["vendredi" {:url "https://repository.hellonico.info/repository/hellonico/"}]]
   :dependencies [[org.clojure/clojure "1.9.0"]
-                 [org.apache.mxnet.contrib.clojure/clojure-mxnet "1.5.0-SNAPSHOT"]
+                 [org.apache.mxnet.contrib.clojure/clojure-mxnet "1.6.0-SNAPSHOT"]
                  [origami "4.0.0-3"]]
   :main neural-style.core)
\ No newline at end of file
diff --git a/contrib/clojure-package/examples/pre-trained-models/project.clj b/contrib/clojure-package/examples/pre-trained-models/project.clj
index e4f6939cb1af..07e0f77b5933 100644
--- a/contrib/clojure-package/examples/pre-trained-models/project.clj
+++ b/contrib/clojure-package/examples/pre-trained-models/project.clj
@@ -21,6 +21,6 @@
   :repositories [["vendredi" {:url "https://repository.hellonico.info/repository/hellonico/"}]]
   :aliases {"predict-image" ["run" "-m" "pre-trained-models.predict-image" ]}
   :dependencies [[org.clojure/clojure "1.9.0"]
-                 [org.apache.mxnet.contrib.clojure/clojure-mxnet "1.5.0-SNAPSHOT"]
+                 [org.apache.mxnet.contrib.clojure/clojure-mxnet "1.6.0-SNAPSHOT"]
                  [origami "4.0.0-3"]]
   :main pre-trained-models.fine-tune)
diff --git a/contrib/clojure-package/examples/profiler/project.clj b/contrib/clojure-package/examples/profiler/project.clj
index cc50482d0418..b5c737b521e2 100644
--- a/contrib/clojure-package/examples/profiler/project.clj
+++ b/contrib/clojure-package/examples/profiler/project.clj
@@ -18,5 +18,5 @@
 (defproject profiler "0.1.0-SNAPSHOT"
   :plugins [[lein-cljfmt "0.5.7"]]
   :dependencies [[org.clojure/clojure "1.9.0"]
-                 [org.apache.mxnet.contrib.clojure/clojure-mxnet "1.5.0-SNAPSHOT"]]
+                 [org.apache.mxnet.contrib.clojure/clojure-mxnet "1.6.0-SNAPSHOT"]]
   :main profiler.core)
diff --git a/contrib/clojure-package/examples/rnn/project.clj b/contrib/clojure-package/examples/rnn/project.clj
index 64f4c290741c..ffbae5da3dd4 100644
--- a/contrib/clojure-package/examples/rnn/project.clj
+++ b/contrib/clojure-package/examples/rnn/project.clj
@@ -19,5 +19,5 @@
   :description "RNN example"
   :plugins [[lein-cljfmt "0.5.7"]]
   :dependencies [[org.clojure/clojure "1.9.0"]
-                 [org.apache.mxnet.contrib.clojure/clojure-mxnet "1.5.0-SNAPSHOT"]]
+                 [org.apache.mxnet.contrib.clojure/clojure-mxnet "1.6.0-SNAPSHOT"]]
   :main rnn.train-char-rnn)
diff --git a/contrib/clojure-package/examples/tutorial/project.clj b/contrib/clojure-package/examples/tutorial/project.clj
index 58a10f04f28b..6bb77bd753d1 100644
--- a/contrib/clojure-package/examples/tutorial/project.clj
+++ b/contrib/clojure-package/examples/tutorial/project.clj
@@ -19,7 +19,7 @@
   :description "MXNET tutorials"
   :plugins [[lein-cljfmt "0.5.7"]]
   :dependencies [[org.clojure/clojure "1.9.0"]
-  				 [org.apache.mxnet.contrib.clojure/clojure-mxnet "1.5.0-SNAPSHOT"]
+  				 [org.apache.mxnet.contrib.clojure/clojure-mxnet "1.6.0-SNAPSHOT"]
 
                  ;; Uncomment the one appropriate for your machine & configuration:
                  #_[org.apache.mxnet.contrib.clojure/clojure-mxnet-linux-cpu "1.4.0"]
diff --git a/contrib/clojure-package/examples/visualization/project.clj b/contrib/clojure-package/examples/visualization/project.clj
index d91ace3188e6..dae61919716e 100644
--- a/contrib/clojure-package/examples/visualization/project.clj
+++ b/contrib/clojure-package/examples/visualization/project.clj
@@ -19,5 +19,5 @@
   :description "Visualization example"
   :plugins [[lein-cljfmt "0.5.7"]]
   :dependencies [[org.clojure/clojure "1.9.0"]
-                 [org.apache.mxnet.contrib.clojure/clojure-mxnet "1.5.0-SNAPSHOT"]]
+                 [org.apache.mxnet.contrib.clojure/clojure-mxnet "1.6.0-SNAPSHOT"]]
   :main visualization.core)
diff --git a/contrib/clojure-package/project.clj b/contrib/clojure-package/project.clj
index e2b999df84d6..3ad0cf6ead46 100644
--- a/contrib/clojure-package/project.clj
+++ b/contrib/clojure-package/project.clj
@@ -15,7 +15,7 @@
 ;; limitations under the License.
 ;;
 
-(defproject org.apache.mxnet.contrib.clojure/clojure-mxnet "1.5.0-SNAPSHOT"
+(defproject org.apache.mxnet.contrib.clojure/clojure-mxnet "1.6.0-SNAPSHOT"
   :description "Clojure package for MXNet"
   :url "https://github.com/apache/incubator-mxnet"
   :license {:name "Apache License"
diff --git a/contrib/clojure-package/scripts/update_versions.sh b/contrib/clojure-package/scripts/update_versions.sh
index 607e3f357bc9..73bbf14fd942 100755
--- a/contrib/clojure-package/scripts/update_versions.sh
+++ b/contrib/clojure-package/scripts/update_versions.sh
@@ -19,7 +19,7 @@
 
 # Run this from the main Clojure project directory with 2 arguments
 # old-version and new-version
-# Ex: scripts/update_version 1.5.0-SNAPSHOT 1.5.0-SNAPSHOT
+# Ex: scripts/update_version 1.6.0-SNAPSHOT 1.6.0-SNAPSHOT
 
 set -evx
 echo "Replacing  $2  with  $2  in the directory  $PWD "
diff --git a/docs/tutorials/scala/mxnet_scala_on_intellij.md b/docs/tutorials/scala/mxnet_scala_on_intellij.md
index f1d15cb3c5b5..b9dc7e6d5152 100644
--- a/docs/tutorials/scala/mxnet_scala_on_intellij.md
+++ b/docs/tutorials/scala/mxnet_scala_on_intellij.md
@@ -228,7 +228,7 @@ The project's `pom.xml` will be open for editing.
     <dependency>
       <groupId>org.apache.mxnet</groupId>
       <artifactId>mxnet-full_2.11-osx-x86_64-cpu</artifactId>
-      <version>[1.3.1,)</version>
+      <version>[1.4.0,)</version>
     </dependency>
     <dependency>
       <groupId>org.scala-lang</groupId>
@@ -403,14 +403,14 @@ If you chose to "Build from Source" when following the [install instructions](ht
       <groupId>org.apache.mxnet</groupId>
       <artifactId>mxnet-core_${scala.version}-${platform}-sources</artifactId>
       <scope>system</scope>
-      <systemPath>/PathToMXNetSource/incubator-mxnet/scala-package/assembly/osx-x86_64-cpu/target/mxnet-full_${scala.version}-osx-x86_64-cpu-1.5.0-SNAPSHOT-sources.jar</systemPath>
+      <systemPath>/PathToMXNetSource/incubator-mxnet/scala-package/assembly/osx-x86_64-cpu/target/mxnet-full_${scala.version}-osx-x86_64-cpu-1.6.0-SNAPSHOT-sources.jar</systemPath>
     </dependency>
 
     <dependency>
       <groupId>org.apache.mxnet</groupId>
       <artifactId>mxnet-full_${scala.version}-${platform}</artifactId>
       <scope>system</scope>
-      <systemPath>/PathToMXNetSource/incubator-mxnet/scala-package/assembly/osx-x86_64-cpu/target/mxnet-full_${scala.version}-osx-x86_64-cpu-1.5.0-SNAPSHOT.jar</systemPath>
+      <systemPath>/PathToMXNetSource/incubator-mxnet/scala-package/assembly/osx-x86_64-cpu/target/mxnet-full_${scala.version}-osx-x86_64-cpu-1.6.0-SNAPSHOT.jar</systemPath>
     </dependency>
 ```
 
diff --git a/julia/NEWS.md b/julia/NEWS.md
index 3cd616288bf8..e678b97176d1 100644
--- a/julia/NEWS.md
+++ b/julia/NEWS.md
@@ -15,7 +15,10 @@
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
 
-# v1.5.0 (#TBD)
+# v1.6.0
+
+
+# v1.5.0
 
 * Following material from `mx` module got exported (#TBD):
     * `NDArray`
diff --git a/python/mxnet/libinfo.py b/python/mxnet/libinfo.py
index fb0859b9954a..8e0ae05f3378 100644
--- a/python/mxnet/libinfo.py
+++ b/python/mxnet/libinfo.py
@@ -111,4 +111,4 @@ def find_include_path():
 
 
 # current version
-__version__ = "1.5.0"
+__version__ = "1.6.0"
diff --git a/scala-package/README.md b/scala-package/README.md
index c0f32e47f080..c3ef4b0c84d0 100644
--- a/scala-package/README.md
+++ b/scala-package/README.md
@@ -45,7 +45,7 @@ To add the MXNet Scala/Java packages to your project, add the dependency as show
 <dependency>
   <groupId>org.apache.mxnet</groupId>
   <artifactId>mxnet-full_2.11-linux-x86_64-gpu</artifactId>
-  <version>[1.3.1,)</version>
+  <version>[1.4.0,)</version>
 </dependency>
 ```
 
@@ -57,7 +57,7 @@ To add the MXNet Scala/Java packages to your project, add the dependency as show
 <dependency>
   <groupId>org.apache.mxnet</groupId>
   <artifactId>mxnet-full_2.11-linux-x86_64-cpu</artifactId>
-  <version>[1.3.1,)</version>
+  <version>[1.4.0,)</version>
 </dependency>
 ```
 
@@ -69,11 +69,11 @@ To add the MXNet Scala/Java packages to your project, add the dependency as show
 <dependency>
   <groupId>org.apache.mxnet</groupId>
   <artifactId>mxnet-full_2.11-osx-x86_64-cpu</artifactId>
-  <version>[1.3.1,)</version>
+  <version>[1.4.0,)</version>
 </dependency>
 ```
 
-**Note:** ```<version>[1.3.1,)<\version>``` indicates that we will fetch packages with version 1.3.1 or higher. This will always ensure that the pom.xml is able to fetch the latest and greatest jar files from Maven.  
+**Note:** ```<version>[1.4.0,)<\version>``` indicates that we will fetch packages with version 1.4.0 or higher. This will always ensure that the pom.xml is able to fetch the latest and greatest jar files from Maven.  
 
 ### Nightly ###
 
@@ -101,7 +101,7 @@ Also, add the dependency which corresponds to your platform to the ```dependenci
 <dependency>
   <groupId>org.apache.mxnet</groupId>
   <artifactId>mxnet-full_2.11-linux-x86_64-gpu</artifactId>
-  <version>[1.5.0-SNAPSHOT,)</version>
+  <version>[1.6.0-SNAPSHOT,)</version>
 </dependency>
 ```
 
@@ -113,7 +113,7 @@ Also, add the dependency which corresponds to your platform to the ```dependenci
 <dependency>
   <groupId>org.apache.mxnet</groupId>
   <artifactId>mxnet-full_2.11-linux-x86_64-cpu</artifactId>
-  <version>[1.5.0-SNAPSHOT,)</version>
+  <version>[1.6.0-SNAPSHOT,)</version>
 </dependency>
 ```
 
@@ -124,11 +124,11 @@ Also, add the dependency which corresponds to your platform to the ```dependenci
 <dependency>
   <groupId>org.apache.mxnet</groupId>
   <artifactId>mxnet-full_2.11-osx-x86_64-cpu</artifactId>
-  <version>[1.5.0-SNAPSHOT,)</version>
+  <version>[1.6.0-SNAPSHOT,)</version>
 </dependency>
 ```
 
-**Note:** ```<version>[1.5.0-SNAPSHOT,)</version>``` indicates that we will fetch packages with version 1.5.0 or higher. This will always ensure that the pom.xml is able to fetch the latest and greatest jar files from Maven Snapshot repository.
+**Note:** ```<version>[1.6.0-SNAPSHOT,)</version>``` indicates that we will fetch packages with version 1.6.0 or higher. This will always ensure that the pom.xml is able to fetch the latest and greatest jar files from Maven Snapshot repository.
 
 Build From Source
 -----------------
@@ -186,7 +186,7 @@ Adding the following configuration in `pom.xml`
 <dependency>
   <groupId>org.apache.mxnet</groupId>
   <artifactId>mxnet-full_2.11-INTERNAL</artifactId>
-  <version>1.5.0</version>
+  <version>1.6.0</version>
   <scope>system</scope>
   <systemPath>path_to_jar/mxnet-full_2.11-INTERNAL.jar</systemPath>
 </dependency>
diff --git a/scala-package/core/src/main/scala/org/apache/mxnet/NDArray.scala b/scala-package/core/src/main/scala/org/apache/mxnet/NDArray.scala
index 717120bcf984..d55ed961d7ea 100644
--- a/scala-package/core/src/main/scala/org/apache/mxnet/NDArray.scala
+++ b/scala-package/core/src/main/scala/org/apache/mxnet/NDArray.scala
@@ -756,7 +756,7 @@ object NDArray extends NDArrayBase {
 class NDArray private[mxnet](private[mxnet] val handle: NDArrayHandle,
                              val writable: Boolean) extends NativeResource {
 
-  @deprecated("Please use ResourceScope instead", "1.5.0")
+  @deprecated("Please use ResourceScope instead", "1.6.0")
   def this(handle: NDArrayHandle,
            writable: Boolean = true,
            addToCollector: Boolean = true) {
diff --git a/scala-package/core/src/main/scala/org/apache/mxnet/NDArrayCollector.scala b/scala-package/core/src/main/scala/org/apache/mxnet/NDArrayCollector.scala
index 0761481cdfe8..5fa0aa5303b6 100644
--- a/scala-package/core/src/main/scala/org/apache/mxnet/NDArrayCollector.scala
+++ b/scala-package/core/src/main/scala/org/apache/mxnet/NDArrayCollector.scala
@@ -64,7 +64,7 @@ import scala.collection.mutable
  *  });
  *  </pre>
  */
-@deprecated("Please use ResourceScope instead", "1.5.0")
+@deprecated("Please use ResourceScope instead", "1.6.0")
 object NDArrayCollector {
   private val logger = LoggerFactory.getLogger(classOf[NDArrayCollector])
 
@@ -76,14 +76,14 @@ object NDArrayCollector {
    * Create a collector which will dispose the collected NDArrays automatically.
    * @return an auto-disposable collector.
    */
-  @deprecated("Please use ResourceScope instead", "1.5.0")
+  @deprecated("Please use ResourceScope instead", "1.6.0")
   def auto(): NDArrayCollector = new NDArrayCollector(true)
 
   /**
    * Create a collector allows users to later dispose the collected NDArray manually.
    * @return a manually-disposable collector.
    */
-  @deprecated("Please use ResourceScope instead", "1.5.0")
+  @deprecated("Please use ResourceScope instead", "1.6.0")
   @Experimental
   def manual(): NDArrayCollector = new NDArrayCollector(false)
 
@@ -91,13 +91,13 @@ object NDArrayCollector {
    * Collect the NDArrays into the collector of the current thread.
    * @param ndArray NDArrays need to be collected.
    */
-  @deprecated("Please use ResourceScope instead", "1.5.0")
+  @deprecated("Please use ResourceScope instead", "1.6.0")
   @varargs def collect(ndArray: NDArray*): Unit = {
     currCollector.get().add(ndArray: _*)
   }
 }
 
-@deprecated("Please use ResourceScope instead", "1.5.0")
+@deprecated("Please use ResourceScope instead", "1.6.0")
 class NDArrayCollector private(private val autoDispose: Boolean = true,
                                private val doCollect: Boolean = true) {
   // native ptr (handle) of the NDArray -> NDArray
@@ -147,7 +147,7 @@ class NDArrayCollector private(private val autoDispose: Boolean = true,
    * @return The result of function <em>codeBlock</em>.
    */
   @Experimental
-  @deprecated("Please use ResourceScope instead", "1.5.0")
+  @deprecated("Please use ResourceScope instead", "1.6.0")
   def withScope[T](codeBlock: => T): T = {
     val old = NDArrayCollector.currCollector.get()
     NDArrayCollector.currCollector.set(this)
diff --git a/scala-package/mxnet-demo/java-demo/README.md b/scala-package/mxnet-demo/java-demo/README.md
index 33cd000b3e6b..40a9a4e829fe 100644
--- a/scala-package/mxnet-demo/java-demo/README.md
+++ b/scala-package/mxnet-demo/java-demo/README.md
@@ -120,5 +120,5 @@ sudo apt install libopencv-imgcodecs3.4
 
 Is there any other version available?
 
-You can find nightly release version from [here](https://repository.apache.org/#nexus-search;gav~org.apache.mxnet~~1.5.0-SNAPSHOT~~).
-Please keep the same version in the pom file or [other versions in here](https://repository.apache.org/#nexus-search;gav~org.apache.mxnet~~~~) to run this demo.
+You can find nightly release version from [here](https://repository.apache.org/#nexus-search;gav~org.apache.mxnet~~1.6.0-SNAPSHOT~~).
+Please keep the same version in the pom file or [other versions in here](https://repository.apache.org/#nexus-search;gav~org.apache.mxnet~~~~) to run this demo.
\ No newline at end of file
diff --git a/scala-package/mxnet-demo/java-demo/pom.xml b/scala-package/mxnet-demo/java-demo/pom.xml
index eb5e043a0dda..a8337b1f3fc6 100644
--- a/scala-package/mxnet-demo/java-demo/pom.xml
+++ b/scala-package/mxnet-demo/java-demo/pom.xml
@@ -27,7 +27,7 @@
     <properties>
         <maven.compiler.source>1.8</maven.compiler.source>
         <maven.compiler.target>1.8</maven.compiler.target>
-        <mxnet.version>[1.5.0-SNAPSHOT, )</mxnet.version>
+        <mxnet.version>[1.6.0-SNAPSHOT, )</mxnet.version>
         <mxnet.scalaprofile>2.11</mxnet.scalaprofile>
     </properties>
 
@@ -41,7 +41,7 @@
                 </repository>
             </repositories>
             <properties>
-                <mxnet.version>[1.5.0-SNAPSHOT, )</mxnet.version>
+                <mxnet.version>[1.6.0-SNAPSHOT, )</mxnet.version>
             </properties>
         </profile>
         <profile>
diff --git a/scala-package/mxnet-demo/scala-demo/pom.xml b/scala-package/mxnet-demo/scala-demo/pom.xml
index b83ba518aa3c..8fab50e8cb9c 100644
--- a/scala-package/mxnet-demo/scala-demo/pom.xml
+++ b/scala-package/mxnet-demo/scala-demo/pom.xml
@@ -35,7 +35,7 @@
                 </repository>
             </repositories>
             <properties>
-                <mxnet.version>[1.5.0-SNAPSHOT, )</mxnet.version>
+                <mxnet.version>[1.6.0-SNAPSHOT, )</mxnet.version>
             </properties>
         </profile>
         <profile>
@@ -65,7 +65,7 @@
 
     <properties>
         <mxnet.scalaprofile>2.11</mxnet.scalaprofile>
-        <mxnet.version>[1.3.1, )</mxnet.version>
+        <mxnet.version>[1.4.0, )</mxnet.version>
         <scala.version>2.11.8</scala.version>
     </properties>
 
diff --git a/scala-package/packageTest/README.md b/scala-package/packageTest/README.md
index ccbafd46a121..edd5344b4aaa 100644
--- a/scala-package/packageTest/README.md
+++ b/scala-package/packageTest/README.md
@@ -55,7 +55,7 @@ You are able to run unit tests, integration tests, or both using this utility. T
 
 For running on GPU, add the flag `USE_CUDA=1`.
 
-An additional option, you can specify the mxnet version with `MXNET_VERSION=1.3.1-SNAPSHOT`.
+An additional option, you can specify the mxnet version with `MXNET_VERSION=1.4.0-SNAPSHOT`.
 
 ## Cleaning Up
 
diff --git a/snapcraft.yaml b/snapcraft.yaml
index 9791cd86fc0e..5e03a0935430 100644
--- a/snapcraft.yaml
+++ b/snapcraft.yaml
@@ -16,7 +16,7 @@
 # under the License.
 
 name: mxnet
-version: '1.5.0'
+version: '1.6.0'
 summary: MXNet is a deep learning framework designed for efficiency and flexibility.
 description: |
   MXNet is a deep learning framework designed for both efficiency and 

From 81ae341f74667dfa9474e3ace2595f9b076d28d9 Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Wed, 24 Jul 2019 16:50:39 -0700
Subject: [PATCH 107/813] [Doc] add squeeze to Array change shape (#15549)

* add squeeze to Array change shape

* Trigger notification

* Trigger notification

* Trigger notification

* Trigger notification
---
 docs/api/python/ndarray/ndarray.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/api/python/ndarray/ndarray.md b/docs/api/python/ndarray/ndarray.md
index 3cebffccaaee..ac18b02b6c9b 100644
--- a/docs/api/python/ndarray/ndarray.md
+++ b/docs/api/python/ndarray/ndarray.md
@@ -149,6 +149,7 @@ The `ndarray` package provides several classes:
     NDArray.expand_dims
     NDArray.split
     NDArray.diag
+    NDArray.squeeze
 ```
 
 ### Array expand elements

From 8158ba4b0f1ebd696ec09a0b1aa6031bacb60740 Mon Sep 17 00:00:00 2001
From: kshitij12345 <kshitijkalambarkar@gmail.com>
Date: Thu, 25 Jul 2019 06:21:02 +0530
Subject: [PATCH 108/813] fix typo (#15648)

---
 python/mxnet/initializer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/mxnet/initializer.py b/python/mxnet/initializer.py
index 277bfd0f4fa5..d0282471ceaa 100755
--- a/python/mxnet/initializer.py
+++ b/python/mxnet/initializer.py
@@ -648,7 +648,7 @@ class MSRAPrelu(Xavier):
     https://arxiv.org/abs/1502.01852.
 
     This initializer is proposed for initialization related to ReLu activation,
-    it maked some changes on top of Xavier method.
+    it makes some changes on top of Xavier method.
 
     Parameters
     ----------

From b00bb81fd4346196d7e059e3b051d1c74ba6e572 Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Thu, 25 Jul 2019 15:12:42 -0700
Subject: [PATCH 109/813] [Opperf] Add array rearrange operators to opperf
 (#15606)

* add array rearrange operators to opperf

* Trigger notification

* 4d tensor, param support

* new line

* add alias logic
---
 benchmark/opperf/nd_operations/README.md      |  5 --
 .../opperf/nd_operations/array_rearrange.py   | 57 +++++++++++++++++++
 .../opperf/nd_operations/binary_operators.py  |  4 +-
 benchmark/opperf/opperf.py                    |  4 ++
 benchmark/opperf/rules/default_params.py      | 15 ++++-
 benchmark/opperf/utils/op_registry_utils.py   | 22 +++++++
 benchmark/opperf/utils/profiler_utils.py      | 20 ++++++-
 7 files changed, 116 insertions(+), 11 deletions(-)
 create mode 100644 benchmark/opperf/nd_operations/array_rearrange.py

diff --git a/benchmark/opperf/nd_operations/README.md b/benchmark/opperf/nd_operations/README.md
index b98a0d3d0c48..321158c48399 100644
--- a/benchmark/opperf/nd_operations/README.md
+++ b/benchmark/opperf/nd_operations/README.md
@@ -66,9 +66,7 @@
 46. linalg_extractdiag
 47. sgd_mom_update
 48. SequenceLast
-50. flip
 51. SequenceReverse
-52. swapaxes
 53. SVMOutput
 54. linalg_trsm
 55. where
@@ -82,7 +80,6 @@
 63. mp_sgd_mom_update
 64. choose_element_0index
 65. tile
-66. space_to_depth
 67. gather_nd
 69. SequenceMask
 70. reshape_like
@@ -110,14 +107,12 @@
 94. broadcast_like
 95. Embedding
 96. linalg_makediag
-97. transpose
 98. linalg_syrk
 99. squeeze
 101. ROIPooling
 102. ftrl_update
 103. SliceChannel
 104. slice_like
-105. depth_to_space
 106. linalg_maketrian
 108. pad
 109. LayerNorm
diff --git a/benchmark/opperf/nd_operations/array_rearrange.py b/benchmark/opperf/nd_operations/array_rearrange.py
new file mode 100644
index 000000000000..151127c7b49a
--- /dev/null
+++ b/benchmark/opperf/nd_operations/array_rearrange.py
@@ -0,0 +1,57 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import mxnet as mx
+from benchmark.opperf.utils.benchmark_utils import run_op_benchmarks
+from benchmark.opperf.utils.op_registry_utils import get_all_rearrange_operators
+
+"""Performance benchmark tests for MXNet NDArray Rearrange Operators.
+
+1. transpose
+2. swapaxes
+3. flip
+4. depth_to_space
+5. space_to_depth
+"""
+
+
+def run_rearrange_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, runs=100):
+    """Runs benchmarks with the given context and precision (dtype) for all the
+    rearrange operators  in MXNet.
+
+    Parameters
+    ----------
+    ctx: mx.ctx
+        Context to run benchmarks
+    dtype: str, default 'float32'
+        Precision to use for benchmarks
+    warmup: int, default 25
+        Number of times to run for warmup
+    runs: int, default 100
+        Number of runs to capture benchmark results
+
+    Returns
+    -------
+    Dictionary of results. Key -> Name of the operator, Value -> Benchmark results.
+
+    """
+    # Fetch all optimizer operators
+    mx_rearrange_ops = get_all_rearrange_operators()
+
+    # Run benchmarks
+    mx_rearrange_op_results = run_op_benchmarks(mx_rearrange_ops, dtype, ctx, warmup, runs)
+    return mx_rearrange_op_results
diff --git a/benchmark/opperf/nd_operations/binary_operators.py b/benchmark/opperf/nd_operations/binary_operators.py
index cca8f9d1f2ad..1898f5dcbe04 100644
--- a/benchmark/opperf/nd_operations/binary_operators.py
+++ b/benchmark/opperf/nd_operations/binary_operators.py
@@ -39,7 +39,7 @@
 
 
 def run_mx_binary_broadcast_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, runs=100):
-    """Runs benchmarks with the given context and precision (dtype)for all the binary
+    """Runs benchmarks with the given context and precision (dtype) for all the binary
     broadcast operators in MXNet.
 
     Parameters
@@ -66,7 +66,7 @@ def run_mx_binary_broadcast_operators_benchmarks(ctx=mx.cpu(), dtype='float32',
 
 
 def run_mx_binary_element_wise_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, runs=100):
-    """Runs benchmarks with the given context and precision (dtype)for all the binary
+    """Runs benchmarks with the given context and precision (dtype) for all the binary
     element_wise operators in MXNet.
 
     Parameters
diff --git a/benchmark/opperf/opperf.py b/benchmark/opperf/opperf.py
index b2258afaaaf7..77b16670b443 100755
--- a/benchmark/opperf/opperf.py
+++ b/benchmark/opperf/opperf.py
@@ -39,6 +39,7 @@
 from benchmark.opperf.nd_operations.nn_conv_operators import run_pooling_operators_benchmarks, \
     run_convolution_operators_benchmarks, run_transpose_convolution_operators_benchmarks
 from benchmark.opperf.nd_operations.nn_basic_operators import run_nn_basic_operators_benchmarks
+from benchmark.opperf.nd_operations.array_rearrange import run_rearrange_operators_benchmarks
 
 from benchmark.opperf.utils.common_utils import merge_map_list, save_to_file
 from benchmark.opperf.utils.op_registry_utils import get_operators_with_no_benchmark, \
@@ -78,6 +79,9 @@ def run_all_mxnet_operator_benchmarks(ctx=mx.cpu(), dtype='float32'):
     # Run all Sorting and Searching operations benchmarks with default input values
     mxnet_operator_benchmark_results.append(run_sorting_searching_operators_benchmarks(ctx=ctx, dtype=dtype))
 
+    # Run all Array Rearrange operations benchmarks with default input values
+    mxnet_operator_benchmark_results.append(run_rearrange_operators_benchmarks(ctx=ctx, dtype=dtype))
+
     # ************************ MXNET NN OPERATOR BENCHMARKS ****************************
 
     # Run all basic NN operations benchmarks with default input values
diff --git a/benchmark/opperf/rules/default_params.py b/benchmark/opperf/rules/default_params.py
index 2c8f3d436e0d..b16cd5429f59 100644
--- a/benchmark/opperf/rules/default_params.py
+++ b/benchmark/opperf/rules/default_params.py
@@ -62,6 +62,15 @@
 # NOTE: Data used is DEFAULT_DATA
 DEFAULT_AXIS = [0]
 
+# For rearrange operators
+# NOTE: Data needs to be a 4D tensor for  operators like space_to_depth and depth_to_space
+# Hence below we append 4d to mark the difference.
+# For depth_to_space, dimension 3 needs to be a multiple of 'block' and 1 should be a multiple of `block^2`
+DEFAULT_DATA_4d = [(1, 4, 2, 4), (10,25,10,100)]
+DEFAULT_DIM_1 = [0, 1, 2, 3]
+DEFAULT_DIM_2 = [1, 2, 3, 0]
+DEFAULT_BLOCK_SIZE = [2, 5]
+
 # Default Inputs. MXNet Op Param Name to Default Input mapping
 DEFAULTS_INPUTS = {"data": DEFAULT_DATA,
                    "lhs": DEFAULT_LHS,
@@ -81,7 +90,11 @@
                    "k_nd": DEFAULT_K_ND,
                    "p_nd": DEFAULT_P_ND,
                    "axis_shape": DEFAULT_AXIS_SHAPE,
-                   "axis": DEFAULT_AXIS}
+                   "axis": DEFAULT_AXIS,
+                   "data_4d": DEFAULT_DATA_4d,
+                   "dim1": DEFAULT_DIM_1,
+                   "dim2": DEFAULT_DIM_2,
+                   "block_size": DEFAULT_BLOCK_SIZE}
 
 # These are names of MXNet operator parameters that is of type NDArray.
 # We maintain this list to automatically recognize these parameters are to be
diff --git a/benchmark/opperf/utils/op_registry_utils.py b/benchmark/opperf/utils/op_registry_utils.py
index 88ea7a16cfe6..f5e75066cafc 100644
--- a/benchmark/opperf/utils/op_registry_utils.py
+++ b/benchmark/opperf/utils/op_registry_utils.py
@@ -115,6 +115,8 @@ def prepare_op_inputs(arg_params):
                                   arg_params["params"]["arg_types"]):
         if "NDArray" in arg_type and arg_name + "_nd" in DEFAULTS_INPUTS:
             arg_values[arg_name] = DEFAULTS_INPUTS[arg_name + "_nd"]
+        elif "NDArray" in arg_type and arg_name + "_4d" in DEFAULTS_INPUTS:
+            arg_values[arg_name] = DEFAULTS_INPUTS[arg_name + "_4d"]
         elif arg_name in DEFAULTS_INPUTS:
             arg_values[arg_name] = DEFAULTS_INPUTS[arg_name]
         elif "float" in arg_type and arg_name + "_float" in DEFAULTS_INPUTS:
@@ -262,6 +264,26 @@ def get_all_sorting_searching_operators():
     return sort_search_mx_operators
 
 
+def get_all_rearrange_operators():
+    """Gets all array rearrange operators registered with MXNet.
+
+    Returns
+    -------
+    {"operator_name": {"has_backward", "nd_op_handle", "params"}}
+    """
+    rearrange_ops = ['transpose','swapaxes','flip','depth_to_space','space_to_depth']
+
+    # Get all mxnet operators
+    mx_operators = _get_all_mxnet_operators()
+
+    # Filter for Array Rearrange operators
+    rearrange_mx_operators = {}
+    for op_name, op_params in mx_operators.items():
+        if op_name in rearrange_ops and op_name not in unique_ops:
+            rearrange_mx_operators[op_name] = mx_operators[op_name]
+    return rearrange_mx_operators
+    
+
 def get_operators_with_no_benchmark(operators_with_benchmark):
     """Gets all MXNet operators with not benchmark.
 
diff --git a/benchmark/opperf/utils/profiler_utils.py b/benchmark/opperf/utils/profiler_utils.py
index a434d3be1e5c..0df67a00ff26 100644
--- a/benchmark/opperf/utils/profiler_utils.py
+++ b/benchmark/opperf/utils/profiler_utils.py
@@ -42,14 +42,28 @@ def _get_memory_profile(memory_profile_results):
 
 def _get_operator_profile(operator_name, operator_profile_results):
     operator_profile = {}
+
+    # alias map : dictionary of the form {"alias" : "registered_name"}
+    # allows to retrieve alias operator profile from the profiler results
+    # TODO handling - "identity" : "_copy"
+    alias_map = {"broadcast_plus" : "broadcast_add", "broadcast_minus" : "broadcast_sub", "flatten" : "Flatten", "max_axis" : "max",
+                 "swapaxes" : "SwapAxis", "flip" : "reverse", "reshape" : "Reshape", "crop" : "slice", "sum_axis" : "sum", "min_axis" : "min"}
+
+    op_name = None
+
+    if operator_name in alias_map:
+        op_name = alias_map[operator_name]
+    else:
+        op_name = operator_name
+
     for line in operator_profile_results:
-        if operator_name in line or operator_name[:3] + " " in line:
+        if op_name in line or op_name[:3] + " " in line:
             operation = line.split()[0]
             operation_avg_time = float(line.split()[-1])
             if "_backward" in operation:
-                operator_profile["avg_time" + operation] = operation_avg_time
+                operator_profile["avg_time_backward_" + operator_name] = operation_avg_time
             else:
-                operator_profile["avg_time_forward_" + operation] = operation_avg_time
+                operator_profile["avg_time_forward_" + operator_name] = operation_avg_time
 
     return operator_profile
 

From e98fea3165670157090f2a2f644890452443803c Mon Sep 17 00:00:00 2001
From: Zhennan Qin <zhennan.qin@intel.com>
Date: Thu, 25 Jul 2019 22:42:04 -0500
Subject: [PATCH 110/813] [MKLDNN] Enable subgraph backend mkldnn by default.
 (#15518)

* Enable subgraph backend mkldnn by default

* Fix lint

* fix ut

* fix scala test

* Fix UT

* Fix lint

* Run CI

* Run CI

* Support MXNET_MKLDNN_ENABLED

* Fix merge

* Run CI
---
 cpp-package/example/inference/README.md       |   3 -
 docs/faq/env_var.md                           |   3 +-
 docs/tutorials/c++/subgraphAPI.md             |  11 +-
 docs/tutorials/mkldnn/MKLDNN_README.md        |  18 +-
 example/quantization/README.md                |  29 +-
 example/ssd/README.md                         |   2 -
 .../org/apache/mxnet/OperatorSuite.scala      |   8 +-
 src/c_api/c_api_symbolic.cc                   |   8 +-
 src/c_api/c_api_test.cc                       |   8 +-
 src/executor/graph_executor.cc                | 399 +++++++++++-------
 src/operator/subgraph/build_subgraph.cc       |  25 +-
 .../subgraph/default_subgraph_property.cc     |   1 +
 .../subgraph/default_subgraph_property_v2.cc  |   2 +
 .../subgraph/mkldnn/mkldnn_conv_property.h    |  14 +-
 .../mkldnn/mkldnn_subgraph_property.cc        |   8 +
 src/operator/subgraph/subgraph_property.h     | 115 ++++-
 src/operator/subgraph/tensorrt/tensorrt.cc    |   2 +
 tests/python/mkl/test_quantization_mkldnn.py  |   4 +
 tests/python/unittest/test_operator.py        | 135 +++---
 19 files changed, 504 insertions(+), 291 deletions(-)

diff --git a/cpp-package/example/inference/README.md b/cpp-package/example/inference/README.md
index 81cf9d856c23..90047e5fe14f 100644
--- a/cpp-package/example/inference/README.md
+++ b/cpp-package/example/inference/README.md
@@ -41,7 +41,6 @@ The following performance numbers are collected via using C++ inference API on A
 ```
 export KMP_AFFINITY=granularity=fine,noduplicates,compact,1,0
 export OMP_NUM_THREADS=$(vCPUs/2)
-export MXNET_SUBGRAPH_BACKEND=MKLDNN
 export MXNET_ENGINE_TYPE=NaiveEngine
 ```
 Also users are recommended to use ```numactl``` or ```taskset``` to bind a running process to the specified cores.
@@ -87,8 +86,6 @@ Follow the below steps to do inference with more models.
 
 The below command lines show how to run inference with FP32/INT8 resnet50_v1 model. Because the C++ inference script provides the almost same command line as this [Python script](https://github.com/apache/incubator-mxnet/blob/master/example/quantization/imagenet_inference.py) and then users can easily go from Python to C++.
 ```
-# set MKLDNN as subgraph backend
-export MXNET_SUBGRAPH_BACKEND=MKLDNN
 
 # FP32 inference
 ./imagenet_inference --symbol_file "./model/resnet50_v1-symbol.json" --params_file "./model/resnet50_v1-0000.params" --dataset "./data/val_256_q90.rec" --rgb_mean "123.68 116.779 103.939" --rgb_std "58.393 57.12 57.375" --batch_size 64 --num_skipped_batches 50 --num_inference_batches 500
diff --git a/docs/faq/env_var.md b/docs/faq/env_var.md
index 05f70b01a69b..24d62f342983 100644
--- a/docs/faq/env_var.md
+++ b/docs/faq/env_var.md
@@ -307,9 +307,10 @@ If ctypes is used, it must be `mxnet._ctypes.ndarray.NDArrayBase`.
   - This variable controls how many CuDNN dropout state resources to create for each GPU context for use in operator.
 
 * MXNET_SUBGRAPH_BACKEND
-  - Values: String ```(default="")```
+  - Values: String ```(default="MKLDNN")``` if MKLDNN is avaliable, otherwise ```(default="")```
   - This variable controls the subgraph partitioning in MXNet.
   - This variable is used to perform MKL-DNN FP32 operator fusion and quantization. Please refer to the [MKL-DNN operator list](../tutorials/mkldnn/operator_list.md) for how this variable is used and the list of fusion passes.
+  - Set ```MXNET_SUBGRAPH_BACKEND=NONE``` to disable subgraph backend.
 
 * MXNET_SAFE_ACCUMULATION
   - Values: Values: 0(false) or 1(true) ```(default=0)```
diff --git a/docs/tutorials/c++/subgraphAPI.md b/docs/tutorials/c++/subgraphAPI.md
index 6b1b477b8021..7403e2654423 100644
--- a/docs/tutorials/c++/subgraphAPI.md
+++ b/docs/tutorials/c++/subgraphAPI.md
@@ -111,7 +111,15 @@ There're 2 built-in attributes that used by MXNet executor.
 
 `inference_only` : bool, apply this property only for inference. Property will be skiped when need_grad=True. Default `false` if this attribute isn't defined.
 
-After defining the subgraph property, we need to register it in .cc file.
+After defining the subgraph property, we need to register it under a backend in .cc file.
+
+Firstly, we need to register the backend
+
+```C++
+MXNET_REGISTER_SUBGRAPH_BACKEND(SgTest);
+```
+
+Then register the property under it.
 
 ```C++
 MXNET_REGISTER_SUBGRAPH_PROPERTY(SgTest, SgProperty);
@@ -124,6 +132,7 @@ It's possible to register multiple properties for same backend. In practice, we
 #include "SgProperty2.h" // Define SgProperty2 class
 #include "SgProperty3.h" // Define SgProperty3 class
 
+MXNET_REGISTER_SUBGRAPH_BACKEND(SgTest);
 MXNET_REGISTER_SUBGRAPH_PROPERTY(SgTest, SgProperty);  // Execution order 1.
 MXNET_REGISTER_SUBGRAPH_PROPERTY(SgTest, SgProperty2); // Execution order 2.
 MXNET_REGISTER_SUBGRAPH_PROPERTY(SgTest, SgProperty3); // Execution order 3.
diff --git a/docs/tutorials/mkldnn/MKLDNN_README.md b/docs/tutorials/mkldnn/MKLDNN_README.md
index 0b8c6b04517b..d8f78ecf71a7 100644
--- a/docs/tutorials/mkldnn/MKLDNN_README.md
+++ b/docs/tutorials/mkldnn/MKLDNN_README.md
@@ -103,7 +103,7 @@ LIBRARY_PATH=$(brew --prefix llvm)/lib/ make -j $(sysctl -n hw.ncpu) CC=$(brew -
 <h2 id="3">Windows</h2>
 
 On Windows, you can use [Micrsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/) and [Microsoft Visual Studio 2017](https://www.visualstudio.com/downloads/) to compile MXNet with Intel MKL-DNN.
-[Micrsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/) is recommended.  
+[Micrsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/) is recommended.
 
 **Visual Studio 2015**
 
@@ -113,8 +113,8 @@ To build and install MXNet yourself, you need the following dependencies. Instal
 2. Download and Install [CMake 3](https://cmake.org/files/v3.14/cmake-3.14.0-win64-x64.msi) if it is not already installed.
 3. Download [OpenCV 3](https://sourceforge.net/projects/opencvlibrary/files/3.4.5/opencv-3.4.5-vc14_vc15.exe/download), and unzip the OpenCV package, set the environment variable ```OpenCV_DIR``` to point to the ```OpenCV build directory``` (e.g.,```OpenCV_DIR = C:\opencv\build ```). Also, add the OpenCV bin directory (```C:\opencv\build\x64\vc14\bin``` for example) to the ``PATH`` variable.
 4. If you have Intel Math Kernel Library (Intel MKL) installed, set ```MKL_ROOT``` to point to ```MKL``` directory that contains the ```include``` and ```lib```. If you want to use MKL blas, you should set ```-DUSE_BLAS=mkl``` when cmake. Typically, you can find the directory in ```C:\Program Files (x86)\IntelSWTools\compilers_and_libraries\windows\mkl```.
-5. If you don't have the Intel Math Kernel Library (MKL) installed, download and install [OpenBLAS](http://sourceforge.net/projects/openblas/files/v0.2.14/), or build the latest version of OpenBLAS from source. Note that you should also download ```mingw64.dll.zip``` along with openBLAS and add them to PATH. 
-6. Set the environment variable ```OpenBLAS_HOME``` to point to the ```OpenBLAS``` directory that contains the ```include``` and ```lib``` directories. Typically, you can find the directory in ```C:\Downloads\OpenBLAS\```. 
+5. If you don't have the Intel Math Kernel Library (MKL) installed, download and install [OpenBLAS](http://sourceforge.net/projects/openblas/files/v0.2.14/), or build the latest version of OpenBLAS from source. Note that you should also download ```mingw64.dll.zip``` along with openBLAS and add them to PATH.
+6. Set the environment variable ```OpenBLAS_HOME``` to point to the ```OpenBLAS``` directory that contains the ```include``` and ```lib``` directories. Typically, you can find the directory in ```C:\Downloads\OpenBLAS\```.
 
 After you have installed all of the required dependencies, build the MXNet source code:
 
@@ -123,17 +123,17 @@ After you have installed all of the required dependencies, build the MXNet sourc
 git clone --recursive https://github.com/apache/incubator-mxnet.git
 cd C:\incubator-mxent
 ```
-2. Enable Intel MKL-DNN by -DUSE_MKLDNN=1. Use [CMake 3](https://cmake.org/) to create a Visual Studio solution in ```./build```. Make sure to specify the architecture in the 
+2. Enable Intel MKL-DNN by -DUSE_MKLDNN=1. Use [CMake 3](https://cmake.org/) to create a Visual Studio solution in ```./build```. Make sure to specify the architecture in the
 command:
 ```
 >mkdir build
 >cd build
 >cmake -G "Visual Studio 14 Win64" .. -DUSE_CUDA=0 -DUSE_CUDNN=0 -DUSE_NVRTC=0 -DUSE_OPENCV=1 -DUSE_OPENMP=1 -DUSE_PROFILER=1 -DUSE_BLAS=open -DUSE_LAPACK=1 -DUSE_DIST_KVSTORE=0 -DCUDA_ARCH_NAME=All -DUSE_MKLDNN=1 -DCMAKE_BUILD_TYPE=Release
 ```
-3. Enable Intel MKL-DNN and Intel MKL as BLAS library by the command:  
+3. Enable Intel MKL-DNN and Intel MKL as BLAS library by the command:
 ```
 >"C:\Program Files (x86)\IntelSWTools\compilers_and_libraries\windows\mkl\bin\mklvars.bat" intel64
->cmake -G "Visual Studio 14 Win64" .. -DUSE_CUDA=0 -DUSE_CUDNN=0 -DUSE_NVRTC=0 -DUSE_OPENCV=1 -DUSE_OPENMP=1 -DUSE_PROFILER=1 -DUSE_BLAS=mkl -DUSE_LAPACK=1 -DUSE_DIST_KVSTORE=0 -DCUDA_ARCH_NAME=All -DUSE_MKLDNN=1 -DCMAKE_BUILD_TYPE=Release -DMKL_ROOT="C:\Program Files (x86)\IntelSWTools\compilers_and_libraries\windows\mkl" 
+>cmake -G "Visual Studio 14 Win64" .. -DUSE_CUDA=0 -DUSE_CUDNN=0 -DUSE_NVRTC=0 -DUSE_OPENCV=1 -DUSE_OPENMP=1 -DUSE_PROFILER=1 -DUSE_BLAS=mkl -DUSE_LAPACK=1 -DUSE_DIST_KVSTORE=0 -DCUDA_ARCH_NAME=All -DUSE_MKLDNN=1 -DCMAKE_BUILD_TYPE=Release -DMKL_ROOT="C:\Program Files (x86)\IntelSWTools\compilers_and_libraries\windows\mkl"
 ```
 4. After the CMake successfully completed, in Visual Studio, open the solution file ```.sln``` and compile it, or compile the MXNet source code by using following command:
 ```r
@@ -154,7 +154,7 @@ User can follow the same steps of Visual Studio 2015 to build MXNET with MKL-DNN
 
 <h2 id="4">Verify MXNet with python</h2>
 
-Preinstall python and some dependent modules: 
+Preinstall python and some dependent modules:
 ```
 pip install numpy graphviz
 set PYTHONPATH=[workdir]\incubator-mxnet\python
@@ -261,7 +261,7 @@ MKL_VERBOSE SGEMM(T,N,12,10,8,0x7f7f927b1378,0x1bc2140,8,0x1ba8040,8,0x7f7f927b1
 
 <h2 id="6">Enable graph optimization</h2>
 
-Graph optimization by subgraph feature are available in master branch. You can build from source and then use below command to enable this *experimental* feature for better performance:
+Graph optimization with subgraph is available and enabled by default in master branch. For MXNet release v1.5, you can manually enable it by:
 
 ```
 export MXNET_SUBGRAPH_BACKEND=MKLDNN
@@ -271,7 +271,7 @@ This limitations of this experimental feature are:
 
 - Use this feature only for inference. When training, be sure to turn the feature off by unsetting the `MXNET_SUBGRAPH_BACKEND` environment variable.
 
-- This feature will only run on the CPU, even if you're using a GPU-enabled build of MXNet. 
+- This feature will only run on the CPU, even if you're using a GPU-enabled build of MXNet.
 
 
 <h2 id="7">Quantization and Inference with INT8</h2>
diff --git a/example/quantization/README.md b/example/quantization/README.md
index 40f1371c33cc..09321beb7997 100644
--- a/example/quantization/README.md
+++ b/example/quantization/README.md
@@ -50,10 +50,7 @@ python imagenet_gen_qsym_mkldnn.py --model=resnet50_v1 --num-calib-batches=5 --c
 The model would be automatically replaced in fusion and quantization format. It is then saved as the quantized symbol and parameter files in the `./model` directory. The following command is to launch inference.
 
 ```
-# USE MKLDNN AS SUBGRAPH BACKEND
-export MXNET_SUBGRAPH_BACKEND=MKLDNN
-
-# Launch FP32 Inference 
+# Launch FP32 Inference
 python imagenet_inference.py --symbol-file=./model/resnet50_v1-symbol.json --param-file=./model/resnet50_v1-0000.params --rgb-mean=123.68,116.779,103.939 --rgb-std=58.393,57.12,57.375 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu
 
 # Launch INT8 Inference
@@ -74,8 +71,6 @@ python imagenet_gen_qsym_mkldnn.py --model=squeezenet1.0 --num-calib-batches=5 -
 The model would be automatically replaced in fusion and quantization format. It is then saved as the quantized symbol and parameter files in the `./model` directory. The following command is to launch inference.
 
 ```
-# USE MKLDNN AS SUBGRAPH BACKEND
-export MXNET_SUBGRAPH_BACKEND=MKLDNN
 
 # Launch FP32 Inference
 python imagenet_inference.py --symbol-file=./model/squeezenet1.0-symbol.json --param-file=./model/squeezenet1.0-0000.params --rgb-mean=123.68,116.779,103.939 --rgb-std=58.393,57.12,57.375 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu
@@ -98,8 +93,6 @@ python imagenet_gen_qsym_mkldnn.py --model=mobilenet1.0 --num-calib-batches=5 --
 The model would be automatically replaced in fusion and quantization format. It is then saved as the quantized symbol and parameter files in the `./model` directory. The following command is to launch inference.
 
 ```
-# USE MKLDNN AS SUBGRAPH BACKEND
-export MXNET_SUBGRAPH_BACKEND=MKLDNN
 
 # Launch FP32 Inference
 python imagenet_inference.py --symbol-file=./model/mobilenet1.0-symbol.json --param-file=./model/mobilenet1.0-0000.params --rgb-mean=123.68,116.779,103.939 --rgb-std=58.393,57.12,57.375 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu
@@ -122,8 +115,6 @@ python imagenet_gen_qsym_mkldnn.py --model=mobilenetv2_1.0 --num-calib-batches=5
 The model would be automatically replaced in fusion and quantization format. It is then saved as the quantized symbol and parameter files in the `./model` directory. The following command is to launch inference.
 
 ```
-# USE MKLDNN AS SUBGRAPH BACKEND
-export MXNET_SUBGRAPH_BACKEND=MKLDNN
 
 # Launch FP32 Inference
 python imagenet_inference.py --symbol-file=./model/mobilenetv2_1.0-symbol.json --param-file=./model/mobilenetv2_1.0-0000.params --rgb-mean=123.68,116.779,103.939 --rgb-std=58.393,57.12,57.375 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu
@@ -146,8 +137,6 @@ python imagenet_gen_qsym_mkldnn.py --model=inceptionv3 --image-shape=3,299,299 -
 The model would be automatically replaced in fusion and quantization format. It is then saved as the quantized symbol and parameter files in the `./model` directory. The following command is to launch inference.
 
 ```
-# USE MKLDNN AS SUBGRAPH BACKEND
-export MXNET_SUBGRAPH_BACKEND=MKLDNN
 
 # Launch FP32 Inference
 python imagenet_inference.py --symbol-file=./model/inceptionv3-symbol.json --param-file=./model/inceptionv3-0000.params --image-shape=3,299,299 --rgb-mean=123.68,116.779,103.939 --rgb-std=58.393,57.12,57.375 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu
@@ -171,10 +160,8 @@ python imagenet_gen_qsym_mkldnn.py --model=imagenet1k-resnet-152 --num-calib-bat
 The model would be automatically replaced in fusion and quantization format. It is then saved as the quantized symbol and parameter files in the `./model` directory. The following command is to launch inference.
 
 ```
-# USE MKLDNN AS SUBGRAPH BACKEND
-export MXNET_SUBGRAPH_BACKEND=MKLDNN
 
-# Launch FP32 Inference 
+# Launch FP32 Inference
 python imagenet_inference.py --symbol-file=./model/imagenet1k-resnet-152-symbol.json --param-file=./model/imagenet1k-resnet-152-0000.params --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu
 
 # Launch INT8 Inference
@@ -196,10 +183,8 @@ python imagenet_gen_qsym_mkldnn.py --model=imagenet1k-inception-bn --num-calib-b
 The model would be automatically replaced in fusion and quantization format. It is then saved as the quantized symbol and parameter files in the `./model` directory. The following command is to launch inference.
 
 ```
-# USE MKLDNN AS SUBGRAPH BACKEND
-export MXNET_SUBGRAPH_BACKEND=MKLDNN
 
-# Launch FP32 Inference 
+# Launch FP32 Inference
 python imagenet_inference.py --symbol-file=./model/imagenet1k-inception-bn-symbol.json --param-file=./model/imagenet1k-inception-bn-0000.params --rgb-mean=123.68,116.779,103.939 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu
 
 # Launch INT8 Inference
@@ -240,10 +225,8 @@ Some tips on quantization configs:
 2. Then, you should run the following command and verify that your fp32 symbolic model runs inference as expected.
 
 ```
-# USE MKLDNN AS SUBGRAPH BACKEND
-export MXNET_SUBGRAPH_BACKEND=MKLDNN
 
-# Launch FP32 Inference 
+# Launch FP32 Inference
 python imagenet_inference.py --symbol-file=./model/custom-symbol.json --param-file=./model/custom-0000.params --rgb-mean=* --rgb-std=* --num-skipped-batches=* --batch-size=* --num-inference-batches=*--dataset=./data/* --ctx=cpu
 ```
 
@@ -260,7 +243,7 @@ python imagenet_gen_qsym_mkldnn.py --model=custom --num-calib-batches=5 --calib-
 6. Finally, you can run INT8 inference:
 
 ```
-# Launch INT8 Inference 
+# Launch INT8 Inference
 python imagenet_inference.py --symbol-file=./model/*.json --param-file=./model/*.params --rgb-mean=* --rgb-std=* --num-skipped-batches=* --batch-size=* --num-inference-batches=*--dataset=./data/* --ctx=cpu
 
 # Launch dummy data Inference
@@ -289,6 +272,6 @@ the console to run model quantization for a specific configuration.
 - `launch_inference.sh` This is a shell script that calculate the accuracies of all the quantized models generated
 by invoking `launch_quantize.sh`.
 
-**NOTE**: 
+**NOTE**:
 - This example has only been tested on Linux systems.
 - Performance is expected to decrease with GPU, however the memory footprint of a quantized model is smaller. The purpose of the quantization implementation is to minimize accuracy loss when converting FP32 models to INT8. MXNet community is working on improving the performance.
diff --git a/example/ssd/README.md b/example/ssd/README.md
index 92a125f1892d..dcb15f4e47a8 100644
--- a/example/ssd/README.md
+++ b/example/ssd/README.md
@@ -234,8 +234,6 @@ python quantization.py
 After quantization, INT8 models will be saved in `model/` dictionary.  Use the following command to launch inference.
 
 ```
-# USE MKLDNN AS SUBGRAPH BACKEND
-export MXNET_SUBGRAPH_BACKEND=MKLDNN
 
 # Launch FP32 Inference on VOC dataset
 python evaluate.py --cpu --num-batch 10 --batch-size 224 --deploy --prefix=./model/ssd_
diff --git a/scala-package/core/src/test/scala/org/apache/mxnet/OperatorSuite.scala b/scala-package/core/src/test/scala/org/apache/mxnet/OperatorSuite.scala
index deb149985ce8..3bb1b2b5b3bc 100644
--- a/scala-package/core/src/test/scala/org/apache/mxnet/OperatorSuite.scala
+++ b/scala-package/core/src/test/scala/org/apache/mxnet/OperatorSuite.scala
@@ -694,8 +694,8 @@ class OperatorSuite extends FunSuite with BeforeAndAfterAll
   }
 
   test("maximum") {
-    val data1 = Symbol.Variable("data")
-    val data2 = Symbol.Variable("data")
+    val data1 = Symbol.Variable("data1")
+    val data2 = Symbol.Variable("data2")
     val shape = Shape(3, 4)
     val dataTmp1 = Random.uniform(0, 100, shape)
     val dataTmp2 = Random.uniform(0, 100, shape)
@@ -712,8 +712,8 @@ class OperatorSuite extends FunSuite with BeforeAndAfterAll
   }
 
   test("minimum") {
-    val data1 = Symbol.Variable("data")
-    val data2 = Symbol.Variable("data")
+    val data1 = Symbol.Variable("data1")
+    val data2 = Symbol.Variable("data2")
     val shape = Shape(3, 4)
     val dataTmp1 = Random.uniform(0, 100, shape)
     val dataTmp2 = Random.uniform(0, 100, shape)
diff --git a/src/c_api/c_api_symbolic.cc b/src/c_api/c_api_symbolic.cc
index 80ae5438c20d..df839b4d296e 100644
--- a/src/c_api/c_api_symbolic.cc
+++ b/src/c_api/c_api_symbolic.cc
@@ -1035,15 +1035,15 @@ int MXSetCalibTableToQuantizedSymbol(SymbolHandle qsym_handle,
   API_END_HANDLE_ERROR(delete s);
 }
 
-int MXGenBackendSubgraph(SymbolHandle sym_handle, const char *backend,
+int MXGenBackendSubgraph(SymbolHandle sym_handle, const char *backend_name,
                          SymbolHandle *ret_sym_handle) {
   nnvm::Symbol *s = new nnvm::Symbol();
   API_BEGIN();
   nnvm::Symbol *sym = static_cast<nnvm::Symbol *>(sym_handle);
   *s = sym->Copy();
-  std::vector<mxnet::op::SubgraphPropertyPtr> properties =
-      mxnet::op::SubgraphPropertyRegistry::Get()->CreateSubgraphProperty(backend);
-  for (auto property : properties) {
+  auto backend = mxnet::op::SubgraphBackendRegistry::Get()->GetSubgraphBackend(backend_name);
+  const auto& subgraph_prop_list = backend->GetSubgraphProperties();
+  for (auto property : subgraph_prop_list) {
     nnvm::Graph g = Symbol2Graph(*s);
     property->SetAttr("graph", g);
     g.attrs["subgraph_property"] = std::make_shared<nnvm::any>(std::move(property));
diff --git a/src/c_api/c_api_test.cc b/src/c_api/c_api_test.cc
index ae36b7af2829..fff92692971d 100644
--- a/src/c_api/c_api_test.cc
+++ b/src/c_api/c_api_test.cc
@@ -41,9 +41,11 @@ int MXBuildSubgraphByOpNames(SymbolHandle sym_handle,
   nnvm::Symbol* sym = static_cast<nnvm::Symbol*>(sym_handle);
   *s = sym->Copy();
   if (!op_name_set.empty()) {
-    std::vector<mxnet::op::SubgraphPropertyPtr> properties =
-        mxnet::op::SubgraphPropertyRegistry::Get()->CreateSubgraphProperty(prop_name);
-    for (auto property : properties) {
+    auto& backend =
+        mxnet::op::SubgraphBackendRegistry::Get()->GetSubgraphBackend(prop_name);
+    LOG(INFO) << "Subgraph backend " << backend->GetName() << " is activated.";
+    const auto& subgraph_prop_list = backend->GetSubgraphProperties();
+    for (auto property : subgraph_prop_list) {
       nnvm::Graph g;
       g.outputs = s->outputs;
       property->SetAttr("graph", g);
diff --git a/src/executor/graph_executor.cc b/src/executor/graph_executor.cc
index 82e591e8f17d..0ba85a9cea55 100644
--- a/src/executor/graph_executor.cc
+++ b/src/executor/graph_executor.cc
@@ -41,11 +41,23 @@ namespace exec {
 
 using namespace mxnet::common;
 
+static const std::string GetDefaultSubgraphBackend() {
+#if MXNET_USE_MKLDNN == 1
+  return std::string("MKLDNN");
+#else
+  return std::string();
+#endif
+}
+
 GraphExecutor::GraphExecutor() {
   log_verbose_ = dmlc::GetEnv("MXNET_EXEC_VERBOSE_LOGGING", false);
   need_grad_ = false;
   is_dynamic_ = false;
-  subgraph_property_ = dmlc::GetEnv("MXNET_SUBGRAPH_BACKEND", std::string());
+  subgraph_property_ = dmlc::GetEnv("MXNET_SUBGRAPH_BACKEND", GetDefaultSubgraphBackend());
+  if (subgraph_property_ == "NONE") {
+    subgraph_property_ = std::string();
+    LOG(INFO) << "MXNET_SUBGRAPH_BACKEND=NONE is detected, subgraph backend is not in use";
+  }
   engine_ref_ = Engine::_GetSharedRef();
 }
 
@@ -1598,15 +1610,18 @@ static nnvm::Graph InferForwardAttrs(nnvm::Graph g,
                                      const Context& default_ctx,
                                      const std::map<std::string, Context>& ctx_map,
                                      const std::vector<Context>& in_arg_ctxes,
-                                     const std::vector<Context>& aux_state_ctxes) {
+                                     const std::vector<Context>& aux_state_ctxes,
+                                     bool partial_shape = false) {
   const auto& indexed_graph = g.indexed_graph();
   const auto num_forward_inputs = indexed_graph.input_nodes().size();
   g = AssignContext(g, default_ctx, ctx_map, in_arg_ctxes, {},
                    aux_state_ctxes, {}, num_forward_inputs, g.outputs.size());
   g = InferShape(std::move(g), std::move(arg_shapes), "__shape__");
   if (g.GetAttr<size_t>("shape_num_unknown_nodes") != 0U) {
-    HandleInferShapeError(num_forward_inputs, indexed_graph,
-                          g.GetAttr<mxnet::ShapeVector>("shape"));
+    if (!partial_shape) {
+      HandleInferShapeError(num_forward_inputs, indexed_graph,
+                            g.GetAttr<mxnet::ShapeVector>("shape"));
+    }
   }
   g = InferType(std::move(g), std::move(arg_dtypes), "__dtype__");
   if (g.GetAttr<size_t>("dtype_num_unknown_nodes") != 0U) {
@@ -1621,10 +1636,46 @@ static nnvm::Graph InferForwardAttrs(nnvm::Graph g,
   return g;
 }
 
+static bool SubgraphBackendCheck(const op::SubgraphBackendPtr& backend,
+                                 const Context& default_ctx,
+                                 bool verbose = false) {
+  if (backend->HasAttr("enable") && (backend->GetAttr<bool>("enable") != true)) {
+    if (verbose) {
+      LOG(INFO) << "Subgraph backend " << backend->GetName()
+                << " isn't activated.";
+    }
+    return false;
+  }
+  if (backend->HasAttr("context") && backend->GetAttr<Context>("context") != default_ctx) {
+    if (verbose) {
+      LOG(INFO) << "Subgraph backend " << backend->GetName()
+                << " isn't activated as context mismatch.";
+    }
+    return false;
+  }
+  return true;
+}
+
+static bool SubgraphPropertyCheck(const std::string& backend_name,
+                                  const op::SubgraphPropertyPtr& prop, bool need_grad,
+                                  bool verbose = false) {
+  if (prop->HasAttr("inference_only") && prop->GetAttr<bool>("inference_only") == true) {
+    if (need_grad) {
+      auto full_name = prop->HasAttr("property_name") ? prop->GetAttr<std::string>("property_name")
+                                                      : std::string();
+      if (verbose) {
+        LOG(INFO) << "skip partitioning graph with subgraph property " << full_name
+                  << " from backend " << backend_name << " as it requires `grad_req=null`.";
+      }
+      return false;
+    }
+  }
+  return true;
+}
+
 // Given input attr arrays, partition the graph using the backend name equal to prop_name.
 // This is a common function for bind and simple_bind flows.
-static nnvm::Symbol BuildSubgraph(const nnvm::Symbol& src,
-                                  mxnet::op::SubgraphPropertyPtr subgraph_prop,
+static nnvm::Symbol BuildSubgraph(const nnvm::Symbol& src, op::SubgraphPropertyPtr subgraph_prop,
                                   const mxnet::ShapeVector& arg_shapes,
                                   const nnvm::DTypeVector& arg_dtypes,
                                   const StorageTypeVector& arg_stypes, const Context& default_ctx,
@@ -1635,7 +1686,7 @@ static nnvm::Symbol BuildSubgraph(const nnvm::Symbol& src,
   nnvm::Graph g;
   g.outputs = ret.outputs;
   g = InferForwardAttrs(g, arg_shapes, arg_dtypes, arg_stypes, default_ctx, ctx_map, in_arg_ctxes,
-                        aux_state_ctxes);
+                        aux_state_ctxes, true);
   subgraph_prop->SetAttr("graph", g);
   g.attrs["subgraph_property"] = std::make_shared<nnvm::any>(std::move(subgraph_prop));
   g = ApplyPass(std::move(g), "BuildSubgraph");
@@ -1643,20 +1694,16 @@ static nnvm::Symbol BuildSubgraph(const nnvm::Symbol& src,
   return ret;
 }
 
-// Given input attr dicts, partition the graph using the backend name equal to prop_name.
+// Given input attr dicts, partition the graph using the backend.
 // This is for simple_bind flow.
-static nnvm::Symbol BuildSubgraph(const nnvm::Symbol& src,
-                                   const std::string& prop_name,
-                                   const std::unordered_map<std::string, mxnet::TShape>
-                                                                             & arg_shape_map,
-                                   const std::unordered_map<std::string, int>& arg_dtype_map,
-                                   const std::unordered_map<std::string, int>& arg_stype_map,
-                                   const Context& default_ctx,
-                                   const std::map<std::string, Context>& ctx_map,
-                                   std::vector<Context>* in_arg_ctxes,
-                                   std::vector<Context>* arg_grad_ctxes,
-                                   std::vector<OpReqType>* grad_req_types,
-                                   std::vector<Context>* aux_state_ctxes) {
+static nnvm::Symbol BuildSubgraph(
+    const nnvm::Symbol& src, const op::SubgraphBackendPtr backend,
+    const std::unordered_map<std::string, mxnet::TShape>& arg_shape_map,
+    const std::unordered_map<std::string, int>& arg_dtype_map,
+    const std::unordered_map<std::string, int>& arg_stype_map, const Context& default_ctx,
+    const std::map<std::string, Context>& ctx_map, std::vector<Context>* in_arg_ctxes,
+    std::vector<Context>* arg_grad_ctxes, std::vector<OpReqType>* grad_req_types,
+    std::vector<Context>* aux_state_ctxes, bool verbose = false) {
   // setup map for in_arg_ctxes, arg_grad_ctxes, aux_state_ctxes and grad_req_types
   std::unordered_map<std::string, Context> in_arg_ctx_map;
   std::unordered_map<std::string, Context> arg_grad_ctx_map;
@@ -1666,7 +1713,7 @@ static nnvm::Symbol BuildSubgraph(const nnvm::Symbol& src,
   auto arg_names = src.ListInputNames(nnvm::Symbol::kReadOnlyArgs);
   auto aux_names = src.ListInputNames(nnvm::Symbol::kAuxiliaryStates);
   for (size_t i = 0; i < arg_names.size(); ++i) {
-    auto name = arg_names[i];
+    const auto& name = arg_names[i];
     in_arg_ctx_map[name] = in_arg_ctxes->at(i);
     arg_grad_ctx_map[name] = arg_grad_ctxes->at(i);
     grad_req_type_map[name] = grad_req_types->at(i);
@@ -1685,81 +1732,73 @@ static nnvm::Symbol BuildSubgraph(const nnvm::Symbol& src,
   }
   nnvm::Symbol ret = src.Copy();
   std::unordered_set<std::string> op_names_set;
-  auto it = op::SubgraphPropertyOpNameSet::Get()->find(prop_name);
+  const auto& backend_name = backend->GetName();
+  const auto it = op::SubgraphPropertyOpNameSet::Get()->find(backend_name);
   // assign a op name set to the subgraph property if it has been provided by users
   if (it != op::SubgraphPropertyOpNameSet::Get()->end()) {
-    LOG(INFO) << "SubgraphPropertyOpNameSet for subgraph property " << prop_name
+    LOG(INFO) << "SubgraphPropertyOpNameSet for subgraph property " << backend_name
               << " has been assigned a value. Please make sure it is initialized"
                  " only for the testing purpose.";
     op_names_set = it->second;
   }
-  std::vector<mxnet::op::SubgraphPropertyPtr> properties =
-      op::SubgraphPropertyRegistry::Get()->CreateSubgraphProperty(prop_name);
-  for (auto subgraph_prop : properties) {
-    if (subgraph_prop->HasAttr("inference_only") &&
-        subgraph_prop->GetAttr<bool>("inference_only") == true) {
-      if (need_grad) {
-        auto full_name = subgraph_prop->HasAttr("property_name")
-                             ? subgraph_prop->GetAttr<std::string>("property_name")
-                             : prop_name;
-        LOG(INFO) << "skip partitioning graph with subgraph property " << full_name
-                  << " as it requires `grad_req=null`.";
-        continue;
-      }
-    }
-    subgraph_prop->SetAttr("op_names", op_names_set);
-    const std::vector<std::string> input_names = ret.ListInputNames(Symbol::kAll);
-    mxnet::ShapeVector arg_shapes(input_names.size(), mxnet::TShape());
-    nnvm::DTypeVector arg_dtypes(input_names.size(), -1);
-    StorageTypeVector arg_stypes(input_names.size(), kUndefinedStorage);
-    for (size_t i = 0; i < input_names.size(); ++i) {
-      const auto& input_name = input_names[i];
-      auto it1 = arg_shape_map.find(input_name);
-      if (arg_shape_map.end() != it1) {
-        arg_shapes[i] = it1->second;
+
+  const auto& subgraph_prop_list = backend->GetSubgraphProperties();
+  for (auto& subgraph_prop : subgraph_prop_list) {
+    if (SubgraphPropertyCheck(backend_name, subgraph_prop, need_grad, verbose)) {
+      subgraph_prop->SetAttr("op_names", op_names_set);
+      const std::vector<std::string> input_names = ret.ListInputNames(Symbol::kAll);
+      mxnet::ShapeVector arg_shapes(input_names.size(), mxnet::TShape());
+      nnvm::DTypeVector arg_dtypes(input_names.size(), -1);
+      StorageTypeVector arg_stypes(input_names.size(), kUndefinedStorage);
+      for (size_t i = 0; i < input_names.size(); ++i) {
+        const auto& input_name = input_names[i];
+        const auto it1 = arg_shape_map.find(input_name);
+        if (arg_shape_map.end() != it1) {
+          arg_shapes[i] = it1->second;
+        }
+        const auto it2 = arg_dtype_map.find(input_name);
+        if (arg_dtype_map.end() != it2) {
+          arg_dtypes[i] = it2->second;
+        }
+        const auto it3 = arg_stype_map.find(input_name);
+        if (arg_stype_map.end() != it3) {
+          arg_stypes[i] = it3->second;
+        }
       }
-      auto it2 = arg_dtype_map.find(input_name);
-      if (arg_dtype_map.end() != it2) {
-        arg_dtypes[i] = it2->second;
+      ret = BuildSubgraph(ret, subgraph_prop, arg_shapes, arg_dtypes, arg_stypes, default_ctx,
+                          ctx_map, *in_arg_ctxes, *aux_state_ctxes);
+      // Reorder in_arg_ctxes, arg_grad_ctxes, aux_state_ctxes and grad_req_types according to
+      // partitioned symbol input sequence
+      in_arg_ctxes->clear();
+      arg_grad_ctxes->clear();
+      aux_state_ctxes->clear();
+      grad_req_types->clear();
+      auto new_arg_names = ret.ListInputNames(nnvm::Symbol::kReadOnlyArgs);
+      auto new_aux_names = ret.ListInputNames(nnvm::Symbol::kAuxiliaryStates);
+      for (const auto& arg_name : new_arg_names) {
+        CHECK(in_arg_ctx_map.count(arg_name));
+        in_arg_ctxes->push_back(in_arg_ctx_map[arg_name]);
+        arg_grad_ctxes->push_back(arg_grad_ctx_map[arg_name]);
+        grad_req_types->push_back(grad_req_type_map[arg_name]);
       }
-      auto it3 = arg_stype_map.find(input_name);
-      if (arg_stype_map.end() != it3) {
-        arg_stypes[i] = it3->second;
+      for (const auto& arg_name : new_aux_names) {
+        CHECK(aux_state_ctx_map.count(arg_name));
+        aux_state_ctxes->push_back(aux_state_ctx_map[arg_name]);
       }
     }
-    ret = BuildSubgraph(ret, subgraph_prop, arg_shapes, arg_dtypes, arg_stypes, default_ctx,
-                         ctx_map, *in_arg_ctxes, *aux_state_ctxes);
-    // Reorder in_arg_ctxes, arg_grad_ctxes, aux_state_ctxes and grad_req_types according to
-    // partitioned symbol input sequence
-    in_arg_ctxes->clear();
-    arg_grad_ctxes->clear();
-    aux_state_ctxes->clear();
-    grad_req_types->clear();
-    auto new_arg_names = ret.ListInputNames(nnvm::Symbol::kReadOnlyArgs);
-    auto new_aux_names = ret.ListInputNames(nnvm::Symbol::kAuxiliaryStates);
-    for (auto arg_name : new_arg_names) {
-      CHECK(in_arg_ctx_map.count(arg_name));
-      in_arg_ctxes->push_back(in_arg_ctx_map[arg_name]);
-      arg_grad_ctxes->push_back(arg_grad_ctx_map[arg_name]);
-      grad_req_types->push_back(grad_req_type_map[arg_name]);
-    }
-    for (auto arg_name : new_aux_names) {
-      CHECK(aux_state_ctx_map.count(arg_name));
-      aux_state_ctxes->push_back(aux_state_ctx_map[arg_name]);
-    }
   }
   return ret;
 }
 
-// Given input ndarrays, partition the graph using the backend name equal to prop_name.
+// Given input ndarrays, partition the graph using backend.
 // This is for bind flow.
-static nnvm::Symbol BuildSubgraph(const nnvm::Symbol& src, const std::string& prop_name,
+static nnvm::Symbol BuildSubgraph(const nnvm::Symbol& src, const op::SubgraphBackendPtr backend,
                                   const Context& default_ctx,
                                   const std::map<std::string, Context>& ctx_map,
                                   std::vector<NDArray>* in_args,
                                   std::vector<NDArray>* arg_grad_store,
                                   std::vector<OpReqType>* grad_req_type,
-                                  std::vector<NDArray>* aux_states) {
+                                  std::vector<NDArray>* aux_states, bool verbose = false) {
   // setup map for in_args, arg_grad_store, grad_req_type and aux_states
   std::unordered_map<std::string, NDArray> in_args_map;
   std::unordered_map<std::string, NDArray> arg_grad_store_map;
@@ -1768,16 +1807,21 @@ static nnvm::Symbol BuildSubgraph(const nnvm::Symbol& src, const std::string& pr
   const std::vector<std::string> arg_names = src.ListInputNames(nnvm::Symbol::kReadOnlyArgs);
   const std::vector<std::string> aux_names = src.ListInputNames(nnvm::Symbol::kAuxiliaryStates);
   for (size_t i = 0; i < arg_names.size(); ++i) {
-    auto name = arg_names[i];
-    in_args_map[name] = in_args->at(i);
-    arg_grad_store_map[name] = arg_grad_store->at(i);
-    grad_req_type_map[name] = grad_req_type->at(i);
+    in_args_map[arg_names[i]] = in_args->at(i);
   }
 
   for (size_t i = 0; i < aux_names.size(); ++i) {
     aux_states_map[aux_names[i]] = aux_states->at(i);
   }
 
+  if (arg_grad_store->size()) {
+    for (size_t i = 0; i < arg_names.size(); ++i) {
+      const auto& name = arg_names[i];
+      arg_grad_store_map[name] = arg_grad_store->at(i);
+      grad_req_type_map[name] = grad_req_type->at(i);
+    }
+  }
+
   bool need_grad = false;
   for (OpReqType req : *grad_req_type) {
     if (req != kNullOp) {
@@ -1787,65 +1831,58 @@ static nnvm::Symbol BuildSubgraph(const nnvm::Symbol& src, const std::string& pr
   }
   nnvm::Symbol ret = src.Copy();
   std::unordered_set<std::string> op_names_set;
-  auto it = op::SubgraphPropertyOpNameSet::Get()->find(prop_name);
+  const auto& backend_name = backend->GetName();
+  auto it = op::SubgraphPropertyOpNameSet::Get()->find(backend_name);
   // assign a op name set to the subgraph property if it has been provided by users
   if (it != op::SubgraphPropertyOpNameSet::Get()->end()) {
-    LOG(INFO) << "SubgraphPropertyOpNameSet for subgraph property " << prop_name
+    LOG(INFO) << "SubgraphPropertyOpNameSet for subgraph property " << backend_name
               << " has been assigned a value. Please make sure it is initialized"
                  " only for the testing purpose.";
     op_names_set = it->second;
   }
-  std::vector<mxnet::op::SubgraphPropertyPtr> properties =
-      op::SubgraphPropertyRegistry::Get()->CreateSubgraphProperty(prop_name);
-  for (auto subgraph_prop : properties) {
-    if (subgraph_prop->HasAttr("inference_only") &&
-        subgraph_prop->GetAttr<bool>("inference_only") == true) {
-      if (need_grad) {
-        auto full_name = subgraph_prop->HasAttr("property_name")
-                             ? subgraph_prop->GetAttr<std::string>("property_name")
-                             : prop_name;
-        LOG(INFO) << "Skip subgraph " << full_name << " as it requires `grad_req=null`.";
-        continue;
-      }
-    }
-    subgraph_prop->SetAttr("op_names", op_names_set);
-    const std::vector<std::string> input_names = ret.ListInputNames(Symbol::kAll);
-    const std::vector<std::string> arg_names = ret.ListInputNames(nnvm::Symbol::kReadOnlyArgs);
-    const std::vector<std::string> aux_names = ret.ListInputNames(nnvm::Symbol::kAuxiliaryStates);
-    CHECK_EQ(arg_names.size(), in_args_map.size());
-    CHECK_EQ(aux_names.size(), aux_states_map.size());
-    mxnet::ShapeVector arg_shapes;  // all input shapes
-    arg_shapes.reserve(input_names.size());
-    nnvm::DTypeVector arg_dtypes;  // all input dtypes
-    arg_dtypes.reserve(input_names.size());
-    StorageTypeVector arg_stypes;  // all input stypes
-    arg_stypes.reserve(input_names.size());
-    std::vector<Context> in_arg_ctxes(in_args_map.size());
-    std::vector<Context> aux_state_ctxes(aux_states_map.size());
-
-    size_t i1 = 0, i2 = 0;
-    for (const auto& input_name : input_names) {
-      if (i2 < aux_names.size() && aux_names[i2] == input_name) {
-        const auto &aux_st = aux_states_map[input_name];
-        arg_shapes.push_back(aux_st.shape());
-        arg_dtypes.push_back(aux_st.dtype());
-        arg_stypes.push_back(aux_st.storage_type());
-        aux_state_ctxes[i2] = aux_st.ctx();
-        ++i2;
-      } else {
-        CHECK(i1 < arg_names.size());
-        CHECK_EQ(arg_names[i1], input_name);
-        const auto &in_arg = in_args_map[input_name];
-        arg_shapes.push_back(in_arg.shape());
-        arg_dtypes.push_back(in_arg.dtype());
-        arg_stypes.push_back(in_arg.storage_type());
-        in_arg_ctxes[i1] = in_arg.ctx();
-        ++i1;
+  const auto& subgraph_prop_list = backend->GetSubgraphProperties();
+
+  for (auto subgraph_prop : subgraph_prop_list) {
+    if (SubgraphPropertyCheck(backend_name, subgraph_prop, need_grad, verbose)) {
+      subgraph_prop->SetAttr("op_names", op_names_set);
+      const std::vector<std::string> input_names = ret.ListInputNames(Symbol::kAll);
+      const std::vector<std::string> arg_names = ret.ListInputNames(nnvm::Symbol::kReadOnlyArgs);
+      const std::vector<std::string> aux_names = ret.ListInputNames(nnvm::Symbol::kAuxiliaryStates);
+      CHECK_EQ(arg_names.size(), in_args_map.size());
+      CHECK_EQ(aux_names.size(), aux_states_map.size());
+      mxnet::ShapeVector arg_shapes;  // all input shapes
+      arg_shapes.reserve(input_names.size());
+      nnvm::DTypeVector arg_dtypes;  // all input dtypes
+      arg_dtypes.reserve(input_names.size());
+      StorageTypeVector arg_stypes;  // all input stypes
+      arg_stypes.reserve(input_names.size());
+      std::vector<Context> in_arg_ctxes(in_args_map.size());
+      std::vector<Context> aux_state_ctxes(aux_states_map.size());
+
+      size_t i1 = 0, i2 = 0;
+      for (const auto& input_name : input_names) {
+        if (i2 < aux_names.size() && aux_names[i2] == input_name) {
+          const auto &aux_st = aux_states_map[input_name];
+          arg_shapes.push_back(aux_st.shape());
+          arg_dtypes.push_back(aux_st.dtype());
+          arg_stypes.push_back(aux_st.storage_type());
+          aux_state_ctxes[i2] = aux_st.ctx();
+          ++i2;
+        } else {
+          CHECK(i1 < arg_names.size());
+          CHECK_EQ(arg_names[i1], input_name);
+          const auto &in_arg = in_args_map[input_name];
+          arg_shapes.push_back(in_arg.shape());
+          arg_dtypes.push_back(in_arg.dtype());
+          arg_stypes.push_back(in_arg.storage_type());
+          in_arg_ctxes[i1] = in_arg.ctx();
+          ++i1;
+        }
       }
-    }
 
-    ret = BuildSubgraph(ret, subgraph_prop, arg_shapes, arg_dtypes, arg_stypes, default_ctx,
-                        ctx_map, in_arg_ctxes, aux_state_ctxes);
+      ret = BuildSubgraph(ret, subgraph_prop, arg_shapes, arg_dtypes, arg_stypes, default_ctx,
+                          ctx_map, in_arg_ctxes, aux_state_ctxes);
+    }
   }
   // Reorder in_args, arg_grad_store, grad_req_type and aux_states according to partitioned symbol
   // input sequence
@@ -1854,19 +1891,25 @@ static nnvm::Symbol BuildSubgraph(const nnvm::Symbol& src, const std::string& pr
   CHECK_EQ(arg_names.size(), new_arg_names.size());
   CHECK_EQ(arg_names.size(), new_arg_names.size());
   in_args->clear();
-  arg_grad_store->clear();
-  grad_req_type->clear();
   aux_states->clear();
-  for (auto arg_name : new_arg_names) {
+  for (const auto& arg_name : new_arg_names) {
     CHECK(in_args_map.count(arg_name));
     in_args->push_back(in_args_map[arg_name]);
-    arg_grad_store->push_back(arg_grad_store_map[arg_name]);
-    grad_req_type->push_back(grad_req_type_map[arg_name]);
   }
-  for (auto arg_name : new_aux_names) {
+
+  for (const auto& arg_name : new_aux_names) {
     CHECK(aux_states_map.count(arg_name));
     aux_states->push_back(aux_states_map[arg_name]);
   }
+
+  if (arg_grad_store->size()) {
+    arg_grad_store->clear();
+    grad_req_type->clear();
+    for (const auto& arg_name : new_arg_names) {
+      arg_grad_store->push_back(arg_grad_store_map[arg_name]);
+      grad_req_type->push_back(grad_req_type_map[arg_name]);
+    }
+  }
   return ret;
 }
 }  // namespace exec
@@ -1888,18 +1931,68 @@ Executor *Executor::SimpleBind(nnvm::Symbol symbol,
                                std::unordered_map<std::string, NDArray>* shared_buffer,
                                Executor* shared_exec) {
   auto exec = new exec::GraphExecutor();
-  std::vector<Context> tmp_in_arg_ctxes = in_arg_ctxes;
-  std::vector<Context> tmp_arg_grad_ctxes = arg_grad_ctxes;
-  std::vector<Context> tmp_aux_state_ctxes = aux_state_ctxes;
-  std::vector<OpReqType> tmp_grad_req_types = grad_req_types;
+  bool init = false;
   if (!exec->subgraph_property().empty()) {
-    symbol = exec::BuildSubgraph(symbol, exec->subgraph_property(), arg_shape_map, arg_dtype_map,
-                                 arg_stype_map, default_ctx, group2ctx, &tmp_in_arg_ctxes,
-                                 &tmp_arg_grad_ctxes, &tmp_grad_req_types, &tmp_aux_state_ctxes);
+    static bool verbose = dmlc::GetEnv("MXNET_SUBGRAPH_VERBOSE", false);
+    const auto& backend_name = exec->subgraph_property();
+    const auto& backend = op::SubgraphBackendRegistry::Get()->GetSubgraphBackend(backend_name);
+    if (exec::SubgraphBackendCheck(backend, default_ctx, verbose)) {
+      LOG(INFO) << "Subgraph backend " << backend_name << " is activated.";
+      std::vector<Context> tmp_in_arg_ctxes = in_arg_ctxes;
+      std::vector<Context> tmp_arg_grad_ctxes = arg_grad_ctxes;
+      std::vector<Context> tmp_aux_state_ctxes = aux_state_ctxes;
+      std::vector<OpReqType> tmp_grad_req_types = grad_req_types;
+      std::vector<NDArray> tmp_in_args;
+      std::vector<NDArray> tmp_arg_grads;
+      std::vector<NDArray> tmp_aux_states;
+      const auto arg_names = symbol.ListInputNames(nnvm::Symbol::kReadOnlyArgs);
+      const auto aux_names = symbol.ListInputNames(nnvm::Symbol::kAuxiliaryStates);
+      symbol = exec::BuildSubgraph(symbol, backend, arg_shape_map, arg_dtype_map, arg_stype_map,
+                                   default_ctx, group2ctx, &tmp_in_arg_ctxes, &tmp_arg_grad_ctxes,
+                                   &tmp_grad_req_types, &tmp_aux_state_ctxes, verbose);
+      exec->Init(symbol, default_ctx, group2ctx, tmp_in_arg_ctxes, tmp_arg_grad_ctxes,
+                 tmp_aux_state_ctxes, arg_shape_map, arg_dtype_map, arg_stype_map,
+                 tmp_grad_req_types, shared_arg_names, &tmp_in_args, &tmp_arg_grads,
+                 &tmp_aux_states, shared_buffer, shared_exec);
+      init = true;
+      const auto new_arg_names = symbol.ListInputNames(nnvm::Symbol::kReadOnlyArgs);
+      const auto new_aux_names = symbol.ListInputNames(nnvm::Symbol::kAuxiliaryStates);
+      std::unordered_map<std::string, size_t> new_arg_names_idx_map;
+      std::unordered_map<std::string, size_t> new_aux_names_idx_map;
+      for (size_t i = 0; i != new_arg_names.size(); ++i) {
+        new_arg_names_idx_map[new_arg_names[i]] = i;
+      }
+      for (size_t i = 0; i != new_aux_names.size(); ++i) {
+        new_aux_names_idx_map[new_aux_names[i]] = i;
+      }
+
+      in_args->reserve(arg_names.size());
+      arg_grads->reserve(arg_names.size());
+      for (size_t i = 0; i != arg_names.size(); ++i) {
+        const auto& arg_name = arg_names[i];
+        const auto& it = new_arg_names_idx_map.find(arg_name);
+        CHECK(it != new_arg_names_idx_map.end())
+            << "Subgraph doesn't support remove any input node for now.";
+        in_args->emplace_back(std::move(tmp_in_args[it->second]));
+        arg_grads->emplace_back(std::move(tmp_arg_grads[it->second]));
+      }
+
+      aux_states->reserve(aux_names.size());
+      for (size_t i = 0; i != aux_names.size(); ++i) {
+        const auto& aux_name = aux_names[i];
+        const auto& it = new_aux_names_idx_map.find(aux_name);
+        CHECK(it != new_aux_names_idx_map.end())
+            << "Subgraph doesn't support remove any input node for now.";
+        aux_states->emplace_back(std::move(tmp_aux_states[it->second]));
+      }
+    }
+  }
+  if (!init) {
+    // init without subgraph
+    exec->Init(symbol, default_ctx, group2ctx, in_arg_ctxes, arg_grad_ctxes, aux_state_ctxes,
+               arg_shape_map, arg_dtype_map, arg_stype_map, grad_req_types, shared_arg_names,
+               in_args, arg_grads, aux_states, shared_buffer, shared_exec);
   }
-  exec->Init(symbol, default_ctx, group2ctx, tmp_in_arg_ctxes, tmp_arg_grad_ctxes,
-             tmp_aux_state_ctxes, arg_shape_map, arg_dtype_map, arg_stype_map, tmp_grad_req_types,
-             shared_arg_names, in_args, arg_grads, aux_states, shared_buffer, shared_exec);
   return exec;
 }
 
@@ -1912,15 +2005,21 @@ Executor *Executor::Bind(nnvm::Symbol symbol,
                          const std::vector<NDArray> &aux_states,
                          Executor* shared_exec) {
   auto exec = new exec::GraphExecutor();
+  static bool verbose = dmlc::GetEnv("MXNET_SUBGRAPH_VERBOSE", false);
   std::vector<NDArray> tmp_in_args = in_args;
   std::vector<NDArray> tmp_arg_grad_store = arg_grad_store;
   std::vector<OpReqType> tmp_grad_req_type = grad_req_type;
   std::vector<NDArray> tmp_aux_states = aux_states;
 
   if (!exec->subgraph_property().empty()) {
-    symbol =
-        exec::BuildSubgraph(symbol, exec->subgraph_property(), default_ctx, group2ctx, &tmp_in_args,
-                            &tmp_arg_grad_store, &tmp_grad_req_type, &tmp_aux_states);
+    const auto& backend_name = exec->subgraph_property();
+    const auto& backend = op::SubgraphBackendRegistry::Get()->GetSubgraphBackend(backend_name);
+    if (exec::SubgraphBackendCheck(backend, default_ctx, verbose)) {
+      LOG(INFO) << "Subgraph backend " << backend_name << " is activated.";
+      symbol = exec::BuildSubgraph(symbol, backend, default_ctx, group2ctx, &tmp_in_args,
+                                   &tmp_arg_grad_store, &tmp_grad_req_type, &tmp_aux_states,
+                                   verbose);
+    }
   }
   exec->Init(symbol, default_ctx, group2ctx, tmp_in_args, tmp_arg_grad_store, tmp_grad_req_type,
              tmp_aux_states, reinterpret_cast<Executor*>(shared_exec));
diff --git a/src/operator/subgraph/build_subgraph.cc b/src/operator/subgraph/build_subgraph.cc
index e0fb615a7ac0..0420e2248077 100644
--- a/src/operator/subgraph/build_subgraph.cc
+++ b/src/operator/subgraph/build_subgraph.cc
@@ -297,8 +297,11 @@ void PreSelectSubgraphNodes(const nnvm::Graph& g, SubgraphSelectorV2Ptr subgraph
       for (auto node : excluded_nodes) {
         excluded_node_names += node->node->attrs.name + ", ";
       }
-      LOG(INFO) << "Found a cycle when BFS from node " << simple_nodes[snid]->node->attrs.name
-                << ". Excluding nodes " << excluded_node_names << "and retrying";
+      static bool verbose = dmlc::GetEnv("MXNET_SUBGRAPH_VERBOSE", false);
+      if (verbose) {
+        LOG(INFO) << "Found a cycle when BFS from node " << simple_nodes[snid]->node->attrs.name
+                  << ". Excluding nodes " << excluded_node_names << "and retrying";
+      }
       subgraph_selector->Reset();
     }
     ++count;
@@ -673,17 +676,23 @@ void TopSortEntries(const nnvm::Graph& g,
 }
 
 nnvm::Graph BuildSubgraph(nnvm::Graph&& g) {
+    static bool verbose = dmlc::GetEnv("MXNET_SUBGRAPH_VERBOSE", false);
   if (!g.HasAttr("subgraph_property")) {  // treat the whole graph as a subgraph
-    LOG(INFO) << "The graph has no attribute of subgraph_property attached. "
-                 "The original graph is returned.";
+    if (verbose) {
+      LOG(INFO) << "The graph has no attribute of subgraph_property attached. "
+                   "The original graph is returned.";
+    }
     return g;
   }
   using namespace sg;
+
   const SubgraphPropertyPtr& subg_prop = g.GetAttr<SubgraphPropertyPtr>("subgraph_property");
-  const std::string& prop_name = subg_prop->HasAttr("property_name")
-                                     ? subg_prop->GetAttr<std::string>("property_name")
-                                     : "partition graph";
-  LOG(INFO) << "start to execute " << prop_name << ".";
+  if (verbose) {
+    const std::string& prop_name = subg_prop->HasAttr("property_name")
+                                       ? subg_prop->GetAttr<std::string>("property_name")
+                                       : "partition graph";
+    LOG(INFO) << "start to execute " << prop_name << ".";
+  }
   // top sort NodeEntry of all the nodes' inputs
   std::unordered_map<const nnvm::NodeEntry*, size_t> entry_top_order_map;
   TopSortEntries(g, &entry_top_order_map);
diff --git a/src/operator/subgraph/default_subgraph_property.cc b/src/operator/subgraph/default_subgraph_property.cc
index 5a2c52e61729..246b2945ecec 100644
--- a/src/operator/subgraph/default_subgraph_property.cc
+++ b/src/operator/subgraph/default_subgraph_property.cc
@@ -73,6 +73,7 @@ class DefaultSubgraphProperty: public SubgraphProperty {
   }
 };
 
+MXNET_REGISTER_SUBGRAPH_BACKEND(default);
 MXNET_REGISTER_SUBGRAPH_PROPERTY(default, DefaultSubgraphProperty);
 
 }  // namespace op
diff --git a/src/operator/subgraph/default_subgraph_property_v2.cc b/src/operator/subgraph/default_subgraph_property_v2.cc
index bf8ccfe5ba6f..c8cc3b144890 100644
--- a/src/operator/subgraph/default_subgraph_property_v2.cc
+++ b/src/operator/subgraph/default_subgraph_property_v2.cc
@@ -78,6 +78,8 @@ class DefaultSubgraphProperty: public SubgraphProperty {
   }
 };
 
+MXNET_REGISTER_SUBGRAPH_BACKEND(default_v2);
+
 MXNET_REGISTER_SUBGRAPH_PROPERTY(default_v2, DefaultSubgraphProperty);
 
 }  // namespace op
diff --git a/src/operator/subgraph/mkldnn/mkldnn_conv_property.h b/src/operator/subgraph/mkldnn/mkldnn_conv_property.h
index d7a237e08c87..bf278ab75718 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_conv_property.h
+++ b/src/operator/subgraph/mkldnn/mkldnn_conv_property.h
@@ -24,6 +24,7 @@
 #include <string>
 #include <vector>
 #include "../../nn/activation-inl.h"
+#include "../../nn/convolution-inl.h"
 #include "../../nn/mkldnn/mkldnn_ops-inl.h"
 #include "../../tensor/matrix_op-inl.h"
 #include "../common.h"
@@ -62,10 +63,13 @@ class SgMKLDNNConvSelector : public SubgraphSelector {
 
   bool Select(const nnvm::Node &n) override {
     if (n.op() && n.op()->name == "Convolution") {
-      status_ = disable_all_ ? kSuccess : kStart;
-      matched_list_.clear();
-      matched_list_.push_back(&n);
-      return true;
+      const auto &param = nnvm::get<ConvolutionParam>(n.attrs.parsed);
+      if (param.kernel.ndim() == 2) {
+        status_ = disable_all_ ? kSuccess : kStart;
+        matched_list_.clear();
+        matched_list_.push_back(&n);
+        return true;
+      }
     }
     return false;
   }
@@ -224,7 +228,7 @@ class SgMKLDNNConvProperty : public SubgraphProperty {
   }
 
   SubgraphSelectorPtr CreateSubgraphSelector() const override {
-    int quantize = HasAttr("quantize") ? GetAttr<int>("quantize") : 0;
+    bool quantize = HasAttr("quantize") ? GetAttr<bool>("quantize") : false;
     auto selector = std::make_shared<SgMKLDNNConvSelector>(
         disable_all_, disable_conv_bn_, disable_conv_act_, disable_conv_sum_, quantize);
     return selector;
diff --git a/src/operator/subgraph/mkldnn/mkldnn_subgraph_property.cc b/src/operator/subgraph/mkldnn/mkldnn_subgraph_property.cc
index 7fbc859cc8d1..d0d2b51918b1 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_subgraph_property.cc
+++ b/src/operator/subgraph/mkldnn/mkldnn_subgraph_property.cc
@@ -28,10 +28,18 @@
 namespace mxnet {
 namespace op {
 
+MXNET_REGISTER_SUBGRAPH_BACKEND(MKLDNN)
+.set_attr("enable", MKLDNNEnvSet())
+.set_attr("context", Context::CPU());
+
 MXNET_REGISTER_SUBGRAPH_PROPERTY(MKLDNN, SgMKLDNNConvProperty);
 
 MXNET_REGISTER_SUBGRAPH_PROPERTY(MKLDNN, SgMKLDNNFCProperty);
 
+
+MXNET_REGISTER_SUBGRAPH_BACKEND(MKLDNN_QUANTIZE)
+.set_attr("context", Context::CPU());
+
 MXNET_REGISTER_SUBGRAPH_PROPERTY(MKLDNN_QUANTIZE, SgMKLDNNConvProperty)
 .set_attr("quantize", true);
 
diff --git a/src/operator/subgraph/subgraph_property.h b/src/operator/subgraph/subgraph_property.h
index 460055f9ed86..0683e5ed65f1 100644
--- a/src/operator/subgraph/subgraph_property.h
+++ b/src/operator/subgraph/subgraph_property.h
@@ -345,8 +345,10 @@ using SubgraphPropertyPtr = std::shared_ptr<SubgraphProperty>;
 class SubgraphPropertyEntry {
  public:
   explicit SubgraphPropertyEntry(std::shared_ptr<SubgraphProperty> entry) : entry_(entry) {}
-  SubgraphPropertyEntry set_attr(const std::string& name, const int value) const {
-    entry_->SetAttr<int>(name, value);
+
+  template<typename T>
+  SubgraphPropertyEntry set_attr(const std::string& name, const T value) const {
+    entry_->SetAttr<T>(name, value);
     return *this;
   }
 
@@ -354,31 +356,102 @@ class SubgraphPropertyEntry {
   std::shared_ptr<SubgraphProperty> entry_;
 };
 
-class SubgraphPropertyRegistry {
+class SubgraphBackend {
+ public:
+  explicit SubgraphBackend(std::string name) : name_(name) {}
+  /*!
+   * \brief Set an attr with name in the attr map.
+   */
+  template<typename T>
+  SubgraphBackend& SetAttr(const std::string& name, const T& value) {
+    attrs_[name] = std::make_shared<dmlc::any>(value);
+    return *this;
+  }
+  /*!
+   * \brief Get the attr with the name.
+   */
+  template<typename T>
+  const T& GetAttr(const std::string& name) const {
+    auto it = attrs_.find(name);
+    CHECK(it != attrs_.end()) << "Cannot find attribute " << name << " in SubgraphProperty";
+    return nnvm::get<T>(*it->second);
+  }
+  /*!
+   * \brief Check if the attr exists.
+   */
+  bool HasAttr(const std::string& name) const {
+    auto it = attrs_.find(name);
+    return it != attrs_.end();
+  }
+
+  SubgraphPropertyPtr& RegisterSubgraphProperty(const SubgraphPropertyPtr prop) {
+    prop_ptr_.push_back(prop);
+    return prop_ptr_.back();
+  }
+
+  const std::string& GetName() const { return name_; }
+
+  const std::vector<SubgraphPropertyPtr>& GetSubgraphProperties() const { return prop_ptr_; }
+
+ private:
+  const std::string name_;
+  std::unordered_map<std::string, std::shared_ptr<nnvm::any>> attrs_;
+  std::vector<SubgraphPropertyPtr> prop_ptr_;
+};
+
+using SubgraphBackendPtr = std::shared_ptr<SubgraphBackend>;
+
+class SubgraphBackendEntry {
  public:
+  explicit SubgraphBackendEntry(SubgraphBackendPtr entry) : entry_(entry) {}
+
+  template<typename T>
+  SubgraphBackendEntry set_attr(const std::string& name, const T value) const {
+    entry_->SetAttr<T>(name, value);
+    return *this;
+  }
+
+ private:
+  SubgraphBackendPtr entry_;
+};
+
+class SubgraphBackendRegistry {
   typedef SubgraphPropertyPtr (*SubgraphPropertyCreateFn)(void);
-  static SubgraphPropertyRegistry* Get() {
-    static SubgraphPropertyRegistry inst;
+
+ public:
+  static SubgraphBackendRegistry* Get() {
+    static SubgraphBackendRegistry inst;
     return &inst;
   }
 
-  std::vector<SubgraphPropertyPtr> CreateSubgraphProperty(const std::string& name) {
-    auto it = prop_ptr_map_.find(name);
-    CHECK(it != prop_ptr_map_.end()) << "SubgraphProperty " << name
-                                    << " is not found in SubgraphPropertyRegistry";
+  SubgraphBackendPtr& GetSubgraphBackend(const std::string& name) {
+    auto it = backend_map_.find(name);
+    CHECK(it != backend_map_.end()) << "SubgraphProperty " << name
+                                    << " is not found in SubgraphBackendRegistry";
     return it->second;
   }
 
-  SubgraphPropertyEntry __REGISTER__(const std::string& name, SubgraphPropertyCreateFn fn) {
-    prop_ptr_map_[name].emplace_back(fn());
-    return SubgraphPropertyEntry(prop_ptr_map_[name].back());
+  SubgraphBackendEntry __REGISTER_BACKEND__(const std::string& name) {
+    auto it = backend_map_.find(name);
+    CHECK(it == backend_map_.end()) << "Subgraph backend " << name << " is already registered";
+    backend_map_[name] = std::make_shared<SubgraphBackend>(name);
+    return SubgraphBackendEntry(backend_map_[name]);
+  }
+
+  SubgraphPropertyEntry __REGISTER_PROPERTY__(const std::string& name,
+                                              SubgraphPropertyCreateFn fn) {
+    auto it = backend_map_.find(name);
+    CHECK(it != backend_map_.end())
+        << "Subgraph backend " << name << " is not found in SubgraphBackendRegistry";
+    auto prop = it->second->RegisterSubgraphProperty(fn());
+    return SubgraphPropertyEntry(prop);
   }
 
-  SubgraphPropertyRegistry() = default;
-  SubgraphPropertyRegistry(const SubgraphPropertyRegistry&) = delete;
-  SubgraphPropertyRegistry(SubgraphPropertyRegistry&&) = delete;
-  SubgraphPropertyRegistry& operator=(const SubgraphPropertyRegistry&) = delete;
-  std::unordered_map<std::string, std::vector<SubgraphPropertyPtr>> prop_ptr_map_;
+  SubgraphBackendRegistry() = default;
+  SubgraphBackendRegistry(const SubgraphBackendRegistry&) = delete;
+  SubgraphBackendRegistry(SubgraphBackendRegistry&&) = delete;
+  SubgraphBackendRegistry& operator=(const SubgraphBackendRegistry&) = delete;
+  std::unordered_map<std::string, SubgraphBackendPtr> backend_map_;
 };
 
 // This op name set is for setting the names of operators that should be grouped into
@@ -395,7 +468,13 @@ typedef dmlc::ThreadLocalStore<std::unordered_map<std::string, std::unordered_se
 
 #define MXNET_REGISTER_SUBGRAPH_PROPERTY(Name, SubgraphPropertyType) \
   DECLARE_PROPERTY(Name, SubgraphPropertyType, __LINE__) =           \
-      SubgraphPropertyRegistry::Get()->__REGISTER__(#Name, &SubgraphPropertyType::Create)
+      SubgraphBackendRegistry::Get()->__REGISTER_PROPERTY__(#Name, &SubgraphPropertyType::Create)
+
+#define DECLARE_BACKEND(Name) \
+  static const DMLC_ATTRIBUTE_UNUSED auto __make_##Name##__
+
+#define MXNET_REGISTER_SUBGRAPH_BACKEND(Name) \
+  DECLARE_BACKEND(Name) = SubgraphBackendRegistry::Get()->__REGISTER_BACKEND__(#Name)
 
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/subgraph/tensorrt/tensorrt.cc b/src/operator/subgraph/tensorrt/tensorrt.cc
index 30fcee007cfc..eac4ba7fc6fc 100644
--- a/src/operator/subgraph/tensorrt/tensorrt.cc
+++ b/src/operator/subgraph/tensorrt/tensorrt.cc
@@ -329,6 +329,8 @@ NNVM_REGISTER_OP(_TensorRT)
     .set_attr<FCreateOpState>("FCreateOpState", TRTCreateState)
     .set_attr<FInferStorageType>("FInferStorageType", TRTInferStorageType);
 
+MXNET_REGISTER_SUBGRAPH_BACKEND(TensorRT);
+
 MXNET_REGISTER_SUBGRAPH_PROPERTY(TensorRT, TensorrtProperty);
 }  // namespace op
 }  // namespace mxnet
diff --git a/tests/python/mkl/test_quantization_mkldnn.py b/tests/python/mkl/test_quantization_mkldnn.py
index 290f1a195c24..3c8cee465ec5 100644
--- a/tests/python/mkl/test_quantization_mkldnn.py
+++ b/tests/python/mkl/test_quantization_mkldnn.py
@@ -19,6 +19,7 @@
 import mxnet as mx
 
 os.environ['ENABLE_MKLDNN_QUANTIZATION_TEST'] = '1'
+os.environ['MXNET_SUBGRAPH_BACKEND'] = 'NONE'
 curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
 sys.path.insert(0, os.path.join(curr_path, '../quantization'))
 from test_quantization import *
@@ -26,3 +27,6 @@
 if __name__ == '__main__':
     import nose
     nose.runmodule()
+
+del os.environ['ENABLE_MKLDNN_QUANTIZATION_TEST']
+del os.environ['MXNET_SUBGRAPH_BACKEND']
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index d195ea9ef2f3..49a3475638c0 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -1227,8 +1227,8 @@ def test_rsqrt_cos_sin():
 
 @with_seed()
 def test_maximum_minimum():
-    data1 = mx.symbol.Variable('data')
-    data2 = mx.symbol.Variable('data')
+    data1 = mx.symbol.Variable('data1')
+    data2 = mx.symbol.Variable('data2')
     shape = (3, 4)
     data_tmp1 = np.random.rand(3,4)
     data_tmp2 = np.random.rand(3,4)
@@ -1241,7 +1241,7 @@ def test_maximum_minimum():
     arr_grad1 = mx.nd.empty(shape)
     arr_grad2 = mx.nd.empty(shape)
 
-    test =  mx.sym.maximum(data1,data2) + mx.sym.minimum(data1,data2);
+    test =  mx.sym.maximum(data1,data2) + mx.sym.minimum(data1,data2)
     exe_test = test.bind(default_context(), args=[arr_data1,arr_data2], args_grad=[arr_grad1,arr_grad2])
     exe_test.forward(is_train=True)
     out = exe_test.outputs[0].asnumpy()
@@ -2028,9 +2028,9 @@ def test_convolution_independent_gradients():
         x = mx.sym.Variable('x')
         w = mx.sym.Variable('w')
         b = mx.sym.Variable('b') if not no_bias else None
-        conv = mx.sym.Convolution(x, w, b, num_filter=num_filter, 
+        conv = mx.sym.Convolution(x, w, b, num_filter=num_filter,
             kernel=kernel, stride=stride, pad=pad, no_bias=no_bias)
-        
+
         for req_kind in reqs:
             # Binding args for conv with possible dependent gradients
             base_args = {
@@ -2049,7 +2049,7 @@ def test_convolution_independent_gradients():
             exe1 = conv.bind(ctx, args1, args_grad=grad1, grad_req=grad_req1)
             exe1.forward(is_train=True)
             exe1.backward(exe1.outputs[0])
-            
+
             for x_req, w_req, b_req in itertools.product(reqs, repeat=3):
                 # Binding args for conv with independent gradients
                 args2 = copy.deepcopy(base_args)    # Deepcopy the same params of `exe1`
@@ -2059,11 +2059,11 @@ def test_convolution_independent_gradients():
                     'b': mx.nd.zeros(shape=(num_filter, )) if not no_bias else None}
                 grad_req2 = {"x": x_req, "w": w_req, "b": b_req}
                 exe2 = conv.bind(ctx, args2, args_grad=grad2, grad_req=grad_req2)
-                    
+
                 exe2.forward(is_train=True)
                 np.testing.assert_allclose(exe1.outputs[0].asnumpy(),
                     exe2.outputs[0].asnumpy(), rtol=rtol, atol=atol)
-                
+
                 exe2.backward(exe2.outputs[0])
                 for var_name in var_names:
                     if var_name == "b" and no_bias:
@@ -3960,8 +3960,8 @@ def mathematical_core_binary(name,
                              data1_init=2.,
                              data2_init=3.,
                              grad_init=2.):
-    data1 = mx.symbol.Variable('data')
-    data2 = mx.symbol.Variable('data')
+    data1 = mx.symbol.Variable('data1')
+    data2 = mx.symbol.Variable('data2')
     shape = (3, 4)
     data_tmp1 = np.random.rand(3, 4)
     data_tmp2 = np.random.rand(3, 4)
@@ -7995,39 +7995,46 @@ def get_output_names_callback(name, arr):
             pass
         for output_name, expected_name in zip(output_names, expected_names):
             assert output_name == expected_name
+    is_windows = sys.platform.startswith('win')
+    if (is_windows):
+        # Windows doesn't support set environment variable on the fly, so disable it for now
+        pass
+    else:
+        # Disable subgraph in case subgraph will replace symbol
+        os.environ['MXNET_SUBGRAPH_BACKEND'] = "NONE"
+        data = mx.sym.Variable('data', shape=(10, 3, 10, 10))
+        conv_sym = mx.sym.Convolution(data, kernel=(2, 2), num_filter=1, name='conv')
+        check_name(conv_sym, ['conv_output'])
 
-    data = mx.sym.Variable('data', shape=(10, 3, 10, 10))
-    conv_sym = mx.sym.Convolution(data, kernel=(2, 2), num_filter=1, name='conv')
-    check_name(conv_sym, ['conv_output'])
-
-    deconv_sym = mx.sym.Deconvolution(data, kernel=(2, 2), num_filter=1, name='deconv')
-    check_name(deconv_sym, ['deconv_output'])
+        deconv_sym = mx.sym.Deconvolution(data, kernel=(2, 2), num_filter=1, name='deconv')
+        check_name(deconv_sym, ['deconv_output'])
 
-    fc_sym = mx.sym.FullyConnected(data, num_hidden=10, name='fc')
-    check_name(fc_sym, ['fc_output'])
+        fc_sym = mx.sym.FullyConnected(data, num_hidden=10, name='fc')
+        check_name(fc_sym, ['fc_output'])
 
-    lrn_sym = mx.sym.LRN(data, nsize=1, name='lrn')
-    check_name(lrn_sym, ['lrn_output', 'lrn_tmp_norm'])
+        lrn_sym = mx.sym.LRN(data, nsize=1, name='lrn')
+        check_name(lrn_sym, ['lrn_output', 'lrn_tmp_norm'])
 
-    act_sym = mx.sym.Activation(data, act_type='relu', name='act')
-    check_name(act_sym, ['act_output'])
+        act_sym = mx.sym.Activation(data, act_type='relu', name='act')
+        check_name(act_sym, ['act_output'])
 
-    cc_sym = mx.sym.concat(data, data, dim=0, name='concat')
-    check_name(cc_sym, ['concat_output'])
+        cc_sym = mx.sym.concat(data, data, dim=0, name='concat')
+        check_name(cc_sym, ['concat_output'])
 
-    sm_sym = mx.sym.softmax(data, name='softmax')
-    check_name(sm_sym, ['softmax_output'])
+        sm_sym = mx.sym.softmax(data, name='softmax')
+        check_name(sm_sym, ['softmax_output'])
 
-    sa_sym = mx.sym.SoftmaxActivation(data, name='softmax')
-    check_name(sa_sym, ['softmax_output'])
+        sa_sym = mx.sym.SoftmaxActivation(data, name='softmax')
+        check_name(sa_sym, ['softmax_output'])
 
-    us_sym = mx.sym.UpSampling(data, scale=2, sample_type='nearest',
-                               name='upsampling')
-    check_name(us_sym, ['upsampling_output'])
+        us_sym = mx.sym.UpSampling(data, scale=2, sample_type='nearest',
+                                name='upsampling')
+        check_name(us_sym, ['upsampling_output'])
 
-    us_sym = mx.sym.Pooling(data, kernel=(2, 2), pool_type='avg',
-                            name='pooling')
-    check_name(us_sym, ['pooling_output'])
+        us_sym = mx.sym.Pooling(data, kernel=(2, 2), pool_type='avg',
+                                name='pooling')
+        check_name(us_sym, ['pooling_output'])
+        del os.environ['MXNET_SUBGRAPH_BACKEND']
 
 def test_op_all_names_monitor():
     def check_name(op_sym, expected_names):
@@ -8046,43 +8053,51 @@ def get_output_names_callback(name, arr):
             pass
         for output_name, expected_name in zip(output_names, expected_names):
             assert output_name == expected_name
+    is_windows = sys.platform.startswith('win')
+    if (is_windows):
+        # Windows doesn't support set environment variable on the fly, so disable it for now
+        pass
+    else:
+        # Disable subgraph in case subgraph will replace symbol
+        os.environ['MXNET_SUBGRAPH_BACKEND'] = "NONE"
 
-    data = mx.sym.Variable('data', shape=(10, 3, 10, 10))
-    conv_sym = mx.sym.Convolution(data, kernel=(2, 2), num_filter=1, name='conv')
-    check_name(conv_sym, ['data', 'conv_data', 'conv_weight', 'conv_weight', 'conv_bias', 'conv_bias', 'conv_output'])
+        data = mx.sym.Variable('data', shape=(10, 3, 10, 10))
+        conv_sym = mx.sym.Convolution(data, kernel=(2, 2), num_filter=1, name='conv')
+        check_name(conv_sym, ['data', 'conv_data', 'conv_weight', 'conv_weight', 'conv_bias', 'conv_bias', 'conv_output'])
 
-    deconv_sym = mx.sym.Deconvolution(data, kernel=(2, 2), num_filter=1, name='deconv')
-    check_name(deconv_sym, ['data', 'deconv_data', 'deconv_weight', 'deconv_weight', 'deconv_output'])
+        deconv_sym = mx.sym.Deconvolution(data, kernel=(2, 2), num_filter=1, name='deconv')
+        check_name(deconv_sym, ['data', 'deconv_data', 'deconv_weight', 'deconv_weight', 'deconv_output'])
 
-    fc_sym = mx.sym.FullyConnected(data, num_hidden=10, name='fc')
-    check_name(fc_sym, ['data', 'fc_data', 'fc_weight', 'fc_weight', 'fc_bias', 'fc_bias', 'fc_output'])
+        fc_sym = mx.sym.FullyConnected(data, num_hidden=10, name='fc')
+        check_name(fc_sym, ['data', 'fc_data', 'fc_weight', 'fc_weight', 'fc_bias', 'fc_bias', 'fc_output'])
 
-    lrn_sym = mx.sym.LRN(data, nsize=1, name='lrn')
-    check_name(lrn_sym, ['data', 'lrn_data', 'lrn_output', 'lrn_tmp_norm'])
+        lrn_sym = mx.sym.LRN(data, nsize=1, name='lrn')
+        check_name(lrn_sym, ['data', 'lrn_data', 'lrn_output', 'lrn_tmp_norm'])
 
-    act_sym = mx.sym.Activation(data, act_type='relu', name='act')
-    check_name(act_sym, ['data', 'act_input0', 'act_output'])
+        act_sym = mx.sym.Activation(data, act_type='relu', name='act')
+        check_name(act_sym, ['data', 'act_input0', 'act_output'])
 
-    cc_sym = mx.sym.concat(data, data, dim=0, name='concat')
-    check_name(cc_sym, ['data', 'concat_arg0', 'data', 'concat_arg1', 'concat_output'])
+        cc_sym = mx.sym.concat(data, data, dim=0, name='concat')
+        check_name(cc_sym, ['data', 'concat_arg0', 'data', 'concat_arg1', 'concat_output'])
 
-    sm_sym = mx.sym.softmax(data, name='softmax')
-    check_name(sm_sym, ['data', 'softmax_data', 'softmax_output'])
+        sm_sym = mx.sym.softmax(data, name='softmax')
+        check_name(sm_sym, ['data', 'softmax_data', 'softmax_output'])
 
-    length = mx.sym.Variable("length", shape=(10, 10, 10))
-    sm_sym = mx.sym.softmax(data, length, axis=1, use_length=True, name='softmax')
-    check_name(sm_sym, ['data', 'softmax_data', 'length', 'softmax_length', 'softmax_output'])
+        length = mx.sym.Variable("length", shape=(10, 10, 10))
+        sm_sym = mx.sym.softmax(data, length, axis=1, use_length=True, name='softmax')
+        check_name(sm_sym, ['data', 'softmax_data', 'length', 'softmax_length', 'softmax_output'])
 
-    sa_sym = mx.sym.SoftmaxActivation(data, name='softmax')
-    check_name(sa_sym, ['data', 'softmax_input0', 'softmax_output'])
+        sa_sym = mx.sym.SoftmaxActivation(data, name='softmax')
+        check_name(sa_sym, ['data', 'softmax_input0', 'softmax_output'])
 
-    us_sym = mx.sym.UpSampling(data, scale=2, sample_type='nearest',
-                               name='upsampling')
-    check_name(us_sym, ['data', 'upsampling_arg0', 'upsampling_output'])
+        us_sym = mx.sym.UpSampling(data, scale=2, sample_type='nearest',
+                                name='upsampling')
+        check_name(us_sym, ['data', 'upsampling_arg0', 'upsampling_output'])
 
-    us_sym = mx.sym.Pooling(data, kernel=(2, 2), pool_type='avg',
-                            name='pooling')
-    check_name(us_sym, ['data', 'pooling_data', 'pooling_output'])
+        us_sym = mx.sym.Pooling(data, kernel=(2, 2), pool_type='avg',
+                                name='pooling')
+        check_name(us_sym, ['data', 'pooling_data', 'pooling_output'])
+        del os.environ['MXNET_SUBGRAPH_BACKEND']
 
 @with_seed()
 @unittest.skip("test fails intermittently. temporarily disabled till it gets fixed. tracked at https://github.com/apache/incubator-mxnet/issues/13915")

From 5e6ba7b9812d1cf76b82c291264f794365c9563b Mon Sep 17 00:00:00 2001
From: Zixuan Wei <zixuan.wei@intel.com>
Date: Fri, 26 Jul 2019 16:34:24 +0800
Subject: [PATCH 111/813] [Flaky test] Skip
 test_operator_gpu.test_convolution_independent_gradients (#15631)

* Skip test_convolution_independent_gradirents

* Add an issue link

* Fix inconsistent context of input array and binding op

* Trigger CI

* Retrigger CI
---
 tests/python/unittest/test_operator.py | 28 ++++++++++++++------------
 1 file changed, 15 insertions(+), 13 deletions(-)

diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 49a3475638c0..e8c9d6cbd061 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -2001,10 +2001,12 @@ def test_depthwise_convolution():
 
 @with_seed()
 def test_convolution_independent_gradients():
-    ctx = default_context()
-    # set a low bar for autotuned cudnn conv
-    atol = 1.0e-1 if ctx.device_type == "gpu" else 1.0e-3
-    rtol = 1.0e-2 if ctx.device_type == "gpu" else 1.0e-3
+    # NOTE(zixuanweeei): Flaky test tracked by https://github.com/apache/incubator-mxnet/issues/15603.
+    # GPU context will be enabled after figuring out the possible issue tracked at
+    # https://github.com/apache/incubator-mxnet/issues/15638.
+    ctx = mx.cpu()
+    atol = 1.0e-3
+    rtol = 1.0e-3
     reqs = ["null", "write", "add"]
     var_names = ["x", "w", "b"]
     dims = [1, 2]
@@ -2034,14 +2036,14 @@ def test_convolution_independent_gradients():
         for req_kind in reqs:
             # Binding args for conv with possible dependent gradients
             base_args = {
-                'x': mx.nd.random.normal(shape=x_shape),
-                'w': mx.nd.random.normal(shape=w_shape),
-                'b': mx.nd.random.normal(shape=(num_filter, )) if not no_bias else None}
+                'x': mx.nd.random.normal(shape=x_shape, ctx=ctx),
+                'w': mx.nd.random.normal(shape=w_shape, ctx=ctx),
+                'b': mx.nd.random.normal(shape=(num_filter, ), ctx=ctx) if not no_bias else None}
             args1 = copy.deepcopy(base_args)
             grad1 = {
-                'x': mx.nd.zeros(shape=x_shape),
-                'w': mx.nd.zeros(shape=w_shape),
-                'b': mx.nd.zeros(shape=(num_filter, )) if not no_bias else None}
+                'x': mx.nd.zeros(shape=x_shape, ctx=ctx),
+                'w': mx.nd.zeros(shape=w_shape, ctx=ctx),
+                'b': mx.nd.zeros(shape=(num_filter, ), ctx=ctx) if not no_bias else None}
 
             grad_req1 = [req_kind] * 3
             grad_req1 = dict(zip(var_names, grad_req1))
@@ -2054,9 +2056,9 @@ def test_convolution_independent_gradients():
                 # Binding args for conv with independent gradients
                 args2 = copy.deepcopy(base_args)    # Deepcopy the same params of `exe1`
                 grad2 = {
-                    'x': mx.nd.zeros(shape=x_shape),
-                    'w': mx.nd.zeros(shape=w_shape),
-                    'b': mx.nd.zeros(shape=(num_filter, )) if not no_bias else None}
+                    'x': mx.nd.zeros(shape=x_shape, ctx=ctx),
+                    'w': mx.nd.zeros(shape=w_shape, ctx=ctx),
+                    'b': mx.nd.zeros(shape=(num_filter, ), ctx=ctx) if not no_bias else None}
                 grad_req2 = {"x": x_req, "w": w_req, "b": b_req}
                 exe2 = conv.bind(ctx, args2, args_grad=grad2, grad_req=grad_req2)
 

From c310763e0c54180279cda48a89f5ab4488ad4514 Mon Sep 17 00:00:00 2001
From: Zhaoqi Zhu <zhaoqizh@usc.edu>
Date: Fri, 26 Jul 2019 08:53:36 -0700
Subject: [PATCH 112/813] update profiler tutorial (#15580)

* update profiler tutorial

* Update profiler.md

* Update profiler.md

* Update profiler.md

* Re-Trigger build

* Re-Trigger build

* Re-Trigger build

* Update profiler.md
---
 docs/tutorials/python/profiler.md | 27 +++++++++++++++------------
 1 file changed, 15 insertions(+), 12 deletions(-)

diff --git a/docs/tutorials/python/profiler.md b/docs/tutorials/python/profiler.md
index 5be6bf83ac5d..91a74e4f49cf 100644
--- a/docs/tutorials/python/profiler.md
+++ b/docs/tutorials/python/profiler.md
@@ -195,10 +195,10 @@ print(profiler.dumps())
 You can also dump the information collected by the profiler into a `json` file using the `profiler.dump()` function and view it in a browser.
 
 ```python
-profiler.dump()
+profiler.dump(finished=False)
 ```
 
-`dump()` creates a `json` file which can be viewed using a trace consumer like `chrome://tracing` in the Chrome browser. Here is a snapshot that shows the output of the profiling we did above.
+`dump()` creates a `json` file which can be viewed using a trace consumer like `chrome://tracing` in the Chrome browser. Here is a snapshot that shows the output of the profiling we did above. Note that setting the `finished` parameter to `False` will prevent the profiler from finishing dumping to file. If you just use `profiler.dump()`, you will no longer be able to profile the remaining sections of your model. 
 
 ![Tracing Screenshot](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tutorials/python/profiler/profiler_output_chrome.png)
 
@@ -214,11 +214,6 @@ Should the existing NDArray operators fail to meet all your model's needs, MXNet
 Let's try profiling custom operators with the following code example:
 
 ```python
-
-import mxnet as mx
-from mxnet import nd
-from mxnet import profiler
-
 class MyAddOne(mx.operator.CustomOp):
     def forward(self, is_train, req, in_data, out_data, aux):  
         self.assign(out_data[0], req[0], in_data[0]+1)
@@ -246,7 +241,8 @@ class CustomAddOneProp(mx.operator.CustomOpProp):
 
 inp = mx.nd.zeros(shape=(500, 500))
 
-profiler.set_config(profile_all=True, continuous_dump = True)
+profiler.set_config(profile_all=True, continuous_dump=True, \
+                    aggregate_stats=True)
 profiler.set_state('run')
 
 w = nd.Custom(inp, op_type="MyAddOne")
@@ -254,7 +250,8 @@ w = nd.Custom(inp, op_type="MyAddOne")
 mx.nd.waitall()
 
 profiler.set_state('stop')
-profiler.dump()
+print(profiler.dumps())
+profiler.dump(finished=False)
 ```
 
 Here, we have created a custom operator called `MyAddOne`, and within its `forward()` function, we simply add one to the input. We can visualize the dump file in `chrome://tracing/`:
@@ -267,10 +264,10 @@ Please note that: to be able to see the previously described information, you ne
 
 ```python 
 # Set profile_all to True
-profiler.set_config(profile_all=True, aggregate_stats=True, continuous_dump = True)
+profiler.set_config(profile_all=True, aggregate_stats=True, continuous_dump=True)
 # OR, Explicitly Set profile_symbolic and profile_imperative to True
-profiler.set_config(profile_symbolic = True, profile_imperative = True, \
-    aggregate_stats=True, continuous_dump = True)
+profiler.set_config(profile_symbolic=True, profile_imperative=True, \
+                    aggregate_stats=True, continuous_dump=True)
 
 profiler.set_state('run')
 # Use Symbolic Mode
@@ -280,9 +277,15 @@ c = b.bind(mx.cpu(), {'a': inp})
 y = c.forward()
 mx.nd.waitall()
 profiler.set_state('stop')
+print(profiler.dumps())
 profiler.dump()
 ```
 
+### Some Rules to Pay Attention to
+1. Always use `profiler.dump(finished=False)` if you do not intend to finish dumping to file. Otherwise, calling `profiler.dump()` in the middle of your model may lead to unexpected behaviors; and if you subsequently call `profiler.set_config()`, the program will error out.
+
+2. You can only dump to one file. Do not change the target file by calling `profiler.set_config(filename='new_name.json')` in the middle of your model. This will lead to incomplete dump outputs.
+
 ## Advanced: Using NVIDIA Profiling Tools
 
 MXNet's Profiler is the recommended starting point for profiling MXNet code, but NVIDIA also provides a couple of tools for low-level profiling of CUDA code: [NVProf](https://devblogs.nvidia.com/cuda-pro-tip-nvprof-your-handy-universal-gpu-profiler/), [Visual Profiler](https://developer.nvidia.com/nvidia-visual-profiler) and [Nsight Compute](https://developer.nvidia.com/nsight-compute). You can use these tools to profile all kinds of executables, so they can be used for profiling Python scripts running MXNet. And you can use these in conjunction with the MXNet Profiler to see high-level information from MXNet alongside the low-level CUDA kernel information.

From 7bc3db8e8e2daa2647fb2229a788b935e2a6fe17 Mon Sep 17 00:00:00 2001
From: Zach Kimberg <kimbergz@amazon.com>
Date: Fri, 26 Jul 2019 14:06:24 -0700
Subject: [PATCH 113/813] Bump Scala version to 1.6 (#15660)

Fix API deprecation versions
---
 .../src/main/scala/org/apache/mxnet/NDArray.scala    |  2 +-
 .../scala/org/apache/mxnet/NDArrayCollector.scala    | 12 ++++++------
 scala-package/pom.xml                                |  2 +-
 3 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/scala-package/core/src/main/scala/org/apache/mxnet/NDArray.scala b/scala-package/core/src/main/scala/org/apache/mxnet/NDArray.scala
index d55ed961d7ea..717120bcf984 100644
--- a/scala-package/core/src/main/scala/org/apache/mxnet/NDArray.scala
+++ b/scala-package/core/src/main/scala/org/apache/mxnet/NDArray.scala
@@ -756,7 +756,7 @@ object NDArray extends NDArrayBase {
 class NDArray private[mxnet](private[mxnet] val handle: NDArrayHandle,
                              val writable: Boolean) extends NativeResource {
 
-  @deprecated("Please use ResourceScope instead", "1.6.0")
+  @deprecated("Please use ResourceScope instead", "1.5.0")
   def this(handle: NDArrayHandle,
            writable: Boolean = true,
            addToCollector: Boolean = true) {
diff --git a/scala-package/core/src/main/scala/org/apache/mxnet/NDArrayCollector.scala b/scala-package/core/src/main/scala/org/apache/mxnet/NDArrayCollector.scala
index 5fa0aa5303b6..0761481cdfe8 100644
--- a/scala-package/core/src/main/scala/org/apache/mxnet/NDArrayCollector.scala
+++ b/scala-package/core/src/main/scala/org/apache/mxnet/NDArrayCollector.scala
@@ -64,7 +64,7 @@ import scala.collection.mutable
  *  });
  *  </pre>
  */
-@deprecated("Please use ResourceScope instead", "1.6.0")
+@deprecated("Please use ResourceScope instead", "1.5.0")
 object NDArrayCollector {
   private val logger = LoggerFactory.getLogger(classOf[NDArrayCollector])
 
@@ -76,14 +76,14 @@ object NDArrayCollector {
    * Create a collector which will dispose the collected NDArrays automatically.
    * @return an auto-disposable collector.
    */
-  @deprecated("Please use ResourceScope instead", "1.6.0")
+  @deprecated("Please use ResourceScope instead", "1.5.0")
   def auto(): NDArrayCollector = new NDArrayCollector(true)
 
   /**
    * Create a collector allows users to later dispose the collected NDArray manually.
    * @return a manually-disposable collector.
    */
-  @deprecated("Please use ResourceScope instead", "1.6.0")
+  @deprecated("Please use ResourceScope instead", "1.5.0")
   @Experimental
   def manual(): NDArrayCollector = new NDArrayCollector(false)
 
@@ -91,13 +91,13 @@ object NDArrayCollector {
    * Collect the NDArrays into the collector of the current thread.
    * @param ndArray NDArrays need to be collected.
    */
-  @deprecated("Please use ResourceScope instead", "1.6.0")
+  @deprecated("Please use ResourceScope instead", "1.5.0")
   @varargs def collect(ndArray: NDArray*): Unit = {
     currCollector.get().add(ndArray: _*)
   }
 }
 
-@deprecated("Please use ResourceScope instead", "1.6.0")
+@deprecated("Please use ResourceScope instead", "1.5.0")
 class NDArrayCollector private(private val autoDispose: Boolean = true,
                                private val doCollect: Boolean = true) {
   // native ptr (handle) of the NDArray -> NDArray
@@ -147,7 +147,7 @@ class NDArrayCollector private(private val autoDispose: Boolean = true,
    * @return The result of function <em>codeBlock</em>.
    */
   @Experimental
-  @deprecated("Please use ResourceScope instead", "1.6.0")
+  @deprecated("Please use ResourceScope instead", "1.5.0")
   def withScope[T](codeBlock: => T): T = {
     val old = NDArrayCollector.currCollector.get()
     NDArrayCollector.currCollector.set(this)
diff --git a/scala-package/pom.xml b/scala-package/pom.xml
index 147d7615a380..e1887d7c343b 100644
--- a/scala-package/pom.xml
+++ b/scala-package/pom.xml
@@ -57,7 +57,7 @@
     <scala.version>2.11.8</scala.version>
     <build.platform/>
     <scala.binary.version>2.11</scala.binary.version>
-    <base.revision>1.5.0</base.revision>
+    <base.revision>1.6.0</base.revision>
     <build.platform />
     <cxx>g++</cxx>
     <dollar>$</dollar>

From 6b22aa455e562db0e2744e94dc6a583191081b05 Mon Sep 17 00:00:00 2001
From: Haohuan Wang <haohuanw@umich.edu>
Date: Fri, 26 Jul 2019 18:07:15 -0700
Subject: [PATCH 114/813] handle fix_gamma in tensorrt subgraph conversion
 correctly (#15645)

---
 .../subgraph/tensorrt/nnvm_to_onnx-inl.h      | 21 ++++--
 .../subgraph/tensorrt/nnvm_to_onnx.cc         | 29 ++++++++-
 src/operator/subgraph/tensorrt/tensorrt.cc    |  2 +-
 .../tensorrt/test_tensorrt_batchnorm.py       | 65 +++++++++++++++++++
 4 files changed, 109 insertions(+), 8 deletions(-)
 create mode 100644 tests/python/tensorrt/test_tensorrt_batchnorm.py

diff --git a/src/operator/subgraph/tensorrt/nnvm_to_onnx-inl.h b/src/operator/subgraph/tensorrt/nnvm_to_onnx-inl.h
index edf4d357e922..55b3d938df0a 100644
--- a/src/operator/subgraph/tensorrt/nnvm_to_onnx-inl.h
+++ b/src/operator/subgraph/tensorrt/nnvm_to_onnx-inl.h
@@ -33,6 +33,8 @@
 
 #include <onnx/onnx_pb.h>
 
+#include <unordered_map>
+#include <vector>
 #include <string>
 
 namespace mxnet {
@@ -72,15 +74,12 @@ typedef void (*ConverterFunction)(NodeProto *node_proto,
                                   const nnvm::IndexedGraph &ig,
                                   const array_view<IndexedGraph::NodeEntry> &inputs);
 
-
 // Forward declarations
-void ConvertConvolution(
-                        NodeProto *node_proto,
+void ConvertConvolution(NodeProto *node_proto,
                         const NodeAttrs &attrs,
                         const nnvm::IndexedGraph &ig,
                         const array_view<IndexedGraph::NodeEntry> &inputs);
 
-
 void ConvertPooling(NodeProto *node_proto,
                     const NodeAttrs &attrs,
                     const nnvm::IndexedGraph &ig,
@@ -152,7 +151,7 @@ void ConvertPad(NodeProto* node_proto,
                 const array_view<IndexedGraph::NodeEntry> &inputs);
 
 std::string ConvertNnvmGraphToOnnx(const nnvm::Graph &g,
-    const std::unordered_map<std::string, NDArray>* const params_map);
+    std::unordered_map<std::string, NDArray>* params_map);
 
 static const std::unordered_map<std::string, ConverterFunction> converter_map = {
   {"Activation", ConvertActivation},
@@ -172,6 +171,18 @@ static const std::unordered_map<std::string, ConverterFunction> converter_map =
   {"SoftmaxOutput", ConvertSoftmaxOutput}
 };
 
+typedef void (*PreprocessFunction)(const NodeAttrs &attrs,
+                                   const std::vector<nnvm::NodeEntry> &inputs,
+                                   std::unordered_map<std::string, NDArray> *params_map);
+
+void PreprocessBatchNorm(const NodeAttrs &attrs,
+                         const std::vector<nnvm::NodeEntry> &inputs,
+                         std::unordered_map<std::string, NDArray> *params_map);
+
+static const std::unordered_map<std::string, PreprocessFunction> preprocess_map = {
+  {"BatchNorm", PreprocessBatchNorm}
+};
+
 }  // namespace nnvm_to_onnx
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/subgraph/tensorrt/nnvm_to_onnx.cc b/src/operator/subgraph/tensorrt/nnvm_to_onnx.cc
index 9d98a48c2ec2..6116f296e300 100644
--- a/src/operator/subgraph/tensorrt/nnvm_to_onnx.cc
+++ b/src/operator/subgraph/tensorrt/nnvm_to_onnx.cc
@@ -54,7 +54,7 @@ namespace nnvm_to_onnx {
 
 std::string ConvertNnvmGraphToOnnx(
     const nnvm::Graph& g,
-    const std::unordered_map<std::string, NDArray>* const params_map) {
+    std::unordered_map<std::string, NDArray>* params_map) {
 
   static std::atomic_ulong subgraph_count = { 0 };
 
@@ -88,8 +88,21 @@ std::string ConvertNnvmGraphToOnnx(
   auto placeholder_shapes = GetPlaceholderShapes(shape_inputs, ig);
   auto placeholder_dtypes = GetPlaceholderDTypes(dtype_inputs, ig);
   auto output_lookup = GetOutputLookup(ig);
-  uint32_t current_input = 0;
 
+  for (uint32_t node_idx = 0; node_idx < ig.num_nodes(); ++node_idx) {
+      const IndexedGraph::Node& node = ig[node_idx];
+      const nnvm::Node* source = node.source;
+      // If this is a op
+      if (!source->is_variable()) {
+        auto mightNeedPreprocessNode = preprocess_map.find(source->op()->name);
+        // if this op is defined in preprocess_map
+        if (mightNeedPreprocessNode != preprocess_map.end()) {
+          mightNeedPreprocessNode->second(source->attrs, source->inputs, params_map);
+        }
+      }
+  }
+
+  uint32_t current_input = 0;
   // Can't do a foreach over IndexedGraph since it doesn't implement begin(), etc.
   for (uint32_t node_idx = 0; node_idx < ig.num_nodes(); ++node_idx) {
     const IndexedGraph::Node& node = ig[node_idx];
@@ -642,6 +655,18 @@ void ConvertDropout(NodeProto* node_proto, const NodeAttrs& attrs,
   node_proto->set_op_type("Dropout");
 }
 
+void PreprocessBatchNorm(const NodeAttrs &attrs,
+                         const std::vector<nnvm::NodeEntry> &inputs,
+                         std::unordered_map<std::string, NDArray> *params_map) {
+  const auto& param = nnvm::get<op::BatchNormParam>(attrs.parsed);
+  if (param.fix_gamma) {
+    // if mxnet is specify fix_gamma, we will need to preprocess the params map
+    // to convert the gamma associate with this batch norm layer to 1.
+    std::string gammaNodeName = inputs[batchnorm::kGamma].node->attrs.name;
+    (*params_map)[gammaNodeName] = 1.0f;
+  }
+}
+
 }  // namespace nnvm_to_onnx
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/subgraph/tensorrt/tensorrt.cc b/src/operator/subgraph/tensorrt/tensorrt.cc
index eac4ba7fc6fc..8b64c2a6b6ac 100644
--- a/src/operator/subgraph/tensorrt/tensorrt.cc
+++ b/src/operator/subgraph/tensorrt/tensorrt.cc
@@ -272,7 +272,7 @@ OpStatePtr TRTCreateState(const nnvm::NodeAttrs& attrs, Context ctx,
               << " instead of: " << max_batch_size;
     max_batch_size = in_shape[0][0];
   }
-  const auto& params_map = node_param.params_map;
+  std::unordered_map<std::string, NDArray> params_map = node_param.params_map;
   const auto& inputs_to_idx = node_param.inputs_to_idx;
   const auto& outputs_to_idx = node_param.outputs_to_idx;
   const auto& idx_g = graph.indexed_graph();
diff --git a/tests/python/tensorrt/test_tensorrt_batchnorm.py b/tests/python/tensorrt/test_tensorrt_batchnorm.py
new file mode 100644
index 000000000000..62af3bbf329b
--- /dev/null
+++ b/tests/python/tensorrt/test_tensorrt_batchnorm.py
@@ -0,0 +1,65 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import mxnet as mx
+from mxnet.test_utils import assert_almost_equal
+
+def get_params():
+    arg_params = {}
+    aux_params = {}
+    arg_params["trt_bn_test_conv_weight"] = mx.nd.ones((1, 1, 3, 3))
+    arg_params["trt_bn_test_bn_gamma"] = mx.nd.zeros((1,))
+    arg_params["trt_bn_test_bn_beta"] = mx.nd.zeros((1,))
+    aux_params["trt_bn_test_bn_moving_mean"] = mx.nd.ones(1)
+    aux_params["trt_bn_test_bn_moving_var"] = mx.nd.ones(1)
+    return arg_params, aux_params
+
+def get_symbol():
+    data = mx.sym.Variable("data")
+    conv = mx.sym.Convolution(data=data, kernel=(3,3), no_bias=True, num_filter=1, num_group=1,
+                              name="trt_bn_test_conv")
+    bn = mx.sym.BatchNorm(data=conv, fix_gamma=True, use_global_stats=False, name="trt_bn_test_bn")
+    return bn
+
+def test_batch_norm_runs_correctly_with_fix_gamma():
+    arg_params, aux_params = get_params()
+    arg_params_trt, aux_params_trt = get_params()
+
+    sym = get_symbol()
+    sym_trt = get_symbol().get_backend_symbol("TensorRT")
+
+    mx.contrib.tensorrt.init_tensorrt_params(sym_trt, arg_params_trt, aux_params_trt)
+
+    executor = sym.simple_bind(ctx=mx.gpu(), data=(1, 1, 3, 3), grad_req='null', force_rebind=True)
+    executor.copy_params_from(arg_params, aux_params)
+
+    executor_trt = sym_trt.simple_bind(ctx=mx.gpu(), data=(1, 1, 3, 3), grad_req='null',
+                                  force_rebind=True)
+    executor_trt.copy_params_from(arg_params_trt, aux_params_trt)
+
+    input_data = mx.nd.random.uniform(low=0, high=1, shape=(1, 1, 3, 3))
+
+    y = executor.forward(is_train=False, data=input_data)
+    y_trt = executor_trt.forward(is_train=False, data=input_data)
+
+    print(y[0].asnumpy())
+    print(y_trt[0].asnumpy())
+    assert_almost_equal(y[0].asnumpy(), y_trt[0].asnumpy(), 1e-4, 1e-4)
+
+if __name__ == '__main__':
+    import nose
+    nose.runmodule()

From 08fd98d54bf342a1293ba17444b22118dc323099 Mon Sep 17 00:00:00 2001
From: Lai Wei <royweilai@gmail.com>
Date: Sat, 27 Jul 2019 11:33:49 -0700
Subject: [PATCH 115/813] update website for 1.5.0 (#15641)

* update website

* update description

* update cuda
---
 docs/_static/js/options.js          |   2 +-
 docs/_static/mxnet-theme/index.html |   6 +-
 docs/install/download.md            |   1 +
 docs/install/index.md               | 104 +++++++++++++++++++---------
 4 files changed, 76 insertions(+), 37 deletions(-)

diff --git a/docs/_static/js/options.js b/docs/_static/js/options.js
index ca4ac363dc41..d400d1ce2c9f 100644
--- a/docs/_static/js/options.js
+++ b/docs/_static/js/options.js
@@ -19,7 +19,7 @@
  */
 
 /* Installation page display functions for install selector */
-var versionSelect   = defaultVersion = 'v1.4.1';
+var versionSelect   = defaultVersion = 'v1.5.0';
 var platformSelect    = 'Linux';
 var languageSelect  = 'Python';
 var processorSelect = 'CPU';
diff --git a/docs/_static/mxnet-theme/index.html b/docs/_static/mxnet-theme/index.html
index a5f0bed636e9..d9311d7ae39f 100644
--- a/docs/_static/mxnet-theme/index.html
+++ b/docs/_static/mxnet-theme/index.html
@@ -23,9 +23,9 @@
   <div class="container">
     <div class="row">
       <div class="col-lg-4 col-sm-12">
-        <h3>MXNet 1.4.1 Released</h3>
-        <p>This patch release features bug fixes and performance improvements.</p>
-        <a href="https://github.com/apache/incubator-mxnet/releases/tag/1.4.1">Learn More</a>
+        <h3>MXNet 1.5.0 Released</h3>
+        <p>This release features Automatic Mixed Precision, MKL-DNN updates, CUDA10.1 support and more. </p>
+        <a href="https://github.com/apache/incubator-mxnet/releases/tag/1.5.0">Learn More</a>
       </div>
       <div class="col-lg-4 col-sm-12">
         <h3>A 60-minute Gluon Crash Course</h3>
diff --git a/docs/install/download.md b/docs/install/download.md
index 808b4b8a72e5..ead73e5a18c2 100644
--- a/docs/install/download.md
+++ b/docs/install/download.md
@@ -21,6 +21,7 @@ These source archives are generated from tagged releases. Updates and patches wi
 
 | Version | Source                                                                                                      | PGP                                                                                                             | SHA                                                                                                                |
 |---------|-------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------|
+| 1.5.0   | [Download](https://apache.org/dist/incubator/mxnet/1.5.0/apache-mxnet-src-1.5.0-incubating.tar.gz)                 | [Download](https://apache.org/dist/incubator/mxnet/1.5.0/apache-mxnet-src-1.5.0-incubating.tar.gz.asc)    |  [Download](https://apache.org/dist/incubator/mxnet/1.5.0/apache-mxnet-src-1.5.0-incubating.tar.gz.sha512)     |
 | 1.4.1   | [Download](https://www.apache.org/dyn/closer.cgi/incubator/mxnet/1.4.1/apache-mxnet-src-1.4.1-incubating.tar.gz)   | [Download](https://apache.org/dist/incubator/mxnet/1.4.1/apache-mxnet-src-1.4.1-incubating.tar.gz.asc)    | [Download](https://apache.org/dist/incubator/mxnet/1.4.1/apache-mxnet-src-1.4.1-incubating.tar.gz.sha512)      |
 | 1.4.0   | [Download](https://www.apache.org/dyn/closer.cgi/incubator/mxnet/1.4.0/apache-mxnet-src-1.4.0-incubating.tar.gz)   | [Download](https://apache.org/dist/incubator/mxnet/1.4.0/apache-mxnet-src-1.4.0-incubating.tar.gz.asc)    | [Download](https://apache.org/dist/incubator/mxnet/1.4.0/apache-mxnet-src-1.4.0-incubating.tar.gz.sha512)      |
 | 1.3.1   | [Download](https://www.apache.org/dyn/closer.cgi/incubator/mxnet/1.3.1/apache-mxnet-src-1.3.1-incubating.tar.gz)   | [Download](https://apache.org/dist/incubator/mxnet/1.3.1/apache-mxnet-src-1.3.1-incubating.tar.gz.asc)    | [Download](https://apache.org/dist/incubator/mxnet/1.3.1/apache-mxnet-src-1.3.1-incubating.tar.gz.sha512)      |
diff --git a/docs/install/index.md b/docs/install/index.md
index ac7bd048a588..4217780c4b1c 100644
--- a/docs/install/index.md
+++ b/docs/install/index.md
@@ -38,10 +38,11 @@
 Indicate your preferred configuration. Then, follow the customized commands to install MXNet.
 
 <div class="dropdown">
-  <button class="btn current-version btn-primary dropdown-toggle" type="button" data-toggle="dropdown">v1.4.1
+  <button class="btn current-version btn-primary dropdown-toggle" type="button" data-toggle="dropdown">v1.5.0
   <span class="caret"></span></button>
   <ul class="dropdown-menu opt-group">
-    <li class="opt active versions"><a href="#">v1.4.1</a></li>
+    <li class="opt active versions"><a href="#">v1.5.0</a></li>
+    <li class="opt versions"><a href="#">v1.4.1</a></li>
     <li class="opt versions"><a href="#">v1.3.1</a></li>
     <li class="opt versions"><a href="#">v1.2.1</a></li>
     <li class="opt versions"><a href="#">v1.1.0</a></li>
@@ -117,22 +118,32 @@ Indicate your preferred configuration. Then, follow the customized commands to i
 <div class="python">
 <div class="cpu">
 <div class="pip">
-<div class="v1-4-1">
+<div class="v1-5-0">
+
+```
+$ pip install mxnet
+```
 
 MKL-DNN enabled pip packages are optimized for Intel hardware. You can find performance numbers in the <a href="http://mxnet.io/faq/perf.html#intel-cpu">MXNet tuning guide</a>.
 
 ```
-$ pip install mxnet
+$ pip install mxnet-mkl
 ```
 
-</div> <!-- End of v1-4-1 -->
-<div class="v1-4-0">
+</div> <!-- End of v1-5-0 -->
+<div class="v1-4-1">
+
+```
+$ pip install mxnet==1.4.1
+```
+
+MKL-DNN enabled pip packages are optimized for Intel hardware. You can find performance numbers in the <a href="http://mxnet.io/faq/perf.html#intel-cpu">MXNet tuning guide</a>.
 
 ```
-$ pip install mxnet==1.4.0
+$ pip install mxnet-mkl==1.4.1
 ```
 
-</div> <!-- End of v1-4-0 -->
+</div> <!-- End of v1-4-1 -->
 <div class="v1-3-1">
 
 ```
@@ -220,7 +231,7 @@ Check the chart below for other options, refer to <a href="https://pypi.org/proj
 
 **NOTES:**
 
-*mxnet-cu92mkl* means the package is built with CUDA/cuDNN and MKL-DNN enabled and the CUDA version is 9.2.
+*mxnet-cu101mkl* means the package is built with CUDA/cuDNN and MKL-DNN enabled and the CUDA version is 10.1.
 
 All MKL pip packages are experimental prior to version 1.3.0.
 
@@ -285,17 +296,24 @@ To build from source, refer to the <a href="ubuntu_setup.html">MXNet Ubuntu inst
 
 <div class="gpu">
 <div class="pip">
-<div class="v1-4-0">
+<div class="v1-5-0">
+
+```
+$ pip install mxnet-cu101
+```
+
+</div> <!-- End of v1-5-0 -->
+<div class="v1-4-1">
 
 ```
-$ pip install mxnet-cu92
+$ pip install mxnet-cu101==1.4.1
 ```
 
-</div> <!-- End of v1-4-0 -->
+</div> <!-- End of v1-4-1 -->
 <div class="v1-3-1">
 
 ```
-$ pip install mxnet==1.3.1
+$ pip install mxnet-cu92==1.3.1
 ```
 
 </div> <!-- End of v1-3-1-->
@@ -342,7 +360,7 @@ $ pip install mxnet-cu80==0.11.0
 <div class="master">
 
 ```
-$ pip install mxnet-cu92 --pre
+$ pip install mxnet-cu101 --pre
 ```
 
 </div> <!-- End of master-->
@@ -354,7 +372,7 @@ Check the chart below for other options, refer to <a href="https://pypi.org/proj
 
 **NOTES:**
 
-*mxnet-cu92mkl* means the package is built with CUDA/cuDNN and MKL-DNN enabled and the CUDA version is 9.2.
+*mxnet-cu101mkl* means the package is built with CUDA/cuDNN and MKL-DNN enabled and the CUDA version is 10.1.
 
 All MKL pip packages are experimental prior to version 1.3.0.
 
@@ -531,13 +549,13 @@ You can use the Maven packages defined in the following dependency to include MX
 <br/>
 You can use the Maven packages defined in the following dependency to include MXNet in your Java project. The Java API is provided as a subset of the Scala API and is intended for inference only. Please refer to the <a href="java_setup.html">MXNet-Java setup guide</a> for a detailed set of instructions to help you with the setup process.
 
-<a href="https://repository.apache.org/#nexus-search;gav~org.apache.mxnet~~1.4.1~~"><img src="https://img.shields.io/badge/org.apache.mxnet-linux gpu-green.svg" alt="maven badge"/></a>
+<a href="https://repository.apache.org/#nexus-search;gav~org.apache.mxnet~~1.5.0~~"><img src="https://img.shields.io/badge/org.apache.mxnet-linux gpu-green.svg" alt="maven badge"/></a>
 
 ```html
 <dependency>
     <groupId>org.apache.mxnet</groupId>
     <artifactId>mxnet-full_2.11-linux-x86_64-gpu</artifactId>
-    <version>[1.4.1, )</version>
+    <version>[1.5.0, )</version>
 </dependency>
 ```
 
@@ -548,13 +566,13 @@ You can use the Maven packages defined in the following dependency to include MX
 <br/>
 You can use the Maven packages defined in the following dependency to include MXNet in your Java project. The Java API is provided as a subset of the Scala API and is intended for inference only. Please refer to the <a href="java_setup.html">MXNet-Java setup guide</a> for a detailed set of instructions to help you with the setup process.
 
-<a href="https://repository.apache.org/#nexus-search;gav~org.apache.mxnet~~1.4.1~~"><img src="https://img.shields.io/badge/org.apache.mxnet-linux cpu-green.svg" alt="maven badge"/></a>
+<a href="https://repository.apache.org/#nexus-search;gav~org.apache.mxnet~~1.5.0~~"><img src="https://img.shields.io/badge/org.apache.mxnet-linux cpu-green.svg" alt="maven badge"/></a>
 
 ```html
 <dependency>
     <groupId>org.apache.mxnet</groupId>
     <artifactId>mxnet-full_2.11-linux-x86_64-cpu</artifactId>
-    <version>[1.4.1, )</version>
+    <version>[1.5.0, )</version>
 </dependency>
 ```
 <br>
@@ -599,12 +617,18 @@ Refer to the <a href="ubuntu_setup.html#install-the-mxnet-package-for-perl">Perl
 <div class="python">
 <div class="cpu">
 <div class="pip">
-<div class="v1-4-0">
+<div class="v1-5-0">
 
 ```
 $ pip install mxnet
 ```
-</div> <!-- End of v1-4-0 -->
+</div> <!-- End of v1-5-0 -->
+<div class="v1-4-1">
+
+```
+$ pip install mxnet==1.4.1
+```
+</div> <!-- End of v1-4-1 -->
 <div class="v1-3-1">
 
 ```
@@ -670,7 +694,7 @@ Check the chart below for other options, refer to <a href="https://pypi.org/proj
 
 **NOTES:**
 
-*mxnet-cu92mkl* means the package is built with CUDA/cuDNN and MKL-DNN enabled and the CUDA version is 9.2.
+*mxnet-cu101mkl* means the package is built with CUDA/cuDNN and MKL-DNN enabled and the CUDA version is 10.1.
 
 All MKL pip packages are experimental prior to version 1.3.0.
 
@@ -839,13 +863,13 @@ Not available at this time. <br>
 </br>
 You can use the Maven packages defined in the following dependency to include MXNet in your Java project. The Java API is provided as a subset of the Scala API and is intended for inference only. Please refer to the <a href="java_setup.html">MXNet-Java setup guide</a> for a detailed set of instructions to help you with the setup process.
 
-<a href="https://repository.apache.org/#nexus-search;gav~org.apache.mxnet~~1.4.1~~"><img src="https://img.shields.io/badge/org.apache.mxnet-mac cpu-green.svg" alt="maven badge"/></a>
+<a href="https://repository.apache.org/#nexus-search;gav~org.apache.mxnet~~1.5.0~~"><img src="https://img.shields.io/badge/org.apache.mxnet-mac cpu-green.svg" alt="maven badge"/></a>
 
 ```html
 <dependency>
     <groupId>org.apache.mxnet</groupId>
     <artifactId>mxnet-full_2.11-linux-x86_64-cpu</artifactId>
-    <version>[1.4.1, )</version>
+    <version>[1.5.0, )</version>
 </dependency>
 ```
 <br>
@@ -892,13 +916,20 @@ For more installation options, refer to the <a href="osx_setup.html">MXNet macOS
 <div class="python">
 <div class="cpu">
 <div class="pip">
-<div class="v1-4-0">
+<div class="v1-5-0">
 
 ```
 $ pip install mxnet
 ```
 
-</div> <!-- End of v1-4-0 -->
+</div> <!-- End of v1-5-0 -->
+<div class="v1-4-1">
+
+```
+$ pip install mxnet==1.4.1
+```
+
+</div> <!-- End of v1-4-1 -->
 <div class="v1-3-1">
 
 ```
@@ -961,7 +992,7 @@ Check the chart below for other options, refer to <a href="https://pypi.org/proj
 
 **NOTES:**
 
-*mxnet-cu92mkl* means the package is built with CUDA/cuDNN and MKL-DNN enabled and the CUDA version is 9.2.
+*mxnet-cu101mkl* means the package is built with CUDA/cuDNN and MKL-DNN enabled and the CUDA version is 10.1.
 
 All MKL pip packages are experimental prior to version 1.3.0.
 
@@ -1023,17 +1054,24 @@ Refer to the <a href="windows_setup.html">MXNet Windows installation guide</a>
 
 <div class="gpu">
 <div class="pip">
-<div class="v1-4-0">
+<div class="v1-5-0">
 
 ```
-$ pip install mxnet-cu92
+$ pip install mxnet-cu101
 ```
 
-</div> <!-- End of v1-4-0 -->
+</div> <!-- End of v1-5-0 -->
+<div class="v1-4-1">
+
+```
+$ pip install mxnet-cu101==1.4.1
+```
+
+</div> <!-- End of v1-4-1 -->
 <div class="v1-3-1">
 
 ```
-$ pip install mxnet==1.3.1
+$ pip install mxnet-cu92==1.3.1
 ```
 
 </div> <!-- End of v1-3-1 -->
@@ -1080,7 +1118,7 @@ $ pip install mxnet-cu80==0.11.0
 <div class="master">
 
 ```
-$ pip install mxnet-cu92 --pre
+$ pip install mxnet-cu101 --pre
 ```
 
 </div> <!-- End of master-->
@@ -1092,7 +1130,7 @@ Check the chart below for other options, refer to <a href="https://pypi.org/proj
 
 **NOTES:**
 
-*mxnet-cu92mkl* means the package is built with CUDA/cuDNN and MKL-DNN enabled and the CUDA version is 9.2.
+*mxnet-cu101mkl* means the package is built with CUDA/cuDNN and MKL-DNN enabled and the CUDA version is 10.1.
 
 All MKL pip packages are experimental prior to version 1.3.0.
 

From 3ececb332c992e8decc7a05d3d75fa7f00d9aaea Mon Sep 17 00:00:00 2001
From: Sheng Zha <szha@users.noreply.github.com>
Date: Sat, 27 Jul 2019 19:33:37 -0700
Subject: [PATCH 116/813] in-place reshape ops (#14053)

* in-place reshape ops

* add inplace option

* add dummy arguments to symbol
---
 python/mxnet/ndarray/ndarray.py       | 126 +++++++++++++++++++++++---
 python/mxnet/symbol/symbol.py         |  12 +--
 tests/python/unittest/test_ndarray.py |  59 ++++++++++++
 3 files changed, 176 insertions(+), 21 deletions(-)

diff --git a/python/mxnet/ndarray/ndarray.py b/python/mxnet/ndarray/ndarray.py
index 3fb1af6a7336..53c563854511 100644
--- a/python/mxnet/ndarray/ndarray.py
+++ b/python/mxnet/ndarray/ndarray.py
@@ -1259,13 +1259,38 @@ def sign(self, *args, **kwargs):
         """
         return op.sign(self, *args, **kwargs)
 
-    def flatten(self, *args, **kwargs):
-        """Convenience fluent method for :py:func:`flatten`.
+    def flatten(self, inplace=False):
+        """Flatten this array without altering any data.
 
-        The arguments are the same as for :py:func:`flatten`, with
-        this array as data.
+        Parameters
+        ----------
+        inplace : bool, default False
+            If True, this method returns a **view** of this array
+            that shares data with this array. Otherwise, a copy is returned.
+
+        Returns
+        -------
+        NDArray
+            An array with flattened shape `(d1, d2*...*dk)` that shares data with
+            this array with shape `(d1, d2, ..., dk)`.
+
+        Examples
+        --------
+        >>> x = mx.nd.arange(30).reshape(5,2,3)
+        >>> y = x.flatten(inplace=True)
+        >>> z = x.flatten()
+        >>> y.shape
+        (5, 6)
+        >>> y[0].asnumpy()
+        array([0., 1., 2., 3., 4., 5.], dtype=float32)
+        >>> y[:] = -1
+        >>> x[0].asnumpy()
+        array([[-1., -1., -1.],
+               [-1., -1., -1.]], dtype=float32)
+        >>> z[0].asnumpy()
+        array([0., 1., 2., 3., 4., 5.], dtype=float32)
         """
-        return op.flatten(self, *args, **kwargs)
+        return op.flatten(self) if not inplace else self.reshape((0, -1))
 
     def shape_array(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`shape_array`.
@@ -1283,13 +1308,52 @@ def size_array(self, *args, **kwargs):
         """
         return op.size_array(self, *args, **kwargs)
 
-    def expand_dims(self, *args, **kwargs):
-        """Convenience fluent method for :py:func:`expand_dims`.
+    def expand_dims(self, axis, inplace=False):
+        """Adds an additional dimension to the current array without altering any data.
 
-        The arguments are the same as for :py:func:`expand_dims`, with
-        this array as data.
+        Parameters
+        ----------
+        axis : int
+            Position where new axis is to be inserted.
+            Suppose that the input NDArray's dimension is ndim,
+            the range of the inserted axis is [-ndim, ndim].
+        inplace : bool, default False
+            If True, this method returns a **view** of this array
+            that shares data with this array. Otherwise, a copy is returned.
+
+        Returns
+        -------
+        NDArray
+            An array with expanded shape `(d1, d2, ..., 1, di, ..., dk)`
+            that shares data with this array with shape `(d1, d2, ..., dk)`,
+            given input axis `i`.
+
+        Examples
+        --------
+        >>> x = mx.nd.arange(6).reshape(2,3)
+        >>> y = x.expand_dims(1, inplace=True)
+        >>> z = x.expand_dims(1)
+        >>> y.shape
+        (2, 1, 3)
+        >>> y[0].asnumpy()
+        array([[0., 1., 2.]], dtype=float32)
+        >>> y[:] = -1
+        >>> x.asnumpy()
+        array([[-1., -1., -1.],
+               [-1., -1., -1.]], dtype=float32)
+        >>> z[0].asnumpy()
+        array([[0., 1., 2.]], dtype=float32)
         """
-        return op.expand_dims(self, *args, **kwargs)
+        if not inplace:
+            return op.expand_dims(self, axis=axis)
+        else:
+            new_shape = list(self.shape)
+            assert -len(new_shape)-1 <= axis <= len(new_shape), \
+                    "axis {} is out of range for {}d array".format(axis, len(new_shape))
+            if axis < 0:
+                axis += len(new_shape) + 1
+            new_shape.insert(axis, 1)
+            return self.reshape(new_shape)
 
     def tile(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`tile`.
@@ -1699,13 +1763,45 @@ def softmin(self, *args, **kwargs):
         """
         return op.softmin(self, *args, **kwargs)
 
-    def squeeze(self, *args, **kwargs):
-        """Convenience fluent method for :py:func:`squeeze`.
+    def squeeze(self, axis=None, inplace=False):
+        """Remove dimensions with size 1 from this array without altering any data.
 
-        The arguments are the same as for :py:func:`squeeze`, with
-        this array as data.
+        Parameters
+        ----------
+        axis : int, tuple of int, or None
+            Selects a subset of the single-dimensional entries in the shape.
+            If an axis is selected with shape entry greater than one, an error is raised.
+        inplace : bool, default False
+            If True, this method returns a **view** of this array
+            that shares data with this array. Otherwise, a copy is returned.
         """
-        return op.squeeze(self, *args, **kwargs)
+        if not inplace:
+            return op.squeeze(self, axis=axis)
+        else:
+            new_shape = list(self.shape)
+            axes = axis # rename variable for readability
+            if isinstance(axes, int):
+                axes = [axes]
+            if axes:
+                assert len(axes) == len(set(axes)), \
+                    "axis {} contains duplicate which is not allowed.".format(axes)
+                resolved_axes = [i if i >= 0 else i+len(self.shape) for i in axes]
+                for arg_axis, actual_axis in zip(axes, resolved_axes):
+                    assert -len(new_shape) <= arg_axis < len(new_shape), \
+                        "axis {} is out of range for {}d array".format(arg_axis, len(new_shape))
+                    axis_size = new_shape[actual_axis]
+                    assert axis_size == 1, \
+                        "Squeeze target axis {} must be size 1, got {}.".format(arg_axis, axis_size)
+                for i in sorted(resolved_axes, reverse=True):
+                    del new_shape[i]
+            else:
+                for i in reversed(range(len(new_shape))):
+                    if new_shape[i] == 1:
+                        del new_shape[i]
+            if not new_shape:
+                new_shape.append(1)
+
+            return self.reshape(new_shape)
 
     # pylint: disable= undefined-variable
     def broadcast_to(self, shape):
diff --git a/python/mxnet/symbol/symbol.py b/python/mxnet/symbol/symbol.py
index d3cd519b9a8c..deedf0fe83d2 100644
--- a/python/mxnet/symbol/symbol.py
+++ b/python/mxnet/symbol/symbol.py
@@ -2083,13 +2083,13 @@ def sign(self, *args, **kwargs):
         """
         return op.sign(self, *args, **kwargs)
 
-    def flatten(self, *args, **kwargs):
+    def flatten(self, inplace=False, **kwargs): # pylint: disable=unused-argument
         """Convenience fluent method for :py:func:`flatten`.
 
         The arguments are the same as for :py:func:`flatten`, with
         this array as data.
         """
-        return op.flatten(self, *args, **kwargs)
+        return op.flatten(self, **kwargs)
 
     def shape_array(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`shape_array`.
@@ -2107,13 +2107,13 @@ def size_array(self, *args, **kwargs):
         """
         return op.size_array(self, *args, **kwargs)
 
-    def expand_dims(self, *args, **kwargs):
+    def expand_dims(self, axis, inplace=False, **kwargs): # pylint: disable=unused-argument
         """Convenience fluent method for :py:func:`expand_dims`.
 
         The arguments are the same as for :py:func:`expand_dims`, with
         this array as data.
         """
-        return op.expand_dims(self, *args, **kwargs)
+        return op.expand_dims(self, axis=axis, **kwargs)
 
     def broadcast_to(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`broadcast_to`.
@@ -2539,13 +2539,13 @@ def softmin(self, *args, **kwargs):
         """
         return op.softmin(self, *args, **kwargs)
 
-    def squeeze(self, *args, **kwargs):
+    def squeeze(self, axis, inplace=False, **kwargs): # pylint: disable=unused-argument
         """Convenience fluent method for :py:func:`squeeze`.
 
         The arguments are the same as for :py:func:`squeeze`, with
         this array as data.
         """
-        return op.squeeze(self, *args, **kwargs)
+        return op.squeeze(self, axis=axis, **kwargs)
 
     def get_backend_symbol(self, backend):
         """Return symbol for target backend.
diff --git a/tests/python/unittest/test_ndarray.py b/tests/python/unittest/test_ndarray.py
index f40bb3053358..56db1ebd640d 100644
--- a/tests/python/unittest/test_ndarray.py
+++ b/tests/python/unittest/test_ndarray.py
@@ -193,6 +193,65 @@ def test_ndarray_reshape():
     assert same(tensor.reshape(-1, 0, reverse=True).asnumpy(), true_res.reshape(6, 5).asnumpy())
 
 
+@with_seed()
+def test_ndarray_flatten():
+    tensor = (mx.nd.arange(30) + 1).reshape(2, 3, 5)
+    copy = tensor.flatten()
+    ref = tensor.flatten(inplace=True)
+    assert same(copy.asnumpy(), tensor.reshape(2, 15).asnumpy())
+    assert same(ref.asnumpy(), tensor.reshape(2, 15).asnumpy())
+
+    tensor[0] = -1
+    assert not same(copy.asnumpy(), tensor.reshape(2, 15).asnumpy())
+    assert same(ref.asnumpy(), tensor.reshape(2, 15).asnumpy())
+
+
+@with_seed()
+def test_ndarray_squeeze():
+    def check_squeeze(shape, axis=None):
+        data = mx.random.uniform(low=-10.0, high=10.0, shape=shape)
+        copy = data.squeeze(axis=axis)
+        ref = data.squeeze(axis=axis, inplace=True)
+        out_expected = np.squeeze(data.asnumpy(), axis=axis)
+        if copy.shape == (1,):  # as an exception (1, 1, 1) will be squeezed to (1,)
+            out_expected = np.squeeze(data.asnumpy(), axis=tuple([i for i in range(1, len(shape))]))
+        assert same(copy.asnumpy(), out_expected)
+        assert same(ref.asnumpy(), out_expected)
+        data[0][0] = -1
+        assert same(copy.asnumpy(), out_expected)
+        assert not same(ref.asnumpy(), out_expected)
+
+    # check forward
+    check_squeeze((1, 5, 1, 3, 1), 0)
+    check_squeeze((1, 5, 1, 3, 1), 2)
+    check_squeeze((1, 5, 1, 3, 1), 4)
+    check_squeeze((1, 5, 1, 3, 1), (0, 4))
+    check_squeeze((1, 5, 1, 3, 1), (0, 2, 4))
+    check_squeeze((1, 5, 1, 3, 1), -5)
+    check_squeeze((1, 5, 1, 3, 1), -3)
+    check_squeeze((1, 5, 1, 3, 1), -1)
+    check_squeeze((1, 5, 1, 3, 1), (0, 4))
+    check_squeeze((1, 5, 1, 3, 1), (0, 2, 4))
+    check_squeeze((1, 5, 1, 3, 1))
+    check_squeeze((1, 1, 1, 1))
+
+
+@with_seed()
+def test_ndarray_expand_dims():
+    for ndim in range(1, 6):
+        for axis in range(-ndim-1, ndim+1):
+            shape = list(np.random.randint(1, 10, size=ndim))
+            data = mx.random.normal(shape=shape)
+            copy = data.expand_dims(axis=axis)
+            ref = data.expand_dims(axis=axis, inplace=True)
+            out_expected = np.expand_dims(data.asnumpy(), axis=axis)
+            assert same(copy.asnumpy(), out_expected)
+            assert same(ref.asnumpy(), out_expected), (shape, axis, ref.asnumpy().shape, out_expected.shape)
+            data[0] = -1
+            assert same(copy.asnumpy(), out_expected)
+            assert not same(ref.asnumpy(), out_expected)
+
+
 @with_seed()
 def test_ndarray_choose():
     shape = (100, 20)

From 33ea2dbe4cb179ab3f3a76cfd9dcfb0930887ed1 Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Sat, 27 Jul 2019 21:30:01 -0700
Subject: [PATCH 117/813] [OpPerf] PDF Random ops fix (#15661)

* fix pdf sample for opperf benchmark

* change sample array

* Trigger notification
---
 benchmark/opperf/README.md               | 2 +-
 benchmark/opperf/rules/default_params.py | 4 +++-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/benchmark/opperf/README.md b/benchmark/opperf/README.md
index e6a367d38f28..7e708d3cbe1d 100644
--- a/benchmark/opperf/README.md
+++ b/benchmark/opperf/README.md
@@ -75,7 +75,7 @@ For example, you want to run benchmarks for all NDArray Broadcast Binary Operato
 
 ```
 #!/usr/bin/python
-from benchmark.opperf.tensor_operations.binary_broadcast_operators import run_mx_binary_broadcast_operators_benchmarks
+from benchmark.opperf.nd_operations.binary_broadcast_operators import run_mx_binary_broadcast_operators_benchmarks
 
 # Run all Binary Broadcast operations benchmarks with default input values
 print(run_mx_binary_broadcast_operators_benchmarks())
diff --git a/benchmark/opperf/rules/default_params.py b/benchmark/opperf/rules/default_params.py
index b16cd5429f59..322fde2fbc8c 100644
--- a/benchmark/opperf/rules/default_params.py
+++ b/benchmark/opperf/rules/default_params.py
@@ -34,6 +34,7 @@
 
 # For operators like - random_uniform, random_normal etc..
 DEFAULT_SHAPE = [(1024, 1024), (10000, 1), (10000, 100)]
+DEFAULT_SAMPLE = [(2,)]
 DEFAULT_LOW = [0]
 DEFAULT_HIGH = [5]
 DEFAULT_K = [1]
@@ -73,6 +74,7 @@
 
 # Default Inputs. MXNet Op Param Name to Default Input mapping
 DEFAULTS_INPUTS = {"data": DEFAULT_DATA,
+                   "sample": DEFAULT_SAMPLE,
                    "lhs": DEFAULT_LHS,
                    "rhs": DEFAULT_RHS,
                    "shape": DEFAULT_SHAPE,
@@ -101,6 +103,6 @@
 # given as NDArray and translate users inputs such as a shape tuple, Numpy Array or
 # a list to MXNet NDArray. This is just a convenience added so benchmark utility users
 # can just say shape of the tensor, and we automatically create Tensors.
-PARAMS_OF_TYPE_NDARRAY = ["lhs", "rhs", "data", "base", "exp",
+PARAMS_OF_TYPE_NDARRAY = ["lhs", "rhs", "data", "base", "exp", "sample",
                           "mu", "sigma", "lam", "alpha", "beta", "gamma", "k", "p",
                           "low", "high", "weight", "bias", "moving_mean", "moving_var"]

From 3d366a3aa24c2aabc8b67b82d2b834844449d1f7 Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Sat, 27 Jul 2019 21:31:31 -0700
Subject: [PATCH 118/813] [Opperf] Add  optimizer update operator benchmarks to
 opperf (#15522)

* optimizer for opperf benchmark

* Trigger notification

* missed function call

* added params

* minor typos

* Trigger notification

* resolve default params

* temp remove multi op

* take care of #15643

* numbering typo
---
 benchmark/opperf/nd_operations/README.md      | 13 +---
 .../nd_operations/nn_optimizer_operators.py   | 64 +++++++++++++++++++
 benchmark/opperf/opperf.py                    |  3 +
 benchmark/opperf/rules/default_params.py      | 54 +++++++++++++++-
 benchmark/opperf/utils/op_registry_utils.py   | 21 ++++++
 5 files changed, 142 insertions(+), 13 deletions(-)
 create mode 100644 benchmark/opperf/nd_operations/nn_optimizer_operators.py

diff --git a/benchmark/opperf/nd_operations/README.md b/benchmark/opperf/nd_operations/README.md
index 321158c48399..95958662ae8c 100644
--- a/benchmark/opperf/nd_operations/README.md
+++ b/benchmark/opperf/nd_operations/README.md
@@ -28,9 +28,7 @@
 6. reshape
 7. one_hot
 8. linalg_potri
-9. mp_sgd_update
 10. multi_sgd_update
-11. signum_update
 12. Convolution_v1
 13. repeat
 14. Custom
@@ -38,7 +36,6 @@
 16. SwapAxis
 17. norm
 18. Softmax
-19. rmspropalex_update
 20. fill_element_0index
 21. cast
 22. UpSampling
@@ -52,7 +49,6 @@
 30. Activation
 31. LinearRegressionOutput
 32. Pooling_v1
-33. ftml_update
 34. Crop
 35. ElementWiseSum
 36. diag
@@ -60,24 +56,20 @@
 38. Pad
 39. linalg_gemm2
 40. crop
-41. rmsprop_update
 43. RNN
 45. SoftmaxOutput
 46. linalg_extractdiag
-47. sgd_mom_update
 48. SequenceLast
 51. SequenceReverse
 53. SVMOutput
 54. linalg_trsm
 55. where
 56. SoftmaxActivation
-57. signsgd_update
 58. slice
 59. linalg_gelqf
 60. softmin
 61. linalg_gemm
 62. BilinearSampler
-63. mp_sgd_mom_update
 64. choose_element_0index
 65. tile
 67. gather_nd
@@ -110,7 +102,6 @@
 98. linalg_syrk
 99. squeeze
 101. ROIPooling
-102. ftrl_update
 103. SliceChannel
 104. slice_like
 106. linalg_maketrian
@@ -127,6 +118,4 @@
 119. normal
 120. take
 121. MakeLoss
-122. sgd_update
-123. adam_update
-124. concat
\ No newline at end of file
+124. concat
diff --git a/benchmark/opperf/nd_operations/nn_optimizer_operators.py b/benchmark/opperf/nd_operations/nn_optimizer_operators.py
new file mode 100644
index 000000000000..130ab85300dc
--- /dev/null
+++ b/benchmark/opperf/nd_operations/nn_optimizer_operators.py
@@ -0,0 +1,64 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import mxnet as mx
+from benchmark.opperf.utils.benchmark_utils import run_op_benchmarks
+from benchmark.opperf.utils.op_registry_utils import get_all_optimizer_operators
+
+"""Performance benchmark tests for MXNet Neural Network Optimizer Update Operators.
+
+1. Stochastic Gradient Descent (SGD)
+    1.1 mp_sgd_update
+    1.2 sgd_mom_update
+    1.3 signsgd_update
+    1.4 mp_sgd_mom_update
+    1.5 sgd_update
+2. signum_update
+3. rmspropalex_update
+4. ftml_update
+5. rmsprop_update
+6. ftrl_update
+7. adam_update
+"""
+
+
+def run_optimizer_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, runs=100):
+    """Runs benchmarks with the given context and precision (dtype) for all the neural network
+    optimizer update operators in MXNet.
+
+    Parameters
+    ----------
+    ctx: mx.ctx
+        Context to run benchmarks
+    dtype: str, default 'float32'
+        Precision to use for benchmarks
+    warmup: int, default 25
+        Number of times to run for warmup
+    runs: int, default 100
+        Number of runs to capture benchmark results
+
+    Returns
+    -------
+    Dictionary of results. Key -> Name of the operator, Value -> Benchmark results.
+
+    """
+    # Fetch all optimizer operators
+    mx_optimizer_ops = get_all_optimizer_operators()
+
+    # Run benchmarks
+    mx_optimizer_op_results = run_op_benchmarks(mx_optimizer_ops, dtype, ctx, warmup, runs)
+    return mx_optimizer_op_results
diff --git a/benchmark/opperf/opperf.py b/benchmark/opperf/opperf.py
index 77b16670b443..b8055d7d14ae 100755
--- a/benchmark/opperf/opperf.py
+++ b/benchmark/opperf/opperf.py
@@ -39,6 +39,7 @@
 from benchmark.opperf.nd_operations.nn_conv_operators import run_pooling_operators_benchmarks, \
     run_convolution_operators_benchmarks, run_transpose_convolution_operators_benchmarks
 from benchmark.opperf.nd_operations.nn_basic_operators import run_nn_basic_operators_benchmarks
+from benchmark.opperf.nd_operations.nn_optimizer_operators import run_optimizer_operators_benchmarks
 from benchmark.opperf.nd_operations.array_rearrange import run_rearrange_operators_benchmarks
 
 from benchmark.opperf.utils.common_utils import merge_map_list, save_to_file
@@ -96,6 +97,8 @@ def run_all_mxnet_operator_benchmarks(ctx=mx.cpu(), dtype='float32'):
     # Run all Convolution operations benchmarks with default input values
     mxnet_operator_benchmark_results.append(run_convolution_operators_benchmarks(ctx=ctx, dtype=dtype))
 
+    # Run all Optimizer operations benchmarks with default input values
+    mxnet_operator_benchmark_results.append(run_optimizer_operators_benchmarks(ctx=ctx, dtype=dtype))
     # Run all Transpose Convolution operations benchmarks with default input values
     mxnet_operator_benchmark_results.append(run_transpose_convolution_operators_benchmarks(ctx=ctx, dtype=dtype))
 
diff --git a/benchmark/opperf/rules/default_params.py b/benchmark/opperf/rules/default_params.py
index 322fde2fbc8c..c864c7d829b6 100644
--- a/benchmark/opperf/rules/default_params.py
+++ b/benchmark/opperf/rules/default_params.py
@@ -63,6 +63,31 @@
 # NOTE: Data used is DEFAULT_DATA
 DEFAULT_AXIS = [0]
 
+# For optimizer operators
+DEFAULT_WEIGHT = [(1024, 1024), (10000, 1), (10000, 100)]
+DEFAULT_GRAD = [(1024, 1024), (10000, 1), (10000, 100)]
+DEFAULT_MOM = [(1024, 1024), (10000, 1), (10000, 100)]
+DEFAULT_MEAN = [(1024, 1024), (10000, 1), (10000, 100)]
+DEFAULT_VAR = [(1024, 1024), (10000, 1), (10000, 100)]
+DEFAULT_N = [(1024, 1024), (10000, 1), (10000, 100)]
+DEFAULT_D = [(1024, 1024), (10000, 1), (10000, 100)]
+DEFAULT_V = [(1024, 1024), (10000, 1), (10000, 100)]
+DEFAULT_Z = [(1024, 1024), (10000, 1), (10000, 100)]
+DEFAULT_G = [(1024, 1024), (10000, 1), (10000, 100)]
+DEFAULT_DELTA = [(1024, 1024), (10000, 1), (10000, 100)]
+DEFAULT_LRS = [(0.1,0.1)]
+DEFAULT_LR = [0.1,0.5,0.9]
+DEFAULT_GAMMA_1 = [0.1,0.5,0.9]
+DEFAULT_GAMMA_2 = [0.1,0.5,0.9]
+DEFAULT_EPSILON = [1e-08]
+DEFAULT_BETA_1 = [0.1,0.5,0.9]
+DEFAULT_BETA_2 = [0.1,0.5,0.9]
+DEFAULT_T = [1,5]
+DEFAULT_RESCALE_GRAD = [0.4, 0.77]
+DEFAULT_CLIP_GRADIENT = [-1.0,0.8]
+DEFAULT_CLIP_WEIGHTS = [-1.0,0.8]
+DEFAULT_LAZY_UPDATE = [0,1]
+
 # For rearrange operators
 # NOTE: Data needs to be a 4D tensor for  operators like space_to_depth and depth_to_space
 # Hence below we append 4d to mark the difference.
@@ -72,6 +97,7 @@
 DEFAULT_DIM_2 = [1, 2, 3, 0]
 DEFAULT_BLOCK_SIZE = [2, 5]
 
+
 # Default Inputs. MXNet Op Param Name to Default Input mapping
 DEFAULTS_INPUTS = {"data": DEFAULT_DATA,
                    "sample": DEFAULT_SAMPLE,
@@ -93,11 +119,36 @@
                    "p_nd": DEFAULT_P_ND,
                    "axis_shape": DEFAULT_AXIS_SHAPE,
                    "axis": DEFAULT_AXIS,
+                   "weight" : DEFAULT_WEIGHT,
+                   "weight32" : DEFAULT_WEIGHT,
+                   "grad" : DEFAULT_GRAD,
+                   "mean" : DEFAULT_MEAN,
+                   "var" : DEFAULT_VAR,
+                   "mom" : DEFAULT_MOM,
+                   "n" : DEFAULT_N,
+                   "d" : DEFAULT_D,
+                   "v" : DEFAULT_V,
+                   "z" : DEFAULT_Z,
+                   "g" : DEFAULT_G,
+                   "delta" : DEFAULT_DELTA,
+                   "lr" : DEFAULT_LR,
+                   "lrs" : DEFAULT_LRS,
+                   "wds" : DEFAULT_LRS,
+                   "gamma1" : DEFAULT_GAMMA_1,
+                   "gamma2" : DEFAULT_GAMMA_2,
+                   "epsilon" : DEFAULT_EPSILON,
+                   "beta1" : DEFAULT_BETA_1,
+                   "beta2" : DEFAULT_BETA_2,
+                   "t" : DEFAULT_T,
+                   "rescale_grad" : DEFAULT_RESCALE_GRAD,
+                   "clip_grad" : DEFAULT_CLIP_GRADIENT,
+                   "lazy_update" : DEFAULT_LAZY_UPDATE,
                    "data_4d": DEFAULT_DATA_4d,
                    "dim1": DEFAULT_DIM_1,
                    "dim2": DEFAULT_DIM_2,
                    "block_size": DEFAULT_BLOCK_SIZE}
 
+
 # These are names of MXNet operator parameters that is of type NDArray.
 # We maintain this list to automatically recognize these parameters are to be
 # given as NDArray and translate users inputs such as a shape tuple, Numpy Array or
@@ -105,4 +156,5 @@
 # can just say shape of the tensor, and we automatically create Tensors.
 PARAMS_OF_TYPE_NDARRAY = ["lhs", "rhs", "data", "base", "exp", "sample",
                           "mu", "sigma", "lam", "alpha", "beta", "gamma", "k", "p",
-                          "low", "high", "weight", "bias", "moving_mean", "moving_var"]
+                          "low", "high", "weight", "bias", "moving_mean", "moving_var",
+                          "weight", "weight32", "grad", "mean", "var", "mom", "n", "d", "v", "z", "g", "delta"]
diff --git a/benchmark/opperf/utils/op_registry_utils.py b/benchmark/opperf/utils/op_registry_utils.py
index f5e75066cafc..860b83a4dace 100644
--- a/benchmark/opperf/utils/op_registry_utils.py
+++ b/benchmark/opperf/utils/op_registry_utils.py
@@ -244,6 +244,27 @@ def get_all_reduction_operators():
     return reduction_mx_operators
 
 
+def get_all_optimizer_operators():
+    """Gets all Optimizer operators registered with MXNet.
+
+     Returns
+     -------
+     {"operator_name": {"has_backward", "nd_op_handle", "params"}}
+     """
+    optimizer_ops = ['mp_sgd_update', 'signum_update', 'rmspropalex_update', 'ftml_update', 'rmsprop_update',
+                     'sgd_mom_update', 'signsgd_update', 'mp_sgd_mom_update', 'ftrl_update', 'sgd_update',
+                     'adam_update']
+
+    # Get all mxnet operators
+    mx_operators = _get_all_mxnet_operators()
+
+    # Filter for Optimizer operators
+    optimizer_mx_operators = {}
+    for op_name, op_params in mx_operators.items():
+         if op_name in optimizer_ops and op_name not in unique_ops:
+             optimizer_mx_operators[op_name] = mx_operators[op_name]
+    return optimizer_mx_operators
+
 def get_all_sorting_searching_operators():
     """Gets all Sorting and Searching operators registered with MXNet.
 

From d464a47486cd4ec5470afbb291fbbe67b85044a0 Mon Sep 17 00:00:00 2001
From: Zhennan Qin <zhennan.qin@intel.com>
Date: Sun, 28 Jul 2019 12:34:00 +0800
Subject: [PATCH 119/813] Fix subgraph with custom_op (#15671)

---
 src/c_api/c_api_symbolic.cc               |  4 +-
 src/c_api/c_api_test.cc                   |  4 +-
 src/executor/graph_executor.cc            |  4 +-
 src/operator/subgraph/subgraph_property.h | 19 ++++++++
 tests/python/unittest/test_subgraph.py    | 53 +++++++++++++++++++++++
 5 files changed, 81 insertions(+), 3 deletions(-)

diff --git a/src/c_api/c_api_symbolic.cc b/src/c_api/c_api_symbolic.cc
index df839b4d296e..627acf46cc6b 100644
--- a/src/c_api/c_api_symbolic.cc
+++ b/src/c_api/c_api_symbolic.cc
@@ -1046,8 +1046,10 @@ int MXGenBackendSubgraph(SymbolHandle sym_handle, const char *backend_name,
   for (auto property : subgraph_prop_list) {
     nnvm::Graph g = Symbol2Graph(*s);
     property->SetAttr("graph", g);
-    g.attrs["subgraph_property"] = std::make_shared<nnvm::any>(std::move(property));
+    g.attrs["subgraph_property"] = std::make_shared<nnvm::any>(property);
     g = ApplyPass(std::move(g), "BuildSubgraph");
+    property->RemoveAttr("graph");
+    g.attrs.erase("subgraph_property");
     s->outputs = g.outputs;
   }
   *ret_sym_handle = s;
diff --git a/src/c_api/c_api_test.cc b/src/c_api/c_api_test.cc
index fff92692971d..a1f12fad60f0 100644
--- a/src/c_api/c_api_test.cc
+++ b/src/c_api/c_api_test.cc
@@ -50,8 +50,10 @@ int MXBuildSubgraphByOpNames(SymbolHandle sym_handle,
       g.outputs = s->outputs;
       property->SetAttr("graph", g);
       property->SetAttr("op_names", op_name_set);
-      g.attrs["subgraph_property"] = std::make_shared<nnvm::any>(std::move(property));
+      g.attrs["subgraph_property"] = std::make_shared<nnvm::any>(property);
       g = nnvm::ApplyPass(std::move(g), "BuildSubgraph");
+      property->RemoveAttr("graph");
+      g.attrs.erase("subgraph_property");
       s->outputs = g.outputs;
     }
   }
diff --git a/src/executor/graph_executor.cc b/src/executor/graph_executor.cc
index 0ba85a9cea55..c5da0a0cf90d 100644
--- a/src/executor/graph_executor.cc
+++ b/src/executor/graph_executor.cc
@@ -1688,8 +1688,10 @@ static nnvm::Symbol BuildSubgraph(const nnvm::Symbol& src, op::SubgraphPropertyP
   g = InferForwardAttrs(g, arg_shapes, arg_dtypes, arg_stypes, default_ctx, ctx_map, in_arg_ctxes,
                         aux_state_ctxes, true);
   subgraph_prop->SetAttr("graph", g);
-  g.attrs["subgraph_property"] = std::make_shared<nnvm::any>(std::move(subgraph_prop));
+  g.attrs["subgraph_property"] = std::make_shared<nnvm::any>(subgraph_prop);
   g = ApplyPass(std::move(g), "BuildSubgraph");
+  subgraph_prop->RemoveAttr("graph");
+  g.attrs.erase("subgraph_property");
   ret.outputs = g.outputs;
   return ret;
 }
diff --git a/src/operator/subgraph/subgraph_property.h b/src/operator/subgraph/subgraph_property.h
index 0683e5ed65f1..e2c861ef512a 100644
--- a/src/operator/subgraph/subgraph_property.h
+++ b/src/operator/subgraph/subgraph_property.h
@@ -330,6 +330,15 @@ class SubgraphProperty {
     auto it = attrs_.find(name);
     return it != attrs_.end();
   }
+  /*!
+   * \brief Remove attr if the attr exists.
+   */
+  void RemoveAttr(const std::string& name) {
+    auto it = attrs_.find(name);
+    if (it != attrs_.end()) {
+      attrs_.erase(it);
+    }
+  }
   /*!
    * \brief Get the property type.
    */
@@ -384,6 +393,16 @@ class SubgraphBackend {
     return it != attrs_.end();
   }
 
+  /*!
+   * \brief Remove attr if the attr exists.
+   */
+  void RemoveAttr(const std::string& name) {
+    auto it = attrs_.find(name);
+    if (it != attrs_.end()) {
+      attrs_.erase(it);
+    }
+  }
+
   SubgraphPropertyPtr& RegisterSubgraphProperty(const SubgraphPropertyPtr prop) {
     prop_ptr_.push_back(prop);
     return prop_ptr_.back();
diff --git a/tests/python/unittest/test_subgraph.py b/tests/python/unittest/test_subgraph.py
index b5577d4d0ff5..4c13f9c70dfc 100644
--- a/tests/python/unittest/test_subgraph.py
+++ b/tests/python/unittest/test_subgraph.py
@@ -144,6 +144,59 @@ def make_subgraph4(stype):
                         rtol=0.001, atol=0.0001)
 
 
+def test_subgraph_with_customOp():
+    class MyAdd(mx.operator.CustomOp):
+        def forward(self, is_train, req, in_data, out_data, aux):
+            self.assign(out_data[0], req[0], in_data[0] + 1)
+
+        def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
+            self.assign(in_grad[0], req[0], out_grad[0])
+
+    @mx.operator.register('MyAdd1')
+    class MyAdd1Prop(mx.operator.CustomOpProp):
+        def __init__(self):
+            super(MyAdd1Prop, self).__init__(need_top_grad=True)
+
+        def list_arguments(self):
+            return ['data']
+
+        def list_outputs(self):
+            return ['output']
+
+        def infer_shape(self, in_shape):
+            # inputs, outputs, aux
+            return [in_shape[0]], [in_shape[0]], []
+
+        def create_operator(self, ctx, shapes, dtypes):
+            return MyAdd()
+
+    @mx.operator.register('MyAdd2')
+    class MyAdd2Prop(mx.operator.CustomOpProp):
+        def __init__(self):
+            super(MyAdd2Prop, self).__init__(need_top_grad=True)
+
+        def list_arguments(self):
+            return ['data']
+
+        def list_outputs(self):
+            return ['output']
+
+        def infer_shape(self, in_shape):
+            # inputs, outputs, aux
+            return [in_shape[0]], [in_shape[0]], []
+
+        def create_operator(self, ctx, shapes, dtypes):
+            return MyAdd()
+
+    inp = mx.nd.zeros(shape=(100, 100))
+    a = mx.symbol.Variable('a')
+    b = a + 1
+    b = mx.symbol.Custom(data=a, op_type='MyAdd1')
+    c = mx.symbol.Custom(data=a, op_type='MyAdd2')
+    b.bind(mx.cpu(), {'a': inp}).forward()
+    c.bind(mx.cpu(), {'a': inp}).forward()
+    mx.nd.waitall()
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From a26af2b20f96d160d6960ac34d44f89e63cde378 Mon Sep 17 00:00:00 2001
From: Rahul Huilgol <rahulhuilgol@gmail.com>
Date: Sat, 27 Jul 2019 22:56:05 -0700
Subject: [PATCH 120/813] Remove myself from CODEOWNERS (#15617)

* Update CODEOWNERS

* retrigger CI
---
 CODEOWNERS | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/CODEOWNERS b/CODEOWNERS
index 7007e80b2035..74caea2c4cc7 100644
--- a/CODEOWNERS
+++ b/CODEOWNERS
@@ -42,8 +42,8 @@
 /plugin/          @pllarroy
 
 # CMake
-CMakeLists.txt    @szha @rahul003 @pllarroy
-/cmake/           @szha @rahul003 @pllarroy
+CMakeLists.txt    @szha @pllarroy
+/cmake/           @szha @pllarroy
 
 # MXNet CI
 dev_menu.py         @pllarroy
@@ -71,4 +71,3 @@ prepare_mkl.sh    @szha
 
 # Github templates
 /.github/         @szha
-

From 8e31dad7b3513c6298a865e3a46d581dd498a722 Mon Sep 17 00:00:00 2001
From: kshitij12345 <kshitijkalambarkar@gmail.com>
Date: Tue, 30 Jul 2019 03:00:37 +0530
Subject: [PATCH 121/813] [MXNET-978] Add higher order gradient support `tan`,
 `tanh` (#15253)

* init to reset

* issue: higher order backward sigmoid

* update gradient code.

update code as per #15288.

* undo changes

* relax tolerance of gradient mismatch for tanh

* update comments

* update comments
---
 src/operator/tensor/elemwise_unary_op_trig.cc | 57 ++++++++++++++++++-
 .../python/unittest/test_higher_order_grad.py | 40 ++++++++++++-
 2 files changed, 93 insertions(+), 4 deletions(-)

diff --git a/src/operator/tensor/elemwise_unary_op_trig.cc b/src/operator/tensor/elemwise_unary_op_trig.cc
index 13410e9422a5..ff210040d552 100644
--- a/src/operator/tensor/elemwise_unary_op_trig.cc
+++ b/src/operator/tensor/elemwise_unary_op_trig.cc
@@ -139,7 +139,33 @@ The storage type of ``tan`` output depends upon the input storage type:
 )code" ADD_FILELINE)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{ "_backward_tan" });
 
-MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_tan, unary_bwd<mshadow_op::tan_grad>);
+MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_tan, unary_bwd<mshadow_op::tan_grad>)
+.set_attr<nnvm::FGradient>("FGradient",
+  [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
+      // NodeEntry{n} : y_grad * f'(x)
+      // n->inputs[0] : y_grad (dL/dy)
+      // n->inputs[1] : y = f(x) = tan(x) (ElemwiseGradUseOut)
+      // ograds[0] : head_grads (dL/dxgrad)
+      // f'(x) = sec^2(x)
+      // f''(x) = 2 * f'(x) * f(x)
+      //
+      // Note: When building gradient graph, the backward node of n->inputs[1] will be
+      // added to the graph again, therefore f`(x) will be multiplied
+      // So we need to compute only -> 2 * f(x) * dL/dy_grad * y_grad
+      const std::unordered_map<std::string, std::string> args = {{"scalar", "2.0"}};
+      auto two_y = MakeNode("_mul_scalar", n->attrs.name + "_mul_two", {n->inputs[1]}, &args, &n);
+      auto grad_grad_mid = MakeNode("elemwise_mul", n->attrs.name + "_grad_mul",
+                                    {n->inputs[0], nnvm::NodeEntry{two_y}}, nullptr, &n);
+      auto dydx = MakeNode("elemwise_div", n->attrs.name + "_grad_div",
+                           {nnvm::NodeEntry{n}, n->inputs[0]}, nullptr, &n);
+
+      std::vector<nnvm::NodeEntry> ret;
+      ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "backward_grad_grad",
+                                {ograds[0], nnvm::NodeEntry{dydx}}, nullptr, &n));
+      ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "backward_grad_grad_in",
+                                {ograds[0], nnvm::NodeEntry{grad_grad_mid}}, nullptr, &n));
+      return ret;
+  });
 
 // arcsin
 MXNET_OPERATOR_REGISTER_UNARY_WITH_RSP_CSR(arcsin, cpu, mshadow_op::arcsin)
@@ -290,7 +316,34 @@ The storage type of ``tanh`` output depends upon the input storage type:
 )code" ADD_FILELINE)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{ "_backward_tanh" });
 
-MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_tanh, unary_bwd<mshadow_op::tanh_grad>);
+MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_tanh, unary_bwd<mshadow_op::tanh_grad>)
+.set_attr<nnvm::FGradient>("FGradient",
+  [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
+      // NodeEntry{n} : y_grad * f'(x)
+      // n->inputs[0] : y_grad (dL/dy)
+      // n->inputs[1] : y = f(x) = tanh(x) (ElemwiseGradUseOut)
+      // ograds[0] : head_grads dL/dxgrad
+      // f'(x) = sech^2(x)
+      // f''(x) = -2 * f'(x) * f(x)
+      //
+      // Note: when building gradient graph, the backward node of n->inputs[1] will be
+      // added to the graph again, therefore f`(x) will be multiplied
+      // So we need to compute only -> -2 * f(x) * dL/dy_grad * y_grad
+      const std::unordered_map<std::string, std::string> args = {{"scalar", "-2.0"}};
+      auto neg_two_y = MakeNode("_mul_scalar", n->attrs.name + "_mul_neg_two",
+                                {n->inputs[1]}, &args, &n);
+      auto grad_grad_mid = MakeNode("elemwise_mul", n->attrs.name + "_grad_mul",
+                                    {n->inputs[0], nnvm::NodeEntry{neg_two_y}}, nullptr, &n);
+      auto dydx = MakeNode("elemwise_div", n->attrs.name + "_grad_div",
+                           {nnvm::NodeEntry{n}, n->inputs[0]}, nullptr, &n);
+
+      std::vector<nnvm::NodeEntry> ret;
+      ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "backward_grad_grad",
+                                {ograds[0], nnvm::NodeEntry{dydx}}, nullptr, &n));
+      ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "backward_grad_grad_in",
+                                {ograds[0], nnvm::NodeEntry{grad_grad_mid}}, nullptr, &n));
+      return ret;
+  });
 
 // arcsinh
 MXNET_OPERATOR_REGISTER_UNARY_WITH_RSP_CSR(arcsinh, cpu, mshadow_op::arcsinh)
diff --git a/tests/python/unittest/test_higher_order_grad.py b/tests/python/unittest/test_higher_order_grad.py
index 0f07d014d435..429070de5896 100644
--- a/tests/python/unittest/test_higher_order_grad.py
+++ b/tests/python/unittest/test_higher_order_grad.py
@@ -50,6 +50,41 @@ def grad_grad_op(x):
         check_second_order_unary(array, cos, grad_grad_op)
 
 
+@with_seed()
+def test_tan():
+    def tan(x):
+        return nd.tan(x)
+
+    def grad_op(x):
+        return 1 / nd.cos(x)**2
+
+    def grad_grad_op(x):
+        return 2 * tan(x) * grad_op(x)
+
+    for dim in range(1, 5):
+        shape = rand_shape_nd(dim)
+        array = random_arrays(shape)
+        check_second_order_unary(array, tan, grad_grad_op)
+
+
+@with_seed()
+def test_tanh():
+    def tanh(x):
+        return nd.tanh(x)
+
+    def grad_op(x):
+        return 1 / nd.cosh(x)**2
+
+    def grad_grad_op(x):
+        return -2 * tanh(x) * grad_op(x)
+
+    for dim in range(1, 5):
+        shape = rand_shape_nd(dim)
+        array = random_arrays(shape)
+        check_second_order_unary(
+            array, tanh, grad_grad_op, rtol=1e-6, atol=1e-6)
+
+
 @with_seed()
 def test_relu():
     def relu(x):
@@ -150,7 +185,7 @@ def grad_grad_op(x):
         check_second_order_unary(array, sigmoid, grad_grad_op)
 
 
-def check_second_order_unary(x, op, grad_grad_op):
+def check_second_order_unary(x, op, grad_grad_op, rtol=None, atol=None):
     x = nd.array(x)
     grad_grad_x = grad_grad_op(x)
     x.attach_grad()
@@ -171,7 +206,8 @@ def check_second_order_unary(x, op, grad_grad_op):
         y_grad.asnumpy()
 
     # Validate the gradients.
-    assert_almost_equal(expected_grad_grad, x.grad.asnumpy())
+    assert_almost_equal(expected_grad_grad,
+                        x.grad.asnumpy(), rtol=rtol, atol=atol)
 
 
 if __name__ == '__main__':

From 90f881d6c243be2dfdc17b1ec9ccf29ca050387b Mon Sep 17 00:00:00 2001
From: Pedro Larroy <pedro.larroy.lists@gmail.com>
Date: Mon, 29 Jul 2019 21:50:45 -0700
Subject: [PATCH 122/813] Fix USE_BLAS == openblas check (#15691)

Fixes #15652
---
 Makefile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index ce840a3d30aa..0ac20d971713 100644
--- a/Makefile
+++ b/Makefile
@@ -252,7 +252,7 @@ ifeq ($(USE_CUDNN), 1)
 	LDFLAGS += -lcudnn
 endif
 
-ifeq ($(USE_BLAS), open)
+ifeq ($(USE_BLAS), openblas)
 	CFLAGS += -DMXNET_USE_BLAS_OPEN=1
 else ifeq ($(USE_BLAS), atlas)
 	CFLAGS += -DMXNET_USE_BLAS_ATLAS=1

From 93708eb803348136c157c441102f47ec44a3d832 Mon Sep 17 00:00:00 2001
From: SusangKim <healess1@gmail.com>
Date: Wed, 31 Jul 2019 01:22:59 +0900
Subject: [PATCH 123/813] Fix url (404 error) (#15683)

Fix page url for face recognition - page not found error(404)
---
 example/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/example/README.md b/example/README.md
index be9903099e19..2b9c366849e0 100644
--- a/example/README.md
+++ b/example/README.md
@@ -151,7 +151,7 @@ If your tutorial depends on specific packages, simply add them to this provision
 
 ### <a name="deep-learning-examples-other"></a>Other Deep Learning Examples with MXNet
 
-* [Face Recognition with ArcFace](https://github.com/onnx/models/tree/master/models/face_recognition/ArcFace) - ONNX model for face recognition with notebooks for training, validating and running inference in MXNet by [abhinavs95](https://github.com/abhinavs95)
+* [Face Recognition with ArcFace](https://github.com/onnx/models/tree/master/vision/body_analysis/arcface) - ONNX model for face recognition with notebooks for training, validating and running inference in MXNet by [abhinavs95](https://github.com/abhinavs95)
 * [Chinese plate recognition](https://github.com/imistyrain/mxnet-mr) - Recognize Chinese vehicle plate, by [imistyrain](https://github.com/imistyrain)
 * [Fast R-CNN](https://github.com/precedenceguo/mx-rcnn) by [Jian Guo](https://github.com/precedenceguo)
 * "End2End Captcha Recognition (OCR)" by [xlvector](https://github.com/xlvector) [github link](https://github.com/xlvector/learning-dl/tree/master/mxnet/ocr) [Blog in Chinese](http://blog.xlvector.net/2016-05/mxnet-ocr-cnn/)

From 0f28f5b827c718dcab7bbf2617e16a59ad3f601c Mon Sep 17 00:00:00 2001
From: Pedro Larroy <pedro.larroy.lists@gmail.com>
Date: Tue, 30 Jul 2019 14:18:20 -0700
Subject: [PATCH 124/813] Add -DMXNET_USE_OPENMP to Makefiles so libinfo gets
 updated accordingly (#15498)

---
 Makefile | 1 +
 1 file changed, 1 insertion(+)

diff --git a/Makefile b/Makefile
index 0ac20d971713..b54df174acde 100644
--- a/Makefile
+++ b/Makefile
@@ -190,6 +190,7 @@ endif
 
 ifeq ($(USE_OPENMP), 1)
 	CFLAGS += -fopenmp
+	CFLAGS += -DMXNET_USE_OPENMP=1
 endif
 
 ifeq ($(USE_NNPACK), 1)

From f0b6d723b114acc82c138456e5091c6656830a9e Mon Sep 17 00:00:00 2001
From: Zhaoqi Zhu <zhaoqizh@usc.edu>
Date: Tue, 30 Jul 2019 16:38:12 -0700
Subject: [PATCH 125/813] update previous flaky naive engine test (#15651)

* update precious flaky naive engine test

* retriever tests

* retrigger tests

* retrigger tests

* retrigger tests

* retrigger tests

* retrigger tests

* Update test_profiler.py

* retrigger tests

* retrigger tests

* retrigger tests

* retrigger tests

* retrigger tests

* retrigger tests

* Update test_profiler.py

* retrigger tests

* retrigger tests

* retrigger tests

* retrigger tests

* Update test_profiler.py

* retrigger tests

* Update test_profiler.py
---
 tests/python/unittest/test_profiler.py | 88 ++++++++++++--------------
 1 file changed, 40 insertions(+), 48 deletions(-)

diff --git a/tests/python/unittest/test_profiler.py b/tests/python/unittest/test_profiler.py
index b9447f951764..33f59845557b 100644
--- a/tests/python/unittest/test_profiler.py
+++ b/tests/python/unittest/test_profiler.py
@@ -267,21 +267,23 @@ def check_sorting(debug_str, sort_by, ascending):
     test_profile_event(False)
     for sb in sort_by_options:
         for asc in ascending_options:
-            debug_str = profiler.dumps(format = 'json', sort_by = sb, ascending = asc)
+            debug_str = profiler.dumps(format='json', sort_by=sb, ascending=asc)
             check_sorting(debug_str, sb, asc)
     profiler.set_state('stop')
 
 def test_aggregate_duplication():
     file_name = 'test_aggregate_duplication.json'
-    enable_profiler(profile_filename = file_name, run=True, continuous_dump=True, \
+    enable_profiler(profile_filename=file_name, run=True, continuous_dump=True, \
                     aggregate_stats=True)
+    # clear aggregate stats
+    profiler.dumps(reset=True)
     inp = mx.nd.zeros(shape=(100, 100))
     y = mx.nd.sqrt(inp)
     inp = inp + 1
     inp = inp + 1
     mx.nd.waitall()
     profiler.dump(False)
-    debug_str = profiler.dumps(format = 'json')
+    debug_str = profiler.dumps(format='json')
     target_dict = json.loads(debug_str)
     assert 'Time' in target_dict and 'operator' in target_dict['Time'] \
         and 'sqrt' in target_dict['Time']['operator'] \
@@ -293,7 +295,7 @@ def test_aggregate_duplication():
     assert target_dict['Time']['operator']['_plus_scalar']['Count'] == 2
     profiler.set_state('stop')
     
-def test_custom_operator_profiling(seed = None, file_name = None):
+def test_custom_operator_profiling(seed=None, file_name=None):
     class Sigmoid(mx.operator.CustomOp):
         def forward(self, is_train, req, in_data, out_data, aux):
             x = in_data[0].asnumpy()
@@ -330,8 +332,10 @@ def create_operator(self, ctx, in_shapes, in_dtypes):
 
     if file_name is None:
         file_name = 'test_custom_operator_profiling.json'
-    enable_profiler(profile_filename = file_name, run=True, continuous_dump=True,\
+    enable_profiler(profile_filename=file_name, run=True, continuous_dump=True,\
                     aggregate_stats=True)
+    # clear aggregate stats
+    profiler.dumps(reset=True)
     x = mx.nd.array([0, 1, 2, 3])
     x.attach_grad()
     with mx.autograd.record():
@@ -339,7 +343,7 @@ def create_operator(self, ctx, in_shapes, in_dtypes):
     y.backward()
     mx.nd.waitall()
     profiler.dump(False)
-    debug_str = profiler.dumps(format = 'json')
+    debug_str = profiler.dumps(format='json')
     target_dict = json.loads(debug_str)
     assert 'Time' in target_dict and 'Custom Operator' in target_dict['Time'] \
         and 'MySigmoid::pure_python' in target_dict['Time']['Custom Operator'] \
@@ -347,8 +351,15 @@ def create_operator(self, ctx, in_shapes, in_dtypes):
         and 'MySigmoid::_zeros' in target_dict['Time']['Custom Operator']
     profiler.set_state('stop')
 
-def test_custom_operator_profiling_multiple_custom_ops_imperative(seed = None, \
-        mode = 'imperative', file_name = None):
+def check_custom_operator_profiling_multiple_custom_ops_output(debug_str):
+    target_dict = json.loads(debug_str)
+    assert 'Time' in target_dict and 'Custom Operator' in target_dict['Time'] \
+        and 'MyAdd1::pure_python' in target_dict['Time']['Custom Operator'] \
+        and 'MyAdd2::pure_python' in target_dict['Time']['Custom Operator'] \
+        and 'MyAdd1::_plus_scalar' in target_dict['Time']['Custom Operator'] \
+        and 'MyAdd2::_plus_scalar' in target_dict['Time']['Custom Operator']
+
+def custom_operator_profiling_multiple_custom_ops(seed, mode, file_name):
     class MyAdd(mx.operator.CustomOp):
         def forward(self, is_train, req, in_data, out_data, aux):        
             self.assign(out_data[0], req[0], in_data[0] + 1)
@@ -392,65 +403,46 @@ def infer_shape(self, in_shape):
         def create_operator(self, ctx, shapes, dtypes):
             return MyAdd()
 
-    if file_name is None:
-        file_name = 'test_custom_operator_profiling_multiple_custom_ops_imperative.json'
-    enable_profiler(profile_filename = file_name, run=True, continuous_dump=True,\
+    enable_profiler(profile_filename=file_name, run=True, continuous_dump=True,\
                     aggregate_stats=True)
+    # clear aggregate stats
+    profiler.dumps(reset=True)
     inp = mx.nd.zeros(shape=(100, 100))
     if mode == 'imperative':
-        x = inp + 1
         y = mx.nd.Custom(inp, op_type='MyAdd1')
         z = mx.nd.Custom(inp, op_type='MyAdd2')
     elif mode == 'symbolic':
         a = mx.symbol.Variable('a')
-        b = a + 1
-        c = mx.symbol.Custom(data=a, op_type='MyAdd1')
-        d = mx.symbol.Custom(data=a, op_type='MyAdd2')
-        b.bind(mx.cpu(), {'a': inp}).forward()
-        c.bind(mx.cpu(), {'a': inp}).forward()
-        d.bind(mx.cpu(), {'a': inp}).forward()
+        b = mx.symbol.Custom(data=a, op_type='MyAdd1')
+        c = mx.symbol.Custom(data=a, op_type='MyAdd2')
+        y = b.bind(mx.cpu(), {'a': inp})
+        z = c.bind(mx.cpu(), {'a': inp})
+        yy = y.forward()
+        zz = z.forward()
     mx.nd.waitall()
     profiler.dump(False)
-    debug_str = profiler.dumps(format = 'json')
-    target_dict = json.loads(debug_str)
-    '''
-    We are calling _plus_scalar within MyAdd1 and MyAdd2 and outside both the custom 
-    operators, so in aggregate stats we should have three different kinds of 
-    _plus_scalar under domains "Custom Operator" and "operator"
-    '''
-    assert 'Time' in target_dict and 'Custom Operator' in target_dict['Time'] \
-        and 'MyAdd1::pure_python' in target_dict['Time']['Custom Operator'] \
-        and 'MyAdd2::pure_python' in target_dict['Time']['Custom Operator'] \
-        and 'MyAdd1::_plus_scalar' in target_dict['Time']['Custom Operator'] \
-        and 'MyAdd2::_plus_scalar' in target_dict['Time']['Custom Operator'] \
-        and '_plus_scalar' not in target_dict['Time']['Custom Operator'] \
-        and 'operator' in target_dict['Time'] \
-        and '_plus_scalar' in target_dict['Time']['operator']
+    debug_str = profiler.dumps(format='json')
+    check_custom_operator_profiling_multiple_custom_ops_output(debug_str)
     profiler.set_state('stop')
-
-@unittest.skip("Flaky test https://github.com/apache/incubator-mxnet/issues/15406")
+    
 def test_custom_operator_profiling_multiple_custom_ops_symbolic():
-    run_in_spawned_process(test_custom_operator_profiling_multiple_custom_ops_imperative, \
-            {'MXNET_EXEC_BULK_EXEC_INFERENCE' : 0, \
-            'MXNET_EXEC_BULK_EXEC_TRAIN' : 0}, \
-            'symbolic', \
+    custom_operator_profiling_multiple_custom_ops(None, 'symbolic', \
             'test_custom_operator_profiling_multiple_custom_ops_symbolic.json')
 
-@unittest.skip("Flaky test https://github.com/apache/incubator-mxnet/issues/15406")
+def test_custom_operator_profiling_multiple_custom_ops_imperative():
+    custom_operator_profiling_multiple_custom_ops(None, 'imperative', \
+            'test_custom_operator_profiling_multiple_custom_ops_imperative.json')
+
 def test_custom_operator_profiling_naive_engine():
     # run the three tests above using Naive Engine
     run_in_spawned_process(test_custom_operator_profiling, \
             {'MXNET_ENGINE_TYPE' : "NaiveEngine"}, \
             'test_custom_operator_profiling_naive.json')
-    run_in_spawned_process(test_custom_operator_profiling_multiple_custom_ops_imperative, \
-            {'MXNET_ENGINE_TYPE' : "NaiveEngine"}, \
-            'imperative', \
+    run_in_spawned_process(custom_operator_profiling_multiple_custom_ops, \
+            {'MXNET_ENGINE_TYPE' : "NaiveEngine"}, 'imperative', \
             'test_custom_operator_profiling_multiple_custom_ops_imperative_naive.json')
-    run_in_spawned_process(test_custom_operator_profiling_multiple_custom_ops_imperative, \
-            {'MXNET_ENGINE_TYPE' : "NaiveEngine", \
-            'MXNET_EXEC_BULK_EXEC_INFERENCE' : 0, \
-            'MXNET_EXEC_BULK_EXEC_TRAIN' : 0}, \
-            'symbolic', \
+    run_in_spawned_process(custom_operator_profiling_multiple_custom_ops, \
+            {'MXNET_ENGINE_TYPE' : "NaiveEngine"}, 'symbolic', \
             'test_custom_operator_profiling_multiple_custom_ops_symbolic_naive.json')
 
 if __name__ == '__main__':

From f2ac85a7efe24278c76184e3651c13091875dfd0 Mon Sep 17 00:00:00 2001
From: Ksoy <ksoyam95@gmail.com>
Date: Wed, 31 Jul 2019 09:58:03 +0800
Subject: [PATCH 126/813] update julia install doc (#15609)

---
 docs/install/index.md | 64 +++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 62 insertions(+), 2 deletions(-)

diff --git a/docs/install/index.md b/docs/install/index.md
index 4217780c4b1c..db8dae8e46d7 100644
--- a/docs/install/index.md
+++ b/docs/install/index.md
@@ -109,7 +109,22 @@ Indicate your preferred configuration. Then, follow the customized commands to i
 </div>
 </div>
 </div>
+
+<!-- Linux Julia Options -->
+
+<div class="linux macos windows">
+<div class="julia">
+<div class="cpu gpu">
+<div class="btn-group opt-group" role="group">
+  <button type="button" class="btn btn-default environs opt active">Pkg</button>
+  <button type="button" class="btn btn-default environs opt">Build from Source</button>
+</div>
+</div>
+</div>
+</div>
+
 <hr>
+
 <!-- END - Main Menu -->
 
 <!-- START - Linux Python CPU Installation Instructions -->
@@ -583,8 +598,23 @@ You can use the Maven packages defined in the following dependency to include MX
 <div class="julia">
 <div class="cpu gpu">
 </br>
+<div class="pkg">
+
+```
+]add MXNet#v1.5.0
+```
+
+Install the latest release:
+
+```
+]add MXNet
+```
+
+</div>
+<div class="build-from-source">
 Refer to the <a href="ubuntu_setup.html#install-the-mxnet-package-for-julia">Julia section of the MXNet Ubuntu installation guide</a>.
 
+</div>
 </div> <!-- End of cpu gpu -->
 </div> <!-- End of julia -->
 
@@ -594,7 +624,7 @@ Refer to the <a href="ubuntu_setup.html#install-the-mxnet-package-for-julia">Jul
 Refer to the <a href="ubuntu_setup.html#install-the-mxnet-package-for-perl">Perl section of the MXNet Ubuntu installation guide</a>.
 
 </div> <!-- End of cpu gpu -->
-</div> <!-- End of julia -->
+</div> <!-- End of perl -->
 
 
 
@@ -885,8 +915,23 @@ Not available at this time. <br>
 <div class="julia">
 <div class="cpu gpu">
 </br>
+<div class="pkg">
+
+```
+]add MXNet#v1.5.0
+```
+
+Install the latest release:
+
+```
+]add MXNet
+```
+
+</div>
+<div class="build-from-source">
 Refer to the <a href="osx_setup.html#install-the-mxnet-package-for-julia">Julia section of the MXNet macOS installation guide</a>.
 
+</div>
 </div> <!-- End of cpu gpu -->
 </div> <!-- End of julia -->
 
@@ -1227,8 +1272,23 @@ MXNet-Java for Windows is not yet available.
 <div class="julia">
 <div class="cpu gpu">
 </br>
+<div class="pkg">
+
+```
+]add MXNet#v1.5.0
+```
+
+Install the latest release:
+
+```
+]add MXNet
+```
+
+</div>
+<div class="build-from-source">
 Refer to the <a href="windows_setup.html#install-the-mxnet-package-for-julia">Julia section of the MXNet Windows installation guide</a>.
 
+</div>
 </div> <!-- End of cpu gpu -->
 </div> <!-- End of julia -->
 
@@ -1238,7 +1298,7 @@ Refer to the <a href="windows_setup.html#install-the-mxnet-package-for-julia">Ju
 Refer to the <a href="windows_setup.html#install-the-mxnet-package-for-perl">Perl section of the MXNet Windows installation guide</a>.
 
 </div> <!-- End of cpu gpu -->
-</div> <!-- End of julia -->
+</div> <!-- End of perl -->
 
 <div class="cpp">
 <div class="cpu gpu">

From f0c69f5da565389aad48a142d499f451de78434c Mon Sep 17 00:00:00 2001
From: Leonard Lausen <lausen@amazon.com>
Date: Wed, 31 Jul 2019 20:26:55 +0200
Subject: [PATCH 127/813] Add missing default axis value to symbol.squeeze op
 (#15707)

* Add missing default arg

* Add test

* add test
---
 python/mxnet/symbol/symbol.py        |  2 +-
 tests/python/unittest/test_gluon.py  | 24 ++++++++++++++++++++----
 tests/python/unittest/test_symbol.py |  1 +
 3 files changed, 22 insertions(+), 5 deletions(-)

diff --git a/python/mxnet/symbol/symbol.py b/python/mxnet/symbol/symbol.py
index deedf0fe83d2..1e2defab3713 100644
--- a/python/mxnet/symbol/symbol.py
+++ b/python/mxnet/symbol/symbol.py
@@ -2539,7 +2539,7 @@ def softmin(self, *args, **kwargs):
         """
         return op.softmin(self, *args, **kwargs)
 
-    def squeeze(self, axis, inplace=False, **kwargs): # pylint: disable=unused-argument
+    def squeeze(self, axis=None, inplace=False, **kwargs): # pylint: disable=unused-argument
         """Convenience fluent method for :py:func:`squeeze`.
 
         The arguments are the same as for :py:func:`squeeze`, with
diff --git a/tests/python/unittest/test_gluon.py b/tests/python/unittest/test_gluon.py
index b59ce2d0864c..af30980b10ea 100644
--- a/tests/python/unittest/test_gluon.py
+++ b/tests/python/unittest/test_gluon.py
@@ -115,7 +115,7 @@ def test_parameter_dict():
     params1.get('w1', shape=(10, 10), stype='row_sparse')
     params1.load('test_parameter_dict.params', ctx)
     trainer1 = mx.gluon.Trainer(params1, 'sgd')
-    
+
     # compare the values before and after save/load
     cur_w0 = params1.get('w0').data(ctx)
     cur_w1 = params1.get('w1').row_sparse_data(all_row_ids)
@@ -134,7 +134,7 @@ def test_parameter_dict():
     cur_w1 = params2.get('w1').data(ctx)
     mx.test_utils.assert_almost_equal(prev_w0.asnumpy(), cur_w0.asnumpy())
     mx.test_utils.assert_almost_equal(prev_w1.asnumpy(), cur_w1.asnumpy())
-    
+
     # test the dtype casting functionality
     params0 = gluon.ParameterDict('')
     params0.get('w0', shape=(10, 10), dtype='float32')
@@ -386,7 +386,7 @@ def hybrid_forward(self, F, x):
         if 'conv' in param_name and 'weight' in param_name:
             break
     assert np.dtype(net_fp64.params[param_name].dtype) == np.dtype(np.float64)
-    
+
     # 3.b Verify same functionnality with the imports API
     net_fp_64 = mx.gluon.SymbolBlock.imports(sym_file, 'data', params_file, ctx=ctx)
 
@@ -2788,7 +2788,7 @@ def test_gluon_param_load():
     net.cast('float16')
     net.load_parameters('test_gluon_param_load.params', cast_dtype=True)
     mx.nd.waitall()
-    
+
 @with_seed()
 def test_gluon_param_load_dtype_source():
     net = mx.gluon.nn.Dense(10, in_units=10)
@@ -2800,6 +2800,22 @@ def test_gluon_param_load_dtype_source():
     assert net.weight.dtype == np.float16
     mx.nd.waitall()
 
+@with_seed()
+def test_squeeze_consistency():
+    class Foo(gluon.HybridBlock):
+        def __init__(self, inplace, **kwargs):
+            super(Foo, self).__init__(**kwargs)
+            self.inplace = inplace
+
+        def forward(self, x):
+            return x.squeeze(inplace=self.inplace)
+
+    for inplace in (True, False):
+        block = Foo(inplace)
+        block.hybridize()
+        shape = (np.random.randint(1, 10), np.random.randint(1, 10), 1)
+        block(mx.nd.ones(shape))
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()
diff --git a/tests/python/unittest/test_symbol.py b/tests/python/unittest/test_symbol.py
index 2dfe3e44eedb..0c97c68b0880 100644
--- a/tests/python/unittest/test_symbol.py
+++ b/tests/python/unittest/test_symbol.py
@@ -242,6 +242,7 @@ def check_fluent_regular(func, kwargs, shape=(5, 17, 1), equal_nan=False):
     check_fluent_regular('reshape', {'shape': (17, 1, 5)})
     check_fluent_regular('broadcast_to', {'shape': (5, 17, 47)})
     check_fluent_regular('squeeze', {'axis': (1, 3)}, shape=(2, 1, 3, 1, 4))
+    check_fluent_regular('squeeze', {}, shape=(2, 1, 3, 1, 4))
 
 def check_symbol_consistency(sym1, sym2, ctx, skip_grad=False, equal_nan=False):
     assert sym1.list_arguments() == sym2.list_arguments()

From 776d9c4a9e5ae521e1d5ef402c03abb0a94ccfbb Mon Sep 17 00:00:00 2001
From: Haohuan Wang <haohuanw@umich.edu>
Date: Wed, 31 Jul 2019 14:36:14 -0700
Subject: [PATCH 128/813] add deconv in TRT subgraph (#15666)

---
 .../subgraph/tensorrt/nnvm_to_onnx-inl.h      | 19 +++++-
 .../subgraph/tensorrt/nnvm_to_onnx.cc         | 46 ++++++++++----
 src/operator/subgraph/tensorrt/tensorrt-inl.h |  2 +
 tests/python/tensorrt/test_tensorrt_deconv.py | 63 +++++++++++++++++++
 4 files changed, 116 insertions(+), 14 deletions(-)
 create mode 100644 tests/python/tensorrt/test_tensorrt_deconv.py

diff --git a/src/operator/subgraph/tensorrt/nnvm_to_onnx-inl.h b/src/operator/subgraph/tensorrt/nnvm_to_onnx-inl.h
index 55b3d938df0a..5a433f1d9820 100644
--- a/src/operator/subgraph/tensorrt/nnvm_to_onnx-inl.h
+++ b/src/operator/subgraph/tensorrt/nnvm_to_onnx-inl.h
@@ -41,6 +41,8 @@ namespace mxnet {
 namespace op {
 namespace nnvm_to_onnx {
 
+enum ConvDeconvType {Convolution, Deconvolution};
+
 using namespace nnvm;
 using namespace ::onnx;
 using int64 = ::google::protobuf::int64;
@@ -48,8 +50,7 @@ using int64 = ::google::protobuf::int64;
 std::unordered_map<std::string, mxnet::TShape> GetPlaceholderShapes(const ShapeVector& shape_inputs,
     const nnvm::IndexedGraph& ig);
 
-std::unordered_map<std::string, int> GetPlaceholderDTypes(const DTypeVector&
-dtype_inputs,
+std::unordered_map<std::string, int> GetPlaceholderDTypes(const DTypeVector& dtype_inputs,
     const nnvm::IndexedGraph& ig);
 
 std::unordered_map<std::string, uint32_t> GetOutputLookup(const nnvm::IndexedGraph& ig);
@@ -74,12 +75,25 @@ typedef void (*ConverterFunction)(NodeProto *node_proto,
                                   const nnvm::IndexedGraph &ig,
                                   const array_view<IndexedGraph::NodeEntry> &inputs);
 
+template <class ConvDeconvParam>
+void ConvDeconvConvertHelper(NodeProto *node_proto,
+                             const NodeAttrs &attrs,
+                             const nnvm::IndexedGraph &ig,
+                             const array_view<IndexedGraph::NodeEntry> &inputs,
+                             const ConvDeconvParam& param,
+                             ConvDeconvType type);
+
 // Forward declarations
 void ConvertConvolution(NodeProto *node_proto,
                         const NodeAttrs &attrs,
                         const nnvm::IndexedGraph &ig,
                         const array_view<IndexedGraph::NodeEntry> &inputs);
 
+void ConvertDeconvolution(NodeProto *node_proto,
+                        const NodeAttrs &attrs,
+                        const nnvm::IndexedGraph &ig,
+                        const array_view<IndexedGraph::NodeEntry> &inputs);
+
 void ConvertPooling(NodeProto *node_proto,
                     const NodeAttrs &attrs,
                     const nnvm::IndexedGraph &ig,
@@ -158,6 +172,7 @@ static const std::unordered_map<std::string, ConverterFunction> converter_map =
   {"BatchNorm", ConvertBatchNorm},
   {"clip", ConvertClip},
   {"Convolution", ConvertConvolution},
+  {"Deconvolution", ConvertDeconvolution},
   {"Concat", ConvertConcatenate},
   {"Dropout", ConvertDropout},
   {"elemwise_add", ConvertElementwiseAdd},
diff --git a/src/operator/subgraph/tensorrt/nnvm_to_onnx.cc b/src/operator/subgraph/tensorrt/nnvm_to_onnx.cc
index 6116f296e300..84580d0b05d0 100644
--- a/src/operator/subgraph/tensorrt/nnvm_to_onnx.cc
+++ b/src/operator/subgraph/tensorrt/nnvm_to_onnx.cc
@@ -31,6 +31,7 @@
 #include <mxnet/base.h>
 #include <nnvm/graph.h>
 #include <nnvm/pass_functions.h>
+#include <operator/nn/deconvolution-inl.h>
 
 #include "../../../common/utils.h"
 #include "../../../ndarray/ndarray_function.h"
@@ -170,20 +171,25 @@ std::string ConvertNnvmGraphToOnnx(
   return serialized_onnx_graph;
 }
 
-void ConvertConvolution(NodeProto* node_proto, const NodeAttrs& attrs,
-                        const nnvm::IndexedGraph& /*ig*/,
-                        const array_view<IndexedGraph::NodeEntry>& /*inputs*/) {
-  const auto& conv_param = nnvm::get<op::ConvolutionParam>(attrs.parsed);
-
-  node_proto->set_op_type("Conv");
+template <class ConvDeconvParam>
+void ConvDeconvConvertHelper(NodeProto* node_proto, const NodeAttrs& attrs,
+                             const nnvm::IndexedGraph& /*ig*/,
+                             const array_view<IndexedGraph::NodeEntry>& /*input*/,
+                             const ConvDeconvParam& param,
+                             ConvDeconvType type) {
+  if (type == ConvDeconvType::Convolution) {
+    node_proto->set_op_type("Conv");
+  } else {
+    node_proto->set_op_type("ConvTranspose");
+  }
 
-  const mxnet::TShape kernel = conv_param.kernel;
-  const mxnet::TShape stride = conv_param.stride;
-  const mxnet::TShape dilate = conv_param.dilate;
-  const mxnet::TShape pad = conv_param.pad;
-  const uint32_t num_group = conv_param.num_group;
+  const mxnet::TShape kernel = param.kernel;
+  const mxnet::TShape stride = param.stride;
+  const mxnet::TShape dilate = param.dilate;
+  const mxnet::TShape pad = param.pad;
+  const uint32_t num_group = param.num_group;
   // const bool no_bias = conv_param.no_bias;
-  const dmlc::optional<int> layout = conv_param.layout;
+  const dmlc::optional<int> layout = param.layout;
 
   // dilations
   AttributeProto* const dilations = node_proto->add_attribute();
@@ -226,8 +232,24 @@ void ConvertConvolution(NodeProto* node_proto, const NodeAttrs& attrs,
   for (const dim_t kval : stride) {
     strides->add_ints(static_cast<int64>(kval));
   }
+}
+
+void ConvertConvolution(NodeProto* node_proto, const NodeAttrs& attrs,
+                        const nnvm::IndexedGraph& ig,
+                        const array_view<IndexedGraph::NodeEntry>& inputs) {
+  const auto& conv_param = nnvm::get<op::ConvolutionParam>(attrs.parsed);
+  ConvDeconvConvertHelper(node_proto, attrs, ig, inputs, conv_param,
+      ConvDeconvType::Convolution);
 }  // end ConvertConvolution
 
+void ConvertDeconvolution(NodeProto* node_proto, const NodeAttrs& attrs,
+                          const nnvm::IndexedGraph& ig,
+                          const array_view<IndexedGraph::NodeEntry>& inputs) {
+  const auto& deconv_param = nnvm::get<op::DeconvolutionParam>(attrs.parsed);
+  ConvDeconvConvertHelper(node_proto, attrs, ig, inputs, deconv_param,
+      ConvDeconvType::Deconvolution);
+}  // end ConvertDeconvolution
+
 void ConvertPooling(NodeProto* node_proto, const NodeAttrs& attrs,
                     const nnvm::IndexedGraph& /*ig*/,
                     const array_view<IndexedGraph::NodeEntry>& /*inputs*/) {
diff --git a/src/operator/subgraph/tensorrt/tensorrt-inl.h b/src/operator/subgraph/tensorrt/tensorrt-inl.h
index e258d892aaba..a6b93f10598a 100644
--- a/src/operator/subgraph/tensorrt/tensorrt-inl.h
+++ b/src/operator/subgraph/tensorrt/tensorrt-inl.h
@@ -88,6 +88,7 @@ class TensorrtSelector : public SubgraphSelector {
     "clip",
     "Concat",
     "Convolution",
+    "Deconvolution",
     "Dropout",
     "elemwise_add",
     "elemwise_sub",
@@ -104,6 +105,7 @@ class TensorrtSelector : public SubgraphSelector {
   const std::unordered_set<std::string> withWeightsOps = {
     "BatchNorm",
     "Convolution",
+    "Deconvolution",
     "FullyConnected"
   };
 
diff --git a/tests/python/tensorrt/test_tensorrt_deconv.py b/tests/python/tensorrt/test_tensorrt_deconv.py
new file mode 100644
index 000000000000..ef567d1dae3c
--- /dev/null
+++ b/tests/python/tensorrt/test_tensorrt_deconv.py
@@ -0,0 +1,63 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import mxnet as mx
+from mxnet.test_utils import assert_almost_equal
+
+def get_params():
+    arg_params = {}
+    aux_params = {}
+    arg_params["trt_bn_test_conv_weight"] = mx.nd.ones((1, 1, 3, 3))
+    arg_params["trt_bn_test_deconv_weight"] = mx.nd.ones((1, 1, 3, 3))
+    return arg_params, aux_params
+
+def get_symbol():
+    data = mx.sym.Variable("data")
+    conv = mx.sym.Convolution(data=data, kernel=(3,3), no_bias=True, num_filter=1, num_group=1,
+                              name="trt_bn_test_conv")
+    deconv = mx.sym.Deconvolution(data=conv, kernel=(3, 3), no_bias=True, num_filter=1,
+                                  num_group=1, name="trt_bn_test_deconv")
+    return deconv
+
+def test_deconvolution_produce_same_output_as_tensorrt():
+    arg_params, aux_params = get_params()
+    arg_params_trt, aux_params_trt = get_params()
+
+    sym = get_symbol()
+    sym_trt = get_symbol().get_backend_symbol("TensorRT")
+
+    mx.contrib.tensorrt.init_tensorrt_params(sym_trt, arg_params_trt, aux_params_trt)
+
+    executor = sym.simple_bind(ctx=mx.gpu(), data=(1, 1, 3, 3), grad_req='null', force_rebind=True)
+    executor.copy_params_from(arg_params, aux_params)
+
+    executor_trt = sym_trt.simple_bind(ctx=mx.gpu(), data=(1, 1, 3, 3), grad_req='null',
+                                  force_rebind=True)
+    executor_trt.copy_params_from(arg_params_trt, aux_params_trt)
+
+    input_data = mx.nd.random.uniform(low=0, high=1, shape=(1, 1, 3, 3))
+
+    y = executor.forward(is_train=False, data=input_data)
+    y_trt = executor_trt.forward(is_train=False, data=input_data)
+
+    print(y[0].asnumpy())
+    print(y_trt[0].asnumpy())
+    assert_almost_equal(y[0].asnumpy(), y_trt[0].asnumpy(), 1e-4, 1e-4)
+
+if __name__ == '__main__':
+    import nose
+    nose.runmodule()

From 9d7fc7cbee09de2694022995d0601cb4316e4988 Mon Sep 17 00:00:00 2001
From: Cody Allen <ceedubs@gmail.com>
Date: Wed, 31 Jul 2019 16:09:23 -0700
Subject: [PATCH 129/813] Fix Scala Symbolic API some/Some typo (#15687)

---
 docs/api/scala/symbol.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/api/scala/symbol.md b/docs/api/scala/symbol.md
index aaddc2a8a2f0..f92548e4820d 100644
--- a/docs/api/scala/symbol.md
+++ b/docs/api/scala/symbol.md
@@ -41,7 +41,7 @@ The following example configures a two-layer neural network.
     val data = Symbol.Variable("data")
     val fc1 = Symbol.api.FullyConnected(Some(data), num_hidden = 128, name = "fc1")
     val act1 = Symbol.api.Activation(Some(fc1), "relu", "relu1")
-    val fc2 = Symbol.api.FullyConnected(some(act1), num_hidden = 64, name = "fc2")
+    val fc2 = Symbol.api.FullyConnected(Some(act1), num_hidden = 64, name = "fc2")
     val net = Symbol.api.SoftmaxOutput(Some(fc2), name = "out")
     :type net
     // org.apache.mxnet.Symbol

From 811d5168507785a00dba81262a84ff4e043c5b00 Mon Sep 17 00:00:00 2001
From: Xinyu Chen <xinyu1.chen@intel.com>
Date: Thu, 1 Aug 2019 12:43:36 +0800
Subject: [PATCH 130/813] Add MKLDNN 4c layout to fix gluoncv
 se_resnext101_64x4d (#15692)

* add 4c type

* trigger
---
 src/operator/nn/mkldnn/mkldnn_base.cc | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src/operator/nn/mkldnn/mkldnn_base.cc b/src/operator/nn/mkldnn/mkldnn_base.cc
index e36a0f008821..a13337b122c3 100644
--- a/src/operator/nn/mkldnn/mkldnn_base.cc
+++ b/src/operator/nn/mkldnn/mkldnn_base.cc
@@ -329,6 +329,7 @@ mkldnn_memory_format_t GetDefaultFormat(const mkldnn::memory::desc &desc) {
       case mkldnn_nchw:
       case mkldnn_nhwc:
       case mkldnn_chwn:
+      case mkldnn_nChw4c:
       case mkldnn_nChw8c:
       case mkldnn_nChw16c:
         return mkldnn_nchw;
@@ -338,6 +339,7 @@ mkldnn_memory_format_t GetDefaultFormat(const mkldnn::memory::desc &desc) {
       case mkldnn_iohw:
       case mkldnn_oIhw8i:
       case mkldnn_oIhw16i:
+      case mkldnn_OIhw4i4o:
       case mkldnn_OIhw8i8o:
       case mkldnn_hwio_s8s8:
       case mkldnn_OIhw16i16o:
@@ -376,6 +378,7 @@ mkldnn_memory_format_t GetDefaultFormat(const mkldnn::memory::desc &desc) {
       case mkldnn_giohw:
       case mkldnn_hwigo:
       case mkldnn_hwigo_s8s8:
+      case mkldnn_gOIhw4i4o:
       case mkldnn_gOIhw8i8o:
       case mkldnn_gOIhw16i16o:
       case mkldnn_gOIhw4i16o4i:
@@ -383,6 +386,7 @@ mkldnn_memory_format_t GetDefaultFormat(const mkldnn::memory::desc &desc) {
       case mkldnn_gOIhw8i16o2i:
       case mkldnn_gOIhw8o16i2o:
       case mkldnn_gOIhw8o8i:
+      case mkldnn_gOIhw4o4i:
       case mkldnn_gOIhw16o16i:
       case mkldnn_gIOhw16o16i:
       case mkldnn_gOihw8o:

From 42a47b1cbd53026b7c69c915d17d507f6bc512d6 Mon Sep 17 00:00:00 2001
From: Zhennan Qin <zhennan.qin@intel.com>
Date: Thu, 1 Aug 2019 13:54:15 +0800
Subject: [PATCH 131/813] Fix _copy_to on MKLDNN backend (#15637)

* Fix _copy_to

* Add comment
---
 src/imperative/imperative_utils.h | 34 ++++++++++++++++++++++++++++---
 1 file changed, 31 insertions(+), 3 deletions(-)

diff --git a/src/imperative/imperative_utils.h b/src/imperative/imperative_utils.h
index b867162abc9b..07fe04782bd0 100644
--- a/src/imperative/imperative_utils.h
+++ b/src/imperative/imperative_utils.h
@@ -419,7 +419,14 @@ inline void PushFCompute(const FCompute& fn,
       // mapping from index in input_blobs to index in pre_temp_dst
       std::unordered_map<uint32_t, uint32_t> in_temp_idx_map;
 #if MXNET_USE_MKLDNN == 1
-      InvalidateOutputs(outputs, req);
+      if (exec_type != ExecType::kCrossDeviceCopy) {
+        // kCrossDeviceCopy is used for `_copy_to` operator, which doesn't compute immediately in
+        // its FCcomputeEx, but AsyncPush the copy operation to engine.
+        // So for the case that A is holding mkldnn memory, and then copy A to B, and then copy B
+        // back to A, we shouldn't invalidate outputs for copying B back to A, because at this time,
+        // copying A to B may not happen, and will corrupt A's memory.
+        InvalidateOutputs(outputs, req);
+      }
 #endif
       std::vector<OpReqType> tmp_req = req;
       // setup blobs
@@ -461,7 +468,14 @@ inline void PushFComputeEx(const FComputeEx& fn,
   const auto& run = [=](RunContext rctx) {
       OpContext opctx{need_grad, is_train, rctx, engine::CallbackOnComplete(), requested};
 #if MXNET_USE_MKLDNN == 1
-      InvalidateOutputs(outputs, req);
+      if (exec_type != ExecType::kCrossDeviceCopy) {
+        // kCrossDeviceCopy is used for `_copy_to` operator, which doesn't compute immediately in
+        // its FCcomputeEx, but AsyncPush the copy operation to engine.
+        // So for the case that A is holding mkldnn memory, and then copy A to B, and then copy B
+        // back to A, we shouldn't invalidate outputs for copying B back to A, because at this time,
+        // copying A to B may not happen, and will corrupt A's memory.
+        InvalidateOutputs(outputs, req);
+      }
 #endif
       fn(attrs, opctx, inputs, req, outputs);
       if (ctx.dev_mask() == gpu::kDevMask && exec_type == ExecType::kSync && !rctx.is_bulk) {
@@ -508,7 +522,14 @@ inline void PushOperator(const OpStatePtr& state,
                           engine::CallbackOnComplete on_complete) {
       OpContext opctx{need_grad, is_train, rctx, on_complete, requested};
 #if MXNET_USE_MKLDNN == 1
-      InvalidateOutputs(outputs, req);
+      if (exec_type != ExecType::kCrossDeviceCopy) {
+        // kCrossDeviceCopy is used for `_copy_to` operator, which doesn't compute immediately in
+        // its FCcomputeEx, but AsyncPush the copy operation to engine.
+        // So for the case that A is holding mkldnn memory, and then copy A to B, and then copy B
+        // back to A, we shouldn't invalidate outputs for copying B back to A, because at this time,
+        // copying A to B may not happen, and will corrupt A's memory.
+        InvalidateOutputs(outputs, req);
+      }
 #endif
       fcompute_ex(state, opctx, inputs, req, outputs);
       if (ctx.dev_mask() == gpu::kDevMask && exec_type == ExecType::kSync
@@ -547,7 +568,14 @@ inline void PushOperator(const OpStatePtr& state,
         // mapping from index in input_blobs to index in pre_temp_dst
         std::unordered_map<uint32_t, uint32_t> in_temp_idx_map;
 #if MXNET_USE_MKLDNN == 1
+      if (exec_type != ExecType::kCrossDeviceCopy) {
+        // kCrossDeviceCopy is used for `_copy_to` operator, which doesn't compute immediately in
+        // its FCcomputeEx, but AsyncPush the copy operation to engine.
+        // So for the case that A is holding mkldnn memory, and then copy A to B, and then copy B
+        // back to A, we shouldn't invalidate outputs for copying B back to A, because at this time,
+        // copying A to B may not happen, and will corrupt A's memory.
         InvalidateOutputs(outputs, req);
+      }
 #endif
         std::vector<OpReqType> tmp_req = req;
         // populate input blobs and output blobs

From d5fcd984dadc662d8502d61b5602e41a83ea2eb1 Mon Sep 17 00:00:00 2001
From: Pedro Larroy <pedro.larroy.lists@gmail.com>
Date: Thu, 1 Aug 2019 12:59:15 -0700
Subject: [PATCH 132/813] [DOC] refine autograd docs (#15109)

* refine autograd docs

* CR comments

* Fix examples

* CR comments

* Followup CR

* CR
---
 docs/api/python/autograd/autograd.md | 75 +++++++++++++++++++++++++---
 python/mxnet/autograd.py             |  3 ++
 python/mxnet/ndarray/ndarray.py      |  2 +
 3 files changed, 74 insertions(+), 6 deletions(-)

diff --git a/docs/api/python/autograd/autograd.md b/docs/api/python/autograd/autograd.md
index 1905831b16d9..82da4eac05b5 100644
--- a/docs/api/python/autograd/autograd.md
+++ b/docs/api/python/autograd/autograd.md
@@ -42,16 +42,28 @@ to allocate space for the gradient. Then, start a `with autograd.record()` block
 and do some computation. Finally, call `backward()` on the result:
 
 ```python
->>> x = mx.nd.array([1,2,3,4])
->>> x.attach_grad()
->>> with mx.autograd.record():
-...     y = x * x + 1
->>> y.backward()
->>> print(x.grad)
+import mxnet as mx
+x = mx.nd.array([1,2,3,4])
+x.attach_grad()
+with mx.autograd.record():
+    y = x * x + 1
+y.backward()
+print(x.grad)
+```
+
+Which outputs:
+
+```
 [ 2.  4.  6.  8.]
 <NDArray 4 @cpu(0)>
 ```
 
+Gradient recording is enabled during the scope of the `with mx.autograd.record():` statement, then
+disabled when we go out of that scope.
+
+It can be also set manually by executing `mx.autograd.set_recording(True)`, and turning it off after
+we no longer want to record operations with `mx.autograd.set_recording(False)`.
+
 
 ## Train mode and Predict Mode
 
@@ -76,8 +88,59 @@ Detailed tutorials are available in Part 1 of
 [the MXNet gluon book](http://gluon.mxnet.io/).
 
 
+# Higher order gradient
+
+Some operators support higher order gradients. Some operators support differentiating multiple
+times, and others two, most just once.
+
+For calculating higher order gradients, we can use the `mx.autograd.grad` function while recording
+and then call backward, or call `mx.autograd.grad` two times. If we do the latter, is important that
+the first call uses `create_graph=True` and `retain_graph=True` and the second call uses
+`create_graph=False` and `retain_graph=True`. Otherwise we will not get the results that we want. If
+we would be to recreate the graph in the second call, we would end up with a graph of just the
+backward nodes, not the full initial graph that includes the forward nodes.
+
+The pattern to calculate higher order gradients is the following:
+
+```python
+from mxnet import ndarray as nd
+from mxnet import autograd as ag
+x = nd.array([1,2,3])
+x.attach_grad()
+def f(x):
+    # Any function which supports higher oder gradient
+    return nd.log(x)
+```
+
+If the operators used in `f` don't support higher order gradients you will get an error like
+`operator ... is non-differentiable because it didn't register FGradient attribute.`. This means
+that it doesn't support getting the gradient of the gradient. Which is, running backward on
+the backward graph.
+
+Using mxnet.autograd.grad multiple times:
+
+```python
+with ag.record():
+    y = f(x)
+    x_grad = ag.grad(heads=y, variables=x, create_graph=True, retain_graph=True)[0]
+    x_grad_grad = ag.grad(heads=x_grad, variables=x, create_graph=False, retain_graph=False)[0]
+```
+
+Running backward on the backward graph:
+
+```python
+with ag.record():
+    y = f(x)
+    x_grad = ag.grad(heads=y, variables=x, create_graph=True, retain_graph=True)[0]
+x_grad.backward()
+x_grad_grad = x.grad
+```
 
+Both methods are equivalent, except that in the second case, retain_graph on running backward is set
+to False by default. But both calls are running a backward pass as on the graph as usual to get the
+gradient of the first gradient `x_grad` with respect to `x` evaluated at the value of `x`.
 
+For more examples, check the [higher order gradient unit tests](https://github.com/apache/incubator-mxnet/blob/master/tests/python/unittest/test_higher_order_grad.py).
 
 
 <script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
diff --git a/python/mxnet/autograd.py b/python/mxnet/autograd.py
index f461b77e2818..6f1cc4367821 100644
--- a/python/mxnet/autograd.py
+++ b/python/mxnet/autograd.py
@@ -197,6 +197,9 @@ def predict_mode():
 def mark_variables(variables, gradients, grad_reqs='write'):
     """Mark NDArrays as variables to compute gradient for autograd.
 
+    This is equivalent to the function .attach_grad() in a variable, but with this
+    call we can set the gradient to any value.
+
     Parameters
     ----------
     variables: NDArray or list of NDArray
diff --git a/python/mxnet/ndarray/ndarray.py b/python/mxnet/ndarray/ndarray.py
index 53c563854511..3d8a7aa98c94 100644
--- a/python/mxnet/ndarray/ndarray.py
+++ b/python/mxnet/ndarray/ndarray.py
@@ -2243,6 +2243,8 @@ def attach_grad(self, grad_req='write', stype=None):
         """Attach a gradient buffer to this NDArray, so that `backward`
         can compute gradient with respect to it.
 
+        The gradient is initialized to zeros.
+
         Parameters
         ----------
         grad_req : {'write', 'add', 'null'}

From 773f4dc3042921a1e9d07503f6bf750554696212 Mon Sep 17 00:00:00 2001
From: Zhennan Qin <zhennan.qin@intel.com>
Date: Fri, 2 Aug 2019 04:37:58 +0800
Subject: [PATCH 133/813] Fix quantized concat when inputs are mixed int8 and
 uint8 (#15693)

---
 .../quantization/mkldnn/mkldnn_quantized_concat.cc   | 12 +++++++++++-
 tests/python/mkl/test_subgraph.py                    | 11 +++++++++++
 2 files changed, 22 insertions(+), 1 deletion(-)

diff --git a/src/operator/quantization/mkldnn/mkldnn_quantized_concat.cc b/src/operator/quantization/mkldnn/mkldnn_quantized_concat.cc
index d9e884e82806..2a4c6d612e65 100644
--- a/src/operator/quantization/mkldnn/mkldnn_quantized_concat.cc
+++ b/src/operator/quantization/mkldnn/mkldnn_quantized_concat.cc
@@ -64,22 +64,32 @@ static void MKLDNNQuantizedConcatForward(const nnvm::NodeAttrs& attrs, const OpC
   std::vector<const mkldnn::memory*> data_mem;
   // new_data_mem is for auto-free new created mkldnn memory
   std::vector<std::shared_ptr<mkldnn::memory>> new_data_mem;
+  const auto out_dtype = out_data[quantized_concat_enum::kOut].dtype();
   for (int i = 0; i < param_.num_args; ++i) {
     auto i_scale = GetScale(in_data[i], data_min[i], data_max[i]);
     if (i_scale == out_scale) {
+      CHECK(in_data[i].dtype() == out_dtype);
       auto mem = in_data[i].GetMKLDNNData();
       data_mem.push_back(mem);
       data_md.push_back(mem->get_primitive_desc());
     } else {
       auto mem = in_data[i].GetMKLDNNData();
       auto pd = mem->get_primitive_desc();
+      if (in_data[i].dtype() != out_dtype) {
+        auto mem_desc = pd.desc();
+        mkldnn::memory::desc new_md(
+            mkldnn::memory::dims(mem_desc.data.dims, mem_desc.data.dims + mem_desc.data.ndims),
+            get_mkldnn_type(out_dtype), static_cast<mkldnn::memory::format>(mem_desc.data.format));
+        pd = mkldnn::memory::primitive_desc(new_md, CpuEngine::Get()->get_engine());
+      }
       const auto rescaled_mem = std::make_shared<mkldnn::memory>(pd);
       new_data_mem.push_back(rescaled_mem);
       std::vector<float> reorder_scale = {out_scale / i_scale};
       primitive_attr reorder_attr;
       reorder_attr.set_int_output_round_mode(round_mode::round_nearest);
       reorder_attr.set_output_scales(0, reorder_scale);
-      const auto reorder_pd = mkldnn::reorder::primitive_desc(pd, pd, reorder_attr);
+      const auto reorder_pd =
+          mkldnn::reorder::primitive_desc(mem->get_primitive_desc(), pd, reorder_attr);
       MKLDNNStream::Get()->RegisterPrim(mkldnn::reorder(reorder_pd, *mem, *rescaled_mem));
       data_mem.push_back(rescaled_mem.get());
       data_md.push_back(pd);
diff --git a/tests/python/mkl/test_subgraph.py b/tests/python/mkl/test_subgraph.py
index b25fefc6cc0e..563fff1a6aa1 100644
--- a/tests/python/mkl/test_subgraph.py
+++ b/tests/python/mkl/test_subgraph.py
@@ -401,6 +401,15 @@ def single_concat(data_shape, input_num, dim):
   concat = mx.symbol.Concat(*inputs, name="concat", dim=dim)
   return concat
 
+def single_concat_pos_neg(data_shape):
+  data, weight = head_symbol(data_shape)
+  conv = mx.symbol.Convolution(data=data, weight=weight, name='conv', num_filter=4,
+                               kernel=(1, 1), stride=(1, 1), no_bias=True)
+  relu = mx.symbol.Activation(data=conv, name='relu', act_type='relu')
+  inputs = [data, relu]
+  concat = mx.symbol.Concat(*inputs, name="concat", dim=1)
+  return concat
+
 # concat scale alignment case
 def concat_scale_align(data_shape):
   data, weight = head_symbol(data_shape)
@@ -738,6 +747,8 @@ def test_pos_single_concat():
       net = single_concat(data_shape, 4, 3)
       check_quantize(net, data_shape, out_type, name='conv', check_calibration=False)
       check_quantize(net, data_shape, out_type, name='conv', check_calibration=False, gluon_forward=True)
+      net = single_concat_pos_neg(data_shape)
+      check_quantize(net, data_shape, out_type, name='', check_calibration=False)
 
 @with_seed()
 def test_pos_concat_scale_align():

From b3064c5914ba9bebf2c0489f29d21368dd549ae8 Mon Sep 17 00:00:00 2001
From: Xinyu Chen <xinyu1.chen@intel.com>
Date: Fri, 2 Aug 2019 04:41:35 +0800
Subject: [PATCH 134/813] [MKLDNN]Enhance Quantization APIs and Tutorial
 (#15448)

* enhance api and new tutorial

* Update MKLDNN_QUANTIZATION.md

update

* fix lint

* modify pics

* skip test

* add quantize layer in graph

* update

* remove center css flag

* change requantize color

* fix markdown pics

* change to use png

* Update MKLDNN_QUANTIZATION.md

update

* enable ipython script

* fix png

* fix lint

* Update MKLDNN_QUANTIZATION.md

* change title

* trigger

* use lower case

* some typo

* some typo

* use dmlc web data

* trigger

* trigger
---
 docs/tutorials/index.md                       |   3 +-
 docs/tutorials/mkldnn/mkldnn_quantization.md  | 259 ++++++++++++++++++
 example/quantization/README.md                |  95 +++++--
 .../quantization/imagenet_gen_qsym_mkldnn.py  | 168 ++++++------
 python/mxnet/contrib/quantization.py          | 237 ++++++++++++++++
 tests/tutorials/test_tutorials.py             |   3 +
 6 files changed, 657 insertions(+), 108 deletions(-)
 create mode 100644 docs/tutorials/mkldnn/mkldnn_quantization.md

diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md
index 6e31e825e2ca..e01a30dbe68c 100644
--- a/docs/tutorials/index.md
+++ b/docs/tutorials/index.md
@@ -105,6 +105,8 @@ Select API:&nbsp;
     * [Module to Gluon API](/tutorials/python/module_to_gluon.html)
     * [Gluon end to end from training to inference](/tutorials/gluon/gluon_from_experiment_to_deployment.html)
     * [Automatic Mixed Precision in Gluon](/tutorials/amp/amp_tutorial.html)
+    * [How to build and install MXNet with MKL-DNN backend](/tutorials/mkldnn/MKLDNN_README.html)
+    * [How to quantize custom models with MKL-DNN backend](/tutorials/mkldnn/mkldnn_quantization.html)<span style="color:red"> (new!) </span>
 * API Guides
     * Core APIs
         * NDArray
@@ -157,7 +159,6 @@ Select API:&nbsp;
     * [Large-Scale Multi-Host Multi-GPU Image Classification](/tutorials/vision/large_scale_classification.html)
     * [Importing an ONNX model into MXNet](/tutorials/onnx/super_resolution.html)
     * [Optimizing Deep Learning Computation Graphs with TensorRT](/tutorials/tensorrt/inference_with_trt.html)
-    * [How to build and install MXNet with MKL-DNN backend](/tutorials/mkldnn/MKLDNN_README.html)
 * API Guides
     * Core APIs
         * NDArray
diff --git a/docs/tutorials/mkldnn/mkldnn_quantization.md b/docs/tutorials/mkldnn/mkldnn_quantization.md
new file mode 100644
index 000000000000..459bf2a17d40
--- /dev/null
+++ b/docs/tutorials/mkldnn/mkldnn_quantization.md
@@ -0,0 +1,259 @@
+
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Quantize custom models with MKL-DNN backend
+
+This document is to introduce how to quantize the customer models from FP32 to INT8 with Apache/MXNet toolkit and APIs under Intel CPU.
+
+If you are not familiar with Apache/MXNet quantization flow, please reference [quantization blog](https://medium.com/apache-mxnet/model-quantization-for-production-level-neural-network-inference-f54462ebba05) first, and the performance data is shown in [Apache/MXNet C++ interface](https://github.com/apache/incubator-mxnet/tree/master/cpp-package/example/inference) and [GluonCV](https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html). 
+
+## Installation and Prerequisites
+
+Installing MXNet with MKLDNN backend is an easy and essential process. You can follow [How to build and install MXNet with MKL-DNN backend](https://mxnet.incubator.apache.org/tutorials/mkldnn/MKLDNN_README.html) to build and install MXNet from source. Also, you can install the release or nightly version via PyPi and pip directly by running:
+
+```
+# release version
+pip install mxnet-mkl
+# nightly version
+pip install mxnet-mkl --pre
+```
+
+## Image Classification Demo
+
+A quantization script [imagenet_gen_qsym_mkldnn.py](https://github.com/apache/incubator-mxnet/blob/master/example/quantization/imagenet_gen_qsym_mkldnn.py) has been designed to launch quantization for image-classification models. This script is  integrated with [Gluon-CV modelzoo](https://gluon-cv.mxnet.io/model_zoo/classification.html), so that all pre-trained models can be downloaded from Gluon-CV and then converted for quantization. For details, you can refer [Model Quantization with Calibration Examples](https://github.com/apache/incubator-mxnet/blob/master/example/quantization/README.md).
+
+## Integrate Quantization Flow to Your Project
+
+Quantization flow works for both symbolic and Gluon models. If you're using Gluon, you can first refer [Saving and Loading Gluon Models](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/save_load_params.html) to hybridize your computation graph and export it as a symbol before running quantization.
+
+In general, the quantization flow includes 4 steps. The user can get the acceptable accuracy from step 1 to 3 with minimum effort. Most of thing in this stage is out-of-box and the data scientists and researchers only need to focus on how to represent data and layers in their model. After a quantized model is generated, you may want to deploy it online and the performance will be the next key point. Thus, step 4, calibration, can improve the performance a lot by reducing lots of runtime calculation.
+
+![quantization flow](https://github.com/dmlc/web-data/raw/master/mxnet/tutorials/mkldnn/quantization/quantization.png)
+
+Now, we are going to take Gluon ResNet18 as an example to show how each step work.
+
+### Initialize Model
+
+```python
+import logging
+import mxnet as mx
+from mxnet.gluon.model_zoo import vision
+from mxnet.contrib.quantization import *
+
+logging.basicConfig()
+logger = logging.getLogger('logger')
+logger.setLevel(logging.INFO)
+
+batch_shape = (1, 3, 224, 224)
+resnet18 = vision.resnet18_v1(pretrained=True)
+resnet18.hybridize()
+resnet18.forward(mx.nd.zeros(batch_shape))
+resnet18.export('resnet18_v1')
+sym, arg_params, aux_params = mx.model.load_checkpoint('resnet18_v1', 0)
+# (optional) visualize float32 model
+mx.viz.plot_network(sym)
+```
+First, we download resnet18-v1 model from gluon modelzoo and export it as a symbol. You can visualize float32 model. Below is a raw residual block.
+
+![float32 model](https://github.com/dmlc/web-data/raw/master/mxnet/tutorials/mkldnn/quantization/fp32_raw.png)
+
+#### Model Fusion
+
+```python
+sym = sym.get_backend_symbol('MKLDNN_QUANTIZE')
+# (optional) visualize fused float32 model
+mx.viz.plot_network(sym)
+```
+It's important to add this line to enable graph fusion before quantization to get better performance. Below is a fused residual block. Batchnorm, Activation and elemwise_add are fused into Convolution.
+
+![float32 fused model](https://github.com/dmlc/web-data/raw/master/mxnet/tutorials/mkldnn/quantization/fp32_fusion.png)
+
+### Quantize Model
+
+A python interface `quantize_graph` is provided for the user. Thus, it is very flexible for the data scientist to construct the expected models based on different requirements in a real deployment.
+
+```python
+# quantize configs
+# set exclude layers
+excluded_names = []
+# set calib mode.
+calib_mode = 'none'
+# set calib_layer
+calib_layer = None
+# set quantized_dtype
+quantized_dtype = 'auto'
+logger.info('Quantizing FP32 model Resnet18-V1')
+qsym, qarg_params, aux_params, collector = quantize_graph(sym=sym, arg_params=arg_params, aux_params=aux_params,
+                                                          excluded_sym_names=excluded_names,
+                                                          calib_mode=calib_mode, calib_layer=calib_layer,
+                                                          quantized_dtype=quantized_dtype, logger=logger)
+# (optional) visualize quantized model
+mx.viz.plot_network(qsym)
+# save quantized model
+mx.model.save_checkpoint('quantized-resnet18_v1', 0, qsym, qarg_params, aux_params)
+```
+
+By applying `quantize_graph` to the symbolic model, a new quantized model can be generated, named `qsym` along with its parameters. We can see `_contrib_requantize` operators are inserted after `Convolution` to convert the INT32 output to FP32. 
+
+![none calibrated model](https://github.com/dmlc/web-data/raw/master/mxnet/tutorials/mkldnn/quantization/none_calib.png)
+
+Below table gives some descriptions.
+
+| param              | type            | description|
+|--------------------|-----------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| excluded_sym_names | list of strings | A list of strings representing the names of the symbols that users want to excluding from being quantized.|
+| calib_mode         | str             | If calib_mode='none', no calibration will be used and the thresholds for requantization after the corresponding layers will be calculated at runtime by calling min  and max operators. The quantized models generated in this mode are normally 10-20% slower than those with  calibrations during inference.<br>If calib_mode='naive', the min and max values of the layer outputs from a calibration dataset will be directly taken as the thresholds for quantization.<br>If calib_mode='entropy', the thresholds for quantization will be derived such that the KL divergence between the distributions of FP32 layer outputs and  quantized layer outputs is minimized based upon the calibration dataset. |
+| calib_layer        | function        | Given a layer's output name in string, return True or False for deciding whether to calibrate this layer.<br>If yes, the statistics of the layer's output will be collected; otherwise, no information of the layer's output will be collected.<br>If not provided, all the layers' outputs that need requantization will be collected.|
+| quantized_dtype    | str             | The quantized destination type for input data. Currently support 'int8', 'uint8' and 'auto'.<br>'auto' means automatically select output type according to calibration result.|
+
+### Evaluate & Tune
+
+Now, you get a pair of quantized symbol and params file for inference. For Gluon inference, only difference is to load model and params by a SymbolBlock as below example:
+
+```python
+quantized_net = mx.gluon.SymbolBlock.imports('quantized-resnet18_v1-symbol.json', 'data', 'quantized-resnet18_v1-0000.params')
+quantized_net.hybridize(static_shape=True, static_alloc=True)
+batch_size = 1
+data = mx.nd.ones((batch_size,3,224,224))
+quantized_net(data)
+```
+
+Now, you can get the accuracy from a quantized network. Furthermore, you can try to select different layers or OPs to be quantized by `excluded_sym_names` parameter and figure out an acceptable accuracy.
+
+### Calibrate Model (optional for performance)
+
+The quantized model generated in previous steps can be very slow during inference since it will calculate min and max at runtime. We recommend using offline calibration for better performance by setting `calib_mode` to `naive` or `entropy`. And then calling `set_monitor_callback` api to collect layer information with a subset of the validation datasets before int8 inference.
+
+```python
+# quantization configs
+# set exclude layers
+excluded_names = []
+# set calib mode.
+calib_mode = 'naive'
+# set calib_layer
+calib_layer = None
+# set quantized_dtype
+quantized_dtype = 'auto'
+logger.info('Quantizing FP32 model resnet18-V1')
+cqsym, cqarg_params, aux_params, collector = quantize_graph(sym=sym, arg_params=arg_params, aux_params=aux_params,
+                                                          excluded_sym_names=excluded_names,
+                                                          calib_mode=calib_mode, calib_layer=calib_layer,
+                                                          quantized_dtype=quantized_dtype, logger=logger)
+
+# download imagenet validation dataset
+mx.test_utils.download('http://data.mxnet.io/data/val_256_q90.rec', 'dataset.rec')
+# set rgb info for data
+mean_std = {'mean_r': 123.68, 'mean_g': 116.779, 'mean_b': 103.939, 'std_r': 58.393, 'std_g': 57.12, 'std_b': 57.375}
+# set batch size
+batch_size = 16
+# create DataIter
+data = mx.io.ImageRecordIter(path_imgrec='dataset.rec', batch_size=batch_size, data_shape=batch_shape[1:], rand_crop=False, rand_mirror=False, **mean_std)
+# create module
+mod = mx.mod.Module(symbol=sym, label_names=None, context=mx.cpu())
+mod.bind(for_training=False, data_shapes=data.provide_data, label_shapes=None)
+mod.set_params(arg_params, aux_params)
+
+# calibration configs
+# set num_calib_batches
+num_calib_batches = 5
+max_num_examples = num_calib_batches * batch_size
+# monitor FP32 Inference
+mod._exec_group.execs[0].set_monitor_callback(collector.collect, monitor_all=True)
+num_batches = 0
+num_examples = 0
+for batch in data:
+    mod.forward(data_batch=batch, is_train=False)
+    num_batches += 1
+    num_examples += batch_size
+    if num_examples >= max_num_examples:
+        break
+if logger is not None:
+    logger.info("Collected statistics from %d batches with batch_size=%d"
+                % (num_batches, batch_size))
+```
+
+After that, layer information will be filled into the `collector` returned by `quantize_graph` api. Then, you need to write the layer information into int8 model by calling `calib_graph` api.
+
+
+```python
+# write scaling factor into quantized symbol
+cqsym, cqarg_params, aux_params = calib_graph(qsym=cqsym, arg_params=arg_params, aux_params=aux_params,
+                                            collector=collector, calib_mode=calib_mode,
+                                            quantized_dtype=quantized_dtype, logger=logger)
+# (optional) visualize quantized model
+mx.viz.plot_network(cqsym)
+```
+
+Below is a quantized residual block with naive calibration. We can see `min_calib_range` and `max_calib_range` are written into `_contrib_requantize` operators.
+
+![naive calibrated model](https://github.com/dmlc/web-data/raw/master/mxnet/tutorials/mkldnn/quantization/naive_calib.png)
+
+When you get a quantized model with calibration, keeping sure to call fusion api again since this can fuse some `requantize` or `dequantize` operators for further performance improvement.
+
+```python
+# perform post-quantization fusion
+cqsym = cqsym.get_backend_symbol('MKLDNN_QUANTIZE')
+# (optional) visualize post-quantized model
+mx.viz.plot_network(cqsym)
+# save quantized model
+mx.model.save_checkpoint('quantized-resnet18_v1', 0, cqsym, cqarg_params, aux_params)
+```
+
+Below is a post-quantized residual block. We can see `_contrib_requantize` operators are fused into `Convolution` operators.
+
+![post-quantized model](https://github.com/dmlc/web-data/raw/master/mxnet/tutorials/mkldnn/quantization/post_quantize.png)
+
+BTW, You can also modify the `min_calib_range` and `max_calib_range` in the JSON file directly.
+
+```
+    {
+      "op": "_sg_mkldnn_conv", 
+      "name": "quantized_sg_mkldnn_conv_bn_act_6", 
+      "attrs": {
+        "max_calib_range": "3.562147", 
+        "min_calib_range": "0.000000", 
+        "quantized": "true", 
+        "with_act": "true", 
+        "with_bn": "true"
+      }, 
+......
+```
+
+### Tips for Model Calibration
+
+#### Accuracy Tuning
+
+- Try to use `entropy` calib mode;
+
+- Try to exclude some layers which may cause obvious accuracy drop;
+
+- Change calibration dataset by setting different `num_calib_batches` or shuffle your validation dataset;
+
+#### Performance Tuning
+
+- Keep sure to perform graph fusion before quantization;
+
+- If lots of `requantize` layers exist, keep sure to perform post-quantization fusion after calibration;
+
+- Compare the MXNet profile or `MKLDNN_VERBOSE` of float32 and int8 inference;
+
+## Deploy with Python/C++
+
+MXNet also supports deploy quantized models with C++. Refer [MXNet C++ Package](https://github.com/apache/incubator-mxnet/blob/master/cpp-package/README.md) for more details.
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/example/quantization/README.md b/example/quantization/README.md
index 09321beb7997..1ae58fbb3a69 100644
--- a/example/quantization/README.md
+++ b/example/quantization/README.md
@@ -9,13 +9,76 @@ This folder contains examples of quantizing a FP32 model with Intel® MKL-DNN or
 
 <h2 id="1">Model Quantization with Intel® MKL-DNN</h2>
 
-Intel® MKL-DNN supports quantization with subgraph features on Intel® CPU Platform and can bring performance improvements on the [Intel® Xeon® Scalable Platform](https://www.intel.com/content/www/us/en/processors/xeon/scalable/xeon-scalable-platform.html). A new quantization script `imagenet_gen_qsym_mkldnn.py` has been designed to launch quantization for CNN models with Intel® MKL-DNN. This script integrates with [Gluon-CV modelzoo](https://gluon-cv.mxnet.io/model_zoo/classification.html), so that more pre-trained models can be downloaded from Gluon-CV and then converted for quantization. This script also supports custom models.
-
-Calibration is used for generating a calibration table for the quantized symbol. The quantization script supports three methods:
-
-- **none:** No calibration will be used. The thresholds for quantization will be calculated on the fly. This will result in inference speed slowdown and loss of accuracy in general.
-- **naive:** Simply take min and max values of layer outputs as thresholds for quantization. In general, the inference accuracy worsens with more examples used in calibration. It is recommended to use `entropy` mode as it produces more accurate inference results.
-- **entropy:** Calculate KL divergence of the fp32 output and quantized output for optimal thresholds. This mode is expected to produce the best inference accuracy of all three kinds of quantized models if the calibration dataset is representative enough of the inference dataset.
+Intel® MKL-DNN supports quantization with subgraph features on Intel® CPU Platform and can bring performance improvements on the [Intel® Xeon® Scalable Platform](https://www.intel.com/content/www/us/en/processors/xeon/scalable/xeon-scalable-platform.html). A new quantization script `imagenet_gen_qsym_mkldnn.py` has been designed to launch quantization for image-classification models with Intel® MKL-DNN. This script integrates with [Gluon-CV modelzoo](https://gluon-cv.mxnet.io/model_zoo/classification.html), so that more pre-trained models can be downloaded from Gluon-CV and then converted for quantization. To apply quantization flow to your project directly, please refer [Quantize custom models with MKL-DNN backend](https://mxnet.incubator.apache.org/tutorials/mkldnn/mkldnn_quantization.html).
+
+```
+usage: imagenet_gen_qsym_mkldnn.py [-h] [--model MODEL] [--epoch EPOCH]
+                                   [--no-pretrained] [--batch-size BATCH_SIZE]
+                                   [--label-name LABEL_NAME]
+                                   [--calib-dataset CALIB_DATASET]
+                                   [--image-shape IMAGE_SHAPE]
+                                   [--data-nthreads DATA_NTHREADS]
+                                   [--num-calib-batches NUM_CALIB_BATCHES]
+                                   [--exclude-first-conv] [--shuffle-dataset]
+                                   [--shuffle-chunk-seed SHUFFLE_CHUNK_SEED]
+                                   [--shuffle-seed SHUFFLE_SEED]
+                                   [--calib-mode CALIB_MODE]
+                                   [--quantized-dtype {auto,int8,uint8}]
+                                   [--enable-calib-quantize ENABLE_CALIB_QUANTIZE]
+
+Generate a calibrated quantized model from a FP32 model with Intel MKL-DNN
+support
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --model MODEL         model to be quantized.
+  --epoch EPOCH         number of epochs, default is 0
+  --no-pretrained       If enabled, will not download pretrained model from
+                        MXNet or Gluon-CV modelzoo.
+  --batch-size BATCH_SIZE
+  --label-name LABEL_NAME
+  --calib-dataset CALIB_DATASET
+                        path of the calibration dataset
+  --image-shape IMAGE_SHAPE
+  --data-nthreads DATA_NTHREADS
+                        number of threads for data decoding
+  --num-calib-batches NUM_CALIB_BATCHES
+                        number of batches for calibration
+  --exclude-first-conv  excluding quantizing the first conv layer since the
+                        input data may have negative value which doesn't
+                        support at moment
+  --shuffle-dataset     shuffle the calibration dataset
+  --shuffle-chunk-seed SHUFFLE_CHUNK_SEED
+                        shuffling chunk seed, see https://mxnet.incubator.apac
+                        he.org/api/python/io/io.html?highlight=imager#mxnet.io
+                        .ImageRecordIter for more details
+  --shuffle-seed SHUFFLE_SEED
+                        shuffling seed, see https://mxnet.incubator.apache.org
+                        /api/python/io/io.html?highlight=imager#mxnet.io.Image
+                        RecordIter for more details
+  --calib-mode CALIB_MODE
+                        calibration mode used for generating calibration table
+                        for the quantized symbol; supports 1. none: no
+                        calibration will be used. The thresholds for
+                        quantization will be calculated on the fly. This will
+                        result in inference speed slowdown and loss of
+                        accuracy in general. 2. naive: simply take min and max
+                        values of layer outputs as thresholds for
+                        quantization. In general, the inference accuracy
+                        worsens with more examples used in calibration. It is
+                        recommended to use `entropy` mode as it produces more
+                        accurate inference results. 3. entropy: calculate KL
+                        divergence of the fp32 output and quantized output for
+                        optimal thresholds. This mode is expected to produce
+                        the best inference accuracy of all three kinds of
+                        quantized models if the calibration dataset is
+                        representative enough of the inference dataset.
+  --quantized-dtype {auto,int8,uint8}
+                        quantization destination data type for input data
+  --enable-calib-quantize ENABLE_CALIB_QUANTIZE
+                        If enabled, the quantize op will be calibrated offline
+                        if calibration mode is enabled
+```
 
 Use the following command to install [Gluon-CV](https://gluon-cv.mxnet.io/):
 
@@ -23,12 +86,13 @@ Use the following command to install [Gluon-CV](https://gluon-cv.mxnet.io/):
 pip install gluoncv
 ```
 
-The following models have been tested on Linux systems.
+Below are some quantization demos. These models have been tested on Linux systems.
 
 | Model | Source | Dataset | FP32 Accuracy (top-1/top-5)| INT8 Accuracy (top-1/top-5)|
 |:---|:---|---|:---:|:---:|
 | [ResNet18-V1](#3)  | [Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)  | [Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)  |70.15%/89.38%|69.92%/89.26%|
 | [ResNet50-V1](#3)  | [Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)  | [Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)  | 76.34%/93.13%  |  75.91%/92.95% |
+| [ResNet50-V1b](#3)  | [Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)  | [Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)  | 76.82%/93.38% |  76.39%/93.24% |
 | [ResNet101-V1](#3)  | [Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)  | [Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)  | 77.33%/93.59%  | 77.05%/93.43%  |
 |[Squeezenet 1.0](#4)|[Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|56.98%/79.20%|52.98%/77.21%|
 |[MobileNet 1.0](#5)|[Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|72.23%/90.64%|72.03%/90.42%|
@@ -39,7 +103,7 @@ The following models have been tested on Linux systems.
 | [SSD-VGG16](#10) | [example/ssd](https://github.com/apache/incubator-mxnet/tree/master/example/ssd)  | VOC2007/2012  | 0.8366 mAP  | 0.8364 mAP  |
 | [SSD-VGG16](#10) | [example/ssd](https://github.com/apache/incubator-mxnet/tree/master/example/ssd)  | COCO2014  | 0.2552 mAP  | 0.253 mAP  |
 
-<h3 id='3'>ResNet18/50/101-V1</h3>
+<h3 id='3'>ResNetV1</h3>
 
 The following command is to download the pre-trained model from Gluon-CV and transfer it into the symbolic model which would be finally quantized. The [validation dataset](http://data.mxnet.io/data/val_256_q90.rec) is available for testing the pre-trained models:
 
@@ -47,7 +111,7 @@ The following command is to download the pre-trained model from Gluon-CV and tra
 python imagenet_gen_qsym_mkldnn.py --model=resnet50_v1 --num-calib-batches=5 --calib-mode=naive
 ```
 
-The model would be automatically replaced in fusion and quantization format. It is then saved as the quantized symbol and parameter files in the `./model` directory. The following command is to launch inference.
+The model would be automatically replaced in fusion and quantization format. It is then saved as the quantized symbol and parameter files in the `./model` directory. Set `--model` to `resnet18_v1/resnet50_v1b/resnet101_v1` to quantize other models. The following command is to launch inference.
 
 ```
 # Launch FP32 Inference
@@ -204,17 +268,14 @@ SSD model is located in [example/ssd](https://github.com/apache/incubator-mxnet/
 This script also supports custom symbolic models. You can easily add some quantization layer configs in `imagenet_gen_qsym_mkldnn.py` like below:
 
 ```
-elif args.model == 'custom':
+else:
+    logger.info('Please set proper RGB configs for model %s' % args.model)
     # add rgb mean/std of your model.
     rgb_mean = '0,0,0'
     rgb_std = '0,0,0'
-    calib_layer = lambda name: name.endswith('_output')
     # add layer names you donnot want to quantize.
-    # add conv/pool layer names that has negative inputs
-    # since Intel® MKL-DNN only support uint8 quantization temporary.
-    # add all fc layer names since Intel® MKL-DNN does not support temporary.
+    logger.info('Please set proper excluded_sym_names for model %s' % args.model)
     excluded_sym_names += ['layers']
-    # add your first conv layer names since Intel® MKL-DNN only support uint8 quantization temporary.
     if exclude_first_conv:
         excluded_sym_names += ['layers']
 ```
@@ -230,7 +291,7 @@ Some tips on quantization configs:
 python imagenet_inference.py --symbol-file=./model/custom-symbol.json --param-file=./model/custom-0000.params --rgb-mean=* --rgb-std=* --num-skipped-batches=* --batch-size=* --num-inference-batches=*--dataset=./data/* --ctx=cpu
 ```
 
-3. Then, you should add `rgb_mean`, `rgb_std` and `excluded_sym_names` in this script. Notice that you should exclude conv/pool layers that have negative data since Intel® MKL-DNN only supports `uint8` quantization temporarily. You should also exclude all fc layers in your model.
+3. Then, you should add `rgb_mean`, `rgb_std` and `excluded_sym_names` in this script.
 
 4. Then, you can run the following command for quantization:
 
diff --git a/example/quantization/imagenet_gen_qsym_mkldnn.py b/example/quantization/imagenet_gen_qsym_mkldnn.py
index 482127ba355c..302a04449885 100644
--- a/example/quantization/imagenet_gen_qsym_mkldnn.py
+++ b/example/quantization/imagenet_gen_qsym_mkldnn.py
@@ -92,21 +92,12 @@ def save_params(fname, arg_params, aux_params, logger=None):
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser(description='Generate a calibrated quantized model from a FP32 model with Intel MKL-DNN support')
-    parser.add_argument('--model', type=str, choices=['resnet18_v1',
-                                                      'resnet50_v1',
-                                                      'resnet101_v1',
-                                                      'inceptionv3',
-                                                      'squeezenet1.0',
-                                                      'mobilenet1.0',
-                                                      'mobilenetv2_1.0',
-                                                      'imagenet1k-resnet-152',
-                                                      'imagenet1k-inception-bn',
-                                                      'custom'],
-                        help='currently only supports imagenet1k-resnet-50_v1, imagenet1k-resnet-152 or imagenet1k-inception-bn.'
-                             'you can set to custom to load your pre-trained model.')
-    parser.add_argument('--use-gluon-model', type=bool, default=False,
-                        help='If enabled, will download pretrained model from Gluon-CV '
-                             'and convert to symbolic model ')
+    parser.add_argument('--model', type=str, default='resnet50_v1',
+                        help='model to be quantized.')
+    parser.add_argument('--epoch', type=int, default=0,
+                        help='number of epochs, default is 0')
+    parser.add_argument('--no-pretrained', action='store_true', default=False,
+                        help='If enabled, will not download pretrained model from MXNet or Gluon-CV modelzoo.')
     parser.add_argument('--batch-size', type=int, default=32)
     parser.add_argument('--label-name', type=str, default='softmax_label')
     parser.add_argument('--calib-dataset', type=str, default='data/val_256_q90.rec',
@@ -155,6 +146,7 @@ def save_params(fname, arg_params, aux_params, logger=None):
     logger = logging.getLogger('logger')
     logger.setLevel(logging.INFO)
 
+    logger.info(args)
     logger.info('shuffle_dataset=%s' % args.shuffle_dataset)
 
     calib_mode = args.calib_mode
@@ -165,29 +157,24 @@ def save_params(fname, arg_params, aux_params, logger=None):
         download_calib_dataset('http://data.mxnet.io/data/val_256_q90.rec', args.calib_dataset)
 
     # download model
-    if args.model in ['resnet18_v1',
-                      'resnet50_v1',
-                      'resnet101_v1',
-                      'squeezenet1.0',
-                      'mobilenet1.0',
-                      'mobilenetv2_1.0',
-                      'inceptionv3']:
-        logger.info('model %s is converted from GluonCV' % args.model)
-        args.use_gluon_model = True
-    if args.use_gluon_model == True:
-        prefix = convert_from_gluon(model_name=args.model, image_shape=args.image_shape, classes=1000, logger=logger)
-        epoch = 0
-        sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)
-    elif args.model == 'custom':
+    if not args.no_pretrained:
+        logger.info('Get pre-trained model from MXNet or Gluoncv modelzoo.')
+        logger.info('If you want to use custom model, please set --no-pretrained.')
+        if args.model in ['imagenet1k-resnet-152', 'imagenet1k-inception-bn']:
+            logger.info('model %s is downloaded from MXNet modelzoo' % args.model)
+            prefix, epoch = download_model(model_name=args.model, logger=logger)
+        else:
+            logger.info('model %s is converted from GluonCV' % args.model)
+            prefix = convert_from_gluon(model_name=args.model, image_shape=args.image_shape, classes=1000, logger=logger)
+            rgb_mean = '123.68,116.779,103.939'
+            rgb_std = '58.393, 57.12, 57.375'
+            epoch = 0
+    else:
         dir_path = os.path.dirname(os.path.realpath(__file__))
         prefix = os.path.join(dir_path, 'model', args.model)
-        epoch = 0
-        sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)
-    else:
-        prefix, epoch = download_model(model_name=args.model, logger=logger)
-        sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)
+        epoch = args.epoch
 
-    sym = sym.get_backend_symbol('MKLDNN_QUANTIZE')
+    sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)
 
     # get batch size
     batch_size = args.batch_size
@@ -212,57 +199,59 @@ def save_params(fname, arg_params, aux_params, logger=None):
         logger.info('quantized dtype is set to uint8, will exclude first conv.')
         exclude_first_conv = True
     excluded_sym_names = []
-    if args.model == 'imagenet1k-resnet-152':
-        rgb_mean = '0,0,0'
-        rgb_std = '1,1,1'
-        excluded_sym_names += ['flatten0']
-        if exclude_first_conv:
-            excluded_sym_names += ['conv0']
-    elif args.model == 'imagenet1k-inception-bn':
-        rgb_mean = '123.68,116.779,103.939'
-        rgb_std = '1,1,1'
-        excluded_sym_names += ['flatten']
-        if exclude_first_conv:
-            excluded_sym_names += ['conv_1']
-    elif args.model in ['resnet18_v1', 'resnet50_v1', 'resnet101_v1']:
-        rgb_mean = '123.68,116.779,103.939'
-        rgb_std = '58.393, 57.12, 57.375'
-        if exclude_first_conv:
-            excluded_sym_names += ['resnetv10_conv0_fwd']
-    elif args.model == 'squeezenet1.0':
-        rgb_mean = '123.68,116.779,103.939'
-        rgb_std = '58.393, 57.12, 57.375'
-        excluded_sym_names += ['squeezenet0_flatten0_flatten0']
-        if exclude_first_conv:
-            excluded_sym_names += ['squeezenet0_conv0_fwd']
-    elif args.model == 'mobilenet1.0':
-        rgb_mean = '123.68,116.779,103.939'
-        rgb_std = '58.393, 57.12, 57.375'
-        excluded_sym_names += ['mobilenet0_flatten0_flatten0',
-                               'mobilenet0_pool0_fwd']
-        if exclude_first_conv:
-            excluded_sym_names += ['mobilenet0_conv0_fwd']
-    elif args.model == 'mobilenetv2_1.0':
-        rgb_mean = '123.68,116.779,103.939'
-        rgb_std = '58.393, 57.12, 57.375'
-        excluded_sym_names += ['mobilenetv20_output_flatten0_flatten0']
-        if exclude_first_conv:
-            excluded_sym_names += ['mobilenetv20_conv0_fwd']
-    elif args.model == 'inceptionv3':
-        rgb_mean = '123.68,116.779,103.939'
-        rgb_std = '58.393, 57.12, 57.375'
-        if exclude_first_conv:
-            excluded_sym_names += ['inception30_conv0_fwd']
-    elif args.model == 'custom':
+    if not args.no_pretrained:
+        if args.model == 'imagenet1k-resnet-152':
+            rgb_mean = '0,0,0'
+            rgb_std = '1,1,1'
+            excluded_sym_names += ['flatten0']
+            if exclude_first_conv:
+                excluded_sym_names += ['conv0']
+        elif args.model == 'imagenet1k-inception-bn':
+            rgb_mean = '123.68,116.779,103.939'
+            rgb_std = '1,1,1'
+            excluded_sym_names += ['flatten']
+            if exclude_first_conv:
+                excluded_sym_names += ['conv_1']
+        elif args.model.find('resnet') != -1 and args.model.find('v1') != -1:
+            if exclude_first_conv:
+                excluded_sym_names += ['resnetv10_conv0_fwd']
+        elif args.model.find('resnet') != -1 and args.model.find('v2') != -1:
+            excluded_sym_names += ['resnetv20_flatten0_flatten0']
+            if exclude_first_conv:
+                excluded_sym_names += ['resnetv20_conv0_fwd']
+        elif args.model.find('vgg') != -1:
+            if exclude_first_conv:
+                excluded_sym_names += ['vgg0_conv0_fwd']
+        elif args.model.find('squeezenet1') != -1:
+            excluded_sym_names += ['squeezenet0_flatten0_flatten0']
+            if exclude_first_conv:
+                excluded_sym_names += ['squeezenet0_conv0_fwd']
+        elif args.model.find('mobilenet') != -1 and args.model.find('v2') == -1:
+            excluded_sym_names += ['mobilenet0_flatten0_flatten0',
+                                'mobilenet0_pool0_fwd']
+            if exclude_first_conv:
+                excluded_sym_names += ['mobilenet0_conv0_fwd']
+        elif args.model.find('mobilenet') != -1 and args.model.find('v2') != -1:
+            excluded_sym_names += ['mobilenetv20_output_flatten0_flatten0']
+            if exclude_first_conv:
+                excluded_sym_names += ['mobilenetv20_conv0_fwd']
+        elif args.model == 'inceptionv3':
+            if exclude_first_conv:
+                excluded_sym_names += ['inception30_conv0_fwd']
+        else:
+            raise ValueError('Currently, model %s is not supported in this script' % args.model)
+    else:
+        logger.info('Please set proper RGB configs for model %s' % args.model)
         # add rgb mean/std of your model.
         rgb_mean = '0,0,0'
         rgb_std = '0,0,0'
         # add layer names you donnot want to quantize.
+        logger.info('Please set proper excluded_sym_names for model %s' % args.model)
         excluded_sym_names += ['layers']
         if exclude_first_conv:
             excluded_sym_names += ['layers']
-    else:
-        raise ValueError('model %s is not supported in this script' % args.model)
+
+    logger.info('These layers have been excluded %s' % excluded_sym_names)
 
     label_name = args.label_name
     logger.info('label_name = %s' % label_name)
@@ -281,10 +270,10 @@ def save_params(fname, arg_params, aux_params, logger=None):
     combine_mean_std.update(std_args)
     if calib_mode == 'none':
         logger.info('Quantizing FP32 model %s' % args.model)
-        qsym, qarg_params, aux_params = quantize_model(sym=sym, arg_params=arg_params, aux_params=aux_params,
-                                                       ctx=ctx, excluded_sym_names=excluded_sym_names,
-                                                       calib_mode=calib_mode, quantized_dtype=args.quantized_dtype,
-                                                       logger=logger)
+        qsym, qarg_params, aux_params = quantize_model_mkldnn(sym=sym, arg_params=arg_params, aux_params=aux_params,
+                                                              ctx=ctx, excluded_sym_names=excluded_sym_names,
+                                                              calib_mode=calib_mode, quantized_dtype=args.quantized_dtype,
+                                                              logger=logger)
         sym_name = '%s-symbol.json' % (prefix + '-quantized')
     else:
         logger.info('Creating ImageRecordIter for reading calibration dataset')
@@ -301,12 +290,12 @@ def save_params(fname, arg_params, aux_params, logger=None):
                                      seed=args.shuffle_seed,
                                      **combine_mean_std)
 
-        qsym, qarg_params, aux_params = quantize_model(sym=sym, arg_params=arg_params, aux_params=aux_params,
-                                                        ctx=ctx, excluded_sym_names=excluded_sym_names,
-                                                        calib_mode=calib_mode, calib_data=data,
-                                                        num_calib_examples=num_calib_batches * batch_size,
-                                                        calib_layer=calib_layer, quantized_dtype=args.quantized_dtype,
-                                                        label_names=(label_name,), logger=logger)
+        qsym, qarg_params, aux_params = quantize_model_mkldnn(sym=sym, arg_params=arg_params, aux_params=aux_params,
+                                                              ctx=ctx, excluded_sym_names=excluded_sym_names,
+                                                              calib_mode=calib_mode, calib_data=data,
+                                                              num_calib_examples=num_calib_batches * batch_size,
+                                                              calib_layer=calib_layer, quantized_dtype=args.quantized_dtype,
+                                                              label_names=(label_name,), logger=logger)
         if calib_mode == 'entropy':
             suffix = '-quantized-%dbatches-entropy' % num_calib_batches
         elif calib_mode == 'naive':
@@ -315,7 +304,6 @@ def save_params(fname, arg_params, aux_params, logger=None):
             raise ValueError('unknow calibration mode %s received, only supports `none`, `naive`, and `entropy`'
                              % calib_mode)
         sym_name = '%s-symbol.json' % (prefix + suffix)
-    qsym = qsym.get_backend_symbol('MKLDNN_QUANTIZE')
     save_symbol(sym_name, qsym, logger)
     param_name = '%s-%04d.params' % (prefix + '-quantized', epoch)
     save_params(param_name, qarg_params, aux_params, logger)
diff --git a/python/mxnet/contrib/quantization.py b/python/mxnet/contrib/quantization.py
index b94b5a8da32a..fa2ab1842f5f 100644
--- a/python/mxnet/contrib/quantization.py
+++ b/python/mxnet/contrib/quantization.py
@@ -543,3 +543,240 @@ def quantize_model(sym, arg_params, aux_params,
     qarg_params = _quantize_params(qsym, arg_params, th_dict)
 
     return qsym, qarg_params, aux_params
+
+def quantize_model_mkldnn(sym, arg_params, aux_params,
+                          data_names=('data',), label_names=('softmax_label',),
+                          ctx=cpu(), excluded_sym_names=None, calib_mode='entropy',
+                          calib_data=None, num_calib_examples=None, calib_layer=None,
+                          quantized_dtype='int8', logger=logging):
+    """User-level API for generating a fusion + quantized model from a FP32 model
+    w/ or w/o calibration with Intel MKL-DNN.
+    The backend quantized operators are only enabled for Linux systems. Please do not run
+    inference using the quantized models on Windows for now.
+
+    Parameters
+    ----------
+    sym : str or Symbol
+        Defines the structure of a neural network for FP32 data types.
+    arg_params : dict
+        Dictionary of name to `NDArray`.
+    aux_params : dict
+        Dictionary of name to `NDArray`.
+    data_names : a list of strs
+        Data names required for creating a Module object to run forward propagation on the
+        calibration dataset.
+    label_names : a list of strs
+        Label names required for creating a Module object to run forward propagation on the
+        calibration dataset.
+    ctx : Context
+        Defines the device that users want to run forward propagation on the calibration
+        dataset for collecting layer output statistics. Currently, only supports single context.
+    excluded_sym_names : list of strings
+        A list of strings representing the names of the symbols that users want to excluding
+        from being quantized.
+    calib_mode : str
+        If calib_mode='none', no calibration will be used and the thresholds for
+        requantization after the corresponding layers will be calculated at runtime by
+        calling min and max operators. The quantized models generated in this
+        mode are normally 10-20% slower than those with calibrations during inference.
+        If calib_mode='naive', the min and max values of the layer outputs from a calibration
+        dataset will be directly taken as the thresholds for quantization.
+        If calib_mode='entropy' (default mode), the thresholds for quantization will be
+        derived such that the KL divergence between the distributions of FP32 layer outputs and
+        quantized layer outputs is minimized based upon the calibration dataset.
+    calib_data : DataIter
+        A data iterator initialized by the calibration dataset.
+    num_calib_examples : int or None
+        The maximum number of examples that user would like to use for calibration. If not provided,
+        the whole calibration dataset will be used.
+    calib_layer : function
+        Given a layer's output name in string, return True or False for deciding whether to
+        calibrate this layer. If yes, the statistics of the layer's output will be collected;
+        otherwise, no information of the layer's output will be collected. If not provided,
+        all the layers' outputs that need requantization will be collected.
+    quantized_dtype : str
+        The quantized destination type for input data. Currently support 'int8'
+        , 'uint8' and 'auto'. 'auto' means automatically select output type according to calibration result.
+        Default value is 'int8'.
+    logger : Object
+        A logging object for printing information during the process of quantization.
+
+    Returns
+    -------
+    tuple
+        A tuple of quantized symbol, quantized arg_params, and aux_params.
+    -------
+    """
+    if ctx != cpu():
+        raise ValueError(
+            'quantize_model_mkldnn only support Intel cpu platform with MKL-DNN Backend')
+
+    sym = sym.get_backend_symbol('MKLDNN_QUANTIZE')
+
+    qsym, qarg_params, aux_params = quantize_model(sym=sym, arg_params=arg_params, aux_params=aux_params,
+                                                   data_names=data_names, label_names=label_names,
+                                                   ctx=ctx, excluded_sym_names=excluded_sym_names,
+                                                   calib_mode=calib_mode, calib_data=calib_data,
+                                                   num_calib_examples=num_calib_examples, calib_layer=calib_layer,
+                                                   quantized_dtype=quantized_dtype, logger=logger)
+
+    qsym = qsym.get_backend_symbol('MKLDNN_QUANTIZE')
+
+    return qsym, qarg_params, aux_params
+
+def quantize_graph(sym, arg_params, aux_params,
+                   excluded_sym_names=None, calib_mode='entropy',
+                   calib_layer=None, quantized_dtype='int8', logger=logging):
+    """User-level API for generating a quantized model from a FP32 model w/o calibration
+    and a collector for naive or entropy calibration.
+    The backend quantized operators are only enabled for Linux systems. Please do not run
+    inference using the quantized models on Windows for now.
+    The quantization implementation adopts the TensorFlow's approach:
+    https://www.tensorflow.org/performance/quantization.
+    The calibration implementation borrows the idea of Nvidia's 8-bit Inference with TensorRT:
+    http://on-demand.gputechconf.com/gtc/2017/presentation/s7310-8-bit-inference-with-tensorrt.pdf
+    and adapts the method to MXNet.
+    Parameters
+    ----------
+    sym : str or Symbol
+        Defines the structure of a neural network for FP32 data types.
+    arg_params : dict
+        Dictionary of name to `NDArray`.
+    aux_params : dict
+        Dictionary of name to `NDArray`.
+    excluded_sym_names : list of strings
+        A list of strings representing the names of the symbols that users want to excluding
+        from being quantized.
+    calib_mode : str
+        If calib_mode='none', no calibration will be used and the thresholds for
+        requantization after the corresponding layers will be calculated at runtime by
+        calling min and max operators. The quantized models generated in this
+        mode are normally 10-20% slower than those with calibrations during inference.
+        If calib_mode='naive', the min and max values of the layer outputs from a calibration
+        dataset will be directly taken as the thresholds for quantization.
+        If calib_mode='entropy' (default mode), the thresholds for quantization will be
+        derived such that the KL divergence between the distributions of FP32 layer outputs and
+        quantized layer outputs is minimized based upon the calibration dataset.
+    calib_layer : function
+        Given a layer's output name in string, return True or False for deciding whether to
+        calibrate this layer. If yes, the statistics of the layer's output will be collected;
+        otherwise, no information of the layer's output will be collected. If not provided,
+        all the layers' outputs that need requantization will be collected.
+    quantized_dtype : str
+        The quantized destination type for input data. Currently support 'int8'
+        , 'uint8' and 'auto'. 'auto' means automatically select output type according to calibration result.
+        Default value is 'int8'.
+    logger : Object
+        A logging object for printing information during the process of quantization.
+    Returns
+    -------
+    tuple
+        A tuple of quantized symbol, quantized arg_params, aux_params and collector.
+    -------
+    """
+    if excluded_sym_names is None:
+        excluded_sym_names = []
+    if not isinstance(excluded_sym_names, list):
+        raise ValueError('excluded_sym_names must be a list of strings representing'
+                         ' the names of the symbols that will not be quantized,'
+                         ' while received type %s' % str(type(excluded_sym_names)))
+
+    logger.info('Quantizing graph')
+    if quantized_dtype not in ('int8', 'uint8', 'auto'):
+        raise ValueError('unknown quantized_dtype %s received,'
+                         ' expected `int8`, `uint8` or `auto`' % quantized_dtype)
+    qsym = _quantize_symbol(sym, excluded_symbols=excluded_sym_names,
+                            offline_params=list(arg_params.keys()),
+                            quantized_dtype=quantized_dtype)
+
+    th_dict = {}
+    collector = None
+    if calib_mode is not None and calib_mode != 'none':
+        if calib_mode == 'entropy':
+            collector = _LayerOutputCollector(
+                include_layer=calib_layer, logger=logger)
+            logger.info(
+                'Create a layer output collector for entropy calibration.')
+        elif calib_mode == 'naive':
+            collector = _LayerOutputMinMaxCollector(
+                include_layer=calib_layer, logger=logger)
+            logger.info(
+                'Create a layer output minmax collector for naive calibration')
+        else:
+            raise ValueError('unknown calibration mode %s received,'
+                             ' expected `none`, `naive`, or `entropy`' % calib_mode)
+        logger.info('Collector created, please use set_monitor_callback'
+                    ' to collect calibration information.')
+
+    logger.info('Quantizing parameters')
+    qarg_params = _quantize_params(qsym, arg_params, th_dict)
+
+    return qsym, qarg_params, aux_params, collector
+
+def calib_graph(qsym, arg_params, aux_params, collector,
+                calib_mode='entropy', quantized_dtype='int8', logger=logging):
+    """User-level API for calibrating a quantized model using a filled collector.
+    The backend quantized operators are only enabled for Linux systems. Please do not run
+    inference using the quantized models on Windows for now.
+    The quantization implementation adopts the TensorFlow's approach:
+    https://www.tensorflow.org/performance/quantization.
+    The calibration implementation borrows the idea of Nvidia's 8-bit Inference with TensorRT:
+    http://on-demand.gputechconf.com/gtc/2017/presentation/s7310-8-bit-inference-with-tensorrt.pdf
+    and adapts the method to MXNet.
+    Parameters
+    ----------
+    qsym : str or Symbol
+        Defines the structure of a neural network for INT8 data types.
+    arg_params : dict
+        Dictionary of name to `NDArray`.
+    aux_params : dict
+        Dictionary of name to `NDArray`.
+    collector : function
+        layer collector for naive or entropy calibration.
+    calib_mode : str
+        If calib_mode='none', no calibration will be used and the thresholds for
+        requantization after the corresponding layers will be calculated at runtime by
+        calling min and max operators. The quantized models generated in this
+        mode are normally 10-20% slower than those with calibrations during inference.
+        If calib_mode='naive', the min and max values of the layer outputs from a calibration
+        dataset will be directly taken as the thresholds for quantization.
+        If calib_mode='entropy' (default mode), the thresholds for quantization will be
+        derived such that the KL divergence between the distributions of FP32 layer outputs and
+        quantized layer outputs is minimized based upon the calibration dataset.
+    calib_layer : function
+        Given a layer's output name in string, return True or False for deciding whether to
+        calibrate this layer. If yes, the statistics of the layer's output will be collected;
+        otherwise, no information of the layer's output will be collected. If not provided,
+        all the layers' outputs that need requantization will be collected.
+    quantized_dtype : str
+        The quantized destination type for input data. Currently support 'int8'
+        , 'uint8' and 'auto'. 'auto' means automatically select output type according to calibration result.
+        Default value is 'int8'.
+    logger : Object
+        A logging object for printing information during the process of quantization.
+    Returns
+    -------
+    tuple
+        A tuple of calibrated symbol, quantized arg_params, aux_params.
+    -------
+    """
+    th_dict = {}
+    if calib_mode is not None and calib_mode != 'none':
+        if calib_mode == 'entropy':
+            logger.info('Calculating optimal thresholds for quantization')
+            th_dict = _get_optimal_thresholds(
+                collector.nd_dict, quantized_dtype, logger=logger)
+        elif calib_mode == 'naive':
+            th_dict = collector.min_max_dict
+        else:
+            raise ValueError('unknown calibration mode %s received,'
+                             ' expected `none`, `naive`, or `entropy`' % calib_mode)
+        logger.info('Calibrating quantized symbol')
+        qsym = _calibrate_quantized_sym(qsym, th_dict)
+    else:
+        raise ValueError('please set calibration mode to naive or entropy.')
+
+    logger.info('Quantizing parameters')
+    qarg_params = _quantize_params(qsym, arg_params, th_dict)
+
+    return qsym, qarg_params, aux_params
diff --git a/tests/tutorials/test_tutorials.py b/tests/tutorials/test_tutorials.py
index 0c4954acbd8b..5fe6a03eae7b 100644
--- a/tests/tutorials/test_tutorials.py
+++ b/tests/tutorials/test_tutorials.py
@@ -213,3 +213,6 @@ def test_control_flow():
 
 def test_amp():
     assert _test_tutorial_nb('amp/amp_tutorial')
+
+def test_mkldnn_quantization():
+    assert _test_tutorial_nb('mkldnn/mkldnn_quantization')
\ No newline at end of file

From 90954ec64c284d0e3a0757c992e7be0c30a0423e Mon Sep 17 00:00:00 2001
From: dtracz <41399548+dtracz@users.noreply.github.com>
Date: Thu, 1 Aug 2019 14:41:13 -0700
Subject: [PATCH 135/813] make TransposeShape infer shape form both sides
 (#15713)

* make TransposeShape infer shape form both sides

* small fixes

* remove redundant lines

* unit tests
---
 src/operator/tensor/matrix_op-inl.h    | 19 +++++++++++++++++--
 tests/python/unittest/test_operator.py | 20 ++++++++++++++++++++
 2 files changed, 37 insertions(+), 2 deletions(-)

diff --git a/src/operator/tensor/matrix_op-inl.h b/src/operator/tensor/matrix_op-inl.h
index 5cd7bf6652d3..cd98cb020c6b 100644
--- a/src/operator/tensor/matrix_op-inl.h
+++ b/src/operator/tensor/matrix_op-inl.h
@@ -344,19 +344,34 @@ inline bool TransposeShape(const nnvm::NodeAttrs& attrs,
   CHECK_EQ(in_attrs->size(), 1U);
   CHECK_EQ(out_attrs->size(), 1U);
   mxnet::TShape& shp = (*in_attrs)[0];
+  mxnet::TShape& out_shp = (*out_attrs)[0];
   CHECK_LE(shp.ndim(), 6) << "Transpose support at most 6 dimensions";
-  mxnet::TShape ret(shp.ndim(), -1);
+  CHECK_NE(shp.ndim(), 0) << "Number of dimensions cannot be 0";
+  CHECK_NE(out_shp.ndim(), 0) << "Number of dimensions cannot be 0";
+  if (shp.ndim() == -1 && out_shp.ndim() == -1)
+    return false;  // none of the shapes is known
+  if (out_shp.ndim() > 0 && shp.ndim() > 0)
+    CHECK_EQ(out_shp.ndim(), shp.ndim());
+  mxnet::TShape get(std::max(shp.ndim(), out_shp.ndim()), -1);
+  mxnet::TShape ret(std::max(shp.ndim(), out_shp.ndim()), -1);
   if (param.axes.ndim() == 0) {
     for (int i = 0; i < shp.ndim(); ++i) {
       ret[i] = shp[shp.ndim()-1-i];
     }
+    for (int i = 0; i < out_shp.ndim(); ++i) {
+      get[shp.ndim()-1-i] = out_shp[i];
+    }
   } else {
-    CHECK_EQ(shp.ndim(), param.axes.ndim());
+    CHECK_EQ(std::max(shp.ndim(), out_shp.ndim()), param.axes.ndim());
     for (int i = 0; i < shp.ndim(); ++i) {
       CHECK(param.axes[i] < static_cast<int64_t>(shp.ndim()));
       ret[i] = shp[param.axes[i]];
     }
+    for (int i = 0; i < out_shp.ndim(); ++i) {
+      get[param.axes[i]] = out_shp[i];
+    }
   }
+  SHAPE_ASSIGN_CHECK(*in_attrs, 0, get);
   SHAPE_ASSIGN_CHECK(*out_attrs, 0, ret);
   return shape_is_known(ret);
 }
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index e8c9d6cbd061..8f1c2533c62c 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -8970,6 +8970,26 @@ def test_get_operator_arguments():
     ok_(operator_arguments.narg == 2)
 
 
+def test_transpose_infer_shape_back():
+    o1 = mx.sym.ones(shape=[2,3])
+    o2 = mx.sym.ones(shape=[-1,-1])
+    t = mx.sym.transpose(o2)
+    b = o1 + t
+    x = b.bind(mx.cpu(), args={})
+    y = x.forward()
+    assert(y[0].shape == (2,3))
+
+
+def test_transpose_infer_shape_mixed():
+    o1 = mx.sym.ones(shape=[2,-1])
+    o2 = mx.sym.ones(shape=[3,-1])
+    t = mx.sym.transpose(o2)
+    b = o1 + t
+    x = b.bind(mx.cpu(), args={})
+    y = x.forward()
+    assert(y[0].shape == (2,3))
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 4b07386bc273fab27c3f7ce223f39f274f9b82e9 Mon Sep 17 00:00:00 2001
From: Lai Wei <royweilai@gmail.com>
Date: Thu, 1 Aug 2019 15:09:04 -0700
Subject: [PATCH 136/813] [MXNET-1358] Fit api tutorial (#15353)

* Added tutorial for FIT API

* Added tests for Fit API tutorial

* Updated index.md for the new tutorial to show up

* Addressed PR feedback

* Addressed PR feedback

* Removed spurious comment for Py2 and Py3 compatibility

* Address PR feedback

* Addressed PR feedback

* Fixed typo

* Added example to showcase custom event handler

* Fixed imports as estimator moved to contrib package

* Added a side note to inform about estimator reference being updated by the handlers

* Corrected typo

* update tutorial

* address comments

* new line

* fix import

* fix cached graph

* fix import

* address comments

* fix doc gen

* add softmax

* add to website index

* fix doc string

* Fix doc gen (#12)

* fix warining

* fix test

* fix

* fix

* fix print

* fix test (#13)

* fix warning (#14)

* fix href (#15)
---
 docs/api/python/gluon/contrib.md              |  30 ++
 docs/tutorials/gluon/fit_api_tutorial.md      | 271 ++++++++++++++++++
 docs/tutorials/index.md                       |   2 +
 python/mxnet/gluon/contrib/__init__.py        |   2 +
 .../mxnet/gluon/contrib/estimator/__init__.py |   2 +
 .../gluon/contrib/estimator/estimator.py      |  64 +++--
 .../gluon/contrib/estimator/event_handler.py  |  23 +-
 tests/python/unittest/test_gluon_estimator.py |   7 +-
 tests/tutorials/test_tutorials.py             |   3 +
 9 files changed, 367 insertions(+), 37 deletions(-)
 create mode 100644 docs/tutorials/gluon/fit_api_tutorial.md

diff --git a/docs/api/python/gluon/contrib.md b/docs/api/python/gluon/contrib.md
index a940f697de69..22cdebb53b85 100644
--- a/docs/api/python/gluon/contrib.md
+++ b/docs/api/python/gluon/contrib.md
@@ -114,6 +114,33 @@ In the rest of this document, we list routines provided by the `gluon.contrib` p
     WikiText103
 ```
 
+### Estimator
+
+```eval_rst
+.. currentmodule:: mxnet.gluon.contrib.estimator
+
+.. autosummary::
+    :nosignatures:
+    
+    Estimator
+```
+
+#### EventHandler
+
+```eval_rst
+.. currentmodule:: mxnet.gluon.contrib.estimator
+
+.. autosummary::
+    :nosignatures:
+
+    StoppingHandler
+    MetricHandler
+    ValidationHandler
+    LoggingHandler
+    CheckpointHandler
+    EarlyStoppingHandler
+```
+
 ## API Reference
 
 <script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
@@ -144,6 +171,9 @@ In the rest of this document, we list routines provided by the `gluon.contrib` p
     :members:
     :imported-members:
 
+.. automodule:: mxnet.gluon.contrib.estimator
+    :members:
+    :imported-members:
 ```
 
 <script>auto_index("api-reference");</script>
diff --git a/docs/tutorials/gluon/fit_api_tutorial.md b/docs/tutorials/gluon/fit_api_tutorial.md
new file mode 100644
index 000000000000..bc50690ac1a2
--- /dev/null
+++ b/docs/tutorials/gluon/fit_api_tutorial.md
@@ -0,0 +1,271 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+
+# MXNet Gluon Fit API
+
+In this tutorial, you will learn how to use the [Gluon Fit API](https://cwiki.apache.org/confluence/display/MXNET/Gluon+Fit+API+-+Tech+Design) which is the easiest way to train deep learning models using the [Gluon API](http://mxnet.incubator.apache.org/versions/master/gluon/index.html) in Apache MXNet. 
+
+With the Fit API, you can train a deep learning model with a minimal amount of code. Just specify the network, loss function and the data you want to train on. You don't need to worry about the boiler plate code to loop through the dataset in batches (often called as 'training loop'). Advanced users can train with bespoke training loops, and many of these use cases will be covered by the Fit API.
+
+To demonstrate the Fit API, you will train an image classification model using the [ResNet-18](https://arxiv.org/abs/1512.03385) neural network architecture. The model will be trained using the [Fashion-MNIST dataset](https://research.zalando.com/welcome/mission/research-projects/fashion-mnist/). 
+
+## Prerequisites
+
+To complete this tutorial, you will need:
+
+- [MXNet](https://mxnet.incubator.apache.org/install/#overview) (The version of MXNet will be >= 1.5.0, you can use `pip install mxnet` to get 1.5.0 release pip package or build from source with master, refer to [MXNet installation](http://mxnet.incubator.apache.org/versions/master/install/index.html?platform=Linux&language=Python&processor=CPU)
+- [Jupyter Notebook](https://jupyter.org/index.html) (For interactively running the provided .ipynb file)
+
+
+
+
+```python
+import mxnet as mx
+from mxnet import gluon
+from mxnet.gluon.model_zoo import vision
+from mxnet.gluon.contrib.estimator import estimator
+from mxnet.gluon.contrib.estimator.event_handler import TrainBegin, TrainEnd, EpochEnd, CheckpointHandler
+
+gpu_count = mx.context.num_gpus()
+ctx = [mx.gpu(i) for i in range(gpu_count)] if gpu_count > 0 else mx.cpu()
+```
+
+## Dataset
+
+[Fashion-MNIST](https://research.zalando.com/welcome/mission/research-projects/fashion-mnist/) dataset consists of fashion items divided into ten categories: t-shirt/top, trouser, pullover, dress, coat, sandal, shirt, sneaker, bag and ankle boot. 
+
+- It has 60,000 grayscale images of size 28 * 28 for training.  
+- It has 10,000 grayscale images of size 28 * 28 for testing/validation. 
+
+We will use the ```gluon.data.vision``` package to directly import the Fashion-MNIST dataset and perform pre-processing on it.
+
+
+```python
+# Get the training data 
+fashion_mnist_train = gluon.data.vision.FashionMNIST(train=True)
+
+# Get the validation data
+fashion_mnist_val = gluon.data.vision.FashionMNIST(train=False)
+```
+
+
+```python
+transforms = [gluon.data.vision.transforms.Resize(224), # We pick 224 as the model we use takes an input of size 224.
+                gluon.data.vision.transforms.ToTensor()]
+
+# Now we will stack all these together.
+transforms = gluon.data.vision.transforms.Compose(transforms)
+```
+
+
+```python
+# Apply the transformations
+fashion_mnist_train = fashion_mnist_train.transform_first(transforms)
+fashion_mnist_val = fashion_mnist_val.transform_first(transforms)
+```
+
+
+```python
+batch_size = 256 # Batch size of the images
+num_workers = 4 # The number of parallel workers for loading the data using Data Loaders.
+
+train_data_loader = gluon.data.DataLoader(fashion_mnist_train, batch_size=batch_size, 
+                                          shuffle=True, num_workers=num_workers)
+val_data_loader = gluon.data.DataLoader(fashion_mnist_val, batch_size=batch_size, 
+                                        shuffle=False, num_workers=num_workers)
+```
+
+## Model and Optimizers
+
+Let's load the resnet-18 model architecture from [Gluon Model Zoo](http://mxnet.apache.org/api/python/gluon/model_zoo.html) and initialize its parameters. The Gluon Model Zoo contains a repository of pre-trained models as well the model architecture definitions. We are using the model architecture from the model zoo in order to train it from scratch.
+
+
+```python
+resnet_18_v1 = vision.resnet18_v1(pretrained=False, classes = 10)
+resnet_18_v1.initialize(init = mx.init.Xavier(), ctx=ctx)
+```
+
+We will be using `SoftmaxCrossEntropyLoss` as the loss function since this is a multi-class classification problem. We will be using `sgd` (Stochastic Gradient Descent) as the optimizer. 
+You can experiment with a [different loss](http://mxnet.incubator.apache.org/versions/master/api/python/gluon/loss.html) or [optimizer](http://mxnet.incubator.apache.org/versions/master/api/python/optimization/optimization.html) as well. 
+
+
+```python
+loss_fn = gluon.loss.SoftmaxCrossEntropyLoss()
+```
+
+Let's define the trainer object for training the model.
+
+
+```python
+learning_rate = 0.04 # You can experiment with your own learning rate here
+num_epochs = 2 # You can run training for more epochs
+trainer = gluon.Trainer(resnet_18_v1.collect_params(), 
+                        'sgd', {'learning_rate': learning_rate})
+```
+
+## Train using Fit API
+
+As stated earlier, the Fit API greatly simplifies the boiler plate code and complexity for training using MXNet Gluon.
+
+In the basic usage example, with just 2 lines of code, we will set up our model for training.
+
+### Basic Usage
+
+
+```python
+train_acc = mx.metric.Accuracy() # Metric to monitor
+
+# Define the estimator, by passing to it the model, loss function, metrics, trainer object and context
+est = estimator.Estimator(net=resnet_18_v1, 
+                          loss=loss_fn, 
+                          metrics=train_acc, 
+                          trainer=trainer, 
+                          context=ctx)
+
+# ignore warnings for nightly test on CI only
+import warnings
+with warnings.catch_warnings():
+    warnings.simplefilter("ignore")
+    # Magic line
+    est.fit(train_data=train_data_loader,
+        epochs=num_epochs)
+```
+
+    Training begin: using optimizer SGD with current learning rate 0.0400 <!--notebook-skip-line-->
+    Train for 2 epochs. <!--notebook-skip-line-->
+    
+    [Epoch 0] finished in 25.110s: train_accuracy : 0.7877 train_softmaxcrossentropyloss0 : 0.5905 <!--notebook-skip-line-->
+    
+    [Epoch 1] finished in 23.595s: train_accuracy : 0.8823 train_softmaxcrossentropyloss0 : 0.3197 <!--notebook-skip-line-->
+    Train finished using total 48s at epoch 1. train_accuracy : 0.8823 train_softmaxcrossentropyloss0 : 0.3197 <!--notebook-skip-line-->
+
+
+### Advanced Usage
+
+The Fit API is also customizable with several `Event Handlers` which give a fine grained control over the steps in training and exposes callback methods that provide control over the stages involved in training. Available callback methods are: `train_begin`, `train_end`, `batch_begin`, `batch_end`, `epoch_begin` and `epoch_end`.
+
+You can use built-in event handlers such as `LoggingHandler`, `CheckpointHandler` or `EarlyStoppingHandler` to log and save the model at certain time-steps during training. You can also stop the training when the model's performance plateaus. 
+There are also some default utility handlers that will be added to your estimator by default. For example, `StoppingHandler` is used to control when the training ends, based on number of epochs or number of batches trained. 
+`MetricHandler` is used to calculate training metrics at end of each batch and epoch. 
+`ValidationHandler` is used to validate your model on test data at each epoch's end and then calculate validation metrics.
+You can create these utility handlers with different configurations and pass to estimator. This will override the default handler configuration.
+You can create a custom handler by inheriting one or multiple 
+[base event handlers](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/contrib/estimator/event_handler.py#L32)
+ including: `TrainBegin`, `TrainEnd`, `EpochBegin`, `EpochEnd`, `BatchBegin`, `BatchEnd`.
+
+
+### Custom Event Handler
+
+Here we will showcase an example custom event handler the inherits features from a few base handler classes. 
+Our custom event handler is a simple one: record the loss values at the end of every epoch in our training phase.
+
+Note: For each of the method, the `Estimator` object is passed along, so you can access training metrics.
+
+```python
+class LossRecordHandler(TrainBegin, TrainEnd, EpochEnd):
+    def __init__(self):
+        super(LossRecordHandler, self).__init__()
+        self.loss_history = {}
+
+    def train_begin(self, estimator, *args, **kwargs):
+        print("Training begin")
+
+    def train_end(self, estimator, *args, **kwargs):
+        # Print all the losses at the end of training
+        print("Training ended")
+        for loss_name in self.loss_history:
+            for i, loss_val in enumerate(self.loss_history[loss_name]):
+                print("Epoch: {}, Loss name: {}, Loss value: {}".format(i, loss_name, loss_val))
+
+    def epoch_end(self, estimator, *args, **kwargs):
+        for metric in estimator.train_metrics:
+            # look for train Loss in training metrics
+            # we wrapped loss value as a metric to record it
+            if isinstance(metric, mx.metric.Loss):
+                loss_name, loss_val = metric.get()
+                # append loss value for this epoch
+                self.loss_history.setdefault(loss_name, []).append(loss_val)
+```
+
+
+```python
+# Let's reset the model, trainer and accuracy objects from above
+
+resnet_18_v1.initialize(force_reinit=True, init = mx.init.Xavier(), ctx=ctx)
+trainer = gluon.Trainer(resnet_18_v1.collect_params(), 
+                        'sgd', {'learning_rate': learning_rate})
+train_acc = mx.metric.Accuracy()
+```
+
+
+```python
+# Define the estimator, by passing to it the model, loss function, metrics, trainer object and context
+est = estimator.Estimator(net=resnet_18_v1,
+                          loss=loss_fn,
+                          metrics=train_acc,
+                          trainer=trainer, 
+                          context=ctx)
+
+# Define the handlers, let's say in built Checkpointhandler
+checkpoint_handler = CheckpointHandler(model_dir='./',
+                                       model_prefix='my_model',
+                                       monitor=train_acc,  # Monitors a metric
+                                       save_best=True)  # Save the best model in terms of
+# Let's instantiate another handler which we defined above 
+loss_record_handler = LossRecordHandler()
+# ignore warnings for nightly test on CI only
+import warnings
+with warnings.catch_warnings():
+    warnings.simplefilter("ignore")
+    # Magic line
+    est.fit(train_data=train_data_loader,
+            val_data=val_data_loader,
+            epochs=num_epochs,
+            event_handlers=[checkpoint_handler, loss_record_handler]) # Add the event handlers
+```
+
+    Training begin: using optimizer SGD with current learning rate 0.0400 <!--notebook-skip-line-->
+    Train for 2 epochs. <!--notebook-skip-line-->
+    
+    [Epoch 0] finished in 25.236s: train_accuracy : 0.7917 train_softmaxcrossentropyloss0 : 0.5741 val_accuracy : 0.6612 val_softmaxcrossentropyloss0 : 0.8627 <!--notebook-skip-line-->
+    
+    [Epoch 1] finished in 24.892s: train_accuracy : 0.8826 train_softmaxcrossentropyloss0 : 0.3229 val_accuracy : 0.8474 val_softmaxcrossentropyloss0 : 0.4262 <!--notebook-skip-line-->
+    
+    Train finished using total 50s at epoch 1. train_accuracy : 0.8826 train_softmaxcrossentropyloss0 : 0.3229 val_accuracy : 0.8474 val_softmaxcrossentropyloss0 : 0.4262 <!--notebook-skip-line-->
+
+    Training begin <!--notebook-skip-line-->
+    Epoch 1, loss 0.5741 <!--notebook-skip-line-->
+    Epoch 2, loss 0.3229 <!--notebook-skip-line-->
+
+You can load the saved model, by using the `load_parameters` API in Gluon. For more details refer to the [Loading model parameters from file tutorial](save_load_params.html#saving-model-parameters-to-file)
+
+
+```python
+resnet_18_v1 = vision.resnet18_v1(pretrained=False, classes=10)
+resnet_18_v1.load_parameters('./my_model-best.params', ctx=ctx)
+```
+
+## Summary
+
+- To learn more about deep learning with MXNeT, see [Dive Into Deep Learning](http://gluon.io)
+
+## Next Steps 
+
+- For more hands on learning about deep learning, check out [Dive into Deep Learning](https://d2l.ai)
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md
index e01a30dbe68c..f773a79f63a7 100644
--- a/docs/tutorials/index.md
+++ b/docs/tutorials/index.md
@@ -139,6 +139,8 @@ Select API:&nbsp;
             * [Data Transforms](/tutorials/gluon/transforms.html)
             * [Applying Data Augmentation](/tutorials/gluon/data_augmentation.html)
             * [Data Augmentation with Masks (for Object Segmentation)](https://mxnet.incubator.apache.org/tutorials/python/data_augmentation_with_masks.html)
+        * Fit API
+            * [Using Fit API](/tutorials/gluon/fit_api_tutorial.html)
 </div> <!--end of gluon-->
 
 <div class="module">
diff --git a/python/mxnet/gluon/contrib/__init__.py b/python/mxnet/gluon/contrib/__init__.py
index 83be8a39ba32..7590eb740f67 100644
--- a/python/mxnet/gluon/contrib/__init__.py
+++ b/python/mxnet/gluon/contrib/__init__.py
@@ -25,3 +25,5 @@
 from . import cnn
 
 from . import data
+
+from . import estimator
diff --git a/python/mxnet/gluon/contrib/estimator/__init__.py b/python/mxnet/gluon/contrib/estimator/__init__.py
index 58600dadffb4..bb0a0917c363 100644
--- a/python/mxnet/gluon/contrib/estimator/__init__.py
+++ b/python/mxnet/gluon/contrib/estimator/__init__.py
@@ -17,5 +17,7 @@
 
 # pylint: disable=wildcard-import
 """Gluon Estimator Module"""
+from . import estimator
+from . import event_handler
 from .estimator import *
 from .event_handler import *
diff --git a/python/mxnet/gluon/contrib/estimator/estimator.py b/python/mxnet/gluon/contrib/estimator/estimator.py
index da1a3915caec..b6142e100d96 100644
--- a/python/mxnet/gluon/contrib/estimator/estimator.py
+++ b/python/mxnet/gluon/contrib/estimator/estimator.py
@@ -24,9 +24,15 @@
 
 from .event_handler import MetricHandler, ValidationHandler, LoggingHandler, StoppingHandler
 from .event_handler import TrainBegin, EpochBegin, BatchBegin, BatchEnd, EpochEnd, TrainEnd
-from .... import gluon, autograd
+from ...data import DataLoader
+from ...loss import SoftmaxCrossEntropyLoss
+from ...loss import Loss as gluon_loss
+from ...trainer import Trainer
+from ...utils import split_and_load
+from .... import autograd
 from ....context import Context, cpu, gpu, num_gpus
-from ....metric import EvalMetric, Loss, Accuracy
+from ....metric import EvalMetric, Accuracy
+from ....metric import Loss as metric_loss
 
 __all__ = ['Estimator']
 
@@ -69,9 +75,9 @@ def __init__(self, net,
         self.trainer = self._check_trainer(trainer)
 
     def _check_loss(self, loss):
-        if isinstance(loss, gluon.loss.Loss):
+        if isinstance(loss, gluon_loss):
             loss = [loss]
-        elif isinstance(loss, list) and all([isinstance(l, gluon.loss.Loss) for l in loss]):
+        elif isinstance(loss, list) and all([isinstance(l, gluon_loss) for l in loss]):
             loss = loss
         else:
             raise ValueError("loss must be a Loss or a list of Loss, "
@@ -146,9 +152,9 @@ def _check_trainer(self, trainer):
         if not trainer:
             warnings.warn("No trainer specified, default SGD optimizer "
                           "with learning rate 0.001 is used.")
-            trainer = gluon.Trainer(self.net.collect_params(),
-                                    'sgd', {'learning_rate': 0.001})
-        elif not isinstance(trainer, gluon.Trainer):
+            trainer = Trainer(self.net.collect_params(),
+                              'sgd', {'learning_rate': 0.001})
+        elif not isinstance(trainer, Trainer):
             raise ValueError("Trainer must be a Gluon Trainer instance, refer to "
                              "gluon.Trainer:{}".format(trainer))
         return trainer
@@ -165,8 +171,8 @@ def _is_initialized(self):
     def _get_data_and_label(self, batch, ctx, batch_axis=0):
         data = batch[0]
         label = batch[1]
-        data = gluon.utils.split_and_load(data, ctx_list=ctx, batch_axis=batch_axis)
-        label = gluon.utils.split_and_load(label, ctx_list=ctx, batch_axis=batch_axis)
+        data = split_and_load(data, ctx_list=ctx, batch_axis=batch_axis)
+        label = split_and_load(label, ctx_list=ctx, batch_axis=batch_axis)
         return data, label
 
     def prepare_loss_and_metrics(self):
@@ -179,13 +185,13 @@ def prepare_loss_and_metrics(self):
         """
         if any(not hasattr(self, attribute) for attribute in
                ['train_metrics', 'val_metrics']):
-            # Use default mx.metric.Accuracy() for gluon.loss.SoftmaxCrossEntropyLoss()
-            if not self.train_metrics and any([isinstance(l, gluon.loss.SoftmaxCrossEntropyLoss) for l in self.loss]):
+            # Use default mx.metric.Accuracy() for SoftmaxCrossEntropyLoss()
+            if not self.train_metrics and any([isinstance(l, SoftmaxCrossEntropyLoss) for l in self.loss]):
                 self.train_metrics = [Accuracy()]
             self.val_metrics = []
             for loss in self.loss:
                 # remove trailing numbers from loss name to avoid confusion
-                self.train_metrics.append(Loss(loss.name.rstrip('1234567890')))
+                self.train_metrics.append(metric_loss(loss.name.rstrip('1234567890')))
             for metric in self.train_metrics:
                 val_metric = copy.deepcopy(metric)
                 metric.name = "train " + metric.name
@@ -208,10 +214,10 @@ def evaluate(self,
          batch_axis : int, default 0
              Batch axis to split the validation data into devices.
          """
-        if not isinstance(val_data, gluon.data.DataLoader):
+        if not isinstance(val_data, DataLoader):
             raise ValueError("Estimator only support input as Gluon DataLoader. Alternatively, you "
                              "can transform your DataIter or any NDArray into Gluon DataLoader. "
-                             "Refer to gluon.data.dataloader")
+                             "Refer to gluon.data.DataLoader")
 
         for metric in val_metrics:
             metric.reset()
@@ -222,7 +228,7 @@ def evaluate(self,
             loss = [self.loss[0](y_hat, y) for y_hat, y in zip(pred, label)]
             # update metrics
             for metric in val_metrics:
-                if isinstance(metric, Loss):
+                if isinstance(metric, metric_loss):
                     metric.update(0, loss)
                 else:
                     metric.update(label, pred)
@@ -254,7 +260,7 @@ def fit(self, train_data,
         batch_axis : int, default 0
             Batch axis to split the training data into devices.
         """
-        if not isinstance(train_data, gluon.data.DataLoader):
+        if not isinstance(train_data, DataLoader):
             raise ValueError("Estimator only support input as Gluon DataLoader. Alternatively, you "
                              "can transform your DataIter or any NDArray into Gluon DataLoader. "
                              "Refer to gluon.data.dataloader")
@@ -328,28 +334,36 @@ def fit(self, train_data,
     def _prepare_default_handlers(self, val_data, event_handlers):
         event_handlers = event_handlers or []
         default_handlers = []
-        train_metrics, val_metrics = self.prepare_loss_and_metrics()
+        self.prepare_loss_and_metrics()
 
         # no need to add to default handler check as StoppingHandler does not use metrics
         event_handlers.append(StoppingHandler(self.max_epoch, self.max_batch))
+        default_handlers.append("StoppingHandler")
 
         if not any(isinstance(handler, MetricHandler) for handler in event_handlers):
-            event_handlers.append(MetricHandler(train_metrics=train_metrics))
+            event_handlers.append(MetricHandler(train_metrics=self.train_metrics))
             default_handlers.append("MetricHandler")
 
-        if val_data and not any(isinstance(handler, ValidationHandler) for handler in event_handlers):
-            event_handlers.append(ValidationHandler(val_data=val_data, eval_fn=self.evaluate,
-                                                    val_metrics=val_metrics))
-            default_handlers.append("ValidationHandler")
+        if not any(isinstance(handler, ValidationHandler) for handler in event_handlers):
+            # no validation handler
+            if val_data:
+                # add default validation handler if validation data found
+                event_handlers.append(ValidationHandler(val_data=val_data, eval_fn=self.evaluate,
+                                                        val_metrics=self.val_metrics))
+                default_handlers.append("ValidationHandler")
+                val_metrics = self.val_metrics
+            else:
+                # set validation metrics to None if no validation data and no validation handler
+                val_metrics = []
 
         if not any(isinstance(handler, LoggingHandler) for handler in event_handlers):
-            event_handlers.append(LoggingHandler(train_metrics=train_metrics,
+            event_handlers.append(LoggingHandler(train_metrics=self.train_metrics,
                                                  val_metrics=val_metrics))
             default_handlers.append("LoggingHandler")
 
         # if there is a mix of user defined event handlers and default event handlers
         # they should have the same set of loss and metrics
-        if default_handlers:
+        if default_handlers and len(event_handlers) != len(default_handlers):
             msg = "You are training with the following default event handlers: %s. " \
                   "They use loss and metrics from estimator.prepare_loss_and_metrics(). " \
                   "Please use the same set of metrics for all your other handlers." % \
@@ -368,7 +382,7 @@ def _prepare_default_handlers(self, val_data, event_handlers):
             # remove None metric references
             references = set([ref for ref in references if ref])
             for metric in references:
-                if metric not in train_metrics + val_metrics:
+                if metric not in self.train_metrics + self.val_metrics:
                     msg = "We have added following default handlers for you: %s and used " \
                           "estimator.prepare_loss_and_metrics() to pass metrics to " \
                           "those handlers. Please use the same set of metrics " \
diff --git a/python/mxnet/gluon/contrib/estimator/event_handler.py b/python/mxnet/gluon/contrib/estimator/event_handler.py
index ed97c7bc3d19..da2c84455e35 100644
--- a/python/mxnet/gluon/contrib/estimator/event_handler.py
+++ b/python/mxnet/gluon/contrib/estimator/event_handler.py
@@ -26,7 +26,12 @@
 
 import numpy as np
 
-from ....metric import EvalMetric, Loss
+from ....metric import EvalMetric
+from ....metric import Loss as metric_loss
+
+__all__ = ['TrainBegin', 'TrainEnd', 'EpochBegin', 'EpochEnd', 'BatchBegin', 'BatchEnd',
+           'StoppingHandler', 'MetricHandler', 'ValidationHandler',
+           'LoggingHandler', 'CheckpointHandler', 'EarlyStoppingHandler']
 
 
 class TrainBegin(object):
@@ -127,7 +132,7 @@ def batch_end(self, estimator, *args, **kwargs):
         label = kwargs['label']
         loss = kwargs['loss']
         for metric in self.train_metrics:
-            if isinstance(metric, Loss):
+            if isinstance(metric, metric_loss):
                 # metric wrapper for loss values
                 metric.update(0, loss)
             else:
@@ -135,7 +140,7 @@ def batch_end(self, estimator, *args, **kwargs):
 
 
 class ValidationHandler(TrainBegin, BatchEnd, EpochEnd):
-    """"Validation Handler that evaluate model on validation dataset
+    """Validation Handler that evaluate model on validation dataset
 
     :py:class:`ValidationHandler` takes validation dataset, an evaluation function,
     metrics to be evaluated, and how often to run the validation. You can provide custom
@@ -430,7 +435,7 @@ def train_begin(self, estimator, *args, **kwargs):
         self.current_epoch = 0
         self.current_batch = 0
         if self.save_best:
-            self.best = np.Inf if self.monitor_op == np.less else -np.Inf # pylint: disable=comparison-with-callable
+            self.best = np.Inf if self.monitor_op == np.less else -np.Inf  # pylint: disable=comparison-with-callable
         if self.resume_from_checkpoint:
             error_msg = "To use resume from checkpoint, you must only specify " \
                         "the same type of period you used for training." \
@@ -506,12 +511,12 @@ def _save_checkpoint(self, estimator):
 
     def _save_symbol(self, estimator):
         symbol_file = os.path.join(self.model_dir, self.model_prefix + '-symbol.json')
-        if hasattr(estimator.net, '_cached_graph'):
+        if hasattr(estimator.net, '_cached_graph') and estimator.net._cached_graph:
             sym = estimator.net._cached_graph[1]
             sym.save(symbol_file)
         else:
-            self.logger.info("Model architecture(symbol file) is not saved, please use HybridBlock"
-                             "to construct your model, can call net.hybridize() before passing to"
+            self.logger.info("Model architecture(symbol file) is not saved, please use HybridBlock "
+                             "to construct your model, can call net.hybridize() before passing to "
                              "Estimator in order to save model architecture as %s.", symbol_file)
 
     def _save_params_and_trainer(self, estimator, file_prefix):
@@ -666,7 +671,7 @@ def __init__(self,
                                  "if you want otherwise", self.monitor.get()[0])
                 self.monitor_op = np.less
 
-        if self.monitor_op == np.greater: # pylint: disable=comparison-with-callable
+        if self.monitor_op == np.greater:  # pylint: disable=comparison-with-callable
             self.min_delta *= 1
         else:
             self.min_delta *= -1
@@ -679,7 +684,7 @@ def train_begin(self, estimator, *args, **kwargs):
         if self.baseline is not None:
             self.best = self.baseline
         else:
-            self.best = np.Inf if self.monitor_op == np.less else -np.Inf # pylint: disable=comparison-with-callable
+            self.best = np.Inf if self.monitor_op == np.less else -np.Inf  # pylint: disable=comparison-with-callable
 
     def epoch_end(self, estimator, *args, **kwargs):
         monitor_name, monitor_value = self.monitor.get()
diff --git a/tests/python/unittest/test_gluon_estimator.py b/tests/python/unittest/test_gluon_estimator.py
index d2e8c082aa08..ae47d925670f 100644
--- a/tests/python/unittest/test_gluon_estimator.py
+++ b/tests/python/unittest/test_gluon_estimator.py
@@ -19,11 +19,13 @@
 
 import sys
 import unittest
+import warnings
 
 import mxnet as mx
 from mxnet import gluon
 from mxnet.gluon import nn
 from mxnet.gluon.contrib.estimator import *
+from mxnet.gluon.contrib.estimator.event_handler import *
 from nose.tools import assert_raises
 
 
@@ -335,10 +337,9 @@ def test_default_handlers():
                     metrics=train_acc,
                     trainer=trainer,
                     context=ctx)
-    # no handler
+    # no handler(all default handlers), no warning
     with warnings.catch_warnings(record=True) as w:
         est.fit(train_data=train_data, epochs=num_epochs)
-        assert 'You are training with the' in str(w[-1].message)
 
     # handler with prepared loss and metrics
     # use mix of default and user defined handlers
@@ -353,7 +354,7 @@ def test_default_handlers():
     # handler with all user defined metrics
     # use mix of default and user defined handlers
     metric = MetricHandler(train_metrics=[train_acc])
-    logging = LoggingHandler(train_metrics=[train_acc], val_metrics=[mx.metric.RMSE("val acc")])
+    logging = LoggingHandler(train_metrics=[train_acc])
     est.fit(train_data=train_data, epochs=num_epochs, event_handlers=[metric, logging])
 
     # handler with mixed metrics, some handler use metrics prepared by estimator
diff --git a/tests/tutorials/test_tutorials.py b/tests/tutorials/test_tutorials.py
index 5fe6a03eae7b..c2173a7dc071 100644
--- a/tests/tutorials/test_tutorials.py
+++ b/tests/tutorials/test_tutorials.py
@@ -133,6 +133,9 @@ def test_gluon_learning_rate_schedules_advanced():
 def test_gluon_info_gan():
     assert _test_tutorial_nb('gluon/info_gan')
 
+def test_gluon_fit_api_fashion_mnist():
+    assert _test_tutorial_nb('gluon/fit_api_tutorial')
+
 def test_nlp_cnn():
     assert _test_tutorial_nb('nlp/cnn')
 

From 8c641b855107104f236b2dc8fc7b112fd67d276b Mon Sep 17 00:00:00 2001
From: Sheng Zha <zhasheng@amazon.com>
Date: Thu, 18 Jul 2019 20:06:12 -0700
Subject: [PATCH 137/813] remove mshadow submodule

---
 .gitmodules      | 3 ---
 3rdparty/mshadow | 1 -
 2 files changed, 4 deletions(-)
 delete mode 160000 3rdparty/mshadow

diff --git a/.gitmodules b/.gitmodules
index e0ffec11bfd0..90ef157f0eec 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -1,6 +1,3 @@
-[submodule "3rdparty/mshadow"]
-	path = 3rdparty/mshadow
-	url = https://github.com/dmlc/mshadow.git
 [submodule "3rdparty/dmlc-core"]
 	path = 3rdparty/dmlc-core
 	url = https://github.com/dmlc/dmlc-core.git
diff --git a/3rdparty/mshadow b/3rdparty/mshadow
deleted file mode 160000
index 1d79ecfdb4c9..000000000000
--- a/3rdparty/mshadow
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit 1d79ecfdb4c9234537e1bf5148f44a1af54501ec

From 1434b98e26ace5300f17465fbb2942272a3dfd77 Mon Sep 17 00:00:00 2001
From: Sheng Zha <zhasheng@amazon.com>
Date: Thu, 18 Jul 2019 20:11:57 -0700
Subject: [PATCH 138/813] import mshadow source tree

---
 3rdparty/mshadow/.gitignore                   |   21 +
 3rdparty/mshadow/.travis.yml                  |   43 +
 3rdparty/mshadow/CHANGES.md                   |   12 +
 3rdparty/mshadow/CMakeLists.txt               |    6 +
 3rdparty/mshadow/LICENSE                      |   13 +
 3rdparty/mshadow/README.md                    |   37 +
 3rdparty/mshadow/cmake/Cuda.cmake             |  324 +++
 3rdparty/mshadow/cmake/Utils.cmake            |  398 +++
 3rdparty/mshadow/cmake/mshadow.cmake          |   91 +
 3rdparty/mshadow/cmake/mshadowUtils.cmake     |    2 +
 3rdparty/mshadow/doc/Doxyfile                 | 2358 +++++++++++++++++
 3rdparty/mshadow/doc/README.md                |  321 +++
 3rdparty/mshadow/doc/mkdoc.sh                 |    4 +
 3rdparty/mshadow/guide/.gitignore             |    3 +
 3rdparty/mshadow/guide/Makefile               |   37 +
 3rdparty/mshadow/guide/README.md              |  226 ++
 3rdparty/mshadow/guide/basic.cpp              |  161 ++
 3rdparty/mshadow/guide/basic_stream.cu        |   35 +
 3rdparty/mshadow/guide/config.mk              |   39 +
 3rdparty/mshadow/guide/defop.cpp              |   49 +
 .../mshadow/guide/exp-template/.gitignore     |    1 +
 3rdparty/mshadow/guide/exp-template/Makefile  |   20 +
 3rdparty/mshadow/guide/exp-template/README.md |  340 +++
 .../mshadow/guide/exp-template/exp_lazy.cpp   |   45 +
 .../guide/exp-template/exp_template.cpp       |   72 +
 .../guide/exp-template/exp_template_op.cpp    |   92 +
 3rdparty/mshadow/guide/mshadow-ps/.gitignore  |    4 +
 .../mshadow/guide/mshadow-ps/2-levels.png     |  Bin 0 -> 59413 bytes
 3rdparty/mshadow/guide/mshadow-ps/Makefile    |   45 +
 3rdparty/mshadow/guide/mshadow-ps/README.md   |  227 ++
 3rdparty/mshadow/guide/mshadow-ps/config.mk   |   40 +
 3rdparty/mshadow/guide/mshadow-ps/dbstr.h     |   35 +
 .../guide/mshadow-ps/dist_async_sum-inl.h     |  124 +
 .../guide/mshadow-ps/dist_async_sum.cpp       |   11 +
 3rdparty/mshadow/guide/mshadow-ps/local.sh    |   39 +
 .../mshadow/guide/mshadow-ps/local_sum-inl.h  |  119 +
 .../mshadow/guide/mshadow-ps/local_sum.cpp    |    4 +
 .../mshadow/guide/mshadow-ps/local_sum.cu     |    4 +
 3rdparty/mshadow/guide/neuralnet/Makefile     |   38 +
 3rdparty/mshadow/guide/neuralnet/README.md    |   16 +
 3rdparty/mshadow/guide/neuralnet/config.mk    |   35 +
 3rdparty/mshadow/guide/neuralnet/convnet.cu   |  282 ++
 3rdparty/mshadow/guide/neuralnet/nnet.cu      |  202 ++
 3rdparty/mshadow/guide/neuralnet/nnet_ps.cu   |  312 +++
 3rdparty/mshadow/guide/neuralnet/util.h       |   86 +
 3rdparty/mshadow/make/README.md               |   18 +
 3rdparty/mshadow/make/mshadow.mk              |  166 ++
 3rdparty/mshadow/mshadow-ps/.gitignore        |    3 +
 3rdparty/mshadow/mshadow-ps/README.md         |    4 +
 3rdparty/mshadow/mshadow-ps/mshadow_ps.h      |  358 +++
 3rdparty/mshadow/mshadow-ps/ps_dist-inl.h     |  126 +
 3rdparty/mshadow/mshadow-ps/ps_local-inl.h    |  814 ++++++
 3rdparty/mshadow/mshadow-ps/ps_rabit-inl.h    |  113 +
 3rdparty/mshadow/mshadow-ps/thread.h          |  261 ++
 3rdparty/mshadow/mshadow-ps/thread_util.h     |  169 ++
 3rdparty/mshadow/mshadow/README.md            |    8 +
 3rdparty/mshadow/mshadow/base.h               | 1110 ++++++++
 3rdparty/mshadow/mshadow/cuda/reduce.cuh      |  120 +
 .../mshadow/mshadow/cuda/tensor_gpu-inl.cuh   |  828 ++++++
 3rdparty/mshadow/mshadow/dot_engine-inl.h     |  936 +++++++
 3rdparty/mshadow/mshadow/expr_engine-inl.h    |  482 ++++
 3rdparty/mshadow/mshadow/expr_scalar-inl.h    |  165 ++
 3rdparty/mshadow/mshadow/expression.h         |  416 +++
 3rdparty/mshadow/mshadow/extension.h          |   41 +
 .../mshadow/mshadow/extension/broadcast.h     |  165 ++
 .../mshadow/extension/broadcast_with_axis.h   |  258 ++
 .../mshadow/mshadow/extension/channel_pool.h  |  108 +
 .../mshadow/extension/channel_unpool.h        |  137 +
 3rdparty/mshadow/mshadow/extension/choose.h   |   90 +
 3rdparty/mshadow/mshadow/extension/complex.h  |  525 ++++
 3rdparty/mshadow/mshadow/extension/concat.h   |  194 ++
 3rdparty/mshadow/mshadow/extension/crop.h     |  119 +
 3rdparty/mshadow/mshadow/extension/fill.h     |  103 +
 3rdparty/mshadow/mshadow/extension/flip.h     |  132 +
 .../mshadow/mshadow/extension/implicit_gemm.h |  128 +
 3rdparty/mshadow/mshadow/extension/mask.h     |   97 +
 3rdparty/mshadow/mshadow/extension/mirror.h   |   62 +
 3rdparty/mshadow/mshadow/extension/one_hot.h  |   87 +
 .../mshadow/extension/pack_col2patch.h        |  154 ++
 3rdparty/mshadow/mshadow/extension/pad.h      |  111 +
 3rdparty/mshadow/mshadow/extension/range.h    |  118 +
 .../mshadow/extension/reduce_with_axis.h      |  136 +
 .../mshadow/mshadow/extension/reduceto1d.h    |  104 +
 3rdparty/mshadow/mshadow/extension/reshape.h  |   87 +
 3rdparty/mshadow/mshadow/extension/slice.h    |  156 ++
 3rdparty/mshadow/mshadow/extension/slice_ex.h |  135 +
 .../mshadow/mshadow/extension/spatial_pool.h  |  152 ++
 .../mshadow/extension/spatial_unpool.h        |  135 +
 .../extension/spatial_upsampling_nearest.h    |   71 +
 3rdparty/mshadow/mshadow/extension/swapaxis.h |  110 +
 3rdparty/mshadow/mshadow/extension/take.h     |   99 +
 .../mshadow/mshadow/extension/take_grad.h     |  111 +
 .../mshadow/mshadow/extension/transpose.h     |  200 ++
 .../mshadow/extension/unpack_patch2col.h      |  151 ++
 3rdparty/mshadow/mshadow/half.h               |  354 +++
 3rdparty/mshadow/mshadow/half2.h              |  143 +
 3rdparty/mshadow/mshadow/io.h                 |  137 +
 3rdparty/mshadow/mshadow/logging.h            |  234 ++
 3rdparty/mshadow/mshadow/packet-inl.h         |  413 +++
 3rdparty/mshadow/mshadow/packet/plain-inl.h   |   76 +
 3rdparty/mshadow/mshadow/packet/sse-inl.h     |  147 +
 3rdparty/mshadow/mshadow/random.h             |  570 ++++
 3rdparty/mshadow/mshadow/stream_gpu-inl.h     |  214 ++
 3rdparty/mshadow/mshadow/tensor.h             | 1081 ++++++++
 3rdparty/mshadow/mshadow/tensor_container.h   |  208 ++
 3rdparty/mshadow/mshadow/tensor_cpu-inl.h     |  627 +++++
 3rdparty/mshadow/mshadow/tensor_gpu-inl.h     |  245 ++
 3rdparty/mshadow/scripts/travis_script.sh     |   19 +
 3rdparty/mshadow/test/Makefile                |   35 +
 3rdparty/mshadow/test/pairtest.cu             |  105 +
 3rdparty/mshadow/test/pool.cu                 |   69 +
 3rdparty/mshadow/test/reshape.cu              |   74 +
 3rdparty/mshadow/test/test.cu                 |   79 +
 3rdparty/mshadow/test/test.h                  |   67 +
 3rdparty/mshadow/test/unpack.cu               |   85 +
 115 files changed, 21728 insertions(+)
 create mode 100644 3rdparty/mshadow/.gitignore
 create mode 100644 3rdparty/mshadow/.travis.yml
 create mode 100644 3rdparty/mshadow/CHANGES.md
 create mode 100644 3rdparty/mshadow/CMakeLists.txt
 create mode 100644 3rdparty/mshadow/LICENSE
 create mode 100644 3rdparty/mshadow/README.md
 create mode 100644 3rdparty/mshadow/cmake/Cuda.cmake
 create mode 100644 3rdparty/mshadow/cmake/Utils.cmake
 create mode 100644 3rdparty/mshadow/cmake/mshadow.cmake
 create mode 100644 3rdparty/mshadow/cmake/mshadowUtils.cmake
 create mode 100644 3rdparty/mshadow/doc/Doxyfile
 create mode 100644 3rdparty/mshadow/doc/README.md
 create mode 100755 3rdparty/mshadow/doc/mkdoc.sh
 create mode 100644 3rdparty/mshadow/guide/.gitignore
 create mode 100644 3rdparty/mshadow/guide/Makefile
 create mode 100644 3rdparty/mshadow/guide/README.md
 create mode 100644 3rdparty/mshadow/guide/basic.cpp
 create mode 100644 3rdparty/mshadow/guide/basic_stream.cu
 create mode 100644 3rdparty/mshadow/guide/config.mk
 create mode 100644 3rdparty/mshadow/guide/defop.cpp
 create mode 100644 3rdparty/mshadow/guide/exp-template/.gitignore
 create mode 100644 3rdparty/mshadow/guide/exp-template/Makefile
 create mode 100644 3rdparty/mshadow/guide/exp-template/README.md
 create mode 100644 3rdparty/mshadow/guide/exp-template/exp_lazy.cpp
 create mode 100644 3rdparty/mshadow/guide/exp-template/exp_template.cpp
 create mode 100644 3rdparty/mshadow/guide/exp-template/exp_template_op.cpp
 create mode 100644 3rdparty/mshadow/guide/mshadow-ps/.gitignore
 create mode 100644 3rdparty/mshadow/guide/mshadow-ps/2-levels.png
 create mode 100644 3rdparty/mshadow/guide/mshadow-ps/Makefile
 create mode 100644 3rdparty/mshadow/guide/mshadow-ps/README.md
 create mode 100644 3rdparty/mshadow/guide/mshadow-ps/config.mk
 create mode 100644 3rdparty/mshadow/guide/mshadow-ps/dbstr.h
 create mode 100644 3rdparty/mshadow/guide/mshadow-ps/dist_async_sum-inl.h
 create mode 100644 3rdparty/mshadow/guide/mshadow-ps/dist_async_sum.cpp
 create mode 100755 3rdparty/mshadow/guide/mshadow-ps/local.sh
 create mode 100644 3rdparty/mshadow/guide/mshadow-ps/local_sum-inl.h
 create mode 100644 3rdparty/mshadow/guide/mshadow-ps/local_sum.cpp
 create mode 100644 3rdparty/mshadow/guide/mshadow-ps/local_sum.cu
 create mode 100644 3rdparty/mshadow/guide/neuralnet/Makefile
 create mode 100644 3rdparty/mshadow/guide/neuralnet/README.md
 create mode 100644 3rdparty/mshadow/guide/neuralnet/config.mk
 create mode 100644 3rdparty/mshadow/guide/neuralnet/convnet.cu
 create mode 100644 3rdparty/mshadow/guide/neuralnet/nnet.cu
 create mode 100644 3rdparty/mshadow/guide/neuralnet/nnet_ps.cu
 create mode 100644 3rdparty/mshadow/guide/neuralnet/util.h
 create mode 100644 3rdparty/mshadow/make/README.md
 create mode 100644 3rdparty/mshadow/make/mshadow.mk
 create mode 100644 3rdparty/mshadow/mshadow-ps/.gitignore
 create mode 100644 3rdparty/mshadow/mshadow-ps/README.md
 create mode 100644 3rdparty/mshadow/mshadow-ps/mshadow_ps.h
 create mode 100644 3rdparty/mshadow/mshadow-ps/ps_dist-inl.h
 create mode 100644 3rdparty/mshadow/mshadow-ps/ps_local-inl.h
 create mode 100644 3rdparty/mshadow/mshadow-ps/ps_rabit-inl.h
 create mode 100644 3rdparty/mshadow/mshadow-ps/thread.h
 create mode 100644 3rdparty/mshadow/mshadow-ps/thread_util.h
 create mode 100644 3rdparty/mshadow/mshadow/README.md
 create mode 100755 3rdparty/mshadow/mshadow/base.h
 create mode 100644 3rdparty/mshadow/mshadow/cuda/reduce.cuh
 create mode 100755 3rdparty/mshadow/mshadow/cuda/tensor_gpu-inl.cuh
 create mode 100644 3rdparty/mshadow/mshadow/dot_engine-inl.h
 create mode 100644 3rdparty/mshadow/mshadow/expr_engine-inl.h
 create mode 100644 3rdparty/mshadow/mshadow/expr_scalar-inl.h
 create mode 100644 3rdparty/mshadow/mshadow/expression.h
 create mode 100644 3rdparty/mshadow/mshadow/extension.h
 create mode 100644 3rdparty/mshadow/mshadow/extension/broadcast.h
 create mode 100644 3rdparty/mshadow/mshadow/extension/broadcast_with_axis.h
 create mode 100644 3rdparty/mshadow/mshadow/extension/channel_pool.h
 create mode 100644 3rdparty/mshadow/mshadow/extension/channel_unpool.h
 create mode 100644 3rdparty/mshadow/mshadow/extension/choose.h
 create mode 100644 3rdparty/mshadow/mshadow/extension/complex.h
 create mode 100644 3rdparty/mshadow/mshadow/extension/concat.h
 create mode 100644 3rdparty/mshadow/mshadow/extension/crop.h
 create mode 100644 3rdparty/mshadow/mshadow/extension/fill.h
 create mode 100644 3rdparty/mshadow/mshadow/extension/flip.h
 create mode 100644 3rdparty/mshadow/mshadow/extension/implicit_gemm.h
 create mode 100644 3rdparty/mshadow/mshadow/extension/mask.h
 create mode 100644 3rdparty/mshadow/mshadow/extension/mirror.h
 create mode 100644 3rdparty/mshadow/mshadow/extension/one_hot.h
 create mode 100644 3rdparty/mshadow/mshadow/extension/pack_col2patch.h
 create mode 100644 3rdparty/mshadow/mshadow/extension/pad.h
 create mode 100644 3rdparty/mshadow/mshadow/extension/range.h
 create mode 100644 3rdparty/mshadow/mshadow/extension/reduce_with_axis.h
 create mode 100644 3rdparty/mshadow/mshadow/extension/reduceto1d.h
 create mode 100644 3rdparty/mshadow/mshadow/extension/reshape.h
 create mode 100644 3rdparty/mshadow/mshadow/extension/slice.h
 create mode 100644 3rdparty/mshadow/mshadow/extension/slice_ex.h
 create mode 100644 3rdparty/mshadow/mshadow/extension/spatial_pool.h
 create mode 100644 3rdparty/mshadow/mshadow/extension/spatial_unpool.h
 create mode 100644 3rdparty/mshadow/mshadow/extension/spatial_upsampling_nearest.h
 create mode 100644 3rdparty/mshadow/mshadow/extension/swapaxis.h
 create mode 100644 3rdparty/mshadow/mshadow/extension/take.h
 create mode 100644 3rdparty/mshadow/mshadow/extension/take_grad.h
 create mode 100644 3rdparty/mshadow/mshadow/extension/transpose.h
 create mode 100644 3rdparty/mshadow/mshadow/extension/unpack_patch2col.h
 create mode 100644 3rdparty/mshadow/mshadow/half.h
 create mode 100755 3rdparty/mshadow/mshadow/half2.h
 create mode 100644 3rdparty/mshadow/mshadow/io.h
 create mode 100644 3rdparty/mshadow/mshadow/logging.h
 create mode 100644 3rdparty/mshadow/mshadow/packet-inl.h
 create mode 100644 3rdparty/mshadow/mshadow/packet/plain-inl.h
 create mode 100644 3rdparty/mshadow/mshadow/packet/sse-inl.h
 create mode 100644 3rdparty/mshadow/mshadow/random.h
 create mode 100644 3rdparty/mshadow/mshadow/stream_gpu-inl.h
 create mode 100755 3rdparty/mshadow/mshadow/tensor.h
 create mode 100644 3rdparty/mshadow/mshadow/tensor_container.h
 create mode 100755 3rdparty/mshadow/mshadow/tensor_cpu-inl.h
 create mode 100755 3rdparty/mshadow/mshadow/tensor_gpu-inl.h
 create mode 100755 3rdparty/mshadow/scripts/travis_script.sh
 create mode 100644 3rdparty/mshadow/test/Makefile
 create mode 100644 3rdparty/mshadow/test/pairtest.cu
 create mode 100644 3rdparty/mshadow/test/pool.cu
 create mode 100644 3rdparty/mshadow/test/reshape.cu
 create mode 100644 3rdparty/mshadow/test/test.cu
 create mode 100644 3rdparty/mshadow/test/test.h
 create mode 100644 3rdparty/mshadow/test/unpack.cu

diff --git a/3rdparty/mshadow/.gitignore b/3rdparty/mshadow/.gitignore
new file mode 100644
index 000000000000..3da5172aeb2a
--- /dev/null
+++ b/3rdparty/mshadow/.gitignore
@@ -0,0 +1,21 @@
+# Compiled Object files
+*.slo
+*.lo
+*.o
+
+# Compiled Dynamic libraries
+*.so
+*.dylib
+
+# Compiled Static libraries
+*.lai
+*.la
+*.a
+*~
+doc/html
+doc/latex
+rabit
+dmlc-core
+*.db
+*.bak
+build
diff --git a/3rdparty/mshadow/.travis.yml b/3rdparty/mshadow/.travis.yml
new file mode 100644
index 000000000000..a4d6223d8ea7
--- /dev/null
+++ b/3rdparty/mshadow/.travis.yml
@@ -0,0 +1,43 @@
+# disable sudo to use container based build
+sudo: false
+
+# Use Build Matrix to do lint and build seperately
+env:
+  matrix:
+    - TASK=lint LINT_LANG=cpp
+    - TASK=doc
+    - TASK=build CXX=g++
+
+# dependent apt packages
+addons:
+  apt:
+    packages:
+      - doxygen
+      - wget
+      - unzip
+      - libblas-dev
+      - python3-pip
+
+before_install:
+  - git clone https://github.com/dmlc/dmlc-core
+  - export TRAVIS=dmlc-core/scripts/travis
+  - source ${TRAVIS}/travis_setup_env.sh
+
+install:
+  - pip3 install --upgrade pip --user
+  - pip3 install  --user  cpplint pylint
+  
+script: scripts/travis_script.sh
+
+before_cache:
+  - ${TRAVIS}/travis_before_cache.sh
+
+cache:
+  directories:
+    - ${HOME}/.cache/usr
+
+notifications:
+  email:
+    on_success: change
+    on_failure: always
+
diff --git a/3rdparty/mshadow/CHANGES.md b/3rdparty/mshadow/CHANGES.md
new file mode 100644
index 000000000000..03bb16936acd
--- /dev/null
+++ b/3rdparty/mshadow/CHANGES.md
@@ -0,0 +1,12 @@
+Change Log
+=====
+
+mshadow-1.0
+=====
+* Initial release
+
+mshadow-2.0: in progress
+=====
+* Support multiple data type
+* Great refactoring of code
+* Parameter server interface for MultiGPU and distributed learning
diff --git a/3rdparty/mshadow/CMakeLists.txt b/3rdparty/mshadow/CMakeLists.txt
new file mode 100644
index 000000000000..b89e9028a77b
--- /dev/null
+++ b/3rdparty/mshadow/CMakeLists.txt
@@ -0,0 +1,6 @@
+cmake_minimum_required(VERSION 2.8.7)
+
+project(mshadow C CXX)
+
+set(mshadow_LINT_DIRS mshadow mshadow-ps)
+add_custom_target(mshadow_lint COMMAND ${CMAKE_COMMAND} -DMSVC=${MSVC} -DPYTHON_EXECUTABLE=${PYTHON_EXECUTABLE} -DLINT_DIRS=${mshadow_LINT_DIRS} -DPROJECT_SOURCE_DIR=${PROJECT_SOURCE_DIR} -DPROJECT_NAME=mshadow -P ${PROJECT_SOURCE_DIR}/../dmlc-core/cmake/lint.cmake)
diff --git a/3rdparty/mshadow/LICENSE b/3rdparty/mshadow/LICENSE
new file mode 100644
index 000000000000..ebf9611d76cd
--- /dev/null
+++ b/3rdparty/mshadow/LICENSE
@@ -0,0 +1,13 @@
+Copyright (c) 2014 by Contributors
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+    
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
diff --git a/3rdparty/mshadow/README.md b/3rdparty/mshadow/README.md
new file mode 100644
index 000000000000..cc18964a65f7
--- /dev/null
+++ b/3rdparty/mshadow/README.md
@@ -0,0 +1,37 @@
+mshadow: Matrix Shadow
+======
+[![Build Status](https://travis-ci.org/dmlc/mshadow.svg?branch=master)](https://travis-ci.org/dmlc/mshadow)
+
+MShadow is a lightweight CPU/GPU Matrix/Tensor Template Library in C++/CUDA. The goal of mshadow is to support ***efficient***,
+***device invariant*** and ***simple*** tensor library for machine learning project that aims for maximum performance and control, while also emphasize simplicity.
+
+MShadow also provides interface that allows writing Multi-GPU and distributed deep learning programs in an easy and unified way.
+
+* [Contributors](https://github.com/tqchen/mshadow/graphs/contributors)
+* [Tutorial](guide)
+* [Documentation](doc)
+* [Parameter Server Interface for GPU Tensor](guide/mshadow-ps)
+
+Features
+--------
+* Efficient: all the expression you write will be lazily evaluated and compiled into optimized code
+  - No temporal memory allocation will happen for expression you write
+  - mshadow will generate specific kernel for every expression you write in compile time.
+* Device invariant: you can write one code and it will run on both CPU and GPU
+* Simple: mshadow allows you to write machine learning code using expressions.
+* Whitebox: put a float* into the Tensor struct and take the benefit of the package, no memory allocation is happened unless explicitly called
+* Lightweight library: light amount of code to support frequently used functions in machine learning
+* Extendable: user can write simple functions that plugs into mshadow and run on GPU/CPU, no experience in CUDA is required.
+* MultiGPU and Distributed ML: mshadow-ps interface allows user to write efficient MultiGPU and distributed programs in an unified way.
+
+Version
+-------
+* This version mshadow-2.x, there are a lot of changes in the interface and it is not backward compatible with mshadow-1.0
+  - If you use older version of cxxnet, you will need to use the legacy mshadow code
+* For legacy code, refer to [Here](https://github.com/tqchen/mshadow/releases/tag/v1.1)
+* Change log in [CHANGES.md](CHANGES.md)
+
+Projects Using MShadow
+----------------------
+* [MXNet: Efficient and Flexible Distributed Deep Learning Framework](https://github.com/dmlc/mxnet)
+* [CXXNet: A lightweight  C++ based deep learnig framework](https://github.com/dmlc/cxxnet)
diff --git a/3rdparty/mshadow/cmake/Cuda.cmake b/3rdparty/mshadow/cmake/Cuda.cmake
new file mode 100644
index 000000000000..bc09a3905076
--- /dev/null
+++ b/3rdparty/mshadow/cmake/Cuda.cmake
@@ -0,0 +1,324 @@
+if(NOT USE_CUDA)
+  return()
+endif()
+
+include(CheckCXXCompilerFlag)
+check_cxx_compiler_flag("-std=c++11"   SUPPORT_CXX11)
+
+################################################################################################
+# A function for automatic detection of GPUs installed  (if autodetection is enabled)
+# Usage:
+#   mshadow_detect_installed_gpus(out_variable)
+function(mshadow_detect_installed_gpus out_variable)
+set(CUDA_gpu_detect_output "")
+  if(NOT CUDA_gpu_detect_output)
+    message(STATUS "Running GPU architecture autodetection")
+    set(__cufile ${PROJECT_BINARY_DIR}/detect_cuda_archs.cu)
+
+    file(WRITE ${__cufile} ""
+      "#include <cstdio>\n"
+      "#include <iostream>\n"
+      "using namespace std;\n"
+      "int main()\n"
+      "{\n"
+      "  int count = 0;\n"
+      "  if (cudaSuccess != cudaGetDeviceCount(&count)) { return -1; }\n"
+      "  if (count == 0) { cerr << \"No cuda devices detected\" << endl; return -1; }\n"
+      "  for (int device = 0; device < count; ++device)\n"
+      "  {\n"
+      "    cudaDeviceProp prop;\n"
+      "    if (cudaSuccess == cudaGetDeviceProperties(&prop, device))\n"
+      "      std::printf(\"%d.%d \", prop.major, prop.minor);\n"
+      "  }\n"
+      "  return 0;\n"
+      "}\n")
+    if(MSVC)
+      #find vcvarsall.bat and run it building msvc environment
+      get_filename_component(MY_COMPILER_DIR ${CMAKE_CXX_COMPILER} DIRECTORY)
+      find_file(MY_VCVARSALL_BAT vcvarsall.bat "${MY_COMPILER_DIR}/.." "${MY_COMPILER_DIR}/../..")
+      execute_process(COMMAND ${MY_VCVARSALL_BAT} && ${CUDA_NVCC_EXECUTABLE} -arch sm_30 --run  ${__cufile}
+                      WORKING_DIRECTORY "${PROJECT_BINARY_DIR}/CMakeFiles/"
+                      RESULT_VARIABLE __nvcc_res OUTPUT_VARIABLE __nvcc_out
+                      OUTPUT_STRIP_TRAILING_WHITESPACE)
+    else()
+      if(CUDA_LIBRARY_PATH)
+        set(CUDA_LINK_LIBRARY_PATH "-L${CUDA_LIBRARY_PATH}")
+      endif()
+      execute_process(COMMAND ${CUDA_NVCC_EXECUTABLE} -arch sm_30 --run ${__cufile} ${CUDA_LINK_LIBRARY_PATH}
+                      WORKING_DIRECTORY "${PROJECT_BINARY_DIR}/CMakeFiles/"
+                      RESULT_VARIABLE __nvcc_res OUTPUT_VARIABLE __nvcc_out
+                      OUTPUT_STRIP_TRAILING_WHITESPACE)
+    endif()
+    if(__nvcc_res EQUAL 0)
+      # nvcc outputs text containing line breaks when building with MSVC.
+      # The line below prevents CMake from inserting a variable with line
+      # breaks in the cache
+      message(STATUS "Found CUDA arch ${__nvcc_out}")
+      string(REGEX MATCH "([1-9].[0-9])" __nvcc_out "${__nvcc_out}")
+      string(REPLACE "2.1" "2.1(2.0)" __nvcc_out "${__nvcc_out}")
+      set(CUDA_gpu_detect_output ${__nvcc_out} CACHE INTERNAL "Returned GPU architetures from mshadow_detect_gpus tool" FORCE)
+    else()
+      message(WARNING "Running GPU detection script with nvcc failed: ${__nvcc_out}")
+    endif()
+  endif()
+
+  if(NOT CUDA_gpu_detect_output)
+    message(WARNING "Automatic GPU detection failed. Building for all known architectures (${mshadow_known_gpu_archs}).")
+    set(${out_variable} ${mshadow_known_gpu_archs} PARENT_SCOPE)
+  else()
+    set(${out_variable} ${CUDA_gpu_detect_output} PARENT_SCOPE)
+  endif()
+endfunction()
+
+
+################################################################################################
+# Function for selecting GPU arch flags for nvcc based on CUDA_ARCH_NAME
+# Usage:
+#   mshadow_select_nvcc_arch_flags(out_variable)
+function(mshadow_select_nvcc_arch_flags out_variable)
+  # List of arch names
+  set(__archs_names "Fermi" "Kepler" "Maxwell" "Pascal" "Volta" "All" "Manual")
+  set(__archs_name_default "All")
+  if(NOT CMAKE_CROSSCOMPILING)
+    list(APPEND __archs_names "Auto")
+    set(__archs_name_default "Auto")
+  endif()
+
+  # set CUDA_ARCH_NAME strings (so it will be seen as dropbox in CMake-Gui)
+  set(CUDA_ARCH_NAME ${__archs_name_default} CACHE STRING "Select target NVIDIA GPU achitecture.")
+  set_property( CACHE CUDA_ARCH_NAME PROPERTY STRINGS "" ${__archs_names} )
+  mark_as_advanced(CUDA_ARCH_NAME)
+
+  # verify CUDA_ARCH_NAME value
+  if(NOT ";${__archs_names};" MATCHES ";${CUDA_ARCH_NAME};")
+    string(REPLACE ";" ", " __archs_names "${__archs_names}")
+    message(FATAL_ERROR "Only ${__archs_names} architeture names are supported.")
+  endif()
+
+  if(${CUDA_ARCH_NAME} STREQUAL "Manual")
+    set(CUDA_ARCH_BIN ${mshadow_known_gpu_archs} CACHE STRING "Specify 'real' GPU architectures to build binaries for, BIN(PTX) format is supported")
+    set(CUDA_ARCH_PTX "50"                     CACHE STRING "Specify 'virtual' PTX architectures to build PTX intermediate code for")
+    mark_as_advanced(CUDA_ARCH_BIN CUDA_ARCH_PTX)
+  else()
+    unset(CUDA_ARCH_BIN CACHE)
+    unset(CUDA_ARCH_PTX CACHE)
+  endif()
+
+  if(${CUDA_ARCH_NAME} STREQUAL "Fermi")
+    set(__cuda_arch_bin "20 21(20)")
+  elseif(${CUDA_ARCH_NAME} STREQUAL "Kepler")
+    set(__cuda_arch_bin "30 35")
+  elseif(${CUDA_ARCH_NAME} STREQUAL "Maxwell")
+    set(__cuda_arch_bin "50")
+  elseif(${CUDA_ARCH_NAME} STREQUAL "Pascal")
+    set(__cuda_arch_bin "60 61")
+  elseif(${CUDA_ARCH_NAME} STREQUAL "Volta")
+    set(__cuda_arch_bin "70")
+  elseif(${CUDA_ARCH_NAME} STREQUAL "All")
+    set(__cuda_arch_bin ${mshadow_known_gpu_archs})
+  elseif(${CUDA_ARCH_NAME} STREQUAL "Auto")
+    mshadow_detect_installed_gpus(__cuda_arch_bin)
+  else()  # (${CUDA_ARCH_NAME} STREQUAL "Manual")
+    set(__cuda_arch_bin ${CUDA_ARCH_BIN})
+  endif()
+
+  # remove dots and convert to lists
+  string(REGEX REPLACE "\\." "" __cuda_arch_bin "${__cuda_arch_bin}")
+  string(REGEX REPLACE "\\." "" __cuda_arch_ptx "${CUDA_ARCH_PTX}")
+  string(REGEX MATCHALL "[0-9()]+" __cuda_arch_bin "${__cuda_arch_bin}")
+  string(REGEX MATCHALL "[0-9]+"   __cuda_arch_ptx "${__cuda_arch_ptx}")
+  mshadow_list_unique(__cuda_arch_bin __cuda_arch_ptx)
+
+  set(__nvcc_flags "")
+  set(__nvcc_archs_readable "")
+
+  # Tell NVCC to add binaries for the specified GPUs
+  foreach(__arch ${__cuda_arch_bin})
+    if(__arch MATCHES "([0-9]+)\\(([0-9]+)\\)")
+      # User explicitly specified PTX for the concrete BIN
+      list(APPEND __nvcc_flags -gencode arch=compute_${CMAKE_MATCH_2},code=sm_${CMAKE_MATCH_1})
+      list(APPEND __nvcc_archs_readable sm_${CMAKE_MATCH_1})
+    else()
+      # User didn't explicitly specify PTX for the concrete BIN, we assume PTX=BIN
+      list(APPEND __nvcc_flags -gencode arch=compute_${__arch},code=sm_${__arch})
+      list(APPEND __nvcc_archs_readable sm_${__arch})
+    endif()
+  endforeach()
+
+  # Tell NVCC to add PTX intermediate code for the specified architectures
+  foreach(__arch ${__cuda_arch_ptx})
+    list(APPEND __nvcc_flags -gencode arch=compute_${__arch},code=compute_${__arch})
+    list(APPEND __nvcc_archs_readable compute_${__arch})
+  endforeach()
+
+  string(REPLACE ";" " " __nvcc_archs_readable "${__nvcc_archs_readable}")
+  set(${out_variable}          ${__nvcc_flags}          PARENT_SCOPE)
+  set(${out_variable}_readable ${__nvcc_archs_readable} PARENT_SCOPE)
+endfunction()
+
+################################################################################################
+# Short command for cuda comnpilation
+# Usage:
+#   mshadow_cuda_compile(<objlist_variable> <cuda_files>)
+macro(mshadow_cuda_compile objlist_variable)
+  foreach(var CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_RELEASE CMAKE_CXX_FLAGS_DEBUG)
+    set(${var}_backup_in_cuda_compile_ "${${var}}")
+
+    # we remove /EHa as it generates warnings under windows
+    string(REPLACE "/EHa" "" ${var} "${${var}}")
+
+  endforeach()
+  if(UNIX OR APPLE)
+    list(APPEND CUDA_NVCC_FLAGS -Xcompiler -fPIC)
+  endif()
+
+  if(APPLE)
+    list(APPEND CUDA_NVCC_FLAGS -Xcompiler -Wno-unused-function)
+  endif()
+
+  set(CUDA_NVCC_FLAGS_DEBUG "${CUDA_NVCC_FLAGS_DEBUG} -G")
+
+  if(MSVC)
+    # disable noisy warnings:
+    # 4819: The file contains a character that cannot be represented in the current code page (number).
+    list(APPEND CUDA_NVCC_FLAGS -Xcompiler "/wd4819")
+    foreach(flag_var
+        CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_DEBUG CMAKE_CXX_FLAGS_RELEASE
+        CMAKE_CXX_FLAGS_MINSIZEREL CMAKE_CXX_FLAGS_RELWITHDEBINFO)
+      if(${flag_var} MATCHES "/MD")
+        string(REGEX REPLACE "/MD" "/MT" ${flag_var} "${${flag_var}}")
+      endif(${flag_var} MATCHES "/MD")
+    endforeach(flag_var)
+  endif()
+
+  # If the build system is a container, make sure the nvcc intermediate files
+  # go into the build output area rather than in /tmp, which may run out of space
+  if(IS_CONTAINER_BUILD)
+    set(CUDA_NVCC_INTERMEDIATE_DIR "${CMAKE_CURRENT_BINARY_DIR}")
+    message(STATUS "Container build enabled, so nvcc intermediate files in: ${CUDA_NVCC_INTERMEDIATE_DIR}")
+    list(APPEND CUDA_NVCC_FLAGS "--keep --keep-dir ${CUDA_NVCC_INTERMEDIATE_DIR}")
+  endif()
+
+  cuda_compile(cuda_objcs ${ARGN})
+
+  foreach(var CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_RELEASE CMAKE_CXX_FLAGS_DEBUG)
+    set(${var} "${${var}_backup_in_cuda_compile_}")
+    unset(${var}_backup_in_cuda_compile_)
+  endforeach()
+
+  set(${objlist_variable} ${cuda_objcs})
+endmacro()
+
+################################################################################################
+# Short command for cuDNN detection. Believe it soon will be a part of CUDA toolkit distribution.
+# That's why not FindcuDNN.cmake file, but just the macro
+# Usage:
+#   detect_cuDNN()
+function(detect_cuDNN)
+  set(CUDNN_ROOT "" CACHE PATH "CUDNN root folder")
+
+  find_path(CUDNN_INCLUDE cudnn.h
+            PATHS ${CUDNN_ROOT} $ENV{CUDNN_ROOT} ${CUDA_TOOLKIT_INCLUDE}
+            DOC "Path to cuDNN include directory." )
+
+  get_filename_component(__libpath_hist ${CUDA_CUDART_LIBRARY} PATH)
+  find_library(CUDNN_LIBRARY NAMES libcudnn.so cudnn.lib # libcudnn_static.a
+                             PATHS ${CUDNN_ROOT} $ENV{CUDNN_ROOT} ${CUDNN_INCLUDE} ${__libpath_hist}
+                             DOC "Path to cuDNN library.")
+
+  if(CUDNN_INCLUDE AND CUDNN_LIBRARY)
+    set(HAVE_CUDNN  TRUE PARENT_SCOPE)
+    set(CUDNN_FOUND TRUE PARENT_SCOPE)
+
+    mark_as_advanced(CUDNN_INCLUDE CUDNN_LIBRARY CUDNN_ROOT)
+    message(STATUS "Found cuDNN (include: ${CUDNN_INCLUDE}, library: ${CUDNN_LIBRARY})")
+  endif()
+endfunction()
+
+
+################################################################################################
+###  Non macro section
+################################################################################################
+
+# Try to prime CUDA_TOOLKIT_ROOT_DIR by looking for libcudart.so
+if(NOT CUDA_TOOLKIT_ROOT_DIR)
+  find_library(CUDA_LIBRARY_PATH libcudart.so PATHS ENV LD_LIBRARY_PATH PATH_SUFFIXES lib lib64)
+  if(CUDA_LIBRARY_PATH)
+    get_filename_component(CUDA_LIBRARY_PATH ${CUDA_LIBRARY_PATH} DIRECTORY)
+    set(CUDA_TOOLKIT_ROOT_DIR "${CUDA_LIBRARY_PATH}/..")
+  endif()
+endif()
+
+find_package(CUDA 5.5 QUIET REQUIRED)
+find_cuda_helper_libs(curand)  # cmake 2.8.7 compartibility which doesn't search for curand
+
+if(NOT CUDA_FOUND)
+  return()
+endif()
+
+set(HAVE_CUDA TRUE)
+message(STATUS "CUDA detected: " ${CUDA_VERSION})
+include_directories(SYSTEM ${CUDA_INCLUDE_DIRS})
+list(APPEND mshadow_LINKER_LIBS ${CUDA_CUDART_LIBRARY}
+                              ${CUDA_curand_LIBRARY} ${CUDA_CUBLAS_LIBRARIES})
+
+# Known NVIDIA GPU achitectures mshadow can be compiled for.
+# This list will be used for CUDA_ARCH_NAME = All option
+if(CUDA_ARCH_ALL)
+  set(mshadow_known_gpu_archs "${CUDA_ARCH_ALL}")
+else()
+  if(${CUDA_VERSION} EQUAL 9.0 OR ${CUDA_VERSION} GREATER 9.0)
+    set(mshadow_known_gpu_archs "30 35 50 52 60 61 70")
+  elseif(${CUDA_VERSION} EQUAL 8.0 OR ${CUDA_VERSION} GREATER 8.0)
+    set(mshadow_known_gpu_archs "30 35 50 52 60 61")
+  else()
+    set(mshadow_known_gpu_archs "30 35 50 52")
+  endif()
+endif()
+
+# cudnn detection
+if(USE_CUDNN)
+  detect_cuDNN()
+  if(HAVE_CUDNN)
+    add_definitions(-DUSE_CUDNN)
+    include_directories(SYSTEM ${CUDNN_INCLUDE})
+    list(APPEND mshadow_LINKER_LIBS ${CUDNN_LIBRARY})
+  endif()
+endif()
+
+# setting nvcc arch flags
+mshadow_select_nvcc_arch_flags(NVCC_FLAGS_EXTRA)
+list(APPEND CUDA_NVCC_FLAGS ${NVCC_FLAGS_EXTRA})
+message(STATUS "Added CUDA NVCC flags for: ${NVCC_FLAGS_EXTRA_readable}")
+
+# Boost 1.55 workaround, see https://svn.boost.org/trac/boost/ticket/9392 or
+# https://github.com/ComputationalRadiationPhysics/picongpu/blob/master/src/picongpu/CMakeLists.txt
+if(Boost_VERSION EQUAL 105500)
+  message(STATUS "Cuda + Boost 1.55: Applying noinline work around")
+  # avoid warning for CMake >= 2.8.12
+  set(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} \"-DBOOST_NOINLINE=__attribute__((noinline))\" ")
+endif()
+
+# disable some nvcc diagnostic that apears in boost, glog, glags, opencv, etc.
+foreach(diag cc_clobber_ignored integer_sign_change useless_using_declaration set_but_not_used)
+  list(APPEND CUDA_NVCC_FLAGS -Xcudafe --diag_suppress=${diag})
+endforeach()
+
+# setting default testing device
+if(NOT CUDA_TEST_DEVICE)
+  set(CUDA_TEST_DEVICE -1)
+endif()
+
+mark_as_advanced(CUDA_BUILD_CUBIN CUDA_BUILD_EMULATION CUDA_VERBOSE_BUILD)
+mark_as_advanced(CUDA_SDK_ROOT_DIR CUDA_SEPARABLE_COMPILATION)
+
+# Handle clang/libc++ issue
+if(APPLE)
+  mshadow_detect_darwin_version(OSX_VERSION)
+
+  # OSX 10.9 and higher uses clang/libc++ by default which is incompartible with old CUDA toolkits
+  if(OSX_VERSION VERSION_GREATER 10.8)
+    # enabled by default if and only if CUDA version is less than 7.0
+    mshadow_option(USE_libstdcpp "Use libstdc++ instead of libc++" (CUDA_VERSION VERSION_LESS 7.0))
+  endif()
+endif()
diff --git a/3rdparty/mshadow/cmake/Utils.cmake b/3rdparty/mshadow/cmake/Utils.cmake
new file mode 100644
index 000000000000..dc464f0092f5
--- /dev/null
+++ b/3rdparty/mshadow/cmake/Utils.cmake
@@ -0,0 +1,398 @@
+################################################################################################
+# Command alias for debugging messages
+# Usage:
+#   dmsg(<message>)
+function(dmsg)
+  message(STATUS ${ARGN})
+endfunction()
+
+################################################################################################
+# Removes duplicates from list(s)
+# Usage:
+#   mshadow_list_unique(<list_variable> [<list_variable>] [...])
+macro(mshadow_list_unique)
+  foreach(__lst ${ARGN})
+    if(${__lst})
+      list(REMOVE_DUPLICATES ${__lst})
+    endif()
+  endforeach()
+endmacro()
+
+################################################################################################
+# Clears variables from list
+# Usage:
+#   mshadow_clear_vars(<variables_list>)
+macro(mshadow_clear_vars)
+  foreach(_var ${ARGN})
+    unset(${_var})
+  endforeach()
+endmacro()
+
+################################################################################################
+# Removes duplicates from string
+# Usage:
+#   mshadow_string_unique(<string_variable>)
+function(mshadow_string_unique __string)
+  if(${__string})
+    set(__list ${${__string}})
+    separate_arguments(__list)
+    list(REMOVE_DUPLICATES __list)
+    foreach(__e ${__list})
+      set(__str "${__str} ${__e}")
+    endforeach()
+    set(${__string} ${__str} PARENT_SCOPE)
+  endif()
+endfunction()
+
+################################################################################################
+# Prints list element per line
+# Usage:
+#   mshadow_print_list(<list>)
+function(mshadow_print_list)
+  foreach(e ${ARGN})
+    message(STATUS ${e})
+  endforeach()
+endfunction()
+
+################################################################################################
+# Function merging lists of compiler flags to single string.
+# Usage:
+#   mshadow_merge_flag_lists(out_variable <list1> [<list2>] [<list3>] ...)
+function(mshadow_merge_flag_lists out_var)
+  set(__result "")
+  foreach(__list ${ARGN})
+    foreach(__flag ${${__list}})
+      string(STRIP ${__flag} __flag)
+      set(__result "${__result} ${__flag}")
+    endforeach()
+  endforeach()
+  string(STRIP ${__result} __result)
+  set(${out_var} ${__result} PARENT_SCOPE)
+endfunction()
+
+################################################################################################
+# Converts all paths in list to absolute
+# Usage:
+#   mshadow_convert_absolute_paths(<list_variable>)
+function(mshadow_convert_absolute_paths variable)
+  set(__dlist "")
+  foreach(__s ${${variable}})
+    get_filename_component(__abspath ${__s} ABSOLUTE)
+    list(APPEND __list ${__abspath})
+  endforeach()
+  set(${variable} ${__list} PARENT_SCOPE)
+endfunction()
+
+################################################################################################
+# Reads set of version defines from the header file
+# Usage:
+#   mshadow_parse_header(<file> <define1> <define2> <define3> ..)
+macro(mshadow_parse_header FILENAME FILE_VAR)
+  set(vars_regex "")
+  set(__parnet_scope OFF)
+  set(__add_cache OFF)
+  foreach(name ${ARGN})
+    if("${name}" STREQUAL "PARENT_SCOPE")
+      set(__parnet_scope ON)
+    elseif("${name}" STREQUAL "CACHE")
+      set(__add_cache ON)
+    elseif(vars_regex)
+      set(vars_regex "${vars_regex}|${name}")
+    else()
+      set(vars_regex "${name}")
+    endif()
+  endforeach()
+  if(EXISTS "${FILENAME}")
+    file(STRINGS "${FILENAME}" ${FILE_VAR} REGEX "#define[ \t]+(${vars_regex})[ \t]+[0-9]+" )
+  else()
+    unset(${FILE_VAR})
+  endif()
+  foreach(name ${ARGN})
+    if(NOT "${name}" STREQUAL "PARENT_SCOPE" AND NOT "${name}" STREQUAL "CACHE")
+      if(${FILE_VAR})
+        if(${FILE_VAR} MATCHES ".+[ \t]${name}[ \t]+([0-9]+).*")
+          string(REGEX REPLACE ".+[ \t]${name}[ \t]+([0-9]+).*" "\\1" ${name} "${${FILE_VAR}}")
+        else()
+          set(${name} "")
+        endif()
+        if(__add_cache)
+          set(${name} ${${name}} CACHE INTERNAL "${name} parsed from ${FILENAME}" FORCE)
+        elseif(__parnet_scope)
+          set(${name} "${${name}}" PARENT_SCOPE)
+        endif()
+      else()
+        unset(${name} CACHE)
+      endif()
+    endif()
+  endforeach()
+endmacro()
+
+################################################################################################
+# Reads single version define from the header file and parses it
+# Usage:
+#   mshadow_parse_header_single_define(<library_name> <file> <define_name>)
+function(mshadow_parse_header_single_define LIBNAME HDR_PATH VARNAME)
+  set(${LIBNAME}_H "")
+  if(EXISTS "${HDR_PATH}")
+    file(STRINGS "${HDR_PATH}" ${LIBNAME}_H REGEX "^#define[ \t]+${VARNAME}[ \t]+\"[^\"]*\".*$" LIMIT_COUNT 1)
+  endif()
+
+  if(${LIBNAME}_H)
+    string(REGEX REPLACE "^.*[ \t]${VARNAME}[ \t]+\"([0-9]+).*$" "\\1" ${LIBNAME}_VERSION_MAJOR "${${LIBNAME}_H}")
+    string(REGEX REPLACE "^.*[ \t]${VARNAME}[ \t]+\"[0-9]+\\.([0-9]+).*$" "\\1" ${LIBNAME}_VERSION_MINOR  "${${LIBNAME}_H}")
+    string(REGEX REPLACE "^.*[ \t]${VARNAME}[ \t]+\"[0-9]+\\.[0-9]+\\.([0-9]+).*$" "\\1" ${LIBNAME}_VERSION_PATCH "${${LIBNAME}_H}")
+    set(${LIBNAME}_VERSION_MAJOR ${${LIBNAME}_VERSION_MAJOR} ${ARGN} PARENT_SCOPE)
+    set(${LIBNAME}_VERSION_MINOR ${${LIBNAME}_VERSION_MINOR} ${ARGN} PARENT_SCOPE)
+    set(${LIBNAME}_VERSION_PATCH ${${LIBNAME}_VERSION_PATCH} ${ARGN} PARENT_SCOPE)
+    set(${LIBNAME}_VERSION_STRING "${${LIBNAME}_VERSION_MAJOR}.${${LIBNAME}_VERSION_MINOR}.${${LIBNAME}_VERSION_PATCH}" PARENT_SCOPE)
+
+    # append a TWEAK version if it exists:
+    set(${LIBNAME}_VERSION_TWEAK "")
+    if("${${LIBNAME}_H}" MATCHES "^.*[ \t]${VARNAME}[ \t]+\"[0-9]+\\.[0-9]+\\.[0-9]+\\.([0-9]+).*$")
+      set(${LIBNAME}_VERSION_TWEAK "${CMAKE_MATCH_1}" ${ARGN} PARENT_SCOPE)
+    endif()
+    if(${LIBNAME}_VERSION_TWEAK)
+      set(${LIBNAME}_VERSION_STRING "${${LIBNAME}_VERSION_STRING}.${${LIBNAME}_VERSION_TWEAK}" ${ARGN} PARENT_SCOPE)
+    else()
+      set(${LIBNAME}_VERSION_STRING "${${LIBNAME}_VERSION_STRING}" ${ARGN} PARENT_SCOPE)
+    endif()
+  endif()
+endfunction()
+
+########################################################################################################
+# An option that the user can select. Can accept condition to control when option is available for user.
+# Usage:
+#   mshadow_option(<option_variable> "doc string" <initial value or boolean expression> [IF <condition>])
+function(mshadow_option variable description value)
+  set(__value ${value})
+  set(__condition "")
+  set(__varname "__value")
+  foreach(arg ${ARGN})
+    if(arg STREQUAL "IF" OR arg STREQUAL "if")
+      set(__varname "__condition")
+    else()
+      list(APPEND ${__varname} ${arg})
+    endif()
+  endforeach()
+  unset(__varname)
+  if("${__condition}" STREQUAL "")
+    set(__condition 2 GREATER 1)
+  endif()
+
+  if(${__condition})
+    if("${__value}" MATCHES ";")
+      if(${__value})
+        option(${variable} "${description}" ON)
+      else()
+        option(${variable} "${description}" OFF)
+      endif()
+    elseif(DEFINED ${__value})
+      if(${__value})
+        option(${variable} "${description}" ON)
+      else()
+        option(${variable} "${description}" OFF)
+      endif()
+    else()
+      option(${variable} "${description}" ${__value})
+    endif()
+  else()
+    unset(${variable} CACHE)
+  endif()
+endfunction()
+
+################################################################################################
+# Utility macro for comparing two lists. Used for CMake debugging purposes
+# Usage:
+#   mshadow_compare_lists(<list_variable> <list2_variable> [description])
+function(mshadow_compare_lists list1 list2 desc)
+  set(__list1 ${${list1}})
+  set(__list2 ${${list2}})
+  list(SORT __list1)
+  list(SORT __list2)
+  list(LENGTH __list1 __len1)
+  list(LENGTH __list2 __len2)
+
+  if(NOT ${__len1} EQUAL ${__len2})
+    message(FATAL_ERROR "Lists are not equal. ${__len1} != ${__len2}. ${desc}")
+  endif()
+
+  foreach(__i RANGE 1 ${__len1})
+    math(EXPR __index "${__i}- 1")
+    list(GET __list1 ${__index} __item1)
+    list(GET __list2 ${__index} __item2)
+    if(NOT ${__item1} STREQUAL ${__item2})
+      message(FATAL_ERROR "Lists are not equal. Differ at element ${__index}. ${desc}")
+    endif()
+  endforeach()
+endfunction()
+
+################################################################################################
+# Command for disabling warnings for different platforms (see below for gcc and VisualStudio)
+# Usage:
+#   mshadow_warnings_disable(<CMAKE_[C|CXX]_FLAGS[_CONFIGURATION]> -Wshadow /wd4996 ..,)
+macro(mshadow_warnings_disable)
+  set(_flag_vars "")
+  set(_msvc_warnings "")
+  set(_gxx_warnings "")
+
+  foreach(arg ${ARGN})
+    if(arg MATCHES "^CMAKE_")
+      list(APPEND _flag_vars ${arg})
+    elseif(arg MATCHES "^/wd")
+      list(APPEND _msvc_warnings ${arg})
+    elseif(arg MATCHES "^-W")
+      list(APPEND _gxx_warnings ${arg})
+    endif()
+  endforeach()
+
+  if(NOT _flag_vars)
+    set(_flag_vars CMAKE_C_FLAGS CMAKE_CXX_FLAGS)
+  endif()
+
+  if(MSVC AND _msvc_warnings)
+    foreach(var ${_flag_vars})
+      foreach(warning ${_msvc_warnings})
+        set(${var} "${${var}} ${warning}")
+      endforeach()
+    endforeach()
+  elseif((CMAKE_COMPILER_IS_GNUCXX OR CMAKE_COMPILER_IS_CLANGXX) AND _gxx_warnings)
+    foreach(var ${_flag_vars})
+      foreach(warning ${_gxx_warnings})
+        if(NOT warning MATCHES "^-Wno-")
+          string(REPLACE "${warning}" "" ${var} "${${var}}")
+          string(REPLACE "-W" "-Wno-" warning "${warning}")
+        endif()
+        set(${var} "${${var}} ${warning}")
+      endforeach()
+    endforeach()
+  endif()
+  mshadow_clear_vars(_flag_vars _msvc_warnings _gxx_warnings)
+endmacro()
+
+################################################################################################
+# Helper function get current definitions
+# Usage:
+#   mshadow_get_current_definitions(<definitions_variable>)
+function(mshadow_get_current_definitions definitions_var)
+  get_property(current_definitions DIRECTORY PROPERTY COMPILE_DEFINITIONS)
+  set(result "")
+
+  foreach(d ${current_definitions})
+    list(APPEND result -D${d})
+  endforeach()
+
+  mshadow_list_unique(result)
+  set(${definitions_var} ${result} PARENT_SCOPE)
+endfunction()
+
+################################################################################################
+# Helper function get current includes/definitions
+# Usage:
+#   mshadow_get_current_cflags(<cflagslist_variable>)
+function(mshadow_get_current_cflags cflags_var)
+  get_property(current_includes DIRECTORY PROPERTY INCLUDE_DIRECTORIES)
+  mshadow_convert_absolute_paths(current_includes)
+  mshadow_get_current_definitions(cflags)
+
+  foreach(i ${current_includes})
+    list(APPEND cflags "-I${i}")
+  endforeach()
+
+  mshadow_list_unique(cflags)
+  set(${cflags_var} ${cflags} PARENT_SCOPE)
+endfunction()
+
+################################################################################################
+# Helper function to parse current linker libs into link directories, libflags and osx frameworks
+# Usage:
+#   mshadow_parse_linker_libs(<mshadow_LINKER_LIBS_var> <directories_var> <libflags_var> <frameworks_var>)
+function(mshadow_parse_linker_libs mshadow_LINKER_LIBS_variable folders_var flags_var frameworks_var)
+
+  set(__unspec "")
+  set(__debug "")
+  set(__optimized "")
+  set(__framework "")
+  set(__varname "__unspec")
+
+  # split libs into debug, optimized, unspecified and frameworks
+  foreach(list_elem ${${mshadow_LINKER_LIBS_variable}})
+    if(list_elem STREQUAL "debug")
+      set(__varname "__debug")
+    elseif(list_elem STREQUAL "optimized")
+      set(__varname "__optimized")
+    elseif(list_elem MATCHES "^-framework[ \t]+([^ \t].*)")
+      list(APPEND __framework -framework ${CMAKE_MATCH_1})
+    else()
+      list(APPEND ${__varname} ${list_elem})
+      set(__varname "__unspec")
+    endif()
+  endforeach()
+
+  # attach debug or optimized libs to unspecified according to current configuration
+  if(CMAKE_BUILD_TYPE MATCHES "Debug")
+    set(__libs ${__unspec} ${__debug})
+  else()
+    set(__libs ${__unspec} ${__optimized})
+  endif()
+
+  set(libflags "")
+  set(folders "")
+
+  # convert linker libraries list to link flags
+  foreach(lib ${__libs})
+    if(TARGET ${lib})
+      list(APPEND folders $<TARGET_LINKER_FILE_DIR:${lib}>)
+      list(APPEND libflags -l${lib})
+    elseif(lib MATCHES "^-l.*")
+      list(APPEND libflags ${lib})
+    elseif(IS_ABSOLUTE ${lib})
+      get_filename_component(name_we ${lib} NAME_WE)
+      get_filename_component(folder  ${lib} PATH)
+
+      string(REGEX MATCH "^lib(.*)" __match ${name_we})
+      list(APPEND libflags -l${CMAKE_MATCH_1})
+      list(APPEND folders    ${folder})
+    else()
+      message(FATAL_ERROR "Logic error. Need to update cmake script")
+    endif()
+  endforeach()
+
+  mshadow_list_unique(libflags folders)
+
+  set(${folders_var} ${folders} PARENT_SCOPE)
+  set(${flags_var} ${libflags} PARENT_SCOPE)
+  set(${frameworks_var} ${__framework} PARENT_SCOPE)
+endfunction()
+
+################################################################################################
+# Helper function to detect Darwin version, i.e. 10.8, 10.9, 10.10, ....
+# Usage:
+#   mshadow_detect_darwin_version(<version_variable>)
+function(mshadow_detect_darwin_version output_var)
+  if(APPLE)
+    execute_process(COMMAND /usr/bin/sw_vers -productVersion
+                    RESULT_VARIABLE __sw_vers OUTPUT_VARIABLE __sw_vers_out
+                    ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
+
+    set(${output_var} ${__sw_vers_out} PARENT_SCOPE)
+  else()
+    set(${output_var} "" PARENT_SCOPE)
+  endif()
+endfunction()
+
+################################################################################################
+# Convenient command to setup source group for IDEs that support this feature (VS, XCode)
+# Usage:
+#   caffe_source_group(<group> GLOB[_RECURSE] <globbing_expression>)
+function(mshadow_source_group group)
+  cmake_parse_arguments(CAFFE_SOURCE_GROUP "" "" "GLOB;GLOB_RECURSE" ${ARGN})
+  if(CAFFE_SOURCE_GROUP_GLOB)
+    file(GLOB srcs1 ${CAFFE_SOURCE_GROUP_GLOB})
+    source_group(${group} FILES ${srcs1})
+  endif()
+
+  if(CAFFE_SOURCE_GROUP_GLOB_RECURSE)
+    file(GLOB_RECURSE srcs2 ${CAFFE_SOURCE_GROUP_GLOB_RECURSE})
+    source_group(${group} FILES ${srcs2})
+  endif()
+endfunction()
\ No newline at end of file
diff --git a/3rdparty/mshadow/cmake/mshadow.cmake b/3rdparty/mshadow/cmake/mshadow.cmake
new file mode 100644
index 000000000000..1ef76988d8d0
--- /dev/null
+++ b/3rdparty/mshadow/cmake/mshadow.cmake
@@ -0,0 +1,91 @@
+set(mshadow_LINKER_LIBS "")
+
+set(BLAS "Open" CACHE STRING "Selected BLAS library")
+set_property(CACHE BLAS PROPERTY STRINGS "Atlas;Open;MKL")
+
+if(DEFINED USE_BLAS)
+  set(BLAS "${USE_BLAS}") 
+else()
+  if(USE_MKL_IF_AVAILABLE)
+    if(NOT MKL_FOUND)
+      find_package(MKL)
+    endif()
+    if(MKL_FOUND)
+      set(BLAS "MKL")
+    endif()
+  endif()
+endif()
+
+if(BLAS STREQUAL "Atlas" OR BLAS STREQUAL "atlas")
+  find_package(Atlas REQUIRED)
+  include_directories(SYSTEM ${Atlas_INCLUDE_DIR})
+  list(APPEND mshadow_LINKER_LIBS ${Atlas_LIBRARIES})
+  add_definitions(-DMSHADOW_USE_CBLAS=1)
+  add_definitions(-DMSHADOW_USE_MKL=0)
+elseif(BLAS STREQUAL "Open" OR BLAS STREQUAL "open")
+  find_package(OpenBLAS REQUIRED)
+  include_directories(SYSTEM ${OpenBLAS_INCLUDE_DIR})
+  list(APPEND mshadow_LINKER_LIBS ${OpenBLAS_LIB})
+  add_definitions(-DMSHADOW_USE_CBLAS=1)
+  add_definitions(-DMSHADOW_USE_MKL=0)
+elseif(BLAS STREQUAL "MKL" OR BLAS STREQUAL "mkl")
+  find_package(MKL REQUIRED)
+  include_directories(SYSTEM ${MKL_INCLUDE_DIR})
+  list(APPEND mshadow_LINKER_LIBS ${MKL_LIBRARIES})
+  add_definitions(-DMSHADOW_USE_CBLAS=0)
+  add_definitions(-DMSHADOW_USE_MKL=1)
+elseif(BLAS STREQUAL "apple")
+  find_package(Accelerate REQUIRED)
+  include_directories(SYSTEM ${Accelerate_INCLUDE_DIR})
+  list(APPEND mshadow_LINKER_LIBS ${Accelerate_LIBRARIES})
+  add_definitions(-DMSHADOW_USE_MKL=0)
+  add_definitions(-DMSHADOW_USE_CBLAS=1)
+endif()
+
+if(SUPPORT_MSSE2)
+	add_definitions(-DMSHADOW_USE_SSE=1)
+else()
+	add_definitions(-DMSHADOW_USE_SSE=0)
+endif()
+
+if(NOT DEFINED SUPPORT_F16C AND NOT MSVC)
+    check_cxx_compiler_flag("-mf16c"     COMPILER_SUPPORT_MF16C)
+    if(CMAKE_SYSTEM_NAME STREQUAL "Linux")
+        execute_process(COMMAND cat /proc/cpuinfo
+                COMMAND grep flags
+                COMMAND grep f16c
+                OUTPUT_VARIABLE CPU_SUPPORT_F16C)
+    elseif(CMAKE_SYSTEM_NAME STREQUAL "Darwin")
+        execute_process(COMMAND sysctl -a
+                COMMAND grep machdep.cpu.features
+                COMMAND grep F16C
+                OUTPUT_VARIABLE CPU_SUPPORT_F16C)
+    endif()
+    if(NOT CPU_SUPPORT_F16C)
+        message("CPU does not support F16C instructions")
+    endif()
+    if(CPU_SUPPORT_F16C AND COMPILER_SUPPORT_MF16C)
+        set(SUPPORT_F16C TRUE)
+    endif()
+endif()
+
+if(SUPPORT_F16C)
+    set(CMAKE_CXX_FLAGS  "${CMAKE_CXX_FLAGS} -mf16c")
+else()
+    add_definitions(-DMSHADOW_USE_F16C=0)
+endif()
+
+if(USE_CUDA)
+	find_package(CUDA 5.5 QUIET)
+	find_cuda_helper_libs(curand)
+	if(NOT CUDA_FOUND)
+		message(FATAL_ERROR "-- CUDA is disabled.")
+	endif()
+	add_definitions(-DMSHADOW_USE_CUDA=1)
+	add_definitions(-DMSHADOW_FORCE_STREAM)
+	include_directories(SYSTEM ${CUDA_INCLUDE_DIRS})
+    list(APPEND mshadow_LINKER_LIBS ${CUDA_CUDART_LIBRARY}
+                              ${CUDA_curand_LIBRARY} ${CUDA_CUBLAS_LIBRARIES})
+else()
+  add_definitions(-DMSHADOW_USE_CUDA=0)
+endif()
diff --git a/3rdparty/mshadow/cmake/mshadowUtils.cmake b/3rdparty/mshadow/cmake/mshadowUtils.cmake
new file mode 100644
index 000000000000..d4b8bfc89b7a
--- /dev/null
+++ b/3rdparty/mshadow/cmake/mshadowUtils.cmake
@@ -0,0 +1,2 @@
+include("${CMAKE_CURRENT_LIST_DIR}/Utils.cmake")
+
diff --git a/3rdparty/mshadow/doc/Doxyfile b/3rdparty/mshadow/doc/Doxyfile
new file mode 100644
index 000000000000..3e83d471844c
--- /dev/null
+++ b/3rdparty/mshadow/doc/Doxyfile
@@ -0,0 +1,2358 @@
+# Doxyfile 1.8.8
+
+# This file describes the settings to be used by the documentation system
+# doxygen (www.doxygen.org) for a project.
+#
+# All text after a double hash (##) is considered a comment and is placed in
+# front of the TAG it is preceding.
+#
+# All text after a single hash (#) is considered a comment and will be ignored.
+# The format is:
+# TAG = value [value, ...]
+# For lists, items can also be appended using:
+# TAG += value [value, ...]
+# Values that contain spaces should be placed between quotes (\" \").
+
+#---------------------------------------------------------------------------
+# Project related configuration options
+#---------------------------------------------------------------------------
+
+# This tag specifies the encoding used for all characters in the config file
+# that follow. The default is UTF-8 which is also the encoding used for all text
+# before the first occurrence of this tag. Doxygen uses libiconv (or the iconv
+# built into libc) for the transcoding. See http://www.gnu.org/software/libiconv
+# for the list of possible encodings.
+# The default value is: UTF-8.
+
+DOXYFILE_ENCODING      = UTF-8
+
+# The PROJECT_NAME tag is a single word (or a sequence of words surrounded by
+# double-quotes, unless you are using Doxywizard) that should identify the
+# project for which the documentation is generated. This name is used in the
+# title of most generated pages and in a few other places.
+# The default value is: My Project.
+
+PROJECT_NAME           = "mshadow"
+
+# The PROJECT_NUMBER tag can be used to enter a project or revision number. This
+# could be handy for archiving the generated documentation or if some version
+# control system is used.
+
+PROJECT_NUMBER         =
+
+# Using the PROJECT_BRIEF tag one can provide an optional one line description
+# for a project that appears at the top of each page and should give viewer a
+# quick idea about the purpose of the project. Keep the description short.
+
+PROJECT_BRIEF          =
+
+# With the PROJECT_LOGO tag one can specify an logo or icon that is included in
+# the documentation. The maximum height of the logo should not exceed 55 pixels
+# and the maximum width should not exceed 200 pixels. Doxygen will copy the logo
+# to the output directory.
+
+PROJECT_LOGO           =
+
+# The OUTPUT_DIRECTORY tag is used to specify the (relative or absolute) path
+# into which the generated documentation will be written. If a relative path is
+# entered, it will be relative to the location where doxygen was started. If
+# left blank the current directory will be used.
+
+OUTPUT_DIRECTORY       = doc
+
+# If the CREATE_SUBDIRS tag is set to YES, then doxygen will create 4096 sub-
+# directories (in 2 levels) under the output directory of each output format and
+# will distribute the generated files over these directories. Enabling this
+# option can be useful when feeding doxygen a huge amount of source files, where
+# putting all generated files in the same directory would otherwise causes
+# performance problems for the file system.
+# The default value is: NO.
+
+CREATE_SUBDIRS         = NO
+
+# If the ALLOW_UNICODE_NAMES tag is set to YES, doxygen will allow non-ASCII
+# characters to appear in the names of generated files. If set to NO, non-ASCII
+# characters will be escaped, for example _xE3_x81_x84 will be used for Unicode
+# U+3044.
+# The default value is: NO.
+
+ALLOW_UNICODE_NAMES    = NO
+
+# The OUTPUT_LANGUAGE tag is used to specify the language in which all
+# documentation generated by doxygen is written. Doxygen will use this
+# information to generate all constant output in the proper language.
+# Possible values are: Afrikaans, Arabic, Armenian, Brazilian, Catalan, Chinese,
+# Chinese-Traditional, Croatian, Czech, Danish, Dutch, English (United States),
+# Esperanto, Farsi (Persian), Finnish, French, German, Greek, Hungarian,
+# Indonesian, Italian, Japanese, Japanese-en (Japanese with English messages),
+# Korean, Korean-en (Korean with English messages), Latvian, Lithuanian,
+# Macedonian, Norwegian, Persian (Farsi), Polish, Portuguese, Romanian, Russian,
+# Serbian, Serbian-Cyrillic, Slovak, Slovene, Spanish, Swedish, Turkish,
+# Ukrainian and Vietnamese.
+# The default value is: English.
+
+OUTPUT_LANGUAGE        = English
+
+# If the BRIEF_MEMBER_DESC tag is set to YES doxygen will include brief member
+# descriptions after the members that are listed in the file and class
+# documentation (similar to Javadoc). Set to NO to disable this.
+# The default value is: YES.
+
+BRIEF_MEMBER_DESC      = YES
+
+# If the REPEAT_BRIEF tag is set to YES doxygen will prepend the brief
+# description of a member or function before the detailed description
+#
+# Note: If both HIDE_UNDOC_MEMBERS and BRIEF_MEMBER_DESC are set to NO, the
+# brief descriptions will be completely suppressed.
+# The default value is: YES.
+
+REPEAT_BRIEF           = YES
+
+# This tag implements a quasi-intelligent brief description abbreviator that is
+# used to form the text in various listings. Each string in this list, if found
+# as the leading text of the brief description, will be stripped from the text
+# and the result, after processing the whole list, is used as the annotated
+# text. Otherwise, the brief description is used as-is. If left blank, the
+# following values are used ($name is automatically replaced with the name of
+# the entity):The $name class, The $name widget, The $name file, is, provides,
+# specifies, contains, represents, a, an and the.
+
+ABBREVIATE_BRIEF       =
+
+# If the ALWAYS_DETAILED_SEC and REPEAT_BRIEF tags are both set to YES then
+# doxygen will generate a detailed section even if there is only a brief
+# description.
+# The default value is: NO.
+
+ALWAYS_DETAILED_SEC    = NO
+
+# If the INLINE_INHERITED_MEMB tag is set to YES, doxygen will show all
+# inherited members of a class in the documentation of that class as if those
+# members were ordinary class members. Constructors, destructors and assignment
+# operators of the base classes will not be shown.
+# The default value is: NO.
+
+INLINE_INHERITED_MEMB  = NO
+
+# If the FULL_PATH_NAMES tag is set to YES doxygen will prepend the full path
+# before files name in the file list and in the header files. If set to NO the
+# shortest path that makes the file name unique will be used
+# The default value is: YES.
+
+FULL_PATH_NAMES        = YES
+
+# The STRIP_FROM_PATH tag can be used to strip a user-defined part of the path.
+# Stripping is only done if one of the specified strings matches the left-hand
+# part of the path. The tag can be used to show relative paths in the file list.
+# If left blank the directory from which doxygen is run is used as the path to
+# strip.
+#
+# Note that you can specify absolute paths here, but also relative paths, which
+# will be relative from the directory where doxygen is started.
+# This tag requires that the tag FULL_PATH_NAMES is set to YES.
+
+STRIP_FROM_PATH        =
+
+# The STRIP_FROM_INC_PATH tag can be used to strip a user-defined part of the
+# path mentioned in the documentation of a class, which tells the reader which
+# header file to include in order to use a class. If left blank only the name of
+# the header file containing the class definition is used. Otherwise one should
+# specify the list of include paths that are normally passed to the compiler
+# using the -I flag.
+
+STRIP_FROM_INC_PATH    =
+
+# If the SHORT_NAMES tag is set to YES, doxygen will generate much shorter (but
+# less readable) file names. This can be useful is your file systems doesn't
+# support long names like on DOS, Mac, or CD-ROM.
+# The default value is: NO.
+
+SHORT_NAMES            = NO
+
+# If the JAVADOC_AUTOBRIEF tag is set to YES then doxygen will interpret the
+# first line (until the first dot) of a Javadoc-style comment as the brief
+# description. If set to NO, the Javadoc-style will behave just like regular Qt-
+# style comments (thus requiring an explicit @brief command for a brief
+# description.)
+# The default value is: NO.
+
+JAVADOC_AUTOBRIEF      = NO
+
+# If the QT_AUTOBRIEF tag is set to YES then doxygen will interpret the first
+# line (until the first dot) of a Qt-style comment as the brief description. If
+# set to NO, the Qt-style will behave just like regular Qt-style comments (thus
+# requiring an explicit \brief command for a brief description.)
+# The default value is: NO.
+
+QT_AUTOBRIEF           = NO
+
+# The MULTILINE_CPP_IS_BRIEF tag can be set to YES to make doxygen treat a
+# multi-line C++ special comment block (i.e. a block of //! or /// comments) as
+# a brief description. This used to be the default behavior. The new default is
+# to treat a multi-line C++ comment block as a detailed description. Set this
+# tag to YES if you prefer the old behavior instead.
+#
+# Note that setting this tag to YES also means that rational rose comments are
+# not recognized any more.
+# The default value is: NO.
+
+MULTILINE_CPP_IS_BRIEF = NO
+
+# If the INHERIT_DOCS tag is set to YES then an undocumented member inherits the
+# documentation from any documented member that it re-implements.
+# The default value is: YES.
+
+INHERIT_DOCS           = YES
+
+# If the SEPARATE_MEMBER_PAGES tag is set to YES, then doxygen will produce a
+# new page for each member. If set to NO, the documentation of a member will be
+# part of the file/class/namespace that contains it.
+# The default value is: NO.
+
+SEPARATE_MEMBER_PAGES  = NO
+
+# The TAB_SIZE tag can be used to set the number of spaces in a tab. Doxygen
+# uses this value to replace tabs by spaces in code fragments.
+# Minimum value: 1, maximum value: 16, default value: 4.
+
+TAB_SIZE               = 8
+
+# This tag can be used to specify a number of aliases that act as commands in
+# the documentation. An alias has the form:
+# name=value
+# For example adding
+# "sideeffect=@par Side Effects:\n"
+# will allow you to put the command \sideeffect (or @sideeffect) in the
+# documentation, which will result in a user-defined paragraph with heading
+# "Side Effects:". You can put \n's in the value part of an alias to insert
+# newlines.
+
+ALIASES                =
+
+# This tag can be used to specify a number of word-keyword mappings (TCL only).
+# A mapping has the form "name=value". For example adding "class=itcl::class"
+# will allow you to use the command class in the itcl::class meaning.
+
+TCL_SUBST              =
+
+# Set the OPTIMIZE_OUTPUT_FOR_C tag to YES if your project consists of C sources
+# only. Doxygen will then generate output that is more tailored for C. For
+# instance, some of the names that are used will be different. The list of all
+# members will be omitted, etc.
+# The default value is: NO.
+
+OPTIMIZE_OUTPUT_FOR_C  = YES
+
+# Set the OPTIMIZE_OUTPUT_JAVA tag to YES if your project consists of Java or
+# Python sources only. Doxygen will then generate output that is more tailored
+# for that language. For instance, namespaces will be presented as packages,
+# qualified scopes will look different, etc.
+# The default value is: NO.
+
+OPTIMIZE_OUTPUT_JAVA   = NO
+
+# Set the OPTIMIZE_FOR_FORTRAN tag to YES if your project consists of Fortran
+# sources. Doxygen will then generate output that is tailored for Fortran.
+# The default value is: NO.
+
+OPTIMIZE_FOR_FORTRAN   = NO
+
+# Set the OPTIMIZE_OUTPUT_VHDL tag to YES if your project consists of VHDL
+# sources. Doxygen will then generate output that is tailored for VHDL.
+# The default value is: NO.
+
+OPTIMIZE_OUTPUT_VHDL   = NO
+
+# Doxygen selects the parser to use depending on the extension of the files it
+# parses. With this tag you can assign which parser to use for a given
+# extension. Doxygen has a built-in mapping, but you can override or extend it
+# using this tag. The format is ext=language, where ext is a file extension, and
+# language is one of the parsers supported by doxygen: IDL, Java, Javascript,
+# C#, C, C++, D, PHP, Objective-C, Python, Fortran (fixed format Fortran:
+# FortranFixed, free formatted Fortran: FortranFree, unknown formatted Fortran:
+# Fortran. In the later case the parser tries to guess whether the code is fixed
+# or free formatted code, this is the default for Fortran type files), VHDL. For
+# instance to make doxygen treat .inc files as Fortran files (default is PHP),
+# and .f files as C (default is Fortran), use: inc=Fortran f=C.
+#
+# Note For files without extension you can use no_extension as a placeholder.
+#
+# Note that for custom extensions you also need to set FILE_PATTERNS otherwise
+# the files are not read by doxygen.
+
+EXTENSION_MAPPING      =
+
+# If the MARKDOWN_SUPPORT tag is enabled then doxygen pre-processes all comments
+# according to the Markdown format, which allows for more readable
+# documentation. See http://daringfireball.net/projects/markdown/ for details.
+# The output of markdown processing is further processed by doxygen, so you can
+# mix doxygen, HTML, and XML commands with Markdown formatting. Disable only in
+# case of backward compatibilities issues.
+# The default value is: YES.
+
+MARKDOWN_SUPPORT       = YES
+
+# When enabled doxygen tries to link words that correspond to documented
+# classes, or namespaces to their corresponding documentation. Such a link can
+# be prevented in individual cases by by putting a % sign in front of the word
+# or globally by setting AUTOLINK_SUPPORT to NO.
+# The default value is: YES.
+
+AUTOLINK_SUPPORT       = YES
+
+# If you use STL classes (i.e. std::string, std::vector, etc.) but do not want
+# to include (a tag file for) the STL sources as input, then you should set this
+# tag to YES in order to let doxygen match functions declarations and
+# definitions whose arguments contain STL classes (e.g. func(std::string);
+# versus func(std::string) {}). This also make the inheritance and collaboration
+# diagrams that involve STL classes more complete and accurate.
+# The default value is: NO.
+
+BUILTIN_STL_SUPPORT    = NO
+
+# If you use Microsoft's C++/CLI language, you should set this option to YES to
+# enable parsing support.
+# The default value is: NO.
+
+CPP_CLI_SUPPORT        = NO
+
+# Set the SIP_SUPPORT tag to YES if your project consists of sip (see:
+# http://www.riverbankcomputing.co.uk/software/sip/intro) sources only. Doxygen
+# will parse them like normal C++ but will assume all classes use public instead
+# of private inheritance when no explicit protection keyword is present.
+# The default value is: NO.
+
+SIP_SUPPORT            = NO
+
+# For Microsoft's IDL there are propget and propput attributes to indicate
+# getter and setter methods for a property. Setting this option to YES will make
+# doxygen to replace the get and set methods by a property in the documentation.
+# This will only work if the methods are indeed getting or setting a simple
+# type. If this is not the case, or you want to show the methods anyway, you
+# should set this option to NO.
+# The default value is: YES.
+
+IDL_PROPERTY_SUPPORT   = YES
+
+# If member grouping is used in the documentation and the DISTRIBUTE_GROUP_DOC
+# tag is set to YES, then doxygen will reuse the documentation of the first
+# member in the group (if any) for the other members of the group. By default
+# all members of a group must be documented explicitly.
+# The default value is: NO.
+
+DISTRIBUTE_GROUP_DOC   = NO
+
+# Set the SUBGROUPING tag to YES to allow class member groups of the same type
+# (for instance a group of public functions) to be put as a subgroup of that
+# type (e.g. under the Public Functions section). Set it to NO to prevent
+# subgrouping. Alternatively, this can be done per class using the
+# \nosubgrouping command.
+# The default value is: YES.
+
+SUBGROUPING            = YES
+
+# When the INLINE_GROUPED_CLASSES tag is set to YES, classes, structs and unions
+# are shown inside the group in which they are included (e.g. using \ingroup)
+# instead of on a separate page (for HTML and Man pages) or section (for LaTeX
+# and RTF).
+#
+# Note that this feature does not work in combination with
+# SEPARATE_MEMBER_PAGES.
+# The default value is: NO.
+
+INLINE_GROUPED_CLASSES = NO
+
+# When the INLINE_SIMPLE_STRUCTS tag is set to YES, structs, classes, and unions
+# with only public data fields or simple typedef fields will be shown inline in
+# the documentation of the scope in which they are defined (i.e. file,
+# namespace, or group documentation), provided this scope is documented. If set
+# to NO, structs, classes, and unions are shown on a separate page (for HTML and
+# Man pages) or section (for LaTeX and RTF).
+# The default value is: NO.
+
+INLINE_SIMPLE_STRUCTS  = NO
+
+# When TYPEDEF_HIDES_STRUCT tag is enabled, a typedef of a struct, union, or
+# enum is documented as struct, union, or enum with the name of the typedef. So
+# typedef struct TypeS {} TypeT, will appear in the documentation as a struct
+# with name TypeT. When disabled the typedef will appear as a member of a file,
+# namespace, or class. And the struct will be named TypeS. This can typically be
+# useful for C code in case the coding convention dictates that all compound
+# types are typedef'ed and only the typedef is referenced, never the tag name.
+# The default value is: NO.
+
+TYPEDEF_HIDES_STRUCT   = NO
+
+# The size of the symbol lookup cache can be set using LOOKUP_CACHE_SIZE. This
+# cache is used to resolve symbols given their name and scope. Since this can be
+# an expensive process and often the same symbol appears multiple times in the
+# code, doxygen keeps a cache of pre-resolved symbols. If the cache is too small
+# doxygen will become slower. If the cache is too large, memory is wasted. The
+# cache size is given by this formula: 2^(16+LOOKUP_CACHE_SIZE). The valid range
+# is 0..9, the default is 0, corresponding to a cache size of 2^16=65536
+# symbols. At the end of a run doxygen will report the cache usage and suggest
+# the optimal cache size from a speed point of view.
+# Minimum value: 0, maximum value: 9, default value: 0.
+
+LOOKUP_CACHE_SIZE      = 0
+
+#---------------------------------------------------------------------------
+# Build related configuration options
+#---------------------------------------------------------------------------
+
+# If the EXTRACT_ALL tag is set to YES doxygen will assume all entities in
+# documentation are documented, even if no documentation was available. Private
+# class members and static file members will be hidden unless the
+# EXTRACT_PRIVATE respectively EXTRACT_STATIC tags are set to YES.
+# Note: This will also disable the warnings about undocumented members that are
+# normally produced when WARNINGS is set to YES.
+# The default value is: NO.
+
+EXTRACT_ALL            = NO
+
+# If the EXTRACT_PRIVATE tag is set to YES all private members of a class will
+# be included in the documentation.
+# The default value is: NO.
+
+EXTRACT_PRIVATE        = NO
+
+# If the EXTRACT_PACKAGE tag is set to YES all members with package or internal
+# scope will be included in the documentation.
+# The default value is: NO.
+
+EXTRACT_PACKAGE        = NO
+
+# If the EXTRACT_STATIC tag is set to YES all static members of a file will be
+# included in the documentation.
+# The default value is: NO.
+
+EXTRACT_STATIC         = NO
+
+# If the EXTRACT_LOCAL_CLASSES tag is set to YES classes (and structs) defined
+# locally in source files will be included in the documentation. If set to NO
+# only classes defined in header files are included. Does not have any effect
+# for Java sources.
+# The default value is: YES.
+
+EXTRACT_LOCAL_CLASSES  = YES
+
+# This flag is only useful for Objective-C code. When set to YES local methods,
+# which are defined in the implementation section but not in the interface are
+# included in the documentation. If set to NO only methods in the interface are
+# included.
+# The default value is: NO.
+
+EXTRACT_LOCAL_METHODS  = NO
+
+# If this flag is set to YES, the members of anonymous namespaces will be
+# extracted and appear in the documentation as a namespace called
+# 'anonymous_namespace{file}', where file will be replaced with the base name of
+# the file that contains the anonymous namespace. By default anonymous namespace
+# are hidden.
+# The default value is: NO.
+
+EXTRACT_ANON_NSPACES   = NO
+
+# If the HIDE_UNDOC_MEMBERS tag is set to YES, doxygen will hide all
+# undocumented members inside documented classes or files. If set to NO these
+# members will be included in the various overviews, but no documentation
+# section is generated. This option has no effect if EXTRACT_ALL is enabled.
+# The default value is: NO.
+
+HIDE_UNDOC_MEMBERS     = NO
+
+# If the HIDE_UNDOC_CLASSES tag is set to YES, doxygen will hide all
+# undocumented classes that are normally visible in the class hierarchy. If set
+# to NO these classes will be included in the various overviews. This option has
+# no effect if EXTRACT_ALL is enabled.
+# The default value is: NO.
+
+HIDE_UNDOC_CLASSES     = YES
+
+# If the HIDE_FRIEND_COMPOUNDS tag is set to YES, doxygen will hide all friend
+# (class|struct|union) declarations. If set to NO these declarations will be
+# included in the documentation.
+# The default value is: NO.
+
+HIDE_FRIEND_COMPOUNDS  = NO
+
+# If the HIDE_IN_BODY_DOCS tag is set to YES, doxygen will hide any
+# documentation blocks found inside the body of a function. If set to NO these
+# blocks will be appended to the function's detailed documentation block.
+# The default value is: NO.
+
+HIDE_IN_BODY_DOCS      = NO
+
+# The INTERNAL_DOCS tag determines if documentation that is typed after a
+# \internal command is included. If the tag is set to NO then the documentation
+# will be excluded. Set it to YES to include the internal documentation.
+# The default value is: NO.
+
+INTERNAL_DOCS          = NO
+
+# If the CASE_SENSE_NAMES tag is set to NO then doxygen will only generate file
+# names in lower-case letters. If set to YES upper-case letters are also
+# allowed. This is useful if you have classes or files whose names only differ
+# in case and if your file system supports case sensitive file names. Windows
+# and Mac users are advised to set this option to NO.
+# The default value is: system dependent.
+
+CASE_SENSE_NAMES       = YES
+
+# If the HIDE_SCOPE_NAMES tag is set to NO then doxygen will show members with
+# their full class and namespace scopes in the documentation. If set to YES the
+# scope will be hidden.
+# The default value is: NO.
+
+HIDE_SCOPE_NAMES       = NO
+
+# If the SHOW_INCLUDE_FILES tag is set to YES then doxygen will put a list of
+# the files that are included by a file in the documentation of that file.
+# The default value is: YES.
+
+SHOW_INCLUDE_FILES     = YES
+
+# If the SHOW_GROUPED_MEMB_INC tag is set to YES then Doxygen will add for each
+# grouped member an include statement to the documentation, telling the reader
+# which file to include in order to use the member.
+# The default value is: NO.
+
+SHOW_GROUPED_MEMB_INC  = NO
+
+# If the FORCE_LOCAL_INCLUDES tag is set to YES then doxygen will list include
+# files with double quotes in the documentation rather than with sharp brackets.
+# The default value is: NO.
+
+FORCE_LOCAL_INCLUDES   = NO
+
+# If the INLINE_INFO tag is set to YES then a tag [inline] is inserted in the
+# documentation for inline members.
+# The default value is: YES.
+
+INLINE_INFO            = YES
+
+# If the SORT_MEMBER_DOCS tag is set to YES then doxygen will sort the
+# (detailed) documentation of file and class members alphabetically by member
+# name. If set to NO the members will appear in declaration order.
+# The default value is: YES.
+
+SORT_MEMBER_DOCS       = YES
+
+# If the SORT_BRIEF_DOCS tag is set to YES then doxygen will sort the brief
+# descriptions of file, namespace and class members alphabetically by member
+# name. If set to NO the members will appear in declaration order. Note that
+# this will also influence the order of the classes in the class list.
+# The default value is: NO.
+
+SORT_BRIEF_DOCS        = NO
+
+# If the SORT_MEMBERS_CTORS_1ST tag is set to YES then doxygen will sort the
+# (brief and detailed) documentation of class members so that constructors and
+# destructors are listed first. If set to NO the constructors will appear in the
+# respective orders defined by SORT_BRIEF_DOCS and SORT_MEMBER_DOCS.
+# Note: If SORT_BRIEF_DOCS is set to NO this option is ignored for sorting brief
+# member documentation.
+# Note: If SORT_MEMBER_DOCS is set to NO this option is ignored for sorting
+# detailed member documentation.
+# The default value is: NO.
+
+SORT_MEMBERS_CTORS_1ST = NO
+
+# If the SORT_GROUP_NAMES tag is set to YES then doxygen will sort the hierarchy
+# of group names into alphabetical order. If set to NO the group names will
+# appear in their defined order.
+# The default value is: NO.
+
+SORT_GROUP_NAMES       = NO
+
+# If the SORT_BY_SCOPE_NAME tag is set to YES, the class list will be sorted by
+# fully-qualified names, including namespaces. If set to NO, the class list will
+# be sorted only by class name, not including the namespace part.
+# Note: This option is not very useful if HIDE_SCOPE_NAMES is set to YES.
+# Note: This option applies only to the class list, not to the alphabetical
+# list.
+# The default value is: NO.
+
+SORT_BY_SCOPE_NAME     = NO
+
+# If the STRICT_PROTO_MATCHING option is enabled and doxygen fails to do proper
+# type resolution of all parameters of a function it will reject a match between
+# the prototype and the implementation of a member function even if there is
+# only one candidate or it is obvious which candidate to choose by doing a
+# simple string match. By disabling STRICT_PROTO_MATCHING doxygen will still
+# accept a match between prototype and implementation in such cases.
+# The default value is: NO.
+
+STRICT_PROTO_MATCHING  = NO
+
+# The GENERATE_TODOLIST tag can be used to enable ( YES) or disable ( NO) the
+# todo list. This list is created by putting \todo commands in the
+# documentation.
+# The default value is: YES.
+
+GENERATE_TODOLIST      = YES
+
+# The GENERATE_TESTLIST tag can be used to enable ( YES) or disable ( NO) the
+# test list. This list is created by putting \test commands in the
+# documentation.
+# The default value is: YES.
+
+GENERATE_TESTLIST      = YES
+
+# The GENERATE_BUGLIST tag can be used to enable ( YES) or disable ( NO) the bug
+# list. This list is created by putting \bug commands in the documentation.
+# The default value is: YES.
+
+GENERATE_BUGLIST       = YES
+
+# The GENERATE_DEPRECATEDLIST tag can be used to enable ( YES) or disable ( NO)
+# the deprecated list. This list is created by putting \deprecated commands in
+# the documentation.
+# The default value is: YES.
+
+GENERATE_DEPRECATEDLIST= YES
+
+# The ENABLED_SECTIONS tag can be used to enable conditional documentation
+# sections, marked by \if <section_label> ... \endif and \cond <section_label>
+# ... \endcond blocks.
+
+ENABLED_SECTIONS       =
+
+# The MAX_INITIALIZER_LINES tag determines the maximum number of lines that the
+# initial value of a variable or macro / define can have for it to appear in the
+# documentation. If the initializer consists of more lines than specified here
+# it will be hidden. Use a value of 0 to hide initializers completely. The
+# appearance of the value of individual variables and macros / defines can be
+# controlled using \showinitializer or \hideinitializer command in the
+# documentation regardless of this setting.
+# Minimum value: 0, maximum value: 10000, default value: 30.
+
+MAX_INITIALIZER_LINES  = 30
+
+# Set the SHOW_USED_FILES tag to NO to disable the list of files generated at
+# the bottom of the documentation of classes and structs. If set to YES the list
+# will mention the files that were used to generate the documentation.
+# The default value is: YES.
+
+SHOW_USED_FILES        = YES
+
+# Set the SHOW_FILES tag to NO to disable the generation of the Files page. This
+# will remove the Files entry from the Quick Index and from the Folder Tree View
+# (if specified).
+# The default value is: YES.
+
+SHOW_FILES             = YES
+
+# Set the SHOW_NAMESPACES tag to NO to disable the generation of the Namespaces
+# page. This will remove the Namespaces entry from the Quick Index and from the
+# Folder Tree View (if specified).
+# The default value is: YES.
+
+SHOW_NAMESPACES        = YES
+
+# The FILE_VERSION_FILTER tag can be used to specify a program or script that
+# doxygen should invoke to get the current version for each file (typically from
+# the version control system). Doxygen will invoke the program by executing (via
+# popen()) the command command input-file, where command is the value of the
+# FILE_VERSION_FILTER tag, and input-file is the name of an input file provided
+# by doxygen. Whatever the program writes to standard output is used as the file
+# version. For an example see the documentation.
+
+FILE_VERSION_FILTER    =
+
+# The LAYOUT_FILE tag can be used to specify a layout file which will be parsed
+# by doxygen. The layout file controls the global structure of the generated
+# output files in an output format independent way. To create the layout file
+# that represents doxygen's defaults, run doxygen with the -l option. You can
+# optionally specify a file name after the option, if omitted DoxygenLayout.xml
+# will be used as the name of the layout file.
+#
+# Note that if you run doxygen from a directory containing a file called
+# DoxygenLayout.xml, doxygen will parse it automatically even if the LAYOUT_FILE
+# tag is left empty.
+
+LAYOUT_FILE            =
+
+# The CITE_BIB_FILES tag can be used to specify one or more bib files containing
+# the reference definitions. This must be a list of .bib files. The .bib
+# extension is automatically appended if omitted. This requires the bibtex tool
+# to be installed. See also http://en.wikipedia.org/wiki/BibTeX for more info.
+# For LaTeX the style of the bibliography can be controlled using
+# LATEX_BIB_STYLE. To use this feature you need bibtex and perl available in the
+# search path. See also \cite for info how to create references.
+
+CITE_BIB_FILES         =
+
+#---------------------------------------------------------------------------
+# Configuration options related to warning and progress messages
+#---------------------------------------------------------------------------
+
+# The QUIET tag can be used to turn on/off the messages that are generated to
+# standard output by doxygen. If QUIET is set to YES this implies that the
+# messages are off.
+# The default value is: NO.
+
+QUIET                  = NO
+
+# The WARNINGS tag can be used to turn on/off the warning messages that are
+# generated to standard error ( stderr) by doxygen. If WARNINGS is set to YES
+# this implies that the warnings are on.
+#
+# Tip: Turn warnings on while writing the documentation.
+# The default value is: YES.
+
+WARNINGS               = YES
+
+# If the WARN_IF_UNDOCUMENTED tag is set to YES, then doxygen will generate
+# warnings for undocumented members. If EXTRACT_ALL is set to YES then this flag
+# will automatically be disabled.
+# The default value is: YES.
+
+WARN_IF_UNDOCUMENTED   = YES
+
+# If the WARN_IF_DOC_ERROR tag is set to YES, doxygen will generate warnings for
+# potential errors in the documentation, such as not documenting some parameters
+# in a documented function, or documenting parameters that don't exist or using
+# markup commands wrongly.
+# The default value is: YES.
+
+WARN_IF_DOC_ERROR      = YES
+
+# This WARN_NO_PARAMDOC option can be enabled to get warnings for functions that
+# are documented, but have no documentation for their parameters or return
+# value. If set to NO doxygen will only warn about wrong or incomplete parameter
+# documentation, but not about the absence of documentation.
+# The default value is: NO.
+
+WARN_NO_PARAMDOC       = YES
+
+# The WARN_FORMAT tag determines the format of the warning messages that doxygen
+# can produce. The string should contain the $file, $line, and $text tags, which
+# will be replaced by the file and line number from which the warning originated
+# and the warning text. Optionally the format may contain $version, which will
+# be replaced by the version of the file (if it could be obtained via
+# FILE_VERSION_FILTER)
+# The default value is: $file:$line: $text.
+
+WARN_FORMAT            = "$file:$line: $text"
+
+# The WARN_LOGFILE tag can be used to specify a file to which warning and error
+# messages should be written. If left blank the output is written to standard
+# error (stderr).
+
+WARN_LOGFILE           =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the input files
+#---------------------------------------------------------------------------
+
+# The INPUT tag is used to specify the files and/or directories that contain
+# documented source files. You may enter file names like myfile.cpp or
+# directories like /usr/src/myproject. Separate the files or directories with
+# spaces.
+# Note: If this tag is empty the current directory is searched.
+
+INPUT                  = mshadow \
+                         mshadow-ps
+
+# This tag can be used to specify the character encoding of the source files
+# that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses
+# libiconv (or the iconv built into libc) for the transcoding. See the libiconv
+# documentation (see: http://www.gnu.org/software/libiconv) for the list of
+# possible encodings.
+# The default value is: UTF-8.
+
+INPUT_ENCODING         = UTF-8
+
+# If the value of the INPUT tag contains directories, you can use the
+# FILE_PATTERNS tag to specify one or more wildcard patterns (like *.cpp and
+# *.h) to filter out the source-files in the directories. If left blank the
+# following patterns are tested:*.c, *.cc, *.cxx, *.cpp, *.c++, *.java, *.ii,
+# *.ixx, *.ipp, *.i++, *.inl, *.idl, *.ddl, *.odl, *.h, *.hh, *.hxx, *.hpp,
+# *.h++, *.cs, *.d, *.php, *.php4, *.php5, *.phtml, *.inc, *.m, *.markdown,
+# *.md, *.mm, *.dox, *.py, *.f90, *.f, *.for, *.tcl, *.vhd, *.vhdl, *.ucf,
+# *.qsf, *.as and *.js.
+
+FILE_PATTERNS          =
+
+# The RECURSIVE tag can be used to specify whether or not subdirectories should
+# be searched for input files as well.
+# The default value is: NO.
+
+RECURSIVE              = NO
+
+# The EXCLUDE tag can be used to specify files and/or directories that should be
+# excluded from the INPUT source files. This way you can easily exclude a
+# subdirectory from a directory tree whose root is specified with the INPUT tag.
+#
+# Note that relative paths are relative to the directory from which doxygen is
+# run.
+
+EXCLUDE                =
+
+# The EXCLUDE_SYMLINKS tag can be used to select whether or not files or
+# directories that are symbolic links (a Unix file system feature) are excluded
+# from the input.
+# The default value is: NO.
+
+EXCLUDE_SYMLINKS       = NO
+
+# If the value of the INPUT tag contains directories, you can use the
+# EXCLUDE_PATTERNS tag to specify one or more wildcard patterns to exclude
+# certain files from those directories.
+#
+# Note that the wildcards are matched against the file with absolute path, so to
+# exclude all test directories for example use the pattern */test/*
+
+EXCLUDE_PATTERNS       = *-inl.* \
+                         utils.h \
+                         thread_util.h \
+                         thread.h \
+                         kv_array.h
+
+# The EXCLUDE_SYMBOLS tag can be used to specify one or more symbol names
+# (namespaces, classes, functions, etc.) that should be excluded from the
+# output. The symbol name can be a fully qualified name, a word, or if the
+# wildcard * is used, a substring. Examples: ANamespace, AClass,
+# AClass::ANamespace, ANamespace::*Test
+#
+# Note that the wildcards are matched against the file with absolute path, so to
+# exclude all test directories use the pattern */test/*
+
+EXCLUDE_SYMBOLS        = mshadow::expr::Plan* \
+                         mshadow::expr::*Engine*
+
+# The EXAMPLE_PATH tag can be used to specify one or more files or directories
+# that contain example code fragments that are included (see the \include
+# command).
+
+EXAMPLE_PATH           =
+
+# If the value of the EXAMPLE_PATH tag contains directories, you can use the
+# EXAMPLE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp and
+# *.h) to filter out the source-files in the directories. If left blank all
+# files are included.
+
+EXAMPLE_PATTERNS       =
+
+# If the EXAMPLE_RECURSIVE tag is set to YES then subdirectories will be
+# searched for input files to be used with the \include or \dontinclude commands
+# irrespective of the value of the RECURSIVE tag.
+# The default value is: NO.
+
+EXAMPLE_RECURSIVE      = NO
+
+# The IMAGE_PATH tag can be used to specify one or more files or directories
+# that contain images that are to be included in the documentation (see the
+# \image command).
+
+IMAGE_PATH             =
+
+# The INPUT_FILTER tag can be used to specify a program that doxygen should
+# invoke to filter for each input file. Doxygen will invoke the filter program
+# by executing (via popen()) the command:
+#
+# <filter> <input-file>
+#
+# where <filter> is the value of the INPUT_FILTER tag, and <input-file> is the
+# name of an input file. Doxygen will then use the output that the filter
+# program writes to standard output. If FILTER_PATTERNS is specified, this tag
+# will be ignored.
+#
+# Note that the filter must not add or remove lines; it is applied before the
+# code is scanned, but not when the output code is generated. If lines are added
+# or removed, the anchors will not be placed correctly.
+
+INPUT_FILTER           =
+
+# The FILTER_PATTERNS tag can be used to specify filters on a per file pattern
+# basis. Doxygen will compare the file name with each pattern and apply the
+# filter if there is a match. The filters are a list of the form: pattern=filter
+# (like *.cpp=my_cpp_filter). See INPUT_FILTER for further information on how
+# filters are used. If the FILTER_PATTERNS tag is empty or if none of the
+# patterns match the file name, INPUT_FILTER is applied.
+
+FILTER_PATTERNS        =
+
+# If the FILTER_SOURCE_FILES tag is set to YES, the input filter (if set using
+# INPUT_FILTER ) will also be used to filter the input files that are used for
+# producing the source files to browse (i.e. when SOURCE_BROWSER is set to YES).
+# The default value is: NO.
+
+FILTER_SOURCE_FILES    = NO
+
+# The FILTER_SOURCE_PATTERNS tag can be used to specify source filters per file
+# pattern. A pattern will override the setting for FILTER_PATTERN (if any) and
+# it is also possible to disable source filtering for a specific pattern using
+# *.ext= (so without naming a filter).
+# This tag requires that the tag FILTER_SOURCE_FILES is set to YES.
+
+FILTER_SOURCE_PATTERNS =
+
+# If the USE_MDFILE_AS_MAINPAGE tag refers to the name of a markdown file that
+# is part of the input, its contents will be placed on the main page
+# (index.html). This can be useful if you have a project on for instance GitHub
+# and want to reuse the introduction page also for the doxygen output.
+
+USE_MDFILE_AS_MAINPAGE =
+
+#---------------------------------------------------------------------------
+# Configuration options related to source browsing
+#---------------------------------------------------------------------------
+
+# If the SOURCE_BROWSER tag is set to YES then a list of source files will be
+# generated. Documented entities will be cross-referenced with these sources.
+#
+# Note: To get rid of all source code in the generated output, make sure that
+# also VERBATIM_HEADERS is set to NO.
+# The default value is: NO.
+
+SOURCE_BROWSER         = NO
+
+# Setting the INLINE_SOURCES tag to YES will include the body of functions,
+# classes and enums directly into the documentation.
+# The default value is: NO.
+
+INLINE_SOURCES         = NO
+
+# Setting the STRIP_CODE_COMMENTS tag to YES will instruct doxygen to hide any
+# special comment blocks from generated source code fragments. Normal C, C++ and
+# Fortran comments will always remain visible.
+# The default value is: YES.
+
+STRIP_CODE_COMMENTS    = YES
+
+# If the REFERENCED_BY_RELATION tag is set to YES then for each documented
+# function all documented functions referencing it will be listed.
+# The default value is: NO.
+
+REFERENCED_BY_RELATION = NO
+
+# If the REFERENCES_RELATION tag is set to YES then for each documented function
+# all documented entities called/used by that function will be listed.
+# The default value is: NO.
+
+REFERENCES_RELATION    = NO
+
+# If the REFERENCES_LINK_SOURCE tag is set to YES and SOURCE_BROWSER tag is set
+# to YES, then the hyperlinks from functions in REFERENCES_RELATION and
+# REFERENCED_BY_RELATION lists will link to the source code. Otherwise they will
+# link to the documentation.
+# The default value is: YES.
+
+REFERENCES_LINK_SOURCE = YES
+
+# If SOURCE_TOOLTIPS is enabled (the default) then hovering a hyperlink in the
+# source code will show a tooltip with additional information such as prototype,
+# brief description and links to the definition and documentation. Since this
+# will make the HTML file larger and loading of large files a bit slower, you
+# can opt to disable this feature.
+# The default value is: YES.
+# This tag requires that the tag SOURCE_BROWSER is set to YES.
+
+SOURCE_TOOLTIPS        = YES
+
+# If the USE_HTAGS tag is set to YES then the references to source code will
+# point to the HTML generated by the htags(1) tool instead of doxygen built-in
+# source browser. The htags tool is part of GNU's global source tagging system
+# (see http://www.gnu.org/software/global/global.html). You will need version
+# 4.8.6 or higher.
+#
+# To use it do the following:
+# - Install the latest version of global
+# - Enable SOURCE_BROWSER and USE_HTAGS in the config file
+# - Make sure the INPUT points to the root of the source tree
+# - Run doxygen as normal
+#
+# Doxygen will invoke htags (and that will in turn invoke gtags), so these
+# tools must be available from the command line (i.e. in the search path).
+#
+# The result: instead of the source browser generated by doxygen, the links to
+# source code will now point to the output of htags.
+# The default value is: NO.
+# This tag requires that the tag SOURCE_BROWSER is set to YES.
+
+USE_HTAGS              = NO
+
+# If the VERBATIM_HEADERS tag is set the YES then doxygen will generate a
+# verbatim copy of the header file for each class for which an include is
+# specified. Set to NO to disable this.
+# See also: Section \class.
+# The default value is: YES.
+
+VERBATIM_HEADERS       = YES
+
+# If the CLANG_ASSISTED_PARSING tag is set to YES, then doxygen will use the
+# clang parser (see: http://clang.llvm.org/) for more accurate parsing at the
+# cost of reduced performance. This can be particularly helpful with template
+# rich C++ code for which doxygen's built-in parser lacks the necessary type
+# information.
+# Note: The availability of this option depends on whether or not doxygen was
+# compiled with the --with-libclang option.
+# The default value is: NO.
+
+CLANG_ASSISTED_PARSING = NO
+
+# If clang assisted parsing is enabled you can provide the compiler with command
+# line options that you would normally use when invoking the compiler. Note that
+# the include paths will already be set by doxygen for the files and directories
+# specified with INPUT and INCLUDE_PATH.
+# This tag requires that the tag CLANG_ASSISTED_PARSING is set to YES.
+
+CLANG_OPTIONS          =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the alphabetical class index
+#---------------------------------------------------------------------------
+
+# If the ALPHABETICAL_INDEX tag is set to YES, an alphabetical index of all
+# compounds will be generated. Enable this if the project contains a lot of
+# classes, structs, unions or interfaces.
+# The default value is: YES.
+
+ALPHABETICAL_INDEX     = YES
+
+# The COLS_IN_ALPHA_INDEX tag can be used to specify the number of columns in
+# which the alphabetical index list will be split.
+# Minimum value: 1, maximum value: 20, default value: 5.
+# This tag requires that the tag ALPHABETICAL_INDEX is set to YES.
+
+COLS_IN_ALPHA_INDEX    = 5
+
+# In case all classes in a project start with a common prefix, all classes will
+# be put under the same header in the alphabetical index. The IGNORE_PREFIX tag
+# can be used to specify a prefix (or a list of prefixes) that should be ignored
+# while generating the index headers.
+# This tag requires that the tag ALPHABETICAL_INDEX is set to YES.
+
+IGNORE_PREFIX          =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the HTML output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_HTML tag is set to YES doxygen will generate HTML output
+# The default value is: YES.
+
+GENERATE_HTML          = YES
+
+# The HTML_OUTPUT tag is used to specify where the HTML docs will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it.
+# The default directory is: html.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_OUTPUT            = html
+
+# The HTML_FILE_EXTENSION tag can be used to specify the file extension for each
+# generated HTML page (for example: .htm, .php, .asp).
+# The default value is: .html.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_FILE_EXTENSION    = .html
+
+# The HTML_HEADER tag can be used to specify a user-defined HTML header file for
+# each generated HTML page. If the tag is left blank doxygen will generate a
+# standard header.
+#
+# To get valid HTML the header file that includes any scripts and style sheets
+# that doxygen needs, which is dependent on the configuration options used (e.g.
+# the setting GENERATE_TREEVIEW). It is highly recommended to start with a
+# default header using
+# doxygen -w html new_header.html new_footer.html new_stylesheet.css
+# YourConfigFile
+# and then modify the file new_header.html. See also section "Doxygen usage"
+# for information on how to generate the default header that doxygen normally
+# uses.
+# Note: The header is subject to change so you typically have to regenerate the
+# default header when upgrading to a newer version of doxygen. For a description
+# of the possible markers and block names see the documentation.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_HEADER            =
+
+# The HTML_FOOTER tag can be used to specify a user-defined HTML footer for each
+# generated HTML page. If the tag is left blank doxygen will generate a standard
+# footer. See HTML_HEADER for more information on how to generate a default
+# footer and what special commands can be used inside the footer. See also
+# section "Doxygen usage" for information on how to generate the default footer
+# that doxygen normally uses.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_FOOTER            =
+
+# The HTML_STYLESHEET tag can be used to specify a user-defined cascading style
+# sheet that is used by each HTML page. It can be used to fine-tune the look of
+# the HTML output. If left blank doxygen will generate a default style sheet.
+# See also section "Doxygen usage" for information on how to generate the style
+# sheet that doxygen normally uses.
+# Note: It is recommended to use HTML_EXTRA_STYLESHEET instead of this tag, as
+# it is more robust and this tag (HTML_STYLESHEET) will in the future become
+# obsolete.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_STYLESHEET        =
+
+# The HTML_EXTRA_STYLESHEET tag can be used to specify additional user-defined
+# cascading style sheets that are included after the standard style sheets
+# created by doxygen. Using this option one can overrule certain style aspects.
+# This is preferred over using HTML_STYLESHEET since it does not replace the
+# standard style sheet and is therefor more robust against future updates.
+# Doxygen will copy the style sheet files to the output directory.
+# Note: The order of the extra stylesheet files is of importance (e.g. the last
+# stylesheet in the list overrules the setting of the previous ones in the
+# list). For an example see the documentation.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_EXTRA_STYLESHEET  =
+
+# The HTML_EXTRA_FILES tag can be used to specify one or more extra images or
+# other source files which should be copied to the HTML output directory. Note
+# that these files will be copied to the base HTML output directory. Use the
+# $relpath^ marker in the HTML_HEADER and/or HTML_FOOTER files to load these
+# files. In the HTML_STYLESHEET file, use the file name only. Also note that the
+# files will be copied as-is; there are no commands or markers available.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_EXTRA_FILES       =
+
+# The HTML_COLORSTYLE_HUE tag controls the color of the HTML output. Doxygen
+# will adjust the colors in the stylesheet and background images according to
+# this color. Hue is specified as an angle on a colorwheel, see
+# http://en.wikipedia.org/wiki/Hue for more information. For instance the value
+# 0 represents red, 60 is yellow, 120 is green, 180 is cyan, 240 is blue, 300
+# purple, and 360 is red again.
+# Minimum value: 0, maximum value: 359, default value: 220.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_COLORSTYLE_HUE    = 220
+
+# The HTML_COLORSTYLE_SAT tag controls the purity (or saturation) of the colors
+# in the HTML output. For a value of 0 the output will use grayscales only. A
+# value of 255 will produce the most vivid colors.
+# Minimum value: 0, maximum value: 255, default value: 100.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_COLORSTYLE_SAT    = 100
+
+# The HTML_COLORSTYLE_GAMMA tag controls the gamma correction applied to the
+# luminance component of the colors in the HTML output. Values below 100
+# gradually make the output lighter, whereas values above 100 make the output
+# darker. The value divided by 100 is the actual gamma applied, so 80 represents
+# a gamma of 0.8, The value 220 represents a gamma of 2.2, and 100 does not
+# change the gamma.
+# Minimum value: 40, maximum value: 240, default value: 80.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_COLORSTYLE_GAMMA  = 80
+
+# If the HTML_TIMESTAMP tag is set to YES then the footer of each generated HTML
+# page will contain the date and time when the page was generated. Setting this
+# to NO can help when comparing the output of multiple runs.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_TIMESTAMP         = YES
+
+# If the HTML_DYNAMIC_SECTIONS tag is set to YES then the generated HTML
+# documentation will contain sections that can be hidden and shown after the
+# page has loaded.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_DYNAMIC_SECTIONS  = NO
+
+# With HTML_INDEX_NUM_ENTRIES one can control the preferred number of entries
+# shown in the various tree structured indices initially; the user can expand
+# and collapse entries dynamically later on. Doxygen will expand the tree to
+# such a level that at most the specified number of entries are visible (unless
+# a fully collapsed tree already exceeds this amount). So setting the number of
+# entries 1 will produce a full collapsed tree by default. 0 is a special value
+# representing an infinite number of entries and will result in a full expanded
+# tree by default.
+# Minimum value: 0, maximum value: 9999, default value: 100.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_INDEX_NUM_ENTRIES = 100
+
+# If the GENERATE_DOCSET tag is set to YES, additional index files will be
+# generated that can be used as input for Apple's Xcode 3 integrated development
+# environment (see: http://developer.apple.com/tools/xcode/), introduced with
+# OSX 10.5 (Leopard). To create a documentation set, doxygen will generate a
+# Makefile in the HTML output directory. Running make will produce the docset in
+# that directory and running make install will install the docset in
+# ~/Library/Developer/Shared/Documentation/DocSets so that Xcode will find it at
+# startup. See http://developer.apple.com/tools/creatingdocsetswithdoxygen.html
+# for more information.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+GENERATE_DOCSET        = NO
+
+# This tag determines the name of the docset feed. A documentation feed provides
+# an umbrella under which multiple documentation sets from a single provider
+# (such as a company or product suite) can be grouped.
+# The default value is: Doxygen generated docs.
+# This tag requires that the tag GENERATE_DOCSET is set to YES.
+
+DOCSET_FEEDNAME        = "Doxygen generated docs"
+
+# This tag specifies a string that should uniquely identify the documentation
+# set bundle. This should be a reverse domain-name style string, e.g.
+# com.mycompany.MyDocSet. Doxygen will append .docset to the name.
+# The default value is: org.doxygen.Project.
+# This tag requires that the tag GENERATE_DOCSET is set to YES.
+
+DOCSET_BUNDLE_ID       = org.doxygen.Project
+
+# The DOCSET_PUBLISHER_ID tag specifies a string that should uniquely identify
+# the documentation publisher. This should be a reverse domain-name style
+# string, e.g. com.mycompany.MyDocSet.documentation.
+# The default value is: org.doxygen.Publisher.
+# This tag requires that the tag GENERATE_DOCSET is set to YES.
+
+DOCSET_PUBLISHER_ID    = org.doxygen.Publisher
+
+# The DOCSET_PUBLISHER_NAME tag identifies the documentation publisher.
+# The default value is: Publisher.
+# This tag requires that the tag GENERATE_DOCSET is set to YES.
+
+DOCSET_PUBLISHER_NAME  = Publisher
+
+# If the GENERATE_HTMLHELP tag is set to YES then doxygen generates three
+# additional HTML index files: index.hhp, index.hhc, and index.hhk. The
+# index.hhp is a project file that can be read by Microsoft's HTML Help Workshop
+# (see: http://www.microsoft.com/en-us/download/details.aspx?id=21138) on
+# Windows.
+#
+# The HTML Help Workshop contains a compiler that can convert all HTML output
+# generated by doxygen into a single compiled HTML file (.chm). Compiled HTML
+# files are now used as the Windows 98 help format, and will replace the old
+# Windows help format (.hlp) on all Windows platforms in the future. Compressed
+# HTML files also contain an index, a table of contents, and you can search for
+# words in the documentation. The HTML workshop also contains a viewer for
+# compressed HTML files.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+GENERATE_HTMLHELP      = NO
+
+# The CHM_FILE tag can be used to specify the file name of the resulting .chm
+# file. You can add a path in front of the file if the result should not be
+# written to the html output directory.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+CHM_FILE               =
+
+# The HHC_LOCATION tag can be used to specify the location (absolute path
+# including file name) of the HTML help compiler ( hhc.exe). If non-empty
+# doxygen will try to run the HTML help compiler on the generated index.hhp.
+# The file has to be specified with full path.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+HHC_LOCATION           =
+
+# The GENERATE_CHI flag controls if a separate .chi index file is generated (
+# YES) or that it should be included in the master .chm file ( NO).
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+GENERATE_CHI           = NO
+
+# The CHM_INDEX_ENCODING is used to encode HtmlHelp index ( hhk), content ( hhc)
+# and project file content.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+CHM_INDEX_ENCODING     =
+
+# The BINARY_TOC flag controls whether a binary table of contents is generated (
+# YES) or a normal table of contents ( NO) in the .chm file. Furthermore it
+# enables the Previous and Next buttons.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+BINARY_TOC             = NO
+
+# The TOC_EXPAND flag can be set to YES to add extra items for group members to
+# the table of contents of the HTML help documentation and to the tree view.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+TOC_EXPAND             = NO
+
+# If the GENERATE_QHP tag is set to YES and both QHP_NAMESPACE and
+# QHP_VIRTUAL_FOLDER are set, an additional index file will be generated that
+# can be used as input for Qt's qhelpgenerator to generate a Qt Compressed Help
+# (.qch) of the generated HTML documentation.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+GENERATE_QHP           = NO
+
+# If the QHG_LOCATION tag is specified, the QCH_FILE tag can be used to specify
+# the file name of the resulting .qch file. The path specified is relative to
+# the HTML output folder.
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QCH_FILE               =
+
+# The QHP_NAMESPACE tag specifies the namespace to use when generating Qt Help
+# Project output. For more information please see Qt Help Project / Namespace
+# (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#namespace).
+# The default value is: org.doxygen.Project.
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHP_NAMESPACE          = org.doxygen.Project
+
+# The QHP_VIRTUAL_FOLDER tag specifies the namespace to use when generating Qt
+# Help Project output. For more information please see Qt Help Project / Virtual
+# Folders (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#virtual-
+# folders).
+# The default value is: doc.
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHP_VIRTUAL_FOLDER     = doc
+
+# If the QHP_CUST_FILTER_NAME tag is set, it specifies the name of a custom
+# filter to add. For more information please see Qt Help Project / Custom
+# Filters (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#custom-
+# filters).
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHP_CUST_FILTER_NAME   =
+
+# The QHP_CUST_FILTER_ATTRS tag specifies the list of the attributes of the
+# custom filter to add. For more information please see Qt Help Project / Custom
+# Filters (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#custom-
+# filters).
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHP_CUST_FILTER_ATTRS  =
+
+# The QHP_SECT_FILTER_ATTRS tag specifies the list of the attributes this
+# project's filter section matches. Qt Help Project / Filter Attributes (see:
+# http://qt-project.org/doc/qt-4.8/qthelpproject.html#filter-attributes).
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHP_SECT_FILTER_ATTRS  =
+
+# The QHG_LOCATION tag can be used to specify the location of Qt's
+# qhelpgenerator. If non-empty doxygen will try to run qhelpgenerator on the
+# generated .qhp file.
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHG_LOCATION           =
+
+# If the GENERATE_ECLIPSEHELP tag is set to YES, additional index files will be
+# generated, together with the HTML files, they form an Eclipse help plugin. To
+# install this plugin and make it available under the help contents menu in
+# Eclipse, the contents of the directory containing the HTML and XML files needs
+# to be copied into the plugins directory of eclipse. The name of the directory
+# within the plugins directory should be the same as the ECLIPSE_DOC_ID value.
+# After copying Eclipse needs to be restarted before the help appears.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+GENERATE_ECLIPSEHELP   = NO
+
+# A unique identifier for the Eclipse help plugin. When installing the plugin
+# the directory name containing the HTML and XML files should also have this
+# name. Each documentation set should have its own identifier.
+# The default value is: org.doxygen.Project.
+# This tag requires that the tag GENERATE_ECLIPSEHELP is set to YES.
+
+ECLIPSE_DOC_ID         = org.doxygen.Project
+
+# If you want full control over the layout of the generated HTML pages it might
+# be necessary to disable the index and replace it with your own. The
+# DISABLE_INDEX tag can be used to turn on/off the condensed index (tabs) at top
+# of each HTML page. A value of NO enables the index and the value YES disables
+# it. Since the tabs in the index contain the same information as the navigation
+# tree, you can set this option to YES if you also set GENERATE_TREEVIEW to YES.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+DISABLE_INDEX          = NO
+
+# The GENERATE_TREEVIEW tag is used to specify whether a tree-like index
+# structure should be generated to display hierarchical information. If the tag
+# value is set to YES, a side panel will be generated containing a tree-like
+# index structure (just like the one that is generated for HTML Help). For this
+# to work a browser that supports JavaScript, DHTML, CSS and frames is required
+# (i.e. any modern browser). Windows users are probably better off using the
+# HTML help feature. Via custom stylesheets (see HTML_EXTRA_STYLESHEET) one can
+# further fine-tune the look of the index. As an example, the default style
+# sheet generated by doxygen has an example that shows how to put an image at
+# the root of the tree instead of the PROJECT_NAME. Since the tree basically has
+# the same information as the tab index, you could consider setting
+# DISABLE_INDEX to YES when enabling this option.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+GENERATE_TREEVIEW      = NO
+
+# The ENUM_VALUES_PER_LINE tag can be used to set the number of enum values that
+# doxygen will group on one line in the generated HTML documentation.
+#
+# Note that a value of 0 will completely suppress the enum values from appearing
+# in the overview section.
+# Minimum value: 0, maximum value: 20, default value: 4.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+ENUM_VALUES_PER_LINE   = 4
+
+# If the treeview is enabled (see GENERATE_TREEVIEW) then this tag can be used
+# to set the initial width (in pixels) of the frame in which the tree is shown.
+# Minimum value: 0, maximum value: 1500, default value: 250.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+TREEVIEW_WIDTH         = 250
+
+# When the EXT_LINKS_IN_WINDOW option is set to YES doxygen will open links to
+# external symbols imported via tag files in a separate window.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+EXT_LINKS_IN_WINDOW    = NO
+
+# Use this tag to change the font size of LaTeX formulas included as images in
+# the HTML documentation. When you change the font size after a successful
+# doxygen run you need to manually remove any form_*.png images from the HTML
+# output directory to force them to be regenerated.
+# Minimum value: 8, maximum value: 50, default value: 10.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+FORMULA_FONTSIZE       = 10
+
+# Use the FORMULA_TRANPARENT tag to determine whether or not the images
+# generated for formulas are transparent PNGs. Transparent PNGs are not
+# supported properly for IE 6.0, but are supported on all modern browsers.
+#
+# Note that when changing this option you need to delete any form_*.png files in
+# the HTML output directory before the changes have effect.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+FORMULA_TRANSPARENT    = YES
+
+# Enable the USE_MATHJAX option to render LaTeX formulas using MathJax (see
+# http://www.mathjax.org) which uses client side Javascript for the rendering
+# instead of using prerendered bitmaps. Use this if you do not have LaTeX
+# installed or if you want to formulas look prettier in the HTML output. When
+# enabled you may also need to install MathJax separately and configure the path
+# to it using the MATHJAX_RELPATH option.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+USE_MATHJAX            = NO
+
+# When MathJax is enabled you can set the default output format to be used for
+# the MathJax output. See the MathJax site (see:
+# http://docs.mathjax.org/en/latest/output.html) for more details.
+# Possible values are: HTML-CSS (which is slower, but has the best
+# compatibility), NativeMML (i.e. MathML) and SVG.
+# The default value is: HTML-CSS.
+# This tag requires that the tag USE_MATHJAX is set to YES.
+
+MATHJAX_FORMAT         = HTML-CSS
+
+# When MathJax is enabled you need to specify the location relative to the HTML
+# output directory using the MATHJAX_RELPATH option. The destination directory
+# should contain the MathJax.js script. For instance, if the mathjax directory
+# is located at the same level as the HTML output directory, then
+# MATHJAX_RELPATH should be ../mathjax. The default value points to the MathJax
+# Content Delivery Network so you can quickly see the result without installing
+# MathJax. However, it is strongly recommended to install a local copy of
+# MathJax from http://www.mathjax.org before deployment.
+# The default value is: http://cdn.mathjax.org/mathjax/latest.
+# This tag requires that the tag USE_MATHJAX is set to YES.
+
+MATHJAX_RELPATH        = http://www.mathjax.org/mathjax
+
+# The MATHJAX_EXTENSIONS tag can be used to specify one or more MathJax
+# extension names that should be enabled during MathJax rendering. For example
+# MATHJAX_EXTENSIONS = TeX/AMSmath TeX/AMSsymbols
+# This tag requires that the tag USE_MATHJAX is set to YES.
+
+MATHJAX_EXTENSIONS     =
+
+# The MATHJAX_CODEFILE tag can be used to specify a file with javascript pieces
+# of code that will be used on startup of the MathJax code. See the MathJax site
+# (see: http://docs.mathjax.org/en/latest/output.html) for more details. For an
+# example see the documentation.
+# This tag requires that the tag USE_MATHJAX is set to YES.
+
+MATHJAX_CODEFILE       =
+
+# When the SEARCHENGINE tag is enabled doxygen will generate a search box for
+# the HTML output. The underlying search engine uses javascript and DHTML and
+# should work on any modern browser. Note that when using HTML help
+# (GENERATE_HTMLHELP), Qt help (GENERATE_QHP), or docsets (GENERATE_DOCSET)
+# there is already a search function so this one should typically be disabled.
+# For large projects the javascript based search engine can be slow, then
+# enabling SERVER_BASED_SEARCH may provide a better solution. It is possible to
+# search using the keyboard; to jump to the search box use <access key> + S
+# (what the <access key> is depends on the OS and browser, but it is typically
+# <CTRL>, <ALT>/<option>, or both). Inside the search box use the <cursor down
+# key> to jump into the search results window, the results can be navigated
+# using the <cursor keys>. Press <Enter> to select an item or <escape> to cancel
+# the search. The filter options can be selected when the cursor is inside the
+# search box by pressing <Shift>+<cursor down>. Also here use the <cursor keys>
+# to select a filter and <Enter> or <escape> to activate or cancel the filter
+# option.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+SEARCHENGINE           = YES
+
+# When the SERVER_BASED_SEARCH tag is enabled the search engine will be
+# implemented using a web server instead of a web client using Javascript. There
+# are two flavors of web server based searching depending on the EXTERNAL_SEARCH
+# setting. When disabled, doxygen will generate a PHP script for searching and
+# an index file used by the script. When EXTERNAL_SEARCH is enabled the indexing
+# and searching needs to be provided by external tools. See the section
+# "External Indexing and Searching" for details.
+# The default value is: NO.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+SERVER_BASED_SEARCH    = NO
+
+# When EXTERNAL_SEARCH tag is enabled doxygen will no longer generate the PHP
+# script for searching. Instead the search results are written to an XML file
+# which needs to be processed by an external indexer. Doxygen will invoke an
+# external search engine pointed to by the SEARCHENGINE_URL option to obtain the
+# search results.
+#
+# Doxygen ships with an example indexer ( doxyindexer) and search engine
+# (doxysearch.cgi) which are based on the open source search engine library
+# Xapian (see: http://xapian.org/).
+#
+# See the section "External Indexing and Searching" for details.
+# The default value is: NO.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+EXTERNAL_SEARCH        = NO
+
+# The SEARCHENGINE_URL should point to a search engine hosted by a web server
+# which will return the search results when EXTERNAL_SEARCH is enabled.
+#
+# Doxygen ships with an example indexer ( doxyindexer) and search engine
+# (doxysearch.cgi) which are based on the open source search engine library
+# Xapian (see: http://xapian.org/). See the section "External Indexing and
+# Searching" for details.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+SEARCHENGINE_URL       =
+
+# When SERVER_BASED_SEARCH and EXTERNAL_SEARCH are both enabled the unindexed
+# search data is written to a file for indexing by an external tool. With the
+# SEARCHDATA_FILE tag the name of this file can be specified.
+# The default file is: searchdata.xml.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+SEARCHDATA_FILE        = searchdata.xml
+
+# When SERVER_BASED_SEARCH and EXTERNAL_SEARCH are both enabled the
+# EXTERNAL_SEARCH_ID tag can be used as an identifier for the project. This is
+# useful in combination with EXTRA_SEARCH_MAPPINGS to search through multiple
+# projects and redirect the results back to the right project.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+EXTERNAL_SEARCH_ID     =
+
+# The EXTRA_SEARCH_MAPPINGS tag can be used to enable searching through doxygen
+# projects other than the one defined by this configuration file, but that are
+# all added to the same external search index. Each project needs to have a
+# unique id set via EXTERNAL_SEARCH_ID. The search mapping then maps the id of
+# to a relative location where the documentation can be found. The format is:
+# EXTRA_SEARCH_MAPPINGS = tagname1=loc1 tagname2=loc2 ...
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+EXTRA_SEARCH_MAPPINGS  =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the LaTeX output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_LATEX tag is set to YES doxygen will generate LaTeX output.
+# The default value is: YES.
+
+GENERATE_LATEX         = YES
+
+# The LATEX_OUTPUT tag is used to specify where the LaTeX docs will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it.
+# The default directory is: latex.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_OUTPUT           = latex
+
+# The LATEX_CMD_NAME tag can be used to specify the LaTeX command name to be
+# invoked.
+#
+# Note that when enabling USE_PDFLATEX this option is only used for generating
+# bitmaps for formulas in the HTML output, but not in the Makefile that is
+# written to the output directory.
+# The default file is: latex.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_CMD_NAME         = latex
+
+# The MAKEINDEX_CMD_NAME tag can be used to specify the command name to generate
+# index for LaTeX.
+# The default file is: makeindex.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+MAKEINDEX_CMD_NAME     = makeindex
+
+# If the COMPACT_LATEX tag is set to YES doxygen generates more compact LaTeX
+# documents. This may be useful for small projects and may help to save some
+# trees in general.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+COMPACT_LATEX          = NO
+
+# The PAPER_TYPE tag can be used to set the paper type that is used by the
+# printer.
+# Possible values are: a4 (210 x 297 mm), letter (8.5 x 11 inches), legal (8.5 x
+# 14 inches) and executive (7.25 x 10.5 inches).
+# The default value is: a4.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+PAPER_TYPE             = a4
+
+# The EXTRA_PACKAGES tag can be used to specify one or more LaTeX package names
+# that should be included in the LaTeX output. To get the times font for
+# instance you can specify
+# EXTRA_PACKAGES=times
+# If left blank no extra packages will be included.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+EXTRA_PACKAGES         =
+
+# The LATEX_HEADER tag can be used to specify a personal LaTeX header for the
+# generated LaTeX document. The header should contain everything until the first
+# chapter. If it is left blank doxygen will generate a standard header. See
+# section "Doxygen usage" for information on how to let doxygen write the
+# default header to a separate file.
+#
+# Note: Only use a user-defined header if you know what you are doing! The
+# following commands have a special meaning inside the header: $title,
+# $datetime, $date, $doxygenversion, $projectname, $projectnumber,
+# $projectbrief, $projectlogo. Doxygen will replace $title with the empy string,
+# for the replacement values of the other commands the user is refered to
+# HTML_HEADER.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_HEADER           =
+
+# The LATEX_FOOTER tag can be used to specify a personal LaTeX footer for the
+# generated LaTeX document. The footer should contain everything after the last
+# chapter. If it is left blank doxygen will generate a standard footer. See
+# LATEX_HEADER for more information on how to generate a default footer and what
+# special commands can be used inside the footer.
+#
+# Note: Only use a user-defined footer if you know what you are doing!
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_FOOTER           =
+
+# The LATEX_EXTRA_FILES tag can be used to specify one or more extra images or
+# other source files which should be copied to the LATEX_OUTPUT output
+# directory. Note that the files will be copied as-is; there are no commands or
+# markers available.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_EXTRA_FILES      =
+
+# If the PDF_HYPERLINKS tag is set to YES, the LaTeX that is generated is
+# prepared for conversion to PDF (using ps2pdf or pdflatex). The PDF file will
+# contain links (just like the HTML output) instead of page references. This
+# makes the output suitable for online browsing using a PDF viewer.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+PDF_HYPERLINKS         = YES
+
+# If the USE_PDFLATEX tag is set to YES, doxygen will use pdflatex to generate
+# the PDF file directly from the LaTeX files. Set this option to YES to get a
+# higher quality PDF documentation.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+USE_PDFLATEX           = YES
+
+# If the LATEX_BATCHMODE tag is set to YES, doxygen will add the \batchmode
+# command to the generated LaTeX files. This will instruct LaTeX to keep running
+# if errors occur, instead of asking the user for help. This option is also used
+# when generating formulas in HTML.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_BATCHMODE        = NO
+
+# If the LATEX_HIDE_INDICES tag is set to YES then doxygen will not include the
+# index chapters (such as File Index, Compound Index, etc.) in the output.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_HIDE_INDICES     = NO
+
+# If the LATEX_SOURCE_CODE tag is set to YES then doxygen will include source
+# code with syntax highlighting in the LaTeX output.
+#
+# Note that which sources are shown also depends on other settings such as
+# SOURCE_BROWSER.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_SOURCE_CODE      = NO
+
+# The LATEX_BIB_STYLE tag can be used to specify the style to use for the
+# bibliography, e.g. plainnat, or ieeetr. See
+# http://en.wikipedia.org/wiki/BibTeX and \cite for more info.
+# The default value is: plain.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_BIB_STYLE        = plain
+
+#---------------------------------------------------------------------------
+# Configuration options related to the RTF output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_RTF tag is set to YES doxygen will generate RTF output. The
+# RTF output is optimized for Word 97 and may not look too pretty with other RTF
+# readers/editors.
+# The default value is: NO.
+
+GENERATE_RTF           = NO
+
+# The RTF_OUTPUT tag is used to specify where the RTF docs will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it.
+# The default directory is: rtf.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+RTF_OUTPUT             = rtf
+
+# If the COMPACT_RTF tag is set to YES doxygen generates more compact RTF
+# documents. This may be useful for small projects and may help to save some
+# trees in general.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+COMPACT_RTF            = NO
+
+# If the RTF_HYPERLINKS tag is set to YES, the RTF that is generated will
+# contain hyperlink fields. The RTF file will contain links (just like the HTML
+# output) instead of page references. This makes the output suitable for online
+# browsing using Word or some other Word compatible readers that support those
+# fields.
+#
+# Note: WordPad (write) and others do not support links.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+RTF_HYPERLINKS         = NO
+
+# Load stylesheet definitions from file. Syntax is similar to doxygen's config
+# file, i.e. a series of assignments. You only have to provide replacements,
+# missing definitions are set to their default value.
+#
+# See also section "Doxygen usage" for information on how to generate the
+# default style sheet that doxygen normally uses.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+RTF_STYLESHEET_FILE    =
+
+# Set optional variables used in the generation of an RTF document. Syntax is
+# similar to doxygen's config file. A template extensions file can be generated
+# using doxygen -e rtf extensionFile.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+RTF_EXTENSIONS_FILE    =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the man page output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_MAN tag is set to YES doxygen will generate man pages for
+# classes and files.
+# The default value is: NO.
+
+GENERATE_MAN           = NO
+
+# The MAN_OUTPUT tag is used to specify where the man pages will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it. A directory man3 will be created inside the directory specified by
+# MAN_OUTPUT.
+# The default directory is: man.
+# This tag requires that the tag GENERATE_MAN is set to YES.
+
+MAN_OUTPUT             = man
+
+# The MAN_EXTENSION tag determines the extension that is added to the generated
+# man pages. In case the manual section does not start with a number, the number
+# 3 is prepended. The dot (.) at the beginning of the MAN_EXTENSION tag is
+# optional.
+# The default value is: .3.
+# This tag requires that the tag GENERATE_MAN is set to YES.
+
+MAN_EXTENSION          = .3
+
+# The MAN_SUBDIR tag determines the name of the directory created within
+# MAN_OUTPUT in which the man pages are placed. If defaults to man followed by
+# MAN_EXTENSION with the initial . removed.
+# This tag requires that the tag GENERATE_MAN is set to YES.
+
+MAN_SUBDIR             =
+
+# If the MAN_LINKS tag is set to YES and doxygen generates man output, then it
+# will generate one additional man file for each entity documented in the real
+# man page(s). These additional files only source the real man page, but without
+# them the man command would be unable to find the correct page.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_MAN is set to YES.
+
+MAN_LINKS              = NO
+
+#---------------------------------------------------------------------------
+# Configuration options related to the XML output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_XML tag is set to YES doxygen will generate an XML file that
+# captures the structure of the code including all documentation.
+# The default value is: NO.
+
+GENERATE_XML           = NO
+
+# The XML_OUTPUT tag is used to specify where the XML pages will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it.
+# The default directory is: xml.
+# This tag requires that the tag GENERATE_XML is set to YES.
+
+XML_OUTPUT             = xml
+
+# If the XML_PROGRAMLISTING tag is set to YES doxygen will dump the program
+# listings (including syntax highlighting and cross-referencing information) to
+# the XML output. Note that enabling this will significantly increase the size
+# of the XML output.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_XML is set to YES.
+
+XML_PROGRAMLISTING     = YES
+
+#---------------------------------------------------------------------------
+# Configuration options related to the DOCBOOK output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_DOCBOOK tag is set to YES doxygen will generate Docbook files
+# that can be used to generate PDF.
+# The default value is: NO.
+
+GENERATE_DOCBOOK       = NO
+
+# The DOCBOOK_OUTPUT tag is used to specify where the Docbook pages will be put.
+# If a relative path is entered the value of OUTPUT_DIRECTORY will be put in
+# front of it.
+# The default directory is: docbook.
+# This tag requires that the tag GENERATE_DOCBOOK is set to YES.
+
+DOCBOOK_OUTPUT         = docbook
+
+# If the DOCBOOK_PROGRAMLISTING tag is set to YES doxygen will include the
+# program listings (including syntax highlighting and cross-referencing
+# information) to the DOCBOOK output. Note that enabling this will significantly
+# increase the size of the DOCBOOK output.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_DOCBOOK is set to YES.
+
+DOCBOOK_PROGRAMLISTING = NO
+
+#---------------------------------------------------------------------------
+# Configuration options for the AutoGen Definitions output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_AUTOGEN_DEF tag is set to YES doxygen will generate an AutoGen
+# Definitions (see http://autogen.sf.net) file that captures the structure of
+# the code including all documentation. Note that this feature is still
+# experimental and incomplete at the moment.
+# The default value is: NO.
+
+GENERATE_AUTOGEN_DEF   = NO
+
+#---------------------------------------------------------------------------
+# Configuration options related to the Perl module output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_PERLMOD tag is set to YES doxygen will generate a Perl module
+# file that captures the structure of the code including all documentation.
+#
+# Note that this feature is still experimental and incomplete at the moment.
+# The default value is: NO.
+
+GENERATE_PERLMOD       = NO
+
+# If the PERLMOD_LATEX tag is set to YES doxygen will generate the necessary
+# Makefile rules, Perl scripts and LaTeX code to be able to generate PDF and DVI
+# output from the Perl module output.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_PERLMOD is set to YES.
+
+PERLMOD_LATEX          = NO
+
+# If the PERLMOD_PRETTY tag is set to YES the Perl module output will be nicely
+# formatted so it can be parsed by a human reader. This is useful if you want to
+# understand what is going on. On the other hand, if this tag is set to NO the
+# size of the Perl module output will be much smaller and Perl will parse it
+# just the same.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_PERLMOD is set to YES.
+
+PERLMOD_PRETTY         = YES
+
+# The names of the make variables in the generated doxyrules.make file are
+# prefixed with the string contained in PERLMOD_MAKEVAR_PREFIX. This is useful
+# so different doxyrules.make files included by the same Makefile don't
+# overwrite each other's variables.
+# This tag requires that the tag GENERATE_PERLMOD is set to YES.
+
+PERLMOD_MAKEVAR_PREFIX =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the preprocessor
+#---------------------------------------------------------------------------
+
+# If the ENABLE_PREPROCESSING tag is set to YES doxygen will evaluate all
+# C-preprocessor directives found in the sources and include files.
+# The default value is: YES.
+
+ENABLE_PREPROCESSING   = NO
+
+# If the MACRO_EXPANSION tag is set to YES doxygen will expand all macro names
+# in the source code. If set to NO only conditional compilation will be
+# performed. Macro expansion can be done in a controlled way by setting
+# EXPAND_ONLY_PREDEF to YES.
+# The default value is: NO.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+MACRO_EXPANSION        = NO
+
+# If the EXPAND_ONLY_PREDEF and MACRO_EXPANSION tags are both set to YES then
+# the macro expansion is limited to the macros specified with the PREDEFINED and
+# EXPAND_AS_DEFINED tags.
+# The default value is: NO.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+EXPAND_ONLY_PREDEF     = NO
+
+# If the SEARCH_INCLUDES tag is set to YES the includes files in the
+# INCLUDE_PATH will be searched if a #include is found.
+# The default value is: YES.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+SEARCH_INCLUDES        = YES
+
+# The INCLUDE_PATH tag can be used to specify one or more directories that
+# contain include files that are not input files but should be processed by the
+# preprocessor.
+# This tag requires that the tag SEARCH_INCLUDES is set to YES.
+
+INCLUDE_PATH           =
+
+# You can use the INCLUDE_FILE_PATTERNS tag to specify one or more wildcard
+# patterns (like *.h and *.hpp) to filter out the header-files in the
+# directories. If left blank, the patterns specified with FILE_PATTERNS will be
+# used.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+INCLUDE_FILE_PATTERNS  =
+
+# The PREDEFINED tag can be used to specify one or more macro names that are
+# defined before the preprocessor is started (similar to the -D option of e.g.
+# gcc). The argument of the tag is a list of macros of the form: name or
+# name=definition (no spaces). If the definition and the "=" are omitted, "=1"
+# is assumed. To prevent a macro definition from being undefined via #undef or
+# recursively expanded use the := operator instead of the = operator.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+PREDEFINED             =
+
+# If the MACRO_EXPANSION and EXPAND_ONLY_PREDEF tags are set to YES then this
+# tag can be used to specify a list of macro names that should be expanded. The
+# macro definition that is found in the sources will be used. Use the PREDEFINED
+# tag if you want to use a different macro definition that overrules the
+# definition found in the source code.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+EXPAND_AS_DEFINED      =
+
+# If the SKIP_FUNCTION_MACROS tag is set to YES then doxygen's preprocessor will
+# remove all references to function-like macros that are alone on a line, have
+# an all uppercase name, and do not end with a semicolon. Such function macros
+# are typically used for boiler-plate code, and will confuse the parser if not
+# removed.
+# The default value is: YES.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+SKIP_FUNCTION_MACROS   = YES
+
+#---------------------------------------------------------------------------
+# Configuration options related to external references
+#---------------------------------------------------------------------------
+
+# The TAGFILES tag can be used to specify one or more tag files. For each tag
+# file the location of the external documentation should be added. The format of
+# a tag file without this location is as follows:
+# TAGFILES = file1 file2 ...
+# Adding location for the tag files is done as follows:
+# TAGFILES = file1=loc1 "file2 = loc2" ...
+# where loc1 and loc2 can be relative or absolute paths or URLs. See the
+# section "Linking to external documentation" for more information about the use
+# of tag files.
+# Note: Each tag file must have a unique name (where the name does NOT include
+# the path). If a tag file is not located in the directory in which doxygen is
+# run, you must also specify the path to the tagfile here.
+
+TAGFILES               =
+
+# When a file name is specified after GENERATE_TAGFILE, doxygen will create a
+# tag file that is based on the input files it reads. See section "Linking to
+# external documentation" for more information about the usage of tag files.
+
+GENERATE_TAGFILE       =
+
+# If the ALLEXTERNALS tag is set to YES all external class will be listed in the
+# class index. If set to NO only the inherited external classes will be listed.
+# The default value is: NO.
+
+ALLEXTERNALS           = NO
+
+# If the EXTERNAL_GROUPS tag is set to YES all external groups will be listed in
+# the modules index. If set to NO, only the current project's groups will be
+# listed.
+# The default value is: YES.
+
+EXTERNAL_GROUPS        = YES
+
+# If the EXTERNAL_PAGES tag is set to YES all external pages will be listed in
+# the related pages index. If set to NO, only the current project's pages will
+# be listed.
+# The default value is: YES.
+
+EXTERNAL_PAGES         = YES
+
+# The PERL_PATH should be the absolute path and name of the perl script
+# interpreter (i.e. the result of 'which perl').
+# The default file (with absolute path) is: /usr/bin/perl.
+
+PERL_PATH              = /usr/bin/perl
+
+#---------------------------------------------------------------------------
+# Configuration options related to the dot tool
+#---------------------------------------------------------------------------
+
+# If the CLASS_DIAGRAMS tag is set to YES doxygen will generate a class diagram
+# (in HTML and LaTeX) for classes with base or super classes. Setting the tag to
+# NO turns the diagrams off. Note that this option also works with HAVE_DOT
+# disabled, but it is recommended to install and use dot, since it yields more
+# powerful graphs.
+# The default value is: YES.
+
+CLASS_DIAGRAMS         = YES
+
+# You can define message sequence charts within doxygen comments using the \msc
+# command. Doxygen will then run the mscgen tool (see:
+# http://www.mcternan.me.uk/mscgen/)) to produce the chart and insert it in the
+# documentation. The MSCGEN_PATH tag allows you to specify the directory where
+# the mscgen tool resides. If left empty the tool is assumed to be found in the
+# default search path.
+
+MSCGEN_PATH            =
+
+# You can include diagrams made with dia in doxygen documentation. Doxygen will
+# then run dia to produce the diagram and insert it in the documentation. The
+# DIA_PATH tag allows you to specify the directory where the dia binary resides.
+# If left empty dia is assumed to be found in the default search path.
+
+DIA_PATH               =
+
+# If set to YES, the inheritance and collaboration graphs will hide inheritance
+# and usage relations if the target is undocumented or is not a class.
+# The default value is: YES.
+
+HIDE_UNDOC_RELATIONS   = YES
+
+# If you set the HAVE_DOT tag to YES then doxygen will assume the dot tool is
+# available from the path. This tool is part of Graphviz (see:
+# http://www.graphviz.org/), a graph visualization toolkit from AT&T and Lucent
+# Bell Labs. The other options in this section have no effect if this option is
+# set to NO
+# The default value is: YES.
+
+HAVE_DOT               = NO
+
+# The DOT_NUM_THREADS specifies the number of dot invocations doxygen is allowed
+# to run in parallel. When set to 0 doxygen will base this on the number of
+# processors available in the system. You can set it explicitly to a value
+# larger than 0 to get control over the balance between CPU load and processing
+# speed.
+# Minimum value: 0, maximum value: 32, default value: 0.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_NUM_THREADS        = 0
+
+# When you want a differently looking font in the dot files that doxygen
+# generates you can specify the font name using DOT_FONTNAME. You need to make
+# sure dot is able to find the font, which can be done by putting it in a
+# standard location or by setting the DOTFONTPATH environment variable or by
+# setting DOT_FONTPATH to the directory containing the font.
+# The default value is: Helvetica.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_FONTNAME           = Helvetica
+
+# The DOT_FONTSIZE tag can be used to set the size (in points) of the font of
+# dot graphs.
+# Minimum value: 4, maximum value: 24, default value: 10.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_FONTSIZE           = 10
+
+# By default doxygen will tell dot to use the default font as specified with
+# DOT_FONTNAME. If you specify a different font using DOT_FONTNAME you can set
+# the path where dot can find it using this tag.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_FONTPATH           =
+
+# If the CLASS_GRAPH tag is set to YES then doxygen will generate a graph for
+# each documented class showing the direct and indirect inheritance relations.
+# Setting this tag to YES will force the CLASS_DIAGRAMS tag to NO.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+CLASS_GRAPH            = YES
+
+# If the COLLABORATION_GRAPH tag is set to YES then doxygen will generate a
+# graph for each documented class showing the direct and indirect implementation
+# dependencies (inheritance, containment, and class references variables) of the
+# class with other documented classes.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+COLLABORATION_GRAPH    = YES
+
+# If the GROUP_GRAPHS tag is set to YES then doxygen will generate a graph for
+# groups, showing the direct groups dependencies.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+GROUP_GRAPHS           = YES
+
+# If the UML_LOOK tag is set to YES doxygen will generate inheritance and
+# collaboration diagrams in a style similar to the OMG's Unified Modeling
+# Language.
+# The default value is: NO.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+UML_LOOK               = NO
+
+# If the UML_LOOK tag is enabled, the fields and methods are shown inside the
+# class node. If there are many fields or methods and many nodes the graph may
+# become too big to be useful. The UML_LIMIT_NUM_FIELDS threshold limits the
+# number of items for each type to make the size more manageable. Set this to 0
+# for no limit. Note that the threshold may be exceeded by 50% before the limit
+# is enforced. So when you set the threshold to 10, up to 15 fields may appear,
+# but if the number exceeds 15, the total amount of fields shown is limited to
+# 10.
+# Minimum value: 0, maximum value: 100, default value: 10.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+UML_LIMIT_NUM_FIELDS   = 10
+
+# If the TEMPLATE_RELATIONS tag is set to YES then the inheritance and
+# collaboration graphs will show the relations between templates and their
+# instances.
+# The default value is: NO.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+TEMPLATE_RELATIONS     = NO
+
+# If the INCLUDE_GRAPH, ENABLE_PREPROCESSING and SEARCH_INCLUDES tags are set to
+# YES then doxygen will generate a graph for each documented file showing the
+# direct and indirect include dependencies of the file with other documented
+# files.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+INCLUDE_GRAPH          = YES
+
+# If the INCLUDED_BY_GRAPH, ENABLE_PREPROCESSING and SEARCH_INCLUDES tags are
+# set to YES then doxygen will generate a graph for each documented file showing
+# the direct and indirect include dependencies of the file with other documented
+# files.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+INCLUDED_BY_GRAPH      = YES
+
+# If the CALL_GRAPH tag is set to YES then doxygen will generate a call
+# dependency graph for every global function or class method.
+#
+# Note that enabling this option will significantly increase the time of a run.
+# So in most cases it will be better to enable call graphs for selected
+# functions only using the \callgraph command.
+# The default value is: NO.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+CALL_GRAPH             = NO
+
+# If the CALLER_GRAPH tag is set to YES then doxygen will generate a caller
+# dependency graph for every global function or class method.
+#
+# Note that enabling this option will significantly increase the time of a run.
+# So in most cases it will be better to enable caller graphs for selected
+# functions only using the \callergraph command.
+# The default value is: NO.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+CALLER_GRAPH           = NO
+
+# If the GRAPHICAL_HIERARCHY tag is set to YES then doxygen will graphical
+# hierarchy of all classes instead of a textual one.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+GRAPHICAL_HIERARCHY    = YES
+
+# If the DIRECTORY_GRAPH tag is set to YES then doxygen will show the
+# dependencies a directory has on other directories in a graphical way. The
+# dependency relations are determined by the #include relations between the
+# files in the directories.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DIRECTORY_GRAPH        = YES
+
+# The DOT_IMAGE_FORMAT tag can be used to set the image format of the images
+# generated by dot.
+# Note: If you choose svg you need to set HTML_FILE_EXTENSION to xhtml in order
+# to make the SVG files visible in IE 9+ (other browsers do not have this
+# requirement).
+# Possible values are: png, png:cairo, png:cairo:cairo, png:cairo:gd, png:gd,
+# png:gd:gd, jpg, jpg:cairo, jpg:cairo:gd, jpg:gd, jpg:gd:gd, gif, gif:cairo,
+# gif:cairo:gd, gif:gd, gif:gd:gd and svg.
+# The default value is: png.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_IMAGE_FORMAT       = png
+
+# If DOT_IMAGE_FORMAT is set to svg, then this option can be set to YES to
+# enable generation of interactive SVG images that allow zooming and panning.
+#
+# Note that this requires a modern browser other than Internet Explorer. Tested
+# and working are Firefox, Chrome, Safari, and Opera.
+# Note: For IE 9+ you need to set HTML_FILE_EXTENSION to xhtml in order to make
+# the SVG files visible. Older versions of IE do not have SVG support.
+# The default value is: NO.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+INTERACTIVE_SVG        = NO
+
+# The DOT_PATH tag can be used to specify the path where the dot tool can be
+# found. If left blank, it is assumed the dot tool can be found in the path.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_PATH               =
+
+# The DOTFILE_DIRS tag can be used to specify one or more directories that
+# contain dot files that are included in the documentation (see the \dotfile
+# command).
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOTFILE_DIRS           =
+
+# The MSCFILE_DIRS tag can be used to specify one or more directories that
+# contain msc files that are included in the documentation (see the \mscfile
+# command).
+
+MSCFILE_DIRS           =
+
+# The DIAFILE_DIRS tag can be used to specify one or more directories that
+# contain dia files that are included in the documentation (see the \diafile
+# command).
+
+DIAFILE_DIRS           =
+
+# When using plantuml, the PLANTUML_JAR_PATH tag should be used to specify the
+# path where java can find the plantuml.jar file. If left blank, it is assumed
+# PlantUML is not used or called during a preprocessing step. Doxygen will
+# generate a warning when it encounters a \startuml command in this case and
+# will not generate output for the diagram.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+PLANTUML_JAR_PATH      =
+
+# The DOT_GRAPH_MAX_NODES tag can be used to set the maximum number of nodes
+# that will be shown in the graph. If the number of nodes in a graph becomes
+# larger than this value, doxygen will truncate the graph, which is visualized
+# by representing a node as a red box. Note that doxygen if the number of direct
+# children of the root node in a graph is already larger than
+# DOT_GRAPH_MAX_NODES then the graph will not be shown at all. Also note that
+# the size of a graph can be further restricted by MAX_DOT_GRAPH_DEPTH.
+# Minimum value: 0, maximum value: 10000, default value: 50.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_GRAPH_MAX_NODES    = 50
+
+# The MAX_DOT_GRAPH_DEPTH tag can be used to set the maximum depth of the graphs
+# generated by dot. A depth value of 3 means that only nodes reachable from the
+# root by following a path via at most 3 edges will be shown. Nodes that lay
+# further from the root node will be omitted. Note that setting this option to 1
+# or 2 may greatly reduce the computation time needed for large code bases. Also
+# note that the size of a graph can be further restricted by
+# DOT_GRAPH_MAX_NODES. Using a depth of 0 means no depth restriction.
+# Minimum value: 0, maximum value: 1000, default value: 0.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+MAX_DOT_GRAPH_DEPTH    = 0
+
+# Set the DOT_TRANSPARENT tag to YES to generate images with a transparent
+# background. This is disabled by default, because dot on Windows does not seem
+# to support this out of the box.
+#
+# Warning: Depending on the platform used, enabling this option may lead to
+# badly anti-aliased labels on the edges of a graph (i.e. they become hard to
+# read).
+# The default value is: NO.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_TRANSPARENT        = NO
+
+# Set the DOT_MULTI_TARGETS tag to YES allow dot to generate multiple output
+# files in one run (i.e. multiple -o and -T options on the command line). This
+# makes dot run faster, but since only newer versions of dot (>1.8.10) support
+# this, this feature is disabled by default.
+# The default value is: NO.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_MULTI_TARGETS      = YES
+
+# If the GENERATE_LEGEND tag is set to YES doxygen will generate a legend page
+# explaining the meaning of the various boxes and arrows in the dot generated
+# graphs.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+GENERATE_LEGEND        = YES
+
+# If the DOT_CLEANUP tag is set to YES doxygen will remove the intermediate dot
+# files that are used to generate the various graphs.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_CLEANUP            = YES
diff --git a/3rdparty/mshadow/doc/README.md b/3rdparty/mshadow/doc/README.md
new file mode 100644
index 000000000000..2fed3444a38c
--- /dev/null
+++ b/3rdparty/mshadow/doc/README.md
@@ -0,0 +1,321 @@
+MShadow Documentation
+=====
+This is the documentation for mshadow: A Lightweight CPU/GPU Matrix/Tensor Template Library in C++/CUDA. 
+
+### Links to Topics
+
+* [Tutorial](../guide)
+* [API Documentation](http://homes.cs.washington.edu/~tqchen/mshadow/doc)
+  - You can run ```./mkdoc.sh``` to make the document locally
+* [Tutorial about Expression Template](../guide/exp-template)
+* [Writing Multi-GPU and Distributed ML](../guide/mshadow-ps)
+* [Compile Configuration script](../make)
+* [Expression API](#expression-api)
+  - Expression api introduces the concept of expression in mshadow
+
+Expression API
+=====
+Expression is the key concept in mshadow, a common operation of mshadow is ```tensor = some code to construct expression```
+
+There are three major types of expression:
+* Mapper expression: only contain element-wise operations of Mapper expressions  
+  - Mapper expression can used as composition component of other operations.
+  - Tensor, scalar are Mapper expressions
+  - Example: ``` weight =  - eta * (grad + lambda * weight)```  is a Mapper expression.
+  - Mapper expressions are translated using expression template code implemented by mshadow.
+  - ***Assign safety***: Element-wise mapping are assign safe, which means, we can write ```A = A * 2 + B```, making lvalue appear in expression, the results are still correct.
+* Chainer expression: may contain element-wise operation such as reduction and broadcast
+  - Example: ```dst = mirror(src)``` is a chainer expression
+  - ***Assign safety***: Most of the chainer extensions are not assignment safe, which means user should avoid putting target in source epression.
+* Complex expression: complex operations, need special translation rule to translate to specific implementations.
+   - Complex expression can not be used as composition component of other operations.
+   - Example: ``` dot(lhs.T(), rhs)```,  is complex expression, we can not write
+``` dst =  1.0 + dot(lhs.T(), rhs)```
+   - But limited syntax is supported depending on specification, for example, we do support ``` dst +=  2.0f * dot(lhs.T(), rhs)```
+   - Complex expressions are translated into specific implementations such as BLAS.
+
+### Element-wise Operations
+The basic binary operators are overloaded to composite Mapper expressions, so we can write 
+```c++
+weight = (-eta) * (grad + lambda * weight);
+```
+We can also use customized binary operators, and unary operators:
+```c++
+struct maximum {
+  MSHADOW_XINLINE static float Map(float a, float b) {
+    return a > b ? a : b;
+  }
+};
+template<typename xpu>
+void ExampleMaximum(Tensor<xpu, 2> out,
+                    const Tensor<xpu, 2> &A,
+                    const Tensor<xpu, 2> &B) {
+  out= 10.0f * F<maximum>(A+1.0f, B); 
+}
+struct sigmoid {
+  MSHADOW_XINLINE static float Map(float a) {
+    return 1.0f/(1.0f+expf(-a));
+  }
+};
+template<typename xpu>
+void ExampleSigmoid(Tensor<xpu, 2> out, const Tensor<xpu, 2> &in) {
+  // equivalent to out = sigmoid(in*2) + 1; 
+  out = F<op::plus>(F<sigmoid>(in * 2.0f), ScalarExp(1.0f));
+}
+```
+### Matrix Multiplications
+Matrix multiplications are supported by following syntax, with things brackets [] are optional
+```
+dst <sv> [scale*] dot(lhs [.T()] , rhs [.T()]), <sv> can be =,+=,-=
+```
+Example:
+```c++
+template<typename xpu>
+void Backprop(Tensor<xpu, 2> gradin,
+              const Tensor<xpu, 2> &gradout,
+              const Tensor<xpu, 2> &netweight) {
+  gradin = 2.0 * dot(gradout, netweight.T());
+}
+```
+
+### Introducing Expression Extensions
+Naming conventions:
+* ```Tensor<xpu, dim>``` to refer to any Tensor with device any device and dimension. 
+* ```xpu```, ```dim```, are implicit template parameters. 
+* ```Expr<xpu, dim>``` will be used to refer to any mapper expression with type ```Tensor<xpu,dim>```.
+
+List of functions:
+* [reshape](#reshape): reshapes a tensor to another shape, number of content must be same
+* [broadcast<?>](#broadcast): replicate a 1 dimension tensor in certain dimension
+* [repmat](#repmat), special case of broadcast<0>: repeat vector over rows to form a matrix
+* [sumall_except_dim<?>](#sumall_except_dim): sum over all the dimensions, except the dimension specified in template parameter
+* [sum_rows](#sum_rows): special case of sumall_except_dim<0>, sum of rows in the matrix
+* [unpack_patch2col](#unpack_patch2col): unpack local (overlap) patches of image to column of mat, can be used to implement convolution
+* [pack_col2patch](#pack_col2patch): reverse operation of unpack_patch2col, can be used to implement deconvolution
+* [pool](#pool): do pooling on image
+* [unpool](#unpool): get gradient of pooling result
+* [crop](#crop): crop the original image to a smaller size
+* [mirror](#mirror): get the mirrored result of input expression
+
+======
+##### reshape
+* ```reshape(Expr<xpu,dim> src, Shape<dimdst> oshape)```
+* reshapes a tensor to another shape, total number of elements must be same
+* parameters:
+  - src:  input data
+  - oshape: target shape
+* result expression type: ```Tensor<xpu, dimdst>``` with ```shape=oshape```, is Mapper expression
+```c++
+void ExampleReshape(void) {
+  Tensor<cpu, 2> dst = NewTensor<cpu>(Shape2(4, 5));
+  Tensor<cpu, 1> src = NewTensor<cpu>(Shape1(20), 1.0f); 
+  dst = reshape(src, dst.shape_);
+  ...
+}
+```
+======
+
+##### broadcast
+* ```broadcast<dimcast>(Tensor<xpu,1> src, Shape<dimdst> oshape)```
+* replicate a 1 dimension tensor certain dimension, specified by template parameter dimcast
+* parameters:
+  - src: input 1 dimensional tensor
+  - oshape: shape of output
+* return expression type: ```Tensor<xpu, dimdst>```, ```shape = oshape```, is Chainer expression 
+```c++
+void ExampleBroadcast(void) {
+  Tensor<cpu, 2> dst = NewTensor<cpu>(Shape2(2, 3));
+  Tensor<cpu, 1> src = NewTensor<cpu>(Shape1(2), 1.0f);
+  src[0] = 2.0f; src[1] = 1.0f;
+  dst = broadcast<0>(src, dst.shape_);
+  // dst[0][0] = 2, dst[0][1] = 2; dst[1][0]=1, dst[1][1] = 1
+  ...
+}
+```
+======
+##### repmat
+* ```repmat(Tensor<xpu, 1> src, int nrows) ```
+* special case of broadcast, repeat 1d tensor over rows
+* input parameters:
+  - src: input vector
+  - nrows: number of rows in target
+* return expression type:  ```Tensor<xpu, 2>```, with ```shape=(nrows, src.size(0))```,  is Chainer expression
+```c++
+void ExampleRepmat(void) {
+  Tensor<cpu,2> dst = NewTensor<cpu>(Shape2(3, 2));
+  Tensor<cpu,1> src = NewTensor<cpu>(Shape1(2), 1.0f);
+  src[0] = 2.0f; src[1] = 1.0f;
+  dst = repmat(src, 3);
+  // dst[0][0] = 2, dst[0][1] = 1; dst[1][0]=2, dst[1][1] = 1
+  ...
+}
+```
+======
+##### sumall_except_dim
+* ```sumall_except_dim<dimkeep>(Expr<xpu,dim> src) ```
+* sum over all dimensions, except dimkeep
+* input parameters:
+  - src: input mapper expression
+* return expression type:  ```Tensor<xpu, 1>```, with ```shape=(src.size(dimkeep))```,  is Complex expression
+* Syntax: ```dst [sv] [scale*] sumall_except_dim<dimkeep>(src) , <sv> can be =, +=, -=, *=, /=````
+```c++
+void ExampleSumAllExceptDim(void) {
+  Tensor<cpu,3> src = NewTensor<cpu>(Shape3(2, 3, 2), 1.0f);
+  Tensor<cpu,1> dst = NewTensor<cpu>(Shape1(3), 1.0f);
+  dst += sum_all_except<1>(src * 2.0f);
+  // dst[0] = 1.0 + 4.0 *2.0 = 9.0
+  ...
+}
+```
+======
+##### sum_rows
+* ```sum_rows(Expr<xpu, 2> src) ```
+* sum of rows in the matrix
+* input parameters:
+  - src: input mapper  expression
+* return expression type:  ```Tensor<xpu,1>```, with ```shape=(src.size(0))```,  is Complex expression
+* Syntax: ```dst [sv] [scale*] sum_rows(src) , <sv> can be =,+=,-=,*=,/=````
+```c++
+void ExampleSumRows(void) {
+  Tensor<cpu, 2> src = NewTensor<cpu>(Shape2(3, 2), 1.0f);
+  Tensor<cpu, 1> dst = NewTensor<cpu>(Shape1(2), 1.0f);
+  dst += sum_rows(src + 1.0f);
+  // dst[0] = 1.0 + 3.0 *(1.0+1.0) = 7.0
+  ...
+}
+```
+======
+##### unpack_patch2col
+* ```unpack_patch2col(Expr<xpu,3> img, int psize_y, int p_size_x, int pstride) ```
+* unpack local (overlap) patches of image to column of mat, can be used to implement convolution, after getting unpacked mat, we can use: ```output = dot(weight, mat)``` to get covolved results, the relations:
+  - weight; shape[0]: out_channel, shape[1]: ichannel * psize_y * psize_x
+  - output; shape[0]: out_channel, shape[1]: out_height * out_width * num_of_images
+  -  out_height = (in_height - psize_y) / pstride + 1, this means we pad inperfect patch with 0
+  - out_width  = (in_width - psize_x) / pstride + 1
+* input parameters:
+  - img: source image, can be expression; (in_channels, in_height, in_width)
+  - psize_y height of each patch
+  - psize_x width of each patch
+  - pstride: stride of each patch
+* return expression type:  ```Tensor<xpu, 2>```, with ```shape=(in_channel*psize_x*psize_y, out_height*out_width)```,  is Chainer expression
+```c++
+void ExampleCovolution(Tensor<cpu, 3> dst, Tensor<cpu, 3> src,
+                       Tensor<cpu, 2> weight, int ksize, int stride) {
+  int o_height = (src.size(1)- ksize) / stride + 1;
+  int o_width  = (src.size(2)- ksize) / stride + 1;
+  utils::Assert(weight.size(1) == src.size(0) * ksize * ksize);
+  TensorContainer<cpu, 2> tmp_col(Shape2(src.size(0) * ksize * ksize,
+                                         o_height * o_width)); 
+  TensorContainer<cpu, 2> tmp_dst(Shape2(weight.size(0),
+                                         o_height * o_width)); 
+  tmp_col = unpack_patch2col(src, ksize, ksize, stride);
+  tmp_dst = dot(weight, tmp_col);
+  dst = reshape(tmp_dst, dst.shape_);
+}
+```
+
+======
+##### pack_col2patch
+* ```pack_col2patch(Tensor<xpu, 2> mat, Shape<3> imshape, int psize_y, int psize_x, int pstride) ````
+* reverse operation of unpack_patch2col, can be used to implement deconvolution
+* input parameters:
+  - mat: source mat, same shape as output of unpack_patch2col
+  - imshape: shape of target image
+  - psize_y height of each patch
+  - psize_x width of each patch
+  - pstride: stride of each patch
+* return expression type:  ```Tensor<xpu, 3>```, with ```shape = imshape```,  is Chainer expression
+```c++
+void ExampleDecovolution(Tensor<cpu, 3> bottom, Tensor<cpu, 3> top,
+                         Tensor<cpu, 2> weight, int ksize, int stride) {
+  int o_height = (bottom.size(1)- ksize) / stride + 1;
+  int o_width  = (bottom.size(2)- ksize) / stride + 1;
+  utils::Assert(weight.size(1) == bottom.size(0) * ksize * ksize);
+  TensorContainer<cpu, 2> tmp_col(Shape2(bottom.size(0) * ksize * ksize,
+                                         o_height * o_width)); 
+  TensorContainer<cpu, 2> tmp_dst(Shape2(weight.size(0), o_height*o_width)); 
+  tmp_dst = reshape(top, tmp_dst.shape_);
+  tmp_col = dot(weight.T(), tmp_dst);
+  bottom = pack_col2patch(tmp_col, bottom.shape_, ksize, ksize, stride);
+}
+```
+
+======
+##### pool
+* ```pool<Reducer>(Expr<xpu, dim> img, [Shape<2> pshape,] int ksize_y, int ksize_x, int kstride)```
+* Pooling on image with specify kernel size and stride, can be used to implement max pooilng and other pooling layer
+* input parameters:
+  - Reducer: operation can be max or sum
+  - img: source image, can be expression; (in_channels, in_height, in_width)
+  - [optional] Shape<2> pshape, output shape
+  - ksize_y height of each patch
+  - ksize_x width of each patch
+  - kstride: stride of each patch
+* return expression:  ```Expr<xpu, dim>```, with ```shape = (in_channel, (out_height - ksize) / kstride + 1, (out_width - ksize) / kstride + 1)```, or expression in pshape
+  - Chainer expression
+```c++
+void ExampleMaxPooling(TensorContainer<cpu, 3> &data, int ksize, int stride) {
+  TensorContainer<cpu, 3> pooled(Shape3(data.size(0),
+                                        (data.size(2) - ksize) / kstride + 1), 
+                                        (data.size(1) - ksize) / kstride + 1));
+  pooled = pool<red::maximum>(data, ksize, ksize, stride);
+}
+```
+
+======
+##### unpool
+* ```unpool<Reducer>(Tensor<xpu, 4> data_src, Tensor<xpu, 4> data_pooled, Tensor<xpu, 4> grad_pooled, int ksize_y,  int ksize_x, int kstride)```
+* Unpooling on image with specify kernel size and stride, can be used to implement backprop of max pooilng and other pooling layer
+* input parameters:
+  - Reducer: operation can be max or sum
+  - data_src: source image batch. 
+  - data_pooled: pooled image batch. 
+  - grad_pooled: gradient of upper layer
+  - ksize_y height of each patch
+  - ksize_x width of each patch
+  - kstride: stride of each patch
+* return:
+  Expression, same shape to data_src
+```c++
+void ExampleMaxUnpooling(Tensor<cpu, 4> &data_src, Tensor<cpu, 4> &data_pooled, 
+                         Tensor<cpu, 4> &grad_pooled, int ksize, int kstride) {
+  TensorContainer<cpu, 4> grad(data_src.shape_);
+  grad = unpool<red::maximum>(data_src, data_pooled,
+                              grad_pooled, ksize, ksize, kstride);
+}
+```
+
+======
+##### crop
+* ```crop(Expr<xpu, dim> src, Shape<2> oshape, int start_height, int start_width)```
+* input parameters:
+ - src: input expression 
+ - oshape: output shape after crop
+ - start_height: start height for cropping
+ - start_width: start width for cropping
+* Can also be ```crop(Expr<xpu, dim> src, Shape<2> oshape)``` where the crop will happen in center. 
+* return
+ - cropped expression
+```c++
+void ExampleCrop(TensorContainer<cpu, 3> img, int start_height, int start_width) {
+  TensorContainer<cpu> cropped(Shape3(img.size(0),
+                                      img.size(1) - start_height,
+                                      img.size(2) - start_width));
+  cropped = crop(img, start_height, start_width);
+}
+```
+
+======
+##### mirror
+* ```mirrow(Expr<xpu, dim> src)```
+* input:
+    - src, source expression to be mirrored
+* output:
+    - expression of mirrored result
+```c++
+void ExampleMirror(TensorContainer<cpu, 3> img) {
+  TensorContainer<cpu> mirrored(img.shape_);
+  mirrored = mirror(img);
+}
+```
+
diff --git a/3rdparty/mshadow/doc/mkdoc.sh b/3rdparty/mshadow/doc/mkdoc.sh
new file mode 100755
index 000000000000..3ee3d71b8ce8
--- /dev/null
+++ b/3rdparty/mshadow/doc/mkdoc.sh
@@ -0,0 +1,4 @@
+#!/bin/bash
+cd ..
+doxygen doc/Doxyfile
+cd doc
diff --git a/3rdparty/mshadow/guide/.gitignore b/3rdparty/mshadow/guide/.gitignore
new file mode 100644
index 000000000000..ef7796743059
--- /dev/null
+++ b/3rdparty/mshadow/guide/.gitignore
@@ -0,0 +1,3 @@
+defop
+basic
+config.mk
diff --git a/3rdparty/mshadow/guide/Makefile b/3rdparty/mshadow/guide/Makefile
new file mode 100644
index 000000000000..bad7a8e94b1d
--- /dev/null
+++ b/3rdparty/mshadow/guide/Makefile
@@ -0,0 +1,37 @@
+# set LD_LIBRARY_PATH
+export CC  = gcc
+export CXX = g++
+export NVCC =nvcc
+include config.mk
+include ../make/mshadow.mk
+export CFLAGS = -Wall -O3 -std=c++11 -I../ $(MSHADOW_CFLAGS)
+export LDFLAGS= -lm $(MSHADOW_LDFLAGS)
+export NVCCFLAGS = -O3 --use_fast_math -ccbin $(CXX) $(MSHADOW_NVCCFLAGS)
+
+# specify tensor path
+BIN = basic defop
+OBJ =
+CUOBJ =
+CUBIN =
+.PHONY: clean all
+
+all: $(BIN) $(OBJ) $(CUBIN) $(CUOBJ)
+
+basic: basic.cpp
+defop: defop.cpp
+basic_stream: basic_stream.cu
+
+$(BIN) :
+	$(CXX) $(CFLAGS) -o $@ $(filter %.cpp %.o %.c, $^)  $(LDFLAGS)
+
+$(OBJ) :
+	$(CXX) -c $(CFLAGS) -o $@ $(firstword $(filter %.cpp %.c, $^) )
+
+$(CUOBJ) :
+	$(NVCC) -c -o $@ $(NVCCFLAGS) -Xcompiler "$(CFLAGS)" $(filter %.cu, $^)
+
+$(CUBIN) :
+	$(NVCC) -o $@ $(NVCCFLAGS) -Xcompiler "$(CFLAGS)" -Xlinker "$(LDFLAGS)" $(filter %.cu %.cpp %.o, $^)
+
+clean:
+	$(RM) $(OBJ) $(BIN) $(CUBIN) $(CUOBJ) *~
diff --git a/3rdparty/mshadow/guide/README.md b/3rdparty/mshadow/guide/README.md
new file mode 100644
index 000000000000..3f2e6819b7ec
--- /dev/null
+++ b/3rdparty/mshadow/guide/README.md
@@ -0,0 +1,226 @@
+Tutorial of mshadow
+=====
+This is a beginner's tutorial for mshadow. If you like mshadow and have ideas to improve this tutorial, you are more than welcome to contribute :)
+Please send a pull-request if you would like to share your experience.
+
+See also other related materials about mshadow
+* [Expression Template Tutorial](exp-template)
+* [Writing Multi-GPU and Distributed ML](mshadow-ps)
+
+**List of Topics**
+* [Tensor Data Structure](#tensor-data-structure)
+* [Memory Allocation](#memory-allocation)
+* [Elementwise Operations](#elementwise-operations)
+* [One code for both CPU and GPU](#one-code-for-both-cpu-and-gpu)
+* [Matrix Multiplications](#matrix-multiplications)
+* [User Defined Operator](#user-defined-operator)
+
+Tensor Data Structure
+====
+The basic data structure of mshadow is Tensor. The following is a simplified equivalent version of
+the declaration in [mashadow/tensor.h](../mshadow/tensor.h)
+```c++
+typedef unsigned index_t;
+template<int dimension>
+struct Shape {
+  index_t shape_[dimension];
+};
+template<typename Device, int dimension, typename DType = float>
+struct Tensor {
+  DType *dptr_;
+  Shape<dimension> shape_;
+  Stream<Device> stream_;
+  index_t stride_;
+};
+// this is how shape object declaration look like
+Shape<2> shape2;
+// this is how tensor object declaration look like
+// you can
+Tensor<cpu, 2> ts2;
+Tensor<gpu, 3, float> ts3;
+```
+``` Tensor<cpu,2>``` is a two dimensional tensor in host memory, while ```Tensor<gpu,3>``` is a three dimensional tensor in device memory.
+```Shape<k>``` gives the shape information of a k-dimensional tensor. The declarations use templates and
+can be specialized to tensors on a specific device and of a specific dimension. This is what a two dimensional tensor would look like:
+```c++
+struct Shape<2> {
+  index_t shape_[2];
+};
+struct Tensor<cpu, 2, float> {
+  float *dptr_;
+  Shape<2> shape_;
+  index_t stride_;
+};
+```
+* ``` Tensor<cpu, 2>``` contains ```dptr_```, which points to the space that backs up the tensor.
+* ```Shape<2>``` is a structure that stores shape information, the convention is the same as numpy.
+* ```stride_``` gives the number of cell spaces allocated in the smallest dimension (if we use numpy convention, the dimension corresponds to shape_[-1]).
+This is introduced when we introduce some padding cells in lowest dimension to make sure memory is aligned. ```stride_``` is automatically set during
+memory allocation of a tensor in mshadow.
+
+To understand the data structure, consider the following code:
+``` c++
+float data[9] = {0, 1, 2, 3, 4, 5, 6, 7, 8};
+Tensor<cpu, 2> ts;
+ts.dptr_ = data;
+ts.shape_ = mshadow::Shape2(3, 2);
+ts.stride_ = 3;
+// now: ts[0][0] == 0, ts[0][1] == 1 , ts[1][0] == 3, ts[1][1] == 4
+for (index_t i = 0; i < ts.size(0); ++i) {
+  for (index_t j = 0; j < ts.size(1); ++j) {
+    printf("ts[%u][%u]=%f\n", i, j, ts[i][j]);
+  }
+}
+```
+The result ts should be a 3 * 2 matrix, where data[2], data[5], data[8] are padding cells that are ignored. If you want a continuous memory, set ```stride_=shape_[1]```.
+
+NOTICE: We highly recommend use stream in ```gpu``` mode, there will be an error thrown out if no stream is set. Check [basic_stream.cu](basic_stream.cu) for more detail.
+
+Memory Allocation
+====
+An important design choice in mshadow was making the data structure ```Tensor``` a **whitebox**,
+it works so long as we set the space pointer ```dptr_``` corresponding ```shape_``` and ```stride_```:
+* For ```Tensor<cpu, k>``` ```dptr_``` must point to space created by ```new float[]``` or to some existing space such as the float array in the last example.
+* For ```Tensor<gpu, k>``` ```dptr_``` must point to space on the device created by ```cudaMallocPitch```.
+
+mshadow also provides an explicit memory allocation routine, as shown in following code:
+``` c++
+// create a 5 x 3 tensor on the device, and allocate space
+Tensor<gpu, 2> ts2(Shape2(5, 3));
+AllocSpace(&ts2);
+// allocate 5 x 3 x 2 tensor on the host, initialized by 0
+Tensor<cpu, 3> ts3 = NewTensor<cpu>(Shape3(5,3,2), 0.0f);
+// free space
+FreeSpace(&ts2); FreeSpace(&ts3);
+```
+All memory allocations in mshadow are **explicit**. There are **no** implicit memory allocations or de-allocations during any operations.
+This means ```Tensor<cpu, k>``` variable is more like a reference handle(pointer), instead of a object. If we assign a tensor to another variable, the two share the same content space.
+
+This also allows user to use mshadow in their existing project easily, simply give mshadow the pointer of the memory and you can get the benefit of all the mshadow expressions with zero cost:)
+
+We also have STL style container object called ```TensorContainer```, they behave exactly the same as Tensors, but the memory will be automatically freed during destruction.
+
+Elementwise Operations
+====
+All the operators(+, -, *, /, += etc.) in mshadow are element-wise. Consider the following SGD update code:
+```c++
+void UpdateSGD(Tensor<cpu, 2> weight, Tensor<cpu, 2> grad, float eta, float lambda) {
+  weight -= eta * (grad + lambda * weight);
+}
+```
+During compilation, this code will be translated to the following form:
+```c++
+void UpdateSGD(Tensor<cpu,2> weight, Tensor<cpu,2> grad, float eta, float lambda) {
+  for (index_t y = 0; y < weight.size(0); ++y) {
+    for (index_t x = 0; x < weight.size(1); ++x) {
+      weight[y][x] -= eta * (grad[y][x] + lambda * weight[y][x]);
+    }
+  }
+}
+```
+As we can see, *no memory allocation* happens in the translated code. For ```Tensor<gpu, k>```, the corresponding function will be translated into a CUDA kernel of the same spirit.
+Using an [Expression Template](exp-template), the translation happens at compile time. We can write simple lines of code while getting the full performance of the translated code.
+
+One code for both CPU and GPU
+====
+Since mshadow has an identical interface for ```Tensor<cpu, k>``` and ```Tensor<gpu, k>```, we can easily write code that works on both the CPU and GPU.
+For example, the following code compiles for both GPU and CPU Tensors.
+```c++
+template<typename xpu>
+void UpdateSGD(Tensor<xpu, 2> weight, const Tensor<xpu, 2> &grad,
+               float eta, float lambda) {
+  weight -= eta * (grad + lambda * weight);
+}
+```
+Matrix Multiplications
+====
+We also have a shorthand for dot product that will be translated to call standard packages such as MKL and CuBLAS.
+```c++
+template<typename xpu>
+void Backprop(Tensor<xpu, 2> gradin,
+              const Tensor<xpu, 2> &gradout,
+              const Tensor<xpu, 2> &netweight) {
+  gradin = dot(gradout, netweight.T());
+}
+```
+Again, the code can compile for both GPU and CPU Tensors.
+
+User Defined Operator
+====
+There are common cases when we want to define our own function. For example, assume we do not have an element-wise sigmoid transformation in mshadow.
+We simply use the following code to add ```sigmoid``` to mshadow
+```c++
+struct sigmoid {
+  MSHADOW_XINLINE static float Map(float a) {
+    return 1.0f / (1.0f + expf(-a));
+  }
+};
+template<typename xpu>
+void ExampleSigmoid(Tensor<xpu, 2> out, const Tensor<xpu, 2> &in) {
+  out = F<sigmoid>(in * 2.0f) + 1.0f;
+}
+```
+The translated code for CPU is given by
+```c++
+template<typename xpu>
+void ExampleSigmoid(Tensor<xpu, 2> out, const Tensor<xpu, 2> &in) {
+  for (index_t y = 0; y < out.size(0); ++y) {
+    for(index_t x = 0; x < out.size(1); ++x) {
+      out[y][x] = sigmoid::Map(in[y][x] * 2.0f) + 1.0f;
+    }
+  }
+}
+```
+Also note that the defined operation can be **composited into expressions**, not only we can write ```out = F<sigmoid>(in)```,
+we can also write ```out = F<sigmoid>+2.0``` or ```out = F<sigmoid>(F<sigmoid>(in))```.
+
+There will also be a translated CUDA kernel version that runs on the GPU. Check out [defop.cpp](defop.cpp) for a complete example.
+
+Complete Example
+====
+The following code is from [basic.cpp](basic.cpp). It illustrates basic usage of mshadow.
+
+```c++
+// header file to use mshadow
+#include "mshadow/tensor.h"
+// this namespace contains all data structures, functions
+using namespace mshadow;
+// this namespace contains all operator overloads
+using namespace mshadow::expr;
+
+int main(void) {
+  // intialize tensor engine before using tensor operation, needed for CuBLAS
+  InitTensorEngine<cpu>();
+  // assume we have a float space
+  float data[20];
+  // create a 2 x 5 x 2 tensor, from existing space
+  Tensor<cpu, 3> ts(data, Shape3(2,5,2));
+    // take first subscript of the tensor
+  Tensor<cpu, 2> mat = ts[0];
+  // Tensor object is only a handle, assignment means they have same data content
+  // we can specify content type of a Tensor, if not specified, it is float bydefault
+  Tensor<cpu, 2, float> mat2 = mat;
+
+  // shape of matrix, note size order is the same as numpy
+  printf("%u X %u matrix\n", mat.size(0), mat.size(1));
+
+  // initialize all element to zero
+  mat = 0.0f;
+  // assign some values
+  mat[0][1] = 1.0f; mat[1][0] = 2.0f;
+  // elementwise operations
+  mat += (mat + 10.0f) / 10.0f + 2.0f;
+
+  // print out matrix, note: mat2 and mat1 are handles(pointers)
+  for (index_t i = 0; i < mat.size(0); ++i) {
+    for (index_t j = 0; j < mat.size(1); ++j) {
+      printf("%.2f ", mat2[i][j]);
+    }
+    printf("\n");
+  }
+  // shutdown tensor enigne after usage
+  ShutdownTensorEngine<cpu>();
+  return 0;
+}
+```
+
diff --git a/3rdparty/mshadow/guide/basic.cpp b/3rdparty/mshadow/guide/basic.cpp
new file mode 100644
index 000000000000..8e90ac966053
--- /dev/null
+++ b/3rdparty/mshadow/guide/basic.cpp
@@ -0,0 +1,161 @@
+// header file to use mshadow
+#include "mshadow/tensor.h"
+// this namespace contains all data structures, functions
+using namespace mshadow;
+// this namespace contains all operator overloads
+using namespace mshadow::expr;
+
+int main(void) {
+  // intialize tensor engine before using tensor operation, needed for CuBLAS
+  InitTensorEngine<cpu>();
+  // assume we have a float space
+  float data[20];
+  // create a 2 x 5 x 2 tensor, from existing space
+  Tensor<cpu, 3> ts(data, Shape3(2,5,2));
+  // take first subscript of the tensor
+  Tensor<cpu, 2> mat = ts[0];
+  // Tensor object is only a handle, assignment means they have same data content
+  // we can specify content type of a Tensor, if not specified, it is float bydefault
+  Tensor<cpu, 2, float> mat2 = mat;
+  mat = Tensor<cpu, 1>(data, Shape1(10)).FlatTo2D();
+
+  // shaape of matrix, note size order is same as numpy
+  printf("%u X %u matrix\n", mat.size(0), mat.size(1));
+
+  // initialize all element to zero
+  mat = 0.0f;
+  // assign some values
+  mat[0][1] = 1.0f; mat[1][0] = 2.0f;
+  // elementwise operations
+  mat += (mat + 10.0f) / 10.0f + 2.0f;
+
+  // print out matrix, note: mat2 and mat1 are handles(pointers)
+  for (index_t i = 0; i < mat.size(0); ++i) {
+    for (index_t j = 0; j < mat.size(1); ++j) {
+      printf("%.2f ", mat2[i][j]);
+    }
+    printf("\n");
+  }
+
+  TensorContainer<cpu, 2> lhs(Shape2(2, 3)), rhs(Shape2(2, 3)), ret(Shape2(2,2));
+  lhs = 1.0;
+  rhs = 1.0;
+  ret = implicit_dot(lhs, rhs.T());
+  VectorDot(ret[0].Slice(0, 1), lhs[0], rhs[0]);
+  printf("vdot=%f\n", ret[0][0]);
+  int cnt = 0;
+  for (index_t i = 0; i < ret.size(0); ++i) {
+    for (index_t j = 0; j < ret.size(1); ++j) {
+      printf("%.2f ", ret[i][j]);
+    }
+    printf("\n");
+  }
+
+  printf("\n");
+
+  for (index_t i = 0; i < lhs.size(0); ++i) {
+    for (index_t j = 0; j < lhs.size(1); ++j) {
+      lhs[i][j] = cnt++;
+      printf("%.2f ", lhs[i][j]);
+    }
+    printf("\n");
+  }
+  printf("\n");
+  TensorContainer<cpu, 1> index(Shape1(2)), choosed(Shape1(2));
+  index[0] = 1; index[1] = 2;
+  choosed = mat_choose_row_element(lhs, index);
+  for (index_t i = 0; i < choosed.size(0); ++i) {
+    printf("%.2f ", choosed[i]);
+  }
+  printf("\n");
+
+  TensorContainer<cpu, 2> recover_lhs(Shape2(2, 3)), small_mat(Shape2(2, 3));
+  small_mat = -100.0f;
+  recover_lhs = mat_fill_row_element(small_mat, choosed, index);
+  for (index_t i = 0; i < recover_lhs.size(0); ++i) {
+    for (index_t j = 0; j < recover_lhs.size(1); ++j) {
+      printf("%.2f ", recover_lhs[i][j] - lhs[i][j]);
+    }
+  }
+  printf("\n");
+
+  rhs = one_hot_encode(index, 3);
+
+  for (index_t i = 0; i < lhs.size(0); ++i) {
+    for (index_t j = 0; j < lhs.size(1); ++j) {
+      printf("%.2f ", rhs[i][j]);
+    }
+    printf("\n");
+  }
+  printf("\n");
+  TensorContainer<cpu, 1> idx(Shape1(3));
+  idx[0] = 8;
+  idx[1] = 0;
+  idx[2] = 1;
+
+  TensorContainer<cpu, 2> weight(Shape2(10, 5));
+  TensorContainer<cpu, 2> embed(Shape2(3, 5));
+
+  for (index_t i = 0; i < weight.size(0); ++i) {
+    for (index_t j = 0; j < weight.size(1); ++j) {
+      weight[i][j] = i;
+    }
+  }
+  embed = take(idx, weight);
+  for (index_t i = 0; i < embed.size(0); ++i) {
+    for (index_t j = 0; j < embed.size(1); ++j) {
+      printf("%.2f ", embed[i][j]);
+    }
+    printf("\n");
+  }
+  printf("\n\n");
+  weight = take_grad(idx, embed, 10);
+  for (index_t i = 0; i < weight.size(0); ++i) {
+    for (index_t j = 0; j < weight.size(1); ++j) {
+      printf("%.2f ", weight[i][j]);
+    }
+    printf("\n");
+  }
+
+  printf("upsampling\n");
+  TensorContainer<cpu, 2> small(Shape2(2, 2));
+  small[0][0] = 1.0f;
+  small[0][1] = 2.0f;
+  small[1][0] = 3.0f;
+  small[1][1] = 4.0f;
+  TensorContainer<cpu, 2> large(Shape2(6, 6));
+  large = upsampling_nearest(small, 3);
+  for (index_t i = 0; i < large.size(0); ++i) {
+    for (index_t j = 0; j < large.size(1); ++j) {
+      printf("%.2f ", large[i][j]);
+    }
+    printf("\n");
+  }
+  small = pool<red::sum>(large, small.shape_, 3, 3, 3, 3);
+  // shutdown tensor enigne after usage
+  for (index_t i = 0; i < small.size(0); ++i) {
+    for (index_t j = 0; j < small.size(1); ++j) {
+      printf("%.2f ", small[i][j]);
+    }
+    printf("\n");
+  }
+
+  printf("mask\n");
+  TensorContainer<cpu, 2> mask_data(Shape2(6, 8));
+  TensorContainer<cpu, 2> mask_out(Shape2(6, 8));
+  TensorContainer<cpu, 1> mask_src(Shape1(6));
+
+  mask_data = 1.0f;
+  for (int i = 0; i < 6; ++i) {
+    mask_src[i] = static_cast<float>(i);
+  }
+  mask_out = mask(mask_src, mask_data);
+  for (index_t i = 0; i < mask_out.size(0); ++i) {
+    for (index_t j = 0; j < mask_out.size(1); ++j) {
+      printf("%.2f ", mask_out[i][j]);
+    }
+    printf("\n");
+  }
+  ShutdownTensorEngine<cpu>();
+  return 0;
+}
diff --git a/3rdparty/mshadow/guide/basic_stream.cu b/3rdparty/mshadow/guide/basic_stream.cu
new file mode 100644
index 000000000000..930fc54710f7
--- /dev/null
+++ b/3rdparty/mshadow/guide/basic_stream.cu
@@ -0,0 +1,35 @@
+// header file to use mshadow
+#include "mshadow/tensor.h"
+// this namespace contains all data structures, functions
+using namespace mshadow;
+// this namespace contains all operator overloads
+using namespace mshadow::expr;
+
+int main(void) {
+  // intialize tensor engine before using tensor operation, needed for CuBLAS
+  InitTensorEngine<gpu>();
+  // create a 2 x 5 tensor, from existing space
+  Stream<gpu> *sm1 = NewStream<gpu>();
+  Stream<gpu> *sm2 = NewStream<gpu>();
+  Tensor<gpu, 2, float> ts1 = NewTensor<gpu, float>(Shape2(2, 5), 0.0f, sm1);
+  Tensor<gpu, 2, float> ts2 = NewTensor<gpu, float>(Shape2(2, 5), 0.0f, sm2);
+  ts1 = 1; // Should use stream 0.
+  ts2 = 2; // Should use stream 1. Can run in parallel with stream 0.
+  Tensor<gpu, 2> res = NewTensor<gpu, float>(Shape2(2, 2), 0.0f);
+  res.stream_ = NewStream<gpu>();
+  res = dot(ts1, ts2.T()); //Should use stream 2.
+
+  Tensor<cpu, 2> cpu_res = NewTensor<cpu, float>(Shape2(2, 2), 0.0f);
+  Copy(cpu_res, res); // default stream, should be 0.
+  for (index_t i = 0; i < cpu_res.size(0); ++i){
+    for (index_t j = 0; j < cpu_res.size(1); ++j){
+      printf("%.2f ", cpu_res[i][j]);
+    }
+    printf("\n");
+  }
+  // shutdown tensor enigne after usage
+  DeleteStream(sm1);
+  DeleteStream(sm2);
+  ShutdownTensorEngine<gpu>();
+  return 0;
+}
diff --git a/3rdparty/mshadow/guide/config.mk b/3rdparty/mshadow/guide/config.mk
new file mode 100644
index 000000000000..26431bb06b3f
--- /dev/null
+++ b/3rdparty/mshadow/guide/config.mk
@@ -0,0 +1,39 @@
+#---------------------------------------------------------------------------------------
+#  mshadow: the configuration compile script
+#
+#  This is configuration script that you can use to compile mshadow
+#  Usage:
+#
+#  include config.mk in your Makefile, or directly include the definition of variables
+#  include mshadow.mk after the variables are set
+#
+#  Add MSHADOW_CFLAGS to the compile flags
+#  Add MSHADOW_LDFLAGS to the linker flags
+#  Add MSHADOW_NVCCFLAGS to the nvcc compile flags
+#----------------------------------------------------------------------------------------
+
+# whether use CUDA during compile
+USE_CUDA = 0
+
+# add the path to CUDA libary to link and compile flag
+# if you have already add them to enviroment variable, leave it as NONE
+USE_CUDA_PATH = NONE
+
+#
+# choose the version of blas you want to use
+# can be: mkl, blas, atlas, openblas, apple
+USE_BLAS = blas
+#
+# add path to intel library, you may need it
+# for MKL, if you did not add the path to enviroment variable
+#
+USE_INTEL_PATH = NONE
+
+# whether compile with parameter server
+USE_DIST_PS = 0
+PS_PATH = NONE
+PS_THIRD_PATH = NONE
+
+# whether compile with rabit allreduce
+USE_RABIT_PS = 0
+RABIT_PATH = NONE
diff --git a/3rdparty/mshadow/guide/defop.cpp b/3rdparty/mshadow/guide/defop.cpp
new file mode 100644
index 000000000000..8aa1b5c8f371
--- /dev/null
+++ b/3rdparty/mshadow/guide/defop.cpp
@@ -0,0 +1,49 @@
+#include <cmath>
+// header file to use mshadow
+#include "mshadow/tensor.h"
+// this namespace contains all data structures, functions
+using namespace mshadow;
+// this namespace contains all operator overloads
+using namespace mshadow::expr;
+
+// user defined unary operator addone
+struct addone {
+  // map can be template function
+  template<typename DType>
+  MSHADOW_XINLINE static DType Map(DType a) {
+    return  a + static_cast<DType>(1);
+  }
+};
+// user defined binary operator max of two
+struct maxoftwo {
+  // map can also be normal functions,
+  // however, this can only be applied to float tensor
+  MSHADOW_XINLINE static float Map(float a, float b) {
+    if(a > b) return a;
+    else return b;
+  }
+};
+
+int main(void) {
+  // intialize tensor engine before using tensor operation, needed for CuBLAS
+  InitTensorEngine<cpu>();
+  // take first subscript of the tensor
+  Stream<cpu> *stream_ = NewStream<cpu>(0);
+  Tensor<cpu,2, float> mat = NewTensor<cpu>(Shape2(2,3), 0.0f, stream_);
+  Tensor<cpu,2, float> mat2= NewTensor<cpu>(Shape2(2,3), 0.0f, stream_);
+
+  mat[0][0] = -2.0f;
+  mat = F<maxoftwo>(F<addone>(mat) + 0.5f, mat2);
+
+  for (index_t i = 0; i < mat.size(0); ++i) {
+    for (index_t j = 0; j < mat.size(1); ++j) {
+      printf("%.2f ", mat[i][j]);
+    }
+    printf("\n");
+  }
+  FreeSpace(&mat); FreeSpace(&mat2);
+  DeleteStream(stream_);
+  // shutdown tensor enigne after usage
+  ShutdownTensorEngine<cpu>();
+  return 0;
+}
diff --git a/3rdparty/mshadow/guide/exp-template/.gitignore b/3rdparty/mshadow/guide/exp-template/.gitignore
new file mode 100644
index 000000000000..fc070ad5bd7e
--- /dev/null
+++ b/3rdparty/mshadow/guide/exp-template/.gitignore
@@ -0,0 +1 @@
+exp_*
\ No newline at end of file
diff --git a/3rdparty/mshadow/guide/exp-template/Makefile b/3rdparty/mshadow/guide/exp-template/Makefile
new file mode 100644
index 000000000000..90639896f751
--- /dev/null
+++ b/3rdparty/mshadow/guide/exp-template/Makefile
@@ -0,0 +1,20 @@
+# set LD_LIBRARY_PATH
+export CC  = gcc
+export CXX = g++
+export CFLAGS = -Wall -O3 
+# specify tensor path
+BIN = exp_lazy exp_template exp_template_op
+
+.PHONY: clean all
+
+all: $(BIN) 
+
+exp_lazy: exp_lazy.cpp
+exp_template: exp_template.cpp
+exp_template_op: exp_template_op.cpp
+
+$(BIN) :
+	$(CXX) $(CFLAGS) -o $@ $(filter %.cpp %.o %.c, $^)
+
+clean:
+	rm -rf $(BIN) *~
diff --git a/3rdparty/mshadow/guide/exp-template/README.md b/3rdparty/mshadow/guide/exp-template/README.md
new file mode 100644
index 000000000000..46e0019e9583
--- /dev/null
+++ b/3rdparty/mshadow/guide/exp-template/README.md
@@ -0,0 +1,340 @@
+Expression Template Tutorial
+====
+This page explains how mshadow works. The main trick behind mshadow is called [Expression Template](http://en.wikipedia.org/wiki/Expression_templates).
+We will explain how it will affect the performance of compiled code. Expression template is the major trick behind the C++ matrix libraries such as Eigen, GSL, boost.uBLAS.
+
+How to write efficient machine learning code
+====
+Before we start, let us think of the question above. Assume we want to write down the update rule (for illustration purpose here, while typical update would be `weight += - eta * (grad + lambda * weight)` )
+```c++
+weight =  - eta * (grad + lambda * weight);
+```
+Where weight and grad are vectors of length ```n```. When you choose C++ as your programming language,
+I guess the major concern is efficiency. There is one principle that is important and used in most C/C++ programs:
+* Pre-allocate necessary memory, **no temporal memory allocation** during running.
+
+An example code is like
+```c++
+void UpdateWeight (const float *grad, float eta, float lambda,
+                   int n, float *weight) {
+  for (int i = 0; i < n; ++i) {
+    weight[i] =  - eta * (grad[i] + lambda * weight[i]);
+  }
+}
+``` 
+The function takes the pre-allocated space grad, and weight, and run the calculation. Writing these functions are simple,
+however, it can be annoying when we write them repeatedly. So the question is, can we write as follows, and get same performance as previous code?
+```c++
+void UpdateWeight (const Vec& grad, float eta, float lambda, Vec& weight) {
+  weight = -eta * (grad + lambda * weight);
+} 
+```
+The answer is yes, but not by the most obvious solution.
+
+A Naive Bad Solution
+====
+Let us first take a look at a most straight forward solution: operator overloading.
+```c++
+// Naive solution for vector operation overloading 
+struct Vec {
+  int len;
+  float* dptr;
+  Vec(int len) : len(len) { 
+    dptr = new float[len];
+  }
+  Vec(const Vec& src) : len(src.len) {
+    dptr = new float[len];
+    memcpy(dptr, src.dptr, sizeof(float)*len ); 
+  }
+  ~Vec(void) {
+    delete [] dptr;
+  }
+};
+
+inline Vec operator+(const Vec &lhs, const Vec &rhs) {
+  Vec res(lhs.len);
+  for (int i = 0; i < lhs.len; ++i) {
+    res.dptr[i] = lhs.dptr[i] + rhs.dptr[i];
+  } 
+  return res;
+} 
+```
+If we add more operators overloading in the same style, we can get what we want, and write equations instead of loop.
+However, this kind of approach is inefficient, because temporal memory is allocated and de-allocated during each operation, while we could have done better.
+
+An alternative, more effective way is only overload operator+=, operator-=, which can be implemented without temporal memory allocation. But this limits the equations we can write.
+
+We will discuss why we still need expression template although C++11 provides move assignment operator and rvalue reference at the end of this tutorial. 
+
+Lazy Evaluation
+====
+Let us think why we need temporal memory allocation when doing operator+. This is because we *do not know* the target that will be assigned to in operator+,
+otherwise we could have directly storing into target memory instead of temporal memory. 
+
+What if we can know the target? The following code ([exp_lazy.cpp](exp_lazy.cpp)) achieves this. 
+```c++
+// Example Lazy evaluation code
+// for simplicity, we use struct and make all members public
+#include <cstdio>
+struct Vec;
+// expression structure holds the expression
+struct BinaryAddExp {
+  const Vec &lhs;
+  const Vec &rhs;
+  BinaryAddExp(const Vec &lhs, const Vec &rhs)
+  : lhs(lhs), rhs(rhs) {}
+};
+// no constructor and destructor to allocate and de-allocate memory,
+//  allocation done by user
+struct Vec {
+  int len;
+  float* dptr;
+  Vec(void) {}
+  Vec(float *dptr, int len)
+      : len(len), dptr(dptr) {}
+  // here is where evaluation happens
+  inline Vec &operator=(const BinaryAddExp &src) {
+    for (int i = 0; i < len; ++i) {
+      dptr[i] = src.lhs.dptr[i] + src.rhs.dptr[i];
+    }
+    return *this;
+  }
+};
+// no evaluation happens here
+inline BinaryAddExp operator+(const Vec &lhs, const Vec &rhs) {
+  return BinaryAddExp(lhs, rhs);
+}
+
+const int n = 3;
+int main(void) {
+  float sa[n] = {1, 2, 3};
+  float sb[n] = {2, 3, 4};
+  float sc[n] = {3, 4, 5};
+  Vec A(sa, n), B(sb, n), C(sc, n);
+  // run expression
+  A = B + C;
+  for (int i = 0; i < n; ++i) {
+    printf("%d:%f==%f+%f\n", i, A.dptr[i], B.dptr[i], C.dptr[i]);
+  }
+  return 0;
+}
+```
+The idea is that we do not actually do computation in operator+, but only return a expression structure (like abstract syntax tree),
+and when we overload operator=, we see the target, as well as all the operands, and we can run computation without introducing extra memory!
+Similarly, we can define a DotExp and lazily evaluate at operator=, and redirect matrix(vector) multiplications to BLAS.
+
+
+More Lengthy Expressions and Expression Template
+====
+By using lazy evaluation, we are cool by avoiding temporal memory allocations. But the ability of the code is limited:
+* We can only write ```A=B+C```, but not more lengthy expressions.
+* When we add more expression, we need to write more operator= to evaluate each equations.
+
+Here is where the magic of template programming comes to rescue. The following code ([exp_template.cpp](exp_template.cpp)),
+which is a bit more lengthy, also allows you to write lengthy equations.
+```c++
+// Example code, expression template, and more length equations
+// for simplicity, we use struct and make all members public
+#include <cstdio>
+
+// this is expression, all expressions must inheritate it,
+//  and put their type in subtype
+template<typename SubType>
+struct Exp {
+  // returns const reference of the actual type of this expression
+  inline const SubType& self(void) const {
+    return *static_cast<const SubType*>(this);
+  }
+};
+
+// binary add expression
+// note how it is inheritates from Exp
+// and put its own type into the template argument
+template<typename TLhs, typename TRhs>
+struct BinaryAddExp: public Exp<BinaryAddExp<TLhs, TRhs> > {
+  const TLhs &lhs;
+  const TRhs &rhs;
+  BinaryAddExp(const TLhs& lhs, const TRhs& rhs)
+      : lhs(lhs), rhs(rhs) {}
+  // evaluation function, evaluate this expression at position i
+  inline float Eval(int i) const {
+    return lhs.Eval(i) + rhs.Eval(i);
+  }
+};
+// no constructor and destructor to allocate
+// and de-allocate memory, allocation done by user
+struct Vec: public Exp<Vec> {
+  int len;
+  float* dptr;
+  Vec(void) {}
+  Vec(float *dptr, int len)
+      :len(len), dptr(dptr) {}
+  // here is where evaluation happens
+  template<typename EType>
+  inline Vec& operator= (const Exp<EType>& src_) {
+    const EType &src = src_.self();
+    for (int i = 0; i < len; ++i) {
+      dptr[i] = src.Eval(i);
+    }
+    return *this;
+  }
+  // evaluation function, evaluate this expression at position i
+  inline float Eval(int i) const {
+    return dptr[i];
+  }
+};
+// template add, works for any expressions
+template<typename TLhs, typename TRhs>
+inline BinaryAddExp<TLhs, TRhs>
+operator+(const Exp<TLhs> &lhs, const Exp<TRhs> &rhs) {
+  return BinaryAddExp<TLhs, TRhs>(lhs.self(), rhs.self());
+}
+
+const int n = 3;
+int main(void) {
+  float sa[n] = {1, 2, 3};
+  float sb[n] = {2, 3, 4};
+  float sc[n] = {3, 4, 5};
+  Vec A(sa, n), B(sb, n), C(sc, n);
+  // run expression, this expression is longer:)
+  A = B + C + C;
+  for (int i = 0; i < n; ++i) {
+    printf("%d:%f == %f + %f + %f\n", i,
+           A.dptr[i], B.dptr[i],
+           C.dptr[i], C.dptr[i]);
+  }
+  return 0;
+}
+```
+The key idea of the code is the template ```Exp<SubType>``` takes type of its derived class as template argument, so it can convert itself to
+the SubType via ```self()```.  BinaryAddExp now is a template class that can composite expressions together, like a template version of Composite pattern.
+The evaluation is done through function Eval, which is done in a recursive way in BinaryAddExp.
+* Due to inlining, the function calls of ```src.Eval(i)``` in ```operator=``` will be compiled into ```B.dptr[i] + C.dptr[i] + C.dptr[i]``` in compile time.
+* We can write equations for element-wise operations with same efficiency as if we write a loop  
+
+Make it more flexible
+====
+As we can find in the previous example, template programming is a powerful to make things flexible in compile time, our final example,
+which is closer to mshadow, allows user customized binary operators ([exp_template_op.cpp](exp_template_op.cpp)). 
+```c++
+// Example code, expression template
+// with binary operator definition and extension
+// for simplicity, we use struct and make all members public
+#include <cstdio>
+
+// this is expression, all expressions must inheritate it,
+// and put their type in subtype
+template<typename SubType>
+struct Exp{
+  // returns const reference of the actual type of this expression
+  inline const SubType& self(void) const {
+    return *static_cast<const SubType*>(this);
+  }
+};
+
+// binary operators
+struct mul{
+  inline static float Map(float a, float b) {
+    return a * b;
+  }
+};
+
+// binary add expression
+// note how it is inheritates from Exp
+// and put its own type into the template argument
+template<typename OP, typename TLhs, typename TRhs>
+struct BinaryMapExp: public Exp<BinaryMapExp<OP, TLhs, TRhs> >{
+  const TLhs& lhs;
+  const TRhs& rhs;
+  BinaryMapExp(const TLhs& lhs, const TRhs& rhs)
+      :lhs(lhs), rhs(rhs) {}
+  // evaluation function, evaluate this expression at position i
+  inline float Eval(int i) const {
+    return OP::Map(lhs.Eval(i), rhs.Eval(i));
+  }
+};
+// no constructor and destructor to allocate and de-allocate memory
+// allocation done by user
+struct Vec: public Exp<Vec>{
+  int len;
+  float* dptr;
+  Vec(void) {}
+  Vec(float *dptr, int len)
+      : len(len), dptr(dptr) {}
+  // here is where evaluation happens
+  template<typename EType>
+  inline Vec& operator=(const Exp<EType>& src_) {
+    const EType &src = src_.self();
+    for (int i = 0; i < len; ++i) {
+      dptr[i] = src.Eval(i);
+    }
+    return *this;
+  }
+  // evaluation function, evaluate this expression at position i
+  inline float Eval(int i) const {
+    return dptr[i];
+  }
+};
+// template binary operation, works for any expressions
+template<typename OP, typename TLhs, typename TRhs>
+inline BinaryMapExp<OP, TLhs, TRhs>
+F(const Exp<TLhs>& lhs, const Exp<TRhs>& rhs) {
+  return BinaryMapExp<OP, TLhs, TRhs>(lhs.self(), rhs.self());
+}
+
+template<typename TLhs, typename TRhs>
+inline BinaryMapExp<mul, TLhs, TRhs>
+operator*(const Exp<TLhs>& lhs, const Exp<TRhs>& rhs) {
+  return F<mul>(lhs, rhs);
+}
+
+// user defined operation
+struct maximum{
+  inline static float Map(float a, float b) {
+    return a > b ? a : b;
+  }
+};
+
+const int n = 3;
+int main(void) {
+  float sa[n] = {1, 2, 3};
+  float sb[n] = {2, 3, 4};
+  float sc[n] = {3, 4, 5};
+  Vec A(sa, n), B(sb, n), C(sc, n);
+  // run expression, this expression is longer:)
+  A = B * F<maximum>(C, B);
+  for (int i = 0; i < n; ++i) {
+    printf("%d:%f == %f * max(%f, %f)\n",
+           i, A.dptr[i], B.dptr[i], C.dptr[i], B.dptr[i]);
+  }
+  return 0;
+}
+```
+
+Summary
+=====
+Up to this point, you should have understand basic ideas how it works:
+* Lazy evaluation, to allow us see all the operands and target
+* Template composition and recursive evaluation, to allows us evaluate arbitrary composite expressions for element-wise operations.
+* Due to template and inlining, writing expressions are as efficient as if we directly write a for loop to implement the update rule:)
+
+So write expressions when you write machine learning codes, and focus your energy on the algorithm part that matters.
+
+The Expression Template in MShadow
+=====
+Expression template in mshadow use the same key points as we introduced in the tutorial, with some minor differences:
+* We separate evaluation code from expression construction and composition code.  
+    - Instead of putting Eval in Exp class. A Plan class is created from expression, and used to evaluate the result. 
+    - This allows us to put less variables in Plan, for example, we do not need array length when we evaluate a data.
+    - One important reason is CUDA kernel cannot take class with const references 
+    - This design choice is debatable, but we find it is useful so far.
+* Lazy support for complex expressions such as matrix dot product
+    - Besides element-wise expressions, we also want to support sugars such as ```A = dot(B.T(), C)```,  again, lazy evaluation is used and no extra memory is allocated.
+* Type checking and array length checking.
+
+Notes
+====
+* Expression Template and C++11: in C++11, move constructor can be used to save repetitive allocation memory, which removes some need to expression template. However, the space still needs to be allocated at least once. 
+   - This only removes the need of expression template then expression generate space, say dst = A+B+C, dst does not contain space allocated before assignment.
+   - If we want to keep the syntax that everything is pre-allocated, and expression executes without memory allocation (which is what we did in mshadow), we still need expression template.
+
diff --git a/3rdparty/mshadow/guide/exp-template/exp_lazy.cpp b/3rdparty/mshadow/guide/exp-template/exp_lazy.cpp
new file mode 100644
index 000000000000..4e6a6b14b9de
--- /dev/null
+++ b/3rdparty/mshadow/guide/exp-template/exp_lazy.cpp
@@ -0,0 +1,45 @@
+// Example Lazy evaluation code
+// for simplicity, we use struct and make all members public
+#include <cstdio>
+struct Vec;
+// expression structure holds the expression
+struct BinaryAddExp {
+  const Vec &lhs;
+  const Vec &rhs;
+  BinaryAddExp(const Vec &lhs, const Vec &rhs)
+  : lhs(lhs), rhs(rhs) {}
+};
+// no constructor and destructor to allocate and de-allocate memory,
+//  allocation done by user
+struct Vec {
+  int len;
+  float* dptr;
+  Vec(void) {}
+  Vec(float *dptr, int len)
+      : len(len), dptr(dptr) {}
+  // here is where evaluation happens
+  inline Vec &operator=(const BinaryAddExp &src) {
+    for (int i = 0; i < len; ++i) {
+      dptr[i] = src.lhs.dptr[i] + src.rhs.dptr[i];
+    }
+    return *this;
+  }
+};
+// no evaluation happens here
+inline BinaryAddExp operator+(const Vec &lhs, const Vec &rhs) {
+  return BinaryAddExp(lhs, rhs);
+}
+
+const int n = 3;
+int main(void) {
+  float sa[n] = {1, 2, 3};
+  float sb[n] = {2, 3, 4};
+  float sc[n] = {3, 4, 5};
+  Vec A(sa, n), B(sb, n), C(sc, n);
+  // run expression
+  A = B + C;
+  for (int i = 0; i < n; ++i) {
+    printf("%d:%f==%f+%f\n", i, A.dptr[i], B.dptr[i], C.dptr[i]);
+  }
+  return 0;
+}
diff --git a/3rdparty/mshadow/guide/exp-template/exp_template.cpp b/3rdparty/mshadow/guide/exp-template/exp_template.cpp
new file mode 100644
index 000000000000..556b10316a3b
--- /dev/null
+++ b/3rdparty/mshadow/guide/exp-template/exp_template.cpp
@@ -0,0 +1,72 @@
+// Example code, expression template, and more length equations
+// for simplicity, we use struct and make all members public
+#include <cstdio>
+
+// this is expression, all expressions must inheritate it,
+//  and put their type in subtype
+template<typename SubType>
+struct Exp {
+  // returns const reference of the actual type of this expression
+  inline const SubType& self(void) const {
+    return *static_cast<const SubType*>(this);
+  }
+};
+
+// binary add expression
+// note how it is inheritates from Exp
+// and put its own type into the template argument
+template<typename TLhs, typename TRhs>
+struct BinaryAddExp: public Exp<BinaryAddExp<TLhs, TRhs> > {
+  const TLhs &lhs;
+  const TRhs &rhs;
+  BinaryAddExp(const TLhs& lhs, const TRhs& rhs)
+      : lhs(lhs), rhs(rhs) {}
+  // evaluation function, evaluate this expression at position i
+  inline float Eval(int i) const {
+    return lhs.Eval(i) + rhs.Eval(i);
+  }
+};
+// no constructor and destructor to allocate
+// and de-allocate memory, allocation done by user
+struct Vec: public Exp<Vec> {
+  int len;
+  float* dptr;
+  Vec(void) {}
+  Vec(float *dptr, int len)
+      :len(len), dptr(dptr) {}
+  // here is where evaluation happens
+  template<typename EType>
+  inline Vec& operator= (const Exp<EType>& src_) {
+    const EType &src = src_.self();
+    for (int i = 0; i < len; ++i) {
+      dptr[i] = src.Eval(i);
+    }
+    return *this;
+  }
+  // evaluation function, evaluate this expression at position i
+  inline float Eval(int i) const {
+    return dptr[i];
+  }
+};
+// template add, works for any expressions
+template<typename TLhs, typename TRhs>
+inline BinaryAddExp<TLhs, TRhs>
+operator+(const Exp<TLhs> &lhs, const Exp<TRhs> &rhs) {
+  return BinaryAddExp<TLhs, TRhs>(lhs.self(), rhs.self());
+}
+
+const int n = 3;
+int main(void) {
+  float sa[n] = {1, 2, 3};
+  float sb[n] = {2, 3, 4};
+  float sc[n] = {3, 4, 5};
+  Vec A(sa, n), B(sb, n), C(sc, n);
+  // run expression, this expression is longer:)
+  A = B + C + C;
+  for (int i = 0; i < n; ++i) {
+    printf("%d:%f == %f + %f + %f\n", i,
+           A.dptr[i], B.dptr[i],
+           C.dptr[i], C.dptr[i]);
+  }
+  return 0;
+}
diff --git a/3rdparty/mshadow/guide/exp-template/exp_template_op.cpp b/3rdparty/mshadow/guide/exp-template/exp_template_op.cpp
new file mode 100644
index 000000000000..ecc1be804f98
--- /dev/null
+++ b/3rdparty/mshadow/guide/exp-template/exp_template_op.cpp
@@ -0,0 +1,92 @@
+// Example code, expression template
+// with binary operator definition and extension
+// for simplicity, we use struct and make all members public
+#include <cstdio>
+
+// this is expression, all expressions must inheritate it,
+// and put their type in subtype
+template<typename SubType>
+struct Exp{
+  // returns const reference of the actual type of this expression
+  inline const SubType& self(void) const {
+    return *static_cast<const SubType*>(this);
+  }
+};
+
+// binary operators
+struct mul{
+  inline static float Map(float a, float b) {
+    return a * b;
+  }
+};
+
+// binary add expression
+// note how it is inheritates from Exp
+// and put its own type into the template argument
+template<typename OP, typename TLhs, typename TRhs>
+struct BinaryMapExp: public Exp<BinaryMapExp<OP, TLhs, TRhs> >{
+  const TLhs& lhs;
+  const TRhs& rhs;
+  BinaryMapExp(const TLhs& lhs, const TRhs& rhs)
+      :lhs(lhs), rhs(rhs) {}
+  // evaluation function, evaluate this expression at position i
+  inline float Eval(int i) const {
+    return OP::Map(lhs.Eval(i), rhs.Eval(i));
+  }
+};
+// no constructor and destructor to allocate and de-allocate memory
+// allocation done by user
+struct Vec: public Exp<Vec>{
+  int len;
+  float* dptr;
+  Vec(void) {}
+  Vec(float *dptr, int len)
+      : len(len), dptr(dptr) {}
+  // here is where evaluation happens
+  template<typename EType>
+  inline Vec& operator=(const Exp<EType>& src_) {
+    const EType &src = src_.self();
+    for (int i = 0; i < len; ++i) {
+      dptr[i] = src.Eval(i);
+    }
+    return *this;
+  }
+  // evaluation function, evaluate this expression at position i
+  inline float Eval(int i) const {
+    return dptr[i];
+  }
+};
+// template binary operation, works for any expressions
+template<typename OP, typename TLhs, typename TRhs>
+inline BinaryMapExp<OP, TLhs, TRhs>
+F(const Exp<TLhs>& lhs, const Exp<TRhs>& rhs) {
+  return BinaryMapExp<OP, TLhs, TRhs>(lhs.self(), rhs.self());
+}
+
+template<typename TLhs, typename TRhs>
+inline BinaryMapExp<mul, TLhs, TRhs>
+operator*(const Exp<TLhs>& lhs, const Exp<TRhs>& rhs) {
+  return F<mul>(lhs, rhs);
+}
+
+// user defined operation
+struct maximum{
+  inline static float Map(float a, float b) {
+    return a > b ? a : b;
+  }
+};
+
+const int n = 3;
+int main(void) {
+  float sa[n] = {1, 2, 3};
+  float sb[n] = {2, 3, 4};
+  float sc[n] = {3, 4, 5};
+  Vec A(sa, n), B(sb, n), C(sc, n);
+  // run expression, this expression is longer:)
+  A = B * F<maximum>(C, B);
+  for (int i = 0; i < n; ++i) {
+    printf("%d:%f == %f * max(%f, %f)\n",
+           i, A.dptr[i], B.dptr[i], C.dptr[i], B.dptr[i]);
+  }
+  return 0;
+}
diff --git a/3rdparty/mshadow/guide/mshadow-ps/.gitignore b/3rdparty/mshadow/guide/mshadow-ps/.gitignore
new file mode 100644
index 000000000000..351b5b4d3dfc
--- /dev/null
+++ b/3rdparty/mshadow/guide/mshadow-ps/.gitignore
@@ -0,0 +1,4 @@
+log
+*cpu
+*gpu
+core*
diff --git a/3rdparty/mshadow/guide/mshadow-ps/2-levels.png b/3rdparty/mshadow/guide/mshadow-ps/2-levels.png
new file mode 100644
index 0000000000000000000000000000000000000000..4f8a0e8081c485be2d8f45debca88bdaeaf5da5a
GIT binary patch
literal 59413
zcmcG$by!qg`!CKgz)%AMN;eYH(hY)wfOI!VOLxNzrId7chkyu@l7hq_B_&crgM=`2
zpY8KL@AsVF@4C*PXEt*&vsi2Gwbs7#bKf^n>Z%G*TuNLtG&HE<%jcSCXz1=}XdrKF
zETHALo)<0f3*@1xAd6NzO1%So!Et?Q;DLtr=-uBpsBc!L0O&AnudVN?ukuRF(#46}
z!ph~fHMg&mE6^JaO~O|U_~>NqX+iJn<ml`n<}1ngPY*HR^WSD3M*4rccsfWj>Z_>J
z%elB)(+hG7aPu-s;nLI7OSoIvh-p5T|93d>O_I^h)6-RqhsVdqhuepr+r{0M=c%Zu
zC=V|m4<8>F(1Xjv&)L($m&@6M=|7YFcb@0g9+vL*uAcTT&h&rhwRr8~<tfR?`1eNt
z^ZL&^J?(A&_nn+Q{@oU^L7u;V;d#o<%kw{T14AYLwu;HQIJvr8dw2lzO9@K+)ARq+
z_P_7*pZ@B0E}kyHDY)BPDmr^wy90wgE&i@fitpdi|MwpM|GCuM?X7`b|1<jOzoY-p
zwtxFe@ciBQ|8WriIpu#^fn%1!mEifGM<#_^x5l@Kh9-li`23l+FK90pGw1QQ(}C*)
z*Yn;#1cbB#`YpXCaxkQS55r6J$1RWbL$Q2t;4%rwb=Ob_Z0EOMhKEM)uXE>p?mJuO
z7J(~sZqYw~%%!Rgc>O;8p!d;K=iJJms1N@C^1@n`$;YZvu2Nc+QKn`~oxE`?Trc{u
zmNdCj_tt}+he}8=#F(9`bU-pku+s05J3SBorLU;plXTq`C(Wil_-|Kwo|5S&Pg9db
z#@z_QCFJcCFMU&txw$^7E3aLyu`xv~W`h~PM8C2E+ZR6Bm)0jp3*|`*g%srtgEmS9
zGoMJZvLc2QN>U>w(>*G!q2J){p}*80y-M9VqQ<7+ggW_}sb?Lym1MG}lpnE#&`&aU
z2+(dpNxXOn`aF2ZHa8eUq*j{*tiJkIR`<20$$nIvx)n`GCoxKv)?LQfSS#gANl$$D
zR>H5tK9P1HGKv_|77q3U?I%%{$dW(|kus+6NNUQ+BT_e>vGPXs1^sW8vd|ZAw%tsJ
z<Rws_wM~^=s0mUKN`uO#+^>=Qv&Q>_TD6a!4!(DmqNq<uPkqU+EM;2To-%VmjeS{H
zMnu?(63Mq|C%)9+PRUG&|6!>bCrRek`#cJ)vmxW~)o_}s^eZtWOY{L^vNxeiy@Xal
zBBgT0*j$m9f1A)I%RCgv=E+0cV)=O5Td4s$q27z$kt6lGry?J$Xj-7mlcnlkE#Loe
zVR9?b)?F3hp`vuYJb58Picr+hWyx{<Y9Jp+c`ArPf$Zr(y(=;Xm&D=K;eqSc%sh0L
zBI(S(v8dH`Lm8dP)xE!72<JdK@umceDjcw{>E--Pit#1UQ<yf2v?Etra`K*r0CZR7
zCc~hbBBG8*&}Y8eA$!1L*XhQl^QiI&rw3&|62+2bmrKMXkH0-xu4a*CUq&H#{GFd{
zGsE(IaKosWLsue`g3X`K>w^lPCBO2WUP9@;>(f~?BU3p1+HZ7G_D1ONvpqf5PT)(F
zei-+|TKlNq!*ET5BM~g44W{+Ji*<8seVVmk8mxJWtF!%ifnI!mHV%q<K9I$-OXnqv
zoT@wnj|yGTiN-Q;6K8a*H`-mnx3n8sMy;j>eviVd*$AJKzy02amScsvDIoHM>REp(
zL$789e;RtB!zQa~hX3zMh0{OFUA)(O4d>ZTO><Wa9KW+B2FL9@Rq}Z{FTU<x737;f
zldg1dhERF~B%o^Q9IK2_FUUv;CWidI+Q1OmC^0lVXk%1Qi8(H>U<T=T0s>2!yBzf}
zr@p0tKPD|}@5OPYZw|o(sWUsnJYAf|9wUMgPR)x=IK?jE*5*?kiY*)&w6DNr7zB68
zXuE~7q=kD?ELBVpRQ;;Q&~Wq^x0SW&YPrSdTN!<Q*B(p-YWG2gh_|%<6|nRsuV*WJ
z5V}D1mcy@jcvLR!N85_5xt=A~^c-qL&gf-e299K+v0)|h#2J1KOLzL%#_f3$4Hd7r
z+hF#B+Dx3ix3v=ux*2Z$6bgIw!`u;6&Mk$oiRH2zULv*y&q<YtLb<o3Ho$`|sx7Q0
zeBuyNz^Z?FWcpipV?c~;xDZ@p`LAqp?t$NQR})Xdv_4g8TdI5GScW`YQ7}INp1qOp
zN(9+2?XntcSKg3|mA80Z7vFf~HM~i}PGZ8l&?!Ba^f0NyJlSrnzW1bvX`1Q=vM_M?
zS%%Q>46G?$+aR8lQ~T|G8X#Q?zTTgCUO3_*eRpBG(lqbPQT_Rc(u*(xiuU|~YLLLT
ztgZk?iqURrtZ2q~L5lFIK#uIJ2+lYx8??8ILC9k?c{cANeKo|I@2DmJ3vqZdsYd3#
z<hRAbLRy@dzp}74J95sR`@n8I;^wg>fz4wj>X+1Zjs)SFI&KlRFV>!<{EqFLm9Ok9
z)m(HNpve{!XG_;=VgD^SYpfMk3rh-0IV`7l8L!;asU!HIVOtdSW)P12Qoq_T{!U95
zMVmt77o4vmanx5#Aa#NKx|JVddf?{6z7MR4hqLMDYhWLiNAd^KWY}Tc60P>Ukx!uV
zPjS@WPYFsf#kqLJ^`~Gh;BIH#6AL-D?UWVYpLgARDd{3iZcxKKWJ1e<$EzszbN{G-
z+xE+)nI4=dFU_VJ%fUd@!Pi(o@<f5-zQNV22oQ`C94iqX=AQPJE;e;cx~6^`8UUTY
z7VXfV{(OD#<s<L;f=B!mIdX{#4LZ~w%5~b}DyBgOXaSCmc2`8SvQ9aytfqO%|MPH>
ziK2>%$~R{JGsQS7-r%_Fwo4N^daD<rI0jKY870rXR%Il>8|67<swNGOA}^a`xZAb|
z2k1iDIlh(apJ}N{>gd0YVwQIw`_ppnEn`xwFdn5?jiIl1T70V3PyOm;ib#UUxCpiS
z(^N{gQF$6{9lL`Famd57Uk)ExgtC2FU&m3^DYkCs_QIxi-O<Khj_VI{=tIH}M~PIX
zBB-$qhN#?DUbvy<W%nF<r&p^=o=*A33gRsLu0hY2M9D)`B!wUdCH(%4G(*wcihk;M
zQ8JZ8TS+bb&GSO<F^f-ATAOQhPj1hb$#Q&MUCe?nNb0yh5kFWmiEAAf9Lflz0Q3kk
z<o6hyJfoLn3sz3Fg;qf%j2-{;*8BvM%vhZxS8OzBLD~lihxBJ#ZfTaQ*HL;Dn(Nd)
zHDbxCz!P~;Pb{BPPdt1;=@zp_6kuIdSw`tMYvL1kjPKxu?DsKyXTGxxy7Vbv?p<zr
z_q=fpSYG+f&yZZ+H+>@rkL$O<GQ|&C45PmLo{R33B;z6oNkX$LCLKq-8s5)Li?NRV
z_YBFYvA<Eu=O|-a^ojD0!DX+vtddoPe)mA=sMS`c#a32hcxEy@ngRtfFhz{GPruKl
z31~&)=#(X-@~)-;Z-KPUo}VFUW74sKr_*{4T$F1IW2H08w+B9m=>CN>nO^Dk7J|M@
z>zT(Kye0AvNkCABe7U9Fk3F8G{gZaf&ufGi{da2)#&m*5Aq}DWRq;`lOBrjP{|b&@
z9`sl?P&#2s@s<dpt!@d(MRJ3s*}Tg`v8(Okm<H?LP0;ZSt)4VW4+3&~7_hFQ_UgX7
zAz)cao6l8iv#pX8<s|{4jbmhw;A;9Q`w`JR<$^sJ;MSX=b(UP+9Zs1LNJ=U5JL|CT
z+e}>0OOBkNvpFb}G?|H~Mm0LM%%Xrmjf3x|W0+HvH<8L;qDv4_HzKHjQ4h*~N~3fW
zcdxBgrslHul+sv5yM>-o;iLL<z@H=K_beE)DsTZp!wCzaW_pi`$5fd$3kgz=ujbx4
z_=t+rO$%5q3yy)cKi!h4t`2{yAfQ3!M$dx&o6^tpO`lVD9)iwi-l@5)4O@vrcw<s2
zxTx>8H5=$&uJEfV{AoJNdtN4uRhd<peUXA6ZfL_i$s1;l`UYx|*>Oe_ZeHT2`_0Rw
zq^7<WkZoc6-oS3qFUcTEa!L<&z4^2hj#t}{BdSvfzJ4q9K`i~aeXc;`rM<Xb*hoc4
zC)Lio=s9_b-q$hZg*HB4UCqI<qVtL8u>l;%<hd%wUc4%X0PLh-SJ7w4{b?Gn^jS7c
z-66i7dn@ph!?cbb%?ldo(pqJKS<$Z2UZK(`rvI?2x;TM|dBodq6xM7r`l}gl;g|Av
zn>JQl%j6Cp(}MRKXI~QzfBM`55_9=3D)Yg35)Dcs_Kbl8#b@=c8L#(UHksvBNbvjq
zAp1C_V2Af&G5g&)^s%BH;|v^UQGj9yXV==MWJOo47g!Yt7g*gy-+Co=xn6uh)g=EY
z|7&@Eu;C#l?Qeeuatze9hvjO9#1~$ce@@RyM7>J?^i>Oy@wciZLpp)W*UyTR^JOi{
zZZJ<xs4t=(#%Vxs)13t*e!QY!@eGyKJrPTx3Xi$Yq76EkkS9z1itGtfu<-1Z5#I!H
zCvKeUvUAwe|7DL1Y`Z>t-mPXAkxN66+797CfRm@#N;nBF=co4tqtcMeA(^`k$hC47
zes}mk64+z_u3k-GxMJa^s7d%urVcII>u3A%sCa$SeOGleYGbSp@6G}3REp+1l)4~y
z-y!FX@8gfuueRwUs>6jxLCOXb$D-Jr;T9)nkyOq<6Ndkk)?W@X#fR&uu4$<ykdU$R
zB9u^wo^b90U}=Lpm$TWsR?2dNTts@R;`^VOy6&!ae+*2!4%Dh1-q-qX)H>CE;R*ZJ
zH_<fLFF*mqiTbq(3r%8kD=4OZM^Ws)j>dJ;gbn>tI#JLi`ltQ)3T2@~K$WeZG!zWz
z@J>R>lX1o=Bg|{bcOY@PUk~C*bVlKT#E7J+K-Je%NGu=H-Crn}VYMKXYqV6GaIJ2-
zY$kU<c6B}&?hYxYRFTPiH=Y#{``pg3=)GHa6b##>_-5VOj$6Wb;O;0sBnNiuM)O4u
z`tkLTq)+X?&du(xW^WeGgbqd3RZ&RSYJ3W{e_2(BAmzAjJCS_Xf+6@89glO|SNy$E
zX8!dUA&H7WqG_vTPdj~@zH*0qj*>P5L{^vDSMbn;SW9<-szlg<2&-w!FQCOuxIZ<K
z;=VV%ErzCN;lJId6bVF##@|g|%vx^XvH0Je_TOUUNR9|h##%AzZYm1eB=J}Z1Z;2@
z+~El{e-qlV{~oZhCk?0t!*y8hU)EI+EeIuI|9NDC*b}Duj;w~1%|E#Rm|0VgV5d<2
zDp&D2@-4QD<!An1i%!3$(H=qbFbTt&Bl-C%xCZGmv&-kl)k3Z&zo+|F93o`EOjgXl
z?8x^4-H}Y~+D^POEy(Hbm5GgSVbDou#3bey^ULIY@OPm(t7M!Ek=m~&#nLIiDd*>F
zu3qemWE?!CR)0y%OyC&~E;MG;21O+4v~nXpoK02(s+{J@m)cp!x*n|Cj9#7>%z+cn
zFn3{&*NzS+nRdd8QNNHfhY0mSwM#aa)YMc1QjY4z8N=sa$d1lUNcbKQ7S0$xK)f7`
z-XCtx?tM<PE^|0B9QM)TkWKD36Z`uYfatc4y$*F7J6_$2yzYAkU(gvV3S!`qE_8TF
z%c0!QQMjDE^FxVmhF(qK_v)}5d$=IHU2zH*f(rei)a1$TR=^3-&MEZ8x74-2@DQSn
zH}u~r9X9=$_+08iHdogTiejU!>#-4FNz_lOijM?2(LAaB=Ad1>7f=r!21D@ICRDFu
zx4px#ylPo7mr>xlqo@ms5haOPbY)AOO1fpt?*!s|b>D+BZ^WMq0ltf^!^aNq{`*GV
z(wjhr-P$j;;7?T35LiNp^^2c2z0Y&EWVR{&veFAzpQ91_Tf_;@Y~Pq<DOaS{u4KDY
zEImKh`8~~5?vRrIew$Jl*BPG@{!5E2J@pTWF3bpi4OvD{2ahqc_kzS<0P63|UFI8O
zN<Ow+k64CExVvE<g}bx_7qxfZ-PLSY8-C)`<I%G@+x%F<ejAT5NuQ^k)4E31zT;vc
z$(zJ~U98xSpkfhYkcfJPxutp2vkB+<yb3RZ&nz4$Z&|B#TmNT9JZ1F_2#~e)jfS7l
zn8dMF;3mVoWxQm)&vKBkQPAmh1INeq`ub4z?}Ao?39%dP6INzKKWXO^tuR@%6J-a;
zv0R`rk^Cg^4Zog3-i;Jzl|6}R&DmFCJ0%qT#vnO?j}BA9ev%|tWyf(2H^c`e(UgrL
zSRY`y-M!`<$XN`4;vN%dM}gT%A60_&g4eDjsClq-yA!TaeI#nt2yRPhf|%xhigVwM
z+q?6w*-#FNo5S9<+}&4S(~O)e-GjpGO<sHPHy#8@8}GV11V%rmaOW+C;u^T^OeD^v
zQ`>-#(Yt1w3D^$LOl@gre>(gDe61phoRGUq%gNZAi?#TO7i1=%VTn?z{(=O&SGTko
zF16gCh0nEbxjQ+8aonzzk{)G@gK(Kti$~OEwwvO}sz_Px_|uxq<^%q8d^lpmI~^B<
z(E1kSOZv|@!6Lfp&8eIm+E2`--w-aUZhkasHTNFX89>o^B+nomK5%P{T)z7mG8P@a
zIlkp=B(a638KopLZo0zODnWDhnxMj*rr>pfHz`F1`Pke;ZhM_Dz!M0IZB}fsPB-yx
zU-h1|PInv;oc-Q;_4~uos2595TLZ<M@F2BJkx7#<mi2t@72&H2Sg2kkN|TlnVdR%H
z!l&$Ya*W4w5x3+hVPH`=M$Q{ET(cZ<AM{kMF_@RYDSnv>Ki+sSW<M_Ybw-6H_24n@
zmHFUUxW4P8j&kttLWgSc<++pW=n0lyJKGZ)zi|T!)D^f#!Cd5mS_12Y1+pm9N3dE_
zl2ZG4MV!~?I7$h@!ByP2wvnZ_q?@#3^xnl;)k8D4A{>mX2vGEUSNexB7CUJB1vAZt
zh{llnyG)nlHoAj@FMo7*;%ffnc#LK)de1u<2%Zn<w|Lol(;{?AtK8#+0&*(c2{4vX
z^~7O1N5yW_d{=;jboaXE!UMIJs%J`39%<y#;iaK#JX*yvBDPx{^z`a<CNhUyK0CSE
zGj9&1iCfGvwuR$c`3CW3NKMi+&Z`{ipL=3BFJxg<-1Yog@%O{rk!e=gm!3_eaQC%8
zwM*%3Zj0&qh)kZb-yF6Crm2IJhN9ny0SE4#LakZYwUYaAJTGdAT{N9w1@`r?TI`@!
z{}k?yv+?_r8^jLMxXR+uJDPO8cM<b(^{xfrOs+Mx;Rdf616J{~pOy`JW<iY&-_kow
zH>&g!ll|kgA2;#D5*As`yGUpZQ?s#hAfk3q-tuQ6k8H9k+4#89HCHG)8vN}AFSfE>
zIvvLgp3MOsZng>FJ5!k5e5^}3v!2|xrC2J}-e9_t!yhEIF(UEG0W-)&_L#}}Nzl-?
z22W=R%*f3O%q}*GR(?{MXc`xP>}434Ru*dlqiyDbW6j%}TbmDvP`{i7#|xj*D^L;p
zV0mhI7?y5qMBDLSv5Ag?%u?1D^4CB}(?kEER!?lm!wkT3q1k|;NDzWp+>_A(JXNQp
zMXB?4#c}n`%faGbwu@r;I>}NyPJSp;T(3okH4aP%do;ElG<~CR5CwDpay}()9D$Rk
ztY^OiIVee3sgTP$rK>+9i2b^^;4wCm2e4}F#~WMe%OSOT0KIrRZ(p9vCt*8RS)OP3
z?8*LcQ8W3U8u!;<WOet*k}UqhE@FM~gzRx)201OGtg!+^Z3mO&4_~fbj!`6hRz^%q
z6E@D1#{GWGn1SDqXu3PPDV4c%P4PA{V?yaAee)*KQrNj%ULClFEV{I-6L4{*kIK?Q
z$E4xu-Ax5(|A;HkBry9!1(S2Iecet}2DqiHl#d+$3_YSe&%cVDD#3`ok7u{R*k*wH
zW`!`s?upUcA3QC{P;~hoHiRh&psOT2K*D#eICFEAO3e+pX^j4y5!EeL>-41q&zKMJ
zK*KNw<a6~vib6TUs?eCg3HX@e!@ov+zCFBarV3ewXxQtzzjM~5Qx(^RNP`Jpg~@~z
zL?oK}!f<FF$B}!DtecA=Lw>!n;v~>T&xU_h5nj~31n<g>)=mi?g%a#vJaX;&_1c`)
z+{mfHDgVJ>jM9!b-){pvl)g#j?CuJ2`(hyX83O7oB^9l%xqOSweO!?FOUu;W<n9H<
zi;hL=H}soBM;kufS0BREK+}Ow!&d!C8x?r{b%(Va#D+oE18~Ea49byaS;+pCH{;Cd
z#+o<5#ydYU%w}e<`EE}pZQC0S<Ba0CdfSdSYohzkuv23m?#KDwwPY~d#`eM!60s#I
z>KWC#_zDf{>j6>lNLtY{^I7M1LHY|q8cW}Px}|~2s_;Ku_npIs`I;H~^|>2>;bWW{
za8@K+6cTZRK_@A6$245X6ofTh;J;0|JBm>pbrk}3On!Gp%%!>GR-#8KmYAfSLYRuA
zd@q<Lh4G^U$#sZFSfB#yKY2kbpcR0KabRrEdv}hvzPL{Lap~UokX|3-25UV|lYnGH
zv_OFYFx>(~3p9tc*k~cVYs*C$^2G(OWrnw$d=~!5*;ia@_S$5Ng6>1y6TA&ZtsCBG
z9(N;6q4P|0o0<>RO7e998&!ARUWS$oDr#oD!+GSkgzxgk`{roC7Vx|0M819d6yj^9
zvP$JuY+!hs!<SsGo&4$EOxH9rcG%WS`I9uNXh`@KM%nHC9!>nT+^k4cA#|CKf~|@5
zmkzOayM|?LV5*GEk#u*EWm5kgLR@m7H(To@%>*l$kQBoR*4)keU@8x_nuR?U|5CBy
z(}h~y<DV49Xv8|*aCM1CRw@R>kp2}J(NasS$)K1&^KbkNZv3T|(T4wGVc#hl0ya)Y
zB>@peaX*$Bgc@R<+sytFQS2YipeOSwc*eNsw&Z7Rl6Xd1h0|YC<3+r2!^(oUqSN{z
z2@|CUU{APv-`}2-NgEYY-yP@Qb0XsD0!%F;9{pk~4Ju#u+oFMV7LjGC8{qg2k)>EM
zkHwvRlM8;`j%!Mpb@rlhy<D}8rS}6lio_{e2vS}&;<m7Kc(;i+!_}%Drcao=#Iqr?
z{bO_RAp8SNS>`S<VAz4XcG}!&lpCrF8G7fCs<UIYDzhb0AUlyP{=}7ZORC_RIYS+%
zK8<4}!#Vn}L9uupP3aypfF9exL!#9aW5IWeg@?^uw>5i$xr*y5^@$6+@<@kEX-G_%
z-?QN5X#PQR<996VE3kS#26f#s5Pw)JY?#>}GJ}zQeY7n{DEXN`-XX^Um~b=H{n?!5
z%96<4T;U;r%Ec!)H$VcH>$>Up>VF!T)6_BQ<9~<Rfc(Whq3j$eCXH*6g2ENr-X}e=
zGR^yHuxq!su(+O}7yyr}7?m1s>6~ec{A2lER!Xo;=#6u-Q#M`8xi6OaJS!HeNRxdr
zeY0^4{k(V|XQ8~RdI^1x^$Se1UKP<)x4c;?xsf`<Y^Ev&fYdV^uU+NwGO|yeGgH5e
zE)8ecsZ$|f<M)0IC^6PYvDt1k)n#Z0xvt|&Dfg`ldjLSaw~MlE#QvkuZ*Cl@?9~ke
zeIsly!uX&Q_=$Si*Vu~4DQ?g1kZs)Iai>#1VYoDUT!)6ti*&m{f(vtjs|a`E`l5Dh
zR&)e{7LV)mIO@=(wF>vhq;}P3-85jo(Gj31p3X(%eP$gFe*jLhHFEqE5jN&*E~2iw
zFm?=e9mhA&P)#8_W~s^n7Xs!brKhZ9@r@mlP1-;9%oiZ7Ywg_t_muQ$+1bFWo$|R@
z{?Q7kA3^gJa)Q77276;GprDollu|fRKwMwF0hhpaC-#4;FG-BOjHv)Se)i-xE4(W-
zpvPQYDK$;6mb=v2?*?1HY$3|{*I3kYzPPG}NI@t71C0|Ah|qs4(B{75%*U9!$qP8B
z{5{}rn;&%c%9D9z!fgyeNDlreu*~|4Y(H*3omRmgcVI*2aBRx`XzT8J`M!3&+n~mF
z_?RQuzi%wwlX>U1xA;P6_>iDo^Yce-A*J|%b=9s^S9)JEi{MqOUm=;_1sC0ixo*7|
z+y;~oAK28mJmao|8Rid-Yc(f2?OfJ}tnK;RY%;0;BsO7<e`(@Xx48!kkPc7GJ&sPR
z_<=%9ml|i*Za@Bw2D$P^W_c>E%9!>HLgQbUf`SIo&ZFcte3;I(j<$%3b*e{UPFkHN
zLah0lZbfOL>QUA0-Kcf8JD0`{P0B-j9ucd+vmzX(1N*A-^2a=669<8Y;HDxY?I*tW
z)=ad4rxyXHzW$506I_PLA097@aPsc>#ypm6VUXKob1Ju=E}DI^EzgVp*{ZK`%XFIi
zqvW}&---<1dJ?&33YeXe2zT0jRX;)?3v@8ob$|P(crkFvWE+5#5q|-fPEIe0Y=`*$
zdX<UIgC9Bam9~dmR^Kd#EH8hXqke6qn;p0{7k)Wt<FMR?DTj=q&0F0<bJ1Q-j6PM^
zY38RM99A7vJPZ_AI<|H0IGx#R7+bw98C|Fnn!UOoe`T73%_hwtJ~1j%t784bq)iZ#
z#4uY!XJ*1~CMNRDZNj%$z7U50=YG35{_3Kp`9St9U9c~Fu0xr?f897zAFrj{>QNoK
zK6(r1Q{fEE76`MG1`NlV&5B9&o7<og`G81s*4=G4nDB%~$WedY>EbX!j({Srx?TRo
zX@y<;TweQ)!u3mT&Ck;BwM4ifq#NqZdy7aA@@|S1o?HbRI4E^rP7C;0i@6KGH~$`Y
zGns$){ByuK5YJgRHM8z(iUa@>wsqEZmahjB-P*%hZUt&12Pw`|$sz@0vahfgx(~3{
zeODNS=)WYIIF5uAbN8~!UA6ACC+>VBGdtP95vDa$k^F;k48MKG9GOKmxA^mp&$YJ}
z>|gZMo$($qgwQVeV$i|HLH^>J2M9=bNFDW&K&5-yS0bsR=_tw$La-<)%+Q3ZLNFIf
z4m2}S^qz_wbc!tIvK%EjEq@TrTJwx?lx~!+`RVSTK|H!O-;BzGKAbLpNSR|kIU1m|
z4HW)_BX_{<T=2AN4<oQr>2gA==H>=FC7*Zi9g7n6MF@dF``4TMsxp1B8+}TJg)e=M
z2BqE{IpM+%D`nfNG<U0l&XAh9`Kv(HLPGGPEjgr5H~gn}qdX<M7R*S7vhF@#%g-PR
zKFDkJLW;~fA}Ff)Q14}sU=n5K0+rABGf13`*>cR(u^?{~w9hGh|5Kc7667!H6(~+P
zI5zXLGi$bUhh?GY=E2f%!{tgv@%uNv!E`<gZWOf7p`R^AP6;WZn_k>woBTL*Dx!({
z-3PZCoQ^t@L^iRL65+BwR0ras$rsccSG;DwT>Sq?vd+gTa_(9Kn0_weRQFBA3hBOU
zlk{g%E-V{@;iPtvFMF9wx#aGzfuD-N=XN)OvBl|^6@Rw>bbainbyV6z7rVbXzQs^A
zTmNO<osf)O95f*<p{gwk8D;vgbeHy0>K?5L;uD7a5_A@D^~Yo@*T=NqXq;Mz<?^zG
ze^jJ6s_ibD^0!}%5IH*!JfV<xQzVtm%{-9cv7B>YUP%f<<Uv?NtB!=lcxCMCCqq4R
z7hMLj7yba-III$W7j@)Z<w-MUarjDgE+!a&uKx$N?}zV*W6XElLY;X}SKwAN?cP|c
zVkl}5^^7AeF1kL{&2tw3_@uJNUJ9%uH@c$sTDS6MRKOu3gTrmN-QGV<=WV4{+=`k+
zkw1Q~BKg%1E{BcM>4eb_lJSBE_;~~0sU-$>OAEx_-FW2g#=Yo7){Tno>i>!F0n`7k
zZU5Q1O}0s-7;G+EG$T6x>wEbr$K>47DCk^IjEGnJeJoXfFkA1ta|;;lXlDX5dC9eN
z{SXVZL6`*0Z)BaSlNCuwclSs+U`xZ+%yZlvPvn}6;1_qcGrpsq{qm81@pH^u+?Qd(
zuk2W?0i=zG_28rNLwpdyXjsqfp2kf|JeY?M2(p|RxP5!1_y&J9tYJ=xqh{)4(Pz0s
zh2~zhMnLSOA`s1!0oPhagg%D+-7p19jw%KO3kL4CHn%+#Vq0V_Ph5L!?{p|u*R|ml
zJK*fH{kGQcc;2PUWL5h9QmqRRfo+7Qrb)YMQylZYYjN3mKy;eOcWty>J|Eq>U()S&
z_PC(M!rW&2({wBB$j6lVVBEhEdcfz7{glAaEAjOmp0#*r&!6F~ya4BCcNuMCfDy8s
z7@&Kbwcj*f8zszq>%8#o;T?l|WtFi`HIPsMM572EJ0pK#*o8lz*Qs*>N#a04b78m_
zI_4Bn{p2^D`}OEQ(f1$(n3S%{usfu70mD8XIOqTf>cW+|8nyE;H?!k=uaffC0sprq
zD(+-iU^x09Pqe$UyO{+7kAm^7i$W}*q8l=gdudtLUv?B|coU0|aN^fu&BHBwoMrqH
z*Vkgqy$U9(tyyH3_0!tV^(~zR<R>BR=8fdn9@l@N#=;LGI?`eQP|~2x)A3nmv9^fx
zcT5K7=BdhdR{PD*JFC9wKNEsCxt8>AE~3kDg{UFg?+fX5N;KJYQ>*Q173^;C;sN}6
zRMa-tYeq_$yY=zgQwFRx$FAEmj=JSaLafcksa58J*Ku=h<evU|0;{jnZitQ?U_g{7
zZr?p&rnru5T#KLoCsl?J$9UKw&bE^P4b&96hfkeD^SK#aIcyFdR{Qv#8zVj}q3~Rv
zF*_ojU#<B&-|)#wmzJmw-PLBcOZ|w@eDxtRjPT8&nG)9$1cC4{f5uBij-d-`Iwcz!
zX!_PpFg27AhBJ;<N7zD*kLh1}hvtnTEKKL{`4u<~+)sz@fPVT`OrcYr9Q9h{*|F%_
z^2Emw-}OX=P21t;O(+CMvbHp;flYzdujEk+R01kHTVX3Z8%|U{I*~x~6z3%h%uk%U
z1w+sO2jExFeDhfmku<>g@J)Cgo=BZazur&V>-A(cT!-pU@+0X|8Kt&Vj&;vS=s4tO
z$(#$N*mYAY5rPk^m8L;I)xZ6&7P0B~U%JB*!l2~vPiz9`&6-a-_+Zslk91qexP*qy
zICS?^mQU6S97-K>)IA(`<w$*$6fF@Dt?rVuC8vtN{2J$tjnqSlMQtsWY1`Hf>RAV6
zAa@~BUS@JFURsKn>A>*d;COg=$ddXaSU@Q?NRi2ks18#E%@Iw&oH{l&1hTdC{5Pn>
zg*A-7=XEzue9$JwAwJv2&~C{-WKWq2bX`~y8@`0&_bG7AFUWGiAsFd`4yh+j_@vNG
zcrPTUxj$G#HSgG<vceN`86(_5D1nVaSsF`{wDpTQbVt-C@*q@P#wi5&H55c1M3*zo
zmO1zfLsD|pE7(=ig{G2{VTwIgK(>c9f2`VKP8q8M&RK1B(^p)MlU4xmzE+jKJ$dXU
zd0EraIBWVP>i+bGh-rmx;fSjaw*{{q+jwS@ce3spD;z-KtE_aY;S}$dahxGt;Ek8G
zq7U{lvB2q(clg|xbfI}lh)}FwH!_!O3IwVsgFj{MI`U<R2*i7L0dmeS09V@t8n=xB
zVCr{^*<?D_GiLI$zM!aTJKu}F6sYy{xr=&H<F|+O$Odi&f<8oS;7@Q4rau^3kVxqu
z2hX#Ybl8G@fF~C>4Lb0E5sGH?AsdDDrJWiNrUV&1<5m#nL5GihaX8=UId@=^>YJnA
z#W(USyD(Mm4y9N4?i5Ac=J~t3Y`(w8L<DSSB$NW;X=>*-U7FIvV*JrNRwZh-VUTSH
z5D*bVz{y)=hc<iPaai_4M_h~pqlII6lG=sO0pN3naO2m2w1}-}oYpk!xk#?xW+vcr
zx-9k0JLW&--PH(yh|rILywSitMo6auP&wZtO1H0|Zy*EBPgTBLTR7&TT!~n#%t+jV
zZ*AhPNIL9xGc|$j_31E~v7E6l)Taz?E{77>t!&VdW7>-(kQ?`1_K$d>a|&K_G({6R
zf5%Hw10m1tar6+LbJDOn`32T)+dcmHage^KA2lb`27SY`%1PZhhjS0?&u4E}169Sc
z4gJ<VZ7%ML&bAfA)XJGD?9x3rpc&q7H??yqfl-YWNRSTN;qV-xI=OD;X+QiLWs~F9
zFBRx3wp*1>pFnp0)w+FZLs|QvvOQ9)9V>;b5dVdp@#hi;$B;Y-HDC#mr-Qwt{{`NP
z83nc7#FV|8Bye2QI8Qo7l}CF&cb~9L>CB-?*d@LowHE`-wCmc|&8(bSf(XqG%PtkV
zz{6*evLWx2-hym<3-xRM<jdd_kFKkMH$4hw-f2yUk>kasjI!y?*TNh4^&d!JsbX+5
ziBkPh^yG{aB?rPrl2ARt{NdqfQ;|!OLU(=xk6(_yy1HwbXJ$amAp7oOK>ExCh`5O8
z(0n~k%k}<cPeJD}jW^&VFRe607)I{f3!5x1+6%|`3l=MAXHzb9(y?jZWJSu2lh~uh
z{i4OkY=FJ(7Dc`>U#`T8m?*2AFxOuGj_(Z;N4I9VI(*dk2ZYcZ9G`miP;bNc4i1Kp
zrqG>9>3X5Io7qCH=a+>#0DOF*YHWBr`%CNc7QGeQy1W5yMwx=4iRC@nN}SeMg$sb?
zeIA5sMohl$_hfV{z$6G~D1Qyb6o2Zrg0@f3sa;m64F%`%=ZBr3@ben#7k>TCemUQ8
zYKZMRU)8>j5}v6Ca9C0CjTEh{i}fh?TFs#FL#6^vXgJ?u{O3~mzIqg;6y6<%GdiV5
z*#~`^j1Mn%HOEcOy$+v<K#eh$Fc))Y%0XM9JMb}?JSEf-O`SwSx}O0~E3c<H!Q4}P
zgUo<X92R|okH0_rO7bm99Q0C^0<k;4w%e1hcsy2j*?mBsQg!o8Gj4uy%0M?6{d!-I
z5SB$R?{Dfr$r=4Q>K2Ns9Mi?HlLi?z#{8`;b2fc4yE;3WxzTi)M#av^r@}l(K|M|M
zCj=9W_79y1zf;NQpdL>N-eyX04|vf2O&23O1FV-$PPh&KShdQ};H$%4LjE*I{=1H{
zolM)j!tG8M&9mN=6DjOVUVIZva@0fAh99)$@NQl~04XMJU-Is`WG;B)p{kmT#~U**
zAf0iV_hA*kb)k=JAAL0=G;GwjBwGPfoGSwq=4}7aB4m}qg_HE+Omi6kt*siyqymH{
zU-4VxujT<5ZT4@cfa4M22EZJWv>Y=b30Tptpu};}&?R?e_a#E`e|)vYviM9y&0@Uc
z)#_8E3w>;BVCS2@Q@a~1M`#(k9(=OLvf#_al=j8rI{p^g9Q;T)4*W48;8J9~$|eW0
zAFY`1MdKs*h%ei1f~_oY@Y{BN_L=Gfh9Pcy-;eYtnavQVQQltsHLm;}uK)mLxV4Sk
zn=x`ZU6#oDIXp#{d2Ai2$EFqRkg#t3LR7;>)|63T1f<WTD)M07bUw}H!^T`1xI44p
zOLBdiBss1Ph%nTnXIe(elbK6l1riE{--=(n`uq|xBZx#VsKt>}1#&GnHU@^)__p89
zWvsU@7)}YQ;w~tZTj*W6_YEd8@k51KqXTKnD;zGkbC#OXuQnyEUYZ=!Vqa=VrKG2=
z4|w|7-0`kE>TOd%TLD>7<4#ZEUQ^GSTb%%2Nu5l<vsA_W@Dvd5#5(Y*mvV)aKITH&
zb9cBBm{RDbwpVBm*U?h;_wZ+4MAUrE0SryN+v{_lgfh+j-YtgqkVN(N<zmO>Rhf3E
z>@&(LUcg#J+k#lF>4iQscl7IHPOS|3hI$Vs1E)IcB?SJ$*~8p~9FT|+8AGZAiYI?-
zk3XB*YKB};B@mzwkXc$Ttv0G4qQ`WGje+DaAS;%H2<$c(T__QKgNTm&Lyz@C6vx)W
zyvaDO1)X-WsQLmYF<$Dbf-vM(@NABe!;FDLYErm6K<4tWH7z;yu=|O4|A?i;I!D^F
zE%S)2%{5H|SAe1`#`tak-cKU7t;MK7c?Y;jPn*oz39c)tFOH|+NI$d|iv!KCW+8>i
z&-u0!-R}~|Pcv9HGKFU2rQZ(`n(jVx)stv@O?Y!@3~CLIItHMIDo8kql350&>Pd|a
z8(~A(Jv@P7@;SLje=6OzJN^2ow|#e|OSqFF02Z-8qS5DUYEu~83cLuy-TAdw#1km1
z9OX19t7LR4A_+Vr{a1_`Gs(7zw1z6pJ7&h~!!N0)j;$;9v?B{SJI?S5__T4V$aRv4
z`jOdP{Vhr}u9B|!<l;X`#<bwz`3v#G@|!5`=#MX3A)#I{@MI<u{gBj705vz5yx61C
z4Y~(ESmzJKzTniwLOso^856T&UT0qK+p4MV`(&|{fG4V<8|X}{2Ki1ls~^cnG-l|R
zn2(6r`RZ@qhhxoR^{t(OC(?9HbnPkHfES|J+6l^yp4N)*OZif+yR^IQ0L>W`r%EX@
zA695idPpxqW(;)<;Tr1nv*Qp>Sw@{SciYmAN%P<(mf>GgrwCiihaY4``o5xP>_So2
z7j!<zhx=|aNPkK27WXAy-{^OUn9Za`C8slZrJ*b{WrLCdlN5j?a{xo3st05}1fLoQ
z8J4nXC$N7f4}{7MjlMMAkRVGSz(}*mYnj=#>>gVVTW74?Ludc09?9NEZmyHYh=shN
zr?F?(-N7k%Th3hH8Dh6%MOHc{-+CSD)Ps+tMAq+y6%g6+rnWq=lesMuRpq4}QH~z7
z5IGcutWt2WK5bzKGh%64v%Ft3CJ#bH9~us8o8qUEP0$5m%c{NxQmv_BsgHUSlGChS
zP~iCCgpG|m$ek7KQN8>^mV=L44}UJVO@`Dy6gSU|dyaDFs^b{{qT}#~s@XAdFXSy+
zzJsV?Zl9ja;7h<~*$d~)Bt?<(m2gHH`xCA`v(W#>PXOdnlyL4r_lj*{1MvRrF=2es
zHPA7KgImYOi@Co!>+GvATpl1YAt+ja!iz}f5&rR-*K7P9FN3CnQ^$EWl-zju$q?_T
zb|8C?MEla3dJ;ifp+uZG32CB@oj?94yY}SC9VZlpo72lk^YxF$$dMtInMZ?ok3EtG
ziS+0FNv(gM4wtADw70s&xp^dkOMN3~YRD)_Zk8rm`6>mWVroh~B_X8EjjCH@;IK%@
zWJ!U@LC(+N=Y$kb1ZQN0)@_yRiR(8S)0kG5E<r!rmarn>n)Ue5qjMRFUdTHKyP0e(
zbr37Q9$H+BTDOI{f-C>U!>Q~$70j!!QWzh}dP92r@00}Q{IEf3sx`mMqc#ayZ<K|E
zgIs<P{d$jjriOPKNDG`kxyX(p#m-G0qqFvR+0`;sNCH&7c0W8&p<*vOh2t*++Mzws
zQzgbf<~OnJdQIypnnCMp>TW&{c}^>5JMdLv#Tp09!SkgML+(ixxg|Z}22~+KJHmk+
zcdor3YKwl4S(;3}+l)O<ZinICE%Lw-&2iYQ=Mt%-j%S#cZH#Y&)rGm#)v?}ye<2Yx
zgObeapcP(Q@x$X$rp}f|g#$6^%*aXDZI2~V{sH()J!48Pxi%AitDT}QquHRrpjr4s
z1EM*?TC^B=Soco%)pXLH5hd~qIxBWQc-Iq<RsxhG3K5Z@jXnU60#msM64t3O2uBi)
z1+2<C<EK&_lSkHm3LMsr3|*ZIUwOo5MJ~4P(-(p+TYJWM_gFnm)|oMOmrE_&u|Mdg
z5+Is{AWD!AO&?#10wlBsL%e*Ep{LYOhUWj+__d}zmZ;qVuJ*g9GLfMIDjQ@7zK4Z~
ziD$ynNO=jH!OKH&RP-OXbsx-0;$TCji9UlYoLD;cf={M+=MB>9w9Kax7AQfcKepe^
zVD5JK)R)8)Q}mDq%#Ah%ii3TTu~js1I~*R49Y}Jwl)!4owxo|8-^xgT6d!`M309?a
z1~U*cup(qjA_9G>-Hs!2$PI(LujLo3&8E`rUaXfOUl+jL1oT0J!!9y<lW5N4+e^)w
zv=`KGK^p-E^!Iw`Ka`Q(uN}XuNs!46ska_w{TLS@8q;aTx%eLUmU?}IWfjQRAOF#3
z3qgF+hb~|)fi==LzDdeCEA1+$0U9&aP3D<0P}$%_5Khb=n^_}2?~e2@uw{1XtDJHM
z4S;R?^Y~*a>Ic$DNaA_xUb3@qtB10GQ%{Iwf0gPXB|!U5BC>FSYt0sEY90u2eKOwi
z1tMZ@*Iv;=6}3L`5k`xao@<K98phG1S3tQ=?}bB-MgZbIMO=?2@G%CKef7Qb+uG>y
z^;J)St|6Y6PzW!&#~RJ#_uzN?=Yj4fsn6%wsie*mh&H+byYGbDcTK+)NA&6ESP79m
z-kQPG^su3xQ6!eSQ_@GoL9Mw7vSb6$b?UWLnVZG#y)gNODx?-Y4VVB^9BrQ1-O4Bx
zB)1{o%8TFUCMDovyw#Y-{6yi|QF|)u26KTU8|#cU_B#j3dqI7=cKq*_hJ*2i)&{i}
z0J`K2q<$CdcZ!$*$=JA%lp2Yd%?rv0(R?A5N{8r3SWyu)g0fIa@ZksR$fU=H+QEg`
zI+5)C>a~&VqngkC*N?MW-rH$ZO1-QgI`kpQvVZ#3qPsNd6vmH|f85~?2~~mZi;?9J
znxfG#<VS(07?4DDlD?0$V^HqI;7=BV(P1@iavSvc!tqR8vd1qd@JQ}S64rb9k%aW?
z-?m)ZP!<)`SvGcAM}c;<lPUai!%_9`ytB&6F!HB)ddzx2Nbzw4N*U0<itA8H4;(iw
zUw&DE<W+yu_oCyR$(`jev#)_Lh)C@D<={Z7{UmC~ZHcd4iYJ|#v~nr|FY|C|^+ADi
zT{XS+%RjqskSgX<AMxg%KK$#{_JJ@30fDUNE<-U-ZHy&5l(wW+>P_B)TY;ui%dXk0
zUR9_s$(?GH`XQP(rRqq%x+dcCrk`2vV?KHySjnD(_yGMpEDomBrZKe9C)urPuIh>>
zLbCu`H()L~@_*vWD@mP$!QPQpz0a8MAYmp~C~>hDx3%&0^N(jDC<?=w9&?okVO6I~
z<l7@rwk}z2`IZhA!G}<aBSY)bU!Mgr-(<8CHleG*&XSxCYL}V=jpq4QQGRi67s5G*
z-nLWw8Am647^HGv_u|^gN}#fS7M?eeptWqzwyAjOPk2LpVVp`e{NV3Kzx{Wk=C5&Q
zdkd1ZS5lcCZOzl4<pe4ltA$5uKQU(UtYk!f@&iN<r(7T*r`~qZ19qK46g1-1XZ<en
zjA075&7=MAUft%0!scHnQw8+jqkF))dIXTVhhjgeUO@~Q=Qj#Y$P;{|F<D`?)V{A>
zW5xq6a{_IoIC_cb^UBABcsLN@15)D0A8}D*1MY8O38@K0$3Wt=)i?L{@_MRz;#2-T
z+_oH9!2)RzVHjA(OdSVGKe>W*awR#Wa3Qn-U&GR6d<xGf-I|7NAFoopFgvG+<b0xl
zNEj2hdTB5?tY+HvT!N0BpK)E~l4$dZ0%c4?s5tzV_qU<|@RYYi)WznvjqP^;*gs9Q
zfWh^#>&JItMg2y3SRRL*ye1;9QGv#Of?Pb!?0Yh73d6!9ys?cptv7!4u{t-VT2E?}
z`m=ky%&E*T7vZEuN!MX!=ze}gA~fyTq2E1l^f9QR?;)sa-Sy)Uro2|5stgh*FcuZq
zHsHUcicwmRBDMd3p5Hk3-8;gOje~)zECMhhC^ma6(vzIT@Tt*h(GYXu1u5%cOU;Gd
ziv3$iKo)GPMp8}FDAPP$?Kge(oFV}~71Rs6;ZwQ8TFoi6&x_xEM>=H76VV=S>`U$b
zneJ#ArWsGI_)Q`$2wHc~{I~e!CG~b1wMd%bfcXxmC{{nbj}@X7TG{j2+@F^Os8j=u
z+C?ly$dpiw;(wP0kCUu?_hCo!?O2%;7ra$}!7Xb+vj3oFScj?mQl@k{_a*H;K|5|B
z);S#7^GwF&Te6H?ZC(nUV~{Dqd#Nr(_?Q3>htzR<wM5^iNc}-Qsi~hp=~I1L`lDf>
zd<p;T)W2W4Sn4ButlNg(4lS{_C!qUG5>4Q2UBjpBvf}0o(!PLT8$SX_>)w8pEG%W@
z6~|{KeK;48PXXsknHA*Pq1sUBSQq?FY5mf~16!SSeS?by!zZy*s!kOk@qa40m?7FF
zE}?kcYCUn?bJfbU>_64(2}=Bmxl?Konx&OLpy9+i6nMvC-EWU<4AMRj74VVe3L@EF
zb2`bsdJ)wBGv(mXgvci-Vc4NS%gq|v4BJ%Fvn`nMo2kniK^Z6IVBWZvu)Rybl;kYe
z*8T0&tx@`^<dqqg<G?<W6dArva!AH2y^W)-!o)omuNkHq^Z-R7pBt&aO(f?PgKc72
zK|l;xMROt=+QDb&zrp!&7ep1yY3To5y(A>^2YTwSh1N7q)f>(SVP%C%x^}RLdL6|o
zSGk=x6_~GL;6KM5iefd_YB<{2yH<nQ_eo@Ym@Ze4$27h;#(13=1TtIj7te|lytr(q
zQW1#6b*<5_z8ui5)f_A{Q3D*&*nq?u20OGy5%lfU21%)pz3`xt^6@}{62rWfG~ZPZ
zVAzwtRfHC#SIl&2lY&y<k3L0hG>4Zz)4d@Lpz{fu|73(dCh&A1SnqJ-5`2s`M*fQF
z#IUg55hsz=rNu8OMe(z;J*(0zo~LK}QfXVn*bBFFbV+Q=LX@sM@(OxE=c?|H(NaRT
z6*ZH)+8P;4ezlhx)(ks!h8npq-CXV#ze=JCtzPy=vLu{$)_gzWE%@Y*T?bvv-7E(E
z2`z``eKZ6ZKDI3(KcO(~BzC9J0sk0fX`mXYi|Hqp9X1Q!BKe!N3M|nGNG$Ky`H`XC
z#NN~XgG;BtZRzH;NV4@)f3M4~4jlInN4z)YGK{RJzJS><ounBAjUwzWEJ#c<sT|d>
zH#rUs2@B}%7#T<UwTcE47K-3cY3a5E#R$Oo6=3Hul&ADhto05T#Fw$x7`|xu7>lW!
z#jA}%tA?vO+lko6{w%9*s+X0|BS{(Z$K1lwoU?wA{6nUcx!_UbWehSQ{}&(ByTDK6
zN;`ZWA$~_QZkCLT!z1U+V8e>3<Me#FSjN95dD}2Jp>5z+?L9s{rsTYW(`S|EPIp4=
z9gnw6`WxbmJ|{L4sR<_$&8FT^$glW#Za_8X{uYZxflY?s@BP=>>Qzk)&i*`U$JzUl
zD!d~zH}4S#zWZR#JWk|pHb;x3DCoI*_!C$A&4Z-I&8fl21+*4747-u*8PeW`W-rj8
zpD}3RewL=9ncPh(Mg=a`BLtwr{|FXc>TQ*!mYc9kCWwDW(<tlAlLO7|E*N;o3=R$v
zwDZDkC2AQsAaQ*>zBX$&Wfy6z(Ba~A<i*5yLA`{*5h`D{WYu>`9k}@iHYxc}sTg3N
z<cbsskHUlV4zzdJr>9F_&fmqbr_Lt6UYPphzMXBZGyX_GJR%QU3ccyv#T;{-ZHg_@
zSg;+KR=aqSSt7reNCsRqQPE7{)^jiSjx9#(xh(GgoVown658K0|56d`94<u|i?-`E
zA!*3&&7x{dXzZMPuN7Kb7S-CrjWj?8<wL|_Z~V}nRLN<p$*Ia!F$FA(O^t8Hq==ut
zxx>B#Z(_yCc1*uZzCTmUAEKrGr_d0PwFJ~ZQ<Z*GY{xCN_)2xIcvD<}S{@qU{R+<x
z#9snA%&taS9~|-jsKuoGWQLZWwQH?D)tm4k;vAaRq!i<J)`fJabNV3a;88D3<<_&J
z9Ch*PpM=K81OgHe|1ZMa_pmEBSLg-ljYLQh;%Mi|n6*lWMS7UMOozP<yDO(Jp#Xfh
z`$55Mg|0S-TrKTVbCd7(@RNfWZI(NNGR)HgtG>9Ew*0FjgKFLQw+RQC|CCCN(-J&(
zS!h}KQP+VJh-rB3Jl~?H6Z{!uAZ^zlnQErnkkb?*ekkHff1FIwg{1|rEvpq;#~Oz*
z!TPJ5zR)>>jo&ywG)#{U#Et|*cMLW8R{J;3n5geO7D_z-Q{geLPmS%e@FGs9rfs$#
zeye&YdSRdXrg&CG%0XuPbdqCs`!mL)cJ!8oz53-R=Adz%sYj8<mFFX+dtI>0FI7Z3
zY|YZvH+UC9r*7TBzos9`FK8`Ph!5K69rvOHQH$<;<&b}6C}kq}i)Q6gr*LiQ79R!c
z0hr(huQ8?a>q+C_IROycVs9@?tRIoFCV;qrp%d9~CwDUo!OARv>e;<9d<k7*FB1Oy
zzX0@YJ6w#y<WZd(xw`grqrrrEXeRc)EKIdGmg>5D4I%6yVx=tq$eF2h@DSCk(0x$Y
zi8L~Z*JXZgV0h)!77UHrg}d~6*}4<1kRqR%XPZI-!t9?3Ry0H!`z{LzoZ5(D3%8xu
zaCM4C`C_%Wcx*NC`5*)NRt;@`eag{&&tflWB$oZ3YPJC25gA)c_$ZzzBJ)b&1)7Et
zu`Z!wK+0?R#8-iB#sc^ejR=?%S4@sZa2?Bil1awrtW)EbRB>#)vO8V<4(B96Hw+LG
zLS$R?F&{|9Kj#W={AJ>o)D<+4B;neic5h@L?KV8CCQg}k>&CwO*=&$%LulSB$~H*Y
zEX-8woYWcZWT`Rzm*rsUap!F&)tj6XZNsog`)b^^+``M23zLMizQ&Ok(qn7Ge+y{-
zUX+pGFY3vkf!X7G$2{8cO);G1c@#L3F8&uyXBigP(zI(}fWbAm2iKs%-QC@Ty9Jlv
zu0exCaF^ij5?lfVcXx*XXYuauT=RcsR<G)+>gsy#qMIU380v^v`LyqT844Mr#h(WD
z#}h78iy-5S?~R4VXTy)Z52_3>C|?s&pt~6z*-jLa{F@ccg}Z=)FK1w8_vogVSoh!B
zf5m(M``Z<+KX#EX2#33t_FsQs20Yi@NR7jLv5hNkPeS_j?t&eOOH7v<C`GF{z1EGi
z%YPAA=>PG9ZH}u_k&)0V%*5x+A%3JEl9!SE&vo8z3SAAl#-6)4J@R&Q!WYB(*_?gb
zAgG}}0Aa*z`hW8N2YWCu_w$nZ0mhM4IaUU_CBSsld;M&?nbUK9^pYm0sHcMP$^7{2
z5tFe$5q$y5gieZBhWM?qoYKJuM!bOGzn=l5C?4BPXFkdut<c~pRR7;$Kf61GK%bO7
zQ7l)g!1iD-#&vL5ptevp`wn~Ps);)rbF~A~-A#uBdu^X-4vIsKH>)lyJMdnOv2i|S
z2CjeVHqN4_($D&DUvr}bbK0*>U;&~n11OYc!wFxRvxtlj6lKvM{PyC2$sRaIcr64Z
zXr?|Mt)mXvHO{mt6nqf_YHX4~SJ^6A2|ajG9*=`y5r=l!m;ZaE))SBxXVpPbHh%lr
zE~1_X(LNL&xVF&jd^ha!NEqr9kb=$aoF15#%~pVjK&TIBkmP1&Onnfnyizq;f|p?3
zG7?E1hOc35^-La>9e5AY6ez!<KyC5MW<trz`FAcx=uDm%2OK=OH=r5k?yl-qNXr9C
z>kJTR#F@ea)$%JJ;>z(ZDeWiz{V2)^p22{uW;uaQsGBK%n5$3wj%g)9gv}wxXXhIY
zhjst)Hc(u;3;h>*28fvQ?hQ(M?sPB6JqVM$MID(B{XX1VqrnnSi*cjS1lgVXK1cGo
zL){N>A2uTtC<=53qfC|Q{TyqZyg-`*QylSu|K!0@lSH|7mZ*umi#oT2b_o}MRE!X4
za(8{)hY}-(Jlo|V{ME>5E<bTx*#=!8j7h~lD<r~iyXk9VcdpysaI~-I2IF<qYtjUA
zN;Sa*+Uox~AJLjNo!gL3{=F-tl_VfM`2D!h{b;u%Tl`<#DjN*giLnu+SO4=IaNfga
z<fH_D=;ZvivXpFk*5gTc*_mwPhwJ?4;<W<=mDx^GC%!a93cAUFc66NIrS#u#kuTsK
z4K4B~oHjW1&N%-So$9f?pMTyzqg?UrPRW<F&g1esez`c?T+HZ}s4w5g42}uxJ7QpG
zHe@;+c0E)l;-h@((JnpPQT3SGQIH^gh4NVlX)O9-9Ezy6R2qmCsPPkn#E7>2VKYNl
zAc@-e^ppSdS=``W0(a@%)SJJV_|R=2tQ$llt10Ebv52e9)tl3^r9ao2oPDgu<D>p{
zDt-L6T3w=i^${20--Dw=(VysMKEuW>A%QOFl(8JCOE;$tLg%*rvU@<m`@fY>bt3iL
zrZYK(s9Qz8xyeLY`8lsW(>yiBRZ$Paac~PNT}asAH{v}9;lpDF#i~$->UG+73A$4{
zI^=(nc{tevcf`<fYz2M-MI5-gwLFo=N28}rDO{O>cl}=63{vZ;|7V$MW5D8<N2~KD
z$dhlFZTMAK&vbq$s$1_$5BXDDanbn_#bbH%<W?#>jE^y&dx!7rG>I2Mh!P6o&wHis
zPtWDVrST-U=lQEgdrSV4&qZ;c-v}kX#Fwx+^`xok{Cg6w6Djb?b$)m5$L;AcvrSOo
z*I8lB4s=gAGn=tf*j9Svg00<l2Tb)AVZgEH<Kw6628Q|beM3HhDT3rr;IZ!C^{iz8
zS3?<6IDNCoALYJ4tSb|oY2v2e*B*Tj?@DCv{?S#!uFncw#o28%HUISkr_$#6)I4X|
zmq*~M{YFK$^UB|2Ql|lvuRe4q=**iB#DL_5pxzgbXdoxNgOPjbO_qj10qnxN1oxg&
zI}3TB68Zl5I|P}E=<8BP4_>{SFJW<>l;g06K+)QV0@vsyJU8jZuH<WQo^1HSI%kFM
zdkYDyf>)kDqaRq86_Me%W~B`GY#<wXpFy12I9t`Wsc^(J^Pzs;-eAk6zP5P8q+R<6
z_M80iNr=%^Ix1P3`uzvJVBeqSpUc;!1I!J9GLBJ;!>hUa<{<Cy_@}xCw-=qsCT^Oz
z&Q3X<VxM99{9#v@Hz1fn2MTlQ5_%U#ZXd6txaXw~CtyF#pF1B-68S-QUS`w$_Y&v`
zrPi9AmJWn`ksUAG9*57Z=A)h*x1;<cznyxie{^3D=)YcT^5(8ezGu{oshzH3-x*vB
z9##Y{!EH7qb8aV?pzyO=|At51+1wlr5JD0|fP{h8Tt~d(Y=_6MJc<8*Pux^AGdNoT
zyW${|V73*t$J?j_)~YmUM_mZ?MT+RCAler0$|2kBcCAS!Lz`Zd>4*&reVxV+&+<SJ
z&JXgRhW=0G%IIdFw$z;s@eP+p^;faTxIRcjTxCBEJZcW2|6J-<APXnp47HlY;F8s7
zv1V`BuSYUHcb-XWCH4Vn99*@<wDbrC{`8lq^eJt+kD-f}Z1}vB59J$fd9aS+Ln}^9
z1f}=c2D&T^9q07U?(Ev1)rGR8STbXolYw7h{ABk3q5;qQ{vhaUb%iGk7lb;CuvSYC
zk6cAyH%K|km-$?GU#XXPY&N`m%*qKa265K3LHn^Bl<kX7+o|x!H2dt{Y(1@ZbN0xP
zPB#-S#)=v;XPB(){9nqb#QmP|PMFnQ2V@7?1dv47eN9h7cqQ;bz^?pa23FDd>AgP6
z(S(lFQnIikb_YEPGCN|~xZ3K^)tIA^(EH_%{n6cjTIkU^Ki(1v{ErhCd3768^gpxZ
z=?z#ri_s-2>Of`>U#S2J8$mrC+Go4ExtLhz_JCKCx1$hcGVJ2uYKA{>nx6HBP9yqC
z$&rqZW9N$J_3Z-W0X_Df2Xs3@<GVDl-(jA=`)V&Zuv-BU3M<{)nwaX;B3l44Kqlgc
zT}@bVFBsa_-<UD*wj}-0_7hkY0)qMYs8$qGXN(8vXlTuVUEzjOWne{kZ%L0U|KZ!B
zVwf#uG7Iy!`+kx5&)<1-P3d+x(f1WB1bF{&vqbH~XRrQb$_HA5MuDqTRs;;+#_%VB
zv$93%v>S{fBsID~(`kV0ZdT!$QNK9BuBco=71^5QCoFM(8$5%d@yw+Es}7Z<fjY(s
z_KhAbrl5U(*OaF=H;a#3Qk~rhULery>k~?ODPDF?Cta4b0YN4bb#H@22zAw>n_mSa
zELp;f=k$12*%zP-3JoHA#Oz?Uf95!w6*Rf&4a-T-eDA*x<~*y5)muTX){f=rg4UaO
zbmgr0MyZ~iELR<f<NwL_M(vzHo*=IECmBx6MGFy_-O>k7XxpB-B&oF=Fg+IxXSSNt
zZ_t<lYcg+zo2mDI28-R^UGuLvP{#nl{+=-y43021B0U&k_aE#4tvQ6U{qydr7E&Z8
zZL236`bqnJ(!%=!`l7`u9u?+n-!`JP4L#Li!GO>QZ2JZU{AHR>0=pM<%N3wy=RFy=
zO!(jTa<*q~`XSx{p-!>zqH|RQ#Gw;1xS}ZRkeh8Zyqh!2N~i}<IA^y!Vq_3soNd}K
z-iA$a#a=Uch9A*EGiQ14`Id*%z|Fz&?%YA|N1uO0&5gWztYpc27|r(wkV-rd-{(v$
zNf~KZpNsD8ST^>V&cKO5*$VtXG~6$=*n_)3adaql(5i;_%14*1k7Q@*s!hFx-C~QD
zKI7>6mRBfn4(}x&r7HSvcz*{VlY^d1ypGnmkZ~<;)`LL8hEub{7R_XE=gUL=w~JiO
z`_cx@5wA?l(ywBu(f4YiOvK5PKC%~Rj!W~>qmCOYUW$eDR*T+3lXZhO4rI@@63^xx
zg*~NN?`i7kb~lQt`;y)3o&F!-9UY*+kaz#wpau>eLik_V9UaTRo@dB@#piy5!VJik
z;a>%wV2|`%7Ck5Myqk05>&ACe#ezetxm%&Wy$`ufRh(0H>{p#@Q&V$;PtM&h67Z{0
zU{41<a{jgnnYy5~bwg*t>2qPhDb*5{b6uNL{e;9b?k8|fd$v?ZPyB4SHx3`nzq52M
z=yrJalXHmUplpga_F^f~NA=Jg7(^UZue(Vv_DPFYsY!ctYyC<tG*G9k8YCFBX;7fT
zb*yM9M|?6?KTS~lvEr7EE;*6QZ3;fvCu9t-QDRRJ;vrQK#Xc%9TLfe^JiWo6E7ApS
zz8~NJgRYYtrDxv>HQbaf0X$$v9Qck2j-2W3--%#GptpbB71bii!T6Ch1u8Lk^fH^j
z)Y$Cr7ZP8hOx-;_nWGRwOB-f}q3^g-{5Y3NujnZTIFB;3vrZ<CLT2Y{EU859Wva=9
zrh!*Zvs;UlAGSR`3?lgk_hE2AsVH$^NzgqqaD;T;k@L~4p>q7TT)07a!Fs?@DGK=p
zoeCNA#3#so@~42zotdj#w<+Iem%}JDAg;SisKqkkM&TTh{plGpr-85b{2n?zUV;#m
z*}aP52EoK}FE)cKh%g4*c@TjtX9p;C@U@avb~uoN<jv3y(eJt9X0FJ;S$XkPk$}}s
ziXpr3&wisc6tz#s$LYo~uYJ5}ztECG$6rT$uxnoEaod2O%CaI&UN6O~M_WUj04+ep
z%v#?S$IzbBZODCdm{OJ6-a~VAQ?ycY;mxdBL1y350OthBSkkf5s*3jT3H{)~v>ZN8
zMvd&mH7?-v;z~o{8M&m)Ch8dn?Ccl&f&K;9(OJ<(&sy=BOX-l*sFSbPayA#Ua42EH
zC3!8VBI2kUGH3NgC`gjL1f2$nqc7a1LVTr%>YJ?(L^E3RzU+V-?gRQ)N$Zj4BFKA$
zZ~?5B3OxdH#TJ*!Lc58PsP0msPa2A($VA|F7cUW$^yOSgH}FN}PJ>XPM5Zr|;<0lL
zuNGE2>MH^h#1#Wg6-%^7rp=s^sAS(usu20D&_<n&;>X(_v|LRMqO_7!=Uf#t64)P)
zM5Yv?=!2O~lVQp@WYXfJ>+~dX*Zh<xOp#0vFNhz9f;hSf610k$CwaH&iuoisNNk2-
z_`1lsAlt!>pf||X1Xz{Btn`HY&X*weWn$l$gT)B-etu2HzoCH>5LY*g)>t}2&TE@%
zfGp}*E6jS$ix}Y<XDVEmM9sQCp@Cs3Y!6^_dF|ht>Lcs9`9cAP^=gHCxfKEXx)jjL
zO{)xRtLvSbaBZS4prGtpEM6M${4qH|_sAea4Imqku7_P+-led)nXms1L8zAa!4B!z
z%{)e)V{Q<)jlcbf5+CPMCaGLZEJ4Jz$>z*G(A0ra+4rK`YPRN)Sd7nN+>aM)8#*{~
zXoQu!nFl`PC3Q`;Fcze1x<A#MF@%|ZLpqh6&bb<*z!_mzM*bTi@(LFVb=Ax}d#v_q
z>8R{(FXpnkPxWgOAnibPLJ;NoyoEO>{v7GiFp{i+@IZR_R}JdqqWB@GBxiX+;_1e%
z1yw@+ij+StHk5ZD_{dB$X%Tzlqf@!<?$YPb1KZH))N0rUNYvJ@d}$_9L$Ox-g$o#+
zH{!?2H|}g=z4^$}O7M!jZ4WpS);-{StcFX)Tz9&4R=hvwI%l+^+`{GE{wWkW&HbW-
z>qHy}L1w6nAqeXO^#}SM>YCnRQ<(H`4SRh0>0GonT;<zmq4SK;GGEHS))~P=y)M&T
z(cTKIOl88FD#J+|%%ta^pjLm($tSw)!z$ei3n?xY3!Trz<<{Fpc79pE<X&F)`Ge!1
z3{)b&IAS<Dlim1$fs*{JjhZ8+BiGdL_RotbJIe{@yo1-ibR?7e<hrXP`%dsv2aQ<~
z_!*#SL!@ip$!)%*dM^8VadZ0^z8n&vUJq5;P)G|d7H0W0YS>KYAmn094hXPM)ft&5
zeC7(ot+FL$$nwL1c`$eaXv5mzgF=F(s}c0C7Q*#0IB!r%;@&HH?YawdUTboZ`i8f-
z4rSagZpixU+wSvEt7*w?o=MF_J^N)$LX@gf^BxWc+3FeEjWYbT`oB&1QUR$z!w6A#
zYVOYuFCR%Hx>y+3@1uE_mSGM`GZ6F;Z~O(3#z=P2>^yV=@C{Hh3l^z5GVX{*re0@t
za34oQissYjASe9^y7L-3JzEVk2#4=uW;(Z=qTPX(isDoQ)w@u#ZZpO2xS$YFJe;U}
zpS&DYCVG`10P?X{q4}`)j$<TLDr_7jPNaYN69W1v5t9xf$2u*B>ATS6U<gx!E=@M^
zbbHKMS6X^_3h0(!eNi-_nJE8ZBZ2VU$usg!21wPE!KB|9ElD|-O7S**U8qj|EnkAZ
zpw+I2^q6;DFLvU*e0nn3VTramb)P@;#&=%!Cl_{KiTatD3BTj+Ucg=YE3s<QKePce
zbwa7uP+90E0aqyhTuhJ^X>lm2jnN-=Du2Vu5&wbUK;aXHW%QHEP<6NG24}a~_JQQC
z$g^5>*z|%_nU`Vx*xihP9`gqV_|{n$O9$RIm)8TA=>??gi}sgl_Hh&1UKUBpE<0Ku
z7)~SV2?EDKYO{ELzng>Ua=Y3A?4|FrOiSSH+#T%0nrW`?>rn;n-6Y#s?!93j`xpg4
z7^&?B(4ou#sfEIyV#S<KKbZf0=jlRJ>l87O;#%w{E!dFUC)07M>#6{|!Pr27H)VxA
z*27~(?*?!gu6!q)**8ucLLskl-IZA4h8|xt0sk;tz|us<g}<_JIAg8bEx$4W`8@b{
zt&z?>BTGdA@+@ST#0R-d*tlOi#g_^e4U8Fgsu(H=^{n2_eq90K1_BUvvb#F%Fs+Do
z;^G_^f>6s)$g>CDcl6uSP0<<VU93sb;7EAarytqRYlyLs`129?VIUS*@o&H5WCwqq
z;~`v&ldu!NuvSr9`KU<I=b>3%gKq}y0*g%>fZpJi$=2g4U1@GwMUdkLz)+j$eAtRQ
z0jO$YK&yNJY>D!3^=1H8R}VDV^7n0L0OAwvC1EfT`vB*<&rheXgi6C$UU|;Q;^oej
zILJGLyc{OjB3LJXHb}aAYAGWj$^N4IDwxQVPcS0gamP`EuOY#tzi;rbqb$9HDbz!l
zia(_O^g~sEhzmLsx%$G&Ebm_G=)#|z^E2Rgi6mr>1IOQZk`GrPNhB(KyV0dM(w*;?
z30r@kxm&?nL#sgR2<ZmK`r!ug)Uat=6%=ZaY>)2-Zf=;ez_Q;E&$egUUc()Cp9Cp&
zy?elEw|tCJo?4g_mr)KpLf=?t3g^y=%V^3I-_9uC=nqo^TEjnSAN;lhYvEhMVQ9Ls
znZ7VP(W)16asPJ4dAmyO$~*_qk~WoubyU_LP!3{dJ#@`~nAOo&HRmgraTxI5^qvAr
zF>%Ee4PCqBEWfA2rC+lOx=8n{?uMy4VU7ve5Xou3;WvNOi^9dU)rly2HH^IG-P_S<
zfqw1!lK=KF{f0Tco<F&C9{gfYS3_+uB7Z5PzznFhVlx3aoUMgCT(-r!@BL~pxn%4K
z@8eElT)|tIbLq$nuUF!v;G54E500{w_9ILEZ61x!Pt3+Y!tGFDJop2X<GAJv^Cr{w
zdDEW-e!nR0nO6t-yv;P%e+!w$d6Db0kgdVdf@+6QfT6={5@E*||Hk&~tjlJQJoq^<
zF2L9E<A&$;E^=#g_KwDo^5jAw_7;@|<ngN-qkY<$G7I<%VO@0zTL_>b58Z_1jJ~9w
z7~)7V@!?ZZ0xV7Enttxfm!$Dr0Di)a=alSNBYb7_)uOuV)yr*GK8Dw+-X2iF+Jx4A
z*Z=l%yCgwi$DZOX8j3W6YnvpfAE5{e;r~$KT*ikV+AT@_vGj;N9>d)_G&{n(=T9!<
zIf3@5${hcbb}+tkV$ItPQgm@h901kys~`WYdpAm7EoR7|tEEZ7Gu1ZsG9&v*z94|#
zaw@l~Szi4&-&Qzz-8?`y>D^7yRq*5B^98sDOP>2#spEXtV@ngf=PMR~@NO1Um#tT8
z@?Dl-X6X00+jZIQzBf1t@tgqHH||&5jxft{!DK&J7mWYGYwu<)8+8J<I#7c2m;tYh
zz96ytrQ2%_CKloic6tcuG|p2Wb3wv0vx)$Df8uJvj=Flj68ms)mkZ5gibesS^A`y`
zGH0IcQrH(Hw()i+I{}5O=DBnTx*F^wWHkyDDzB|*MaUvh)9cImx5>}Twru$kz$I$T
zS<Ew_kE;i0fYfa}-(@3NcU6v%*v6PqbXTeO*90uTU7$U{`hc$tf-Z1>(f!k#MXwV~
z@>*viJJq{zmPFV|$5mqBg++S)VZbH&yo&81?RO2(*lzT#OAEOwNH%Ln-dS8H00DOV
z3{aohE}0QE5u_FYqCTYUre?AA<_1Dlw&>!q5t+Bwm-=@}bA~<7yzS{o$Hd&v!`dNY
z?|T52sn&C>9wkt8zbt_LG6qRYr64w>^6g6-(A_+?VJNhHrho?eFrBW7loIQ7$DOw?
zF9(W+Bmf<IlRR)Z>ISIsn5PiOji~1s9-lWxeG+cjSI*HXY#R+|Z<k!T=-P1G*bx?;
z5HRnmi}SQyzgkrCkk?cEibUI8nIKYDGbChkz`?SJCO-#sw><8qa#nDj0kqPE=lDx=
zYqxjpKEqPefQFgU5>l|UhJ<*76)DUak{fir|GSm_T;pV7l${<rv1lB3Q|O=h8~H9M
z({Lc4z;830?uY`UqJE8kbZpGt0Q6IR&g)Haw#*zn3VS5F1?I)8`MdPmkne|!BNsW`
zPkDzx=R+r5-&4^mLdaN{49HY{qF)A(#R0NFa~*L(C){E-?gG1iER@1%CPUS`W+V2m
zV;{043NCZ*9M=&7e;*^S1pYdJaItO#oU~%+QCazu4+aM|c#x&r!vJQmgE9y3JISR*
zT&*pwd#dO34ui?6qUcs)Z#f^<8fkdS^Pn8^ScZSO+VQND5>64gkFgAWSXZ$tz#;4e
z1YalqAe7+=fI^96_9M$>f?M1{48|65*9Moh%r?}S;|!^Ytsql#`B|e%K>3^KG11G|
zge?)(7o#jN%xOz#ZUl8U*Ym#M+mFhQyT42LHWdl00Pkx_1hGY)8W5mONlEC<earUT
zi#Q<tNCIibvNPK(k@ex~SV%RNovz9@6uT?uHy1h~DiiG4=@Ur_$L{hM!`YvdR3>$G
zmvU_5YIEWM`)dpuKn<Qlzhi@QM9N%=mVLVrKeaoGJZ%TxO^d~h2M7Y(y(Pw`l`Vv!
zBahCkhl5&_bKPJdSl-pwsU3EsfufD2>WH|NIX8iOr`L-xL3X*AFl2%h#@2)UAHT1e
zimh>*3)IL$<N&<cte6F041}OH*ac83=WXXWED8jXzR2zjHtk<byP6z$a2otlIXmR@
zwoZF9G4*0w^5vk{XMo~qQC``oQ^fMU<b}{ujC?}lAlZg$*O2>H&X2TIWt3%c`~G#}
zF%<Tle?B9KLW)_5ROi#lA6)MR(3^5R^-gSpk7w8SS^(fOm5~+qJBKMMaU<vL-~JN7
z7V0@1EChfvjMw5Unj9Fes3;Wxp?XPKl4yW1Z@s$Y@>jP&Q5~n+=j2(9XYq1s7o7c&
zHIbien3b6;mgl)I@2Z@4UO{~f=UQ{K`)(MC9tyE2=Zd4~<#@lMz#|0!W8JRndcCc7
zqVom_iS_TuLOlCzTLA4f@{TsB-T&wegdsD4#i&Z<^7?OI5GNI5J985NTP0hK*#c1C
z0UqY%(Hy2hAeUM?2`7&o{#StDAk|vxlgVe7MMCXDghK4G0ci+_>%D(5Xa@jCkS|-%
zIaSX)?$-wP9};u^QFy;T?$-loX*XM6`e>SsJbzJvZ56X$9R+d@NcJ$*0eKrT%!0U3
zRZ!$Yn%_^u8^u<bd0aHt+C<IjPm<ojTuTh~V=6w`?|783(Zfm{jxu!3E54*ITs0Bm
zYiFJB9r=BVBd&yLh|MQFEY0wX_j)rYqvHWbU}J)OeUwam=RYPacfOw)0VERMvr7H?
z>s`~*H-H0kGOH-)y2R?DvQpB2<>2(Y56>cVx<p;>7BCjg&$z|Of>|)5xz2d+r&Q^%
z%gf<tJ7qs5q|JMLhdHJ?5TV<m@xan^d39&{CAWWm?VjAwt?wZ~6=PzmZrQvCfDIFR
zUWx2b<M)7rvJiY;nv7&4UlaMw1mt8}p5Gj4aX1m4HTJp#K=p|VJhbmb>Zs7b{TKUm
zZ%=j<fFAm^Wp2l5eI1%o9JRwOV4JBe$d3#N_w}6&XKboV4Ic<S2==3X9R1sVWksfl
zF-LB_7#w*9(g!0TF{T&bOjUKIe}*-urrBZZy~E~+2eQe&9@kv#HBS54kK!b3(U8AZ
zeJclM+BW*xXXO$%JBEEqjgr+hX#JX75baf!6YgGso##x*<)p;9Gn=(DpV1nDPMwj|
zhpPsh#~4XC9lWjBar6*jgT%YDHl!|4xWEhuWzvsN_;GGa5<O){!7g<#rkMgw{TQ;Q
zfH9gn*^B~_PBn?raKc*bs$Sj;!;_^9y*%|uEfnOZQ~slfdhGZmUeSyL&66I1aZGsJ
zL311$7J{>f0Z{{_>kl;uWf)Nsy(qh_<FGkM;6UgN3G!q)1$Y`vMF7gxU|{>53!Vmv
zHT!^~7SPm$0O+<}7LTJh256Z*K3s1qFm*>my{R#OMqL-CV;=s1b`nT{_`?{B`4xfY
zuGaG)DFk@`wafqy@(Vs&6;wv^Q1*}v6GPqb#}I(xSebuq@~J|#|LN(Jwv$6+huoVN
zfk892FV#yeCKVUq`Mm2}t-XgDqK7??TFho&7|wDCArZ<Sh|%p{pqh)Cq`9jhUa^r%
zJ{fX99<XELh>?am7j#nL1ziNe26ScbfdyfL0=@;@!#EhYg<CYV9src*Pc#mI{>*SQ
z2vwkD-fpNR?rPG4H<I-WNw^={C6_tvx6ET}-|l@~gm<icK*X*VzveN|FV`+T!)Fj{
z$wX5%H!Ko#EWB!vw6KkD%<L)EI-x6KP`yRYE^`g-g+LbNb9y%FI&{<PQ8Xm!V59nh
z&8HI}&1I|$Q~wF&FpIMXpz)ySg&6nB_u;9oLf&Bj+3HgF_F%BH?`hNUmW&Y#^Cw4!
zsb&{FM8#E)?R1e<bo}aE8ASL6hn<B&E+9Y4cW0Pc$#4QR;jzBAYYP|p_n%(IBG_y#
z#1;82#ikR(dNlPkVw@l1&8N4wU4ewp!%2FfP+I^6U%QUTeTC(^O`d64Mpd3eeY$NU
zoALLbhBFm9Sab1r!+*G*mL$RIa2!^u#rDDmX!d<yo>w>%44o_0IrO0xFe7WTjnQxf
zwetwSeiwlyuPA5^Fy(<o0xtx3d9Rm<M47go<b=)>)9AKaStvZLvhg3uMMfI9r{-JB
z-)X{bZoM)+3*7{T6SQJ%{E75Z=Dg~MiAw5=7vE*s@Z6p+`b0AokqlabHKJ&SZHGjG
z?G#_uu3MXCdQOc+%uBHFirQJ;j)m`J|Lk$Gv4x$brEGnfkDoI|zaJUBEu3#Z0BNT5
zs7fU&pqnhIyI>B>rk<Eq8>hMQ?|M>j!Z|v~$8WZ6FdV^a085i_Undon^1fqWrZ{8&
zdgrQKwvJA5WIXrm7{1>y%4$p+N6%6CQUG9vicZIWJ-mI_UVkAm)ZLE?nY-Kv&U!@q
zXDso?mvO!(4H~dnpjHR3|4~W}CdXdFy-#Yx-corKh}s^yQP9^oEgnRBu!Q0k&>XQ2
zjyfDx@;@RBE`Bn}Ej{R!7}?=`{YMy)NVjfYIdnA(_-0PGatT_)Op2$sUWKb8;nk_|
zMEPA)7Sjg$r2~oe4FBwVm~Lbt(TRvfaU<y|ygFph#dAWbWs3XYR?fLoUJ)YA*3{N<
zkyhp)@7S!G2jfGr)kx5&p&Uc6!0+FdOM_yAb=0yL<uguwHN&Z=0?kVn3qe}hKDvsE
zDkrH?dd8VnHv<G3>weF6nM|P!4Ih$QMHv65)&{nZAokf3pdu`e^!DaEr@!}tSdFrv
zuIX!Cn#WV1{GS1-S`Uk>_hJ^;U1E})5gf^UFZR10>yV<(bQBppz8K&S)AyKEd7RsI
zXIDA|8k-m=Q)b-6s3-QV4jyuagCeuJ&8E-CY6tR)Ze8mR45zps=_&cC6=r~Sm<#`1
zFB6(FYVqo$IhYku9=xjLP5OZ6LD-yBN9kxIQux6xyc6q`^($%2b!Eza@Yi?pL+96G
z0oVDSj<gsdRXG;%z2HIh`1Yo$kzdc9m>dk?q@H#@{7F^qC5G{&u5J%FFDtK1`qVY+
zbSA4VB$Az+rmzg28JWI|_7>Yj>U9bg?c)#=65Y$BoL_J=9+x_)ioPJ}8SniJNl0Wt
zgBM07@S(7C<C$&_eu}|9HH%N%pX$XXSAjF#$*G>}w?$q=58lY@bH!c85D{8zLMTcP
ze+)jMKv{Tu;h?tE4GK#Pie@mimcLMt>Iw$j*+5}43=Pba!zAx|m;kl+{aK5}q&9ck
zzQE*uz5As8kF3Yju?e;~33;Qrh;3-64j>;Q1o0zo{e;YxVue?P6n%%%|EW;z<q78H
zAlZcUGueG@U5{k&y`C?Z)sN|F<!riwDmY;X*W!BT17jR;Pd=gP@mNoX_d6CQGYgNT
z%1rc!3~6_6xcdBUdR?#oHwgB%>&olu8g)2-v<qK1zttt2`FtqA<vG~W<%N8WTExqM
z2FS#zn+}UIB59Rw1F>I(o_l;8w_`XXMM+dd_d0T245P}-<0@nP{jP0J$<I$B6J}9}
zT}?Fi^Q)?fMMEOIW(h>2?C^<lNTW_{(CP5o6%$S_gm*wOk|+^#ms4U`$K>&{)BxpQ
zbpSi|DxrWSAs+L)QK1}-hfJOIJA-2dIJKU(gF?vX;8pl3zA=AX0dEhwG?NU*LMom6
zR6gx{W(JhcjBL1Q^LuS;j_9YW=Sua4cXy+^!<(a6QDb^Zd^kC%f3zSxXtMY>C`ojA
z+~{X=8#{e7>E-&?M<WQ0Tklc-G`#!$(*n4C>~u5u@1?{sQTC8KEbJ$X`A(nu$auGM
z5bojI>`msBsXE<jXWPHV?Df5Cf<27q$5Z_-{nMu17mc{Ub+i}#^X%bkOP{p3i(ZRP
z|CYY~+~i=+N8lKFd{JkXJ?E%zT&YffY&m%r?0?sH(K#F9gqmACKo2oV%$MZ#B+u_%
zqm6se6xyNDk);b$Eq-R!M?MF}y5*al3|=Thea?H+xcBgUtif`QvnVk=Pb}4JM^?DS
z=OgRjsn%(<=%f?Sle%r^e5&?PkITHDp<VL+xjzb=$h0~Jt1IXP2tzZk-Pr5yH>K!2
zl7((3G!d<*Q76+h_{gbmHKLcEJv^{J<&70+WItAJD35qw+CL@Z7<iB+?kib#x9w}K
zGksR0U!1976<lyw95>yR-j5oO)>4w+Y+8Rt7CmeE7Ycd6>Zd#Ikl|5esxsZ7VbgH~
z9EU5mD7&#7C=6~<Pz{d@S2sl?-iCts`LNS(Q|udA%`Oxw=NqTAM4uW0*Oh$7j+?aq
zg}T$O<&0t7Y-(V-3M0I=uRp*F>^7ag+|}#HylqB!<%fz>282Ci`k6GeB&};)F2nj&
zysRQ(Tlc8kb6rQ{;D-<00QEe*OT@I+`vT7Z_my%FNuk4Mzsngbrh|oQ!}PfNIu#*O
z=0CBQzK)DtwP^5h9D5uZ#mt_yw3reT;G<-7)yq@^uTnBNIK~U0u%h-)rug>->{@5#
z7P;dBLxzjMlKX({^2cg2zKFO!)fRy0LAO?viwROY_c6`mCPHEzd6oZ7dUyH2>iq;{
zzdDyH&5>^p>Y*q^I+xbhW7*<82BXC!RB8<W_Do{t?EJdWW&B49ZKDb4Hc81jo8AoZ
zTIOHQRoI-{IUTN)F4wlOKH<eH2@wWq>3P8Wiua+u_^@Yn8Q_=riubO0BwO>qgvne^
zXkj_H6Y%JiCjE%`$r3QKtF;bJPUy?1D2dLj)=4%RWrlqg?Ajc0%U{<boj2L}EH^rA
zC3zw2SvZOLCDfB16`#X8>BbXF?cq*i$p2M>*;tJM8p@<nFIunG$TsJ%heZquXo1(=
zbcQSK2$&G0(JmugP$r;dPBodQlqTwstE+8O*$`;wJ@zeaYN*~>d(PqdVH~$*Y)#}U
zup`xQp!}Bdl^luCcbLHRH!tO@-e0lDex;iikpgn<f!HAQ5NTvq7c+}351fFJ!szJC
zZ>J(sE1{3b<|svHuw7)kRg?*`F$o~ioPm-+^YB;nITR-*`g}pJ>wt}>x;_Vov+wgi
zD_5w{;i4OgaJe<8CG3LzQAg=2duok=Vz~Vz|GjOq>n)!S{OwHFw{QFNuv^3ad7p@N
ztrAl+WumE&=6qF*!(!A)Z^!swJ>R6Be`pxE!FfpNebF_}I0E^}e)VB(6k5#RB21uS
zft|a<ZkuBGGGuHhEA^4&Lg(abZ5&;TiD-ItE}U7%IlWWbI$Tsu)VkMGOzh_#hvi4)
zhT~8Fiaz|S97<o?whiOwbldcaiV!Z&t4e43H4!6b-p!QxN+NDfjuRqd96SB?3-w4q
zuSH?#=la)&#-vy3uA<OOsnX#_Mpy@fPqC?sHqbW%peEuTe-<gH(TgRv_cE;ddCKs$
zTwO2?e4?SP)C0DP?Ip`qqvF<L_w~%jO!>iBB6?$bsRRjr#~CB%n<MU~9$12cTN?Ue
z7&ztxblZ$60gp+mu0dhys9)N_M*ek?MDAxT``^ixvja4rkIJi=SOy%k16gkItadVA
zthk{BpYZjLn#^AQtUG*qwrgI`zF9Wv+Qgl=3cMM@z+9Kiym0(S=D{C}=DzBxx#etw
z<<B+=pkt`kWe9zG`}6xsF5sY_^I!k?7LS^MQwgjtR#%9%0>LZ^oANtE0xKX?KGL%-
zj}>c(FLqF%I>|U<jD=WSZI6wr)}u0_I<<{j8FPeD>(BkYz%U>37Hx;-P1rE#LW{|4
z6`0;ewAc_H4BkTkC!Xq`sMA&8UEr|RDasdVC5RzN!6~&hWBMWS&+M6ED8viC1d3Gv
zt5XsjNKuQhpo96sDnSv4Dq0=8cH0thteDmL7uY<Ofn8J2DTKwzs-yqrH{N-a&tHcU
z{e&62Q%)|w*!8|Z4#dC_>bXBmZ6{m9mcd3Q{o&|LQ8ZA1cHh9eatzaB(wd}De{k6B
z(oI*_yeeAHCPSy9n9Yi1YLR^PqZ3~=CZw9Qs~G8*J7yN<&$cD<#((1F&<_&SOqgBz
zTlxkF=gFfr>a7(5Nv{)(#fSQ3eh8%Imc)j`ge3*MJ>Q*XAf)k`tTa1)bTw(<0S0b0
z3kSir=)14Zi<?16@8)Sp{YDeg8G{a`;2iF^3Q6-($|`33LB48Ok4R9dJ7+Na05jPG
z-CdABc#EQ*RW}V?-LTFmxuI$pl;GF4=n(#-Y>afdUYOHvmjRti4N^+)=Zy7lmnzUk
z>ocu2qEu7-+rP4T-5Z&NYYe%rOT$1R_1vOiP>UwDYSSRi+uhhY&ZdJ!l9oVeI3W9n
z&Lin5w*|RrT6UPupi;F5aS?SfGOkdw1>jNhdO&|xp$Xj-$kHx$L>9=&#hvg<_83s?
zkNKc|{TIk<%AJ8Sjb>uTGp;VyC-~^yA`(^v{pIcE_Vux)JgUC~T*7d$T@dkpitE)4
zkEAnLIX#7kGTyMYTR42IMLu%Xk#Tsmc}_mD>yetfk)lurMIPDIWZ7&$t!<8-?tUzj
z3krsa^gCZQ);;Xt-`ZDSmOpTUD%jQPhVxP)3*lj0gz2Fq+EZ^-|Ax!_c!xikQeVFE
zVQP{QVrM^UqoJ?_|L!$D0qCX&ABe~*EuPX!vG^>4@;t6kBED<r{%y$QF|pHF|HLsj
z`F7p)?XYRlG+HmRV{lnQt%J$;Y$1{}Qlau|b;^fxK>>la=Ekl0eB{(q^}>Wij5S3A
z`8h|=O@a%)Tl7H~&?rDB!lVE8_}h?f?T*pMD)kM_^N*#Int*(PQfGiinkEOJm&rY8
zJE~ls*N!euhxr=Khti>JhiTSpzG&irb*Q2Uw?v*cO6DybCuHB~qD4te_4p78ymGr1
zmz~^b6myEb?MP~wIw%UJw-B9tjVfHJCG5$2<2;_X5Z1-cEh%5-!G+ZxrvV;1#gES6
zIUWajVKN?sUoz|9O2r#`&E{JLJpKV#+>#oah1WwDGO*(EIX1bHMFk%D!qz=`9JL%6
z6I}kWT4uaLrn%KSgB7pBCwxnWsC`jifP!Z@$eW6SB24NeJ&$6hsbn99r=7)F86tTD
zcZ^pK$3z5=_|%2@QK~iGW1f6V8sj^xdCYTo-1|TTgv)K8l!VB8np36NCgif312QAJ
zJSm^MFuI_9@(o$Sek1?Os%*s*v7_qSKm4Zwc$BzKDP*3lWx6{1K)73GamGi(r6QDO
zk`p{n^V=fAp<6>?WlSq1WanUxVH;;x9pEi<rCZMU6&^gb9Ph@1bMMbNS?ZBtVKoGd
zbVIxU+{OfkK8ofr*>Uy&1BtpFx|qr!MGykV#Ex2h4Xi>uZ67ax_+mE74Ax`tUVchU
zdeT?t{MYwUKbiwyxl&VZDn;|&o~frcV-ljEO2mddQr9+gzx)n)Cb6E@5v#G!`L0Ms
zL8TR#3tL}5*ZS#*%XfY`Q=`4(yIj&*XsI(Wc}>me$r?R6Hx_xduN|F!Z27wev$rJ}
zA-ci-_b|-*Pj|JFhYt=|b8oh5DNVh#zu;)`q7a`x)NXtwJ0r(cGtaR5%pLnETOVb8
zB)y(#Q`JJD<yy0rB;7Ump{H2uLvr0Do-P?8G@iC4eH6u)O>|V1V{C>#Kh8LztXlJE
zz3VBew#0}Kb>7kld!XED8p7ekna~ekcSJ-*S0Gg;zScsjQkRRV=BW5Nx{8LzH|Wu(
z|E{v!IcOh>Xbrc8l$rAN<*+zM<9);j0_|l|-ZimbmX4SgV=@=H?==}AkAC8gzdH|x
z1X2d&T<v0}M1ngL20W>6=D2sFX8XNy2z*l(4fCJq1jKf&0w?MmpE{}7M*%NXZx#Y+
z*^o2Qu;F|tHoSsXW_P-lqOQD6X^|33<AZnsL*PyhBbPv$)K0iH#uSYa35hV8eh;nS
z55;18QKjkh)=wok6G<w{pQdngcs7H>ltE1`Z9me`jLAbK2WIKMgoGVMSUxFBEG*8z
z;Bmajw=xRg{)@9ipMmu=c_1iaD0JAgxcrveM?j7PqZAR>14o9e<lUH?@Ca0|XsoHJ
zFrVyFX6&ADF>y`&eW#cSCE<w(771An%@e2_;i_5QpD4<MOsckB(=tRH><a~Z@IH^D
zhEz(XL@8C*zU8P_qHFxbxY04mGR0>nX!O0%YK8UKNbaaq+{U#{cVIb`AI8Hn86UP%
z!u_9xrUx}Vwmp<2&Z1v(j3nyHS72P)id&<pJsOb*JS7XmZ5NRsko3v+9|&x9E(S7B
zxo&-#huwzVCG(ByW%@q@MULGh@&%aAQBAbt!dapul<1y%1dZ%Q{>EZZ0Zoi|;_-14
zhGaP`M}=<1-CArCRTIA@Ky^S=jG%}KVM~uKhz6#WY&2<iO-t=xxY?;eh4%$b4w6$r
z2(+uiD4rF#d@#YHZhU9f@4?U2dhRAyUdzb1woU+F#dO|uSpPBnMJxd+E8#0CfA>;g
zz);!mp&U%<j5t=NJ7JF(GHJEm3`If0o)=&uRC#tWv)i~w_7Pk968PPrj6-PUdux3#
zrWbn?y-n}`T(V>?2n?0{A{zNCge6N1W5$+9$JNjXa9KFllr>YNN#Uqsj4vrls=*r8
zYSxH}p<b%$WjzkeUD|rUgf2ewN;<3@vSQZLU$YuTz{Oz8Cp!Ypy$^H0cAN(Ipo%`z
zjKu0s+j|-RZ<}Pl1VY~;)dACLm?nn?y|6LS=*q`A*Yzk|7IDxGY2=Wk(&{h5L<NaY
z>T7%v7k>VE3qWy*)?<wSyTg?TSw=wBtASC*T5!T8LK<<#^Z^&JNREe@$5@O!2wv7-
z_)^`#apSL<N5TfVPt{x*6~&S67)x5aR}n9Be2H=kaaeZ6DVMPjK)Mr2FzxFvqu^cq
z{Je@6z++O$M1C*(78s)<)ctK^_4WDu+$UFe|3~2m8M!7n6|4m5-twr-CQfGortgT7
zjQ2rP?^hS!ahQ(|JD;WhfE}QjTIf3}CFacB#?YjqArj7|U*$!-t_m8z`;3Xc4^NXW
z!g7A`Qasnt7|Pa%f^P4ZtGkJ3O5mHmF?L|uXdj88_(dY<=QFP1;cd?8bz?bn|EFBT
z#$X8k%IbPG_9H870(LYKh0k+GV(_&}ONp#1?_QzPccSnU#<k5!Ppq1qRQXd#4J8Vw
z!C~><nTMXAaRPrmZ=<fus|Fn=4pCJ6SuQ#VTCN-IiLzy0J0L-@QmwcGqz&N#G74(s
zqNMcDJ~g)cg-&Mx2s+_4eclD1HdQ<>?wpeLO6%k+hXg+DLY3V&=wTux%-z%PSxvGo
z0?duq#$v%E0V7v*!zjQ^=Lm*0?gOH|O!v&hYvwQm1nRCw5a=GbjA}OS3ssLhM6C{R
zdgr2gFYniw^Lb8c@+bdZ%Q)d8p_00yN+h0?v>)DWS-mA294@@r!;_=Ppe{D)z*0kq
zlU0Lu-3i(nSf6)pcQ3zYx{38EHlS4EC%z4e3u|gBX+el?9g4++azekLgB?*l+V1B*
zhs3>a)T=U4xpfozPV%s55;wm4<1YSAxSMx^t9tLN0TCUyM#98nci%?{E}&0fr|m>N
zZ=%?sg6Tv29@+erLB+(72c{kBqYfGNJOj-`k+<#yosxksUDOrerPn;e5ci4D%l5DS
z1yDRYe0$EA!-Ir!Wf_HqG}%$<z6CDVzrNpC8W(`|IU+`e!_*uFUZSsIt3MhW4T!1i
zddX#`L=6)s2PBZie}<CUOuLYxIJ1kIVG?liTr*LpAdMzDS5Ir^qtEsJxN#bt<<9Jq
zw)4P1UB@MHwZ~sTglI#F%TaaRQYFqGH9Bv>fG(Yhs>gVwE2r1E?&l50JRl0(;mK)K
zz$O&X3BaEJ&W|C+&Lz+vaeaxC)%3$gn*Z-hUgGYr)3urCzx$YLQKK-^PUzIxCFaiz
z$P+_d7^Q^e9BP$P7>}gE_sT=fgJ7Omy1&2z%ai+$!s}+2SGCf)=w=tuA3Cf6mnkcc
z`#I;##r~V@tkCJ=5T%*-0$7QJzj)F<1V_Mp{)>?lrEbfieZT-*zSbG1#{3uyhR4iA
z9Xs|ZA>L<8ZU%-HbwB3*+056u5*#CJTq^PM6>=!eSkf$hri)IH4W}-r%n=xPq*;WQ
z9X)qD7vA+@b=cp23lEa;Q*5NhgK9t*>X>VDci?o~=>Cg?wSk0)r&bOCQ)8~-CyDNa
zBtnHSpxfOWEnREWV?R>ZEw(<^DEH36WMLn~30w!3ywnbe!bBk(B*3}Wpk|-6Q<sl%
zm&C=)h~|2<M&0~Pk(DmN?TGT6=i*2WE4n}<(VKr<@tP>kkj`VL??sae?FKd)-3+W+
z3a%?o)`f@|9n`gzDO^E-f1Kb#&kr&^s<F&CvjgT3;SPKXLMopc!zQ3|(n{pENaj%n
zxX^-W3EK)oU{F}^TsaZlmuS7frUTWi@B~^pWgT|66D{qAhwhjO3j;J!b-t|kady9m
z$#zML!h9;omzGIHbCcnW&Vz`MvuDjn$7p<X$ZN4~OZ!Ka%*ssy&@b??SJGHaj(8F_
zZA0s!zgNDa>i&LIds)nxrjC*m*bcewIV5u1l{h3biq?Z-NEjr;pw>{t5B3FphF;_|
z3JZFIU2ye#hDcCXEMm_Nk;Ehcx#&oN<mt)bp`<W@V>0pKw)E)8vjN3rNwY1|Zj8?}
zKdJ@W+)sh$*yH)~)U@LVh_i{}=c73-LQ<bC?}V|(Ur`tfXs8Z+UjXv+nZg3jp+A#<
z4Q=tb1kmjK*%{tZjN=fKpslnholclmaD8-xB8@q~D}uZrn1y}<z9f^`a@W%eo!A5V
zo`V%yCjw${Nx(HzR<$lm(wlipq7IpSzdmp0Qp#H*<D)-CYYKaM3*<8Qpp?h;qiWV<
z6ywnE{5?snfNjt<(WZ3tck=hgMnbp4gyupP0az3ybF#*Nfx|jQbG~9JmO%2s8~0vX
z-%1ZAnI&g&0E9Ls-1_^OsX#eX&YD~z8cmVAmzQ)&XvS$u7+grjzYzH`ncN53By}Gb
z56X(%f1IWl>T}L|tLkO&?FDzIez|DO*D@^}Fq*;!sZ{sYuR&j&IGbnHA|3gQNaG!#
z(JTd;Qm)f=?}r682lAcc{hD`mKWL`DxdfO+lJ&82*2L=M(m=e@3<cFH*UF4N3Ic->
zH=iBu5C#+avpaAln8OQnl2H_Z?kb@16nOQ9f#Fl+mpFJiz!q48LyOL?A!l|m`^c^s
ztjtm${B<pz<$b*)VcC*kjd>k^#GQuv5`K1Htf>S#e9nR~6^0ouN?7x-3s_<}EzQ`2
zAj<NA$=K8lj_@VcK$a!o{zuzF+GhypR@0Qj45w2}L+cGGkQS>RDuM@n1tN+Av{7&x
zeI&Mh0Wr$rE6XHMeH*r|T{6#@@2Fb4p5e+VVEWYc&$)=(A|}D+3VEA97_pgER33YF
z^YfRs+SXqCn4OiK-+gUi!T0}i8u!as^9F}qJ&^AdW8o~<XFJnwrhS<6Y9gQ@rQj$1
z5t=kAs?2lbR~v}NQ;hHNpvFld$b;3=zUo_{A<{$Xvne?4nE3tt$6&iu6XAQ&10K-+
zsj*rM37D6QaXGEDehuQzb0QXSN~5S-WULvGx52t+fmY}je~zTDivQ=;kKAjL9K33^
zpFP)_N=YuRhjiL{MVd?YVMYgyv8Eep#v?QM;sg-`(nmSqz1g&<FXEf3yPf1#Zv})X
zFiAC$O5S;UDDG3GHq_pSw7d9_e0}&qTw}RE(SV##{*F{b2sgXv77`c|_?watj{7Pw
zIN>w=neoNNclpF$XIAq0R)5AvkBH$)DCjE%nt@&<?#!SXHu3K<WVgU|8};;Ap9wu<
zu>8kV?C%;1<Zx(S!2?_YrL(#1;~2P{B_U2y)+tje#_0*zh3dqvy-7iy1lJ`MBe*Tl
zaK#O#&-4GE1@H)&W1emmtQ^IfF2M-BEk#$uE={fc*Vq*97gT_v;ENiR;m<+op!o0g
z7d_@Ngekz-lz6Fnci_4NPf#pHG2;TefC#X*Y`B6BG=~svk@}ev&NYv|+v9~v7Kb{m
zK5a*!VZ;eQPA9fQeVHTklq|%fs*`a<ad0X0+C6l2x?hC&t`M{6=MCHwtt_W^PV~mH
zBWGbgLlVisM<(c^v-S17^U=?Mdv*8nin>VhHjwi6=0WYy@|P&a?*c`AFLPP&>}gHa
zeK2=%WbrIeKH<uOq}o!d16{)(qqrJHQMknf&VX@XZCw0>j@^(|z}PqY%K;q4O0!^?
z{Qv%X4H|U{849PB(iB6$#tqjcjd4nM%4ZI8Bw_g$0F^Gqh21_&3%Wf4#vPslL%}?q
z+_w1gdT{Tf73(Ny^?|uDUmU8qv$m5KN{~Og@&I$Db`!?-wf9pvBox-|F}*2FJ?aXC
z9WNGpYRkGMn50bINo}DFDy%T_AAc44SpAtCwf+XJm5?ZU1#o8*)m>+>fe?F#G08~d
zDViTcjy^KLQbSeamL5%rSti|(*vZ}2r3|eVP97nGkOf<+-3yEnq%eo)_r6OyL1*x}
zARPZvI}<GyyRpn{!R(MmEd1UMoW_%Y3}1;Ih%QGLuE}Y?(u|^QQq^|GV|)b{jyk}o
z$i#F&qFPDi!9wxL9Ox!i`gEvnw6_L~>Z`pZQ=+5$7~MFVGB|BJ^KlxMTr^i5js{d<
z{aSD<)Zh%PWt1APUW&QV51=!suNdQHGB`aY#xOr}aeNg4GLvWqNn{nF9{Na;cMBu?
zr^gXf!<>pbmBRS|En4qyERjH|4Ey#=iN_p4E>zZ>-km&1%Yt@mvbSg9VEDOTtsMY8
zepA-@d7Z);K376S0vAKYy7r57)Npfm3Z?PvHGqXTKf=f|14bm>2?^nmrsc60m^MFL
zZw~sI?+hnYxu37)OZ2qGjiHq2J^}SZIuGW&5~moX06uh~!bl2UyU|&OfG5s;d<|q=
z$j+Z%_Im(vSwK8ZRr}TG$2<VLe7-WLs(7kYE>d=DUBK7Ea$yn2e}^_-N!)ve!eLE6
z4D6^9lbvWL;t}n1AePe&bSCqd7zzj5{coK%<_Eyox=L3mRE5CNZYG9^_YW``+3yN{
z;p(9q)Bgr&tdLfZ6oxnl`mXDPHh&4s-h~RKz>$s*y5Y|TOV~B%AI;O%LpO{^m8m|Y
zS$Cm*t*Gfm*qn8?1?FK@5*N$&m-T7Ha^pA9>Cn`53dN*oGA_Q6W()=*GHLM#!%LO}
z|1Ws>XB2?z{X6&<zFhs@t$(&cBTypO)u9d%DIf{mzvrVXHi^!cu0r8x=@Vb2yl&G3
zgm5Jat8%r{WAZq@EjSwyg6RLPSNeGSH2{7t_;Orf4&N7wj42stq+7=>E96C-ngnTb
z=?>hnyEVt$_$>N@?H)g`epPJ(B3e`{)*h<sK|Gmg**%Aj!L7s_XsACwercG}2lML(
z4RN|Uc`ZGrCd_SEi7H!_e!cG6u$@?lXq@sr_U(2uJE|MF31km{i=KYS58H4S1skJ$
z?hvN|s~7>lPGwnS{eJ}2dI3q&Dlp!f5l<GV#F!iF)UnD=x%GQSl*k$nYYBze9XrBN
zd>8=|qcZWH|3}qZheg${?Zd<XgVfM9!~`SlkPacz-O?pp(k+70T|<{jDUEcibP1ve
zh=fuGDWD)G-#vT3`#65zzt5vGtXb<`=XKUKzwu>68K0&lyc_e)uJWTPmFN<k(>B91
z7$z0CoQ&s+%UlIWTaXS%`_03&E;UUHEi?2{c&GjKh#S%|_*hE-c~8`dFmXplOS08U
zp7}>WxlQg&I5MAR9C37NQO-J$ye|a(J-C@JBPm+0Ja(4%m8?#WO@B%TEKK3E<Mdqi
zD`c9lkHZa7dV-B>JG?Eo9`|<#&wP^wgM=byftzY?P;LdI63>&~T|t7Cv)gR<dum3`
z!j<a~L;BO5H{&fQR~R=QCA74Q1-OLYI=Eq;wuhH)E7VN2FmZS?C9kxU0d8A^QVPN*
z3|jw>b79=4W9`*OpHXPf0-JzjY;z$6g68rU(N9cq+)4)=XfG$qw)kBs?XcJGw+CBO
zPgcCPjl&qfyh~<?1pA(3y6=%&7$x`n8rA1lEWX`cxCUD%O-)~YRjy+D%A#kY@$wI`
zx>9woh?G_Cdh4GsdwL~!`@O$=H*06)z$+-Js)2pme%t<8(pQVrBi(pnD#i~~j^oz|
z^uw~MZA)xAawu*6<1+K8z!NjuUcmxD3#fOLzmH5Wy_#%;au4=D0OlSMX|gyRzaYU*
zzWG|?agt<zY09#Gz4=cV?{&e>D@rQc$gY_4jHPYfcZZ%x!2r$%pN|?ulpMP7>^5=w
zGw-DyKD?{b*91x5SQ_b?$q}g-t>uqWC;L@zIZy>mgBNR=Vy87mmGD|UQD6?t)z*7b
z==R{Y`p6VmV^FfhQf1zn=L|(_5V=AhLq9<=wO@ip=rxkOGszJm;gOAZ50Z(Lwv+QT
zEz-s5Tey$AZNWB=DU*Yt#Raw=rkgZ9E40_X8)~)1)4Liatv?Stv`<v6VFKqv5_Ev6
z94P|~OQ{x5J@0eh`{O@35CA;u)x|uWzleT2KqaGyFqFbk&E7(!8|<m<3|o<pi(Ca&
z@~CcVfZOG+T8#*0Yij5;aJbzM*hy#oF-&(|(^k?nxi*?({MF$E1+kZ<K5*QLy~hxT
z&?g(JSbKNmEseI|!~vF0CklDD<l%H-L*5Gt*GFAxDEp0Mp=_uE+KdI`!ZZsm{50Fv
z1VP*m7yZh^bN9Sl7YF3U(lE&K?&Ysn?PqT>5u1phdllBBNLPW)=EIkRB0B?F1^Lyb
z0=>LE0jyb=4*kG;cFMP459BssVNCwwqxZvF4)N$az`n?=Q6Bzw<!2c9iL7+h2~9(f
zI2Lw+)%vnOov)fj_T>j*Ln2^xXEsxze2$KAi>iuub1F@=86xVlp!zGUo?4ct5N*}p
zVyr`qaEe6@J3EP*Qq;ir>8oMQj(5yVI20nfM>2#`zc<(5682ouY}oKXUi3r$)FnhW
zl2c~TS>^-lV4<81iL5ACgFu^*@f43RWPxBwyTL1mk8S7I-jgc=NmjO@F<D@6>0uCJ
zu)YDlGfgPg6|1308h0ODP(M^5H{u#Pq{=9qaN)S~?`s>E@L>4%Ns>N{eQhw)?G9w*
z^#}V>eZWsGSWf~*)5z-Zcf=}XU!bu0Z%^+1t^dy}##l#WK|GH>^~xB7LwsSJpF8+|
zQj&-s%YQBn%vdVQ;J(&tXo?P#eo2$|5a@-?hQYTIr@#*!wnn>50EPuE=4;=|0=Cte
zR4y?x;lq@L8r!q6$=i+c0F8*4+0cGn<i>N6Kvwwv!V*@$9{{o&)0e20`v*W8nTnyd
zb7Vol>!%*5V*A8ib`o^JWi9#N$&8m-Do(5ZD7R+t#=>mXcL=l7uR`^F&Gv8%q)-%t
zIT^t}r_B?R!T3*R=%^+GlX^8*?O?v8E4xO9JklK1YgTKs+PW{RQZ@md#%-mED<t)2
zbn#0oYVNdRSX7n@p8?mm_+zlnE46{g<0B_YEwA6QM|BUgnZ+OtaL0d~hNmL-Z6||e
z!B{*=Q=*M(%%X)_q%_g`Iscxmt$rXFn_8OPkZ<vEID+8y%I3-FzpaUA=HayzyK_>0
z7d%LZU{=rm3j~YxLg(}6KwI@$3x3Po6-X`;!T?}vA3|}8*F+u2H{CV3C*7Nu@EHdn
z-QbfV@L02>M>q?5rt@+PtPj$Yf5q+w{=6m3zJC>%_ZdPi-QcXU7mhi>#IZQrMzI+{
zX0HB+3u+zgP^r~?)!gFnNHyRlw2w!+KNR?UsT{r#?9m#{MuqLb0$a5Uk8b43rgS-k
zm9$MMIV#!+Q3=fv`~0rfqvv%dZdsY|Pf@x`3#}Scv62Q$-O-*>MFt5poES!4P<;l#
ztlE8pxSF+9Z3uyX)u^}=L*`U+1{{Mgx*bF(W5?3iQJu@~sS}&_spVcqv!5GXR}z#9
zGFl|-5rsH;%j(jADbGm!m@Qx-Oqqo$-1>D1Yngw5^)|92b0aZZSJSwJ;D>|uOnHJX
zw`}S?%g=^k?oN<GSo6gnus=}r>=q8$JYngSF0~50vK#LszRg%PUcSy}_fhloQ}HcP
zF$3QBNaFxUf6Lu63+%EQnL5!V{6Pb5x#f#P8aB~c%RQ6r2D&{l8coq4=5LAo7x)fn
z#z|tG@?gVxC(81gs+~L^1}^&xyww{MW%rG8>X<T!Yeyhs+hmcy54tK`M|~zHU|;m1
z0}<(Lptsjk@vaj(K(njm`Y1ZO(5jGYA8gw`1aI&htfFwZU-3EznnkmNC-S>dq6EVo
zxR9Y*QL=LAuGYbaMca^fqvZ!m-(usuUWjz%#v2xkjF(6IC#+p1p>(VsQnbR9OByxV
zE;`eepGk#0ri4uRYS)SOn^ImK+E%=rm_@!m3MTOn6N{S1Xr_BW9fY@R{`&FtN*6NO
z0xZoIkyJ)0E_XaqC;H4SKj6<4W9j6yp$Zq7*Yv!R(nex@_~Gwi=+L?duopZ2CPyV{
z2)a@V^0^ts&&O!_Ca}BmODUM$R^d$|dP!GEmxYG}LdFKc@@1hw_2+m0j<CpuMV5Xj
zq&C8&l{}swe#S(hFjiu8mv9&nFZw<`&9@F-Fdfaw4N`y!<vh4{XUf-1^U2e*V)SZ4
z(XBXpYJ?Y$A&tPs!uUG6JsFR9^%BZ!X;06XYf?Ffu)p$PRk&=EVGdV%xtR+Y!bQb5
zCYt&PEJ2p`5evDGFbsphFuds~DhTFUjrF@XUo8iC%g3B4ti3if6Zqb^M_KSb1=Fqg
z59~$F8!oGg!B~pas^3*P`V2Wd@E>U>N&{>?x&C8&=}N@0k9Fah=d<_zq3TRACHBo@
z>g+NY4sy2GOAFZyvyQxupJSK7_wB9$`SK$y=h^WX=jLdgw=?2R4dSX(!ctJ!Fn7e!
z;1Jso9QityedO6VnkuFFV1<nM7j6h5tsBwY=Q`Y}H*Ajx8&D-qKCRNqiE}_1)G<7!
z!mZIiXZmzy;=5Rr-$);bjiE#8-aGx?hhf!4)x0jwx}GOb<qi^Uv7rBrdk}wJ;Ee=f
zqrrQ7|I~BSHA_yCBA$#~G-O!#ulbL2(HMAIWgO5%S0KT*lIuxpCEAZn`NChTv1*0I
zlCjz}m)D_2hxelupJRQnLAjJ|NyDVlA;2_JF#60BYTDPW3$;Id19H1YiH^lb;PsR0
zbiWw0?w9B+%f>)8lP!z2&xm4`5j5o+E7zXEpCeACr&tK|^H4$FUjiq<sM5$}gQiZP
zq+(F7@TXKi0*>ECy9@{ttX@!FqNZUNS3P-WdZnEwL2;74%(s#ODW55t{kYguFp-_~
zXBq;|!-3e}=N*IDg=n_n2L>mOso}iP)tQ*dv`$W?#U>Fd5@8FF8ohGWVF<!bu~c|+
z&&JU|KIe5ss;E^uy9PN$l2|I&dte0<{AOh6$=VgsC(2Fose^;ATJ(vsa#Uea2B&kw
z&{agmqL${Yzp$u-IjivXbL$sTM)Lp8x6~RM$Gv=^x$MNhBr|9<i(|JacGw?$NRxvm
zlUUna5)Y^;OwywWtQl!b8ja7DJGZ&!dq<^g8rp0RvTS;*00W84@D@eBI+WF4vESw$
zgSuWfd}8-h);%t3sRcSbIm^!p0rWDlc`0@CJH*nJGYTctzWuRL4soo2Xsveb`%|FI
z+fr<~IYV=~h|xYEM;M%_Mjcrima55+i?6&7Pu0;px_erGbMj-FUjOsq5yxa*VQjX8
zNBFtb?+-A3MiLx<*~VahVzb$Ztc$#q^3+7R2=WXmXxd6o)`r9SG#6NdxZ&uZYW&H;
z^8W#!Uh|C|6%GWOtQp!+Btg`|0>uDf>%j?flhcNT<E~2%@ssblJg139@?wL&7*cw}
zTr2Fi?LbCEe|q80*Qcy~%bMG{_$`bsa|N4Hp$t41OMX=xvP?`Y-(d+?5_W_sHz(E(
zD~O5?4k)D=c<E2}4}k$vJ&V8OC)H)j{r>bhHSin;9p@$!_uVJ3CH>}m%$eOX+G-rZ
zJnw^)c2wr2!qcM3ZqyFOo_2$~yOlR8ZokkeBe}t6zOZ&QNsVQlZfHVmklzUN&bT7!
zIB1ZJ6-V`qD<1f;z!j0)2d5QRT?7aMnO%(gh>F&#X6zM}G`FmPeYLAVD_Rwh=K8E9
zknBt=(DgqUWz68;0L7#XN0V!V30bCo@@XZt!j|?&>=>;MrlRg*<-<R-w05nlK_F73
zVe2?f9j%Ot8i}$<wlxY9<|X!3MQSv!^P=7^w0&PC-~<`V(|aqNBreKCdNgOA4$)CB
zb=)JM<;(>!Y49J-Y>qDxkDSlhOWpd75<!A$iQe}mKNsO%q0kT@i}hG-Jl-6H-2tEg
zM#qa`6~6{EDw%Wx;qt7#;G>abzQk=&?E}UJg-*&UL1lUUxlPIJWQrGE9}*Pppji(x
zrl}0!jATtt*)><yyY&S`-&7y(SY!=O6s4Hbw@1E%4U|(<$6<3Gr6My7N^xQ#Z9{3L
z?=_hc`D<l9vm40!{y9Hx!q?DPbsoqOAeftOu|mu>I^AV`xr6!Iv=1Pn$Ut9Wlc6h3
zjp>j3^?ir&MaBV0=GJO&6)wSSRhE#YAl+?D{`yNx@^A<^MfoL2M*L8@w4aDIKgD!<
zbu5#i5-nA1ls#WD?<l8cq2qX!VhASe-s@nCyzQFnXK;0qQE#u((sZy13(~}@Y4>$k
zNe67~<%v=Ae7>ighc#addZWkEuW+dhlK}`-h`Ui&Rwrc3$fQnlY)CcnIe>YiY`KA0
zdx(+qY1=Dhs`x8kzh7r~_CmBz*U=Vnf7mam^PMYW?FtB_ig=hMHK%vMNJqeVPXU2i
zXe$~ON*4C<s#K?$HQ&ct`$M&e1Rr2)zo%~jYQAmhOABr@^vLF1f$#{5li-lBNnX$C
zxV;&YM8yIBntWB!=?SrrFzM(n{}2n0M|qn}zHdiV!98E7xX`x`7O67$nv(FU)6bE4
zZAdTK$JV?F&K%tL1{7Sjj;X00*vaTjafh7zQYXdO#6tWVQGR-ost%OzR=5Aw2>S*&
zrbE%h8Vj{mjA}NSa_NZbv~qpF+*HIluq5-pzf-7!J$ottO*b+ZJUYgRD2UJcK(zQp
z9D9QmP4cbF=Baq9{1;2A)@s_5fa1JawLkdnOil(!x_sk?g#vI+zo1vi2peHL*(#!9
zK*kSO7yr9HmB*=0`viUCi8M|9nguVUnMZ9d{YQuRtrG9fB4^t#AD%xC8*q3`RH?>7
zK1Vr<#|+dUm^F&^(uDeV42znc|NG_Gpoc^7^&d6MG*+i9?0Ne_nJA-g=*x^=I~pK0
zeN!49tYY{IiW={x&i<GAhRZ39j4g&%7ajiVyWO^wv^HyWhA;10YoDG6?y4|=?-`Vs
z6bt&-MaeBW7z7?whWdG!3$t|M_c4}H8#!}PC;Dot978~4_lu9gp2=QQGfOoL)!Zcz
z6-pl(*^si!G&~+4dH@E9p?*EU6*t{{ewV;r>;(zc{=j<k{z(F(nKZ=jr9%tBNMOcw
z(CbP1&2$xpE~G|$ydgqgWCU}~3WZn8jj>_qVcabQH_Uu|%dK2)!j&A!j!0v}Z+4gM
z7Wv^v76)%hd8^YV!<pbg)X4d+v5EK(F@JBWL!d4LB*~*%S<UfI^rYXIF?rCq%(uVq
zf!>JVO!*VdYKs7z+<>NWftCNK-8}ElgjrM(O^Ti=<X?ABbcIezS!Wf>*>~D+AMvNF
z)R+mlC31!M0sm(R=d8Jxq2-^r<Q_^bS$!4%h5ilk{fn0Xq`hU&Nyy+$w+1+IEsB`b
z^L^rSFc8^72=ngDtzsFaqMFztN5A=c2ptBV0xp6X#G*q&3>K`QTa%JSV*e(eWpKh_
zoa19h;t1WsF3r`|ti`PcxC(D5#J<%6BIa8@sZ>D-GHdO>0bb^*nE0R6iM_aTr7Sb2
zDZh<)R@pJSx*KEL!EbHN+q~nB1~-U3`u<A-5NV8z>+%y4Fa?iz7su{irb0$&;XYCx
zcR@oPjeCS)UE<%A*8w%pxI>IzA0Fwp2E)XtwOmBgyNj03JFhD>s=}GSS_Wt_?2dyo
zM~GzG_`ak^HrBU28cKx?B;+o+WYY1nsC1N@XDL=LEU@0nR19%<2iPn={rfq4Z?~NG
zj&6)SQS$N;AQwV$ZWoe?QNYWxfeCG?Q_}5U_N-qJCaWezJ9*Tbo<=*&nr2Ag*bB!d
z1hhM^aFS>~|MJ{u4!0cBE~?#FTy?)t-9C`j<+4VPZV(||rb2upXMX%p3Noi21+$22
z++FJn?QpMpFYGvE^1_5a!uI^0xW)h+IZ&epy`FXMAmNq^2Ux3kB#WrL!Dm-%>&R^M
zoRG=#I?=NIkZ>PvSGSQzvunCr?ZDD2I{K1>WK`LHzQ)MWNoBtti^mh6!cuG6DNkY3
zRwfqlQi~SD1Q7|7r^6W}bmc1Kuy;>1`Qu}{5d`r<4SC_)Ev>V#h_;Dc?WfxPH~P&T
zwvTwPYA1kSUvE7Tk2#zV+nR5C9gQEp+hk^ujp6>V!GaE8t4>&`uZi}%7*m)9SDcXq
zaxwae{PYFjuh(jKM=F%>19164|8q8X6keq`it$EamLV)9j`GrHKEP|}Ld!%)#b`@#
zd^9(h`S5RU6~C*ri}%<U{EJS5XE3;TrezZ}W5#p(tX$%#YESdHL|6vYvexu=2mo1d
z(GL#lv<7nI4YlTd;VL0bNl^QnvGeIm{zh?;@Xm50UAy_ybU2VAexhIbiJS^f^U35o
zNCF;S9HD3!4xMaeU{AI<+QJoJ%ISw%Duk3s0_VYA?ysUiF#G|cosWN>`<Um81<*VT
zs3NP{SO$m>KG)ooq)(lrHeJ_{WJpzGKK!6wK66l2U(U?&KeIZoE=gUHl`+ZwR;yvX
zpn~Q{PqBcw05(bYEVg(FBfuEBC=pS<1KbPWV+AX>j?7!q&YAg)?`J^@)=}<#UU=Re
zNo)EI&9q_-@U|dt8Ft|)X?Mzyd3<=k#uT5THu|RaP9Q0(s!P~<h)FN?tEM!4&agCF
zPpESSQHE0JhsZ8ek_Qvw<6SVYO1N!LMC)cO@FO`PA=e<h(&CV*7@{;A!$$3=_=!gz
zJgg%mr*-7ywA^(6T<;?HDL*kR-RG1d-0(E(>e=3Ne%@gBb+FuZ^(wmSfBYVMnl{R2
zE0H_nECdd>!ZwP+P4TUfujzUJ5F-?`FdFnc5&L>=&6#gLBk(HA36gRtST_qGRL$%l
zF2Uu@G$Bbs+T5F1R5e47uwWyiYgZ(!uo7yKHdCtAQ*wt9SgfKr9|VO()1FdB?Y#zh
zPeFV=C26)40v|hRGQ6<<tq^9(ypSzQ@J}Zw3egYT0G{S6!E#^w+f)lP>!>_U_qPoF
zUDX1o#S)huEE+#Ku_@*DOM<REb~U{$^cJQWLo|njQ$f$_RnO^4Lt3*Z07V!ygj>Av
z#F(ew9N*3;H&^2i5Ljw5JHAP58f|J&I`X5&)ZFg&N7^%}7|+6y3$?0oz3RKk4fp6O
zTG9Y;2TwrPRaM(>2PiKh;ap$>zquFWIo{WrvxU&y&YC6=M77u6ToQ-0Dif(^gjEl&
zKkYqw;*wgdZfp2N+;#8vZP2&!$5B6e(ij^W#u5C5)&($=$N5^XV=vQW&}b8=I5cs_
zmLScW@qx)XYty~a2Sfvqu(WJi*zFRSK%Jdh|I50>eY!v3%T0+P`pZChW)nEWZ25>z
z;d}smd)7VJ0NrrFpxWqZAe8a;LZKk7+zWoFeCxB2S1^7Ok`Xq(L~Lp%Z|%6CM@K8t
z<S~?ev#L|>*Oy9{m!O9SS>K!AW8<`qe9zqya-@qo{|qDzG|S5*dX)2M)bb;4hlZJT
zgp!{fQ{INs!*mE~B84#r{X&WRPc&|<auJ*fBqYMhnlvUtIi$hPE)P2*OC8INuRpL%
zJy*wSS@LIFLEjxCc@=pUOX~)I+4CeyU-r&iSeMXb*3x~0+chQm%=qvWJd2(38%i*z
zKho-=BxpwqM2x!IbFgbW^%5Cp+c2%1@aFEzM8o<ai$`1~)!%Y3JcIhQMlyrZ2yy|T
z5};L@m<%nw8Z09hNX6L<o*Z-`VDXA3wCYv<+Y$$YCUlGpTPc%>TgV6!V;>gzj-+sy
zLUeCpdOLoK6HvU0QSCNVqTTkFwL<f`>jy)8dp_nP>C<usi3NUuNnwYajvFpKboVWR
zTYyHrkNjclkd!Ncj8Dd}98M7xR~U|Oe#t7ZGOeG5j6;5cutzU|3UeX=!!UfMP0&wH
zW74j1YMYWSpKX2$KG=Xjbt&?=0ef>`FdF?H41^sJqC>&wsI*e;sfsDR-xk;8WFCx<
z#F8?lUQHT!y!3kU#W|7{xO8|aAQeLyM@z!j%wlJTY+}lWmj)J5pB{mLzm{%v(l2`^
zI`B3@`h|m#VO_R}z7T2LFNziRQa1bi3aB;2%k{){JBynaS)Ea1$WCqbwI;mSO6n4`
zTs{Q})))7QpiMD&#$m~D+ei0AVl0tk@aULtNVXnA3gd-MR^d&1-xO`=Cz?szgD(Fn
zy4xMY58g!5^IkqCj0^e>8b=m?fpRMT&4LE0Pfp=jJZAJHfCtZFX63=Bk+t6Rsd-(Z
z_qQ+jgyBT@ucu^n4hkRpyT#M1IR_5ZGmi-xI5zVtn9nZ6(#lSF1L!XG$Y*Tg<Ij+N
zOC^Y*oTK%a21gDV+PmhTnA1SM#24qPAO189+sx)RMszOE+y=)aZfR{;Hs251Om3}F
zVNh@wiu1f*F+aYL19-*>DDkI<_86Wj|EgRnoz|NX$7;1ogfAPj`E=@hz)OIZ9?f>6
z3;>MK-c@Cf;Fu9$TOEQ-DvDf@-2ViuG~7S^-a5_~CP~5^Yrafh(71&uiYp#Yl)dON
z8`|GuqFni$PQ)N1Wxf8ccNe1$&-Q|+5L6%9g&2J0Jw_^_E1Q-h4O9{VVJ|noL~X$y
zgm8!=mN3ii&d}}j0Rk#*{yH+B@GN<fWUkRK1hKzmf7JTjf~J!zk>XQnx3P?IGJV(u
zV1PnBeJ?TMfJaSqn33$^yZd*6BRYH~4)-dVFDxZbhLN&9#cG4TIa8^)pv%g1Z*rZ_
zIflVlcK<YsJHb2i1k3g|eCa(5iX_Tmlo~VH{0rC|aJO5FhNZ?%zPQm?N0-@}<5V%y
zlB9Ms_j}eInT*`m6R-(JV)^Ur)`d&doYK0jIa%e9;E#ldE-$He+|w>(ZdyszpV!8~
zMtQl>_$w*{ryR$yYLI`aGJ}9d>FE+HRSFHOH}w)tqN0scv0<N39|Tq0*z}BJH@xfP
zR5M=kJivX+^>VibL`{^U1+u!Pc}ekOimW@Z0lXb{^i{vR)f`?Eu9R@ye)}B58-D{?
z4|!+IV&z`0>Ct89wri?3baoqo1?rzU&!Y$P!u%6Xy+8rcLB})d;}CD)^$VBc?yf&g
z*`e!f@`om0-RXml7@S{W(TDv={M?57*zrvY;Pv-DOekQ+U71v@aWf}JX%LMFZ=J-|
zt!#YY%ePSeXD%mmE%_~szAe^R#%Y_0i|~buu~aIIq;cvp4pAXk3`<qia<AgwFdOtX
zhv$7)nNNkO7(J$%XPm46{>;lHZPVg9&i7Q6r+++*|4U2(&ng-BY(4<n=MzvDq3~LG
zi$%+aJ`h~4N-Enffii$%y}Qj{d@feldJI_==`u|eDY>q7x^^fmhI0v;&KEZt*BjGe
zSiR0+^@)^EWcQieDRLb1djyAPJ1=OWc}a}21u^XVw}-Tz_Ru9Ht7!|d@Cd&*{FcBj
zv%65KL1*-Hu~@!xc`_1!8Z&6tWOY7o^cK2VL-_{)NS`-bIJ^KhH|e*1H%Xt8bUWoM
zk@0yA8IQ}RpSvVGIAkzRi04@oh#?n8nZ1TKxZ@XGTo+8*BM#}#=Wv-df5M38KED`x
zoPVX1%j#!-?~k^HFqtG0pt(1y)1Uc+&AXYhc`wA~>wkQ>cW=AR(8VD(Zc;6*#f_my
zb7J2<0ovRRm0g4I1Ow$pE8=xOIPii8?lAwJ1Mh0Su<l<aqa4$ABCfeqEqlW{tM1}p
zAk!ODzUy#vjG|~g9`ZX)r^eJ$YyOfBLsx0lTZhYuZ`7E?H2v->P64#Zq#Pnl2N_C(
zpIzzOZ`IkZoTSi&H<(ETXkw4Pry{G?u_c;4v13~pe5&$`43}i0w9@6Voxs9bupRkY
z7dQ+j5vLZ9K77&>P9dy07hR~2pdy$U>C@s^HRf3{v*-Y>52a!OT*q1cbwL#Xgh&QG
zK6@UvC=cGbBBu482U!zhKU>u3LnuH~PEoB{<v@#54{ua@=^P>vKIbCKJzp2mZSBBQ
z9c%+;IG1C_M|KOhpwzmy_^EoyL3BV$#=$pbq6N>aFR7ZTlNA+l<ANEogKCKnUWC&*
zjjdW;c|l73VI-N6kIAU8NX6-~@o2-Ib^b{@p6Y2WFx^g_`WmK7_^jfLY&T6_c^W4s
z^2^CIaz@7hD!}dUTi_LGg1V<knY|xQr2BgEPP6yOepIv9G|rnYI;qcbyN1)__{i0?
zwqI-8>U6C4Yao7tjFMBL@G)>SKMTh_0goF+!rd}EH7O42Jmy~2fA{bG`^m?Tm9Fc>
z&Fy*Cx}HXau3Yq*(zu%HmvSm()JRyj$ZFOfu(?;%tpXhYe!o4x(V%TsWiu2w1++FD
z(B7xb$n!1G?t+?&c70g%F|sC%)i%h;2r!=0HFpqE6dd&@px5HL&oDlK;5rO`^5Pc>
z8d4M0a{ZEl;GkO$cP9EH+pQ8+(WJD$FG-`zRB8L%V@Pt!(VCju(7o4!6$&`$#&;OC
z1d}Nb8&DvWt8D@%`4)5({(ir~e$y5CT>>rQu<gh7X;@Q`-th&jwK6|c`*eo+4@H`Q
zK6(4+1Wq3qXH=8IxqpdDF#|@tibVG=byTYp+177h8BcbaAOr!hLsGj6Q0&<ipxlYF
zBVP@O(pa;dkarbZ_`$=!lsn!_`KfFk$5DtC91P2_we<xISvzdtA2oS~42c%Y>q{{s
zU91iRfZK%2m+tv~QfpV%yhQoSIx`75_#;M^2S^nQKj~K9g*&y_y*mha#+L^7yk>_9
z_lfz}!RT-^G37M(hW^gn*=7Bb<bpO}9@eHco$orxPbco9VR%%Ty({Pt4sX~m{smok
zRy?`wsMV3Jp3bp=7%^z<Ei?Nl_xjTBZZK%6@4FnIB={kfpQZ-!zT8%t7JjjBxHq$W
z+yGR}R|fuJikr-f?`ym*?PSbubui8_8zGuwQC#M5<UQQ3nWSBzTiJtj36`x~+p}8%
zZrke>lxGyfZ+vv&_oL}><f|Uo0D@n&+v5Y!9=yj#q8C8D(bus|o-6o15Mai8K__4N
z^c`Syp`Lm!`mMLE!XKTqsMF9@@}SMWu=%0<*<^#<3*MQ<{22Y2Zo<`&6(=F^5*Sz)
zRtlNKC}g(MB<jXf{>4TV+sD@xd}Vmarhbr4gz(R}c4l!EVDS)7{xg5~&`<Y6-|K%q
zkym`>`%DEwk?~@}=sAcwOq519kI0)}e%`XmE~AhYR^INIcdh5$yMM@PxQALcM_!ES
z?1O836TY2NKDIkcCk{gK$AS+`)OV0apiyX|E{__5JF)k*1eIL#>Qxn-*TUO1x$K|K
z+3Vwp7$~y6Xh-59^|K1Uh1XcK13i|$N1jYxKAe`AWomd3aWdIy!}N*zRyogA=LuNX
zYjsYw#g;q(U@n#aQxm-EI?Wae{&cD=F6>$ThouF?Br2-=^(=Jz<wPXtt=pWM)o!t+
zl#}M)K@*ud-*R`08H_6Q0%Y`s-d5DN@$3JL%vuhlKQ&(<B%zNvaBOJAe3@o^GFfsY
zC!?Mm{am*~<gadE#j94~PYLmKGFp!565ps9C>>Q4N|LlO8YzvdlF_ze4Zfz#>SYC<
zTMf%~RogfFVr)G(46d1a**i>GluBu<wLTfM=DHK|OylXDA3p5@$xXBO@j52feeYmc
zh$Cts5#K<Hp7yzlp@lWGtHsB~(&xA_bZmfU`^rm7$bbGv1J;RHs5Diy_Y2bKiT^6U
zEd+X<^i8Y^4{@larU$M#=;6GLhj1Y!l;^4%N<SivQqo-m?jQHPynT$K&S)C#haAE?
zfN@Fqbm=#sS=YenjOKU@EEH=5jeh2773NS}y@~SGK8s2jH8krI#gO51+vLu2g{sD-
zX(^qNcE=%aw_2<NPhsKYpH^Vxl%&dAGbbhf#oKA^Y&0NrohJBRy2i)wLNX$O?-SI~
zXkXw;(fjiU2uZf^pZqx|vHph)tnY_Nj)q2kSRXTa->@ss5$5%yy4ibe88*Q{Ow+%T
zF^3z~d&ISDcJ2KaZaV+8fj8OGlE41QH<>59;MA<RXAfy-mw=s9H8Oq8ybtUD>%AXQ
z%#JL;?{L~1T9h8ZTj2firNZz1PKV6_NQYFKz=r7;y4!fMfZGV~SuqGTvC06<T?YdF
zaSS5fsqHC~{ahY^L3SKJ1BH2L&-Z;_pX~wG{Hcv;X0qVw$PD%wu6RK^@0}{%_Wj(0
zYfUdOD_Y>6z!Lr?-fNKNWEDZ((zX!3p<>063SH$m-q`Td-Mp$o<q!4VcmAHf4Xmve
z3+Nf}qWj;^HitC7iCZ@1&{&z@=k;(cKa9#dBlQ+KchP#}xD7?GKDl{a%;zNolgMvC
z6J%RRjo@^aZ+A@hpWSv+0|z{yweA;~a~{YfL?P^<Y$OSap&Df-Y4D~uaW^K4Ymtwc
zs8PpVM+$>d#c4Ebss-^UL)ym!r^PnST3fe0Zg*b&?<D}AKl&FmHw9n+p){yzAo9G(
z?E(FkDH~Hoxj`Xz@Bp;T?Z}s<Ya?=rn|2`S!gt1^q{BrPng08$NBhJmmb1$ke}adt
zM-!|#p}?c;v@JA9ngIP;;?<fgcQS$EIxfrA^t%>zWKY0Vdx&>|G~>sml#KrQ`m#Id
z<ftb9+6giO>g)mT8TCzU{8xdY3#}Gp6ym4DaL~s*^Iigkjp+V$y%6{eUNaE2g}=I+
z%N^Zs`ptFhl|=!fP_U&t?LS{Azc4Cv=TSjQ?q|FPw|`X~@)I>>eXJ@66G_Yi@Mh0u
z<8V#Ly*6?jt?8@A-37`h6~ap7z?p~Eqj~*uIIe)wKVT713$)ds%$GQsJF}|EJ(t|9
z#?irtP=4k4&962QpWzsGimbRBc;&WB`Z7gyzX{<}*gQl$U_Gw;zfXZi{Q$-VT)-&)
zoHg$g;DA2^BZr><*F|&kHnq4NAXyt$DS>j7azbU`8fd8zjiv%6gZHR%y1rDThGc#6
zhwpGtE>EIZF=oA4y;gg-gN58;gcu&hGc#y-(P@cQ1Oe4gyGb#?kb|AXlGomq`q)UV
zZ2%bz(s98~;~DAqDs4T$P2ruch+yyW-x}-D%E4&Sss-Q17@vWP0pmz^#>@Il0x-|F
zU=VK&FO?5PlVflwNNkLW!9qQ#WXyy}!2zFFN_U}TN`)U6(-eG)s&>Nji!^BJwF6G7
zkJG2Se7PUEO3c_?OvTI%mNGk4cr!d7sKM|{AZC9{W8j7TO)-gt3|pjk4xRR8H_?#Z
znx~US9`nF@Z&Dl|$UU;j<3JvhS;cO2QsaBXe!C>%XkWZgj42zBLW)zbde%3xL?kwW
zI);S{sTy9eO`52Iv*(bvPzE~5>9TW)WykKJLZr>t_fJ1YoX>(*HKD{9L3P^o??f}$
zVd~Z^Qx7FumH|cR;)e<zQDL3-6zf;>c)oy=A@Mi)CTy=+sy7#`@Gp%5t@54Sk*|n-
zc>70I{3OMr%4mD9*3*NP&=b-F^ICdEMrFwa;MbS@7GnPZ5SS8^p^>1&4{zcKK#b$3
zS-m~t?|*?Q3$kZTE|KuEchsKB>U+tb<RBjm6$Z+;*(2bQp|-!4Z{bLJ&1Nm9nWY$r
zhHHd2j5$)1IN(kWEExMOczgI<)}l%Kd;98ctkoZsUPI?<fR2&J@IW-DU5)Od`a!Cq
zp;YE_hT1H_a;4eKT?HEO<f_&qzKgj<Ws!r1;9sp4z;#e8whs=j0J|AB3+na~b`_C^
z7z~f5g^6{PYZTb*?9=T)k(1eNL0NV-4%3Vxdd=!bE&r*|X%&EO<cA+L(o2f_gJQOG
z&x+USy#s~5U1QLxTAxl_de2&PNdS-X;S9R?`y+Hfls|m&mZ8Id-%i3d5z2mKL0gl?
zkjvJ8lo_cv;9tQa7Ci`WsiS4cAv{bUSuCCL>l^tOX7IUzOCBf&Jrh?=QU7zpvCfY)
z9haPA6yXc<bkBgHWetFo8!nr;&iCPaJ>1vAC&n`_X3_>J+fug-mN7i*x4^BFwV9$x
zlvV%)${V_oLn1mWY0)PH+2JY(GxQS1ZWgn9=fga4wJ08b{ab2PW`z+^D(|M*E=^7l
zU@=2n0hag5)9=~CcLG4G%OSw)O=nVUf*6KUxM<HdL!$Er_-a`2$DbeGJ9Gu@tGgWy
zG53D-edqxpYYQ}#;4?h6Ij89K$KC~A)O<FUEipOIdH%$wY!1>X>K;yND?GrC777ec
zxWEAZ<rV$s3gC7FrJ&DMwG$&#im}aZ4cJ8!K4(|zmo6?R_<to`4A#*^TQobg#ptRJ
zAPV)xSs4^1l6tFEGG~mH%?0mIq^dWH|0GImEh0P{zZag|H9eJ7vyF|T#vg%MVX^#)
zZN%}Y59p)}1TCXEHTGgdcHlp#6*Kk2@M4w}_Gg>e%UFMGa@fGocL)14R*_i&jz{Ut
zX$T=HtcJJi{Cg<G^xI%Q`3#$3>f9`wN)2bs3V<vh4f~XpB*^!_V4c`ZhHI1dOqWyH
zRkEh+$6yV<FrlM3usRQi*ti@-AKg~2-jZFiZ?HqJ$T2pQWbs(DXXm$rTWc9RsXnE<
z2DCh0owAV$(KVl@L~JCHVD*{}cL6|cXv!G&g>c)%CTL`^vaA20l#^+N7pie$vFPYM
zn2~>;l?JDAcocPwmXRzQx7k<TG!T-ZMW);sIL;NSNx?UJ`E41?Hi{insBjo>RI3pd
zN1vcVBZQwqa0v@ON?!22`;8Od3N8gJ!FQOn=1lEASkN7){!xi=P#wRjH@~96LMP>5
zZ}8m7#?NMQn(eMMmDb9(%5g{JZF+nE;XiRR(jRn^e1)fNvs$rE1GnjBShBxk#ct+{
zagZKNkkn%pT+W|2r@_||lonuGQrmqzZOv2@)+UYvhx)UcH+np=Mk6<#P^*_Uj@5Z&
z9gO88VH#2fWOTr`O!*@gFZmJ%S{mcxD2DPO0^Qj~V$Mz{<>$-K`k}n^!^528WxNh|
z8go0?NzeQokn`s3m155Erfd(z|J>AkvLH_67A#3Hq?pbON0m2i+{3U;vq29k<9>*<
zD;$NP@;Dh8S}2SjIx03+xl%7Rj!8KwHpa4(#>G{9aiu<OR{oXg%y5I+@n#IO07UE^
z)VR`u>}$t|k1C2%aCf<wDy1(nERs$GkL7eZxP@wpGa1u~1KEFBFQpMzEAHMHbEmd+
zU>@tIzTiZeCv-W3k0P$BXbN!ZMZYc@$1wQHVX7?7LHc%Z3+w1J3TtctOhaqR!Fg#j
zt5|GcTCCj270<Ws-)x+d3Es@(4y)j1&+jawZ-@y?csd{Zc|TN)KFYG#v#xfeh<dWq
z7|Wrul&17VNkpglH)tlAo&iTdK;b9}RRiudTQ{)=g>shtmGQtbAeG!OX$q@iO$I05
zvfXY0tC`vmQs4GcqgDDFqP{VJ*s@VDbE(;>XFv?!yv;f~B4y1e;pEOD)AvLeinVJ-
zV5p`DjX;Q2sHsZR(anDWCnlxfiDaW6hn<QlJvC|>nn(P#1FFxO_zU41{A|`9Cr13d
zj%Ka_Uz2ag+PV_Z*ndG;Ix5O(7}$q5l3Y@PXsZ!JR(hbTZeb{kP`&Bz19CJKCukc=
z2XD_Jv61w#DDU?m0DNUGKz_{Oy@leS3DW-x!{GPL7OG(89;8r1N}O70Z=t6Yt#(NI
ze?eaqA`3>W%U~EkRP*%W-|wM`!4b09c{i6a#f6r4aX%3p5J4Rp;@oD-qE+tkG=xq=
zyO9gl2@CR5`3E!v2u#3#aM*xd(}URPVzDY)uENRI?OoHH%y2evt3Cb9^0B|xEcFD_
z!2Lg*N%+kqlN1lr;M^XdKEASh`>Ms;IU6vDLOGMr5VMgf4>#nJxauV-)c^1?K-$#N
zU|ux#9Gt*+l-nlTJi<B*D%Q@l=lz~Eix#`p$3X0&6|27P_W%DY`Pcvgr-90#6}Z2g
zo|GsBj9SUz2EFxYRXcSOMQ+d!td%0L|8#_gQ`)PLZT&I(R7EpQXA1lOuD<{(OAe*M
zJ7iR;u4R9H^DXeBQwdzHl^UgYJ_bxyFd;*)x#h=Ype^wdS1AWZWAsbdAkVN-Pfq@a
zy5G+){@poVwF1^MKDhxf8m-jTwVwt`J=_9shxeTa^PL{nCEol_=@B^xUQ=Z|MT8fI
zt3&H=fJMuqpzR~K^8P#ms$z4uO{M2YRKt!=N)){=`!u=FhCiPgs0-;Bmi7VqRUn}(
zZa5V~vka<JR0TCmd{-B>W*EBkjb`0ihg{@p|Js?n^UoCfSz&j%V;TVToosE<ErqT=
z+qWRB&2COmM6uHcOLvzOBKdZPvSC!fP5k+K>wq(L7Yg5e|0{(97Xg`;MZ(-EQ4|pn
z8Fb_fup}RXf@-6mNi1ZGEl%UIb9)5n&{8i6Y|*&6Qph*`v1dIm<4MV>{Rk0-su09K
z1BPBenxMPUM$fF(G_ModtY1{k)yW1Wen$R%OqXLt6_`-*XhZ84ZS#|nw2iV@+Fcm?
z^P+&{+793>)wydKQ^g`O7b)RAcL(c$_;EmRkTKl7=JWeIy}L$WdApII)zL=#ipK3_
zx)aY^FW6)_bWr9N4rK4npF>Be6z}%{kBY4EBc}Mm+~nz*{5XJIXs^1z;swU--^{Yr
zF4@w{Pjl#TV2g{hk@8dtyK7;&_j?(JZy+3NT=feqN+?=ghdd|D%>?p*!1?R9Lk^Gv
z|G>LQ&3d5F_alkuV7<Vf#t8GD>NzA-IrtE?=?q{a_LGaNdv1s^YkM-_Ymn^kb+1RY
z!uC~7`yQp)n8aH}dI2lhA-JL;1cX&zpFR2Qm{u)bbu$nnTzRG*HTjX?W&-iH!tF40
zwG;M<Q@xeJ{jBNDS8w?L{&;?occdolnYCZ77G`k$d_DFsL+;Rz@Ls6-!)a^e<R}I@
zEV@A9e4otcQi;YoDO=jFJdAkYM3ZUx)w81=6w4__v$>M;QwOV~A}7VA#~i7}-r~9<
z{UPETB`JlON5%)&y`0g=cAA8%Pke#!UEZasAUE*!O|`)n-8+B!21uxqOn|)eKuo>q
znw>|38I#tD9*%1sbne~lSqa~3bXvGRn`)?Z*+}IkAW|Gr*68HB56*+%x^1i{{JrYY
zd^F}(K1cf<U?Z)JMS|I>g|Fo`IUS2e_5G0Is!ODmz@Xfv2RGW^PNd@z{pn@d@JS7b
z4Ot2J75i8kWsQ~3H^~vlI|Q53nMZC%l?inHOPopfy^DX3>OxnikS_gSpZ(sn^Lg7r
zNg|wYzX{ke-{k36+Lslo7O}eN+PT9|VgeL?ZZirb##4Ih#99o{*QJti-F9oyQ46=p
ztf4c3G%9yDUHrIraoJ)+Gl}s7c+Oa!q1!I{q)>?*%ug&&*CT-Yc$9v9HE?!@`UuJN
zr~3L|EfbBw-alJB7ce*o1N5#t{Pu=cWe^d!U7U|!PuWGB4pABMronejz25l6hyz~J
z3qUZR8L78SH^^gp2trlQRc=a-kV??4<FZ-y9ji--k~UjunbtY*(&E;MYEj>?X923I
zH5xcg0%G$afNM3XN>Wsc9vq_+miSp~tr#x{CeKl){OGPpOi2=}NSZFNr=LVn^e9ld
z(NYJB?PK^QvSs8QwDRKD0O8*AEW_s~cK;P17RIUK@lL`2Ua8O}hlX@TOs;jmj!-$C
z@@3X5#Tl{n0I)nnXz_2XQ*Zb@bhK{IRFJiu$$3?uMbotx%igg{N@8#~Xzw0gDtdux
zY-Yq`c=+qv=l?q7fW|cX=E<p)ZZ&yBn8n|Q?#&d(hAASqib4Giz?d*&GV%&W3$!VG
zg&u)Mj#>-(b_vwDa;^CjyT^8oYc%`13D7c2M{d-%VITctC8#Zk%l3xcvZ^=>x}%<v
zL0s^}|0dMzcfs>NYVv^=GrT>5avSNPFJ(R_eaAP`XeYIHtR>B^@U6yl&Ym@DL<x(A
z^+f`RXUq<KCT_J?X=s)w#l|M=13_q}4@f1CPOpr5zFLI#gQ1mn_EpH=2bJRgK-A*U
zZ%N`8d$iBY#9y$A;;Xadqz3C4Es~XAI>_a+nQKz{E%u}Uc%KZ2-+M!v6H%{3-+MUU
z#O>Bpj&!OW7{9q|XEasW2+oEEoPJBzrd*5(T&o+1Tzn*&+bqCFEX?zIuJyGrfGS>?
zJgWY9pV^7vj|;J%wfaVS2DxE*222u(CaqsOnW7bLUDt^tDvvPyrugL9uR842u))El
z@~IC58!w>dOe$E^%$e!rhibh5NitO9m_~!wieC!Q){3q5lmR@BCn8GK2d~Cb8{GSS
zylD6cIwR4q@mgtb@g=S1wn+zDgb`Lc$@d3f;nz2eFa90)-rFO{vJ>YPH5Mzk*9(4)
z=JlTh&PWwNzAm`BpA-Gl*qj*?s|s<!1hC~u5x<FFzpD1=cRP(K>m1=~h(Mr$fxuuf
z^`pcW_u$dj^Y7UQ!gx}-{@BR@)pef}G~kVt{OtGr`}>T#+_qyFsjl2iS6vyD7F{g@
z!jX3ocG8I5>=;-;)L;BFAdSYNScP-Q7#aF<3>0L-x^tfwDYmF^ZNK@jP*D*NG%N!-
zJ8p;t8`iws@{%a8-)ih+Z28uT{vxIE_YNxhO(zhG!jCfEo_JRV1oOwX#Y={Az2LEa
z>*+T-ZLb(WRw)5CgGfW1Ek)IB(+YgD@J`C!8g$jI{W&2`Zsr1>7OJ-3mtVkTxQsgh
zLs2Mb<WG&$fM#6rHaEBUA_XomhY}HnW<#a>`BSh)#BpkX%?TBtB!>;a(QLGY1Aa<d
zP;Xi?H=2yX_))ON?wNGJV1a&OU)JLUA|k#HEmb8Gdq;$YM<0W<Y{OzqXEa32MDB5j
zcUMQf6qFaAirlwpUh(RRc&@2I*sl@Ps`lt<SOV~5cpw#F%T}Hyg>t{RESZ)E7w>>Y
z63o@*^eFXia0D9In_x2TnZR-|&s+pvu=QL#6_6#~Q3kN)X={Jq<GsnKfqGgkIO&?a
zSxLg2PeUw`E{*XaZ;N}`1%2eRHE1n`C0B*6^z`fUwf7-n%fpT}i^`>2m+uD|iwPDt
zvk>BmOGJx;V{AQE6YW3!OSyPu2L$UIHi136P}=u+b|y9&^^Dci1q1(PalrEAsY`mA
zqdoRjMvirhOP>Cbz4&NWPPYH!5!~>t)d`p;XEdPCgRF2z)GRyD%g}|_v?CQq;JwRw
zDi9dL6wjJ&^tOFVEN9|pmv%ACrfBDUEk6I|-P>Kj;;a*xj^7jYeJXV@La&%ichu}K
zynOdKij?BO%#~KkoJpm=8|eA&EW~j3AqM*~_HWBz<JP=aOMHK3mYUO8TCL^PHej>T
z`~5%>si8?kox3s!Ykz$;zLEI2f7+?TXnX&cI?OPET#C-Kj1U6Eujt$s$&U6b6>{3v
z{wYXxbLaijJLas^R+e7l(Ban^40V9Qxom8!Te8yf^p}3$awreI3FHzsAdcDs>;Xc)
z?7TqOyqyJXMrX*O`$d<<AC}F=WDXuv!84~dwUhLN9l(R$j8qz%FfDK95kN*U39<!8
zkukobo6uTrQsn^Wv&p8OT%QE$?c5OxeChZ5z*Sfya5R&9)(bmx&FQGzNibm^0%??$
z-lKXgpMY>kxRGj9sjqVs_J9>w-%3^G2cto%5-<nL$@)on`a<Rd^Ilz5lDc)L>(}Q+
z8g6-uA$IzI#HIz{&>|x#oazS%gCQYK?G1=7-XC?kWcq28^`<bw3}iHAJg$Co49%P9
zNW-J<&j3#CGe)l%T^nv6HKhwgw`k3-EI<0s303brV6i7aVxy;f&U5e2>qd~M_Zf&H
z_F!6T71s=l`2Jes#rP*60|gOmxG#1c>0WXZqXWWX>-AjE41`}YW+~VKfRSOp+96Ms
zdc(S2%aVRT{}8MZ%TX93xeX5aE4OYqzU9=~1{QJWf8b%fqPqLR)_dEEF!vk)>rMui
zjm@c3r%X4k+v6=*)FvduCpN9Ct&&K~e23_az>e$zd&?Y>%Krka-{JQ#GXqhSA0Xb>
zmF{MW7!a}GW1|Su3N_!|*%}|521Z6pL>Vm|SdNf4LOHW5qb9^E@B^^%yP)tuD6Ld$
zJ0M)8<_3ObaD=eHOy-)@!*Q^AN3!DINLfjLDgmZcP&^07*XR6b;}9^EIez5HmxPvP
zs>~gTkm;3I2#H6sdWSovpF#Sc>22iDT`YN9l~p<MTsWrQ$1BQkwhg9P!q(;0a28AX
z_03({k1pv8gw_N?49=B$ZGi=s`opYU96x~gdkrk28HsLmKYh`vux$nMy*hl?3d8~|
zkzD-00n5b_?Ah0&c8k&Iq5U^d@1*!2f;!!_`ZMRd>)&6gIPyLN54>c6wMSF@F<<X>
z-78w-1llX_@u5pCKluO~d7FDN2<yfZ*D+H*6ZS@m{RQ6PuZtE=>ANvlnuIT4?@fW`
z)5$~S0mnkM)(5#o_Npx^m@SC$QU8I%i!|G!-3+u(Wx8~>uSe1DeKT9jdHgeQXtl!{
zZ@d(G(vM^e>8rU>S^Q#(bw%1FL6>}n!Qymxp+444zs-gDRH2G&>EE?)(-O(ef}X#v
zY@dKlNpClt2UIf-bmi!&-x6|r0x3W)?HbW@GEy2al2(SdQ#qXYgW`lsj1;lUT&-{0
zU-aFo;$pk#x|MZ+!!;WcOlq4p%OP*KeNYs!j6v)^a;lNBLD<)e=y|SY<E?qWxzFhC
zcTcaQqFCS3Yrqi<Jt0n;?g-<rAi%5r{a01mDp=Je?B7L5Id?j44p4gV8G~mh(#$Ix
z$h3K0P)b}RG_i|-qGef~zqRk{s0~X3Ox}}cW~IeK0)VoMCmjL)A3#gflWAO4Wm&7v
zqDrZd7?HC9oHSuQUeAe44_K4xbSqN=(kntj2pATMc>!OTcj_R(q`T@F@Et&wHmZ6W
zdO;CWx6Fs=Q*n0*_*D3XD*2V7_^b3}!0+#4>D<t<XvMH19INrs4DB5=sa&D+;Wp^*
z;X0eqqHII8i$zps)%o@xk1;5tJ$f4ZFTi%30Qc7xk{hdW_eBK1%7eNKdBaXOHWCrV
zb)X65RCl(q$95ioo4{aX!vDH-$#$TZxs`o1S(S(GG)pP&Z&z9n=<_=aT#{zQ)ZI^t
zJJtL+fhbLI;*dUx+gH|3`)Mfm8pMOYnAzQSY00|`x8(l*LY~`k77!9ZP}6}JdVt5T
zQI%kX4<zh<SrPNMpNgTh*4eyE@cky+|Jz99L4R7Kyj=P!L>E4g)dwFd$kM6F{Ko6F
zeWLj8fx~}km{*-pSQZOq7(mU-o#`WWvCuCM;`~TPFdPe>eGJ~Q4x{?wbmHowbh!uY
z|3`6nr$&u&miqMWfWN@OE$9K)J{9(MlOXpl>6F^jcSy5Ygj)anf9VkCIK^5vnf_>)
zZS{Rgg-g~W6@ZfNmIJ*MqoV5|D4o*V&^&%JvI``2`p`QMv~+R*56>|{5p8_C@h(uV
zH|cXb93*sCfbxN98Uah`Vu$}ln_MMyO9<)kC-yfZp{SOrBx2PRHb*mWux~G0{L$JL
z*(V9r1A+g297S)4HXpuzEiGs^tMu+%mDb^-0=s~R_x|-byYuoR#|ib6K|E%ga~R!1
zUS0TmjU~M(8arzOQ1;^UDK=gEucA2s<0m7Dcio060AY`!7dYf>=Sz%|z!%zgNJ#bw
zyaEE-4k5n3>oW44T|TTC<#{KoJ7J5%O(=}E<ABiQCbK7b8EmV5%2$o@c}p-?ADs;S
z(NAIS!5M&BC;fwHfmowd<R++PKLayLuqx3wTYjkG_pkl^!4<nM<JqXJYR~u6Y@5%o
zuoV7Bk%Fu3`Z)hb>U3c50o2WWBPjAd1Rn(d$Ai$nFw#Q#Oq{Lh`rjRcjPq+XiVm*%
zwjq`TUqBBv9WqAJ4zyehuB?J7dW+zGzY3b|f^jdeYpzSL`l)EO(aYEF+UPxJmVdkZ
z!NY#EiYV|mxW&v$VpDNxK}^Fh)cPNSF;L5-E}SviR7*ksNVD<;nN{G)EkQoS0aMIV
zBPwG41g`Whn<#0_Z3nfpWRMG%6(`RkaR>cg1g<G)(+}n{u(azAeu)lxel$wsR|Y99
zZos?!wL0x7*X=!mOql$lLJtu<0j46so*8@Y3E#waFOY*~_27s;w?Bct)PSk*Wqi9J
zSb>bu*P4C<p#+*!Ef$E&n1XpM0IP%zj+{@V0UU{PT9-+k9;>F%vsoMeRy6`@*+_m?
zAUL5q(JqP6TTjK_fpWD|7*--79IB^4m(vIO0w#YXHIoBJ+H-f_b2E-SR$^nhjgj#m
z(sbd#La}CQZp){1`yp_A%X1ekOPtIz<!b%1)l>()NB)kLoq$kDIhKEF4<Mo+!B*Cx
zk|oS}%H{di9(gl6b~tRq|I%O?@4}5!LC<7>SmtOrBsE{Y{T&E?Jvo#5hF<)>M?_)x
zkB`9$A`QUWh_GCvc=N;t5UoS}R{$&zE>W?{e*X8s_YhDGdGkU*{tsEZvmdj`3rO`@
zBs3v%udyoxT}yYyEaCL!T=b3f&jAVeB+ixEoySkm@kHZJl*kjK+w_Vnu|=0YycACU
zjJ<i%s8Ny13yCI66<ENz{QM(1CA||WnG)~3JwY~+cxm=EZ@HP_+?P4R5}$2Nv;lGj
zY?gEDQN7QQ)o--8)Z+9JH+K!Kg>|!~=K-U?&5i7zt%)A!dseUcfrtkxB=Mf&7Elz4
zt3SKlP<zxn&}F%9@v$hk%>CM<kjTxNPZuDOlOoCC_Ww2Z)nQS7QMa@V-CYBS2q*&x
z3@9Nbji59FGL+N+3L-sp3n+qu0!m1?gdpA0NJ&YT!q7F$cjkBR_uPN){5Q|MapsM)
z&t7}&wU}IHNP{&iozE$}bM3oetP3LA@+QS?@N3QY)8rcg$(=Jnr!*WRP9)3gK5ds8
zy|x9KgOky*riAs6qPt+ehZ_mZFAuPN0+`#jr%5;qwn`=IN>a7HK#-vn&c!<O{8xmG
z-txeRfw!q&Z$}-s_&Fo}%&XF7)bC@p8vugkEbt+O8Yn*gK9$XoGTK1~5u{+D;Hdi(
z-DmqGwp^hjo!PLEuF&PG84PHZvrUDakr|cc#P!=?p1HR;l!CB^8_0twlO?Qpfo9dW
zBv4A+JJ+d=LVvcW0CPuKhOq<*6Cu1kOk;p0q`Tjy>mhkjR`0b!@n1|?tW%(xtP;X>
z=PW9g(VjEx2=%Z480!Twrdy9IOnqZZxrPb?aLnfke~^97g6<MV*3p=X=io$9_R+(}
zaiVGDq?;#Tj=g!@k0YAa{DGre6I(NIx8>LJIbp#9STO$c1_VEuFxwN*XiK~H54<IB
zJc=io>|W<BzB7080BQ|Q)-;A?)fEPt(rpk}$m4SiP)SxX%wFvV70^dWeR-4nWHU4y
zl3!ugD=(Bi72C{qsNN>B4MR(DDi;Ea@Davr*^s0^xs1R@gEB^$&1B?Rl4Irm1J7uF
z@jj?Z#c~61QQK<%C1()w-u{C|TFBP@3w?DW@F*0(2k!$WscuU2@)QW_>(*V?LOc=(
z_qH8-*L@U!_%5rAa7BtTncHJ3qT#H?#7Mjl)iKbcAp7i7Wny3`RrIb{G$|c9^%iZ-
zFq!3K%tFa1Lv{LL`t`%Nf$0-Sxyy+$WbCP{X>-YDEh@0c=et;$<!ikkgvS0oF}IO>
zLZLAZ4~57@$`w1cKqKgKsn7Dym{fT~AAxnKLek@VU-aw2l3O^?&J10Y6q8D0*w`8n
zxi2RzI<!(BXgK!A;*@*$G4wxH&dU3H(si%g<ovbru}ufzZ_zD;)GuTwd+<d$i%x&}
zrPk)er=)fn`tvOGJ$|HPcrv$t8}lG*NkNUVETP25{9{ZimHKbF&`G<dqa2%6R(-kg
zRm-vy%pyy33C=F(+t-CP^zWa67oiIqwTBCpQG`LO+p^&cfxETuFQsZ1nD&1IE15Z9
zC4=150D4;6bm@B<%}%K=|F|OC@(ZyCpL`<c-Ku@#xN)5avszf2OZNd#EldaJeKF-v
zvk~(Wmh9KmU3K^oEHsU;PU4<)ri=CLmR_B9nsjP$tgu=VSfG!KpFElWlW`nbhskdr
zKm{%Nr!HBqGRKCnFW-n_#9o}3pkujTIaQeu7h0f4$2Avz1PBMxmHI^7_7U2zwP8H@
zo-=vj%e^h#HA5C?{u6m{PN`3Wa20A6n;^T%uy;iECZXopWi%^BDu01SOmwx@5c4gG
z+d1gO8<E5^A-lnP=T-B()c1+&o*5P5K#s=vg|@Fmc+4b2Z&RF1j~}uhzRxBI<lh1-
z_$Pf3K{9K(3yh+YWuHz$$Zt+QVExKfbkZ*2!6<tDooCZ{(flgoUuusFy%K_D|J1Z0
z0Nu{Sq$(LVZbmS49;^fMe?i$ko+)BAH<J;m%L~lI4+hQAv3X_#TkaMWF&9rpw*#=T
zY|d5Z%d!(!nb^5!#a^<DqE0eNw2E>%`jc7m{6;ut>qNHOCltNt?KnyOR$dL|7jbuP
zs=H4`<?8eO+HV=(_J|7}gfCOjN+J-<vs`{VVolXF7u6MJK+K^toA9T8x|b?T|34t-
zXA>O1<p4l04`A=ia3nI#6&LdwP$amEU5DdPzhp6SVqp$G?(G!#HNp&_h?zhoK{8(W
zg!Y6_r<)ZbL6Wm}n9FOxQCvr&v_#hd`7Yz~05VM|8S@K>$o)$Ie1dvotTlbyz~{{I
z6;t2Y<4%=eF%%QwBkns)G^`$N3w`%;7Su^9?w`coes8`VuzjFbE%+g%QoWhHw`b&8
zd3!5YBsB&^slQ<U^Fr65UCOkDEJDrJamc=p`aGZnc~TOFzqU=YKCfd|%N16V>+q`i
zh9aa@VTv8?=+k<_ze4(~_q6AQs`ehN5&j@+3)S0sKxSVW_4&MXX7tHWr~Ux_-3oxI
z{sSpT$p=`A|Hv+mP9;-%3aBh?1gd~A0h<u9|JAF777(F-{CLfH`<dU31BQhAQ{Rbi
zIu5B(=(;?HRAp27??4)L1qF{>TqmNb{lu3fD>npu3B8}E@iN9Q!O?c{jA_;p#8?!B
za6$8w(B&jnEvkcD3r5)L<5nXyQ{|x7J@K(1#iXKPoYDSe;59p!yX5Th4`H@b|9qa4
zBsm_S$>^SnT8~o6tk5L2(ZN65`yQ47(TpLDydUxUKT<`5WjupOUOUp|rr7DRJ%u5}
ze=h`dw2Tyt8SMEK1$MW_K<IwoUox|K6UIQ_oe5Qr=pm5kahD8Xl=<{N*dqZB@<LTY
zlq)Z*sdb@87EDNrpaEu|#KGScOt;{X5-1VTw(=|9Q&4aue!C-5h2|O0w-k|<k*|r|
z-=i4b22OhV{3H|3S@6fQAT@<B_8h#=xM9}8pM*Moq#-M4hE1+hppGB#lsa+kt^zDY
zH>U(<3=2??e3<F`J4xuFeb=G|6X4jmHd3a*ty^8b#J5}|E;vGC(me8JZtg_#wA7)$
zB@g~i^HSvB=t#{yOn0ON@};Sr%;=Svz7Bj{VM$`Ux1fb(@~Qf?LWJT8jzz^aBxUq9
z>>BhEpPpboxp=P~&_y=W{e#7x29Q2MRpV(03S5T~<Dy}ES<96u4}r)B^;bQoz<1l3
zzmkA!Fei;d+)%w)CgG4>Qh0=(lro%%iQPR{jlW?wl$B1O1@?$nTf<$s(~O@?vl-$O
z&nb8mKgy63&uAdcAEMrDTD(87oNi>h`7+zENOjA^h*>%7BW+aChZ|K8v6Kdh&Q2ln
zH`M%}^3-l2ciSuQFEm`rtaq#zZy%t1AueTMhv|!39X@=L{N7%+h_BZ%0$E788$ZBu
z&B(<RsQd7WXp7LbRq(Y>uADVHky#sg_^brwjKc5P$ERpEdwf<|D(QGh9Z?!J3L2#j
zZ%xntZ%)!fgU>JLV~@yNRMw3O;KL6Jt<W(%*Ri$=x%8w)g+8@!BSN$qy)H*90ks<g
z9ek+(kx%6c#DwE*nx@vO)39&<i6DvI(uJxw#kxcy<1&s(fqKRN&7EX;f}1Sl9&o(@
zGR@&)oLp5ch1#RYs-*s>!!vyU(F^~}O<QPs2tfBorQ)x?xOhDDIgiSU+W%%>!O&qA
z0+6w@(AoHyc4Ya+-#5N+Y7EJ;Hi?)qo|Gpvn$oY9ptWTZa0B`p$aQ<O-~W4Dlr~Mz
z$E0>gdNv7rZ2?LlrR*<i@pN5b!C4wnCQnojj?ZS286Hjfp}TlttAKVu|33apIb-er
zD|Hj!&k|PCO&&Y<y(+%gGI=UDiFS;`0wrXo=-lT(RJk5U)iZe7Sy#f4dU=qEeH(S$
zK}xnB?wW@W#M&C$F>EhiEUDGd)22si<E!)v7zEJg1U$UR|A+mpV(R)T*0-M4GwX@Q
z?+E=DbOge;gf(PTe;7A0)mSmqml{cfN`KUAMLsKqkAlmomdl#F+HYF8Qk?~!kKRq0
zQ;Xs7hTW)pW#!&V?Df21A~<jCX^vk<6;y#+zxBK3tMSdX5H21=ie$CXpB7-I=S_01
zEUY=VEm_u)&&#N#^&{gokRku-%|AmzTgU_PP#_o6W=+-{eKXG2?{`BtL+}qfPNGeO
z8({}-rs5#!owlA_BT3J}q}ars-YIoJ8hwy!MM=LM39-&iF-ks^d4`e%9!KgtJrH_<
zo-pkYUoRT<ZPr(*%C~jP&0K4Kg`o#7&gNEMfk5;9>uC~IS!JJ)>$J-Z2|^rj1O%L{
zKrY4cO5{NT$NQK&a{6}Sb??L1_WrbA-w=jO(I?(YrVDncWdqF2VLCNkcBKw`sW0cG
zfSzJM{*DwTv!zBD`o?b0r#-$I&^;}H6Z{;==9YmB*@V<+9*zwI(AlY^{C$wxK4VGU
zqa?DsPCNuchhRmK`s=rEWhfNB7-E~jVyx0ppl%ZoVaBv2Loi7fM?rJakL=CoLcbB6
zgLaGGg-=?$E&;uHUsNd;2WpK5X1VHw7-#sgKV4y|b_%j3gi|$m>%1c!z81<pR2j&s
zlu>;b=aKYv9UamYhac_pUc>MJ*K54;+QO}V)!^A6aOLFZ90dm#El7I!;6_c8R;~?%
z><q}c@+;x#qkH>jij2#Ma=l(T*K+x#MZ>=J#DXNf$Q$z5RIiX#d35%N`mk88FF-rC
zNmOqO8&y-9k2ew-9<kK#sX$6$&pD7*<W6SK9={-}MjGg02Id9B@E~6SYNiwE%N6|-
z5HT4VK3ibhq5xYj*~VPs@aPUCdKO^HJ2;WYNdBT7DjIdRXoldex+odmG5?p4KF!td
zpfGFRLl8`S|NC^#lAy9#1yI6+R^+hJM*vu?pVMnx-$v_G>bTH-tmB3elOA9ScmUo5
z&G+L7H@E#IcpQi7whDy4n}j}pR0cZ+ngKcS|Asc`ts*k~ggt$ExV3gxW_}sn)?v{&
z0Zzf&J@4%6jf|i)!}TIte%b3E3O4}Syx|7YRa0x6>`rn~6=&KyFsAQ==Gs3!0F^n(
zaGldU0P^cGA=JlOoq+s-NK-rCpK31O4JfYN3di5A2T$YO*&R66nR{IB#L?<kK)&ae
zn5S<jQ=y;T+F>KmL20DT65VPGX*$-KTYr*yCo|u5SSru-TZ$d&oftd}&8WxmLC|q6
zA%TjBVoi*S8=$2#Ke(NvXD1+uXdw*5>EQrb--0`lM2D~_!fR9v*D`>0=inbAV|xRM
zh#vtLF}>FY?OBr&x}DG-EHWP-zZg{^_-tOo0|Yf!;_w3Yu$OsnV!i+E$*((I_svL<
zycH&;)POxxxNs(2iN(Vt%YQ}0=5HhCo)`b7*C*+UXJ4NO#<Dn14;g_Iae8k{xE4;D
zvOpREm^8F&d*RHMiSU9g?WFTiPa&Eci6E03y9MA@899%A1l!Y<x>$`zH->}sZg&N;
z64di$VG^5;`*|K2e}kxP_kZ$sG!JQher=k|k(B8C^RdoxLk+uPwXj%g&JKVuqx(0h
zh<R4Ev_>a6_hOt789%9Hvw1KNA*2^UuiP-*pcX4sXmQt`)Hb9AV7LXVfq#OAsZ+8a
zMMNGH_{ZJ+N}S`BFPA7@sDO0{qC{QdE`4q0l}6@xV4r2sMP@EviGm;Jb2~S^P0L`z
zI(mXu`RPsDag;9*A^P$L;%70R*o8Ulx4PP=%#_u<+idCVr_3WkgoA5m8z7E{4GSBQ
zE{9rwzt~-oZsXZ&Z?0&3Pg4QH4o1){gjP4}(eMwm33T3(v33n-!{jokQ|_zq^yrN+
zd)?lswDRuk(SLO-Y(X~d{o|>?!;!W!b_x5RqM#@x65ZUP`d`Q6ELevA0|pz1w9u>P
zp+Q~Emn8JeZyAQCH}iMA4ivPM<D^S!zl9%{`|LyH;I^PmHvumo(IfA<qPwLaB7a=A
zYEmrU)dmS9&B^fpzG)3C@EQp$!Wy4EneN%t*<tN~1vFq=u~*>>|Mo_cEaxR(Ide;|
zGRxbKh`Lmwzp~`U;I;BKL0wkCLKzeO8Y(r<oM0twAd^BRtemfHQ8+Y|4eK&-AB%p|
zD&l&;;=d2r1sG^#!xV_d_ZI=(#)$uvmIEO>I3>L4pN&m@TIMyO@u>SFm#8v(al+ql
zya1S!|A^*CKG~W&5F14&(l>Wto-lWqmrFAkkObz+JJa3wR_174<V$FtcGBUiJl#?>
z7L;B+Xop`rc1nm|UX@W>UvMMYPinB3a)%9pb2)Mdi;R>2i>y56kwVW-zgnwDo@=%%
zb_j_*X1S-}y)&hdz#_IZ33+b!j*__u8B0?KP%#+dv^QvZ;mGh|G>6~c%yI3DTh09{
zX2_55P`^PO&aD_+p=xEDF+%NC>@|I3Tg;PzV7cp42-I#R@M?EGvT5zc2WdbU^Ah0v
zW_6~++Q1{x-30C-4a)r*rMD%fu5Gv+iXwO8_+nMLZgzh2B(nc7l1$GGc`Tb);|eQI
z4FbipZI`+~5s%I;3!1e*2;}cMnh={u;OSpJh-XuEo5%VJxV<fl`XIIe|6<kk!P;yh
z>YRE4-Ia2grxW)*$PYg(GB^l4eEu|~j^eZi8*uVLhs%hKqK{v>__`@))uAxk7cY)&
zs+Lb^)_uywJu;}pJuazP=}#o6j%%d|&OLrn{gm$+-}B1dt;iqR?DBF+^nNji^{<`^
zDiB@{m2*WvbN=Qv#bv+7tn4#$n+9wE=}YO@)IQAQHJ%!=RaCU{*Y)@<KE)U3K<1Mu
z-9e%Dak*+!A5sWEA6dES^QHOSvy8JX&42<4SL-48*m2Lx&ev77Kvn*X7=wl%d};m(
ziqH`)0R7A(_TLH`z7d*!tk;w`mR2;WU(+s;^pHixArhWXi1LIF=Ct#77)#WhV9BN@
zA19RuFc8diPdP7)HhH_GNuRS}57L{!;4hSO`De&R1iHwUZ`WmR81@rlD(rw^S-K47
z_yYUn3nJe6OP9&GSWtn!w6fwws+2i)(xd&8Sh&utR-j)yQa$vnw1_n{L9VGgGr6Mg
zhX{k4w5!b+vj2_z4RkQ@a#0NV^DbB4XlS3;9R!3#^0Ul&)L}dJsC{o%ZF!@1=H*$l
zric8VtPHFmOqvBQkhGXL={TJsAx^Z!llAef2EkI$N7&Rh|L33SS7npLC$m-pHjOZE
z7SWv~cZnZ?2hFZASWd){m|XYKB~`**&Z?{OBq&XEZ~~*Q_@@_-?A2JMj|=~yvd185
z-CWDbl~a9+vQjJ(vR#4OK@z2TmkY;tk?cN&Lwd98poEG&!bRbz8898>#0N{K;xhA&
zIDE+I%^&_bi`YZ<H3}DO^T#R;Pqr8oj9sd@ErBv}k;5Xw&x1v`h0>%dJ4VUsp4A4r
z<2^T1#~Y&*5jnYO#LI#QF%Z7>a?kNAc(y9XHw&a`Y^FVG*WG@sm92Z`ahDufg(j^J
z1<Hj)>KZ%oJ0*eAT`rF>$>d2>-pQaaKF{t}3i4W(Sr^$m9O*QEYke~Hc^}_`J%WQT
zHRY!T_y4|)@4z3rpI~uzW0&hPQJAuYvF^=m2twO=<NL_T7b`V|p@%0I?(krUf#ogu
z;VF_7R@d|%+i~K3#kE^@CPWw%{bze)NM9a)=0ze>lggdJwf9#>Rl}_?9?_QHIuLNo
zfGBm2ZMjaw(N<|v^>oUoE+X`!PITJ(@~+NeOylR?#k)VndT6zrZ@+8ubXEL%j}`w_
zs242Mc^b~KDE{uua=BOGkc@7XG6QAh_yfH*kfA)+jaG;cWJz%_sgEdOKxvV-&=Qe1
zKRk^H<i<#yBgXLup;Z40)_#*aY?n3WklwN`O(u@dT&kHw|17W~8iXoV@Y3-FkWZ7?
zBpmxJ=~8==lOs9S_Bc1ni{sy?uP+(Lv>-@ZSnDHHpRsfV5Vo)>oc!!PxM3&F>#&l>
zoxri?fA>K~nw1@l+Olt8?%oUJVKWp@?_pRg#qHLck#|IpKQL^{KZqTue_Y2lPJ}UM
z-@Iqsc(bR4x+f1`j0UkLGQt(xGDh2DlN&{gn2F=0_YUk??{nw^JM_x1LBhIuo&V9k
zi9Ai=#Un69)aRAbI8oZU4y?|Y*&+i<Ab-^wgEDDLTW)m7EhvpzPbzbqt;4&|Qg{==
zpu-y40I~ikg^;s`<b-2!<*6m&+it%WiA-6G4s@m_Xtwcgbv8fSktj6rsCw{wb?xBX
zwru7|-pwE$Nhf-Z{(}$s*Ntuxn3I|~Pd(mFNQ)<0fKN(r2O-;<+G#VRFH$A$7%bnr
zJMfNL>j$la4<GY3kMzGy|4U6;8hj^>>~}Oa6HFO4jtIS2r2j1a<i1&lo(=jFfme(o
zZ5<iWihy^?Hp$Y}M7&=~38u?RfgoLH@!!a_-gRw>`<DH9wf6=*-%6N7iWgfwY1z6M
z`f9en^441^g||F9c$OChrP3{ssiQ??W>bkp9-xQF!p&|Hs4kw<1lN%TN>TX#;&5GK
zExH9pZwr#inKW-sho+ak4*et=gm%`5%!sYT2ik+HJko#sW^JF`B#3Tlm@qMaPT?`m
zD)#mqG30elFE5J84dO~wi*2dlP0Y~2n(fw})F==+@<w@^HVuAhXMKlH>G80A4KKC6
zx9RXT^F}r)5kc=4j$!qu1LYCo!%!-uNyqbdzuqIr_b3D@8Bx3siDFs?cou`Z2)7ZO
zJL2zToHM<0NsDRSY1(<su1O)@J0w&rN1I<Gt2Q747fBHOp9xxbYx--G(>LPZKQbr!
z6skz&_-VcT+x&Ca`Md#wIqfYq+-6y%3@COb9zVo6q}Ax&^DSn4+9|f5an1u}xBkzM
nxKsakK-`x9zhB%BNH6gZp7CbvYl_X`fscl&u1cwrb;$n!`B=2J

literal 0
HcmV?d00001

diff --git a/3rdparty/mshadow/guide/mshadow-ps/Makefile b/3rdparty/mshadow/guide/mshadow-ps/Makefile
new file mode 100644
index 000000000000..58d64a304009
--- /dev/null
+++ b/3rdparty/mshadow/guide/mshadow-ps/Makefile
@@ -0,0 +1,45 @@
+# set LD_LIBRARY_PATH
+export CC  = gcc
+export CXX = g++
+export NVCC =nvcc
+include config.mk
+include ../../make/mshadow.mk
+export CFLAGS = -Wall -O3 -std=c++11 -fopenmp -I../../ $(MSHADOW_CFLAGS)
+export LDFLAGS= -lm $(MSHADOW_LDFLAGS)
+export NVCCFLAGS = -O3 --use_fast_math -ccbin $(CXX) $(MSHADOW_NVCCFLAGS)
+
+# specify tensor path local_sum.cpu
+BIN = local_sum.cpu
+OBJ =
+CUOBJ =
+CUBIN = local_sum.gpu
+
+ifeq ($(USE_DIST_PS),1)
+BIN = dist_async_sum.cpu
+LDFLAGS += -lunwind
+endif
+
+.PHONY: clean all
+
+all: $(BIN) #$(CUBIN)
+
+local_sum.cpu: local_sum.cpp
+local_sum.gpu: local_sum.cu
+
+dist_async_sum.cpu: dist_async_sum.cpp dist_async_sum-inl.h
+dist_sync_sum.cpu: dist_sync_sum.cpp
+
+$(BIN) :
+	$(CXX) $(CFLAGS) -o $@ $(filter %.cpp %.o %.c, $^)  $(LDFLAGS) $(PS_LIB)
+
+$(OBJ) :
+	$(CXX) -c $(CFLAGS) -o $@ $(firstword $(filter %.cpp %.c, $^) )
+
+$(CUOBJ) :
+	$(NVCC) -c -o $@ $(NVCCFLAGS) -Xcompiler "$(CFLAGS)" $(filter %.cu, $^)
+
+$(CUBIN) :
+	$(NVCC) -o $@ $(NVCCFLAGS) -Xcompiler "$(CFLAGS)" -Xlinker "$(LDFLAGS)" $(filter %.cu %.cpp %.o, $^)
+
+clean:
+	$(RM) $(OBJ) $(BIN) $(CUBIN) $(CUOBJ) *~
diff --git a/3rdparty/mshadow/guide/mshadow-ps/README.md b/3rdparty/mshadow/guide/mshadow-ps/README.md
new file mode 100644
index 000000000000..146c7f04498e
--- /dev/null
+++ b/3rdparty/mshadow/guide/mshadow-ps/README.md
@@ -0,0 +1,227 @@
+mshadow-ps
+====
+### Parameter Server Interface for GPU Tensor
+
+mshadow-ps provides asynchronize parameter server interface for mshadow GPU/CPU Tensor.
+This allows you to do ***multi-GPU*** and ***disrtibuted*** (deep) learning in
+an ***easy*** and ***unified*** way.
+
+mshadow-ps implemented a two-level parameter server. The architecture is shown
+in the following figure. Typically, a GPU card or a cpu core runs a worker node,
+then a level-1 server node communicates with the worker nodes on the same
+machine.  Inter-machine communication is then via the level-2 server nodes.
+
+The rational is that both the bandwidth and latency between the worker nodes in
+a single machine is usually 10x better than the inter-machine ones. By using the
+two level parameter server, we can use different consistency models at different
+level to better trade-off the algorithm efficiency and system performance. For
+example, we can use a sequential consistency model, also known as
+[BSP](http://en.wikipedia.org/wiki/Bulk_synchronous_parallel), on level 1 for
+guaranteed algorithm convergence, but use a
+[eventual consistency model](http://en.wikipedia.org/wiki/Eventual_consistency)
+on level 2 to hide the network latency. See our
+[OSDI'14 paper](https://www.usenix.org/conference/osdi14/technical-sessions/presentation/li_mu)
+for more details.
+
+![Arch](2-levels.png?raw=true "arch")
+
+####List of Resources
+* [API Documentation](http://homes.cs.washington.edu/~tqchen/mshadow/doc/namespacemshadow_1_1mshadow_ps.html)
+* [Library Interface Header](../../mshadow-ps/mshadow_ps.h)
+* Tutorial in this page
+
+Working with Level-1 Server
+====
+Suppose that we are now implementing a Multi-GPU learning program.
+One way to do that is through data parallelism. We can launch many
+threads, with each thread compute gradient on one GPU, and aggregate
+the statistics together.
+However, the gradient synchronization step could be cost time, and in
+many cases, we can do the computation in an smarter way, so that
+we ***overlaps the computation with the synchronization***.
+
+mshadow-ps provides interface to do such synchronization in an easy way.
+The following documents provides a way
+
+### Getting Sum from Multiple GPUs
+We first get familiar with the interface of mshadow-mshadow_ps. Through the following
+program in [local_sum-inl.h](local_sum-inl.h). You can compile the program
+by setup the [config.mk](config.mk) according to your computers's enviroment, and type make.
+
+In the following program, each thread first does some computation locally, then tries to get the sum
+of ```data``` through mshadow-ps interface.
+There are four key functions in ```ISharedModel``` interface
+* [InitKey](../../mshadow-ps/mshadow_ps.h#L76) allocates a key to specific tensor shape
+* [Push](../../mshadow-ps/mshadow_ps.h#L100) pushes out the local data to the synchronization interface
+  - The data pushed by different devices will be aggregated together by key
+  - Push is an asynchronize call and returns immediately
+* [PullReq](../../mshadow-ps/mshadow_ps.h#L122) requests the result of synchronization to be copied back
+  - In the local default case, the synchronized result is the sum of pushed data
+  - mshadow-ps also support the weight update on server side, where the result of PullReq is the updated weight instead of sum of gradient
+  - PullReq is also asynchronize
+* [PullWait](../../mshadow-ps/mshadow_ps.h#L87) wait until the pull request of corresponding key finishes
+
+```c++
+// this function is runed by specific thread
+template<typename xpu>
+inline void RunWorkerThread(int devid,
+                            mshadow::ps::ISharedModel<xpu, float> *ps) {
+  // initialize tensor engine
+  mshadow::InitTensorEngine<xpu>(devid);
+  mshadow::Stream<xpu> *stream  = mshadow::NewStream<xpu>();
+  // allocate tensor on xpu
+  mshadow::TensorContainer<xpu, 2> data(mshadow::Shape2(2, 3));
+  // set the computation stream to the new allocated stream
+  // this will make subsequent computation whose target is data
+  // to use the stream, stream is needed for async execution in GPU
+  data.set_stream(stream);
+  // assume these operations sets the content of dataient
+  data[0] = 1.0f;
+  data[1] = devid + data[0];
+  printf("dev%d: before sync, data:\n", devid);
+  // use print to show result, do not call
+  // print normally since Copy will block
+  Print(data);
+  printf("====================\n");
+  // intiaialize the key, register the shape on parameter server
+  ps->InitKey(data[0].shape_, 0, devid);
+  ps->InitKey(data[1].shape_, 1, devid);
+  // push data[0] out, for update, or aggregation
+  // 0 is the key of the data, devid is the current device id
+  ps->Push(data[0], 0, devid);
+  // pull request is used to request the data to be copied back
+  // once computation is done
+  ps->PullReq(data[0], 0, devid);
+  // computation can be done here..
+  // the pull request handler will be overlapped with
+  // similar as previous call
+  ps->Push(data[1], 1, devid);
+  ps->PullReq(data[1], 1, devid);
+  // more computation can be done here...
+  // the computation will be overlapped
+  // PullWait will block until these request finishes
+  ps->PullWait(0, devid);
+  ps->PullWait(1, devid);
+  printf("dev%d: after sync, data:\n", devid);
+  // use print to show result, do not call
+  // print normally since Copy will block
+  Print(data);
+  printf("====================\n");
+  mshadow::DeleteStream(stream);
+  mshadow::ShutdownTensorEngine<xpu>();
+}
+
+template<typename xpu>
+inline int Run(int argc, char *argv[]) {
+  if (argc < 2) {
+    printf("Usage: device list\n"\
+           "\tfor CPU the device list can be arbitrary\n"\
+           "\tfor GPU the device list need to be actual device index\n");
+    return 0;
+  }
+  // list of device ids
+  std::vector<int> devs;
+  // initialization
+  for (int i = 1; i < argc; ++i) {
+    // record the device id
+    devs.push_back(atoi(argv[i]));
+  }
+  mshadow::ps::ISharedModel<xpu, float>
+      *ps = mshadow::ps::CreateSharedModel<xpu, float>("local");
+  // intiaialize the ps
+  ps->Init(devs);
+  // use openmp to launch #devs threads
+  #pragma omp parallel num_threads(devs.size())
+  {
+    int tid = omp_get_thread_num();
+    RunWorkerThread<xpu>(devs[tid], ps);
+  }
+  delete ps;
+  return 0;
+}
+```
+In the above example, we did not do weight update on server side, so the synchronization result is
+simply the sum of data on each device. The key property of this interface is that the Push and PullReq are asynchronize.
+* We can call these two functions once the gradient is ready, and the mshadow-ps will do the data synchronization in the background.
+* When we need the result of synchronization, we simply call PullWait to wait the synchronization task to finish.
+* Such interface allows us to do additional computation between the Push/PullReq and PullWait
+
+### A MultiGPU Neural Net
+To get a more concrete understanding of the interface. We give an example of multi-GPU two layer neuralnet
+in [../neuralnet/nnet_ps.cu](../neuralnet/nnet_ps.cu). The general idea is follows
+* Push and PullReq is called once we get the gradient of certain layer
+* PullWait is called before we do forward on that layer next time
+* This creates a ***time lag*** between the backprop and next forward to that layer
+  - mshadow-ps do synchronization concurrently with computations during the time lag
+  - The time lag is big for latter layers, which also usually need more time to synchronize
+
+There are several note of the mshadow-ps on the neural net code
+* Callback function in PullReq
+  - A callback function can be pass to PullReq to be called when the request complete
+  - We place weight update in the callback to perform update when we get the gradient sum
+* Computing stream
+  - Due to GPU's programming model, we need to do computation on non-default stream
+  - Use set_stream in mshadow tensors to set stream to computation stream
+  - To report error when you did not use stream, you can compile with -DMSHADOW_FORCE_STREAM
+
+We should note thate because the example runs on MNIST, which is an quite small dataset, you may not observe
+speedup with multiple cards. However, you will find significant speedup when you run on other tasks.
+The newest version of [cxxnet](https://github.com/antinucleon/cxxnet)
+
+### Moving Parameter Update to the Server
+In all the examples so far, we use mshadow-ps to get the aggregated sum of gradients, and update
+weights locally on each GPU. For more advanced usage of mshadow-ps, we can move the weight update
+to the server. The communication pattern is as follows
+* Each thread still call Push to push out gradient
+* The server will apply the update rule to update the weight
+* Each thread call PullReq to pull back the weight from server
+
+Such update pattern is suitable under distributed setting. To do so, user need to implement an
+[IModelUpdater](../../mshadow-ps/mshadow_ps.h#L202) interface. And define the following CreateModelUpdater function
+in the program
+```c++
+namespace mshadow {
+namespace ps {
+template<>
+IModelUpdater<float> *CreateModelUpdater() {
+  return new MyModelUpdater();
+}
+}
+}
+```
+Before calling ISharedModel.Init, user need to call ```ps->SetParam("update_on_server", "1")``` to set the update
+mode on the server side. If user uses distributed shared model, user must define ModelUpdater.
+
+Working with Level-2 Server
+====
+
+First build the parameter server (replace `ps_dir` to any convenient directory)
+
+```bash
+git clone https://github.com/dmlc/parameter_server -b dev ps_dir
+cd ps_dir
+./script/install_third.sh
+make -j8
+```
+
+Next change `config.mk` to
+```bash
+USE_DIST_PS = 1
+PS_PATH = ps_dir
+```
+
+Then `make`.
+
+Next start 1 server node, 3 worker nodes with 2 devices in each worker node:
+```bash
+./local.sh 1 3 ./dist_async_sum.cpu 1 2
+```
+
+The `dist_async_sum-inl.h` is similar to `local_sum-inl.h`. The main differences
+are 1) we create the server at a remote node, and set
+`update_on_server` to be true.
+```c++
+auto* ps = mshadow::ps::CreateSharedModel<xpu, float>("dist");
+ps->SetParam("update_on_server", "1");
+```
+2) we explicitly create server node and worker node at `dist_async_sum.cpp`
diff --git a/3rdparty/mshadow/guide/mshadow-ps/config.mk b/3rdparty/mshadow/guide/mshadow-ps/config.mk
new file mode 100644
index 000000000000..ed7af28a5096
--- /dev/null
+++ b/3rdparty/mshadow/guide/mshadow-ps/config.mk
@@ -0,0 +1,40 @@
+#---------------------------------------------------------------------------------------
+#  mshadow: the configuration compile script
+#
+#  This is configuration script that you can use to compile mshadow
+#  Usage:
+#
+#  include config.mk in your Makefile, or directly include the definition of variables
+#  include mshadow.mk after the variables are set
+#
+#  Add MSHADOW_CFLAGS to the compile flags
+#  Add MSHADOW_LDFLAGS to the linker flags
+#  Add MSHADOW_NVCCFLAGS to the nvcc compile flags
+#----------------------------------------------------------------------------------------
+
+# whether use CUDA during compile
+USE_CUDA = 0
+
+# add the path to CUDA libary to link and compile flag
+# if you have already add them to enviroment variable, leave it as NONE
+USE_CUDA_PATH = NONE
+
+#
+# choose the version of blas you want to use
+# can be: mkl, blas, atlas, openblas, apple
+USE_BLAS = blas
+#
+# add path to intel library, you may need it
+# for MKL, if you did not add the path to enviroment variable
+#
+USE_INTEL_PATH = NONE
+
+# whether compile with parameter server
+USE_DIST_PS = 0
+PS_PATH = ../../../parameter_server/
+PS_THIRD_PATH = NONE
+
+# whether compile with rabit allreduce
+USE_RABIT_PS = 1
+RABIT_PATH = ../../rabit
+
diff --git a/3rdparty/mshadow/guide/mshadow-ps/dbstr.h b/3rdparty/mshadow/guide/mshadow-ps/dbstr.h
new file mode 100644
index 000000000000..59a7135cd540
--- /dev/null
+++ b/3rdparty/mshadow/guide/mshadow-ps/dbstr.h
@@ -0,0 +1,35 @@
+#pragma once
+#include <mshadow/tensor.h>
+#include <sstream>
+
+template<typename DType>
+std::string dbstr(mshadow::Tensor<mshadow::cpu, 1, DType> ts) {
+  std::stringstream ss;
+  for (mshadow::index_t i = 0; i < ts.size(0); ++i)
+    ss << ts[i] << " ";
+  ss << "\n";
+  return ss.str();
+}
+
+template<typename DType>
+std::string dbstr(mshadow::Tensor<mshadow::cpu, 2, DType> ts) {
+  std::stringstream ss;
+  for (mshadow::index_t i = 0; i < ts.size(0); ++i) {
+    for (mshadow::index_t j = 0; j < ts.size(1); ++j) {
+      ss << ts[i][j] << " ";
+    }
+    ss << "\n";
+  }
+  ss << "\n";
+  return ss.str();
+}
+
+template<typename DType>
+std::string dbstr(mshadow::Tensor<mshadow::cpu, 3, DType> ts) {
+  std::stringstream ss;
+  for (mshadow::index_t i = 0; i < ts.size(0); ++i) {
+    ss << dbstr(ts[i]) << "\n";
+  }
+  ss << "\n";
+  return ss.str();
+}
diff --git a/3rdparty/mshadow/guide/mshadow-ps/dist_async_sum-inl.h b/3rdparty/mshadow/guide/mshadow-ps/dist_async_sum-inl.h
new file mode 100644
index 000000000000..9b60460e3c44
--- /dev/null
+++ b/3rdparty/mshadow/guide/mshadow-ps/dist_async_sum-inl.h
@@ -0,0 +1,124 @@
+/**
+ * @brief  Simple test of KVLayer
+ */
+#include "ps.h"
+#include "parameter/kv_layer.h"
+#include <cstdio>
+#include <iostream>
+#include <omp.h>
+#include <map>
+#include <mshadow/tensor.h>
+#include <mshadow-ps/mshadow_ps.h>
+#include "dbstr.h"
+#include "glog/logging.h"
+
+namespace mshadow {
+namespace ps {
+
+
+template<typename DType>
+class Updater : public IModelUpdater<DType> {
+ protected:
+  void InitModel_(int key, Tensor<cpu, 1, DType> data) {
+    data = 0;
+    data_[key] = data;
+  }
+
+  void Update_(int key, Tensor<cpu, 1, DType> data) {
+    data_[key] += data;
+    // LOG(ERROR) << dbstr(data_[key]);
+  }
+  std::map<int, Tensor<cpu, 1, DType> > data_;
+};
+
+template<typename DType>
+IModelUpdater<DType> *CreateModelUpdater(void) {
+  return new Updater<DType>();
+}
+
+}  // namespace ps
+}  // namespace mshadow
+
+// this function is runed by specific thread
+template<typename xpu>
+inline void RunWorkerThread(int devid,
+                            mshadow::ps::ISharedModel<xpu, float> *ps) {
+  // initialize tensor engine
+  mshadow::InitTensorEngine<xpu>(devid);
+  mshadow::Stream<xpu> *stream  = mshadow::NewStream<xpu>();
+  // allocate tensor on xpu
+  mshadow::TensorContainer<xpu, 2> data(mshadow::Shape2(2, 3));
+  // set the computation stream to the new allocated stream
+  // this will make subsequent computation whose target is data
+  // to use the stream, stream is needed for async execution in GPU
+  data.set_stream(stream);
+  // intiaialize the key, register the shape on parameter server
+  ps->InitKey(data[0].shape_, 0, devid);
+  ps->InitKey(data[1].shape_, 1, devid);
+  // first step, pull the data back from server
+  ps->PullReq(data[0], 0, devid);
+  ps->PullReq(data[1], 1, devid);
+
+  // PullWait will block until these request finishes
+  ps->PullWait(0, devid);
+  ps->PullWait(1, devid);
+
+  data[1] = devid + data[0];
+
+  LOG(ERROR) << "node " << ::ps::MyNodeID() << ", dev " << devid << ": before sync\n"
+             << dbstr(data);
+
+  // push data[0] out, for update, or aggregation
+  // 0 is the key of the data, devid is the current device id
+  ps->Push(data[0], 0, devid);
+  // pull request is used to request the data to be copied back
+  // once computation is done
+  ps->PullReq(data[0], 0, devid);
+  // computation can be done here..
+  // the pull request handler will be overlapped with
+  // similar as previous call
+  ps->PullWait(0, devid);
+
+  ps->Push(data[1], 1, devid);
+  ps->PullReq(data[1], 1, devid);
+  // more computation can be done here...
+  // the computation will be overlapped
+  // PullWait will block until these request finishes
+  ps->PullWait(1, devid);
+
+  LOG(ERROR) << "node " << ::ps::MyNodeID() << ", dev " << devid
+             << ": after sync\n" << dbstr(data);
+
+  mshadow::DeleteStream(stream);
+  mshadow::ShutdownTensorEngine<xpu>();
+}
+
+template<typename xpu>
+inline int Run(int argc, char *argv[]) {
+  if (argc < 2) {
+    printf("Usage: device list\n"\
+           "\tfor CPU the device list can be arbitrary\n"\
+           "\tfor GPU the device list need to be actual device index\n");
+    return 0;
+  }
+  // list of device ids
+  std::vector<int> devs;
+  // initialization
+  for (int i = 1; i < argc; ++i) {
+    // record the device id
+    devs.push_back(atoi(argv[i]));
+  }
+  mshadow::ps::ISharedModel<xpu, float>
+      *ps = mshadow::ps::CreateSharedModel<xpu, float>("dist");
+  // intiaialize the ps
+  ps->SetParam("update_on_server", "1");
+  ps->Init(devs);
+  // use openmp to launch #devs threads
+  #pragma omp parallel num_threads(devs.size())
+  {
+    int tid = omp_get_thread_num();
+    RunWorkerThread<xpu>(devs[tid], ps);
+  }
+  delete ps;
+  return 0;
+}
diff --git a/3rdparty/mshadow/guide/mshadow-ps/dist_async_sum.cpp b/3rdparty/mshadow/guide/mshadow-ps/dist_async_sum.cpp
new file mode 100644
index 000000000000..11891053fd4b
--- /dev/null
+++ b/3rdparty/mshadow/guide/mshadow-ps/dist_async_sum.cpp
@@ -0,0 +1,11 @@
+#include "./dist_async_sum-inl.h"
+
+int CreateServerNode(int argc, char *argv[]) {
+  mshadow::ps::MShadowServerNode<float> server(argc, argv);
+  return 0;
+}
+
+
+int WorkerNodeMain(int argc, char *argv[]) {
+  return Run<mshadow::cpu>(argc, argv);
+}
diff --git a/3rdparty/mshadow/guide/mshadow-ps/local.sh b/3rdparty/mshadow/guide/mshadow-ps/local.sh
new file mode 100755
index 000000000000..a965ea86805f
--- /dev/null
+++ b/3rdparty/mshadow/guide/mshadow-ps/local.sh
@@ -0,0 +1,39 @@
+#!/bin/bash
+# set -x
+# export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:../third_party/lib
+if [ $# -lt 3 ]; then
+    echo "usage: $0 num_servers num_workers bin [args..]"
+    exit -1;
+fi
+
+num_servers=$1
+shift
+num_workers=$1
+shift
+bin=$1
+shift
+arg="-num_servers ${num_servers} -num_workers ${num_workers} -log_dir log $@"
+
+
+# killall -q $(basename ${bin})
+# killall -q ${bin}
+
+# start the scheduler
+Sch="role:SCHEDULER,hostname:'127.0.0.1',port:8001,id:'H'"
+${bin} -my_node ${Sch} -scheduler ${Sch} ${arg} &
+
+# start servers
+for ((i=0; i<${num_servers}; ++i)); do
+    port=$((9600 + ${i}))
+    N="role:SERVER,hostname:'127.0.0.1',port:${port},id:'S${i}'"
+    ${bin} -my_node ${N} -scheduler ${Sch} ${arg} &
+done
+
+# start workers
+for ((i=0; i<${num_workers}; ++i)); do
+    port=$((9500 + ${i}))
+    N="role:WORKER,hostname:'127.0.0.1',port:${port},id:'W${i}'"
+    ${bin} -my_node ${N} -scheduler ${Sch} ${arg} &
+done
+
+wait
diff --git a/3rdparty/mshadow/guide/mshadow-ps/local_sum-inl.h b/3rdparty/mshadow/guide/mshadow-ps/local_sum-inl.h
new file mode 100644
index 000000000000..cfd503706c76
--- /dev/null
+++ b/3rdparty/mshadow/guide/mshadow-ps/local_sum-inl.h
@@ -0,0 +1,119 @@
+// This is an example demonstrating the usage of mshadow ps
+#include <cstdio>
+// use openmp to launch multiple threads
+#include <omp.h>
+#include <mshadow/tensor.h>
+#include <mshadow-ps/mshadow_ps.h>
+
+// simple util to print result
+void Print_(mshadow::Tensor<mshadow::cpu, 2, float> ts) {
+  for (mshadow::index_t i = 0; i < ts.size(0); ++i) {
+    for (mshadow::index_t j = 0; j < ts.size(1); ++j) {
+      printf("%g ", ts[i][j]);
+    }
+    printf("\n");
+  }
+}
+template<typename xpu>
+inline void Print(mshadow::Tensor<xpu, 2, float> ts) {
+  mshadow::TensorContainer<mshadow::cpu, 2, float> tmp;
+  tmp.Resize(ts.shape_);
+  mshadow::Copy(tmp, ts);
+  Print_(tmp);
+}
+
+// this function is runed by specific thread
+template<typename xpu>
+inline void RunWorkerThread(int devid,
+                            mshadow::ps::ISharedModel<xpu, float> *ps) {
+  // initialize tensor engine
+  mshadow::InitTensorEngine<xpu>(devid);
+  mshadow::Stream<xpu> *stream  = mshadow::NewStream<xpu>(devid);
+  // allocate tensor on xpu
+  mshadow::TensorContainer<xpu, 2> data(mshadow::Shape2(2, 3));
+  // set the computation stream to the new allocated stream
+  // this will make subsequent computation whose target is data
+  // to use the stream, stream is needed for async execution in GPU
+  data.set_stream(stream);
+  // assume these operations sets the content of dataient
+  data[0] = 1.0f;
+  data[1] = devid + data[0];
+  printf("dev%d: before sync, data:\n", devid);
+  // use print to show result, do not call
+  // print normally since Copy will block
+  Print(data);
+  printf("====================\n");
+  // intiaialize the key, register the shape on parameter server
+  ps->InitKey(data[0].shape_, 0, devid);
+  ps->InitKey(data[1].shape_, 1, devid);
+  // push data[0] out, for update, or aggregation
+  // 0 is the key of the data, devid is the current device id
+  ps->Push(data[0], 0, devid);
+  // pull request is used to request the data to be copied back
+  // once computation is done
+  ps->PullReq(data[0], 0, devid);
+  // computation can be done here..
+  // the pull request handler will be overlapped with
+  // similar as previous call
+  ps->Push(data[1], 1, devid);
+  ps->PullReq(data[1], 1, devid);
+  // more computation can be done here...
+  // the computation will be overlapped
+  // PullWait will block until these request finishes
+  ps->PullWait(0, devid);
+  ps->PullWait(1, devid);
+  printf("dev%d: after sync, data:\n", devid);
+  // use print to show result, do not call
+  // print normally since Copy will block
+  Print(data);
+  printf("====================\n");
+  mshadow::DeleteStream(stream);
+  mshadow::ShutdownTensorEngine<xpu>();
+}
+
+namespace mshadow {
+namespace ps {
+// model updater is used when update is happening on server side
+// if we only use parameter server for sum aggregation
+// this is not needed, but we must declare this function to return NULL
+template<>
+IModelUpdater<float> *CreateModelUpdater(void) {
+  return NULL;
+}
+}
+}
+
+template<typename xpu>
+inline int Run(int argc, char *argv[]) {
+  if (argc < 2) {
+    printf("Usage: device list\n"\
+           "\tfor CPU the device list can be arbitrary\n"\
+           "\tfor GPU the device list need to be actual device index\n");
+    return 0;
+  }
+#if MSHADOW_RABIT_PS
+  rabit::Init(argc, argv);
+#endif
+  // list of device ids
+  std::vector<int> devs;
+  // initialization
+  for (int i = 1; i < argc; ++i) {
+    // record the device id
+    devs.push_back(atoi(argv[i]));
+  }
+  mshadow::ps::ISharedModel<xpu, float>
+      *ps = mshadow::ps::CreateSharedModel<xpu, float>("local");
+  // intiaialize the ps
+  ps->Init(devs);
+  // use openmp to launch #devs threads
+  #pragma omp parallel num_threads(devs.size())
+  {
+    int tid = omp_get_thread_num();
+    RunWorkerThread<xpu>(devs[tid], ps);
+  }
+  delete ps;
+#if MSHADOW_RABIT_PS
+  rabit::Finalize();
+#endif
+  return 0;
+}
diff --git a/3rdparty/mshadow/guide/mshadow-ps/local_sum.cpp b/3rdparty/mshadow/guide/mshadow-ps/local_sum.cpp
new file mode 100644
index 000000000000..7f0eed0df42e
--- /dev/null
+++ b/3rdparty/mshadow/guide/mshadow-ps/local_sum.cpp
@@ -0,0 +1,4 @@
+#include "./local_sum-inl.h"
+int main(int argc, char *argv[]) {
+  return Run<mshadow::cpu>(argc, argv);
+}
diff --git a/3rdparty/mshadow/guide/mshadow-ps/local_sum.cu b/3rdparty/mshadow/guide/mshadow-ps/local_sum.cu
new file mode 100644
index 000000000000..6e839601a265
--- /dev/null
+++ b/3rdparty/mshadow/guide/mshadow-ps/local_sum.cu
@@ -0,0 +1,4 @@
+#include "./local_sum-inl.h"
+int main(int argc, char *argv[]) {
+  return Run<mshadow::gpu>(argc, argv);
+}
diff --git a/3rdparty/mshadow/guide/neuralnet/Makefile b/3rdparty/mshadow/guide/neuralnet/Makefile
new file mode 100644
index 000000000000..826384b5f3b0
--- /dev/null
+++ b/3rdparty/mshadow/guide/neuralnet/Makefile
@@ -0,0 +1,38 @@
+# set LD_LIBRARY_PATH
+export CC  = gcc
+export CXX = g++
+export NVCC =nvcc
+include config.mk
+include ../../make/mshadow.mk
+export CFLAGS = -Wall -O3 -I../../ -fopenmp $(MSHADOW_CFLAGS)
+export LDFLAGS= -lm $(MSHADOW_LDFLAGS)
+export NVCCFLAGS = -O3 --use_fast_math -ccbin $(CXX) $(MSHADOW_NVCCFLAGS)
+
+# specify tensor path
+BIN =
+OBJ =
+CUOBJ =
+CUBIN = nnet convnet nnet_ps
+.PHONY: clean all
+
+all: $(BIN) $(OBJ) $(CUBIN) $(CUOBJ)
+
+nnet: nnet.cu
+nnet_ps: nnet_ps.cu
+convnet: convnet.cu
+
+$(BIN) :
+	$(CXX) $(CFLAGS) -o $@ $(filter %.cpp %.o %.c, $^)  $(LDFLAGS)
+
+$(OBJ) :
+	$(CXX) -c $(CFLAGS) -o $@ $(firstword $(filter %.cpp %.c, $^) )
+
+$(CUOBJ) :
+	$(NVCC) -c -o $@ $(NVCCFLAGS) -Xcompiler "$(CFLAGS)" $(filter %.cu, $^)
+
+$(CUBIN) :
+	$(NVCC) -o $@ $(NVCCFLAGS) -Xcompiler "$(CFLAGS)" -Xlinker "$(LDFLAGS)" $(filter %.cu %.cpp %.o, $^)
+
+clean:
+	$(RM) $(OBJ) $(BIN) $(CUBIN) $(CUOBJ) *~
+
diff --git a/3rdparty/mshadow/guide/neuralnet/README.md b/3rdparty/mshadow/guide/neuralnet/README.md
new file mode 100644
index 000000000000..dd181e758c65
--- /dev/null
+++ b/3rdparty/mshadow/guide/neuralnet/README.md
@@ -0,0 +1,16 @@
+Example Neural Net code with MShadow
+====
+
+To compile the code, modify ```config.mk``` to the setting you like and type make
+* You will need to have CUDA and  a version of BLAS
+
+To run the demo, download  MNIST dataset from: http://yann.lecun.com/exdb/mnist/
+unzip all the files into current folder
+
+and run by  ./nnet cpu or ./nnet gpu. ./convnet cpu or ./convnet gpu
+
+MultiGPU Version
+====
+* If you have two GPUs, you can run it by ```./nnet_ps gpu 0 1```.
+* You can run it using CPUs ```./nnet_ps cpu 0 1```.
+* This is an demonstration of mshadow-ps interface, see introduction in [../mshadow-ps](../mshadow-ps)
diff --git a/3rdparty/mshadow/guide/neuralnet/config.mk b/3rdparty/mshadow/guide/neuralnet/config.mk
new file mode 100644
index 000000000000..6c10b79903bf
--- /dev/null
+++ b/3rdparty/mshadow/guide/neuralnet/config.mk
@@ -0,0 +1,35 @@
+#---------------------------------------------------------------------------------------
+#  mshadow: the configuration compile script
+#
+#  This is configuration script that you can use to compile mshadow
+#  Usage:
+#
+#  include config.mk in your Makefile, or directly include the definition of variables
+#  include mshadow.mk after the variables are set
+#
+#  Add MSHADOW_CFLAGS to the compile flags
+#  Add MSHADOW_LDFLAGS to the linker flags
+#  Add MSHADOW_NVCCFLAGS to the nvcc compile flags
+#----------------------------------------------------------------------------------------
+
+# whether use CUDA during compile
+USE_CUDA = 1
+
+# add the path to CUDA libary to link and compile flag
+# if you have already add them to enviroment variable, leave it as NONE
+USE_CUDA_PATH = NONE
+
+#
+# choose the version of blas you want to use
+# can be: mkl, blas, atlas, openblas, apple
+USE_BLAS = openblas
+#
+# add path to intel library, you may need it
+# for MKL, if you did not add the path to enviroment variable
+#
+USE_INTEL_PATH = NONE
+
+# whether compile with parameter server
+USE_DIST_PS = 0
+PS_PATH = NONE
+PS_THIRD_PATH = NONE
diff --git a/3rdparty/mshadow/guide/neuralnet/convnet.cu b/3rdparty/mshadow/guide/neuralnet/convnet.cu
new file mode 100644
index 000000000000..5276338ec71c
--- /dev/null
+++ b/3rdparty/mshadow/guide/neuralnet/convnet.cu
@@ -0,0 +1,282 @@
+// this implements a simple convolution neural net: conv-maxpool-fullc
+#include <vector>
+// header file to use mshadow
+#include "mshadow/tensor.h"
+// helper function to load mnist dataset
+#include "util.h"
+// this namespace contains all data structures, functions
+using namespace mshadow;
+// this namespace contains all operator overloads
+using namespace mshadow::expr;
+
+// define operations
+struct relu{
+  MSHADOW_XINLINE static real_t Map(real_t a) {
+    using namespace std;
+    return max(a, 0.0f);
+  }
+};
+struct relu_grad {
+  MSHADOW_XINLINE static real_t Map(real_t a) {
+    return a > 0.0f ? 1.0f : 0.0f;
+  }
+};
+
+/*! \brief interface for nnet, interfacd allows use to use GPU/CPU implementation in a unified way */
+class INNet{
+ public:
+  virtual void Forward(const Tensor<cpu, 4, real_t>& inbatch, Tensor<cpu, 2, real_t> &oubatch) = 0;
+  virtual void Backprop(const Tensor<cpu, 2, real_t>& gradout) = 0;
+  virtual void Update(void) = 0;
+  virtual ~INNet() {}
+};
+
+/*!
+ * \brief simple two layer conv-net conv-pool-flat-fullc
+ *        this implementation is device invariant
+ */
+template<typename xpu>
+class ConvNet : public INNet {
+ public:
+  // initialize the network
+  ConvNet(int batch_size, int insize, int nchannel, int ksize, int kstride, int psize, int num_out)
+      :rnd(0), ksize(ksize), kstride(kstride), psize(psize) {
+    // setup stream
+    Stream<xpu> *stream = NewStream<xpu>();
+    ninput.set_stream(stream);
+    nhidden.set_stream(stream);
+    nhiddenbak.set_stream(stream);
+    npool.set_stream(stream);
+    npoolbak.set_stream(stream);
+    nflat.set_stream(stream);
+    nout.set_stream(stream);
+    hbias.set_stream(stream); g_hbias.set_stream(stream);
+    obias.set_stream(stream);  g_obias.set_stream(stream);
+    Ki2h.set_stream(stream);  g_Ki2h.set_stream(stream);
+    Wh2o.set_stream(stream);   g_Wh2o.set_stream(stream);
+    tmp_col.set_stream(stream);
+    tmp_dst.set_stream(stream);
+    // setup nodes
+    ninput.Resize(Shape4(batch_size, 1, insize, insize));
+    nhidden.Resize(Shape4(batch_size, nchannel, (insize - ksize)/kstride+1, (insize -ksize)/kstride+1));
+    nhiddenbak.Resize(nhidden.shape_);
+    npool.Resize(Shape4(batch_size, nchannel, (nhidden.size(2)+1-psize)/psize, (nhidden.size(3)+1-psize)/psize));
+    npoolbak.Resize(npool.shape_);
+    nflat.Resize(Shape2(batch_size, npool.size(1)*npool.size(2)*npool.size(3)));
+    nout.Resize(Shape2(batch_size, num_out));
+    // setup bias
+    hbias.Resize(Shape1(nchannel)); g_hbias.Resize(hbias.shape_);
+    obias.Resize(Shape1(num_out));  g_obias.Resize(obias.shape_);
+    hbias = 0.0f; obias = 0.0f;
+    // setup weights
+    Ki2h.Resize(Shape2(nchannel, ksize*ksize));  g_Ki2h.Resize(Ki2h.shape_);
+    Wh2o.Resize(Shape2(nflat.size(1), num_out));   g_Wh2o.Resize(Wh2o.shape_);
+    rnd.SampleGaussian(&Ki2h, 0, 0.01f);
+    rnd.SampleGaussian(&Wh2o, 0, 0.01f);
+
+    printf("conv=%d, pool=%d\n", nhidden.size(3), npool.size(3));
+  }
+  virtual ~ConvNet() {}
+  // forward propagation
+  virtual void Forward(const Tensor<cpu, 4, real_t>& inbatch, Tensor<cpu, 2, real_t> &oubatch) {
+    index_t batch_size = inbatch.size(0);
+    // copy data to input layer
+    Copy(ninput, inbatch, ninput.stream_);
+    // first layer, conv, use stride=2
+    ConvForward(ninput, Ki2h, nhidden, ksize, kstride, tmp_col, tmp_dst);
+    // add bias
+    nhidden += broadcast<1>(hbias, nhidden.shape_);
+    // activation, relu, backup activation in nhidden
+    nhidden = F<relu>(nhidden);
+    Copy(nhiddenbak, nhidden, nhiddenbak.stream_);
+    // max pooling
+    npool = pool<red::maximum>(nhiddenbak, npool[0][0].shape_, psize, psize, psize);
+    Copy(npoolbak, npool, npoolbak.stream_);
+    // flat
+    nflat = reshape(npool, nflat.shape_);
+    // second layer fullc
+    nout = dot(nflat, Wh2o);
+    nout += repmat(obias, batch_size);
+    // softmax calculation
+    Softmax(nout, nout);
+    // copy result out
+    Copy(oubatch, nout, nout.stream_);
+  }
+  // back propagation
+  virtual void Backprop(const Tensor<cpu, 2, real_t>& gradout) {
+    // copy gradient to output layer
+    Copy(nout, gradout, nout.stream_);
+    // calc grad of final layer
+    g_obias = sum_rows(nout);
+    g_Wh2o  = dot(nflat.T(), nout);
+    // backprop to previous layer
+    nflat = dot(nout, Wh2o.T());
+    npool = reshape(nflat, npool.shape_);
+    // backprop pooling layer
+    nhiddenbak = unpool<red::maximum>(nhiddenbak, npoolbak, npool, psize, psize, psize);
+    // calculate gradient of relu layer
+    nhidden = F<relu_grad>(nhidden) * nhiddenbak;
+    // calc grad of layer 1
+    g_hbias = sumall_except_dim<1>(nhidden);
+    ConvBackWard(nhidden, Ki2h, g_Ki2h, ninput, ksize, kstride, tmp_col, tmp_dst);
+  }
+  // update weight
+  virtual void Update(void) {
+    // run SGD
+    const float eta = 0.1;
+    const float wd = 0.00001;
+    // update weight
+    Ki2h -= eta * (wd * Ki2h + g_Ki2h);
+    Wh2o -= eta * (wd * Wh2o + g_Wh2o);
+    // no regularization for bias
+    hbias-= eta * g_hbias;
+    obias-= eta * g_obias;
+  }
+ private:
+  // forward convolution, tmp_col and tmp_dst are helper structure
+  inline static void ConvForward(const Tensor<xpu, 4, real_t> &in,
+                                 const Tensor<xpu, 2, real_t> &kernel,
+                                 Tensor<xpu, 4, real_t> &out,
+                                 int ksize, int kstride,
+                                 TensorContainer<xpu, 2, real_t> &tmp_col,
+                                 TensorContainer<xpu, 2, real_t> &tmp_dst) {
+    index_t oheight  = (in.size(2) - ksize)/kstride + 1;
+    index_t owidth   = (in.size(3) - ksize)/kstride + 1;
+    index_t nbatch   = in.size(0);
+    index_t nchannel = out.size(1);
+    // we directly unpack all local patches and do a dot product
+    // this cost lots of memory, normally for large image, only unpack several image at a time
+    tmp_col.Resize(Shape2(in.size(1)*ksize*ksize, nbatch*oheight*owidth));
+    tmp_dst.Resize(Shape2(nchannel, nbatch*oheight*owidth));
+    // unpack local patches , stride=1
+	tmp_col = unpack_patch2col(in, ksize, ksize, kstride, kstride, 1, 1);
+    tmp_dst = dot(kernel, tmp_col);
+    // reshape, then swap axis, we chain equations together
+    out = swapaxis<1,0>(reshape(tmp_dst, Shape4(nchannel, nbatch, oheight, owidth)));
+  }
+  // backward convolution, calculate gradient of kernel, and backprop back to in
+  inline static void ConvBackWard(const Tensor<xpu, 4, real_t> &out,
+                                  const Tensor<xpu, 2, real_t> &kernel,
+                                  Tensor<xpu, 2, real_t> &g_kernel,
+                                  Tensor<xpu, 4, real_t> &in,
+                                  int ksize, int kstride,
+                                  TensorContainer<xpu, 2, real_t> &tmp_col,
+                                  TensorContainer<xpu, 2, real_t> &tmp_dst) {
+    index_t oheight  = (in.size(2) - ksize)/kstride + 1;
+    index_t owidth   = (in.size(3) - ksize)/kstride + 1;
+    index_t nbatch   = in.size(0);
+    index_t nchannel = out.size(1);
+    // we directly unpack all local patches and do a dot product
+    // this cost lots of memory, normally for large image, only unpack several image at a time
+    tmp_col.Resize(Shape2(in.size(1) * ksize * ksize,
+                          nbatch * oheight * owidth));
+    tmp_dst.Resize(Shape2(nchannel, nbatch * oheight * owidth));
+    // unpack local patches
+    tmp_col = unpack_patch2col(in, ksize, ksize, kstride, kstride, 1, 1);
+    tmp_dst = reshape(swapaxis<1,0>(out), tmp_dst.shape_);
+    g_kernel = dot(tmp_dst, tmp_col.T());
+        // backpropgation: not necessary for first layer, but included anyway
+    tmp_col = dot(kernel.T(), tmp_dst);
+    in = pack_col2patch(tmp_col, in.shape_, ksize, ksize, kstride, kstride, 1, 1);
+  }
+ private:
+  // random seed generator
+  Random<xpu, real_t> rnd;
+  // kernel size, pooling size
+  int ksize, kstride, psize;
+  // nodes in neural net
+  TensorContainer<xpu, 4, real_t> ninput, nhidden, nhiddenbak, npool, npoolbak;
+  TensorContainer<xpu, 2, real_t> nflat, nout;
+  // temp helper structure
+  TensorContainer<xpu, 2, real_t> tmp_col, tmp_dst;
+  // hidden bias, gradient
+  TensorContainer<xpu, 1, real_t> hbias, obias, g_hbias, g_obias;
+  // weight, gradient: Ki2h is actually convoltuion kernel, with shape=(num_channel,ksize*ksize)
+  TensorContainer<xpu, 2, real_t> Ki2h,  Wh2o, g_Ki2h, g_Wh2o;
+};
+
+// helper function to get the max inde
+inline int MaxIndex(Tensor<cpu, 1, real_t> pred) {
+  int maxidx = 0;
+  for (index_t i = 1; i < pred.size(0); ++i) {
+    if(pred[i] > pred[maxidx]) maxidx = (int)i;
+  }
+  return maxidx;
+}
+
+int main(int argc, char *argv[]) {
+  if(argc < 2) {
+    printf("Usage: cpu or gpu\n"); return 0;
+  }
+  srand(0);
+  // settings
+  int batch_size = 100;
+  int insize = 28;
+  int nchannel = 10;
+  int ksize = 5;
+  int kstride = 1;
+  int psize = 2;
+  int num_out = 10;
+
+  // choose which version to use
+  INNet *net;
+  if (!strcmp(argv[1], "gpu")) {
+    InitTensorEngine<gpu>();
+    net = new ConvNet<gpu>(batch_size, insize, nchannel, ksize, kstride, psize, num_out);
+  } else {
+    InitTensorEngine<cpu>();
+    net = new ConvNet<cpu>(batch_size, insize, nchannel, ksize, kstride, psize, num_out);
+  }
+
+  // temp output layer
+  TensorContainer<cpu, 2, real_t> pred;
+  pred.Resize(Shape2(batch_size, num_out));
+
+  // label
+  std::vector<int> ytrain, ytest;
+  // data
+  TensorContainer<cpu, 2, real_t> xtrain_, xtest_;
+  LoadMNIST("train-images-idx3-ubyte", "train-labels-idx1-ubyte", ytrain, xtrain_, true);
+  LoadMNIST("t10k-images-idx3-ubyte", "t10k-labels-idx1-ubyte", ytest, xtest_, false);
+
+  TensorContainer<cpu, 4, real_t> xtrain(Shape4(xtrain_.size(0), 1, insize, insize));
+  TensorContainer<cpu, 4, real_t> xtest(Shape4(xtest_.size(0),  1, insize, insize));
+  xtrain = reshape(xtrain_, xtrain.shape_);
+  xtest = reshape(xtest_, xtest.shape_);
+
+  int num_iter = 20;
+
+  for (int i = 0; i < num_iter; ++ i) {
+    // training
+    for (index_t j = 0; j + batch_size <= xtrain.size(0); j += batch_size) {
+      net->Forward(xtrain.Slice(j, j + batch_size), pred);
+      // set gradient into pred
+      for (int k = 0; k < batch_size; ++ k) {
+        pred[k][ ytrain[k+j] ] -= 1.0f;
+      }
+      // scale gradient by batchs zie
+      pred *= 1.0f / batch_size;
+      // run backprop
+      net->Backprop(pred);
+      // update net parameters
+      net->Update();
+    }
+    // evaluation
+    long nerr = 0;
+    for (index_t j = 0; j + batch_size <= xtest.size(0); j += batch_size) {
+      net->Forward(xtest.Slice(j, j + batch_size), pred);
+      for (int k = 0; k < batch_size; ++ k) {
+        nerr += MaxIndex(pred[k]) != ytest[j+k];
+      }
+    }
+    printf("round %d: test-err=%f\n", i, (float)nerr/xtest.size(0));
+  }
+  delete net;
+
+  if (!strcmp(argv[1], "gpu")) {
+    ShutdownTensorEngine<gpu>();
+  } else {
+    ShutdownTensorEngine<cpu>();
+  }
+  return 0;
+}
diff --git a/3rdparty/mshadow/guide/neuralnet/nnet.cu b/3rdparty/mshadow/guide/neuralnet/nnet.cu
new file mode 100644
index 000000000000..6ef8b0db3f64
--- /dev/null
+++ b/3rdparty/mshadow/guide/neuralnet/nnet.cu
@@ -0,0 +1,202 @@
+// this implements a simple two layer neural net
+#include <vector>
+#include <cmath>
+// header file to use mshadow
+#include "mshadow/tensor.h"
+// helper function to load mnist dataset
+#include "util.h"
+// this namespace contains all data structures, functions
+using namespace mshadow;
+// this namespace contains all operator overloads
+using namespace mshadow::expr;
+
+// define sigmoid operation
+struct sigmoid{
+  MSHADOW_XINLINE static real_t Map(real_t a) {
+    return  1.0f/(1.0f+expf(-a));
+  }
+};
+
+/*! \brief interface for nnet, interfacd allows use to use GPU/CPU implementation in a unified way */
+class INNet{
+ public:
+  virtual void Forward(const Tensor<cpu, 2, real_t>& inbatch, Tensor<cpu, 2, real_t> &oubatch) = 0;
+  virtual void Backprop(const Tensor<cpu, 2, real_t>& gradout) = 0;
+  virtual void Update(void) = 0;
+  virtual ~INNet() {}
+};
+
+/*!
+ * \brief simple two layer neural net
+ *        this implementation is device invariant
+ */
+template<typename xpu>
+class NNet : public INNet {
+ public:
+  // initialize the network
+  NNet(int batch_size, int num_in, int num_hidden, int num_out) : rnd(0) {
+    // setup stream
+    Stream<xpu> *stream = NewStream<xpu>();
+    ninput.set_stream(stream);
+    nhidden.set_stream(stream);
+    nhiddenbak.set_stream(stream);
+    nout.set_stream(stream);
+    hbias.set_stream(stream);
+    g_hbias.set_stream(stream);
+    g_obias.set_stream(stream);
+    obias.set_stream(stream);
+    Wi2h.set_stream(stream);
+    Wh2o.set_stream(stream);
+    g_Wi2h.set_stream(stream);
+    g_Wh2o.set_stream(stream);
+    // setup nodes
+    ninput.Resize(Shape2(batch_size, num_in));
+    nhidden.Resize(Shape2(batch_size, num_hidden));
+    nhiddenbak.Resize(nhidden.shape_);
+    nout.Resize(Shape2(batch_size, num_out));
+    // setup bias
+    hbias.Resize(Shape1(num_hidden)); g_hbias.Resize(hbias.shape_);
+    obias.Resize(Shape1(num_out)); g_obias.Resize(obias.shape_);
+    hbias = 0.0f; obias = 0.0f;
+    // setup weights
+    Wi2h.Resize(Shape2(num_in, num_hidden));  g_Wi2h.Resize(Wi2h.shape_);
+    Wh2o.Resize(Shape2(num_hidden, num_out)); g_Wh2o.Resize(Wh2o.shape_);
+    rnd.SampleGaussian(&Wi2h, 0, 0.01f);
+    rnd.SampleGaussian(&Wh2o, 0, 0.01f);
+  }
+  virtual ~NNet() {}
+  // forward propagation
+  virtual void Forward(const Tensor<cpu, 2, real_t>& inbatch,
+                       Tensor<cpu, 2, real_t> &oubatch) {
+    // size is same conventsion as numpy
+    index_t batch_size = inbatch.size(0);
+    // copy data to input layer
+    Copy(ninput, inbatch, ninput.stream_);
+    // first layer, fullc
+    nhidden = dot(ninput, Wi2h);
+    nhidden+= repmat(hbias, batch_size);
+    // activation, sigmloid, backup activation in nhidden
+    nhidden = F<sigmoid>(nhidden);
+    Copy(nhiddenbak, nhidden, nhiddenbak.stream_);
+    // second layer fullc
+    nout = dot(nhiddenbak, Wh2o);
+    nout += repmat(obias, batch_size);
+    // softmax calculation
+    Softmax(nout, nout);
+    // copy result out
+    Copy(oubatch, nout, nout.stream_);
+  }
+  // back propagation
+  virtual void Backprop(const Tensor<cpu, 2, real_t>& gradout) {
+    // copy gradient to output layer
+    Copy(nout, gradout, nout.stream_);
+    // calc grad of layer 2
+    g_obias = sum_rows(nout);
+    g_Wh2o  = dot(nhiddenbak.T(), nout);
+    // backprop to layer 1
+    nhiddenbak = dot(nout, Wh2o.T());
+    // calculate gradient of sigmoid layer
+    nhidden = nhidden * (1.0f-nhidden) * nhiddenbak;
+    // calc grad of layer 1
+    g_hbias = sum_rows(nhidden);
+    g_Wi2h  = dot(ninput.T(), nhidden);
+  }
+  // update weight
+  virtual void Update(void) {
+    // run SGD
+    const float eta = 0.8;
+    const float wd = 0.00001;
+    // update weight
+    Wi2h -= eta * (wd * Wi2h + g_Wi2h);
+    Wh2o -= eta * (wd * Wh2o + g_Wh2o);
+    // no regularization for bias
+    hbias-= eta * g_hbias;
+    obias-= eta * g_obias;
+  }
+ private:
+  // random seed generator
+  Random<xpu, real_t> rnd;
+  // nodes in neural net
+  TensorContainer<xpu, 2, real_t> ninput, nhidden, nhiddenbak, nout;
+  // hidden bias, gradient
+  TensorContainer<xpu, 1, real_t> hbias, obias, g_hbias, g_obias;
+  // weight gradient
+  TensorContainer<xpu, 2, real_t> Wi2h, Wh2o, g_Wi2h, g_Wh2o;
+};
+// helper function to get the max inde
+inline int MaxIndex(Tensor<cpu, 1, real_t> pred) {
+  int maxidx = 0;
+  for(index_t i = 1; i < pred.size(0); ++i) {
+    if(pred[i] > pred[maxidx]) maxidx = (int)i;
+  }
+  return maxidx;
+}
+
+int main(int argc, char *argv[]) {
+  if(argc < 2) {
+    printf("Usage: cpu or gpu\n"); return 0;
+  }
+  srand(0);
+
+  // settings
+  int batch_size = 100;
+  int num_in = 28 * 28;
+  int num_hidden = 100;
+  int num_out = 10;
+  // choose which version to use
+  INNet *net;
+  if (!strcmp(argv[1], "gpu")) {
+    InitTensorEngine<gpu>();
+    net = new NNet<gpu>(batch_size, num_in, num_hidden, num_out);
+  } else {
+    InitTensorEngine<cpu>();
+    net = new NNet<cpu>(batch_size, num_in, num_hidden, num_out);
+  }
+
+  // temp output layer
+  TensorContainer<cpu, 2, real_t> pred;
+  pred.Resize(Shape2(batch_size, num_out));
+
+  // label
+  std::vector<int> ytrain, ytest;
+  // data
+  TensorContainer<cpu,2> xtrain, xtest;
+  LoadMNIST("train-images-idx3-ubyte", "train-labels-idx1-ubyte", ytrain, xtrain, true);
+  LoadMNIST("t10k-images-idx3-ubyte", "t10k-labels-idx1-ubyte", ytest, xtest, false);
+
+  int num_iter = 20;
+
+  for (int i = 0; i < num_iter; ++ i) {
+    // training
+    for (index_t j = 0; j + batch_size <= xtrain.size(0); j += batch_size) {
+      net->Forward(xtrain.Slice(j, j + batch_size), pred);
+      // set gradient into pred
+      for (int k = 0; k < batch_size; ++ k) {
+        pred[k][ ytrain[k+j] ] -= 1.0f;
+      }
+      // scale gradient by batchs zie
+      pred *= 1.0f / batch_size;
+      // run backprop
+      net->Backprop(pred);
+      // update net parameters
+      net->Update();
+    }
+    // evaluation
+    long nerr = 0;
+    for (index_t j = 0; j + batch_size <= xtest.size(0); j += batch_size) {
+      net->Forward(xtest.Slice(j, j + batch_size), pred);
+      for (int k = 0; k < batch_size; ++ k) {
+        nerr += MaxIndex(pred[k]) != ytest[j+k];
+
+      }
+    }
+    printf("round %d: test-err=%f\n", i, (float)nerr/xtest.size(0));
+  }
+  delete net;
+  if (!strcmp(argv[1], "gpu")) {
+    ShutdownTensorEngine<gpu>();
+  } else {
+    ShutdownTensorEngine<cpu>();
+  }
+  return 0;
+}
diff --git a/3rdparty/mshadow/guide/neuralnet/nnet_ps.cu b/3rdparty/mshadow/guide/neuralnet/nnet_ps.cu
new file mode 100644
index 000000000000..5c03fb10daab
--- /dev/null
+++ b/3rdparty/mshadow/guide/neuralnet/nnet_ps.cu
@@ -0,0 +1,312 @@
+// this implements a simple two layer Multi-GPU neural net
+// this implementation uses mshadow-ps to get gradient aggregation
+// between cards
+// this code is modified from nnet.cu
+#include <vector>
+#include <cmath>
+#include <omp.h>
+// header file to use mshadow
+#include <mshadow/tensor.h>
+#include <mshadow-ps/mshadow_ps.h>
+// helper function to load mnist dataset
+#include "./util.h"
+// this namespace contains all data structures, functions
+using namespace mshadow;
+// this namespace contains all operator overloads
+using namespace mshadow::expr;
+
+// define sigmoid operation
+struct sigmoid {
+  MSHADOW_XINLINE static real_t Map(real_t a) {
+    return 1.0f / (1.0f + expf(-a));
+  }
+};
+
+/*! \brief interface for nnet, interfacd allows use to use GPU/CPU implementation in a unified way */
+class INNet{
+ public:
+  virtual void Forward(const Tensor<cpu, 2, real_t>& inbatch,
+                       Tensor<cpu, 2, real_t> &oubatch) = 0;
+  virtual void Backprop(const Tensor<cpu, 2, real_t>& gradout) = 0;
+  virtual ~INNet() {}
+};
+
+/*!
+ * \brief simple two layer neural net
+ *        this implementation is device invariant
+ */
+template<typename xpu>
+class NNet : public INNet {
+ public:
+  // initialize the network
+  NNet(int batch_size, int num_in, int num_hidden, int num_out,
+       int devid, mshadow::ps::ISharedModel<xpu, real_t> *ps)
+      : rnd(0), devid(devid), ps(ps) {
+    mshadow::SetDevice<xpu>(devid);
+    stream = mshadow::NewStream<xpu>();
+    // set the computing streams
+    ninput.set_stream(stream);
+    nhidden.set_stream(stream);
+    nhiddenbak.set_stream(stream);
+    nout.set_stream(stream);
+    hbias.set_stream(stream);
+    obias.set_stream(stream);
+    g_hbias.set_stream(stream);
+    g_obias.set_stream(stream);
+    Wi2h.set_stream(stream);
+    Wh2o.set_stream(stream);
+    g_Wi2h.set_stream(stream);
+    g_Wh2o.set_stream(stream);
+    rnd.set_stream(stream);
+    // setup nodes
+    ninput.Resize(Shape2(batch_size, num_in));
+    nhidden.Resize(Shape2(batch_size, num_hidden));
+    nhiddenbak.Resize(nhidden.shape_);
+    nout.Resize(Shape2(batch_size, num_out));
+    // setup bias
+    hbias.Resize(Shape1(num_hidden)); g_hbias.Resize(hbias.shape_);
+    obias.Resize(Shape1(num_out)); g_obias.Resize(obias.shape_);
+    hbias = 0.0f; obias = 0.0f;
+    // setup weights
+    Wi2h.Resize(Shape2(num_in, num_hidden));  g_Wi2h.Resize(Wi2h.shape_);
+    Wh2o.Resize(Shape2(num_hidden, num_out)); g_Wh2o.Resize(Wh2o.shape_);
+    rnd.SampleGaussian(&Wi2h, 0, 0.01f);
+    rnd.SampleGaussian(&Wh2o, 0, 0.01f);
+    // initialize the key
+    ps->InitKey(Wi2h.shape_, 0, devid);
+    ps->InitKey(hbias.shape_, 1, devid);
+    ps->InitKey(Wh2o.shape_, 2, devid);
+    ps->InitKey(obias.shape_, 3, devid);
+  }
+  virtual ~NNet() {
+    mshadow::SetDevice<xpu>(devid);
+    mshadow::DeleteStream(stream);
+  }
+  // forward propagation
+  virtual void Forward(const Tensor<cpu, 2, real_t> &inbatch,
+                       Tensor<cpu, 2, real_t> &oubatch) {
+    // size is same conventsion as numpy
+    index_t batch_size = inbatch.size(0);
+    // copy data to input layer
+    Copy(ninput, inbatch, stream);
+    // wait the last pull requst on layer to complete
+    ps->PullWait(0, devid);
+    // first layer, fullc
+    nhidden = dot(ninput, Wi2h);
+    // wait the pull request on hbias to complete
+    ps->PullWait(1, devid);
+    nhidden+= repmat(hbias, batch_size);
+    // activation, sigmloid, backup activation in nhidden
+    nhidden = F<sigmoid>(nhidden);
+    Copy(nhiddenbak, nhidden, stream);
+    // second layer fullc
+    ps->PullWait(2, devid);
+    nout = dot(nhiddenbak, Wh2o);
+    ps->PullWait(3, devid);
+    nout += repmat(obias, batch_size);
+    // softmax calculation
+    Softmax(nout, nout);
+    // copy result out
+    Copy(oubatch, nout, stream);
+    // Copy with stream is non-blocking, use wait to wait until copy finishes
+    stream->Wait();
+  }
+  // back propagation
+  virtual void Backprop(const Tensor<cpu, 2, real_t> &gradout) {
+    // copy gradient to output layer
+    Copy(nout, gradout, stream);
+    // calc grad of layer 2
+    g_obias = sum_rows(nout);
+    // sync proc defines the synchronization step
+    this->SyncProc(obias, g_obias, 3);
+    // update second layer weights
+    g_Wh2o = dot(nhiddenbak.T(), nout);
+    // backprop to layer 1
+    nhiddenbak = dot(nout, Wh2o.T());
+    this->SyncProc(Wh2o, g_Wh2o, 2);
+    // calculate gradient of sigmoid layer
+    nhidden = nhidden * (1.0f-nhidden) * nhiddenbak;
+    // calc grad of layer 1
+    g_hbias = sum_rows(nhidden);
+    this->SyncProc(hbias, g_hbias, 1);
+    g_Wi2h = dot(ninput.T(), nhidden);
+    this->SyncProc(Wi2h, g_Wi2h, 0);
+  }
+  // synchronization function
+  template<int dim>
+  inline void SyncProc(mshadow::Tensor<xpu, dim> weight,
+                       mshadow::Tensor<xpu, dim> grad,
+                       int data_key) {
+    // wait till last computation finishes
+    stream->Wait();
+    ps->Push(grad, data_key, devid, -data_key);
+    ps->PullReq(grad, data_key, devid, -data_key,
+                UpdateEntry::ApplyUpdate,
+                new UpdateEntry(weight.FlatTo2D(), grad.FlatTo2D(), dim == 1));
+  }
+  // data structure defined to help using callback function
+  struct UpdateEntry {
+    mshadow::Tensor<xpu, 2> weight;
+    mshadow::Tensor<xpu, 2> grad;
+    bool is_bias;
+    // constructor
+    UpdateEntry(mshadow::Tensor<xpu, 2> weight,
+                mshadow::Tensor<xpu, 2> grad,
+                bool is_bias)
+        : weight(weight), grad(grad),
+          is_bias(is_bias) {}
+    inline void Update(mshadow::Stream<xpu> *stream) {
+      weight.set_stream(stream);
+      const float wd = 0.00001;
+      const float eta = 0.8;
+      if (!is_bias) {
+        weight -= eta * (wd * weight + grad);
+      } else {
+        weight -= eta * grad;
+      }
+    }
+    // callback function to apply update
+    inline static void ApplyUpdate(mshadow::Stream<xpu> *stream, void *arg) {
+      UpdateEntry *e = static_cast<UpdateEntry*>(arg);
+      e->Update(stream);
+      delete e;
+    }
+  };
+
+ private:
+  // computing stream
+  mshadow::Stream<xpu> *stream;
+  // device id
+  int devid;
+  // parameter server interface
+  mshadow::ps::ISharedModel<xpu, real_t> *ps;
+  // random seed generator
+  Random<xpu, real_t> rnd;
+  // nodes in neural net
+  TensorContainer<xpu, 2, real_t> ninput, nhidden, nhiddenbak, nout;
+  // hidden bias, gradient
+  TensorContainer<xpu, 1, real_t> hbias, obias, g_hbias, g_obias;
+  // weight gradient
+  TensorContainer<xpu, 2, real_t> Wi2h, Wh2o, g_Wi2h, g_Wh2o;
+};
+
+// helper function to get the max inde
+inline int MaxIndex(Tensor<cpu, 1, real_t> pred) {
+  int maxidx = 0;
+  for(index_t i = 1; i < pred.size(0); ++i) {
+    if(pred[i] > pred[maxidx]) maxidx = (int)i;
+  }
+  return maxidx;
+}
+
+namespace mshadow {
+namespace ps {
+// model updater is used when update is happening on server side
+// if we only use parameter server for sum aggregation
+// this is not needed, but we must declare this function to return NULL
+template<>
+IModelUpdater<float> *CreateModelUpdater(void) {
+  return NULL;
+}
+}
+}
+
+template<typename xpu>
+inline int Run(int argc, char *argv[]) {
+  srand(0);
+  // settings
+  int batch_size = 100;
+  int num_in = 28 * 28;
+  int num_hidden = 100;
+  int num_out = 10;
+  int ndev = argc - 2;
+  if (batch_size % ndev != 0) {
+    fprintf(stderr, "choose number of devices ndev such that 100 MOD ndev == 0\n");
+    return 0;
+  }
+  // choose which version to use
+  std::vector<int> devs;
+  for (int i = 2; i < argc; ++i) {
+    devs.push_back(atoi(argv[i]));
+  }
+  mshadow::ps::ISharedModel<xpu, real_t>
+      *ps = mshadow::ps::CreateSharedModel<xpu, real_t>("local");
+  ps->Init(devs);
+
+  std::vector<INNet *> nets(ndev);
+  for (int i = 0; i < ndev; ++i) {
+    mshadow::InitTensorEngine<xpu>(devs[i]);
+    nets[i] = new NNet<xpu>(batch_size / ndev, num_in, num_hidden, num_out, devs[i], ps);
+  }
+
+  // label
+  std::vector<int> ytrain, ytest;
+  // data
+  TensorContainer<cpu,2> xtrain, xtest;
+  LoadMNIST("train-images-idx3-ubyte", "train-labels-idx1-ubyte", ytrain, xtrain, true);
+  LoadMNIST("t10k-images-idx3-ubyte", "t10k-labels-idx1-ubyte", ytest, xtest, false);
+  int num_iter = 20;
+
+  for (int i = 0; i < num_iter; ++ i) {
+    // mini-batch per device
+    int step = batch_size / ndev;
+    // running parallel threads
+    #pragma omp parallel num_threads(ndev)
+    {
+      // temp output layer
+      TensorContainer<cpu, 2, real_t> pred;
+      pred.Resize(Shape2(step, num_out));
+      int tid = omp_get_thread_num();
+      mshadow::SetDevice<xpu>(devs[tid]);
+      for (index_t j = 0; j + batch_size <= xtrain.size(0); j += batch_size) {
+        nets[tid]->Forward(xtrain.Slice(j + tid * step, j + (tid + 1) * step), pred);
+        // set gradient into pred
+        for (int k = 0; k < step; ++ k) {
+          pred[k][ytrain[j + tid * step + k]] -= 1.0f;
+        }
+        // scale gradient by batchs zie
+        pred *= 1.0f / batch_size;
+        // run backprop
+        nets[tid]->Backprop(pred);
+      }
+    }
+    // evaluation
+    long nerr = 0;
+    #pragma omp parallel num_threads(ndev) reduction(+:nerr)
+    {
+      // temp output layer
+      TensorContainer<cpu, 2, real_t> pred;
+      pred.Resize(Shape2(step, num_out));
+      int tid = omp_get_thread_num();
+      mshadow::SetDevice<xpu>(devs[tid]);
+      for (index_t j = 0; j + batch_size <= xtest.size(0); j += batch_size) {
+        nets[tid]->Forward(xtest.Slice(j + tid * step, j + (tid + 1) * step), pred);
+        for (int k = 0; k < step; ++ k) {
+          nerr += MaxIndex(pred[k]) != ytest[j + tid * step + k];
+        }
+      }
+    }
+    printf("round %d: test-err=%f\n", i, (float)nerr/xtest.size(0));
+  }
+
+  for(int i = 0; i < ndev; ++i) {
+    mshadow::SetDevice<xpu>(devs[i]);
+    delete nets[i];
+    ShutdownTensorEngine<xpu>();
+  }
+  return 0;
+}
+int main(int argc, char *argv[]) {
+  if (argc < 3) {
+    printf("Usage: <device> devicelist\n"\
+           "\tExample1: ./nnet_ps cpu 1 2 3\n"\
+           "\tExample2: ./nnet_ps gpu 0 1\n");
+    return 0;
+  }
+  if (!strcmp(argv[1], "cpu")) {
+    Run<mshadow::cpu>(argc, argv);
+  } else {
+    Run<mshadow::gpu>(argc, argv);
+  }
+  return 0;
+}
diff --git a/3rdparty/mshadow/guide/neuralnet/util.h b/3rdparty/mshadow/guide/neuralnet/util.h
new file mode 100644
index 000000000000..f58203c7667a
--- /dev/null
+++ b/3rdparty/mshadow/guide/neuralnet/util.h
@@ -0,0 +1,86 @@
+#pragma once
+#include <assert.h>
+#include <cstdio>
+#include <cstdlib>
+#include "mshadow/tensor.h"
+
+typedef float real_t;
+
+using namespace mshadow;
+
+int pack(unsigned char zz[4]){
+    return (int)(zz[3]) 
+        | (((int)(zz[2])) << 8)
+        | (((int)(zz[1])) << 16)
+        | (((int)(zz[0])) << 24);
+}
+
+template<typename T>
+inline void shuffle(T *data, size_t sz){
+  if(sz == 0) return;
+  for(size_t i = sz - 1; i > 0; i--){
+    std::swap(data[i], data[rand() % (i+1)]);
+  } 
+}
+// random shuffle the data inside, require PRNG 
+template<typename T>
+inline void shuffle(std::vector<T> &data){
+  shuffle(&data[0], data.size());
+}
+
+// simple function to load in mnist
+inline void LoadMNIST(const char *path_img, const char *path_label,
+                      std::vector<int> &ylabel,
+                      TensorContainer<cpu, 2, real_t> &xdata,
+                      bool do_shuffle){
+  // load in data
+  FILE *fi = fopen(path_img, "rb");
+  if (fi == NULL) {
+    printf("cannot open %s\n", path_img);
+    exit(-1);
+  }
+  unsigned char zz[4];
+  unsigned char *t_data, *l_data;
+  int num_image, width, height, nlabel;            
+  assert(fread(zz, 4 , 1, fi));
+  assert(fread(zz, 4 , 1, fi));    
+  num_image = pack(zz);
+  assert(fread(zz, 4 , 1, fi));                
+  width = pack(zz);
+  assert(fread(zz, 4 , 1, fi));                    
+  height = pack(zz);
+  
+  int step = width * height;
+  t_data = new unsigned char[num_image * step];    
+  assert(fread(t_data, step*num_image , 1 , fi));
+  fclose(fi);
+  
+  // load in label
+  fi = fopen(path_label, "rb");
+  assert(fread(zz, 4 , 1, fi));
+  assert(fread(zz, 4 , 1, fi));    
+  nlabel = pack(zz);
+  assert(num_image == nlabel);
+  l_data = new unsigned char[num_image];
+  assert(fread(l_data, num_image , 1 , fi));    
+  // try to do shuffle 
+  std::vector<int> rindex;
+  for (int i = 0; i < num_image; ++ i) {
+    rindex.push_back(i);
+  }
+  if (do_shuffle) {
+    shuffle(rindex);
+  }
+  
+  // save out result
+  ylabel.resize(num_image);
+  xdata.Resize(Shape2(num_image, width * height));
+  for (int i = 0 ; i < num_image ; ++i) {
+    for(int j = 0; j < step; ++j) {
+      xdata[i][j] = (float)(t_data[rindex[i]*step + j]) / 256.0f;            
+    }        
+    ylabel[i] = l_data[rindex[i]];
+  }
+  delete[] t_data; delete [] l_data;
+  printf("finish loading %dx%d matrix from %s, shuffle=%d\n", num_image, step, path_img, (int)do_shuffle);
+}
diff --git a/3rdparty/mshadow/make/README.md b/3rdparty/mshadow/make/README.md
new file mode 100644
index 000000000000..6ef24d6d467c
--- /dev/null
+++ b/3rdparty/mshadow/make/README.md
@@ -0,0 +1,18 @@
+Makefile Configuration of MShadow
+=====
+MShadow is a template library, you only need to include mshadow to use it. So this folder is not used to build mshadow library file.
+
+However, mshadow is a flexible library that allows you to compile with different configurations. For example,
+you can compile mshadow without CUDA, and specify your own choice of BLAS.
+There are different compile flags that you might need to set in your own configuration.
+This folder provides a Makefile script to help you do that.
+
+Usage
+=====
+* Set the configurations via variables in your Makefile, see example in [../guide/config.mk](../guide/config.mk)
+* include [mshadow.mk](mshadow.mk) in your Makefile
+* mshadow.mk will give you compiler variables that you can include when compiling
+  - Add MSHADOW_CFLAGS to the compile flags
+  - Add MSHADOW_LDFLAGS to the linker flags
+  - Add MSHADOW_NVCCFLAGS to the nvcc compile flags
+* For example Makefile, see [../guide/Makefile](../guide/Makefile)
diff --git a/3rdparty/mshadow/make/mshadow.mk b/3rdparty/mshadow/make/mshadow.mk
new file mode 100644
index 000000000000..86155eaaadcf
--- /dev/null
+++ b/3rdparty/mshadow/make/mshadow.mk
@@ -0,0 +1,166 @@
+#---------------------------------------------------------------------------------------
+#  mshadow configuration script
+#
+#  include mshadow.mk after the variables are set
+#
+#  Add MSHADOW_CFLAGS to the compile flags
+#  Add MSHADOW_LDFLAGS to the linker flags
+#  Add MSHADOW_NVCCFLAGS to the nvcc compile flags
+#----------------------------------------------------------------------------------------
+
+MSHADOW_CFLAGS = -funroll-loops -Wno-unused-parameter -Wno-unknown-pragmas -Wno-unused-local-typedefs
+MSHADOW_LDFLAGS = -lm
+MSHADOW_NVCCFLAGS =
+
+
+# atlas blas library has different name on CentOS
+OS := $(shell cat /etc/system-release 2>/dev/null)
+ifeq ($(findstring CentOS,$(OS)), CentOS)
+  ATLAS_LDFLAGS := -lsatlas -L/usr/lib64/atlas
+else
+  ATLAS_LDFLAGS := -lcblas
+endif
+
+ifndef USE_SSE
+	USE_SSE=1
+endif
+
+ifeq ($(USE_SSE), 1)
+	MSHADOW_CFLAGS += -msse3
+else
+	MSHADOW_CFLAGS += -DMSHADOW_USE_SSE=0
+endif
+
+# whether to use F16C instruction set extension for fast fp16 compute on CPU
+# if cross compiling you may want to explicitly turn it off if target system does not support it
+ifndef USE_F16C
+    ifneq ($(OS),Windows_NT)
+        detected_OS := $(shell uname -s)
+        ifeq ($(detected_OS),Darwin)
+            F16C_SUPP = $(shell sysctl -a | grep machdep.cpu.features | grep F16C)
+        endif
+        ifeq ($(detected_OS),Linux)
+            F16C_SUPP = $(shell cat /proc/cpuinfo | grep flags | grep f16c)
+        endif
+	ifneq ($(strip $(F16C_SUPP)),)
+                USE_F16C=1
+        else
+                USE_F16C=0
+        endif
+    endif
+    # if OS is Windows, check if your processor and compiler support F16C architecture.
+    # One way to check if processor supports it is to download the tool 
+    # https://docs.microsoft.com/en-us/sysinternals/downloads/coreinfo.
+    # If coreinfo -c shows F16C and compiler supports it, 
+    # then you can set USE_F16C=1 explicitly to leverage that capability"
+endif
+
+ifeq ($(USE_F16C), 1)
+        MSHADOW_CFLAGS += -mf16c
+else
+        MSHADOW_CFLAGS += -DMSHADOW_USE_F16C=0
+endif
+
+ifeq ($(USE_CUDA), 0)
+	MSHADOW_CFLAGS += -DMSHADOW_USE_CUDA=0
+else
+	MSHADOW_LDFLAGS += -lcudart -lcublas -lcurand -lcusolver
+endif
+ifneq ($(USE_CUDA_PATH), NONE)
+	MSHADOW_CFLAGS += -I$(USE_CUDA_PATH)/include
+	MSHADOW_LDFLAGS += -L$(USE_CUDA_PATH)/lib64 -L$(USE_CUDA_PATH)/lib
+endif
+
+ifeq ($(USE_BLAS), mkl)
+ifneq ($(USE_INTEL_PATH), NONE)
+	UNAME_S := $(shell uname -s)
+	ifeq ($(UNAME_S),Darwin)
+		MSHADOW_LDFLAGS += -L$(USE_INTEL_PATH)/mkl/lib
+		MSHADOW_LDFLAGS += -L$(USE_INTEL_PATH)/lib
+	else
+		MSHADOW_LDFLAGS += -L$(USE_INTEL_PATH)/mkl/lib/intel64
+		MSHADOW_LDFLAGS += -L$(USE_INTEL_PATH)/compiler/lib/intel64
+		MSHADOW_LDFLAGS += -L$(USE_INTEL_PATH)/lib/intel64
+	endif
+	MSHADOW_CFLAGS += -I$(USE_INTEL_PATH)/mkl/include
+endif
+ifneq ($(USE_STATIC_MKL), NONE)
+ifeq ($(USE_INTEL_PATH), NONE)
+	MKLROOT = /opt/intel/mkl
+else
+	MKLROOT = $(USE_INTEL_PATH)/mkl
+endif
+	MSHADOW_LDFLAGS += -L${MKLROOT}/../compiler/lib/intel64 -Wl,--start-group ${MKLROOT}/lib/intel64/libmkl_intel_lp64.a ${MKLROOT}/lib/intel64/libmkl_core.a ${MKLROOT}/lib/intel64/libmkl_intel_thread.a -Wl,--end-group -liomp5 -ldl -lpthread -lm
+else
+ifneq ($(USE_MKLML), 1)
+  MSHADOW_LDFLAGS += -lmkl_intel_lp64 -lmkl_intel_thread -lmkl_core -liomp5
+endif
+endif
+else
+ifneq ($(USE_BLAS), NONE)
+	MSHADOW_CFLAGS += -DMSHADOW_USE_CBLAS=1 -DMSHADOW_USE_MKL=0
+endif
+endif
+
+ifeq ($(USE_MKLML), 1)
+	MSHADOW_CFLAGS += -I$(MKLROOT)/include
+	ifneq ($(shell uname),Darwin)
+		MSHADOW_LDFLAGS += -Wl,--as-needed -lmklml_intel -lmklml_gnu
+	else
+		MSHADOW_LDFLAGS += -lmklml
+	endif
+	MSHADOW_LDFLAGS += -liomp5 -L$(MKLROOT)/lib/
+endif
+
+ifeq ($(USE_BLAS), openblas)
+	MSHADOW_LDFLAGS += -lopenblas
+else ifeq ($(USE_BLAS), perfblas)
+	MSHADOW_LDFLAGS += -lperfblas
+else ifeq ($(USE_BLAS), atlas)
+	MSHADOW_LDFLAGS += $(ATLAS_LDFLAGS)
+else ifeq ($(USE_BLAS), blas)
+	MSHADOW_LDFLAGS += -lblas
+else ifeq ($(USE_BLAS), apple)
+	MSHADOW_CFLAGS += -I/System/Library/Frameworks/Accelerate.framework/Versions/Current/Frameworks/vecLib.framework/Versions/Current/Headers/
+	MSHADOW_LDFLAGS += -framework Accelerate
+endif
+
+ifeq ($(PS_PATH), NONE)
+	PS_PATH = ..
+endif
+ifeq ($(PS_THIRD_PATH), NONE)
+	PS_THIRD_PATH = $(PS_PATH)/third_party
+endif
+
+ifndef RABIT_PATH
+	RABIT_PATH = rabit
+endif
+
+ifeq ($(RABIT_PATH), NONE)
+	RABIT_PATH = rabit
+endif
+
+ifeq ($(USE_RABIT_PS),1)
+	MSHADOW_CFLAGS += -I$(RABIT_PATH)/include
+	MSHADOW_LDFLAGS += -L$(RABIT_PATH)/lib -lrabit_base
+	MSHADOW_CFLAGS += -DMSHADOW_RABIT_PS=1
+else
+	MSHADOW_CFLAGS += -DMSHADOW_RABIT_PS=0
+endif
+
+ifeq ($(USE_DIST_PS),1)
+MSHADOW_CFLAGS += -DMSHADOW_DIST_PS=1 -std=c++11 \
+	-I$(PS_PATH)/src -I$(PS_THIRD_PATH)/include
+PS_LIB = $(addprefix $(PS_PATH)/build/, libps.a libps_main.a) \
+	$(addprefix $(PS_THIRD_PATH)/lib/, libgflags.a libzmq.a libprotobuf.a \
+	libglog.a libz.a libsnappy.a)
+	# -L$(PS_THIRD_PATH)/lib -lgflags -lzmq -lprotobuf -lglog -lz -lsnappy
+MSHADOW_NVCCFLAGS += --std=c++11
+else
+	MSHADOW_CFLAGS+= -DMSHADOW_DIST_PS=0
+endif
+
+# MSHADOW_USE_PASCAL=1 used to enable true-fp16 gemms.  Now, mshadow
+# only uses pseudo-fp16 gemms, so this flag will be removed after
+# dependent projects no longer reference it.
+MSHADOW_CFLAGS += -DMSHADOW_USE_PASCAL=0
diff --git a/3rdparty/mshadow/mshadow-ps/.gitignore b/3rdparty/mshadow/mshadow-ps/.gitignore
new file mode 100644
index 000000000000..076c1aa82e8b
--- /dev/null
+++ b/3rdparty/mshadow/mshadow-ps/.gitignore
@@ -0,0 +1,3 @@
+Makefile
+test
+test.cpp
diff --git a/3rdparty/mshadow/mshadow-ps/README.md b/3rdparty/mshadow/mshadow-ps/README.md
new file mode 100644
index 000000000000..9c90cc9f3c9d
--- /dev/null
+++ b/3rdparty/mshadow/mshadow-ps/README.md
@@ -0,0 +1,4 @@
+mshadow-ps
+====
+This folder contains mshadow-ps parameter server interface for mshadow GPU/CPU Tensor. See [guide on mshadow-ps](../guide/mshadow-ps) for introduction of the interface.
+
diff --git a/3rdparty/mshadow/mshadow-ps/mshadow_ps.h b/3rdparty/mshadow/mshadow-ps/mshadow_ps.h
new file mode 100644
index 000000000000..a00e79aefda2
--- /dev/null
+++ b/3rdparty/mshadow/mshadow-ps/mshadow_ps.h
@@ -0,0 +1,358 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file mshadow_ps.h
+ * \brief parameter server abstraction for mshadow tensor
+ *  this is a plugin of mshadow that can be used to syncrhonize
+ *  parameters across device and machines
+ *
+ * \author Tianqi Chen, Mu Li
+ */
+#ifndef MSHADOW_PS_H_  // NOLINT(*)
+#define MSHADOW_PS_H_  // NOLINT(*)
+#include <vector>
+// optionally support of lambda function in C++11, if available
+#if __cplusplus >= 201103L
+#include <functional>
+#endif  // C++11
+#include "../mshadow/tensor.h"
+
+/*! \brief whether to adapt distributed PS from parameter-server */
+#ifndef MSHADOW_DIST_PS
+#define MSHADOW_DIST_PS 1
+#endif
+
+/*! \brief whether to support BSP rabit API of PS*/
+#ifndef MSHADOW_RABIT_PS
+#define MSHADOW_RABIT_PS 1
+#endif
+
+namespace mshadow {
+/*! \brief namespace of mshadow-ps */
+namespace ps {
+/*!
+ * \brief interface of parameter server
+ * \tparam xpu the device of the data lies
+ * \tparam DType the type of element in the tensor
+ */
+template<typename xpu,
+         typename DType MSHADOW_DEFAULT_DTYPE>
+class ISharedModel {
+ public:
+  /*!
+   * \brief callback function that will be executed when pull request finishes
+   *        before calling the callback, the thread context is already switched
+   *        to the device of pullrequest
+   * \param stream the stream of callback thread, it is recommended to operate using this stream
+   * \param arg the argument of callback function
+   */
+  typedef void (CallbackFunction) (Stream<xpu> *stream, void *arg);
+  /*! \brief virtual destructor */
+  virtual ~ISharedModel(void) {}
+  /*!
+   * \brief Set param for the layer from string
+   * \param name parameter name
+   * \param val string for configuration
+   */
+  virtual void SetParam(const char *name, const char *val) {}
+  /*!
+   * \brief initialize the paramerver server client
+   * \param devices specifies the possible device id
+   *   to be input from Push and Pull,
+   */
+  virtual void Init(const std::vector<int> &devices) {}
+  /*!
+   * \brief initialize the paramerver server client
+   * without specifying the devices, only device 0 is allowed
+   */
+  inline void Init(void) {
+    std::vector<int> dev;
+    dev.push_back(0);
+    this->Init(dev);
+  }
+  /*!
+   * \brief initialize a key with certain shape
+   *  must be called before using Push/PullReq/PullWait
+   *  on the corresponding key
+   * \param shape the shape content of the key
+   * \param key the unique key to indicate the tensor
+   *        this is unique per device
+   * \param devid the device id this tensor lies in
+   */
+  template<int dim>
+  inline void InitKey(Shape<dim> shape,
+                      int key, int devid) {
+    this->InitKey_(shape.FlatTo2D(), key, devid);
+  }
+  /*!
+   * \brief wait until the pull event finishes
+   * if there was no pull request, wait will directly returns
+   * \param key the unique key to indicate the tensor
+   *        this is unique per device
+   * \param devid the device id this tensor lies in
+   */
+  virtual void PullWait(int key, int devid) = 0;
+  /*!
+   * \brief check if the weight was correct on the current device
+   *
+   * \param data the data
+   * \param key the unique key to indicate the tensor
+   *        this is unique per device
+   * \param devid the device id this tensor lies in
+   */
+  template<int dim>
+  inline void CheckWeight(Tensor<xpu, dim, DType> data,
+                          int key,
+                          int devid) {
+    this->CheckWeight_(data.FlatTo2D(), key, devid);
+  }
+  /*!
+   * \brief push out a tensor to parameter server
+   *  this call is asynchronize and returns immediately
+   *
+   * \param data the data
+   * \param key the unique key to indicate the tensor
+   *        this is unique per device
+   * \param devid the device id this tensor lies in
+   * \param priority the priority of this operation,
+   *   the bigger the number is the higher the priority will be
+   */
+  template<int dim>
+  inline void Push(Tensor<xpu, dim, DType> data,
+                   int key,
+                   int devid,
+                   int priority = 0) {
+    this->Push_(data.FlatTo2D(), key, devid, priority);
+  }
+  /*!
+   * \brief send a pull request, to pull parameter into data
+   *  this call is asynchronize and returns immediately
+   *  use PullWait to wait the event of copy finish
+   *
+   * \param data the data
+   * \param key the unique key to indicate the tensor,
+   *        this is unique per device
+   * \param devid the device id this tensor lies in
+   * \param priority the priority of this operation,
+   *   the bigger the number is the higher the priority will be
+   * \param callback the callback function that will
+   *                 be invoked when the request finishes
+   * \param callback_arg the argument to pass to callback
+   */
+  template<int dim>
+  inline void PullReq(Tensor<xpu, dim, DType> data,
+                      int key,
+                      int devid,
+                      int priority = 0,
+                      CallbackFunction callback = NULL,
+                      void *callback_arg = NULL) {
+    this->PullReq_(data.FlatTo2D(), key,
+                   devid, priority, callback, callback_arg);
+  }
+#if __cplusplus >= 201103L
+  /*!
+   * \brief send a pull request, to pull parameter into data
+   *  this call is asynchronize and returns immediately
+   *  use PullWait to wait the event of copy finish
+   *  this is the c++11 version that allows lambda function as callback
+   * \param data the data
+   * \param key the unique key to indicate the tensor,
+   *        this is unique per device
+   * \param devid the device id this tensor lies in
+   * \param priority the priority of this operation,
+   *   the bigger the number is the higher the priority will be
+   * \param callback the callback function
+   */
+  template<int dim>
+  inline void PullReq(Tensor<xpu, dim, DType> data,
+                      int key,
+                      int devid,
+                      int priority,
+                      std::function<void(Stream<xpu> *stream)> callback) {
+    // need to allocate space, because callback can happen latter..
+    auto calbk = new std::function<void(Stream<xpu> *stream)>();
+    *calbk = callback;
+    this->PullReq(data, key, devid, priority, InvokeLambda_, calbk);
+  }
+#endif  // C++11
+
+  /*!
+   * \brief set weight of corresponding key in server
+   *   this is a debug function that was not necessarily
+   *   implemented by the server
+   * \param data the data to set
+   * \param key the unique key to indicate the tensor
+   *        this is unique per device
+   * \param devid the device id this tensor lies in
+   */
+  virtual void SetWeight_(Tensor<xpu, 2, DType> data,
+                          int key,
+                          int devid) = 0;
+  /*!
+   * \brief check if the weight matches the server side
+   *   this is a debug function that was not necessarily
+   *   implemented by the server
+   * \param data the data to set
+   * \param key the unique key to indicate the tensor
+   *        this is unique per device
+   * \param devid the device id this tensor lies in
+   */
+  virtual void CheckWeight_(Tensor<xpu, 2, DType> data,
+                            int key,
+                            int devid) = 0;
+
+ protected:
+  /*!
+   * \brief initialize a key with certain shape
+   * \param shape the shape content of the key
+   * \param key the unique key to indicate the tensor
+   *        this is unique per device
+   * \param devid the device id this tensor lies in
+   */
+  virtual void InitKey_(Shape<2> shape,
+                        int key, int devid) = 0;
+  /*!
+   * \brief push out a tensor to parameter server
+   *  this call is asynchronize and returns immediately
+   *
+   * \param data the data
+   * \param key the unique key to indicate the tensor
+   *        this is unique per device
+   * \param devid the device id this tensor lies in
+   * \param priority the priority of this operation,
+   *   the bigger the number is the higher the priority will be
+   */
+  virtual void Push_(Tensor<xpu, 2, DType> data,
+                     int key,
+                     int devid,
+                     int priority = 0) = 0;
+  /*!
+   * \brief send a pull request, to pull parameter into data
+   *  this call is asynchronize and returns immediately
+   *  use PullWait to wait the event of copy finish
+   *
+   * \param data the data
+   * \param key the unique key to indicate the tensor,
+   *        this is unique per device
+   * \param devid the device id this tensor lies in
+   * \param priority the priority of this operation,
+   *   the bigger the number is the higher the priority will be
+   * \param callback the callback function that will
+   *                 be invoked when the request finishes
+   * \param callback_arg the argument to pass to callback
+   */
+  virtual void PullReq_(Tensor<xpu, 2, DType> data,
+                        int key,
+                        int devid,
+                        int priority,
+                        CallbackFunction callback,
+                        void *callback_arg) = 0;
+
+ private:
+// C++11 support for lambda prepare function
+#if __cplusplus >= 201103L
+  /*! \brief hack function to convert lambda to callback function */
+  inline static void InvokeLambda_(Stream<xpu> *stream, void *fun) {
+    auto *fp = static_cast<std::function<void(Stream<xpu> *stream)>*>(fun);
+    (*fp)(stream);
+    delete fp;
+  }
+#endif  // C++11
+};
+/*! \brief interface for customized mshadow server */
+template<typename DType>
+class IModelUpdater {
+ public:
+  virtual ~IModelUpdater(void) {}
+  /*!
+   * \brief set parameters from outside
+   * \param name name of parameter
+   * \param val value of parameter
+   */
+  virtual void SetParam(const char *name, const char *val) {}
+  /*!
+   * \brief init the model updater
+   * \param rank the rank of the node
+   * \param argc number of arguments
+   * \param argv arguments
+   */
+  virtual void InitUpdater(int rank, int argc, char *argv[]) {}
+  /*!
+   * \brief initialize the model
+   * \param key the key of data we point to
+   * \param dptr the data pointer
+   * \param size size of the parameter key
+   */
+  virtual void InitModel(int key, DType *dptr, size_t size) {
+    this->InitModel_(key, Tensor<cpu, 1, DType>(dptr, Shape1(size)));
+  }
+  /*!
+   * update the model
+   * \param key the key of data we point to
+   * \param dptr the data pointer
+   * \param size size of the parameter key
+   */
+  virtual void Update(int key, DType *dptr, size_t size) {
+    this->Update_(key, Tensor<cpu, 1, DType>(dptr, Shape1(size)));
+  }
+
+ protected:
+  /*!
+   * \brief initialize the model, user can implement this one
+   *   to take advantage of tensor operations
+   * \param key the key of data we point to
+   * \param data the tensor data corresponding to the data we want to initialize
+   */
+  virtual void InitModel_(int key, Tensor<cpu, 1, DType> data) {
+    LOG(FATAL) << "InitModel: not implemented";
+  }
+  /*!
+   * \brief update the model, user can implement this one
+   *    to take advantage of tensor operations
+   * \param key the key of data we point to
+   * \param data the tensor data corresponding to the data we want to initialize
+   */
+  virtual void Update_(int key, Tensor<cpu, 1, DType> data) {
+    LOG(FATAL) << "InitModel: not implemented";
+  }
+};
+/*!
+ * \brief create customized server
+ * this is a server defined by user
+ * \return new server
+ */
+template<typename DType>
+IModelUpdater<DType> *CreateModelUpdater(void);
+}  // namespace ps
+}  // namespace mshadow
+
+#include "./ps_local-inl.h"
+#include "./ps_dist-inl.h"
+#include "./ps_rabit-inl.h"
+namespace mshadow {
+namespace ps {
+/*!
+ * \brief create a parameter server implementation
+ * \param type the type of paramerver server
+ *     can either be "local" or "dist"
+ * \return the ISharedModel that can be used to synchronize weights
+ */
+template<typename xpu, typename DType>
+inline ISharedModel<xpu, DType> *CreateSharedModel(const char *type) {
+  if (!strcmp("local", type)) {
+#if MSHADOW_RABIT_PS
+    // allreduce on one machine pays no cost
+    if (rabit::IsDistributed()) {
+      return new RabitModel<xpu, DType>();
+    }
+#endif
+    return new LocalModel<xpu, DType>();
+  }
+#if MSHADOW_DIST_PS
+  if (!strcmp("dist", type)) return new DistModel<xpu, DType>();
+#endif
+  LOG(FATAL) << "unknown server type " << type;
+  return NULL;
+}
+}  // namespace ps
+}  // namespace mshadow
+#endif  // MSHADOW_PS_H_  NOLINT(*)
diff --git a/3rdparty/mshadow/mshadow-ps/ps_dist-inl.h b/3rdparty/mshadow/mshadow-ps/ps_dist-inl.h
new file mode 100644
index 000000000000..9bce0e13adbc
--- /dev/null
+++ b/3rdparty/mshadow/mshadow-ps/ps_dist-inl.h
@@ -0,0 +1,126 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file ps_dist-inl.h
+ * \brief distributed version of PS
+ *
+ * \author Tianqi Chen, Mu Li
+ */
+#ifndef MSHADOW_PS_DIST_INL_H_ // NOLINT(*)
+#define MSHADOW_PS_DIST_INL_H_ // NOLINT(*)
+
+#include <vector>
+#include "./mshadow_ps.h"
+#include "./ps_local-inl.h"
+
+#if MSHADOW_DIST_PS
+#include "parameter/kv_layer.h"
+namespace mshadow {
+namespace ps {
+
+/**
+ * @brief bridge IModelUpdater to KVLayerUpdater
+ */
+template<typename DType>
+class UpdaterWrapper {
+ public:
+  explicit UpdaterWrapper(IModelUpdater<DType> * updater)
+      : updater_(updater) { }
+  ~UpdaterWrapper() { delete updater_; }
+
+  /// @brief initialize the data
+  void Init(int id, size_t size, DType* data) {
+    updater_->InitModel(id, data, size);
+  }
+
+  /// @brief update the model by using received data
+  void Update(int id, size_t size, const DType* recv_data, DType* data) {
+    updater_->Update(id, (DType*)recv_data, size);  // NOLINT(*)
+  }
+ private:
+  IModelUpdater<DType> *updater_;
+};
+
+
+template<typename xpu, typename DType>
+class DistModel : public LocalModel<xpu, DType> {
+ public:
+  // parent type
+  typedef LocalModel<xpu, DType> Parent;
+
+  // initialize the parameter server
+  virtual void Init(const std::vector<int> &devices) {
+    Parent::Init(devices);
+    if (this->custom_server != NULL) {
+      delete this->custom_server;
+      this->custom_server = NULL;
+    }
+  }
+  virtual ~DistModel(void) {
+  }
+
+ protected:
+  // do nothing
+  virtual void InitCustomerServer(void) {
+  }
+  virtual void ServerInitKey(Tensor<cpu, 2> weight, int key) {
+    // this is called when key get initialized for the first time
+    // weight can be used to hold the model that pulled back
+    // use this to initialize the key on serverside
+    shared_model_.Pull(
+        ::ps::Parameter::Request(key), weight.dptr_, weight.MSize(),
+        [this, weight, key]() {
+          // call PullReady to notify LocalServer pulling is ready
+          this->PullReady(weight, key);
+        });
+  }
+  // override this function, to use parameter server
+  virtual void HandlePushFinish(Tensor<cpu, 3, DType> data,
+                                int key) {
+    // summation the data fron all devices
+    LocalModel<xpu, DType>::ReduceSum(data);
+
+    // push and pull
+    Tensor<cpu, 2> sendrecv = data[0];
+    CHECK_EQ(data[0].CheckContiguous(), true) << "data must be contiguous";
+
+    int ts = shared_model_.Push(
+        ::ps::Parameter::Request(key), sendrecv.dptr_, sendrecv.MSize(), false);
+
+    // let this pull request wait the push finish at the server node
+    shared_model_.Pull(
+        ::ps::Parameter::Request(key, -1, {ts}), sendrecv.dptr_, sendrecv.MSize(),
+        [this, sendrecv, key]() {
+          // call PullReady to notify LocalServer pulling is ready
+          this->PullReady(sendrecv, key);
+        });
+  }
+
+ private:
+  ::ps::KVLayer<DType, UpdaterWrapper<DType> > shared_model_;
+};
+
+
+template<typename DType>
+class MShadowServerNode {
+ public:
+  // conf: get from the flag -app_conf
+  MShadowServerNode(int argc, char *argv[]) {
+    IModelUpdater<DType> *updater = CreateModelUpdater<DType>();
+    updater->InitUpdater(::ps::MyRank(), argc, argv);
+
+    UpdaterWrapper<DType> *wrapper = new UpdaterWrapper<DType>(updater);
+    typedef ::ps::KVLayer<DType, UpdaterWrapper<DType> > PSServer;
+    PSServer *shared_model_ = new PSServer();
+    shared_model_->set_updater(wrapper);
+    ::ps::Postoffice::instance().manager().TransferCustomer(
+         CHECK_NOTNULL(shared_model_));
+  }
+  virtual ~MShadowServerNode() { }
+};
+
+// NOTE: do not add PS::CreateServer here add it in the program that uses
+// mshadow-ps
+}  // namespace ps
+}  // namespace mshadow
+#endif
+#endif  // MSHADOW_PS_DIST_INL_H_  NOLINT(*)
diff --git a/3rdparty/mshadow/mshadow-ps/ps_local-inl.h b/3rdparty/mshadow/mshadow-ps/ps_local-inl.h
new file mode 100644
index 000000000000..ee4c55d92487
--- /dev/null
+++ b/3rdparty/mshadow/mshadow-ps/ps_local-inl.h
@@ -0,0 +1,814 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file ps_local-inl.h
+ * \brief local multi-threading implementation of PS abstraction
+ *
+ * \author Tianqi Chen, Mu Li
+ */
+#ifndef MSHADOW_PS_LOCAL_INL_H_  // NOLINT(*)
+#define MSHADOW_PS_LOCAL_INL_H_  // NOLINT(*)
+#include <map>
+#include <utility>
+#include <string>
+#include <vector>
+#if defined(_OPENMP)
+#include <omp.h>
+#ifdef _MSC_VER
+typedef int ms_omp_uint;
+#else
+typedef unsigned ms_omp_uint;
+#endif
+#endif
+
+#include "./thread.h"
+#include "./thread_util.h"
+
+namespace mshadow {
+namespace ps {
+// multi-threaded implementation of
+template<typename xpu, typename DType>
+class LocalModel : public ISharedModel<xpu, DType> {
+ public:
+  // redefine callback function
+  typedef typename ISharedModel<xpu, DType>::CallbackFunction
+  CallbackFunction;
+  // constructor
+  LocalModel(void) {
+    init_end = 0;
+    perdev_pull_thread = 1;
+    perdev_push_thread = 1;
+    use_fifo_push_queue = 0;
+    bigarray_bound = 1000 * 1000;
+    nthread_reduction = 8;
+    use_pin_memory = 1;
+    test_on_server = 0;
+    update_on_server = 0;
+    destroy_signal = false;
+    custom_server = NULL;
+  }
+  // destructor
+  virtual ~LocalModel(void) {
+    this->Destroy();
+  }
+  inline void Destroy(void) {
+    if (init_end != 0) {
+      destroy_signal = true;
+      for (size_t i = 0; i < push_queues.size(); ++i) {
+        push_queues[i].Abort(1);
+      }
+      for (size_t i = 0; i < pull_queues.size(); ++i) {
+        pull_queues[i].Abort(1);
+      }
+      for (size_t i = 0; i < thread_push_handler.size(); ++i) {
+        thread_push_handler[i].Join();
+      }
+      for (size_t i = 0; i < thread_pull_handler.size(); ++i) {
+        thread_pull_handler[i].Join();
+      }
+      for (size_t i = 0; i < push_queues.size(); ++i) {
+        push_queues[i].Destroy();
+      }
+      push_map.Destroy();
+      push_lock.Destroy();
+      for (size_t i = 0; i < pull_queues.size(); ++i) {
+        pull_queues[i].Destroy();
+      }
+      pull_map.Destroy();
+      request_lock.Destroy();
+      wait_lock.Destroy();
+      wait_cond.Destroy();
+      init_end = 0;
+    }
+    if (custom_server != NULL) {
+      delete custom_server;
+      custom_server = NULL;
+    }
+  }
+  virtual void SetParam(const char *name, const char *val) {
+    int key;
+    if (sscanf(name, "push_op[%d]", &key) == 1) {
+      if (!strcmp(val, "gather")) {
+        request_lock.Lock();
+        push_operation[key] = kGather;
+        request_lock.Unlock();
+        return;
+      }
+      if (!strcmp(val, "sum")) {
+        push_operation[key] = kSum; return;
+      }
+      LOG(FATAL) << "unknown push operation " << val;
+    }
+    if (!strcmp(name, "reduce_thread")) {
+      nthread_reduction = atoi(val);
+    }
+    if (!strcmp(name, "use_pin_memory")) {
+      use_pin_memory = atoi(val);
+    }
+    if (!strcmp(name, "bigarray_bound")) {
+      bigarray_bound = static_cast<size_t>(atol(val));
+    }
+    if (!strcmp(name, "pull_thread")) {
+      if (!strcmp(val, "ndev")) {
+        perdev_pull_thread = 1;
+      } else if (!strcmp(val, "one")) {
+        perdev_pull_thread = 0;
+      } else {
+        LOG(FATAL) << "invalid value for parameter pull_thread," << " can only be ndev or one";
+      }
+    }
+    if (!strcmp(name, "push_thread")) {
+      if (!strcmp(val, "ndev")) {
+        perdev_push_thread = 1;
+      } else if (!strcmp(val, "one")) {
+        perdev_push_thread = 0;
+      } else {
+        LOG(FATAL) << "invalid value for parameter push_thread," << " can only be ndev or one";
+      }
+    }
+    if (!strcmp(name, "update_on_server")) {
+      update_on_server = atoi(val);
+    }
+    if (!strcmp(name, "test_on_server")) {
+      test_on_server = atoi(val);
+    }
+    // ignore message parameter
+    if (!strncmp(name, "msg:", 4)) return;
+    cfgvec.push_back(std::make_pair(std::string(name),
+                                    std::string(val)));
+  }
+  virtual void PullWait(int key, int devid) {
+    const int wid = GetWorkIndex(devid);
+    PullEntry *p = pull_map.Get(key);
+    if (p == NULL || p->wait.size() == 0) return;
+    PullEntry &e = *p;
+    // wake up waiters if any
+    CHECK_EQ(e.wait.size(), devices.size()) << "PullWait: must initialize the wait";
+    PullWaitRecord &w = e.wait[wid];
+    if (!w.finished) {
+      wait_lock.Lock();
+      w.nwait += 1;
+      while (!w.finished) {
+        wait_cond.Wait(&wait_lock);
+      }
+      w.nwait -= 1;
+      CHECK_GE(w.nwait, 0) << "boundary check";
+      wait_lock.Unlock();
+    }
+  }
+  virtual void Init(const std::vector<int> &devices) {
+    CHECK_EQ(init_end, 0) << "LocalServer.Init can only call Init once";
+    CHECK_NE(devices.size(), 0) << "LocalServer.Init: must at least contain 1 devices";
+    this->devices = devices;
+    destroy_signal = false;
+    // initialize device id to local index
+    dev2index.clear();
+    for (size_t i = 0; i < devices.size(); ++i) {
+      int devid = devices[i];
+      CHECK_GE(devid, 0) << "device id must be bigger than 0";
+      if (devid >= static_cast<int>(dev2index.size())) {
+        dev2index.resize(devid + 1, -1);
+      }
+      dev2index[devid] = static_cast<int>(i);
+    }
+    // allocate space
+    pull_stream.resize(devices.size());
+    push_stream.resize(devices.size());
+    // initialize all the thread related things
+    if (perdev_push_thread != 0) {
+      push_queues.resize(devices.size());
+    } else {
+      push_queues.resize(1);
+    }
+    for (size_t i = 0; i < push_queues.size(); ++i) {
+      push_queues[i].Init(use_fifo_push_queue != 0);
+    }
+    push_map.Init();
+    push_lock.Init();
+    pull_map.Init();
+    request_lock.Init();
+    wait_lock.Init();
+    wait_cond.Init();
+    if (perdev_pull_thread != 0) {
+      pull_queues.resize(devices.size());
+    } else {
+      pull_queues.resize(1);
+    }
+    for (size_t i = 0; i < pull_queues.size(); ++i) {
+      pull_queues[i].Init();
+    }
+    // initialize the thread
+    if (perdev_push_thread != 0) {
+      thread_push_handler.resize(devices.size());
+      for (size_t i = 0; i < devices.size(); ++i) {
+        std::pair<LocalModel*, size_t> *p
+            = new std::pair<LocalModel*, size_t>();
+        *p = std::make_pair(this, i);
+        thread_push_handler[i].Start(PushLocalThread, p);
+      }
+    } else {
+      thread_push_handler.resize(1);
+      thread_push_handler[0].Start(PushGlobalThread, this);
+    }
+    // initialize pull handler
+    if (perdev_pull_thread != 0) {
+      thread_pull_handler.resize(devices.size());
+      for (size_t i = 0; i < devices.size(); ++i) {
+        std::pair<LocalModel*, size_t> *p
+            = new std::pair<LocalModel*, size_t>();
+        *p = std::make_pair(this, i);
+        thread_pull_handler[i].Start(PullLocalThread, p);
+      }
+    } else {
+      thread_pull_handler.resize(1);
+      thread_pull_handler[0].Start(PullGlobalThread, this);
+    }
+    this->InitCustomerServer();
+    this->init_end = 1;
+  }
+
+  // set weight
+  virtual void SetWeight_(Tensor<xpu, 2, DType> data,
+                          int key,
+                          int devid) {
+    PushEntry &e = push_map.GetRef(key);
+    Stream<xpu> s;
+    push_lock.Lock();
+    mshadow::Copy(e.weight, data, &s);
+    push_lock.Unlock();
+  }
+  virtual void CheckWeight_(Tensor<xpu, 2, DType> data,
+                            int key,
+                            int devid) {
+    CHECK_NE(test_on_server, 0) << "must be in pair debug mode";
+    PushEntry &e = push_map.GetRef(key);
+    mshadow::TensorContainer<cpu, 2, DType> tmp(false);
+    tmp.Resize(data.shape_);
+    Stream<xpu> s;
+    push_lock.Lock();
+    // copy data
+    mshadow::Copy(tmp, data, &s);
+    index_t count = tmp.shape_.Size();
+    double diff = 0.0, ssum = 0.0, maxdiff = 0.0;
+    index_t mxidx = 0;
+    for (index_t i = 0; i < count; ++i) {
+      double d = std::abs(tmp.dptr_[i] - e.weight.dptr_[i]);
+      if (d > maxdiff) {
+        maxdiff = d; mxidx = i;
+      }
+      diff += d;
+      ssum += std::abs(tmp.dptr_[i]);
+    }
+    push_lock.Unlock();
+    // relative absolute error
+    double rerr = diff / ssum;
+    if (rerr > 1e-5 || diff != diff) {
+      fprintf(stderr, "PSLocal:key=%d,dev=%d: err=%f, maxd[%u]=%f, diff=%f, ssum=%f\n",
+              key, devid, rerr, mxidx, maxdiff, diff, ssum);
+    } else {
+      fprintf(stderr, "PSLocal:key=%d,dev=%d:check pass\n", key, devid);
+    }
+  }
+
+ protected:
+  /*! \brief operation performed locally in PS */
+  enum LocalOp {
+    /*! \brief take sum of all devices over the same key */
+    kSum = 0,
+    /*!
+     * \brief concatenate(gather),
+     *  the tensors in all devices with same key
+     */
+    kGather = 1
+  };
+  virtual void InitKey_(Shape<2> shape,
+                        int key, int devid) {
+    this->InitPullMap(key);
+    this->InitPushMap(key, shape);
+  }
+  virtual void Push_(Tensor<xpu, 2, DType> data,
+                     int key, int devid, int priority) {
+    PullEntry &e = pull_map.GetRef(key);
+    e.req[GetWorkIndex(devid)].ready = false;
+    if (perdev_push_thread != 0) {
+      int wid = GetWorkIndex(devid);
+      push_queues[wid].Push(PullTask(data, key, devid), priority);
+    } else {
+      push_queues[0].Push(PullTask(data, key, devid), priority);
+    }
+  }
+  virtual void PullReq_(Tensor<xpu, 2, DType> data,
+                        int key, int devid, int priority,
+                        CallbackFunction callback,
+                        void *callback_arg) {
+    PullEntry &e = pull_map.GetRef(key);
+    CHECK_EQ(e.req.size(), devices.size()) << "PullReq: must initialize the key, req";
+    CHECK_EQ(e.wait.size(), devices.size()) << "PullReq: must initialize the key, wait";
+    const int wid = GetWorkIndex(devid);
+    PullReqRecord &r = e.req[wid];
+    r.dest = data;
+    r.priority = priority;
+    r.callback = callback;
+    r.callback_arg = callback_arg;
+    // reset pull request finish mark
+    wait_lock.Lock();
+    e.wait[wid].finished = false;
+    wait_lock.Unlock();
+    // check ready event
+    request_lock.Lock();
+    CHECK_EQ(!r.pending, true) << "key = " << key
+      << "cannot send duplicate pull request before it finishes";
+    if (e.req[wid].ready) {
+      if (perdev_pull_thread != 0) {
+        pull_queues[wid].Push(std::make_pair(key, devid));
+      } else {
+        pull_queues[0].Push(std::make_pair(key, devid));
+      }
+    } else {
+      r.pending = true;
+    }
+    request_lock.Unlock();
+  }
+  /*!
+   * \brief called to notify that the data is ready for pull
+   * \param data the data that can be pulled back
+   * \param the key of the data
+   */
+  virtual void PullReady(Tensor<cpu, 2> data, int key) {
+    PullEntry &e = pull_map.GetRef(key);
+    CHECK_EQ(e.req.size(), devices.size()) << "PullReady: must initialize the key, req";
+    request_lock.Lock();
+    e.src = data;
+    for (index_t i = 0; i < e.req.size(); ++i) {
+      e.req[i].ready = true;
+      if (e.req[i].pending) {
+        if (perdev_pull_thread != 0) {
+          pull_queues[i].Push(std::make_pair(key, devices[i]));
+        } else {
+          pull_queues[0].Push(std::make_pair(key, devices[i]));
+        }
+        e.req[i].pending = false;
+      }
+    }
+    request_lock.Unlock();
+  }
+  virtual void ServerInitKey(Tensor<cpu, 2> weight, int key) {
+    if (custom_server != NULL) {
+      // intialize server, and ready for pullback
+      custom_server->InitModel(key, weight.dptr_, weight.MSize());
+      if (update_on_server != 0) {
+        this->PullReady(weight, key);
+      }
+    }
+  }
+  /*!
+   * \brief event handler for push finish
+   *  called when all the data with same key comes int
+   * \param data the buffer holds the data in all devices
+   * \param key the key of the data
+   */
+  virtual void HandlePushFinish(Tensor<cpu, 3, DType> data,
+                                int key) {
+    // LOG(ERROR) << dbstr(data);
+    LocalOp op = kSum;
+    typename std::map<int, LocalOp>::const_iterator
+        it = push_operation.find(key);
+    if (it != push_operation.end() && it->first == key) {
+      op = it->second;
+    }
+    // customized server
+    if (custom_server != NULL) {
+      this->ReduceSum(data);
+      custom_server->Update(key, data[0].dptr_, data[0].MSize());
+      if (update_on_server != 0) {
+        PushEntry &e = push_map.GetRef(key);
+        this->PullReady(e.weight, key);
+      } else {
+        CHECK_NE(test_on_server, 0) << "test mode";
+        this->PullReady(data[0], key);
+      }
+      return;
+    }
+    switch (op) {
+      case kSum: {
+        this->ReduceSum(data);
+        this->PullReady(data[0], key);
+        return;
+      }
+      case kGather: {
+        this->PullReady(data.FlatTo2D(), key);
+        return;
+      }
+      default: LOG(FATAL) << "unknown LocalOp";
+    }
+  }
+  /*!
+   * \brief event handler for reduce finish
+   *  called when all the data with same key finishes the reduction
+   * \param data the buffer holds the reduction result
+   * \param key the key of the data
+   */
+  inline void HandleReduceFinish(Tensor<cpu, 2, DType> data,
+                                 int key) {
+    if (custom_server != NULL) {
+      custom_server->Update(key, data.dptr_, data.MSize());
+      if (update_on_server != 0) {
+        PushEntry &e = push_map.GetRef(key);
+        this->PullReady(e.weight, key);
+      } else {
+        CHECK_NE(test_on_server, 0) << "test mode";
+        this->PullReady(data, key);
+      }
+    } else {
+      this->PullReady(data, key);
+    }
+  }
+  virtual void InitCustomerServer(void) {
+    if (update_on_server != 0 || test_on_server != 0) {
+      custom_server = CreateModelUpdater<DType>();
+      for (size_t j = 0; j < cfgvec.size(); ++j) {
+        custom_server->SetParam(cfgvec[j].first.c_str(),
+                                cfgvec[j].second.c_str());
+      }
+      custom_server->InitUpdater(0, 0, NULL);
+    }
+  }
+
+ protected:
+  // customized server
+  IModelUpdater<DType> *custom_server;
+  // whether use fifo push queue
+  int use_fifo_push_queue;
+
+  // perform sum reduction
+  inline void ReduceSum(Tensor<cpu, 3, DType> data) {
+    #if defined(_OPENMP)
+    if (data[0].MSize() >= bigarray_bound &&
+        nthread_reduction != 0) {
+      ms_omp_uint ntask = static_cast<ms_omp_uint>(data.size(1));
+      #pragma omp parallel for schedule(static) num_threads(nthread_reduction)
+      for (ms_omp_uint j = 0; j < ntask; ++j) {
+        for (index_t i = 1; i < data.size(0); ++i) {
+          data[0][j] += data[i][j];
+        }
+      }
+    } else  //NOLINT(*)
+      #endif
+    {
+      for (index_t i = 1; i < data.size(0); ++i) {
+        data[0] += data[i];
+      }
+    }
+  }
+
+ private:
+  /*! \brief task running */
+  struct PullTask {
+    /*! \brief the task data source */
+    Tensor<xpu, 2, DType> data;
+    /*! \brief the key to the tensor */
+    int key;
+    /*!
+     * \brief the device id, (key,devid),
+     * uniquely identifies a mem location
+     */
+    int devid;
+    PullTask(void) {}
+    PullTask(Tensor<xpu, 2, DType> data, int key, int devid)
+        : data(data), key(key), devid(devid) {}
+  };
+  /*! \brief data structure to hold temporal push result */
+  struct PushEntry {
+    // temporal space to hold input data
+    Tensor<cpu, 4, DType> data;
+    // temporal space to hold weight, if needed
+    Tensor<cpu, 2, DType> weight;
+    // indicator whether the certain devices is already copied in
+    std::vector<bool> copied;
+    // number of data copied in
+    int num_copied;
+    // version number of data used to hold incomming data in push
+    int copyin_version;
+    // use pinned memory
+    bool pin_memory;
+    // constructor
+    PushEntry(void)
+        : copyin_version(0) {
+      weight.dptr_ = NULL;
+    }
+    ~PushEntry(void) {
+      if (data.dptr_ != NULL) {
+        if (pin_memory) {
+          mshadow::FreeHost<xpu>(&data);
+          if (weight.dptr_ != NULL) {
+            mshadow::FreeHost<xpu>(&weight);
+          }
+        } else {
+          mshadow::FreeSpace(&data);
+          if (weight.dptr_ != NULL) {
+            mshadow::FreeSpace(&weight);
+          }
+        }
+      }
+    }
+    // constructor
+    inline void Init(int ndevice, Shape<2> shape,
+                     bool pin_memory, bool need_weight) {
+      this->pin_memory = pin_memory;
+      data.shape_ = Shape4(2, ndevice, shape[0], shape[1]);
+      weight.shape_ = shape;
+      if (pin_memory) {
+        mshadow::AllocHost<xpu>(&data);
+        if (need_weight) mshadow::AllocHost<xpu>(&weight);
+      } else {
+        mshadow::AllocSpace(&data, false);
+        if (need_weight) mshadow::AllocSpace(&weight);
+      }
+      CHECK_EQ(data.CheckContiguous(), true) << "Data must be contiguous";
+      CHECK(!need_weight || weight.CheckContiguous()) << "Weight must be contiguous";
+      num_copied = 0;
+      copied.resize(ndevice, false);
+    }
+  };
+  // a record to remember things related to pull request
+  struct PullReqRecord {
+    // whether this record contains a pending request
+    // whether pull is ready to go
+    bool ready;
+    // waiting for pull ready
+    bool pending;
+    // the destination to pull data into
+    Tensor<xpu, 2, DType> dest;
+    // the priority of the
+    int priority;
+    // callback function
+    CallbackFunction *callback;
+    // argument for callback
+    void *callback_arg;
+    PullReqRecord(void) : ready(false), pending(false) {
+    }
+  };
+  // a record to help handle pullwait
+  struct PullWaitRecord {
+    // number of thread that waits for the request to finish
+    int nwait;
+    // the request was finished
+    bool finished;
+    PullWaitRecord(void)
+        : nwait(0), finished(true) {
+      // set finished to true so pull without pull request returns
+    }
+  };
+  /*! \brief data structure to hold pull request */
+  struct PullEntry {
+    // data to be pulled back
+    Tensor<cpu, 2, DType> src;
+    // pullrequest record
+    std::vector<PullReqRecord> req;
+    // whether there is thread waiting on this event
+    std::vector<PullWaitRecord> wait;
+    PullEntry(void) {
+    }
+  };
+  // signal to notify all the thread about class destruction
+  bool destroy_signal;
+  // vector of devices
+  std::vector<int> devices;
+  // device index to local index
+  std::vector<int> dev2index;
+  //----- data structure used to support push ----
+  // stream used by push thread each device for memcpy
+  std::vector<Stream<xpu>*> push_stream;
+  // the queue used for push task
+  std::vector<utils::ThreadPQueue<PullTask> > push_queues;
+  // thread to handle push task
+  std::vector<utils::Thread> thread_push_handler;
+  // lock to lock push field
+  utils::Mutex push_lock;
+  // the map of push buffer
+  utils::ThreadSafeMap<PushEntry> push_map;
+  // customized local reduction operation
+  std::map<int, LocalOp> push_operation;
+  //----- data structure used to support pull ----
+  // the queue used for pull task
+  std::vector<utils::ThreadPQueue<std::pair<int, int> > > pull_queues;
+  // stream used by pull thread each device for memcpy
+  std::vector<Stream<xpu>*> pull_stream;
+  // the map to store pull status
+  utils::ThreadSafeMap<PullEntry> pull_map;
+  // thread to handle pull task
+  std::vector<utils::Thread> thread_pull_handler;
+  // lock to lock request field
+  utils::Mutex request_lock;
+  // lock to lock wait field
+  utils::Mutex wait_lock;
+  // conditional variable to do waiting
+  utils::ConditionVariable wait_cond;
+  // ---------configurations of server-------
+  int init_end;
+  // whether perform update on serverside
+  int update_on_server;
+  // debug option
+  int test_on_server;
+  // use pinned memory
+  int use_pin_memory;
+  // number of reduction thread
+  int nthread_reduction;
+  // the threshold for big array
+  size_t bigarray_bound;
+  // whether use pull thread per device
+  int perdev_pull_thread;
+  // whether use push thread per device
+  int perdev_push_thread;
+  /*! \brief history of configurations */
+  std::vector< std::pair<std::string, std::string> > cfgvec;
+  // push handler
+  inline void PushProc(utils::ThreadPQueue<PullTask> *queue) {
+    while (!destroy_signal) {
+      PullTask tsk;
+      if (queue->Pop(&tsk)) {
+        const int wid = GetWorkIndex(tsk.devid);
+        PushEntry &e = push_map.GetRef(tsk.key);
+        CHECK_EQ(e.data[0][0].shape_, tsk.data.shape_)
+          << "Tensor with same key must share same shape "
+          << e.data[0][0].shape_
+          << " vs "
+          << tsk.data.shape_;
+        CHECK_EQ(!e.copied[wid], true) << "data inconsistency";
+        // start copy
+        SetDevice<xpu>(tsk.devid);
+        Copy(e.data[e.copyin_version][wid], tsk.data, push_stream[wid]);
+        // wait till the copy finishes
+        push_stream[wid]->Wait();
+        // mark copied
+        e.copied[wid] = true;
+        push_lock.Lock();
+        e.num_copied += 1;
+        int cp_version = e.copyin_version;
+        bool push_finish = e.num_copied >= static_cast<int>(devices.size());
+        if (push_finish) {
+          // switch version
+          e.copyin_version = (e.copyin_version + 1) % e.data.size(0);
+          std::fill(e.copied.begin(), e.copied.end(), false);
+          e.num_copied = 0;
+        }
+        push_lock.Unlock();
+        if (push_finish) {
+          this->HandlePushFinish(e.data[cp_version], tsk.key);
+        }
+      } else {
+        CHECK_EQ(destroy_signal, true) << "abort but not destroy";
+      }
+    }
+  }
+  inline void PushHandlerGlobal(void) {
+    // allocate stream resources
+    for (size_t i = 0; i < devices.size(); ++i) {
+      SetDevice<xpu>(devices[i]);
+      push_stream[i] = NewStream<xpu>(devices[i]);
+    }
+    this->PushProc(&push_queues[0]);
+    // free resources
+    for (size_t i = 0; i < devices.size(); ++i) {
+      SetDevice<xpu>(devices[i]);
+      DeleteStream(push_stream[i]);
+    }
+  }
+  inline void PushHandlerLocal(size_t tid) {
+    CHECK_LT(tid, devices.size()) << "threadid exceed boundary";
+    CHECK_EQ(push_queues.size(), devices.size()) << "must have one pull_queue per device";
+    // allocate stream resources
+    SetDevice<xpu>(devices[tid]);
+    push_stream[tid] = NewStream<xpu>(devices[tid]);
+    this->PushProc(&push_queues[tid]);
+    SetDevice<xpu>(devices[tid]);
+    DeleteStream(push_stream[tid]);
+  }
+  /*!\brief entry point of loader thread */
+  inline static MSHADOW_THREAD_PREFIX PushGlobalThread(void *pthread) {
+    static_cast<LocalModel*>(pthread)->PushHandlerGlobal();
+    utils::ThreadExit(NULL);
+    return NULL;
+  }
+  inline static MSHADOW_THREAD_PREFIX PushLocalThread(void *arg) {
+    std::pair<LocalModel*, size_t> *p
+        = static_cast<std::pair<LocalModel*, size_t>*>(arg);
+    p->first->PushHandlerLocal(p->second);
+    delete p;
+    return NULL;
+  }
+  // push handler procedure
+  inline void PullProc(utils::ThreadPQueue<std::pair<int, int> > *queue) {
+    while (!destroy_signal) {
+      std::pair<int, int> tsk;
+      if (queue->Pop(&tsk)) {
+        const int key = tsk.first;
+        const int devid = tsk.second;
+        const int wid = GetWorkIndex(devid);
+        PullEntry &e = pull_map.GetRef(key);
+        {
+          // handle request
+          CHECK_EQ(e.req.size(), devices.size()) << "PullHandler: must initialize the key, req";
+          PullReqRecord &r = e.req[wid];
+          SetDevice<xpu>(devid);
+          Copy(r.dest, e.src, pull_stream[wid]);
+          // callback, if any
+          if (r.callback != NULL) {
+            (*r.callback)(pull_stream[wid], r.callback_arg);
+          }
+          // wait till the operation finishes
+          pull_stream[wid]->Wait();
+        }
+        {
+          // wake up waiters if any
+          CHECK_EQ(e.wait.size(), devices.size()) << "PullHandler, must initialize the key, req";
+          PullWaitRecord &w = e.wait[wid];
+          wait_lock.Lock();
+          w.finished = true;
+          if (w.nwait != 0) {
+            wait_cond.Broadcast();
+          }
+          wait_lock.Unlock();
+        }
+      } else {
+        CHECK_EQ(destroy_signal, true) << "abort but not destroy";
+      }
+    }
+  }
+  // use one thread for all pull actions
+  inline void PullHandlerGlobal(void) {
+    // allocate stream resources
+    for (size_t i = 0; i < devices.size(); ++i) {
+      SetDevice<xpu>(devices[i]);
+      pull_stream[i] = NewStream<xpu>(devices[i]);
+    }
+    this->PullProc(&pull_queues[0]);
+    // free resources
+    for (size_t i = 0; i < devices.size(); ++i) {
+      SetDevice<xpu>(devices[i]);
+      DeleteStream(pull_stream[i]);
+    }
+  }
+  inline void PullHandlerLocal(size_t tid) {
+    CHECK_LT(tid, devices.size()) << "threadid exceed boundary";
+    CHECK_EQ(pull_queues.size(), devices.size()) << "must have one pull_queue per device";
+    // allocate stream resources
+    SetDevice<xpu>(devices[tid]);
+    pull_stream[tid] = NewStream<xpu>(devices[tid]);
+    this->PullProc(&pull_queues[tid]);
+    SetDevice<xpu>(devices[tid]);
+    DeleteStream(pull_stream[tid]);
+  }
+  /*!\brief entry point of pull thread, one thread for all devices */
+  inline static MSHADOW_THREAD_PREFIX PullGlobalThread(void *arg) {
+    static_cast<LocalModel*>(arg)->PullHandlerGlobal();
+    return NULL;
+  }
+  inline static MSHADOW_THREAD_PREFIX PullLocalThread(void *arg) {
+    std::pair<LocalModel*, size_t> *p
+        = static_cast<std::pair<LocalModel*, size_t>*>(arg);
+    p->first->PullHandlerLocal(p->second);
+    delete p;
+    return NULL;
+  }
+  // get internal index of device
+  inline int GetWorkIndex(int devid) const {
+    CHECK(devid >= 0 &&
+          devid < static_cast<int>(dev2index.size()) &&
+          dev2index[devid] >= 0) << "Push: invalid devid";
+    return dev2index[devid];
+  }
+  // functions to handle pull
+  inline void InitPullMap(int key) {
+    pull_map.Init(key);
+    PullEntry &e = pull_map.GetRef(key);
+    request_lock.Lock();
+    // must recheck after lock
+    if (e.req.size() == 0) {
+      e.req.resize(devices.size(), PullReqRecord());
+    }
+    request_lock.Unlock();
+    // check wait map
+    wait_lock.Lock();
+    // must recheck after lock
+    if (e.wait.size() == 0) {
+      e.wait.resize(devices.size(), PullWaitRecord());
+    }
+    wait_lock.Unlock();
+  }
+  // functions to handle pull
+  inline void InitPushMap(int key, Shape<2> shape) {
+    push_map.Init(key);
+    PushEntry &e = push_map.GetRef(key);
+    push_lock.Lock();
+    if (e.copied.size() == 0) {
+      e.Init(devices.size(), shape,
+             use_pin_memory != 0,
+             update_on_server != 0 || test_on_server != 0);
+    }
+    this->ServerInitKey(e.weight, key);
+    push_lock.Unlock();
+  }
+};
+}  // namespace ps
+}  // namespace mshadow
+#endif // MSHADOW_PS_LOCAL_INL_H_  NOLINT(*)
diff --git a/3rdparty/mshadow/mshadow-ps/ps_rabit-inl.h b/3rdparty/mshadow/mshadow-ps/ps_rabit-inl.h
new file mode 100644
index 000000000000..238932e38982
--- /dev/null
+++ b/3rdparty/mshadow/mshadow-ps/ps_rabit-inl.h
@@ -0,0 +1,113 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file ps_rabit-inl.h
+ * \brief distributed version of PS using BSP
+ *     synchronization in the backend
+ * \author Tianqi Chen, Mu Li
+ */
+#ifndef MSHADOW_PS_RABIT_INL_H_ // NOLINT(*)
+#define MSHADOW_PS_RABIT_INL_H_ // NOLINT(*)
+#include <vector>
+#include "./mshadow_ps.h"
+#include "./ps_local-inl.h"
+
+#if MSHADOW_RABIT_PS
+#include <rabit.h>
+namespace mshadow {
+namespace ps {
+// multi-threaded implementation of
+template<typename xpu, typename DType>
+class RabitModel : public LocalModel<xpu, DType> {
+ public:
+  // parent type
+  typedef LocalModel<xpu, DType> Parent;
+  // constructor
+  RabitModel() {
+    // enforce usage of fifo queue
+    this->use_fifo_push_queue = 1;
+    destroy_reduce_thread_ = false;
+    disable_allreduce_ = 0;
+    this->init_reducer_ = 0;
+  }
+  virtual ~RabitModel(void) {
+    Parent::Destroy();
+    if (init_reducer_ != 0) {
+      destroy_reduce_thread_ = true;
+      reduce_queue_.Abort(1);
+      thread_reduce_handler_.Join();
+      reduce_queue_.Destroy();
+    }
+  }
+  // initialize the parameter server
+  virtual void Init(const std::vector<int> &devices) {
+    this->use_fifo_push_queue = 1;
+    // use fifo
+    reduce_queue_.Init(true);
+    thread_reduce_handler_.Start(ReduceGlobalThread, this);
+    init_reducer_ = 1;
+    // initialize other things
+    Parent::Init(devices);
+  }
+  // set parameters
+  virtual void SetParam(const char *name, const char *val) {
+    if (!strcmp(name, "msg:disable_allreduce")) {
+      disable_allreduce_ = atoi(val);
+    }
+    Parent::SetParam(name, val);
+  }
+  // override this function, to use parameter server
+  virtual void HandlePushFinish(Tensor<cpu, 3, DType> data,
+                                int key) {
+    // summation the data fron all devices
+    LocalModel<xpu, DType>::ReduceSum(data);
+    CHECK_EQ(data[0].CheckContiguous(), true) << "data must be contiguous";
+    ReduceTask tsk;
+    tsk.data = data[0]; tsk.key = key;
+    reduce_queue_.Push(tsk, 0);
+  }
+
+ private:
+  // reduce task
+  struct ReduceTask {
+    int key;
+    mshadow::Tensor<cpu, 2> data;
+  };
+  // destroy reduce
+  bool destroy_reduce_thread_;
+  // whether reducer is initialized
+  int init_reducer_;
+  // check disable_allreduce functionalities
+  int disable_allreduce_;
+  // reduce handler thread
+  utils::Thread thread_reduce_handler_;
+  // queue for allreduce task
+  utils::ThreadPQueue<ReduceTask> reduce_queue_;
+  // reduce handler
+  inline void ReduceHandler(void) {
+    while (!destroy_reduce_thread_) {
+      ReduceTask tsk;
+      if (reduce_queue_.Pop(&tsk)) {
+        CHECK_EQ(disable_allreduce_, 0) << "Allreduce disabled error";
+        int key = tsk.key;
+        rabit::Allreduce<rabit::op::Max>(&key, 1);
+        CHECK_EQ(key, tsk.key) << "Allreduce not concensus";
+        rabit::Allreduce<rabit::op::Sum>
+            (tsk.data.dptr_, tsk.data.MSize());
+        tsk.data *= 1.0f / rabit::GetWorldSize();
+        CHECK_EQ(disable_allreduce_, 0) << "Allreduce disabled error";
+        this->HandleReduceFinish(tsk.data, tsk.key);
+      } else {
+        CHECK_EQ(destroy_reduce_thread_, true) << "abort but not destroy";
+      }
+    }
+  }
+  /*!\brief entry point of reduce thread */
+  inline static MSHADOW_THREAD_PREFIX ReduceGlobalThread(void *pthread) {
+    static_cast<RabitModel*>(pthread)->ReduceHandler();
+    return NULL;
+  }
+};
+}  // namespace ps
+}  // namespace mshadow
+#endif  // MSHADOW_RABIT_PS
+#endif  // MSHADOW_PS_RABIT_INL_H_ // NOLINT(*)
diff --git a/3rdparty/mshadow/mshadow-ps/thread.h b/3rdparty/mshadow/mshadow-ps/thread.h
new file mode 100644
index 000000000000..a80dffc22fb6
--- /dev/null
+++ b/3rdparty/mshadow/mshadow-ps/thread.h
@@ -0,0 +1,261 @@
+/*!
+ * Copyright by Contributors
+ * \file thread.h
+ * \brief this header include the minimum necessary resource
+ * for multi-threading that can be compiled in windows, linux, mac
+ * \author Tianqi Chen
+ */
+#ifndef MSHADOW_PS_THREAD_H_ // NOLINT(*)
+#define MSHADOW_PS_THREAD_H_ // NOLINT(*)
+
+#ifdef _MSC_VER
+#include <windows.h>
+#include <process.h>
+#include "../mshadow/logging.h"
+namespace mshadow {
+namespace utils {
+/*! \brief simple semaphore used for synchronization */
+class Semaphore {
+ public :
+  inline void Init(int init_val) {
+    sem = CreateSemaphore(NULL, init_val, 10, NULL);
+    CHECK_NE(sem, NULL) << "create Semaphore error";
+  }
+  inline void Destroy(void) {
+    CloseHandle(sem);
+  }
+  inline void Wait(void) {
+    CHECK_EQ(WaitForSingleObject(sem, INFINITE), WAIT_OBJECT_0)
+      << "WaitForSingleObject error";
+  }
+  inline void Post(void) {
+    CHECK_NE(ReleaseSemaphore(sem, 1, NULL), 0) << "ReleaseSemaphore error";
+  }
+
+ private:
+  HANDLE sem;
+};
+
+/*! \brief mutex under windows */
+class Mutex {
+ public:
+  inline void Init(void) {
+    CHECK_NE(InitializeCriticalSectionAndSpinCount(&mutex, 0x00000400), 0)
+      << "Mutex::Init fail";
+  }
+  inline void Lock(void) {
+    EnterCriticalSection(&mutex);
+  }
+  inline void Unlock(void) {
+    LeaveCriticalSection(&mutex);
+  }
+  inline void Destroy(void) {
+    DeleteCriticalSection(&mutex);
+  }
+
+ private:
+  friend class ConditionVariable;
+  CRITICAL_SECTION mutex;
+};
+
+// conditional variable that uses pthread
+class ConditionVariable {
+ public:
+  // initialize conditional variable
+  inline void Init(void) {
+    InitializeConditionVariable(&cond);
+  }
+  // destroy the thread
+  inline void Destroy(void) {
+    // DeleteConditionVariable(&cond);
+  }
+  // wait on the conditional variable
+  inline void Wait(Mutex *mutex) {
+    CHECK_NE(SleepConditionVariableCS(&cond, &(mutex->mutex), INFINITE), 0)
+      << "ConditionVariable:Wait fail";
+  }
+  inline void Broadcast(void) {
+    WakeAllConditionVariable(&cond);
+  }
+  inline void Signal(void) {
+    WakeConditionVariable(&cond);
+  }
+
+ private:
+  CONDITION_VARIABLE cond;
+};
+
+/*! \brief simple thread that wraps windows thread */
+class Thread {
+ private:
+  HANDLE    thread_handle;
+  unsigned  thread_id;
+ public:
+  inline void Start(unsigned int __stdcall entry(void*p), void *param) {
+    thread_handle = (HANDLE)_beginthreadex(NULL, 0, entry, param, 0, &thread_id);
+  }
+  inline int Join(void) {
+    WaitForSingleObject(thread_handle, INFINITE);
+    return 0;
+  }
+};
+/*! \brief exit function called from thread */
+inline void ThreadExit(void *status) {
+  _endthreadex(0);
+}
+#define MSHADOW_THREAD_PREFIX unsigned int __stdcall
+}  // namespace utils
+}  // namespace mshadow
+#else
+// thread interface using g++
+#include <semaphore.h>
+#include <pthread.h>
+#include <errno.h>
+namespace mshadow {
+namespace utils {
+/*!\brief semaphore class */
+class Semaphore {
+  #ifdef __APPLE__
+
+ private:
+  sem_t* semPtr;
+  char sema_name[20];
+
+ private:
+  inline void GenRandomString(char *s, const int len) {
+    static const char alphanum[] = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ";
+    for (int i = 0; i < len; ++i) {
+      s[i] = alphanum[rand() % (sizeof(alphanum) - 1)];
+    }
+    s[len] = 0;
+  }
+
+ public:
+  inline void Init(int init_val) {
+    sema_name[0] = '/';
+    sema_name[1] = 's';
+    sema_name[2] = 'e';
+    sema_name[3] = '/';
+    GenRandomString(&sema_name[4], 16);
+    if ((semPtr = sem_open(sema_name, O_CREAT, 0644, init_val)) == SEM_FAILED) {
+      perror("sem_open");
+      exit(1);
+    }
+    CHECK_NE(semPtr, NULL) << "create Semaphore error";
+  }
+  inline void Destroy(void) {
+    if (sem_close(semPtr) == -1) {
+      perror("sem_close");
+      exit(EXIT_FAILURE);
+    }
+    if (sem_unlink(sema_name) == -1) {
+      perror("sem_unlink");
+      exit(EXIT_FAILURE);
+    }
+  }
+  inline void Wait(void) {
+    sem_wait(semPtr);
+  }
+  inline void Post(void) {
+    sem_post(semPtr);
+  }
+  #else
+
+ private:
+  sem_t sem;
+
+ public:
+  inline void Init(int init_val) {
+    if (sem_init(&sem, 0, init_val) != 0) {
+      LOG(FATAL) << "Semaphore.Init: " << strerror(errno);
+    }
+  }
+  inline void Destroy(void) {
+    if (sem_destroy(&sem) != 0) {
+      LOG(FATAL) << "Semaphore.Destroy: " << strerror(errno);
+    }
+  }
+  inline void Wait(void) {
+    if (sem_wait(&sem) != 0) {
+      LOG(FATAL) << "Semaphore.Wait: " << strerror(errno);
+    }
+  }
+  inline void Post(void) {
+    if (sem_post(&sem) != 0) {
+      LOG(FATAL) << "Semaphore.Post: " << strerror(errno);
+    }
+  }
+  #endif
+};
+
+// mutex that works with pthread
+class Mutex {
+ public:
+  inline void Init(void) {
+    pthread_mutex_init(&mutex, NULL);
+  }
+  inline void Lock(void) {
+    pthread_mutex_lock(&mutex);
+  }
+  inline void Unlock(void) {
+    pthread_mutex_unlock(&mutex);
+  }
+  inline void Destroy(void) {
+    pthread_mutex_destroy(&mutex);
+  }
+
+ private:
+  friend class ConditionVariable;
+  pthread_mutex_t mutex;
+};
+
+// conditional variable that uses pthread
+class ConditionVariable {
+ public:
+  // initialize conditional variable
+  inline void Init(void) {
+    pthread_cond_init(&cond, NULL);
+  }
+  // destroy the thread
+  inline void Destroy(void) {
+    pthread_cond_destroy(&cond);
+  }
+  // wait on the conditional variable
+  inline void Wait(Mutex *mutex) {
+    pthread_cond_wait(&cond, &(mutex->mutex));
+  }
+  inline void Broadcast(void) {
+    pthread_cond_broadcast(&cond);
+  }
+  inline void Signal(void) {
+    pthread_cond_signal(&cond);
+  }
+
+ private:
+  pthread_cond_t cond;
+};
+
+/*!\brief simple thread class */
+class Thread {
+ private:
+  pthread_t thread;
+ public :
+  inline void Start(void * entry(void*), void *param) { // NOLINT(*)
+    pthread_attr_t attr;
+    pthread_attr_init(&attr);
+    pthread_attr_setdetachstate(&attr, PTHREAD_CREATE_JOINABLE);
+    pthread_create(&thread, &attr, entry, param);
+  }
+  inline int Join(void) {
+    void *status;
+    return pthread_join(thread, &status);
+  }
+};
+inline void ThreadExit(void *status) {
+  pthread_exit(status);
+}
+}  // namespace utils
+}  // namespace mshadow
+#define MSHADOW_THREAD_PREFIX void *
+#endif  // Linux
+#endif  // MSHADOW_PS_THREAD_H_  NOLINT(*)
diff --git a/3rdparty/mshadow/mshadow-ps/thread_util.h b/3rdparty/mshadow/mshadow-ps/thread_util.h
new file mode 100644
index 000000000000..185cfcfa0728
--- /dev/null
+++ b/3rdparty/mshadow/mshadow-ps/thread_util.h
@@ -0,0 +1,169 @@
+/*!
+ * Copyright by Contributors
+ * \file thread_util.h
+ * \brief data structures for multi-threading communication
+ * \author Tianqi Chen
+ */
+#ifndef MSHADOW_PS_THREAD_UTIL_H_  // NOLINT(*)
+#define MSHADOW_PS_THREAD_UTIL_H_  // NOLINT(*)
+
+#include <utility>
+#include <queue>
+#include <map>
+#include "./thread.h"
+namespace mshadow {
+namespace utils {
+/*!
+ * \brief thread safe queue that can be used for customer consumer model
+ * in the future, it will support priority scheduling
+ * \tparam DType the content of the queue
+ */
+template<typename DType>
+class ThreadPQueue {
+ public:
+  // constructor
+  ThreadPQueue() : use_fifo_(false) {
+  }
+  /*! \brief intitialize the queue, must call this before use */
+  inline void Init(bool use_fifo = false) {
+    use_fifo_ = use_fifo;
+    lock_.Init();
+    counter_.Init(0);
+  }
+  /*! \brief destroy the resources on the queue */
+  inline void Destroy(void) {
+    lock_.Destroy();
+    counter_.Destroy();
+  }
+  /*!
+   * \brief Destroy the queue
+   *        wake up all the threads waits on pop
+   *  this is usually used in class destructor
+   * \param max_nthread the maximum number of thread that
+   *  could be waiting on the queue
+   */
+  inline void Abort(int max_nthread = 1) {
+    for (int i = 0; i < max_nthread; ++i) {
+      counter_.Post();
+    }
+  }
+  /*!
+   * \brief push an element to the queue
+   * \param data the data to be puhed into queue
+   * \param optionally priority level to hint which
+   *        element should be poped first
+   */
+  inline void Push(const DType &data, int priority = 0) {
+    lock_.Lock();
+    if (use_fifo_) {
+      fqueue_.push(data);
+    } else {
+      pqueue_.push(Entry(data, priority));
+    }
+    lock_.Unlock();
+    counter_.Post();
+  }
+  /*!
+   * \brief pop an element from the queue
+   * this will block the thread if the queue is empty
+   * \param data_out the address to put output of the queue
+   * \return true if a correct element is returned
+   *  false if abort is called and no element was left in queue
+   */
+  inline bool Pop(DType *data_out) {
+    counter_.Wait();
+    lock_.Lock();
+    if (use_fifo_) {
+      if (fqueue_.size() == 0) {
+        lock_.Unlock(); return false;
+      }
+    } else {
+      if (pqueue_.size() == 0) {
+        lock_.Unlock(); return false;
+      }
+    }
+    if (use_fifo_) {
+      CHECK_NE(fqueue_.size(), 0) << "Queue.Pop";
+      *data_out = fqueue_.front();
+      fqueue_.pop();
+    } else {
+      CHECK_NE(pqueue_.size(), 0) << "Queue.Pop";
+      *data_out = pqueue_.top().data;
+      pqueue_.pop();
+    }
+    lock_.Unlock();
+    return true;
+  }
+
+ private:
+  // entry in the queue
+  struct Entry {
+    DType data;
+    int priority;
+    Entry(const DType &data, int priority)
+        : data(data), priority(priority) {}
+    inline bool operator<(const Entry &b) const {
+      return priority < b.priority;
+    }
+  };
+  // whether use FIFO queue
+  bool use_fifo_;
+  // a priority queue
+  std::priority_queue<Entry> pqueue_;
+  // a FIFO queue
+  std::queue<DType> fqueue_;
+  // lock for accessing the queue
+  utils::Mutex lock_;
+  // counter to count number of push tasks
+  utils::Semaphore counter_;
+};
+
+// naive implementation of threadsafe map
+template<typename TValue>
+class ThreadSafeMap {
+ public:
+  inline void Init(void) {
+    lock_.Init();
+  }
+  inline void Destroy(void) {
+    for (typename std::map<int, TValue*>::iterator
+             it = map_.begin(); it != map_.end(); ++it) {
+      delete it->second;
+    }
+    lock_.Destroy();
+  }
+  inline TValue *Get(int key) {
+    TValue *ret;
+    lock_.Lock();
+    typename std::map<int, TValue*>::const_iterator
+        it = map_.find(key);
+    if (it == map_.end() || it->first != key) {
+      ret = NULL;
+    } else {
+      ret = it->second;
+    }
+    lock_.Unlock();
+    return ret;
+  }
+  inline TValue &GetRef(int key) {
+    TValue *ret = this->Get(key);
+    CHECK_NE(ret, NULL) << "key = " << key << " does not exist";
+    return *ret;
+  }
+  inline void Init(int key) {
+    lock_.Lock();
+    if (map_.count(key) == 0) {
+      map_[key] = new TValue();
+    }
+    lock_.Unlock();
+  }
+
+ private:
+  // lock for accessing the queue
+  utils::Mutex lock_;
+  std::map<int, TValue*> map_;
+};
+
+}  // namespace utils
+}  // namespace mshadow
+#endif  // MSHADOW_PS_THREAD_UTIL_H_
diff --git a/3rdparty/mshadow/mshadow/README.md b/3rdparty/mshadow/mshadow/README.md
new file mode 100644
index 000000000000..86276af013e2
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/README.md
@@ -0,0 +1,8 @@
+Code Guide
+====
+This readme contains notes about code in mshadow. MShadow generally follows Google's C++ Style.
+
+Convention
+====
+* Basically, all the files ends in ```-inl.h, -inl.cuh``` are implementations, and can be ignored if only using mshadow
+* The files ends in ```.h``` are heavily commented with [doxyen format](http://www.doxygen.org/), and can be used to generate the corresponding document.
diff --git a/3rdparty/mshadow/mshadow/base.h b/3rdparty/mshadow/mshadow/base.h
new file mode 100755
index 000000000000..d08efd387c7e
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/base.h
@@ -0,0 +1,1110 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file base.h
+ * \brief definitions of base types, operators, macros functions
+ *
+ * \author Bing Xu, Tianqi Chen
+ */
+#ifndef MSHADOW_BASE_H_
+#define MSHADOW_BASE_H_
+#ifdef _MSC_VER
+#ifndef _CRT_SECURE_NO_WARNINGS
+#define _CRT_SECURE_NO_WARNINGS
+#endif
+#ifndef _CRT_SECURE_NO_DEPRECATE
+#define _CRT_SECURE_NO_DEPRECATE
+#endif
+#ifndef NOMINMAX
+#define NOMINMAX
+#endif
+#endif
+#include <cmath>
+#include <cstdio>
+#include <cfloat>
+#include <climits>
+#include <algorithm>
+#include <functional>
+#include <sstream>
+#include <string>
+
+#ifdef _MSC_VER
+//! \cond Doxygen_Suppress
+typedef signed char int8_t;
+typedef __int16 int16_t;
+typedef __int32 int32_t;
+typedef __int64 int64_t;
+typedef unsigned char uint8_t;
+typedef unsigned __int16 uint16_t;
+typedef unsigned __int32 uint32_t;
+typedef unsigned __int64 uint64_t;
+//! \endcond
+#else
+#include <inttypes.h>
+#endif
+// macro defintiions
+/*!
+ * \brief if this macro is define to be 1,
+ * mshadow should compile without any of other libs
+ */
+#ifndef MSHADOW_STAND_ALONE
+#define MSHADOW_STAND_ALONE 0
+#endif
+/*! \brief whether do padding during allocation */
+#ifndef MSHADOW_ALLOC_PAD
+#define MSHADOW_ALLOC_PAD true
+#endif
+/*!
+ * \brief
+ *  x dimension of data must be bigger pad_size * ratio to be alloced padded memory,
+ *  otherwise use tide allocation
+ *  for example, if pad_ratio=2, GPU memory alignement size is 32,
+ *  then we will only allocate padded memory if x dimension > 64
+ *  set it to 0 then we will always allocate padded memory
+ */
+#ifndef MSHADOW_MIN_PAD_RATIO
+  #define MSHADOW_MIN_PAD_RATIO 2
+#endif
+
+#if MSHADOW_STAND_ALONE
+  #define MSHADOW_USE_CBLAS 0
+  #define MSHADOW_USE_MKL   0
+  #define MSHADOW_USE_CUDA  0
+#endif
+
+/*!
+ * \brief force user to use GPU stream during computation
+ *  error will be shot when default stream NULL is used
+ */
+#ifndef MSHADOW_FORCE_STREAM
+#define MSHADOW_FORCE_STREAM 1
+#endif
+
+/*! \brief use CBLAS for CBLAS */
+#ifndef MSHADOW_USE_CBLAS
+  #define MSHADOW_USE_CBLAS 0
+#endif
+/*! \brief use MKL for BLAS */
+#ifndef MSHADOW_USE_MKL
+  #define MSHADOW_USE_MKL   1
+#endif
+
+/*!
+ * \brief use CUDA support, must ensure that the cuda include path is correct,
+ * or directly compile using nvcc
+ */
+#ifndef MSHADOW_USE_CUDA
+  #define MSHADOW_USE_CUDA   1
+#endif
+
+/*!
+ * \brief use CUDNN support, must ensure that the cudnn include path is correct
+ */
+#ifndef MSHADOW_USE_CUDNN
+  #define MSHADOW_USE_CUDNN 0
+#endif
+
+/*!
+ * \brief use CUSOLVER support
+ */
+#ifndef MSHADOW_USE_CUSOLVER
+  #define MSHADOW_USE_CUSOLVER MSHADOW_USE_CUDA
+#endif
+
+/*!
+ * \brief seems CUDAARCH is deprecated in future NVCC
+ * set this to 1 if you want to use CUDA version smaller than 2.0
+ */
+#ifndef MSHADOW_OLD_CUDA
+#define MSHADOW_OLD_CUDA 0
+#endif
+
+/*!
+ * \brief macro to decide existence of c++11 compiler
+ */
+#ifndef MSHADOW_IN_CXX11
+  #if (defined(__GXX_EXPERIMENTAL_CXX0X__) ||\
+      __cplusplus >= 201103L || defined(_MSC_VER))
+    #define MSHADOW_IN_CXX11 1
+  #else
+    #define MSHADOW_IN_CXX11 0
+  #endif
+#endif
+
+/*! \brief whether use SSE */
+#ifndef MSHADOW_USE_SSE
+  #define MSHADOW_USE_SSE 1
+#endif
+
+/*! \brief whether use F16C instruction set architecture extension */
+#ifndef MSHADOW_USE_F16C
+  #if defined(_MSC_VER) || defined(__CUDACC__)
+    #define MSHADOW_USE_F16C 0
+  #elif defined(__clang__) && \
+        ((__clang_major__ < 8) || ((__clang_major__ == 8) && (__clang_minor__ < 1)))
+    #define MSHADOW_USE_F16C 0
+  #else
+    #define MSHADOW_USE_F16C 1
+  #endif
+#endif
+
+/*! \brief whether use NVML to get dynamic info */
+#ifndef MSHADOW_USE_NVML
+  #define MSHADOW_USE_NVML 0
+#endif
+// SSE is conflict with cudacc
+#ifdef __CUDACC__
+  #undef MSHADOW_USE_SSE
+  #define MSHADOW_USE_SSE 0
+#endif
+
+#if MSHADOW_USE_CBLAS
+extern "C" {
+    #include <cblas.h>
+}
+#elif MSHADOW_USE_MKL
+  #include <mkl_blas.h>
+  #include <mkl_cblas.h>
+  #include <mkl_vsl.h>
+  #include <mkl_vsl_functions.h>
+  #include <mkl_version.h>
+#endif
+
+#if MSHADOW_USE_CUDA
+  #include <cuda.h>
+  #include <cublas_v2.h>
+  #include <curand.h>
+#endif
+
+#if MSHADOW_USE_CUDNN == 1
+  #include <cudnn.h>
+#endif
+
+#if MSHADOW_USE_CUSOLVER == 1
+  #include <cusolverDn.h>
+#endif
+
+#if MSHADOW_USE_NVML
+  #include <nvml.h>
+#endif
+
+// --------------------------------
+// MSHADOW_XINLINE is used for inlining template code for both CUDA and CPU code
+#ifdef MSHADOW_XINLINE
+  #error "MSHADOW_XINLINE must not be defined"
+#endif
+#ifdef _MSC_VER
+#define MSHADOW_FORCE_INLINE __forceinline
+#pragma warning(disable : 4068)
+#else
+#define MSHADOW_FORCE_INLINE inline __attribute__((always_inline))
+#endif
+#ifdef __CUDACC__
+  #define MSHADOW_XINLINE MSHADOW_FORCE_INLINE __device__ __host__
+#else
+  #define MSHADOW_XINLINE MSHADOW_FORCE_INLINE
+#endif
+/*! \brief cpu force inline */
+#define MSHADOW_CINLINE MSHADOW_FORCE_INLINE
+
+#if defined(__GXX_EXPERIMENTAL_CXX0X) ||\
+    defined(__GXX_EXPERIMENTAL_CXX0X__) || __cplusplus >= 201103L
+  #define MSHADOW_CONSTEXPR constexpr
+#else
+  #define MSHADOW_CONSTEXPR const
+#endif
+
+/*!
+ * \brief default data type for tensor string
+ *  in code release, change it to default_real_t
+ *  during development, change it to empty string so that missing
+ *  template arguments can be detected
+ */
+#ifndef MSHADOW_DEFAULT_DTYPE
+#define MSHADOW_DEFAULT_DTYPE = ::mshadow::default_real_t
+#endif
+
+/*!
+ * \brief DMLC marco for logging
+ */
+#ifndef MSHADOW_USE_GLOG
+#define MSHADOW_USE_GLOG DMLC_USE_GLOG
+#endif  // MSHADOW_USE_GLOG
+
+#if DMLC_USE_CXX11
+#define MSHADOW_THROW_EXCEPTION noexcept(false)
+#define MSHADOW_NO_EXCEPTION  noexcept(true)
+#else
+#define MSHADOW_THROW_EXCEPTION
+#define MSHADOW_NO_EXCEPTION
+#endif
+
+#if defined(_MSC_VER)
+#define MSHADOW_ALIGNED(x) __declspec(align(x))
+#else
+#define MSHADOW_ALIGNED(x) __attribute__ ((aligned(x)))
+#endif
+
+/*!
+ * \brief Protected cuda call in mshadow
+ * \param func Expression to call.
+ * It checks for CUDA errors after invocation of the expression.
+ */
+#define MSHADOW_CUDA_CALL(func)                                    \
+  {                                                                \
+    cudaError_t e = (func);                                        \
+    if (e == cudaErrorCudartUnloading) {                           \
+      throw dmlc::Error(cudaGetErrorString(e));                    \
+    }                                                              \
+    CHECK(e == cudaSuccess)                                        \
+        << "CUDA: " << cudaGetErrorString(e);                      \
+  }
+
+/*!
+ * \brief Run function and catch error, log unknown error.
+ * \param func Expression to call.
+ */
+#define MSHADOW_CATCH_ERROR(func)                                     \
+  {                                                                   \
+    try {                                                             \
+      (func);                                                         \
+    } catch (const dmlc::Error &e) {                                    \
+      std::string what = e.what();                                      \
+      if (what.find("driver shutting down") == std::string::npos) {     \
+        LOG(ERROR) << "Ignore CUDA Error " << what;                     \
+      }                                                                 \
+    }                                                                   \
+  }
+
+#include "./half.h"
+#include "./half2.h"
+#include "./logging.h"
+/*! \brief namespace for mshadow */
+namespace mshadow {
+/*! \brief buffer size for each random number generator */
+const unsigned kRandBufferSize = 1000000;
+/*! \brief pi  */
+const float kPi = 3.1415926f;
+/*! \brief type that will be used for index */
+#if MSHADOW_INT64_TENSOR_SIZE == 1
+  typedef int64_t index_t;
+#else
+  typedef int32_t index_t;
+#endif
+
+#ifdef _WIN32
+  /*! \brief openmp index for windows */
+  typedef int64_t openmp_index_t;
+#else
+  /*! \brief openmp index for linux */
+  typedef index_t openmp_index_t;
+#endif
+
+/*! \brief float point type that will be used in default by mshadow */
+typedef float default_real_t;
+
+/*! \brief data type flag */
+enum TypeFlag {
+  kFloat32 = 0,
+  kFloat64 = 1,
+  kFloat16 = 2,
+  kUint8 = 3,
+  kInt32 = 4,
+  kInt8  = 5,
+  kInt64 = 6,
+};
+
+template<typename DType>
+struct DataType;
+
+template<>
+struct DataType<float> {
+  static const int kFlag = kFloat32;
+  static const int kLanes = 1;
+#if MSHADOW_USE_CUDA
+#if (CUDA_VERSION >= 8000)
+  static const cudaDataType_t kCudaFlag = CUDA_R_32F;
+#endif
+#if MSHADOW_USE_CUDNN
+  static const cudnnDataType_t kCudnnFlag = CUDNN_DATA_FLOAT;
+  typedef float ScaleType;
+#endif
+#endif
+};
+template<>
+struct DataType<double> {
+  static const int kFlag = kFloat64;
+  static const int kLanes = 1;
+#if MSHADOW_USE_CUDA
+#if (CUDA_VERSION >= 8000)
+  static const cudaDataType_t kCudaFlag = CUDA_R_64F;
+#endif
+#if MSHADOW_USE_CUDNN
+  static const cudnnDataType_t kCudnnFlag = CUDNN_DATA_DOUBLE;
+  typedef double ScaleType;
+#endif
+#endif
+};
+template<>
+struct DataType<half::half_t> {
+  static const int kFlag = kFloat16;
+  static const int kLanes = 1;
+#if MSHADOW_USE_CUDA
+#if (CUDA_VERSION >= 8000)
+  static const cudaDataType_t kCudaFlag = CUDA_R_16F;
+#endif
+#if MSHADOW_USE_CUDNN
+  static const cudnnDataType_t kCudnnFlag = CUDNN_DATA_HALF;
+  typedef float ScaleType;
+#endif
+#endif
+};
+template<>
+struct DataType<half::half2_t> {
+  static const int kFlag = kFloat16;
+  static const int kLanes = 2;
+};
+template<>
+struct DataType<uint8_t> {
+  static const int kFlag = kUint8;
+  static const int kLanes = 1;
+#if MSHADOW_USE_CUDA
+#if (CUDA_VERSION >= 8000)
+  static const cudaDataType_t kCudaFlag = CUDA_R_8U;
+#endif
+#if (MSHADOW_USE_CUDNN == 1 && CUDNN_MAJOR >= 6)
+  // no uint8 in cudnn for now
+  static const cudnnDataType_t kCudnnFlag = CUDNN_DATA_INT8;
+  typedef uint8_t ScaleType;
+#endif
+#endif
+};
+template<>
+struct DataType<int8_t> {
+  static const int kFlag = kInt8;
+  static const int kLanes = 1;
+#if MSHADOW_USE_CUDA
+#if (CUDA_VERSION >= 8000)
+  static const cudaDataType_t kCudaFlag = CUDA_R_8I;
+#endif
+#if (MSHADOW_USE_CUDNN == 1 && CUDNN_MAJOR >= 6)
+  static const cudnnDataType_t kCudnnFlag = CUDNN_DATA_INT8;
+  typedef int8_t ScaleType;
+#endif
+#endif
+};
+template<>
+struct DataType<int32_t> {
+  static const int kFlag = kInt32;
+  static const int kLanes = 1;
+#if MSHADOW_USE_CUDA
+#if (CUDA_VERSION >= 8000)
+  static const cudaDataType_t kCudaFlag = CUDA_R_32I;
+#endif
+#if (MSHADOW_USE_CUDNN == 1 && CUDNN_MAJOR >= 6)
+  static const cudnnDataType_t kCudnnFlag = CUDNN_DATA_INT32;
+  typedef int32_t ScaleType;
+#endif
+#endif
+};
+template<>
+struct DataType<int64_t> {
+  static const int kFlag = kInt64;
+  static const int kLanes = 1;
+};
+
+/*! \brief type enum value for default real type */
+const int default_type_flag = DataType<default_real_t>::kFlag;
+
+/*! layout flag */
+enum LayoutFlag {
+  kNCHW = 0,
+  kNHWC,
+  kCHWN,
+
+  kNCW = 1 << 3,
+  kNWC,
+  kCWN,
+
+  kNCDHW = 1 << 5,
+  kNDHWC,
+  kCDHWN
+};
+
+template<int layout>
+struct LayoutType;
+
+template<>
+struct LayoutType<kNCHW> {
+  static const index_t kNdim = 4;
+#if (MSHADOW_USE_CUDA && MSHADOW_USE_CUDNN == 1 && CUDNN_MAJOR >= 4)
+  static const cudnnTensorFormat_t kCudnnFlag = CUDNN_TENSOR_NCHW;
+#else
+  static const int kCudnnFlag = -1;
+#endif
+};
+
+template<>
+struct LayoutType<kNHWC> {
+  static const index_t kNdim = 4;
+#if (MSHADOW_USE_CUDA && MSHADOW_USE_CUDNN == 1 && CUDNN_MAJOR >= 4)
+  static const cudnnTensorFormat_t kCudnnFlag = CUDNN_TENSOR_NHWC;
+#else
+  static const int kCudnnFlag = -1;
+#endif
+};
+
+/*! \brief default layout for 4d tensor */
+const int default_layout = kNCHW;
+
+template<>
+struct LayoutType<kNCDHW> {
+  static const index_t kNdim = 5;
+#if (MSHADOW_USE_CUDA && MSHADOW_USE_CUDNN == 1 && CUDNN_MAJOR >= 4)
+  static const cudnnTensorFormat_t kCudnnFlag = CUDNN_TENSOR_NCHW;
+#else
+  static const int kCudnnFlag = -1;
+#endif
+};
+
+template<>
+struct LayoutType<kNDHWC> {
+  static const index_t kNdim = 5;
+#if (MSHADOW_USE_CUDA && MSHADOW_USE_CUDNN == 1 && CUDNN_MAJOR >= 4)
+  static const cudnnTensorFormat_t kCudnnFlag = CUDNN_TENSOR_NHWC;
+#else
+  static const int kCudnnFlag = -1;
+#endif
+};
+
+/*! \brief default layout for 5d tensor */
+const int default_layout_5d = kNCDHW;
+
+/*! \brief namespace for operators */
+namespace op {
+// binary operator
+/*! \brief mul operator */
+struct mul{
+  /*! \brief map a, b to result using defined operation */
+  template<typename DType>
+  MSHADOW_XINLINE static DType Map(DType a, DType b) {
+    return a * b;
+  }
+};
+/*! \brief plus operator */
+struct plus {
+  /*! \brief map a, b to result using defined operation */
+  template<typename DType>
+  MSHADOW_XINLINE static DType Map(DType a, DType b) {
+    return a + b;
+  }
+};
+/*! \brief minus operator */
+struct minus {
+  /*! \brief map a, b to result using defined operation */
+  template<typename DType>
+  MSHADOW_XINLINE static DType Map(DType a, DType b) {
+    return a - b;
+  }
+};
+/*! \brief divide operator */
+struct div {
+  /*! \brief map a, b to result using defined operation */
+  template<typename DType>
+  MSHADOW_XINLINE static DType Map(DType a, DType b) {
+    return a / b;
+  }
+};
+/*! \brief get rhs */
+struct right {
+  /*! \brief map a, b to result using defined operation */
+  template<typename DType>
+  MSHADOW_XINLINE static DType Map(DType a, DType b) {
+    return b;
+  }
+};
+// unary operator/ function: example
+// these operators can be defined by user,
+// in the same style as binary and unary operator
+// to use, simply write F<op::identity>( src )
+/*! \brief identity function that maps a real number to it self */
+struct identity{
+  /*! \brief map a to result using defined operation */
+  template<typename DType>
+  MSHADOW_XINLINE static DType Map(DType a) {
+    return a;
+  }
+};
+}  // namespace op
+/*! \brief namespace for savers */
+namespace sv {
+/*! \brief save to saver: = */
+struct saveto {
+  /*! \brief save b to a using save method */
+  template<typename DType>
+  MSHADOW_XINLINE static void Save(DType &a, DType b) { // NOLINT(*)
+    a = b;
+  }
+  /*! \brief helper constant to use BLAS, alpha */
+  inline static default_real_t AlphaBLAS(void) { return 1.0f; }
+  /*! \brief helper constant to use BLAS, beta */
+  inline static default_real_t BetaBLAS(void) { return 0.0f; }
+  /*! \brief corresponding binary operator type */
+  typedef op::right OPType;
+};
+/*! \brief save to saver: += */
+struct plusto {
+  /*! \brief save b to a using save method */
+  template<typename DType>
+  MSHADOW_XINLINE static void Save(DType &a, DType b) { // NOLINT(*)
+    a += b;
+  }
+  /*! \brief helper constant to use BLAS, alpha */
+  inline static default_real_t AlphaBLAS(void) { return 1.0f; }
+  /*! \brief helper constant to use BLAS, beta */
+  inline static default_real_t BetaBLAS(void) { return 1.0f; }
+  /*! \brief corresponding binary operator type */
+  typedef op::plus OPType;
+};
+/*! \brief minus to saver: -= */
+struct minusto {
+  /*! \brief save b to a using save method */
+  template<typename DType>
+  MSHADOW_XINLINE static void Save(DType &a, DType b) { // NOLINT(*)
+    a -= b;
+  }
+  /*! \brief helper constant to use BLAS, alpha */
+  inline static default_real_t AlphaBLAS(void) { return -1.0f; }
+  /*! \brief helper constant to use BLAS, beta */
+  inline static default_real_t BetaBLAS(void) { return 1.0f; }
+  /*! \brief corresponding binary operator type */
+  typedef op::minus OPType;
+};
+/*! \brief multiply to saver: *= */
+struct multo {
+  /*! \brief save b to a using save method */
+  template<typename DType>
+  MSHADOW_XINLINE static void Save(DType &a, DType b) { // NOLINT(*)
+    a *= b;
+  }
+  /*! \brief corresponding binary operator type */
+  typedef op::mul OPType;
+};
+/*! \brief divide to saver: /= */
+struct divto {
+  /*! \brief save b to a using save method */
+  template<typename DType>
+  MSHADOW_XINLINE static void Save(DType& a, DType b) { // NOLINT(*)
+    a /= b;
+  }
+  /*! \brief corresponding binary operator type */
+  typedef op::div OPType;
+};
+}  // namespace sv
+/*! \brief namespace for potential reducer operations */
+namespace red {
+namespace limits {
+/*!
+ * \brief minimum value of certain types
+ * \tparam DType data type
+ */
+template<typename DType>
+MSHADOW_XINLINE DType MinValue(void);
+/*! \brief minimum value of float */
+template<>
+MSHADOW_XINLINE float MinValue<float>(void) {
+  return -FLT_MAX;
+}
+/*! \brief minimum value of double */
+template<>
+MSHADOW_XINLINE double MinValue<double>(void) {
+  return -DBL_MAX;
+}
+/*! \brief minimum value of half */
+template<>
+MSHADOW_XINLINE half::half_t MinValue<half::half_t>(void) {
+  return MSHADOW_HALF_MIN;
+}
+/*! \brief minimum value of uint8_t */
+template<>
+MSHADOW_XINLINE uint8_t MinValue<uint8_t>(void) {
+  return 0;
+}
+/*! \brief minimum value of int8_t */
+template<>
+MSHADOW_XINLINE int8_t MinValue<int8_t>(void) {
+  return SCHAR_MIN;
+}
+/*! \brief minimum value of int32_t */
+template<>
+MSHADOW_XINLINE int MinValue<int32_t>(void) {
+  return INT_MIN;
+}
+/*! \brief minimum value of int64_t */
+template<>
+MSHADOW_XINLINE int64_t MinValue<int64_t>(void) {
+  return LLONG_MIN;
+}
+
+/*!
+ * \brief maximum value of certain types
+ * \tparam DType data type
+ */
+template<typename DType>
+MSHADOW_XINLINE DType MaxValue(void);
+/*! \brief maximum value of float */
+template<>
+MSHADOW_XINLINE float MaxValue<float>(void) {
+  return FLT_MAX;
+}
+/*! \brief maximum value of double */
+template<>
+MSHADOW_XINLINE double MaxValue<double>(void) {
+  return DBL_MAX;
+}
+/*! \brief maximum value of half */
+template<>
+MSHADOW_XINLINE half::half_t MaxValue<half::half_t>(void) {
+  return MSHADOW_HALF_MAX;
+}
+/*! \brief maximum value of uint8_t */
+template<>
+MSHADOW_XINLINE uint8_t MaxValue<uint8_t>(void) {
+  return UCHAR_MAX;
+}
+/*! \brief maximum value of int8_t */
+template<>
+MSHADOW_XINLINE int8_t MaxValue<int8_t>(void) {
+  return SCHAR_MAX;
+}
+/*! \brief maximum value of int32_t */
+template<>
+MSHADOW_XINLINE int MaxValue<int32_t>(void) {
+  return INT_MAX;
+}
+/*! \brief maximum value of int64_t */
+template<>
+MSHADOW_XINLINE int64_t MaxValue<int64_t>(void) {
+  return LLONG_MAX;
+}
+}  // namespace limits
+
+/*! \brief sum reducer */
+struct sum {
+  /*! \brief do reduction into dst */
+  template<typename DType>
+  MSHADOW_XINLINE static void Reduce(volatile DType& dst,  volatile DType src) { // NOLINT(*)
+    dst += src;
+  }
+  /*! \brief do stable reduction into dst */
+  template<typename DType>
+  MSHADOW_XINLINE static void Reduce(volatile DType& dst,  volatile DType src, volatile DType& residual) { // NOLINT(*)
+    DType y = src - residual;
+    DType t = dst + y;
+    residual = (t - dst) - y;
+    dst = t;
+  }
+  /*! \brief combine the results of two reducers */
+  template<typename DType>
+  MSHADOW_XINLINE static void Merge(volatile DType& dst_val, volatile DType& src_val) { // NOLINT(*)
+    Reduce(dst_val, src_val);
+  }
+  /*! \brief combine the results of two reducers */
+  template<typename DType>
+  MSHADOW_XINLINE static void Merge(volatile DType& dst_val, volatile DType& dst_residual, volatile DType& src_val, volatile DType& src_residual) { // NOLINT(*)
+    DType t1 = dst_val + src_val;
+    DType e = t1 - dst_val;
+    DType t2 = ((src_val - e) + (dst_val - (t1 - e))) + dst_residual + src_residual;
+    dst_val = t1 + t2;
+    dst_residual = t2 - (dst_val - t1);
+  }
+  /*! \brief finalize reduction */
+  template<typename DType>
+  MSHADOW_XINLINE static void Finalize(volatile DType& dst) {} // NOLINT(*)
+  /*! \brief finalize reduction */
+  template<typename DType>
+  MSHADOW_XINLINE static void Finalize(volatile DType& dst, volatile DType& residual) {} // NOLINT(*)
+  /*!
+   *\brief calculate gradient of redres with respect to redsrc,
+   * redres: reduced result, redsrc: one of reduction element
+   */
+  template<typename DType>
+  MSHADOW_XINLINE static DType PartialGrad(DType redres, DType redsrc) {
+    return 1;
+  }
+  /*!
+   *\brief set the initial value during reduction
+   */
+  template<typename DType>
+  MSHADOW_XINLINE static void SetInitValue(DType &initv) { // NOLINT(*)
+    initv = 0;
+  }
+  /*!
+   *\brief set the initial value during reduction
+   */
+  template<typename DType>
+  MSHADOW_XINLINE static void SetInitValue(DType &initv, DType &residual) { // NOLINT(*)
+    SetInitValue(initv);
+    residual = 0;
+  }
+};
+/*! \brief maximum reducer */
+struct maximum {
+  /*! \brief do reduction into dst */
+  template<typename DType>
+  MSHADOW_XINLINE static void Reduce(volatile DType& dst,  volatile DType src) { // NOLINT(*)
+    using namespace std;
+#ifdef __CUDACC__
+    dst = ::max(dst, src);
+#else
+    dst = max(dst, src);
+#endif  // __CUDACC__
+  }
+  /*! \brief do reduction into dst */
+  template<typename DType>
+  MSHADOW_XINLINE static void Reduce(volatile DType& dst,  volatile DType src, volatile DType &none) { // NOLINT(*)
+    Reduce(dst, src);
+  }
+  /*! \brief combine the results of two reducers */
+  template<typename DType>
+  MSHADOW_XINLINE static void Merge(volatile DType& dst_val, volatile DType& src_val) { // NOLINT(*)
+    Reduce(dst_val, src_val);
+  }
+  /*! \brief combine the results of two reducers */
+  template<typename DType>
+  MSHADOW_XINLINE static void Merge(volatile DType& dst_val, volatile DType& dst_residual, volatile DType& src_val, volatile DType& src_residual) { // NOLINT(*)
+    Reduce(dst_val, src_val);
+  }
+  /*! \brief finalize reduction */
+  template<typename DType>
+  MSHADOW_XINLINE static void Finalize(volatile DType& dst) {} // NOLINT(*)
+  /*! \brief finalize reduction */
+  template<typename DType>
+  MSHADOW_XINLINE static void Finalize(volatile DType& dst, volatile DType& residual) {} // NOLINT(*)
+  /*!
+   * \brief calculate gradient of redres with respect to redsrc,
+   * redres: reduced result, redsrc: one of reduction element
+   */
+  template<typename DType>
+  MSHADOW_XINLINE static DType PartialGrad(DType redres, DType redsrc) {
+    return redres == redsrc ? 1: 0;
+  }
+  /*!
+   *\brief set the initial value during reduction
+   */
+  template<typename DType>
+  MSHADOW_XINLINE static void SetInitValue(DType &initv) { // NOLINT(*)
+    initv = limits::MinValue<DType>();
+  }
+  /*!
+   *\brief set the initial value during reduction
+   */
+  template<typename DType>
+  MSHADOW_XINLINE static void SetInitValue(DType &initv, DType &none) { // NOLINT(*)
+    SetInitValue(initv);
+  }
+};
+/*! \brief minimum reducer */
+struct minimum {
+  /*! \brief do reduction into dst */
+  template<typename DType>
+  MSHADOW_XINLINE static void Reduce(volatile DType& dst,  volatile DType src) { // NOLINT(*)
+    using namespace std;
+#ifdef __CUDACC__
+    dst = ::min(dst, src);
+#else
+    dst = min(dst, src);
+#endif  // __CUDACC__
+  }
+  /*! \brief do reduction into dst */
+  template<typename DType>
+  MSHADOW_XINLINE static void Reduce(volatile DType& dst,  volatile DType src, volatile DType &none) { // NOLINT(*)
+    Reduce(dst, src);
+  }
+  /*! \brief combine the results of two reducers */
+  template<typename DType>
+  MSHADOW_XINLINE static void Merge(volatile DType& dst_val, volatile DType& src_val) { // NOLINT(*)
+    Reduce(dst_val, src_val);
+  }
+  /*! \brief combine the results of two reducers */
+  template<typename DType>
+  MSHADOW_XINLINE static void Merge(volatile DType& dst_val, volatile DType& dst_residual, volatile DType& src_val, volatile DType& src_residual) { // NOLINT(*)
+    Reduce(dst_val, src_val);
+  }
+  /*! \brief finalize reduction */
+  template<typename DType>
+  MSHADOW_XINLINE static void Finalize(volatile DType& dst) {} // NOLINT(*)
+  /*! \brief finalize reduction */
+  template<typename DType>
+  MSHADOW_XINLINE static void Finalize(volatile DType& dst, volatile DType& residual) {} // NOLINT(*)
+  /*!
+   * \brief calculate gradient of redres with respect to redsrc,
+   * redres: reduced result, redsrc: one of reduction element
+   */
+  template<typename DType>
+  MSHADOW_XINLINE static DType PartialGrad(DType redres, DType redsrc) {
+    return redres == redsrc ? 1: 0;
+  }
+  /*!
+   *\brief set the initial value during reduction
+   */
+  template<typename DType>
+  MSHADOW_XINLINE static void SetInitValue(DType &initv) { // NOLINT(*)
+    initv = limits::MaxValue<DType>();
+  }
+  /*!
+   *\brief set the initial value during reduction
+   */
+  template<typename DType>
+  MSHADOW_XINLINE static void SetInitValue(DType &initv, DType &none) { // NOLINT(*)
+    SetInitValue(initv);
+  }
+};
+}  // namespace red
+
+#define MSHADOW_TYPE_SWITCH(type, DType, ...)       \
+  switch (type) {                                   \
+  case mshadow::kFloat32:                           \
+    {                                               \
+      typedef float DType;                          \
+      {__VA_ARGS__}                                 \
+    }                                               \
+    break;                                          \
+  case mshadow::kFloat64:                           \
+    {                                               \
+      typedef double DType;                         \
+      {__VA_ARGS__}                                 \
+    }                                               \
+    break;                                          \
+  case mshadow::kFloat16:                           \
+    {                                               \
+      typedef mshadow::half::half_t DType;          \
+      {__VA_ARGS__}                                 \
+    }                                               \
+    break;                                          \
+  case mshadow::kUint8:                             \
+    {                                               \
+      typedef uint8_t DType;                        \
+      {__VA_ARGS__}                                 \
+    }                                               \
+    break;                                          \
+  case mshadow::kInt8:                              \
+    {                                               \
+      typedef int8_t DType;                         \
+      {__VA_ARGS__}                                 \
+    }                                               \
+    break;                                          \
+  case mshadow::kInt32:                             \
+    {                                               \
+      typedef int32_t DType;                        \
+      {__VA_ARGS__}                                 \
+    }                                               \
+    break;                                          \
+  case mshadow::kInt64:                             \
+    {                                               \
+      typedef int64_t DType;                        \
+      {__VA_ARGS__}                                 \
+    }                                               \
+    break;                                          \
+  default:                                          \
+    LOG(FATAL) << "Unknown type enum " << type;     \
+  }
+
+#define MSHADOW_TYPE_SWITCH_WITH_HALF2(type, DType, ...)  \
+  switch (type) {                                         \
+  case mshadow::kFloat32:                                 \
+    {                                                     \
+      typedef float DType;                                \
+      {__VA_ARGS__}                                       \
+    }                                                     \
+    break;                                                \
+  case mshadow::kFloat64:                                 \
+    {                                                     \
+      typedef double DType;                               \
+      {__VA_ARGS__}                                       \
+    }                                                     \
+    break;                                                \
+  case mshadow::kFloat16:                                 \
+    {                                                     \
+      typedef mshadow::half::half2_t DType;               \
+      {__VA_ARGS__}                                       \
+    }                                                     \
+    break;                                                \
+  case mshadow::kUint8:                                   \
+    {                                                     \
+      typedef uint8_t DType;                              \
+      {__VA_ARGS__}                                       \
+    }                                                     \
+    break;                                                \
+  case mshadow::kInt32:                                   \
+    {                                                     \
+      typedef int32_t DType;                              \
+      {__VA_ARGS__}                                       \
+    }                                                     \
+    break;                                                \
+  case mshadow::kInt64:                                   \
+    {                                                     \
+      typedef int64_t DType;                              \
+      {__VA_ARGS__}                                       \
+    }                                                     \
+    break;                                                \
+  default:                                                \
+    LOG(FATAL) << "Unknown type enum " << type;           \
+  }
+
+#define MSHADOW_SGL_DBL_TYPE_SWITCH(type, DType, ...)  \
+  switch (type) {                                      \
+  case mshadow::kFloat32:                              \
+    {                                                  \
+      typedef float DType;                             \
+      {__VA_ARGS__}                                    \
+    }                                                  \
+    break;                                             \
+  case mshadow::kFloat64:                              \
+    {                                                  \
+      typedef double DType;                            \
+      {__VA_ARGS__}                                    \
+    }                                                  \
+    break;                                             \
+  default:                                             \
+    LOG(FATAL) << "This operation only supports "      \
+                  "32-bit and 64-bit floating point";  \
+  }
+
+#define MSHADOW_REAL_TYPE_SWITCH(type, DType, ...)  \
+  switch (type) {                                   \
+  case mshadow::kFloat32:                           \
+    {                                               \
+      typedef float DType;                          \
+      {__VA_ARGS__}                                 \
+    }                                               \
+    break;                                          \
+  case mshadow::kFloat64:                           \
+    {                                               \
+      typedef double DType;                         \
+      {__VA_ARGS__}                                 \
+    }                                               \
+    break;                                          \
+  case mshadow::kFloat16:                           \
+    {                                               \
+      typedef mshadow::half::half_t DType;          \
+      {__VA_ARGS__}                                 \
+    }                                               \
+    break;                                          \
+  case mshadow::kUint8:                             \
+    LOG(FATAL) << "This operation only support "    \
+                  "floating point types not uint8"; \
+    break;                                          \
+  case mshadow::kInt8:                              \
+    LOG(FATAL) << "This operation only support "    \
+                  "floating point types not int8";  \
+    break;                                          \
+  case mshadow::kInt32:                             \
+    LOG(FATAL) << "This operation only support "    \
+                  "floating point types, not int32";\
+    break;                                          \
+  case mshadow::kInt64:                             \
+    LOG(FATAL) << "This operation only support "    \
+                  "floating point types, not int64";\
+    break;                                          \
+  default:                                          \
+    LOG(FATAL) << "Unknown type enum " << type;     \
+  }
+
+#define MSHADOW_REAL_TYPE_SWITCH_EX(type$, DType$, DLargeType$, ...)  \
+  switch (type$) {                                  \
+  case mshadow::kFloat32:                           \
+    {                                               \
+      typedef float DType$;                         \
+      typedef float DLargeType$;                    \
+      {__VA_ARGS__}                                 \
+    }                                               \
+    break;                                          \
+  case mshadow::kFloat64:                           \
+    {                                               \
+      typedef double DType$;                        \
+      typedef double DLargeType$;                   \
+      {__VA_ARGS__}                                 \
+    }                                               \
+    break;                                          \
+  case mshadow::kFloat16:                           \
+    {                                               \
+      typedef mshadow::half::half_t DType$;         \
+      typedef float DLargeType$;                    \
+      {__VA_ARGS__}                                 \
+    }                                               \
+    break;                                          \
+  case mshadow::kUint8:                             \
+    LOG(FATAL) << "This operation only support "    \
+                  "floating point types not uint8"; \
+    break;                                          \
+  case mshadow::kInt8:                              \
+    LOG(FATAL) << "This operation only support "    \
+                  "floating point types not int8";  \
+    break;                                          \
+  case mshadow::kInt32:                             \
+    LOG(FATAL) << "This operation only support "    \
+                  "floating point types, not int32";\
+    break;                                          \
+  case mshadow::kInt64:                             \
+    LOG(FATAL) << "This operation only support "    \
+                  "floating point types, not int64";\
+    break;                                          \
+  default:                                          \
+    LOG(FATAL) << "Unknown type enum " << type$;    \
+  }
+
+#define MSHADOW_LAYOUT_SWITCH(layout, Layout, ...)  \
+  switch (layout) {                                 \
+  case mshadow::kNCHW:                              \
+    {                                               \
+      const int Layout = kNCHW;                     \
+      {__VA_ARGS__}                                 \
+    }                                               \
+    break;                                          \
+  case mshadow::kNHWC:                              \
+    {                                               \
+      const int Layout = kNHWC;                     \
+      {__VA_ARGS__}                                 \
+    }                                               \
+    break;                                          \
+  case mshadow::kNCDHW:                             \
+    {                                               \
+      const int Layout = kNCDHW;                    \
+      {__VA_ARGS__}                                 \
+    }                                               \
+    break;                                          \
+  case mshadow::kNDHWC:                             \
+    {                                               \
+      const int Layout = kNDHWC;                    \
+      {__VA_ARGS__}                                 \
+    }                                               \
+    break;                                          \
+  default:                                          \
+    LOG(FATAL) << "Unknown layout enum " << layout; \
+  }
+
+/*!
+ * \brief Only supports int64 index type for aux_data
+ * in NDArray class fow now.
+ */
+#define MSHADOW_IDX_TYPE_SWITCH(type, DType, ...)   \
+  switch (type) {                                   \
+  case mshadow::kInt64:                             \
+    {                                               \
+      typedef int64_t DType;                        \
+      {__VA_ARGS__}                                 \
+    }                                               \
+    break;                                          \
+  default:                                          \
+    LOG(FATAL) << "Unknown type enum " << type;     \
+  }
+
+/*! \brief get data type size from type enum */
+inline size_t mshadow_sizeof(int type) {
+  int size = 0;
+  MSHADOW_TYPE_SWITCH(type, DType, size = sizeof(DType););
+  return size;
+}
+
+}  // namespace mshadow
+#endif  // MSHADOW_BASE_H_
diff --git a/3rdparty/mshadow/mshadow/cuda/reduce.cuh b/3rdparty/mshadow/mshadow/cuda/reduce.cuh
new file mode 100644
index 000000000000..921d5ad5e0c0
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/cuda/reduce.cuh
@@ -0,0 +1,120 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file reduce.cuh
+ * \brief helper functions to do reduction
+ * \author Tianqi Chen
+ */
+#ifndef MSHADOW_CUDA_REDUCE_CUH_
+#define MSHADOW_CUDA_REDUCE_CUH_
+
+namespace mshadow {
+namespace cuda {
+/*
+ * \brief reduce over the dimension x
+ * \tparam Reducer reducer
+ * \tparam x_bits dimension = 1<<x_bits
+ * \tparam DType content data type
+ */
+template<typename Reducer, int x_bits, typename DType>
+inline __device__ void Reduce1D(volatile DType buf[1 << x_bits]);
+/*
+ * \brief reduce over the dimension x
+ * \tparam Reducer reducer
+ * \tparam xmax_bits maximum size of buffer
+ * \tparam DType content data type
+ * \param xsize size of x dimension, not sure if aligned
+ */
+template<typename Reducer, int xmax_bits, typename DType>
+inline __device__ void
+Reduce1DNotAlign(volatile DType buf[1 << xmax_bits], int xsize);
+// ===============================================x===
+//  implementations afterwards,
+//  no need to read if only use the functions
+// --------------------------------------------------
+#ifdef  __DEVICE_EMULATION__
+#define __syncwarp() __syncthreads()
+#else
+#if CUDA_VERSION < 9000
+#define __syncwarp()
+#endif
+#endif
+
+template<typename Reducer, int x_bits, typename DType>
+inline __device__ void ReduceX(volatile DType  buf[], int tid) {
+  if (x_bits >= 10) {
+    if (tid < 512) Reducer::Reduce(buf[tid] , buf[tid + 512]);
+    __syncthreads();
+  }
+  if (x_bits >= 9) {
+    if (tid < 256) Reducer::Reduce(buf[tid] , buf[tid + 256]);
+    __syncthreads();
+  }
+  if (x_bits >= 8) {
+    if (tid < 128) Reducer::Reduce(buf[tid] , buf[tid + 128]);
+    __syncthreads();
+  }
+  if (x_bits >= 7) {
+    if (tid < 64) Reducer::Reduce(buf[tid] , buf[tid + 64]);
+    __syncthreads();
+  }
+  if (x_bits >= 6) {
+    if (tid < 32) Reducer::Reduce(buf[tid] , buf[tid + 32]);
+    __syncthreads();
+  }
+  // in warp optimization
+  if (x_bits >= 5) {
+    if (tid < 16) Reducer::Reduce(buf[tid] , buf[tid + 16]);
+#if MSHADOW_OLD_CUDA
+    __syncthreads();
+#else
+    __syncwarp();
+#endif
+  }
+  if (x_bits >= 4) {
+    if (tid < 8) Reducer::Reduce(buf[tid] , buf[tid + 8]);
+    __syncwarp();
+  }
+  if (x_bits >= 3) {
+    if (tid < 4) Reducer::Reduce(buf[tid] , buf[tid + 4]);
+    __syncwarp();
+  }
+  if (x_bits >= 2) {
+    if (tid < 2) Reducer::Reduce(buf[tid] , buf[tid + 2]);
+    __syncwarp();
+  }
+  if (x_bits >= 1) {
+    if (tid < 1) Reducer::Reduce(buf[tid] , buf[tid + 1]);
+    __syncwarp();
+  }
+}
+template<typename Reducer, int x_bits, typename DType>
+inline __device__ void Reduce1D(volatile DType buf[1 << x_bits]) {
+  ReduceX<Reducer, x_bits>(buf, threadIdx.x);
+}
+// reduce with a upper bound
+#define __RD_NON_ALIGN(els, x_bits)                                     \
+  els                                                                   \
+  if (xmax_bits >= x_bits && x_size >= (1 << x_bits)) {                 \
+    if (tid < (1 << x_bits) && tid + (1 << x_bits) < x_size) {          \
+      Reducer::Reduce(buf[tid] , buf[tid + (1 << x_bits)]);             \
+    }                                                                   \
+    __syncthreads();                                                    \
+    ReduceX<Reducer, x_bits>(buf, tid);                                 \
+  }                                                                     \
+
+template<typename Reducer, int xmax_bits, typename DType>
+inline __device__ void Reduce1DNotAlign(volatile DType buf[], int x_size) {
+  int tid = threadIdx.x;
+  __RD_NON_ALIGN(, 8)
+  __RD_NON_ALIGN(else, 7)
+  __RD_NON_ALIGN(else, 6)
+  __RD_NON_ALIGN(else, 5)
+  __RD_NON_ALIGN(else, 4)
+  __RD_NON_ALIGN(else, 3)
+  __RD_NON_ALIGN(else, 2)
+  __RD_NON_ALIGN(else, 1)
+}
+}  // namespace cuda
+}  // namespace mshadow
+#endif  // MSHADOW_CUDA_REDUCE_CUH_
+
diff --git a/3rdparty/mshadow/mshadow/cuda/tensor_gpu-inl.cuh b/3rdparty/mshadow/mshadow/cuda/tensor_gpu-inl.cuh
new file mode 100755
index 000000000000..72e4b7eb9ee9
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/cuda/tensor_gpu-inl.cuh
@@ -0,0 +1,828 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file tensor_gpu-inl.cuh
+ * \brief implementation of GPU code using CUDA
+ * \author Bing Xu, Tianqi Chen
+ */
+#ifndef MSHADOW_CUDA_TENSOR_GPU_INL_CUH_
+#define MSHADOW_CUDA_TENSOR_GPU_INL_CUH_
+#include <thrust/device_ptr.h>
+#include <thrust/sort.h>
+#if CUDA_VERSION >= 7000
+#include <thrust/system/cuda/execution_policy.h>
+#endif
+#include "../tensor.h"
+#include "./reduce.cuh"
+#define MSHADOW_CUDA_POST_KERNEL_CHECK(x) \
+  /* Code block avoids redefinition of cudaError_t err */ \
+  do { \
+    cudaError err = cudaPeekAtLastError(); \
+    CHECK_EQ(err, cudaSuccess) << "Name: " << #x << " ErrStr:" << cudaGetErrorString(err); \
+  } while (0)
+namespace mshadow {
+namespace cuda {
+/* load unit for memory access, if CUDAARCH not defined, this is advanced nvcc */
+#if MSHADOW_OLD_CUDA
+const int kMemUnitBits = 4;
+const int kMaxThreadsPerBlock = 512;
+#else
+const int kMemUnitBits = 5;
+const int kMaxThreadsPerBlock = 1024;
+#endif
+/*! \brief number of units that can do synchronized update, half warp size */
+const int kMemUnit = 1 << kMemUnitBits;
+/*! \brief mask that could be helpful sometime */
+const int kMemUnitMask = kMemUnit - 1;
+/*! \brief suggested thread number(logscale) for mapping kernel */
+const int kBaseThreadBits = 8;
+/*! \brief suggested thread number for mapping kernel */
+const int kBaseThreadNum  = 1 << kBaseThreadBits;
+/*! \brief maximum value of grid */
+const int kMaxGridNum = 65535;
+/*! \brief maximum value of grid within each dimension */
+const int kMaxGridDim = 65535;
+/*! \brief suggested grid number for mapping kernel */
+const int kBaseGridNum = 1024;
+/*! \brief get align stride for given size in x dimension */
+inline index_t GetAlignStride(index_t xsize) {
+  if (xsize >= MSHADOW_MIN_PAD_RATIO * 32) {
+    return ((xsize  + kMemUnit - 1) >> kMemUnitBits) << kMemUnitBits;
+  } else {
+    // if originally space is not aligned, no necessary to to alligned thread allocation
+    return xsize;
+  }
+}
+inline void CheckLaunchParam(dim3 dimGrid, dim3 dimBlock, const char *estr = "") {
+  if (dimBlock.x * dimBlock.y * dimBlock.z > static_cast<unsigned>(kMaxThreadsPerBlock) ||
+      dimGrid.x > kMaxGridDim || dimGrid.y > kMaxGridDim) {
+    LOG(FATAL) << "too large launch parameter: "
+      << estr << "["
+      << dimGrid.x << ","
+      << dimGrid.y << "], ["
+      << dimBlock.x << ","
+      << dimBlock.y << ","
+      << dimBlock.z << "]";
+  }
+}
+template<typename Saver, typename DstPlan,
+         typename Plan, int block_dim_bits>
+__device__ void MapPlanProc(DstPlan dst, index_t xstride,
+                            Shape<2> dshape, const Plan plan, int block_idx) {
+  const index_t tid = (block_idx << block_dim_bits) + threadIdx.x;
+  const int y = tid / xstride;
+  const int x = tid % xstride;
+  if (y < dshape[0] && x < dshape[1]) {
+    Saver::Save(dst.REval(y, x), plan.Eval(y, x));
+  }
+}
+template<typename Saver, int block_dim_bits,
+         typename DstPlan, typename Plan>
+__global__ void MapPlanKernel(DstPlan dst, index_t xstride,
+                              Shape<2> dshape, const Plan plan) {
+  MapPlanProc<Saver, DstPlan, Plan, block_dim_bits>
+      (dst, xstride, dshape, plan, blockIdx.x);
+}
+template<typename Saver, int block_dim_bits, int grid_size,
+         typename DstPlan, typename Plan>
+__global__ void MapPlanLargeKernel(DstPlan dst, index_t xstride,
+                                   Shape<2> dshape, const Plan plan, int repeat) {
+  for (int i = 0; i < repeat; ++i) {
+  MapPlanProc<Saver, DstPlan, Plan, block_dim_bits>
+      (dst, xstride, dshape, plan, blockIdx.x + i * grid_size);
+  }
+}
+
+template<typename Saver, typename DstExp, typename E, typename DType>
+inline void MapPlan(expr::Plan<DstExp, DType> dst,
+                    const expr::Plan<E, DType> &plan,
+                    Shape<2> dshape,
+                    cudaStream_t stream) {
+  const index_t xstride = GetAlignStride(dshape[1]);
+  const int num_block = (dshape[0] * xstride + kBaseThreadNum-1) / kBaseThreadNum;
+  dim3 dimBlock(kBaseThreadNum, 1, 1);
+
+  if (num_block < kMaxGridNum) {
+    dim3 dimGrid(num_block, 1, 1);
+    MapPlanKernel<Saver, kBaseThreadBits,
+                  expr::Plan<DstExp, DType>,
+                  expr::Plan<E, DType> >
+        <<<dimGrid, dimBlock, 0, stream>>>(dst, xstride, dshape, plan);
+    MSHADOW_CUDA_POST_KERNEL_CHECK(MapPlanKernel);
+  } else {
+    int repeat = (num_block + kBaseGridNum-1) / kBaseGridNum;
+    dim3 dimGrid(kBaseGridNum, 1 , 1);
+    MapPlanLargeKernel<Saver, kBaseThreadBits, kBaseGridNum,
+                       expr::Plan<DstExp, DType>,
+                       expr::Plan<E, DType> >
+        <<<dimGrid, dimBlock, 0, stream>>>(dst, xstride, dshape, plan, repeat);
+    MSHADOW_CUDA_POST_KERNEL_CHECK(MapPlanLargeKernel);
+  }
+}
+
+template<typename Saver, typename Reducer, int warp_bits,
+         typename DType, typename DstPlan, typename Plan>
+__global__ void
+__launch_bounds__(kMemUnit*kMemUnit, 1)
+MapRedKeepLowestKernel(DstPlan dst, Plan plan,
+                       DType scale, Shape<2> eshape) {
+  const unsigned warp_size = 1 << warp_bits;
+  const unsigned x = (blockIdx.x << warp_bits) + threadIdx.x;
+  // to avoid bank conflict
+  __shared__ DType s_res[warp_size][warp_size + 1];
+  // note: reverse store [y][x], so that we can reduce over threadIdx.x, use warp optimization
+  if (threadIdx.y < eshape[0] && x < eshape[1]) {
+    s_res[threadIdx.x][threadIdx.y] = plan.Eval(threadIdx.y, x);
+  }
+  for (unsigned y = warp_size; y < eshape[0]; y += warp_size) {
+    if (threadIdx.y + y < eshape[0] && x < eshape[1]) {
+      Reducer::Reduce(s_res[threadIdx.x][threadIdx.y], plan.Eval(threadIdx.y + y, x));
+    }
+  }
+  __syncthreads();
+  if (eshape[0] >= warp_size) {
+    Reduce1D<Reducer, warp_bits>(s_res[threadIdx.y]);
+  } else {
+    Reduce1DNotAlign<Reducer, warp_bits>(s_res[threadIdx.y], eshape[0]);
+  }
+  __syncthreads();
+
+  if (threadIdx.y == 0 && x < eshape[1]) {
+    Saver::Save(dst.REval(0, x),  DType(s_res[threadIdx.x][0] * scale));
+  }
+}
+
+template<typename Saver, typename Reducer,
+         typename DstExp, typename E, typename DType>
+inline void MapReduceKeepLowest(expr::Plan<DstExp, DType> dst,
+                                const expr::Plan<E, DType> &plan,
+                                DType scale, Shape<2> eshape,
+                                cudaStream_t stream) {
+  dim3 dimBlock(kMemUnit, kMemUnit);
+  dim3 dimGrid((eshape[1] + kMemUnit - 1) >> kMemUnitBits);
+  CheckLaunchParam(dimGrid, dimBlock, "MapRedKeepLowestKernel");
+  MapRedKeepLowestKernel<Saver, Reducer, kMemUnitBits, DType,
+                         expr::Plan<DstExp, DType>,
+                         expr::Plan<E, DType> >
+      <<<dimGrid, dimBlock, 0, stream>>>(dst, plan, scale, eshape);
+  MSHADOW_CUDA_POST_KERNEL_CHECK(MapRedKeepLowestKernel);
+}
+
+template<typename Saver, typename Reducer, int block_dim_bits,
+         typename DType, typename DstPlan, typename Plan>
+__global__ void MapReduceKeepDim1Kernel(DstPlan dst, Plan plan, DType scale, Shape<4> pshape) {
+  const int block_size = 1 << block_dim_bits;
+  __shared__ DType s_rec[block_size];
+  const int c = blockIdx.x + blockIdx.y * gridDim.x;
+  const index_t tot = pshape[3] * pshape[2] * pshape[0];
+
+  if (c < pshape[1]) {
+    DType res; Reducer::SetInitValue(res);
+    for (index_t i_offset = 0; i_offset < tot; i_offset += block_size) {
+      index_t i = i_offset + threadIdx.x;
+      if (i< tot) {
+        const index_t x = i % pshape[3];
+        i /= pshape[3];
+        const index_t y = i % pshape[2];
+        const index_t n = i / pshape[2];
+        Reducer::Reduce(res, plan.Eval((n * pshape[1] + c) * pshape[2] + y, x));
+      }
+    }
+    s_rec[threadIdx.x] = res;
+    __syncthreads();
+    Reduce1D<Reducer, block_dim_bits>(s_rec);
+    if (threadIdx.x == 0) {
+      Saver::Save(dst.REval(0, c), DType(s_rec[0] * scale));
+    }
+  }
+}
+
+template<typename Saver, typename Reducer, typename DstExp, typename E, typename DType>
+inline void MapReduceKeepDim1(expr::Plan<DstExp, DType> dst,
+                              const expr::Plan<E, DType> &plan,
+                              DType scale, Shape<4> pshape,
+                              cudaStream_t stream) {
+  dim3 dimBlock(kBaseThreadNum);
+  const int grid_dim_x = (pshape[1] > kMaxGridNum) ? kMaxGridNum : pshape[1];
+  const int grid_dim_y = (pshape[1] > kMaxGridNum) ? (pshape[1] + kMaxGridNum - 1) / kMaxGridNum
+                                                   : 1;
+  dim3 dimGrid(grid_dim_x, grid_dim_y);
+  CheckLaunchParam(dimGrid, dimBlock, "MapReduceKeepDim1");
+  MapReduceKeepDim1Kernel<Saver, Reducer, kBaseThreadBits, DType,
+                          expr::Plan<DstExp, DType>,
+                          expr::Plan<E, DType> >
+      <<<dimGrid, dimBlock, 0, stream>>>(dst, plan, scale, pshape);
+  MSHADOW_CUDA_POST_KERNEL_CHECK(MapReduceKeepDim1Kernel);
+}
+
+template<int x_bits, typename DType>
+__global__ void GetBatchedViewKernel(DType **dst, DType *src, int num, int stride) {
+  const int x_size = 1 << x_bits;
+  const int start = threadIdx.x;
+  // Copy the addresses of src to dst every stride steps
+  for (int i = start; i < num; i += x_size) {
+    dst[i] = src + i * stride;
+  }
+}
+
+template<typename DType>
+inline void GetBatchedView(DType **dst, DType *src, int num, int stride,
+                           Stream<gpu> *stream) {
+  cudaStream_t stream_ = Stream<gpu>::GetStream(stream);
+  dim3 dimBlock(kBaseThreadNum);
+  dim3 dimGrid(1);
+  CheckLaunchParam(dimGrid, dimBlock, "GetBatchedView");
+  GetBatchedViewKernel<kBaseThreadBits, DType>
+    <<<dimGrid, dimBlock, 0, stream_>>> (dst, src, num, stride);
+  MSHADOW_CUDA_POST_KERNEL_CHECK(GetBatchedViewKernel);
+}
+
+template<int x_bits, typename DType, typename DstPlan, typename SrcPlan1, typename SrcPlan2>
+__global__ void SoftmaxGradKernel(DstPlan dst, SrcPlan1 src, SrcPlan2 label, index_t xmax) {
+  const unsigned x_size = 1 << x_bits;
+  const int y = blockIdx.x;
+  const int k = static_cast<int>(label.Eval(0, y));
+
+  // calculate normalizer, with writeback
+  for (unsigned x = 0; x < xmax; x += x_size) {
+    const unsigned xindex = x + threadIdx.x;
+    if (xindex < xmax) {
+      if (xindex == k) {
+        dst.REval(y, xindex) = src.Eval(y, xindex) - 1.0f;
+      } else {
+        dst.REval(y, xindex) = src.Eval(y, xindex);
+      }
+    }
+  }
+}
+
+template<int x_bits, typename DType, typename DstPlan, typename SrcPlan1, typename SrcPlan2>
+__global__ void SmoothSoftmaxGradKernel(DstPlan dst, SrcPlan1 src, SrcPlan2 label, index_t xmax,
+                                        float alpha) {
+  const unsigned x_size = 1 << x_bits;
+  const int y = blockIdx.x;
+  const int k = static_cast<int>(label.Eval(0, y));
+  // xmax is the number of classes in our distribution
+  const float smooth_grad = (alpha / (xmax - 1));
+
+  // calculate normalizer, with writeback
+  for (unsigned x = 0; x < xmax; x += x_size) {
+    const unsigned xindex = x + threadIdx.x;
+    if (xindex < xmax) {
+      if (xindex == k) {
+        dst.REval(y, xindex) = src.Eval(y, xindex) - 1.0f + alpha;
+      } else {
+        dst.REval(y, xindex) = src.Eval(y, xindex) - smooth_grad;
+      }
+    }
+  }
+}
+
+template<int x_bits, typename DType, typename DstPlan, typename SrcPlan1, typename SrcPlan2>
+__global__ void SoftmaxGradKernel(DstPlan dst, SrcPlan1 src, SrcPlan2 label, index_t xmax,
+                                  DType ignore_label) {
+  const unsigned x_size = 1 << x_bits;
+  const int y = blockIdx.x;
+  const int k = static_cast<int>(label.Eval(0, y));
+
+  // calculate normalizer, with writeback
+  for (unsigned x = 0; x < xmax; x += x_size) {
+    const unsigned xindex = x + threadIdx.x;
+    if (xindex < xmax) {
+      if (static_cast<int>(ignore_label) == k) {
+        dst.REval(y, xindex) = 0.0f;
+      } else {
+        if (xindex == k) {
+          dst.REval(y, xindex) = src.Eval(y, xindex) - 1.0f;
+        } else {
+          dst.REval(y, xindex) = src.Eval(y, xindex);
+        }
+      }
+    }
+  }
+}
+
+template<int x_bits, typename DType, typename DstPlan, typename SrcPlan1, typename SrcPlan2>
+__global__ void SmoothSoftmaxGradKernel(DstPlan dst, SrcPlan1 src, SrcPlan2 label, index_t xmax,
+                                  DType ignore_label, float alpha) {
+  const unsigned x_size = 1 << x_bits;
+  const int y = blockIdx.x;
+  const int k = static_cast<int>(label.Eval(0, y));
+  // xmax is the number of classes in our distribution
+  const float smooth_grad = (alpha / (xmax - 1));
+
+  // calculate normalizer, with writeback
+  for (unsigned x = 0; x < xmax; x += x_size) {
+    const unsigned xindex = x + threadIdx.x;
+    if (xindex < xmax) {
+      if (static_cast<int>(ignore_label) == k) {
+        dst.REval(y, xindex) = 0.0f;
+      } else {
+        if (xindex == k) {
+          dst.REval(y, xindex) = src.Eval(y, xindex) - 1.0f + alpha;
+        } else {
+          dst.REval(y, xindex) = src.Eval(y, xindex) - smooth_grad;
+        }
+      }
+    }
+  }
+}
+
+template<int x_bits, typename DType,  typename DstPlan, typename SrcPlan>
+__global__ void SoftmaxKernel(DstPlan dst, SrcPlan src, index_t xmax) {
+  const unsigned x_size = 1 << x_bits;
+  const int y = blockIdx.x;
+  __shared__ DType s_rec[x_size];
+  // step 1: get max
+  if (threadIdx.x < xmax) {
+    s_rec[threadIdx.x] = src.Eval(y, threadIdx.x);
+  }
+  for (unsigned x = x_size; x < xmax; x += x_size) {
+    if (x + threadIdx.x < xmax) {
+      DType a = src.Eval(y, x + threadIdx.x);
+      s_rec[threadIdx.x] = max(a, s_rec[threadIdx.x]);
+    }
+  }
+  __syncthreads();
+  if (threadIdx.x >= xmax) {
+    s_rec[threadIdx.x] = s_rec[0];
+  }
+  __syncthreads();
+  Reduce1D<red::maximum, x_bits>(s_rec);
+  __syncthreads();
+  DType smax = s_rec[0];
+  __syncthreads();
+  s_rec[threadIdx.x] = 0.0f;
+  __syncthreads();
+
+  // calculate normalizer, with writeback
+  for (unsigned x = 0; x < xmax; x += x_size) {
+    if (x + threadIdx.x < xmax) {
+      DType p = expf(src.Eval(y, x + threadIdx.x) - smax);
+      s_rec[threadIdx.x] += p;
+      // write back first, will fetch later
+      dst.REval(y, x + threadIdx.x) = p;
+    }
+  }
+  // calculate normalizer
+  __syncthreads();
+  Reduce1D<red::sum, x_bits>(s_rec);
+  __syncthreads();
+  DType ssum = s_rec[0];
+
+  for (unsigned x = 0; x < xmax; x += x_size) {
+    if (x + threadIdx.x < xmax) {
+      dst.REval(y, x + threadIdx.x) /= ssum;
+    }
+  }
+}
+
+template<typename DType>
+inline void Softmax(const Tensor<gpu, 2, DType> &dst,
+                    const Tensor<gpu, 2, DType> &src) {
+  dim3 dimBlock(kBaseThreadNum);
+  dim3 dimGrid(dst.size(0));
+  CHECK_EQ(dst.shape_, src.shape_) << "Softmax: shape mismatch";
+  CheckLaunchParam(dimGrid, dimBlock, "Softmax");
+  cudaStream_t stream = Stream<gpu>::GetStream(dst.stream_);
+  SoftmaxKernel<kBaseThreadBits, DType>
+      <<<dimGrid, dimBlock, 0, stream>>>
+      (expr::MakePlan(dst),
+       expr::MakePlan(src),
+       dst.size(1));
+  MSHADOW_CUDA_POST_KERNEL_CHECK(SoftmaxKernel);
+}
+
+template<typename DType>
+inline void SoftmaxGrad(const Tensor<gpu, 2, DType> &dst,
+                        const Tensor<gpu, 2, DType> &src,
+                        const Tensor<gpu, 1, DType> &label) {
+  dim3 dimBlock(kBaseThreadNum);
+  dim3 dimGrid(dst.size(0));
+  CHECK_EQ(dst.shape_, src.shape_) << "SoftmaxGrad: shape mismatch";
+  CHECK_EQ(dst.size(0), label.size(0)) << "SoftmaxGrad: label shape mismatch";
+  CheckLaunchParam(dimGrid, dimBlock, "SoftmaxGrad");
+  cudaStream_t stream = Stream<gpu>::GetStream(dst.stream_);
+  SoftmaxGradKernel<kBaseThreadBits, DType>
+      <<<dimGrid, dimBlock, 0, stream>>>
+      (expr::MakePlan(dst),
+       expr::MakePlan(src),
+       expr::MakePlan(label),
+       dst.size(1));
+  MSHADOW_CUDA_POST_KERNEL_CHECK(SoftmaxGradKernel);
+}
+
+template<typename DType>
+inline void SmoothSoftmaxGrad(const Tensor<gpu, 2, DType> &dst,
+                              const Tensor<gpu, 2, DType> &src,
+                              const Tensor<gpu, 1, DType> &label,
+                              const float alpha) {
+  dim3 dimBlock(kBaseThreadNum);
+  dim3 dimGrid(dst.size(0));
+  CHECK_EQ(dst.shape_, src.shape_) << "SoftmaxGrad: shape mismatch";
+  CHECK_EQ(dst.size(0), label.size(0)) << "SoftmaxGrad: label shape mismatch";
+  CheckLaunchParam(dimGrid, dimBlock, "SoftmaxGrad");
+  cudaStream_t stream = Stream<gpu>::GetStream(dst.stream_);
+  SmoothSoftmaxGradKernel<kBaseThreadBits, DType>
+      <<<dimGrid, dimBlock, 0, stream>>>
+      (expr::MakePlan(dst),
+       expr::MakePlan(src),
+       expr::MakePlan(label),
+       dst.size(1),
+       alpha);
+  MSHADOW_CUDA_POST_KERNEL_CHECK(SoftmaxGradKernel);
+}
+
+template<typename DType>
+inline void SoftmaxGrad(const Tensor<gpu, 2, DType> &dst,
+                        const Tensor<gpu, 2, DType> &src,
+                        const Tensor<gpu, 1, DType> &label,
+                        const DType &ignore_label) {
+  dim3 dimBlock(kBaseThreadNum);
+  dim3 dimGrid(dst.size(0));
+  CHECK_EQ(dst.shape_, src.shape_) << "SoftmaxGrad: shape mismatch";
+  CHECK_EQ(dst.size(0), label.size(0)) << "SoftmaxGrad: label shape mismatch";
+  CheckLaunchParam(dimGrid, dimBlock, "SoftmaxGrad");
+  cudaStream_t stream = Stream<gpu>::GetStream(dst.stream_);
+  SoftmaxGradKernel<kBaseThreadBits, DType>
+      <<<dimGrid, dimBlock, 0, stream>>>
+      (expr::MakePlan(dst),
+       expr::MakePlan(src),
+       expr::MakePlan(label),
+       dst.size(1),
+       ignore_label);
+  MSHADOW_CUDA_POST_KERNEL_CHECK(SoftmaxGradKernel);
+}
+
+template<typename DType>
+inline void SmoothSoftmaxGrad(const Tensor<gpu, 2, DType> &dst,
+                              const Tensor<gpu, 2, DType> &src,
+                              const Tensor<gpu, 1, DType> &label,
+                              const DType &ignore_label,
+                              const float alpha) {
+  dim3 dimBlock(kBaseThreadNum);
+  dim3 dimGrid(dst.size(0));
+  CHECK_EQ(dst.shape_, src.shape_) << "SoftmaxGrad: shape mismatch";
+  CHECK_EQ(dst.size(0), label.size(0)) << "SoftmaxGrad: label shape mismatch";
+  CheckLaunchParam(dimGrid, dimBlock, "SoftmaxGrad");
+  cudaStream_t stream = Stream<gpu>::GetStream(dst.stream_);
+  SmoothSoftmaxGradKernel<kBaseThreadBits, DType>
+      <<<dimGrid, dimBlock, 0, stream>>>
+      (expr::MakePlan(dst),
+       expr::MakePlan(src),
+       expr::MakePlan(label),
+       dst.size(1),
+       ignore_label,
+       alpha);
+  MSHADOW_CUDA_POST_KERNEL_CHECK(SoftmaxGradKernel);
+}
+
+template<int n_bits, typename DType>
+__global__ void Softmax3DGradKernel(Tensor<gpu, 3, DType> dst,
+                                    const Tensor<gpu, 3, DType> src,
+                                    const Tensor<gpu, 2, DType> label) {
+  const index_t xmax = dst.size(1);
+  const index_t nmax = dst.size(2);
+  const unsigned n_size = 1 << n_bits;
+  const int y = blockIdx.x;
+  const int n = threadIdx.x;
+
+  for (index_t n_index = n; n_index < nmax; n_index += n_size) {
+    const int k = static_cast<int>(label[y][n_index]);
+    for (index_t i = 0; i < xmax; ++i) {
+      if (i == k) {
+        dst[y][i][n_index] = src[y][i][n_index] - 1.0f;
+      } else {
+        dst[y][i][n_index] = src[y][i][n_index];
+      }
+    }
+  }
+}
+
+template<int n_bits, typename DType>
+__global__ void Softmax3DGradKernel(Tensor<gpu, 3, DType> dst,
+                                    const Tensor<gpu, 3, DType> src,
+                                    const Tensor<gpu, 2, DType> label,
+                                    DType ignore_label) {
+  const index_t xmax = dst.size(1);
+  const index_t nmax = dst.size(2);
+  const unsigned n_size = 1 << n_bits;
+  const int y = blockIdx.x;
+  const int n = threadIdx.x;
+  for (index_t n_index = n; n_index < nmax; n_index += n_size) {
+    int k = static_cast<int>(label[y][n_index]);
+    if (k == static_cast<int>(ignore_label)) {
+      for (index_t i = 0; i < xmax; ++i) {
+        dst[y][i][n_index] = 0.0f;
+      }
+    } else {
+      for (index_t i = 0; i < xmax; ++i) {
+        if (i == k) {
+          dst[y][i][n_index] = src[y][i][n_index] - 1.0f;
+        } else {
+          dst[y][i][n_index] = src[y][i][n_index];
+        }
+      }
+    }
+  }
+}
+
+template<int n_bits, typename DType>
+__global__ void Softmax3DKernel(Tensor<gpu, 3, DType> dst,
+                    const Tensor<gpu, 3, DType> src) {
+  const index_t xmax = dst.size(1);
+  const index_t nmax = dst.size(2);
+  const unsigned n_size = 1 << n_bits;
+  const int y = blockIdx.x;
+  const int n = threadIdx.x;
+
+  for (index_t n_index = n; n_index < nmax; n_index += n_size) {
+    DType smax = src[y][0][n_index];
+    for (index_t i = 1; i < xmax; ++i) {
+      smax = max(smax, src[y][i][n_index]);  // NOLINT(*)
+    }
+    DType ssum = 0.0f;
+    for (index_t i = 0; i < xmax; ++i) {
+      DType p = expf(src[y][i][n_index] - smax);
+      ssum += p;
+      dst[y][i][n_index] = p;
+    }
+    for (index_t i = 0; i < xmax; ++i) {
+      dst[y][i][n_index] /= ssum;
+    }
+  }
+}
+
+template<typename DType>
+inline void Softmax(const Tensor<gpu, 3, DType> &dst,
+                    const Tensor<gpu, 3, DType> &src) {
+  dim3 dimBlock(kBaseThreadNum);
+  dim3 dimGrid(dst.size(0));
+  CHECK_EQ(dst.shape_, src.shape_) << "Softmax: shape mismatch";
+  CheckLaunchParam(dimGrid, dimBlock, "Softmax");
+  cudaStream_t stream = Stream<gpu>::GetStream(dst.stream_);
+  Softmax3DKernel<kBaseThreadBits, DType><<<dimGrid, dimBlock, 0, stream>>>(dst, src);
+  MSHADOW_CUDA_POST_KERNEL_CHECK(Softmax3DKernel);
+}
+
+template<typename DType>
+inline void SoftmaxGrad(const Tensor<gpu, 3, DType> &dst,
+                        const Tensor<gpu, 3, DType> &src,
+                        const Tensor<gpu, 2, DType> &label) {
+  dim3 dimBlock(kBaseThreadNum);
+  dim3 dimGrid(dst.size(0));
+  CHECK_EQ(dst.shape_, src.shape_) << "SoftmaxGrad: shape mismatch";
+  CHECK_EQ(dst.size(0), label.size(0)) << "SoftmaxGrad: label shape mismatch";
+  CHECK_EQ(dst.size(2), label.size(1)) << "SoftmaxGrad: label shape mismatch";
+  CheckLaunchParam(dimGrid, dimBlock, "SoftmaxGrad");
+  cudaStream_t stream = Stream<gpu>::GetStream(dst.stream_);
+  Softmax3DGradKernel<kBaseThreadBits, DType><<<dimGrid, dimBlock, 0, stream>>>(dst, src, label);
+  MSHADOW_CUDA_POST_KERNEL_CHECK(Softmax3DGradKernel);
+}
+
+template<typename DType>
+inline void SoftmaxGrad(const Tensor<gpu, 3, DType> &dst,
+                        const Tensor<gpu, 3, DType> &src,
+                        const Tensor<gpu, 2, DType> &label,
+                        const DType &ignore_label) {
+  dim3 dimBlock(kBaseThreadNum);
+  dim3 dimGrid(dst.size(0));
+  CHECK_EQ(dst.shape_, src.shape_) << "SoftmaxGrad: shape mismatch";
+  CHECK_EQ(dst.size(0), label.size(0)) << "SoftmaxGrad: label shape mismatch";
+  CHECK_EQ(dst.size(2), label.size(1)) << "SoftmaxGrad: label shape mismatch";
+  CheckLaunchParam(dimGrid, dimBlock, "SoftmaxGrad");
+  cudaStream_t stream = Stream<gpu>::GetStream(dst.stream_);
+  Softmax3DGradKernel<kBaseThreadBits, DType><<<dimGrid, dimBlock, 0, stream>>>(
+    dst, src, label, ignore_label);
+  MSHADOW_CUDA_POST_KERNEL_CHECK(Softmax3DGradKernel);
+}
+
+template<int x_bits, typename DType, typename DstPlan, typename SrcPlan1, typename SrcPlan2>
+__global__ void AddTakeGradKernel(DstPlan dst,
+                                  SrcPlan1 index, SrcPlan2 src,
+                                  index_t ymax, index_t xmax, const int K) {
+  const unsigned x_size = 1 << x_bits;
+  const int xindex = blockIdx.x * x_size + threadIdx.x;
+  __shared__ int ptr;
+  for (unsigned y = 0; y < ymax; ++y) {
+    if (threadIdx.x == 0) {
+      ptr = index.Eval(0, y);
+      if (ptr <= 0) ptr = 0;
+      else if (ptr >= K) ptr = K - 1;
+    }
+    __syncthreads();
+    if (xindex < xmax) {
+      dst.REval(ptr, xindex) += src.Eval(y, xindex);
+    }
+  }
+}
+
+template<int warp_bits, int SZ, typename DType, typename IdxType>
+__global__ void AddTakeGradLargeBatchKernel(DType* dst,
+                                            const IdxType *sorted, const IdxType *index,
+                                            const DType *src,
+                                            int ymax, int xmax) {
+  // Based on Torch's Version https://github.com/torch/cunn/blob/master/lib/THCUNN/LookupTable.cu
+  // Each warp is responsible for an input into the LookupTable.
+  // If the preceeding input has the same as this input, then the warp
+  // exits immediately. The warp also processes subsequent inputs with the
+  // same value.
+  //
+  // Input Warp
+  // 1     <warp 1>
+  // 1     <warp 1> (<warp 2> exits without doing any work)
+  // 5     <warp 3>
+  // 8     <warp 4>
+  // Also, all warp will loop for SZ times to increase the throughput.
+
+  const int warp_size = 1 << warp_bits;
+  int idx = blockIdx.x * blockDim.y + threadIdx.y;
+
+  if (idx < ymax
+    && (idx == 0 || sorted[idx] != sorted[idx - 1])) {
+    do {
+      const int start_feature = threadIdx.x + blockIdx.y * blockDim.x * SZ;
+      const int dst_row = static_cast<int>(sorted[idx]) * xmax;
+      const int src_row = static_cast<int>(index[idx]) * xmax;
+      float grad_out[SZ];
+      float grad_weight[SZ];
+      #pragma unroll
+      for (int ii = 0; ii < SZ; ii++) {
+        int feature_dim = start_feature + ii * warp_size;
+        if (feature_dim < xmax) {
+          grad_out[ii] = src[src_row + feature_dim];
+          grad_weight[ii] = dst[dst_row + feature_dim];
+        }
+      }
+
+      #pragma unroll
+      for (int ii = 0; ii < SZ; ii++) {
+        grad_weight[ii] += grad_out[ii];
+      }
+
+      #pragma unroll
+      for (int ii = 0; ii < SZ; ii++) {
+        int feature_dim = start_feature + ii * warp_size;
+        if (feature_dim < xmax) {
+          dst[dst_row + feature_dim] = grad_weight[ii];
+        }
+      }
+      idx++;
+    } while (idx < ymax && (sorted[idx] == sorted[idx - 1]));
+  }
+}
+
+template<typename IndexType, typename DType>
+inline void AddTakeGrad(Tensor<gpu, 2, DType> dst,
+                        const Tensor<gpu, 1, IndexType>& index,
+                        const Tensor<gpu, 2, DType> &src) {
+  CHECK_EQ(dst.CheckContiguous(), true);
+  CHECK_EQ(index.CheckContiguous(), true);
+  CHECK_EQ(src.CheckContiguous(), true);
+  const int kUnitBits = kMemUnitBits + 1;
+  dim3 dimBlock(1 << kUnitBits);
+  dim3 dimGrid((dst.size(1) + (1 << kUnitBits) - 1) >> kUnitBits);
+
+  CHECK_EQ(dst.size(1), src.size(1)) << "AddTakeGrad: shape mismatch";
+  CHECK_EQ(index.size(0), src.size(0)) << "AddTakeGrad: shape mismatch";
+  CheckLaunchParam(dimGrid, dimBlock, "AddTakeGrad");
+  cudaStream_t stream = Stream<gpu>::GetStream(dst.stream_);
+  const int K = dst.shape_[0];
+
+  AddTakeGradKernel<kUnitBits, DType>
+      <<<dimGrid, dimBlock, 0, stream>>>
+      (expr::MakePlan(dst),
+       expr::MakePlan(index),
+       expr::MakePlan(src),
+       src.size(0),
+       src.size(1), K);
+  MSHADOW_CUDA_POST_KERNEL_CHECK(AddTakeGradKernel);
+}
+
+template<typename IndexType, typename DType>
+inline void AddTakeGradLargeBatch(Tensor<gpu, 2, DType> dst,
+                                  const Tensor<gpu, 1, IndexType>& sorted,
+                                  const Tensor<gpu, 1, IndexType>& index,
+                                  const Tensor<gpu, 2, DType> &src) {
+  CHECK_EQ(dst.CheckContiguous(), true);
+  CHECK_EQ(sorted.CheckContiguous(), true);
+  CHECK_EQ(index.CheckContiguous(), true);
+  CHECK_EQ(src.CheckContiguous(), true);
+  const int kWarpBits = kMemUnitBits;
+  const int SZ = 4;
+  const int block_dim_x = 1 << kWarpBits;
+  const int block_dim_y = 4;
+  const int grid_dim_x = (src.size(0) + block_dim_y - 1) / block_dim_y;
+  const int grid_dim_y = (src.size(1) + block_dim_x * SZ - 1) / (block_dim_x * SZ);
+  dim3 dimBlock(block_dim_x, block_dim_y);
+  dim3 dimGrid(grid_dim_x, grid_dim_y);
+
+  CHECK_EQ(dst.size(1), src.size(1)) << "AddTakeGradLargeBatch: shape mismatch";
+  CHECK_EQ(index.size(0), src.size(0)) << "AddTakeGradLargeBatch: shape mismatch";
+  CheckLaunchParam(dimGrid, dimBlock, "AddTakeGradLargeBatch");
+  cudaStream_t stream = Stream<gpu>::GetStream(dst.stream_);
+
+  AddTakeGradLargeBatchKernel<kWarpBits, SZ, DType>
+      <<<dimGrid, dimBlock, 0, stream>>>
+      (dst.dptr_,
+       sorted.dptr_,
+       index.dptr_,
+       src.dptr_,
+       static_cast<int>(src.size(0)),
+       static_cast<int>(src.size(1)));
+  MSHADOW_CUDA_POST_KERNEL_CHECK(AddTakeGradLargeBatchKernel);
+}
+
+template<int warp_bits, typename DType, typename DstPlan, typename IndexPlan, typename SrcPlan>
+__global__ void IndexFillKernel(DstPlan dst,
+                                const IndexPlan index,
+                                const SrcPlan src,
+                                const int ymax,
+                                const int xmax) {
+  int bid = blockIdx.y * blockDim.x + blockIdx.x;
+  int tid = bid * blockDim.x * blockDim.y + threadIdx.y * blockDim.x + threadIdx.x;
+  if (tid < ymax * xmax) {
+    int i = tid / xmax;
+    int j = tid % xmax;
+    int k = static_cast<int>(index.Eval(0, i));
+    dst.REval(k, j) = src.Eval(i, j);
+  }
+}
+
+template<typename IndexType, typename DType>
+inline void IndexFill(Tensor<gpu, 2, DType> dst,
+                      const Tensor<gpu, 1, IndexType>& index,
+                      const Tensor<gpu, 2, DType> &src) {
+  CHECK_EQ(dst.CheckContiguous(), true);
+  CHECK_EQ(index.CheckContiguous(), true);
+  CHECK_EQ(src.CheckContiguous(), true);
+  CHECK_EQ(dst.size(1), src.size(1)) << "IndexFill: shape mismatch";
+  CHECK_EQ(index.size(0), src.size(0)) << "IndexFill: shape mismatch";
+  const int block_dim_x = 1 << kMemUnitBits;
+  const int block_dim_y = 1 << kMemUnitBits;
+  const int block_size = block_dim_x * block_dim_y;
+  int grid_dim_x = (src.size(0) * src.size(1) + block_size - 1) / block_size;
+  int grid_dim_y = 1;
+  while (grid_dim_x > kMaxGridDim) {
+    grid_dim_x = (grid_dim_x + 1) / 2;
+    grid_dim_y *= 2;
+  }
+  dim3 dimBlock(block_dim_x, block_dim_y);
+  dim3 dimGrid(grid_dim_x, grid_dim_y);
+  CheckLaunchParam(dimGrid, dimBlock, "IndexFill");
+  cudaStream_t stream = Stream<gpu>::GetStream(dst.stream_);
+
+  IndexFillKernel<kMemUnitBits, DType>
+      <<<dimGrid, dimBlock, 0, stream>>>
+      (expr::MakePlan(dst),
+       expr::MakePlan(index),
+       expr::MakePlan(src),
+       src.size(0),
+       src.size(1));
+  MSHADOW_CUDA_POST_KERNEL_CHECK(IndexFillKernel);
+}
+
+template<typename KDType, typename VDType>
+inline void SortByKey(Tensor<gpu, 1, KDType> keys, Tensor<gpu, 1, VDType> values,
+                      bool is_ascend) {
+  CHECK_EQ(keys.CheckContiguous(), true);
+  CHECK_EQ(values.CheckContiguous(), true);
+#if CUDA_VERSION >= 7000
+  cudaStream_t stream = Stream<gpu>::GetStream(keys.stream_);
+  thrust::device_ptr<KDType> key_iter = thrust::device_pointer_cast(keys.dptr_);
+  thrust::device_ptr<VDType> value_iter = thrust::device_pointer_cast(values.dptr_);
+  if (is_ascend) {
+    thrust::stable_sort_by_key(
+      thrust::cuda::par.on(stream),
+      key_iter, key_iter + keys.size(0), value_iter, thrust::less<KDType>());  // NOLINT(*)
+  } else {
+    thrust::stable_sort_by_key(
+      thrust::cuda::par.on(stream),
+      key_iter, key_iter + keys.size(0), value_iter, thrust::greater<KDType>());  // NOLINT(*)
+  }
+  MSHADOW_CUDA_POST_KERNEL_CHECK(SortByKey);
+#else
+  LOG(FATAL) << "SortByKey is only supported for CUDA version >=7.0!";
+#endif
+}
+
+template<typename DType>
+inline void SortByKey(Tensor<gpu, 1, mshadow::half::half_t> keys, Tensor<gpu, 1, DType> values,
+                      bool is_ascend) {
+  LOG(FATAL) << "SortByKey for half_t is not implemented!";
+}
+
+template<typename DType>
+inline void SortByKey(Tensor<gpu, 1, DType> keys, Tensor<gpu, 1, mshadow::half::half_t> values,
+  bool is_ascend) {
+  LOG(FATAL) << "SortByKey for half_t is not implemented!";
+}
+
+// break ambiguous template deduction for <half_t, half_t>
+inline void SortByKey(Tensor<gpu, 1, mshadow::half::half_t> keys,
+  Tensor<gpu, 1, mshadow::half::half_t> values,
+  bool is_ascend) {
+  LOG(FATAL) << "SortByKey for half_t is not implemented!";
+}
+}  // namespace cuda
+}  // namespace mshadow
+#endif  // MSHADOW_CUDA_TENSOR_GPU_INL_CUH_
diff --git a/3rdparty/mshadow/mshadow/dot_engine-inl.h b/3rdparty/mshadow/mshadow/dot_engine-inl.h
new file mode 100644
index 000000000000..21816f209e40
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/dot_engine-inl.h
@@ -0,0 +1,936 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file dot_engine-inl.h
+ * \brief definitions of how Matrix Multiplications can be evaluated
+ * \author Tianqi Chen
+ */
+#ifndef MSHADOW_DOT_ENGINE_INL_H_
+#define MSHADOW_DOT_ENGINE_INL_H_
+
+#include <vector>
+#include "./base.h"
+#include "./extension/implicit_gemm.h"
+
+#ifdef __CUDACC__
+#include "./cuda/tensor_gpu-inl.cuh"
+#endif  // #ifdef __CUDACC__
+
+namespace mshadow {
+ /*!
+* \brief CPU/GPU: Get a batched view of the src array. dst[i] = src + i * stride
+* \param dst 2D pointer
+* \param src 1D pointer
+* \param num number of batches
+* \param stride size of each batch
+* \param stream
+*/
+template<typename Device, typename DType>
+inline void GetBatchedView(DType **dst, DType *src, int num, int stride,
+                           Stream<Device> *stream);
+template<typename DType>
+inline void GetBatchedView(DType **dst, DType *src, int num, int stride,
+                           Stream<cpu> *stream) {
+  for (int i = 0; i < num; i++) {
+    dst[i] = src + i * stride;
+  }
+}
+#ifdef __CUDACC__
+namespace cuda {};
+template<typename DType>
+inline void GetBatchedView(DType **dst, DType *src, int num, int stride,
+                           Stream<gpu> *stream) {
+  cuda::GetBatchedView(dst, src, num, stride, stream);
+}
+#endif  // #ifdef __CUDACC__
+
+namespace expr {
+//---------------------------------------------------------------------
+// Matrix Multiplications, depends on BLAS Engine
+//---------------------------------------------------------------------
+template<typename SV, typename Device, int ddim, int ldim,
+         int rdim, bool ltrans, bool rtrans, typename DType>
+struct DotEngine {
+  inline static void Eval(Tensor<Device, ddim, DType> *p_dst,
+                          const Tensor<Device, ldim, DType> &lhs,
+                          const Tensor<Device, rdim, DType> &rhs,
+                          DType scale);
+};
+// handles the dot, use CblasColMajor
+template<typename Device, typename DType = default_real_t>
+struct BLASEngine {
+  inline static bool GetT(bool t) {
+    return t ? true : false;
+  }
+  inline static void SetStream(Stream<Device> *stream) {
+  }
+  inline static void gemm(Stream<Device> *stream,
+                          bool transa, bool transb,
+                          int m, int n, int k, DType alpha,
+                          const DType *A, int lda, const DType *B, int ldb,
+                          DType beta, DType *C, int ldc) {
+    LOG(FATAL) << "Not implmented!";
+  }
+  inline static void batched_gemm(Stream<Device> *stream,
+                                  bool transa, bool transb,
+                                  int m, int n, int k, DType alpha,
+                                  const DType *A, int lda, const DType *B, int ldb,
+                                  DType beta, DType *C, int ldc, int batch_count,
+                                  DType **workspace) {
+    LOG(FATAL) << "Not implmented!";
+  }
+  inline static void gemv(Stream<Device> *stream,
+                          bool trans, int m, int n,
+                          DType alpha, const DType *A, int lda,
+                          const DType *X, int incX,
+                          DType beta, DType *Y, int incY) {
+    LOG(FATAL) << "Not implmented!";
+  }
+  inline static void batched_gemv(Stream<Device> *stream,
+                                  bool trans, int m, int n,
+                                  DType alpha, const DType *A, int lda,
+                                  const DType *X, int incX,
+                                  DType beta, DType *Y, int incY, int batch_count) {
+    LOG(FATAL) << "Not implmented!";
+  }
+  inline static void ger(Stream<Device> *stream,
+                         int m, int n, DType alpha,
+                         const DType *X, int incX,
+                         const DType *Y, int incY, DType *A, int lda) {
+    LOG(FATAL) << "Not implmented!";
+  }
+  inline static void batched_ger(Stream<Device> *stream,
+                         int m, int n, DType alpha,
+                         const DType *X, int incX,
+                         const DType *Y, int incY, DType *A, int lda, int batch_count) {
+    LOG(FATAL) << "Not implmented!";
+  }
+  inline static void dot(Stream<Device> *stream,
+                         int n,
+                         const DType* X, int incX,
+                         const DType* Y, int incY,
+                         DType* ret) {
+    LOG(FATAL) << "Not implmented!";
+  }
+};
+
+#if MSHADOW_STAND_ALONE
+template<>
+struct BLASEngine<cpu, float> {
+  inline static bool GetT(bool t) {
+    return t ? true : false;
+  }
+  inline static void SetStream(Stream<cpu> *stream) {
+  }
+  inline static void gemm(Stream<cpu> *stream,
+                          bool transa, bool transb,
+                          int m, int n, int k, float alpha,
+                          const float *A, int lda, const float *B, int ldb,
+                          float beta, float *C, int ldc) {
+    if (alpha == 1.0f && beta == 0.0f) {
+      bool transpose_left = transb;
+      bool transpose_right = transa;
+      Tensor<cpu, 2, float> lhs((float*)B, Shape2(transpose_left ? k : n, transpose_left ? n : k));  // NOLINT(*)
+      Tensor<cpu, 2, float> rhs((float*)A, Shape2(transpose_right ? m : k, transpose_right ? k : m));  // NOLINT(*)
+      Tensor<cpu, 2, float> dst(C, Shape2(m, n));
+      if (!transpose_left && !transpose_right) {
+        dst = expr::implicit_dot(lhs, rhs); return;
+      } else if (!transpose_left && transpose_right) {
+        dst = expr::implicit_dot(lhs, rhs.T()); return;
+      } else if (transpose_left && !transpose_right) {
+        dst = expr::implicit_dot(lhs.T(), rhs); return;
+      } else {
+        LOG(FATAL) << "Not implmented!";
+      }
+    } else {
+      LOG(FATAL) << "Not implmented!";
+    }
+  }
+  inline static void batched_gemm(Stream<cpu> *stream,
+                                  bool transa, bool transb,
+                                  int m, int n, int k, float alpha,
+                                  const float *A, int lda, const float *B, int ldb,
+                                  float beta, float *C, int ldc, int batch_count,
+                                  float **workspace) {
+    for (int i = 0; i < batch_count; ++i) {
+      gemm(stream, transa, transb, m, n, k, alpha,
+           A + i * m * k, lda, B + i * k * n, ldb,
+           beta, C + i * m * n, ldc);
+    }
+  }
+  inline static void gemv(Stream<cpu> *stream,
+                          bool trans, int m, int n,
+                          float alpha, const float *A, int lda,
+                          const float *X, int incX,
+                          float beta, float *Y, int incY) {
+    LOG(FATAL) << "Not implmented!";
+  }
+  inline static void batched_gemv(Stream<cpu> *stream,
+                                  bool trans, int m, int n,
+                                  float alpha, const float *A, int lda,
+                                  const float *X, int incX,
+                                  float beta, float *Y, int incY, int batch_count) {
+    LOG(FATAL) << "Not implmented!";
+  }
+  inline static void ger(Stream<cpu> *stream,
+                         int m, int n, float alpha,
+                         const float *X, int incX,
+                         const float *Y, int incY, float *A, int lda) {
+    LOG(FATAL) << "Not implmented!";
+  }
+  inline static void batched_ger(Stream<cpu> *stream,
+                         int m, int n, float alpha,
+                         const float *X, int incX,
+                         const float *Y, int incY, float *A, int lda, int batch_count) {
+    LOG(FATAL) << "Not implmented!";
+  }
+  inline static void dot(Stream<cpu> *stream,
+                         int n,
+                         const float* X, int incX,
+                         const float* Y, int incY,
+                         float* ret) {
+    LOG(FATAL) << "Not implmented!";
+  }
+};
+
+template<>
+struct BLASEngine<cpu, double> {
+  inline static bool GetT(bool t) {
+    return t ? true : false;
+  }
+  inline static void SetStream(Stream<cpu> *stream) {
+  }
+  inline static void gemm(Stream<cpu> *stream,
+                          bool transa, bool transb,
+                          int m, int n, int k, double alpha,
+                          const double *A, int lda, const double *B, int ldb,
+                          double beta, double *C, int ldc) {
+    if (alpha == 1.0f && beta == 0.0f) {
+      bool transpose_left = transb;
+      bool transpose_right = transa;
+      Tensor<cpu, 2, double> lhs((double*)B, Shape2(transpose_left ? k : n, transpose_left ? n : k));  // NOLINT(*)
+      Tensor<cpu, 2, double> rhs((double*)A, Shape2(transpose_right ? m : k, transpose_right ? k : m));  // NOLINT(*)
+      Tensor<cpu, 2, double> dst(C, Shape2(m, n));
+      if (!transpose_left && !transpose_right) {
+        dst = expr::implicit_dot(lhs, rhs); return;
+      } else if (!transpose_left && transpose_right) {
+        dst = expr::implicit_dot(lhs, rhs.T()); return;
+      } else if (transpose_left && !transpose_right) {
+        dst = expr::implicit_dot(lhs.T(), rhs); return;
+      } else {
+        LOG(FATAL) << "Not implmented!";
+      }
+    } else {
+      LOG(FATAL) << "Not implmented!";
+    }
+  }
+  inline static void batched_gemm(Stream<cpu> *stream,
+                                  bool transa, bool transb,
+                                  int m, int n, int k, double alpha,
+                                  const double *A, int lda, const double *B, int ldb,
+                                  double beta, double *C, int ldc, int batch_count,
+                                  double **workspace) {
+    for (int i = 0; i < batch_count; ++i) {
+      gemm(stream, transa, transb, m, n, k, alpha,
+           A + i * m * k, lda, B + i * k * n, ldb,
+           beta, C + i * m * n, ldc);
+    }
+  }
+  inline static void gemv(Stream<cpu> *stream,
+                          bool trans, int m, int n,
+                          double alpha, const double *A, int lda,
+                          const double *X, int incX,
+                          double beta, double *Y, int incY) {
+    LOG(FATAL) << "Not implmented!";
+  }
+  inline static void batched_gemv(Stream<cpu> *stream,
+                                  bool trans, int m, int n,
+                                  double alpha, const double *A, int lda,
+                                  const double *X, int incX,
+                                  double beta, double *Y, int incY, int batch_count) {
+    LOG(FATAL) << "Not implmented!";
+  }
+  inline static void ger(Stream<cpu> *stream,
+                         int m, int n, double alpha,
+                         const double *X, int incX,
+                         const double *Y, int incY, double *A, int lda) {
+    LOG(FATAL) << "Not implmented!";
+  }
+  inline static void batched_ger(Stream<cpu> *stream,
+                         int m, int n, double alpha,
+                         const double *X, int incX,
+                         const double *Y, int incY, double *A, int lda, int batch_count) {
+    LOG(FATAL) << "Not implmented!";
+  }
+  inline static void dot(Stream<cpu> *stream,
+                         int n,
+                         const double* X, int incX,
+                         const double* Y, int incY,
+                         double* ret) {
+    LOG(FATAL) << "Not implmented!";
+  }
+};
+
+#elif (MSHADOW_USE_MKL || MSHADOW_USE_CBLAS)  // NOLINT(*)
+template<>
+struct BLASEngine<cpu, float> {
+  inline static CBLAS_TRANSPOSE GetT(bool t) {
+    return t ? CblasTrans : CblasNoTrans;
+  }
+  inline static void SetStream(Stream<cpu> *stream) {
+  }
+  inline static void gemm(Stream<cpu> *stream,
+                          bool transa, bool transb,
+                          int m, int n, int k, float alpha,
+                          const float *A, int lda, const float *B, int ldb,
+                          float beta, float *C, int ldc) {
+    cblas_sgemm(CblasColMajor, GetT(transa), GetT(transb),
+                m, n, k, alpha, A, lda, B, ldb, beta, C, ldc);
+  }
+  inline static void batched_gemm(Stream<cpu> *stream,
+                                  bool transa, bool transb,
+                                  int m, int n, int k, float alpha,
+                                  const float *A, int lda, const float *B, int ldb,
+                                  float beta, float *C, int ldc, int batch_count,
+                                  float **workspace) {
+#if (MSHADOW_USE_MKL && INTEL_MKL_VERSION >= 20160000)
+  // since same m/n/k is used for all single gemms, so we put all gemms into one group
+  const int GROUP_SIZE = 1;
+  MKL_INT p_m[GROUP_SIZE] = {m};
+  MKL_INT p_n[GROUP_SIZE] = {n};
+  MKL_INT p_k[GROUP_SIZE] = {k};
+  MKL_INT p_lda[GROUP_SIZE] = {lda};
+  MKL_INT p_ldb[GROUP_SIZE] = {ldb};
+  MKL_INT p_ldc[GROUP_SIZE] = {ldc};
+
+  float p_alpha[GROUP_SIZE] = {alpha};
+  float p_beta[GROUP_SIZE] = {beta};
+
+  CBLAS_TRANSPOSE cblas_a_trans = GetT(transa);
+  CBLAS_TRANSPOSE cblas_b_trans = GetT(transb);
+
+  MKL_INT p_group_sizeb[GROUP_SIZE] = {batch_count};
+  CBLAS_TRANSPOSE p_transa[GROUP_SIZE] = {cblas_a_trans};
+  CBLAS_TRANSPOSE p_transb[GROUP_SIZE] = {cblas_b_trans};
+
+  std::vector<const float*> pp_A;
+  std::vector<const float*> pp_B;
+  std::vector<float*> pp_C;
+  pp_A.reserve(batch_count);
+  pp_B.reserve(batch_count);
+  pp_C.reserve(batch_count);
+
+  auto m_k = m * k;
+  auto k_n = k * n;
+  auto m_n = m * n;
+
+  for (int i = 0; i < batch_count; i++) {
+    pp_A[i] = A + i * m_k;
+    pp_B[i] = B + i * k_n;
+    pp_C[i] = C + i * m_n;
+  }
+
+  cblas_sgemm_batch(CblasColMajor, p_transa, p_transb,
+                    p_m, p_n, p_k, p_alpha, pp_A.data(), p_lda, pp_B.data(),
+                    p_ldb, p_beta, pp_C.data(), p_ldc, GROUP_SIZE, p_group_sizeb);
+#else
+    for (int i = 0; i < batch_count; ++i) {
+      gemm(stream, transa, transb, m, n, k, alpha,
+           A + i * m * k, lda, B + i * k * n, ldb,
+           beta, C + i * m * n, ldc);
+    }
+#endif
+  }
+  inline static void gemv(Stream<cpu> *stream,
+                          bool trans, int m, int n,
+                          float alpha, const float *A, int lda,
+                          const float *X, int incX,
+                          float beta, float *Y, int incY) {
+    cblas_sgemv(CblasColMajor, GetT(trans), m, n, alpha,
+                A, lda, X, incX, beta, Y, incY);
+  }
+  inline static void batched_gemv(Stream<cpu> *stream,
+                                  bool trans, int m, int n,
+                                  float alpha, const float *A, int lda,
+                                  const float *X, int incX,
+                                  float beta, float *Y, int incY, int batch_count) {
+    for (int i = 0; i < batch_count; ++i) {
+      gemv(stream, trans, m, n, alpha, A + i * m * n, lda,
+           X + i * (trans ? m : n) * incX, incX,
+           beta, Y + i * (trans ? n : m) * incY, incY);
+    }
+  }
+  inline static void ger(Stream<cpu> *stream,
+                         int m, int n, float alpha,
+                         const float *X, int incX,
+                         const float *Y, int incY, float *A, int lda) {
+    cblas_sger(CblasColMajor, m, n, alpha, X, incX, Y, incY, A, lda);
+  }
+  inline static void batched_ger(Stream<cpu> *stream,
+                         int m, int n, float alpha,
+                         const float *X, int incX,
+                         const float *Y, int incY, float *A, int lda, int batch_count) {
+    for (int i = 0; i < batch_count; ++i) {
+      ger(stream, m, n, alpha, X + i * m * incX, incX, Y + i * n * incY, incY,
+          A + i * lda * n, lda);
+    }
+  }
+  inline static void dot(Stream<cpu> *stream,
+                         int n,
+                         const float* X, int incX,
+                         const float* Y, int incY,
+                         float* ret) {
+    *ret = cblas_sdot(n, X, incX, Y, incY);
+  }
+};
+
+template<>
+struct BLASEngine<cpu, double> {
+  inline static CBLAS_TRANSPOSE GetT(bool t) {
+    return t ? CblasTrans : CblasNoTrans;
+  }
+  inline static void SetStream(Stream<cpu> *stream) {
+  }
+  inline static void gemm(Stream<cpu> *stream,
+                          bool transa, bool transb,
+                          int m, int n, int k, double alpha,
+                          const double *A, int lda, const double *B, int ldb,
+                          double beta, double *C, int ldc) {
+    cblas_dgemm(CblasColMajor, GetT(transa), GetT(transb),
+                m, n, k, alpha, A, lda, B, ldb, beta, C, ldc);
+  }
+  inline static void batched_gemm(Stream<cpu> *stream,
+                                  bool transa, bool transb,
+                                  int m, int n, int k, double alpha,
+                                  const double *A, int lda, const double *B, int ldb,
+                                  double beta, double *C, int ldc, int batch_count,
+                                  double **workspace) {
+#if (MSHADOW_USE_MKL && INTEL_MKL_VERSION >= 20160000)
+  // since same m/n/k is used for all single gemms, so we put all gemms into one group
+  const int GROUP_SIZE = 1;
+  MKL_INT p_m[GROUP_SIZE] = {m};
+  MKL_INT p_n[GROUP_SIZE] = {n};
+  MKL_INT p_k[GROUP_SIZE] = {k};
+  MKL_INT p_lda[GROUP_SIZE] = {lda};
+  MKL_INT p_ldb[GROUP_SIZE] = {ldb};
+  MKL_INT p_ldc[GROUP_SIZE] = {ldc};
+
+  double p_alpha[GROUP_SIZE] = {alpha};
+  double p_beta[GROUP_SIZE] = {beta};
+
+  CBLAS_TRANSPOSE cblas_a_trans = GetT(transa);
+  CBLAS_TRANSPOSE cblas_b_trans = GetT(transb);
+
+  MKL_INT p_group_sizeb[GROUP_SIZE] = {batch_count};
+  CBLAS_TRANSPOSE p_transa[GROUP_SIZE] = {cblas_a_trans};
+  CBLAS_TRANSPOSE p_transb[GROUP_SIZE] = {cblas_b_trans};
+
+  std::vector<const double*> pp_A;
+  std::vector<const double*> pp_B;
+  std::vector<double*> pp_C;
+  pp_A.reserve(batch_count);
+  pp_B.reserve(batch_count);
+  pp_C.reserve(batch_count);
+
+  auto m_k = m * k;
+  auto k_n = k * n;
+  auto m_n = m * n;
+
+  for (int i = 0; i < batch_count; i++) {
+    pp_A[i] = A + i * m_k;
+    pp_B[i] = B + i * k_n;
+    pp_C[i] = C + i * m_n;
+  }
+
+  cblas_dgemm_batch(CblasColMajor, p_transa, p_transb,
+                    p_m, p_n, p_k, p_alpha, pp_A.data(), p_lda, pp_B.data(),
+                    p_ldb, p_beta, pp_C.data(), p_ldc, GROUP_SIZE, p_group_sizeb);
+#else
+    for (int i = 0; i < batch_count; ++i) {
+      gemm(stream, transa, transb, m, n, k, alpha,
+           A + i * m * k, lda, B + i * k * n, ldb,
+           beta, C + i * m * n, ldc);
+    }
+#endif
+  }
+  inline static void gemv(Stream<cpu> *stream,
+                          bool trans, int m, int n, double alpha,
+                          const double *A, int lda,
+                          const double *X, int incX,
+                          double beta, double *Y, int incY) {
+    cblas_dgemv(CblasColMajor, GetT(trans), m, n, alpha,
+                A, lda, X, incX, beta, Y, incY);
+  }
+  inline static void batched_gemv(Stream<cpu> *stream,
+                                  bool trans, int m, int n,
+                                  double alpha, const double *A, int lda,
+                                  const double *X, int incX,
+                                  double beta, double *Y, int incY, int batch_count) {
+    for (int i = 0; i < batch_count; ++i) {
+      gemv(stream, trans, m, n, alpha, A + i * m * n, lda,
+           X + i * (trans ? m : n) * incX, incX,
+           beta, Y + i * (trans ? n : m) * incY, incY);
+    }
+  }
+  inline static void ger(Stream<cpu> *stream,
+                         int m, int n, double alpha,
+                         const double *X, int incX,
+                         const double *Y, int incY, double *A, int lda) {
+    cblas_dger(CblasColMajor, m, n, alpha, X, incX, Y, incY, A, lda);
+  }
+  inline static void batched_ger(Stream<cpu> *stream,
+                         int m, int n, double alpha,
+                         const double *X, int incX,
+                         const double *Y, int incY, double *A, int lda, int batch_count) {
+    for (int i = 0; i < batch_count; ++i) {
+      ger(stream, m, n, alpha, X + i * m * incX, incX, Y + i * n * incY, incY,
+          A + i * lda * n, lda);
+    }
+  }
+  inline static void dot(Stream<cpu> *stream,
+                         int n,
+                         const double* X, int incX,
+                         const double* Y, int incY,
+                         double* ret) {
+    *ret = cblas_ddot(n, X, incX, Y, incY);
+  }
+};
+#endif  // MSHADOW_USE_CBLAS || MSHADOW_USE_MKL || MSHADOW_STAND_ALONE
+// CuBLAS redirect code
+#if MSHADOW_USE_CUDA
+// All CuBLAS goes to here, use legacy API: not threadsafe
+template<>
+struct BLASEngine<gpu, half::half_t> {
+  inline static cublasOperation_t GetT(bool t) {
+    return t ? CUBLAS_OP_T : CUBLAS_OP_N;
+  }
+  inline static void SetStream(Stream<gpu> *stream) {
+    cublasStatus_t err = cublasSetStream(Stream<gpu>::GetBlasHandle(stream),
+                    Stream<gpu>::GetStream(stream));
+    CHECK_EQ(err, CUBLAS_STATUS_SUCCESS) << "Cublas set stream fail";
+  }
+  inline static void gemm(Stream<gpu> *stream,
+                          bool transa, bool transb,
+                          int m, int n, int k, half::half_t alpha,
+                          const half::half_t *A, int lda,
+                          const half::half_t *B, int ldb, half::half_t beta,
+                          half::half_t *C, int ldc) {
+#if defined(CUDA_VERSION) && CUDA_VERSION >= 7050
+  // Always use pseudo-fp16: fp32 compute with fp16 I/O.
+  float alpha_f = float(alpha);  // NOLINT(*)
+  float beta_f = float(beta);  // NOLINT(*)
+  #if CUDA_VERSION >= 8000
+    cublasStatus_t err = cublasSgemmEx(Stream<gpu>::GetBlasHandle(stream),
+                                       GetT(transa), GetT(transb), m, n, k, &alpha_f,
+                                       A, CUDA_R_16F, lda, B, CUDA_R_16F,
+                                       ldb, &beta_f, C, CUDA_R_16F, ldc);
+    CHECK_EQ(err, CUBLAS_STATUS_SUCCESS) << "Cublas SgemmEx fail";
+  #else
+    cublasStatus_t err = cublasSgemmEx(Stream<gpu>::GetBlasHandle(stream),
+                                       GetT(transa), GetT(transb), m, n, k, &alpha_f,
+                                       A, CUBLAS_DATA_HALF, lda, B, CUBLAS_DATA_HALF,
+                                       ldb, &beta_f, C, CUBLAS_DATA_HALF, ldc);
+    CHECK_EQ(err, CUBLAS_STATUS_SUCCESS) << "Cublas SgemmEx fail";
+  #endif  // CUDA_VERSION >= 8000
+#else
+    LOG(FATAL) << "Require CUDA version >= 7.5!";
+#endif  // defined(CUDA_VERSION) && CUDA_VERSION >= 7050
+  }
+  inline static void batched_gemm(Stream<gpu> *stream,
+                                  bool transa, bool transb,
+                                  int m, int n, int k, half::half_t alpha,
+                                  const half::half_t *A, int lda, const half::half_t *B, int ldb,
+                                  half::half_t beta, half::half_t *C, int ldc, int batch_count,
+                                  half::half_t **workspace) {
+#if defined(__CUDACC__) && CUDA_VERSION >= 9000
+    int major = stream->prop.major;
+    int minor = stream->prop.minor;
+    // fp16 is not supported before ARCH 53
+    if ((major > 5) || (major == 5 && minor >= 3)) {
+      const __half* A_h = reinterpret_cast<const __half*>(A);
+      const __half* B_h = reinterpret_cast<const __half*>(B);
+      __half* alpha_h = reinterpret_cast<__half*>(&alpha);
+      __half* beta_h = reinterpret_cast<__half*>(&beta);
+      __half* C_h = reinterpret_cast<__half*>(C);
+      cublasStatus_t err = cublasHgemmStridedBatched(Stream<gpu>::GetBlasHandle(stream),
+        GetT(transa), GetT(transb), m, n, k, alpha_h,
+        A_h, lda, m * k,
+        B_h, ldb, k * n,
+        beta_h, C_h, ldc, m * n,
+        batch_count);
+      CHECK_EQ(err, CUBLAS_STATUS_SUCCESS) << "Cublas: HgemmStridedBatched fail";
+      return;
+    }
+#endif
+    for (int i = 0; i < batch_count; ++i) {
+      gemm(stream, transa, transb, m, n, k, alpha,
+           A + i * m * k, lda, B + i * k * n, ldb,
+           beta, C + i * m * n, ldc);
+    }
+  }
+  inline static void gemv(Stream<gpu> *stream,
+                          bool trans, int m, int n, half::half_t alpha,
+                          const half::half_t *A, int lda,
+                          const half::half_t *X, int incX, half::half_t beta,
+                          half::half_t *Y, int incY) {
+    LOG(FATAL) << "Not implmented!";
+  }
+  inline static void batched_gemv(Stream<gpu> *stream,
+                                  bool trans, int m, int n,
+                                  half::half_t alpha, const half::half_t *A, int lda,
+                                  const half::half_t *X, int incX,
+                                  half::half_t beta, half::half_t *Y, int incY, int batch_count) {
+    LOG(FATAL) << "Not implmented!";
+  }
+  inline static void ger(Stream<gpu> *stream,
+                         int m, int n, half::half_t alpha,
+                         const half::half_t *X, int incX,
+                         const half::half_t *Y, int incY, half::half_t *A, int lda) {
+    LOG(FATAL) << "Not implmented!";
+  }
+  inline static void batched_ger(Stream<gpu> *stream,
+                         int m, int n, half::half_t alpha,
+                         const half::half_t *X, int incX, const half::half_t *Y, int incY,
+                         half::half_t *A, int lda, int batch_count) {
+    LOG(FATAL) << "Not implmented!";
+  }
+  inline static void dot(Stream<gpu> *stream,
+                         int n,
+                         const half::half_t* X, int incX,
+                         const half::half_t* Y, int incY,
+                         half::half_t *ret) {
+    LOG(FATAL) << "Not implmented!";
+  }
+};
+
+template<>
+struct BLASEngine<gpu, float> {
+  inline static cublasOperation_t GetT(bool t) {
+    return t ? CUBLAS_OP_T : CUBLAS_OP_N;
+  }
+  inline static void SetStream(Stream<gpu> *stream) {
+    cublasStatus_t err = cublasSetStream(Stream<gpu>::GetBlasHandle(stream),
+                    Stream<gpu>::GetStream(stream));
+    CHECK_EQ(err, CUBLAS_STATUS_SUCCESS) << "Cublas: set stream fail";
+  }
+  inline static void gemm(Stream<gpu> *stream,
+                          bool transa, bool transb,
+                          int m, int n, int k, float alpha,
+                          const float *A, int lda,
+                          const float *B, int ldb, float beta,
+                          float *C, int ldc) {
+    cublasStatus_t err = cublasSgemm(Stream<gpu>::GetBlasHandle(stream),
+                GetT(transa), GetT(transb), m, n, k, &alpha,
+                A, lda, B, ldb, &beta, C, ldc);
+    CHECK_EQ(err, CUBLAS_STATUS_SUCCESS) << "Cublas: Sgemm fail";
+  }
+  inline static void batched_gemm(Stream<gpu> *stream,
+                                  bool transa, bool transb,
+                                  int m, int n, int k, float alpha,
+                                  const float *A, int lda, const float *B, int ldb,
+                                  float beta, float *C, int ldc, int batch_count,
+                                  float **workspace) {
+#if defined(__CUDACC__) && CUDA_VERSION >= 4010 && CUDA_VERSION < 8000
+    // Cast DType* to DType** using workspace as a buffer
+    bool alloc_workspace = false;
+    if (workspace == NULL) {
+      // Allocate the workspace if it's NULL.
+      // TODO(sxjscience) Try to move the allocation inside Tensor, which is thread-safe.
+      cudaMalloc(reinterpret_cast<void**>(&workspace), 3 * batch_count * sizeof(float*));
+      alloc_workspace = true;
+    }
+    GetBatchedView(workspace, const_cast<float*>(A), batch_count, m * k, stream);
+    GetBatchedView(workspace + batch_count,
+                   const_cast<float*>(B), batch_count, k * n, stream);
+    GetBatchedView(workspace + 2 * batch_count, C, batch_count, m * n, stream);
+    cublasStatus_t err = cublasSgemmBatched(Stream<gpu>::GetBlasHandle(stream),
+                                            GetT(transa), GetT(transb), m, n, k, &alpha,
+                                            (const float**)workspace, lda,
+                                            (const float**)(workspace + batch_count), ldb,
+                                            &beta, workspace + 2 * batch_count, ldc, batch_count);
+    CHECK_EQ(err, CUBLAS_STATUS_SUCCESS) << "Cublas: SgemmBatched fail";
+    if (alloc_workspace) {
+      cudaFree(workspace);
+    }
+#elif defined(__CUDACC__) && CUDA_VERSION >= 8000
+    cublasStatus_t err = cublasSgemmStridedBatched(Stream<gpu>::GetBlasHandle(stream),
+      GetT(transa), GetT(transb), m, n, k, &alpha,
+      A, lda, m * k,
+      B, ldb, k * n,
+      &beta, C, ldc, m * n,
+      batch_count);
+    CHECK_EQ(err, CUBLAS_STATUS_SUCCESS) << "Cublas: SgemmStridedBatched fail";
+#else
+    for (int i = 0; i < batch_count; ++i) {
+      gemm(stream, transa, transb, m, n, k, alpha,
+           A + i * m * k, lda, B + i * k * n, ldb,
+           beta, C + i * m * n, ldc);
+    }
+#endif  // defined(__CUDACC__) && CUDA_VERSION >= 4010
+  }
+  inline static void gemv(Stream<gpu> *stream,
+                          bool trans, int m, int n, float alpha,
+                          const float *A, int lda,
+                          const float *X, int incX, float beta,
+                          float *Y, int incY) {
+    cublasStatus_t err = cublasSgemv(Stream<gpu>::GetBlasHandle(stream),
+                GetT(trans), m, n, &alpha, A, lda, X, incX, &beta, Y, incY);
+    CHECK_EQ(err, CUBLAS_STATUS_SUCCESS) << "Cublas: Sgemv fail";
+  }
+  inline static void batched_gemv(Stream<gpu> *stream,
+                                  bool trans, int m, int n,
+                                  float alpha, const float *A, int lda,
+                                  const float *X, int incX,
+                                  float beta, float *Y, int incY, int batch_count) {
+    for (int i = 0; i < batch_count; ++i) {
+      gemv(stream, trans, m, n, alpha, A + i * m * n, lda,
+           X + i * (trans ? m : n) * incX, incX,
+           beta, Y + i * (trans ? n : m) * incY, incY);
+    }
+  }
+  inline static void ger(Stream<gpu> *stream,
+                         int m, int n, float alpha,
+                         const float *X, int incX,
+                         const float *Y, int incY, float *A, int lda) {
+    cublasStatus_t err = cublasSger(Stream<gpu>::GetBlasHandle(stream),
+                                    m, n, &alpha, X, incX, Y, incY, A, lda);
+    CHECK_EQ(err, CUBLAS_STATUS_SUCCESS) << "Cublas: Sger fail";
+  }
+  inline static void batched_ger(Stream<gpu> *stream,
+                         int m, int n, float alpha,
+                         const float *X, int incX,
+                         const float *Y, int incY, float *A, int lda, int batch_count) {
+    for (int i = 0; i < batch_count; ++i) {
+      ger(stream, m, n, alpha, X + i * m * incX, incX, Y + i * n * incY, incY,
+          A + i * lda * n, lda);
+    }
+  }
+  inline static void dot(Stream<gpu> *stream,
+                         int n,
+                         const float* X, int incX,
+                         const float* Y, int incY,
+                         float *ret) {
+    cublasSetPointerMode(Stream<gpu>::GetBlasHandle(stream),
+                         CUBLAS_POINTER_MODE_DEVICE);
+    cublasStatus_t err = cublasSdot(Stream<gpu>::GetBlasHandle(stream),
+                                    n, X, incX, Y, incY, ret);
+    CHECK_EQ(err, CUBLAS_STATUS_SUCCESS) << "Cublas: Dot fail";
+    cublasSetPointerMode(Stream<gpu>::GetBlasHandle(stream),
+                         CUBLAS_POINTER_MODE_HOST);
+  }
+};
+
+template<>
+struct BLASEngine<gpu, double> {
+  inline static cublasOperation_t GetT(bool t) {
+    return t ? CUBLAS_OP_T : CUBLAS_OP_N;
+  }
+  inline static void SetStream(Stream<gpu> *stream) {
+    cublasStatus_t err = cublasSetStream(Stream<gpu>::GetBlasHandle(stream),
+                    Stream<gpu>::GetStream(stream));
+    CHECK_EQ(err, CUBLAS_STATUS_SUCCESS) << "Cublas: set stream fail";
+  }
+  inline static void gemm(Stream<gpu> *stream,
+                          bool transa, bool transb,
+                          int m, int n, int k, double alpha,
+                          const double *A, int lda,
+                          const double *B, int ldb,
+                          double beta, double *C, int ldc) {
+    cublasStatus_t err = cublasDgemm(Stream<gpu>::GetBlasHandle(stream),
+                GetT(transa), GetT(transb), m, n, k, &alpha,
+                A, lda, B, ldb, &beta, C, ldc);
+    CHECK_EQ(err, CUBLAS_STATUS_SUCCESS) << "Cublas: Dgemm fail";
+  }
+  inline static void batched_gemm(Stream<gpu> *stream,
+                                  bool transa, bool transb,
+                                  int m, int n, int k, double alpha,
+                                  const double *A, int lda, const double *B, int ldb,
+                                  double beta, double *C, int ldc, int batch_count,
+                                  double **workspace) {
+#if defined(__CUDACC__) && CUDA_VERSION >= 4010 && CUDA_VERSION < 8000
+    // Cast DType* to DType** using workspace as a buffer
+    bool alloc_workspace = false;
+    if (workspace == NULL) {
+      // Allocate the workspace if it's NULL.
+      // TODO(sxjscience) Try to move the allocation inside Tensor, which is thread-safe.
+      cudaMalloc(reinterpret_cast<void**>(&workspace), 3 * batch_count * sizeof(double*));
+      alloc_workspace = true;
+    }
+    GetBatchedView(workspace, const_cast<double*>(A), batch_count, m * k, stream);
+    GetBatchedView(workspace + batch_count,
+                   const_cast<double*>(B), batch_count, k * n, stream);
+    GetBatchedView(workspace + 2 * batch_count, C, batch_count, m * n, stream);
+    cublasStatus_t err = cublasDgemmBatched(Stream<gpu>::GetBlasHandle(stream),
+                                            GetT(transa), GetT(transb), m, n, k, &alpha,
+                                            (const double**)workspace, lda,
+                                            (const double**)(workspace + batch_count), ldb,
+                                            &beta, workspace + 2 * batch_count, ldc, batch_count);
+    CHECK_EQ(err, CUBLAS_STATUS_SUCCESS) << "Cublas: DgemmBatched fail";
+    if (alloc_workspace) {
+      cudaFree(workspace);
+    }
+#elif defined(__CUDACC__) && CUDA_VERSION >= 8000
+    cublasStatus_t err = cublasDgemmStridedBatched(Stream<gpu>::GetBlasHandle(stream),
+      GetT(transa), GetT(transb), m, n, k, &alpha,
+      A, lda, m * k,
+      B, ldb, k * n,
+      &beta, C, ldc, m * n,
+      batch_count);
+    CHECK_EQ(err, CUBLAS_STATUS_SUCCESS) << "Cublas: DgemmStridedBatched fail";
+#else
+    for (int i = 0; i < batch_count; ++i) {
+      gemm(stream, transa, transb, m, n, k, alpha,
+           A + i * m * k, lda, B + i * k * n, ldb,
+           beta, C + i * m * n, ldc);
+    }
+#endif  // defined(__CUDACC__) && CUDA_VERSION >= 4010
+  }
+  inline static void gemv(Stream<gpu> *stream,
+                          bool trans, int m, int n, double alpha,
+                          const double *A, int lda,
+                          const double *X, int incX,
+                          double beta, double *Y, int incY) {
+    cublasStatus_t err = cublasDgemv(Stream<gpu>::GetBlasHandle(stream),
+                GetT(trans), m, n, &alpha, A, lda, X, incX, &beta, Y, incY);
+    CHECK_EQ(err, CUBLAS_STATUS_SUCCESS) << "Cublas: Dgemv fail";
+  }
+  inline static void batched_gemv(Stream<gpu> *stream,
+                                  bool trans, int m, int n,
+                                  double alpha, const double *A, int lda,
+                                  const double *X, int incX,
+                                  double beta, double *Y, int incY, int batch_count) {
+    for (int i = 0; i < batch_count; ++i) {
+      gemv(stream, trans, m, n, alpha, A + i * m * n, lda,
+           X + i * (trans ? m : n) * incX, incX,
+           beta, Y + i * (trans ? n : m) * incY, incY);
+    }
+  }
+  inline static void ger(Stream<gpu> *stream,
+                         int m, int n, double alpha,
+                         const double *X, int incX,
+                         const double *Y, int incY, double *A, int lda) {
+    cublasStatus_t err = cublasDger(Stream<gpu>::GetBlasHandle(stream),
+                                    m, n, &alpha, X, incX, Y, incY, A, lda);
+    CHECK_EQ(err, CUBLAS_STATUS_SUCCESS) << "Cublas: Dger fail";
+  }
+  inline static void batched_ger(Stream<gpu> *stream,
+                         int m, int n, double alpha,
+                         const double *X, int incX,
+                         const double *Y, int incY, double *A, int lda, int batch_count) {
+    for (int i = 0; i < batch_count; ++i) {
+      ger(stream, m, n, alpha, X + i * m * incX, incX, Y + i * n * incY, incY,
+          A + i * lda * n, lda);
+    }
+  }
+  inline static void dot(Stream<gpu> *stream,
+                         int n,
+                         const double* X, int incX,
+                         const double* Y, int incY,
+                         double *ret) {
+    cublasSetPointerMode(Stream<gpu>::GetBlasHandle(stream),
+                         CUBLAS_POINTER_MODE_DEVICE);
+    cublasStatus_t err = cublasDdot(Stream<gpu>::GetBlasHandle(stream),
+                                    n, X, incX, Y, incY, ret);
+    CHECK_EQ(err, CUBLAS_STATUS_SUCCESS) << "Cublas: Dot fail";
+    cublasSetPointerMode(Stream<gpu>::GetBlasHandle(stream),
+                         CUBLAS_POINTER_MODE_HOST);
+  }
+};
+#endif  // MSHADOW_USE_CUDA
+// helper function to decide which shape we are in
+inline Shape<2> GetShape(const Shape<2> &shape, bool transpose) {
+  return transpose ? Shape2(shape[1], shape[0]) : shape;
+}
+// dst = dot(lhs[.T], rhs[.T])
+template<typename SV, typename xpu,
+         bool transpose_left, bool transpose_right, typename DType>
+struct DotEngine<SV, xpu, 2, 2, 2, transpose_left, transpose_right, DType> {
+  inline static void Eval(Tensor<xpu, 2, DType> *p_dst,
+                          const Tensor<xpu, 2, DType> &lhs,
+                          const Tensor<xpu, 2, DType> &rhs,
+                          DType scale) {
+    Tensor<xpu, 2, DType> &dst = *p_dst;
+#if MSHADOW_STAND_ALONE
+    if (xpu::kDevMask == cpu::kDevMask && scale == 1.0f) {
+      if (!transpose_left && !transpose_right) {
+        dst = expr::implicit_dot(lhs, rhs); return;
+      } else if (!transpose_left && transpose_right) {
+        dst = expr::implicit_dot(lhs, rhs.T()); return;
+      } else if (transpose_left && !transpose_right) {
+        dst = expr::implicit_dot(lhs.T(), rhs); return;
+      }
+    }
+#endif
+    // set kernel stream
+    // if there is no stream, crush
+    BLASEngine<xpu, DType>::SetStream(dst.stream_);
+    Shape<2> sleft = GetShape(lhs.shape_, transpose_left);
+    Shape<2> sright = GetShape(rhs.shape_, transpose_right);
+    CHECK(dst.size(0) == sleft[0] && dst.size(1) == sright[1] && sleft[1] == sright[0])
+      << "dot-gemm: matrix shape mismatch";
+    // use column major argument to compatible with most BLAS
+    BLASEngine<xpu, DType>::gemm
+        (dst.stream_,
+         transpose_right , transpose_left,
+         transpose_right ? rhs.size(0) : rhs.size(1),
+         transpose_left  ? lhs.size(1) : lhs.size(0),
+         transpose_right ? rhs.size(1) : rhs.size(0),
+         DType(scale * SV::AlphaBLAS()),
+         rhs.dptr_, rhs.stride_,
+         lhs.dptr_, lhs.stride_,
+         DType(SV::BetaBLAS()),
+         dst.dptr_, dst.stride_);
+  }
+};
+template<typename SV, typename xpu, bool transpose_right, typename DType>
+struct DotEngine<SV, xpu, 1, 1, 2, false, transpose_right, DType> {
+  inline static void Eval(Tensor<xpu, 1, DType> *p_dst,
+                          const Tensor<xpu, 1, DType> &lhs,
+                          const Tensor<xpu, 2, DType> &rhs,
+                          DType scale) {
+    Tensor<xpu, 1, DType> &dst = *p_dst;
+    // set kernel stream
+    // if there is no stream, crush
+    BLASEngine<xpu, DType>::SetStream(dst.stream_);
+    Shape<2> sright = GetShape(rhs.shape_, transpose_right);
+    CHECK(dst.size(0) == sright[1] && lhs.size(0) == sright[0])
+      << "dot-gemv: matrix shape mismatch"
+      << "dst: " << dst.shape_ << "\n"
+      << "lhs: " << lhs.shape_ << "\n"
+      << "rhs: " << sright << "\n";
+    BLASEngine<xpu, DType>::gemv
+        (dst.stream_,
+         transpose_right,
+         rhs.size(1), rhs.size(0), scale * SV::AlphaBLAS(),
+         rhs.dptr_, rhs.stride_,
+         lhs.dptr_, 1, SV::BetaBLAS(),
+         dst.dptr_, 1);
+  }
+};
+template<typename SV, typename xpu, typename DType>
+struct DotEngine<SV, xpu, 2, 1, 1, true, false, DType> {
+  inline static void Eval(Tensor<xpu, 2, DType> *p_dst,
+                          const Tensor<xpu, 1, DType> &lhs,
+                          const Tensor<xpu, 1, DType> &rhs,
+                          DType scale) {
+    Tensor<xpu, 2, DType> &dst = *p_dst;
+    // set kernel stream
+    // if there is no stream, crush
+    BLASEngine<xpu, DType>::SetStream(dst.stream_);
+    CHECK(dst.size(0) == lhs.size(0) && dst.size(1) == rhs.size(0))
+      << "dot-ger: matrix shape mismatch"
+      << "dst: " << dst.shape_ << "\n"
+      << "lhs: " << lhs.shape_ << "\n"
+      << "rhs: " << rhs.shape_;
+    if (SV::BetaBLAS() == 0.0f) {
+      BLASEngine<xpu, DType>::ger
+          (dst.stream_, rhs.size(0), lhs.size(0), scale * SV::AlphaBLAS(),
+           rhs.dptr_, 1, lhs.dptr_, 1, dst.dptr_, dst.stride_);
+    } else {
+      DotEngine<SV, xpu, 2, 2, 2, true, false,
+                DType>::Eval(p_dst, lhs.FlatTo2D(), rhs.FlatTo2D(), scale);
+    }
+  }
+};
+}  // namespace expr
+}  // namespace mshadow
+#endif  // MSHADOW_DOT_ENGINE_INL_H_
diff --git a/3rdparty/mshadow/mshadow/expr_engine-inl.h b/3rdparty/mshadow/mshadow/expr_engine-inl.h
new file mode 100644
index 000000000000..6421ebcff812
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/expr_engine-inl.h
@@ -0,0 +1,482 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file expr_engine-inl.h
+ * \brief definitions of how expressions should be evaluated
+ * \author Tianqi Chen, Bing Xu
+ */
+#ifndef MSHADOW_EXPR_ENGINE_INL_H_
+#define MSHADOW_EXPR_ENGINE_INL_H_
+#include <utility>
+#include <algorithm>
+#include "./logging.h"
+#include "./expression.h"
+#include "./tensor.h"
+
+namespace mshadow {
+namespace expr {
+/*!
+ * \brief a general class that allows extension that makes tensors of some shape
+ * \tparam SubType type of subclass
+ * \tparam SrcExp source expression of the MakeTensorExp, the source of operation
+ * \tparam dim dimension of the expression
+ * \tparam DType the type of elements
+ */
+template<typename SubType, typename SrcExp, int dim, typename DType>
+struct MakeTensorExp
+    : public Exp<MakeTensorExp<SubType, SrcExp, dim, DType>,
+                 DType, type::kChainer> {
+  /*! \brief the shape of this expression */
+  Shape<dim> shape_;
+  /*! \brief true self of subtype */
+  inline const SubType& real_self(void) const{
+    return *static_cast<const SubType*>(this);
+  }
+};
+//----------------------------------------------------------------------
+// This part of code gives plan that can be used to carry out execution
+//---------------------------------------------------------------------
+// Declarations of plans
+template<typename ExpType, typename DType>
+class Plan {
+ public:
+  /*!
+   * \brief evaluate the expression at index [y][x]
+   *  to be implemented by SubType, for RValue, the return type will be DType &
+   */
+  MSHADOW_XINLINE DType Eval(index_t y, index_t x) const;
+};
+// tensor plan
+template <typename Device, int dim, typename DType>
+class Plan<Tensor<Device, dim, DType>, DType> {
+ public:
+  explicit Plan(const Tensor<Device, dim, DType> &t)
+      : dptr_(t.dptr_), stride_(t.stride_) {}
+  // for RValue, the return type should be reference
+  MSHADOW_XINLINE DType &REval(index_t y, index_t x) {
+    return dptr_[y * stride_ + x];
+  }
+  // const evaluation
+  MSHADOW_XINLINE const DType &Eval(index_t y, index_t x) const {
+    return dptr_[y * stride_ + x];
+  }
+
+ private:
+  DType  *dptr_;
+  index_t stride_;
+};
+// special evaluation case for 1d tensor, no stride
+template <typename Device, typename DType>
+class Plan<Tensor<Device, 1, DType>, DType> {
+ public:
+  explicit Plan(const Tensor<Device, 1, DType> &t) : dptr_(t.dptr_) {}
+  MSHADOW_XINLINE DType &REval(index_t y, index_t x) {
+    return dptr_[x];
+  }
+  MSHADOW_XINLINE const DType &Eval(index_t y, index_t x) const {
+    return dptr_[x];
+  }
+
+ private:
+  DType  *dptr_;
+};
+// scalar
+template<typename DType>
+class Plan<ScalarExp<DType>, DType> {
+ public:
+  explicit Plan(DType scalar) : scalar_(scalar) {}
+  MSHADOW_XINLINE DType Eval(index_t y, index_t x) const {
+    return scalar_;
+  }
+
+ private:
+  DType scalar_;
+};
+// unary expression
+template<typename DstDType, typename SrcDType,
+         typename EType, int etype>
+class Plan<TypecastExp<DstDType, SrcDType, EType, etype>, DstDType> {
+ public:
+  explicit Plan(const Plan<EType, SrcDType> &src) : src_(src) {}
+  MSHADOW_XINLINE DstDType Eval(index_t y, index_t x) const {
+    return DstDType(src_.Eval(y, x));  // NOLINT(*)
+  }
+
+ private:
+  Plan<EType, SrcDType> src_;
+};
+
+// ternary expression
+template<typename OP, typename TA, typename TB, typename TC, int etype, typename DType>
+class Plan<TernaryMapExp<OP, TA, TB, TC, DType, etype>, DType> {
+ public:
+  explicit Plan(const Plan<TA, DType> &item1, const Plan<TB, DType> &item2,
+       const Plan<TC, DType> &item3)
+      : item1_(item1), item2_(item2), item3_(item3) {}
+  MSHADOW_XINLINE DType Eval(index_t y, index_t x) const {
+    return OP::Map(item1_.Eval(y, x), item2_.Eval(y, x), item3_.Eval(y, x));
+  }
+
+ private:
+  Plan<TA, DType> item1_;
+  Plan<TB, DType> item2_;
+  Plan<TC, DType> item3_;
+};
+// binary expression
+template<typename OP, typename TA, typename TB, int etype, typename DType>
+class Plan<BinaryMapExp<OP, TA, TB, DType, etype>, DType> {
+ public:
+  explicit Plan(const Plan<TA, DType> &lhs, const Plan<TB, DType> &rhs)
+      : lhs_(lhs), rhs_(rhs) {}
+  MSHADOW_XINLINE DType Eval(index_t y, index_t x) const {
+    return OP::Map(lhs_.Eval(y, x), rhs_.Eval(y, x));
+  }
+
+ private:
+  Plan<TA, DType> lhs_;
+  Plan<TB, DType> rhs_;
+};
+// unary expression
+template<typename OP, typename TA, int etype, typename DType>
+class Plan<UnaryMapExp<OP, TA, DType, etype>, DType> {
+ public:
+  explicit Plan(const Plan<TA, DType> &src) : src_(src) {}
+  MSHADOW_XINLINE DType Eval(index_t y, index_t x) const {
+    return OP::Map(src_.Eval(y, x));
+  }
+
+ private:
+  Plan<TA, DType> src_;
+};
+// remaps map tensor expression to subtype's plan
+template<typename SubType, typename SrcExp, int dim, typename DType>
+struct Plan<MakeTensorExp<SubType, SrcExp, dim, DType>, DType> {
+ public:
+  Plan(const Plan<SubType, DType> &src) : src_(src) {}
+  MSHADOW_XINLINE DType Eval(index_t y, index_t x) const {
+    return src_.Eval(y, x);
+  }
+
+ private:
+  Plan<SubType, DType> src_;
+};
+// tranpsoe
+template<typename EType, typename DType>
+class Plan<TransposeExp<EType, DType>, DType> {
+ public:
+  explicit Plan(const Plan<EType, DType> &src) : src_(src) {}
+  MSHADOW_XINLINE DType Eval(index_t y, index_t x) const {
+    return src_.Eval(x, y);
+  }
+
+ private:
+  Plan<EType, DType> src_;
+};
+//----------------------------------------------------------------------
+// Mappings from expression to plans
+//---------------------------------------------------------------------
+template<typename OP, typename TA, typename TB, typename DType, int etype>
+inline Plan<BinaryMapExp<OP, TA, TB, DType, etype>, DType>
+MakePlan(const BinaryMapExp<OP, TA, TB, DType, etype> &e);
+
+template<typename OP, typename TA, typename TB, typename TC, typename DType, int etype>
+inline Plan<TernaryMapExp<OP, TA, TB, TC, DType, etype>, DType>
+MakePlan(const TernaryMapExp<OP, TA, TB, TC, DType, etype> &e);
+
+template<typename DType>
+inline Plan<ScalarExp<DType>, DType> MakePlan(const ScalarExp<DType> &e) {
+  return Plan<ScalarExp<DType>, DType>(e.scalar_);
+}
+
+template<typename DstDType, typename SrcDType, typename EType, int etype>
+inline Plan<TypecastExp<DstDType, SrcDType, EType, etype>, DstDType>
+MakePlan(const TypecastExp<DstDType, SrcDType, EType, etype> &e) {
+  return Plan<TypecastExp<DstDType, SrcDType, EType, etype>, DstDType>(MakePlan(e.exp));
+}
+
+template<typename T, typename DType>
+inline Plan<T, DType> MakePlan(const RValueExp<T, DType> &e) {
+  return Plan<T, DType>(e.self());
+}
+
+template<typename T, typename DType>
+inline Plan<TransposeExp<T, DType>, DType>
+MakePlan(const TransposeExp<T, DType> &e) {
+  return Plan<TransposeExp<T, DType>, DType>(MakePlan(e.exp));
+}
+
+template<typename T, typename SrcExp, int dim, typename DType>
+inline Plan<T, DType>
+MakePlan(const MakeTensorExp<T, SrcExp, dim, DType> &e) {
+  return Plan<T, DType>(e.real_self());
+}
+
+template<typename OP, typename TA, typename DType, int etype>
+inline Plan<UnaryMapExp<OP, TA, DType, etype>, DType>
+MakePlan(const UnaryMapExp<OP, TA, DType, etype> &e) {
+  return Plan<UnaryMapExp<OP, TA, DType, etype>, DType>(MakePlan(e.src_));
+}
+
+template<typename OP, typename TA, typename TB, typename DType, int etype>
+inline Plan<BinaryMapExp<OP, TA, TB, DType, etype>, DType>
+MakePlan(const BinaryMapExp<OP, TA, TB, DType, etype> &e) {
+  return Plan<BinaryMapExp<OP, TA, TB, DType, etype>,
+              DType>(MakePlan(e.lhs_), MakePlan(e.rhs_));
+}
+
+// Ternary
+template<typename OP, typename TA, typename TB, typename TC, typename DType, int etype>
+inline Plan<TernaryMapExp<OP, TA, TB, TC, DType, etype>, DType>
+MakePlan(const TernaryMapExp<OP, TA, TB, TC, DType, etype> &e) {
+  return Plan<TernaryMapExp<OP, TA, TB, TC, DType, etype>,
+              DType>(MakePlan(e.item1_), MakePlan(e.item2_), MakePlan(e.item3_));
+}
+//----------------------------------------------------------------
+// Static Type inference and Type Checking
+//----------------------------------------------------------------
+/*!
+ * \brief static type inference template,
+ *        used to get the dimension of each expression,
+ *        if ExpInfo<E>::kDim == -1, this means here are mismatch in expression
+ *        if (ExpInfo<E>::kDevMask & cpu::kDevMask) != 0, this means this expression can be assigned to cpu
+ * \tparam E expression
+ */
+template<typename E>
+struct ExpInfo {
+  static const int kDim = -1;
+  static const int kDevMask = 0;
+};
+template<typename DType>
+struct ExpInfo< ScalarExp<DType> > {
+  static const int kDim = 0;
+  static const int kDevMask = 0xffff;
+};
+template<typename E, typename DType>
+struct ExpInfo<TransposeExp<E, DType> > {
+  static const int kDim = ExpInfo<E>::kDim;
+  static const int kDevMask = ExpInfo<E>::kDevMask;
+};
+template<typename DstDType, typename SrcDType, typename EType, int etype>
+struct ExpInfo<TypecastExp<DstDType, SrcDType, EType, etype> > {
+  static const int kDim = ExpInfo<EType>::kDim;
+  static const int kDevMask = ExpInfo<EType>::kDevMask;
+};
+template<typename Device, int dim, typename DType>
+struct ExpInfo<Tensor<Device, dim, DType> > {
+  static const int kDim = dim;
+  static const int kDevMask = Device::kDevMask;
+};
+template<typename T, typename SrcExp, int dim, typename DType>
+struct ExpInfo<MakeTensorExp<T, SrcExp, dim, DType> > {
+  static const int kDimSrc = ExpInfo<SrcExp>::kDim;
+  static const int kDim = kDimSrc >= 0 ? dim : -1;
+  static const int kDevMask = ExpInfo<SrcExp>::kDevMask;
+};
+template<typename OP, typename TA, typename DType, int etype>
+struct ExpInfo<UnaryMapExp<OP, TA, DType, etype> > {
+  static const int kDim = ExpInfo<TA>::kDim;
+  static const int kDevMask = ExpInfo<TA>::kDevMask;
+};
+template<typename OP, typename TA, typename TB, typename DType, int etype>
+struct ExpInfo<BinaryMapExp<OP, TA, TB, DType, etype> > {
+  static const int kDimLhs = ExpInfo<TA>::kDim;
+  static const int kDimRhs = ExpInfo<TB>::kDim;
+  static const int kDim = (kDimLhs >= 0 && kDimRhs >= 0) ?\
+      (kDimLhs == 0 ?\
+       kDimRhs :\
+       ((kDimRhs == 0 || kDimLhs == kDimRhs) ? kDimLhs : -1)) : -1;
+  static const int kDevMask = ExpInfo<TA>::kDevMask & ExpInfo<TB>::kDevMask;
+};
+template<typename OP, typename TA, typename TB, typename TC, typename DType, int etype>
+struct ExpInfo<TernaryMapExp<OP, TA, TB, TC, DType, etype> > {
+  static const int kDimItem1 = ExpInfo<TA>::kDim;
+  static const int kDimItem2 = ExpInfo<TB>::kDim;
+  static const int kDimItem3 = ExpInfo<TC>::kDim;
+  static const int kDim = kDimItem1;
+  static const int kDevMask = ExpInfo<TA>::kDevMask & ExpInfo<TB>::kDevMask & ExpInfo<TC>::kDevMask;
+};
+
+/*! \brief template to do type check */
+template<typename Device, int dim, typename DType, typename E>
+struct TypeCheck {
+  /*! \brief dimension of expression*/
+  static const int kExpDim = ExpInfo<E>::kDim;
+  /*! \brief whether the expression device type matches */
+  static const bool kDevPass = (ExpInfo<E>::kDevMask & Device::kDevMask) != 0;
+  /*! \brief whether the expression can be mapped to expression of dim */
+  static const bool kMapPass = (kExpDim == 0 || kExpDim == dim) && kDevPass;
+  /*! \brief whether the expression can be reduced to expression of dim */
+  static const bool kRedPass = (kExpDim > dim) && kDevPass;
+};
+/*! \brief used to help static type check*/
+template<bool kPass>
+struct TypeCheckPass;
+// Todo : add static assert using C++11
+template<>
+struct TypeCheckPass<false> {};
+template<>
+struct TypeCheckPass<true> {
+  inline static void Error_All_Tensor_in_Exp_Must_Have_Same_Type(void) {}
+  inline static void Error_TypeCheck_Not_Pass_For_Reduce_Exp(void) {}
+  inline static void Error_Expression_Does_Not_Meet_Dimension_Req(void) {}
+};
+
+//----------------------------------------------------------------
+// Runtime Stream Getting
+//----------------------------------------------------------------
+template<typename Device, typename E>
+struct StreamInfo {
+  inline static Stream<Device> *Get(const E &t);
+};
+template<int dim, typename Device, typename DType>
+struct StreamInfo<Device, Tensor<Device, dim, DType> > {
+  inline static Stream<Device> *Get(const Tensor<Device, dim, DType> &t) {
+    return t.stream_;
+  }
+};
+//----------------------------------------------------------------
+// Runtime Shape Checking
+//----------------------------------------------------------------
+/*!
+ * \brief runtime shape checking template
+ *    get the shape of an expression, report error if shape mismatch
+ * \tparam dim the dimension of the shape
+ * \tparam E expression
+ */
+template<int dim, typename E>
+struct ShapeCheck {
+  inline static Shape<dim> Check(const E &t);
+};
+template<int dim, typename DType>
+struct ShapeCheck<dim, ScalarExp<DType> > {
+  inline static Shape<dim> Check(const ScalarExp<DType> &exp) {
+    // use lowest dimension to mark scalar exp
+    Shape<dim> shape;
+    for (int i = 0; i < dim; ++i) {
+      shape[i] = 0;
+    }
+    return shape;
+  }
+};
+template<int dim, typename DstDType, typename SrcDType, typename EType, int etype>
+struct ShapeCheck<dim, TypecastExp<DstDType, SrcDType, EType, etype> > {
+  inline static Shape<dim>
+  Check(const TypecastExp<DstDType, SrcDType, EType, etype> &exp) {
+    return ShapeCheck<dim, EType>::Check(exp.exp);
+  }
+};
+template<int dim, typename E, typename DType>
+struct ShapeCheck<dim, TransposeExp<E, DType> > {
+  inline static Shape<dim> Check(const TransposeExp<E, DType> &e) {
+    // swap the lowest two dimensions
+    Shape<dim> s = ShapeCheck<dim, E>::Check(e.exp);
+    std::swap(s[0], s[1]);
+    return s;
+  }
+};
+template<int dim, typename Device, typename DType>
+struct ShapeCheck<dim, Tensor<Device, dim, DType> > {
+  inline static Shape<dim> Check(const Tensor<Device, dim, DType> &t) {
+    return t.shape_;
+  }
+};
+template<int dim, typename SrcExp, typename T, typename DType>
+struct ShapeCheck<dim, MakeTensorExp<T, SrcExp, dim, DType> > {
+  inline static Shape<dim>
+  Check(const MakeTensorExp<T, SrcExp, dim, DType> &t) {
+    return t.shape_;
+  }
+};
+template<int dim, typename OP, typename TA, typename DType, int etype>
+struct ShapeCheck<dim, UnaryMapExp<OP, TA, DType, etype> > {
+  inline static Shape<dim> Check(const UnaryMapExp<OP, TA, DType, etype> &t) {
+    Shape<dim> s = ShapeCheck<dim, TA>::Check(t.src_);
+    return s;
+  }
+};
+
+template<int dim, typename OP, typename TA, typename TB,
+         typename DType, int etype>
+struct ShapeCheck<dim, BinaryMapExp<OP, TA, TB, DType, etype> > {
+  inline static Shape<dim>
+  Check(const BinaryMapExp<OP, TA, TB, DType, etype> &t) {
+    Shape<dim> shape1 = ShapeCheck<dim, TA>::Check(t.lhs_);
+    Shape<dim> shape2 = ShapeCheck<dim, TB>::Check(t.rhs_);
+    if (shape1[0] == 0) return shape2;
+    if (shape2[0] == 0) return shape1;
+    CHECK_EQ(shape1, shape2) << "BinaryMapExp: Shapes of operands are not the same, " <<
+      "Shape1=" << shape1 << ", Shape2=" << shape2;
+    return shape1;
+  }
+};
+
+template<int dim, typename OP, typename TA, typename TB, typename TC,
+         typename DType, int etype>
+struct ShapeCheck<dim, TernaryMapExp<OP, TA, TB, TC, DType, etype> > {
+  inline static Shape<dim>
+  Check(const TernaryMapExp<OP, TA, TB, TC, DType, etype> &t) {
+    Shape<dim> shape1 = ShapeCheck<dim, TA>::Check(t.item1_);
+    Shape<dim> shape2 = ShapeCheck<dim, TB>::Check(t.item2_);
+    Shape<dim> shape3 = ShapeCheck<dim, TC>::Check(t.item3_);
+    bool same = (shape1 == shape2) && (shape2 == shape3);
+    CHECK(same) << "TernaryMapExp: Shapes of operands are not the same, " <<
+      "Shape1=" << shape1 << ", Shape2=" << shape2 << ", Shape3=" << shape3;
+
+    return shape1;
+  }
+};
+}  // namespace expr
+
+}  // namespace mshadow
+// include definition of dot engine
+#include "./dot_engine-inl.h"
+
+namespace mshadow {
+namespace expr {
+/*! \brief some engine that evaluate complex expression */
+template<typename SV, typename RV, typename E, typename DType>
+struct ExpComplexEngine {
+  inline static void Eval(RV *dst, const E &exp);
+};
+/*! \brief the engine that dispatches simple operations*/
+template<typename SV, typename RV, typename DType>
+struct ExpEngine {
+  template<typename E>
+  inline static void Eval(RV *dst,
+                          const Exp<E, DType, type::kMapper> &exp) {
+    MapExp<SV>(dst, exp);
+  }
+  template<typename E>
+  inline static void Eval(RV *dst,
+                          const Exp<E, DType, type::kChainer> &exp) {
+    MapExp<SV>(dst, exp);
+  }
+  template<typename E>
+  inline static void Eval(RV *dst,
+                          const Exp<E, DType, type::kRValue> &exp) {
+    MapExp<SV>(dst, exp);
+  }
+  template<typename E>
+  inline static void Eval(RV *dst,
+                          const Exp<E, DType, type::kComplex> &exp) {
+    ExpComplexEngine<SV, RV, E, DType>::Eval(dst->ptrself(), exp.self());
+  }
+};
+template<typename SV, typename Device, int dim, int ldim,
+         int rdim, bool ltrans, bool rtrans, typename DType>
+struct ExpComplexEngine<SV,
+                        Tensor<Device, dim, DType>,
+                        DotExp<Tensor<Device, ldim, DType>,
+                               Tensor<Device, rdim, DType>,
+                               ltrans, rtrans, DType>,
+                        DType> {
+  inline static void Eval(Tensor<Device, dim, DType> *dst,
+                          const DotExp<Tensor<Device, ldim, DType>,
+                                       Tensor<Device, rdim, DType>,
+                                       ltrans, rtrans, DType> &exp) {
+    DotEngine<SV, Device, dim, ldim, rdim,
+              ltrans, rtrans, DType>::Eval(dst, exp.lhs_, exp.rhs_, exp.scale_);
+  }
+};
+}  // namespace expr
+}  // namespace mshadow
+#endif  // MSHADOW_EXPR_ENGINE_INL_H_
diff --git a/3rdparty/mshadow/mshadow/expr_scalar-inl.h b/3rdparty/mshadow/mshadow/expr_scalar-inl.h
new file mode 100644
index 000000000000..1ddaba412543
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/expr_scalar-inl.h
@@ -0,0 +1,165 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file expr_scalar-inl.h
+ * \brief definitions of operators in expression with respect to scalar
+ *  this file will be included several times, each time with MACRO MSHADOW_SCALAR_ to be different types
+ *
+ * DO NOT add pragma once or macro guard
+ * \author Tianqi Chen, Bing Xu
+ */
+// macro guard is harmful, used to pass the cpplint
+#ifndef MSHADOW_EXPR_SCALAR_INL_H_
+#define MSHADOW_EXPR_SCALAR_INL_H_
+// undef the guard so it can be included multiple times
+#undef MSHADOW_EXPR_SCALAR_INL_H_
+
+namespace mshadow {
+namespace expr {
+// DotExp
+/*! \brief dot operator def */
+template<typename TA, typename TB, bool ltrans, bool rtrans>
+inline DotExp<TA, TB, ltrans, rtrans, MSHADOW_SCALAR_>
+operator*(const DotExp<TA, TB, ltrans, rtrans, MSHADOW_SCALAR_> &lhs,
+          MSHADOW_SCALAR_ rhs) {
+  return DotExp<TA, TB, ltrans, rtrans,
+                MSHADOW_SCALAR_>(lhs.lhs_, lhs.rhs_, lhs.scale_ * rhs);
+}
+/*! \brief scale of dot operation */
+template<typename TA, typename TB, bool ltrans, bool rtrans>
+inline DotExp<TA, TB, ltrans, rtrans, MSHADOW_SCALAR_>
+operator*(MSHADOW_SCALAR_ lhs,
+          const DotExp<TA, TB, ltrans, rtrans, MSHADOW_SCALAR_> &rhs) {
+  return DotExp<TA, TB, ltrans, rtrans,
+                MSHADOW_SCALAR_>(rhs.lhs_, rhs.rhs_, rhs.scale_ * lhs);
+}
+
+/*! \brief operator overload */
+template<typename E, typename DType, typename R, int d>
+inline ReduceTo1DExp<E, DType, R, d>
+operator*(const ReduceTo1DExp<E, DType, R, d> &e, MSHADOW_SCALAR_ scale) {
+  return ReduceTo1DExp<E, DType, R, d>(e.src_, e.scale_ * scale);
+}
+/*! \brief operator overload */
+template<typename E, typename DType, typename R, int d>
+inline ReduceTo1DExp<E, DType, R, d>
+operator*(MSHADOW_SCALAR_ scale, const ReduceTo1DExp<E, DType, R, d> &e) {
+  return ReduceTo1DExp<E, DType, R, d>(e.src_, e.scale_ * scale);
+}
+
+/*! \brief operator overload for const */
+template<typename OP, typename TA, int ta>
+inline BinaryMapExp<OP, TA, ScalarExp<MSHADOW_SCALAR_>,
+                    MSHADOW_SCALAR_, (ta|type::kMapper)>
+F(const Exp<TA, MSHADOW_SCALAR_, ta> &lhs, const ScalarExp<MSHADOW_SCALAR_> &rhs) {
+  return MakeExp<OP>(lhs, rhs);
+}
+/*! \brief operator overload for const */
+template<typename OP, typename TB, int tb>
+inline BinaryMapExp<OP, ScalarExp<MSHADOW_SCALAR_>, TB,
+                    MSHADOW_SCALAR_, (tb|type::kMapper)>
+F(const ScalarExp<MSHADOW_SCALAR_> &lhs, const Exp<TB, MSHADOW_SCALAR_, tb> &rhs) {
+  return MakeExp<OP>(lhs, rhs);
+}
+/*! \brief operator overload for const */
+template<typename OP>
+inline BinaryMapExp<OP, ScalarExp<MSHADOW_SCALAR_>, ScalarExp<MSHADOW_SCALAR_>,
+                    MSHADOW_SCALAR_, (1|type::kMapper)>
+F(const ScalarExp<MSHADOW_SCALAR_> &lhs, const ScalarExp<MSHADOW_SCALAR_> &rhs) {
+  return MakeExp<OP>(lhs, rhs);
+}
+// constant operators
+/*! \brief operator overload */
+template<typename TA, int ta>
+inline BinaryMapExp<op::plus, TA, ScalarExp<MSHADOW_SCALAR_>,
+                    MSHADOW_SCALAR_, (ta|type::kMapper)>
+operator+(const Exp<TA, MSHADOW_SCALAR_, ta> &lhs,
+          const ScalarExp<MSHADOW_SCALAR_> &rhs) {
+  return MakeExp<op::plus>(lhs, rhs);
+}
+/*! \brief operator overload */
+template<typename TA, int ta>
+inline BinaryMapExp<op::minus, TA, ScalarExp<MSHADOW_SCALAR_>,
+                    MSHADOW_SCALAR_, (ta|type::kMapper)>
+operator-(const Exp<TA, MSHADOW_SCALAR_, ta> &lhs,
+          const ScalarExp<MSHADOW_SCALAR_> &rhs) {
+  return MakeExp<op::minus>(lhs, rhs);
+}
+/*! \brief operator overload */
+template<typename TA, int ta>
+inline BinaryMapExp<op::mul, TA, ScalarExp<MSHADOW_SCALAR_>,
+                    MSHADOW_SCALAR_, (ta|type::kMapper)>
+operator*(const Exp<TA, MSHADOW_SCALAR_, ta> &lhs,
+          const ScalarExp<MSHADOW_SCALAR_> &rhs) {
+  return MakeExp<op::mul>(lhs, rhs);
+}
+/*! \brief operator overload */
+template<typename TA, int ta>
+inline BinaryMapExp<op::div, TA, ScalarExp<MSHADOW_SCALAR_>,
+                    MSHADOW_SCALAR_, (ta|type::kMapper)>
+operator/(const Exp<TA, MSHADOW_SCALAR_, ta> &lhs,
+          const ScalarExp<MSHADOW_SCALAR_> &rhs) {
+  return MakeExp<op::div>(lhs, rhs);
+}
+// constant operators 2
+/*! \brief operator overload */
+template<typename TB, int tb>
+inline BinaryMapExp<op::plus, ScalarExp<MSHADOW_SCALAR_>, TB,
+                    MSHADOW_SCALAR_, (tb|type::kMapper)>
+operator+(const ScalarExp<MSHADOW_SCALAR_> &lhs,
+          const Exp<TB, MSHADOW_SCALAR_, tb> &rhs) {
+  return MakeExp<op::plus>(lhs, rhs);
+}
+/*! \brief operator overload */
+template<typename TB, int tb>
+inline BinaryMapExp<op::minus, ScalarExp<MSHADOW_SCALAR_>, TB,
+                    MSHADOW_SCALAR_, (tb|type::kMapper)>
+operator-(const ScalarExp<MSHADOW_SCALAR_> &lhs,
+          const Exp<TB, MSHADOW_SCALAR_, tb> &rhs) {
+  return MakeExp<op::minus>(lhs, rhs);
+}
+/*! \brief operator overload */
+template<typename TB, int tb>
+inline BinaryMapExp<op::mul, ScalarExp<MSHADOW_SCALAR_>, TB,
+                    MSHADOW_SCALAR_, (tb|type::kMapper)>
+operator*(const ScalarExp<MSHADOW_SCALAR_> &lhs,
+          const Exp<TB, MSHADOW_SCALAR_, tb> &rhs) {
+  return MakeExp<op::mul>(lhs, rhs);
+}
+/*! \brief operator overload */
+template<typename TB, int tb>
+inline BinaryMapExp<op::div, ScalarExp<MSHADOW_SCALAR_>, TB,
+                    MSHADOW_SCALAR_, (tb|type::kMapper)>
+operator/(const ScalarExp<MSHADOW_SCALAR_> &lhs, const Exp<TB, MSHADOW_SCALAR_, tb> &rhs) {
+  return MakeExp<op::div>(lhs, rhs);
+}
+// constant operators 3
+/*! \brief operator overload */
+inline BinaryMapExp<op::plus, ScalarExp<MSHADOW_SCALAR_>, ScalarExp<MSHADOW_SCALAR_>,
+                    MSHADOW_SCALAR_, (1|type::kMapper)>
+operator+(const ScalarExp<MSHADOW_SCALAR_> &lhs,
+          const ScalarExp<MSHADOW_SCALAR_> &rhs) {
+  return MakeExp<op::plus>(lhs, rhs);
+}
+/*! \brief operator overload */
+inline BinaryMapExp<op::minus, ScalarExp<MSHADOW_SCALAR_>, ScalarExp<MSHADOW_SCALAR_>,
+                    MSHADOW_SCALAR_, (1|type::kMapper)>
+operator-(const ScalarExp<MSHADOW_SCALAR_> &lhs,
+          const ScalarExp<MSHADOW_SCALAR_> &rhs) {
+  return MakeExp<op::minus>(lhs, rhs);
+}
+/*! \brief operator overload */
+inline BinaryMapExp<op::mul, ScalarExp<MSHADOW_SCALAR_>, ScalarExp<MSHADOW_SCALAR_>,
+                    MSHADOW_SCALAR_, (1|type::kMapper)>
+operator*(const ScalarExp<MSHADOW_SCALAR_> &lhs,
+          const ScalarExp<MSHADOW_SCALAR_> &rhs) {
+  return MakeExp<op::mul>(lhs, rhs);
+}
+/*! \brief operator overload */
+inline BinaryMapExp<op::div, ScalarExp<MSHADOW_SCALAR_>, ScalarExp<MSHADOW_SCALAR_>,
+                    MSHADOW_SCALAR_, (1|type::kMapper)>
+operator/(const ScalarExp<MSHADOW_SCALAR_> &lhs, const ScalarExp<MSHADOW_SCALAR_> &rhs) {
+  return MakeExp<op::div>(lhs, rhs);
+}
+}  // namespace expr
+}  // namespace mshadow
+#endif  // MSHADOW_EXPR_SCALAR_INL_H_
diff --git a/3rdparty/mshadow/mshadow/expression.h b/3rdparty/mshadow/mshadow/expression.h
new file mode 100644
index 000000000000..77f943165088
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/expression.h
@@ -0,0 +1,416 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file expression.h
+ * \brief definitions of abstract expressions and expressions template
+ * \author Tianqi Chen, Bing Xu
+ */
+#ifndef MSHADOW_EXPRESSION_H_
+#define MSHADOW_EXPRESSION_H_
+#include "./base.h"
+
+namespace mshadow {
+/*!
+ * \brief namespace for abstract expressions and expressions template,
+ *        have no dependency on tensor.h,
+ *        These data structure takes no charge in computations,
+ *        they are only used to define operations and represent expression in a symbolic way
+ */
+namespace expr {
+/*! \brief type of expressions */
+namespace type {
+// type expression type are defined as bitmask
+// subtype relationshop kRValue < kMapper < kPull < kComplex
+/*!
+ * \brief this expression directly correspnds to a data class,
+ *   can be used to assign data
+ */
+const int kRValue = 0;
+/*!
+ * \brief expression contains element-wise tensor operations,
+ *   map a expression to same shape
+ */
+const int kMapper = 1;
+/*!
+ * \brief expression that can be chained with other expressiones
+ *    Usually it have function Eval(i,j) defined, which pulls the result (i, j) from input
+ *    expression and output the result at certain position.
+ */
+const int kChainer = 3;
+/*! \brief othercase: e.g dot product */
+const int kComplex = 7;
+}  // namespace type
+/*!
+ * \brief expression engine that actually interprets these expressions
+ *   this is a function template that needed to be implemented for specific expressions
+ * \tparam Saver the save method
+ * \tparam RValue the type of RValue to be saved
+ * \sa namespace sv
+ */
+template<typename Saver, typename RValue, typename DType>
+struct ExpEngine;
+/*! \brief defines how expression exp can be evaluated and stored into dst */
+// template<typename EType>
+// inline static void Eval(RValue *dst, const EType &exp);
+/*!
+ * \brief base class for expression
+ * \tparam SubType inheritated class must put their type into this parameter
+ * \tparam DType the data type of each element in the expression
+ * \tparam exp_type expression type, see namespace type
+ */
+template<typename SubType, typename DType, int exp_type>
+struct Exp {
+ public:
+  /*! \return  subtype instance of current class */
+  inline const SubType& self(void) const {
+    return *static_cast<const SubType*>(this);
+  }
+  /*! \return reference of subtype instance of current class */
+  inline SubType* ptrself(void) {
+    return static_cast<SubType*>(this);
+  }
+};
+/*!
+ * \brief scalar expression
+ * \tparam DType the data type of the scalar
+ */
+template<typename DType>
+struct ScalarExp: public Exp<ScalarExp<DType>, DType, type::kMapper> {
+  /*! \brief scalar value */
+  DType scalar_;
+  /*! \brief implicit constructor, MUST NOT BE explicit */
+  ScalarExp(DType scalar) : scalar_(scalar) {}  // NOLINT(*)
+};
+/*! \brief create an scalar expression */
+template<typename DType>
+inline ScalarExp<DType> scalar(DType s) {
+  return ScalarExp<DType>(s);
+}
+/*!
+ * \brief typecast expression, cast the type of elements
+ * \tparam DstDType the target type we want to cast into
+ * \tparam SrcDType the target type we want to cast from
+ * \tparam EType the type of the source expression
+ * \tparam etype the type of expression after cast
+ */
+template<typename DstDType, typename SrcDType, typename EType, int etype>
+struct TypecastExp:
+      public Exp<TypecastExp<DstDType, SrcDType, EType, etype>,
+                 DstDType, etype> {
+  /*! \brief expression to be typecasted */
+  const EType &exp;
+  /*! \brief constructor */
+  explicit TypecastExp(const EType &e) : exp(e) {}
+};
+/*! \brief create an scalar expression */
+template<typename DstDType, typename SrcDType,
+         typename EType, int etype>
+inline TypecastExp<DstDType, SrcDType, EType, (etype|type::kMapper)>
+tcast(const Exp<EType, SrcDType, etype> &exp) {
+  return TypecastExp<DstDType, SrcDType, EType, (etype|type::kMapper)>(exp.self());
+}
+/*! \brief represent a transpose expression of a container */
+template<typename EType, typename DType>
+struct TransposeExp: public Exp<TransposeExp<EType, DType>,
+                                DType, type::kChainer> {
+  /*! \brief expression to be transposed */
+  const EType &exp;
+  /*! \brief constructor */
+  explicit TransposeExp(const EType &e) : exp(e) {}
+  /*! \brief transpose expression */
+  inline const EType &T(void) const {
+    return exp;
+  }
+};
+/*!
+ * \brief base class of all rvalues
+ * \tparam Container the actually class of data container, e.g. Tensor1D
+ * \tparam DataType the element data type of each element in the container
+ */
+template<typename Container, typename DType>
+class RValueExp: public Exp<Container, DType, type::kRValue> {
+ public:
+  /*!
+   *\brief transpose of a matrix
+   *\return transpose of current expression
+   */
+  inline const TransposeExp<Container, DType> T(void) const {
+    return TransposeExp<Container, DType>(this->self());
+  }
+  /*! \brief operator overload */
+  inline Container &operator+=(DType s) {
+    ExpEngine<sv::plusto, Container, DType>::Eval(this->ptrself(), scalar<DType>(s));
+    return *(this->ptrself());
+  }
+  /*! \brief operator overload */
+  inline Container &operator-=(DType s) {
+    ExpEngine<sv::minusto, Container, DType>::Eval(this->ptrself(), scalar<DType>(s));
+    return *(this->ptrself());
+  }
+  /*! \brief operator overload */
+  inline Container &operator*=(DType s) {
+    ExpEngine<sv::multo, Container, DType>::Eval(this->ptrself(), scalar<DType>(s));
+    return *(this->ptrself());
+  }
+  /*! \brief operator overload */
+  inline Container &operator/=(DType s) {
+    ExpEngine<sv::divto, Container, DType>::Eval(this->ptrself(), scalar<DType>(s));
+    return *(this->ptrself());
+  }
+  /*! \brief operator overload */
+  inline Container &__assign(DType s) {
+    ExpEngine<sv::saveto, Container, DType>::Eval(this->ptrself(), scalar<DType>(s));
+    return *(this->ptrself());
+  }
+  /*! \brief  we can not define container = container */
+  template<typename E, int etype>
+  inline Container &__assign(const Exp<E, DType, etype> &exp) {
+    ExpEngine<sv::saveto, Container, DType>::Eval(this->ptrself(), exp.self());
+    return *(this->ptrself());
+  }
+  /*! \brief operator overload, assign */
+  inline Container &__assign(const Exp<Container, DType, type::kRValue> &exp);
+  /*! \brief implementation of operator+= */
+  template<typename E, int etype>
+  inline Container &operator+=(const Exp<E, DType, etype> &exp) {
+    ExpEngine<sv::plusto, Container, DType>::Eval(this->ptrself(), exp.self());
+    return *(this->ptrself());
+  }
+  /*! \brief implementation of operator-= */
+  template<typename E, int etype>
+  inline Container &operator-=(const Exp<E, DType, etype> &exp) {
+    ExpEngine<sv::minusto, Container, DType>::Eval(this->ptrself(), exp.self());
+    return *(this->ptrself());
+  }
+  /*! \brief implementation of operator*= */
+  template<typename E, int etype>
+  inline Container &operator*=(const Exp<E, DType, etype> &exp) {
+    ExpEngine<sv::multo, Container, DType>::Eval(this->ptrself(), exp.self());
+    return *(this->ptrself());
+  }
+  /*! \brief implementation of operator/= */
+  template<typename E, int etype>
+  inline Container &operator/=(const Exp<E, DType, etype> &exp) {
+    ExpEngine<sv::divto, Container, DType>::Eval(this->ptrself(), exp.self());
+    return *(this->ptrself());
+  }
+};
+/*!
+ * \brief matrix multiplication expression dot(lhs[.T], rhs[.T])
+ * \tparam TA type of lhs
+ * \tparam TB type of rhs
+ * \tparam ltrans whether lhs is transposed
+ * \tparam rtrans whether rhs is transposed
+ * \tparam DType the data type of the scalar
+ */
+template<typename TA, typename TB, bool ltrans, bool rtrans, typename DType>
+struct DotExp: public Exp<DotExp<TA, TB, ltrans, rtrans, DType>,
+                          DType, type::kComplex> {
+  /*! \brief left operand */
+  const TA &lhs_;
+  /*! \brief right operand */
+  const TB &rhs_;
+  /*! \brief scale over result */
+  DType scale_;
+  /*! \brief constructor */
+  explicit DotExp(const TA &lhs, const TB &rhs, DType scale)
+      : lhs_(lhs), rhs_(rhs), scale_(scale) {}
+};
+// definition of dot expression
+/*! \brief dot operator def */
+template<typename TA, typename TB, typename DType>
+inline DotExp<TA, TB, false, false, DType>
+dot(const RValueExp<TA, DType> &lhs, const RValueExp<TB, DType> &rhs) {
+  return DotExp<TA, TB, false, false, DType>(lhs.self(), rhs.self(), DType(1.0f));
+}
+/*! \brief dot operator def */
+template<typename TA, typename TB, typename DType>
+inline DotExp<TA, TB, true, false, DType>
+dot(const TransposeExp<TA, DType> &lhs, const RValueExp<TB, DType> &rhs) {
+  return DotExp<TA, TB, true, false, DType>(lhs.exp, rhs.self(), DType(1.0f));
+}
+/*! \brief dot operator def */
+template<typename TA, typename TB, typename DType>
+inline DotExp<TA, TB, false, true, DType>
+dot(const RValueExp<TA, DType> &lhs, const TransposeExp<TB, DType> &rhs) {
+  return DotExp<TA, TB, false, true, DType>(lhs.self(), rhs.exp, DType(1.0f));
+}
+/*! \brief dot operator def */
+template<typename TA, typename TB, typename DType>
+inline DotExp<TA, TB, true, true, DType>
+dot(const TransposeExp<TA, DType> &lhs, const TransposeExp<TB, DType> &rhs) {
+  return DotExp<TA, TB, true, true, DType>(lhs.exp, rhs.exp, DType(1.0f));
+}
+/*! \brief batch_dot operator def */
+template<bool transpose_left, bool transpose_right, typename TA, typename TB, typename DType>
+inline DotExp<TA, TB, transpose_left, transpose_right, DType>
+batch_dot(const RValueExp<TA, DType> &lhs, const RValueExp<TB, DType> &rhs) {
+  return DotExp<TA, TB, transpose_left, transpose_right, DType>(
+    lhs.self(), rhs.self(), DType(1.0f));
+}
+//---------------
+// TernaryMapExp
+// --------------
+/*!
+ * \brief ternary map expression
+ * \tparam OP operator
+ * \tparam TA type of item1
+ * \tparam TB type of item2
+ * \tparam etype expression type, sa namespace::type
+ */
+template<typename OP, typename TA, typename TB, typename TC, typename DType, int etype>
+struct TernaryMapExp: public Exp<TernaryMapExp<OP, TA, TB, TC, DType, etype>,
+                                DType, etype> {
+  /*! \brief first operand */
+  const TA &item1_;
+  /*! \brief second operand */
+  const TB &item2_;
+  /*! \brief third  operand */
+  const TC &item3_;
+  /*! \brief constructor */
+  explicit TernaryMapExp(const TA &item1, const TB &item2, const TC &item3)
+      :item1_(item1), item2_(item2), item3_(item3) {}
+};
+
+/*! \brief make expression */
+template<typename OP, typename TA, typename TB, typename TC, typename DType, int ta, int tb, int tc>
+inline TernaryMapExp<OP, TA, TB, TC, DType, (ta|tb|tc|type::kMapper)>
+MakeExp(const Exp<TA, DType, ta> &item1, const Exp<TB, DType, tb> &item2,
+ const Exp<TC, DType, tc> &item3) {
+  return TernaryMapExp<OP, TA, TB, TC, DType,
+                      (ta|tb|tc|type::kMapper)>(item1.self(), item2.self(), item3.self());
+}
+/*!
+ * \brief short hand for MakeExp, usage F<op>(item1,item2,item3). create a ternary operation expression
+ * \param item1 first operand
+ * \param item2 second operand
+ * \param item3 third operand
+ * \return the result expression
+ * \tparam ternary operator
+ * \tparam TA item1 expression
+ * \tparam ta item1 expression type
+ * \tparam TB item2 expression
+ * \tparam tb item2 expression type
+ * \tparam TC item3 expression
+ * \tparam tc item3 expression type
+ * \sa mshadow::op
+ */
+
+// Ternary
+template<typename OP, typename TA, typename TB, typename TC, typename DType, int ta, int tb, int tc>
+inline TernaryMapExp<OP, TA, TB, TC, DType, (ta|tb|tc|type::kMapper)>
+F(const Exp<TA, DType, ta> &item1, const Exp<TB, DType, tb> &item2,
+ const Exp<TC, DType, tc> &item3) {
+  return MakeExp<OP>(item1, item2, item3);
+}
+//---------------
+// BinaryMapExp
+// --------------
+/*!
+ * \brief binary map expression lhs [op] rhs
+ * \tparam OP operator
+ * \tparam TA type of lhs
+ * \tparam TB type of rhs
+ * \tparam etype expression type, sa namespace::type
+ */
+template<typename OP, typename TA, typename TB, typename DType, int etype>
+struct BinaryMapExp: public Exp<BinaryMapExp<OP, TA, TB, DType, etype>,
+                                DType, etype> {
+  /*! \brief left operand */
+  const TA &lhs_;
+  /*! \brief right operand */
+  const TB &rhs_;
+  /*! \brief constructor */
+  explicit BinaryMapExp(const TA &lhs, const TB &rhs)
+      :lhs_(lhs), rhs_(rhs) {}
+};
+
+/*! \brief make expression */
+template<typename OP, typename TA, typename TB, typename DType, int ta, int tb>
+inline BinaryMapExp<OP, TA, TB, DType, (ta|tb|type::kMapper)>
+MakeExp(const Exp<TA, DType, ta> &lhs, const Exp<TB, DType, tb> &rhs) {
+  return BinaryMapExp<OP, TA, TB, DType,
+                      (ta|tb|type::kMapper)>(lhs.self(), rhs.self());
+}
+/*!
+ * \brief short hand for MakeExp, usage F<op>(lhs, rhs). create a binary operation expression
+ * \param lhs left operand
+ * \param rhs right operand
+ * \return the result expression
+ * \tparam binary operator
+ * \tparam TA lhs expression
+ * \tparam ta lhs expression type
+ * \tparam TB rhs expression
+ * \tparam tb rhs expression type
+ * \sa mshadow::op
+ */
+template<typename OP, typename TA, typename TB, typename DType, int ta, int tb>
+inline BinaryMapExp<OP, TA, TB, DType, (ta|tb|type::kMapper)>
+F(const Exp<TA, DType, ta> &lhs, const Exp<TB, DType, tb> &rhs) {
+  return MakeExp<OP>(lhs, rhs);
+}
+// operator rules
+/*! \brief operator overload */
+template<typename TA, typename TB, typename DType, int ta, int tb>
+inline BinaryMapExp<op::plus, TA, TB, DType, (ta|tb|type::kMapper)>
+operator+(const Exp<TA, DType, ta> &lhs, const Exp<TB, DType, tb> &rhs) {
+  return MakeExp<op::plus>(lhs, rhs);
+}
+/*! \brief operator overload */
+template<typename TA, typename TB, typename DType, int ta, int tb>
+inline BinaryMapExp<op::minus, TA, TB, DType, (ta|tb|type::kMapper)>
+operator-(const Exp<TA, DType, ta> &lhs, const Exp<TB, DType, tb> &rhs) {
+  return MakeExp<op::minus>(lhs, rhs);
+}
+/*! \brief operator overload */
+template<typename TA, typename TB, typename DType, int ta, int tb>
+inline BinaryMapExp<op::mul, TA, TB, DType, (ta|tb|type::kMapper)>
+operator*(const Exp<TA, DType, ta> &lhs, const Exp<TB, DType, tb> &rhs) {
+  return MakeExp<op::mul>(lhs, rhs);
+}
+/*! \brief operator overload */
+template<typename TA, typename TB, typename DType, int ta, int tb>
+inline BinaryMapExp<op::div, TA, TB, DType, (ta|tb|type::kMapper)>
+operator/(const Exp<TA, DType, ta> &lhs, const Exp<TB, DType, tb> &rhs) {
+  return MakeExp<op::div>(lhs, rhs);
+}
+//---------------
+// UnaryMapExp
+// --------------
+/*!
+ * \brief unary map expression op(src)
+ * \tparam OP operator
+ * \tparam TA type of src
+ * \tparam etype expression type, sa namespace::type
+ */
+template<typename OP, typename TA, typename DType, int etype>
+struct UnaryMapExp: public Exp<UnaryMapExp<OP, TA, DType, etype>,
+                               DType, etype> {
+  /*! \brief source expression */
+  const TA &src_;
+  /*! \brief constructor */
+  explicit UnaryMapExp(const TA &src) : src_(src) {}
+};
+
+/*! \brief make expression */
+template<typename OP, typename TA, typename DType, int ta>
+inline UnaryMapExp<OP, TA, DType, (ta|type::kMapper)>
+MakeExp(const Exp<TA, DType, ta> &src) {
+  return UnaryMapExp<OP, TA, DType, (ta|type::kMapper)>(src.self());
+}
+/*!
+ * \brief short hand for MakeExp, usage F<op>(src), create a unary operation expression
+ * \param src source expression
+ * \return the result expression
+ * \tparam operator
+ * \tparam TA source expression
+ * \tparam ta source expression type
+ * \sa mshadow::op
+ */
+template<typename OP, typename TA, typename DType, int ta>
+inline UnaryMapExp<OP, TA, DType, (ta|type::kMapper)>
+F(const Exp<TA, DType, ta> &src) {
+  return MakeExp<OP>(src);
+}
+}  // namespace expr
+}  // namespace mshadow
+#endif  // MSHADOW_EXPRESSION_H_
diff --git a/3rdparty/mshadow/mshadow/extension.h b/3rdparty/mshadow/mshadow/extension.h
new file mode 100644
index 000000000000..7af0f56f7699
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/extension.h
@@ -0,0 +1,41 @@
+/*!
+ * Copyright by Contributors
+ * \file extension.h
+ * \brief some extension of expressions,
+ *  used to support something beyond elementwise op
+ * \author Tianqi Chen, Bing Xu
+ */
+#ifndef MSHADOW_EXTENSION_H_
+#define MSHADOW_EXTENSION_H_
+#include "./expr_engine-inl.h"
+#include "./extension/broadcast.h"
+#include "./extension/unpack_patch2col.h"
+#include "./extension/pack_col2patch.h"
+#include "./extension/reshape.h"
+#include "./extension/swapaxis.h"
+#include "./extension/reduceto1d.h"
+#include "./extension/spatial_pool.h"
+#include "./extension/spatial_unpool.h"
+#include "./extension/channel_pool.h"
+#include "./extension/channel_unpool.h"
+#include "./extension/pad.h"
+#include "./extension/crop.h"
+#include "./extension/mirror.h"
+#include "./extension/concat.h"
+#include "./extension/implicit_gemm.h"
+#include "./extension/choose.h"
+#include "./extension/fill.h"
+#include "./extension/one_hot.h"
+#include "./extension/slice.h"
+#include "./extension/slice_ex.h"
+#include "./extension/take.h"
+#include "./extension/take_grad.h"
+#include "./extension/reduce_with_axis.h"
+#include "./extension/broadcast_with_axis.h"
+#include "./extension/spatial_upsampling_nearest.h"
+#include "./extension/transpose.h"
+#include "./extension/flip.h"
+#include "./extension/complex.h"
+#include "./extension/range.h"
+#include "./extension/mask.h"
+#endif  // MSHADOW_EXTENSION_H_
diff --git a/3rdparty/mshadow/mshadow/extension/broadcast.h b/3rdparty/mshadow/mshadow/extension/broadcast.h
new file mode 100644
index 000000000000..ea138ccd9e4d
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/extension/broadcast.h
@@ -0,0 +1,165 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file broadcast.h
+ * \brief support for broadcast and repmat
+ * \author Tianqi Chen
+ */
+#ifndef MSHADOW_EXTENSION_BROADCAST_H_
+#define MSHADOW_EXTENSION_BROADCAST_H_
+#include "../extension.h"
+namespace mshadow {
+namespace expr {
+/*!
+ * \brief broadcast Tensor1D into a higher dimension Tensor
+ * input: Tensor<Device,1>: ishape[0]
+ * output: Tensor<Device,dimdst> : oshape[dimcast] = ishape[0]
+ * \tparam SrcExp type of input expression
+ * \tparam DType the type of elements
+ * \tparam dimdst  target tensor dimension
+ * \tparam dimcast_m_dst  dimdst - dimcast
+ */
+template<typename SrcExp, typename DType, int dimdst, int dimdst_m_cast>
+struct Broadcast1DExp:
+      public MakeTensorExp<Broadcast1DExp<SrcExp, DType, dimdst, dimdst_m_cast>,
+                           SrcExp, dimdst, DType> {
+  /*! \brief source operand */
+  const SrcExp &src_;
+  /*! \brief constructor */
+  Broadcast1DExp(const SrcExp &src, Shape<dimdst> shape)
+      : src_(src) {
+    this->shape_ = shape;
+  }
+};
+
+/*!
+ * \brief broadcast scalar into a higher dimension Tensor
+ * input: Tensor<Device,1>: ishape = {1}
+ * output: Tensor<Device, dimdst> : oshape[dimcast] = ishape[0]
+ * \tparam SrcExp type of input expression
+ * \tparam DType the type of elements
+ * \tparam dimdst  target tensor dimension
+ */
+template<typename SrcExp, typename DType, int dimdst>
+struct BroadcastScalarExp:
+      public MakeTensorExp<BroadcastScalarExp<SrcExp, DType, dimdst>,
+                           SrcExp, dimdst, DType> {
+  /*! \brief source operand */
+  const SrcExp &src_;
+  /*! \brief constructor */
+  BroadcastScalarExp(const SrcExp &src, Shape<dimdst> shape)
+      : src_(src) {
+    this->shape_ = shape;
+  }
+};
+
+/*!
+ * \brief a expression that replicate a 1 dimension tensor in dimension dimcast
+ * \param src Tensor<Device,1>: shape[0]
+ * \param shape shape of output
+ * \return a expresion with type Tensor<Device,dimdst>
+ * \tparam dimcast target dimension where the 1D tensor will be broadcasted
+ * \tparam SrcExp type of input expression
+ * \tparam DType the type of elements
+ * \tparam dimdst dimension of destination tensor
+ * \tparam dimcast_lowest the dimension we want to cast the data into
+ */
+template<int dimcast, typename SrcExp, typename DType,
+         int etype, int dimdst>
+inline Broadcast1DExp<SrcExp, DType, dimdst, dimdst - dimcast>
+broadcast(const expr::Exp<SrcExp, DType, etype> &src, Shape<dimdst> shape) {
+  TypeCheckPass<dimcast < dimdst && ExpInfo<SrcExp>::kDim == 1>
+                ::Error_Expression_Does_Not_Meet_Dimension_Req();
+  typedef ShapeCheck<1, SrcExp> ShapeCheckDim1SrcExp;
+  CHECK_EQ(ShapeCheckDim1SrcExp::Check(src.self())[0], shape[dimcast])
+    << "broadcast, shape mismatch";
+  return Broadcast1DExp<SrcExp, DType, dimdst,
+                        dimdst - dimcast>(src.self(), shape);
+}
+
+/*!
+ * \brief a expression that replicate a scalar tensor to target dimension.
+ * \param src Tensor<Device,1>: shape[0] == 1
+ * \param shape shape of output
+ * \return a expresion with type Tensor<Device, dimdst>
+ * \tparam dimcast target dimension where the 1D tensor will be broadcasted
+ * \tparam SrcExp type of input expression
+ * \tparam DType the type of elements
+ * \tparam dimdst dimension of destination tensor
+ */
+template<typename SrcExp, typename DType, int etype, int dimdst>
+inline BroadcastScalarExp<SrcExp, DType, dimdst>
+broadcast_scalar(const expr::Exp<SrcExp, DType, etype> &src, Shape<dimdst> shape) {
+  TypeCheckPass<ExpInfo<SrcExp>::kDim == 1>
+                ::Error_Expression_Does_Not_Meet_Dimension_Req();
+  typedef ShapeCheck<1, SrcExp> ShapeCheckDim1SrcExp;
+  CHECK_EQ(ShapeCheckDim1SrcExp::Check(src.self())[0], 1U)
+      << "broadcast_scalar, source need to be scalar expression";
+  return BroadcastScalarExp<SrcExp, DType, dimdst>(src.self(), shape);
+}
+// short cut functions
+/*!
+ * \brief a expression that replicate a 1 dimension tensor for nrow times
+ * \param src Tensor<Device,1>: shape[0]
+ * \param nrow number of rows to replicate
+ * \return a expresion with type Tensor<Device,2> size(1), size(0) = nrow
+ * \tparam Device which device it lies
+ */
+template<typename SrcExp, typename DType, int etype>
+inline Broadcast1DExp<SrcExp, DType, 2, 1>
+repmat(const expr::Exp<SrcExp, DType, etype> &src, index_t nrow) {
+  return broadcast<1>
+      (src, Shape2(nrow, ShapeCheck<1, SrcExp>::Check(src.self())[0]));
+}
+//----------------------
+// Execution plan
+//----------------------
+template<typename SrcExp, typename DType, int dimdst, int dimdst_m_cast>
+struct Plan<Broadcast1DExp<SrcExp, DType, dimdst, dimdst_m_cast>, DType> {
+ public:
+  static const int dimcast = dimdst - dimdst_m_cast;
+  explicit Plan(const Broadcast1DExp<SrcExp, DType, dimdst, dimdst_m_cast> &e)
+      : src_(MakePlan(e.src_)),
+        ystride_(e.shape_.ProdShape(dimcast + 1, dimdst - 1)),
+        length_(e.shape_[dimcast]) {
+    TypeCheckPass<dimcast != dimdst - 1>
+        ::Error_Expression_Does_Not_Meet_Dimension_Req();
+  }
+  MSHADOW_XINLINE DType Eval(index_t y, index_t x) const {
+    return src_.Eval(0, (y / ystride_) % length_);
+  }
+
+ private:
+  expr::Plan<SrcExp, DType> src_;
+  const index_t  ystride_, length_;
+};
+
+/*! \brief execution plan of Broadcast1DExp */
+template<typename SrcExp, typename DType, int dimdst>
+struct Plan<Broadcast1DExp<SrcExp, DType, dimdst, 1>, DType>{
+ public:
+  explicit Plan(const Broadcast1DExp<SrcExp, DType, dimdst, 1> &e)
+      : src_(MakePlan(e.src_)) {}
+  MSHADOW_XINLINE DType Eval(index_t y, index_t x) const {
+    return src_.Eval(0, x);
+  }
+
+ private:
+  expr::Plan<SrcExp, DType> src_;
+};
+
+/*! \brief execution plan of Broadcast1DExp */
+template<typename SrcExp, typename DType, int dimdst>
+struct Plan<BroadcastScalarExp<SrcExp, DType, dimdst>, DType>{
+ public:
+  explicit Plan(const BroadcastScalarExp<SrcExp, DType, dimdst> &e)
+      : src_(MakePlan(e.src_)) {}
+  MSHADOW_XINLINE DType Eval(index_t y, index_t x) const {
+    return src_.Eval(0, 0);
+  }
+
+ private:
+  expr::Plan<SrcExp, DType> src_;
+};
+}  // namespace expr
+}  // namespace mshadow
+#endif  // MSHADOW_EXTENSION_BROADCAST_H_
diff --git a/3rdparty/mshadow/mshadow/extension/broadcast_with_axis.h b/3rdparty/mshadow/mshadow/extension/broadcast_with_axis.h
new file mode 100644
index 000000000000..49605af67d32
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/extension/broadcast_with_axis.h
@@ -0,0 +1,258 @@
+/*!
+ * Copyright (c) 2016 by Contributors
+ * \file broadcast_with_axis.h
+ * \brief
+ * \author Junyuan Xie, Xingjian Shi
+*/
+#ifndef MSHADOW_EXTENSION_BROADCAST_WITH_AXIS_H_
+#define MSHADOW_EXTENSION_BROADCAST_WITH_AXIS_H_
+
+#include <vector>
+#include "../extension.h"
+
+namespace mshadow {
+namespace expr {
+
+  /*!
+  * \brief Broadcasting the tensor in the given axis. If keepdim is off, insert the broadcasting dim after axis. Otherwise broadcasting axis.
+  * \tparam SrcExp source expression
+  * \tparam DType  data type
+  * \tparam dimsrc source dimension
+  * \tparam dimdst destination dimension
+  */
+template<typename SrcExp, typename DType, int dimsrc, int dimdst>
+struct BroadcastWithAxisExp:
+    public MakeTensorExp<BroadcastWithAxisExp<SrcExp, DType, dimsrc, dimdst>,
+                         SrcExp, dimdst, DType> {
+  /*! \brief data oprand */
+  const SrcExp &src_;
+  /*! \brief size of the last dimension of dst */
+  index_t dst_last_;
+  /*! \brief product of the dimensions after the broadcasting axis */
+  index_t trailing_;
+  /*! \brief new dimension of the broadcasting axis*/
+  index_t size_;
+  /*! \brief size of the last dimension of src*/
+  index_t last_;
+  /*! constructor */
+  BroadcastWithAxisExp(const SrcExp &src, const int axis, const index_t size)
+    : src_(src), size_(size) {
+    bool keepdim = (dimsrc == dimdst);
+    Shape<dimsrc> src_shape = ShapeCheck<dimsrc, SrcExp>::Check(src_);
+    this->trailing_ = 1;
+
+    if (!keepdim) {
+      CHECK(dimsrc > axis && axis >= -1) << "broadcast axis (no keepdim) out of bound, "  <<
+        "axis must be between -1 and" << dimsrc - 1 << ", given=" << axis << ".";
+      for (int i = 0; i <= axis; ++i) {
+        this->shape_[i] = src_shape[i];
+      }
+      this->shape_[axis + 1] = size_;
+      for (int i = axis + 1; i < dimsrc; ++i) {
+        this->trailing_ *= src_shape[i];
+        this->shape_[i + 1] = src_shape[i];
+      }
+    } else {
+      CHECK(dimdst > axis && axis >= 0) << "broadcast axis (keepdim) out of bound, " <<
+        "axis must be between 0 and" << dimdst - 1 << ", given=" << axis << ".";
+      CHECK_EQ(src_shape[axis], 1U) << "Size of the dimension of the broadcasting axis must be 1" <<
+        " when keepdim is on, src_shape[" << axis << "]=" << src_shape[axis] << ".";
+      for (int i = 0; i <= axis - 1; ++i) {
+        this->shape_[i] = src_shape[i];
+      }
+      this->shape_[axis] = size_;
+      for (int i = axis + 1; i < dimdst; ++i) {
+        this->trailing_ *= src_shape[i];
+        this->shape_[i] = src_shape[i];
+      }
+    }
+
+    this->last_ = src_shape[dimsrc - 1];
+    this->dst_last_ = this->shape_[dimdst - 1];
+  }
+};  // struct BroadcastWithAxisExp
+
+/*!
+ * \brief Broadcasting the tensor after given axis.
+ * \tparam SrcExp source expression
+ * \tparam DType data type
+ * \tparam etype type of the expression
+ */
+template<typename SrcExp, typename DType, int etype>
+inline BroadcastWithAxisExp<SrcExp, DType, ExpInfo<SrcExp>::kDim,
+  ExpInfo<SrcExp>::kDim + 1>
+broadcast_with_axis(const Exp<SrcExp, DType, etype> &src, const int axis, const index_t size) {
+  return BroadcastWithAxisExp<SrcExp, DType, ExpInfo<SrcExp>::kDim,
+    ExpInfo<SrcExp>::kDim + 1>(src.self(), axis, size);
+}
+
+/*!
+* \brief Broadcasting the tensor in the given axis (keepdim turned on)
+* \tparam SrcExp source expression
+* \tparam DType data type
+* \tparam etype type of the expression
+*/
+template<typename SrcExp, typename DType, int etype>
+inline BroadcastWithAxisExp<SrcExp, DType, ExpInfo<SrcExp>::kDim,
+  ExpInfo<SrcExp>::kDim>
+  broadcast_keepdim(const Exp<SrcExp, DType, etype> &src, const int axis, const index_t size) {
+  return BroadcastWithAxisExp<SrcExp, DType, ExpInfo<SrcExp>::kDim,
+    ExpInfo<SrcExp>::kDim>(src.self(), axis, size);
+}
+
+/*!
+* \brief Broadcasting the tensor in multiple axes. The dimension of the source tensor
+         in the given axes must be 1.
+* \tparam SrcExp source expression
+* \tparam DType  data type
+* \tparam dimsrc source dimension
+* \tparam axesnum number of broadcasting dimensions
+*/
+template<typename SrcExp, typename DType, int dimsrc>
+struct BroadcastWithMultiAxesExp :
+      public MakeTensorExp<BroadcastWithMultiAxesExp<SrcExp, DType, dimsrc>,
+  SrcExp, dimsrc, DType> {
+  /*! \brief data oprand */
+  const SrcExp &src_;
+  /*! \brief size of the last dimension of dst */
+  index_t dst_last_;
+  /*! \brief number of broadcasting axes*/
+  index_t axesnum_;
+  /*! \brief product of the dimensions after the broadcasting axses */
+  Shape<dimsrc> trailings_;
+  /*! \brief new dimension of the broadcasting axes*/
+  Shape<dimsrc> sizes_;
+  /*! \brief size of the last dimension of src*/
+  index_t last_;
+  /*! constructor */
+  template<typename TShape>
+  BroadcastWithMultiAxesExp(const SrcExp &src, const TShape& axes, const TShape& sizes)
+    : src_(src) {
+    Shape<dimsrc> src_shape = ShapeCheck<dimsrc, SrcExp>::Check(src_);
+    CHECK(axes.ndim() == sizes.ndim()) << "ndim of axes and sizes must be equal.";
+    this->axesnum_ = axes.ndim();
+    CHECK(this->axesnum_ <= dimsrc) << "Number of broadcasting axes must be smaller than"
+      "the source ndim, number of axes=" << this->axesnum_ << " dimsrc=" << dimsrc;
+    for (index_t i = 0; i < this->axesnum_; i++) {
+      CHECK(dimsrc > axes[i]) << "broadcast axis (keepdim) out of bound, " <<
+        "all axes must be between 0 and" << dimsrc - 1 << ", given axes[" << i << "] = " << axes[i]
+        << ".";
+      CHECK_EQ(src_shape[axes[i]], 1U) << "Size of the dimension of the broadcasting axis must be 1"
+        << ", src_shape[" << axes[i] << "]=" << src_shape[axes[i]] << ".";
+      if (i < this->axesnum_ - 1) {
+        CHECK(axes[i] < axes[i + 1]) << "The given axes must be in increasing order.";
+      }
+    }
+    for (index_t i = 0; i < dimsrc; i++) {
+      this->shape_[i] = src_shape[i];
+      this->sizes_[i] = 1;
+      this->trailings_[i] = 1;
+    }
+    for (index_t i = 0; i < this->axesnum_; i++) {
+      this->shape_[axes[i]] = sizes[i];
+      this->sizes_[i] = sizes[i];
+    }
+    for (index_t i = 0; i < this->axesnum_; i++) {
+      this->trailings_[i] = 1;
+      for (index_t j = axes[i] + 1; j < dimsrc; ++j) {
+        this->trailings_[i] *= this->shape_[j];
+      }
+    }
+    this->last_ = src_shape[dimsrc - 1];
+    this->dst_last_ = this->shape_[dimsrc - 1];
+  }
+};  // struct BroadcastWithMultiAxesExp
+
+/*!
+* \brief Broadcasting the tensor in the given axis (keepdim turned on)
+* \param src source
+* \param axes broadcasting axes
+* \param sizes sizes of the broadcasting axes
+* \tparam SrcExp source expression
+* \tparam DType data type
+* \tparam etype type of the expression
+* \tparam TShape the flexible shape type
+*/
+template<typename SrcExp, typename DType, int etype, typename TShape>
+inline BroadcastWithMultiAxesExp<SrcExp, DType, ExpInfo<SrcExp>::kDim>
+broadcast_multi_axes(const Exp<SrcExp, DType, etype> &src,
+const TShape &axes, const TShape &sizes) {
+  return BroadcastWithMultiAxesExp<SrcExp, DType, ExpInfo<SrcExp>::kDim>(src.self(), axes, sizes);
+}
+
+/*!
+* \brief Broadcasting the tensor to the target shape,
+         dimension of different sizes must be 1 in the original tensor.
+* \param src source
+* \param target_shape shape of the target broadcasting tensor
+* \tparam SrcExp source expression
+* \tparam DType data type
+* \tparam etype type of the expression
+* \tparam TShape the flexible shape type
+*/
+template<typename SrcExp, typename DType, int etype, typename TShape>
+inline BroadcastWithMultiAxesExp<SrcExp, DType, ExpInfo<SrcExp>::kDim>
+broadcast_to(const Exp<SrcExp, DType, etype> &src, const TShape &target_shape) {
+  static const size_t dimsrc = ExpInfo<SrcExp>::kDim;
+  CHECK_EQ(target_shape.ndim(), dimsrc);
+  std::vector<index_t> axes_vec, sizes_vec;
+  Shape<dimsrc> src_shape = ShapeCheck<dimsrc, SrcExp>::Check(src.self());
+  for (size_t i = 0; i < dimsrc; ++i) {
+    if (src_shape[i] != target_shape[i]) {
+      CHECK_EQ(src_shape[i], 1U) << "broadcasting axis must have size 1, received shape="
+        << src_shape << " target_shape=" << target_shape;
+      axes_vec.push_back(i);
+      sizes_vec.push_back(target_shape[i]);
+    }
+  }
+  TShape axes = TShape(axes_vec.begin(), axes_vec.end());
+  TShape sizes = TShape(sizes_vec.begin(), sizes_vec.end());
+  return BroadcastWithMultiAxesExp<SrcExp, DType, ExpInfo<SrcExp>::kDim>(src.self(), axes, sizes);
+}
+
+//----------------------
+// Execution plan
+//----------------------
+template<typename SrcExp, typename DType, int dimsrc, int dimdst>
+struct Plan<BroadcastWithAxisExp<SrcExp, DType, dimsrc, dimdst>, DType> {
+ public:
+  explicit Plan(const BroadcastWithAxisExp<SrcExp, DType, dimsrc, dimdst> &e)
+       : src_(MakePlan(e.src_)), dst_last_(e.dst_last_),
+         trailing_(e.trailing_), size_(e.size_), last_(e.last_) {}
+  MSHADOW_XINLINE DType Eval(index_t i, index_t j) const {
+    index_t x = (i * dst_last_ + j) / trailing_ / size_;
+    index_t y = (i * dst_last_ + j) % trailing_;
+    index_t z = x * trailing_ + y;
+    return src_.Eval(z / last_, z % last_);
+  }
+
+ private:
+  Plan<SrcExp, DType> src_;
+  const index_t dst_last_, trailing_, size_, last_;
+};
+
+template<typename SrcExp, typename DType, int dimsrc>
+struct Plan<BroadcastWithMultiAxesExp<SrcExp, DType, dimsrc>, DType> {
+ public:
+  explicit Plan(const BroadcastWithMultiAxesExp<SrcExp, DType, dimsrc> &e)
+    : src_(MakePlan(e.src_)), dst_last_(e.dst_last_), last_(e.last_), axesnum_(e.axesnum_),
+    trailings_(e.trailings_), sizes_(e.sizes_) {}
+  MSHADOW_XINLINE DType Eval(index_t i, index_t j) const {
+    index_t indx = i * dst_last_ + j;
+    for (index_t p = 0; p < dimsrc; ++p) {
+      if (p >= axesnum_) {
+        break;
+      }
+      indx = (indx / trailings_[p] / sizes_[p]) * trailings_[p] + (indx % trailings_[p]);
+    }
+    return src_.Eval(indx / last_, indx % last_);
+  }
+
+ private:
+  Plan<SrcExp, DType> src_;
+  const index_t dst_last_, last_, axesnum_;
+  const Shape<dimsrc> trailings_, sizes_;
+};
+}  // namespace expr
+}  // namespace mshadow
+#endif  // MSHADOW_EXTENSION_BROADCAST_WITH_AXIS_H_
diff --git a/3rdparty/mshadow/mshadow/extension/channel_pool.h b/3rdparty/mshadow/mshadow/extension/channel_pool.h
new file mode 100644
index 000000000000..60d1112f4a61
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/extension/channel_pool.h
@@ -0,0 +1,108 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file channel_pool.h
+ * \brief support for chpool
+ * \author Tianqi Chen
+ */
+#ifndef MSHADOW_EXTENSION_CHANNEL_POOL_H_
+#define MSHADOW_EXTENSION_CHANNEL_POOL_H_
+#include <algorithm>
+#include "../extension.h"
+namespace mshadow {
+namespace expr {
+/*!
+ * \brief channel pooling expression, do reduction over (local nearby) channels,
+ *        used to implement local response normalization
+ * \tparam Reducer reduction method during pooling
+ * \tparam SrcExp source expression to be pooled from
+ * \tparam DType the type of elements
+ * \tparam srcdim dimension of src
+ */
+template<typename Reducer, typename SrcExp, typename DType, int srcdim>
+struct ChannelPoolingExp:
+      public MakeTensorExp<ChannelPoolingExp<Reducer, SrcExp, DType, srcdim>,
+                           SrcExp, srcdim, DType> {
+  /*! \brief source operand */
+  const SrcExp &src_;
+  /*! \brief neighbor size */
+  index_t nsize_;
+  /*! \brief stride of pooling */
+  index_t stride_;
+  /*! \brief pad of pooling of each side */
+  index_t pad_;
+  index_t src_channel_;
+  /*! \brief constructor */
+  ChannelPoolingExp(const SrcExp &src, index_t nsize, index_t stride, index_t pad)
+      : src_(src), nsize_(nsize), stride_(stride), pad_(pad) {
+    this->shape_ = ShapeCheck<srcdim, SrcExp>::Check(src_);
+    this->src_channel_ = this->shape_[srcdim - 3];
+    CHECK_GE(this->shape_[srcdim - 3], nsize_)
+      << "chpool: local size must be smaller than nchannels";
+    this->shape_[srcdim - 3] = (this->src_channel_ - nsize + pad * 2 + 1) / stride;
+  }
+};
+/*!
+ * \brief  channel pooling, do reduction over (local nearby) channels,
+ *         used to implement local response normalization
+ * \param src source data
+ * \param nsize neighbor size
+ * \return expression of pooled result
+ * \tparam Reducer reducer type
+ * \tparam SrcExp source expression
+ * \tparam DType the type of elements
+ * \tparam etype type of expression
+ */
+template<typename Reducer, typename SrcExp, typename DType, int etype>
+inline ChannelPoolingExp<Reducer, SrcExp, DType, ExpInfo<SrcExp>::kDim>
+chpool(const Exp<SrcExp, DType, etype> &src, index_t nsize) {
+  TypeCheckPass<ExpInfo<SrcExp>::kDim >= 3>
+      ::Error_Expression_Does_Not_Meet_Dimension_Req();
+  CHECK_EQ(nsize % 2, 1U) << "chpool: if no pad is specified, local size must be odd";
+  return ChannelPoolingExp<Reducer, SrcExp,
+                           DType, ExpInfo<SrcExp>::kDim>(src.self(), nsize, 1, nsize / 2);
+}
+
+template<typename Reducer, typename SrcExp, typename DType, int etype>
+inline ChannelPoolingExp<Reducer, SrcExp, DType, ExpInfo<SrcExp>::kDim>
+chpool(const Exp<SrcExp, DType, etype> &src, index_t nsize, index_t stride, index_t pad) {
+  TypeCheckPass<ExpInfo<SrcExp>::kDim >= 3>
+      ::Error_Expression_Does_Not_Meet_Dimension_Req();
+  return ChannelPoolingExp<Reducer, SrcExp,
+                           DType, ExpInfo<SrcExp>::kDim>(src.self(), nsize, stride, pad);
+}
+
+//----------------------
+// Execution plan
+//----------------------
+template<typename Reducer, typename SrcExp, typename DType, int srcdim>
+struct Plan<ChannelPoolingExp<Reducer, SrcExp, DType, srcdim>, DType> {
+ public:
+  explicit Plan(const ChannelPoolingExp<Reducer, SrcExp, DType, srcdim> &e)
+      : src_(MakePlan(e.src_)), channel_(e.shape_[srcdim - 3]),
+        height_(e.shape_[srcdim - 2]), width_(e.shape_[srcdim - 1]),
+        hnsize_(e.nsize_), stride_(e.stride_), pad_(e.pad_),
+        src_channel_(e.src_channel_) {}
+  MSHADOW_XINLINE DType Eval(index_t i, index_t j) const {
+    using namespace std;
+    const index_t y = i % height_;
+    i /= height_;
+    const index_t c = i % channel_;
+    const index_t n = i / channel_;
+    const index_t x = j;
+    const index_t cstart = c * stride_ < pad_ ? 0  : c * stride_ - pad_;
+    const index_t cend   = min(c * stride_ - pad_ + hnsize_, channel_);
+    DType res; Reducer::SetInitValue(res);
+    for (index_t cc = cstart; cc < cend; ++cc) {
+      Reducer::Reduce(res, src_.Eval((n * src_channel_ + cc) * height_ + y, x));
+    }
+    return res;
+  }
+
+ private:
+  Plan<SrcExp, DType> src_;
+  const index_t channel_, height_, width_, hnsize_, stride_, pad_, src_channel_;
+};
+}  // namespace expr
+}  // namespace mshadow
+#endif  // MSHADOW_EXTENSION_CHANNEL_POOL_H_
+
diff --git a/3rdparty/mshadow/mshadow/extension/channel_unpool.h b/3rdparty/mshadow/mshadow/extension/channel_unpool.h
new file mode 100644
index 000000000000..00ba279c1760
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/extension/channel_unpool.h
@@ -0,0 +1,137 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file channel_pool.h
+ * \brief support for chpool
+ * \author Tianqi Chen
+ */
+#ifndef MSHADOW_EXTENSION_CHANNEL_UNPOOL_H_
+#define MSHADOW_EXTENSION_CHANNEL_UNPOOL_H_
+#include <algorithm>
+#include "../extension.h"
+namespace mshadow {
+namespace expr {
+/*!
+ * \brief channel pooling expression, do reduction over (local nearby) channels,
+ *        used to implement local response normalization
+ * \tparam Reducer reduction method during pooling
+ * \tparam SrcExp source expression to be pooled from
+ * \tparam DType the type of elements
+ * \tparam srcdim dimension of src
+ */
+template<typename Reducer, typename SrcExp, typename DType, int srcdim>
+struct ChannelUnpoolingExp:
+      public MakeTensorExp<ChannelUnpoolingExp<Reducer, SrcExp, DType, srcdim>,
+                           SrcExp, srcdim, DType> {
+  /*! \brief source input, corresponds to src in pooling */
+  const SrcExp &data_src_;
+  /*! \brief result of pooled data, corresponds to result of pooling */
+  const SrcExp &data_pooled_;
+  /*! \brief gradient data of pooled part, to be propgate down */
+  const SrcExp &grad_pooled_;
+  /*! \brief channel of pooled expression */
+  index_t pchannel_;
+  /*! \brief kernel size in height */
+  index_t nsize_;
+  /*! \brief kernel size in width */
+  index_t kstride_;
+  /*! \brief pad */
+  index_t pad_;
+  /*! \brief constructor */
+  ChannelUnpoolingExp(const SrcExp &data_src,
+               const SrcExp &data_pooled,
+               const SrcExp &grad_pooled,
+               index_t nsize, index_t kstride, index_t pad)
+      : data_src_(data_src), data_pooled_(data_pooled),
+        grad_pooled_(grad_pooled),
+        nsize_(nsize), kstride_(kstride), pad_(pad) {
+    Shape<srcdim> pshape = ShapeCheck<srcdim, SrcExp>::Check(grad_pooled);
+    typedef ShapeCheck<srcdim, SrcExp> ShapeCheckSrcDimSrcExp;
+    CHECK_EQ(pshape, ShapeCheckSrcDimSrcExp::Check(data_pooled))
+      << "ChannelUnPoolingExp: data and grad shape mismatch";
+    Shape<srcdim> sshape = ShapeCheck<srcdim, SrcExp>::Check(data_src);
+    for (int k = 0; k < srcdim; ++k) {
+      if (k == 1) {
+        continue;
+      }
+      CHECK_EQ(pshape[k], sshape[k])
+        << "ChannelUnPoolingExp: pooled tensor and src tensor shape mismatch"
+        << pshape[k]
+        << " vs "
+        << sshape[k];
+    }
+    pchannel_ = pshape[1];
+    this->shape_ = sshape;
+  }
+};
+/*!
+ * \brief  channel unpooling, do unroll over (local nearby) channels
+ * \param src source data
+ * \param nsize neighbor size
+ * \param stride stride of the pooling
+ * \param pad number of padding at each side
+ * \return expression of pooled result
+ * \tparam Reducer reducer type
+ * \tparam SrcExp source expression
+ * \tparam DType the type of elements
+ * \tparam etype type of expression
+ */
+template<typename Reducer, typename SrcExp, typename DType, int etype>
+inline ChannelUnpoolingExp<Reducer, SrcExp, DType, ExpInfo<SrcExp>::kDim>
+ch_unpool(const Exp<SrcExp, DType, etype> &data_src,
+       const Exp<SrcExp, DType, etype> &data_pooled,
+       const Exp<SrcExp, DType, etype> &grad_pooled,
+      index_t nsize, index_t stride, index_t pad) {
+  TypeCheckPass<ExpInfo<SrcExp>::kDim >= 3>
+      ::Error_Expression_Does_Not_Meet_Dimension_Req();
+  return ChannelUnpoolingExp<Reducer, SrcExp, DType, ExpInfo<SrcExp>::kDim>
+        (data_src.self(), data_pooled.self(), grad_pooled.self(), nsize, stride, pad);
+}
+
+template<typename Reducer, typename SrcExp, typename DType, int etype>
+inline ChannelUnpoolingExp<Reducer, SrcExp, DType, ExpInfo<SrcExp>::kDim>
+ch_unpool(const Exp<SrcExp, DType, etype> &data_src,
+       const Exp<SrcExp, DType, etype> &data_pooled,
+       const Exp<SrcExp, DType, etype> &grad_pooled, index_t nsize) {
+  return ch_unpool(data_src, data_pooled, grad_pooled, nsize, 1, nsize / 2);
+}
+
+
+//----------------------
+// Execution plan
+//----------------------
+template<typename Reducer, typename SrcExp, typename DType, int srcdim>
+struct Plan<ChannelUnpoolingExp<Reducer, SrcExp, DType, srcdim>, DType> {
+ public:
+  explicit Plan(const ChannelUnpoolingExp<Reducer, SrcExp, DType, srcdim> &e)
+      : data_src_(e.data_src_), data_pooled_(e.data_pooled_),
+        grad_pooled_(e.grad_pooled_), channel_(e.shape_[srcdim - 3]),
+        height_(e.shape_[srcdim - 2]), pchannel_(e.pchannel_),
+        hnsize_(e.nsize_), stride_(e.kstride_), pad_(e.pad_) {}
+  MSHADOW_XINLINE DType Eval(index_t i, index_t j) const {
+    using namespace std;
+    const DType vsrc = data_src_.Eval(i, j);
+    const index_t y = i % height_;
+    i /= height_;
+    const index_t c = i % channel_;
+    const index_t n = i / channel_;
+    const index_t x = j;
+    const index_t cstart = c < hnsize_ - pad_ ? 0
+                        : (c - (hnsize_ - pad_) + stride_) / stride_;
+    const index_t cend = min((c + pad_ + stride_) / stride_, channel_);
+    DType val = static_cast<DType>(0);
+    for (index_t cc = cstart; cc < cend; ++cc) {
+      val += Reducer::PartialGrad(vsrc,
+                                  data_pooled_.Eval((n * pchannel_ + cc) * height_ + y, x)) *
+                                  grad_pooled_.Eval((n * pchannel_ + cc) * height_ + y, x);
+    }
+    return val;
+  }
+
+ private:
+  Plan<SrcExp, DType> data_src_, data_pooled_, grad_pooled_;
+  const index_t channel_, height_, pchannel_, hnsize_, stride_, pad_;
+};
+}  // namespace expr
+}  // namespace mshadow
+#endif  // MSHADOW_EXTENSION_CHANNEL_UNPOOL_H_
+
diff --git a/3rdparty/mshadow/mshadow/extension/choose.h b/3rdparty/mshadow/mshadow/extension/choose.h
new file mode 100644
index 000000000000..b1391724d400
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/extension/choose.h
@@ -0,0 +1,90 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file choose.h
+ * \brief support for implicit array selection operation
+ * \author Tianqi Chen
+ */
+#ifndef MSHADOW_EXTENSION_CHOOSE_H_
+#define MSHADOW_EXTENSION_CHOOSE_H_
+
+#include "../extension.h"
+
+namespace mshadow {
+namespace expr {
+/*!
+ * \brief Make a choice of index in the lowest changing dimension.
+ * \tparam SrcExp type of lhs expression
+ * \tparam IndexExp type of index expression
+ * \tparam DType the type of elements
+ */
+template<typename SrcExp, typename IndexExp, typename DType>
+struct MatChooseRowElementExp:
+      public Exp<MatChooseRowElementExp<SrcExp, IndexExp, DType>,
+                 DType, type::kChainer> {
+  /*! \brief source operand */
+  const SrcExp &src_;
+  /*! \brief index operand */
+  const IndexExp &index_;
+  /*! \brief constructor */
+  MatChooseRowElementExp(const SrcExp &src, const IndexExp &index)
+      : src_(src), index_(index) {}
+};
+
+template<typename SrcExp, typename IndexExp,
+         typename DType, typename IDType, int e1, int e2>
+inline MatChooseRowElementExp<SrcExp, IndexExp, DType>
+mat_choose_row_element(const Exp<SrcExp, DType, e1> &src,
+                       const Exp<IndexExp, IDType, e2> &index) {
+  TypeCheckPass<ExpInfo<SrcExp>::kDim == 2 && ExpInfo<IndexExp>::kDim == 1>
+      ::Error_Expression_Does_Not_Meet_Dimension_Req();
+  return MatChooseRowElementExp<SrcExp, IndexExp, DType>(src.self(), index.self());
+}
+
+//----------------------
+// Execution plan
+//----------------------
+template<typename SrcExp, typename IndexExp, typename DType>
+struct Plan<MatChooseRowElementExp<SrcExp, IndexExp, DType>, DType> {
+ public:
+  explicit Plan(const MatChooseRowElementExp<SrcExp, IndexExp, DType> &e)
+      : src_(MakePlan(e.src_)),
+        index_(MakePlan(e.index_)) {
+  }
+  MSHADOW_XINLINE DType Eval(index_t y, index_t x) const {
+    index_t idx = static_cast<index_t>(index_.Eval(0, x));
+    return src_.Eval(x, idx);
+  }
+
+ private:
+  expr::Plan<SrcExp, DType> src_;
+  expr::Plan<IndexExp, DType> index_;
+};
+
+template<typename SrcExp, typename IndexExp, typename DType>
+inline Plan<MatChooseRowElementExp<SrcExp, IndexExp, DType>, DType>
+MakePlan(const MatChooseRowElementExp<SrcExp, IndexExp, DType> &exp) {
+  return Plan<MatChooseRowElementExp<SrcExp, IndexExp, DType>, DType>(exp);
+}
+
+template<int dim, typename SrcExp, typename IndexExp, typename DType>
+struct ShapeCheck<dim, MatChooseRowElementExp<SrcExp, IndexExp, DType> > {
+  inline static Shape<dim>
+  Check(const MatChooseRowElementExp<SrcExp, IndexExp, DType> &t) {
+    CHECK(dim == 1)
+        << "MatChooseRowElementExp only support 1 dimension output";
+    Shape<2> shape1 = ShapeCheck<2, SrcExp>::Check(t.src_);
+    Shape<dim> shape2 = ShapeCheck<dim, IndexExp>::Check(t.index_);
+    CHECK_EQ(shape1[0], shape2[0])
+        << "mat_choose_row_element index length and number of rows in matrix";
+    return shape2;
+  }
+};
+
+template<typename SrcExp, typename IndexExp, typename DType>
+struct ExpInfo<MatChooseRowElementExp<SrcExp, IndexExp, DType> > {
+  static const int kDim = 1;
+  static const int kDevMask = ExpInfo<SrcExp>::kDevMask & ExpInfo<IndexExp>::kDevMask;
+};
+}  // namespace expr
+}  // namespace mshadow
+#endif  // MSHADOW_EXTENSION_CHOOSE_H_
diff --git a/3rdparty/mshadow/mshadow/extension/complex.h b/3rdparty/mshadow/mshadow/extension/complex.h
new file mode 100644
index 000000000000..8e79b7eb819c
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/extension/complex.h
@@ -0,0 +1,525 @@
+/*!
+ *  Copyright (c) 2016 by Contributors
+ * \file complex.h
+ * \brief support for complex operations
+ * \author Xingjian Shi
+ */
+#ifndef MSHADOW_EXTENSION_COMPLEX_H_
+#define MSHADOW_EXTENSION_COMPLEX_H_
+#include <algorithm>
+#include "../extension.h"
+
+namespace mshadow {
+namespace op {
+namespace complex {
+enum BinaryCalculationType { kBinaryCC, kBinaryCR, kBinaryRC};
+enum UnitaryCalculationType { kUnitaryC2R, kUnitaryC2C, kUnitaryR2C };
+struct mul {
+  /*! \brief map a_real, a_imag, b_real, b_imag to result using defined operation */
+  template<typename DType>
+  MSHADOW_XINLINE static DType RealMap(DType a_real, DType a_imag,
+    DType b_real, DType b_imag) {
+    return a_real * b_real - a_imag * b_imag;
+  }
+  template<typename DType>
+  MSHADOW_XINLINE static DType ImagMap(DType a_real, DType a_imag,
+    DType b_real, DType b_imag) {
+    return a_real * b_imag + b_real * a_imag;
+  }
+};
+
+struct div {
+  /*! \brief map a_real, a_imag, b_real, b_imag to result using defined operation */
+  template<typename DType>
+  MSHADOW_XINLINE static DType RealMap(DType a_real, DType a_imag,
+    DType b_real, DType b_imag) {
+    return (a_real * b_real + a_imag * b_imag) / (b_real * b_real + b_imag * b_imag);
+  }
+  template<typename DType>
+  MSHADOW_XINLINE static DType ImagMap(DType a_real, DType a_imag,
+    DType b_real, DType b_imag) {
+    return (b_real * a_imag - a_real * b_imag) / (b_real * b_real + b_imag * b_imag);
+  }
+};
+
+struct conjugate {
+  template<typename TA, typename DType>
+  MSHADOW_XINLINE static DType RealMap(const expr::Plan<TA, DType> &src_,
+    index_t real_i, index_t real_j, index_t imag_i, index_t imag_j) {
+    return src_.Eval(real_i, real_j);
+  }
+  template<typename TA, typename DType>
+  MSHADOW_XINLINE static DType ImagMap(const expr::Plan<TA, DType> &src_,
+    index_t real_i, index_t real_j, index_t imag_i, index_t imag_j) {
+    return -src_.Eval(imag_i, imag_j);
+  }
+};
+
+struct exchange {
+  template<typename TA, typename DType>
+  MSHADOW_XINLINE static DType RealMap(const expr::Plan<TA, DType> &src_,
+    index_t real_i, index_t real_j, index_t imag_i, index_t imag_j) {
+    return src_.Eval(imag_i, imag_j);
+  }
+  template<typename TA, typename DType>
+  MSHADOW_XINLINE static DType ImagMap(const expr::Plan<TA, DType> &src_,
+    index_t real_i, index_t real_j, index_t imag_i, index_t imag_j) {
+    return src_.Eval(real_i, real_j);
+  }
+};
+
+// r2c operator
+struct pad_imag {
+  template<typename TA, typename DType>
+  MSHADOW_XINLINE static DType RealMap(const expr::Plan<TA, DType> &src_,
+    index_t real_i, index_t real_j) {
+    return src_.Eval(real_i, real_j);
+  }
+  template<typename TA, typename DType>
+  MSHADOW_XINLINE static DType ImagMap(const expr::Plan<TA, DType> &src_,
+    index_t real_i, index_t real_j) {
+    return 0;
+  }
+};
+
+// c2r operator
+struct toreal {
+  template<typename TA, typename DType>
+  MSHADOW_XINLINE static DType RealMap(const expr::Plan<TA, DType> &src_,
+    index_t real_i, index_t real_j, index_t imag_i, index_t imag_j) {
+    DType real_val = src_.Eval(real_i, real_j);
+    return real_val;
+  }
+};
+
+struct abs_square {
+  template<typename TA, typename DType>
+  MSHADOW_XINLINE static DType RealMap(const expr::Plan<TA, DType> &src_,
+    index_t real_i, index_t real_j, index_t imag_i, index_t imag_j) {
+    DType real_val = src_.Eval(real_i, real_j);
+    DType image_val = src_.Eval(imag_i, imag_j);
+    return real_val * real_val + image_val * image_val;
+  }
+};
+
+struct sum_real_imag {
+  template<typename TA, typename DType>
+  MSHADOW_XINLINE static DType RealMap(const expr::Plan<TA, DType> &src_,
+    index_t real_i, index_t real_j, index_t imag_i, index_t imag_j) {
+    DType real_val = src_.Eval(real_i, real_j);
+    DType image_val = src_.Eval(imag_i, imag_j);
+    return real_val + image_val;
+  }
+};
+}  // namespace complex
+}  // namespace op
+
+namespace expr {
+//--------------------
+// ComplexBinaryMapExp
+//--------------------
+  /*!
+* \brief binary map expression lhs [op] rhs where lhs and rhs are complex tensors
+* \tparam OP operator
+* \tparam calctype type of the calculation
+* \tparam TA type of lhs
+* \tparam TB type of rhs
+* \tparam etype expression type, sa namespace::type
+*/
+template<int calctype, typename OP, typename TA, typename TB, typename DType, int etype>
+struct ComplexBinaryMapExp : public Exp<ComplexBinaryMapExp<calctype, OP, TA, TB, DType, etype>,
+  DType, etype> {
+  /*! \brief left operand */
+  const TA &lhs_;
+  /*! \brief right operand */
+  const TB &rhs_;
+  /*! \brief constructor */
+  explicit ComplexBinaryMapExp(const TA &lhs, const TB &rhs)
+    :lhs_(lhs), rhs_(rhs) {}
+};
+
+//-------------------
+// ComplexConjExp
+//-------------------
+/*!
+* \brief compute conj(src) where src is a complex tensor
+* \tparam TA type of src
+* \tparam etype expression type, sa namespace::type
+*/
+template<int calctype, typename OP, typename TA, typename DType, int etype>
+struct ComplexUnitaryExp : public Exp<ComplexUnitaryExp<calctype, OP, TA, DType, etype>,
+  DType, etype> {
+  /*! \brief source expression */
+  const TA &src_;
+  /*! \brief constructor */
+  explicit ComplexUnitaryExp(const TA &src) : src_(src) {}
+};
+
+
+
+template<int calctype, typename OP, typename TA, typename TB, typename DType, int ta, int tb>
+inline ComplexBinaryMapExp<calctype, OP, TA, TB, DType, (ta | tb | type::kMapper)>
+ComplexF(const Exp<TA, DType, ta> &lhs, const Exp<TB, DType, tb> &rhs) {
+  return ComplexBinaryMapExp<calctype, OP, TA, TB, DType,
+    (ta | tb | type::kMapper)>(lhs.self(), rhs.self());
+}
+
+/*!
+* \brief conj Negation the imaginary part of A where A is a complex tensor
+* \param src source tensor
+* \tparam e1 type of source expression
+*/
+template<int calctype, typename OP, typename SrcExp, typename DType, int e1>
+inline ComplexUnitaryExp<calctype, OP, SrcExp, DType, (e1 | type::kMapper)>
+ComplexF(const Exp<SrcExp, DType, e1> &src) {
+  return ComplexUnitaryExp<calctype, OP, SrcExp, DType, (e1 | type::kMapper)>(src.self());
+}
+
+/*!
+* \brief complex_mul_cc Complex multipilication two complex tensors, A * B
+*/
+template<typename TA, typename TB, typename DType, int ta, int tb>
+inline ComplexBinaryMapExp<op::complex::kBinaryCC, op::complex::mul,
+  TA, TB, DType, (ta | tb | type::kMapper)>
+complex_mul_cc(const Exp<TA, DType, ta> &lhs, const Exp<TB, DType, tb> &rhs) {
+  return ComplexF<op::complex::kBinaryCC, op::complex::mul>(lhs, rhs);
+}
+
+/*!
+* \brief complex_mul_cr Complex multipilication a complex tensor A and a real tensor B
+*/
+template<typename TA, typename TB, typename DType, int ta, int tb>
+inline ComplexBinaryMapExp<op::complex::kBinaryCR, op::complex::mul,
+  TA, TB, DType, (ta | tb | type::kMapper)>
+complex_mul_cr(const Exp<TA, DType, ta> &lhs, const Exp<TB, DType, tb> &rhs) {
+  return ComplexF<op::complex::kBinaryCR, op::complex::mul>(lhs, rhs);
+}
+
+/*!
+* \brief complex_mul_rc Complex multipilication of a real tensor B and a complex tensor A
+*/
+template<typename TA, typename TB, typename DType, int ta, int tb>
+inline ComplexBinaryMapExp<op::complex::kBinaryRC, op::complex::mul,
+  TA, TB, DType, (ta | tb | type::kMapper)>
+complex_mul_rc(const Exp<TA, DType, ta> &lhs, const Exp<TB, DType, tb> &rhs) {
+  return ComplexF<op::complex::kBinaryRC, op::complex::mul>(lhs, rhs);
+}
+
+/*!
+* \brief complex_mul_cc Complex multipilication two complex tensors, A * B
+*/
+template<typename TA, typename TB, typename DType, int ta, int tb>
+inline ComplexBinaryMapExp<op::complex::kBinaryCC, op::complex::div,
+  TA, TB, DType, (ta | tb | type::kMapper)>
+complex_div_cc(const Exp<TA, DType, ta> &lhs, const Exp<TB, DType, tb> &rhs) {
+  return ComplexF<op::complex::kBinaryCC, op::complex::div>(lhs, rhs);
+}
+
+/*!
+* \brief complex_mul_cr Complex multipilication a complex tensor A and a real tensor B
+*/
+template<typename TA, typename TB, typename DType, int ta, int tb>
+inline ComplexBinaryMapExp<op::complex::kBinaryCR, op::complex::div,
+  TA, TB, DType, (ta | tb | type::kMapper)>
+complex_div_cr(const Exp<TA, DType, ta> &lhs, const Exp<TB, DType, tb> &rhs) {
+  return ComplexF<op::complex::kBinaryCR, op::complex::div>(lhs, rhs);
+}
+
+/*!
+* \brief complex_mul_rc Complex multipilication of a real tensor A and a complex tensor B
+*/
+template<typename TA, typename TB, typename DType, int ta, int tb>
+inline ComplexBinaryMapExp<op::complex::kBinaryRC, op::complex::div,
+  TA, TB, DType, (ta | tb | type::kMapper)>
+complex_div_rc(const Exp<TA, DType, ta> &lhs, const Exp<TB, DType, tb> &rhs) {
+  return ComplexF<op::complex::kBinaryRC, op::complex::div>(lhs, rhs);
+}
+
+/*!
+* \brief conj Negation the imaginary part of A where A is a complex tensor
+* \param src source tensor
+* \tparam e1 type of source expression
+*/
+template<typename SrcExp, typename DType, int e1>
+inline ComplexUnitaryExp<op::complex::kUnitaryC2C, op::complex::conjugate,
+  SrcExp, DType, (e1|type::kMapper)>
+conj(const Exp<SrcExp, DType, e1> &src) {
+  return ComplexF<op::complex::kUnitaryC2C, op::complex::conjugate>(src);
+}
+
+/*!
+* \brief complex_exchange Exchange the real and imaginary part of A where A is a complex tensor
+* \param src source tensor
+* \tparam e1 type of source expression
+*/
+template<typename SrcExp, typename DType, int e1>
+inline ComplexUnitaryExp<op::complex::kUnitaryC2C, op::complex::exchange,
+  SrcExp, DType, (e1|type::kMapper)>
+complex_exchange(const Exp<SrcExp, DType, e1> &src) {
+  return ComplexF<op::complex::kUnitaryC2C, op::complex::exchange>(src);
+}
+
+/*!
+* \brief complex_pad_imag Transform real matrix into complex matrix
+* \param src source tensor
+* \tparam e1 type of source expression
+*/
+template<typename SrcExp, typename DType, int e1>
+inline ComplexUnitaryExp<op::complex::kUnitaryR2C, op::complex::pad_imag,
+  SrcExp, DType, (e1|type::kMapper)>
+complex_pad_imag(const Exp<SrcExp, DType, e1> &src) {
+  return ComplexF<op::complex::kUnitaryR2C, op::complex::pad_imag>(src);
+}
+
+/*!
+* \brief complex_toreal convert complex matrix to real matrix, keep only real part
+* \param src source tensor
+* \tparam e1 type of source expression
+*/
+template<typename SrcExp, typename DType, int e1>
+inline ComplexUnitaryExp<op::complex::kUnitaryC2R, op::complex::toreal,
+  SrcExp, DType, (e1 | type::kMapper)>
+complex_toreal(const Exp<SrcExp, DType, e1> &src) {
+  return ComplexF<op::complex::kUnitaryC2R, op::complex::toreal>(src);
+}
+
+/*!
+* \brief complex_abs_square calculate the square of the modulus of A where A is a complex tensor
+* \param src source tensor
+* \tparam e1 type of source expression
+*/
+template<typename SrcExp, typename DType, int e1>
+inline ComplexUnitaryExp<op::complex::kUnitaryC2R, op::complex::abs_square,
+  SrcExp, DType, (e1 | type::kMapper)>
+complex_abs_square(const Exp<SrcExp, DType, e1> &src) {
+  return ComplexF<op::complex::kUnitaryC2R, op::complex::abs_square>(src);
+}
+
+template<typename SrcExp, typename DType, int e1>
+inline ComplexUnitaryExp<op::complex::kUnitaryC2R, op::complex::sum_real_imag,
+  SrcExp, DType, (e1 | type::kMapper)>
+complex_sum_real_imag(const Exp<SrcExp, DType, e1> &src) {
+  return ComplexF<op::complex::kUnitaryC2R, op::complex::sum_real_imag>(src);
+}
+
+template<int dim, int calctype, typename OP, typename TA, typename TB,
+  typename DType, int etype>
+struct ShapeCheck<dim, ComplexBinaryMapExp<calctype, OP, TA, TB, DType, etype> > {
+  inline static Shape<dim>
+    Check(const ComplexBinaryMapExp<calctype, OP, TA, TB, DType, etype> &t) {
+    Shape<dim> shape1 = ShapeCheck<dim, TA>::Check(t.lhs_);
+    Shape<dim> shape2 = ShapeCheck<dim, TB>::Check(t.rhs_);
+    if (shape1[0] == 0) return shape2;
+    if (shape2[0] == 0) return shape1;
+    if (calctype == op::complex::kBinaryCC) {
+      CHECK_EQ(shape1, shape2) << "ComplexBinaryMapExp (CC): Shapes of operands are not the same.";
+      CHECK_EQ(shape1[dim - 1] % 2, 0) <<
+        "ComplexBinaryMapExp (CC): Shape of the last dimension is not even. "
+        "We must have real part + imaginary part.";
+      return shape1;
+    } else if (calctype == op::complex::kBinaryCR) {
+      for (int i = 0; i < dim - 1; ++i) {
+        CHECK_EQ(shape1.shape_[i], shape2.shape_[i]) <<
+          "ComplexBinaryMapExp (CR): Shapes of operands are not the same.";
+      }
+      CHECK_EQ(shape1[dim - 1], shape2[dim - 1] * 2) <<
+        "ComplexBinaryMapExp (CR): Shapes of operands do not match.";
+      return shape1;
+    } else if (calctype == op::complex::kBinaryRC) {
+      for (int i = 0; i < dim - 1; ++i) {
+        CHECK_EQ(shape1.shape_[i], shape2.shape_[i]) <<
+          "ComplexBinaryMapExp (RC): Shapes of operands are not the same.";
+      }
+      CHECK_EQ(shape2[dim - 1], shape1[dim - 1] * 2) <<
+        "ComplexBinaryMapExp (RC): Shapes of operands do not match.";
+      return shape2;
+    } else {
+      LOG(FATAL) << "ComplexBinaryMapExp: Unexpected Calculation Type!";
+      return shape1;
+    }
+  }
+};
+
+template<int dim, int calctype, typename OP, typename TA, typename DType, int etype>
+struct ShapeCheck<dim, ComplexUnitaryExp<calctype, OP, TA, DType, etype> > {
+  inline static Shape<dim> Check(const ComplexUnitaryExp<calctype, OP, TA, DType, etype> &t) {
+    Shape<dim> s = ShapeCheck<dim, TA>::Check(t.src_);
+    CHECK_EQ(s[dim - 1] % 2, 0) << "ComplexUnitaryExp: Shape of the last dimension is not even. "
+      "We must have real + imaginary.";
+    if (calctype == op::complex::kUnitaryC2C) {
+      return s;
+    } else if (calctype == op::complex::kUnitaryC2R) {
+      Shape<dim> s_ret = s;
+      s_ret[dim - 1] /= 2;
+      return s_ret;
+    } else if (calctype == op::complex::kUnitaryR2C) {
+      Shape<dim> s_ret = s;
+      s_ret[dim-1] *= 2;
+      return s_ret;
+    } else {
+      LOG(FATAL) << "ComplexUnitaryExp: Unexpected Calculation Type!";
+      return s;
+    }
+  }
+};
+
+
+
+// complex binary expression (cc)
+template<typename OP, typename TA, typename TB, int etype, typename DType>
+class Plan<ComplexBinaryMapExp<op::complex::kBinaryCC, OP, TA, TB, DType, etype>, DType> {
+ public:
+  explicit Plan(const Plan<TA, DType> &lhs, const Plan<TB, DType> &rhs)
+    : lhs_(lhs), rhs_(rhs) {}
+  MSHADOW_XINLINE DType Eval(index_t y, index_t x) const {
+    const index_t base_x = static_cast<index_t>(x / 2) * 2;
+    if (x % 2 == 0) {
+      return OP::RealMap(lhs_.Eval(y, base_x), lhs_.Eval(y, base_x + 1),
+        rhs_.Eval(y, base_x), rhs_.Eval(y, base_x + 1));
+    } else {
+      return OP::ImagMap(lhs_.Eval(y, base_x), lhs_.Eval(y, base_x + 1),
+        rhs_.Eval(y, base_x), rhs_.Eval(y, base_x + 1));
+    }
+  }
+
+ private:
+  Plan<TA, DType> lhs_;
+  Plan<TB, DType> rhs_;
+};
+
+// complex binary expression (cr)
+template<typename OP, typename TA, typename TB, int etype, typename DType>
+class Plan<ComplexBinaryMapExp<op::complex::kBinaryCR, OP, TA, TB, DType, etype>, DType> {
+ public:
+  explicit Plan(const Plan<TA, DType> &lhs, const Plan<TB, DType> &rhs)
+    : lhs_(lhs), rhs_(rhs) {}
+  MSHADOW_XINLINE DType Eval(index_t y, index_t x) const {
+    const index_t base_x = static_cast<index_t>(x / 2) * 2;
+    if (x % 2 == 0) {
+      return OP::RealMap(lhs_.Eval(y, base_x), lhs_.Eval(y, base_x + 1),
+        rhs_.Eval(y, base_x / 2), static_cast<DType>(0));
+    } else {
+      return OP::ImagMap(lhs_.Eval(y, base_x), lhs_.Eval(y, base_x + 1),
+        rhs_.Eval(y, base_x / 2), static_cast<DType>(0));
+    }
+  }
+
+ private:
+  Plan<TA, DType> lhs_;
+  Plan<TB, DType> rhs_;
+};
+
+
+// complex binary expression (rc)
+template<typename OP, typename TA, typename TB, int etype, typename DType>
+class Plan<ComplexBinaryMapExp<op::complex::kBinaryRC, OP, TA, TB, DType, etype>, DType> {
+ public:
+  explicit Plan(const Plan<TA, DType> &lhs, const Plan<TB, DType> &rhs)
+    : lhs_(lhs), rhs_(rhs) {}
+  MSHADOW_XINLINE DType Eval(index_t y, index_t x) const {
+    const index_t base_x = static_cast<index_t>(x / 2) * 2;
+    if (x % 2 == 0) {
+      return OP::RealMap(lhs_.Eval(y, base_x / 2), static_cast<DType>(0),
+        rhs_.Eval(y, base_x), rhs_.Eval(y, base_x + 1));
+    } else {
+      return OP::ImagMap(lhs_.Eval(y, base_x / 2), static_cast<DType>(0),
+        rhs_.Eval(y, base_x), rhs_.Eval(y, base_x + 1));
+    }
+  }
+
+ private:
+  Plan<TA, DType> lhs_;
+  Plan<TB, DType> rhs_;
+};
+
+
+// complex unitary expression (c2c)
+template<typename OP, typename TA, int etype, typename DType>
+class Plan<ComplexUnitaryExp<op::complex::kUnitaryC2C, OP, TA, DType, etype>, DType> {
+ public:
+  explicit Plan(const Plan<TA, DType> &src) : src_(src) {}
+  MSHADOW_XINLINE DType Eval(index_t y, index_t x) const {
+    const index_t base_x = static_cast<index_t>(x / 2) * 2;
+    if (0 == x % 2) {
+      return OP::RealMap(src_, y, base_x, y, base_x + 1);
+    } else {
+      return OP::ImagMap(src_, y, base_x, y, base_x + 1);
+    }
+  }
+
+ private:
+  Plan<TA, DType> src_;
+};
+
+// complex unitary expression (r2c)
+template<typename OP, typename TA, int etype, typename DType>
+class Plan<ComplexUnitaryExp<op::complex::kUnitaryR2C, OP, TA, DType, etype>, DType> {
+ public:
+  explicit Plan(const Plan<TA, DType> &src) : src_(src) {}
+  MSHADOW_XINLINE DType Eval(index_t y, index_t x) const {
+    const index_t real_x = static_cast<index_t>(x / 2);
+    if (0 == x%2) {
+      // x,y should be coordinates in the complex matrix
+      // this defines how we will give value to the real part from the real matrix src_,
+      // thus the index has only 2 dimensions
+      return OP::RealMap(src_, y, real_x);
+    } else {
+      return OP::ImagMap(src_, y, real_x);
+    }
+  }
+
+ private:
+  Plan<TA, DType> src_;
+};
+
+// complex unitary expression (c2r)
+template<typename OP, typename TA, int etype, typename DType>
+class Plan<ComplexUnitaryExp<op::complex::kUnitaryC2R, OP, TA, DType, etype>, DType> {
+ public:
+  explicit Plan(const Plan<TA, DType> &src) : src_(src) {}
+  MSHADOW_XINLINE DType Eval(index_t y, index_t x) const {
+    return OP::RealMap(src_, y, x * 2, y, x * 2 + 1);
+  }
+
+ private:
+  Plan<TA, DType> src_;
+};
+
+
+
+template<int calctype, typename OP, typename TA, typename TB, typename DType, int etype>
+inline Plan<ComplexBinaryMapExp<calctype, OP, TA, TB, DType, etype>, DType>
+MakePlan(const ComplexBinaryMapExp<calctype, OP, TA, TB, DType, etype> &e) {
+  return Plan<ComplexBinaryMapExp<calctype, OP, TA, TB, DType, etype>,
+    DType>(MakePlan(e.lhs_), MakePlan(e.rhs_));
+}
+
+template<int calctype, typename OP, typename TA, typename DType, int etype>
+inline Plan<ComplexUnitaryExp<calctype, OP, TA, DType, etype>, DType>
+MakePlan(const ComplexUnitaryExp<calctype, OP, TA, DType, etype> &e) {
+  return Plan<ComplexUnitaryExp<calctype, OP, TA, DType, etype>,
+    DType>(MakePlan(e.src_));
+}
+
+
+
+template<int calctype, typename OP, typename TA, typename TB, typename DType, int etype>
+struct ExpInfo<ComplexBinaryMapExp<calctype, OP, TA, TB, DType, etype> > {
+  static const int kDimLhs = ExpInfo<TA>::kDim;
+  static const int kDimRhs = ExpInfo<TB>::kDim;
+  static const int kDim = (kDimLhs >= 0 && kDimRhs >= 0) ? \
+    (kDimLhs == 0 ? \
+  kDimRhs : \
+            ((kDimRhs == 0 || kDimLhs == kDimRhs) ? kDimLhs : -1)) : -1;
+  static const int kDevMask = ExpInfo<TA>::kDevMask & ExpInfo<TB>::kDevMask;
+};
+
+template<int calctype, typename OP, typename TA, typename DType, int etype>
+struct ExpInfo<ComplexUnitaryExp<calctype, OP, TA, DType, etype> > {
+  static const int kDim = ExpInfo<TA>::kDim;
+  static const int kDevMask = ExpInfo<TA>::kDevMask;
+};
+
+}  // namespace expr
+}  // namespace mshadow
+#endif  // MSHADOW_EXTENSION_COMPLEX_H_
diff --git a/3rdparty/mshadow/mshadow/extension/concat.h b/3rdparty/mshadow/mshadow/extension/concat.h
new file mode 100644
index 000000000000..c51b1dcb0a26
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/extension/concat.h
@@ -0,0 +1,194 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file concat.h
+ * \brief support for concatenation
+ */
+#ifndef MSHADOW_EXTENSION_CONCAT_H_
+#define MSHADOW_EXTENSION_CONCAT_H_
+
+#include "../extension.h"
+
+namespace mshadow {
+namespace expr {
+/*!
+ * \brief concat expression, concat two tensor's channel
+ * \tparam LhsExp left expression
+ * \tparam RhsExp right expression
+ * \tparam DType the type of elements
+ * \tparam srcdim dimension of src
+ * \tparam dimsrc_m_cat dimsrc - dimcat
+ */
+template<typename LhsExp, typename RhsExp,
+         typename Device, typename DType,
+         int srcdim, int dimsrc_m_cat>
+struct ConcatExp : public TRValue<ConcatExp<LhsExp, RhsExp,
+                                            Device, DType,
+                                            srcdim, dimsrc_m_cat>,
+                                  Device, srcdim, DType> {
+  static const int dimcat = srcdim - dimsrc_m_cat;
+  const LhsExp &src1_;
+  const RhsExp &src2_;
+  index_t dcat_src1_;
+  index_t dcat_src2_;
+  Shape<4> shape_;
+  ConcatExp(const LhsExp &src1, const RhsExp &src2) : src1_(src1), src2_(src2) {
+    Shape<srcdim> sshape1 = ShapeCheck<srcdim, LhsExp>::Check(src1_);
+    Shape<srcdim> sshape2 = ShapeCheck<srcdim, RhsExp>::Check(src2_);
+    #pragma unroll
+    for (int i = 0; i < srcdim; ++i) {
+      if (i != dimcat) {
+        CHECK_EQ(sshape1[i], sshape2[i]) << "ConcatExp: shape mismatch";
+      }
+    }
+    this->shape_ = sshape1;
+    this->shape_[dimcat] = sshape1[dimcat] + sshape2[dimcat];
+    this->dcat_src1_ = sshape1[dimcat];
+    this->dcat_src2_ = sshape2[dimcat];
+  }
+  template<typename E, int etype>
+  inline void
+  operator=(const expr::Exp<E, DType, etype> &exp) {
+    this->__assign(exp);
+  }
+  inline void
+  operator=(const DType &exp) {
+    this->__assign(exp);
+  }
+};  // struct ConcatExp
+/*!
+ * \brief concat two 4D tensor
+ * \param src1 source tensor1
+ * \param src2 source tensor2
+ * \return concated 4D tensor
+ * \tparam cdim the dimension to concatnate on
+ * \tparam SrcExp source expression
+ * \tparam DType the type of elements
+ * \tparam etype type of expression
+ */
+template<int cdim, typename LhsExp, typename RhsExp,
+         typename Device, typename DType, int srcdim>
+inline ConcatExp<LhsExp, RhsExp, Device, DType, srcdim, srcdim - cdim>
+concat(const TRValue<LhsExp, Device, srcdim, DType> &src1,
+       const TRValue<RhsExp, Device, srcdim, DType> &src2) {
+  TypeCheckPass<ExpInfo<LhsExp>::kDim == ExpInfo<RhsExp>::kDim>
+      ::Error_Expression_Does_Not_Meet_Dimension_Req();
+  TypeCheckPass<cdim < srcdim && ExpInfo<LhsExp>::kDim == srcdim>
+      ::Error_Expression_Does_Not_Meet_Dimension_Req();
+  return ConcatExp<LhsExp, RhsExp, Device, DType, srcdim, srcdim - cdim>
+      (src1.self(), src2.self());
+}
+//------------------------
+//  engine plugin
+//------------------------
+// runtime shapecheck
+template<typename LhsExp, typename RhsExp,
+         typename Device, typename DType,
+         int srcdim, int dimsrc_m_cat>
+struct ShapeCheck<srcdim, ConcatExp<LhsExp, RhsExp, Device, DType, srcdim, dimsrc_m_cat> >{
+  inline static Shape<srcdim> Check(const ConcatExp<LhsExp, RhsExp,
+                                    Device, DType, srcdim, dimsrc_m_cat> &t) {
+    return t.shape_;
+  }
+};
+template<typename LhsExp, typename RhsExp,
+         typename Device, typename DType,
+         int srcdim, int dimsrc_m_cat>
+struct StreamInfo<Device, ConcatExp<LhsExp, RhsExp, Device, DType, srcdim, dimsrc_m_cat> >{
+  inline static Stream<Device> *
+  Get(const ConcatExp<LhsExp, RhsExp, Device, DType, srcdim, dimsrc_m_cat> &t) {
+    Stream<Device> *lhs = StreamInfo<Device, LhsExp>::Get(t.src1_);
+    Stream<Device> *rhs = StreamInfo<Device, RhsExp>::Get(t.src2_);
+    if (lhs != rhs) return NULL;
+    return lhs;
+  }
+};
+// static typecheck
+template<typename LhsExp, typename RhsExp,
+         typename Device, typename DType,
+         int srcdim, int dimsrc_m_cat>
+struct ExpInfo<ConcatExp<LhsExp, RhsExp, Device, DType, srcdim, dimsrc_m_cat> >{
+  static const int kDimLhs = ExpInfo<LhsExp>::kDim;
+  static const int kDimRhs = ExpInfo<RhsExp>::kDim;
+  // copy from binarymap
+  static const int kDim = (kDimLhs >= 0 && kDimRhs >= 0) ?\
+      (kDimLhs == 0 ?\
+       kDimRhs :\
+       ((kDimRhs == 0 || kDimLhs == kDimRhs) ? kDimLhs : -1)) : -1;
+  static const int kDevMask = ExpInfo<LhsExp>::kDevMask & ExpInfo<RhsExp>::kDevMask;
+};
+//----------------------
+// Execution plan
+//---------------------
+template<typename LhsExp, typename RhsExp,
+         typename Device, typename DType,
+         int srcdim, int dimsrc_m_cat>
+struct Plan<ConcatExp<LhsExp, RhsExp, Device, DType, srcdim, dimsrc_m_cat>, DType> {
+ public:
+  static const int dimcat = srcdim - dimsrc_m_cat;
+  explicit Plan(const ConcatExp<LhsExp, RhsExp, Device, DType, srcdim, dimsrc_m_cat> &e)
+      : src1_(MakePlan(e.src1_)), src2_(MakePlan(e.src2_)),
+        height_(e.shape_.ProdShape(dimcat + 1, srcdim - 1)),
+        ch_src1_(e.dcat_src1_), ch_src2_(e.dcat_src2_), ch_(e.shape_[dimcat]) {}
+  MSHADOW_XINLINE DType Eval(index_t i, index_t j) const {
+    const index_t y = i % height_;
+    i /= height_;
+    const index_t c = i % ch_;
+    const index_t b = i / ch_;
+    const index_t x = j;
+    if (c < ch_src1_) {
+      return src1_.Eval((b * ch_src1_ + c) * height_ + y, x);
+    } else {
+      return src2_.Eval((b * ch_src2_ + c - ch_src1_) * height_ + y, x);
+    }
+  }
+  MSHADOW_XINLINE DType &REval(index_t i, index_t j) {
+    const index_t y = i % height_;
+    i /= height_;
+    const index_t c = i % ch_;
+    const index_t b = i / ch_;
+    const index_t x = j;
+    if (c < ch_src1_) {
+      return src1_.REval((b * ch_src1_ + c) * height_ + y, x);
+    } else {
+      return src2_.REval((b * ch_src2_ + c - ch_src1_) * height_ + y, x);
+    }
+  }
+
+ private:
+  Plan<LhsExp, DType> src1_;
+  Plan<RhsExp, DType> src2_;
+  const index_t height_, ch_src1_, ch_src2_, ch_;
+};  // struct Plan
+
+// specialize for concat in x
+template<typename LhsExp, typename RhsExp,
+         typename Device, typename DType,
+         int srcdim>
+struct Plan<ConcatExp<LhsExp, RhsExp, Device, DType, srcdim, 1>, DType> {
+ public:
+  explicit Plan(const ConcatExp<LhsExp, RhsExp, Device, DType, srcdim, 1> &e)
+      : src1_(MakePlan(e.src1_)), src2_(MakePlan(e.src2_)),
+        width_src1_(e.dcat_src1_) {}
+  MSHADOW_XINLINE DType Eval(index_t y, index_t x) const {
+    if (x < width_src1_) {
+      return src1_.Eval(y, x);
+    } else {
+      return src2_.Eval(y, x - width_src1_);
+    }
+  }
+  MSHADOW_XINLINE DType &REval(index_t y, index_t x) {
+    if (x < width_src1_) {
+      return src1_.REval(y, x);
+    } else {
+      return src2_.REval(y, x - width_src1_);
+    }
+  }
+
+ private:
+  Plan<LhsExp, DType> src1_;
+  Plan<RhsExp, DType> src2_;
+  const index_t width_src1_;
+};
+}  // namespace expr
+}   // namespace mshadow
+#endif  // MSHADOW_EXTENSION_CONCAT_H_
diff --git a/3rdparty/mshadow/mshadow/extension/crop.h b/3rdparty/mshadow/mshadow/extension/crop.h
new file mode 100644
index 000000000000..80096a2d22d3
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/extension/crop.h
@@ -0,0 +1,119 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file crop.h
+ * \brief support for crop
+ * \author Tianqi Chen
+ */
+#ifndef MSHADOW_EXTENSION_CROP_H_
+#define MSHADOW_EXTENSION_CROP_H_
+#include "../extension.h"
+namespace mshadow {
+namespace expr {
+/*!
+ * \brief crop expression, cut off the boundary region, reverse operation of padding
+ * \tparam SrcExp source expression to be pooled from
+ * \tparam DType the type of elements
+ * \tparam srcdim dimension of src
+ */
+template<typename SrcExp, typename DType, int srcdim>
+struct CroppingExp:
+      public MakeTensorExp<CroppingExp<SrcExp, DType, srcdim>,
+                           SrcExp, srcdim, DType> {
+  /*! \brief source operand */
+  const SrcExp &src_;
+  /*! \brief pad height */
+  index_t pad_height_;
+  /*! \brief pad height */
+  index_t pad_width_;
+  /*! \brief src height */
+  index_t src_height_;
+  /*! \brief constructor */
+  explicit CroppingExp(const SrcExp &src, Shape<2> cshape)
+      : src_(src) {
+    this->shape_ = ShapeCheck<srcdim, SrcExp>::Check(src_);
+    CHECK_GE(this->shape_[srcdim - 2], cshape[0]) << "CroppingExp: height requirement not met";
+    CHECK_GE(this->shape_[srcdim - 1], cshape[1]) << "CroppingExp: width requirement not met";
+    pad_height_ = (this->shape_[srcdim - 2] - cshape[0]) / 2;
+    pad_width_ = (this->shape_[srcdim - 1] - cshape[1]) / 2;
+    src_height_ = this->shape_[srcdim - 2];
+    this->shape_[srcdim - 2] = cshape[0];  // height
+    this->shape_[srcdim - 1] = cshape[1];  // width
+  }
+  /*! \brief constructor */
+  explicit CroppingExp(const SrcExp &src, Shape<2> cshape,
+                       index_t start_height, index_t start_width)
+      : src_(src), pad_height_(start_height), pad_width_(start_width) {
+    this->shape_ = ShapeCheck<srcdim, SrcExp>::Check(src_);
+    CHECK_GE(this->shape_[srcdim - 2], cshape[0] + start_height)
+      << "CroppingExp: height requirement not met";
+    CHECK_GE(this->shape_[srcdim - 1], cshape[1] + start_width)
+      << "CroppingExp: width requirement not met";
+    src_height_ = this->shape_[srcdim - 2];
+    this->shape_[srcdim - 2] = cshape[0];  // height
+    this->shape_[srcdim - 1] = cshape[1];  // width
+  }
+};  // struct CroppingExp
+/*!
+ * \brief revserse operationg of padding, cut off boundaries,
+ *   crop output from center of input
+ * \param src original image batches
+ * \param oshape output shape to be cropped
+ * \return expression corresponding to padded result
+ * \tparam SrcExp source expression
+ * \tparam DType the type of elements
+ * \tparam etype type of expression
+ */
+template<typename SrcExp, typename DType, int etype>
+inline CroppingExp<SrcExp, DType, ExpInfo<SrcExp>::kDim>
+crop(const Exp<SrcExp, DType, etype> &src, Shape<2> oshape) {
+  TypeCheckPass<ExpInfo<SrcExp>::kDim >= 2>
+      ::Error_Expression_Does_Not_Meet_Dimension_Req();
+  return CroppingExp<SrcExp, DType, ExpInfo<SrcExp>::kDim>(src.self(), oshape);
+}
+/*!
+ * \brief same as crop, but can specify starting position to do cropping
+ * \param src original image batches
+ * \param oshape output shape to be cropped
+ * \param start_height start height position to do cropping
+ * \param start_width  start width position to do cropping
+ * \return expression corresponding to padded result
+ * \tparam SrcExp source expression
+ * \tparam DType the type of elements
+ * \tparam etype type of expression
+ */
+template<typename SrcExp, typename DType, int etype>
+inline CroppingExp<SrcExp, DType, ExpInfo<SrcExp>::kDim>
+crop(const Exp<SrcExp, DType, etype> &src, Shape<2> oshape,
+     index_t start_height, index_t start_width) {
+  TypeCheckPass<ExpInfo<SrcExp>::kDim >= 2>
+      ::Error_Expression_Does_Not_Meet_Dimension_Req();
+  return CroppingExp<SrcExp, DType, ExpInfo<SrcExp>::kDim>
+      (src.self(), oshape, start_height, start_width);
+}
+//----------------------
+// Execution plan
+//----------------------
+template<typename SrcExp, typename DType, int srcdim>
+struct Plan<CroppingExp<SrcExp, DType, srcdim>, DType> {
+ public:
+  explicit Plan(const CroppingExp<SrcExp, DType, srcdim> &e)
+      : src_(MakePlan(e.src_)),
+        pad_height_(e.pad_height_), pad_width_(e.pad_width_),
+        new_height_(e.shape_[srcdim - 2]), src_height_(e.src_height_) {}
+  MSHADOW_XINLINE DType Eval(index_t i, index_t j) const {
+    const index_t x = j;
+    const index_t y = i % new_height_;
+    const index_t c = i / new_height_;
+    const index_t h = y + pad_height_;
+    const index_t w = x + pad_width_;
+    return src_.Eval(c * src_height_ + h, w);
+  }
+ private:
+  Plan<SrcExp, DType> src_;
+  const index_t pad_height_, pad_width_;
+  const index_t new_height_;
+  const index_t src_height_;
+};
+}  // namespace expr
+}  // namespace mshadow
+#endif  // MSHADOW_EXTENSION_CROP_H_
diff --git a/3rdparty/mshadow/mshadow/extension/fill.h b/3rdparty/mshadow/mshadow/extension/fill.h
new file mode 100644
index 000000000000..4ac62c1673e5
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/extension/fill.h
@@ -0,0 +1,103 @@
+/*!
+ *  Copyright (c) 2015 by Contributors
+ * \file fill.h
+ * \brief support for implicit array filling operation
+ * \author Xingjian Shi
+ */
+#ifndef MSHADOW_EXTENSION_FILL_H_
+#define MSHADOW_EXTENSION_FILL_H_
+
+#include "../extension.h"
+
+
+namespace mshadow {
+namespace expr {
+/*!
+ * \brief Set value of a specific element in each line of the data matrix.
+ * \tparam SrcExp type of src expression
+ * \tparam ValExp type of val expression
+ * \tparam IndexExp type of index expression
+ * \tparam DType the type of ret expression
+ */
+template<typename SrcExp, typename ValExp, typename IndexExp, typename DType>
+struct MatFillRowElementExp:
+      public Exp<MatFillRowElementExp<SrcExp, ValExp, IndexExp, DType>,
+                 DType, type::kChainer> {
+  /*! \brief src operand */
+  const SrcExp &src_;
+  const ValExp &val_;
+  /*! \brief index operand */
+  const IndexExp &index_;
+  /*! \brief constructor */
+  MatFillRowElementExp(const SrcExp &src, const ValExp &val, const IndexExp &index)
+      : src_(src), val_(val), index_(index) {}
+};
+
+template<typename SrcExp, typename ValExp, typename IndexExp,
+        typename SDType, typename VDType, typename IDType, int e1, int e2, int e3>
+inline MatFillRowElementExp<SrcExp, ValExp, IndexExp, SDType>
+mat_fill_row_element(const Exp<SrcExp, SDType, e1> &src,
+                     const Exp<ValExp, VDType, e2> &val,
+                     const Exp<IndexExp, IDType, e3> &index) {
+  TypeCheckPass<ExpInfo<SrcExp>::kDim == 2 && ExpInfo<ValExp>::kDim == 1
+                && ExpInfo<IndexExp>::kDim == 1>::Error_Expression_Does_Not_Meet_Dimension_Req();
+  return MatFillRowElementExp<SrcExp, ValExp, IndexExp, SDType>(src.self(),
+                                                                val.self(), index.self());
+}
+
+//----------------------
+// Execution plan
+//----------------------
+template<typename SrcExp, typename ValExp, typename IndexExp, typename DType>
+struct Plan<MatFillRowElementExp<SrcExp, ValExp, IndexExp, DType>, DType> {
+ public:
+  explicit Plan(const MatFillRowElementExp<SrcExp, ValExp, IndexExp, DType> &e)
+      : src_(MakePlan(e.src_)),
+        val_(MakePlan(e.val_)),
+        index_(MakePlan(e.index_)) {
+  }
+  MSHADOW_XINLINE DType Eval(index_t y, index_t x) const {
+    index_t idx = static_cast<index_t>(index_.Eval(0, y));
+    if (idx == x) {
+      return static_cast<DType>(val_.Eval(0, y));
+    } else {
+      return static_cast<DType>(src_.Eval(y, x));
+    }
+  }
+
+ private:
+  expr::Plan<SrcExp, DType> src_;
+  expr::Plan<ValExp, DType> val_;
+  expr::Plan<IndexExp, DType> index_;
+};
+
+template<typename SrcExp, typename ValExp, typename IndexExp, typename DType>
+inline Plan<MatFillRowElementExp<SrcExp, ValExp, IndexExp, DType>, DType>
+MakePlan(const MatFillRowElementExp<SrcExp, ValExp, IndexExp, DType> &exp) {
+  return Plan<MatFillRowElementExp<SrcExp, ValExp, IndexExp, DType>, DType>(exp);
+}
+
+template<int dim, typename SrcExp, typename ValExp, typename IndexExp, typename DType>
+struct ShapeCheck<dim, MatFillRowElementExp<SrcExp, ValExp, IndexExp, DType> > {
+  inline static Shape<dim>
+  Check(const MatFillRowElementExp<SrcExp, ValExp, IndexExp, DType> &t) {
+    CHECK(dim == 2)
+        << "MatFillRowElementExp only support 2 dimension output";
+    Shape<2> shape_src = ShapeCheck<2, SrcExp>::Check(t.src_);
+    Shape<1> shape_val = ShapeCheck<1, ValExp>::Check(t.val_);
+    Shape<1> shape_index = ShapeCheck<1, IndexExp>::Check(t.index_);
+    CHECK((shape_src[0] == shape_index[0]) && (shape_index[0] == shape_val[0]))
+        << "mat_fill_row_element index length, val length and number of rows in matrix";
+    return shape_src;
+  }
+};
+
+template<typename SrcExp, typename ValExp, typename IndexExp, typename DType>
+struct ExpInfo<MatFillRowElementExp<SrcExp, ValExp, IndexExp, DType> > {
+  static const int kDim = 2;
+  static const int kDevMask =
+          ExpInfo<SrcExp>::kDevMask & ExpInfo<ValExp>::kDevMask & ExpInfo<IndexExp>::kDevMask;
+};
+}  // namespace expr
+}  // namespace mshadow
+#endif  // MSHADOW_EXTENSION_FILL_H_
diff --git a/3rdparty/mshadow/mshadow/extension/flip.h b/3rdparty/mshadow/mshadow/extension/flip.h
new file mode 100644
index 000000000000..17d1894530fc
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/extension/flip.h
@@ -0,0 +1,132 @@
+/*!
+ *  Copyright (c) 2016 by Contributors
+ * \file flip.h
+ * \brief support for flip a certain dimension.
+ * \author Junyuan Xie
+ */
+#ifndef MSHADOW_EXTENSION_FLIP_H_
+#define MSHADOW_EXTENSION_FLIP_H_
+
+#include "../extension.h"
+
+namespace mshadow {
+namespace expr {
+/*!
+ * \brief slice expression, slice a tensor's channel
+ * \tparam SrcExp left expression
+ * \tparam DType the type of elements
+ * \tparam srcdim dimension of src
+ * \tparam dimsrc_m_cat dimsrc - dimcat
+ */
+template<typename SrcExp, typename Device,
+         typename DType, int srcdim>
+struct FlipExp : public TRValue<FlipExp<SrcExp,
+                                        Device, DType,
+                                        srcdim>,
+                                Device, srcdim, DType> {
+  const SrcExp &src_;
+  index_t trailing_;
+  index_t stride_;
+  index_t stride_j_;
+  Shape<srcdim> shape_;
+  FlipExp(const SrcExp &src, int dim)
+      : src_(src) {
+    shape_ = ShapeCheck<srcdim, SrcExp>::Check(src_);
+    stride_ = shape_[dim];
+    stride_j_ = shape_[srcdim-1];
+    trailing_ = 1;
+    for (int i = dim + 1; i < srcdim; ++i) {
+      trailing_ *= shape_[i];
+    }
+  }
+  template<typename E, int etype>
+  inline void
+  operator=(const expr::Exp<E, DType, etype> &exp) {
+    this->__assign(exp);
+  }
+  inline void
+  operator=(const DType &exp) {
+    this->__assign(exp);
+  }
+};  // struct Flip
+
+/*!
+ * \brief Flip a Tensor
+ * \param src source tensor
+ * \param begin The beginning slice.
+ * \param end The end slice.
+ * \return sliced tensor
+ * \tparam sdim the dimension to slice on
+ * \tparam SrcExp source expression
+ * \tparam DType the type of elements
+ * \tparam etype type of expression
+ */
+template<typename SrcExp, typename Device,
+         typename DType, int srcdim>
+inline FlipExp<SrcExp, Device, DType, srcdim>
+flip(const TRValue<SrcExp, Device, srcdim, DType> &src, int dim) {
+  return FlipExp<SrcExp, Device, DType, srcdim>(src.self(), dim);
+}
+//------------------------
+//  engine plugin
+//------------------------
+// runtime shapecheck
+template<typename SrcExp, typename Device,
+         typename DType, int srcdim>
+struct ShapeCheck<srcdim, FlipExp<SrcExp, Device, DType, srcdim> >{
+  inline static Shape<srcdim> Check(const FlipExp<SrcExp,
+                                    Device, DType, srcdim> &t) {
+    return t.shape_;
+  }
+};
+template<typename SrcExp, typename Device,
+         typename DType, int srcdim>
+struct StreamInfo<Device, FlipExp<SrcExp, Device, DType, srcdim> >{
+  inline static Stream<Device> *
+  Get(const FlipExp<SrcExp, Device, DType, srcdim> &t) {
+    return StreamInfo<Device, SrcExp>::Get(t.src_);
+  }
+};
+// static typecheck
+template<typename SrcExp, typename Device,
+         typename DType, int srcdim>
+struct ExpInfo<FlipExp<SrcExp, Device, DType, srcdim> >{
+  static const int kDim = ExpInfo<SrcExp>::kDim;
+  static const int kDevMask = ExpInfo<SrcExp>::kDevMask;
+};
+//----------------------
+// Execution plan
+//---------------------
+template<typename SrcExp, typename Device,
+         typename DType, int srcdim>
+struct Plan<FlipExp<SrcExp, Device, DType, srcdim>, DType> {
+ public:
+  explicit Plan(const FlipExp<SrcExp, Device, DType, srcdim> &e)
+      : src_(MakePlan(e.src_)), stride_j_(e.stride_j_),
+        trailing_(e.trailing_), stride_(e.stride_) {}
+  MSHADOW_XINLINE DType Eval(index_t i, index_t j) const {
+    index_t idx = i*stride_j_+j;
+    const index_t low = idx%trailing_;
+    index_t high = idx/trailing_;
+    const index_t x = high%stride_;
+    high /= stride_;
+    idx = (high*stride_+stride_-1-x)*trailing_+low;
+    return src_.Eval(idx/stride_j_, idx%stride_j_);
+  }
+  MSHADOW_XINLINE DType &REval(index_t i, index_t j) const {
+    index_t idx = i*stride_j_+j;
+    const index_t low = idx%trailing_;
+    index_t high = idx/trailing_;
+    const index_t x = high%stride_;
+    high /= stride_;
+    idx = (high*stride_+stride_-1-x)*trailing_+low;
+    return src_.REval(idx/stride_j_, idx%stride_j_);
+  }
+
+ private:
+  Plan<SrcExp, DType> src_;
+  const index_t stride_j_, trailing_, stride_;
+};  // struct Plan
+}  // namespace expr
+}   // namespace mshadow
+#endif  // MSHADOW_EXTENSION_FLIP_H_
diff --git a/3rdparty/mshadow/mshadow/extension/implicit_gemm.h b/3rdparty/mshadow/mshadow/extension/implicit_gemm.h
new file mode 100644
index 000000000000..b4b88ea326c8
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/extension/implicit_gemm.h
@@ -0,0 +1,128 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file implicit_gemm.h
+ * \brief support for implicit GEMM operation
+ * \author Tianqi Chen
+ */
+#ifndef MSHADOW_EXTENSION_IMPLICIT_GEMM_H_
+#define MSHADOW_EXTENSION_IMPLICIT_GEMM_H_
+
+#include "../extension.h"
+#include "../packet-inl.h"
+
+namespace mshadow {
+namespace expr {
+/*!
+ * \brief Matrix multiplication.
+ * \tparam LhsExp type of lhs expression
+ * \tparam LhsExp type of rhs expression
+ * \tparam DType the type of elements
+ */
+template<typename LhsExp, typename RhsExp, typename DType>
+struct ImplicitGEMMExp:
+      public Exp<ImplicitGEMMExp<LhsExp, RhsExp, DType>,
+                 DType, type::kChainer> {
+  /*! \brief lhs operand */
+  const LhsExp &lhs_;
+  /*! \brief rhs operand */
+  const RhsExp &rhs_;
+  /*! \brief internal production size*/
+  index_t prod_size_;
+  /*! \brief the shape of this expression */
+  Shape<2> shape_;
+  /*! \brief constructor */
+  ImplicitGEMMExp(const LhsExp &lhs, const RhsExp &rhs)
+      : lhs_(lhs), rhs_(rhs) {
+    Shape<2> slhs = ShapeCheck<2, LhsExp>::Check(lhs_);
+    Shape<2> srhs = ShapeCheck<2, RhsExp>::Check(rhs_);
+    this->shape_ = mshadow::Shape2(slhs[0], srhs[1]);
+    prod_size_ = slhs[1];
+  }
+};
+
+
+template<typename LhsExp, typename RhsExp, typename DType, int e1, int e2>
+inline ImplicitGEMMExp<LhsExp, RhsExp, DType>
+implicit_dot(const Exp<LhsExp, DType, e1> &lhs,
+             const Exp<RhsExp, DType, e2> &rhs) {
+  TypeCheckPass<ExpInfo<LhsExp>::kDim == 2 && ExpInfo<RhsExp>::kDim == 2>
+      ::Error_Expression_Does_Not_Meet_Dimension_Req();
+  return ImplicitGEMMExp<LhsExp, RhsExp, DType>(lhs.self(), rhs.self());
+}
+
+//----------------------
+// Execution plan
+//----------------------
+template<typename LhsExp, typename RhsExp, typename DType>
+struct Plan<ImplicitGEMMExp<LhsExp, RhsExp, DType>, DType> {
+ public:
+  explicit Plan(const ImplicitGEMMExp<LhsExp, RhsExp, DType> &e)
+      : lhs_(MakePlan(e.lhs_)),
+        rhs_(MakePlan(e.rhs_)),
+        prod_size_(e.prod_size_),
+        prod_size_lower_align_(packet::LowerAlign<DType, MSHADOW_DEFAULT_PACKET>(e.prod_size_)) {
+  }
+
+  MSHADOW_XINLINE DType Eval(index_t y, index_t x) const {
+    typedef packet::Packet<DType> Packet;
+    Packet sum = Packet::Fill(0);
+
+    const size_t packetSize = Packet::size;
+    DType lhs_temp[packetSize], rhs_temp[packetSize];
+
+    for (index_t i = 0; i < prod_size_lower_align_; i += packetSize) {
+      // unroll
+      for (index_t j = 0; j < packetSize; ++j) {
+        lhs_temp[j] = lhs_.Eval(y, i + j);
+      }
+      for (index_t j = 0; j < packetSize; ++j) {
+        rhs_temp[j] = rhs_.Eval(i + j, x);
+      }
+      sum = sum + Packet::LoadUnAligned(lhs_temp) * Packet::LoadUnAligned(rhs_temp);
+    }
+    DType ret_result = sum.Sum();
+
+    for (index_t i =  prod_size_lower_align_; i < prod_size_; ++i) {
+      ret_result += lhs_.Eval(y, i) * rhs_.Eval(i, x);
+    }
+    return ret_result;
+  }
+
+ private:
+  expr::Plan<LhsExp, DType> lhs_;
+  expr::Plan<RhsExp, DType> rhs_;
+  const index_t prod_size_;
+  const index_t prod_size_lower_align_;
+};
+
+template<typename LhsExp, typename RhsExp, typename DType>
+inline Plan<ImplicitGEMMExp<LhsExp, RhsExp, DType>, DType>
+MakePlan(const ImplicitGEMMExp<LhsExp, RhsExp, DType> &exp) {
+  return Plan<ImplicitGEMMExp<LhsExp, RhsExp, DType>, DType>(exp);
+}
+
+
+template<int dim, typename LhsExp, typename RhsExp, typename DType>
+struct ShapeCheck<dim, ImplicitGEMMExp<LhsExp, RhsExp, DType> > {
+  inline static Shape<dim>
+  Check(const ImplicitGEMMExp<LhsExp, RhsExp, DType> &t) {
+    CHECK(dim == 2)
+        << "ImplicitGEMMExp only support 2 dimension";
+    Shape<dim> shape1 = ShapeCheck<dim, LhsExp>::Check(t.lhs_);
+    Shape<dim> shape2 = ShapeCheck<dim, RhsExp>::Check(t.rhs_);
+    CHECK_EQ(shape1[1], shape2[0])
+      << "implicit_dot The matrix shape do  not match";
+    return t.shape_;
+  }
+};
+
+template<typename LhsExp, typename RhsExp, typename DType>
+struct ExpInfo<ImplicitGEMMExp<LhsExp, RhsExp, DType> > {
+  static const int kDim = 2;
+  static const int kDevMask = ExpInfo<LhsExp>::kDevMask & ExpInfo<RhsExp>::kDevMask;
+};
+
+}  // namespace expr
+}  // namespace mshadow
+#endif  // MSHADOW_EXTENSION_IMPLICIT_GEMM_H_
+
diff --git a/3rdparty/mshadow/mshadow/extension/mask.h b/3rdparty/mshadow/mshadow/extension/mask.h
new file mode 100644
index 000000000000..0fd4cc6db72e
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/extension/mask.h
@@ -0,0 +1,97 @@
+/*!
+ * Copyright (c) 2016 by Contributors
+ * \file mask.h
+ * \brief
+ * \author Bing Xu
+*/
+#ifndef MSHADOW_EXTENSION_MASK_H_
+#define MSHADOW_EXTENSION_MASK_H_
+
+#include "../extension.h"
+
+namespace mshadow {
+namespace expr {
+
+/*! \brief Broadcast a mask and do element-wise multiplication
+ *  \tparam IndexExp type of index expression
+ *  \tparam SrcExp type of src expression
+ *  \tparam DType data type
+ */
+template<typename IndexExp, typename SrcExp, typename DType>
+struct MaskExp: public Exp<MaskExp<IndexExp, SrcExp, DType>,
+                           DType, type::kChainer> {
+  /*! \brief index oprand */
+  const IndexExp &index_;
+  /*! \brief matrix oprand */
+  const SrcExp &src_;
+  /*! constructor */
+  MaskExp(const IndexExp &index, const SrcExp &src)
+    : index_(index), src_(src) {}
+};  // struct MaskExp
+
+
+
+template<typename IndexExp,
+         typename SrcExp,
+         typename DType,
+         int e1, int e2>
+inline MaskExp<IndexExp, SrcExp, DType>
+mask(const Exp<IndexExp, DType, e1> &index,
+     const Exp<SrcExp, DType, e2> &src) {
+  return MaskExp<IndexExp, SrcExp, DType>(index.self(), src.self());
+}
+
+
+//----------------------
+// Execution plan
+//----------------------
+
+template<typename IndexExp, typename SrcExp, typename DType>
+struct Plan<MaskExp<IndexExp, SrcExp, DType>, DType> {
+ public:
+  explicit Plan(const MaskExp<IndexExp, SrcExp, DType> &e)
+    : index_(MakePlan(e.index_)), src_(MakePlan(e.src_)) {
+  }
+
+  MSHADOW_XINLINE DType Eval(index_t y, index_t x) const {
+    return static_cast<DType>(src_.Eval(y, x) * index_.Eval(0, y));
+  }
+
+ private:
+  expr::Plan<IndexExp, DType> index_;
+  expr::Plan<SrcExp, DType> src_;
+};  // struct Plan
+
+template<typename IndexExp, typename SrcExp, typename DType>
+inline Plan<MaskExp<IndexExp, SrcExp, DType>, DType>
+MakePlan(const MaskExp<IndexExp, SrcExp, DType> &exp) {
+  return Plan<MaskExp<IndexExp, SrcExp, DType>, DType>(exp);
+}
+
+template<int dim, typename IndexExp, typename SrcExp, typename DType>
+struct ShapeCheck<dim, MaskExp<IndexExp, SrcExp, DType> > {
+  inline static Shape<dim>
+  Check(const MaskExp<IndexExp, SrcExp, DType> &t) {
+    CHECK(dim == 2)
+      << "MaskExp only support 2D output";
+    Shape<1> dshape = ShapeCheck<1, IndexExp>::Check(t.index_);
+    Shape<2> wshape = ShapeCheck<2, SrcExp>::Check(t.src_);
+    CHECK_EQ(dshape[0], wshape[0]) << "MaskExp require inputs in same first dimention";
+    Shape<dim> ret;
+    ret[0] = wshape[0];
+    ret[1] = wshape[1];
+    return ret;
+  }
+};
+
+
+template<typename IndexExp, typename SrcExp, typename DType>
+struct ExpInfo<MaskExp<IndexExp, SrcExp, DType> > {
+  static const int kDim = 2;
+  static const int kDevMask = ExpInfo<IndexExp>::kDevMask;
+};
+
+}  // namespace expr
+}  // namespace mshadow
+
+#endif  // MSHADOW_EXTENSION_MASK_H_
diff --git a/3rdparty/mshadow/mshadow/extension/mirror.h b/3rdparty/mshadow/mshadow/extension/mirror.h
new file mode 100644
index 000000000000..9e9edc9b6f70
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/extension/mirror.h
@@ -0,0 +1,62 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file mirror.h
+ * \brief support for mirror
+ * \author Tianqi Chen
+ */
+#ifndef MSHADOW_EXTENSION_MIRROR_H_
+#define MSHADOW_EXTENSION_MIRROR_H_
+#include "../extension.h"
+namespace mshadow {
+namespace expr {
+/*!
+ * \brief mirror expression, mirror a image in width
+ * \tparam SrcExp source expression to be mirrored
+ * \tparam DType the type of elements
+ * \tparam srcdim dimension of src
+ */
+template<typename SrcExp, typename DType, int srcdim>
+struct MirroringExp:
+      public MakeTensorExp<MirroringExp<SrcExp, DType, srcdim>,
+                           SrcExp, srcdim, DType> {
+  /*! \brief source operand */
+  const SrcExp &src_;
+  /*! \brief constructor */
+  explicit MirroringExp(const SrcExp &src) : src_(src) {
+    this->shape_ = ShapeCheck<srcdim, SrcExp>::Check(src_);
+  }
+};
+/*!
+ * \brief mirroring expression, mirror images in width
+ * \param src original image batches
+ * \return expression corresponding to mirrored result
+ * \tparam SrcExp source expression
+ * \tparam DType the type of elements
+ * \tparam etype type of expression
+ */
+template<typename SrcExp, typename DType, int etype>
+inline MirroringExp<SrcExp, DType, ExpInfo<SrcExp>::kDim>
+mirror(const Exp<SrcExp, DType, etype> &src) {
+  TypeCheckPass<ExpInfo<SrcExp>::kDim >= 2>
+      ::Error_Expression_Does_Not_Meet_Dimension_Req();
+  return MirroringExp<SrcExp, DType, ExpInfo<SrcExp>::kDim>(src.self());
+}
+//----------------------
+// Execution plan
+//----------------------
+template<typename SrcExp, typename DType, int srcdim>
+struct Plan<MirroringExp<SrcExp, DType, srcdim>, DType> {
+ public:
+  explicit Plan(const MirroringExp<SrcExp, DType, srcdim> &e)
+      : src_(MakePlan(e.src_)), width_(e.shape_[srcdim - 1]) {}
+  MSHADOW_XINLINE DType Eval(index_t i, index_t j) const {
+    return src_.Eval(i, width_ - j - 1);
+  }
+
+ private:
+  Plan<SrcExp, DType> src_;
+  const index_t width_;
+};
+}  // namespace expr
+}  // namespace mshadow
+#endif  // MSHADOW_EXTENSION_MIRROR_H_
diff --git a/3rdparty/mshadow/mshadow/extension/one_hot.h b/3rdparty/mshadow/mshadow/extension/one_hot.h
new file mode 100644
index 000000000000..326d4c3560eb
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/extension/one_hot.h
@@ -0,0 +1,87 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file one_hot.h
+ * \brief Create one-hot indicator array based on the index.
+ * \author Tianqi Chen
+ */
+#ifndef MSHADOW_EXTENSION_ONE_HOT_H_
+#define MSHADOW_EXTENSION_ONE_HOT_H_
+
+#include "../extension.h"
+
+
+namespace mshadow {
+namespace expr {
+/*!
+ * \brief Create a one-hot indicator array.
+ * \tparam IndexExp type of index expression
+ * \tparam DType the type of elements
+ */
+template<typename IndexExp, typename DType>
+struct OneHotEncodeExp:
+      public Exp<OneHotEncodeExp<IndexExp, DType>,
+                 DType, type::kChainer> {
+  /*! \brief index operand */
+  const IndexExp &index_;
+  /*! \brief number of choices we can have. */
+  index_t num_choices_;
+  /*! \brief constructor */
+  OneHotEncodeExp(const IndexExp &index, index_t num_choices)
+      : index_(index), num_choices_(num_choices) {}
+};
+
+template<typename IndexExp,
+         typename IDType, int e1>
+inline OneHotEncodeExp<IndexExp, default_real_t>
+one_hot_encode(const Exp<IndexExp, IDType, e1> &index, index_t num_choices) {
+  TypeCheckPass<ExpInfo<IndexExp>::kDim == 1>
+      ::Error_Expression_Does_Not_Meet_Dimension_Req();
+  return OneHotEncodeExp<IndexExp, default_real_t>(index.self(), num_choices);
+}
+
+//----------------------
+// Execution plan
+//----------------------
+template<typename IndexExp, typename DType>
+struct Plan<OneHotEncodeExp<IndexExp, DType>, DType> {
+ public:
+  explicit Plan(const OneHotEncodeExp<IndexExp, DType> &e)
+      : index_(MakePlan(e.index_)) {
+  }
+  MSHADOW_XINLINE DType Eval(index_t y, index_t x) const {
+    index_t idx = static_cast<index_t>(index_.Eval(0, y));
+    return static_cast<DType>(x == idx);
+  }
+
+ private:
+  expr::Plan<IndexExp, DType> index_;
+};
+
+template<typename IndexExp, typename DType>
+inline Plan<OneHotEncodeExp<IndexExp, DType>, DType>
+MakePlan(const OneHotEncodeExp<IndexExp, DType> &exp) {
+  return Plan<OneHotEncodeExp<IndexExp, DType>, DType>(exp);
+}
+
+template<int dim, typename IndexExp, typename DType>
+struct ShapeCheck<dim, OneHotEncodeExp<IndexExp, DType> > {
+  inline static Shape<dim>
+  Check(const OneHotEncodeExp<IndexExp, DType> &t) {
+    CHECK(dim == 2)
+        << "OneHotEncodeExp only support 2 dimension output";
+    Shape<1> shape = ShapeCheck<1, IndexExp>::Check(t.index_);
+    Shape<dim> ret;
+    ret[0] = shape[0];
+    ret[1] = t.num_choices_;
+    return ret;
+  }
+};
+
+template<typename IndexExp, typename DType>
+struct ExpInfo<OneHotEncodeExp<IndexExp, DType> > {
+  static const int kDim = 2;
+  static const int kDevMask = ExpInfo<IndexExp>::kDevMask;
+};
+}  // namespace expr
+}  // namespace mshadow
+#endif  // MSHADOW_EXTENSION_ONE_HOT_H_
diff --git a/3rdparty/mshadow/mshadow/extension/pack_col2patch.h b/3rdparty/mshadow/mshadow/extension/pack_col2patch.h
new file mode 100644
index 000000000000..37f1a699ead5
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/extension/pack_col2patch.h
@@ -0,0 +1,154 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file pack_col2patch.h
+ * \brief support for pack
+ * \author Tianqi Chen
+ */
+#ifndef MSHADOW_EXTENSION_PACK_COL2PATCH_H_
+#define MSHADOW_EXTENSION_PACK_COL2PATCH_H_
+#include <algorithm>
+#include "../extension.h"
+namespace mshadow {
+namespace expr {
+/*!
+ * \brief reverse operation of UnpackPatchToCol,
+ *    used to backprop gradient back
+ *    this is a version supporting multiple images
+ * \tparam SrcExp source expression
+ * \tparam DType the type of elements
+ * \tparam dstdim destination dimension
+ */
+template<typename SrcExp, typename DType, int dstdim>
+struct PackColToPatchXExp:
+      public MakeTensorExp<PackColToPatchXExp<SrcExp, DType, dstdim>,
+                           SrcExp, dstdim, DType> {
+  /*! \brief source operand */
+  const SrcExp &src_;
+  /*! \brief patch height */
+  index_t psize_y_;
+  /*! \brief patch height */
+  index_t psize_x_;
+  /*! \brief patch stride */
+  index_t pstride_y_;
+  index_t pstride_x_;
+  /*! \brief patch dilate */
+  index_t pdilate_y_;
+  index_t pdilate_x_;
+  /*! \brief constructor */
+  PackColToPatchXExp(const SrcExp &src, Shape<dstdim> imshape,
+                     index_t psize_y, index_t psize_x,
+                     index_t pstride_y, index_t pstride_x,
+                     index_t pdilate_y, index_t pdilate_x)
+      :src_(src), psize_y_(psize_y), psize_x_(psize_x),
+       pstride_y_(pstride_y), pstride_x_(pstride_x),
+       pdilate_y_(pdilate_y), pdilate_x_(pdilate_x){
+    this->shape_ = imshape;
+    const index_t o_height = (imshape[dstdim - 2] -
+        (pdilate_y * (psize_y - 1)+ 1))/pstride_y + 1;
+    const index_t o_width  = (imshape[dstdim - 1] -
+        (pdilate_x * (psize_x - 1) + 1)) / pstride_x + 1;
+    Shape<2> sshape = ShapeCheck<2, SrcExp>::Check(src_);
+    CHECK_EQ(sshape[1], o_height * o_width * imshape.ProdShape(0, dstdim - 3))
+      << "PackColToPatchExp: src.size(1) mismatch";
+    CHECK_EQ(sshape[0], psize_y * psize_x * imshape[dstdim - 3])
+      << "PackColToPatchExp: src.size(0) mismatch";
+  }
+};
+/*!
+ * \brief reverse operation of pack_col2patch, can be used to implement deconvolution
+ * \return packed img expression
+ * \param mat source matrix
+ * \param imshape shape of target img
+ * \param psize_y height of each patch
+ * \param psize_x height of each patch
+ * \param pstride stride of each patch
+ * \tparam SrcExp source expression
+ * \tparam DType the type of elements
+ * \tparam dstdim destination dimension
+ * \tparam etype type of expression
+ */
+template<typename SrcExp, typename DType, int dstdim, int etype>
+inline PackColToPatchXExp<SrcExp, DType, dstdim>
+pack_col2patch(const expr::Exp<SrcExp, DType, etype> &src,
+               Shape<dstdim> imshape, index_t psize_y,
+               index_t psize_x, index_t pstride, index_t pdilate) {
+  TypeCheckPass<ExpInfo<SrcExp>::kDim == 2>
+      ::Error_Expression_Does_Not_Meet_Dimension_Req();
+  CHECK(imshape[dstdim - 1] >= psize_x && imshape[dstdim - 2] >= psize_y)
+    << "PackColToPatch:image shape smaller than patch size";
+  return PackColToPatchXExp<SrcExp, DType, dstdim>(src.self(), imshape,
+                                                   psize_y, psize_x, pstride, pstride,
+                                                   pdilate, pdilate);
+}
+/*!
+ *if you want to specify kstride_y and kstride_x
+ */
+template<typename SrcExp, typename DType, int dstdim, int etype>
+inline PackColToPatchXExp<SrcExp, DType, dstdim>
+pack_col2patch(const expr::Exp<SrcExp, DType, etype> &src,
+               Shape<dstdim> imshape, index_t psize_y,
+               index_t psize_x, index_t pstride_y, index_t pstride_x,
+               index_t pdilate_y, index_t pdilate_x) {
+  TypeCheckPass<ExpInfo<SrcExp>::kDim == 2>
+      ::Error_Expression_Does_Not_Meet_Dimension_Req();
+  CHECK(imshape[dstdim - 1] >= psize_x && imshape[dstdim - 2] >= psize_y)
+    << "PackColToPatch:image shape smaller than patch size";
+  return PackColToPatchXExp<SrcExp, DType, dstdim>(src.self(), imshape,
+                                                   psize_y, psize_x, pstride_y, pstride_x,
+                                                   pdilate_y, pdilate_x);
+}
+
+//----------------------
+// Execution plan
+//----------------------
+template<typename SrcExp, typename DType, int dstdim>
+struct Plan<PackColToPatchXExp<SrcExp, DType, dstdim>, DType> {
+ public:
+  explicit Plan(const PackColToPatchXExp<SrcExp, DType, dstdim> &e)
+      :src_(MakePlan(e.src_)), psize_y_(e.psize_y_),
+       psize_x_(e.psize_x_), pstride_y_(e.pstride_y_), pstride_x_(e.pstride_x_),
+       i_channel_(e.shape_[dstdim - 3]), pdilate_y_(e.pdilate_y_), pdilate_x_(e.pdilate_x_),
+       i_height_(e.shape_[dstdim - 2]),
+       o_height_((e.shape_[dstdim - 2] - (pdilate_y_ * (psize_y_ - 1) + 1)) /
+               pstride_y_ + 1),
+       o_width_((e.shape_[dstdim - 1] - (pdilate_x_ * (psize_x_ - 1) + 1)) /
+               pstride_x_ + 1) {
+    // note: i/o convention are same as unpack
+  }
+  MSHADOW_XINLINE DType Eval(index_t i, index_t j) const {
+    using namespace std;
+    const index_t y = i % i_height_;
+    const index_t idivh = i / i_height_;
+    const index_t c = idivh % i_channel_;
+    const index_t n = idivh / i_channel_;
+    const index_t x = j;
+
+    const index_t psize_y_dilate = (pdilate_y_ * (psize_y_ - 1) + 1);
+    const index_t psize_x_dilate = (pdilate_x_ * (psize_x_ - 1) + 1);
+
+    const index_t py_min =
+        y < psize_y_dilate ? y % pdilate_y_ : (y-psize_y_dilate + pstride_y_) / pstride_y_;
+    const index_t px_min =
+        x < psize_x_dilate ? x % pdilate_x_ : (x-psize_x_dilate + pstride_x_) / pstride_x_;
+    const index_t py_max = min((y + pstride_y_) / pstride_y_, o_height_);
+    const index_t px_max = min((x + pstride_x_) / pstride_x_, o_width_);
+    DType res = static_cast<DType>(0);
+    for (index_t py = py_min; py < py_max; py += pdilate_y_) {
+      for (index_t px = px_min; px < px_max; px += pdilate_x_) {
+        res += src_.Eval(((c * psize_y_ + (y - py*pstride_y_) / pdilate_y_) * psize_x_ +
+                         (x - px * pstride_x_) / pdilate_x_),
+                         (n * o_height_ + py) * o_width_ + px);
+      }
+    }
+    return res;
+  }
+
+ private:
+  Plan<SrcExp, DType> src_;
+  const index_t psize_y_, psize_x_, pstride_y_, pstride_x_, i_channel_;
+  const index_t pdilate_y_, pdilate_x_;
+  const index_t i_height_, o_height_, o_width_;
+};
+}  // namespace expr
+}  // namespace mshadow
+#endif  // MSHADOW_EXTENSION_PACK_COL2PATCH_H_
diff --git a/3rdparty/mshadow/mshadow/extension/pad.h b/3rdparty/mshadow/mshadow/extension/pad.h
new file mode 100644
index 000000000000..6622a022acc8
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/extension/pad.h
@@ -0,0 +1,111 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file pad.h
+ * \brief support for pad
+ * \author Tianqi Chen
+ */
+#ifndef MSHADOW_EXTENSION_PAD_H_
+#define MSHADOW_EXTENSION_PAD_H_
+#include "../extension.h"
+namespace mshadow {
+namespace expr {
+/*!
+ * \brief padding expression, pad a image with zeros
+ * \tparam SrcExp source expression
+ * \tparam DType the type of elements
+ * \tparam srcdim dimension of src
+ */
+template<typename SrcExp, typename DType, int srcdim>
+struct PaddingExp:
+      public MakeTensorExp<PaddingExp<SrcExp, DType, srcdim>,
+                           SrcExp, srcdim, DType> {
+  /*! \brief source operand */
+  const SrcExp &src_;
+  /*! \brief pad size in y */
+  index_t pad_y_;
+  /*! \brief pad size in x */
+  index_t pad_x_;
+  /*! \brief source tensor height */
+  index_t src_height_;
+  /*! \brief source tensor width */
+  index_t src_width_;
+  /*! \brief constructor */
+  PaddingExp(const SrcExp &src, index_t pad_y, index_t pad_x)
+      : src_(src), pad_y_(pad_y), pad_x_(pad_x) {
+    this->shape_ = ShapeCheck<srcdim, SrcExp>::Check(src_);
+    src_height_ = this->shape_[srcdim - 2];
+    src_width_  = this->shape_[srcdim - 1];
+    this->shape_[srcdim - 2] += pad_y * 2;  // height
+    this->shape_[srcdim - 1] += pad_x * 2;  // width
+  }
+};
+/*!
+ * \brief padding expression, pad a image with zeros on boundaries, padding affects shape[0], and shape[1]
+ * \param src original image batches
+ * \param pad padding size
+ * \return expression corresponding to padded result
+ * \tparam SrcExp source expression
+ * \tparam DType the content data type
+ * \tparam etype type of expression
+ */
+template<typename SrcExp, typename DType, int etype>
+inline PaddingExp<SrcExp, DType, ExpInfo<SrcExp>::kDim>
+pad(const Exp<SrcExp, DType, etype> &src, index_t pad) {
+  TypeCheckPass<ExpInfo<SrcExp>::kDim >= 2>
+      ::Error_Expression_Does_Not_Meet_Dimension_Req();
+  return PaddingExp<SrcExp, DType, ExpInfo<SrcExp>::kDim>(src.self(), pad, pad);
+}
+/*!
+ * \brief padding expression, pad a image with zeros on boundaries, padding affects shape[0], and shape[1]
+ * \param src original image batches
+ * \param pad_y padding size in y
+ * \param pad_x padding size in x
+ * \return expression corresponding to padded result
+ * \tparam SrcExp source expression
+ * \tparam DType the content data type
+ * \tparam etype type of expression
+ */
+template<typename SrcExp, typename DType, int etype>
+inline PaddingExp<SrcExp, DType, ExpInfo<SrcExp>::kDim>
+pad(const Exp<SrcExp, DType, etype> &src, index_t pad_y, index_t pad_x) {
+  TypeCheckPass<ExpInfo<SrcExp>::kDim >= 2>
+      ::Error_Expression_Does_Not_Meet_Dimension_Req();
+  return PaddingExp<SrcExp, DType, ExpInfo<SrcExp>::kDim>
+      (src.self(), pad_y, pad_x);
+}
+//----------------------
+// Execution plan
+//----------------------
+template<typename SrcExp, typename DType, int srcdim>
+struct Plan<PaddingExp<SrcExp, DType, srcdim>, DType> {
+ public:
+  explicit Plan(const PaddingExp<SrcExp, DType, srcdim> &e)
+      : src_(MakePlan(e.src_)),
+        pad_y_(e.pad_y_), pad_x_(e.pad_x_),
+        new_height_(e.shape_[srcdim - 2]),
+        src_height_(e.src_height_), src_width_(e.src_width_) {}
+  MSHADOW_XINLINE DType Eval(index_t i, index_t j) const {
+    const index_t x = j;
+    const index_t y = i % new_height_;
+    const index_t c = i / new_height_;
+    if (y < pad_y_ || x < pad_x_) return static_cast<DType>(0);
+    const index_t h = y - pad_y_;
+    const index_t w = x - pad_x_;
+    if (h < src_height_ && w < src_width_) {
+      return src_.Eval(c * src_height_ + h, w);
+    } else {
+      return static_cast<DType>(0);
+    }
+  }
+
+ private:
+  Plan<SrcExp, DType> src_;
+  const index_t pad_y_;
+  const index_t pad_x_;
+  const index_t new_height_;
+  const index_t src_height_;
+  const index_t src_width_;
+};
+}  // namespace expr
+}  // namespace mshadow
+#endif  // MSHADOW_EXTENSION_PAD_H_
diff --git a/3rdparty/mshadow/mshadow/extension/range.h b/3rdparty/mshadow/mshadow/extension/range.h
new file mode 100644
index 000000000000..ab49b6e3cf18
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/extension/range.h
@@ -0,0 +1,118 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file range.h
+ * \brief support generating a range vector
+ * \author Xingjian Shi
+ */
+#ifndef MSHADOW_EXTENSION_RANGE_H_
+#define MSHADOW_EXTENSION_RANGE_H_
+
+#include "../extension.h"
+
+namespace mshadow {
+namespace expr {
+/*!
+ * \brief Generate a range vector similar to python: range(start, stop[, step][, repeat]).
+          If step is positive, the last element is the largest start + i * step less than stop
+          If step is negative, the last element is the smallest start + i * step greater than stop.
+          All elements are repeated for `repeat` times, e.g range(0, 4, 2, 3) --> 0, 0, 0, 2, 2, 2
+ * \tparam SrcExp type of lhs expression
+ * \tparam IndexExp type of index expression
+ * \tparam DType the type of elements
+ */
+template<typename DType>
+struct RangeExp:
+      public Exp<RangeExp<DType>, DType, type::kMapper> {
+  const DType start_;
+  const DType stop_;
+  const DType step_;
+  const int repeat_;
+  /*! \brief constructor */
+  RangeExp(DType start, DType stop, DType step, int repeat)
+      : start_(start), stop_(stop), step_(step), repeat_(repeat) {}
+};
+
+template<typename DType>
+inline RangeExp<DType>
+range(DType start, DType stop, DType step = 1, int repeat = 1) {
+  return RangeExp<DType>(start, stop, step, repeat);
+}
+
+//----------------------
+// Execution plan
+//----------------------
+template<typename DType>
+struct Plan<RangeExp<DType>, DType> {
+ public:
+  explicit Plan(const RangeExp<DType> &e)
+      : start_(e.start_),
+        stop_(e.stop_),
+        step_(e.step_),
+        repeat_(e.repeat_) {
+  }
+  MSHADOW_XINLINE DType Eval(index_t y, index_t x) const {
+    return start_ + static_cast<DType>((static_cast<int>(x) / repeat_)) * step_;
+  }
+
+ private:
+  const DType start_;
+  const DType stop_;
+  const DType step_;
+  const int repeat_;
+};
+
+template<typename DType>
+inline Plan<RangeExp<DType>, DType>
+MakePlan(const RangeExp<DType> &exp) {
+  return Plan<RangeExp<DType>, DType>(exp);
+}
+
+
+template<typename DType>
+inline int RangeOutSize(DType start, DType stop, DType step, int repeat) {
+  return repeat * ((stop - start - 1) / step + 1);
+}
+
+template<>
+inline int RangeOutSize<float>(float start, float stop, float step, int repeat) {
+  double d_start = static_cast<double>(start);
+  double d_stop = static_cast<double>(stop);
+  double d_step = static_cast<double>(step);
+  return repeat * static_cast<int>(ceil((d_stop - d_start) / d_step));
+}
+
+template<>
+inline int RangeOutSize<double>(double start, double stop, double step, int repeat) {
+  return repeat * static_cast<int>(ceil((stop - start) / step));
+}
+
+
+template<int dim, typename DType>
+struct ShapeCheck<dim, RangeExp<DType> > {
+  inline static Shape<dim>
+  Check(const RangeExp<DType> &t) {
+    CHECK(dim == 1)
+        << "RangeExp only support 1 dimension output, received " << dim;
+    CHECK(t.step_ != 0)
+        << "RangeExp does not support step=0, received " << t.step_;
+    CHECK(t.repeat_ > 0)
+      << "RangeExp only supports repeat > 0, received " << t.repeat_;
+    if (t.step_ > 0) {
+      CHECK(t.start_ < t.stop_) << "RangeExp does not support (start, stop, step) = "
+                                << "(" << t.start_ << "," << t.stop_ << "," << t.step_ << ")";
+    } else {
+      CHECK(t.start_ > t.stop_) << "RangeExp does not support (start, stop, step)= "
+                                << "(" << t.start_ << "," << t.stop_ << "," << t.step_ << ")";
+    }
+    return Shape1(RangeOutSize<DType>(t.start_, t.stop_, t.step_, t.repeat_));
+  }
+};
+
+template<typename DType>
+struct ExpInfo<RangeExp<DType> > {
+  static const int kDim = 1;
+  static const int kDevMask = 0xffff;
+};
+}  // namespace expr
+}  // namespace mshadow
+#endif  // MSHADOW_EXTENSION_RANGE_H_
diff --git a/3rdparty/mshadow/mshadow/extension/reduce_with_axis.h b/3rdparty/mshadow/mshadow/extension/reduce_with_axis.h
new file mode 100644
index 000000000000..54bcc750cfc5
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/extension/reduce_with_axis.h
@@ -0,0 +1,136 @@
+/*!
+ * Copyright (c) 2015 by Contributors
+ * \file reduce_with_axis.h
+ * \brief
+ * \author Junyuan Xie
+*/
+#ifndef MSHADOW_EXTENSION_REDUCE_WITH_AXIS_H_
+#define MSHADOW_EXTENSION_REDUCE_WITH_AXIS_H_
+
+#include "../extension.h"
+
+namespace mshadow {
+namespace expr {
+
+/*! \brief reduce out the dimension of src labeled by axis.
+ *  \tparam Reducer type of reducer
+ *  \tparam SrcExp type of source expression
+ *  \tparam DType data type
+ */
+template<typename Reducer, typename SrcExp, typename DType, int dimsrc, bool mask, int dimdst>
+struct ReduceWithAxisExp:
+    public MakeTensorExp<ReduceWithAxisExp<Reducer, SrcExp, DType, dimsrc, mask, dimdst>,
+                         SrcExp, dimdst, DType> {
+  /*! \brief source oprand */
+  const SrcExp &src_;
+  /*! \brief size of last destination dimension */
+  index_t last_dst_dim_;
+  /*! \brief size of trailing dimensions */
+  index_t trailing_;
+  /*! \brief size of axis dimension */
+  index_t size_;
+  /*! \brief size of last src dimension */
+  index_t last_;
+  /*! constructor */
+  explicit ReduceWithAxisExp(const SrcExp &src, int axis)
+    : src_(src) {
+    bool keepdim = (dimsrc == dimdst);
+    CHECK(dimsrc > axis) << "reduce axis out of bound";
+    Shape<dimsrc> src_shape = ShapeCheck<dimsrc, SrcExp>::Check(src_);
+    for (int i = 0; i < axis; ++i) {
+      this->shape_[i] = src_shape[i];
+    }
+    this->size_ = src_shape[axis];
+    this->trailing_ = 1;
+    if (!keepdim) {
+      for (int i = axis + 1; i < dimsrc; ++i) {
+        this->trailing_ *= src_shape[i];
+        this->shape_[i - 1] = src_shape[i];
+      }
+    } else {
+      this->shape_[axis] = 1;
+      for (index_t i = axis + 1; i < dimsrc; ++i) {
+        this->trailing_ *= src_shape[i];
+        this->shape_[i] = src_shape[i];
+      }
+    }
+
+    this->last_ = src_shape[dimsrc - 1];
+    this->last_dst_dim_ = this->shape_[dimdst - 1];
+  }
+};  // struct ReduceWithAxisExp
+
+/*!
+ * \brief reduce out the dimension of src labeled by axis.
+ * \param Reducer type of the reducing operation
+ * \param mask whether to output the unmask indices
+ * \tparam SrcExp source expression
+ * \tparam DType data type
+ * \tparam etype type of the expression
+ */
+template<typename Reducer, bool mask, typename SrcExp, typename DType, int etype>
+inline ReduceWithAxisExp<Reducer, SrcExp, DType, ExpInfo<SrcExp>::kDim, mask,
+  ExpInfo<SrcExp>::kDim - 1>
+reduce_with_axis(const Exp<SrcExp, DType, etype> &src, int axis) {
+  return ReduceWithAxisExp<Reducer, SrcExp, DType, ExpInfo<SrcExp>::kDim, mask,
+    ExpInfo<SrcExp>::kDim- 1>(src.self(), axis);
+}
+
+/*!
+* \brief reduce out the dimension of src labeled by axis, keepdim turned on.
+* \param Reducer type of the reducing operation
+* \param mask whether to output the unmask indices
+* \tparam SrcExp source expression
+* \tparam DType data type
+* \tparam etype type of the expression
+*/
+template<typename Reducer, bool mask, typename SrcExp, typename DType, int etype>
+inline ReduceWithAxisExp<Reducer, SrcExp, DType, ExpInfo<SrcExp>::kDim, mask,
+  ExpInfo<SrcExp>::kDim>
+  reduce_keepdim(const Exp<SrcExp, DType, etype> &src, int axis) {
+  return ReduceWithAxisExp<Reducer, SrcExp, DType, ExpInfo<SrcExp>::kDim, mask,
+    ExpInfo<SrcExp>::kDim>(src.self(), axis);
+}
+
+//----------------------
+// Execution plan
+//----------------------
+template<typename Reducer, typename SrcExp, typename DType, int dimsrc, bool mask, int dimdst>
+struct Plan<ReduceWithAxisExp<Reducer, SrcExp, DType, dimsrc, mask, dimdst>, DType> {
+ public:
+  explicit Plan(const ReduceWithAxisExp<Reducer, SrcExp, DType, dimsrc, mask, dimdst> &e)
+      : src_(MakePlan(e.src_)), last_dst_dim_(e.last_dst_dim_), trailing_(e.trailing_),
+        size_(e.size_), last_(e.last_) {}
+  MSHADOW_XINLINE DType Eval(index_t i, index_t j) const {
+    index_t x = (i*last_dst_dim_ + j)/trailing_;
+    index_t y = (i*last_dst_dim_ + j)%trailing_;
+
+    if (mask) {
+      index_t idx = 0;
+      DType res; Reducer::SetInitValue(res);
+      for (index_t k = 0; k < size_; ++k) {
+        index_t z = (x*size_+k)*trailing_+y;
+        DType tmp = res;
+        Reducer::Reduce(res, src_.Eval(z/last_, z%last_));
+        if (tmp != res) {
+          idx = k;
+        }
+      }
+      return static_cast<DType>(static_cast<int>(idx));
+    } else {
+      DType res; Reducer::SetInitValue(res);
+      for (index_t k = 0; k < size_; ++k) {
+        index_t z = (x*size_+k)*trailing_+y;
+        Reducer::Reduce(res, src_.Eval(z/last_, z%last_));
+      }
+      return res;
+    }
+  }
+
+ private:
+  Plan<SrcExp, DType> src_;
+  const index_t last_dst_dim_, trailing_, size_, last_;
+};
+}  // namespace expr
+}  // namespace mshadow
+#endif  // MSHADOW_EXTENSION_REDUCE_WITH_AXIS_H_
diff --git a/3rdparty/mshadow/mshadow/extension/reduceto1d.h b/3rdparty/mshadow/mshadow/extension/reduceto1d.h
new file mode 100644
index 000000000000..09a478ab311e
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/extension/reduceto1d.h
@@ -0,0 +1,104 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file reduceto1d.h
+ * \brief support for sum_rows and sumall_except_dim
+ * \author Tianqi Chen
+ */
+#ifndef MSHADOW_EXTENSION_REDUCETO1D_H_
+#define MSHADOW_EXTENSION_REDUCETO1D_H_
+#include "../extension.h"
+namespace mshadow {
+namespace expr {
+/*!
+ * \brief reduction to 1 dimension tensor
+ * input: Tensor<Device,k>: ishape
+ * output: Tensor<Device,1> shape[0] = ishape[dimkeep];
+ *
+ * \tparam SrcExp type of expression to be reduced
+ * \tparam DType the data type of the scalar
+ * \tparam Reducer which reducer to use
+ * \tparam m_dimkeep which dimension to be kept, encoded with dimsrc - dimkeep
+ */
+template<typename SrcExp, typename DType, typename Reducer, int m_dimkeep>
+struct ReduceTo1DExp:
+      public Exp<ReduceTo1DExp<SrcExp, DType, Reducer, m_dimkeep>,
+                 DType, type::kComplex> {
+  /*! \brief source operand */
+  const SrcExp &src_;
+  /*! \brief source operand, scale of the  */
+  DType scale_;
+  /*! \brief construct a repmat expression from src and nrow */
+  ReduceTo1DExp(const SrcExp& src, DType scale) : src_(src), scale_(scale) {}
+};
+/*!
+ * \brief a sum over all dimensions, except dimkeep
+ * \param exp input expression that must be a matrix Tensor<?,2>
+ * \return a expresion with type Tensor<Device,1>
+ * \tparam dimkeep the dimension that will be kept
+ * \tparam SrcExp expression
+ * \tparam etype type of expression
+ */
+template<int dimkeep,  typename SrcExp, typename DType, int etype>
+inline ReduceTo1DExp<SrcExp, DType, red::sum,
+                     ExpInfo<SrcExp>::kDim - dimkeep>
+sumall_except_dim(const Exp<SrcExp, DType, etype> &exp) {
+  return ReduceTo1DExp<SrcExp, DType, red::sum,
+                       ExpInfo<SrcExp>::kDim - dimkeep>(exp.self(), DType(1));
+}
+/*!
+ * \brief reduce over all dimensions, except dimkeep
+ * \param exp input expression that must be a matrix Tensor<?,2>
+ * \return a expresion with type Tensor<Device,1>
+ * \tparam dimkeep the dimension that will be kept
+ * \tparam SrcExp expression
+ * \tparam etype type of expression
+ */
+template<int dimkeep, typename Reducer, typename SrcExp, typename DType, int etype>
+inline ReduceTo1DExp<SrcExp, DType, Reducer,
+                     ExpInfo<SrcExp>::kDim - dimkeep>
+reduce_except_dim(const Exp<SrcExp, DType, etype> &exp) {
+  return ReduceTo1DExp<SrcExp, DType, Reducer,
+                       ExpInfo<SrcExp>::kDim - dimkeep>(exp.self(), DType(1));
+}
+/*!
+ * \brief a expression that sum over rows of a matrix
+ * \param exp input expression that must be a matrix Tensor<?, 2>
+ * \return a expresion with type Tensor<Device, 1>
+ * \tparam SrcExp expression
+ * \tparam etype type of expression
+ */
+template<typename SrcExp, typename DType, int etype>
+inline ReduceTo1DExp<SrcExp, DType, red::sum, 1>
+sum_rows(const Exp<SrcExp, DType, etype> &exp) {
+  TypeCheckPass<ExpInfo<SrcExp>::kDim ==2>
+      ::Error_Expression_Does_Not_Meet_Dimension_Req();
+  return sumall_except_dim<1>(exp);
+}
+template<typename SV, typename Device, typename DType,
+         typename SrcExp, typename Reducer, int m_dimkeep>
+struct ExpComplexEngine<SV,
+                        Tensor<Device, 1, DType>,
+                        ReduceTo1DExp<SrcExp, DType, Reducer, m_dimkeep>,
+                        DType> {
+  static const int dimkeep = ExpInfo<SrcExp>::kDim - m_dimkeep;
+  inline static void Eval(Tensor<Device, 1, DType> *dst,
+                          const ReduceTo1DExp<SrcExp, DType,
+                                              Reducer, m_dimkeep> &exp) {
+    TypeCheckPass<m_dimkeep != 1>
+        ::Error_Expression_Does_Not_Meet_Dimension_Req();
+    MapReduceKeepHighDim<SV, Reducer, dimkeep>(dst, exp.src_, exp.scale_);
+  }
+};
+template<typename SV, typename Device, typename DType,
+         typename SrcExp, typename Reducer>
+struct ExpComplexEngine<SV,
+                        Tensor<Device, 1, DType>,
+                        ReduceTo1DExp<SrcExp, DType, Reducer, 1>, DType> {
+  inline static void Eval(Tensor<Device, 1, DType> *dst,
+                          const ReduceTo1DExp<SrcExp, DType, Reducer, 1> &exp) {
+    MapReduceKeepLowest<SV, Reducer>(dst, exp.src_, exp.scale_);
+  }
+};
+}  // namespace expr
+}  // namespace mshadow
+#endif  // MSHADOW_EXTENSION_REDUCETO1D_H_
diff --git a/3rdparty/mshadow/mshadow/extension/reshape.h b/3rdparty/mshadow/mshadow/extension/reshape.h
new file mode 100644
index 000000000000..b310fe69291a
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/extension/reshape.h
@@ -0,0 +1,87 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file reshape.h
+ * \brief support for reshape
+ * \author Tianqi Chen
+ */
+#ifndef MSHADOW_EXTENSION_RESHAPE_H_
+#define MSHADOW_EXTENSION_RESHAPE_H_
+#include "../extension.h"
+namespace mshadow {
+namespace expr {
+/*!
+ * \brief reshape the content to another shape
+ * input: Tensor<Device,dimsrc>: ishape
+ * output: Tensor<Device,dimdst> ishape.Size() == oshape.Size()
+ * \tparam SrcExp source expression
+ * \tparam dimdst target dimension
+ * \tparam dimsrc source dimension
+ */
+template<typename SrcExp, typename DType, int dimdst, int dimsrc>
+struct ReshapeExp:
+      public MakeTensorExp<ReshapeExp<SrcExp, DType, dimdst, dimsrc>,
+                           SrcExp, dimdst, DType> {
+  /*! \brief source expression */
+  const SrcExp &src_;
+  /*! \brief smallest dimension of input */
+  index_t ishapex_;
+  /*! \brief constructor */
+  ReshapeExp(const SrcExp &src, Shape<dimdst> shape)
+      : src_(src) {
+    Shape<dimsrc> ishape = ShapeCheck<dimsrc, SrcExp>::Check(src_);
+    CHECK_EQ(ishape.Size(), shape.Size()) << "reshape size must match";
+    ishapex_ = ishape[dimsrc - 1];
+    this->shape_ = shape;
+  }
+};
+/*!
+ * \brief a expression that reshapes a tensor to another shape
+ * \param src Tensor<Device,dimsrc>:
+ * \param oshape target shape
+ * \return a expresion with type Tensor<Device,dimdst>
+ * \tparam SrcExp source expression
+ * \tparam etype source expression type
+ * \tparam dimdst target dimension
+ */
+template<typename SrcExp, typename DType, int etype, int dimdst>
+inline ReshapeExp<SrcExp, DType, dimdst, ExpInfo<SrcExp>::kDim>
+reshape(const Exp<SrcExp, DType, etype> &src, Shape<dimdst> oshape) {
+  return ReshapeExp<SrcExp, DType, dimdst, ExpInfo<SrcExp>::kDim>
+      (src.self(), oshape);
+}
+//----------------------
+// Execution plan
+//----------------------
+template<typename SrcExp, typename DType, int dimdst, int dimsrc>
+struct Plan<ReshapeExp<SrcExp, DType, dimdst, dimsrc>, DType> {
+ public:
+  explicit Plan(const ReshapeExp<SrcExp, DType, dimdst, dimsrc> &e)
+      : src_(MakePlan(e.src_)),
+        oshapex_(e.shape_[dimdst - 1]), ishapex_(e.ishapex_) {}
+  MSHADOW_XINLINE DType Eval(index_t y, index_t x) const {
+    const index_t idx = y * oshapex_ + x;
+    return src_.Eval(idx / ishapex_, idx % ishapex_);
+  }
+
+ private:
+  Plan<SrcExp, DType> src_;
+  const index_t oshapex_, ishapex_;
+};
+// special work plan for 1 dimensional data
+template<typename SrcExp, typename DType, int dimdst>
+struct Plan<ReshapeExp<SrcExp, DType, dimdst, 1>, DType> {
+ public:
+  explicit Plan(const ReshapeExp<SrcExp, DType, dimdst, 1> &e)
+      : src_(MakePlan(e.src_)), oshapex_(e.shape_[dimdst - 1]) {
+  }
+  MSHADOW_XINLINE DType Eval(index_t y, index_t x) const {
+    return src_.Eval(0, y * oshapex_ + x);
+  }
+
+ private:
+  Plan<SrcExp, DType> src_;
+  const index_t oshapex_;
+};
+}  // namespace expr
+}  // namespace mshadow
+#endif  // MSHADOW_EXTENSION_RESHAPE_H_
diff --git a/3rdparty/mshadow/mshadow/extension/slice.h b/3rdparty/mshadow/mshadow/extension/slice.h
new file mode 100644
index 000000000000..cb2eff4548aa
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/extension/slice.h
@@ -0,0 +1,156 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file slice.h
+ * \brief support for slice a certain dimension.
+ */
+#ifndef MSHADOW_EXTENSION_SLICE_H_
+#define MSHADOW_EXTENSION_SLICE_H_
+
+#include "../extension.h"
+
+namespace mshadow {
+namespace expr {
+/*!
+ * \brief slice expression, slice a tensor's channel
+ * \tparam SrcExp left expression
+ * \tparam DType the type of elements
+ * \tparam srcdim dimension of src
+ * \tparam dimsrc_m_cat dimsrc - dimcat
+ */
+template<typename SrcExp,
+         typename Device, typename DType,
+         int srcdim, int dimsrc_m_slice>
+struct SliceExp : public TRValue<SliceExp<SrcExp,
+                                          Device, DType,
+                                          srcdim, dimsrc_m_slice>,
+                                 Device, srcdim, DType> {
+  static const int dimslice = srcdim - dimsrc_m_slice;
+  const SrcExp &src_;
+  index_t ch_begin_;
+  index_t ch_old_;
+  Shape<srcdim> shape_;
+  SliceExp(const SrcExp &src, index_t begin, index_t end)
+      : src_(src), ch_begin_(begin) {
+    shape_ = ShapeCheck<srcdim, SrcExp>::Check(src_);
+    ch_old_ = shape_[dimslice];
+    CHECK(begin < shape_[dimslice] && end <= shape_[dimslice])
+        << "The slice went out of range";
+    shape_[dimslice] = end - begin;
+  }
+  template<typename E, int etype>
+  inline void
+  operator=(const expr::Exp<E, DType, etype> &exp) {
+    this->__assign(exp);
+  }
+  inline void
+  operator=(const DType &exp) {
+    this->__assign(exp);
+  }
+};  // struct Slice
+
+/*!
+ * \brief Slice a Tensor
+ * \param src source tensor
+ * \param begin The beginning slice.
+ * \param end The end slice.
+ * \return sliced tensor
+ * \tparam sdim the dimension to slice on
+ * \tparam SrcExp source expression
+ * \tparam DType the type of elements
+ * \tparam etype type of expression
+ */
+template<int sdim, typename SrcExp,
+         typename Device, typename DType, int srcdim>
+inline SliceExp<SrcExp, Device, DType, srcdim, srcdim - sdim>
+slice(const TRValue<SrcExp, Device, srcdim, DType> &src, index_t begin, index_t end) {
+  TypeCheckPass<sdim < srcdim && ExpInfo<SrcExp>::kDim == srcdim>
+      ::Error_Expression_Does_Not_Meet_Dimension_Req();
+  return SliceExp<SrcExp, Device, DType, srcdim, srcdim - sdim>(src.self(), begin, end);
+}
+//------------------------
+//  engine plugin
+//------------------------
+// runtime shapecheck
+template<typename SrcExp,
+         typename Device, typename DType,
+         int srcdim, int dimsrc_m_slice>
+struct ShapeCheck<srcdim, SliceExp<SrcExp, Device, DType, srcdim, dimsrc_m_slice> >{
+  inline static Shape<srcdim> Check(const SliceExp<SrcExp,
+                                    Device, DType, srcdim, dimsrc_m_slice> &t) {
+    return t.shape_;
+  }
+};
+template<typename SrcExp,
+         typename Device, typename DType,
+         int srcdim, int dimsrc_m_slice>
+struct StreamInfo<Device, SliceExp<SrcExp, Device, DType, srcdim, dimsrc_m_slice> >{
+  inline static Stream<Device> *
+  Get(const SliceExp<SrcExp, Device, DType, srcdim, dimsrc_m_slice> &t) {
+    return StreamInfo<Device, SrcExp>::Get(t.src_);
+  }
+};
+// static typecheck
+template<typename SrcExp,
+         typename Device, typename DType,
+         int srcdim, int dimsrc_m_slice>
+struct ExpInfo<SliceExp<SrcExp, Device, DType, srcdim, dimsrc_m_slice> >{
+  static const int kDim = ExpInfo<SrcExp>::kDim;
+  static const int kDevMask = ExpInfo<SrcExp>::kDevMask;
+};
+//----------------------
+// Execution plan
+//---------------------
+template<typename SrcExp,
+         typename Device, typename DType,
+         int srcdim, int dimsrc_m_slice>
+struct Plan<SliceExp<SrcExp, Device, DType, srcdim, dimsrc_m_slice>, DType> {
+ public:
+  static const int dimslice = srcdim - dimsrc_m_slice;
+  explicit Plan(const SliceExp<SrcExp, Device, DType, srcdim, dimsrc_m_slice> &e)
+      : src_(MakePlan(e.src_)),
+        height_(e.shape_.ProdShape(dimslice + 1, srcdim - 1)),
+        ch_begin_(e.ch_begin_), ch_old_(e.ch_old_), ch_(e.shape_[dimslice]) {}
+  MSHADOW_XINLINE DType Eval(index_t i, index_t j) const {
+    const index_t y = i % height_;
+    i /= height_;
+    const index_t c = i % ch_ + ch_begin_;
+    const index_t b = i / ch_;
+    const index_t x = j;
+    return src_.Eval((b * ch_old_ + c) * height_ + y, x);
+  }
+  MSHADOW_XINLINE DType &REval(index_t i, index_t j) {
+    const index_t y = i % height_;
+    i /= height_;
+    const index_t c = i % ch_ + ch_begin_;
+    const index_t b = i / ch_;
+    const index_t x = j;
+    return src_.REval((b * ch_old_ + c) * height_ + y, x);
+  }
+
+ private:
+  Plan<SrcExp, DType> src_;
+  const index_t height_, ch_begin_, ch_old_, ch_;
+};  // struct Plan
+
+template<typename SrcExp,
+         typename Device, typename DType,
+         int srcdim>
+struct Plan<SliceExp<SrcExp, Device, DType, srcdim, 1>, DType> {
+ public:
+  explicit Plan(const SliceExp<SrcExp, Device, DType, srcdim, 1> &e)
+      : src_(MakePlan(e.src_)),
+        ch_begin_(e.ch_begin_) {}
+  MSHADOW_XINLINE DType Eval(index_t y, index_t x) const {
+    return src_.Eval(y, x + ch_begin_);
+  }
+  MSHADOW_XINLINE DType &REval(index_t y, index_t x) {
+    return src_.REval(y, x + ch_begin_);
+  }
+
+ private:
+  Plan<SrcExp, DType> src_;
+  const index_t ch_begin_;
+};
+}  // namespace expr
+}   // namespace mshadow
+#endif  // MSHADOW_EXTENSION_SLICE_H_
diff --git a/3rdparty/mshadow/mshadow/extension/slice_ex.h b/3rdparty/mshadow/mshadow/extension/slice_ex.h
new file mode 100644
index 000000000000..7f464097fb3b
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/extension/slice_ex.h
@@ -0,0 +1,135 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file slice.h
+ * \brief support for slice a certain dimension.
+ */
+#ifndef MSHADOW_EXTENSION_SLICE_EX_H_
+#define MSHADOW_EXTENSION_SLICE_EX_H_
+
+#include "../extension.h"
+
+namespace mshadow {
+namespace expr {
+/*!
+ * \brief slice expression, slice a tensor's channel
+ * \tparam SrcExp left expression
+ * \tparam DType the type of elements
+ * \tparam srcdim dimension of src
+ * \tparam dimsrc_m_cat dimsrc - dimcat
+ */
+template<typename SrcExp, typename Device,
+         typename DType, int srcdim>
+struct SliceExExp : public TRValue<SliceExExp<SrcExp,
+                                              Device, DType,
+                                              srcdim>,
+                                   Device, srcdim, DType> {
+  const SrcExp &src_;
+  Shape<srcdim> src_shape_;
+  Shape<srcdim> shape_;
+  const Shape<srcdim> begin_;
+  const Shape<srcdim> end_;
+  SliceExExp(const SrcExp &src, Shape<srcdim> begin, Shape<srcdim> end)
+      : src_(src), begin_(begin), end_(end) {
+    src_shape_ = ShapeCheck<srcdim, SrcExp>::Check(src_);
+    for (int i = 0; i < srcdim; ++i) {
+      shape_[i] = end_[i] - begin_[i];
+    }
+  }
+  template<typename E, int etype>
+  inline void
+  operator=(const expr::Exp<E, DType, etype> &exp) {
+    this->__assign(exp);
+  }
+  inline void
+  operator=(const DType &exp) {
+    this->__assign(exp);
+  }
+};  // struct SliceEx
+
+/*!
+ * \brief SliceEx a Tensor
+ * \param src source tensor
+ * \param begin The beginning slice.
+ * \param end The end slice.
+ * \return sliced tensor
+ * \tparam sdim the dimension to slice on
+ * \tparam SrcExp source expression
+ * \tparam DType the type of elements
+ * \tparam etype type of expression
+ */
+template<typename SrcExp, typename Device,
+         typename DType, int srcdim>
+inline SliceExExp<SrcExp, Device, DType, srcdim>
+slice(const TRValue<SrcExp, Device, srcdim, DType> &src, Shape<srcdim> begin, Shape<srcdim> end) {
+  TypeCheckPass<ExpInfo<SrcExp>::kDim == srcdim>
+      ::Error_Expression_Does_Not_Meet_Dimension_Req();
+  return SliceExExp<SrcExp, Device, DType, srcdim>(src.self(), begin, end);
+}
+//------------------------
+//  engine plugin
+//------------------------
+// runtime shapecheck
+template<typename SrcExp, typename Device,
+         typename DType, int srcdim>
+struct ShapeCheck<srcdim, SliceExExp<SrcExp, Device, DType, srcdim> >{
+  inline static Shape<srcdim> Check(const SliceExExp<SrcExp,
+                                    Device, DType, srcdim> &t) {
+    return t.shape_;
+  }
+};
+
+template<typename SrcExp, typename Device,
+         typename DType, int srcdim>
+struct StreamInfo<Device, SliceExExp<SrcExp, Device, DType, srcdim> >{
+  inline static Stream<Device> *
+  Get(const SliceExExp<SrcExp, Device, DType, srcdim> &t) {
+    return StreamInfo<Device, SrcExp>::Get(t.src_);
+  }
+};
+// static typecheck
+template<typename SrcExp, typename Device,
+         typename DType, int srcdim>
+struct ExpInfo<SliceExExp<SrcExp, Device, DType, srcdim> >{
+  static const int kDim = ExpInfo<SrcExp>::kDim;
+  static const int kDevMask = ExpInfo<SrcExp>::kDevMask;
+};
+//----------------------
+// Execution plan
+//---------------------
+template<typename SrcExp, typename Device,
+         typename DType, int srcdim>
+struct Plan<SliceExExp<SrcExp, Device, DType, srcdim>, DType> {
+ public:
+  explicit Plan(const SliceExExp<SrcExp, Device, DType, srcdim> &e)
+      : src_(MakePlan(e.src_)), begin_(e.begin_),
+        src_shape_(e.src_shape_), shape_(e.shape_) {}
+  MSHADOW_XINLINE DType Eval(index_t i, index_t j) const {
+    index_t idx = 0;
+    index_t stride = 1;
+    #pragma unroll
+    for (int k = srcdim-2; k >= 0; --k) {
+      idx += stride * (i%shape_[k] + begin_[k]);
+      i /= shape_[k];
+      stride *= src_shape_[k];
+    }
+    return src_.Eval(idx, j + begin_[srcdim-1]);
+  }
+  MSHADOW_XINLINE DType &REval(index_t i, index_t j) {
+    index_t idx = 0;
+    index_t stride = 1;
+    #pragma unroll
+    for (int k = srcdim-2; k >= 0; --k) {
+      idx += stride * (i%shape_[k] + begin_[k]);
+      i /= shape_[k];
+      stride *= src_shape_[k];
+    }
+    return src_.REval(idx, j + begin_[srcdim-1]);
+  }
+
+ private:
+  Plan<SrcExp, DType> src_;
+  const Shape<srcdim> begin_, src_shape_, shape_;
+};  // struct Plan
+}  // namespace expr
+}   // namespace mshadow
+#endif  // MSHADOW_EXTENSION_SLICE_EX_H_
diff --git a/3rdparty/mshadow/mshadow/extension/spatial_pool.h b/3rdparty/mshadow/mshadow/extension/spatial_pool.h
new file mode 100644
index 000000000000..c833fb40ad58
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/extension/spatial_pool.h
@@ -0,0 +1,152 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file spatial_pool.h
+ * \brief support for spatial pooling
+ * \author Tianqi Chen
+ */
+#ifndef MSHADOW_EXTENSION_SPATIAL_POOL_H_
+#define MSHADOW_EXTENSION_SPATIAL_POOL_H_
+#include <algorithm>
+#include "../extension.h"
+namespace mshadow {
+namespace expr {
+/*!
+ * \brief pooling expression, do reduction over local patches of a image
+ * \tparam Reducer reduction method during pooling
+ * \tparam SrcExp source expression to be pooled from
+ * \tparam DType the content data type
+ * \tparam srcdim dimension of src
+ */
+template<typename Reducer, typename SrcExp, typename DType, int srcdim>
+struct PoolingExp:
+      public MakeTensorExp<PoolingExp<Reducer, SrcExp, DType, srcdim>,
+                           SrcExp, srcdim, DType> {
+  /*! \brief source operand */
+  const SrcExp &src_;
+  /*! \brief kernel size in height */
+  index_t ksize_y_;
+  /*! \brief kernel size in width */
+  index_t ksize_x_;
+  /*! \brief kernel stride in y directory */
+  index_t kstride_y_;
+  /*! \brief kernel stride in x directory */
+  index_t kstride_x_;
+  /*! \brief source height shape[1] */
+  index_t src_height_;
+  /*! \brief source width shape[0] */
+  index_t src_width_;
+  /*! \brief constructor */
+  PoolingExp(const SrcExp &src,
+             index_t ksize_y, index_t ksize_x, index_t kstride_y, index_t kstride_x)
+             : src_(src), ksize_y_(ksize_y), ksize_x_(ksize_x),
+               kstride_y_(kstride_y), kstride_x_(kstride_x) {
+    Shape<srcdim> sshape = ShapeCheck<srcdim, SrcExp>::Check(src_);
+    CHECK(sshape[srcdim - 1] >= ksize_x && sshape[srcdim - 2] >= ksize_y)
+      << "PoolingExp: kernel must be smaller than image";
+    this->src_height_ = sshape[srcdim - 2];
+    this->src_width_  = sshape[srcdim - 1];
+    this->shape_ = sshape;
+    this->shape_[srcdim - 2] = (src_height_ - ksize_y) / kstride_y + 1;
+    this->shape_[srcdim - 1] = (src_width_  - ksize_x) / kstride_x + 1;
+  }
+  /*! \brief constructor, specify shape */
+  PoolingExp(const SrcExp &src, Shape<2> pshape,
+             index_t ksize_y, index_t ksize_x, index_t kstride_y, index_t kstride_x)
+             : src_(src), ksize_y_(ksize_y), ksize_x_(ksize_x),
+               kstride_y_(kstride_y), kstride_x_(kstride_x) {
+    Shape<srcdim> sshape = ShapeCheck<srcdim, SrcExp>::Check(src_);
+    CHECK(sshape[srcdim - 1] >= ksize_x && sshape[srcdim - 2] >= ksize_y)
+      << "PoolingExp: kernel must be smaller than image";
+    this->src_height_ = sshape[srcdim - 2];
+    this->src_width_  = sshape[srcdim - 1];
+    this->shape_ = sshape;
+    this->shape_[srcdim - 2] = pshape[0];
+    this->shape_[srcdim - 1] = pshape[1];
+  }
+};
+/*!
+ * \brief pooling subregion results together
+ * \param src source image, shape: (batch, channel, height, width)
+ * \param ksize_y kernel size in height
+ * \param ksize_x kernel size in width
+ * \param kstride_y stride in y directory
+ * \param kstride_x stride in x directory
+ * \return expression of pooled result
+ * \tparam Reducer reducer type
+ * \tparam SrcExp source expression
+ * \tparam DType the content data type
+ * \tparam etype type of expression
+ */
+template<typename Reducer, typename SrcExp, typename DType, int etype>
+inline PoolingExp<Reducer, SrcExp, DType, ExpInfo<SrcExp>::kDim>
+pool(const Exp<SrcExp, DType, etype> &src,
+     index_t ksize_y, index_t ksize_x, index_t kstride_y, index_t kstride_x) {
+  TypeCheckPass<ExpInfo<SrcExp>::kDim >= 2>
+      ::Error_Expression_Does_Not_Meet_Dimension_Req();
+  return PoolingExp<Reducer, SrcExp, DType, ExpInfo<SrcExp>::kDim>
+      (src.self(), ksize_y, ksize_x, kstride_y, kstride_x);
+}
+/*!
+ * \brief same as pool, except the output shape is specified by pshape
+ * \param src source image
+ * \param pshape ouput shape
+ * \param ksize_y kernel size in y
+ * \param ksize_x kernel size in x
+ * \param kstride_y stride in y directory
+ * \param kstride_x stride in x directory
+ * \return expression of pooled result
+ * \tparam Reducer reducer type
+ * \tparam SrcExp source expression
+ * \tparam DType the content data type
+ * \tparam etype type of expression
+ */
+template<typename Reducer, typename SrcExp,
+         typename DType, int etype>
+inline PoolingExp<Reducer, SrcExp, DType, ExpInfo<SrcExp>::kDim>
+pool(const Exp<SrcExp, DType, etype> &src, Shape<2> pshape,
+     index_t ksize_y, index_t ksize_x, index_t kstride_y, index_t kstride_x) {
+  TypeCheckPass<ExpInfo<SrcExp>::kDim >= 2>
+      ::Error_Expression_Does_Not_Meet_Dimension_Req();
+  return PoolingExp<Reducer, SrcExp, DType, ExpInfo<SrcExp>::kDim>
+     (src.self(), pshape, ksize_y, ksize_x, kstride_y, kstride_x);
+}
+//----------------------
+// Execution plan
+//----------------------
+template<typename Reducer, typename SrcExp, typename DType, int srcdim>
+struct Plan<PoolingExp< Reducer, SrcExp, DType, srcdim>, DType> {
+ public:
+  explicit Plan(const PoolingExp<Reducer, SrcExp, DType, srcdim> &e)
+      : src_(MakePlan(e.src_)),
+      ksize_y_(e.ksize_y_), ksize_x_(e.ksize_x_),
+      kstride_y_(e.kstride_y_), kstride_x_(e.kstride_x_),
+        src_height_(e.src_height_), src_width_(e.src_width_),
+        new_height_(e.shape_[srcdim - 2]) {}
+  MSHADOW_XINLINE DType Eval(index_t i, index_t j) const {
+    using namespace std;
+    const index_t py = i % new_height_;
+    const index_t y_start = py * kstride_y_;
+    const index_t y_end = min(y_start + ksize_y_, src_height_);
+    const index_t px = j;
+    const index_t x_start = px * kstride_x_;
+    const index_t x_end = min(x_start + ksize_x_, src_width_);
+    const index_t c = i / new_height_;
+
+    DType res; Reducer::SetInitValue(res);
+    for (index_t y = y_start; y < y_end; ++y) {
+      for (index_t x = x_start; x < x_end; ++x) {
+        Reducer::Reduce(res, src_.Eval(c * src_height_ + y, x));
+      }
+    }
+    return res;
+  }
+
+ private:
+  Plan<SrcExp, DType> src_;
+  const index_t ksize_y_, ksize_x_, kstride_y_, kstride_x_;
+  const index_t src_height_, src_width_;
+  const index_t new_height_;
+};
+}  // namespace expr
+}  // namespace mshadow
+#endif  // MSHADOW_EXTENSION_SPATIAL_POOL_H_
diff --git a/3rdparty/mshadow/mshadow/extension/spatial_unpool.h b/3rdparty/mshadow/mshadow/extension/spatial_unpool.h
new file mode 100644
index 000000000000..e9ca2dfd035b
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/extension/spatial_unpool.h
@@ -0,0 +1,135 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file spatial_unpool.h
+ * \brief support for unpool
+ * \author Tianqi Chen
+ */
+#ifndef MSHADOW_EXTENSION_SPATIAL_UNPOOL_H_
+#define MSHADOW_EXTENSION_SPATIAL_UNPOOL_H_
+#include <algorithm>
+#include "../extension.h"
+namespace mshadow {
+namespace expr {
+/*!
+ * \brief unpooling expr reverse operation of pooling, used to pass gradient back
+ * \tparam Reducer reduction method during pooling
+ * \tparam SrcExp source expression to be pooled from
+ * \tparam DType the content data type
+ * \tparam srcdim dimension of src
+ */
+template<typename Reducer, typename SrcExp, typename DType, int srcdim>
+struct UnPoolingExp:
+      public MakeTensorExp<UnPoolingExp<Reducer, SrcExp, DType, srcdim>,
+                           SrcExp, srcdim, DType> {
+  /*! \brief source input, corresponds to src in pooling */
+  const SrcExp &data_src_;
+  /*! \brief result of pooled data, corresponds to result of pooling */
+  const SrcExp &data_pooled_;
+  /*! \brief gradient data of pooled part, to be propgate down */
+  const SrcExp &grad_pooled_;
+  /*! \brief shape of pooled expression */
+  index_t pshape_y_;
+  /*! \brief shape of pooled expression */
+  index_t pshape_x_;
+  /*! \brief kernel size in height */
+  index_t ksize_y_;
+  /*! \brief kernel size in width */
+  index_t ksize_x_;
+  /*! \brief kernel stride in y directory */
+  index_t kstride_y_;
+  /*! \brief kernel stride in x directory */
+  index_t kstride_x_;
+  /*! \brief constructor */
+  UnPoolingExp(const SrcExp &data_src,
+               const SrcExp &data_pooled,
+               const SrcExp &grad_pooled,
+               index_t ksize_y, index_t ksize_x, index_t kstride_y, index_t kstride_x)
+      : data_src_(data_src), data_pooled_(data_pooled),
+        grad_pooled_(grad_pooled),
+    ksize_y_(ksize_y), ksize_x_(ksize_x),
+    kstride_y_(kstride_y), kstride_x_(kstride_x) {
+    Shape<srcdim> pshape = ShapeCheck<srcdim, SrcExp>::Check(grad_pooled);
+    typedef ShapeCheck<srcdim, SrcExp> ShapeCheckSrcDimSrcExp;
+    CHECK_EQ(pshape, ShapeCheckSrcDimSrcExp::Check(data_pooled))
+      << "UnPoolingExp: pooled shape mismatch";
+    Shape<srcdim> sshape = ShapeCheck<srcdim, SrcExp>::Check(data_src);
+    for (int k = 0;  k < srcdim - 2; ++k) {
+      CHECK_EQ(pshape[k], sshape[k]) << "UnPoolingExp: pool and src shape mismatch";
+    }
+    pshape_x_ = pshape[srcdim - 1];
+    pshape_y_ = pshape[srcdim - 2];
+    this->shape_ = sshape;
+  }
+};
+/*!
+ * \brief unpooling gradient for 4D, backprop gradient value back, revserse operation of pooling,
+ *   same as unpooling, but allows unequal size of kernel
+ * \param data_src  source input, corresponds to src in pooling
+ * \param data_pooled result of pooled data, corresponds to result of pooling
+ * \param grad_pooled gradient data of pooled part, to be propgate down
+ * \param ksize_y kernel height
+ * \param ksize_x kernel width
+ * \param kstride_y stride in y directory
+ * \param kstride_x stride in x directory
+ * \return expression corresponding to unpooled 4D Tensor, storing backproped gradient
+ * \tparam Reducer reducer type
+ * \tparam SrcExp source expression
+ * \tparam DType the content data type
+ * \tparam etype type of expression
+ */
+template<typename Reducer, typename SrcExp, typename DType, int etype>
+inline UnPoolingExp<Reducer, SrcExp, DType, ExpInfo<SrcExp>::kDim>
+unpool(const Exp<SrcExp, DType, etype> &data_src,
+       const Exp<SrcExp, DType, etype> &data_pooled,
+       const Exp<SrcExp, DType, etype> &grad_pooled,
+       index_t ksize_y, index_t ksize_x, index_t kstride_y, index_t kstride_x) {
+  return UnPoolingExp<Reducer, SrcExp, DType, ExpInfo<SrcExp>::kDim>
+      (data_src.self(), data_pooled.self(), grad_pooled.self(),
+       ksize_y, ksize_x, kstride_y, kstride_x);
+}
+//----------------------
+// Execution plan
+//----------------------
+template<typename Reducer, typename SrcExp, typename DType, int srcdim>
+struct Plan<UnPoolingExp<Reducer, SrcExp, DType, srcdim>, DType> {
+ public:
+  explicit Plan(const UnPoolingExp<Reducer, SrcExp, DType, srcdim> &e)
+      : data_src_(MakePlan(e.data_src_)), data_pooled_(MakePlan(e.data_pooled_)),
+        grad_pooled_(MakePlan(e.grad_pooled_)), sshape_y_(e.shape_[srcdim - 2]),
+        pshape_y_(e.pshape_y_),  pshape_x_(e.pshape_x_),
+        ksize_y_(e.ksize_y_), ksize_x_(e.ksize_x_),
+        kstride_y_(e.kstride_y_), kstride_x_(e.kstride_x_) {}
+  MSHADOW_XINLINE DType Eval(index_t i, index_t j) const {
+    using namespace std;
+    const index_t x = j;
+    const index_t y = i % sshape_y_;
+    const index_t c = i / sshape_y_;
+    const DType vsrc = data_src_.Eval(i, j);
+    const index_t py_min =
+        y < ksize_y_ ? 0 : (y - ksize_y_ + kstride_y_) / kstride_y_;
+    const index_t px_min =
+        x < ksize_x_ ? 0 : (x - ksize_x_ + kstride_x_) / kstride_x_;
+    const index_t py_max = min((y + kstride_y_) / kstride_y_, pshape_y_);
+    const index_t px_max = min((x + kstride_x_) / kstride_x_, pshape_x_);
+
+    DType val = static_cast<DType>(0);
+    for (index_t py = py_min; py < py_max; ++py) {
+      for (index_t px = px_min; px < px_max; ++px) {
+        val += Reducer::PartialGrad(vsrc,
+                                    data_pooled_.Eval(c * pshape_y_ + py, px)) *
+                                    grad_pooled_.Eval(c * pshape_y_ + py, px);
+      }
+    }
+
+    return val;
+  }
+
+ private:
+  Plan<SrcExp, DType> data_src_, data_pooled_, grad_pooled_;
+  const index_t sshape_y_, pshape_y_, pshape_x_;
+  const index_t ksize_y_, ksize_x_;
+  const index_t kstride_y_, kstride_x_;
+};
+}  // namespace expr
+}  // namespace mshadow
+#endif  // MSHADOW_EXTENSION_SPATIAL_UNPOOL_H_
diff --git a/3rdparty/mshadow/mshadow/extension/spatial_upsampling_nearest.h b/3rdparty/mshadow/mshadow/extension/spatial_upsampling_nearest.h
new file mode 100644
index 000000000000..534fbdd9ebe0
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/extension/spatial_upsampling_nearest.h
@@ -0,0 +1,71 @@
+/*!
+ * Copyright (c) 2015 by Contributors
+ * \file spatial_upsampling.h
+ * \brief
+ * \author Bing Xu
+*/
+#ifndef MSHADOW_EXTENSION_SPATIAL_UPSAMPLING_NEAREST_H_
+#define MSHADOW_EXTENSION_SPATIAL_UPSAMPLING_NEAREST_H_
+#include "../extension.h"
+
+namespace mshadow {
+namespace expr {
+
+/*! \brief nearest neighboor upsampling
+ *         out(x, y) = in(int(x / scale_x), int(y / scale_y))
+ *  \tparam SrcExp source expression
+ *  \tparam DType data type
+ *  \tparam srcdim source dimension
+ */
+template<typename SrcExp, typename DType, int srcdim>
+struct UpSamplingNearestExp :
+  public MakeTensorExp<UpSamplingNearestExp<SrcExp, DType, srcdim>,
+                       SrcExp, srcdim, DType> {
+  /*! \brief source oprand */
+  const SrcExp &src_;
+  /*! \brief up sampling scale */
+  index_t scale_;
+  /*! \brief constructor */
+  UpSamplingNearestExp(const SrcExp &src, index_t scale)
+    : src_(src), scale_(scale) {
+    this->shape_ = ShapeCheck<srcdim, SrcExp>::Check(src_);
+    this->shape_[srcdim - 2] *= scale_;
+    this->shape_[srcdim - 1] *= scale_;
+  }
+};
+
+
+template<typename SrcExp, typename DType, int etype>
+inline UpSamplingNearestExp<SrcExp, DType, ExpInfo<SrcExp>::kDim>
+upsampling_nearest(const Exp<SrcExp, DType, etype> &src, index_t scale) {
+  TypeCheckPass<ExpInfo<SrcExp>::kDim >= 2>
+    ::Error_Expression_Does_Not_Meet_Dimension_Req();
+  return UpSamplingNearestExp<SrcExp, DType, ExpInfo<SrcExp>::kDim>(src.self(), scale);
+}
+
+template<typename SrcExp, typename DType, int srcdim>
+struct Plan<UpSamplingNearestExp<SrcExp, DType, srcdim>, DType> {
+ public:
+  explicit Plan(const UpSamplingNearestExp<SrcExp, DType, srcdim> &e)
+    : src_(MakePlan(e.src_)),
+      scale_(e.scale_),
+      new_height_(e.shape_[srcdim - 2]),
+      src_height_(static_cast<index_t>(e.shape_[srcdim - 2] / e.scale_)) {}
+  MSHADOW_XINLINE DType Eval(index_t i, index_t j) const {
+    const index_t x = j;
+    const index_t y = i % new_height_;
+    const index_t c = i / new_height_;
+    const index_t h = static_cast<index_t>(y / scale_);
+    const index_t w = static_cast<index_t>(x / scale_);
+    return src_.Eval(c * src_height_ + h, w);
+  }
+
+ private:
+  Plan<SrcExp, DType> src_;
+  const index_t scale_;
+  const index_t new_height_;
+  const index_t src_height_;
+};
+}  // namespace expr
+}  // namespace mshadow
+#endif  // MSHADOW_EXTENSION_SPATIAL_UPSAMPLING_NEAREST_H_
diff --git a/3rdparty/mshadow/mshadow/extension/swapaxis.h b/3rdparty/mshadow/mshadow/extension/swapaxis.h
new file mode 100644
index 000000000000..b79aba441175
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/extension/swapaxis.h
@@ -0,0 +1,110 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file swapaxis.h
+ * \brief support for swapaxis
+ * \author Tianqi Chen
+ */
+#ifndef MSHADOW_EXTENSION_SWAPAXIS_H_
+#define MSHADOW_EXTENSION_SWAPAXIS_H_
+#include <algorithm>
+#include <utility>
+#include "../extension.h"
+namespace mshadow {
+namespace expr {
+/*!
+ * \brief swap two axis of a tensor
+ * input: Tensor<Device,dim>: ishape
+ * output: Tensor<Device,dimdst> oshape[a1],oshape[a2] = ishape[a2],oshape[a1]
+ *
+ * \tparam SrcExp type of source expression
+ * \tparam DType the type of elements 
+ * \tparam dimsrc source dimension, assert a1 > a2
+ * \tparam m_a1 one dimension to be swapped, encoded by dimsrc - a1 
+ * \tparam a2 second dimension to be swapped, encoded by a2
+ */
+template<typename SrcExp, typename DType, int dimsrc, int m_a1, int a2>
+struct SwapAxisExp:
+      public MakeTensorExp<SwapAxisExp<SrcExp, DType, dimsrc, m_a1, a2>,
+                           SrcExp, dimsrc, DType> {
+  // decode the a1, a2
+  static const int a1 = dimsrc - m_a1;
+  /*! \brief source expression */
+  const SrcExp &src_;
+  /*! \brief constructor */
+  explicit SwapAxisExp(const SrcExp &src) : src_(src) {
+    this->shape_ = ShapeCheck<dimsrc, SrcExp>::Check(src);
+    std::swap(this->shape_[a1], this->shape_[a2]);
+  }
+};
+/*!
+ * \brief a expression that reshapes a tensor to another shape
+ * \param src Tensor<Device,dimsrc>:
+ * \return a expresion with type Tensor<Device,dimdst>
+ * \tparam a1 higher dimension to be swapped, assert a1 > a2
+ * \tparam a2 lower dimension to be swapped
+ * \tparam SrcExp source expression
+ * \tparam DType the type of elements 
+ * \tparam etype source expression type
+ */
+template<int a1, int a2, typename SrcExp, typename DType, int etype>
+inline SwapAxisExp<SrcExp, DType, ExpInfo<SrcExp>::kDim,
+                   ExpInfo<SrcExp>::kDim - a1, a2>
+swapaxis(const Exp<SrcExp, DType, etype> &src) {
+  typedef ExpInfo<SrcExp> Info;
+  TypeCheckPass<Info::kDim >= a1 + 1 && Info::kDim >= a2 + 1 &&
+                a2 < a1>::Error_Expression_Does_Not_Meet_Dimension_Req();
+  return SwapAxisExp<SrcExp, DType, ExpInfo<SrcExp>::kDim,
+                     ExpInfo<SrcExp>::kDim - a1, a2>(src.self());
+}
+template<typename SrcExp, typename DType, int dimsrc, int m_a1, int a2>
+struct Plan<SwapAxisExp<SrcExp, DType, dimsrc, m_a1, a2>, DType> {
+ public:
+  // decode the a1
+  static const int a1 = dimsrc - m_a1;
+  explicit Plan(const SwapAxisExp<SrcExp, DType, dimsrc, m_a1, a2> &e)
+      : src_(MakePlan(e.src_)),
+        shapey_(e.shape_.ProdShape(a1 + 1, dimsrc - 1)),
+        shapez_(e.shape_[a1]),
+        shapec_(e.shape_.ProdShape(a2 + 1, a1)),
+        shapen_(e.shape_[a2]) {}
+  MSHADOW_XINLINE DType Eval(index_t i, index_t j) const {
+    const index_t y = i % shapey_;
+    i /= shapey_;
+    const index_t z = i % shapez_;
+    i /= shapez_;
+    const index_t c = i % shapec_;
+    i /= shapec_;
+    const index_t n = i % shapen_;
+    // swap z and n
+    return src_.Eval(((((i / shapen_) * shapez_ + z) * shapec_ +
+                          c) * shapen_ + n) * shapey_ + y, j);
+  }
+
+ private:
+  Plan<SrcExp, DType> src_;
+  const index_t shapey_, shapez_, shapec_, shapen_;
+};
+template<typename SrcExp, typename DType, int dimsrc, int a2>
+struct Plan<SwapAxisExp<SrcExp, DType, dimsrc, 1, a2>, DType> {
+ public:
+  explicit Plan(const SwapAxisExp<SrcExp, DType, dimsrc, 1, a2> &e)
+      : src_(MakePlan(e.src_)),
+        shapex_(e.shape_[dimsrc - 1]),
+        shapey_(e.shape_.ProdShape(a2 + 1, dimsrc - 1)),
+        shapez_(e.shape_[a2]) {}
+  MSHADOW_XINLINE DType Eval(index_t i, index_t x) const {
+    // swap x and z
+    const index_t y = i % shapey_;
+    i /= shapey_;
+    const index_t z = i % shapez_;
+    const index_t n = i / shapez_;
+    return src_.Eval((n * shapex_ + x) * shapey_ + y , z);
+  }
+
+ private:
+  Plan<SrcExp, DType> src_;
+  const index_t shapex_, shapey_, shapez_;
+};
+}  // namespace expr
+}  // namespace mshadow
+#endif  // MSHADOW_EXTENSION_SWAPAXIS_H_
diff --git a/3rdparty/mshadow/mshadow/extension/take.h b/3rdparty/mshadow/mshadow/extension/take.h
new file mode 100644
index 000000000000..76c4f4729491
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/extension/take.h
@@ -0,0 +1,99 @@
+/*!
+ * Copyright (c) 2015 by Contributors
+ * \file take.h
+ * \brief
+ * \author Bing Xu
+*/
+#ifndef MSHADOW_EXTENSION_TAKE_H_
+#define MSHADOW_EXTENSION_TAKE_H_
+
+#include "../extension.h"
+
+namespace mshadow {
+namespace expr {
+
+/*! \brief Take a column from a matrix
+ *  \tparam IndexExp type of index expression
+ *  \tparam SrcExp type of src expression
+ *  \tparam DType data type
+ */
+template<typename IndexExp, typename SrcExp, typename DType>
+struct TakeExp: public Exp<TakeExp<IndexExp, SrcExp, DType>,
+                           DType, type::kChainer> {
+  /*! \brief index oprand */
+  const IndexExp &index_;
+  /*! \brief embediing oprand */
+  const SrcExp &src_;
+  /*! constructor */
+  TakeExp(const IndexExp &index, const SrcExp &src)
+    : index_(index), src_(src) {}
+};  // struct TakeExp
+
+
+
+template<typename IndexExp,
+         typename SrcExp,
+         typename DType,
+         int e1, int e2>
+inline TakeExp<IndexExp, SrcExp, DType>
+take(const Exp<IndexExp, DType, e1> &index,
+     const Exp<SrcExp, DType, e2> &src) {
+  return TakeExp<IndexExp, SrcExp, DType>(index.self(), src.self());
+}
+
+
+//----------------------
+// Execution plan
+//----------------------
+
+template<typename IndexExp, typename SrcExp, typename DType>
+struct Plan<TakeExp<IndexExp, SrcExp, DType>, DType> {
+ public:
+  explicit Plan(const TakeExp<IndexExp, SrcExp, DType> &e)
+    : index_(MakePlan(e.index_)), src_(MakePlan(e.src_)) {
+  }
+
+  // TODO(xx): discuss W shape: in * out or out * in
+  // Now I use in * out
+  MSHADOW_XINLINE DType Eval(index_t y, index_t x) const {
+    index_t idx = static_cast<index_t>(index_.Eval(0, y));
+    return static_cast<DType>(src_.Eval(idx, x));
+  }
+
+ private:
+  expr::Plan<IndexExp, DType> index_;
+  expr::Plan<SrcExp, DType> src_;
+};  // struct Plan
+
+template<typename IndexExp, typename SrcExp, typename DType>
+inline Plan<TakeExp<IndexExp, SrcExp, DType>, DType>
+MakePlan(const TakeExp<IndexExp, SrcExp, DType> &exp) {
+  return Plan<TakeExp<IndexExp, SrcExp, DType>, DType>(exp);
+}
+
+template<int dim, typename IndexExp, typename SrcExp, typename DType>
+struct ShapeCheck<dim, TakeExp<IndexExp, SrcExp, DType> > {
+  inline static Shape<dim>
+  Check(const TakeExp<IndexExp, SrcExp, DType> &t) {
+    CHECK(dim == 2)
+      << "TakeExp only support 2D output";
+    Shape<1> dshape = ShapeCheck<1, IndexExp>::Check(t.index_);
+    Shape<2> wshape = ShapeCheck<2, SrcExp>::Check(t.src_);
+    Shape<dim> ret;
+    ret[0] = dshape[0];
+    ret[1] = wshape[1];
+    return ret;
+  }
+};
+
+
+template<typename IndexExp, typename SrcExp, typename DType>
+struct ExpInfo<TakeExp<IndexExp, SrcExp, DType> > {
+  static const int kDim = 2;
+  static const int kDevMask = ExpInfo<IndexExp>::kDevMask;
+};
+
+}  // namespace expr
+}  // namespace mshadow
+
+#endif  // MSHADOW_EXTENSION_TAKE_H_
diff --git a/3rdparty/mshadow/mshadow/extension/take_grad.h b/3rdparty/mshadow/mshadow/extension/take_grad.h
new file mode 100644
index 000000000000..4479b3e0cd9d
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/extension/take_grad.h
@@ -0,0 +1,111 @@
+/*!
+ * Copyright (c) 2015 by Contributors
+ * \file take_grad.h
+ * \brief
+ * \author Bing Xu
+*/
+#ifndef MSHADOW_EXTENSION_TAKE_GRAD_H_
+#define MSHADOW_EXTENSION_TAKE_GRAD_H_
+
+#include "../extension.h"
+
+namespace mshadow {
+namespace expr {
+
+/*! \brief Calculate embedding gradient
+ *  \tparam IndexExp type of index expression
+ *  \tparam SrcExp type of src expression
+ *  \tparam DType data type
+ */
+
+template<typename IndexExp, typename SrcExp, typename DType>
+struct TakeGradExp : public Exp<TakeGradExp<IndexExp, SrcExp, DType>,
+                                DType, type::kChainer> {
+  /*! \brief index oprand */
+  const IndexExp &index_;
+  /*! \brief out gradient oprand */
+  const SrcExp &src_;
+  /*! \brief batch size */
+  const index_t input_dim_;
+  /*! \brief constructor */
+  TakeGradExp(const IndexExp &index, const SrcExp &src, const index_t input_dim)
+    : index_(index), src_(src), input_dim_(input_dim) {}
+};  // struct TakeGradExp
+
+
+template<typename IndexExp,
+         typename SrcExp,
+         typename DType,
+         int e1, int e2>
+inline TakeGradExp<IndexExp, SrcExp, DType>
+take_grad(const Exp<IndexExp, DType, e1> &index,
+          const Exp<SrcExp, DType, e2> &src,
+          const index_t input_dim) {
+  return TakeGradExp<IndexExp, SrcExp, DType>(index.self(),
+                                                       src.self(),
+                                                       input_dim);
+}
+
+//----------------------
+// Execution plan
+//----------------------
+
+template<typename IndexExp, typename SrcExp, typename DType>
+struct Plan<TakeGradExp<IndexExp, SrcExp, DType>, DType> {
+ public:
+  explicit Plan(const TakeGradExp<IndexExp, SrcExp, DType> &e)
+    : index_(MakePlan(e.index_)),
+      src_(MakePlan(e.src_)),
+      batch_size_(ShapeCheck<1, IndexExp>::Check(e.index_)[0]) {
+  }
+
+  // now return shape: in * out
+  MSHADOW_XINLINE DType Eval(index_t y, index_t x) const {
+    DType ret = 0.f;
+    for (index_t i = 0; i < batch_size_; ++i) {
+      index_t idx = static_cast<index_t>(index_.Eval(0, i));
+      if (idx == y) {
+        ret += static_cast<DType>(src_.Eval(i, x));
+      }
+    }
+    return ret;
+  }
+
+ private:
+  expr::Plan<IndexExp, DType> index_;
+  expr::Plan<SrcExp, DType> src_;
+  const index_t batch_size_;
+};  // struct Plan
+
+
+template<typename IndexExp, typename SrcExp, typename DType>
+inline Plan<TakeGradExp<IndexExp, SrcExp, DType>, DType>
+MakePlan(const TakeGradExp<IndexExp, SrcExp, DType> &exp) {
+  return Plan<TakeGradExp<IndexExp, SrcExp, DType>, DType>(exp);
+}
+
+template<int dim, typename IndexExp, typename SrcExp, typename DType>
+struct ShapeCheck<dim, TakeGradExp<IndexExp, SrcExp, DType> > {
+  inline static Shape<dim>
+  Check(const TakeGradExp<IndexExp, SrcExp, DType> &t) {
+    CHECK(dim == 2)
+      << "TakeGradExp only support 2D output";
+    // Shape<1> dshape = ShapeCheck<1, IndexExp>::Check(t.index_);
+    Shape<2> gshape = ShapeCheck<2, SrcExp>::Check(t.src_);
+    Shape<dim> ret;
+    ret[0] = t.input_dim_;
+    ret[1] = gshape[1];
+    return ret;
+  }
+};  // struct ShapeCheck
+
+template<typename IndexExp, typename SrcExp, typename DType>
+struct ExpInfo<TakeGradExp<IndexExp, SrcExp, DType> > {
+  static const int kDim = 2;
+  static const int kDevMask = ExpInfo<IndexExp>::kDevMask;
+};
+
+}  // namespace expr
+}  // namespace mshadow
+
+#endif  // MSHADOW_EXTENSION_TAKE_GRAD_H_
diff --git a/3rdparty/mshadow/mshadow/extension/transpose.h b/3rdparty/mshadow/mshadow/extension/transpose.h
new file mode 100644
index 000000000000..6640153f2100
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/extension/transpose.h
@@ -0,0 +1,200 @@
+/*!
+ *  Copyright (c) 2016 by Contributors
+ * \file transpose.h
+ * \brief support for transpose
+ * \author Junyuan Xie
+ */
+#ifndef MSHADOW_EXTENSION_TRANSPOSE_H_
+#define MSHADOW_EXTENSION_TRANSPOSE_H_
+#include <algorithm>
+#include "../extension.h"
+namespace mshadow {
+namespace expr {
+/*!
+ * \brief transpose axes of a tensor
+ * input: Tensor<Device,dim>: ishape
+ * output: Tensor<Device,dimdst> oshape[a1],oshape[a2] = ishape[a2],oshape[a1]
+ *
+ * \tparam SrcExp type of source expression
+ * \tparam DType the type of elements
+ * \tparam dimsrc source dimension, assert a1 > a2
+ * \tparam m_a1 one dimension to be swapped, encoded by dimsrc - a1
+ * \tparam a2 second dimension to be swapped, encoded by a2
+ */
+template<typename SrcExp, typename DType, int dimsrc>
+struct TransposeExExp:
+      public MakeTensorExp<TransposeExExp<SrcExp, DType, dimsrc>,
+                           SrcExp, dimsrc, DType> {
+  /*! \brief source expression */
+  const SrcExp &src_;
+  const Shape<dimsrc> axes_;
+  Shape<dimsrc> dst_in_src_stride_;  // Holds the corresponding stride of the dst axes in src
+  index_t src_stride_;
+  /*! \brief constructor */
+  explicit TransposeExExp(const SrcExp &src, Shape<dimsrc> axes) : src_(src), axes_(axes) {
+    Shape<dimsrc> src_shape = ShapeCheck<dimsrc, SrcExp>::Check(src);
+    src_stride_ = src_shape[dimsrc - 1];
+    Shape<dimsrc> src_stride;
+    src_stride[dimsrc-1] = 1;
+    for (int i = dimsrc-2; i >= 0; --i) src_stride[i] = src_shape[i+1]*src_stride[i+1];
+    for (int i = 0; i < dimsrc; ++i) {
+      dst_in_src_stride_[i] = src_stride[axes[i]];
+      this->shape_[i] = src_shape[axes[i]];
+    }
+  }
+};
+/*!
+ * \brief a expression that reshapes a tensor to another shape
+ * \param src Tensor<Device,dimsrc>:
+ * \return a expresion with type Tensor<Device,dimdst>
+ * \tparam a1 higher dimension to be swapped, assert a1 > a2
+ * \tparam a2 lower dimension to be swapped
+ * \tparam SrcExp source expression
+ * \tparam DType the type of elements
+ * \tparam etype source expression type
+ */
+template<typename SrcExp, typename DType, int etype>
+inline TransposeExExp<SrcExp, DType, ExpInfo<SrcExp>::kDim>
+transpose(const Exp<SrcExp, DType, etype> &src, Shape<ExpInfo<SrcExp>::kDim> axes) {
+  return TransposeExExp<SrcExp, DType, ExpInfo<SrcExp>::kDim>(src.self(), axes);
+}
+
+template<typename SrcExp, typename DType, int dimsrc>
+struct Plan<TransposeExExp<SrcExp, DType, dimsrc>, DType> {
+ public:
+  explicit Plan(const TransposeExExp<SrcExp, DType, dimsrc> &e)
+      : src_(MakePlan(e.src_)),
+        src_stride_(e.src_stride_),
+        dst_in_src_stride_(e.dst_in_src_stride_),
+        dst_shape_(e.shape_) {}
+  MSHADOW_XINLINE DType Eval(index_t i, index_t j) const {
+    index_t idx = j * dst_in_src_stride_[dimsrc - 1];
+    #pragma unroll
+    for (int k = dimsrc-2; k >= 0; --k) {
+      idx += (i % dst_shape_[k]) * dst_in_src_stride_[k];
+      i /= dst_shape_[k];
+    }
+    return src_.Eval(idx/src_stride_, idx%src_stride_);
+  }
+
+ private:
+  Plan<SrcExp, DType> src_;
+  const index_t src_stride_;
+  const Shape<dimsrc> dst_in_src_stride_, dst_shape_;
+};
+
+/*!
+ * \brief transform contiguous indices of the source tensor to indices of the transposed tensor.
+ * input: Tensor<Device, k>: ishape
+ * output: Tensor<Device, k>: oshape = ishape
+ *
+ * \tparam SrcExp type of source expression
+ * \tparam DType the type of elements
+ * \tparam dimsrc source dimension
+ * \tparam etype source type
+ */
+template<typename SrcExp, typename DType, int dimsrc, int etype>
+struct TransposeIndicesExp:
+      public Exp<TransposeIndicesExp<SrcExp, DType, dimsrc, etype>, DType, etype> {
+  /*! \brief source expression */
+  const SrcExp &src_indices_;  // Expression of the source indices
+  Shape<dimsrc> src_shape_;  // Holds the corresponding stride of the source axes in dst
+  const Shape<dimsrc> axes_;  // The transpose axes
+  Shape<dimsrc> src_in_dst_stride_;  // Holds the corresponding stride of the source axes in dst
+  /*! \brief constructor */
+  explicit TransposeIndicesExp(const SrcExp &src_indices,
+                               Shape<dimsrc> src_shape,
+                               Shape<dimsrc> axes) : src_indices_(src_indices),
+                                                     src_shape_(src_shape), axes_(axes) {
+    Shape<dimsrc> dst_shape_;
+    Shape<dimsrc> dst_stride_;
+    bool axes_checking_flag[dimsrc] = { 0 };
+    for (int i = 0; i < dimsrc; ++i) {
+      CHECK_LT(static_cast<int>(axes[i]), dimsrc)
+        << "Invalid axes input! All elements of axes must be between 0 and " << dimsrc
+        << ", find axes=" << axes;
+      dst_shape_[i] = src_shape[axes[i]];
+      axes_checking_flag[axes[i]] = true;
+    }
+    // check if the input axes is valid
+    for (int i = 0; i < dimsrc; ++i) {
+      CHECK_EQ(axes_checking_flag[i], true)
+        << "Invalid axes input! All elements of axes must be between 0 and " << dimsrc
+        << ", find axes=" << axes;
+    }
+    dst_stride_[dimsrc - 1] = 1;
+    for (int i = dimsrc - 2; i >= 0; --i) dst_stride_[i] = dst_shape_[i+1] * dst_stride_[i+1];
+    for (int i = 0; i < dimsrc; ++i) {
+      src_in_dst_stride_[axes[i]] = dst_stride_[i];
+    }
+  }
+};
+
+/*!
+ * \brief a expression that reshapes a tensor to another shape
+ * \param src Tensor<Device,dimsrc>:
+ * \return a expresion with type Tensor<Device,dimdst>
+ * \tparam a1 higher dimension to be swapped, assert a1 > a2
+ * \tparam a2 lower dimension to be swapped
+ * \tparam SrcExp source expression
+ * \tparam DType the type of elements
+ * \tparam etype source expression type
+ */
+template<typename SrcExp, typename DType, int dimsrc, int etype>
+inline TransposeIndicesExp<SrcExp, DType, dimsrc, etype>
+transpose_indices(const Exp<SrcExp, DType, etype> &src_indices,
+                  Shape<dimsrc> src_shape,
+                  Shape<dimsrc> axes) {
+  return TransposeIndicesExp<SrcExp, DType, dimsrc, etype>(src_indices.self(), src_shape, axes);
+}
+
+template<typename SrcExp, typename DType, int dimsrc, int etype>
+struct Plan<TransposeIndicesExp<SrcExp, DType, dimsrc, etype>, DType> {
+ public:
+  explicit Plan(const TransposeIndicesExp<SrcExp, DType, dimsrc, etype> &e)
+      : src_indices_(MakePlan(e.src_indices_)),
+        src_in_dst_stride_(e.src_in_dst_stride_),
+        src_shape_(e.src_shape_) {}
+  MSHADOW_XINLINE DType Eval(index_t i, index_t j) const {
+    index_t src_idx = static_cast<index_t>(src_indices_.Eval(i, j));
+    index_t dst_idx = 0;
+    #pragma unroll
+    for (int k = dimsrc - 1; k >= 0; --k) {
+      dst_idx += (src_idx % src_shape_[k]) * src_in_dst_stride_[k];
+      src_idx /= src_shape_[k];
+    }
+    return static_cast<DType>(dst_idx);
+  }
+
+ private:
+  Plan<SrcExp, DType> src_indices_;
+  const Shape<dimsrc> src_in_dst_stride_, src_shape_;
+};
+
+//----------------------
+// Execution plan
+//----------------------
+/*! \brief make expression */
+template<typename SrcExp, typename DType, int dimsrc, int etype>
+inline Plan<TransposeIndicesExp<SrcExp, DType, dimsrc, etype>, DType>
+MakePlan(const TransposeIndicesExp<SrcExp, DType, dimsrc, etype> &e) {
+  return Plan<TransposeIndicesExp<SrcExp, DType, dimsrc, etype>, DType>(e);
+}
+
+template<int dim, typename SrcExp, typename DType, int dimsrc, int etype>
+struct ShapeCheck<dim, TransposeIndicesExp<SrcExp, DType, dimsrc, etype> > {
+  inline static Shape<dim>
+  Check(const TransposeIndicesExp<SrcExp, DType, dimsrc, etype> &t) {
+    Shape<dim> s = ShapeCheck<dim, SrcExp>::Check(t.src_indices_);
+    return s;
+  }
+};
+
+template<typename SrcExp, typename DType, int dimsrc, int etype>
+struct ExpInfo<TransposeIndicesExp<SrcExp, DType, dimsrc, etype> > {
+  static const int kDim = ExpInfo<SrcExp>::kDim;
+  static const int kDevMask = ExpInfo<SrcExp>::kDevMask;
+};
+}  // namespace expr
+}  // namespace mshadow
+#endif  // MSHADOW_EXTENSION_TRANSPOSE_H_
diff --git a/3rdparty/mshadow/mshadow/extension/unpack_patch2col.h b/3rdparty/mshadow/mshadow/extension/unpack_patch2col.h
new file mode 100644
index 000000000000..ed473f81d496
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/extension/unpack_patch2col.h
@@ -0,0 +1,151 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file unpack_patch2col.h
+ * \brief support for unpack
+ * \author Tianqi Chen
+ */
+#ifndef MSHADOW_EXTENSION_UNPACK_PATCH2COL_H_
+#define MSHADOW_EXTENSION_UNPACK_PATCH2COL_H_
+#include "../extension.h"
+namespace mshadow {
+namespace expr {
+/*!
+ * \brief unpack local (overlap) patches of image to column of mat,
+ *  can be used to implement convolution, this expression allow unpack of a batch
+ *  this is a version support unpacking multiple images
+ *  after getting unpacked mat, we can use: output = dot(weight, mat) to get covolved results, the relations:
+ * \tparam SrcExp source expression
+ * \tparam dstdim destination dimension
+ */
+template<typename SrcExp, typename DType, int srcdim>
+struct UnpackPatchToColXExp:
+      public MakeTensorExp<UnpackPatchToColXExp<SrcExp, DType, srcdim>,
+                           SrcExp, 2, DType>{
+  /*! \brief source operand */
+  const SrcExp &img_;
+  /*! \brief patch height */
+  index_t psize_y_;
+  /*! \brief patch width */
+  index_t psize_x_;
+  /*! \brief patch stride */
+  index_t pstride_y_;
+  index_t pstride_x_;
+  /*! \brief patch dilate */
+  index_t pdilate_y_;
+  index_t pdilate_x_;
+  /*! \brief number of input channel */
+  index_t i_channel_;
+  /*! \brief height of img */
+  index_t i_height_;
+  /*! \brief width of img */
+  index_t i_width_;
+  /*! \brief constructor */
+  UnpackPatchToColXExp(const SrcExp &img,
+                       index_t psize_y,
+                       index_t psize_x,
+                       index_t pstride_y,
+                       index_t pstride_x,
+                       index_t pdilate_y,
+                       index_t pdilate_x)
+      : img_(img), psize_y_(psize_y), psize_x_(psize_x),
+      pstride_y_(pstride_y), pstride_x_(pstride_x),
+      pdilate_y_(pdilate_y), pdilate_x_(pdilate_x){
+    Shape<srcdim> imshape = ShapeCheck<srcdim, SrcExp>::Check(img_);
+    CHECK(imshape[srcdim - 1] >= psize_x && imshape[srcdim - 2] >= psize_y)
+      << "UnpackPatchToCol:image shape smaller than patch size";
+    this->i_channel_ = imshape[srcdim - 3];
+    this->i_height_  = imshape[srcdim - 2];
+    this->i_width_   = imshape[srcdim - 1];
+    // calculate number of batches
+    const index_t num = imshape.ProdShape(0, srcdim - 3);
+    const index_t o_height = (i_height_ -
+        (pdilate_y * (psize_y - 1) + 1)) / pstride_y + 1;
+    const index_t o_width  = (i_width_  -
+        (pdilate_x * (psize_x - 1) + 1)) / pstride_x + 1;
+    this->shape_[1] = o_height * o_width * num;
+    this->shape_[0] = psize_y * psize_x * i_channel_;
+  }
+};
+
+/*!
+ * \brief  unpack local (overlap) patches of image to column of mat, can be used to implement convolution
+ *  after getting unpacked mat, we can use: output = dot(weight, mat) to get covolved results, the relations:
+ *
+ *  weight; shape[0]: out_channel, shape[1]: ichannel * psize_y * psize_x
+ *  output; shape[0]: out_channel, shape[1]: out_height * out_width * num_of_images
+ *  out_height = (in_height - psize_y) / pstride + 1, this means we pad inperfect patch with 0
+ *  out_width  = (in_width - psize_x) / pstride + 1
+ *
+ * \return mat target matrix; shape[0]: in_channel*psize_y*psize_x  shape[1]: out_height*out_width * num_of_images
+ * \param img source image; shape[-3]: in_channels, shape[-2]: in_height, shape[-1]: in_width, can be 3D or 4D tensor(multiple images)
+ * \param psize_y height of each patch
+ * \param psize_x width of each patch
+ * \param pstride stride of each patch
+ * \param pdilate dilate of each patch
+ * \tparam SrcExp source expression
+ * \tparam DType the type of elements
+ * \tparam etype type of expression
+ */
+template<typename SrcExp, typename DType, int etype>
+inline UnpackPatchToColXExp<SrcExp, DType, ExpInfo<SrcExp>::kDim>
+unpack_patch2col(const Exp<SrcExp, DType, etype> &img,
+                 index_t psize_y, index_t psize_x, index_t pstride, index_t pdilate) {
+  TypeCheckPass<ExpInfo<SrcExp>::kDim >= 3>
+      ::Error_Expression_Does_Not_Meet_Dimension_Req();
+  return UnpackPatchToColXExp<SrcExp, DType, ExpInfo<SrcExp>::kDim>
+      (img.self(), psize_y, psize_x, pstride, pstride, pdilate, pdilate);
+}
+
+/*!
+ *if you want to specify stride_x and stride_y
+ */
+template<typename SrcExp, typename DType, int etype>
+inline UnpackPatchToColXExp<SrcExp, DType, ExpInfo<SrcExp>::kDim>
+unpack_patch2col(const Exp<SrcExp, DType, etype> &img,
+                 index_t psize_y, index_t psize_x, index_t pstride_y_, index_t pstride_x_,
+                 index_t pdilate_y_, index_t pdilate_x_) {
+  TypeCheckPass<ExpInfo<SrcExp>::kDim >= 3>
+      ::Error_Expression_Does_Not_Meet_Dimension_Req();
+  return UnpackPatchToColXExp<SrcExp, DType, ExpInfo<SrcExp>::kDim>
+      (img.self(), psize_y, psize_x, pstride_y_, pstride_x_, pdilate_y_, pdilate_x_);
+}
+//----------------------
+// Execution plan
+//----------------------
+template<typename SrcExp, typename DType, int srcdim>
+struct Plan<UnpackPatchToColXExp<SrcExp, DType, srcdim>, DType> {
+ public:
+  explicit Plan(const UnpackPatchToColXExp<SrcExp, DType, srcdim> &e)
+      :src_(MakePlan(e.img_)),
+       psize_y_(e.psize_y_), psize_x_(e.psize_x_),
+       pstride_y_(e.pstride_y_), pstride_x_(e.pstride_x_),
+       i_channel_(e.i_channel_), pdilate_y_(e.pdilate_y_), pdilate_x_(e.pdilate_x_),
+       i_height_(e.i_height_), i_width_(e.i_width_),
+       o_height_((i_height_ - (pdilate_y_ * (psize_y_ - 1) + 1)) / pstride_y_ + 1),
+       o_width_((i_width_ - (pdilate_x_ * (psize_x_ - 1) + 1)) / pstride_x_ + 1) {}
+  MSHADOW_XINLINE DType Eval(index_t i, index_t j) const {
+    const index_t x_offset = i % psize_x_ * pdilate_x_;
+    const index_t idivp    = i / psize_x_;
+    const index_t y_offset = idivp % psize_y_ * pdilate_y_;
+    const index_t c = idivp / psize_y_;
+    const index_t x = (j % o_width_) * pstride_x_ + x_offset;
+    const index_t jdivw = j / o_width_;
+    const index_t y = (jdivw % o_height_) * pstride_y_ + y_offset;
+    const index_t n = jdivw / o_height_;
+
+    if (x < i_width_ && y < i_height_) {
+      return src_.Eval((n * i_channel_  + c) * i_height_ + y, x);
+    } else {
+      return DType(0.0f);
+    }
+  }
+
+ private:
+  Plan<SrcExp, DType> src_;
+  const index_t psize_y_, psize_x_, pstride_y_, pstride_x_, i_channel_;
+  const index_t pdilate_y_, pdilate_x_;
+  const index_t i_height_, i_width_, o_height_, o_width_;
+};
+}  // namespace expr
+}  // namespace mshadow
+#endif  // MSHADOW_EXTENSION_UNPACK_PATCH2COL_H_
diff --git a/3rdparty/mshadow/mshadow/half.h b/3rdparty/mshadow/mshadow/half.h
new file mode 100644
index 000000000000..2dded0a7752e
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/half.h
@@ -0,0 +1,354 @@
+/*!
+ *  Copyright (c) 2015 by Contributors
+ * \file half.h
+ * \brief definition of half (float16) type.
+ *
+ * \author Junyuan Xie
+ */
+#ifndef MSHADOW_HALF_H_
+#define MSHADOW_HALF_H_
+#include "./base.h"
+
+#if MSHADOW_USE_F16C
+  #include <x86intrin.h>
+#endif  // MSHADOW_USE_F16C
+
+// This flag dictates rounding for the float2half() routine only (used generally on Windows),
+// not the f16c lib or cuda v7.5 (or later) behavior which is fixed at round-to-nearest-even.
+#ifndef MSHADOW_HALF_ROUND_TO_NEAREST
+#define MSHADOW_HALF_ROUND_TO_NEAREST 1
+#endif
+
+#if (MSHADOW_USE_CUDA && CUDA_VERSION >= 7050)
+  #define MSHADOW_CUDA_HALF 1
+  #include <cuda_fp16.h>
+  #if defined(__CUDA_ARCH__)
+    /*! \brief __half2float_warp */
+    __host__ __device__ float __half2float_warp(const volatile __half& h) { /* NOLINT(*) */
+      __half val;
+#if CUDA_VERSION >= 9000
+      val = const_cast<__half&>(h);
+#else
+      val.x = h.x;
+#endif
+      return __half2float(val);
+    }
+  #endif
+#else
+  #define MSHADOW_CUDA_HALF 0
+#endif
+
+/*! \brief namespace for mshadow */
+namespace mshadow {
+/* \brief name space for host/device portable half-precision floats */
+namespace half {
+#define MSHADOW_HALF_OPERATOR(RTYPE, OP)                                  \
+  MSHADOW_XINLINE RTYPE operator OP (half_t a, half_t b) {                \
+    return RTYPE(float(a) OP float(b));  /* NOLINT(*) */                  \
+  }                                                                       \
+  template<typename T>                                                    \
+  MSHADOW_XINLINE RTYPE operator OP (half_t a, T b) {                     \
+    return RTYPE(float(a) OP float(b));  /* NOLINT(*) */                  \
+  }                                                                       \
+  template<typename T>                                                    \
+  MSHADOW_XINLINE RTYPE operator OP (T a, half_t b) {                     \
+    return RTYPE(float(a) OP float(b));  /* NOLINT(*) */                  \
+  }
+
+#define MSHADOW_HALF_ASSIGNOP(AOP, OP)                                    \
+  template<typename T>                                                    \
+  MSHADOW_XINLINE half_t operator AOP (const T& a) {                      \
+    return *this = half_t(float(*this) OP float(a));  /* NOLINT(*)*/      \
+  }                                                                       \
+  template<typename T>                                                    \
+  MSHADOW_XINLINE half_t operator AOP (const volatile T& a) volatile {    \
+    return *this = half_t(float(*this) OP float(a));  /* NOLINT(*)*/      \
+  }
+
+#if (MSHADOW_CUDA_HALF && defined(__CUDA_ARCH__))
+#define MSHADOW_HALF_CONVERSIONOP(T)                                      \
+  MSHADOW_XINLINE operator T() const {                                    \
+    return T(__half2float(cuhalf_));  /* NOLINT(*)*/                      \
+  }                                                                       \
+  MSHADOW_XINLINE operator T() const volatile {                           \
+    return T(__half2float_warp(cuhalf_));  /* NOLINT(*)*/                 \
+  }
+#elif(MSHADOW_USE_F16C)
+#define MSHADOW_HALF_CONVERSIONOP(T)                                      \
+  MSHADOW_XINLINE operator T() const {                                    \
+    return T(_cvtsh_ss(half_));   /* NOLINT(*)*/                          \
+  }                                                                       \
+  MSHADOW_XINLINE operator T() const volatile {                           \
+    return T(_cvtsh_ss(half_));   /* NOLINT(*)*/                          \
+  }
+#else
+#define MSHADOW_HALF_CONVERSIONOP(T)                                      \
+  MSHADOW_XINLINE operator T() const {                                    \
+    return T(half2float(half_));  /* NOLINT(*)*/                          \
+  }                                                                       \
+  MSHADOW_XINLINE operator T() const volatile {                           \
+    return T(half2float(half_));  /* NOLINT(*)*/                          \
+  }
+#endif  // (MSHADOW_CUDA_HALF && defined(__CUDA_ARCH__))
+
+class MSHADOW_ALIGNED(2) half_t {
+ public:
+  union {
+    uint16_t half_;
+#if MSHADOW_CUDA_HALF
+    __half cuhalf_;
+#endif  // MSHADOW_CUDA_HALF
+  };
+
+  static MSHADOW_XINLINE half_t Binary(uint16_t value) {
+    half_t res;
+    res.half_ = value;
+    return res;
+  }
+
+  MSHADOW_XINLINE half_t() {}
+
+#if MSHADOW_CUDA_HALF
+  MSHADOW_XINLINE explicit half_t(const __half& value) {
+    cuhalf_ = value;
+  }
+#endif  // MSHADOW_CUDA_HALF
+
+  MSHADOW_XINLINE half_t(const float& value) { constructor(value); }
+  MSHADOW_XINLINE explicit half_t(const double& value) { constructor(value); }
+  MSHADOW_XINLINE explicit half_t(const int8_t& value) { constructor(value); }
+  MSHADOW_XINLINE explicit half_t(const uint8_t& value) { constructor(value); }
+  MSHADOW_XINLINE explicit half_t(const int32_t& value) { constructor(value); }
+  MSHADOW_XINLINE explicit half_t(const uint32_t& value) { constructor(value); }
+  MSHADOW_XINLINE explicit half_t(const int64_t& value) { constructor(value); }
+  MSHADOW_XINLINE explicit half_t(const uint64_t& value) { constructor(value); }
+
+  MSHADOW_HALF_CONVERSIONOP(float)
+
+  MSHADOW_HALF_ASSIGNOP(+=, +)
+  MSHADOW_HALF_ASSIGNOP(-=, -)
+  MSHADOW_HALF_ASSIGNOP(*=, *)
+  MSHADOW_HALF_ASSIGNOP(/=, /)
+
+  MSHADOW_XINLINE half_t operator+() {
+    return *this;
+  }
+
+  MSHADOW_XINLINE half_t operator-() {
+    return half_t(-float(*this));  // NOLINT(*)
+  }
+
+  MSHADOW_XINLINE half_t operator=(const half_t& a) {
+    half_ = a.half_;
+    return a;
+  }
+
+  template<typename T>
+  MSHADOW_XINLINE half_t operator=(const T& a) {
+    return *this = half_t(a);  /* NOLINT(*)*/
+  }
+
+  MSHADOW_XINLINE half_t operator=(const half_t& a) volatile {
+    half_ = a.half_;
+    return a;
+  }
+
+  template<typename T>
+  MSHADOW_XINLINE half_t operator=(const T& a) volatile {
+    return *this = half_t(a);  /* NOLINT(*)*/
+  }
+
+ private:
+  union Bits {
+    float f;
+    int32_t si;
+    uint32_t ui;
+  };
+
+  static int const fp16FractionBits = 10;
+  static int const fp32FractionBits = 23;
+  static int32_t const fp32FractionMask = ~(~0u << fp32FractionBits);  // == 0x7fffff
+  static int32_t const fp32HiddenBit = 1 << fp32FractionBits;         // == 0x800000
+  static int const shift = fp32FractionBits - fp16FractionBits;       // == 13
+  static int const shiftSign = 16;
+  static int32_t const expAdjust = 127 - 15;    // exp32-127 = exp16-15, so exp16 = exp32 - (127-15)
+
+  static int32_t const infN = 0x7F800000;  // flt32 infinity
+  static int32_t const maxN = 0x477FFFFF;  // max flt32 that's a flt16 normal after >> by shift
+  static int32_t const minN = 0x38800000;  // min flt16 normal as a flt32
+  static int32_t const maxZ = 0x33000000;  // max fp32 number that's still rounded to zero in fp16
+  static int32_t const signN = 0x80000000;  // flt32 sign bit
+
+  static int32_t const infC = infN >> shift;
+  static int32_t const nanN = (infC + 1) << shift;  // minimum flt16 nan as a flt32
+  static int32_t const maxC = maxN >> shift;
+  static int32_t const minC = minN >> shift;
+  static int32_t const signC = signN >> shiftSign;  // flt16 sign bit
+
+  static int32_t const mulN = 0x52000000;  // (1 << 23) / minN
+  static int32_t const mulC = 0x33800000;  // minN / (1 << (23 - shift))
+
+  static int32_t const subC = 0x003FF;  // max flt32 subnormal down shifted
+  static int32_t const norC = 0x00400;  // min flt32 normal down shifted
+
+  static int32_t const maxD = infC - maxC - 1;
+  static int32_t const minD = minC - subC - 1;
+
+  MSHADOW_XINLINE uint16_t float2half(const float& value) const {
+    Bits v;
+    v.f = value;
+    uint32_t sign = v.si & signN;    // grab sign bit
+    v.si ^= sign;                    // clear sign bit from v
+    sign >>= shiftSign;              // logical shift sign to fp16 position
+
+    if (v.si <= maxZ) {
+      // Handle eventual zeros here to ensure vshift will not exceed 32 below.
+      v.ui = 0;
+    } else if (v.si < minN) {
+      // Handle denorms
+      uint32_t exp32 = v.ui >> fp32FractionBits;
+      int32_t exp16 = exp32 - expAdjust;
+      // If exp16 == 0 (just into the denorm range), then significant should be shifted right 1.
+      // Smaller (so negative) exp16 values should result in greater right shifts.
+      uint32_t vshift = 1 - exp16;
+      uint32_t significand = fp32HiddenBit | (v.ui & fp32FractionMask);
+      v.ui = significand >> vshift;
+      // The only time it's *not* OK to add 0x1000 (i.e. half the flt16 fraction lsb) is
+      // when the lsb of the flt16 fraction == 0 (so not rounding up to even) and the additional
+      // bits to the right of the lsb are 1000... (including flt32 significand bits
+      // that may be lost during the above vshift).  The first term below will always
+      // be true for vshift >=12 (since even the 'hidden bit' has been shifted to the
+      // right of the '1' bit in 0x1000). And when vshift <= 11, both terms combine to make
+      // the proper test of the flt32 significand bits, including those lost during the vshift.
+#if MSHADOW_HALF_ROUND_TO_NEAREST == 1
+      // Rounding may increase the exponent to 1, but that's OK.
+      v.ui += (v.ui & 0x3fff) != 0x1000 || (significand & 0x7ff) ? 0x1000 : 0;
+#endif
+    } else if (v.si <= maxN) {
+      // Handle norms
+#if MSHADOW_HALF_ROUND_TO_NEAREST == 1
+      // Rounding may increase the exponent, possibly creating an inf, but that's OK.
+      v.ui += (v.ui & 0x3fff) != 0x1000 ? 0x1000 : 0;
+#endif
+      v.ui -= expAdjust << fp32FractionBits;
+    } else if (v.si <= infN) {
+      v.si = infN;
+    } else if (v.si < nanN) {
+      v.si = nanN;
+    }
+
+    v.ui >>= shift;
+    return sign | (v.ui & 0x7fff);
+  }
+
+  // Same as above routine, except for addition of volatile keyword
+  MSHADOW_XINLINE uint16_t float2half(const volatile float& value) const volatile {  // NOLINT (*)
+    Bits v;
+    v.f = value;
+    uint32_t sign = v.si & signN;    // grab sign bit
+    v.si ^= sign;                    // clear sign bit from v
+    sign >>= shiftSign;              // logical shift sign to fp16 position
+
+    if (v.si <= maxZ) {
+      // Handle eventual zeros here to ensure vshift will not exceed 32 below.
+      v.ui = 0;
+    } else if (v.si < minN) {
+      // Handle denorms
+      uint32_t exp32 = v.ui >> fp32FractionBits;
+      int32_t exp16 = exp32 - expAdjust;
+      // If exp16 == 0 (just into the denorm range), then significant should be shifted right 1.
+      // Smaller (so negative) exp16 values should result in greater right shifts.
+      uint32_t vshift = 1 - exp16;
+      uint32_t significand = fp32HiddenBit | (v.ui & fp32FractionMask);
+      v.ui = significand >> vshift;
+#if MSHADOW_HALF_ROUND_TO_NEAREST == 1
+      // Rounding may increase the exponent to 1, but that's OK.
+      v.ui += (v.ui & 0x3fff) != 0x1000 || (significand & 0x7ff) ? 0x1000 : 0;
+#endif
+    } else if (v.si <= maxN) {
+      // Handle norms
+#if MSHADOW_HALF_ROUND_TO_NEAREST == 1
+      // Rounding may increase the exponent, possibly creating an inf, but that's OK.
+      v.ui += (v.ui & 0x3fff) != 0x1000 ? 0x1000 : 0;
+#endif
+      v.ui -= expAdjust << fp32FractionBits;
+    } else if (v.si <= infN) {
+      v.si = infN;
+    } else if (v.si < nanN) {
+      v.si = nanN;
+    }
+
+    v.ui >>= shift;
+    return sign | (v.ui & 0x7fff);
+  }
+
+  MSHADOW_XINLINE float half2float(const uint16_t& value) const {
+    Bits v;
+    v.ui = value;
+    int32_t sign = v.si & signC;
+    v.si ^= sign;
+    sign <<= shiftSign;
+    v.si ^= ((v.si + minD) ^ v.si) & -(v.si > subC);
+    v.si ^= ((v.si + maxD) ^ v.si) & -(v.si > maxC);
+    Bits s;
+    s.si = mulC;
+    s.f *= v.si;
+    int32_t mask = -(norC > v.si);
+    v.si <<= shift;
+    v.si ^= (s.si ^ v.si) & mask;
+    v.si |= sign;
+    return v.f;
+  }
+
+  MSHADOW_XINLINE float half2float(const volatile uint16_t& value) const volatile {  // NOLINT(*)
+    Bits v;
+    v.ui = value;
+    int32_t sign = v.si & signC;
+    v.si ^= sign;
+    sign <<= shiftSign;
+    v.si ^= ((v.si + minD) ^ v.si) & -(v.si > subC);
+    v.si ^= ((v.si + maxD) ^ v.si) & -(v.si > maxC);
+    Bits s;
+    s.si = mulC;
+    s.f *= v.si;
+    int32_t mask = -(norC > v.si);
+    v.si <<= shift;
+    v.si ^= (s.si ^ v.si) & mask;
+    v.si |= sign;
+    return v.f;
+  }
+
+  template<typename T>
+  MSHADOW_XINLINE void constructor(const T& value) {
+#if (MSHADOW_CUDA_HALF && defined(__CUDA_ARCH__))
+    cuhalf_ = __float2half(float(value));  // NOLINT(*)
+#elif(MSHADOW_USE_F16C)
+    half_ = _cvtss_sh(static_cast<float>(value), 0);
+#else /* !MSHADOW_CUDA_HALF && !MSHADOW_USE_F16C */
+    half_ = float2half(float(value));  // NOLINT(*)
+#endif /* !MSHADOW_CUDA_HALF && !MSHADOW_USE_F16C */
+  }
+};
+
+/*! \brief overloaded + operator for half_t */
+MSHADOW_HALF_OPERATOR(half_t, +)
+/*! \brief overloaded - operator for half_t */
+MSHADOW_HALF_OPERATOR(half_t, -)
+/*! \brief overloaded * operator for half_t */
+MSHADOW_HALF_OPERATOR(half_t, *)
+/*! \brief overloaded / operator for half_t */
+MSHADOW_HALF_OPERATOR(half_t, /)
+/*! \brief overloaded > operator for half_t */
+MSHADOW_HALF_OPERATOR(bool, >)
+/*! \brief overloaded < operator for half_t */
+MSHADOW_HALF_OPERATOR(bool, <)
+/*! \brief overloaded >= operator for half_t */
+MSHADOW_HALF_OPERATOR(bool, >=)
+/*! \brief overloaded <= operator for half_t */
+MSHADOW_HALF_OPERATOR(bool, <=)
+
+#define MSHADOW_HALF_MIN mshadow::half::half_t::Binary(0xFBFF);
+#define MSHADOW_HALF_MAX mshadow::half::half_t::Binary(0x7BFF);
+}  // namespace half
+}  // namespace mshadow
+#endif  // MSHADOW_HALF_H_
diff --git a/3rdparty/mshadow/mshadow/half2.h b/3rdparty/mshadow/mshadow/half2.h
new file mode 100755
index 000000000000..3e130c85ba63
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/half2.h
@@ -0,0 +1,143 @@
+/*!
+ *  Copyright (c) 2017 by Contributors
+ * \file half2.h
+ * \brief definition of vector float16, half2 type.
+ *
+ * \author Antti-Pekka Hynninen
+ */
+#ifndef MSHADOW_HALF2_H_
+#define MSHADOW_HALF2_H_
+
+#if (defined(__CUDACC__) && __CUDA_ARCH__ >= 530 && MSHADOW_USE_CUDA && CUDA_VERSION >= 7050)
+  #define MSHADOW_CUDA_HALF2 1
+  #include <cuda_fp16.h>
+#else
+  #define MSHADOW_CUDA_HALF2 0
+#endif
+
+#include<math.h>
+
+/*! \brief namespace for mshadow */
+namespace mshadow {
+/* \brief name space for host/device portable half-precision floats */
+namespace half {
+
+#define MSHADOW_HALF2_ASSIGNOP(AOP, OP)                                   \
+  template<typename T>                                                    \
+  MSHADOW_XINLINE half2_t operator AOP (const T& a) {                     \
+    return *this = half2_t(*this OP a);  /* NOLINT(*)*/                   \
+  }                                                                       \
+
+class MSHADOW_ALIGNED(4) half2_t {
+ public:
+#if MSHADOW_CUDA_HALF2
+  half2 half2_;
+#else
+  half_t half_t2[2];
+#endif
+
+  MSHADOW_XINLINE half2_t() {}
+
+#if MSHADOW_CUDA_HALF2
+  MSHADOW_XINLINE explicit half2_t(half2 a) : half2_(a) {}
+#else
+  MSHADOW_XINLINE explicit half2_t(half_t a, half_t b) {
+    half_t2[0] = a;
+    half_t2[1] = b;
+  }
+#endif
+
+  MSHADOW_XINLINE explicit half2_t(int a) {
+#if MSHADOW_CUDA_HALF2
+    half2_ = __half2half2(__int2half_rz(a));
+#else
+    half_t2[0] = (half_t)a;
+    half_t2[1] = (half_t)a;
+#endif
+  }
+
+  MSHADOW_XINLINE half2_t operator+() {
+    return *this;
+  }
+
+  MSHADOW_XINLINE half2_t operator-() {
+#if MSHADOW_CUDA_HALF2
+    return half2_t(__hneg2(half2_));
+#else
+    return half2_t(-half_t2[0], -half_t2[1]);
+#endif
+  }
+
+  MSHADOW_XINLINE half2_t operator=(const half2_t& a) {
+#if MSHADOW_CUDA_HALF2
+    half2_ = a.half2_;
+#else
+    half_t2[0] = a.half_t2[0];
+    half_t2[1] = a.half_t2[1];
+#endif
+    return a;
+  }
+
+  MSHADOW_HALF2_ASSIGNOP(+=, +)
+  MSHADOW_HALF2_ASSIGNOP(-=, -)
+  MSHADOW_HALF2_ASSIGNOP(*=, *)
+  MSHADOW_HALF2_ASSIGNOP(/=, /)
+};
+
+/*! \brief overloaded + operator for half2_t */
+MSHADOW_XINLINE half2_t operator+(half2_t a, half2_t b) {
+#if MSHADOW_CUDA_HALF2
+  return half2_t(__floats2half2_rn(__low2float(a.half2_) + __low2float(b.half2_),
+                                   __high2float(a.half2_) + __high2float(b.half2_)));
+#else
+  return half2_t(a.half_t2[0] + b.half_t2[0], a.half_t2[1] + b.half_t2[1]);
+#endif
+}
+/*! \brief overloaded - operator for half2_t */
+MSHADOW_XINLINE half2_t operator-(half2_t a, half2_t b) {
+#if MSHADOW_CUDA_HALF2
+  return half2_t(__floats2half2_rn(__low2float(a.half2_) - __low2float(b.half2_),
+                                   __high2float(a.half2_) - __high2float(b.half2_)));
+#else
+  return half2_t(a.half_t2[0] - b.half_t2[0], a.half_t2[1] - b.half_t2[1]);
+#endif
+}
+/*! \brief overloaded * operator for half2_t */
+MSHADOW_XINLINE half2_t operator*(half2_t a, half2_t b) {
+#if MSHADOW_CUDA_HALF2
+  return half2_t(__floats2half2_rn(__low2float(a.half2_) * __low2float(b.half2_),
+                                   __high2float(a.half2_) * __high2float(b.half2_)));
+#else
+  return half2_t(a.half_t2[0] * b.half_t2[0], a.half_t2[1] * b.half_t2[1]);
+#endif
+}
+/*! \brief overloaded / operator for half2_t */
+MSHADOW_XINLINE half2_t operator/(half2_t a, half2_t b) {
+#if MSHADOW_CUDA_HALF2
+  return half2_t(__floats2half2_rn(__low2float(a.half2_) / __low2float(b.half2_),
+                                   __high2float(a.half2_) / __high2float(b.half2_)));
+#else
+  return half2_t(a.half_t2[0] / b.half_t2[0], a.half_t2[1] / b.half_t2[1]);
+#endif
+}
+/*! \brief overloaded % operator for half2_t */
+MSHADOW_XINLINE half2_t operator%(half2_t a, half2_t b) {
+#if MSHADOW_CUDA_HALF2
+  return half2_t(__floats2half2_rn(::fmod(__low2float(a.half2_), __low2float(b.half2_)),
+                                   ::fmod(__high2float(a.half2_), __high2float(b.half2_))));
+#else
+  return half2_t(::fmod(a.half_t2[0], b.half_t2[0]), ::fmod(a.half_t2[1], b.half_t2[1]));
+#endif
+}
+/*! \brief overloaded == operator for half2_t */
+MSHADOW_XINLINE bool operator==(half2_t a, half2_t b) {
+#if MSHADOW_CUDA_HALF2
+  return __hbeq2(a.half2_, b.half2_);
+#else
+  return (a.half_t2[0] == b.half_t2[0] && a.half_t2[1] == b.half_t2[1]);
+#endif
+}
+
+}  // namespace half
+}  // namespace mshadow
+#endif  // MSHADOW_HALF2_H_
diff --git a/3rdparty/mshadow/mshadow/io.h b/3rdparty/mshadow/mshadow/io.h
new file mode 100644
index 000000000000..2d0efc3aa56b
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/io.h
@@ -0,0 +1,137 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file io.h
+ * \brief definitions of I/O functions for mshadow tensor
+ * \author Tianqi Chen
+ */
+#ifndef MSHADOW_IO_H_
+#define MSHADOW_IO_H_
+#include "./tensor.h"
+
+namespace mshadow {
+namespace utils {
+/*!
+ * \brief interface of stream I/O, used to serialize data,
+ *   mshadow does not restricted to only this interface in SaveBinary/LoadBinary
+ *   mshadow accept all class that implements Read and Write
+ */
+class IStream {
+ public:
+  /*!
+   * \brief read data from stream
+   * \param ptr pointer to memory buffer
+   * \param size size of block
+   * \return usually is the size of data readed
+   */
+  virtual size_t Read(void *ptr, size_t size) = 0;
+  /*!
+   * \brief write data to stream
+   * \param ptr pointer to memory buffer
+   * \param size size of block
+   */
+  virtual void Write(const void *ptr, size_t size) = 0;
+  /*! \brief virtual destructor */
+  virtual ~IStream(void) {}
+};
+}  // namespace utils
+/*!
+ * \brief CPU/GPU: save a tensor by binary format, for GPU version, a temp Tensor<cpu,dim> storage will be allocated
+ * \param fo output binary stream
+ * \param src source data file
+ * \tparam dim dimension of tensor
+ * \tparam DType type of element in tensor
+ * \tparam TStream type of stream, need to support Read, Write, one example is utils::IStream.
+ */
+template<int dim, typename DType, typename TStream>
+inline void SaveBinary(TStream &fo, const Tensor<cpu, dim, DType> &src);  // NOLINT(*)
+/*!
+ * \brief CPU/GPU: save a tensor by binary format, for GPU version, a temp Tensor<cpu,dim> storage will be allocated
+ * \param fo output binary stream
+ * \param src source data file
+ * \tparam dim dimension of tensor
+ * \tparam DType type of element in tensor
+ * \tparam TStream type of stream, need to support Read, Write, one example is utils::IStream.
+ */
+template<int dim, typename DType, typename TStream>
+inline void SaveBinary(TStream &fo, const Tensor<gpu, dim, DType> &src); // NOLINT(*)
+/*!
+ * \brief CPU/GPU: load a tensor by binary format, for GPU version, a temp Tensor<cpu,dim> storage will be allocated
+ *       if pre_alloc is true , then space in dst is preallocated, and must have same shape of the tensor loaded
+ *       if pre_alloc is false, then dst originally does not have space allocated, LoadBinary will allocate space for dst
+ * \param fi output binary stream
+ * \param dst destination file
+ * \param pre_alloc whether space is pre-allocated, if false, space allocation will happen
+ * \tparam dim dimension of tensor
+ * \tparam DType type of element in tensor
+ * \tparam TStream type of stream, need to support Read, Write, one example is utils::IStream.
+ */
+template<int dim, typename DType, typename TStream>
+inline void LoadBinary(TStream &fi,  // NOLINT(*)
+                       Tensor<cpu, dim, DType> *dst, bool pre_alloc);
+/*!
+ * \brief CPU/GPU: load a tensor by binary format, for GPU version, a temp Tensor<cpu,dim> storage will be allocated
+ *       if pre_alloc is true , then space in dst is preallocated, and must have same shape of the tensor loaded
+ *       if pre_alloc is false, then dst originally does not have space allocated, LoadBinary will allocate space for dst
+ * \param fi output binary stream
+ * \param dst destination file
+ * \param pre_alloc whether space is pre-allocated, if false, space allocation will happen
+ * \tparam dim dimension of tensor
+ * \tparam DType type of element in tensor
+ * \tparam TStream type of stream, need to support Read, Write, one example is utils::IStream.
+ */
+
+template<int dim, typename DType, typename TStream>
+inline void LoadBinary(TStream &fi, // NOLINT(*)
+                       Tensor<gpu, dim, DType> *dst, bool pre_alloc);
+
+// implementations
+template<int dim, typename DType, typename TStream>
+inline void SaveBinary(TStream &fo, const Tensor<cpu, dim, DType> &src_) { // NOLINT(*)
+  fo.Write(&src_.shape_, sizeof(src_.shape_));
+  Tensor<cpu, 2, DType> src = src_.FlatTo2D();
+  for (index_t i = 0; i < src.size(0); ++i) {
+    fo.Write(src[i].dptr_, sizeof(DType) * src.size(1));
+  }
+}
+template<int dim, typename DType, typename TStream>
+inline void SaveBinary(TStream &fo, const Tensor<gpu, dim, DType> &src) { // NOLINT(*)
+  // copy to CPU, then save
+  Tensor<cpu, dim, DType> tmp(src.shape_);
+  AllocSpace(&tmp);
+  Stream<gpu> stream;
+  Copy(tmp, src, &stream);
+  SaveBinary(fo, tmp);
+  FreeSpace(&tmp);
+}
+template<int dim, typename DType, typename TStream>
+inline void LoadBinary(TStream &fi, // NOLINT(*)
+                       Tensor<cpu, dim, DType> *dst_, bool pre_alloc) {
+  Shape<dim> shape;
+  CHECK_NE(fi.Read(&shape, sizeof(shape)), 0) << "mshadow::LoadBinary";
+  if (pre_alloc) {
+    CHECK_EQ(shape, dst_->shape_) << "LoadBinary, shape do not match pre-allocated shape";
+  } else {
+    dst_->shape_ = shape; AllocSpace(dst_);
+  }
+  Tensor<cpu, 2, DType> dst = dst_->FlatTo2D();
+  if (dst.size(0) == 0) return;
+  for (index_t i = 0; i < dst.size(0); ++i) {
+    CHECK_NE(fi.Read(dst[i].dptr_, sizeof(DType) * dst.size(1)), 0) << "mshadow::LoadBinary";
+  }
+}
+template<int dim, typename DType, typename TStream>
+inline void LoadBinary(TStream &fi, // NOLINT(*)
+                       Tensor<gpu, dim, DType> *dst, bool pre_alloc) {
+  Tensor<cpu, dim, DType> tmp;
+  LoadBinary(fi, &tmp, false);
+  if (pre_alloc) {
+    CHECK_EQ(tmp.shape, dst->shape_) << "LoadBinary, shape do not match pre-allocated shape";
+  } else {
+    dst->shape = tmp.shape; AllocSpace(dst);
+  }
+  Stream<gpu> stream;
+  Copy(*dst, tmp, &stream);
+  FreeSpace(&tmp);
+}
+}  // namespace mshadow
+#endif  // MSHADOW_IO_H_
diff --git a/3rdparty/mshadow/mshadow/logging.h b/3rdparty/mshadow/mshadow/logging.h
new file mode 100644
index 000000000000..002b90097595
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/logging.h
@@ -0,0 +1,234 @@
+/*!
+ *  Copyright (c) 2015 by Contributors
+ * \file logging.h
+ * \brief defines logging macros of dmlc
+ *  allows use of GLOG, fall back to internal
+ *  implementation when disabled
+ */
+#ifndef MSHADOW_LOGGING_H_
+#define MSHADOW_LOGGING_H_
+#ifndef DMLC_LOGGING_H_
+#define DMLC_LOGGING_H_
+
+#include <cstdio>
+#include <cstdlib>
+#include <string>
+#include <vector>
+#include <stdexcept>
+#include "./base.h"
+
+namespace dmlc {
+/*! \brief taken from DMLC directly */
+
+/*!
+ * \brief exception class that will be thrown by
+ *  default logger if DMLC_LOG_FATAL_THROW == 1
+ */
+struct Error : public std::runtime_error {
+  /*!
+   * \brief constructor
+   * \param s the error message
+   */
+  explicit Error(const std::string &s) : std::runtime_error(s) {}
+};
+}  // namespace dmlc
+
+#if defined(_MSC_VER) && _MSC_VER < 1900
+#define noexcept(a)
+#endif
+
+#if DMLC_USE_GLOG
+#include <glog/logging.h>
+
+namespace dmlc {
+/*! \brief taken from DMLC directly */
+inline void InitLogging(const char* argv0) {
+  google::InitGoogleLogging(argv0);
+}
+}  // namespace dmlc
+
+#else
+// use a light version of glog
+#include <assert.h>
+#include <iostream>
+#include <sstream>
+#include <ctime>
+
+#if defined(_MSC_VER)
+#pragma warning(disable : 4722)
+#endif
+
+namespace dmlc {
+inline void InitLogging(const char* argv0) {
+  // DO NOTHING
+}
+
+// Always-on checking
+#define CHECK(x)                                           \
+  if (!(x))                                                \
+    dmlc::LogMessageFatal(__FILE__, __LINE__).stream() << "Check "  \
+      "failed: " #x << ' '
+#define CHECK_LT(x, y) CHECK((x) < (y))
+#define CHECK_GT(x, y) CHECK((x) > (y))
+#define CHECK_LE(x, y) CHECK((x) <= (y))
+#define CHECK_GE(x, y) CHECK((x) >= (y))
+#define CHECK_EQ(x, y) CHECK((x) == (y))
+#define CHECK_NE(x, y) CHECK((x) != (y))
+#define CHECK_NOTNULL(x) \
+  ((x) == NULL ? dmlc::LogMessageFatal(__FILE__, __LINE__).stream() << "Check  notnull: "  #x << ' ', (x) : (x)) // NOLINT(*)
+// Debug-only checking.
+#ifdef NDEBUG
+#define DCHECK(x) \
+  while (false) CHECK(x)
+#define DCHECK_LT(x, y) \
+  while (false) CHECK((x) < (y))
+#define DCHECK_GT(x, y) \
+  while (false) CHECK((x) > (y))
+#define DCHECK_LE(x, y) \
+  while (false) CHECK((x) <= (y))
+#define DCHECK_GE(x, y) \
+  while (false) CHECK((x) >= (y))
+#define DCHECK_EQ(x, y) \
+  while (false) CHECK((x) == (y))
+#define DCHECK_NE(x, y) \
+  while (false) CHECK((x) != (y))
+#else
+#define DCHECK(x) CHECK(x)
+#define DCHECK_LT(x, y) CHECK((x) < (y))
+#define DCHECK_GT(x, y) CHECK((x) > (y))
+#define DCHECK_LE(x, y) CHECK((x) <= (y))
+#define DCHECK_GE(x, y) CHECK((x) >= (y))
+#define DCHECK_EQ(x, y) CHECK((x) == (y))
+#define DCHECK_NE(x, y) CHECK((x) != (y))
+#endif  // NDEBUG
+
+#define LOG_INFO dmlc::LogMessage(__FILE__, __LINE__)
+#define LOG_ERROR LOG_INFO
+#define LOG_WARNING LOG_INFO
+#define LOG_FATAL dmlc::LogMessageFatal(__FILE__, __LINE__)
+#define LOG_QFATAL LOG_FATAL
+
+// Poor man version of VLOG
+#define VLOG(x) LOG_INFO.stream()
+
+#define LOG(severity) LOG_##severity.stream()
+#define LG LOG_INFO.stream()
+#define LOG_IF(severity, condition) \
+  !(condition) ? (void)0 : dmlc::LogMessageVoidify() & LOG(severity)
+
+#ifdef NDEBUG
+#define LOG_DFATAL LOG_ERROR
+#define DFATAL ERROR
+#define DLOG(severity) true ? (void)0 : dmlc::LogMessageVoidify() & LOG(severity)
+#define DLOG_IF(severity, condition) \
+  (true || !(condition)) ? (void)0 : dmlc::LogMessageVoidify() & LOG(severity)
+#else
+#define LOG_DFATAL LOG_FATAL
+#define DFATAL FATAL
+#define DLOG(severity) LOG(severity)
+#define DLOG_IF(severity, condition) LOG_IF(severity, condition)
+#endif
+
+// Poor man version of LOG_EVERY_N
+#define LOG_EVERY_N(severity, n) LOG(severity)
+
+class DateLogger {
+ public:
+  DateLogger() {
+#if defined(_MSC_VER)
+    _tzset();
+#endif
+  }
+  const char* HumanDate() {
+#if defined(_MSC_VER)
+    _strtime_s(buffer_, sizeof(buffer_));
+#else
+    time_t time_value = time(NULL);
+    struct tm now;
+    localtime_r(&time_value, &now);
+    snprintf(buffer_, sizeof(buffer_), "%02d:%02d:%02d", now.tm_hour,
+             now.tm_min, now.tm_sec);
+#endif
+    return buffer_;
+  }
+ private:
+  char buffer_[9];
+};
+
+class LogMessage {
+ public:
+  LogMessage(const char* file, int line)
+      :
+#ifdef __ANDROID__
+        log_stream_(std::cout)
+#else
+        log_stream_(std::cerr)
+#endif
+  {
+    log_stream_ << "[" << pretty_date_.HumanDate() << "] " << file << ":"
+                << line << ": ";
+  }
+  ~LogMessage() { log_stream_ << "\n"; }
+  std::ostream& stream() { return log_stream_; }
+
+ protected:
+  std::ostream& log_stream_;
+
+ private:
+  DateLogger pretty_date_;
+  LogMessage(const LogMessage&);
+  void operator=(const LogMessage&);
+};
+
+#if DMLC_LOG_FATAL_THROW == 0
+class LogMessageFatal : public LogMessage {
+ public:
+  LogMessageFatal(const char* file, int line) : LogMessage(file, line) {}
+  ~LogMessageFatal() {
+    log_stream_ << "\n";
+    abort();
+  }
+
+ private:
+  LogMessageFatal(const LogMessageFatal&);
+  void operator=(const LogMessageFatal&);
+};
+#else
+class LogMessageFatal {
+ public:
+  LogMessageFatal(const char* file, int line) {
+    log_stream_ << "[" << pretty_date_.HumanDate() << "] " << file << ":"
+                << line << ": ";
+  }
+  std::ostringstream &stream() { return log_stream_; }
+  ~LogMessageFatal() DMLC_THROW_EXCEPTION {
+    // throwing out of destructor is evil
+    // hopefully we can do it here
+    throw Error(log_stream_.str());
+  }
+
+ private:
+  std::ostringstream log_stream_;
+  DateLogger pretty_date_;
+  LogMessageFatal(const LogMessageFatal&);
+  void operator=(const LogMessageFatal&);
+};
+#endif
+
+// This class is used to explicitly ignore values in the conditional
+// logging macros.  This avoids compiler warnings like "value computed
+// is not used" and "statement has no effect".
+class LogMessageVoidify {
+ public:
+  LogMessageVoidify() {}
+  // This has to be an operator with a precedence lower than << but
+  // higher than "?:". See its usage.
+  void operator&(std::ostream&) {}
+};
+
+}  // namespace dmlc
+
+#endif
+#endif  // DMLC_LOGGING_H_
+#endif  // MSHADOW_LOGGING_H_
+
diff --git a/3rdparty/mshadow/mshadow/packet-inl.h b/3rdparty/mshadow/mshadow/packet-inl.h
new file mode 100644
index 000000000000..f5a89bfa8421
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/packet-inl.h
@@ -0,0 +1,413 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file packet-inl.h
+ * \brief Generic packet vectorization code
+ */
+#ifndef MSHADOW_PACKET_INL_H_
+#define MSHADOW_PACKET_INL_H_
+
+#ifdef __APPLE__
+#include <stdlib.h>
+#else
+#include <malloc.h>
+#endif
+#include "./base.h"
+#include "./tensor.h"
+#include "./expression.h"
+
+
+namespace mshadow {
+/*! \brief namespace of packet math*/
+namespace packet {
+
+enum PacketArch {
+  kPlain,
+  kSSE2,
+};
+
+#if MSHADOW_USE_SSE
+#define MSHADOW_DEFAULT_PACKET  ::mshadow::packet::kSSE2
+#else
+#define MSHADOW_DEFAULT_PACKET  ::mshadow::packet::kPlain
+#endif
+
+// whether packet operator is enabled.
+/*!
+ * \brief Generic packet type
+ * \tparam DType The data type of the packet.
+ * \tparam Arch the Arch of the packet.
+ */
+template<typename DType, PacketArch Arch = MSHADOW_DEFAULT_PACKET>
+struct Packet;
+
+template<PacketArch Arch>
+struct AlignBytes {
+  static const index_t value = 4;
+};
+
+}  // namespace packet
+}  // namespace mshadow
+
+namespace mshadow {
+namespace packet {
+/*!
+ * \brief analog to cudaMallocPitch, allocate a aligned space with num_line * lspace cells
+ * \param out_pitch output parameter, the actuall space allocated for each line
+ * \param lspace number of cells required for each line
+ * \param num_line number of lines to be allocated
+ */
+inline void* AlignedMallocPitch(size_t *out_pitch,
+                                size_t lspace,
+                                size_t num_line) {
+  const index_t bits = AlignBytes<MSHADOW_DEFAULT_PACKET>::value;
+  const index_t mask = (1 << bits) - 1;
+
+  size_t pitch = ((lspace + mask) >> bits) << bits;
+  *out_pitch = pitch;
+#ifdef _MSC_VER
+  void *res = _aligned_malloc(pitch * num_line, 1 << bits);
+#else
+  void *res;
+  int ret = posix_memalign(&res, 1 << bits, pitch * num_line);
+  CHECK_EQ(ret, 0) << "AlignedMallocPitch failed";
+#endif
+  if (res == NULL) {
+    LOG(FATAL) << "AlignedMallocPitch failed";
+  }
+  return res;
+}
+
+/*!
+ * \brief free aligned space
+ * \param ptr pointer to space to be freed
+ */
+inline void AlignedFree(void *ptr) {
+#ifdef _MSC_VER
+  _aligned_free(ptr);
+#else
+  free(ptr);
+#endif
+}
+
+/*! \brief check if a pointer is aligned */
+template<PacketArch Arch>
+inline bool CheckAlign(size_t pitch) {
+  const index_t bits = AlignBytes<Arch>::value;
+  return !(pitch & ((1 << bits) - 1));
+}
+
+/*! \brief check if a pointer is aligned */
+template<PacketArch Arch>
+inline bool CheckAlign(void *ptr) {
+  return CheckAlign<Arch>(reinterpret_cast<size_t>(ptr));
+}
+
+/*!
+ * \brief get upper bound of aligned index of size
+ * \param size size of the array
+ * \param fsize size of float
+ */
+template<typename DType, PacketArch Arch>
+inline index_t UpperAlign(index_t size) {
+  const index_t bits = AlignBytes<MSHADOW_DEFAULT_PACKET>::value;
+  const index_t mask = (1 << bits) - 1;
+  const index_t fsize = sizeof(DType);
+  return (((size * fsize + mask) >> bits) << bits) / fsize;
+}
+
+/*!
+ * \brief get lower bound of aligned index of size
+ * \param size size of the array
+ * \param fsize size of float
+ */
+template<typename DType, PacketArch Arch>
+inline index_t LowerAlign(index_t size) {
+  const index_t bits = AlignBytes<MSHADOW_DEFAULT_PACKET>::value;
+  const index_t fsize = sizeof(DType);
+  return (((size * fsize) >> bits) << bits) / fsize;
+}
+
+/*!
+ * \brief generic Packet operator
+ * \tparam OP The operator
+ * \tparam DType The data type
+ * \tparam Arch The architecture.
+ */
+template<typename OP, typename DType, PacketArch Arch>
+struct PacketOp {
+  static const bool kEnabled = false;
+};
+// specialization of operators
+template<typename DType, PacketArch Arch>
+struct PacketOp<op::plus, DType, Arch> {
+  static const bool kEnabled = true;
+  MSHADOW_CINLINE static Packet<DType, Arch> Map(const Packet<DType, Arch>& lhs,
+                                                   const Packet<DType, Arch>& rhs) {
+    return lhs + rhs;
+  }
+};
+template<typename DType, PacketArch Arch>
+struct PacketOp<op::minus, DType, Arch> {
+  static const bool kEnabled = true;
+  MSHADOW_CINLINE static Packet<DType, Arch> Map(const Packet<DType, Arch>& lhs,
+                                                  const Packet<DType, Arch>& rhs) {
+    return lhs - rhs;
+  }
+};
+template<typename DType, PacketArch Arch>
+struct PacketOp<op::mul, DType, Arch> {
+  static const bool kEnabled = true;
+  MSHADOW_CINLINE static Packet<DType, Arch> Map(const Packet<DType, Arch>& lhs,
+                                                  const Packet<DType, Arch>& rhs) {
+    return lhs * rhs;
+  }
+};
+template<typename DType, PacketArch Arch>
+struct PacketOp<op::div, DType, Arch> {
+  static const bool kEnabled = true;
+  MSHADOW_CINLINE static Packet<DType, Arch> Map(const Packet<DType, Arch>& lhs,
+                                                  const Packet<DType, Arch>& rhs) {
+    return lhs / rhs;
+  }
+};
+
+template<typename DType, PacketArch Arch>
+struct PacketOp<op::identity, DType, Arch> {
+  static const bool kEnabled = true;
+  MSHADOW_CINLINE static Packet<DType, Arch> Map(const Packet<DType, Arch>& src) {
+    return src;
+  }
+};
+
+
+// savers to do storage
+template<typename SV, typename TFloat, PacketArch Arch>
+struct Saver{
+  MSHADOW_CINLINE static void Save(TFloat *dst, const Packet<TFloat, Arch>& src) {
+    Packet<TFloat, Arch> lhs = Packet<TFloat, Arch>::Load(dst);
+    Packet<TFloat, Arch> ans = PacketOp<typename SV::OPType, TFloat, Arch>::Map(lhs, src);
+    ans.Store(dst);
+  }
+};
+template<typename TFloat, PacketArch Arch>
+struct Saver<sv::saveto, TFloat, Arch> {
+  MSHADOW_CINLINE static void Save(TFloat *dst, const Packet<TFloat, Arch>& src) {
+    src.Store(dst);
+  }
+};
+}  // namespace packet
+}  // namespace mshadow
+
+#include "packet/plain-inl.h"
+#if MSHADOW_USE_SSE && !defined(__CUDACC__)
+#include "packet/sse-inl.h"
+#endif
+
+namespace mshadow {
+namespace expr {
+
+typedef packet::PacketArch PacketArch;
+
+// same as plan, but use packet
+template<typename ExpType, typename DType, PacketArch Arch>
+class PacketPlan {
+ public:
+  /*!
+   * \brief evaluate the expression at index [y][x],
+   * x will be aligned to Packet<DType, Arch>::Size()
+   */
+  MSHADOW_CINLINE packet::Packet<DType, Arch> EvalPacket(index_t y, index_t x) const;
+  MSHADOW_CINLINE DType Eval(index_t y, index_t x) const;
+};
+
+template <typename Device, int dim, typename DType, PacketArch Arch>
+class PacketPlan<Tensor<Device, dim, DType>, DType, Arch> {
+ public:
+  explicit PacketPlan(const Tensor<Device, dim, DType> &t)
+      :dptr_(t.dptr_), stride_(t.stride_) {}
+  MSHADOW_CINLINE packet::Packet<DType, Arch> EvalPacket(index_t y, index_t x) const {
+    return packet::Packet<DType, Arch>::Load(&dptr_[y * stride_ + x]);
+  }
+  MSHADOW_CINLINE DType Eval(index_t y, index_t x) const {
+    return dptr_[y * stride_ + x];
+  }
+
+ private:
+  const DType  *dptr_;
+  index_t stride_;
+};
+
+template<typename DType, PacketArch Arch>
+class PacketPlan<ScalarExp<DType>, DType, Arch> {
+ public:
+  explicit PacketPlan(DType scalar) : scalar_(scalar) {}
+  MSHADOW_CINLINE packet::Packet<DType, Arch> EvalPacket(index_t y, index_t x) const {
+    return packet::Packet<DType, Arch>::Fill(scalar_);
+  }
+  MSHADOW_CINLINE DType Eval(index_t y, index_t x) const {
+    return scalar_;
+  }
+
+ private:
+  DType scalar_;
+};
+
+template<typename OP, typename TA, typename TB, int etype, typename DType, PacketArch Arch>
+class PacketPlan<BinaryMapExp<OP, TA, TB, DType, etype>, DType, Arch> {
+ public:
+  PacketPlan(const PacketPlan<TA, DType, Arch> &lhs, const PacketPlan<TB, DType, Arch> &rhs)
+      : lhs_(lhs), rhs_(rhs) {}
+  MSHADOW_CINLINE packet::Packet<DType, Arch> EvalPacket(index_t y, index_t x) const {
+    return packet::PacketOp<OP, DType, Arch>::Map(lhs_.EvalPacket(y, x), rhs_.EvalPacket(y, x));
+  }
+  MSHADOW_CINLINE DType Eval(index_t y, index_t x) const {
+    return OP::Map(lhs_.Eval(y, x), rhs_.Eval(y, x));
+  }
+
+ private:
+  PacketPlan<TA, DType, Arch> lhs_;
+  PacketPlan<TB, DType, Arch> rhs_;
+};
+
+template<typename OP, typename TA, int etype, typename DType, PacketArch Arch>
+class PacketPlan<UnaryMapExp<OP, TA, DType, etype>, DType, Arch> {
+ public:
+  PacketPlan(const PacketPlan<TA, DType, Arch> &src) : src_(src) {}
+  MSHADOW_CINLINE packet::Packet<DType> EvalPacket(index_t y, index_t x) const {
+    return packet::PacketOp<OP, DType, Arch>::Map(src_.EvalPacket(y, x));
+  }
+  MSHADOW_CINLINE DType Eval(index_t y, index_t x) const {
+    return OP::Map(src_.Eval(y, x));
+  }
+
+ private:
+  PacketPlan<TA, DType, Arch> src_;
+};
+
+template<PacketArch Arch, typename OP, typename TA, typename TB, typename DType, int etype>
+inline PacketPlan<BinaryMapExp<OP, TA, TB, DType, etype>, DType, Arch>
+MakePacketPlan(const BinaryMapExp<OP, TA, TB, DType, etype> &e);
+
+template<PacketArch Arch, typename DType>
+inline PacketPlan<ScalarExp<DType>, DType, Arch> MakePacketPlan(const ScalarExp<DType> &e) {
+  return PacketPlan<ScalarExp<DType>, DType, Arch>(e.scalar_);
+}
+template<PacketArch Arch, typename T, typename DType>
+inline PacketPlan<T, DType, Arch> MakePacketPlan(const RValueExp<T, DType> &e) {
+  return PacketPlan<T, DType, Arch>(e.self());
+}
+template<PacketArch Arch, typename T, int dim, typename DType>
+inline PacketPlan<T, DType, Arch>
+MakePacketPlan(const MakeTensorExp<T, cpu, dim, DType> &e) {
+  return PacketPlan<T, DType, Arch>(e.real_self());
+}
+template<PacketArch Arch, typename OP, typename TA, typename DType, int etype>
+inline PacketPlan<UnaryMapExp<OP, TA, DType, etype>, DType, Arch>
+MakePacketPlan(const UnaryMapExp<OP, TA, DType, etype> &e) {
+  return PacketPlan<UnaryMapExp<OP, TA, DType, etype>, DType, Arch>(MakePacketPlan<Arch>(e.src_));
+}
+template<PacketArch Arch, typename OP, typename TA, typename TB, typename DType, int etype>
+inline PacketPlan<BinaryMapExp<OP, TA, TB, DType, etype>, DType, Arch>
+MakePacketPlan(const BinaryMapExp<OP, TA, TB, DType, etype> &e) {
+  return PacketPlan<BinaryMapExp<OP, TA, TB, DType, etype>,
+                    DType, Arch>(MakePacketPlan<Arch>(e.lhs_), MakePacketPlan<Arch>(e.rhs_));
+}
+
+/*!
+ * \brief static check packet enable
+ *
+ * \tparam Device the type of Device
+ * \tparam dim dimension of the tensor
+ * \tparam E expression
+ */
+template<typename E, PacketArch Arch>
+struct PacketCheck{
+  static const bool kPass = false;
+};
+template<PacketArch Arch>
+struct PacketCheck<float, Arch> {
+  static const bool kPass = true;
+};
+template<PacketArch Arch>
+struct PacketCheck<double, Arch> {
+  static const bool kPass = true;
+};
+template<typename DType, PacketArch Arch>
+struct PacketCheck<ScalarExp<DType>, Arch> {
+  static const bool kPass = PacketCheck<DType, Arch>::kPass;
+};
+template<int dim, typename DType, PacketArch Arch>
+struct PacketCheck<Tensor<cpu, dim, DType>, Arch> {
+  static const bool kPass = PacketCheck<DType, Arch>::kPass;
+};
+template<typename OP, typename TA, typename DType, int etype, PacketArch Arch>
+struct PacketCheck<UnaryMapExp<OP, TA, DType, etype>, Arch> {
+  static const bool kPass = PacketCheck<TA, Arch>::kPass &&
+      packet::PacketOp<OP, DType, Arch>::kEnabled;
+};
+template<typename OP, typename TA, typename TB, typename DType, int etype, PacketArch Arch>
+struct PacketCheck< BinaryMapExp<OP, TA, TB, DType, etype>, Arch> {
+  static const bool kPass = packet::PacketOp<OP, DType, Arch>::kEnabled &&
+      PacketCheck<TA, Arch>::kPass && PacketCheck<TB, Arch>::kPass;
+};
+//----------------------------------------------------
+// Check if data is aligned and allow packet operation
+//----------------------------------------------------
+template<int dim, typename E, PacketArch Arch>
+struct PacketAlignCheck {
+  inline static bool Check(const E &exp) {
+    return false;
+  }
+};
+template<int dim, typename DType, PacketArch Arch>
+struct PacketAlignCheck<dim, ScalarExp<DType>, Arch> {
+  inline static bool Check(const ScalarExp<DType> &exp) {
+    return true;
+  }
+};
+template<int dim, typename DType, PacketArch Arch>
+struct PacketAlignCheck<dim, Tensor<cpu, dim, DType>, Arch> {
+  inline static bool Check(const Tensor<cpu, dim, DType> &t) {
+    return packet::CheckAlign<Arch>(t.dptr_) &&
+        packet::CheckAlign<Arch>(t.stride_ * sizeof(DType));
+  }
+};
+template<int dim, typename OP, typename TA, typename DType, int etype, PacketArch Arch>
+struct PacketAlignCheck<dim, UnaryMapExp<OP, TA, DType, etype>, Arch> {
+  inline static bool Check(const UnaryMapExp<OP, TA, DType, etype> &t) {
+    return PacketAlignCheck<dim, TA, Arch>::Check(t.src_);
+  }
+};
+template<int dim, typename OP, typename TA, typename TB,
+         typename DType, int etype, PacketArch Arch>
+struct PacketAlignCheck<dim, BinaryMapExp<OP, TA, TB, DType, etype>, Arch> {
+  inline static bool Check(const BinaryMapExp<OP, TA, TB, DType, etype> &t) {
+    return PacketAlignCheck<dim, TA, Arch>::Check(t.lhs_) &&
+        PacketAlignCheck<dim, TB, Arch>::Check(t.rhs_);
+  }
+};
+
+/*!
+ * \brief use PacketPlan to compute result
+ */
+template<typename SV, typename E, int dim, typename DType, PacketArch Arch>
+inline void MapPacketPlan(Tensor<cpu, dim, DType> _dst,
+                          const expr::PacketPlan<E, DType, Arch>& plan) {
+  Tensor<cpu, 2, DType> dst = _dst.FlatTo2D();
+  const index_t xlen = packet::LowerAlign<DType, Arch>(dst.size(1));
+  const size_t packetSize = packet::Packet<DType, Arch>::size;
+#ifndef __CUDACC__
+  #pragma omp parallel for
+#endif
+  for (openmp_index_t y = 0; y < dst.size(0); ++y) {
+    for (index_t x = 0; x < xlen; x += packetSize) {
+      packet::Saver<SV, DType, Arch>::Save(&dst[y][x], plan.EvalPacket(y, x));
+    }
+    for (index_t x = xlen; x < dst.size(1); ++x) {
+      SV::Save(dst[y][x], plan.Eval(y, x));
+    }
+  }
+}
+}  // namespace expr
+}  // namespace mshadow
+#endif  // MSHADOW_PACKET_INL_H_
diff --git a/3rdparty/mshadow/mshadow/packet/plain-inl.h b/3rdparty/mshadow/mshadow/packet/plain-inl.h
new file mode 100644
index 000000000000..de28ad7b4894
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/packet/plain-inl.h
@@ -0,0 +1,76 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file plain-inl.h
+ * \brief support of plain packet that use the plain datatype.
+ */
+#ifndef MSHADOW_PACKET_PLAIN_INL_H_
+#define MSHADOW_PACKET_PLAIN_INL_H_
+
+#include "../base.h"
+#include "../packet-inl.h"
+
+namespace mshadow {
+namespace packet {
+template<typename DType>
+struct Packet<DType, kPlain> {
+ public:
+  /*! \brief number of float in vector */
+  static constexpr index_t size = 1;
+  /*! \brief The internal data */
+  DType data_;
+  // enable default copy constructor
+  Packet(void) {}
+  // constructor from the intrinsic type
+  explicit Packet(DType data) : data_(data) {}
+  // create a fill with the target value s
+  MSHADOW_CINLINE static Packet<DType, kPlain> Fill(DType s) {
+    return Packet<DType, kPlain>(s);
+  }
+  // load from address
+  MSHADOW_CINLINE static Packet<DType, kPlain> Load(const DType* src) {
+    return Packet<DType, kPlain>(*src);
+  }
+  // load from address
+  MSHADOW_CINLINE static Packet<DType, kPlain> LoadUnAligned(const DType* src) {
+    return Packet<DType, kPlain>(*src);
+  }
+  // fill it with value s
+  MSHADOW_CINLINE Packet<DType, kPlain>& operator=(DType s) {
+    data_ = s;
+    return *this;
+  }
+  // store data into dst
+  MSHADOW_CINLINE void Store(DType* dst) const {
+    *dst = data_;
+  }
+  // get the sum of all contents
+  MSHADOW_CINLINE DType Sum() const {
+    return data_;
+  }
+};
+
+template<typename DType>
+MSHADOW_CINLINE Packet<DType, kPlain> operator+(const Packet<DType, kPlain>& lhs,
+                                                const Packet<DType, kPlain>& rhs) {
+  return Packet<DType, kPlain>(lhs.data_ + rhs.data_);
+}
+
+template<typename DType>
+MSHADOW_CINLINE Packet<DType, kPlain> operator-(const Packet<DType, kPlain>& lhs,
+                                                const Packet<DType, kPlain>& rhs) {
+  return Packet<DType, kPlain>(lhs.data_ - rhs.data_);
+}
+template<typename DType>
+MSHADOW_CINLINE Packet<DType, kPlain> operator*(const Packet<DType, kPlain>& lhs,
+                                                    const Packet<DType, kPlain>& rhs) {
+  return Packet<DType, kPlain>(lhs.data_ * rhs.data_);
+}
+
+template<typename DType>
+MSHADOW_CINLINE Packet<DType, kPlain> operator/(const Packet<DType, kPlain>& lhs,
+                                                    const Packet<DType, kPlain>& rhs) {
+  return Packet<DType, kPlain>(lhs.data_ / rhs.data_);
+}
+}  // namespace packet
+}  // namespace mshadow
+#endif  // MSHADOW_PACKET_PLAIN_INL_H_
diff --git a/3rdparty/mshadow/mshadow/packet/sse-inl.h b/3rdparty/mshadow/mshadow/packet/sse-inl.h
new file mode 100644
index 000000000000..923a5f60de38
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/packet/sse-inl.h
@@ -0,0 +1,147 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file sse-inl.h
+ * \brief support of sse2 packet optimization of some operations
+ * \author Tianqi Chen
+ */
+#ifndef MSHADOW_PACKET_SSE_INL_H_
+#define MSHADOW_PACKET_SSE_INL_H_
+
+#include <emmintrin.h>
+#include "../base.h"
+#include "../packet-inl.h"
+
+namespace mshadow {
+namespace packet {
+template<>
+struct Packet<float, kSSE2> {
+ public:
+  /*! \brief number of float in vector */
+  static constexpr index_t size = 4;
+  /*! \brief The internal data */
+  __m128 data_;
+  // enable default copy constructor
+  Packet(void) {}
+  // constructor from the intrinsic type
+  explicit Packet(__m128 data) : data_(data) {}
+  // create a fill with the target value s
+  MSHADOW_CINLINE static Packet<float, kSSE2> Fill(float s) {
+    return Packet<float, kSSE2>(_mm_set1_ps(s));
+  }
+  // load from address
+  MSHADOW_CINLINE static Packet<float, kSSE2> Load(const float* src) {
+    return Packet<float, kSSE2>(_mm_load_ps(src));
+  }
+  // load from address
+  MSHADOW_CINLINE static Packet<float, kSSE2> LoadUnAligned(const float* src) {
+    return Packet<float, kSSE2>(_mm_loadu_ps(src));
+  }
+  // fill it with value s
+  MSHADOW_CINLINE Packet<float, kSSE2>& operator=(float s) {
+    data_ = _mm_set1_ps(s);
+    return *this;
+  }
+  // store data into dst
+  MSHADOW_CINLINE void Store(float* dst) const {
+    _mm_store_ps(dst, data_);
+  }
+  // get the sum of all contents
+  MSHADOW_CINLINE float Sum() const {
+    __m128 ans  = _mm_add_ps(data_, _mm_movehl_ps(data_, data_));
+    __m128 rst  = _mm_add_ss(ans, _mm_shuffle_ps(ans, ans, 1));
+#if defined(_MSC_VER) && (_MSC_VER <= 1500) && defined(_WIN64)
+    return rst.m128_f32[0];
+#else
+    float rr = _mm_cvtss_f32(rst);
+    return rr;
+#endif
+  }
+};
+
+
+/*! \brief vector real type for float */
+template<>
+struct Packet<double, kSSE2> {
+  /*! \brief number of float in vector */
+  static constexpr index_t size = 2;
+  // internal data
+  __m128d data_;
+  // constructor
+  Packet(void) {}
+  explicit Packet(__m128d data) : data_(data) {}
+  // create a fill with the target value s
+  MSHADOW_CINLINE static Packet<double, kSSE2> Fill(double s) {
+    return Packet<double, kSSE2>(_mm_set1_pd(s));
+  }
+  // load from address
+  MSHADOW_CINLINE static Packet<double, kSSE2> Load(const double* src) {
+    return Packet<double, kSSE2>(_mm_load_pd(src));
+  }
+  MSHADOW_CINLINE static Packet<double, kSSE2> LoadUnAligned(const double* src) {
+    return Packet<double, kSSE2>(_mm_loadu_pd(src));
+  }
+  // fill it with value s
+  MSHADOW_CINLINE Packet<double, kSSE2>& operator=(double s) {
+    data_ = _mm_set1_pd(s);
+    return *this;
+  }
+  // store data into dst
+  MSHADOW_CINLINE void Store(double* dst) const {
+    _mm_store_pd(dst, data_);
+  }
+  // get sum of all content
+  inline double Sum(void) const {
+    __m128d tmp =  _mm_add_sd(data_, _mm_unpackhi_pd(data_, data_));
+#if defined(_MSC_VER) && (_MSC_VER <= 1500) && defined(_WIN64)
+    return tmp.m128d_f64[0];
+#else
+    double ans = _mm_cvtsd_f64(tmp);
+    return ans;
+#endif
+  }
+};
+
+MSHADOW_CINLINE Packet<float, kSSE2> operator+(const Packet<float, kSSE2>& lhs,
+                                                    const Packet<float, kSSE2>& rhs) {
+  return Packet<float, kSSE2>(_mm_add_ps(lhs.data_, rhs.data_));
+}
+
+MSHADOW_CINLINE Packet<double, kSSE2> operator+(const Packet<double, kSSE2>& lhs,
+                                                     const Packet<double, kSSE2>& rhs) {
+  return Packet<double, kSSE2>(_mm_add_pd(lhs.data_, rhs.data_));
+}
+
+MSHADOW_CINLINE Packet<float, kSSE2> operator-(const Packet<float, kSSE2>& lhs,
+                                                    const Packet<float, kSSE2>& rhs) {
+  return Packet<float, kSSE2>(_mm_sub_ps(lhs.data_, rhs.data_));
+}
+
+MSHADOW_CINLINE Packet<double, kSSE2> operator-(const Packet<double, kSSE2>& lhs,
+                                                     const Packet<double, kSSE2>& rhs) {
+  return Packet<double, kSSE2>(_mm_sub_pd(lhs.data_, rhs.data_));
+}
+
+MSHADOW_CINLINE Packet<float, kSSE2> operator*(const Packet<float, kSSE2>& lhs,
+                                                    const Packet<float, kSSE2>& rhs) {
+  return Packet<float, kSSE2>(_mm_mul_ps(lhs.data_, rhs.data_));
+}
+
+MSHADOW_CINLINE Packet<double, kSSE2> operator*(const Packet<double, kSSE2>& lhs,
+                                                     const Packet<double, kSSE2>& rhs) {
+  return Packet<double, kSSE2>(_mm_mul_pd(lhs.data_, rhs.data_));
+}
+
+
+MSHADOW_CINLINE Packet<float, kSSE2> operator/(const Packet<float, kSSE2>& lhs,
+                                                    const Packet<float, kSSE2>& rhs) {
+  return Packet<float, kSSE2>(_mm_div_ps(lhs.data_, rhs.data_));
+}
+
+MSHADOW_CINLINE Packet<double, kSSE2> operator/(const Packet<double, kSSE2>& lhs,
+                                                     const Packet<double, kSSE2>& rhs) {
+  return Packet<double, kSSE2>(_mm_div_pd(lhs.data_, rhs.data_));
+}
+
+}  // namespace packet
+}  // namespace mshadow
+#endif  // MSHADOW_PACKET_SSE_INL_H_
diff --git a/3rdparty/mshadow/mshadow/random.h b/3rdparty/mshadow/mshadow/random.h
new file mode 100644
index 000000000000..c136f4f67809
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/random.h
@@ -0,0 +1,570 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ *  \file random.h
+ *  \brief Random inline functions for tensor.
+ *  \author Bing Xu, Tianqi Chen
+ *   Based on curand|MKL|stdlib
+ */
+#ifndef MSHADOW_RANDOM_H_
+#define MSHADOW_RANDOM_H_
+
+#include <cstdlib>
+#include <algorithm>
+#include <random>
+#include "./base.h"
+#include "./tensor.h"
+#include "./tensor_container.h"
+
+#if MSHADOW_IN_CXX11
+#include <random>  // use cxx11 random by default
+#endif
+
+#if _MSC_VER
+#define rand_r(x) rand()
+#endif
+
+
+namespace mshadow {
+/*!
+ * \brief random number generator
+ * \tparam Device the device of random number generator
+ * \tparam DType the target data type of random number can be float for double
+ */
+template<typename Device, typename DType MSHADOW_DEFAULT_DTYPE>
+class Random {};
+
+/*! \brief CPU random number generator */
+template<typename DType>
+class Random<cpu, DType> {
+ public:
+  /*!
+   * \brief constructor of random engine
+   * \param seed random number seed
+   */
+  explicit Random(int seed) {
+    this->Seed(seed);
+    buffer_.Resize(Shape1(kRandBufferSize));
+  }
+  ~Random(void) {
+  }
+  /*!
+   * \brief seed random number generator using this seed
+   * \param seed seed of prng
+   */
+  inline void Seed(int seed) {
+#if MSHADOW_IN_CXX11
+    rnd_engine_.seed(seed);
+#endif
+    this->rseed_ = static_cast<unsigned>(seed);
+  }
+  /*!
+   * \brief get random seed used in random generator
+   * \return seed in unsigned
+   */
+  inline unsigned GetSeed() const {
+    return rseed_;
+  }
+  /*!
+   * \brief set the stream of computation
+   * \param stream computation stream
+   */
+  inline void set_stream(Stream<cpu> *stream) {
+  }
+
+// These samplers are only avail in C++11.
+#if MSHADOW_IN_CXX11
+
+  /*!
+   * \brief get some random integer
+   * \return integer as unsigned
+   */
+  inline unsigned GetRandInt() {
+    return rnd_engine_();
+  }
+
+  /*!
+   * \brief get a set of random integers
+   */
+  inline void GetRandInt(const Tensor<cpu, 1, unsigned>& dst) {
+    std::generate_n(dst.dptr_, dst.size(0), [&](){ return rnd_engine_(); });
+  }
+
+  /*!
+   * \brief generate data from a distribution
+   * \param dst destination
+   * \tparam dim dimension of tensor
+   * \param sampler sampler of the distribution
+   */
+  template<int dim, class Sampler>
+  inline void SampleDistribution(Tensor<cpu, dim, DType> *dst, Sampler sampler) {
+    if (dst->CheckContiguous()) {
+      std::generate_n(dst->dptr_, dst->shape_.Size(), sampler);
+    } else {
+      Tensor<cpu, 2, DType> mat = dst->FlatTo2D();
+      for (index_t i = 0; i < mat.size(0); ++i) {
+        std::generate_n(mat[i].dptr_, mat.size(1), sampler);
+      }
+    }
+  }
+
+  /*!
+   * \brief generate data from uniform [a,b)
+   * \param dst destination
+   * \param a lower bound of uniform
+   * \param b upper bound of uniform
+   * \tparam dim dimension of tensor
+   */
+  template<int dim, typename PType>
+  inline void SampleUniform(Tensor<cpu, dim, DType> *dst,
+                            PType a = 0.0f , PType b = 1.0f ) {
+    // Ensure that half_t is handled correctly.
+    typedef typename std::conditional<std::is_floating_point<DType>::value,
+                                      DType, double>::type FType;
+    typedef typename std::conditional<std::is_integral<DType>::value,
+                                      std::uniform_int_distribution<DType>,
+                                      std::uniform_real_distribution<FType>>::type GType;
+    GType dist_uniform(a, b);
+    SampleDistribution(dst, [&](){ return dist_uniform(rnd_engine_);});
+  }
+
+  /*!
+   * \brief generate data from standard gaussian
+   * \param dst destination
+   * \param mu mean variable
+   * \param sigma standard deviation
+   * \tparam dim dimension of tensor
+   */
+  template<int dim, typename PType>
+  inline void SampleGaussian(Tensor<cpu, dim, DType> *dst,
+                             PType mu = 0.0f, PType sigma = 1.0f ) {
+    if (sigma <= 0) {
+      *dst = mu; return;
+    }
+    typedef typename std::conditional<std::is_floating_point<DType>::value,
+                                      DType, double>::type GType;
+    std::normal_distribution<GType> dist_normal(mu, sigma);
+    SampleDistribution(dst, [&](){ return dist_normal(rnd_engine_);});
+  }
+
+  /*!
+   * \brief generate data from a gamma distribution
+   * \param dst destination
+   * \param alpha (shape) parameter
+   * \param beta (scale) parameter
+   * \tparam dim dimension of tensor
+   */
+  template<int dim, typename PType>
+  inline void SampleGamma(Tensor<cpu, dim, DType> *dst,
+                          PType alpha, PType beta) {
+    typedef typename std::conditional<std::is_floating_point<DType>::value,
+                                      DType, double>::type GType;
+    std::gamma_distribution<GType> dist_gamma(alpha, beta);
+    SampleDistribution(dst, [&](){ return dist_gamma(rnd_engine_);});
+  }
+
+  /*!
+   * \brief generate data from an exponential distribution
+   * \param dst destination
+   * \param lambda parameter (rate) of the distribution
+   * \tparam dim dimension of tensor
+   */
+  template<int dim, typename PType>
+  inline void SampleExponential(Tensor<cpu, dim, DType> *dst, PType lambda ) {
+    typedef typename std::conditional<std::is_floating_point<DType>::value,
+                                      DType, double>::type GType;
+    std::exponential_distribution<GType> dist_exp(lambda);
+    SampleDistribution(dst, [&](){ return dist_exp(rnd_engine_);});
+  }
+
+  /*!
+   * \brief generate data from a poisson distribution
+   * \param dst destination
+   * \param lambda parameter (rate) of the distribution
+   * \tparam dim dimension of tensor
+   */
+  template<int dim, typename PType>
+  inline void SamplePoisson(Tensor<cpu, dim, DType> *dst, PType lambda) {
+    typedef typename std::conditional<std::is_integral<DType>::value, DType, int>::type GType;
+    std::poisson_distribution<GType> dist_poisson(lambda);
+    SampleDistribution(dst, [&](){ return static_cast<DType>(dist_poisson(rnd_engine_));});
+  }
+
+  /*!
+   * \brief generate data from a negative binomial distribution
+   * \param dst destination
+   * \param k limit on number of failures
+   * \param p success probability
+   * \tparam dim dimension of tensor
+   */
+  template<int dim, typename PType1, typename PType2>
+  inline void SampleNegativeBinomial(Tensor<cpu, dim, DType> *dst, PType1 k, PType2 p) {
+    typedef typename std::conditional<std::is_integral<DType>::value, DType, int>::type GType;
+    std::negative_binomial_distribution<GType> dist_negbinomial(k, p);
+    SampleDistribution(dst, [&](){ return static_cast<DType>(dist_negbinomial(rnd_engine_));});
+  }
+
+  /*!
+   * \brief generate data from a generalized negative binomial distribution
+   * \param dst destination
+   * \param mu parameter (mean) of the distribution
+   * \param alpha parameter (over dispersion) of the distribution
+   *   (for alpha=0 this gives a Poisson)
+   * \tparam dim dimension of tensor
+   */
+  template<int dim, typename PType>
+  inline void SampleGeneralizedNegativeBinomial(Tensor<cpu, dim, DType> *dst,
+                                                PType mu, PType alpha) {
+    if (alpha == PType(0)) {
+      SamplePoisson(dst, mu);  // limit of Poisson
+    } else {
+      PType r(PType(1) / alpha);
+      PType beta = mu * alpha;
+      std::gamma_distribution<> dist_gamma(r, beta);
+      typedef typename std::conditional<std::is_integral<DType>::value, DType, int>::type GType;
+      SampleDistribution(dst,
+        [&](){ std::poisson_distribution<GType> dist_poisson(dist_gamma(rnd_engine_));
+               return static_cast<DType>(dist_poisson(rnd_engine_));});
+    }
+  }
+#endif
+
+  /*!
+   * \brief return a temporal expression storing standard gaussian random variables
+   *        the temporal tensor is only valid before next call of gaussian or uniform
+   *        can be used as part of expression
+   *  Caution: this means expression such as A = gaussian(s1) * gaussian(s2) will give invalid result,
+   *           since second call of gaussian(s2) makes gaussian(s1) invalid
+   *           A = gaussian(s1)*B+C; is correct; use one gaussian/uniform in each expression
+   * \param shape shape of the tensor
+   * \return a temporal expression storing standard gaussian random variables
+   * \tparam dim dimension of tensor
+   */
+  template<int dim>
+  inline expr::ReshapeExp<Tensor<cpu, 1, DType>, DType, dim, 1>
+  gaussian(Shape<dim> shape) {
+    buffer_.Resize(Shape1(shape.Size()));
+    this->SampleGaussian(&buffer_, 0.0f, 1.0f);
+    return expr::reshape(buffer_, shape);
+  }
+  /*!
+   * \brief return a temporal expression storing standard uniform [0,1)
+   *        the temporal tensor is only valid before next call of gaussian or uniform
+   *        can be used as part of expression
+   *  Caution: this means expression such as A = uniform(s1) * uniform(s2) will give invalid result,
+   *           since second call of gaussian(s2) makes gaussian(s1) invalid
+   *           A = gaussian(s1)*B+C; is correct; use one gaussian/uniform in each expression
+   * \param shape shape of the tensor
+   * \return a temporal expression storing standard uniform [0,1)
+   * \tparam dim dimension of tensor
+   */
+  template<int dim>
+  inline expr::ReshapeExp<Tensor<cpu, 1, DType>, DType, dim, 1>
+  uniform(Shape<dim> shape) {
+    buffer_.Resize(Shape1(shape.Size()));
+    this->SampleUniform(&buffer_, 0.0f, 1.0f);
+    return expr::reshape(buffer_, shape);
+  }
+
+  std::mt19937 &GetRndEngine() {
+    return rnd_engine_;
+  }
+
+ private:
+#if MSHADOW_IN_CXX11
+  /*! \brief use c++11 random engine. */
+  std::mt19937 rnd_engine_;
+  /*! \brief random number seed used in random engine */
+  unsigned rseed_;
+
+#else
+
+  /*! \brief random number seed used by PRNG */
+  unsigned rseed_;
+  // functions
+  template<int dim>
+  inline void SampleUniform(Tensor<cpu, dim, DType> *dst,
+                            DType a = 0.0f, DType b = 1.0f) {
+    if (dst->CheckContiguous()) {
+      this->GenUniform(dst->dptr_, dst->shape_.Size(), a, b);
+    } else {
+      Tensor<cpu, 2, DType> mat = dst->FlatTo2D();
+      for (index_t i = 0; i < mat.size(0); ++i) {
+        this->GenUniform(mat[i].dptr_, mat.size(1), a, b);
+      }
+    }
+  }
+  template<int dim>
+  inline void SampleGaussian(Tensor<cpu, dim, DType> *dst,
+                             DType mu = 0.0f, DType sigma = 1.0f) {
+    if (sigma <= 0.0f) {
+      *dst = mu; return;
+    }
+    if (dst->CheckContiguous()) {
+      this->GenGaussian(dst->dptr_, dst->shape_.Size(), mu, sigma);
+    } else {
+      Tensor<cpu, 2, DType> mat = dst->FlatTo2D();
+      for (index_t i = 0; i < mat.size(0); ++i) {
+        this->GenGaussian(mat[i].dptr_, mat.size(1), mu, sigma);
+      }
+    }
+  }
+  inline void GenUniform(float *dptr, index_t size, float a, float b) {
+    for (index_t j = 0; j < size; ++j) {
+      dptr[j] = static_cast<float>(RandNext()) * (b - a) + a;
+    }
+  }
+  inline void GenUniform(double *dptr, index_t size, double a, double b) {
+    for (index_t j = 0; j < size; ++j) {
+      dptr[j] = static_cast<double>(RandNext()) * (b - a) + a;
+    }
+  }
+  inline void GenGaussian(float *dptr, index_t size, float mu, float sigma) {
+    this->GenGaussianX(dptr, size, mu, sigma);
+  }
+  inline void GenGaussian(double *dptr, index_t size, double mu, double sigma) {
+    this->GenGaussianX(dptr, size, mu, sigma);
+  }
+  inline void GenGaussianX(DType *dptr, index_t size, DType mu, DType sigma) {
+    DType g1 = 0.0f, g2 = 0.0f;
+    for (index_t j = 0; j < size; ++j) {
+      if ((j & 1) == 0) {
+        this->SampleNormal2D(&g1, &g2);
+        dptr[j] = mu + g1 * sigma;
+      } else {
+        dptr[j] = mu + g2 * sigma;
+      }
+    }
+  }
+  /*! \brief get next random number from rand */
+  inline DType RandNext(void) {
+    return static_cast<DType>(rand_r(&rseed_)) /
+        (static_cast<DType>(RAND_MAX) + 1.0f);
+  }
+  /*! \brief return a real numer uniform in (0,1) */
+  inline DType RandNext2(void) {
+    return (static_cast<DType>(rand_r(&rseed_)) + 1.0f) /
+        (static_cast<DType>(RAND_MAX) + 2.0f);
+  }
+  /*!
+   * \brief sample iid xx,yy ~N(0,1)
+   * \param xx first  gaussian output
+   * \param yy second gaussian output
+   */
+  inline void SampleNormal2D(DType *xx_, DType *yy_) {
+    DType &xx = *xx_, &yy = *yy_;
+    DType x, y, s;
+    do {
+      x = 2.0f * RandNext2() - 1.0f;
+      y = 2.0f * RandNext2() - 1.0f;
+      s = x * x + y * y;
+    } while (s >= 1.0f || s == 0.0f);
+    DType t = std::sqrt(-2.0f * std::log(s) / s);
+    xx = x * t; yy = y * t;
+  }
+#endif
+  /*! \brief temporal space used to store random numbers */
+  TensorContainer<cpu, 1, DType> buffer_;
+};  // class Random<cpu, DType>
+
+// only allow GPU PRNG when cuda is enabled
+#if MSHADOW_USE_CUDA
+/*! \brief GPU random number generator */
+template<typename DType>
+class Random<gpu, DType> {
+ public:
+  /*!
+   * \brief constructor of random engine
+   * \param seed random number seed
+   */
+  explicit Random(int seed) : gen_(NULL) {
+    this->Seed(seed);
+    buffer_.Resize(Shape1(kRandBufferSize));
+  }
+  ~Random(void) MSHADOW_THROW_EXCEPTION {
+    DeleteGenerator();
+  }
+  /*!
+   * \brief set the stream of computation
+   * \param stream computation stream
+   */
+  inline void set_stream(Stream<gpu> *stream) {
+    curandStatus_t status;
+    status = curandSetStream(gen_, Stream<gpu>::GetStream(stream));
+
+    CHECK_EQ(status, CURAND_STATUS_SUCCESS) << "set_stream CURAND failed";
+  }
+  /*!
+   * \brief seed random number generator using this seed
+   * \param seed seed of prng
+   */
+  inline void Seed(int seed) {
+    // Create a new rng, either initially or if the RNG type can't reset its offset.
+    if (gen_ == NULL || (curandSetGeneratorOffset(gen_, 0ULL) != CURAND_STATUS_SUCCESS))
+      CreateGenerator();
+    // Now set the seed.
+    curandStatus_t status;
+    status = curandSetPseudoRandomGeneratorSeed(gen_, static_cast<uint64_t>(seed));
+    CHECK_EQ(status, CURAND_STATUS_SUCCESS) << "Set CURAND seed failed.";
+  }
+  /*!
+   * \brief get a set of random integers
+   */
+  inline void GetRandInt(const Tensor<gpu, 1, unsigned>& dst) {
+    curandStatus_t status = curandGenerate(gen_, dst.dptr_, dst.size(0));
+    CHECK_EQ(status, CURAND_STATUS_SUCCESS) << "CURAND Gen rand ints failed.";
+  }
+  /*!
+   * \brief generate data from uniform [a,b)
+   * \param dst destination
+   * \param a lower bound of uniform
+   * \param b upper bound of uniform
+   * \tparam dim dimension of tensor
+   */
+  template<int dim>
+  inline void SampleUniform(Tensor<gpu, dim, DType> *dst,
+                            DType a = 0.0f, DType b = 1.0f);
+
+  /*!
+   * \brief generate data from standard gaussian
+   * \param dst destination
+   * \param mu mean variable
+   * \param sigma standard deviation
+   * \tparam dim dimension of tensor
+   */
+  template<int dim>
+  inline void SampleGaussian(Tensor<gpu, dim, DType> *dst,
+                             DType mu = 0.0f, DType sigma = 1.0f);
+  /*!
+   * \brief return a temporal expression storing standard gaussian random variables
+   *        the temporal tensor is only valid before next call of gaussian or uniform
+   *        can be used as part of expression
+   *  Caution: this means expression such as A = gaussian(s1) * gaussian(s2) will give invalid result,
+   *           since second call of gaussian(s2) makes gaussian(s1) invalid
+   *           A = gaussian(s1)*B+C; is correct; use one gaussian/uniform in each expression
+   * \param shape shape of the tensor
+   * \param mu mean
+   * \param sigma variance
+   * \return a temporal expression storing standard gaussian random variables
+   * \tparam dim dimension of tensor
+   */
+  template<int dim>
+  inline expr::ReshapeExp<Tensor<gpu, 1, DType>, DType, dim, 1>
+  gaussian(Shape<dim> shape, DType mu = 0.0f, DType sigma = 1.0f);
+  /*!
+   * \brief return a temporal expression storing standard uniform [0,1)
+   *        the temporal tensor is only valid before next call of gaussian or uniform
+   *        can be used as part of expression
+   *  Caution: this means expression such as A = gaussian(s1) * gaussian(s2) will give invalid result,
+   *           since second call of gaussian(s2) makes gaussian(s1) invalid
+   *           A = gaussian(s1)*B+C; is correct; use one gaussian/uniform in each expression
+   * \param shape shape of the tensor
+   * \return a temporal expression storing standard uniform [0,1)
+   * \tparam dim dimension of tensor
+   */
+  template<int dim>
+  inline expr::ReshapeExp<Tensor<gpu, 1, DType>, DType, dim, 1>
+  uniform(Shape<dim> shape);
+
+ private:
+  inline void GenGaussian(float *dptr, size_t size, float mu, float sigma) {
+    curandStatus_t status;
+    status = curandGenerateNormal(gen_, dptr, size, mu, sigma);
+    CHECK_EQ(status, CURAND_STATUS_SUCCESS) << "CURAND Gen Normal float failed."
+                                            << " size = " << size
+                                            << ",mu = " << mu
+                                            << ",sigma = " << sigma;
+  }
+  inline void GenGaussian(double *dptr, size_t size, double mu, double sigma) {
+    curandStatus_t status;
+    status = curandGenerateNormalDouble(gen_, dptr, size, mu, sigma);
+    CHECK_EQ(status, CURAND_STATUS_SUCCESS) << "CURAND Gen Normal double failed."
+                                            << " size = " << size
+                                            << ",mu = " << mu
+                                            << ",sigma = " << sigma;
+  }
+  inline void GenUniform(float *dptr, size_t size) {
+    curandStatus_t status;
+    status = curandGenerateUniform(gen_, dptr, size);
+    CHECK_EQ(status, CURAND_STATUS_SUCCESS) << "CURAND Gen Uniform float failed."
+                                            << " size = " << size;
+  }
+  inline void GenUniform(double *dptr, size_t size) {
+    curandStatus_t status;
+    status = curandGenerateUniformDouble(gen_, dptr, size);
+    CHECK_EQ(status, CURAND_STATUS_SUCCESS) << "CURAND Gen Uniform double failed."
+                                            << " size = " << size;
+  }
+  inline void CreateGenerator() {
+    if (gen_ != NULL)
+      DeleteGenerator();
+    curandStatus_t status;
+    status = curandCreateGenerator(&gen_, CURAND_RNG_PSEUDO_DEFAULT);
+    CHECK_EQ(status, CURAND_STATUS_SUCCESS) << "Cannot create CURAND Generator";
+  }
+  inline void DeleteGenerator() {
+    if (gen_ != NULL) {
+      curandStatus_t status;
+      status = curandDestroyGenerator(gen_);
+      CHECK_EQ(status, CURAND_STATUS_SUCCESS) << "Destory CURAND Gen failed";
+      gen_ = NULL;
+    }
+  }
+  /*! \brief random number generator */
+  curandGenerator_t gen_;
+  /*! \brief templ buffer */
+  TensorContainer<gpu, 1, DType> buffer_;
+};  // class Random<gpu, DType>
+#endif  // MSHADOW_USE_CUDA
+
+#ifdef __CUDACC__
+// implementations that depends on cuda kernels
+template<typename DType>
+template<int dim>
+inline void Random<gpu, DType>::SampleUniform(
+    Tensor<gpu, dim, DType> *dst, DType a, DType b) {
+  if (a == 0.0f && b == 1.0f) {
+    if (dst->CheckContiguous()) {
+      this->GenUniform(dst->dptr_, dst->shape_.Size());
+    } else {
+      *dst = this->uniform(dst->shape_);
+    }
+  } else {
+    *dst = this->uniform(dst->shape_) * (b - a) + a;
+  }
+}
+template<typename DType>
+template<int dim>
+inline void Random<gpu, DType>::SampleGaussian(
+    Tensor<gpu, dim, DType> *dst, DType mu, DType sigma) {
+  // We need to check whether the shape size is even since CuRand supports only normal distribution
+  // generation of even number of elements.
+  if (dst->CheckContiguous() && (dst->shape_.Size() % 2 == 0)) {
+    this->GenGaussian(dst->dptr_, dst->shape_.Size(), mu, sigma);
+  } else {
+    *dst = this->gaussian(dst->shape_, mu, sigma);
+  }
+}
+
+template<typename DType>
+template<int dim>
+inline expr::ReshapeExp<Tensor<gpu, 1, DType>, DType, dim, 1>
+Random<gpu, DType>::gaussian(Shape<dim> shape, DType mu, DType sigma) {
+  size_t aligned_sz = ((shape.Size() + 1UL) >> 1) << 1;
+  // allocate alligned size
+  buffer_.Resize(Shape1(aligned_sz));
+  buffer_.Resize(Shape1(shape.Size()));
+  this->GenGaussian(buffer_.dptr_, aligned_sz, mu, sigma);
+  return expr::reshape(buffer_, shape);
+}
+
+template<typename DType>
+template<int dim>
+inline expr::ReshapeExp<Tensor<gpu, 1, DType>, DType, dim, 1>
+Random<gpu, DType>::uniform(Shape<dim> shape) {
+  buffer_.Resize(Shape1(shape.Size()));
+  this->GenUniform(buffer_.dptr_, buffer_.size(0));
+  return expr::reshape(buffer_, shape);
+}
+#endif  // __CUDACC__
+}  // namespace mshadow
+#endif  // MSHADOW_RANDOM_H_
diff --git a/3rdparty/mshadow/mshadow/stream_gpu-inl.h b/3rdparty/mshadow/mshadow/stream_gpu-inl.h
new file mode 100644
index 000000000000..603da9767e81
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/stream_gpu-inl.h
@@ -0,0 +1,214 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file stream_gpu-inl.h
+ * \brief implementation of GPU code
+ * \author Bing Xu, Tianqi Chen
+ */
+#ifndef MSHADOW_STREAM_GPU_INL_H_
+#define MSHADOW_STREAM_GPU_INL_H_
+#include <memory>
+#include "./base.h"
+#include "./tensor.h"
+#include "./logging.h"
+
+namespace mshadow {
+#if MSHADOW_USE_CUDA == 1
+// Stream alocation
+// actual implementation of GPU stream in CUDA
+template<>
+struct Stream<gpu> {
+  /*! \brief handle state */
+  enum HandleState {
+    NoHandle = 0,
+    OwnHandle = 1,
+  };
+  /*! \brief cudaStream */
+  cudaStream_t stream_;
+  /*! \brief cublas handle */
+  cublasHandle_t blas_handle_;
+  /*! \brief cusolver handle */
+  #if MSHADOW_USE_CUSOLVER == 1
+  cusolverDnHandle_t solver_handle_;
+  #endif
+  /*! \brief cudnn handle */
+  #if MSHADOW_USE_CUDNN == 1
+  cudnnHandle_t dnn_handle_;
+  #endif
+  /*! \brief cublas handle ownership */
+  HandleState blas_handle_ownership_;
+  /*! \brief cusolver handle ownership */
+  HandleState solver_handle_ownership_;
+  /*! \brief cudnn handle ownership */
+  HandleState dnn_handle_ownership_;
+  /*! \brief cudaDeviceProp */
+  cudaDeviceProp prop;
+  /*! \brief dev id */
+  int dev_id;
+
+  Stream(void)
+    : stream_(0)
+      , blas_handle_(0)
+#if MSHADOW_USE_CUDNN == 1
+      , dnn_handle_(0)
+#endif
+      , blas_handle_ownership_(NoHandle)
+      , solver_handle_ownership_(NoHandle)
+      , dnn_handle_ownership_(NoHandle) {}
+  /*!
+   * \brief wait for all the computation associated
+   *  with this stream to complete
+   */
+  inline void Wait(void) {
+    MSHADOW_CUDA_CALL(cudaStreamSynchronize(stream_));
+  }
+  /*!
+   * \brief query whether the the stream is idle
+   * \return true if the stream is idle and all the job have been completed
+   */
+  inline bool CheckIdle(void) {
+    cudaError_t err = cudaStreamQuery(stream_);
+    if (err == cudaSuccess) return true;
+    if (err == cudaErrorNotReady) return false;
+    LOG(FATAL) << cudaGetErrorString(err);
+    return false;
+  }
+  /*!
+   * \brief returns actual cudaStream_t given an input GPU stream pointer
+   * \param stream pointer to GPU stream
+   */
+  inline static cudaStream_t GetStream(Stream<gpu> *stream) {
+    if (stream == NULL) {
+#if MSHADOW_FORCE_STREAM
+      LOG(FATAL) << "Default GPU stream was used when MSHADOW_FORCE_STREAM was on";
+#endif
+      return 0;
+    } else {
+      return stream->stream_;
+    }
+  }
+  /*!
+   * \brief return actual cublasHandle
+   * \param pointer to GPU stream
+   */
+  inline static cublasHandle_t GetBlasHandle(Stream<gpu> *stream) {
+    if (stream == NULL) {
+      return 0;
+    } else {
+      CHECK_NE(stream->blas_handle_ownership_, NoHandle)
+        << "No handle exist in source stream";
+      return stream->blas_handle_;
+    }
+  }
+  /*! \brief Destory cublas handle if own it */
+  inline void DestroyBlasHandle() {
+    if (blas_handle_ownership_ == OwnHandle) {
+      cublasStatus_t err = cublasDestroy(blas_handle_);
+      blas_handle_ownership_ = NoHandle;
+      CHECK_EQ(err, CUBLAS_STATUS_SUCCESS) << "Destory cublas handle failed";
+    }
+  }
+  /*! \brief Destory original blas handle and create a new one */
+  inline void CreateBlasHandle() {
+    this->DestroyBlasHandle();
+    cublasStatus_t err = cublasCreate(&blas_handle_);
+    blas_handle_ownership_ = OwnHandle;
+    CHECK_EQ(err, CUBLAS_STATUS_SUCCESS) << "Create cublas handle failed";
+    err = cublasSetStream(blas_handle_, stream_);
+    CHECK_EQ(err, CUBLAS_STATUS_SUCCESS) << "Setting cublas stream failed";
+  }
+#if MSHADOW_USE_CUSOLVER == 1
+  inline static cusolverDnHandle_t GetSolverHandle(Stream<gpu> *stream) {
+    if (stream == NULL) {
+      return 0;
+    } else {
+      CHECK_NE(stream->solver_handle_ownership_, NoHandle) << "No handle exist in source stream";
+      return stream->solver_handle_;
+    }
+  }
+#endif
+  inline void DestroySolverHandle() {
+#if MSHADOW_USE_CUSOLVER == 1
+    if (solver_handle_ownership_ == OwnHandle) {
+      cusolverStatus_t err = cusolverDnDestroy(solver_handle_);
+      CHECK_EQ(err, CUSOLVER_STATUS_SUCCESS) << "Destory cusolver handle failed";
+    }
+#endif
+  }
+  inline void CreateSolverHandle() {
+#if MSHADOW_USE_CUSOLVER == 1
+    this->DestroySolverHandle();
+    cusolverStatus_t err = cusolverDnCreate(&solver_handle_);
+    CHECK_EQ(err, CUSOLVER_STATUS_SUCCESS) << "Create cusolver handle failed";
+    err = cusolverDnSetStream(solver_handle_, stream_);
+    CHECK_EQ(err, CUSOLVER_STATUS_SUCCESS) << "Setting cusolver stream failed";
+    this->solver_handle_ownership_ = OwnHandle;
+#endif
+  }
+// #if MSHADOW_USE_CUDNN && defined(__CUDACC__)
+#if MSHADOW_USE_CUDNN == 1
+  inline static cudnnHandle_t GetDnnHandle(Stream<gpu> *stream) {
+    if (stream == NULL) {
+      return 0;
+    } else {
+      CHECK_NE(stream->dnn_handle_ownership_, NoHandle) << "No handle exist in source stream";
+      return stream->dnn_handle_;
+    }
+  }
+#endif
+  inline void DestroyDnnHandle() {
+// #if MSHADOW_USE_CUDNN && defined(__CUDACC__)
+#if MSHADOW_USE_CUDNN == 1
+    if (dnn_handle_ownership_ == OwnHandle) {
+      cudnnStatus_t err = cudnnDestroy(dnn_handle_);
+      this->dnn_handle_ownership_ = NoHandle;
+      CHECK_EQ(err, CUDNN_STATUS_SUCCESS) << cudnnGetErrorString(err);
+    }
+#endif
+  }
+  inline void CreateDnnHandle() {
+// #if MSHADOW_USE_CUDNN == 1 && defined(__CUDACC__)
+#if MSHADOW_USE_CUDNN == 1
+    this->DestroyDnnHandle();
+    cudnnStatus_t err = cudnnCreate(&dnn_handle_);
+    CHECK_EQ(err, CUDNN_STATUS_SUCCESS) << cudnnGetErrorString(err);
+    // At this point, we have the resource which may need to be freed
+    this->dnn_handle_ownership_ = OwnHandle;
+    err = cudnnSetStream(dnn_handle_, stream_);
+    CHECK_EQ(err, CUDNN_STATUS_SUCCESS) << cudnnGetErrorString(err);
+#endif
+  }
+};
+template<>
+inline void DeleteStream<gpu>(Stream<gpu> *stream) {
+  if (stream) {
+    MSHADOW_CUDA_CALL(cudaStreamDestroy(stream->stream_));
+    stream->DestroyBlasHandle();
+    stream->DestroySolverHandle();
+    stream->DestroyDnnHandle();
+    delete stream;
+  }
+}
+template<>
+inline Stream<gpu> *NewStream<gpu>(bool create_blas_handle,
+                                   bool create_dnn_handle,
+                                   int dev_id) {
+  // RAII on Cuda exception
+  struct StreamDeleter { void operator()(Stream<gpu> *ptr) const { DeleteStream<gpu>(ptr); } };
+  std::unique_ptr<Stream<gpu>, StreamDeleter> st(new Stream<gpu>());
+  MSHADOW_CUDA_CALL(cudaStreamCreate(&st->stream_));
+  if (create_blas_handle) {
+    st->CreateBlasHandle();
+    st->CreateSolverHandle();
+  }
+  if (create_dnn_handle) {
+    st->CreateDnnHandle();
+  }
+  st->dev_id = dev_id;
+  if (dev_id != -1) {
+    MSHADOW_CUDA_CALL(cudaGetDeviceProperties(&st->prop, dev_id));
+  }
+  return st.release();
+}
+#endif
+}  // namespace mshadow
+#endif  // MSHADOW_STREAM_GPU_INL_H_
diff --git a/3rdparty/mshadow/mshadow/tensor.h b/3rdparty/mshadow/mshadow/tensor.h
new file mode 100755
index 000000000000..0d662621aa4d
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/tensor.h
@@ -0,0 +1,1081 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file tensor.h
+ * \brief header file of tensor data structure and functions
+ *  This lib requires explicit memory allocation and de-allocation
+ *  all the data structure Tensor<cpu,1>, Tensor<gpu,1> are like handles(pointers),
+ *  no memory allocation is happening during calculation
+ *
+ *  For STL style tensor, see tensor_container.h
+ * \author Bing Xu, Tianqi Chen
+ */
+#ifndef MSHADOW_TENSOR_H_
+#define MSHADOW_TENSOR_H_
+#include <string>
+#include <iostream>
+#include "./base.h"
+#include "./expression.h"
+
+namespace mshadow {
+/*! \brief device name CPU */
+struct cpu {
+  /*! \brief whether this device is CPU or not */
+  static const bool kDevCPU = true;
+  /*! \brief device flag number, identifies this device */
+  static const int kDevMask = 1 << 0;
+};
+/*! \brief device name GPU */
+struct gpu {
+  /*! \brief whether this device is CPU or not */
+  static const bool kDevCPU = false;
+  /*! \brief device flag number, identifies this device */
+  static const int kDevMask = 1 << 1;
+};
+template<int ndim>
+struct Shape;
+
+/*!
+ * \brief allow string printing of the shape
+ * \param os the output stream
+ * \param shape the shape
+ * \return the ostream
+ */
+template<int ndim>
+inline std::ostream &operator<<(std::ostream &os, const Shape<ndim> &shape); // NOLINT(*)
+
+/*!
+ * \brief shape of a tensor
+ * \tparam dimension dimension of tensor
+ */
+template<int dimension>
+struct Shape {
+  /*! \brief dimension of current shape */
+  static const int kDimension = dimension;
+  /*! \brief dimension of current shape minus one */
+  static const int kSubdim = dimension - 1;
+  /*! \brief storing the dimension information */
+  index_t shape_[kDimension];
+  /*! \brief default constructor, do nothing */
+  MSHADOW_XINLINE Shape(void) {}
+  /*! \brief constuctor */
+  MSHADOW_XINLINE Shape(const Shape<kDimension> &s) {
+    #pragma unroll
+    for (int i = 0; i < kDimension; ++i) {
+      this->shape_[i] = s[i];
+    }
+  }
+  /*!
+   * \brief get corresponding index
+   * \param idx dimension index
+   * \return the corresponding dimension size
+   */
+  MSHADOW_XINLINE index_t &operator[](index_t idx) {
+    return shape_[idx];
+  }
+  /*!
+   * \brief get corresponding index
+   * \param idx dimension index
+   * \return the corresponding dimension size
+   */
+  MSHADOW_XINLINE const index_t &operator[](index_t idx) const {
+    return shape_[idx];
+  }
+  /*!
+   * \return whether two shape equals
+   * \param s the shape to compare against
+   */
+  MSHADOW_XINLINE bool operator==(const Shape<kDimension> &s) const {
+    #pragma unroll
+    for (int i = 0; i < kDimension; ++i) {
+      if (s.shape_[i] != this->shape_[i]) return false;
+    }
+    return true;
+  }
+  /*!
+   * \return whether two shape not equal
+   * \param s the shape to compare against
+   */
+  MSHADOW_XINLINE bool operator!=(const Shape<kDimension> &s) const {
+    return !(*this == s);
+  }
+  /*!
+   * flatten the tensor, return a 1D shape
+   * \return the flat 1d shape
+   */
+  MSHADOW_XINLINE Shape<1> FlatTo1D(void) const {
+    Shape<1> s;
+    s[0] = this->Size();
+    return s;
+  }
+  /*!
+   * flatten the higher dimension to second dimension, return a 2D shape
+   * \return the flat 2d shape
+   */
+  MSHADOW_XINLINE Shape<2> FlatTo2D(void) const {
+    Shape<2> s;
+    s.shape_[1] = this->shape_[kDimension - 1];
+    index_t ymax = 1;
+    #pragma unroll
+    for (int i = 0; i < kDimension - 1; ++i) {
+      ymax *= this->shape_[i];
+    }
+    s.shape_[0] = ymax;
+    return s;
+  }
+  /*! \return number of valid elements */
+  MSHADOW_XINLINE index_t Size(void) const {
+    index_t size = this->shape_[0];
+    #pragma unroll
+    for (int i = 1; i < kDimension; ++i) {
+      size *= this->shape_[i];
+    }
+    return size;
+  }
+  /*!
+   * \return product shape in [dimstart,dimend)
+   * \param dimstart start dimension
+   * \param dimend end dimension
+   */
+  MSHADOW_XINLINE index_t ProdShape(int dimstart, int dimend) const {
+    index_t num = 1;
+    #pragma unroll
+    for (int i = dimstart; i < dimend; ++i) {
+      num *= this->shape_[i];
+    }
+    return num;
+  }
+  /*!
+   * \brief get subshape that takes off largest dimension
+v   * \return subshape
+   */
+  MSHADOW_XINLINE Shape<kSubdim> SubShape(void) const {
+    Shape<kSubdim> s;
+    // for cuda
+    #pragma unroll
+    for (int i = 0; i < kSubdim; ++i) {
+      s.shape_[i] = this->shape_[i + 1];
+    }
+    return s;
+  }
+  /*!
+   * \brief slice the shape from start to end
+   * \tparam dimstart start dimension
+   * \tparam dimend end dimension
+   * \return the sliced shape
+   */
+  template<int dimstart, int dimend>
+  MSHADOW_XINLINE Shape<dimend - dimstart> Slice(void) const {
+    Shape<dimend - dimstart> s;
+    #pragma unroll
+    for (int i = dimstart; i < dimend; ++i) {
+      s[i - dimstart] = this->shape_[i];
+    }
+    return s;
+  }
+  //! \cond Doxygen_Suppress
+  template<int dim>
+  friend std::ostream &operator<<(std::ostream &os, const Shape<dim> &shape); // NOLINT(*)
+  //! \endcond
+};  // Shape
+//------------------------------------------------
+// useful construction functions to generate shape
+//-------------------------------------------------
+/*!
+ * \brief construct a one dimension shape, stride will equal s0
+ * \param s0 size of dimension 0
+ * \return the shape construction
+ */
+MSHADOW_XINLINE Shape<1> Shape1(index_t s0) {
+  Shape<1> s; s[0] = s0;
+  return s;
+}
+/*!
+ * \brief construct a two dimension shape, stride will equal s0
+ * \param s0 size of dimension 0
+ * \param s1 size of dimension 1
+ * \return the shape construction
+ */
+MSHADOW_XINLINE Shape<2> Shape2(index_t s0, index_t s1) {
+  Shape<2> s; s[0] = s0; s[1] = s1;
+  return s;
+}
+/*!
+ * \brief construct a three dimension shape, stride will equal s0
+ * \param s0 size of dimension 0
+ * \param s1 size of dimension 1
+ * \param s2 size of dimension 2
+ * \return the shape construction
+ */
+MSHADOW_XINLINE Shape<3> Shape3(index_t s0, index_t s1, index_t s2) {
+  Shape<3> s;
+  s[0] = s0; s[1] = s1; s[2] = s2;
+  return s;
+}
+/*!
+ * \brief construct a four dimension shape, stride will equal s0
+ * \param s0 size of dimension 0
+ * \param s1 size of dimension 1
+ * \param s2 size of dimension 2
+ * \param s3 size of dimension 3
+ * \return the shape construction
+ */
+MSHADOW_XINLINE Shape<4> Shape4(index_t s0, index_t s1,
+                                index_t s2, index_t s3) {
+  Shape<4> s;
+  s[0] = s0; s[1] = s1; s[2] = s2; s[3] = s3;
+  return s;
+}
+/*!
+* \brief construct a five dimension shape, stride will equal s0
+* \param s0 size of dimension 0
+* \param s1 size of dimension 1
+* \param s2 size of dimension 2
+* \param s3 size of dimension 3
+* \param s4 size of dimension 4
+* \return the shape construction
+*/
+MSHADOW_XINLINE Shape<5> Shape5(index_t s0, index_t s1, index_t s2,
+                                index_t s3, index_t s4) {
+  Shape<5> s;
+  s[0] = s0; s[1] = s1; s[2] = s2; s[3] = s3; s[4] = s4;
+  return s;
+}
+
+/*!
+* \brief Convert shape in src_layout to shape in dst_layout
+* \param src original shape
+* \param src_layout layout of original shape
+* \param dst_layout target layout
+* \return shape in target layout
+*/
+inline Shape<3> ConvertLayout(const Shape<3>& src, int src_layout, int dst_layout) {
+  Shape<3> dst;
+  switch (src_layout) {
+  case kNCW:
+    dst = src;
+    break;
+  case kNWC:
+    dst[0] = src[0];
+    dst[1] = src[2];
+    dst[2] = src[1];
+    break;
+  default:
+    LOG(FATAL) << "Invalid layout for 3d shape " << src_layout;
+  }
+  switch (dst_layout) {
+  case kNCW:
+    return dst;
+  case kNWC:
+    {
+      index_t tmp = dst[1];
+      dst[1] = dst[2];
+      dst[2] = tmp;
+    }
+    break;
+  default:
+    LOG(FATAL) << "Invalid layout for 3d shape " << src_layout;
+  }
+  return dst;
+}
+
+/*!
+* \brief Convert shape in src_layout to shape in dst_layout
+* \param src original shape
+* \param src_layout layout of original shape
+* \param dst_layout target layout
+* \return shape in target layout
+*/
+inline Shape<4> ConvertLayout(const Shape<4>& src, int src_layout, int dst_layout) {
+  Shape<4> dst;
+  switch (src_layout) {
+  case kNCHW:
+    dst = src;
+    break;
+  case kNHWC:
+    dst[0] = src[0];
+    dst[2] = src[1];
+    dst[3] = src[2];
+    dst[1] = src[3];
+    break;
+  default:
+    LOG(FATAL) << "Invalid layout for 4d shape " << src_layout;
+    dst = src;  // fixes compiler warning
+  }
+  Shape<4> dst2;
+  switch (dst_layout) {
+  case kNCHW:
+    return dst;
+  case kNHWC:
+    dst2[0] = dst[0];
+    dst2[1] = dst[2];
+    dst2[2] = dst[3];
+    dst2[3] = dst[1];
+    break;
+  default:
+    LOG(FATAL) << "Invalid layout for 4d shape " << src_layout;
+    dst2 = src;  // fixes compiler warning
+  }
+  return dst2;
+}
+
+/*!
+* \brief Convert shape in src_layout to shape in dst_layout
+* \param src original shape
+* \param src_layout layout of original shape
+* \param dst_layout target layout
+* \return shape in target layout
+*/
+inline Shape<5> ConvertLayout(const Shape<5>& src, int src_layout, int dst_layout) {
+  Shape<5> dst;
+  switch (src_layout) {
+  case kNCDHW:
+    dst = src;
+    break;
+  case kNDHWC:
+    dst[0] = src[0];
+    dst[2] = src[1];
+    dst[3] = src[2];
+    dst[4] = src[3];
+    dst[1] = src[4];
+    break;
+  default:
+    LOG(FATAL) << "Invalid layout for 5d shape " << src_layout;
+  }
+  Shape<5> dst2;
+  switch (dst_layout) {
+  case kNCDHW:
+    return dst;
+  case kNDHWC:
+    dst2[0] = dst[0];
+    dst2[1] = dst[2];
+    dst2[2] = dst[3];
+    dst2[3] = dst[4];
+    dst2[4] = dst[1];
+    break;
+  default:
+    LOG(FATAL) << "Invalid layout for 5d shape " << src_layout;
+  }
+  return dst2;
+}
+
+/*!
+ * \brief computaion stream structure, used for asynchronous computations
+ */
+template<typename Device>
+struct Stream {
+  // this is only a dummy implementation for CPU
+  // for GPU, the actual implementation will be specialized in tensor_gpu-inl.h
+  /*!
+   * \brief wait for all the computations associated
+   *  with this stream to complete
+   */
+  inline void Wait(void) {}
+  /*!
+   * \brief query whether the the stream is idle
+   * \return true if the stream is idle and all the jobs have been completed
+   */
+  inline bool CheckIdle(void) {
+    return true;
+  }
+  /*! \brief create a blas handle */
+  inline void CreateBlasHandle() {}
+};
+/*!
+ * \brief Tensor RValue, this is the super type of all kinds of possible tensors
+ * \tparam Container the tensor type
+ * \tparam Device which device the tensor is on
+ * \tparam dimension dimension of the tensor
+ * \tparam DType the type of elements in the tensor
+ */
+template<typename Container, typename Device, int dimension, typename DType>
+struct TRValue: public expr::RValueExp<Container, DType> {
+};
+// more compact template
+/*!
+ * \brief general tensor
+ * \tparam Device which device the tensor is on
+ * \tparam dimension dimension of the tensor
+ * \tparam DType the type of elements in the tensor
+ */
+template<typename Device, int dimension,
+         typename DType MSHADOW_DEFAULT_DTYPE>
+struct Tensor: public TRValue<Tensor<Device, dimension, DType>,
+                              Device, dimension, DType> {
+ public:
+  //--------------------------------
+  // struct memembers
+  //--------------------------------
+  /*! \brief whether current type lies in cpu */
+  static const bool kDevCPU = Device::kDevCPU;
+  /*! \brief dimension of subtype */
+  static const int  kSubdim = dimension - 1;
+  //--------------------------------
+  // struct memembers
+  //--------------------------------
+  /*! \brief pointer to the data */
+  DType *dptr_ = nullptr;
+  /*! \brief shape of the tensor */
+  Shape<dimension> shape_;
+  /*!
+   * \brief storing the stride information in x dimension
+   *    this is used to deal with pitch allocation in gpu or sse(align x dimension to 64bit) for efficiency
+   */
+  index_t stride_;
+  /*!
+   * \brief stream where the computation lies
+   * stream is a device dependency concept where each computation
+   */
+  Stream<Device> *stream_;
+  //--------------------------------
+  // functions
+  //--------------------------------
+  /*! \brief default constructor */
+  MSHADOW_XINLINE Tensor(void) : stream_(NULL) {}
+  /*! \brief constructor from shape  */
+  MSHADOW_XINLINE Tensor(const Shape<dimension> &shape)
+      : shape_(shape), stream_(NULL) {}
+  /*! \brief constructor from data pointer and shape, without stride */
+  MSHADOW_XINLINE Tensor(DType *dptr, const Shape<dimension> &shape)
+      : dptr_(dptr), shape_(shape), stride_(shape[kSubdim]), stream_(NULL) {}
+  /*! \brief constructor from data pointer and shape, without stride */
+  MSHADOW_XINLINE Tensor(DType *dptr, const Shape<dimension> &shape,
+                         Stream<Device> *stream)
+    : dptr_(dptr), shape_(shape), stride_(shape[kSubdim]), stream_(stream) {}
+  /*! \brief constructor from data pointer and shape  */
+  MSHADOW_XINLINE Tensor(DType *dptr,
+                         const Shape<dimension> &shape,
+                         index_t stride, Stream<Device> *stream)
+      : dptr_(dptr), shape_(shape), stride_(stride), stream_(stream) {}
+  /*!
+   * \brief set the stream to do computation of current tensor
+   * \param stream the computation stream
+   */
+  inline void set_stream(Stream<Device> *stream) {
+    this->stream_ = stream;
+  }
+  /*!
+   * \return memory cost of the tensor, including the aligned x dimension
+   * \tparam startdim the starting dimension
+   */
+  template<int startdim>
+  MSHADOW_XINLINE index_t MemSize(void) const {
+    index_t memsz = this->stride_;
+    #pragma unroll
+    for (int i = startdim; i < kSubdim; ++i) {
+      memsz *= this->shape_[i];
+    }
+    return memsz;
+  }
+  /*!
+   * \return whether the tensor's memory is continuous
+   * x dimension same as stride
+   */
+  MSHADOW_XINLINE bool CheckContiguous(void) const {
+    return this->shape_[dimension - 1] == stride_;
+  }
+  /*!
+   * \return memory cost of the tensor, including the aligned x dimension
+   */
+  MSHADOW_XINLINE index_t MSize(void) const {
+    return this->MemSize<0>();
+  }
+  /*!
+   * \brief return size of i-th dimension, start counting from highest dimension
+   * \param idx the dimension count from the highest dimensin
+   * \return the size
+   */
+  MSHADOW_XINLINE index_t size(index_t idx) const {
+    return shape_[idx];
+  }
+  /*!
+   * \brief flatten the tensor to 1 dimension
+   * \return tensor after flatten
+   */
+  MSHADOW_XINLINE Tensor<Device, 1, DType> FlatTo1D(void) const {
+    return Tensor<Device, 1, DType>(dptr_, shape_.FlatTo1D(), stride_, stream_);
+  }
+  /*!
+   * \brief flatten the tensor to 2 dimension, collapse the higher dimensions together
+   * \return tensor after flatten
+   */
+  MSHADOW_XINLINE Tensor<Device, 2, DType> FlatTo2D(void) const {
+    return Tensor<Device, 2, DType>(dptr_, shape_.FlatTo2D(), stride_, stream_);
+  }
+  /*!
+   * \brief get a element of dimension - 1
+   * \param idx index
+   * \return the result tensor
+   */
+  MSHADOW_XINLINE Tensor<Device, kSubdim, DType> operator[](index_t idx) const {
+    return Tensor<Device, kSubdim, DType>(dptr_ + this->MemSize<1>() * idx,
+                                          shape_.SubShape(), stride_, stream_);
+  }
+  /*!
+   * \brief slice the tensor in highest dimension [begin,end)
+   * \param begin begin position of slice
+   * \param end end position of slice
+   * \return tensor after slice
+   */
+  MSHADOW_XINLINE Tensor<Device, dimension, DType>
+  Slice(index_t begin, index_t end) const {
+    Shape<dimension> s = this->shape_;
+    s[0] = end - begin;
+    return Tensor<Device, dimension, DType>(dptr_ + this->MemSize<1>() * begin,
+                                            s, stride_, stream_);
+  }
+  /*!\brief implement the assignment of same type */
+  inline Tensor<Device, dimension, DType> &
+  operator=(const Tensor<Device, dimension, DType> &exp) {
+    dptr_ = exp.dptr_;
+    shape_ = exp.shape_;
+    stride_ = exp.stride_;
+    stream_ = exp.stream_;
+    return *this;
+  }
+  /*!\brief functions to fit expression template */
+  template<typename E, int etype>
+  inline Tensor<Device, dimension, DType> &
+  operator=(const expr::Exp<E, DType, etype> &exp) {
+    return this->__assign(exp);
+  }
+  /*!\brief functions to fit expression template */
+  inline Tensor<Device, dimension, DType> &operator=(const DType &exp) {
+    return this->__assign(exp);
+  }
+};
+/*
+ *  respecialized class Tensor1D, thei is due to different implementation in operator[]
+ */
+template<typename Device, typename DType>
+struct Tensor<Device, 1, DType>:
+      public TRValue<Tensor<Device, 1, DType>, Device, 1, DType> {
+ public:
+  DType *dptr_;
+  Shape<1> shape_;
+  index_t stride_;
+  Stream<Device> *stream_;
+  // constructor
+  MSHADOW_XINLINE Tensor(void) : stream_(NULL) {}
+  MSHADOW_XINLINE Tensor(const Shape<1> &shape)
+      : shape_(shape), stream_(NULL) {}
+  MSHADOW_XINLINE Tensor(DType *dptr, Shape<1> shape)
+      : dptr_(dptr), shape_(shape), stride_(shape[0]), stream_(NULL) {}
+  MSHADOW_XINLINE Tensor(DType *dptr, Shape<1> shape, Stream<Device> *stream)
+      : dptr_(dptr), shape_(shape), stride_(shape[0]), stream_(stream) {}
+  MSHADOW_XINLINE Tensor(DType *dptr, Shape<1> shape,
+                         index_t stride, Stream<Device> *stream)
+      : dptr_(dptr), shape_(shape), stride_(stride), stream_(stream) {}
+  inline void set_stream(Stream<Device> *stream) {
+    this->stream_ = stream;
+  }
+  MSHADOW_XINLINE Tensor<Device, 1, DType> FlatTo1D(void) const {
+    return *this;
+  }
+  MSHADOW_XINLINE Tensor<Device, 2, DType> FlatTo2D(void) const {
+    return Tensor<Device, 2, DType>(dptr_, shape_.FlatTo2D(), stride_, stream_);
+  }
+  MSHADOW_XINLINE Tensor<Device, 1, DType> Slice(index_t begin, index_t end) const {
+    Shape<1> s;
+    s[0] = end  - begin;
+    return Tensor<Device, 1, DType>(dptr_ + begin, s, s[0], stream_);
+  }
+  MSHADOW_XINLINE bool CheckContiguous(void) const {
+    return true;
+  }
+  MSHADOW_XINLINE index_t MSize(void) const {
+    return shape_[0];
+  }
+  MSHADOW_XINLINE index_t size(index_t i) const {
+    return shape_[0];
+  }
+  MSHADOW_XINLINE DType &operator[](index_t idx) {
+    return dptr_[idx];
+  }
+  MSHADOW_XINLINE const DType &operator[](index_t idx) const {
+    return dptr_[idx];
+  }
+  /*!\brief implement the assignment of same type */
+  inline Tensor<Device, 1, DType> &
+  operator=(const Tensor<Device, 1, DType> &exp) {
+    dptr_ = exp.dptr_;
+    shape_ = exp.shape_;
+    stride_ = exp.stride_;
+    stream_ = exp.stream_;
+    return *this;
+  }
+  template<typename E, int etype>
+  inline Tensor<Device, 1, DType> &
+  operator=(const expr::Exp<E, DType, etype> &exp) {
+    return this->__assign(exp);
+  }
+  inline Tensor<Device, 1, DType> &operator=(const DType &exp) {
+    return this->__assign(exp);
+  }
+};
+//------------------------
+// Function Declarations
+//-----------------------
+/*!
+ * \brief initialize tensor engine, used to call intialization functions of dependent libs
+ *        this function should be called before all GPU tensor operations,
+ *        for using tensors in CPU, this call is actually not needed
+ * \param device_id GPU device id to be choosed
+ * \tparam Device the device type
+ */
+template<typename Device>
+inline void InitTensorEngine(int device_id = 0);
+/*!
+ * \brief Shutdown tensor engine on current device
+ *     this function should be called after all GPU tensor operations,
+ *     for using tensors in CPU, this call is actually not needed
+ * \tparam Device the device type
+ */
+template<typename Device>
+inline void ShutdownTensorEngine(void);
+/*!
+ * \brief set the device of current thread to work on
+ * \param devid the device id
+ * \tparam Device the device type
+ */
+template<typename Device>
+inline void SetDevice(int devid);
+/*!
+ * \brief create a new stream from system
+ * \param create_blas_handle whether create blas & cusolver handle in stream
+ * \param create_dnn_handle whether create cudnn handle in stream
+ * \param dev_id device id
+ * \return a pointer to the created stream
+ * \tparam Device the device type
+ */
+template<typename Device>
+inline Stream<Device> *NewStream(bool create_blas_handle,
+                                 bool create_dnn_handle,
+                                 int dev_id = -1);
+/*! \brief default behavior: create cublas handle
+ *  \param dev_id device id
+ *  \return a pointer to the created stream
+ */
+template<typename Device>
+inline Stream<Device> *NewStream(int dev_id) {
+  return NewStream<Device>(true, false, dev_id);
+}
+/*!
+ * \brief delete the computing stream
+ * \param stream the stream parameter to be deleted
+ */
+template<typename Device>
+inline void DeleteStream(Stream<Device> *stream);
+/*!
+ * \brief CPU/CPU: allocate space for CTensor, according to the shape in the obj
+ *        this function is responsible to set the stride_ in each obj.shape
+ * \param obj the tensor object, with shape specified
+ * \param pad whether padding dimension 0, to make last dimension aligned,
+ *            padding may help improve efficiency of matrix multiplications
+ *            if true, will allocate space with stride_ that may not equals shape[0]
+ *            if false, will allocate continuous space
+ * \tparam dim specify the dim of tensor
+ * \tparam DType type of element in tensor
+ */
+template<int dim, typename DType>
+inline void AllocSpace(Tensor<cpu, dim, DType> *obj,
+                       bool pad = MSHADOW_ALLOC_PAD);
+/*!
+ * \brief CPU/CPU: allocate space for CTensor, according to the shape in the obj
+ *        this function is responsible to set the stride_ in each obj.shape
+ * \param obj the tensor object, with shape specified
+ * \param pad whether padding dimension 0, to make last dimension aligned,
+ *            padding may help improve efficiency of matrix multiplications
+ *            if true, will allocate space with stride_ that may not equals shape[0]
+ *            if false, will allocate continuous space
+ * \tparam dim specify the dim of tensor
+ * \tparam DType type of element in tensor
+ */
+template<int dim, typename DType>
+inline void AllocSpace(Tensor<gpu, dim, DType> *obj,
+                       bool pad = MSHADOW_ALLOC_PAD);
+/*!
+ * \brief CPU/GPU: free the space of tensor, will set obj.dptr to NULL
+ * \param obj the tensor object
+ * \tparam dim specify the dim of tensor
+ * \tparam DType type of element in tensor
+ */
+template<int dim, typename DType>
+inline void FreeSpace(Tensor<cpu, dim, DType> *obj);
+/*!
+ * \brief CPU/GPU: free the space of tensor, will set obj.dptr to NULL
+ * \param obj the tensor object
+ * \tparam dim specify the dim of tensor
+ * \tparam DType type of element in tensor
+ */
+template<int dim, typename DType>
+inline void FreeSpace(Tensor<gpu, dim, DType> *obj);
+/*!
+ * \brief CPU/GPU: short cut to allocate and initialize a Tensor
+ * \param shape: shape of tensor
+ * \param initv: initialization value
+ * \param pad : padding option
+ * \param stream : stream of tensor
+ * \tparam Device device of tensor
+ * \tparam DType type of element in tensor
+ * \tparam dim dimention of tensor
+ * \return a new allocated tensor
+ * \sa AllocSpace
+ */
+template<typename Device, typename DType, int dim>
+inline Tensor<Device, dim, DType> NewTensor(const Shape<dim> &shape,
+                                            DType initv,
+                                            bool pad = MSHADOW_ALLOC_PAD,
+                                            Stream<Device> *stream = NULL);
+/*!
+ * \brief copy data from one tensor to another, with same shape
+ * \param dst target tensor
+ * \param src source tensor
+ * \param stream the stream, when specified, the copy can exhibit asynchronize behavior
+ * \tparam dim specify the dim of tensor
+ * \tparam DType type of element in tensor
+ */
+template<int dim, typename DType>
+inline void Copy(Tensor<cpu, dim, DType> dst,
+                 const Tensor<cpu, dim, DType> &src,
+                 Stream<cpu> *stream = NULL);
+/*!
+ * \brief copy data from one tensor to another, with same shape
+ * \param dst target tensor
+ * \param src source tensor
+ * \param stream the stream, when specified, the copy can exhibit asynchronize behavior
+ * \tparam dim specify the dim of tensor
+ * \tparam DType type of element in tensor
+ */
+template<int dim, typename DType>
+inline void Copy(Tensor<cpu, dim, DType> dst,
+                 const Tensor<gpu, dim, DType> &src,
+                 Stream<gpu> *stream = NULL);
+/*!
+ * \brief copy data from one tensor to another, with same shape
+ * \param dst target tensor
+ * \param src source tensor
+ * \param stream the stream, when specified, the copy can exhibit asynchronize behavior
+ * \tparam dim specify the dim of tensor
+ * \tparam DType type of element in tensor
+ */
+template<int dim, typename DType>
+inline void Copy(Tensor<gpu, dim, DType> dst,
+                 const Tensor<cpu, dim, DType> &src,
+                 Stream<gpu> *stream = NULL);
+/*!
+ * \brief copy data from one tensor to another, with same shape
+ * \param dst target tensor
+ * \param src source tensor
+ * \param stream the stream, when specified, the copy can exhibit asynchronize behavior
+ * \tparam dim specify the dim of tensor
+ * \tparam DType type of element in tensor
+ */
+template<int dim, typename DType>
+inline void Copy(Tensor<gpu, dim, DType> dst,
+                 const Tensor<gpu, dim, DType> &src,
+                 Stream<gpu> *stream = NULL);
+/*!
+ * \brief CPU/GPU: normalize softmax: dst[i][j] = exp(energy[i][j]) /(sum_j exp(energy[i][j]))
+ * \param dst destination
+ * \param energy input energy
+ */
+template<typename DType>
+inline void Softmax(Tensor<cpu, 2, DType> dst, const Tensor<cpu, 2, DType> &energy);
+/*!
+ * \brief CPU/GPU: normalize softmax: dst[i][j] = exp(energy[i][j]) /(sum_j exp(energy[i][j]))
+ * \param dst destination
+ * \param energy input energy
+ */
+template<typename DType>
+inline void Softmax(Tensor<gpu, 2, DType> dst, const Tensor<gpu, 2, DType> &energy);
+
+/*!
+ * \brief CPU/GPU: softmax gradient
+ * \param dst destination
+ * \param src source output
+ * \param label label info
+ */
+template<typename DType>
+inline void SoftmaxGrad(Tensor<cpu, 2, DType> dst,
+                        const Tensor<cpu, 2, DType> &src,
+                        const Tensor<cpu, 1, DType> &label);
+/*!
+ * \brief CPU/GPU: softmax gradient
+ * \param dst destination
+ * \param src source output
+ * \param label label info
+ */
+template<typename DType>
+inline void SoftmaxGrad(const Tensor<gpu, 2, DType> &dst,
+                        const Tensor<gpu, 2, DType> &src,
+                        const Tensor<gpu, 1, DType> &label);
+/*!
+ * \brief CPU/GPU: Gradient accumulate of embedding matrix.
+                   dst[index[i]] += src[i]
+                   Called when the featuredim of src is much larger than the batchsize
+ * \param dst destination
+ * \param index index to take
+ * \param src source output
+ */
+template<typename IndexType, typename DType>
+inline void AddTakeGrad(Tensor<cpu, 2, DType> dst,
+                        const Tensor<cpu, 1, IndexType>& index,
+                        const Tensor<cpu, 2, DType> &src);
+/*!
+ * \brief CPU/GPU: Gradient accumulate of embedding matrix.
+                   dst[index[i]] += src[i]
+                   Called when the featuredim of src is much larger than the batchsize
+ * \param dst destination
+ * \param index index to take
+ * \param src source output
+ */
+template<typename IndexType, typename DType>
+inline void AddTakeGrad(Tensor<gpu, 2, DType> dst,
+                        const Tensor<gpu, 1, IndexType>& index,
+                        const Tensor<gpu, 2, DType> &src);
+/*!
+ * \brief CPU/GPU: Gradient accumulate of embedding matrix.
+                   dst[sorted[i]] += src[index[i]]
+                   Called when the batchsize of src is larger than the featuredim
+ * \param dst destination
+ * \param sorted the sorted indices
+ * \param index original index of the sorted indices
+ * \param src source output
+ */
+template<typename IndexType, typename DType>
+inline void AddTakeGradLargeBatch(Tensor<cpu, 2, DType> dst,
+                                  const Tensor<cpu, 1, IndexType>& sorted,
+                                  const Tensor<cpu, 1, IndexType>& index,
+                                  const Tensor<cpu, 2, DType> &src);
+/*!
+ * \brief CPU/GPU: Gradient accumulate of embedding matrix.
+                   dst[sorted[i]] += src[index[i]]
+                   Called when the batchsize of src is larger than the featuredim
+ * \param dst destination
+ * \param sorted the sorted indices
+ * \param index original index of the sorted indices
+ * \param src source output
+ */
+template<typename IndexType, typename DType>
+inline void AddTakeGradLargeBatch(Tensor<gpu, 2, DType> dst,
+                                  const Tensor<gpu, 1, IndexType>& sorted,
+                                  const Tensor<gpu, 1, IndexType>& index,
+                                  const Tensor<gpu, 2, DType> &src);
+/*!
+ * \brief CPU/GPU: Fill the values of the destination matrix to specific rows in the source matrix.
+                   dst[index[i]] = src[i]
+                   Will use atomicAdd in the inner implementation and the result may not be deterministic.
+ * \param dst destination
+ * \param index the index to accumulate value
+ * \param src source output
+ */
+template<typename IndexType, typename DType>
+inline void IndexFill(Tensor<cpu, 2, DType> dst,
+                      const Tensor<cpu, 1, IndexType>& index,
+                      const Tensor<cpu, 2, DType> &src);
+/*!
+ * \brief CPU/GPU: Fill the values of the destination matrix to specific rows in the source matrix.
+                   dst[index[i]] = src[i]
+                   Will use atomicAdd in the inner implementation and the result may not be deterministic.
+ * \param dst destination
+ * \param index the index to accumulate value
+ * \param src source output
+ */
+template<typename IndexType, typename DType>
+inline void IndexFill(Tensor<gpu, 2, DType> dst,
+                      const Tensor<gpu, 1, IndexType>& index,
+                      const Tensor<gpu, 2, DType> &src);
+/*!
+ * \brief CPU/GPU: Sort key-value pairs stored in separate places. (Stable sort is performed!)
+ * \param keys the keys to sort
+ * \param values the values that sorts w.r.t the key
+ * \param is_ascend whether to sort key in ascending order
+ */
+template<typename KDType, typename VDType>
+inline void SortByKey(Tensor<cpu, 1, KDType> keys, Tensor<cpu, 1, VDType> values,
+                      bool is_ascend = true);
+/*!
+ * \brief CPU/GPU: Sort key-value pairs stored in separate places. (Stable sort is performed!)
+ * \param keys the keys to sort
+ * \param values the values that sorts w.r.t the key
+ * \param is_ascend whether to sort key in ascending order
+ */
+template<typename KDType, typename VDType>
+inline void SortByKey(Tensor<gpu, 1, KDType> keys, Tensor<gpu, 1, VDType> values,
+                      bool is_ascend = true);
+/*!
+ * \brief CPU/GPU: Sort the keys within each segment. (Stable sort is performed!)
+                   Segments is defined as an ascending ordered vector like [0, 0, 0, 1, 1, 2, 3, 3, 3,...]
+                   We sort separately the keys labeled by 0 and 1, 2, 3, etc.
+                   Currently only supports sorting in ascending order !!
+ * \param values the data to sort
+ * \param segments segment indicator
+ */
+template<typename Device, typename VDType, typename SDType>
+inline void VectorizedSort(Tensor<Device, 1, VDType> values, Tensor<Device, 1, SDType> segments);
+
+// function declarations to support expression, no need to understand them
+// these functions do not need to be directly used
+/*!
+ * \brief CPU/GPU: map a expression to a tensor, this function calls MapPlan
+ * \tparam Saver specify storage method
+ * \tparam R specifies the storage type of the tensor
+ * \tparam dim dim of the tensor, during usage, there is no need to specify this parameter
+ * \tparam DType the type of elements in the tensor
+ * \tparam E specifies the expression type, not need to specify this parameter during usage
+ * \tparam etype expression type
+ * \param dst destination
+ * \param exp expression
+ * \sa namespace mshadow:sv, mshadow::op, mshadow::expr
+ */
+template<typename Saver, typename R, int dim,
+         typename DType, typename E, int etype>
+inline void MapExp(TRValue<R, cpu, dim, DType> *dst,
+                   const expr::Exp<E, DType, etype> &exp);
+/*!
+ * \brief CPU/GPU: map a expression to a tensor, this function calls MapPlan
+ * \tparam Saver specify storage method
+ * \tparam R specifies the storage type of the tensor
+ * \tparam dim dim of the tensor, during usage, there is no need to specify this parameter
+ * \tparam DType the type of elements in the tensor
+ * \tparam E specifies the expression type, not need to specify this parameter during usage
+ * \tparam etype expression type
+ * \param dst destination
+ * \param exp expression
+ * \sa namespace mshadow:sv, mshadow::op, mshadow::expr
+ */
+template<typename Saver, typename R, int dim,
+         typename DType, typename E, int etype>
+inline void MapExp(TRValue<R, gpu, dim, DType> *dst,
+                   const expr::Exp<E, DType, etype> &exp);
+/*!
+ * \brief CPU/GPU: map a expression, do reduction to 1D Tensor in lowest dimension (dimension 0)
+ * \tparam Saver specify storage method
+ * \tparam Reducer specify a reducer method
+ * \tparam R specifies the storage type of the tensor
+ * \tparam DType the type of elements in the tensor
+ * \tparam E specifies the expression type, not need to specify this parameter during usage
+ * \tparam etype expression type
+ * \param dst destination
+ * \param exp expression
+ * \param scale scale the result before save
+ * \sa namespace mshadow:sv, mshadow::op, mshadow::red, mshadow::expr
+ */
+template<typename Saver, typename Reducer,
+         typename R, typename DType, typename E, int etype>
+inline void MapReduceKeepLowest(TRValue<R, cpu, 1, DType> *dst,
+                                const expr::Exp<E, DType, etype> &exp,
+                                DType scale = 1);
+/*!
+ * \brief CPU/GPU: map a expression, do reduction to 1D Tensor in lowest dimension (dimension 0)
+ * \tparam Saver specify storage method
+ * \tparam Reducer specify a reducer method
+ * \tparam R specifies the storage type of the tensor
+ * \tparam DType the type of elements in the tensor
+ * \tparam E specifies the expression type, not need to specify this parameter during usage
+ * \tparam etype expression type
+ * \param dst destination
+ * \param exp expression
+ * \param scale scale the result before save
+ * \sa namespace mshadow:sv, mshadow::op, mshadow::red, mshadow::expr
+ */
+template<typename Saver, typename Reducer, typename R,
+         typename DType, typename E, int etype>
+inline void MapReduceKeepLowest(TRValue<R, gpu, 1, DType> *dst,
+                                const expr::Exp<E, DType, etype> &exp,
+                                DType scale = 1);
+/*!
+ * \brief CPU/GPU: map a expression, do reduction to 1D Tensor in third dimension (dimension 2)
+ * \tparam Saver specify storage method
+ * \tparam Reducer specify a reducer method
+ * \tparam R specifies the storage type of the tensor
+ * \tparam DType the type of elements in the tensor
+ * \tparam dimkeep the target dimension to be kept, should be larger than 0, for 0, use MapReduceKeepLowest
+ * \tparam E specifies the expression type, not need to specify this parameter during usage
+ * \tparam etype expression type
+ * \param dst destination
+ * \param exp expression
+ * \param scale scale the result before save
+ * \sa namespace mshadow:sv, mshadow::op, mshadow::red, mshadow::expr
+ */
+template<typename Saver, typename Reducer, int dimkeep,
+         typename R, typename DType, typename E, int etype>
+inline void MapReduceKeepHighDim(TRValue<R, cpu, 1, DType> *dst,
+                                 const expr::Exp<E, DType, etype> &exp,
+                                 DType scale = 1);
+/*!
+ * \brief CPU/GPU: map a expression, do reduction to 1D Tensor in third dimension (dimension 2)
+ * \tparam Saver specify storage method
+ * \tparam Reducer specify a reducer method
+ * \tparam R specifies the storage type of the tensor
+ * \tparam DType the type of elements in the tensor
+ * \tparam dimkeep the target dimension to be kept, should be larger than 0, for 0, use MapReduceKeepLowest
+ * \tparam E specifies the expression type, not need to specify this parameter during usage
+ * \tparam etype expression type
+ * \param dst destination
+ * \param exp expression
+ * \param scale scale the result before save
+ * \sa namespace mshadow:sv, mshadow::op, mshadow::red, mshadow::expr
+ */
+template<typename Saver, typename Reducer, int dimkeep,
+         typename R, typename DType, typename E, int etype>
+inline void MapReduceKeepHighDim(TRValue<R, gpu, 1, DType> *dst,
+                                 const expr::Exp<E, DType, etype> &exp,
+                                 DType scale = 1);
+/*!
+ * \brief CPU/GPU: 1 dimension vector dot
+ * \param dst Length 1 vector, used to hold the result.
+ * \param lhs Left operand vector
+ * \param rhs Right operand vector
+ */
+template<typename Device, typename DType>
+inline void VectorDot(Tensor<Device, 1, DType> dst,
+                      const Tensor<Device, 1, DType> &lhs,
+                      const Tensor<Device, 1, DType> &rhs);
+/*!
+ * \brief CPU/GPU: dst = alpha * op(lhs) op(rhs) + beta * dst
+ * \param dst Length 3 tensor, used to hold the result
+ * \param lhs Left operand vector
+ * \param rhs Right operand vector
+ * \param alpha multiplier of op(lhs)op(rhs)
+ * \param beta multiplier of dst
+ * \param workspace Workspace for casting DType* to DType** (batched-view), must have size >= 3 * batch_size
+ */
+template<bool transpose_left, bool transpose_right, typename Device, typename DType>
+inline void BatchGEMM(Tensor<Device, 3, DType> dst,
+                      const Tensor<Device, 3, DType> &lhs,
+                      const Tensor<Device, 3, DType> &rhs,
+                      DType alpha,
+                      DType beta,
+                      Tensor<Device, 1, DType*> workspace);
+}  // namespace mshadow
+// include headers
+#include "./stream_gpu-inl.h"
+#include "./extension.h"
+#include "./expr_engine-inl.h"
+#include "./tensor_cpu-inl.h"
+#include "./tensor_gpu-inl.h"
+#include "./io.h"
+#include "./tensor_container.h"
+#include "./random.h"
+// add definition of scalar related operators
+#ifdef MSHADOW_SCALAR_
+  #error "MSHADOW_SCALAR_ must not be defined"
+#endif
+// enumerate all the scalar data type we aim to be good at
+#define MSHADOW_SCALAR_ float
+#include "./expr_scalar-inl.h"
+#undef MSHADOW_SCALAR_
+#define MSHADOW_SCALAR_ double
+#include "./expr_scalar-inl.h"
+#undef MSHADOW_SCALAR_
+#define MSHADOW_SCALAR_ int32_t
+#include "./expr_scalar-inl.h"
+#undef MSHADOW_SCALAR_
+#define MSHADOW_SCALAR_ int64_t
+#include "./expr_scalar-inl.h"
+#undef MSHADOW_SCALAR_
+#define MSHADOW_SCALAR_ mshadow::half::half_t
+#include "./expr_scalar-inl.h"
+#undef MSHADOW_SCALAR_
+#endif  // MSHADOW_TENSOR_H_
diff --git a/3rdparty/mshadow/mshadow/tensor_container.h b/3rdparty/mshadow/mshadow/tensor_container.h
new file mode 100644
index 000000000000..8dee1de10eb9
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/tensor_container.h
@@ -0,0 +1,208 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file tensor_container.h
+ * \brief tensor container that does memory allocation and resize like STL
+ * \author Tianqi Chen
+ */
+#ifndef MSHADOW_TENSOR_CONTAINER_H_
+#define MSHADOW_TENSOR_CONTAINER_H_
+#include "./tensor.h"
+#include "./io.h"
+
+namespace mshadow {
+/*!
+ * \brief tensor container that does memory allocation and resize like STL,
+ *        use it to save the lines of FreeSpace in class.
+ *        Do not abuse it, efficiency can come from pre-allocation and no re-allocation
+ *
+ * \tparam Device which device the tensor is on
+ * \tparam dimension dimension of the tensor
+ */
+template<typename Device, int dimension, typename DType = default_real_t>
+class TensorContainer: public Tensor<Device, dimension, DType> {
+ public:
+  /*!
+   * \brief constructor
+   * \param pad whether use padding alignment in space allocation
+   */
+  explicit TensorContainer(bool pad = MSHADOW_ALLOC_PAD) {
+    this->pad_ = pad;
+    this->dptr_ = data_.dptr_ = NULL;
+    this->shape_[0] = 0;
+    this->stride_ = 0;
+    this->data_.stride_ = 0;
+    this->data_.shape_[0] = 0;
+  }
+  /*!
+   * \brief constructor
+   * \param shape intial shape
+   */
+  explicit TensorContainer(const Shape<dimension> &shape) {
+    this->pad_ = MSHADOW_ALLOC_PAD;
+    data_.dptr_ = NULL;
+    this->AllocByShape(shape);
+  }
+  /*!
+   * \brief constructor
+   * \param shape intial shape
+   * \param initv intial value
+   */
+  explicit TensorContainer(const Shape<dimension> &shape, DType initv) {
+    this->pad_ = MSHADOW_ALLOC_PAD;
+    data_.dptr_ = NULL;
+    this->AllocByShape(shape);
+    (*this) = initv;
+  }
+  /*!
+   * \brief copy constructor
+   * \param src source value
+   */
+  TensorContainer
+  (const TensorContainer<Device, dimension, DType> &src)
+      : pad_(src.pad_) {
+    this->dptr_ = data_.dptr_ = NULL;
+    this->shape_[0] = 0;
+    this->stride_ = 0;
+    this->data_.stride_ = 0;
+    this->data_.shape_[0] = 0;
+    this->stream_ = src.stream_;
+    if (src.dptr_ != NULL) {
+      this->AllocByShape(src.shape_);
+      mshadow::Copy(*this, src, this->stream_);
+    }
+  }
+  ~TensorContainer(void) MSHADOW_THROW_EXCEPTION {
+    this->Release();
+  }
+  /*!
+   * \brief resize the container to given shape, content is NOT preserved
+   * \param shape target shape
+   */
+  inline void Resize(const Shape<dimension> &shape) {
+    Shape<2> s2 = shape.FlatTo2D();
+    if (s2.shape_[1] > data_.stride_ || s2.shape_[0] > data_.size(0)) {
+      this->AllocByShape(shape);
+    } else {
+      this->shape_ = shape;
+      if (this->pad_) {
+        this->stride_ = data_.stride_;
+      } else {
+        this->stride_ = s2.shape_[1];
+      }
+    }
+  }
+  /*!
+   * \brief resize the container to given shape, and initialize, content is NOT preserved
+   * \param shape target shape
+   * \param initv initialization value
+   */
+  inline void Resize(const Shape<dimension> &shape, DType initv) {
+    this->Resize(shape);
+    (*this) = initv;
+  }
+  /*! \brief set whether padding is allowed in tensor */
+  inline void set_pad(bool pad) {
+    this->pad_ = pad;
+  }
+  /*!
+   * \brief save by binary format
+   * \param fo output binary stream
+   * \tparam TStream type of stream, need to support Read, Write, one example is utils::IStream.
+   */
+  template<typename TStream>
+  inline void SaveBinary(TStream &fo) const { // NOLINT(*)
+    mshadow::SaveBinary(fo, *this);
+  }
+  /*!
+   * \brief load by binary format, a temp Tensor<cpu,dim> storage will be allocated
+   * \param fi input binary stream
+   * \tparam TStream type of stream, need to support Read, Write, one example is utils::IStream.
+   */
+  template<typename TStream>
+  inline void LoadBinary(TStream &fi) { // NOLINT(*)
+    Tensor<cpu, dimension, DType> tmp;
+    mshadow::LoadBinary(fi, &tmp, false);
+    this->Resize(tmp.shape_);
+    Stream<Device> stream;
+    Copy(*this, tmp, &stream);
+    mshadow::FreeSpace(&tmp);
+  }
+  /*!
+   * \brief assign operator from TensorContainer
+   * \param src source value
+   * \return reference of self
+   */
+  inline TensorContainer &operator=
+  (const TensorContainer<Device, dimension, DType> &src) {
+    this->pad_ = src.pad_;
+    this->stream_ = src.stream_;
+    if (src.dptr_ != NULL) {
+      this->Resize(src.shape_);
+      mshadow::Copy(*this, src, this->stream_);
+    }
+    return *this;
+  }
+  /*!\brief functions to fit expression template */
+  inline Tensor<Device, dimension, DType> &operator=(DType s) {
+    return this->__assign(s);
+  }
+  /*!\brief functions to fit expression template */
+  template<typename E>
+  inline Tensor<Device, dimension, DType> &
+  operator=(const expr::Exp<E, DType, expr::type::kMapper> &exp) {
+    return this->__assign(exp);
+  }
+  /*!\brief functions to fit expression template */
+  template<typename E>
+  inline Tensor<Device, dimension, DType> &
+  operator=(const expr::Exp<E, DType, expr::type::kChainer> &exp) {
+    return this->__assign(exp);
+  }
+  /*!\brief functions to fit expression template */
+  template<typename E>
+  inline Tensor<Device, dimension, DType> &
+  operator=(const expr::Exp<E, DType, expr::type::kComplex> &exp) {
+    return this->__assign(exp);
+  }
+  /*!
+   * \brief Release the llocated space,
+   *  The TensorContainer is still functionable,
+   *  but will restart allocating space when Resize is called.
+   */
+  inline void Release(void) {
+    if (data_.dptr_ != NULL) {
+      this->shape_[0] = 0;
+      this->stride_ = 0;
+      this->data_.stride_ = 0;
+      this->data_.shape_[0] = 0;
+      try {
+        mshadow::FreeSpace(&data_);
+      } catch (const dmlc::Error &e) {
+        this->dptr_ = data_.dptr_ = NULL;
+        throw e;
+      }
+      this->dptr_ = data_.dptr_ = NULL;
+    }
+  }
+
+ private:
+  /*! \brief whether we do padding in the space */
+  bool pad_;
+  /*! \brief the shape of data_ is actually current data space */
+  Tensor<Device, 2, DType> data_;
+
+  inline void AllocByShape(const Shape<dimension>& shape) {
+    if (data_.dptr_ != NULL) this->Release();
+    data_.shape_ = shape.FlatTo2D();
+    mshadow::AllocSpace(&data_, pad_);
+    this->dptr_ = data_.dptr_;
+    this->shape_ = shape;
+    if (this->pad_) {
+      this->stride_ = data_.stride_;
+    } else {
+      this->stride_ = data_.size(1);
+    }
+  }
+};
+}  // namespace mshadow
+#endif  // MSHADOW_TENSOR_CONTAINER_H_
diff --git a/3rdparty/mshadow/mshadow/tensor_cpu-inl.h b/3rdparty/mshadow/mshadow/tensor_cpu-inl.h
new file mode 100755
index 000000000000..ab5f9a68df14
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/tensor_cpu-inl.h
@@ -0,0 +1,627 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file tensor_cpu-inl.h
+ * \brief implementation of CPU host code
+ * \author Bing Xu, Tianqi Chen
+ */
+#ifndef MSHADOW_TENSOR_CPU_INL_H_
+#define MSHADOW_TENSOR_CPU_INL_H_
+#include <cstring>
+#include <functional>
+#include <utility>
+#include <vector>
+#include "./base.h"
+#include "./tensor.h"
+#include "./packet-inl.h"
+#include "./dot_engine-inl.h"
+
+namespace mshadow {
+template<>
+inline void InitTensorEngine<cpu>(int dev_id) {
+}
+template<>
+inline void ShutdownTensorEngine<cpu>(void) {
+}
+
+template<>
+inline void SetDevice<cpu>(int devid) {
+}
+template<>
+inline Stream<cpu> *NewStream<cpu>(bool create_blas_handle,
+                                   bool create_dnn_handle,
+                                   int dev_id) {
+  return new Stream<cpu>();
+}
+template<>
+inline void DeleteStream<cpu>(Stream<cpu> *stream) {
+  delete stream;
+}
+
+template<int ndim>
+inline std::ostream &operator<<(std::ostream &os, const Shape<ndim> &shape) { // NOLINT(*)
+  os << '(';
+  for (int i = 0; i < ndim; ++i) {
+    if (i != 0) os << ',';
+    os << shape[i];
+  }
+  // python style tuple
+  if (ndim == 1) os << ',';
+  os << ')';
+  return os;
+}
+
+template<typename xpu>
+inline void *AllocHost_(size_t size);
+template<typename xpu>
+inline void FreeHost_(void * dptr);
+
+#ifdef __CUDACC__
+template<>
+inline void *AllocHost_<gpu>(size_t size) {
+  void *dptr;
+  MSHADOW_CUDA_CALL(cudaMallocHost(&dptr, size, cudaHostAllocPortable));
+  return dptr;
+}
+template<>
+inline void FreeHost_<gpu>(void *dptr) {
+  MSHADOW_CUDA_CALL(cudaFreeHost(dptr));
+}
+#endif
+
+template<>
+inline void *AllocHost_<cpu>(size_t size) {
+  size_t pitch;
+  return packet::AlignedMallocPitch(&pitch, size, 1);
+}
+template<>
+inline void FreeHost_<cpu>(void *dptr) {
+  packet::AlignedFree(dptr);
+}
+
+template<typename xpu, int dim, typename DType>
+inline void AllocHost(Tensor<cpu, dim, DType> *obj) {
+  obj->stride_ = obj->size(dim - 1);
+  CHECK_EQ(obj->CheckContiguous(), true) << "AllocHost";
+  void *dptr = AllocHost_<xpu>(obj->MSize() * sizeof(DType));
+  obj->dptr_ = reinterpret_cast<DType*>(dptr);
+}
+template<typename xpu, int dim, typename DType>
+inline void FreeHost(Tensor<cpu, dim, DType> *obj) {
+  if (obj->dptr_ == NULL) {
+    LOG(FATAL) << "FreeHost:: double free";
+  }
+  FreeHost_<xpu>(obj->dptr_);
+  obj->dptr_ = NULL;
+}
+
+template<int dim, typename DType>
+inline void AllocSpace(Tensor<cpu, dim, DType> *obj, bool pad) {
+  size_t pitch;
+  void *dptr;
+  if (pad) {
+    dptr = packet::AlignedMallocPitch
+        (&pitch, obj->size(dim - 1) * sizeof(DType), obj->shape_.FlatTo2D()[0]);
+    obj->stride_ = static_cast<index_t>(pitch / sizeof(DType));
+  } else {
+    obj->stride_ = obj->size(dim - 1);
+    dptr = packet::AlignedMallocPitch
+        (&pitch, obj->shape_.Size() * sizeof(DType), 1);
+  }
+  obj->dptr_ = reinterpret_cast<DType*>(dptr);
+}
+template<typename Device, typename DType, int dim>
+inline Tensor<Device, dim, DType>
+NewTensor(const Shape<dim> &shape, DType initv, bool pad, Stream<Device> *stream_) {
+  Tensor<Device, dim, DType> obj(shape);
+  obj.stream_ = stream_;
+  AllocSpace(&obj, pad);
+  MapExp<sv::saveto>(&obj, expr::ScalarExp<DType>(initv));
+  return obj;
+}
+template<int dim, typename DType>
+inline void FreeSpace(Tensor<cpu, dim, DType> *obj) {
+  packet::AlignedFree(obj->dptr_);
+  obj->dptr_ = NULL;
+}
+template<int dim, typename DType>
+inline void Copy(Tensor<cpu, dim, DType> _dst,
+                 const Tensor<cpu, dim, DType> &_src,
+                 Stream<cpu> *stream) {
+  CHECK_EQ(_dst.shape_, _src.shape_)
+      << "Copy:shape mismatch:" << _dst.shape_ << " vs " << _src.shape_;
+  if (_dst.CheckContiguous() && _src.CheckContiguous()) {
+    memcpy(_dst.dptr_, _src.dptr_, sizeof(DType) * _dst.shape_.Size());
+  } else {
+    Tensor<cpu, 2, DType> dst = _dst.FlatTo2D();
+    Tensor<cpu, 2, DType> src = _src.FlatTo2D();
+    for (index_t y = 0; y < dst.size(0); ++y) {
+      memcpy(dst[y].dptr_, src[y].dptr_, sizeof(DType) * dst.size(1));
+    }
+  }
+}
+
+template<typename Saver, typename R, int dim,
+         typename DType, typename E>
+inline void MapPlan(TRValue<R, cpu, dim, DType> *dst,
+                    const expr::Plan<E, DType> &plan) {
+  Shape<2> shape = expr::ShapeCheck<dim, R>::Check(dst->self()).FlatTo2D();
+  expr::Plan<R, DType> dplan = expr::MakePlan(dst->self());
+#ifndef __CUDACC__
+  #pragma omp parallel for
+#endif
+  // temp remove openmp, as default setting throttles CPU
+  for (openmp_index_t y = 0; y < shape[0]; ++y) {
+    for (index_t x = 0; x < shape[1]; ++x) {
+      // trust your compiler! -_- they will optimize it
+      Saver::template Save<DType>(dplan.REval(y, x), plan.Eval(y, x));
+    }
+  }
+}
+// code to handle SSE optimization
+template<bool pass_check, typename Saver,
+         typename R, int dim,
+         typename DType, typename E, int etype>
+struct MapExpCPUEngine {
+  inline static void Map(TRValue<R, cpu, dim, DType> *dst,
+                         const expr::Exp<E, DType, etype> &exp) {
+    MapPlan<Saver>(dst, MakePlan(exp.self()));
+  }
+};
+
+template<typename SV, int dim, typename DType, typename E, int etype>
+struct MapExpCPUEngine<true, SV, Tensor<cpu, dim, DType>,
+                       dim, DType, E, etype> {
+  inline static void Map(Tensor<cpu, dim, DType> *dst,
+                         const expr::Exp<E, DType, etype> &exp) {
+    if (expr::PacketAlignCheck<dim, E, MSHADOW_DEFAULT_PACKET>::Check(exp.self()) &&
+        expr::PacketAlignCheck<dim, Tensor<cpu, dim, DType>, MSHADOW_DEFAULT_PACKET>::Check(*dst)) {
+      expr::MapPacketPlan<SV>(dst->self(),
+                              expr::MakePacketPlan<MSHADOW_DEFAULT_PACKET>(exp.self()));
+    } else {
+      MapPlan<SV>(dst, MakePlan(exp.self()));
+    }
+  }
+};
+
+
+template<typename Saver, typename R, int dim,
+         typename DType, typename E, int etype>
+inline void MapExp(TRValue<R, cpu, dim, DType> *dst,
+                   const expr::Exp<E, DType, etype> &exp) {
+  expr::TypeCheckPass<expr::TypeCheck<cpu, dim, DType, E>::kMapPass>
+      ::Error_All_Tensor_in_Exp_Must_Have_Same_Type();
+  Shape<dim> eshape = expr::ShapeCheck<dim, E>::Check(exp.self());
+  Shape<dim> dshape = expr::ShapeCheck<dim, R>::Check(dst->self());
+  CHECK(eshape[0] == 0 || eshape == dshape)
+      << "Assignment: Shape of Tensors are not consistent with target, "
+      << "eshape: " << eshape << " dshape:" << dshape;
+  MapExpCPUEngine<expr::PacketCheck<E, MSHADOW_DEFAULT_PACKET>::kPass,
+                  Saver, R, dim, DType, E, etype>
+  ::Map(dst->ptrself(), exp);
+}
+
+template<typename Saver, typename Reducer,
+         typename R, typename DType, typename E, int etype>
+inline void MapReduceKeepLowest(TRValue<R, cpu, 1, DType> *dst,
+                                const expr::Exp<E, DType, etype> &exp,
+                                DType scale) {
+  expr::TypeCheckPass<expr::TypeCheck<cpu, 1, DType, E>::kRedPass>
+      ::Error_TypeCheck_Not_Pass_For_Reduce_Exp();
+  Shape<2> eshape = expr::ShapeCheck<expr::ExpInfo<E>::kDim, E>
+      ::Check(exp.self()).FlatTo2D();
+  Shape<1> dshape = expr::ShapeCheck<1, R>::Check(dst->self());
+  CHECK_EQ(eshape[1], dshape[0]) << "MapReduceKeepLowest::reduction dimension do not match";
+  CHECK_NE(eshape[0], 0U) << "can not reduce over empty tensor";
+  // execution
+  expr::Plan<R, DType> dplan = MakePlan(dst->self());
+  expr::Plan<E, DType> splan = MakePlan(exp.self());
+#ifndef __CUDACC__
+  #pragma omp parallel for
+#endif
+  for (openmp_index_t x = 0; x < eshape[1]; ++x) {
+    DType res = splan.Eval(0, x);
+    for (index_t y = 1; y < eshape[0]; ++y) {
+      Reducer::Reduce(res, splan.Eval(y, x));
+    }
+    Saver::template Save<DType>(dplan.REval(0, x), res * scale);
+  }
+}
+
+template<typename Saver, typename Reducer, int dimkeep,
+         typename R, typename DType, typename E, int etype>
+inline void MapReduceKeepHighDim(TRValue<R, cpu, 1, DType> *dst,
+                                 const expr::Exp<E, DType, etype> &exp,
+                                 DType scale) {
+  expr::TypeCheckPass<expr::TypeCheck<cpu, dimkeep, DType, E>::kRedPass>
+      ::Error_TypeCheck_Not_Pass_For_Reduce_Exp();
+  typedef Shape<expr::ExpInfo<E>::kDim> EShape;
+  EShape eshape = expr::ShapeCheck<expr::ExpInfo<E>::kDim, E>
+      ::Check(exp.self());
+  Shape<1> dshape = expr::ShapeCheck<1, R>::Check(dst->self());
+  CHECK_EQ(eshape[dimkeep], dshape[0])
+    << "MapReduceKeepHighDim::reduction dimension do not match";
+  // use equvalent form
+  Shape<4> pshape = Shape4(eshape.ProdShape(0, dimkeep),
+                           eshape[dimkeep],
+                           eshape.ProdShape(dimkeep + 1, EShape::kSubdim),
+                           eshape[EShape::kSubdim]);
+  // execution
+  expr::Plan<R, DType> dplan = MakePlan(dst->self());
+  expr::Plan<E, DType> splan = MakePlan(exp.self());
+#ifndef __CUDACC__
+  #pragma omp parallel for
+#endif
+  for (openmp_index_t c = 0; c < pshape[1]; ++c) {
+    DType res; Reducer::SetInitValue(res);
+    for (index_t n = 0; n < pshape[0]; ++n) {
+      DType tres; Reducer::SetInitValue(tres);
+      for (index_t y = 0; y < pshape[2]; ++y) {
+        for (index_t x = 0; x < pshape[3]; ++x) {
+          Reducer::Reduce(tres,
+                          splan.Eval((n * pshape[1] + c) * pshape[2] + y, x));
+        }
+      }
+      Reducer::Reduce(res, tres);
+    }
+    Saver::template Save<DType>(dplan.REval(0, c), DType(res * scale));
+  }
+}
+
+template<typename DType>
+inline void Softmax(Tensor<cpu, 1, DType> dst,
+                    const Tensor<cpu, 1, DType> &energy) {
+  DType mmax = energy[0];
+  for (index_t x = 1; x < dst.size(0); ++x) {
+    if (mmax < energy[x]) mmax = energy[x];
+  }
+  DType sum = DType(0.0f);
+  for (index_t x = 0; x < dst.size(0); ++x) {
+    dst[x] = std::exp(energy[x] - mmax);
+    sum += dst[x];
+  }
+  for (index_t x = 0; x < dst.size(0); ++x) {
+    dst[x] /= sum;
+  }
+}
+
+template<typename DType>
+inline void SoftmaxGrad(Tensor<cpu, 2, DType> dst,
+                        const Tensor<cpu, 2, DType> &src,
+                        const Tensor<cpu, 1, DType> &label) {
+#pragma omp parallel for
+  for (openmp_index_t y = 0; y < dst.size(0); ++y) {
+    const index_t k = static_cast<int>(label[y]);
+    for (index_t x = 0; x < dst.size(1); ++x) {
+      if (x == k) {
+        dst[y][k] = src[y][k] - 1.0f;
+      } else {
+        dst[y][x] = src[y][x];
+      }
+    }
+  }
+}
+
+template<typename DType>
+inline void SmoothSoftmaxGrad(Tensor<cpu, 2, DType> dst,
+                        const Tensor<cpu, 2, DType> &src,
+                        const Tensor<cpu, 1, DType> &label,
+                        const float alpha) {
+  const float smooth_grad = (alpha / (dst.size(1) - 1));
+#pragma omp parallel for
+  for (openmp_index_t y = 0; y < dst.size(0); ++y) {
+    const index_t k = static_cast<int>(label[y]);
+    for (index_t x = 0; x < dst.size(1); ++x) {
+      if (x == k) {
+        dst[y][k] = src[y][k] - 1.0f + alpha;
+      } else {
+        dst[y][x] = src[y][x] - smooth_grad;
+      }
+    }
+  }
+}
+
+
+template<typename DType>
+inline void SoftmaxGrad(Tensor<cpu, 2, DType> dst,
+                        const Tensor<cpu, 2, DType> &src,
+                        const Tensor<cpu, 1, DType> &label,
+                        const DType &ignore_label) {
+#pragma omp parallel for
+  for (openmp_index_t y = 0; y < dst.size(0); ++y) {
+    const int k = static_cast<int>(label[y]);
+    for (int x = 0; x < static_cast<int>(dst.size(1)); ++x) {
+      if (static_cast<int>(ignore_label) == k) {
+        dst[y][x] = 0.0f;
+      } else {
+        if (x == k) {
+          dst[y][k] = src[y][k] - 1.0f;
+        } else {
+          dst[y][x] = src[y][x];
+        }
+      }
+    }
+  }
+}
+
+template<typename DType>
+inline void SmoothSoftmaxGrad(Tensor<cpu, 2, DType> dst,
+                              const Tensor<cpu, 2, DType> &src,
+                              const Tensor<cpu, 1, DType> &label,
+                              const DType &ignore_label,
+                              const float alpha) {
+  const float smooth_grad = (alpha / (dst.size(1) - 1));
+#pragma omp parallel for
+  for (openmp_index_t y = 0; y < dst.size(0); ++y) {
+    const int k = static_cast<int>(label[y]);
+    for (int x = 0; x < static_cast<int>(dst.size(1)); ++x) {
+      if (static_cast<int>(ignore_label) == k) {
+        dst[y][x] = 0.0f;
+      } else {
+        if (x == k) {
+          dst[y][k] = src[y][k] - 1.0f + alpha;
+        } else {
+          dst[y][x] = src[y][x] - smooth_grad;
+        }
+      }
+    }
+  }
+}
+
+template<typename DType>
+inline void SoftmaxGrad(Tensor<cpu, 3, DType> dst,
+                        const Tensor<cpu, 3, DType> &src,
+                        const Tensor<cpu, 2, DType> &label) {
+#pragma omp parallel for
+  for (openmp_index_t n = 0; n < dst.size(2); ++n) {
+    for (index_t y = 0; y < dst.size(0); ++y) {
+      const int k = static_cast<int>(label[y][n]);
+      for (int x = 0; x < static_cast<int>(dst.size(1)); ++x) {
+        if (x == k) {
+          dst[y][k][n] = src[y][k][n] - 1.0f;
+        } else {
+          dst[y][x][n] = src[y][x][n];
+        }
+      }
+    }
+  }
+}
+
+template<typename DType>
+inline void SmoothSoftmaxGrad(Tensor<cpu, 3, DType> dst,
+                        const Tensor<cpu, 3, DType> &src,
+                        const Tensor<cpu, 2, DType> &label,
+                        const float alpha) {
+  const float smooth_grad = (alpha / (dst.size(1) - 1));
+#pragma omp parallel for
+  for (openmp_index_t n = 0; n < dst.size(2); ++n) {
+    for (index_t y = 0; y < dst.size(0); ++y) {
+      const int k = static_cast<int>(label[y][n]);
+      for (int x = 0; x < static_cast<int>(dst.size(1)); ++x) {
+        if (x == k) {
+          dst[y][k][n] = src[y][k][n] - 1.0f + alpha;
+        } else {
+          dst[y][x][n] = src[y][x][n] - smooth_grad;
+        }
+      }
+    }
+  }
+}
+
+template<typename DType>
+inline void SoftmaxGrad(Tensor<cpu, 3, DType> dst,
+                        const Tensor<cpu, 3, DType> &src,
+                        const Tensor<cpu, 2, DType> &label,
+                        const DType &ignore_label) {
+#pragma omp parallel for
+  for (openmp_index_t n = 0; n < dst.size(2); ++n) {
+    for (index_t y = 0; y < dst.size(0); ++y) {
+      const int k = static_cast<int>(label[y][n]);
+      if (k == static_cast<int>(ignore_label)) {
+        for (int x = 0; x < static_cast<int>(dst.size(1)); ++x) {
+          dst[y][x][n] = DType(0.0f);
+        }
+      } else {
+        for (int x = 0; x < static_cast<int>(dst.size(1)); ++x) {
+          if (x == k) {
+            dst[y][k][n] = src[y][k][n] - 1.0f;
+          } else {
+            dst[y][x][n] = src[y][x][n];
+          }
+        }
+      }
+    }
+  }
+}
+
+template<typename DType>
+inline void SmoothSoftmaxGrad(Tensor<cpu, 3, DType> dst,
+                        const Tensor<cpu, 3, DType> &src,
+                        const Tensor<cpu, 2, DType> &label,
+                        const DType &ignore_label,
+                        const float alpha) {
+  const float smooth_grad = (alpha / (dst.size(1) - 1));
+#pragma omp parallel for
+  for (openmp_index_t n = 0; n < dst.size(2); ++n) {
+    for (index_t y = 0; y < dst.size(0); ++y) {
+      const int k = static_cast<int>(label[y][n]);
+      if (k == static_cast<int>(ignore_label)) {
+        for (int x = 0; x < static_cast<int>(dst.size(1)); ++x) {
+          dst[y][x][n] = DType(0.0f);
+        }
+      } else {
+        for (int x = 0; x < static_cast<int>(dst.size(1)); ++x) {
+          if (x == k) {
+            dst[y][k][n] = src[y][k][n] - 1.0f + alpha;
+          } else {
+            dst[y][x][n] = src[y][x][n] - smooth_grad;
+          }
+        }
+      }
+    }
+  }
+}
+
+template<typename DType>
+inline void Softmax(Tensor<cpu, 2, DType> dst,
+                    const Tensor<cpu, 2, DType> &energy) {
+  CHECK_EQ(dst.shape_, energy.shape_) << "Softmax: shape mismatch";
+#pragma omp parallel for
+  for (openmp_index_t y = 0; y < dst.size(0); ++y) {
+    Softmax(dst[y], energy[y]);
+  }
+}
+
+template<typename DType>
+inline void Softmax(Tensor<cpu, 3, DType> dst,
+                    const Tensor<cpu, 3, DType> &energy) {
+  CHECK_EQ(dst.shape_, energy.shape_) << "Softmax: shape mismatch";
+#pragma omp parallel for
+  for (openmp_index_t y = 0; y < dst.size(0); ++y) {
+    for (index_t n = 0; n < dst.size(2); ++n) {
+      DType mmax = energy[y][0][n];
+      for (index_t x = 1; x < dst.size(1); ++x) {
+        if (mmax < energy[y][x][n]) mmax = energy[y][x][n];
+      }
+      DType sum = DType(0.0f);
+      for (index_t x = 0; x < dst.size(1); ++x) {
+        dst[y][x][n] = std::exp(energy[y][x][n] - mmax);
+        sum += dst[y][x][n];
+      }
+      for (index_t x = 0; x < dst.size(1); ++x) {
+        dst[y][x][n] /= sum;
+      }
+    }
+  }
+}
+
+template<typename IndexType, typename DType>
+inline void AddTakeGrad(Tensor<cpu, 2, DType> dst,
+                        const Tensor<cpu, 1, IndexType>& index,
+                        const Tensor<cpu, 2, DType> &src) {
+  const int K = dst.shape_[0];
+  for (index_t y = 0; y < index.size(0); ++y) {
+    int j = index[y];
+    if (j <= 0) j = 0;
+    else if (j >= K) j = K - 1;
+    dst[j] += src[y];
+  }
+}
+
+template<typename IndexType, typename DType>
+inline void AddTakeGradLargeBatch(Tensor<cpu, 2, DType> dst,
+                                  const Tensor<cpu, 1, IndexType>& sorted,
+                                  const Tensor<cpu, 1, IndexType>& index,
+                                  const Tensor<cpu, 2, DType> &src) {
+  for (index_t y = 0; y < sorted.size(0); ++y) {
+    dst[sorted[y]] += src[index[y]];
+  }
+}
+
+template<typename IndexType, typename DType>
+inline void IndexFill(Tensor<cpu, 2, DType> dst,
+                      const Tensor<cpu, 1, IndexType>& index,
+                      const Tensor<cpu, 2, DType> &src) {
+  for (index_t y = 0; y < index.size(0); ++y) {
+    for (index_t j = 0; j < src.size(1); j++) {
+      dst[index[y]][j] = src[y][j];
+    }
+  }
+}
+
+template<typename KDType, typename VDType>
+inline void SortByKey(Tensor<cpu, 1, KDType> keys, Tensor<cpu, 1, VDType> values,
+                      bool is_ascend) {
+  CHECK_EQ(keys.CheckContiguous(), true);
+  CHECK_EQ(values.CheckContiguous(), true);
+  CHECK_EQ(keys.size(0), values.size(0))
+    << "The sizes of key/value are not equal! keys_size: " << keys.size(0)
+    << "values_size: " << values.size(0);
+  std::vector<size_t> idx(keys.size(0));
+  std::vector<KDType> keys_vec(keys.size(0));
+  std::vector<VDType> values_vec(values.size(0));
+  for (int i = 0; i < keys.size(0); i++) {
+    idx[i] = i;
+    keys_vec[i] = keys[i];
+    values_vec[i] = values[i];
+  }
+  if (is_ascend) {
+    std::stable_sort(idx.begin(), idx.end(),
+                     [&keys_vec](size_t i1, size_t i2)
+                       {return keys_vec[i1] < keys_vec[i2]; });
+  } else {
+    std::stable_sort(idx.begin(), idx.end(),
+                     [&keys_vec](size_t i1, size_t i2)
+                       {return keys_vec[i1] > keys_vec[i2]; });
+  }
+  for (index_t i = 0; i < values.size(0); i++) {
+    keys[i] = keys_vec[idx[i]];
+    values[i] = values_vec[idx[i]];
+  }
+}
+
+template<typename Device, typename VDType, typename SDType>
+inline void VectorizedSort(Tensor<Device, 1, VDType> values, Tensor<Device, 1, SDType> segments) {
+  // We can sort each segments using two stable sorts
+  SortByKey(values, segments, true);
+  SortByKey(segments, values, true);
+}
+
+// blas related
+template<typename Device, typename DType>
+inline void VectorDot(Tensor<Device, 1, DType> dst,
+                      const Tensor<Device, 1, DType> &lhs,
+                      const Tensor<Device, 1, DType> &rhs) {
+  CHECK_EQ(lhs.size(0), rhs.size(0))
+      << "VectorDot: Shape mismatch";
+  CHECK_EQ(dst.size(0), 1U)
+      << "VectorDot: expect dst to be scalar";
+  expr::BLASEngine<Device, DType>::SetStream(lhs.stream_);
+  mshadow::expr::BLASEngine<Device, DType>::dot(
+      lhs.stream_, lhs.size(0), lhs.dptr_, 1, rhs.dptr_, 1, dst.dptr_);
+}
+
+template<bool transpose_left, bool transpose_right, typename Device, typename DType>
+inline void BatchGEMM(Tensor<Device, 3, DType> dst,
+                      const Tensor<Device, 3, DType> &lhs,
+                      const Tensor<Device, 3, DType> &rhs,
+                      DType alpha,
+                      DType beta,
+                      Tensor<Device, 1, DType*> workspace) {
+  index_t batch_size = dst.shape_[0];
+  expr::BLASEngine<Device, DType>::SetStream(dst.stream_);
+  Shape<3> sleft = transpose_left ? Shape3(lhs.shape_[0], lhs.shape_[2], lhs.shape_[1])
+    : lhs.shape_;
+  Shape<3> sright = transpose_right ? Shape3(rhs.shape_[0], rhs.shape_[2], rhs.shape_[1])
+    : rhs.shape_;
+  CHECK_EQ(dst.CheckContiguous(), true);
+  CHECK_EQ(lhs.CheckContiguous(), true);
+  CHECK_EQ(rhs.CheckContiguous(), true);
+  CHECK(sleft[0] == batch_size && sright[0] == batch_size)
+    << "BatchGEMM: batchsize must be equal."
+    << "dst: " << dst.shape_ << "\n"
+    << "lhs: " << sleft << "\n"
+    << "rhs: " << sright << "\n";
+  CHECK(dst.size(1) == sleft[1] && dst.size(2) == sright[2] && sleft[2] == sright[1])
+    << "BatchGEMM: matrix shape mismatch"
+    << "dst: " << dst.shape_ << "\n"
+    << "lhs: " << sleft << "\n"
+    << "rhs: " << sright << "\n";
+  CHECK(workspace.size(0) >= 3 * batch_size)
+    << "Workspace Size must be bigger than " << 3 * batch_size;
+  CHECK_EQ(workspace.CheckContiguous(), true);
+  // use column major argument to compatible with most BLAS
+  expr::BLASEngine<Device, DType>::batched_gemm
+    (dst.stream_,
+    transpose_right, transpose_left,
+    transpose_right ? rhs.size(1) : rhs.size(2),
+    transpose_left ? lhs.size(2) : lhs.size(1),
+    transpose_right ? rhs.size(2) : rhs.size(1),
+    alpha,
+    rhs.dptr_, rhs.stride_,
+    lhs.dptr_, lhs.stride_,
+    beta,
+    dst.dptr_, dst.stride_, batch_size,
+    workspace.dptr_);
+}
+}  // namespace mshadow
+#endif  // MSHADOW_TENSOR_CPU_INL_H_
diff --git a/3rdparty/mshadow/mshadow/tensor_gpu-inl.h b/3rdparty/mshadow/mshadow/tensor_gpu-inl.h
new file mode 100755
index 000000000000..94fdb0527e72
--- /dev/null
+++ b/3rdparty/mshadow/mshadow/tensor_gpu-inl.h
@@ -0,0 +1,245 @@
+/*!
+ *  Copyright (c) 2014 by Contributors
+ * \file tensor_gpu-inl.h
+ * \brief implementation of GPU host code
+ * \author Bing Xu, Tianqi Chen
+ */
+#ifndef MSHADOW_TENSOR_GPU_INL_H_
+#define MSHADOW_TENSOR_GPU_INL_H_
+#include "./base.h"
+#include "./tensor.h"
+
+namespace mshadow {
+#if MSHADOW_USE_CUDA
+template<>
+inline void InitTensorEngine<gpu>(int dev_id) {
+  cudaDeviceProp prop;
+  int device_id = 0;
+  int device_count = 0;
+  cudaGetDeviceCount(&device_count);
+  CHECK_GT(device_count, 0) << "Cannot find CUDA device. Please check CUDA-Configuration";
+  if (dev_id < 0) {
+    device_id = 0;
+  } else {
+    device_id = dev_id;
+  }
+  CHECK_LT(device_id, device_count) << "Incorrect Device ID";
+  MSHADOW_CUDA_CALL(cudaSetDevice(device_id));
+  MSHADOW_CUDA_CALL(cudaGetDeviceProperties(&prop, device_id));
+}
+template<>
+inline void ShutdownTensorEngine<gpu>(void) {
+}
+template<>
+inline void SetDevice<gpu>(int devid) {
+  MSHADOW_CUDA_CALL(cudaSetDevice(devid));
+}
+template<int dim, typename DType>
+inline void AllocSpace(Tensor<gpu, dim, DType> *obj, bool pad) {
+  size_t pitch;
+  // common choice for cuda mem align unit is 32
+  if (pad && obj->size(dim - 1) >= MSHADOW_MIN_PAD_RATIO * 32) {
+    MSHADOW_CUDA_CALL(cudaMallocPitch(reinterpret_cast<void**>(&(obj->dptr_)), &pitch,
+                                      obj->size(dim - 1) * sizeof(DType),
+                                      obj->shape_.FlatTo2D()[0]));
+    obj->stride_ = static_cast<index_t>(pitch / sizeof(DType));
+  } else {
+    obj->stride_ = obj->size(dim - 1);
+    MSHADOW_CUDA_CALL(cudaMallocPitch(reinterpret_cast<void**>(&(obj->dptr_)), &pitch,
+                                      obj->shape_.Size() * sizeof(DType), 1));
+  }
+}
+template<int dim, typename DType>
+inline void FreeSpace(Tensor<gpu, dim, DType> *obj) {
+  MSHADOW_CUDA_CALL(cudaFree(obj->dptr_));
+  obj->dptr_ = NULL;
+}
+template<typename A, typename B, int dim, typename DType>
+inline void Copy(Tensor<A, dim, DType> _dst,
+                 Tensor<B, dim, DType> _src,
+                 cudaMemcpyKind kind,
+                 Stream<gpu> *stream) {
+  CHECK_EQ(_dst.shape_, _src.shape_) << "Copy:shape mismatch";
+  Tensor<A, 2, DType> dst = _dst.FlatTo2D();
+  Tensor<B, 2, DType> src = _src.FlatTo2D();
+  MSHADOW_CUDA_CALL(cudaMemcpy2DAsync(dst.dptr_, dst.stride_ * sizeof(DType),
+                                      src.dptr_, src.stride_ * sizeof(DType),
+                                      dst.size(1) * sizeof(DType),
+                                      dst.size(0), kind,
+                                      Stream<gpu>::GetStream(stream)));
+  // use synchronize call behavior for zero stream
+  if (stream == NULL) {
+    MSHADOW_CUDA_CALL(cudaStreamSynchronize(0));
+  }
+}
+template<int dim, typename DType>
+inline void Copy(Tensor<cpu, dim, DType> dst,
+                 const Tensor<gpu, dim, DType> &src,
+                 Stream<gpu> *stream) {
+  Copy(dst, src, cudaMemcpyDeviceToHost, stream);
+}
+template<int dim, typename DType>
+inline void Copy(Tensor<gpu, dim, DType> dst,
+                 const Tensor<gpu, dim, DType> &src,
+                 Stream<gpu> *stream) {
+  Copy(dst, src, cudaMemcpyDeviceToDevice, stream);
+}
+template<int dim, typename DType>
+inline void Copy(Tensor<gpu, dim, DType> dst,
+                 const Tensor<cpu, dim, DType> &src,
+                 Stream<gpu> *stream) {
+  Copy(dst, src, cudaMemcpyHostToDevice, stream);
+}
+#endif  // MSHADOW_USE_CUDA
+}  // namespace mshadow
+
+// the following part is included only if compiler is nvcc
+#ifdef __CUDACC__
+#include "./cuda/tensor_gpu-inl.cuh"
+
+namespace mshadow {
+template<typename Saver, typename R, int dim,
+         typename DType, typename E, int etype>
+inline void MapExp(TRValue<R, gpu, dim, DType> *dst,
+                   const expr::Exp<E, DType, etype> &exp) {
+  expr::TypeCheckPass<expr::TypeCheck<gpu, dim, DType, E>::kMapPass>
+      ::Error_All_Tensor_in_Exp_Must_Have_Same_Type();
+  Shape<dim> eshape = expr::ShapeCheck<dim, E>::Check(exp.self());
+  Shape<dim> dshape = expr::ShapeCheck<dim, R>::Check(dst->self());
+  CHECK(eshape[0] == 0 || eshape == dshape)
+    << "Assignment: Shape of Tensors are not consistent with target, "
+    << "eshape: " << eshape << " dshape:" << dshape;
+  cuda::MapPlan<Saver>(MakePlan(dst->self()),
+                       MakePlan(exp.self()),
+                       dshape.FlatTo2D(),
+                       Stream<gpu>::GetStream(expr::StreamInfo<gpu, R>::Get(dst->self())));
+}
+
+template<typename Saver, typename Reducer,
+         typename R, typename DType, typename E, int etype>
+inline void MapReduceKeepLowest(TRValue<R, gpu, 1, DType> *dst,
+                                const expr::Exp<E, DType, etype> &exp,
+                                DType scale) {
+  expr::TypeCheckPass<expr::TypeCheck<gpu, 1, DType, E>::kRedPass>
+      ::Error_TypeCheck_Not_Pass_For_Reduce_Exp();
+  Shape<2> eshape = expr::ShapeCheck<expr::ExpInfo<E>::kDim, E>
+      ::Check(exp.self()).FlatTo2D();
+  Shape<1> dshape = expr::ShapeCheck<1, R>::Check(dst->self());
+  CHECK_EQ(eshape[1], dshape[0]) << "MapReduceKeepLowest::reduction dimension do not match";
+  CHECK_NE(eshape[0], 0U) << "can not reduce over empty tensor";
+  cuda::MapReduceKeepLowest<Saver, Reducer>
+      (MakePlan(dst->self()), MakePlan(exp.self()), scale, eshape,
+       Stream<gpu>::GetStream(expr::StreamInfo<gpu, R>::Get(dst->self())));
+}
+
+template<typename Saver, typename Reducer, int dimkeep,
+         typename R, typename DType, typename E, int etype>
+inline void MapReduceKeepHighDim(TRValue<R, gpu, 1, DType> *dst,
+                                 const expr::Exp<E, DType, etype> &exp,
+                                 DType scale) {
+  expr::TypeCheckPass<expr::TypeCheck<gpu, dimkeep, DType, E>::kRedPass>
+      ::Error_TypeCheck_Not_Pass_For_Reduce_Exp();
+  typedef Shape<expr::ExpInfo<E>::kDim> EShape;
+  EShape eshape = expr::ShapeCheck<expr::ExpInfo<E>::kDim, E>
+      ::Check(exp.self());
+    Shape<1> dshape = expr::ShapeCheck<1, R>::Check(dst->self());
+  CHECK_EQ(eshape[dimkeep], dshape[0]) << "MapReduceKeepHighDim::reduction dimension do not match";
+  // use equvalent form
+  Shape<4> pshape = Shape4(eshape.ProdShape(0, dimkeep),
+                           eshape[dimkeep],
+                           eshape.ProdShape(dimkeep + 1, EShape::kSubdim),
+                           eshape[EShape::kSubdim]);
+  // call equavalent map red dim 2
+  cuda::MapReduceKeepDim1<Saver, Reducer>
+      (MakePlan(dst->self()), MakePlan(exp.self()), scale, pshape,
+       Stream<gpu>::GetStream(expr::StreamInfo<gpu, R>::Get(dst->self())));
+}
+template<typename DType>
+inline void Softmax(Tensor<gpu, 2, DType> dst,
+                    const Tensor<gpu, 2, DType>& src) {
+  cuda::Softmax(dst, src);
+}
+
+template<typename DType>
+inline void Softmax(Tensor<gpu, 3, DType> dst,
+                    const Tensor<gpu, 3, DType>& src) {
+  cuda::Softmax(dst, src);
+}
+
+template<typename DType>
+inline void SoftmaxGrad(const Tensor<gpu, 2, DType> &dst,
+                        const Tensor<gpu, 2, DType> &src,
+                        const Tensor<gpu, 1, DType> &label) {
+  cuda::SoftmaxGrad(dst, src, label);
+}
+
+template<typename DType>
+inline void SmoothSoftmaxGrad(const Tensor<gpu, 2, DType> &dst,
+                              const Tensor<gpu, 2, DType> &src,
+                              const Tensor<gpu, 1, DType> &label,
+                              const float alpha) {
+  cuda::SmoothSoftmaxGrad(dst, src, label, alpha);
+}
+
+template<typename DType>
+inline void SoftmaxGrad(const Tensor<gpu, 2, DType> &dst,
+                        const Tensor<gpu, 2, DType> &src,
+                        const Tensor<gpu, 1, DType> &label,
+                        const DType &ignore_label) {
+  cuda::SoftmaxGrad(dst, src, label, ignore_label);
+}
+
+template<typename DType>
+inline void SmoothSoftmaxGrad(const Tensor<gpu, 2, DType> &dst,
+                              const Tensor<gpu, 2, DType> &src,
+                              const Tensor<gpu, 1, DType> &label,
+                              const DType &ignore_label,
+                              const float alpha) {
+  cuda::SmoothSoftmaxGrad(dst, src, label, ignore_label, alpha);
+}
+
+template<typename DType>
+inline void SoftmaxGrad(const Tensor<gpu, 3, DType> &dst,
+                        const Tensor<gpu, 3, DType> &src,
+                        const Tensor<gpu, 2, DType> &label) {
+  cuda::SoftmaxGrad(dst, src, label);
+}
+
+template<typename DType>
+inline void SoftmaxGrad(const Tensor<gpu, 3, DType> &dst,
+                        const Tensor<gpu, 3, DType> &src,
+                        const Tensor<gpu, 2, DType> &label,
+                        const DType &ignore_label) {
+  cuda::SoftmaxGrad(dst, src, label, ignore_label);
+}
+
+template<typename IndexType, typename DType>
+inline void AddTakeGrad(Tensor<gpu, 2, DType> dst,
+                        const Tensor<gpu, 1, IndexType>& index,
+                        const Tensor<gpu, 2, DType> &src) {
+  cuda::AddTakeGrad(dst, index, src);
+}
+
+template<typename IndexType, typename DType>
+inline void AddTakeGradLargeBatch(Tensor<gpu, 2, DType> dst,
+                                  const Tensor<gpu, 1, IndexType>& sorted,
+                                  const Tensor<gpu, 1, IndexType>& index,
+                                  const Tensor<gpu, 2, DType> &src) {
+  cuda::AddTakeGradLargeBatch(dst, sorted, index, src);
+}
+
+template<typename KDType, typename VDType>
+inline void SortByKey(Tensor<gpu, 1, KDType> keys, Tensor<gpu, 1, VDType> values,
+                      bool is_ascend) {
+  cuda::SortByKey(keys, values, is_ascend);
+}
+
+template<typename IndexType, typename DType>
+inline void IndexFill(Tensor<gpu, 2, DType> dst,
+                      const Tensor<gpu, 1, IndexType>& index,
+                      const Tensor<gpu, 2, DType> &src) {
+  cuda::IndexFill(dst, index, src);
+}
+}  // namespace mshadow
+#endif  // __CUDACC__
+#endif  // MSHADOW_TENSOR_GPU_INL_H_
diff --git a/3rdparty/mshadow/scripts/travis_script.sh b/3rdparty/mshadow/scripts/travis_script.sh
new file mode 100755
index 000000000000..c0220eb6c589
--- /dev/null
+++ b/3rdparty/mshadow/scripts/travis_script.sh
@@ -0,0 +1,19 @@
+# main script of travis
+if [ ${TASK} == "lint" ]; then
+    python3 dmlc-core/scripts/lint.py mshadow all mshadow mshadow-ps || exit -1
+fi
+
+if [ ${TASK} == "doc" ]; then
+    doxygen doc/Doxyfile 2>log.txt
+    (cat log.txt| grep -v ENABLE_PREPROCESSING |grep -v "unsupported tag" |grep nothing) && exit -1
+fi
+
+if [ ${TASK} == "build" ]; then
+    cd guide
+    echo "USE_BLAS=blas" >> config.mk
+    make all || exit -1
+    cd mshadow-ps
+    echo "USE_BLAS=blas" >> config.mk
+    echo "USE_RABIT_PS=0" >> config.mk    
+    make local_sum.cpu || exit -1
+fi
diff --git a/3rdparty/mshadow/test/Makefile b/3rdparty/mshadow/test/Makefile
new file mode 100644
index 000000000000..dc2d0552deb4
--- /dev/null
+++ b/3rdparty/mshadow/test/Makefile
@@ -0,0 +1,35 @@
+# set LD_LIBRARY_PATH
+export CC  = gcc
+export CXX = g++
+export NVCC =nvcc
+export CFLAGS = -Wall -O3 -g -msse3 -Wno-unknown-pragmas -funroll-loops -I../
+export LDFLAGS= -g -lm -lcublas -lcudart -lcusolver
+export NVCCFLAGS = -O3 --use_fast_math -ccbin $(CXX)
+
+# specify tensor path
+BIN = test_tblob
+OBJ =
+CUOBJ =
+CUBIN = test
+.PHONY: clean all
+
+all: $(CUBIN) $(BIN)
+
+test: test.cu
+
+test_tblob: test_tblob.cc
+
+$(BIN) :
+	$(CXX) $(CFLAGS) -std=c++0x -o $@ $(filter %.cpp %.o %.c %.cc, $^)  $(LDFLAGS)
+
+$(OBJ) :
+	$(CXX) -c $(CFLAGS) -o $@ $(firstword $(filter %.cpp %.c, $^) )
+
+$(CUOBJ) :
+	$(NVCC) -c -o $@ $(NVCCFLAGS) -Xcompiler "$(CFLAGS)" $(filter %.cu, $^)
+
+$(CUBIN) :
+	$(NVCC) -o $@ $(NVCCFLAGS) -Xcompiler "$(CFLAGS)" -Xlinker "$(LDFLAGS)" $(filter %.cu %.cpp %.o, $^)
+
+clean:
+	$(RM) $(OBJ) $(BIN) $(CUBIN) $(CUOBJ) *~
diff --git a/3rdparty/mshadow/test/pairtest.cu b/3rdparty/mshadow/test/pairtest.cu
new file mode 100644
index 000000000000..56b0380747a7
--- /dev/null
+++ b/3rdparty/mshadow/test/pairtest.cu
@@ -0,0 +1,105 @@
+#include "mshadow/tensor.h"
+#include "old/tensor.h"
+#include "assert.h"
+#include <cstring>
+
+using mshadow::index_t;
+template<typename T>
+void Print(T const & ist, int I, int J) {
+  for (int i = 0; i < I; ++i) {
+    for (int j = 0; j < J; ++j) {
+      printf("%.2f ", ist[i][j]);
+    }
+    printf("\n");
+  }
+}
+
+bool Check(mshadow::TensorContainer<mshadow::cpu, 2, float> &mct, \
+           Xmshadow::TensorContainer<Xmshadow::cpu, 2> &xct) {
+  for (index_t i = 0; i < mct.size(0); ++i) {
+    for (index_t j = 0; j < mct.size(1); ++j) {
+      assert(mct[i][j] == xct[i][j]);
+    }
+  }
+  return true;
+}
+
+template<typename xpua, typename xpub>
+void RunTask() {
+  const int X = 6;
+  const int K = 2;
+  const int O = (X - K) / 2 + 1;
+  mshadow::TensorContainer<mshadow::cpu, 4, float> srcm(mshadow::Shape4(1,1,X, X));
+  Xmshadow::TensorContainer<Xmshadow::cpu, 4> srcx(Xmshadow::Shape4(1,1,X, X));
+  for (int i = 0; i < X; ++i) {
+    for (int j = 0; j < X; ++j) {
+      srcm[0][0][i][j] = i * 0.1f + j * 0.1f;
+      srcx[0][0][i][j] = i * 0.1f + j * 0.1f;
+    }
+  }
+  printf("Source:\n");
+  Print(srcm[0][0], X, X);
+  printf("\n");
+  mshadow::TensorContainer<xpua, 4, float> mct(mshadow::Shape4(1,1,X, X));
+  Xmshadow::TensorContainer<xpub, 4> xct(Xmshadow::Shape4(1,1,X, X));
+  mshadow::Copy(mct, srcm);
+  Xmshadow::Copy(xct, srcx);
+
+  
+  mshadow::TensorContainer<xpua, 4, float> pool_ct(mshadow::Shape4(1,1, O, O));
+  Xmshadow::TensorContainer<xpub, 4> pool_xct(Xmshadow::Shape4(1,1,O,O));
+
+  pool_ct = mshadow::expr::pool<mshadow::red::maximum>(mct, K, K, K);
+  pool_xct = Xmshadow::expr::pool<Xmshadow::red::maximum>(xct, K, K);
+
+  printf("New pool:\n");
+  Print(pool_ct[0][0], O, O);
+  printf("\nOld pool:\n");
+  Print(pool_xct[0][0], O, O);
+  printf("\n");
+  mshadow::TensorContainer<mshadow::cpu, 4, float> gpool_src(mshadow::Shape4(1,1, O, O));
+  Xmshadow::TensorContainer<Xmshadow::cpu, 4> gpool_xsrc(Xmshadow::Shape4(1,1,O,O));
+  for (int i = 0; i < O; ++i) {
+    for (int j = 0; j < O; ++j) {
+      gpool_src[0][0][i][j] = 0.1f;
+      gpool_xsrc[0][0][i][j] = 0.1f;
+    }
+  }
+  mshadow::TensorContainer<xpua, 4, float> gpool_ct(mshadow::Shape4(1,1, O, O));
+  Xmshadow::TensorContainer<xpub, 4> gpool_xct(Xmshadow::Shape4(1,1,O,O));
+  mshadow::Copy(gpool_ct, gpool_src);
+  Xmshadow::Copy(gpool_xct, gpool_xsrc);
+
+  mshadow::TensorContainer<xpua, 4, float> mout(mshadow::Shape4(1,1,X, X));
+  Xmshadow::TensorContainer<xpub, 4> xout(Xmshadow::Shape4(1,1,X, X));
+
+  mout = mshadow::expr::unpool<mshadow::red::maximum>(mct, pool_ct, gpool_ct, K, K, K);
+  xout = Xmshadow::expr::unpool<Xmshadow::red::maximum>(xct, pool_xct, gpool_xct, K, K);
+
+  mshadow::Copy(srcm, mout);
+  Xmshadow::Copy(srcx, xout);
+
+  mshadow::TensorContainer<mshadow::cpu, 2> l1(mshadow::Shape2(X,X));
+  Xmshadow::TensorContainer<Xmshadow::cpu, 2> l2(Xmshadow::Shape2(X, X));
+  l1 = mshadow::expr::reshape(srcm, l1.shape_);
+  l2 = Xmshadow::expr::reshape(srcx, l2.shape);
+  printf("New unpool\n");
+  Print(l1, l1.size(0), l1.size(1));
+  printf("\nOld unpool\n");
+  Print(l2, X, X);
+  if (Check(l1, l2)) {
+    printf("Pass\n");
+  }
+}
+
+int main(int argc, char** argv) {
+  if (argc < 1) {
+    printf("Usage: dev\n");
+    exit(-1);
+  }
+  if (!strcmp(argv[1], "cpu")) {
+    RunTask<mshadow::cpu, Xmshadow::cpu>();
+  } else {
+    RunTask<mshadow::gpu, Xmshadow::gpu>();
+  }
+}
\ No newline at end of file
diff --git a/3rdparty/mshadow/test/pool.cu b/3rdparty/mshadow/test/pool.cu
new file mode 100644
index 000000000000..9641d53c9c45
--- /dev/null
+++ b/3rdparty/mshadow/test/pool.cu
@@ -0,0 +1,69 @@
+#include "mshadow/tensor.h"
+#include "old/tensor.h"
+#include "assert.h"
+#include <cstring>
+
+using mshadow::index_t;
+template<typename T>
+void Print(T const & ist) {
+  for (int i = 0; i < ist.size(0); ++i) {
+    for (int j = 0; j < ist.size(1); ++j) {
+      printf("%.2f ", ist[i][j]);
+    }
+    printf("\n");
+  }
+}
+
+bool Check(mshadow::TensorContainer<mshadow::cpu, 2, float> &mct, \
+           Xmshadow::TensorContainer<Xmshadow::cpu, 2> &xct) {
+  for (index_t i = 0; i < mct.size(0); ++i) {
+    for (index_t j = 0; j < mct.size(1); ++j) {
+      assert(mct[i][j] == xct[i][j]);
+    }
+  }
+  return true;
+}
+
+template<typename xpua, typename xpub>
+void RunTask() {
+  const int X = 6;
+  const int K = 2;
+  mshadow::TensorContainer<mshadow::cpu, 2, float> srcm(mshadow::Shape2(X, X));
+  Xmshadow::TensorContainer<Xmshadow::cpu, 2> srcx(Xmshadow::Shape2(X, X));
+  
+  mshadow::TensorContainer<xpua, 2, float> mct(mshadow::Shape2(X, X));
+  Xmshadow::TensorContainer<xpub, 2> xct(Xmshadow::Shape2(X, X));
+  for (int i = 0; i < X; ++i) {
+    for (int j = 0; j < X; ++j) {
+      srcm[i][j] = i * 0.1f + j * 0.1f;
+      srcx[i][j] = i * 0.1f + j * 0.1f;
+    }
+  }
+  mshadow::Copy(mct, srcm);
+  Xmshadow::Copy(xct, srcx);
+  mshadow::TensorContainer<xpua, 2, float> pool_ct(mshadow::Shape2((X-K)/2+1, (X-K)/2+1));
+  Xmshadow::TensorContainer<xpub, 2> pool_xct(Xmshadow::Shape2((X-K)/2+1, (X-K)/2+1));
+
+  pool_ct = mshadow::expr::pool<mshadow::red::maximum>(mct, K, K, K);
+  pool_xct = Xmshadow::expr::pool<Xmshadow::red::maximum>(xct, K, K);
+
+  mshadow::TensorContainer<mshadow::cpu, 2, float> cpool_ct(mshadow::Shape2((X-K)/2+1, (X-K)/2+1));
+  Xmshadow::TensorContainer<Xmshadow::cpu, 2> cpool_xct(Xmshadow::Shape2((X-K)/2+1, (X-K)/2+1));
+  mshadow::Copy(cpool_ct, pool_ct);
+  Xmshadow::Copy(cpool_xct, pool_xct);
+  if (Check(cpool_ct, cpool_xct)) {
+    printf("Pass\n");
+  }
+}
+
+int main(int argc, char** argv) {
+  if (argc < 2) {
+    printf("Usage: dev\n");
+    exit(-1);
+  }
+  if (!strcmp(argv[1], "cpu")) {
+    RunTask<mshadow::cpu, Xmshadow::cpu>();
+  } else {
+    RunTask<mshadow::gpu, Xmshadow::gpu>();
+  }
+}
\ No newline at end of file
diff --git a/3rdparty/mshadow/test/reshape.cu b/3rdparty/mshadow/test/reshape.cu
new file mode 100644
index 000000000000..c1ad52e07c40
--- /dev/null
+++ b/3rdparty/mshadow/test/reshape.cu
@@ -0,0 +1,74 @@
+#include "mshadow/tensor.h"
+#include "old/tensor.h"
+#include "assert.h"
+#include <cstring>
+
+using mshadow::index_t;
+template<typename T>
+void Print(T const & ist) {
+  for (int i = 0; i < ist.size(0); ++i) {
+    for (int j = 0; j < ist.size(1); ++j) {
+      printf("%.2f ", ist[i][j]);
+    }
+    printf("\n");
+  }
+}
+
+bool Check(mshadow::TensorContainer<mshadow::cpu, 2, float> &mct, \
+           Xmshadow::TensorContainer<Xmshadow::cpu, 2> &xct) {
+  for (index_t i = 0; i < mct.size(0); ++i) {
+    for (index_t j = 0; j < mct.size(1); ++j) {
+      assert(mct[i][j] == xct[i][j]);
+    }
+  }
+  return true;
+}
+
+template<typename xpua, typename xpub>
+void RunTask() {
+  const int X = 6;
+  const int K = 2;
+  mshadow::TensorContainer<mshadow::cpu, 2, float> srcm(mshadow::Shape2(X, X));
+  Xmshadow::TensorContainer<Xmshadow::cpu, 2> srcx(Xmshadow::Shape2(X, X));
+  
+  mshadow::TensorContainer<xpua, 2, float> mct(mshadow::Shape2(X, X));
+  Xmshadow::TensorContainer<xpub, 2> xct(Xmshadow::Shape2(X, X));
+  for (int i = 0; i < X; ++i) {
+    for (int j = 0; j < X; ++j) {
+      srcm[i][j] = i * 0.1f + j * 0.1f;
+      srcx[i][j] = i * 0.1f + j * 0.1f;
+    }
+  }
+  mshadow::Copy(mct, srcm);
+  Xmshadow::Copy(xct, srcx);
+
+  mshadow::TensorContainer<xpua, 4, float> mct4d(mshadow::Shape4(1, 1, X / K, X * K));
+  Xmshadow::TensorContainer<xpub, 4> xct4d(Xmshadow::Shape4(X / K, X * K, 1, 1));
+  
+  mct4d = mshadow::expr::reshape(mct, mct4d.shape_);
+  xct4d = Xmshadow::expr::reshape(xct, xct4d.shape);
+  
+  mct = mshadow::expr::reshape(mct4d, mct.shape_);
+  xct = Xmshadow::expr::reshape(xct4d, xct.shape);
+  
+  mshadow::TensorContainer<mshadow::cpu, 2, float> m_ct(mshadow::Shape2(X, X));
+  Xmshadow::TensorContainer<Xmshadow::cpu, 2> x_ct(Xmshadow::Shape2(X, X));
+  
+  mshadow::Copy(m_ct, mct);
+  Xmshadow::Copy(x_ct, xct);
+  if (Check(m_ct, x_ct)) {
+    printf("Pass\n");
+  }
+}
+
+int main(int argc, char** argv) {
+  if (argc < 2) {
+    printf("Usage: dev\n");
+    exit(-1);
+  }
+  if (!strcmp(argv[1], "cpu")) {
+    RunTask<mshadow::cpu, Xmshadow::cpu>();
+  } else {
+    RunTask<mshadow::gpu, Xmshadow::gpu>();
+  }
+}
\ No newline at end of file
diff --git a/3rdparty/mshadow/test/test.cu b/3rdparty/mshadow/test/test.cu
new file mode 100644
index 000000000000..13f26dcfd6ea
--- /dev/null
+++ b/3rdparty/mshadow/test/test.cu
@@ -0,0 +1,79 @@
+#include "test.h"
+
+using namespace mshadow;
+
+
+int main() {
+  InitTensorEngine<cpu>();
+  InitTensorEngine<gpu>();
+  Tensor<cpu, 3, float> tc = NewTensor<cpu, float>(Shape3(3, 2, 4), 0.0f);
+  Tensor<gpu, 3, float> tg = NewTensor<gpu, float>(tc.shape_, 0.0f);
+  // init
+  for (index_t i = 0; i < tc.size(0); ++i) {
+    for (index_t j = 0; j < tc.size(1); ++j) {
+      for (index_t k = 0; k < tc.size(2); ++k) {
+        tc[i][j][k] = i * 0.1f + j * 0.2f + k * 0.1f;
+      }
+    }
+  }
+  Copy(tg, tc);
+  // print
+  printf("\n#print batch 0 of cpu tensor:\n");
+  Print2DTensor(tc[0]);
+  printf("\n");
+  Print2DTensor(tc[1]);
+  printf("\n");
+  Print2DTensor(tc[2]);
+  // check
+  if (Check2DTensor(tg[1], tc[1])) {
+    printf("batch 1 of gpu & cpu tensor are same.\n");
+  }
+  // sum of row
+  Tensor<cpu, 1, float> tmp_tc = NewTensor<cpu, float>(Shape1(tc[0].size(1)), 0.0f);
+  Tensor<gpu, 1, float> tmp_tg = NewTensor<gpu, float>(Shape1(tg[0].size(1)), 0.0f);
+  printf("\n#sum_rows of batch 0:\n");
+  tmp_tc = sum_rows(tc[0]);
+  tmp_tg = sum_rows(tg[0]);
+  Print1DTensor(tmp_tc);
+  if (Check1DTensor(tmp_tg, tmp_tc)) {
+    printf("cpu & gpu result consists\n");
+  }
+  FreeSpace(&tmp_tc);
+  FreeSpace(&tmp_tg);
+  // sumall_except_dim
+  printf("\n#sumall_except_dim<0> of batch 0:\n");
+  Tensor<cpu, 1, float> red_tc = NewTensor<cpu, float>(Shape1(tc.size(0)), 0.0f);
+  Tensor<gpu, 1, float> red_tg = NewTensor<gpu, float>(Shape1(tg.size(0)), 0.0f);
+  red_tc = sumall_except_dim<0>(tc);
+  red_tg = sumall_except_dim<0>(tg);
+  Print1DTensor(red_tc);
+  if (Check1DTensor(red_tg, red_tc)) {
+    printf("cpu & gpu result consists\n");
+  }
+  FreeSpace(&red_tc);
+  FreeSpace(&red_tg);
+  // softmax
+  printf("\n#Softmax\n");
+  Tensor<cpu, 2, float> sm_tc = NewTensor<cpu, float>(tc[0].shape_, 0.0f);
+  Tensor<gpu, 2, float> sm_tg = NewTensor<gpu, float>(tg[0].shape_, 0.0f);
+  Softmax(sm_tc, tc[0]);
+  Softmax(sm_tg, tg[0]);
+  if (Check2DTensor(sm_tg, sm_tc)) {
+    printf("cpu & gpu result consists\n");
+  }
+  // mirror
+  printf("\n#mirror\n");
+  sm_tc = mirror(tc[0]);
+  sm_tg = mirror(tg[0]);
+  if (Check2DTensor(sm_tg, sm_tc)) {
+    printf("cpu & gpu result consists\n");
+  }
+  FreeSpace(&sm_tc);
+  FreeSpace(&sm_tg);
+  // reshape
+  
+  FreeSpace(&tc);
+  FreeSpace(&tg);
+  ShutdownTensorEngine<cpu>();
+  ShutdownTensorEngine<gpu>();
+}
diff --git a/3rdparty/mshadow/test/test.h b/3rdparty/mshadow/test/test.h
new file mode 100644
index 000000000000..2cfc515957ca
--- /dev/null
+++ b/3rdparty/mshadow/test/test.h
@@ -0,0 +1,67 @@
+#ifndef TEST_H
+#define TEST_H
+
+#include "mshadow/tensor.h"
+#include "assert.h"
+
+#define EPS 0.0001
+using namespace mshadow;
+using namespace mshadow::expr;
+
+
+template<typename xpu>
+void Print2DTensor(Tensor<xpu, 2, float> const &ts);
+
+template<typename xpu>
+void Print1DTensor(Tensor<xpu, 1, float> const &ts);
+
+template<>
+void Print1DTensor(Tensor<cpu, 1, float> const &ts) {
+  for (index_t i = 0; i < ts.size(0); ++i) {
+    printf("%.2f ", ts[i]);
+  }
+  printf("\n");
+}
+
+
+template<>
+void Print2DTensor(Tensor<cpu, 2, float> const &ts) {
+  for (index_t i = 0; i < ts.size(0); ++i) {
+    Print1DTensor(ts[i]);
+  }
+}
+
+template<>
+void Print2DTensor(Tensor<gpu, 2, float> const &tg) {
+  Tensor<cpu, 2, float> tc = NewTensor<cpu, float>(tg.shape_, 0.0f);
+  Copy(tc, tg);
+  Print2DTensor(tc);
+  FreeSpace(&tc);
+}
+
+
+
+bool Check2DTensor(Tensor<gpu, 2, float> const &tg, Tensor<cpu, 2, float> const &tc) {
+  Tensor<cpu, 2, float> tcc = NewTensor<cpu, float>(tg.shape_, 0.0f);
+  Copy(tcc, tg);
+  for (index_t i = 0; i < tc.size(0); ++i) {
+    for (index_t j = 0; j < tc.size(1); ++j) {
+      assert(abs(tcc[i][j] - tc[i][j]) < EPS);
+    }
+  }
+  FreeSpace(&tcc);
+  return true;
+}
+
+bool Check1DTensor(Tensor<gpu, 1, float> const &tg, Tensor<cpu, 1, float> const &tc) {
+  Tensor<cpu, 1, float> tcc = NewTensor<cpu, float>(tc.shape_, 0.0f);
+  Copy(tcc, tg);
+  printf("gpu result:\n");
+  Print1DTensor(tcc);
+  for (index_t i = 0; i < tc.size(0); ++i) {
+    assert(abs(tcc[i] - tc[i]) < EPS);
+  }
+  FreeSpace(&tcc);
+  return true;
+}
+#endif
diff --git a/3rdparty/mshadow/test/unpack.cu b/3rdparty/mshadow/test/unpack.cu
new file mode 100644
index 000000000000..dd0c2b9c5821
--- /dev/null
+++ b/3rdparty/mshadow/test/unpack.cu
@@ -0,0 +1,85 @@
+#include "mshadow/tensor.h"
+#include "old/tensor.h"
+#include "assert.h"
+#include <cstring>
+
+using mshadow::index_t;
+template<typename T>
+void Print(T const & ist) {
+  for (int i = 0; i < ist.size(0); ++i) {
+    for (int j = 0; j < ist.size(1); ++j) {
+      printf("%.2f ", ist[i][j]);
+    }
+    printf("\n");
+  }
+}
+
+bool Check(mshadow::TensorContainer<mshadow::cpu, 2, float> &mct, \
+           Xmshadow::TensorContainer<Xmshadow::cpu, 2> &xct) {
+  for (index_t i = 0; i < mct.size(0); ++i) {
+    for (index_t j = 0; j < mct.size(1); ++j) {
+      assert(mct[i][j] == xct[i][j]);
+    }
+  }
+  return true;
+}
+
+template<typename xpua, typename xpub>
+void RunTask() {
+  const int ksize = 3;
+  const int kstride = 2;
+  const int X = 6;
+  Xmshadow::TensorContainer<Xmshadow::cpu, 4> xsrc(Xmshadow::Shape4(1, 1, X, X));
+  mshadow::TensorContainer<mshadow::cpu, 4> src(mshadow::Shape4(1, 1, X, X));
+
+  for (int i = 0; i < X; ++i) {
+    for (int j = 0; j < X; ++j) {
+      xsrc[0][0][i][j] = i * 0.1f + j * 0.2f;
+      src[0][0][i][j] = i * 0.1f + j * 0.2f;
+    }
+  }
+  Xmshadow::TensorContainer<xpub, 4> xin(Xmshadow::Shape4(1, 1, X, X));
+  mshadow::TensorContainer<xpua, 4> in(mshadow::Shape4(1, 1, X, X));
+
+  mshadow::Copy(in, src);
+  Xmshadow::Copy(xin, xsrc);
+
+  Xmshadow::TensorContainer<xpub, 2> xtmp_col;
+  mshadow::TensorContainer<xpua, 2> tmp_col;
+  
+
+  index_t oheight  = (in.size(2) - ksize)/kstride + 1;
+  index_t owidth   = (in.size(3) - ksize)/kstride + 1;
+  index_t nbatch   = in.size(0);
+
+  
+  xtmp_col.Resize( Xmshadow::Shape2( xin.shape[2]*ksize*ksize, nbatch*oheight*owidth ) );
+  tmp_col.Resize(mshadow::Shape2(in.size(1)*ksize*ksize, nbatch*oheight*owidth));
+  xtmp_col = Xmshadow::expr::unpack_patch2col( xin, ksize, kstride );
+  tmp_col = mshadow::expr::unpack_patch2col(in, ksize, ksize, kstride);
+
+  Xmshadow::TensorContainer<Xmshadow::cpu, 2> xtc;
+  mshadow::TensorContainer<mshadow::cpu, 2> tc;
+
+  xtc.Resize( Xmshadow::Shape2( xin.shape[2]*ksize*ksize, nbatch*oheight*owidth ) );
+  tc.Resize(mshadow::Shape2(in.size(1)*ksize*ksize, nbatch*oheight*owidth));
+
+  mshadow::Copy(tc, tmp_col);
+  Xmshadow::Copy(xtc, xtmp_col);
+  if (Check(tc, xtc)) {
+    printf("Pass\n");
+  }
+  
+}
+
+int main(int argc, char** argv) {
+  if (argc < 2) {
+    printf("Usage: dev\n");
+    exit(-1);
+  }
+  if (!strcmp(argv[1], "cpu")) {
+    RunTask<mshadow::cpu, Xmshadow::cpu>();
+  } else {
+    RunTask<mshadow::gpu, Xmshadow::gpu>();
+  }
+}
\ No newline at end of file

From b07211f41b6e8a0e967d2170500be7e508ff3c2a Mon Sep 17 00:00:00 2001
From: Zhaoqi Zhu <zhaoqizh@usc.edu>
Date: Thu, 1 Aug 2019 20:29:22 -0700
Subject: [PATCH 139/813] Skip Flaky Test (#15722)

* Update test_profiler.py

* retrigger tests
---
 tests/python/unittest/test_profiler.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tests/python/unittest/test_profiler.py b/tests/python/unittest/test_profiler.py
index 33f59845557b..d04f390f219b 100644
--- a/tests/python/unittest/test_profiler.py
+++ b/tests/python/unittest/test_profiler.py
@@ -433,6 +433,7 @@ def test_custom_operator_profiling_multiple_custom_ops_imperative():
     custom_operator_profiling_multiple_custom_ops(None, 'imperative', \
             'test_custom_operator_profiling_multiple_custom_ops_imperative.json')
 
+@unittest.skip("Flaky test https://github.com/apache/incubator-mxnet/issues/15406")
 def test_custom_operator_profiling_naive_engine():
     # run the three tests above using Naive Engine
     run_in_spawned_process(test_custom_operator_profiling, \

From cf28b46ecb2342e3010f9ed1c6b17ee3533246f9 Mon Sep 17 00:00:00 2001
From: kshitij12345 <kshitijkalambarkar@gmail.com>
Date: Fri, 2 Aug 2019 10:23:09 +0530
Subject: [PATCH 140/813] Add magic method `abs` to NDArray and Symbol.
 (#15680)

* add magic method abs to ndarray

* add relevant tests

* add magic method abs to symbol

* add relevant tests

* retrigger CI

* retrigger CI
---
 python/mxnet/ndarray/ndarray.py       |  4 ++++
 python/mxnet/symbol/symbol.py         |  4 ++++
 tests/python/unittest/test_ndarray.py |  9 +++++++++
 tests/python/unittest/test_symbol.py  | 17 ++++++++++++++++-
 4 files changed, 33 insertions(+), 1 deletion(-)

diff --git a/python/mxnet/ndarray/ndarray.py b/python/mxnet/ndarray/ndarray.py
index 3d8a7aa98c94..0b7dca4ebba4 100644
--- a/python/mxnet/ndarray/ndarray.py
+++ b/python/mxnet/ndarray/ndarray.py
@@ -205,6 +205,10 @@ def _to_shared_mem(self):
             self.handle, ctypes.byref(shared_pid), ctypes.byref(shared_id)))
         return shared_pid.value, shared_id.value, self.shape, self.dtype
 
+    def __abs__(self):
+        """x.__abs__() <=> abs(x) <=> x.abs() <=> mx.nd.abs(x, y)"""
+        return self.abs()
+
     def __add__(self, other):
         """x.__add__(y) <=> x+y <=> mx.nd.add(x, y) """
         return add(self, other)
diff --git a/python/mxnet/symbol/symbol.py b/python/mxnet/symbol/symbol.py
index 1e2defab3713..68322297c8bb 100644
--- a/python/mxnet/symbol/symbol.py
+++ b/python/mxnet/symbol/symbol.py
@@ -93,6 +93,10 @@ def __iter__(self):
         """
         return (self[i] for i in range(len(self)))
 
+    def __abs__(self):
+        """x.__abs__() <=> abs(x) <=> x.abs() <=> mx.symbol.abs(x, y)"""
+        return self.abs()
+
     def __add__(self, other):
         """x.__add__(y) <=> x+y
 
diff --git a/tests/python/unittest/test_ndarray.py b/tests/python/unittest/test_ndarray.py
index 56db1ebd640d..0f154bd67a1a 100644
--- a/tests/python/unittest/test_ndarray.py
+++ b/tests/python/unittest/test_ndarray.py
@@ -172,6 +172,15 @@ def test_ndarray_negate():
     assert_almost_equal(npy, arr.asnumpy())
 
 
+@with_seed()
+def test_ndarray_magic_abs():
+    for dim in range(1, 7):
+        shape = rand_shape_nd(dim)
+        npy = np.random.uniform(-10, 10, shape)
+        arr = mx.nd.array(npy)
+        assert_almost_equal(abs(arr).asnumpy(), arr.abs().asnumpy())
+
+
 @with_seed()
 def test_ndarray_reshape():
     tensor = (mx.nd.arange(30) + 1).reshape(2, 3, 5)
diff --git a/tests/python/unittest/test_symbol.py b/tests/python/unittest/test_symbol.py
index 0c97c68b0880..963b32493b44 100644
--- a/tests/python/unittest/test_symbol.py
+++ b/tests/python/unittest/test_symbol.py
@@ -22,7 +22,7 @@
 import numpy as np
 from common import assertRaises, models
 from mxnet.base import NotImplementedForSymbol
-from mxnet.test_utils import discard_stderr
+from mxnet.test_utils import discard_stderr, rand_shape_nd
 import pickle as pkl
 
 def test_symbol_basic():
@@ -188,6 +188,21 @@ def test_symbol_infer_shape_var():
     assert arg_shapes[1] == overwrite_shape
     assert out_shapes[0] == overwrite_shape
 
+
+def test_symbol_magic_abs():
+    for dim in range(1, 7):
+        with mx.name.NameManager():
+            data = mx.symbol.Variable('data')
+            method = data.abs(name='abs0')
+            magic = abs(data)
+            regular = mx.symbol.abs(data, name='abs0')
+            ctx = {'ctx': mx.context.current_context(), 'data': rand_shape_nd(dim)}
+            mx.test_utils.check_consistency(
+                [method, magic], ctx_list=[ctx, ctx])
+            mx.test_utils.check_consistency(
+                [regular, magic], ctx_list=[ctx, ctx])
+
+
 def test_symbol_fluent():
     has_grad = set(['flatten', 'expand_dims', 'flip', 'tile', 'transpose', 'sum', 'nansum', 'prod',
                     'nanprod', 'mean', 'max', 'min', 'reshape', 'broadcast_to', 'split',

From 87425d2adf22ca4bb7bbd19016ee0a8d9bafdcb2 Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Thu, 1 Aug 2019 22:12:00 -0700
Subject: [PATCH 141/813] fix boolean_mask for 0-size output (#15731)

---
 include/mxnet/ndarray.h                |  6 +-----
 src/imperative/imperative.cc           |  4 +++-
 src/ndarray/ndarray.cc                 |  7 +++++++
 src/operator/contrib/boolean_mask.cc   |  1 +
 src/operator/contrib/boolean_mask.cu   | 15 +++++++++------
 tests/python/unittest/test_operator.py | 15 +++++++++++++++
 6 files changed, 36 insertions(+), 12 deletions(-)

diff --git a/include/mxnet/ndarray.h b/include/mxnet/ndarray.h
index 428245b56d0e..176aa0aaa197 100644
--- a/include/mxnet/ndarray.h
+++ b/include/mxnet/ndarray.h
@@ -190,11 +190,7 @@ class NDArray {
   /*!
    * \brief set the correct shape of NDArray directly from the storage_shape of its own chunk.
    */
-  void SetShapeFromChunk() {
-    if (!(ptr_->storage_shape.ndim() == 1 && ptr_->storage_shape[0] == 0)) {
-      shape_ = ptr_->storage_shape;
-    }
-  }
+  void SetShapeFromChunk();
   /*
    * This indicates whether an array is a view of another array (created by
    * reshape or slice). If an array is a view and the data is stored in
diff --git a/src/imperative/imperative.cc b/src/imperative/imperative.cc
index e2c0c9d4c9d4..c00021c44d1d 100644
--- a/src/imperative/imperative.cc
+++ b/src/imperative/imperative.cc
@@ -313,7 +313,9 @@ std::vector<NDArray*> Imperative::Backward(
     } else {
       info.outputs.emplace_back(outputs[i]->shape(), outputs[i]->ctx(),
                                 true, outputs[i]->dtype());
-      info.outputs.back() = static_cast<real_t>(1.0);
+      if (info.outputs.back().shape().Size() != 0) {
+        info.outputs.back() = static_cast<real_t>(1.0);
+      }
     }
   }
 
diff --git a/src/ndarray/ndarray.cc b/src/ndarray/ndarray.cc
index bee8bef37b44..37c32c09cebb 100644
--- a/src/ndarray/ndarray.cc
+++ b/src/ndarray/ndarray.cc
@@ -96,6 +96,13 @@ NDArray::NDArray(const NDArrayStorageType stype, const mxnet::TShape &shape, Con
         dtype, aux_types, aux_shapes);
 }
 
+void NDArray::SetShapeFromChunk() {
+  if (Imperative::Get()->is_np_shape() ||
+      !(ptr_->storage_shape.ndim() == 1 && ptr_->storage_shape[0] == 0)) {
+    shape_ = ptr_->storage_shape;
+  }
+}
+
 struct ChunkMem {
   Storage::Handle h;
   std::vector<Storage::Handle> aux_h;
diff --git a/src/operator/contrib/boolean_mask.cc b/src/operator/contrib/boolean_mask.cc
index 4d66e1ec0a69..f431d77f26ce 100644
--- a/src/operator/contrib/boolean_mask.cc
+++ b/src/operator/contrib/boolean_mask.cc
@@ -143,6 +143,7 @@ inline void BooleanMaskForward<cpu>(const nnvm::NodeAttrs& attrs,
   // set the output shape forcefully
   mxnet::TShape s = data.shape();
   s[axis] = valid_num;
+
   const_cast<NDArray &>(out).Init(s);
   // do the copy
   MSHADOW_TYPE_SWITCH(data.dtype(), DType, {
diff --git a/src/operator/contrib/boolean_mask.cu b/src/operator/contrib/boolean_mask.cu
index 47335bfd6b79..c4a06d25d70a 100644
--- a/src/operator/contrib/boolean_mask.cu
+++ b/src/operator/contrib/boolean_mask.cu
@@ -79,7 +79,6 @@ inline void BooleanMaskForward<gpu>(const nnvm::NodeAttrs& attrs,
                                 Stream<gpu>::GetStream(s));
   CUDA_CALL(cudaMemcpy(&valid_num, &prefix_sum[idx_size - 1], sizeof(int32_t),
                        cudaMemcpyDeviceToHost));
-  CHECK(valid_num > 0) << "boolean_mask behavior not defined when all masks are 0";
   // Set the output shape forcefully
   mxnet::TShape data_shape = data.shape();
   data_shape[axis] = valid_num;
@@ -88,8 +87,10 @@ inline void BooleanMaskForward<gpu>(const nnvm::NodeAttrs& attrs,
   size_t col_size = input_size / idx.shape()[0];
   // Do the copy
   MSHADOW_TYPE_SWITCH(out.dtype(), DType, {
-    mxnet_op::Kernel<BooleanMaskForwardKernel, gpu>::Launch(
-      s, input_size, out.data().dptr<DType>(), data.data().dptr<DType>(), prefix_sum, col_size);
+    if (valid_num > 0) {
+      mxnet_op::Kernel<BooleanMaskForwardKernel, gpu>::Launch(
+        s, input_size, out.data().dptr<DType>(), data.data().dptr<DType>(), prefix_sum, col_size);
+    }
   });
 }
 
@@ -143,9 +144,11 @@ inline void BooleanMaskBackward<gpu>(const nnvm::NodeAttrs& attrs,
   size_t col_size = input_size / idx_size;
   // Backward pass
   MSHADOW_TYPE_SWITCH(igrad_data.dtype(), DType, {
-    mxnet_op::Kernel<BooleanMaskBackwardKernel, gpu>::Launch(
-      s, input_size, igrad_data.data().dptr<DType>(), req[0], ograd.data().dptr<DType>(),
-      prefix_sum, col_size);
+    if (input_size > 0) {
+      mxnet_op::Kernel<BooleanMaskBackwardKernel, gpu>::Launch(
+        s, input_size, igrad_data.data().dptr<DType>(), req[0], ograd.data().dptr<DType>(),
+        prefix_sum, col_size);
+    }
   });
 }
 
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 8f1c2533c62c..72bf5864ff4b 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -5589,6 +5589,21 @@ def test_boolean_mask():
     assert same(out.asnumpy(), expected)
     assert same(data.grad.asnumpy(), expected_grad)
 
+    # test 0-size output
+    mx.set_np_shape(True)
+    data = mx.nd.array([[1, 2, 3],[4, 5, 6],[7, 8, 9]])
+    index = mx.nd.array([0, 0, 0])
+    data.attach_grad()
+    with mx.autograd.record():
+        out = mx.nd.contrib.boolean_mask(data, index)
+    out.backward()
+    data.grad.wait_to_read()
+    expected = np.zeros((0, 3))
+    expected_grad = np.array([[0, 0, 0], [0, 0, 0], [0, 0, 0]])
+    assert same(out.asnumpy(), expected)
+    assert same(data.grad.asnumpy(), expected_grad)
+    mx.set_np_shape(False)
+
     # test gradient
     shape = (100, 30)
     a = mx.nd.random.randint(0, 100, shape=shape)

From 24cce9e3c99e499b696b779cbb3b863145f473f1 Mon Sep 17 00:00:00 2001
From: Caenorst <cfujitsang@nvidia.com>
Date: Fri, 2 Aug 2019 19:14:43 -0400
Subject: [PATCH 142/813] prevent TRT_Logger to be destroyed before TRT engine
 (#14898)

* prevent TRT_Logger to be destroyed before TRT engine

* use unique_ptr for trt_logger/parser/engine/executor ownership

* reduce line length for lint
---
 .../subgraph/tensorrt/onnx_to_tensorrt.cc     | 35 ++++------
 .../subgraph/tensorrt/onnx_to_tensorrt.h      | 66 ++++++++++++-------
 src/operator/subgraph/tensorrt/tensorrt-inl.h | 25 +++----
 src/operator/subgraph/tensorrt/tensorrt.cc    |  4 +-
 4 files changed, 69 insertions(+), 61 deletions(-)

diff --git a/src/operator/subgraph/tensorrt/onnx_to_tensorrt.cc b/src/operator/subgraph/tensorrt/onnx_to_tensorrt.cc
index 7dbc54bc1a63..27f6da436a8f 100644
--- a/src/operator/subgraph/tensorrt/onnx_to_tensorrt.cc
+++ b/src/operator/subgraph/tensorrt/onnx_to_tensorrt.cc
@@ -48,23 +48,6 @@ using std::endl;
 
 namespace onnx_to_tensorrt {
 
-struct InferDeleter {
-  template<typename T>
-    void operator()(T* obj) const {
-      if ( obj ) {
-        obj->destroy();
-      }
-    }
-};
-
-template<typename T>
-inline std::shared_ptr<T> InferObject(T* obj) {
-  if ( !obj ) {
-    throw std::runtime_error("Failed to create object");
-  }
-  return std::shared_ptr<T>(obj, InferDeleter());
-}
-
 std::string onnx_ir_version_string(int64_t ir_version = onnx::IR_VERSION) {
   int onnx_ir_major = ir_version / 1000000;
   int onnx_ir_minor = ir_version % 1000000 / 10000;
@@ -83,7 +66,9 @@ void PrintVersion() {
     << NV_TENSORRT_PATCH << endl;
 }
 
-std::tuple<nvinfer1::ICudaEngine*, nvonnxparser::IParser*> onnxToTrtCtx(
+std::tuple<unique_ptr<nvinfer1::ICudaEngine>,
+           unique_ptr<nvonnxparser::IParser>,
+           std::unique_ptr<TRT_Logger> > onnxToTrtCtx(
         const std::string& onnx_model,
         int32_t max_batch_size,
         size_t max_workspace_size,
@@ -91,10 +76,10 @@ std::tuple<nvinfer1::ICudaEngine*, nvonnxparser::IParser*> onnxToTrtCtx(
         bool debug_builder) {
   GOOGLE_PROTOBUF_VERIFY_VERSION;
 
-  TRT_Logger trt_logger(verbosity);
-  auto trt_builder = InferObject(nvinfer1::createInferBuilder(trt_logger));
-  auto trt_network = InferObject(trt_builder->createNetwork());
-  auto trt_parser  = nvonnxparser::createParser(trt_network.get(), trt_logger);
+  auto trt_logger = std::unique_ptr<TRT_Logger>(new TRT_Logger(verbosity));
+  auto trt_builder = nvinfer1::createInferBuilder(*trt_logger);
+  auto trt_network = trt_builder->createNetwork();
+  auto trt_parser  = InferObject(nvonnxparser::createParser(trt_network, *trt_logger));
   ::ONNX_NAMESPACE::ModelProto parsed_model;
   // We check for a valid parse, but the main effect is the side effect
   // of populating parsed_model
@@ -139,8 +124,10 @@ std::tuple<nvinfer1::ICudaEngine*, nvonnxparser::IParser*> onnxToTrtCtx(
   trt_builder->setMaxBatchSize(max_batch_size);
   trt_builder->setMaxWorkspaceSize(max_workspace_size);
   trt_builder->setDebugSync(debug_builder);
-  nvinfer1::ICudaEngine* trt_engine = trt_builder->buildCudaEngine(*trt_network.get());
-  return std::make_tuple(trt_engine, trt_parser);
+  auto trt_engine = InferObject(trt_builder->buildCudaEngine(*trt_network));
+  trt_builder->destroy();
+  trt_network->destroy();
+  return std::make_tuple(std::move(trt_engine), std::move(trt_parser), std::move(trt_logger));
 }
 
 }  // namespace onnx_to_tensorrt
diff --git a/src/operator/subgraph/tensorrt/onnx_to_tensorrt.h b/src/operator/subgraph/tensorrt/onnx_to_tensorrt.h
index 3e8ea1bf9ee1..b89422f59069 100644
--- a/src/operator/subgraph/tensorrt/onnx_to_tensorrt.h
+++ b/src/operator/subgraph/tensorrt/onnx_to_tensorrt.h
@@ -32,6 +32,7 @@
 #include <NvInfer.h>
 
 #include <fstream>
+#include <memory>
 #include <iostream>
 #include <sstream>
 #include <string>
@@ -40,33 +41,51 @@
 
 namespace onnx_to_tensorrt {
 
+struct InferDeleter {
+  template<typename T>
+    void operator()(T* obj) const {
+      if ( obj ) {
+        obj->destroy();
+      }
+    }
+};
+
+template<typename T>
+using unique_ptr = std::unique_ptr<T, InferDeleter>;
+
+template<typename T>
+inline unique_ptr<T> InferObject(T* obj) {
+  if ( !obj ) {
+    throw std::runtime_error("Failed to create object");
+  }
+  return unique_ptr<T>(obj, InferDeleter());
+}
+
 class TRT_Logger : public nvinfer1::ILogger {
-        nvinfer1::ILogger::Severity _verbosity;
-        std::ostream* _ostream;
+  nvinfer1::ILogger::Severity _verbosity;
+  std::ostream* _ostream;
  public:
-        TRT_Logger(Severity verbosity = Severity::kWARNING,
-                   std::ostream& ostream = std::cout)
-                : _verbosity(verbosity), _ostream(&ostream) {}
-        void log(Severity severity, const char* msg) override {
-                if ( severity <= _verbosity ) {
-                        time_t rawtime = std::time(0);
-                        char buf[256];
-                        strftime(&buf[0], 256,
-                                 "%Y-%m-%d %H:%M:%S",
-                                 std::gmtime(&rawtime));
-                        const char* sevstr = (severity == Severity::kINTERNAL_ERROR ? "    BUG" :
-                                              severity == Severity::kERROR          ? "  ERROR" :
-                                              severity == Severity::kWARNING        ? "WARNING" :
-                                              severity == Severity::kINFO           ? "   INFO" :
-                                              "UNKNOWN");
-                        (*_ostream) << "[" << buf << " " << sevstr << "] "
-                                    << msg
-                                    << std::endl;
-                }
-        }
+  TRT_Logger(Severity verbosity = Severity::kWARNING,
+             std::ostream& ostream = std::cout) :
+               _verbosity(verbosity), _ostream(&ostream) {}
+  void log(Severity severity, const char* msg) override {
+    if (severity <= _verbosity) {
+      time_t rawtime = std::time(0);
+      char buf[256];
+      strftime(&buf[0], 256, "%Y-%m-%d %H:%M:%S", std::gmtime(&rawtime));
+      const char* sevstr = (severity == Severity::kINTERNAL_ERROR ? "    BUG" :
+                            severity == Severity::kERROR          ? "  ERROR" :
+                            severity == Severity::kWARNING        ? "WARNING" :
+                            severity == Severity::kINFO           ? "   INFO" :
+                            "UNKNOWN");
+      (*_ostream) << "[" << buf << " " << sevstr << "] " << msg << std::endl;
+    }
+  }
 };
 
-std::tuple<nvinfer1::ICudaEngine*, nvonnxparser::IParser*> onnxToTrtCtx(
+std::tuple<unique_ptr<nvinfer1::ICudaEngine>,
+           unique_ptr<nvonnxparser::IParser>,
+           std::unique_ptr<TRT_Logger> > onnxToTrtCtx(
         const std::string& onnx_model,
         int32_t max_batch_size = 32,
         size_t max_workspace_size = 1L << 30,
@@ -75,5 +94,4 @@ std::tuple<nvinfer1::ICudaEngine*, nvonnxparser::IParser*> onnxToTrtCtx(
 }  // namespace onnx_to_tensorrt
 
 #endif  // MXNET_USE_TENSORRT
-
 #endif  // MXNET_OPERATOR_SUBGRAPH_TENSORRT_ONNX_TO_TENSORRT_H_
diff --git a/src/operator/subgraph/tensorrt/tensorrt-inl.h b/src/operator/subgraph/tensorrt/tensorrt-inl.h
index a6b93f10598a..7b0dcc1bd4a5 100644
--- a/src/operator/subgraph/tensorrt/tensorrt-inl.h
+++ b/src/operator/subgraph/tensorrt/tensorrt-inl.h
@@ -51,10 +51,14 @@ struct TRTParam {
 };
 
 struct TRTEngineParam {
-  TRTEngineParam(nvinfer1::ICudaEngine* trt_engine,
-                 nvonnxparser::IParser* _parser,
-                 const std::unordered_map<std::string, uint32_t> input_map,
-                 const std::unordered_map<std::string, uint32_t> output_map) {
+  TRTEngineParam(onnx_to_tensorrt::unique_ptr<nvinfer1::ICudaEngine> _trt_engine,
+                 onnx_to_tensorrt::unique_ptr<nvonnxparser::IParser> _trt_parser,
+                 std::unique_ptr<onnx_to_tensorrt::TRT_Logger> _trt_logger,
+                 const std::unordered_map<std::string, uint32_t>& input_map,
+                 const std::unordered_map<std::string, uint32_t>& output_map) {
+    trt_engine = std::move(_trt_engine);
+    trt_logger = std::move(_trt_logger);
+    trt_parser = std::move(_trt_parser);
     binding_order = std::make_shared<std::vector<std::pair<uint32_t, bool> > >();
     bindings = std::make_shared<std::vector<void*> >();
     binding_order->reserve(trt_engine->getNbBindings());
@@ -67,16 +71,13 @@ struct TRTEngineParam {
         binding_order->emplace_back(output_map.at(binding_name), false);
       }
     }
-    trt_executor = trt_engine->createExecutionContext();
-    trt_parser = _parser;
+    trt_executor = onnx_to_tensorrt::InferObject(trt_engine->createExecutionContext());
   }
 
-  ~TRTEngineParam() {
-    trt_parser->destroy();
-    trt_executor->destroy();
-  }
-  nvinfer1::IExecutionContext* trt_executor;
-  nvonnxparser::IParser* trt_parser;
+  onnx_to_tensorrt::unique_ptr<nvinfer1::ICudaEngine> trt_engine;
+  onnx_to_tensorrt::unique_ptr<nvinfer1::IExecutionContext> trt_executor;
+  onnx_to_tensorrt::unique_ptr<nvonnxparser::IParser> trt_parser;
+  std::unique_ptr<onnx_to_tensorrt::TRT_Logger> trt_logger;
   std::shared_ptr<std::vector<std::pair<uint32_t, bool> > > binding_order;
   std::shared_ptr<std::vector<void*> > bindings;
 };
diff --git a/src/operator/subgraph/tensorrt/tensorrt.cc b/src/operator/subgraph/tensorrt/tensorrt.cc
index 8b64c2a6b6ac..4ef1d7dece9d 100644
--- a/src/operator/subgraph/tensorrt/tensorrt.cc
+++ b/src/operator/subgraph/tensorrt/tensorrt.cc
@@ -312,7 +312,9 @@ OpStatePtr TRTCreateState(const nnvm::NodeAttrs& attrs, Context ctx,
   graph.attrs["shape"]        = std::make_shared<nnvm::any>(std::move(shapes));
   auto onnx_graph = op::nnvm_to_onnx::ConvertNnvmGraphToOnnx(graph, &params_map);
   auto trt_tuple = ::onnx_to_tensorrt::onnxToTrtCtx(onnx_graph, max_batch_size, 1 << 30);
-  return OpStatePtr::Create<TRTEngineParam>(std::get<0>(trt_tuple), std::get<1>(trt_tuple),
+  return OpStatePtr::Create<TRTEngineParam>(std::move(std::get<0>(trt_tuple)),
+                                            std::move(std::get<1>(trt_tuple)),
+                                            std::move(std::get<2>(trt_tuple)),
                                             inputs_to_idx, outputs_to_idx);
 }
 

From 7c26810c18f299b16ebd8a0b78c0f96413c90bd8 Mon Sep 17 00:00:00 2001
From: Wuxun Zhang <wuxun.zhang@intel.com>
Date: Sun, 4 Aug 2019 21:50:12 +0800
Subject: [PATCH 143/813] fix dropout mask output (#15697)

---
 src/operator/nn/dropout-inl.h | 17 +++++++++++------
 src/operator/nn/dropout.cc    |  5 +++--
 2 files changed, 14 insertions(+), 8 deletions(-)

diff --git a/src/operator/nn/dropout-inl.h b/src/operator/nn/dropout-inl.h
index cb02be6e0338..cb1cd428bd5e 100644
--- a/src/operator/nn/dropout-inl.h
+++ b/src/operator/nn/dropout-inl.h
@@ -130,11 +130,18 @@ class DropoutOp {
     DType *dataptr = data.dptr_;
     auto maskptr = reinterpret_cast<int *>(mask.dptr_);
     int count = mask.shape_[0] * mask.shape_[1];
+    if (sizeof(DType) > sizeof(int)) {
+      // allocating new buffer to avoiding memory overlapping between `mask.dptr_` and `maskptr`
+      Tensor<xpu, 1, int> temp = ctx.requested[1].get_space_typed<xpu, 1, int>(Shape1(count), s);
+      maskptr = temp.dptr_;
+    }
     BernoulliGenerate(*pgen, count, this->pkeep_, maskptr);
     const float pk_1 = 1.0f / this->pkeep_;
 #pragma omp parallel for num_threads(engine::OpenMP::Get()->GetRecommendedOMPThreadCount())
     for (int i = 0; i < count; ++i) {
-      outptr[i] = dataptr[i] * maskptr[i] * pk_1;
+      const DType maskVal = static_cast<DType>(maskptr[i]) * pk_1;
+      outptr[i] = dataptr[i] * maskVal;
+      mask.dptr_[i] = maskVal;
     }
   }
 
@@ -149,12 +156,11 @@ class DropoutOp {
     Tensor<xpu, 2, DType> gdata = in_grad[dropout::kData].FlatTo2D<xpu, DType>(s);
     DType *ingradptr = gdata.dptr_;
     const DType *outgradptr = grad.dptr_;
-    auto maskptr = reinterpret_cast<int *>(mask.dptr_);
-    int count = mask.shape_[0] * mask.shape_[1];
-    const float pk_1 = 1.0f / this->pkeep_;
+    const DType *maskptr = mask.dptr_;
+    const int count = mask.shape_[0] * mask.shape_[1];
 #pragma omp parallel for num_threads(engine::OpenMP::Get()->GetRecommendedOMPThreadCount())
     for (int i = 0; i < count; ++i) {
-      ingradptr[i] = outgradptr[i] * maskptr[i] * pk_1;
+      ingradptr[i] = outgradptr[i] * maskptr[i];
     }
   }
 
@@ -529,5 +535,4 @@ void DropoutGradCompute(const OpStatePtr& state,
 }  // namespace op
 }  // namespace mxnet
 
-#undef MXNET_USE_MKL_DROPOUT
 #endif  // MXNET_OPERATOR_NN_DROPOUT_INL_H_
diff --git a/src/operator/nn/dropout.cc b/src/operator/nn/dropout.cc
index 63da5613df84..d2fe3a5651a4 100644
--- a/src/operator/nn/dropout.cc
+++ b/src/operator/nn/dropout.cc
@@ -28,8 +28,6 @@
 #include "../operator_common.h"
 #include "mxnet/op_attr_types.h"
 
-
-
 namespace mxnet {
 namespace op {
 
@@ -163,6 +161,9 @@ Example::
 #endif
     }
     request.emplace_back(ResourceRequest::kParallelRandom);
+#if MXNET_USE_MKL_DROPOUT
+    request.emplace_back(ResourceRequest::kTempSpace);
+#endif
     return request;
   })
 .add_argument("data", "NDArray-or-Symbol", "Input array to which dropout will be applied.")

From 3255d879674a7c10ba30982baa09d3b30f91f8e8 Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Sun, 4 Aug 2019 23:09:32 -0700
Subject: [PATCH 144/813] fix broadcast op param (#15714)

---
 benchmark/opperf/rules/default_params.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/benchmark/opperf/rules/default_params.py b/benchmark/opperf/rules/default_params.py
index c864c7d829b6..8f3973244aa2 100644
--- a/benchmark/opperf/rules/default_params.py
+++ b/benchmark/opperf/rules/default_params.py
@@ -28,9 +28,9 @@
 # For Unary operators like abs, arccos, arcsin etc..
 DEFAULT_DATA = [(1024, 1024), (10000, 1), (10000, 100)]
 
-# For Binary broadcast operators like - broadcast_add/sub/mode/logical_and etc..
-DEFAULT_LHS = [[(1024, 1024), (10000, 10), (10000, 1)]]
-DEFAULT_RHS = [[(1024, 1024), (10000, 10), (10000, 1)]]
+# For Binary broadcast operators like - broadcast_add/sub/mod/logical_and etc..
+DEFAULT_LHS = [(1024, 1024), (10000, 10), (10000, 1)]
+DEFAULT_RHS = [(1024, 1024), (10000, 10), (10000, 1)]
 
 # For operators like - random_uniform, random_normal etc..
 DEFAULT_SHAPE = [(1024, 1024), (10000, 1), (10000, 100)]

From 3112893f80fdda2c9d8f6832562146bec97de205 Mon Sep 17 00:00:00 2001
From: Manu Seth <22492939+mseth10@users.noreply.github.com>
Date: Mon, 5 Aug 2019 12:20:15 -0700
Subject: [PATCH 145/813] Dynamic Library Loading Support (#15489)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Accelerator APIs header file

* adding example to test accelerator loading

* adding c_api function header

* modifying header file path

* creating templates for call to lib fns

* modifying target to mxnet_static for libdl

* rebaseing with master

* returning nullptr handle if library not loaded

* refactoring code to load libraries dynamically

* addressing review comments

* using static cast

* pylint fix

* moving library.h file to src/common/

* adding dynamic loading support for windows

* updating header guard

* fixing headers

* fixing windows casting error

* declaring library functions for windows

* adding library testing module in examples

* adding unit test to test library loading

* correcting file names

* updating error messages

* getting error message from DL library

* adding unit test to gpu suite

* correcting windows pointer

* requiring absolute path to library

* changing file description

* addressing review comments - adding more docs, windows error msg

* addressing PR comments

* checking machine type for unit test

* “re-trigger”

* added map to store loaded libraries

* added dlclose calls in naive & threaded engines

* removed library map declaration in cc file

* added windows free

* fixed formatting

* added cast to HMODULE for void* for windows

* retrigger CI for flaky unix_cpu
---
 CMakeLists.txt                                |   1 +
 Makefile                                      |   2 +-
 example/lib_api/Makefile                      |  31 +++++
 example/lib_api/libtest.cc                    |  78 +++++++++++
 example/lib_api/mylib.cc                      |  37 ++++++
 example/lib_api/test.py                       |  31 +++++
 include/mxnet/c_api.h                         |   7 +
 include/mxnet/lib_api.h                       |  50 +++++++
 python/mxnet/__init__.py                      |   1 +
 python/mxnet/base.py                          |   2 +-
 python/mxnet/library.py                       |  49 +++++++
 src/c_api/c_api.cc                            |  15 +++
 src/common/library.cc                         | 125 ++++++++++++++++++
 src/common/library.h                          |  57 ++++++++
 src/engine/naive_engine.cc                    |   5 +
 src/engine/threaded_engine.h                  |   5 +
 tests/python/gpu/test_operator_gpu.py         |   1 +
 tests/python/unittest/test_library_loading.py |  38 ++++++
 18 files changed, 533 insertions(+), 2 deletions(-)
 create mode 100644 example/lib_api/Makefile
 create mode 100644 example/lib_api/libtest.cc
 create mode 100644 example/lib_api/mylib.cc
 create mode 100644 example/lib_api/test.py
 create mode 100644 include/mxnet/lib_api.h
 create mode 100644 python/mxnet/library.py
 create mode 100644 src/common/library.cc
 create mode 100644 src/common/library.h
 create mode 100644 tests/python/unittest/test_library_loading.py

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 7c479f756295..a86435f9900f 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -701,6 +701,7 @@ if(UNIX)
   add_library(mxnet SHARED ${DUMMY_SOURCE})
   target_link_libraries(mxnet PRIVATE ${BEGIN_WHOLE_ARCHIVE} $<TARGET_FILE:mxnet_static> ${END_WHOLE_ARCHIVE})
   target_link_libraries(mxnet PRIVATE mxnet_static)
+  target_link_libraries(mxnet_static PUBLIC ${CMAKE_DL_LIBS})
   set_target_properties(mxnet_static PROPERTIES OUTPUT_NAME mxnet)
 else()
   add_library(mxnet SHARED ${SOURCE})
diff --git a/Makefile b/Makefile
index b54df174acde..f1e21d6fa945 100644
--- a/Makefile
+++ b/Makefile
@@ -107,7 +107,7 @@ else
 	CFLAGS += -O3 -DNDEBUG=1
 endif
 CFLAGS += -I$(TPARTYDIR)/mshadow/ -I$(TPARTYDIR)/dmlc-core/include -fPIC -I$(NNVM_PATH)/include -I$(DLPACK_PATH)/include -I$(TPARTYDIR)/tvm/include -Iinclude $(MSHADOW_CFLAGS)
-LDFLAGS = -pthread $(MSHADOW_LDFLAGS) $(DMLC_LDFLAGS)
+LDFLAGS = -pthread -ldl $(MSHADOW_LDFLAGS) $(DMLC_LDFLAGS)
 
 ifeq ($(ENABLE_TESTCOVERAGE), 1)
         CFLAGS += --coverage
diff --git a/example/lib_api/Makefile b/example/lib_api/Makefile
new file mode 100644
index 000000000000..e5893c8065c4
--- /dev/null
+++ b/example/lib_api/Makefile
@@ -0,0 +1,31 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+all:
+	g++ -shared -fPIC mylib.cc -o mylib.so -I ../../include/mxnet
+
+test:
+	g++ -std=c++11 -O3 -o libtest libtest.cc -ldl -I ../../include/mxnet
+
+windows:
+	cl /LD mylib.cc
+
+win_test:
+	cl libtest.cc
+
+clean:
+	rm -rf mylib.so libtest
diff --git a/example/lib_api/libtest.cc b/example/lib_api/libtest.cc
new file mode 100644
index 000000000000..8bdf36c05d37
--- /dev/null
+++ b/example/lib_api/libtest.cc
@@ -0,0 +1,78 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2015 by Contributors
+ * \file libtest.cc
+ * \brief This test checks if the library is implemented correctly
+ * and does not involve dynamic loading of library into MXNet
+ * This test is supposed to be run before test.py
+ */
+
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+#include <windows.h>
+#else
+#include <dlfcn.h>
+#endif
+
+#include <iostream>
+#include "lib_api.h"
+
+#define MXNET_VERSION 10500
+
+int main(void) {
+  // Get a handle to the library.
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+  HINSTANCE handle;
+  handle = LoadLibrary(TEXT("mylib.dll"));
+#else
+  void *handle;
+  handle = dlopen("mylib.so", RTLD_LAZY);
+#endif
+
+  if (!handle) {
+    std::cerr << "Unable to load library" << std::endl;
+    return 1;
+  }
+
+  // get initialize function address from the library
+  initialize_t init_lib;
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+  init_lib = (initialize_t) GetProcAddress(handle, MXLIB_INITIALIZE_STR);
+#else
+  init_lib = (initialize_t) dlsym(handle, MXLIB_INITIALIZE_STR);
+#endif
+
+  if (!init_lib) {
+    std::cerr << "Unable to get function 'intialize' from library" << std::endl;
+    return 1;
+  }
+
+  // Call the function.
+  (init_lib)(MXNET_VERSION);
+
+  // Deallocate memory.
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+  FreeLibrary(handle);
+#else
+  dlclose(handle);
+#endif
+
+  return 0;
+}
diff --git a/example/lib_api/mylib.cc b/example/lib_api/mylib.cc
new file mode 100644
index 000000000000..e67560a87f3d
--- /dev/null
+++ b/example/lib_api/mylib.cc
@@ -0,0 +1,37 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2015 by Contributors
+ * \file mylib.cc
+ * \brief Sample library file
+ */
+
+#include <iostream>
+#include "lib_api.h"
+
+int initialize(int version) {
+  if (version >= 10400) {
+    std::cout << "MXNet version " << version << " supported" << std::endl;
+    return 1;
+  } else {
+    std::cout << "MXNet version " << version << " not supported" << std::endl;
+    return 0;
+  }
+}
diff --git a/example/lib_api/test.py b/example/lib_api/test.py
new file mode 100644
index 000000000000..d73d85c02ced
--- /dev/null
+++ b/example/lib_api/test.py
@@ -0,0 +1,31 @@
+#!/usr/bin/env python3
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# coding: utf-8
+# pylint: disable=arguments-differ
+
+# This test checks if dynamic loading of library into MXNet is successful
+
+import mxnet as mx
+import os
+
+if (os.name=='posix'):
+    mx.library.load('mylib.so')
+elif (os.name=='nt'):
+    mx.library.load('mylib.dll')
diff --git a/include/mxnet/c_api.h b/include/mxnet/c_api.h
index 058f859ae7bc..376c7e9d1063 100644
--- a/include/mxnet/c_api.h
+++ b/include/mxnet/c_api.h
@@ -226,6 +226,13 @@ MXNET_DLL const char *MXGetLastError();
 // Part 0: Global State setups
 //-------------------------------------
 
+/*!
+ * \brief Load library dynamically
+ * \param path to the library .so file
+ * \return 0 when success, -1 when failure happens.
+ */
+MXNET_DLL int MXLoadLib(const char *path);
+
 /*!
  * \brief Get list of features supported on the runtime
  * \param libFeature pointer to array of LibFeature
diff --git a/include/mxnet/lib_api.h b/include/mxnet/lib_api.h
new file mode 100644
index 000000000000..ca3b2952eafa
--- /dev/null
+++ b/include/mxnet/lib_api.h
@@ -0,0 +1,50 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2015 by Contributors
+ * \file lib_api.h
+ * \brief APIs to interact with libraries
+ */
+#ifndef MXNET_LIB_API_H_
+#define MXNET_LIB_API_H_
+
+/*!
+ * \brief Following are the APIs implemented in the external library
+ * Each API has a #define string that is used to lookup the function in the library
+ * Followed by the function declaration
+ */
+#define MXLIB_INITIALIZE_STR "initialize"
+typedef int (*initialize_t)(int);
+
+extern "C" {
+    /*!
+     * \brief Checks if the MXNet version is supported by the library.
+     * If supported, initializes the library.
+     * \param version MXNet version number passed to library and defined as:
+     *                MXNET_VERSION = (MXNET_MAJOR*10000 + MXNET_MINOR*100 + MXNET_PATCH)
+     * \return Non-zero value on error i.e. library incompatible with passed MXNet version
+     */
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+    __declspec(dllexport) int __cdecl initialize(int);
+#else
+    int initialize(int);
+#endif
+}
+#endif  // MXNET_LIB_API_H_
diff --git a/python/mxnet/__init__.py b/python/mxnet/__init__.py
index ab4bffde28a9..233bb2a1f57e 100644
--- a/python/mxnet/__init__.py
+++ b/python/mxnet/__init__.py
@@ -26,6 +26,7 @@
 from .base import MXNetError
 from .util import is_np_shape, set_np_shape, np_shape, use_np_shape
 from . import base
+from . import library
 from . import contrib
 from . import ndarray
 from . import ndarray as nd
diff --git a/python/mxnet/base.py b/python/mxnet/base.py
index bf8026359d02..17819bde28b2 100644
--- a/python/mxnet/base.py
+++ b/python/mxnet/base.py
@@ -86,7 +86,7 @@ def __repr__(self):
 
 
 class MXNetError(Exception):
-    """Error that will be throwed by all mxnet functions."""
+    """Error that will be thrown by all mxnet functions."""
     pass
 
 
diff --git a/python/mxnet/library.py b/python/mxnet/library.py
new file mode 100644
index 000000000000..9ebf2c2bc580
--- /dev/null
+++ b/python/mxnet/library.py
@@ -0,0 +1,49 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# coding: utf-8
+"""Library management API of mxnet."""
+from __future__ import absolute_import
+import ctypes
+import os
+from .base import _LIB, check_call, MXNetError
+
+def load(path):
+    """Loads library dynamically.
+
+    Parameters
+    ---------
+    path : Path to library .so/.dll file
+
+    Returns
+    ---------
+    void
+    """
+    #check if path exists
+    if not os.path.exists(path):
+        raise MXNetError("load path %s does NOT exist" % path)
+    #check if path is an absolute path
+    if not os.path.isabs(path):
+        raise MXNetError("load path %s is not an absolute path" % path)
+    #check if path is to a library file
+    _, file_ext = os.path.splitext(path)
+    if not file_ext in ['.so', '.dll']:
+        raise MXNetError("load path %s is NOT a library file" % path)
+
+    byt_obj = path.encode('utf-8')
+    chararr = ctypes.c_char_p(byt_obj)
+    check_call(_LIB.MXLoadLib(chararr))
diff --git a/src/c_api/c_api.cc b/src/c_api/c_api.cc
index 5207bdfa444b..5b23a8c694f6 100644
--- a/src/c_api/c_api.cc
+++ b/src/c_api/c_api.cc
@@ -45,11 +45,13 @@
 #include "mxnet/storage.h"
 #include "mxnet/libinfo.h"
 #include "mxnet/imperative.h"
+#include "mxnet/lib_api.h"
 #include "./c_api_common.h"
 #include "../operator/custom/custom-inl.h"
 #include "../operator/tensor/matrix_op-inl.h"
 #include "../operator/tvmop/op_module.h"
 #include "../common/utils.h"
+#include "../common/library.h"
 
 using namespace mxnet;
 
@@ -90,6 +92,19 @@ inline int MXAPIGetFunctionRegInfo(const FunRegType *e,
 
 // NOTE: return value is added in API_END
 
+// Loads library and initializes it
+int MXLoadLib(const char *path) {
+  API_BEGIN();
+  void *lib = load_lib(path);
+  if (!lib)
+    LOG(FATAL) << "Unable to load library";
+
+  initialize_t initialize = get_func<initialize_t>(lib, const_cast<char*>(MXLIB_INITIALIZE_STR));
+  if (!initialize(static_cast<int>(MXNET_VERSION)))
+    LOG(FATAL) << "Library failed to initialize";
+  API_END();
+}
+
 int MXLibInfoFeatures(const struct LibFeature **lib_features, size_t *size) {
   using namespace features;
   API_BEGIN();
diff --git a/src/common/library.cc b/src/common/library.cc
new file mode 100644
index 000000000000..9e79b5dbe1bc
--- /dev/null
+++ b/src/common/library.cc
@@ -0,0 +1,125 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2015 by Contributors
+ * \file library.cc
+ * \brief Dynamically loading accelerator library
+ * and accessing its functions
+ */
+
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+#include <windows.h>
+#else
+#include <dlfcn.h>
+#endif
+
+#include <string>
+#include "library.h"
+
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+/*!
+ * \brief Retrieve the system error message for the last-error code
+ * \param err string that gets the error message
+ */
+void win_err(char **err) {
+  uint32_t dw = GetLastError();
+  FormatMessage(
+        FORMAT_MESSAGE_ALLOCATE_BUFFER |
+        FORMAT_MESSAGE_FROM_SYSTEM |
+        FORMAT_MESSAGE_IGNORE_INSERTS,
+        NULL,
+        dw,
+        MAKELANGID(LANG_NEUTRAL, SUBLANG_DEFAULT),
+        reinterpret_cast<char*>(err),
+        0, NULL);
+}
+#endif
+
+
+/*!
+ * \brief Loads the dynamic shared library file
+ * \param path library file location
+ * \return handle a pointer for the loaded library, nullptr if loading unsuccessful
+ */
+void* load_lib(const char* path) {
+  void *handle = nullptr;
+  std::string path_str(path);
+  // check if library was already loaded
+  if (loaded_libs.find(path_str) == loaded_libs.end()) {
+    // if not, load it
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+    handle = LoadLibrary(path);
+    if (!handle) {
+      char *err_msg = nullptr;
+      win_err(&err_msg);
+      LOG(FATAL) << "Error loading library: '" << path << "'\n" << err_msg;
+      LocalFree(err_msg);
+      return nullptr;
+    }
+#else
+    handle = dlopen(path, RTLD_LAZY);
+    if (!handle) {
+      LOG(FATAL) << "Error loading library: '" << path << "'\n" << dlerror();
+      return nullptr;
+    }
+#endif  // _WIN32 or _WIN64 or __WINDOWS__
+    // then store the pointer to the library
+    loaded_libs[path_str] = handle;
+  } else {
+    // otherwise just look up the pointer
+    handle = loaded_libs[path_str];
+  }
+  return handle;
+}
+
+/*!
+ * \brief Closes the loaded dynamic shared library file
+ * \param handle library file handle
+ */
+void close_lib(void* handle) {
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+  FreeLibrary((HMODULE)handle);
+#else
+  dlclose(handle);
+#endif  // _WIN32 or _WIN64 or __WINDOWS__
+}
+
+/*!
+ * \brief Obtains address of given function in the loaded library
+ * \param handle pointer for the loaded library
+ * \param func function pointer that gets output address
+ * \param name function name to be fetched
+ */
+void get_sym(void* handle, void** func, char* name) {
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+  *func = GetProcAddress((HMODULE)handle, name);
+  if (!(*func)) {
+    char *err_msg = nullptr;
+    win_err(&err_msg);
+    LOG(FATAL) << "Error getting function '" << name << "' from library\n" << err_msg;
+    LocalFree(err_msg);
+  }
+#else
+  *func = dlsym(handle, name);
+  if (!(*func)) {
+    LOG(FATAL) << "Error getting function '" << name << "' from library\n" << dlerror();
+  }
+#endif  // _WIN32 or _WIN64 or __WINDOWS__
+}
diff --git a/src/common/library.h b/src/common/library.h
new file mode 100644
index 000000000000..d6eff4184191
--- /dev/null
+++ b/src/common/library.h
@@ -0,0 +1,57 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2015 by Contributors
+ * \file library.h
+ * \brief Defining library loading functions
+ */
+#ifndef MXNET_COMMON_LIBRARY_H_
+#define MXNET_COMMON_LIBRARY_H_
+
+#include <iostream>
+#include <map>
+#include <string>
+#include "dmlc/io.h"
+
+// map of libraries loaded
+static std::map<std::string, void*> loaded_libs;
+
+void* load_lib(const char* path);
+void close_lib(void* handle);
+void get_sym(void* handle, void** func, char* name);
+
+/*!
+ * \brief a templated function that fetches from the library
+ * a function pointer of any given datatype and name
+ * \param T a template parameter for data type of function pointer
+ * \param lib library handle
+ * \param func_name function name to search for in the library
+ * \return func a function pointer
+ */
+template<typename T>
+T get_func(void *lib, char *func_name) {
+  T func;
+  get_sym(lib, reinterpret_cast<void**>(&func), func_name);
+  if (!func)
+    LOG(FATAL) << "Unable to get function '" << func_name << "' from library";
+  return func;
+}
+
+#endif  // MXNET_COMMON_LIBRARY_H_
diff --git a/src/engine/naive_engine.cc b/src/engine/naive_engine.cc
index 92b38a8ea368..371a6eb96303 100644
--- a/src/engine/naive_engine.cc
+++ b/src/engine/naive_engine.cc
@@ -30,6 +30,7 @@
 #include "./openmp.h"
 #include "../common/object_pool.h"
 #include "../profiler/custom_op_profiler.h"
+#include "../../src/common/library.h"
 
 namespace mxnet {
 namespace engine {
@@ -84,6 +85,10 @@ class NaiveEngine final : public Engine {
       }
     }
 #endif
+    // close opened libraries
+    for (auto const& lib : loaded_libs) {
+      close_lib(lib.second);
+    }
   }
 
   void Stop() override {
diff --git a/src/engine/threaded_engine.h b/src/engine/threaded_engine.h
index bf74485ba442..e9ced8898305 100644
--- a/src/engine/threaded_engine.h
+++ b/src/engine/threaded_engine.h
@@ -44,6 +44,7 @@
 #include "./openmp.h"
 #include "../common/object_pool.h"
 #include "../profiler/custom_op_profiler.h"
+#include "../../src/common/library.h"
 
 namespace mxnet {
 namespace engine {
@@ -331,6 +332,10 @@ class ThreadedEngine : public Engine {
       kill_.store(true);
     }
     finished_cv_.notify_all();
+    // close opened libraries
+    for (auto const& lib : loaded_libs) {
+      close_lib(lib.second);
+    }
   }
 
  protected:
diff --git a/tests/python/gpu/test_operator_gpu.py b/tests/python/gpu/test_operator_gpu.py
index 91ba9fb68750..a8aa1ff853f7 100644
--- a/tests/python/gpu/test_operator_gpu.py
+++ b/tests/python/gpu/test_operator_gpu.py
@@ -45,6 +45,7 @@
 from test_subgraph_op import *
 from test_contrib_operator import test_multibox_target_op
 from test_tvm_op import *
+from test_library_loading import *
 
 set_default_context(mx.gpu(0))
 del test_support_vector_machine_l1_svm  # noqa
diff --git a/tests/python/unittest/test_library_loading.py b/tests/python/unittest/test_library_loading.py
new file mode 100644
index 000000000000..3d56d0c750a6
--- /dev/null
+++ b/tests/python/unittest/test_library_loading.py
@@ -0,0 +1,38 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# This test checks if dynamic loading of library into MXNet is successful
+
+import os
+import platform
+import unittest
+import mxnet as mx
+from mxnet.test_utils import download
+
+def check_platform():
+    return platform.machine() not in ['x86_64', 'AMD64']
+
+@unittest.skipIf(check_platform(), "not all machine types supported")
+def test_library_loading():
+    if (os.name=='posix'):
+        lib = 'mylib.so'
+    elif (os.name=='nt'):
+        lib = 'mylib.dll'
+
+    fname = mx.test_utils.download('https://mxnet-demo-models.s3.amazonaws.com/lib_binary/'+lib)
+    fname = os.path.abspath(fname)
+    mx.library.load(fname)

From 67acbe5f68ccafcc1cf899ff1e7791a4555f7453 Mon Sep 17 00:00:00 2001
From: Haibin Lin <linhaibin.eric@gmail.com>
Date: Tue, 6 Aug 2019 07:04:32 -0800
Subject: [PATCH 146/813] [DOC] Fix many arguments in the doc: reshape_like,
 arange_like, shape_array (#15752)

* Update init_op.cc

* Update elemwise_unary_op_basic.cc
---
 src/operator/tensor/elemwise_unary_op_basic.cc | 6 +++---
 src/operator/tensor/init_op.cc                 | 3 ++-
 2 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/src/operator/tensor/elemwise_unary_op_basic.cc b/src/operator/tensor/elemwise_unary_op_basic.cc
index 6da384d3679b..3caf840f1ebf 100644
--- a/src/operator/tensor/elemwise_unary_op_basic.cc
+++ b/src/operator/tensor/elemwise_unary_op_basic.cc
@@ -546,7 +546,8 @@ Negative indices are supported, and `None` can be used for either `lhs_end` or `
       return lhs;
     })
 .add_argument("lhs", "NDArray-or-Symbol", "First input.")
-.add_argument("rhs", "NDArray-or-Symbol", "Second input.");
+.add_argument("rhs", "NDArray-or-Symbol", "Second input.")
+.add_arguments(ReshapeLikeParam::__FIELDS__());
 
 void ShapeComputeCPU(const nnvm::NodeAttrs& attrs,
                      const OpContext& ctx,
@@ -594,8 +595,7 @@ Example::
     TYPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::kInt64);
     return out_attrs->at(0) != -1;
   })
-.add_argument("data", "NDArray-or-Symbol", "Input Array.")
-.add_arguments(ReshapeLikeParam::__FIELDS__());
+.add_argument("data", "NDArray-or-Symbol", "Input Array.");
 
 void SizeComputeCPU(const nnvm::NodeAttrs& attrs,
                     const OpContext& ctx,
diff --git a/src/operator/tensor/init_op.cc b/src/operator/tensor/init_op.cc
index 0cbdaa43c198..239cff798e59 100644
--- a/src/operator/tensor/init_op.cc
+++ b/src/operator/tensor/init_op.cc
@@ -134,7 +134,8 @@ Examples::
     [](const NodeAttrs& attrs) { return std::vector<uint32_t>(1, 0); })
 .set_attr<FCompute>("FCompute<cpu>", RangeCompute<cpu, RangeLikeParam>)
 .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
-.add_argument("data", "NDArray-or-Symbol", "The input");
+.add_argument("data", "NDArray-or-Symbol", "The input")
+.add_arguments(RangeLikeParam::__FIELDS__());
 
 NNVM_REGISTER_OP(_linspace)
 .describe("Return evenly spaced numbers over a specified interval. Similar to Numpy")

From 8c2bef558a58c97591372490dbba030ecd237e76 Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Tue, 6 Aug 2019 11:06:34 -0700
Subject: [PATCH 147/813] Add Gather_nd Scatter_nd to NDArray API category doc
 (#15689)

* gather scatter to NDArray doc

* Trigger notification
---
 docs/api/python/ndarray/ndarray.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/docs/api/python/ndarray/ndarray.md b/docs/api/python/ndarray/ndarray.md
index ac18b02b6c9b..5addf92cd150 100644
--- a/docs/api/python/ndarray/ndarray.md
+++ b/docs/api/python/ndarray/ndarray.md
@@ -462,6 +462,8 @@ The `ndarray` package provides several classes:
     where
     ravel_multi_index
     unravel_index
+    gather_nd
+    scatter_nd
 ```
 
 ## Mathematical functions

From 51c07e55faadb30e81551b483fa34beeafe27eed Mon Sep 17 00:00:00 2001
From: Jake Lee <gstu1130@gmail.com>
Date: Tue, 6 Aug 2019 11:50:13 -0700
Subject: [PATCH 148/813] [Dependency Update] Dependency update doc (#15045)

* first draft

* 2nd draft

* complete the draft

* capitalize the comment

* address the comment

* address comment
---
 tools/dependencies/README.md | 298 +++++++++++++++++++++++++++++++++++
 1 file changed, 298 insertions(+)

diff --git a/tools/dependencies/README.md b/tools/dependencies/README.md
index a31caef05daa..db35aabfe133 100644
--- a/tools/dependencies/README.md
+++ b/tools/dependencies/README.md
@@ -40,3 +40,301 @@ This issue appeared in the OSX build with XCode version 8.0 above (reproduced on
 ```
 --without-libidn2
 ``` 
+
+***
+
+# Dependency Update Runbook
+
+MXNet is built on top of many dependencies. Managing these dependencies could be a big headache. This goal of this document is to give a overview of those dependencies and how to upgrade when new version of those are rolled out.
+
+## Overview
+
+The dependencies could be categorized by several groups: BLAS libraries, CPU-based performance boost library, i.e. MKLDNN and GPU-based performance boosting library including CUDA, cuDNN, NCCL. and others including OpenCV, Numpy, S3-related, PS-lite dependencies. The list below shows all the dependencies and their version. Except for CUDA, cuDNN, NCCL which the user is required to install on their environments, we statically link those dependencies into libmxnet.so when we build PyPi package. By doing this, the user can take advantage of these dependencies without being worry about it.
+
+| Dependencies  | MXNet Version |
+| :------------: |:-------------:| 
+|OpenBLAS| 0.3.3 |
+|MKLDNN| 0.19 | 
+|CUDA| 10.1 |
+|cuDNN| 7.5.1 |
+|NCCL| 2.4.2 |
+|numpy| >1.16.0,<2.0.0 |
+|request| >=2.20.0,< 3.0.0 |
+|graphviz| <0.9.0,>=0.8.1 |
+|OpenCV| 3.4.2 |
+|zlib| 1.2.6 |
+|libjpeg-turbo| 2.0.2 |
+|libpng| 1.6.35 |
+|libtiff| 4-0-10 |
+|eigen| 3.3.4 |
+|libcurl| 7.61.0 |
+|libssl-dev| 1.1.1b |
+|zmq| 4.2.2 |
+|protobuf| 3.5.1 |
+|lz4| r130 |
+|cityhash| 1.1.1 |
+
+## How to update them?
+
+### MKL, MKLDNN
+
+@pengzhao-intel (https://github.com/apache/incubator-mxnet/commits?author=pengzhao-intel) and his team are tracking and updating these versions. Kudos to them!
+
+### CUDA, cuDNN, NCCL
+#### 1. Environment Setup
+We will install all the prerequsite software.
+We demonstrate with CUDA10/cuDNN7.5/NCCL 2.4.2.
+You might want to change these versions to suit your needs.
+
+```
+# Take Ubuntu 16.04 for example.
+sudo apt update
+sudo apt-get install -y git \
+    cmake \
+    libcurl4-openssl-dev \
+    unzip \
+    gcc-4.8 \
+    g++-4.8 \
+    gfortran \
+    gfortran-4.8 \
+    binutils \
+    nasm \
+    libtool \
+    curl \
+    wget \
+    sudo \
+    gnupg \
+    gnupg2 \
+    gnupg-agent \
+    pandoc \
+    python3-pip \
+    automake \
+    pkg-config \
+    openjdk-8-jdk
+    
+# CUDA installation 
+# Take CUDA 10 for example, please follow the instructions on https://developer.nvidia.com/cuda-downloads
+# Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 410.48?
+# (y)es/(n)o/(q)uit: y
+# 
+# Do you want to install the OpenGL libraries?
+# (y)es/(n)o/(q)uit [ default is yes ]:
+#
+# Do you want to run nvidia-xconfig?
+# This will update the system X configuration file so that the NVIDIA X driver
+# is used. The pre-existing X configuration file will be backed up.
+# This option should not be used on systems that require a custom
+# X configuration, such as systems with multiple GPU vendors.
+# (y)es/(n)o/(q)uit [ default is no ]:
+# 
+# Install the CUDA 10.0 Toolkit?
+# (y)es/(n)o/(q)uit: y
+#
+# Enter Toolkit Location
+# [ default is /usr/local/cuda-10.0 ]:
+#
+# Do you want to install a symbolic link at /usr/local/cuda?
+# (y)es/(n)o/(q)uit: y
+#
+# Install the CUDA 10.0 Samples?
+# (y)es/(n)o/(q)uit: n
+
+# Set LD_LIBRARY_PATH
+export LD_LIBRARY_PATH=/usr/local/cuda/lib64:${LD_LIBRARY_PATH}
+
+# Check installation
+nvidia-smi
+
+# cuDNN Setup 
+# Take cuDNN 7.5.0 with CUDA 10 for example
+# https://developer.nvidia.com/rdp/cudnn-download
+# Register with NVIDIA and download cudnn-10.0-linux-x64-v7.5.0.56.tgz
+# scp it to your instance
+# https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html
+tar -xvzf cudnn-10.0-linux-x64-v7.5.0.56.tgz
+sudo cp cuda/include/cudnn.h /usr/local/cuda/include
+sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
+sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
+# Check cuDNN version
+cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2 
+# #define CUDNN_MAJOR 7
+# #define CUDNN_MINOR 5
+# #define CUDNN_PATCHLEVEL 0
+# --
+# #define CUDNN_VERSION (CUDNN_MAJOR * 1000 + CUDNN_MINOR * 100 + CUDNN_PATCHLEVEL)
+#
+# #include "driver_types.h"
+
+# install NCCL
+# take NCCL 2.4.2 for example
+# https://developer.nvidia.com/nccl/nccl2-download-survey
+# Register with NVIDIA and download nccl-repo-ubuntu1604-2.4.2-ga-cuda10.0_1-1_amd64.deb
+sudo dpkg -i nccl-repo-ubuntu1604-2.4.2-ga-cuda10.0_1-1_amd64.deb
+sudo apt-key add /var/nccl-repo-2.4.2-ga-cuda10.0/7fa2af80.pub
+sudo apt update
+sudo apt install libnccl2 libnccl-dev
+# we will check the NCCL version later
+```
+#### 2. Build
+We will build MXNet with statically linked dependencies.
+```
+# Clone MXNet repo
+git clone --recursive https://github.com/apache/incubator-mxnet.git
+cd incubator-mxnet
+# Make sure you pin to specific commit for all the performance sanity check to make fair comparison
+# Make corresponding change on tools/setup_gpu_build_tools.sh
+# to upgrade CUDA version, please refer to PR #14887.
+# Make sure you add new makefile and right debs CUDA uses on the website
+# http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/
+
+# Build PyPi package
+tools/staticbuild/build.sh cu100mkl pip
+
+# Wait for 10 - 30 mins, you will find libmxnet.so under the incubator-mxnet/lib
+
+# Install python frontend
+pip install -e python
+# Test MXNet
+>>> import mxnet as mx
+>>> mx.runtime.feature_list()
+
+# Test NCCL version
+export NCCL_DEBUG=VERSION
+vim tests/python/gpu/test_nccl.py
+# Remove @unittest.skip("Test requires NCCL library installed and enabled during build") then run
+nosetests --verbose tests/python/gpu/test_nccl.py
+# test_nccl.test_nccl_pushpull ... NCCL version 2.4.2+cuda10.0
+# ok
+# ----------------------------------------------------------------------
+# Ran 1 test in 67.666s
+
+OK
+```
+#### 3. Performance Sanity Check
+Please run performance test aginast the MXNet you build before raising the PR.
+
+#### 4. Raise a PR
+1. Update the tools/setup_gpu_build_tools.sh please refer to PR [#14988](https://github.com/apache/incubator-mxnet/pull/14988), [#14887](https://github.com/apache/incubator-mxnet/pull/14887/files)
+2. (optional) Update the CI-related configuration/shell script/Dockerfile. Please refer to PR [#14986](https://github.com/apache/incubator-mxnet/pull/14986/files), [#14950](https://github.com/apache/incubator-mxnet/pull/14950/files)
+
+#### 5. CI Test
+1. Our CI would test PyPi and Scala publish of latest CUDA version i.e. mxnet-cu101mkl
+
+### numpy, requests, graphviz (python dependencies)
+1. Please refer to [#14588](https://github.com/apache/incubator-mxnet/pull/14588/files) and make sure the version have both of upper bound and lower bound
+#### Checklist
+- [ ] Python/setup.py
+- [ ] tools/pip/setup.py
+- [ ] ci/docker/install/docs_requirements
+- [ ] ci/docker/install/ubuntu_publish.sh
+- [ ] ci/docker/install/ubuntu_python.sh
+- [ ] ci/qemu/mxnet_requirements.txt
+- [ ] docs/install/requirements.txt 
+
+2. Build from source to do sanity check
+```
+# Compile mxnet to get libmxnet.so
+pip install -e python
+python
+>>> import mxnet as mx
+>>> mx.nd.ones((1, 2))
+[[1. 1.]]
+<NDArray 1x2 @cpu(0)>
+```
+
+### OpenCV and its dependencies: zlib, libjpeg-turbo, libpng, libtiff, eigen
+
+#### Update the build script
+1. Find the library under `tools/dependencies` and update the version.
+
+#### Sanity Check
+1. Environment Setup
+```python
+# Take Ubuntu 16.04 for example
+sudo apt update
+sudo apt-get install -y git \
+    cmake \
+    libcurl4-openssl-dev \
+    unzip \
+    gcc-4.8 \
+    g++-4.8 \
+    gfortran \
+    gfortran-4.8 \
+    binutils \
+    nasm \
+    libtool \
+    curl \
+    wget \
+    sudo \
+    gnupg \
+    gnupg2 \
+    gnupg-agent \
+    pandoc \
+    python3-pip \
+    automake \
+    pkg-config \
+    openjdk-8-jdk
+```
+2. Build PyPi package
+```
+# Update the dependency under tools/dependencies, then
+tools/staticbuild/build.sh mkl pip
+
+# Wait for 10 - 30 mins, you will find libmxnet.so under the incubator-mxnet/lib
+
+# Install python frontend
+pip install -e python
+# Test MXNet
+>>> import mxnet as mx
+>>> mx.runtime.feature_list()
+```
+
+3. Run performance tests against image related tasks
+
+
+### Other dependencies under tools/dependencies
+
+#### Update the build script
+1. Find the library under `tools/dependencies` and update the version.
+
+#### Sanity Check
+1. Environment Setup
+```python
+# Take Ubuntu 16.04 for example
+sudo apt update
+sudo apt-get install -y git \
+    cmake \
+    libcurl4-openssl-dev \
+    unzip \
+    gcc-4.8 \
+    g++-4.8 \
+    gfortran \
+    gfortran-4.8 \
+    binutils \
+    nasm \
+    libtool \
+    curl \
+    wget \
+    sudo \
+    gnupg \
+    gnupg2 \
+    gnupg-agent \
+    pandoc \
+    python3-pip \
+    automake \
+    pkg-config \
+    openjdk-8-jdk
+```
+2. Build PyPi package
+```
+# Update the dependency under tools/dependencies, then
+tools/staticbuild/build.sh mkl pip
+
+# Wait for 10 - 30 mins, you will find libmxnet.so under the incubator-mxnet/lib
+
+# Install python frontend
+pip install -e python
+# Test MXNet
+>>> import mxnet as mx
+>>> mx.runtime.feature_list()
+```

From ce62873ae24299242a9c809fd7b83060a0374163 Mon Sep 17 00:00:00 2001
From: Xinyu Chen <xinyu1.chen@intel.com>
Date: Wed, 7 Aug 2019 13:29:43 +0800
Subject: [PATCH 149/813] Add quantization support for GluonCV (#15754)

* enhance quantization api

* integrate gluoncv solution

* support gluon ssd

* enhance api

* [TODO]split to another PR

* enhance example script

* add wildcard match for exclude layers

* support int8 dtype parameter

* enable dataiter api

* use try method

* add unit test for quantize gluon

* fix lint

* fix lint 2

* fix temporary directory in python2

* fix lint

* fix try import and add todo

* trigger
---
 python/mxnet/contrib/quantization.py          | 212 +++++++++++++++++-
 tests/python/mkl/test_quantization_mkldnn.py  |   5 +-
 .../python/quantization/test_quantization.py  |  48 ++++
 3 files changed, 261 insertions(+), 4 deletions(-)

diff --git a/python/mxnet/contrib/quantization.py b/python/mxnet/contrib/quantization.py
index fa2ab1842f5f..262cc075bc89 100644
--- a/python/mxnet/contrib/quantization.py
+++ b/python/mxnet/contrib/quantization.py
@@ -26,6 +26,7 @@
 import ctypes
 import logging
 import os
+import shutil
 import numpy as np
 from ..base import _LIB, check_call, py_str
 from ..base import c_array, c_str, mx_uint, c_str_array
@@ -34,8 +35,9 @@
 from ..symbol import load as sym_load
 from .. import ndarray
 from ..ndarray import load as nd_load
+from ..ndarray import save as nd_save
 from ..ndarray import NDArray
-from ..io import DataIter
+from ..io import DataIter, DataDesc, DataBatch
 from ..context import cpu, Context
 from ..module import Module
 
@@ -420,6 +422,44 @@ def _load_params(params, logger=logging):
         raise ValueError('Unsupported params provided. Must be either a path to the param file or'
                          ' a pair of dictionaries representing arg_params and aux_params')
 
+# pylint: disable=super-init-not-called
+class _DataIterWrapper(DataIter):
+    """DataIter wrapper for general iterator, e.g., gluon dataloader"""
+    def __init__(self, calib_data):
+        self._data = calib_data
+        try:
+            calib_iter = iter(calib_data)
+        except TypeError as e:
+            raise TypeError('calib_data is not a valid iterator. {}'.format(str(e)))
+        data_example = next(calib_iter)
+        if isinstance(data_example, (list, tuple)):
+            data_example = list(data_example)
+        else:
+            data_example = [data_example]
+        # suppose there must be one label in data_example
+        num_data = len(data_example)
+        assert num_data > 0
+        self.provide_data = [DataDesc(name='data', shape=(data_example[0].shape))]
+        self.provide_data += [DataDesc(name='data{}'.format(i), shape=x.shape) for i, x in enumerate(data_example[1:])]
+        self.batch_size = data_example[0].shape[0]
+        self.reset()
+
+    def reset(self):
+        self._iter = iter(self._data)
+
+    def next(self):
+        return DataBatch(data=next(self._iter))
+# pylint: enable=super-init-not-called
+
+def _as_data_iter(calib_data):
+    """Convert normal iterator to mx.io.DataIter while parsing the data_shapes"""
+    if isinstance(calib_data, DataIter):
+        # already validated DataIter, just return
+        return calib_data, calib_data.provide_data
+
+    calib_data = _DataIterWrapper(calib_data)
+    return calib_data, calib_data.provide_data
+
 def quantize_model(sym, arg_params, aux_params,
                    data_names=('data',), label_names=('softmax_label',),
                    ctx=cpu(), excluded_sym_names=None, calib_mode='entropy',
@@ -780,3 +820,173 @@ def calib_graph(qsym, arg_params, aux_params, collector,
     qarg_params = _quantize_params(qsym, arg_params, th_dict)
 
     return qsym, qarg_params, aux_params
+
+def quantize_net(network, quantized_dtype='auto', exclude_layers=None, exclude_layers_match=None, calib_data=None,
+                 data_shapes=None, calib_mode='none', num_calib_examples=None, ctx=cpu(), logger=logging):
+    """User-level API for Gluon users to generate a quantized SymbolBlock from a FP32 HybridBlock w/ or w/o calibration.
+    The backend quantized operators are only enabled for Linux systems. Please do not run
+    inference using the quantized models on Windows for now.
+    The quantization implementation adopts the TensorFlow's approach:
+    https://www.tensorflow.org/performance/quantization.
+    The calibration implementation borrows the idea of Nvidia's 8-bit Inference with TensorRT:
+    http://on-demand.gputechconf.com/gtc/2017/presentation/s7310-8-bit-inference-with-tensorrt.pdf
+    and adapts the method to MXNet.
+
+    Parameters
+    ----------
+    network : Gluon HybridBlock
+        Defines the structure of a neural network for FP32 data types.
+    quantized_dtype : str
+        The quantized destination type for input data. Currently support 'int8'
+        , 'uint8' and 'auto'. 'auto' means automatically select output type according to calibration result.
+        Default value is 'int8'.
+    exclude_layers : list of strings
+        A list of strings representing the names of the symbols that users want to excluding
+    exclude_layers_match : list of strings
+        A list of strings wildcard matching the names of the symbols that users want to excluding
+        from being quantized.
+    calib_data : mx.io.DataIter or gluon.DataLoader
+        A iterable data loading object.
+    data_shapes : list
+        List of DataDesc, required if calib_data is not provided
+    calib_mode : str
+        If calib_mode='none', no calibration will be used and the thresholds for
+        requantization after the corresponding layers will be calculated at runtime by
+        calling min and max operators. The quantized models generated in this
+        mode are normally 10-20% slower than those with calibrations during inference.
+        If calib_mode='naive', the min and max values of the layer outputs from a calibration
+        dataset will be directly taken as the thresholds for quantization.
+        If calib_mode='entropy' (default mode), the thresholds for quantization will be
+        derived such that the KL divergence between the distributions of FP32 layer outputs and
+        quantized layer outputs is minimized based upon the calibration dataset.
+    calib_layer : function
+        Given a layer's output name in string, return True or False for deciding whether to
+        calibrate this layer. If yes, the statistics of the layer's output will be collected;
+        otherwise, no information of the layer's output will be collected. If not provided,
+        all the layers' outputs that need requantization will be collected.
+    num_calib_examples : int or None
+        The maximum number of examples that user would like to use for calibration. If not provided,
+        the whole calibration dataset will be used.
+    ctx : Context
+        Defines the device that users want to run forward propagation on the calibration
+        dataset for collecting layer output statistics. Currently, only supports single context.
+    logger : Object
+        A logging object for printing information during the process of quantization.
+
+    Returns
+    -------
+    network : Gluon SymbolBlock
+        Defines the structure of a neural network for INT8 data types.
+    -------
+    """
+
+    logger.info('Export HybridBlock')
+    network.hybridize()
+    import mxnet as mx
+    if calib_data is not None:
+        if isinstance(calib_data, DataIter):
+            dshapes = calib_data.provide_data
+        else:
+            calib_data, dshapes = _as_data_iter(calib_data)
+    if not data_shapes:
+        data_shapes = dshapes
+    if not data_shapes:
+        raise ValueError('data_shapes required')
+    data_nd = []
+    for shape in data_shapes:
+        data_nd.append(mx.nd.zeros(shape.shape))
+    while True:
+        try:
+            network(*data_nd)
+        except TypeError:
+            del data_nd[-1]
+            del calib_data.provide_data[-1]
+            continue
+        else:
+            break
+
+    import tempfile
+    try:
+        from tempfile import TemporaryDirectory
+    except ImportError:
+        # really simple implementation of TemporaryDirectory
+        class TemporaryDirectory(object):
+            def __init__(self, suffix='', prefix='', dir=''):
+                self._dirname = tempfile.mkdtemp(suffix, prefix, dir)
+
+            def __enter__(self):
+                return self._dirname
+
+            def __exit__(self, exc_type, exc_value, traceback):
+                shutil.rmtree(self._dirname)
+    # TODO(xinyu-intel): tmp solution to save and reload for mxnet.mod.Module.
+    # will enhance `export` function to return `sym, args, auxs` directly.
+    with TemporaryDirectory() as tmpdirname:
+        prefix = os.path.join(tmpdirname, 'tmp')
+        network.export(prefix, epoch=0)
+        symnet, args, auxs = mx.model.load_checkpoint(prefix, 0)
+
+    if exclude_layers is None:
+        exclude_layers = []
+    if exclude_layers_match is None:
+        exclude_layers_match = []
+    for name_match in exclude_layers_match:
+        for layers in list(symnet.get_internals()):
+            if layers.name.find(name_match) != -1:
+                exclude_layers.append(layers.name)
+    logger.info('These layers have been excluded %s' % exclude_layers)
+
+    if ctx == mx.cpu():
+        symnet = symnet.get_backend_symbol('MKLDNN_QUANTIZE')
+
+    qsym, qarg_params, aux_params, collector = quantize_graph(
+        sym=symnet, arg_params=args, aux_params=auxs, excluded_sym_names=exclude_layers,
+        calib_mode=calib_mode, calib_layer=None, quantized_dtype=quantized_dtype, logger=logger)
+
+    if calib_mode is not None and calib_mode != 'none':
+        if not isinstance(ctx, Context):
+            raise ValueError(
+                'currently only supports single ctx, while received %s' % str(ctx))
+        if calib_data is None:
+            raise ValueError(
+                'calib_data must be provided when calib_mode=%s' % calib_mode)
+        if calib_mode in ['naive', 'entropy']:
+            data_names = [pair[0] for pair in calib_data.provide_data]
+            mod = Module(symbol=symnet, context=ctx,
+                         data_names=data_names, label_names=None)
+            mod.bind(for_training=False, data_shapes=data_shapes)
+            mod.set_params(args, auxs, allow_missing=False, force_init=True)
+            num_examples = _collect_layer_statistics(mod, calib_data, collector,
+                                                     num_calib_examples, logger)
+            logger.info('Collected layer output values from FP32 model using %d examples'
+                        % num_examples)
+            qsym, qarg_params, aux_params = calib_graph(
+                qsym=qsym, arg_params=args, aux_params=auxs, collector=collector,
+                calib_mode=calib_mode, quantized_dtype=quantized_dtype, logger=logger)
+        else:
+            raise ValueError(
+                'please set calibration mode to naive or entropy.')
+    elif calib_mode is not None and calib_mode == 'none':
+        data_names = [pair[0] for pair in data_shapes]
+
+    if ctx == mx.cpu():
+        qsym = qsym.get_backend_symbol('MKLDNN_QUANTIZE')
+
+    from ..gluon import SymbolBlock
+    data_sym = []
+    for name in data_names:
+        data_sym.append(mx.sym.var(name))
+    net = SymbolBlock(qsym, data_sym)
+    # TODO(xinyu-intel): tmp solution to save param_dict and reload for SymbolBlock
+    # will enhance SymbolBlock to load args, auxs directly.
+    with TemporaryDirectory() as tmpdirname:
+        prefix = os.path.join(tmpdirname, 'tmp')
+        param_name = '%s-%04d.params' % (prefix + 'net-quantized', 0)
+        save_dict = {('arg:%s' % k): v.as_in_context(cpu())
+                     for k, v in qarg_params.items()}
+        save_dict.update({('aux:%s' % k): v.as_in_context(cpu())
+                          for k, v in aux_params.items()})
+        nd_save(param_name, save_dict)
+        net.collect_params().load(param_name, cast_dtype=True, dtype_source='saved')
+        net.collect_params().reset_ctx(ctx)
+    return net
diff --git a/tests/python/mkl/test_quantization_mkldnn.py b/tests/python/mkl/test_quantization_mkldnn.py
index 3c8cee465ec5..8ba2f2b01feb 100644
--- a/tests/python/mkl/test_quantization_mkldnn.py
+++ b/tests/python/mkl/test_quantization_mkldnn.py
@@ -27,6 +27,5 @@
 if __name__ == '__main__':
     import nose
     nose.runmodule()
-
-del os.environ['ENABLE_MKLDNN_QUANTIZATION_TEST']
-del os.environ['MXNET_SUBGRAPH_BACKEND']
+    del os.environ['ENABLE_MKLDNN_QUANTIZATION_TEST']
+    del os.environ['MXNET_SUBGRAPH_BACKEND']
diff --git a/tests/python/quantization/test_quantization.py b/tests/python/quantization/test_quantization.py
index 294e10763220..a991417b28c9 100644
--- a/tests/python/quantization/test_quantization.py
+++ b/tests/python/quantization/test_quantization.py
@@ -21,6 +21,7 @@
 import os
 import mxnet as mx
 import numpy as np
+from mxnet.gluon.model_zoo import vision
 from mxnet.test_utils import assert_almost_equal, assert_exception, rand_ndarray, rand_shape_nd, same, DummyIter
 from common import with_seed
 from mxnet.module import Module
@@ -898,6 +899,53 @@ def check_qsym_forward(qsym, qarg_params, qaux_params, data_shape, label_shape=N
     for qdtype in ['int8', 'uint8']:
         check_quantize_model(qdtype)
 
+@with_seed()
+def test_quantize_gluon_with_forward():
+    def check_quantize_net(qdtype):
+        if is_test_for_native_cpu():
+            print('skipped testing test_quantize_model_with_forward for native cpu since it is not supported yet')
+            return
+        elif qdtype == 'uint8' and is_test_for_gpu():
+            print('skipped testing test_quantize_model_with_forward for gpu uint8 since it is not supported yet')
+            return
+
+        data_shape = (32, 3, 224, 224)
+        data_shapes = [mx.io.DataDesc(name='data', shape=data_shape)]
+        label_shape = (32, 1)
+        batch_size = 1
+        resnet18_v1 = vision.resnet18_v1(pretrained=True)
+        resnet18_v1.collect_params().reset_ctx(mx.current_context())
+        excluded_names_match = []
+        if mx.current_context() == mx.gpu():
+            excluded_names_match += ['activation', 'relu', 'conv0']
+        num_calib_examples = 5
+
+        random_data = mx.random.uniform(shape=data_shape)
+        random_label = mx.random.uniform(shape=label_shape)
+        dataset = mx.gluon.data.dataset.ArrayDataset(random_data, random_label)
+        calib_data = mx.gluon.data.DataLoader(dataset, batch_size=batch_size)
+
+        quantized_resnet18_v1 = mx.contrib.quant.quantize_net(resnet18_v1, quantized_dtype=qdtype,
+                                                              exclude_layers=None,
+                                                              exclude_layers_match=excluded_names_match,
+                                                              calib_mode='none',
+                                                              data_shapes=data_shapes,
+                                                              ctx=mx.current_context())
+        quantized_resnet18_v1.hybridize(static_alloc=True, static_shape=True)
+        quantized_resnet18_v1(random_data)
+
+        quantized_resnet18_v1 = mx.contrib.quant.quantize_net(resnet18_v1, quantized_dtype=qdtype,
+                                                              exclude_layers=None,
+                                                              exclude_layers_match=excluded_names_match,
+                                                              calib_data=calib_data,
+                                                              calib_mode='naive',
+                                                              num_calib_examples=num_calib_examples,
+                                                              ctx=mx.current_context())
+        quantized_resnet18_v1.hybridize(static_alloc=True, static_shape=True)
+        quantized_resnet18_v1(random_data)
+
+    for qdtype in ['int8', 'uint8']:
+        check_quantize_net(qdtype)
 
 @with_seed()
 def test_quantize_sym_with_calib():

From 71861238743fcd8177afe52d1562d9078ac547de Mon Sep 17 00:00:00 2001
From: Marco de Abreu <marcoabreu@users.noreply.github.com>
Date: Wed, 7 Aug 2019 14:05:08 +0200
Subject: [PATCH 150/813] Revert "Dynamic Library Loading Support" (#15755)

* Revert "Dynamic Library Loading Support (#15489)"

This reverts commit 3112893f80fdda2c9d8f6832562146bec97de205.

* Retrigger CI

* CI

* retrigger CI

* retrigger CI

* retrigger CI
---
 CMakeLists.txt                                |   1 -
 Makefile                                      |   2 +-
 example/lib_api/Makefile                      |  31 -----
 example/lib_api/libtest.cc                    |  78 -----------
 example/lib_api/mylib.cc                      |  37 ------
 example/lib_api/test.py                       |  31 -----
 include/mxnet/c_api.h                         |   7 -
 include/mxnet/lib_api.h                       |  50 -------
 python/mxnet/__init__.py                      |   1 -
 python/mxnet/base.py                          |   2 +-
 python/mxnet/library.py                       |  49 -------
 src/c_api/c_api.cc                            |  15 ---
 src/common/library.cc                         | 125 ------------------
 src/common/library.h                          |  57 --------
 src/engine/naive_engine.cc                    |   5 -
 src/engine/threaded_engine.h                  |   5 -
 tests/python/gpu/test_operator_gpu.py         |   1 -
 tests/python/unittest/test_library_loading.py |  38 ------
 18 files changed, 2 insertions(+), 533 deletions(-)
 delete mode 100644 example/lib_api/Makefile
 delete mode 100644 example/lib_api/libtest.cc
 delete mode 100644 example/lib_api/mylib.cc
 delete mode 100644 example/lib_api/test.py
 delete mode 100644 include/mxnet/lib_api.h
 delete mode 100644 python/mxnet/library.py
 delete mode 100644 src/common/library.cc
 delete mode 100644 src/common/library.h
 delete mode 100644 tests/python/unittest/test_library_loading.py

diff --git a/CMakeLists.txt b/CMakeLists.txt
index a86435f9900f..7c479f756295 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -701,7 +701,6 @@ if(UNIX)
   add_library(mxnet SHARED ${DUMMY_SOURCE})
   target_link_libraries(mxnet PRIVATE ${BEGIN_WHOLE_ARCHIVE} $<TARGET_FILE:mxnet_static> ${END_WHOLE_ARCHIVE})
   target_link_libraries(mxnet PRIVATE mxnet_static)
-  target_link_libraries(mxnet_static PUBLIC ${CMAKE_DL_LIBS})
   set_target_properties(mxnet_static PROPERTIES OUTPUT_NAME mxnet)
 else()
   add_library(mxnet SHARED ${SOURCE})
diff --git a/Makefile b/Makefile
index f1e21d6fa945..b54df174acde 100644
--- a/Makefile
+++ b/Makefile
@@ -107,7 +107,7 @@ else
 	CFLAGS += -O3 -DNDEBUG=1
 endif
 CFLAGS += -I$(TPARTYDIR)/mshadow/ -I$(TPARTYDIR)/dmlc-core/include -fPIC -I$(NNVM_PATH)/include -I$(DLPACK_PATH)/include -I$(TPARTYDIR)/tvm/include -Iinclude $(MSHADOW_CFLAGS)
-LDFLAGS = -pthread -ldl $(MSHADOW_LDFLAGS) $(DMLC_LDFLAGS)
+LDFLAGS = -pthread $(MSHADOW_LDFLAGS) $(DMLC_LDFLAGS)
 
 ifeq ($(ENABLE_TESTCOVERAGE), 1)
         CFLAGS += --coverage
diff --git a/example/lib_api/Makefile b/example/lib_api/Makefile
deleted file mode 100644
index e5893c8065c4..000000000000
--- a/example/lib_api/Makefile
+++ /dev/null
@@ -1,31 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-all:
-	g++ -shared -fPIC mylib.cc -o mylib.so -I ../../include/mxnet
-
-test:
-	g++ -std=c++11 -O3 -o libtest libtest.cc -ldl -I ../../include/mxnet
-
-windows:
-	cl /LD mylib.cc
-
-win_test:
-	cl libtest.cc
-
-clean:
-	rm -rf mylib.so libtest
diff --git a/example/lib_api/libtest.cc b/example/lib_api/libtest.cc
deleted file mode 100644
index 8bdf36c05d37..000000000000
--- a/example/lib_api/libtest.cc
+++ /dev/null
@@ -1,78 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- */
-
-/*!
- * Copyright (c) 2015 by Contributors
- * \file libtest.cc
- * \brief This test checks if the library is implemented correctly
- * and does not involve dynamic loading of library into MXNet
- * This test is supposed to be run before test.py
- */
-
-#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
-#include <windows.h>
-#else
-#include <dlfcn.h>
-#endif
-
-#include <iostream>
-#include "lib_api.h"
-
-#define MXNET_VERSION 10500
-
-int main(void) {
-  // Get a handle to the library.
-#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
-  HINSTANCE handle;
-  handle = LoadLibrary(TEXT("mylib.dll"));
-#else
-  void *handle;
-  handle = dlopen("mylib.so", RTLD_LAZY);
-#endif
-
-  if (!handle) {
-    std::cerr << "Unable to load library" << std::endl;
-    return 1;
-  }
-
-  // get initialize function address from the library
-  initialize_t init_lib;
-#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
-  init_lib = (initialize_t) GetProcAddress(handle, MXLIB_INITIALIZE_STR);
-#else
-  init_lib = (initialize_t) dlsym(handle, MXLIB_INITIALIZE_STR);
-#endif
-
-  if (!init_lib) {
-    std::cerr << "Unable to get function 'intialize' from library" << std::endl;
-    return 1;
-  }
-
-  // Call the function.
-  (init_lib)(MXNET_VERSION);
-
-  // Deallocate memory.
-#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
-  FreeLibrary(handle);
-#else
-  dlclose(handle);
-#endif
-
-  return 0;
-}
diff --git a/example/lib_api/mylib.cc b/example/lib_api/mylib.cc
deleted file mode 100644
index e67560a87f3d..000000000000
--- a/example/lib_api/mylib.cc
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- */
-
-/*!
- * Copyright (c) 2015 by Contributors
- * \file mylib.cc
- * \brief Sample library file
- */
-
-#include <iostream>
-#include "lib_api.h"
-
-int initialize(int version) {
-  if (version >= 10400) {
-    std::cout << "MXNet version " << version << " supported" << std::endl;
-    return 1;
-  } else {
-    std::cout << "MXNet version " << version << " not supported" << std::endl;
-    return 0;
-  }
-}
diff --git a/example/lib_api/test.py b/example/lib_api/test.py
deleted file mode 100644
index d73d85c02ced..000000000000
--- a/example/lib_api/test.py
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/usr/bin/env python3
-
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-# coding: utf-8
-# pylint: disable=arguments-differ
-
-# This test checks if dynamic loading of library into MXNet is successful
-
-import mxnet as mx
-import os
-
-if (os.name=='posix'):
-    mx.library.load('mylib.so')
-elif (os.name=='nt'):
-    mx.library.load('mylib.dll')
diff --git a/include/mxnet/c_api.h b/include/mxnet/c_api.h
index 376c7e9d1063..058f859ae7bc 100644
--- a/include/mxnet/c_api.h
+++ b/include/mxnet/c_api.h
@@ -226,13 +226,6 @@ MXNET_DLL const char *MXGetLastError();
 // Part 0: Global State setups
 //-------------------------------------
 
-/*!
- * \brief Load library dynamically
- * \param path to the library .so file
- * \return 0 when success, -1 when failure happens.
- */
-MXNET_DLL int MXLoadLib(const char *path);
-
 /*!
  * \brief Get list of features supported on the runtime
  * \param libFeature pointer to array of LibFeature
diff --git a/include/mxnet/lib_api.h b/include/mxnet/lib_api.h
deleted file mode 100644
index ca3b2952eafa..000000000000
--- a/include/mxnet/lib_api.h
+++ /dev/null
@@ -1,50 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- */
-
-/*!
- * Copyright (c) 2015 by Contributors
- * \file lib_api.h
- * \brief APIs to interact with libraries
- */
-#ifndef MXNET_LIB_API_H_
-#define MXNET_LIB_API_H_
-
-/*!
- * \brief Following are the APIs implemented in the external library
- * Each API has a #define string that is used to lookup the function in the library
- * Followed by the function declaration
- */
-#define MXLIB_INITIALIZE_STR "initialize"
-typedef int (*initialize_t)(int);
-
-extern "C" {
-    /*!
-     * \brief Checks if the MXNet version is supported by the library.
-     * If supported, initializes the library.
-     * \param version MXNet version number passed to library and defined as:
-     *                MXNET_VERSION = (MXNET_MAJOR*10000 + MXNET_MINOR*100 + MXNET_PATCH)
-     * \return Non-zero value on error i.e. library incompatible with passed MXNet version
-     */
-#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
-    __declspec(dllexport) int __cdecl initialize(int);
-#else
-    int initialize(int);
-#endif
-}
-#endif  // MXNET_LIB_API_H_
diff --git a/python/mxnet/__init__.py b/python/mxnet/__init__.py
index 233bb2a1f57e..ab4bffde28a9 100644
--- a/python/mxnet/__init__.py
+++ b/python/mxnet/__init__.py
@@ -26,7 +26,6 @@
 from .base import MXNetError
 from .util import is_np_shape, set_np_shape, np_shape, use_np_shape
 from . import base
-from . import library
 from . import contrib
 from . import ndarray
 from . import ndarray as nd
diff --git a/python/mxnet/base.py b/python/mxnet/base.py
index 17819bde28b2..bf8026359d02 100644
--- a/python/mxnet/base.py
+++ b/python/mxnet/base.py
@@ -86,7 +86,7 @@ def __repr__(self):
 
 
 class MXNetError(Exception):
-    """Error that will be thrown by all mxnet functions."""
+    """Error that will be throwed by all mxnet functions."""
     pass
 
 
diff --git a/python/mxnet/library.py b/python/mxnet/library.py
deleted file mode 100644
index 9ebf2c2bc580..000000000000
--- a/python/mxnet/library.py
+++ /dev/null
@@ -1,49 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-# coding: utf-8
-"""Library management API of mxnet."""
-from __future__ import absolute_import
-import ctypes
-import os
-from .base import _LIB, check_call, MXNetError
-
-def load(path):
-    """Loads library dynamically.
-
-    Parameters
-    ---------
-    path : Path to library .so/.dll file
-
-    Returns
-    ---------
-    void
-    """
-    #check if path exists
-    if not os.path.exists(path):
-        raise MXNetError("load path %s does NOT exist" % path)
-    #check if path is an absolute path
-    if not os.path.isabs(path):
-        raise MXNetError("load path %s is not an absolute path" % path)
-    #check if path is to a library file
-    _, file_ext = os.path.splitext(path)
-    if not file_ext in ['.so', '.dll']:
-        raise MXNetError("load path %s is NOT a library file" % path)
-
-    byt_obj = path.encode('utf-8')
-    chararr = ctypes.c_char_p(byt_obj)
-    check_call(_LIB.MXLoadLib(chararr))
diff --git a/src/c_api/c_api.cc b/src/c_api/c_api.cc
index 5b23a8c694f6..5207bdfa444b 100644
--- a/src/c_api/c_api.cc
+++ b/src/c_api/c_api.cc
@@ -45,13 +45,11 @@
 #include "mxnet/storage.h"
 #include "mxnet/libinfo.h"
 #include "mxnet/imperative.h"
-#include "mxnet/lib_api.h"
 #include "./c_api_common.h"
 #include "../operator/custom/custom-inl.h"
 #include "../operator/tensor/matrix_op-inl.h"
 #include "../operator/tvmop/op_module.h"
 #include "../common/utils.h"
-#include "../common/library.h"
 
 using namespace mxnet;
 
@@ -92,19 +90,6 @@ inline int MXAPIGetFunctionRegInfo(const FunRegType *e,
 
 // NOTE: return value is added in API_END
 
-// Loads library and initializes it
-int MXLoadLib(const char *path) {
-  API_BEGIN();
-  void *lib = load_lib(path);
-  if (!lib)
-    LOG(FATAL) << "Unable to load library";
-
-  initialize_t initialize = get_func<initialize_t>(lib, const_cast<char*>(MXLIB_INITIALIZE_STR));
-  if (!initialize(static_cast<int>(MXNET_VERSION)))
-    LOG(FATAL) << "Library failed to initialize";
-  API_END();
-}
-
 int MXLibInfoFeatures(const struct LibFeature **lib_features, size_t *size) {
   using namespace features;
   API_BEGIN();
diff --git a/src/common/library.cc b/src/common/library.cc
deleted file mode 100644
index 9e79b5dbe1bc..000000000000
--- a/src/common/library.cc
+++ /dev/null
@@ -1,125 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- */
-
-/*!
- * Copyright (c) 2015 by Contributors
- * \file library.cc
- * \brief Dynamically loading accelerator library
- * and accessing its functions
- */
-
-#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
-#include <windows.h>
-#else
-#include <dlfcn.h>
-#endif
-
-#include <string>
-#include "library.h"
-
-#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
-/*!
- * \brief Retrieve the system error message for the last-error code
- * \param err string that gets the error message
- */
-void win_err(char **err) {
-  uint32_t dw = GetLastError();
-  FormatMessage(
-        FORMAT_MESSAGE_ALLOCATE_BUFFER |
-        FORMAT_MESSAGE_FROM_SYSTEM |
-        FORMAT_MESSAGE_IGNORE_INSERTS,
-        NULL,
-        dw,
-        MAKELANGID(LANG_NEUTRAL, SUBLANG_DEFAULT),
-        reinterpret_cast<char*>(err),
-        0, NULL);
-}
-#endif
-
-
-/*!
- * \brief Loads the dynamic shared library file
- * \param path library file location
- * \return handle a pointer for the loaded library, nullptr if loading unsuccessful
- */
-void* load_lib(const char* path) {
-  void *handle = nullptr;
-  std::string path_str(path);
-  // check if library was already loaded
-  if (loaded_libs.find(path_str) == loaded_libs.end()) {
-    // if not, load it
-#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
-    handle = LoadLibrary(path);
-    if (!handle) {
-      char *err_msg = nullptr;
-      win_err(&err_msg);
-      LOG(FATAL) << "Error loading library: '" << path << "'\n" << err_msg;
-      LocalFree(err_msg);
-      return nullptr;
-    }
-#else
-    handle = dlopen(path, RTLD_LAZY);
-    if (!handle) {
-      LOG(FATAL) << "Error loading library: '" << path << "'\n" << dlerror();
-      return nullptr;
-    }
-#endif  // _WIN32 or _WIN64 or __WINDOWS__
-    // then store the pointer to the library
-    loaded_libs[path_str] = handle;
-  } else {
-    // otherwise just look up the pointer
-    handle = loaded_libs[path_str];
-  }
-  return handle;
-}
-
-/*!
- * \brief Closes the loaded dynamic shared library file
- * \param handle library file handle
- */
-void close_lib(void* handle) {
-#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
-  FreeLibrary((HMODULE)handle);
-#else
-  dlclose(handle);
-#endif  // _WIN32 or _WIN64 or __WINDOWS__
-}
-
-/*!
- * \brief Obtains address of given function in the loaded library
- * \param handle pointer for the loaded library
- * \param func function pointer that gets output address
- * \param name function name to be fetched
- */
-void get_sym(void* handle, void** func, char* name) {
-#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
-  *func = GetProcAddress((HMODULE)handle, name);
-  if (!(*func)) {
-    char *err_msg = nullptr;
-    win_err(&err_msg);
-    LOG(FATAL) << "Error getting function '" << name << "' from library\n" << err_msg;
-    LocalFree(err_msg);
-  }
-#else
-  *func = dlsym(handle, name);
-  if (!(*func)) {
-    LOG(FATAL) << "Error getting function '" << name << "' from library\n" << dlerror();
-  }
-#endif  // _WIN32 or _WIN64 or __WINDOWS__
-}
diff --git a/src/common/library.h b/src/common/library.h
deleted file mode 100644
index d6eff4184191..000000000000
--- a/src/common/library.h
+++ /dev/null
@@ -1,57 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- */
-
-/*!
- * Copyright (c) 2015 by Contributors
- * \file library.h
- * \brief Defining library loading functions
- */
-#ifndef MXNET_COMMON_LIBRARY_H_
-#define MXNET_COMMON_LIBRARY_H_
-
-#include <iostream>
-#include <map>
-#include <string>
-#include "dmlc/io.h"
-
-// map of libraries loaded
-static std::map<std::string, void*> loaded_libs;
-
-void* load_lib(const char* path);
-void close_lib(void* handle);
-void get_sym(void* handle, void** func, char* name);
-
-/*!
- * \brief a templated function that fetches from the library
- * a function pointer of any given datatype and name
- * \param T a template parameter for data type of function pointer
- * \param lib library handle
- * \param func_name function name to search for in the library
- * \return func a function pointer
- */
-template<typename T>
-T get_func(void *lib, char *func_name) {
-  T func;
-  get_sym(lib, reinterpret_cast<void**>(&func), func_name);
-  if (!func)
-    LOG(FATAL) << "Unable to get function '" << func_name << "' from library";
-  return func;
-}
-
-#endif  // MXNET_COMMON_LIBRARY_H_
diff --git a/src/engine/naive_engine.cc b/src/engine/naive_engine.cc
index 371a6eb96303..92b38a8ea368 100644
--- a/src/engine/naive_engine.cc
+++ b/src/engine/naive_engine.cc
@@ -30,7 +30,6 @@
 #include "./openmp.h"
 #include "../common/object_pool.h"
 #include "../profiler/custom_op_profiler.h"
-#include "../../src/common/library.h"
 
 namespace mxnet {
 namespace engine {
@@ -85,10 +84,6 @@ class NaiveEngine final : public Engine {
       }
     }
 #endif
-    // close opened libraries
-    for (auto const& lib : loaded_libs) {
-      close_lib(lib.second);
-    }
   }
 
   void Stop() override {
diff --git a/src/engine/threaded_engine.h b/src/engine/threaded_engine.h
index e9ced8898305..bf74485ba442 100644
--- a/src/engine/threaded_engine.h
+++ b/src/engine/threaded_engine.h
@@ -44,7 +44,6 @@
 #include "./openmp.h"
 #include "../common/object_pool.h"
 #include "../profiler/custom_op_profiler.h"
-#include "../../src/common/library.h"
 
 namespace mxnet {
 namespace engine {
@@ -332,10 +331,6 @@ class ThreadedEngine : public Engine {
       kill_.store(true);
     }
     finished_cv_.notify_all();
-    // close opened libraries
-    for (auto const& lib : loaded_libs) {
-      close_lib(lib.second);
-    }
   }
 
  protected:
diff --git a/tests/python/gpu/test_operator_gpu.py b/tests/python/gpu/test_operator_gpu.py
index a8aa1ff853f7..91ba9fb68750 100644
--- a/tests/python/gpu/test_operator_gpu.py
+++ b/tests/python/gpu/test_operator_gpu.py
@@ -45,7 +45,6 @@
 from test_subgraph_op import *
 from test_contrib_operator import test_multibox_target_op
 from test_tvm_op import *
-from test_library_loading import *
 
 set_default_context(mx.gpu(0))
 del test_support_vector_machine_l1_svm  # noqa
diff --git a/tests/python/unittest/test_library_loading.py b/tests/python/unittest/test_library_loading.py
deleted file mode 100644
index 3d56d0c750a6..000000000000
--- a/tests/python/unittest/test_library_loading.py
+++ /dev/null
@@ -1,38 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-# This test checks if dynamic loading of library into MXNet is successful
-
-import os
-import platform
-import unittest
-import mxnet as mx
-from mxnet.test_utils import download
-
-def check_platform():
-    return platform.machine() not in ['x86_64', 'AMD64']
-
-@unittest.skipIf(check_platform(), "not all machine types supported")
-def test_library_loading():
-    if (os.name=='posix'):
-        lib = 'mylib.so'
-    elif (os.name=='nt'):
-        lib = 'mylib.dll'
-
-    fname = mx.test_utils.download('https://mxnet-demo-models.s3.amazonaws.com/lib_binary/'+lib)
-    fname = os.path.abspath(fname)
-    mx.library.load(fname)

From aadef2d666b4ca4c5b89247778146f79dd482baa Mon Sep 17 00:00:00 2001
From: Przemyslaw Tredak <ptredak@nvidia.com>
Date: Wed, 7 Aug 2019 09:22:48 -0700
Subject: [PATCH 151/813] Fix flaky test test_global_metric (#15756)

* Fix flaky test test_global_metric

* Retrigger CI

* retrigger CI

* retrigger CI

* retrigger CI
---
 tests/python/unittest/test_metric.py | 18 +++++++++++++++++-
 1 file changed, 17 insertions(+), 1 deletion(-)

diff --git a/tests/python/unittest/test_metric.py b/tests/python/unittest/test_metric.py
index d8dca753bda4..a70ffdb88987 100644
--- a/tests/python/unittest/test_metric.py
+++ b/tests/python/unittest/test_metric.py
@@ -18,6 +18,7 @@
 import mxnet as mx
 import numpy as np
 import json
+import math
 from common import with_seed
 from copy import deepcopy
 
@@ -56,6 +57,21 @@ def _create_pred_label():
             label[:shape[0] // 2] = 0
         return pred, label
 
+    def _compare_metric_result(m1, m2):
+        # Compare names
+        assert m1[0] == m2[0]
+        # Compare values
+        if isinstance(m1[1], (list, tuple)):
+            assert len(m1[1]) == len(m2[1])
+            for r1, r2 in zip(m1[1], m2[1]):
+                assert r1 == r2 or \
+                       (math.isnan(r1) and
+                        math.isnan(r2))
+        else:
+            assert m1[1] == m2[1] or \
+                   (math.isnan(m1[1]) and
+                    math.isnan(m2[1]))
+
     shape = kwargs.pop('shape', (10,10))
     use_same_shape = kwargs.pop('use_same_shape', False)
     m1 = mx.metric.create(metric, *args, **kwargs)
@@ -78,7 +94,7 @@ def _create_pred_label():
     pred, label = _create_pred_label()
     m1.update([label], [pred])
     m2.update([label], [pred])
-    assert m1.get() == m2.get()
+    _compare_metric_result(m1.get(), m2.get())
 
 @with_seed()
 def test_global_metric():

From 45db8eac70f8a7cc9647884b5cc29bb900cf5394 Mon Sep 17 00:00:00 2001
From: Wang Jiajun <wangjiajun.phy@gmail.com>
Date: Wed, 7 Aug 2019 17:18:33 -0500
Subject: [PATCH 152/813] Add matrix determinant operator in linalg (#15007)

* add backbone

* cpu forward det

* refactor for gpu forward det

* fix

* register gpu det forward

* add gpu det backward

* register gpu det backward

* fix

* add logdet slogdet backward

* stop grad for zero det

* fix

* fix

* reduce grad transfer

* fix docs

* update comments

* fix docs

* fix lint

* add test

* update docs

* add operator

* update test

* trigger CI

* remove slash

* update comments and docs

* update det helper function

* update operator check

* remove logdet

* add no grad when det = 0

* update comments and docs

* remove remaining logdet
---
 docs/api/python/symbol/linalg.md         |   2 +
 python/mxnet/contrib/amp/lists/symbol.py |   4 +
 src/operator/linalg.h                    |  50 +++--
 src/operator/linalg_impl.h               | 243 +++++++++++++++--------
 src/operator/tensor/la_op-inl.h          | 136 ++++++++++++-
 src/operator/tensor/la_op.cc             | 166 +++++++++++++---
 src/operator/tensor/la_op.cu             |  12 ++
 src/operator/tensor/la_op.h              | 180 +++++++++++++++++
 tests/python/unittest/test_operator.py   |  23 ++-
 9 files changed, 679 insertions(+), 137 deletions(-)

diff --git a/docs/api/python/symbol/linalg.md b/docs/api/python/symbol/linalg.md
index 436bab78c451..5b9afbcbb782 100644
--- a/docs/api/python/symbol/linalg.md
+++ b/docs/api/python/symbol/linalg.md
@@ -60,6 +60,8 @@ In the rest of this document, we list routines provided by the `symbol.linalg` p
     extracttrian
     maketrian
     inverse
+    det
+    slogdet
 ```
 
 ## API Reference
diff --git a/python/mxnet/contrib/amp/lists/symbol.py b/python/mxnet/contrib/amp/lists/symbol.py
index c6cc3d1b1f00..397f4775f8cd 100644
--- a/python/mxnet/contrib/amp/lists/symbol.py
+++ b/python/mxnet/contrib/amp/lists/symbol.py
@@ -433,6 +433,8 @@
     '_linalg_maketrian',
     '_linalg_extracttrian',
     '_linalg_inverse',
+    '_linalg_det',
+    '_linalg_slogdet',
     'linalg_syrk',
     'linalg_potrf',
     'linalg_potri',
@@ -446,6 +448,8 @@
     'linalg_maketrian',
     'linalg_extracttrian',
     'linalg_inverse',
+    'linalg_det',
+    'linalg_slogdet',
     '_NDArray',
     '_Native',
     '_contrib_count_sketch',
diff --git a/src/operator/linalg.h b/src/operator/linalg.h
index ee713e5548c0..8f1eedae03ac 100644
--- a/src/operator/linalg.h
+++ b/src/operator/linalg.h
@@ -195,50 +195,68 @@ int linalg_syevd_workspace_query(const Tensor<xpu, 2, DType>& A,
 
 // CPU/GPU-versions of LAPACK function "getrf". Please refer to the
 // LAPACK documentation for further details.
-// Note that this is A = getrf(A), so A is input and output parameter.
 
+// Note:
+// - A is input and output parameter (overwritten by LU)
+// - Param check_singular is only useful in cpu version. If check_singular is false,
+//   don't throw error when A is non-invertible matrix.
 template<typename xpu, typename DType>
 void linalg_getrf(const Tensor<xpu, 2, DType>& A,
-                  const Tensor<xpu, 1, DType>& work,
+                  const Tensor<xpu, 1, int>& pivot,
+                  bool check_singular,
                   Stream<xpu> *s = 0);
 
 template<typename xpu, typename DType>
 void linalg_batch_getrf(const Tensor<xpu, 3, DType>& A,
-                        const Tensor<xpu, 1, DType>& work,
+                        const Tensor<xpu, 2, int>& pivot,
+                        bool check_singular,
                         Stream<xpu> *s = 0);
 
 //////////////////////////////// GETRI ////////////////////////////////////////////
 
 // CPU/GPU-versions of LAPACK function "getri". Please refer to the
 // LAPACK documentation for further details.
-// Note that this is A = getri(A), so A is input and output parameter.
 
+// Note:
+// - pivot and LU is the output of getrf(A)
+// - LU is also the output parameter (overwritten by inverse(A))
 template<typename xpu, typename DType>
-void linalg_getri(const Tensor<xpu, 2, DType>& A,
+void linalg_getri(const Tensor<xpu, 2, DType>& LU,
+                  const Tensor<xpu, 1, int>& pivot, \
                   const Tensor<xpu, 1, DType>& work,
                   Stream<xpu> *s = 0);
 
+// Note that this function only implements GPU version with "getriBatched" in cuBLAS.
+// Unlike lapack routines in cpu, it is computed out-of-place, so the final matrix
+// inverse is stored in A.
 template<typename xpu, typename DType>
 void linalg_batch_getri(const Tensor<xpu, 3, DType>& A,
-                        const Tensor<xpu, 3, DType>& B,
-                        const Tensor<xpu, 1, DType>& work,
+                        const Tensor<xpu, 3, DType>& LU,
+                        const Tensor<xpu, 2, int>& pivot,
                         Stream<xpu> *s = 0);
 
-// This function determines the amount of workspace needed for linalg_getri to operate
-// on a batch of matrices which is returned as number of elements of type DType.
-template<typename xpu, typename DType>
-int linalg_getri_workspace_query(const Tensor<xpu, 3, DType>& A,
-                                 Stream<xpu> *s = 0);
-
 //////////////////////////////// INVERSE ////////////////////////////////////////////
 
-// CPU/GPU-versions of matrix inversion combining LAPACK function "getrf" and "getri"
+// CPU/GPU-versions of matrix inverse combining LAPACK function "getrf" and "getri"
 // Note that A = inverse(B)
 template<typename xpu, typename DType>
 void linalg_batch_inverse(const Tensor<xpu, 3, DType>& A,
                           const Tensor<xpu, 3, DType>& B,
-                          const Tensor<xpu, 1, DType>& work,
-                          Stream<xpu> *s = 0);
+                          const mxnet::OpContext& ctx);
+
+//////////////////////////////// DET ////////////////////////////////////////////
+
+// CPU/GPU-versions of helper functions used in matrix determinant operators
+
+// Helper function in determinant backward computation: compute matrix inverse
+// from LU and pivot using temp workspace, the result is stored back to LU
+template<typename xpu, typename DType>
+void linalg_batch_det_backward_helper(const Tensor<xpu, 3, DType>& LU,
+                                      const Tensor<xpu, 2, int>& pivot,
+                                      const Tensor<xpu, 1, DType>& det,
+                                      const Tensor<xpu, 3, DType>& temp,
+                                      const DType zero_det,
+                                      const mxnet::OpContext& ctx);
 
 #include "linalg_impl.h"
 
diff --git a/src/operator/linalg_impl.h b/src/operator/linalg_impl.h
index 718e3f9c5aa0..958e95555502 100644
--- a/src/operator/linalg_impl.h
+++ b/src/operator/linalg_impl.h
@@ -1243,23 +1243,40 @@ LINALG_GPU_SYEVD_WORKSPACE_QUERY(DnDsyevd, double)
 #define LINALG_CPU_GETRF(fname, DType) \
 template<> inline \
 void linalg_getrf<cpu, DType>(const Tensor<cpu, 2, DType>& A, \
-                              const Tensor<cpu, 1, DType>& work, \
-                              Stream<cpu> *s) { \
-  int *ipiv = reinterpret_cast<int *>(work.dptr_); \
+                              const Tensor<cpu, 1, int>& pivot, \
+                              bool check_singular, Stream<cpu> *s) { \
   int ret(MXNET_LAPACK_##fname(MXNET_LAPACK_COL_MAJOR, A.size(1), A.size(0), \
-                               A.dptr_, A.stride_, ipiv)); \
-  CHECK_EQ(ret, 0) << #fname << " failed in lapack on cpu."; \
+                               A.dptr_, A.stride_, pivot.dptr_)); \
+  CHECK_GE(ret, 0) << #fname << " failed in lapack on cpu."; \
+  if (check_singular) { \
+    CHECK_EQ(ret, 0) << "the input matrix is non-convertible"; \
+  } \
 }
 
 LINALG_CPU_GETRF(sgetrf, float)
 LINALG_CPU_GETRF(dgetrf, double)
 
+
+#define LINALG_CPU_BATCH_GETRF(fname, DType) \
+template<> inline \
+void linalg_batch_getrf<cpu, DType>(const Tensor<cpu, 3, DType>& A, \
+                                    const Tensor<cpu, 2, int>& pivot, \
+                                    bool check_singular, \
+                                    Stream<cpu> *s) { \
+  for (index_t i = 0; i < A.size(0); ++i) { \
+    linalg_getrf(A[i], pivot[i], check_singular); \
+  } \
+}
+
+LINALG_CPU_BATCH_GETRF(sgetrf, float)
+LINALG_CPU_BATCH_GETRF(dgetrf, double)
+
 #ifdef __CUDACC__
 
 // "getrfBatched" and "getriBatched" in cuBLAS must have DType *matrices[] as input
 // to store the pointers of each batch matrix. This kernel is used to build the
 // pointer array.
-struct set_matrix : public mxnet::op::mxnet_op::tunable {
+struct set_matrix {
   template<typename DType>
   MSHADOW_XINLINE static void Map(int i, DType **p, DType *m, int step) {
     p[i] = m + i * step;
@@ -1277,23 +1294,22 @@ struct set_matrix : public mxnet::op::mxnet_op::tunable {
 #define LINALG_GPU_BATCH_GETRF(fname, DType) \
 template<> inline \
 void linalg_batch_getrf<gpu, DType>(const Tensor<gpu, 3, DType>& A, \
-                                    const Tensor<gpu, 1, DType>& work, \
+                                    const Tensor<gpu, 2, int>& pivot, \
+                                    bool check_singular, \
                                     Stream<gpu> *s) { \
   using namespace mxnet; \
   using namespace mxnet::op::mxnet_op; \
   CHECK_NOTNULL(s); \
+  Storage::Handle info = Storage::Get()->Alloc(sizeof(int) * A.size(0), Context::GPU()); \
   Storage::Handle A_ptr_buf = Storage::Get()->Alloc(sizeof(DType *) * A.size(0), Context::GPU()); \
   DType **A_ptr = static_cast<DType **>(A_ptr_buf.dptr); \
-  const Tensor<gpu, 3, DType> temp(work.dptr_, A.shape_, s); \
-  int *pivot = reinterpret_cast<int *>(temp.dptr_ + temp.shape_.Size()); \
-  int *info = pivot + A.size(0) * A.size(1); \
-  Copy(temp, A, s); \
-  Kernel<set_matrix, gpu>::Launch(s, temp.size(0), \
-                                  A_ptr, temp.dptr_, \
-                                  temp.size(1) * temp.size(2)); \
+  Kernel<set_matrix, gpu>::Launch(s, A.size(0), \
+                                  A_ptr, A.dptr_, \
+                                  A.size(1) * A.size(2)); \
   CUBLAS_CALL(cublas##fname(Stream<gpu>::GetBlasHandle(s), \
-                            A.size(1), A_ptr, A.size(2), pivot, \
-                            info, A.size(0))) \
+                            A.size(1), A_ptr, A.size(2), pivot.dptr_, \
+                            static_cast<int *>(info.dptr), A.size(0))) \
+  Storage::Get()->Free(info); \
   Storage::Get()->Free(A_ptr_buf); \
 }
 
@@ -1302,7 +1318,8 @@ void linalg_batch_getrf<gpu, DType>(const Tensor<gpu, 3, DType>& A, \
 #define LINALG_GPU_BATCH_GETRF(fname, DType) \
 template<> inline \
 void linalg_batch_getrf<gpu, DType>(const Tensor<gpu, 3, DType>& A, \
-                                    const Tensor<gpu, 1, DType>& work, \
+                                    const Tensor<gpu, 2, int>& pivot, \
+                                    bool check_singular, \
                                     Stream<gpu> *s) { \
   LOG(FATAL) << "batched getrf requires CUDA version >= 8.0!"; \
 }
@@ -1319,38 +1336,37 @@ LINALG_GPU_BATCH_GETRF(DgetrfBatched, double)
 // CPU/GPU-versions of LAPACK function "getri"
 
 // The input of this function should be col-major for performance.
-// Tensor work holds space for ipiv, work in getri
 #define LINALG_CPU_GETRI(fname, DType) \
 template<> inline \
-void linalg_getri<cpu, DType>(const Tensor<cpu, 2, DType>& A, \
+void linalg_getri<cpu, DType>(const Tensor<cpu, 2, DType>& LU, \
+                              const Tensor<cpu, 1, int>& pivot, \
                               const Tensor<cpu, 1, DType>& work, \
                               Stream<cpu> *s) { \
-  DType wkopt; \
-  MXNET_LAPACK_##fname(MXNET_LAPACK_COL_MAJOR, A.size(0), A.dptr_, \
-                       A.stride_, nullptr, &wkopt, -1); \
-  int lwork(static_cast<int>(wkopt)); \
-  int *ipiv = reinterpret_cast<int *>(work.dptr_); \
-  DType *pwork = reinterpret_cast<DType *>(ipiv + A.size(0)); \
-  int ret(MXNET_LAPACK_##fname(MXNET_LAPACK_COL_MAJOR, A.size(0), A.dptr_, \
-                               A.stride_, ipiv, pwork, lwork)); \
+  int ret(MXNET_LAPACK_##fname(MXNET_LAPACK_COL_MAJOR, LU.size(0), LU.dptr_, \
+                               LU.stride_, pivot.dptr_, work.dptr_, work.size(0))); \
   CHECK_EQ(ret, 0) << #fname << " failed in lapack on cpu."; \
 }
 LINALG_CPU_GETRI(sgetri, float)
 LINALG_CPU_GETRI(dgetri, double)
 
-// Query workspace for the whole batch of matrices.For cpu version, the workspace
-// is re-used, so space for only one matrix is enough.
+template<typename xpu, typename DType>
+int linalg_getri_workspace_query(const Tensor<xpu, 2, DType>& A, \
+                                 Stream<cpu> *s) {
+  LOG(FATAL) << "it only takes float or double Tensor";
+  return 0;
+}
+
+// Query workspace for "getri"
 #define LINALG_CPU_GETRI_WORKSPACE_QUERY(func, DType) \
 template<> inline \
-int linalg_getri_workspace_query<cpu, DType>(const Tensor<cpu, 3, DType>& A, \
+int linalg_getri_workspace_query<cpu, DType>(const Tensor<cpu, 2, DType>& A, \
                                              Stream<cpu> *s) { \
-  const Tensor<cpu, 2, DType>& matrix = A[0]; \
   DType lwork(0); \
-  MXNET_LAPACK_##func(MXNET_LAPACK_COL_MAJOR, matrix.size(0), matrix.dptr_, \
-                      matrix.stride_, nullptr, &lwork, -1); \
-  int ipiv = (sizeof(int) * matrix.size(0) + sizeof(DType) - 1) / sizeof(DType); \
-  return ipiv + static_cast<int>(lwork); \
+  MXNET_LAPACK_##func(MXNET_LAPACK_COL_MAJOR, A.size(0), A.dptr_, \
+                      A.stride_, nullptr, &lwork, -1); \
+  return lwork; \
 }
+
 LINALG_CPU_GETRI_WORKSPACE_QUERY(sgetri, float)
 LINALG_CPU_GETRI_WORKSPACE_QUERY(dgetri, double)
 
@@ -1367,41 +1383,30 @@ LINALG_CPU_GETRI_WORKSPACE_QUERY(dgetri, double)
 #define LINALG_GPU_BATCH_GETRI(fname, DType) \
 template<> inline \
 void linalg_batch_getri<gpu, DType>(const Tensor<gpu, 3, DType>& A, \
-                                    const Tensor<gpu, 3, DType>& B, \
-                                    const Tensor<gpu, 1, DType>& work, \
+                                    const Tensor<gpu, 3, DType>& LU, \
+                                    const Tensor<gpu, 2, int>& pivot, \
                                     Stream<gpu> *s) { \
   using namespace mxnet; \
   using namespace mxnet::op::mxnet_op; \
   CHECK_NOTNULL(s); \
+  Storage::Handle info = Storage::Get()->Alloc(sizeof(int) * A.size(0), Context::GPU()); \
   Storage::Handle A_ptr_buf = Storage::Get()->Alloc(sizeof(DType *) * A.size(0), Context::GPU()); \
   DType **A_ptr = static_cast<DType **>(A_ptr_buf.dptr); \
-  Storage::Handle B_ptr_buf = Storage::Get()->Alloc(sizeof(DType *) * A.size(0), Context::GPU()); \
-  DType **B_ptr = static_cast<DType **>(B_ptr_buf.dptr); \
-  Tensor<gpu, 3, DType> temp(work.dptr_, A.shape_, s); \
-  int *pivot = reinterpret_cast<int *>(temp.dptr_ + temp.shape_.Size()); \
-  int *info = pivot + A.size(0) * A.size(1); \
+  Storage::Handle LU_ptr_buf = Storage::Get()->Alloc(sizeof(DType *) * A.size(0), Context::GPU()); \
+  DType **LU_ptr = static_cast<DType **>(LU_ptr_buf.dptr); \
   Kernel<set_matrix, gpu>::Launch(s, A.size(0), \
                                   A_ptr, A.dptr_, \
                                   A.size(1) * A.size(2)); \
-  Kernel<set_matrix, gpu>::Launch(s, temp.size(0), \
-                                  B_ptr, temp.dptr_, \
-                                  temp.size(1) * temp.size(2)); \
-  CUBLAS_CALL(cublas##fname(Stream<gpu>::GetBlasHandle(s), \
-                            A.size(1), const_cast<const DType **>(B_ptr), \
-                            B.size(2), const_cast<const int *>(pivot), \
-                            A_ptr, A.size(2), info, A.size(0))) \
+  Kernel<set_matrix, gpu>::Launch(s, LU.size(0), \
+                                  LU_ptr, LU.dptr_, \
+                                  LU.size(1) * LU.size(2)); \
+  CUBLAS_CALL(cublas##fname(Stream<gpu>::GetBlasHandle(s), A.size(1), \
+                            const_cast<const DType **>(LU_ptr), LU.size(2), \
+                            const_cast<const int *>(pivot.dptr_), A_ptr, A.size(2), \
+                            static_cast<int *>(info.dptr), A.size(0))) \
+  Storage::Get()->Free(info); \
   Storage::Get()->Free(A_ptr_buf); \
-  Storage::Get()->Free(B_ptr_buf); \
-}
-
-#define LINALG_GPU_GETRI_WORKSPACE_QUERY(fname, DType) \
-template<> inline \
-int linalg_getri_workspace_query<gpu, DType>(const Tensor<gpu, 3, DType>& A, \
-                                             Stream<gpu> *s) { \
-  int pivot_size = sizeof(int) * A.size(0) * A.size(1); \
-  int info_size = sizeof(int) * A.size(0); \
-  int matrix_size = sizeof(DType) * A.shape_.Size(); \
-  return (pivot_size + info_size + matrix_size + sizeof(DType) - 1) / sizeof(DType); \
+  Storage::Get()->Free(LU_ptr_buf); \
 }
 
 #else
@@ -1409,49 +1414,49 @@ int linalg_getri_workspace_query<gpu, DType>(const Tensor<gpu, 3, DType>& A, \
 #define LINALG_GPU_BATCH_GETRI(fname, DType) \
 template<> inline \
 void linalg_batch_getri<gpu, DType>(const Tensor<gpu, 3, DType>& A, \
-                                    const Tensor<gpu, 3, DType>& B, \
-                                    const Tensor<gpu, 1, DType>& work, \
+                                    const Tensor<gpu, 3, DType>& LU, \
+                                    const Tensor<gpu, 2, int>& pivot, \
                                     Stream<gpu> *s) { \
   LOG(FATAL) << "batched getri requires CUDA version >= 8.0!"; \
 }
 
-#define LINALG_GPU_GETRI_WORKSPACE_QUERY(fname, DType) \
-template<> inline \
-int linalg_getri_workspace_query<gpu, DType>(const Tensor<gpu, 3, DType>& A, \
-                                             Stream<gpu> *s) { \
-  LOG(FATAL) << "batched getri requires CUDA version >= 8.0!"; \
-}
-
 #endif  // CUDA_VERSION >= 8000
 
 LINALG_GPU_BATCH_GETRI(SgetriBatched, float)
 LINALG_GPU_BATCH_GETRI(DgetriBatched, double)
 
-LINALG_GPU_GETRI_WORKSPACE_QUERY(SgetriBatched, float)
-LINALG_GPU_GETRI_WORKSPACE_QUERY(DgetriBatched, double)
-
 #endif  // __CUDACC__
 
 //////////////////////////////// INVERSE ////////////////////////////////////////////
 
-// CPU/GPU-versions of matrix inversion combining LAPACK function "getrf" and "getri"
+// CPU/GPU-versions of matrix inverse combining LAPACK function "getrf" and "getri"
 
 // Note A = inverse(B)
 #define LINALG_CPU_BATCH_INVERSE(xpu, DType) \
 template<> inline \
 void linalg_batch_inverse<xpu, DType>(const Tensor<xpu, 3, DType>& A, \
                                       const Tensor<xpu, 3, DType>& B, \
-                                      const Tensor<xpu, 1, DType>& work, \
-                                      Stream<cpu> *s) { \
+                                      const mxnet::OpContext& ctx) { \
+  Stream<xpu> *s = ctx.get_stream<xpu>(); \
+  int lwork(linalg_getri_workspace_query(A[0], s)); \
+  int workspace_size = (sizeof(int) * A.size(1) + sizeof(DType) * lwork + \
+    sizeof(DType) - 1) / sizeof(DType); \
+  Tensor<xpu, 1, DType> workspace = ctx.requested[0].\
+    get_space_typed<xpu, 1, DType>(Shape1(workspace_size), s); \
+  const Tensor<xpu, 1, int> pivot(reinterpret_cast<int *>(workspace.dptr_), \
+                                  Shape1(A.size(1))); \
+  const Tensor<xpu, 1, DType> work(reinterpret_cast<DType *>(pivot.dptr_ + pivot.MSize()), \
+                                   Shape1(lwork)); \
   if (A.dptr_ != B.dptr_) Copy(A, B, s); \
   for (index_t i = 0; i < A.size(0); ++i) { \
-    linalg_getrf(A[i], work, s); \
-    linalg_getri(A[i], work, s); \
+    linalg_getrf(A[i], pivot, true, s); \
+    linalg_getri(A[i], pivot, work, s); \
   } \
 }
 LINALG_CPU_BATCH_INVERSE(cpu, float)
 LINALG_CPU_BATCH_INVERSE(cpu, double)
 
+
 #ifdef __CUDACC__
 
 // GETRF and GETRI only available with cuda8 or higher.
@@ -1461,10 +1466,21 @@ LINALG_CPU_BATCH_INVERSE(cpu, double)
 template<> inline \
 void linalg_batch_inverse<xpu, DType>(const Tensor<xpu, 3, DType>& A, \
                                       const Tensor<xpu, 3, DType>& B, \
-                                      const Tensor<xpu, 1, DType>& work, \
-                                      Stream<gpu> *s) { \
-  linalg_batch_getrf(B, work, s); \
-  linalg_batch_getri(A, B, work, s); \
+                                      const mxnet::OpContext& ctx) { \
+  Stream<xpu> *s = ctx.get_stream<xpu>(); \
+  int pivot_size = sizeof(int) * A.size(0) * A.size(1); \
+  int matrix_size = sizeof(DType) * A.shape_.Size(); \
+  int workspace_size = (pivot_size + matrix_size + \
+    sizeof(DType) - 1) / sizeof(DType); \
+  Tensor<xpu, 1, DType> workspace = ctx.requested[0].\
+    get_space_typed<xpu, 1, DType>(Shape1(workspace_size), s); \
+  const Tensor<xpu, 2, int> pivot(reinterpret_cast<int *>(workspace.dptr_), \
+                                  Shape2(A.size(0), A.size(1))); \
+  const Tensor<xpu, 3, DType> LU(reinterpret_cast<DType *>(pivot.dptr_ + pivot.MSize()), \
+                                 A.shape_); \
+  Copy(LU, B, s); \
+  linalg_batch_getrf(LU, pivot, true, s); \
+  linalg_batch_getri(A, LU, pivot, s); \
 }
 
 #else
@@ -1473,9 +1489,8 @@ void linalg_batch_inverse<xpu, DType>(const Tensor<xpu, 3, DType>& A, \
 template<> inline \
 void linalg_batch_inverse<xpu, DType>(const Tensor<xpu, 3, DType>& A, \
                                       const Tensor<xpu, 3, DType>& B, \
-                                      const Tensor<xpu, 1, DType>& work, \
-                                      Stream<gpu> *s) { \
-  LOG(FATAL) << "batched getrf and getri requires CUDA version >= 8.0!"; \
+                                      const mxnet::OpContext& ctx) { \
+  LOG(FATAL) << "gpu matrix inverse requires CUDA version >= 8.0!"; \
 }
 
 #endif  // CUDA_VERSION >= 8000
@@ -1485,4 +1500,64 @@ LINALG_GPU_BATCH_INVERSE(gpu, double)
 
 #endif  // __CUDACC__
 
+//////////////////////////////// DET ////////////////////////////////////////////
+
+// CPU/GPU-versions of helper functions used in matrix determinant operators
+
+#define LINALG_CPU_BATCH_DET_HELPER(xpu, DType) \
+template<> inline \
+void linalg_batch_det_backward_helper<xpu, DType>(const Tensor<xpu, 3, DType>& LU, \
+                                         const Tensor<xpu, 2, int>& pivot, \
+                                         const Tensor<xpu, 1, DType>& det, \
+                                         const Tensor<xpu, 3, DType>& temp, \
+                                         const DType zero_det, \
+                                         const mxnet::OpContext& ctx) { \
+  Stream<xpu> *s = ctx.get_stream<xpu>(); \
+  int lwork(linalg_getri_workspace_query(LU[0], s)); \
+  Tensor<xpu, 1, DType> work = ctx.requested[0].\
+    get_space_typed<xpu, 1, DType>(Shape1(lwork), s); \
+  for (index_t i = 0; i < LU.size(0); ++i) { \
+    if (det[i] != zero_det) { \
+      linalg_getri(LU[i], pivot[i], work, s); \
+    } \
+  } \
+}
+
+LINALG_CPU_BATCH_DET_HELPER(cpu, float)
+LINALG_CPU_BATCH_DET_HELPER(cpu, double)
+
+// GETRF and GETRI only available with cuda8 or higher.
+#if CUDA_VERSION >= 8000
+
+#define LINALG_GPU_BATCH_DET_HELPER(xpu, DType) \
+template<> inline \
+void linalg_batch_det_backward_helper<xpu, DType>(const Tensor<xpu, 3, DType>& LU, \
+                                         const Tensor<xpu, 2, int>& pivot, \
+                                         const Tensor<xpu, 1, DType>& det, \
+                                         const Tensor<xpu, 3, DType>& temp, \
+                                         const DType zero_det, \
+                                         const mxnet::OpContext& ctx) { \
+  Stream<xpu> *s = ctx.get_stream<xpu>(); \
+  linalg_batch_getri(temp, LU, pivot, s); \
+  Copy(LU, temp, s); \
+}
+
+#else
+
+#define LINALG_GPU_BATCH_DET_HELPER(xpu, DType) \
+template<> inline \
+void linalg_batch_det_backward_helper<xpu, DType>(const Tensor<xpu, 3, DType>& LU, \
+                                         const Tensor<xpu, 2, int>& pivot, \
+                                         const Tensor<xpu, 1, DType>& det, \
+                                         const Tensor<xpu, 3, DType>& temp, \
+                                         const DType zero_det, \
+                                         const mxnet::OpContext& ctx) { \
+  LOG(FATAL) << "gpu matrix inverse requires CUDA version >= 8.0!"; \
+}
+
+#endif  // CUDA_VERSION >= 8000
+
+LINALG_GPU_BATCH_DET_HELPER(gpu, float)
+LINALG_GPU_BATCH_DET_HELPER(gpu, double)
+
 #endif  // MXNET_OPERATOR_LINALG_IMPL_H_
diff --git a/src/operator/tensor/la_op-inl.h b/src/operator/tensor/la_op-inl.h
index 4dead87b3dce..42d1f4527575 100644
--- a/src/operator/tensor/la_op-inl.h
+++ b/src/operator/tensor/la_op-inl.h
@@ -458,14 +458,73 @@ struct inverse {
   template<typename xpu, typename DType>
   static void op(const Tensor<xpu, 3, DType>& B, const Tensor<xpu, 3, DType>& A,
                  const OpContext& ctx, const nnvm::NodeAttrs& attrs) {
-    Stream<xpu> *s = ctx.get_stream<xpu>();
-    // Reserve workspace (size determined by query)
-    int lwork(linalg_getri_workspace_query(A, s));
-    Tensor<xpu, 1, DType> work = ctx.requested[0]
-      .get_space_typed<xpu, 1, DType>(Shape1(lwork), s);
     // Since inverse(A) = trans(inverse(trans(A))), so we don't need to transpose
     // A even if we are using the col-major version of getrf and getri routines.
-    linalg_batch_inverse(A, B, work, s);
+    linalg_batch_inverse(A, B, ctx);
+  }
+};
+
+// this kernel computes sign(det(A)), log(abs(det(A))) from LU decomposition
+struct SignedLogDet {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int i, int N, int* pivot,
+                                  DType *LU, DType* sign, DType *logdet) {
+    int changes(0);
+    DType diag_sign(1);
+    DType diag_logsum(0);
+    int *pivot_mat = pivot + i * N;
+    DType *LU_mat = LU + i * N * N;
+    for (int j = 0; j < N; ++j) {
+      changes += (pivot_mat[j] != (j + 1));
+      DType diag = LU_mat[j * (N + 1)];
+      diag_sign *= ((DType(0) < diag) - (diag < DType(0)));
+      diag_logsum += std::log(std::abs(diag));
+    }
+    sign[i] = (changes % 2 == 1 ? DType(-1) : DType(1)) * diag_sign;
+    logdet[i] = diag_logsum;
+  }
+};
+
+// det = det(A), the computation method is based on partial pivoting LU decomposition:
+//     A = PLU, so det(A) = det(P) * det(L) * det(U),
+//     det(P) depends on number of row changes in P
+//     det(L) = 1 since L has unit diagnal elemements
+//     det(U) = prod(diag(U))
+// LU and pivot store the LU decomposition output which will be used in computing gradient
+struct det {
+  template<typename xpu, typename DType>
+  static void op(const Tensor<xpu, 3, DType>& A, const Tensor<xpu, 1, DType>& det,
+                 const Tensor<xpu, 3, DType>& LU, const Tensor<xpu, 2, int>& pivot,
+                 const OpContext& ctx, const nnvm::NodeAttrs& attrs) {
+    Stream<xpu> *s = ctx.get_stream<xpu>();
+    Tensor<xpu, 1, DType> sign = ctx.requested[0]
+      .get_space_typed<xpu, 1, DType>(det.shape_, s);
+    Copy(LU, A, s);
+    // since det(A) = det(trans(A)), so we'll use col-major blas routines here
+    linalg_batch_getrf(LU, pivot, false, s);
+    using namespace mxnet_op;
+    using namespace mshadow::expr;
+    Kernel<SignedLogDet, xpu>::Launch(s, pivot.size(0), pivot.size(1), pivot.dptr_,
+                                      LU.dptr_, sign.dptr_, det.dptr_);
+    const_cast<Tensor<xpu, 1, DType>&>(det) = sign * F<mshadow_op::exp>(det);
+  }
+};
+
+// sign = sign(det(A))
+// logabsdet = log(abs(det(A)))
+struct slogdet {
+  template<typename xpu, typename DType>
+  static void op(const Tensor<xpu, 3, DType>& A, const Tensor<xpu, 1, DType>& sign,
+                 const Tensor<xpu, 1, DType>& logabsdet, const Tensor<xpu, 3, DType>& LU,
+                 const Tensor<xpu, 2, int>& pivot, const OpContext& ctx,
+                 const nnvm::NodeAttrs& attrs) {
+    Stream<xpu> *s = ctx.get_stream<xpu>();
+    Copy(LU, A, s);
+    linalg_batch_getrf(LU, pivot, false, s);
+    using namespace mxnet_op;
+    using namespace mshadow::expr;
+    Kernel<SignedLogDet, xpu>::Launch(s, pivot.size(0), pivot.size(1), pivot.dptr_,
+                                      LU.dptr_, sign.dptr_, logabsdet.dptr_);
   }
 };
 
@@ -825,6 +884,71 @@ struct inverse_backward {
   }
 };
 
+// Here we set grad to zero if det = 0
+struct StopZeroDetGrad {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int i, int grad_step, DType *grad, DType *det, DType zero_det) {
+    int batch_ind = i / grad_step;
+    if (det[batch_ind] == zero_det) {
+      grad[i] = DType(0);
+    }
+  }
+};
+
+// Backward of det(A) is derived from Jacobi's formula.
+// The closed form solution is pretty easy when A is invertible.
+// For non-invertible A, grad is not backwarded.
+struct det_backward {
+  template<typename xpu, typename DType>
+  static void op(const Tensor<xpu, 1, DType>& ddet,
+                 const Tensor<xpu, 1, DType>& det,
+                 const Tensor<xpu, 3, DType>& LU,
+                 const Tensor<xpu, 2, int>& pivot,
+                 const Tensor<xpu, 3, DType>& dA,
+                 const OpContext& ctx, const nnvm::NodeAttrs& attrs) {
+    using namespace mshadow;
+    using namespace mshadow::expr;
+    using namespace mxnet_op;
+    // compute inverse(A) and stores it to LU
+    linalg_batch_det_backward_helper(LU, pivot, det, dA, DType(0), ctx);
+    const_cast<Tensor<xpu, 3, DType>&>(dA) = broadcast_to(reshape(det * ddet, \
+      Shape3(det.size(0), 1, 1)), mxnet::TShape(LU.shape_)) * \
+      transpose(LU, Shape3(0, 2, 1));
+    Stream<xpu> *s = ctx.get_stream<xpu>();
+    // stop grad for zero det temporarily
+    Kernel<StopZeroDetGrad, xpu>::Launch(s, dA.shape_.Size(), dA.size(1) * dA.size(2), \
+                                         dA.dptr_, det.dptr_, DType(0));
+  }
+};
+
+// Backward of slogdet(A) is derived from Jacobi's formula.
+// The closed form solution is pretty easy when A is invertible.
+// For non-invertible A, grad is not backwarded.
+// Grad is not properly defined on sign, so it's not backwarded either.
+struct slogdet_backward {
+  template<typename xpu, typename DType>
+  static void op(const Tensor<xpu, 1, DType>& dlogabsdet,
+                 const Tensor<xpu, 1, DType>& sign,
+                 const Tensor<xpu, 1, DType>& logabsdet,
+                 const Tensor<xpu, 3, DType>& LU,
+                 const Tensor<xpu, 2, int>& pivot,
+                 const Tensor<xpu, 3, DType>& dA,
+                 const OpContext& ctx, const nnvm::NodeAttrs& attrs) {
+    using namespace mshadow;
+    using namespace mshadow::expr;
+    using namespace mxnet_op;
+    // compute inverse(A) and stores it to LU
+    linalg_batch_det_backward_helper(LU, pivot, logabsdet, dA, DType(-INFINITY), ctx);
+    const_cast<Tensor<xpu, 3, DType>&>(dA) = broadcast_to(reshape(dlogabsdet, \
+      Shape3(logabsdet.size(0), 1, 1)), mxnet::TShape(LU.shape_)) * \
+      transpose(LU, Shape3(0, 2, 1));
+    Stream<xpu> *s = ctx.get_stream<xpu>();
+    // stop grad for zero det
+    Kernel<StopZeroDetGrad, xpu>::Launch(s, dA.shape_.Size(), dA.size(1) * dA.size(2), \
+                                         dA.dptr_, logabsdet.dptr_, DType(-INFINITY));
+  }
+};
+
 }  // namespace op
 }  // namespace mxnet
 
diff --git a/src/operator/tensor/la_op.cc b/src/operator/tensor/la_op.cc
index 2fa1fd3a1cb2..ce7d1d5de692 100644
--- a/src/operator/tensor/la_op.cc
+++ b/src/operator/tensor/la_op.cc
@@ -73,14 +73,14 @@ Tensor Cores on suitable NVIDIA GPUs. This can sometimes give significant speedu
 
 Examples::
 
-   // Single matrix multiply-add
+   Single matrix multiply-add
    A = [[1.0, 1.0], [1.0, 1.0]]
    B = [[1.0, 1.0], [1.0, 1.0], [1.0, 1.0]]
    C = [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]
    gemm(A, B, C, transpose_b=True, alpha=2.0, beta=10.0)
            = [[14.0, 14.0, 14.0], [14.0, 14.0, 14.0]]
 
-   // Batch matrix multiply-add
+   Batch matrix multiply-add
    A = [[[1.0, 1.0]], [[0.1, 0.1]]]
    B = [[[1.0, 1.0]], [[0.1, 0.1]]]
    C = [[[10.0]], [[0.01]]]
@@ -149,13 +149,13 @@ Tensor Cores on suitable NVIDIA GPUs. This can sometimes give significant speedu
 
 Examples::
 
-   // Single matrix multiply
+   Single matrix multiply
    A = [[1.0, 1.0], [1.0, 1.0]]
    B = [[1.0, 1.0], [1.0, 1.0], [1.0, 1.0]]
    gemm2(A, B, transpose_b=True, alpha=2.0)
             = [[4.0, 4.0, 4.0], [4.0, 4.0, 4.0]]
 
-   // Batch matrix multiply
+   Batch matrix multiply
    A = [[[1.0, 1.0]], [[0.1, 0.1]]]
    B = [[[1.0, 1.0]], [[0.1, 0.1]]]
    gemm2(A, B, transpose_b=True, alpha=2.0)
@@ -204,11 +204,11 @@ If *n>2*, *potrf* is performed separately on the trailing two dimensions for all
 
 Examples::
 
-   // Single matrix factorization
+   Single matrix factorization
    A = [[4.0, 1.0], [1.0, 4.25]]
    potrf(A) = [[2.0, 0], [0.5, 2.0]]
 
-   // Batch matrix factorization
+   Batch matrix factorization
    A = [[[4.0, 1.0], [1.0, 4.25]], [[16.0, 4.0], [4.0, 17.0]]]
    potrf(A) = [[[2.0, 0], [0.5, 2.0]], [[4.0, 0], [1.0, 4.0]]]
 )code" ADD_FILELINE)
@@ -264,11 +264,11 @@ If *n>2*, *potri* is performed separately on the trailing two dimensions for all
 
 Examples::
 
-   // Single matrix inverse
+   Single matrix inverse
    A = [[2.0, 0], [0.5, 2.0]]
    potri(A) = [[0.26563, -0.0625], [-0.0625, 0.25]]
 
-   // Batch matrix inverse
+   Batch matrix inverse
    A = [[[2.0, 0], [0.5, 2.0]], [[4.0, 0], [1.0, 4.0]]]
    potri(A) = [[[0.26563, -0.0625], [-0.0625, 0.25]],
                [[0.06641, -0.01562], [-0.01562, 0,0625]]]
@@ -320,12 +320,12 @@ If *n>2*, *trmm* is performed separately on the trailing two dimensions for all
 
 Examples::
 
-   // Single triangular matrix multiply
+   Single triangular matrix multiply
    A = [[1.0, 0], [1.0, 1.0]]
    B = [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]
    trmm(A, B, alpha=2.0) = [[2.0, 2.0, 2.0], [4.0, 4.0, 4.0]]
 
-   // Batch triangular matrix multiply
+   Batch triangular matrix multiply
    A = [[[1.0, 0], [1.0, 1.0]], [[1.0, 0], [1.0, 1.0]]]
    B = [[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], [[0.5, 0.5, 0.5], [0.5, 0.5, 0.5]]]
    trmm(A, B, alpha=2.0) = [[[2.0, 2.0, 2.0], [4.0, 4.0, 4.0]],
@@ -382,12 +382,12 @@ If *n>2*, *trsm* is performed separately on the trailing two dimensions for all
 
 Examples::
 
-   // Single matrix solve
+   Single matrix solve
    A = [[1.0, 0], [1.0, 1.0]]
    B = [[2.0, 2.0, 2.0], [4.0, 4.0, 4.0]]
    trsm(A, B, alpha=0.5) = [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]
 
-   // Batch matrix solve
+   Batch matrix solve
    A = [[[1.0, 0], [1.0, 1.0]], [[1.0, 0], [1.0, 1.0]]]
    B = [[[2.0, 2.0, 2.0], [4.0, 4.0, 4.0]],
         [[4.0, 4.0, 4.0], [8.0, 8.0, 8.0]]]
@@ -435,11 +435,11 @@ inputs (batch mode).
 
 Examples::
 
-   // Single matrix reduction
+   Single matrix reduction
    A = [[1.0, 1.0], [1.0, 7.0]]
    sumlogdiag(A) = [1.9459]
 
-   // Batch matrix reduction
+   Batch matrix reduction
    A = [[[1.0, 1.0], [1.0, 7.0]], [[3.0, 0], [0, 17.0]]]
    sumlogdiag(A) = [1.9459, 3.9318]
 )code" ADD_FILELINE)
@@ -476,7 +476,7 @@ If *n>2*, then *A* represents a batch of square matrices on the trailing two dim
 
 Examples::
 
-    // Single matrix diagonal extraction
+    Single matrix diagonal extraction
     A = [[1.0, 2.0],
          [3.0, 4.0]]
 
@@ -484,7 +484,7 @@ Examples::
 
     extractdiag(A, 1) = [2.0]
 
-    // Batch matrix diagonal extraction
+    Batch matrix diagonal extraction
     A = [[[1.0, 2.0],
           [3.0, 4.0]],
          [[5.0, 6.0],
@@ -526,7 +526,7 @@ If *n>1*, then *A* represents a batch of diagonals of square matrices. The batch
 
 Examples::
 
-    // Single diagonal matrix construction
+    Single diagonal matrix construction
     A = [1.0, 2.0]
 
     makediag(A)    = [[1.0, 0.0],
@@ -536,7 +536,7 @@ Examples::
                       [0.0, 0.0, 2.0],
                       [0.0, 0.0, 0.0]]
 
-    // Batch diagonal matrix construction
+    Batch diagonal matrix construction
     A = [[1.0, 2.0],
          [3.0, 4.0]]
 
@@ -585,7 +585,7 @@ The *offset* and *lower* parameters determine the triangle to be extracted:
 
 Examples::
 
-    // Single triagonal extraction
+    Single triagonal extraction
     A = [[1.0, 2.0],
          [3.0, 4.0]]
 
@@ -594,7 +594,7 @@ Examples::
     extracttrian(A, 1) = [2.0]
     extracttrian(A, -1) = [3.0]
 
-    // Batch triagonal extraction
+    Batch triagonal extraction
     A = [[[1.0, 2.0],
           [3.0, 4.0]],
          [[5.0, 6.0],
@@ -639,7 +639,7 @@ If *n>1*, then *A* represents a batch of triangular sub-matrices. The batch of c
 
 Examples::
 
-    // Single  matrix construction
+    Single  matrix construction
     A = [1.0, 2.0, 3.0]
 
     maketrian(A)              = [[1.0, 0.0],
@@ -655,7 +655,7 @@ Examples::
                                  [1.0, 0.0, 0.0],
                                  [2.0, 3.0, 0.0]]
 
-    // Batch matrix construction
+    Batch matrix construction
     A = [[1.0, 2.0, 3.0],
          [4.0, 5.0, 6.0]]
 
@@ -714,7 +714,7 @@ inputs (batch mode).
 
 Examples::
 
-   // Single matrix multiply
+   Single matrix multiply
    A = [[1., 2., 3.], [4., 5., 6.]]
    syrk(A, alpha=1., transpose=False)
             = [[14., 32.],
@@ -724,7 +724,7 @@ Examples::
                [22., 29., 36.],
                [27., 36., 45.]]
 
-   // Batch matrix multiply
+   Batch matrix multiply
    A = [[[1., 1.]], [[0.1, 0.1]]]
    syrk(A, alpha=2., transpose=False) = [[[4.]], [[0.04]]]
 )code" ADD_FILELINE)
@@ -775,7 +775,7 @@ inputs (batch mode).
 
 Examples::
 
-   // Single LQ factorization
+   Single LQ factorization
    A = [[1., 2., 3.], [4., 5., 6.]]
    Q, L = gelqf(A)
    Q = [[-0.26726124, -0.53452248, -0.80178373],
@@ -783,7 +783,7 @@ Examples::
    L = [[-3.74165739, 0.],
         [-8.55235974, 1.96396101]]
 
-   // Batch LQ factorization
+   Batch LQ factorization
    A = [[[1., 2., 3.], [4., 5., 6.]],
         [[7., 8., 9.], [10., 11., 12.]]]
    Q, L = gelqf(A)
@@ -847,14 +847,14 @@ mode). In this case, *U* has *n* dimensions like *A*, and *L* has *n-1* dimensio
 
 Examples::
 
-   // Single symmetric eigendecomposition
+   Single symmetric eigendecomposition
    A = [[1., 2.], [2., 4.]]
    U, L = syevd(A)
    U = [[0.89442719, -0.4472136],
         [0.4472136, 0.89442719]]
    L = [0., 5.]
 
-   // Batch symmetric eigendecomposition
+   Batch symmetric eigendecomposition
    A = [[[1., 2.], [2., 4.]],
         [[1., 2.], [2., 5.]]]
    U, L = syevd(A)
@@ -905,11 +905,11 @@ for all inputs (batch mode).
 
 Examples::
 
-   // Single matrix inversion
+   Single matrix inverse
    A = [[1., 4.], [2., 3.]]
    inverse(A) = [[-0.6, 0.8], [0.4, -0.2]]
 
-   // Batch matrix inversion
+   Batch matrix inverse
    A = [[[1., 4.], [2., 3.]],
         [[1., 3.], [2., 4.]]]
    inverse(A) = [[[-0.6, 0.8], [0.4, -0.2]],
@@ -939,5 +939,111 @@ NNVM_REGISTER_OP(_backward_linalg_inverse)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
 .set_attr<FCompute>("FCompute<cpu>", LaOpBackward<cpu, 2, 2, 2, 1, inverse_backward>);
 
+NNVM_REGISTER_OP(_linalg_det)
+.add_alias("linalg_det")
+.describe(R"code(Compute the determinant of a matrix.
+Input is a tensor *A* of dimension *n >= 2*.
+
+If *n=2*, *A* is a square matrix. We compute:
+
+  *out* = *det(A)*
+
+If *n>2*, *det* is performed separately on the trailing two dimensions
+for all inputs (batch mode).
+
+.. note:: The operator supports float32 and float64 data types only.
+.. note:: There is no gradient backwarded when A is non-invertible (which is
+          equivalent to det(A) = 0) because zero is rarely hit upon in float
+          point computation and the Jacobi's formula on determinant gradient
+          is not computationally efficient when A is non-invertible.
+
+Examples::
+
+   Single matrix determinant
+   A = [[1., 4.], [2., 3.]]
+   det(A) = [-5.]
+
+   Batch matrix determinant
+   A = [[[1., 4.], [2., 3.]],
+        [[2., 3.], [1., 4.]]]
+   det(A) = [-5., 5.]
+)code" ADD_FILELINE)
+.set_num_inputs(1)
+.set_num_outputs(3)
+.set_attr<nnvm::FListInputNames>("FListInputNames", [](const NodeAttrs& attrs)
+  { return std::vector<std::string>{"A"}; })
+.set_attr<nnvm::FNumVisibleOutputs>("FNumVisibleOutputs", [](const NodeAttrs& attrs) {
+  return 1; })
+.set_attr<mxnet::FInferShape>("FInferShape", DetShape<1>)
+.set_attr<nnvm::FInferType>("FInferType", DetType<1>)
+.set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& attrs)
+  { return std::vector<ResourceRequest>{ResourceRequest::kTempSpace}; })
+.set_attr<FCompute>("FCompute<cpu>", LaOpDetForward<cpu, 1, det>)
+.set_attr<nnvm::FGradient>("FGradient", ReduceDetGrad<1>{"_backward_linalg_det"})
+.add_argument("A", "NDArray-or-Symbol", "Tensor of square matrix");
+
+NNVM_REGISTER_OP(_backward_linalg_det)
+.set_num_inputs(4)
+.set_num_outputs(1)
+.set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& attrs)
+  { return std::vector<ResourceRequest>{ResourceRequest::kTempSpace}; })
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FCompute>("FCompute<cpu>", LaOpDetBackward<cpu, 1, det_backward>);
+
+NNVM_REGISTER_OP(_linalg_slogdet)
+.add_alias("linalg_slogdet")
+.describe(R"code(Compute the sign and log of the determinant of a matrix.
+Input is a tensor *A* of dimension *n >= 2*.
+
+If *n=2*, *A* is a square matrix. We compute:
+
+  *sign* = *sign(det(A))*
+  *logabsdet* = *log(abs(det(A)))*
+
+If *n>2*, *slogdet* is performed separately on the trailing two dimensions
+for all inputs (batch mode).
+
+.. note:: The operator supports float32 and float64 data types only.
+.. note:: The gradient is not properly defined on sign, so the gradient of
+          it is not backwarded.
+.. note:: No gradient is backwarded when A is non-invertible. Please see
+          the docs of operator det for detail.
+
+Examples::
+
+   Single matrix signed log determinant
+   A = [[2., 3.], [1., 4.]]
+   sign, logabsdet = slogdet(A)
+   sign = [1.]
+   logabsdet = [1.609438]
+
+   Batch matrix signed log determinant
+   A = [[[2., 3.], [1., 4.]],
+        [[1., 2.], [2., 4.]],
+        [[1., 2.], [4., 3.]]]
+   sign, logabsdet = slogdet(A)
+   sign = [1., 0., -1.]
+   logabsdet = [1.609438, -inf, 1.609438]
+)code" ADD_FILELINE)
+.set_num_inputs(1)
+.set_num_outputs(4)
+.set_attr<nnvm::FListInputNames>("FListInputNames", [](const NodeAttrs& attrs)
+  { return std::vector<std::string>{"A"}; })
+.set_attr<nnvm::FNumVisibleOutputs>("FNumVisibleOutputs", [](const NodeAttrs& attrs) {
+  return 2; })
+.set_attr<mxnet::FInferShape>("FInferShape", DetShape<2>)
+.set_attr<nnvm::FInferType>("FInferType", DetType<2>)
+.set_attr<FCompute>("FCompute<cpu>", LaOpDetForward<cpu, 2, slogdet>)
+.set_attr<nnvm::FGradient>("FGradient", ReduceDetGrad<2>{"_backward_linalg_slogdet"})
+.add_argument("A", "NDArray-or-Symbol", "Tensor of square matrix");
+
+NNVM_REGISTER_OP(_backward_linalg_slogdet)
+.set_num_inputs(5)
+.set_num_outputs(1)
+.set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& attrs)
+  { return std::vector<ResourceRequest>{ResourceRequest::kTempSpace}; })
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FCompute>("FCompute<cpu>", LaOpDetBackward<cpu, 2, slogdet_backward>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/tensor/la_op.cu b/src/operator/tensor/la_op.cu
index 3ef714e00c18..68c33180e3d5 100644
--- a/src/operator/tensor/la_op.cu
+++ b/src/operator/tensor/la_op.cu
@@ -99,6 +99,18 @@ NNVM_REGISTER_OP(_linalg_inverse)
 NNVM_REGISTER_OP(_backward_linalg_inverse)
 .set_attr<FCompute>("FCompute<gpu>", LaOpBackward<gpu, 2, 2, 2, 1, inverse_backward>);
 
+NNVM_REGISTER_OP(_linalg_det)
+.set_attr<FCompute>("FCompute<gpu>", LaOpDetForward<gpu, 1, det>);
+
+NNVM_REGISTER_OP(_backward_linalg_det)
+.set_attr<FCompute>("FCompute<gpu>", LaOpDetBackward<gpu, 1, det_backward>);
+
+NNVM_REGISTER_OP(_linalg_slogdet)
+.set_attr<FCompute>("FCompute<gpu>", LaOpDetForward<gpu, 2, slogdet>);
+
+NNVM_REGISTER_OP(_backward_linalg_slogdet)
+.set_attr<FCompute>("FCompute<gpu>", LaOpDetBackward<gpu, 2, slogdet_backward>);
+
 #if MXNET_USE_CUSOLVER == 1
 
 NNVM_REGISTER_OP(_linalg_potrf)
diff --git a/src/operator/tensor/la_op.h b/src/operator/tensor/la_op.h
index 5b0c7e3562dc..e024693e3819 100644
--- a/src/operator/tensor/la_op.h
+++ b/src/operator/tensor/la_op.h
@@ -406,10 +406,57 @@ inline bool InverseShape(const nnvm::NodeAttrs& attrs,
   CHECK_EQ(in_attrs->size(), 1);
   CHECK_EQ(out_attrs->size(), 1);
   const mxnet::TShape& in = (*in_attrs)[0];
+  if (!ndim_is_known(in)) return false;
   const int ndim(in.ndim());
   CHECK_GE(ndim, 2) << "Input A's dimension must be >= 2";
   CHECK_EQ(in[ndim-2], in[ndim-1]) << "Input A's last two dimension must be equal";
   SHAPE_ASSIGN_CHECK(*out_attrs, 0, in);
+  return shape_is_known(in);
+}
+
+// Shape inference function for det functions in linalg
+template<int onum>
+inline bool DetShape(const nnvm::NodeAttrs& attrs,
+                     mxnet::ShapeVector* in_attrs,
+                     mxnet::ShapeVector* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1);
+  CHECK_EQ(out_attrs->size(), onum + 2);
+  const mxnet::TShape& in = (*in_attrs)[0];
+  if (!ndim_is_known(in)) return false;
+  const int ndim(in.ndim());
+  CHECK_GE(ndim, 2) << "Input A's dimension must be >= 2";
+  CHECK_EQ(in[ndim-2], in[ndim-1]) << "Input A's last two dimension must be equal";
+  mxnet::TShape out;
+  if (ndim == 2) {
+    out = mxnet::TShape(1, 1);
+  } else {
+    out = mxnet::TShape(in.begin(), in.end() - 2);
+  }
+  for (int i = 0; i < onum; ++i) {
+    SHAPE_ASSIGN_CHECK(*out_attrs, i, out); /* sign or det or logdet */
+  }
+  SHAPE_ASSIGN_CHECK(*out_attrs, onum, in); /* LU */
+  SHAPE_ASSIGN_CHECK(*out_attrs, onum + 1, mxnet::TShape(in.begin(), in.end() - 1)); /* pivot */
+  return shape_is_known(in);
+}
+
+// Type inference function for det functions in linalg
+template<int onum>
+inline bool DetType(const nnvm::NodeAttrs& attrs,
+                    std::vector<int>* in_type,
+                    std::vector<int>* out_type) {
+  using namespace mshadow;
+  CHECK_EQ(in_type->size(), 1);
+  CHECK_EQ(out_type->size(), onum + 2);
+  const int dtype = (*in_type)[0];
+  if (dtype == -1) return false;
+  CHECK(dtype == kFloat32 || dtype == kFloat64)
+    << "This operation only supports 32-bit and 64-bit floating point";
+  for (int i = 0; i < onum; ++i) {
+    TYPE_ASSIGN_CHECK(*out_type, i, dtype);  /* sign or det or logdet */
+  }
+  TYPE_ASSIGN_CHECK(*out_type, onum, dtype);  /* LU */
+  TYPE_ASSIGN_CHECK(*out_type, onum + 1, kInt32);  /* pivot */
   return true;
 }
 
@@ -753,6 +800,139 @@ void LaOpBackwSyevd(const nnvm::NodeAttrs& attrs,
   });
 }
 
+
+template<typename xpu, typename DType, int onum, typename laop>
+struct LaOpDetForwardCaller {
+  static void op(const std::vector<TBlob>& inputs,
+                 const std::vector<TBlob>& outputs,
+                 const nnvm::NodeAttrs& attrs,
+                 const OpContext& ctx) {
+      CHECK(false) << "no specialized LaOpDetForward defined for template parameters";
+  }
+};
+template<typename xpu, typename DType, typename laop>
+struct LaOpDetForwardCaller<xpu, DType, 1, laop> {
+  static void op(const std::vector<TBlob>& inputs,
+                 const std::vector<TBlob>& outputs,
+                 const nnvm::NodeAttrs& attrs,
+                 const OpContext& ctx) {
+    mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+    laop::op(inputs[0].FlatToKD<xpu, 3, DType>(s),
+             outputs[0].FlatToKD<xpu, 1, DType>(s),
+             outputs[1].FlatToKD<xpu, 3, DType>(s),
+             outputs[2].FlatToKD<xpu, 2, int>(s), ctx, attrs);
+  }
+};
+template<typename xpu, typename DType, typename laop>
+struct LaOpDetForwardCaller<xpu, DType, 2, laop> {
+  static void op(const std::vector<TBlob>& inputs,
+                 const std::vector<TBlob>& outputs,
+                 const nnvm::NodeAttrs& attrs,
+                 const OpContext& ctx) {
+    mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+    laop::op(inputs[0].FlatToKD<xpu, 3, DType>(s),
+             outputs[0].FlatToKD<xpu, 1, DType>(s),
+             outputs[1].FlatToKD<xpu, 1, DType>(s),
+             outputs[2].FlatToKD<xpu, 3, DType>(s),
+             outputs[3].FlatToKD<xpu, 2, int>(s), ctx, attrs);
+  }
+};
+template<typename xpu, int onum, typename laop>
+void LaOpDetForward(const nnvm::NodeAttrs& attrs,
+                    const OpContext& ctx,
+                    const std::vector<TBlob>& inputs,
+                    const std::vector<OpReqType>& req,
+                    const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  CHECK_EQ(inputs.size(), 1);
+  CHECK_EQ(outputs.size(), onum + 2);
+  MSHADOW_SGL_DBL_TYPE_SWITCH(outputs[0].type_flag_, OType, {
+    LaOpDetForwardCaller<xpu, OType, onum, laop>::op(inputs, outputs, attrs, ctx);
+  });
+}
+
+template<typename xpu, typename DType, int onum, typename laop>
+struct LaOpDetBackwardCaller {
+  static void op(const std::vector<TBlob>& inputs,
+                 const std::vector<TBlob>& outputs,
+                 const nnvm::NodeAttrs& attrs,
+                 const OpContext& ctx) {
+      CHECK(false) << "no specialized LaOpDetBackward defined for template parameters";
+  }
+};
+template<typename xpu, typename DType, typename laop>
+struct LaOpDetBackwardCaller<xpu, DType, 1, laop> {
+  static void op(const std::vector<TBlob>& inputs,
+                 const std::vector<TBlob>& outputs,
+                 const nnvm::NodeAttrs& attrs,
+                 const OpContext& ctx) {
+    mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+    laop::op(inputs[0].FlatToKD<xpu, 1, DType>(s),
+             inputs[1].FlatToKD<xpu, 1, DType>(s),
+             inputs[2].FlatToKD<xpu, 3, DType>(s),
+             inputs[3].FlatToKD<xpu, 2, int>(s),
+             outputs[0].FlatToKD<xpu, 3, DType>(s), ctx, attrs);
+  }
+};
+template<typename xpu, typename DType, typename laop>
+struct LaOpDetBackwardCaller<xpu, DType, 2, laop> {
+  static void op(const std::vector<TBlob>& inputs,
+                 const std::vector<TBlob>& outputs,
+                 const nnvm::NodeAttrs& attrs,
+                 const OpContext& ctx) {
+    mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+    laop::op(inputs[0].FlatToKD<xpu, 1, DType>(s),
+             inputs[1].FlatToKD<xpu, 1, DType>(s),
+             inputs[2].FlatToKD<xpu, 1, DType>(s),
+             inputs[3].FlatToKD<xpu, 3, DType>(s),
+             inputs[4].FlatToKD<xpu, 2, int>(s),
+             outputs[0].FlatToKD<xpu, 3, DType>(s), ctx, attrs);
+  }
+};
+template<typename xpu, int onum, typename laop>
+void LaOpDetBackward(const nnvm::NodeAttrs& attrs,
+                     const OpContext& ctx,
+                     const std::vector<TBlob>& inputs,
+                     const std::vector<OpReqType>& req,
+                     const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  CHECK_EQ(inputs.size(), onum + 3);
+  CHECK_EQ(outputs.size(), 1);
+  MSHADOW_SGL_DBL_TYPE_SWITCH(outputs[0].type_flag_, OType, {
+    std::vector<TBlob> tspace(outputs);
+    for ( int i = 0; i < onum; ++i ) {
+      if ( req[i] == kAddTo ) {
+        tspace[i].dptr_ = ctx.requested[0]
+                             .get_space_typed<xpu, 1, OType>(Shape1(outputs[i].Size()), s).dptr_;
+      }
+    }
+    LaOpDetBackwardCaller<xpu, OType, onum, laop>::op(inputs, tspace, attrs, ctx);
+    for ( int i = 0; i < onum; ++i ) {
+      if ( req[i] == kAddTo ) {
+        Tensor<xpu, 1, OType> out = outputs[i].FlatTo1D<xpu, OType>(s);
+        out += tspace[i].FlatTo1D<xpu, OType>(s);
+      }
+    }
+  });
+}
+
+// Only transfer ddet and outputs to gradient
+template<int onum>
+struct ReduceDetGrad {
+  const char *op_name;
+  std::vector<nnvm::NodeEntry> operator()(const nnvm::NodePtr& n,
+                                          const std::vector<nnvm::NodeEntry>& ograds) {
+    std::vector<nnvm::NodeEntry> heads;
+    heads.push_back(ograds[onum - 1]);
+    uint32_t n_out = n->num_outputs();
+    for (uint32_t i = 0; i < n_out; ++i) {
+      heads.emplace_back(nnvm::NodeEntry{n, i, 0});
+    }
+    return MakeGradNode(op_name, n, heads, n->attrs.dict);
+  }
+};
+
 }  // namespace op
 }  // namespace mxnet
 
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 72bf5864ff4b..027ec3d12a4b 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -6842,8 +6842,14 @@ def test_laop_6():
         check_numeric_gradient(sym, location, numeric_eps=num_eps, rtol=rtol_bw,
                                atol=atol_bw, dtype=dtype)
 
-    a = np.sqrt(np.arange(4 * 4)).reshape(4, 4)
+    ## det(I + dot(v, v.T)) = 1 + dot(v.T, v) >= 1, so it's always invertible;
+    ## det is away from zero, so the value of logdet is stable
+    v = np.random.random(4)
+    a = np.eye(4) + np.outer(v, v)
     a = np.tile(a, (3, 1, 1))
+    permute_mat = np.eye(4)[[1, 0, 2, 3]]
+
+    # test matrix inverse
     r = np.eye(4)
     r = np.tile(r, (3, 1, 1))
     test_inverse = mx.sym.linalg.inverse(data)
@@ -6851,6 +6857,21 @@ def test_laop_6():
     check_fw(test_eye, [a], [r])
     check_grad(test_inverse, [a])
 
+    # test matrix determinant
+    # det
+    r = np.linalg.det(a)
+    test_det = mx.sym.linalg.det(data)
+    check_fw(test_det, [a], [r])
+    check_grad(test_det, [a])
+    # test slogdet
+    r1 = np.array([1., 1., 1.])
+    r2 = np.log(np.abs(np.linalg.det(a)))
+    test_sign, test_logabsdet = mx.sym.linalg.slogdet(data)
+    check_fw(test_sign, [a], [r1])
+    check_fw(test_sign, [np.dot(a, permute_mat)], [-r1])
+    check_fw(test_logabsdet, [a], [r2])
+    check_grad(test_logabsdet, [a])
+
 @with_seed()
 def test_stack():
     for _ in range(100):

From 07eb482670c5e7891b6baa0184f361a9b9621786 Mon Sep 17 00:00:00 2001
From: Haozheng Fan <fhztc1997618@gmail.com>
Date: Thu, 8 Aug 2019 07:56:26 +0800
Subject: [PATCH 153/813] fix tvm cmake (#15781)

---
 CMakeLists.txt                      | 2 +-
 cmake/BuildTVM.cmake                | 2 +-
 src/operator/contrib/tvmop/ufunc.cc | 4 ++--
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 7c479f756295..b33d195001ef 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -751,7 +751,7 @@ if(USE_TVM_OP)
   add_custom_command(TARGET mxnet POST_BUILD
     COMMAND ${CMAKE_COMMAND} -E env
           PYTHONPATH="${CMAKE_CURRENT_SOURCE_DIR}/3rdparty/tvm/python:${CMAKE_CURRENT_SOURCE_DIR}/3rdparty/tvm/topi/python:${CMAKE_CURRENT_SOURCE_DIR}/contrib"
-          LD_LIBRARY_PATH="${CMAKE_CURRENT_BINARY_DIR}/3rdparty/tvm/build"
+          LD_LIBRARY_PATH="${CMAKE_CURRENT_BINARY_DIR}/3rdparty/tvm"
           ${Python3_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/contrib/tvmop/compile.py -o${CMAKE_CURRENT_BINARY_DIR}/libtvmop.so
     )
 endif()
diff --git a/cmake/BuildTVM.cmake b/cmake/BuildTVM.cmake
index ad8517cafe26..db8b33b84596 100644
--- a/cmake/BuildTVM.cmake
+++ b/cmake/BuildTVM.cmake
@@ -16,7 +16,7 @@
 # under the License.
 
 message(STATUS "Prepare external packages for TVM...")
-execute_process(COMMAND "${CMAKE_CURRENT_SOURCE_DIR}/contrib/tvmop/prepare_tvm.sh")
+execute_process(COMMAND "sh" "${CMAKE_CURRENT_SOURCE_DIR}/contrib/tvmop/prepare_tvm.sh")
 
 # Whether enable ROCM runtime
 #
diff --git a/src/operator/contrib/tvmop/ufunc.cc b/src/operator/contrib/tvmop/ufunc.cc
index faba671322e2..3475a211cfec 100644
--- a/src/operator/contrib/tvmop/ufunc.cc
+++ b/src/operator/contrib/tvmop/ufunc.cc
@@ -56,10 +56,10 @@ NNVM_REGISTER_OP(_contrib_tvm_vadd)
     .add_argument("b", "NDArray-or-Symbol", "second input")
     .set_attr<mxnet::FInferShape>("FInferShape", BinaryBroadcastShape)
     .set_attr<nnvm::FInferType>("FInferType", mxnet::op::ElemwiseType<2, 1>)
-    .set_attr<mxnet::FCompute>("FCompute<cpu>", mxnet::op::TVMBroadcastCompute<func_vadd_cpu>)
 #if MXNET_USE_CUDA
-    .set_attr<mxnet::FCompute>("FCompute<gpu>", mxnet::op::TVMBroadcastCompute<func_vadd_gpu>);
+    .set_attr<mxnet::FCompute>("FCompute<gpu>", mxnet::op::TVMBroadcastCompute<func_vadd_gpu>)
 #endif  // MXNET_USE_CUDA
+    .set_attr<mxnet::FCompute>("FCompute<cpu>", mxnet::op::TVMBroadcastCompute<func_vadd_cpu>);
 
 }  // namespace op
 }  // namespace mxnet

From a2b11aed6851c20f7fcf419849dbb3f4b8c4c192 Mon Sep 17 00:00:00 2001
From: Manu Seth <22492939+mseth10@users.noreply.github.com>
Date: Wed, 7 Aug 2019 17:34:29 -0700
Subject: [PATCH 154/813] Fix PR #15489 (Dynamic Library Loading Support)
 (#15760)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Dynamic Library Loading Support (#15489)

* Accelerator APIs header file

* adding example to test accelerator loading

* adding c_api function header

* modifying header file path

* creating templates for call to lib fns

* modifying target to mxnet_static for libdl

* rebaseing with master

* returning nullptr handle if library not loaded

* refactoring code to load libraries dynamically

* addressing review comments

* using static cast

* pylint fix

* moving library.h file to src/common/

* adding dynamic loading support for windows

* updating header guard

* fixing headers

* fixing windows casting error

* declaring library functions for windows

* adding library testing module in examples

* adding unit test to test library loading

* correcting file names

* updating error messages

* getting error message from DL library

* adding unit test to gpu suite

* correcting windows pointer

* requiring absolute path to library

* changing file description

* addressing review comments - adding more docs, windows error msg

* addressing PR comments

* checking machine type for unit test

* “re-trigger”

* added map to store loaded libraries

* added dlclose calls in naive & threaded engines

* removed library map declaration in cc file

* added windows free

* fixed formatting

* added cast to HMODULE for void* for windows

* retrigger CI for flaky unix_cpu

* build library and stash it in CI

* modifying unittest to use CI built binary

* Retrigger CI

* adding dlclose to initialize destructor

* adding sample_lib target to all in Makefile
---
 CMakeLists.txt                                |   6 +
 Makefile                                      |   7 +-
 ci/jenkins/Jenkins_steps.groovy               |  10 +-
 example/lib_api/Makefile                      |  31 +++++
 example/lib_api/libtest.cc                    |  78 +++++++++++
 example/lib_api/mylib.cc                      |  37 ++++++
 example/lib_api/test.py                       |  31 +++++
 include/mxnet/c_api.h                         |   7 +
 include/mxnet/lib_api.h                       |  50 +++++++
 python/mxnet/__init__.py                      |   1 +
 python/mxnet/base.py                          |   2 +-
 python/mxnet/library.py                       |  49 +++++++
 src/c_api/c_api.cc                            |  15 +++
 src/common/library.cc                         | 125 ++++++++++++++++++
 src/common/library.h                          |  57 ++++++++
 src/initialize.cc                             |   8 ++
 tests/python/gpu/test_operator_gpu.py         |   1 +
 tests/python/unittest/test_library_loading.py |  48 +++++++
 18 files changed, 555 insertions(+), 8 deletions(-)
 create mode 100644 example/lib_api/Makefile
 create mode 100644 example/lib_api/libtest.cc
 create mode 100644 example/lib_api/mylib.cc
 create mode 100644 example/lib_api/test.py
 create mode 100644 include/mxnet/lib_api.h
 create mode 100644 python/mxnet/library.py
 create mode 100644 src/common/library.cc
 create mode 100644 src/common/library.h
 create mode 100644 tests/python/unittest/test_library_loading.py

diff --git a/CMakeLists.txt b/CMakeLists.txt
index b33d195001ef..85f302f867e2 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -691,6 +691,8 @@ else()
 
 endif()
 
+add_library(sample_lib SHARED ${CMAKE_CURRENT_SOURCE_DIR}/example/lib_api/mylib.cc)
+target_include_directories(sample_lib PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/include/mxnet)
 set(MXNET_INSTALL_TARGETS mxnet)
 if(UNIX)
   # Create dummy file since we want an empty shared library before linking
@@ -701,9 +703,13 @@ if(UNIX)
   add_library(mxnet SHARED ${DUMMY_SOURCE})
   target_link_libraries(mxnet PRIVATE ${BEGIN_WHOLE_ARCHIVE} $<TARGET_FILE:mxnet_static> ${END_WHOLE_ARCHIVE})
   target_link_libraries(mxnet PRIVATE mxnet_static)
+  target_link_libraries(mxnet_static PUBLIC ${CMAKE_DL_LIBS})
+  target_compile_options(sample_lib PUBLIC -shared)
   set_target_properties(mxnet_static PROPERTIES OUTPUT_NAME mxnet)
 else()
   add_library(mxnet SHARED ${SOURCE})
+  target_compile_options(sample_lib PUBLIC /LD)
+  set_target_properties(sample_lib PROPERTIES PREFIX "lib")
 endif()
 
 if(USE_CUDA)
diff --git a/Makefile b/Makefile
index b54df174acde..a14c29452f6e 100644
--- a/Makefile
+++ b/Makefile
@@ -107,7 +107,7 @@ else
 	CFLAGS += -O3 -DNDEBUG=1
 endif
 CFLAGS += -I$(TPARTYDIR)/mshadow/ -I$(TPARTYDIR)/dmlc-core/include -fPIC -I$(NNVM_PATH)/include -I$(DLPACK_PATH)/include -I$(TPARTYDIR)/tvm/include -Iinclude $(MSHADOW_CFLAGS)
-LDFLAGS = -pthread $(MSHADOW_LDFLAGS) $(DMLC_LDFLAGS)
+LDFLAGS = -pthread -ldl $(MSHADOW_LDFLAGS) $(DMLC_LDFLAGS)
 
 ifeq ($(ENABLE_TESTCOVERAGE), 1)
         CFLAGS += --coverage
@@ -454,7 +454,7 @@ endif
 .PHONY: clean all extra-packages test lint docs clean_all rcpplint rcppexport roxygen\
 	cython2 cython3 cython cyclean
 
-all: lib/libmxnet.a lib/libmxnet.so $(BIN) extra-packages
+all: lib/libmxnet.a lib/libmxnet.so $(BIN) extra-packages sample_lib
 
 SRC = $(wildcard src/*/*/*/*.cc src/*/*/*.cc src/*/*.cc src/*.cc)
 OBJ = $(patsubst %.cc, build/%.o, $(SRC))
@@ -659,6 +659,9 @@ cpplint:
 pylint:
 	python3 -m pylint --rcfile=$(ROOTDIR)/ci/other/pylintrc --ignore-patterns=".*\.so$$,.*\.dll$$,.*\.dylib$$" python/mxnet tools/caffe_converter/*.py
 
+sample_lib:
+	$(CXX) -shared -fPIC example/lib_api/mylib.cc -o libsample_lib.so -I include/mxnet
+
 doc: docs
 
 docs:
diff --git a/ci/jenkins/Jenkins_steps.groovy b/ci/jenkins/Jenkins_steps.groovy
index c27a61383e46..f653e0c5cb63 100644
--- a/ci/jenkins/Jenkins_steps.groovy
+++ b/ci/jenkins/Jenkins_steps.groovy
@@ -23,8 +23,8 @@
 utils = load('ci/Jenkinsfile_utils.groovy')
 
 // mxnet libraries
-mx_lib = 'lib/libmxnet.so, lib/libmxnet.a, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a'
-mx_lib_cython = 'lib/libmxnet.so, lib/libmxnet.a, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
+mx_lib = 'lib/libmxnet.so, lib/libmxnet.a, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a'
+mx_lib_cython = 'lib/libmxnet.so, lib/libmxnet.a, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
 
 // Python wheels
 mx_pip = 'build/*.whl'
@@ -33,11 +33,11 @@ mx_pip = 'build/*.whl'
 mx_cmake_lib = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so'
 mx_cmake_lib_cython = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
 // mxnet cmake libraries, in cmake builds we do not produce a libnvvm static library by default.
-mx_cmake_lib_debug = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests'
+mx_cmake_lib_debug = 'build/libmxnet.so, build/libmxnet.a, build/libsample_lib.so, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests'
 mx_cmake_mkldnn_lib = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so, build/3rdparty/mkldnn/src/libmkldnn.so.0'
-mx_mkldnn_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libiomp5.so, lib/libmkldnn.so.0, lib/libmklml_intel.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a'
+mx_mkldnn_lib = 'lib/libmxnet.so, lib/libmxnet.a, libsample_lib.so, lib/libiomp5.so, lib/libmkldnn.so.0, lib/libmklml_intel.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a'
 mx_tensorrt_lib = 'build/libmxnet.so, lib/libnvonnxparser_runtime.so.0, lib/libnvonnxparser.so.0, lib/libonnx_proto.so, lib/libonnx.so'
-mx_lib_cpp_examples = 'lib/libmxnet.so, lib/libmxnet.a, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, 3rdparty/ps-lite/build/libps.a, deps/lib/libprotobuf-lite.a, deps/lib/libzmq.a, build/cpp-package/example/*, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
+mx_lib_cpp_examples = 'lib/libmxnet.so, lib/libmxnet.a, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, 3rdparty/ps-lite/build/libps.a, deps/lib/libprotobuf-lite.a, deps/lib/libzmq.a, build/cpp-package/example/*, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
 mx_lib_cpp_examples_cpu = 'build/libmxnet.so, build/cpp-package/example/*'
 
 // Python unittest for CPU
diff --git a/example/lib_api/Makefile b/example/lib_api/Makefile
new file mode 100644
index 000000000000..e5893c8065c4
--- /dev/null
+++ b/example/lib_api/Makefile
@@ -0,0 +1,31 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+all:
+	g++ -shared -fPIC mylib.cc -o mylib.so -I ../../include/mxnet
+
+test:
+	g++ -std=c++11 -O3 -o libtest libtest.cc -ldl -I ../../include/mxnet
+
+windows:
+	cl /LD mylib.cc
+
+win_test:
+	cl libtest.cc
+
+clean:
+	rm -rf mylib.so libtest
diff --git a/example/lib_api/libtest.cc b/example/lib_api/libtest.cc
new file mode 100644
index 000000000000..8bdf36c05d37
--- /dev/null
+++ b/example/lib_api/libtest.cc
@@ -0,0 +1,78 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2015 by Contributors
+ * \file libtest.cc
+ * \brief This test checks if the library is implemented correctly
+ * and does not involve dynamic loading of library into MXNet
+ * This test is supposed to be run before test.py
+ */
+
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+#include <windows.h>
+#else
+#include <dlfcn.h>
+#endif
+
+#include <iostream>
+#include "lib_api.h"
+
+#define MXNET_VERSION 10500
+
+int main(void) {
+  // Get a handle to the library.
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+  HINSTANCE handle;
+  handle = LoadLibrary(TEXT("mylib.dll"));
+#else
+  void *handle;
+  handle = dlopen("mylib.so", RTLD_LAZY);
+#endif
+
+  if (!handle) {
+    std::cerr << "Unable to load library" << std::endl;
+    return 1;
+  }
+
+  // get initialize function address from the library
+  initialize_t init_lib;
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+  init_lib = (initialize_t) GetProcAddress(handle, MXLIB_INITIALIZE_STR);
+#else
+  init_lib = (initialize_t) dlsym(handle, MXLIB_INITIALIZE_STR);
+#endif
+
+  if (!init_lib) {
+    std::cerr << "Unable to get function 'intialize' from library" << std::endl;
+    return 1;
+  }
+
+  // Call the function.
+  (init_lib)(MXNET_VERSION);
+
+  // Deallocate memory.
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+  FreeLibrary(handle);
+#else
+  dlclose(handle);
+#endif
+
+  return 0;
+}
diff --git a/example/lib_api/mylib.cc b/example/lib_api/mylib.cc
new file mode 100644
index 000000000000..e67560a87f3d
--- /dev/null
+++ b/example/lib_api/mylib.cc
@@ -0,0 +1,37 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2015 by Contributors
+ * \file mylib.cc
+ * \brief Sample library file
+ */
+
+#include <iostream>
+#include "lib_api.h"
+
+int initialize(int version) {
+  if (version >= 10400) {
+    std::cout << "MXNet version " << version << " supported" << std::endl;
+    return 1;
+  } else {
+    std::cout << "MXNet version " << version << " not supported" << std::endl;
+    return 0;
+  }
+}
diff --git a/example/lib_api/test.py b/example/lib_api/test.py
new file mode 100644
index 000000000000..d73d85c02ced
--- /dev/null
+++ b/example/lib_api/test.py
@@ -0,0 +1,31 @@
+#!/usr/bin/env python3
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# coding: utf-8
+# pylint: disable=arguments-differ
+
+# This test checks if dynamic loading of library into MXNet is successful
+
+import mxnet as mx
+import os
+
+if (os.name=='posix'):
+    mx.library.load('mylib.so')
+elif (os.name=='nt'):
+    mx.library.load('mylib.dll')
diff --git a/include/mxnet/c_api.h b/include/mxnet/c_api.h
index 058f859ae7bc..376c7e9d1063 100644
--- a/include/mxnet/c_api.h
+++ b/include/mxnet/c_api.h
@@ -226,6 +226,13 @@ MXNET_DLL const char *MXGetLastError();
 // Part 0: Global State setups
 //-------------------------------------
 
+/*!
+ * \brief Load library dynamically
+ * \param path to the library .so file
+ * \return 0 when success, -1 when failure happens.
+ */
+MXNET_DLL int MXLoadLib(const char *path);
+
 /*!
  * \brief Get list of features supported on the runtime
  * \param libFeature pointer to array of LibFeature
diff --git a/include/mxnet/lib_api.h b/include/mxnet/lib_api.h
new file mode 100644
index 000000000000..ca3b2952eafa
--- /dev/null
+++ b/include/mxnet/lib_api.h
@@ -0,0 +1,50 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2015 by Contributors
+ * \file lib_api.h
+ * \brief APIs to interact with libraries
+ */
+#ifndef MXNET_LIB_API_H_
+#define MXNET_LIB_API_H_
+
+/*!
+ * \brief Following are the APIs implemented in the external library
+ * Each API has a #define string that is used to lookup the function in the library
+ * Followed by the function declaration
+ */
+#define MXLIB_INITIALIZE_STR "initialize"
+typedef int (*initialize_t)(int);
+
+extern "C" {
+    /*!
+     * \brief Checks if the MXNet version is supported by the library.
+     * If supported, initializes the library.
+     * \param version MXNet version number passed to library and defined as:
+     *                MXNET_VERSION = (MXNET_MAJOR*10000 + MXNET_MINOR*100 + MXNET_PATCH)
+     * \return Non-zero value on error i.e. library incompatible with passed MXNet version
+     */
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+    __declspec(dllexport) int __cdecl initialize(int);
+#else
+    int initialize(int);
+#endif
+}
+#endif  // MXNET_LIB_API_H_
diff --git a/python/mxnet/__init__.py b/python/mxnet/__init__.py
index ab4bffde28a9..233bb2a1f57e 100644
--- a/python/mxnet/__init__.py
+++ b/python/mxnet/__init__.py
@@ -26,6 +26,7 @@
 from .base import MXNetError
 from .util import is_np_shape, set_np_shape, np_shape, use_np_shape
 from . import base
+from . import library
 from . import contrib
 from . import ndarray
 from . import ndarray as nd
diff --git a/python/mxnet/base.py b/python/mxnet/base.py
index bf8026359d02..17819bde28b2 100644
--- a/python/mxnet/base.py
+++ b/python/mxnet/base.py
@@ -86,7 +86,7 @@ def __repr__(self):
 
 
 class MXNetError(Exception):
-    """Error that will be throwed by all mxnet functions."""
+    """Error that will be thrown by all mxnet functions."""
     pass
 
 
diff --git a/python/mxnet/library.py b/python/mxnet/library.py
new file mode 100644
index 000000000000..9ebf2c2bc580
--- /dev/null
+++ b/python/mxnet/library.py
@@ -0,0 +1,49 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# coding: utf-8
+"""Library management API of mxnet."""
+from __future__ import absolute_import
+import ctypes
+import os
+from .base import _LIB, check_call, MXNetError
+
+def load(path):
+    """Loads library dynamically.
+
+    Parameters
+    ---------
+    path : Path to library .so/.dll file
+
+    Returns
+    ---------
+    void
+    """
+    #check if path exists
+    if not os.path.exists(path):
+        raise MXNetError("load path %s does NOT exist" % path)
+    #check if path is an absolute path
+    if not os.path.isabs(path):
+        raise MXNetError("load path %s is not an absolute path" % path)
+    #check if path is to a library file
+    _, file_ext = os.path.splitext(path)
+    if not file_ext in ['.so', '.dll']:
+        raise MXNetError("load path %s is NOT a library file" % path)
+
+    byt_obj = path.encode('utf-8')
+    chararr = ctypes.c_char_p(byt_obj)
+    check_call(_LIB.MXLoadLib(chararr))
diff --git a/src/c_api/c_api.cc b/src/c_api/c_api.cc
index 5207bdfa444b..5b23a8c694f6 100644
--- a/src/c_api/c_api.cc
+++ b/src/c_api/c_api.cc
@@ -45,11 +45,13 @@
 #include "mxnet/storage.h"
 #include "mxnet/libinfo.h"
 #include "mxnet/imperative.h"
+#include "mxnet/lib_api.h"
 #include "./c_api_common.h"
 #include "../operator/custom/custom-inl.h"
 #include "../operator/tensor/matrix_op-inl.h"
 #include "../operator/tvmop/op_module.h"
 #include "../common/utils.h"
+#include "../common/library.h"
 
 using namespace mxnet;
 
@@ -90,6 +92,19 @@ inline int MXAPIGetFunctionRegInfo(const FunRegType *e,
 
 // NOTE: return value is added in API_END
 
+// Loads library and initializes it
+int MXLoadLib(const char *path) {
+  API_BEGIN();
+  void *lib = load_lib(path);
+  if (!lib)
+    LOG(FATAL) << "Unable to load library";
+
+  initialize_t initialize = get_func<initialize_t>(lib, const_cast<char*>(MXLIB_INITIALIZE_STR));
+  if (!initialize(static_cast<int>(MXNET_VERSION)))
+    LOG(FATAL) << "Library failed to initialize";
+  API_END();
+}
+
 int MXLibInfoFeatures(const struct LibFeature **lib_features, size_t *size) {
   using namespace features;
   API_BEGIN();
diff --git a/src/common/library.cc b/src/common/library.cc
new file mode 100644
index 000000000000..9e79b5dbe1bc
--- /dev/null
+++ b/src/common/library.cc
@@ -0,0 +1,125 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2015 by Contributors
+ * \file library.cc
+ * \brief Dynamically loading accelerator library
+ * and accessing its functions
+ */
+
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+#include <windows.h>
+#else
+#include <dlfcn.h>
+#endif
+
+#include <string>
+#include "library.h"
+
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+/*!
+ * \brief Retrieve the system error message for the last-error code
+ * \param err string that gets the error message
+ */
+void win_err(char **err) {
+  uint32_t dw = GetLastError();
+  FormatMessage(
+        FORMAT_MESSAGE_ALLOCATE_BUFFER |
+        FORMAT_MESSAGE_FROM_SYSTEM |
+        FORMAT_MESSAGE_IGNORE_INSERTS,
+        NULL,
+        dw,
+        MAKELANGID(LANG_NEUTRAL, SUBLANG_DEFAULT),
+        reinterpret_cast<char*>(err),
+        0, NULL);
+}
+#endif
+
+
+/*!
+ * \brief Loads the dynamic shared library file
+ * \param path library file location
+ * \return handle a pointer for the loaded library, nullptr if loading unsuccessful
+ */
+void* load_lib(const char* path) {
+  void *handle = nullptr;
+  std::string path_str(path);
+  // check if library was already loaded
+  if (loaded_libs.find(path_str) == loaded_libs.end()) {
+    // if not, load it
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+    handle = LoadLibrary(path);
+    if (!handle) {
+      char *err_msg = nullptr;
+      win_err(&err_msg);
+      LOG(FATAL) << "Error loading library: '" << path << "'\n" << err_msg;
+      LocalFree(err_msg);
+      return nullptr;
+    }
+#else
+    handle = dlopen(path, RTLD_LAZY);
+    if (!handle) {
+      LOG(FATAL) << "Error loading library: '" << path << "'\n" << dlerror();
+      return nullptr;
+    }
+#endif  // _WIN32 or _WIN64 or __WINDOWS__
+    // then store the pointer to the library
+    loaded_libs[path_str] = handle;
+  } else {
+    // otherwise just look up the pointer
+    handle = loaded_libs[path_str];
+  }
+  return handle;
+}
+
+/*!
+ * \brief Closes the loaded dynamic shared library file
+ * \param handle library file handle
+ */
+void close_lib(void* handle) {
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+  FreeLibrary((HMODULE)handle);
+#else
+  dlclose(handle);
+#endif  // _WIN32 or _WIN64 or __WINDOWS__
+}
+
+/*!
+ * \brief Obtains address of given function in the loaded library
+ * \param handle pointer for the loaded library
+ * \param func function pointer that gets output address
+ * \param name function name to be fetched
+ */
+void get_sym(void* handle, void** func, char* name) {
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+  *func = GetProcAddress((HMODULE)handle, name);
+  if (!(*func)) {
+    char *err_msg = nullptr;
+    win_err(&err_msg);
+    LOG(FATAL) << "Error getting function '" << name << "' from library\n" << err_msg;
+    LocalFree(err_msg);
+  }
+#else
+  *func = dlsym(handle, name);
+  if (!(*func)) {
+    LOG(FATAL) << "Error getting function '" << name << "' from library\n" << dlerror();
+  }
+#endif  // _WIN32 or _WIN64 or __WINDOWS__
+}
diff --git a/src/common/library.h b/src/common/library.h
new file mode 100644
index 000000000000..d6eff4184191
--- /dev/null
+++ b/src/common/library.h
@@ -0,0 +1,57 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2015 by Contributors
+ * \file library.h
+ * \brief Defining library loading functions
+ */
+#ifndef MXNET_COMMON_LIBRARY_H_
+#define MXNET_COMMON_LIBRARY_H_
+
+#include <iostream>
+#include <map>
+#include <string>
+#include "dmlc/io.h"
+
+// map of libraries loaded
+static std::map<std::string, void*> loaded_libs;
+
+void* load_lib(const char* path);
+void close_lib(void* handle);
+void get_sym(void* handle, void** func, char* name);
+
+/*!
+ * \brief a templated function that fetches from the library
+ * a function pointer of any given datatype and name
+ * \param T a template parameter for data type of function pointer
+ * \param lib library handle
+ * \param func_name function name to search for in the library
+ * \return func a function pointer
+ */
+template<typename T>
+T get_func(void *lib, char *func_name) {
+  T func;
+  get_sym(lib, reinterpret_cast<void**>(&func), func_name);
+  if (!func)
+    LOG(FATAL) << "Unable to get function '" << func_name << "' from library";
+  return func;
+}
+
+#endif  // MXNET_COMMON_LIBRARY_H_
diff --git a/src/initialize.cc b/src/initialize.cc
index 7236ced52e93..04952be7072a 100644
--- a/src/initialize.cc
+++ b/src/initialize.cc
@@ -27,6 +27,7 @@
 #include <mxnet/engine.h>
 #include "./engine/openmp.h"
 #include "./operator/custom/custom-inl.h"
+#include "./common/library.h"
 #if MXNET_USE_OPENCV
 #include <opencv2/opencv.hpp>
 #endif  // MXNET_USE_OPENCV
@@ -80,6 +81,13 @@ class LibraryInitializer {
 #endif
   }
 
+  ~LibraryInitializer() {
+    // close opened libraries
+    for (auto const& lib : loaded_libs) {
+      close_lib(lib.second);
+    }
+  }
+
   static LibraryInitializer* Get();
 };
 
diff --git a/tests/python/gpu/test_operator_gpu.py b/tests/python/gpu/test_operator_gpu.py
index 91ba9fb68750..a8aa1ff853f7 100644
--- a/tests/python/gpu/test_operator_gpu.py
+++ b/tests/python/gpu/test_operator_gpu.py
@@ -45,6 +45,7 @@
 from test_subgraph_op import *
 from test_contrib_operator import test_multibox_target_op
 from test_tvm_op import *
+from test_library_loading import *
 
 set_default_context(mx.gpu(0))
 del test_support_vector_machine_l1_svm  # noqa
diff --git a/tests/python/unittest/test_library_loading.py b/tests/python/unittest/test_library_loading.py
new file mode 100644
index 000000000000..596d124c89d6
--- /dev/null
+++ b/tests/python/unittest/test_library_loading.py
@@ -0,0 +1,48 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# This test checks if dynamic loading of library into MXNet is successful
+
+import os
+import platform
+import unittest
+import mxnet as mx
+from mxnet.base import MXNetError
+from mxnet.test_utils import download
+
+def check_platform():
+    return platform.machine() not in ['x86_64', 'AMD64']
+
+@unittest.skipIf(check_platform(), "not all machine types supported")
+def test_library_loading():
+    if (os.name=='posix'):
+        lib = 'libsample_lib.so'
+        if os.path.exists(lib):
+            fname = lib
+        elif os.path.exists('build/'+lib):
+            fname = 'build/'+lib
+        else:
+            raise MXNetError("library %s not found " % lib)
+    elif (os.name=='nt'):
+        lib = 'libsample_lib.dll'
+        if os.path.exists('windows_package\\lib\\'+lib):
+            fname = 'windows_package\\lib\\'+lib
+        else:
+            raise MXNetError("library %s not found " % lib)
+
+    fname = os.path.abspath(fname)
+    mx.library.load(fname)

From be49b3b2230d02e57b837355d0322b468ed520bf Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Wed, 7 Aug 2019 19:54:02 -0700
Subject: [PATCH 155/813] Numpy-compatible Infra (#15581)

* [Do not review] [Do not merge] New numpy-compatible sum (#14739)

* Add numpy namespace and initial impl of np.sum (not complete)

* Clean up

* Fix import error

* numpy sum

* add test and backward data type support

* add license to test_numpy_op.py

* improve test to reduce flakiness

* fix sanity build

* extra numeric test and imperative test

* add error message for initial argument

* [numpy] Infra for supporting numpy ops in imperative mode and Gluon APIs (#14758)

* Infra of new ndarray and symbol types for numpy operators

* Rename

* Fix import problem

* Refactor

* Remove redundant code

* Add docstring

* More on numpy ndarray and symbol

* Override unimplemented methdos for ndarray and _NumpySymbol

* Fix built-in methods of ndarray and _NumpySymbol

* Fix test and sanity check

* Fix pylint

* Address cr comments

* Add unit tests for ndarray and _NumpySymbol

* Add _true_divide

* Fix gpu build

* Add future import division

* More correct way of checking if an output is from a np compat op

* Fix gpu build

* Fix output ndarray/symbol types with at least one new ndarray/symbol

* Modify true_divide doc

* Fix flaky copying zero-size arrays via gpus

* Fix zero size in gluon hybridize and zeros/ones symbol not creating new symbol type

* Fix doc

* Enable np op compat check with name prefix (#14897)

* [numpy] Numpy dot (#14831)

* Numpy Dot case 1-4 + case 3.5 forward and 0.5 backward

* Backward computation and test coverage

* numpy-compatible mean (#14859)

* [numpy] Some np ops for d2l (#14924)

* Add np transpose

More ops and namespaces for submodules

Add relu and sigmoid

Add reshape

Fix symbolic name mismatch

Add maximum and minimum

* Add convenience fluent method

* Add ndarray.item()

* Fix CI

* Fix lint

* Fix lint

* Fix reshape gpu

* Add example

* Remove python notebook outputs

* Remove notebook output

* Add one more example

* [numpy] Refactor np modules (#14989)

* Refactor

* Initial refactoring

* Fix notebook

* Move numpy op check from backend to frontend

* Add homogeneous ndarray check

* Fix grouping inhomogeneous types of symbols

* Improve error handling of different types of symbols as outputs

* Fix test

* Fix numpy test

* Fix ci

* Try to fix gpu ci failure

* [numpy] Refactor np module (example runs through) (#15055)

* Refactor notebook

* notebook working with hybrid block

* More refactoring

* Remove unnecessary use_np_compat

* Use class decorator to initialize numpy ndarrays in parameter.py

* Clear notebook outputs

* Improve np decorator

* Remove npe op from optimizer

* Fix CI

* Fix functools.wraps issue in Python2

* Fix ci

* Change np_compat to np_shape

* Temporarily disable test_amp

* Numpy-compatible stack (#15027)

* numpy stack

* migrate to use_np_shape

* Numpy Unary Ops (#15010)

* Unary Ops

* new version of unit tests

* [numpy] Fix np branch after rebase (#15086)

* Add np_array semantics for Gluon

Fix notebook

Fix sanity

Fix gluon deferred infer shape

Add np.random.uniform

Add random normal

Add boolean comparison ops

Add np.ndarray indexing

Reformat test ndarray indexing

Fix unit tests

Add one more test of indexing

Fix sanity

Enable amp test

Add np.arange

Revert cython unit test to ctypes

Delete unnecessary use_np_shape decorator from test

Rebase with numpy branch

support range as index

Fix python2 range type check

Add argmax

Disable clojure test

* Fix ci

* Add np.linalg.norm for ord='fro'

* Fix pylint

* numpy concatenate (#15104)

* [WIP][numpy] Fix for D2L Chapters 2/3/4 (#15139)

* Fix

* Fix linear regression gluon

* More fix

* Fix pylint

* Fix for chapter 4

* Add np.add mul div mod pow sub and shuffle

* Fix model selection, underfitting, overfitting

* Fix weight decay

* Fix dropout

* Fix

* Fix chapter 4

* [numpy] Fix d2l performance regression (#15173)

* Add np array adapter decorator for layers

* Fix performance regression caused by too many conversions between nd.NDArray and np.ndarray

* Fix pylint

* Fix test backward compatibility issue

* Fix test_lambda

* Fix (#15188)

* fix for chapter6 conv nn (#15224)

* [numpy] Fix d2l chapter8 (#15237)

* Add np op doc

* Fix several issues

* Add a N-D dot b 2D support

* Simplify array creation api

* Add swapaxes

* Fix rnn gluon

* More fix

* Fix pylint

* Delete

* Fix mp windows

* fix for ch11 (#15244)

* Numpy-compatible split (#15049)

* numpy split

* numpy split

* unit test

* unit test

* [numpy] [DO NOT MERGE] Fix d2l chapters 9 and 13 (#15246)

* Add npx batch_dot and topk

* Text embedding uses numpy

* Fix SoftmaxCrossEntropyLoss with np

* Fix sentiment cnn

* Fix pylint

* Fix dot attention

* Fix seq2seq attention

* Add np.tile

* Fix transformer

* Fix ci

* Fix ci and rebase

* [numpy] Fix d2l chapter 5 (#15264)

* Fix parameter initializer

* Add np.save and np.load

* Fix read-write

* Fix lint

* Numpy compatible max (#15161)

* numpy amax

* weird cu file diff

* fix the unit test error

* fix gpu bug

* minor fix

* fix lint

* remove scalar value check

* fix the bug on unit test

* fix the case () that breaks the kernel launch

* add zero dimension unit test

* revert the tuple change

* use mshadow maximum

* remove test zero

* change the macro for now

* change the cuda to use mashadow op

* fix the broadcast_reduce_op_value.cu wrong kernel

* add more logic in shape to detect the invalid situation

* change back to type swtich

* change to as_nd_ndarray

* add missing @npx.use_np_shape

* retrigger CI

* address the comment

* undo algorithm import

* remove the numeric gradient check

* Numpy compatible multinomial (#15219)

* draft of multinomial

* rename to more concise name

* finish shape

* complete the forward function

* complete forward without handle 0 dimension & scalar

* handle 0 dimension

* add new line

* fix lint

* fix the build error

* fix lint

* finish unit test

* change the registration

* make multinomial support pvals as mx.ndarray

* delete newline

* fix lint error

* support input as list, mx.ndarray, np.ndarray & unit test

* fix lint

* fix the include error

* fix lint

* refactor & pass the tensor instead of tuple to kernel

* fix lint

* updata the doc

* address the comment

* Numpy compatible linspace (#15256)

* draft

* finish linspace implementation

* finish linspace

* delete newline

* fix pylint

* add more unit test

* address comment

* add more test case

* disable too-many-arguments

* resolve confliction

* add ctx

* numpy-compatible cumsum (#15309)

* [numpy] Misc fix for other chapters (#15332)

* Add np.prod

* Fix ndarray.reshape accepting positional integers as arguments

* Rebase

* Fix rebase error

* Add np.ndarray.flatten

* Fix

* Add broadcast_to

* Add meshgrid and broadcast_arrays

* Fix sin, cos, sinh, cosh not supporting scalars

* Add more unary ops supporting python scalars

* Fix

* Fix

* Fix ci

* Fix sanity

* [numpy] Change d2l chapters cv and gan to use numpy (#15368)

* Change op name style to lower case underscore

* Add ops under image to npx

* Add image submodule to npx

* Fix split_and_load use np

* Fix fine tuning

* Fix bbox and anchor

* Fix odd

* Fix ssd and rcnn

* Remove restriction on binary element-wise scalar

* Fix gan

* Fix sanity

* Try to fix website build failure

* Add npx.random.seed

* Fix doc

* add doc for multinomial, dot, cumsum, clip, abs, exp, arctan (#15386)

* [numpy] Fix several places in numpy (#15398)

* Fix

* More fix

* [numpy] fix cython (#15418)

* add cython support for numpy

* stay with original API for backward compatibility

* fix after rebase

* get rid of coverage in clang60 mkldnn

* fix lint issues

* fix flaky test and get rid of extra print

* remove numpy examples

* revert #15309 #15256 #15219 #15161

* remove numpy docs

* remove changes to contrib/text/embedding.py

* remove numpy changes to gluon peripherals

* Revert "remove numpy docs"

This reverts commit c104695b28a26738b8700d80c70814e0f583ac55.

* get rid of most operators

* Revert "get rid of coverage in clang60 mkldnn"

This reverts commit 77dc90520b6a2282716ba41987a1f37522daf078.

* remove np-compatible from mxnet.image mxnet.initializer

* address comments
---
 include/mxnet/base.h                          |    4 +-
 include/mxnet/c_api.h                         |   12 +
 include/mxnet/tuple.h                         |   15 +
 python/mxnet/__init__.py                      |    5 +
 python/mxnet/_ctypes/ndarray.py               |   36 +-
 python/mxnet/_ctypes/symbol.py                |   13 +-
 python/mxnet/_numpy_op_doc.py                 |   54 +
 python/mxnet/base.py                          |  118 +-
 python/mxnet/cython/ndarray.pyx               |   27 +-
 python/mxnet/cython/symbol.pyx                |   16 +-
 python/mxnet/gluon/block.py                   |   49 +-
 python/mxnet/gluon/data/dataloader.py         |    2 +
 python/mxnet/gluon/parameter.py               |   52 +-
 python/mxnet/gluon/rnn/rnn_layer.py           |    2 -
 python/mxnet/gluon/utils.py                   |   45 +-
 python/mxnet/image/image.py                   |    2 +-
 python/mxnet/ndarray/__init__.py              |    6 +-
 python/mxnet/ndarray/_internal.py             |   11 +-
 python/mxnet/ndarray/ndarray.py               |   26 +-
 python/mxnet/ndarray/numpy/__init__.py        |   26 +
 python/mxnet/ndarray/numpy/_internal.py       |   20 +
 python/mxnet/ndarray/numpy/_op.py             |  295 ++++
 python/mxnet/ndarray/numpy/_register.py       |   28 +
 python/mxnet/ndarray/numpy/linalg.py          |   22 +
 python/mxnet/ndarray/numpy/random.py          |   21 +
 .../mxnet/ndarray/numpy_extension/__init__.py |   25 +
 python/mxnet/ndarray/numpy_extension/_op.py   |   21 +
 .../ndarray/numpy_extension/_register.py      |   25 +
 python/mxnet/ndarray/numpy_extension/image.py |   20 +
 python/mxnet/ndarray/register.py              |  116 +-
 python/mxnet/ndarray/utils.py                 |    7 +
 python/mxnet/numpy/__init__.py                |   30 +
 python/mxnet/numpy/_op.py                     |   20 +
 python/mxnet/numpy/_register.py               |   27 +
 python/mxnet/numpy/linalg.py                  |   22 +
 python/mxnet/numpy/multiarray.py              | 1551 +++++++++++++++++
 python/mxnet/numpy/random.py                  |   22 +
 python/mxnet/numpy/utils.py                   |   35 +
 python/mxnet/numpy_extension/__init__.py      |   32 +
 python/mxnet/numpy_extension/_op.py           |   20 +
 python/mxnet/numpy_extension/_register.py     |   27 +
 python/mxnet/numpy_extension/image.py         |   22 +
 python/mxnet/numpy_extension/utils.py         |  122 ++
 python/mxnet/optimizer/optimizer.py           |   34 +-
 python/mxnet/symbol/__init__.py               |    7 +-
 python/mxnet/symbol/_internal.py              |   10 +-
 python/mxnet/symbol/numpy/__init__.py         |   28 +
 python/mxnet/symbol/numpy/_internal.py        |   20 +
 python/mxnet/symbol/numpy/_op.py              |   20 +
 python/mxnet/symbol/numpy/_register.py        |   28 +
 python/mxnet/symbol/numpy/_symbol.py          | 1013 +++++++++++
 python/mxnet/symbol/numpy/linalg.py           |   22 +
 python/mxnet/symbol/numpy/random.py           |   22 +
 .../mxnet/symbol/numpy_extension/__init__.py  |   25 +
 python/mxnet/symbol/numpy_extension/_op.py    |   21 +
 .../mxnet/symbol/numpy_extension/_register.py |   24 +
 python/mxnet/symbol/numpy_extension/image.py  |   20 +
 python/mxnet/symbol/register.py               |   84 +-
 python/mxnet/symbol/symbol.py                 |   38 +-
 python/mxnet/test_utils.py                    |   28 +-
 python/mxnet/util.py                          |  395 ++++-
 src/c_api/c_api.cc                            |    9 +
 src/c_api/c_api_common.h                      |    1 +
 src/c_api/c_api_symbolic.cc                   |   13 +-
 src/imperative/imperative_utils.h             |    1 -
 src/io/image_io.cc                            |    3 +
 src/ndarray/ndarray.cc                        |   17 +-
 src/operator/contrib/multibox_detection.cc    |    4 +
 src/operator/contrib/multibox_prior.cc        |    3 +
 src/operator/contrib/multibox_target.cc       |    4 +
 src/operator/image/crop.cc                    |    1 +
 src/operator/image/image_random.cc            |   13 +
 src/operator/image/resize.cc                  |    1 +
 src/operator/leaky_relu.cc                    |    1 +
 src/operator/nn/activation.cc                 |    1 +
 src/operator/nn/batch_norm.cc                 |    1 +
 src/operator/nn/concat.cc                     |   13 +-
 src/operator/nn/convolution.cc                |    1 +
 src/operator/nn/deconvolution.cc              |    1 +
 src/operator/nn/dropout.cc                    |    1 +
 src/operator/nn/fully_connected.cc            |    1 +
 src/operator/nn/layer_norm.cc                 |    1 +
 src/operator/nn/pooling.cc                    |    3 +-
 src/operator/nn/softmax.cc                    |    2 +
 .../numpy/np_elemwise_broadcast_op.cc         |  186 ++
 .../numpy/np_elemwise_broadcast_op.cu         |   82 +
 src/operator/numpy/np_init_op.cc              |  111 ++
 src/operator/numpy/np_init_op.cu              |   44 +
 src/operator/numpy/np_true_divide.cc          |  127 ++
 src/operator/numpy/np_true_divide.cu          |   41 +
 src/operator/quantization/quantized_concat.cc |   12 +-
 src/operator/random/sample_op.cc              |    2 +
 src/operator/random/shuffle_op.cc             |    1 +
 src/operator/rnn.cc                           |    1 +
 src/operator/roi_pooling.cc                   |    4 +
 src/operator/sequence_mask.cc                 |    3 +
 src/operator/swapaxis-inl.h                   |   42 +-
 src/operator/swapaxis.cc                      |    2 +-
 src/operator/tensor/broadcast_reduce_op.h     |  183 +-
 .../tensor/broadcast_reduce_op_index.cc       |    1 +
 src/operator/tensor/dot.cc                    |    1 +
 .../tensor/elemwise_binary_broadcast_op.h     |    1 +
 .../elemwise_binary_broadcast_op_logic.cc     |    6 +
 src/operator/tensor/elemwise_binary_op.h      |   16 +-
 .../elemwise_binary_scalar_op_extended.cc     |    3 +-
 .../tensor/elemwise_binary_scalar_op_logic.cc |    6 +
 src/operator/tensor/elemwise_unary_op.h       |    6 +-
 .../tensor/elemwise_unary_op_basic.cc         |    3 +
 src/operator/tensor/indexing_op.cc            |    2 +
 src/operator/tensor/matrix_op-inl.h           |   40 +-
 src/operator/tensor/matrix_op.cc              |    6 +
 src/operator/tensor/ordering_op.cc            |    1 +
 tests/python/gpu/test_operator_gpu.py         |    1 +
 tests/python/unittest/test_numpy_ndarray.py   |  672 +++++++
 tests/python/unittest/test_thread_local.py    |   36 +
 115 files changed, 6396 insertions(+), 232 deletions(-)
 create mode 100644 python/mxnet/_numpy_op_doc.py
 create mode 100644 python/mxnet/ndarray/numpy/__init__.py
 create mode 100644 python/mxnet/ndarray/numpy/_internal.py
 create mode 100644 python/mxnet/ndarray/numpy/_op.py
 create mode 100644 python/mxnet/ndarray/numpy/_register.py
 create mode 100644 python/mxnet/ndarray/numpy/linalg.py
 create mode 100644 python/mxnet/ndarray/numpy/random.py
 create mode 100644 python/mxnet/ndarray/numpy_extension/__init__.py
 create mode 100644 python/mxnet/ndarray/numpy_extension/_op.py
 create mode 100644 python/mxnet/ndarray/numpy_extension/_register.py
 create mode 100644 python/mxnet/ndarray/numpy_extension/image.py
 create mode 100644 python/mxnet/numpy/__init__.py
 create mode 100644 python/mxnet/numpy/_op.py
 create mode 100644 python/mxnet/numpy/_register.py
 create mode 100644 python/mxnet/numpy/linalg.py
 create mode 100644 python/mxnet/numpy/multiarray.py
 create mode 100644 python/mxnet/numpy/random.py
 create mode 100644 python/mxnet/numpy/utils.py
 create mode 100644 python/mxnet/numpy_extension/__init__.py
 create mode 100644 python/mxnet/numpy_extension/_op.py
 create mode 100644 python/mxnet/numpy_extension/_register.py
 create mode 100644 python/mxnet/numpy_extension/image.py
 create mode 100644 python/mxnet/numpy_extension/utils.py
 create mode 100644 python/mxnet/symbol/numpy/__init__.py
 create mode 100644 python/mxnet/symbol/numpy/_internal.py
 create mode 100644 python/mxnet/symbol/numpy/_op.py
 create mode 100644 python/mxnet/symbol/numpy/_register.py
 create mode 100644 python/mxnet/symbol/numpy/_symbol.py
 create mode 100644 python/mxnet/symbol/numpy/linalg.py
 create mode 100644 python/mxnet/symbol/numpy/random.py
 create mode 100644 python/mxnet/symbol/numpy_extension/__init__.py
 create mode 100644 python/mxnet/symbol/numpy_extension/_op.py
 create mode 100644 python/mxnet/symbol/numpy_extension/_register.py
 create mode 100644 python/mxnet/symbol/numpy_extension/image.py
 create mode 100644 src/operator/numpy/np_elemwise_broadcast_op.cc
 create mode 100644 src/operator/numpy/np_elemwise_broadcast_op.cu
 create mode 100644 src/operator/numpy/np_init_op.cc
 create mode 100644 src/operator/numpy/np_init_op.cu
 create mode 100644 src/operator/numpy/np_true_divide.cc
 create mode 100644 src/operator/numpy/np_true_divide.cu
 create mode 100644 tests/python/unittest/test_numpy_ndarray.py

diff --git a/include/mxnet/base.h b/include/mxnet/base.h
index c1e2da7a9db3..02dd204fc77d 100644
--- a/include/mxnet/base.h
+++ b/include/mxnet/base.h
@@ -451,7 +451,9 @@ inline int32_t Context::GetGPUCount() {
   }
   int32_t count;
   cudaError_t e = cudaGetDeviceCount(&count);
-  if (e == cudaErrorNoDevice) {
+  // TODO(junwu): Remove e == cudaErrorInsufficientDriver
+  // This is skipped for working around wheel build system with older CUDA driver.
+  if (e == cudaErrorNoDevice || e == cudaErrorInsufficientDriver) {
     return 0;
   }
   CHECK_EQ(e, cudaSuccess) << " CUDA: " << cudaGetErrorString(e);
diff --git a/include/mxnet/c_api.h b/include/mxnet/c_api.h
index 376c7e9d1063..9d647c3b69d7 100644
--- a/include/mxnet/c_api.h
+++ b/include/mxnet/c_api.h
@@ -2909,6 +2909,18 @@ MXNET_DLL int MXEnginePushSync(EngineSyncFunc sync_func, void* func_param,
                                EngineVarHandle mutable_vars_handle, int num_mutable_vars,
                                EngineFnPropertyHandle prop_handle DEFAULT(NULL),
                                int priority DEFAULT(0), const char* opr_name DEFAULT(NULL));
+/*!
+ * \brief Create an NDArray from source sharing the same data chunk.
+ * \param src source NDArray
+ * \param out new NDArray sharing the same data chunck with src
+ */
+MXNET_DLL int MXShallowCopyNDArray(NDArrayHandle src, NDArrayHandle* out);
+/*!
+ * \brief Create an Symbol from source sharing the same graph structure.
+ * \param src source Symbol
+ * \param out new Symbol sharing the same graph structure with src
+ */
+MXNET_DLL int MXShallowCopySymbol(SymbolHandle src, SymbolHandle * out);
 
 /*!
   * \brief Push an asynchronous operation to the engine.
diff --git a/include/mxnet/tuple.h b/include/mxnet/tuple.h
index bc630f153744..f018c8faabea 100644
--- a/include/mxnet/tuple.h
+++ b/include/mxnet/tuple.h
@@ -272,6 +272,14 @@ class Tuple {
       is.get();
       if (ch == '(' || ch == '[') break;
       if (!isspace(ch)) {
+        if (ch == 'N') {
+          std::string tmp_val;
+          is >> tmp_val;
+          if (tmp_val == "one") {  // is stores "None"
+            t.SetDim(-1);
+            return is;
+          }
+        }
         is.setstate(std::ios::failbit);
         return is;
       }
@@ -653,6 +661,13 @@ inline bool shape_is_known(const TShape& x) {
   return true;
 }
 
+inline bool shape_is_known(const std::vector<TShape>& shapes) {
+  for (const TShape& shape : shapes) {
+    if (!shape_is_known(shape)) return false;
+  }
+  return true;
+}
+
 /*! \brief helper function to cast type of container elements */
 template<typename SrcIter, typename DstIter>
 inline DstIter ShapeTypeCast(const SrcIter begin,
diff --git a/python/mxnet/__init__.py b/python/mxnet/__init__.py
index 233bb2a1f57e..e9c1229d7f2f 100644
--- a/python/mxnet/__init__.py
+++ b/python/mxnet/__init__.py
@@ -25,11 +25,16 @@
 from . import engine
 from .base import MXNetError
 from .util import is_np_shape, set_np_shape, np_shape, use_np_shape
+from .util import is_np_array, np_array, use_np_array, use_np
 from . import base
 from . import library
 from . import contrib
 from . import ndarray
 from . import ndarray as nd
+from . import numpy
+from . import numpy_extension
+from . import numpy as np
+from . import numpy_extension as npx
 from . import name
 # use mx.sym as short for symbol
 from . import symbol as sym
diff --git a/python/mxnet/_ctypes/ndarray.py b/python/mxnet/_ctypes/ndarray.py
index f324545a2352..b1a38c1d2621 100644
--- a/python/mxnet/_ctypes/ndarray.py
+++ b/python/mxnet/_ctypes/ndarray.py
@@ -55,6 +55,8 @@ def __reduce__(self):
 
 
 _ndarray_cls = None
+_np_ndarray_cls = None
+
 
 def _set_ndarray_class(cls):
     """Set the symbolic class to be cls"""
@@ -62,7 +64,13 @@ def _set_ndarray_class(cls):
     _ndarray_cls = cls
 
 
-def _imperative_invoke(handle, ndargs, keys, vals, out):
+def _set_np_ndarray_class(cls):
+    """Set the symbolic class to be cls"""
+    global _np_ndarray_cls
+    _np_ndarray_cls = cls
+
+
+def _imperative_invoke(handle, ndargs, keys, vals, out, is_np_op):
     """ctypes implementation of imperative invoke wrapper"""
     if out is not None:
         original_output = out
@@ -91,23 +99,27 @@ def _imperative_invoke(handle, ndargs, keys, vals, out):
         c_str_array([str(s) for s in vals]),
         ctypes.byref(out_stypes)))
 
+    create_ndarray_fn = _np_ndarray_cls if is_np_op else _ndarray_cls
     if original_output is not None:
         return original_output
     if num_output.value == 1:
-        return _ndarray_cls(ctypes.cast(output_vars[0], NDArrayHandle),
-                            stype=out_stypes[0])
+        return create_ndarray_fn(ctypes.cast(output_vars[0], NDArrayHandle),
+                                 stype=out_stypes[0])
     else:
-        return [_ndarray_cls(ctypes.cast(output_vars[i], NDArrayHandle),
-                             stype=out_stypes[i])
-                for i in range(num_output.value)]
+        return [create_ndarray_fn(ctypes.cast(output_vars[i], NDArrayHandle),
+                                  stype=out_stypes[i]) for i in range(num_output.value)]
 
 
 class CachedOp(object):
     """Cached operator handle."""
-    __slots__ = ["handle"]
+    __slots__ = ["handle", "is_np_sym"]
+
     def __init__(self, sym, flags=()):
         self.handle = CachedOpHandle()
 
+        from ..symbol.numpy._symbol import _Symbol
+        self.is_np_sym = bool(isinstance(sym, _Symbol))
+
         check_call(_LIB.MXCreateCachedOpEx(
             sym.handle,
             len(flags),
@@ -151,10 +163,10 @@ def __call__(self, *args, **kwargs):
 
         if original_output is not None:
             return original_output
+        create_ndarray_fn = _np_ndarray_cls if self.is_np_sym else _ndarray_cls
         if num_output.value == 1:
-            return _ndarray_cls(ctypes.cast(output_vars[0], NDArrayHandle),
-                                stype=out_stypes[0])
+            return create_ndarray_fn(ctypes.cast(output_vars[0], NDArrayHandle),
+                                     stype=out_stypes[0])
         else:
-            return [_ndarray_cls(ctypes.cast(output_vars[i], NDArrayHandle),
-                                 stype=out_stypes[i])
-                    for i in range(num_output.value)]
+            return [create_ndarray_fn(ctypes.cast(output_vars[i], NDArrayHandle),
+                                      stype=out_stypes[i]) for i in range(num_output.value)]
diff --git a/python/mxnet/_ctypes/symbol.py b/python/mxnet/_ctypes/symbol.py
index fe4cb950ed14..01ba18b38963 100644
--- a/python/mxnet/_ctypes/symbol.py
+++ b/python/mxnet/_ctypes/symbol.py
@@ -26,7 +26,9 @@
 from ..base import SymbolHandle
 from ..base import check_call
 
+# The symbol class to be used (Cython or Ctypes)
 _symbol_cls = None
+_np_symbol_cls = None
 
 class SymbolBase(object):
     """Symbol is symbolic graph."""
@@ -115,7 +117,13 @@ def _set_symbol_class(cls):
     _symbol_cls = cls
 
 
-def _symbol_creator(handle, args, kwargs, keys, vals, name):
+def _set_np_symbol_class(cls):
+    """Set the numpy-compatible symbolic class to be cls"""
+    global _np_symbol_cls
+    _np_symbol_cls = cls
+
+
+def _symbol_creator(handle, args, kwargs, keys, vals, name, is_np_op):
     sym_handle = SymbolHandle()
     check_call(_LIB.MXSymbolCreateAtomicSymbol(
         ctypes.c_void_p(handle),
@@ -128,7 +136,8 @@ def _symbol_creator(handle, args, kwargs, keys, vals, name):
         raise TypeError(
             'Operators with variable length input can only accept input'
             'Symbols either as positional or keyword arguments, not both')
-    s = _symbol_cls(sym_handle)
+    create_symbol_fn = _np_symbol_cls if is_np_op else _symbol_cls
+    s = create_symbol_fn(sym_handle)
     if args:
         s._compose(*args, name=name)
     elif kwargs:
diff --git a/python/mxnet/_numpy_op_doc.py b/python/mxnet/_numpy_op_doc.py
new file mode 100644
index 000000000000..5543ebc8e8c9
--- /dev/null
+++ b/python/mxnet/_numpy_op_doc.py
@@ -0,0 +1,54 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# pylint: skip-file
+
+"""Doc placeholder for numpy ops with prefix _np."""
+
+
+def _np_ones_like(a):
+    """Return an array of ones with the same shape and type as a given array.
+
+    Parameters
+    ----------
+    a : ndarray
+        The shape and data-type of `a` define these same attributes of
+        the returned array.
+
+    Returns
+    -------
+    out : ndarray
+        Array of ones with the same shape and type as `a`.
+    """
+    pass
+
+
+def _np_zeros_like(a):
+    """Return an array of zeros with the same shape and type as a given array.
+
+    Parameters
+    ----------
+    a : ndarray
+        The shape and data-type of `a` define these same attributes of
+        the returned array.
+
+    Returns
+    -------
+    out : ndarray
+        Array of zeros with the same shape and type as `a`.
+    """
+    pass
diff --git a/python/mxnet/base.py b/python/mxnet/base.py
index 17819bde28b2..848c36fb4d89 100644
--- a/python/mxnet/base.py
+++ b/python/mxnet/base.py
@@ -16,7 +16,7 @@
 # under the License.
 
 # coding: utf-8
-# pylint: disable=invalid-name, no-member, trailing-comma-tuple, bad-mcs-classmethod-argument, unnecessary-pass, wrong-import-position
+# pylint: disable=invalid-name, no-member, trailing-comma-tuple, bad-mcs-classmethod-argument, unnecessary-pass, too-many-lines, wrong-import-position
 """ctypes library of mxnet and helper functions."""
 from __future__ import absolute_import
 
@@ -598,7 +598,9 @@ def _init_op_module(root_namespace, module_name, make_op_func):
                                      ctypes.byref(plist)))
     op_names = []
     for i in range(size.value):
-        op_names.append(py_str(plist[i]))
+        op_name = py_str(plist[i])
+        if not _is_np_op(op_name):
+            op_names.append(op_name)
 
     module_op = sys.modules["%s.%s.op" % (root_namespace, module_name)]
     module_internal = sys.modules["%s.%s._internal" % (root_namespace, module_name)]
@@ -692,7 +694,9 @@ def write_all_str(module_file, module_all_list):
                                      ctypes.byref(plist)))
     op_names = []
     for i in range(size.value):
-        op_names.append(py_str(plist[i]))
+        op_name = py_str(plist[i])
+        if not _is_np_op(op_name):
+            op_names.append(op_name)
 
     module_op_file = get_module_file("%s.%s.op" % (root_namespace, module_name))
     module_op_all = []
@@ -735,7 +739,115 @@ def write_all_str(module_file, module_all_list):
 ctypes.pythonapi.PyCapsule_New.restype = ctypes.py_object
 ctypes.pythonapi.PyCapsule_GetPointer.restype = ctypes.c_void_p
 
+
 from .runtime import Features
 if Features().is_enabled("TVM_OP"):
     _LIB_TVM_OP = libinfo.find_lib_path("libtvmop")
     check_call(_LIB.MXLoadTVMOp(c_str(_LIB_TVM_OP[0])))
+
+
+_NP_OP_PREFIX = '_np_'
+_NP_OP_SUBMODULE_LIST = ['_random_', '_linalg_']
+
+_NP_EXT_OP_PREFIX = '_npx_'
+_NP_EXT_OP_SUBMODULE_LIST = ['_image_']
+
+_NP_INTERNAL_OP_PREFIX = '_npi_'
+
+
+def _is_np_op(op_name):
+    return op_name.startswith(_NP_OP_PREFIX) or op_name.startswith(_NP_EXT_OP_PREFIX)\
+           or op_name.startswith(_NP_INTERNAL_OP_PREFIX)
+
+
+def _get_op_submodule_name(op_name, op_name_prefix, submodule_name_list):
+    """Get the submodule name of a specific op"""
+    assert op_name.startswith(op_name_prefix)
+    for submodule_name in submodule_name_list:
+        if op_name[len(op_name_prefix):].startswith(submodule_name):
+            return submodule_name
+    return ""
+
+
+def _init_np_op_module(root_module_name, np_module_name, mx_module_name, make_op_func):
+    """
+    Register numpy operators in namespaces `mxnet.numpy`, `mxnet.ndarray.numpy`
+    and `mxnet.symbol.numpy`. They are used in imperative mode, Gluon APIs w/o hybridization,
+    and Gluon APIs w/ hybridization, respectively. Essentially, operators with the same name
+    registered in three namespaces, respectively share the same functionality in C++ backend.
+    Different namespaces are needed for dispatching operator calls in Gluon's `HybridBlock` by `F`.
+
+    Parameters
+    ----------
+    root_module_name : str
+        Top level module name, `mxnet` in the current cases.
+    np_module_name : str
+        Second level module name, `numpy` or `numpy_extension` in the current case.
+    make_op_func : function
+        Function for creating op functions.
+    """
+    from . import _numpy_op_doc as _np_op_doc
+    if np_module_name == 'numpy':
+        op_name_prefix = _NP_OP_PREFIX
+        submodule_name_list = _NP_OP_SUBMODULE_LIST
+    elif np_module_name == 'numpy_extension':
+        op_name_prefix = _NP_EXT_OP_PREFIX
+        submodule_name_list = _NP_EXT_OP_SUBMODULE_LIST
+    elif np_module_name == 'numpy._internal':
+        op_name_prefix = _NP_INTERNAL_OP_PREFIX
+        submodule_name_list = []
+    else:
+        raise ValueError('unsupported np module name {}'.format(np_module_name))
+
+    plist = ctypes.POINTER(ctypes.c_char_p)()
+    size = ctypes.c_uint()
+    check_call(_LIB.MXListAllOpNames(ctypes.byref(size), ctypes.byref(plist)))
+    op_names = []
+    for i in range(size.value):
+        name = py_str(plist[i])
+        if name.startswith(op_name_prefix):
+            op_names.append(name)
+
+    if mx_module_name is None:
+        # register np/npx ops for imperative programming
+        op_module_name = "%s.%s._op" % (root_module_name, np_module_name)  # e.g. mxnet.numpy._op
+        op_submodule_name = "%s.%s" % (root_module_name, np_module_name)  # e.g. mxnet.numpy.random
+    elif mx_module_name in ('ndarray', 'symbol'):
+        # register numpy internal ops and np/npx ops for use in Gluon
+        # np internal ops are registered in mxnet.ndarray/symbol.numpy._internal
+        # np ops are registered in mxnet.ndarray/symbol.numpy._op
+        # npx ops are registered in mxnet.ndarray/symbol.numpy_extension._op
+        op_module_name = "%s.%s.%s" % (root_module_name, mx_module_name, np_module_name)
+        if op_name_prefix != _NP_INTERNAL_OP_PREFIX:
+            op_module_name += '._op'
+        # e.g. mxnet.symbol.numpy.random
+        op_submodule_name = "%s.%s.%s" % (root_module_name, mx_module_name, np_module_name)
+    else:
+        raise ValueError('unsupported mxnet module {}'.format(mx_module_name))
+    op_submodule_name += '.%s'
+
+    op_module = sys.modules[op_module_name]
+    submodule_dict = {}
+    for submodule_name in submodule_name_list:
+        submodule_dict[submodule_name] = sys.modules[op_submodule_name % submodule_name[1:-1]]
+    for name in op_names:
+        hdl = OpHandle()
+        check_call(_LIB.NNGetOpHandle(c_str(name), ctypes.byref(hdl)))
+        submodule_name = _get_op_submodule_name(name, op_name_prefix, submodule_name_list)
+        if len(submodule_name) > 0:
+            func_name = name[(len(op_name_prefix) + len(submodule_name)):]
+            cur_module = submodule_dict[submodule_name]
+            module_name_local = op_submodule_name % submodule_name[1:-1]
+        else:
+            func_name = name[len(op_name_prefix):]
+            cur_module = op_module
+            module_name_local =\
+                op_module_name[:-len('._op')] if op_module_name.endswith('._op') else op_module_name
+
+        function = make_op_func(hdl, name, func_name)
+        function.__module__ = module_name_local
+        setattr(cur_module, function.__name__, function)
+        cur_module.__all__.append(function.__name__)
+
+        if hasattr(_np_op_doc, name):
+            function.__doc__ = getattr(_np_op_doc, name).__doc__
diff --git a/python/mxnet/cython/ndarray.pyx b/python/mxnet/cython/ndarray.pyx
index f9279889b504..50791e9b9a86 100644
--- a/python/mxnet/cython/ndarray.pyx
+++ b/python/mxnet/cython/ndarray.pyx
@@ -64,21 +64,27 @@ cdef class NDArrayBase:
 
 
 _ndarray_cls = None
+_np_ndarray_cls = None
 
 def _set_ndarray_class(cls):
     global _ndarray_cls
     _ndarray_cls = cls
 
 
-cdef NewArray(NDArrayHandle handle, int stype=-1):
+def _set_np_ndarray_class(cls):
+    global _np_ndarray_cls
+    _np_ndarray_cls = cls
+
+
+cdef NewArray(NDArrayHandle handle, int stype=-1, int is_np_array=0):
     """Create a new array given handle"""
-    return _ndarray_cls(_ctypes.cast(<unsigned long long>handle, _ctypes.c_void_p), stype=stype)
+    create_array_fn = _np_ndarray_cls if is_np_array else _ndarray_cls
+    return create_array_fn(_ctypes.cast(<unsigned long long>handle, _ctypes.c_void_p), stype=stype)
 
 
 cdef class CachedOp:
     """Cached operator handle."""
     cdef CachedOpHandle chandle
-
     cdef _set_handle(self, handle):
         cdef unsigned long long ptr
         if handle is None:
@@ -96,6 +102,8 @@ cdef class CachedOp:
         def __set__(self, value):
             self._set_handle(value)
 
+    cdef int is_np_sym
+
     def __init__(self, sym, flags=()):
         cdef vector[string] s_flag_keys
         cdef vector[string] s_flag_vals
@@ -106,6 +114,9 @@ cdef class CachedOp:
         cdef vector[const char*] c_flag_keys = SVec2Ptr(s_flag_keys)
         cdef vector[const char*] c_flag_vals = SVec2Ptr(s_flag_vals)
 
+        from ..symbol.numpy._symbol import _Symbol
+        self.is_np_sym = bool(isinstance(sym, _Symbol))
+
         CALL(MXCreateCachedOpEx(
             <SymbolHandle>(<unsigned long long>sym.handle.value),
             len(flags),
@@ -154,12 +165,12 @@ cdef class CachedOp:
         if original_output is not None:
             return original_output
         if num_output == 1:
-            return NewArray(p_output_vars[0], p_output_stypes[0])
+            return NewArray(p_output_vars[0], p_output_stypes[0], self.is_np_sym)
         else:
-            return [NewArray(p_output_vars[i], p_output_stypes[i]) for i in range(num_output)]
+            return [NewArray(p_output_vars[i], p_output_stypes[i], self.is_np_sym) for i in range(num_output)]
 
 
-def _imperative_invoke(handle, ndargs, keys, vals, out):
+def _imperative_invoke(handle, ndargs, keys, vals, out, is_np_op=0):
     """cython implementation of imperative invoke wrapper"""
     cdef unsigned long long ihandle = handle
     cdef OpHandle chandle = <OpHandle>ihandle
@@ -211,6 +222,6 @@ def _imperative_invoke(handle, ndargs, keys, vals, out):
     if original_output is not None:
         return original_output
     if num_output == 1:
-        return NewArray(p_output_vars[0], p_output_stypes[0])
+        return NewArray(p_output_vars[0], p_output_stypes[0], is_np_op)
     else:
-        return [NewArray(p_output_vars[i], p_output_stypes[i]) for i in range(num_output)]
+        return [NewArray(p_output_vars[i], p_output_stypes[i], is_np_op) for i in range(num_output)]
diff --git a/python/mxnet/cython/symbol.pyx b/python/mxnet/cython/symbol.pyx
index 1bdea6c6c547..86fe8ae6db4f 100644
--- a/python/mxnet/cython/symbol.pyx
+++ b/python/mxnet/cython/symbol.pyx
@@ -84,19 +84,27 @@ cdef SymbolSetAttr(SymbolHandle handle, dict kwargs):
 
 
 _symbol_cls = SymbolBase
+_np_symbol_cls = None
 
 def _set_symbol_class(cls):
     global _symbol_cls
     _symbol_cls = cls
 
-cdef NewSymbol(SymbolHandle handle):
+
+def _set_np_symbol_class(cls):
+    global _np_symbol_cls
+    _np_symbol_cls = cls
+
+
+cdef NewSymbol(SymbolHandle handle, int is_np_sym=0):
     """Create a new symbol given handle"""
-    sym = _symbol_cls(None)
+    create_symbol_fn = _np_symbol_cls if is_np_sym else _symbol_cls
+    sym = create_symbol_fn(None)
     (<SymbolBase>sym).chandle = handle
     return sym
 
 
-def _symbol_creator(handle, args, kwargs, keys, vals, name):
+def _symbol_creator(handle, args, kwargs, keys, vals, name, is_np_op=0):
     cdef unsigned long long ihandle = handle
     cdef OpHandle chandle = <OpHandle>ihandle
     cdef vector[string] ckeys
@@ -143,4 +151,4 @@ def _symbol_creator(handle, args, kwargs, keys, vals, name):
         &csym_keys[0] if csym_keys.size() != 0 else NULL,
         &sym_args[0] if sym_args.size() != 0 else NULL))
 
-    return NewSymbol(ret_handle)
+    return NewSymbol(ret_handle, is_np_op)
diff --git a/python/mxnet/gluon/block.py b/python/mxnet/gluon/block.py
index bd22cf85d561..97e6e8b68453 100644
--- a/python/mxnet/gluon/block.py
+++ b/python/mxnet/gluon/block.py
@@ -26,7 +26,6 @@
 import re
 from collections import OrderedDict
 
-
 from ..base import mx_real_t, MXNetError
 from .. import symbol, ndarray, initializer
 from ..symbol import Symbol
@@ -34,6 +33,10 @@
 from .. import name as _name
 from .parameter import Parameter, ParameterDict, DeferredInitializationError
 from .utils import _indent, _brief_print_list, HookHandle
+from .utils import _check_same_symbol_type, _check_all_np_ndarrays
+from .. import numpy_extension as _mx_npx
+from .. import numpy as _mx_np
+from .. util import is_np_array, np_shape, np_array
 
 
 class _BlockScope(object):
@@ -332,7 +335,8 @@ def save_parameters(self, filename):
         """
         params = self._collect_params_with_prefix()
         arg_dict = {key : val._reduce() for key, val in params.items()}
-        ndarray.save(filename, arg_dict)
+        save_fn = _mx_npx.save if is_np_array() else ndarray.save
+        save_fn(filename, arg_dict)
 
     def save_params(self, filename):
         """[Deprecated] Please use save_parameters. Note that if you want load
@@ -381,7 +385,28 @@ def load_parameters(self, filename, ctx=None, allow_missing=False,
         `Saving and Loading Gluon Models \
         <https://mxnet.incubator.apache.org/tutorials/gluon/save_load_params.html>`_
         """
-        loaded = ndarray.load(filename)
+        if is_np_array():
+            # failure may happen when loading parameters saved as NDArrays within
+            # NumPy semantics. Check the failure type and recover from it if it happens.
+            try:
+                loaded = _mx_npx.load(filename)
+            except MXNetError as e:
+                err_msg = str(e)
+                if 'is_np_shape' in err_msg:
+                    # Loading failure due to parameters saved without numpy semantics.
+                    # Temporarily disable numpy semantics and load parameters. After it's
+                    # done, resume the numpy semantics. This is fine because the cases
+                    # numpy ndarray covers is a superset of the legacy ndarray's.
+                    with np_array(False):
+                        with np_shape(False):
+                            loaded_nds = ndarray.load(filename)
+                    assert isinstance(loaded_nds, dict),\
+                        'expecting a dict type, got {}'.format(str(type(loaded_nds)))
+                    loaded = {k: loaded_nds[k].as_np_ndarray() for k in loaded_nds}
+                else:
+                    raise ValueError(err_msg)
+        else:
+            loaded = ndarray.load(filename)
         params = self._collect_params_with_prefix()
         if not loaded and not params:
             return
@@ -549,7 +574,8 @@ def __call__(self, *args):
 
         for hook in self._forward_hooks.values():
             hook(self, args, out)
-
+        if _mx_npx.is_np_array():
+            _check_all_np_ndarrays(out)
         return out
 
     def forward(self, *args):
@@ -739,9 +765,13 @@ def _get_graph(self, *args):
         if not self._cached_graph:
             args, self._in_format = _flatten(args, "input")
             if len(args) > 1:
-                inputs = [symbol.var('data%d'%i) for i in range(len(args))]
+                inputs = [symbol.var('data%d' % i).as_np_ndarray()
+                          if isinstance(args[i], _mx_np.ndarray)
+                          else symbol.var('data%d' % i) for i in range(len(args))]
             else:
-                inputs = [symbol.var('data')]
+                inputs = [symbol.var('data').as_np_ndarray()
+                          if isinstance(args[0], _mx_np.ndarray)
+                          else symbol.var('data')]
             grouped_inputs = _regroup(inputs, self._in_format)[0]
 
             params = {i: j.var() for i, j in self._reg_params.items()}
@@ -749,7 +779,7 @@ def _get_graph(self, *args):
                 out = self.hybrid_forward(symbol, *grouped_inputs, **params)  # pylint: disable=no-value-for-parameter
             out, self._out_format = _flatten(out, "output")
 
-            self._cached_graph = inputs, symbol.Group(out)
+            self._cached_graph = inputs, symbol.Group(out, _check_same_symbol_type(out))
 
         return self._cached_graph
 
@@ -905,7 +935,8 @@ def export(self, path, epoch=0, remove_amp_cast=True):
             else:
                 assert name in aux_names
                 arg_dict['aux:%s'%name] = param._reduce()
-        ndarray.save('%s-%04d.params'%(path, epoch), arg_dict)
+        save_fn = _mx_npx.save if is_np_array() else ndarray.save
+        save_fn('%s-%04d.params'%(path, epoch), arg_dict)
 
     def forward(self, x, *args):
         """Defines the forward computation. Arguments can be either
@@ -1058,7 +1089,7 @@ def __init__(self, outputs, inputs, params=None):
 
         syms, self._in_format = _flatten(inputs, "input")
         out, self._out_format = _flatten(outputs, "output")
-        out = symbol.Group(out)
+        out = symbol.Group(out, _check_same_symbol_type(out))
 
         input_names = set()
         for i in syms:
diff --git a/python/mxnet/gluon/data/dataloader.py b/python/mxnet/gluon/data/dataloader.py
index 65fd7d84b5bb..4dfa94f72c50 100644
--- a/python/mxnet/gluon/data/dataloader.py
+++ b/python/mxnet/gluon/data/dataloader.py
@@ -124,6 +124,7 @@ def __init__(self, *args, **kwargs):
         self._send = self._writer.send
         self._recv = self._reader.recv
 
+
 def default_batchify_fn(data):
     """Collate data into batch."""
     if isinstance(data[0], nd.NDArray):
@@ -382,6 +383,7 @@ def same_process_iter():
     def __len__(self):
         return len(self._batch_sampler)
 
+
 _worker_dataset = None
 def _worker_initializer(dataset):
     """Initialier for processing pool."""
diff --git a/python/mxnet/gluon/parameter.py b/python/mxnet/gluon/parameter.py
index a174d82341af..89a3c338173c 100644
--- a/python/mxnet/gluon/parameter.py
+++ b/python/mxnet/gluon/parameter.py
@@ -18,6 +18,8 @@
 # coding: utf-8
 # pylint: disable=unnecessary-pass, too-many-lines
 """Neural network parameter."""
+from __future__ import absolute_import
+
 __all__ = ['DeferredInitializationError', 'Parameter', 'Constant',
            'ParameterDict', 'tensor_types']
 
@@ -31,7 +33,8 @@
 from ..context import Context, cpu
 from .. import autograd
 from .utils import _indent, _brief_print_list, shape_is_known
-from .. import is_np_shape
+from ..util import is_np_shape, is_np_array
+from .. import numpy as _mx_np  # pylint: disable=reimported
 
 # pylint: disable= invalid-name
 tensor_types = (symbol.Symbol, ndarray.NDArray)
@@ -131,7 +134,6 @@ def __init__(self, name, grad_req='write', shape=None, dtype=mx_real_t,
         self._grad_stype = grad_stype
         self._stype = stype
 
-
     def __repr__(self):
         s = 'Parameter {name} (shape={shape}, dtype={dtype})'
         return s.format(name=self.name, shape=self.shape, dtype=self.dtype)
@@ -191,9 +193,9 @@ def shape(self, new_shape):
             return
 
         assert len(self._shape) == len(new_shape) and \
-            all(j in (0, i) for i, j in zip(new_shape, self._shape)), \
+            all(j in (-1, 0, i) for i, j in zip(new_shape, self._shape)), \
             "Expected shape %s is incompatible with given shape %s."%(
-                str(new_shape), str(self._shape))
+                str(new_shape), str(self._shape))  # -1 means unknown dim size in np_shape mode
 
         self._shape = new_shape
 
@@ -272,12 +274,14 @@ def _load_init(self, data, ctx, cast_dtype=False, dtype_source='current'):
         if cast_dtype:
             assert dtype_source in ['current', 'saved']
         if self.shape:
+            unknown_dim_size = -1 if is_np_shape() else 0
             for self_dim, data_dim in zip(self.shape, data.shape):
-                assert self_dim in (0, data_dim), \
+                assert self_dim in (unknown_dim_size, data_dim), \
                     "Failed loading Parameter '%s' from saved params: " \
                     "shape incompatible expected %s vs saved %s"%(
                         self.name, str(self.shape), str(data.shape))
-            self.shape = tuple(i if i != 0 else j for i, j in zip(self.shape, data.shape))
+            self.shape = tuple(i if i != unknown_dim_size else j
+                               for i, j in zip(self.shape, data.shape))
         if self.dtype:
             if cast_dtype and np.dtype(self.dtype).type != data.dtype:
                 if dtype_source == 'current':
@@ -318,6 +322,7 @@ def _finish_deferred_init(self):
             return
         init, ctx, default_init, data = self._deferred_init
         self._deferred_init = ()
+
         assert shape_is_known(self.shape), \
             "Cannot initialize Parameter '%s' because it has " \
             "invalid shape: %s. Please specify in_units, " \
@@ -326,8 +331,16 @@ def _finish_deferred_init(self):
 
         with autograd.pause():
             if data is None:
-                data = ndarray.zeros(shape=self.shape, dtype=self.dtype,
-                                     ctx=context.cpu(), stype=self._stype)
+                kwargs = {'shape': self.shape, 'dtype': self.dtype, 'ctx': context.cpu()}
+                if is_np_array():
+                    if self._stype != 'default':
+                        raise ValueError("mxnet.numpy.zeros does not support stype = {}"
+                                         .format(self._stype))
+                    zeros_fn = _mx_np.zeros
+                else:
+                    kwargs['stype'] = self._stype
+                    zeros_fn = ndarray.zeros
+                data = zeros_fn(**kwargs)
                 initializer.create(default_init)(
                     initializer.InitDesc(self.name, {'__init__': init}), data)
 
@@ -352,8 +365,15 @@ def _init_grad(self):
             self._grad = None
             return
 
-        self._grad = [ndarray.zeros(shape=i.shape, dtype=i.dtype, ctx=i.context,
-                                    stype=self._grad_stype) for i in self._data]
+        if is_np_array():
+            if self._grad_stype != 'default':
+                raise ValueError("mxnet.numpy.zeros does not support stype = {}"
+                                 .format(self._grad_stype))
+            self._grad = [_mx_np.zeros(shape=i.shape, dtype=i.dtype, ctx=i.context)
+                          for i in self._data]
+        else:
+            self._grad = [ndarray.zeros(shape=i.shape, dtype=i.dtype, ctx=i.context,
+                                        stype=self._grad_stype) for i in self._data]
 
         autograd.mark_variables(self._check_and_get(self._data, list),
                                 self._grad, self.grad_req)
@@ -363,7 +383,10 @@ def _reduce(self):
         ctx = context.cpu()
         if self._stype == 'default':
             block = self.list_data()
-            data = ndarray.add_n(*(w.copyto(ctx) for w in block)) / len(block)
+            if is_np_array():
+                data = sum([w.copyto(ctx) for w in block]) / len(block)
+            else:
+                data = ndarray.add_n(*(w.copyto(ctx) for w in block)) / len(block)
         else:
             # fetch all rows for 'row_sparse' param
             all_row_ids = ndarray.arange(0, self.shape[0], dtype='int64', ctx=ctx)
@@ -463,7 +486,6 @@ def reset_ctx(self, ctx):
             raise ValueError("Cannot reset context for Parameter '%s' because it "
                              "has not been initialized."%self.name)
 
-
     def set_data(self, data):
         """Sets this parameter's value on all contexts."""
         self.shape = data.shape
@@ -602,6 +624,8 @@ def var(self):
             self._var = symbol.var(self.name, shape=self.shape, dtype=self.dtype,
                                    lr_mult=self.lr_mult, wd_mult=self.wd_mult,
                                    init=self.init, stype=self._stype)
+            if is_np_array():
+                self._var = self._var.as_np_ndarray()
         return self._var
 
     def cast(self, dtype):
@@ -763,12 +787,12 @@ def get(self, name, **kwargs):
                         inferred_shape = []
                         matched = True
                         for dim1, dim2 in zip(v, existing):
-                            if dim1 != dim2 and dim1 * dim2 != 0:
+                            if dim1 != dim2 and dim1 > 0 and dim2 > 0:
                                 matched = False
                                 break
                             elif dim1 == dim2:
                                 inferred_shape.append(dim1)
-                            elif dim1 == 0:
+                            elif dim1 in (0, -1):  # -1 means unknown dim size in np_shape mode
                                 inferred_shape.append(dim2)
                             else:
                                 inferred_shape.append(dim1)
diff --git a/python/mxnet/gluon/rnn/rnn_layer.py b/python/mxnet/gluon/rnn/rnn_layer.py
index b3cc596282a7..2a9cd88bb214 100644
--- a/python/mxnet/gluon/rnn/rnn_layer.py
+++ b/python/mxnet/gluon/rnn/rnn_layer.py
@@ -236,7 +236,6 @@ def __call__(self, inputs, states=None, sequence_length=None, **kwargs):
         else:
             return super(_RNNLayer, self).__call__(inputs, states, **kwargs)
 
-
     def hybrid_forward(self, F, inputs, states, sequence_length=None, **kwargs):
         if F is ndarray:
             batch_size = inputs.shape[self._layout.find('N')]
@@ -285,7 +284,6 @@ def _forward_kernel(self, F, inputs, states, sequence_length, **kwargs):
                     lstm_state_clip_max=self._lstm_state_clip_max,
                     lstm_state_clip_nan=self._lstm_state_clip_nan)
 
-
         if self._mode == 'lstm':
             outputs, states = rnn[0], [rnn[1], rnn[2]]
         else:
diff --git a/python/mxnet/gluon/utils.py b/python/mxnet/gluon/utils.py
index 2060f61a0212..f12f15e7d41a 100644
--- a/python/mxnet/gluon/utils.py
+++ b/python/mxnet/gluon/utils.py
@@ -40,6 +40,7 @@ class requests_failed_to_import(object):
 from .. import ndarray
 from ..util import is_np_shape
 
+
 def split_data(data, num_slice, batch_axis=0, even_split=True):
     """Splits an NDArray into `num_slice` slices along `batch_axis`.
     Usually used for data parallelism where each slices is sent
@@ -108,7 +109,7 @@ def split_and_load(data, ctx_list, batch_axis=0, even_split=True):
 
     Returns
     -------
-    list of NDArray
+    list of NDArrays
         Each corresponds to a context in `ctx_list`.
     """
     if not isinstance(data, ndarray.NDArray):
@@ -414,6 +415,7 @@ def __enter__(self):
     def __exit__(self, ptype, value, trace):
         self.detach()
 
+
 def shape_is_known(shape):
     """Check whether a shape is completely known with or without np semantics.
 
@@ -430,3 +432,44 @@ def shape_is_known(shape):
         assert dim_size > unknown_dim_size, "shape dimension size cannot be less than {}, while " \
                                             "received {}".format(unknown_dim_size, dim_size)
     return True
+
+
+def _check_same_symbol_type(symbols):
+    """Check whether all the symbols in the list are of the same type.
+    Raise type error if the types are different. Return the class of
+    the symbols."""
+    from ..symbol.numpy import _Symbol as np_symbol
+    from ..symbol import Symbol as nd_symbol
+    is_np_sym = isinstance(symbols[0], np_symbol)
+    for s in symbols[1:]:
+        if is_np_sym != isinstance(s, np_symbol):
+            raise TypeError('Found both classic symbol (mx.sym.Symbol) and numpy symbol '
+                            '(mx.sym.np._Symbol) in outputs. This will prevent you from building '
+                            'a computation graph by grouping them since different types of symbols '
+                            'are not allowed to be grouped in Gluon to form a computation graph. '
+                            'You will need to convert them to the same type of symbols, either '
+                            'classic or numpy following this rule: if you want numpy ndarray '
+                            'output(s) from the computation graph, please convert all the classic '
+                            'symbols in the list to numpy symbols by calling `as_np_ndarray()` '
+                            'on each of them; if you want classic ndarray output(s) from the '
+                            'computation graph, please convert all the numpy symbols in the list '
+                            'to classic symbols by calling `as_nd_ndarray()` on each of them.')
+    return np_symbol if is_np_sym else nd_symbol
+
+
+def _check_all_np_ndarrays(out):
+    """Check if ndarrays/symbols in out are all np.ndarray/np._Symbol."""
+    from ..numpy import ndarray as np_ndarray
+    from ..symbol.numpy import _Symbol as np_symbol
+    from ..symbol import Symbol as nd_symbol
+    from ..ndarray import NDArray as nd_ndarray
+
+    # pylint: disable=no-else-raise
+    if isinstance(out, (nd_ndarray, nd_symbol)) and not isinstance(out, (np_ndarray, np_symbol)):
+        raise TypeError("Block's output ndarrays/symbols must be of type `mxnet.numpy.ndarray`"
+                        " or `mxnet.symbol.numpy._Symbol`, while got output type {}"
+                        .format(str(type(out))))
+    elif isinstance(out, (list, tuple)):
+        for i in out:
+            _check_all_np_ndarrays(i)
+    # pylint: enable=no-else-raise
diff --git a/python/mxnet/image/image.py b/python/mxnet/image/image.py
index bffd286cb538..5c3f02cac1ab 100644
--- a/python/mxnet/image/image.py
+++ b/python/mxnet/image/image.py
@@ -428,7 +428,7 @@ def fixed_crop(src, x0, y0, w, h, size=None, interp=2):
     NDArray
         An `NDArray` containing the cropped image.
     """
-    out = nd.slice(src, begin=(y0, x0, 0), end=(y0 + h, x0 + w, int(src.shape[2])))
+    out = src[y0:y0+h, x0:x0+w]
     if size is not None and (w, h) != size:
         sizes = (h, w, size[1], size[0])
         out = imresize(out, *size, interp=_get_interp_method(interp, sizes))
diff --git a/python/mxnet/ndarray/__init__.py b/python/mxnet/ndarray/__init__.py
index f09908e894d5..f6b8712a2513 100644
--- a/python/mxnet/ndarray/__init__.py
+++ b/python/mxnet/ndarray/__init__.py
@@ -17,7 +17,7 @@
 
 """NDArray API of MXNet."""
 
-from . import _internal, contrib, linalg, op, random, sparse, utils, image, ndarray
+from . import _internal, contrib, linalg, op, random, sparse, utils, image, ndarray, numpy
 # pylint: disable=wildcard-import, redefined-builtin
 try:
     from .gen_op import * # pylint: disable=unused-wildcard-import
@@ -30,6 +30,8 @@
 from .utils import load, load_frombuffer, save, zeros, empty, array
 from .sparse import _ndarray_cls
 from .ndarray import _GRAD_REQ_MAP, _DTYPE_MX_TO_NP, _DTYPE_NP_TO_MX, _new_empty_handle
+from . import numpy as np
+from . import numpy_extension as npx
 
 __all__ = op.__all__ + ndarray.__all__ + utils.__all__ + \
-          ['contrib', 'linalg', 'random', 'sparse', 'image']
+          ['contrib', 'linalg', 'random', 'sparse', 'image', 'numpy', 'numpy_extension']
diff --git a/python/mxnet/ndarray/_internal.py b/python/mxnet/ndarray/_internal.py
index 8045d9bd2b14..d48255647939 100644
--- a/python/mxnet/ndarray/_internal.py
+++ b/python/mxnet/ndarray/_internal.py
@@ -23,18 +23,18 @@
 try:
     if int(_os.environ.get("MXNET_ENABLE_CYTHON", True)) == 0:
         from .._ctypes.ndarray import NDArrayBase, CachedOp
-        from .._ctypes.ndarray import _set_ndarray_class, _imperative_invoke
+        from .._ctypes.ndarray import _set_ndarray_class, _imperative_invoke, _set_np_ndarray_class
     elif _sys.version_info >= (3, 0):
         from .._cy3.ndarray import NDArrayBase, CachedOp
-        from .._cy3.ndarray import _set_ndarray_class, _imperative_invoke
+        from .._cy3.ndarray import _set_ndarray_class, _imperative_invoke, _set_np_ndarray_class
     else:
         from .._cy2.ndarray import NDArrayBase, CachedOp
-        from .._cy2.ndarray import _set_ndarray_class, _imperative_invoke
+        from .._cy2.ndarray import _set_ndarray_class, _imperative_invoke, _set_np_ndarray_class
 except ImportError:
     if int(_os.environ.get("MXNET_ENFORCE_CYTHON", False)) != 0:
         raise ImportError("Cython Module cannot be loaded but MXNET_ENFORCE_CYTHON=1")
     from .._ctypes.ndarray import NDArrayBase, CachedOp
-    from .._ctypes.ndarray import _set_ndarray_class, _imperative_invoke
+    from .._ctypes.ndarray import _set_ndarray_class, _imperative_invoke, _set_np_ndarray_class
 
 from ..base import _Null
 try:
@@ -42,4 +42,5 @@
 except ImportError:
     pass
 
-__all__ = ['NDArrayBase', 'CachedOp', '_imperative_invoke', '_set_ndarray_class']
+__all__ = ['NDArrayBase', 'CachedOp', '_imperative_invoke', '_set_ndarray_class',
+           '_set_np_ndarray_class']
diff --git a/python/mxnet/ndarray/ndarray.py b/python/mxnet/ndarray/ndarray.py
index 0b7dca4ebba4..c1c45f04ead0 100644
--- a/python/mxnet/ndarray/ndarray.py
+++ b/python/mxnet/ndarray/ndarray.py
@@ -184,6 +184,24 @@ class NDArray(NDArrayBase):
     # See C++ side of definition(kTVMNDArrayTypeCode) at include/mxmet/tensor_blob.h
     _tvm_tcode = 19
     # pylint: disable= no-member, undefined-variable
+
+    def as_np_ndarray(self):
+        """Convert mxnet.ndarray.NDArray to mxnet.numpy.ndarray."""
+        storage_type = self.stype
+        if storage_type != 'default':
+            raise ValueError('cannot convert ndarray of stype {} to numpy ndarray'
+                             .format(str(type(storage_type))))
+        from ..numpy import ndarray
+        hdl = NDArrayHandle()
+        check_call(_LIB.MXShallowCopyNDArray(self.handle, ctypes.byref(hdl)))
+        return ndarray(handle=hdl, writable=self.writable)
+
+    def as_nd_ndarray(self):
+        """A convenience function for creating a classic ndarray from the current
+        ndarray with zero copy. For this class, it just returns itself since it is
+        already a classic ndarray."""
+        return self
+
     @property
     def _tvm_handle(self):
         return self.handle.value
@@ -912,7 +930,7 @@ def _slice(self, start, stop):
 
         check_call(_LIB.MXNDArraySlice(
             self.handle, mx_uint(start), mx_uint(stop), ctypes.byref(handle)))
-        return NDArray(handle=handle, writable=self.writable)
+        return self.__class__(handle=handle, writable=self.writable)
 
     def _at(self, idx):
         """Returns a view of the array sliced at `idx` in the first dim.
@@ -946,7 +964,7 @@ def _at(self, idx):
                                  % (idx-length, length))
         check_call(_LIB.MXNDArrayAt(
             self.handle, mx_uint(idx), ctypes.byref(handle)))
-        return NDArray(handle=handle, writable=self.writable)
+        return self.__class__(handle=handle, writable=self.writable)
 
     def reshape(self, *shape, **kwargs):
         """Returns a **view** of this array with a new shape without altering any data.
@@ -1069,7 +1087,7 @@ def reshape(self, *shape, **kwargs):
                                            c_array(ctypes.c_int64, shape),
                                            reverse,
                                            ctypes.byref(handle)))
-        return NDArray(handle=handle, writable=self.writable)
+        return self.__class__(handle=handle, writable=self.writable)
 
     def reshape_like(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`reshape_like`.
@@ -2490,7 +2508,7 @@ def _get_broadcast_shape(shape1, shape2):
     for a, b in zip(shape1[::-1], shape2[::-1]):
         if a != 1 and b != 1 and a != b:
             raise ValueError('shape1=%s is not broadcastable to shape2=%s' % (shape1, shape2))
-        shape[i] = max(a, b)
+        shape[i] = b if a == 1 else a
         i -= 1
     return tuple(shape)
 
diff --git a/python/mxnet/ndarray/numpy/__init__.py b/python/mxnet/ndarray/numpy/__init__.py
new file mode 100644
index 000000000000..7eb478f792f5
--- /dev/null
+++ b/python/mxnet/ndarray/numpy/__init__.py
@@ -0,0 +1,26 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Module for numpy ops under mxnet.ndarray."""
+
+from . import random
+from . import linalg
+from . import _op, _internal
+from . import _register
+from ._op import *  # pylint: disable=wildcard-import
+
+__all__ = _op.__all__
diff --git a/python/mxnet/ndarray/numpy/_internal.py b/python/mxnet/ndarray/numpy/_internal.py
new file mode 100644
index 000000000000..c5f292842b3b
--- /dev/null
+++ b/python/mxnet/ndarray/numpy/_internal.py
@@ -0,0 +1,20 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Namespace for numpy internal ops."""
+
+__all__ = []
diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
new file mode 100644
index 000000000000..d7c06e76c182
--- /dev/null
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -0,0 +1,295 @@
+# pylint: disable=C0302
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Namespace for numpy operators used in Gluon dispatched by F=ndarray."""
+
+from __future__ import absolute_import
+import numpy as _np
+from ...base import numeric_types
+from ...util import set_module
+from ...context import current_context
+from . import _internal as _npi
+
+__all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power']
+
+
+@set_module('mxnet.ndarray.numpy')
+def zeros(shape, dtype=_np.float32, order='C', ctx=None):
+    """Return a new array of given shape and type, filled with zeros.
+    This function currently only supports storing multi-dimensional data
+    in row-major (C-style).
+
+    Parameters
+    ----------
+    shape : int or tuple of int
+        The shape of the empty array.
+    dtype : str or numpy.dtype, optional
+        An optional value type. Default is `numpy.float32`. Note that this
+        behavior is different from NumPy's `ones` function where `float64`
+        is the default value, because `float32` is considered as the default
+        data type in deep learning.
+    order : {'C'}, optional, default: 'C'
+        How to store multi-dimensional data in memory, currently only row-major
+        (C-style) is supported.
+    ctx : Context, optional
+        An optional device context (default is the current default context).
+
+    Returns
+    -------
+    out : ndarray
+        Array of zeros with the given shape, dtype, and ctx.
+    """
+    if order != 'C':
+        raise NotImplementedError
+    if ctx is None:
+        ctx = current_context()
+    dtype = _np.float32 if dtype is None else dtype
+    return _npi.zeros(shape=shape, ctx=ctx, dtype=dtype)
+
+
+@set_module('mxnet.ndarray.numpy')
+def ones(shape, dtype=_np.float32, order='C', ctx=None):
+    """Return a new array of given shape and type, filled with ones.
+    This function currently only supports storing multi-dimensional data
+    in row-major (C-style).
+
+    Parameters
+    ----------
+    shape : int or tuple of int
+        The shape of the empty array.
+    dtype : str or numpy.dtype, optional
+        An optional value type. Default is `numpy.float32`. Note that this
+        behavior is different from NumPy's `ones` function where `float64`
+        is the default value, because `float32` is considered as the default
+        data type in deep learning.
+    order : {'C'}, optional, default: 'C'
+        How to store multi-dimensional data in memory, currently only row-major
+        (C-style) is supported.
+    ctx : Context, optional
+        An optional device context (default is the current default context).
+
+    Returns
+    -------
+    out : ndarray
+        Array of zeros with the given shape, dtype, and ctx.
+    """
+    if order != 'C':
+        raise NotImplementedError
+    if ctx is None:
+        ctx = current_context()
+    dtype = _np.float32 if dtype is None else dtype
+    return _npi.ones(shape=shape, ctx=ctx, dtype=dtype)
+
+
+#pylint: disable= too-many-arguments, no-member, protected-access
+def _ufunc_helper(lhs, rhs, fn_array, fn_scalar, lfn_scalar, rfn_scalar=None, out=None):
+    """ Helper function for element-wise operation.
+    The function will perform numpy-like broadcasting if needed and call different functions.
+
+    Parameters
+    --------
+    lhs : ndarray or numeric value
+        Left-hand side operand.
+
+    rhs : ndarray or numeric value
+        Right-hand operand,
+
+    fn_array : function
+        Function to be called if both lhs and rhs are of ``ndarray`` type.
+
+    fn_scalar : function
+        Function to be called if both lhs and rhs are numeric values.
+
+    lfn_scalar : function
+        Function to be called if lhs is ``ndarray`` while rhs is numeric value
+
+    rfn_scalar : function
+        Function to be called if lhs is numeric value while rhs is ``ndarray``;
+        if none is provided, then the function is commutative, so rfn_scalar is equal to lfn_scalar
+
+    Returns
+    --------
+    mxnet.numpy.ndarray or scalar
+        result array or scalar
+    """
+    from ...numpy import ndarray
+    if isinstance(lhs, numeric_types):
+        if isinstance(rhs, numeric_types):
+            return fn_scalar(lhs, rhs, out=out)
+        else:
+            if rfn_scalar is None:
+                # commutative function
+                return lfn_scalar(rhs, float(lhs), out=out)
+            else:
+                return rfn_scalar(rhs, float(lhs), out=out)
+    elif isinstance(rhs, numeric_types):
+        return lfn_scalar(lhs, float(rhs), out=out)
+    elif isinstance(rhs, ndarray):
+        return fn_array(lhs, rhs, out=out)
+    else:
+        raise TypeError('type {} not supported'.format(str(type(rhs))))
+#pylint: enable= too-many-arguments, no-member, protected-access
+
+
+@set_module('mxnet.ndarray.numpy')
+def add(x1, x2, out=None):
+    """Add arguments element-wise.
+
+    Parameters
+    ----------
+    x1, x2 : ndarrays or scalar values
+        The arrays to be added. If x1.shape != x2.shape, they must be broadcastable to
+        a common shape (which may be the shape of one or the other).
+
+    out : ndarray
+        A location into which the result is stored. If provided, it must have a shape
+        that the inputs broadcast to. If not provided or None, a freshly-allocated array
+        is returned.
+
+    Returns
+    -------
+    add : ndarray or scalar
+        The sum of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars.
+    """
+    return _ufunc_helper(x1, x2, _npi.add, _np.add, _npi.add_scalar, None, out)
+
+
+@set_module('mxnet.ndarray.numpy')
+def subtract(x1, x2, out=None):
+    """Subtract arguments element-wise.
+
+    Parameters
+    ----------
+    x1, x2 : ndarrays or scalar values
+        The arrays to be subtracted from each other. If x1.shape != x2.shape,
+        they must be broadcastable to a common shape (which may be the shape
+        of one or the other).
+
+    out : ndarray
+        A location into which the result is stored. If provided, it must have a shape
+        that the inputs broadcast to. If not provided or None, a freshly-allocated array
+        is returned.
+
+    Returns
+    -------
+    subtract : ndarray or scalar
+        The difference of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars.
+    """
+    return _ufunc_helper(x1, x2, _npi.subtract, _np.subtract, _npi.subtract_scalar,
+                         _npi.rsubtract_scalar, out)
+
+
+@set_module('mxnet.ndarray.numpy')
+def multiply(x1, x2, out=None):
+    """Multiply arguments element-wise.
+
+    Parameters
+    ----------
+    x1, x2 : ndarrays or scalar values
+        The arrays to be multiplied. If x1.shape != x2.shape, they must be broadcastable to
+        a common shape (which may be the shape of one or the other).
+
+    out : ndarray
+        A location into which the result is stored. If provided, it must have a shape
+        that the inputs broadcast to. If not provided or None, a freshly-allocated array
+        is returned.
+
+    Returns
+    -------
+    out : ndarray or scalar
+        The multiplication of x1 and x2, element-wise. This is a scalar if both x1 and x2
+        are scalars.
+    """
+    return _ufunc_helper(x1, x2, _npi.multiply, _np.multiply, _npi.multiply_scalar, None, out)
+
+
+@set_module('mxnet.ndarray.numpy')
+def divide(x1, x2, out=None):
+    """Returns a true division of the inputs, element-wise.
+
+    Parameters
+    ----------
+    x1 : ndarray or scalar
+        Dividend array.
+
+    x2 : ndarray or scalar
+        Divisor array.
+
+    out : ndarray
+        A location into which the result is stored. If provided, it must have a shape
+        that the inputs broadcast to. If not provided or None, a freshly-allocated array
+        is returned.
+
+    Returns
+    -------
+    out : ndarray or scalar
+        This is a scalar if both x1 and x2 are scalars.
+    """
+    return _ufunc_helper(x1, x2, _npi.true_divide, _np.divide, _npi.true_divide_scalar,
+                         _npi.rtrue_divide_scalar, out)
+
+
+@set_module('mxnet.ndarray.numpy')
+def mod(x1, x2, out=None):
+    """Return element-wise remainder of division.
+
+    Parameters
+    ----------
+    x1 : ndarray or scalar
+        Dividend array.
+
+    x2 : ndarray or scalar
+        Divisor array.
+
+    out : ndarray
+        A location into which the result is stored. If provided, it must have a shape
+        that the inputs broadcast to. If not provided or None, a freshly-allocated array
+        is returned.
+
+    Returns
+    -------
+    out : ndarray or scalar
+        This is a scalar if both x1 and x2 are scalars.
+    """
+    return _ufunc_helper(x1, x2, _npi.mod, _np.mod, _npi.mod_scalar, _npi.rmod_scalar, out)
+
+
+@set_module('mxnet.ndarray.numpy')
+def power(x1, x2, out=None):
+    """First array elements raised to powers from second array, element-wise.
+
+    Parameters
+    ----------
+    x1 : ndarray or scalar
+        The bases.
+
+    x2 : ndarray or scalar
+        The exponent.
+
+    out : ndarray
+        A location into which the result is stored. If provided, it must have a shape
+        that the inputs broadcast to. If not provided or None, a freshly-allocated array
+        is returned.
+
+    Returns
+    -------
+    out : ndarray or scalar
+        The bases in x1 raised to the exponents in x2.
+        This is a scalar if both x1 and x2 are scalars.
+    """
+    return _ufunc_helper(x1, x2, _npi.power, _np.power, _npi.power_scalar, _npi.rpower_scalar, out)
diff --git a/python/mxnet/ndarray/numpy/_register.py b/python/mxnet/ndarray/numpy/_register.py
new file mode 100644
index 000000000000..3ac464e24217
--- /dev/null
+++ b/python/mxnet/ndarray/numpy/_register.py
@@ -0,0 +1,28 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Registering numpy ops."""
+
+from ...base import _init_np_op_module
+from ..register import _make_ndarray_function
+
+
+_init_np_op_module(root_module_name='mxnet', np_module_name='numpy',
+                   mx_module_name='ndarray', make_op_func=_make_ndarray_function)
+
+_init_np_op_module(root_module_name='mxnet', np_module_name='numpy._internal',
+                   mx_module_name='ndarray', make_op_func=_make_ndarray_function)
diff --git a/python/mxnet/ndarray/numpy/linalg.py b/python/mxnet/ndarray/numpy/linalg.py
new file mode 100644
index 000000000000..0222bb45d148
--- /dev/null
+++ b/python/mxnet/ndarray/numpy/linalg.py
@@ -0,0 +1,22 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Namespace for operators used in Gluon dispatched by F=ndarray."""
+
+from __future__ import absolute_import
+
+__all__ = []
diff --git a/python/mxnet/ndarray/numpy/random.py b/python/mxnet/ndarray/numpy/random.py
new file mode 100644
index 000000000000..339fb1e77920
--- /dev/null
+++ b/python/mxnet/ndarray/numpy/random.py
@@ -0,0 +1,21 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Namespace for operators used in Gluon dispatched by F=ndarray."""
+from __future__ import absolute_import
+
+__all__ = []
diff --git a/python/mxnet/ndarray/numpy_extension/__init__.py b/python/mxnet/ndarray/numpy_extension/__init__.py
new file mode 100644
index 000000000000..5be34ac9b3d5
--- /dev/null
+++ b/python/mxnet/ndarray/numpy_extension/__init__.py
@@ -0,0 +1,25 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Module for the ops not belonging to the official numpy package."""
+
+from . import _op
+from . import image
+from . import _register
+from ._op import *  # pylint: disable=wildcard-import
+
+__all__ = _op.__all__
diff --git a/python/mxnet/ndarray/numpy_extension/_op.py b/python/mxnet/ndarray/numpy_extension/_op.py
new file mode 100644
index 000000000000..22738a0f1950
--- /dev/null
+++ b/python/mxnet/ndarray/numpy_extension/_op.py
@@ -0,0 +1,21 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Namespace for the operators not belonging to the official numpy package
+used in Gluon dispatched by F=ndarray module."""
+
+__all__ = []
diff --git a/python/mxnet/ndarray/numpy_extension/_register.py b/python/mxnet/ndarray/numpy_extension/_register.py
new file mode 100644
index 000000000000..32cd0686551c
--- /dev/null
+++ b/python/mxnet/ndarray/numpy_extension/_register.py
@@ -0,0 +1,25 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Registering numpy_extension ops."""
+
+from ...base import _init_np_op_module
+from ..register import _make_ndarray_function
+
+
+_init_np_op_module(root_module_name='mxnet', np_module_name='numpy_extension',
+                   mx_module_name='ndarray', make_op_func=_make_ndarray_function)
diff --git a/python/mxnet/ndarray/numpy_extension/image.py b/python/mxnet/ndarray/numpy_extension/image.py
new file mode 100644
index 000000000000..b3bd27fc503c
--- /dev/null
+++ b/python/mxnet/ndarray/numpy_extension/image.py
@@ -0,0 +1,20 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Image pre-processing operators."""
+
+__all__ = []
diff --git a/python/mxnet/ndarray/register.py b/python/mxnet/ndarray/register.py
index 1ccf228698ba..bdbfa1584ca6 100644
--- a/python/mxnet/ndarray/register.py
+++ b/python/mxnet/ndarray/register.py
@@ -24,12 +24,97 @@
 from ._internal import NDArrayBase, _imperative_invoke # pylint: disable=unused-import
 from ..ndarray_doc import _build_doc
 
-from ..base import mx_uint, check_call, _LIB, py_str, _init_op_module, _Null # pylint: disable=unused-import
+from ..base import mx_uint, check_call, _LIB, py_str, _init_op_module, _Null, _is_np_op  # pylint: disable=unused-import
+from ..util import use_np_shape  # pylint: disable=unused-import
+
+
+def _verify_all_np_ndarrays(op_name, func_name, args, out):
+    """Verify if all the arrays are numpy ndarrays.
+
+    Parameters
+    ----------
+    op_name : str
+        Operator full name registered in backend.
+    func_name : str
+        Operator name exposed to users. This is usually the name by stripping off
+        the prefix of the full operator names registered in backend.
+    args : list of arrays
+        Input ndarray arguments to be checked.
+    out : ndarray or None or list of ndarrays
+        User-provided output ndarrays.
+    """
+    from ..numpy import ndarray as np_ndarray
+    for arr in args:
+        if (arr is not None) and (not isinstance(arr, np_ndarray)):
+            raise TypeError('Operator `{}` registered in backend is known as `{}` in Python. '
+                            'This is a numpy operator which can only accept '
+                            'MXNet numpy ndarrays, while received a legacy ndarray. '
+                            'Please ensure that you have activated numpy semantics by calling '
+                            '`npx.set_np()` in your code. If you still see this error with numpy '
+                            'semantics activated, please call `as_np_ndarray()` upon the legacy '
+                            'ndarray to convert it to an MXNet numpy ndarray, and then feed the '
+                            'converted array to this operator.'
+                            .format(op_name, func_name))
+    if out is None:
+        return
+    if not isinstance(out, (list, tuple)):
+        out = [out]
+    for arr in out:
+        if (arr is not None) and (not isinstance(arr, np_ndarray)):
+            raise TypeError('Operator `{}` registered in backend is known as `{}` in Python. '
+                            'This is a numpy operator which can only accept '
+                            'MXNet numpy ndarrays, while received a legacy ndarray. '
+                            'Please ensure that you have activated numpy semantics by calling '
+                            '`npx.set_np()` in your code. If you still see this error with numpy '
+                            'semantics activated, please call `as_np_ndarray()` upon the legacy '
+                            'ndarray to convert it to an MXNet numpy ndarray, and then feed the '
+                            'converted array to this operator.'
+                            .format(op_name, func_name))
+
+
+def _verify_all_legacy_ndarrays(op_name, func_name, args, out):
+    """Verify if all the arrays are legacy ndarrays.
+
+    Parameters
+    ----------
+    op_name : str
+        Operator full name registered in backend.
+    func_name : str
+        Operator name exposed to users. This is usually the name by stripping off
+        the prefix of the full operator names registered in backend.
+    args : list of arrays
+        Input ndarray arguments to be checked.
+    out : ndarray or None or list of ndarrays
+        User-provided output ndarrays.
+    """
+    from ..numpy import ndarray as np_ndarray
+    for arr in args:
+        if (arr is not None) and (isinstance(arr, np_ndarray)):
+            raise TypeError('Operator `{}` registered in backend is known as `{}` in Python. '
+                            'This is a legacy operator which can only accept '
+                            'legacy ndarrays, while received an MXNet numpy ndarray. '
+                            'Please call `as_nd_ndarray()` upon the numpy ndarray to '
+                            'convert it to a legacy ndarray, and then feed the converted '
+                            'array to this operator.'
+                            .format(op_name, func_name))
+    if out is None:
+        return
+    if not isinstance(out, (list, tuple)):
+        out = [out]
+    for arr in out:
+        if (arr is not None) and (isinstance(arr, np_ndarray)):
+            raise TypeError('Operator `{}` registered in backend is known as `{}` in Python. '
+                            'This is a legacy operator which can only write to '
+                            'legacy ndarrays, while received an MXNet numpy ndarray. '
+                            'Please call `as_nd_ndarray()` upon the numpy ndarray to '
+                            'convert it to a legacy ndarray, and then feed the converted '
+                            'array to this operator.'
+                            .format(op_name, func_name))
 
 
 # pylint: disable=too-many-locals
-def _generate_ndarray_function_code(handle, name, func_name, signature_only=False):
-    """Generate function for ndarray op by handle and function name."""
+def _generate_ndarray_function_code(handle, op_name, func_name, signature_only=False):
+    """Generate function for ndarray op by handle and function op_name."""
     real_name = ctypes.c_char_p()
     desc = ctypes.c_char_p()
     num_args = mx_uint()
@@ -52,7 +137,7 @@ def _generate_ndarray_function_code(handle, name, func_name, signature_only=Fals
     arg_types = [py_str(arg_types[i]) for i in range(narg)]
     key_var_num_args = py_str(key_var_num_args.value)
     ret_type = py_str(ret_type.value) if ret_type.value is not None else ''
-    doc_str = _build_doc(name,
+    doc_str = _build_doc(op_name,
                          py_str(desc.value),
                          arg_names,
                          arg_types,
@@ -90,6 +175,10 @@ def _generate_ndarray_function_code(handle, name, func_name, signature_only=Fals
     signature = ndsignature + signature
 
     code = []
+    is_np_op = _is_np_op(op_name)
+    doc_str_idx = 1
+    if is_np_op:
+        doc_str_idx = 2
     if arr_name:
         code.append("""
 def %s(*%s, **kwargs):"""%(func_name, arr_name))
@@ -134,15 +223,26 @@ def %s(%s):"""%(func_name, ', '.join(signature)))
         vals.append(%s)"""%(name, name, name))
             # dtype
             if dtype_name is not None:
-                code.append("""
+                if is_np_op:
+                    code.append("""
+    if %s is not _Null and %s is not None:
+        keys.append('%s')
+        vals.append(_np.dtype(%s).name)"""%(dtype_name, dtype_name, dtype_name, dtype_name))
+                else:
+                    code.append("""
     if %s is not _Null:
         keys.append('%s')
         vals.append(_np.dtype(%s).name)"""%(dtype_name, dtype_name, dtype_name))
 
+    verify_ndarrays_fn =\
+        _verify_all_np_ndarrays.__name__ if is_np_op else _verify_all_legacy_ndarrays.__name__
     if not signature_only:
         code.append("""
-    return _imperative_invoke(%d, ndargs, keys, vals, out)"""%(
-        handle.value))
+    {verify_fn}("{op_name}", "{func_name}", ndargs, out)
+        """.format(verify_fn=verify_ndarrays_fn, op_name=op_name, func_name=func_name))
+        code.append("""
+    return _imperative_invoke(%d, ndargs, keys, vals, out, %s)"""%(
+        handle.value, str(is_np_op)))
     else:
         code.append("""
     return (0,)""")
@@ -150,7 +250,7 @@ def %s(%s):"""%(func_name, ', '.join(signature)))
     doc_str_lines = _os.linesep+''.join(['    '+s if s.strip() else s
                                          for s in 'r"""{doc_str}"""'.format(doc_str=doc_str)
                                          .splitlines(True)])
-    code.insert(1, doc_str_lines)
+    code.insert(doc_str_idx, doc_str_lines)
     return ''.join(code), doc_str
 
 
diff --git a/python/mxnet/ndarray/utils.py b/python/mxnet/ndarray/utils.py
index ff93d0be6d73..730f2172c4f4 100644
--- a/python/mxnet/ndarray/utils.py
+++ b/python/mxnet/ndarray/utils.py
@@ -248,6 +248,7 @@ def save(fname, data):
     >>> mx.nd.load('my_dict')
     {'y': <NDArray 1x4 @cpu(0)>, 'x': <NDArray 2x3 @cpu(0)>}
     """
+    from ..numpy import ndarray as np_ndarray
     if isinstance(data, NDArray):
         data = [data]
         handles = c_array(NDArrayHandle, [])
@@ -257,11 +258,17 @@ def save(fname, data):
         if any(not isinstance(k, string_types) for k in str_keys) or \
            any(not isinstance(v, NDArray) for v in nd_vals):
             raise TypeError('save only accept dict str->NDArray or list of NDArray')
+        if any(isinstance(v, np_ndarray) for v in nd_vals):
+            raise TypeError('cannot save mxnet.numpy.ndarray using mxnet.ndarray.save;'
+                            ' use mxnet.numpy.save instead.')
         keys = c_str_array(str_keys)
         handles = c_handle_array(nd_vals)
     elif isinstance(data, list):
         if any(not isinstance(v, NDArray) for v in data):
             raise TypeError('save only accept dict str->NDArray or list of NDArray')
+        if any(isinstance(v, np_ndarray) for v in data):
+            raise TypeError('cannot save mxnet.numpy.ndarray using mxnet.ndarray.save;'
+                            ' use mxnet.numpy.save instead.')
         keys = None
         handles = c_handle_array(data)
     else:
diff --git a/python/mxnet/numpy/__init__.py b/python/mxnet/numpy/__init__.py
new file mode 100644
index 000000000000..979e8d61a5ab
--- /dev/null
+++ b/python/mxnet/numpy/__init__.py
@@ -0,0 +1,30 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""MXNet NumPy module."""
+
+from __future__ import division, absolute_import, print_function
+
+from . import random
+from . import linalg
+from .multiarray import *  # pylint: disable=wildcard-import
+from . import _op
+from . import _register
+from ._op import *  # pylint: disable=wildcard-import
+from .utils import *  # pylint: disable=wildcard-import
+
+__all__ = []
diff --git a/python/mxnet/numpy/_op.py b/python/mxnet/numpy/_op.py
new file mode 100644
index 000000000000..8f6f9cc053e4
--- /dev/null
+++ b/python/mxnet/numpy/_op.py
@@ -0,0 +1,20 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Namespace for registering numpy ops for imperative programming."""
+
+__all__ = []
diff --git a/python/mxnet/numpy/_register.py b/python/mxnet/numpy/_register.py
new file mode 100644
index 000000000000..8a2d2ea61c24
--- /dev/null
+++ b/python/mxnet/numpy/_register.py
@@ -0,0 +1,27 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Registering ops in mxnet.numpy for imperative programming."""
+
+from __future__ import absolute_import
+
+from ..base import _init_np_op_module
+from ..ndarray.register import _make_ndarray_function
+
+
+_init_np_op_module(root_module_name='mxnet', np_module_name='numpy',
+                   mx_module_name=None, make_op_func=_make_ndarray_function)
diff --git a/python/mxnet/numpy/linalg.py b/python/mxnet/numpy/linalg.py
new file mode 100644
index 000000000000..c4109378e146
--- /dev/null
+++ b/python/mxnet/numpy/linalg.py
@@ -0,0 +1,22 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Namespace for ops used in imperative programming."""
+
+from __future__ import absolute_import
+
+__all__ = []
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
new file mode 100644
index 000000000000..54b1069b1029
--- /dev/null
+++ b/python/mxnet/numpy/multiarray.py
@@ -0,0 +1,1551 @@
+#!/usr/bin/env python
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# pylint: disable=too-many-lines
+"""numpy ndarray and util functions."""
+
+from __future__ import absolute_import
+from __future__ import division
+
+try:
+    from __builtin__ import slice as py_slice
+except ImportError:
+    from builtins import slice as py_slice
+
+from array import array as native_array
+import sys
+import ctypes
+import warnings
+import numpy as _np
+from ..ndarray import NDArray, _DTYPE_NP_TO_MX, _GRAD_REQ_MAP
+from ..ndarray._internal import _set_np_ndarray_class
+from . import _op as _mx_np_op
+from ..base import check_call, _LIB, NDArrayHandle
+from ..base import mx_real_t, c_array_buf, mx_uint, numeric_types, integer_types
+from ..util import _sanity_check_params, set_module
+from ..context import current_context
+from ..ndarray import numpy as _mx_nd_np
+from ..ndarray.numpy import _internal as _npi
+
+__all__ = ['ndarray', 'empty', 'array', 'zeros', 'ones', 'add', 'subtract', 'multiply', 'divide',
+           'mod', 'power']
+
+
+# This function is copied from ndarray.py since pylint
+# keeps giving false alarm error of undefined-all-variable
+def _new_alloc_handle(shape, ctx, delay_alloc, dtype=mx_real_t):
+    """Return a new handle with specified shape and context.
+
+    Empty handle is only used to hold results.
+
+    Returns
+    -------
+    handle
+        A new empty `ndarray` handle.
+    """
+    hdl = NDArrayHandle()
+    check_call(_LIB.MXNDArrayCreateEx(
+        c_array_buf(mx_uint, native_array('I', shape)),
+        mx_uint(len(shape)),
+        ctypes.c_int(ctx.device_typeid),
+        ctypes.c_int(ctx.device_id),
+        ctypes.c_int(int(delay_alloc)),
+        ctypes.c_int(int(_DTYPE_NP_TO_MX[_np.dtype(dtype).type])),
+        ctypes.byref(hdl)))
+    return hdl
+
+
+# Have to use 0 as default value for stype since pylint does not allow
+# importing _STORAGE_TYPE_DEFAULT from ndarray.py.
+def _np_ndarray_cls(handle, writable=True, stype=0):
+    if stype != 0:
+        raise ValueError('_np_ndarray_cls currently only supports default storage '
+                         'type, while received stype = {}'.format(stype))
+    return ndarray(handle, writable=writable)
+
+
+_set_np_ndarray_class(_np_ndarray_cls)
+
+
+def _get_index(idx):
+    if isinstance(idx, NDArray) and not isinstance(idx, ndarray):
+        raise TypeError('Cannot have mx.nd.NDArray as index')
+    if isinstance(idx, ndarray):
+        return idx.as_nd_ndarray()
+    elif sys.version_info[0] > 2 and isinstance(idx, range):
+        return array(_np.arange(idx.start, idx.stop, idx.step, dtype=_np.int32)).as_nd_ndarray()
+    else:
+        return idx
+
+
+@set_module('mxnet.numpy')  # pylint: disable=invalid-name
+class ndarray(NDArray):
+    """An array object represents a multidimensional, homogeneous array of fixed-size items.
+    An associated data-type object describes the format of each element in the array
+    (its byte-order, how many bytes it occupies in memory, whether it is an integer, a
+    floating point number, or something else, etc.). Arrays should be constructed using
+    `array`, `zeros` or `empty`. Currently, only c-contiguous arrays are supported."""
+
+    # pylint: disable=too-many-return-statements
+    def __getitem__(self, key):
+        # TODO(junwu): calling base class __getitem__ is a temp solution
+        ndim = self.ndim
+        shape = self.shape
+        if ndim == 0:
+            if key != ():
+                raise IndexError('scalar tensor can only accept `()` as index')
+        if isinstance(key, tuple) and len(key) == 0:
+            return self
+        elif isinstance(key, tuple) and len(key) == ndim\
+                and all(isinstance(idx, integer_types) for idx in key):
+            out = self
+            for idx in key:
+                out = out[idx]
+            return out
+        elif isinstance(key, integer_types):
+            if key > shape[0] - 1:
+                raise IndexError(
+                    'index {} is out of bounds for axis 0 with size {}'.format(
+                        key, shape[0]))
+            return self._at(key)
+        elif isinstance(key, py_slice):
+            if key.step is not None and key.step != 1:
+                if key.step == 0:
+                    raise ValueError("slice step cannot be zero")
+                return self.as_nd_ndarray()._get_nd_basic_indexing(key).as_np_ndarray()
+            elif key.start is not None or key.stop is not None:
+                return self._slice(key.start, key.stop)
+            else:
+                return self
+
+        if isinstance(key, ndarray):
+            key = key.as_nd_ndarray()
+        elif isinstance(key, tuple):
+            key = [_get_index(idx) for idx in key]
+            key = tuple(key)
+        elif isinstance(key, list):
+            key = [_get_index(idx) for idx in key]
+        elif sys.version_info[0] > 2 and isinstance(key, range):
+            key = _get_index(key)
+        return self.as_nd_ndarray().__getitem__(key).as_np_ndarray()
+    # pylint: enable=too-many-return-statements
+
+    def __setitem__(self, key, value):
+        # TODO(junwu): calling base class __setitem__ is a temp solution
+        if isinstance(value, NDArray) and not isinstance(value, ndarray):
+            raise TypeError('Cannot assign mx.nd.NDArray to mxnet.numpy.ndarray')
+        if self.ndim == 0:
+            if not isinstance(key, tuple) or len(key) != 0:
+                raise IndexError('scalar tensor can only accept `()` as index')
+        if isinstance(value, ndarray):
+            value = value.as_nd_ndarray()
+        # TODO(junwu): Better handling of this situation
+        if isinstance(key, tuple) and len(key) == 0:
+            self.as_nd_ndarray().__setitem__(slice(None), value)
+            return
+
+        if isinstance(key, ndarray):
+            key = key.as_nd_ndarray()
+        elif isinstance(key, tuple):
+            key = [_get_index(idx) for idx in key]
+            key = tuple(key)
+        elif isinstance(key, list):
+            key = [_get_index(idx) for idx in key]
+        elif sys.version_info[0] > 2 and isinstance(key, range):
+            key = _get_index(key)
+        self.as_nd_ndarray().__setitem__(key, value)
+
+    def __add__(self, other):
+        """x.__add__(y) <=> x + y"""
+        return add(self, other)
+
+    def __iadd__(self, other):
+        """x.__iadd__(y) <=> x += y"""
+        if not self.writable:
+            raise ValueError('trying to add to a readonly ndarray')
+        return add(self, other, out=self)
+
+    def __sub__(self, other):
+        """x.__sub__(y) <=> x - y"""
+        return subtract(self, other)
+
+    def __isub__(self, other):
+        """x.__isub__(y) <=> x -= y"""
+        if not self.writable:
+            raise ValueError('trying to subtract from a readonly ndarray')
+        return subtract(self, other, out=self)
+
+    def __rsub__(self, other):
+        """x.__rsub__(y) <=> y - x"""
+        return subtract(other, self)
+
+    def __mul__(self, other):
+        """x.__mul__(y) <=> x * y"""
+        return multiply(self, other)
+
+    def __neg__(self):
+        return self.__mul__(-1.0)
+
+    def __imul__(self, other):
+        """x.__imul__(y) <=> x *= y"""
+        if not self.writable:
+            raise ValueError('trying to add to a readonly ndarray')
+        return multiply(self, other, out=self)
+
+    def __rmul__(self, other):
+        """x.__rmul__(y) <=> y * x"""
+        return self.__mul__(other)
+
+    def __div__(self, other):
+        raise AttributeError('ndarray.__div__ is replaced by __truediv__. If you are using'
+                             ' Python2, please use the statement from __future__ import division'
+                             ' to change the / operator to mean true division throughout the'
+                             ' module. If you are using Python3, this error should not have'
+                             ' been encountered.')
+
+    def __rdiv__(self, other):
+        raise AttributeError('ndarray.__rdiv__ is replaced by __rtruediv__. If you are using'
+                             ' Python2, please use the statement from __future__ import division'
+                             ' to change the / operator to mean true division throughout the'
+                             ' module. If you are using Python3, this error should not have'
+                             ' been encountered.')
+
+    def __idiv__(self, other):
+        raise AttributeError('ndarray.__idiv__ is replaced by __irtruediv__. If you are using'
+                             ' Python2, please use the statement from __future__ import division'
+                             ' to change the / operator to mean true division throughout the'
+                             ' module. If you are using Python3, this error should not have'
+                             ' been encountered.')
+
+    def __truediv__(self, other):
+        """x.__truediv__(y) <=> x / y"""
+        return divide(self, other)
+
+    def __rtruediv__(self, other):
+        """x.__rtruediv__(y) <=> y / x"""
+        return divide(other, self)
+
+    def __itruediv__(self, other):
+        return divide(self, other, out=self)
+
+    def __mod__(self, other):
+        """x.__mod__(y) <=> x % y"""
+        return mod(self, other)
+
+    def __rmod__(self, other):
+        """x.__rmod__(y) <=> y % x"""
+        return mod(other, self)
+
+    def __imod__(self, other):
+        """x.__imod__(y) <=> x %= y"""
+        return mod(self, other, out=self)
+
+    def __pow__(self, other):
+        """x.__pow__(y) <=> x ** y"""
+        return power(self, other)
+
+    def __rpow__(self, other):
+        """x.__rpow__(y) <=> y ** x"""
+        return power(other, self)
+
+    def __eq__(self, other):
+        """x.__eq__(y) <=> x == y"""
+        # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported
+        if isinstance(other, ndarray):
+            return _npi.equal(self, other)
+        elif isinstance(other, numeric_types):
+            return _npi.equal_scalar(self, float(other))
+        else:
+            raise TypeError("ndarray does not support type {} as operand".format(str(type(other))))
+
+    def __hash__(self):
+        raise NotImplementedError
+
+    def __ne__(self, other):
+        """x.__ne__(y) <=> x != y"""
+        # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported
+        if isinstance(other, ndarray):
+            return _npi.not_equal(self, other)
+        elif isinstance(other, numeric_types):
+            return _npi.not_equal_scalar(self, float(other))
+        else:
+            raise TypeError("ndarray does not support type {} as operand".format(str(type(other))))
+
+    def __gt__(self, other):
+        """x.__gt__(y) <=> x > y"""
+        # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported
+        if isinstance(other, ndarray):
+            return _npi.greater(self, other)
+        elif isinstance(other, numeric_types):
+            return _npi.greater_scalar(self, float(other))
+        else:
+            raise TypeError("ndarray does not support type {} as operand".format(str(type(other))))
+
+    def __ge__(self, other):
+        """x.__ge__(y) <=> x >= y"""
+        # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported
+        if isinstance(other, ndarray):
+            return _npi.greater_equal(self, other)
+        elif isinstance(other, numeric_types):
+            return _npi.greater_equal_scalar(self, float(other))
+        else:
+            raise TypeError("ndarray does not support type {} as operand".format(str(type(other))))
+
+    def __lt__(self, other):
+        """x.__lt__(y) <=> x < y"""
+        # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported
+        if isinstance(other, ndarray):
+            return _npi.less(self, other)
+        elif isinstance(other, numeric_types):
+            return _npi.less_scalar(self, float(other))
+        else:
+            raise TypeError("ndarray does not support type {} as operand".format(str(type(other))))
+
+    def __le__(self, other):
+        """x.__le__(y) <=> x <= y"""
+        # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported
+        if isinstance(other, ndarray):
+            return _npi.less_equal(self, other)
+        elif isinstance(other, numeric_types):
+            return _npi.less_equal_scalar(self, float(other))
+        else:
+            raise TypeError("ndarray does not support type {} as operand".format(str(type(other))))
+
+    def __bool__(self):
+        num_elements = self.size
+        if num_elements == 0:
+            warnings.simplefilter('default')
+            warnings.warn('The truth value of an empty array is ambiguous. Returning False, but in'
+                          ' future this will result in an error.', DeprecationWarning)
+            return False
+        elif num_elements == 1:
+            return bool(self.item())
+        else:
+            raise ValueError("The truth value of an ndarray with multiple elements is ambiguous.")
+
+    __nonzero__ = __bool__
+
+    def __float__(self):
+        num_elements = self.size
+        if num_elements != 1:
+            raise TypeError('only size-1 arrays can be converted to Python scalars')
+        return float(self.item())
+
+    def __int__(self):
+        num_elements = self.size
+        if num_elements != 1:
+            raise TypeError('only size-1 arrays can be converted to Python scalars')
+        return int(self.item())
+
+    def __len__(self):
+        """Number of elements along the first axis."""
+        shape = self.shape
+        if len(shape) == 0:
+            raise TypeError('len() of unsized object')
+        return self.shape[0]
+
+    def __reduce__(self):
+        return ndarray, (None,), self.__getstate__()
+
+    def item(self, *args):
+        """Copy an element of an array to a standard Python scalar and return it.
+
+        Parameters
+        ----------
+        *args : Arguments (variable number and type)
+            none: in this case, the method only works for arrays with one element (a.size == 1),
+            which element is copied into a standard Python scalar object and returned.
+
+            int_type: this argument is interpreted as a flat index into the array, specifying which
+            element to copy and return.
+
+            tuple of int_types: functions as does a single int_type argument, except that the
+            argument is interpreted as an nd-index into the array.
+
+        Returns
+        -------
+        z : Standard Python scalar object
+            A copy of the specified element of the array as a suitable Python scalar.
+        """
+        # TODO(junwu): no need to call asnumpy() on the whole array.
+        return self.asnumpy().item(*args)
+
+    @property
+    # pylint: disable= invalid-name, undefined-variable
+    def T(self):
+        """Same as self.transpose(). This always returns a copy of self."""
+        return self.transpose()
+    # pylint: enable= invalid-name, undefined-variable
+
+    def all(self, axis=None, out=None, keepdims=False):
+        raise NotImplementedError
+
+    def any(self, axis=None, out=None, keepdims=False):
+        raise NotImplementedError
+
+    def as_nd_ndarray(self):
+        """Convert mxnet.numpy.ndarray to mxnet.ndarray.NDArray to use its fluent methods."""
+        hdl = NDArrayHandle()
+        check_call(_LIB.MXShallowCopyNDArray(self.handle, ctypes.byref(hdl)))
+        return NDArray(handle=hdl, writable=self.writable)
+
+    def as_np_ndarray(self):
+        """A convenience function for creating a numpy ndarray from the current ndarray
+        with zero copy. For this class, it just returns itself since it's already a
+        numpy ndarray."""
+        return self
+
+    def __repr__(self):
+        """
+        Returns a string representation of the array. The dtype of the ndarray will not
+        be appended to the string if it is `float32`. The context of the ndarray will
+        be appended for devices other than CPU.
+
+        Examples
+        --------
+        >>> from mxnet import np, npx
+        >>> a = np.random.uniform(size=(2, 3))
+        >>> a
+        array([[0.5488135 , 0.5928446 , 0.71518934],
+               [0.84426576, 0.60276335, 0.8579456 ]])
+        >>> print(a)
+        [[0.5488135  0.5928446  0.71518934]
+         [0.84426576 0.60276335 0.8579456 ]]
+        >>> a.dtype
+        <class 'numpy.float32'>
+        >>> b = a.astype(np.float64)
+        >>> b
+        array([[0.54881352, 0.59284461, 0.71518934],
+               [0.84426576, 0.60276335, 0.85794562]], dtype=float64)
+        >>> print(b)
+        [[0.54881352 0.59284461 0.71518934]
+         [0.84426576 0.60276335 0.85794562]]
+        >>> b.dtype
+        <class 'numpy.float64'>
+        >>> c = a.copyto(npx.gpu(0))
+        >>> c
+        array([[0.5488135 , 0.5928446 , 0.71518934],
+               [0.84426576, 0.60276335, 0.8579456 ]], ctx=gpu(0))
+        >>> print(c)
+        [[0.5488135  0.5928446  0.71518934]
+         [0.84426576 0.60276335 0.8579456 ]] @gpu(0)
+        >>> d = b.copyto(npx.gpu(0))
+        >>> d
+        array([[0.54881352, 0.59284461, 0.71518934],
+               [0.84426576, 0.60276335, 0.85794562]], dtype=float64, ctx=gpu(0))
+        >>> print(d)
+        [[0.54881352 0.59284461 0.71518934]
+         [0.84426576 0.60276335 0.85794562]] @gpu(0)
+        """
+        array_str = self.asnumpy().__repr__()
+        dtype = self.dtype
+        if 'dtype=' in array_str:
+            if dtype == _np.float32:
+                array_str = array_str[:array_str.rindex(',')] + ')'
+        elif dtype != _np.float32:
+            array_str = array_str[:-1] + ', dtype={})'.format(dtype.__name__)
+
+        context = self.context
+        if context.device_type == 'cpu':
+            return array_str
+        return array_str[:-1] + ', ctx={})'.format(str(context))
+
+    def __str__(self):
+        """Returns a string representation of the array."""
+        array_str = self.asnumpy().__str__()
+        context = self.context
+        if context.device_type == 'cpu' or self.ndim == 0:
+            return array_str
+        return '{array} @{ctx}'.format(array=array_str, ctx=context)
+
+    def attach_grad(self, grad_req='write'):  # pylint: disable=arguments-differ
+        """Attach a gradient buffer to this ndarray, so that `backward`
+        can compute gradient with respect to it.
+
+        Parameters
+        ----------
+        grad_req : {'write', 'add', 'null'}
+            How gradient will be accumulated.
+            - 'write': gradient will be overwritten on every backward.
+            - 'add': gradient will be added to existing value on every backward.
+            - 'null': do not compute gradient for this NDArray.
+        """
+        grad = _mx_np_op.zeros_like(self)  # pylint: disable=undefined-variable
+        grad_req = _GRAD_REQ_MAP[grad_req]
+        check_call(_LIB.MXAutogradMarkVariables(
+            1, ctypes.pointer(self.handle),
+            ctypes.pointer(mx_uint(grad_req)),
+            ctypes.pointer(grad.handle)))
+
+    @property
+    def grad(self):
+        """Returns gradient buffer attached to this ndarray."""
+        hdl = NDArrayHandle()
+        check_call(_LIB.MXNDArrayGetGrad(self.handle, ctypes.byref(hdl)))
+        if hdl.value is None:
+            return None
+        return _np_ndarray_cls(hdl)
+
+    def detach(self):
+        """Returns a new ndarray, detached from the current graph."""
+        hdl = NDArrayHandle()
+        check_call(_LIB.MXNDArrayDetach(self.handle, ctypes.byref(hdl)))
+        return _np_ndarray_cls(hdl)
+
+    def astype(self, dtype, *args, **kwargs):  # pylint: disable=arguments-differ,unused-argument
+        """
+        Copy of the array, cast to a specified type.
+
+        Parameters
+        ----------
+        dtype : str or dtype
+            Typecode or data-type to which the array is cast.
+        copy : bool, optional
+            Default `True`. By default, astype always returns a newly
+            allocated ndarray on the same context. If this is set to
+            `False`, and the dtype requested is the same as the ndarray's
+            dtype, the ndarray is returned instead of a copy.
+
+        Returns
+        -------
+        arr_t : ndarray
+            Unless `copy` is False and the other conditions for returning the input
+            array are satisfied (see description for `copy` input parameter), `arr_t`
+            is a new array of the same shape as the input array with `dtype`.
+        """
+        _sanity_check_params('astype', ['order', 'casting', 'subok'], kwargs)
+        copy = kwargs.get('copy', True)
+        if not copy and _np.dtype(dtype) == self.dtype:
+            return self
+
+        res = empty(self.shape, dtype=dtype, ctx=self.context)
+        self.copyto(res)
+        return res
+
+    def copyto(self, other):
+        """Copies the value of this array to another array.
+
+        If ``other`` is a ``ndarray`` object, then ``other.shape`` and
+        ``self.shape`` should be the same. This function copies the value from
+        ``self`` to ``other``.
+
+        If ``other`` is a context, a new ``NDArray`` will be first created on
+        the target context, and the value of ``self`` is copied.
+
+        Parameters
+        ----------
+        other : ndarray or Context
+            The destination array or context.
+
+        Returns
+        -------
+        ndarray
+            The copied array. If ``other`` is an ``ndarray``, then the return value
+            and ``other`` will point to the same ``ndarray``.
+
+        Examples
+        --------
+        >>> x = np.ones((2,3))
+        >>> y = np.zeros((2,3), mx.gpu(0))
+        >>> z = x.copyto(y)
+        >>> z is y
+        True
+        >>> y.asnumpy()
+        array([[ 1.,  1.,  1.],
+               [ 1.,  1.,  1.]], dtype=float32)
+        """
+        if isinstance(other, ndarray):
+            other = other.as_nd_ndarray()
+        return self.as_nd_ndarray().copyto(other).as_np_ndarray()
+
+    def asscalar(self):
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute asscalar')
+
+    def argmax(self, axis=None, out=None):  # pylint: disable=arguments-differ
+        raise NotImplementedError
+
+    def as_in_context(self, context):
+        """Returns an array on the target device with the same value as this array.
+
+        If the target context is the same as ``self.context``, then ``self`` is
+        returned.  Otherwise, a copy is made.
+
+        Parameters
+        ----------
+        context : Context
+            The target context.
+
+        Returns
+        -------
+        ndarray
+            The target array.
+        """
+        if self.context == context:
+            return self
+        return self.copyto(context)
+
+    def copy(self, order='C'):  # pylint: disable=arguments-differ
+        if order != 'C':
+            raise NotImplementedError('ndarray.copy only supports order=\'C\', while '
+                                      'received {}'.format(str(order)))
+        return super(ndarray, self).copy().as_np_ndarray()
+
+    def dot(self, b, out=None):
+        raise NotImplementedError
+
+    def reshape(self, *args, **kwargs):  # pylint: disable=arguments-differ
+        """Returns an array containing the same data with a new shape.
+
+        Notes
+        -----
+        Unlike the free function `numpy.reshape`, this method on `ndarray` allows
+        the elements of the shape parameter to be passed in as separate arguments.
+        For example, ``a.reshape(10, 11)`` is equivalent to
+        ``a.reshape((10, 11))``.
+        """
+        order = 'C'
+        if len(kwargs) > 1:
+            raise TypeError('function takes at most 1 keyword argument')
+        if len(kwargs) == 1:
+            if 'order' not in kwargs:
+                raise TypeError('{} is an invalid keyword argument for this function'
+                                .format(kwargs.keys()[0]))
+            order = kwargs.pop('order', 'C')
+            if order != 'C':
+                raise NotImplementedError('only supports C-order,'
+                                          ' while received {}'.format(order))
+        if len(args) == 0:
+            raise TypeError('reshape() takes exactly 1 argument (0 given)')
+        raise NotImplementedError
+
+    def reshape_like(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`reshape_like`.
+
+        The arguments are the same as for :py:func:`reshape_like`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute reshape_like')
+
+    def zeros_like(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`zeros_like`.
+
+        The arguments are the same as for :py:func:`zeros_like`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute zeros_like')
+
+    def ones_like(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`ones_like`.
+
+        The arguments are the same as for :py:func:`ones_like`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute ones_like')
+
+    def broadcast_axes(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`broadcast_axes`.
+
+        The arguments are the same as for :py:func:`broadcast_axes`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute broadcast_like')
+
+    def repeat(self, repeats, axis=None):  # pylint: disable=arguments-differ
+        """Repeat elements of an array."""
+        raise NotImplementedError
+
+    def pad(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`pad`.
+
+        The arguments are the same as for :py:func:`pad`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute pad')
+
+    def swapaxes(self, axis1, axis2):  # pylint: disable=arguments-differ
+        """Return a copy of the array with axis1 and axis2 interchanged.
+        Refer to `mxnet.numpy.swapaxes` for full documentation.
+        """
+        raise NotImplementedError
+
+    def split(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`split`.
+
+        The arguments are the same as for :py:func:`split`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute split')
+
+    def split_v2(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`split_v2`.
+
+        The arguments are the same as for :py:func:`split_v2`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute split_v2')
+
+    def slice(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`slice`.
+
+        The arguments are the same as for :py:func:`slice`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute slice')
+
+    def slice_axis(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`slice_axis`.
+
+        The arguments are the same as for :py:func:`slice_axis`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute slice_axis')
+
+    def slice_like(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`slice_like`.
+
+        The arguments are the same as for :py:func:`slice_like`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute slice_like')
+
+    def take(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`take`.
+
+        The arguments are the same as for :py:func:`take`, with
+        this array as data.
+        """
+        raise NotImplementedError
+
+    def one_hot(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`one_hot`.
+
+        The arguments are the same as for :py:func:`one_hot`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute one_hot')
+
+    def pick(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`pick`.
+
+        The arguments are the same as for :py:func:`pick`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute pick')
+
+    def sort(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`sort`.
+
+        The arguments are the same as for :py:func:`sort`, with
+        this array as data.
+        """
+        raise NotImplementedError
+
+    def topk(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`topk`.
+
+        The arguments are the same as for :py:func:`topk`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute topk')
+
+    def argsort(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`argsort`.
+
+        The arguments are the same as for :py:func:`argsort`, with
+        this array as data.
+        """
+        raise NotImplementedError
+
+    def argmax_channel(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`argmax_channel`.
+
+        The arguments are the same as for :py:func:`argmax_channel`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute argmax_channel')
+
+    def argmin(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`argmin`.
+
+        The arguments are the same as for :py:func:`argmin`, with
+        this array as data.
+        """
+        raise NotImplementedError
+
+    def clip(self, min=None, max=None, out=None):  # pylint: disable=arguments-differ
+        """Return an array whose values are limited to [min, max].
+        One of max or min must be given.
+        """
+        raise NotImplementedError
+
+    def abs(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`abs`.
+
+        The arguments are the same as for :py:func:`abs`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute abs')
+
+    def sign(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`sign`.
+
+        The arguments are the same as for :py:func:`sign`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute abs')
+
+    def flatten(self, order='C'):  # pylint: disable=arguments-differ
+        """Return a copy of the array collapsed into one dimension."""
+        raise NotImplementedError
+
+    def shape_array(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`shape_array`.
+
+        The arguments are the same as for :py:func:`shape_array`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute shape_array')
+
+    def size_array(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`size_array`.
+
+        The arguments are the same as for :py:func:`size_array`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute size_array')
+
+    def expand_dims(self, *args, **kwargs):  # pylint: disable=arguments-differ
+        """Convenience fluent method for :py:func:`expand_dims`.
+
+        The arguments are the same as for :py:func:`expand_dims`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute expand_dims')
+
+    def tile(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`tile`.
+
+        The arguments are the same as for :py:func:`tile`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute tile')
+
+    def transpose(self, *axes):  # pylint: disable=arguments-differ
+        """Permute the dimensions of an array."""
+        raise NotImplementedError
+
+    def flip(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`flip`.
+
+        The arguments are the same as for :py:func:`flip`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute flip')
+
+    def depth_to_space(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`depth_to_space`.
+
+        The arguments are the same as for :py:func:`depth_to_space`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute depth_to_space')
+
+    def space_to_depth(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`space_to_depth`.
+
+        The arguments are the same as for :py:func:`space_to_depth`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute space_to_depth')
+
+    def diag(self, k=0, **kwargs):
+        """Convenience fluent method for :py:func:`diag`.
+
+        The arguments are the same as for :py:func:`diag`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute diag')
+
+    def sum(self, axis=None, dtype=None, out=None, keepdims=False):  # pylint: disable=arguments-differ
+        """Convenience fluent method for :py:func:`sum`.
+
+        The arguments are the same as for :py:func:`sum`, with
+        this array as data.
+        """
+        raise NotImplementedError
+
+    def nansum(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`nansum`.
+
+        The arguments are the same as for :py:func:`nansum`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute nansum')
+
+    def prod(self, axis=None, dtype=None, out=None, keepdims=False):  # pylint: disable=arguments-differ
+        """Return the product of the array elements over the given axis."""
+        raise NotImplementedError
+
+    def nanprod(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`nanprod`.
+
+        The arguments are the same as for :py:func:`nanprod`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute nanprod')
+
+    def mean(self, axis=None, dtype=None, out=None, keepdims=False):  # pylint: disable=arguments-differ
+        """Returns the average of the array elements along given axis."""
+        raise NotImplementedError
+
+    # TODO(junwu): Use mxnet std op instead of onp.std
+    def std(self, axis=None, dtype=None, out=None, ddof=0, keepdims=False):  # pylint: disable=arguments-differ
+        """Returns the standard deviation of the array elements along given axis."""
+        ret_np = self.asnumpy().std(axis=axis, dtype=dtype, out=out, ddof=ddof, keepdims=keepdims)
+        return array(ret_np, dtype=ret_np.dtype, ctx=self.context)
+
+    def cumsum(self, axis=None, dtype=None, out=None):
+        """Return the cumulative sum of the elements along the given axis."""
+        raise NotImplementedError
+
+    def tolist(self):
+        return self.asnumpy().tolist()
+
+    def max(self, axis=None, out=None, keepdims=False):  # pylint: disable=arguments-differ
+        """Return the maximum along a given axis."""
+        raise NotImplementedError
+
+    def min(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`min`.
+
+        The arguments are the same as for :py:func:`min`, with
+        this array as data.
+        """
+        raise NotImplementedError
+
+    def norm(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`norm`.
+
+        The arguments are the same as for :py:func:`norm`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute norm')
+
+    def round(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`round`.
+
+        The arguments are the same as for :py:func:`round`, with
+        this array as data.
+        """
+        raise NotImplementedError
+
+    def rint(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`rint`.
+
+        The arguments are the same as for :py:func:`rint`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute rint')
+
+    def fix(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`fix`.
+
+        The arguments are the same as for :py:func:`fix`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute fix')
+
+    def floor(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`floor`.
+
+        The arguments are the same as for :py:func:`floor`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute floor')
+
+    def ceil(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`ceil`.
+
+        The arguments are the same as for :py:func:`ceil`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute ceil')
+
+    def trunc(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`trunc`.
+
+        The arguments are the same as for :py:func:`trunc`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute trunc')
+
+    def sin(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`sin`.
+
+        The arguments are the same as for :py:func:`sin`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute sin')
+
+    def cos(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`cos`.
+
+        The arguments are the same as for :py:func:`cos`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute cos')
+
+    def tan(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`tan`.
+
+        The arguments are the same as for :py:func:`tan`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute tan')
+
+    def arcsin(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`arcsin`.
+
+        The arguments are the same as for :py:func:`arcsin`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute arcsin')
+
+    def arccos(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`arccos`.
+
+        The arguments are the same as for :py:func:`arccos`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute arccos')
+
+    def arctan(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`arctan`.
+
+        The arguments are the same as for :py:func:`arctan`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute arctan')
+
+    def degrees(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`degrees`.
+
+        The arguments are the same as for :py:func:`degrees`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute degrees')
+
+    def radians(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`radians`.
+
+        The arguments are the same as for :py:func:`radians`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute radians')
+
+    def sinh(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`sinh`.
+
+        The arguments are the same as for :py:func:`sinh`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute sinh')
+
+    def cosh(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`cosh`.
+
+        The arguments are the same as for :py:func:`cosh`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute cosh')
+
+    def tanh(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`tanh`.
+
+        The arguments are the same as for :py:func:`tanh`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute tanh')
+
+    def arcsinh(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`arcsinh`.
+
+        The arguments are the same as for :py:func:`arcsinh`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute arcsinh')
+
+    def arccosh(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`arccosh`.
+
+        The arguments are the same as for :py:func:`arccosh`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute arccosh')
+
+    def arctanh(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`arctanh`.
+
+        The arguments are the same as for :py:func:`arctanh`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute arctanh')
+
+    def exp(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`exp`.
+
+        The arguments are the same as for :py:func:`exp`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute exp')
+
+    def expm1(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`expm1`.
+
+        The arguments are the same as for :py:func:`expm1`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute expm1')
+
+    def log(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`log`.
+
+        The arguments are the same as for :py:func:`log`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute log')
+
+    def log10(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`log10`.
+
+        The arguments are the same as for :py:func:`log10`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute log10')
+
+    def log2(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`log2`.
+
+        The arguments are the same as for :py:func:`log2`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute log2')
+
+    def log1p(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`log1p`.
+
+        The arguments are the same as for :py:func:`log1p`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute log1p')
+
+    def sqrt(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`sqrt`.
+
+        The arguments are the same as for :py:func:`sqrt`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute sqrt')
+
+    def rsqrt(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`rsqrt`.
+
+        The arguments are the same as for :py:func:`rsqrt`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute rsqrt')
+
+    def cbrt(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`cbrt`.
+
+        The arguments are the same as for :py:func:`cbrt`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute cqrt')
+
+    def rcbrt(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`rcbrt`.
+
+        The arguments are the same as for :py:func:`rcbrt`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute rcqrt')
+
+    def square(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`square`.
+
+        The arguments are the same as for :py:func:`square`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute square')
+
+    def reciprocal(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`reciprocal`.
+
+        The arguments are the same as for :py:func:`reciprocal`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute reciprocal')
+
+    def relu(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`relu`.
+
+        The arguments are the same as for :py:func:`relu`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute relu')
+
+    def sigmoid(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`sigmoid`.
+
+        The arguments are the same as for :py:func:`sigmoid`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute sigmoid')
+
+    def softmax(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`softmax`.
+
+        The arguments are the same as for :py:func:`softmax`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute softmax')
+
+    def log_softmax(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`log_softmax`.
+
+        The arguments are the same as for :py:func:`log_softmax`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute log_softmax')
+
+    def softmin(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`softmin`.
+
+        The arguments are the same as for :py:func:`softmin`, with
+        this array as data.
+        """
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute softmin')
+
+    def squeeze(self, axis=None):  # pylint: disable=arguments-differ
+        """Remove single-dimensional entries from the shape of a.
+        """
+        raise NotImplementedError
+
+    def broadcast_to(self, shape):
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute broadcast_to')
+
+    def broadcast_like(self, other):
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute broadcast_like')
+
+    @property
+    def shape(self):
+        return super(ndarray, self).shape
+
+    @property
+    def ndim(self):
+        """Number of array dimensions."""
+        return len(self.shape)
+
+    @property
+    def size(self):
+        """Number of elements in the array."""
+        return super(ndarray, self).size
+
+    def tostype(self, stype):
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute tostype')
+
+
+@set_module('mxnet.numpy')
+def empty(shape, dtype=float, order='C', ctx=None):
+    """Return a new array of given shape and type, without initializing entries.
+
+    Parameters
+    ----------
+    shape : int or tuple of int Shape of the empty array, e.g., ``(2, 3)`` or ``2``.
+    dtype : data-type, optional
+        Desired output data-type for the array, e.g, `numpy.int8`. Default is
+        `numpy.float32`. Note that this behavior is different from NumPy's `empty`
+        function where `float64` is the default value, because `float32` is
+        considered as the default data type in deep learning.
+    order : {'C'}, optional, default: 'C'
+        How to store multi-dimensional data in memory, currently only row-major
+        (C-style) is supported.
+    ctx : device context, optional
+        Device context on which the memory is allocated. Default is
+        `mxnet.context.current_context()`.
+
+    Returns
+    -------
+    out : ndarray
+        Array of uninitialized (arbitrary) data of the given shape, dtype, and order.
+    """
+    if order != 'C':
+        raise NotImplementedError
+    if ctx is None:
+        ctx = current_context()
+    if dtype is None:
+        dtype = _np.float32
+    if isinstance(shape, int):
+        shape = (shape,)
+    return ndarray(handle=_new_alloc_handle(shape, ctx, False, dtype))
+
+
+@set_module('mxnet.numpy')
+def array(object, dtype=None, ctx=None):
+    """
+    Create an array.
+
+    Parameters
+    ----------
+    object : array_like or `numpy.ndarray` or `mxnet.numpy.ndarray`
+        An array, any object exposing the array interface, an object whose
+        __array__ method returns an array, or any (nested) sequence.
+    dtype : data-type, optional
+        The desired data-type for the array. Default is `float32`.
+    ctx : device context, optional
+        Device context on which the memory is allocated. Default is
+        `mxnet.context.current_context()`.
+
+    Returns
+    -------
+    out : ndarray
+        An array object satisfying the specified requirements.
+    """
+    if ctx is None:
+        ctx = current_context()
+    if isinstance(object, ndarray):
+        dtype = object.dtype if dtype is None else dtype
+    else:
+        dtype = mx_real_t if dtype is None else dtype
+        if not isinstance(object, (ndarray, _np.ndarray)):
+            try:
+                object = _np.array(object, dtype=dtype)
+            except Exception as e:
+                raise TypeError('{}'.format(str(e)))
+    ret = empty(object.shape, dtype=dtype, ctx=ctx)
+    if len(object.shape) == 0:
+        ret[()] = object
+    else:
+        ret[:] = object
+    return ret
+
+
+@set_module('mxnet.numpy')
+def zeros(shape, dtype=_np.float32, order='C', ctx=None):
+    """Return a new array of given shape and type, filled with zeros.
+    This function currently only supports storing multi-dimensional data
+    in row-major (C-style).
+
+    Parameters
+    ----------
+    shape : int or tuple of int
+        The shape of the empty array.
+    dtype : str or numpy.dtype, optional
+        An optional value type (default is `numpy.float32`). Note that this
+        behavior is different from NumPy's `ones` function where `float64`
+        is the default value, because `float32` is considered as the default
+        data type in deep learning.
+    order : {'C'}, optional, default: 'C'
+        How to store multi-dimensional data in memory, currently only row-major
+        (C-style) is supported.
+    ctx : Context, optional
+        An optional device context (default is the current default context).
+
+    Returns
+    -------
+    out : ndarray
+        Array of zeros with the given shape, dtype, and ctx.
+    """
+    return _mx_nd_np.zeros(shape, dtype, order, ctx)
+
+
+@set_module('mxnet.numpy')
+def ones(shape, dtype=_np.float32, order='C', ctx=None):
+    """Return a new array of given shape and type, filled with zeros.
+    This function currently only supports storing multi-dimensional data
+    in row-major (C-style).
+
+    Parameters
+    ----------
+    shape : int or tuple of int
+        The shape of the empty array.
+    dtype : str or numpy.dtype, optional
+        An optional value type. Default is `numpy.float32`. Note that this
+        behavior is different from NumPy's `ones` function where `float64`
+        is the default value, because `float32` is considered as the default
+        data type in deep learning.
+    order : {'C'}, optional, default: 'C'
+        How to store multi-dimensional data in memory, currently only row-major
+        (C-style) is supported.
+    ctx : Context, optional
+        An optional device context (default is the current default context).
+
+    Returns
+    -------
+    out : ndarray
+        Array of zeros with the given shape, dtype, and ctx.
+    """
+    return _mx_nd_np.ones(shape, dtype, order, ctx)
+
+
+@set_module('mxnet.numpy')
+def add(x1, x2, out=None):
+    """Add arguments element-wise.
+
+    Parameters
+    ----------
+    x1, x2 : ndarrays or scalar values
+        The arrays to be added. If x1.shape != x2.shape, they must be broadcastable to
+        a common shape (which may be the shape of one or the other).
+
+    out : ndarray
+        A location into which the result is stored. If provided, it must have a shape
+        that the inputs broadcast to. If not provided or None, a freshly-allocated array
+        is returned.
+
+    Returns
+    -------
+    add : ndarray or scalar
+        The sum of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars.
+    """
+    return _mx_nd_np.add(x1, x2, out)
+
+
+@set_module('mxnet.numpy')
+def subtract(x1, x2, out=None):
+    """Subtract arguments element-wise.
+
+    Parameters
+    ----------
+    x1, x2 : ndarrays or scalar values
+        The arrays to be subtracted from each other. If x1.shape != x2.shape,
+        they must be broadcastable to a common shape (which may be the shape
+        of one or the other).
+
+    out : ndarray
+        A location into which the result is stored. If provided, it must have a shape
+        that the inputs broadcast to. If not provided or None, a freshly-allocated array
+        is returned.
+
+    Returns
+    -------
+    subtract : ndarray or scalar
+        The difference of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars.
+    """
+    return _mx_nd_np.subtract(x1, x2, out)
+
+
+@set_module('mxnet.numpy')
+def multiply(x1, x2, out=None):
+    """Multiply arguments element-wise.
+
+    Parameters
+    ----------
+    x1, x2 : ndarrays or scalar values
+        The arrays to be multiplied. If x1.shape != x2.shape, they must be broadcastable to
+        a common shape (which may be the shape of one or the other).
+
+    out : ndarray
+        A location into which the result is stored. If provided, it must have a shape
+        that the inputs broadcast to. If not provided or None, a freshly-allocated array
+        is returned.
+
+    Returns
+    -------
+    out : ndarray or scalar
+        The difference of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars.
+    """
+    return _mx_nd_np.multiply(x1, x2, out)
+
+
+@set_module('mxnet.numpy')
+def divide(x1, x2, out=None):
+    """Returns a true division of the inputs, element-wise.
+
+    Parameters
+    ----------
+    x1 : ndarray or scalar
+        Dividend array.
+
+    x2 : ndarray or scalar
+        Divisor array.
+
+    out : ndarray
+        A location into which the result is stored. If provided, it must have a shape
+        that the inputs broadcast to. If not provided or None, a freshly-allocated array
+        is returned.
+
+    Returns
+    -------
+    out : ndarray or scalar
+        This is a scalar if both x1 and x2 are scalars.
+    """
+    return _mx_nd_np.divide(x1, x2, out=out)
+
+
+@set_module('mxnet.numpy')
+def mod(x1, x2, out=None):
+    """Return element-wise remainder of division.
+
+    Parameters
+    ----------
+    x1 : ndarray or scalar
+        Dividend array.
+
+    x2 : ndarray or scalar
+        Divisor array.
+
+    out : ndarray
+        A location into which the result is stored. If provided, it must have a shape
+        that the inputs broadcast to. If not provided or None, a freshly-allocated array
+        is returned.
+
+    Returns
+    -------
+    out : ndarray or scalar
+        This is a scalar if both x1 and x2 are scalars.
+    """
+    return _mx_nd_np.mod(x1, x2, out=out)
+
+
+@set_module('mxnet.numpy')
+def power(x1, x2, out=None):
+    """First array elements raised to powers from second array, element-wise.
+
+    Parameters
+    ----------
+    x1 : ndarray or scalar
+        The bases.
+
+    x2 : ndarray or scalar
+        The exponent.
+
+    out : ndarray
+        A location into which the result is stored. If provided, it must have a shape
+        that the inputs broadcast to. If not provided or None, a freshly-allocated array
+        is returned.
+
+    Returns
+    -------
+    out : ndarray or scalar
+        The bases in x1 raised to the exponents in x2.
+        This is a scalar if both x1 and x2 are scalars.
+    """
+    return _mx_nd_np.power(x1, x2, out=out)
diff --git a/python/mxnet/numpy/random.py b/python/mxnet/numpy/random.py
new file mode 100644
index 000000000000..c4109378e146
--- /dev/null
+++ b/python/mxnet/numpy/random.py
@@ -0,0 +1,22 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Namespace for ops used in imperative programming."""
+
+from __future__ import absolute_import
+
+__all__ = []
diff --git a/python/mxnet/numpy/utils.py b/python/mxnet/numpy/utils.py
new file mode 100644
index 000000000000..920897efc80b
--- /dev/null
+++ b/python/mxnet/numpy/utils.py
@@ -0,0 +1,35 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Util functions for the numpy module."""
+
+
+from __future__ import absolute_import
+
+import numpy as onp
+
+__all__ = ['float16', 'float32', 'float64', 'uint8', 'int32', 'int8', 'int64', 'pi']
+
+float16 = onp.float16
+float32 = onp.float32
+float64 = onp.float64
+uint8 = onp.uint8
+int32 = onp.int32
+int8 = onp.int8
+int64 = onp.int64
+
+pi = onp.pi
diff --git a/python/mxnet/numpy_extension/__init__.py b/python/mxnet/numpy_extension/__init__.py
new file mode 100644
index 000000000000..d71d65f08de2
--- /dev/null
+++ b/python/mxnet/numpy_extension/__init__.py
@@ -0,0 +1,32 @@
+#!/usr/bin/env python
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Module for ops not belonging to the official numpy package for imperative programming."""
+
+from __future__ import absolute_import
+from . import _op
+from . import image
+from . import _register
+from ._op import *  # pylint: disable=wildcard-import
+from ..context import *  # pylint: disable=wildcard-import
+from ..util import is_np_shape, is_np_array, set_np, reset_np
+from ..ndarray import waitall
+from .utils import *  # pylint: disable=wildcard-import
+
+__all__ = []
diff --git a/python/mxnet/numpy_extension/_op.py b/python/mxnet/numpy_extension/_op.py
new file mode 100644
index 000000000000..a995e480221a
--- /dev/null
+++ b/python/mxnet/numpy_extension/_op.py
@@ -0,0 +1,20 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Namespace for registering numpy_extension ops for imperative programming."""
+
+__all__ = []
diff --git a/python/mxnet/numpy_extension/_register.py b/python/mxnet/numpy_extension/_register.py
new file mode 100644
index 000000000000..8abb7254057c
--- /dev/null
+++ b/python/mxnet/numpy_extension/_register.py
@@ -0,0 +1,27 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Registering ops in mxnet.numpy_extension for imperative programming."""
+
+from __future__ import absolute_import
+
+from ..base import _init_np_op_module
+from ..ndarray.register import _make_ndarray_function
+
+
+_init_np_op_module(root_module_name='mxnet', np_module_name='numpy_extension',
+                   mx_module_name=None, make_op_func=_make_ndarray_function)
diff --git a/python/mxnet/numpy_extension/image.py b/python/mxnet/numpy_extension/image.py
new file mode 100644
index 000000000000..00a028b3c18f
--- /dev/null
+++ b/python/mxnet/numpy_extension/image.py
@@ -0,0 +1,22 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Image pre-processing operators."""
+
+from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
+
+__all__ = []
diff --git a/python/mxnet/numpy_extension/utils.py b/python/mxnet/numpy_extension/utils.py
new file mode 100644
index 000000000000..0aa89badbb58
--- /dev/null
+++ b/python/mxnet/numpy_extension/utils.py
@@ -0,0 +1,122 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Util functions for the numpy module."""
+
+
+from __future__ import absolute_import
+
+import ctypes
+from .. util import is_np_array, is_np_shape
+from .. base import _LIB, check_call, string_types, c_str_array
+from .. base import c_handle_array, c_str, mx_uint, NDArrayHandle, py_str
+from ..numpy import ndarray
+
+__all__ = ['save', 'load']
+
+
+def save(file, arr):
+    """Saves a list of `ndarray`s or a dict of `str`->`ndarray` to file.
+
+    Examples of filenames:
+
+    - ``/path/to/file``
+    - ``s3://my-bucket/path/to/file`` (if compiled with AWS S3 supports)
+    - ``hdfs://path/to/file`` (if compiled with HDFS supports)
+
+    Parameters
+    ----------
+    file : str
+        Filename to which the data is saved.
+    arr : `ndarray` or list of `ndarray`s or dict of `str` to `ndarray`
+        The data to be saved.
+
+    Notes
+    -----
+    This function can only be called within numpy semantics, i.e., `npx.is_np_shape()`
+    and `npx.is_np_array()` must both return true.
+    """
+    if not (is_np_shape() and is_np_array()):
+        raise ValueError('Cannot save `mxnet.numpy.ndarray` in legacy mode. Please activate'
+                         ' numpy semantics by calling `npx.set_np()` in the global scope'
+                         ' before calling this function.')
+    if isinstance(arr, ndarray):
+        arr = [arr]
+    if isinstance(arr, dict):
+        str_keys = arr.keys()
+        nd_vals = arr.values()
+        if any(not isinstance(k, string_types) for k in str_keys) or \
+                any(not isinstance(v, ndarray) for v in nd_vals):
+            raise TypeError('Only accepts dict str->ndarray or list of ndarrays')
+        keys = c_str_array(str_keys)
+        handles = c_handle_array(nd_vals)
+    elif isinstance(arr, list):
+        if any(not isinstance(v, ndarray) for v in arr):
+            raise TypeError('Only accepts dict str->ndarray or list of ndarrays')
+        keys = None
+        handles = c_handle_array(arr)
+    else:
+        raise ValueError("data needs to either be a ndarray, dict of (str, ndarray) pairs "
+                         "or a list of ndarrays.")
+    check_call(_LIB.MXNDArraySave(c_str(file),
+                                  mx_uint(len(handles)),
+                                  handles,
+                                  keys))
+
+
+def load(file):
+    """Loads an array from file.
+
+    See more details in ``save``.
+
+    Parameters
+    ----------
+    file : str
+        The filename.
+
+    Returns
+    -------
+    result : list of ndarrays or dict of str -> ndarray
+        Data stored in the file.
+
+    Notes
+    -----
+    This function can only be called within numpy semantics, i.e., `npx.is_np_shape()`
+    and `npx.is_np_array()` must both return true.
+    """
+    if not (is_np_shape() and is_np_array()):
+        raise ValueError('Cannot load `mxnet.numpy.ndarray` in legacy mode. Please activate'
+                         ' numpy semantics by calling `npx.set_np()` in the global scope'
+                         ' before calling this function.')
+    if not isinstance(file, string_types):
+        raise TypeError('file required to be a string')
+    out_size = mx_uint()
+    out_name_size = mx_uint()
+    handles = ctypes.POINTER(NDArrayHandle)()
+    names = ctypes.POINTER(ctypes.c_char_p)()
+    check_call(_LIB.MXNDArrayLoad(c_str(file),
+                                  ctypes.byref(out_size),
+                                  ctypes.byref(handles),
+                                  ctypes.byref(out_name_size),
+                                  ctypes.byref(names)))
+    if out_name_size.value == 0:
+        return [ndarray(NDArrayHandle(handles[i])) for i in range(out_size.value)]
+    else:
+        assert out_name_size.value == out_size.value
+        return dict(
+            (py_str(names[i]), ndarray(NDArrayHandle(handles[i])))
+            for i in range(out_size.value))
diff --git a/python/mxnet/optimizer/optimizer.py b/python/mxnet/optimizer/optimizer.py
index c2c1aa6a76f4..d953e9247900 100644
--- a/python/mxnet/optimizer/optimizer.py
+++ b/python/mxnet/optimizer/optimizer.py
@@ -18,6 +18,7 @@
 
 # pylint: disable=too-many-lines
 """Weight updating functions."""
+from __future__ import absolute_import
 import logging
 import math
 import pickle
@@ -33,6 +34,7 @@
                        multi_mp_sgd_mom_update)
 from ..ndarray import sparse
 from ..random import normal
+from ..util import is_np_array
 
 __all__ = [
     'AdaDelta', 'AdaGrad', 'Adam', 'Adamax', 'DCASGD', 'FTML', 'Ftrl', 'LBSGD',
@@ -119,6 +121,7 @@ def __init__(self, rescale_grad=1., param_idx2name=None, wd=0.,
         self.idx2name = param_idx2name.copy()
         self.sym_info = (sym.attr_dict(), sym.list_arguments()) if sym is not None else ()
         self.param_dict = param_dict if param_dict else {}
+        self.allow_np_array = is_np_array()
 
         self.set_lr_mult({})
         self.set_wd_mult({})
@@ -1644,6 +1647,28 @@ def update(self, index, weight, grad, state):
 # backward compatibility wrapper for Optimizer.CreateOptimizer
 create = Optimizer.create_optimizer  # pylint: disable=invalid-name
 
+
+def _as_classic(a, allow_np):
+    # TODO(junwu): This is a temp solution for allowing converting
+    # np.ndarray to mx.nd.NDArray to be fed into the optimizer since
+    # users may have custom optimizers implemented using mx.nd.NDArray ops.
+    from ..numpy import ndarray as np_ndarray
+    if isinstance(a, (tuple, list)):
+        if any(isinstance(x, np_ndarray) for x in a):
+            if allow_np:
+                return [x.as_nd_ndarray() for x in a]
+            else:
+                raise ValueError('Converting np.ndarray to mx.nd.NDArray is not allowed')
+    else:
+        if isinstance(a, np_ndarray):
+            if allow_np:
+                return a.as_nd_ndarray()
+            else:
+                raise ValueError('Converting np.ndarray to mx.nd.NDArray is not allowed')
+    return a
+
+
+
 class Updater(object):
     """Updater for kvstore."""
     def __init__(self, optimizer):
@@ -1654,14 +1679,15 @@ def __init__(self, optimizer):
 
     def __call__(self, index, grad, weight):
         """Updates weight given gradient and index."""
+        allow_np = self.optimizer.allow_np_array
         if not isinstance(index, (list, tuple)):
             indices = [index]
-            grads = [grad]
-            weights = [weight]
+            grads = [_as_classic(grad, allow_np)]
+            weights = [_as_classic(weight, allow_np)]
         else:
             indices = index
-            grads = grad
-            weights = weight
+            grads = _as_classic(grad, allow_np)
+            weights = _as_classic(weight, allow_np)
         if weights:
             self.optimizer._set_current_context(weights[0].context.device_id)
         for i, idx in enumerate(indices):
diff --git a/python/mxnet/symbol/__init__.py b/python/mxnet/symbol/__init__.py
index f438e4954aa9..2ce395bdd279 100644
--- a/python/mxnet/symbol/__init__.py
+++ b/python/mxnet/symbol/__init__.py
@@ -17,7 +17,7 @@
 
 """Symbol API of MXNet."""
 
-from . import _internal, contrib, linalg, op, random, sparse, image, symbol
+from . import _internal, contrib, linalg, op, random, sparse, image, symbol, numpy
 # pylint: disable=wildcard-import, redefined-builtin
 try:
     from .gen_op import * # pylint: disable=unused-wildcard-import
@@ -27,5 +27,8 @@
 from .op import *
 from .symbol import *
 # pylint: enable=wildcard-import
+from . import numpy as np
+from . import numpy_extension as npx
 
-__all__ = op.__all__ + symbol.__all__ + ['contrib', 'linalg', 'random', 'sparse', 'image']
+__all__ = op.__all__ + symbol.__all__\
+          + ['contrib', 'linalg', 'random', 'sparse', 'image', 'numpy', 'numpy_extension']
diff --git a/python/mxnet/symbol/_internal.py b/python/mxnet/symbol/_internal.py
index 7e9787e32b1c..d46c0e64e6f1 100644
--- a/python/mxnet/symbol/_internal.py
+++ b/python/mxnet/symbol/_internal.py
@@ -24,18 +24,18 @@
 
 try:
     if int(_os.environ.get("MXNET_ENABLE_CYTHON", True)) == 0:
-        from .._ctypes.symbol import SymbolBase, _set_symbol_class
+        from .._ctypes.symbol import SymbolBase, _set_symbol_class, _set_np_symbol_class
         from .._ctypes.symbol import _symbol_creator
     elif _sys.version_info >= (3, 0):
-        from .._cy3.symbol import SymbolBase, _set_symbol_class
+        from .._cy3.symbol import SymbolBase, _set_symbol_class, _set_np_symbol_class
         from .._cy3.symbol import _symbol_creator
     else:
-        from .._cy2.symbol import SymbolBase, _set_symbol_class
+        from .._cy2.symbol import SymbolBase, _set_symbol_class, _set_np_symbol_class
         from .._cy2.symbol import _symbol_creator
 except ImportError:
     if int(_os.environ.get("MXNET_ENFORCE_CYTHON", False)) != 0:
         raise ImportError("Cython Module cannot be loaded but MXNET_ENFORCE_CYTHON=1")
-    from .._ctypes.symbol import SymbolBase, _set_symbol_class
+    from .._ctypes.symbol import SymbolBase, _set_symbol_class, _set_np_symbol_class
     from .._ctypes.symbol import _symbol_creator
 from ..attribute import AttrScope
 from ..base import _Null
@@ -45,4 +45,4 @@
 except ImportError:
     pass
 
-__all__ = ['SymbolBase', '_set_symbol_class', '_symbol_creator']
+__all__ = ['SymbolBase', '_set_symbol_class', '_symbol_creator', '_set_np_symbol_class']
diff --git a/python/mxnet/symbol/numpy/__init__.py b/python/mxnet/symbol/numpy/__init__.py
new file mode 100644
index 000000000000..857849c4ae62
--- /dev/null
+++ b/python/mxnet/symbol/numpy/__init__.py
@@ -0,0 +1,28 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Module for numpy ops under mxnet.symbol."""
+
+from . import random
+from . import linalg
+from . import _op, _symbol, _internal
+from ._symbol import _Symbol
+from . import _register
+from ._op import *  # pylint: disable=wildcard-import
+from ._symbol import *  # pylint: disable=wildcard-import
+
+__all__ = _op.__all__ + _symbol.__all__
diff --git a/python/mxnet/symbol/numpy/_internal.py b/python/mxnet/symbol/numpy/_internal.py
new file mode 100644
index 000000000000..c5f292842b3b
--- /dev/null
+++ b/python/mxnet/symbol/numpy/_internal.py
@@ -0,0 +1,20 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Namespace for numpy internal ops."""
+
+__all__ = []
diff --git a/python/mxnet/symbol/numpy/_op.py b/python/mxnet/symbol/numpy/_op.py
new file mode 100644
index 000000000000..a4a979f30b18
--- /dev/null
+++ b/python/mxnet/symbol/numpy/_op.py
@@ -0,0 +1,20 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Namespace for operators used in Gluon dispatched by F=symbol module."""
+
+__all__ = []
diff --git a/python/mxnet/symbol/numpy/_register.py b/python/mxnet/symbol/numpy/_register.py
new file mode 100644
index 000000000000..3245c8d6d638
--- /dev/null
+++ b/python/mxnet/symbol/numpy/_register.py
@@ -0,0 +1,28 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Registering numpy ops."""
+
+from ...base import _init_np_op_module
+from ..register import _make_symbol_function
+
+_init_np_op_module(root_module_name='mxnet', np_module_name='numpy',
+                   mx_module_name='symbol', make_op_func=_make_symbol_function)
+
+
+_init_np_op_module(root_module_name='mxnet', np_module_name='numpy._internal',
+                   mx_module_name='symbol', make_op_func=_make_symbol_function)
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
new file mode 100644
index 000000000000..616f3066d98d
--- /dev/null
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -0,0 +1,1013 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# pylint: disable=too-many-lines
+"""numpy namespace for operators used in Gluon APIs dispatched by F=symbol module."""
+
+from __future__ import absolute_import
+import ctypes
+import numpy as _np
+from ...base import _LIB, SymbolHandle, numeric_types, mx_uint
+from ...util import check_call, set_module
+from ...context import current_context
+from ..symbol import Symbol
+from .._internal import _set_np_symbol_class
+from . import _internal as _npi
+
+__all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power']
+
+
+def _num_outputs(sym):
+    return len(sym.as_nd_ndarray())
+
+
+@set_module('mxnet.symbol.numpy')
+class _Symbol(Symbol):
+    def __getitem__(self, key):
+        num_outputs = _num_outputs(self)
+        if num_outputs == 1:
+            raise NotImplementedError
+        if not isinstance(key, int):
+            raise NotImplementedError
+        if key >= num_outputs:
+            # Important, python determines the end by this exception
+            raise IndexError
+        handle = SymbolHandle()
+        check_call(_LIB.MXSymbolGetOutput(
+            self.handle, mx_uint(key), ctypes.byref(handle)))
+        return _Symbol(handle=handle)
+
+    def __setitem__(self, key, value):
+        raise NotImplementedError
+
+    def __iter__(self):
+        raise AttributeError('_Symbol object has no attribute __iter__')
+
+    def __add__(self, other):
+        """x.__add__(y) <=> x + y"""
+        return add(self, other)
+
+    def __sub__(self, other):
+        """x.__sub__(y) <=> x - y"""
+        return subtract(self, other)
+
+    def __rsub__(self, other):
+        """x.__rsub__(y) <=> y - x"""
+        return subtract(other, self)
+
+    def __mul__(self, other):
+        """x.__mul__(y) <=> x * y"""
+        return multiply(self, other)
+
+    def __rmul__(self, other):
+        """x.__rmul__(y) <=> y * x"""
+        return multiply(other, self)
+
+    def __div__(self, other):
+        raise AttributeError('_Symbol.__div__ is replaced by __truediv__. If you are using'
+                             ' Python2, please use the statement from __future__ import division'
+                             ' to change the / operator to mean true division throughout the'
+                             ' module. If you are using Python3, this error should not have'
+                             ' been encountered.')
+
+    def __rdiv__(self, other):
+        raise AttributeError('_Symbol.__rdiv__ is replaced by __rtruediv__. If you are using'
+                             ' Python2, please use the statement from __future__ import division'
+                             ' to change the / operator to mean true division throughout the'
+                             ' module. If you are using Python3, this error should not have'
+                             ' been encountered.')
+
+    def __mod__(self, other):
+        """x.__mod__(y) <=> x % y"""
+        return mod(self, other)
+
+    def __rmod__(self, other):
+        """x.__rmod__(y) <=> y % x"""
+        return mod(other, self)
+
+    def __idiv__(self, other):
+        raise NotImplementedError
+
+    def __truediv__(self, other):
+        """x.__truediv__(y) <=> x / y"""
+        return divide(self, other)
+
+    def __rtruediv__(self, other):
+        """x.__rtruediv__(y) <=> y / x"""
+        return divide(other, self)
+
+    def __itruediv__(self, other):
+        raise NotImplementedError
+
+    def __pow__(self, other):
+        """x.__pow__(y) <=> x ** y"""
+        return power(self, other)
+
+    def __rpow__(self, other):
+        return power(other, self)
+
+    def __neg__(self):
+        """x.__neg__() <=> - x"""
+        return self.__mul__(-1.0)
+
+    def __deepcopy__(self, _):
+        return super(_Symbol, self).as_np_ndarray()
+
+    def __eq__(self, other):
+        """x.__eq__(y) <=> x == y"""
+        # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported
+        if isinstance(other, _Symbol):
+            return _npi.equal(self, other)
+        elif isinstance(other, numeric_types):
+            return _npi.equal_scalar(self, float(other))
+        else:
+            raise TypeError("_Symbol does not support type {} as operand".format(str(type(other))))
+
+    def __ne__(self, other):
+        """x.__ne__(y) <=> x != y"""
+        # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported
+        if isinstance(other, _Symbol):
+            return _npi.not_equal(self, other)
+        elif isinstance(other, numeric_types):
+            return _npi.not_equal_scalar(self, float(other))
+        else:
+            raise TypeError("_Symbol does not support type {} as operand".format(str(type(other))))
+
+    def __gt__(self, other):
+        """x.__gt__(y) <=> x > y"""
+        # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported
+        if isinstance(other, _Symbol):
+            return _npi.greater(self, other)
+        elif isinstance(other, numeric_types):
+            return _npi.greater_scalar(self, float(other))
+        else:
+            raise TypeError("_Symbol does not support type {} as operand".format(str(type(other))))
+
+    def __ge__(self, other):
+        """x.__ge__(y) <=> x >= y"""
+        # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported
+        if isinstance(other, _Symbol):
+            return _npi.greater_equal(self, other)
+        elif isinstance(other, numeric_types):
+            return _npi.greater_equal_scalar(self, float(other))
+        else:
+            raise TypeError("_Symbol does not support type {} as operand".format(str(type(other))))
+
+    def __lt__(self, other):
+        """x.__lt__(y) <=> x < y"""
+        # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported
+        if isinstance(other, _Symbol):
+            return _npi.less(self, other)
+        elif isinstance(other, numeric_types):
+            return _npi.less_scalar(self, float(other))
+        else:
+            raise TypeError("_Symbol does not support type {} as operand".format(str(type(other))))
+
+    def __le__(self, other):
+        """x.__le__(y) <=> x <= y"""
+        # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported
+        if isinstance(other, _Symbol):
+            return _npi.less_equal(self, other)
+        elif isinstance(other, numeric_types):
+            return _npi.less_equal_scalar(self, float(other))
+        else:
+            raise TypeError("_Symbol does not support type {} as operand".format(str(type(other))))
+
+    def __len__(self):
+        raise NotImplementedError
+
+    def as_nd_ndarray(self):
+        """Convert _Symbol to mxnet.symbol.Symbol to use its convenience fluent methods."""
+        hdl = SymbolHandle()
+        check_call(_LIB.MXShallowCopySymbol(self.handle, ctypes.byref(hdl)))
+        return Symbol(handle=hdl)
+
+    def as_np_ndarray(self):
+        """For the convenience of conversion between legacy and np symbols."""
+        return self
+
+    @property
+    # pylint: disable= invalid-name, undefined-variable
+    def T(self):
+        """Same as self.transpose()."""
+        return self.transpose()
+    # pylint: enable= invalid-name, undefined-variable
+
+    def astype(self, dtype, **kwargs):  # pylint: disable=arguments-differ
+        raise NotImplementedError
+
+    def dot(self, b, out=None):
+        raise NotImplementedError
+
+    def reshape(self, *args, **kwargs):  # pylint: disable=arguments-differ
+        """Returns an array containing the same data with a new shape.
+
+        Notes
+        -----
+        Unlike the free function `numpy.reshape`, this method on `ndarray` allows
+        the elements of the shape parameter to be passed in as separate arguments.
+        For example, ``a.reshape(10, 11)`` is equivalent to
+        ``a.reshape((10, 11))``.
+        """
+        order = 'C'
+        if len(kwargs) > 1:
+            raise TypeError('function takes at most 1 keyword argument')
+        if len(kwargs) == 1:
+            if 'order' not in kwargs:
+                raise TypeError('{} is an invalid keyword argument for this function'
+                                .format(kwargs.keys()[0]))
+            order = kwargs.pop('order', 'C')
+            if order != 'C':
+                raise NotImplementedError('only supports C-order,'
+                                          ' while received {}'.format(order))
+        if len(args) == 0:
+            raise TypeError('reshape() takes exactly 1 argument (0 given)')
+        raise NotImplementedError
+
+    def argmax(self, axis=None, out=None):  # pylint: disable=arguments-differ
+        raise NotImplementedError
+
+    def reshape_like(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`reshape_like`.
+
+        The arguments are the same as for :py:func:`reshape_like`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute reshape_like')
+
+    def zeros_like(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`zeros_like`.
+
+        The arguments are the same as for :py:func:`zeros_like`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute zeros_like')
+
+    def ones_like(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`ones_like`.
+
+        The arguments are the same as for :py:func:`ones_like`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute ones_like')
+
+    def broadcast_axes(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`broadcast_axes`.
+
+        The arguments are the same as for :py:func:`broadcast_axes`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute broadcast_like')
+
+    def repeat(self, repeats, axis=None):  # pylint: disable=arguments-differ
+        """Repeat elements of an array."""
+        raise NotImplementedError
+
+    def pad(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`pad`.
+
+        The arguments are the same as for :py:func:`pad`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute pad')
+
+    def swapaxes(self, axis1, axis2):  # pylint: disable=arguments-differ
+        """Return a copy of the array with axis1 and axis2 interchanged.
+        Refer to `mxnet.numpy.swapaxes` for full documentation.
+        """
+        raise NotImplementedError
+
+    def split(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`split`.
+
+        The arguments are the same as for :py:func:`split`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute split')
+
+    def split_v2(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`split_v2`.
+
+        The arguments are the same as for :py:func:`split_v2`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute split_v2')
+
+    def slice(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`slice`.
+
+        The arguments are the same as for :py:func:`slice`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute slice')
+
+    def slice_axis(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`slice_axis`.
+
+        The arguments are the same as for :py:func:`slice_axis`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute slice_axis')
+
+    def slice_like(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`slice_like`.
+
+        The arguments are the same as for :py:func:`slice_like`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute slice_like')
+
+    def take(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`take`.
+
+        The arguments are the same as for :py:func:`take`, with
+        this array as data.
+        """
+        raise NotImplementedError
+
+    def one_hot(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`one_hot`.
+
+        The arguments are the same as for :py:func:`one_hot`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute one_hot')
+
+    def pick(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`pick`.
+
+        The arguments are the same as for :py:func:`pick`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute pick')
+
+    def sort(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`sort`.
+
+        The arguments are the same as for :py:func:`sort`, with
+        this array as data.
+        """
+        raise NotImplementedError
+
+    def topk(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`topk`.
+
+        The arguments are the same as for :py:func:`topk`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute topk')
+
+    def argsort(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`argsort`.
+
+        The arguments are the same as for :py:func:`argsort`, with
+        this array as data.
+        """
+        raise NotImplementedError
+
+    def argmax_channel(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`argmax_channel`.
+
+        The arguments are the same as for :py:func:`argmax_channel`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute argmax_channel')
+
+    def argmin(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`argmin`.
+
+        The arguments are the same as for :py:func:`argmin`, with
+        this array as data.
+        """
+        raise NotImplementedError
+
+    def clip(self, min=None, max=None, out=None):  # pylint: disable=arguments-differ
+        """Return an array whose values are limited to [min, max].
+        One of max or min must be given.
+        """
+        raise NotImplementedError
+
+    def abs(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`abs`.
+
+        The arguments are the same as for :py:func:`abs`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute abs')
+
+    def sign(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`sign`.
+
+        The arguments are the same as for :py:func:`sign`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute abs')
+
+    def flatten(self, order='C'):  # pylint: disable=arguments-differ
+        """Return a copy of the array collapsed into one dimension."""
+        return self.reshape(-1, order=order)
+
+    def shape_array(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`shape_array`.
+
+        The arguments are the same as for :py:func:`shape_array`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute shape_array')
+
+    def size_array(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`size_array`.
+
+        The arguments are the same as for :py:func:`size_array`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute size_array')
+
+    def expand_dims(self, *args, **kwargs):  # pylint: disable=arguments-differ
+        """Convenience fluent method for :py:func:`expand_dims`.
+
+        The arguments are the same as for :py:func:`expand_dims`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute expand_dims')
+
+    def tile(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`tile`.
+
+        The arguments are the same as for :py:func:`tile`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute tile')
+
+    def transpose(self, *axes):  # pylint: disable=arguments-differ
+        """Convenience fluent method for :py:func:`transpose`.
+
+        The arguments are the same as for :py:func:`transpose`, with
+        this array as data.
+        """
+        raise NotImplementedError
+
+    def flip(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`flip`.
+
+        The arguments are the same as for :py:func:`flip`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute flip')
+
+    def depth_to_space(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`depth_to_space`.
+
+        The arguments are the same as for :py:func:`depth_to_space`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute depth_to_space')
+
+    def space_to_depth(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`space_to_depth`.
+
+        The arguments are the same as for :py:func:`space_to_depth`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute space_to_depth')
+
+    def diag(self, k=0, **kwargs):
+        """Convenience fluent method for :py:func:`diag`.
+
+        The arguments are the same as for :py:func:`diag`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute diag')
+
+    def sum(self, axis=None, dtype=None, out=None, keepdims=False):  # pylint: disable=arguments-differ
+        """Convenience fluent method for :py:func:`sum`.
+
+        The arguments are the same as for :py:func:`sum`, with
+        this array as data.
+        """
+        raise NotImplementedError
+
+    def nansum(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`nansum`.
+
+        The arguments are the same as for :py:func:`nansum`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute nansum')
+
+    def prod(self, axis=None, dtype=None, out=None, keepdims=False):  # pylint: disable=arguments-differ
+        """Return the product of the array elements over the given axis."""
+        raise NotImplementedError
+
+    def nanprod(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`nanprod`.
+
+        The arguments are the same as for :py:func:`nanprod`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute nanprod')
+
+    def mean(self, axis=None, dtype=None, out=None, keepdims=False):  # pylint: disable=arguments-differ
+        """Convenience fluent method for :py:func:`mean`.
+
+        The arguments are the same as for :py:func:`mean`, with
+        this array as data.
+        """
+        raise NotImplementedError
+
+    def cumsum(self, axis=None, dtype=None, out=None):
+        """Return the cumulative sum of the elements along the given axis."""
+        raise NotImplementedError
+
+    def max(self, axis=None, out=None, keepdims=False):  # pylint: disable=arguments-differ
+        """Return the maximum along a given axis."""
+        raise NotImplementedError
+
+    def min(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`min`.
+
+        The arguments are the same as for :py:func:`min`, with
+        this array as data.
+        """
+        raise NotImplementedError
+
+    def norm(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`norm`.
+
+        The arguments are the same as for :py:func:`norm`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute norm')
+
+    def round(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`round`.
+
+        The arguments are the same as for :py:func:`round`, with
+        this array as data.
+        """
+        raise NotImplementedError
+
+    def rint(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`rint`.
+
+        The arguments are the same as for :py:func:`rint`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute rint')
+
+    def fix(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`fix`.
+
+        The arguments are the same as for :py:func:`fix`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute fix')
+
+    def floor(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`floor`.
+
+        The arguments are the same as for :py:func:`floor`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute floor')
+
+    def ceil(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`ceil`.
+
+        The arguments are the same as for :py:func:`ceil`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute ceil')
+
+    def trunc(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`trunc`.
+
+        The arguments are the same as for :py:func:`trunc`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute trunc')
+
+    def sin(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`sin`.
+
+        The arguments are the same as for :py:func:`sin`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute sin')
+
+    def cos(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`cos`.
+
+        The arguments are the same as for :py:func:`cos`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute cos')
+
+    def tan(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`tan`.
+
+        The arguments are the same as for :py:func:`tan`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute tan')
+
+    def arcsin(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`arcsin`.
+
+        The arguments are the same as for :py:func:`arcsin`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute arcsin')
+
+    def arccos(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`arccos`.
+
+        The arguments are the same as for :py:func:`arccos`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute arccos')
+
+    def arctan(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`arctan`.
+
+        The arguments are the same as for :py:func:`arctan`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute arctan')
+
+    def degrees(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`degrees`.
+
+        The arguments are the same as for :py:func:`degrees`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute degrees')
+
+    def radians(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`radians`.
+
+        The arguments are the same as for :py:func:`radians`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute radians')
+
+    def sinh(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`sinh`.
+
+        The arguments are the same as for :py:func:`sinh`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute sinh')
+
+    def cosh(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`cosh`.
+
+        The arguments are the same as for :py:func:`cosh`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute cosh')
+
+    def tanh(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`tanh`.
+
+        The arguments are the same as for :py:func:`tanh`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute tanh')
+
+    def arcsinh(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`arcsinh`.
+
+        The arguments are the same as for :py:func:`arcsinh`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute arcsinh')
+
+    def arccosh(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`arccosh`.
+
+        The arguments are the same as for :py:func:`arccosh`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute arccosh')
+
+    def arctanh(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`arctanh`.
+
+        The arguments are the same as for :py:func:`arctanh`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute arctanh')
+
+    def exp(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`exp`.
+
+        The arguments are the same as for :py:func:`exp`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute exp')
+
+    def expm1(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`expm1`.
+
+        The arguments are the same as for :py:func:`expm1`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute expm1')
+
+    def log(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`log`.
+
+        The arguments are the same as for :py:func:`log`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute log')
+
+    def log10(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`log10`.
+
+        The arguments are the same as for :py:func:`log10`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute log10')
+
+    def log2(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`log2`.
+
+        The arguments are the same as for :py:func:`log2`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute log2')
+
+    def log1p(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`log1p`.
+
+        The arguments are the same as for :py:func:`log1p`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute log1p')
+
+    def sqrt(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`sqrt`.
+
+        The arguments are the same as for :py:func:`sqrt`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute sqrt')
+
+    def rsqrt(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`rsqrt`.
+
+        The arguments are the same as for :py:func:`rsqrt`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute rsqrt')
+
+    def cbrt(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`cbrt`.
+
+        The arguments are the same as for :py:func:`cbrt`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute cqrt')
+
+    def rcbrt(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`rcbrt`.
+
+        The arguments are the same as for :py:func:`rcbrt`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute rcqrt')
+
+    def square(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`square`.
+
+        The arguments are the same as for :py:func:`square`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute square')
+
+    def reciprocal(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`reciprocal`.
+
+        The arguments are the same as for :py:func:`reciprocal`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute reciprocal')
+
+    def relu(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`relu`.
+
+        The arguments are the same as for :py:func:`relu`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute relu')
+
+    def sigmoid(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`sigmoid`.
+
+        The arguments are the same as for :py:func:`sigmoid`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute sigmoid')
+
+    def softmax(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`softmax`.
+
+        The arguments are the same as for :py:func:`softmax`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute softmax')
+
+    def log_softmax(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`log_softmax`.
+
+        The arguments are the same as for :py:func:`log_softmax`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute log_softmax')
+
+    def softmin(self, *args, **kwargs):
+        """Convenience fluent method for :py:func:`softmin`.
+
+        The arguments are the same as for :py:func:`softmin`, with
+        this array as data.
+        """
+        raise AttributeError('_Symbol object has no attribute softmin')
+
+    def squeeze(self, axis=None):  # pylint: disable=arguments-differ
+        """Remove single-dimensional entries from the shape of a.
+        """
+        raise NotImplementedError
+
+    def broadcast_to(self, *args, **kwargs):
+        raise AttributeError('_Symbol object has no attribute broadcast_to')
+
+    def broadcast_like(self, *args, **kwargs):
+        raise AttributeError('_Symbol object has no attribute broadcast_like')
+
+
+@set_module('mxnet.symbol.numpy')
+def zeros(shape, dtype=_np.float32, order='C', ctx=None):
+    """Return a new array of given shape and type, filled with zeros.
+    This function currently only supports storing multi-dimensional data
+    in row-major (C-style).
+
+    Parameters
+    ----------
+    shape : int or tuple of int
+        The shape of the empty array.
+    dtype : str or numpy.dtype, optional
+        An optional value type. Default is `numpy.float32`. Note that this
+        behavior is different from NumPy's `zeros` function  where `float64`
+        is the default value, because `float32` is considered as the default
+        data type in deep learning.
+    order : {'C'}, optional, default: 'C'
+        How to store multi-dimensional data in memory, currently only row-major
+        (C-style) is supported.
+    ctx : Context, optional
+        An optional device context (default is the current default context).
+
+    Returns
+    -------
+    out : Symbol
+        Array of zeros with the given shape, dtype, and ctx.
+    """
+    if order != 'C':
+        raise NotImplementedError
+    if ctx is None:
+        ctx = current_context()
+    dtype = _np.float32 if dtype is None else dtype
+    return _npi.zeros(shape=shape, ctx=ctx, dtype=dtype)
+
+
+@set_module('mxnet.symbol.numpy')
+def ones(shape, dtype=_np.float32, order='C', ctx=None):
+    """Return a new array of given shape and type, filled with zeros.
+    This function currently only supports storing multi-dimensional data
+    in row-major (C-style).
+
+    Parameters
+    ----------
+    shape : int or tuple of int
+        The shape of the empty array.
+    dtype : str or numpy.dtype, optional
+        An optional value type. Default is `numpy.float32`. Note that this
+        behavior is different from NumPy's `ones` function where `float64`
+        is the default value, because `float32` is considered as the default
+        data type in deep learning.
+    order : {'C'}, optional, default: 'C'
+        How to store multi-dimensional data in memory, currently only row-major
+        (C-style) is supported.
+    ctx : Context, optional
+        An optional device context (default is the current default context).
+
+    Returns
+    -------
+    out : ndarray
+        Array of zeros with the given shape, dtype, and ctx.
+    """
+    if order != 'C':
+        raise NotImplementedError
+    if ctx is None:
+        ctx = current_context()
+    dtype = _np.float32 if dtype is None else dtype
+    return _npi.ones(shape=shape, ctx=ctx, dtype=dtype)
+
+
+#pylint: disable= too-many-arguments, no-member, protected-access
+def _ufunc_helper(lhs, rhs, fn_array, fn_scalar, lfn_scalar, rfn_scalar=None, out=None):
+    """ Helper function for element-wise operation.
+    The function will perform numpy-like broadcasting if needed and call different functions.
+
+    Parameters
+    --------
+    lhs : Symbol or numeric value
+        Left-hand side operand.
+
+    rhs : Symbol or numeric value
+        Right-hand operand,
+
+    fn_array : function
+        Function to be called if both lhs and rhs are of ``Symbol`` type.
+
+    fn_scalar : function
+        Function to be called if both lhs and rhs are numeric values.
+
+    lfn_scalar : function
+        Function to be called if lhs is ``Symbol`` while rhs is numeric value
+
+    rfn_scalar : function
+        Function to be called if lhs is numeric value while rhs is ``Symbol``;
+        if none is provided, then the function is commutative, so rfn_scalar is equal to lfn_scalar
+
+    Returns
+    --------
+    mxnet.numpy.ndarray
+        result array
+    """
+    if isinstance(lhs, numeric_types):
+        if isinstance(rhs, numeric_types):
+            return fn_scalar(lhs, rhs, out=out)
+        else:
+            if rfn_scalar is None:
+                # commutative function
+                return lfn_scalar(rhs, float(lhs), out=out)
+            else:
+                return rfn_scalar(rhs, float(lhs), out=out)
+    elif isinstance(rhs, numeric_types):
+        return lfn_scalar(lhs, float(rhs), out=out)
+    elif isinstance(rhs, Symbol):
+        return fn_array(lhs, rhs, out=out)
+    else:
+        raise TypeError('type %s not supported' % str(type(rhs)))
+#pylint: enable= too-many-arguments, no-member, protected-access
+
+
+@set_module('mxnet.symbol.numpy')
+def add(x1, x2, out=None):
+    return _ufunc_helper(x1, x2, _npi.add, _np.add, _npi.add_scalar, None, out)
+
+
+@set_module('mxnet.symbol.numpy')
+def subtract(x1, x2, out=None):
+    return _ufunc_helper(x1, x2, _npi.subtract, _np.subtract, _npi.subtract_scalar,
+                         _npi.rsubtract_scalar, out)
+
+
+@set_module('mxnet.symbol.numpy')
+def multiply(x1, x2, out=None):
+    return _ufunc_helper(x1, x2, _npi.multiply, _np.multiply, _npi.multiply_scalar, None, out)
+
+
+@set_module('mxnet.symbol.numpy')
+def divide(x1, x2, out=None):
+    return _ufunc_helper(x1, x2, _npi.true_divide, _np.divide, _npi.true_divide_scalar,
+                         _npi.rtrue_divide_scalar, out)
+
+
+@set_module('mxnet.symbol.numpy')
+def mod(x1, x2, out=None):
+    return _ufunc_helper(x1, x2, _npi.mod, _np.mod, _npi.mod_scalar, _npi.rmod_scalar, out)
+
+
+@set_module('mxnet.symbol.numpy')
+def power(x1, x2, out=None):
+    return _ufunc_helper(x1, x2, _npi.power, _np.power, _npi.power_scalar, _npi.rpower_scalar, out)
+
+
+_set_np_symbol_class(_Symbol)
diff --git a/python/mxnet/symbol/numpy/linalg.py b/python/mxnet/symbol/numpy/linalg.py
new file mode 100644
index 000000000000..28cfd0f3806a
--- /dev/null
+++ b/python/mxnet/symbol/numpy/linalg.py
@@ -0,0 +1,22 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Namespace for operators used in Gluon dispatched by F=symbol."""
+
+from __future__ import absolute_import
+
+__all__ = []
diff --git a/python/mxnet/symbol/numpy/random.py b/python/mxnet/symbol/numpy/random.py
new file mode 100644
index 000000000000..28cfd0f3806a
--- /dev/null
+++ b/python/mxnet/symbol/numpy/random.py
@@ -0,0 +1,22 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Namespace for operators used in Gluon dispatched by F=symbol."""
+
+from __future__ import absolute_import
+
+__all__ = []
diff --git a/python/mxnet/symbol/numpy_extension/__init__.py b/python/mxnet/symbol/numpy_extension/__init__.py
new file mode 100644
index 000000000000..5be34ac9b3d5
--- /dev/null
+++ b/python/mxnet/symbol/numpy_extension/__init__.py
@@ -0,0 +1,25 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Module for the ops not belonging to the official numpy package."""
+
+from . import _op
+from . import image
+from . import _register
+from ._op import *  # pylint: disable=wildcard-import
+
+__all__ = _op.__all__
diff --git a/python/mxnet/symbol/numpy_extension/_op.py b/python/mxnet/symbol/numpy_extension/_op.py
new file mode 100644
index 000000000000..82eaa8e6ec9f
--- /dev/null
+++ b/python/mxnet/symbol/numpy_extension/_op.py
@@ -0,0 +1,21 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Namespace for operators not belonging to the official numpy package
+used in Gluon APIs dispatched by F=symbol module."""
+
+__all__ = []
diff --git a/python/mxnet/symbol/numpy_extension/_register.py b/python/mxnet/symbol/numpy_extension/_register.py
new file mode 100644
index 000000000000..b118987b1fd3
--- /dev/null
+++ b/python/mxnet/symbol/numpy_extension/_register.py
@@ -0,0 +1,24 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Registering numpy_extension ops."""
+
+from ...base import _init_np_op_module
+from ..register import _make_symbol_function
+
+_init_np_op_module(root_module_name='mxnet', np_module_name='numpy_extension',
+                   mx_module_name='symbol', make_op_func=_make_symbol_function)
diff --git a/python/mxnet/symbol/numpy_extension/image.py b/python/mxnet/symbol/numpy_extension/image.py
new file mode 100644
index 000000000000..b3bd27fc503c
--- /dev/null
+++ b/python/mxnet/symbol/numpy_extension/image.py
@@ -0,0 +1,20 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Image pre-processing operators."""
+
+__all__ = []
diff --git a/python/mxnet/symbol/register.py b/python/mxnet/symbol/register.py
index ac59f8b97f15..a17dd79048d4 100644
--- a/python/mxnet/symbol/register.py
+++ b/python/mxnet/symbol/register.py
@@ -27,12 +27,60 @@
 from ..attribute import AttrScope
 from ..base import mx_uint, check_call, _LIB, py_str
 from ..symbol_doc import _build_doc
-from ..base import _Null, _init_op_module
+from ..base import _Null, _init_op_module, _is_np_op
 from ..name import NameManager
 # pylint: enable=unused-import
 
 
-def _generate_symbol_function_code(handle, name, func_name, signature_only=False):
+def _verify_np_symbol(op_name, func_name, sym):
+    """Verify if the sym is a numpy symbol.
+
+    Parameters
+    ----------
+    op_name : str
+        Operator full name registered in backend.
+    func_name : str
+        Operator name exposed to users. This is usually the name by stripping off
+        the prefix of the full operator names registered in backend.
+    sym : symbol to be verified
+    """
+    from .numpy._symbol import _Symbol as np_symbol
+    if not isinstance(sym, np_symbol):
+        raise TypeError('Operator `{}` registered in backend is known as `{}` in Python. '
+                        'This is a numpy operator which can only accept '
+                        'MXNet numpy ndarrays, while received a legacy ndarray. '
+                        'Please ensure that you have activated numpy semantics by calling '
+                        '`npx.set_np()` in your code. If you still see this error with numpy '
+                        'semantics activated, please call `as_np_ndarray()` upon the legacy '
+                        'ndarray to convert it to an MXNet numpy ndarray, and then feed the '
+                        'converted array to this operator.'
+                        .format(op_name, func_name))
+
+
+def _verify_legacy_symbol(op_name, func_name, sym):
+    """Verify if the sym is a legacy symbol.
+
+    Parameters
+    ----------
+    op_name : str
+        Operator full name registered in backend.
+    func_name : str
+        Operator name exposed to users. This is usually the name by stripping off
+        the prefix of the full operator names registered in backend.
+    sym : symbol to be verified
+    """
+    from .numpy._symbol import _Symbol as np_symbol
+    if isinstance(sym, np_symbol):
+        raise TypeError('Operator `{}` registered in backend is known as `{}` in Python. '
+                        'This is a legacy operator which can only accept '
+                        'legacy ndarrays, while received an MXNet numpy ndarray. '
+                        'Please call `as_nd_ndarray()` upon the numpy ndarray to '
+                        'convert it to a legacy ndarray, and then feed the converted '
+                        'array to this operator.'
+                        .format(op_name, func_name))
+
+
+def _generate_symbol_function_code(handle, op_name, func_name, signature_only=False):
     """Generate function for symbol op by handle and function name."""
     real_name = ctypes.c_char_p()
     desc = ctypes.c_char_p()
@@ -56,7 +104,7 @@ def _generate_symbol_function_code(handle, name, func_name, signature_only=False
     arg_types = [py_str(arg_types[i]) for i in range(narg)]
     key_var_num_args = py_str(key_var_num_args.value)
     ret_type = py_str(ret_type.value) if ret_type.value is not None else ''
-    doc_str = _build_doc(name,
+    doc_str = _build_doc(op_name,
                          py_str(desc.value),
                          arg_names,
                          arg_types,
@@ -95,6 +143,8 @@ def _generate_symbol_function_code(handle, name, func_name, signature_only=False
     signature.append('**kwargs')
     signature = ndsignature + signature
 
+    is_np_op = _is_np_op(op_name)
+    verify_symbol_fn = _verify_np_symbol.__name__ if is_np_op else _verify_legacy_symbol.__name__
     code = []
     if arr_name:
         code.append("""
@@ -106,7 +156,8 @@ def %s(*%s, **kwargs):"""%(func_name, arr_name))
         assert isinstance(i, SymbolBase), \\
             "Positional arguments must be Symbol instances, " \\
             "but got %s"%str(i)
-        sym_args.append(i)""".format(arr_name))
+        {}('{}', '{}', i)
+        sym_args.append(i)""".format(arr_name, verify_symbol_fn, op_name, func_name))
             if dtype_name is not None:
                 code.append("""
     if '%s' in kwargs:
@@ -128,9 +179,10 @@ def %s(*%s, **kwargs):"""%(func_name, arr_name))
     for k, v in kwargs.items():
         if isinstance(v, SymbolBase):
             sym_kwargs[k] = v
+            %s('%s', '%s', v)
         else:
             keys.append(k)
-            vals.append(v)"""%(func_name.lower()))
+            vals.append(v)"""%(func_name.lower(), verify_symbol_fn, op_name, func_name))
             if key_var_num_args: # pylint: disable=using-constant-test
                 code.append("""
     if '%s' not in kwargs:
@@ -139,8 +191,8 @@ def %s(*%s, **kwargs):"""%(func_name, arr_name))
             key_var_num_args, key_var_num_args))
 
             code.append("""
-    return _symbol_creator(%d, sym_args, sym_kwargs, keys, vals, name)"""%(
-        handle.value))
+    return _symbol_creator(%d, sym_args, sym_kwargs, keys, vals, name, %s)"""%(
+        handle.value, str(is_np_op)))
     else:
         code.append("""
 def %s(%s):"""%(func_name, ', '.join(signature)))
@@ -155,9 +207,10 @@ def %s(%s):"""%(func_name, ', '.join(signature)))
     for _k, _v in kwargs.items():
         if isinstance(_v, SymbolBase):
             sym_kwargs[_k] = _v
+            {}('{}', '{}', _v)
         else:
             _keys.append(_k)
-            _vals.append(_v)""")
+            _vals.append(_v)""".format(verify_symbol_fn, op_name, func_name))
             # NDArray args
             for name in ndarg_names: # pylint: disable=redefined-argument-from-local
                 code.append("""
@@ -165,6 +218,9 @@ def %s(%s):"""%(func_name, ', '.join(signature)))
         assert isinstance({name}, SymbolBase), \\
             "Argument {name} must be Symbol instances, but got %s"%str({name})
         sym_kwargs['{name}'] = {name}""".format(name=name))
+                code.append("""
+        {}('{}', '{}', {name})
+                """.format(verify_symbol_fn, op_name, func_name, name=name))
             # kwargs
             for name in kwarg_names: # pylint: disable=redefined-argument-from-local
                 code.append("""
@@ -173,7 +229,13 @@ def %s(%s):"""%(func_name, ', '.join(signature)))
         _vals.append(%s)"""%(name, name, name))
             # dtype
             if dtype_name is not None:
-                code.append("""
+                if is_np_op:
+                    code.append("""
+    if %s is not _Null and %s is not None:
+        _keys.append('%s')
+        _vals.append(_np.dtype(%s).name)"""%(dtype_name, dtype_name, dtype_name, dtype_name))
+                else:
+                    code.append("""
     if %s is not _Null:
         _keys.append('%s')
         _vals.append(_np.dtype(%s).name)"""%(dtype_name, dtype_name, dtype_name))
@@ -182,8 +244,8 @@ def %s(%s):"""%(func_name, ', '.join(signature)))
     if not hasattr(NameManager._current, "value"):
         NameManager._current.value = NameManager()
     name = NameManager._current.value.get(name, '%s')
-    return _symbol_creator(%d, None, sym_kwargs, _keys, _vals, name)"""%(
-        func_name.lower(), handle.value))
+    return _symbol_creator(%d, None, sym_kwargs, _keys, _vals, name, %s)"""%(
+        func_name.lower(), handle.value, str(is_np_op)))
 
     if signature_only:
         code.append("""
diff --git a/python/mxnet/symbol/symbol.py b/python/mxnet/symbol/symbol.py
index 68322297c8bb..542b3796a9fe 100644
--- a/python/mxnet/symbol/symbol.py
+++ b/python/mxnet/symbol/symbol.py
@@ -30,7 +30,7 @@
 import warnings
 from numbers import Number
 
-import numpy as _numpy
+import numpy as _numpy  # pylint: disable=relative-import
 
 from ..attribute import AttrScope
 from ..base import _LIB, numeric_types, c_array, c_array_buf, c_str, c_str_array, c_handle_array
@@ -61,6 +61,17 @@ class Symbol(SymbolBase):
     # Make numpy functions return Symbol instead of numpy object array
     __array_priority__ = 1000.0
 
+    def as_np_ndarray(self):
+        """Convert mx.sym.Symbol to mx.sym.np._Symbol."""
+        from .numpy import _Symbol
+        hdl = SymbolHandle()
+        check_call(_LIB.MXShallowCopySymbol(self.handle, ctypes.byref(hdl)))
+        return _Symbol(hdl)
+
+    def as_nd_ndarray(self):
+        """Returns self. For the convenience of conversion between legacy and np symbols."""
+        return self
+
     def __repr__(self):
         """Gets a string representation of the symbol."""
         name = self.name
@@ -148,6 +159,8 @@ def __rsub__(self, other):
         array([[-2., -2., -2.],
                [-2., -2., -2.]], dtype=float32)
         """
+        if isinstance(other, Symbol):
+            return other.__sub__(self)
         if isinstance(other, Number):
             return _internal._RMinusScalar(self, scalar=other)
         else:
@@ -196,6 +209,8 @@ def __rdiv__(self, other):
         array([[ 0.33333334,  0.33333334,  0.33333334],
                [ 0.33333334,  0.33333334,  0.33333334]], dtype=float32)
         """
+        if isinstance(other, Symbol):
+            return other.__truediv__(self)
         if isinstance(other, Number):
             return _internal._RDivScalar(self, scalar=other)
         else:
@@ -226,6 +241,8 @@ def __rmod__(self, other):
         array([[ 1.,  1.,  1.,
                [ 1.,  1.,  1., dtype=float32)
         """
+        if isinstance(other, Symbol):
+            return other.__mod__(self)
         if isinstance(other, Number):
             return _internal._RModScalar(self, scalar=other)
         else:
@@ -256,7 +273,13 @@ def __pow__(self, other):
             raise TypeError('type %s not supported' % str(type(other)))
 
     def __rpow__(self, other):
-        raise NotImplementedForSymbol(self.__rpow__, 'y**x', other)
+        """x.__rpow__(y) <=> y ** x"""
+        if isinstance(other, Symbol):
+            return other.__pow__(self)
+        elif isinstance(other, Number):
+            return _internal._rpower_scalar(self, scalar=other)
+        else:
+            raise TypeError('type %s not supported' % str(type(other)))
 
     def __neg__(self):
         """x.__neg__() <=> -x
@@ -2671,8 +2694,12 @@ def var(name, attr=None, shape=None, lr_mult=None, wd_mult=None, dtype=None,
 Variable = var
 
 
-def Group(symbols):
+def Group(symbols, create_fn=Symbol):
     """Creates a symbol that contains a collection of other symbols, grouped together.
+    A classic symbol (`mx.sym.Symbol`) will be returned if all the symbols in the list
+    are of that type; a numpy symbol (`mx.sym.np._Symbol`) will be returned if all the
+    symbols in the list are of that type. A type error will be raised if a list of mixed
+    classic and numpy symbols are provided.
 
     Example
     -------
@@ -2686,6 +2713,9 @@ def Group(symbols):
     symbols : list
         List of symbols to be grouped.
 
+    create_fn : mx.sym.Symbol or mx.sym.np._Symbol
+        Symbol class for creating the grouped symbol.
+
     Returns
     -------
     sym : Symbol
@@ -2697,7 +2727,7 @@ def Group(symbols):
     check_call(_LIB.MXSymbolCreateGroup(
         mx_uint(len(symbols)),
         c_handle_array(symbols), ctypes.byref(handle)))
-    return Symbol(handle)
+    return create_fn(handle)
 
 
 def load(fname):
diff --git a/python/mxnet/test_utils.py b/python/mxnet/test_utils.py
index 0e260ceb7676..2275f4d5da0f 100644
--- a/python/mxnet/test_utils.py
+++ b/python/mxnet/test_utils.py
@@ -48,6 +48,8 @@
 from .ndarray.ndarray import _STORAGE_TYPE_STR_TO_ID
 from .ndarray import array
 from .symbol import Symbol
+from .symbol.numpy import _Symbol as np_symbol
+from .util import use_np  # pylint: disable=unused-import
 
 
 def default_context():
@@ -89,7 +91,8 @@ def get_etol(etol=None):
 
 def random_arrays(*shapes):
     """Generate some random numpy arrays."""
-    arrays = [np.random.randn(*s).astype(default_dtype())
+    arrays = [np.array(np.random.randn(), dtype=default_dtype())
+              if len(s) == 0 else np.random.randn(*s).astype(default_dtype())
               for s in shapes]
     if len(arrays) == 1:
         return arrays[0]
@@ -408,16 +411,20 @@ def create_sparse_array_zd(shape, stype, density, data_init=None,
                                density=density,
                                shuffle_csr_indices=shuffle_csr_indices)
 
-def rand_shape_2d(dim0=10, dim1=10):
-    return rnd.randint(1, dim0 + 1), rnd.randint(1, dim1 + 1)
 
+def rand_shape_2d(dim0=10, dim1=10, allow_zero_size=False):
+    low = 0 if allow_zero_size else 1
+    return rnd.randint(low, dim0 + 1), rnd.randint(low, dim1 + 1)
 
-def rand_shape_3d(dim0=10, dim1=10, dim2=10):
-    return rnd.randint(1, dim0 + 1), rnd.randint(1, dim1 + 1), rnd.randint(1, dim2 + 1)
 
+def rand_shape_3d(dim0=10, dim1=10, dim2=10, allow_zero_size=False):
+    low = 0 if allow_zero_size else 1
+    return rnd.randint(low, dim0 + 1), rnd.randint(low, dim1 + 1), rnd.randint(low, dim2 + 1)
 
-def rand_shape_nd(num_dim, dim=10):
-    return tuple(rnd.randint(1, dim+1, size=num_dim))
+
+def rand_shape_nd(num_dim, dim=10, allow_zero_size=False):
+    low = 0 if allow_zero_size else 1
+    return tuple(rnd.randint(low, dim+1, size=num_dim))
 
 
 def rand_coord_2d(x_low, x_high, y_low, y_high):
@@ -829,7 +836,7 @@ def as_stype(var, stype, dtype):
             continue
         stype = executor.arg_dict[k].stype
         old_value = v.copy()
-        for i in range(np.prod(v.shape)):
+        for i in range(int(np.prod(v.shape))):
             # inplace update
             v.ravel()[i] += eps/2.0
             executor.arg_dict[k][:] = as_stype(v, stype, dtype=dtype)
@@ -941,7 +948,12 @@ def random_projection(shape):
     input_shape = {k: v.shape for k, v in location.items()}
     _, out_shape, _ = sym.infer_shape(**input_shape)
     proj = mx.sym.Variable("__random_proj")
+    is_np_sym = bool(isinstance(sym, np_symbol))
+    if is_np_sym:  # convert to np symbol for using element-wise multiplication
+        proj = proj.as_np_ndarray()
     out = sym * proj
+    if is_np_sym:  # convert to classic symbol so that make_loss can be used
+        out = out.as_nd_ndarray()
     out = mx.sym.make_loss(out)
 
     location = dict(list(location.items()) +
diff --git a/python/mxnet/util.py b/python/mxnet/util.py
index 5bc1dc809c88..d4e95e0c0c9c 100644
--- a/python/mxnet/util.py
+++ b/python/mxnet/util.py
@@ -20,6 +20,9 @@
 import os
 import sys
 import functools
+import itertools
+import inspect
+import threading
 
 from .base import _LIB, check_call
 
@@ -76,14 +79,24 @@ def set_np_shape(active):
     >>> print(mx.is_np_shape())
     True
     """
+    if active:
+        import logging
+        logging.info('NumPy-shape semantics has been activated in your code. '
+                     'This is required for creating and manipulating scalar and zero-size '
+                     'tensors, which were not supported in MXNet before, as in the official '
+                     'NumPy library. Please DO NOT manually deactivate this semantics while '
+                     'using `mxnet.numpy` and `mxnet.numpy_extension` modules.')
+    elif is_np_array():
+        raise ValueError('Deactivating NumPy shape semantics while NumPy array semantics is still'
+                         ' active is not allowed. Please consider calling `npx.reset_np()` to'
+                         ' deactivate both of them.')
     prev = ctypes.c_int()
     check_call(_LIB.MXSetIsNumpyShape(ctypes.c_int(active), ctypes.byref(prev)))
     return bool(prev.value)
 
 
 def is_np_shape():
-    """
-    Checks whether the NumPy shape semantics is currently turned on.
+    """Checks whether the NumPy shape semantics is currently turned on.
     In NumPy shape semantics, `()` represents the shape of scalar tensors,
     and tuples with `0` elements, for example, `(0,)`, `(1, 0, 2)`, represent
     the shapes of zero-size tensors. This is turned off by default for keeping
@@ -213,39 +226,379 @@ def np_shape(active=True):
     return _NumpyShapeScope(active)
 
 
+def wraps_safely(wrapped, assigned=functools.WRAPPER_ASSIGNMENTS):
+    """This function is safe version of `functools.wraps` in Python2 which skips wrapping functions
+    for the attributes that do not exist."""
+    if sys.version_info[0] > 2:
+        return functools.wraps(wrapped)
+    else:
+        return functools.wraps(wrapped,
+                               assigned=itertools.ifilter(
+                                   functools.partial(hasattr, wrapped), assigned))
+
+
 def use_np_shape(func):
-    """Wraps a function with an activated NumPy-shape scope. This ensures
-    that the execution of the function is guaranteed with the support of
-    scalar and zero-size tensors as in NumPy.
+    """A decorator wrapping a function or class with activated NumPy-shape semantics.
+    When `func` is a function, this ensures that the execution of the function is scoped with NumPy
+    shape semantics, such as the support for zero-dim and zero size tensors. When
+    `func` is a class, it ensures that all the methods, static functions, and properties
+    of the class are executed with the NumPy shape semantics.
+
+    Example::
+        import mxnet as mx
+        @mx.use_np_shape
+        def scalar_one():
+            return mx.nd.ones(())
+        print(scalar_one())
+
+        @np.use_np_shape
+        class ScalarTensor(object):
+            def __init__(self, val=None):
+                if val is None:
+                    val = ScalarTensor.random().value
+                self._scalar = mx.nd.ones(()) * val
+
+            def __repr__(self):
+                print("Is __repr__ in np_shape semantics? {}!".format(str(np.is_np_shape())))
+                return str(self._scalar.asnumpy())
+
+            @staticmethod
+            def random():
+                val = mx.nd.random.uniform().asnumpy().item()
+                return ScalarTensor(val)
+
+            @property
+            def value(self):
+                print("Is value property in np_shape semantics? {}!".format(str(np.is_np_shape())))
+                return self._scalar.asnumpy().item()
+
+
+        print("Is global scope of np_shape activated? {}!".format(str(np.is_np_shape())))
+        scalar_tensor = ScalarTensor()
+        print(scalar_tensor)
+
+    Parameters
+    ----------
+    func : a user-provided callable function or class to be scoped by the NumPy-shape semantics.
+
+    Returns
+    -------
+    Function or class
+        A function or class wrapped in the NumPy-shape scope.
+    """
+
+    if inspect.isclass(func):
+        for name, method in inspect.getmembers(
+                func,
+                predicate=
+                lambda f: inspect.isfunction(f) or inspect.ismethod(f) or isinstance(f, property)):
+            if isinstance(method, property):
+                setattr(func, name, property(use_np_shape(method.__get__),
+                                             method.__set__,
+                                             method.__delattr__,
+                                             method.__doc__))
+            else:
+                setattr(func, name, use_np_shape(method))
+        return func
+    elif callable(func):
+        @wraps_safely(func)
+        def _with_np_shape(*args, **kwargs):
+            with np_shape(active=True):
+                return func(*args, **kwargs)
+        return _with_np_shape
+    else:
+        raise TypeError('use_np_shape can only decorate classes and callable objects, '
+                        'while received a {}'.format(str(type(func))))
+
+
+def _sanity_check_params(func_name, unsupported_params, param_dict):
+    for param_name in unsupported_params:
+        if param_name in param_dict:
+            raise NotImplementedError("function {} does not support parameter {}"
+                                      .format(func_name, param_name))
+
+
+def set_module(module):
+    """Decorator for overriding __module__ on a function or class.
+
+    Example usage::
+
+        @set_module('mxnet.numpy')
+        def example():
+            pass
+
+        assert example.__module__ == 'numpy'
+    """
+    def decorator(func):
+        if module is not None:
+            func.__module__ = module
+        return func
+    return decorator
+
+
+class _NumpyArrayScope(object):
+    """Scope for managing NumPy array creation. This is often used
+    with `is_np_array=True` in initializer to enforce array creation
+    as type `mxnet.numpy.ndarray`, instead of `mx.nd.NDArray` in Gluon.
+
+    Do not use this class directly. Use `np_array(active)` instead.
+    """
+    _current = threading.local()
+
+    def __init__(self, is_np_array):  # pylint: disable=redefined-outer-name
+        self._old_scope = None
+        self._is_np_array = is_np_array
+
+    def __enter__(self):
+        if not hasattr(_NumpyArrayScope._current, "value"):
+            _NumpyArrayScope._current.value = _NumpyArrayScope(False)
+        self._old_scope = _NumpyArrayScope._current.value
+        _NumpyArrayScope._current.value = self
+        return self
+
+    def __exit__(self, ptype, value, trace):
+        assert self._old_scope
+        _NumpyArrayScope._current.value = self._old_scope
+
+
+def np_array(active=True):
+    """Returns an activated/deactivated NumPy-array scope to be used in 'with' statement
+    and captures code that needs the NumPy-array semantics.
+
+    Currently, this is used in Gluon to enforce array creation in `Block`s as type
+    `mxnet.numpy.ndarray`, instead of `mx.nd.NDArray`.
+
+    It is recommended to use the decorator `use_np_array` to decorate the classes
+    that need this semantics, instead of using this function in a `with` statement
+    unless you know exactly what has been scoped by this semantics.
 
     Please note that this is designed as an infrastructure for the incoming
     MXNet-NumPy operators. Legacy operators registered in the modules
     `mx.nd` and `mx.sym` are not guaranteed to behave like their counterparts
     in NumPy even within this scope.
 
+    Parameters
+    ----------
+    active : bool
+        Indicates whether to activate NumPy-array semantics.
+
+    Returns
+    -------
+    _NumpyShapeScope
+        A scope object for wrapping the code w/ or w/o NumPy-shape semantics.
+    """
+    return _NumpyArrayScope(active)
+
+
+def is_np_array():
+    """Checks whether the NumPy-array semantics is currently turned on.
+    This is currently used in Gluon for checking whether an array of type `mxnet.numpy.ndarray`
+    or `mx.nd.NDArray` should be created. For example, at the time when a parameter
+    is created in a `Block`, an `mxnet.numpy.ndarray` is created if this returns true; else
+    an `mx.nd.NDArray` is created.
+
+    Normally, users are not recommended to use this API directly unless you known exactly
+    what is going on under the hood.
+
+    Please note that this is designed as an infrastructure for the incoming
+    MXNet-NumPy operators. Legacy operators registered in the modules
+    `mx.nd` and `mx.sym` are not guaranteed to behave like their counterparts
+    in NumPy within this semantics.
+
+    Returns
+    -------
+        A bool value indicating whether the NumPy-array semantics is currently on.
+    """
+    return _NumpyArrayScope._current.value._is_np_array if hasattr(
+        _NumpyArrayScope._current, "value") else False
+
+
+def use_np_array(func):
+    """A decorator wrapping Gluon `Block`s and all its methods, properties, and static functions
+    with the semantics of NumPy-array, which means that where ndarrays are created,
+    `mxnet.numpy.ndarray`s should be created, instead of legacy ndarrays of type `mx.nd.NDArray`.
+    For example, at the time when a parameter is created in a `Block`, an `mxnet.numpy.ndarray`
+    is created if it's decorated with this decorator.
+
+    Example::
+        import mxnet as mx
+        from mxnet import gluon, np
+
+
+        class TestHybridBlock1(gluon.HybridBlock):
+            def __init__(self):
+                super(TestHybridBlock1, self).__init__()
+                self.w = self.params.get('w', shape=(2, 2))
+
+            def hybrid_forward(self, F, x, w):
+                return F.dot(x, w)
+
+
+        x = mx.nd.ones((2, 2))
+        net1 = TestHybridBlock1()
+        net1.initialize()
+        out = net1.forward(x)
+        for _, v in net1.collect_params().items():
+            assert type(v.data()) is mx.nd.NDArray
+        assert type(out) is mx.nd.NDArray
+
+
+        @np.use_np_array
+        class TestHybridBlock2(gluon.HybridBlock):
+            def __init__(self):
+                super(TestHybridBlock2, self).__init__()
+                self.w = self.params.get('w', shape=(2, 2))
+
+            def hybrid_forward(self, F, x, w):
+                return F.np.dot(x, w)
+
+
+        x = np.ones((2, 2))
+        net2 = TestHybridBlock2()
+        net2.initialize()
+        out = net2.forward(x)
+        for _, v in net2.collect_params().items():
+            print(type(v.data()))
+            assert type(v.data()) is np.ndarray
+        assert type(out) is np.ndarray
 
     Parameters
     ----------
-    func : a user-provided callable function to be scoped by the NumPy-shape semantics.
+    func : a user-provided callable function or class to be scoped by the NumPy-array semantics.
 
     Returns
     -------
-    Function
-        A function for wrapping the user functions in the NumPy-shape semantics.
+    Function or class
+        A function or class wrapped in the NumPy-array scope.
+    """
+    if inspect.isclass(func):
+        for name, method in inspect.getmembers(
+                func,
+                predicate=
+                lambda f: inspect.isfunction(f) or inspect.ismethod(f) or isinstance(f, property)):
+            if isinstance(method, property):
+                setattr(func, name, property(use_np_array(method.__get__),
+                                             method.__set__,
+                                             method.__delattr__,
+                                             method.__doc__))
+            else:
+                setattr(func, name, use_np_array(method))
+        return func
+    elif callable(func):
+        @wraps_safely(func)
+        def _with_np_array(*args, **kwargs):
+            with np_array(active=True):
+                return func(*args, **kwargs)
+        return _with_np_array
+    else:
+        raise TypeError('use_np_array can only decorate classes and callable objects, '
+                        'while received a {}'.format(str(type(func))))
 
 
-    Examples
-    --------
-    >>> import mxnet as mx
-    >>> @mx.use_np_shape
-    ... def scalar_one():
-    ...     return mx.nd.ones(())
-    ...
-    >>> print(scalar_one())
+def use_np(func):
+    """A convenience decorator for wrapping user provided functions and classes in the scope of
+    both NumPy-shape and NumPy-array semantics, which means that (1) empty tuples `()` and tuples
+    with zeros, such as `(0, 1)`, `(1, 0, 2)`, will be treated as scalar tensors' shapes and
+    zero-size tensors' shapes in shape inference functions of operators, instead of as unknown
+    in legacy mode; (2) ndarrays of type `mxnet.numpy.ndarray` should be created instead of
+    `mx.nd.NDArray`.
+
+    Example::
+        import mxnet as mx
+        from mxnet import gluon, np
+
+
+        class TestHybridBlock1(gluon.HybridBlock):
+            def __init__(self):
+                super(TestHybridBlock1, self).__init__()
+                self.w = self.params.get('w', shape=(2, 2))
+
+            def hybrid_forward(self, F, x, w):
+                return F.dot(x, w) + F.ones((1,))
+
+
+        x = mx.nd.ones((2, 2))
+        net1 = TestHybridBlock1()
+        net1.initialize()
+        out = net1.forward(x)
+        for _, v in net1.collect_params().items():
+            assert type(v.data()) is mx.nd.NDArray
+        assert type(out) is mx.nd.NDArray
+
+
+        @np.use_np
+        class TestHybridBlock2(gluon.HybridBlock):
+            def __init__(self):
+                super(TestHybridBlock2, self).__init__()
+                self.w = self.params.get('w', shape=(2, 2))
+
+            def hybrid_forward(self, F, x, w):
+                return F.np.dot(x, w) + F.np.ones(())
+
+
+        x = np.ones((2, 2))
+        net2 = TestHybridBlock2()
+        net2.initialize()
+        out = net2.forward(x)
+        for _, v in net2.collect_params().items():
+            print(type(v.data()))
+            assert type(v.data()) is np.ndarray
+        assert type(out) is np.ndarray
+
+    Parameters
+    ----------
+    func : a user-provided callable function or class to be scoped by the
+    NumPy-shape and NumPy-array semantics.
+
+    Returns
+    -------
+    Function or class
+        A function or class wrapped in the Numpy-shape and NumPy-array scope.
     """
-    @functools.wraps(func)
-    def _with_np_shape(*args, **kwargs):
-        with np_shape(active=True):
-            return func(*args, **kwargs)
+    return use_np_shape(use_np_array(func))
+
+
+def _set_np_array(active):
+    """Turns on/off NumPy array semantics for the current thread in which `mxnet.numpy.ndarray`
+    is expected to be created, instead of the legacy `mx.nd.NDArray`.
+
+    Parameters
+    ---------
+    active : bool
+        A boolean value indicating whether the NumPy-array semantics should be turned on or off.
+
+    Returns
+    -------
+        A bool value indicating the previous state of NumPy array semantics.
+    """
+    if active:
+        import logging
+        logging.info('NumPy array semantics has been activated in your code. This allows you'
+                     ' to use operators from MXNet NumPy and NumPy Extension modules as well'
+                     ' as MXNet NumPy `ndarray`s.')
+    cur_state = is_np_array()
+    _NumpyArrayScope._current.value = _NumpyArrayScope(active)
+    return cur_state
+
+
+def set_np(shape=True, array=True):
+    """Setting NumPy shape and array semantics at the same time.
+    It is required to keep NumPy shape semantics active when activating NumPy array semantics.
+    Deactivating NumPy shape semantics while NumPy array semantics is still active is not allowed.
+
+    Parameters
+    ----------
+    shape : bool
+        A boolean value indicating whether the NumPy-shape semantics should be turned on or off.
+    array : bool
+        A boolean value indicating whether the NumPy-array semantics should be turned on or off.
+    """
+    if not shape and array:
+        raise ValueError('NumPy Shape semantics is required in using NumPy array semantics.')
+    _set_np_array(array)
+    set_np_shape(shape)
+
 
-    return _with_np_shape
+def reset_np():
+    """Deactivate NumPy shape and array semantics at the same time."""
+    set_np(shape=False, array=False)
diff --git a/src/c_api/c_api.cc b/src/c_api/c_api.cc
index 5b23a8c694f6..dfb01dca94d9 100644
--- a/src/c_api/c_api.cc
+++ b/src/c_api/c_api.cc
@@ -1604,3 +1604,12 @@ int MXStorageEmptyCache(int dev_type, int dev_id) {
   Storage::Get()->ReleaseAll(ctx);
   API_END();
 }
+
+int MXShallowCopyNDArray(NDArrayHandle src_handle, NDArrayHandle* out) {
+  NDArray* ret = nullptr;
+  API_BEGIN();
+  NDArray* src_array = static_cast<NDArray*>(src_handle);
+  ret = new NDArray(*src_array);
+  *out = ret;
+  API_END_HANDLE_ERROR(delete ret);
+}
diff --git a/src/c_api/c_api_common.h b/src/c_api/c_api_common.h
index 013ecab93da8..233acc85f36b 100644
--- a/src/c_api/c_api_common.h
+++ b/src/c_api/c_api_common.h
@@ -31,6 +31,7 @@
 #include <mxnet/c_api.h>
 #include <mxnet/c_api_error.h>
 #include <mxnet/base.h>
+#include <mxnet/op_attr_types.h>
 #include <nnvm/graph.h>
 #include <vector>
 #include <string>
diff --git a/src/c_api/c_api_symbolic.cc b/src/c_api/c_api_symbolic.cc
index 627acf46cc6b..020c0d17f0d1 100644
--- a/src/c_api/c_api_symbolic.cc
+++ b/src/c_api/c_api_symbolic.cc
@@ -1061,11 +1061,20 @@ int MXGenAtomicSymbolFromSymbol(SymbolHandle sym_handle, SymbolHandle *ret_sym_h
   API_BEGIN();
   nnvm::Symbol *source = static_cast<nnvm::Symbol *>(sym_handle);
   CHECK_EQ(source->outputs.size(), 1U)
-    << "Generating atomic symbol from other symbol only works for nongrouped symbol.";
-  const auto& node = source->outputs[0];
+      << "Generating atomic symbol from other symbol only works for nongrouped symbol.";
+  const auto &node = source->outputs[0];
   const auto *op = node.node->op();
   const auto attrs = source->ListAttrs(nnvm::Symbol::ListAttrOption::kShallow);
   *s = nnvm::Symbol::CreateFunctor(op, attrs);
   *ret_sym_handle = s;
   API_END_HANDLE_ERROR(delete s);
 }
+
+int MXShallowCopySymbol(SymbolHandle src, SymbolHandle* out) {
+  nnvm::Symbol* out_sym = new nnvm::Symbol;
+  API_BEGIN();
+  nnvm::Symbol* src_sym = static_cast<nnvm::Symbol*>(src);
+  *out_sym = *src_sym;
+  *out = out_sym;
+  API_END_HANDLE_ERROR(delete out_sym);
+}
diff --git a/src/imperative/imperative_utils.h b/src/imperative/imperative_utils.h
index 07fe04782bd0..477139fd84b8 100644
--- a/src/imperative/imperative_utils.h
+++ b/src/imperative/imperative_utils.h
@@ -884,7 +884,6 @@ inline std::multimap<size_t, NDArray> AllocateMemory(
     }
     CHECK_EQ(stypes[i], kDefaultStorage);
     if (mem_plan[i].root == i) {
-      CHECK_GT(mem_plan[i].size, 0);
       auto iter = pool.lower_bound(mem_plan[i].size);
       if (iter != pool.end()) {
         *arrays[i] = iter->second.AsArray(shapes[i], dtypes[i]);
diff --git a/src/io/image_io.cc b/src/io/image_io.cc
index c0357998f31c..db9ac7682287 100644
--- a/src/io/image_io.cc
+++ b/src/io/image_io.cc
@@ -357,6 +357,7 @@ inline void copyMakeBorder(const nnvm::NodeAttrs& attrs,
 }
 
 NNVM_REGISTER_OP(_cvimdecode)
+.add_alias("_npi_cvimdecode")
 .describe("Decode image with OpenCV. \n"
           "Note: return image in RGB by default, "
           "instead of OpenCV's default BGR.")
@@ -368,6 +369,7 @@ NNVM_REGISTER_OP(_cvimdecode)
 .add_arguments(ImdecodeParam::__FIELDS__());
 
 NNVM_REGISTER_OP(_cvimread)
+.add_alias("_npi_cvimread")
 .describe("Read and decode image with OpenCV. \n"
           "Note: return image in RGB by default, "
           "instead of OpenCV's default BGR.")
@@ -378,6 +380,7 @@ NNVM_REGISTER_OP(_cvimread)
 .add_arguments(ImreadParam::__FIELDS__());
 
 NNVM_REGISTER_OP(_cvimresize)
+.add_alias("_npi_cvimresize")
 .describe("Resize image with OpenCV. \n")
 .set_num_inputs(1)
 .set_num_outputs(1)
diff --git a/src/ndarray/ndarray.cc b/src/ndarray/ndarray.cc
index 37c32c09cebb..7fca6aa3f733 100644
--- a/src/ndarray/ndarray.cc
+++ b/src/ndarray/ndarray.cc
@@ -319,7 +319,7 @@ NDArray NDArray::AtWithRecord(index_t idx) {
   CHECK(storage_type() == kDefaultStorage)
       << "Storage type " << storage_type() << " doesn't support At()";
   NDArray ret = this->SliceWithRecord(idx, idx+1);
-  if (shape_.ndim() > 1) {
+  if (shape_.ndim() > 1 || Imperative::Get()->is_np_shape()) {
     return ret.ReshapeWithRecord(mxnet::TShape(shape_.data()+1, shape_.data()+shape_.ndim()));
   } else {
     return ret;
@@ -1212,7 +1212,10 @@ void CopyFromTo(const NDArray& from, const NDArray& to, int priority, bool is_op
       << "from.shape = " << from.shape() << " to.shape=" << to.shape();
   CHECK(!mxnet::op::shape_is_none(from.shape()))
       << "source operands have undefined shape";
-  if (from.shape().Size() == 0U) return;
+  // zero-size array, no need to copy
+  if (from.shape().Size() == 0U) {
+    return;
+  }
   // important: callback must always capture by value
   const Context from_ctx = from.ctx();
   const int a = from_ctx.dev_mask();
@@ -1732,7 +1735,7 @@ bool NDArray::Load(dmlc::Stream *strm) {
     CHECK(!Imperative::Get()->is_np_shape())
         << "ndarray was not saved in np shape semantics, but being loaded in np shape semantics."
            " Please turn off np shape semantics in Python using `with np_shape(False)`"
-           " to scope of the code of loading the ndarray.";
+           " to scope the code of loading the ndarray.";
   }
   if (magic != NDARRAY_V2_MAGIC && magic != NDARRAY_V3_MAGIC) {
     return LegacyLoad(strm, magic);
@@ -1872,6 +1875,10 @@ void NDArray::SyncCopyFromCPU(const void *data, size_t size) const {
   mxnet::TShape dshape = this->shape();
   CHECK_EQ(dshape.Size(), size)
       << "Memory size do not match";
+  // zero-size array, no need to copy
+  if (size == 0U) {
+    return;
+  }
   TBlob src((void*)data, dshape, cpu::kDevMask, this->dtype_, 0); // NOLINT(*)
 
   if (this->ctx().dev_mask() == cpu::kDevMask) {
@@ -2003,6 +2010,10 @@ void NDArray::SyncCopyToCPU(void *data, size_t size) const {
   mxnet::TShape dshape = this->shape();
   CHECK_EQ(dshape.Size(), size)
       << "Memory size do not match";
+  // zero-size array, no need to copy
+  if (size == 0U) {
+    return;
+  }
   TBlob dst(data, dshape, cpu::kDevMask, this->dtype_, 0); // NOLINT(*)
 
   if (this->ctx().dev_mask() == cpu::kDevMask) {
diff --git a/src/operator/contrib/multibox_detection.cc b/src/operator/contrib/multibox_detection.cc
index 37bb5a500d71..cb2dfe34bfc3 100644
--- a/src/operator/contrib/multibox_detection.cc
+++ b/src/operator/contrib/multibox_detection.cc
@@ -220,5 +220,9 @@ MXNET_REGISTER_OP_PROPERTY(_contrib_MultiBoxDetection, MultiBoxDetectionProp)
 .add_argument("loc_pred", "NDArray-or-Symbol", "Location regression predictions.")
 .add_argument("anchor", "NDArray-or-Symbol", "Multibox prior anchor boxes")
 .add_arguments(MultiBoxDetectionParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_contrib_MultiBoxDetection)
+.add_alias("_npx_multibox_detection");
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/contrib/multibox_prior.cc b/src/operator/contrib/multibox_prior.cc
index 2ad173a2dd93..66fd2c11517a 100644
--- a/src/operator/contrib/multibox_prior.cc
+++ b/src/operator/contrib/multibox_prior.cc
@@ -100,5 +100,8 @@ MXNET_REGISTER_OP_PROPERTY(_contrib_MultiBoxPrior, MultiBoxPriorProp)
 .add_arguments(MultiBoxPriorParam::__FIELDS__())
 .describe("Generate prior(anchor) boxes from data, sizes and ratios.");
 
+NNVM_REGISTER_OP(_contrib_MultiBoxPrior)
+.add_alias("_npx_multibox_prior");
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/contrib/multibox_target.cc b/src/operator/contrib/multibox_target.cc
index a1808c5a7c81..feab3977f82c 100644
--- a/src/operator/contrib/multibox_target.cc
+++ b/src/operator/contrib/multibox_target.cc
@@ -307,5 +307,9 @@ MXNET_REGISTER_OP_PROPERTY(_contrib_MultiBoxTarget, MultiBoxTargetProp)
 .add_argument("label", "NDArray-or-Symbol", "Object detection labels.")
 .add_argument("cls_pred", "NDArray-or-Symbol", "Class predictions.")
 .add_arguments(MultiBoxTargetParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_contrib_MultiBoxTarget)
+.add_alias("_npx_multibox_target");
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/image/crop.cc b/src/operator/image/crop.cc
index 52d2f11a464b..6067f89d7033 100644
--- a/src/operator/image/crop.cc
+++ b/src/operator/image/crop.cc
@@ -35,6 +35,7 @@ namespace image {
 DMLC_REGISTER_PARAMETER(CropParam);
 
 NNVM_REGISTER_OP(_image_crop)
+.add_alias("_npx__image_crop")
 .describe(R"code(Crop an image NDArray of shape (H x W x C) or (N x H x W x C) 
 to the given size.
 Example:
diff --git a/src/operator/image/image_random.cc b/src/operator/image/image_random.cc
index 34f4cb4d395c..0c4603ecc475 100644
--- a/src/operator/image/image_random.cc
+++ b/src/operator/image/image_random.cc
@@ -39,6 +39,7 @@ DMLC_REGISTER_PARAMETER(RandomLightingParam);
 DMLC_REGISTER_PARAMETER(RandomColorJitterParam);
 
 NNVM_REGISTER_OP(_image_to_tensor)
+.add_alias("_npx__image_to_tensor")
 .describe(R"code(Converts an image NDArray of shape (H x W x C) or (N x H x W x C) 
 with values in the range [0, 255] to a tensor NDArray of shape (C x H x W) or (N x C x H x W)
 with values in the range [0, 1]
@@ -102,6 +103,7 @@ with values in the range [0, 1]
 .add_argument("data", "NDArray-or-Symbol", "Input ndarray");
 
 NNVM_REGISTER_OP(_image_normalize)
+.add_alias("_npx__image_normalize")
 .describe(R"code(Normalize an tensor of shape (C x H x W) or (N x C x H x W) with mean and
     standard deviation.
 
@@ -189,28 +191,34 @@ NNVM_REGISTER_OP(_backward_image_normalize)
 .set_attr<FCompute>("FCompute<cpu>", NormalizeOpBackward<cpu>);
 
 MXNET_REGISTER_IMAGE_AUG_OP(_image_flip_left_right)
+.add_alias("_npx__image_flip_left_right")
 .describe(R"code()code" ADD_FILELINE)
 .set_attr<FCompute>("FCompute<cpu>", FlipLeftRight);
 
 MXNET_REGISTER_IMAGE_RND_AUG_OP(_image_random_flip_left_right)
+.add_alias("_npx__image_random_flip_left_right")
 .describe(R"code()code" ADD_FILELINE)
 .set_attr<FCompute>("FCompute<cpu>", RandomFlipLeftRight);
 
 MXNET_REGISTER_IMAGE_AUG_OP(_image_flip_top_bottom)
+.add_alias("_npx__image_flip_top_bottom")
 .describe(R"code()code" ADD_FILELINE)
 .set_attr<FCompute>("FCompute<cpu>", FlipTopBottom);
 
 MXNET_REGISTER_IMAGE_RND_AUG_OP(_image_random_flip_top_bottom)
+.add_alias("_npx__image_random_flip_top_bottom")
 .describe(R"code()code" ADD_FILELINE)
 .set_attr<FCompute>("FCompute<cpu>", RandomFlipTopBottom);
 
 MXNET_REGISTER_IMAGE_RND_AUG_OP(_image_random_brightness)
+.add_alias("_npx__image_random_brightness")
 .describe(R"code()code" ADD_FILELINE)
 .set_attr_parser(ParamParser<RandomEnhanceParam>)
 .set_attr<FCompute>("FCompute<cpu>", RandomBrightness)
 .add_arguments(RandomEnhanceParam::__FIELDS__());
 
 MXNET_REGISTER_IMAGE_RND_AUG_OP(_image_random_contrast)
+.add_alias("_npx__image_random_contrast")
 .describe(R"code()code" ADD_FILELINE)
 .set_attr_parser(ParamParser<RandomEnhanceParam>)
 .set_attr<FCompute>("FCompute<cpu>", RandomContrast)
@@ -218,6 +226,7 @@ MXNET_REGISTER_IMAGE_RND_AUG_OP(_image_random_contrast)
 
 
 MXNET_REGISTER_IMAGE_RND_AUG_OP(_image_random_saturation)
+.add_alias("_npx__image_random_saturation")
 .describe(R"code()code" ADD_FILELINE)
 .set_attr_parser(ParamParser<RandomEnhanceParam>)
 .set_attr<FCompute>("FCompute<cpu>", RandomSaturation)
@@ -225,6 +234,7 @@ MXNET_REGISTER_IMAGE_RND_AUG_OP(_image_random_saturation)
 
 
 MXNET_REGISTER_IMAGE_RND_AUG_OP(_image_random_hue)
+.add_alias("_npx__image_random_hue")
 .describe(R"code()code" ADD_FILELINE)
 .set_attr_parser(ParamParser<RandomEnhanceParam>)
 .set_attr<FCompute>("FCompute<cpu>", RandomHue)
@@ -232,6 +242,7 @@ MXNET_REGISTER_IMAGE_RND_AUG_OP(_image_random_hue)
 
 
 MXNET_REGISTER_IMAGE_RND_AUG_OP(_image_random_color_jitter)
+.add_alias("_npx__image_random_color_jitter")
 .describe(R"code()code" ADD_FILELINE)
 .set_attr_parser(ParamParser<RandomColorJitterParam>)
 .set_attr<FCompute>("FCompute<cpu>", RandomColorJitter)
@@ -239,6 +250,7 @@ MXNET_REGISTER_IMAGE_RND_AUG_OP(_image_random_color_jitter)
 
 
 MXNET_REGISTER_IMAGE_AUG_OP(_image_adjust_lighting)
+.add_alias("_npx__image_adjust_lighting")
 .describe(R"code(Adjust the lighting level of the input. Follow the AlexNet style.)code" ADD_FILELINE)
 .set_attr_parser(ParamParser<AdjustLightingParam>)
 .set_attr<FCompute>("FCompute<cpu>", AdjustLighting)
@@ -246,6 +258,7 @@ MXNET_REGISTER_IMAGE_AUG_OP(_image_adjust_lighting)
 
 
 MXNET_REGISTER_IMAGE_RND_AUG_OP(_image_random_lighting)
+.add_alias("_npx__image_random_lighting")
 .describe(R"code(Randomly add PCA noise. Follow the AlexNet style.)code" ADD_FILELINE)
 .set_attr_parser(ParamParser<RandomLightingParam>)
 .set_attr<FCompute>("FCompute<cpu>", RandomLighting)
diff --git a/src/operator/image/resize.cc b/src/operator/image/resize.cc
index d93769faa8b3..d2397ea72685 100644
--- a/src/operator/image/resize.cc
+++ b/src/operator/image/resize.cc
@@ -34,6 +34,7 @@ namespace image {
 DMLC_REGISTER_PARAMETER(ResizeParam);
 
 NNVM_REGISTER_OP(_image_resize)
+.add_alias("_npx__image_resize")
 .describe(R"code(Resize an image NDArray of shape (H x W x C) or (N x H x W x C) 
 to the given size
 Example:
diff --git a/src/operator/leaky_relu.cc b/src/operator/leaky_relu.cc
index 214e41a84611..c25833b799d0 100644
--- a/src/operator/leaky_relu.cc
+++ b/src/operator/leaky_relu.cc
@@ -71,6 +71,7 @@ The following modified ReLU Activation functions are supported:
 .add_arguments(LeakyReLUParam::__FIELDS__());
 
 NNVM_REGISTER_OP(LeakyReLU)
+.add_alias("_npx_leaky_relu")
 .set_attr<nnvm::FSetInputVarAttrOnCompose>("FSetInputVarAttrOnCompose",
     [](const nnvm::NodeAttrs& attrs, nnvm::NodePtr var, const int index) {
       if (index == 1 && var->attrs.dict.find("__init__") == var->attrs.dict.end()) {
diff --git a/src/operator/nn/activation.cc b/src/operator/nn/activation.cc
index 5b6cece4a92e..5abb6670c9b0 100644
--- a/src/operator/nn/activation.cc
+++ b/src/operator/nn/activation.cc
@@ -154,6 +154,7 @@ inline static bool BackwardActStorageType(const nnvm::NodeAttrs& attrs,
 
 
 MXNET_OPERATOR_REGISTER_UNARY(Activation)
+.add_alias("_npx_activation")
 .describe(R"code(Applies an activation function element-wise to the input.
 
 The following activation functions are supported:
diff --git a/src/operator/nn/batch_norm.cc b/src/operator/nn/batch_norm.cc
index 2564609c6b90..6382d46d272d 100644
--- a/src/operator/nn/batch_norm.cc
+++ b/src/operator/nn/batch_norm.cc
@@ -520,6 +520,7 @@ std::vector<nnvm::NodeEntry> BatchNormGrad(const nnvm::NodePtr& n,
 }
 
 NNVM_REGISTER_OP(BatchNorm)
+.add_alias("_npx_batch_norm")
 .describe(R"code(Batch normalization.
 
 Normalizes a data batch by mean and variance, and applies a scale ``gamma`` as
diff --git a/src/operator/nn/concat.cc b/src/operator/nn/concat.cc
index 8fb229889332..80469b5385eb 100644
--- a/src/operator/nn/concat.cc
+++ b/src/operator/nn/concat.cc
@@ -32,9 +32,9 @@
 namespace mxnet {
 namespace op {
 
-static bool ConcatShape(const nnvm::NodeAttrs& attrs,
-                        mxnet::ShapeVector *in_shape,
-                        mxnet::ShapeVector *out_shape) {
+bool ConcatShape(const nnvm::NodeAttrs& attrs,
+                 mxnet::ShapeVector *in_shape,
+                 mxnet::ShapeVector *out_shape) {
   using namespace mshadow;
   const ConcatParam& param_ = nnvm::get<ConcatParam>(attrs.parsed);
   CHECK_EQ(in_shape->size(), static_cast<size_t>(param_.num_args));
@@ -138,9 +138,9 @@ static bool RNNParamConcatShape(const nnvm::NodeAttrs& attrs,
   return shape_is_known(dshape);
 }
 
-static bool ConcatType(const nnvm::NodeAttrs& attrs,
-                       std::vector<int> *in_type,
-                       std::vector<int> *out_type) {
+bool ConcatType(const nnvm::NodeAttrs& attrs,
+                std::vector<int> *in_type,
+                std::vector<int> *out_type) {
   const ConcatParam& param_ = nnvm::get<ConcatParam>(attrs.parsed);
   int dtype = -1;
 
@@ -403,6 +403,7 @@ NNVM_REGISTER_OP(_backward_Concat)
 // which handles the case where the first one or two inputs may have
 // unknown shape that can be inferred from output shape.
 NNVM_REGISTER_OP(_rnn_param_concat)
+.add_alias("_npi_rnn_param_concat")
 #if MXNET_USE_MKLDNN == 1
 .set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& n) {
   return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
diff --git a/src/operator/nn/convolution.cc b/src/operator/nn/convolution.cc
index 536e9a731171..32ed93e4a463 100644
--- a/src/operator/nn/convolution.cc
+++ b/src/operator/nn/convolution.cc
@@ -397,6 +397,7 @@ struct ConvolutionGrad {
 };
 
 NNVM_REGISTER_OP(Convolution)
+.add_alias("_npx_convolution")
 .describe(R"code(Compute *N*-D convolution on *(N+2)*-D input.
 
 In the 2-D convolution, given input data with shape *(batch_size,
diff --git a/src/operator/nn/deconvolution.cc b/src/operator/nn/deconvolution.cc
index 09b255d009e0..9f461f4e9de3 100644
--- a/src/operator/nn/deconvolution.cc
+++ b/src/operator/nn/deconvolution.cc
@@ -408,6 +408,7 @@ struct DeconvolutionGrad {
 DMLC_REGISTER_PARAMETER(DeconvolutionParam);
 
 NNVM_REGISTER_OP(Deconvolution)
+.add_alias("_npx_deconvolution")
 .describe("Computes 1D or 2D transposed convolution (aka fractionally strided convolution) of the "
     "input tensor. This operation can be seen as the gradient of Convolution operation with "
     "respect to its input. Convolution usually reduces the size of the input. Transposed "
diff --git a/src/operator/nn/dropout.cc b/src/operator/nn/dropout.cc
index d2fe3a5651a4..c4ad4250853e 100644
--- a/src/operator/nn/dropout.cc
+++ b/src/operator/nn/dropout.cc
@@ -63,6 +63,7 @@ struct DropoutGrad {
 DMLC_REGISTER_PARAMETER(DropoutParam);
 
 NNVM_REGISTER_OP(Dropout)
+.add_alias("_npx_dropout")
 .describe(R"(Applies dropout operation to input array.
 
 - During training, each element of the input is set to zero with probability p.
diff --git a/src/operator/nn/fully_connected.cc b/src/operator/nn/fully_connected.cc
index 27f6595aee9e..06ad6d034398 100644
--- a/src/operator/nn/fully_connected.cc
+++ b/src/operator/nn/fully_connected.cc
@@ -244,6 +244,7 @@ DMLC_REGISTER_PARAMETER(FullyConnectedParam);
 
 NNVM_REGISTER_OP(FullyConnected)
 MXNET_ADD_SPARSE_OP_ALIAS(FullyConnected)
+.add_alias("_npx_fully_connected")
 .describe(R"code(Applies a linear transformation: :math:`Y = XW^T + b`.
 
 If ``flatten`` is set to be true, then the shapes are:
diff --git a/src/operator/nn/layer_norm.cc b/src/operator/nn/layer_norm.cc
index e95f47255d7a..0b53d5091194 100644
--- a/src/operator/nn/layer_norm.cc
+++ b/src/operator/nn/layer_norm.cc
@@ -127,6 +127,7 @@ void LayerNormGradCompute<cpu>(const nnvm::NodeAttrs& attrs,
 }
 
 NNVM_REGISTER_OP(LayerNorm)
+.add_alias("_npx_layer_norm")
 .describe(R"code(Layer normalization.
 
 Normalizes the channels of the input tensor by mean and variance, and applies a scale ``gamma`` as
diff --git a/src/operator/nn/pooling.cc b/src/operator/nn/pooling.cc
index 41a486e141d1..485fc1345dfd 100644
--- a/src/operator/nn/pooling.cc
+++ b/src/operator/nn/pooling.cc
@@ -364,7 +364,8 @@ inline static bool BackwardPoolingStorageType(const nnvm::NodeAttrs &attrs,
 DMLC_REGISTER_PARAMETER(PoolingParam);
 
 NNVM_REGISTER_OP(Pooling)
-    .describe(R"code(Performs pooling on the input.
+.add_alias("_npx_pooling")
+.describe(R"code(Performs pooling on the input.
 
 The shapes for 1-D pooling are
 
diff --git a/src/operator/nn/softmax.cc b/src/operator/nn/softmax.cc
index 5a581e4ea5ef..b9a31ccc9313 100644
--- a/src/operator/nn/softmax.cc
+++ b/src/operator/nn/softmax.cc
@@ -77,6 +77,7 @@ inline static bool SoftmaxStorageType(const nnvm::NodeAttrs& attrs,
 
 
 NNVM_REGISTER_OP(softmax)
+.add_alias("_npx_softmax")
 .describe(R"code(Applies the softmax function.
 
 The resulting array contains elements in the range (0,1) and the elements along the given axis sum up to 1.
@@ -205,6 +206,7 @@ NNVM_REGISTER_OP(_backward_softmin)
                                                         mxnet_op::softmax_bwd, true>);
 
 NNVM_REGISTER_OP(log_softmax)
+.add_alias("_npx_log_softmax")
 .describe(R"code(Computes the log softmax of the input.
 This is equivalent to computing softmax followed by log.
 
diff --git a/src/operator/numpy/np_elemwise_broadcast_op.cc b/src/operator/numpy/np_elemwise_broadcast_op.cc
new file mode 100644
index 000000000000..c36423dff9fd
--- /dev/null
+++ b/src/operator/numpy/np_elemwise_broadcast_op.cc
@@ -0,0 +1,186 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file np_elemwise_binary_op.cc
+ * \brief CPU Implementation of basic functions for elementwise numpy binary broadcast operator.
+ */
+
+#include "../tensor/elemwise_binary_broadcast_op.h"
+#include "../tensor/elemwise_binary_scalar_op.h"
+
+namespace mxnet {
+namespace op {
+
+bool NumpyBinaryScalarType(const nnvm::NodeAttrs& attrs,
+                           std::vector<int>* in_attrs,
+                           std::vector<int>* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
+  TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
+  return in_attrs->at(0) != -1;
+}
+
+#define MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(name)              \
+  NNVM_REGISTER_OP(name)                                            \
+  .set_num_inputs(1)                                                \
+  .set_num_outputs(1)                                               \
+  .set_attr_parser([](NodeAttrs* attrs) {                           \
+      attrs->parsed = std::stod(attrs->dict["scalar"]);             \
+    })                                                              \
+  .set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>) \
+  .set_attr<nnvm::FInferType>("FInferType", NumpyBinaryScalarType)  \
+  .set_attr<nnvm::FInplaceOption>("FInplaceOption",                 \
+    [](const NodeAttrs& attrs){                                     \
+      return std::vector<std::pair<int, int> >{{0, 0}};             \
+    })                                                              \
+  .add_argument("data", "NDArray-or-Symbol", "source input")        \
+  .add_argument("scalar", "float", "scalar input")
+
+
+MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_add)
+.describe(R"code(Add arguments element-wise with broadcasting if necessary.
+
+Example::
+
+   x = [[ 1.,  1.,  1.],
+        [ 1.,  1.,  1.]]
+
+   y = [[ 0.],
+        [ 1.]]
+
+   add(x, y) = [[ 1.,  1.,  1.],
+                [ 2.,  2.,  2.]]
+
+)code" ADD_FILELINE)
+.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, op::mshadow_op::plus>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_broadcast_add"});
+
+MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_subtract)
+.describe(R"code(Subtract arguments element-wise with broadcasting if necessary.
+
+Example::
+
+   x = [[ 1.,  1.,  1.],
+        [ 1.,  1.,  1.]]
+
+   y = [[ 0.],
+        [ 1.]]
+
+   subtract(x, y) = [[ 1.,  1.,  1.],
+                     [ 0.,  0.,  0.]]
+
+)code" ADD_FILELINE)
+.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, op::mshadow_op::minus>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_broadcast_sub"});
+
+MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_multiply)
+.describe(R"code(Multiply arguments with broadcasting if necessary.
+
+Example::
+
+   x = [[ 1.,  1.,  1.],
+        [ 1.,  1.,  1.]]
+
+   y = [[ 0.],
+        [ 1.]]
+
+   multiply(x, y) = [[ 0.,  0.,  0.],
+                     [ 1.,  1.,  1.]]
+
+)code" ADD_FILELINE)
+.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, op::mshadow_op::mul>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_broadcast_mul"});
+
+MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_mod)
+.describe(R"code(Return element-wise remainder of division.
+It is equivalent to the Python modulus operator``x1 % x2`` and has the same sign as the divisor x2.
+
+Example::
+
+   x = [[ 8.,  8.,  8.],
+        [ 8.,  8.,  8.]]
+
+   y = [[ 2.],
+        [ 3.]]
+
+   mod(x, y) = [[ 0.,  0.,  0.],
+                [ 2.,  2.,  2.]]
+
+)code" ADD_FILELINE)
+.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, mshadow_op::mod>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_broadcast_mod"});
+
+MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_power)
+.describe(R"code(First array elements raised to powers from second array, element-wise.
+
+Raise each base in x1 to the positionally-corresponding power in x2. x1 and x2 must be
+broadcastable to the same shape.
+
+Example::
+
+   x = [[ 1.,  1.,  1.],
+        [ 1.,  1.,  1.]]
+
+   y = [[ 0.],
+        [ 1.]]
+
+   power(x, y) = [[ 2.,  2.,  2.],
+                  [ 4.,  4.,  4.]]
+
+)code" ADD_FILELINE)
+.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, mshadow_op::power>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_broadcast_power"});
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_add_scalar)
+.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, op::mshadow_op::plus>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_copy"});
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_subtract_scalar)
+.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, op::mshadow_op::minus>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_copy"});
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_rsubtract_scalar)
+.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::rminus>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"negative"});
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_multiply_scalar)
+.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, op::mshadow_op::mul>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_mul_scalar"});
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_mod_scalar)
+.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::mod>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_mod_scalar"});
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_rmod_scalar)
+.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::rmod>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_rmod_scalar"});
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_power_scalar)
+.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::power>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_power_scalar"});
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_rpower_scalar)
+.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::rpower>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{"_backward_rpower_scalar"});
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_elemwise_broadcast_op.cu b/src/operator/numpy/np_elemwise_broadcast_op.cu
new file mode 100644
index 000000000000..c858b3a4987a
--- /dev/null
+++ b/src/operator/numpy/np_elemwise_broadcast_op.cu
@@ -0,0 +1,82 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file np_elemwise_broadcast_op.cu
+ * \brief GPU Implementation of basic functions for elementwise binary broadcast operator.
+ */
+#include "../tensor/elemwise_binary_broadcast_op.h"
+#include "../tensor/elemwise_binary_scalar_op.h"
+
+namespace mxnet {
+namespace op {
+NNVM_REGISTER_OP(_npi_add)
+.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, op::mshadow_op::plus>);
+
+NNVM_REGISTER_OP(_npi_subtract)
+.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, op::mshadow_op::minus>);
+
+NNVM_REGISTER_OP(_npi_multiply)
+.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, op::mshadow_op::mul>);
+
+NNVM_REGISTER_OP(_npi_mod)
+.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, mshadow_op::mod>);
+
+NNVM_REGISTER_OP(_npi_power)
+.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, mshadow_op::power>);
+
+NNVM_REGISTER_OP(_npi_maximum)
+.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, mshadow_op::maximum>);
+
+NNVM_REGISTER_OP(_npi_minimum)
+.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, mshadow_op::minimum>);
+
+NNVM_REGISTER_OP(_npi_add_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, op::mshadow_op::plus>);
+
+NNVM_REGISTER_OP(_npi_subtract_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, op::mshadow_op::minus>);
+
+NNVM_REGISTER_OP(_npi_rsubtract_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::rminus>);
+
+NNVM_REGISTER_OP(_npi_multiply_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, op::mshadow_op::mul>);
+
+NNVM_REGISTER_OP(_npi_mod_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::mod>);
+
+NNVM_REGISTER_OP(_npi_rmod_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::rmod>);
+
+NNVM_REGISTER_OP(_npi_power_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::power>);
+
+NNVM_REGISTER_OP(_npi_rpower_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::rpower>);
+
+NNVM_REGISTER_OP(_npi_maximum_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::maximum>);
+
+NNVM_REGISTER_OP(_npi_minimum_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::minimum>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_init_op.cc b/src/operator/numpy/np_init_op.cc
new file mode 100644
index 000000000000..83a44c8ae280
--- /dev/null
+++ b/src/operator/numpy/np_init_op.cc
@@ -0,0 +1,111 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file np_init_op.cc
+ * \brief CPU Implementation of numpy init op
+ */
+#include "../tensor/init_op.h"
+#include "../tensor/elemwise_unary_op.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(_npi_zeros)
+.describe("Return a new array of given shape, type, and context, filled with zeros.")
+.set_num_inputs(0)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<InitOpParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", InitShape<InitOpParam>)
+.set_attr<nnvm::FInferType>("FInferType", InitType<InitOpParam>)
+.set_attr<FInferStorageType>("FInferStorageType", InitStorageType<InitOpParam, true, true>)
+.set_attr<FCompute>("FCompute<cpu>", FillCompute<cpu, 0>)
+.add_arguments(InitOpParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_npi_ones)
+.describe("Return a new array of given shape, type, and context, filled with ones.")
+.set_num_inputs(0)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<InitOpParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", InitShape<InitOpParam>)
+.set_attr<nnvm::FInferType>("FInferType", InitType<InitOpParam>)
+.set_attr<FCompute>("FCompute<cpu>", FillCompute<cpu, 1>)
+.add_arguments(InitOpParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_np_zeros_like)
+.describe(R"code(Return an array of zeros with the same shape and type as a given array.
+
+Examples::
+
+  x = [[ 1.,  1.,  1.],
+       [ 1.,  1.,  1.]]
+
+  zeros_like(x) = [[ 0.,  0.,  0.],
+                   [ 0.,  0.,  0.]]
+
+)code")
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
+.set_attr<nnvm::FIgnoreInputs>("FIgnoreInputs",
+  [](const NodeAttrs& attrs) {
+    return std::vector<uint32_t>(1, 0);
+  })
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"a"};
+  })
+.set_attr<FCompute>("FCompute<cpu>", FillCompute<cpu, 0>)
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.add_argument("a", "NDArray-or-Symbol",
+              "The shape and data-type of a define these same attributes of the returned array.");
+
+NNVM_REGISTER_OP(_np_ones_like)
+.describe(R"code(Return an array of ones with the same shape and type as a given array.
+
+Examples::
+
+  x = [[ 0.,  0.,  0.],
+       [ 0.,  0.,  0.]]
+
+  ones_like(x) = [[ 1.,  1.,  1.],
+                  [ 1.,  1.,  1.]]
+
+)code")
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
+.set_attr<nnvm::FIgnoreInputs>("FIgnoreInputs",
+  [](const NodeAttrs& attrs) {
+    return std::vector<uint32_t>(1, 0);
+  })
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"a"};
+  })
+.set_attr<FCompute>("FCompute<cpu>", FillCompute<cpu, 1>)
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.add_argument("a", "NDArray-or-Symbol",
+              "The shape and data-type of a define these same attributes of the returned array.");
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_init_op.cu b/src/operator/numpy/np_init_op.cu
new file mode 100644
index 000000000000..2eb8ed6d83b7
--- /dev/null
+++ b/src/operator/numpy/np_init_op.cu
@@ -0,0 +1,44 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file np_init_op.cu
+ * \brief GPU Implementation of numpy init op
+ */
+
+#include "../tensor/init_op.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(_npi_zeros)
+.set_attr<FCompute>("FCompute<gpu>", FillCompute<gpu, 0>);
+
+NNVM_REGISTER_OP(_npi_ones)
+.set_attr<FCompute>("FCompute<gpu>", FillCompute<gpu, 1>);
+
+NNVM_REGISTER_OP(_np_zeros_like)
+.set_attr<FCompute>("FCompute<gpu>", FillCompute<gpu, 0>);
+
+NNVM_REGISTER_OP(_np_ones_like)
+.set_attr<FCompute>("FCompute<gpu>", FillCompute<gpu, 1>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_true_divide.cc b/src/operator/numpy/np_true_divide.cc
new file mode 100644
index 000000000000..429762778700
--- /dev/null
+++ b/src/operator/numpy/np_true_divide.cc
@@ -0,0 +1,127 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file np_true_divide.cc
+ * \brief CPU Implementation of true_divide operator.
+ */
+#include "../tensor/elemwise_binary_broadcast_op.h"
+#include "../tensor/elemwise_binary_scalar_op.h"
+
+namespace mxnet {
+namespace op {
+
+template <int num_inputs>
+bool TrueDivideType(const nnvm::NodeAttrs& attrs,
+                    std::vector<int>* in_attrs,
+                    std::vector<int>* out_attrs) {
+  CHECK_EQ(in_attrs->size(), static_cast<size_t>(num_inputs));
+  CHECK_EQ(out_attrs->size(), 1U);
+  for (const int dtype : *in_attrs) {
+    if (dtype == -1) return false;
+  }
+  if (num_inputs == 2) {
+    const int lhs_dtype = in_attrs->at(0);
+    const int rhs_dtype = in_attrs->at(1);
+    CHECK_EQ(lhs_dtype, rhs_dtype)
+        << "_true_divide currently only supports same dtype for dividend and divisor";
+  }
+  auto is_float = [](const int dtype) {
+    return dtype == mshadow::kFloat32 || dtype == mshadow::kFloat64 || dtype == mshadow::kFloat16;
+  };
+
+  for (const int dtype : *in_attrs) {
+    CHECK(is_float(dtype)) << "_true_divide currently only supports float dtype";
+  }
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
+  return true;
+}
+
+NNVM_REGISTER_OP(_npi_true_divide)
+.describe(R"code(
+Returns a true division of the inputs, element-wise.
+
+It currently only supports dtype float16, float32, and float64.
+
+Example::
+
+   x = [[ 6.,  6.,  6.],
+        [ 6.,  6.,  6.]]
+
+   y = [[ 2.],
+        [ 3.]]
+
+   _true_divide(x, y) = [[ 3.,  3.,  3.],
+                         [ 2.,  2.,  2.]]
+
+)code" ADD_FILELINE)
+.set_num_inputs(2)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"lhs", "rhs"};
+  })
+.set_attr<mxnet::FInferShape>("FInferShape", BinaryBroadcastShape)
+.set_attr<nnvm::FInferType>("FInferType", TrueDivideType<2>)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs){
+    return std::vector<std::pair<int, int> >{{0, 0}, {1, 0}};
+  })
+.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, op::mshadow_op::div>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_broadcast_div"})
+.add_argument("lhs", "NDArray-or-Symbol", "Dividend array")
+.add_argument("rhs", "NDArray-or-Symbol", "Divisor array");
+
+NNVM_REGISTER_OP(_npi_true_divide_scalar)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser([](NodeAttrs* attrs) {
+    attrs->parsed = std::stod(attrs->dict["scalar"]);
+  })
+.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
+.set_attr<nnvm::FInferType>("FInferType", TrueDivideType<1>)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::pair<int, int> >{{0, 0}};
+  })
+.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, op::mshadow_op::div>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_div_scalar"})
+.add_argument("data", "NDArray-or-Symbol", "source input")
+.add_argument("scalar", "float", "scalar input");
+
+NNVM_REGISTER_OP(_npi_rtrue_divide_scalar)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser([](NodeAttrs* attrs) {
+  attrs->parsed = std::stod(attrs->dict["scalar"]);
+  })
+.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
+.set_attr<nnvm::FInferType>("FInferType", TrueDivideType<1>)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::pair<int, int> >{{0, 0}};
+  })
+.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::rdiv>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_rdiv_scalar"})
+.add_argument("data", "NDArray-or-Symbol", "source input")
+.add_argument("scalar", "float", "scalar input");
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_true_divide.cu b/src/operator/numpy/np_true_divide.cu
new file mode 100644
index 000000000000..be10c44f92a1
--- /dev/null
+++ b/src/operator/numpy/np_true_divide.cu
@@ -0,0 +1,41 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file np_true_divide.cu
+ * \brief GPU Implementation of true_divide operator.
+ */
+#include "../tensor/elemwise_binary_broadcast_op.h"
+#include "../tensor/elemwise_binary_scalar_op.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(_npi_true_divide)
+.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, mshadow_op::div>);
+
+NNVM_REGISTER_OP(_npi_true_divide_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::div>);
+
+NNVM_REGISTER_OP(_npi_rtrue_divide_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::rdiv>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/quantization/quantized_concat.cc b/src/operator/quantization/quantized_concat.cc
index f7a810b1e404..5835701497d9 100644
--- a/src/operator/quantization/quantized_concat.cc
+++ b/src/operator/quantization/quantized_concat.cc
@@ -28,8 +28,8 @@
 namespace mxnet {
 namespace op {
 
-static bool ConcatShape(const nnvm::NodeAttrs& attrs, mxnet::ShapeVector* in_shape,
-                        mxnet::ShapeVector* out_shape) {
+static bool QuantizedConcatShape(const nnvm::NodeAttrs& attrs, mxnet::ShapeVector* in_shape,
+                                 mxnet::ShapeVector* out_shape) {
   const ConcatParam& param_ = nnvm::get<ConcatParam>(attrs.parsed);
   CHECK_EQ(in_shape->size(), static_cast<size_t>(param_.num_args * 3));
   CHECK_EQ(out_shape->size(), 3U);
@@ -74,8 +74,8 @@ static bool ConcatShape(const nnvm::NodeAttrs& attrs, mxnet::ShapeVector* in_sha
   return shape_is_known(dshape);
 }
 
-static bool ConcatType(const nnvm::NodeAttrs& attrs, std::vector<int>* in_type,
-                       std::vector<int>* out_type) {
+static bool QuantizedConcatType(const nnvm::NodeAttrs& attrs, std::vector<int>* in_type,
+                                std::vector<int>* out_type) {
   const ConcatParam& param_ = nnvm::get<ConcatParam>(attrs.parsed);
   CHECK_EQ(in_type->size(), static_cast<size_t>(param_.num_args * 3));
   CHECK_EQ(out_type->size(), 3U);
@@ -130,8 +130,8 @@ If any input holds int8, then the output will be int8. Otherwise output will be
 // TODO(Xinyu): a temp solution to enable GluonCV INT8 flow,
 // will be reverted after the improvement of CachedOP is done.
 .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
-.set_attr<nnvm::FInferType>("FInferType", ConcatType)
-.set_attr<mxnet::FInferShape>("FInferShape", ConcatShape)
+.set_attr<nnvm::FInferType>("FInferType", QuantizedConcatType)
+.set_attr<mxnet::FInferShape>("FInferShape", QuantizedConcatShape)
 .set_attr<std::string>("key_var_num_args", "num_args")
 .add_argument("data", "NDArray-or-Symbol[]", "List of arrays to concatenate")
 .add_arguments(ConcatParam::__FIELDS__());
diff --git a/src/operator/random/sample_op.cc b/src/operator/random/sample_op.cc
index 56a162be5da4..543146257ddf 100644
--- a/src/operator/random/sample_op.cc
+++ b/src/operator/random/sample_op.cc
@@ -81,6 +81,7 @@ DMLC_REGISTER_PARAMETER(SampleGenNegBinomialLikeParam);
 MXNET_OPERATOR_REGISTER_SAMPLE(_random_uniform, SampleUniformParam)
 .add_alias("uniform")
 .add_alias("random_uniform")
+.add_alias("_npi_random_uniform")
 .describe(R"code(Draw random samples from a uniform distribution.
 
 .. note:: The existing alias ``uniform`` is deprecated.
@@ -99,6 +100,7 @@ Example::
 MXNET_OPERATOR_REGISTER_SAMPLE(_random_normal, SampleNormalParam)
 .add_alias("normal")
 .add_alias("random_normal")
+.add_alias("_npi_random_normal")
 .describe(R"code(Draw random samples from a normal (Gaussian) distribution.
 
 .. note:: The existing alias ``normal`` is deprecated.
diff --git a/src/operator/random/shuffle_op.cc b/src/operator/random/shuffle_op.cc
index 70315716dea2..86797c136bab 100644
--- a/src/operator/random/shuffle_op.cc
+++ b/src/operator/random/shuffle_op.cc
@@ -122,6 +122,7 @@ void ShuffleForwardCPU(const nnvm::NodeAttrs& attrs,
 
 NNVM_REGISTER_OP(_shuffle)
 .add_alias("shuffle")
+.add_alias("_np__random_shuffle")
 .describe(R"code(Randomly shuffle the elements.
 
 This shuffles the array along the first axis.
diff --git a/src/operator/rnn.cc b/src/operator/rnn.cc
index 6a0dbd7a4e23..244e39335a91 100644
--- a/src/operator/rnn.cc
+++ b/src/operator/rnn.cc
@@ -634,6 +634,7 @@ static void RNNStatefulComputeCPU(const OpStatePtr& state_ptr,
 #endif
 
 NNVM_REGISTER_OP(RNN)
+.add_alias("_npx_rnn")
 .describe(R"code(Applies recurrent layers to input data. Currently, vanilla RNN, LSTM and GRU are
 implemented, with both multi-layer and bidirectional support.
 
diff --git a/src/operator/roi_pooling.cc b/src/operator/roi_pooling.cc
index bba3bea5ce6a..56c872500822 100644
--- a/src/operator/roi_pooling.cc
+++ b/src/operator/roi_pooling.cc
@@ -230,5 +230,9 @@ Example::
 "corners of designated region of interest. `batch_index` indicates the index of corresponding "
 "image in the input array")
 .add_arguments(ROIPoolingParam::__FIELDS__());
+
+NNVM_REGISTER_OP(ROIPooling)
+.add_alias("_npx_roi_pooling");
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/sequence_mask.cc b/src/operator/sequence_mask.cc
index f4f81a801e70..d7731026ce21 100644
--- a/src/operator/sequence_mask.cc
+++ b/src/operator/sequence_mask.cc
@@ -191,5 +191,8 @@ Example::
                   "vector of sequence lengths of the form [batch_size]")
     .add_arguments(SequenceMaskParam::__FIELDS__());
 
+NNVM_REGISTER_OP(SequenceMask)
+.add_alias("_npx_sequence_mask");
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/swapaxis-inl.h b/src/operator/swapaxis-inl.h
index b17a81f75bc6..fd9872db6ec8 100644
--- a/src/operator/swapaxis-inl.h
+++ b/src/operator/swapaxis-inl.h
@@ -47,7 +47,7 @@ enum SwapAxisOpOutputs {kOut};
 
 struct SwapAxisParam : public dmlc::Parameter<SwapAxisParam> {
   // use int for enumeration
-  uint32_t dim1, dim2;
+  int dim1, dim2;
   DMLC_DECLARE_PARAMETER(SwapAxisParam) {
     DMLC_DECLARE_FIELD(dim1)
     .set_default(0)
@@ -106,8 +106,6 @@ class SwapAxisOp : public Operator {
                 const std::vector<OpReqType> &req) {
     using namespace mshadow;
     using namespace mshadow::expr;
-    int dim1 = param_.dim1;
-    int dim2 = param_.dim2;
 
     TBlob data_in = in_data[swapaxisenum::kData];
     TBlob data_out = out_data[swapaxisenum::kData];
@@ -115,10 +113,27 @@ class SwapAxisOp : public Operator {
 
     mxnet::TShape shape_in = data_in.shape_;
     mxnet::TShape shape_out = data_out.shape_;
+    int axis1 = param_.dim1;
+    if (axis1 < 0) {
+      axis1 += shape_in.ndim();
+    }
+    CHECK(axis1 >= 0 && axis1 < shape_in.ndim())
+        << "axis1: axis " << param_.dim1 << " is out of bounds for array of ndim "
+        << shape_in.ndim();
+
+    int axis2 = param_.dim2;
+    if (axis2 < 0) {
+      axis2 += shape_in.ndim();
+    }
+    CHECK(axis2 >= 0 && axis2 < shape_in.ndim())
+        << "axis2: axis " << param_.dim2 << " is out of bounds for array of ndim "
+        << shape_in.ndim();
+
+    if (shape_in.Size() == 0U) return;
 
     Shape<5> inter_shape;
 
-    Reshape2Five(&inter_shape, shape_in, dim1, dim2);
+    Reshape2Five(&inter_shape, shape_in, axis1, axis2);
 
     Tensor<xpu, 5, DType> inter_data_in = data_in.get_with_shape<xpu, 5, DType>(inter_shape, s);
 
@@ -187,13 +202,28 @@ class SwapAxisProp : public OperatorProperty {
     CHECK_EQ(in_shape->size(), 1U);
 
     mxnet::TShape &shape0 = (*in_shape)[swapaxisenum::kData];
+    if (!ndim_is_known(shape0)) return false;
+    int axis1 = param_.dim1;
+    if (axis1 < 0) {
+      axis1 += shape0.ndim();
+    }
+    CHECK(axis1 >= 0 && axis1 < shape0.ndim())
+        << "axis1: axis " << param_.dim1 << " is out of bounds for array of ndim " << shape0.ndim();
+
+    int axis2 = param_.dim2;
+    if (axis2 < 0) {
+      axis2 += shape0.ndim();
+    }
+    CHECK(axis2 >= 0 && axis2 < shape0.ndim())
+        << "axis2: axis " << param_.dim2 << " is out of bounds for array of ndim " << shape0.ndim();
+
     out_shape->clear();
     out_shape->push_back(shape0);
     mxnet::TShape &shape1 = (*out_shape)[swapaxisenum::kOut];
 
-    std::swap(shape1[param_.dim1], shape1[param_.dim2]);
+    std::swap(shape1[axis1], shape1[axis2]);
 
-    return true;
+    return shape_is_known(*out_shape);
   }
 
   bool InferType(std::vector<int> *in_type,
diff --git a/src/operator/swapaxis.cc b/src/operator/swapaxis.cc
index 45bcca4db9ae..32b26cc14f0c 100644
--- a/src/operator/swapaxis.cc
+++ b/src/operator/swapaxis.cc
@@ -69,6 +69,6 @@ Examples::
                        [ 3, 7]]]
 )code" ADD_FILELINE);
 
-NNVM_REGISTER_OP(SwapAxis).add_alias("swapaxes");
+NNVM_REGISTER_OP(SwapAxis).add_alias("swapaxes").add_alias("_npi_swapaxes");
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/tensor/broadcast_reduce_op.h b/src/operator/tensor/broadcast_reduce_op.h
index c7c49937730c..27edf0195a1c 100644
--- a/src/operator/tensor/broadcast_reduce_op.h
+++ b/src/operator/tensor/broadcast_reduce_op.h
@@ -168,15 +168,27 @@ struct BroadcastLikeParam : public dmlc::Parameter<BroadcastLikeParam> {
   }
 };
 
-inline int CheckAxis(int axis, int ndim) {
-  CHECK(axis < ndim && axis >= -ndim)
-    << "axis " << axis << " exceeds the input dimension of " << ndim;
-  return (axis + ndim)%ndim;
+/*
+ * Check whether the axis is within the legal range.
+ */
+inline int CheckAxis(const int axis, const int ndim) {
+  if (ndim == 0) {
+    CHECK(axis == 0 || axis == -1) << "axis " << axis
+                                   << " is out of bounds for 0-dimension tensor";
+    return 0;
+  } else {
+    CHECK(axis < ndim && axis >= -ndim)
+        << "axis " << axis << " exceeds the input dimension of " << ndim;
+    return (axis + ndim) % ndim;
+  }
 }
 
 inline mxnet::TShape AxisShapeCompact(mxnet::TShape shape, int *axis, bool allow_2d) {
   int ndim = shape.ndim();
-  index_t leading = 1, trailing = 1, M = shape[*axis];
+  index_t leading = 1, trailing = 1, M = 1;
+  if (shape.ndim() > *axis) {
+    M = shape[*axis];
+  }
   for (int i = 0; i < *axis; ++i) leading *= shape[i];
   for (int i = *axis + 1; i < ndim; ++i) trailing *= shape[i];
   if (allow_2d && trailing == 1) {
@@ -553,14 +565,37 @@ void SearchAxisCompute(const nnvm::NodeAttrs& attrs,
   using namespace mshadow::expr;
   const ReduceAxisParam& param = nnvm::get<ReduceAxisParam>(attrs.parsed);
   Stream<xpu> *s = ctx.get_stream<xpu>();
-  if (!param.axis) LOG(FATAL) << "Global reduction not supported yet";
+  int axis = inputs[0].ndim();
+  TBlob input = inputs[0];
+  if (param.axis.has_value()) {
+    axis = param.axis.value();
+  } else {
+    // If global reduction, reshape the input tensor into 2D shape (1, inputs[0].shape_.Size())
+    // and search on axis = 1.
+    mxnet::TShape shape_2d(2, 1);
+    shape_2d[1] = input.shape_.Size();
+    input = TBlob(input.dptr_, shape_2d, input.dev_mask(), input.type_flag_, input.dev_id());
+    axis = 1;
+  }
 
-  int axis = CheckAxis(param.axis.value(), inputs[0].shape_.ndim());
-  mxnet::TShape shape = AxisShapeCompact(inputs[0].shape_, &axis, false);
+  axis = CheckAxis(axis, input.shape_.ndim());
+  if (inputs[0].shape_.ndim() != 0) {
+    if (param.axis.has_value()) {
+      // cannot do argmax in an empty dimension
+      CHECK_NE(inputs[0].shape_[axis], 0)
+          << "searching input tensor of shape " << inputs[0].shape_
+          << " along axis = " << axis << " of zero dim-size is not allowed";
+    } else {
+      // cannot do argmax on an empty array
+      CHECK_NE(inputs[0].shape_.Size(), 0U) << "attempt to search an empty sequence";
+    }
+  }
+  if (input.shape_.Size() == 0U) return;  // zero-size tensor
+  mxnet::TShape shape = AxisShapeCompact(input.shape_, &axis, false);
   MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, DType, {
     Tensor<xpu, 2, DType> out = outputs[0].get_with_shape<xpu, 2, DType>(
       Shape2(shape[0], shape[2]), s);
-    Tensor<xpu, 3, DType> in = inputs[0].get_with_shape<xpu, 3, DType>(
+    Tensor<xpu, 3, DType> in = input.get_with_shape<xpu, 3, DType>(
       shape.get<3>(), s);
     CHECK(req[0] != kAddTo) << "AddTo is not supported";
     ASSIGN_DISPATCH(out, req[0], (reduce_with_axis<reducer, true>(in, 1)));
@@ -865,8 +900,8 @@ struct reduce_axes_backward_broadcast {
                                   OType *out,
                                   DType *igrad,
                                   OType *ograd,
-                                  mshadow::Shape<5> in_shape,
-                                  mshadow::Shape<5> out_shape,
+                                  mshadow::Shape<MXNET_SPECIAL_MAX_NDIM> in_shape,
+                                  mshadow::Shape<MXNET_SPECIAL_MAX_NDIM> out_shape,
                                   const uint32_t ndim) {
     size_t in_stride = 1;
     size_t out_stride = 1;
@@ -902,9 +937,9 @@ void ReduceAxesBackwardUseInOutImpl(const OpContext& ctx,
 
   MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, DType, {
     MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, OType, {
-      mshadow::Shape<5> in_shape;
-      mshadow::Shape<5> out_shape;
-      for (int i = 0; i < 5; ++i) {
+      mshadow::Shape<MXNET_SPECIAL_MAX_NDIM> in_shape;
+      mshadow::Shape<MXNET_SPECIAL_MAX_NDIM> out_shape;
+      for (int i = 0; i < MXNET_SPECIAL_MAX_NDIM; ++i) {
         if (i < dst_shape.ndim()) {
           in_shape[i] = src_shape[i];
           out_shape[i] = dst_shape[i];
@@ -914,36 +949,36 @@ void ReduceAxesBackwardUseInOutImpl(const OpContext& ctx,
         }
       }
       if (dst_shape.ndim() == 2) {
-        Tensor<xpu, 2, DType> igrad =
-          outputs[0].get_with_shape<xpu, 2, DType>(src_shape.get<2>(), s);
-        Tensor<xpu, 2, OType> ograd =
-          inputs[0].get_with_shape<xpu, 2, OType>(dst_shape.get<2>(), s);
-        Tensor<xpu, 2, DType> data =
-          inputs[1].get_with_shape<xpu, 2, DType>(src_shape.get<2>(), s);
-        Tensor<xpu, 2, OType> out =
-          inputs[2].get_with_shape<xpu, 2, OType>(dst_shape.get<2>(), s);
+        Tensor<xpu, 2, OType> igrad =
+          outputs[0].get_with_shape<xpu, 2, OType>(src_shape.get<2>(), s);
+        Tensor<xpu, 2, DType> ograd =
+          inputs[0].get_with_shape<xpu, 2, DType>(dst_shape.get<2>(), s);
+        Tensor<xpu, 2, OType> data =
+          inputs[1].get_with_shape<xpu, 2, OType>(src_shape.get<2>(), s);
+        Tensor<xpu, 2, DType> out =
+          inputs[2].get_with_shape<xpu, 2, DType>(dst_shape.get<2>(), s);
         MXNET_REQ_TYPE_SWITCH(req[0], Req, {
           Kernel<reduce_axes_backward_broadcast<Req, OP>, xpu>::Launch(
             s, outputs[0].shape_.Size(), data.dptr_, out.dptr_, igrad.dptr_, ograd.dptr_,
             in_shape, out_shape, src_shape.ndim());
         });
-        if (normalize) igrad /= scalar<DType>(src_shape.Size()/dst_shape.Size());
+        if (normalize) igrad /= scalar<OType>(src_shape.Size()/dst_shape.Size());
       } else {
         const int ndim = MXNET_SPECIAL_MAX_NDIM;
-        Tensor<xpu, ndim, DType> igrad =
-          outputs[0].get_with_shape<xpu, ndim, DType>(src_shape.get<ndim>(), s);
-        Tensor<xpu, ndim, OType> ograd =
-          inputs[0].get_with_shape<xpu, ndim, OType>(dst_shape.get<ndim>(), s);
-        Tensor<xpu, ndim, DType> data =
-          inputs[1].get_with_shape<xpu, ndim, DType>(src_shape.get<ndim>(), s);
-        Tensor<xpu, ndim, OType> out =
-          inputs[2].get_with_shape<xpu, ndim, OType>(dst_shape.get<ndim>(), s);
+        Tensor<xpu, ndim, OType> igrad =
+          outputs[0].get_with_shape<xpu, ndim, OType>(src_shape.get<ndim>(), s);
+        Tensor<xpu, ndim, DType> ograd =
+          inputs[0].get_with_shape<xpu, ndim, DType>(dst_shape.get<ndim>(), s);
+        Tensor<xpu, ndim, OType> data =
+          inputs[1].get_with_shape<xpu, ndim, OType>(src_shape.get<ndim>(), s);
+        Tensor<xpu, ndim, DType> out =
+          inputs[2].get_with_shape<xpu, ndim, DType>(dst_shape.get<ndim>(), s);
         MXNET_REQ_TYPE_SWITCH(req[0], Req, {
           Kernel<reduce_axes_backward_broadcast<Req, OP>, xpu>::Launch(
             s, outputs[0].shape_.Size(), data.dptr_, out.dptr_, igrad.dptr_, ograd.dptr_,
             in_shape, out_shape, src_shape.ndim());
         });
-        if (normalize) igrad /= scalar<DType>(src_shape.Size()/dst_shape.Size());
+        if (normalize) igrad /= scalar<OType>(src_shape.Size()/dst_shape.Size());
       }
     });
   });
@@ -968,6 +1003,34 @@ void ReduceAxesBackwardUseInOut(const nnvm::NodeAttrs& attrs,
   ReduceAxesBackwardUseInOutImpl<xpu, OP, normalize>(ctx, small, inputs, req, outputs);
 }
 
+template<typename OP>
+struct broadcast_kernel {
+  template<typename IType, typename OType>
+  MSHADOW_XINLINE static void Map(index_t i,
+                                  IType *input,
+                                  OType *output,
+                                  mshadow::Shape<MXNET_SPECIAL_MAX_NDIM> in_shape,
+                                  mshadow::Shape<MXNET_SPECIAL_MAX_NDIM> out_shape,
+                                  const OpReqType req,
+                                  const uint32_t ndim) {
+    size_t in_stride = 1;
+    size_t out_stride = 1;
+    index_t idx = i;
+    index_t in_idx = i;
+    for (int iter = ndim - 1; iter >= 0; --iter) {
+      size_t dim_idx = idx % out_shape[iter];
+      in_idx -= dim_idx * out_stride;
+      if (in_shape[iter] != 1) {
+        in_idx += dim_idx * in_stride;
+      }
+      idx /= out_shape[iter];
+      in_stride *= in_shape[iter];
+      out_stride *= out_shape[iter];
+    }
+    KERNEL_ASSIGN(output[i], req, OP::Map(input[in_idx]));
+  }
+};
+
 template<typename xpu>
 inline void BroadcastComputeImpl(const nnvm::NodeAttrs& attrs,
                                  const OpContext& ctx,
@@ -977,24 +1040,40 @@ inline void BroadcastComputeImpl(const nnvm::NodeAttrs& attrs,
                                  const mxnet::TShape& small) {
   using namespace mshadow;
   using namespace mshadow::expr;
+  using namespace mxnet_op;
   mxnet::TShape src_shape, dst_shape;
   BroadcastReduceShapeCompact(outputs[0].shape_, small, &dst_shape, &src_shape);
   Stream<xpu> *s = ctx.get_stream<xpu>();
-  MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
-    if (dst_shape.ndim() == 2) {
-      Tensor<xpu, 2, DType> out =
-        outputs[0].get_with_shape<xpu, 2, DType>(dst_shape.get<2>(), s);
-      Tensor<xpu, 2, DType> data =
-        inputs[0].get_with_shape<xpu, 2, DType>(src_shape.get<2>(), s);
-      ASSIGN_DISPATCH(out, req[0], broadcast_to(data, dst_shape));
-    } else {
-      const int ndim = MXNET_SPECIAL_MAX_NDIM;
-      Tensor<xpu, ndim, DType> out =
-        outputs[0].get_with_shape<xpu, ndim, DType>(dst_shape.get<ndim>(), s);
-      Tensor<xpu, ndim, DType> data =
-        inputs[0].get_with_shape<xpu, ndim, DType>(src_shape.get<ndim>(), s);
-      ASSIGN_DISPATCH(out, req[0], broadcast_to(data, dst_shape));
-    }
+  MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, IType, {
+    MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, OType, {
+      mshadow::Shape<MXNET_SPECIAL_MAX_NDIM> in_shape;
+      mshadow::Shape<MXNET_SPECIAL_MAX_NDIM> out_shape;
+      for (int i = 0; i < MXNET_SPECIAL_MAX_NDIM; ++i) {
+        if (i < dst_shape.ndim()) {
+          in_shape[i] = src_shape[i];
+          out_shape[i] = dst_shape[i];
+        } else {
+          in_shape[i] = 1;
+          out_shape[i] = 1;
+        }
+      }
+      if (dst_shape.ndim() == 2) {
+        Tensor<xpu, 2, OType> out =
+          outputs[0].get_with_shape<xpu, 2, OType>(dst_shape.get<2>(), s);
+        Tensor<xpu, 2, IType> data =
+          inputs[0].get_with_shape<xpu, 2, IType>(src_shape.get<2>(), s);
+        Kernel<broadcast_kernel<mshadow_op::identity>, xpu>::Launch(
+          s, out.shape_.Size(), data.dptr_, out.dptr_, in_shape, out_shape, req[0], 2);
+      } else {
+        const int ndim = MXNET_SPECIAL_MAX_NDIM;
+        Tensor<xpu, ndim, OType> out =
+          outputs[0].get_with_shape<xpu, ndim, OType>(dst_shape.get<ndim>(), s);
+        Tensor<xpu, ndim, IType> data =
+          inputs[0].get_with_shape<xpu, ndim, IType>(src_shape.get<ndim>(), s);
+        Kernel<broadcast_kernel<mshadow_op::identity>, xpu>::Launch(
+          s, out.shape_.Size(), data.dptr_, out.dptr_, in_shape, out_shape, req[0], ndim);
+      }
+    });
   });
 }
 
@@ -1215,8 +1294,8 @@ struct norm_backward_broadcast {
                                   DType *igrad,
                                   OType *ograd,
                                   DType *data,
-                                  mshadow::Shape<5> in_shape,
-                                  mshadow::Shape<5> out_shape,
+                                  mshadow::Shape<MXNET_SPECIAL_MAX_NDIM> in_shape,
+                                  mshadow::Shape<MXNET_SPECIAL_MAX_NDIM> out_shape,
                                   const uint32_t ndim) {
     size_t in_stride = 1;
     size_t out_stride = 1;
@@ -1258,9 +1337,9 @@ void LpNormGradCompute(const nnvm::NodeAttrs& attrs,
     mxnet::TShape src_shape, dst_shape;
     BroadcastReduceShapeCompact(outputs[0].shape_, small, &src_shape, &dst_shape);
     Stream<xpu> *s = ctx.get_stream<xpu>();
-    mshadow::Shape<5> in_shape;
-    mshadow::Shape<5> out_shape;
-    for (int i = 0; i < 5; ++i) {
+    mshadow::Shape<MXNET_SPECIAL_MAX_NDIM> in_shape;
+    mshadow::Shape<MXNET_SPECIAL_MAX_NDIM> out_shape;
+    for (int i = 0; i < MXNET_SPECIAL_MAX_NDIM; ++i) {
       if (i < dst_shape.ndim()) {
         in_shape[i] = src_shape[i];
         out_shape[i] = dst_shape[i];
diff --git a/src/operator/tensor/broadcast_reduce_op_index.cc b/src/operator/tensor/broadcast_reduce_op_index.cc
index 56af3887c763..52082f759e7a 100644
--- a/src/operator/tensor/broadcast_reduce_op_index.cc
+++ b/src/operator/tensor/broadcast_reduce_op_index.cc
@@ -110,6 +110,7 @@ Examples::
 
 NNVM_REGISTER_OP(pick)
 .add_alias("choose_element_0index")
+.add_alias("_npx_pick")
 .describe(R"code(Picks elements from an input array according to the input indices along the given axis.
 
 Given an input array of shape ``(d0, d1)`` and indices of shape ``(i0,)``, the result will be
diff --git a/src/operator/tensor/dot.cc b/src/operator/tensor/dot.cc
index 7d7b6c06c846..11a056146e1d 100644
--- a/src/operator/tensor/dot.cc
+++ b/src/operator/tensor/dot.cc
@@ -111,6 +111,7 @@ NNVM_REGISTER_OP(_backward_dot)
 .add_arguments(DotParam::__FIELDS__());
 
 NNVM_REGISTER_OP(batch_dot)
+.add_alias("_npx_batch_dot")
 .describe(R"doc(Batchwise dot product.
 
 ``batch_dot`` is used to compute dot product of ``x`` and ``y`` when ``x`` and
diff --git a/src/operator/tensor/elemwise_binary_broadcast_op.h b/src/operator/tensor/elemwise_binary_broadcast_op.h
index f84767dd4b2f..8a81bbc1c475 100644
--- a/src/operator/tensor/elemwise_binary_broadcast_op.h
+++ b/src/operator/tensor/elemwise_binary_broadcast_op.h
@@ -292,6 +292,7 @@ void BinaryBroadcastCompute(const nnvm::NodeAttrs& attrs,
                             const std::vector<TBlob>& inputs,
                             const std::vector<OpReqType>& req,
                             const std::vector<TBlob>& outputs) {
+  if (outputs[0].shape_.Size() == 0U) return;
   mxnet::TShape new_lshape, new_rshape, new_oshape;
   int ndim = BinaryBroadcastShapeCompact(inputs[0].shape_, inputs[1].shape_, outputs[0].shape_,
                                          &new_lshape, &new_rshape, &new_oshape);
diff --git a/src/operator/tensor/elemwise_binary_broadcast_op_logic.cc b/src/operator/tensor/elemwise_binary_broadcast_op_logic.cc
index cd433e00a770..e3c2e0e898d9 100644
--- a/src/operator/tensor/elemwise_binary_broadcast_op_logic.cc
+++ b/src/operator/tensor/elemwise_binary_broadcast_op_logic.cc
@@ -30,6 +30,7 @@ namespace mxnet {
 namespace op {
 
 MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(broadcast_equal)
+.add_alias("_npi_equal")
 .describe(R"code(Returns the result of element-wise **equal to** (==) comparison operation with broadcasting.
 
 Example::
@@ -48,6 +49,7 @@ Example::
 .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes);
 
 MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(broadcast_not_equal)
+.add_alias("_npi_not_equal")
 .describe(R"code(Returns the result of element-wise **not equal to** (!=) comparison operation with broadcasting.
 
 Example::
@@ -66,6 +68,7 @@ Example::
 .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes);
 
 MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(broadcast_greater)
+.add_alias("_npi_greater")
 .describe(R"code(Returns the result of element-wise **greater than** (>) comparison operation with broadcasting.
 
 Example::
@@ -84,6 +87,7 @@ Example::
 .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes);
 
 MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(broadcast_greater_equal)
+.add_alias("_npi_greater_equal")
 .describe(R"code(Returns the result of element-wise **greater than or equal to** (>=) comparison operation with broadcasting.
 
 Example::
@@ -102,6 +106,7 @@ Example::
 .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes);
 
 MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(broadcast_lesser)
+.add_alias("_npi_less")
 .describe(R"code(Returns the result of element-wise **lesser than** (<) comparison operation with broadcasting.
 
 Example::
@@ -120,6 +125,7 @@ Example::
 .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes);
 
 MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(broadcast_lesser_equal)
+.add_alias("_npi_less_equal")
 .describe(R"code(Returns the result of element-wise **lesser than or equal to** (<=) comparison operation with broadcasting.
 
 Example::
diff --git a/src/operator/tensor/elemwise_binary_op.h b/src/operator/tensor/elemwise_binary_op.h
index 2fe3fd9919cf..9c1d8b17fdea 100644
--- a/src/operator/tensor/elemwise_binary_op.h
+++ b/src/operator/tensor/elemwise_binary_op.h
@@ -487,9 +487,11 @@ class ElemwiseBinaryOp : public OpBase {
         MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
           const size_t size = (minthree(outputs[0].Size(), inputs[0].Size(), inputs[1].Size())
           + DataType<DType>::kLanes - 1) / DataType<DType>::kLanes;
-          Kernel<mxnet_op::op_with_req<OP, Req>, xpu>::Launch(s, size,
-          outputs[0].dptr<DType>(),
-          inputs[0].dptr<DType>(), inputs[1].dptr<DType>());
+          if (size != 0) {
+            Kernel<mxnet_op::op_with_req<OP, Req>, xpu>::Launch(s, size,
+            outputs[0].dptr<DType>(),
+            inputs[0].dptr<DType>(), inputs[1].dptr<DType>());
+          }
         });
       });
     }
@@ -510,9 +512,11 @@ class ElemwiseBinaryOp : public OpBase {
         MSHADOW_TYPE_SWITCH_WITH_HALF2(outputs[0].type_flag_, DType, {
           const size_t size = (minthree(outputs[0].Size(), inputs[0].Size(), inputs[1].Size())
           + DataType<DType>::kLanes - 1) / DataType<DType>::kLanes;
-          Kernel<mxnet_op::op_with_req<OP, Req>, xpu>::Launch(s, size,
-          outputs[0].dptr<DType>(),
-          inputs[0].dptr<DType>(), inputs[1].dptr<DType>());
+          if (size != 0) {
+            Kernel<mxnet_op::op_with_req<OP, Req>, xpu>::Launch(s, size,
+            outputs[0].dptr<DType>(),
+            inputs[0].dptr<DType>(), inputs[1].dptr<DType>());
+          }
         });
       });
     }
diff --git a/src/operator/tensor/elemwise_binary_scalar_op_extended.cc b/src/operator/tensor/elemwise_binary_scalar_op_extended.cc
index f027665a549b..3a687c2aa062 100644
--- a/src/operator/tensor/elemwise_binary_scalar_op_extended.cc
+++ b/src/operator/tensor/elemwise_binary_scalar_op_extended.cc
@@ -84,7 +84,8 @@ MXNET_OPERATOR_REGISTER_BINARY(_backward_hypot_scalar)
   cpu, mshadow_op::hypot_grad_left>);
 
 NNVM_REGISTER_OP(smooth_l1)
-  .describe(R"code(Calculate Smooth L1 Loss(lhs, scalar) by summing
+.add_alias("_npx_smooth_l1")
+.describe(R"code(Calculate Smooth L1 Loss(lhs, scalar) by summing
 
 .. math::
 
diff --git a/src/operator/tensor/elemwise_binary_scalar_op_logic.cc b/src/operator/tensor/elemwise_binary_scalar_op_logic.cc
index 17e76153ebb2..87ba394c99b2 100644
--- a/src/operator/tensor/elemwise_binary_scalar_op_logic.cc
+++ b/src/operator/tensor/elemwise_binary_scalar_op_logic.cc
@@ -71,26 +71,32 @@ static bool BinaryScalarLogicStorageType(const nnvm::NodeAttrs& attrs,
 
 
 MXNET_OPERATOR_REGISTER_BINARY_SCALAR_LOGIC(_equal_scalar, mshadow_op::eq)
+.add_alias("_npi_equal_scalar")
 .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
 .add_alias("_EqualScalar");
 
 MXNET_OPERATOR_REGISTER_BINARY_SCALAR_LOGIC(_not_equal_scalar, mshadow_op::ne)
+.add_alias("_npi_not_equal_scalar")
 .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
 .add_alias("_NotEqualScalar");
 
 MXNET_OPERATOR_REGISTER_BINARY_SCALAR_LOGIC(_greater_scalar, mshadow_op::gt)
+.add_alias("_npi_greater_scalar")
 .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
 .add_alias("_GreaterScalar");
 
 MXNET_OPERATOR_REGISTER_BINARY_SCALAR_LOGIC(_greater_equal_scalar, mshadow_op::ge)
+.add_alias("_npi_greater_equal_scalar")
 .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
 .add_alias("_GreaterEqualScalar");
 
 MXNET_OPERATOR_REGISTER_BINARY_SCALAR_LOGIC(_lesser_scalar, mshadow_op::lt)
+.add_alias("_npi_less_scalar")
 .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
 .add_alias("_LesserScalar");
 
 MXNET_OPERATOR_REGISTER_BINARY_SCALAR_LOGIC(_lesser_equal_scalar, mshadow_op::le)
+.add_alias("_npi_less_equal_scalar")
 .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
 .add_alias("_LesserEqualScalar");
 
diff --git a/src/operator/tensor/elemwise_unary_op.h b/src/operator/tensor/elemwise_unary_op.h
index 458106e02671..87964ac246f0 100644
--- a/src/operator/tensor/elemwise_unary_op.h
+++ b/src/operator/tensor/elemwise_unary_op.h
@@ -243,8 +243,10 @@ class UnaryOp : public OpBase {
     mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
     MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
       MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
-        mxnet_op::Kernel<mxnet_op::op_with_req<OP, Req>, xpu>::Launch(
-          s, inputs[0].Size(), outputs[0].dptr<DType>(), inputs[0].dptr<DType>());
+        if (inputs[0].Size() != 0) {
+          mxnet_op::Kernel<mxnet_op::op_with_req<OP, Req>, xpu>::Launch(
+            s, inputs[0].Size(), outputs[0].dptr<DType>(), inputs[0].dptr<DType>());
+        }
       });
     });
   }
diff --git a/src/operator/tensor/elemwise_unary_op_basic.cc b/src/operator/tensor/elemwise_unary_op_basic.cc
index 3caf840f1ebf..c293ee6e22bd 100644
--- a/src/operator/tensor/elemwise_unary_op_basic.cc
+++ b/src/operator/tensor/elemwise_unary_op_basic.cc
@@ -511,6 +511,7 @@ Negative indices are supported, and `None` can be used for either `lhs_end` or `
   - lhs shape = (30, 12), rhs shape = (4, 2, 2, 3), lhs_begin=-1, lhs_end=None, rhs_begin=1, rhs_end=None, output shape = (30, 2, 2, 3)
 
 )code" ADD_FILELINE)
+.add_alias("_npx_reshape_like")
 .set_num_inputs(2)
 .set_attr_parser(ParamParser<ReshapeLikeParam>)
 .set_attr<nnvm::FListInputNames>("FListInputNames",
@@ -649,6 +650,7 @@ Example::
 DMLC_REGISTER_PARAMETER(CastParam);
 NNVM_REGISTER_OP(Cast)
 .add_alias("cast")
+.add_alias("_npx_cast")
 .describe(R"code(Casts all elements of the input to a new type.
 
 .. note:: ``Cast`` is deprecated. Use ``cast`` instead.
@@ -1289,6 +1291,7 @@ MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_expm1, unary_bwd<msh
 // gamma
 MXNET_OPERATOR_REGISTER_UNARY_WITH_SPARSE_DR(gamma, cpu, mshadow_op::gamma)
 MXNET_ADD_SPARSE_OP_ALIAS(gamma)
+.add_alias("_npx_gamma")
 .describe(R"code(Returns the gamma function (extension of the factorial function \
 to the reals), computed element-wise on the input array.
 
diff --git a/src/operator/tensor/indexing_op.cc b/src/operator/tensor/indexing_op.cc
index 396d1c612cd2..ad4e54db54f1 100644
--- a/src/operator/tensor/indexing_op.cc
+++ b/src/operator/tensor/indexing_op.cc
@@ -466,6 +466,7 @@ DMLC_REGISTER_PARAMETER(ScatterNDParam);
 
 NNVM_REGISTER_OP(Embedding)
 MXNET_ADD_SPARSE_OP_ALIAS(Embedding)
+.add_alias("_npx_embedding")
 .describe(R"code(Maps integer indices to vector representations (embeddings).
 
 This operator maps words to real-valued vectors in a high-dimensional space,
@@ -764,6 +765,7 @@ Examples::
 .add_argument("indices", "NDArray-or-Symbol", "The index array");
 
 NNVM_REGISTER_OP(one_hot)
+.add_alias("_npx_one_hot")
 .describe(R"code(Returns a one-hot array.
 
 The locations represented by `indices` take value `on_value`, while all
diff --git a/src/operator/tensor/matrix_op-inl.h b/src/operator/tensor/matrix_op-inl.h
index cd98cb020c6b..a670c85b49f7 100644
--- a/src/operator/tensor/matrix_op-inl.h
+++ b/src/operator/tensor/matrix_op-inl.h
@@ -265,11 +265,17 @@ void TransposeImpl(RunContext ctx,
   using namespace mshadow;
   using namespace mshadow::expr;
   CHECK_EQ(src.type_flag_, ret.type_flag_);
+  // zero-size tensor, no need to compute
+  if (src.shape_.Size() == 0U) return;
   Stream<xpu> *s = ctx.get_stream<xpu>();
   MSHADOW_TYPE_SWITCH(ret.type_flag_, DType, {
     switch (axes.ndim()) {
-     case 0:
+     case 0: {
+      Tensor<xpu, 1, DType> in = src.get_with_shape<xpu, 1, DType>(mshadow::Shape1(1), s);
+      Tensor<xpu, 1, DType> out = ret.get_with_shape<xpu, 1, DType>(mshadow::Shape1(1), s);
+      Copy(out, in, s);
       break;
+     }
      case 1: {
       Tensor<xpu, 1, DType> in = src.get<xpu, 1, DType>(s);
       Tensor<xpu, 1, DType> out = ret.get<xpu, 1, DType>(s);
@@ -1796,9 +1802,6 @@ inline bool TileOpShape(const nnvm::NodeAttrs& attrs,
     SHAPE_ASSIGN_CHECK(*out_attrs, 0, ishape);
     return true;
   }
-  for (int i = 0; i < reps.ndim(); ++i) {
-    CHECK_GT(reps[i], 0) << "invalid reps=" << i << ", dim size must be greater than zero";
-  }
   mxnet::TShape oshape(std::max(ishape.ndim(), reps.ndim()), -1);
   int i1 = ishape.ndim() - 1;
   int i2 = reps.ndim() - 1;
@@ -1811,6 +1814,11 @@ inline bool TileOpShape(const nnvm::NodeAttrs& attrs,
       oshape[i] = reps[i2--];
     }
   }
+  // If reps contains 0s, oshape is a zero-size shape.
+  // Need to distinguish between np_shape mode and legacy mode.
+  if (!Imperative::Get()->is_np_shape()) {
+    common::ConvertToNumpyShape(&oshape);
+  }
   SHAPE_ASSIGN_CHECK(*out_attrs, 0, oshape);
   return shape_is_known(oshape);
 }
@@ -1829,7 +1837,7 @@ inline bool TileOpType(const nnvm::NodeAttrs& attrs,
 
 /*!
  * \brief Reshape the input and output tensors for
- * using broadcast_to to achieve the funcitonality
+ * using broadcast_to to achieve the functionality
  * of operator tile.
  * \return a pair of mxnet::TShape's, first is the reshaped
  * input shape, second is the reshaped output shape.
@@ -1837,7 +1845,7 @@ inline bool TileOpType(const nnvm::NodeAttrs& attrs,
 inline std::pair<mxnet::TShape, mxnet::TShape> ReshapeInputOutputForTileOp(
   const mxnet::TShape& ishape,
   const mxnet::Tuple<int>& reps) {
-  if (ishape.ndim() == 0 || reps.ndim() == 0) {
+  if (reps.ndim() == 0) {
     return std::make_pair(ishape, ishape);
   }
 
@@ -2192,7 +2200,7 @@ inline size_t SqueezeShapeHelper(mxnet::TShape* shape) {
   CHECK(shape != nullptr);
   size_t count = 0;
   for (int i = 0; i < shape->ndim(); ++i) {
-    if ((*shape)[i] == 0) {
+    if ((*shape)[i] == -1) {
       ++count;
     } else {
       std::swap((*shape)[i], (*shape)[i-count]);
@@ -2225,12 +2233,12 @@ inline bool SqueezeShape(const nnvm::NodeAttrs& attrs,
       CHECK_EQ(dshape[axes[i]], 1)
         << "cannot select an axis to squeeze out which has size="
         << dshape[axes[i]] << " not equal to one";
-      CHECK_NE(oshape[axes[i]], 0) << "duplicate value in axis";
-      oshape[axes[i]] = 0;
+      CHECK_NE(oshape[axes[i]], -1) << "duplicate value in axis";
+      oshape[axes[i]] = -1;
     }
   } else {
     for (int i = 0; i < oshape.ndim(); ++i) {
-      if (oshape[i] == 1) oshape[i] = 0;
+      if (oshape[i] == 1) oshape[i] = -1;
     }
   }
   size_t oshape_size = SqueezeShapeHelper(&oshape);
@@ -2646,10 +2654,14 @@ inline bool SplitOpShape(const nnvm::NodeAttrs& attrs,
   for (int i = 0; i < num_outputs; ++i) {
     int start = indices[i];
     int end = (i < num_outputs - 1) ? indices[i + 1] : ishape[real_axis];
-    CHECK(start < end)
-      << "start " << start << " is not less than end " << end << "for subarray " << i;
-    CHECK(end <= ishape[real_axis])
-      << "end " << end << " is no less than the size of the axis " << ishape[real_axis];
+    if (ishape[real_axis] == 0U) {
+      end = start;
+    } else {
+      CHECK(start < end)
+        << "start " << start << " is not less than end " << end << "for subarray " << i;
+      CHECK(end <= ishape[real_axis])
+        << "end " << end << " is no less than the size of the axis " << ishape[real_axis];
+    }
     dshape[real_axis] = (end - start);
     if (param.squeeze_axis) {
       CHECK_EQ(end - start, 1U) << "expected axis size of 1 but got " << end - start;
diff --git a/src/operator/tensor/matrix_op.cc b/src/operator/tensor/matrix_op.cc
index e78050aec009..59e8386d6679 100644
--- a/src/operator/tensor/matrix_op.cc
+++ b/src/operator/tensor/matrix_op.cc
@@ -263,6 +263,7 @@ static inline bool FlattenStorageType(const nnvm::NodeAttrs& attrs,
 
 NNVM_REGISTER_OP(Flatten)
 .add_alias("flatten")
+.add_alias("_npx_batch_flatten")
 .describe(R"code(Flattens the input array into a 2-D array by collapsing the higher dimensions.
 
 .. note:: `Flatten` is deprecated. Use `flatten` instead.
@@ -408,6 +409,7 @@ Examples::
 
 
 NNVM_REGISTER_OP(expand_dims)
+.add_alias("_npi_expand_dims")
 .describe(R"code(Inserts a new axis of size 1 into the array shape
 
 For example, given ``x`` with shape ``(2,3,4)``, then ``expand_dims(x, axis=1)``
@@ -696,6 +698,7 @@ NNVM_REGISTER_OP(_backward_slice_like)
 
 NNVM_REGISTER_OP(clip)
 MXNET_ADD_SPARSE_OP_ALIAS(clip)
+.add_alias("_npi_clip")
 .describe(R"code(Clips (limits) the values in an array.
 
 Given an interval, values outside the interval are clipped to the interval edges.
@@ -770,6 +773,7 @@ NNVM_REGISTER_OP(_backward_clip)
 .set_attr<FCompute>("FCompute<cpu>", ClipGrad_<cpu>);
 
 NNVM_REGISTER_OP(repeat)
+.add_alias("_np_repeat")
 .describe(R"code(Repeats elements of an array.
 
 By default, ``repeat`` flattens the input array into 1-D and then repeats the
@@ -820,6 +824,7 @@ NNVM_REGISTER_OP(_backward_repeat)
 });
 
 NNVM_REGISTER_OP(tile)
+.add_alias("_npi_tile")
 .describe(R"code(Repeats the whole array multiple times.
 
 If ``reps`` has length *d*, and input array has dimension of *n*. There are
@@ -1121,6 +1126,7 @@ Example::
 .add_arguments(DepthToSpaceParam::__FIELDS__());
 
 NNVM_REGISTER_OP(_split_v2)
+.add_alias("_npi_split")
 .describe(R"code(Splits an array along a particular axis into multiple sub-arrays.
 
 Example::
diff --git a/src/operator/tensor/ordering_op.cc b/src/operator/tensor/ordering_op.cc
index b0ade2076a2c..58c98f3a380c 100644
--- a/src/operator/tensor/ordering_op.cc
+++ b/src/operator/tensor/ordering_op.cc
@@ -34,6 +34,7 @@ DMLC_REGISTER_PARAMETER(SortParam);
 DMLC_REGISTER_PARAMETER(ArgSortParam);
 
 NNVM_REGISTER_OP(topk)
+.add_alias("_npx_topk")
 .describe(R"code(Returns the top *k* elements in an input array along the given axis.
  The returned elements will be sorted.
 
diff --git a/tests/python/gpu/test_operator_gpu.py b/tests/python/gpu/test_operator_gpu.py
index a8aa1ff853f7..0b5142dd8c8b 100644
--- a/tests/python/gpu/test_operator_gpu.py
+++ b/tests/python/gpu/test_operator_gpu.py
@@ -35,6 +35,7 @@
 from common import setup_module, with_seed, teardown, assert_raises_cudnn_not_satisfied
 from common import run_in_spawned_process
 from test_operator import *
+from test_numpy_ndarray import *
 from test_optimizer import *
 from test_random import *
 from test_exc_handling import *
diff --git a/tests/python/unittest/test_numpy_ndarray.py b/tests/python/unittest/test_numpy_ndarray.py
new file mode 100644
index 000000000000..6024ac9b4acd
--- /dev/null
+++ b/tests/python/unittest/test_numpy_ndarray.py
@@ -0,0 +1,672 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# pylint: skip-file
+from __future__ import absolute_import
+from __future__ import division
+import os
+import numpy as _np
+import mxnet as mx
+from mxnet import np, npx, autograd
+from mxnet.gluon import HybridBlock
+from mxnet.test_utils import same, assert_almost_equal, rand_shape_nd, rand_ndarray, retry, assert_exception, use_np
+from common import with_seed, TemporaryDirectory
+
+
+@with_seed()
+@use_np
+def test_array_creation():
+    dtypes = [_np.int8, _np.int32, _np.float16, _np.float32, _np.float64, None]
+    objects = [
+        [],
+        (),
+        [[1, 2], [3, 4]],
+        _np.random.uniform(size=rand_shape_nd(3)),
+        _np.random.uniform(size=(3, 0, 4))
+    ]
+    for dtype in dtypes:
+        for src in objects:
+            mx_arr = np.array(src, dtype=dtype)
+            assert mx_arr.context == mx.current_context()
+            if isinstance(src, mx.nd.NDArray):
+                np_arr = _np.array(src.asnumpy(), dtype=dtype if dtype is not None else _np.float32)
+            else:
+                np_arr = _np.array(src, dtype=dtype if dtype is not None else _np.float32)
+            assert mx_arr.dtype == np_arr.dtype
+            assert same(mx_arr.asnumpy(), np_arr)
+
+
+@with_seed()
+@use_np
+def test_zeros():
+    # test np.zeros in Gluon
+    class TestZeros(HybridBlock):
+        def __init__(self, shape, dtype=None):
+            super(TestZeros, self).__init__()
+            self._shape = shape
+            self._dtype = dtype
+
+        def hybrid_forward(self, F, x, *args, **kwargs):
+            return x + F.np.zeros(shape, dtype)
+
+    class TestZerosOutputType(HybridBlock):
+        def hybrid_forward(self, F, x, *args, **kwargs):
+            return x, F.np.zeros(shape=())
+
+    # test np.zeros in imperative
+    def check_zero_array_creation(shape, dtype):
+        np_out = _np.zeros(shape=shape, dtype=dtype)
+        mx_out = np.zeros(shape=shape, dtype=dtype)
+        assert same(mx_out.asnumpy(), np_out)
+        if dtype is None:
+            assert mx_out.dtype == _np.float32
+            assert np_out.dtype == _np.float64
+
+    shapes = [(0,), (2, 0, 2), (0, 0, 0, 0), ()]
+    shapes += [rand_shape_nd(ndim, allow_zero_size=True) for ndim in range(5)]
+    dtypes = [_np.int8, _np.int32, _np.float16, _np.float32, _np.float64, None]
+    for shape in shapes:
+        for dtype in dtypes:
+            check_zero_array_creation(shape, dtype)
+            x = np.array(_np.random.uniform(size=shape), dtype=dtype)
+            if dtype is None:
+                x = x.astype('float32')
+            for hybridize in [True, False]:
+                test_zeros = TestZeros(shape, dtype)
+                test_zeros_output_type = TestZerosOutputType()
+                if hybridize:
+                    test_zeros.hybridize()
+                    test_zeros_output_type.hybridize()
+                y = test_zeros(x)
+                assert type(y) == np.ndarray
+                assert same(x.asnumpy(), y.asnumpy())
+                y = test_zeros_output_type(x)
+                assert type(y[1]) == np.ndarray
+
+
+@with_seed()
+@use_np
+def test_ones():
+    # test np.ones in Gluon
+    class TestOnes(HybridBlock):
+        def __init__(self, shape, dtype=None):
+            super(TestOnes, self).__init__()
+            self._shape = shape
+            self._dtype = dtype
+
+        def hybrid_forward(self, F, x, *args, **kwargs):
+            return x * F.np.ones(shape, dtype)
+
+    class TestOnesOutputType(HybridBlock):
+        def hybrid_forward(self, F, x, *args, **kwargs):
+            return x, F.np.ones(shape=())
+
+    # test np.ones in imperative
+    def check_ones_array_creation(shape, dtype):
+        np_out = _np.ones(shape=shape, dtype=dtype)
+        mx_out = np.ones(shape=shape, dtype=dtype)
+        assert same(mx_out.asnumpy(), np_out)
+        if dtype is None:
+            assert mx_out.dtype == _np.float32
+            assert np_out.dtype == _np.float64
+
+    shapes = [(0,), (2, 0, 2), (0, 0, 0, 0), ()]
+    shapes += [rand_shape_nd(ndim, allow_zero_size=True) for ndim in range(5)]
+    dtypes = [_np.int8, _np.int32, _np.float16, _np.float32, _np.float64, None]
+    for shape in shapes:
+        for dtype in dtypes:
+            check_ones_array_creation(shape, dtype)
+            x = mx.nd.array(_np.random.uniform(size=shape), dtype=dtype).as_np_ndarray()
+            if dtype is None:
+                x = x.astype('float32')
+            for hybridize in [True, False]:
+                test_ones = TestOnes(shape, dtype)
+                test_ones_output_type = TestOnesOutputType()
+                if hybridize:
+                    test_ones.hybridize()
+                    test_ones_output_type.hybridize()
+                y = test_ones(x)
+                assert type(y) == np.ndarray
+                assert same(x.asnumpy(), y.asnumpy())
+                y = test_ones_output_type(x)
+                assert type(y[1]) == np.ndarray
+
+
+@with_seed()
+def test_ndarray_binary_element_wise_ops():
+    np_op_map = {
+        '+': _np.add,
+        '*': _np.multiply,
+        '-': _np.subtract,
+        '/': _np.divide,
+        'mod': _np.mod,
+        'pow': _np.power,
+        '==': _np.equal,
+        '>': _np.greater,
+        '>=': _np.greater_equal,
+        '<': _np.less,
+        '<=': _np.less_equal
+    }
+
+    def get_np_ret(x1, x2, op):
+        return np_op_map[op](x1, x2)
+
+    @use_np
+    class TestBinaryElementWiseOp(HybridBlock):
+        def __init__(self, op, scalar=None, reverse=False):
+            super(TestBinaryElementWiseOp, self).__init__()
+            self._op = op
+            self._scalar = scalar
+            self._reverse = reverse  # if false, scalar is the right operand.
+
+        def hybrid_forward(self, F, x, *args):
+            if self._op == '+':
+                if self._scalar is not None:
+                    return x + self._scalar if not self._reverse else self._scalar + x
+                else:
+                    return x + args[0] if not self._reverse else args[0] + x
+            elif self._op == '*':
+                if self._scalar is not None:
+                    return x * self._scalar if not self._reverse else self._scalar * x
+                else:
+                    return x * args[0] if not self._reverse else args[0] * x
+            elif self._op == '-':
+                if self._scalar is not None:
+                    return x - self._scalar if not self._reverse else self._scalar - x
+                else:
+                    return x - args[0] if not self._reverse else args[0] - x
+            elif self._op == '/':
+                if self._scalar is not None:
+                    return x / self._scalar if not self._reverse else self._scalar / x
+                else:
+                    return x / args[0] if not self._reverse else args[0] / x
+            elif self._op == 'mod':
+                if self._scalar is not None:
+                    return x % self._scalar if not self._reverse else self._scalar % x
+                else:
+                    return x % args[0] if not self._reverse else args[0] % x
+            elif self._op == 'pow':
+                if self._scalar is not None:
+                    return x ** self._scalar if not self._reverse else self._scalar ** x
+                else:
+                    return x ** args[0] if not self._reverse else args[0] ** x
+            elif self._op == '>':
+                if self._scalar is not None:
+                    return x > self._scalar if not self._reverse else self._scalar > x
+                else:
+                    return x > args[0]
+            elif self._op == '>=':
+                if self._scalar is not None:
+                    return x >= self._scalar if not self._reverse else self._scalar >= x
+                else:
+                    return x >= args[0]
+            elif self._op == '<':
+                if self._scalar is not None:
+                    return x < self._scalar if not self._reverse else self._scalar < x
+                else:
+                    return x < args[0]
+            elif self._op == '<=':
+                if self._scalar is not None:
+                    return x <= self._scalar if not self._reverse else self._scalar <= x
+                else:
+                    return x <= args[0]
+            elif self._op == '==':
+                if self._scalar is not None:
+                    return x == self._scalar if not self._reverse else self._scalar == x
+                else:
+                    return x == args[0]
+            else:
+                print(self._op)
+                assert False
+
+    @use_np
+    def check_binary_op_result(shape1, shape2, op, dtype=None):
+        if shape1 is None:
+            mx_input1 = abs(_np.random.uniform()) + 1
+            np_input1 = mx_input1
+        else:
+            mx_input1 = rand_ndarray(shape1, dtype=dtype).abs() + 1
+            np_input1 = mx_input1.asnumpy()
+        if shape2 is None:
+            mx_input2 = abs(_np.random.uniform()) + 1
+            np_input2 = mx_input2
+        else:
+            mx_input2 = rand_ndarray(shape2, dtype=dtype).abs() + 1
+            np_input2 = mx_input2.asnumpy()
+
+        scalar = None
+        reverse = False
+        if isinstance(mx_input1, mx.nd.NDArray) and not isinstance(mx_input2, mx.nd.NDArray):
+            scalar = mx_input2
+            reverse = False
+        elif isinstance(mx_input2, mx.nd.NDArray) and not isinstance(mx_input1, mx.nd.NDArray):
+            scalar = mx_input1
+            reverse = True
+
+        np_out = get_np_ret(np_input1, np_input2, op)
+        for hybridize in [True, False]:
+            if scalar is None:
+                get_mx_ret_np = TestBinaryElementWiseOp(op)
+                get_mx_ret_classic = TestBinaryElementWiseOp(op)
+                if hybridize:
+                    get_mx_ret_np.hybridize()
+                    get_mx_ret_classic.hybridize()
+                mx_out = get_mx_ret_np(mx_input1.as_np_ndarray(), mx_input2.as_np_ndarray())
+                assert type(mx_out) == np.ndarray
+                assert np_out.shape == mx_out.shape
+                assert_almost_equal(mx_out.asnumpy(), np_out, atol=1e-6, rtol=1e-5)
+            else:
+                get_mx_ret = TestBinaryElementWiseOp(op, scalar=scalar, reverse=reverse)
+                if hybridize:
+                    get_mx_ret.hybridize()
+                if reverse:
+                    mx_out = get_mx_ret(mx_input2.as_np_ndarray())
+                    assert type(mx_out) == np.ndarray
+                else:
+                    mx_out = get_mx_ret(mx_input1.as_np_ndarray())
+                    assert type(mx_out) == np.ndarray
+                assert np_out.shape == mx_out.shape
+                assert_almost_equal(mx_out.asnumpy(), np_out, atol=1e-6, rtol=1e-5)
+
+    dtypes = [_np.float32, _np.float64, None]
+    ops = np_op_map.keys()
+    for dtype in dtypes:
+        for op in ops:
+            check_binary_op_result((3, 4), (3, 4), op, dtype)
+            check_binary_op_result(None, (3, 4), op, dtype)
+            check_binary_op_result((3, 4), None, op, dtype)
+            check_binary_op_result((1, 4), (3, 1), op, dtype)
+            check_binary_op_result(None, (3, 1), op, dtype)
+            check_binary_op_result((1, 4), None, op, dtype)
+            check_binary_op_result((1, 4), (3, 5, 4), op, dtype)
+            check_binary_op_result((), (3, 5, 4), op, dtype)
+            check_binary_op_result((), None, op, dtype)
+            check_binary_op_result(None, (), op, dtype)
+            check_binary_op_result((0, 2), (1, 1), op, dtype)
+            check_binary_op_result((0, 2), None, op, dtype)
+            check_binary_op_result(None, (0, 2), op, dtype)
+
+
+@with_seed()
+def test_hybrid_block_multiple_outputs():
+    @use_np
+    class TestAllNumpyOutputs(HybridBlock):
+        def hybrid_forward(self, F, x, *args, **kwargs):
+            return F.np.add(x, x), F.np.multiply(x, x)
+
+    class TestAllClassicOutputs(HybridBlock):
+        def hybrid_forward(self, F, x, *args, **kwargs):
+            return x.as_nd_ndarray() + x.as_nd_ndarray(), x.as_nd_ndarray() * x.as_nd_ndarray()
+
+    data_np = np.ones((2, 3))
+    for block, expected_out_type in [(TestAllClassicOutputs, mx.nd.NDArray),
+                                     (TestAllNumpyOutputs, np.ndarray)]:
+        net = block()
+        for hybridize in [True, False]:
+            if hybridize:
+                net.hybridize()
+            out1, out2 = net(data_np)
+            assert type(out1) is expected_out_type
+            assert type(out2) is expected_out_type
+
+    @use_np
+    class TestMixedTypeOutputsFailure(HybridBlock):
+        def hybrid_forward(self, F, x, *args, **kwargs):
+            return x.as_nd_ndarray() + x.as_nd_ndarray(), F.np.multiply(x, x)
+
+    net = TestMixedTypeOutputsFailure()
+    assert_exception(net, TypeError, data_np)
+    net.hybridize()
+    assert_exception(net, TypeError, data_np)
+
+
+@with_seed()
+@use_np
+def test_grad_ndarray_type():
+    data = np.array(2, dtype=_np.float32)
+    data.attach_grad()
+    assert type(data.grad) == np.ndarray
+    assert type(data.detach()) == np.ndarray
+
+
+@with_seed()
+def test_np_ndarray_astype():
+    mx_data = np.array([2, 3, 4, 5], dtype=_np.int32)
+    np_data = mx_data.asnumpy()
+
+    def check_astype_equal(dtype, copy, expect_zero_copy=False):
+        mx_ret = mx_data.astype(dtype=dtype, copy=copy)
+        assert type(mx_ret) is np.ndarray
+        np_ret = np_data.astype(dtype=dtype, copy=copy)
+        assert mx_ret.dtype == np_ret.dtype
+        assert same(mx_ret.asnumpy(), np_ret)
+        if expect_zero_copy:
+            assert id(mx_ret) == id(mx_data)
+            assert id(np_ret) == id(np_data)
+
+    for dtype in [_np.int8, _np.uint8, _np.int32, _np.float16, _np.float32, _np.float64]:
+        for copy in [True, False]:
+            check_astype_equal(dtype, copy, copy is False and mx_data.dtype == dtype)
+
+
+@with_seed()
+def test_np_ndarray_copy():
+    mx_data = np.array([2, 3, 4, 5], dtype=_np.int32)
+    assert_exception(mx_data.copy, NotImplementedError, order='F')
+    mx_ret = mx_data.copy()
+    np_ret = mx_data.asnumpy().copy()
+    assert same(mx_ret.asnumpy(), np_ret)
+
+
+@with_seed()
+@use_np
+def test_np_ndarray_indexing():
+    def test_getitem(np_array, index):
+        """`is_scalar` indicates whether we should expect a scalar for the result.
+        If so, the indexed array of NDArray should call asscalar to compare
+        with numpy's indexed array."""
+        np_index = index
+        if isinstance(index, np.ndarray):
+            np_index = index.asnumpy()
+        if isinstance(index, tuple):
+            np_index = []
+            for idx in index:
+                if isinstance(idx, np.ndarray):
+                    np_index.append(idx.asnumpy())
+                else:
+                    np_index.append(idx)
+            np_index = tuple(np_index)
+
+        np_indexed_array = np_array[np_index]
+        mx_array = np.array(np_array, dtype=np_array.dtype)
+        mx_indexed_array = mx_array[index].asnumpy()
+        assert same(np_indexed_array, mx_indexed_array), 'Failed with index=%s' % str(index)
+
+    def test_setitem(np_array, index):
+        def assert_same(np_array, np_index, mx_array, mx_index, mx_value, np_value=None):
+            if np_value is not None:
+                np_array[np_index] = np_value
+            elif isinstance(mx_value, np.ndarray):
+                np_array[np_index] = mx_value.asnumpy()
+            else:
+                np_array[np_index] = mx_value
+            mx_array[mx_index] = mx_value
+            assert same(np_array, mx_array.asnumpy())
+
+        np_index = index
+        if isinstance(index, np.ndarray):
+            np_index = index.asnumpy()
+        if isinstance(index, tuple):
+            np_index = []
+            for idx in index:
+                if isinstance(idx, np.ndarray):
+                    np_index.append(idx.asnumpy())
+                else:
+                    np_index.append(idx)
+            np_index = tuple(np_index)
+
+        mx_array = np.array(np_array, dtype=np_array.dtype)
+        np_array = mx_array.asnumpy()
+        indexed_array_shape = np_array[np_index].shape
+        np_indexed_array = _np.random.randint(low=-10000, high=0, size=indexed_array_shape)
+        # test value is a numpy array without broadcast
+        assert_same(np_array, np_index, mx_array, index, np_indexed_array)
+        # test value is an numeric_type
+        assert_same(np_array, np_index, mx_array, index, _np.random.randint(low=-10000, high=0))
+        if len(indexed_array_shape) > 1:
+            # test ndarray with broadcast
+            assert_same(np_array, np_index, mx_array, index,
+                        _np.random.uniform(low=-10000, high=0, size=(indexed_array_shape[-1],)))
+            # test numpy array with broadcast
+            assert_same(np_array, np_index, mx_array, index,
+                        _np.random.randint(low=-10000, high=0, size=(indexed_array_shape[-1],)))
+            # test list with broadcast
+            assert_same(np_array, np_index, mx_array, index,
+                        [_np.random.randint(low=-10000, high=0)] * indexed_array_shape[-1])
+
+    def test_getitem_autograd(np_array, index):
+        x = np.array(np_array, dtype=np_array.dtype)
+        x.attach_grad()
+        with autograd.record():
+            y = x[index]
+        y.backward()
+        value = np.ones_like(y)
+        x_grad = np.zeros_like(x)
+        x_grad[index] = value
+        assert same(x_grad.asnumpy(), x.grad.asnumpy())
+
+    def test_setitem_autograd(np_array, index):
+        x = np.array(np_array, dtype=np_array.dtype)
+        out_shape = x[index].shape
+        y = np.array(_np.random.uniform(size=out_shape))
+        y.attach_grad()
+        try:
+            with autograd.record():
+                x[index] = y
+                assert False  # should not reach here
+        except mx.base.MXNetError as err:
+            assert str(err).find('Inplace operations (+=, -=, x[:]=, etc) are not supported when recording with') != -1
+
+    def np_int(index, int_type=_np.int32):
+        def convert(num):
+            if num is None:
+                return num
+            else:
+                return int_type(num)
+
+        if isinstance(index, slice):
+            return slice(convert(index.start), convert(index.stop), convert(index.step))
+        elif isinstance(index, tuple):  # tuple of slices and integers
+            ret = []
+            for elem in index:
+                if isinstance(elem, slice):
+                    ret.append(slice(convert(elem.start), convert(elem.stop), convert(elem.step)))
+                else:
+                    ret.append(convert(elem))
+            return tuple(ret)
+        else:
+            assert False
+
+    shape = (8, 16, 9, 9)
+    np_array = _np.arange(_np.prod(shape), dtype='int32').reshape(shape)
+    index_list = [
+        (),
+        0,
+        _np.int32(0),
+        _np.int64(0),
+        5,
+        _np.int32(5),
+        _np.int64(5),
+        -1,
+        _np.int32(-1),
+        _np.int64(-1),
+        slice(5),
+        np_int(slice(5), _np.int32),
+        np_int(slice(5), _np.int64),
+        slice(1, 5),
+        np_int(slice(1, 5), _np.int32),
+        np_int(slice(1, 5), _np.int64),
+        slice(1, 5, 2),
+        np_int(slice(1, 5, 2), _np.int32),
+        np_int(slice(1, 5, 2), _np.int64),
+        slice(7, 0, -1),
+        np_int(slice(7, 0, -1)),
+        np_int(slice(7, 0, -1), _np.int64),
+        slice(None, 6),
+        np_int(slice(None, 6)),
+        np_int(slice(None, 6), _np.int64),
+        slice(None, 6, 3),
+        np_int(slice(None, 6, 3)),
+        np_int(slice(None, 6, 3), _np.int64),
+        slice(1, None),
+        np_int(slice(1, None)),
+        np_int(slice(1, None), _np.int64),
+        slice(1, None, 3),
+        np_int(slice(1, None, 3)),
+        np_int(slice(1, None, 3), _np.int64),
+        slice(None, None, 2),
+        np_int(slice(None, None, 2)),
+        np_int(slice(None, None, 2), _np.int64),
+        slice(None, None, -1),
+        np_int(slice(None, None, -1)),
+        np_int(slice(None, None, -1), _np.int64),
+        slice(None, None, -2),
+        np_int(slice(None, None, -2), _np.int32),
+        np_int(slice(None, None, -2), _np.int64),
+        (slice(None), slice(None), 1, 8),
+        (slice(None), slice(None), -1, 8),
+        (slice(None), slice(None), 1, -8),
+        (slice(None), slice(None), -1, -8),
+        np_int((slice(None), slice(None), 1, 8)),
+        np_int((slice(None), slice(None), 1, 8), _np.int64),
+        (slice(None), slice(None), 1, 8),
+        np_int((slice(None), slice(None), -1, -8)),
+        np_int((slice(None), slice(None), -1, -8), _np.int64),
+        (slice(None), 2, slice(1, 5), 1),
+        np_int((slice(None), 2, slice(1, 5), 1)),
+        np_int((slice(None), 2, slice(1, 5), 1), _np.int64),
+        (1, 2, 3),
+        np_int((1, 2, 3)),
+        np_int((1, 2, 3), _np.int64),
+        (-1, -2, -3),
+        np_int((-1, -2, -3)),
+        np_int((-1, -2, -3), _np.int64),
+        (1, 2, 3, 4),
+        np_int((1, 2, 3, 4)),
+        np_int((1, 2, 3, 4), _np.int64),
+        (-4, -3, -2, -1),
+        np_int((-4, -3, -2, -1)),
+        np_int((-4, -3, -2, -1), _np.int64),
+        (slice(None, None, -1), 2, slice(1, 5), 1),
+        np_int((slice(None, None, -1), 2, slice(1, 5), 1)),
+        np_int((slice(None, None, -1), 2, slice(1, 5), 1), _np.int64),
+        (slice(None, None, -1), 2, slice(1, 7, 2), 1),
+        np_int((slice(None, None, -1), 2, slice(1, 7, 2), 1)),
+        np_int((slice(None, None, -1), 2, slice(1, 7, 2), 1), _np.int64),
+        (slice(1, 8, 2), slice(14, 2, -2), slice(3, 8), slice(0, 7, 3)),
+        np_int((slice(1, 8, 2), slice(14, 2, -2), slice(3, 8), slice(0, 7, 3))),
+        np_int((slice(1, 8, 2), slice(14, 2, -2), slice(3, 8), slice(0, 7, 3)), _np.int64),
+        (slice(1, 8, 2), 1, slice(3, 8), 2),
+        np_int((slice(1, 8, 2), 1, slice(3, 8), 2)),
+        np_int((slice(1, 8, 2), 1, slice(3, 8), 2), _np.int64),
+        [1],
+        [1, 2],
+        [2, 1, 3],
+        [7, 5, 0, 3, 6, 2, 1],
+        _np.array([6, 3], dtype=_np.int32),
+        _np.array([[3, 4], [0, 6]], dtype=_np.int32),
+        _np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=_np.int32),
+        _np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=_np.int64),
+        _np.array([[2], [0], [1]], dtype=_np.int32),
+        _np.array([[2], [0], [1]], dtype=_np.int64),
+        np.array([4, 7], dtype=_np.int32),
+        np.array([4, 7], dtype=_np.int64),
+        np.array([[3, 6], [2, 1]], dtype=_np.int32),
+        np.array([[3, 6], [2, 1]], dtype=_np.int64),
+        np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=_np.int32),
+        np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=_np.int64),
+        (1, [2, 3]),
+        (1, [2, 3], _np.array([[3], [0]], dtype=_np.int32)),
+        (1, [2, 3]),
+        (1, [2, 3], _np.array([[3], [0]], dtype=_np.int64)),
+        (1, [2], _np.array([[5], [3]], dtype=_np.int32), slice(None)),
+        (1, [2], _np.array([[5], [3]], dtype=_np.int64), slice(None)),
+        (1, [2, 3], _np.array([[6], [0]], dtype=_np.int32), slice(2, 5)),
+        (1, [2, 3], _np.array([[6], [0]], dtype=_np.int64), slice(2, 5)),
+        (1, [2, 3], _np.array([[4], [7]], dtype=_np.int32), slice(2, 5, 2)),
+        (1, [2, 3], _np.array([[4], [7]], dtype=_np.int64), slice(2, 5, 2)),
+        (1, [2], _np.array([[3]], dtype=_np.int32), slice(None, None, -1)),
+        (1, [2], _np.array([[3]], dtype=_np.int64), slice(None, None, -1)),
+        (1, [2], _np.array([[3]], dtype=_np.int32), np.array([[5, 7], [2, 4]], dtype=_np.int64)),
+        (1, [2], np.array([[4]], dtype=_np.int32), np.array([[1, 3], [5, 7]], dtype='int64')),
+        [0],
+        [0, 1],
+        [1, 2, 3],
+        [2, 0, 5, 6],
+        ([1, 1], [2, 3]),
+        ([1], [4], [5]),
+        ([1], [4], [5], [6]),
+        ([[1]], [[2]]),
+        ([[1]], [[2]], [[3]], [[4]]),
+        (slice(0, 2), [[1], [6]], slice(0, 2), slice(0, 5, 2)),
+        ([[[[1]]]], [[1]], slice(0, 3), [1, 5]),
+        ([[[[1]]]], 3, slice(0, 3), [1, 3]),
+        ([[[[1]]]], 3, slice(0, 3), 0),
+        ([[[[1]]]], [[2], [12]], slice(0, 3), slice(None)),
+        ([1, 2], slice(3, 5), [2, 3], [3, 4]),
+        ([1, 2], slice(3, 5), (2, 3), [3, 4]),
+        range(4),
+        range(3, 0, -1),
+        (range(4,), [1]),
+        # slice(0, 0) does not support output zero-size tensor yet
+    ]
+    for index in index_list:
+        test_getitem(np_array, index)
+        test_setitem(np_array, index)
+        test_getitem_autograd(np_array, index)
+        if not isinstance(index, tuple) or len(index) != 0:
+            # When index = (), this is same a[()] = b is equivalent to b.copyto(a)
+            # which should have no problem to do autograd
+            test_setitem_autograd(np_array, index)
+
+
+@with_seed()
+@use_np
+def test_np_save_load_ndarrays():
+    shapes = [(2, 0, 1), (0,), (), (), (0, 4), (), (3, 0, 0, 0), (2, 1), (0, 5, 0), (4, 5, 6), (0, 0, 0)]
+    array_list = [_np.random.randint(0, 10, size=shape) for shape in shapes]
+    array_list = [np.array(arr, dtype=arr.dtype) for arr in array_list]
+    # test save/load single ndarray
+    for i, arr in enumerate(array_list):
+        with TemporaryDirectory() as work_dir:
+            fname = os.path.join(work_dir, 'dataset.npy')
+            npx.save(fname, arr)
+            arr_loaded = npx.load(fname)
+            assert isinstance(arr_loaded, list)
+            assert len(arr_loaded) == 1
+            assert _np.array_equal(arr_loaded[0].asnumpy(), array_list[i].asnumpy())
+
+    # test save/load a list of ndarrays
+    with TemporaryDirectory() as work_dir:
+        fname = os.path.join(work_dir, 'dataset.npy')
+        npx.save(fname, array_list)
+        array_list_loaded = mx.nd.load(fname)
+        assert isinstance(arr_loaded, list)
+        assert len(array_list) == len(array_list_loaded)
+        assert all(isinstance(arr, np.ndarray) for arr in arr_loaded)
+        for a1, a2 in zip(array_list, array_list_loaded):
+            assert _np.array_equal(a1.asnumpy(), a2.asnumpy())
+
+    # test save/load a dict of str->ndarray
+    arr_dict = {}
+    keys = [str(i) for i in range(len(array_list))]
+    for k, v in zip(keys, array_list):
+        arr_dict[k] = v
+    with TemporaryDirectory() as work_dir:
+        fname = os.path.join(work_dir, 'dataset.npy')
+        npx.save(fname, arr_dict)
+        arr_dict_loaded = npx.load(fname)
+        assert isinstance(arr_dict_loaded, dict)
+        assert len(arr_dict_loaded) == len(arr_dict)
+        for k, v in arr_dict_loaded.items():
+            assert k in arr_dict
+            assert _np.array_equal(v.asnumpy(), arr_dict[k].asnumpy())
+
+
+if __name__ == '__main__':
+    import nose
+    nose.runmodule()
diff --git a/tests/python/unittest/test_thread_local.py b/tests/python/unittest/test_thread_local.py
index b553299ab4d7..ee56ba780a95 100644
--- a/tests/python/unittest/test_thread_local.py
+++ b/tests/python/unittest/test_thread_local.py
@@ -23,6 +23,7 @@
 from mxnet.attribute import AttrScope
 from mxnet.name import NameManager
 from mxnet.test_utils import set_default_context
+from mxnet.util import _NumpyArrayScope
 
 def test_context():
     ctx_list = []
@@ -163,6 +164,41 @@ def f():
     thread.join()
     assert status[0], "Failed to execute a symbolic graph within a thread"
 
+
+def test_np_array_scope():
+    np_array_scope_list = []
+    _NumpyArrayScope._current = _NumpyArrayScope(False)
+    np_array_scope_list.append(_NumpyArrayScope._current)
+
+    def f():
+        _NumpyArrayScope._current = _NumpyArrayScope(True)
+        np_array_scope_list.append(_NumpyArrayScope._current)
+
+    thread = threading.Thread(target=f)
+    thread.start()
+    thread.join()
+    assert len(np_array_scope_list) == 2
+    assert not np_array_scope_list[0]._is_np_array
+    assert np_array_scope_list[1]._is_np_array
+
+    event = threading.Event()
+    status = [False]
+
+    def g():
+        with mx.np_array(False):
+            event.wait()
+            if not mx.is_np_array():
+                status[0] = True
+
+    thread = threading.Thread(target=g)
+    thread.start()
+    _NumpyArrayScope._current = _NumpyArrayScope(True)
+    event.set()
+    thread.join()
+    event.clear()
+    assert status[0], "Spawned thread didn't set status correctly"
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 79d8d8656691c19502b7b71bf8c7d9001cdc3a4a Mon Sep 17 00:00:00 2001
From: JackieWu <wkcn@live.cn>
Date: Thu, 8 Aug 2019 11:15:48 +0800
Subject: [PATCH 156/813] Fix the bug of `MXEnginePushAsyncND` and
 `MXEnginePushSyncND` (#15751)

* fix push sync nd api

* align code

* update test for syncnd

* fix bug in tests/cpp/engine/threaded_engine_test

* add more testcases for MXEnginePushSyncND and MXEnginePushAsyncND

* fix test

* fix

* fix

* lint

* ci

* retrigger CI
---
 include/mxnet/c_api.h                    |  22 ++---
 src/c_api/c_api.cc                       |  40 ++++----
 tests/cpp/engine/threaded_engine_test.cc | 117 ++++++++++++++---------
 3 files changed, 105 insertions(+), 74 deletions(-)

diff --git a/include/mxnet/c_api.h b/include/mxnet/c_api.h
index 9d647c3b69d7..20b2aa2d5c9b 100644
--- a/include/mxnet/c_api.h
+++ b/include/mxnet/c_api.h
@@ -2940,12 +2940,12 @@ MXNET_DLL int MXShallowCopySymbol(SymbolHandle src, SymbolHandle * out);
   * \param wait Whether this is a WaitForVar operation.
   */
 MXNET_DLL int MXEnginePushAsyncND(EngineAsyncFunc async_func, void* func_param,
-                                EngineFuncParamDeleter deleter, ContextHandle ctx_handle,
-                                NDArrayHandle const_nds_handle, int num_const_nds,
-                                NDArrayHandle mutable_nds_handle, int num_mutable_nds,
-                                EngineFnPropertyHandle prop_handle DEFAULT(NULL),
-                                int priority DEFAULT(0), const char* opr_name DEFAULT(NULL),
-                                bool wait DEFAULT(false));
+                                  EngineFuncParamDeleter deleter, ContextHandle ctx_handle,
+                                  NDArrayHandle* const_nds_handle, int num_const_nds,
+                                  NDArrayHandle* mutable_nds_handle, int num_mutable_nds,
+                                  EngineFnPropertyHandle prop_handle DEFAULT(NULL),
+                                  int priority DEFAULT(0), const char* opr_name DEFAULT(NULL),
+                                  bool wait DEFAULT(false));
 
 /*!
   * \brief Push a synchronous operation to the engine.
@@ -2963,11 +2963,11 @@ MXNET_DLL int MXEnginePushAsyncND(EngineAsyncFunc async_func, void* func_param,
   * \param opr_name The operation name.
   */
 MXNET_DLL int MXEnginePushSyncND(EngineSyncFunc sync_func, void* func_param,
-                               EngineFuncParamDeleter deleter, ContextHandle ctx_handle,
-                               NDArrayHandle const_nds_handle, int num_const_nds,
-                               NDArrayHandle mutable_nds_handle, int num_mutable_nds,
-                               EngineFnPropertyHandle prop_handle DEFAULT(NULL),
-                               int priority DEFAULT(0), const char* opr_name DEFAULT(NULL));
+                                 EngineFuncParamDeleter deleter, ContextHandle ctx_handle,
+                                 NDArrayHandle* const_nds_handle, int num_const_nds,
+                                 NDArrayHandle* mutable_nds_handle, int num_mutable_nds,
+                                 EngineFnPropertyHandle prop_handle DEFAULT(NULL),
+                                 int priority DEFAULT(0), const char* opr_name DEFAULT(NULL));
 
 #ifdef __cplusplus
 }
diff --git a/src/c_api/c_api.cc b/src/c_api/c_api.cc
index dfb01dca94d9..13f22191955d 100644
--- a/src/c_api/c_api.cc
+++ b/src/c_api/c_api.cc
@@ -1559,18 +1559,18 @@ int MXEnginePushSync(EngineSyncFunc sync_func, void* func_param,
 }
 
 int MXEnginePushAsyncND(EngineAsyncFunc async_func, void* func_param,
-                      EngineFuncParamDeleter deleter, ContextHandle ctx_handle,
-                      NDArrayHandle const_nds_handle, int num_const_nds,
-                      NDArrayHandle mutable_nds_handle, int num_mutable_nds,
-                      EngineFnPropertyHandle prop_handle, int priority,
-                      const char* opr_name, bool wait) {
-  API_BEGIN();
-  NDArray* const_nds = static_cast<NDArray*>(const_nds_handle);
-  NDArray* mutable_nds = static_cast<NDArray*>(mutable_nds_handle);
+                        EngineFuncParamDeleter deleter, ContextHandle ctx_handle,
+                        NDArrayHandle* const_nds_handle, int num_const_nds,
+                        NDArrayHandle* mutable_nds_handle, int num_mutable_nds,
+                        EngineFnPropertyHandle prop_handle, int priority,
+                        const char* opr_name, bool wait) {
+  API_BEGIN();
+  NDArray** const_nds = reinterpret_cast<NDArray**>(const_nds_handle);
+  NDArray** mutable_nds = reinterpret_cast<NDArray**>(mutable_nds_handle);
   std::vector<VarHandle> const_var_vec(num_const_nds);
-  for (int i = 0; i < num_const_nds; ++i) const_var_vec[i] = (const_nds+i)->var();
+  for (int i = 0; i < num_const_nds; ++i) const_var_vec[i] = const_nds[i]->var();
   std::vector<VarHandle> mutable_var_vec(num_mutable_nds);
-  for (int i = 0; i < num_mutable_nds; ++i) mutable_var_vec[i] = (mutable_nds+i)->var();
+  for (int i = 0; i < num_mutable_nds; ++i) mutable_var_vec[i] = mutable_nds[i]->var();
   return MXEnginePushAsync(async_func, func_param, deleter, ctx_handle,
                            const_var_vec.data(), num_const_nds,
                            mutable_var_vec.data(), num_mutable_nds,
@@ -1579,18 +1579,18 @@ int MXEnginePushAsyncND(EngineAsyncFunc async_func, void* func_param,
 }
 
 int MXEnginePushSyncND(EngineSyncFunc sync_func, void* func_param,
-                     EngineFuncParamDeleter deleter, ContextHandle ctx_handle,
-                     NDArrayHandle const_nds_handle, int num_const_nds,
-                     NDArrayHandle mutable_nds_handle, int num_mutable_nds,
-                     EngineFnPropertyHandle prop_handle, int priority,
-                     const char* opr_name) {
-  API_BEGIN();
-  NDArray* const_nds = static_cast<NDArray*>(const_nds_handle);
-  NDArray* mutable_nds = static_cast<NDArray*>(mutable_nds_handle);
+                       EngineFuncParamDeleter deleter, ContextHandle ctx_handle,
+                       NDArrayHandle* const_nds_handle, int num_const_nds,
+                       NDArrayHandle* mutable_nds_handle, int num_mutable_nds,
+                       EngineFnPropertyHandle prop_handle, int priority,
+                       const char* opr_name) {
+  API_BEGIN();
+  NDArray** const_nds = reinterpret_cast<NDArray**>(const_nds_handle);
+  NDArray** mutable_nds = reinterpret_cast<NDArray**>(mutable_nds_handle);
   std::vector<VarHandle> const_var_vec(num_const_nds);
-  for (int i = 0; i < num_const_nds; ++i) const_var_vec[i] = (const_nds+i)->var();
+  for (int i = 0; i < num_const_nds; ++i) const_var_vec[i] = const_nds[i]->var();
   std::vector<VarHandle> mutable_var_vec(num_mutable_nds);
-  for (int i = 0; i < num_mutable_nds; ++i) mutable_var_vec[i] = (mutable_nds+i)->var();
+  for (int i = 0; i < num_mutable_nds; ++i) mutable_var_vec[i] = mutable_nds[i]->var();
   return MXEnginePushSync(sync_func, func_param, deleter, ctx_handle,
                           const_var_vec.data(), num_const_nds,
                           mutable_var_vec.data(), num_mutable_nds,
diff --git a/tests/cpp/engine/threaded_engine_test.cc b/tests/cpp/engine/threaded_engine_test.cc
index 6b863f8efdc0..cea92a01e799 100644
--- a/tests/cpp/engine/threaded_engine_test.cc
+++ b/tests/cpp/engine/threaded_engine_test.cc
@@ -257,49 +257,80 @@ TEST(Engine, PushFunc) {
 
 TEST(Engine, PushFuncND) {
   auto ctx = mxnet::Context{};
-  mxnet::NDArray nd(ctx);
-
-  // Test #1
-  LOG(INFO) << "===== Test #1: PushAsyncND param and deleter =====";
-  int* a = new int(100);
-  int res = MXEnginePushAsyncND(FooAsyncFunc, a, FooFuncDeleter, &ctx, &nd, 1, nullptr, 0);
-  EXPECT_EQ(res, 0);
-
-  // Test #2
-  LOG(INFO) << "===== Test #2: PushAsyncND NULL param and NULL deleter =====";
-  res = MXEnginePushAsyncND(FooAsyncFunc, nullptr, nullptr, &ctx, nullptr, 0, &nd, 0);
-  EXPECT_EQ(res, 0);
-
-  // Test #3
-  LOG(INFO) << "===== Test #3: PushAsyncND invalid number of const nds =====";
-  res = MXEnginePushAsyncND(FooAsyncFunc, nullptr, nullptr, &ctx, &nd, -1, nullptr, 0);
-  EXPECT_EQ(res, -1);
-
-  // Test #4
-  LOG(INFO) << "===== Test #4: PushAsyncND invalid number of mutable nds =====";
-  res = MXEnginePushAsyncND(FooAsyncFunc, nullptr, nullptr, &ctx, nullptr, 0, &nd, -1);
-  EXPECT_EQ(res, -1);
-
-  // Test #5
-  LOG(INFO) << "===== Test #5: PushSyncND param and deleter =====";
-  int* b = new int(101);
-  res = MXEnginePushSyncND(FooSyncFunc, b, FooFuncDeleter, &ctx, &nd, 1, nullptr, 0);
-  EXPECT_EQ(res, 0);
-
-  // Test #6
-  LOG(INFO) << "===== Test #6: PushSyncND NULL param and NULL deleter =====";
-  res = MXEnginePushSyncND(FooSyncFunc, nullptr, nullptr, &ctx, nullptr, 0, &nd, 1);
-  EXPECT_EQ(res, 0);
-
-  // Test #7
-  LOG(INFO) << "===== Test #7: PushSyncND invalid number of const nds =====";
-  res = MXEnginePushSyncND(FooSyncFunc, nullptr, nullptr, &ctx, &nd, -1, nullptr, 0);
-  EXPECT_EQ(res, -1);
-
-  // Test #8
-  LOG(INFO) << "===== Test #8: PushSyncND invalid number of mutable nds =====";
-  res = MXEnginePushSyncND(FooSyncFunc, nullptr, nullptr, &ctx, nullptr, 0, &nd, -1);
-  EXPECT_EQ(res, -1);
+  std::vector<mxnet::NDArray*> nds;
+  const int num_nds = 5;
+  for (int i = 0; i < num_nds; ++i) {
+      mxnet::NDArray *pnd = new mxnet::NDArray(ctx);
+      nds.push_back(pnd);
+  }
+  for (int num_const_nds = 0; num_const_nds <= num_nds; ++num_const_nds) {
+      int num_mutable_nds = num_nds - num_const_nds;
+      void** const_nds_handle = num_const_nds > 0 ?
+          reinterpret_cast<void**>(nds.data()) : nullptr;
+      void** mutable_nds_handle = num_mutable_nds > 0 ?
+          reinterpret_cast<void**>(nds.data() + num_const_nds) : nullptr;
+
+      // Test #1
+      LOG(INFO) << "===== Test #1: PushAsyncND param and deleter =====";
+      int* a = new int(100);
+      int res = MXEnginePushAsyncND(FooAsyncFunc, a, FooFuncDeleter, &ctx,
+              const_nds_handle, num_const_nds,
+              mutable_nds_handle, num_mutable_nds);
+      EXPECT_EQ(res, 0);
+
+      // Test #2
+      LOG(INFO) << "===== Test #2: PushAsyncND NULL param and NULL deleter =====";
+      res = MXEnginePushAsyncND(FooAsyncFunc, nullptr, nullptr, &ctx,
+              const_nds_handle, num_const_nds,
+              mutable_nds_handle, num_mutable_nds);
+      EXPECT_EQ(res, 0);
+
+      // Test #3
+      LOG(INFO) << "===== Test #3: PushAsyncND invalid number of const nds =====";
+      res = MXEnginePushAsyncND(FooAsyncFunc, nullptr, nullptr, &ctx,
+              const_nds_handle, -1,
+              mutable_nds_handle, num_mutable_nds);
+      EXPECT_EQ(res, -1);
+
+      // Test #4
+      LOG(INFO) << "===== Test #4: PushAsyncND invalid number of mutable nds =====";
+      res = MXEnginePushAsyncND(FooAsyncFunc, nullptr, nullptr, &ctx,
+              const_nds_handle, num_const_nds,
+              mutable_nds_handle, -1);
+      EXPECT_EQ(res, -1);
+
+      // Test #5
+      LOG(INFO) << "===== Test #5: PushSyncND param and deleter =====";
+      int* b = new int(101);
+      res = MXEnginePushSyncND(FooSyncFunc, b, FooFuncDeleter, &ctx,
+              const_nds_handle, num_const_nds,
+              mutable_nds_handle, num_mutable_nds);
+      EXPECT_EQ(res, 0);
+
+      // Test #6
+      LOG(INFO) << "===== Test #6: PushSyncND NULL param and NULL deleter =====";
+      res = MXEnginePushSyncND(FooSyncFunc, nullptr, nullptr, &ctx,
+              const_nds_handle, num_const_nds,
+              mutable_nds_handle, num_mutable_nds);
+      EXPECT_EQ(res, 0);
+
+      // Test #7
+      LOG(INFO) << "===== Test #7: PushSyncND invalid number of const nds =====";
+      res = MXEnginePushSyncND(FooSyncFunc, nullptr, nullptr, &ctx,
+              const_nds_handle, -1,
+              mutable_nds_handle, num_mutable_nds);
+      EXPECT_EQ(res, -1);
+
+      // Test #8
+      LOG(INFO) << "===== Test #8: PushSyncND invalid number of mutable nds =====";
+      res = MXEnginePushSyncND(FooSyncFunc, nullptr, nullptr, &ctx,
+              const_nds_handle, num_const_nds,
+              mutable_nds_handle, -1);
+      EXPECT_EQ(res, -1);
+  }
+  for (mxnet::NDArray* pnd : nds) {
+      delete pnd;
+  }
 }
 
 TEST(Engine, basics) {

From b6972bb055fc44481b072db3abb90e26ee27c787 Mon Sep 17 00:00:00 2001
From: YixinBao <yixin.bao@intel.com>
Date: Thu, 8 Aug 2019 18:20:19 +0800
Subject: [PATCH 157/813] add int8 bn mkldnn implementation and test (#15664)

* add int8 bn mkldnn implementation and test

* fix lint

* fix ci

* enable int8 bn test only in mkldnn backend

* disable int8 bn forward test with gpu backend

* update int8 bn with reference to comments

* fix lint

* disable int8 bn gluon forward test with gpu backend

* disable uint8 bn forward test with mkldnn backend

* restore support mkldnn bn condition

* rm duplicate code
---
 cpp-package/scripts/OpWrapperGenerator.py     |   1 +
 src/operator/nn/batch_norm-inl.h              |  52 +++++--
 .../nn/mkldnn/mkldnn_batch_norm-inl.h         |  29 ++--
 .../mkldnn/mkldnn_quantized_batch_norm.cc     | 123 ++++++++++++++++
 .../quantization/quantize_graph_pass.cc       |  42 +++++-
 .../quantization/quantized_batch_norm.cc      | 137 ++++++++++++++++++
 .../python/quantization/test_quantization.py  |  90 +++++++++++-
 7 files changed, 446 insertions(+), 28 deletions(-)
 create mode 100644 src/operator/quantization/mkldnn/mkldnn_quantized_batch_norm.cc
 create mode 100644 src/operator/quantization/quantized_batch_norm.cc

diff --git a/cpp-package/scripts/OpWrapperGenerator.py b/cpp-package/scripts/OpWrapperGenerator.py
index 65ba247c25c8..853c519a73d4 100644
--- a/cpp-package/scripts/OpWrapperGenerator.py
+++ b/cpp-package/scripts/OpWrapperGenerator.py
@@ -93,6 +93,7 @@ class Arg:
         'int (non-negative)': 'uint32_t',\
         'long (non-negative)': 'uint64_t',\
         'int or None':'dmlc::optional<int>',\
+        'float or None':'dmlc::optional<float>',\
         'long':'int64_t',\
         'double':'double',\
         'double or None':'dmlc::optional<double>',\
diff --git a/src/operator/nn/batch_norm-inl.h b/src/operator/nn/batch_norm-inl.h
index a82771cd4401..17a16db5adcd 100644
--- a/src/operator/nn/batch_norm-inl.h
+++ b/src/operator/nn/batch_norm-inl.h
@@ -53,11 +53,19 @@ enum BatchNormOpOutputs {kOut, kMean, kVar};  // req, out_data
 enum BatchNormOpResource {kTempSpace};
 enum BatchNormOpAuxiliary {kMovingMean, kMovingVar};  // aux_states
 
-/*! \brief Default channel axis if none specified int he params */
+/*! \brief Default channel axis if none specified in the params */
 constexpr int DEFAULT_AXIS = 1;
 }  // namespace batchnorm
 
 /*! \brief Parameters for BatchNorm operator */
+namespace quantized_batchnorm {
+enum QuantizedBatchNormOpInputs {kData, kGamma, kBeta, kInMovingMean,
+  kInMovingVar, kDataMin, kDataMax};
+enum QuantizedBatchNormOutputs {kOut, kOutMin, kOutMax};
+enum QuantizedBatchNormOpAuxiliary {kMovingMean, kMovingVar};
+}  // quantized_batchnorm
+
+/*! \brief Parameters for BatchNoram operator */
 struct BatchNormParam : public dmlc::Parameter<BatchNormParam> {
   double eps;
   float momentum;
@@ -66,6 +74,10 @@ struct BatchNormParam : public dmlc::Parameter<BatchNormParam> {
   bool output_mean_var;
   int axis;
   bool cudnn_off;
+
+  dmlc::optional<float> min_calib_range;  // min float value calculated from calibration dataset
+  dmlc::optional<float> max_calib_range;  // max float value calculated from calibration dataset
+
   DMLC_DECLARE_PARAMETER(BatchNormParam) {
     DMLC_DECLARE_FIELD(eps).set_default(1e-3f)
     .describe("Epsilon to prevent div 0. "
@@ -81,19 +93,37 @@ struct BatchNormParam : public dmlc::Parameter<BatchNormParam> {
     DMLC_DECLARE_FIELD(output_mean_var).set_default(false)
     .describe("Output the mean and inverse std ");
     DMLC_DECLARE_FIELD(axis).set_default(mxnet::op::batchnorm::DEFAULT_AXIS)
-      .describe("Specify which shape axis the channel is specified");
+    .describe("Specify which shape axis the channel is specified");
     DMLC_DECLARE_FIELD(cudnn_off).set_default(false)
-      .describe("Do not select CUDNN operator, if available");
+    .describe("Do not select CUDNN operator, if available");
+    DMLC_DECLARE_FIELD(min_calib_range)
+    .set_default(dmlc::optional<float>())
+    .describe("The minimum scalar value in the form of float32 obtained "
+              "through calibration. If present, it will be used to by "
+              "quantized batch norm op to calculate primitive scale."
+              "Note: this calib_range is to calib bn output.");
+    DMLC_DECLARE_FIELD(max_calib_range)
+    .set_default(dmlc::optional<float>())
+    .describe("The maximum scalar value in the form of float32 obtained "
+              "through calibration. If present, it will be used to by "
+              "quantized batch norm op to calculate primitive scale."
+              "Note: this calib_range is to calib bn output.");
   }
 
-  bool operator==(const BatchNormParam& other) const {
-    return this->eps == other.eps &&
-           this->momentum == other.momentum &&
-           this->fix_gamma == other.fix_gamma &&
-           this->use_global_stats == other.use_global_stats &&
-           this->output_mean_var == other.output_mean_var &&
-           this->axis == other.axis &&
-           this->cudnn_off == other.cudnn_off;
+  bool operator==(const BatchNormParam &other) const {
+    bool flag = this->eps == other.eps && this->momentum == other.momentum &&
+                this->fix_gamma == other.fix_gamma &&
+                this->use_global_stats == other.use_global_stats &&
+                this->output_mean_var == other.output_mean_var && this->axis == other.axis &&
+                this->cudnn_off == other.cudnn_off &&
+                this->min_calib_range.has_value() == other.min_calib_range.has_value() &&
+                this->max_calib_range.has_value() == other.max_calib_range.has_value();
+    if (this->min_calib_range.has_value() && other.min_calib_range.has_value() &&
+        this->max_calib_range.has_value() && other.max_calib_range.has_value()) {
+      flag = flag && this->min_calib_range.value() == other.min_calib_range.value() &&
+             this->max_calib_range.value() == other.max_calib_range.value();
+    }
+    return flag;
   }
 };
 
diff --git a/src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h b/src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h
index 403baaa94ab4..f294153ecc24 100644
--- a/src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h
@@ -132,8 +132,8 @@ class MKLDNNBNForward {
     return *var_m;
   }
 
-  void SetDataHandle(const NDArray &data, const NDArray &mean,
-                     const NDArray &var, const mkldnn::memory &out) {
+  void SetDataHandle(const NDArray &data, const mkldnn::memory *mean,
+                     const mkldnn::memory *var, const mkldnn::memory *out) {
     auto _data = data.GetMKLDNNData();
     if (data_m) {
       data_m->set_data_handle(_data->get_data_handle());
@@ -142,24 +142,22 @@ class MKLDNNBNForward {
                                       _data->get_data_handle()));
     }
     if (out_m) {
-      out_m->set_data_handle(out.get_data_handle());
+      out_m->set_data_handle(out->get_data_handle());
     } else {
-      out_m.reset(new mkldnn::memory(out.get_primitive_desc(),
-                                     out.get_data_handle()));
+      out_m.reset(new mkldnn::memory(out->get_primitive_desc(),
+                                     out->get_data_handle()));
     }
-    auto mean_ptr = mean.data().dptr_;
     if (mean_m) {
-      mean_m->set_data_handle(mean_ptr);
+      mean_m->set_data_handle(mean->get_data_handle());
     } else {
-      mean_m.reset(new mkldnn::memory(pd.mean_primitive_desc(),
-                                      mean_ptr));
+      mean_m.reset(new mkldnn::memory(mean->get_primitive_desc(),
+                                      mean->get_data_handle()));
     }
-    auto var_ptr = var.data().dptr_;
     if (var_m) {
-      var_m->set_data_handle(var_ptr);
+      var_m->set_data_handle(var->get_data_handle());
     } else {
-      var_m.reset(new mkldnn::memory(pd.variance_primitive_desc(),
-                                     var_ptr));
+      var_m.reset(new mkldnn::memory(var->get_primitive_desc(),
+                                     var->get_data_handle()));
     }
 
     if (fwd == nullptr) {
@@ -175,6 +173,11 @@ class MKLDNNBNForward {
     }
   }
 
+  void SetDataHandle(const NDArray &data, const NDArray &mean,
+                     const NDArray &var, const mkldnn::memory &out) {
+    SetDataHandle(data, mean.GetMKLDNNData(), var.GetMKLDNNData(), &out);
+  }
+
   const mkldnn::batch_normalization_forward &GetFwd() const {
     return *fwd;
   }
diff --git a/src/operator/quantization/mkldnn/mkldnn_quantized_batch_norm.cc b/src/operator/quantization/mkldnn/mkldnn_quantized_batch_norm.cc
new file mode 100644
index 000000000000..df5e48744f2d
--- /dev/null
+++ b/src/operator/quantization/mkldnn/mkldnn_quantized_batch_norm.cc
@@ -0,0 +1,123 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file mkldnn_quantized_batch_norm.cc
+ * \brief
+ * \author Yixin Bao
+*/
+
+#if MXNET_USE_MKLDNN == 1
+#include "../../nn/mkldnn/mkldnn_batch_norm-inl.h"
+#include "../quantization_utils.h"
+
+namespace mxnet {
+namespace op {
+
+static void MKLDNNQuantizedBatchNormForward(const nnvm::NodeAttrs &attrs, const OpContext &ctx,
+                                            const std::vector<NDArray> &in_data,
+                                            const std::vector<OpReqType> &req,
+                                            const std::vector<NDArray> &outputs) {
+  CHECK_EQ(in_data.size(), 7U);
+  CHECK_EQ(outputs.size(), 3U);
+
+  TmpMemMgr::Get()->Init(ctx.requested[batchnorm::kTempSpace]);
+  const BatchNormParam &param = nnvm::get<BatchNormParam>(attrs.parsed);
+  const NDArray &data = in_data[quantized_batchnorm::kData];
+  const size_t channelAxis = static_cast<size_t>(
+      param.axis < 0 ? static_cast<int>(data.shape().ndim()) + param.axis : param.axis);
+  const int channel_count = data.shape()[channelAxis];
+  const float min_data = in_data[quantized_batchnorm::kDataMin].data().dptr<float>()[0];
+  const float max_data = in_data[quantized_batchnorm::kDataMax].data().dptr<float>()[0];
+  const float max_abs_data = std::max(std::abs(min_data), std::abs(max_data));
+
+  float *min_output_ptr = outputs[quantized_batchnorm::kOutMin].data().dptr<float>();
+  float *max_output_ptr = outputs[quantized_batchnorm::kOutMax].data().dptr<float>();
+  if (param.min_calib_range.has_value() && param.max_calib_range.has_value()) {
+    *max_output_ptr = param.max_calib_range.value();
+    *min_output_ptr = param.min_calib_range.value();
+  } else {
+    LOG(FATAL) << "min_calib_range or max_calib_range is not available. Quantized BN currently "
+                  "don't support calib_mode=None";
+  }
+  const float max_abs_output = std::max(std::abs(*min_output_ptr), std::abs(*max_output_ptr));
+
+  unsigned flags = mkldnn::use_global_stats | mkldnn::use_scale_shift;
+  auto &fwd = GetBNForward<float>(param, ctx, data, flags);
+  const mkldnn::memory &weight_mem = fwd.GetWeight();
+  CHECK_EQ(weight_mem.get_primitive_desc().get_size(), channel_count * sizeof(float) * 2);
+  float *weight_buf = reinterpret_cast<float *>(weight_mem.get_data_handle());
+
+  float *gamma_ptr = in_data[quantized_batchnorm::kGamma].data().dptr<float>();
+  float *beta_ptr = in_data[quantized_batchnorm::kBeta].data().dptr<float>();
+
+  const NDArray &moving_mean = in_data[quantized_batchnorm::kInMovingMean];
+  const NDArray &moving_var = in_data[quantized_batchnorm::kInMovingVar];
+  float *moving_mean_ptr = moving_mean.data().dptr<float>();
+  float *moving_var_ptr = moving_var.data().dptr<float>();
+
+  // rescale gamma and beta, to make mean=0 and var=1
+  auto rescaled_mean_mem =
+      TmpMemMgr::Get()->Alloc(moving_mean.GetMKLDNNData()->get_primitive_desc());
+  auto rescaled_var_mem = TmpMemMgr::Get()->Alloc(moving_var.GetMKLDNNData()->get_primitive_desc());
+  float *rescaled_mean_ptr = reinterpret_cast<float *>(rescaled_mean_mem->get_data_handle());
+  float *rescaled_var_ptr = reinterpret_cast<float *>(rescaled_var_mem->get_data_handle());
+
+#pragma omp parallel for num_threads(engine::OpenMP::Get()->GetRecommendedOMPThreadCount())
+  for (int channel = 0; channel < channel_count; ++channel) {
+    float invstd = 1.0 / std::sqrt(moving_var_ptr[channel] + param.eps);
+    weight_buf[channel] = gamma_ptr[channel] * invstd * max_abs_data / max_abs_output;
+    weight_buf[channel_count + channel] =
+        (beta_ptr[channel] - moving_mean_ptr[channel] * gamma_ptr[channel] * invstd) * kInt8Range /
+        max_abs_output;
+    rescaled_mean_ptr[channel] = 0.0f;
+    rescaled_var_ptr[channel] = 1.0f;
+  }
+
+  auto out_mem = CreateMKLDNNMem(outputs[batchnorm::kOut],
+      fwd.GetPd().dst_primitive_desc(), req[batchnorm::kOut], &data);
+  fwd.SetDataHandle(data, rescaled_mean_mem, rescaled_var_mem, out_mem.second);
+
+  MKLDNNStream::Get()->RegisterPrim(fwd.GetFwd());
+  MKLDNNStream::Get()->Submit();
+}
+
+inline static bool QuantizedBatchNormStorageType(const nnvm::NodeAttrs &attrs, const int dev_mask,
+                                                 DispatchMode *dispatch_mode,
+                                                 std::vector<int> *in_attrs,
+                                                 std::vector<int> *out_attrs) {
+  bool dispatched = false;
+  if (!dispatched) {
+    dispatched = MKLDNNStorageType(attrs, dev_mask, true, dispatch_mode, in_attrs, out_attrs);
+  }
+  return dispatched;
+}
+
+NNVM_REGISTER_OP(_contrib_quantized_batch_norm)
+.set_attr<FInferStorageType>("FInferStorageType", QuantizedBatchNormStorageType)
+.set_attr<FComputeEx>("FComputeEx<cpu>", MKLDNNQuantizedBatchNormForward)
+.set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& n) {
+  return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+})
+.set_attr<bool>("TIsMKLDNN", true);
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_USE_MKLDNN == 1
diff --git a/src/operator/quantization/quantize_graph_pass.cc b/src/operator/quantization/quantize_graph_pass.cc
index 412e78e70fff..31e3539b8a84 100644
--- a/src/operator/quantization/quantize_graph_pass.cc
+++ b/src/operator/quantization/quantize_graph_pass.cc
@@ -37,6 +37,19 @@ using nnvm::NodePtr;
 using nnvm::NodeEntry;
 using nnvm::Graph;
 
+inline size_t GetNumOutputs(NodePtr node) {
+  // Get NumOutputs, check if current node has NumVisibleOutputs function, if yes, return
+  // num_visible_outputs
+  size_t num_outputs = node->num_outputs();
+  static const auto& num_visible_outputs_attr =
+      nnvm::Op::GetAttr<nnvm::FNumVisibleOutputs>("FNumVisibleOutputs");
+  auto num_visible_output_func = num_visible_outputs_attr.get(node->op(), nullptr);
+  if (num_visible_output_func != nullptr) {
+    num_outputs = num_visible_output_func(node->attrs);
+  }
+  return num_outputs;
+}
+
 NodePtr CreateNode(std::string op_name, std::string node_name) {
   NodePtr node = Node::Create();
   node->attrs.name = node_name;
@@ -223,7 +236,7 @@ Graph QuantizeGraph(Graph &&src) {
           // calculate min/max index from mirror node) based on assumption that
           // there is only 1min and 1max output from mirror node (which is
           // currently true)
-          size_t num_outputs = mirror_node->num_outputs() - 2;
+          size_t num_outputs = GetNumOutputs(mirror_node) - 2;
           min_index = num_outputs + 2 * e.index;
           max_index = num_outputs + 2 * e.index + 1;
         } else {
@@ -276,7 +289,7 @@ Graph QuantizeGraph(Graph &&src) {
           // calculate min/max index from mirror node) based on assumption that
           // there is only 1 min and 1 max output from mirror node (which is
           // currently true)
-          size_t num_outputs = mirror_node->num_outputs() - 2;
+          size_t num_outputs = GetNumOutputs(mirror_node) - 2;
           uint32_t min_index = num_outputs + 2 * e.index;
           uint32_t max_index = num_outputs + 2 * e.index + 1;
           NodePtr dequantize_node = CreateNode("_contrib_dequantize",
@@ -309,7 +322,7 @@ Graph QuantizeGraph(Graph &&src) {
       // calculate min/max index from mirror node) based on assumption that
       // there is only 1 min and 1 max output from mirror node (which is
       // currently true)
-      size_t num_outputs = e.node->num_outputs();
+      size_t num_outputs = GetNumOutputs(e.node);
       uint32_t min_index = num_outputs + 2 * e.index;
       uint32_t max_index = num_outputs + 2 * e.index + 1;
 
@@ -403,6 +416,29 @@ Graph SetCalibTableToQuantizedGraph(Graph&& g) {
                        << "` has negative input, consider use `auto` or `int8` as out_type";
         }
       }
+    } else if (node->op() == Op::Get("_contrib_quantized_batch_norm")) {
+      auto quantized_op_idx = node->inputs[0].index;
+      const std::string prefix = "quantized_";
+      std::string out_data_name = node->attrs.name.substr(prefix.size());
+      if (node->op()) {
+        auto list_output_names_func = flist_outputs.get(node->op(), nullptr);
+        // We want to get the pre-calculated min_range and max_range from the calibration table for
+        // out_data. Here we create the output data name same as its constructed in
+        // GraphExecutor::ExecuteMonCallback.
+        if (list_output_names_func != nullptr) {
+          std::vector<std::string> names = list_output_names_func(node->attrs);
+          out_data_name += "_" + names[quantized_op_idx];
+        } else {
+          out_data_name += "_" + std::to_string(quantized_op_idx);
+        }
+      }
+
+      const auto calib_table_iter = calib_table.find(out_data_name);
+      if (calib_table_iter != calib_table.end()) {
+        node->attrs.dict["min_calib_range"] = std::to_string(calib_table_iter->second.first);
+        node->attrs.dict["max_calib_range"] = std::to_string(calib_table_iter->second.second);
+        node->op()->attr_parser(&(node->attrs));
+      }
     }
   });
   return g;
diff --git a/src/operator/quantization/quantized_batch_norm.cc b/src/operator/quantization/quantized_batch_norm.cc
new file mode 100644
index 000000000000..3187826fe996
--- /dev/null
+++ b/src/operator/quantization/quantized_batch_norm.cc
@@ -0,0 +1,137 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2017 by Contributors
+ * \file quantized_batch_norm.cc
+ * \brief
+ * \author Yixin Bao
+*/
+#include <mxnet/op_attr_types.h>
+#include "../nn/batch_norm-inl.h"
+#if MXNET_USE_MKLDNN == 1
+#include "../nn/mkldnn/mkldnn_batch_norm-inl.h"
+#endif
+
+namespace mxnet {
+namespace op {
+
+bool QuantizedBatchNormShape(const nnvm::NodeAttrs& attrs, mxnet::ShapeVector* in_shape,
+                             mxnet::ShapeVector* out_shape) {
+  const BatchNormParam& param = nnvm::get<BatchNormParam>(attrs.parsed);
+  using namespace mshadow;
+  CHECK_EQ(in_shape->size(), 7U)
+      << "Input:[data, gamma, beta, moving_mean, moving_var, min_data, max_data]";
+  CHECK_EQ(out_shape->size(), 3U);
+
+  const mxnet::TShape& dshape = in_shape->at(batchnorm::kData);
+  if (!mxnet::ndim_is_known(dshape)) {
+    return false;
+  }
+  const int channelAxis = param.axis < 0 ? dshape.ndim() + param.axis : param.axis;
+  CHECK_LT(channelAxis, dshape.ndim()) << "Channel axis out of range: " << param.axis;
+  const int channelCount = dshape[channelAxis];
+
+  SHAPE_ASSIGN_CHECK(*in_shape, 1, mxnet::TShape(Shape1(channelCount)))  // gamma,beta
+  SHAPE_ASSIGN_CHECK(*in_shape, 2, mxnet::TShape(Shape1(channelCount)))
+  SHAPE_ASSIGN_CHECK(*in_shape, 3, mxnet::TShape(Shape1(channelCount)));  // moving_mean, moving_var
+  SHAPE_ASSIGN_CHECK(*in_shape, 4, mxnet::TShape(Shape1(channelCount)))
+  SHAPE_ASSIGN_CHECK(*in_shape, 5, mxnet::TShape(1, 1));  // min_data, max_data
+  SHAPE_ASSIGN_CHECK(*in_shape, 6, mxnet::TShape(1, 1));
+
+  SHAPE_ASSIGN_CHECK(*out_shape, 0, dshape);
+  SHAPE_ASSIGN_CHECK(*out_shape, 1, mxnet::TShape(1, 1));  // min_output, max_output
+  SHAPE_ASSIGN_CHECK(*out_shape, 2, mxnet::TShape(1, 1));
+  return true;
+}
+
+bool QuantizedBatchNormType(const nnvm::NodeAttrs& attrs, std::vector<int>* in_type,
+                            std::vector<int>* out_type) {
+  using namespace mshadow;
+  CHECK_EQ(in_type->size(), 7U);
+  CHECK_EQ(out_type->size(), 3U);
+
+  TYPE_ASSIGN_CHECK(*in_type, 0, mshadow::kInt8);
+  for (size_t i = 1; i < 7; ++i) {
+    TYPE_ASSIGN_CHECK(*in_type, i, mshadow::kFloat32);
+  }
+
+  TYPE_ASSIGN_CHECK(*out_type, 0, mshadow::kInt8);
+  TYPE_ASSIGN_CHECK(*out_type, 1, mshadow::kFloat32);
+  TYPE_ASSIGN_CHECK(*out_type, 2, mshadow::kFloat32);
+
+  return true;
+}
+
+NNVM_REGISTER_OP(_contrib_quantized_batch_norm)
+.describe(R"code(BatchNorm operator for input and output data type of int8.
+The input and output data comes with min and max thresholds for quantizing
+the float32 data into int8.
+
+.. Note::
+    This operator only supports forward propogation. DO NOT use it in training.
+)code" ADD_FILELINE)
+.set_num_inputs(7)
+.set_num_outputs(3)
+.set_attr_parser(ParamParser<BatchNormParam>)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"data", "gamma", "beta",
+    "moving_mean", "moving_var", "min_data", "max_data"};
+  })
+.set_attr<nnvm::FListOutputNames>("FListOutputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"output", "min_output", "max_output"};
+  })
+.set_attr<nnvm::FMutateInputs>("FMutateInputs", [](const nnvm::NodeAttrs& attrs) {
+  return std::vector<uint32_t>{3, 4};
+})
+.set_attr<mxnet::FInferShape>("FInferShape", QuantizedBatchNormShape)
+.set_attr<nnvm::FInferType>("FInferType", QuantizedBatchNormType)
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.set_attr<FNeedRequantize>("FNeedRequantize", [](const NodeAttrs& attrs) { return false; })
+.add_argument("data", "NDArray-or-Symbol", "Input data.")
+.add_argument("gamma", "NDArray-or-Symbol", "gamma.")
+.add_argument("beta", "NDArray-or-Symbol", "beta.")
+.add_argument("moving_mean", "NDArray-or-Symbol", "moving_mean.")
+.add_argument("moving_var", "NDArray-or-Symbol", "moving_var.")
+.add_argument("min_data", "NDArray-or-Symbol", "Minimum value of data.")
+.add_argument("max_data", "NDArray-or-Symbol", "Maximum value of data.")
+.add_arguments(BatchNormParam::__FIELDS__());
+
+NNVM_REGISTER_OP(BatchNorm)
+.set_attr<FQuantizedOp>("FQuantizedOp", [](const NodeAttrs& attrs) {
+    nnvm::NodePtr node = nnvm::Node::Create();
+    node->attrs.op = Op::Get("_contrib_quantized_batch_norm");
+    node->attrs.name = "quantized_" + attrs.name;
+    node->attrs.dict = attrs.dict;
+    if (node->op()->attr_parser != nullptr) {
+      node->op()->attr_parser(&(node->attrs));
+    }
+    return node;
+  })
+.set_attr<FAvoidQuantizeInput>("FAvoidQuantizeInput", [](const NodeAttrs &attrs, size_t index) {
+  if (index == 0)
+    return false;
+  else
+    return true;
+});
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/tests/python/quantization/test_quantization.py b/tests/python/quantization/test_quantization.py
index a991417b28c9..0cf38b7375a8 100644
--- a/tests/python/quantization/test_quantization.py
+++ b/tests/python/quantization/test_quantization.py
@@ -594,6 +594,88 @@ def check_quantized_act(data_shape, qdtype):
         check_quantized_act((10, 15, 18), qdtype)
         check_quantized_act((3, 4, 23, 23), qdtype)
 
+@with_seed()
+def test_quantized_bn():
+    def get_mean_var(data):
+        mean = mx.ndarray.mean(data, axis=1, exclude=1)
+        mean_broad = mx.ndarray.expand_dims(mean, axis=0)
+        mean_broad = mx.ndarray.expand_dims(mean_broad, axis=2)
+        mean_broad = mx.ndarray.expand_dims(mean_broad, axis=3)
+        mean_broad = mx.ndarray.broadcast_like(mean_broad, data)
+        var = mx.ndarray.multiply(data - mean_broad, data - mean_broad)
+        var = mx.ndarray.mean(var, axis=1, exclude=1)
+        return mean, var
+
+    def check_quantized_bn(data_shape, qdtype):
+        if qdtype == 'uint8':
+            print('skipped testing quantize_bn for uint8 since it is not supported yet')
+            return
+        elif is_test_for_native_cpu():
+            print('skipped testing quantize_bn for native cpu since it is not supported yet')
+            return
+        elif is_test_for_gpu():
+            print('skipped testing quantize_bn for gpu since it is not supported yet')
+            return
+
+        # qdtype = int8
+        data_low = -127.0
+        data_high = 127.0
+        quantized_range = 127.0
+        # run fp32 bn
+        data_sym = mx.sym.Variable(name='data', shape=data_shape, dtype='float32')
+        bn_fp32 = mx.sym.BatchNorm(data=data_sym, name='bn', use_global_stats=True, fix_gamma=False)
+        arg_shapes, out_shapes, aux_shapes = bn_fp32.infer_shape(data=data_shape)
+        arg_names = bn_fp32.list_arguments()
+        aux_names = bn_fp32.list_auxiliary_states()
+
+        data = mx.nd.random.uniform(low=data_low, high=data_high, shape=data_shape)
+        gamma = mx.nd.random.uniform(low=data_low, high=data_high, shape=arg_shapes[1])
+        beta = mx.nd.random.uniform(low=data_low, high=data_high, shape=arg_shapes[2])
+        moving_mean, moving_var = get_mean_var(data)
+
+        bn_fp32_exe = bn_fp32.simple_bind(ctx=mx.current_context(), grad_req='null')
+        bn_fp32_exe.arg_dict[arg_names[0]][:] = data
+        bn_fp32_exe.arg_dict[arg_names[1]][:] = gamma
+        bn_fp32_exe.arg_dict[arg_names[2]][:] = beta
+        bn_fp32_exe.aux_dict[aux_names[0]][:] = moving_mean
+        bn_fp32_exe.aux_dict[aux_names[1]][:] = moving_var
+        min_data = mx.nd.min(data)
+        max_data = mx.nd.max(data)
+        data_range = mx.nd.maximum(mx.nd.abs(min_data), mx.nd.abs(max_data))
+
+        output= bn_fp32_exe.forward()[0]
+
+        # generate int8 bn from fp32 bn
+        arg_params = dict()
+        for k,v in bn_fp32_exe.arg_dict.items():
+            if 'data' in k or 'softmax_label' in k:
+                continue
+            arg_params[k] = v
+
+        calib_data = NDArrayIter(data=data, batch_size=data_shape[0])
+        calib_data = DummyIter(calib_data)
+        qsym, qarg_params, qaux_params = mx.contrib.quant.quantize_model(sym=bn_fp32,
+                                                                             arg_params=arg_params,
+                                                                             aux_params=bn_fp32_exe.aux_dict,
+                                                                             ctx=mx.current_context(),
+                                                                             quantized_dtype=qdtype,
+                                                                             calib_mode='naive',
+                                                                             calib_data=calib_data,
+                                                                             num_calib_examples=20)
+
+        mod = mx.mod.Module(symbol=qsym, label_names=None, context=mx.current_context())
+        mod.bind(for_training=False, data_shapes=[('data', data_shape)])
+        mod.set_params(qarg_params, qaux_params)
+        batch = mx.io.DataBatch([data], [])
+        mod.forward(batch, is_train=False)
+        output_int8_to_fp32= mod.get_outputs()[0]
+
+        assert_almost_equal(output.asnumpy(), output_int8_to_fp32.asnumpy(), rtol=1e-1, atol=3)
+
+    check_quantized_bn((32, 512, 4, 4), 'int8')
+    check_quantized_bn((32, 1024, 8, 8), 'int8')
+    check_quantized_bn((32, 3, 224, 224), 'int8')
+
 @with_seed()
 def test_quantize_params():
     data = mx.sym.Variable('data')
@@ -835,6 +917,12 @@ def check_qsym_forward(qsym, qarg_params, qaux_params, data_shape, label_shape=N
             if qdtype == 'int8' and is_test_for_mkldnn() and name in ['sym1', 'sym2', 'sym3']:
               print('skipped testing test_quantize_model_with_forward for mkldnn cpu int8 since it is not supported yet')
               continue
+            elif qdtype == 'uint8' and is_test_for_mkldnn() and name in ['sym1']:
+              print('skipping test_quantize_model_with_forward for mkldnn cpu uint8 since it is not supported yet')
+              continue
+            elif qdtype == 'int8' and is_test_for_gpu() and name in ['sym1']:
+              print('skipped testing test_quantize_model_with_forward for gpu int8 since it is not supported yet')
+              continue
 
             if lshape is None:
                 mod = Module(symbol=s, label_names=None)
@@ -905,7 +993,7 @@ def check_quantize_net(qdtype):
         if is_test_for_native_cpu():
             print('skipped testing test_quantize_model_with_forward for native cpu since it is not supported yet')
             return
-        elif qdtype == 'uint8' and is_test_for_gpu():
+        elif is_test_for_gpu():
             print('skipped testing test_quantize_model_with_forward for gpu uint8 since it is not supported yet')
             return
 

From b77f52463eccd1b3cc6533405ce56803bb35ef4f Mon Sep 17 00:00:00 2001
From: Holger Kohr <ho.kohr@zoho.com>
Date: Thu, 8 Aug 2019 19:01:24 +0200
Subject: [PATCH 158/813] [MXNET-1206] Support NDArray indexing with None and
 Ellipsis (#13143)

* Support NDArray indexing with None and Ellipsis

* Update NDArray.__setitem__ docs with None and Ellipsis

* Fix boolean flag in NDArray.__getitem__, add doctests

* Add setitem test for None and Ellipsis

* Fix wrong slice used, add cases to test_indexing

* Revamp NDArray.__getitem__ and __setitem__

* Fix typo in error message of SetSliceOpOutputDimSize

* Fix setting of array with integer indices

* Fix basic __setitem__ for all test cases

* WIP: fixing advanced indexing

* REMOVE: printing in tests

* Re-implement advanced indexing with None and Ellipsis

* Fix lint errors

* WIP: fix basic indexing

* WIP: fix basic indexing

* TEMP: print statements in tests

* Fix op.slice with step<0 and end==-1

* Implement copy-free general contiguous indexing

* Improve doctest of __getitem__

* Fix missing staticmethod

* Remove superfluous _at and _slice

* Fix lint errors

* WIP: basic indexing

* Remove print statements from tests

* Fix call into op.slice in basic indexing, add doctest

* Print failing index in setitem tests

* Simplify implementation of advanced index broadcasting

* Better printing for failing setitem tests

* Remove list indexing restriction, fix value shape broadcasting

* Fix bad string formatting

* Fix bug in test_uniform

* Test mutability of sliced array if contiguous

* Fix whitespace error in matrix_op-inl.h

* "Fix" pylint complaints

* Temporarily disable failing unittests

* Silence another pylint complaint

* Fix size-0 array creation

* Make scalar tensor assignment test check for IndexError

* Re-activate operator tests with 0-size arrays

* Use np.compat in tests with zeros in shape or empty shape

* Change comment in autograd indexing test

* Add more None-containing index tuples to indexing test

* Disable None in advanced indexing test since it has not been supported

* Fix sanity

* Fix ci

* Fix unit test failure

* Fix __getitem__
---
 python/mxnet/ndarray/ndarray.py         | 1137 +++++++++++++++--------
 python/mxnet/numpy/multiarray.py        |    4 +-
 python/mxnet/test_utils.py              |    6 +-
 src/operator/tensor/matrix_op-inl.h     |    9 +-
 tests/python/unittest/test_dgl_graph.py |    2 +-
 tests/python/unittest/test_ndarray.py   |  122 ++-
 tests/python/unittest/test_operator.py  |   31 +-
 7 files changed, 882 insertions(+), 429 deletions(-)

diff --git a/python/mxnet/ndarray/ndarray.py b/python/mxnet/ndarray/ndarray.py
index c1c45f04ead0..171ba0a5008c 100644
--- a/python/mxnet/ndarray/ndarray.py
+++ b/python/mxnet/ndarray/ndarray.py
@@ -18,9 +18,10 @@
 # coding: utf-8
 # pylint: disable=too-many-lines, protected-access
 # pylint: disable=import-error, no-name-in-module, undefined-variable
+
 """NDArray API of MXNet."""
-from __future__ import absolute_import
-from __future__ import division
+
+from __future__ import absolute_import, division
 
 try:
     from __builtin__ import slice as py_slice
@@ -410,19 +411,15 @@ def __setstate__(self, state):
         else:
             self.handle = None
 
-    # pylint: disable=line-too-long
     def __setitem__(self, key, value):
         """x.__setitem__(i, y) <=> x[i]=y
 
-        Sets value to self[key]. This functions supports advanced indexing defined in the following reference with
-        some restrictions.
+        Sets ``self[key]`` to ``value``.
 
-        https://docs.scipy.org/doc/numpy-1.13.0/reference/arrays.indexing.html#combining-advanced-and-basic-indexing
-
-        - If key is a list type, only a list of integers is supported, e.g. key=[1, 2] is supported,
-          while not for key=[[1, 2]].
-        - Ellipsis (...) and np.newaxis are not supported.
-        - Boolean array indexing is not supported.
+        This functions supports advanced indexing as defined in `the NumPy
+        advanced indexing documentation
+        <https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#advanced-indexing>`_,
+        with the restriction that boolean array indexing is not supported.
 
         Parameters
         ----------
@@ -433,27 +430,24 @@ def __setitem__(self, key, value):
 
         Examples
         --------
-        >>> x = mx.nd.zeros((2,3))
+        >>> x = mx.nd.zeros((2, 3))
         >>> x[:] = 1
         >>> x.asnumpy()
         array([[ 1.,  1.,  1.],
                [ 1.,  1.,  1.]], dtype=float32)
-        >>> x.asnumpy()
-        array([[ 1.,  1.,  1.],
-               [ 1.,  1.,  1.]], dtype=float32)
-        >>> x[:,1:2] = 2
+        >>> x[:, 1:2] = 2
         >>> x.asnumpy()
         array([[ 1.,  2.,  1.],
                [ 1.,  2.,  1.]], dtype=float32)
-        >>> x[1:2,1:] = 3
+        >>> x[1:2, 1:] = 3
         >>> x.asnumpy()
         array([[ 1.,  2.,  1.],
                [ 1.,  3.,  3.]], dtype=float32)
-        >>> x[1:,0:2] = mx.nd.zeros((1,2))
+        >>> x[1:, 0:2] = mx.nd.zeros((1, 2))
         >>> x.asnumpy()
         array([[ 1.,  2.,  1.],
                [ 0.,  0.,  3.]], dtype=float32)
-        >>> x[1,2] = 4
+        >>> x[1, 2] = 4
         >>> x.asnumpy()
         array([[ 1.,  2.,  1.],
                [ 0.,  0.,  4.]], dtype=float32)
@@ -466,31 +460,51 @@ def __setitem__(self, key, value):
         array([[ 6.,  5.,  5.],
                [ 6.,  0.,  4.]], dtype=float32)
         """
-        indexing_dispatch_code = _get_indexing_dispatch_code(key)
+        if self.ndim == 0 and key == ():
+            _internal._full(shape=self.shape, value=float(value), ctx=self.context,
+                            dtype=self.dtype, out=self)
+            return
+        key = _indexing_key_expand_implicit_axes(key, self.shape)
+        slc_key = tuple(idx for idx in key if idx is not None)
+
+        if len(slc_key) < self.ndim:
+            raise RuntimeError(
+                'too few indices after normalization: expected `ndim` ({}) '
+                'but got {}. This is a bug, please report it!'
+                ''.format(self.ndim, len(slc_key))
+            )
+        if len(slc_key) > self.ndim:
+            raise IndexError(
+                'too many indices ({}) for array with {} dimensions'
+                ''.format(len(slc_key), self.ndim)
+            )
+
+        indexing_dispatch_code = _get_indexing_dispatch_code(slc_key)
         if indexing_dispatch_code == _NDARRAY_BASIC_INDEXING:
-            self._set_nd_basic_indexing(key, value)
+            self._set_nd_basic_indexing(slc_key, value)
         elif indexing_dispatch_code == _NDARRAY_ADVANCED_INDEXING:
-            self._set_nd_advanced_indexing(key, value)
+            self._set_nd_advanced_indexing(slc_key, value)
         else:
-            raise ValueError('Indexing NDArray with index=%s and type=%s is not supported'
-                             % (str(key), str(type(key))))
-    # pylint: enable=line-too-long
+            raise ValueError(
+                'Indexing NDArray with index {} of type {} is not supported'
+                ''.format(key, type(key))
+            )
 
-    # pylint: disable=line-too-long
     def __getitem__(self, key):
         """x.__getitem__(i) <=> x[i]
 
-        Returns a sliced view of this array if the elements fetched are contiguous in memory;
-        otherwise, returns a newly created NDArray.
-        This functions supports advanced indexing defined in the following reference with
-        some restrictions.
+        Returns the subarray ``self[key]``.
 
-        https://docs.scipy.org/doc/numpy-1.13.0/reference/arrays.indexing.html#combining-advanced-and-basic-indexing
+        For basic indexing, i.e., if ``key`` consists only of integers,
+        ``slice``, ``Ellipsis`` (``...``) and ``None``, a mutable view is
+        returned that shares memory with this array if the accessed portion is
+        contiguous in memory.
+        Otherwise, a newly created ``NDArray`` is returned.
 
-        - If key is a list type, only a list of integers is supported, e.g. key=[1, 2] is supported,
-          while not for key=[[1, 2]].
-        - Ellipsis (...) and np.newaxis are not supported.
-        - Boolean array indexing is not supported.
+        This functions supports advanced indexing as defined in `the NumPy
+        advanced indexing documentation
+        <https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#advanced-indexing>`_,
+        with the restriction that boolean array indexing is not supported.
 
         Parameters
         ----------
@@ -499,196 +513,142 @@ def __getitem__(self, key):
 
         Examples
         --------
-        >>> x = mx.nd.arange(0,6).reshape((2,3))
+        The default is to give explicit indices for all axes:
+
+        >>> x = mx.nd.arange(0, 6).reshape((2, 3))
         >>> x.asnumpy()
         array([[ 0.,  1.,  2.],
                [ 3.,  4.,  5.]], dtype=float32)
-        >>> x[1].asnumpy()
-        array([ 3.,  4.,  5.], dtype=float32)
-        >>> y = x[0:1]
-        >>> y[:] = 2
+        >>> x[0, :].asnumpy()
+        array([0., 1., 2.], dtype=float32)
+        >>> x[0, :2].asnumpy()
+        array([0., 1.], dtype=float32)
+        >>> x[:, :-1].asnumpy()
+        array([[0., 1.],
+               [3., 4.]], dtype=float32)
+
+        If fewer indices are given, they are automatically supplemented by an
+        appropriate number of ``slice(None)`` ("``:``") to the right. For
+        instance, a single integer indexes along the first axis:
+
+        >>> x = mx.nd.arange(0, 6).reshape((2, 3))
+        >>> x[0].asnumpy()
+        array([0., 1., 2.], dtype=float32)
+        >>> x[1:].asnumpy()
+        array([[3., 4., 5.]], dtype=float32)
+
+        To omit a range of axes that should be kept as-is, an `Ellipsis`
+        ("``...``") can be used:
+
+        >>> x = mx.nd.arange(0, 16).reshape((2, 2, 2, 2))
+        >>> x[0, ..., 1].asnumpy()
+        array([[1., 3.],
+               [5., 7.]], dtype=float32)
+        >>> x[0, :, :, 1].asnumpy()  # equivalent
+        array([[1., 3.],
+               [5., 7.]], dtype=float32)
+
+        New axes of length 1 can be created by inserting ``None``
+        (`numpy.newaxis`) in the index:
+
+        >>> x = mx.nd.arange(0, 6).reshape((2, 3))
+        >>> x[None, :, :].asnumpy()
+        array([[[0., 1., 2.],
+                [3., 4., 5.]]], dtype=float32)
+        >>> x[None, :, :].shape
+        (1, 2, 3)
+
+        If the indexed portion of the array is contiguous in memory, no data
+        is copied. Instead, a shared-memory view of the original array is
+        returned, and changes to that view affect the original array:
+
+        >>> x = mx.nd.arange(0, 8).reshape((2, 2, 2))
+        >>> y = x[0]  # contiguous
+        >>> y.asnumpy()
+        array([[0., 1.],
+               [2., 3.]], dtype=float32)
+        >>> y[:] = -1
         >>> x.asnumpy()
-        array([[ 2.,  2.,  2.],
-               [ 3.,  4.,  5.]], dtype=float32)
-        >>> x = mx.nd.arange(0, 8, dtype='int32').reshape((2, 2, 2))
-        >>> x[[0, 1]]
-        [[[0 1]
-          [2 3]]
-         [[4 5]
-          [6 7]]]
-        >>> x[1:, [0, 1]]
-        [[[4 5]
-          [6 7]]]
+        array([[[-1., -1.],
+                [-1., -1.]],
+        <BLANKLINE>
+               [[ 4.,  5.],
+                [ 6.,  7.]]], dtype=float32)
+        >>> x = mx.nd.arange(0, 8).reshape((2, 2, 2))
+        >>> y = x[1, :1, :]  # contiguous
+        >>> y.asnumpy()
+        array([[4., 5.]], dtype=float32)
+        >>> y[:] = -1
+        >>> x.asnumpy()
+        array([[[ 0.,  1.],
+                [ 2.,  3.]],
+        <BLANKLINE>
+               [[-1., -1.],
+                [ 6.,  7.]]], dtype=float32)
+        >>> x = mx.nd.arange(0, 8).reshape((2, 2, 2))
+        >>> y = x[:, :, 1]  # not contiguous
+        >>> y.asnumpy()
+        array([[1., 3.],
+               [5., 7.]], dtype=float32)
+        >>> y[:] = -1
+        >>> x.asnumpy()
+        array([[[0., 1.],
+                [2., 3.]],
+        <BLANKLINE>
+               [[4., 5.],
+                [6., 7.]]], dtype=float32)
+
+        If the indexing key contains `list`, `numpy.ndarray` or `NDArray`
+        objects, advanced indexing is triggered, which always returns a
+        copy:
+
+        >>> x = mx.nd.arange(0, 8).reshape((2, 2, 2))
+        >>> x[[0, 1]].asnumpy()
+        array([[[0., 1.],
+                [2., 3.]],
+        <BLANKLINE>
+               [[4., 5.],
+                [6., 7.]]], dtype=float32)
+        >>> x[[0, 1], :].asnumpy()  # equivalent
+        array([[[0., 1.],
+                [2., 3.]],
+        <BLANKLINE>
+               [[4., 5.],
+                [6., 7.]]], dtype=float32)
         >>> y = np.array([0, 1], dtype='int32')
-        >>> x[1:, y]
-        [[[4 5]
-          [6 7]]]
+        >>> x[1:, y].asnumpy()
+        array([[[4., 5.],
+                [6., 7.]]], dtype=float32)
         >>> y = mx.nd.array([0, 1], dtype='int32')
-        >>> x[1:, y]
-        [[[4 5]
-          [6 7]]]
+        >>> x[1:, y].asnumpy()
+        array([[[4., 5.],
+                [6., 7.]]], dtype=float32)
         """
+        if self.ndim == 0 and key == ():
+            return self
+        key = _indexing_key_expand_implicit_axes(key, self.shape)
+        if len(key) == 0:
+            raise ValueError('indexing key cannot be an empty tuple')
+
         indexing_dispatch_code = _get_indexing_dispatch_code(key)
         if indexing_dispatch_code == _NDARRAY_BASIC_INDEXING:
             return self._get_nd_basic_indexing(key)
         elif indexing_dispatch_code == _NDARRAY_ADVANCED_INDEXING:
             return self._get_nd_advanced_indexing(key)
         else:
-            raise ValueError('Indexing NDArray with index=%s and type=%s is not supported'
-                             % (str(key), str(type(key))))
-    # pylint: enable=line-too-long
+            raise RuntimeError
 
-    def _get_index_nd(self, key):
-        """Returns an index array for use in scatter_nd and gather_nd."""
-        def _is_advanced_index(index):
-            """The definition of advanced index here includes integers as well, while
-            integers are considered as basic index type when the key contains only
-            slices and integers."""
-            return not isinstance(index, py_slice)
-
-        if isinstance(key, (NDArray, np.ndarray, list, integer_types, py_slice)):
-            key = (key,)
-
-        assert isinstance(key, tuple),\
-            'index=%s must be a NDArray, or np.ndarray, or list, or tuple ' \
-            ' type to use advanced indexing, received type=%s' % (str(key), str(type(key)))
-
-        assert len(key) > 0, "Cannot slice with empty indices"
-        shape = self.shape
-        assert len(shape) >= len(key),\
-            "Slicing dimensions exceeds array dimensions, %d vs %d" % (len(key), len(shape))
-        indices = []
-        dtype = 'int32'  # index data type passed to gather_nd op
-        need_broadcast = (len(key) != 1)
-        advanced_indices = []  # include list, NDArray, np.ndarray, integer
-        basic_indices = []  # include only slices
-        advanced_index_bshape = None  # final advanced index shape
-        for i, idx_i in enumerate(key):
-            is_advanced_index = True
-            if isinstance(idx_i, (np.ndarray, list, tuple)):
-                idx_i = array(idx_i, ctx=self.context, dtype=dtype)
-                advanced_indices.append(i)
-            elif isinstance(idx_i, py_slice):
-                start, stop, step = _get_index_range(idx_i.start, idx_i.stop, shape[i], idx_i.step)
-                idx_i = arange(start, stop, step, ctx=self.context, dtype=dtype)
-                basic_indices.append(i)
-                is_advanced_index = False
-            elif isinstance(idx_i, integer_types):
-                start, stop, step = _get_index_range(idx_i, idx_i+1, shape[i], 1)
-                idx_i = arange(start, stop, step, ctx=self.context, dtype=dtype)
-                advanced_indices.append(i)
-            elif isinstance(idx_i, NDArray):
-                if dtype != idx_i.dtype:
-                    idx_i = idx_i.astype(dtype)
-                advanced_indices.append(i)
-            else:
-                raise IndexError('Indexing NDArray with index=%s of type=%s is not supported'
-                                 % (str(key), str(type(key))))
-            if is_advanced_index:
-                if advanced_index_bshape is None:
-                    advanced_index_bshape = idx_i.shape
-                elif advanced_index_bshape != idx_i.shape:
-                    need_broadcast = True
-                    advanced_index_bshape = _get_broadcast_shape(advanced_index_bshape, idx_i.shape)
-            indices.append(idx_i)
-
-        # Get final index shape for gather_nd. See the following reference
-        # for determining the output array shape.
-        # https://docs.scipy.org/doc/numpy-1.13.0/reference/arrays.indexing.html#combining-advanced-and-basic-indexing  # pylint: disable=line-too-long
-        if len(advanced_indices) == 0:
-            raise ValueError('Advanced index tuple must contain at least one of the following types:'
-                             ' list, tuple, NDArray, np.ndarray, integer, received index=%s' % key)
-        # determine the output array's shape by checking whether advanced_indices are all adjacent
-        # or separated by slices
-        advanced_indices_adjacent = True
-        for i in range(0, len(advanced_indices)-1):
-            if advanced_indices[i] + 1 != advanced_indices[i+1]:
-                advanced_indices_adjacent = False
-                break
-
-        index_bshape_list = []  # index broadcasted shape
-        if advanced_indices_adjacent:
-            for i in range(0, advanced_indices[0]):
-                index_bshape_list.extend(indices[i].shape)
-                if not need_broadcast and indices[i].shape != advanced_index_bshape:
-                    need_broadcast = True
-            index_bshape_list.extend(advanced_index_bshape)
-            for i in range(advanced_indices[-1]+1, len(indices)):
-                if not need_broadcast and indices[i].shape != advanced_index_bshape:
-                    need_broadcast = True
-                index_bshape_list.extend(indices[i].shape)
-        else:
-            index_bshape_list.extend(advanced_index_bshape)
-            for i in basic_indices:
-                index_bshape_list.extend(indices[i].shape)
-                if not need_broadcast and indices[i].shape != advanced_index_bshape:
-                    need_broadcast = True
-        index_bshape = tuple(index_bshape_list)
-
-        # Need to broadcast all ndarrays in indices to the final shape.
-        # For example, suppose an array has shape=(5, 6, 7, 8) and
-        # key=(slice(1, 5), [[1, 2]], slice(2, 5), [1]).
-        # Since key[1] and key[3] are two advanced indices here and they are
-        # separated by basic indices key[0] and key[2], the output shape
-        # is (1, 2, 4, 3), where the first two elements come from the shape
-        # that key[1] and key[3] should broadcast to, which is (1, 2), and
-        # the last two elements come from the shape of two basic indices.
-        # In order to broadcast all basic and advanced indices to the output shape,
-        # we need to reshape them based on their axis. For example, to broadcast key[0],
-        # with shape=(4,), we first need to reshape it into (1, 1, 4, 1), and then
-        # broadcast the reshaped array to (1, 2, 4, 3); to broadcast key[1], we first
-        # reshape it into (1, 2, 1, 1), then broadcast the reshaped array to (1, 2, 4, 3).
-        if need_broadcast:
-            broadcasted_indices = []
-            idx_rshape = [1] * len(index_bshape)
-            if advanced_indices_adjacent:
-                advanced_index_bshape_start = advanced_indices[0]  # start index of advanced_index_bshape in index_shape
-                advanced_index_bshape_stop = advanced_index_bshape_start + len(advanced_index_bshape)
-                for i, idx in enumerate(key):
-                    if _is_advanced_index(idx):
-                        k = advanced_index_bshape_stop
-                        # find the reshaped shape for indices[i]
-                        for dim_size in indices[i].shape[::-1]:
-                            k -= 1
-                            idx_rshape[k] = dim_size
-                    else:
-                        if i < advanced_indices[0]:  # slice is on the left side of advanced indices
-                            idx_rshape[i] = indices[i].shape[0]
-                        elif i > advanced_indices[-1]:  # slice is on the right side of advanced indices
-                            idx_rshape[i-len(key)] = indices[i].shape[0]
-                        else:
-                            raise ValueError('basic index i=%d cannot be between advanced index i=%d and i=%d'
-                                             % (i, advanced_indices[0], advanced_indices[-1]))
-                    # broadcast current index to the final shape
-                    broadcasted_indices.append(indices[i].reshape(tuple(idx_rshape)).broadcast_to(index_bshape))
-                    # reset idx_rshape to ones
-                    for j, _ in enumerate(idx_rshape):
-                        idx_rshape[j] = 1
-            else:
-                basic_index_offset = len(advanced_index_bshape)
-                for i, idx in enumerate(key):
-                    if _is_advanced_index(idx):
-                        k = len(advanced_index_bshape)
-                        for dim_size in indices[i].shape[::-1]:
-                            k -= 1
-                            idx_rshape[k] = dim_size
-                    else:
-                        idx_rshape[basic_index_offset] = indices[i].shape[0]
-                        basic_index_offset += 1
-                    # broadcast current index to the final shape
-                    broadcasted_indices.append(indices[i].reshape(tuple(idx_rshape)).broadcast_to(index_bshape))
-                    # reset idx_rshape to ones
-                    for j, _ in enumerate(idx_rshape):
-                        idx_rshape[j] = 1
-
-            indices = broadcasted_indices
-        return op.stack(*indices)
-
-    def _prepare_value_nd(self, value, vshape):
-        """Given value and vshape, create an `NDArray` from value with the same
-        context and dtype as the current one and broadcast it to vshape."""
+    def _prepare_value_nd(self, value, new_axes, bcast_shape):
+        """Return a broadcast `NDArray` with same context and dtype as ``self``.
+
+        Before broadcasting, ``new_axes`` of length 1 will be added to
+        ``value``. This is done in contrast to blindly reshaping based on
+        ``bcast_shape``, since the latter would silently ignore wrongly shaped
+        ``value`` arrays, e.g. ``nd.zeros((2, 3))[:, :1] = nd.ones(2)``.
+        """
         if isinstance(value, numeric_types):
-            value_nd = full(shape=vshape, val=value, ctx=self.context, dtype=self.dtype)
+            value_nd = full(bcast_shape, value, ctx=self.context, dtype=self.dtype)
+            new_axes = []  # ignore for scalar
         elif isinstance(value, NDArray):
             value_nd = value.as_in_context(self.context)
             if value_nd.dtype != self.dtype:
@@ -697,168 +657,461 @@ def _prepare_value_nd(self, value, vshape):
             try:
                 value_nd = array(value, ctx=self.context, dtype=self.dtype)
             except:
-                raise TypeError('NDArray does not support assignment with non-array-like'
-                                ' object %s of type %s' % (str(value), str(type(value))))
-        if value_nd.shape != vshape:
-            value_nd = value_nd.broadcast_to(vshape)
+                raise TypeError('NDArray does not support assignment with non-array-like '
+                                'object {} of type {}'.format(value, type(value)))
+
+        # First reshape `value_nd` to a new shape that incorporates existing
+        # axes, new axes and broadcasting axes in the right way.
+        tmp_shape = _shape_for_bcast(
+            value_nd.shape, target_ndim=len(bcast_shape), new_axes=new_axes
+        )
+        value_nd = value_nd.reshape(tmp_shape)
+
+        if value_nd.shape != bcast_shape:
+            value_nd = value_nd.broadcast_to(bcast_shape)
         return value_nd
 
+    # pylint: disable=invalid-name
+    @staticmethod
+    def _basic_indexing_key_to_begin_end_step(idcs, shape, keep_none=True):
+        """Map a tuple of ``slice`` and ``None`` (ignored) to begin, end, step tuples."""
+        idcs = [idx for idx in idcs if idx is not None]
+        idcs = [idx if isinstance(idx, py_slice) else _int_to_slice(idx)
+                for idx in idcs]
+
+        if keep_none:
+            sss_list = [(slc.start, slc.stop, slc.step) for slc, n in zip(idcs, shape)]
+        else:
+            sss_list = [slc.indices(n) for slc, n in zip(idcs, shape)]
+        return tuple(zip(*sss_list))
+    # pylint: enable=invalid-name
+
+    # pylint: disable=invalid-name
+    @staticmethod
+    def _basic_indexing_key_int_to_slice(idcs):
+        """Return the converted indexing tuple and the integer axes."""
+        int_axes = []
+        conv_idcs = []
+        for ax, idx in enumerate(idcs):
+            if isinstance(idx, integer_types):
+                conv_idcs.append(_int_to_slice(idx))
+                int_axes.append(ax)
+            else:
+                conv_idcs.append(idx)
+
+        return tuple(conv_idcs), tuple(int_axes)
+    # pylint: enable=invalid-name
+
+    @staticmethod
+    def _new_axes_after_basic_indexing(axes, key_nd):
+        """Return indices of ``axes`` after slicing with ``key_nd``.
+
+        This function is used to calculate the positions where new axes should
+        end up after indexing, taking into account the removal of axes by
+        integer indexing.
+
+        The ``key_nd`` sequence should contain slices and integers only, no
+        ``None`` entries.
+        """
+        steps = [0] + [0 if isinstance(idx, integer_types) else 1
+                       for idx in key_nd]
+        cum_steps = np.cumsum(steps)
+        axes_in_bounds = [ax for ax in axes if ax < len(cum_steps)]
+        axes_out_of_bounds = [ax for ax in axes if ax >= len(cum_steps)]
+        axes_after = tuple(cum_steps[axes_in_bounds])
+        oob_offsets = [ax - len(key_nd) for ax in axes_out_of_bounds]
+        axes_after += tuple(cum_steps[-1] + offset for offset in oob_offsets)
+        return axes_after
+
+    # pylint: disable=invalid-name
+    @staticmethod
+    def _basic_indexing_slice_is_contiguous(slc_key, shape):
+        """Whether indexing with the given key results in a contiguous array.
+
+        The rule is: From right to left, if in an axis, a slice produces a
+        proper subset, no later axis can produce a proper subset or use
+        a step different from 1.
+
+        The ``slc_key`` sequence must have the same length as ``shape`` and
+        only contain `slice` objects.
+        """
+        assert len(slc_key) == len(shape)
+        subset = False
+        for idx, n in zip(reversed(slc_key), reversed(shape)):
+            start, stop, step = idx.indices(n)
+            if step > 0:
+                num = int(np.ceil(max(stop - start, 0) / step))
+            else:
+                num = int(np.ceil(min(stop - start, 0) / step))
+
+            if num != 1 and (subset or step != 1):
+                return False
+            if num != n:
+                subset = True
+
+        return True
+    # pylint: enable=invalid-name
+
+    @staticmethod
+    def _basic_indexing_sliced_shape(slc_key, shape):
+        """Return the shape after slicing with the given key."""
+        assert len(slc_key) == len(shape)
+        sliced_shape = []
+        for idx, n in zip(slc_key, shape):
+            start, stop, step = idx.indices(n)
+            if step > 0:
+                num = int(np.ceil(max(stop - start, 0) / step))
+            else:
+                num = int(np.ceil(min(stop - start, 0) / step))
+            sliced_shape.append(num)
+
+        return tuple(sliced_shape)
+
+    # pylint: disable=invalid-name
+    @staticmethod
+    def _basic_indexing_contiguous_flat_begin_end(slc_key, shape):
+        """Return the flat indices of begin and end for contiguous slicing."""
+        assert len(slc_key) == len(shape)
+        begin, end, _ = slc_key[0].indices(shape[0])
+        flat_begin, flat_end = begin, end - 1
+        for idx, n in zip(slc_key[1:], shape[1:]):
+            flat_begin *= n
+            flat_end *= n
+            begin, end, _ = idx.indices(n)
+            flat_begin += begin
+            flat_end += end - 1
+
+        return flat_begin, flat_end + 1
+    # pylint: enable=invalid-name
+
     def _set_nd_basic_indexing(self, key, value):
-        """This function is called by __setitem__ when key is a basic index, i.e.
-        an integer, or a slice, or a tuple of integers and slices. No restrictions
-        on the values of slices' steps."""
-        shape = self.shape
-        if isinstance(key, integer_types):
-            if key < 0:
-                key += shape[0]
-            if key < 0 or key >= shape[0]:
-                if key < 0:
-                    key -= shape[0]
-                raise IndexError('index %d is out of bounds for axis 0 with size %d'
-                                 % (key, shape[0]))
-            key = py_slice(key, key+1)  # key must be >= 0 here
-
-        if isinstance(key, py_slice):
-            assign_to_self = key.step is None or key.step == 1
-            assign_to_self &= key.start is None or key.start == 0
-            assign_to_self &= key.stop is None or key.stop == shape[0]
-            if assign_to_self:  # trivial case, assign value to self
-                if isinstance(value, NDArray):
-                    if value.handle is not self.handle:
-                        if value.shape != shape:
-                            value = value.broadcast_to(shape)
-                        value.copyto(self)
-                elif isinstance(value, numeric_types):
-                    _internal._full(shape=shape, ctx=self.context,
-                                    dtype=self.dtype, value=float(value), out=self)
-                elif isinstance(value, (np.ndarray, np.generic)):
-                    if isinstance(value, np.generic) or value.shape != shape:
-                        value = np.broadcast_to(value, shape)
-                    self._sync_copyfrom(value)
-                else:  # value might be a list or a tuple
-                    value_nd = self._prepare_value_nd(value, shape)
-                    value_nd.copyto(self)
-                return
-            else:  # non-trivial case, use _slice_assign or _slice_assign_scalar
-                key = (key,)
-
-        assert isinstance(key, tuple), "key=%s must be a tuple of slices and integers" % str(key)
-
-        assert len(key) <= len(shape), "Indexing dimensions exceed array dimensions, %d vs %d"\
-                                       % (len(key), len(shape))
-        begin = []
-        end = []
-        steps = []
-        oshape = []  # output shape of slice using key
-        vshape = []  # value shape of data[key]
-        for i, slice_i in enumerate(key):
-            dim_size = 1
-            if isinstance(slice_i, py_slice):
-                begin.append(slice_i.start)
-                end.append(slice_i.stop)
-                steps.append(slice_i.step)
-                start, stop, step = _get_index_range(slice_i.start, slice_i.stop,
-                                                     shape[i], slice_i.step)
-                dim_size = _get_dim_size(start, stop, step)
-                vshape.append(dim_size)
-            elif isinstance(slice_i, integer_types):
-                begin.append(slice_i)
-                end.append(slice_i+1 if slice_i != -1 else self.shape[i])
-                steps.append(1)
+        """This function indexes ``self`` with a tuple of ``slice`` objects only."""
+        for idx in key:
+            if not isinstance(idx, (py_slice, integer_types)):
+                raise RuntimeError(
+                    '`key` may only contain `slice` or integer objects in the '
+                    'basic implementation, got object of type {}. '
+                    'This is a bug, please report it!'
+                    ''.format(type(idx)))
+        int_axes = [
+            ax for ax in range(len(key)) if isinstance(key[ax], integer_types)
+        ]
+        begin, end, step = self._basic_indexing_key_to_begin_end_step(
+            key, self.shape, keep_none=False
+        )
+        indexed_shape = tuple(
+            _get_dim_size(b, e, s) for b, e, s in zip(begin, end, step)
+        )
+        can_assign_directly = (
+            (indexed_shape == self.shape) and all(s > 0 for s in step)
+        )
+        begin, end, step = self._basic_indexing_key_to_begin_end_step(
+            key, self.shape, keep_none=True
+        )
+
+        if can_assign_directly:
+            # Easy case, overwrite whole array.
+            if isinstance(value, NDArray):
+                if value.handle is not self.handle:
+                    # Need to do this before `broadcast_to`.
+                    tmp_shape = _shape_for_bcast(
+                        value.shape, target_ndim=self.ndim, new_axes=int_axes
+                    )
+                    value = value.reshape(tmp_shape)
+
+                    if value.shape != self.shape:
+                        value = value.broadcast_to(self.shape)
+                    value.copyto(self)
+
+            elif isinstance(value, numeric_types):
+                _internal._full(
+                    shape=self.shape, value=float(value), ctx=self.context,
+                    dtype=self.dtype, out=self
+                )
+
+            elif isinstance(value, (np.ndarray, np.generic)):
+                tmp_shape = _shape_for_bcast(
+                    value.shape, target_ndim=self.ndim, new_axes=int_axes
+                )
+                value = value.reshape(tmp_shape)
+
+                if isinstance(value, np.generic) or value.shape != self.shape:
+                    value = np.broadcast_to(value, self.shape)
+                self._sync_copyfrom(value)
+
             else:
-                raise ValueError("basic indexing does not support index=%s of type=%s"
-                                 % (str(slice_i), str(type(slice_i))))
-            oshape.append(dim_size)
-
-        oshape.extend(shape[len(key):])
-        vshape.extend(shape[len(key):])
-        # if key contains all integers, vshape should be (1,)
-        if len(vshape) == 0:
-            vshape.append(1)
-        oshape = tuple(oshape)
-        vshape = tuple(vshape)
+                # Other array-like
+                value_nd = self._prepare_value_nd(
+                    value, new_axes=int_axes, bcast_shape=self.shape
+                )
+                value_nd.copyto(self)
 
-        if isinstance(value, numeric_types):
-            _internal._slice_assign_scalar(self, out=self, begin=begin, end=end,
-                                           step=steps, scalar=float(value))
+        elif isinstance(value, numeric_types):
+            _internal._slice_assign_scalar(
+                self, float(value), begin, end, step, out=self
+            )
+
+        else:
+            value_nd = self._prepare_value_nd(
+                value, new_axes=int_axes, bcast_shape=indexed_shape
+            )
+            _internal._slice_assign(self, value_nd, begin, end, step, out=self)
+
+    def _get_nd_basic_indexing(self, key):
+        """This function indexes ``self`` with a tuple of `slice` objects only."""
+        key_nd = tuple(idx for idx in key if idx is not None)
+        if len(key_nd) < self.ndim:
+            raise RuntimeError(
+                'too few indices after normalization: expected `ndim` ({}) '
+                'but got {}. This is a bug, please report it!'
+                ''.format(self.ndim, len(key_nd))
+            )
+        if len(key_nd) > self.ndim:
+            raise IndexError(
+                'too many indices ({}) for array with {} dimensions'
+                ''.format(len(key_nd), self.ndim)
+            )
+
+        none_axes = [ax for ax in range(len(key)) if key[ax] is None]  # pylint: disable=invalid-name
+        slc_key, int_axes = self._basic_indexing_key_int_to_slice(key_nd)
+        new_axes = self._new_axes_after_basic_indexing(none_axes, key_nd)
+
+        # Check bounds for integer axes
+        for ax in int_axes:  # pylint: disable=invalid-name
+            if not -self.shape[ax] <= key_nd[ax] < self.shape[ax]:
+                raise IndexError(
+                    'index {} is out of bounds for axis {} with size {}'
+                    ''.format(key_nd[ax], ax, self.shape[ax]))
+
+        # Make sure we don't accidentally have advanced indexing or
+        # unsupported entries.
+        for idx in slc_key:
+            if not isinstance(idx, py_slice):
+                raise RuntimeError(
+                    'found object of type {} instead of `slice`. '
+                    'This is a bug, please report it!'
+                    ''.format(type(idx)))
+
+        # Convert to begin, end and step, and return immediately if the slice
+        # is empty
+        begin, end, step = self._basic_indexing_key_to_begin_end_step(
+            slc_key, self.shape, keep_none=False
+        )
+        # Pylint is wrong about this
+        # pylint: disable=bad-continuation
+        if any(
+            b >= e and s > 0 or b <= e and s < 0 for b, e, s in zip(begin, end, step)
+        ):
+            return array([], self.context, self.dtype)
+        # pylint: enable=bad-continuation
+
+        if self._basic_indexing_slice_is_contiguous(slc_key, self.shape):
+            # Create a shared-memory view by using low-level flat slicing
+            flat_begin, flat_end = self._basic_indexing_contiguous_flat_begin_end(
+                slc_key, self.shape
+            )
+            handle = NDArrayHandle()
+            flat_self = self.reshape(-1)
+            check_call(
+                _LIB.MXNDArraySlice(
+                    flat_self.handle,
+                    mx_uint(flat_begin),
+                    mx_uint(flat_end),
+                    ctypes.byref(handle),
+                )
+            )
+            sliced_shape = self._basic_indexing_sliced_shape(slc_key, self.shape)
+            sliced = NDArray(handle=handle, writable=self.writable).reshape(sliced_shape)
         else:
-            value_nd = self._prepare_value_nd(value, vshape)
-            if vshape != oshape:
-                value_nd = value_nd.reshape(oshape)
-            _internal._slice_assign(self, value_nd, begin, end, steps, out=self)
+            begin, end, step = self._basic_indexing_key_to_begin_end_step(
+                slc_key, self.shape, keep_none=True
+            )
+            sliced = op.slice(self, begin, end, step)
+
+        # Reshape to final shape due to integer and `None` entries in `key`.
+        final_shape = [sliced.shape[i] for i in range(sliced.ndim)
+                       if i not in int_axes]
+        for ax in new_axes:  # pylint: disable=invalid-name
+            final_shape.insert(ax, 1)
+
+        if final_shape == []:
+            # Override for single element indexing
+            final_shape = [1]
+
+        return sliced.reshape(final_shape)
+
+    @staticmethod
+    def _advanced_index_to_array(idx, ax_len, ctx):
+        """Convert ``idx`` to `NDArray` for advanced indexing.
+
+        The ``ax_len`` is used to convert `slice` objects to integer arrays.
+        """
+        idx_dtype = 'int32'
+        if isinstance(idx, NDArray):
+            if idx.dtype != idx_dtype:
+                idx = idx.astype(idx_dtype)
+            return idx.as_in_context(ctx)
+
+        elif isinstance(idx, (np.ndarray, list, tuple)):
+            return array(idx, ctx, idx_dtype)
+
+        elif isinstance(idx, integer_types):
+            return array([idx], ctx, idx_dtype)
+
+        elif isinstance(idx, py_slice):
+            start, stop, step = idx.indices(ax_len)
+            return arange(start, stop, step, ctx=ctx, dtype=idx_dtype)
+
+        else:
+            raise RuntimeError('illegal index type {}'.format(type(idx)))
+
+    # pylint: disable=invalid-name
+    @staticmethod
+    def _broadcast_advanced_indices(arrays, block_axes):
+        """Broadcast arrays according to position in the sequence.
+
+        Here, "according to position" means that an array of dimension 1
+        (which is the case for all except ``block_axes``) will have shape
+        ``(1, ..., 1, N, 1, ..., 1)``, where ``N`` is the length, and the
+        position of ``N`` in the shape is the same as the position of the
+        array in the ``arrays`` sequence, plus extra dimensions of the
+        advanced block if it is left of the array.
+
+        The arrays at ``block_axes`` are the advanced indices. They are assumed to
+        be ready for mutual broadcasting to produce the advanced indexing block.
+        It is further assumed that the numbers in ``block_axes`` are consecutive.
+
+        The return value is a tuple containing the arrays with broadcast shapes.
+        """
+        block_shape = _broadcast_shapes([arrays[ax] for ax in block_axes])
+        ndim_blk = len(block_shape)
+        ndim_blk_delta = ndim_blk - len(block_axes)
+        ndim_lead = block_axes[0]
+        ndim_trail = len(arrays) - (block_axes[-1] + 1)
+
+        bcast_shape = (
+            tuple(arrays[ax].shape[0] for ax in range(ndim_lead)) +
+            block_shape +
+            tuple(arrays[ax].shape[0] for ax in range(block_axes[-1] + 1, len(arrays)))
+        )
+
+        bcast_arrays = [None] * len(arrays)
+        for ax in block_axes:
+            arr = arrays[ax].broadcast_to(block_shape)
+            shp = (1,) * ndim_lead + block_shape + (1,) * ndim_trail
+            bcast_arrays[ax] = arr.reshape(shp).broadcast_to(bcast_shape)
+
+        for ax in set(range(len(arrays))) - set(block_axes):
+            shp = [1] * len(bcast_shape)
+            if ax < ndim_lead:
+                shp[ax] = arrays[ax].shape[0]
+            else:
+                shp[ax + ndim_blk_delta] = arrays[ax].shape[0]
+            bcast_arrays[ax] = arrays[ax].reshape(shp).broadcast_to(bcast_shape)
+
+        return tuple(bcast_arrays)
+    # pylint: enable=invalid-name
+
+    @staticmethod
+    def _drop_slice_none_at_end(key):
+        """Remove ``slice(None)`` at the end of a key.
+
+        This is used for efficiency in advanced indexing, to avoid generating
+        ``arange(n)`` arrays for these axes. The `gather_nd` and `scatter_nd`
+        handle implicit full trailing axes automatically.
+        """
+        key = list(key)
+        while isinstance(key[-1], py_slice) and key[-1] == slice(None):
+            key.pop()
+        return tuple(key)
+
+    def _get_index_nd(self, key):
+        """Return an index array for use in `scatter_nd` and `gather_nd`."""
+        key_nd = tuple(idx for idx in key if idx is not None)
+        if len(key_nd) < self.ndim:
+            raise RuntimeError(
+                'too few indices after normalization: expected `ndim` ({}) '
+                'but got {}. This is a bug, please report it!'
+                ''.format(self.ndim, len(key_nd))
+            )
+        if len(key_nd) > self.ndim:
+            raise IndexError(
+                'too many indices ({}) for array with {} dimensions'
+                ''.format(len(key_nd), self.ndim)
+            )
+        ndim = len(key_nd)
+
+        # --- Preparation --- #
+
+        # - Make lists for bookkeeping of advanced indices & axes
+        # - Drop trailing `slice(None)` entries in `key` for efficiency
+        # - Determine whether the advanced indices are adjacent in `key`
+        # - Depending on that, make index permutations to move around indices
+
+        adv_axs = [ax for ax, idx in enumerate(key) if _is_advanced_index(idx)]
+        adv_axs_nd = [ax for ax, idx in enumerate(key_nd) if _is_advanced_index(idx)]
+        adv_idcs_are_adjacent = bool(np.all(np.diff(adv_axs) == 1))
+        nonadv_axs_nd = [ax for ax in range(ndim) if ax not in adv_axs_nd]
+        adv_idcs_nd = [key_nd[ax] for ax in adv_axs_nd]
+        idcs_short = self._drop_slice_none_at_end(key_nd)
+        dropped_axs = list(range(len(idcs_short), ndim))
+
+        if adv_idcs_are_adjacent:
+            # The easy case: the advanced block can stay at its position, and no
+            # permutation needs to be done (identity permutation)
+            axs_nd_permut = axs_nd_permut_inv = tuple(range(ndim))
+            idcs_permut_short = idcs_short
+            block_axs_nd = adv_axs_nd
+        else:
+            # The more complicated case: during broadcasting, we need to use the
+            # indices in the *permuted* order, where the advanced block is
+            # at the beginning, while the final index for `gather_nd` is stacked
+            # in the *original* order, so that the association of index with
+            # array axis remains the same.
+
+            # This order is used for broadcasting: advanced block at the beginning
+            idcs_permut_short = (
+                adv_idcs_nd +
+                [key_nd[ax] for ax in range(ndim)
+                 if ax not in adv_axs_nd and ax not in dropped_axs]
+            )
+            block_axs_nd = list(range(len(adv_axs_nd)))
+            axs_nd_permut = adv_axs_nd + nonadv_axs_nd
+            axs_nd_permut_inv = list(np.argsort(axs_nd_permut))
+
+        # --- Conversion, broadcasting and index stacking --- #
+
+        # - Convert all indices in `key` to arrays: integers to 1-element arrays,
+        #   `slice` objects to arrays with explicit indices
+        # - Reshape arrays for broadcasting according to their position in the
+        #   *permuted* key
+        # - Broadcast and stack the indices in the *original* order
+
+        shape_nd_permut = tuple(self.shape[ax] for ax in axs_nd_permut)
+        converted_idcs_short = [
+            self._advanced_index_to_array(idx, ax_len, self.context)
+            for idx, ax_len in zip(idcs_permut_short, shape_nd_permut)
+        ]
+        bcast_idcs_permut_short = self._broadcast_advanced_indices(
+            converted_idcs_short, block_axes=block_axs_nd
+        )
+        # Undo the permutation to restore the original order
+        bcast_idcs_short = [
+            bcast_idcs_permut_short[ax]
+            for ax in axs_nd_permut_inv
+            if axs_nd_permut[ax] not in dropped_axs
+        ]
+
+        return op.stack(*bcast_idcs_short)
 
     def _set_nd_advanced_indexing(self, key, value):
         """This function is called by __setitem__ when key is an advanced index."""
         indices = self._get_index_nd(key)
         vshape = _get_oshape_of_gather_nd_op(self.shape, indices.shape)
-        value_nd = self._prepare_value_nd(value, vshape)
-        _internal._scatter_set_nd(lhs=self, rhs=value_nd, indices=indices,
-                                  shape=self.shape, out=self)
-
-    def _get_nd_basic_indexing(self, key):
-        """This function is called when key is a slice, or an integer,
-        or a tuple of slices or integers"""
-        shape = self.shape
-        if isinstance(key, integer_types):
-            if key > shape[0] - 1:
-                raise IndexError(
-                    'index {} is out of bounds for axis 0 with size {}'.format(
-                        key, shape[0]))
-            return self._at(key)
-        elif isinstance(key, py_slice):
-            if key.step is not None and key.step != 1:
-                if key.step == 0:
-                    raise ValueError("slice step cannot be zero")
-                return op.slice(self, begin=(key.start,), end=(key.stop,), step=(key.step,))
-            elif key.start is not None or key.stop is not None:
-                return self._slice(key.start, key.stop)
-            else:
-                return self
-
-        if not isinstance(key, tuple):
-            raise ValueError('index=%s must be a slice, or an ineger, or a tuple'
-                             ' of slices and integers to use basic indexing, received type=%s'
-                             % (str(key), str(type(key))))
-        assert len(key) != 0, 'basic index cannot be an empty tuple'
-        begin = []
-        end = []
-        step = []
-        kept_axes = []  # axes where slice_i is a slice
-        i = -1
-        for i, slice_i in enumerate(key):
-            if isinstance(slice_i, integer_types):
-                begin.append(slice_i)
-                end.append(slice_i+1 if slice_i != -1 else self.shape[i])
-                step.append(1)
-            elif isinstance(slice_i, py_slice):
-                if slice_i.step == 0:
-                    raise ValueError('basic index=%s cannot have slice=%s with step = 0'
-                                     % (str(key), str(slice_i)))
-                begin.append(slice_i.start)
-                end.append(slice_i.stop)
-                step.append(slice_i.step)
-                kept_axes.append(i)
-            else:
-                raise ValueError('basic_indexing does not support slicing with '
-                                 'index=%s of type=%s.' % (str(slice_i), str(type(slice_i))))
-        kept_axes.extend(range(i+1, len(shape)))
-        sliced_nd = op.slice(self, begin, end, step)
-        if len(kept_axes) == len(shape):
-            return sliced_nd
-        # squeeze sliced_shape to remove the axes indexed by integers
-        oshape = []
-        sliced_shape = sliced_nd.shape
-        for axis in kept_axes:
-            oshape.append(sliced_shape[axis])
-        # if key is a tuple of integers, still need to keep 1 dim
-        # while in Numpy, the output will become an value instead of an ndarray
-        if len(oshape) == 0:
-            oshape.append(1)
-        oshape = tuple(oshape)
-        assert np.prod(oshape) == np.prod(sliced_shape), 'oshape=%s has different size'\
-                                                         ' than sliced_shape=%s'\
-                                                         % (oshape, sliced_shape)
-        return sliced_nd.reshape(oshape)
+        value_nd = self._prepare_value_nd(value, new_axes=[], bcast_shape=vshape)
+        _internal._scatter_set_nd(
+            lhs=self, rhs=value_nd, indices=indices, shape=self.shape, out=self
+        )
 
     def _get_nd_advanced_indexing(self, key):
         """Get item when key is a tuple of any objects of the following types:
@@ -2400,30 +2653,123 @@ def to_dlpack_for_write(self):
         """
         return to_dlpack_for_write(self)
 
+
+def _indexing_key_expand_implicit_axes(key, shape):
+    """Make implicit axes explicit by adding ``slice(None)``.
+
+    Examples
+    --------
+    >>> shape = (3, 4, 5)
+    >>> _indexing_key_expand_implicit_axes(np.s_[2, 1, 1], shape)
+    (2, 1, 1)
+    >>> _indexing_key_expand_implicit_axes(np.s_[0], shape)
+    (0, slice(None, None, None), slice(None, None, None))
+    >>> _indexing_key_expand_implicit_axes(np.s_[0, ...], shape)  # equivalent
+    (0, slice(None, None, None), slice(None, None, None))
+    >>> _indexing_key_expand_implicit_axes(np.s_[:2, None, 0, ...], shape)
+    (slice(None, 2, None), None, 0, slice(None, None, None))
+    """
+    if not isinstance(key, tuple):
+        key = (key,)
+
+    # We need to loop explicitly since tuple functions like `index()` or
+    # `count()` use `==` internally, which doesn't play well with fancy
+    # indexing.
+    ell_idx = None
+    num_none = 0
+    nonell_key = []
+    for i, idx in enumerate(key):
+        if idx is Ellipsis:
+            if ell_idx is not None:
+                raise IndexError(
+                    'Cannot use more than one ellipsis (`...`) for indexing'
+                )
+            ell_idx = i
+        else:
+            if idx is None:
+                num_none += 1
+            nonell_key.append(idx)
+
+    nonell_key = tuple(nonell_key)
+
+    if ell_idx is None:
+        # This handles the case of "too few" indices, e.g., `nd.zeros((2, 3))[0]`,
+        # where the ellipsis is implicitly after the last entry.
+        ell_idx = len(nonell_key)
+
+    ell_ndim = len(shape) + num_none - len(nonell_key)
+    expanded_key = (nonell_key[:ell_idx] +
+                    (slice(None),) * ell_ndim +
+                    nonell_key[ell_idx:])
+
+    return expanded_key
+
+
+def _int_to_slice(idx):
+    """Return a slice that indexes the same entries as a single int."""
+    if idx == -1:
+        # Avoid slice(-1, 0)
+        return slice(-1, None)
+    else:
+        return slice(idx, idx + 1)
+
+
+def _shape_for_bcast(shape, target_ndim, new_axes):
+    """Return shape with added axes for broadcasting in ``target_ndim`` dimensions.
+
+    If ``shape`` is shorter than ``target_ndim``, fixed ``1`` entries are inserted
+    into the returned shape, in locations indexed by ``new_axes``. The rest is
+    filled from the back with ``shape`` while possible.
+    """
+    new_shape = [None] * target_ndim
+    if len(shape) < target_ndim:
+        for new_ax in new_axes:
+            new_shape[new_ax] = 1
+
+    # Replace `None` from the right with `shape` entries from the right as
+    # long as possible, thereafter with 1.
+    ax_s = 1
+    for ax in range(1, target_ndim + 1):
+        if new_shape[-ax] is None:
+            try:
+                new_shape[-ax] = shape[-ax_s]
+                ax_s += 1
+            except IndexError:
+                new_shape[-ax] = 1
+
+    return tuple(new_shape)
+
+
+def _is_advanced_index(idx):
+    """Return whether ``idx`` is an advanced index (array-like or integer).
+
+    Note that in contrast to basic indexing, integers are considered advanced
+    indices in the context of advanced indexing as they participate in
+    broadcasting.
+    """
+    if isinstance(idx, (NDArray, np.ndarray, integer_types, list, tuple)):
+        return True
+    elif isinstance(idx, py_slice) or idx is None:
+        return False
+    else:
+        raise RuntimeError('illegal index type {}'.format(type(idx)))
+
+
 def _get_indexing_dispatch_code(key):
     """Returns a dispatch code for calling basic or advanced indexing functions."""
-    if isinstance(key, (NDArray, np.ndarray)):
-        return _NDARRAY_ADVANCED_INDEXING
-    elif isinstance(key, list):
-        # TODO(junwu): Add support for nested lists besides integer list
-        for i in key:
-            if not isinstance(i, integer_types):
-                raise TypeError('Indexing NDArray only supports a list of integers as index'
-                                ' when key is of list type, received element=%s of type=%s'
-                                % (str(i), str(type(i))))
-        return _NDARRAY_ADVANCED_INDEXING
-    elif isinstance(key, (integer_types, py_slice)):
-        return _NDARRAY_BASIC_INDEXING
-    elif isinstance(key, tuple):
-        for idx in key:
-            if isinstance(idx, (NDArray, np.ndarray, list, tuple)):
-                return _NDARRAY_ADVANCED_INDEXING
-            elif not isinstance(idx, (py_slice, integer_types)):
-                raise ValueError("NDArray does not support slicing with key %s of type %s."
-                                 % (str(idx), str(type(idx))))
-        return _NDARRAY_BASIC_INDEXING
-    else:
-        return _NDARRAY_UNSUPPORTED_INDEXING
+    assert isinstance(key, tuple)
+
+    for idx in key:
+        if isinstance(idx, (NDArray, np.ndarray, list, tuple)):
+            return _NDARRAY_ADVANCED_INDEXING
+
+        elif not (isinstance(idx, (py_slice, integer_types)) or idx is None):
+            raise ValueError(
+                'NDArray does not support slicing with key {} of type {}.'
+                ''.format(idx, type(idx))
+            )
+
+    return _NDARRAY_BASIC_INDEXING
 
 
 def _get_index_range(start, stop, length, step=1):
@@ -2483,6 +2829,8 @@ def _get_dim_size(start, stop, step):
     """Given start, stop, and stop, calculate the number of elements
     of this slice."""
     assert step != 0
+    if stop == start:
+        return 0
     if step > 0:
         assert start < stop
         dim_size = (stop - start - 1) // step + 1
@@ -2513,6 +2861,14 @@ def _get_broadcast_shape(shape1, shape2):
     return tuple(shape)
 
 
+def _broadcast_shapes(seq):
+    """Return the broadcast shape of all advanced indices in ``seq``.
+
+    All entries are assumed to have a ``shape`` property.
+    """
+    return reduce(_get_broadcast_shape, [x.shape for x in seq], ())
+
+
 def onehot_encode(indices, out):
     """One-hot encoding indices into matrix out.
 
@@ -2625,9 +2981,18 @@ def array(source_array, ctx=None, dtype=None):
                 source_array = np.array(source_array, dtype=dtype)
             except:
                 raise TypeError('source_array must be array like object')
-    arr = empty(source_array.shape, ctx, dtype)
-    arr[:] = source_array
-    return arr
+
+    if source_array.shape == ():
+        # In this case we can't assign, so we need to go through an auxiliary array
+        arr = empty((1,), ctx, dtype)
+        arr[:] = source_array
+        return arr.reshape(())
+    elif source_array.size == 0:
+        return empty(source_array.shape, ctx, dtype)
+    else:
+        arr = empty(source_array.shape, ctx, dtype)
+        arr[:] = source_array
+        return arr
 
 
 def moveaxis(tensor, source, destination):
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 54b1069b1029..9e0c52dbfd68 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -128,7 +128,7 @@ def __getitem__(self, key):
             if key.step is not None and key.step != 1:
                 if key.step == 0:
                     raise ValueError("slice step cannot be zero")
-                return self.as_nd_ndarray()._get_nd_basic_indexing(key).as_np_ndarray()
+                return self.as_nd_ndarray().__getitem__(key).as_np_ndarray()
             elif key.start is not None or key.stop is not None:
                 return self._slice(key.start, key.stop)
             else:
@@ -157,7 +157,7 @@ def __setitem__(self, key, value):
             value = value.as_nd_ndarray()
         # TODO(junwu): Better handling of this situation
         if isinstance(key, tuple) and len(key) == 0:
-            self.as_nd_ndarray().__setitem__(slice(None), value)
+            self.as_nd_ndarray().__setitem__(key, value)
             return
 
         if isinstance(key, ndarray):
diff --git a/python/mxnet/test_utils.py b/python/mxnet/test_utils.py
index 2275f4d5da0f..89013f4392e8 100644
--- a/python/mxnet/test_utils.py
+++ b/python/mxnet/test_utils.py
@@ -1074,7 +1074,11 @@ def check_symbolic_forward(sym, location, expected, rtol=1E-4, atol=None,
 
     executor = sym.bind(ctx=ctx, args=location, args_grad=args_grad_data, aux_states=aux_states)
     for g in executor.grad_arrays:
-        g[:] = 0
+        print(g.shape)
+        if g.ndim == 0:
+            g[()] = 0
+        else:
+            g[:] = 0
 
     executor.forward(is_train=False)
 
diff --git a/src/operator/tensor/matrix_op-inl.h b/src/operator/tensor/matrix_op-inl.h
index a670c85b49f7..49847502258f 100644
--- a/src/operator/tensor/matrix_op-inl.h
+++ b/src/operator/tensor/matrix_op-inl.h
@@ -711,7 +711,10 @@ inline void GetIndexRange(const mxnet::TShape& dshape,
 
       // checking upper and lower bounds for end
       if (e < 0 && param_end[i].has_value()) {
-        e += len;
+        if (!(s < 0 && e == -1)) {
+          // Keep end=-1 as one-beyond-limits index for negative stride
+          e += len;
+        }
         CHECK_GE(e, 0) << "slicing with end[" << i << "]=" << e - len
                        << " exceeds limit of input dimension[" << i << "]=" << len;
       }
@@ -740,11 +743,11 @@ inline void SetSliceOpOutputDimSize(const index_t i, const int b,
                                     mxnet::TShape* oshape) {
   if (e != b) {
     if (s > 0) {
-      CHECK_LT(b, e) << "slicing with begin=[" << i << "]=" << b << ", end[" << i << "]="
+      CHECK_LT(b, e) << "slicing with begin[" << i << "]=" << b << ", end[" << i << "]="
                      << e << ", and step[" << i << "]=" << s << " is invalid";
       (*oshape)[i] = (e - b - 1) / s + 1;
     } else {
-      CHECK_LT(e, b) << "slicing with begin=[" << i << "]=" << b << ", end[" << i << "]="
+      CHECK_LT(e, b) << "slicing with begin[" << i << "]=" << b << ", end[" << i << "]="
                      << e << ", and step[" << i << "]=" << s << " is invalid";
       (*oshape)[i] = (b - e - 1) / (-s) + 1;
     }
diff --git a/tests/python/unittest/test_dgl_graph.py b/tests/python/unittest/test_dgl_graph.py
index e24cf4deb756..805adc2dac6f 100644
--- a/tests/python/unittest/test_dgl_graph.py
+++ b/tests/python/unittest/test_dgl_graph.py
@@ -61,7 +61,7 @@ def check_compact(csr, id_arr, num_nodes):
     compact = mx.nd.contrib.dgl_graph_compact(csr, id_arr, graph_sizes=num_nodes, return_mapping=False)
     assert compact.shape[0] == num_nodes
     assert compact.shape[1] == num_nodes
-    assert mx.nd.sum(compact.indptr == csr.indptr[0:(num_nodes + 1)]).asnumpy() == num_nodes + 1
+    assert mx.nd.sum(compact.indptr == csr.indptr[0:int(num_nodes + 1)]).asnumpy() == num_nodes + 1
     sub_indices = compact.indices.asnumpy()
     indices = csr.indices.asnumpy()
     id_arr = id_arr.asnumpy()
diff --git a/tests/python/unittest/test_ndarray.py b/tests/python/unittest/test_ndarray.py
index 0f154bd67a1a..87d3a41fd5bf 100644
--- a/tests/python/unittest/test_ndarray.py
+++ b/tests/python/unittest/test_ndarray.py
@@ -18,11 +18,11 @@
 import mxnet as mx
 import numpy as np
 from distutils.version import LooseVersion
+from itertools import permutations, combinations_with_replacement
 import os
 import pickle as pkl
-import unittest
-from nose.tools import raises
-from common import setup_module, with_seed, assertRaises, TemporaryDirectory, teardown
+from nose.tools import assert_raises, raises
+from common import with_seed, assertRaises, TemporaryDirectory
 from mxnet.test_utils import almost_equal
 from mxnet.test_utils import assert_almost_equal, assert_exception
 from mxnet.test_utils import default_context
@@ -101,6 +101,26 @@ def test_ndarray_setitem():
     x_np[-1] = 1
     assert same(x.asnumpy(), x_np)
 
+    # Ellipsis
+    x = mx.nd.zeros(shape)
+    x[2, ...] = 1
+    x_np = np.zeros(shape, dtype=x.dtype)
+    x_np[2, ...] = 1
+    assert same(x.asnumpy(), x_np)
+
+    x = mx.nd.zeros(shape)
+    x[..., 1] = 1
+    x_np = np.zeros(shape, dtype=x.dtype)
+    x_np[..., 1] = 1
+    assert same(x.asnumpy(), x_np)
+
+    # `None` should be ignored
+    x = mx.nd.zeros(shape)
+    x[None, 0, None, None, 0, 0, None] = 1
+    x_np = np.zeros(shape, dtype=x.dtype)
+    x_np[None, 0, None, None, 0, 0, None] = 1
+    assert same(x.asnumpy(), x_np)
+
     # short all-dim indexing
     x = mx.nd.zeros(shape)
     val = mx.nd.ones((3, 2))
@@ -121,13 +141,15 @@ def test_ndarray_setitem():
     x_np[:, -3:-1, -2:-1] = 1
     assert same(x.asnumpy(), x_np)
 
-    # numpy assignment for empty axis
-    for trivial_shape in [(), (1,), (1, 1), (1, 1, 1)]:
-        if trivial_shape == tuple():
-            with mx.np_shape():
-                x = mx.nd.zeros(trivial_shape)
-        else:
-            x = mx.nd.zeros(trivial_shape)
+    # Scalar array, no assignment allowed
+    with mx.np_shape():
+        x = mx.nd.zeros(())
+        with assert_raises(IndexError):
+            x[:] = 1
+
+    # Assignments for empty axes
+    for trivial_shape in [(1,), (1, 1), (1, 1, 1)]:
+        x = mx.nd.zeros(trivial_shape)
         x[:] = np.ones(trivial_shape)
         x_np = np.ones(trivial_shape, dtype=x.dtype)
         assert x.shape == trivial_shape
@@ -1286,6 +1308,42 @@ def test_bool():
     assert bool(mx.nd.ones((1,)))
 
 
+def test_basic_indexing_is_contiguous():
+    x_np = np.arange(np.prod((6, 7, 8, 9))).reshape((6, 7, 8, 9))
+    x_mx = mx.nd.array(x_np)
+
+    slices = [
+        slice(None),
+        slice(2),
+        slice(20),
+        slice(1, 4),
+        slice(None, None, 2),
+        slice(None, None, 20),
+        slice(0, 1),
+        slice(None, None, -1),
+        slice(3, None, -2),
+    ]
+
+    is_contiguous = mx.nd.NDArray._basic_indexing_slice_is_contiguous
+
+    for idx in combinations_with_replacement(slices, 4):
+        for slc in permutations(idx):
+            # Check helper function
+            contig_pred = is_contiguous(slc, x_np.shape)
+            contig_true = x_np[slc].flags.contiguous
+            assert contig_pred == contig_true, (
+                "failed with slc={}, pred ({}) != actual ({})"
+                "".format(slc, contig_pred, contig_true)
+            )
+
+            if contig_pred:
+                # Check mutation behavior
+                y_mx = x_mx.copy()
+                y_mx_slc = y_mx[slc]
+                y_mx_slc[:] = 0
+                assert (y_mx[slc].asnumpy() == 0).all()
+
+
 @with_seed()
 def test_ndarray_indexing():
     def test_getitem(np_array, index, is_scalar=False):
@@ -1296,22 +1354,24 @@ def test_getitem(np_array, index, is_scalar=False):
         if isinstance(index, mx.nd.NDArray):
             np_index = index.asnumpy()
         if isinstance(index, tuple):
-            np_index = []
-            for idx in index:
-                if isinstance(idx, mx.nd.NDArray):
-                    np_index.append(idx.asnumpy())
-                else:
-                    np_index.append(idx)
-            np_index = tuple(np_index)
+            np_index = tuple(
+                idx.asnumpy() if isinstance(idx, mx.nd.NDArray) else idx
+                for idx in index
+            )
 
         np_indexed_array = np_array[np_index]
         mx_array = mx.nd.array(np_array, dtype=np_array.dtype)
-        mx_indexed_array = mx_array[index]
+        try:
+            mx_indexed_array = mx_array[index]
+        except Exception as e:
+            print('Failed with index = {}'.format(index))
+            raise e
         if is_scalar:
             mx_indexed_array = mx_indexed_array.asscalar()
         else:
             mx_indexed_array = mx_indexed_array.asnumpy()
-        assert same(np_indexed_array, mx_indexed_array), 'Failed with index=%s' % str(index)
+
+        assert same(np_indexed_array, mx_indexed_array), 'Failed with index = {}'.format(index)
 
     def test_setitem(np_array, index, is_scalar):
         def assert_same(np_array, np_index, mx_array, mx_index, mx_value, np_value=None):
@@ -1321,7 +1381,13 @@ def assert_same(np_array, np_index, mx_array, mx_index, mx_value, np_value=None)
                 np_array[np_index] = mx_value.asnumpy()
             else:
                 np_array[np_index] = mx_value
-            mx_array[mx_index] = mx_value
+
+            try:
+                mx_array[mx_index] = mx_value
+            except Exception as e:
+                print('Failed with index = {}, value.shape = {}'.format(mx_index, mx_value.shape))
+                raise e
+
             assert same(np_array, mx_array.asnumpy())
 
         np_index = index
@@ -1380,7 +1446,9 @@ def test_setitem_autograd(np_array, index):
         try:
             with mx.autograd.record():
                 x[index] = y
-                assert False  # should not reach here
+                # `a[None] = v` is equivalent to `a[...] = v` which doesn't raise
+                if index is not None:
+                    assert False, 'failed with index = {}'.format(index)  # should not reach here
         except mx.base.MXNetError as err:
             assert str(err).find('Inplace operations (+=, -=, x[:]=, etc) are not supported when recording with') != -1
 
@@ -1502,7 +1570,17 @@ def convert(num):
                   (([[[[1]]]], 3, slice(0, 3), 0), False),
                   (([[[[1]]]], [[2], [12]], slice(0, 3), slice(None)), False),
                   (([1, 2], slice(3, 5), [2, 3], [3, 4]), False),
-                  (([1, 2], slice(3, 5), (2, 3), [3, 4]), False)]
+                  (([1, 2], slice(3, 5), (2, 3), [3, 4]), False),
+                  ((1, Ellipsis, -1), False),
+                  ((slice(2), Ellipsis, None, 0), False),
+                  (None, False),
+                  ((1, None, -2, 3, -4), False),
+                  # TODO(zoeygxy): Support None in advanced indexing
+                  # (([1, 2], slice(3, 5), None, None, [3, 4]), False),
+                  # ((slice(None), slice(3, 5), None, None, [2, 3], [3, 4]), False),
+                  # ((slice(None), slice(3, 5), None, [2, 3], None, [3, 4]), False),
+                  # ((None, slice(None), slice(3, 5), [2, 3], None, [3, 4]), False),
+    ]
     for index in index_list:
         test_getitem(np_array, index[0], index[1])
         test_setitem(np_array, index[0], index[1])
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 027ec3d12a4b..13ab03b94de9 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -4793,13 +4793,14 @@ def test_normal_case():
 
     def test_empty_tensor():
         shape = (2, 3, 0, 4)
-        a = np.array([], dtype=np.int32).reshape(shape)
-        b = mx.nd.array(a, ctx=default_context(), dtype=a.dtype)
-        reps = (2, 4, 6)
+        with mx.np_shape():
+            a = np.array([], dtype=np.int32).reshape(shape)
+            b = mx.nd.array(a, ctx=default_context(), dtype=a.dtype)
 
-        a_tiled = np.tile(a, reps)
-        b_tiled = mx.nd.tile(b, reps).asnumpy()
-        assert same(a_tiled, b_tiled)
+            reps = (2, 4, 6)
+            a_tiled = np.tile(a, reps)
+            b_tiled = mx.nd.tile(b, reps).asnumpy()
+            assert same(a_tiled, b_tiled)
 
     def test_empty_reps():
         a = np.array([[2, 3, 4], [5, 6, 7]], dtype=np.int32)
@@ -4889,13 +4890,15 @@ def test_normal_case(index_type=np.int32):
 
     def test_empty_indices():
         shape = (2, 0, 9, 3)
-        indices = np.array([]).reshape(shape)
-        depth = 10
-        mx_one_hot_array = mx.nd.one_hot(
-            mx.nd.array(indices, ctx=default_context(), dtype=np.int32),
-            depth=depth, dtype=np.int32).asnumpy()
-        expected_array = np.array([], dtype=np.int32).reshape(shape + (depth, ))
-        assert same(expected_array, mx_one_hot_array)
+        with mx.np_shape():
+            indices = np.array([]).reshape(shape)
+            depth = 10
+            mx_one_hot_array = mx.nd.one_hot(
+                mx.nd.array(indices, ctx=default_context(), dtype=np.int32),
+                depth=depth, dtype=np.int32
+            ).asnumpy()
+            expected_array = np.array([], dtype=np.int32).reshape(shape + (depth,))
+            assert same(expected_array, mx_one_hot_array)
 
     def test_zero_depth():
         shape = (2, 4, 9, 3)
@@ -8859,7 +8862,7 @@ def test_index_array_default():
 
     @mx.use_np_shape
     def test_index_array_default_zero_dim():
-        data  = mx.symbol.Variable("data")
+        data = mx.symbol.Variable("data")
         index_array = mx.sym.contrib.index_array(data)
 
         input_array = np.ones(())

From a3babc4a4d79397acc14d1f38c20e6b8b1ffeb23 Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Thu, 8 Aug 2019 20:30:50 -0700
Subject: [PATCH 159/813] numpy-compatible sum (#15810)

---
 src/operator/numpy/np_broadcast_reduce_op.h   | 218 ++++++++++++++++++
 .../numpy/np_broadcast_reduce_op_value.cc     |  78 +++++++
 .../numpy/np_broadcast_reduce_op_value.cu     |  36 +++
 tests/python/gpu/test_operator_gpu.py         |   1 +
 tests/python/unittest/test_numpy_op.py        |  97 ++++++++
 5 files changed, 430 insertions(+)
 create mode 100644 src/operator/numpy/np_broadcast_reduce_op.h
 create mode 100644 src/operator/numpy/np_broadcast_reduce_op_value.cc
 create mode 100644 src/operator/numpy/np_broadcast_reduce_op_value.cu
 create mode 100644 tests/python/unittest/test_numpy_op.py

diff --git a/src/operator/numpy/np_broadcast_reduce_op.h b/src/operator/numpy/np_broadcast_reduce_op.h
new file mode 100644
index 000000000000..c516e6b0689a
--- /dev/null
+++ b/src/operator/numpy/np_broadcast_reduce_op.h
@@ -0,0 +1,218 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2015 by Contributors
+ * \file broadcast_reduce_op.h
+ * \brief Function definition of broadcast and reduce operators
+ */
+#ifndef MXNET_OPERATOR_NUMPY_NP_BROADCAST_REDUCE_OP_H_
+#define MXNET_OPERATOR_NUMPY_NP_BROADCAST_REDUCE_OP_H_
+
+#include <algorithm>
+#include <vector>
+#include "../tensor/broadcast_reduce_op.h"
+
+namespace mxnet {
+namespace op {
+
+struct NumpyReduceAxesParam : public dmlc::Parameter<NumpyReduceAxesParam> {
+  dmlc::optional<mxnet::Tuple<int>> axis;
+  dmlc::optional<int> dtype;
+  bool keepdims;
+  dmlc::optional<double> initial;
+  DMLC_DECLARE_PARAMETER(NumpyReduceAxesParam) {
+    DMLC_DECLARE_FIELD(axis)
+      .set_default(dmlc::optional<mxnet::Tuple<int>>())
+      .describe("Axis or axes along which a sum is performed. The default, axis=None, will sum "
+                "all of the elements of the input array. If axis is negative it counts from the "
+                "last to the first axis.");
+    DMLC_DECLARE_FIELD(dtype)
+      .add_enum("float16", mshadow::kFloat16)
+      .add_enum("float32", mshadow::kFloat32)
+      .add_enum("float64", mshadow::kFloat64)
+      .add_enum("int8", mshadow::kInt8)
+      .add_enum("int32", mshadow::kInt32)
+      .add_enum("int64", mshadow::kInt64)
+      .set_default(dmlc::optional<int>())
+      .describe("The type of the returned array and of the accumulator in which the elements are "
+                "summed. The dtype of a is used by default unless a has an integer dtype of less "
+                "precision than the default platform integer. In that case, if a is signed then "
+                "the platform integer is used while if a is unsigned then an unsigned integer of "
+                "the same precision as the platform integer is used.");
+    DMLC_DECLARE_FIELD(keepdims).set_default(false)
+      .describe("If this is set to `True`, the reduced axes are left "
+                "in the result as dimension with size one.");
+    DMLC_DECLARE_FIELD(initial).set_default(dmlc::optional<double>())
+      .describe("Starting value for the sum.");
+  }
+};
+
+inline TShape NumpyReduceAxesShapeImpl(const TShape& ishape,
+                                       const dmlc::optional<mxnet::Tuple<int>>& axis,
+                                       bool keepdims) {
+  // TODO(junwu): improve the logic
+  // If input is a scalar, output should be a scalar too
+  if (ishape.ndim() == 0) {
+    if (axis.has_value()) {
+      const mxnet::Tuple<int>& axes = axis.value();
+      if (axes.ndim() > 0) {
+        CHECK_EQ(axes.ndim(), 1);
+        CHECK(axes[0] == 0 || axes[0] == -1);
+      }
+    }
+    return TShape(0, -1);
+  }
+
+  // axis=None, do global reduction
+  if (!axis.has_value()) {
+    if (keepdims) {
+      return TShape(ishape.ndim(), 1);
+    } else {
+      return TShape(0, -1);
+    }
+  }
+
+  // axis = (), will return identity(input)
+  if (axis.value().ndim() == 0) {
+    return ishape;
+  }
+
+  // axis has value
+  mxnet::Tuple<int> axes(axis.value());
+  for (index_t i = 0; i < axes.ndim(); i++) {
+    if (axes[i] < 0) {
+      axes[i] += ishape.ndim();
+    }
+  }
+  std::sort(axes.begin(), axes.end());
+
+  for (index_t i = 1; i < axes.ndim(); i++) {
+    CHECK_LT(axes[i-1], axes[i])
+        << "Reduction axes have duplicates "
+        << axes;
+  }
+  CHECK_LT(axes[axes.ndim()-1], ishape.ndim())
+      << "Reduction axis " << axes[axes.ndim()-1]
+      << " Exceeds input dimensions " << ishape;
+  CHECK_GE(axes[0], 0)
+      << "Reduction axis " << axis.value()
+      << " Exceeds input dimensions " << ishape;
+
+  TShape oshape;
+  if (keepdims) {
+    oshape = TShape(ishape);
+  } else {
+    oshape = TShape(ishape.ndim() - axes.ndim(), -1);
+  }
+
+  if (keepdims) {
+    for (index_t i = 0; i < axes.ndim(); ++i) {
+      oshape[axes[i]] = 1;
+    }
+  } else {
+    for (index_t i = 0, j = 0, k = 0; i < ishape.ndim(); ++i) {
+      if (j < axes.ndim() && i == axes[j]) {
+        ++j;
+        continue;
+      }
+      oshape[k++] = ishape[i];
+    }
+  }
+  return oshape;
+}
+
+inline bool NumpyReduceAxesShape(const nnvm::NodeAttrs& attrs,
+                                 std::vector<TShape> *in_attrs,
+                                 std::vector<TShape> *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  if (!shape_is_known(in_attrs->at(0))) {
+    return false;
+  }
+  const NumpyReduceAxesParam& param = nnvm::get<NumpyReduceAxesParam>(attrs.parsed);
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0,
+                     NumpyReduceAxesShapeImpl((*in_attrs)[0], param.axis, param.keepdims));
+  return shape_is_known(out_attrs->at(0));
+}
+
+template<bool safe_acc_hint = false>
+inline bool NeedSafeAcc(int itype, int otype) {
+  bool rule = (itype != otype) || (itype != mshadow::kFloat32 && itype != mshadow::kFloat64);
+  return safe_acc_hint && rule;
+}
+
+template<typename xpu, typename reducer, bool safe_acc_hint = false, bool normalize = false,
+         typename OP = op::mshadow_op::identity>
+void NumpyReduceAxesCompute(const nnvm::NodeAttrs& attrs,
+                            const OpContext& ctx,
+                            const std::vector<TBlob>& inputs,
+                            const std::vector<OpReqType>& req,
+                            const std::vector<TBlob>& outputs) {
+  const NumpyReduceAxesParam& param = nnvm::get<NumpyReduceAxesParam>(attrs.parsed);
+  if (param.initial.has_value()) {
+    LOG(FATAL) << "initial is not supported yet";
+  }
+  if (param.axis.has_value() && param.axis.value().ndim() == 0) {
+    UnaryOp::IdentityCompute<xpu>(attrs, ctx, inputs, req, outputs);
+  }
+  TShape small;
+  if (param.keepdims) {
+    small = outputs[0].shape_;
+  } else {
+    small = NumpyReduceAxesShapeImpl(inputs[0].shape_, param.axis, true);
+  }
+
+  if (NeedSafeAcc<safe_acc_hint>(inputs[0].type_flag_, outputs[0].type_flag_)) {
+    ReduceAxesComputeImpl<xpu, reducer, true, normalize, OP>(ctx, inputs, req, outputs, small);
+  } else {
+    ReduceAxesComputeImpl<xpu, reducer, false, normalize, OP>(ctx, inputs, req, outputs, small);
+  }
+}
+
+template<typename xpu, bool normalize = false>
+inline void NumpyReduceAxesBackwardUseNone(const nnvm::NodeAttrs& attrs,
+                                           const OpContext& ctx,
+                                           const std::vector<TBlob>& inputs,
+                                           const std::vector<OpReqType>& req,
+                                           const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mshadow::expr;
+  const NumpyReduceAxesParam& param = nnvm::get<NumpyReduceAxesParam>(attrs.parsed);
+  TShape small;
+  if (param.keepdims) {
+    small = inputs[0].shape_;
+  } else {
+    small = NumpyReduceAxesShapeImpl(outputs[0].shape_, param.axis, true);
+  }
+
+  BroadcastComputeImpl<xpu>(attrs, ctx, inputs, req, outputs, small);
+  if (normalize) {
+    Stream<xpu> *s = ctx.get_stream<xpu>();
+    MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, IType, {
+      Tensor<xpu, 1, IType> igrad = outputs[0].FlatTo1D<xpu, IType>(s);
+      printf("output size: %lu input_size: %lu\n", outputs[0].Size(), inputs[0].Size());
+      igrad /= scalar<IType>(outputs[0].Size()/inputs[0].Size());
+    });
+  }
+}
+
+}  // namespace op
+}  // namespace mxnet
+#endif  // MXNET_OPERATOR_NUMPY_NP_BROADCAST_REDUCE_OP_H_
diff --git a/src/operator/numpy/np_broadcast_reduce_op_value.cc b/src/operator/numpy/np_broadcast_reduce_op_value.cc
new file mode 100644
index 000000000000..fd227de168e1
--- /dev/null
+++ b/src/operator/numpy/np_broadcast_reduce_op_value.cc
@@ -0,0 +1,78 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file np_reduce_op_value.cc
+ * \brief CPU Implementation of broadcast and reduce functions based on value.
+ */
+
+#include "np_broadcast_reduce_op.h"
+
+namespace mxnet {
+namespace op {
+
+DMLC_REGISTER_PARAMETER(NumpyReduceAxesParam);
+
+inline bool NumpySumType(const nnvm::NodeAttrs& attrs,
+                         std::vector<int> *in_attrs,
+                         std::vector<int> *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  const NumpyReduceAxesParam &param = nnvm::get<NumpyReduceAxesParam>(attrs.parsed);
+
+  if (param.dtype.has_value()) {
+    TYPE_ASSIGN_CHECK(*out_attrs, 0, param.dtype.value());
+  } else {
+    TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
+    TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
+  }
+
+  return out_attrs->at(0) != -1 && in_attrs->at(0) != -1;
+}
+
+NNVM_REGISTER_OP(_np_sum)
+.describe(R"code()code" ADD_FILELINE)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<NumpyReduceAxesParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyReduceAxesShape)
+.set_attr<nnvm::FInferType>("FInferType", NumpySumType)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"a"};
+  })
+.add_argument("a", "NDArray-or-Symbol", "The input")
+.add_arguments(NumpyReduceAxesParam::__FIELDS__())
+.set_attr<FCompute>("FCompute<cpu>", NumpyReduceAxesCompute<cpu, mshadow_op::sum, true>)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_np_sum"});
+
+NNVM_REGISTER_OP(_backward_np_sum)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<NumpyReduceAxesParam>)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_num_inputs(1)
+.set_attr<FCompute>("FCompute<cpu>", NumpyReduceAxesBackwardUseNone<cpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_broadcast_reduce_op_value.cu b/src/operator/numpy/np_broadcast_reduce_op_value.cu
new file mode 100644
index 000000000000..1eb773009a79
--- /dev/null
+++ b/src/operator/numpy/np_broadcast_reduce_op_value.cu
@@ -0,0 +1,36 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file np_reduce_op_value.cu
+ * \brief GPU Implementation of reduce functions based on value.
+ */
+#include "np_broadcast_reduce_op.h"
+
+namespace mxnet {
+namespace op {
+NNVM_REGISTER_OP(_np_sum)
+.set_attr<FCompute>("FCompute<gpu>", NumpyReduceAxesCompute<gpu, mshadow_op::sum, true>);
+
+NNVM_REGISTER_OP(_backward_np_sum)
+.set_attr<FCompute>("FCompute<gpu>", NumpyReduceAxesBackwardUseNone<gpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/tests/python/gpu/test_operator_gpu.py b/tests/python/gpu/test_operator_gpu.py
index 0b5142dd8c8b..f8d8b4496afc 100644
--- a/tests/python/gpu/test_operator_gpu.py
+++ b/tests/python/gpu/test_operator_gpu.py
@@ -36,6 +36,7 @@
 from common import run_in_spawned_process
 from test_operator import *
 from test_numpy_ndarray import *
+from test_numpy_op import *
 from test_optimizer import *
 from test_random import *
 from test_exc_handling import *
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
new file mode 100644
index 000000000000..b179f67e6128
--- /dev/null
+++ b/tests/python/unittest/test_numpy_op.py
@@ -0,0 +1,97 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# pylint: skip-file
+from __future__ import absolute_import
+import numpy as _np
+import mxnet as mx
+from mxnet import np, npx
+from mxnet.base import MXNetError
+from mxnet.gluon import HybridBlock
+from mxnet.base import MXNetError
+from mxnet.test_utils import same, assert_almost_equal, rand_shape_nd, rand_ndarray
+from mxnet.test_utils import check_numeric_gradient, use_np
+from common import assertRaises, with_seed
+import random
+import collections
+
+
+@with_seed()
+@use_np
+def test_np_sum():
+    class TestSum(HybridBlock):
+        def __init__(self, axis=None, dtype=None, keepdims=False):
+            super(TestSum, self).__init__()
+            self._axis = axis
+            self._dtype = dtype
+            self._keepdims = keepdims
+
+        def hybrid_forward(self, F, a, *args, **kwargs):
+            return F.np.sum(a, axis=self._axis, dtype=self._dtype, keepdims=self._keepdims)
+
+    def is_int(dtype):
+        return 'int' in dtype
+
+    in_data_dim = random.choice([2, 3, 4])
+    shape = rand_shape_nd(in_data_dim, dim=3)
+    acc_type = {'float16': 'float32', 'float32': 'float64', 'float64': 'float64',
+                'int8': 'int32', 'int32': 'int64', 'int64': 'int64'}
+    for hybridize in [False, True]:
+        for keepdims in [True, False]:
+            for axis in ([i for i in range(in_data_dim)] + [(), None]):
+                for itype in ['float16', 'float32', 'float64', 'int8', 'int32', 'int64']:
+                    for dtype in ['float16', 'float32', 'float64', 'int8', 'int32', 'int64']:
+                        if is_int(dtype) and not is_int(itype):
+                            continue
+                        # test gluon
+                        test_sum = TestSum(axis=axis, dtype=dtype, keepdims=keepdims)
+                        if hybridize:
+                            test_sum.hybridize()
+                        if is_int(itype):
+                            x = _np.random.randint(-128, 128, shape, dtype=itype)
+                            x = mx.nd.array(x)
+                        else:
+                            x = mx.nd.random.uniform(-1.0, 1.0, shape=shape, dtype=itype)
+                        x = x.as_np_ndarray()
+                        x.attach_grad()
+                        expected_ret = _np.sum(x.asnumpy(), axis=axis, dtype=acc_type[itype], keepdims=keepdims)
+                        expected_ret = expected_ret.astype(dtype)
+                        with mx.autograd.record():
+                            y = test_sum(x)
+                        assert y.shape == expected_ret.shape
+                        assert_almost_equal(y.asnumpy(), expected_ret, rtol=1e-3 if dtype == 'float16' else 1e-3,
+                                            atol=1e-5 if dtype == 'float16' else 1e-5)
+
+                        y.backward()
+                        assert same(x.grad.asnumpy(), _np.ones(shape=x.shape, dtype=x.dtype))
+
+                        # test numeric
+                        if itype == 'float32' and dtype == 'float32':
+                            x_sym = mx.sym.Variable("x").as_np_ndarray()
+                            mx_sym = mx.sym.np.sum(x_sym, axis=axis, dtype=dtype, keepdims=keepdims).as_nd_ndarray()
+                            check_numeric_gradient(mx_sym, [x.as_nd_ndarray()],
+                                                   numeric_eps=1e-3, rtol=1e-3, atol=1e-4, dtype=_np.float32)
+
+                        # test imperative
+                        mx_out = np.sum(x, axis=axis, dtype=dtype, keepdims=keepdims)
+                        np_out = _np.sum(x.asnumpy(), axis=axis, dtype=acc_type[itype], keepdims=keepdims).astype(dtype)
+                        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+
+
+if __name__ == '__main__':
+    import nose
+    nose.runmodule()

From 19916af5f7de2784e03883c8626e8506086703b2 Mon Sep 17 00:00:00 2001
From: Tao Lv <tao.a.lv@intel.com>
Date: Fri, 9 Aug 2019 23:52:06 +0800
Subject: [PATCH 160/813] fix license issues (#15806)

---
 LICENSE                              | 355 +++++++++++++++++++++++----
 src/operator/special_functions-inl.h |  19 ++
 2 files changed, 325 insertions(+), 49 deletions(-)

diff --git a/LICENSE b/LICENSE
index 0673e0e40f7b..4532449470e8 100644
--- a/LICENSE
+++ b/LICENSE
@@ -458,6 +458,37 @@
     10. ONNX Export module
     For details, see, python/mxnet/contrib/onnx/mx2onnx/LICENSE
 
+    This module is based on https://github.com/NVIDIA/mxnet_to_onnx/blob/master/mx2onnx_converter/#
+    The original code has the following license:
+
+    #  Copyright (c) 2017, NVIDIA CORPORATION. All rights reserved.
+    #
+    #  Redistribution and use in source and binary forms, with or without
+    #  modification, are permitted provided that the following conditions
+    #  are met:
+    #  * Redistributions of source code must retain the above copyright
+    #    notice, this list of conditions and the following disclaimer.
+    #  * Redistributions in binary form must reproduce the above copyright
+    #    notice, this list of conditions and the following disclaimer in the
+    #    documentation and/or other materials provided with the distribution.
+    #  * Neither the name of NVIDIA CORPORATION nor the names of its
+    #    contributors may be used to endorse or promote products derived
+    #    from this software without specific prior written permission.
+    #
+    #  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
+    #  EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+    #  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+    #  PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+    #  CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+    #  EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+    #  PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+    #  PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
+    #  OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    #  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    #  OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+    Now it has been changed by Apache MXNet project. Modifications have the following license:
+
     # Licensed to the Apache Software Foundation (ASF) under one
     # or more contributor license agreements.  See the NOTICE file
     # distributed with this work for additional information
@@ -657,56 +688,282 @@
     =======================================================================================
 
     14. FindJeMalloc.cmake
-    For details, see, cmake/Modules/FindJeMalloc.cmake
-
-    Licensed to the Apache Software Foundation (ASF) under one
-    or more contributor license agreements.  See the NOTICE file
-    distributed with this work for additional information
-    regarding copyright ownership.  The ASF licenses this file
-    to you under the Apache License, Version 2.0 (the
-    "License"); you may not use this file except in compliance
-    with the License.  You may obtain a copy of the License at
-
-      http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing,
-    software distributed under the License is distributed on an
-    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-    KIND, either express or implied.  See the License for the
-    specific language governing permissions and limitations
-    under the License.
-
-
-    Copyright (c)      2014 Thomas Heller
-    Copyright (c) 2007-2012 Hartmut Kaiser
-    Copyright (c) 2010-2011 Matt Anderson
-    Copyright (c) 2011      Bryce Lelbach
-
-    Distributed under the Boost Software License, Version 1.0.
-    Boost Software License - Version 1.0 - August 17th, 2003
-
-    Permission is hereby granted, free of charge, to any person or organization
-    obtaining a copy of the software and accompanying documentation covered by
-    this license (the "Software") to use, reproduce, display, distribute,
-    execute, and transmit the Software, and to prepare derivative works of the
-    Software, and to permit third-parties to whom the Software is furnished to
-    do so, all subject to the following:
-
-    The copyright notices in the Software and this entire statement, including
-    the above license grant, this restriction and the following disclaimer,
-    must be included in all copies of the Software, in whole or in part, and
-    all derivative works of the Software, unless such copies or derivative
-    works are solely in the form of machine-executable object code generated by
-    a source language processor.
+    For details, see cmake/Modules/FindJeMalloc.cmake
 
-    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-    FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT
-    SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE
-    FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE,
-    ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
-    DEALINGS IN THE SOFTWARE.
+    This file is based on https://github.com/STEllAR-GROUP/hpx/blob/master/cmake/FindJemalloc.cmake
+    The original code has the following license:
+
+    # Copyright (c)      2014 Thomas Heller
+    # Copyright (c) 2007-2012 Hartmut Kaiser
+    # Copyright (c) 2010-2011 Matt Anderson
+    # Copyright (c) 2011      Bryce Lelbach
+    #
+    # Distributed under the Boost Software License, Version 1.0. (See accompanying
+    # file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
+
+    # Boost Software License - Version 1.0 - August 17th, 2003
+
+    # Permission is hereby granted, free of charge, to any person or organization
+    # obtaining a copy of the software and accompanying documentation covered by
+    # this license (the "Software") to use, reproduce, display, distribute,
+    # execute, and transmit the Software, and to prepare derivative works of the
+    # Software, and to permit third-parties to whom the Software is furnished to
+    # do so, all subject to the following:
+    # 
+    # The copyright notices in the Software and this entire statement, including
+    # the above license grant, this restriction and the following disclaimer,
+    # must be included in all copies of the Software, in whole or in part, and
+    # all derivative works of the Software, unless such copies or derivative
+    # works are solely in the form of machine-executable object code generated by
+    # a source language processor.
+    # 
+    # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    # FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT
+    # SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE
+    # FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE,
+    # ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+    # DEALINGS IN THE SOFTWARE.
+
+    Now it has been changed by Apache MXNet project. Modifications have the following license: 
+
+    # Licensed to the Apache Software Foundation (ASF) under one
+    # or more contributor license agreements.  See the NOTICE file
+    # distributed with this work for additional information
+    # regarding copyright ownership.  The ASF licenses this file
+    # to you under the Apache License, Version 2.0 (the
+    # "License"); you may not use this file except in compliance
+    # with the License.  You may obtain a copy of the License at
+    #
+    #   http://www.apache.org/licenses/LICENSE-2.0
+    #
+    # Unless required by applicable law or agreed to in writing,
+    # software distributed under the License is distributed on an
+    # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    # KIND, either express or implied.  See the License for the
+    # specific language governing permissions and limitations
+    # under the License.
+    
+    
+    # Copyright (c)      2014 Thomas Heller
+    # Copyright (c) 2007-2012 Hartmut Kaiser
+    # Copyright (c) 2010-2011 Matt Anderson
+    # Copyright (c) 2011      Bryce Lelbach
+    #
+    #----
+    # Distributed under the Boost Software License, Version 1.0.
+    # Boost Software License - Version 1.0 - August 17th, 2003
+    #
+    # Permission is hereby granted, free of charge, to any person or organization
+    # obtaining a copy of the software and accompanying documentation covered by
+    # this license (the "Software") to use, reproduce, display, distribute,
+    # execute, and transmit the Software, and to prepare derivative works of the
+    # Software, and to permit third-parties to whom the Software is furnished to
+    # do so, all subject to the following:
+    #
+    # The copyright notices in the Software and this entire statement, including
+    # the above license grant, this restriction and the following disclaimer,
+    # must be included in all copies of the Software, in whole or in part, and
+    # all derivative works of the Software, unless such copies or derivative
+    # works are solely in the form of machine-executable object code generated by
+    # a source language processor.
+    #
+    # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    # FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT
+    # SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE
+    # FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE,
+    # ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+    # DEALINGS IN THE SOFTWARE.
 
+    =======================================================================================
+
+    15. FindPythonLibsNew.cmake
+
+    For details, see 3rdparty/onnx-tensorrt/third_party/onnx/third_party/pybind11/tools/FindPythonLibsNew.cmake
+
+    # Copyright 2001-2009 Kitware, Inc.
+    # Copyright 2012 Continuum Analytics, Inc.
+    #
+    # All rights reserved.
+    #
+    # Redistribution and use in source and binary forms, with or without
+    # modification, are permitted provided that the following conditions
+    # are met:
+    #
+    # * Redistributions of source code must retain the above copyright
+    # notice, this list of conditions and the following disclaimer.
+    #
+    # * Redistributions in binary form must reproduce the above copyright
+    # notice, this list of conditions and the following disclaimer in the
+    # documentation and/or other materials provided with the distribution.
+    #
+    # * Neither the names of Kitware, Inc., the Insight Software Consortium,
+    # nor the names of their contributors may be used to endorse or promote
+    # products derived from this software without specific prior written
+    # permission.
+    #
+    # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    # # A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    # HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    # THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+    =======================================================================================
+
+    16. erfinv-inl.h
+
+    For details, see /src/operator/contrib/erfinv-inl.h
+
+    /*
+     * Copyright (c) 2014 Indiana University
+     * All rights reserved.
+     * Written by Prof. Gary L. Pavlis, Dept. of Geol. Sci.,
+     *           Indiana University, Bloomington, IN
+     * This software is licensed under the New BSD license:
+     * Redistribution and use in source and binary forms,
+     * with or without modification, are permitted provided
+     * that the following conditions are met:
+     * Redistributions of source code must retain the above
+     * copyright notice, this list of conditions and the
+     * following disclaimer.
+     * Redistributions in binary form must reproduce the
+     * above copyright notice, this list of conditions and
+     * the following disclaimer in the documentation and/or
+     * other materials provided with the distribution.
+     * Neither the name of Indiana University nor
+     * the names of its contributors may be used to endorse
+     * or promote products derived from this software without
+     * specific prior written permission.
+     * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND
+     * CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED
+     * WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+     * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
+     * PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL
+     * THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY
+     * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+     * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+     * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF
+     * USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+     * HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER
+     * IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+     * NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE
+     * USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+     * POSSIBILITY OF SUCH DAMAGE.
+     */
+
+    =======================================================================================
+
+    17. mersenne.h
+
+    For details, see /3rdparty/nvidia_cub/test/mersenne.h
+
+    /*
+     A C-program for MT19937, with initialization improved 2002/1/26.
+     Coded by Takuji Nishimura and Makoto Matsumoto.
+    
+     Before using, initialize the state by using init_genrand(seed)
+     or init_by_array(init_key, key_length).
+    
+     Copyright (C) 1997 - 2002, Makoto Matsumoto and Takuji Nishimura,
+     All rights reserved.
+    
+     Redistribution and use in source and binary forms, with or without
+     modification, are permitted provided that the following conditions
+     are met:
+    
+     1. Redistributions of source code must retain the above copyright
+     notice, this list of conditions and the following disclaimer.
+    
+     2. Redistributions in binary form must reproduce the above copyright
+     notice, this list of conditions and the following disclaimer in the
+     documentation and/or other materials provided with the distribution.
+    
+     3. The names of its contributors may not be used to endorse or promote
+     products derived from this software without specific prior written
+     permission.
+    
+     THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+     "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+     LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+     A PARTICAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+     CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+     EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+     PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+     PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+     LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+     NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+     SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+    
+    
+     Any feedback is very welcome.
+     http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html
+     email: m-mat @ math.sci.hiroshima-u.ac.jp (remove space)
+     */
+
+    =======================================================================================
+
+    18. FindEigen3.cmake
+
+    For details, see /3rdparty/onnx-tensorrt/third_party/onnx/third_party/pybind11/tools/FindEigen3.cmake
+
+    # Copyright (c) 2006, 2007 Montel Laurent, <montel@kde.org>
+    # Copyright (c) 2008, 2009 Gael Guennebaud, <g.gael@free.fr>
+    # Copyright (c) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+    # Redistribution and use is allowed according to the terms of the 2-clause BSD license.
+
+    =======================================================================================
+
+    19. protoc-gen-mypy.py
+
+    For details, see /3rdparty/onnx-tensorrt/third_party/onnx/tools/protoc-gen-mypy.py
+
+    # Taken from https://github.com/dropbox/mypy-protobuf/blob/d984389124eae6dbbb517f766b9266bb32171510/python/protoc-gen-mypy
+    # (Apache 2.0 License)
+    # with own fixes to
+    # - appease flake8
+    # - exit without error when protobuf isn't installed
+    # - fix recognition of whether an identifier is defined locally
+    #   (unfortunately, we use a python package name ONNX_NAMESPACE_FOO_BAR_FOR_CI
+    #    on CI, which by the original protoc-gen-mypy script was recognized to be
+    #    camel case and therefore handled as an entry in the local package)
+
+    =======================================================================================
+
+    20. rang
+
+    For details, see /3rdparty/tvm/3rdparty/rang/LICENSE
+
+    This is free and unencumbered software released into the public domain.
+    
+    Anyone is free to copy, modify, publish, use, compile, sell, or
+    distribute this software, either in source code form or as a compiled
+    binary, for any purpose, commercial or non-commercial, and by any
+    means.
+    
+    In jurisdictions that recognize copyright laws, the author or authors
+    of this software dedicate any and all copyright interest in the
+    software to the public domain. We make this dedication for the benefit
+    of the public at large and to the detriment of our heirs and
+    successors. We intend this dedication to be an overt act of
+    relinquishment in perpetuity of all present and future rights to this
+    software under copyright law.
+    
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+    IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR
+    OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
+    ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+    OTHER DEALINGS IN THE SOFTWARE.
+    
+    For more information, please refer to <http://unlicense.org>
 
 
     =======================================================================================
@@ -745,4 +1002,4 @@
 
     2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
 
-    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
\ No newline at end of file
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/src/operator/special_functions-inl.h b/src/operator/special_functions-inl.h
index 743391e0fce0..f51cfeec9f36 100644
--- a/src/operator/special_functions-inl.h
+++ b/src/operator/special_functions-inl.h
@@ -1,3 +1,22 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
 /*!
  * Copyright (c) 2015 by Contributors
  * \file special_functions-inl.h

From bfd3bb8972b7e4a9cd8487c4ab6e6583202f3259 Mon Sep 17 00:00:00 2001
From: Pedro Larroy <pedro.larroy.lists@gmail.com>
Date: Fri, 9 Aug 2019 14:12:55 -0700
Subject: [PATCH 161/813] Refactor LibraryInitializer so it's thread safe.
 Fixes random sporadical concurrency crashes. (#15762)

* Refactor LibraryInitializer so it's thread safe.
Fixes #13438
Fixes #14979

* Refactor around lib loading

* Fix lint

* CR

* Add option to choose between OMP implementations

* Fix bug

* Fix from CR
---
 CMakeLists.txt                          |  12 +-
 docs/faq/env_var.md                     |  10 +-
 src/c_api/c_api.cc                      |   4 +-
 src/common/library.cc                   | 125 ------------
 src/common/library.h                    |  57 ------
 src/common/utils.h                      |  12 ++
 src/engine/threaded_engine_perdevice.cc |   4 +-
 src/initialize.cc                       | 247 +++++++++++++++++++-----
 src/initialize.h                        | 126 ++++++++++++
 src/profiler/profiler.h                 |  15 +-
 10 files changed, 359 insertions(+), 253 deletions(-)
 delete mode 100644 src/common/library.cc
 delete mode 100644 src/common/library.h
 create mode 100644 src/initialize.h

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 85f302f867e2..976c736f5f35 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -24,6 +24,7 @@ mxnet_option(USE_OLDCMAKECUDA     "Build with old cmake cuda" OFF)
 mxnet_option(USE_NCCL             "Use NVidia NCCL with CUDA" OFF)
 mxnet_option(USE_OPENCV           "Build with OpenCV support" ON)
 mxnet_option(USE_OPENMP           "Build with Openmp support" ON)
+mxnet_option(USE_OPENMP_BUNDLED_LLVM "Build with bundled llvm openmp from 3rdparty" OFF)
 mxnet_option(USE_CUDNN            "Build with cudnn support"  ON) # one could set CUDNN_ROOT for search path
 mxnet_option(USE_SSE              "Build with x86 SSE instruction support" ON IF NOT ARM)
 mxnet_option(USE_F16C             "Build with x86 F16C instruction support" ON) # autodetects support if ON
@@ -433,11 +434,11 @@ if(USE_OPENMP)
   find_package(OpenMP REQUIRED)
   # This should build on Windows, but there's some problem and I don't have a Windows box, so
   # could a Windows user please fix?
-  if(EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/3rdparty/openmp/CMakeLists.txt
-     AND SYSTEM_ARCHITECTURE STREQUAL "x86_64"
-     AND NOT MSVC
-     AND NOT CMAKE_CROSSCOMPILING)
-
+  if(USE_OPENMP_BUNDLED_LLVM AND EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/3rdparty/openmp/CMakeLists.txt
+      AND SYSTEM_ARCHITECTURE STREQUAL "x86_64"
+      AND NOT MSVC
+      AND NOT CMAKE_CROSSCOMPILING)
+    message("Using bundlded LLVM OpenMP")
     # Intel/llvm OpenMP: https://github.com/llvm-mirror/openmp
     set(OPENMP_STANDALONE_BUILD TRUE)
     set(LIBOMP_ENABLE_SHARED TRUE)
@@ -451,6 +452,7 @@ if(USE_OPENMP)
     set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
     add_definitions(-DMXNET_USE_OPENMP=1)
   else()
+    message("Using platform provided OpenMP")
     if(OPENMP_FOUND)
       set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}")
       set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
diff --git a/docs/faq/env_var.md b/docs/faq/env_var.md
index 24d62f342983..b33a104fd315 100644
--- a/docs/faq/env_var.md
+++ b/docs/faq/env_var.md
@@ -39,6 +39,9 @@ $env:MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
 
 ## Set the Number of Threads
 
+* MXNET_OMP_MAX_THREADS
+  - Values: Int ```(default=Number of processors / Number of processors * 2 in X86)```
+  - Maximum number of threads to use in individual operators through OpenMP. If not set, OMP_NUM_THREADS is considered after.
 * MXNET_GPU_WORKER_NTHREADS
   - Values: Int ```(default=2)```
   - The maximum number of threads to use on each GPU. This parameter is used to parallelize the computation within a single GPU card.
@@ -47,7 +50,7 @@ $env:MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
   - The maximum number of concurrent threads that do the memory copy job on each GPU.
 * MXNET_CPU_WORKER_NTHREADS
   - Values: Int ```(default=1)```
-  - The maximum number of scheduling threads on CPU. It specifies how many operators can be run in parallel. Note that most CPU operators are parallelized by OpenMP. To change the number of threads used by individual operators, please set `OMP_NUM_THREADS` instead.
+  - The maximum number of scheduling threads on CPU. It specifies how many operators can be run in parallel. Note that most CPU operators are parallelized by OpenMP. To change the number of threads used by individual operators, please set `MXNET_OMP_MAX_THREADS` instead.
 * MXNET_CPU_PRIORITY_NTHREADS
   - Values: Int ```(default=4)```
   - The number of threads given to prioritized CPU jobs.
@@ -56,10 +59,13 @@ $env:MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
   - The number of threads used for NNPACK. NNPACK package aims to provide high-performance implementations of some layers for multi-core CPUs. Checkout [NNPACK](http://mxnet.io/faq/nnpack.html) to know more about it.
 * MXNET_MP_WORKER_NTHREADS
   - Values: Int ```(default=1)```
-  - The number of scheduling threads on CPU given to multiprocess workers. Enlarge this number allows more operators to run in parallel in individual workers but please consider reducing the overall `num_workers` to avoid thread contention (not available on Windows).
+  - The number of scheduling threads on CPU given to multiprocess workers (after fork). Enlarge this number allows more operators to run in parallel in individual workers but please consider reducing the overall `num_workers` to avoid thread contention (not available on Windows).
 * MXNET_MP_OPENCV_NUM_THREADS
   - Values: Int ```(default=0)```
   - The number of OpenCV execution threads given to multiprocess workers. OpenCV multithreading is disabled if `MXNET_MP_OPENCV_NUM_THREADS` < 1 (default). Enlarge this number may boost the performance of individual workers when executing underlying OpenCV functions but please consider reducing the overall `num_workers` to avoid thread contention (not available on Windows).
+* MXNET_GPU_COPY_NTHREADS
+  - Values:: Int ```(default=2)```
+  - Number of threads for copying data from CPU to GPU.
 
 ## Memory Options
 
diff --git a/src/c_api/c_api.cc b/src/c_api/c_api.cc
index 13f22191955d..17648458ba22 100644
--- a/src/c_api/c_api.cc
+++ b/src/c_api/c_api.cc
@@ -46,12 +46,12 @@
 #include "mxnet/libinfo.h"
 #include "mxnet/imperative.h"
 #include "mxnet/lib_api.h"
+#include "../initialize.h"
 #include "./c_api_common.h"
 #include "../operator/custom/custom-inl.h"
 #include "../operator/tensor/matrix_op-inl.h"
 #include "../operator/tvmop/op_module.h"
 #include "../common/utils.h"
-#include "../common/library.h"
 
 using namespace mxnet;
 
@@ -95,7 +95,7 @@ inline int MXAPIGetFunctionRegInfo(const FunRegType *e,
 // Loads library and initializes it
 int MXLoadLib(const char *path) {
   API_BEGIN();
-  void *lib = load_lib(path);
+  void *lib = LibraryInitializer::Get()->lib_load(path);
   if (!lib)
     LOG(FATAL) << "Unable to load library";
 
diff --git a/src/common/library.cc b/src/common/library.cc
deleted file mode 100644
index 9e79b5dbe1bc..000000000000
--- a/src/common/library.cc
+++ /dev/null
@@ -1,125 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- */
-
-/*!
- * Copyright (c) 2015 by Contributors
- * \file library.cc
- * \brief Dynamically loading accelerator library
- * and accessing its functions
- */
-
-#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
-#include <windows.h>
-#else
-#include <dlfcn.h>
-#endif
-
-#include <string>
-#include "library.h"
-
-#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
-/*!
- * \brief Retrieve the system error message for the last-error code
- * \param err string that gets the error message
- */
-void win_err(char **err) {
-  uint32_t dw = GetLastError();
-  FormatMessage(
-        FORMAT_MESSAGE_ALLOCATE_BUFFER |
-        FORMAT_MESSAGE_FROM_SYSTEM |
-        FORMAT_MESSAGE_IGNORE_INSERTS,
-        NULL,
-        dw,
-        MAKELANGID(LANG_NEUTRAL, SUBLANG_DEFAULT),
-        reinterpret_cast<char*>(err),
-        0, NULL);
-}
-#endif
-
-
-/*!
- * \brief Loads the dynamic shared library file
- * \param path library file location
- * \return handle a pointer for the loaded library, nullptr if loading unsuccessful
- */
-void* load_lib(const char* path) {
-  void *handle = nullptr;
-  std::string path_str(path);
-  // check if library was already loaded
-  if (loaded_libs.find(path_str) == loaded_libs.end()) {
-    // if not, load it
-#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
-    handle = LoadLibrary(path);
-    if (!handle) {
-      char *err_msg = nullptr;
-      win_err(&err_msg);
-      LOG(FATAL) << "Error loading library: '" << path << "'\n" << err_msg;
-      LocalFree(err_msg);
-      return nullptr;
-    }
-#else
-    handle = dlopen(path, RTLD_LAZY);
-    if (!handle) {
-      LOG(FATAL) << "Error loading library: '" << path << "'\n" << dlerror();
-      return nullptr;
-    }
-#endif  // _WIN32 or _WIN64 or __WINDOWS__
-    // then store the pointer to the library
-    loaded_libs[path_str] = handle;
-  } else {
-    // otherwise just look up the pointer
-    handle = loaded_libs[path_str];
-  }
-  return handle;
-}
-
-/*!
- * \brief Closes the loaded dynamic shared library file
- * \param handle library file handle
- */
-void close_lib(void* handle) {
-#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
-  FreeLibrary((HMODULE)handle);
-#else
-  dlclose(handle);
-#endif  // _WIN32 or _WIN64 or __WINDOWS__
-}
-
-/*!
- * \brief Obtains address of given function in the loaded library
- * \param handle pointer for the loaded library
- * \param func function pointer that gets output address
- * \param name function name to be fetched
- */
-void get_sym(void* handle, void** func, char* name) {
-#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
-  *func = GetProcAddress((HMODULE)handle, name);
-  if (!(*func)) {
-    char *err_msg = nullptr;
-    win_err(&err_msg);
-    LOG(FATAL) << "Error getting function '" << name << "' from library\n" << err_msg;
-    LocalFree(err_msg);
-  }
-#else
-  *func = dlsym(handle, name);
-  if (!(*func)) {
-    LOG(FATAL) << "Error getting function '" << name << "' from library\n" << dlerror();
-  }
-#endif  // _WIN32 or _WIN64 or __WINDOWS__
-}
diff --git a/src/common/library.h b/src/common/library.h
deleted file mode 100644
index d6eff4184191..000000000000
--- a/src/common/library.h
+++ /dev/null
@@ -1,57 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- */
-
-/*!
- * Copyright (c) 2015 by Contributors
- * \file library.h
- * \brief Defining library loading functions
- */
-#ifndef MXNET_COMMON_LIBRARY_H_
-#define MXNET_COMMON_LIBRARY_H_
-
-#include <iostream>
-#include <map>
-#include <string>
-#include "dmlc/io.h"
-
-// map of libraries loaded
-static std::map<std::string, void*> loaded_libs;
-
-void* load_lib(const char* path);
-void close_lib(void* handle);
-void get_sym(void* handle, void** func, char* name);
-
-/*!
- * \brief a templated function that fetches from the library
- * a function pointer of any given datatype and name
- * \param T a template parameter for data type of function pointer
- * \param lib library handle
- * \param func_name function name to search for in the library
- * \return func a function pointer
- */
-template<typename T>
-T get_func(void *lib, char *func_name) {
-  T func;
-  get_sym(lib, reinterpret_cast<void**>(&func), func_name);
-  if (!func)
-    LOG(FATAL) << "Unable to get function '" << func_name << "' from library";
-  return func;
-}
-
-#endif  // MXNET_COMMON_LIBRARY_H_
diff --git a/src/common/utils.h b/src/common/utils.h
index 251a8fe3c190..9dad5df84fd2 100644
--- a/src/common/utils.h
+++ b/src/common/utils.h
@@ -50,9 +50,21 @@
 #include "../operator/nn/mkldnn/mkldnn_base-inl.h"
 #endif
 
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+#include <windows.h>
+#else
+#include <unistd.h>
+#endif
+
+
 namespace mxnet {
 namespace common {
 
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+inline size_t current_process_id() { return ::GetCurrentProcessId(); }
+#else
+inline size_t current_process_id() { return getpid(); }
+#endif
 /*!
  * \brief IndPtr should be non-negative, in non-decreasing order, start with 0
  *           and end with value equal with size of indices.
diff --git a/src/engine/threaded_engine_perdevice.cc b/src/engine/threaded_engine_perdevice.cc
index bcb101e9e1bb..2184d784a414 100644
--- a/src/engine/threaded_engine_perdevice.cc
+++ b/src/engine/threaded_engine_perdevice.cc
@@ -28,6 +28,7 @@
 #include <dmlc/parameter.h>
 #include <dmlc/concurrency.h>
 #include <dmlc/thread_group.h>
+#include "../initialize.h"
 #include "./threaded_engine.h"
 #include "./thread_pool.h"
 #include "../common/lazy_alloc_array.h"
@@ -76,7 +77,8 @@ class ThreadedEnginePerDevice : public ThreadedEngine {
   void Start() override {
     if (is_worker_) return;
     gpu_worker_nthreads_ = common::GetNumThreadsPerGPU();
-    cpu_worker_nthreads_ = dmlc::GetEnv("MXNET_CPU_WORKER_NTHREADS", 1);
+    // MXNET_CPU_WORKER_NTHREADS
+    cpu_worker_nthreads_ = LibraryInitializer::Get()->cpu_worker_nthreads_;
     gpu_copy_nthreads_ = dmlc::GetEnv("MXNET_GPU_COPY_NTHREADS", 2);
     // create CPU task
     int cpu_priority_nthreads = dmlc::GetEnv("MXNET_CPU_PRIORITY_NTHREADS", 4);
diff --git a/src/initialize.cc b/src/initialize.cc
index 04952be7072a..071e8d32e548 100644
--- a/src/initialize.cc
+++ b/src/initialize.cc
@@ -1,4 +1,4 @@
-/*
+  /*
  * Licensed to the Apache Software Foundation (ASF) under one
  * or more contributor license agreements.  See the NOTICE file
  * distributed with this work for additional information
@@ -22,17 +22,43 @@
  * \file initialize.cc
  * \brief initialize mxnet library
  */
+#include "initialize.h"
 #include <signal.h>
 #include <dmlc/logging.h>
 #include <mxnet/engine.h>
 #include "./engine/openmp.h"
 #include "./operator/custom/custom-inl.h"
-#include "./common/library.h"
 #if MXNET_USE_OPENCV
 #include <opencv2/opencv.hpp>
 #endif  // MXNET_USE_OPENCV
+#include "common/utils.h"
+#include "engine/openmp.h"
+
+
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+#include <windows.h>
+/*!
+ * \brief Retrieve the system error message for the last-error code
+ * \param err string that gets the error message
+ */
+void win_err(char **err) {
+  uint32_t dw = GetLastError();
+  FormatMessage(
+        FORMAT_MESSAGE_ALLOCATE_BUFFER |
+        FORMAT_MESSAGE_FROM_SYSTEM |
+        FORMAT_MESSAGE_IGNORE_INSERTS,
+        NULL,
+        dw,
+        MAKELANGID(LANG_NEUTRAL, SUBLANG_DEFAULT),
+        reinterpret_cast<char*>(err),
+        0, NULL);
+}
+#else
+#include <dlfcn.h>
+#endif
 
 namespace mxnet {
+
 #if MXNET_USE_SIGNAL_HANDLER && DMLC_LOG_STACK_TRACE
 static void SegfaultLogger(int sig) {
   fprintf(stderr, "\nSegmentation fault: %d\n\n", sig);
@@ -41,65 +67,188 @@ static void SegfaultLogger(int sig) {
 }
 #endif
 
-class LibraryInitializer {
- public:
-  LibraryInitializer() {
-    dmlc::InitLogging("mxnet");
-#if MXNET_USE_SIGNAL_HANDLER && DMLC_LOG_STACK_TRACE
-    struct sigaction sa;
-    sigaction(SIGSEGV, nullptr, &sa);
-    if (sa.sa_handler == nullptr) {
-        signal(SIGSEGV, SegfaultLogger);
+// pthread_atfork handlers, delegated to LibraryInitializer members.
+
+void pthread_atfork_prepare() {
+  LibraryInitializer* library_initializer = LibraryInitializer::Get();
+  library_initializer->atfork_prepare();
+}
+
+void pthread_atfork_parent() {
+  LibraryInitializer* library_initializer = LibraryInitializer::Get();
+  library_initializer->atfork_parent();
+}
+
+void pthread_atfork_child() {
+  LibraryInitializer* library_initializer = LibraryInitializer::Get();
+  library_initializer->atfork_child();
+}
+
+// LibraryInitializer member functions
+
+LibraryInitializer::LibraryInitializer()
+  : original_pid_(common::current_process_id()),
+    mp_worker_nthreads_(dmlc::GetEnv("MXNET_MP_WORKER_NTHREADS", 1)),
+    cpu_worker_nthreads_(dmlc::GetEnv("MXNET_CPU_WORKER_NTHREADS", 1)),
+    mp_cv_num_threads_(dmlc::GetEnv("MXNET_MP_OPENCV_NUM_THREADS", 0)) {
+  dmlc::InitLogging("mxnet");
+  engine::OpenMP::Get();   // force OpenMP initialization
+  install_signal_handlers();
+  install_pthread_atfork_handlers();
+}
+
+LibraryInitializer::~LibraryInitializer() {
+  close_open_libs();
+}
+
+bool LibraryInitializer::lib_is_loaded(const std::string& path) const {
+  return loaded_libs.count(path) > 0;
+}
+
+/*!
+ * \brief Loads the dynamic shared library file
+ * \param path library file location
+ * \return handle a pointer for the loaded library, throws dmlc::error if library can't be loaded
+ */
+void* LibraryInitializer::lib_load(const char* path) {
+  void *handle = nullptr;
+  // check if library was already loaded
+  if (!lib_is_loaded(path)) {
+    // if not, load it
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+    handle = LoadLibrary(path);
+    if (!handle) {
+      char *err_msg = nullptr;
+      win_err(&err_msg);
+      LOG(FATAL) << "Error loading library: '" << path << "'\n" << err_msg;
+      LocalFree(err_msg);
+      return nullptr;
     }
-#endif
+#else
+    handle = dlopen(path, RTLD_LAZY);
+    if (!handle) {
+      LOG(FATAL) << "Error loading library: '" << path << "'\n" << dlerror();
+      return nullptr;
+    }
+#endif  // _WIN32 or _WIN64 or __WINDOWS__
+    // then store the pointer to the library
+    loaded_libs[path] = handle;
+  } else {
+    handle = loaded_libs.at(path);
+  }
+  return handle;
+}
 
-// disable openmp for multithreaded workers
-#ifndef _WIN32
-    using op::custom::CustomOperator;
-    pthread_atfork(
-      []() {
-        CustomOperator::Get()->Stop();
-        Engine::Get()->Stop();
-      },
-      []() {
-        Engine::Get()->Start();
-        CustomOperator::Get()->Start();
-      },
-      []() {
-        // Conservative thread management for multiprocess workers
-        const size_t mp_worker_threads = dmlc::GetEnv("MXNET_MP_WORKER_NTHREADS", 1);
-        dmlc::SetEnv("MXNET_CPU_WORKER_NTHREADS", mp_worker_threads);
-        dmlc::SetEnv("OMP_NUM_THREADS", 1);
+/*!
+ * \brief Closes the loaded dynamic shared library file
+ * \param handle library file handle
+ */
+void LibraryInitializer::lib_close(void* handle) {
+  std::string libpath;
+  for (const auto& l : loaded_libs) {
+    if (l.second == handle) {
+      libpath = l.first;
+      break;
+    }
+  }
+  CHECK(!libpath.empty());
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+  FreeLibrary((HMODULE)handle);
+#else
+  if (dlclose(handle)) {
+    LOG(WARNING) << "LibraryInitializer::lib_close: couldn't close library at address: " << handle
+        << " loaded from: '" << libpath << "': " << dlerror();
+  }
+#endif  // _WIN32 or _WIN64 or __WINDOWS__
+  loaded_libs.erase(libpath);
+}
+
+/*!
+ * \brief Obtains address of given function in the loaded library
+ * \param handle pointer for the loaded library
+ * \param func function pointer that gets output address
+ * \param name function name to be fetched
+ */
+void LibraryInitializer::get_sym(void* handle, void** func, char* name) {
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+  *func = GetProcAddress((HMODULE)handle, name);
+  if (!(*func)) {
+    char *err_msg = nullptr;
+    win_err(&err_msg);
+    LOG(FATAL) << "Error getting function '" << name << "' from library\n" << err_msg;
+    LocalFree(err_msg);
+  }
+#else
+  *func = dlsym(handle, name);
+  if (!(*func)) {
+    LOG(FATAL) << "Error getting function '" << name << "' from library\n" << dlerror();
+  }
+#endif  // _WIN32 or _WIN64 or __WINDOWS__
+}
+
+bool LibraryInitializer::was_forked() const {
+  return common::current_process_id() != original_pid_;
+}
+
+void LibraryInitializer::atfork_prepare() {
+  using op::custom::CustomOperator;
+  CustomOperator::Get()->Stop();
+  Engine::Get()->Stop();
+}
+
+void LibraryInitializer::atfork_parent() {
+  using op::custom::CustomOperator;
+  Engine::Get()->Start();
+  CustomOperator::Get()->Start();
+}
+
+void LibraryInitializer::atfork_child() {
+  using op::custom::CustomOperator;
+  // Conservative thread management for multiprocess workers
+  this->cpu_worker_nthreads_ = this->mp_worker_nthreads_;
 #if MXNET_USE_OPENCV && !__APPLE__
-        const size_t mp_cv_num_threads = dmlc::GetEnv("MXNET_MP_OPENCV_NUM_THREADS", 0);
-        cv::setNumThreads(mp_cv_num_threads);  // disable opencv threading
+  cv::setNumThreads(mp_cv_num_threads_);
 #endif  // MXNET_USE_OPENCV
-        engine::OpenMP::Get()->set_enabled(false);
-        Engine::Get()->Start();
-        CustomOperator::Get()->Start();
-      });
+  engine::OpenMP::Get()->set_thread_max(1);
+  engine::OpenMP::Get()->set_enabled(false);
+  Engine::Get()->Start();
+  CustomOperator::Get()->Start();
+}
+
+
+void LibraryInitializer::install_pthread_atfork_handlers() {
+#ifndef _WIN32
+  pthread_atfork(pthread_atfork_prepare, pthread_atfork_parent, pthread_atfork_child);
 #endif
-  }
+}
 
-  ~LibraryInitializer() {
-    // close opened libraries
-    for (auto const& lib : loaded_libs) {
-      close_lib(lib.second);
-    }
+void LibraryInitializer::install_signal_handlers() {
+#if MXNET_USE_SIGNAL_HANDLER && DMLC_LOG_STACK_TRACE
+  struct sigaction sa;
+  sigaction(SIGSEGV, nullptr, &sa);
+  if (sa.sa_handler == nullptr) {
+      signal(SIGSEGV, SegfaultLogger);
   }
+#endif
+}
 
-  static LibraryInitializer* Get();
-};
-
-LibraryInitializer* LibraryInitializer::Get() {
-  static LibraryInitializer inst;
-  return &inst;
+void LibraryInitializer::close_open_libs() {
+  for (const auto& l : loaded_libs) {
+    lib_close(l.second);
+  }
 }
 
+/**
+ * Perform static initialization
+ */
 #ifdef __GNUC__
-// Don't print an unused variable message since this is intentional
+// In GCC we use constructor to perform initialization before any static initializer is able to run
+__attribute__((constructor)) static void LibraryInitializerEntry() {
 #pragma GCC diagnostic ignored "-Wunused-variable"
+  volatile LibraryInitializer* library_init = LibraryInitializer::Get();
+}
+#else
+static LibraryInitializer* __library_init = LibraryInitializer::Get();
 #endif
 
-static LibraryInitializer* __library_init = LibraryInitializer::Get();
 }  // namespace mxnet
diff --git a/src/initialize.h b/src/initialize.h
new file mode 100644
index 000000000000..8a6dc3aa5f7f
--- /dev/null
+++ b/src/initialize.h
@@ -0,0 +1,126 @@
+  /*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file initialize.h
+ * \brief Library initialization
+ */
+
+#include <cstdlib>
+#include <string>
+#include <map>
+#include "dmlc/io.h"
+
+
+#ifndef MXNET_INITIALIZE_H_
+#define MXNET_INITIALIZE_H_
+
+namespace mxnet {
+
+
+
+void pthread_atfork_prepare();
+void pthread_atfork_parent();
+void pthread_atfork_child();
+
+/**
+ * Perform library initialization and control multiprocessing behaviour.
+ */
+class LibraryInitializer {
+ public:
+  typedef std::map<std::string, void*> loaded_libs_t;
+  static LibraryInitializer* Get() {
+    static LibraryInitializer inst;
+    return &inst;
+  }
+
+  /**
+   * Library initialization. Called on library loading via constructor attributes or
+   * C++ static initialization.
+   */
+  LibraryInitializer();
+
+  ~LibraryInitializer();
+
+  /**
+   * @return true if the current pid doesn't match the one that initialized the library
+   */
+  bool was_forked() const;
+
+
+  // Library loading
+  bool lib_is_loaded(const std::string& path) const;
+  void* lib_load(const char* path);
+  void lib_close(void* handle);
+  static void get_sym(void* handle, void** func, char* name);
+
+  /**
+   * Original pid of the process which first loaded and initialized the library
+   */
+  size_t original_pid_;
+  size_t mp_worker_nthreads_;
+  size_t cpu_worker_nthreads_;
+  size_t omp_num_threads_;
+  size_t mp_cv_num_threads_;
+
+  // Actual code for the atfork handlers as member functions.
+  void atfork_prepare();
+  void atfork_parent();
+  void atfork_child();
+
+ private:
+  /**
+   * Pthread atfork handlers are used to reset the concurrency state of modules like CustomOperator
+   * and Engine when forking. When forking only the thread that forks is kept alive and memory is
+   * copied to the new process so state is inconsistent. This call install the handlers.
+   * Has no effect on Windows.
+   *
+   * https://pubs.opengroup.org/onlinepubs/009695399/functions/pthread_atfork.html
+   */
+  void install_pthread_atfork_handlers();
+
+  /**
+   * Install signal handlers (UNIX). Has no effect on Windows.
+   */
+  void install_signal_handlers();
+
+  void close_open_libs();
+
+  loaded_libs_t loaded_libs;
+};
+
+/*!
+ * \brief fetches from the library a function pointer of any given datatype and name
+ * \param T a template parameter for data type of function pointer
+ * \param lib library handle
+ * \param func_name function name to search for in the library
+ * \return func a function pointer
+ */
+template<typename T>
+T get_func(void *lib, char *func_name) {
+  T func;
+  LibraryInitializer::Get()->get_sym(lib, reinterpret_cast<void**>(&func), func_name);
+  if (!func)
+    LOG(FATAL) << "Unable to get function '" << func_name << "' from library";
+  return func;
+}
+
+}  // namespace mxnet
+#endif  // MXNET_INITIALIZE_H_
diff --git a/src/profiler/profiler.h b/src/profiler/profiler.h
index 6df7ce4339da..f9f997fe8bb8 100644
--- a/src/profiler/profiler.h
+++ b/src/profiler/profiler.h
@@ -36,22 +36,13 @@
 #include "./vtune.h"
 #include "./aggregate_stats.h"
 #include "./nvtx.h"
+#include "../common/utils.h"
 
-#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
-#include <windows.h>
-#else
-#include <unistd.h>
-#include <cstdint>
-#endif
 
 namespace mxnet {
 namespace profiler {
 
-#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
-inline size_t current_process_id() { return ::GetCurrentProcessId(); }
-#else
-inline size_t current_process_id() { return getpid(); }
-#endif
+
 
 /*!
  * \brief Constant-sized character array class with simple string API to avoid allocations
@@ -132,7 +123,7 @@ struct ProfileStat {
   bool enable_aggregate_ = true;
 
   /* !\brief Process id */
-  size_t process_id_ = current_process_id();
+  size_t process_id_ = common::current_process_id();
 
   /*! \brief id of thread which operation run on.
    *

From 0eb213d9a704a4bad6073db7b5bf30ec0be7526f Mon Sep 17 00:00:00 2001
From: Przemyslaw Tredak <ptredak@nvidia.com>
Date: Fri, 9 Aug 2019 16:05:25 -0700
Subject: [PATCH 162/813] Fix backward_clip num inputs and type of clip params
 (#15688)

* Fix backward_clip num inputs and type of clip params

* Clip test

* Trigger CI

* Changes to clip docs

* Fix docstring

* Trigger CI
---
 src/operator/tensor/matrix_op-inl.h    |  8 ++++----
 src/operator/tensor/matrix_op.cc       |  8 +++++---
 tests/python/unittest/test_operator.py | 16 +++++++++++++---
 3 files changed, 22 insertions(+), 10 deletions(-)

diff --git a/src/operator/tensor/matrix_op-inl.h b/src/operator/tensor/matrix_op-inl.h
index 49847502258f..597c92a8b124 100644
--- a/src/operator/tensor/matrix_op-inl.h
+++ b/src/operator/tensor/matrix_op-inl.h
@@ -1481,7 +1481,7 @@ struct ClipParam : public dmlc::Parameter<ClipParam> {
 struct clip {
   template<typename DType>
   MSHADOW_XINLINE static void Map(index_t i, DType* out, const DType* datas,
-                                  DType a_min, DType a_max) {
+                                  const float a_min, const float a_max) {
     DType data = datas[i];
     if (data > a_max) {
       out[i] = a_max;
@@ -1497,7 +1497,7 @@ struct clip {
 struct clip_grad {
   template<typename DType>
   MSHADOW_XINLINE static void Map(index_t i, DType* out, const DType* grad, const DType* datas,
-                                  DType a_min, DType a_max) {
+                                  const float a_min, const float a_max) {
     DType data = datas[i];
     if (data > a_max) {
       out[i] = 0;
@@ -1524,7 +1524,7 @@ void Clip(const nnvm::NodeAttrs& attrs,
   MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
     mxnet_op::Kernel<mxnet::op::clip, xpu>::Launch(s, outputs[0].Size(), outputs[0].dptr<DType>(),
                                                    inputs[0].dptr<DType>(),
-                                                   DType(param.a_min), DType(param.a_max));
+                                                   param.a_min, param.a_max);
   });
 }
 
@@ -1553,7 +1553,7 @@ void ClipGrad_(const nnvm::NodeAttrs& attrs,
   Stream<xpu> *s = ctx.get_stream<xpu>();
   MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
     Kernel<clip_grad, xpu>::Launch(s, outputs[0].Size(), outputs[0].dptr<DType>(),
-    inputs[0].dptr<DType>(), inputs[1].dptr<DType>(), DType(param.a_min), DType(param.a_max));
+    inputs[0].dptr<DType>(), inputs[1].dptr<DType>(), param.a_min, param.a_max);
   });
 }
 
diff --git a/src/operator/tensor/matrix_op.cc b/src/operator/tensor/matrix_op.cc
index 59e8386d6679..c8e7b60d9548 100644
--- a/src/operator/tensor/matrix_op.cc
+++ b/src/operator/tensor/matrix_op.cc
@@ -702,9 +702,11 @@ MXNET_ADD_SPARSE_OP_ALIAS(clip)
 .describe(R"code(Clips (limits) the values in an array.
 
 Given an interval, values outside the interval are clipped to the interval edges.
-Clipping ``x`` between `a_min` and `a_x` would be::
+Clipping ``x`` between `a_min` and `a_max` would be::
 
-   clip(x, a_min, a_max) = max(min(x, a_max), a_min))
+.. math::
+
+   clip(x, a_min, a_max) = \max(\min(x, a_max), a_min))
 
 Example::
 
@@ -766,7 +768,7 @@ parameter values:
 .add_arguments(ClipParam::__FIELDS__());
 
 NNVM_REGISTER_OP(_backward_clip)
-.set_num_inputs(1)
+.set_num_inputs(2)
 .set_num_outputs(1)
 .set_attr_parser(ParamParser<ClipParam>)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 13ab03b94de9..8072cb88085f 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -4174,15 +4174,25 @@ def test_special_functions_using_scipy():
 
 
 @with_seed()
-@unittest.skip("Flaky test, tracked at https://github.com/apache/incubator-mxnet/issues/12901")
 def test_clip():
     data = mx.symbol.Variable('data')
     shape = (30, 30)
-    data_tmp = np.random.uniform(-1, 1, shape)
+    data_tmp = np.random.uniform(-1, 1, shape).astype('float32')
     test = mx.sym.clip(data, a_max=0.6, a_min=-0.6)
     check_symbolic_forward(test, [data_tmp], [np.clip(data_tmp, -0.6, 0.6)])
     check_symbolic_backward(test, [data_tmp], [np.ones(shape)],
-                            [np.where(data_tmp < 0.6, [1], [0]) * np.where(data_tmp > -0.6, [1], [0])])
+                            [np.where(data_tmp <= 0.6, [1], [0]) * np.where(data_tmp >= -0.6, [1], [0])])
+
+    # Test monitor on symbol using clip
+
+    def simple_callback(name, arr):
+        pass
+
+    exe = test.simple_bind(ctx=mx.current_context(), data=shape)
+    exe.set_monitor_callback(simple_callback, monitor_all=True)
+    exe.forward(is_train=True)
+    exe.backward(out_grads=mx.nd.ones(shape))
+    mx.nd.waitall()
 
 
 @with_seed()

From 44a7fca2cc0afe7c30658be96efcbd81c2be0dad Mon Sep 17 00:00:00 2001
From: Mike <maowenxu@connect.hku.hk>
Date: Sat, 10 Aug 2019 11:56:34 +0800
Subject: [PATCH 163/813] [Numpy] Numpy compatible slicing (#15798)

* Modify ndarray slice to have numpy compatbile behaviou

* Minor syntax fix

* Fix slice inconsistency

* Allow empty outputs after slicing ndarrays

* Fix
---
 src/operator/tensor/matrix_op-inl.h    | 67 +++++++++++++-------------
 src/operator/tensor/matrix_op.cc       |  1 +
 tests/python/unittest/test_numpy_op.py | 57 ++++++++++++++++++++++
 tests/python/unittest/test_operator.py |  9 ----
 4 files changed, 91 insertions(+), 43 deletions(-)

diff --git a/src/operator/tensor/matrix_op-inl.h b/src/operator/tensor/matrix_op-inl.h
index 597c92a8b124..96c86c42a6c4 100644
--- a/src/operator/tensor/matrix_op-inl.h
+++ b/src/operator/tensor/matrix_op-inl.h
@@ -685,13 +685,13 @@ inline void GetIndexRange(const mxnet::TShape& dshape,
     << "Static array size=" << ndim
     << " is not equal to data shape ndim=" << dshape.ndim();
 
-  if (param_step.ndim() != 0) {
+  if (param_step.ndim() > 0) {
     CHECK_EQ(param_step.ndim(), param_begin.ndim())
       << "step and begin must have the same length";
   }
 
   for (int i = 0; i < param_begin.ndim(); ++i) {
-    index_t s = param_step.ndim() != 0U && param_step[i].has_value() ? param_step[i].value() : 1;
+    index_t s = param_step.ndim() > 0 && param_step[i].has_value() ? param_step[i].value() : 1;
     CHECK_NE(s, 0) << "slice op step[" << i << "] cannot be 0";
 
     index_t b = 0, e = 0;
@@ -700,30 +700,23 @@ inline void GetIndexRange(const mxnet::TShape& dshape,
       b = param_begin[i].has_value() ? param_begin[i].value() : (s < 0 ? len - 1 : 0);
       e = param_end[i].has_value() ? param_end[i].value() : (s < 0 ? -1 : len);
 
-      // checking upper and lower bounds for begin
       if (b < 0) {
         b += len;
-        CHECK_GE(b, 0) << "slicing with begin[" << i << "]=" << b - len
-                       << " exceeds limit of input dimension[" << i << "]=" << len;
       }
-      CHECK_LT(b, len) << "slicing with begin[" << i << "]=" << b
-                       << " exceeds limit of input dimension[" << i << "]=" << len;
-
-      // checking upper and lower bounds for end
       if (e < 0 && param_end[i].has_value()) {
-        if (!(s < 0 && e == -1)) {
-          // Keep end=-1 as one-beyond-limits index for negative stride
-          e += len;
-        }
-        CHECK_GE(e, 0) << "slicing with end[" << i << "]=" << e - len
-                       << " exceeds limit of input dimension[" << i << "]=" << len;
+        e += len;
       }
-      CHECK_LE(e, len) << "slicing with end[" << i << "]=" << e
-                       << " exceeds limit of input dimension[" << i << "]=" << len;
 
-      // checking begin==end case which is not supported
-      CHECK_NE(b, e) << "slicing with begin[" << i << "]=end[" << i << "]="
-                     << e << " results in an empty tensor and is not supported";
+      // move the begin and end to correct position for calculating dim size
+      b = (b < 0 && s > 0) ? 0 : b;
+      b = (b > len - 1 && s < 0) ? len - 1 : b;
+      // if the start value lead to empty tensor under step s, use -1 for indication
+      b = (b < 0 || b > len - 1) ? -1 : b;
+      e = e > -1 ? e : -1;
+      e = e > len ? len : e;
+    } else if (len == 0) {
+      b = 0;
+      e = 0;
     }
 
     (*begin)[i] = b;
@@ -731,27 +724,30 @@ inline void GetIndexRange(const mxnet::TShape& dshape,
     (*step)[i] = s;
   }
 
-  for (index_t i = param_begin.ndim(); i < dshape.ndim(); ++i) {
+  for (int i = param_begin.ndim(); i < dshape.ndim(); ++i) {
     (*begin)[i] = 0;
     (*end)[i] = dshape[i];
     (*step)[i] = 1;
   }
 }
 
-inline void SetSliceOpOutputDimSize(const index_t i, const int b,
+inline void SetSliceOpOutputDimSize(const mxnet::TShape& dshape,
+                                    const index_t i, const int b,
                                     const int e, const int s,
                                     mxnet::TShape* oshape) {
-  if (e != b) {
+  if (!mxnet::dim_size_is_known(dshape, i)) {
+    (*oshape)[i] = -1;
+    return;
+  }
+  if (e != b && b >= 0) {
     if (s > 0) {
-      CHECK_LT(b, e) << "slicing with begin[" << i << "]=" << b << ", end[" << i << "]="
-                     << e << ", and step[" << i << "]=" << s << " is invalid";
-      (*oshape)[i] = (e - b - 1) / s + 1;
+      (*oshape)[i] = e > b ? (e - b - 1) / s + 1 : 0;
     } else {
-      CHECK_LT(e, b) << "slicing with begin[" << i << "]=" << b << ", end[" << i << "]="
-                     << e << ", and step[" << i << "]=" << s << " is invalid";
-      (*oshape)[i] = (b - e - 1) / (-s) + 1;
+      (*oshape)[i] = e < b ? (b - e - 1) / (-s) + 1 : 0;
     }
-  }  // else leave oshape[i] as 0 for partial infer
+  } else {
+      (*oshape)[i] = 0;
+  }
 }
 
 inline bool SliceOpShape(const nnvm::NodeAttrs& attrs,
@@ -761,6 +757,7 @@ inline bool SliceOpShape(const nnvm::NodeAttrs& attrs,
   CHECK_EQ(out_attrs->size(), 1U);
   const mxnet::TShape& dshape = (*in_attrs)[0];
   if (!mxnet::ndim_is_known(dshape)) return false;
+  CHECK_GT(dshape.ndim(), 0) << "slice only works for ndim > 0";
   const SliceParam& param = nnvm::get<SliceParam>(attrs.parsed);
   mxnet::TShape oshape = dshape;
 
@@ -769,12 +766,12 @@ inline bool SliceOpShape(const nnvm::NodeAttrs& attrs,
     GetIndexRange(dshape, param.begin, param.end, param.step, &begin, &end, &step);
     for (int i = 0; i < param.begin.ndim(); ++i) {
       const int b = begin[i], e = end[i], s = step[i];
-      SetSliceOpOutputDimSize(i, b, e, s, &oshape);
+      SetSliceOpOutputDimSize(dshape, i, b, e, s, &oshape);
     }
   })
 
   SHAPE_ASSIGN_CHECK(*out_attrs, 0, oshape);
-  return shape_is_known(oshape);
+  return shape_is_known(dshape) && shape_is_known(oshape);
 }
 
 template<int ndim, int req, typename xpu>
@@ -852,6 +849,7 @@ void SliceOpForward(const nnvm::NodeAttrs& attrs,
   Stream<xpu>* s = ctx.get_stream<xpu>();
   const TBlob& data = inputs[0];
   const TBlob& out = outputs[0];
+  if (out.Size() == 0) return;
   const SliceParam& param = nnvm::get<SliceParam>(attrs.parsed);
   MXNET_NDIM_SWITCH(data.ndim(), ndim, {
     common::StaticArray<index_t, ndim> begin, end, step;
@@ -951,6 +949,7 @@ void SliceOpBackward(const nnvm::NodeAttrs& attrs,
   } else if (req[0] == kWriteInplace) {
     LOG(FATAL) << "_slice_backward does not support kWriteInplace";
   }
+  if (ograd.Size() == 0) return;
   MXNET_NDIM_SWITCH(ograd.ndim(), ndim, {
     common::StaticArray<index_t, ndim> begin, end, step;
     GetIndexRange(igrad.shape_, param.begin, param.end, param.step, &begin, &end, &step);
@@ -982,7 +981,7 @@ inline bool SliceAssignOpShape(const nnvm::NodeAttrs& attrs,
     GetIndexRange(dshape, param.begin, param.end, param.step, &begin, &end, &step);
     for (int i = 0; i < param.begin.ndim(); ++i) {
       const int b = begin[i], e = end[i], s = step[i];
-      SetSliceOpOutputDimSize(i, b, e, s, &vshape);
+      SetSliceOpOutputDimSize(dshape, i, b, e, s, &vshape);
     }
   })
   SHAPE_ASSIGN_CHECK(*in_attrs, 1, vshape);
@@ -1121,7 +1120,7 @@ void SliceAssignScalarOpForward(const nnvm::NodeAttrs& attrs,
     GetIndexRange(data.shape_, param.begin, param.end, param.step, &begin, &end, &step);
     for (index_t i = 0; i < param.begin.ndim(); ++i) {
       const int b = begin[i], e = end[i], s = step[i];
-      SetSliceOpOutputDimSize(i, b, e, s, &vshape);
+      SetSliceOpOutputDimSize(data.shape_, i, b, e, s, &vshape);
     }
     MSHADOW_TYPE_SWITCH(out.type_flag_, DType, {
       mxnet_op::Kernel<slice_assign_scalar<ndim>, xpu>::Launch(s, vshape.FlatTo2D()[0],
diff --git a/src/operator/tensor/matrix_op.cc b/src/operator/tensor/matrix_op.cc
index c8e7b60d9548..f02a38ac07c4 100644
--- a/src/operator/tensor/matrix_op.cc
+++ b/src/operator/tensor/matrix_op.cc
@@ -506,6 +506,7 @@ Example::
                                                             [5.,  7.],
                                                             [1.,  3.]]
 )code" ADD_FILELINE)
+.add_alias("_npx_slice")
 .set_attr_parser(ParamParser<SliceParam>)
 .set_attr<mxnet::FInferShape>("FInferShape", SliceOpShape)
 .set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index b179f67e6128..c172336be32e 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -92,6 +92,63 @@ def is_int(dtype):
                         assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
 
 
+@with_seed()
+@use_np
+def test_npx_slice():
+    class TestSlice(HybridBlock):
+        def __init__(self, begin, end, step):
+            super(TestSlice, self).__init__()
+            self._begin = begin
+            self._end = end
+            self._step = step
+
+        def hybrid_forward(self, F, a):
+            return F.npx.slice(a, begin=self._begin, end=self._end, step=self._step)
+
+    shape = (8, 16, 9, 9)
+    np_array = _np.arange(_np.prod(shape), dtype='int32').reshape(shape)
+    configs = [
+        ([], [], None),
+        ([], [], []),
+        ([1], [4], None),
+        ([1], [10], [3]),
+        ([10], [0], [-2]),
+        ([None], [None], [None]),
+        ([None], [None], [-1]),
+        ([10], [None], [-1]),
+        ([1, 0, 3], [-2, 10, -4], [None, 2, 3]),
+        ([-2, -3, -5, -6], [1, 3, 4, 5], None),
+        ([-2, -3, -5, -6], [1, 3, 4, 5], [-1, -2, -3, -4]),
+        ([2, -3, -5, -6], [2, 3, 4, 5], None),
+        ([2, -3, -5, 5], [3, 3, 4, 5], None),
+    ]
+
+    for hybridize in [True, False]:
+        for config in configs:
+            start, end, step = config[0], config[1], config[2]
+            test_slice = TestSlice(begin=start, end=end, step=step)
+            if hybridize:
+                test_slice.hybridize()
+
+            a = np.array(np_array, dtype=np_array.dtype)
+            a.attach_grad()
+            basic_index = tuple([
+                slice(start[i], end[i], step[i]) if step is not None else slice(start[i], end[i])
+                for i in range(len(start))
+            ])
+            expected_ret = np_array[basic_index]
+            with mx.autograd.record():
+                y = test_slice(a)
+
+            assert same(y.asnumpy(), expected_ret)
+
+            # test backward
+            mx.autograd.backward(y)
+            expected_grad = _np.zeros(shape)
+            expected_grad[basic_index] = 1
+            assert same(a.grad.asnumpy(), expected_grad)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 8072cb88085f..1991b16be317 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -7529,15 +7529,6 @@ def test_slice_forward_backward(a, index):
     for index in index_list:
         test_slice_forward_backward(arr, index)
 
-    def test_begin_equals_end(shape, begin, end, step):
-        in_arr = mx.nd.arange(np.prod(shape)).reshape(shape=shape)
-        out_arr = mx.nd.slice(in_arr, begin=begin, end=end, step=step)
-
-    assertRaises(MXNetError, test_begin_equals_end, (4,), (2,), (2,), (1,))
-    assertRaises(MXNetError, test_begin_equals_end, (1, 5), (None, 3), (None, 3), (-1, 1))
-    assertRaises(MXNetError, test_begin_equals_end, (3, 4, 5), (1, 3, 1), (3, 3, 1), (1, -3, 2))
-    assertRaises(MXNetError, test_begin_equals_end, (2, 4), (None, 2), (None, 2), (1, -1))
-
     # check numeric gradient
     in_data = np.arange(36).reshape(2, 2, 3, 3)
     data = mx.sym.Variable('data')

From 614cba3042567fd8de84b9cd8122feabb9e85b5c Mon Sep 17 00:00:00 2001
From: Rohit Kumar Srivastava <srivastava.141@osu.edu>
Date: Sat, 10 Aug 2019 01:49:49 -0700
Subject: [PATCH 164/813] Making Features as a singleton for improved caching
 (#15835)

---
 python/mxnet/runtime.py               | 6 ++++++
 tests/python/unittest/test_runtime.py | 9 +++++++++
 2 files changed, 15 insertions(+)

diff --git a/python/mxnet/runtime.py b/python/mxnet/runtime.py
index e47cca93dace..edfa24525ab9 100644
--- a/python/mxnet/runtime.py
+++ b/python/mxnet/runtime.py
@@ -73,6 +73,12 @@ class Features(collections.OrderedDict):
     """
     OrderedDict of name to Feature
     """
+    instance = None
+    def __new__(cls):
+        if cls.instance is None:
+            cls.instance = super(Features, cls).__new__(cls)
+        return cls.instance
+
     def __init__(self):
         super(Features, self).__init__([(f.name, f) for f in feature_list()])
 
diff --git a/tests/python/unittest/test_runtime.py b/tests/python/unittest/test_runtime.py
index 5b06d0c3c36c..82e2314532b8 100644
--- a/tests/python/unittest/test_runtime.py
+++ b/tests/python/unittest/test_runtime.py
@@ -21,12 +21,20 @@
 from mxnet.base import MXNetError
 from nose.tools import *
 
+
 def test_features():
     features = Features()
     print(features)
     ok_('CUDA' in features)
     ok_(len(features) >= 30)
 
+
+def test_is_singleton():
+    x = Features()
+    y = Features()
+    assert x is y
+
+
 def test_is_enabled():
     features = Features()
     for f in features:
@@ -35,6 +43,7 @@ def test_is_enabled():
         else:
             ok_(not features.is_enabled(f))
 
+
 @raises(RuntimeError)
 def test_is_enabled_not_existing():
     features = Features()

From 57927a932610581a351e4aed4fb7c9209d0e57c2 Mon Sep 17 00:00:00 2001
From: Carin Meier <cmeier@gigasquidsoftware.com>
Date: Sun, 11 Aug 2019 00:47:47 -0400
Subject: [PATCH 165/813] Clojure package remove source images (#15828)

* remove test images

* add script and .gitignore

* add test helper to download images

* remove unlicensed pic

* add license header
---
 contrib/clojure-package/.gitignore            |   1 +
 .../infer/objectdetector/images/marcel.jpg    | Bin 116161 -> 0 bytes
 .../scripts/get_test_images.sh                |  33 ++++++++++++++++++
 .../org/apache/clojure_mxnet/image_test.clj   |   6 +++-
 .../infer/imageclassifier_test.clj            |   5 ++-
 .../test/test-images/Pug-Cookie.jpg           | Bin 104323 -> 0 bytes
 .../test/test-images/kitten.jpg               | Bin 110969 -> 0 bytes
 contrib/clojure-package/test/test_helper.clj  |  26 ++++++++++++++
 8 files changed, 69 insertions(+), 2 deletions(-)
 delete mode 100644 contrib/clojure-package/examples/infer/objectdetector/images/marcel.jpg
 create mode 100755 contrib/clojure-package/scripts/get_test_images.sh
 delete mode 100644 contrib/clojure-package/test/test-images/Pug-Cookie.jpg
 delete mode 100644 contrib/clojure-package/test/test-images/kitten.jpg
 create mode 100644 contrib/clojure-package/test/test_helper.clj

diff --git a/contrib/clojure-package/.gitignore b/contrib/clojure-package/.gitignore
index 884834e3843e..c304e7926588 100644
--- a/contrib/clojure-package/.gitignore
+++ b/contrib/clojure-package/.gitignore
@@ -46,5 +46,6 @@ test/test-symbol.clj
 test/test-symbol-random.clj
 test/test-symbol-random-api.clj
 test/test-symbol-api.clj
+test/test-images/*
 src/org/apache/clojure_mxnet/gen/*
 
diff --git a/contrib/clojure-package/examples/infer/objectdetector/images/marcel.jpg b/contrib/clojure-package/examples/infer/objectdetector/images/marcel.jpg
deleted file mode 100644
index 1bf7387e097d829643140c8922911f3aeffb258a..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 116161
zcmeFYXIN89*FPMpbSVi!1cg9C2rWS9qLk1=KoSy4DAFOJNVPYr5Q>xpLIj17gd}tX
z1T06Y6hT8%Q9NQnK+&UEj_vr5_jBLR|9U^ZAKp*zJ^Q-$nmv1FWqxaBuUTu&<ky2=
zPXWppcQ1E<m>2-yDY^i^Rskh$$H;L2fR7IV0RRAG08(PA00~h}OmqRnzyPU#<pBVU
z*zW($<Hg|rt|JZroF@Y$|6PY9qQ8@f^Y7OGrV<Te|CeKf_<w3k0vaU#lb85iThtrb
zxrEHjRC_bClnm3zn8Q)ArqPF!&5lK;nwgs-%>erj97~OiPK?deiHeOUr#M31UHb!~
zLymET1X}qZeNtUx6Ud$?(qsKk_!6Q|Bu3lCKo0EJ*>}wTSaND|Y-Xg+vE-zb4Etk_
zkbg0^7v+B!n?ZE`RV6dg5fbF%r{j7!Jyyrc6lIEph+3z|#M$G~?*DEpdUAyPyOT$c
z9yL8`X?i$4-pt(2&dv;JVP;`rBC27MaXck6@|a0VhTeZTpkp(l)5)os<ijaCzd1%m
z9nQ*hgou9mUtLN~IGlMnBjIrB|1$r7NZ@~8|4R-Ko&SBd_e#mgOwWq`%{Rjl;_l*u
zMw%m$$Vgi&WQ>R+t?c4Zze{XwVxy2KB+?=}&hj^nM#aVbrjbb7Sfn))850{5i;NXf
zE9*F9oT#0xs9Z#&ZDK5bQ=2H8NORG9h?z(vX8)GS@9h5ycfZbm(|;xKUkUtI0{@l3
ze<ko=3H<+00{_=Mi%k*PWJg8D7vR?xpxy63#*L1pDH34o;^iax+hPO&j%bOI01~^Q
z9$F$hQXC+zB@PEf{eN%6{Hq7hfAv5aOeu48@;0$K`3#(C{*STKuVH|SjD(mZKtc=x
z5LXeCP!anz3fL>MXQjmcmHyQX0FV%u6q5o-?~sv|lNVJOQ3i-fh{_}+r6qSrOUg)R
zh=~IvR3ue(rOeeJ%F@m_3t(iL-i~aY3Kvi#-m*(S3aLJnE_AI#<;bl1J3*s&S^ZU&
z!SX|Q=e~y#tYc`I!x3|NO&Y1s4BTvDuMo4?^pX4z^X^B^J@*KR>yeey1j7vxws!WO
zUYNk3U{ZVnnR4{l@e>8-3yY}b)isP-W&?-Y%;O6>d;6~T4-DQK9UH$rF*$W_VR7mH
zgNMscU%Xs<wZ5^r{qfW1FMogi_8X<hPX8Z}qWiya{sti_^?y;S0K~;bI3>g-q{OAf
zB_+>`iDV?9tg0)iWA3abg@jn(fKJkudXX-mG(0NXPrstkwM%_Rl$HNqLTJPgdNuw1
zuAFG=RG1r~(ttH55}wAtaM*}|xO<485CeXLiHVK-jVB{B>&S0R=W_G%{|8qUjs8E-
zn%Msa*L|hu-}r_{{)2G-KM<ci|2N1F|BLbwz!e}V{u_d%=q4#jQnJ!gq9iXPBQ2{a
zuc)Xfub`l$3{+9tskT!=K~+Ol4Wzzn*DfU$&E1;nV4(Ug^<N?;V!vBTNy$k|%c<{F
z*s1>iI{jJ(D2Uu1k@!?Zlj{{1M(0xEK*6L$tPh4oIYJ~Cwe|?OsHI1Q@oa3POXxTm
zQKUX!YM;u5QB(;F<Y5K@42$rhBhE{?p-qhtAe<$v=s^gU34`^FE<KWqO!LZdgUD6s
z;>7jKun8_LuqX~<p&prlS&iA4fr0WEggMk^VF3Fj#TTE5_3SGpMF%_r>-r@uBQia!
z`YbXx^V$)5lso}K7l#9jZbT|pXh5N(7w-6Aw=fLme6LvopTVpx;y=PWMHmwsX#9Ll
zOZGfhife_bLiL`b)!G`l#L-!9MNky<A*IVk43thUE$<<`U}G3nQRpJXGAq3$mV&;s
zn@knjH(})w<~>IEs3@+#AT!dPVLcD5V>9tR@=%;#4;)`#%F0Th6_cs+8uPosFsp<*
zxUf_W+=DQ*k75CtlglgvJZdZe>tpJV>V*<5<@iEWTQ8;w&xLXEf_ZR_CaMQpWZ78*
z$5qz}ii&uQMH*cW2II^VYc;=%#g>ep&nAWhmp#ilf3WZc|1|X6{u3kZFGlHoPG;qc
z;$78BK@NK9_=SBCPa5U}L7IW9qVtveEr=QznxIO>3LI7CK!|lqR-H?42=*hTR2DuY
zQ?be};f%H>!l)0~*ZLu<wH7$))s&z<$*k89_VJdDpkBOuH<vg>mur|REYU=cgQeiB
zP;GLp?9K3HZY0rM4bPrIk5aAu8ojEpv(_|0Pl9?qu(git*LfM-`q8UH!+J?;vCiRY
z0*-gvr1aD;Ky6QZ%dEo}<-ZoPfEY?u#1LjW=viwh!A^QS*e%G1a_1)&)|#POzLi`o
zwN8ZXf|WSWMHI1PovW&u*ex6v0Rwp>Se46<QMc5MOT?pPZ`R}Fa%1kuMN@>m$>5$*
zO|J(ybI7u#|89Kd7#d4(XS(AlU0zImE*Vc3%??uwGQ}m|qSoT0XtorShiiQ)amb!^
zi;wV-q-4Vd9s@W|Wh|G|OA}lWC3AhF`VX>B#h=`Xu0G$MmA_(MbNp_I+_Smr;kvJZ
z?I^ulA%8q~zUmW|eMZnt?713*z+)i{baNjH;Xgl<@xV4CBw{G|cur<nGj`4(8tto^
zxrs&?0R!pkfis2Pev-9qmDEd%R}cj&@i2FAr{NrP8OgB(^^Gq>xG1@3H{S>IDgu9V
z5=>DR?^cDR^m2$sE_@7jj)M@*?f?<B#0c|l$f2g<=wL*&j}eqe*B~8fvp2$|(RUNv
zee`$4bW8d&E}ePB!D-$0S04rs9dfKXsyJoxIs`F%1LQ{6TMhhr{_1w#an`qUx$X|{
ziO$B4i6`?em5pt!UuSkK{peQ-{cv>RzLI1B@s4gzr@UJpmMB*#B}c`^Pzg6D7FV1q
zh-1tq=_@Gpv*BvtxF95GD|ue7xpr6`@1xwRzd<dmOPCj*WVERXAZe^<`GoOD(P*r3
zJ(5zX7$=%g{CvU||2Tq=2VqJD@jlpb4%WxF2$&3;t5xkL`ZwWMEy#r$MPi;M=8!9~
za&22fF>%wGdi&M~Ok0Vr7Q14l)D_GBM4`-*kMC|G+3dSlhYA{)+VlN9GAnp=bH08<
zz&Ok&Kh#8YkmdYSU%u9qk=B><%f?slt^##9`?w~&0cO_%K^@(keiHmySg<%NeL^`K
ztH6AoeTZEd(jDxV89E{O=vA3NJdy9rLd`{Gp<{}Zt-!FJ<REDcyEJ|Dc$;c<Q!-H!
zEsROz7|L-lL=0^c&oU(a{<;YwKoo+n`XsK|UA4+km7Rw6ERQPjQoWZq*C^XcyX9ES
zyk8=lvsSz~|9BB{&~V|N?;dL()1|lxgHzz(e5(lImCd-vJEdN3y&G^1`BE3iqUK)S
zNI>*o@b~%C+{%{S6Bi$(&We~F%^c}$9_08PN*~X@s(e}dm7B|80V*noG0Pi;gg_?5
zqh`lGrMKJP)+t!Mk>)@5P$;WXZdj@^NxPMZ5%}BZ<WTAjd9rTka(XP;J0v;803V6N
zseuS=6OJ;4GeyL+eGI)8_}IBVW<Hd_$1Y3}F~WSVn)kg56C|}_b%C9rqI1l9cm|=&
ztOMrGhiyAvlpat0aDj|FLRs&IQzmZigSTlZoI`*x8blY@33YPn)PR!r+&cfRA8hf^
z>;r${o`vN_{P<sY1>Z2Fh>uveB+M(?`;pVWUqjxWVLm?J)p+h!b6aAO+jM12H$#`~
z7AX#=b7ObCWEPrKj_W4ptMnRR@$qiHz9qwPnwCHYAs~QSYG1`gK(Y+5nuY;_bj3JQ
z6>7j`15YR1p#&_`MN_a8gNM<p@~$&DXs4P==Hv(uSfZ_IWv+q|@(uA=n&HpPupI~D
z+`1~?+&%P)a9u|xM;>zmyirP3X6#E@qwEdXZ#iB(ET`_Ql(Ss;_<QU%)@vq(;w5OG
z?c0IPkIEoOy>tu<TRJ4$7B;tGrhXzaIP=z>a9ia{pfv1GkuQM5<k>W}2Ro(?$@}J|
zZzrb=5mn!pLM{3B%5opkFsS(>w6#!Uj)6@MNw6->XGONs^>ICNEIhZGji`YW4LzBm
zA{AGw%CR6b7WlrLgnTXz#U91atN2+#;J!{A^ZX_s_j}1IARDV)lJcw<KLJaQp*cG$
za$v0veiUh%Hr_>PS7OV}D@Ph^b1-zgw<KEz4-0+YQnEaG!OM-k@pX#VbZTNnp8C4W
z;cyJ|WlIOyCo7s5VPKlaG`VQUl2r@t%63ZlryE+pg1)AVN4DlH>ONkc%j~wZL~2d?
z{d4nTDGoTwmUQ{cuu!A8ijlx*rNhk4A;QMI@$7_UE^0UWR#l%MtI@|WIt)8H_fnqE
zSh!xxNys*Xp??pJn;Fb7*b-ms<`N6>EG%Tj>TGEgm0HQxyY+TliM)|cpHv^UB&m)z
zP*f5zdo;o<^pBa5pL0K3Q_^>_G`*$si(C>$KM^tGb66v;UV9n;a7*^u_x%Bbm$K{I
zv^^*kObav3=gQHKLAvCNRdV^#i&wa)JLcy25mX~Eix@mtNjX!!cWUtlRc1}#us^Ov
zl}%Odk$0!&wTqQD78IS!Ovd%Nx>FfKe~~thb{CSMR=z#(sh}E+NV(yyz-s*CB83g9
z*TCe>_?BV{siBR;O7<8SudhpHCuAo#%&UPa2XuZOHrg0Jvwc7($ANNApOP9VEp@uT
zDb!mV)7jCLt(1dXvTM|7%NrkDZfAlnj_j#vY7@L|uH2wpy!iU-@_Gl^evs^Z`tH5u
zm1=>uYQkY!q-36XZR5IOi?{V6=KPzD9+QgWE{Ae<w4Il~z3@d9XfB;hJnkR59TFE6
zTm7WlTD=so&VX2pfRL=)zzB;9O<`jnRoK-Xhp2b0f)^4I{wNNMe8S4iosZ8#s4`*h
zsx&(e)|1|Zud*+vvkWmpPnSyZIHH%K;;w3~H=UdI7i@Yv&>;p3Xe}ZD&X@9{_>tL;
ziI7v4tyBJ4{(l*R%2`GERx64D`&%{?6KAvPX8f|W&Ih`@+IlVc1?YHnLcofRTdrOj
zJIAQM_*@gS5ZmZpq2&Sywc#2+ZdlB!2j}AZyFiLJa-gYc>eY>mJBYV%N@G>QGc?8-
zi<M6(L_ieSTev0y*PjG4a;Mg-h(^huEmX<(bmp@#up+|+H>HGHHE)_{4V=lCLak?G
z$AMEY5t`$c!3ZCyq!V0IT7OGF78Vsl^~@4(pO(G$+Ltumo->PHl^74rAM=Iefq(^u
zc_byv6DTo428vl0-MEHszgo9(N3fepT07iw*IjUBap4d##9#gHxDN74R7&__6Y~t)
zw8x1@l}i||m~)Dp-xs6Vqe3vZ&jvDjsbs6Yc1!7>d{xEBlh`<v3&bj6nPabKUM1>&
z6bfolg>4Qfq}GG`goH}E<_Rk`cMdjEG(f$I?)VA_UDu_ykowFVjBi9(MBR|h>7h(A
zRHe~=&muG!jN!^Ct(Cb?c_HDY?+bdaHgOusL++8ryfBjlqOzA}H8;COKM->HYQlIM
z*&W<-ea&?AQ>{E<>P(ZRb}nKwDPe0ghGV6$2WjO%Q>U`Usn#t`2#~igQOCE}(v|Hm
zQn8Yn5U&>nmBZLSFRv#WmBgm`bygYfKqn-F?JNs@h1$pp-$Gr_Wpi^N&zA$U!W?M>
z%dmr9O#9vUrioS?W<aU85XJXZK~Y3d0t!p+Q0l>Qr2?4Fyc*!vQlWmLr+Lqir*HA4
z>9Ti0St%~00uM#8i5d@EMfcJ?HysqWA~bfs>qF4DtoRD&8{}8T`r+%p0Ar5e!S8P=
zf5`_LD?~fqmBKH+#$357IEltyZR#CDN<J)g0J)y{!$e~68q|B)5HF}y=*iM0AX}q=
zcZ9SR)P}6?#qKk=7WVz=?G96cOGoRIAc#!oW(Xxp4O7BE=(+o|)L4l0ENrrdP?_zw
z^szMtC|WIPGpaa#I$hp(5rM76sOI%!vI)r{?!Y_Ih7PXDd^ibPQ2}T)%62b3_){(a
zqx-IDg#{~`$aGf$lPAIL^<s+fecvxTFuPgX+<>PcA49!$EgjSqlS+iUR}R~nxzUa&
zm?1`U7kwAkH(qj1K8Lo9OBa|W2%4wi{Os<iO3lR;fZJ=w19xv;+w*3zuX`YUnfzdN
zMy3F8PWYS>b6l4i9A@2em1}jtG1<NInU1C}wUAbfc0;IEsj5i_pmDnPG{&NNl`L(P
zSS3i0;w|OdR_bOoN1FNx!izWwL`+p)e^D!Y22;VSwWl{p*hA<l%HdjbRfat5m`fl8
z*l&Je{nUZD6uMuwl%#W?6K|UGPuZz7NcT!x+D-4xaHo)tkB4mb*l+JUd~iB*TQJLg
zu-8+-d42rTtDnCBYX_RGeA2>FiEdtkF0%{HN6w=y+Ri*K|NB7CsVlo7AFubB`A+L4
zy|ce$wBorf{qw9@_02!BUWG-6Arq(L<4!dBZ(FCrWkVPg7;tJSB9PQ+qv<iX&614C
zRZ%v~8^fXl3Kz|l$rB8$d&v~6Sr6Z64~{bQDg~1<b*fQhe^qGAjEN`nw<Dwq#l&a|
z3+uVvEHOe7nu|AlXkP2CI<KNw#SCTV=#^i$yxDZ|b*XSTeaI_BQ?Yt5?8eE#BTjb8
z%h$=FuS!o#45{7P_sn7c8EUw{(5Uxz*^N^Hfp9a!g4vt(U%zkcDPfHsQN3z^-_@8s
zDe@lgKk>gfecfa3$E^dc$NxM%@$^T;<@=e>UM*a|-y@&7JaYNf`kNDP_4*zrg;^er
zN0@Tjiukuy#dC@>V~gSz-#)CE@cXFsp#t5AZA?Hxz1YK5BwZAH+#1XFZwjcGF@h=d
zjB|!z%?3o5dDC6yDE<S{bVc9?=fSi_o=7Vqgh&EfGuXaHufkWTQRIVl2adw%wlCuG
ze@~yWp}lM7ualiq!LAuprrj(;8)dam&p_O>eoQt%$!Kg}?wj`H4rbE#&CuSL*&Frm
z_vqdGE^pu)C^YP7_^TXItAa?6t3&7uL*j!D!`>g{4NP8de^xM3b0(nUUeHFV<7@6$
zJG+4J+U}3q5-fYyqJ8@1U6Q`LmnN$)xxnX8B4aQ<^woiR0?}fRg|u3wA$BYVyBiY4
zvT*D3F|?>kR))I7%&S;+r*ic)^IB|+>hWwrU!5_V=ieuCXbD_r0?V^r8H%P5&~E7_
z-g%qLI+(d=pW>rz?WrV1YnLuiY4Ooc*LbA-vD93}<#5@`$D3kUk|vF$`^b0}Z>QR=
z9-}+8d84$&JTP=|pJf*mg&vjRMy=mQ>feq^^J{5uSU8jMI=6!<*x%UV@wMnrp_1qJ
zVRRQVASRuu)t-=OUvkeZWM0Qk%DvQMA{SaQiU9AQy*UtfAM`0aM^d8{#DZ|_jbKqt
zXt^jzPeNM{zR@DNi7=eC)4@nu^%H6-4DJJcJiqTf>XGd^=Q8FVW51_et~m+vYG}V8
z<qz{XHNG8*7O>fTS8y45wX3z$s=m!<y|qMb$X2NGY=XTWh9z@+sdiw>$y&q(u`ta9
zqMd&PIB{XYySk+Ggdo;^D&h74r`Odd&0GeLwrii;^jmYn!Ht|MEyl#<`K@*fi#t{0
zKJe9R{XLz9a@BoFVnD6T9G8;CrWbOZM~pXFO>A=LbE84Vm3+nI-J)PhPs{>0s!vFQ
z85Ghc4e^vH^S;8CISfNu%ab{sFQfbn9=*>$W^iF<XMudoppQ-NW^xHrRZHI~$46R`
ze;o|j*AKKiUpDYD$f`B!9_itg!DBB>by_)oFOLE5j9P;sudUs*6+;$FI;mZ0p`=V{
zxh4U~O^y4B==vaF_e^U-KkChAlZo(-urBhxq3&-wH|5tmrZ=rKmtNHkAX?7>d{=eV
zdaRh~6+W79uTlVWU$%RwO9D1YvuH4YDTcn{Z4<#wt`CsC;r;pJLq2#uE*@M#rWP`I
z^V#kU2qdb>m%V{qh9XNFN_Wev7h%m7@6H_RxIDi8s?M)ta;7y5GSIubsqDDZN6Wi{
z4l7g8=sYig-(G%*L#us~4B02;0z((R1z&dhXSna-$ZP99alOxXpT2w)-;0=&d5+eV
z2T64?-G-f|owh}TrhY*;%{%jblxBfI&#(Nhq&c<a+{e#u*FRigJ?;MFK$`eyV)u3b
z(<PhT`8A+gr#pSVc$mDJdGJgc|7|?#W6A7%{#BHIkh!50irX%MeadU_Qt{g*#=Vo2
z9r`KIn}o2QO&n78&4=XANP9BMh`iJrVnYy_DXMQh5PCvps*nYe#Cg8VjyX{(!whAi
z`RH1F2?T>IswlzI2``=!8U^$!U}y<s)r%VNO83#{`>?EIvv+303npaK^)%j|OH;*H
znR>_@+YMBN$C<l`gL`%-%Ec8E{k_X?s7h{<Yh&E$*>qBmhGAr!<-xLV9`$A(4WIX)
zz4tJ4jdR5?jyuhAh=z3)NVfST>H4<<nq$aTanDLOTE`y{J|!s_B&N6m6SZZbR5>Mk
zWsE&#@i`@#^pn>@-dg5!y@#5tTu0BxE6qz!W>#2x&BeIpE6-`U+2pQ@D^az~G`v!&
zFJp}Uw3v7qg@IK!&LD6oj)82tC`u7C$EN5*akbdF9v^HIehsDmM!n#8R&Z*fk@XSa
z>g#xA!%>pC%kKasn%f{rfxS^Yq@s9>x3pk}c-j^w1nz^{AlKn%a^)nK3aFdUNxB?u
zi|1j;-mP!H0LVA&jmLqA@a!ZL@-)}0#s6MqtB15;_H1*D;3{0>QaR4!PDOx+;Os%{
zgPzRcQdN$Vgl&`x$S-AwS82%ZKJkXw!@kC_36Bip7nX&RPJiRMIJ=y<7#CMs(REv9
zN{zjV?&I>=Jx=>$aCd#ZX^~)bqbiP9E2m1!m&Mz0cofuBnh^~%J;4n3wOF8wH%ZMK
z6~iu+re%0!1(-Q)R=>ZvjL3N=Wm|*B&OnV66`KmCNXU%vI4{c+A!`OLBOmSxK7Jf;
z0rpY-RGY-WeatIvv8HX?)Rz}Oox51qn`)ccZs%>A8F+o{Q<wu=J2QE6($~Av06S%$
z1a~eo_v9GQc%*~o=dT!Enn3MYo8lCBc+x1Ip_V1RZpZ2Z7{nREkyE1-q+jbAY2Ciy
zfiG=V=B(A<yR}1*R~9>cS{EZpV+5BchW8ZZ&fD{$8t5Qe?36xwl1bpA+cc)|Fr&#^
zW-w<$GoEWsH)KMFGaLpZ7s|h_a!PG&Yb3OcnxR;kHJ1`Eaejii<ix=dV%B}EfMx@#
zXy*+iyQV<rug#v`3M*F!P6phvv7LP)<^Crdgs*+C=xURM=-hROLJQ3BNhYbchj05L
zRun>sjYj20ev7<W!Vs_CR{lh9yX%zqw>hG4^T|9t+Z|GY@?362P|wk#<P93EjO0lt
z<d2iF{3klZdkpHTU2Nox*-jktS*duHigTD5nxIf9kVW^4(Dcd@dLd+rR-wp^5kd&f
zZWz%gSft8P!_d4WD+C5-hG%!xn>idwjHh?-g%|5`fl88rx@qpd+ELiKLYZcOJG<9j
zEM{)5uU5F+&Bed3ms$@ca@ySTgDyareMa{9-K<M?Dl@>VKURVj3ItaaqX@e-q|Et7
zsRBxk9{1@PU82eJ6G^LSE;UAcop8MNGoc(vxS$4Bw^Mf+Cg-s@{5iz8YkgKTf#HF>
z>)N`TRdy7}acnV@J|>1fs!~8th00jIP_b}R@`3NgK>J31j;Qk_ST)<n#IpiBZt>DC
z7865{SvSS$bU?_*En<z`a*Hvy<8J=JS>Jg6x#OunyTHF(3ypvk`q{RrQZ?v+iF=so
zkTkj8?`r0|t9u^!?r<VayPsP5xo-sI8h8Q(>5A8SpH0p(i@Kmkv<Bry>y85PFL!WE
zBrPIwK8ppqL(Wd-JKPX;ep*9(`Rb48;e^>>d%kmJvJACC^M(r~LCgqe#qDnuq#SN<
zwn5VK+%R}qoglPJpqKwOwZNMhDEgaNa+Qrlr>kh0qxY$PAmY1b)L`&h3#cJje_}Y4
z6~QZkuIB{8tCX_|JC6*%xgSWl|M8!pE5qt+_3AXw;tEuH<5=XKxez;zD-08hq%`p{
zda-}ttI`b%*Lf>Cun53^5JGL8y6azOG3WG>a34s!Q|5|RE3`bS+y&CNvPOcuU2X^Y
z^L9Fs+D+)heX08MkKwFya27+&8<rd(k&Jq0Lv%f^ZDxonxIiL0$djUpiz^vDL(Fq}
z{ONK^Rx!2hN;<|FMTkLgJVX2&rS<%g66K7AHWsZ>y}N?Pw3u&{HqQ4}NB6h;ONaaB
zaBS(G?<F1LLD8o@9uO}(jX%y+k>^y%smMSVXr>zvE2-}Je(ufQlK12a+MTdP&k=1&
zs~kHkzs-3|vhPJ}>XCRE?&$6*d;k@RM`T2pFSPaEu!&9#@fL%y+dl12f!v#GoI`uE
zuD2f#Abjp!h*3?5NViXVFv`)qCJ1mL<4gUK_Ow_$A#X_?Wb^89;_FiRw-LAFY6{Y`
z=n5diCJusEhnTBmMk-FE-outg2G|dx^Iah4QG6|-{)ysp0Rie#gl!}=%TyMT(~B%t
zZKoy~Cfg44VOhkQg`e|BmCc7;5$1wjk}8+1j=HD#c;b;96`lNeH(k?m7fKRo@#HRk
zvH4)EE5(a#UySg#l)eP!E?xAhUyNu~^Hk0C?rb@EF!*|;yCxG~Q{^+f*?pN)C9v0D
z*k$q<msoe-|GqIOz|8Q^#S_V!d?EM}T_dwsI$`E<!9`O@6<ma%fGdGoBzjmb;r-?F
zO>0;LDzY#`Crn%BPP#iH1cl6ImbU3;OUKLFO7fAE7<1p)J~fWz!iV6gyBTicYay7~
z*ujz{Lv#J09C${6wui$*tF*JhQ3mb7*CL+gr1`TXn~LJn<Hl9`h@N3`#YQRm75&*=
zpbP<!LnKHXu(2=hBiw3-vD;e7#k&@JZgXF~<3A<L6~g*}(g6zmgTd9Ux7<Gp=2hzz
zta}57SLgwC83zw7Ugtr^ToWP*c47#uW{b1#3b8Jx)yhZbT$qt>W8vUM$u_=|8kp&3
zZK?iB+baaFHX-Zx_!bYJ<>IF<2}sO~({-k;z^#S+ueyJA)_18PK`6Ms%<&ADu?54>
zIo))9z8{$}9FAt3r);)=oAuz@1pETj|IuP3ol=<I9|rTXqKOy2q&Pd4^W1gh)pRqY
zC)3R3VnO1khO0$x6e{0`EN3+)mHLFa_!!0QcyP4^GWRI(U|^PutNNM~Oi;a;oH*Ry
zUi8Xj-Y<MOp+Qa1015MVEwxIn0x6im6aMO*)AekAor2Nuq~4Br+qpyy9zwalP}Ci$
zAkvR!a@p)oMVHzc&E&P>T!@AE#8N4o0e*Q=hE;0R`k=ugy%&uoPQM5Gyq+D!+hOLx
z&W}9%>Lq6xoHR)7G)2ES0;C-$rqE=2jeEW2ZC^1*tvT7(wbI7VKn|jcQ!jhjxZe4g
z_H~ZX(}>`h!Dns>ZiS)7%>{2-_=DEX<yHYlEc`4_l3pZkJ(QjKv^O-KFmlSnz!V@8
z^ha9*$iQ-X$~!w0*~>Y1NtR8b4<M#4ZQiU?k!#Mh>G=esP~dl{=acSwj)Ekq+CY&5
zRv{={86gb?Q8YC`&K~#Yz$Hq|i=h0VcEP4oe4AetoEH|;W>rFyG1m~Pf$S{9yyHQ9
zl=vKc>Q*zB8VB_i!h~v=T2G5EK5Z>2Pzn2Rl7{WS*wnh&n2{Xx<~%3rqi?yMMtLa)
z-soL|6q;;w$ebx0#@!nn4zW+MWCLwA5SrQpc`s9V>FPB1NB&o<!hAv=<S?@yPcQ3i
z1v%evT#>{Dy|4G~1lI)z2Hy{K7T)(ajop^BpP+W<BN}t3z6}V@JYh~%`MT3^<80wm
zC`&BEb2v<&qG+oz@0H@`7h>CC9j8v1XRaU54akqO7J}?$G0u<*nn2G(QL>+)iDWNe
z$1<%_g5uF{xY15M9HKecayPz4URu>hY-%;9(8|{ET;HVwEg4~;yXKjujh{ecL3Wg5
zb~992S+LyXVNkE!=99PG3%PLbrdt#GMP6<4PA2D^HT+?%FJOYTF>Uy#{3g_~xq2zr
zCb8!y2IG|subR(qXN6igSEH4Ezk&B~>jlimWuy-7tL(1oKw6&?g}e-WcCofJ-$twQ
zoF0`9U({D2`>VN>AS(F1yL7S?wXL{~)aN#@?7@ikSmRPa&>fNIVmyO}5@BNeZVSGH
zn=hlamU_(5w!SYQj}MKD1|J!o%Ew_<g|QyTkXvZWyNkr>)3B(kjH#~Z*f5h`;<>i!
z2QO@oL38}_Aq8*MoX+g(jrvqOk=a>YNGmI9gb3lvl`)kPG4qAURo}5`je)DsELnXw
zDT%P)Sj5Ev0OIcD!Pw9TkGWm%$w%(I<83`qmljyAZ!h_{1}IJ-3?DHs>c8O~Q3#(8
z!U&+2-tZdl5o$=F1RIr(0Q_Ybm7;gh?z9(Si(!LgIvjS<YFJi7C0BSed~BT=RGE^5
zQmZ~{;c}_oL)BFtMij_0_irTXd$pLvMp&A}<w6ps<aiI|D6xp9;KfJOms{F5NRI<=
zSk^&`5TP;&rmb(>EMH#L*RE)9)|X)3!b=X!>0gDjp885h>rmR0rmR8<Bun=F2lzQH
z>#k;j+EmQaoZu4QtmT4#-aVzN`7SNs)cY$Gy|w)IV?)fdF+(dBWU^}jFJ-M^>M(|~
zQEz;Xxvz-O<xNTrcuO8s2TSZ~Q#=&9meVHXLfUJ#L}XrY;^oq6<();RAL!6AR~IV1
zhcL?R&I}OddCk#$%i}VssMhS(V&oLQ*3{g`2>%+}>N;IzVCasiLE&|8wRRw$EY35p
z**d^wqE?NAJ#V#*WR$-G5Z)W_B?=x@a$Hl5)%w~DGeA<)&N^gSYE5L*NEY?x&7s;}
zo{!Hvqc#M$&o$8ZQ$&k?ih@K5?c+=sk+9Fo=(3Sq8r(5Rqk)ZEnZvcdHsLGYLkw5o
zZpHPyXbWA6d*AE5q{LS!4fE(oUzi!VnOk=r)*s<ReIVDG=t)&*gJJm~1V*3*1^(%>
zVQ$FBjwL(@@ns<lEcjDEBX|+Sb8e!N8EC`{h2wcdc%gY?2F!Pv<}9GTr!BTDJKzpI
z#9z<vX4+dazzdU+SzI+)@d`CN%7>kymYKpjz%3m~t&BBw7v`%)+Qa#Hp`}4dLmqR`
zRO4o{??X+|^6j2!fIT0$007yY#cp2SpjxhVz!%k>1+c!>gg{Uqkf4hi{Nlt8G6(6p
zQZ2LO>Ut}1E6CGsKyDsej(cOoAE4%XkhxUGYq7r>F^M%E#<IA6gZ84GhA{2iz8o4~
zdLGPikV9=gqU;Zt*s@Rx1?70gE9}-BQc$grlI*V%_mTE05C|(UK}C21q7RE}71YbE
z-L3CyFMWA()7*szWCbDf%4j_Z+G?#}=8*wb`>B>}O3#?@!$IZcer=t?h1Eti&6b{6
zZ_FnfV~V_ii6KK=kV@yWQ=|e?UccG6viusH0cQ~NsKvxKRNceOexXmN)|@nM!N!49
zl;p4Zd`AvK)m|_CD)OOy2e)qJoi6b$C1xpyLW$NFtE593$tLdfdv_p(5_1Wq0_(+Q
zA6T4GGH?~wNoFvBTP3TH3d+Fg0k1gaiRKm{5{zu;#ltM<SD99*R>45PUTYtRjpHrZ
zSvxmF7b}(>vY)BS++z!yeRQ5W?oXXb+DmVQcNeLjxbxAyeK!^D{tUH_g6Wq}i2`v?
zvIE>{_nxIjW3=QI=Mh<yp&6Si{O;yR2Sh^SK;zPl7S5m-#puZ*{<8UTCr`RJ<tZ$l
z26mX5pYMUd+vKK-!?*H@>8A}WiJpGT!50N35V_8mC=P^jWWg)s#2!Xl&w>%9R4v54
zO^rWWKl}AqYj@JiMy+;+^^un|Hs))2Rgj#jhGFOIU<4CWCC>0tI9J=7lsA8I&;ulV
zl%cKDu4Uoq00HVJ1A~TJ-xxLH7sgKn6zEQk4h91-@HI*6mms^tIw}Ovn9Fk6L|*%;
z9U$veOZ+99Zf>r=@bx3r)^MU@3*`%)&g~xV^{ICZ8MbmG-Ux#aFKEgtu=Vi42jV?-
zo|Oa)mfb5|r@u8qT&mx~)rxIXv__c(C|>!7KaDxhEm<1ADLQSEITj;87*Lk2u*trQ
z^x3&g`z-%P?dJmt8IF2Z^Vb#hT**!2P{|Z4$&`y4o`idarI?h+`Ai<l)gmhl^v?9J
zFzr9==KLr20Gmx~^7#uxh*)J|U024A{3MdadES)5oN~iGbl<Z~%9n<|;{k6AHX114
zI-s`M`Uh!Rr%C4H^M}1e5ys{P0jIfM3bbh6<~Kv>b61-xw%;tw(`+i>wC<BT@<hTv
z7Z?PFKlS6^aLZQ=u(Wi=_*KPhu%TWYcx;;*2r5s7o;*hofV_$pHE|?VKoK=ZpSDU4
zP<T3I$@dgTMfKt8H7hqZFJ7WOHQgYSB<V#~Of7enN|JKjjHP!$59}?;FF<nsHA#MX
zt9`vS?-u}&qt8Wz7R{)}xL3dl9!c8eKW1coQv;%m(FJjl;_4mDBBfz-t0Q-W=_?b4
z0DE|p5QDnfY(^kOJpAadk6`BNWyZI^rXfV}h|2@*R{NFJaAJ$9eLdx#@R3qKE0IIw
z0}sAo+0fg7+^vj48A{iy`Q@|NxnaH-L6}WCj67)$MerV?+EnliYQ3C_o^!p4{6rng
zSf6DyO;V4l+tG~Hl!D2YLg4v2whCyzm73_Jm7q$riQz)oXtQ(?qSx*I$r)^+T#Vd@
z^uavx&pr17bgf_$zcO;7#wQGU%(+}uIVPf8AxCMs{NXgk+ap=b8SSTiHT8*}ikpvC
zUxt+Bu~|pUcoV))n3Vu?lA2*>yA$Ig#ByPUIQ41^r7AVxiSKgXl$GT0_Xe?8f<kWN
zm1DC4ayz-?&JHO>dFSp*0fxY&Lx~6suRu0$JPO+{I^Y8`#FNdDa3nSXQ*}smicm&$
zR#Qw${Otke-qfRej~&_mKJvBd%gBNB`Vr%!dIRqQ!$&S`zxulUL-_snPbY`3pZCrb
z9JuJn|B|o0e^uDXzwm13W8F(5y=NH*?pE)A|K{Z0PnvHIEO07?4~`s2J$)<d!|H>h
zj+GkVs435gtj@dJsDEOv{KMNjv-6Yf8(Z@q_ue?{><?BvaOXn5qPF;u@P20`n|OOv
z&cv?<n1##B>A6>C(Dha``us}sHtH9^FYSBNPyE@sy_skCc|6`blk2{pw&x$`(R93C
z%T*t7lgdNavO<@QG^2IBeYA@0GWKBk`PZLd_x7Jz7XkZt<;%fuv|j*g2c?nRy)&1u
z9a=ke^Dh%C=@?lDj$7N9?hy}jt;{IgZnUt?5vux-h0CI_2X5nb=1wlMKYK*HUT**6
z&ffmB%;yjHPb313dkFkf9=$(?egSZMuRBN|VC>zDeDK*T&1Awa+}OQO#fbV4?|Ac}
zry5vRK#GOqE_!lb_vfoMoqSXyPjn3wf6WWIi}w5was85S<6`cQnFICv)ZR4b@i*Mk
zxz4bb7D$l@@g?mGPaN;2Jw7e`r>&)BO&mIw6AEj8s2viH8#_~PfwJXu(6_~Jl|AvT
zohz!k9(3_|=j5y^xcv2kQjv+=@qHtzI~d3JG5n?S?Yq8^zZ5&(jd@>^$<`8rB3^)}
zRaD%>E7eqsY04G<bnKno`6<}A#hX7^dS~*;mt7c#tkE@08R=%FEUyS!R<J~pnWqI%
zbV$6pHT{)GZ~tl^I~o|;D|om5^I6A}+CdYW{O_x0KL1=_w25P_ccnk?Ps7M-A%+>N
zKmM#amLG$b3o4j)KCG1n<jiP5p4s<Pc0r_u`b);SCwG3bd-vjY<ezuHR(#p9Ep~a$
z+y5p$C$hvhpTyJ9E8n?ODYAc6W%6iOk(*5B#PZACQ)OxdkMA>cGrMrd+J|r$mw^Nf
zRl&%N)+q$nrK-;G$vyt+NhEdMQ+<e<!zL${%>R1#>WjpIqtle`(7C8WVXgn;X1j^b
zyo_RWW6IjTP)kGAaLLEZaR07<B7VOAvHm^xYsG`JeA-fQSa6Xyxr<g2OW=-j-uOkN
z5Z!{kKW2RNy4TloYNa(^b9KG&-Wb$R{?8-pZ=Z(GmHYQLNN!E+{FvmXmsH9flO<Fq
zqy~@U-li=&oTB)w&Ch2g@n}~Qn_QiuZ)CkWx$EFSn4XvE@B|K^yGvEzHqofv&xx;r
zBG@DM-t}fnZ6yX;yhqi3-E;fr$DM1}&qw|kGC|vg4W?I*OLB6~ZTonEIv1bxgL1A+
ziqlKVBgNbwP`winjD>vnPE{iJb+WTBef_!p1d9$6Sn91m$6>CdJ*Hjrs)Db)xyOFu
z!p6kF9ZuDL)-L8xwOY(??YZ3>>PevX_M#!?#Lz4qk(&?^Q{k4>6VN^Tys9>FPjiAt
z_{R6`2fqM<^O0XXzU7^@2@LSYB|eXVZ{yB-l2mlfUufss1!k>D;Mp%OrKW9uR9cBl
zM(FZ&ebR`?_9o??@Q@Eny;<TfSamV7j_lB8(98K(Kn5bQ3IV&STKq`$jV@eK`vPb^
z3S^ku?(9PQ_@2Pr<)J{S4lxSas#xfy_@jzSJbTmE3={DSiyu=G&)55|ykS|+c6(xX
zcU=9aDVY3E%-NP{=;Gpw4-}LAE3_=nk{u0l{-qStyWA<VeWA;+s^6e~|EiA*EyrAG
z?uANt#c<F%uUdkx>zbN3qEp>m+fda$8fT;8h0iidFa>2ce4Jcb2b5~ssd?}~l;_cV
zJe<}(vmGTobX$De0F$u$6ox-c;5pG2InhNQ%}*)9(aElO7LLZbeFy(}Fct`6uDWVZ
z)D?etd*;(whVF&IGf&?-TzGYR8<Z7`t@egiZccmj#<l00ych!tHB)~9sNUc2Lb^YB
zQ#}`=+{R@<9=cMu8CbQfG<i3${=T+3U6UR@fim+b%XAXu{(KR7i}lj##Hz|>hp>aA
zS;yM&+9Zu|u(F7NKQ+eB*u-xTZa~8ag(__a5BI(pZ_2^pUl^9EVV&wGuDbE~M&9`2
zMM$2V3qJWNGp%=&rvFoov|ai2IOg>irLw`VM?&0A@)uv+-fnt(VQok157AlS7U*K-
zWPH~fZjMXeCoO$1vUee%p9Q+`BaCcEgWn9G6pY}eJiTr~D)lt{aH%D4m7iaht_!H8
zT+9dGzShJ)QQxMCkol+J4KBUeP|~SZ1<fP+A==jleD)}r-v^(Nfw_60Ns#WLxt5NU
z7fZpuH<w4H#(=5)<O1(YZd1y&&5F^CVL8uuXsr%0qOab?NTzB3TiUg(9a(l$IZswX
z53ZOpzxYKQb-W$@W8&?By1ldf#l^)-FKYq_qyRy(fJc_%JW`@5)vBhyr0OcU)ErJc
z(yTp*UzE$R&FNXoYjb|w%qVs<fvh2W{0hGSV`!rRN3O*&)88co>S{jf*f40sRB!nZ
zc;DXH*F_9KWidYfqD>J(rE-e)&^K42>D0a2Oqa^`*vE4*G4*xqQ@r&&^spYQ3Vcbn
z5u>+n&K)1{Yz<D2Hm;GAEi>3xx7g9O`s3l&!B?)@x4#vBX=!PJUb=nbr0V1wrEB4#
z6VEotpLISzS7-`)=B>UZu2D<hMi4m2!5l9YPkA4s{9SNi=27*>6I&)ZDG^St{o&HV
zV7gybNtae#(Rd>Ir}2qm#%q&-mTEzHi;7$q79&ZIHKy|aaLM^RVOb6}MC|pdmZ|je
zF4D-Urb(;$@s-Q<D~mZhuR*_$#!lxj1C!{k)_xr@*5dDvfBwskZ;Zk2yMH>af9Bmu
zZP3l3ahKdc99RXqIY)JP`R}4eiMpISw+#KEMJ|`xPRM5wE8Gkol;au}ACKB`oNk}F
zS+wLbmeG5acVV4fUS-mA4hk#m|NP$2Mdf2R)lR>m(=kBUUCqPJJsg=j2)N!IY=}fB
zsJRn6tjri*uOzReF0?*$!EoK?;RE8(I^m(CgrwW<?W3nhehhs-|F!GO`ELa`rj4)s
zSj>y^S_-Uh_a?s(1IN%G4J%v3PW1u;H7S=cll$mp=|SP;QN0zpc(GCdPJUy8Jt?TH
ziwSP2`f?OAWjKy|)U>|U<NGXMJTKo(b@OF_dH*1%->L0SOq_szRnN&?@<o)>n(|&?
zt2$owxtFYN8=4;(TR2Y`uAEoE4(I%QvTM3bfPrUAp9WyS&x=~hD<GSTx4Z0jzB;(Q
zqcift-~ndKfjiZ~|4>HO#}=q^j}8hSIPt5`G0tC>=G;4|7y1!Oq32JA_I3rY0S#4m
zvP<^?7KDExjPDH`bm4gJLNpb(C)#?+f>)?0<Med)mmTXS6|=IXhDV9xe=C%0bBlJM
z19gWqF>|eG>Nv7&0JyeMBCh-`w9-%WW9YWu-Snx2=1Zn`p+=4rrf&^Y^FV6XkD0Rz
z68&%X{{qx($5sb_zdaasNf0#qF6!!9X9ZXk31AH2j9cR^2XJ!O1&cW}i|uM^VEn9%
z7laCJLQ?>*%a!x1+`z10_`8EXITqD3KtI=;nvL<*5fT^8_rL4o5d2yTEW%zK@*G@}
zphgpk>XmrGj)o4WaRS)RFAzFuoL`NDlnzyxKIPeNlkNzn2FF!7!MP>b2W7Qt#dM`=
z4h?!wA9{8D+TQ*HwR_Y5nB6mW>D4=ZhlB4=b04o9x|J6^Tt{1QQF(N1QnoBS=BUi1
zzyf$KO$Hy3U5){kGoLxf=bSjt>~;ARd#wFEq2Af;q5e2tV7qHBN^teq2JA`qHHPlY
zvVlxq7Ix9_8qnE0ThmL`s^!PVYdkM+><!;T=ALT)fLKhY!2Wfaujjni)3_D4fTgz@
zg9<4z%+SWnfV}r_&b)Nk`(^sjn)iVjy@%O$q`!|$TK82L2JSjDMQjO7WOMwSclp|K
zNy_s1-mO^zEmt%W!BoGJ!`X-tK6aqRY(3Kw;9wY2JA3+Ey%73vB!6*`ELC|z9nM^Q
zviaI0g$c7{pOWE~0uz10rHe{pGO-^dd!<wfdHO17;R{+bCus~{$o4AL&y$mq24uLu
zgN%7xc%twN(Bko}<nw_$PM;Gq^e-|Oio<T|YVG%+j+pwYa3<C*Dv^!KEPTQ}rWLFd
z+<)!h_wGck#;Hr?h=~oE)}s@Ksm8p7@b^VW-e-+`FOE44x{Cqc6a)}M>h7I)k`$xZ
zORLTmG<LiSz6a$WZyhh-*vZW;5cXQ&RV=*n3CLOX&Z}b`In?@b9q;@AuVegji36Jl
z4-9HV|MWe%_YaSp;v(H0BR>P8tV{{dWc_h1MAtRqi@&XQ<PeTWavh0#s@Hg439&q3
z%el{-RahAw|K_e06zKDplzQe=KkL2Lg4vfPS3OF-%^u}u>%#T-ncwh;C?9dMpOt5f
z)eg7->dDHN%qHTIiDfKZ@WGzZ7{n{&=WAyO#MniixIrljulxcbg~1`toZY;9VD0XS
z!7qDHeL0XOp(j;BO>>P_`?4~)BX%H`Vk(=h%8U=J;#1lv6vdzgI7QYQsH4m6x|4+`
zGfYTqFpG};VX$a9Wxjl)_k2Hbxg>4^VRZ9i7S$^Pm51AX;ff?#c4&J3p<z~wqF8+p
zg%ddB%r}0fuxaYq&P@$rJdi{!UOdHdF_-mk5l%w2N2fC;SYG#k5*&7}?Ko`qNi4oU
z8Zb6hc^_45SW>M{yD=aiO>mQkH#AwraSG09!{1ZV5}pO63w`qdSv)pfn!aT__hJAL
zyd3p%KrL`kZuG*F5N7G)XXLtTNhCJ8-ykUWL;vcnkp=i1*tQJHwzDsJ0iDQc(0$ol
zT|Q!4ATV#3=7X{#g?DN)wQ}*F-gtatpXHyr`9A*b4tE)EM2lDBqnF`F=%a9ahljf>
z$4TKvuos)41dH0RbKj)zQEJ=Kd;7`}pX5sYR7iH#(JdLxhbIG{!yBAmC7+o{X9--)
zV;r*4<%CtB?9;K}07z?l>yF$&{L<f_4{2=ft+bfb6>^|oL)in=$LEQvn_<Df0K~=n
ze_USKJ1c!=^?UDDpr&R}JC&MMqaC)V@xHH#Ro47eS^kG*F(<jX#tA8BUQ?Tfe}8yQ
zvs$0zwqGcfm2#s;A*`|t)>4VeO5SLW+HiO6UqBQQBC0Q0ZQ*b&k~{94ro{41BC}aH
z8mb#vdw1+sC^hh{IkQlAaK-V$lLMmvPFZMtfBw_+<lez-BGehS=sVHof-?7LLMe1=
zC`cSMa||+xP+QuI9BgcF<R^QNBAon>wk&^>4O0;C2HpH2sB9_ZXcg7=LCgAwh0hf;
zLrz4{8}Drd$jToZVIhSQr)9Qqjj2724LH7;c96HLsna{<8l=L{4|l(RF#b73Sg#Q)
zHt9R*7l9`L?)6GZ(|KJ1mGS`Oh^wGB>=3emFQw3MJzIuU9uwprHU^q)A`PXspp6aT
z3uX^Wh*O9GD_NgiaS5)7xwe!jvb(&#^njGzxgKy&dkLR-0$I~Q0Z{;hluS8cX;2g9
zkhFL3Q5}_|PJ}b>>d*dZa`uwrg|rq}eG|xm;5@{lEQFR2P~huXo;&$DK|wohbm#D7
z6kj~?AMZnjy3Uu!{7`>iTDd(ql<G5IfUQWn0eMQdNL0Ka<$-REy6~=I&tM5<b24$U
zN<epyiT7uaLtGwKtW|D1om%IWcpuaXM#Ws|h)MWx>LjAw<sFXAI{$X>CuH>3>>q!B
ze_2fmc3kC$FVS%l?krJAsG!u-kF3`jSF*mehLA`fM5n#I#;!8jQ2!cB+t0q}GaNKK
zoeGly`rIHXburtQ9DULTem*8KvQA`=s_}8@MqD@Hq?J~Ur{oIswuy_kn><DWY+pb>
zH`L;7T>d_mkT6d5I~CL-;r17UoG!4N@;)Fk-+6b_L>3bJ%7f1b((kgj5R)=szyhi2
zLBd_RYWn#~ZCAS6w64<U57a-~106>Lo?5D;Lk*=84Agl&4^2yZ-|VoF`Q~}ShZP#S
zQ@(9y_qAk)T?Wc)C)=KxJb(Pd`95s>2>0+iBc*pI!EL8pGw20R2dWh2&TGx71G}F;
zHN;kK^Ib6YfJDku!ICj!vQ#U7>gow^+E3AmCH{|VO3#1#p7}&|KbekKr8|<kw*oX;
zCONv;(QoB1VOQCCZxanqCU;f`_pK_{ufJrixk7I5KEut+I`oM4heYW17H&v9M_>5e
zP|Nf@Ni8s`3jLS=bHkH2rc4_DylU6+c5IMQs>?;~diyEy-1%hVzWJo9E^8W<&xHA%
z95s=0hbxpX60Mf%L|d&W%56n>r}mWg6z~4%&yjyv2Qp9Jy8Vs)#gQNA5zG3#NQdqN
z@$B~)5AGYm_+k7I6|N9GzJL9TMBp>o{Z;XW9=)R%Emzw<`=3vQo;LZJ&pc&r?0oWU
z&Re_Py#TDtX33SI6@!h)gFgMNgtsVQ`o{~ciWBXxl6vLcCIGVnN3YIcM8SP#@kONn
z72j(P({^H1dn7jnuaF*d=N!r_a3Oe%?_@=p4|IL<)Zrh5_T48P-wIE!{@4<^aZCk$
zRD-RGYu{f^L?AaI3_fdBF<yyHXrdO0>;k*@hAW6KHx4J!-15Gtre96r6)R2PZppoJ
z7r&ht?ojrpSJU1b`5Nq#(E;p$aH5K8g6G_NPwJm3YRPdT1>1k?QdpaCvzAEm?)8)J
zd$jiG$hCG7`w8nLp_n?QS!O60*3<n^9h&w`NRoDrf%WdRH*Wz_GXA=uJBbm^zv7NR
z71F-v{v>H6q=Fp>VuyV#_5~~T#6u$Q>g(SVzeN$Y5>iy8E&$Q&=_^m-<BwGCvYiMW
zjS5vZOtD?J{`<DY<{{Ry|D6@<F8XzIKo7ExDV=R!E|(UaKc$2^8&ngN{Qc~^x5|o~
z2^QybuJhu_(v=k&+4SA6ZbOUUM|m80L`|E!55h<MyWxAC9UeoxYZu~E6Q7-39QjK9
zvi(ogA2ZTmSEp1McL+$fS9LIR8BlbQFKZ>TO6_~C7VP|h`wm{sXzLDR3!iwT*GBO#
zJ-$y1#l1e(;|h8){_L#M!~et5yMQzOzyIS7WjRF2`GX}XXC>Zg%t#D#9>&55@0bqM
z$eAc|oU>AnGqVjtR;-*V=MKgep>mEnpUtV?-rxWKdA+Xf!fUSQ?mh3veczA!uw09`
zo$vsG@HjMsZp4?a%hM<9Wgn=Ft-96XTaz*>CeF7Ye8mGg(rf7Ry-Mz?O7@r40rUFQ
zOEStImC23{e7PFAqF>x$a%5+M-h2!CcGEtA9lt|9VrR)H23?m8tjJq^r}sg$!mQyz
ziT!2b69GlDTnT}A0j1uo4@}1r-}4p@k@gc$E&olP?e%+f{7QHNxpHAeAnztb1of+0
zU+B`se@8zHi~sus&#ZE)NV*na9ijGgEExOg&A9`Qo7Vo1>fCa`-?^0w&gGFk@vdz~
zaiZd3Ag@zr9vSxC?@KsK!e2--7YecGo-P=2*i$ZmBifmt-N(P%p}%c(v6DGL7<oqo
zy>pV^&m>KQ`&0H!$AA+)ud^*QOv`1~`%V+_N_4^-X{pKi)7QKOY^Gk1{5<)w{c_LM
zP5Bj#*0#*!ci+jSJ<F0g&83W3xqF7dg$FTIpNLAG1gA7A{T%sj{q0WNw#K0U`w{%;
zN>;7CEcR!=I6y;qeewE*ynv?n7pccePuZOqQS=g0n5;0d8?tjmcYD@Wz~F^Qn}+X>
zcpLL>-{M&j4n=ec!qDBP<2!jR-TeJvZ_{gjo>cL%Cq7|T@uhiy91>EMp5*1wbWV!$
z(&y$96k#r&+6C7F96Ov$&0ag7Ak>SMopvztejl4V#dvqW+`a07V)$x~HYw_D$ZMxh
zLqWx1w9JvkogK^J_Xf}1o9h*AMDp4QKDE+lv~XcIG|g1``wYYFqhqyzg|-Au!tdKd
zj`}XeFoXBIVK9?ay(~BXAOIImh&89`p`pT^VA4^3*-T1^59zun)=tLdBzKqOHPJrQ
zN!vda@F@`H-=-s#{feaRbGghb6-|{YuPqIflkY#ivlJLFUT-%LyM~c#yM;A#5Jbwv
zYnf$i`Bv5&2jE^PN>3PTo0bhgIh^jsZmpcWmLwFKf+K|cLv22Rq6WG)xUB{J?EGAU
zG`h%dVCJ_da?0JFI>HmIF7*5O-l{y<v04{Rnib((Qn-&(@{kf-x-7XTNIBs}_T$_Q
z?h|lVrJ?yVi+Q2)pZLs3=05g=R6$&ckMM{tKu>`|w_qj@^@LO7WRfuCMw5(;UeeGf
zjy0%dDC8J;XaVBe$Ja3bttGPvI2H1o;5m>rVUi|%Ue6@nD4jcf{&@OCIx`IPzR<-h
z8(Mg*Uh-Ung+{>lu;X_x2pVVjs!tZ}HRxEIemr|Kex!z28hY8T{dQAig#FxHo(Zn2
z7UOd*9<Q$)Fe~#DIrp8Ud&(;5S(CTvzs5u$%QHc=*Fyx5(lPnL71lA?>n<lJC6ow-
znnBSZkhbBhrcMLbl<=iV;~<4j87c3^6@;^(Ix+YC?|)*=VQEee%-Q9yRi7*2b<bd)
zK9!{5-74R1+|`<Q@!>(&JuunHa(VY@6xG%1F(Dr^c$~XZxTdP8hm)b`rEOFEc@e&W
zLb@z*oP7ZEbG*eB3$<hBB!1o7hp>#`Tj|ZDZ&CX88FO>J?$W~Ph0i!3QOQxbRK0##
z3*oZ^{q#0Iz)v!{he@=C{c9#z>8DWRm_StZcH%V5KjQ^TGYVp}maBVz{pEUV-{Y%_
ziqF=D|C7?BW||M$dEtAqcV>8LD7P^va6_rL<ijs7YotMnfF?EA^Yy654}XtdW_5u?
zO?%#3#iG)UG;z)PN?o*=>+QoE5rSERn2y1n#1*Yy!1s{Q$n6V}vloPVftN0y4NJ`K
z2BnW&**Ss1gwk{p;--{JKTL5>`2TFt0PL+k-HK`r({2}qWcb;2^GI9GvJ<^B#yu9q
z5`JZ(n7Pim?VqCHuKS%Z^}AC=_wjFB+(hrLRfj~l@V|c3QG%&3lJ;|c4b4{R&pMGg
zEpI%{`Vdbx_tah(*43>)D0DsPmBZ|lesltd^c%8t>glT6|HA=QG>hd9BZf&xcvz{o
zctw6-<2R&oH;VOI0%q&3{4oreZSkHp{O|gF>5aH^;+dzqfu)_i*WJ82RX@@h+As!9
zTjry}G_UG06>GGszd1qjSztxQB}R#S_;IH%{}AoDRll2x;>D&L2O93@q0_EdSp4JJ
zH2t>T5-#MW$^C?ZHuBQSGjYh1KL518-tmM-(|gi~=brWE!N--{$-_)i>LJsfXk5wc
zb(E!FFgE!2p9*)wB{}^;s^QFkkM0pe>^<85t@lM%5?TF|#~M$KTg%8=_zCGaB}h*>
z%cLk(HCel=tLx<B&bPef5lFS*NBiWx9|72EKE;G7k!+f5&Rv7M_l|4++5*~6EP6Ir
z((GuZ;^>T6AFWq|7hd?|@}wBcXX0MdFFL8Fi8iQ%YBBahAdbpcQ3G~ha2Ma|FYTCf
znErel#_hvZ>>m@8Cwix3jdTK&G`Q^XX)^8+PT#Aj67@sQ_3~zw%uXqj(+%hOGAppq
zbI)A@a=5?tnp%o7-Vnvl?WzSmzeV+Z^j0}hl<}lplW5l=FlQ|@bu7ck+IZGrY)+K_
zlwH+cy)GUphRy}eIk)?3R)v7OeThV3rEZg`a>=yx6?wb}&swN~yiiGfq8V>T##-om
zcWD_qojyp7z|N{2>~cQ5rTz)nWAUJVy?D87oRAZfHUGl~s_=kXM@*IZWE7TA=t9qO
zbwy`1R1$ME$=EXG5AHpzEF%TA4sF=x{|b9e(lGe}1%&%9W$33H$%y+(NF@g+F{*!7
zwN7aHQ?mtV$g6B+O%*X&w78hMq}_(Uin?FBKX`taraf=wz(_oY9GDgP(hmYW3w`fu
z(`dkZx#ebij^WMrarS*ayVX#`n?f<9DcDnC$ve5qCH_+We&f7C5=A4ge^~o=yX97<
zR|h2ga{>AJxMdJM(Qd)n$oFi+tqezPH{3PCfWONNU!wcfx5ffZ4tZ@682p)=eVZ8;
zgQD#cFu#ub0>_#hOQsritj6CDlxk!03|?hQB$A0s4l;t*8^lVM^-q0Y7~V#@)y*5+
zLnio?YID>e@ry_SP{+_SvW<<sv@UYNktOHuZgTNWYM#`J$JVm`5W8+zCn1sN<SnD1
z*kfLJ{3l&?ox0&|jk|YkEJX9wQ*Pe43o{Dj456@;nki9N#a=imljrVas%V;gZH}1N
zQ10#~W0Pf-srnq#S?HPRHX0G@_U{u7SJz&ormp3HRji5>CiAgH*fmuK`bw}5v%pZD
zM?lcRv(^RNg9i8bW<ym+zPNu1mb4d>cJ#Qf&3?KeI4k-2mIE7~l=4UNTjb8y$Q{n(
z|6MEZ4aYf|v^E!wV|qC<5=$0=$0cG120)oVxD%cA?CI6S>TeEg-|N>;^(y7u$U(d)
z&#(D`Mn=u3o3EET%zbZuz}=^r-E*V21wUQ!oP6$dR!yea-O51#LQ6EuVQE%xR9WB2
z=~U`3O94|l%xcpvL*r}%vCs6RLvN)+gHJ=j3%~N3YSrTk`0{P?^&+c0Tc7&8#*y`t
zmd;KA@O=9iVGY!0#IjTNjw)+`lgfG}d4_5XPd+7NagsYBou1yF&ONu6qT+k}dBK$V
zgObXe@@uCom(O$e5|v!i)o!b=cayJ|-9kD{1&Bx1&stditE71&x9RLHIVW+RQ=&=d
zNI415Ni$F+E;L^tCD~FxB3aiw9{e@o+=qrf8&9}nvW44>s-2)I+Ws=Pm#Bijqh&f&
zW&|e7^Bit)z0h^hq?#Me-5q-)lT_bX!7q%I1ed`IwBy{P58XK_cNR)>s@6~Vhs+OC
zr~r~zmRI%+iCn@c*LFv`fK%W%Gn|Mzk7Tb2+{}1ZC>umXcLM~UG>(|K9Czp<osx+~
z8wn|1c#_;Fp^Q|mw1*o+xdikK<&sXDa$V1`NIGtruKNDjq6OS)MC%6A{!(R)vC?@_
zp$8X?&j|?d`@yfj6K)@T`(m-AC*a}JYsmwjnGd&=&`3+>XUoPzR;wxpDGvvSqt<r1
zHvGhc6mQ-D2tP~1;|bUduhMh@trTu%0^Xj594UxL9>3=_cn)cYmwv`?dqS^s5`-}A
zSGpU1Y1+<z+o7PT(`0|C@}VNpc=CfuM{jy#=4LHUe8ME9;)3~RrtF8*43fN8TAA@V
zOLN&9q8XAB8YeZt@$1p1MIDv8iDw@sU+e!+dB0p*2}xm-XtgftA?km*bRZ06&EHN&
zZe~eX=ez`crE(o@lmsuh9#>ONmEg4#_OALSTGq@h9IMyZIS`>yT}?FYBDIT)SXs7Y
z%1FQPgOh~|%0cM5?<R1wbRYYi*~_>gCDRwr3f4aqh)(vXpS=VGIA;&nBH!gZBjXD|
zN#4p2``-;yLD~U3<dK@@Sfwt0|EKH48bl56{~Y<sc%wkSNzG#m|4U2I)A8#-Vj__a
z-+Uzz&UhU_$5%{t+@NzyM#<dV#}w#E^kX6po~vPTyZme|Cf8BG))9%nYfNYLcLZ1)
z<agvG+sDZaCMiDO)rl~bzNBpldJdD*N+FYl<iy&)H>X@Q`rZ=&fBB^H(Pf4kG-)H+
zrWJEOU-sl_uDLckz4F>AmEpRBVQ+TEzyLwCC&b*p|C0<a>_4drQ&Mi?G^>@#PRe*{
z(@B{$nSyu6L<gn4IF3-YL`B}4a%PT`sS@7C4B+TNXWsC0_~4<zou?-uQ$W(c=-iM~
zsKkGgdm_8DT8YRg=eUubNTNf*YrFN^k3H+Z_u#V9O|MC0t(>}?f1TA6931TF)NvAd
zj*EMETd01BlaBB5VJ+eU?eqpsPWTB4xA_(5vIg5YlI|!H@K5WEy}|I8fwjdl1o<Ng
zu^4}IQm(^OyA8~2@Hxv`QWhrl8m~DyQ$vwc$XRNW#65+Ok!iP(p`L_+bMh<wZ7*Lv
z{hTTNSpjbis+YpK1ee-WBc=K-)X<ouG%GUgRr*6S{JUCqf%SEmIaygtbWKw)>GDUj
zGq#QTfh$_vN0nronq!SR5~Uxqth@-di@L#n8sPUMjb|OR2e3bj@MHO^m`wGuSVlu9
zA>j$130!`p!Ti|s4uS6zOqc@s2Mi_E*@!l4&F@XsDG;|eB8XR*`MD!gj<1ExCI(vH
zdS<AJFVFhitg2Gx=uv$UPG+bu@z&O8f96mfv-<OFi6gCPz=4?Fq-UZN%IuQzHy5UK
z(`ef;b>j50Bw-8~UDNuta|&K(W5(3ItVBrkbcr@ejr1O@o19jcBm+uxBOHYB<>xIu
zEKi=vU{`7>IN2Sq(lyU15H%{f@R+|0US?ub;jeogJ!LIgKV|B0hTd)|bAj4-LnTY*
z<M$`jT9_pjJNnYxF+<{USwX0r(+wgh{$ANosWJVAWP8yM-R%uH*_E8DE7^Tk)m3!*
z)tNw^0ytBupHH;w0jq}*a-8ErM}E1OjcS1~6!4>GtDWio0dg|>fc6AIvaGjEXTdcm
z2kC&o5-uvB`2N?&PHyCC<DL%7U`?gT^Ut&JqElv;mTo^ab;M;Y!U0PvNNPmLfu;{B
z(V^PkbnXQdX$}7&Ye{>I)=^!u`Cd^ow<&T<bOu)P5Lcd!<UjRzjOHj%&lvoOy?+i_
zFa&*g{pBa8Z;n;!jb_&TOQ>dRWQuCQO=LlNNfBy`>&QnBj(BTg{4a;H^)@R6_-v<X
zXwoFBRe=&s@`OgFeO|Rsv2mJ*dZGe-a_V^Uv7Y?}w8kSjH3LyW7ca1@U+j|ZwOhq#
zm8f#SKiKY7J4TO5k%fRddYZl#vZuO+EW0jdEtVwJA1jvR;81D@zy5?ZZ!?kceQ>0v
z`mR~JgP_ZC{p4iXl+PXiOFGNdrxai5xvie(_`L#8ttQ^{m)7MdP;l^)$455j5KuUp
zqev>jy_Gqu(*U_NpnTA;YE})7ho@dswe%*J$YsmH(|8V_05r+Co0(Ru{=%T|C)TXe
zZU0q?6K~X;Jg*^UBOXdGbRU>Y@lJh+-ZcwGaX%ua$~r3VR-nB2()h{@ukz2yDn2r6
z&cJA>mL_4YoYmM@wY2~lH1yS5Ul%WbAECm`nt6ClqTwaU@<}@*$DW=zUDHN;U&uM+
znRf$xt<mTuC<2kqNqhf)i!w$*(`Y-f;6{DDSIA?*hUYJJ79M1Dp&Rf=K1x}AMkg)F
zPJ+3GiiV72ALSPNPy;tV?xBlU`TueS1OF%U;GW~HAoHL+Bd0=S0jJx-p`IcJ-N&c9
zfrtV%A%)lNO&^za7DWdJd6Zd8O)2;iOlD?I(=Wn4G0adry)G*+Cxuh<oQOF4<6}I>
zdLN>>)q#x_(G%)<)N4LycVte_@*BQV8P7gW@Hf34W>9qhVs_6oi^W<PsS2W6TNhk6
zZ5sF|`$kvW<&wqpb^;9vMP=<406v@h^w!4)O_Jlyx0ED$_%pbe$jNo{@XAsdYf_D1
znr@cPp~iYs9152v+ant^DKP}SQ}CiS)PH%uDDS&vPj{h4u4(tH{>N35Pp)+GI_EOF
zRTZ}@DxD&rBmZE1F*8;6XmyEFoCN{xw5mkx?yX!-gEnVOXR7{7g$q~7cjTM7YFIun
zmmR1l<x1F&Y3kG#O33`=<e)i0?0;|MmD`=ecVIWAE=DP%eARr>1weCm0h68cU<x^W
zqN;8M&{G5B0X#ei1C|nu>`=)FPIVe>PkDi~_+VU33$Jz*syELT?oK6Sa%=wknshqT
zEh$OW<7HHDK$xh+c-}AZ9xH$2e_NX7UeuVv*~HR4olj_Hr}mXs=r(UNq+V&&;b~o~
z#2W?M2p>@y7!uus(=;xxamrXZbza;7QRoULu@=TE0qQ*`9|>E$$jnBxo!K#~I8P1C
zczy6wPEOyRPJhy%fAtRS#1P{4hf{;3=fbITtO3>_+197-gAO$Wm)Wgz9g{J0b=uwX
z*;#eT`}dlVis`4Q+nE>7{4a+-T9f^YF?Po(IpR}gQ<lk|L9WW})U?}<)o3e7iwxd;
zPtD$REXxV9OM2?Y{WZ<2pAZ*?7wV5{EQrUAH&pVx?hn<_HTP#YvExTowId`iJu=tH
zA!=);h~db`hp4WZ3STf8voxh#A}WZxH+s`J?N~?UYfj;tBZqtOhw-{#LS}xPt2mt+
zw13u)Pg=vQc|a;S*l#@3*xAKXH^?E|_0K%CK@8cTs%dDjCCTV=s3X2goi;r-7Slq!
z=DuEQC3+keVlCRm+x~C0ak)QFkVKUErw89_iZpj8{A9#QQzY|F*xl=!Ay3Erb(G@1
z)pv2JNoLv93rLNn)_#PO)$__`xZZW^X6j|38G|DhS^2tyZoL|o7i*Vg8Y~!x!vhRA
zR<O@sE|8RPD!`1FKPc_4*&7w_5vWXqHY$6(znww0Y;ZEu=~v#CYvh;7tr7DVx9{)m
zt9WLNQ7Y9zJKVY+Uf#r$MIarEGJv!cLX4WMECsB(bESxG7w7_0vyYw5R1AMxo~4f{
zzPK>2?EWPJNk0Ee$jN`d9=*i(8e1-_;DezT|9jlNSGj(gsWR1}^~E4EA9uWp8fgFE
z-4mW^Ynha+z)am9jR&F&KfzgysRzZg7c@<Fpc@wI5`r8er)=tNr^Opn;B%c5OYZI{
z`5rA*Q11Uc7lu!o2%BEBcJt)ZMOmIXN6KBTs4IQ)`kBCUT}hvGGXC0`O@C|Abo~cU
z@`mQhs^lkCv=52Q&D$RRCq2H$q^W?f`dx7KJ!Q)NU6|4+O>|5?M!p$!`Yg-vvzuf1
zDcA7<QbK`P=Jl+h4l9+JQgy0dZ-c`~j$j5L7)N{2T^~K+T^GF6t_nJwxt7D^gwP3j
zf-vq8)9Px`CFUk)e3O%+aW;2lZ95v_D=AzdEc6J(ONMuF(mOpr_yeqYm}aE>62H`H
zi*mmL%LZ(j%RGOG!}`oS+3uybh=gaEpD|utWw}dt`Fq*7Z~l7Hi$1dix6pBVHV15Z
z&$3b^qC3xNTkVPIuEG$a0}(uogM%dWGRz*$?45SjgmHQ=Gp|}Oy_fO%98LWkA=0ef
zkg;$ue0!NsN?jsu0{7Ib%{~8(_@JafgRTmpHc~drpy!{j7oFu?m{oI8If-UveQ+Z`
zIfM{3UraaP#fe~t0;(My;LLk2%awdK{Csn1@*K-Lf-l_wUR#!^p8p_&>_2Fv;|8j5
zS@%3K<xu%tJo`HO9nSOy=r)W%r~JSs7t8nfGV|Sif|uXCK|s%C5rXZO1xQ34WP1P(
zoFWuL)=)Gpj6UeS&90UEZ|U>NxcI@}YO3%ypa!6yB&6XmZh96Ma)b#N2!3<&V(n5}
z&~1mI5;($ZvArtakU<*43;R31KIUtKRKht-g7BZ^vS>BIZiRO4$(KO+9qG#3>Wnid
z<MQ6IZRuZAvuYh09gP!*pKlUfRk;R9v#eJc&m5|<VnSMd{3w@ysIQ@Ydp^`qgK_7t
z$>Lls0^H#trV^%z;T`kaRH>;8NgFswxCv#$7$3_aRHRI1IE2L$1DE5U-1tZ%36*dt
z4?)TkEc0C^{psX5>?uA$DgfbCVnfc%Si_L=mY`1G(~#H1mb||_7|>CR!w70BGGso!
zozDlg_65GmZf8F(_g~_voP=szQL6^bU)8Hzf@eE$MlOZMW+aNfcA2~3U)ro|-GoCe
zm%$Bx7e8z_@PTP1J1>|y_VnlfQ^pyw@JpE-C#EEnk7|@&IrWfJ_<e*4vG7+9;U27&
zt8DYs4}!{kFs%)193*ipdq?Il0C-~ed_!$d$w>nrjf*B7)m@#o=-(sn9;)Q~N~QDL
z1Z-rRl}*HC2oT~@(r$ce*s{5t$-h|WW=?#p{8Umh{ioZf<HZArvnqTy@H3f7o`Q6f
ze^p;N7bIRFVMkt?-|ejT^JeDh@s<XA+1T_liA8Eo{BX61)UXRvCZBcGdWh=j9ax&q
z^~S$kR}iRKBaFY$B@Um}8W>`e{*4&IplH_sqy}noF^duB^@cqgF1e%qzQ(Q{;9}81
z!Uawte|9@&pSs;LtB*<M>Mzt3Q=JexVhv-=3(ZK0qcrQ2z0uDvS~`8wO|N{YV(BNX
zo+)G(@rZX$jRcc*Ea(Wqn>}L;ye}B@t{LA{G@stqXvVJzSJCR}mG-3%uAP)UH2<S(
zLA=@WM5SB65k^+0U6NkTtRmRbVv2+=wRsx2zzDxiD&Vi{mAb0DfyEjWxL}s`6`1&G
z*2GYKpU~^Jg0qPq&Nps4&6cW=Wo_z|bgx>r0}y|B+`7u>I?0~>(OE;I?_kfPuHdlP
zObfs3UTFIQ<IU`4GTpeei~LUYy07$yGWWXgP#H`<(Lv_I{h#ffaK=d!`+;hu^sDkr
zBxBavE&0>$vjb{zpEqTvt2`7<4=20)b}e0kmt%^BbP_V=+B`v^OaGDf0Qvaj>dYHA
zYmvH(auI2ANWF_tM23(iUQU^;0Z!3M@p$a}+3$%e@mzXAdB17Z+fK6Pd7tu=;*7>Y
z^ObyPo6E`jCwP-{e+XYKb8okI4A*l=QTEN~eZyNTt+_%B+!rk(+oE$V(~~BU@6t~<
zfh!xYeEg!-`>$ZWv$Mm!$_6*Ni}nJ^w|Xn#y0Sf%aoIbSZA^C+2<w!b6pY_XLm|O%
zVp;z@TCYtP`dT_DFWxAQb+AuQfc0G`HS(#emV&cne%yY-L&{EWZc5MFt4d^->sS*9
z7K0mv;Ghp@-a@mMofWM}s!k7_3WBf?UY7CZxa0gaz{Wx1VkefKj#c>T^{vM~xI3e%
zxQXB)zop}L;A&2Vr#ro!ZZLw=+!?yI;xhb#$jS!~FS)aV9vOscV|r#?;gm7CGGX7C
zN+VB>Af@&{QVsuP_?3h;Iw9^2cvu<oG{;7AX;?DWWc2p=iWI6?6*zoNrAzQa?&!tq
zI?l<(Pid9!+8(4j1+!RrPTem<eV<p?G!8?Z+{R-6WM8@W-cb+^vk3f7(s_81bfaO-
zIlU9~)Z*1Zp>Sq(=d8zR9|p#_#K6ukYid$2Ox_T!o_|$b%~-iAx&6^u^TKu|K@AO5
z5O{txPQ0Udw##c+LZ8oYl~cRO3C*q-zDv9H>}G1+aEMU8JAWk7EmE}W#P3WrhyoX}
z^mTr+VA+VVJbV#F47SWo$|p%d2i*MBTf=mHvb;&m8yzaAVIBEmsc1dDtbvlgLXfhf
zxm-h7ZjW7&f#YlEibA239(IwgPd#SCR&3RnZc5Dhj?6`<wYtLoY(RAqA3pu8CY4tm
zJ-+{p(Hp}sx+LQwr1XRa0)ce4!KAMH7oFp)0&q`ll$<(#fD>=^3{btzvPq6p{=oxO
zG&ml*{5=>hvQBW+_{$XzB3+co)*=QHB+rdBjuAUi89q95mEhd;=Q)m(zCBeQXlo0d
zpkyA>B|kwpvcP0xu}0Oo>T+A4IyKNh!o9ar^AG6;%PPUxQMvq!DfJ|jFM512T~scn
z7-#O!IetP)^v8IP5i*?cN-&&60=3gWGe2Qqa#=k-^+=g_3SA{8HKmk#m+ceisXrH<
zy$SH%Q4x6zX*NrXvi9l@dZ1cyby`{m?h?vPm(M?)EUtUBgnFNy?9dZTDhT~-1biV2
zXb7e1uE~nJ-L9xAbXv^DWxR{K)C#JiZI+qUlvE(!34g4etQHOZ%Vi*gL`Epl>FIx#
z*E}y7E@Q8XTUMu^6XtgGBGkX|!o|J2F))CE_0=ur)d)nx3)Odf$C><73ImER?-?^L
z4@gyONk#ma=)lWmYBwn>gw#aal@yltEBHC8sfBmBRYhXE_<{jUgOe?pQkTGyzCJ}3
ziN4GA$Dkd%d{ih+-pkNVIH%Qu=<m-l9!%EOehvn+96bRp(Fy78?xZORkd6Jl3SGU}
zpvD3u>^L(4saL+GOw|s~Pa}x4mS7wCKjL?czmkq;F_;~)GWuEw9$9ip9joAqD=cT8
zJvbmqMo>=-e)S%Bdv9p*JkxT@U9!XdF(*UqhR>$V<o*h5L{AN0sa!Uk_UaBk3Qy(Z
z<)+6D<UNsi{)>}~#Nh~Iw%)__5rKZhOKPW*WRG`!ya3Gfu1`5BBhXL?{{fD-@Fmwx
z0cypo-=1FL<h+&5YUa=_z2Yj~$-LRd0{j6kNoUj>qtpNQ(3pLDJ5iS8yA9EClk2Jt
zy?G60VgQ?*mI-6WvDPbHeHIYc9tC_iL8t~VXOmv+|IS%EiquIE<!nahyiTG`SJR@q
zoZ+}Hk;yJ~yk&hCZT;r4s-K2|{*YPGFMlsbKXnkTwo6<$P*dsB(n+_1OW_q0JcW}F
z(oEhK15S~7lI+yn>c<Q6C1n_&(DmVt;WJs}XZrDj9+ihV=;f${G!9)*mQZb4LY1iF
z1l_E%9<=6+<0H#s#6o1YO&2wSFCrl-zKh-dLqnlNry(IPPmoNej49A#`@kPHtZ46F
zL2p{iTGF)x?pGdX7WGVc^ehV#HNc6U5f)wDXFk)Mj@KmR=1PFZ!J5(-nz<Drk+0C|
z^t|?fOdia+tS}2kodQ+9z?YiUE8cKdBX2y#bOyitfe;P%W>|%1g*#7xA4pz&jo>V2
zf@&e7@QMUXsq%3ATWI5#kL<RO9Q%mJ#|^l^0i3O7ju9G~yO*3@eVOf0*#`fDnr@%T
z{wpJCri2up!X(dzhPs_IL}yaYW_goJ4otPcMm$jF+F>a!xAMJZD!<h|i=yT@SJ+M4
z|3#f-FuY7*Z^fS%@DDNN%O$cF94RCI{oV9M*di_tYl$^=k3cxTy((BFh`!w_5rHpS
zOT4@YDd27N_e$r`Rsvz~3z58pm~$0xq<lbEepuBb5oYI=M&4=it4p~1E&Sk4h>(aU
zAzggqiGJgl0A@9DG<w@)z}7*;svw)_jAzYccFHt_%=egTLgV?v?0>2g&Ba2F7=2|D
zHI=Yx8Q9l!vj{-DcA;udaQ|v>v~M@&g-tN4q9S_ook*ipzZUj4B#<wH58LE1S*@Za
z!D}k5$?-k9F&N*q0GK+J3Ti04(>Qwi1~IM+!<UB<=irc_vA?<sbJZ(RVacp^Y8>yx
z{^V9z-p{3SZe0Z?r+^OepDRd;bftIK9$Vk+RK=AvKrArLPMx{|=L*Eja?8XKmvP44
zKUC40&3)j<!2y~xHuwFls8Jt>qdo;8j)wq0<MX7v@ErN%<>e(#e>5C4{PsM+yKYi8
z-9yv4M#JEa-l*P))UqDFr%hLP)x<=;dt7R~2oTp;-*=GO=r#tWUX%tu8=id2t_Huv
z-jmd#hB7+=7X@vu8vcI%P%l)aV}Bw2@+#!r$w*u3tR6W<tGh{VG(T=PFCu;F?MrO(
zu7rMZi=Im2|Mfs}zNZ7i{g>-$tF=FBp8S3EKKv+qbI;r4D7yyY$9s4>QXBGm`7hUv
zd-o5QA~?^0v=fwJ;^~lj@uO$*r-~rHqUA3+zmEI8Sajy7h_CSX>N}$-jb^p^p}iZ|
z03&Iau<Gw(Z+FR@F+UgtZl;>&_+L^eJCY*M3?whjZf-@ILgxQxh8VkZD0n8#@W&0V
z^<L-SdygWs)&6o>aV=>?Gf{I^5s|gevh_@Jy#H(?mmsyp!#LIOusHd$;0q2Hw+jYZ
z`$Ny)a(xM@JE>BwX6awi+8;ELU;IN0cE5gfuWS3}{q0Mb=KHX_Z~k)qI&9tVH+_r$
z%T)p0AHMpR%Rf5iR~ux-Gv@z<!TH{j|Ab*Ww8!<At2s70<1d$uaNlw@=e55VAMNW#
zf258qg(Uj<R|A%K4~3ANTT%Nzytgg)K5n1>%O!X=`d18H@o+KwD1ox6d+~5_;V)N2
zV39v}d0+ouE`pcVJGVbwe*Lx6zhZAj4$&cVFXGR5NgG>zJat=bC1av3O2<&DaPTje
z#s1}}lC@2xGyE=5bc-I9zz_d}?nU^>@OJG59<{{;0Bc{~d#Q26fZPJsSzTV+4~{|~
zrW*t6<TT!l-oJUbb(45fXV9H`R8TYWy>*Z=GzgiS+63(X{x}ERY>pDmd4w&4(5ZOc
zE$rTjL$$kaMhCVkTIsmMMS(rZO&-V~<4Fwu8>foX8t$Gmv=?5$5yK#=7P}d^NZDNF
z{D9r51?)v0iy?F^nnLD&|NqUjd=MVao75x^QX$ikI7&7tdI*Zra*hVBgZ^?UN3CEZ
z<Ik>YCGx?Df4QPK9j%$lr>9xPeurQ4+YmD&E$*Z>%of^07V;Y$fn|#lXAVT9br9tK
z@<U2IeXgi<&Z9JA%QFnfXs-q%#iw0?fDF#PO0G*D%uTK9b6s6WH`41+Df1b?`0rKa
zM{iDOIc&(^nfU~^9a#evPk1_%)S~A~OMM2*X&|NSoyNnj5LW!UpL|K}Ts|V7EALg~
zAc`%j3<IrtON$u}Id)g14%&szwT&~9(Z@Y3CuaA=9g9T76J5UXIfQB_Q_rTk$_8T$
zTs2!Cp_;o6%?<CxM#?$ic>5vaOY4|u6DX&JH4_-G5T&}l+YBdf4AM)A70H7=6^`tR
zryHWd8}bw&1X`0U1k^&<{&QmKV`X3fYW+ndkZ0FWBr#efe(2CjqFGcooNIHd<d*W5
zJ*GO!-Bx(SZ*zQI7JBfEp)jBgv$2o?$;rQRtt8yf#2h4#d#DMQhVOw+t#3|l9iTeE
zIsQbUa>Dp-2p$Od2yI`D)OXUFIKuT`K2O^N%*mZ8Lz>HR=H})WcW`>oBcr?4Jky8S
zx-s2pmHUY;AVlBfS}9xphd{}m6Ww&kwr<VaSvr?@g@?m4_770v6{n6$I~2A~Lhr!a
z7jUZU<G>=VG<i4qr~PXfo>+rc5%y`awL9RxFH7dx5_w)rI&ow{wt<~+MPaTw0{#ON
zO6kdPmAAs$d|Yu1xyX*4A8EyyWo#fM9dtx&gj>fGBC!H{5JUyGD!E0=XlruKH}bg7
zZXMwuNry~%S@zVU00_15F0TY$I!c<P9f_z$5kpmwdo;-<r)lZM2z%z!5FEi*#AZy+
za2?6`NEqwjscC(2Pzd02z*7I%Qdvgw*wOGr#-=$&L~JY4RrUcW@63D$mVFv9M)+AO
z9&AZo6}s?*+?)PFDLOWBGf9OFs(?PLhVOmsi17?w57+(6b<x?s6DvCRQJ8|C8$cFe
z7ym6XOAR5Q4<b(Qg!u!-FYn*pf{bH;{-NyrWDIU<+fXFFEKO|F+9JlhM&2TvTP&I+
z8$(+&jXqxwXz(~Y2kf(DpH>_F_NJzE@T~wK!HZFovo3P1f^-BMc>LWG3muTt^&B7~
zc<0toytA7zAC9{6ag-$k7x`2|KP_>?6AX6s;@<#opgp61gl5bm2*rUiisOD?_{y2n
z6;5`i=F)k;6h`~5(FX{N!D=P)lwkq3VFs4Eg$8rs<sHoty%ZJ1Zp)MY@H~fj3R%_g
z2j?J*E$o0jaBm)OwPX_7Cq;=)(-}uEj88y*^>1>4hygn5>)}d!AmGIT`QzVn6zg_d
zGjUeW7iQPxZU1o*3l^E2YY#neH~5;4{WEH#los)rb!2+rF21OU?U>pt;*WAOcwP+z
z;N@dmeHnJ(Q@FkOby)W+=0+Isc291BIIRe{u4o4;lrLzJX*C@2+x6LF5^(dpZ*da{
zD9NJ+s~Q*_9@bK1Xj>UHc_d=3543B?Xw9zgYL~71#?*I6ZQ*c+)sP-^2^+*G778fA
zvBAMwV&uwtHHXlAhfo@Qf$*jOJaJw^i|81Z8$}J*f;!n6uSd5#flchMEAQpn#$seR
z$2q}cpw%v#>5jqhl+K~xtlP(EHAeXrw<q`PqFrd7rQ3!VIUB0CT@XRlZu8M3ue0Vh
zDw-=<q1q@^P3q8>1B2pkwLpL^g83med!z-cILlf;B$!y{A$CFV6xE6r6yU`q!Bz`u
zw@FH2(U}W~Phbhvy;DGE<3Z7RlM&K?xG4s3Totj~GK~UW=P@DWA_h2iY|H!<n3sO+
z;MX%THtpEa+v|D4PZ<}NmP#uzBFER(W3Jl&=}7OG?--=xV?UR=od^S-b%B|+SVQUk
zgV1e5ty4$7L)`{OxRQQ=v&uF|#My$8jU#e^8-NDbh@^SYAi9#((N58}c;BP-R`2cD
zzg$ZFV9BHPy&*FFShHy6U#@e~h*=QdDVz7Ozg+hbDBG*a9u$u5qB{0((BKHh<?tB6
zGBR^^6X-pZvaX3sMB5Mby}9=a{k7#@5e{K8jcRKW4IZe8r&pvyQj}IB^_t~e#6#Iv
z)?z-iAGCh>Mj`KJw@;heqKUckBecm)-qk3*gJO?!MNju_jk?0;IXh6^MSnvSRS96&
z`F+m<4n$5IF=;1lDR<BZMM=Q;4$p%VVPWZ-IRN?-%0U(j`em)3&2IMaUk}Z%yj9RF
zz1ytIoS)sC1djAM)9{byq$!z!#h^Tq4o(M6Lq8;6{<owNgJ2ptj3b$PSQ37WP7XMI
zr1u-<Fz3WRT$ixzS<}MXB))l4wCBXJ{O44$9Z$+J@$faT6Wb|ek-PcNmd``81;D%Z
z-*ebthtB3^=qfnkpb$FJ=MD~z)bYAKM8jsK%*o$b=pe9FJVDKJ@-{8$4H#K&U(Dp1
z(!9~;I7GYf;i2u9q1AAZK;&<W=zff5690jG?O(3AJ<<8=A}<M1p)uV_2%|zrHevR#
znzt-^f~+&XwH^-gQ~e;;w=0>nj#<v0{&rVHvbZwJoyc1AXX}VZy6o0drfY-o@`Wu5
z1I7Q?PK?Im==}z~^5r4Bw0uUWK`X$CYd3Z`aiCs=72V+;!P0l+cnF!Nct2F1DjC$&
z*}N5xjg})W{D#hXG<ZA1tqlsJ=Y(3bOV>5i({azjKzoKR^2kfJ+3k1{k1;t^U%W73
zsIF)AHvJ&+7T)klz7q&DG(cHzzI-5`S@IqMWZNbuvTbs!fjpaF>cpt=Z}=`4@_VJ;
zrMLBhL{5-rr2`flB{u)-y+=(d_S~lM)A5MNLYL6us|Q-nQK$~)LDrhjfmGgmvoyt?
z!{!w7E-mV-95p-y*Fk674}F=oX`yVK*}z1~^AzkgTchW9bhYruT75)bGHtQ@2PMZ2
z3Y{;>m$%}ZAm{TpL)gWt1@H5U5N&)C#h<tKbN5FKL62~yx5w8u*2CB2TC9}wOP3H`
zNip8nqjdcV#o-eA=yi_Nw+WOnHaK!0?Fu@OJH7XN2FKFXvSF$mL<C0aZ`y%!W&U!t
zu3WsK{FjTDd5@$=U>@?s^dTgavx2`@pDVH<v=oO^#<zlF+Kv>M3=p;{u2F@40PILZ
z!ckz4SE~Iicf;kX@TlG$9B$IXAf|dwK1T#mx<vS;6aJU0`-%3gbPA$tIdG+8?hx{L
zHQbLC8eR+USR14dwqR{EaYL)#LvP;ml7W0`ofNI}hm)=r8yLzMorO7)-`c{+k!OCp
z8?FG_3nVFHQ%9WK^3n%NxyZ{h9TA6J4x?;`68OvIJ9h|tZ8B;zhFQv1l@-LXAusKH
zjzp%9d=HDGTOUxMpG00n=^{F@4nRo)y69I;dd_~%66N5TBgS?F#e=G|8R1ONw{7}}
zC?#(Iuht~Kso|i*6@<}du)I}HvQz(i#z*1e&wfDrCCh%$<L!JRrjs%?r;lDZ_)ZKL
zVW09d92#=wv4hdUd+r=Vg^AQh?<OuCk4zyTM&y%8QF0FM{K3G)JI{lC52QA^=Z6tP
z>|!V$M%O}$)cxhcjbg`-<c{Q65eqxIB1XG0t$V^-ZBbuD?ujkUk0y>y3KD_$Un;qy
zMubT!wqN?=qE&P~eMpf|0R601N@Wb6?)s8#JhSMxHzijJ^4W1`9GG3f!ANs<;?iF(
z_;$*81ZxY!v0(lmTT=Z;6JrqV^tEtFN|ZqHp3uy6uYFl`%){$#_LZLT9zE<U3sBa=
z>V84g;KFFic3WhG6D#KCdL&?ci(En&0oHA|=!S0(wG=jlH%I)ZfdW-gf4Q*a2}H-*
zO4k<cHhJd!9GI{c2}Fc9FgA-c&IinP;T*^muT|f{Wxo#n+-gVMo*c@eYnpZ2)et}P
znRw(}bzBWdjXQ7qgFIjq?H4ZA4Bz`*yG7p8vTRoQji^X}5!0Qn6R97xf}`(+u;}x9
zzXcBDX(0}YROH1awBiqW-y}}#+rID%i}=iIdo_;g7#^ZXf4sv>`RSOV!~M$Q#5gZ&
zh2ZJpjQ-2TpI#q*-`cQi>C6#~uK8Nc_J@cigcYd_l<azg#f}jUp1FcUqgIw_5Z^f<
zpq*Jf?r+X+=#DY0xjEnMmUZ#8ZFgiA@LOX@et&PSrBC_D7Eg=@m^TMFTp1ds!DO)C
zzvG_oI7}74vbHGi6mzyC=XT;77W5@Vco$e#sOxiz4cm6?tf8zY8dbXoL9{s$gQiwF
z;?L;RL6>v!y29Kp*kp1myjg5Z>+ur7+gebaQ}h#V>i5}yJHWwk#skgRYa*AlWx1h&
z4Ii+_5;Hf(!BUiLVz5?<aN$#fBE{Y`2jCBMuA4Kd_1_}u?M4L7baAfg!Q%oICJ?7)
zZ5o+km9!p2bX8Mu)&aE$1Y6ynb=*2c2jgKZaAUJIO60w<h%Nh6LwIph2{2&0Z+)LS
zweFMn8D}Lhnd>OwaXu(Ce-(4`>>g|O*h2p6)u8!~`^O%aFsExYIUb$QUcKL1jAloC
z+iNKUn1vx_(YI*TScl{oc;Ojf?+^v&ouoVN>&ixJ7QQd^V_C~npzSQkw8i*GynfKq
z4O(Rjkg^;TybpOC^V8QDLL_P!n#G2U3o_|lOze`brwH7NT9!fi=T$?bp>0anu6%K{
zSavCKDbCthh|{E#u?Plu%4v()+T)BA)0?R>kG4H>_B~1!_HE&a>8<dnFPzwg1A3Qd
zMvX_4Fu~mYbGzJYeCerQ?qsYvILZ^&qHY-KdR;0fdOY6HwBfYGNJ0F(ZJUfaig+Fr
z?FoV@=N?yjGDE<TpxNV_I-w<C4+1pK0Zm0m3mIy`ccKU-;YA||PBf@xGp4^5sd|`C
zgkbN9pmx;=!ehL9E*XTJGY`5-!`-2_NQ0W?{IiESLLUEYcOP4W{KUY-<ve_!V0#xv
zEkR--_|W85zr)WWJnJ&Y_6*%P@+%6jIbZA-7WqRofbjA{I)0VVv6;gNrv*iJM4%pF
z?;T%V=~}VlAa6Y7Cv)}aVIsO&pRNdRFG!P%aL8GFZ4D*7WE}i3EV#U*8&>YOhBRis
z*2tWu^arZP#kM}viLtm-yLMfi(*9KC7%yw#oUvvsd^s=@@@nu+P3y{EE|0P39g#mQ
zcd$DUNL{`Cr!pL^Lg<(U=Uzc+vuuA-(30*zM-|9)^Eo3nU~x6l@DG5pvFEqR0g5y6
z69+pS2{4&)kws}ntNuu9POoerp-_-O-ffoC-#h#T{5pTo+1$Xn8IZVf0C`dECtZrh
zm~@?b5L(<m(0T!S58QY-(gKOVZj1qi4<NsnTUqiVP9cOX$fieg17_U^x@72BXTT=f
zj(iv(9heNgl>z0V4@+D&*kHq>)<@W1QwPhfD=W6Mp;13kl7wgi?mPA~Si*3q2EP`m
zFXL|(?ila~YV9}s^md6IVLU?9yu&dQ*YkixnEXV_ng57zQbaD0CkS^V)*?}oT`T^h
zs6QACVvoZ4&pUd5xmsfU_ff>7`|X@RH|%at{6>P^Vdweq+&H@ptrQi*JAQ_D9L3>V
zm#t%-G6$QDaYHLXoFP^m1u$bAeg!uIIu^);sEAbzZhSXdGkz_U5bLY={k?0}Wx`7l
zv1rZm1!%^2_xgHpaCpeJ2Qd;QyajxO-54<}<-la(;lV0rZWTHrEWc!l7%n^lfOgij
z+`}3>tjR|94IKgN2d#y%DLe&h8p^%YaaRK6e&R36OEv_7qX60u^yiOC=nF?jRQjCZ
za&b%=@CCbBhZDEI6&?Xc&l27|r8D>TK-ne06n0=Rc~@tq5@2Q`wvUn@iTY_W)a<Z@
z+27-wycELJ4kz2Ii<{bo*tX8@ZWF@|jG2tO%Awc*LUzv}Q|nCocN!#q7X&(7(%UW_
zKNLAgYGlVih>;M<edzc?8|dQ791A6r-@)=*XamRa`7t*Z3nPb_1#D1UcNiGh!Qkwt
z2FIaGww9jl$Gn)@`3j<)*#@m0aqZ4V%kZfg6*Ct0zWoNVT*B94xW2d$UT{kB-hN>i
zaAF>43VWM=AYc5DV$YH@!yQ_qcINXZI5Tx9=v8_Ap;X_bhoz4B28in@$>4)tb@+LM
z`frENT5m7P-!Eqz(IY$jibu4>z`B}6F%Kx5dYuF{w!6Q_@7q!GHASUZ=V&b;^!n!b
z)=Eczv*6U@L(}=PW`mT{N=@qi2nTftW0Q04Mt`}YF`GyC9X9(xAw$rGQR5f8t1&Pq
zwcg7|ztR&@2**UcAV0&iaMSYe0gK2o*)tnq8YqQnIo?6k3}~k^bT8`L%Oe1M*8tnY
zWYX#Qfhq}+ENbx966&HOO?0kB-gGOuyu}B4p1qeyZV$&F^~=Y+(DDF0mKB^jDr0Ve
z%9~?;KaGME?uHDuP;81Pw8T=OtIdEt5h<IN2Qd#@POoktmO=<I`9CRqPSyZE5i<WS
zjk|qyzc@f~vU|g(G;^MUexJbtQ*05YEcrse4O`q9qh?6_EUN%|@%g%zc%q}6yBL@K
z`T^-^B0TU&_%9dm&tClCoA)u(MnlU^NI)`1u99~K_hSP>-3i!PJ3zH-EyV;7eu9bH
zQN8IRY}AK&%2uMwTyC*e@7vGGM3u>6o%a&DN5Jg95nBr3+Z#2eVU-G~oKwH88>Lmr
z@y(xcoE-=3vDFR3)`w82?K}8z<{U$9r(r)gcx82i!{{WAY=x4Kq~ol`r(01kNv2}p
z@>VQ`OfnUpEna?9`Ijq!ql%6(t#UC88L+#YoSeMM#$?PZ0!6+JV8hs-z`ipLTsoj=
zrIbDP^tFsojZ`lE6y7=$2F4&(o1q-nqSF6hwsj@_@xrM6!BKO=${K`qW~UkQq62js
z9esad5kTNsu#WucPab+x%n}w(Z(aF7SUrkCILyh}-f7kg8Q+T35BLp*@2KsV5H<>;
zzRkpl+#lDPnq|#-Bp#Y%U^vVr!^?7C&US2wy=cNcNN3QT=C&Z1N1q_EnEftxntMdh
z{$uaA9S8ESpgaq<&w~Lec#l_xN5x-&J3rxziKtmlvqjPhg%hC+wstK$Hp{`f*<d};
zUoL%0R7Ak9RxCPA402OvkP!}c*Izi82yb?W?k{p(da?@q!W>+7JY0m%>{B6uAOo!M
z9te_uN3^RgWZ$Fs0XAib3PsZ%5eVo+yA2VYwT-Zg&nETfM`w19P@IjoEVhcgL%l<3
zGWK%~<cl9v<+&Rk@&b9jxC@r7K;b_gNZ@`oFZ@n{h`j4~JVL~1;@%Y4&!&MrOqPHV
z9K5cF{Pyz{(cGiuqh|fKFyTAI71XS11p34KBxae{$1jY-FDMNIUf%8WYJT~9@pO7}
z-2$p<0}?6E*j!oFni^GTpXPM%&_X7T_O<t0xp#sNqL-qVYvL2pB26EceJD+~=h%V=
zzrMAP1G1Go`Ud-Y`VEC2PY&>sRfFmyB%#}f^x<vF&+Z@$W@>Kp=r#g3)C}EU;$PW_
zfpF#-hPG&ZnNyZ;o-_E3kR;s2grm2D!Cha5woR@tEcv-u2SD2y+XUg?$Jnj$yyFAV
zjI-Oj28ohplxEvj6sPodHRi*@%rD#u!@zM9veE!5V#u*#ikPeWIbq=tg;8h+U4iN5
zQOJrclCODn0`<}bHN3e}f{}F<$yMk-BDkLCH|3+`rI)mt1r1Wcs4qh;uWZLaf%~68
zF<0z&NF3XwOP)GnC#@JCmCD7V?#Z>tQy8sKga_l~`8}%01#yr3{=J-q4mv36vm?%^
zeRB!H;q^M=HiI==A5>v!?$Jw-mz<bOx9f0&dtvm?!LQ=axAZ`Hbql{p<8q=9fWzDe
z4{!?bpNfq6S$Y4h(iK!w_Hou19XTA75{_*$i=Nj^FMiO{`h}xmGT|{-$%88kB^VKB
z!x{blpcwg?xKPkuUT#bNVFlD{O!(6y5wU$0`rN`WZCkw<)#}l<P1ryl{)GH?5z61#
z&pmqN!CYF4fi!bGMq2SpHt6**C*XqcMG3MG527NDJ?>Z^TnfvFkJZZ?MkVdWTsGMW
zSU8w1=14Z`i%0R-A$FlYnibu41nxp}IwK8U$XA6QZf<{3gzs2{MT^*Kj?+SbEL+>1
zpQt9(7C%SDO2V{^_H;D~E1r?Rd!9yZnMV{cZlxDtA=+Evmx`E+kmr;QS&!C-sGSi}
zFtG2C13@%>d&XLL@oOrlOI2*kH-{EA{bUuV<Y7bu(d2)G(H@lq2mhaIG%kZTG&zKF
z3Z&FVDBQn_10mGnX58uMK}bVrUY^}XuwnN~O?r_PnK|ttT$migFz}m;`JJd?5JU_H
zlVTX-S5{Z@H=uQLbGzDTZ3A0e8AKvC?0uB0!1x-J>qt0N8Ptk2_%^&S#?c<NgHDST
z%Kj21!`HMwhJ@{^CF?AqXc6{PI}PFa&pg9*-Ht@G634c@ROp(7Wv-rJ$Y=O6BZy;5
z0jt5niELU-3rN`TPIF8lfdhKl?jAPH#GT58;i5mw{h*`U#_cJX2$jiCQEmR#(TDmX
zEh4{EVI5Ga>cRmt=C?d5p0F2J{5f`h^vsU-`hH2Ii*R9M2<7J>_Aa=rcW6n%F>?Rl
z9gqXxe1^OGRLqjesJjUVoTG}lIsVDex8*^0z5e4_4}}epJGt$1ocSwzfGjGU^lq+e
zNo`r4fjJ7VDSptp@+Q47abqOr3d*o+IdXW(qr(F``@sWBb(&t|iwz<C8f+G%Z#b2i
zT%_s`emgWyTC!UhDPC6`tlN*R8>s??F#7`+OGG45&F~V8tuQlE-;N!mt|Z^RPu&bY
zitu2@Na6^`0k*UM5r6-HQz#owK0vJ|C^R{G3$T}v-n=z6QoPa~L$dWAp=iZZHXN5W
zF#FV)i>NJAc`S;31_oxuyd-lImirC<vmIa?#*#<Zk?FZUh0I~jq*MSw(%ENozX|l;
z)R1m0VXtyqy<YkV-5O62m&8&eZ8n!|X9Z%Wy%e&(jp4e(I8k_fnY)MS!dL+f!1<6l
zyK?6kGRFU->AK^ReBUk&vobYzrRg`zZJN1rkmbm|McgB|<=!SOGoduMf{A-8j)ntw
zWDYb3jtU1VrshC0K@s2Q_s8pBejY%4?s2Yj&UIb)!|AHM_jeQX1Ft45GO~0Gx^5b0
zyIg(>IO$_hf(~yx!Y5DwzMazff}SGxY?=<|BnSKgzAzm;&thDhu&`Me`>&Ac|9${?
z6A)n4zy%`P?Z;n0iWg6SPJvGT@8=Y7k}c>Y+X*hvH3c0@_mF34MU6c>V;2`G|J}N4
z;THV#U13A__l5sI^A>!KvX!j~`x3-J2p)sPr6O-9?lv01`iZFrDK$QL$x6MyjoG&u
z7J-kab`bLn*aCR>wjXI1#h8XuBiBNcOJu#&8bdspsSGf+DLv7g*JhO7o7*ggFlVsW
ztHKYO(NrI1m@B_5Z1T}^Rkkb)=(B(E@h8y(mmMU16>3)FNO0<Kwf2z$?np&+@19>t
z6cUf%jCzY<0GG_QjzN9EFm+#fe+=3YO*;l@`vy)WTGcHcgV=k<_s~>~fOm&AvOrc}
zHH*Ak0sn;%&}fQ)8S|39jIn*89)r#Yg<90nJLcq|Y$|@Zm$Tpnu451vKlnExx|EKk
zdP`44AA^vxuzsfI)LsH9O=QOtLnj8EC+)|`jUHK>)77yp7=fA_HgW=O$Dsb8Y8-5}
zJ<SIv58H8xiwSD!_+ECqAjs})V_cbB=AMyqav7puR)jLID#73-WmU_V<F{Z7_sb%T
z7hRQ*@?griDq%(1oPL?hD)O~kF_wDoYz<$K*vq}x#$(V`Z{TiIl^TtC$#6;g3cm5p
zb&6?;Dr%mw^;bm`f*_z(tBye_bFC$-On@W2cfqiuh~j&;uvOT8Or3+qF^C7YN*JY6
z9X<oUzn>CEN40e=2vlp=PXvXR1wMj0_F`c@G2pdwq}9%f?;j_sJ%*v0qR8AjrDaw6
zph;deg~)I@y(u@vonGw9f#lW`Hd;;3EmgA67te~oy{|S4a++%a{&En4lfHh?_@KUq
zXOuz4PNQiQ%7ZrKc)4kG+2h%RW*D$GKHg5#@GMJJZJ)2cIOYnexolr%oRzXIvWF3t
zhu}tJoq)e%z=)n^Mgb;}OOTC>J3<8M!vLoq`g$F9c&!3EXr?eh9!!;-5F{)#SfFW2
zMxU@D#uP`K=#2_xKTBk)ddbVZ7|EFqD+%1o);%LumZYyUx;GXJYW_<)Ea4ROB)3)!
zKq`bLwVb{i<25TCxhPScpX`F`)Jc0eS=s><kIzX*Db=nK4`E;6Gd~KdK3-cr2KA_0
zHv<4LD(&OZMd2YQq0~p@t4$!x5!f7tKge}A5;*C_b!T;+(M7>1Q)5x8#{H3@eM~d_
z?_&@GyxeR~3pfUyDX4`FJ9RLiREXaMQ$Rl6YSh+{)Y-Mbn8QQ_9&~QsgEm$_X1NkS
zWuj|z{Q+G=P&;gWfR*ka?>G=%Z?ok|;CkCX0iB7reY$u~p!$&3@Ksb!#lhR|>Qs7)
ztpk7`FRhv~<Hx1V8E>1BUMkJPwJjai%qK2^SEur=+9+1?VwVzJcZKbX#Fnw8NpOk8
zEs78G=KHz?*zz&xOLky}2sM>99`G!#`SbslHF${{;{7?L%BX&r;1$=|wHk8_BJmh7
z<6X8@Wgh`MBykJ<cL`0i+rwRssp)6D+EjJ@7;=`Di0Y1!a!_meZhi%4|MG`kJ-<MZ
zk=I43cd4k`d;Nns-V5PVb34rwID6!7E!ZOLUa#e%mmKt+OKh?GgH&3E<cuCC13rON
zg8kV{_F?J`coOxqI$*u^*6>Fu8xeP{!GiqOnb-0ceVT7(B)X)u28>*rh<#l+4G+U@
zFirO#7|$Pt;>r6!8A27yL@Xd1a|dC4fNtQIS(3IU4N4jJGuBjJ^(gfgz!;}|n7|v^
zh;Om5xj(?FV<T+Uxkf~O9xQ!xU4#Dty<BYJE(3Z;wB^sJ&8G7E)(ok87nRW$)E6xt
zhAh~3AMvk?OQ^b^99BwKxLbMu7*xjb06{SLxZ@Ob8BN!O$H@91p>rE^(htZxsL$)!
zGSyQ!gvgHkJY$+MsX4bF<EA<79r47<I(h5sgJHC?{@E3W@f0ibo7-0R5+@KJYrG4#
zaf|f2AR=rP(yj`Wf?~(fA`~DVT$YLLn96zkx;f9d&)jwA2W&Vs61)V5^<n{lV+AiN
z`Y`<`5X1@CHfi4jytIznkHs(|o(eN8aOUsl0|)rzRm9aRzPyu<<5ULho>K(xM{f1Z
zd)QAY@tIbkWX*XuH*9ap6}}>z{hMNi31@{1Hzo|prAFl4@*vC7cs#fDmyQaCxuzuw
zah_ZVK#PF##ktBoR}?Q$7z3;xVoFv!8UG)>zOdo2!-wmMLCfyc5bH%vTgc^0<yCWe
zkF)RRpq7a5yUtVZ+6)o4@KhIBmkkkWN5{AT0UlSYCq1_vg^xxoD&NLmnWuq&lSZ@D
z$Oje2pr?fvBDB!`*qZ8zQAR557&Po+gW8KLxfz4{n2W&~;Cyl$o^^6#tahX8-?j&t
zgh@%r@wY#by+S_KMXA50FL|})chx7TYsBpbBppKfPDhkinpUUO>T}dqf58ug$bTwm
zUTY4qC+GG5B-DI$_T)PT<rb5?OVUfPRH)lWs}h?3TtX30J|Ag~ZtxNCA7CfEL(~CK
zt&MTAPqt8YKfmBF9+B#`JhOqD2*|XG9~>fA@4D<~ztwQ&R*CEZLRXI}2?t(ErOzLs
zX)zepRJuB#?nY_!&h031=`ywdfySZ_GXyUcTT?TC40@&zOWuLa!&Tb{%)97B2bCw*
z@J?C0y>vat_g!L6r(Vv}>#r;I3;XkGLTDk@!KPZXsl2vqyq;0wn$0ylsf@-q=7Z^u
zlY9yi?SyOvEh*Cxlc+fPf9<szC3fbQXw_$Rp#P<h<a*uS?)`R_Ug0vWEq_{;i(H#l
z8e-pyZ5%o?rxZxA9_4*bn+uj&f7kKI7%$CvUH5e7+3IvqAyL>>#a1LF$|fg{nfWA)
zg=p#ebUmHBz@;{(Z~l2&MXqfBW4%-%(;(GBHDH()LV%j5W6&Fjtk>HL#x|4kWPFsd
zP$AHic$FGgCOf_<JG=e|*2nZrrP*bN0(RNJ4_()+#W1GUT#Y%J<|3!TuCrL9ITLdW
z7^l3R*fIAT*tueyeRAbQ-)QwnzK}2{;?6Vrc}NS@TPkM3zbK0_`SpIifcWf)df`a}
zP8(2Aw9ssYx*W53w3?AZuKvDKtR0Z-89X?3V?~_E_vS>PbQK+vwN+KWndK4PV2F^p
zR<Y*1&5WrmFO$i1&_v%H46f3hGtX#qy4D?CTYhav*uUYV2x+=Q<Vt3JQ9t9{q`mFp
zy}<H|A<~A8ql|)M&?3Xd;fnzfvL~2P*>TO87?FREqkKX~8Aw5UGywhJ9S!hK%Z@ED
zI^b0sMP|IUV+N2sOQjQG3#5YIRyj5M9*#GrD)8ov2y*o`x$_qvG*~N1$y~_RR5H@i
z!z-eUeB+9SwBky~75~`2kr+_&($~^wOg1t1k3>+fGXK1v(!<>Lp4SSlaH>8*DWtzC
z)CjNQE))yBPkeqw2s@eg1NvUQ{;hX!UmJ@_Gaod+x<u@5OknSwun1xCmqj8w3zuW6
z*9*l0d9$HCV4vdWEukodFJIWhhmku`UbjBoIB9dzc5%#ybYjf*16~w=|9QC<q@NY}
zNV6koytCFYda$1nWGi1obS8Z~1{J{8=W2wY1`N^&MBdu&Lsf;QYbwndYdz19KmyC-
zOimX~Oz0Stk?n_f8u}E5&{X&z8RV}A21sJA8Iu@bS9S8c06{+hxioBJoai{%$L%6s
zL`<_6sF0NkiX_@9PWCyyaZPho#|jEm*~umR5k4Jh^|FU*afK7|DM-6xiu7RR?uESO
zli#U!wCbuaroQP1y6tT3N(lV*w%qpJ=Xb$;9%AQw_>6_VXFO=Mq0d6N@TvY@x6Bb7
zX`Xy4&s^4C<U<EJg#Ww>$j<QE;UrTjcsTeJ-^px~tU0usA3NuBvLlWA+em`%S4KTQ
z&sQb;?v>asLUQm64y=F=t(s!?3r^KDZR!_T$3lRU6B))mx}<$HwV-yI1qx<J4N@a}
zk+2b0CTqA~zY@ScdInvjwy5Uxd4?*14vdZWupM&zaQztc)fiyDVjk2```fq`%&*ql
z-z2UQgKK(J8sPCA@k%P59o+v`?TQCpQm_4;JuxuxR=xVV;FYT?ewCsLqwvGjy|pqT
zyPMn!CGOI@sTNhae;?*HE)SPq7sUVEsT&|z?l=U#GAF;hN^H_CyXuyD^RBRquj3cJ
zL|THh<!?e=(Z5;~`*(89g&H0CEy=|dPe9sp=kKnfalF(?SO?q5kv)+^kECz;o%Md#
zDY}zMC+Y_|x%3MdR)5c}VR}i^vM#PNNWl+4d0CjwiUI;9@(xPEMRMlgRXJ`0Fj2$u
z#ID6^WSZ{>2{Xjhrv`iRZa%a51YY`r8(HHEl^JE^k+JhcakpboQ78GZ6}*U-X9pk@
z4f`9UZ085?U?xAF@O0l*1*o`S@us`@cQ(`sO)Ap$%vLEn_qV!@*_Z8X&MVy?Q0797
z$;1T4r{X28!Y;dJAA>F&HhAv-hkZdM_oy3O#*x%P{?^@q2{|yxQ(l&T@x_2S$B8Io
zjtS1oCbkJnS($qKHmG&RHG26W3-=xQuTlx(7wSrPEUo+DZ?r63%Y`v-f2(Sz@>+o&
zEfld^ij}mDk6gBB30Ee4O@Ugtr6UZ}PFkI<y+p<pYDL=3H?^&DoGo=L$l~nRFSw++
z`<r!;{89J`>yK~Ww8lh&J|0=1q8|r(NWtGxd!w}d9`n@`NgoXFS|@aol-ROHf;egL
z2?6lh?H!k{CHyvm2t5X!O{Jx*8EH8K<ZZdb07i|&$^uI%3B2E~5||YPaeTqNYM1qO
zz*5sTpYPc-!!HT)n)Z);$X%+LHy`{y&>K2&PlwcQKv0`g6UPlcVVVDJ;mp+JTxniy
zHFQiBR)jw#QItw2qay*&;eziWCoV<smJ&2QziD_B*;i?py7m2Z(nUM&LtD@Q7t1$Y
zaRWy8rN0|JChuH7K2X0I*1J;mboIb#jy;^y#yvM=&;9xsYqkDr-}<*=mu=*BLXC0v
z_v?wgr3)tywx}L{9hf)He3~5ndZ5!H3r*_LYRx^P5M9T@q~@BeL{h<peUD7x|7y+l
zZ;H$nY}Z4BVVLU4cb8sQl{sIH2m0%W?CN5UK`*C1uLD8pcHKJ}2p|AcZC}@IGlc`l
z*hhhrRn%x!MuhPY3a1Y2H`8NKx+_YR=ywb<0Kn+J5AzCa!OHeW^%y`gB^Z2EH=<C9
zvY{Ajid2lx27LNuwQ0<QfBx7`7clQsaQGm<>L>LQ?zQg%MM|e(e6lF3)7OV5EkgP{
zHY6nPt_9jEj*_=Z)J5!IT=V*WL54e{O1x(`s86!+m!tB~NRd@G4aM~wn=P47=I2UN
zBz8L}J>f~&=P$L3<=zLQ_`M2Wh;tH?^A=Pmzx1~)8@%{&^u|AJF8i&4w%m<{kgaD0
zPD#jR9ToKo1)E)Srj4OQ4$_MCPrH$JjuP|vu06HSYEAQFEjUv$kRxj$p6><h^oh#C
z`zJfOs=Bq|u!STfB|*M=#VQ}N=Z&V+nx@j?I$Snzz^=<D3;`5cYpTA=Og<urA##{z
zCLdm*G#JD{&H|5V0JJ9-hI0yWe&pOtu7^$S)<qXO=ho+vq|Wn*M0{bKXv&-A3q6q2
zFEZ)e@LULjNT1>i?S6Q5KzS*7{=8d4kIzpw;Fva?RCXYTD^dMHLSVbi@;TAJf@rU9
zMuiu5v3K?*WuYe)?`z9q^5`{kOXmDK;NR2GHuugC9Ilbqv>&OMzt?F2FIKZq9u_5`
z?)X0+>RVLuOhqX8t5LkmbWT`VWG=*1sI<mPopGmGj768^mag=b;KAQ3ecFH5zB0%z
z2rgUMik9VU3Yr3pWPbMT?5*1t>-`ZsQp0V-k5co(M@egG@$U)nV-U_RIYGt~kWbT`
zjHNcLGnVQ|FtYnh#^cp1lsbN;z&288Utkk}bCGm}#G}AFPP4<!dA1PP4g}upPzp&s
zB<fIZ&nn4zzNWJ4lNP9<Pk3rH%!vb+G(QI-8!#ZTAWdQ4H)V?Hhl_5u+Hs}btQpAA
zlfjk&4U?6j3<1X1z!%r(&M7*xeY#HfN?n`2-dPUl-Y~%5@M48`D>MHJPxS9SJb~!M
zT7{g3>%4=kFBaVXz_=bT6mE`EP2=#tKuwW;rKa~RD8Rt*7LS&mQ9p?I^gr>YvqDdz
z9?-zJZ)ejD&=Cm`^XD1ktUCe)L-K#i6nQoq3d~4f%$LkEzc-=JKZejllk389oX+;I
zMHfnyX?_+4NA~yDE=yBORzkB`c2pk0Y)NURK}z6N*MFhA_RX;ss?PHe`KX_Oju6d?
zfQa^1^dH65-5$i}ZjBxV?HMqIZB6ql5ZRHR{P%6K{#f9YqG3+@UM#sTx&do>lJgoN
zQnnN|L<ntK`_o1%1f{=OyZIoR*1}8NB%1ek4-`GEFr2MOT7))$P}rKKyQ3wxw}NPL
zmpz;DnKxeKZsmUb6KQ#_*f@EXl)}@Kf=MpB6d5Co;U4HmT>VRUb1Iv!FzUO24rk4f
z`?L68gL;h#cT((g2H85*`nDRi9rI_3{S^czJlN<lPB)dJI3)EO$735sEQ8z3Oj+Hj
zM%b35o3dtzDeh6${U@6qqkJ)j(|JGivS`>=P9B?LhXiE@%!lPRiyDgy`zp|Hkz*H)
zv`K!WC&C9h;dKZ>L&3Yy&STJq_Yhw(+I1K9r^=O1G~1dwY~>AV|4Q{Psv{u(=T^=6
zN9Mpw30>{eDvn^vF{rpYm9bbhPlGM~?3k!)`Lo`C_xF)iuZGy!&;)t-rY2&WY4+`c
zB^$i_?aJbVGYo0^)7Gem!FTLdohYWyd>72O3Tpvy^g;D#^SsKn{z-amK#FbJz4HT?
zgZPj7I9=hEO<-Yy65sHQO^!(S>`Bc|-;6i;Rl^o)7XnV9*ASH=V~q-k)XO5o|1@P<
zTV$OS)5{m02P1^kb%2r|^JH*TY=nGgtyk|Y=5G$;l228!d5g+{%fAj^lu~RH?Ag+2
z2?6~dnq6`$pKz;Ly(t(%&e_gX#%t=`ujE*gOyeB$S-4(FvHNFw!`AX$I)x=M#9+vV
z^cf3CCUY3o1AwI~f?LQ@1|ZSi!*HoJD?R}v8h;d13?NJ#a)7bM6xkzcx&R^Yc}y9K
zc%5nLO8@x82?`i$>PFNjQaCtlPIQ<R+`D^vH0%7?2&@D6B<;Svd!c<-_0UXu6^FTP
zxT2QI+5DzB@T_N^M)U{w8wFBa*9JO>j-XfO_)ER|%LTPw+wDDQ@m|gj*|kOgr}sqP
zvEI|)GGJXS4;?zQm?5(tvyoYFJ}l$;LEz*5Q)FL|xx&BikEisndQBp!W>x__>tE!g
zb>4;B57lE(9Hq;$<nk|Q9LixQwkeGI4{FHbsdON9f(HE(^E={h_q<Gx4%gGrvJ}Vf
zrX>H&^cV!b24xu$9TDS^AWwg`{snkgY2~wMu}8q*u%rY<z(&>~QSpHPGl85&DEPEJ
z(wg0m+;gP?5<bP(ZnP~#|E<<Ib1MNx-ZN4O{R{`*N@K<UBxb7&+XnamF068gH4#Nj
zWd1p<*T4PQEBR8V!#h`U`Q5)TK91KbyL05#7o_B4Q0B$tD|UJJf@N4>=bbvrWRL2l
z^j0}xQTM{90!zbi0(b6grP%y>`w)Hx;Q0#wAZ<LDBz!@aS3s+Ovsk~xdew7PX_McL
zL&zmHn9@aaR>=WRk->qK(Jk7KvZRvw^@Y{s4sIEHQJ;Ux!NlBukJl7)8&z?Vs|DkE
z({QVJdYeJwF=#W>Sg5pzUU)xPEQhn^y9`MdJN@EmBZytm<>hTpxO&+7?^|~$X7>r7
z?oWB!z61X^`z_U!D9wY0rx*oyXg@ZrDc}>RxlDEapI{fj9BeQn%m5o-Yl3Og9*aCI
zfds+!$@|_Z6U=@Fc?sZD6KFax&f+<3{S4WxJ%?%L6?r=-f^g6=4fwXY;U9EIP|}_>
z8M$9P_y;GyYZRTfqhq%`UTUxTL`^_z6NDN{wB!#Q<;*g~V`MI|CRdtu%%#ZRl&RzS
zOWI8lct%=#n|Wk~h~(8oEmfZru7}Pzvp#%2z`iJG)GbEYLcjjF0qm8+$NW6IOnJ-Y
znf2E%Dzw)h@(dYwrL5ig*}U|OxhBC^GSF>LKRuIB7}HTuf_zqNxK3844P7~B>Kdu_
z=qvtKE*J*iH=LYm5?)+~o1o8Ie_8cXQ7g1YEF68;;KUZ)P#S+^RgqqXEE2(?s%wKt
zC2E7mpkIJF{}51n%}(v!oQL@6X5+04<&sR2fAukl5{dB64id5k^v&;0SGsC_Aibj6
zWW3cC+0OujcZe0>-7qG=v4;LjjAvPu=`Q@Pfb!I(R@h8b`}TO~GG&Q!si$jSA6cKm
zM<+4m&%SC6U#n+v8?PK5(UzfbhQtWOx@`IQ>g_h}I-lx5Tx|&>9pviGK+)34;bH5m
zYM1REe3d})VPF)lzZIzhF;lh6Vg~!hEay(}b4L?a4K%#uZ`p$e+9oGlPUYm)+L=eY
zn1)h`)VsD?bfb<BknW=y_R`mHT1pCP`OOjU42rlv`Ex(nx8&Cz$(%H!)|3dmW1-yb
z8JpO8y1t?`d7-KJ_J=fvt*^O_%*`p&hd*#Ehq60$L3~{TZ7!IwW00_SK)Wp|KZpTa
z-Da91KlyGm;On`TgCWKW`cxnM1{2NyWZDHItq8%{!+N`@#Jpor_9s__2vyVhYc$V&
zB;#z`@a|f|29HLDo%^XXqqwZ9=uWx8$jY#NuYFyWBZ6@k>FFxfb7%biA*rrSmsrNr
z>(XcDdo!g%ENR|I^O94^hI5PjPu$(&TLBw*^yl!#5mc+2;;!|LL&@<0`P>hd!|n>^
zbx&j`h514H?E;jK-5kCpR6{@Fc;a*_CtKP?drnC7z2uSVLrVe)HP99FNb>!B+rgW7
zJ!y5D+S5}laxOEBv9g5oNf?<h^@8flSuvFt>iVfgF^Rb*RvB#TJ((Rn-p0GNYprq3
z5OD^yIj$~nMXmW5#EF{MfV2}~q+`%XedyF3EY+EG2<=iTG4;c}#{zSxVTYGsik$^e
ze?;ESgE4~vQ&)qhgNUNj%W>qTYc%IXK#<)ldY;O8eF>MP{xI9g?Cc4GH@}v}0<yb6
zYQ_9?C%cd4Or@M!E5|E{l*q?ZDJI_DJsjl)M;1>f@-(6P^nT>U-}4^CzY+moDk_L4
zB#16&u8l^$MAsFcjm{Y$Z0WKW%3s6Pcv$uYOPq^Ceog8uaniBOdxjTHADHLAki6M;
zPbG&7pA{eQ?$wR<T#rCbej82;0%FWgY!$<Q3vD!h4Os0suhMe{VIP9IoltThzR>IB
zW6<uOaImU4_dyl+XQ8rFI^E-Y*0a{|_oV*B`EwRk-ZJEaX75128~CZ(Ms)>@--xO>
z@*cE!Y@W7WATPlHFV9h#;2j2l(?3E0U*)T76ym1oWdOIJKup|W-*`;4H|s(aonTD=
zi`TMzht^Z4Fr}?^XPPr@`cGG<MzE_f<v7l{kG~2|Reh7UEsOI%<qYmA%ct}5GEa^^
zk9W2&+kw@bv@=F?hHDBnK3vHY)dxqVLk<6!fN@UB1;REh_EYb4zblRwr@hd$R(puf
z?#6zjI!yH3=e|aMEk{siE%<HF=8ry%8I?Iz8s%BNCUzG{^L)E0E`JL7>?9ZAb-_|D
zwlqkp>-1Jr)Vhr3iEh+u<M+->@HDpqcx{2N#e1gwtk;-sTnxs@KQ|VsclGgR8qdR(
z+A3whJ;1rr2O>uqt^%m_eO1v>1}p-@Sf@I~O0FPvu(D$%*1W*z4OfrMO5uKZ%{C`t
zXp}gBX)TFRFan7Ip?*(fW2`Rey*P*T`By=41Tl?(RR;GrjsYX&v+VDMZ8hHYFW}pe
zZ}XG5up9AEnyz+GQKm@9NLt7ryLqOntb6sag?xmB?xGQ2)AeEH$*b`PzR_oa`9R~f
zlKMQoazs>H!&F2QDxX}cyqPhW-X|sQM=DPUf*_q|?|HquAveU4%JTa)hs=U=%JWuz
zUNBKFmx>XC+|gp)8orpIGE_?;`N^JQ--}3ILfa0%+lkWc_stEZxc@G`;qlu0N`bZ9
zw_2|~Z_Bs6fSEFy1<ZOH1Va;li=3V3gG0~<QNYp&i*^R~*#aPJ)vSi=a)<DmmheaG
zwFo6Z^+smaPv>{c0g0^Hdg?Lg5_~=}NUCO<f&BQ58{a!W;Po^a_x!HS^Vk;s$9P3@
z{<Q2JQV#!hsarnq6Q4?h)2(zz7&aWnHxi7mFpPLZ4;~PJ^pr-kun#=o$CJ&6X)5lD
zkgt?r>kEd^3=WkX4`^k^;NT3XwAH!v?&GYLeGADcg#T?{a?Z<%u1HO$G%nlYujwYn
z&y{8P?!2PMxrm(45(9^g6Bzs-?JT$j6clWeqP+9v6Zgyap92_kJFBDNPE?(!w)<IT
zDGs`(8ri~;Z3>2xih8ml)GUVW2L3&kh^A%bV&@62hx5vTrU`**KyJVFQz(9YIfx-2
zW?IojgQ9R(<oW-XChHl(-v-oO@8&4I2k;_^NUDp>lgBdD__=t?yHB8mxoN}u1o}yS
zUW0bdJ7%;(`SV{z!e0rlVcUhP)ZC9jS!p~vml!iCoH!EXhG=`@7Qc*0(d<ixA|os0
z+K~6)-M@2E9m>n8>>=!GNzR=X@LBIY)w{WyBte;8HD1NO(1j@1?wvNTuBo=_!1;pu
zx&O-4;|$|1{Z9Ovwx*pIw|zo@6-m!*FB-h)g5YKMm{VRE3sF`N2KJpZ`Vk}lN%b^t
zy<lU(Sp|FIZ;j%fTSmLtt$%zv;6Um82)IH!7hpZHm{CUHEJ~>cAY!8od*GrfhW-KG
zWHkaC0Qv|RYMr$!S3;tTkzfiLw!C^o?%ECf9Q3GTo1qKqttojiic-0ga;1XX=Csb9
ztLOkeDSAG4=6$wYX?)JiKV+J)xpDy?d)gMZrFs3r^4A5ueXn0;r!7vr+xrM9xpDN?
z<b45Tmt3Jo{ckc|Ao;7LhlyJ8d~<bq^)2q}e1F$(Q>!P{&ok`$r|jo#uf~+_fhLBp
z{DYCrT05-c-ty#6IlO*uZhI-wL0mIBucLo>R{Z6lt00nv4F6}~AoJqo9=KXq!9a?S
zO)^&Si+pG*nG(L<)TB5ZbvNS2v(3PM`;IW1s%DjkNJtVe+gY|96z{hv07+i>Fe7||
zpbkK^*1&-GqN_?>+mtX!F@Px7cLBTOn<OE>Or>i-G3FRlIt8SAg}O*Q9Zvrl?6w+%
zAa`uu;M8`g7UYEWL#c+<d9jDjJD$h?x+i;-)qIhD<r+0kW=C4z+}J4XUvBN(+=Qh>
zBt5%|c=>gl%&4r_dY^q7-u<gfwQi>!X!Qj_G>=Ef?&W`8=JH~98GF%)2iG$#mMJWQ
zx@<C}ue$M=w9M&U?7L`Wu?W1ux!dy_^ziD2!$QBJ$!+Uj*37!7uVc$EM!h&B|4QFu
zak;#Qzm~tl?^Fh}NkRzRjN2^nU?amzp8N`aG97)cSd%vz>Inam#I!LDu<KF@iU^#g
zIQ$BTYl0emd_0Sqnq9{n)NZ~n$+8-O4vAd_5aSr6pARh|$V13fPuQY&nyZa_r|ZtV
zhTt*C3D6myXnrrS9;eKO9-YCfp6ks-bRXfx&_dy@{^g+Yn4-Y*3O7DD88rFjR?JC0
zaAVjEql6u+PA$)?i}2lkLGA`uXOcb%2&GFfCKRL3ZUtVwSiO6Om0TEf^H$Fp*?Uo+
zu)GW8IBZC(%IO!XZPybr>>M2H536pdSLi}mmjhe0rIx6W?Gp;_th46_Bm#@qUjO0T
z)VtQv9PSo<qk4sID{L;$a?j#O57kV}5=z2PF|yA*nqUac?DmiXe|ol`t7tzhf^3y1
zVU<{0Dg{L0rFy`OBOK<Un~<#n=RbupV>@eSe@szEj~ut~C?bj=Wx!BUVL+{!yyX6f
zxnt0(Es*(V03YgbT4J&yDb~z52;e)a%Dg2vT($@~|Lf7%ZMjKko^*j@xCam6e)S1b
zybIsnG3e)wu)pO!-<Old0t!TUFI9g3*V@E^8Sr4g>|!j+-0Pcm-4V(b1-#dSM@N$h
zwO^?$$Dmsjr)wCcDHkbd4g1)>K(4ZA7VY9hk9-%Oo9}jVAd9)Cz0(^BbmZx2sTiZH
z_geGT+AHofuocA1ns*yEEIdJ)osLV~t|CTe?OcRy-Y+Svi27}tew0YQ+crh07UpZX
zCIa5wuzMxVqP*u%!cGUy2@oBX0a##cjiTu)FcQvDwXN&VreN;~)fjJWXNjh381qM_
z%$5UeZ5y@&TZBq?jMw8xhjp;ERp!Ydo2twfs=?svVC-vzw9cCQmz)bike8o)_&c8_
zkG8+oApPN%cwhc9=(CF4U59bjJ5vLA*u}U2v`d!nOz(}YugJrTYLUwYo__8Mlq=l@
zEd{^jceZ}tw2G^^IJ5NAJ2jp$U7%)atdjFV1jiC%-S?@53x6j0!gV*^At&^2Te*xZ
z-VK&Ds`tGQy+c3NZfH3-psPRTRjXrcNGA^i0C6f~A+uoY$~pJ>?)H_^KiI#;6|$i-
zM|9f?x&AOxIz_KQMjHmHs=KVeaA++Y;4^<DXY~<yjEMf|5lVYIGL#yN;fsfsZ0*|s
zCIyn0Vs0BRvJ=AlceZSl78X+i#1tkkz|>foa^?)v=#q~lJem&d99D)f%dK9V7F&s7
zU;NyOn)31tN%m}M5ml@ElUDY25A{UO{j+j1CuA9Xvluu%apEbN@WVa!(QUtBMe@gj
zp;vWHm+*=i>164}C3(JzQ)lvAWz`*Dge^rp`0ac&w%b;2d3(Dj`0Aa;{^#jy<4R8%
z1Ti=C0L85_@!OtD!<if%yGrIYtDcL^-i~E93V2_A>WN6Ia$?d{u`bIVsb<db>AuU>
zg);%EyDK&vcL1uP%!$rJXlx3Bd3~=fG@2cmMx8vbZT$sOs%MZybSuCVFI6wvji2g)
z;2fyRb;$XsF1pqu05(OAK_}}>k+a<Rb;Otvj0V67C(VcPwriS!$}qA6FT1R{0LqZR
z-UVPRjb2KdUuT_+x61$HOsVv8zd_aE`9iC*{5k&fFR!|;w!`BK5sSsq=56O#<+qZJ
z4WFgNYgZCfxZil&-><+6SN5QZ$_(54f3*Xwxyavz|0^80$dE)<M6wi}QscjK@fRgq
z2eVTVOA`*jhx|N(HX1Rm@-7AU26boH-zOSYN*HIGFj@UM|8QA^WS+l9ZAogsg;sua
z-65Ll)pjpuCGy`C$l^_@zA;aNZzovno|^%@aK}E^^iSh2ZO2om7C&3N{NOPB%MS@V
zEa`h-sQqeSXXXwU)t$G)#Q;@qKn<zcZJX+%JjTah7%)<!b)Of2G(h|*i$bV`!MiB-
zB_9U8<k?0=c2=1p)bw67FM0Gx7^p~!0xuzyBk?E={W>I!2DJoBpA@O#Jk$HFS>ik{
zd-=>kDTm*esb57-n8gP7L-~bO^`-~8G?Q7y_fsO|8YrDl^rgxTRoy*z=Zjh&Vjp(3
zr+M6%i<5w7g9Z;h>h8Ke!Y@44p;J9RpBveh`OJ|TZugDi_RMpvSi(UqL5m=qmP&Kv
zcQOgg`$aXBvzP8!!#1h>ozZ4ZoY-~QDAI70y^&l$yacbqvn(8gB4grFxb?(?`eTqs
zx^6$=#TT&h8JI)<kyDjxuj|8jaZ3(^Pm4kwnfoU(S(@2;c@raCkrZBlxJob!Wa6;Z
z+egvOKB4ut`kw+<@{d8u7`#&lK+&vmXqyVW+CESL)ZDex{2>TGN+Wd4ZHLp3L2tL2
znzM8NBXoU%i72KL3*T^|#^Y-A_-K-rjpS&E$<nT0RM%y<=o**DVx2l}e}gV#t}2&4
zJUqW)Lj5mudD<%A_o%3+d6%-}J7GU@&X0#Ix5rMqa5Nr4Mka)r0b*$riBAXc?MINl
zRC4iKj-~7ZLn^_mUINW~FFfAL!;i~TJi7TcDIhNtYnpa(V3Ahnmp{Bp>R@bU=MO_k
zbgl=aZS`_ac46_IOM!7UR#^h9C0l<bU$gakRCk@AhAv6qHcT40#1My7np`H~rkW`0
z9c<P*^e?I(`HsrR3$`(854N52C#2Fg!Vuck&}Kd?zampWa;`RWNF`_x2-Y|${Q{ua
zpv#7|hueB92$20|XbEX#7J;FCtR4XdaQ0_)CwR3rE`G{mncN*H%X<vkvpWU_$WEEX
zG+#}SO>*@=sj#olb*iOemoabnV~0mCXN8D=I?sQlB28NH`eM7oo@w@fyAnX``Xtx?
zG=W~yu`KXoYX}5{Q$L3y-0#`Daj`7|*ZAlTzdqost`Q}`s<&^c5>_X>jSjDqq(#MF
zGrnuil)?j}tMe|L(TljDu>A|8BgvV-xz%3iwXAYYkP!M)6#Ttz<YbJsL)L#*^#Uw0
z-`WDq)Q{}J<IM9JZz;ApJB<b3Xr-asukg6ccUohYqTE8+ZB916UHkxOxy8CCwire{
ztREty=mP^vbHw+30$k)FwH@{Y#hC!zU1bV*G9{Q%TPyEN?C>ZBAMkQ^lq@c9o2kge
zzv&G4E=#m+)wL#o9`Y*^{pDHL>4R4bW8<w${r@5lCPnt$TyHQXpD+XSB{i9Wz0yXH
zJw&yGF2w)Z-HtHe@HMi>?-{y3=XQe*$hbEPE2x}|xNA`4+h~;fXqYDhFuw*Fe)nX$
zaXj?_dgTU`7<H~s^7GZ^DKdmVOzEc&zFGoGvYk%LVA%e6uRiUAJO<UV%>qZ;^ZJeQ
z3rljaW#2jao!`~Q<NK?*{!`e4x8QK2+a(to&Y_jB@fe?iwnJUUyhIL9)sd<hbK<oG
zfW3WuGEG3K1C|tw6i>+h-{yIB<Wg(ZMXeU0O4@HTGf9~gj}NtCBeev~QItR%r;3Z8
z8r-`~*Y)NPPL<P$8$~p!yRmG_>tCDiPCkjB%>`2~Hkk7}tm4wPhERQOO>YK(2lV|6
zydK-e&9LWyYhF(O`W~6MD7a`*Kpa)EH>ZSzyDH=h%ZO$@bRT*+uupJdO_tAKp3?2d
z4c}SDs683mvFvt9Uv)6l`oMnizf7JJ_va%dj-Y0kABf(Rn;x)l54IZYHNci}A>rgv
z7U^BEo#t}qNuvlHIk}0GW}pMDx8K^G1#qHOnGd!U*O*fUNE{#Dy|5tLKxCsq0xwP$
zM)`_rB)kIuUi4(1Y;^o2iM`I$v}0s7jScEuMhJpksi*a9HRN*c#3^ebb(5<u*$ld8
z^76L?_}sGzvKi%75FtJ#_1}L;>zr60*($`L278T;Z1P$0t3pqHd9uI1ZTHtJ@I|`U
z_p{yjGt%GHBy}rFh2}2oMBYxfaOJc$r4XzxNH%t<c7G6<#xXv>dNuQ0`cjpeUdR_^
zh)B)#7t&R@nC(GH;VmxpLSf&d+MjVO>80;*KLuoKcl2L<F5Y3WvzA5%+*~R$r8eiZ
zkA}D&5HQN5Js>GmCuf?Ch4s!*++-QheG`CcEGX90fS1#Z%=;M^;35&Y6gbVk>XWQ}
zRo3kTtL);^eQ!3F6#fQ<IxlOT6~+W16^t~sFL{4nIh1C;!PFpk15`E<!)vK-Xelm%
zEII%Fyml%M!sU3C0kzt(6dDdouu`ude=!^L&~Tax)sV>f^i+t~{G!EsU2^I2h;895
zGSbL^=dS)0P8Ac^i5R@SmTk)J(2FdyD;PoNFndCak%FgiwyW~(7j6s}0cmaV=GdZw
zs)vCRa=xqiohys&7XEe<rsr*LeSTTwkVOH~RsX|vn)Vt#G^=BpDFm&FZS#BBVPlE2
zmCq4ad?J_tpD-p-5gQKUM=Gq0u%WuQq_$=ZqcNMmeB5b_qW5s2o-y5g4B|fZ7dyn=
z=e_Ku?^|=#OPpzw#IPhkoyGF@A9k3O$G9G`B&QuVVpEk8V)ocXV1llX<+(E~&-YZF
zJ*dR(I&<-~xm{c~EHv1(x4I0yY~0S?W5r`&Aoi?b|G^|)_CCFqeZ6dCYYc}*?DE0(
z;=Wct*3%tvG96NIVpw+<hS96`Y`h6+%dR|zs?kj=!QYqx<QF3JTOq;v&-6lNCGW@l
z4v>CAYywg>%pf>mSFFU;<=3nO35P4As3BK+sfE^LrEDgjfUtiCn!+nA8RonKyqGMQ
z!}!R_V3}#uhe!v`jMBCK%>pb-LO9v-Y-2dqGS>0OWOOZs>*}0IOsmlanYtj7{RkLx
z1u;&`%0Rx@qJEFvEQG~k8>emNudN{ubZNR!z(b=vIEa;$xLp2Nn~ghP=u_A^oFA>K
zD~nmrUKAsq(1MLwsaUh=^hM<FA6PBsoUXZh`<7Q+iCpb7cAKY7dS_lu>~={xwb~cA
za;vz_G?EUbHJU$a`Z6IsT=9LVYgkn&ztpydY`+VrpLnT{5|KQ-T%=-%<uk*@oMMO#
zs<`(vtvRQS>HsM5y7lsz{}~l;OZ>K8wC~6_QNA)TBd=mAD~ByNi&A{k5+PAU>tEA&
zck-g+If9EV-<b-pR2rBftz|D-6kmNbmOsZk6x^XGdtLT=EmT&#G$c9{N<U}}t&!e^
z?0amznKm2E(%F%bzrX%JPfQC6p7dirtu{$8t)*CTXnlTO(HbOB6VD$`0^F08$F>V2
zY%kx>^K%&0)K8tYcM3H>f5qzkruW@b)3l%yq$1UN84tabrD|CPRF-e0R8VfoxuG$2
zv+GRIVgSF?)jmV6hoX^dT<l9?25+0G(1`@qB+c({>|y`~F&G}x3cVxA8#E#x+jN_r
zg@}g)8h&u;T7@8p8_W{$(q?Tquou|(;+p8i%>ZwfX$oq^CYKcWX;hs4Q0UZbAT)`P
zTIn1DtN!Q-4|@>$J+3j8mddzPfqXfJvmmJZgt(mv@*_6}H_IC7%pBHUQkJ_I7Z?hz
zUNy(d)#&-{QR58=UqpC370?K@XuKC$exRr)n>B*oaQUybCVvkP&b>l@GPdf?F{sV2
zzgo66hMAcrfy_Uzb@~9qeL9to7dxYKV$|p?&*pMvp=8;wNW30q=kcxD*2RDZm|vw^
zMX~A^VSnyCDYZfLzWjxK*Y*jEy?9Gm;_QBOnBID2{Dic7m!?g&QI3C+qB~X^d!-<>
zCKPH3G<0}0dVFAJ%R1W!4dUK5f+?GB!s58USRV$T%43A~6(!ihY{`e>_*qFpGlaN;
zrb@`d%|;}Lkrto*2}mTKH0a@L>@oF6=-2dQMRJK+P2!tH2=Xf5iyAE6b<Isu&D{Ev
z^VwmC^yxkVzrc{*rO%cuLM6mi+yc{xGYMxUsKgDo;GoCbyHQ>0lOC=S3NnR+ZG$D)
z&ahtH>gVBEVv66cWgmAR=d!r3_!u<3zMMq%bqd*!);cV5CxqZUbYu7muNG>Z8s4=~
zN)VJ?-ou@njVaxZ*OnqImb~f{13ab=Kpn|P)4nz;E-^5I?<;u0Y6e?Bf=p%k`cuEH
z?#3*XcL_cPINZHu^K_tCns49YO(W2D^mYTO1YGT7@_A#efmhKq3C1@d_^Skoaq<Gm
z?_^(Dy`8EO@G`d2Dxps&5BZSR>gwO}-5SZYP&>cgo)xfsk`}PZx}I9`1)dvBEIus!
zE{wVO!y(Lb@h+D4i9lW*#T@c7jo~8B)A{4{NuL_2H(p7+#6_zIC)t4KvPL^mLLrv-
z)K04XoozvUB)(2HmRpNdXkw40>U^ZT3jFI7+kVy;Rt4t2AjVU1&`1%vPBcd^HNAXM
z5EKS4$(~%SS!HVTI9~qr5IA$#fL9!W=#%t(?9YGy<68s|PJMtq)?om5)7-X#$vq3;
z!ke)MEGF@1e9i4aMnKqyDa(>9Snmk5+0M@dLIxy5&rc=NrdkC<tyv|E$r(O<40`>o
zLxe|1C!5h3;35m&$#)W~;<rRortDAG+J;Z<G`z=feY)mF7eRe{ay8J@@Et-KfEu>`
zJ$>)Ej(dmX!ksfao{*gEp#~qNxz|8-W<zn4A`T}S5O95E>8I|wEWBX^Im|T3S+%u&
z`l-XK)SJ@2%lmO^h>?^S=>qp991PxO$Q$i#to_gC!EW$aoN#Py?m?TcS^p^NjD$e<
zkp%^eEH#Or=tFF@I0tOYVl4(kyC^ZTKSKi_XZsbJ*uC??2jb(id3h}@9WP8m`0od>
zjWB|;;w#_V)36)H^9(^^esd;_!E&9cEelPh^9y!@xBD5uU=(25Os1T%rsuL1dVZ32
zl76-)F{0tW0I*qAwL8(MzdemyN~xXRZm!jeqE!<XN7W7%4uf{ScI_Mp(tFWc3l}4?
z0PHE?ffA5)oS9w&3nD}5A)62`ZGB;P3|O_S6euM+<#Dy^&oKyurNH;>Ao2<qpJ9k)
zI@D39HZ+1tt~N7udDAL8!tJ+}2nw=6$pz>X2+J3=1JUc$O#HZ@1pfK`e=fSNK1vSV
z45n;5@O3`F4l$?ttJ_Cx(?;8W+Q0Nd>b+NODajTBde1y)V#_TXGUvP%fVQrCW`5b^
z2($inv?>Q|wQS##Bk*$RuN)apCGiXkyt$nbzX@zXx*9Lon*xNjXB9^xf1%Ajz-|F3
zWBqS5E&Hqti@|ww=2^03qZ{u22J=Fgen@n@w0lb+LCY(W*W_;$VAb(+$GbCsUVN!0
zB*OB*>3WvuvOo5q1^m;8Q}SEwE0GLR#Suy-|2tm}5vr%w8QWxfd#dW=SxNNos;eoc
z`9hDj?2KEoBFZhuc?$S@qM8%zJ%Yo!q4G%@v~~{zzB8nzN1SI|-XgJ+f;cwGCsW;i
zc<4hU!=7K#uw?z6Zi4Yv3u?xG9Qt9EaO!G~n|<Y-FsFWV(m?q^o8B_595%i+9O6Eh
z^s1Nqmh-&q<ZF+wdv-#<T3di<cIw;g94Zr?2Uy;m;d(i!tzY{}IVybn*jCKPu#O2P
zcsHJq2I!<LXo(L~9REX!j7L76t$-^4fhLoU18oDP(P6`{Nhuq4`n$MY2s<CaV6*cT
zp9DVrk&l!)3$5lH0?EgXh0+>4fiR7ugxxV=fcI%#7gkTKcZ<AZW;`DmnN_DXcW8Tz
z^k(2wea8Q_?&*lM^*IlWk}8=+Ub9k9-XfNV_Rp+kN_m_kHJ^W$hWGa2zhlXFC4yI|
z4MsROc6~oY8?D+ndZs(LY&ej%=bm3VBDQ8;EqF1W5N+KzVHPcde8U>Ug!>xcVQd6j
zu2AvJ=nF)hQJX%+tcUnMhoE43wamP;v!WuCODN3d!_QyHIH^6HPZ_IZNU~#Tu@rbW
zaJD8BMsjS{Zv(1G=}og+0p`$}GHt&uY7TIr#D@Uf-3!3bLes(=r>jG}(K{tp#_H9q
zq=KDaB0F6A!_ala=+-_jXIq>Lk57?B2qUMrv3PL(^F5bsTBS_LqB}#Q(Ca67H1WTv
z1<#{FtNZBh{NQi8477G_a_sK2J(m{F@K<5mL!^z_m4*^u%%%CSM*Ru@E5+2&VoXMC
z^{MQ<RDFIVxl6fc6fJ_d?f6s2`AW(KUmw%b7ZXnRKe>dBqP|=4iz-zLHR#=HPg5`x
z_vBv93(l4p)B<xN$>)wi<Mu_T8#Si(ysiGCKM#aUed5oZMacnXM7y}sSHZCuSN$UZ
zJ)M)C@+V!i*!hL9FQj+o+s~<~9*VhzcP;v_IB3hxZZk9N-hLS(L|5sLHG5|VPQaD?
z5->p4EyO@Jt^?MWN*nlJeHQIpdUYs%l<lSFJeZML>NGgQQEA2>{;h2FiS1$S`^tlI
zY4j!p)fF`%@F5AkgOK_b!?|LTxEalF3f+&77GC3bJauk!N7zBZnJUOkG&*cSFEm0C
z5)M;rf1B0mewU4<RbX&dp|Gf<(dU0X>ZO2by7?edB=W?#dq+t2)d1vW8%WCM-$p~J
zi)yf%W&!z>?N@;<{eoN7+zQ}VA4ux)@v_Xk59R_{>ie{Pdhw*0Y{E&PF~={Y3>=Qq
z`C{j^c?37|+EtGBlW%)tAX1`-j)*53X-z<J>mQ$|@7*uBay4x$t;4I?Y?A9<alKXr
zniptwLxGtmr#Wf9ge$r(8^G*^0ky>sfnaP@1=Psc0MVHIR&vircIB{XY?PLjzATz9
zax<i#&ufbCV_T~6!qysMtxMoa0$fibEdK3(xQRO1gj&B-1Z_EK?0&>KH4Q(mqTk!h
zlgr?epI+fKr!GQv)ruXJ);PA`QqB2@=&W9?9K;uz{EUR;U!DvVA;xIg$*03jwtWY9
zCAEupkn7j|R>;0D`cnJ!b%3LE??jOB)&^-?Z?5jNUWx(!J^%EVn)#ho<>c!$FkzKX
zJg8@bhSmM2Mwdf7sqlucaTZc7_O0k&uBN&SK4bj>4~6dv-zf@y^eR=;B_L19Al&`u
z+P#K@CWHp*+m{J1hD@W0qEn^GY%`EooNe|VaP>b1{kUT?wnZ&b0$Te-Xt2rILFf-?
z8PE#^Wa@#chHU8qnbRyUW2+MF%z2|3V+D^f0mK_Pc;ZW29~puDxZ;`^S>&RdoP`&k
zQGuMRDFTR3lmuA}?{b*UF8t}HdwfbDWi6(s+YX;ebzf0jkEAsQyL+1?CvByk&w1RB
zCvTh5!dyE2aL;M>np0tgqR~yS|Dc*W;kNJ<4onjwR|d{=T5b35<Trct$B2_Ks)fYD
z{Oo#ZpuU#&6^bnry!O!JO^mR?tA->E8F`w#eRGym(0Pnat5h{(Q+*I`;lQ2|KerfJ
zC<~Bv2`2vl=3`z!FrQ#J>C4nN>3%+Q8oxDQ)s@7A82EYn7Dy6)RUn9Hpko4vO3gX0
z6s0D;hp|??hPb7Cr|*xR=2k)EX=htH9!1leqiW7}D82ctd733@&r;VS%lB$m*2WT(
zU87E@Zt~H)5qw&W>bLJVFSmynZc7|kbMm`3U`tS(9WE;~2N)h9Rkp3CXL^II+wPNo
zh^g`15WEbKnOE$|!x-w)gwgG%*T5xV2onj!GKYu@nkprB2b1dYWf^D?$$@;P$2Fy$
zw5U?(WKF37J|)jcr9!f@R$A7nas`28qYSEby#41zw(Fd9At{U!;KC#W_BMoB8w9^d
z0kQz*_{DSjzj@4H<ST$r_rElNHQc3ZBZ)DzHOjbRM{W0bs$*aJZmam@2XhHKps8WT
zQsqBNM880e(hK&K#E|ICCA$WjX*SRuN!{~h$bdX7WcD}3UCqT-pgM^?4{N{B&Ncn$
znUk1F{Rx=LxfmdyFFTLy;qgf$2UgAE@P^(SY#$$j7in;q-UW$*D)c+V;1#Wj8+jq)
z1yMt|F@A5gOOt9l%xckFN5(Jzi;nqcDe&yDRcBsV=UmLo>2qzB$DoGuyu8z3jnO-m
zsy3$Y+Z{;M(H}XI$WMZ$vJ++FNT5Pj8WF_P2pFWg?DqiL@Fug94{)OZ4qg(8S01{p
zCk>qF^*l7$p#Nyt{a!nky@wBxQ;kCL?b>%BeoY3K;c$<ApHs^vO`jIH$doqzbq1ba
zTLRaCo%(4({TMr)eZ-!m>~l#^Ji{e7g8&uz=yh#qJ9ZDUAj_zgMp{5jf-Ta&9y(5;
zg!YkJRym9$u`>rO$?`HagF!1{FHtwOU%h;?w>9GaY8kwEG-se)>xBKm{d=d;ZDiwN
z9=U3Ev3QsFlzMY{=O4DMAG$<I-N{$-3}Nqru0QE5qNa)dRprAERc}U^I>yS9B*!jb
z`68PI3TD!Sq(ksqD=->Q91tQ6{KxTvDvk>JZw0lb9yWj09B&X^^YSia>HpY=PqV7~
z+M7B4r`@XNJQZcK`I-%%7hJFfWI;q3QrXcBMYyH+R^5h-DtQSzJC-1ypAW{Q-)Z5|
zEc@}0X4G#;Zd1qAi9s*j`}l`IpPV&^Rl8`P^Q`?=UtM``+Ks>w&AEhKz7uNIZdk7f
zH&SV>$=g;1qR4Bd3OMIw=HvxKCt@PMgmH?1%Z9ErtGjy)kTHyn!`y-Af3b|8RxGbA
z*b_aJk_)__E&80I+x4eDB(%11OaeIGTuM0yw+8!-{O$o>Gf)a*Ys-!WFr5#w%FOd&
z@(tqAhXj-oaQEK(p);f*8GtL;a<CpW>T&hH&5c6M##l?6Ng`iqRDr4yLine3B1+eA
zix4cIvG-649AA;YL|j)zX^@(x60&>?f~4YqoKdCOi;;Hn>8**{Js7fO-RWvj9}d?G
zwPWidAgxlqZ2livZygoo_q~A*ozf|dG)Sk!kWwOD(k%)iJqSwIkkTbXx1=H^J@kN-
zfQZ0Q65<RaGRQEW`+k3St^3dYhXwB%)_KpIefHkZv+M6hgqxdHFkBcWXhCbu7JcnX
z5Hz0N6BzhSbiQGi1766%mdIe4#**-^Fgr>qJ2Vs*z4i0_qdn@_7g4#r0GG31f4ST^
zSt8Y8kuJYtrw3zs=)_=FFlP~eI9EG5%_3IZGWuczGXF93*@w6QaardR<MNg~RRC|Q
zwcOEvULAe*6(O-|EAsV&-v$upWNnu3%4_xpJkIzyYbXKP#o$x=^K6l~ff#MZEluOW
zHvi*rBFY1187m`5=fApuhCIw#+(<y68}G(Hs?G>YNigzlr>DtL_~SYvj49`V>)!!h
z+QXD(2@(M}ctBOOJUQA-;0kBA-8a{@-&~zGuE8RTXCdo5T<0iOt*WL7!C->R&pfgt
z1_CGh8i_8oRqR0*N^#5TWl(u#d?Ufw#vWc@H-7l{mMiT_z6sj){3+>%69(e+w`LDw
z2vI=wNKUL)STB06Nx}2d?jt`=G!+!4k*uv2A~?J98|ClTyf%|C{-nA)uov28{0R(b
zFfsr|Ob;-3dH*k{3%iKMky+!e)L*arum}_Qut$|$x=E^AM*uc8UNPai*8sjD1Apou
zbAMZV_Y*6L*h5NDwJ)>evj$MOU}5LRcop-&u>#S8x~18wmi<umasNIUtL_!rK0%Qz
zr$EDViI=vZi|S<0B)o>sc3p-w=y7-GA!oBQt9{usVGrbBIhC+@{^;*Jx<w7GEV6&1
zoPF~D!Fl`5zS0<p>X{F}TnBo`?(5Xs0%18lT%mm{qpp0zpx*6i5{LTN`w3A$=z${?
zc4ivLuMlsAVPfi}5MH&v@no#5F8=~qBI)mki;FpmMuB&NP|en*N^Spvh?fvW@7VxL
zzz{u6aXyY3xA$^CM3r+z);Z^1`6QP&00pdr;JqEn3r^;d!X~4?TpKLGT#ibPc9!Pu
z;`26-sED6b>UfphXJc~&00@P}fO6-s%Tt>!QWdKOdIYLZOh|Id%7syz$ml{C?pjna
zTH!pq8MVxR3C|<W@#Cia#Gq4<{)WqzF87QB`()9C<~alVo%8b?nQv<JrB=fgWdmTQ
zv)9`%WuMP&)G6srGP^r=d{98`Z6!(K6eefK(GC?^$j<!9)UNECY7(TPZZm@M#aR;m
z-_E8$>fu~Q5qH$OC}nwp-fPjcF`&SJsb(!2{Wp5*w^Pu%q)HFK)5bwX0ch|)dOFrp
zz6)s00f}Dn{iNLj)*k!lv-J%fo5wQ}YN}LndOvgkw4&X5c94H&rW;wwcy~Q3bXIFY
zl9R-Z=;Eu4`KJ;=sl#C{uj8{u7!YsxvNvKAJSgdEi^5nbeJC-=3V7D8wzA&+T7qKd
zW6weTM62dPIQePc_s#BsHWu@PIs#d!*-`sIM0?WvCzEM=fzBb$K+^p>{G_a>OiIEs
z=NHjPf&Q**RAw7$fTA-cq$*5e3a0V%k@R1@1pVFyGWTPZzO9qS%;%yjM;)hoCG?Xx
zvUu^luXmH-<JP;il29rc7Hy8a>v}11Rqyx59RuI5D>*lKF29)<Jh53-)CVH4K{HZE
zfLFkSC+EQio3rSk-ygFkYWVl!IxfReff4GLUgz_RWH77ZQg9oVS~PuIsH*e>OIkrO
z&Ct=xQpIb%(T1QO1rI~}SNH6-|I|Mv`Z##|yGfsE!A5yKFp|aZWc-<%$fhWW-0=0V
zjQW>2FO;q8*CWbGQm&h3z|r`4;9OVdv+uaK*b>TO59Ag{p>^J!S$9rh1dog!NA!bP
zd!d@Ix=JMGEOHfFG6G8eif<KZ*nrrUvQ6GJQJ3N2IW*SAe!n`@lS3sF6-~0?j350O
z3uNC%tLMi*qm=AxY)U)q)N@Ijzjow7bAOXOJViX$DtxZ+AE>unf<rV>@Oe|dgqfdH
z&4ync6gr;|NJbnmUAgIKzm60BG|ai4lZS~|R9Gvr;G!i+9IOYU#bxW`WrGc4o|9x?
ze6U?UB*<sz{IZfBm4N$Qly;d4tR#X|8mBbE{qprrPoust-r*K|t<doG0aJaN)d$Pb
zi%5(VQyOFc0dwECg>duu?`@^1>E|+dpVXNIT*8LCk@C~a_ia42|A7b)oLm0BruorG
zqW-jJ#irKTL6Y9${ermzZ;VT(e-6pGi;ww^Mx4yOz6{<9yCH(p7_%4^_3w6miR-rr
zYy^Ff_qX%S!Xip)zf@JtHZ>xAU|0?lAUzpWr=aKKyaqr<774o!cNhwug=NJFEVF=`
zTkT4}MpQ;IP;t-cU8r^m>tVjHb9UJ^OTB*wR(!pEuP=o2yHFbzsAJg>6{w~<&NaX;
zTw7#Xr&|f!n|_J^HjjFa4GnQx@#B_K)U{Bb5h>jt4b{CsA1;_D#PyYnPm(n)S>Asd
z5GGx;bo`Ra>wEsYrP3EPU)=KZFFXDBLn?q|;jf;Dedo4})%$wmvonZ~$B*8;I9Rco
zANw6bRSp??aZTM~i)=3Y3hT=p{b5_vm0?1~jOMRtn@8B$pFgAO`ILC;EZAz)a`hjG
zV<Qx5^ATX1mJR$6yS6|IHo&q1&yBYzpA|%7WmV|SjbfFgx15P)KRxRT{@|VU9Kjm*
z>%#)Gn$2TN>DI8hnf_|TOxeGIu1V`3cWaY#B@7`>_Rf4Aw!;w(am0bTnMBu}v6}j1
z_fynZA!z+jZ#fy$k)j?yiTyu{3MwP3sb9U$-yzb64vzcXmAq!GWez!r1xbd5FPt@R
zEzb0r9!~r?lrx@pN&h_KJ@~Zh_x_n0KnQnMRJ(j1JaZAYAy>4OYwAbqb3L73RdF7(
zvat%|!LR{}>p|S7_Rq7Tm8E>mMu#=+KQ6BFtgFGuEd>u-91GBgvlK84WS@`!hv-M)
z0G=JT6x9WO0^|_j<tBBmHNrE6${>WNOXnA*d(GFuI(}(VqMJFB?Del+LhP#Xrt%1i
zh~{|5G(7w-RmE|%HOZret-~&((#Hy963#L*y3=P{nXS$GM^CHKZ-o2?>|s@nxl`X|
zMn0U}o4fLC=1r<rmvPibuyH?6uuZx0rjarqZMpgRZmkcwety}pIw@Wz$pNu>v^;b-
z>i1f%;ZO0by9br7Sz(?&{VaDuFI+-k*?<M6kJ#@zL|oXUcj0TAsmMf+uA6=m|L}^i
zc6m~dFaNe~XTtc&?*Gzp?%|n6$GicnjRK=AJ^~3h0FXd&b;jcfi|KWzw#(#oz7MJ5
zBs+^-q?oo|f0eYfd}zd+=6md?ZkMF!T~K1$R5=s8i~G8{O8;tcvA|OX3F&MLZTc!j
zr5(}cz+`$qTDB>|+e+z|rw2>PK(-|VCX^ygr5d*1#Bd`aKuqt)9%8W-l#|f!?a%!X
zS;*F|vzik>QJduAl>fv(sEvw-jEOHSXe86duE2kZCbCX3*fFSmx(C~lu{e>e&KgtR
zp!tKcS9cJG2GnA#(%0ES9Em~4z<Y<1gtP!Y?+<{kwoH3q^Il4j2MyRP%AGzXQydkG
zNHq2dfKyO3CVLKA0|0aGy5woEzvb9G8o27kv>Vj&lqcL+j%26!@a9?NoyCP~!uJ*$
zlFk3Bj3nm<raglOD0ATaa)*_)&^VF5>naDUQe)Smo7^E?<{`Y5w)#2|)|*>^gG>t*
z93a1L_Q;Z7<kVBBmb!<a=?a)ss4YU)^15k=iw&$QbjXiJygyUX{BSbd`F7#CFs5r~
zd^;O_K;1giprmGZx8C-Nz|8wc|A7!Q&+~))fE?BFWGVIcir(W+NsXrO+rj8omgwUq
z{^ADNao`TPU||>J_P+JEfJSx%;wwPMne+TrAWmLI`ow^=$$E3RTS897RLFOBRlUvJ
zVasv$#-2H529A(nx^6&rv{Th0Z#wvpu=|3-oX%+nAzRqljG$VwXbi^{l!H69|H<5G
zcHb+h-!1C)X1k@3=~>_Wm+PEv5V8=y&7%+s<S0A*owIh2=T^T)i@S2yAG`0_L8T|>
zlch3WM$axc?~>E_)?L2bkg+qyzj)r()Q-#&3<|Izf}xC(BxQ5yt2w`--b+l2IQjEp
zt8zM`X6Ho;<nw`pB7r)$Saz9vFWf%=52SKG3k&SEBznz(xuq>1YYpi{>)`eBLt6*F
zc;-7Vf}52dTX;I1uG+`t17eXR^}#%?*uK;h#wxD*(fmrcX|(BKqzhE605vb+xyVre
z%Z1UBQoS%ajb^NA#k4Q(fu|pGz{-7*6(w=HRD~?&NMv{8ZoFO&D|&Zy4d@1do5@TY
z2wNPp3$?S>rEdjq5k;45+`8x^kY-^k?OSnOj!@s-!6yQfB#Eq_e@BY`c$#6C^DG{6
za-F#H*->dx-|>`e)t^Q79i+$CVh?uBd4XI0_<8Xh+5gf~VGPJF9k#?T;BwMC+=l?&
zi-Jn5yfxm?n;PN}L1y7)?;4FD{L3e%zsp=K4-Y<HvcK#oTfDmws8AyIJy-`mV#8sB
z2=#-1ThP<XC|(5^IohSP#YglJiSP7{<$w9D;DaC4!SvD-_kX}xv^`4YADgmJK3<J~
z!JiQLk=@xN;;rX4LeRa4y0xn*sJiZahA}<wgPo@G#Q*Bz2FhS8Dw}sic>2B=ko<kb
zs)7|KzU_8z6X>><tUmb{J%-FT6+_-R<Cw)0+`YK-?b@pR*oI4-#-sTGIrq1ChrS!@
z=7$cWO}54Z8xHgHw!Q#6A~A$%!vauh-xN!1w_d5`iq?V0PvWwe+9K0dJmP18`PA)@
zE;G#*)uxU|zsT!bTYw~gfoIdD*5*N~?@dIkwoJ)z4}hRKd(|6elqkM-RZ*)J(#pf<
zB=P7~FML_Dl=Ey8aN#E>w(q_Cv8AcnshuA?CdGgpP8NS>eEY1w_h9R7%F<24y~nmW
zT5jEEXuWq{IrHm@p33W4q}<|hjrE1{1{YhVsxNsioU2Qr5vEKW<}UNovszAu`r;*Z
ztX!WzW%zdZIU|DZ?AV6mc(6xC{XwFLgH=UqAgL+(3=mdJ*#DXo@9F@ilq&ixPXW-r
zcz^lViG%va@KJLa#e1viHZ;Dh_+TI&B;pmpgZUc(@Tj9msfWE4sm?R1O*Z2NB@&@@
zSf$VpVww#UY1D~tYl6PEvtc`<TrljFgA#AJU9gOf3blLrzO!@Iy(jQz+n~)?XR6oj
zLcb&qoOPO0xz+<IB6>%I-fdghzVNZ}cD!@#p(p@(pTSBC#+bjvHieayYCP;VP0Qs3
z-n(*6MhAowS&9(03Fe>$<A{w;hMk$Dm`h%fP;mf!KqCN)5`gXzw^FJ+*Gf=1h%;36
zjxf~TI}z3Ie}C?_QRTaBYK20bL{7j=USXdvJu~O>QiboTt)l2p>&*;IfKik008^{(
zkNJHLlKiw-vw@n@yWx-JPNrv_^J5$+&Ng_&={?8}&mw#MSt#G?{RjFGRKkdAi4gkv
z_wFAT*>_jw2zxzVDa4sXT%y)}zM#YOW0f$|l7+8@+^s^--8356Z#rWmZ67IV&qp}{
zNM5yoPPG?STJ=X!BA?BB&Lxa_`lL>!+fK14dIw>1^;it3ODxI(Ldj#6-pyb<s1hJ}
zl5bCvzSKVj$SxEZN!3hV{hF%nhEO-K9FY(uF~(JxC@XFgXh?^|wXyjD6>sBrYM<xX
zQg&Z6s~y2-4|ATc53cU{{ot68(Clg=xRq4+p)l|iYA1+(RJLoQGP`GKF*XUUR!Vd+
z*VFs<8e#BWxg0u5b^KTqZOUw~xhxpefHfntrUYy{W$j4`!<JIhU|*2TyH{P%gua3d
zQ|N2)JDRATmQw{daUP`E$v;V@{!TFTTZzm`i&H<wNTC0*NX&jeuKDDAIZd3}+T17X
z^bR0aZyzSCljIBu^+w|u0ne7xtkON{|J)qZZ<9Tp1soX2Y)yjayzN?!_+@4qCf%|Q
zb^Q3l(igfG<m;?3+EH&VT1$%1)OfbhCGlNS%l1xri)LZ?mt{Xg@1*&$=Bo|Id<z@3
z;8&w{xslC`$t?EU(B#+;u8p0L#Ms9d<Ldc8+SqLnX=;lGy<L_9xAwu@v`jfwD|VSL
zhH08{VM3n~JL1Wsm^Ur$mw{A68AX>irfKRSCT~gG*|lM`;lKQD05?so*Mlo5>Cbah
zNQb?*b&1$;uvg2m#SnVh(;HDP;(ziH;K}o9p^!=-jHLiR;?o<$8|&38C4!OQ9eZ-s
zgPMhzzH=5<rTGM$lZ#4~p}i6e8x|gQHTSPk%Z1=Wul$a9PC0#a#`zCNi%d%?>-Z<P
zgpH#=XIM%8)^EAi9!WLYF>bjp%*VWL&=gXx?Rve@i*ZR1`J%C=-~&0C3*r#G1s^{D
z6WK9PekvLu<QO8+D~k>|TX7qMik&}89xb;l*uVDU+kYcP8$R{zDLmM{c~1;&4Iafr
z)ZfXUU5k{W=(BOOzltQ;k|QqzMDbh7vjcC$qm6se+`z}a2&<V(AatAt%wUl*#XbOI
z?gpFGs&stvmuKz;WPP^I`OfEjUkmraZ+t%I(})Owch2TP*)k`t3LczS99Ww_Rur0+
zAG9TbyHQo+O`yBF)_*$CqHa#{B~{%wr+I(B1|Qaoi|S;AsrF6lS(-aYLf%WSU!T$<
zSYJW(Fu>ZpOI>L-T5TkB=0(=c%A70~mWk=mhfS97-7vJrk$1cJ);p7L&4svOi)1nS
z)1(>avLEi7N<`Lq4}Y99K3RiZD|gxA$y1`I<ALzJ5f2_@QL|F{{>djr58!lhq`!8W
z+Hj9Q#Si={(1<>nXr6^wAFzp>XIOk$xQ0%PH1fS7!bdnpH4d9WpWk0bTOgX=ca^Ku
zlwiK~Aj`xX(Z&kM3x*WhJ5tlL{PvhAM<<iddeA7r@Yg`T*Rs#g=KItthb5m|Yr<b2
z3#5&Or++==y6JCp4@S^F58c{(Glti+oUx^Bkkc3YcD=^X_UztPpb5lDZ1G)i*x7lT
zsrrWBkisMPs(O9cKZ#2~uM-<h1Ev;0V*hhjj5tiO0`*J-7NSt*F?`a{k&UEeC)Ux{
zZ?%;}<uH3s!MiTP_OQxtmgFdSpx?7@;L>5KYxiW}u5kXIQh|k(a8dMrxul4wtW!Rs
ze+{pR>8Rn~3xA7oXk(=jN$rY6yw=Cv0ebiCG!r1Fc(4ZfyXHKh2G^|`HZIx|`cBQ2
zmwHcxc9?zef{ytSpPM7sfYgN;qo?!(531_4*HTIuHYnPaU;Lq!6u-MP`a;h#Kj}Jp
zKD-U<e)5iK<v-B16b_6N5U++<Kv~3G5&`$KjTwC6j_?jZ(A%M%=r5;A2|t&&iPr8q
zxu9}vGn$|6R9*nxbll+iFN{P8Jm?RLOkj#?DxZy)kr#DaM2ROZ^6Z8U>?ZHFcv!{K
zC>cM0b^I>q$;p3{V3&e%j#dm6fTWdFO-y1S!HKd3yaV4dmR<h>P+RXMCh|vk>!sbr
zKGsp=x3BxBrl{)<;tc?%6zXw&Qt-2`PXZQB?XpQ7Z05le;Q_BZ?LUwf;PHwzH3Cqm
z{O5;1Kt>FUgnk(NC168Hn1k_@m{;xgoeD$7+LFNM<{wmx&3reB&69(Zx0NifNs0Eu
zCq&aFU%Uf~;r2xfA9RA6(LDx-eGD(n)n-M%MszO}9E5G9S><syW?gey5k1f2`dYX%
z)0sna>=+y#(AZxPG}0&arg3$_mb5Z$u2O-d_x!kt*>6<|%1pnx1^q40x)mkt@|RJ3
z)wZYB8V6+kO}w{`%5P@&0<tt(9P6fE!0^m~b%g?5eEk6wnu2TrXooaOiHJ1N`)(L>
zJ>|y%MMTJeAM>Vc>mBB!h^}}mv-Sbs{N{!J)fvf6q*VfIqvHo}RMq9`%Rt0g6J)?Q
z?mtkt@3Ta-{g%O-lLS9jf#Q(Y3PjM+yMLvK0NKU2wsoFgg$in!R-5U4dzsny!=vEF
znm6hzWdO;rmsyG2OT@eQ=0v@}J?6TrrW#UI|7@b~wr*otu}b1|Eu=eR7G_`G#YT?>
z@LaG(?;qyZ;{<@tlO6wS#j*T<mSw3_eg5}r8&;+$OQwNfe6Z`9_eFiao3jK02F4Tr
z>5{i;tF<_zNyZ5quew0N7Y99%&VmRR@U=i27-{HDZ;NxG-O7us;aaq!2!Fl$<)9wt
zn|S_H2-$hj8GyEUZYPoX^VEw=By3C3$KjC<jD-DKk>Nr6GXx6$5~JhId+D~OkkJ5U
zo=qd|l#K45`zU(e=yZgSG}8zT_8Ti4&2BZWaSS+h0k!$bezgMc_02V!z`?tlM9~*u
z;0X9X0Jr+?qPxiUKOzLBAGaS53JOyU!+5ywMh)$jzkmW2VN8JDa%W!6+0vPx<)X79
z@i+g>dgr9^sh%35)CJ{&>sk#7yRt4UEwcSA<YN8IYv<&5Qg*|+TFYr6Eg2%etCsEW
zGngvFopwVx1cOv|7M3ci$KE8zLDD3F<|+#BaMx(G-<&Ypfn=3(6bIKf*|D#+^51tp
zxpoGlhToq-tNn;KR9<y1;0UK|K6X8f{s~4|rmO)qG@XDctOxIytkjJ*!){H^uAbP;
zRBO{T7<W^0x84o*z!Nz-0Tui}CbXD-DJ3bUZh~7}Aza&mV9>aULc;L5;-eaCz)Qd_
zXNDnm+VFhDRMWd~aWvIf@Q9;bE~8VzUd9e-BXfguC;ZXqOrVCt26L{sJEU37i*mN_
z$z#i$uKqXtC<Wmlh2Mj-6oZY)woMz3dJYN#P0LnBc&>6lV~%fFA;LFoZWWARe;l>~
z0or~N!1G(4?r>(nT>-xTAxz&!n+-nz6N*i&fH&pZRtGB`7={~$YkBXK6z93O3HCb4
zDg4GYe*_Q@;~H*4GGBF7rq$gyeVw{9+_sPJyiw}@%r!jFD{U4}iiogh)(<m^<dzgX
zY_&BQ;5|Wz;A(T6qxVlg|1dEfb1opM6c`S6^&Y)a=@GP#Xy3P9b$G9X#B_NInT$lL
zb`c?)4yPdfteGY6Y$cno;Oww01S6pTDgOvH2{L}dGh_$=EkXDv6?LsYuG-=`f4E@s
zm4W6CFr1{{Qy@@Yh%23SZThHYeV%bwzOv@hNL)0HOS*zZVQXuPevpMpFAFQZRmkiV
z?Eb#`(F<FSg@90JDQkfrdOn3x*t{=g?RnH9rTL>f$XZ&iHQNzgsV#0!PTRt!@8%$Q
z7Gl`Kpm~IAW5a!vsykwqr*ojVbS6o8n>jIG@LM?EaA#xpaLhK<!1%yk#V)6_Y8Knm
z?NMUsr1vhhGEgsNv+mc^-wH39zqJA8jEwf5R5T`(!{L8MIU2YMJlM$A_dx1Ic=9cv
zc{5$m^M8R`o&z`ph0zz!6AysTfl<2=ucOEJ@G7D}K1l5z1egwT@L3A&{6B3*6AnAV
zJMv)Q5#YVC$5N!IA-KcB&w;EooGrFZ0dS(56U8roD<SqGq^RJ4*s&%bqM~eiVM=eK
zOEA~lyV0i?KX}5E@xOtUVi$0i11@R9L(6!cu~*SppRDUiJo7f-y$*pfg}2c-%Y2|O
zJT(ykh>)I$9t5IWHWH{Hm8e`$W;Y!$xFbR-A}8%9P$9$w;+0;n$k}aCn{9;l%Sb$@
zBHbP444PD1)F+N1WYUR~*O_NAtBU*bQ^cV`WJy$H#)MZyE5^{2;jM(E_-c0-rH~x8
zNNlE>%z|_~g+L$)+shO}Xlqj6aBHYrd1Q(brn5C$jF54^r=m_iSN2u4LCWTlawR33
zyPjHhES(u#`2<1EW<*J!@vqj@>{{q89jO#$W0K{VVkbeulYN$F88zA`<kgu11jB3$
z)zrKE<mecQjnVG<qT*HV&xJ=TI^CD7t*vryC8~p@VoyGZj4`plze7jH8JjLLlJ)>O
z!bbGGuAPK8c9w3JUXj*a8rgy;rF_gkm-EDAcf+&QNGMB>xOthpZzMrpGeMdmeE_>#
zlqS-FHIb38-DlU6FGeUhdeB`!)ALo01qB$V&o3L}SbhZac8to)J|>qY?pdthByev%
zy>C(dHQb$mzfq*ed?)6CJHe#16Ns-NHEAnXdzf;wWXVW^P?wrux~S|>>e=qh^M?CW
zFyiH(+8!eErN2tDjz%Za&ej6T<DQU<X=!Y?mhqd?S?Wu7H73T-OUQ}Q{qv>@2oUQu
zse1Chd{m#my<npEn6Xu0qvA>HvMuNsq`EXBQh7o*qQON%$?9XA3jgqFze019s^~&Y
zNP5#urlvIHZKnfS+o`W>m**WrPqCbIYAq@Oj&*f~3#*Fw`#eD{i)7Dc_ugXshZzr~
z`KT#h{0F*?(6tT|vFc+b9ZF4dCn7da&2gK%GwR)}P5Uj9xtRIE=!sjltHB3DXkRrk
z&n%VBGs4=l<9)kCs~c$|5E;mfmKqwvL^gHD>d1LimyN-blks=49Px*p`_j3->b^Il
zom0}sYZ)JY_`LRR>d+*1-z#Dde)fck!8^&dy_W8$UMr)?fTk(8%CpYB(e5~x?vkvE
z+F{MoQR_jNq?W$W1-X<a%?its)(H(6;p_vDCowsp(G;~<q<_U|ylk<4da9AL_r5{Q
znuIi|Ku@K7@HpLhV*TKEBJ*Vh8Dr(Z=R%Ve;h@U)v3u&liEM<;P<!nVB9Jmeh)V2=
z@I?zTp=6#y7B{-QNQOv&FjanWT7LFXuK0Na-{C$FQ;qDe1_RKEm~d-{w*13+nnjWU
zjBad_rlKLneSh)cj22zsbK{AsH$|y!=2l7VKs5A(W>@kNcBjX+o9X4omq5+3LxM;5
z*~@733$y>mG#ki2`zeXiYEn5MoDl?-WvRqY)-~LoTe@m{(DX6JQSa&|RL2p2Pw=fa
zAg1O|8J%Hj3V4h8_VHLdXShzsGy-Jo#<X$#IY5w(!;3T+%9g81*tjV>r1qslw4{;n
z6NEA)CRWIjx`;@|pr?@kOMm9KDgpkM#3drSC6TV#FLy1In~(T<=t~E!ez3R^lD&nq
zX(tfJ#j;_$aA`gTX*DbaKe%ISx*5h_1<%r5>Sk4)%L=S@^N!yv#HYsml)HTtq9dMO
zvQrz_F#a&UoS;_Y?ZfsZ@i<x$v&<~!oLNE&@r)L_a>Bv%4KQ*s_Vz~Rn`V}P@rkUG
zx43<M`MSScP&}svC1JZ*O>aHm*LGxL-J5c*4s@Fa?NV-JhnjYx*IyE{$O@Mc1mpYM
z#1`XujYmN7P&SG+1>x8yjs1#fQM#8V+ffuni>fKgL2Rz`xHu>S<h*E}-Fx^cXY7;!
zZylFgH)+DGj9yW$N!1TVcm<?0YZ*yGHK6t~#?jq0VfYbm4|$LN-0O)K4A=zfd&auS
ztPi>fLk-AbuHJ|32HZz2Ahp@5r!zv5FHyRE?}@(niOp!x3xh!SxZP{YciwY?NT?a-
zrZkgesVE*05XgUD$+<{VCq#ih^=AlK(RJ!^w)4v~La7;NdICow5XrI>9gWD?@tDV=
zFO8epDFxCVhZLq4UHS=YpR+SUHR9&g_|C>7#q$;$soM=mOIk`Q^^_SK7>wqb8B2wB
zg+%DD8T8QuS^Ty9N-?~b-<I^$zL3lFCNp)ClvF=zFRo*o+0X7u%rqcP`NCn5Cdo81
zG1Go`;M-HHc}6=vg{hr+>Z&-lZ0%0E5#AjliOv}&!WhZvgLJ#LIK+dfiv^L`TJ^MQ
zq9i?pSx7~hH=l_fwHpz6g}-7|ytG>!6a6sLn8^dpxyqUFFM4wleMU)a@<OMgKFs|7
z8N_rWY?%$|0&k~Cp;MgIBTUN^Y+X$~gvNCZpl)7?kEWU;apVI+NwBp9mQ*S?*Kr4w
zwS*WsC@_vPG`)mPFJ@Ppt|L=IT9Zlp>BqL=yQz<fUxGMa-WB%UY{C5ETxhrFf)u<2
z$ukUBdBGEQ?y3<^fiMJWjEPMDw1hfzn5NUIn{C18xq5$bO{Ml*#Hqqhj@^Ah<V%#i
zF9Pb^#ss7ZnE15FQRx!*pv3KD(#2ZxF=Rup_HDn|6Q6)i%HtT&t-1`eMHj>5z6acx
z+@g_0wR=Q#Z%rhZo2B2<P|G5uhwtw{*eqhCO4mA!0w%xj0>LuvS<l=AWf{JdetT*y
zYGUnFUM;d3qVzmhJ(|i)a!3!I$F5s3EhiR1NT)@HO^FcIk*JiE&%MK`H4_8U1M$YH
z$&kywB5mM*Brp3&vcp@2F_BKRjo84zzKr^^|8UjSpG_4+$0xzdrbqmCnsb+&Ex4L&
z#;rmdN*+iiONj__r;#?A1ubVy|4X-W^qvm;ai1Q_OFqU~nWUCGNsJb(!5R?0qM#c|
z$OB1a=I7GBe8yX4%uDDAlB8jJg1ABCo9!A)l;}yc=MaA(RJNneaVN=iH)h-2C&#2p
z$X~g3CezWcs^u8BGf9Z~l9T0s!Ng4p$RL=OX_d_@`+}xGI&o#lOzB-?<*GQkaCT}c
zzC1G;WzA$_hD3>?Mm5#nr<=)~VhEZFGBL}h7&$^-0)hus6QE^H>Lnr(z0Mv6VJMZc
zK+KqIX*?&@jHYr%s6!>EmMQszs%$=YULA6IdXW!ub0G6c<;78Iorj=oV`&PIgp7dz
z<EgA1)N{w0gj<_4avoIrxJV|8csW_1Iw{3Gfm~!>;^jz@l@wFuGZxG+$Z}Z2;Y8F-
zFs?zc!E!amN$)FKZ-_7}&y14kj!^^A4CqNB$l*8Wf75vA34kFyL?8k}5)$AS0s;yG
z5Ivs^9WaiEHxTtUwz6Y!WEcJao5n*#PXML^SUA9GpWJ=}zU7FfiT;Pi8UxS}Me!f#
zkDB_d1g4*N!=}aC2f*3^Kz7X*Mj3bat>UH)@_Y)0E5*{{?Sa6+d1NW}^&3HowW?^U
z^JvsGfao#I?*ta)0RM9n;A`J?-a$p;^h=KwqpyX7M_5Y%Sf3axxtUuH#s(k(caTaQ
zOw$?qy&tO&{&#MCsHG09zJR~RVo(P_L|b2xm1R3ZgYgIf!<pg@kI(I~>^Ilj#WI!3
zWt-sZ-$)7FQV-?!SCKHh5cZMY+K&Ixiiz|uDWKi}pp5ONRB?3P+hc>j{{sc1!m(j$
zn+)M#94-FdORUby)$w7(Q0k{K0NF=&Z(YM~7x043X{Hhb`1>%!)c-)|ksZQ7!sOA;
ztlxj2!&fE+Z#a8^HY+eZu(z@3f85=hhu1^6I0Oa&NqaEd6wdw-5&^q~jpJp1^({H<
zRvY5nF#a{D<^Rh-odsVX?QBMElOcdu!;<`MYk5>_CAQnuGa}7(e{X4FA{w(j0pV79
zbJPO(^W&aWL(1O4_xkI>sjl4-A=nFwQ=cT$JuuFD)&n(!8&@-6E5K9u*oUKE7CboX
z4GMR=kIm}?PE@7*KcEi&>TnZ`i^8drOdvKs0b~?#tOu!bbK4`?Z#c7P$NvNU)l1qb
zf#F~i5wIAvbLJbaejJ}yfju|@6OGMO_1o!@h^=s}S@;j+k3wVEf9|Vd;ghAiEZ9K*
zR2;`)G{&d)vudpQp)CwJLEe~hV-5~Ak3BGpz5h^}KN?t#$rJi{2+zT5*HN5Y$?SvN
z^y^#z^bZ<*O-#_u#dB*ax0ob^J@9kb#rdIdWMLY@C;+BxdI%;(Uk}3Ksk|lsIYi?|
zz!}}!L9>9lonDw6N>h)f0H>+Fp1z94!fwNXI0*I;jtqT-+;+j={0I6;Re<Na%o@Lj
z#Q<-`7=6Q&3dRD`OE%B{`aU34eg)fjsZEq8aA;Zofxzlhz(b^i-=Kf@4aR2|e9M^T
z1$<RWkp*ugaO^zz6jNYi)^`jb0<F>LZH;K)S{!nduG?wtZSO{TtbGB5|HT_lK-}d<
z7POF2;Eb!+;9C(xp;yJ{fZ#8v0e|?Q6gQ)>0XzS<d>OF6<F|WQTA+yuAY8x;MU!c0
z_&tI|UjWxYj-m@#=#5lq6i4*+5g@ya;B=hd8-9P_RrMo*hJD7s!^P0)Xq+u-2oD{?
z`K)Y{M&DY&F~?Z)bB~E@oDU!zQvWw<i}3%w+=m_8$tA|>k!__rrm^tuo0jr`&k=1c
z5)$_%J}s(q0f#{rkKCv8dMgp^1%JCzUmAEE7)T%x?2nNurA>J2KZevg4+cb=P&GFo
z&0})x5IYA>i0JS8^x}Q8nnbGG6*&c%aU~jSO#JWi)D0yr^8zh!^5~OSwqG~7;utXf
zL@lD5b|)fU?T61W5BL3{Tl&|bIO^Q5syXg+D3z|`-}82Ueu%bOg-IJMhudX0l`u-~
z?GE}*##^b$`YgOUUZQi`KF%Li%pXnaSxTe)sh``cJmCN%Rbw}4NwWC-cxB)_WYPs!
z6tzt>=+`~@eqihnri2(+aQ3{;u@^*R6=xwZMOsMi3yP=(#V|QOO!PGlyQ|^fZCLZF
zKs5y8d5vis8Hzz;n)|^-_a+ghO50iX$3Q&)bnJWa9^FDQBLWud!ow~6y&q@K>2SpT
zS^viZZSdSI|GZ~B0GAQLs~9!mA}|s5jZbOYSOK0J5H@EGzf#UuJjswu9B?^hu-l3}
zirgNqKi*Ggg->kbzi&v$Ag2XXuy?wK)fXmu{fAOB{r>2!OuqTFfD}N1>HW7;NebSO
z>boR{kENb~xec-1QQNe(s7CiFFe5v1gk&v!Tt)HKrf~7(&WTe73@=so=FXp401Pok
z*=uk|{WFsEPn!4XDWXlm(PrW_3&9B}s!|xdd@yWX-D62@VXT;RniHbYAte(~N_~5~
zx9rS)gvzSF(>ba5hHR^F)ZL+Afrs?|B@WnDG{O0GH=?t9Tt#wPdUFui^Dv(E>o$cV
zl>m+dyTEx*z@TevxeY$(licfx21wp;r2QVtzJ^b|B{mo#J=hXQ@b9fmlzysmc)*vQ
z*%#`+mnQ^@fLoT#Jrv*UkCEtHpeRyWfQ_|d3QYcCH+RmmG{U9=l@0EE_#&OeW(L8L
zv`^NfhT_#;Zw`fzwJEw4tVdw(0qg>Yq#BaLkIO|P^ax)1bk^L3Tf#=jVc_4*TMeJe
z<A6P5wbvX`rY?Z|X4z8AeR8z_dj=Y7&eAV+*GTh{yq9g@jywJCvGihE(ho;ge6GK<
z@o8dSqT3&xf>Q;@r@8f`awJ>*XZ^7Sp?pz--IDt_E^H|{*&nCwaKAg}dhmsV-%w1}
zLsL9Iggam^{C(1^P48KWO?CCzXizlEnA_gbn+~Zo|L$+EwrMJ1BLxwK<Nw6i{KSm~
zx`t8e2B^2_pw~wz;iQT6!{E8jS>dRMipFfKIARzN?bzT!>G!2mke1w9aWGH9Ww(P@
z($ZEphkrK@yOQeR*j)QrwxOq893od}fq@3h7MU%e+&LFDhPu;@=NtrM4NSTbr`$Zl
zWqR?%os+WkKOsZ0I6kZ#9GexczA(xY>tAYk6&5e)69z{y)VHM-tWP>?NQ9GB2kbaR
zv%i?!JuwDGFTWv0E-)Iak)>!tMn_GH&av0Y>6qQE0?4GIJ@*yKTCvaPh@p&tv}g(}
z$Nj;mvB{<1%e14DiVGEgP$<|)Z9kTl`pT#1WZkbbn&!IuA4CJ3!W~XKT6!3KN}H^t
zAHRoXEd_IL9@&gvqj^#=S;LNyh@8Mft#Z=0la0r7g*LM+5cYEjo>T?=Z}8XP5zywU
z7#35$-;t}%?v)$Rb0)+is|KdRviwg62Tftr&L<|;fCeKu1!`md-DA=?Q<oS#{{kMc
zrc+**t{cd0cqxHku1ryntlFJ;@O585PQUm+&<c(izMXI{$rU4U1wk*b^x5T2snO%a
zgH<tDzye60u8rFJB<}=8*?D#Ib}dhSKa8SM&YHL$;^x8AABFivqggh*a5NTU(wlp~
zJtr9eRV@VAjA<}Y&aYgrTuOE+mj{B$sB{?a)7ohCzjAIKoOMe?_n51MWHqXOFPaoE
zmfN)Wk=<S3pBQMV_*vD|U%w{rJc*J5te5~sZ2_-}#Bx(RaOv_Spiy3*{BokHPLCGq
zftkl+LvnznfVA_zp~LB?VpSp6rQlm7;C5*E^zx+O*u4gm0g4s{n_ztuG)=a<p!6N*
zlks-f2Pg`NQ}@}i!`;a?P31}K1ukUA|J<VQgH*h;{x~w^cN|gnO~Io#tmtwcTo%56
zj#qtFZF^#yCk8P7rYwfAjLz3!EWOVzz^lh};G}@>ainW5VK$ly*g7W${Er9lpZ3+S
zVR+%-?~vnNb(y2N%P7;VtOPiQ3r~jT<{rf1X;ZKMzsZnv<SM554w6?MYU&?k4xNwV
z!p|#3Rksuc*mY+Y*fqJQ=RO;b*pd<d%A&vAGI*EU=S+t9a_y^AQLeT=*rr~1X<pPS
zQ*kiD^$u|kY5#8A8!}5B#$*6h%rST<!te+6)!h-}$;*CT`5ck#kI~wT3wC$CsiV7d
zQ(X{xQcY14n;9V@n>s&+M9oE}z3WFFZfJ;)3C!O8LI;}9vC>U({>DHU1*xeSGD2gz
zEeU8E2_Iy-Pqc;62_wb5g#%L?<AmP)+~pSMOrN{2y(dV@{P=15Be5wu1}{$P=?RRb
zNn!EHT%_|w>WLu6E!oKjo;`}QAi~_YiWd8A&Ms$`vIa7nKjF<{^Y`|Z-%N@8OfxQ}
zm)b?EYeN_cPUFWQpK4|T-O04*z|0jvYEGba%<8<pScd5NR0B`2t0A(0_>DvACESMn
z(uWs;JQlKV>&W7=U>(OGr^lkcYG*Evv&##A#r6^hYZEx_J&7v;(}q0Yd2TjcSX|RD
z6hoSjyXk2b{BdM7V;L!ulF1#`Zpbge;7MInDYhWuL>|ATqqmKCmse1s-_M&KyH+aN
zziXAPtlItpuQ=s(R!6AzU10VV*_fN-5a&j9>x*Rz;YT&uDb6j-4~&Igtz<rXD^t%R
znc!rma=U^uZWfJwjZj&~4$3u?)?BxIg!7v{q0mi{+jJ-TTw$KWv&vW2(3CnT!p8Np
zWSn-yqCMB!I`=Sj-#O>!_4_dd<F=YjirVK57H;nnPP1u4Y{LM>t8W-JeMOxS*y_cD
z%MjR?_M~JOi%^v>VN~#kygJ&g;&_!H3{n^t9uuf`Wi@YjSg*iBW=dgI5@EQ-`LBvk
zcCbm|6;~?s&u);_ao1@R-FoiR$9KL;uzju3k4~eSRd;?cbW)KA<HT4~EU)f_ub8PZ
zYRM-2$WFz$b9NJYZd(%rO`<JAvM$|psSaBNts3WrBK(G=G4u|c2uERel@<magg0jz
z+lz(buA|R=FSD5WiQk=dCMczxbWA0$I;sdKH7W--MbI|!R;si3bkrF#R8mGUyRIe+
zNZre4kU#3JiR4<mN3Y1}mU?iQaC2QXquM8{i|(e|i9hU{)uA|?E6U-2eBx8w#J=7>
zOa{+R{?nJ+>$P^Ysv@UAC*0V~k9yP6%A(HkGC}T8xuqfaSDkD=yinaL-2+)Tq2`|+
z4lcRE>XGzzrtJUn6F1k%N^YX~dd@drsvZ+jm2_L1oX9Yn3#TX?QV?w)IG`Id<~leT
zo%Ou^(xY(}<E|FjH<Fs#a8QuxE;j7R>N5*n-D`Cq-WAVlr1>H`;!IKV{eE!QtMrAF
z>3m4L@Fy6vG}@i>Ygl{V=aqQ6SN)+`O})u?)1pJml+DiZOg5T6-q^U8iMrFAHNKB)
zx+Xh6<+WN-s3TNq!+j$lm*%n$M`NK)m1f^KuWxF;Et~IOy4u<QuIg%uzy~N2iMpoj
zS@spzrygdi_f!&b|4qr9f3i4z>6HN4oPuecjMTtFbYCD87>wqYWRlT_4_H^FJDmTu
z?_x_;lF?BQqjGfv2VYR*rL<Pb(h==7K<2geXxW}))Lpvd=ukJwe>LOfVt>P@J$vVA
zI$;!9_d`D4^i|l4OUwazdLnQ$0-jZYhr$R#<zi&#DYj|H7-yvn{+rauV!<lqYsp*y
zrn>*rJG_~9>ZrF-;9YPqjfu(UGS*mkMp4!*#N#I>Pw-S(0@!vnxX$#TZ%u-2+^6il
zlAQJ^lQHtw)~aL(;$xUKxh{o?4Y}Hee4s1$VMw#O5=JX{WYO!fhS0cC^=iVq>+5z3
z<zNfa&)zE}9`EW4S1E$*h~sc}2jh@lxd63G&2Wt$qU~Fiyth?N_qG*{)6X!G0yq=D
zr~euw)_e3!OMl*Aj8w$`HL~kJsMKP6$pT67K;+FCLE7}P``0I%a_No6>%mY1W9|T!
zaY$$IkbIZqWUIux1dOvA{7QjN^G7qg9P-o=ebSO$8n2IGahrogNJaD?_7(3nR-NOy
zBE>?IZ5Spw1#P?BL`)msr!3@{(Sd@3pyF4q9=aNheCf$?gAYW4qBNd<NEX?9FJ>G+
zsYFN$1XA^MnFY}3DC6o?JAyPKC6bg}c_NgR%p^L(H90~uH@uXdXEb4ay^f=oT_>63
zbz-||<jG8?hIcW=TGJM|^sa%dRlkalwWjj-8ipS4p$!B@$exJf-oGYEc5;zfm5y+)
z$$#xR{5-5Z%66mV^GzZdoyDmGiF?qRn6Zxpe@0)}IhuJ!ey!fL)3SU_#h;FLB$8tB
zeh8^px~Vld8VX?)`7y%Zl(K!^E4^Tn?4;4mHf}<{F+Cv4#wfL#P)vZasC{{uJ#w^4
z(Cj8?Cpo}AzLtX7E}=>8_*Q?fzJ#vZ{TKNj6za!9C@R@?YtMx|wy;Gac)#fsi)SnS
zZXOr(NL9bvJJ~97wuf!li%X`e<D=maR=oV^YB<0!Bau7jZ%!m4bKJ6CVrAM=Nqp0y
zd^*2gG&ALfag~03WoPi_XOcXX@@Z4$2#e3gcN3e50ea7z+`U!jd#BKiKvAJLR`qIE
zb{1%i88SLV{OYE0uX!jnu9P#7#V$L{;y5+=<jVbEGK(oU0Kdu;;O|8sp*3S6^6<>W
zOeoItH!y)OrJT;WLgjSycVo=hEHF-+^ud^Wr<Uac+>D%fAuUCpKSj3~D6o~AwBC6_
zEO)wP!3Kj0P)1aw{y<i-x&C}o80W%DEr0avagdO|fez8O^>l9JjHKM+BY0-jFlO+_
zPg1h)OV3hO+C@T7{s^l%8}cobs4VG2wY+6DJNC)@xAA6>gH`f)G^>7c7VABBn%0uf
zXtm|B?It5p7BbX5DB<)Fs}R4Km17o5lxtb6o8079zG{GC5Wln9ig3jn$~;%?h$@Nl
zy;53gWR>HBqM6q}v0Mfvi_Ejpz(J0w-z1je^)@6~T+Fmw>-UC2tcx<f78#aDtqNxM
zs^OL>%#)KE1v6c1I>t^~yhSW^!WyUMXNl-X5Rqy)C(o;(d#2FX8gs}|7K?f2bkj`e
zZ1=NZ)mo1Q7UtjcW^>!;P;Jh^c1T^3wp$=xXd20PI6@M4Pm0*!Y3JCZ+>;sU(3@sD
zEl&F`5ws4^hC7=8H_j2HsMyB_<t#qdmgOtS6pzb-mSb<tzj16Ccv|b0Q51JMWrag~
z1KjsYJHh`tb>69VAi2EC7%v|NuCjyn;4!l|){toVMIseA`Ab4Dbc1ED?T>H%7<D1P
z;59NE^pGG!z>@gU-bS$f6liR_#2-dZ69xFXDIoI8e1XxZ>Mie-mujf!ko%Mjk%Jr{
zVY=+|`Iy{C`t&|Fgyh4u#MPA^@DTb1g@;67X2TIPD!dnX_4Olbq;6(YuH&O9>lZD@
zy0SGNyl953BBNIH@13+?Y!b-TGrH;G-~!HSQP!L)OMY(?5wq)$bb}%Pgv-UuvW$TS
z^(@Ymkby#PVG%#7gKxUVjqR>qD=8PO1!CXXxpB)jEHlKLL@UL-zZ<U2woBO0PLsV+
z!rB&^#4Nxge-HFEIBbg3#~v<vOCx4~sM<B~_}4?3c=zF01W0#&<qtdB#tMy$bC;x^
z?2{%W>+Ep1>jLtZzNywgI@t7hLh^`G(ncfP32XF!!_9b_!UCZx4Ab893XNw2Z|M~b
zlx|m*UvSf3w}1U=&Uhy|HtOTJjlC;60+RttycB!{TnA^erw4>#W?HMOOj<q5L8f7L
zZkRwmy!h>eb&^W?`FJciO`>D^q;lL#bkDtBU=Q&lm(1(J)P`o{(t;1HqUjOd_ke7n
ziO%4)g=zVh-Wa8=$D)brqdHfH1f~VQsSX#44(mLl!}B7)neb+p-+ko;ip8EcFC4Av
z)vq-<jpanqPL@D<2<fj?uO(6imzmMjij>T)u;oZ**TD0t)?FUu@7DD)*z5K_ZSnzy
z^2K*zG-mxesh8)&bd2kJsE?aj-Wam;ckD3+hCq4ss5S2u_OMd7Xu-OsuVK8x^<K$%
z@6WHlpjgov#;y^g2&FP)ncZ?w0FR;p-)9p)Zq=4AXte%Y!C&WNee3{+n2|oefK;VQ
zso9$ptJEyb+&ruC(y~`^^Lu;&>0v8wV4FnbxBM2i(jY!5@_f^xdG1u%szmwMvB=BE
zPR)|yC=_kCySVhuJEh9t%u1NO*E~glDJbU16MTfwILMJr$YjtLUL{A#N3HOAIMd!%
z^SsnJW~iY?RPi04bx$8&cY<g7-mazn5r)USh20;!#kT70AfM){Xn3~|MF0Bc>eDaR
zZOo5|T7HS?w=Mvt`fx70<jgsB(uNrN9VvR;I~=28vCte%oTt6>GP$HEcIVr*J3rN!
zVNyyESy<pbtvr6k@hPW2O70z7X#z=MN7+}*Fq$a)sqbnV9)BBjPEa<u;qNtz#5e5=
z0*HGCi5LTZ_O_LG2Vc*)jhpzpW|8q1pVwaA^vR~YdFI9K<Axk1!|26iCH>JknVZE*
zW#iP$Lk<JAKw15xT2xzk73B?NdQH>prMy~OcE1i|XC*Zk*jaSHFUb4!Fb@5F<+tCw
zfojpMq{~o3Hq(ca*Kc2cz_KvXyE*bc4TkCx{5wX6nwfFtcASdy33<u=g;bl8Jd~_Q
zSSdaz>b<EGEoKYE9DXDnez&$Xl$4w`$(*W`DS=1{R_Jg=b1g9xfkQnDE9*oCU)+Ov
zkrDk^`IHyvBTavBK8`fyK68>(^&aM~FOlXerSvvv5e=W!w!P@r*0^ai9PZRDImZ-5
zv}eVaB)mO*HUTs$;-fd0#mGfvsjr!u>O<-I@6B4!YBcyHhbfZG@Fsr=xAhcCmU?m1
z(Q2uAF{wmP{rwb9v_c@=iC3=37nK|(ia(#;uilW{Mg;MCTr!F7^}Ud=iu(!oeK#q=
z>H}j6|Ch!H0>%c0x<a_m)zsJX+xVgu(pa|_Y-l>rA*F%aT;>-f?(?ZIRV^`W$Mk~b
zhtxMgJsU97u+Zhs3Qv_=XEUS5FWm+g>IKdZPK9E;cvew{JtvLCd+!^{Up47^!iW+3
zEN`Cj;q*2V)cj-8WIu$)ac)lSlgZ!9QHP)HEBn=yVLy*c;>859uc~r*ZU?F!N2JLb
zWHf}Uv`k#zI`JB5_>sG}NDI6Y`l<S?CVZ9Rh3S39tb1!AhuS=c3!r~M8yCbw!YC_z
zd<5Te9|76t&Iz-m%x2O@*EQN*g1S>apF0H$ed%c=J0ay_`2v@$rQWKazY(alJ~bFn
z3__^fh5G-a%i(s{&pLq<_*X>SpdX16aau_ns6+WXe1%Fha^~w&v)<!R72`Z#m_3I|
zPvM^y=;m&Gk8awJpOHToUvj(uP{gz9)Dl)L?roSsZ5!vxsAR39vIW6jKV6Btly|KN
zF9V9<AL&@nURTTMtg<ulu-g?iuY_7m1Ro&BTXLtaTvVBaSR<)uLJlQ&1s)&Jo%b>b
zP1=b4r8(tSc~!y7n`I?8em#@^`?!MiwE{P-#X97?m^E+pPsFB3oFFPNbjg?`W~xpi
zreL7Vle*ajZfZaPa06wH*{xZJtxiwf0Oj4@POhJFbZgBP^!Q5*R#XomJDFWU+Ay}^
zIvx_WNY7EB`3e*EfL6gUi6uaKc`MeE!OAsx5$-vzax&<s0uB{W_dX!jF~}8p%%b&t
zm{g6GbP0p3A(D^JLN0uW+MqM->lTiJLp{`%e{q_>ME@MfNjzPZ(p^CA?q7QE@<f@5
zcK}uBkotR?mzpti2)1e;e)<oEE6beKYfXmF&+9?7PG4PrDRg!ac~QgQblz`*rh2h`
zf(10*I~!;wicGN-Z}<4e@I2qi&ML^iGyw{jyx~~6YK;I>Q3i}Dhf^nHjIGfy=oLCp
z<;sE!_v2m$1|gf-cNv*y^l;sK;iy?$S>5Tr7o9%)Y7Xf_;1f3~tD>rRcMs)dSzyu~
z_dGTr#2U&krHbFzN@r>Gy^mmO&K(5HSMNVIr9GT*Q;9{p6wByVqvJ5w1XB>ye;|41
z;v9{kh439T-9PjFSY_F)wGe%VZ1%HPt>b(x<i`)5l@QGqk6$4kK4qr38|sk|5ep7?
z`R%#m*&$2L9Z{1cJX6I@G|BQJVjl?u+NJrpo%fhrFiSHwo=^AT0$(BPv>ryhY6)sl
zTmM);tLg2@B~K{CG9D<yxw8A@%`uCI#&bokwhtr7Tr%z#m;Vm{%0M;0KX+@u4lJO~
zL&cFZv`Y^2Svi&fX9Fn8(<DlXp<{sNxtWpFG$-HjMP<6+e68a=C3(KSmcCnBcy?E#
z`KaG3Q7m=n2>jXP=K-jD_P?t(S#YORgn*S^-~22Ayg@U)c%yLcA*gabX(6Kz&BTX;
zX@TJFm|}kF5P!V9X{$&vqwca#^00`B&gl?Ff#QATbP3DniVt1!P$0Wi;ppSV5$mpN
ztjY6HFuL_XIQyUka{XE0bLy)q6Z73XAB!gpercLBRz>D6CrcHGqqAjVZ2VaWc7#M{
zD+nZdrt`Gr2{vHPd8-AQBMthej@vvG&x^)o<0h#VCwZwdW%bciR7C#hry!|@V45sU
z65c>xpA=YLeO`6P&E!t(za?EEcaW&^2gcaqa<dHPygWzl%UnkliP<FL`4j6dD;Q<l
znXC=v-HXDBFdXdS$|QW=-+U#4D1iPFN+gDGEF<@qQ?im{_2c59649bOnML}BWAi`8
z#9opTe`=4?9AmTA+@S^IhVT|}_2>NeaX|qsJN5oWNNog@zEvp8H8Bq`{{R<;jIc0y
z`5%f1^#K@;jY-U4Zbwrd>vm5eaPm<Yizaz#h`NZG1~iZ%0(jY@SVCVeihPimj4m$-
zkf15FG`ItlRCh)J&U0Yk6sKcnMn}=MdCoy0A>xdKTM;E>RJKS<0dXC@)+b*z$RzMQ
zS>K^Ta`3TYIhc5}WcTNaVg!u?uzT(*@FZfJR{}|Tu){~1sgAj-k*tZ}a8MRF@l+WA
zP-ViLH${UG8!tz?<wXhLJ<_Y8kq2}e9@#UGaaqP0hspTEo67@D0rd4<V!l$pUc9${
zh4DYypA^AS`&Xo-9lIF!KvrIaNB-0y!VAk(o*TT-kYO!BNZZhH>Vbn$xNu(^VhQ8T
z)@Aiwc#j>`oi<)?gqUuvc}=K&mQLk7SqS2NQb)}3btm|$8Swrri0?kBIw!@RXRj5W
zG#rtk$|OD1G(^UGxZL;EgueaQ19@)|Ir*BL%;%18GI*@R!&rorOt<Bm?3;JP`mlh0
zyjG+g^w8;L+sAa)BpzziTkhf%Vc*4<#MD=1Jv9Ro*#do0kiQM(rRK}W6kQB=YdCMV
z$;pUqCg-rRM&g7oL3)Lt4q56fCpK&V0zM{$rHF0-TJaopSPa4LW{v6hWtw%F)ouwq
zlV&9Hb60`~#{O%bL{;&YKWNfU=yfpIV=U49ag=E%SxR_-vuV6M{8x!jAM7i;75)&K
zCqyP_UtVvAj3F<%;w){${2c!PRgn7^m!$6R39L-{>a&R|iY)*U)tr|DdY>dw5<w(1
zIpUBA=@I&%6oZ4)LBk#y=7CaR&otmfCQ?2kP}8v*bCXvn6pe1s!a3@^Vu&x|j4?3z
z$)_f0n-Xzjda;J%j;x7dBl)o~$FCH^0C=G8Z&gi@>$=Tg7{fC{o$8H{X6;5}5OuC_
zLs{B5ae5iNQG=yJERl+w*efxK=eDJ=Lm~3TfRas~Dz=_m!;$jXI6n73-HvQ2s4l=n
zzs~6S&=No$_D}BVhO95rM@O&CNZ|Td%Lsyub&KWy09I2X1;jj`RF{SiS?&3<6IKN(
z8m%EdSme4Tu?Mte7(_zz;)sqRhSPQk;=V^(*-c^jqvU#9zz%vWxOMa5j05hos27XC
z>8~d<R`U_nk^{djXN=0Yfk3?#MEP+?{f*Wy=E$mR^TlJ5LTu85kCV+Qa6DAQi7Nzk
z_qT;5c^fbipyQaCvj%#bj<H{H$8~^kqCg7KM}IK|U4ySwgfjEvFI@e!>~z??PN1Xn
zUuoWK<<fG*3B5LSC;tE}^nNKFfgY^73EZL?Wc%i*>VLZ}yjPg#W`VOA#MBbRe^rMI
zKFV{4oU8*pE12Z-bQvD%4S)MVurE;07n|a?j3*OSfW*C50((7Fg$~TD!N($R=BHKU
z3Jzg-czt-YNKYbj^Ti05TslhALT%5Pqa-XHo}sZj*bXT~gdj+NAY`C9f;aL3{%Gv*
zP$nnMH9$x^KHmvu6etJwr3nTO4&$b?=-W(v{^*|~Lwc}#2k2On7_TLhAcr0%h6NaB
z&4uVJeANDv5#q$kLmXIeg~YHJ`!iNFAFuK%dAv@h_>Eb`6rtQ8yIZsuW4<A&gh@Us
z86Ou&Bj!4#gW+eoCxeWz?O21gqnZg(WDt}C$yDgVC%!2xM3V&N+nDv;luvyZ5hO{@
zk1c=Aut<5NP$((>Z5k1FdvqR-T1<45wL5>JqglIHdOm9mO_2ngJouj0#)1+(`m)bp
zCL`>siHVd!@!MiC1LUxEIYIrrSQ|*mA@}CG(-<)~F9-8qAR)aFllIdogN*fabq96`
zhX*ZKE$5k`OurWb5+<TUh^jiCY8m;fu<5YtC<DKmy|s^2$7P-hM5z5#PS~3h!Nmxb
zf89Z~hHPZTX!T=b`KF(nB6#|<L)~CM%J>b617(+pu#x-9Kxa;>b*%4-NFENcmQ<Ko
zr5Tb_ig7yeL{lfleosDT_Y;#?gfES(femBaP}VcqU*MrVXrPb>^L}-bQxzkJd)jt&
zyjDg|^qXO%;)lgZJW1V<IA?uVay^q!z=7_lG>fT?>|OC?T>aQm8J@}@E<IOH1Z|kB
zyfqC^yEMbkG!Vf%o2(neSneYSHLsijnOspl#Q`E+3;ooKiO6{MOc!8(Rz0}95%<^C
zE>Sd}h<pBOgs{yi_-Hy*h+6okZ0w8=iac1$hssxRCzCzI!C=<|AoVZi=n_(C6Wua}
zbJOB#(U4W~4=GSMlz57dQG}ZNsUj^g1a%fnP~lE)=)uD~SS36^)dNTa$xOm`2O-$7
zG%_kmLCby8;A{!=R@3;Q{{Y(RI20e<A&`>HqVa%-4?A-MTE-Sa=z=Rmr1xXbAuRr?
zl)rANssn01^0MP#K3K{qM2f-1NLmoZ4q^!;uz(kJ;|MX&H8O((^7y)vpyW&9?errj
z+vlqS+>vq|6Vd0*U4t+lLxZZyLFfQ@pO~8h9Janb=+Q88a?L?v=_PLo4HXol87D86
z=z}}}JjX?o#GqL{rAWj|l1C)<(tF_U%Hbdo1Hsz$>8v@3C_GPfk<KWiJ?oT$Ir_YZ
z>cScECQxBfIO3KEjvnjmKbs>nN+HZw+=27m0So79?;fj6&s<r<iOV2JM>BYWlc=kk
zr4&6lYAh6r0+1*K8T?a)0`SbFXBI@4y9vpNphGFuL?bGPI_{}swF?X%rr>yzsnq2e
zKsV=>s+_k;0rNm1BNZTHdcB(NDH#<Rj~}{Y)c1TGnTjS6?sDjkSA?kKOXiHA`O4>+
zf+%JOm`W+GPj&J*oKSg_)rm#pimIF%giLi-1D@iOUz)&4Tw{K$F~6I$iT)`hYb>PC
z?xN1~tW!?B@5PKbo2+^`^LAjuzy?oceOP~+;7PjiK*O%NEaV0mpB8M<(c*~~Z%o-1
zeh}x~fc+th{!Zk);qn*A6OXd93&CXapZiNbnxXqXHmD58V4oyd{&-@RdV4CLqd8Cf
zryms(>zTxRUVTv{#vvH4!J-Gb9-gX@zzLm0@kOAKMyz2(R63k?lZIHw?xEEaK<O|c
ziBI=M)HFDV%OnDtJiPsPK%EH(#lWbF1a}Es;F>)wDg|KkTzD8x+2{GHpz;$P9x|dr
zvZu{<-T)ZQz?q~fQ!?%7>WNe&4-Qs?cu&Jxhs1|*V#WkKxR%aW%ewf95gslmg24Iz
z05a+odj)VY=vU^$;gX)mkE&ryK$9<U{;G7U1BXKQ@Uyf)vH<Dgu}H%t{JahCxl0GN
z?XeQfseb7Y0r6se{80*237_!V$?XRPjhJ#`zFsV8xd<2e4{lOH_(}YDJ#q6{MC9gs
zFU2LNfWLdxaZ#0NJjPGv!u%cgN#sF**oL##)M?waq)2JwFL70ey}gb*zH^PsADW<r
zlbVN?w`Qr6hn=g~ie0|jAw2oGxgM&Aob}xe<|o|>Aep8~L~}<VnfFdY>l^NztjCta
zA5T}5o_G?5?jERGKk}%=nr|J}oaN$gj8okuUXN8N-}OnE33+;oq-!sm89aEd@!w5x
z1z@_4pFC6sNl!Pt6>#-nAaO`I_^JcCgADvw7Jlxqn)Urto#J}3ayjm(iQCQ3-<lDL
z_QhagXO3>5^5)JW9wVy<Ecmc2GyblXP!m_9@n%|{V5+k`d#r<l1E%t8J1N-u3Tq8~
z!kACSmP*u-%swbc=pG8mig>92+^|pkrW6oHm)sCm2EH<1DbK2Y;9M;1xIN{HgdH_k
z%Rs%IYWMH%k0*Id{MLD(kS46fBk@Gw7b<hU>%w6om{TL+=Ae#z>GeU9`fa#RF&udK
zvKAmg#&<>#7Qo}Zs9u3~C)In*Ne3U~Gz?4?h)mlcl7NU5O?fPX6VlC~z{go(*oBDb
z>8l}d2=Nz0hNdKL5W++AW)~5Hcg>&KA+H}aW&}!2Mj7tN0Ii=B{{WT%pJ)Wn$Np6T
zCNltM`zm?BQ26m!056w*YZ(zG<u9xVz;i=EKm(V$acBpLNq&wGKNUE!0XzVH3V`wq
zDJL`Q{PRU9-h1Ke-+vd<SS<elZ<>@@=gfE~ua6c747-q*Rh?fp0z-t1d{DG5qP{Nt
z?hu2lnvsplPoUd2NDx5xDE==3Omllb*;@ic4ixZoJr)WHf)nsOQxqDEvh*H)Y>)?S
zOO@!o!m;=Bc7c37O;OC{07<U2SE%zxb_e%dGI8C6!nQn(vHt+eD6SuO%C9k8a0hn`
zVOXX&zf?6zSfFR*u?N>xaRkZVc1K6kNL+Gxp!1-oRq%KDrttc%WOK!GK+Jqq?IpoF
z(4HlZT7(7lvov6=iRPMuPnPoHN#cZOk|{4wJ=9&P)iL$v;a+2gunx_hBk4p=5(=Y4
z*}_~(c|N9+_mxb7B96~--FTj9y1v=BlrC5~W$KDCgz;5zkIfW`A7xs+_^dVh?u)B1
zj?kY0REbr5RAp89qg?iRqA2(#kn+2Ny)Z{%{{U5+S;wEnbxb)Gl}rMCO%R2eD-<a+
z#=nZl<V0|dK%l*fJ(&!s0=Ufwls|E1Ik`|A`FUHlUSwZ!u~?7O2ld%xlh4eY{{R%_
zfrLZiliWvB{M3bo&<Dqp=FIeQ{EgehvK}YT$l2bCnKBkF$R1yI4M1Q$da+Opqu(rk
zsLn!%jSu8v#f~L8Mltb&XVEMi$Gf$Ae~^EY%TRPz5zXEqm}kVq7630Y;s^a)&>@c8
z^<BxRpKtzCh$xiqLyz0{TW0q;NjrHXgO-#rrAE)9xZo+B3`u-9By{d9(gy+Q=lt<n
zB5;qGKG>|uk?q22HwH7~)qfzxJiLDE(tA{VABg*G7c-A1Q9o57Ogv<8J^6mT(vydw
z)X)6)LuSGp4s*qblhT7Pa9xHI1?|^omD>Oi9HfKyREAknUz9d+jQ|6kuRBd4s}gzo
z@8%+a<Z#jO8rfzZBP7nQtr<aG<cFDA{wrn+jLG5S;1}I<$_0S)wH!2?9OYeAnj#Kf
zzAWVLW6hDvUc6l?nV-#^f=x-DYFT{MXJPkJf&B4eb(=DW=4h(Lz-(Azv;=dz3>n&*
z{{Uq^ob_@Vm(NrL5iz@T#Bs%gSp{ca4sQuWRA@WqD*7%1ks6{%>+?cAoD*c;oJ}xJ
zc$<vbrlujT12fB1>Jes4v$aU$)OTexAUxh6bKF%e{Z&U$gPXw$`>;kLE)PEq0A#I_
z#vUThBO=6+4H#PkIiEEdGgP04Zvs^Kuey}rzf9B{KU8CnnnXl+xWhbiV+jajDZ!KD
z%~BBgxa%GXvCOCD$vr#Gm?CDN50#Kts_B_F1BXqEkXQuCP8N+PXkf>uyTFdI(e+~<
z?=kqXSpr9irrWXq0K&nCF+)@7X2}c~hoG>gtd71-OVK<PP6P-rKl^7QTq7$O0&tsi
zt|7>x2y+@U<Km=3c>|5^!3{p`<o;<PIQ8YFF$2kY<w6Sxxes4vR)dT|p&}lcp@KFE
z0sjE2iN#L^4r_AELUIKwl->^I4LR`h3ErxsNi!VR1||i_3;xQG5I7S7d7;RZIzV^z
z78)O|mG}2#kP;paCd34>&J@r2@m-rX%quzkHfspA<vf1tqe+4whfh#7!XV^1S)W@!
zMV3;j@aO7o%J0B?{nm&LWaqOfn{^$MntOb;MqG25RLlN*qF%Y6H>cw0fp~=H{{SZ9
zMwH>fJ|BymWn`p05y4^A95K(bdaVZ*XQS0WDr7l)!Oam-@{xMt@fM|Fi#T=N2jY6W
z%P=9A?Ip6V92NMES{`hlgh{Y;2Vw9&D%8sP+5Z4M(nwX#%jNT9Aw$OVQQ__Ds*-SW
z$B{G9dEvbC5nbVf%~+9ql>`R$HFD&Pq7hO@UDZgOcTx~MWsn$?)q)|(g3*IMW{y{f
z;)E=l8qlto<*vd&9k4lz0}uMEO>BzztSslMfH8{kEIG>v^ZVwrFzekDs;}`=%0JDm
z5IN%F6j&3UN>UyU?B$RQHfPOZVVmWL=7tjQo6w{lt}&}7d9SEMb3E0loUd5rhmj4l
z2yyNxK;B8a+N^Q<um&MJxs_S!EbMbeK1Z6bPFRLlo~sTpby@=oC_i-o9o4S5zC_Qf
zozBo_gY$I<6K1oVu^r!4P9gSGL_?V7xt&{|WBaHasXyye!5$HO<?<i{1KQCO<@$Zt
z3^pUb;>R+26x0~q1~g3lHWE*P^YK=!Qv!Vpwk%r`7v?AGu_W}1<Lbch7&d>ZQW^-=
zJdM&II^_B6?zUK%JttocK@z=re|BWWx&|_RIb&!*4m*mu1t{_4`mq3cB4$tX^?1l&
z<P(3!pM{D&oX6tx{G3kqhvJy7%s7te^<hRCkVo@h0^&~b9d-V!kYLBbtww<(jqtNv
z+$>KEbf9q^<W#0gw)@|~T*MNU<mjVR1|%|T`$e5WBwU{#%~VWj9vFhTmyURoWAuI1
zau#bE2=C#n5QhmkJALs*0HmHHALrd<DRy#CFRHCg07|AZxe3o*8!wiEMS~qW$9^uP
zxe8M5UKhU>S%eb^`93{YW(bELl&+?Q7Bl?!T^QWapN<L~6(trU@lXOtl+H-&n>yrb
z20qh<hF+B=FQ*graP{2J$;FtS5Qv!Xj;r+#3p@N+p%OqQd~;)osm$Zh*#Zv&YWgc)
z5Uc{iYW$Ue`ifEm?m4m?pNOo7Jn=nHbn!G0p7>fUNwJd%>oS-P-{Omx;_4<n`mZxV
z0Ku`e<ZBSV>ZA7DDkq15q77pv#IU2)fWEq<PG(nv;W<Y##YYgw>cz|>-Eo*i-5Nnu
zqa%#5i0<=Rt<v!(;mD7R3J6!l2N=ATMK#xE&LP+JP64U|xUvRCnS^3-8xIE5B!XX?
z&Ij(eK+nxwncJclAJvLD`HLvWJk&2Gl{x}}{HD%Y<C?4~TNwvFV!CFvGlmnZG5-J?
zDAq6Lp06P4r6cuVH{Uf(vHq$lE@eb_y7EYdgSjs~8~srnfC9q!54vM*$G}&Ve1pFs
z{n+v@4j$g`kMQw+svwj#Hk}p@I-){ccliDr9D`<F=))Ifh<pe4Mu-P&Igp-i%I*Z2
z;~r>avO|RV;RMPc28ay$x-eT0QOrF1D)P($=<D4yq?egsz520Ad?dl)@ti)X6>~*0
z{{TM}Cj&sdzGKN#i-{h?s=#o<j!zdC*t=uWd!TB_yC3xRXQfiJz`OhYY@(?>I(DWs
zQJxD5&QOVRta0;r0gvH-*qXy)1EuYne-H+@secr>NwdiF`lZ`QW<eQy&r;wrukwB_
z22_abY9LH1{eAOD;()>^20dXuf6rD@VSwE_@snbakP*#S+n>4=0RRKQ=<rzY5Fm&2
zs(|7=hs8)o(d(dma`~tLq5}id{PRx|j*I+KP(oplY}f}Ob@^+PQVm%6=&;rp<Y&Ih
zGYAsIy}@OHpCkvtM<z=Zn10&=iCi%G+PS(p{{T+xu{2IaeE$IU@u?b%^A+(O7!moe
z*c-6_05wEE!2bY&O@w<(^3vJksj)r#EqsTq%ECFG>Kf!$1b#0DGyPaX7JCYV3>u!#
zQ{8mVtfe0rNlEiNUuirvVFG*0$OF8nIq~AjaQ^^RS4MvqOitAdS(cLzG96r!Me506
zLwSn<!)Y3G&DGL+%8Be|*ODXsS(mnGim713&1?!mVFqQ7yCdShj}{P~kJS?ko)V7r
z6b-^&=z<-4r=Hgq12feo1QGXOlm7s!fc#zw{LNdW&MBdK=BHlnf>5A1`np3rLv~A4
zFF(b?bgQAf#YArPbCdB`<ajJev+lwoO3bOz%jT>|=WRpQfW}Rab?-^#{axr*#JmB}
zSdIgO$^NXT0)t8G<od2uP={XMKNeI8W+P9osmg=H%UM-M2*>Wg5dnuk`%`QQxP7@B
zk!j_R&wi*md4tR3<ol;jzZbkdYiCEmkTCl?^;E$)%sZc7PUm&*Rprod$4qiuD8nEh
zDC_E!TB;g%kH-`PGQu2d{FV?`W#3|ueo5Sie+8L=l6B<aK4}#=gw`F^VFWyb+xK#k
zBs^^>QVWPj#Q`CMf;vycR9ugPk)uKMA;UNQ7I!Tugbpei;#;6hCEA6Ej~ss6kw`J$
z5AK?WAmBh^KLJ@6&W$&t;;J6W+H(GAGlz+eX0v#NEKOloytxCl@0abbkvVcu7)Pk3
zSvFGe#s|gE(C;KCl&WGDV&K3(@~|umn$Az6!SG64_WWIC<oWykDP5p|>)*`)hY-YG
za49fyo*BEz4V5wYqHz)&!TPb*G8o5?nToVUbi(+aD1aWr@~N`25!pX9s?J4`^*c`V
zlIf*N9%0UGshDt)XQlfUNC<T=G}&TrnXwgP#?>0ge>b^Gr7p55I12R@2D(LE0lk$`
zath2xMQ)+awL!xj(m*xgvXzpU;L<ox)jTMY?>*KLhwQ8h@w`<X4^)#4s^hMxdRY&c
zIkE}jx*&c^${iGiXy%Nwj2vA;1tE~MLU&|}F^P|h9ZN6bfP0=J%`?t?_f%IOnt+Cz
zlat+v&Uog%3U<MIjL$c;L`{`W^Ucr`ijn-;jo$`~;PqXmerloe`LY1a$0<;l%-Ew;
zkh7O)kHsKI6RRQv_Z5`$^;MCK?wMTYc&W&F_i^vldJ{%n+3362?h0TOoXlea%rm_V
zYz0CN2Ia;+x$#}Z5*OI_?)f-K$h-FWvdSN9s^cmw{MeTn1oP_1AZ+>gs9-F>7m2ZG
z*;LQC@KvsEsJ@FC9_w<mE0-L2>pvD6hz&3~GqW@1pbB$Ij*H3slno$&1jZd9=bxI4
z3Pk7&C_F-WT$VzP$a#ManTAHz`5(Ig9sFE+v$+a5*z-;(lKhdi`6m)39hPP>KyXfv
z^GGKl$&iHl=96QLk8WbSHD!?=nnoZb2P4G(8zG();V|Zl3V1$5{n$|!en=Dfn*$sG
z!U9ihzf|}nlim?O&EG^adC({EKxdYo2e0mc7yz^e0cJBSK6e7Xks0?xsP}SJIG=$5
z`+pWq03ti+zZIQ;f^zv!Ky}@E4TGuF><4XfM7+z$gz6u=Bd<{M9|v*OlChzHp9AH2
zj&{KuPI6v^ekvoS7=LecLz5>v%N8btb&tPx5%ZAp<?5LyOj0DemI;=55IfqHFk$QN
znhe>JR%Ak9U%Sm2j}b8*-t1-zSo2JaJG_4RpoZ*c2JAL+d8?9e){sDWIkS$RR!76)
zra}znzejd1quoVAo=X|z@pU0)cQWG!cV%BhVdjhSUVAF>NP{x!!Uq#oGnuKt?UXhz
zJl|Achdc{*8;*-4lE_VFI;b(T>-SR%S;-o?;vdz)Lr-*TkGnGWrCc)dS^ogaG8;L?
zYIE>hBm`seUhvH(Bf-;hI!oZV!lY$<k(GVKCse~+)u@y4MwvzIdYaU(5u0Tpf?9rF
z`l=E5XsPb7%hiv%)z8I367_IlWg&Z;(k6JQi4NQl9x`kuck@X+vRS6{-t%1`i7YBR
z4t!RI^1rJ$BP9O-9%`$4mrnf@&ah6eZCp8~VH67>pnUsl4L6s?AaTWUAR_p&2B1@w
zFKC`0@7>R^CTM^6*>>51O?rns$29W=fK1(i*QmU2;<)<k{+jBrSS(Ku>dr>sgvJ_l
z4FX9;0z_-#;o%8~`RcxK2D|Xw!U~coH3f^?N?`RKoPQO4CQ~1N#IgY1W;{_4>T(19
z*gA)pdX_(!v%<uh;%hZgmLnoGPwtQ~l21$rwi=x>KPde)4~&m2r!-t3nCp}HtBC>-
zWSRWgnF#>Cx%}C=%PBQEZhtU-q!IN|o|3~9VVp0Li6yB{XR+}_f+T>z<NpAkG$4Su
z6Y2fZkta0&0O^V1xzi?yQOpPNOMnj*KTP?pXK2elSoXcV*rZPvONjWN)o>x>i1~l2
za2gU1&yR{e=s={$&6gyEt`%TABje|~e3QZ-mNw7abaZ)(=LoYXJ)G4x*H~2k9{&Ip
zoWyk<Qb-p>O@Xd)AL@lE1J4(inB-PC!>cpN<KluO;3{!3^IkKH%n!DbBDDa<{{R#q
ze@eaYRY?=q;>a5(Fw{M8(koGDhgrpx$|u25%NjGFlQqUktPR+EzG0_cYhWg1<}Sg&
zAFBf#iZdvOiVj-!W*oY_px26{Os`ci&A3kbstyX?kb~VGrBrb{)7=!AgD==cU3X#+
z_gF~ayrywC{nAO+UUB$yNFL(O`6u;Fu<219Jw=oR?VbSq-353!Xg9vD@j33oQI_C*
z9Z`&H%VCB61&oQ}YLn->ODRz5@Zb~2bzNs^7EYgvB21|d@UIw*>3Ps3c$+pQWCfJj
zXGU0`FbU5M=+*Mr;B+1@H<EbbEXLZ(Y%fui7ur0JMQIEe0C@FLnB>c<i4aPZMVy3~
zn8fj|YJG*@$qDX;4FH5t=3|)iPU6Mje&?Fw6OuF^edQF0CJp9zqU;Eh)5OJ8z>9;Q
z2~`ZF13ZclB;=kB$JI&y0AljkLqg#>UH<@9HYA1-CX?_|D;KDrlFCIzNymtf^+FBk
z@L41vM9u{N0CXZKAbLssl^D=Zo{QdJh)Lo%_{y*;g2dv*eyIE*Fa!Q=25*ldckxXF
z1tI6%irN(oTLOUy_zb5%k3Oov0)w=7TOfPAHBmM4{7_j-(fA|g&D_F>J`ak>CDjo=
zQ`~U}ou&XJP$PHQ*3W_p6OLHu{)@=C;ll3+`=`WP9Xxzl<zk)jwDq-)xH3uKyFLVv
z!{*3h7$7Sh)t^8Z{4N5{B?0?vVfJ>91)dD>6Y%finAXTP!$};sQ8m!zRe&c+QpAEE
z>1HR%#Rzz2Um|CNt1@v1Y6+G7(S_RfYt<PQoZ>sZj4BkB^;SBcx_EOtUQ$RrS)C#M
zQ0dX`tlWGw<TG^jVc{_T?-_ZECo9K-t447Z&>@VeO+;}QO@}HddGpm(^f_xUI6PU5
z72VoBeW*B@I-vRSJyeQm6;HN!rpGz%%mL#jh(IT}Xs9PW(j^?nbs>;aOsvO>DTZXA
zcy;wtm13*H#^vZmcr~Kj`dSIyvk>#t);TKd0~Tu<^{s){Gl-g$gf$@{WT<1@P@_8g
zyd3=34=fqQnUUeJDV}<}I$V^Wbp(b(n@EJa;H?cL^)(AQ{%R$z8oEay@=qUifC>S`
z@AO{*Flmi{s}KTlwHCZcb7v4o6Li*{4;2Dav3Q@F(3E;iHSR$N$xPu_yI&pr)rle&
z7<-Q+L<k|#oa1CPeV;Ujc@-tPUheQ2d|pWDngr0q4T}(nlqB~R#~K_GDINw{fB8Vk
zo@_ZdZbO1po#0;{DvF4pj()4GV94ig{vEGD<S!$P`k1kuM9;FD^&Em^>}xZ4IF;J#
z67onxEAh>PRv0M7hG`@&PXxgD;%sV$L*@|AGew9P>yB&C5t*T~*2oqRcz>C8kWVqk
zqxq<-CoDz5>pz<8OsO)&?0ajJJg{I32q^s%)qY}N!w$c+SL8VI*CK<0&NJqqW<dN<
zkn9xp_4BnRT5&9t>cxNwgv&Nd)C8Pa37v$%`Qm?yB{CAR=_}8pibR5-Q+{5*6hX|<
z5}plk$aBa)Ko#k<BnBS$DVb*p6XaKqRt{LyAB6SlpnB4<$I(&_o_PwMNxnj$CUXS}
z1_^quHeQaV=33&U%}z(gBFO;*kmcSr&XM<A&=Stu2!P(U%5Nd!q<57T_~IzD9aM*y
zT8S{vi;mUoIWop!=gpKc`>enn?6TA1q=;ejTme1pg(@5-{MWJfwQyRDbDdZS*Y#G9
zehO7iBB#rmj!&AahFC`>_f+h1R0-My*$P5ct&CJVs$-XK^bfneSZc)4l-@R|X>a-#
z0%pnEy->_dlu17+Y#ameUnH}Vts~Ues{)68*>Hp0*(WXHh)K_?IS2m$Dxao*<;Fn`
z3_#TfJU<p1c5idn%~%1-h)c`*xB$7pvCg!XYJR4CuM1o;2qnY0_jzCK3jI)UlMh8I
z9O6!W)8ZZ&L{ZcaTRyB2oO*L-!a~BUkznUF1rZvq{{ZHzgDE0zc2Ef@)zr=>#ZW0w
z_vxTO5QCWEu_YmjmIIQ;FeyP0{{VN;a3jaf9b__mC;9lOl8gtpg25Y_VYBYNf)OWq
zL~al~!QE0|6=y2M2)Ji{KQt3S$TCq0`>ZoL;PEt|!0;TZzncP<Nk4<*t0)qJBVp_H
zcv$e5Oh1hKr<(&D3@_ZvS!e{_j*rziEe2kn_et2G-n*!4OrGP<-8^j9(f$>*=&=5)
zkWZ!&)LD2@k?5We%>&xWgO`0QWNT0to<QojuIbp>a$e5Wm?Zf;*=Up~Y3R6YaU&u>
ziww9s(yj;%B%_(?ks?NDraJfWMwi3}UlW=9*=EMNy{lYam~t<&hy+mph^vO`f+XB`
z#1UdoVq4D?IkU?ZfkE7Wgt2N-;IM@c5|;UjS)aOF)I&k$SS*+wOk?Q1$~FiOSx9VV
z%7j!4r!#p#J3>J{&DAXli$4|V>D1Owj+YB8ywYd9)?Fw1sEnxDpb2Pz4es^}^;e1Y
zM40Y*t>Sp5ociXZY(Vo?300SeV#qX4lC#wHQV-_GCDYbbFpT#WOpTL^C<49FaP4e|
z7Em8+La2Wg;`dmhSn+!`%1S3_iDXd;56n?eN(5lWpb2sNvD$AFqExb&q9@kYpAP1P
zkmoVol_!q{&b)V6uFZT;b&ylXs||5{Hz|@os&}4jj7(9kWHxC7lLNGoEg&y(B($Kv
zfj*6zvbF+F`~HQEgP}LqFfZK<ZyX=#Si#mw<y-QD;>w5bwZ~RH&o#&e;hCyPL70vx
zbV1u4u%Ik)ieResmnxu5Fsf|hh*&G;W4Z=6{{VqwWK9t}`?H$D>Tw=@Sdb=Tv8oSX
zjF^eX&0^99lkr<El``h8cnfidE9Z*o6#Ske#n_clK5I;{2v~6FrAV7TPN(9T`B?Uv
z6fz9a>}u150Epv%x{7iHoe1CE%fjCxhZNLAA7HW{`D4N^4-aSYdmYFfa36~i6CpUQ
zFf7;n>XMot?fuZj!b>4wu!>R%{8{djz*H&7iopnC1DN;X?O?4(Vt_6T`)?&+ck05&
z4mj4pLIO#ho-P!50eC$6voer@hzGvx*$8q7^>i#F<K%t$t^i!xiD2iJp#%a4?hoLv
z`vd)Ii0bS#JckwL`)gnz>fn|dOH3hWo>c8M3o}qa&*sF<^3AdgTxEYP>ufCd>h^*-
zvjmy>tUBC^0Cj8<Z2qo}pPK_$>dhuT8Y6>j2RVIMtb}}&Lxf)*D{&0IDJgaW;A8Yv
z1b++V`7GLyqCPQUA`?e*?yxb(ilBUcuR7<RDo6=CvkBqFJ8L3~${%zQB3<&!AV?tN
zGVqeY+@n-Yuo8LF%aaSq7*p(ux4loxS5Xp|0Qu^O)(wvcBZj!-{M9&|_@JyS@itOr
zJXT?zTEr*yXMBHmml0*guf;egdE&9)&1HiH@kiOsMrJOi7rVgUIYOjv7|DHcMGGG`
z9Wp(K)yf~J5%UN4OAd}DKg&RY^k65Wmc@E05I!7zcFUySWH`y)p9G+an=lycJZJSn
zK`wZ)Nmryfqe_>Ud9o0<%vk{VeBObSpI2oL`kR44B%MuNLevO8elHYBf)BCXD4-?&
zPZS>*fje<MKdU|IM1*!R$?T;f15w|<7i?pH<76Bo4-fjJa>&Q0o3>0q?Dt7Bp98d2
z0~#RSi9a?uBnN>0UaDh&{{Xc9F7c4T@;{2N79w}uo}xP?5`R%h3xp3?6XL+G5RYg}
z-<t-xJ(Z+i3D=IS6qtw{H<9Gzn0!r07En}+#LjBr%KPF@KByr(o=0c>RAi^@%F7Vz
zd$U0$a%xv>9$z(b&nCpv!I?)u`TnWEg^-1z4picJd1=5Q-~fF^=dxRp{{TE)B$4Hp
z%(V&T0~pd7N1hcTB%T-gP-a;*P}WdX=Ye$zet()3fOE{yW>eF_N&5*(E29qfz>+y(
z)h}T1O;xJ-i=@kabx`Dwi=Z6yKzFL4v-MJ-BBZ<t;2HpuvI><<<Gf9mCy$HB4~nIp
z-hEa0RD6Gn9tXOtX~#BYURb+2>{Q_lcf8fkyL&sGsKkbH+2F%HSO#SH?xBu$KCdO|
zvSt;ZJyC!V4>d^#Xri!3c4M#II3{_b5wd?bvc2R|Ai$!|wF3F-&kH|w^G>*=<X2%J
zM^$QLj%kuW<dl{jUZS8tZyy#O%}``*Q(XCIAkUkcKPxP-?^Sh}ZIKwBYn)EB8q+34
z;~)IIFD|ESsa%Ssk3XxxX<R#f)&$VNC-dsQW7VPbtcalT6k-lCuzWnxF2M;@jvz->
z+ZloG!k3gMRRmZ$BfBo__FS6HL#%CVLz^}Q$Z>i1Vj;$1&+5t$k0NZ@IsrZJJr)xJ
zI)6Stnx!In!)_)$KS4ZPyff1$g~EE8!_62s(D|cBq>y<}B|@amN3xHn2AqCq0}L>x
zrAFvrNQW!uswgPrbV^TOG!KVCG5-MAri5g}r+B0YIbd<(e(C^B@Znx)J&Z4y^Zx+0
zdE+*~Hh$vZhy?fN^HjWt7?uA3$}{rBd|4#Eh~k$j6poYCCW-UnfGG^!ppdm0{{T3;
z2%ZN%gT==q2vy_NhJ<ySM4&hxs5qq<UQzK+2=EO)dWxpdVVMsvkM6sZT2f_t@j!D1
zy*Qfyi5CtOree`RH`_@p8I~A#VpC5}{{Y<#1V?Zy>(vQno>%RphbTbh`m=c#APMt(
zYyl8ocvfz6$9F)_v94q>=9(~JPt62SFf8qOK>MbN^Ti@cd1kb=9@?(?;%bs6WA##A
zbG)r#B7|y>n;DtsEO1Z9nj@D^=A0+=Rt-<xZgn+bGYwc79PN(G@kv2qd{<Bqer)bU
z`KW$Zl%$uF93QH~k6n4Mfz4+bo=Yjm;y9>zJ=kG0>WO^%rOOMBabQAzs5yCmi@dH<
zVY{^oS(&(#u~}(ESe84gJZCi_BR$poO(2p6iBQWkbT5m*ksd<ITvi>AYd<>vs-7ba
z_Ex0w^;e%0P$n1Z!Z_#gVa$H+62MdtHSUq_S_AoNq373jJ0KS<IG)~Xp-y=2sKkEX
zyW<iKGlWE<4^sYYn>Mk^D`A^5`&kkV^Fhn){!Mr693C61;C(x?gs@^e{x&k_%no?7
zL+7WOlLWjZM3g26E(nA_yEgZTwe#=YFi;>t`&eFb5sC6S`HBh+xaMEyx~QOo{txf-
zV-kUfr{m-EV(?2b2cnW7-quO#q7tVVPN3?c6Ii^30h$DdpT9Lnj?t5ro}bl7WKQ}X
zrmstHNy+zMiOVj+cYf^CmL?SU{{W?$fC>RJKbli}k$;@Eh!gx|5&5!wz)S6{oY@&l
zI0U_;ilznx%vl_fAN{+C8P6Z>s&fV_Noa&ZKXz@wGB27RxN8)w1mo(@03%UdbT&%<
z>ljPX{vAI>ks;_Se|h_<ArTo)9mH2~9gGwSK#A51m$NC${ZK%`*f}hG>s_Pg#ma&*
z2Z~G#OAjv_L-e{Zt3rc;<fWMm{z`ZW{{RzTat`d-AhLwd%>a!_IIUbmzABhU1s^e7
zJTtj{25LxvY?#N#icqEtucOUZ=&4B<c2*&bQ7nx8RR@@3imyDCN;w)pv-MYeoYc%<
z?x0iKUXEwFv5aTBDU6ByR~&byope$Ro@<2-<(h&rEOeyy((vi#Z<!yf1uph%ig;{!
z9B@>Ur`J^>*`EAXL+9qrB=J1iNe>k>ul~_Mg`VpudpfAI_hc@y*7II0Va<dlDsZ;=
zIqEKJs|RyT$<y^jauu=@E0od+y?5g99X;7XRwOieUJD>}@nI@c&iS&{IKEzd*)SL^
zWP-#Wf$RCA8We!a8(6}S(qv&1-DQWQ<4^)il;L3$sX9~QfRc3ra8v{sIBUoUG<dAT
zLA@x+l+2wz>Jm){y9l#*G4o8mt0Dm7gZV20T3;XD&*HdAWF)J^oBUOQnAfYV>+ZNB
zh=L0F;+UVAAiws3PV3n|f8C2@4JU??WI?IZ)q<D=_$OiJ61JuWT0SoRB0q672t<nG
zNGLFoc55-(0yCH9hoLfvPYd-#oxuI3pu*7Y-OQfyn-^^FQ~@Nl0+YB@O)x{9dZ!6K
zA^uH9$RZHu)!?KIm8}4ij{wwq2%vf$vL}PZAv8fSr=z3uUV;V$&&=@n3ItRW$eR%;
za6nJDxU!MpnB@AbA;~}DBFn@R)jsO9pz69j7bGzC)AF&zz~I2L2rqBy&X3ZHZQ>p-
zn<qzg1oZs=YVRTE!|u#MJ=m5-n~)kiM{&GX951WkOR}c{Z!$Wh5KWR0Ms0%wYod(!
zv%`0)K{4qq(nmO^&Ko1{nndw*PP)s;L%B3S>*uOOV@Fr9pEXyBxRGq7{tBiej^%+f
zwsjf1M}Z`7yCB>Ud7_-q`>DaO^?1mb!iY~l6_MP8^F@b<@ksFZVF^BII4{0w4A}KV
z0w#Rf2!8MGA2k?cp6;M}IjPdjwLpU%^<zQWo%HcVa354*<Net}0al?b^W7O=_gNW;
zs4*6O)sIC)qCBkx^UVyv8KPN~$(*T*Im<klQ39*NDs79mpGT@${N@LBYR(Koeb_I1
z2Og>f1G2(it*T4c$@r*b1`jW)uL6U?^;klQsp`c{hYH0INr-JGNRvhWDrA|^n4~1R
zh#G$D1b56;lw2e~72<gTA0Q;KVo3(5_))xpWI(*@v*x}7N9jXm$&RNUyp~~H1I$N{
zA5|hC$XOG#_1z&d=V>HhNO8z&6yrPQY=9sjH<qLmob)uv%rHPyqKFe7`)=U~2N)Gf
zaw1-zi=qvVQg!>=7zkEMWDdJlHdu<t2aT*j-w6DlHH0l6ssI^y9wL^+w}eaaS$XOH
zKUad>>cjkBY%BbK6!|sj*V?dEQgbY%ey-sL2PfRML-x$CzTZ@yM1l>6l<U=ERbh7j
z0O9JR?sy1T^XAENBs{-g(PY2_m=k;j#2nR-Tpa~=8S?Q<GUS*6t96RLH!cVQB4>)U
zAkCPFalu(GUK+wfoZ@P$0YAD#xthwYR$=Ulqb=wwC&YK=sDo_ARQrm;=`dwL;|HrK
z;|-+Dd9gRw&2uv$*Sf|Kk1d!bmTSgyITwe&Hd$t7py;P7=BK=CIk|pcnxMDhYKg=_
zJY7U5wx!35sr^)9KPMH0iK`6v6HUO<GiTIUWE#=rhyB>NW3svuizpMmX^E9#HfY9k
z=HMv&S(Js!kq;y4$y(m03d^T{D*+ODBF>ONws$p}`7=Z7Jk~t6T*kar9q*5Il9L%m
zgY6ZW3Sxp4=BQ7u>`6J#4_7)KS-`NQ%b0Ka6_*6ZnkJZ^HK@QGryE#+M;s_*gk~98
zLdT%4DEHUuC=yah-swPShjI9<mKJt;X0ioX!H=6T0eEP7vrvh!Xx)iF0sjEnyb!EI
z4E?lZOClBru_CA`IfM_z95#W0&kufKyU+`v{SU)jTIu-og0;d17v?|P=8TYtepWx(
zUcxg9`0<UD97Jz@Gg&~z4okACBL^Ue?f&XAa5dF@1i<0QBC<j+b~maf8Q`%4-^~%!
z!c42q*I6temV_|HbCsq%(meHcOl2pAu*i)fjJv_+sE$fG;H_|o6S)-38zfg<huu<Q
zWD?CfMgr=?y8t}@07X&+M=dhP66ZlD>Xgomb8CPJB$qp!+KZ(IL%xQpActXna?%;3
zgxAtPeAJxY6(_>i<N>-<{{U=+LLCxU;jja9IQ|52N>F3MI-`y_5@F?fph)ASVexQ?
z`kq=Tb$y=|Oif}ZqLx^bxnXggIO68+MDgyT6F;jt?5{j+VKi&539*x8iQwv}@=sTm
z<JIa|OTrGUdV%hw6h8a2oAtDGX6R+b%PenanlKUH6;kRv)ln~+ry+dJh9`N7pzP`5
zt^k>PsEZpGau6em$Cg_osLo=Lf-{++V-xSHL9%>Shx1VE%?6AfDKIiFMFmYK$wk!s
z)E!AXlww(@iHeCI)s>QC+dw2`Ug(-1RiXW?f*H<oqgyLW&xR_&1IL>=5x>o5AL9Q2
z_o7VYQ(<SSlfg07lU{w%yGJ_f2gTxJn+T4$p(B8qj3~+662G-k!oGH(LNVrRV%6i#
zEaU+X6YjlIBVfyv5=MsZg_+7e>rP0`ON>Xc`z*3$7+|p-H;x5BAs9IZ%6DO&DFose
z;a8{;;T%+D45*#b2=ZKi5S2p|?33|Y)50I_{;Qb;NTK_xXY(yqS%oTCM;A0L5+BKY
z_*f{^$qvhqo@!*gehB{ns=Ew|jhCwmfV2EQEGA3F5I>EpiewSWtX0ku${t#Vt0-gN
z`7}xS@9{xzqxHw`$kvcIBfs5HhGe4M0oOK@3Yv+_I3&pF<!lrhw0Kv=QbIx>Zv{o7
zL!wbw7aR9;PnCHG?xbXq(u<>E94aIxrUBa#K}_I(RG+{yf3mJikTNI9AGxy8{V|;A
z=;F>Y91Jhp{I{DCI5RIxT1-@nIR3AKta$hz?YaO%%fbHc(wCKTd+=3kpOu27^A3ul
z9S13*K4_Z_`6BJF{IcWgo8D~T)XY!1<c05cuAdxIS?K-HPKPxS1ZQ-1b{7c}Xg(vG
z$ETllfA-E1UFEB^%wO@AJAKv&pNfJOgakXx(bH`5>)o2pd#eIX<bMkwqsOYs@z*8O
zzh#Nv)nOuj?A4QGiIdF*fuE|8dk<7nn9tP%7$9<xV))~mn^=l_iSb-SKIW4X`=jYR
zRi1LbUQX#YJTZ;JM;0dX`l_J&+C9R27lXu333VAg`l`c+-5axD_t%P1zn>Hf3;o|S
zV89=C9j~u+OEcXQ4nFJ|IvlmG5EI21Vc%g$hQsk-h>9)EFrFsJ6IePTyv%)BLk^*`
zA682*QE=scsDQL1nEgF|Z#{#eKdOYJ9CUl7N6NyFbN~a$@cHhzF?TFWdEzrO&6tFF
zO&^M&$i@Itg@*uCV-s5;K&17|bR5zBz&NWWA>IoU5ayg?)57J1CpQ^0qnhfll3?)<
zBmA`~81T+ts#1AkM-k$;czub3KBkE|OU6Ti_^<>aPi2n;G>@u?&rnC^fF{8&$g@Zo
zAorEwMH{3<QZ}SZ=&<OCvehLi<9MR(;!<4kWnzudH|A@Y6AmePfQ$2d9C<HeApBDB
zwje&uBK#w?z524o!jLD)3H(*eyB@=i56vZ#oICxLFn~H`&UUo%!~TQ1JW+WN{c}Xd
zSHK?ZSHM*#z?o1?4oKkeRwhiD@PAZ}z%YPHe}d@9=r*%Lct$__a7Zk`g?-$Bc}t~K
zaK0O4uMx!UekfDN1p-a<J=p-C?!G-o-6wP*#^YC)>ZzTLqXE{%QA7okP57#kzp69I
z^HhlLs*yd>2ax{&G*lFGU`FxdyB-!vk?~OA!f<)AELGVdo@w(3hUvRzbvX5rQ3=fB
zUZKSqoc&Ne{ZZ_`Vs^q~y8;9oV0aIj$R{Vw2eAI^LTGCKjqFZ1+sB<*qr(9CooL*S
zEW5GatKcdDFb+J@Wlf$c#HZ%3MESY_?`w`J82O@d#RVAGrjSIZ8c`WVVTW4|bo|j5
zlk<F>QjP0`?x1w5La>K0p*nn7L@?f{Bl5iu-HA^#=8FghJ@H&11k7;M8;8Y61a3xH
zob=?9UgRY21qM(&lmV3EgFwI+V2`>Qx;Q2m*NOF9V++IIwjZL57$-&RnqjR#0iWyh
zU=Z`P{8Vv&C5Zn3J=k+0ZV&<7ETzC5oT+^KW&5Q-FwY{MTNi`CBCFV(3A|N=%aT|n
zxw0-39fGKrBs_*6#V{ExY&uej1$WB8{%ldmG32HoK*}6|m3!Qv6Uu=c2EUpJ79>3J
z7I9D9Gg@fpXawl;@dZ5~mxr8>?#5^eka9&HDa&C8A?;|xkt+%1^ZZo6*q(X0teDyd
zBgHCIJvmebU4}Hv335<L>7n4m36|%Qx|n20Q}-qb?d6(95as*=fk`El>(S0q1V%D^
zy#0`)z*KY(L~~_P4vZg);t>!*KQl*g<o5g?XvhF~f>`_R%ax7^=D3TN!OzVif*drn
zK_c=s;%hS|E|)#~FGc;cC?lDuN+tJFwURiSFAOTDxjL><#o&j0RTxR}wo=URy0Me<
zXUu!6d7n*KV^%!f?8Ngmd<2fCiZk0lOzufjti;(;3W**()P@|IDWBbzWZnSrMR(tt
zq=@{`AXE6W91QmLQ#O96JEo_TUhJuy@nmR}0QEEk%=cy>4Nh#2D%YYZ4fj0K8P)8p
zDHq?H(2n@=X95rWqK`HQfR~Ut@kH1lKUO|Z-C{dDvt+!v`?wZ-R5-F2%LW`g)t2ou
zmOC{wJi}7)4oa2j9o~3$g-7P_fflB|KXpbnrzXdBc3??HmAu$s5M7L?-3)~?Oy<o_
zrjXfs&FW^rLLLqlqXq0zLoeI|AAMC$Bm%KWivt5BMKl;Ozu_AckTQSW2OlMuFH^;A
z1``M#$A*SYp)j2scXgS;Jp>iK#l*)FasuNXf}&4D;r{@eU2Dw-m~1*Lvhgmp`;bKB
zD6ufpgmE9uK_`L_U)27rL1onBx}rB>4*b3>hCQdZKLk}Gi^%Xoncx9CSAhQjJD~k-
z1C7t>nj%BT0ImX5OxT_-iz&i5iwp$}rzUu-CLe+xsX=oeY#;8W7X;3)2hz~=j`iax
zp(K#*D5P5bQf!yqVcuVU8stGp>OO4Z1~`d&rBWk!iNyKhlVT=^h^!Ef9bdNU;T~E?
z*3RS5e|2Ufg-@j5E34M6C4rGRocT{bRu~fsVdyGFflN%to*bmK>#GG!x^X^MEMPOp
z9DZ!!3>>WlVjys8^2E8zjh#<X#87qu08H5#BIiYZsumf2RSyf?9Nv1y#DSTtjb(Lc
z*M14&pk4e}IKQeaNHzj^eN=3b;Iqne#h5p`kpgO&neIiAK<!qSc=)P<e;ix{L7QN|
zora_I?y_+h?r6rt(?)a0R&1SdUwFj<;c3c|wk%2`sx!2Bp~4f@&p0J63{Q$ki6|49
zhRf=%3drL%q(2vAj!(qUj#2(@Oz}2Jp`D%8^VQK}X1RjzdA45fyT6M){^=oUIh66E
zmN@rYjwg!H-;t?eCh?SyA9V_6=DVW-)ny_5-WRfpv#*NC$SWQ%$<3D18Hk!?@diG+
z_^b~dMwIe~<iEN@m+HiL7w3Qoe>Pm1r}p#suk(-`KY4v92buiP07W=tpQ^yXcO~0G
z<AGl`Jsp`@_>Xi4G6?-whI1hs?zDh-5y=ncr%<LAz(C+&3GtN$%Pw=aUP{s0>cEg?
zZW089ONr(^aba+yIv>kSa5^6>C+(wPBThg{v&2j0sdQb(oOygw<Pw7(--`!MNcjVn
zh#>ioQJ$_%Raek_Qa*T(EZqXdNzD@mLn5d&%05nis>5OsnVjd<nCP?0bwF>)rUUQ?
z)yEZ(InGO?1OVcqPE1jQJLZovraT6(;%Xc-BKeFN97HH?wbP7=Ecz6k_dDDD+s3UT
zR_s-K#i~t>Aoi%e-M89e-mOj4-U$_}h+TWt>`=63>@-@G4z)vLhN|uR<oOHoL*B>n
zxvuj%&(|5#9N1;ubjR6|AxOm3Pu?!+JR{$uE|b*V5~%8@6g`lAw;_E~z0DNsGm@BT
ztfl?NY${sqRg<Zh(`|(P$x3?&0FrXu2^LaBKjjqrZE7&QK5ml($VF#C)E4i-%Il_R
z6_tx+sr+S4UX_~D3J-)p!&Z{AdN7{2%7A^(oPdX|GQU<a--?kBvk_nFZqOrRXim!T
zjpoJqzRtYWjI5Et6&|=sNyee-%ONH4qdV&$f*tTsmK<B^PnCc$$<bZY^PQ5%*z9Od
zJ(F+jYeA6~Q)&j<kq8r&{Lob9fw}M=VtKc(j>`38xUZ&@$nvvJYUpJj6MvkI)tHZ>
zPc|~Dn+G-^m~1ch+;_F~61XuQa%NY$y+rX^0c$U<9r0(BT`&H79w?HUlGoV+IQ#A4
zQXdzH=gml%vOtJ#D#4u(#3sZ<0=4to+EQ=PGW)J+r{VDT#*y89TkTMMIIoN3Vk6hK
zz`-)SJBoNP^Djo5E9-%33Q=weD~a!_EU^XGooR~VM+28NiOYFaO83qu3>`<Wx+h^d
zX%D+Sp2f}Ik*+2geX>X;%BV2|nQDEX8i!X`*)8W>SatpyZHyJVKclB10~GK~UA9D4
zPF|p6W&4<RzW!osf0!E`-9AA*p+ju{T7eP>FQ2<%42Fj+BuTmCw0-2bN_H^2??VnK
zskX`l{i!dxpy3X$dG#jkECh?9e{hjfD}||)Kwtc#dv82Gb9h7JpIv%&flzu0rY2P<
zr6N5Fs@FAp<eAJ0U}y8ut1A@0020e!CPOqJWQ?0a<Eaey+)fX--Fs7M3Z=J9qtt8;
zXmYgT@*D(W$uFDxopc)yYFPw)io}wbg`7mCa|x$PjEy6ir6i%Gd;y|`WA~=1BctmK
z%K+b}(m)>2kRJ-w>P8c6BY8`Rj~mnN2PStl9lk3el%O(!WsO?x$Brdvmk1zkwvS+S
z5(SyM^cz)FY+J~WE|q0gcJ{v*Ag}45+W6!#pT%Nx(I=ohF^YXCqKIm*{fK});>Q#>
zPDBb!JkU}09rF?XhM&RWWxRQQ#a?&kg<o`+G8qjY?v`(db>@8dcD^~~<ngIxKV_iM
zSUdk9$>_G34yy6~W34VSr9wG3gKIipxk`l&eASoZ)q=?>1<0S-0fJbzaW#4~<BK`w
z<noyOqm9a9B7U>CJkXNaR}>4_ceg@qcCOydQw|im^zG_y{KrAA{ut`p@WkDe!NQrm
zhf|cCf^+c3RgQt`T0&r()7f*%B5jVwnuSM|k2BD=D*xdc;JTA3hY8z@U5UpExx<ny
z%V>s*T*c6i)pCaR;**)Z*x;1XjGp1e66!*vvSA`zT>#88d{|-knoh2$l5v|cR+lv=
zoXsiO8mrCcx?iue<rj6lhwtLb=AYTf!~%tJ(KHle$14Y}KiD6esV*ts16VF*6S^=A
z*}r1?rn&X^Ppwp9fBzsi9g(L93ZV(xt^;nftdQQL2+O~BCoZCyBL-^%a0(E;O1VA}
zH~%AXMSO=OrmnBmpEBkr#w{~gZ}n6zFyUr66@Rv@(J?2`IGNI0Jw-qGV|l=q3f-0R
zNF)E^6qX$-`3qGumhUX3^_|^_>XzIZ1nfDsaZix=W4>VANAgb=7|MXw>C4fyYGWY0
z@U9tGc4}F;Y|{NFiv-V2>?IGo?#v@>G*}V}M;ye4QvONhs%)6~ecd6_3Q~4Xert8Q
z$T9ZUPGRi}&j(B<l}7J^5qOW)OcTcaQt#QX0oD}p(a0&WZT$&JGm&#$5bq+jyQ5Gq
z;KEu)awSI?k3q^RabG)6@=O0h*`)GH7|_W425gp&Xs8LJ6_WqRYP#y#g4vxyjSy$K
zmY5MhV-{^c&o>t?sHi4EANHUA6C`U!#WcB2ywvj6_$RK9fhBy;>Uud!vSi)tb70+*
ztKm^zkZ<3mZE#=1sFY%YPgtSeBlT7$2~nz)zM1W6j)IGo0>J4_)<4^4E)=Z%z^1*$
z16d|Dl~8B7?18w#+$v-85FNf6w1kE2^JHtD)Z$ZDxR!n;R-4W9$o3r8`6`>$c0c1V
zh3`jIHh)EThfNX{G=7HBpO`RPueH&oT%5o7q7Qq2w0smh@f=rc$7;U$8GUL+k;S!p
zf_q=qlUfoS`}fqvZ`_3BA_H1y&oDLYqMCd3!i1~z(TUEYwGip{)#C!S27?&#m^xu;
zNNRD_ZEMo_Q$YxTluu%v8dcKCV|PKoqAMV2?(@Q3&!2pApo?v19+k^<3{v>rbs3(X
zdJ3K|$CyYfOQj+^*goZLi^Be$VtNmcIP(~zbQ0mUX0`T$7?nu>x%beJHRxGtfxmO}
znoy=_x;f%Aqocyj-aj5HAh#JGU$Q^{C90lzO`yMs9_L-D)QMCPEu~P`n3HuCIu8W*
zW0FUemP{c@@Nd`_E@{DP!X2IiJRvO(d=tQ_=4NY?k^f3fOqRAdOFQ<Rj<&qL)3ZZ=
zA-Rl}A*4|bcWTa3Z|EcdiDct^oJHvqOMt;HvWVy93T(VI!cjYdjQCSg#s#l~sr42;
zL#c5ejL(XgLXO+kGMYL+qZa|s^$O5o6Oo3%mA`aRV5^+#n`PvS&~Z^)F+FU{^YZ0`
zOZy9*BE9y9lH3~30fDyU3f8_kCr65W;`3Fdo$?<pI^>&ri-88W`d0K&@NWGr*x~C*
zNQ9$p-eL-$)YyN5<<AguJs%!ha56NP8?ga8GTV|-BA>xLU)6wQW4#8YHvCHF(-F>r
zaoajnmG^AiHF%bXPt#Ndznn~gGc(T2<YT82!i_p7u^d6>X4<TbndBA@(N1fD+NJsT
zsZ-U?S5!YM<liIQT3drF0&i{RiCN;QcT*^%1HwPLijsVSML|WsYc$6tRvnIJ3ohzy
z7vwhR>DsE|_o}ys1Zp!kwOOx3HCZxd6JXKO>%&N@saMa9Qg#onOUJD&d=f8%RN=l<
zJ7_lw%7nz_AG37sEjM7o)%z4<0EpLGJo{PQ-YS}6*v191lr(f^mO~FS9)p5iulHJ6
zjXP&D4LB@a6~BFPRsx31URj#OIeRn<oW)i)%V9<#{v)3Sg6By>QrNanTvwGI!7Keq
zeMl!+ur20ZkbpR+#GzmvFI)=Br3t4;=3jpyR3~slepQn{ApZ|!lDXRJqC4Y>j_|oI
zDtQDPyEFuPjod36rW!8HF58lV3-JA;D!w5Gv=__e3t%+Kd;c1xpmH9vA1EoZ4z|fQ
z`5}>)vB)^9Y(l*}%b=0-<Rd?>4r&~5{&PEQ_uNK@zl=LP)|nyfa1b=Rpn7=n?9|4+
z40l8)|K7MH?5fU7tG_oz{_y42&Z3LJ>Iy1UM}sZcxQDw;4C*xVgPBM_RY!P~Lx-lP
zw0@_6c+3UQPGj#8-{G5QS*yXP@&6Jq&wn+QR{j&+B79Yon#yBo$4(BOqu}qSF2VJ<
zdu^1^G6&)kA*Xif?y(rTV#KHpNb(YV%xm{3qb%*`MGtkPn{osks}f<I@Tm#K)d+l=
zgO`53HQPxioL@2tB(YBAx$Izw=T6E+ZP!pyKD+E?o{1Mv?Y>s)V*q{km-m;b=Jeyf
zSymMsqI>_1uY1z)gV};>0bVHAFsza7z8BtMK+$$KdFluhel?2})U#p6*C&J*qH-+n
zz9}(92qK1-L-Zde1CUT@nSUFY(?ZFAm|Y)qvMfJF-m`vgXQXzwCr4o`ueg}a3|8g9
zg!{xc!={6&G|_ZJbY^kd2s`5l8$rU|E)4~of#G=l;*}lAM~cvok;4TjYQxwrXGy`I
z3x*@a1a4m=vr$D9dqA?4YnXA9>3vn0ai|p8It#^7X}8d;7pT0onS}>J1%gM%f|CC&
z3<asNSM^n8CB7ht<|x=n{DD^Gih5fjm0^ulw^{0@fXtHWv8yArxDHjgXI<t{$<14<
zL~@jT%!wJ|hd8XKIk5NCokU{ni<jg$5lsF!wqd3^sIrMUt^ro}NFv|0ZL+qhli6q|
zh&mQ&pD?NCipXr>)2$9;u@VZwHNR2FezU3sf}cmn*t7B@VmV(@+*((1CX~_bFMs$L
zMCK52*cso#OL0jEhp}ul<&6;xn25}6hT=r??h9>pAFxwo>u0O(chO@X+{A~vccZH8
z(g{03U7+WF1M>WAlOXiayvB#$tMcK)EI!Fvfs~AS6A@`3L~r90tI_)(gDT?kY{$CR
z6?z;73F%@%>|!jT%_85(83MH~n(bgneA{JS>A3V#cj}{>=2Q;f3xqNAWjzJ?;@F=7
z8&+=uz6MB|zJ*JbAja2SY9LtDP>&(KW%8`wX#H=&ui7g<Lp%<wmUOHz4~XiM7C`v-
z_Y4uYj)J<e^lGGB`OM{H&%Ns!?2c%*s-L3<nyfFH#!%<;Hq%~tg=wr?Y>B$gJaaTW
zLkw2>4QvV|a&+uDl&Tr7xVK!KNpf%X+@CfnNJV6b_>T0^`3>`Gto$oCndV9D=2b!r
z8{0CPqKdH(+t-=$W8*(IiFW(Lea&A+ClEZZ+BkDs(l2#mmvW@WLl>Wmurn>#3Ek7T
z)e1<KSf5fJnX>>%<yt_VKZu<XM^$c&nhqYM?4bj5;1d^FOo_r(_&)Y$6u-hVY&r#=
z{$n4DrcLJq^=}|qlmzeP07~!1{?O;<POwCMispjCsb{?<L>Rqlpy~M_=7amU_daoT
zsY_W<>3$~PQWi+c)(R{XQ=9DebP!dhNtRw^7gf7%Y``jyO5m}gf7jg0IM^~zLvHNn
z(J1ZyWvZ_jE|i`|bCOY@<Z@+D>KdOkN%5Uyy+QBTL`}f(?~UDb48vgRBGn7GuDHih
z-c(9faOuqF&$EUTub(PLS9At@$L}Tkz4IL4`g{Iwd33FYQ@;0Qe<1}A)k7+t_<i?m
zRzDEWe<nLYfc10OpTeLl;6_at!Cqj7;I5W;n3~nMNq$vtGX@*z(fs@QTJzg5U?PiE
zPZ+Zkv56J}Rr-mHmNMo%TQ>oWgm`)0(|?qcmJ+1u0=7$*ad(0<M7SE{IKLzYAvL(p
zT;YNnC&94NcmLwx+kfr0glL8Uz=z^ZCkxVv$CeLal{DXHBfG2wKrh?h3l2tYfnpZj
zk=zM=)O6U?+f~wiz2JA#G(@>~Y>?qIvoR4#7Q`y;1JI^d3Xv}Vz40=c(Q@j|XK39{
z*~2X6ys6XUGcz8`sg=KGlM!8}ph+jQyIpx!lMB8~XWdPb;zPWGke=$C6zTMytNd|x
z))+dV8Dq``nEqVjt00H?D?X~aAoFJp+9_dF`24a<x(ZG_ZZP6H{iBk3Di`e#(yZC?
z7#;BplLW`q@n}UWR!3aBiP7W7Oz=+&Qk-W@aqA0qtjCp}F9j8UnoA(yGZgw&{gdy*
z_I^5n1gDw2t*AzIbyG5>&wwt@nZix4Y9i}(eey&X(1MMydp|QI>*-%S&h4_{s<$~Q
zd*NF|+`Xgq8J&f*TN|If>&edXGo#NEHX_=u2oFMj@v~FO;`c1P5CUJxs~lK+KS}ap
zj-z|JyLs(l9hh|_L6;=+Nl4~(FJ!YtG4rXC^-uFnMr39iK~z$5So2K3KoREl&565;
z6k`c*$>XPROhwkSQbb%d6E5mP<2q>RC;R2|hb4Dbe)3%pxM10~KAvCz(Hn_rZe_!;
zI9CdP@2x@Be&K`mF9{q7t=}}cF+rvtv;q&+dc2Pmy+vYT%BF!yb>4*+V&glcaczr+
z_vOvj!&GCxGO`iS$*vwEZ^M7pdj<W3U$()SZfKEQ`RB2$PaW(5nu;!&djrmRfisLL
zCB1NIAIBTHi}*b<X4REiP8gn$NRXM&BU2TJ+Wb8z+{iX@P;*U5e=WyCUs(=Qw=~EW
zXUH{BH6Ak6oK9kl&R~ZGJoEp~#xCCYQOZ(t=NA~15a}#tqtJ2rqRWbQR<p`Mkr~q2
zO~NqBZy%awj;iNm5?S&<MZOtF{fx4-i$mgv7{igb)%Od!{tezTfeO(bSScFid)K`b
zd?poB3l|EiHU>Si+45m7w2<d5CMowV!%}bo@l)lNwroCPT58Q|)R_cppbWRi=(OoN
zSquT1X&@=YJMd-XH+YVl?DfpIBCC{eiE7I@d->;^(4^|<WJxjk5Coa2m_LLrS#qS+
zk%S-S%97FB9bI^Mf}ica-LB$2XZ^We+0J$8OgqSSyw#Pqhq02Yq-|?>vzA5UPgN#J
zJBp$G;hZ_Kn3s8=NxI-<n}@D6TC`AgCO$~g(OEP%mEoA9S#2K^nBC)X^Z?m^!g|P+
zZ6RO;p5H)^Iz6$Qh2U)*=H)4f8$@@dsGd3KvFKsPqry1W?FuRmn06{_s_V>?o0H}Z
zWcM%`{N_;Qq`20LAOgF5<6@!;kB@Rw&K{HcJF8d;R!W*oVRqH-Ax~%sM4w6HwN+J_
zS{6@PJry}ey#H1?EPS9=S@K1ZFwXBrQBnIaQ(L_Ht*yWN*J1HOY}(g6GanB=(Z@A#
z$cDf}314pL4E>}6vR_KViS&CKCBF#9dvaTb6+4ci931FOt`NIIh)@Yk=Tx~iNg4(x
zcXpn{ItcqSQeOtziFp81+n6Io%$`)S7L!0Kc)|#aqVG)$I1$6v6im$&M$&T{)Bxbm
z%7l_v{`jh$(rJMqizmtS>YUGroXz|Ve|fEaT9d6<d%ejss{;7iCIL$IsQ0cVDP+0i
z$3UT&C-<Vrg(-@r(kLtA)aj~qR-f<fWp)kZ@|E|J)z{o47@%#ER5}6W?EbLhwkLz`
z64glu4m0)>hQ|yfR|WPiw+nY{ln4VM4oQDYMJYZ8>UkVt)Qu{V1Mj{l3y|WZJ^3Ff
z2u`qNEUIZ!*#Q;K?dW`0=yGubwI?64pE~H8ocjRGQ(rnEc8ZDU7~WVb?0WEKMt|OF
zQv-l`H+-utrhhsopRgJEH~DjQsx!bd8ako3x3c8<V&TyP3s=9lz2~0Pk3KoM;TF_c
zxIWA&QU+WA6w#uc9Zs$ueCh)L#pFJocxhD&AcyVy+sB2cA-0vC*Wd6X;HxrLgcH5y
zqQE0rqhaq}K<8A!MvCF5<LegT-*w@({nR>|^Xn|^a;cXHz0XRiRIvQU0;uO8#psHb
z3XCQhq9NVmHMd(kO`&;NZ{gNQVqJhtU@MZ^U3qDkkP&V?bz%|sSy0FprEH)7Y2?ZN
zS2cx$yAH~1kE&!MRTZbRjq#Zbe&N|@2I0?cGfT<Z|J|^R8G1n>2JEXr4HuP>WpMC^
z_|i3mb_6+J?v(c@F%3GzzDeQ&QoAh{b^nSai?*trzHIZDL?R@uq&=!MVkE<+G|9;q
zvvRwv?noLQn3D5tqx3gs$UsTdm4ob7ER(9DHp?iMv_Aw-igBBmK#|NnuOqai%V^$G
zq)&3#Mm4zz+Esen949YpH2_q~8(B0Li}LZ>SG%rGVcC>efWp*Uqzkw7-%>Kh2Pi^n
zJsuR`x><K(9UuLw#;5V?s1cQc_bZvgF53ZCT`xL4>o#1IeZAW#Ml_m6z_6B*iSn!*
zI+eFhqBx-%o{MU|ol=Zi*VPPzm@y$L#G}<i0Bc^v#ZDa;ZVUEy79pBTnU8mh2vok^
zIF@~UwmkJZu?c)<7$X#>Xk=T}Cmh!3gRJ3`^y1fWxqIVL*o(8YQ{Bdeo|B*3tMl(x
z6Ss3*_I1RK^_u8P-8UN86D?$f9I_?XWFu&dWwlIFf*id@*BjI1!nT<ZC<3w=f6tYI
zNoy8oQ`4!nSzu1S059HG)VY@veQFljnuiS+@xy|~&h+aQ``Ye170{w+e>^VY>z>_$
zn%OY({(x^N6xaUfZDhh~+CU;$bf>gxC7WG&inGL66=nzf0RH!tYXv@XR9l+d*gkZ{
zJ(2>*7>5?;Yh?G46!Zotc!uA<eK98YiK8ih=xII)=%D*^_m1NRjbL4~9O%?D5?iz>
zq<6#PQhj9&A8&}yk{(tI^}%be6iBUAVZ??@)cNE6(_U+KjCS+`2Ap#<?zh~@2r{Cj
zE5A<~e=D(4t*$YMv(sP8=z8_(;y!2sEe?bTS?H(7oqO+ijR6;>W3~Z8Q4xM}y<k0$
z<&e}jtk9pE64gnYMvL|j-1V0Ue>b!S`^rD=Nd5tzLIrgWv{&+3%Cp<?dOJ@n7`k(l
zIkVTV8mXnG3}}bIKh4-ZDjz8bz&ONJf0>dRs+!=2SToRpCE-H*bAS6)peA5`j6rg|
zeO-B-w`b(ntqjWuz@=e{JNSjosD&4;bRD}%ml<<(_0X5rnUc1O8=~{Z@KSyH69?D|
zT^Z2LqR}@r1$Ky);D&;_OH@k%pn>i<zBpS-PtDI7!-K4PrsFx!s;BMidNwf%bnkIp
zGza*jbyk`Gk=XqcdhndQ!+}FO`X35~Z9uY{qO&UlG*RScVek#}WA!jSe#xf*XcFyi
zzfC<?@_Z7Eu!3gfyX=PyD`_<HkV$Bo!Crl1NW&G}d$dtr$yfTk$_SIg^MG=;vLP?%
z)FkTXfwZ`uM#9WSgOOz!j(o&vZcm~~UUM045nVHFtcFiy(DyO)INxn;OUdpM-+6{s
zi^k#Ws3a@tJ|WMYYKi2XG)qRU*2LM7P6IFSmj=iotj1)ZKIytz54Gur`DX_F;$99l
zuh!o*vH&?PvTF5wnv5k!8uSLOX7x7Ah2<CW%#wsmZWV!_+*NK!3cja5wmFkUSL69y
zVhw5BdB|fxzDs0W^f&Aa{(Ngq{vtE?^wfAlk+Nra=ZbcGHvMFDidDp-Mk2aZCuL$M
zKqywWb?3Ulj2ZN}%HjoDe0y#zW7I?!RKK%?3D2(JOK7q606!|658uF!p8vH^m9I&#
z4<dhF66^e^L)Xt!aQ@v!rweVN)U56RWy&}x(Dcu?xDF}1QM!bFyq@!XDe7h8+J~i$
zq98s%oe|R_52vXoUIRf54uhbW!Dj|6YNv0b*;vq?raI{Ij+htW-azoJZT6xM`G*ho
zmS602PBi)?`>)#n?ipgGbcnia7@E<&xx($-!0f7ip}N_JUcHMW;cutjY3N*MR53cQ
z^J!o86~4vQ&8|q{#*R)H*22x}pa%j!WpnX!Q`(l)8-%y1-h7tltQQOQNmUeZ{w=uH
z&2xs{4ZEtPm?{d(*~@V*Q`d_gI9o+Tpla@YchizI$?KZ~@K{A#FT18$luxLTX_tDq
zN@kX{!2@#9&Vol6$om9kme%uU=l)EOD`nOxVKp)g-i1X-zE(1*nSoAYqI8}pesk^y
zqmiqM*%FPwA((^Cy&1LM;<@7fSfPeL<KDRky(U|smed2CIXHV|s_3AK1zRl<%r`Vp
z@o4^y4{gzq_TdTgVFFq!ww8)u3VWja^GnW3yAI7<*wp|)ifQsnBK38)-0imq(n(Gq
zdWB%;$E<Xv9H8HtOUpsWu@|K))CJ@pjwD`*p41YbN5_TC6m$6(YU-2r)rqSvhAfI9
zb8DlpZfD*u9zMNqz_EA<nnM|*&2dM=&SES<UoufP9(jmf4<|Akm9#DUOx$IfGgDzI
zP3}*_cUeAi{SE$Q1mN1MuN@mB(;6WlSyqS^bAJG@X2nIj4hNjuF7y0}E4Q~Qm8OdG
z>^>oQ?9;t`BbNM3xLEl*7e-yYDqK%W3ga~;OH7^eDPGh7u|$G?%;9@T^Qg^QIpJX`
zUF>2vg|>rk<+)TI<j~Kye=SR8LhpyINlv0wHKmmC2ycO7u9{V{sJ0`*5<OPQ->3YX
zh&OP-%TR>0=TQyRgbU-Ee?=~Jjh(ukSV(f~S{%hREWnH&B=a^xk8|*!0D8&Q5la!$
zviwS%#rRp#JXNO!q@5MQIDJ-iB~@hkXURn?W{U~mG2Lkxi8$hCt@=o-hQ+s!#5u&?
zHBHId5$$Pd&nH*ycgK@D>%tC4%}Gh2F5fey^5Ll8ctRaf6iSaKc!D21BtP|L%513}
zZSTlTde|?<>MSydFn5l$(CTdq5c&|NfA4!Dm8Ig%f+cZUBHLjDENVu1`#ojXbKiTz
z=(Fe8w;|>|y)I;gj(HZ-J!mJAsO7fj)>jM6t>43Lchb2PoWl0~-#<)G#g*Fw7%#G#
zXig5`#;V*DYjKow1c}zWJvb-dCy8;P2wL$H*{O5<jD+0FDvR%X<TZ-gRc9vlMqr(t
z&yUCF6D^53f$7ZG6pCwk7>Kyb?Db`Y+mO0z094&o2c=D~UkJ?m#REKbP)t51Cj0xE
z^AjV&uC^|lj7{qX?8%p6Z5W%-^HWEQRyCo+=x1c$=G!HvB@A>@;m^+!g1)dLgi{1`
zh=v#NXnXNz$=^_%9ZrQ%-oLhJm$TDB{f+K1W$|8J$uMfdkd$gZ8}N=>)bYvwE}A~b
z6ylClN~O+4A_S+8Shu<qF^7r@yAl=iFVs)0>yApg+~2jJ(@%8peh!V%_%4o;mc$~z
zb`~-9>MRXCS*EFQK6}y1c&RMKpUxp9O@`*r^LA)f+*%bG#8dZSoi++X5R%mvVY!L!
zR~}1;L#ZnMl+go{E=(ey90#It(+rmBy_0<1DLe+b{$lu+{~q7JT_9%gw~Kf1y;TwG
z6^8||DkWPc8<ulh7Dl#uT=XfF)On$v)dwq@_b8)fTe}a=TVDZqGf)M8Rhx$ilX*yR
zuUDOhrEJWo#~K)Of1xU_>E$2BJm=)-8$okVj+IK265Xfu`;V^Ft92U-RuV^e*o{FP
zrB*^mbpKUArmh=Fd`Z#n7SW{?lT^mImp%>}Wn9@4idGwZ7SL+<xEHK1E1dExx_<A|
zP78$Ya-_0w-6dU=RZa$1-AJ>Veu&~<ltG1PSTy;#aA@~7Y^JFAZCN2l_b>unET29)
zs)Pb=Sf`3fMKyY=72KSU`Zz=(GK0f*Lw^{iwOKjGYAhPxy(C!6331YXUR!JW)-hFb
zV{a@ae2PPH>1s-!o!R{S#6wEO-?2g^?8C#dlEhF4(=gd5ICp2KEpH3K{vTPP%RHhW
zypb<p0+<-+B4{E<`qq!WysU=twWz6z;LZRdYMo>FkpGb!IJJJBifQ7f0?y0Wn)&BW
zEZ^;0eIX$BQL<E$7mU9X_++JyT(p&g69-Eb#cEU#9F=Yn$<K25I(|l+haba(7=Le+
zks9B8p(ujY*bDG07gI;uW~Gzc&!uE`muY#9$tZvGosH5rb-2GoL}5u^)nu9&l~}|}
z*47z1#{#=Ed3t}%GwmTj@Ysq4apA)u2I1Dl5vfV}eSBSJ!U}29kN5Fkbux&@DZPyz
z7ND}()(K3!6WH8Y9*CcL^{`}yhn1T73%vrYmWiHiJrs#*7!0$!f3x_bOfC1inT!QO
zV@|TYgW&_Nh-N5}iz5o>dys5a#r{dR;)h^2<w7;Bj%f)hhCqn)cWdx6#V!th=ZQ$=
zZUib?Pg{fhe?S;nCi>u+7oDq=uNmKi7fN%@#?a(BEN9T;y(%^#QP+)bjZX=c;u>-h
zui=9HrA>LPuSBsC!`Zx1I^&K)9cxDfm$$@!M9}gWkqB2BGymIoN!d(u%lxsvW0<Jl
zBh$2Ns^JjYrJerHkTE*pOTR6WOSQHJ)7a(tc3L*(zK@z#E~K)AqF_2P+3t$Ska9Pw
z#zQHGz|DKa84r>nwf=c$X7P)u&V62vCnT12Cm$VhO!L*fP_O)pi<T0ra+-wb{D)GJ
zEAS>VVPG0Bjc!7L6Di#zfKR-vi-x3tp=xM^iLF#YIgxo0sNe-}N3@GBQUkb*D}&4^
z6?hIT@;<A>O!wQuTJe(x7QL)W&H~yAfBLtRvbUc#07~SzW9LV@57}5{^tCL{AT_Qo
z=&$q$ExG{qc%uMPMOlgvFNr*rK>?GafS_D{E+Zf>NC{xHiMAT?MW-5PU`T_Vrx!V8
zSL9OxxLOFdF94Fg@tT&QiJ4gjF5s#=_N;$;GdJ+3?sgmpqA~bW&$FpE`mZdqjEktN
z+SJq!-{^r4X9ZWwq>axW6c^RzK?bzMGj+pQ+?d7c(?Nf~Sh=f#-$Dz3{l~xvN^M8s
zb0?O(sFR4dOk7n_&1fL%OrLAe4#fxSxa`h+OfXO)ykWtzkr$rWjq|HKX0i;5kq*Q5
zLF{YHD`P*$wDf2w$&;efEwmzso=284<PEakXrqIzWmyx`U8cGs4{ttd(RR%R65TIp
zL-^Ma;h!rcokz*jv?k7B!|bg3<yEZC@s3HuLc)Wk2g!V1r8)w#@d4ss<^>YenVOxm
zv&F!|5P9M7-`=Z+k;yNEvL`R`HEU30Hui8#U!>>R=`M6aZu8IoNOqGux!gf<Ja<1R
zPPvAO-E2NCVKc`rE76vY-ZS?fR?__mBBguU$^ux^I3qaz8mfUI!&hy06jvr32U~9l
z#yMF(R`)ySdv+Wom;)=3D;9p%=ib5RyN8_&$I~kUqEU*D+cFtMBi#Y@P(fe9WIb5I
z<I0wt&Uy&MPhV7Fkp7}sT|{-cq?<+m*Ie;$negCv+PA2T$e9K{K2t}5JDH!MhVpF!
zmLkV<oH^i(MSVV1!lB!W5OqxNpNp5%JQE4Z(s!OhQY^wr+1iT+eCN1L@K?xE1mv0K
zlBhtW81}QwTqgXF)aG~B;+#GGKFWqT>-j%g;u%RjN(+r!wc<DCc93xnBPKkCBo(U!
z<Ms#J$(I+lh#Yqf--4}an$^CeCo8^nJ73o1`L<5T9+iCuGVIo9zX3>E20Ol2s0iTR
zLC8{9ZUhxEX$3MgM-RBTbd|&&6g=dE4LHYsL5kxu*-q}WRQmPsnA_&1$>n~l+br(=
zs7r;mb3ZIOifKwas%gCH_NwdRejYq_YgfMiR`&z1)k(ie=$Fr@HU|DP9sL>Hx%I!K
zN-_~Ry@@R9wH`sO_GNunJz}nzjQr;!q#<l0rF+&ASp50AuH4M@x=!7z4=$pZ#ni1V
zR{6erR-~TBLDdlf$UA6Kh_gh<(X06}*W4kDD{{PKD;yaUv}8b(=^_koY1QAx1jkuF
zpSI%Xb3^odv~Wy#5yHj0Gk5oP9|Smowgm=JT2cVgL}?A~L080$YoTe3leUtlS7F#j
z)De<ZIrG|ab?4og*-Vy;9g4wJT6`#Fx>`@j4QW74vbNR1nr9U3{dJx5-T|@3y4usa
zNE_iKr6wvw-nA1wBQ;9G7^AK9Tfx)F->zA}M#JQ5IfI$e`&Zn|yVEYY#TupC=7lFO
zUX*y%SxTM6?<+9t!?=JLh>n<%U)UzPGT-${z~;BE(G3}t?1L(~7~BMf`ZMB)RG2Gt
z68_ojH}=gM$xzUBorU&36{4&~li!>UVjLBI{1AUxi9MJ2jRo2_7wUq#x)U{1f@t(;
z_BPQaio3iHbn`b$mqf8-Nsl#|MKeQNd6SDbp(W=h0cx4|<^@2Z#U_rMF_?HQQ}OCJ
zeCL4+nkO>TzTRRF2T#F|gZWE?cwZh4-1sm0xj$vQr(P*J0K$FZd&DPN_DK=;gHmN=
z_Fx;S;*Z44^3VPZIV%FrmjY4Rk%54m8iH1WitZgP%h<W|mfpYU;zgE~uL=a)2NiWu
z&N=&jXX8=cDgKNG&LOvU_uH)DzXC}jl<d@dL_#(tAt7PJE42P00Xh}d)}nIqrC1GV
zQ2co5<BgNo<3{!3^yoIZFJrHyTL^{Irk%p2qx$5!93M5mtfyWN<fhy@C5JMv^vG?~
z85%4}l>mV>x&lM_7yZ=NvJVT>JmD`A_b}O>RIWh=0~xaIEfmb}g&zc`esC<%`T9#B
zL(H=WhLol{w$gFTqpIHii$wHqgiyV&gsf&={pNL2)#h_3RT&rkL#IWS%Bd*SXUTMW
z?sLp3u7y1KK`UY{M{je?_m8gCc1BaQ=E+sh=Y8QbTWF<f(3-Bk#T<*Q@$%_kV#_sP
zm|FOQ8<^i-3o<G1;HNH7{;kF*TH1e;wCjhbE)RvQz^lZq_Yd;S4fjO#$;tGQRU6x4
z6rR#}NWOZ|f1?@yse}|S24JFXVOX>Y&}=#igU+?{=409NJ@v@C`8Zt)Qgv<0ZxY!L
zc=XmN!LIi3xlw?ipBr_;-+||X-L5g(qvj-A=U6^%sI#ar6E3TWQS63@splstzlt9m
zv(BBh72b-yRFls2-CWGX>+@-TQGgWiE|Yjj^NM`*3pLNir($<hO}oSk;8L;-t<FM!
zmF#`L7vzZ+xogd6H`gkd`n@?JfxQ}Wm>v7-jzl%Rq<I?ueTEGuAuy5dhb896FJ6Lg
z*_g_|@p<f*QU*r0dG*U&k8$Yj_2~&pKLqyGDpbnSY0K+3`OX9vp>r}?Y$x9I%>I+O
z6Q!e^B(yz$YWYKab~*H8aZ3C?3LN}GkZQ4b9>a8?Y|B3krQR4W|H;4Q012%@c;rZr
zpV++vS2q1Mx8b>YOI}fEwk;3O0>v#xsffyhF#<Z)0!OXOfTw-Bnmo&0as~6ajod#!
zFF2kz`7nI>2FOf4R9i(SWzH<i?OEIW0Ug%M-z+D)ACD}1Sp9~MFbO0iPbxarH<>XK
z3p*RE2&g|%dVZ7@XRPYZr&_LVIa1ZgdYmj`V}e?+Lpy!{s@q6@r(fLog%gLw9BkkT
z85u)LW9`Sso$G}``*Dkj<DudGCh{5hDl=;>8l6<J`-I2jMS2~j(j^eXRaM}~%yB5M
ztq<cp#T%L;ChCMo?(3T0XX<pYirBT6fD)cuk6R~!)Vj6w@{x~1$HCeyd&FMA%5hWv
zXOYInH?_BRrJR>$+B2uNKY7*Wue8f(K=*OtbZUv&S~Gn1oFN45rBYOaJ1}LB;Pa?c
zSquAEtg7XdEP?|OwiaK$D7qH@E7l0Uj<4hDk>8Ev`=miE6g64NFfOUYr^h|`AIX=2
z$QOcOzPNH*Lt6${m8kuR{Pv*O5JxeZGqg@nwrR%H;VJ3oW_ix7S}J7_CZqP9?>$CX
zwL9jU3_HasNF{*ooZt=%;d}b5F{=a@D^yZFI&7l@3X}94+X~pgc56qa^4seVML6FS
zmlE6B9xm^&+M_D>n79M}$)+<pxA%07Zl&hajzW!{vQ9fZN)@^6PAz}-@rkwbSM}0-
zI*|B={Y9^8(=S+!@ViK>F(ZWBxKBv2gvbMDp(osz)Q0PWx9uh+XW`Lj4kkOLyNLrq
z9YU7zy9{0{+Eg5Q&;R}IOu&!mHYX3-i_o1LK%?Dy@Dp?cBKTl);s{qpZlcL9Y<|jm
zY;eo&)Z~md$MRO8*XRp5pNow*0k@l=l{Qw%F56w4%rJ40twB)+GEKX&#PSbwjQWR#
zOzxR?`}7nW>N4tVYf(0xWx_2y64ANSfT(r<N91YNluRCR(VBN+FC9(c&;Cd9gGJ=Y
zTdSY_iJ=u#{~2ljoVp_HURR47{fi05Q>1nqn#l9<iStzL*w<0ZzXGE*L8yR@p%`4#
z`dTzGZ~5!5z<@Ec-i(Esh<A`y!l&jND)^Z8O>05lV4$Rpb@9T2`8fIME05F?q!x6P
zR*};YzQx<b@M$A4*EX;EtN0kOu)XOpLrzb-Y-0Z<XSuV}+z5TH&IlVufOBn`&oHSp
zL6C52s6=*;l!%yMu@u;nUry*6btJk%*u5E^J_YMpz9FXxopHWB4$YyJZeO`{)7JH;
z;t>X2@YgO1cbb_}qmnJB6Ua~gTWc~Vl6mfwjLotrtkA6MpQJN`1M*2GIvcsn&R29z
zR{R9>T=ERPUHvJ>w$41B4n;Rm-Z*qgX!Nb}M`VYtHBsFYq7>pHgehh-v<fh6HTdUj
zv^kc6n3R*qG5C^hS#-FBNy$|wxmi;M+5d;TAW=EHW|vN?p-LaLG?&aaTB3vIHt}^@
zY-D%4)C~I;|0?GECU(<<G1Pbezz`*r)5Gtmh$cjy2$S_g__xNS6tt0k^nhYfwc^Qq
zMvL6$xl>1rPR><tsJ623&UJTc+wHu1>3KJJ0m`zGD$j>Ep0ze#iJP+%JXMxM<%E8M
z9N|+_mkusok?9qJ2BHy(qO5d>V%@vsRcwr>-34g5O(p+7TG5?ba&hLFnOv_vh{8{G
zM$~cDAR>>biX#!BFQ8TlyzNMy<tWDWl>#v-g+D`*G}g}D>{8S$fuuJl%NC3itmf0f
zO&WYnU8d3ZNoUNXEDAfF!y<<XW3OpkiL4Xml5;{_xL%lqfG+#y9C*Mn%6+0to`+(O
ze{O*cpgb3NYb~l!u9a}oo2WnfT%-D4swUVD>iiU3uVi9UJb#!YA<!P;yS~Jn3}rCx
z?g9$8wd)GXwzuK83CjnZ^0t`^)bMltMx^lQ)qBS6ft%Qh<%rOrn8xE<mo@J^Egfw%
zOS*z5&ohR`_WQk9vCXP&-dGz7s0^422#>#6WWTw0(s4InPHu?hJNk!hf_DW-5OZ4Z
z|F!}j`B<~xLuc*HL<;Js1ja49>b-*bGcHH>O+8Rd2OChFO2ji9?mbSayr}M@@Q!l%
z*}jJ^g_qZJS)3vm6NWuPsEVZ5%r{1agu0H1ZD14fxLD_33Ca#ZBvgSRyXSVq&9>r3
ztOHOW1UHOnp7a`Mozhv!end1cQ90|qC^AgAgffeVrCX&xMDKlwslA9Y-HF<|Y<oFG
zq(hfC0oXsRSTj1D(x<F_U-l^SD)$K<Z2f!vy@fawz_eLA5624B*6NTF8!aA0ERv=2
zw8;;3qyLe}c*n;rr?ln>ki=T+@}7F70i_nDSdhdxGoxkr+6YLijZA9Mmy@5@-aa$r
zk&MxgL%(U62=Gss7T9V#T?6OOBMnq`{Op+A{j6I~_@`Q!U3kQ~aX;V`n)-<NUIDNu
zKNbt2WQw)0)9Cp9ADs3K#{U0t(w{6d67_7~JP<~r!Pa3G6u2mO8qWBXDKbU!xo&i=
zOTuy0${Mcg$;HSsAjY5jpX(=3iKVgEA|>t@#AbolHq6Oz|NN`Ex#5}UdJ~bG<x~{5
zAII>ou^e5r)4%Hi`0>zx%JuEkiXw!%nDc%*x{$-0be2~%i}cbmd4K?2=8_uzb>O@Q
zqYAV`<oL_bKf!tzYUjsYR(W@xC;Xxy&d#o5xAnR^NpaRYzSYt%<D(+NCNkwO#4@S4
z>nLFw?Png}N<L73D)0t|q2~dqU&O~E^a)su5ks`^?UI^Lkq)Wb1Ed0Xr(Q%!W1-Ot
zap84Cft*`sZ}&fCCg!iL;11KKWolYrdr4TQgYk5xkMYMYt$?BY6{(Ihr$qNNYY?&4
zfpyD?S~s6D)q-2diwzArxAVKTs|LcmMK=nNN~-aJ`c`be)O!k|iBXVgoOf7Yj=drl
zP0|CsoF6L2O(&n~vGnLpiWM;CUj73U#AS1D*4*r0SF!!<sZ3C@?opyEie-mT6H%VV
z48DE(j3p)YY!Z9KUV!Sgb#b?wwh@vX2AY@2%rZQD(vS;$jj~aBH|d(+BAL_DO02<v
zJv}Fp1;os|%%~m$%}ANEO(mW+?NYB}B-$Wi|4Ku6mEeK$pa`bU%b1JSiU!p-#w332
zLy*eTQ;u4n$<k!zENVy<k2M(|hDm`@^3UMq&Ad=vz4*Y^h`4bM_xK#ilfR90a)-`2
zdMJW3NeNU#oSf4bI`bb?NJz28rLJGgPb?U47f_-|9ylHN@h*XN-NUn4iy9kALM4>H
zY@`1rL|wH#uf%2a4Xbft+ga%GFF(JIQiS8^MBk83|62wVlt%PLkjI<{*cC|q(2aIy
z^vuT)L&1D)bmqlf!pC=;z^3|vd|dkqhI^^kU#h)Q?#;wEV3}WNQow?2AzPc1VU23u
zc;9$==1n{1`Ux1p06n%V(wppaWw*jfQ+nZMo5X}X4}uMgJo!K3>9C-vU{8WdkX%lz
z^C!bDW5dEN(vxqBkez;3ZY}Y2cHfn+CYKE{&-xN?PhG+Qi_BvfM9RCdj#9F)jU))a
z3aO%Ra+Q`k!736YEl|Z`XB-sbp}9oQiNvr>zYxA@^A#5ybFmCh#C0Jue`d)=uvzj)
z`9}>qrY0GaKG^cY)iB-#KsHzYer!tO^Xn`RxBumikWR2O)Chfnwuk9`)BV^`hn;vJ
z#M2DSnpu&f2$4mQD5eb<%C<DQ47{eK_^bKt-Jn(rC9kmGZ&814hH6~qFm4kSdr{+#
z=(pz;E=5pGm6Elcj+*k<{5h)xe@&KG*gbXW4d-;uB#jm)9U#Xji5D5n&Ci7=^ecM5
zsd|{$5^t*JpqLuqbW$i<Iz%Jm!@lji`$}ZFg#WN9%*3nBpXc7CHb<I|z_C+5d~CiU
zj8-(wX(CI#;7Dv{mZ7iJYxqb!b@Co~F^nV(Z7mv}w{j>*A$pHA)oFNiN;$s~=pZbu
zdS;Tel;7&?fLe^x>FPdxqcF_BX-WA6>!2RQuRI}|{dqKF!r@=ljZZI<=|*CryXHiN
z?MdkVXixM_c^m0!RrR@3%mV3-ukNH%kN#Sgbmj3-{I(klqiw?@g)GQh!SFX3=av);
zV?<c(0b>%V1(25EXs3GAJ)-T{G9oK#->81-DZ^m0alkz@yIrUZaOR=!F^GxwQ*{=8
zh^XYLJ~qTgJny`St`D6Zd-G;;YrWf8v6VqON@GO;QeQ>U^SUTyqNSux>vuKPdA##^
zG}A=C3eC$1$syj$YM3a#o|e+HdZ1u-*|b8v8IRDa4E?0T`t|*(Z{hZ@M;+NM8)EBJ
zS^+R`;(35pHhrM+%($l}JM@1f{St~CCef$55&2Ww&WyxUYtO$gP`()fi%Cm}JPll-
z<pWK+7uB5e0Th706HhyE+s`D+x2+IHS*6o9b^KY(5|i+1ill`IA9+41bRtzHbh4bK
zabrN~qE=6C%ihycXQOMt_rWqhs1>iJ>CEHqL*hqnntsFPu5W}+4D^{$p|ZOD`-DlD
zBbRFR9-AIkOi4)aILJ<X?a;qmE=Z0a=TxDnh=7=_QoWNNCGn<z*w5|z+EOW9*RTMM
zbSgr!qKGz?Mh8&pu|vPlwptU9_gIwv{UMf?LMq{n#B#iE3!ZE1e<Tr`+a#Y==><+4
z#S1VfaqP&Dj3ql&eo5t?fLDe>au~|Z)(PA7XMX$r48}u{X=o`)<Li=~@OYSu`r0|G
z_~*pK*g1JUEJiuH9OEvk56}1?iC>LAKL@!zA5;cOK0zS3$}5C@{1}J`oLa^0JM?_M
zJ~tU94A>;m-l;W^e*V4DoFU?q()qra9o;A2B9|p^g~}Xjrb~GiV64bs5IKnDNXt=q
zZ@jjNp<F%%+jYl1LX+>nYE3DT=DsK#Sr@m2JYglIimsdM%$~w5ggsJ=RjAYY;VRlY
z2c3kvB3QNPhL!#m*un6IFUyL+2@q2+l>qJ#bg*K9!zL2pfmLx`Rr#m9c?G%wjP=>S
zzkd5o$3ml8P)YH2MbmlUy@3Y&yH3lik|2u^6CPj0qr6uM%JI+%bgtYBUplP4*ay3<
z1~V?1Um0&3lt`wM#$q2&mZ&SgqN%612qfJ}6rXnWZB2R89ImlGFwfAsu&N~;{ba)@
z8{%MusF@dwg^5=mOv#Mo`$@Iz!s;@`scEKKKn!hUn)o-V1Mg34JZVltCP<z>z^9Oi
z1>`X-FWuVHk!!N@Md=tr9uG8~x9e)W3ND5_^>5IfuLPT=6s~cp?f5GR#uw|Z;(D#M
zV(tEGBOy2m7e|&;$Ye&HI*5yq&Ky>LC<8vq9`1=8Q($`GY@(o&G8UXP1Cs*}EOS2x
znyVRq_-oDfe7UZ~P7llm0EA(<ENX@NNa?HCTu8T3-?Q_@^mGTsJ1LBcqF=Ta)2q{x
zJ+aaeU4GBUP4|fl$agq7nKJx)4~zKY#b2i2=sWxyjl>9f)mRIIiUEssrAGg3H`c>i
z1Qm-%7j5ZIo@%uxTJUp}TITGotwyY`k1Q7?7jaw2zoH(U#6|{Nz!(naj`M#Pt3NG@
zT~-R;>X{g-ln93(Y)mFa2Yv_DjKE2;Eko&l(eV+*(m&&a%;Z;ejU=iL$A<7IO4zxs
z;Ad=~d6lE0^XwdB2ew6M%|LD3fJ00j_o*obh41@^>!Np?IrvA>ooiES5OXKN9-o~Y
z1b0Vi+cRaq<?vhfYykv)-b7o=BkgVO6)plFEP#M)z|OU5wTXV!itjt+4xI+sQ)C*Y
zhj7WL&0C=Ttb1z1Dpl6~^0MIaDpzUlDyBW$UfQ+Q@0p#f1~WdJ2KxQjo6pokrC*9W
z0vuE|(XJEkB0U61$4;#pEm<&>@l5}wkQbDSgu)OlsVu{;eolL+m&!v^_;De6r~#sY
zRC~*bPJz!=Qeot}Eiq(RONNFMpiInov6`r=mb_;7SccfE8Fxp1A>%V@B{Q~6ph-$9
ziRl!W{-8pg`?`|3>K<9H-+vT7-zn?%FC2%Gh@ZJ&_jn!`&}-Kkh1}1(Im~-jb=A|$
zTyjK_PZh;hEGsPP9r{S5nPie*=~&hGMHMGy7d({8IMmt+Q%w6Pd0?`-x<N$amHMGM
zE)n#gkh<YRqwo1;jbL(!7lH!CGjmhw_dN9JUEpv&v+G^pduTz1`<-PJSGvhJHE`7g
z+uUhOftVWLIU)b|5n41=_(eB+0|^XlXFMl$;!cRnL}J-|Cy(uNLj&y0`IU?K3c!Uq
zt*>E`1&F%CB|~`-<^<W^J_(l0qn0K`#CjXhGfoZ7kgly)t-ydu?wlA(c;!jA`uF+e
zyGJzNkgOw){C!V)&tO)>%+dnbyuz;15A@9^_jcKE(2?@p6!m4_P8r3i9qV#Q=ZH+j
zCdO{6uD{bn!*x%eYl65^Gq?~b&+ddvzw);E!aF|zoFkV_WITfk;R%3O_g4z{uf@pz
zxUwpe5E4oRkgB0GQ+TrE#W+~d_s9I)emG(2OWb~#ptd`WHO8WR3x-A{X4;IxbfL_n
z5~=b<oUeWR1oSmdY+O5Maad+m*IT!D4`k9^-i>FplJQDoR7JtR0(C~Da{zf>h&n;=
z!n8atQ)hm}=??{g&WdD=-(FYnajQEtCXi`l5C7h<fe1suo<eZs2lyllU7fu1N8CCh
z<!5GTf6JGY^ip<yev^|2IT#js^E{?W3=Jb#Bg?^WW`b2T-2SMTZBw-?V0_cvFrrNI
zKKmQ5|475`Z+)_SUpHFlll{d4c-_@H%7c5|%c0<z*X~*U=bZ3Q9+f~Xqk>nMmhm)#
z@*cJf&nyaS$2bMO4jKJ1wNbq!SZsvZ$xtBHYYA^zJVJjdTij8b3ynQ@W#Qwkw6yd+
zvr4r_VW;qk&-P(Wf8L8T>RZM+4WBONwS%;zs4=?D=F<R{uysukTV?c7(5`TiK^_Mv
z0)-qrH=;HZDV%8<>1GAn`$*3PFdy2G#3^C(I=zuffc3*kVBBj520pxDi2M3sPh3?$
ztJ0Z=+KnTH&?klXfOg|<z;(|s&545qQYov{x}Tlmy(BXsYpcIF@zi>^S9582btAX?
z%n`GRFv}c(;j*OIN5S@fUj4M2iy88TCB;E=lxMcN;>132&V=~8{aAtWPftZ;@z}IE
zSakPnzrx+UXum}2QS_6`gJf%A;CZOdCMb~ck|X=nkz}$OT;1U#dMD17cv;_Q&#Tdl
zL(}q*q9IhL?gCs0SB7A}aPoo`Ik9+2(j;6{{2%bw@}c4Ki}#GYXhVOCU*gBp15}Q`
zHpGQ_wZNb0kU0T5wcRn*lxqpy-MQ7eLr(IKg)NccET))=f>V3Q`bD)encJ486P^27
zln%$FGz#uXkHQnnLRC#s<ku6VS7oA4?)`+mm9N`9sOVIgn{d_o9hOBWM|emg-UK=H
z>|~%23Y@c^L6-^HAmjIoN)q*~i>as-J;#WvQ0T11$&v#T5TCv2%(olC`*By_+4r}P
z?ANRy{*<2U2ECo^b6ttYeT8H_1$?o~Wb}DoS{g*wr}huKwI^4<d8r7b{JH9>@OmmF
z7_MZ-l|u#U%j?+e(|>7k;}j)+Aw`EeY@&TYG8JHVxj6$J%_(FoRBAF<t8mV>FoZQh
zzzdJvXTKz~slHN0^ncGVsf=4L_`d+vGAhln{oQ1)fXMs4=6JEdzYt?wE2LOs$wy8f
zRw;a|s*moVnX`yu`6|hcR+K?UL=r!`1c|bdHdZD*&B|Bm$=uV0AlHio=7%E`2Ap>^
zB$%w9#Yl7YOmq9N=ZfrJd{^u&Bxb40DVVHVpWOyWJk*eI$nF0C%ItaRXcPz(IWSDD
z@ONMi*WmMT0;v*teA#l=cq~YlsFfYGOw|ZX%m;H~Ba>0G(G5?<71yGfEDYt4f<VHj
z%le>sN+givY|%9&&v9ay{0Cc|86oKTtK)`;e-<DlupN|gv#6rM0p(@`@!e(!AKgnc
zJB6YW7~+Ix9elLNM3i^#so)@&b`BfF_ceerl9>C-INqxB=zm~*Sw#wZ2i0q+VLQIR
zR6{^1<WtXd%2SnK3j{>)9~A-sO1SpWI1&W4un3W#m$U0{OuXi}!P(PS`mFSj9w(ZW
zHXu;dDW1j$RHT&dOmATiYdGcXsTngk4q0;;R{@3QC<zV=QVRhm^<pOkk2Nz3sXOsS
zb{s$5SSeXO(R0|MGN3s*V@qg5&x-7ul3q%C=p>G4R6~KJBL-Io%B1w2Q<dSNBOc<E
zb$4ZBG59O!dKD8QoGHyDZn&TIbPiKyr^tBfiV%laX1HW{v&2cB&2a2Ib6X)G_}ZZB
zmO8Rh?D?<~sscPwlJ>aRLgUq&JUvi?SpKOA%}gw0*yWx$+Mx&LDM)GVfc%feBFCzn
zz+Pf&E48l-)N@5f<xGsSfS9uIG^#}${m~mYl~K)jMpNF$*?o~VP5$Yc3)%UmtLC4o
zlkSJi(n}Omot?P8!LuGoCn_MR$EC6ZAla-^^6)l7Xoxch{r><IkeiYDZ7RtVhxbV`
z6k#|#+0j!b-D#P!N+IF<u}!;H7g0Ld;*123cX0{aJW>ES=PQC@J@I$|FAo+31CMcK
zvJVvvHT>A}Q(!r;H3ntLD@X`7CZj?YvYsMpq*^gFwB5__vrxmq2hAwJ@Y<t3!~7zw
zlE=<J3Y^kptN<a$x)kcXHcvS)9bRD2eNcr293%Xio}f>Yx5bEx9zcAUs1KX}0ENUE
z=ZZ;Uq|H+xGP_rU!7!~771c&{AA4BI%=rv!Po_^(E?Lj{v}r%91nEO#&2m9F6b%Fr
z9M=XJkoD@0hB+AgQ`?d5%nCmWNgie3^Jc~g&ZNB3Og0FC6nnO1;5d$sEX>D-&eDej
zbw&d0lksMVk98of#tO1}A>wK4)cn(I4f4E|oDbmhSPoJI>G0SHUTE=U=WmzkJy~ZA
zeN=)WupUxWm@zTUK1j}+9BMK<WTX;>nUWrR6jn#+OEoVM&2XMw(Lf&?mE`<f7njAD
z%g)5t2Dc8UPAvN|Ti*pBN6i)iJ=r!W_hiC+__{_%s%AY^R@rdSyk04i8=3Q;H26lb
z!*?uH4*8-JP{ozv%GuaeM-$DC79JLmtkab-d6bVdnrim`=uoQ;Y|t*)I%^m#ug2B}
zb*&u3%hho8z}2J)&(t{o06p0eJOSdMZh-Rmtf{x~QJ~qR94A+b6A&H`6qW<xY!R1+
zW6MAek2I7PAjW|$>+x_==sekA3DQlIoDa6I1LUn=*o)Tz{MZ$V{%%|QDa9hl$Qhn%
z*hyhG)rqx0M;qpgOPEtYK|Q1Kbcn2RTd5eA`m$z8&b?7WAR<PP`E$L+z(MoX1ng7#
z=DuPS$?`=M$Pz+_>Y<q$9tr9AY_u*RLMG{D1gtdq=y>Pqs$0r<{nUInCsr^cLzP&H
zXFn$)_3pD8(~)=wQzrVa)zWQvRr}y9`>JtE?vx&k{8lZs`c^cUALH(*!Fat_1zem<
z8!_gL0v`{^`{I<x4;@$nAsO{smtp3kJd~5!MAQ;H)j>0<;>f&j6%GfZXkk~JvVHQ7
zizpmPA2wGCob7}h9_fdL-0|u3+(~nac%nl5Cx(vN(iz~@*;Iv(mxsmScT8m}8wcXU
zdU#tP%=(HA5hJx6nZa^vY+`HVK=76$cC2I$zVB%y^-bU)RvqltOlHj{O{r4Apm)td
z26D|(=NPWyo0R<2K`oBFwbDPD+4-=Qa!T_R!)Y_wHfcmbCexHP*EI-{V2Q8Y?+OR1
z1N~G-7AZp#89_Wzb|@JlC{%MpUjp`vH>%S$;&Jm-sDK^`ntKiN#8}*%GOm<9@z3IJ
zW`N%XITCqK1wjk~CGGQa6bYNnCq>8ALl6&|q#k&eo2`h<EJP&@1dgHiTXmdeCZ`Vl
z+)TBZO71wUK2S~0Jau9~Ii2p1d3K5fPc9Ki#FKY$55*V=jvj8Xa0F7o)?67Z`=>L-
zY&7PL)#%~z=E2g2lV9~l(a<?+k%X-FqCvuTpu4Y*Dw^kUD^-9&^Byad!oEMos}I|R
ziTuSpq8g)zd*+#oaE*_O;jcRX016G9EPuPR2j3OIyp}2>V8^oSZQ5eP8Gz54)+DLf
zOcyZ&{8R1XqwrTuhxDOJ2tP0WHUL_;-4K!uif&Ubc&!}36%)2_o{J%zBMfBNU>u12
zMVFOmbyA4m?#4&xsRW1&gNk5F4=$=334nWB5todv7)XYmFLlM!Q7o@`6C38K#qz7R
zGl!Zcy}VG6Yo01Z&F94>h}G9t1){`)koD$+As#Zt2OPB_^Wo$1bU+HrIUVs-GvP&Z
z9!i5Wt98lgqLT6Enuji0C)#d1!CwADnz)nds<w7XeOXv@iwBxaY&&Sm9&4rIJ1;o$
zL$(AD9xUVgv6K6u#dsQ{iZXHduo&XNgF!pNan*#5<JBT|#m=ZiiYq-)R1^uTxh2yf
z6+iO8B$=%t^-U+mE)q1xLM({R3JE;P=y7)22oDGT-VG)E)CndtF;uk8W2&S=R*K<6
z)s#plKItImJXz4N?#N7u8(dCvt$hdCK$Xvn1}Way(@VlpB<+k=Civy*%*ldR#W3gS
z?yh$t2PJ5l2!D4)Mr2Cm3<xJOS@ilEn&gaj@ibXD1q5AG>;i-uWs}wQik-ELqy`kj
zh3)2}&J~u*<hqV_mmNg&5PviwL<K{G`nqa7nw?owfQ39~erY0$0X8P(GBNw67hvW&
zd{9WPo-T0<;;6itK!>8mgd?aNDg;hwiKR1d0<X)>Ne0IYF#>0hd9yhs!^NDa;0FzX
z(+eb$E~AS1Nd+#INoHb6F!N>rB5_JmhB>T>63-#kd<4U(T!(Wdf)NM?JEa&Q&-YW5
zN#FdUVCmhLIvhH!5Fry_c>+(L7AK?=Pui2ms;Yq9D-L8V)YD^XD<K+z58`6CT17?W
z6NbkhB(G3S8p9iU1<75kFobtiz^(|r4PHi0B|=sC{{V<X!fP&gs6{CkrVal9aa4(m
zx2q}2HfCE0vMj$4Q&gFr?#lb1h~nZ+GgZ<}rzR>UrZbxiX?QY*e>6*`P%Na0_hbWf
z07Ae>DOzbR!`%>o`-;E}u?$4i=!#g%7)bSG=jwbLdSn)NqO4(@e0r(X$Bem04&ODv
zPN`nJNA82lJETscz@68gtVFXkyhq|Fk_^81v4S}(NKfJ`NJ;LcmwqZKW(E&ceSpdk
zgdFWwFGuvD$x{Ozc&7KR7L5Z3Hho_xI_iip-@`Rz4e{L|j!f@uBv{5rHGFbBs~2RE
z6csp327fj2bQ|*09-1+3n(3#-BBsCOismw`9ZuI|5#Z^LE*udVW3seE9%JX($wjtX
z(TWAv!P3X@l+cttRv;6r0=43@h#(5CUmfO(Ze#ICjUjm=y?|l#DFg!m@|@WzN5yb*
z8kKx`mL^6Jc#~+#@Q?0<i02;^pk4v<KuDq9y;L&b&o_XCY>qkAXzXu2S)j?5JkbQs
z5%{1Ci0Pj+Xky_zu{9$E?Q!7oL(dP}K``VoEX^bt{{Y%xHJ>Lm>LMK5tHJ2n@#Q_z
zM<F?E4P_mDSyr%h5l|uMf{tuG8H$MH^HnYm{8!K>y$T1q&26krUo~0cnCV@Td{#46
zD9<-Y>Yc?%ivm@3R`K^isg#F5yCur1zaQ}DWvR3C5k&l0j?LX(Y&kBdvm|~j1XU09
zWOm45^<%(IU=MUL5%)+bkS|kIwI|gy<iJFGqJUK&x*@OlzneY;4h71@-(1lPfR*!N
zdp#&DHZqn|wlp8Q36@yF>g26FUCQ`v&Z!RPnkCWKiat^CT`kOl!x)w1ppWm>2AJrf
z83TYc$RxpPJjikFvTT`!OG>0M)oD8>da{d=oLJ<2?^Ual*;R)Qg0B%hy?xo^9$?T|
zmXFbSR6zWg;*iclT^MF~*<aNb!2aIsB%}qDjv9Ati18<NQ8;`bG+|@|iHLuS7Y?oN
zfJ;pdk2MBvMlR`@FCa+9)Rm7s99R|5eSeJu`M9n_Ry@DOR#ra;gZQqrI%b#<2|Hor
zCw{!v(<hYoT?x=M06;4a&-%MG?!N1jGco0+Zbu}PS_RQP6ov51Ac-s#7EgxWs&ZN|
zB>KFqaB3t!DvavR+=0gzkT#enU>p<^J3eBz@iWZ>4u0v9gwQ(KJ=9YrF5!OY4am<H
zEgB5%m_`PkuF!}$io_CP@+PUt+Yhpd8;#~Ed2NvzhWM-&Xi`hf?&icCnvOJrg?e7^
zJ+hMlU?F4XsZ+bL)s^1sOdUvR9a9|Q;l(EVD1NIwWdK#&g21z9=A!sk_Ob?$4_sF)
z;XokfyC!nYGCf37FHp#HRE8ED>$@P}W;qH)c>XMkZ0%6uVqO{bLSklnpe1E~=wMIB
z_gp)BDG)XKQXL%F9%14dz>u&9no{hV1(YIhQ8Oq+NR(B5uZT_^T+Y|W6gZ;o?y+Zh
zl-gs7d!4BgeygBiiZJArh%uvxvkB59ypm_%7jl!ppbIC1{6$YDiGSycY8>y~lmlI2
zf+sDUz?scVI3OH&Xz76=(dMfIfj}TG3-~{e;`WBSQ!R~4&C0N~jD+5D1$oATNE>P>
zBrl^9f@i4CQqJx}zp+rCXU)h-NApR6BK=W#aWqZn5kO9nI~~6kLxd3QvMN9^t}A0n
zO=ez6Pz*>po92)j(hfP^Ycs^jIhQ@6apLD72B(;OQ9!;M6q#f9LQE%$2v2!Ab{Zok
zkb{q!6AbZEJ;x;gsfuD_DcKaZbbGSSw=aa7Rr^J)I>d85*Wd)<W@=0YB(KRdAltU4
z3Q3APDv;W9I8*+Yr_017MG~6;=8<`(Y!lUrp6ICS-O8GYYL34&SmL`!R6v^+aw?Mz
z!XrAO&Np`<;_wd^4k}KmiPp4wz42pxJ9nDGE3Z}uUgOn3Ed5g|!`QY7QxWZk8ji?;
zSAEt53DQao3C9Hx6b}{BZG2?b82#0R!Jhl7G=`Ow>xinDd=2)>5F#ms+UE0Qg(NW)
zhN3Hh=ID#$&MX34<!755UBmTYFiY`K!0gM_`N}C0*t_4FJ0a`AO=G7Npv*7k%?(4Z
zis^PA%{4=Ng9^a`?3f~w`G?1->ZgDiQ2r<Yz;r9C+4!;|XOg~~icC7}XwQX0tEB)q
z7Fw7hc}=kX1jxlAZPtMT5$1>pNjtV)(28_oiT<hbK(~Sq_hHP_0m~iO4tQmGumUBe
zKysa{&KyzZun<^b6K864l~ET3LFR&Zo2W`!^Hp0ikG%dY-oCk^EgtA75g`52$o(8c
zTAMCS&~t}$;8!)p6zpp3)w7d%mYod&B&_t+Am^G0PB@`fLjd*Nk{j00%I9ifN0Wk|
zkKZlPo;J*)PD-QzlxSse*Fp!0rbwQB*-;tprA(adfityjOn9bSFp5K(t5hbFw~Otn
zgDm4{#>(^E5@swa+DgqxO5;hPIpU@>o7Gbys3N-F9jnk%56we`Bh%rdbT)oT%@HMd
z5LPmcm}aU$&$@z<#f>@lv=Ow~xbG>V2isaGP#3<RyYV3&UjG1fT1y;QgBB0XfYvyF
zn=ldX_CKm)A@Mba@t=x^E2n;HL4)F@f<7qXUzD~Ykq`5AFfW*{a0>n|0x0nJQ~6Dc
z2FshRKr#gU(i-k0s{%V4&&_3*pW^CdMKtd_H*3mRd`L+eRlI~l)sR^`3IZe<JgI$8
z0|i`}2QCkar=_gc)Yi=5L>X8~w}BC#nky1m@!g*SkeS2HGAAg{lP2=F-$hW72y#kL
zUlEQd0!B{<bt*(>0b@y?E6L@->XFmR@e9wxEsp{N!_iOPUhg#^MnAid%E!$VF*|%$
zpbifaNH|ty%y)K`4D>8KVl~wKTml(e`LiN{6UqKf6b1`94K5!CnV``C&7%=15Edc`
zW6gpBdTfMuHWp~icFed5&1B^|O@Z)Xu*Q=k#YmsATwJ6n9o7=&)Q*g1)Dk=Y02=6D
zW!MmdDV)T3aitDk1uM*A5^3W#DDyywnWNF_z7!QWerTR~u`t@r`c)|EhM_%j3Luv~
zP~~`Si%NmwjG9!7%XX~omKr-)MzK*m+!=A_Y)rCo&6vhMY{}1d1Lnxs+~R|bDd<bp
zFhHR9d8$ekFB~l2$w1)M3H;fJHl^(Gb#RX6iP%0un^AZVgYgz%<#}j>DQ622Bt86C
z29I!YNbq&d=mgHM6X|nEiJq@+Nxs9m>Z?9QJOC{xWb*iFVZR+*80W0A2Pm^cuHdl_
z0M}{>N^zBAVUyEmCmUoV`mBaTvIkcz7>EjK4F%$rW%m)qnZUeHRTQM1)5#>`95kB4
z+ufKImVqd*7hqG{Rq%Y>s$_UYoPmd#ibiN89ZeQaI(^yWa<xcE6rVLfFwYh_)8er-
zp0|pyT(1L)Iwwb}Rl755={`TY>B1oE#qZIFn$`(;_d}eJ`1etdPW$*g(VWBr90!Vp
zVq8k1oN>!T9O4J}SoaspL3B*=Gt~$jG2QWW-!glm5J$(G(EypQ0C7X7QLnox#~o4#
z^2jFS%tnU{j`-R!l0DXrAFfJJN|Dvzmx+p|7(G}LIOwSPF|1i=Mt!_lhk|0B37orV
zu=3(mYa9{8*5flk!g-+cQL#^I(bSAhKdOxz$;1?t9_mWw;_T5(<fgFIzwvZ#L38Ev
zM4mFohR}*hNWDc3`=BI6fWuT_g$cf6nx9+lnUjmqJL1HI?vX<x!<zM|7b9u-6J|tz
z<qUE}o@n_69t<ClN9Knq&76u#1P(mbDJ46kBw+q&jQSQ8c66Rfka-6)P%;RiRpl!Z
ztV_PENC0lHbPOE#5jGSd5#5Uh8j=H2^F~|oK|CAtNf8H|FhkH3gse|6__~vWivTWX
zxTavi<%_EfQlK@;U?WC+n4nlmZ0=KLj*}J;<huE%%h-IU{{SyYi_Koy5tRleeNd69
z_p}1=E-zLAnVjV(IU$*>2Lz{d<|Jkg8xk^sy~M>~1P4RINQjh{W{Pvw@c_@7(ufaK
zB+mh?6x}idqyDJCAVhiaD36qbPX~LfDv8$_)o-CjbM;9O7!Mx{$wXsb{MMlnoIp>C
z%n88{>bf!)nTQU5)msdpCk2&*9C>=LLcGs*H4#SgWDlya5-)Yg9eCR?bazMv*v+_3
zv~Jjjd^e_jEhGR@K)_~2NI-_9OjdQ92_Gv{`DMVQzAWG|!A@Ln1kmvKYJhno`LpFo
zvosnp$wE{FL-kntvF>WzrKpC`XZoX7#Pf6kl&sruL{^BgV~o5ew6DkfVWNFdL>*9q
zlhr53G@kB<6rqDD?kl9yak7XmygxK-8$c7jGRu?IgP43}nT+|ca{NVDL(O6{sxHS|
z&{S&ya#WB=dY7a8@neKk&svvl*ki?&+1=$(HeM`1IN}>P@aLKm5T_KD1M^@aIHYH`
zUaBy|rj+s|ScqkgR_vce?7^JToggZVT$q{>5<X*^fH^F_?5438HYF#o^>Bf&Es|GV
z)k)t4fo^x^vG502usi@U<gZ952e!A)`r9;^8NAI9F;Ng#lig?>#Jh3}G2I9`9&cd9
z>cowlf1Oj9Pfv=Q1cqiXxP2HR!3iV7%75~VfCg}0WRWnM8wwmHipNJ+f-D?G0E}EI
zMN2D$CBluB0E^pQ9s-AEB*T~b;?GQ_zEg_{WEmMyPo*&gsGu4kQ7}?Js3BhByAKCm
zXyPG*#gHyvRm8?<-QU2Z0G)4ju59sp%;jQ2cyhWV4^`k0TikWU5d@cJffA>|dQs(M
z6r4?HjrfbsU#zaw#4BW-5b<M741$0vE125R3zlAbwR8BZkVDN=mW?6qvU*1JrpW;>
zJ44#K^RyKdToXiAL4`$=%}?;B1wr9r9jJ_JCV%R3lHCojDCF45nvjaKvrN-(4-^0=
zD1^LFClNIWK}@{VsSZ!Y&K&!rXU5Rk^Xr?(;buo$=AjH?w7eE`4*9e5F?pCfeji0f
zV8SVe)9#W1^G^nJQK>P3dqgJ)>X=We0S7a-1BovyNM+&cyA%gsivWaBbV2j9GQTr-
zAfJniq3eqhC$qaLW+SSSHVWnawK$k0!jYHMSQ*vksr`T5o<vK5#S0+!VH#lb>bW0e
z32QF`&x%+CgLOiI^-UQQ--^irat6?XL65rg4eX#|NrfbT@Ut<327`1*=JFjMr3Be$
zFOq{bAQ>Ebp~`nXR+-fT!x<>Dg~Kc#WkSU$2=C^BAo4H3f4e`l1yMisdX|mKa{mBU
zH$e%Wx|;IC_fQ??8?Z=0?vkPsPt{mOs81C9=M&e#MU%!WUO~h?)+Gfcoq~c)Gs5h2
z>>m|~<WF<~9;uleeb)~ow!zCX;=)5?nBwVWSJe=njb5pgZ!4@M{?`|f;%@E_x)5Uc
zDG?4o@~69y4Ls05OvJUn2zSk23K8y`2)vxtai6-rY)D%<8#OvgnVZkcWV#M4y>ag0
z9xs*2HdeH={{RY+9T!il#TaEdH?DSXBzInc&x)s|nd0&yqKL|XdHJz&R-Ur?20hXf
z%=KV0u@tft73#_jJk3*r3XqATN62}t0uT5f$2KFDFuwbLHh9w~q^~ML?v^GqzU&@U
zz$)!IyyU1e3GOPkhcz&)<hD|o>MFbEs?(i2D(?r)9fTVY0VgxvFl9`r&LMa!qpo=3
z`%e@OgpQ8s0EO&pP7n1{21(|KVTB2Y4LhI?$24$_KzxmgQV>3@Ao5L^c%Kx?jOprt
zSmipgi$nOWf={B&83yHO*TqR%pfG2Z6lf5>8xd6>@8YP^k0;IIAd>mK$`|uOkRNoQ
z0e`w|gObchP&u$71TP;J7!okyumYY}Zx`k%-bqIl=oT11QBDOP785K}DN!t)k<ngm
z#wXCTIapqLq{l^(lmd$71|eTn-6RqMw{<u*g@C>DPr!cb2xlBsLIgr;O>n7!iRa>^
z8uMH|h<3<`jj<<rO>jYve~M(_^~`O+5fe5xG3KQ)*^cY5Vlb1!P_2pNqD-NNoWL(n
z6r)S$lJTu+6;?ksRt<^!xOkx(MNe0N=CG8aF^Y>p;)R{AD%!1!6y~sDl#sGq8^1RR
zkGd$X(*0MCDtL{#oMi)ypEf6Ypb^KL4re@4LPa2r&Z!vb`LY<C@kB<GVg`TvWS;Le
ze06b_J6{KyjtFPV=k28gRd(q?GRCZKh~Dbj?DF+qCNZa)hip6Lr~IXCQ4-a`<Hcf`
z?DbNKAv!GS>-SFrPR8yL4A+~CyxkgI9oVu%PZn@r2&TZ1cx85D#fu=ld#vybr5B$)
zQNqaA#e4vSn<rvkC_*|D>bPf?P%A2BiQWU%F$bla86gd95<J~m3eG=tk{t0pQgB1c
z{pLJPnG@)6d8p(zJho5ilByIUiP^BQECI#UV3~i<RFR5cJy@+2hQy>#eODNV;-~_j
zsH?M!riyWgZ#0iqpXQZ9Fgf@xAPz1n3doa@Ko94|?g5?6kPzpANfdw-%Wy`!R6-Io
z4ylelZhQ$8g$iRkvylg;oX0D4X8@(9ykfFMa{X9<K|1q8!W7#ci1Q><BsZ3jQ6)Gg
z{I;;rj^*&N@nm8zmRVvCYT-B%vl*#h5B}btXyF%{AUyNwqIvqT0FS#XX&nls!n?0@
z!CFOElD;e|xcV2kTsVH~BmN<cKjX!kf5}bP5q}u0TgC;)c%@1p4em^Nx<p62*!<a(
zC~$K|F%74opxNf7f@N%z3n!9dnv(X(ioqfGOPt?zPv8eWs3Ia6e)_Pilg*eXgYs8^
z;N)3I<!lu8nq{Hu#hUpun-FWn4a_!ZsA6ZGq%!q(0j#7)-jgXnaI%4YU8GkpWtpNd
zKNY|@A(Wv2{O2}91h5qBL<*vh%6PK~OJ-t&=xv+<!1$pqVCk;AvIFM0lb2$^fS318
zsE%jDa$KjyfH*roF1TSMn-DP)danNf5dLfsLywYprVD~1^)yU2@^1`<2x0Y9WiMO8
z{Hb#Z1ao{;Q{n{JnS%j5G-n*~jR~`&g_jsOy~aa`r3jfn0+w1^9iCR1%zLfDJgI_n
z!_5Fxq@MmOW#AnJ(G;Mel0%Oxck+m!5;Px*W{zZZvQV=7ngj_hTFe?m&lDJ*U23??
z33W}=M=u{e*Cb3(fe$8>kf$TPR03EmVW=pgo;R8{1@l;6G_{TLz@F+e5imx}Vc;j4
zhF~i>N~MspN@K_T9yfI^_OT+wtW^w#^P(;tQG&eKrJweoLa%mqk;_zex9XqpEHO4R
zMA$@*ERYpS8nRw1PHhqKW;g1r1(@QbVKoFMSFUK6$EqTH(~<6xhcV}>F^i8Fdb0?u
z6ZpTSIo=kpMjk31J6~5>lJjK;PSFJH1hg37=9(DsjYSJMxRpDW0;K-{Dwbk%)dcA`
z#7`CrB$3TZ3y~id5-h<S(&hQ1AdbE$jdJbiv#<?SmV7?1aW54)*r72SS<ClQD4p54
z0SEI@SIzV4pvdIWqzZPd9L(ONd9W476Y*41lG(X72daa>W`688Dq<Z~puak(H&T!)
zb@dkk7Zu>6W@m@Z4;`c8%poV8EDP+IiJ~*dnuDNtJFyAl^IW}!P#FtC1JBR!QDFoc
zYh?cbqnd$poWaeIBu2|$GY-<CJkkyy+jtT{^X<Bc&50(HPC$>F9NpNIfJ>LioDt%f
z34}{#>mw`!#&A{)l4uO39jPVLS8`|ZK|p~n%`w9jnR)E66DPyXQA_*cpiKK~6PJ_t
zxEvybl;jjPOfY@<yRh9J??LuXv0ca#LuDor7GuV46tbPZ%fha6e0iZ1a^0h?rn1yP
zRh^5IB~q;}f5^+dSsw*&6eE|8uFQ=FCYYjtHbGLE`mbaYtH8X$#W;?8qn$q%7E+xF
zpHzGiK+=9Mf&EwIpMBK^?0LE*v10u-BXiI?@lnC;WDtB*hjGOBVnKcil$JWK$v!2m
zjee;}O#c8C6PK$Dq-S;{Bh6tm$yB-K>R`upu$t$CiTP+qVlL(*zwYdjSYwWF9I4_s
zpeQ079af5A%Ft~j%_@=-U-75`Na0*ys1xGu2*aLfOmWzzJ-tOqpoVCul=C)Vc^)Yj
z<?MIv%3OKo>3TsvZZ2q}LV{Ui@pPaB>&=izoNSyUX2uTVs&!IP;3V@z3YW{PxWxza
zU`fS5^<$J}pA}97zHTcZhE`Z{iN#1XkJ6arf8|D{kwxt8WszJM_@;3>j{a`a;(6wi
ziWSpaktr)&<bb+$bqJU~EAlLz@fLA-isbfQabW#aVzcpH2U9P0HVi?W{m|vdHKPuB
zs}yH(K|>xr*I^$8$$4)VYht@#$0<Nq3B=j(pHDUz!hGExCIOOF!-m1rdCoYbARdf!
zRs4CRrQcrfHYIr!C8i9rjBP_N9~Xum?3WDgs>3!tMd4f3%M(*OsX+aCugCmhajA5(
z1DcarBcC*bJo=^wA66D<q!M#T#6Q)Vo_Z=^r0tK<VuJDN#manG@3PCn%f71w&T&+|
zDnlQ1YdNw~^1C-3-j5v7hn1}rgmp+yfixvhb1yKI_fZKR8_%*+sD@<`#r0DxL_<I&
zDblG$GsjniXFMHTXFris60rXOnPQ{KPEV(UG$cIrTX|L;eN{VW$2X2hj!3%XVIHby
z6Btw|^Vzc#B73YSVR^b2?x5={#S$?Le&~=6B0j3~`3eZKl6b1sWG4guO*0I(NU3-&
zEQsO8R(#%tsDeUgc%X>n^Xh>CEKqQyON9)mv9EDm^<(e4YZ9ax5?DDeSoY9HZIVGq
zq?tQaKh=7q(I;ktd2(eG5Cv-FE?%r!b~oa@5~sz85p1(MIL1<~a5&y+U}2c`T$0Pr
z6oVg|7IDMHpIHH(uHn}dqy~IgS4}LS&el}h%FCGfyrM_usADmCgd;pna`F!oW(17!
zL4Ib;ohlsv03{QzUy{gqhngAv-C_Bz<3S0pg@Wr)B=4&Lr4;Mxq?N!~uRPa^8$&s)
z{ZJYf5jFK&^JR-HSsk?rU>G83W<%!eX7^Bz{M`n`NNip^5SRC2hls0Uq~ZIp{C{<h
ze~S_X&xflD02j?Lm?o5P%g?Kf;xC7u>*%Dqn63-6V@KsQG(GV^Fh~(ym|j@spE<U8
zH5}FgJl3cSFBM1fp*Zk!azJ-}Z(%%f;^6hjS%*<mhLOjbFQKTI@-vF1m59lxT)tCA
z^=GN-m|%?6F2pmtSP9`g_gW#Jn#4Ugp@Q&tO9LW3(h(~TCmSJ-#xj|vLd7UJ9oG~>
zcx>QHC3+^w03&fJz%P78RCx4)peybEcX=h3W`;WZsf<PU-N*1H#6XVjo-oW!Nn%f%
zEHowBLbXXgJk3cH=yi5XzIONuVRRtG$+ZUwcPNg?=k-L=j0AGkOB<14^Y>J!Rr)J0
z9QWOCAwqvDsFTd?l;&1iQ8>JLtir?ivXj}Y)+f`dvo;>6uM~!E+qV2rh-m}#ZP+}c
zMh9i*1tPE<pEZ_n<#Np3qIv%SflR!zz~QBR8bsie9~4A;r?$#BE2C<>wIqoSteDG&
zZ0(Jy;G%$BP4b#^wm{1^c5Dp%*aeTxIr*u%#V#S019s*|Rp3on&%al|QVL7FSs9hn
z0RI5G`?PZ`lSm|!R>aHI6u$EFC7tTLu<>>Q=fwkGs@2EpzIG24d@OWi{n;^Arm@`&
zfhwN$&Z(%tHd^WzIr*|4TsWplU<leS9;!kS)hPHpg%rg!0Qj3Q2*>e2&?DxH5)N|V
zbna^+MDj&LiW?z<9GR=1X>n>-CuW6j5iA}n0V9}dpm8(~L~@$fQlk?Z*x|wEtfIj@
z`lx9T(=!C*Ws*#XD1p?UhU}1MHC|Y6s)Yi*c&P^|;(@YQjN-7#%gu#jdG5o49^|NH
zBu#J$lw8rPcs47@2;tFUU`Wp1=t`bUq90-E%!`*R5^%@OoPpu1H3?H?)|q=uUQ>g?
zO}rf0n0_*uQBTb?4ySb5;4t@4jOQg)&)s5Z%jSZmiS<gQ@#e^TPZmO_4RC8L%6NQO
z(h{QhLkh?`n~5;~#f5gMlPSpcVovx`2%9bLoYj&!q-n#NXXdXbi_TP>7Fp(i$+ArD
z!2D_<;i{3gKk}0_Ae%GAQ+6)3nVJtZ;^T&z-{R{hs{m=T^!mO%nrTMxScvgj#4Okv
zMUYNB(;%NzkWPJJL6_D=GY%f8arWDUM4l{LgzTdM26GlpocO5IXZXB0{7?>IrFcJe
zrnvEVNm{w$;a6geCKsA?f_&GXmm1a}l`?^Fr!_!-H*#k?nk0fDnR~6^>q0?NhFMXW
z!8DlNJW!zbaztko<)oG;&5BEwCcwSNW>f7y2Nr5$*Sn}2!GD`BtHG=5zpJiVoV6)<
zj$<n52;Rk$30EYp6bP>+Gda<H#%j$4NzpAy4uZrGa1=`*L7z2Ai6nokJQ?e@h{sx@
z#Bbt*0B0?yEjLcA;{*dHvKFI;wU{29*rh}#mx=qO5j4T$)j?#WjhCv9!SHuR45nr(
z@hOyesK;E9;>nYk59aL*rvYcjmETn+4iD90V2)YMoJXnSYAgqf#}JGamSB=K+nzG>
zBRjbPlx@;S=7OG5Up6@SLro)-^I6k}^;k!Uc_}T)TKT-G(6H$-bxcX(!!R>jC-GDr
zyw;58QG3?I32<)I0p~Wf##zkyqG)9;Pi-CwrHd~$OIyQ=DVU*jDdFPG?u6d#Ht{=S
z-2f}`dBgK}#GbD>;<5r<obBetm`?a8%x(2UjUS6Be@aux__Lmh$RDNooid~qG|}S8
z&bjqQhGi@Mh2$wEvRsB@yqraX@4EvW@ZG3XjuB8XA5?mA=72NC(?<~T%>m1xix`4n
z&lQ9w`Ld^Zf7B|@G(U6!Bf+pk-#;{$5S)RStj0^{-2tcR&#Q&-@mZ)bR6~&IpfT5&
zvLzkhY~yBDt9kmkBfRlb2VBfugsU_Xz{KT>6$RnukY#*5)g;NjE2_-^>7(W1PRjyx
z&^n?F=h}n1j6G8Y#{p9)`1rFKl6{*wk$}~ufF!IGj1sKLapPGrjhX^yzZEA0)bVEo
zf*|6pIZk(<nn?&d_WuB@DG`x87A)9MN1AS;4k>c(UtQLZnI}hWndoOYp%#ZF@`S&N
zo(7`M&uM$7F|iX+Avoe1*Nz$0GJJxYbh3D)B1my`!agW0J2Uk_LkU<%Z_OqH)jUb!
z!$%!HX_U-0;zo64Fiy(m8Ji(vj#Y9Z@^H`-Gy??m)`K3K;>sY2v&w87yBk}8%JUb%
zSu2(&yt9zoIRlH!cx6c<@$vrvFD6lUl>Y#Q9($_M%Dt30RF&p{Q*{$pGkz}$Z7G4p
z6m#=Hjpul!jC{=n2NO^pXL82L=kY^?zN|?Od+w+rGR;8byE=*nJvS&%2YXK8z2)e?
z`9yO3(IZxTRUkvvBpJ=4bE_xSC(u+O{ZlDh%ZYu`nFng$*KG4yN2+kWOx6fypXaKw
z68owounqX41=#qb6UQ1}2VQF<fOv|<CL_l+!WjsF@k|fbg(72nJkso6RrNe|MgqSx
zOb8E;79N((%32$=Q^?5l+1=VjXtWf?YGjz8SQch3hXXz=K^9))iwSsrSCid=rF`SG
zQU(20DR<2{sbNbQ)k1|0mEM=2EQCW)3;{wC%JDT+^zL&)CSk{l#+95H+JTyDEfGM(
zZ<^XOX`!KlXhd-DJ9mUcO^E5K1>lj0V!1f(D1~??3V`Z(iy-L=NHFS&0Knu}gUEMO
zCG{2=oFz`E_(RPY4?ND@cGjRKcc#K3XLla(uEW>o{$#jPEb;jYvrlwKX3x%OZud#k
zU#c=|2Ev8X;gYPIrIFaO8KzS{uPu60c&KeS_ScfXHYM(eA}nL7tbfjRlrn8c#fg__
z<bLc#{;y-rj33=r9=Xd4oz|PpBc?y9$uo<VFS4nzEcm>$2fM+t>(vOcJkUoF_@Z>f
zJ=G?g%_AxJx;Wp97L)N$hx2Fmq6-w8A7xG6DwW#+nvl@&JqI;He~D^ArI}g*3~fkZ
z`ZC}ydc2Q(?$c<Xazm<8UJHU8{{R%BW${dzK<C8)wmijH2WN?^KG=<_PGsuI<sjvx
z=Drl^)6HOediP#f!;R*Zr9O&dl=Da>6?C#KRglSOVg-(fipZFZeOE;TyDA4Uv;P3f
zmMzXymN2lCS(HI~ESLz6Pc+QY(w0#vlD7@#@K2VsPX4PQC08sg;m?{RS@`OiPk)*a
zP=$|*665}qkYO-P%Xd6kmnZ<B4*IVGFz3aMh_cMJq=}hw_HbP8ELur9LhSxqKm)^t
zjh4*>M3O~DG*PMn6U9<TdGlS84^d=-th=#s$kGVB_hd0D^#z}6yigp+s>&x_H35k5
zRj|}pWOj|nREymYGjfYDI}9MRDoy1R6(@F~-Y9tD!&oO@!YGnLTCgn;b2T!Ws0?3j
zsCA>YCb4-I0TB^N0&I*}NSeNEP=@ts;<+c?aQ^_hqHuM#FqI*`mLm{O?5QADQsK&d
zEX3na;VZDD!ned;sR)L8sew(7uNXOdyz4wt>SMgJ0#0AWSiK%>$Ip$bK|dAnJW)I>
zLE>z=C7$Sx1f&M1T3nR)^Hw-MyjhPv*Gl}^V@W#84w5;KG_Wjst&Y7_Ub8hL-s>^w
z&}y`~%vl3BU|+nY6AMBOpPsAo9V;eths7YuIbt}kGPC5cIApNy#!p)(73R2+{a1C$
zovbI`StJ}M)zi<zNvF#q=$$y972B4!Yn14yLqa6&u7T-|s1y~A@nx9-mR=lk*5fb7
zS9_mUK!_i^5JUqNq|wbDY5*AbK+iHEX1u~k2~ZTX#f4+SyPPajnRu-+j&Wl2o?^nw
zK?v2Oz>^#iK|#sbD5ibY!~p(iGgzoW`s$&C{N-W^4(npc88#YpsxrScBOEy@$tWE~
zY0_M1sT-u;eXP+}-D!?Hz788L9#?{r5ZH+<n006Il@s$wB5b*S>;WxPf}S7E5VBkk
zcgm7bSd;spuvafLW&$FxPlt;?8Dl_ZxBguQtP!zVIX>^kHo+jiYlMqD@N+Z+OgXR#
z;JLyUHbMMcp-60$@l%M5;=w^o5&66hZ&&kTS?8<5XMETu>g}9@e>YdxIeJ$XA}Iv0
zryePR@j(c;2qf66v+kaUrYj?-Rb)ZZ(Rp+oUfA?kQ1Puf0!i+U&;I~!xzV%9U8g2K
z>_(5dph(Yb?RG<wF{;ON@0-6SYJ*T|R|l6<Dg!2aFBGLG;NrTll6kW8CMD{k;V9Sc
z&6+jipA-ltS7<4yi1%<n&jy$X_mu=F*_fO1(G^Do@RcSK`Ff(GlL$Y}n=r#v=PMCO
zIb?^`B_(}@-E3Gr*l7jly5RD7%%Lgoil8t?ON>%d#Ja6@e{=(=<K2KbppKPeSDA{k
za-X|ACVh8E@^+eabKSrL7tZn3*CpBbvf2~OP!x`D1KaMSpWT)bmI(tMZ#t}4Y-V>%
zK<`;xmJ?xPW9q|)fAGeRd{sXNyxCU+wAwg)l);t;{aKyaxnDkf*PcEoyF@vf<@mEy
z{{YAlad3_=b4&fNKg0h3LH>Sk2g7>&UQdg|?(=+JH0gi2dH(>!aQQD6%lzNwzr%lm
z_PiIK{{SI>iT?l&OZ~s#=3alR!T0@MAM$?9@9_Ts1vvXD!{z?3>RumP{7>fofqDFI
zKgs=GU#rPq(SLIP09*Xa`j?9T01NoMf9H4k*N5l*)AuhA&-~BUe|Ll7{az38)&52O
zf9mmmf2+sv-|_mqH~dd`_7|W0>i+;E`MlpR@qa?~`@g+^ar|G|{{TPp{pJ0l{{TPv
zzrMU5;kT3H{omogsQ&;Lm*szY{{S`X_5T1rKg0h3F5fLb<R7%&FZkpBU&p*_gHCJx
r-uIj4z5M?G$l~x*``#P;H<R&sUaPOG$xGef*ScQs2hHvO05AX983?o@

diff --git a/contrib/clojure-package/scripts/get_test_images.sh b/contrib/clojure-package/scripts/get_test_images.sh
new file mode 100755
index 000000000000..e85094848684
--- /dev/null
+++ b/contrib/clojure-package/scripts/get_test_images.sh
@@ -0,0 +1,33 @@
+#!/bin/bash
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+set -evx
+
+MXNET_ROOT=$(cd "$(dirname $0)/.."; pwd)
+
+image_path=$MXNET_ROOT/test/test-images
+
+if [ ! -d "$image_path" ]; then
+    mkdir -p "$image_path"
+fi
+
+if [ ! -f "$image_path/kitten.jpg" ]; then
+    wget https://s3.us-east-2.amazonaws.com/mxnet-scala/scala-example-ci/resnet152/kitten.jpg -P $image_path
+    wget https://s3.amazonaws.com/model-server/inputs/Pug-Cookie.jpg -P $image_path
+fi
diff --git a/contrib/clojure-package/test/org/apache/clojure_mxnet/image_test.clj b/contrib/clojure-package/test/org/apache/clojure_mxnet/image_test.clj
index fd200f18a78f..a5b68033678d 100644
--- a/contrib/clojure-package/test/org/apache/clojure_mxnet/image_test.clj
+++ b/contrib/clojure-package/test/org/apache/clojure_mxnet/image_test.clj
@@ -19,10 +19,14 @@
   (:require [org.apache.clojure-mxnet.image :as image]
             [org.apache.clojure-mxnet.ndarray :as ndarray]
             [clojure.java.io :as io]
-            [clojure.test :refer [deftest is use-fixtures]])
+            [clojure.test :refer [deftest is use-fixtures run-tests]]
+            [test-helper])
   (:import (javax.imageio ImageIO)
            (java.io File)))
 
+
+(test-helper/load-test-images)
+
 (def tmp-dir (System/getProperty "java.io.tmpdir"))
 (def image-path (.getAbsolutePath (io/file tmp-dir "Pug-Cookie.jpg")))
 (def image-src-path "test/test-images/Pug-Cookie.jpg")
diff --git a/contrib/clojure-package/test/org/apache/clojure_mxnet/infer/imageclassifier_test.clj b/contrib/clojure-package/test/org/apache/clojure_mxnet/infer/imageclassifier_test.clj
index b7f468f341cd..5890f754033f 100644
--- a/contrib/clojure-package/test/org/apache/clojure_mxnet/infer/imageclassifier_test.clj
+++ b/contrib/clojure-package/test/org/apache/clojure_mxnet/infer/imageclassifier_test.clj
@@ -22,7 +22,10 @@
             [org.apache.clojure-mxnet.ndarray :as ndarray]
             [clojure.java.io :as io]
             [clojure.java.shell :refer [sh]]
-            [clojure.test :refer :all]))
+            [clojure.test :refer :all]
+            [test-helper]))
+
+(test-helper/load-test-images)
 
 (def model-dir "data/")
 (def model-path-prefix (str model-dir "resnet-18/resnet-18"))
diff --git a/contrib/clojure-package/test/test-images/Pug-Cookie.jpg b/contrib/clojure-package/test/test-images/Pug-Cookie.jpg
deleted file mode 100644
index 56f5dc16ed7a8b5e3c5168906c67be5bd2a02463..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 104323
zcmb4pWl&tfw(j7r!96fI!QI{6-Q8V-OJEq>!r<=imH=UJ_h2DF@WFxv33;4Tb?g3l
zRj=OK{i}QR>fU=V?XUl>|Jw!ND$6U$1K{A`0E%x9;NL4iL`GXqN>@W$n8v~OgR`fd
z1C2iyJ0}g3pO2Hf1C5fh0*$h!oC1wMKQ}WQjhCO5tCMdKjTns(ANs#dfD8Zu9{xY=
zjS$}&5-JiBA|etx3JNkRCORf2208`?7B)UE7B(I>1_mw>F5Wu=LPA1J9AXk80up=z
zLW2JYfkSwkgNTHNgoH+bg@Hxz|4sh}0eGnJl868VI9dQa9vlK5+`kb3B>?cYv;Vy7
z{}2KqJQ5r-3IG-DZCDi-01t<N0FQ`>fP{pMfC7&K2af<i#6!Y=kIeOsR!R$nz{-m|
zG7XiGPP#<fyJcvB2*_g{btRMjlb%;r$HwOxjaavok3YI~_)Qx5+cN)I>wgymyop0Z
zLPmKT{DAkilQ)7xd}|2*Wr2goLwJwRg(yXf^p0E0%8LM*PCAW8JF?{81_1Mo<!u-b
zAOSe!zrL>%Ol4;lv4iR3fe5YkjqTYT6}dq6^hXRa#n&kneCYcF$eh^B)oTo~)K>cz
zoCLQ{&=2^#hXr8W3VXHYv<4xPeFCP^`${bzu<mL?(lh-}^uZLWOh3~|?W@Q8+HB-%
z$E&|C=mWKmLUr8m^+9E{`&3y3B=BHjiC|u3?mBrbEd&C?H9j57TG7(aQoS*CYSR77
zxtysb2)urbg7r+ZNTiw$JMGCJzs?}0i4(02F~OSBUA+nQ;o@-u!<Tk?>P4`~e6wbV
z7Ou|>mVz_)3TUl4VU(%19~9LD4=RJN2Q?=sNabtlfVHyAtB1ij6@>C3^Muu)E$XY<
zW&+Hj3Q|X=z|y7)fE+2WB$tfL;gc2<j=mIKhjlB?YQ)^1RsH75axO_ijicL|xKTL_
zLDHH4(qeRKk{Z_Q8B{7(xSpr^Jgh$Ptb%3E(Q>r=66SP72<<DL4CSfhUQ9*}<`D!`
zWSsVK{v8T(MZRWqs0;KM9vK?qhZ7nS3Q${tt(ROh5seo!kW)KPh%bLwfi1&vyNFbW
zeCgP8ghy^dD5^je5MQBLhC_qe2E6g2KmpP#;AH`Q0g9ClS;Qt?ndMfN^1>#40fR10
z{5e9<y<Bj_UGRW;v6vA8ALw3T0IqO_sOg<yv6$h+jQxO4vFt-No(>~l6ptoc7HzYf
zhMOgL9^S+KmS-n^xKwg9^jI&NjunJwt}aL6%_K{8OOrsMxo({R4y|Qejlf7AjhV_&
z#bsg>9BTj&6L1TS4UooS#R7<mg@3Cy??vrZz}Zt}ax<MXsk4kUz*+K0dkU3xIloJ0
ziG3#Yf?%LqjWVgXv$35?`U=9-TIWrDq)9SB=2A0)XEPkMPrB=belzU*8EVbN!odrX
zq{PTk`^n48(HW9nP<tbEMP?&|*BRk6mGQUVp3%U{g6X3^&ZD9@aLP~C`bBRvv_`=~
zM<2hY!q%JxY93QgkbeTc*66))a>}4t#@8QjFHh9RAG<C8rTPwP!#>3xW2Y6Zq}X9Y
zyO_bwqX(3it6(rC3x%?BABnfqG^GVq*qJk*$g$GSr{8b+S6Q0}Rutb>S(Qk|P!2d%
z=Rgpp^AhBlj8KxCx^ub+NOU32?89%ivS+@$Yy-ih<DHhSFCY~kqos*d0Bh2jzfGag
zjDw+oEzt|NK+`r|4Y<1>5JTVz9AL5u-Yz7EiKwL2@?a)o+EwCwEds4<5D?jM{^78=
zG&g}ic?CJ<%|_oxIC@2>@zzQhXIf9li-a=mT1Q4<5s9m*eZoT4XdtK1oVQOd17&?L
z&}qYf5+VoHBazfpI2S+|t#GZuQE{c<GCof{6j~6nGsaM96G<H%znXqLG7H8|ywLOF
zI2y?;Q$L)hRHP1kO$ZIpp@Yzo6-q@bIHUAFz1&$WlPvi+1dxTlrH^pfjIA^S$H&l0
z?S|PiNBB;XLNPR`6lJb~y|7)D;<qFPzk)L)pA3I@feQj}HlvD8a$B0bOiyZ@O3D|x
zQ9f{NIP+gEHE%Mg#xbZ{IYn4Gz55^4{ohU@zVlVqWn>?7<QDT{`rcOR#S-&kRI-+$
z1_i!T_>dN;ieO0`G_cbVK5oYLS_&)D=!Yb37lkg*jE#R_b>S@>k5M)D_xkAMTg1BW
zP#Ztl-;xerTn0l{S!6ok;t0`mXJi`yjMdaqCko6Ea&{&-ysi$+h)||qVOj76)w&p%
zK4;QaMaq^{Rn7_OB4O<`rdKYQxz7ufV$KW^MxV}Pte%$eG5-M?&~j250PsR3oJS|B
z!t?ndngA<FFI9mCq)&X7aIt8g2>gxTVav&|eMUCMXyE<^c?Vu5LvNj@{^C?=jF<>T
zCA5-EbIK)(+8;3Z&ZM=P<5zh=QgJ|CVSI~NPW{S+nM<&hC}XAT4(gI-@8(6reSnJ5
zKJd0hDb;#YfFAX8QmyNOQZOaO{-v~fvxHvzOyA#R&3?s-Q6PD5kJ4UW7)V?8p$?7s
zM0tGRWV|%vSXvi>&w|^!f!-9P>;?V@sL;(k$x27pwyXMO?xdt&?Tf0|T~SVX^4$P&
zu@xQui``I#Ed^PaA4vt!oC%{ZOtNa<6u?aG1=frC2qLt>1MlT0g=k-;1iP~!@}TcQ
z5`v25)^%{bmuY3Z7@eJnA(*<8ja)L!DDh#wi<-1lh0D_)_d#FM&|Iy#bOfPRsAH?;
z&T&t)Nce2gS_p-spy&>CBy%-F1p+>T5gT55VTL-eeXbM~O^9H`rI3jn3m?E#TACOM
z%GE|PEj=GUOil!$V`|MG5$YxtNzHr7X=zHyYw2?Wi7M;NwV>|^1GCMjq!f~i>&EZP
zm<H<z-QJzExE!;fi)is%H%tI^Y4dfDWull8H_BSKWa|7&n*@tFI!MDZxt$aC31kJU
zBQvzkSyX89x|<LngQk%5N*1Yt%H}dg0!?o5FL&TzGI%7huML(p1k$11O_+NTH&yOJ
zrM0U78cVHr(M8G#gEmxIe1|PR5SOndZP7o&v?yz3IxuKuI+Q||6`a!_N*G+;OaSi9
z3I-(KOruGYs>z^k8RgA1{vVw+aFX%^+N?0GVzK`<9KI}=Rt-}z&s!AuMarvdI*+G2
zvDem?ua$)rl<~WS2(dxIfp;j8;xN0r$57E6Gv<B<sZ`3Dt@xql{T9!e7CQ#af~y2b
z$s~tbIjn1h1V|&q=j3Es^jv?~Yu2I4*8~xXg2-tq(S2m>?n|`%DUa2=D~)`lyr6(S
z@L|k1f^XQ4w!y*k7{a^rrq;QBGiZwyaQYNv??85Of+d_a@aIfy%yFiFnbbvAj7t64
zzCkcRc4NH6gh;Vwa_oC~Ubn@HEX8V2c)7|g?VArqa7siuK9L|d=m(m++Ey0moKUgw
z9>f{5|JtavsYMBip4;-Up2Bv~r-=}8tQX%)(p`=FOfsWGk4y<|GXLQ%A}~)Fh%PSL
zu7G3cO}&(-bCHX311soDFzX7qW*BK#vW6G&L2+(WF?QyMnW>@7k0`<FT06Cd%6j9x
znRVd9dLU_erMVi~hY~@k1cv`Z?Qtmcu^OBW4nscJOG$G1+!PXpoY7oaoI<g<5FM#5
zyH03tD?sc8Q<q1;T-1CZIB<wOk(JTsO$1p1;3FwX$grd10Dqk&52FTt%WFZRR+U7j
znPVyDSG;z>Wr8Jexc-%N;Jui{NHvAQV)q2v-ynOgXlbcJr_Wa`<B6zctK}i923dS$
z{KgpH>B~xA+kvDK%EIV1)xIzYH#&T<4&Hs;XFjP^`*`9LEp^91-74u5QeBu%JyZS4
zQoFrsu<kby*?mofmwbI@#yO*ptQLsmQclXCOfq4HRKGZJa|eV%b%!}>mX0!@iW5wQ
zL&)i@DMbVlA}vACF(9b{vfNjvdTsPKq?xm$G{EJ1X`3U99fvX9mNIB8M$o1xr-LbU
zr7+HPU^8&<?`J~%f;9LT5C~9mAgM4d<5ZaRcq&ZW{0{Fw!9WS}f7klJd|lOu;CcKe
zUBu!AdDLP$^BTNI^ilD4WY(3vNjUmA6dy<$eCc@<-|2&g@&uyKiMA5xzoOb8P<eTl
z^5wS}vD22tOWfHVh_|1VHzF=aIY<4<^3}9EPZGrJu3PSXL-HJc8c3}Q88s@Q`d;oe
zX-2!gzu1iIqJ8q(-F1p~XrgVG%&;+AH@y4q72#>+*#(!~B>0b|r__u2pE?DgZ3A~}
zOzFLdc?tb|n(k5e_M<-5f%7yPf`@k~tL!8uQvG7;DVMsWP9e`<o>yNlA6#wdAi|ln
zJRTZUfI@*{wa;TkpIfy$V^rRjK601E!6!5h=ft#Yj~K>LaqKf;W5;H1ImSlLz)NBK
zR|)nOYaH0pQf$B#iz1mhTC#d7;Oq_L5J+uNUHiDD)6OYhq?8BYXCxMpPK_$}u#RgD
zi`P{YO%4}FbG|En)1VZVZ}bXc&3Z}~gW_CYdOQqX7mZ`jJ>1^^pi5&JE_c;=0ZnZp
zWTagDbwLZ_(vY7*94HF*a~PwfuJZj#A$a{ub#sUgDadC~P%LujCC>S4dGM`^|5Q*A
zn-^N#o!)OjX-`zDioD6PwT$tm8I{+8mEI~N0fPq%5g#CE_J)e|6T&9ID?+R;#3Q*2
zV%NHEG6x*yMU|$E(<b)F{hB3x1-OM7Hofm^(`ZpnStOsWYi@4qayn9^`q?i?Gkd}i
zt_e7ZJXHv8Ywm`&dn;FNmmko*ax8v<Kha3`zMY#o6-dr-_Ab_QI2fYxBoxCFkDlJC
z?24Cgc~)W8Gk`942+f`V1Fn8XqP&-Uc<@OD3?B(K7^G3#dtl}$Yz)HloLs65Y`r%F
zN7P;^9u=-Pr8b1S*q{Eja($oXyNNm|Ah6Cxu+IixXu1z@NLCsZYOE;>n=jz)tbB4(
zt1LnY^!ziEoi>0vC<Ht@7yHDh2f_KCHwN^N|0%>RxG~oH58!ZOE<)NFg+`S%<J$!G
zFK`Gf9a>zH(+sfapLp@7POu(2;IOw!%LnJ{pHX#H8kS2{8OB7@g<~q*$KO)xfL7GR
zK}%-YYSfES<DpLR4l1!*pIAc3rt^G2E%5YA+#D6cCs4S3MhGs$Nt%@1U#C%3bc8BG
zAbRr*TIO%nR)<q2^{>isO2kj|raJ1mdl6^Jcb07Xpm)FDQWpwc?umI6inkH^P$u@C
z+MKtv9U+-H#~8El%L*cBoZCyKdoR~ZGR-lb&`V`ftfmHueILXv*LfJt;-i`ddWX5{
z&Gh`vYlef{y>i<r2xYNWBE44*%6>Lf#XwM(jHUS<hCbdyp`9$UG%F}-o%z|@`H<Ed
zq^)(Pp~$Rc;6xiC?N|Gl18L9wSXoy|0jcAS0N3qgWCm2dWkmE8kZ37VqV#U#=1grL
zcBx{@M36U`K9`#Gmv)4CYUeNnz3Xt}U@9ayD?FIGY+&sl4YBSjI(H^}k#|07?_o+K
zSiDsT9cl#kyh$(x8HhgJ_zd?aur6^OcIFmsHrT(4j;g~}r`1vm*H)LnM@~u)$SYG6
zY=y#0^7kZ@;;$pTe0L?TeKbjB8D-0vcD#GSy>Uu|T?$`p87~Nn+L?Ran!-oH+!+HY
zDZGM`iCI2zdY#YKeklZp+De}Km!>hT=Je-XgF3na6$-u5d#MPfx{YEa++h6<$yADk
zv<O~4m{ARTLDKsp5N)B4%Gh{mG)#}%`LmvQ$9}pq`L#9*Lwyubh>xZNPrC*P$qzQj
zn+{d8q*#?I<|KyjIyhE)P&-4Kp#aIvTB|E;M@3JK4vhgg&VK;o&m#W-EU1@q8peP`
zsG@&$%71*Y*84a2drEU{WPZ!0JYZhG`>aXQr0-?uN!;_~BTZhmLX^tbpl5z3mXvkQ
zpFT}wlxL0O|2p?0?FqD0{HLk%)PmCnNJ5~v(DLnkmSXog;2FWFKh3Q5lsiZ97%Q-B
zSs&}>ut%&5Dk3J$+z6iXpuktcpc7z|EfK(3_^Q0}>u*_mo{HgN5F828xTRhIm)=3J
zKVRaFSC#w}vV8re_Ku~)(oS_pVSURIk&KrgMF^)+0A|YbP$Oae>yrOd=jI1o;0|w#
z^zWVxf;L=@O@yzjUwl`;eAsgFQSmf)iDc8*BNjAZ39wTSl~<Rju*sOTxZ;KG^Is@z
z7*Lb&o-tP%<bBg>m!J6uh*u3`r1~1|^bb&tWBj1@(WR^R%h%5C_Z3~;SDw-0?viq2
z-iB@@7gEn_-}W09?@uZil=hGX_fyjmEvvDWD0Tq^a1-zi!3K2)$*2DS21{0H#>}!m
zF<h@eak}>IoB?i@D6R$=H{2EurTrguLw^Nai>SBs@dw}o1MH##O@?R+k=4CmPoQ6a
zL!RY^94oQ-R$Ae<ivmU^`VI1fy)G>nor~}|>^mW6BiSJZ<(qvJL}~f>-?m_|V*%_p
zRrfRK?RE+e0^XlaP+)^J)#2@a<m2}BQJotI&3aLXuw{Rzb|46)Ho1Km)v5Kp=)tU?
zp>Oxe;6yyvQ*aQX9G4xWst;_8UHS)j*XTEnG-=F*@GS{MIT;fRcy|i@?Emm&Fjj;X
z_TK5NZlvpR5ys8|M>Nj8FnXRhJ}S7bkolY{vNli2he+JnTv<@dChWOXI?#VLdri>l
zK7{eI&MmL-g<FUa?0pbjK2u2Ht-tX77AKxeziltJCrMRR{!kDb`Qx`Ouboaqce-x1
zYwlhusJf4Z_)G~~fl2j_)8^`q2GQY92Gnyr{$g37A^**G8vqk<m$~G240<9=XBu5`
zX?BOUfHJKvz;6p{Atq1Bn?wV&`I8EXt-p+R@Wh2tUZBiB_pM4Hcd%#wNh-Uu`RrDu
z+iRK=pC3y)2uv2*1&6Gjc~m99M+>9Sq{E%VjcObf&98aiaCP{5{skwj0OYti^+;__
z^j)eH2>V3iO&bNN%8PD_H=KL@i{2@dhs2H#A*MW!-93R8*<blivM;N@xgVZsx20~f
zd8UppdQZbqP{2imINUU`PX6A_A>o<7ei543N?XpNY42SswU-}E#aBli`xxqzGQ~F;
z#&%{PW>b3la3B4PjjhXMj5+?R9Tsc#EZX>0&a11<wSB|ka^a4PL_xmTbS%sUM0Bxv
zk1KRBd)#w3`PWf`yrVAc^KnchosvzqL{9RC1V`e5y({%EBwcXfX5<0bAocg8(+`Cb
z3avyn3dWz8BU538$3gBK7x+6W+v=>Wo~NYTp4ix;n+BHSFXr}lB=;rPm7|w?^o7y;
zA2aovhup-b!6<P7ZC`Zny=%Z!^7nIeglg5d$W6;c_-Dd<ikR4YqYHV?6Qc1-Oki&0
zWh&Po+N8W3)?_@J#RvUxT!!94Az;hW=Lz2>u7I>0%wH|3hT?O0J#Yt#Yd!$-GAgC&
zd&W2(ht+L|YjgJe425>2EOr}kwpZNM!^n5>F)G@~+b0SqF^%h4l>!MZ7qOokT&#lo
z&Nf0QXU%}H7z3PBs*Ytp@tTvV0p$~0ys6*X@ncsDvi0!TK`$R7f1tYr&9`rcH7pkC
zW%EjTXX|&~D9WRJB9G$QI@CFQVy^qjueE@8W%-Oz7q&!9Cf0EJ9$$Is4;L<HC8u(y
z*zX7l9z8LIXU$WNGIO?fgxP*q`z#+f^NaY!ql=1bRsI2Du68Vb%!tr!8X65pxDPoU
z2${m2wRDC5X$)NvqjL3dzx<#8rhi2gavN#!5V7gr<(z;W-q-yDWdCSw8V140SzmNd
z%a$fvCPcNqAW&AQqao?OI|EaX7a1Sw{{wvCg9(*8<ts5PGBCR9<^xa~P3Xi2|FQ<2
z>Jf{HUym^dL<!k;vjuG_lIw;d^ZcjGH+8<}->-0MxbT<3>NjT%4jiG}`b#<Z(xzP{
zL>PE2xxgOmf3qjVkS&i+Jd)K}NOD7b;S9e0b<FDHS;^bu)>1=5WbL*2EHwU7yRZM}
zlf!cbEXKsptnq*mu1sIzV^YuGu&gpaff0-b<x|!o;^|1UWRZ!k?)!tIo5onQHi-6U
zFMoshvWgF;{!-<4^5Mr%ADIMaHJV9vR5ngm=be*ol=h`Zrbse!nBeRf&x@&NdqN9)
z==;Oa`#yWhV2yYH!ACbFrLTW`5=ZmG$fTn0sDAu>(l4_6{P)+BfXHh{;hDDU<P=t)
zyPyS=Sj%Ifgz(i_b^6Ypw)MM%bBuJfQDVBMy`G?SrQG!vV19<GvFyH0K7j=%;zNV@
zB=dvqTP)BxbAjPD<NTPO+0H-O`UgldPKnQfMBtn9sE*)K&?h8w-9P*TEcVRw`<VBM
zyxa6*Ts!&e*hA~tQ~(O1cK!rr{=V58zMu4<u7UKJMrIgcXk}?chj5jpK1P*`h~vEF
zTeMN8*VXfw%MM*FohAill9HNc^u&Fv4Y7%GIYg^+vhcE*E50Q>bICRma2LEKJy`Z~
z%u8txOYlvB$%T6EQ7Y&bUMNZa18m<AVhfR6j7ey+_WByYaF%*!>b(ZGyo4(ad7}e`
z#y<N~yWc&Mdo-GvDB8ybM0aa8hS(GhxC(1PlN97a?@wh4jrF|^A?*%K&rH`tFzODn
za81Kg{WCV5MpOzOK)7&#_jq4gG)FhgB9jvBS=zZ1o`<U9Ca;xr{=%QJdr0gno56|%
z_E)Fw4%;qn_fJ51V<SDyP{}!o3g2(z8fp|b!>bE-8O0u|^HeNrZ!X_hzR5Mc;Xu-#
zZ45#12(pe{Wk00Yo%9VCd3Dx1u(1O;>uE18qG_94D1R*vtFOEGMj)`U&mu*&-=P-O
zsraW@*f!L8qI3pcad3_hn3c}B7;$o2N2-&&+&1<D)R782lk}@1<vNk{tuE7LIFs~J
z=l-oSK5z=|K<)LB^c#e!jf*)sg`<_y=>3*Vwf}4dg&Sk2i1aswU@Wom3>Hspv}SOJ
zf+j9i6q$P@X|scCXnB&Ilth-67Pa+^1rpoY|DHI-Ips1tNe$J=d+S0ewA(m>-IdL)
zph{ZIQnhEXb9@f;?}Z=sN^(BgeXLWS>?iY9HW~}Asg9{rQEWdbgXl}`L^yM)jdC&v
z4lsA0vZ2J-7sP9)1rQuqm&Xa<z~?3<OIDP@)TCqbpS9@t$E<HFCC4jDcd?n**I13U
zJ}Z7YQ!D)~ndCLYhyPX)YHkSYe;Y2%)7#4i!-1>}mv6OT$1KT?<4mj*m2`yKGV)xH
zOQ>8YGss*JXTI_K62vldIqqoqSTY}oAPI5>X_tpQ3B~dX&uho!h_kPQvTg!XX$4j&
z(|Auc5R&Y8U5#inAf-TAT?a+yrxelBjwRja)f43Np|u&%KrSug)nu8-aJ&JfC%cf2
zIe%`*W9b75!ncb2Hk@^#t(_7>(k_u_W%({j7HQ5(mnsI8!-H~0{07l#M$9tBOY&J4
zAdx)OUsSu3HaQekC^Gi;2Uf3j1&15M+Z4LIZ_z|97b?aCJAxch6?q4#8{W_wLOvjz
zrmB6VAbc@mz5C!{bBK1ATvk{nkY~F#-+}Q&M<r{+L|kjDNO|5bJRZW3=)(Iz79&zd
z|B-v&i)KG1Z2yjMvSV%o?b5!ND2-dj`71Pu#GOeVBQ%l*?>xXCg`!6^-sawSe@N*3
zNvXp{5rLFZZcH>^Pi$-?33=P7@LJa99{~4e;QAjRyyBzEauH>dZ%-p?&vi+dZELQg
ztxjaBy4T$p;3!4;$mRO*LS+I7F)C1PRS?B|_OHYA&<Fm?Nq!uk^_lVd%I<Nk;CxN{
zl_3<?@G)SaeZ4@FsQ1UuayCh}or1qORpP}@huux)uS@tZ<-a#H20oZTQPn9bg%H-f
z^QpcMJ`mpAw_OLHRWFOok>bVTyial5sEgePdJBpM%&m5~aTtM}>nOJx+1&B6GTHr8
zMf<~@Oeg|&L9<4!bDd_^Q0(NpWr=pN*P6fweBn>MXw|GX03^(YuFX^pLLHB5bV`W^
zKl0}UoM>Kr+;<}AlQD4!XPZ>Ha}{1N=lf9m;YX|QZa1sanrXO0BcQtX`;Xo#aK|iH
z4QD4CdVja@H7Ht?h@>4{mD<72KZBmh#u9VvCzZu$$x_TMoFJH91Iy`KDtov}vjXWe
zk2m4QE4(I&Gkz7FLAG{3aAh?PRd<^o24|TI{E(|`h_+jVTWi_hDj6hkYlU!ruaqW&
ze}Hc!A6$Fqs%#2yCA(Oe)8$0v?e)o9@&RZlm&VmVupR$;VNu|MlYi%V^5~&`nyTyM
z`Nx*(=P;hlFJYOXtR~&b<ORaqsUPR<u(;vt$R5|3#absLiYyhZoo~!kG*<Rcuoxzp
zU<9bHja3D*9s^wtm>Q(l<PvtZ_VXW=@XE#M#@Motd>(WqzvbIj9;GlIuQ=0=K6z+G
zJ;=o^D(P>~!v_?~95Tkg3>Lz}%*FD~)ST9ELdp^_wi)t=owUhaqeatX%X1z!05(N3
zn+)w{-ml;5-Kn4l1;9ufd;zwxhyV_&>>p!RAgZi-4Zj-0sc^qtlZul~N3dC0k~Fbv
z@cmoW0-9;EZwaNVJa%onebF>kOw%BV|FNCXW2t{r&Xsa>^U3D|_t`t7F+Wd!>9&SX
zrOk<QW;#kx58PVO4v_vfv7WZ~24zg$Aud=be+U*=S+Q$d_HeiFZflo`L=0M=)%eZB
z4RlHV2^2eixPL0FTDYE|yfHmqQ2mg*YpUkkBHwr#;qb?*RVJKAe813taZ1V6M00iQ
z(&c$9!MBaOahc9(vpIY7AAsqFNyJJ=sIl%UrLO62hU)S5anj`pym#~s;D*x!X6pBh
ztc<YfBz#6{%Dd|+OINYAiKzVtxY`Qpp`2?TRah{fun|M}`Bi7GV*NX`st)6L+6xg(
zlznJDW}P1lKu3Bet1s>2jTzv9VHYI-D7&2sak`{>C3Vv#SFsPBqOH1iZ!Fh1#FP%g
z@w_UbA?6Dy{m9aBvWf9px$0+lXI>03NY~xL^q!QAU<3s+m4E$=nQgg++W{4}F=|`#
zb<;6i<*;t==vs1XClAb)CU<eiexyv$-5#Ui1&guP-G0(lLK@9s>NNSjjRlMCtBP&~
zDq#502FT8(^NtTI&>c-;`9Q^W-58Y@YWxhO&;9{$*k+qbPqEw((*<G?JxOd~8Nir=
zaro5r_tJp{-zQhXQm!|3=4}hVS7zOzQIub86M%;SgOll=LmKEB-T**!!9PG_crZhv
zoijUIW5**y$mI?sD%#0$SlVnq5oI)!YissnsZ5K|a5B$Y5lZ8eHB&lauSp`p@3jVg
z`N|$Rx#@V4K%t5C?hh08LI4&`Gl;aCUV-{XSj_@`dp1lxuY9uf=IJn`B3tTDl&nd&
zWpU%mET=ShpO6dZhmzAWhD~tiyv4wlem_l*qmnI>JWnn^CN_T*8Y$36z33F_lrn5`
z(;(_dsb#5@s~iH}RJFw6K^>Y*RlrC(S71b#TI=}Wqq|byq0)ekYzRM)UF1<on`^`=
zYdKLwTTGuYw+B2(o&9(+n!8P0Tl%9SjMeb_X)xlpN|572a=PNR0?kHq=}$To!6_lD
z^q_APKY2|40Zc4TdJiLAYF)_6J!y9^>Q3O;R7$WY`&E8S;*q1I;u4YCn8Jt0LXsv=
zf*mFdPslo|<79wqv{LT1#bYFt4?i{Any0L}hO>+-<{N^GDuv_J7+{c77yFRFi%T#1
zfp*6~5J}RP8xN+>v-96Ew`cTNbhRoN9hSr-$^?v^1432mp&tfHP3(7=hZN)P4e;1S
zM>^k)SWN-wNVH}X>py;24&JO`oOk_VA~}D%=}6bp-5vT{1fhq@>}yQ1kYcN+X`4H{
zRLIXc=sE{4IV;8od9`p|Bh*+cE4H>i{-b;<fVr%-JizK>tpK6f&nGY8L`BG)Hto)i
zko^y&a&w>u+oudL0xQLSe$JNCFfMr+d6xpktBAv>1R>1LVVS<=cuJ^|cYQCL$&cRx
zb1v&@=VQ9GEV7o+gt%)VXOr$-$C|S5Xo}@W48Mdld(akEOKXdDaau-pT8=;9XUpxA
z$KF@vbzi?ICu99SqSzoma`#LwHz_=+!I<Xn7+gb6$wu<ZNfj>pI0-*+?pWQ3O!}A@
zd*Xh{Ch*Ru?x3?9Th7c5=ZcIYIA_nI@+}tU*JSB@bR*AE^80a#p<cSoaWVL{w<FT>
z4WIl5ZWebJNsZufSQEc1y>l?$bh?=YrOWwn(35}It;(#$SLY6(c=em%{ZbJ^$q(DH
zL03$~)AZ^A3~D_LKL4#VA2`KmkiIwCk?B;2KGzm${S7yU#i4HJr%yleo9XO**h&}c
zXSYiZc%`wK25+iRPaoYiNI%p9dpq>vzb5`MEtLN#J=pZ-AfNPXf1_Tg$e-fGlP^U>
zn!3Z$y?#y|f+MPgVC#QfI&ndQjQqpSOQLJ|eMCRHyDcn({sFl3)|j+cki9QdLi^zK
z{oA1QXS{>lgzpI~0sgxVPhpw$U0$r*BJNw?f>6Y@jwLP^yUML6&q5gA8$}|}gsU#y
zoHSkyN}@o4NGM&TM|Wl7P1xS2$pRNFfXjb?eS)8ByU7~}B*_8^t=$yOLzMpj62@I4
zjnIa6?M`iEt^RQWPSKF?A3IfU)-l{%dSPBgQz_RK;X%+n{9F<e#Qg!(AO8TvfrxPH
zwkr*IZT3sNcaMQ4HvRYj6p}54J!+}+pjx3_o^9@Av<YhwblN+D1OlxnRi*CQ>tAyp
zbsO$@fAl%Wp|l7QD#)!yOxRvqVjoROx%eAS*ZuJUKcjc%=qNSfeP$^|h&7j_R`#A0
z5omRr#jNOWft`!}15DP8kkwoRZ>oWX!zmwn+BeCs{>+Mfx$|9BL3|$4A4hZ(8hdT8
z?{x+5h(7At(}d5wMLgqAl#ZDO8{g<Ax+xP1aRxlhM3%K3r7*SvRZ$Re)y3-6AV14<
zt>nq&xz5G2QiwgT?p-B}57YnV$t`TR7lFE(Ne&WqoeEGg`*UsYp}sJb`{L73yqJCM
z9J&<3`Ejpv9_Yv1iXRh|6MOGPuUwp#o@(rM2wIR4uMBlr&B(m8&8Y2ek6;B31g!k6
z55X0OK`wFde6Q!G9CtGw!Va*NMFDy(SVaoW<1e@3FlF)=)?GE_8T&4yeNuVSaiO7F
zrL3Qbk==jQS>@7QzUQ|n+=k^!J=^>8?bnVx=m^nR3tmak1W6Q%ue3W-QwMw0*h>-a
zjdkzJPM3U%h)|M9C4A)0dB|S8v|vEF>2wJ`DFO%YfKSj0@NvGyCKw>x5SGqK*yG?!
z7}wvCbO5(e3KSZ>{)#M^bNCc>e576o@35QABP)j-qzx0dJ|>5LY?LH@HZWiO>Y5yF
zm}+q<&^i;DzHm8lw~Nc_2N9n(=MTlBA*JK3j}K~eSSo72l=S0;VRe>?dNU!`8Fz;4
zw;p!+o?)@yqD0xJ;W%tYgzFgBr{$<ZT;_i_1sm%P@(!zKRNmUzJ?KC%qxr0dwbAv}
zm(MCVXvRe~8w~TRC6vvUb=Xm7khMTWgdP=0$Fp8iHW|83_m)d7K`6m`e`W;tg(@g)
z__Q_4mouXW#P}F#Mx9C9g^9dW#sw=O*_i>Q%<LlvP*%>{%5sXux00mP+nbqrl=(!k
z{OGPmmVNR>9_s_m!IDq18Y^Riy>EiDPzCTc^Pp^im1Zx(8#rERLx9=_a^})gij4n6
zo0ps&tAU)C^aib9+KVqApddi>`6~B~Nx>uDa$<P<oy<(1X!GNBzZ;W*ZPD$JIH*Yu
zb2TcY%8rEtDhbFCgDOi+y&ZKS9JCNADGjKqyihR~$zE=ZeU2QI4;PC5CAu2=Zn3!7
zQcDsArq4>)HqK(@GRZW_Ql&&_s>2nT(jfVPi_A-!TAn})FF-fy_Xem21~mq|-Y~FO
z8bmD*FQ+Pr1e7Ao4PaukvO<lSVmsdvp6?AnQja=}dT~?-8OpI)sj~LA0uJ-*^R&_S
zY^__#d2v_xT*;b)jY)JO@9K}J+WFjm1W)Y6&kn!cqTy$f`ZS;@;Oe6f<aB2ddiiZQ
zN60ln3X@a{x-YGr9}8wlJ9R_@;|!2~EXN+1KW9k0EK8uhj((-q^x7iZ;Z@J-=woKG
zchrq)nfk`jn{nG{bObh|#2x<iy(V6xLS^{LK)a~?+=$jslAmfHyjr)qaI%e1d`kA~
zUM(NpcAW%XT~JdAfpH0_Covgy{`Jp|hkP5Wf*XPw4=hPug<8s#V{Z(GQFZ%m%Sd*d
z<jeS>{&186dCJWL)jsvdB;tJz7$0^Yf`l4*q<vlP`GJVJVQT;djsW!rx<BV-w1vo>
zf?gJcgnduIKZ%TQV5CF}Lib0>ThTyU#1`|?oSQX=G5x5T((uI&Yr5ymyiqv++Y!Sx
z;sc^sv$T}8L}sIMI(I*&|8Asl$H3go>0{qOI(au*I)Ad~cP+3xf{^}(yu5BY9KvF{
z5<At!l%|PDVu(u9&&_FQ*BCdr-fB#w6;5XR6DjbZuin@9jXP@vjXM@{_RF1yO;p8+
z#C<O3eQNZd%=`;^$}7RT5Rv>$-^eX9sRrjd4?mxGvNjANP&<&U&7n+w?B?hXS4+GI
zHJ^YumjS7UT(Ctv^C-XYJ!}FoGD?Jcwey2vO@FuHLH?#$5g9V0yVRgtzvu2%s0hNA
z?uQY`m%N|m8}%-9wp-;^z+_bzb~@&xVb&x?(lqK3<OHl0odwy>`cfA1u{TDXLTg7E
zFQ3qG>q3uB0Udd-JQ+pkT+WTUue5W4PX(h+B+)E*a(~qDGYoIM3crliC!cv#2t}y&
zJ9|ZMNA`}14%XJDec-lq{p1#DPC=Qc=#zsJNgd_e8RkR1RHM10-~B*Z;o;9NzOf8o
z3!O-GWzYpxy=MFT#Vst*M8ST}!%NhHj_F5!d;JUvz*}C0QT*FH<il4T%6F1WzBMJ`
z`Ef!Z9(eyIrirn}chm(wo8$?-E}Bbx?@8Zn3T6yrbOkCQMpM1Gn?4af={;C%tDp~6
zC1f2%tun?gs2i12(Y=Nw`}_m&X*6%sR&P^^pPs6GKH%YuJNxq?KVss&>Cq}b;})Ss
zgGlubC9UpN0Rx&H`2;Q25a9|v-Pb*e=sQFMVVB27_b{sj{j@A)e`<}hSHcxx8<SQy
zs-cVXFE*i1NhMN2Q7lR+EjuHBV}bYj=ulxO40gx*^TSE62`UVhwnt7_;SxG29>oo?
zIcpD@7j`h|Z)+1V7+SfD2gVJ%*1`<@cg{yBj@bH=ISKM3`p=991`hnY!iOW|{QPHs
z8oF~dr%GP7I}~~A2J8F5bdEay>@)XnyvZ&}7Dc+Uk*wvq7{n~np1E-LHI5`@KmS05
zf<uvuJuP)JstXu9=Xz{;XecV>Jq-2I%K|@Sx8fS4zCqj?g6OpxORQRp?c|SE%gLqn
z&s8~Ny%{tS&Fy(o;;{GQRHePh2}>|1r^@X!e(mXvT;1;GHb4A$h0NkPbUQ<A#R<LN
z!Qg$Na47k(_>6>pH(A4DP<^SREMA?ro^pgb+=So;vR9PPaZnu^AL~xRLc59K3jMpf
zDeox!YAHq*ZoqKz9}>*P;4A(Ri%)CYDvhTCJkCU&M4Gv_Jc<>TnvFXm@@0Z3!>P2G
z2XGh6Ut<24EmH<Ho^*D4LH#`Q*vgdh{y3M-vHu=KFG;W|QjnN*5iujK?40!wQ0urT
z@e1plv`XTaquP>RvScz1w=)DkXVG<--#REetJOH^Y;22qP4T9Wv8$@mB~xAs`6NZV
zWY1L^<gb~oz2d3()LDE#PUdqXR_@F;DUcnZiMo=vfPS7_mrVi@XdBk(rNyaW!k$ou
z`*c@+X8U(%Zx|jfUwBO0Gc0SWlQH2@u(Bbw(6zi+agB0TuE{dxn^aYXh?y^{#J}38
zsZ1DiPzZbqph=<wzGa^u2xyTgm(yMb_3m;;#T=cwaGf&L{OZmE7EETm7p)CQrx5*K
z*i^ze1GjsS2@T!d7Fy3N2s}Q}Es&W>Q)YMex<<ko9VF%RwCuXZi%x$c_XK(MnmX3Z
z3;boqy07s3updR-*S7dG^2Nzd=u`Ty;;`^&5;(G-%>*9kD*Jc_Z3-+4YZ}nA*VulQ
z25uKKhdK{gqE*gKSydttNe~C74f@9tItrLpep|Lz*RgQNnugQbLqn5_cvJ08+`yN%
zq+i|LJycHo_}J_7cA~r^pB@kOrc$zd9cuYR%na1JmuW{!!d}Jlo2da~*rGh#xI82q
zeRzZ8q|ApK>VKC8TxyJzYQDQ%D-<8cZ2>b#K1yVDZQ!tIel3vnK<7hBWIuO5;{S!e
zLh9Q4XJG!2$!^sqeNXA|mA){FRv^cl{b$DyhpUD)!JEc=V3x$Rb127J?`jL-mxLOQ
ziwDot#>{#mLaE3Xh3$54lq`kJ6lnqjOAU~KnatIlA0@4d8S*6K6&pLBA=uk&OAsBn
z$+&N;{-XGII5PF<NW62&oi9nBW830OZ=G4B@!o^>a=%`8_eMDag|q+2=W{7nj#ruk
zbY&Ljr1wH{o8qablscLV?SDDmeAsG7vVl{W`|y?=84LXR4(o2*?Cwnq$<RhFO@aEQ
zROcBC(J5Qspz}V1qHT<p{CTK7m86phb)0=H_}&3XC4Kq>BTUG}zd)7km4@w}_<^X#
zt;&gW0~M>^77)cJxE>jZ6{RR{`sACk0}(HvA5AMi$*HO;&G-j!y%R58$G7o8B?2NM
zvY}i(vU^&*SM%(5XFCDIoXGwG6kVua*P|<a^>dxq)@sc>WQ#X8M*;X-Ij%%<h_Q?!
zDZo@z_kofZg^rUXY!xgT1v{iK=D(}?%(@c-1G?XLV^8l!JbN4Yw7bn9*Ey6rJz<i?
z#vb?gtrm<B_2zk`%@=&(p^X|j8V8|b%n8l-H0YE*B`TbeyO}fDx?w13)qI$0aq;ib
z^q+~N8(|rV9o#@=iBw`J6}DAkE<46xvj6jm&3o&F>-gRpk7U^o@RfJ|MqMxRDsuMC
zQ^tF6C$H4LxG(t5Hk`0F5LeWH4cb&{aaeBr8ton}M6QDO6$n4@2N2y3FI1%dG5jCE
z(T_2GYiCO}o%N4!8n7+pMP3d-ESgH<Dye}I6-=yqeuf24owrO!J#tmP`ok#-Uy577
zxd>!rfOVuff{$uDWVM3Eo~$%9<}g$cew9%1C0&d#`*yI>Ohzv=&^WYb7SpRQ97>Z<
zq}A1}eGnz+p<^^v=FGjxn>>Uq&`UD)Tu_xoEfal#;{5mBpPq)zlK~0rys|%OCY$(1
zws03745K{z%sZwIm@PkbMDq`d;)9P$J9dhfqm#s?xQj3)8}>9J^O4c{W&6YMz&2^-
z*+P|GcF}c0pshNaX(`y8a~>4<2BhzMV7=6SADnw!>K#*^TWY-C?x*7ZDJ{qT_xqi;
z1nyI^7!RZ_WWuKF3L<TNn<yH7V&;^OC1Inp-Z$ai`I~J=I6I{ah>#^=3M$Rr%%_qV
zZq!7bcB7bThou3N`UD^5oHewYbN$sNxxQxEs!P!=9fxK4PG<GxC9By*UK;6@A?@mJ
zz`xQ_@ffmyTVBG#b(iO6r9u@unLxnXPD9h~ar?ggbggajNOvz0Od1h=TSFdt%C)7v
zguq%`Lr0S`ThzJAiA;{NMONyl9IQ`}DMW}B7TGJ)(8lIQwc<x2>HwMANz7r;?n@DZ
z?_g>~6%BsUaS2H0*(tNFI{%(HVzWtLC8iR<P;8CE2&8};ugx=)`{`QeDUT}N;X?SW
z_sQ~iBve1ijeD9eH$uLpy>-{_$7I<tN&`KRhHIyZS_sjbOY>d74H)iZ)+r>RBJIX7
zUF;csrk1Uu%yQwb%z-hI=Gtuzxq*Y`ok{&Qc-UuFL_g2o94}Y5_1#v8{)s~^)XTBi
zNd6Mnv{g}Rs1{OX-{I#TIjbOEu3g6>tG;~jyfRv2Z<XmvIqC(|%|X|Cq&AMRzcn;5
zM}%uhX@<!OfFwu5vQ*0pzgN}Ypp7aaKzMcg+LnJsJ1JErn|h9Kg_*Bxh!eifLylC`
zFa-p(OieDQ;|{-@_3XplXH&0rcy{ASi54o@=#z!?<JZ+k0%+n;&a~F#7CYK*pMM^W
zxJlICjyKXNzQ|0WZqkQKSe%$ls7;diU(S5|_RMI`{<VT`d~gbFc!m`2AE5W!SHVjQ
zt?@n^p-a%k7$Z}iplx^S7cCV<!OBYBvNb8%)~Cf;r`%8{2STPq;xI>)0m^4hoZzUY
zVh@nEYBb1QeYw40q8>b79?g)E>*@3;vZxbKZJ&0o^@+DXf?|mq9RU-QjCM&{`x>I{
zE>%@Uy_%k;j%Bj&8T<)iY&Tw6uA?%^U0LFo{>5vCnGv}#KcM{Ju9o~gvplmB3c+n@
zdZw4OLg_j4=P~$55-1-D6fh#wA%wozoV>hcyOJaC-BSGq$kiwgoOIWg2bX+4S&wBz
zJs=C_g2I!4breOk7r>TF@j@m3`0Q8$BzxI%hq+QP>BzE6)#AfQvRxe}CU|sb!s6mx
z9p;TVw;0Mg=AC8*3f=uVKKs99L;AzjWmN=(r6o|^c|p@9uE?%4mG_4!R7y2+^z&Am
zpwA@rv<s-?b)=-`E_9$_u<l^(cN4K>R>~Pw%vH%cxtQY!Mm~B$z6?VIzM3$_!pq=j
zY!*rD(yeQ0j$2uN)KXU4`P{bqdStpTXXMdnxBAPMlkj)q9W*~0jOh_B>*I=%hwAfU
z%+FD_RtDGIS2%YAYIE-rO*{i;qp5NaJ7!$=)RK$b=Ln+Q1}5|OT75vUY%Py+k5b1Q
zDNC-uENn$$<niU2Q<I7fY5D$in`fpH`hPkoHjUh|>e6e=@%vgm#XrH!Ov-#+wyD-n
zwYfFikJw2SH)mzow`x#|zNO`Rs(ISq>}Aol5hpQr`msmP8ydAY4zpHR&X6~MbXZT`
zU#31Y5bn|r`<aZEzJ1Adkjb_v_h<JAX?%<(_#ePj&pfiR;!;EL{(-5ja+l$A9KiOK
zmKzlqaNrdkJ-Eha0ct(D(s1&@FtiZY7XsnId3tqUm^O8Wgix*Q*-eGCt&Anz$^TxF
z!1XOaQ7ROf>V6@P>6&(@6}5!X$9alzix?bPjNl{(&yD<AZh&=|Nh3w=e!Ia4_y-uE
z?Yi2*=f><00X*IT(N|v?UzAqFX+Em<EV;k%X9^{V3b3RUqVw;;p`;4n<wtlK=lQ;7
zQGH^`ax`iY<=JTQO_r9FoR*|FYAvVI`b=%AJ0#EpSxhcvb!jv=b$W`046Ye_Br_1T
zmL||-MUJF31+FLKtb4l{J29G35-X*ftY45QU<R^bg8lz!?NYeU@>8MhcN4Zcw~a0H
z!#w#JT3l;SMABX#Q1^YR5Cw1CS>%IUsnm4-)aDqdv0@|!?Oiv$r>T>k9`r!8io}Ma
z@X4nN6hoa$@L721d-8u!i88dM)yyzf9#XA~>i*>k|LZ!R(A|^V-|xgaef9JdV=5P4
zKzAE4*)U4|*PM7ao#*b6c=>#X8TW;Gan7h-;fp+_O)A`bN$!Te$JXxm1E_TK+8d_A
zl@*kG+oQ&ZB)IsxyaBlVX_1i=g5#D7@3XifA-#&DO6x^>Gl);F#=4sAATvySwp^9e
zAVBnZnzsSnH9WGQ@v3mN`G6U{L)_8<h;d?Dlm#&|rLG#*BY8wQzZ-?cQFpun{@Ray
zZ;@lqKqy+5pU?3l6>wPp>raf|lpx(o*^g?ILsEDC$0kdl73IL1y$qEU>8w)2-lQ1%
z25oHTz*mTxG7~<37U7TwvQ7`B7^ez>hl8btPa;(b|Hdcv2?0=2Xr)ZMxqKekHJ$4i
zJK7v)LDd(s_eD_STb@nF`vnW>y*<I9WXI1Dc3l}P;aq)b$Kfosr)=v`-t6@H>@^d6
zkQ_xE0NG+8E)<8)xfQGR*G0$rVV0@|kB1xZm(7s2H{RSKWl^$_ct^D#b`O7v2gVE&
zO^f9&MdQ!z-&>IxPkKMT9eLGneOJ`yAXokpNV=B5<$gmOI$>7)oMx=wV8>Um)oGVI
z!owT#*=8o?cZm|K+-h2>QV>BCMU<0+fg|N7aRsy1loGe88Tak?O-|VZLKW@JN}~G0
zS<n&u`Y?xt`b`AND>BqMMsVTF8uz@1`NlQ9w>BOPl5Pk^y4ZlzU*VB$Hlh|&py5W%
zUeDkpMNyT_#Nc7WdeZ!Toqyu;1LwMka*K$R8yzN3=_?A|*24?bV{V?<Orm^q=alFf
zV{6KVblmkrtE>`Z=hh?$$L2w>_(f!?c+t(~RK#%`n(t!@tdL<<BcPR?L!s#&ryAF&
zCfuOfUVPe@?9mIAP**~N?u-u08;s;Py=<1HpgcBXdzrSSAD_iY*xXy-yq$5D_B>Xm
zPif`8>P{VMwf?N=?qLyy5)EP4Y?yzC@+?r|uL&P1It9n>8Os3Ka){xj*VEvmM(!=J
zajQ8w(OZ8d;+Zj4@=>auEKP~!`!5`?&dL^ID_ec+JnkmZ+$@_4W^)2tS1p*4dsO@S
zy$~&Is<{xk-5&K?w@m(&<u;psyD9WDz-fGld^&~#nOtb=)|W|t*!d0LyZx(vq}SWI
z^dctk;H$Z@u8{P+;y*e*Gj}y4hmvlW$vh#AToF?166~SuScLQBpweg3b0O|xz6w*H
z%gb|a_wT%E8BbDQ>Wh$-mx-mg7q35UV3nFb_qeUrJ`5^WyxsS6+e;`xUr4OctbWD6
z$greAqs&jXohUIAJuwId_Ue5Tn@})}88;6}tYVp=a}XDs{+bkdBa7}qW2I`Gv|u*F
zdhAh3WuARZ8K8cOx}WB*M?*U;s^O`N*D5&1OyDKoGWuD6J^2r*5Vg!sAR1MLkI<L3
z%bzX-Tqe$g*CnrHb(k$t0+uT8MJ55so48HtUlw<B<sOXMjiKfASuoqTqi-6TO!fn&
zC{YxMzPLSA8TlP{<(~qqe*nWRr%*Jaw_Ev-GtTtLdwV}MlVTO$G(-}Y9o`m2mrA~4
zqERhV{lhf~|4ZDDinz4W&11+;{}OMLA5{^2fr3ddP&7`Pvore(vjtw}1(1eb)==NK
z5d5jyR|ux<#(t(B7-O(ezZsW7#ND!C!jfv=8JRp!JIvc}YYEqVdl&=J%#m4xyq;Cx
z8LsYYEADalUn5zkO&Tx&(T`kZW};Ke2Iy*?KQuWyXe@M)o9CUZ6g03C@##L+#0y<p
z%>L&3%@IxN|GKbGryd6GE{9RPYY0Y>JDEzNtGey0OMETP)<GIAbHPtc%sGM+v`hRj
zkUW1OV&xxe9$Nn*w%Tjt+n1!qwz$C`6g7d{^LO9Z0!f=s<dTA4{_>CH5o4dg_YaG~
zVUczoHR~rjJ1Q*I8uw9BuI^dtCwA$aLp$$>*3VmxkP`8+Tp&5hXAe*3#=o+oUFvpD
zCtbDOv_}pNmD6pKBoJ{Q6Wr27a5)2P`6SxZ4Hm9pMbMq{6}~B#2KjBe9JP$WB{#W#
z?}0C?N9p{malb{py#eE+E;YVCM*bSVuXYT!+ukW+U;nid9B=lAn1_^{MHTD`eonNT
z$^b`pV;1eF3}J`~leGW!(C0i&pW91l*tTp>P_m%!=ej8qQAqN>#^lIk+Ut(XYB~)^
z=BpF>(Eb^*H-tQry*XXkwunNp1rvW~BsHviqE?-C&2FF7<oHRY`l~}>QFMuW$5G+F
z$>}??$wVi(&URhkJ7D^_4{yx7qX5jY*8OH!y+!$xk6Pyd2uqkaHLGix9cgB&j-Nu#
zT>uBKmEb!6$`e_>W#LCV-<tmRP<U-3Yk?kGrkVLr%ZEHcZ2|60*~cTOt4&1Tvw>yD
zN@MS&jR#w=-1bg!1wKX}d3^hk;MPZlbSbgdg(Al>QLiyx*w?fLjO5ix7x&(iw~x5s
z=PRtAhJ2U)YrT9iYa9-QgV*1qSqmVK0?7M>)cX-uS^OUWjzDq0*v)IC>QP6c*v3&U
z?px*Cb^_o9JD@<Ij4G=DFgD~V+qDe?P1Cd+c)Fvh^lqi`LP==WpDAG;M;l`isUd%|
z6>?9hAYmW>0A%`Ss_h|b8E!6C+C-9Tc&{1@8$iP)N`S5g2p(0>xf$J9$`a`l8NuzG
zV$0x7roSAp+4T*!op0l`mwKcMmZ>U?c@yox89WlaDd5!VeG$}sKXq#-OX_gnO3)i<
zo$lMhu!T?jMGQ$?06>wplEmy)#@hN5s4r~ndUH$YjShRQCc<e9R~LzAvS}e0U6#@9
zEQoM?`!IPUgSx6Z$D-}viq}uk;9FIkD#8uM>xtvX<CTU$8%G<Efq{a@H5W#g%B>uC
zPQ9O1(l2MfxSmZRhlC!=AsgyN3WFZ=nH@s_HYq%-7vz9ydfTHk%UvpK{a;do-dPy6
zo|777;~UEOjlGyg6l3vjE08dZYrg6I0i$Skr&_hY(e5rGjM~KTun3Ge*ryC%XBozP
z*xCWZR~q<(@ZMgZnp?eFSGKg&ZX{7*WpM}0vhqiZJGd*j1D%0bjD-M^iqbh6W^zhL
z$lY7ew=%rZY4)vpZsZcjG-@WA=(lERNjO~b_`MEIan7OWi~St8&OAvjNHOmOf;)~s
z!@nGXUweE<=^HB@63X6rW_zofe;-o5y`A8bVu6_C4n&|6#?8tPlx;)BeEZb>F>P*U
zIwif7uDen1uOk8&IN~%8JU!XYNAVvc3JrRgvbK4daU<r5ElfYn-|NL1)>)!S6cgaT
z46HJz+O|D^LYmJ}o8~u5CVv~NDRJ%<fNUSAA8M^zIj$SS3E)D%iMI3pr|c`Um7aTx
zOsHk&BcH8E4&2l8>Fox6Ks*lZ`Qw2=Fct1GODv)QuNCdQRLa&>YAlwCTgSb&pS3tN
z-Q0otP_38FzS*s(OtCj6JdO$Gmu`#3DfVkqY3`#A58kV%vfK2{b=#howw~O`tP#|3
z39U}|K|)T<@_UZeJ4IYqqeL+jH71Z181Gg$_9g~7AFWl&yHC=zTkOfI2^{4<)n#LT
z$S|$PwNh?1Byv4y;zvf`9IJB~^MQfK{M1+>!3S>e51DF$%JrcmpDuY7ZyofOlBAKh
zJ;r&@(y@`EkX!5J-c~WSM;JfhP}A?Fg6cV9lfrd85lUW5^AZ^`g5B}kH5+Z>c=nJR
zIRy5qi`mhqnk`nvr3wYYC?^9Mt!nxZz=ASIAlBE?8q#aJy|fK!1UIa}F&rb0UMUW*
z(&p23dx+(O!3Bv>IZ{4{+Ovyk(Yscx)b^PBRN}ajC)^tt^sh}G*2wkrp`Z-dCkN|W
zq-0scevZlvP2zj0?tay445n*%injgDf*6g!{k!6|=;hgr0i5<DgHOk(?vgFh<8B9+
zilbCrO0>_je`H-QYgktH%Hwo$F!50a@H55=_Z<3<&lUDOgtT3NIP~pbApXgEE~%xo
zQmlz3jH%*yWP`X7_}AA1fN}ZySJ%#RF^=B#(Sxfwax0aU^DQeUtvfjNq~sU|y2{X;
zz3F*7(=rMKgA@X9#Ra?5FG~@EXi$TST2M1hKO&ZtRz(GzU{Z6fJs{$gm~d(tc=jbF
zF8tFn&>V63)lFYQlMD)4LG-628O<vvYPrrKFz1R^LBXcw#V0WGnl@UjN@0>|DF!L$
z29uO`sv9nZ<y?wNQBTS#IR|<YI~6k+$)%$i#Wxt_Pz)y+r(jW&N-j92;oxS5Z?Uu=
zr85|%kmi|>fkGJ6W3@RCCX<KKoPcvchLGnJ(wtK9a%qJWC5*&;v~-|GKT2USLIxAf
zOvX8-&~~N}@yE3Q%wx4MH~>(g^rJ67dKkXRdr=}40gT|$Q-fBcXu3g>YG76)=83Zu
zYKi1dG3iXkDRIUzO-6a`LL?anYL!8IRL3+ku&BAIZLA=`sZ;tYN0Czc*MQAr_N|LL
zG2XpiuV)o06(UKfo<8-Hz^D_!tI;M&7@?NItQX>;yC$fR37=}i&9QQLsBQ{5t#7J2
zmJJ@#NhE!tMovu|QC3+i9M(FGY-3^_GBd$7#(Ll5RqmS434}(x;GW0xtsCMG##?)r
zmM@k`S3F_Lu>9+kTkBuGH55oL3W*OeTsQ>t*;}{xSDlwX=<UaWJ)J7|;zp&ZM(iiR
zJmDLVKh~(MI<b>d@TX}SIYvs$lw<V-jC%U_pdN_SZY_n?wk_QVZwg27=2A{PzK8Oy
ztEReFRk<er094U0wHsS?-z?g6WH|#qW{(&>{{Rx;j`^=Pc~kA4j4;;d>Hh!?{vAJ4
z+e3GDmYTMKb^J2#GOvLCf<=5_=Zpp!KZVJ!s{S5yY&|QZjXos1vA9yn@|SlKFfr#F
zS8*ZNj^lCu?Q)KU_*<agboIu&(+MT3NdktOPG#7{58_2}2qi{9A(Vlh=O(U)Ug-MP
zktT~agR1FQF~;ZSET42k8B$b+J9}`r^u{q<vX=I2lX^z=sdWaKVJ*~l6Gc6^&&;*F
zz{zaHV|%KwV!u(2*fsGl#_p-=Il7knreWIRX$iD=-HaE?7i(<(P1ragcIVU|UW4%?
z;zgaR!!_QesHNM+*49@^{5O^P$YKd>{WHkN0={|DI&x8U9sdASE%52P>y68I0Fjr*
z-GSs$_<`W40ggD%F<E1eCv4td1xQQ6mcsh>3)>mA`}mMZ=JwgVdv`ljG05D}XR~rg
z0PW6d<5lU6YQo!Dyy`flv6k>I-9lwyX)?(eA%mbQ*<Uamtb=lak+g5gqs7%4v;lO>
zO>PZD?6vt=TG}aw_5w&B9#Y#%l~u-9ET?WyT=;v_7M*|7ho|kePu`Hl9EzGoqT(x~
z7UAW844vu-1!Y267-X*8rFQN|JA75M=Uq49oL3fhi>q8cqTC^p&rxN-SIz?lR@~?a
z^AVh=-lTwBIVZq4uT7e2x8J(9*~vAwp=+7r+NA=5qy98`Duk#E2~}ha5nH2mH^A*<
z;mlXogH6(}G&|MtQpZ)dSnY$Ixp*$2!b15Ak_xWjxbw8p^jWndq3T**t8;ay%^pjO
zut?w#jO~Nr&Z<`vV2L>0xUojJOlhqgtEM_;D@|_KLFyRtVncIftXaw>LWVua@kjyj
z7lP<H-HpA=1DHAtYCSV?HI3T*_Kc5ddt{R2C}2d<q21<3f8$0$Aek6~JU(`tT~gac
zyS}yRM=hma$SyA4$aoD2au{C1fR*#ItFo(Mj!NedwO4gUhpXrvaujLyx`nOXzst{c
zV(M)*dvX<(LL4ko1lq_H1tc!O11i*iLa#?M)ifPe{{U3gbz55)Q&QISww-Mzz!KcT
z6kIH0v3UVmkc@xDJAEsPrh#pG+P$lZ0Eu_QBxiBME@5x3cPBpo0DAiB>rIbb_)m2e
zg~U^8x;50-cAV~-HQGhY0Kb_-q45`R&Q-xBky!GssOUW*HB0LjN$hVfrw<I)aWRO(
z>$}4WdGeC}Oy>!JPBU7m(MJn71kwDpfwsA~EQ}G?{#=Ymqv{B6&{4W?<gOCp9YMlb
zNIp_&SZ-K^fQaYgw#hKZ9>?Cc==2M#Tb*Li#xGvVH<{50aUzKn6OsIdSTQ4vAFXK}
zO=k_X_t4m*cv1)^1y0nD`5Y10`PdxSL}Z!IDA3KGvL1Mq<9M|z8y1cWxLlTbBRi8A
z_ly|$<oRl+R$m=$rJ6)D#_sYJ{8EBAZas)2^Ql|Gacw<^$iIno;_i>a{{V=%0G$3u
z@*vfudS$e_u9ZF0tlB*2w#|1E5U9c~a-d|8dF|%GBfVd;fpt9+JwvKBv1F|sjkVPK
zq--AxGD)y77#y4edwIq^E2?On38(bGP4w0M$~D%tsIAS@Lu)Fr%R5DEaD5Krp7<E`
zuRlUxblja-!tO10^Hj69is6iaxCV6$;~mJB+Ck5C0RI3rZ(VoOdPV2NC~Z>X<>!Sj
z_D$mf3T$~|80VG_NdR}-^{jGB^lb?4$Mj7G(#uNe&YP0j-pbm0TkS^kf@F>ciZnt1
zz&<AVHiPRT`&Ply`sRhG>bh@DEtGo2<=w)3@PUe;vyJ7HdB6IP8jqs0FQCppaXhfi
z)RRN8wYz|_#s=uE?TW_21?&zMHHhQzsCGCRt!gWdozZRe*6H*6>rjT=ug>f+tgO5s
z=LHZbk@MJu$oFm+E!p0qNa`~6fV;QTE~2<u68_o^Q&g}LG6O3}W^=fa<?%d8iQoiS
zuukPAYu>ceR_1x`-KB$3z01d9apUYeVzY}Q$rN$CYC?>X?!aV&T>k*4ufN)D2U^rM
z+onimaBr@JBX5ezqBvIooy<xSiZGGwU*_9d_O;daS941(#oU@{Wj59@MlfuVzncK@
zjP3(Gx$a2KVDF;#lB2lw6{W{iYb|uM8<~-!n&4W<s*QR;!uWx_s~VWq0-U0#+lIw#
zx>rzX9UIkl@@hJzg}v>xTW!EnW3=%<8--Qm?eeL>0D34s9emT&{{XZi==ZjEx&*%~
zq*_B9QQgRMbV~F5A`g&~GF%c#$<J)+9dq$2$_ra<4@8!GJHa+?Zjb^@i{&M_+{AJ*
zk@<s~l?gE^#<YEG^2ye^oI1|4e|ho~>ITbCjL6aWqBjc~MF8Zs@Is>Gt7VCI0z=aD
zpNjo4(Kh14L6XW{Zq)wnx3e(@iaAyAQ)EG;nnZ2{V>_JiIo)5MwjUWao}HR`EN^GD
znJt#W)>V-BkVJOoC1cq0hi=`#;;s4@<0^E8!rLO9Hp0r$;drc|Gf3{)RR?hdmIrSq
z06XV7V0DaT7k!jzIPs(H-5S$U>RX%1BGzwUwbkb*Rn%u;^ADT>3pbi56^JSjJ4oH|
z<bu6c!s6FS>If4}=}WzLS+`ZT60wa^$##z+5-=9yXa{c4a6bXB&j>$gyPJo2ul39O
zZtwE;mp&A^BWaAw{Hj!PP-g@nDmfj4q(5kxZnR~2{63fI=#(X%m!{i65t|1t<?;v_
z!N4k5<0k-$IacIk@i}TI);C=_rS&&ZH&V@|9b;CBJi3finN|X%@UFq5j4n@`Zda4!
zG{0BrR{AGRMQNjIBG&g)l-pg&ry|~6I6RnsVpciHIUw$1$O=iWVQbeB^;Lzun)6#N
zoN=pKroV9OZ#2v>iE9#tF2EJbC{R0sdySJx_=l`42S}FM-&iQZ*4_oTF<X`jO2Zs@
z3KS~t%b75$&A5S@vdE~<?Mg5DE@iMfZKO6jC5x`5<xzVpTicih%t<@UGEfG{7)`|C
zbHT?x_@D3(r>-<+xawZ1zn4p0$r|YKnBkI6a%7WivfMByC63i6c5#~RJqc%H)5P0D
zWZKTB{{R~6SBevK>oCX(A1<+?4=N{MGEZ_&=d1Mosi^7xC8nu$^HSi>H7!DDyd}qf
z;1Cdw{k<z>wWB_C&&yp);HI&rT|TpIr_C2n+uY?Rlq``(fwCC8ZrXb<opF(j5^-A}
zL;OaT!u8?1wy?8-l-%50yb#@7Tq=())|ra2$QZ%$^RWgne$eX89)6}W=p8L%XRJpj
z7rKn{0$;XOZX{-vo4yGQ2eGb4uKv%Oj)gqSr=1f-mNy<C-Q&PJ4ZnnjD}lSnI2hZ5
zTv;1uekm&6jl-ZmF%GNIZZ55~)zD^wG+&?8p)=h&<Fjx#F&F_900$@7TxlJ3E5d9v
z>#cKGTQ+1-X9=>jV%(Q`l|lai&m!ZV`K~pjYZ|Xd>T9HF{X45`Ftmkb>U)7B>G8%6
zOo~<>6l1e8?etvodhL%`SZH?EuO_7|>;ajixHFcK7mdj^%f_e)<9eV2o_+b8r2BMU
zP>-c$)wjdVC#&v+n%0Dux{REx7Re!vvOnT6knWHS<0OPW{Lvxl*)L>UDB`<oB@DNh
z*DA}B6}N0v!j3YaAtVo=Ide?w{+rdUWYlETMx7cWkptaGmaQgpxRV;J%tkVCCNuaz
zt;40Yr0VN02T#!^n$-UQ!=5Wo4mOWD;p72MaoA(*D%?|4p3FH(UFjU2^QJ6y?Rq=4
zvSSU5Sha-B;GYZwmsE`TRCeBZ@BBm@P@&UqEbK1s#f6onw3g-3BFBj`k%CdjoF^m~
z@eW4=0Oq>>r_**9HrF#xX&s`51lt>jXxcz{W($n4I6H{WK=Wd`-&R>zY5Id&+ysps
z_pEld%p_nz2+bzV;9#8Koc{o81d4|j4#|YMoQ5BTGIb5w85-YA(>x}5;<UUUF(W_V
zjdAAMInDy82LKLO^KXbB2(9%y8<D2!BTdocZw0R)(~pD@=XU8=&mkNGh8bhY9Gd#M
zdVa#&PdZ>|JPW;zF>A97Y+H_Y22<<b%xh2AdR7Rn$=<hi(2zv1v6*7W3b`y57+_;_
zuOrR7J8Pm6Xo*v^^T(;a6*ZLGtgW>TO4?NMZEmhD7TvA@I6hs>0;vo*VmLYNn&p@F
zY|_rV;wqU8u&{g|?+-5r>tA^MX83tEyni#Q++MA_g7RtJJ+KqDFs4@uP?Lef4WJGW
z09VFcTc^Qosmi`JzUE@W2y#{>_8bMkuR;^OGm|7WXw#ogh10^cgXb<B^5w9J>C1K>
z_swg%lwt`QCUlgq8<6>P^sVaeSut5lVPgbw#>uzDV0Vx?Zg2vEPb3e{v<Heyx%Nan
zLQc`QoRWC{wbHve8vO|@CTSHA66`<#deo$gV+0)f<aVy3(mx6Yq|-??%xMG)&m_CB
z5mXZGpRfRXmKCpmvXbihz09_-cyK6kyZ-<QDgkfPa*TVCSF~D@szRGFpYW~grX$J>
zjl&rS&{nOe>2g@ON5;_QH+nG~Ha?l-@~iza@K_{koN#*{L8`9EU!!waZB<o=g>@<6
zl*$g@`gx#U=r=lu-SRVdSn|A3Wk~v9=lWDM%}Fn9VvOnm7!80lNKgL&%sKpxHEpHC
zX)G#kO}j~*z>^YWcHu{Fu4`o_(<;t;u4ttnDmcjmNTpl!HLGeGbda&#6ED{l-!(le
zF(g;XJ1#P^yq+>`^*gX~KRkVFUBBo^uLf-pzD7Z{^&b8i$Uk)Aul*Ei&L1R2ImZ+}
z)w*k4HuiT73ohmy?b=a?-B<%!&CQ5H*vKIEG`-U@YJfo@JZBi@tss}hhyr<DDwmXR
zsHSb^=jtl5L>D&qJ`P3$D+~~85-<}J9z<uIr_z#+LR&P*I34R6qQ%q~?PYZh#j!C;
zj2TM$5$#*1qt!RvPiGu+#IoFTxs`b$xu=ZbgNDH!v0H~tbt~KIF-js`(kF!|Kg<d4
zKBB1Cvv@yDubMI)w+s=%9@MCd#glH}3GM04Ze2m5e9wt{0_>A4(nIbu+*YL}xd5`9
z`qkqk%E@sg*3PkKXetg11O8Ms-w_9oDa{@knY)d|56q9HPE|v+smhbvwHqqBKIr|8
zHA}fP;ig>2y4eAqC72TN9QQ`=x6qE=kF9-V!^wePEB?sKcMgjcz3sRPp=5?ro#0@F
zV0$u=k)KQuc(1CPf%LAN4RA7_Vl%F3IeX%)CdDHt&2MIWkeq`DwrCDG=e;)|rKMbR
zM9QgHqpx}kj&nz2%>rM-O-m*~7>Zs|M<X;T2bQxl6_c7;PC=$*?@P<VlR|xn$~mRq
ziedThKz{X8acK?(DIwyVkZ12n&D-;<>Wl>B7^P-jKPqZoGfGOtip<bS{`924b52W)
z($lUeOhlv@G@OgYJtZkGdKVdwifKFkbhKibjAV)drSzvF<a0^JGfqYLXlBOXT$*tg
z6s5NW8hLrCMH>qrr8xkl;sTn8QKHLiW+BZmzVy692ANud%wjd-m_R5A+K(T46&ONr
zDTv1yrPtn=i=5(sZXQ6U2Rzcza0MPSlZvu3pqps1uWE|<sPU*#kwaA^l?9Aqibi;-
zEfs2eC`lKOb4*6GIL0V|;Lrv7RFrd3S|F$)g<jR}*{rQ(0M&SvGQF$BX0iojEnJcW
z3X68BH+qTfS|S0t$J&JNQjpLX<I;>0Et-fNcB}wTG`^vG6@(2WvMg!`;&Xs1t<i45
z>UXPS3p2LW1Co6!=C6$(5t4bH)+s@VaLwtOwfML3b|}1SST9;d0ESq`6#oFNe81G%
z>{^A(Gvx2*W^?*ipPME8QP+wd*vz*YcCC8NHO?P60XVIzr9K?~{*qZ>b`eHK=5#Cv
z<bT9hEVYGiCPsu6KyY3_-c$9!Cc2+WbxhhGo8b6@IArH=XCM*vz&!i$UQSsww0jt0
zuV+I|8skUPzjk=BUqduSqfOzqoN<Qk2d_A;rSP}m{p8p7cZt*&noK4y7To!x;Eoj}
zQ?Y^LA+er&cCH1YYU8OcUe+1xZxR#Y_<D?w6o&({u<m~Oucdw(Ui9C>{Wd*D-rDNY
z?b1Ufyn2*(T3oTWnL-1&1GTZ`*m4hiS1e<DnbkS!pmj#Oy7+Buvg(mTZx!eX^Hy-E
zic$TijCp-|E05Qj<bM)6pGtxes9Wl9bgEV<jjVxf;m&rs4aXneW1m{t`roM2R3GkL
zFR0jAh>JY-3E{z?R+c#AQ~8x1mGh@o^>0;7l4+V<*d=E0oSQQ4>UaS`LNWDLHAyRU
zx+fpSa$>e>^XiuK*!2V3K|H<$j;kcr(}Tzp$0p%}oaBt-och<$T`6H<)jD9)^=R#F
zuI7ycFYoA?XCnZzNxe?q0od{|_5&ELJD|Q2b*#Ef6X=oJ+$8QUrLfd(q_>lHKM>l?
z!@mYIpQUzP3rW+UnkVYL15&isVs_4?v8z7PxFAUu$r&-}A^WX#LM+Q9lG-b^zk_=A
zoztH&qt6^R7Ywt_ZFwq2ml6;T^^}%YVSq!4KRO7~^zVrGT4b7}H~l-+R*kyft8{GL
zTHUBpyG+nYyM6-Xk~#I{*4<&K+ga)}#i2#3U4{X^TJFt}Tgpk=Q3}2Q9IhC9oE&p~
zZ&c|%n7sMR9Ws8LxL{+M?PW-#o%t=eD}7J$kM9G`Z)r0G+`l5~X}%;&r`*kJHIBCo
zO6E;pO}<#JJX{^7;v5+Bz&Ui?f-u{LPJz_grPaJvh`Lp!mZ6^sEiNqH%fux4!a@#V
zVi|};EgYkE*z@MAZBE~*Z|tJfI#*bSsv$4qM|q~aHxgPfIol(NJa~v12haHq)vd2z
z+4Zfp&XKL_I+sstA_E-P4{k5C`;@`vXh1BWgXUlzPBK`LM&Cq6D(xE9g{xfj=9_t<
z^<IFp^vW4F+^_LJbu$1YR^Gl?Un3-qr*I@TYKi(td^TvcT?<uBOQmn6<~ym~ZSCd9
z5jz4*L<Ztak^u*h2(6S@NuogncD-X|ZFFPsVAS9z<)?5MFNb!V1D4<yjzPJ*<b%&X
z8+C-;C9lt6E|!yxt!t^;*{F2Bi;`Jwh%w8woy8*qBXJ6+4^(|@-Bpg;YWl6@lj{0Z
z*AU)G8(m4_sLR9@vGXqkfH&oMz{mh@6|}|Cb{$h?Yi6*;Zf<1_rro1&OMBRtazP>Q
zf~t$haI6n1GPbK@rz}!kbcNsUoi@rvo*x!cUdmI0v~L+`_hjs1$2n8yz-v|M`C_!w
zHIAK`9>ZN|p_fSj1k<k&4c77@@3+X&9Fk-5f_5Vu)4yh&rDrvh;Ou&LU0HYuE}w0v
z27=99w<J-`6Dfv52Ounr55I&(YyC^`=F3Tksx(H7ThBL3CC2&O12pkT48(#*1jeI*
z>RTP_yxm>Rs<^p;0w<HkxYMP5{{SsIv3-jn$=UdTZ|t4kq*Vu1^zNUo+0Aurb2!vs
z)9$U3CM>zYibUE^q_*BK9l#7fmbOjyQcy?89evYHqfe$xtUr&S+1)HLoHDtyfU)D>
zc|Vb>9-GpXd^5AYgTs*)`^1qOmWtvbBSgwQH-*}LfXDL++5Z4&D?0_T&@~M{<hxqw
zRw_{N2?}ChJ+a`ZBRm}DpZ%TY(6xPB=od4~2Ay?&4U$YxB2+N4Fz1-wOG!SW-2JPm
zPU3ONB_n0&?v=aM^&Ww<o_yY0OFf<RaFER`P)H|L4aPx<!3T_k^`dE(w;e@$tLe8Q
z8@q{&>8IaFq7`|JlkJq}7)E)+^XjYpoK&>?_@TDxiS+5=g8DmH=4iIC{1!e0sz<r`
zWIXz?^fj>Sm)a(s4wbKI4IRz2KP_{2=E8v2lcvr)c_2_o&pG3hT(WNTbV4iHy8LzO
zSmW_JKc{0urfE<YS37o-G=!7M9%F8*paaRgI6Q+~*QG3C)pWfJMTI1aeL_iY@1lUH
zS=1?VK?~$SLyr*f5+cB31pBDm+G<vs_OGZdy}j+snygkf`SD7|-UYHN59LM#fJZBA
zt&Tvd%dVrI)2BL?;T{`1{Z0ge1cZoE&e-D-B8Dd(98qzc1dnD(B-B!oHqlGey0xTQ
z#KOvVeGg7{Htz;j86+7JG+Th&&c*)#i_Xw^?kk#F>DRqisfcV8<h)VC17{3ffGzKU
z4s-3rKhz@T$ELK~Ww_qg5;<fntt4RwAnp7iKs<Y~?aq1^NoZ5(nl`D_R<@5kR?vKn
z&h$HDMtI0&C-D%f8{Bw;rPM{a8qY&~Gql!qn|Y-(HQGw;^9nZz8aUm?(b)a~K*7l%
z4Db(I(LZK;yX{$Y=CqSZwu0^pQ95~?2bn`S#y~96jAuB&B%a5zYTpK6=}Y-~lTLyw
zy%*+m%}&Mbc8J;lmhreMxZ%7<$pf4r0A$zD{bSRU+1zVKOo}UeN$*zf>GQO-;@yfW
zvE&@AY)(5V_NN(kQ<6;hg4f`#pJk+tacwSI;y7+6wziE!?wNN8Ezyq$Vaby@077>h
zwhQ{7!L-r!JreRuOKY20BN7W}rH$cv^5`Z$vf+|rp3Jbj432B)uZeS}htU>Vo}CxT
z^tO+r+gXOVZ<cGvnUSJXFLVx{Eb~abs;q!=Yn^qsNxg?oXct<cdzhd90BP!XS4bLZ
zA|*U1jAVfu6-3(*!9@xQ46C~QYq1=zXUv^>@R4jflTp+VAC$L-E2kvn#wEajO8kR7
z-;Zd{Jj_l8O+CNE-lI)KMPU@v%>)tL#S9TM3s{cf9FPA1)F5sP*+X-K#cX|7@lx|u
z>ZqiTPisqA=9cCwNz898%&(Zqf8^RiEJGe$q<})W6|=?hM^IZ2_CG|9&pMizuJySa
zWwpb&BIf8Z9(~RTS0LmfgiI=f4kYrXQFQNGbry%K&3JVwEd)^EZ(dFEvJ4kszz-(k
zFo*)N3OwEGYqa>G)*5$MTJ<)ut>4?*#@{JoFQXUIfHs4&9E1gc8DZ^!a5GkCqWXVT
z(^cWT5OhA5h+JC~k5TG7^Okqqa_bT)&f}1v<ZdT(Uuz!&ZhD(QxSlN@%R_|5?{TYY
zlcH(*Ex0+rCzocyJ5)0P_>NmA3FB+=?C6GA%W^u$Mt;-Uwwa_f*P7%VMI^4tX<;PL
z+JN6(s&=aSZRglvbG-Dg$GtaK)o#~WpHbEIhCUl<IxmfL97=e@DyWTHB%Bfn+sHf$
z`G=|g3dhr37X{_Hmg;+$5v{*^f|#`2JjNh@hGZuoG0Ldh`1b%kGG3$6byy<Q^qK9t
za@TAlPqh;1dVSobL0C9NX+a#7P<ZzTHO%?Pvqc<JR<zHzAFk|rj><hYd0#`YaLCeY
z8kAOmkZ^X#2GGYm5OPL20<5}wqk3neqGZ#wQ>tnu_X~%J%9BV=LlWL*$9@1GUt0Mx
zPmH?fP2JgD>M&kt`dVy?+SbwuEeogzglW#=q;a^Y7(Y{8Z%F;8v_!IJweeo!TnOHK
zt?(&0$}Z&y7-zd@w<Da@e*M3ZoAKoz!0P>1(R$ZXi+xJ^T`x+zgsVKsc`7mt;Bpj*
zk0kp0b6Re-(mG~|J)Wa`41HT9Opk1)=#U#6Iox--^}>Z5oMMfk{ANp=sFU}kV&2;l
zvcqVXQwBT%8C)WF<90EgMQknc5b7^%)7V9(TdS5b+}uDUU`J!&ou#l(10+^b$&IdF
z%R(7q__d>7>8(oN;uldtdv~IswzGftc%umtc$xfcp-BgU_>ST+-#D%AJ#$glbsw2B
z&bn+Ng2Um19E>~Uu>tXneh|4m%~0KHIv-M6JTuv~+}5p?7q?Nc#(ha*6le4H6}EKx
z6Ju`^HKM^F4H&e8c?1tq6e{D;o@+Q{a(ZQ_1d_G?0K$4yx~;-V6qYwO)};!F;xQtx
zAB)StKbaJzyjK^nHNDl0+I6MBlrfaX^BiEd<=75Az-;^Wt)@Aa-FzW9d3R-dlgEDC
ze}!Nyen0V+E3aP-JkCGr^cB$;eY2gadPg?8(xO|2k2l(tx`xDGFL4?Yc*%EcHy9aQ
zap~z*7rO4XcO|x;qUic|gEGb<O-6XavS1(h3P8Xdk0@++BD(&yrrTdH@oW+}P!<FZ
z1_n1A0muZ7^`KAFwlW314Gs08F>^5}%NfV0+y;66P&@ZEWS%#nYHNL*g7@IYgZs)!
z_5E2o-&R%uY;5h+`JC=z$M~7a1o^Um=Jc+0@r&T(H@Zaj_WDdQ-WA<!yAa&6W1YaC
z;V2*4U;qT-zLh;$)S6PX2Udqiu#<F_>@8zm$US%fF*xnX`qfun={NfBn{g$ct!1S^
z7c=S_+{<rh>_;!(pUFofJFB`pTKJsJ$}i-8LVQl>$@)hA=E`gPXf5B(B#AoQyq`kR
z?o-d8Y<+7}=^Ywd+tVeWGEE;b9>ag3B=*OD(!RC${qTpSZ8Z6vdXG!I)WW$DXci98
z-~dKva!3AL`+fdw(lmSRChe`3LuiIfZQc~QIL2I_2X1Sw6XcBN8g_Lj=;-=qrLFYg
zA-&Y^ki77~eD0?KVN7$DAP=IKJmWNY`ce%#?s#QB09fRCN_kNsi};RDuz9ie3WJ<g
zpTqvD(z<pvyp*J?Y(gOpLqxsV$vFYt@OK}=px|z$?<LeO?5_2FLe<0?qnm=SxMtZH
z_;DhWkcEI8_u4z))zdOp?BpG3(wEnIi14iDODiarC6LBX%uIPIzUOgak4?C(Gh4Bn
zO}#eL{{Y2=1#k}v-^@QE2>XLyOX@c_GA^5AZp|jEcJh41*35jWuj2{F4jG9)%){$k
z7vg@B_8mp2x<atpM=^#V2s{o3KP(EeWE6UJ@l`RG+O5G-po99>$7ONj?O&Q-1o5^+
z%91}YLHk!eis9AwWbO9NY+5d(I?4oiZ&T_`T&ijIbiSIizp|P&w~G)K%PKJ_Ti;<m
zQ^$2Vt<nuL;Gdtl(Yy&H{5Ll9Y-T<1z&2m=+;Li7pRd?h+O&%e#ga%e1Yx6AU<W%-
z9{gk1BCquBx0334gj&N(8C<uAA<O>&d6W$E?^@%Z7E)^ZBh=ByeW#f3^y#jH0tB()
zB#CkAxW{wbXy8{K*Yq1=rJ)`#w)F7aepw*?wc0hEX8I^%7WW1`VZ3Ev%y2trJ-(Hs
z^$x2xp&PEHX=Qp84bof0V37#)BN+PRam{Tx#j7)nS;Q~wOe{#`cJ0j~Jf}6gYdTy=
zrQwcf)b?3C!2OMCcUIdR@rubm*+!2=CP=Fd;0mfhR-B70quQw?EM_)WU<t-)_~-E2
zCR~8L9%`te`&Eo=izgm+$0DmGqVan=hJ&ofsc7#EhtD26cflAUwQKtsbot&HRg|eK
zf-zF*;Cx7)20NR{v(>9=>IHpcPLk?#{CI+fIOLJWYXzos{!uhpLn8Tex7wSxgia$V
z=4|DMuYYPB@e>(PGHO~&G&gXz2|LDD*S%Soqx8@AVumZvhdOMCM6h6#J@y{Ndyd_`
z^X*-hW4Dgxxi{?RWei;nV|!$)922MUB3{Bl!T$hX%vV>KhBe!Td{N6S%vw|FNyurb
zKD3m%{Ht0iRsu%mmX)#%8L4+Xnv{u11NWfFJhW!-LX(k66CqN0pgZ@YK0q|9)<P*T
zZfRMM7^A%{A>*2xCxR7z)CccJY6EwwngDKTNtZdM+dh=6rmk~d#3c5mWL_%5RRmHp
zkO37GP1uB-98%XDnrcDLX<6!~(G-%Ek#m}AN?phqs=F*P9WxhtRw`;P^(YO-3F4iB
zIHe+Oq*IZ<rC7>VITC?QM@qu^9MdRyt12inA18`&DOfxGX}I|Yt9EM;=sQygywd5A
zMK+?KG#M!9c@z~G9`s~6B85g%flNh9r)pv`fI0P`8cx*2V*`p^4-{R-r5UAcWiT;J
zFKSjIO)`e88f&oY=|w||3LYq<y$KG6h&ePQ<WUAV#Q<gOD1)%5Cu~&3K~@xg8p50!
z$Q)KwR*@`_MXBS8f{c)8;d58$uTFt%;)d%@gyOtm+`jZgJu<dcCxJl_l5%R{Ed5C0
zwZ69N%@d$B*Sgg1RxmOGFD%E~6*7`a*(<Qu*O2M9%{1;KjYoE`ojx)AUTIcaTS-K4
zjxsyelkrFPin!|u<&RI@WoR?C-LP?77hKia@tw@iAwSH_YtPA-o{`lETiI!^bx(}4
z5_!&fZ!=b>rQ5c-ti=;7j2(vUr;s!Dsp)oc>Nj2yX-RGiM!Cn|Cbu0rI}Vnyl4&j$
zCsUCc9j^ZXOyJj@l69ndSn=Nd8$FCG(~vxtv%RN=G2^_UKt9|6FmOBOlAlwuyiXfd
zvYsT!A2$MSick9TcK-lc<$Z0{!&1J2OK63wCjp5L$L`n$u6hrmI<u?hH=13Px`fI`
zB)7UnlyT^-A`_l?=QWIRI^`pOx|`z@>d%3C2T$mdE~yrcskEhhmsghd+Kq_G7;KG<
zgdcJbr+V#In&(QiLVapYD$4pISLSr=#T#VhNmAT-iO6m+<oZ_>=v`M(&|gnF_LJ0B
z*OrJ7I@vTAFfKAi*$X4D93vbN_)7LQe-Qp6X}xO<8e{3U`ji(k2;zGr7P7cM+kzqn
z{C697eQPPcEY=a^W3PI?T}NA*2(?KrH1#1_E;dE94lpDfdyMB5W6;`uovvQM*Iz$x
z8!%s$wT{lkatPt(mQWuf`zw>qF;y{X@9_57v<nS#8!M}#b7r!{j`8P$#H+YroDq(A
zKDe&&@Jh!`xV2MjuH3_@4Zzx_oF$D(0Bu=jgfzUI<}riy?^wk<IwsOZ&qxd2osvHb
zL5oDup;mW~!|_%`^eXWt#q#l=6lVju&0T9fP0}`&Qfs<qyRBSYM7|fwTF*MfnLIR#
z7DYY1ee;epR$9ihbJF$`wzH|rs@X^hi6^iND*^ui++DkP#xd*ZoYw>RlkxQF4J%t}
z+U}8Z7cj?TsDws6v$hf!>z%pBuWIUYOGhh<mdn1O>zyO17Upjsr_IDpUQIrDr4k+q
zA!fv61KW^rNXV{VfALPkMbmDjvb30MHp6Ha8k4dGPc~!oWSnCl1IBx26~=EqAYSWw
z!{x6o9(URe#LX;$+arVZ$@JiyfGca$z6k1n5H00sWxlnzx3(9NGEO9oTfW60lNlo`
zl1k(rdsS<8*=76*^*6@54PRG%VjU{(*Hc#7T;7|M101I*oD32-ZuQB<SLq)Y^t-^(
z>Y8+Ea9hP$VT_OB9AV>M<UNN27(ZcI);<!xhdRk|Y-1o4n&#R`BX~#|S0RBady+W^
zYo7dy69iM~FQQ%9B(AQ;3zovm8-HP2H~{hGB&jS2+DOeL({_s}NqRbcwb6$~lJiB5
z4NE2=acL+?Qso?lE(0K8$;LC>dXs}0Ix|#zNoA9!7HF1Lm_aNC8Hx<$6f%r$Vfadb
z*gF`NInjC>LX!3)6`X!N1hJGT63Gc89wx(p;DF$R!>&+}LYnCe+S}@O@M!VeHm_)^
zBe1rdZonCd?cITAU{q}<b2w0rtWe@E^vkr%iSKUTNAa}l71gicQ4^+EwD#l!;Y_D3
zl?}IKq?S@PNWiY*bv~<UqG%d!wG^`%uF3HyXqy)*q_>4K@={d~$0VGFQmhVc`ci1M
zOQ>}_CbIqWC|9`&5*V)c<-AF4<!p_`ibw-(8_R}~8Jenky{6T!H4Q4-813iqh26%L
zc{58A+eUY8hDKoJBN=V4=8Zf-xF~cNaH1}|H%_3nO&dXnP=mzOE%m4*c6{Zu-Bty6
zcp1BxZZ^ufJ@b)THl?qnz3c033JbfNcwSWqL03CW;xUY2m8B!o3_T5WK9s+-z3E1Y
z<C5_$ZHAq9ZFZrZT@a*7-l5Q=K-nDlS??hgs`R%&CsAp(BKAo|<(#*gP1%x9G^-k)
z2&rNCiUm?WgW9U)S`k;-@^8ldGD`{XtVFEQT4F_tG3DB+vvx7+#z)V)8cX37)!$ER
z(P~zanQl6I`aMPbCg;H1t0q+bCBrA=)p7A${_cwBRnx9gON*VNMg$)e5INd;$CwQL
zs+-|$!M)bBB)Ex<z0j7<ceNWCqHt72k=&yksps&ZABMXlxLP@7snOK6VQ165Y1Vf3
z>{)F@w>o^74Vaktg^+f^gzrAV$8IT(o!x2h+gH#hw0SRLSk*Vjw8lizA_9F1$$2^4
zdCGH0&!{G)r^j`75=!4O4ZOK<7sGg6WMF%MvB>IwxRtW?yt3+6iySdr$8B?|JKkI_
zS9C(+=3T3vFaz!<up+T?qfJ>^t33TvVbiyFaF>%%SJ1S(atTz}ZDI~cUNB|BBal_`
zTCc<@bi2zgqnAZdHk)U7-X-fCc+sns@l^*395QY67;Jjh#n$&N(!E!r#bqmLr%Hm(
zA>k7sg62;<fTuZSUi-hD0s2;-s#?jXCacpHF^o0F*{%eeQP*-rfM>qYvT{3QnzALk
zD71Yp<6pQ%js%tF0@eis_;942Sc?5KwCCHsebex(qcpyo=o?9|qFrmJb=^I0?|{qr
zJ_@Yx;YM2?48}IVBo!Qtir}BJmq-5qwmocZwWo_n)AdU!Y%HZ>SHyBlK&VQPBvyt+
z8@*T5fDhKc1+Oi1YimJ%QXNhw)oqMMEJd{-n&2vN=8U06Q=U{0Y9-24jOQ*f9qX-9
zWcX{R*!1?9V+^`<+EiBVzGxj5Ye_-}<1wPhoadZYwbuUt5UjPIhY%vz-9(*XuG<Kp
zOvydOH*;|qB<C$_4cdhVfhuDFirzZkL(u*oX!>_g+FvDwuA!^RYi`kk65b1?F)2Cx
zMB*j`-v^A0=~_O$qm6d=s4r||u+y|{Qs!F%{&rirw%W!NFByv2U|rpVTnu_wO>u1H
zUn-#wiTz-8y<*A^qLSWiH%_>Ha_Yj?GV!T~Vz#!bR0fc|hAaR#FklA+V}pLs-EXON
zM^9)L+Ww_Jn{hXfHTupPLZ~D%@~T3zt1MuVo$?|mJ0k`6WQNbEWWUt)JGZh>zc694
z1hP6Q@WR9o7!n0Y*9BC>qaHGv<^KQ|XN#__dT?pBcJbW&o7_Pyrd{ES#3-0<&dPEW
zuLY5rwxn@^l5g3%OQYqjb?&t7OzI6C;#nDd7nPK&9f|_Ol01Yi20N&3++wVHU#j(O
za$gAyaM)bRE!riIa-eg9BTvM{CmGIg$B<hhUliw-cr@!|Wro_pWVepUNKb>boxp*?
zVo7&n$t~yx2Q$;<SndYt3dA$W^&Y%^f39n>D@QD!8Bz4rh3&4(YdBT4J13S!x3`90
z32z?-gEOe+H6X~u02>@&D%a8X{ZDB%u8;fL{dH|l#%LwA(5@f-+6i0mUEP7m#s`NC
zhs1s`imR~vN6{L=Xk-^REu?5Nk@FG>0?BiR$b?A8DviNnGEUKua0PZgp7`;7q-$&Y
zuGdN$Es*dmw2MgQOK{Q*JcJM8%Oig@jN6F-<X~f-QAwQ<la(ZWkJFBwHX+sZ9TUd6
zo@~q1I_f>7Vn8x}S^yg2SrlUoBMiNVhg)w^{6ISHie<FY=g}`_{v21={w1{U7;I%D
zE~yIV12PtG?MKdQ<&LM+_3eL4iu^98W2VH7bkHPbJ6Xz{{{X^+#TZlBrjZvs?X8!k
zI+gCU-8Hn%s?@dn%Y;_Bj?P<)`4kL)3`c2cKZUm#2XYcdahk1Ok{VBwX60@0Lq+RN
zX|-Kjs&4fGaD`I-(geDWQn)Nl-acL-li{j@yyT3Ux7Pj$vs%kMFqKt7HI1ds%yQeY
z+6S55QW?k@B^}TKj2^+Af8rNVb;Z@Ct<Iy=tt5(kI~28xPnnq)9zi*dcVWkruI4=8
z)n=uArF7p>MQyKi!r#X#_)=YUJ)Cw8JM9HxR=L9EK{+KzIIe7%qV-2sJd<1Gds6sy
z)%u>Fr|Fh9Hh(M=dyn2z-Wa#wIgSV-juk!fSTO+e$)~zI;N|a0>8U22qS<Pf(x5jN
zPr3q;@=i-4?HRy1#(3i#j>XYhm&4e!KNjCh7s&wL{{XwK?Cw$o-N|IIZJ-ujNncKC
z^IN&I)+`?9{g=Adw0oA=@1v?t$Orx-!qNu7K8UP%^{#B5EM$$L=1Mw9(r)Lx31?*_
zc9TsKi4q+)A>hJ5$=diO`y8<LIV5pgUAD2P8*6FeyJ+;K!F!#+E9bw4Nb~Z0gN}GU
zl+}kr!#<ZPX!EUwna2MB-bm0(x3=a+D$2m{GB^U0g2GKbqEoL}>Q*sjklEY@5R71u
zPni1Tm(EGAE`Byh{JkA9&k1}{w}S%PESJ+Qm7{<ojzO7a&$%bm`(yK~D-A+RpsgIR
z+uNYq;Hq+ciF1$8S1*HK5NaPYrr*Aya;&hZiQ+CZ*|HmKW4}1he3OjU%$-xH=>{n-
zHZ|)KyCRY?8vR~I0evy$=kG{lr&LyW?HkNm<4ZJ_^K4kqVIyYF^YlT<A9|(NE+DxJ
zCC-sO%m_myf)L2RKp156^riIvr@NK}xVw!OX5w9vq-ebV0BnLu$G0C^w;e}oe;Sb~
zXpaouCy1mEPjAdua>%D0vytO*mXV|DQt5Wrw)&LjEj<+YhFilL@Hqf@g9FpBHC=Pn
zT`kly#L#FhdvMHVl1rOY9AUG|?nwLd-0_;(wTIEJ?GEu}5q8DA@uYG{#0=p~ig0-J
ztq)GNvb4LJX=9c<Sr^OmXkFcjz}mqIs0TcefO-1WW0Aqt_F2J6U36=I5I!2k)~cG6
zRu;OY%p1It*sMt8p3d&uMlySIjPPsYU)lrVKDN7lPo%|#vZnHyU91Zc9P(s}Lzc!*
z;T#P2uh8$`on37NV@v7Rg4P)Dt!H;^$lk_BF^$BJsOGs}Tj+gd@g7s+^t{g&;u>2=
zw0S*z#l)%!9>Iwp+3#KJB8&K(>g1|S{L{A8Wa(8JYw2mF1~_|*nGcGn2bhp>2JH6l
zi~*lr??Ke{U2{}@OHmIr2^uw;%rh#e17`!U9znO*s_|QQ#;%apni-@{nZLbTJipCO
zU{yxXd_+JX59^%gIj#p~uij{u@*PpHV=_29LWDB7?7NOIgZ((J*iFqG^N$y<kEH${
z=_zrbHQmZautRMntP@JA5tl4-Gl0XyWBu)?ISX7b;ulTaY8tJ!p34iYC-@LXOG$<s
zw@iBi18z7S*n^x?7XJVcwO*(B4L&4_9Wo{esL};V003<y;I?-1dt-rsO>Q#VX|_;L
zEy!uL2_pga<QZAplKAs^vT+{;vy*^pTB9o6pEm0~I`MCo+9vSOoPaaf<0l=vQd(Rx
z+sWdg+x~UfekR@}w}+v_WetpKaU?MST>E4Wz<PRp#tG+hqj-WSt=c{L2nZtpa$BFZ
zO_g!kX$Mh!tOqQ3UJg5-?4P9}b=L)DXG?JlG0yn=AHTonMAHM?&Zao`s)D%B9^8Y$
z`&EBZ+DB}elF)8$QfPdK?91AVvM9TUuJ0y+d?_J(9!Ay3{Jp5x{Y7VZ>Scx@AjVxt
z7~{|%^{!&<Llcdp`wUd!o$?oX!Q+~eyy)te6ZGx&n#*aZ+`)0Mzsk&3NZk71gU99#
zb4_Z+UTi|a&u#_{ZXGwFT3#%MSn%LvGMo?BwJa6%&ZL5OIWao8@j1>a6seHn+Os_$
z@Dr=L#F1mA%132qTms&MHPc7zXV*GsgxZFUI>5k#JSW6J+j0jS{#dWPe`T(&(t5W<
z5=9_;wE)ZDh|$+4kjOESliUI;=?2s6%LzOP!NxL!Zb#)^aDtp}&UY>jsQfwWzlC@G
zDQc5A@f09tz&T$rTzz>JqebOv{et+J@C!`q3kfZatb4BDk}Qr2H;AsJeqq%A0C-pC
zPsXo<`j5jNqP>nwc^1++{31&5jC05T0Ls-8#?maBmeHlyOz;@mH!C7ADnaaPrRX<9
zR<XXdy9l>3BD$0O$J7etHX)h!FY@5l<)!M<E2A>+z`{80E0v?F4??p*BD|6&;gzwE
z@T;DX(c{1BOCciou&jZkT;S~oC;C$Q%mR6m^L8Aaj6W_7U;HJs`HrZ9;#ivI)+7G$
z6lV|N_GTYp%|a^A(vO5)GMzQkkwFBaSR_?&z+gWdHa!U(AFwsLOrU+LgQ1t@x_-(r
zBFz|xY?VgP>*z@IIIFqek8@sxbdkX;v00Bal&#*Hm8m&<{VM2%*u00D1M)edy#@PX
zu4n;|G$%Y}h~vEiQ^Be}ic4q|gb-;b<kJ$KDQE3jmr9T~?Mq9_nq=`y$}kUV5SwgH
zL(MMZnqpq`yPs-;??4;1D<Qz9+-8xQtaFP@{pl$VMK>lvG@RT6P+vmNN>9nA+LV}7
z8lg!kSxq%5rDdTbQS*vwA&OQ$)ZAUo0MtCwaV{xnk7{l{^eICzj?~m+kxNIM{V9m(
zRgOS^#+*(mID1o(u>erA8BveYnMzd-X_)&87a2~}WS}A9jGP)2V=>b)b4td0QxKeS
zLZgxPqQ}lCN!p7ada^0d!f{7aKtbAyrlbc$iZUK)WNJEdKrjsvC;_&uO<Opqx+G~2
zG*L^$XzS*o4FoNaD2YP@jQUiQp4F^+COrpH%^owF5)6;6Ga3QwR%EhjoO4AbAyUo4
zf$K}%+%2WMNLf@U?#C7LPsYF666Zu`vC?l<SOOkI1Nv4o$e^|(4t*Q<#y^j6d_L3U
zTWI4K81k7;bN>KHuY<lJ{8rZb_f~t0yXj_K#DSbD{fDJeb(dP-bp`yAPkAldvlcBY
zXCG?TqPg+IdB7f|R|KZ!?VXXvpoinsnn?+n*}nb0^<$*y7rjGr1S#RdP7Kp|5r02l
z%B%E^HseyfiWp;U&UoUtjel8>;hmU{bq7)lz-P-T#{^*USWa}Qc64Kf?esY%xBD6z
z)8^t?jx#*A#NhjYdHl_CUcKuXZ!elNeI)9q@!$Y{!_u|hrtAGjS(Us?h6==;r-762
z@A=laZXRR&SwnwXW5n0WLndYT$D+<1Q|9Df5#?-l0ZV7}uc-e3WuB?h^u1{z)Yne9
zvbbHFu2N0$eTs$y)04+F!hRfSmRha+aD381C_A$<qN)4tAL;k6v^q;nkvd8jO=`nY
zlx_0wibX7QnD<AL7;(-q&w9^1)KwdJ(B$mD_^qm4bquFXgHX1L?|4{V%JG3X<$Tem
z3GISN``}i;ZSbWuTM6|W{df+bJhXFaJK?#<WM)<b{mJ9dj@5hc@1%N`=T?$?-`~1c
zme1lO(Fq~8#_r140lwJgx_?yZ?W_Z1ZPXKQ0C>`Bl1R`ZfzB13Q@1B3ym@5V?5L!U
z6VTQ-y+?U}YiDknR;dy3B9<$`GpGX$>mV7GKs!pSF9!@)BEF63>)7=^kCNY7ib#<W
zJX3Vh`0N-GrC|%S1_6qhIpKi;m4MK1W6@#Krm(e39dAvx8`|SeP=RF0{vyi}J6&>4
z<ROP}Yv*5x{{R$qI3$L~{`oE=w?iCLS|NzUxaY;#6<nMR#e0#Hh5@Ul617Qe<(l2T
zjnm>c#2F^IWYeV4UIcc90$$=>r0}v~i2(P<VO-iyoYu8Xay>e3oi8-;H1NS`A!#mA
zOBa?87k2&u&fNRsxvk&f??+m7LdNJ}a$#8~j$6Q5-58bLqy~;y&JI>Y2hihiHI(>@
zJ{N2E8a2Um&|zh3tu{4zqmhRq?kHs>!!ocL@Yw*hKuW(4vo964IM-%bC(=gs@RQ(n
zjc&SC?xhqOj*)(R2fhd+n$}jzm?coe4oVe~bAswr;e!s}(;Xt+AJwd~xxBKH&7Ldk
zm{2|4nNhhUZ|0IR$;)SQ5*u(R)vf$6wr;YrYx@Z%>a8wd$$O<Vfo?WBv1t-jIgm28
z?WD6IZV#<@zMSb;w4EV$Ji5<HY7t^9#Aa#jCF~_{SlO~SGUE~kVg`J=sN1s%OOB0x
z{{W=4dyb%OCO9KVwR>fj#`bZ&JXGOhkdR9N-We*~s9}TL*Uvu?KWD8oT<SJnpNr)z
zH7h%b?F2Yov&WFovhWp<uRLRg_3iX7r3|_ay{FDflit}~EOJ}hMYc<U81TMfV;n0q
zb3}}*z#uU4;;Xgm3#<O4(<Y4BT*_}{nPgc3Nuj#5WG(g+t05Ra%bxX~Tzr;`<I5=a
zetPt7yMNJrOLwDRS|z{*qm~&rBPi_qSaO7LK^=<c(zi9z2U^%$UD&H8wG^V?F-jwr
zDYtn7gWqu(+I*zs4oTZ{`($)ebjQR?uBp;@O{Lv4+$nGf!_O}9k;XjUynsis#=Fqh
z7Q!Uf9{O~&5jYYFFtA*b2r`0sfv^$XNHL!cj|v%i+!|+WW~Cc9o}SdD>fKg7LQ8j>
zTUCiKZ7il#xLH5qE!DRhmPcSnAZNtfuCC99-+HO+X82d3YWjWj`*a@)?@!gNV<{t|
zFjhVm2ZtiA;jn@iJ`wWBx#7J>Z!O$cTK0u*^XYnm{_*e?w@4r?JYNm-{{RvfLOlZo
z4Z#N+_d)B>%g|b$t*YvGR}%zz=Z0;_Zh<4kX5H$dLzck>8A%=UqRU9nhdzyg*GB06
zu-CQCBzW2l^2htq+Rb^je5%aR#xhh0-6oAeBp8?s<0kt~)!N&qwfnnwxBclI>fWeB
z<hqt18RS4X#E1dF;~%AHx@G>6r}b3&wvl#L+jCpU*$q9cfmox;nIM?<oa4DhQ;^MC
z7OAdJr$em0kohz|E$yASVi&>@lPqALV9W=xRX(+y?6)30oNumeq?4@l?I6J4A*rE`
zI3s<Iu4M9sY~VMP5uP)Qjwt;i(%9*`Z%{OTB=@dU!+>EVojJ=O<s>*OfH@@y$qibc
z#eGiV{{XA(b!#P%35~qaAY!bJB2ZL&dE<V6)~BKMq?X!~+F!5k^uAFe_)!Jqj}qQ^
zvBI6i9ymLXyH}wYz7%mvOP<cJ)0f(AzoGROi#ElU?&2x!%f8*7Cy2Xo=%d9@G5wXt
z2Dk2qzyAPjtvVjTZ+tuZtA>Uh@b29lldZafqxq5UE*y>tF|K$u%Wm%NuWfY;^@baL
zQaBnkmff(EIoMWJ9Bl>2TpmzM7$9vmZPGnRodMPzJ*){)*3(soQnXm)Bgcy6)6U%F
z6>ZAOc?B?gRe0ZJuk>3clsfxN)isOjnU?l@i<Pt`)<Y<c+rd<G=u2!pvP*q)T%TOI
zmd8-Km2a8eOTlRi%)5z<8CBi6&N8?;@4r38ZF+xE+&-L;>vFfttLhhC6cPndksC%8
zvw%p99D|d9d)AGtYwL5>7S_6q7Ute1iGoH<arkbyf9|k6=dd|ABZ`{+5A6Eu;TDZ?
zY0x?*sc2IEOSZV4$$5%PTc~3bMH}Os$sA0eavSBN!3CQ=gHrf}dc#J8tMu0S4Kh@@
zjyrpl-6Sh<3q}Y7hA~ex&=NbWk&)<Yn)IfTZL0h;{{Uz7S?{9Mtlc$T9W7Y+mzNfg
z9G(DWZYA)WXaM3xF*0riMQr^A(wZx4{YPh`>LxuF-W9sGnj#k7;@ayBxlh6wM8K{9
z2V_~o@Glqfx-rPD{{TgPqt;W|X`c*yc$V);w$OTiSiaQnAH*@o1;p(u5PR*3u*cWJ
zs*bzsNo^w6w7n)#EczCusL7|^i2neHRZs&Ygz>pnMU=Kb@K9s9txKhDueEIx<?bbP
zwzj&v>D&8xL1@G?tj#27r<7)o#wx4^*T5Lb6`|^#cXO=SYjA3iD_^y(+<LY24dJUG
zjiU;q`Bp|~j(tLpYMr}Cs`#V^^Hu)<_Rpg%>@_`dYnd<Y1e&kPIccMU<g<ta9O7AH
zWy=w`c#WPB0OWrYz9~!9Gpssf(rEfSTO!;-phFs!Ob8-W^B9pfqJnbAEI{3gq4=-X
zch~KzTHG{#D#^W-Ny3*Pmr?zuPzgBW#2^8{8VnZ?Hk};z3euHt;i>#1)Q<cPbNPB^
zwotv9Je8zhAi1AJn&Ha9ZEgPUUUVlp{{X`ZGmhl#INU)bp4G1EH@bDbgF_vZqU5k9
z;&;JZdITQ7QcgXy%`2|!BI4vfjAoN)+}HtB<FcsW05CE1stGPj=PX*cNu2VK?Y;7?
z!c9CYbgTaWg(ENx^f(~?hOegUN$%ljQaf!j+;SnYh^9#8a@j0!ea&lnWOkPqKkv6q
zYi<E0qfP97fKysFn3|rWwnH*l#=)kQR1%4hJ$M9?21x@ZtmFGLO`~C6zqyz!w{rO9
zLZI<Ul0uz`+)i=djl;hcdu#EFTQ}I#Wg2u5#6qpSJ9h<NR0M`UR?pPdw|AmLn2nTi
z3r3t<{OpopH;@hqZpuHkft(Ze+~w$d9T?`q<ZH#4TiLy&GB3|h;tZ-r%ejWkWDqg9
z1B2x>X?~5Ar2UQQA0PfD=<7YN`%S6atnmeTWVvwb5d(#9#HvT~sQ~+PTb6_SN8gKk
z4L$8`t|zs9$$fhiQsIv5vj&X0Y_|lt001y>D@5ttFHUV%Hr6jz#R9CxH?tRP%1~js
zI626|pD5sxJ$8<(>WwPbt{~MkeOFM`QrM(|F^g$sl|~2r0U3)Jz}bQTK5tseQBw44
z2{@f!PWaP*q-gBDO>Jv`e-;c|5+f{0__sHZt{dqfIL3Pl>v~7+x6<)x*E)U7HhPu3
zlO&BL{o}NtkO5>^<G~{YjIi_=#eAR9n&RrZ)T^yQeW^XmG|%&{Ri#{R!FkM{TnzpK
zPJ7nLe-@Pmv9Oy>u)S!TCAv~&NTXgz4#^Mk8y<lAb`{GgIGu`0U7vLPApM~u*R`v3
z()ArBbhcLTmd^frySRoj1Fl9u0~k2qa6K#N<J6WK+AXc`Qt8@#{OUF@i1B!;d;b6m
zU<p5OZ_2+at}Jwo6HJ3uv($^3P`AqlnlerYl!l25dJZ_Qm+-6hk%vvwqqEj@?Pg$-
z1>a?TaezOSj3u&hk3h(I^4C8~nzLy}>BXO*mz^i9*i0>w>qpdWRBj=(^Ay{AaNw0E
z>$G!P-2GK;Ykl&NKy=B_vLvdgp@wh*HW-uX<v6N7fcVkV9ao{jbEjU&w*zV`+lGjj
zEw^Uah)~CYu*8I)<u$YQ4@=nISZbGSvR=y~;!A04<7uIM0s@_Z)ML@toc0;6EVx&A
z%`xP5mPB9ZaD40`%=VhS%n!ViA!57^51ez4Oab()wyihK&u?j_+)BW1X=P89g+7Vq
z4#%G3^{!2CsiZJKk*(gJXEJY*wEI$_7lKTYrq>6OcmuKTo_d!{>v|pLn3L(<L2amb
zN<2{()s+eU+B9tHe&;pG1p4UTDpB8HZ|x+%9wg8`gj>8!G}1}D<G2h)4uAam)|GLp
zX*w0?Xe=B_AL7NKWh1#_GI8&U+4XA}b*W0hs_UAj2$0)BENLG-f!w(x{VJDVv)0a^
zBoo?RX_mkR!pOcK(6JH@ez+c;vsp^-k}r*~#8THzvFp2`do(vUwjuDg=^Ww|X8Mlb
z-5W<Wx%AgbXfs<RTcuaEK3|q5IU>Qp02VuO?m7JHo@lyk5Z<h|w~|4r<gt{m%m*io
z=lH9;{41+kT*r5&#$?s?25^8B%4N9iwUBUqE4#<YyJt3hZ90!ev+-A^^sPfpxr*ZA
z>gv`=%Cr|3sTu-DV6Sf3Cyq(rcCXJLiodhV+-e$y=8<PL$}4Tc%9EH^zQ$;y7$6c4
z1M)TcCY@UfT%<Pl3F0K4MYxSi?9cxINUuHkIp(T${+0g#YAx<s>RzO|S>xJewo6rT
zMn{!o860t)xjyE)zUyw{&UF<PQa>NHo}ShEM^};wH(EzADJ1I=LW}CC7y`b7HM9+F
zMuif^e;gNpW-%Ouk+_Y{UpWN&k(NIBHTB2E-`RUdlG5h(%EPH5njT(D*r9M=l5;B}
z9k~9^1$>LuUkf#j3saWjJA9a&$OMKzG5V_Kx3@f3Zam6bW-`Xs<I!)X^lVo8THV1S
z*}AcNonGJ(9DHQ>XfjJ<19O&a_SwaAy<bHJDRoFz_FFcQkY73!g~xN|KZx)@PSv+*
zok3|ZiW~c&*Rh`xORMb6u{ZpPK?S{WjCSU$I;!&TtF;Attw&P7hi*P<>NS!6qiUW9
zve!yx6l&T9(n~au!5@yS009uQ9(_CG?^n;!=Tvz_%8M%gJc1khig_G*lj}$6fKBxF
z))v=m9u_Y&NgAT(mTa8l`wVl8*83ikZ^V(>PGYu=K{F{=k<L30f5>2ZRn>bS;yTa6
zi@RqmtNbyO&ma%IYFGL+mQT-bMgZW~)Ot@uF1T6<be%?Q5Dm<bw0pVdbh3Yusy%n%
zKAf3|+uTHd4k2ZkiTf56F1-;+oGYjLgxKziNASZAJ&j`e;LC6wSTJ72YX1PKJ{z@~
zw}T|D&*7Fmq<eNIwK;7rEM20Ve9P)N1Xh2^RNqI{{{XS~#9LiAs35bvyZzg6SPOkY
zSSeQi!(?EqWQ-rBewXQY&v#^|M_DAm1gOWSsISEjhT7!XR<{F1<{`lfK*#8KHTt*k
zFIa2-kg+pH6!<Dv1sD!JG0OVzMQwskGI21?9dbIX(W{g=#==Oh8~a6k4zT!>()#wx
zMV9vF+`5%qxspFRf%PRo&-};UyHn4O$27Cc_CD3<Z`sWqABtUd(3hVM`ljnmog@(}
z7Pgi|BgKn8R{X^)rOrf-p_BuYgYG+iwfblKMt;rOf5m>D&Adw<t7y#dNH}Ig-E;Kg
z^smjnyy#75;m1(VrRtXQK`d-`y9@>%`K~;e>uBnPi<pF0dP1Co;^c$uN2lJlAA&bi
zSasctCnLpB$>`vmZT#z=X}XYDUNt382XCRSq3|LTbMX^QGDfnaN~*|54P32Oja&A9
zlyuAqYo<bwfUuMdiaRL(0KZDHmAIg3u`CvFqNz|23^#nyC2wl<7e@rU0U@PdpS>PN
z=9PEvQdfS3<jzmE1-p9DWNefEloQQQE75m!3H@nlIR=<>$JUpVnu3CI`qJ)cnW<;0
z8?AzKie6K}qrERDY7#3gF4V-7yq%~3J7SfS*EGYiBA1h#nz@5iEhymeNllDXmjs2R
zr92uCCe);-2Ah!cN=@9=4UnY2YF1imPg+)578{SXH6L?JM@&Y1)B}+o)a*T}C{8g>
zLVUCXahhf|+($IDbj)4GDMnAR$f>kE(nxz#NOMC<p_J|^sE-t)9%-1UNYYW#mrqJM
zP^^%`905#4-~1_cqet9OpyEFi#7B`zFKT8Zv=3ievgoMFoKaNJmzpZnlqQ|&gfs-B
zB?|@|ijs}E#X)GxP)JDm6vB4Ly$Uf#UOgy7CcI<%QKMcvP|#%43Y=EUqAVm}012&o
ztUCKm==%qr?&TuD<0q1S)m_&;Z~n?yPa=3`MN&rd+P(?+-SLB1>U~~V(oq~hk-^SB
z)z6nF9V19Z%d>a*&HGDg+U3l)dPMI9x48UFGwOXS=PgsI?lmixHxj&=K5sEOq~@C9
z`Li^Paq4M1nW2g!8tutHjQ;?YaJ}uFsVJ&OB$cOdv6K4M-j!|<P%53$HWUTPKU`II
zivgZ+rY0zP$sOyc^o^Chm5tt|a0i5|#J#_-zo1q8_^57?crHv8zfwc+^2+AoEM8xg
z+Om|&q4pmC0O4FOtUA8?sI>{M?`LV{cF83F08h1D_4ie_v8db_+rzb1+Z;-LE1g|F
z_9UOkdQ%=de3@L?DRDsFQz0bsezl0#dja@!>q9OF7|E=ptQ#Ybt}C(ha%;1%d?M+M
zKU&r8qQ1DhSlPxJ*_8GkptXIg@WVizb+n3j;gdw+5pJG2JidT=gDLvwgYRDwd=t^*
z)^(WHc{LRZcSgE$A${JTiI@Cq^fTc1O6a#Xt0mMOO7|?Ao?E1jIL91-12F!AzJJY^
z7JUbU8;R5G`fB-x%{mP;O}BCc0@=)xf!LB%GZFQ{A6!!U`{_RlE#6!GGRs!H1ghTa
zQXoc<f%s662_Hd|#{#u}tLsf;qjag`(k<lEbn&^OSt5hPY>|`rFg<^q8sL3H@jt0L
zyHQ;`Q<~Nb0#yjU@uCTwa?K0w9sLjGUQ{Y?;(Bt6kK{A)58@@)SGCkyW~9+C;_$65
zJUC$=+5i9)WcNR<DbhU&rD!ib-&pDPuX2iI7voa}W5~+*!ZYF{9tf5;0Ays3Q)y43
zbWXEpKkj%W)FouOismczIgsFEb;}IrxL-Ho!6O7#v++;jfL(f9d;4uaPMNLZoHgx|
zPaVq=tf9AnKsW~@1Pp=(NUviSH`+YhxtB(_@e@;vr?1j&8qHkB7*7Q9?lP7cGRDc}
z3<g1Xw(ib$=bF%^_?>aDTZmv)xI#<#QXK6iK-zl&*m5vCcEH=Lb#AjHnu}URKamb2
zir!7p4s(>jAY}8wrn*AJK|QRK1=8mnrPQd>LoN@hfOlY?ag*Dz@7$rSoE+PgBkjN0
zZcP_o(^~gKxf%t|lO%IGUWQ2EaKx#Rz!`wPcyBx`#G^7~53AFv<qNObUqLJyfN32T
z&NpbCnm;@j2#3yG7AWh><H`f7t$Y#ihpKJ*YppFcCA20LVHAsEOwmfR9}Y;9Xw0z4
z7#PupHz<ssO8Qr#I;Hgc9a1!zHH{+RMlCI^WngY}@)_lZGT3m?kt~tp4Yg!Z%w-cK
z+I<>MDXTYAeBDtVhb)%=0KIN!u*i%MvdYmb$1F_43gTGIh{RwKBFX>*%ddPp>g$QD
zwQmYW)HRJJ#_||tnD1E#nG9XXz-bZGXB@`)<Yu)#vbc|@HAWV?l-K%vdYzQJqAd*K
zJA)d<D=Od?aK<)%2IYQpHU;jd)A4C`9cwP3s4TjbpUp{ecOD{nSWX0)T<|wY&$qYx
z*BRi}d!q43)jlEVi`9Pyb)n$L6{OmY#L++w=8hSz<hlUokf<dqf%P4SA025r_2))v
zCq}rtduW-}(n-b!P85~{K7{f!#eKKbSGLv{+9sEz2YX9x7|VSm%a(r**dp!sJ~I_#
zfzKY5@ptVZqYWEWisl3`wYZiYp|=4Hb39SGJ@Q!_>5Nt!T&1(4mk8+Jej9ZKwY7!T
zk99lRXtMaz#L7#Er7@lO0~?i^JdhhZQz}7FO~#$oo-O^w<*k$=%F-yU?x3?}R`AZ)
z)T-wMsgX!;!qKn<0HjwR_!sdS=S<TeSb>`M30^oYi+>Jf$B418{{Hf$W0r0~!KQRu
z5qo_msTG-)-c3XkVGERINmR_L8@K#F5ji;pRP(ge);fD5outv!x+?EW)bw3K<eQ7F
zCqSM^wI7rLyh|qu8o1|h*u;^X<ZyfQ&u?!nHQP&DJIlEKVID$~t17NGGBNMm_+*vO
zJNr;Idzd<63+wwx*~4mgNvcb2<%;G8RPeB$$P3CbgN{cYTIYA#eA>nITNGH@>KBR{
z=&^J_cKD3EpTgVlN7ve|_R&dXYe%~MMxlO^*h-P43SwB|Co02hD-8btc!Tu~TBfV4
zMX2fV!*K$w+Dy+J=r973S)<3IhCB~U@!Zvg=AC&x_s>1Fh_W~ZTo4rF%edpd7j`I8
zbiMYbkwp{_EYhkAes=>SF73Jc;Ea2Q_Bic|;;7-1ZJjIO#<!wNrRhgiM){ep<5zJo
zF-Z$4iddvla*R-tuitY0ayK5H=$Y(#=c%-tTb0yrt)Z4{DPcuovOwQ61nLhUF^x&%
z$|aYL+*c6OI(>zfnug_UuQ#j1G_J{%Kg-8FH{oIb05I;LS4rv(ac}ioEppi|bi1Ws
z)4>$NAWDVtk~7&CJBlwLE9@BoYOPmH)-u-6=TM?+-3ikE4U*d5NGG&1H<4V0V-Uw;
z3f)2jcRm|e6Fo@XL9WLw`Ze9mjp4TgbjWZ*@Hk+32OZ6Q5z(4EbvBQs^y*#3HMRWG
zM`b*+D7m$j5?4sFK`I(g41w6>fw)L>^WR<c*|U-@TFD5toSlbhRD5vi0qg_r$NvC>
zNbgK!ZG4gS2gA*NK7;6c*>Cj5*I<;3Yc<^Vvq2-qU2WeO<%3FMd>-;_IAWsy->s#w
zp5Lu*FD1LvwJQhGwKkh<dlA%ynbU3}nST(-+qq;><tQtQd^`B{tLt4d`gW}A)N?^?
z^2-(MXk_q&fzTX};^D%u4V*SR9i(hNHNJx#-S&}k_H*AH$1L)-#f`<hh)2t`ZW*Iu
z699w8!^zuKr_&`#J)4I>_3h_R^@gnurDZL>#pJPTS8z(MMaSWn$3^6pLQTLKB%xqC
zS2gQxPRB!+sC3|v+}qsQUBLp{NLVJv@sc(_%oU*u2=2V*whddVZZ#bvr}do%3t}`I
za}3&i&aLt>s}zwiPED~AGLld5!}(6dQaPtw-Or}m!{P%S?wIB&ZFXSF5Abs8J21dj
z_uLqF<5d$mG;^PdD)4n>^g=k{R&<c8Pq{*}qo+Q;Qn~upwJqaEXt5*tc{s=U{#4&p
zGOn$9H%epi8^UK8ECI$yKT4;PN1PH)22WwY`&U#|BZ<9<NZXjyaZ@ppTOEh3SZTUb
z+@zA=orCa!$t2dADgw^ZO!&Crew_1LE#`qXm2C~g>m{5warp7uvE`Ki0K;#nJdk~c
zeL<mHS+9b-&*C9__Rsm&%hZ<_U017JUESaBX2h_<tF(EJ(1?SDRSLeqB8+eVtelNh
zMEaKZOw;7k^zA<F?<`|fnqsQT7GkPp*_>wtY}`T4!bb!O7O&MBz2k|luWs!kTVoV4
zyzEJXasCn$&eOQ(J^aMxy{2fb9GBOZusrrKyaH(v)NPI2bI%;$jyoF2iOr?_m+u&#
z<`O9~ptCD0oyxfdy)b(Wj@(pOAJjE%X2(gk(~ZoZDxy2zm0WzgNJh{~;|xNAJ$sNb
z%{$b3{<SUDfYmM_f=hUo9k!7~vBam5wP6amz+Ny*V>#?Xx^|&yY|+hkFU*wwGQ!c5
z;k;Xfaj-qpfJk28<GyKWHQNIs;zBkFNp*l>+56+mk)B7m^u-H*Bez<Hp{nXKM{%jk
zb1af=VKOv~tIy&*;DOyiKG~~DdW{krn`nMkVQaQDGnR{TgN&+f2a$t<N$<^QnufIZ
zQcWGYt*xYt#9PM2u%zb<PbVK*P+iDmE`g*Za9@%toNOujI(64mt;{z1e38LncMJHE
zUE9n|asr^J@bQqtwn0B(Tb-oQbw5wF<<C*s{Qat`T-j=px6H_#VWDCPZ>Y~0$j`4H
zUa+~;bmX$WiVLJdc1mrd@s~gSUAxb2*~M7dYmcT_#cdlrmd}RU6#gMhp5Yka9CzdM
zsyudW9JF?>j&o_NTElC3b*0_-+grwgWk6HSj}T$FIP#SwAMJLpyM7FOU(oKf9X1*M
zAWIIZ3p76~egrKtcks)uQ+9Gxl~O^*%=xSHLQjZO^$hlHtIcuL3vR6xGL543V;lt~
zfh?eY7|CTFfWfYt)87<-dRh3eUEQXkX1gGnm%?k9LBWin+!W){2v9~wF<eu}!+o8w
zn#if>{W|KNsJYN(@vP?>j)!vz332kD`>W#tUDb+?cq<|xApR~6Y45b?`l!O!ojV20
zh{_Gj+Hvur0OYKQGqmu35-7%Tk<EO$@b}|xp`}_!ac_O7>y~lH8{S#n6&F@HAvYXy
z<#++nmOyy!R=aK2T3g)O!kV8@{Fa+@923CTQMJR`mLLEmP_h&!)j(xncYxV7=jDo;
zwr`C}4@X|o?DT6pPY(A^w2jPcBa+eFEWW$~I6mB;r3C1mIrEKmZE7aDkYG2BJlnpm
zqD*>naaAzulA8!#d)L;Y7>zDfl1;$z!F3s7k5C&Q%=oK2u8P%Uy^B?fIOMTZB@M$B
z4WH(G2F^#g^zU4aIO!`ji;TA21ZxdLOmvbfTM=Me%@>Cyx(pBHSvbkZa7Jh_TE(k)
z&8g{^7ZP?5!c2e<?PXYkK>Jmk+8mb_sv{%7j1p$I**3s)gvRG%k8E@4Na(WaV*Xg-
z((SL1FleTZBO8J0%!L$ZKTK3g3fJhecAa)x^hUL!EwO^uTX>?0n=vC0@$ER|93Bom
zD4MP9mA%7Dr^{)0N!mfWR&_bx?E~xDl)u+*ui9&S!=cM63_KU?{zdj769?~{R3g>1
z9V*pVP`T;YWjFzBpb-#1yyRxKPgUr{-LUNq#<y~|_YtkUug8qL1d2P15rPNYWA9v_
z;>W@V>MgdKkMB)t<{^k=wq=cQNXFtpS3Gl&eznwPy6LTLO})MN)KxP0mS>e0{{Sc;
zdR0$Rf9*Lfp@v8<VSpXNDVhTpl#C3OAAJ5*Jen@QNca=uPwd&Nbi@(f+1Sl*6ok5+
zib@W1mPnLfW8c%(zIN1lQ&Y8=+q5?`+%P2sa5A9vILH~`9@YA7t>~}Sw%0$p^o5Gl
zgW}!AdE-pj;Foz&@yGV-=6<u$ZLF`Zbn7{+ue?FBNYUkJU9*g{2^m|k9{hS@yCI#U
zDdAE+MVEe!c8Pl<SddSiCoJeky7mX?YoT<9#3=6WK33mYzTL-`XE}eiF`u?6{;}y9
zX4GX36@idvHukp2ilBWIXVcXB*C*C`VRX5t-;>GYs-Jv;?dkq?=*DVC4B+B@DP^on
zeR`{S_IF!&RJx1s0mtD01Nn+8&1Yjgg)dq!C>V;_f{y)$!{6S%ed*m>_iSqbv7O6c
z{{Rjmq=W8P3<3I9!rGMjm|{PKxzGFDvIWnu@iz*)(M7I~g@4rAJeOW2)zUnQKv6tl
zWbgfz!St<1tn`g1sBNddlGg4;M)J=MvZo)}+@q2{yqaAvCY*e;z_P1g6tlYXAE_9@
z$J&{W+UnW~K2rMNN0J@~k&}b&20^9JG!B{3U>!pYaZd6eaB^ZLm*_y|zfS%I>o8q(
z)ud6xvWU<inkEMr^dq{O_z$P7qnliXcgF;%cPx^CLa_EDu>7m-UxZhaM`Z-}PTnG_
zpqeSXRKOS+&UXEQA8Nig^RpbP(cX*TP#R3;g&o5*3S5oEcLN!yG=rM;R?ZNP{*}xA
zG<+7&{w?Uf73H*I`W|7NFPrMWn8>b<?}`(?1s~|2<oF)v_A%77{;8JUHQ#FOm?a&V
zf94*9agp?{-SDIKakJ@*%UwoA9wbaxX&izx{X-ATSJ14$`g89?z)a)R=DFT9os#lC
zjF1$6tt~6m<dceB+nTYMCp;QjL&2sWy)6E3zr8~(1}@&zHtj@XiV-y`sJwwa0z*f0
zK$3<+lJY5c?M%tumU^q~-Dv=NUQR_Y^t8MRf<>HWmzZ%(Jpxlg7^4adynlrq*kYEI
zs`W-KwktRUcBH1@Q<FHPWjQC-pd>9J!TVBk9FtAS$)x7)YNo>SQj&0KsVP}~=t$IN
znv1<FA0MqX7bbvmI&vCHKJ?@i18M0_MZoV#$LUQc6f5j$DVXV4`_oa-u)<SO9Ewsu
zN@^2|0hIKkr2%N`=|CPcOh?8kbfzQkLm3Pw^`;{^6snWOH4(_EM0y!U01TQck;N<s
z$JUDx!&H;URy34Vpsy5UsVEsJ=|D<6bR;}tj-!eUW3Z+Z0+&D}N=`Vc?yTy2KA6)U
zYnjp>4htMrUt0CGw@7I-T~8>CD&%&rg8njmchq{{RFW}t!rfbd4`J<8$&oE0>Ml&$
zems0|i(a~t(@kK*kk6hg=S#U-;#PSayB<zzQr;}G7L&2$k9tD(V#w}*FST*IZ)ZaI
z=@7WPRaQXYd-GMXZMvKge7V5QF40RX;N&6xwPR?DW<1!y?7pPZi*h$Enuf*RDDNba
zJAdYm5t$|9us(z9=~o{SHMFqM(-FrEETv(#Pvsr6k8iK7R&;KhE+vyuxl++U09a!@
zxjvt7b6O9?Zl#x~HK}e}YOml#L*-C782VK>@@C3hV$D-mzBf|t!lL7aL5$Tq6O->p
zo=o%WOOwrYB}n3y&m#9H^)y>(M2;6d{VNhC%#7z8Q~fo)wOCdtqMky)1({T-{dlQ*
zLMq)KUjE8`9rIr=DnX^hr#>W_jnRlO?~G#y)48v=ulkQ$>0X+)8dBH`hhHVOoaM<J
zj4GlQBkD8xR|EI~XK~T}HyYoGA}YJQIFVIJ#z5E!+<WpCwu^mJPt+ziljxV%dNgj}
z+uPdsEqNk>8CpP7Ax?PToQ~D;+2r!(Yti;NVH|joMW)_$H(6^JQ(mTzWoZNET}N1v
zqK+3J;3;vmwmHVq!@esT*Tm7$T{x>Qm1Td8(c0ZbdvO{wVC11#5sxGsmjj*#IIRz_
zJ~z!3&CEJn*P0%e8mbFhYqKIhx_DqLYKI?#X&BCTcdh}cYxnw&zdR^z4xupFx4}D<
z4nnFfG3mzKSEYl;OSF0U{EIq2S$@zn>UNir>9IDMd$gB*E=GYi@EFK9VZCvXdB;50
z0Mw+Ev6adpCnvy>NN-)huOD2VXu6fYr8Ms)^Gx`Co+ZE`cY6{?eh+%6)Nb9Qg4y)z
zdr6gxc%ckWDxXH@X&%I2{cF~Y66o?LHr~ah3E}N&5H<<ibA~?H!RPd;X}5QlZN4^l
zwMh})0_+u!q2t%TJ?gglK)xd=@P_P#bLIov>)+m#g6?>j7@R(MDi0)9oJ^Fd+4Ofz
z{7H{Z)Szo?X>M7bIPdiNRT^8Um@gwEW1J8{$DbhQI3v5+bv~b~-CEyhH}cN~)xytr
z@-hZ%8B3sw<_O5j#_g~&a-vX(Kn2zQG0}B2>buaIhNUf~^kgEIRYi3q?ghBm**VV&
z#GS=SEPIE;EpASd)MA>-_Qz4No++Z5*5ciyx07;VVvvwLIHfydI4V=bP{4R&ILZ|5
z9g8KZXM6a2)fcwiOKm=-YiAhQccI?c+bJ=K{06<Y5R>KrNfh1?Ba8vu8r-_u_rF+O
z+1M3|D^8-)CcKfii5Pe#6(7DZ=_Eb)CLG|An&90N@h4E9tF48^&8a@AC9b0l#mfUU
z3mwFOiBAN}56nziznhcmTZWy~T5gf5d^_s9h%V;SA<>0|nsl=iQ8BlkK^Q#wLrHI(
z4*dPGakgxvhwgQ&xGeQsjZapNYq%|DmN2;5<||8v$9`AEkPP~qpGx_&;?G!I!_l`|
zRMN>T_hKUy@~96bw}~X1n2*F{Y1&Nl+<#<OK<ZAgT}wuSdq^j=j!ixo@Ug0>X%gLE
zaRWOQ)>D#4n4s)P9AD#Rt#zw)Hl3x+L{9=mZ1&K$_~8+;j7B~7l_2|(nye$VGEd?|
z;C`03x}=XS%(kdteXSDiCE1cV@yC?zV}gCKJAir@Pg={O{3H44Nq?WzqPupCFfgnP
z(W-;(x>P?+ow%X=9O%2d8{d(+iR812RkfRtHp*p;MhW!unA`1~d)CX=`Zc2T{LtCm
zOu7WRl>Y#`qSJ?r8ZE`qrYC4p7G*mZCo^&eD${oKPBhZl9Sh;y+U|mFbqHkBt@W8M
zFOzGKW>{fYZHh~6fgPI+cmr|C+ln{CZk*6>dPeT=P`UpA`cP37tE!f|m6k>!UHFXx
z@^g)dS&m3kK-N0SORHJ6Ym2LgvbD4Q&!*Z%E>bHS6haxs(S&(qi+5n&azkVa6|x%-
zg_m*3rpmTAx^%Lv_Xor{oU<7MgMeeVjAVCdSORf@R8eo)qdC7?U&j|s^xd7TJ}txV
z)h?r%3aOfD1aZc><%Dr489kZg_04uJoA_yA@gt`#v|Se4<T{5_foIX3E`XY5MG`%=
z%y|4r<m`L~DDB^88c)Ov?v=U!0Ag&d-XUu)g=>2(PIkAA8id&FZ~~SxoDXdMYiam9
ztlVjRKYd}U*<1B8Y1Z=ITv>m@x=@VidWg%*9fnC2l#RX_Kgx4<^ie8ey05~u)ilJ_
zbsZ{gI^ya`t!|TI{Gptv#L$#2_{c5@1xqZL-O3Hjd^owezPW~79BJasQVY0kt+&Xs
zHr0+)hC+E^B#KlM-9|w`^``3Ge`ya)K81f}KAbfb5n0~a!Gn1piW`4*DtvIwfPe7{
zo<cJ0Iv-nH{966MVxHF4@1|m#L^pS<;Utz*D<l!hNWeRYK`Ginm0JOZOjBifC8mvI
zruti`I%BCU?(|C=twpqZ##v?4W0^i`NXoEuae@q}z-=lIDhS=%+NO%S=R{iDUN!aH
zw-Y+)Gdybcw{Hj$G&|4$&QPJ;JCt0HDHUyd)|Z!^AFW9(%(3<MpK~0IcW~m|tji!_
zD@IPzvc+`jc-k7>PxE4_b>D~=TAq<-ajQ*ZYoxXNYW5mUn-bCd!=Y{oz$lEsXSgRM
z<B6u7m6J_UI0mJo>OBvvrl+M%u{W`bXq{inlDw7Uk-_%_5y2I{Xde@wU(#-L`}@mn
zHfh|b7Xxfp0!Ibold^>j1Mc#bATT*lM$4yko|mocxRwYO(QTue=&Y+2>0}vY2s7nC
zCHC^gpH4XruhQ*xHQn8!m~7TrHY5Hu0Ay|+nfLU?Z+F?x#jT@$Yi&*|yNgpTt*y&k
zGOfeRi28llJ2p!rU<l=047ZhmR^7u_Ew<H#y`9u|a7!)0P|!{r3;^^#;BbHF1B}x>
zKh(Fq2d=4>7#$r97Z$fr36R9#ZdXI!-y9G?Ac8YhN2o&GM=hL$Ttkp2kwFfazy)!G
z#~k;-tx_|TudugM%hgx6dtivXizU!d_z{It@)XJS<n8b1KDzNp%CCl4=a$I)Cnx-|
zOkLlsjzkfyv^$HeKxbh%$OQ9`D95z|&?sgE5;pOVdeFs~NJk?cm9=S>u)zVhW3pCn
z$Jh`96a7Mp*C31;6-F|9R^b+%x>zSC_y7i-v;P1Kk^ny341wRhE{Te=cDhVbtU@^g
z$aY8>kd;-zW-LGD1CjTqF66SaOXQN_Y-O}%Yj(kq&gyoMS0s6q2G8LjV0Wuql#fT!
zp}AumHwHs`q%x9@GqFg|90U2|y=Z+!soZr2u=cjhFBGtfCWbaZ>JJ-G{v7e;`g&Cs
z&X=SYR_kf*QLU|HWV9-Zv4z3f0K)hDAod@hdIp)&{YTap(lwo(-TlnWS=m*c*puAg
z5_$gs8v0lEd;O9VR&><5k`Etg3~Y(Qh3}U>yk@^fJ`?;P&^k^8g4WIpW{eE12&jZ-
zvB2ky*3pb_Y@>rwxgVCjANvvfPU^*qR_X0x7^GJ@j>r}VY1%)C=nh)|^T5gHwL8>5
z{-6H<7xbxaq_OFpO76}#ZwcgzC6&|AvMjHI^aTCu_XkeC(z<@Ysx(7t<YWx}tCoC9
z{iix}qy)n1;@&Y42*_9&3Bk@6@5k$2hAdFX_^AH?`gvJ1%N?|S8})a<Z-^ZZ{{Z`M
zrZpP_$<4*HpdfeTjDK3xBfPuP<vHDNAOw3?>6gSG{{T?E4s@CuOMf46E3uO*J<fSO
z{VU`Th(8qi7vd(VeRHU2P}sceJUJ|3@QPq^PCN2zuP@W_DEx9O{T#Ub{F0Z;8>54)
z=_HKu@^Eptk2j~GrtKt>GOPG_;j(xYZK(8hjnH{?9Z4?0{{Z3T@=1@Td)BqOSf_ND
zGRhmwAA_3l@?w@ZO!YD3jQ101H&1Yg^3H{nU~Cv-20P$?kF{()9o3qhmaA_ixRX@j
zzudsfvZw{JyoSn;W578aR*z!a*oKxkP~$t#0CxMDvzaH;ExbhADBK+;ie2hGJrCQS
z;}y#&r>1o&Q?z|8(p^~s$Zs@j(`f??!#&)JW+@`#NKQ%d6Osr!fyQvYpFn6D{-e_}
zX_oq|S6YnidGglIfM)PI1;V%tOQN#!tlNVC(d33d5qvuMi84WPV>A}GD(<c=B!|r@
z&RC-k%m+9;AF1H|1@Qj>t75a!B#Ilw)NB`Mxzja!b|+!Z%$4IKETMtijerRZapz>i
zPR~moYg<0a_-)iM+up@vr(7ki)Z<{YydX$?<p7FI0VWEPVUT!-4Yar6uAtptt}4dO
zrZv2Ak{H0dmQV=<t0xRF3FGPr;Md4s4L&2;e)blZ`o5p3`08z)HAoDJsDQXI774?$
zh1!3g7${jvx6n3v{k5o@Sn1pSVeD_Mi?BBXmkE%rG0eOHxRTp&eCx>bvSX#K9Wj3|
z*|qgQhHRsn;!8OguZ(4mX`zt)JgjnkFms$^gT-ssTIPXpk;AII+Fj!|Q7)q^1CyM$
z@J4w50H1(!THIY{<4!Kp?@#c7{{VP^+cbV2{tt@`j1Xr4W<ChX!q>G1vFcf-v4RK$
zmWqY!eqQDTM!+GIA2M;<XaMJm;ir1&?NPYw>iTRJ`cxLs+r5Iy<So3`(N1D2I0JJr
z8OAupUZmQRt3b~dsDO>xkfMN3W{<*Q=zVLGbe@JS<CfCrMg8hF=jHD1t*2!^n?$&B
zdB)On?OR;-y+LOaB3s(&+j;P0w2hc49C=%DRr>bLYZptSnrif!dv~lyrO9x!KAopN
zFk*&4hmTU;8f_!(Ro7aI!y3ao*|am0%#%rQWFPsP^x}`DX<bFH<X*wyn6L;~QZPr*
zvjg|<R+`Pf?mac5y47uT_(J)a*AE^Ka1K-T_p7U#<WkgHUd}hyeLG<lo3-wfX{mwd
z2|f14amEjUk2oick;Q!7)H2#%%w2R_O$)(3Saln6t1@mFD#t3`ylp&sn))B9z9YCx
zvu$y4e_;^Jm$S<X<-6fyhhu^dW4j;a%k}Q9mVF9mIw-og+|oVZyPDyPl_YLPZpt&?
z^Q!*<M^a9<a{FGKw$)>6X(!VRYTH)cE58WIoc7}bup^B9Gm6tX=i$}9o|=<~k5RgW
zZWi#yMZKOzcQGX7k3i(-@gHj1x=*MqAnG#h6agP{xtXvzJn*^4+mG_CuU+Y@KDTv>
zCopJ{$O_w9i5;e!oRN+iMkEXjo_m3kJFv;OM;26kFReA^kE%*i@XYA^$c>Soy-9l7
z{^2nz?Q_9$2;cX|2lTI?z9jrVk5ja}hegqBEN!d?+ll13MTXq-gbqj&O5EoeKAAWs
zzIKyJmrT51vLh;-FhjxmeQVW=w0Uxii#JZ0>lv9qXi?blxm8;~m2Jt?F~*`<Bg2q8
z5<cLAS_YPF-UOL~q~PEI#(4D=vcq~FJSZ`oeiGZ1{zo*IMT^ZNQM0<$%#kUHPng?D
z_)pm8zS{Uv)7D2zvy#k75et(Vkm~2(51fy&ubaPTuZDLWL#zv_^l4_a&exFJuZR6{
zoQ^wJ+MPYoT5nEAn;R(YmANRZj8@oS<+GnGk}hqdYj{#TAW_&TEB!qxMTxAj;)Yup
zuSMw`XPLd|Y)@+QH4U1mnNlUNeX0)R`qmw?c&HOreHVYT40A$bijx`>o+^b1vk%sk
zmAN!o7y@Z#IW-K@D?}L3oZx?*O^*~8HB!%@PQ*~1jw(_g%7r1o%?iZ<m${|hjOKvm
zhJa+HrZ}b~YEn~@XaG5-+~gWN((@iTp$*WpoYK=#q$E=Ed6ul*=*Ut|O)V`l=9inX
zs+K}>(vo*H+?1q;zuty|?mN=c4=psunqEv1gIPB01&s13sQc2fj1f)6+*XUAHx)Mz
zYECoSoQK!iqz%S$DVW-nN@hMNX4vFMwKWmWKGdXMl-zjYh0wwpW+BZj7~+|W+M$v%
zz3AycN;>}lN|G34G|Y6e`cnx*A%r=jp{0=Gm`VWa+K8u`8qwoiim`rFgU3uIFC8%%
zG%@xbKT0}K5cZ?TQb^20CYRPVXmnjVYpL4_8k0;X0=a+3sit0zx(PEx3Z3M8dsSPc
z*(qB-KK-P=INx<%y)~p4E|y7^@X@&Z>*v^R<d%Gf?tSU5sk`yDOO8+DJ?fGxj{ue@
z70DHQXIs{dqB$ZN-a|O&8KB%t;{X7{k4mhzoUjdzzj|&-L~#<?=i03(m9S#)Lgkwn
z9kMBVQrGw4ow9+m%?4f59pqpWfWD;Fo|9_NG;yN&*!p@@B}8`3$#oyxeK~bD-J)gK
zf4Y4K)Ylu;E`DNi-=5!U=^c3CG^=}f<R&#CGoI(v*E7073BmsW)}kfk{T1@A=Fe~`
zah>PAIWPc!l`Nwz_pOnWGJUgKCqO{0)E3R=4zH24kbkE?rEAL;;sGH+^)=Ie6LgR7
zZCwdwUif8gq+n#6;NW9C)_G-6W$?!4k@gov>Y8PTPNS<!r`=8jMk`+y;^KAoaLfh|
z921XzDwE<L#cgum$J2D%=3P7?jHF&0ykS(0nL&j^{{YKQ1_p9@s$1@&xv<hkm1T=-
zcu|LliHDiC;1pw-9k6*LYU3Fr6~}%gb#d2hqhH?_dW?|Bd@~{07yyLwrbs6QbC6CB
zLEzVghXR!B_w(lC3K_fjlhl`YFKKD0%Y8KLapGS)7{+-Rk%FlszG*!>T{2uY`zUnH
zSd+f)DW1;cZd~O~P;xjRlbqyNI(=41X1bE)7I~)1KaC24r`>Wer|ZQZ{h>O9;t?#X
zWU5^va35@d9#ugBdE?8DJ?qz4_?|!9zRk|tP_vn1x4(JsrJ7T3c?_u2OBPscmN^jR
zn~{LQw-S4kT1L8)K@vvO8RJ(d7DXV18Nm!jFmQO|7(5ENUy0Y((uLMm3-{WQi`I!&
zR|v(Idx4VUKaBZ^;G9<HuJzW9@dx*=tF72YZ>ij@5=!<_0~PA-3oNBUA}Pq)<f%}0
zxFBv7s?nEc61==ewM%x7!yxY5r@x@c9go(jrE(b8Le=!WCdLTag4{Y>Gc!WFcSLcy
za(<ZG<;cnO#c5j3oVK?LVr6*d1<)znK-@qFfzKY5taeeVF=c#X8<@C}G2zIKw6Et~
zN2I#KTT6$!n(k+rt==OPY*`&2z7%5wjrkbHaB*Ck#ulDcjZQa^D{HjWMl2c=90<$c
z$O$K73?1b9aseK}K<29@W#H+Zi=p*9b?W=udzt=jDPy>_is5;PiIAZo&PwkDu+Di4
zlis)8N35c@iLLcrQc0|TdR|Rz@PgoP@Z)Ai0Dv}`nEwEC1Cv}WwF~_c%JN9$Pm#MZ
ztcWF3;JUFUFqzH<Tmy^{d(gFRplgGrSzD;v3=3<yoNSPWY`%V_yZudbxZ65ZuVv<~
z@e=a#^7`gE96*I4dvzmZqA={a`Uddv{nb6iFVdLSI*$JURlT)YC%>KU;w0fvDR*Z3
zd4I#+sd{rkmsIK-Z9UJ1(#o;zd?w~B6t9~DBX>U7HL>UxV@v7XZsOt=ifAp6$7a#8
zxK@=nI6i~~Bc3_^4wj26eVe~XE~7U}X!kdfn|_{Iu991g#a4O5GD^FF<(TaX2m`i1
z7i&f8dy}e53SG3C73}SI7KX}vGV#W)4~3Dp%azFF@5gn>(0Y5QuJo-oT?^)<y0$^%
zGKVb+NYF~YCf{Ht;QAfkm4jZbk<~7vv$u%^lTOh{#jss`k+*I+1F+81oEpK#nYx;y
z#y+IH5WbsWmXpt@MIM|boe^XZu4F0!;e7IEalrlxW7?zD{aGHjs@_|?iw2*i#b<3M
z*2=U|saZCs_Fa_WeHUmsIISWpl(g1u<bvH~b+TCh0E~Q(Gbl~RCm02TAAP>Yt2KR7
zSGk{E)E;Y#g`9Y6IaVtv-bfk8C)TW?ZpyrYb*NVJQ<G6;c)TV<1aiK1MZbmGI}NfA
z{H<3#Gq2m})1pgrCYunKHmvB(_Uga}WIeJD-(2od+jVGKT-UK$-6gy;OtUH@VF?Oy
z7#`k-J-ZxJ8erCEk~?Neq*N@sTLIJ@Zv%Ea=dm2toIM$G-J`SVKM*y2KcUW(cP*Xu
zrIq<ew?eL9b{k4KAb5~Sr?K-40g<;0sOi@i+Fixn?Vh7;0$j&BPiu3!M4A#vk|IeY
zvY!o?JdCRYmCb7~*=zPXmy0ADR{4HfSQV5BM;HtN>_`VAu^AlVr5>Vf=bqX3*<B=m
zj`0exDhTjcW0Ra=h{!5A6+CD+eVb+WwneV54C*9rmLT067L8GY#8=%KH$}#K07&2p
zo|&mntKM4Z>87Oc&2Xx+N~{oDB%g?S<8LP!{69ln;x3`WO=~N&Kn_%T5<S50*k|oq
z1%|5~uBl|#4q_1(GTkHa*5xs@53z0s_Nb%#3GChOI`-1%PM%v>S5JmpsakimiBz&&
zIW7n%B={yo3;mZPf!$c-{q56Qg!XQlp1F50yS?N1HYoC;f-K_<?D9%kj{#P+Sv4DZ
zq=*O%uzon~ZP@HzE;loKoDAd85m&KmqAda_E;O58{o(HAX<kHoxiC}~z`+U_1wcUV
zeOoq5O#+9iv^liMZErOvTiLH;Y0@MoINowt@IfuR?}BSl)HEq#nNeb7p9IYjLZ%~=
z<-z&^T~_}9T(f?m38cS;aRy$_eJx(#Mo0x!UuqVRF$2WF0QTbon(56l{{UB(<|MY6
zwIK^i>lj%ja!K(nC%Bciz@QwL^72=)%L!6(Msk8@4U!hOlWdq%jFFl=a5G2}F^P^q
zjPOs=x)!t0-UXDf#<0le&+`y7Fky^@9$bIaI43wHVzli?rY|(GvO2hl{{RVPAd&C?
z0OGG1reiE_C#-b#zLFJ;F7g4(HqqZ5&u_hUYdcY;^v0QKc#=h9s88dq<ZYFbV{G`#
z5uYl_AK~^n=M~L5Z&7LETR{kRgzt|y-WveE4t<xm(~fJp+5Fy@(RPTRDJGMqYS#0$
z?xPx)R4ijfMPkB8)k}gl9lKXK7y{+sjyv%QZmn%Bt!256NW=n91`xhRB!*VN<c0^n
zagr;k{?0!O)(g0GeOBCj#H>DCxX#>+{zT%nN#N<-N2%@=Y+6WXiaG7RO@c)P-XLSW
zNMp31kOa<m5OaV<eKF9N7UN0suGtFZQ|s7&opa=u1b6uOzU1wlpQd#fI(#Zc&9>02
z<Nz3+0nh7Jy>I(OX+I70q?b{-U>YFGi2neL<BmJ?>0deZ{{Y0<dQ(iE877V)w1DdQ
z<J%sFzESFqwAA|FQu+Ntc_c(090={5sykF*ypp%kx0vIOG`l{T>rdM|tGd!@E%f_?
zq}UO)RU0lxxj$OwcY2ilMvU5p!`!o+w}`~x{{Y!F$n7<O2X-Txxwq=qZX-Ruz3b@l
z&9%iJBa%DiF3X);rmUHyWk3t(aQ8g(pL)=)bOOdrfCkB4I~;n|{r6GG=t*oh-m0v1
zQtmeljOXiE<$|+(c{FObnpBqVate{oag0zNf1Cm<)+L7vIT>a9`-<MR%W-{X8a`xB
zNBqOoR*!iJ9&BLydshsbi{kn^X*Fp9R%=<rH(p=}!3QCS(EC=#`mcp;6gHZJwUe<w
zDES5!_Q~M=#Z=ho?W)?mJ98=m@gGx;NT5NGK?m}zr$&%043lZMX>}5mD&c{~S3hrB
z=(<N-wymi_1=+f@lJCP&G*fdr@Su5))^0d0+>9~oMuP-iJ*!69BRC3u>blzD__vZq
z@S<YEGTpuQ`UAyel98is9UoNmhf`>_I)$_sQA2wj!y6lk4(O6aE#;TFF|j-l;O%cx
z7?t{4p}r?vbhe#!ZKl~zso59^YiXx@d#Dtzh!tCwLdpZK3&c)Jl>~wL{ipRgyOr1n
zGm?bO3o1N=7(KDc03V|QGC{4^L-@geZKqn<YKXdpv|FAR3fLf$UN=$B!}zkeDn>v!
z2DvifwWG5ZW$gP?O8A)ZX#uo=x}Kd3f(w<5tasL+@~+Uyovbo<C<Zw&q%g@RZM6N#
zcVls@=^Ad5rYPDQp{EzHSyLHPEHaR>cm85mo*UA>OSkxmqjks0OwlwGS}MU4OjOAf
zdCD&J#sZv-fTP?40M|e$zk^+0rF!6=pN=buXScqFe-*R~$V0}hoHIh$GNydZ<t(Iv
zRN#?bK1gvrSW5Pp-YqZfwLJ>f^o=gX)yDgMK*uy57&yw6Ss4afh6kK}AS-FsH8gED
z-7c=WYGldTZmz;icP$b!oyVT)-K&mheL*dYXGpr1-rSXQ8l;{ojOEZtDmV->e3EgD
zVTS^`W}Tww`u&VLl+oN4w{aZOLu4S9-MyM9fblcCIS6}>2YTmQ=+a7^wmW4Wmj$)G
z%y;Re$l1@%p?hV=b}S-g0RI5Zim&QErtk#IAMRaN-2fr<yGEI1$j>EqfO0Z&KGbID
zjWum2mK%H91&o)L^g@qmF+YTm;TSRdpQdP*+HXo}sSprP73>+1hAkI~>w&qLG4;xk
z*z-qyRTS0VX1<+G2UA!{WX9&}U>J_uNtqZQY^;HCocH#wbJtxr4gQ;RJ;s2I0<VT%
z+Vre}eMyapT%TZZ&2(LVQ+qjX<I?R?c;!z$n2|hnKuE}E1f22sMpz#8&93aPRLyeQ
zIJAv&sFO+-RbKdAsvpxCslQCRU!#OP8djG&%c8|LP@fVz#Dy7n^xEK@e|EY>uUO5m
zY4B<G7VzoO#}SHa{BB?vegx!&&#$X?!ww4_X`-Y$eal?wcX!vU0FE}^?2YIg%5#J4
z2eA9{-`?F=FJ!d2yB5+ZWr{f@8Haaujx@;x`vF}Toii+nQGM0=aMj>~!aXYUSCY*P
zR{Bg0A)7z^+2jLkDsng{e;<V~G4qdA^uD8IsJG5(V@sDfDdPhg`(q})lE3R68=~}f
z(XKVy`@8++rMA;{h=C*VO38+1{{WDXed>>+dQ(Q~{cb6tyjj*)+G5o7*&>a}JdDMW
z`QTTnjqZ`<%`VP2(7z9Fde2dhPqw?YidG?2rDOdt4r}b+grBj#jiK7Zd(=Cx^)$l_
zxG^u-4*vk1b<c*qA7P+c#~r<$jgAxn@-QO!gCG3g#(u{Jx(hPKp+Eo*zzX!SVZF%V
zmS0G&(eztw4$UpC!w8UJRIwm^v)Y=GsxnDKXBE`dDn}%?dD@qD=A`dHX3bQo=4{Z}
zt0eJ4W}rOHP%VMQV452m1I*MZ8;Z-eC=VuRN><27*)$h8sWIfxnAHu)G0g?dO^pUa
ziWPkjbCW=j@!E(_)`0DrlCiK(aA+=Qj%X}<)EWYMQd8cTm9QySGyq)wl)S?tj^l$%
zvF3=*OjbcaCnNl+iMt$9QXFQj(Vt)#`G}?FYAc)?T2tPtYY}-{icRUMds40Sp>PdA
zm$9SBX;~X56^&a1NyQ#B%`YEeM?+VO1F>^W!g6UiPXe2W`KsV?DVWA-Sp6xeO$xL(
z9|Th|cBN3~6x38DA(-iyyV9t8Q!#g;BS}VHc=n(r9X}Ncv53VP4-^$NWI5-x3bjGk
zzA1?5XwD5C4G0=hfkuh_GeB0zq9MVn5|yE~J+VhaOCh5n`qY_?CnQr5(vh_@8O;FX
zeXEv!(K9Wl!`(_|89N97S5S=CKKR+B9c!bm;=y%D+1Wk#;C{79{w%b6KPNRyhkLo1
zkMke#s{a5R%nN>%bJo^Rru7x{5-)~Cgzf&dt})AZ;<;neIwjeCZ*XN@z;Hb)H+P$e
zMr@JkRk2=p2tXM=gW907g>A&;>q4d7yX;kF0i28l&*7^ZYpEb?L{9bxFZ4a}TGCwa
zVcnb5)zlYDwE1%(04N*``_wc_?6T@C%caNV{1^%|{cE4yvpnZ<B>n54>ZJbcjGsH8
zB>nMPP2H$T2ppVpezjP-EhJV<hi2j1IG`x=Q<sDo)GwjrQmQk}YPvFEtpJvBpTZBN
zcmDu|+Re_1qD^XStkg`o@h)7a9m6|%0m%dDkT}h8Zkf~PzSLrh+Ak7kI0yS9-#(vu
z?iP`zi__8>4p697<7}4$`IQM%jF34z@y>7sap!7Z6Su}(ZbqZkJ$I;GYO_xb!$8G>
zQyx_A9;BZ5^c>ccsp>Zu@)_>;w1@(3E3^!HWA)G0pXx1I;#;|7a<GSNtZN`_1L#<i
zPu%zQtwT`0c}NAffH*$2raWpZqb!qheT`3djse3S2_3QRN{86Nl$AcBlF28&D)&L@
zEnleLWsQv2BoF*FEsxT=IR1=5!!!~Dy^1q;J^ujg{c6ugzmC%3zls{xP8}OL1o63X
z>&|)aj8@5S@C&SEgT)$afsEjg2>!K2)ISaSkE94~OHH}74Uw0OsBh1)tnGU)lkB~*
zx{6C{h$n_SfMS*zE%HcNHZrTJQ_rCugs2CK)aTM}ucErMR`c22VJ&u&>Ku^79>4|&
z2q!sVk6af?)aSCF2M7rV`#pv|K9#ifJT_W|zL%(3NgQ{Q#Md_xs=nxESKF|XNWluO
zyFlhbJt|`9mXft-<@W8eRYF8$u=A0UzgpR)zOcRmSgd|dL2_qkp&_HeQRPtGfNk7z
zFxd3vZn)7?N9qhmG&99;qTx&j%DayvA48mdF;mgB$nRxDYm*9+0cDA#!21DLlcP0u
z5*zt1uVpfUA)T`CD&)3X>FviA(Ykx5?X5b7-p>66j-w2*h+g6U0E6QP1S>lBM_=du
z>G!Km1K`LyW@NLH`gTW{{{Xnm@H|7E={z~u!x%Zrk2ZT^w)WF)x+KkSs*8)6q=2vP
zjaFBIf^(S6k<GXeK^tFaIV5^lE0y$h#tm(i8m*$(YgX2Jo7{f#S)!KO>eNTIe})3G
zkalehyABwSUO>ZWnueq#I;Fm;boy?jBw;1Hp=hSb^DnN`yUl_z^&C~-S=@B?rGKYO
zqxAgRWQu_qCYvVUcPE&@lbns-qZtCUeNO(&cq?nBLYA>aHwn=0Z#GYuIr@M`IP~^K
ze#^H(ZnbMmJDY2mAY_ff6U5AR?f(FiJmC4U_s6D7F={b2jMjuKmbVKVhvc+!1t8-*
zAH+xaRcw~2acs7;Ot(26Bx@TuNc_2A6yO7#WPm>Ot-n%u7jZ`_?kEBQFC!hW2VqyF
z%-qYd9J0<OSS6K_RaN9T>^M05g<-8OorI1Z6fPABJ4der(zh)xXePF|b0k?KD;uFB
z*eS{N?fF#>vX2OqF7VNZW+<5e_QppZpQTi*U~CstF&Bz&0ubOd`TQ(9bLsCxzR}tr
z_q&;-nnpP+NhO$^XY0-@bLk5~2K$@C;Ky?s>;-aJTx};Ef{f<|wH<%0EtgS}d(A(@
zfXvbqwv)kd+ZZTT843X*yB<_?-lbd758|w-oOmTsE{SXvbtgMWIKlh>0Id~oF6`l*
z9bQQ8cCH%)p83H)<MgT>8(W_Hd~%SnmJHY*iGVzx)c&=+e7tk3{{Un;Dsm45dSi?l
zHN}MQW~|ywH;Wy-GH#iRHvaj*_p04O8?72bCbtG8^0R@t2qS`hxcA0t>sPjmPX<u`
z0L5m=1o8m?03Xkqy3ln?oo`N%nAk&WlRIrMv3SIU9Qk<w`(%64$!^UWJzhB?jxeJN
za=`JQr8#b4n#9DhI!f{rB(1dMa1};*10?5~uh+C`tS%*<D4AdwE{eQIp5OGJLsj;=
zq?XgiBAo3x!Q_62BlQ((_KGZxc5c$mb*0^S(rOXUC^-3;?gXV+uV6tSw%$1WJa#p)
zTI$w)J*H{bml|HBZ8TmVhPJabG2JLTmQ2aJX&GKoamS(Nv`srvlHygENU$`7sbW9}
z`@^<BrCnO<20o)>dtqxiR{}^KOkr?&WHFW7+dLdq8fNNNbzN^$yVTS7)~91Fyc4QT
zZ>VcuIA^?Nz$+8GF@3ydP7kmg3Zc2T&}CR9k}G{e<(Zo2b59Y$&eT%ipD80JkH4)U
zVING(tlS+}R#gl!ZzRhwUA~zqxPnKxVTx;0(d?oTOj7Ob7j`GGx<`s;-f+dw;Ny%H
z2aZ7Iu+^I_qe<7gdiP7Xp7I-MZJ}2x%81IUGm+|h5%0xp<5hicrk+^|lI;06wBRuW
zl0wIv;{bzz1_<>7E=6o~``G2xbUCIMfcOn_JiDa?XL2wYCy~g<dIk4JTiNOmz@y}R
zYjmDD8^lIXQ;~)vJf8mmTG?c+8Srapr0EOEhNW>M$+$d$o5}~6XC9n>Bl_2J>3*RW
z*84#b5Tk?0C+%EPYiM-O3+1DdJGCrlk)#D?^PQ}#k1rSmWaNzToL0#Ov`vg5hw(P|
zUjG0>eJc#>md{7Umkz{!HgzO=n6$QxwtKc4AcN0p<_)RHK#jRkz~Zm^@tO4)oc758
zepRT*`(-)zuCE<&ibtL0Ny=@Ln&_m2S4a2<@e`(gAACpC^}ep@SUSr@fn-^<ndR`-
zLdZh4c?W4A;DR`=TPtp3>sAKIW7E=Hv6=kc_{DoTogOAln{UN#uF^WUuC=XKK+$9A
z%^L3Eq_xtakjR$Np2!&TfA1Vts~nrqu1`JiX(nzy)i@*(j!jxpy_AYk(RB{4@kDdU
z6;&n2%$8=xTRFu6@0DER%~-dS#Loa4;>dSndM<i?>1{Qu1|u<v03drF5BSrP^y9x;
z4I<T2S!TLO;EqWGBX1*UEJzAD10;Yw>W;;U)NRlVo$LNJ)cSkjUr>Bi>MpCBM2-m|
z>Fp%zSLSIJJX^=HTMXQ14V)9sE1p|vBcU&5qpgc&)^!OiA`sa%)0oyXltm|nUQR&l
z4tW@@cT=-5PvbB1dC2v_>_^hN*Tt<~JKmw`{TfX&Jsotd9@kD`C9t=d@V+UMP^>}b
zQ?>93K%0PU*C^@;{#tbicLCYG{{SkdQlurg?46QLaPb>NW|l873Ub*8IUmxtO)FGu
ztHim9qek2eo-P$xk716$=QuxKO4DsM18XEwBfMo>c2M#Uzt*JpO4km2qj4w6_;7u`
zoYb{w$w|eXtD}Ye_Nh8qMPqR)m)#S@5@0s-<;F*^xCDA<C+jbS$t9kbZ*6~Jbv3+k
zZ<^jBsSf-p{w!>Ngxg3e>d3MTD}#;I@J~QKp>xy_L1tQOnHvid`I*=b0QEh+IpV&<
z_&wCFl4+4&UERTJBejb7jUu+6HZt)?A~0n~K5&XhJcTF9e6J!E(d>Bg<)(Ih7hAKs
zmRoxbF)pqa;K3S~OKA!L-b;Cxz>xm{E>I6nJ!^I7`@0*f`-nbv_ouH0ViEkou(?Lg
z2#Ng2;F3I~Z0&=b3J#mr&1a`a4C33XZTHCWN+ylO;0!1Xm}0~5k^?xw;~6zWeX84a
zL=xJB)@?0BMW0W1iv40ijix^n5-1<p-HtPkYs!}t&q|z?8|P5zH_>Wv#XnHpYahb!
z*{&j0oR8)VhBl4C<Ala}<C>uAjW$bb2mRL|SRZPLwEIZXJmh6pYTyt(8Bh3Dn`NYE
z+NO%u+Kd{F;)CJ~n3h0|j&}xJs+GtDD}BefuO(d`#!HpdFD<sef<9*ITY?ju92Q(J
zZclGqb~s8_^lKRL_8IPaiYp{r?PV;%JA65=9I;H`W;9G^0Ats;8LG~z>g(Mu$#nZw
zxY5Q4p5jTT2_l>j0<woB{qgx!2T@$>_JY-Iqn0Un@mASmX$CvFETo)vJf3P&=@LU`
z(@(2IXKxV9i50sBPCK)zr~}XsUMonxsH>|vwz;V38dzACORJgPyvl&P5$H;<9CCJ+
zQOA1a`ku0vnv}M77Lyj<RK+Buu1|BZKf`hRR>$!hU%J$yk}DhiLh2PK#GX)MQoa37
zPI26v@mufgz3~1k+la3xg=DyS1c>efEJ!_v_KEdA+*fuu@@X7dr^OcE3_oPqI)_J#
zQR*9+w80{S6^t<m@VMh}^!LE`u9u{K%~m=L@kwcWc8r7{5oE(D2aJ1<p{~noJX^dd
zPF6lM-<oNg%bNG`c<^1G6!S_&PLW|IooyTv$Yo^%_^NOT`*$DKo_FVyUX}BnXfE0H
zuH>tuob$B^$%@0Up)=K6L8~%k$*3`?lZ@0iZmL-vK;PV(duC(-rD75_ZpDefAE}}-
z=8;Akjr&Rt>w9KoPiK>m$NrvI?pm89nv*jIg(Fmn!0$nj=QRwpRkAMrv<7U7oQJgq
zja1f=7HS(FDosOk#YzvLY;i$zlg&-J=7htXnu42T9E?)!MUe6IpfTcw&^MsDqR40s
z>q0<Ec*QF8#J%ZfGyr65QqITqrX(2|_N8Sw&#g)`kSSRHbexpLjkwbC8krEg7LaC?
zmYbK>lasYn(uJix=~)dkEh{0#2?G9<w1_d9Va)<o;MN|%!VyJMXc3-0sPUS-djpUj
z1vwGPq+mQ#kv1zpI+V;`ij<7gFq|-;9*&xZlS*nPpc;;tjMB(x>DqvDJ_w^J=9i7Q
zqoMrhK*Aa_98$_?sAyHOr8L5xKGd=tulUhZnic43HpdiICYD3mm`2FOSp}vu8~Rbz
z=`}c_#^;V`9DhUTeze4GNh4rW5jPYoO%F!dq`HR0Ug-BxV=VEN0-A`?+;5DIYJ1Q>
zCVyyNjJxTsx4V{fWl7|0arLfB!v?=Vziv;5!sn;0^-E|A+-@u79e}UT*~vgb7-8I3
z7aDYLcd`vGDkf5B1<5(A7dYdJ)`0vkj2z~zY-KV?gLVP#eT8euDF>5Q+I7<Q$1HpO
zD9|>YK%&@26rHD>pK8?Z?s8il&0bwhXN;)8%H#5_e)1y>rC4CrX;Eo07w^gMSrPG)
zYb0|{TEMd5NXb2`Peo0Gqi$te*wG6^8075oll3R<TaQ+1)0;?kt17faN0Z8ojIckJ
zKHk+=@cTts^{9)xM(A>&$GCoZt*_#)opEh$An<2522bMN-4>Em7#NQyJ3aR2KKQN~
zNqxkQ=%%rpZ&;nTGZ5f6DkW^TKgOuvb6|2wJPNkHXw$}76{Md(@frB~@s595u7-b?
z4K@QvPm~`|t#`!XH;;#%F{AwaQ|s3W9AJj<*!Qor{u=6i8EIt|zMR&v`-O0O`d8=7
zD?7+qS7YjOd8<23Qs%&hhE|QU{HF&s*Bpv5;+D>SEz6U7KT#5UUlKL9SlF{<s2K!(
zYhu)TcSq{Whl=*mCr`utNWolvzt+D${u%sp)w&j2V|_fDgecpYU9wZ@pI^OwLD8QS
zZF<h$D}<Uk!yU^BT#otnuQMu^HhVrSqm$xMd}sZQbZuWxmssg0{oiy6&9OotmyC>Y
z<v-HCMw>>y&?eP%ZAu2U)Ga}cqL+fCFysFH>-3L7*1UK~+EqesWymYv7|)>k{{W?Y
z;rm+jOj@n&wza4DcK6W4={j%?!UNq@44eXTYbfMQ>CNM$tsf^{T1l(Dw6aFi2o+-7
z<Ag!j9^7;9U00%P@3gCAxU|zOWj0`MX>DVgFdGBT6<l$i=Q!Z~4IjhalD(Gk?`-Dq
z+SzlxZvI;ygclrvgMxVP-nu=Oji>52x>D*hUB!EK5f;|g`h2$n=MDTKgDK*!@J2(i
z${r6HPEF|?nBb$fY&M+<rM<n?{pVR*!+z@0MPjyz;g)u0^2Z{Bux`oNn|81u=L=c~
zS88(ErRv9j4b8rx8myOg`g%iaBPsM(i};Pe<v<{V$u)U%p#JMfEqa3T>r7XXo>8s_
zWOwARhiMQex1-?X^MP8%xu<^kf-6&~w3<eID{BjTcx0KR4g8SCc8$H6h7=zz?9@Un
zJ1;3J(V^Yyvq1!5)+csm2HIE=STWi%HY&3I5$l}i)SRl8YpEFLYf_g>lP%nmypJmr
z_?ssK^v!2nj?#T#E}0W8ga9mNJ(ySY5;6e0vyyO6-1n))btSFCK_U%~!0(vna0i2&
z{eP`*vzf}v*!1O$7?vR`hHb8?lO|4iu65MO8NPOn3Ke462Zk9Pt9-V*abnY4c#6<I
zGI6mOua_YA9FBS5)`P3Cn^Sn8J}R?#XnUN0pM2)69T~okt<iBwaidz363m;Bl`nz-
zZ0GXN^fWzQS?zD$IPL;Ra>c+_9-f7LJ!{%?oi9pTaMDD9K-{D9{{ULn_3P6vw`)4H
z9F|UbCuzYW=~Sy_-j10bs+M{+<4(%WxD|bw;{{oLH~@@dxF=CueAUrVbGiL{;<~1<
zEQdg2;v7c1w&a}sD~{jD)*7UuWLX(<HnBWpaB>f(bNN-EwlY0Yd!*b;HQHTH=4nfY
zEg?BQy?;vGwA+%`ASI(+yt`Fk0G9DHfwT^Njs<g#A+N8Q5(H;B!tsHCD`v3$=c4L1
z_i^7zX>o5N%@o%2G;+s+4&drRQ^yCrORL#f^`Xw7vYI!vmSm1bo)IK%6RSms8?Z`~
z<{3Ft%HsraRMryBZlXtN8U`yFK*R<I0D=MMfIj%IYkJ<Va@NyLs9ar(NQ5@&Jf*}j
z0!pHTz)_RH_cdO+<lzXC1!o`yW1J3q<J8pG=rYSlji$PtZW2l3o*k;P5rC|F=g@n8
zbUv-=D{UR*YiT@01bL&$KrlT|a!zwZ)3xPQljafkGX?|<fB-yImBz4_%Pert!5lNl
zSb82djPvbLZpdj>8Uz>8TFKpz5rdJz6?1Q@#`{)g!CY*R3~e6VdQx|=c}PT1g^w5+
z{LNR%43Uqgy;;RFiMX8uNtWgvK3mgm2BB`AS!0=)?BrpN)&v8b2JMlI=M{R^si*4k
z39PR)i_M8V0WKq%2qQU1?7-w>8Sm>{zfIQeY=K0Pxf^^u_XKv&`BnCptu@lLiE|=I
zk_la88BFAPkEU~h&w9>KX7QRkgkKPJt5GGr&7|#TD(#jjZQdaa`*2unaE!$Bk^%eG
z8(z+_XmPvR#_*JgNply56<t6ZpJ-fm=Xa+#txr_zOY*bHZ4lCp%tkw=(;UZda-*?$
za#ar<fD7sXt%^NCtU7i}n|($>Et|x^MQ&Cp6L27q4mS>af~TCEn&)sx>QlWJ-ASU^
zS~+<eOgD2g$sE?U_X=iQU~Mb}PP@3=4nfa)((Y1SGC`Cv#@v8=WAE$tuB&U-I*q-U
zTYWwj(k~@RjI-!a%DaM$FdH_W7aMsDGmvVdskEDWi&-Uw^vJ_UCLyzE;tbh1ZwP`=
z<M{ytu^bAoUlwf_?Ho_7?l-IsIKj!SR&sZ4^fkWqzfZ|$p<3QO#jLAwGsGf`c$Ph(
zPUi(c!QFsCJgMMuofbpmrr;Gw7}~Yj1;rd0WzJ2Y8}r-Jp3`rw(&96CB$5?8svdFe
zRjdk==~xU1?_JpEN1Zs?XMJ^TJ;SsS_z`Z%QgT3{M`ty{?*Jrzg}rG4*=}=9!82?b
zbM1=KQ_)z!1orI#=av4@?Nhfnaqa6)v|@P5XOcLjgk~J>1Rq-Dl&tAdqVuJc4P44q
zN(DLGyM`<1euAFbU)ks4UcMd}FYfOx^t*L}?Vy#KNmfF9HA1pRHsB^f1I#;D80j9J
zk#t+lQstw!v$q0C4bn^D+{@i970?_=>$reQ@UC((T~FdKP+D|X!EGC%ZQza_UsJsO
z`K?VItWl~gkt;!LoNpUdgB;)jKp%&KRq|%nYLUdQb8V>09EuWTVvef7jl+Y;;P&+5
zs`aw6y133j#xf{@ij%>ii*Mc&tP1`c5O4}JmOg+51m>}iWXou(Q}5oggUk;d`KF`O
zos)Ms_andBvGT5^PSdpV4l$a!jWljrHk@?(i(8TwSl`SiLzM(}VBbt~rwl!_gI{O-
zE9(6;^vD-eM*6#1j~-o4F(gw31o$@&Jn@DbcFI-I=Pi+dK0WASFKwjK@1>D6=_E#Z
z?d_I%ZN@+>s$B`dB<;=)ImdeJUkhM|PqddtzrDECZLQS&rPL%WX~{lhlx0Zoj2~Pa
zSD&7$+3EQAXVRKqQrS09F?d?u?)p8CWew6pFQ?!;U1ZJ|gSdZWjukKU31ZVHklR~X
zTWTn)9G2S4GPEpkRktYF$-wiXkWMk{T>jqEPSa1$MWf4WW@AJ~g_h#fKm5^>6>u_h
zg&^k`&1~8RuLZw{Wpl12iz4hqw!BMWC@0LSq;62zK2o?mitzF@dvS}=cCqWaw8<8s
zf1v4xbj~ANcx7f${{V*AP|P_#TsPjUW$CyrE^aO_noU(9QM&siF768DnND~haB<qU
z{+pLX(r+S$-=*TckRs)yw@9UYa$kID6Yx)=s_%$;7w<d6e+(L2We8)Z*jdMMH!Z-H
zRNmdaS-$u+jV{aEx@e}+=eD~B-r7A@<ZlY{X}fJ<1Reo=DwF>JQ9Sml&bwVA(aiGc
zcN&4RJ5{bP!^VmZL0E=(;PP>wzcg!E<bvr1u7?SoUusKx21IBMau}QsUP}N6sI3#?
z9;&AH<t}EmF+(AkY1Zny41{pZ6a~p1r#xc1G1D=cc5;5D)lR>wg;?6!-J5WPk;gso
z00WPueM#_Q7U|BFhH(i?i>yO!81Eyn!l)j=a(?ymHk)Y^U5Ty-i78;)Jqvpup!OB@
zr@@^*;^xXdPX7RRj_I=18~*?TA6EYJcKY*O_&+nx@)bha*EDNik+ew5Z*J_z)YGiw
z(4kHM?Sa_(Rv6&-ucU`Z&);SpvqL8}mued|ETeWtL(K(`^r^KC&OWtPUM-QT{X%IS
z($3kMIi(qSm;V3;KUV(n?7v=n15>}$4XH?BPa5uEd?<5{Jz2e!dY`BRB;vhkYx1$N
zXEMzp-z=nlHv4)HVdybXi?HxR9j&}@#DOA>LZS}90l=>@#bb6m)E-Rcq|1-!^B(jD
zJXD#*1>f4B(JCP$WYE~uiJ;s{utg+{aHyq^<xp7meP~W<W6XiDS0gwyCp=Y*o3;&_
zgy%V;XFrt%&NEdmfVm)=3m$52XijKY8yXWF15;+8yI@cOmAuD_UPDEY{{UJ8ngC2`
zNsoV8X^#ezm%sI)Nei*YXfhF+B!_9G<T*JsD3-xL1k!T%rldKgB<yHtn6$@=QbWZv
zDdQBY`qVc?fUy*`=9poDO3n4GfYg4Jbu_$m!uP0MftXGWH5ulTjPfbC&o#IniMSNZ
zZArvwq&PGK@izjQjj1S4lnQ1uKst>_jMA~UYC0ML$j{c8M&^@^xu%fuKrjv{%YjR&
z;*6$+Xlgddqp8L{DM-(nnNvboc>QxmLra6r6&ogmD(GQbwG|Ex0ZwR&Xjo}aG}I>p
z-jj^veQBu9KhmWXmqT&34K{>Q2yu#PCaW15&&3{*hpc*TOF2+5=LGt3Uz*>vH^8gz
zfV-1Wju!bzWWd1p=QaASFw256J*z_aq43JbuQax`xr{oTk;h|-#&N%rG>i0pHIso*
z-Op<79~-}Col~GS3!8{-+}Vdb1>g^HTtZlyDGInMz~F=FRZ>jR=<sTC!bS0yJ+dhS
z+KX=cNGIQlvmrY+?Kf@KqXZkgr0pb}R5g1LWSB7rYvXeI)lH=AvTk1FaBFn2wSx0O
z61o<+dCBLt+<&c8qeZo|N182-kAHe+Pb#+&=eFTSytOGKuc$cfN-FKa^#?TcRO<R_
zJ(jPgtPqJ_R$Z(Z{{TWOYxwOH78W+JrPz5Qj2RLY#ArF&9AM;*<IG9V_~;!4s#sj=
zc7MGZPXP$>#0hylk$s8x^sS%cJ;na1W`V8QU`I1aEQ2maHjUWr&mU3;u&**{{n(zA
zANx$;m(p){Yyf_gT`tFbEqN&Z1Z}l**tRqNpRESrLAa3{(2-Zy(F<Ob(Uf<X%PV8o
z_>8~*0L6OIJi@2u6{N;+Sn_?U&^4{jp)Bx4;oC$4LX2`w1~)g<oS(U>#MAsj2yRL2
zdzwAZl0Av|LGCMfsyXE*#w5Hu&RF-Yzu~W4UG%QMZyL_aX)Em`b^?>Zu6WLJPkNZR
zV4hQkTo7t<MNTxx#WxA2kI*iY_>mp1mGbtoO}2IL_agJ0@t<z~z3WE!t??kGnYV`8
z(kn@oi||>cTm$A|jl(C7NC()~gV0@C&(pTk7ZINfGL}5C#{<|5`kD%A_qRss=|A7C
z;gL2)3?-mu-ZqAA<dQuOeT97bPBLfK%PO8U%P)q>sa$B9xzqYG>U*dl`G&uiQ*Uo(
zi{{=71Lo&}xD%Wx&24FI)wl4ADY}<R>H7&FVRvt%$PL8Y2Rqm?E=VAgEQ&uMD^t|(
zE-m!i2ztG4udio$C9u-$=0y+&0JOy5ki=tdN`)A0ti92&n^D)%9^+75G)V63Ug3i*
zdqzvRCE4U805HuXU<{GRLXWe}a`ZW@Xff%XJv_QxD|K}>%R)69nInYVMzP_dfJ&lG
zv9LR(@(gf7u?t-OXtX#j?vql#H=2#g0rJ{A?Sd8H;K)z$SmPP<DD>a_rR$Aad;MVP
z@hqtcjyPgTN{Hb^?HYlARY4;Q#!1dkdj9~Iu+pvCXf4!2W5St;XJe1V4hJOj^v)}L
zF_y}0M#jKgywOCueY>CG+oZ+XA?R6%%Xj4a(vazlmh;UpWx{_A3KlF+13ZEUInR11
z)E?&NBo>UY$UKj?>06GEJ)5zL=gy@703XChYFjd+nKxK!nq`Ilo{a#4_B4uoz>dTz
zRvTG?C~^lYf_WXds~&;TWxlw5N>h}Dwx;8Zx2Yd$+&ZJ9ZFP9}+aOomJ7<xfS|*vL
zzK?SD5ysMdx*QNOxd7#{_U5&6wW6vGJ&((#TuA_96uTBhOt;|-!*Ch;fydgbdc#Pu
z(_qkE-a#zVMI*?{8yVk^UOR#~?TYrHC_ox8q?W-gTK>2vZC#>bL~wvs7Gs6anXo+p
zVo&E$1sZMkUQOA$BBrgSn3-41qn~fBXlLQMeM)z@pB9P8DaPaMD`e7dn${%)G6FCM
zZ_1jwd0DaIFP%4FH{m$^C+m)Cic}p-LVv*0?c#>f1Xu%U1Dtj|(t4bGVrFsx+B1{f
zRIg-F9^LyJXmxT(9`u#OvR+%OZY6=sWburi)HJG8P)&45EO31(Ww?!3%m(cKFK@Lu
zai)}rJO2O=lYx$EqrkYAl!=)~ZL6BPc3|xdz2}J`!OjPN@~DYRE=yvA9IF(5Wy@n6
z)Yc}NE)EZ{t5E(=H26)vgwa-HaT*m0M{<2?zGY-MCm?#%uzVunfFOVeuik|lu$T5K
z_W@v15R8D$lS@R89`7;19fdfT5|o7tlAx21zj`EwGb<3dMg(m<l?RX3r)2RxZK<$D
z^PFI=6!sp~xW}wL#+0+#&TV$QQ99d@nI!)JGLGQjo;dfdDdLT!1s7rwgO=`nNvIoz
zNk+iRtK6~YBB<l+vXe*Ac5_*3VWVr<w5jfqrfIIAk~?<qh20r#p}4^ype@ecYUfG2
z(={zV>c)HKR|u{&?M7|!u>b&LxKulP4o?Ig8@+N4it1Bk63BHQ3gr}}WgU`Ol>RQ*
z+@xe}2iNi~>4C0wQ@Mi(n=$NT7$-f)W*xqV-xbW^XJlq={ZW5sZ((4JQr%{di8Q@U
zT3B<82F^;b#s)!BFgWaMM(W;^(PX&3ytUJxLYnJ~D;RFW*~KRH-TX;OZx6%WfLNWx
zWv~dY#nJi}pRF;HXL6?4q-m#01oDzLvV}NFl=HbV8<IvyAnF(PnvX<k+J(Kvwau=Z
zI~!~JHqV7JqlO5~nEWlZhHzAm%CyHi(MiLavx)0HId!76X@3}m%Mg_wITlo8C4f7W
zk;z8=KGjO`746M*t!COQyW5MM37u@rjp3}(jOQQ92*G7iOJjgWN2OKG(N8s!&lEu-
z3d5hoyD%KG{x9Yn;~B{GuA5$tM-xTZLw3a(7ah4ZvCq(&y}5F1<p_@?Wtd@iB8PAr
zu_0Iz2RI_6dJ|bl+E%i03PzH&xypoNg>3s0Fb`w-id$uP(L%GBhisbIG`%FHtatjS
z@X#OkcnI@Ieevy0>D?uB6t>RtLm!OT8!*lX89r_}-QUpF#8PQm0^44s$*A7!luFY-
zh!o?<R~XuJ&Ou?%ZU`+GNtgXM9rsVz*=rj9p%gk8)J*Vc63Sfo!XiZe3XoXgLIaR8
zah~G2_P^9u-B+$^8osM?_qvt+#FtkS%gaeCxp!lXa#@M|=z6}XHmh+g@-W=ZmK+X$
z@7sz=WDP3sV7rbB5uANZQcS-@p5Y2?3J{J96Uh|UO&Awkf@y7<@)ZoyTB9<Flqn?{
zm6WOx_=q6mG)oAy)SlI~3)V3_hs9ec!^#vB+jt5)0uE0HKT6m%zMmHG&wB}fn3hF0
z7PF@5o)gKC@FUxgT;%d`w<fBs1gwSMNNNIGYnzB{bibY^SnVO0D26lmNm5BfIT$W`
z5J|xVt#8t|H=2Qr7P(!xGLJbbILh&WzRg!7O1ai`3z;ujV7OpS#GY<&a(A!d#{)cf
z#&KINjicRb-C))qF!zzFFOMyqxI&5nzyRPb0Pb;sImQk%l${zVC)nRed^xp-c!YM>
zw>LnRdwY2Xaq6#+!_GM*V~YBB;a;5<mu0&}=^O1b;SV~U?w}FTaClIx2pHoVj(GY|
zz8`!N{n@8OuXP7XAh2}bZKdA>9mKI5ZFa)~k^T|A+XS7XIT`w6;g>_`+Z|xh;f8d5
zyKj};K*x+lY;qJFU^8cSGnUPHS(}ri)5lQSM>Mt5ZnWP8r@Bop<}$=c*G_IE$jJdq
zxPM;b^X*smu)#gUC6<`_uA?d)^Xgaj_dXXKZjWvTN{&3Iu*Z7tx1S3vCsTKI4T{9c
zi9UB>5*+p+fdG969lKV6eQ3WAwF#!MgFv;gb;|e!R(W!8L_}aK9^4<wy!VZ+`a4!t
zF1s}Q;o-{;pDZ>DbP)Kgwo7iL0nR)%eBk3Guj$^JyXlLaW2WJ;NZ(Gg8E@T|rbu4(
zh(E=4A)8?%>T%m>;<k%@GgInW+3q%JwmU>>+Kj$C0q%hS8;Rtd$Lwmus&u}a>8_cE
z&fi$K)MSN~{!ZTFGz8;=;h`B~Pxf>7t4AAYvpHo}k;iO$c4W4jz`3`!ff&18e5`V$
zK#Y?P@XX%4j1D<&4miieJv!K#)(fO~%l`lgofbBmpTreM;pF*`e@yyzPOh`MxM{8=
z7Tr7@w+6XMR#ArG+jdC7#xsoIb6hJ_&{{1*Bxy*7R>Dg^aHG79oc{ostss?Xw&caM
zew#ykZIg)OWn8Z%-1c9o&-ho?odu<xKTP8e>_;GQPBEXb6~Ov0O&ZTr+T2G8hfA|k
zEx{PwkHsPP%YTscuch=vk_!k!40(9PdR`=Mc6r`xsZnsbQG?&q`_x^rQO}fTlR=V0
z+P%v>E;f1O6ef`8gHdyuTKf9mO@PmLGF!q3UGg>oC$Jvm`T@zPviwkHe_E@()tt*E
zrGh2R^6>l5@ZiYEG7-;iLXJ<+auzb*>!0xaT`I`%WMQ`EQU3raKk&!Y`M!dzEbMLc
z>t(mLiD89uLa6RfB$Mh%<dexfnu5eEY!by}V<EV>l<t<{@wv~ZZU7#NI3KP~TQ%Zy
zP&5Wf%|M#-R0;hk3^(C414L%c4EoR!J@H6hx6IESs`3F6%yHR4%8$&C&>wm=_3GKe
z7%7rhJ0gg8k-Y|eJ<0R{niZY7wuJbMB%VXQSpdfNKEG}N1JD6LFi&$qleJ0T=|Oj?
zvnI9;&pc41KJ-?l<T)mwx(4Qg#)!s*#)W~k0#3$^#+Q`P0dqieL~}uN#Q+&jGsP<@
z&pqhUHfd=pOr^bx%6}?WLUByLCYF+*ngH$Im65yC6Oc2CN<vEWLQol7CV=PlqB)^C
zs!X^SJW`W$>rA<&BsH1?iAO_9#CR0K9w@s55&BbUNg?D@ahlu?LV2drl8E;E(`aZ0
zqBy3bG^B0KGKY!*$c-?a=}6BbG}7^k0faQvWa5;J(=oLG%8|ttO#w)wDWDFGOrvMD
zD3o}cClmuH)MYffnkrBSQ$$uY1vEu814#7BNse>PBC-V>@%O1M<YSDM$M#g35ms79
zCQUGpRST%Zy~Ba^jla^54y$a<j~xN_W3>MOTD6(DyU}wTlwd;&uZm#f%0mxub4<r2
zNmi)Uy8GeXms{zNZ*YSI<luMbHSnkH5&JGnrCnX>T5M-$b=o*Q_BHxD9Ds_c*1BfP
zU9*zj;uVowJ;rM}MLnX*RDL6B8hqMy#E?#gIS}B3N~`VqSKq%JKV*3CZ)dpE;RU8A
zX<i8XSH@ou{u=6C3#!~(%@edSxla&|#CjUdN}V%GU6fXtbO+YAKAzOrSS_hx<t@SQ
z&MQZ<eZEsxws#L}FNlW+kx<D+)$M_i9m`~loS#~wnGVv!?rUnXx{Bu6)?tGrSPV~N
zp7lq656cE$Oyl#a#psrMq%Q@pP}w<^U_l_vr=6qTyHCX1_@~iq?~}rjBDY0Iq*I9y
z5rOV=@ASy$2DvVY&|3DV9Cy|_T=y2}+l-2tUMsC?+9PVcI*q>)s_vRee7I0DG3-6h
z<y;y5GmAU?b#RhCe$}+ZftK>onp;U52X_q%`o`a0_&tc{+zUN@)O7xX7(QsyXxw`r
z6tMn{P`H`&Jx)|qSz?fFk|!QlL+SMQ&v1Da4OZX9wi4U#lFag~--%d`Rk6Tq56>C;
z4@xfSqk*loRn2f>5f$L$AkfX!mpEK=^re`PaC28$bSZ8D2QI^tU1_FrHAPbs>t2t8
z_op=_l)m{F1F$)yqXne_MoBd?(kmX#{{Wz(Yui%<mn{XPPq~>uIQjrc$v)N7#A(sC
zmim<PMjb8}3voG~Ld)59D)4rZkahupcK#Dw&!sf!BLdYWxP~dhe9V!fGGMU4DnAg;
z-u=k@>vZa^IOw;@9-DfXi3)Fl;#|7mmSzAp6fQ6b@{*@+MjZJPS1UcpN}!Rl>)lDz
zT~pQ49d=I^NTX>*i#oZsh<VIT0_|K3GmLWW=M2YfX+2}A+(Ub)>Noci%dF~7&m=L-
z&l(p5V}9atidAvwcPacNQ(B(6YaO`x8*c+ob&NYm#7!^&_w9yqSg~AZb_WBRC~RK-
z`R*<@35Y5pNb$kr01N^7R+=-DGotj>)}d^=hNyvHMiIi&zC1vxFfpIQjAPTk)~R~4
zRWe>1V2YwIDv-p2KAHajL03BFvMgYGYdG561Ljt2{{T-z?^-0%n~S3)gCv~bRika0
zjo6tBlB9v^D*7aE30Vm;U=Rm<det?#aH#6R_VKhDw6VM!(VH=Oh8X$}T8F-iBXRhD
zW2|-GQdo5Em2A@Lmxe`xJ;8UBMsm446NTV+Cj-`&x<+kJ<)xB9V>=I*8&o&3#~_c?
zQo6TL!*zIJhxdKV7WU#cCIwTHpd11`(kUf(^S}q1qOrWaS*|YQn`>Yqyn}NU!5^v5
zda_BCcV)hy0^8dvU;hB@0Xf|PkIG_IEMyOy5;2Xv$;La@t*c#&javR;EUr95v91{J
zDmWXn&T;Qs2D7OayVG@PEnu@)N~=d>DG#{^<MU*xB#-i~X4RJdc^X6yG_AV;U}x#|
zt2IP7=zFL2859PV=LqlSS0I81ILCf_R>KoTe+jpj_<hfd84bC!<;T;~wa9G#NyHa0
zJSywDH`&iVgnvr9vymY1VHV^&e-OU$oumH%LEG(Fy_StF4!nQ75_n=)7ZB$Wvh4>8
zgOiT@cB*S}^3gDN<0l6k56Ygi-Ennu8FPnS!#K(R09vWlBujVN*oXyEsyxNB{MBFJ
z)1o@9yT@&8d_~(gnQ(a=<C^t^x_zt?tkP{LK=O>U9Djvk?th-r#t^7e<zg|Od)BcU
z$#Z=!7^*L?wOK1OZk~&2G}!Npy|S^45Ds|8eJLy3Ce!6$t_U8+t}bI;CD2<<;$&nw
z81I^`xP;s&D#4CBX0E@oT2xfH3_g{Em0o>&ii<lbW$pB-7cY`|KU%TOWhCvNN+DzZ
zwVQ3K;FJy*(E8R`a!44>EV>N@-UBP`Sqcz9VUT{~){&K-X<kNRS&1Z`^wP-AhA@ES
za6sgJXsKzKlojyt$Ru*#O0|Zp1+<QdjNk*DR{7G`>oi13adT~R5b-U9K#<N40OOU$
z2@UkEF>TohWdo0TG&+otT?c8TNr}jjSc9LvWYv=v(e!4O))xT{+_J%YcJgexOW4s;
z>@w}coMD(AbKlhG)1CD{RP_d;?+e`A4K~@@&1YFmo){-41e6oFV<#CPdR0!4Z@RM9
zWEa;zEf7^;WG?fvjuYPj17V230~}W1bW2MOD%#@0;Ud=~VzTHDZMg&lWnD`gFge2m
z$nK<y%9Ku?R%w>{oySc}JT{Ugq@@A~zzmysIQ8#<eT^>NVS7HAX{QHdv$c6;iWg$)
ztn8$P&mnw>ah~9w<kf}8NXxBg?{@;?`YgLy#~JYg<2%47EVu-cxySfwx0;FE`>mpm
zH)6xV+*AX|QIptmJ?hEXl$92+^*#N%U6U-2Ebe4!Ly}aLWh7^uki-+-jjMImqkpH`
z+B~-(!jHmf-d8OwTerr96M(8q5C#D!BxjnSk5OBI!JNe^^SMSz$0Ltgt83;53^!xe
zv{q3U7n+sj#ujV0k|{If5)v_<$DZEbl_m3HHI0$a{OZFvByxW`MBN7%{69leRCbTV
z7-Y^44<qg<KujJ$K2gtlXHT`Vng^2R;%McJj7G4785skY+zR^o8ugWh-OaN{9IG4|
z5*40xjaY%f^AHKG^P;pJJ}c>Ew%7HYKH?=C8%?nn5*H@{Rh776SOqJSlj&71m#|ut
zR$|^LH61S7LbQ>~MFd;qzL}IArdD!FFFC@6&lm%^w$U$Uw3AY~v%R#I;`Vj3wz-wx
z$B{+}0zKJC&JYscMF%;<E7CR{E!5VZ?N{#$y+g%jy3wYUpwtnG$VuC4G<o2is!y94
z2etGaL9N4md9Uf3K8@0XSen>c!xDeHLyfB(YT17gf!mg5b_~o;-NYHHC@*a-<c04r
z(Ok_hj>g@Ia83xxC!TQIfbLOLXMyw&!aXg%8Fc6G4N3n1cXbW=u(Z-VLbnKn1@W#<
z<QCwPr}(Tg6evav2;+S<(w91?RB0N9nZH%GYlVgx{B?)sE_Q(Ir?^#SAQQ+^8ww6j
zRZClYeQ@d4>kRg=rs=Qr%U5Y6lmV28KPl!IR$v>q86ci{y3;j`Tj=^9;Rxt{o3yyq
zdaGD%Q>a=-Yl!Z0T{+qwcHNl3$pDu?2N(bmT~ndHIO`hniw!fQ8q&y0(ZMH|Pl|KN
zEwqCn;~msz>s-s=_rf=`5o-FarIn14i0*X#2G&$*Aq0RXa>0a3!FGu}rH^6{C+Mz+
z_<Lz4ksai^WO11o255m;K>pczd6OADfyp_+CpG5JmDEn8;a4K!+fN#lUojwt8;4N7
zAPYRIeL@z>r`%-yt4!;jlY%RmZz8yW-bR~*_gm#~y|82<NypXL4tTEHr06hOL*pf?
zo<HL|Zf5#{kMf}PE}dk$Tijp#!lLB(w&BA@f3!;Zxc>n2fm$q1Q8D|fGPHcbr1eR+
z)>}@o(=PQW=3>%GWRge;<ZUSzm4_X+@m(`f>TBMY>BhKTnwL%iOUlvs8fqx?G8qcD
z^kZKwe$YK;oh_)uI%@t+I_y5^?yXTH@dg0h8AWd?J==Cj?Z-3@iRuXNp@#DM+S{m?
z9yFKwl!EGE?S_?!iASRnzf9I^{k2T!oOv|Svvs$`doG}Pjn(WwEpEHoO(_l3akp*E
z$(AR+NyjJaTsHSnw~lEp-rrE2rzFKC+)NqCIo?BKjB*Fp*1MNdUR&y-7_KdBwRwE9
z2yJ9`g&!Pm&RNJD?Z^QBmCY|^xYp#jxfZQ|G*h&3<O2%0<LkJA*mfQLD<9<64VxFj
z+leQ<OJiep?0J=mh;3CIFXJA$;=9GRrp+3Yr(H$>^zhpQ{+BhyItyIUdM8IR>pHEb
zojl~7moo*DR_(PD@jp4>1Dfbse_3j}q-r!inZCJ*sKw^5{{XY$1pffa;LJzbEFXIH
z{549?C(CyuZ#tX+dD)@kC&rDF`j7=9t#uxmp(IVHUqNhU;uzVpB*)nhWt{yK8mw-f
z*L7^RvD8g(3GZj1$MVaMV>=d<{m`e{oYDGINa@2X>9!Er%{LQHgEGzk0Njw}oc&mu
z^%22o2#r@+)^YL{AG)oN{{R)LC-G1BU<V`r0P+<-@|tGPK)1BH7jnrh{rcn!tFjr_
z`+xwc{{ToIT-B>)hI>>NF`9ujFG__0;&D(Wq-qV`fQju;eBz{g(9TPG&=4n{DJ?@%
zi%*S9?s+6TVun6mR`yZ;eSqYUIi>D(mY!KHw6sghge7E=n1~@hyV!Rl`wP#$JJBrk
z)wPkQmP2!T<7?bF!Nzm7M`d52`<$L=2wGY=m*(!aHRCSO7nOxi@~`MJ-Pn&n01Z1<
zNud&ZP^+sj$3JQdVy4aMLUTfB9F4^RjTS-;0gVYD^d~hoG$(2T4slAzd(osml(WzS
zdIX1qMRQBYXaKS1mX*0Q!#6af-RM?8S+kl1hKk+jGW(hkD>r&hQ^BU?Jeo>V#S0(}
z-h%%C;YDN31^BGyNC`b@Sv_fqTZ&oDRRO5K2&NO7S~KMo!Ww`bDY&>4qEj&%*#WrC
zHinao@l8Z>DnQI<x4kzR;8JLLrlM?$0l3XG8)lP6rek0z2ID=b=-W*!hKz=QZA}>s
zEg9mDjX*G_iiZ>gG*vVMYG|ryXxo}PngGgZi$PPGGMWI0Gs&W<%?W6OgFrBi#weRZ
z3@`*AdIB~m$la(94K~{$gh3noQRPqcqNS|yjKy~2*h&MB^{FFqMn@H68Cx+O_1(Ta
zYhgP70BYU2{{YH)r)>3mTbVpHlWeE|01iR5ze7Mq2N>p|eLaYbt@FT<fs^wLpbyuz
zNm9<{&MUA3pkfY2Ym0nh{hus4=S#gQ0(i~~3=Z|sM!TijXXLt?!aRTA++HJ~{aYjQ
zr!BhX$F<wU0XXsPr4z;ZmF0giRdI~FEUrBtl)o0f2WnpquMFM;{GnscKMys|17Lk?
z_9x;e!u==WPftq`iaFzu9}ZN*FF#SmeqsE2_(OH@tEwioWN(&px;Dq9b32QpRP5)L
z*M-nS&nF_8nnJ<jn<W1Lom5-7Sy26I!uob_vtt1HO)q8B(f23pC9YWA^yK<Rt>Bw?
zlf#LoBLYL&e#Vjgrur*R=`N$Vv$2*LEvF(xhB*PwPxE7(ag$u@_8RNkT`RA~lYXdT
zxyOdgISrmMUrhemeHyp6b{hSix&n$~npHuZZSs<F_x_caksf%hEu(A`<3H^D>FQlI
z&E3qBC*UWrzy_)Gy+O3enY<*ph18_5OZa48@dS4SatH)g`PBANNdSh#GOD%<GjQD3
zJDhBp(;$#YBzCG{m7>|>PR9C|ry#e1HBBZ-ZuDsip_W9$!6_LrNsa;F;Eos!jB*8K
zwB*JC?z~f$8jZ%0aAkxm#~=$eyr7upOoO%59!5vp5C|ZEYBO0wB0bfZZ<iP{LdP!-
zfByhw{{WlqoF4tkRB|+#UwJ8J!8!ZXEF&`9F5l%K^V>9YYa2?fAS{{51B{Bl&?2-N
zw6dZ3D_!KFbG?ysz|X12sr&m>OD2ef7bA7IlSz+9f?00NNb!)?aUHYV$TCYv{NZ!h
z0DFL-kVqx2xpx+kdm={uHPaa8GRlVmKsfGLgXIUXzzc#n6jMPQx3=vXGOTK^zEjKn
zbLsRSZca(9lUCHj+d*<QSgt16vHYx&7))VC7j8QN+a8t3k=)9g3rX%uz1!}S21Qc&
z+noAvefiF4I)<+dF(W?d9z1!Jfq*?bf7q%^y=`Ni$pm|XbI+~_s>$_dk)-fsb&bE5
zKAy+ku9cYw)%B_MOUIqx#B<4Fpb`2CrM;Y%LO5?+RQyuU+ZnGVv3Ws0Z)(vhE7)i*
zil6{(Vfxm=YZb(ZhXyixU<`hAm4=fP(*}@&ys2D}SL<687S7NtB|Le9?OnJ8k4`_%
zse3hw40PLhq+ge4qM4&mRY#PZa&miQ(${yEw#_r!ME?LT>|GV#b13`c-mV6miE((L
zO2H->R7str`EXc{InR7mPNf79TVOK@R1t9zDgZ!1LWG=>dB~}@%S)qC)URb@&R25h
z1apkmH1;US%iQ*2c%<&;G8SWzkTIWXYfpw50Qo=$zoQ_Xryl;bc60X4x57zm;Awy3
za*}z$8*zpF^G3MRzG?^-)>K)e+}T{=K>PRLcc{SnUY(`saS594^5)s)m16R&FSv-%
zmL2#dv5|%tJ@K0>LL`z{S=_fcEuEv<v1aRMo+AbJ*^6$G0R@IIq;?<rQeU~BQ`MyK
zK$|COCvMfSU+FU4*`WZ!<N%&hG63!g{{X)9jG?Wq-)m%nxtO0ZKT3kkPW}%T6;hiP
zHQR+gql(rc>HDvq4+<AjNjrPuw(Ct*X4`7JrI0R8?%DUv2Ip668;D{+W808QcTj59
z%CwmYG>5sjX&yHUsuXeS?NygC#izryqC1<g^{wwtXVh&jWRgT!*YPWQwmbg-N}%}&
zC%pn_&Qt~}-HG-5stZx6TWJjK33WSUahAsbQyRXH7lk~w;4Db{aC@5B^<Xc{i&&La
zMo2?|0Q=Uyj~r#Ak+(lNK7xgKp?wpkE-#>(V}y<_;P>Qz=xKZ1Doa%iK`6wk=Zu=z
zbj@_y#U-!`RgQDmeMvuROHyy1pJYI71mT;|XO=W0iYlz3*o2<uowu}mqSy>oMsfx~
z_Q~|m*0y~gqhZuE87}0!jwD0Ic_-{EUTcfZFUN)%WkisHA}e_xX~@X+!Q^J8GTdmd
zK1MsR2iBFjQ7*%?2s^jAt6OO!kZhL#`;27NbvU7D^BSrV!E#A2{*@`2wMdLQ?D3>=
z#GV?)F&~4KE6+G1-}SDQ(wBCcy9*sP%))ext%d3%Ol~qVu|EXlle8`ykT89D??h|%
zoh#KA8m^<ES@j*2v+k2j)OQFc7{(iJ4nD?#W2r*-3bR`)z#k<eJ6I23dF|<((W~f1
z(b_L{%S{2EEpMmJv!va#L_A_tBP#}FSC0)G%brS}axxe-b566h(rq<Mr|IkK+uag!
z0sY|${KQ9&9I3$|=L|8C+zjHa^zNs-)KX}7+-kEv)8OvNb}i*IIU@vaIAVMK^IG>%
z+3FG`cRG}56ju)I3&em&xSX<*IVZOV(;Q&dbxhT}Mxi4JTm21M^xlhW)cTBeGir9a
zGsqil-SK6BFa)S}0PO_fcM*loNdqJ)EueIf(l#i7Zwts7I0rv^gc~goGsogWPcFO=
zJ$bFH=ci`HdGPCAx9R2Ae{{ztlLL!un~NzHb&1Y)nL;r!?j4E1ZKoL^(z-60X!jQ}
z39W8(31#u8Yj!x~gk$9|=mP=|VUk5&>C);NR)uV5)HNU6(urbANG2)*x8f)WDCkOn
z%bs!0I5fmPDQT+1aeD@-U~VRD^61YPwVh)mVWK$j-A;cBHthcZB_Hh4jaI_e*8J>e
z@MOqD*rohZZ~}z@WamD<wbs5I+{@5<+zWH0YVEAuTo@Zqf=Mlxk}#<Xg)*}qHj|Fo
zR_#^2Akt}HHDMh3WsR-CCg;S|WwdvX%zQr*EN+Q`18aJKGC8kw{luLs)G}$-OQv6>
z*7$R#+>pK(AgjDiTy9*e4a0`#BB_3jD@O0son6&^N2lFbSm{lt*aa6CR&v6UN&={g
zBPdAYL<7uYj4BM1<zvlgakc)T@^&*51)N03AeKOdaNDFSoN`!_Sd8!hBeh&>U3skO
z6GLJw<I*hcpXMyAVp$R>r6U4$jm}xN_H*0;<|8?^=uU&UhW`LhePc*Bmzsh3t6Lk%
zmf2;CZ~RHK=J1bfah$LlxfMxDnRLHHy*aGeopj4*6#8a~YasbHx7=rP812|IpAoS8
zaB`%Bkk~cZ`U|0=)b+a|YpBZ}sUnMmr)kzdHf`<pk_?y(iG%>Gcw$(M*aH=&bWW9R
ztLoQJcd1?Kvdr!Kx$JFT7;tj0aIt~7sKY9+lmXwIp!*l${f?v1noN2ZMVNG{?vY;m
zSD8^`h9bFQRgUL;m%_1B2PB3$TIG|rbSsKC&WGtORkSN;+QMk9#Q6C;(pFoUTOiAH
z%*@g;#uz&{pa=jM>e@xTpD@<1c9&OCAZaaaUn?6CoXH~$AU>W@eT8bB8oeZRr@LKh
zO-fkUTTQ6F+ep$m^AOSisyP|Q(00aaa_M2Gs<awZR_UjqITqQ#Z(RD5#t9_$=e1Mb
z*^Ii;u}2lrnM2PI+xUW#arP&-=~X>Nr!C}Lnt5&+Pw?P~Ip;n0rzh)@0rji(yMP3Y
zz-0uHxyK)ur5fYqY@%gWj@8(6v?xDrG0kfg?8eWKemz+0_c|t^Ep#i1nL$bQ9a`24
zbPfl2QT&g_0NQsEoVEcK^AAaUNz!zFqqa_^ypL6y7Tl3u#P4p00YHKn%Wpn|h8%)1
z#eMJbZ4K3w-WSL#s<(?J?xM*uW1b>0IBq{W_%7yKPM_;Zby#&90Go(7mJ@&vC^$yQ
zAdXdiJNom<ZqC>?<4050z9VY)mZICIx(en&Brk4mb){V<%HAdeJ0OqvgMr5Vrw5)5
zXft%4yFG+4YI^O(y7I8u=_+h(k$d@AW=8w(8SZO)>F<lNYmh{`eut_QjFA=ntT|>l
z%Q2A@y7Dq`dHrg*^*ooWET1e^Klp|fh)3M_X8QLzW9?Z^U!zz@(JzKwGp6+BwK`ep
zw)TQMbl)UVe->swtmn+xJo0P0ME)F5sbQ8p{Y`OyhgYWN@@wgtb8vUI-*4p~kgl(7
zGf#N{ECf-F%n!NkUH%-jaCwnRdM;a)p|HrJla(H{CdX{oteNIe7w$z2jwr3^Lp|!I
zgeoVBl|=G?tpNkxu)x(6onvivE5oC~F1b3Ltk!t>bUvY{Zy+Po0?q#bEl2l8rK_%W
zd+!Dx{{Vr~*`th)`KIDW{I&z?4I5s7TkfQ_w!D(c1(Q+K6Nk050h%&<c#KF`{{ZSE
z>6%>W*Se@DQ<-76Kl}}w5U2kDxhD+YVYGXOt7|(OT`tirtnK2pwvX^d3Is*_0mVs;
z00^Ou+8E-7HHs+IDk2Ojf!F{$3iF(O>P%>CXb80lvMOZcniEipAjXF0gHmHbcJ!ej
zoY0uj9N^GefQvVJ3mTiAO$m>B05Y`!%@xi~1<e2_G_;4BJNBiV=O%y)o@r@&nqkg(
zr6oRNLK?6x*rnxe;*BAtr6PiW$a6`|dBrm!r6fGmGC((a6o!iY&?GgJb_L}kmUD_c
zhcvW@gFrbErV!GRH)GzJLz;#LQ__zY29`=PHC`ZgHm0F8lx@W^8{pP}c@Ju4CdDR=
zoKpyB2T<mijP|7CV@$?q2O~J6p`{~y(bcF1<7^)E!kSGFYGO9xfN5Hark7JjQ_TRv
z8evWiCasD(H(~2QGKPqx&=t6%D-vi!LjdB4tw0S#R?Pxo)xTOIKQy>F#ZPddBP}{a
zh26p+?#yvXuAz5+acKyG5B~s(xE!DEr?1@Nve2x~8c#4Bs-DB^Or!CFXhjR12`<O`
z_0`qs^;VR|pZQY*_01CBrL<dS-5!%-t^Kc-LVsLSO5a*0{QW9yt6s?_oni()M%y3#
zmHz-r9Q`ez-vjd+9+^HzCQCTm{8o&uSeDK!;=VXCBL4v4mhvCC?0-5G{!Kpf;qON*
z&nAmz`evXHFQ9mlf_<1qLjM3zezn2BY0rh4mb20mT-<5;%#CioTe&X9%s^*6tCx{K
z^JD8@T|u=FDuAfG1p@$nRgFr{JyODJX-4SeQlt^Z9BokLv+*kG17~+E4a3I8O&WW#
zEuzF17;NUdN9{%MhU=nw#%q{unQiB0Zz$uxdg1X77#xG`T))}5ynGV88h^xHB5xGL
zR%I&L$2tE13jJv7j*CAPJ{!KA%jA+ELIMLcnD;$^_wQejpAPhCb$+eW?W2fFvP!DT
z!zxC7YxQ@eejaLlJ<@hx?DtXX-8(8oGixNDJz{tt_!92eKl^Nzq$)|piF{IM{P^o$
zkGarwi~SYg@aDIjq9pQoKA5ga)V(ipq{><1XwGs$`)0nu{iSqGbEf)=-&@x7OLuwP
z7u9qoZ<n~ouY}yAeHEIxSHzp`BV5uJ^GUi{r$yT`tCeCq;gs|JJuA#gSf|I)=(il0
z?)GtD$X5g(vF9h&qXV_I!p#JY6o_)Hr;+bioezk84rw7XB%RL{=vn5LZxY_jS{TGq
z5Lm>VE`4^7Pq*n;y)~i9sH?L0VO5n`<~vb2$3FSzuqObVVzt{@g9To9j#&EV(zjhQ
z+85KLyShuHbN~j4KvpDj8028^53YS`vM&zCucC)Yjge;(Is7ZhP(3m2+}5kAYBv)&
z7X_Gs!9KL^tExTC%Ogx?RN-Y|$sbWwrX{)H23!xw)mFA@-HA>-Lx4s;wItL0Fr|YH
z+=Gl}n!1POv60`lE7ntsd6HI*Wl_jR>}yN1qIXd&D5NR-=e;{?aE%;stVT!1GC0K!
z_D9t(USAKwi*|510-dz9RNCdTK;zib!a<^!Xza?zzI|%)FX0~%)5k%G)?~;Wjy}YH
zl~Wm@AcfBSjy;86Sj!sNBV@=MlAv~fN~1>UQLyPZ+6I@ce(=;ZPu^M#fu4OPE9qeQ
zkO9Ud8DLbhu+DiL){m(`w^na=3ozOOvdBi_#2-&?Ju7sJN4B@sZDYOEZET`3_|x#x
zJYcedr2yr^lE87>xUF~MG?qWVZ(-6eqSP$Xd3-ycl{W72hbpA0B!X2{isXP>zH?e6
z=!tZ5X<N=S+<ViOQZ!^bw$e=}a=DO`!KvFm(cghmawkT`cM4B(s*(b#p>vQ%N4L_p
zoeuKS+6h#ohJAx;gMhyIAE)VBJ-qmc$RrDb7XbN=exB83rt2aGXytNZZt6#^X3^0W
zZ(2`L-my!mRz_P^RE)QnD9IpW^Cp2ZMR#*?9O}})hF{3TB%X6#=_EJ$jPB(Y)q*H2
z-~=bvpQd@QPo}+<*;SPr49Xb}?lX_i{{UK{p;KpSY^0J`!pS0$_a925)GoYB)|9ek
zMgbj(_pQfLX0`Fn+(u@S5gcUqL+Ia@D^}NZu-jTcD}W0wIp@};5cC?(<>a)Dx9uc=
ze>$|%q>5cqFBCU~r)zI)=a2j1wcC4n5sDGY0OV)Vooz{A3{g(!!6CbMr4pEask)7C
zne9|tBMkU)<f!`+YgcJxxtq70k6=61&D5)Ofusc&wtcA$FHDzE)O<KYHV@)a{c388
zzQQG@)Lf}&Zp8Mf26m6bUI{rk?oDjE_e|gE)^OZPprWn<=LGZ59qV41<5vYD@W}2P
z)}rj5gIa1+>5lg9JidtB0N&=IKC`mLqH<(*V#SYap7e~D1)Q@D{{U*kg_%M0`p{m1
zs}+62*RUALqCgzXvRwJc@~G@8+d`T<MwG`0d5ZAvdjZWuQq-coP<#!O<mN?ugZ3Vj
z{)HmCYcyr$n`q?bm9_#&(Nt|r_r+OTI^^#pkLyhD6K~q0B}|Q@f<E;(Mr~0ZpO$N9
zl%z6CYZLCDV+Hd2WcDW<cLd{uT?eJ_Z*@_qu9<GPx_T#zdvA3Qh=~9^$ip3gJhALf
z?mO2cmApAOWcT$42DToO>N)hQ#h%LMZ9e647Vh3gMu-qcamemR=4jLGMpR2x>B#Oc
z(s3hN*<485ZVGQi^ufW;em(iBJ8MhGVv}Ncan8GP;B9a*ouvBxt93pkEhG?|R};iG
z{JVm)ah_MVp#*RTu;A46DOxL*fi&oK4LFf3x^v27O~i6=aukovcpOw+8*x%7wA*N{
zwB&scP`-y*4rP|z4kKh-700^+yVQ(hw>29rVoO^~tv>Bu$~*M}TavOV5t3K~8l9}0
zjz$+eaC_6*-$|fRb}i)7Cb*FJI&6?S#>=0_9H%N&eNG3xZTAv<zfD-^onH>5*B0=}
z9l5xcF*TG#10RAIhFFOtw+sl$TpZ?k1rn=H>Km?->GacYbnAUvPQH(IoUeOu$b@Ik
zu&k<}=OYf8<m2A2ejW8gbgX~-D_PeqHA~6NKOvw383T=tB5gg&keD2u$B~Y8j)#s8
zpXxa-Zgoq0NTF{J=HZYY2;1e@3C;*7jPdVZMc#Zwu(Rn6Ld&L?eA<k0!+oe7M#@=4
zWG?OU!c6Y%i2>RN9v(pLS-Dh;#VR<Z%=Vo<)e`>lOPftm=V)~lxnXfO0~VEBo!gnb
ztCB}!xDacnbl*{Gx_#N!H5{duwK805cV+e;1(bzBo>(^TGvfz)W17~wTFM=3u5EQ4
zPSRNPi+hxNjh4>sD{#!JlN?C-7%d?=IAWv%D|6E|4G&xD?Ppi&OI=3J$(fmL{9W?A
zqa*mvahEJd90E=@kOCa1Bx@M7j>{eNJy$dr5LzwFcdo}uhAVlM?^FU7Tm=PSUksou
z3*a0os2JROdrQ_u)EYg^OR3KL9(!xc*#u3$pD<F-D-Z!Y@&Ifo!RMT5zYuP^W2f)!
zBY8YYA{uy}?nad$lW=8-5~Rw?yecUP7%Xv~E3Nz@w@5AJzSnx5Yk3TTR@Y8UH}O2%
z{6V#RL)^0MRmKZGD~eLLNu5bJ#^ik|)7>#Fw>FlyleCh+k>^RXkHELKUA(V`oT8rK
zGi9@r+zRN1Yh58y)-(`pz}Ty}pJ9dn05ep5Cv~Fqu8VGq<J(QNvfE0=SzA9r&f|=p
zGwOZm{ZFXxHhA`N8MlukPCSG11ZKJuQCsZb+?@{1e6JSo2^0<iZu$P`q6T}bgmywa
zK0_?M{{ZRjTCIhywRv&BP`i@n3L9iGq)~?Ng&qF@QhxP+rcT;?JA^Vo4oQwiR&1X6
z#&Pzl#!c)>G<&Etb=LIxOX+s<Hr7&Zk{Jm&_FzZrUlDv&_+uuQt()6^l!e|*%N%pP
zu--9(LATBZK;U=9eXpv`f2>-)sw8IGU&VqE$Le$XR|5F0(OSLbptgodZW<I|sPe3e
za5&0rW1p|5bBeUzw2d&g7qjLKTdMk^*HelqtSxP!i8icOMVT@{$Y9Q(`gRA9b5s*a
zbFSQ7JeRUzS1k-M#EjVQm5)8M&Q1kgb!;>APOKo+H2pStQTM3S^#CD{9hqV*ia$f?
znxeStKAv+k>ODuGTd6!lZ>U6woabt&&PUq<xf*R~?2Ad+xb$lad-$MT{lUYxu*k06
zWw|1SU;Zm7XXiZs0Qj$)`gh`|NgZLXU#PVC8QkrT+D{bY?72O$*jHe%_^qW*rb`VU
zsP$oywXF0@l}6+Ha-t`Ht!?5dN3#zun^bh5*-`C^!EP%}POs{_(f;SqcQ(p<ORMU3
z@DKd48C&{RMXz1zAQ^Qn3q(SB7Iw1T$fMXajEkSH!}hOnJsv5x@_TY>lUM6K6GprF
zJDo-&e0x~igEh;2qCj8zQVY<r+&Fz(Sk<+b#tKVvV1>T%L$Och4P0rOMTUoJZFJi!
zeLmQo#v53cBq!er0L@u8flm6~y{dV1-4to@AO0^{eT03WLR|frr9rwmH9R)IeLl8$
zCe}~kS_A(8*j^Za_Y0q`Y)o<fRT_X|IMNtWIMri~7iuC5Dvw|XVf3#ttZt}CeJVj0
zZ2hQY)>!k+4UGYYKUyDuy-1oH9x6eG;Lw?>WwAqJP*qqqG#Nc8ta4~FcQh;o=uBvj
zLwW#DL6fnfy#_<Y00%sp3z{QPobf;f%`DxiiF;D=cAx<v;*@@KOvq_w^`HSE7&NT3
z%hI!UG%71AaZ1VFnVYetWT<6<In4q1%^B%u?^pqO4|-ZkVqWH!kawwIW;0F1*&kX(
z8?`kPRIoONj+C+-iZUK5Vg}<m6vStmQZ~&nhP4CGw-m&5oGncwPz}a-remceIpUg)
zs6f<4j+C@*%`&Ehhgv*MkxPTxjS<118bd`>iUN93l+XrRC_EYpnj#!f45{~`DWD^9
zMN>c>NX1Mx4GCz9e_9bil;l*Fgr^ihXh=|VQd$_PuX+ILcm|53W1eYv`{If_Pz)|H
zMoux!ERDq$H_v*tnMiG47^9@4wnl#RhW`L+^nsiLkG0p3PjAkaR*gU6kHURF;zpk&
zH%L4}REEd5did+CKVz%wZl$<}+APA&nB*J=2EP5ayIGl#jgqf)J^tg|QQjnz8`Lo$
z+7(GbH)Z20K4AE3`yAS6nq`&Qovig`WtDCe2G~cTQ^N}H*8O#<bak+YsjNhHQH%|1
zPD0k;5B_R$(EZNk`ik0CO{;)#K9rWVc0OOhw~)hoE>bs%fRRQyARZ0}r`nuUcM#i?
z*}%Rv-)J3wsad9vD82-4lI0{V8j=41E^z+<Q}yp(G<<3JU8GvFJ-l88+=F<P<l%>)
z2LSu=x9gG*9sdALwbOLIveo8c1QzlW6WgR42H<}ZfIP3qBx642xwq{B)0%dnX)VT)
zcDMGD1C%91k-wo*xmFx=oPEKs114AZk9Qs<u<ZE)^G_D?v~ydR0Gvx7mA^6ApZm>K
zoe}S4BF<Rmlm7se-boMHN#y+jHGioYB`3yrpTipi?))R$KYz-lX{QU&{wy+XDivpp
zl`2T%K7?Q%J$bKEGSTNt9_a|_x)sfYh3AFgkR8D%jEw%j=~p+Jjn~Sxw~7+noVY`{
z6ZNk&FJ%z6oxDLB;x|%4uec!g{HaUH1Z{*Nhivdc`&2h&D~VjLrS~~ZspD@3iv+kl
zImtYONnJ4A<RE9#l#+J?jtAQ{dS$vUwI+48B}#-n%YQNY4|-145OU5)9{9~GEzGxj
zOot=@00RQ863Dq#-LsCz+v`QH$ZpAnhA0tLS)^<eo&cw`>qNWSyToMh2^j<L>p_ml
zM!<z6sNIv!K<|n(7)|p=%vK|URX9J9^rB{wf3O!YNovHA;l%h|>&o}!eLGXPH@4Q=
zTxDj5#xia|i=F_%2b_%Y!5?2rQd^eO1B8+BBrO{5%9Rbl#yf%!+nU#}E+a-|+BXg|
z3GZ4}ktuG=8=X<4K1qA>+??mvlh4wDavE!28Zy!W#Ee*HcO?CBk6NZiFDTqdO!61F
z6*WO2J9~`dJ?NJ9X6JeZG8S$!PqhtlxfN{$5?S4&0$iaC8WMBw!Spm)^qKBlG&Yld
z9FRsyKJ_Ma*+-HZiYJkAlls$RO_~<WkVq6BNm6Myj#c5Kem%to%WEB0SR)vARXxX{
z#W<Sm&mu9IvmXj_Ygl)5P`PcO=N{BpBqa-P10ILDs`0YwZ5!O$?E0O6Np2oziAX?C
zVcwLE@Jq4Gq!EFZ<Qmo@y}=#v?kUKx3aP<3ZsXpeDmGJ0+4SpHyVSsmcV!O)o!oZ&
z)7@>;31J*JHtNtxA{I8w=24CT4eOqGs+}WKYa%ALlVQg=JpF3kJ!VPmS}CLwrsIT@
zxX-9HN8XF*(r48snlTX|BKJ@+n%sIu_xB7U@eyR1Ngi+PmFBd)Y9S7$M0^JQ4PR*{
zaT^yUqhJ+sdlSdyR%pg9<+E_O)#9GT46b}L4gr5cd;b7h*JH3Z(5Y1tDQ&zQ;QCcm
znxoo4VP-0<o*M=k<27N^I{yHI(xw+|v6$F5IXLb<wVAD<T}H*>hzQgL9GrXASPV(X
zAdV{XC5_mIkfQpwYahE;F2T2)<T2wSq^}z)?U<yaca(PIW{&0^*&DO@VzDx9Cq2Q&
zYPRnz_(IM0^q~Ap87-|E+x{t8^T5xS6q4P1v1Q%mVS)k9XfeE&<YhtnFs#cwmn)5m
z7uWzQSMpV{d#kl$<!#IDiXElgusD)AHV}O(ZEf6b&%P;nZSjm}^Q)?QC~o~b)fVvH
z**(My{1*;mZelHwmdB<*9lfih>H(tCBe>FTEbiA%oJVVP!B$oA32lK${5iqebF}gR
zR$*Kk*@`#~fIUyOZv7*4%`MHmI)YirEG9ONU`!VYkajn>AP`Tlw|dR0H-xW8bkO?l
z+h5af^etxA=SeVJM=j)H${Cg`frWNo6B5Q-3JUJq%WwuMZF8sfJN*_*PMOfA>bp(e
zBi(8G5(}F(?;!CbMrT8S!_ETZf-6Pn-m<;u9abKp(xB4ky|eK44OYe}o;FEVMQF>0
z^9Du3fEWTtY}Vn^J$J6^I{p5k(~wPPWcMcGf8Fvmh_W&Rl|@$0)g+LrSdh3K>KavO
zw<#x0Xw|ek-kQ3#xVF@7bgLNvz>+Jeqq=fmkW>axM;=nijCVQ3HEE+*Yub!<I%bhJ
zxo<NM4ejmL3!eynJUTNfp800o+wo$$KT-bCcAZfu()x#1)FTm{yR1BU(*w!`klsnd
z00VhhK;V6}s~cz3Zm%!&TZ;s=w<Z_7)Ga|EIKXlqN7&z8*aVJoSWfKT3ifMH3UtR&
zyU3EjA#oZ*9nX*A?Klb~Qcm1)xNrdDoL55VuA{usZXmd~irKAuLiV=Ha8Qs{ql?8-
zR!&dxG36NT&1#y}t-KnoyF;kMuFe!~NUVG{jq&CQ8WI=|`5)necW#yIi#;P$Sg&+g
ze|k)-7tA%Mgy_3SaH);B$EzNFatRg7k-9rEC3`M(Eqh7Sb-Q5#>d7?CCL)60=B_4Z
z%Mgcl8F(9{B}-$3<2CfRNozK?LfSh!H?q{X<|dWCVnrO`rI8e>kOo38U=$Vu%meUy
zLz?eUmr^!*UXo;IS??AR;=>yZi2h@Z(>BgbAiw<U>DY9udrO(@G<_2DPrcT42-IBa
zl4eC%^Y~Q*VNQ73OCIE$l4}UBxw7+@2VIx_0jKIST1zeFt9N-Tk}d3}J`!yWoDi8v
zhH;Jw!0vgi`%&sg8cy2_n@Q&KY>wTx#GssD$V!8fF`WKJwEnBqZTbev;tP2#^t*>q
z9G9Ak?{*xO+@l~V!3(&t=KyvGSJU;aLsHX>`n)$5x@6842DoDk*DQRymL%9lc^L=@
z9kX3cDCpt*pC)v@Humm&aMw4|Th8*B&7^ULU7+#jTmhePky~Vz>}<jD;)30Kv7c)Z
z_c=Al+h5SMczhEzkBPey$1UXdb0J>Y3%P;DIAT84You!Z9?D+U2C%r0{{ZhXT1f|q
z@8&4KPW4FI(-MvB+wV19J~(_7vyWEKB_fF=$j|q|KYnXQ>nmG>6zw&%`dy<9-Xs#j
zh^Nz%ScCQ;*2{0yR(d_1%$7C~O>8-T1=dne_Z5jBrE^ZO*L54oo_~~;Cv+#nc8Mh0
zjGxD~)O%D;N~l6K&zw5bq+!&$l6SJ}3)ZwRx4^i#-@BeWHr!`Ef~(i1wA)9SEQOWa
zsJmlEw^JjxzSZsa$@|w&*1R2N-UxMT3t3>d18v-nj>LEJxd$UY)uHS9e0D1>#lEPi
zwmh+4F2%{lPjmLJcXf7k#-53MI6-fz+%GFE9$YUOZ1Onz*JZPnosLFIk;yr(HSmVr
zCuuH9x`kq62aKHh*KN?@4PuPk?j+~e6|aZ6Y|G5j(Cm%PM72s>7U1MioPl3Lj|_`Y
znADx9WvW3F-mrUCY9+X!FnAOuZ|hlh&)SCG^adE_piO1FR1azc%t6VaF{w2Tk9rYA
zN&b{FYHV>sY7#-H4rs1;A6g5V08D)-Zfb03PIEvB&S)~60Y!QQhJXtnY7_d=7|>kz
zpam^1{iw1YXbxxqN!ZfPXwtW(F6Mv?l%$8fF)6^MWIgBw<?Twjqsr8?nuY-}pi97_
zy#iC|So;F<9GYGIXzp`M$Z<vkCp64Q6r5)i%x&7F4k4qbCYD1-Lr{QqJm#50lf@{f
ziee+cty`o9<2|X^%_AA6q9h6@U~)5xYBNbj-Km(LHDEa#Q!2wWoMxDfzO(~M+|iWg
zmW=kJB>>ttYBC(s(YK`?XalLoG(~C(nwpvcg=#W3MJ%R@jgPee%9<+B6~9U<ngGDm
z)`X^}rxXE{)WOXN9Mpl$08mX8TQm_wQZt$W%Gjcz!Jq^Aikg}b)rSWsCW@?R3VTr%
zs3{Yk-aE7#w<|9y=y1#SG`H^ytu?>ip=+CO{{V=$T(Xb(kaPL2e$?c7ZUD*l6|L(3
z02I1cM7(P&jcN(?74KnU8t&wMJSc-O{KZdY5;mJ_`&~lRPi;JR5T4RTcMs`Wf5Z!0
zYwb39q>b(%P$NDQcF!m6>Frwnr_>kP-P2s?9TC*4cHjJKh;+@~+q_v7n0*-!+O<3D
zPm1&0kDcirpPgB-lTD}rGUxkV8WH+pv&#6vnq!U3x3<$Qtl+h{M2*!*+u@chNB5YV
zpTEC)<^KR3v;lgs-PpNEr^~m6fhoZ5Gu-3Q5`Mj_VZFBaiJ;1HX`#6-krBF{Qm5D*
ztL}KNd)2MgTHckY+3FWMMXW3fY?RF&;0}0fZ3G_Mh$g;bo1NM8TH<`E)OvZfzmP`k
zPDm_M1KS7w^G4~*7Rx)P*^qfuf&u)!t7X^hCAYe1UgF;FK-qPQa@(=oHyrl&ss_8B
zJAAIhiALYY<^%dyvj?Nho|`kT5#SNwqey@mKsZ&w#(mHJwXWUC18qp<d7D0FRNA=o
z#%YaS<<v5=nDdN+y9ugh3hH-b9e`8oTBLHzY>A9J7GG2A-hm+savRJK017Js5LID>
zK7)#dx`vQ&c8|r5IIW57m8>n|Z#{v}l-2A?G8_;e@eXRdGVA~j2V+E<a~emuZ6IJM
z9-j4^R!X7iwG;*>A<|Ut!MGs$ew3Zotp>$k2Xb#LTs9ep7#_nEgx6^E08Z672kBK;
z5~DEzhu)<`hRZ99)RZVl!XlG~##oQ3^~F@}X=FV2HD>)gsM+audZen6*<8&eF`pGT
z??9!pncSlexFi5`#&Bt?T@<7$#Ne^vM?ZSkqQ8)hhMv;6Rm&1NUwXQ<(UlogVa|QH
z6@6!<$EU#nbis1F(oZQMjQ6X^Y@o8Yb+(b3IanM6=E3X_zc@9MpXk*=F3P<xNK4zN
zwq1!4jQ;=`&cxmK^gnuyrKGl!O%H_2By7>kgPag~8LgGA<I=Pb4mA>7%*Vvsx!Au?
zttF$%vcj+(p~(QA#D?R)^{QR=UXewn*c+?K1fRl;sb<D_Bfk`Di!px2Q5qvOz!>8k
zVEP5CSuNP=Gg&I`p4%bYKZ~CK0AEUe!$@Eb<?!$WmnQ{B+LlSK-spODyfMb`pt@(^
zI38us2OgBi`w5|4-&{w3efzrC<>hEv+TX@`CJKBw$lNd)fCaq)&0kGyFErJbW`ack
zJagyHydU-LO4(@9+};Qp>P)KhBJuzukLr6>l)q$gOy-yU8!figUEneDu^wO6sy;$n
zX(UO33=ap;*I;W>A)ei`_R$d&6&=oUPCs61gQ#>=43a=rM0}N8=VFgvwOusK-apZz
z#Uz%9PEQ;YOvR`+;&)*7$f)Z2YumX`H$0zeLOtg`Q<3dTMt0MtF>@b_c^M77anH4F
z7MJl`__4YNjp5mkWA9q3xFB)OSy}24L1{MiCQ>%5XOGg1(Q9LOa4xB62mUe-DeaM(
zq_mQKBKj%9hIrd{))@w@p}O!?7E)Uugp6cRZWvx%>`{g6qN+n_gS*xmM_1tIB!THq
z>AL#u@Xz8PfE%#ls;9A1j?2McLrMr6AYiFHfPHFe$x>yF<=J*TocfA(!fR7%1HxsH
zcTvHr6XGjjff%NuM;*O>wG~8Jo9mTHN~)9|fQoBKk_MO=)G}unZr{>|G{G~gZpl7`
zin6n`vay*&v%bb2W{Vjgk>J%`1Z)p)E~Tf4tTgqNBR(seNLiVEGUwaV+O<ooGLw~-
zGMM0@7{zSYdVR!tXr3cx)^0a(G=4q@_S77X{jpl=lX}Hbquw$}c>?G4;-MPSVg}sX
zP70_Xas!O!k82_MxY<X5atDy@{=S)`gbqwVsbxK~Gy2gjtX-Uf1`pya4hP<ZB~0i&
zE2t)ktlQjNEJo4Cm2Y^rx{n@ZW(2Q%aB{itio2T5B(;-Nw9$;3aFLNO?MX?kWDE$%
z$paZt&QycOE1zkL4b9{bT}>=HbIPV!2Q3$&bAWS<=Qyr|r|CESAoE>x_N@1ou7sJQ
zg_b!YI95p%f)M1WWI0C2!yJs^B}Y_Us%J*hemrX$owPbn#EG>Iq0%p-!VOL1x=_Kk
zmr*J?g~vUxc;JlWS!wo?SqGn|^|&sy@h}Z!OCzLYfN-%+WK?a~+t`3k1}mA|X>Y3N
z(IAUX)X)aU%SyJlma?79L&W&sp4sR5Kwxkv9WB(idJu}t%+~Oh@mXe8KnsD%Rsi7o
z_fx^D%GxaoS~>;Jhj?u6&D3^xaJ0fiySuwK=oOR(DdTY9fN%y5N3JW@hp4YLyIYpj
z=am!)VSBF$Vz!7d+GB|Z$iM@zZ?Dptk5tk1xR&bbD|SnP{CfdzL~uxRiI7HjCmV7A
zBO|f&zt%2nb&YDnPB#{PLW>H<5@|0aw}1i8c9uEpFbAh^ZYjz1cSR<w-R*jm+Q8{?
zdwbMEZP8mtr|KdD5;g%@0}m4z80?^)N4eelTdr(+KSeR7oi&m?tqf32s=T;6aLhu5
z2Wdb3L)a2KS1{AQBWX5P6RKS{pQXV$X{_|+GDE;SRJ5}4q^JX&4tWPPsp=hbVbt@y
zaq0Kk#McsSNVc@^GN%C-!c`8q=NTw7khvt(L4L`bPqU}%{bwbu>gpDDJwd8V1pY;i
z!aY9LCeG8g=25>SU~a;<smZ~sE}rUrdsDZ&n^=a*(5Pn?*0aexOMr0GNWuI<Imj*t
zvVmN}$Kh_f)=O&?dT55~4u5xAf5l8-mSBp;Bgo*D^gh+K-t^~B^rZTAjn=6pro7pC
z?o(4q9@ER^C5Gap6PzoC{V`kdvzF3l(4868HnzPvGg%ll5Tu!*)2#)+jHDhs&WKbW
zV0+ffp!EalS189#)O3)t_$dUFKrQAS@}RjOeMcW!<G&BJK8w;B;TF%-TI8U(p3Y0D
zAq1%MhZ|YjgPt+Y2WsiY$57Q|itbC>%UeVOX*B&o?-B)79N-m@qWXE1cd9tL4f{5`
z{a)Js^{rCo>KWWOFd|4DhJ6`MdGEB2)y{g_H-7+G4L?!0jFBTGDw6~16rV95o<>h$
z#Sd2MZ4KlN1Xi=e(qYyMm@WdgFr-Nk5&_5aC>)M^)O9YSU1}L+-6it?s%_<c(*+!P
zOANA%cMthi4v9+5HM*7ydE}2wp5EH@1`@+Djz(ebqD9;f9gaTrLwFYYNg%qoQE9j4
zE)sI3ij43G;|Bn7=~uePQ0aH}vH@XheCU99w_`7~@Hr=sPAijZmp0PFJhsgu=dvV2
zYMk?y9@U$Zqj;mHd^Na-<tJ!H<0)dQPb-uDWY=nf*xJ}G7s>(T3gMp(Z6u3LQ?Q35
zXFiqNt>?RYZwW@|@qx#sU&NmoJ1ONt&<`8SpL&6lbq?v78<CGeidfHz`Y26E@TCTZ
z*wER4?LaeR)(h5_XG|+OJ*aHPu~8=#AeM7bCZ(E#b3jCzi8-vE)dn;KSn)$+QYN5H
z0TOBkq)iQuC<r<2Kx&&ezj_n56a*a5nxw{t-n0OuG`pHKv<9FC#(?IG{e=QU-hcxf
z(()QKhZMYry#Ta_Co~5*qO}3&1?6sjl&pun9z)J)cRut2vL0v*c%wO>y=Nu>y)7eW
zG{lDjl#taB0_K;H_oGQmNN^264I6!Fh|MP(Q!%wl1F;%q4I>%kQ*pLvz{=AR8dVQ8
z$}j*_w`dHZrlWdA4KR)EMc5xhOh$R6;(X$njk8+;)HK2#)SPD^QxTph2N2QJ(y^T4
zj*UPthaA%=MJT42M&f{BO%Y86PH2LFGSOCoJJC{rVLuHOO$I)~h_nHe@kAb!C8B|v
z0E<#uii(&Rpd_ZIy{aH_Sy}+8+KLY!S{imVp0lmm=oViwbkFB;98koirJ6(dQZ*d2
zA7S@60Ga~ENmAY?mRDG!MJ(>@6;ux7lh{_7s{BTt<4n7~>FXt*PPU1qlUl#rmW4pi
z@Kkx{C$QUvC;94gX)CL=cimw%ycWnq!=lMD{JclFX?-Sf_3|m~3G)>Cm#p-?XHwF1
zt0tdSK{&j*O9)zAtVA*{dvhU=*Rq;gU6poF$?&VKI=aHv{{X3a$Hu*M4=#ZAIMX91
z{{Ri+jzoDe*p_lK4h3u)Mz^heIJHYl-hzksRDG_xDlsZA)Y=fTuh(aZR}$;pMRN?2
zTWEILZMrFTxz+B%1X0OmRL%e#oRNyup}y<RxMlsrtU(5x-Sv-?@U2Mqgf?WK=)+<k
zM>M)5-+FiAhe~yXcR#wd>lvoD1l-!(N#I;tf#1c6@aPBB3g(?l@z0|!^+_x=D=0dT
zQzyguy9p3Lhqs#L@Pi-nvmd2fOZzf(uTspneQPEE0AFgG3JZ;Db}1M>k;H@qAF2A*
zyVjixW$>R)llM1Y-}H^D^Btb4bq(ddf3mAM@j&_qtg_pQY<iIgz3SepvukVqrL@y6
zqbCidL*vW#H!_S9k7)kJx&HuNd^*wWr4sn}SJGq2KC>%ahR?6@2EpIcZ>>^V{9Ni-
zX7D2#UtL7bF*Fg}M91}66n*nd^{-#+x=ox&dOW0)k2HQP1xK5MoQ5CybK1UsP{^f9
zk?z89t7i=B-xe9m_-e!t55-Tz$6?0+asL3Vb4#FOnL?8^nI2%-=iA=5zlfbhb*Kgx
zDweJW9tA=PIrSs)dt}!*o=v3faJlSH73pG4Mw#bjlD!Muw3EoifdC%Ix#!lCmCArJ
zap_FnT$rNp(Xy?U<NRnO{L1Y>z~d~$i~(I|6JU24o40>jZUPkyWpR#sa(?s(2)A*S
zz~i{>Q+tJLfK+*XhjUgmh{Mks&Zh^%1Y{}a9<&*zY19_y+b6L0p~$3>k6zR!46GH2
z@cl^Ul|;P?Ok`H`4WQ7BW?2G7=PgsZo5Um34hCuM9ukB$%*fni`cTa)Wv+{(Z@5&E
z$!_JBlUog(N|y|wM6H~z(UAWDl_;*3mca%aa`}rOJ5M|VQL?;PhFOXeoE&yFlWm<z
zBwFg10!x^Yn{wbjSDxeis>;&m<*lG)%Nzo%NdEw_RMs|0d8urXmIUyb*9QZDNd14#
zr*%6)rQ5P?@zn0%e=ZMyPhQmtTP$yvxB4a5P9RZp``J{;*kuv6JB*)R+z(?~cAJ@Q
z-2nv1!)a0V?fx}!(|V`R*vkuivmJ;8l12u=KBu;OW~=OVrH1O^zFnnI{xV342LprO
zoPDU(5(diXL9fjv!ZpNuyfDSF$7Sc!irj8?t&+r&8I|zG<B$XC-!;yzZUgEIBzU-I
z!74%8Pxf)_Da&4=o+zV)M;ju_WGNzU8TQB0r;e-}EyQNoVU7tl7F?ao2k|qE3dAPz
zY2}_Nqgf}*G8VyN4tsiXII7`yZ1Sl=3#d)|HeiPJ2Z|W#1hBqBVq3_WPWat%h9j{0
z4l1Cw)vnmy2`-~Z9zY3*F9VJ{cNJT0rZwWp93VG@r(}$I3UivqrKno!H>u#E5du!u
zC2&1gCcUE1Z)Y<v%xpl%I|(Fr03JqYX{J!>7NqR9nOAx*4x{msc*#AhSk-MlT6SCj
zM|>VDdDhlOm`EG~2H!6O>G!RE?!-!2oaB9ZC;ck%bjnU9sS_5Jfyfxm729%!=B<C(
z%ef0h4YSQAGCVcO#yx(OMJ(nc;hb+B@y;ttzK%z6?6_6&RaZOZ$3FDsot`6EaC}r^
z7#_q{y=8kU%nG)~^dhZbx;smj1m_3uRiaa|^AZB=7lI0q+=^U4JTk3~xyT2drm~Z4
zV6Mk*a%<PFmu#&NWeFHl^)(dB*jspQ<5mp2$s7akMUPH`CLSV}8T>o8pVq7GE+mgC
ztU|T}0B|_%ny_|ePZM(-a(VQnlA3*tT>QPSf))s39F5?XargHXS11XYK3#A}4bDFF
z^scw}2#R2fBX8l@KQegy>bCv_f>u>BD&w;s^s3Pf7C*h3{dU|*6lkfi1Hix^L6h6o
zgQx2&r>m@X_H%fg6LD@F{zbce#ZpA#GI$*N;;i1z*q}DoMo%a&9m0r@{XTCsL1?ji
zqZ4kCSrG2xBn{;r`83v_sL8owB$p&@Z(59zNd$36G;v209l^|M#E<U`*R<_A(()OL
z-bHT6rGnf7H)H!Ha&!GYwI!Ljbhf8)V}8B^!nZLu5xR^&Jg*~Z`yZ`#3tp|b(5#bF
zi%dFXLEggePk78wUMWnpCC4$y$XjYK<x42sgJpc*2Cpuj=p`N`U;wiI79F_<--_OE
zy6dPl?KWZi<X%BNtadZoHUYYVG7L)N05OcH+J6$90gfuPy@exe>MOf$pt2U$dWXzC
zv1p){>D45P1Q|&DZ^I|{S2*+>WD3&cn)Y>VHs&epH7gfpW|l@mzyLNhMBrl`*Xl4u
z4#TVCI!Lv;ia8>VAd2f4Mr8z@k}%}=;rSzvYBr73)_OLra<E&$fTVn$rv~e5<nF5L
zkYbY<+UbsPM&DR*vtCB~(7i)s))U&?eB>Ic`QW&=WGkKlMnbtO>%(K|TQB=a^4-fF
z^`*1vv9ydD-X=}$k0~gu6$bA6M<cdtg-fqmuA?x5%*AOb$+Ng)A&=Y!JpHrIJJsZ!
zReuaJ4LbhfI9+=ubvVIOl5#P{a>^SzW0H-}t~%P%@I{MTH=ZN0U2U!rz~G(ax0tCM
zWVaP(($QLKx^9~#)}^I~VCQVI+#|%G<7=o~F!oS$$n9L#4PwsX?A+XVmlyI4;3@fa
zR?ny)miOR}^xWYixbbcl8J*(F#}n;U=Yk1t<Jb>cRTIkgeKzN#S5Fp@^}eG$phi}*
z5v{Y5aD1WI@=pMjYz$;JGg=kSq0;&kjQVz)s@S}aFb(_%Yxn;ElyT^GkD#u5qkL29
z9-4-IKTOp17_|8Zp3W<WSYJMj6~=S*8O1ke)wepknC^7@V$G5KMv@J=BhABY$Iz47
zn<}X79VhW3_U_`@=j$7bJ*UVn?%v^|$>iZwj^v*Hzm<0VBd+?dM7M(Q>$exWZ0bZZ
ztj>uiV0Niw$?xuY<2Ca>m#*sZT8nKyEge=6SLbE1w~^WV7{$bKu_OD$b6rEAej{p{
zW}YR8=}fK!cN%r1f-}MVV`v#X_u9vSp4H2e*GEh#=<HH!=JxL0ZeUjNJjm-1xERJ*
zkbLXUe1VTsT8~=VToo5q7tzk7<l8s=c!vZuZk+A<;g_+bBGxT0k)*oQ*j`8EdwE+K
zBeNufo#PxfPx{vCQb+s~X(P+xF=*s$c$<!LjC0?d^TGE9xy_Vp)isTMr*mt*=X7j*
zvJlLp>Pmtr_Au&_*kew!7ZL~m0E*Fq%6p7s{QGy}meq8-ty1JiW-cxzW+bd?;fVAb
za!(zybM!UV`aAY{>dj9@mF}&hwsl?c+1>e4VUFdOEZF|Z!u!=_B(%a#!t`=UYp0@0
z=`4p~k<;*J9Dsd$_Z)reu-ULnBzA1G4C5V#YNqM$gBp&Vrd!E%e3v&2;pd5?aO&T1
z2;#Rl_<MV2VdKdmlLH<Y1xk;gHN>+<4Bk-WP<*6!R|OV77_#SJY!ZH-TCukHdSrBY
z+_jm<@lz=2$MX;LqG-n0%WR)8VZGdbUg;5C8=M%Nb|Cw3N1?6nQ0Z+-H?^Kw);nf9
z<%AQp6moxF)R&VvFN4uVh7o&5KELR@dlMv)T&(f9AyvrSr#yY>3A%O&i?c}4p}|v=
z<o5?P)UC8DQl!Nkax)$tWyw+B828Nr*P^Z=w2<6Hw^Bk2nO#9Lu?HjVgOBS`kIKy_
zYDCKp6uwb8RN4fD=GnyYg~?)YYOU3yc2@;R^fkG4hJ!y#zMBHib#g8MiY1ObTgN7U
z0b$#}bKkvGE!CEzJds~QEv3O^J|;mv%6)nC?_RziK5%uE;mhE{apj1i-B_&ZL}@os
zThA(%kY!nW8jk$^>)pqeIaR@)B=OH1sUywWgvO-J-h|Ct8JH*Zs1sf$pvHjlIH0rS
zR$aX)M9>jqLt|27nj0DbY-$W~Qf|~XGytS-XbxzP>q2^11m=M!YBSREA4&i+Qu3Y+
z9z#n?*chM}l9rc%X!06fW5J*ol9iW^)We+6T!1Q$KvC!qMsv<+E_kZQ0GF|)Bs9#Y
zidIhb2mqFqo3W-`($iHzn2j?TrD7Qr)O=KdggK_;G@Nw86UVPLEDfVk)Y8$@3s8W<
z8ZtI%WnytmL^E1NP#l2MXlYm$nv7Mz%tY;`kmi<z8f66lXwRiEriC120Zc~`CxM@}
zLv#WuqsD3p5go-z-EGF_?NxJ0TV$l)LkUJvK(~=6j5;04eQBuHrAa-Y(_NEZXp2xJ
zAj$WjE%xc-MMB$&QawQiu*u|N!}-dMapB3A@K#wM;89kgf~)`;C$I!j0~A>q<8|4L
z3}m<3tNvaO{n3B%vw!NsDB_9*CN&8A1%3|FHugToi{jir5!?6mb^^L%B`w9-&eM-*
zXmN^{ttM^nq~OgQL;gj{{<V+X%O*T&=bPxxK>Y<+fr<4iTVMFs5ypLsbNW)lUq>JA
zwzoHDwquVA`kV@C*G#sx$AxbipX~+b^{0_=J5U<lsjTXjHZomFZ*OA?a5u4*RwUt7
zQG2mG0qRXBrF1@_s9lX)sC7Gs)L9D0ZEV6g?IS(oQoNFQ+`}2|*b20=U-pArYv%J<
zSUW{%r;zgutdjm*qWzruini7@tv{!%9{W?hg2K%R25W~1xjlf-a(w{g`cMkmbcT&;
z{vzq{%P;=`GI8Rzee&5frkizXYje~Z{p7J(>DJk|6U&51p@$|$C(v&%{{WO#HnY|p
zMSY~*UFdp1(yY=lQ&86tmE}0Xi0y_SlYl%zo%<TAwfIM;e)sA79cJe1sU)>oFQq!1
zyWrfU1q%SgN;k+`J4+pdB=bO4#qk$Q_>FKbR>xB6tvV+UXQ1g8hG-b6%FB5(5f1Le
zL|+NU;9KzSiFffAU;hAuXQFhSyAB@qN7Sxy>E(~Zj34@#R_Pju(IY{%5Cv5ofE|FN
zAm)J4Pr`eRYySZ5KC<dPWn1yu+uaR5*!?zJgknDKY3pBv9UIe@@=K<4zLO2kKlhof
z7HN;(N6Vkqw@BBu5=hoa$~uMway{xE)e&hQH0u|$!_*f|Gh4H;!nDc+Xg@>S=~^dO
z+UhEj71FG3?cdBUyj0vUF~JN@JMe!>=${sC{zG4qSzIJ%J`54A@7LS<*D&jidg}W~
zQ1QVMzDt#b18x5Ros{G#9*2SLn)%F;`LuloIVX)DK72^HOUGEGxIjmjhA+1qascH0
ztC;T%<;Y?i$~NSh-g>LSj(dl93Rs^86dq<CgpcK4Vcdc^^{oZv5Xszc&m?+Rt%*{m
zc{!cR(d)oiD90!1UbG1m`H-y9ftCt*Q~nwKX-oUbE~W{bjAK5hit;%{5h3S1wR9Zp
zZ&J5t-ZDlDuI-+ndS;_<2c35udYt+JMw(<fEaqMQY?I0K%OC4Ys{@ZV0jVG)%%(ES
z!QFr{*?s9b?l%m67RC?OpH&9Z<&Q2f6-nol$^L`&s#~qeKGTfwK_0ZAPTt(hCnp%I
z{XQ?30JkT;MONvMtdlPG+Mcb7wU=*)y$%556-hrvuwKV5-OOA!l#R{)R0yXLM(R6}
z*r*^HPHjSWf;e4|haPq)0AnYb%p|gYbzU@S%VEPi0K1z5jGsz-<_zkP7HoUFai4lg
zZc!p&7tC-z)F+(AjggU4f5C1=){$^wGepu9R#V8&KT0;VV0j{Va?g+Au^3(1;}uVD
za#@ihY{+nTZXo@t!&bll0CkEJCy3cB=Q;eU&WkxA2=1`sCkiw7re(d1p~B(F$CTov
z@!N=EjI2fWj5s(v^UwHIY{g-jly(R7sJbJOuxX7Dk~89N81BOy)-Saf;<)U-ChYi$
z++wIr*O0~-l_xkJ^uLY+j^hWDnue1nk~Y|{gQI1YjDw%oy<$1-d{BlTI-ci&`qEl6
z!z^yQm4cjta68g^rlJ1yRUgEONeiBTtx|~P?CB6qr`+fwA~7QZ<@V>(xChp>>&vS@
zoNtv!@Ru8WPzy2j&N!;wKUn@54RG<yxCldI1K3f&c|~H%rGqPSPI;vobF??9bv5*Q
zMD{k}VOhxWC5U0jAIrPj=~@J~EX>D%d-LA63%5(nhBhV1>_#XFq_n{ARa2h%<ozmB
zqbWy|G?pgbP)Ilw?Th7Ij!LkrE~C>HDEt7{Kq<e7*d5DY<MXX`CulOc@|xXj=4*>D
zt>i4jCnWGW^`?c#hjin1(%*;<z||ZV!D4kMmi6`{wR$_U6_|isj&q++TACc0eFUi-
zrI*Y-ayaMOn6kT><(3<AT^o={<a5PWLws(d@ZpFRBw>57Aa~DtFf&9}L73(%*|^*>
z?Nw^aav^bW(N04Sf0!OAS+10VGI9FQZf+V$36PTfccstLt3!})7!$#!BbR}ZkUc8D
z80=_?F5dm>IF?MXNt!2TfsA0}V|O?sx7M3&g(H$UjCdb_Hh(ed2lS*OaIU1b-hC*y
zmouir;*%Qkjs2guwNSc|a~0L4?WA_iaOF;1i6JDTXO71Yk?sXi1d~sgEUL|KCz}I|
zue#=%wz##sx&HurPYZHw4+z_a#|%ef^`qTbM;wu}TnS^4f5bt`pJVU$1m>WJvDdkZ
zduZ;hd@(9TiB>#<*yDKzu_M2xdG+DjOY2DFwR@{{zmYjbnVW3z{uYPmK;#k1pUWEc
zyhhi>ag{1g*2e_qoxih+50f%X%WZVZ>d3h$joFtZXR|3Ba)f%+qRK;>owcpi(3ee;
zTnE^O?qJO(1~_a5IO8}zqtmsXg!*46SF^q<pqG0$P{8Aj$Ux)VhF%H#)e3_4l15lu
zY?S!1GZ2j21!O<U!~@R+;;eMtQuj&Le}#Q<YU<lFw0Nin4hC>J?SMcdBi5@WeHTpK
zEOkw8<4=Q6(k$k<w2c<-TYE^HhD_izf;Kt9U^fyEU~67klKyus<4Jt^U-zqo@Q!^3
z{y_R};-_@iH_}^6rvTTbU6SKYZvaYw9n53E2-`*u(}Pu)I>dK&%?<U;mlqHau{F9W
zQay6V2W~2kCHXWFzI#h{vAs)}V^g?AzssL--#PaNwqBL8($eIMnP*6xj5wL&Q@f3&
z$aNV%+Ebd;Y?tOQm*mN{a>HV)5><zwV~=01d9J_HeO>#;RkYTU(rK+z<?b!6cUw&9
zj#)}}VUEY|SjfCKZ8|-aYk6-3R?nwSS214O5h&URCv-V;^&w6N@`~vCP2KI?mDFvi
zCaV$Htu55TbaB95TLh2KaL1=LtaJ{Ord}+q4X&R&RuX}mlrG=`He|+0T%OH>4o4!c
zwRMrTS!TDfk_hiu;}Ur8p}h>N#HfGg8-_8K^yJERLvKaho~V{mOKk)a*h;6vds!lB
zS_b~k$K{tf!Q_nbire}};dY_amoX*P^i~6Y=4)l~3C|yd<v_>3HIIfL3bh%ptmL~!
zWE>{tBYJyczM9f{GHYu%;Ao>!xGJvPg%#sw@;H7crH>yj&vcJW>7NcXRQU;#E2&if
z03H}oS-(Z~_RcGN>78m5YNG0OEZmO~vPrF8Zkif!A>{-Cg&y@;Do+dU@d6?YDv!N*
zbMrEC+20=;4mxLB0@}_3L?FigVow=ATIQb<I+i^<O!<j)+t;+lQtrY{%@g(<1L^rw
z5o=nepJ9Y?F~=S=fEVg3ka#QcW3J{{FRvk**a4_ZWLa(41H#~+#Csa<$3AP5Ii5Z*
zX7%uX>rd2Uyy|<~7fZP%F~Q@-;cwf+IZnmBH&;fw)E`iuH<g-2Wx}R#4r^Pp(*&0g
zL$rvZ3mSphRC-q2mdO;mQFjzx`0ZR0%Pfthq;JA8`98|0j(s76MBX+%6lVgqZlBan
zrd{sk9vLB;Hscjj7}*o>cbx9~dsd0nJy3Kh(pXuf@jRt5_(!Sx`&Rg}OA}2P&L49}
zT)m3NSh;&SZB;FzQX+i$i6HJH+tU@m`hrfM>5FYiVuIRK)8kk~b7U4f@aNxlNI4bJ
zw7#r?5nUZ*iV&vYmLJ0mxexUNkIueM{i8a<9Y+4@eJfU)-Xz3RJSZ8v22eh%#FO+Q
zy|n)FIN=A4(PORky|kD1mNv}Ibw20*ArFiLfyZIT-mmnnUJX{&C5LgICB(p<Fnx#9
zzJB%dH%|35&wr=t_X>#Z{5+xn008n7dSbqo_&Er@c1e;r*5WbeV~jA*A79G3)bjXp
z<&B+FhA$!&G(Ke@kjhnuq2i)kFwJjs=<!~KlIGO-jEDXvcpkr&MNn!zEa|>mHr|=s
zV?1pK+P&W&>Z>J3BBppb{+{8yZqYzmhRu(?F+Hrd<wTLPZscwq=nWo4d$F35xU<M{
zadglYZ><cPBgTEI1xOWg0WqPmqZOgL=hA=)G#Bh?lC(C9K9yGJ3m()rrc8LCx*uwz
zb_H61<22i$6c&!FYzW9PN=w}PQ}UtBCpsQ$M8KPx1B}xwv<6;k&!8zKFC{l5coeg*
zdc+AyK$F&)nND*`$WJt2USoqxNNDoX?oCC2681Elxy?5DrKC?$KpHf_rXyO44%EzP
zyA>!Bjd>KprFuB=OeAMLs1HV%^rjIrUP%6w%0zvtK?IARdS)p8oYdlk=e08wRcN~e
zqD%qBGa3_KiV{z5DcEhv9jf+$7~`hmkKcn*ir*c@IFRGGs<{Ah!;^tqE|b#NSBM=6
zj1iNOjE`J%Q?ZW6rhZ!IQY$U&%f1U-a5%^=Pi*^A-w}Q~>0KJ{Zu0==D2bIunCH09
zryic;HRE|cYmdVpXRYISoW5BL`Lt_2HFGkVBnJQ!$u*>PWboNY%_^!z*zV3NSznHN
zRPjL`oqD>J+o3HgKGaX`Hhbfo4_eeN`pWyO^;L?(B%P4$E)}rN^yjdzp3N-TKPG$l
zp^hCRxqLVl+O*#(I;7UqM}s0inm+h%$l|u!?}iu9BbzxA@zCU8?cTpk`j67M9+lM>
zJvXV^E};Y2-9sX}Bu~Vt>$g1T+#juX4w3lFYlxIg7`pEnz|S}w`V9V=!St@r6E<8?
zt2q3u5X$W%QPy1Psf(asJ|@gC$2rITm1)xUOLKN)lY~Ux0UwJ#ziL0lj;hl2OCOzs
z!|`M&#T##xk9?{0K3}m1-nEW}>I=JzYoT{0%eZ5}VBV#3f&F>=R~MftXOfh?9bOa`
zC~-$#w~E5v-F|9N#xcvdAJf{aG~GQl-}l62TUl9*PT(gaw;rCqY*!QbiSfHj(YkhI
z)HOeov}uVg9GRAIfCC(O<nzHll~HtO?I90UU9O)6(#2^UGhGCaZY_{PQByvLo!n&R
zvc=;$N0l1n%A)G_buD^m?5?9hCk2TzAHulrlk4t%sG28HTIyl#?d3@>;ZkLbc;M%p
z4*2a_Hox%$P0~7GY;PPjs&2r`gd2QA{o>dj<nixZ5)X=Z-65&DYj2jFWo1N+4eZV5
zW^Y^q+3)L$^*l$D6ZkzIf0G<}6-^&$S~}c9BwupBp~0p^Llw>b68uJss<b<4BVjRR
z*|#bme{xwz;qT3Lh+-11<sp~cde_uq$@e2?&SZz%l?e!d?N!?R(?eh&wQZNTjxC^J
z{H)#E{*?^h^wrhlL8n?xb#*j$*3hAOCY6GuYCD!EfIg<QLt%UIBVRr!yV5##$}B2m
z!*hM&dbehUHm~8~$a9^b;O=A{HP)1k52P(3y=y#M%YQLHC1zKGtjo-@4^%_J{7kg|
zkI=T(I*Mw#l=5qu+Jm<K@kj7;+(LuP5dP@N^)24En=LBePP2m2(g^KsU{M@ULV*%B
z9hE@f0Za>jag#u6TD(6!s%g4-9z}C`FdeqWM$!4XQ~v--qF*So*R<=V;u%rw*}WBs
z44M0tEB7Pbo6v2(a`RBsPa;R~BL4vG$C3S8ajScsJ{!w<AeuFn83xuy83a&^0I|_l
zF-gT|f2DwfI+e2aEI^${*E~1hgZHMR)a{AHo+J<bnWHLy(u6nq7(~r5vF<`XoK!6?
zAh=+WF_53^slfc}TGe{y>r&M2bRLYfhgsHb+rzuPnTW8{o7!hyUP1o=%1<};ijHVj
znae+DjVDaf>{C;}z80)sY*2>F&fWH+4qKl<57xd?>Wy0SRO$KFXZ_P;SZ#qL{ye9!
z-1y_a;RoyVuetRfg!Z~VpQ&oPo1UoZS>Y=tsi-RCDH!nKjBy%gvM+uzNw18)C)~b|
zCB~O`97}wpvRg+G%w>lGus9%gUrg7Lnmi|@dcHg58f^LJT!tG-wj-7~#vAc>#!uAu
z`w?2cqMIe;j(w|O)zy<#o;FYdwj1e;XZo7g@7zet!;#omOg$LnqPA!7+atSnp`?X<
zYZnp*BzGc*9Ow~)gS*hzLPsF%(}s)@fJg(1kBPTw2XcG+A8JR$&uW`o9|T9~yLDMB
z1A_igPSz`&@+dPSNpJ`m6d2#VGLr;7`_-5%ZRIF-v2O^GxG#FJn)W#Dj4A}m8*;ed
z_Wf$27aN0jUc`HfCVWK!TN&@oWl8MOJqDgwWK=P$ZX?WFwtke{DzQMN(Xdx5jsZVf
zuaZ9kATjv3%|aHL)s$qXf3+wkhc^K-hdIx;dS*zNE<nnWx`U-n1QM2+LmXj29A=ui
zoLo8tTo8E0D`kr2d@<Sx`_vj!u;dd?OBgQCw*U^pl9*!|<27nTk&gM0?PHbt)2POH
zOJgMZnyKWR<BGG=p9tZDIUJ}5fku&3)9l1RmH^~dE?hDK4<AZ)>JqTAR1T$oG4-mb
z?cA@Bf(Ybht5!%gnd8n66_Yar8SU>wWGnBE+|fg*0Z8VihyyI_F@iv-$#~8eocmEH
ziCusk8Wc8U%oK7>D5cS1rOeMVuH(t=n%%A7eJmu*=+Ux<9OZ}HS3ZVgZ6IOJ4Oz#m
z$#i3doQ2ODkF8P0&Eqk>cp@>xo-A%NfPHIExP&S7t%p-*GO%&6*UDC^XHa|9<D_Rd
zVlc$gNT{Q6V}L4hw}hY!6YGyk^F|Ih=7At#!R=a^4qDw5Ko^DwIj1glN$%NRX<k<V
z09@pOj^Apkj1?KCBZ^s1@X)up0<(RBQaeiJ&#BE4Fk2qS)|s(}G|aO;89OF282f&i
zKb2xaw#W*M5_5w<FCt*~#bbCu{&cjr1*AOWX0hVrW74LDhItq{=QQq}c9G4ATlh?+
zTmh4hr`D?j4AFv?833L$RjVz?xJlnkmg3$iQs&xq#^5rtu5t*^Vn0x7`=;)a-r^Z8
zu3FamCmsy0ft8Q50toHy4{FtH4g+pt03K31hV(zJIXtD<%8C?`lAv&ZDyxZf&C{r3
zytULG2h-P1)C^))jy3@<7X*nT&PSMXHj)^2tr}S*xM<-8h}=RUJOWQYZfWa{S#N9-
z)>)+f51J1U?hBq-K7gKn{r&2$?&%%`J|5XYJER2pzJwoNuk@;<OJnXP((KWlUMZi?
z8*_;hb`B40t1dC`_NZU$7Z<)1cL0fDPYW&IXzaZD0e}af;9yeYUM;0HjNnfJV<cey
zC}hAJl=~0>Bi94iQcW{m1>6fb7|0#PS)#Vf?JKD6w3}(I;${f$va2JNWRZUhkEr+H
z`eui1EzR7fHQOxG40)Au-oB&1^`L1wEykd609SF(hFEStVOvI*ph*Rvjdf^cg`@~0
zAcC#zeMjVYtlO^5;G0I%@Yd_)Y~*X*J?|_oM}amru}`v)Mn*}&!S8}Ot;0>}X(!a+
zI(5Ch;ed$(!bQTAUqYuZ8{@j?9N_vIy7)^Q>26ZzP-V8ew%K-(?Zwn;MlcS4iy7n+
z0l@VH*7J9Bp=$!l?@5U?uBd>kqeMJ7@7MWcMqGGuelAx4^N_rX<CK-1kW^R6(!xz+
zPQ20HIK|2V^49Fg5VDMh1C`JGqzqv4IX^-CE$B$JxzZ$!uC*p0OEkmyaSZm!?^=h$
zw}Qibk1EL|iqb;TE*c%&k%buiSvcTi=NTru=fK{Uld0A#*=LeV31!}oG4ot7J^ui@
z0M0AT%=sPo>N{sv=^m06mn-Cr?woIt$ovcF2eoX|+sGtnuy+H4$Lm+kEslq0G|W|2
zAUsJi$v%Vs08gz3JILmY%FE$MSIwT_o^kiDFE%7^42|s6^~~E^$(0SqZ(7jx%jscI
zgrYiNxjYQ}3f{Vo`{XUBxpm&(h9vcKTnnl%?{!^b8+(#tMqu(s0M60sbMN2nUOe#J
zk53%*SM>*6SV5>N$*CU~Bp(kM81%*ut#!|a_Or>QM=Hu9idHU`$rxN``IwIFUp3w9
znogtDcG^ahr$K39CCbLP3cICYo=GRv_x}K~Us81QE|;fSgajTCsU3!R{{SlMlsF|6
zQOlOi>r2w(vyMp}ce7yR(>4fq$((K=w<EW;EoG^eRE|sp+Ax2#*r!%inNUW`%h<oY
zS{Re6Ib|lhEAKXGmQu*?fT~M5t~;+R>tQUnr^A|7FW~|(KxP0D{cEXwLb+MA$?c&b
zQB<dheeqv6{BG6nEcKZl8<NRoY=I$<v6#t*CBM2WrZ^MPSmf1&ZlSc&tfoZNx1)h=
zFh$8=MtL59cl@h?d`i>~qpfNaM-tk!a*Mx~P<9tw5>NMt;Cl>J&W+WdDStK8(Uz63
z#278)JZ%~F=YvG}pP@%ClYe<-YdT-s;pC0H#4ZTmf!qx6YrhsXh3w9A=CpH<hMhZk
z*8O3l=+H!h+sFBZdvJV0fq--@e5=6&2aap*o|@?GH=|qq&_L4%8=EeBfCy4a1mGS<
zGhA!-Ug)b{lhE2}mhmROZ<?O!as-o%;1plwQONf-^nHnsQc0FK0br1LP^sf!9{&L6
zUSE@)%5E{~9eA<vif`C!y+YBYNv1?F4$%(G2helHHGR|eSGMw8TU)Y8jmsJxppUt)
zT;B~o2-!R+=aVR<*CTE++<iEz>(7XGcHT?8K=1(0z#XyVXOrj;)}s=&va;9WYg(^O
zT^S$e!rexpb2M{f1pfdo)7bra&1rY`2FmhVbCOi>ap_%(-&)hP_3-7A-dR}z+DGGF
z;QdE6q;;-{`egqAFfHVoIf-#PWQ6DNW7EIdy{{L@cx6wc%<^!otNApxgV50008iGf
z#i(`4ki+RilGg(wzMe8tNu%dnTwOAq;Gn!DRl8f9_Mo++=e20v5LI|2R2n>0n?gr%
zK$0J&WZke=v~(7a2A_DFnj2fW8479vTO@hrmvlX<$?e=9!$5myK9o%`R=jkikaB9}
ztmHkGmy*@jy3>;M3P~WPnggEIc=l}MfGK$_Tpr(QaweHd@qH;-Vk0$oHG={8O(!Lk
zjgheal}<=gDCCbP6s)lQD*5!ubNCGbwDujrry>d$gFF#SJ-0jx*k4aM<bPTN>Ftix
z<cX$h(pyj---_5;#E#%l9?bK{^r+-1m61SBDd?cU70aXeu{z^6(YM-}PmA*oK2ATm
zCbUsx9Y#1aif$`&#d6rbE?mfehieXfxuZw%lIlM8kmvKOabW7HY=1gx3o>hyug0r^
z!L)?^L8h(#Hr%QI0NkQs^vJ2==pAk=KYDfx5C%xDPjB(f#y4r{`88O<@!~ZjcAN<M
zHAfZ>ryZj4flWnik5B1b${&wbryu(@fd2J3pB^kR&Ydsh)TR!S5n?DCjpu6TjGt=P
z`irY=G)s?`eeXG3szfu4=Oq1rC-SXg<Ku3bdu-7*oht^$L2s|WzqNA>a`RB?E1Qdn
zZ-U7pG)klSV>}Q~^AbC;?_9Z~DJn+zoMS7Ix@%oSsM?{uzj-1y;zrzj_?N?M{{VZF
z{VV6*po>!Yg>`v<sA>@-;|SA|2n7BZ$8Ow{T|206FLk)&jU`B9V1Y+ITp(Qi_$TwN
zY7UCHAOmw50D3KOL&ua|wX@cKMmZ#`)4EHipwcxc?xVPA=7{bl<S~p7Q?MU5+xk~+
zpQ!#-;Y(@8j}QVf?m)@<djb7xO||I>ETs;!D3E&Mulif1EOc0vt~Xsa<0qZ+K_@KP
zJeEDP=qo&)H0JH2Tz*C<Doni8x}V3>jiX<kc3XB03)@@B;xZ0+eDPY%w_nL+0F0K3
zEzD4Z=ZBs_9;!}q54U~=Tk3J$-ms1-W45$U!4fVPKIh)3KiEKSCb)+EqLNYzV}M3G
zaaM<oCFrcYymzS^&V$!usc%Ft3(pLJQHq?Lmcd|g*#2Ccdsfk@^#|@h-lVY?mX_{~
zyYcxlQTy^Z#%q{#=SrOyT8jDZ5xh3u6%>y!>~r)y*2mO1*V&|L3{puKg(QQy8#ex(
zjy>xPz7*uuEgHkk#+KQ|wK(m%>h<sS7@BBcQ4O`IVyF`&vmA4h#Qs>P=jm-Pr|qq8
zEg<<zpfDwc&g^5cKE3O4w)lT#XBgRd+cts7&TuKq{*|@V?UpCFaI1_F--_&5G9^o+
z2jt;y+u1><^_04WwT71CY^y5VO+ToLGIoG{26Agq>m6g|warRsw#fHUczc-V!jC=v
zjKl-^R{os>WdLF?CG!9aW6*kisTuwmX*07Q8e&-PVq5Vab5@6qD7!iGc`%ZsYrhNn
zzfse5nBLamBl&<DVCWr540#H1pZgrw-~At}e2+}E?yso4>WSmYaVn~+Dai@~3~|^G
zuodv~d^pnWBw1p4!zvW~To8NLL+L)fpQ3(K<spmx)<ZOCR|sVEc2UDH=LC)k&wN)(
z$2jgMFE~$AKCZCo`;Mu;gHzTaF<U~yTOB4(<Rg`uVc5y)7qWd@k!hl7`;zkK9Nb*U
z!1p^;>|f}$^9}dyC)Cpx8!f_+0rKRZxjpKPe$m})YZ5_gdmIp;z(F5xdh3Tb`6HT2
zEAeO9qR%{kl_#sDmU?v0AN*oAMm^l{ztq>q6MoWNc-^&4zuk;gE&l-R2iMn(f%S$S
z8;4z}1_<nHW|2egtvf_rHpUB7$w-hV-yO{~EHXdzug}Y$k6#efWjcoB4{krwt8TtK
z{6x56yQyzi1NlfF<xoRbevJHx2e<U4^;_#L2TZusH5<6BY%P$<HO0e(L{aKZes9n5
zli~(O-lfzVJ@F+{>K%L4TD-7Hf34rnU~w#4<|HGI%m-jcy+J4FH_PjP6ET;&=}wbz
zo=JmK)EDd7t)oAbgdgPf-?SYUNa>v_-%Zltw9~9D*JM`K;ZYEM0RI3U=ef;(UB6y+
zUCSKn`izA4&BJH>X*TMfpmshcuc#CCxPSGaKUDQ-?QY=0-N77-pEF7ZKR<f-oA#RM
zy&ph!pGt}e3zG9rkK#Eb`*vLVoc*hTuCc4m%v)2uE$(M;^{U(JjY{V!J=MhOeI7zV
z{cE2tK4zHmJZ{YoskFoU$xMED`SSofo=N>pY1j6%hAbmv^a8s3=?yv^z$qQ5YoP2M
zvr!ywD4EZfqlQama0w<E0sielG~20L%L|{%yTqC(l{UO!eq7Lhb1k_WBP9JRevy&H
zKXu)P3*bNLQ#wWA85Z&%`fIigGzZJX6#Z*AHY>0dPw7vS2ND`R`R#2cf4fG9N4;VT
z!0Y!F*W$C@agXU)YXxt_p#D^L4py3V%Ax)feK@09XxDQ%Q5jL{#BeL3K9~y}E)UwI
zG~fmY>rp`GlWA8fa8$4v?NCiN>o{1S-nuerJm7_4p43TnatJLKhQ9Rx=O<0O-VcJ=
zJm3mq8%vasG?6j%<W~N=V(q}s+*BJ+Y+zuItqbzeqd_&y4<R<5AZ06pzW%hO#ht{s
zJ`1XjF^cGqV7UXg+M?QOWP%B$lg#GE%3=QihTo-4E#-+vmA4by9+l8elOLct&)%aZ
zo`^u&F^Zq$uDdjZienR~56?KIA-9!BDB0{zE1-OumN`%>5?Mxz<s5p{q)#Sjp&!7>
z2LSt0j}dtZDnSR2t#lJ@7&zEJN`qKN&7FhawJDP&?9oJ9yx75RaqV7^cu5<kR#iOa
zw^=lhK_L~7+JKr;%by~yJLFVxW@d@W+<W@gVu_qH05CW_;<u&IZMOhaeZ?F7oVSy<
zKT16VNd&rJjGuE$%?9iY*57lYLmQO}c=o7Cp~T2AxF^=6*YY&Sg_qNc%vQ+f&f!O}
z9M<B!IUdH}l6^5z9WfIeVCU^sGkP@(&5#8#MqeM!ds1?0R}vsYAygmAyb9i%rQ+IE
zjsf(gr0Cmu2t9}Vyj9a6)IMyKKsegC{<JBlXKqjHTY>1PNW!xZsi8~JD4-Dd`sSkl
z0ASPdYsJlNjnQXsk+&bc0s1av&R?3INE-AWX2n?Hql^$v7-Iw5+MJK2ZWQHt`_YDh
zc9Cr;4iCLifk=TwBytDh9^##r@8@@J{{WAiz6Jp&`G?$h`ijJQP27AKi3DP$6@|<$
zhr#n>Yh-s6EUk7_-GZ{Ro-xN1y9|$itzBPf*H;hXY<XR~ll1NVDq2>TG#4AEvq_^W
z%d%2HIUT_ri1a<P-!&Q=Wqz5ZOQ}dg4=VD<oB{5Aht{{gn&_JsQ9b41)49wS2PQTn
z({kqr^2ZfdqG>)I=Eg}C3CwJL52k(nkFT|KSZ21gv{Vs7(Kg~O=0M&F?ST9->@dNy
zG1%5Jf1`Lsdp0@xA);|5_NAuJ6P1x}5wQ)x3zE)9;d~5aFeLXFsJ4}70!aQmaahWu
z!-h#W#N-}VcsS2~FbMk^rcG37@yz;Ly|J5i`*~qTkCBWKj1l(-(}RvH<KH65<+!wI
zF}z2HQ_g=+p{!*V&X~zW=y6!dX$X61L)$S%jmh5j=O7>t&uneogOw}6<!gJ<r60Q{
zX=1dRSx1bC8s{W|x#DlA8Dwv|oUUt_ba%`|jc+CNO>X06$+ErheGeS|TpxPdwEK~!
zO(y9)Sj%owtQ#EU5%=}?u31Id(4?8P+UhdNcMKNou_TZtGtND8k8@vnbR)yldTeP7
zxiG}G%EN^m0@xm$;8()m4lXp^L#o7@tjlX{Yafh+oNjP;cJ;}{b)L2O=-Nh&Wn-ht
zCS$ruBtw(I?f#X^k~Z{?oOLf}(VF*OTlf*$$$cD=#W9vgW039+Pv~$5+PAKh*S>$J
z8tIrxEzFGIFFRK_1Xt$0$5m^ZxU<v3v?)A+1ZyE48WWbu_ax@OKz<y|pX}X*t)i|{
z2*W<?h~onTpL2@ioF1DxTUAJ(Uh5BKWwAgP4mPGw;U2Z~UtQ~8DW+VPx?5{maxQOw
zoTsF54_0qddskxksj8VRt|s5H2@#_n_|G`{n)$Ed8p*6AOvpY`BjLf$H$SNZ*n8J5
zJXez0zdDABFNW7K>K_uiercn249&3#z(oTfpL3e~Lrc6GpH8_(#HCwmgV~00UmScq
z(%(+%iFI<|!)*>>iM+)S@DI?pEnNfR2ghjs80Znbp6x>R=1<|;j?7ygziia`82RN%
z6OGhcXLhvsk#D8gTD9%U8I~g)yPE(%;m&=@t=~u1V!LQ8t>jVRMzXRg=N{+tugcc=
zztkO3)nemQki}&80(Q4v*hk!|Kf=D)_&e5jJzdnguCHkC9i7Fjb`ZzAJ4VvX+4tua
z(+nSPvmBCAR*#@M+ToL<H6I58@WS31$KfnOhW`K>`HSLY2S(^!QJ&~s%|58D5pTz^
zU=n{Ye_Hx`sEwwH(NOKgmrrbw$GH9?ybS#_&3t3=(^85)p?hYXQq>?q6l=-IZOq^G
z6J628$7i0hGm~pN-L<DuYj)7Gq+z^K5$q&x=k6G0whoQ>zb)0(yn2*tY_7%!y&NhA
zVlv-M@P3B5Pf}aG-&bmPFw7)f{vlzUo->N9v%b5v(e5B*F78Pg&PIK|Fe_<dQdCuB
z;_Ukl<ExuW)$Oh2xDdisVp&vxpbt(xI~wc#7u4d)Hd7>Jq*9S7Y>|V(KTd1l`MS?h
z)bxuTJ)<B9!Fd3|&N~zRD`Du*iTcK$d!~Nyj!7f6;aGk@Y!W+)@TcHT`9$^O<-w&E
zefg>DcGnR}J0rUyZi+v@Bz}Nr?_8eyQ-Ub^i&F$fBvz5oNtoRLZOR9>(7F1PTC9H;
ze|*PyvM_=Ng<*>a8%aKI{{WX2%yqt`l4qV++^M-OZjlBdQXq0k9_G4m`14((o_V0U
zX6K=NT^$puy`{>$x<(bFnkEiaSY$H&21Z49TMoOo)p|l}$fS<))_BoayM6%Q%s*b<
z)$rF+$9M9}1&RiZwu6Sjj~<}b+3?Ha7Om0t*EV-bFKlHSd^c=u9^?Hhu3jv=iaCGD
zgLK*Tj260ex*?6(Nl}=;kaOG`v2LGWr^*l8n)%mG_@xJh95*)-;YkT80F_+!`qxCz
zKWGl0(Ci|CvnwV*au{*-_pZMhS#kU%v~l?n#Wl(=XF`)fW7$8Y3Hp7{bU6D}44<^$
zNGwW7oW40wwOc>!SJF|b2?fKqpxa)pGt1Gn9-Kad2iMC&ZkZ=12l86c?mue%2p{2T
z(;R!8RXykJYi6eqXvM$0^$Ig@LrCd|rZP#|e@cX%FwQs_9@WRMe``yo2f)$ckLt~u
zq`v*Db?H}ZR+h1o>$a-lE7^4<cSh-Me+_xMWSr!FweywF?Jw0+0tC>f+B&Dc`2PUa
z)2`F0l=`B9Qn9<yTRxzZr;4sHF-ps!#uVhPYvk*Xi@i^92z_Sj{+Gp4-F5c2Df4QV
zMEzwoWsCVD&#vXuEuuboBVVCmRo7p;ZN_B1fd{#9Um@IeHlcIOx3kIp=?SX&^(mzM
zQnCL4s-J85Ey()X>!^Bu-#hg?QIGzhs!P9!eJ^eZZB_=y_Ia<C4bvX%XmU^Anti1G
zhc<l~b@6kiBR*N0L+q<mH{Todc+c=HC6s%fD}+7B8Rs+yx%R31Q6rN(75B#&$^H$7
z2fGnecRw0$TRSb|P<;(^879xI1*XPLTZC7lEt~b%#ob-<Q^HSd3ZuLDkFC$%=z;7j
zR(bjFL3y`*sl|F0%q)aa705mR06I}yfz1#;D?$fWLZlE#KH`j%F&v!z=>a88ZZ^;l
zi8;*~XZEVqpCc4SmmahQs@$;0`O(toU}u9=!sF8>h_ht!XbZ@vE66-kNqK?K6|bzN
zdodJrxhEN*FQdHNcx)PBZr?q{QAKdL#W`y{aj`B74`65ug}p2QClp1vE7_0hRQ~`n
zrH%nn>6#&!pIQRtH5pvvaTue;cJep@^8VFB71q;(+uoaseTQ<KA9_$PK5dafW4QZJ
z8twOjJhfJno%zmvX`h=7_`A>+qflu8$>yeJV!w!=&Z;AnhR=E{PRG|21<V(FQ)%FO
zW`_4Cca+sy&T;sSW9CUb9w-Zua#(V6Sa~w10;{9F5uD>cO7h=0$iN``&<%LJ{wfW?
zS3)z*0qy|Dy#&f1oX`_)2Hx~P0w>9D@vJ0-=OZ<eayw7~FpktYEz}RfIi|MxH&!->
zQb_|80z%k#;MN|>HwP#W+L$~Fz$ZUS0TW=k7|5YDf~e#M6#FnO#Y#JQXKY0qNPU!M
zp|raTeH6mM41lH<nz*ox<7XUJ!>9Ciuc=6)-%gpy_1lWQkKpfA++%HtL+s6p=a-X;
zE}WPBn@0v&bry4TXaU1<_NcX&sQ$In?!F22MWwq-Et>86HfvgLjMf#(wTy#l#YB9Z
zGwMtJ$(9VU?kv?lMOy;{CcHGzoMRQT+;o<!ZoVO~pJ??7ny+<ej@c2Fy_jIu(VSmQ
z#it(8PWoQqoag8$M@~@K!4-H=XUc0W4r@XUdVM1!hVAYs52tuv%74bTQxDTM@0SiZ
zpfz`}@j(H0k@TPqHIwluIQ6Zm>{QVHMOP#O-%0~nHhCB;iiDbWRA6vDt7$f>RE9zM
ziV(F_QUcH#>S^fQD&ecsTS^AkUBa)8s3$nAHMV_u`&LxYHPLFP>GrIhIYMM^VtuP(
zlE8R<mp^J<Y~C<<AC*c>8uS*ZWIKW5-la8=X5ip`YU*1Z>{~d`wGC{Sej6lI&^7Os
z0FXsttQFXnVm`vQC(}?5Am{5@OCr6%%~%@ZSo!doZ>g!ioCqO+&%JCW&O)T35<S4;
zhx@hI9qM!UsxWH5x^|qG+%wvU)`)S&D`5TF%Eh*xJt{3^Pd*VR?NZ$Wkt|fv1(I#f
z!H>0F!3#v*X#G8^OJ*G8j1Ow9gIZlN6^RHlUY^*f2X4_@9QH&IV^NP&n#6W%-c|&A
zW}?BZjXTGP<b}_z0BP!kuHEDw=C-!CiMUlCdRl7~?l3;pDVB?)T{>f&kOGfmQ;Nuw
zw;cT{_*xCP0A`}FM-ES|2z``orIwRBe_AW)J{kfX6Y4QnAyA$HB-CEn0M9x4)Tc$0
zNgC|>c~%4vR)W@BvBq=nTOqene-9P!mQ&nOyC`d`WI(_mA8McO!bV68-+I3`v5w1=
zMT*^+{v3U30js`~FZMsJMRd2_%Hp>NwZQM1h_qJVf=yj?StnMFRdl9ffG`aiG+sR8
z?Zs^3z$<WvKUyN%F+AfHM3HE0)?w4aje;^y)Kf5No-&aj+wEH?$(92EP{pFfC=zEB
z)6sIBG?b474di=@%h)v1F76Oj`L>K4punNYZU{URRitOgqI+Qx;Gd;VGE`&Qu1$zk
zCusYcQQ$KiDuMMB;)fw|AzWb7G2gl1v8)?yoOi4Xd^SL;xS~{;(%woK9}$P=OfIu>
zhaz_UYPF*yIPF4U-)gS|U%1P-dam{d1#TFT>P>F_HTy$#cT3xwYh6-Bw>Zpfat<q%
zl^{^dVEWY_D8A7vG`$_&{{U%CdsK;1_C>ixCx-ga^$(0EsFF0fv{-gW;pGpZJpTZv
z+PGkrL_ShQ8VjjGIbv%+@u7D`*ONcl-gJ+ORvKjY(@hTZJE2I_@DvOYT9;Gn7W&q$
zJ@9f7n{-jLXTtzsV!7weU<{L*jF-;i8;(Bplkp<uX3NZ)RBV<)%Eo4sC~`16X1X6l
z{9%5Gwt@>M8zP+C1ugiPj>qX-K-@lfVg(Lq<ZoWpvhmSbd2+2EO!Y7AWz>4!1df6w
zRz*eZ0T?6x{p*-({{RtW>Rm;zZr?MI6dYgxJu8|Cq#WdmlE_{A^I9w@=6R&exiy%Q
zR$xKh$>x|E%x91k{p(Ls;epO6xXEBK-nLkhGv_94E3Tcx$`Dg$UzFpJSLs@!O}GU=
zikpBi_2RTpiro}#vuFMFG63Eo$KIwjNt|T4Z2Q)_@;1N)Hw>-ejQ6WjAh%6ISkDfA
z)iu47k2O^W<0hvJ$2`=~ETp++Jd>J(+&l2jNcx&toSsEM+-_jB118~sz+;MtW8BbL
zz`*sa8Nd_)o>DlWw3HT?amlEeLG3^lrarVLkdwtkxHaOtK{=oz(zm@pv2ID`qVq3d
z%>n1d09SG-B<6%>&w7NDg%klKd{9}a5>8KQ3(iLr0h(M=@;N-v%()%tQe0wyleRr*
zCG$gR_fX8hde9SjG0g>>oYWdj`q1QjiUZ7e&S)<6hkvaFnt;N~kF^v|DK&bUoSFgE
zl$s!H(x}*?tI!6{Dr)A24P;OZBh>eCkwA=}dM@!mH4LEed(pRnNDSvAieV+n;k(cc
zq?x8-pBx|7tD|BM(wGviFlY-EVBCr#$&TidjzNwND4sHp<`e_U!G>yLLOpn-(q1s8
zh^-+!;}ipFHh9l8L+2RX#Ufk--<q0CbIkzq+%zBsY|+Kjy6{KkRqHCa_BG{~V{m8(
z66xb?GZqKlm=mjxD!jh@5mOQrJkSla`v!a0lJK75lX*i|l0{zTfNidFYHcA1Nbg9Q
z19qYKjHka81Ic#D-2wYklWQ{ESgo&ysTd&Us@6=8b5>dpOX_`3cCc8)&y)DDaam-P
zV!27^v|{AyC>XynTKGXh8O~@iytgt1jy7}X6cb(Vp}%EaOH^p&)FmN!C2@>bZ_$5e
z9Sf(TjI>Jf0h8;KUq9sibH%~<yJxM3>3Q=0T0T6}J|Ankyo#|{$N}dpF~x5B5A5}<
zYEA{lVHnPFoc>kzR)NrV77%6|WI^_)F0_bkLrb+*zFWxu08#l9{{Y1rN56;Z9y~wt
zJ7>yvf3p;eaEU8!2f41f(BHF;nWpVBM65@vb6pvFhfT6w&g#s*^qBa!r&`EkMpAR$
zyiC7W@-nK)lm3rOAAtyO90zpXg46U#fLmC&{{WYPPhV-#M1j&cln+3lSHIe;YwJ%C
zI1b$LTCTUR+yy=xWeN1JCVBaP%Ff7QmF*T<gF$f!M^q!&imJ5IbctjN*0CS11yCJg
zX%UAYo-<8ZvzSTcU)*}vH{G0FrgeR|wX<-WrL+mHcAF?jBL@Psey{zX`g^J@+{JE*
zHaIvOpS@e@m-3lJW%7N^U#6jP3_)|ndYFEpn;TM$nmp{DEF`I;<9@sSoVAXb@n*DE
znia{~d*kn1f@mePouZXWNT&>>cCXS+NTuzY3Di2U!3Mr^_|foY-ulvQQ%*o8P+Q15
z{SAG$(?8TJEdI}t{{Uyrd7nt*AGwGAkD5agDl7Q2nj0k1Na-Lb>Tp!il`=D5cNUt_
z^6gfcGRj2=GcRsyBx0lo=T{){9w#k9WB}31)C%)JLu!$Vi$fo`KYE!$4;<D+WR3*@
z29F)820V^wrGeYHtULvMMF5jS^yY@W`4s-lcc_J2{vK!run5K~CN(CT5vv~669@)?
zhs_x6%t(OxW|Q3^Jc@9Qrm#lpia=XwtTy~`DXoeJG&Uens{-X1k2eJSR19a(XYEZC
zRusfhN8vZCQh`&*J+V-d5aY4USv9ifCz^?f4DnUs3bnTbj@6Hm!343ZGZcQ@)LI;m
zZ@oBzt)U6;QB6F^`8mhltvn@B$jw2KpMWqa#0VIYGldy6Hq+iuHvA82v`bV@Ta!f#
zzX4Z@t4#u>Y?~bVRFcW0J5(CD7P-b~$r8jcDNGfx>8TjQASdfnI(LbHfGGa}3bGLy
zeuL{qNPrkMEoc>MrNpPtmZ^(njl75{*#;XJqDJT!k%LmY0)jCj;gnDs%|WKzcfrL7
zeQKoL05OCZ0-++Tia{LvifqTYa4Ba}jmX7800blsf29>>ju`OR`%y4T@HwT{*D^D|
zZ)%jb0GDg2I0P@XOT!2-Ii^!AcB1lqs&8yyOt<1RYjg_u;xax0@t#Sh9uNiqrfs5Q
z-+}e16dTVusd4=Q0cDYpwFj^y@G(}Qc&`Y_G~&TV_C#kRB9wbyrEIcW{&XX4$19Oj
z#rp=g^37h@SJ2h-*^K@XLN+<<qNb23Ufty7b3|Ka0B61`+vLcst*$%sQ$VQsMkb~5
zc<<V+%mj=9UKWM1o+>zkoov~TafPEtYM_IT)oRxJ1_w3ew$G;(U{?!#w`yuQLXOnm
z%j43Bh(9_37%jhxxbsh{;BiwBlTs=`=791hK_qiojj(w*6@?%Q6pp#TGy`oecv{Rd
zfC%-VM%Y^MlLt7dOrt|jHo>2I93-<AG^~>s?0Bh1KGBMTQlY7508|!T)U1<p?rSS4
z$9fhRl3ZX{gMukJE(m|6MBEYu0MpD=@k48k!x^QTL+WU4DvswA0c5xeY6f*Z+LMv@
zwrUJ`pcs3V9l4-Az%&++b3!f609Lr6y!>-Qk-#)2kPv7IJ<qV>fLWO4yw0Q6pk?EV
z1I%b_YsLM|L8KU<GFpVk6^4@(CXku~EOKaWKou5$tqI?Q%>e-XiaK#h14T(F29*0$
z!)_F?dLp=>8bgm-DCgFb%}pf50N!e9+;c!l0}(`^8I1AGNVh*q1a0X=$@iccS$w?I
z!{?k*@js?0!GNF`j!;fJQFt`6xb&hdn4k=$knnk?qM4X-X)z|@Swpq5C<mW8&wBF9
z+!2qh1!c$_*Mc19Gy`5J9ffDb-QS7=)Tjox5y&+boxM(ID9xTt7b-_I1mk?3XpGB>
zRKDKSXxUvk1R4ViH#o->c@Q$L(n&s)xMf$xZoLiC&8*!d&Zodrn&$E|$H#-5`bOAr
z%RJN}?ASgPd?K5x?xT|BSzyRNO8T>=ejaIk9cYVZ68M<qvFlo&LCXcEp0HaWj3~xM
zbUhfnvhI=cqtKfCpXy&$&E?I-I*;i61H|w#__12qwP_b}SzG1C+>R^Ft+eyx;K!P(
z)vn8@hY`zqk?BcTIJZ`5AA#y?z;oXdy+L+vS6xSMrdbxqI2F(J-mJRSgQN$KaaBE4
z)QKT6BOGUw^r+~{t!KRmB%Td(Pb+SYyjXayOhIkrVBRYe_c^NTy&e{A`||nstBb2!
zw^uQ#EPE5{TJq=`tdp4~+#?4hXBEvAyFCQ|0FviQlHp`Xqmh6o7|#{HX}V&vug!VI
za{E13<nQB*XMeSHy*_umwo-fIrhcg$^19iv+-W*wFb6>%ezjRPn63+3jIXe*V_LYl
zGVBlAxT_AE)_e#IQnp*ttmPJ{!-GoES*}}LSckZUSTgcfsdP@czQ5E(ywNmdd-krG
zZ#A_0gZ>lfMQVE2?)xj!@d3CV_~xvnlA}np^wEEC#%l})BP*UNn%c%I$R)O%?ncP(
zgG#ql+pW2bzc}EY^{HxIRQ6X}l#(;vqA%sk*+&M|9Ao2`!teHW`LAHWjgC$@;<)fT
z0k5GxE%hPSG@~1m#~C^Pwe!;`jo1;`;=g4250{h3@t53tN9Uhd@#BLv#db<IQ{TN1
zV4rFlNTlNx>0ANFdis=j0T}b%qk&M2?(b9DJ%vaz04B2MVB(|OkSZ=%<062VbBcq$
zYanH4Og5bQP#NBFijMLB0HsM>H>Fq`xK~avn6l(!x39ecSUpIra!-0Hrbo44pkG=7
z4IWoL%@1Kb3L!nQQ$e(IKmnr1dWb=eMHQPZ^sJqraX<sZ0Ou8s-MFH$2hy?HfQH{v
zYsZ?&uViEEKnrIzExb`YJo8>7j8FhE@9$Ia!0l7TPB`;G4PXT1)bIxYVvIlnh7^-P
zc?Z2q0MynpN7As-?G;w&8Bw%xQeQ1adsLNs5^8|SZ2C|flj({(xQt?e#JTN2VMrbM
ztTKXf1rSG3nm~gXpdpY4)|Qo8Mxv{Z<W@<~08jzW${R6riZt;RUBr*pg9~A*0X04W
z;BqQV&pr!s1z@Rjk}7OZ0<M>^WeFe(kcbXXt$8EkH6x#XD&WS_PXdb??fcYLOfE6a
zWfYT|i$L=Xn!#Vvro#rJP6bE?W6z~|q5yZSm-<vWC+R>ypsxfTwK4?_i9M(c?hD2$
zDmNc$@m8#ifj~=v-n>{=7B*wp)QWLHM}OyDJbmf~N}kmvLj8pSF*ySiCXtD$8*;qX
z6*nAE9ujdwMe~wq;t-sk^eG$AP!f(@J~5h{<eXq)y#gv_iRQ5K26K)@0f7nIpIQ?F
z8y>YbbB+lW0C3!z0z_a8)EJ*i%#Hx>Kxv824-^3k`kEPVoz5{sX?FKCtkP~d&S(QY
z&gSjK4oMX9C_#V}7nCue591DKGE7e%lrttfP>h6{0Nb!Rp*+<Ge2NipJ!lNPhcq^q
zgHUHDwL=8=pfkU5&0*XDLTLav6ecCj07)j}iW!!o!xa{wF!E-G(pDUb6ENng*fQ@z
zXBCETN*V7|uuU5^AlNhoI5be%tw6#_nwV~Bb#M(7H2}(U+|fW3ynU$3b3hp}MpNla
zgNl;Lwt!#~D1o@8ym3-VGH3?UUU5aqG?4E`jnAzB$~GGYiX;HiOvO*N8aLi32GUVQ
zR>`D;Z8Uh_w=@G=vFpg9M=ohX{LvAI=Ri0OO-(8%z@kMdZv0RTXEo&+tSJ?iZ)yU0
zRUFn9eDSyX)J6|_CKXqKKu##k(kKHZh#Xhb9}hHJdmfZVbpl3QAM~%9^y`lX>NauZ
z1RDA~qcyvG{VFJxhnMSLA^xFrcvNH2^j}6x?l#Tarf%Z0icnYspL)L1`hn-U@Wh^6
z_6D^XbcZK&Ex32Z7f91Ceq;s8{{VKsFq|!)ZyB#=LpL%cys<w@awvuP@Y+lR#|O1g
z#iw4N^GTjN8kMt6XEye7xbMwzUkCCuUoMGt4vhtp@^?8SgX=)BX>B1JP8a();;A*>
zqidVhLg#2cweR+tFo_y4=YlAs&DuH_D5_@frD|cEB#J(SRqgaiEsJAnmLB!Wk5U_E
z5(XHjk6*mOPz}T1J*qfLF6cGOrdofoK;8?I;~?jn+wAUO)2!Q^f#2&|b+=JTaR$xZ
zyVAOkQpaZlc#;9ZtlPJ;^xm0ecdU&=P<2&1PvJDYw}VfaUCst-zo+#pIf<Ki$^MkL
zP-_<RNwG-C{<WVL-ixZ}*qf@NkogJkil)`|K{T6?<a*|i(NQkhfhUhaNcA1p?tt0J
z^;+32naa8~y>n5%O;Jp2+d~17=soKi_fe>ZD9Ag{;^Vy|2U0B81jvH~<Qi+_<JYwq
znG_N`5nYgqb;)#APm0l5ui7Q{n#@K?AJVwKuL?tdDlR+vS8(guS3$F6W(s!uR{^}#
z1;ymD4nbpz{U7}~d|&AJf339AzRb24wP6P1=FTbtb#CJnP5E9c?$4445NC>!I6bHc
z$94rx+=>9=YziE$zp$aJ`_#NaKwv9YB#f^lb6EwCAlI0d??6RHK9%f~#bgjG-n0fb
zp7j&OWt``Vjtd;n8Njb%vPLW0@-Qd}5$2*4;-j~m)cq&`n9g{wcxTp)#y+*+Ge84o
zc&vCB1m>991w;(c5JmK<?F-yyyZ~B@F=O<gF}HEfF+~Jp7_Tk@;}wB`XbcsjqA5}D
zP&ma5G0gx*ZM}~bo?E_ZIIKI?ZP+qcDeppghH;vX?4D~56!V&>GQY%rO&Ad$dRRyl
z))|@3eJVg^muqBlD04T9=~!0xsTSSXP##T!JP<QcErZ2CoErA;r2s33cJH0O)qns@
zfz5h(GBH_*e-E0EV3IjuiZDUyd8K~~;*1!9QV%=6an2}_xC5Go>c(^0r*A+v41KC#
zkiF<DoO@9hBzsf?IjFTu7(CVsdJ3{&86NeI%A|g^BnjM_xFE3>=CIN1=A`p^u(h50
zyNUw~m*W*Si)S=8lf(Qqly-5+pe4VIR5p^;mRwYKpGpF52G6YyId+cKl~zB9@j{b6
zlmyP{y>Kc?AB1vh5wTkIjLpRXiKkXL6%e`Ru&{09;;>)ypeE#P#Q-o`%Q-xHRB*nu
z228o2mL&06b{>MDJ#)nY={&2N0za71q+QEP5w|#?F*AK=IK@mUW0GqQI5YsAfCht|
z)Iy`ygv>oC4>kvWC=VtN6($9{)Czzzo@fXXO+buROJ<<MKC}mm;)L>&2pm+76dDO^
zngTfYuNTK^@$c<Xd}6BI0A@nXQFh|6%tvYyu_CGL5Oz85QO;^aWK<1Vy9OB0nXyuL
zp%YaGRPjVsrNPBaHC;Oa)a23Bj1Fl<YAT*;z;PQjFm}?Y*rSHrC<ax*9Mtj+E~1JE
z98ePDG(=h+-1Ab6uyAMw2NgAZR09LrvN@m&jl!gg%i4ws?M6(B0%#eJCW@e``cToZ
zXrS&C0apwvKpGF>=QLGuj`ZvYjE`RQVJ<rgLnIvLn2t=2Xa+>X+Ki_oicGL0ikx$@
zoF3EzU8-nzeW_NPH6@%UuQUTL!7Bd%qk7S_YxGBA5S(|dT1$<~-n9Os*B-_~W8jZc
zYlF=3*O3PXbolw@<K$CE-F+|Cx^3JM6LOg&)rNCh_LJd{_ox{-@6CQ;=|30mG|5yM
zj?j3?uGP^$wB^JxEDvx9BLrr@JMzAdpAEQd`z$|D!=JZD+*VrPyJtecvUg$YTK7|4
z%L!tq3*R-v^ly%lLl*x48GQZm>rS`D)z;<OtT`C(UpbS+o!p{(81lycEVtCI=AD`2
zV$x^5EvGHi@{Ps2;2PHlQ@Fes8@=&cj)$+>7LA#LHhC1`_R-el<n&u!>DQLl>hhr|
zJ${sm-bZeQ%i!Z9K9#y^9Y?22q`pdcn|<?|`Tqc{HQ5Ez#|yUH_RkfRKizbTM_rr#
zo!5&6*;4*rq0JLr)Q+)(9E9=SxqgE;%yPp%_^qwAC9t&19!FqJR(Lqd(a@%i+ov??
z?d{Z=KM?Is>K5s3wb}CI_O5wj@e!=ifzAi%Szf*xwEIsO?UPkla(6`@NdC@=Wv-@}
zySJS7;<c`>)~<B~-Tb)XsV=oljm5?XJ?f)S>byiFByIKQn$|d9>t=3g?#nAi-5R;$
z9qP*0uWdSVHanLi)Q;86^}mQV`fZ~rbynxgyEVhQ-{QXD=?tZu@eQ4MT+-PZ##sc?
zP;<f5%rr>}wQ(cmiiNx81}=zN=0Zwn3vnyaX#!D8a>pF|RfePmii#FZmQ3ng+!B{U
z(lYa!qb-_KYU?=n^8fkkyr1{H=l!1dJkOuF_g~LP4$cpWSG(H0tuf2j4d;IAyZuaF
z4ny~v%|he;wMVo^Ah=%_+PXP2k11h5jwW0w9?f0yLC+wuNu8+g=P%6pEBkvL7JrYb
z{K-Bt>L&BzhvVw_b371>cz1B|NwH&5w%*#!@!m)a>fR6$p}y46E3wFpMFDrKa@yq3
z9BcW0GmZy4d85f`{v*|xS)yj~08o>*Wct3+l9O!UGmB`Hv{U$G%SjkJX|>I3P>4n&
z9W6E(Hff5oQ*>kvUi$U2u{(8AOH9Mq(qdMBONs#&d;2sfG7l(De;ZBE#0BAngxY?b
zRw~_5CuNS$J+n#-+)_D;(gphmpasg**A9xf2RhK>s9oFGKJSP-+ZuJR?q?J&Syx5*
zCX*j3ju88C7CTNrnsf){!KV6+UrhrG9y!O@^;(g@dw;+q*g<*mm&^#O(LqyhiN2NG
z<>bDta-(T^X6o*1OrmrCvg%0LcyJ)u|J3Fb63C@y(UkgE6C<+#x9J(XTevm7csGY<
zY9TVZ#|jM6{@B%<ox4*i=Va|F!j8kqojsCkd`lNqc)L%N)M6$g(>5q7R~L7ATf2Z5
zz_WeL4ZxUYYHrJk9+2sYr-<sp<7|od{4A3^^d0CUE>qxV1T%LY(Uk$A{Nn5}oz>9y
z$<O081qto^KfH4V#EALh-5O%|>e+gDo0;=FI%KQ)3OOD<#v0KNmyrQb)qs*or2J4P
zMt5^b_!Xj2sc#t=2S2A!>CaEQV35UZsmJ_ZaQ#4#c7v-tEdHt1*<SkvSAGClRYPEs
zot7}VTDR%7`mPezTQVL2{1E~Y+DxU-6iq^Li$K9)55$$?Wj|~%Zs%9wLjf^FX1bSE
zryo$>lI|sRITd9u8s3V#P?g`$6uLdh{0#-^>l#M1AQc%szj{IPRZ8}&w|?llbx`Gr
z7X7-eNSJOJO9jq<QR=J8(Gu_D8*dEZ3;eDz&)A7~*@zJ@(aq>ThFJlKC(>HWm~~Mz
zc&3`-5tlu|c_)vOV@mlR#|_hoZsl(SR>F8Hqk|+w3Evs(7U8B4>pG$cWXj=GUr?Po
z?jQ*^LNB(*Bsj=Jb}RiUP{HqA2G8}{6S0@=-Uo<v;twa<*jYvYU`r%Qve1ZT^A!B)
zt_&{`r!Yn~K#yMmSHbrRw$1SOCzgJOHB^*n3N?EYZa{vUx+73#S#DfVLAffj?3DL_
z`J&y8M!W4wWdrx-89zhCG2r*w6c#s@m9+Sc-TW@3^fo<rwC%Jbsp-fr;+K|RgT5+!
zt}X<j^EA9&_t#Ay`Ys>rB!uI)4mpGqq_VfyN29(@BRO|6_jNY^h$;y>qjL~8s`>q>
ztIJAs=&h1(hb}B8)Bd^iI`nz)O~dBQvMcM2ZOSOK?YG|B{doDNa{apeDf<TgLugy|
z&WRP<eF=$Dn|D9nGdarkQTzLiYnb!@S-PO1J+gb>;@RvheT@#ey{}raawz!J*BMu>
zLJ;}EkLZ-3xqW?{jbjp*g`gk2gUxRYmhaSW9L){Z9BoNF7^rox2s)?Rm?U0W+ZcV_
z8oY{(9~#s=XxiblT&~btl&pt!pPk<8Q<$}*-s6j%#koUgYDODQw_S31VH;b!qc!Ws
zI->6k-e_Oao2>Fs-0<SpDsMxHxUm!WF7^99WM0ka{+}k?qr!re()ADZQg3}_>;}7u
zezWR~vz>9&sk~?P_co8<zchP$$2elSWm%I&n6x-l@UKpSfe(?7%dX$u)HCoSp8B@7
zpi;7ys4bBov9P?=QMfh?OSiH1o`{)z5HwWMyoLM*tA|$^Rx^VTC2`~a!(W%lKPf{h
z6H37=i5y15r*rhC`zW(*mKV_o4hC$+Q-(u=aaE2s`~q${lCnt|GOyOPQFGc269uxr
z-?<LCs!|6CV-|rgKTEvK#kQ|eMfxj5ngN40cIlYJF@rObg){S#!tFVg1dXql3yeI6
z(DJdk|2~)bu8F}FAiVUz-%P(%=hUk^CB%VM>nsOQO04NZ$4MUs*r<}+cNnNV!Qd~R
z^#EJkGr+>8-aoPIRe;e4tr^FMcryi~)PtVrF$)v+1=Gyh+V1UdPyOMAB5<%^sI9b>
zx*(z&)0_6TGJJJIH~SZUlMc+!$5*+*>Bs|ac^cnMuB3`<8(5o61?V(LV}+!E^;}6K
z5n$UtrLD=LShYZpj=cMeys}%Ol}=6S{2u+r1O!~#<E;trA&M<Lb7O?*DGrL%zeP6%
zH(GLT<X%lFx9@=aWGOkz{%OMuHmGzDCj8IRFsoAxSbOyKd}0OW*ly*KhS&VV|FK!6
zfP=DLDBQ^(x%GEwABnD=u^B~A7iIOwM=hi#Tq5n7I*jXo)Sa|H7ZN@{Hn305v-eh8
zA1uS@nr)`KBhLtH7J;7QUP8jBs+kEhI~1It)_qB2*II7#IZ}j)80@1Pn%rP;aW&q7
zx>r3AwF^a~+=L7WXWnFnGqOB^NJB6NZ1lEyY4bTCKO*JYS@FZP0u^vc2k=$t*2yBV
zI%=rXRK2_9s*nB+K2-G~k5sF%C>Gaj&>>);)@rOrYdNVW$JN6n(3o&U<X4fN9$=ID
zrJ2(lRL(yXfl6#Z!bfLr6+l%wdl_LfoYNu75Ff{F=?W{WF(kS^@@V0eq^IyI(^xN4
zXwo;46VFt9qA_F#1N05v;XTItF^;+~G-&wc^qw`)t3+ULgg-cTcAOG<^k?96gx?wX
zU*;KIRnlXl49Gw{YHPN7FZ@=WXSl&SnQgE%2f*tc7>!Vp`tE-n_r@<}#XfpE>{b!K
zKk=>S-kyiZiR~|eJ6j%CuA{<k8JzREEgNV|e6b@+aXkKc;%n^v*H4=EI9Y&)uAOTR
z+=&soF#TN8pR@wQ<$u;IB%>$Z#?NCUE{jjUQ!K&Dlt+%E+3~>hYZam!fA*|NT4cEN
zPg6^<&|~^|+rS(-R8I4y_>C7!F6}gYqFvN^>`n18L3cAwZgcW3b!q;eo*hime1x8F
z7Eg6(auSby!3=tOSNh-SUxM0NQXcEXNvAK7kBHt%;}Y(h&r+6eHuW2ff19wsfw-W@
zQy!!Wwr#v?clvw@6+S3<UTlYv26-aYM_Q1>dON+y{s)hJf7Tv<ar1r8TtHN&cBxD3
z3&Eur75bxGUR$YSX=QXnT6s(6w!+XNSYY0mc^~!wSORKbky7uDnd1@sztZy5)w~(m
zAKS%-E-$Hl&;P>h>=li<w5&us;E)-2CgYNHe*G9bxlmy6%VitpjA+s=_BCD#?^&VF
zM4?tTjYEf7s$d1X%m9`GVW{4iC<AEnh+pIj-A&L*J-pZq^ih@R-Q-Oqj2StFE+;Qx
z!->LyZ-h9i`$PLXJbrI0TjCD#vQmq(hit6s))!OB(@Yf&V<+HOAw~JDm^0l{TauSJ
zBuD7dECA5u?U(~6F)<-Yptx*nw-4fGb~{kEx1<j1xX1wW`8#m5Z<K<znz;u3U$@ay
zJH?qZq9k|&@`SmtX0#>e-iN;WYb;{OJ;z=$$ywXes;Rn<nBw-fOo%9g=uUmfFI#Cz
zxfTnFoH8|LWCGowxTnI@b99!L(Ob0hDM61v#tfJzN0|oqeqW_R>B}m=$iPG1a8DNb
zte3b*nR&A59=IxNO54n5T31`_@|vrEsB>6A9l;Ww$rwpOzfd1>p30~iL`U<hm&{qD
z=`3XvdV*V&mbJYw=NjqdMHF&N<)DXKUq@tw??0Dy^Np<l?uerUexXRkrNFIbNs7E@
zS2`FD@;KfwT`O|YSJaOpru>G3GkORB&x&N8PIBh=;FrlBa9y!U0^<@1KBw^4k3rye
zbHuC|eP1ifTEaw4cS>NmrQSGL0<IJfs`e<}Xxl(=ziFTc3_VU7*V46c+d1uU%Xo#F
zqYlU(qcj-IsG*>_TUL1_^#>FH;>^cRv@7)^!f<AgG6sE<$FAI+KoOLcj6lJvI;fUS
zV@NzI-<=%X-$Ij#b<V6NS7zCEO)MVAz-U%y8XAjf^JRh5%KNOaL2JX@W=bfTm3EN%
zJ&vXc2g9;5Y_!(A7zZ7_!CRLC)C*4^b=++LSeci*e^8!(<m8y{PMyYENaj`gNndhv
zTMQxy){~HXF&T#5ft%+AKH`R<leo5*x2IzTQ9f6{Q+hSROzpHxeY!Ggr`KgjPrS?{
zuw(y}eJiE=scuHB3BRRd(5*CkGJWXiq*Glu%#ZQJkwmqMk(iBhT)#yRFbbE~X8E30
zH&U=eeQLXW$v4e0=ivgg*|@u|(v*}lqKm?cpjcYDrmnmsP2hYD9QmRZq@rK8-4)Wo
zy;zb7tX4z>QqyG96T^ej6#gH(V^l;unSb$Azk&{Fo}Ig0LH9!I4X8rwUmfTk&9(3d
z%+`bCmAB>A%+kI4vR;Ux<(e+K^=^bBls2%ysqn%%(Z*PUfEZ=m1NbTT1nm{qYm_mX
zsv;S`<o7WCvS>Sr(KUeO;l%==d!Jh84u7yhP6YbiL2)*JlcsB=w>2D_pJUMipXL#!
zKVn}snDp#=zrx?k6!F;eiBM<cbpkjr0O_0>YG32M>F{$|*EN8vq&<>j0AcGisrpx(
z@sEe&DdrLzKuq>M`#2r9J7asHuCD_QWSi{_-uCayc<{3S!JqcYHyL*l(lzbJyUfHP
z%2M{O-AYhGslh(|)ae}xc7ziwQw!TCO#+<EgopQ4!zr@Afs1HLSSG+>QyTB4i40L@
zUMjGFoK6+jD8Utmx$6O)ZHUD>46-V#dqMUDr1dtiy$HEg+-&Fo^|<%*l=R9ay`m3{
z5Ix#lem_u|I#aad8Z$<SLTSgacD=Lr!-Cyh{VF+Y(ynUws%16uYj0PExmBzSjQ#N*
zr=Ww$-!#|JZ!aTS%SExeAVUw&Yy()%eFVykxlES%pVof`JbpwVm4z;p3o|ZwTt#S#
zC!?Qy1H@dDJrVkY9)~!3ye;mOd2pKSW%Dq@DW^%Mf7R3;Er?DRkQor<Nvw{Bs@7c-
zy0BHmFA@`ev6rb1H6<f|8n|jqKL$V;tiTvw{{W$$?kpuZmm`T2fMe$_-u8fb#Cfem
zuR-}$a{a^aqY<X@OGs!ykZ|`LzePZSh7Cw0V$q+c9Bo#K77^iAW1b(QjafuDLwV@o
z8II*Ga}r!X;U)3a7YSUPHMto8LVOnrT7WuN75O0)%$4XJ+A;R|Cd8ElfdUoB8FuDU
zt&OzU(b#2~oXsT(3DjKDbjGcCh^U{>LD0o_{qd0EvMd{B+)`ymppQnBPJSEyuS}q9
z3~QDBV6wT_AZG(}80)<nwY0t2+6WgUzIuc$Kb*`6%Qj8XJKrspf~*;sQ>e1(vp9G2
zWqwwQ1Z}5?*bJ(fJ%@R`#iwv<hO5%T6v>Paajq;PoPQh6Rr;T(DK^J9|2ytMuHhzX
zCt;#cW~f3#K1Kw)lpqbTVNnfk{Rq^;$UZqta2{7vRTNxwE^DesTr?=DfYGn#AL#8+
zaY=y|D+$c<mI$7O%{W*E`rX9`%ZO?Waf@{HR$#Ij%#ts{UF*2HoRw&s%<WC+v^(?2
z5=~eoIcO1Oj{<$Vm*reVKi8D{i7j+}h^qTXzIA6q(<T+TB3MCV`VlOv?&S#GRAdBh
zSzh$%vk)DL_1<XmF3htb=^%XG>u^biR6GNc$751c`JY;0z8ws6mr=3~SDD@^;c-a^
z#`TB$0Zeyd(QKbgd$OJmC`QO>2Z%dmS{(Y(k(8FhE;UM`*B5=3a+L?*=%0bY@HXJ)
z9@9LMxfir<&Az8C^K`h742W&E;%vF3mjO@@#3dR9)w$3$1gfw5n*>Kw+(be{>j`76
zBtG-`&HL>OH<L#&x|T&jS<2DIV4X3OX;;6qgA(Jdiook3N-(NtCHdHuxq?w183a6V
zD(>Q$n>wu~yilcf-NlBK@VVt>>(Je6(wtIah}hIKMbY$=ZT+nFIf+MgfV7wB@jOl8
z7R5Nkh-3F^f)nUV<CqRCa1f=9&xmE~b)Ho$^&>Ui|7^EJmI}zMV(~b$IUr@Yim60s
zBSp}#lo^{`pJSY+M4<!1YZI5DI%<pmfex(R!_XFPaZ46N3mtHn{yR>wRzg!}HQDE#
e(@Bh>$@f_EXT|k*NaoIb6M1N<)-L{kQ~w8%epm<q

diff --git a/contrib/clojure-package/test/test-images/kitten.jpg b/contrib/clojure-package/test/test-images/kitten.jpg
deleted file mode 100644
index ffcd2be2c674fddc4886117c12f3b29e4367aba8..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 110969
zcmb5VcUY4_vo{=iuY!VvW@w=(-9YFiK#%|dsftwTCG;X4M37EEg@j%Oq(}fokRnwD
z450`}7YH4s34$*^=R4<p|NPEe*WQ`z{&r?|_PVpPv)R8>e-{AEaF`wpKypRmB;o+T
z-))jgBNuOfZ$B4rA3mAek^qQ?o&h;I;EG6+fh1M{GA=<KAnAR8n7Ws%r=5o%pQ)X_
zeF&eXoj;$ElfNIIVSvA*x3{k!pT4WVzmpfAW`MsFkTm#ziFf^9;*9_|4D5o9T!WoF
zg#Gv&yuJK=UF`$>uj~Ml`T+!8{QZ3t@7(dU3-<X3x&7GJ$;sK*$<E)$)xkf&*Xg!{
zx91%osn`E+q(6XN)7vM+*VWm@pYLk;|E(NIDgwBn^G`CL&p$Wvd3*goZJYqTD?f?<
z$7ACE?I`j8&ynK)JSF~LUK9W4O7Z^|Dh|6c_di07{;#=z7Xee+!LE-100RSn=+*R4
z1IS630HjwO<yBE3Vg3)#c*SeQ<N_$JK1r?$GQkz_Z#Mt&R#yd}Y3Jqb^<PguZ9@}2
zIZ3U5)&JTZ0hIqw&(av||46BMCJZ3ELXeWA0|4n{|Ix&D^}Q*R=D+Fz%m6?p?f>8h
z{zoF<zqCtBN=Yk9LKLOt_@opRr6d()<*y)s`~Pr%X8@W2GSYwa?@4w=<do$9h?<g;
zf|7=smX?N^hK81&iJq2@k&cFjfrWvQiJ6&&nU<dQ8Y}a)E1vnE5R!jA$;hd%Ix^GI
z&|TI3FX?Y5fQ6cLlysDg<Q9OGg@lZS<nJc{_mzzl|K6B?7XOFHNy#WEsYn3SG*`_g
z%z%H2Ny*8{C@3h&C`g$|NXf_n6fCU#Qk3e~ZW-HAv7w@+Gm6+j8psA}8T+np*9A0v
zV-`&^_io5K_{C~v6_<4H3!3^j(r^el#+CM53FRUIko{Bne|2671(089y=s(X0sM2;
zKdo2e{SSnMl!c6+RZ5-wn(-~WJ!w=!M$tD4w&+Dr*S^NzX#o8d>I%UEPz8L7bZSc`
zmSbApr)s;zbVXyiE1kDM91J%TuHXL{3(26dP%uz`P`Gd{IlLP2%DGe6u!Y^SZJG^T
zO#?M;)PjBaIdLeS<Yb+HcdqO<N*@6%O?qzB>w|#OI>0G(pkwX`0+DM{cCEl_3F9Kd
z2_jFPyfMp=0x4a%L}yYJ%E^6?&dHyena;(dM%XkHyFgLk8r$86Mx6YRqc<u2s+Vpj
z<jKDtu3e4?6ctRtn`Z;(UK-82J;~VpJ*KbRpxxlU;^B$lJ3cHAIPN=KGBC)>W)ieM
z=|3!Uv}IMUYPWzEoSWmEaT;cNSYs$=S5LUzr}7%c9BTBQ^g5$_AE7X4KG(yqSKQVp
zjJmd6)oKm@fyu7dKMWwVt@|yFJoWrBTCEQNDUEGbo5iP2Da^&h$oPkm5g&5yZraSA
z9M_R-8k&mP@2>}*jYZiy;*s*(4d*6_^M0H6;=P86GaPSDZGY(-woW0Vx>oiPe1nKJ
z4|63I)rkT=zuur8fU&ecKe&P{g!`gO&m*M)QO0F^v0gaU<#BTP!CXU<gm3I8$JwOp
zGIZkcJz$C1O4uZw!VH@_CwcBpfz2UvX9V?vNo}KiKWHLJAB2Cg7%4)U@=DSGh&`3t
zCX>~3bRZ8yK2Z`AD5xc45F}G2BIHMvH>Z_#CNLhhz$7ziXAQs#(o|Xo!Tac-6th3m
z?#2*P6UsE4hlJhbGG-KTh8&kaQ|MFOwvd;qfXsw0ApQbQDs)HORxzO_MKvBIn}{Ol
z$(!irS!7mJK&T0S7K5X0xXAjcfa8Ln@z*;k&FuWZH7Z$+Hlali5(9%C{}|E~^Tj)g
ze$C3S-9-0-YQ6_IMu=U!J+6A`tJd|^%p!=K;WZ3;I_03WRN=(-+*%sP=nBu245}3R
zS*7ywr0k8InYuu<^}T>w>b?m9OvCT1tTNS9vo-&8i3(hz_ktbvF9YuCdO1HbG6t#M
z8%+h7Q|7e9B=SGVh1H~_h345Scz8r*sDF6!=~q6%?P<_g#w7&x%(jVdqi^7|wYLQ*
zT@LQbgiNe8Eq5>+!L+H{toNQOE1tIQD_9KbJm!{*UgTRXa{QeWRAS~gWJbEMVzj{l
zH1_fwC<pe*Bpd6}stq9&N`cM=?TWQ~I&9PU$Qx_z(wdgtGQ%hvks17Z?9;VveR98C
zS<1dYD$7rhhmPSNMTy|y0P_HV^8R$Jn!SGtH1#lEV1F(jz{tR_8NPYVK~T90CzNm5
z-^_S8Tu6ByN(Spy8*xyYEQzlE3$QkS$hnjav<n*zr%$X7Q@7x||5U&~ic!@n9#<mZ
ziL+d6Nr$fC)BEunol-ILL=C-qdM5rgeADt{-M@f@42{9Y7@5K~;v=_)Y(J^Z__(Mi
z*@hp!6G-cmW_RT83H-jxuG{flcH@8vhX{`MQwiD7H`cSHdF}8OOv|q}F-|zPm-}t-
z_(mSe6V4A&s1s8Hr*X1xD<~xqbsP|E%rz`FctgI)f=sArGzKw~WT(m9a9o0(nui*V
z$ifyJLSs$4+bC_Ir6zQ|XT3^qN9sIsxqBTx;@|VJ2`it}IfGKFaCKBEy&=MoS>DPw
z1!TsP)XQ8K;6`{F8_w1%_zfeZ)R#<CTL2h(j{g>G15NgPh@g}@tZFtW>PklG38WCf
zdcv^}EOr2uEeCoj8v}jHsE2y9L8aI7=dF-IN)y#n;y$FxVB4_p>O_EdJ4D%~l<r!x
zXUF*R2G|klk2yDUxi0M2e2&ayoFZ*2<#k&}ppqxx(e@3^h`zQ`>`8BcNZ)d(zc3q;
z%$_vxd@Q8`=fh-&6K9-BgUbr03OINY)k5(ky@6OjfXcj^`An`SEeA(mWEtcxq=v*5
zune%raM9ygrACPGJ{NMM`Oro6K0;|J_wh*?GwQO*GUqAv2(O#fwRRD{Y%egOCABX~
zTM$I%jgtUVHIxZ{sIWU6s9>EsntuB&y*qaR`Z(9b0-C&h_U%2TOe&Zb76e7k++4q=
z!(E+=&^s(vQa+vgz_N{VqvZr~l(JyY{j}JG)7(6-EE$6o$4n_^Wnqb#7#{FX><CU8
zq4M+<UlGUgg;*@%_)R9wmdf~-G%GNJbE-_TXRlASl6fli2wT?2=_I*0$+wvlx_y=-
z0D_4?TCJ{*+0wIt+-HqU__CM&2Qs;_$qgA(rq2=ipLRFl-z|m`a|-4|rK%o~)*Me4
zG4DQcZdXdA<%-so&Tw=yD?M;^OUEk6k<NBXUAR6r24Or+yb@O7uPh)`Gr35oEX>WP
z`k(S6tx$J(t@0v;em0s8%mbEXn?7b_li{?B{v7xrf)}<J!~4q&-dYM&!0whmuwiwv
z<6BnfmEWq_PlKC%@^$785*^&tc)v@YtC_~IkE3l-pI7p0w?}4tzfBL^Ha2ATb$Ky2
zL!;d8a>JC-2X^>P>t)Z7tmrt#)^;ov=q#(aKYze(8Wm>V?nma4bs^S+2rhGEy0FJl
zHHdOeHe_Hohd?`ET|`(!cCH-e{L*?7iP=sI_2K)Kw4pXbQ%ZKNLN*Kg7a+<HCbx~&
zMUim;FGQ%j*yJ1+E*}_e=&3YW>a-AbB{DKD@7HwZ%4DUw8boC?v{X#YQPoYnqEYN*
zt9^By&higmQgfFYl9Ec!0M}|%s4TkZ;qTcl_E=SI(w*ZwX?*Ae^^aR`XrE<5X4u3=
zM@c&CM$y_HBRkhKV$sRt(!ZT(X>Y<Jvfm)$Fx;=J1Deb9_=H38QJ7?=DsihuCB?ze
zf{G!z^t3UzGv9M3T7v&t?w6~on9Pqq_P1@kp2$A}j^sIOnlQ0tq^$d-;p@WnY$YV%
zc(t{pN~JDOZ2<@N<(2+>>(A?N&3hQ_yiG^VmC~xuuI5>hMgu0H@r=)O@{ELH1B6rE
zoQ|8;+)_f?FSI#cJKR#%4BZ2bIp|xd|G97#p!YQA1Vx=(D7(*kh*d$LlYzshRRgRV
zlDeEqp74HKSL?uSJ*8j9IiJ#-wjgDWEJw@jf}k0;RhiBzy^M6}Zz-FV<UFqU<TG>G
zi^@I?ELEf%N<pOwU6`+}6c-v;@t$pgY%T}wi8%o4>7AdUlENj<(?u*Om6ZDk^^;u2
zJRHCzBQ-KilB1aIB`@@*vF<kJ!u258dxU_8qXx<~Qd~Rh96s_IBi1|TGL6%4%s?(*
z_W~<3RDm}fL2|W@^08slWDk&t)8lfJkuuf^z%sTn(EU2L9c&?7KaEaTAk9IiN_`cK
zgTtUE(d7Hn=xfInMk$?3xNk4fxUAJn^t!WmC`tn35E>wn3;QCE4rE(^5f)*vB{X`0
zF|e>yvdbHql{`u3knCrevDwQP1s${eKo2g2Mg(-v&Cv2OLw9#|jj14Oc-$+J8lce*
z#usXw3J4;N4G|rl&o=hp+njGG&&dqAa9zrGf3NKNo?1R8sMk-I?JVzTd)FYnXa+Q$
zu*FsMw!T+bPuOoR2Nv|EOyGDfcU?t%({?*oAv%gGl$~t;S234>52WTRr!imm{_MGN
zO{=Ix8PB1$7}Oz2oseky9*RF2HF&q16e2c{6XTRjU*vj>)&ImjOb}}5bMKI+Qu5R7
ztDoltd7!4>JJS0Ze~#olIV%6WmhR>uuYZ@1p>uf-!%CtcRGjx@JF6YS3yQ_*&Bs3K
z5^!AhxHKJ_qsq#qA0SY!=9$d+ipbK@TK5YJ9~I6;a+c4AfoIwoaha;aN%tE)ldOBS
z#%|YrEFbR<+tx$Ay`RLAEWV5RwQa!NKY{fNb;P!dR&gvEL5}L&t^y#&K)D}EN#nZx
zlp9p|XPyd6c>w2JiKnSL9(Wmx`nexpgJo3+4yeI{rTlw{sVaF=2#7SZd?-~})WBGN
zS)uevJyoopKj_wTu4T@{y6fyj<oums*2vpn!RWM}UN2jI!bl}V1wECP)gPK;E&SW?
z=e_HUelg0#5DF<NBWolo!wl-T$j5LXKUXy4oOW16-J{1@G8LwZG*v4S5Vu{*Da*jF
z>rC$EUU1%$h}JhN)K(jT`}m65=;!JvoD&32)G1R)0?ppk7RJ-l7CkDKuP-VMsviIP
zWi7OPj+$TRj35pBrd9A*pD<C5zVQ=rl=G-If-DKC6FTEv$zk@pX1UTK-fd>n#<22J
zUX#Ci)8k=*<V$~ing=1iw~nUQA@<{32|?$H=L1HHv3i|8y^-+OifYHbdhcNfMwKH@
zv9t%FMn+SE#ykz#j$?2xZyaPb9dSV<CmURS)t9iYL)GD4y3A$npAVgEsvb?kWRkrd
zR9eIN#{_XrA{CZ%t&r50xDjcO11?Uk#|8S%+#Tbq=`<XxN%sOoY{?#MVWKAH{sMmL
zifkukF+VUGW(Yd=ZJv(tzn7xc9|1nb%k*8MA8p|oeS)?TLNvO&I7M4Zcpw*T2zaLi
z@hk~QDx=RrkcvJt){v@Crw~K<MITj=xm^V^q^QBU%J``i)sxbN-CjuG)xsA!oSM?0
z7<lGwP{$!0>Qi8%3q4S2($+;}vu$(%Fk~rjiMmEyKwnj>fIE;;Bh5I$M~O;h7Dwzq
z(GoW^vBH^RLOi#ash*f@XEXqB+L4))$!d_e>KQ5Q$jndPdL|N1HM-+UI6o;3(fCj@
z6vS%?52*)R7;Wm2FN|%sv`(kvp(KaJqm)yZ+49}Pf*eyx!FSUsr5>tLdF&dJ$Ihr)
zVWQH9qR7(lnLd#ITBX(bi98amG&h=Ld7TcFLG`-G#Bx<wlselB{u=JA-J^=PfUGpQ
z?r|@3d7A?d0iV}7tW3=LlHNT4nR^)k+U{r5zz5B}GLsHbL2sBk7y8B$i4R10(EFS`
zPcuzmU!Kwi4)74B)0uG0g_M_Q{KeaR1NOJ{Y?w@F1a7;{Bq8PZVJW+}Hf3+Ab8ZMZ
zLkFSTJk8<EBs%<8A0bdKjv_2*Dc^G?`YT0EDia?4f-&gm+{ooqE)T%Li?A2GLZ6hZ
z`A+ui#_a2iqMG#mgxWBR?F$#94_<!B$mdZQx2m@mOBjyTthy&;1`-j6-)&Z6dnIo6
zSlcj7qs?HDBGw%$;ahD>TfVM9oqK$(;gO;mSm?^W0sCrJ<qCEkRd2CqUc^~i^`PLO
zG(LIv3~ySq<1$)-E|h1B`Ea#V*M{i0xm0r#@qn`G<{;Uj)Y(a(O~`UHAvu}hcZJ4t
za-PFNn6gf-d=^eZ!90|IO5Y#`)hw$1^l*XU5)JhK9bKiny^k?1wJpCn;&~wjyQQF%
z{5XsA!!r4==`sr^0S~c!qzu_T(`o|#KE3dhFhYGA_G1~!_-!)--Mzd<7Gt$3p<JnF
z!b7SP75BN66J&>$m!nUr+7C%-2HpX~MmKF^Q^yroQ0ppanQz(ec$%*5u{~Qw%9JJ>
z8x%71%9o=V2m20xg^&TmtHx+m;g={;ww{A)o4smUZTt@VNe>b`*CInNq!alEGoyx&
z$mGq`Iak}{7o_#fRgNBEQB#Yi9LOo<VWTe2)z?9mobZIhk{y|`RmJAx*X{VorC*Mw
zkCCEnVv`lV_d8Ig&}p3Gh1+CMHU=|(>)_Ha%O=9qUz?roy)Qz>i1E%752&|Ei`)i5
z=jioTRt)O*Bzdt7*ywZh%!P<>cDk?YBpd8|5m0GE7N<TX78Q8e6jj6P1x5fp2Y2t4
zo}ny@Vvr=9eMj?jUo%?T1JE&V<PVEUNmQ*?U*4o*SbOp~@v(F&W4`P9{?K@R@4cRu
z*x0L|shbS~WH)I9JfnA!-)g^;4H@WaHJ=HU7tg2xku_nr&iSozE0?svR*cfjqQhTX
z_(JZU8M`#Qah00XxLASRnt+!#;kCk1`EJMJy_)=7xoca<5S<mVut>`Cq&r)fNa{Bc
z1ICw(qG!*URXf3-ra}}W;Yxs|FT}uXR0-SAHk`NHUH5(h)%i#KOTUU=e|WRKNcSIm
zwOO6wbw*9>Cst26zq)w&6Gu+`zY%96duls}g+kl>qHIY;0y=%X3%9L6&5W0vhtsj4
z-8z3{T3SMtEIzoD!V);%8_SJ1Whzzg;mU;nsD(7_Nb3|;UH7PVI+XomDqgvvuFJ^(
zX>Fe`Mow5MdBt?P2Dsr7yFuv&`|9*^Iq~Ty?>tX^h@FVlcugvjl{Timb3Qi1QpZWE
z{D*YIoidD8j6-wzL7>LlmeJ)jqS`z6%x@?<PqpSJ14^YAI-pszUoNNo`&NFgrV)wn
zz;}4rXhayulf6%$4XZEbrgi+ecAD)Gq4YUHYf!3NiTy)QrN2PhqFGit>QB$Q!Q+xe
z?At@LH$viL+PXX%)ZA4Q(-xh8rCL96xZBRp`mRu=nv8|_(|N{x3{k4ReS{f#&%Wk?
zVVKlatmU$pe}n2?Y-$3lFH#rEvLTY*{WdOnc?M_L;vj67oTMTb?QX=w*>${avM}`l
z-iCT8?3kOG;DtH>T;;Y<iTETGzc#ib8|q5*zd@$&PP=`tU8%IYs6@rDqM*-z#l*w^
zSFZ)NQ1fc21S1HZ#{t5(j%dayr_cFqCK3Mv?9aKPUuY16Ofj{Tr6%7Dp+6Z&!#u5J
z5h*V6wBLEoO+VbuwJTj{mJHd=T~904Z)oL}dl)KpT8eH~?{lLuyAdrSk*<RahZ2;*
z+^b3HQ_8=Rs_zs|quL{QeEpKl*NN*)JR{gOE37TBaNGEK(gHN4+HZsLDOiC21JBHT
zA#**6GBF9BFh)_C!yR!QwpVK6V5JIgH=U=3q&2@hhMbMYlEX}qMO9RLxzGD{qQ-7v
zJT3Y}v{@OCq`;YRn54uIh|&bAD3rq(A2hONkWHVFw9(ZU4lYJSjHmbHCV0>qJ!)qx
z+fFho)axoI<+%-@VJ9eb%#QXxtkN@snz^WGrWawdPKxvV=5td$wXT*B{J?Z}Zcj|=
z3Nj$0`6}eaB_mDS-}V}mSnNM?)AR9_`i-P*su(SJ1c!adpGC%n^>$68Ur0(uS~;NB
z%wWpeD6MoFvkks5{CK<~-DKs);nKUT*+{H(P045Mul_O#F%Mn^Vczb7N40lYYK)J7
z=IR2=N@pheD63~k|8sM?1die?vg=E2`?;K6Bm-p<mK^=RM|akgHxgZ}p-awo4+ndt
zQ+4X_`3NHCLX!No&V_A5xzh%Ns|mFMParj-K~Q|lo;{pT)acA1H#d21&gQhT(w6>a
zZW1McLykt$pO^g&6A@{^7bT)wIZG;PkTV?aMo*fae4ui;VJX~9_+>!vOm0k4a}x%*
zLq>3r#eeS$th&L~H`l&y&I;3T)v5+PbZ%IRv9?q~A>Wihm*Pzva@|V8pY?MJ3me|b
zGjY6ji4L8fdu2tr*6v<dvN0265}W)k`lzaDo_W)fZkEZabK%GsCb>Hq+AwqWA(@HI
zq2uW6QR~aOnG$Vm0GWc>yj9(U+ovOWs-bZ&2sA!!ny&^bX<BMi+25VithCX_?!jko
zzY`w$YWC)?nx5j*2pqS{KIV7J>%+~Ce$fl}96t#TirF2-XvMz(tBQw$8{@6JgB8L*
z{0ijEooD6P?W`w-V3x4x3caDkxgtk#c0WVCOd8OwC~NHbxw&|f$hv0I@b{tzARol(
zE&}3*{ngG`tF=Inm*Ib*I-H&|K*ftyEFV<p>J66l>z7T20v&BrMUK^shcTlD$)2!&
z5`D6QGPrTQv=zN=2yoX7`b0JZ<7eEw>PtzYQ0@7e|0=C)kk-{g3ZpRi)C^3!!<8%g
z!&_L7FU-EiM<N8|G3LYitg;!Cj9x9Zd>fl4VX<VD*h$TPyZ7bx&oeV43U;BIsAcL4
z@W_<&+b8!I^F(2kNnm+NHAU2MNvEvQHT&&itif~DuB4CUeSqOyEAdz^0kZ`+ncd?j
z83pt=Js&D(q%no!O&)XnV5^(r+e;g6X}-h${Ur}(dvU)7|H`jI3e%E&j<TvdSIHVu
zg=?IJ3q&yC8>6cSGvAF_JKmka$3~V94p7gGvDe+H2qRvIIESKyy_tU1ewy`ntT`-A
zm_UBg*vY8U<RaN^?L40xo&TQ1UQT|pG)&C1{_HBG!Q-a&f{<{n<%OsO-vTBsEC0xt
zTMkWW{?nxrY`qn}ziHLqWi1nU>?M<DVgMGuGsHz=8x_Rkt%w@_J`?H3Zq36|{#@eM
z68iK7h(TFoXxR<yrBT4Iz@6j|ev~e&-Bx>Me_7Uy?s^ax1=NK1B&p>4w-mJ22v;h`
zu6QF1;AMlTUPtg=DTq5Q4ilqNvW=7qt*aJlMz>MUgUQ|C^x8!=q3pL5qt;qSa6ad=
zO~+w8y<)J~iAeegu7H(*wqRl~a#k}@5H;HBI|}R4%EV;+88jwmJneP3u)fGM!<V*<
zP5SGHEZm@Y=R)7H9OX4MvWP?%bT>qqW~9qqvtrMC&5c@Vt-e><%2E{hc8W2j9nXpE
zSeH}W-kCKTdV}0yR39>}Kj3j%KR=+&h_lT)O_fwROc@d*hRVb_S5>QM+7Fd`d0WkU
zYwk~22hfs(={SaTx6~nW4$)&#akaWUu{^j#??-=lh&%1B9Tmi?Z=fS_4Vsrt>)O59
z(LXb<{S*b=v_Zhs##=bP*@r*%*c99$tFP!umy=wW6}C$c1}V&X+N4!1zOXU8d#7Sy
zTY_c3cEAD5At+4#?VD99TXwoMbh4~9#SLl7Sa{=TE3&XMF^Dhvyb&h<=CsQ!QKXo7
z)18YnNKVe<o~7u>3!<gJSYn9lFJ`}vqsPw!9=?kZ(lZSG;IQ%)r1{$@8l6(r_{$@?
zCi>*{@<VqHdAUkQFF&KZUNyHx$`a&{%Hf|5tXGFpuhQdl@%rBgndrEM`+xxY_yWaY
z$Xvm50tGJ0e59R<ocjfh{L3-cfKr~N*OQESNiAlqQ-eU`lGmEH4Ax?~Dc)69zAM&~
zm=(;)bdHZ<S7qtZU4y1hljwStm`Xp-Xi;Ir?-{qL(5lsk&$2NggS)-XZ>?Mh=0pG?
z`$qW`Q?+c*<3ErSX;!QKZb{JbF<w;Pd#9<6m!!cnalHPMCyIs-3<oW}lU_U(S}?u=
zT;?c$?m}bMu)4i#rm)8vk0vQ7(|N;a@;+$$K`P^|CiPQC%xe20hCbFrXck5(X`u48
zqTl~+pC9(^bomq;{M6H8_k^u}zP{?niux*;_MOt9VSg9j*R0hHb;u2YGF~>FErH3D
zj84IjTP*^;F8<hh82D!AU>_m-4kk8NGcMkh@6x~Jv1Z5Z5QTXzljP=KpH2<f9el%>
zs+M92lq;2S`>B4&>(!q4eW~aEBGP3_XWh&_3}83oV_ovB<J!J6F%Es9OxF7HXZlHc
z%H5{ayeJ!Q#wuDs6_|>HkN(0P8GDi2Ap?$kAmne7&I#_)dNHkkzC{`1%QPk&O1rsd
zUbhYT{U*5;6!oAJ38VB$mkvCFoSE$Dd6qQtq<TMoBKY*dLBB0n^^4r@Zm!9gz)&~V
z54r`QA=`qK|FnL?vf3fgZ|&-dpEIRZx8MZLrmnunLOrW_KG;{tAu_(^FacW|aQs&A
zewr!FwjS`;SCv&<My+K(fS$yFc+yKMEYkv+^31jC>X7PHDcW&uOp6fXlPXN<Uqh67
zP%`o0zBsgmA{i-VEb|-^^(w3pf0E$Wx|XN&HGEJlJlXO$8Iyt*woQyixR#1u0$9H4
zYl0m5=ZYi_?E`OpR75*6_HP?~Gou_A=ppI?oModN+c+S90g7TCrq;fIwdAGen$z#h
z-LmeP#kVB~o{hS5VT`racI3-}WYC~OTCxBIC8J(i<#=F|voPCq-WHOo4ks0LeZJys
zbXZ^+#EaE!#iSm}ByPELX_0Z?YuW<-*?`?O7qLB!{Z;eR-QtHRWGPSjp&q&T<V;wm
zPW84>O43~`fl_`%8p2gNG+Bi8I#aoU!$;gt*+9#YT8Lt%Mj$frL@qyQWp1$eUI&FU
ze1rAjve}byr0*pzW%4Rg-!i*vJm2ifV=Dd0aPS=|A}iy!sbiv(CI2I=Hs=uX-PPz$
zlFdIxZX*5aE14im+3%Ez0I<y(K9fW#m%qHf;-`sM=jg~&L$=a}tjvPiYm3RDW3}>_
zg<m?)u*pIkN&o}?&mm)RDZToFA^;E7T5$oE&U0Euela&EPmvYgq32OF!Ol~rB@M_8
zMEA=S27>_#CFg@mxU5*9Wi5TY>-3vrm)vKN*JUX+L;;71CMeL>KL)bn(n~$&c=3jD
za3-?Ms&--&a5M0zm(DpMlZ5kVx^`mSa_lGDkSwh-?F@$Abk^E-=GOCA05hX#pa3%p
zr<f9zWIx^|!g*Lc=FZ7uRWC&b5eO{SBIyISzGBbt;D8teTlF7|m#j!lnRjS0G*~}_
zPc^6A3$L=DC0VM`V3Z$&GxsaHpvc6CEdW)95VM640}CycPCtF-gfcIYUOTfw81Vv3
zIb85_WV-e-b>C8^9ivn)qmU@PNx)%1^kQ#kxY^DyTVh)(y_=At=1RqduH<g-VrTvN
zL4*yx<>ofQ-E?SZ1P>V4A(w84*aYEy*c<vQD0AGCt$AoyGJfeGXLj$*0bBn9no@5m
z&LWKp*dQouCOSDd>3EQAqM&)!X&O#eqbu6~>NBPS*(f#gxmnZonDAlT>-6WKU(Vsw
zpi^(1Q-Y}}%XdLX4<=j6FeZ3bGdK_Er}*3P@eI(R$3$O9W8~BtiH-}avO?vrx$W_7
zUkHC|-A`Jvve^`S+&tYujkF4Sy2850t0hR+Sv=)jdh64}?q0c<)G*EY_ePG4zP0Iq
z6S8g1Pu?ZGF+Ya)etxd7N-wN`67WW|q(LC++VKoN=9NRkgE#kv8=fdCYjT3c>9Tt&
zM@w7ghcd7N1wTn1S$&*166H7Ewi2vq4~S@ld|7??Jm9T-nf&Z?daOPf2GUI^tp@%!
zV_#=tFS2<_e3bO?4#$s2_X5z>D|eC$%Z!hhcINds;Q=J*b4MR-2boaks@af1Whr~&
zfH#1-vPG*=u9`2oYT^1vdp2t5QrY`QRiV{ok1LK(4AhzovP+e3&Py@gg2}{0*OX?;
zpxM^GX)zSf)^`{;@rd23Wy|}+SV&YU_N!3Lj*|i39F6C<N<WBX1?ufwo#Sde>$nBa
zjtTIE#>iX-9NsXR2@P6$ged;3@Rk)mnoyfL>%o+jp6T;_1WRMqrK>;=EZt7}eXz|#
z#AjAm*D<iYz8jZzO^?QcybvtBK^alpzN@nd$`%>i{jKz64%<C#rI;6%F-zx3zMU0~
zVG4bP>R#cmv9p+$quw6B-RPfQ!=O2hZtEm5h7ZE!VYLH1uj=%4SM&$i2F09$2^!%e
zl&?%SzAd>eqtriRrXM%I0*iWEOrSA96CpoD^X9$E-;7<>z;Ke{+&wqN?e!v2b+g}7
zqg!)1wd(NQ=8M<Yt%oI?sDDJDp@{*ZnOL<l#pbmnJGbZ2@4*HjI>jU(<+O<sc2T(3
zSC#1#D>m8Gi0o?=gN99wG>UgAvz9*JpOa0y)-y^A3#QCbFSYc_T7{=A+G4@;e?D5J
zxRJ*n9^@=^$3}-?_Y4pN&n1q(p!;ayjnjwQF+S>lHhA`CC$=JQkA30}HI<s$M}{c4
zIMXs8ikC^_>y1l=cT+{B?K5AHkwM0C(Fxu;Uvm{ZT)>Dr_!t&ny4`v-_d;l%`#h$k
z(YI9UeOilLnMf0;Z%E+f;v~ApgDKQ~MSZp+ZPU!stbnAcNN2dcPsH_`MNG`}^=$Fv
zETYL^qMl2^O)+1iFHbOTW~3%Za(5iw-r!=Y;jMyHE}(1omIm;#^Y5x@1y#PliW{+n
zqHG7kzD=Nql3A>l?}DHgn0hOyXhof)FRi}notWEA73*y0;kx^wNKkhmiZCN)gZ)q`
z%$toGlJJmof<OFdNTUjm#d38Ed(DhrE(34_TL`A<*Fmx%Ur9>;l&gJ^I*|6efEdpQ
z(|O%PXVZWqVjC0u`+Vz2q=4*@uNp9GovPL=lH@O-5%Z>_rD2lW>!3DVX&q%iQQ5Q=
zSDG#xL(&CZ8x5PpPYkrv?B&c!Tu2h={NB!Z)*o0~<)g}v87iZz4QWBPdh^1eI@vlU
zZB&IT?+nj4cGV&s5Lpdz_fES)GDIyQZX?bpQ7cmglO5YKhyn=+dLg!MlSAyl64MEY
zM51nBpp_o1s8Ff&WN1LlQeigh-L9!#0ecPo3(uB_=D;7EdH|iMG5T6ixFHprOeR9v
z5Br+uJAs#>toh^AH}2N=v^f<IH%?SMcW-YXNjw$i0ba`Bq9NazZ$G*r%f^oiroKB3
z+0*EZe4m2P7^|_eO{Ui5tI-N`?@0|GF|V2X*A^o)+u+#bB{U{j&&VSq+19pNB|(@y
z>gbXAEFXnNi$#7Sr<z^GOFMUkS(gxuxW`>V#k8u|r8ae#jR~O#SpQbpocUmZ(m+J5
zps3yn=iyuxxKB}+jxD#@@-G0_uVLkFrtkcTF^RY^^=xDLL4NXg8u)cgg-B=rOO|v6
zNFCaOLXQH3z05wL(ucPkebf$6)&9Z8_Ci8GyvZO0$>G_jlN8UF48edo`V65!&qr**
ztj;Qn`iUkkBwk8&qOAG7k5&}6b&yFezm(ljnI5jxNP<yD<)weZwPDE!^slrszPC4w
zyg88LjhGXa*y&eB0xU$`Z#@p8Z#<K%G9zMTr-#Kr9*YW74AoUe<j(a=d6XD6C@^f{
zo1MBuY!r;7Vl=C{`>A^rq?a{D$KKD#j^6fn^!$cGmB<EwHqlk*D#hLR5X8<INNFJZ
z7f^M=S;E2>1SQBA0;IBVtO@mRTyK&<KK~)pE{9Q$Q-7X8X_fs2gaRCg90igew>}sj
z1ejVmIoSId3SLz!DLuC<Qe)QRGG|j~Q%%gs>MC>R;PTUmiIdQY0B3vWqTqAs2ohoT
zO8UVZAHR=3HS0Xqazn<q(|#2_Frx^Mb#7{wTo}Q}`*^pi!uiL6fkjs-q4{=%H3#>X
zjj3E$t^Kqitc?nKUg1L2E5Q^+Yh)wxbbPa&;jpXNs;mDEO3l1*l^PT|YMfFcyf@k~
zz5J_WT5l;yYek?!_gJ~X2BA^)7r>y?BDd0KWX`6SdUus8>*{Pea{9xJ1>33G;fdT!
zac3&X_NJkZ3;64=<5Ki-37>#_e<<wsbBb43Q7+G^iRX42Pg*%&3)b?+Yi_xn+d*ik
z@G)v7nvJs5dO)6Wzh+7KgUf8AhFXErr8@b>=+d>ra@}#QKh!;TvA<3KKw>?Tp0^M?
zPpZk4Apnx3VvYK8dq%a2PsV-V3AKK<Gb9sfelpEL55Dc7=eNt(TWDqx4)Q*p?2+tY
zDWnR=o2Om1Fi;jps7Z`uNh}B_*3FZhOmeUcM6%!TQiW&jxzcH<&Bb%Xhuf>%PhO5r
z9t*!O?=kZ9#A5F<RGN^nYWHzYIGhlj8kY2PLxWS@$WNV&Z_UR=@5aRHAEl474+18O
z3mriLA+2QGzl@LA8g$?APE`KJvf9guH_sBqUQP$xe)uhP6^LPHiz{PTBSe*KxZFK^
z{~d1<cX}YCk+YF>Us2y1$G;T0_Zi%+Q_dK^e!D2z?|1ifgq>&*BjtT=&$IwA9Ki_P
zklm8QjZtK4-!b?&Y@sD9Qdy7xSkOaGMcuVx>mUGNcybKx(D^n@mzv_%^@R94r`hX=
z_E^0aD=(rPYyH}KlN()p#%dTosPes7FhKkDpCwD}n7y--W+dAut=iUgVEV6I=IBH7
zxrTj0TGe6sBbzTqw6xiZaDt<Ok+88RV1t2?mBtg&CG^JZ-8IENMTaQ~lT~XYb;46Y
zwhzG*jWf0)VO7D(DM4_vCX|nH7}Yzibqkrf4<GFdlv|`X;?{Ca{l%vEu%~_5+#^EO
zCW^)9^4`xKaNjd1yhH4j*~=tqAw6-et7XwCXn?}-QCZLWPLh}O$LSoW_rH>mR?log
za$=*l(hJ!Od1!{x-iU-82TO`G^$@*cTUryzYp0&2#}~~z=bR;-JLn;fglS~&LgtRi
zM*jksz0H1$UE}}$hUJNY{tM6aCqjfbtecK;6~dnCiZX+jqN8UY%W4bQ-)q3_)k6h`
zL`@dUoJgER1)?!nHB6NHRJ_3C$N|VkLfXvw)NU+rW%+`L?nmLCrmj%HR1osUh9Exf
zUW+rCCFRw*&o}gy%HLF_{slZ82r0WOm|k}|!p964R;vC%$HkmYk{TMEe+CxKZ9O*I
zX$L<ce!tUn=%m2y4Lw*3*KWdyhzA<&s2tHLYJRzqxy&f&qKnK#s*~x?bv$pM-sWWu
z^$bUidEEwWh9G%Uj~D5vN1KRTDg7@`0NgD#4c~Ke>P#fUb-ct|Ev+T0I-kfz#Ykt(
z`#?|X3~!uaii`*9b`N|%f9t>1>b~{Zuxc}|fk(!%wJDx4=r3S`5Jh;Ih5t0LQ-5Bj
zqFuo`-gTkv#ymw_^nLp$(^7*sZsNKr<^3{I$jLzl@-Kj05&Y2Z{BEU$FXyutp2QGM
zkdcWqObxhjqUyHOvssc&1ilIv>#G@DQ48z}Ot^)Fl|Q5i+d?())`DM3wEH|9Y~sJy
zifcAvq=lw@REoHNSFGY^ME5Jy`_F`z?)o;8ikU6!b&%cDOw3=vFgWi?zynY{8Zv`K
zk38=fGV8poQuwpkFsO`~wcgV?Tn0DK{?x<_fGt&66N;beY+ks|tguPMS5<bB*YW{<
z8En=LPY|6>Rd)>aUyC;{(Ln?hWj7HDc=ZuN_-mQU+wx-f2z_I}bhU61tdcC1XqF8H
zwp1IPWUkSk$>8L}tlAtj5RA``#<a30hwf1Sd5u*t$$FJ0d7u3_vnDyCL%Oa~DGB#B
z;<>Mr1`yV5ZRt~iaS8#9iEW5vXyyD!<2+^~W&4I8rF{lS^Bb8iFU>0M7|U!-|5Ekl
z0qaw1WJYYY?!W->5;Mwr`|S%<NHdOKU4ZT6q*llptL)~>+(O!tZ+9E=ff=3hCn`UD
zgbkQyZZK=tuXSVEE0nNQ*49bW2|E0$QlPf9R5?6X07f*G#pXnD_T$Ku*WfuN)uMWX
zhqV#~31j25nne?I5$1`Oivk%JcPn2hD<(=!%uvG#bA|VZ5KOA@QURF$O3{qn0BT|;
zfE;9GED$!Hzp?^x6LXHFyBYNJ;RsRJcTz=6y*S3SG~G0#leSMR^>BL$ZoS9fx`2#A
zq$6NcmrpAou!mWwj%UBO-&dXGq!5;puFY(OzqX&s7IZUBz)-t#3W|Za9#<5sS2w-W
z^Oe29YQm6_nJHUkIe50^o--G<VZBl`cHB(6rXOCp^|nA}8cm`SMe<HK>6gs@#^)YN
z9lWT?3;7ctFNXFPj`#AHEbYqiW;h8gK)Qn5;frpjt;w;6oZinTc&{&r8qBhu%eMf;
z>(ig)j_ivN$CCrOQZ_#CjV#vb{h?{PO9)!&$4;cyI|#a^vwo4(LmqBiiUZixglS0l
zR&u|nNc^$Aao%>U(C#5Y(OaY79b7qty7_EkX_{P;n(J->b9Ay$tF$W1pNqeMuJ6P>
zkq*OO$Fc>9yd0rcdcG4sjtKlTI#2W5l*~;5eeo3@g2Fy=Q3Xf?hj8e%()ZJ+8`ur~
zw#GS8_kL?pXAPZ2jXaB?7OpsPN8X0=ilSSe^Lu{`<B~OR1Ij_4P<>{M`nQ{Kg_JW!
zAQhoTrY9_k(1Oc}P#XtNwO8IbQ@@IGUH~NsYdK?OB#YSkX@ZUqBO(8X3Q}>~oa3!&
z!RTFmC4+GOps4d9-Te~%H2&rXeY1kKx@Nx8u&j<FvUBexMYHF!=M)WnrpduZ={y?g
zuyq1Pg6^;jc9~#*!{CYL2Ici@V3X}s$JhEUVmvqZ*J$lw8tQ*6;9c_`%k1{yL0<JP
zZzc&6HjDh)Vi~lE1P<Z2_d}`W_oEvOGc=<h)uQgiM(gKimC^Rv!=y^rgw|Im#A7M#
z*%GXUPhSPg$55+wHznt5ynDSp{KGZc*X*e?)dL<eeHKk8LZ(J$JEV&$WAkHZ-iEui
zi>HC!CKc#`gMjO8ILaLlm1s)8PsBKHh+^7ey)}GC!d06-kUGNBF)fQC^g!SSzVtvD
znPpsnZ?>V)FNwGu3H&TOvz+{-XD~xvEPd8<3+)Q#7YHJErm_wC`XpuFZ4yPiY<PFL
zIg$3=XyIN!>u8V4b!IjVv!YD+v#z1E@apxts32jk4fim5j!oB=(FZO?ou!UZY}d<p
zlqrYE_e~AAtL-Dk_a72(L)j}2UV3~={^TTAR!BWKTZ%~y6jm}v2;~dwWL%a|mfF%l
zfXLs=2c_g9l_eaY35=K;0!?3;LI?{7%ZA+7poPf~L&||%`(ITIUvS9cGqNl|dU~@=
z@DMWYXJ36lnAIFf$c3u~6*H9=;S6dU_=gymHS*h}%527qiD=>hPAkbbTr(^R-tMK2
zKN0>_fp0py#L1`FRr&*5;-DDr4LLD|sj;tZQ3zKN@*cwl?-923bSUpcacn1JL{nYv
zZtft-cac}hZN}n*gHxCHDFv?0CxyQfV9AWc=f(?@7if+U5rSJOQNp>2UTVfCY}SbF
zqaxj|>0{M`iOe$Rt9u3RW`~8f`<w8uFvP*T5jL*poV&UFKk3gO$DyuH;M&6-oSW0}
z<9}oohi6#YA&W=1ULoJM&XrFMXe2I24yPAwfZ@YIyLzhljE3}dC__8=bd3&>k|HzD
zF^%<vZmXFYXv1n)Vnof9Y+0cMr-n|G%lk^*0p4e0y+Sa9XAC6M?TZXm7hFUdo~Gi-
zhV!vTPqH$VmB(n3k(azfm{IlZ2Otk;KlKuS`Jma*Z_GNoGWRm()4?e7qKLy3j^^nc
zd0@EPu9FV~WS%{L#!(Q$7ma)1$r@!eI32~HF>opSyX9<r2Y;#v)4?~M-|Jv>+jYUn
z&%KiTR$^Vi!830A>~toy)LJOz!?<ID2wU<^$4|xM+~1;PV1k`)q)H9ZuoiKi!(-ZF
zQ}IxIcf5{}(NP6?I;#D#?OuaEJZf7_a4)!mbr0nC+w3hBSw2`@BVNA;C)UT)M9*xN
z@s&(P*RxUmjWSq-^Q-xtmZ#IMIu=t#4D5*34Yod4TP4<@pB8Wah=1sLf9PzcaWAJ-
zDd&$y0Rzu1^~J06#p}{Cf7*Y4bq)_1_qC*>S86`uLBg2Ua4rSk58ug@-Kly(2<+7h
z$4T(;B=Ewob?78uEe=6);xm2g<$@*#FTG96J_{Tve(iRE2dF!0pqX(Mqorx|Jm0x?
zn;B>Y0=^EuFo+Rx8l(n?tgXnM=cN6S#$h3#tgPlvKPyb0qr#omugkC3hSZIPlx`Qm
zvZDfran~yU0tQVc^qq>L(4W@B9>`>is0P||d53}ZJl!nJr>ead>u!vlnM1|`e*Xms
zx4$4%27eqmmG(+M4H<{0|CsP_*Ls={{hFIIzEq{!#hDC7*rt6dN<E!2E4myRda<DW
z%CBW0Ao{1BF6u9UJV^AmyS_89vXnQ1!Xj@23i3g(48P6dk7CE+qB*l|kNuz}*g^8M
zZ6m6s5iP-QZn(Y-iFSPajL~DUN4`tx6KRcRbw2Jm7)Oa23gP!<o4;o)s6^1dI~Cg^
zST8qQp-Z9Le;~~o=RAs4yH>A6FgHCE_&kKXSgyWf0<?{Bu-xKz2?>T6hkPnzXg<I=
z=etjOKa<z1z23vi`sPiU{*d@+K@z%}cNpEILlB6z{Q9eQ*6Hw;PLFks1*b?r-q!>D
zq4eo@6Lk4bK|I1#K+_75c0QPlwJo>K*xk=|54;NMt!uuNsV{Bfxmf#738k{MdPf2n
z`^po}=l7!Cwd+$7`6niNYFh^%l#vXpwrONCp6VgnR?Ol9Ha@A@J)5MHD>i}aKH!!%
z8#3+sK|2b=6#N{XdVRy>j|Qf&G6?aq;T^Z7iC#t(IdC_9tV-0pTutTGQtR4vtZYDU
zbIrRyLZ|hq*bmh6^7hgK0&R>QPR;_(NVA&k=7q^Y0SYmUS;0Q<;zexTndS5N4(}la
z?oNwrZo7FJt+-SD9b^q4b~Arrav(?Sd4|`mhi?prte4`S9DS(ncm7Ez)ZbnkPRDnN
z0ZZ*Hz~hg!|1fTZ(Cb@S&`1mgxM1n)e3!5{XI)|f`M`@)BCM(`Rb!v<;VK<H&E>U$
z$<G29#>y09BH;tcS-v<~`(RqLOVg;Ue;R4Tq$+Q0{8%JPe!goy?$K<(N@mC(*`}#p
zCa7nmM7qGa9Ny3?c8=&X=-Bn#I2G@oWbv&XBjuz)PFBW!xPRCuGn6O?cdqhk+<Km-
z>}9xLsyJI|)dcQ7%PI=(?BoP&&tt2<9Udg<0YlLFU&$)A<=b{O_@mAR-C8?(@HZo=
z(<%gUV5$Ho8siaRU9!-@v!|ZZgpF6OhegI_&2r5?H0q<e=f!or^nU@)hub-hC%5mk
z;!6?w(<D|8H4#k&XSTa!inF@V6t!`~B`ufXkq_-R&66wd$d1|El;vM(TdM1W2Z6Z+
z@~6>;?3?zPIjLab8y=$MZJVo$*EQ>u`JXy`1j@@{YN;(mL1~|&H)xKF6gTz{uF;2>
zlL#8K8u5TZ0Y4w1R;%MP2d{s-sr_9T9M0c8=4u2ZH8R6T&m+xfBR;|_pJ)&h*J^~O
zA3t*oxnV-%*;pn5=I7j(4pyT8EBv%daJZN26^m<-IHEHPgRlr}1a6>nI=lTzgH>Xc
zOdpn?p1n9%*+jR1DYQF-!RDYza>CX={rqF7ujf0siB^p)mLvf$KbC5e62j9Ry>T1;
z08;FyJIURCr|gsXCQG-TRi;$ES<PPn9(*ljY#Z%mLj&FFp`4m|&Zw{?+yf_I9O+X&
zf^a(i&qoY1BVAOMI3`ly)KoSL%MoYlXUlUn*8|Ndsc{I(FuIWJc=GI~1n%SnBWajl
z89r0lk9fb}nGBFv9h>u*DyDUV6fUXMNCGxnuI~dAUJzO8#6NUx;9&QfT9Mg;f}KN>
znh8dNpshkl)u9(Ip%;NKf)ibmGjBCzt7M008mx$5cwqO_a4>(~d%E{CXU4r{SJDYB
zyeL}rZmBX_5Md{4x5A6TX2(EleG_d{KzzFdYm@zcX9t%pt~iB6`_&7G0X<Z33QozP
zRFs`((nGzQuM4ez#y>4U-Bv*V<foy#&A0q+CEWc{qukhal~+%D7GLDsOX>^6#fu1z
zWd<JEXghF&p1kw_*#~^ykuAThtG^E5rDlyw$k<IVt-TQaJSq=uc{s<SvhdrB^nsVm
zM!<+Pmf~vX$^l@-+I$C2=51VPqWy=za3BLe_r9M|t<B-A@7;T)^&(5qU9erm@D&Ks
z`CTV}TrOZE_4%o>qkq}J(~TaA&G>H&zxkVtNSv~L%nAX<TswMwDAVEhr#*JWYT4oB
zb?9s0J9gZ!U$1_kW^8KWNE*QUq2UA_Tldw7hE0oy8fOdeywLY|Rs)89Q)-kbw(Brl
z!&LGMLf|mkM1f50kcKe5pkI3dgI1oz8#B9obFWXUgG|D?6@TE53D8&}d!sLd6`yma
zP>bjFu<tO&HQK@HHzz%YQ|8)vPFMLqfnoGPS=F7+w6eVql@wI8IgCD;#pyJ|e@qMX
z4!2$w^;>N~x6wrrARi#y%DRM#cF&C30@RoX-OezPAI8PYw!62fhHh{vM*0a-Kb=qX
zru|7^P<>Cusc1?9(gQ3jLoUl~7(+VlXa_QMZEj86vN^jo;`*-J{PSPH2ki$=5Nw1a
zxE`6k2HIl{8h{;DTdr`ywHuEG-ZJ!*${#kbS(nO&=<ZLsdLDNoaRLkU`}gV63~XM)
zi!|FF%qLts$>2@%7i8@Bp?AW?WM5r$g9`gnW;+R)l-b*$*?EEt+alshA9LgB)?M*w
zf9@(tQX6ps&?#fF19D!_^*p!4%}<dD#-FXGl4>b#H#$9h;Mb!u2&V;1zypoCKfEJ%
zbO@y~wwB;gtTT%a*8C!&i{$GVDoP8d_7>nFTB&ozHZwfTzEOOZ_zVk;%)UjjsvD}@
zU{1Z*C8Ij0eMcHyrmyJBykxXmnjFunS85YV>3CPW`<qY8SB>wQ&ppe3s(g7m@?rp)
zS)Xb0-APx0`>mn4LACDGYF7Du?;nN)X?{)DQ~EDXrByui*<iu?%*ZyMMb{~Y#zJK#
zStaM`5Q%mgCdo6FYMqptQW0O;XgzY?bCZ_Hr^mnPt}V}5yyaJ&zpLz~Li#pF&Xo>{
z2sB27i8Tm!H%;3&KAn269Dy&8Y;MXN<=io=eRRnX@*BPLYTbS*yZC7Dt7}SVi-J!!
z<ijHmeyInVZk}#N9AFLso>N}z)fG-JmBMF7`0w^N%u7UNsM8&kDkO=Zo4LA(tk&5q
zy;B30AVhVuPE+@ur8Kji8QTsPM<K9Ku_6C@<anLM^0a1_`mIff!6#R*=FJ2K3AL;;
z(OFW!ve(6C#n^OQ8rKEf^vRDUunwo{)y<K&^OxhD`D;)-K7-gR7D<&H=Be~+U95{Q
zcSl;DYb;R>LWa+-2QmOXlZf69?_@PS#jb8~fa*B$!H1=7l9ZCcOK6$(h$eWkcsgxh
z>1*)FVwr#6)D-kDU^;ur)<JHbJa#VA$~;ulMDaz?fb4o08Mar_+Gw6ku9p;=q3ywT
zT_9`{c^Ncppp%`EAMW1u$3wT=m_+2oxpP4%f6H(nh?tw8|9$vs+pGvK%LJ6D32{DQ
zy}O#~y4-cE$jWTlR;9@g;dS@VhVLY-@&0XANrWFxCHtE3wiuu7QmW&2jHp3waz?+x
z;BnCd3xySvVTwow+O?v(*Pi*)H?3Om_YE>hW^$JX%hAP5vK$`%)zg`bvwjA_aefac
zvCVT?nnH>hhRyjjTdq&XTer1AP=FXePj_*vm4%#BO9!`FpUnN7EFzM5MIxRw>mHW+
zvj&&&6O*DsSdX_A4|#Ke?$v>(n>xQr)5gnOauaC2wsd*Jj5iTU$FY3j5`?6}N!E~j
z%D_Z44H*9)0HHu$ze|FNdv0)OCl!N`MkyMhdM2Q}Mlw$IYPCvGBL=9D<P40SeQL>W
zloCM%Qku;!(K}8ut;c$~+O9fqb$ixXY@P-@!_ZZ#-Et!XIQ!8-HH%%$5Io<lS|D73
z1Ac2Jw%x$M*ZS3>-+2c}*TocSG^Q>?ABv4?;0`?1MU8g{0INTGkZS4({6ik1m{yCD
zXa4|;^{10K9rX{+stCGw3a8O?2YlABQEx=;@D)y?aQ<qkBIGgn>Ga%(byl!ZY7>X8
zDO3Ld#Z`~mDIBVXe)5LFA<7mSEEfx)Z3eTRtG^u!4V)`}4Lfw|6(|&?!}c{xyGn9M
zQ6%7ZpxDV2`?S}j&CsPSNm93MESEL?vN83|YaiLxS4mrMS4)dUah{KEac-m>rvs3s
zg(wk?@!GN(jK+Dwi7|qi2ltEluM2GZ<bIW@{2Y(du%dM<a3!>_63r!VG|aWhbR{V{
z3IPGWGmiP7*U~yv#{Mt$E6aFJ>6`p(a8OIBQd%oZs5=pm2`AVL8lkZw`EhMcLFya4
zr{re}jKi*!H~e}^lcW>4{J`R^eHnS_nYNPTE|tVMsv)I4DW^3k^0ctWh_=tVmXqz;
ztq=Tn=<bn1EpVERZ+sysN}Y(cB#(9AtR8=aeQU^g);YH+{{X@4Lo1dw7((CY{FKWV
zMO|(VH6mBTo062Ie(l?%%`IWDFfhwOMI(}RCw=PT{gUMshGHh{@me&cQJ9L@3HSZt
z(w&AmNj`v7E$8DmS3HHwi*>a`jQWC9?09WDr-pxp$6sDQ>HrT)gLmTKaY=UZ)t9*o
zMzv^za%)n6;W`*gWAj<9DZwwKon&w7C3=cSS)wP($La}fg%A<*YA8YyJ7KpFQ}<Ga
z+*V_Ho7641yM^Pap}Q6!SxZb^*-Bq<3~11zokL(%xz8M&)4fUYBcx;p6r(MfODF<G
z+UVL!GyGl|ZD|M7a(kM~Ze3eyxLi>G0E^UDb;imX{gB&>N>Vq|hcu*toazZ2@ljl*
zx+z?ATkW$S>n0ty+~l&kPpwWxkbs{ROZ-c3$_h6bQa{9<KgCrG{jy^WIEU_#Zjj}E
z>6HljQsVd0okMgh+wvq)5U*02a9kg>+#xoX0b$qefu*59<Y8%Z@dJV|0nJ*vg3UVb
zZ;#e6Sa?L97ToyGz)IQ@d2tF#cfy7pMmNHg2I!+r_8YAuJ?U<oeHTl8>d5o2Dl)!R
z=9aG$!cjW~9DWiBKwog64i3kB_;nuBZtCl0_SFo=L$m%yU@2M{OJ+H3dONb>Tqq<Q
zpf5ZnK-C+e?>3aXaOvB#sJFXxgqLH=@eT$Qq@=A!a)hjG;M^qPK!I0FO{L~9Qf4<j
zY%OjG^3uIdr4J=Ki)sMjN<eiBPxG?oMnJ&LH`zO_A^!mFBLS%?xm$>I_{#AaQh+H+
zu$&Rx6(t~UHaXnySZBjjT`tmBYLwaehHwV5b+}Rq(~+E=haJLwYw2%_HydA5baI>$
zVvB@CONjh-j<qu&Xi{{X`BSAXU>x#siuwNlr!6;&4_3u+H0HNdRNBBg0+OKSMov!S
zoPVts`dtSfdo@m-wxzoqj*Gd(L^a%GG^R^R%Sw!t7YYi21T4Ad83S~Y-l`o+r+R?e
z;kLscwB%e~>3Odqpnz0`1x8xH<1DF1ZgihYyxAQV+x+{4>1E*!r^FN}1f|DR;$KT>
zC!gLWzz3ltgIVWNNtGY3t<&v64@S5_i+qk2bdx?i%2-Q@7(2DCvY*5Pxvt+!CsuRj
zNwZ=2UkW|<;cdp@c9@w@+?$^I8Cgj1U5zCLwPYNEqUNj|WOg-_b#3Y+vfMCU`PE0)
z4Kf@9QcsSh<nD3`)V@1nv~P|#DPIYCQazg2GAoEcR9OpbjI{Z2dB!+UNJu>X0qI{V
zLYr}!bdO?f(1|l8w$c!_X$ez{01|$R&2=e0$wgAOT9I=y%c3}OPfG#9p^wA~)wiJp
z{RiHqrgc@h_dZ?R2HJ=V2xVWpTGF(Flb_+xSApE(p!!bnYJ=34%VegO<EV)(Ds3qM
zy84)OAq1YqN=kF|sBWc=Vzu?B><c~B$uLT7#hEX+p-L_=!rW4T#x$vG8hHUewEf!;
zM(H*cy3_FT*?zEI(<U?4XA&4n3RWbtw}X29NOKrF?Y2c`JvkNMiWf;R0*lvK2}xmP
zSW48Tsl_S7>!qhL>PQv7Pw9Ay=o$8w8f`sA2mRVBI!}hnk>Uit0#7Mfkfno+D1dY7
z7fpOTvUE>VUEj1xTUPsWx@{4h8dSGlh?%cGg<x!whgbynB<Iqg@>FehW$;z(w_c!|
zVYKuevGlZ=n%3DZ<Y_Mj1QqvEQugP7b|W-IcNG@d0^u@qujx~crIyC84X4A6meZ0m
zfR^wMF{+=sDr~=qP%jYUt{uK5y~Q}n29m~IN)kaED=N}I>ig6$#OI%ta#gkjLlrA^
z3v@Dbp*oaGQJzLqfPIa5`La1WM`kD`vZ}tV7p|ph!PCllw&oUKsh|p!Ji1O*?USEx
zTKWdlDbAbtTuq`u`N{9bw0`#**6K<;RRj2Pp^#OObfEV&iF`Hbb<gUhNrpU9+vGIz
z2}081#!~)3-%dE=BQ?GJQ@+Zybp5*2+$b>Mz(S<SlOgcZu)KvkpbgYiPLwCiNh&&V
zyd@4=L%fve`R+yLHLlHflKTv{<=*2MhLMH3m6b>iD>&1oDM3mzwxuZJ%UN6toO{Ht
z+A?Ake$RCdmKP~3$B31jl>@@xJ83E<Kn+6&2DRR*>BggcKl!UAqEUWhiHsj81;(30
zUSirXe|mgkfPaLkWR&PNgohcQaFWavKy8SByDC^KZ7W*Bm6QBSP)W+WBm}3qt7C*#
zJ6(~<k;--8*zCPuWOCdGO7!KLWz4U}Q=c!Dh39gmyr*7$c;!J!B%JB1A4q&yCH5{i
z*;YuCqA6`8IcQTQNX`ptemX+iJMf{UK#Y#{l3OhfG^9&IjJooPSY@Czx^tW-2UdMI
zN`+~bxYneylyyD+^tKif9E|*Q+&*7c;VNJEjiCPkD#Fvx9MFpJ34EVH+h2D5WsiI5
zt4u4?G0KY5WVxXdqB?o-oKPVxsX*i`Eg8sA+>sM^x7oUyF}X10+g;6tsSY(R)>)Sw
zc^Pf?T2>YnjX}m1q@Bt^&XEbxy;R%@ETY>c-7`XplO4GaLbwLAf)X7{!=oI8sc75y
zM;f!e8PneoVy6E9Ze^)UbssG1SzByO0RALvLW@gQMpKmX2mIC9iN;zvWpH--GI@>l
zEQpdmc`_wPN{+da-;T6gLNHU|$w>)JgbZN5&?g(@f@(hN5*JvgmZrSVQ6WKUYCuE<
z&RWsu3oYtw7g9XMAOf&R!Agp)mmYxlzta;-{SVc#CdG6Tt9AO?xKjd~-xwez)cXYm
zf=-nbkSUig&visODZBLi2p701E=aYh_L56^8Fn+SfTZC;Ckq;P2O#Wd<61+)OSE8<
zEH7GRWxI{lO)DX@G1SUV`2__@C0Pe4K3>$jb-v<e{Hq}#tv*uPv!wRQa1Z&`ht>Td
zHt!ADw%90s_YjjRJ4^`ET^P!P@rfvLc)`@1>c)H3KFqe_hRcj4hnD74*wv>TZj-tX
z({F0>Jx)Jr<j-RUPkN${qd&8ivU9ii$daT>c`8mG^JN1EIRxYU>*s&%P}f?^_^)@B
z)Sx*TE}xTIZwFEt&J&Z8QhkZz7_X*&70A8V`fhJfD%T~}BSUfa(-jGj>eY?6Dgzkx
zP_K;orYrYL^!wY*mV~#~@)IG%m$Z|R2+2EDeKTv*?)G>;>dkxHSAw13t2+Z;4G>15
zQ-2}b6oUi4nihEt5ka0dz@p>CQq%^V`cd)iOK^-Nnw+=T)N$neg(RBrG-G5`dt%lx
zq|xnJ)u(jTrX7gj5|7TMnXzDF!^rwl6;LQf#(B|Axn*8IyZhFZn+KQheqxaAgkY#Q
z`cX)37txkIXIeoTXeVJTa+=d-SjY#+Dgt$!VE*qPYBVc>NO6KT<KBjGpb!13esnc7
zq6p*KqMmOAjADvHxRI{5N7K+ys|w9YID^L6q1MiL!0k+tHi27Gl4!{8p7g<I2P5^R
zwvwK3e$-fum1Sh$f1Mu6j>l?=j;=6h_E*8lsl9-dVC3(;NU}B=%|tNgn0{3#$<zV$
z?@*EoGPfB!lR>)C;~!d=alCe*-ETQ3*1n+jlOmjKjj4qo=QSeg1q$PShZL4F0x31%
zlby3f7QyZBUhs(o<Mg04Scb+3qMIb1PZY`#gX#3A+fH`P6itP;akUiBBbpH#0g5&<
zNyaloRvy7x2DF?JQZeoe7)rMQ;CobiY07`}Da5Cf=}fFrX%djM`M0hu&0J87k0>ID
zLU1xFh+77cs1kouK{`pvsTWbUJMw4;k%CThSXRcGbxo^0VARAp;UEm)gHTRwk_B<O
zMYUibtt|<oK)2jRMi<AuSRq|70M%KfE13ihRW}yvx8tD9nWjR$X~*v|^{Wgk?lFb?
z)<VtXAdfvuMCt$l%bZqBd-*mvy+Mp&T>k({mXCb|4>sRg$0PL_+xN987pXWNCPgv<
zuD5$n^PkR%YW19DRhBW-VB-#cw0k{6fIRryn#R$mvw0X8P{-DbMbr_EXZ-6jV*Uxo
zh)2?-qu+K1#LWQqUsrWd8y-FC8s~Ue1Ov@Oi_!{nuOfw!a%s`A$@ihrg)Zu%?gF_K
z6#2lQk2dt$NK%UNkbd-uEEgLpIZ35#bgZad*0ngIr00FJS_k%ZhULeuoVP3~EXso+
zl>r$}f>eT>5&;Msoyi~NR9mV;e~TI4nw<DHnGbwHw!@3m?%B+TFrtEVyaxI)s3en*
z;vi9JxUsr+8}O&2+2~G=>L)|%RB197BsZXiM<XcGQZ^V;F|}b`VRE=RG5Jy?MQOy6
z+RWtzw(2>~!%-s}ZJPUAuDTvYjk}ATJ0S>Ehh2Tv+*TWN#86Qw#zK7AI5|#9^{<OQ
zJalxZ36puVz|$H@@ti_V%{-N1#17!(A1UvQcCQC4Kk`#OZ%>w^WEH>SGz&1)&E8AF
z8hk`UVND@m<0(*az!W=qn<Lt!E&l)$z8guxr2IBaWvx5PjdXPxImp!+r8a^+LKd9X
zJ881rdV(-^3js_86sh+d4X~Ua{6%1WhE97Lwp#iIWMr7{TW&MmLIa>Q*T}~p@rPsp
zwf$1q9`$I)f~GvAvO8tH{-eJK-=EQN>@Gn_D{$J(ha|EO1=inMo<IZmf=K@W6+??B
z#p}B_D~0IYX4+drPO~Z0Jo5-iI9N$i(u9l@@}X9)kLl+uv0b~_9<nigh=Sp6(%NTD
zXn<6Z+?*|CSV<aNTRZnx1jv6EZ9Qb=x!$+w>$SznNK<ONO?<H_C<_Qume46>;GZGV
zq-zN|@iwl77&U1e^P5^IRw(^8!pq~R?TtuuDJoMkS1G}Plj0>hqpSY_mg2wat=IcJ
zU;1Lg^~%9}hQk(X>e-0>&MbzP9yG8?W#z<5))10+Iv3dCKAGQ9bQGIo&0OtmH+YQG
z{tA9ou^ky!OKrTDKvPOr;Q?Mns{smGHDvfoi)>4q-A~g!0V+Mt4rBElqGH{7^}tXI
zIuPQD7C;5W;1I1F5TK@&MUG4MIh^9w%}e6P!-zd04Od>Ev8E(Yr_`U9I^#0pP9JZ^
z8>J4mpyO&7NlKNe0Zr9*Erq*7@NJXtGFn&4i-bFXT3l{qc#&Kzc%RIqvb2IfiLkH<
z(Tdw$l-=W@jkW1Au2L<HwK1St!frOq2T`CAhev>a3WlX2DoOLC)+2K1bXa3vtQ{=k
z1ARRvRQn&Cywi}@*06>#f;4!_C9~zilt!GK%4sj98ZEuD)zgpIZ#J0n9JL^!W9R%`
zO8xsK#D|mS+$lOi0bGYwQj(y$ZmwI@l!?;})VS+yfTWm_)Jk-y?p2{fZ$M2^EsK$L
zd2m@vF<H1d4ZC};6&DcPsHMkSSyBH0hk)8tIr|FG`g$AvQ8L-rLg31iE2mR%b#FG6
zfa9?OWVNFi@q8fShjIZm&atUFE$(qfLw<#RbF9~EwMu=VYLwN*N@oczElnh)7$XVE
zY^(xv=OfyxSDVB;m&b0MeWsM)c63Km9ZS-c>rivTQietpRii$N-_oyt6K~ebC8}ih
zBRa#*-eU*6OzPB?6@{xA&IE-3PrtQ%+QstWH{wA@%)1iohmf`(Nb^QpCsK|F7;Ply
zw})Mr<58qdzE0V_{{Uz<=g8^%W$VXGseM5aH#trr1NW}7*NZ9TD}oAS0kFrD9O9_I
z55HwxI$x-IfY_4t#SxoC2RTqu+(RxnpK+9s2L*ohru;y*C#bqAP0nzNi<8!;8MLys
z97Hcd4=F0ZAvr=pDF<TWM%)a0;ZIPs)$w*a!z`hwJvk*Ug5U{5@e<6S>CO)S04}_3
zx|TgFDLF^(Z6-#>Tqk<^H-jU=Oje7YAzrMy%VA+4o&3v2Gs(`>&&3uW>D%mUqM;@@
zx-Cz%>OsSaZKj(fpI;RZZGd?vIjHwwy=&=bZgODn1=ue$Icw76?z%sEfI9#ZoO`Q1
z>f9r<@i(RHod<D{Ot>?@cPVO0Kxx>nI<Vr#;|gr2AaF_NHEvbE0?U0H&q#brS4#Xb
ziPzUeJ0^4TZ9pS(OaU4;u;Ph2RHB2q87jxxxA6Y}<C8OOw|f=o#i@qgxZIZEAgCqc
zz|o#ny78XL2hxZ5&3m&s@Nx>8wmS}QP%bO7-Uz~3ffm{yZD9%E7as^G9HeJ(1$^=F
zR^blk))v+;&M7wOueTCv3xPR~>d+U39{Att1xt;_*`n2-T-z^DM^m$4xJhxxWkYf4
zO(V<YG8=C~3E=8yP|q9F$mu5fmN(7aA9aaRf(L*az;G$m_+>rPk}?TD;oNGf?^X02
z(Q;d#5@4BeOqW6nS<Z)w(#r}8IZj+sz{bFNyVRW7H+v1a)}Oi}YwR@JY)a5lR+n+%
zB|3dTLt#ARgR!n0kZqk1nn<02(>D&Dl2aK;We&EaE5ld!E;fZFu%b8fHUh9enC;%9
zde5nthiQe?-9Tc;xX8JvIVELIGadsB68h9vooXl!0Ca(^cmSMIX7#PHH<_2o*C&j7
zW39|^vW-boib53Qji)#&Q#oxTI|c2Hs*`qRFIZh<^%d&XA|FU}i|$<~q4x-CT*W<z
z@Yza+brmHDSRaJ}M>$xdixZQnbS&~Ev|6Y2eB1v3S4#MM5!rFrw%U-?+k&HnDldwV
zw+O+&KfEDW8+d3)8<SY=yP={_vjfu%xY`o2ArWG>nNMnTX^zsAt(5A<2^{D7Ql0k}
z(`@*X{pRN?+~g~5&Bmd|F`FTGSM()r6el{LC<-G=e1T4HsEwh%DRmRQJq_Q6{{Rha
z;@;aTDZAVnPBgl-oW}}l%;TgFF~|e}k%Z+&-q!R-+;v7|!zZK`s3>{qGJyX8ew51!
zR-ZTb0ci?I1BGeIlz){w84G_~^fOm5i;|-?af$#ANQzSIbd$mqk_g}UicU5gRSqG4
z!<5HNPFK#P_)_Ow<0?mhjD8fYg`l!de-gRutZrdniPaFnRqk!oFj!7om1~w?2qa)>
zFCk<go>Srwc){Yd@hNqoqc*!fr|I8`-8FTF<Qqp)N4!U9rC%d0^gV^=1Su*hQciP)
zrD<2FttX^pSg#H(%RFUU=eRKP1eS~2V+6MDmleL@E^7w|&Aw~mYi~r}Zmq8CWb2Nb
zYTyc6Y+O>yPORz9l$4~n0UWofLp&sGnwpPs>#mud@)sn`a%>jD)o+m6jjiMydE&`R
zl<INLoHv8QhTv#p;rdynQ|$Zk0z#SjDSdd=y~cu+Sbfx?qqx~jJ{F7udth#GMRCra
z>R}YPn2@ga_{mrAV7!Sj7poyjTw|}n$mc@9--`Iprhm1^#7lIvS~}~cZC7}0BgoA5
zi^;}tmA2_6dyHg-xGO}n{Ac)0)IqDFpmkSB-qeL4Na>62QH{s`?)J)1&dDTa81rpi
zD<e*%P2qkGM)UCg!*Fyk<$~pIFu-yZ!qVzyrIya&1(H(wP&>3jlAcggRL-R6Ter=k
zcZUk#!a!M;!CGHd1^`kV30~*;Navqgwb{DYr25X-!)y45XzIsetL2d@d_~!>ER1C#
zdJY^p1QHTA^{#clP2FC!1;K7r`4PW-BCABlkP<d1CrQrZak(9~%|p~eKA~~&Uj|uv
zcYaB$t>i=g&zIRR!kGXT+luQ;XTnm#zk~u#kaJ%h77`Rf5E46A(|v1nzjUX<K9#q)
z<1pO2ux<{r<4Tgk)UH$JAwvl%*rfd{=c|j%%am5k2$7ZiLLpy<v+5F}bo~b2!lgK<
z#YK2L&r>gQcF3_D5sz<5d6+&Sz#m<xhS8}&4)r^1wv~`dYn_mc0qwS8tvOFV)oZo%
z{-8g1`TEp_qRyP86o1;TwyQc$pD4yS#YvWqIxFz{cW=SB(xq7G@qy$&m0V(3vH=Ls
zy-77^7{ZgEwG{@+s3ME|1P}OU9=M_3v8Vq44EmE=D(yMgr}Uv7wC6j1l|>5dZYay<
z*vInr^s3F$&7)V6wfWYxfN+n`imF}hX~8-`$m12bf>82ex4T&xP)Qi)ZR(#oI-6cF
zd98Z=Z2>Dfd-_&+dV&EY3Ur7{1yyP6GAX4ed}AL<DdzO3=M=(*PH{$vN+8Ndert&b
zzVzb9w$zmHDOw{WEe9U7Q!Xnx^q~`s)9n%uZ(MUo#U$Bt1OjSa8-YUyYJzE9!2`7&
z5#=C^5PsDOBNZo6wkS87!Q!OcLQcc_&`$yMucbY7M!1rFaYL=V@t>ii+)ij`5x&4u
zGKqo|0qASG2cY7fDDU}VxKKtxAFUv4V*xwq#X8=@Z>=R`JbKrf=bB(K^m-bRia6VP
zk8cG@+r3IfP6B-?tU%WG98-x%$l{p?1A0(&0x6IvK0XF0v5a%IMkVl`4HVP*)11{%
zjach|VBk;^5;KegMk_eT=7xA@YHAUYj*d?M07?zscI`*V8b|f0_3m&^$F&M1*~Ca&
zV>vWqS3-fwpb7`lnRg*42OmmFAX`kD{j-6)nETU=-}Ec-ezj40LXnY@2fYC0$!)9g
zdsMEOOEn1nMZfo)d(rXwlS$cilQVoHZAbH?n7$r-Pu8fNg&O>RrpO+By>rb;vDAZM
z;(lharZ41U_mw9XsJO$2erdyI#>Y^ed`+rJi`v}b%zf)3jnqjv^X_V5CsDW*07+Q(
zJWys<Y6!f`N_SpxIZ3IDRQ~{(NlS$1oReAAy6U+AVQNXp{6ekPrOQr(;v*gD80%%?
zI-6}8vyn-gF|yGcjlC){D||J&w)UpvO3OqQ=N{Y8>~yHC-{cjzQVI5_x*bfjeYd?a
zJ|hgsN|urU^5=d*{*)EP=F9m5JMU69xoSg*TS)UHg#*|hZ%VI$6Lx)d)KeLrmSkJo
z@mTOig=tX)5rQ_x-)vXT9}v+ccRAlGsID$SIk((e3qb&RwC7?;0D!EIL&a$w9n}{J
z$qSs*h*57spDu?QQ6tdqKEzcot!>dIDnyICHm0nVArcvP3qo*kr6e4jdvEKJUp?xR
zSr1PIBz%+A##hdn%aeFZZjs`{@my&PHh=KI!Af!2I}CA0o|A4(lr8q;LW43}P?IT5
z6$Twn=~IVep=2X-woY@tOZ{~Lgvoihw|@Y5o@Ki|LO4NANYSL69&iV9wS5)+p6)ie
zeJs_VtzC<Hw3d+)E$$LhwMI&^5guD-30g_WOPD#v4i(2vD$5&SoSrhP@W0?cQM~(e
zEY~LWp)R*JD~=ZQi~>?N){=#UoL~?Xl_y|DYRr5;=y2;7--vxRGAzjDUq0evpq5=)
zbQjg5`^HoO1!&czoTv<wS_6F&o|=^!>C<;lBs7MKUTCS%l<tJ91H?LYZJk3&BUTBc
z9_kx>$gQcO(quSGdnhRhc}D{n)Cvh8WO1oE+Nzqz(Fsaf9k45>z`Jvn6#l}L%g7C<
z<lT=Y$nGQ#6qgaJBYY_(DoF=)k(8Wz=V$7Cm7><BdWOoUJOa3W5~ic^qvhXaLrjD%
zB&S1#w?m3ZN)`K|qO0Q^ZkUTM6vmi>rWD?$QnfS`vH?nx(aJ(qry)o>c2FP@Sx;T{
z!?zpKE>;QGG&gaG&yNpKC|8bD60IkP8&YwoY_I}Vns0e@Bymdi4}bW`__xB3Wwyq)
z^*mV3++j4*RAa3H#Vz)8&Im5Jlq>?31%bY#(jwlVS*`+mEJ<c1>LGFqP9qG(Q5qg(
z9Oo%?v~kad;z-I>q)kh<!0YrvnR9@|tJ@NbXbd;us%k?ZI~m0jq&TlSnqQPCoeDaz
zqgClknr@c?&^HE_w;78aFq}g{B+X&4mQuXnB~ExDmG>HwbLFX8mX}}qD_lr|)T=V4
z+TzEOZP6h)9uwuvw&aJ~@mT->Hp)VO=5a%{Uu+3w-jqj8rtamOSbCD!_1X6x327{}
z#DADbKf;B!9S4vy6Tztdn3&z?qHQ+|laB`Nw@2SWQ#eXAy8NJAN#6}R(ndkpI0s>q
z`o7g>wI&^w$8C9N>lR|fej!ha7E<qsryV<aLrXwB3_M9bQCTXGJ28KcH@NqHkVnc*
zE?nl`XGokJSD7)xQCvVLpEHY5Pi!Y{2&xCco}3+R@zOQw@f~ehZ#PLX+*^C$3QzA7
zfI$f;Qjx3F;(_?H7pYt7xz-3V-IUzrGcr4jp+yeGETlG&f0s%L9OUs^Pr<H;w{%6L
zqq>svwZfYc;PhLA%%CZ5A+@Q^hBBlnDv!Q7Q1v3J+J-;e3+j<?_?tG}b&Ynu+0TZ=
zZlAj$9t-|Oy7RFa4uD4}Y-kDPUK+7gPQCb7)AqlK-AB|eO(8e5ssh-++EvCfo+U~I
z=}1fLqyfI6fO}EzToKegH)-jYm~I=h=Qk7O3w|D)X;j24jtW%Em^s|Ju&O^obv?(g
zelOgv(;JY|rcP#92|4*GNsTB+LP6O;5Xkn@F}+SUm+Uhk;IicYm9_LW@CZ-Kl5UO%
z@P2KmZ9C!ZjD+D#<9{{@`Z@5Ip)PM;onj(nl)9N0)+IFfN<>E74L0b`e9B7XCm278
zC$&!WUrW8$cl(vn@UPjkEmk=%E^d(L%e2xMOK8$g>se@nz5?7=(zR}ZgKv{}wavB|
ztXuAOFZOvZqspk!(IVnnZ6IX$g;CL|Kx6lS5;>>IJ}IOuV%xHj@#n5OcdI&r>(P;|
zZNZNMBJHu-OPFq3Sq``pqi-^aM#Ex|dsoi=A^RpLTiUKJvK)gh0a0x$C=JSCK?+82
z2+EL?#@;CU)#Lj_^p)^l6V<j`e3?u|P_{r@3Bi0c^GqdMV7R3fFC=bNzZIKw7gIxO
z`wrtUB1Cs(B16H&5|&>ijHM&8n@V+igr3z^AEQEDI%nP&E~&Iy-nZW!nwF%^jTzYu
z6`^jn>KR+wRtdsgB09kYlAU!_z2DxE8o|`PM{kxIgK=rqLx_@3%wVkzA$Z*&A)=GQ
zd^3P|6&=&<*}CFOjzV=Xo4#L@F{tBCrkx8)T2gRw3fxK)jtU6pQKx#I@p@IED}g1Z
z=Ank1@S?V%8T6_|h-?rOpW-1)^yiA_;F$K(q7SGnuE(@Yi6OK<a6plEj~$h$GIS}8
zD8@6nUX!cLPQz>pl4|0I`cBV3tU7J!@-J5vTq9aguo0=ZLroV#kC7>Gy!y(r1`=15
z6QsL*Yn+>`X9Q(8G)`r!dns^)FykSl$du}pokU>1oRTmUui+V;>DZkW8L3@QbcEE1
zw&gi)>HA!l6j0<=;s+ZJl@JzEg!qe3a^qxzNfhw<V}3-I-50ZVvGl}<@a>V`yLFW7
zYfgjbOj|XfASmfS@S9LcIsWm|Qb`HiR-tq=-w~dXV}o>JJ(?s%E%l*~4Wx&OQr&m(
zB_+h<=qgi%v>+`&Y6c$H)l=pOPxT~8jH>LnZa3c%M^w2AD8R_UEVkE*tvqRM9<?>o
zpBe2oOEi-z(jT?NWyuVil9!pU6{L9>NZlZ52g(AKfH<tBT0m){v(-NcVO*{EBhr!N
zw{L_N?9022NKhJ6{oHK}B>56HDF_8=0R#^=3u&|bMCo}-g51T2<;ZCU+=dvqC&D>W
zl=hLM%ruQWl6TF0hMukKkBe}jbj&M=wZf5YYDC$qj*?t))2l;qf|M50H2_PeO4c&3
z6954vbQev+YJHcY`lI$+ExZ*zU&x{P4?e7FenYyDTy;uNT3aMG+DJYb+bzm6Ku%FU
zS-<rMO|D$0?wu6PtSw4x+^x(?w<)xt!rF-qzT-*-K;URe+iFA~h!&VOSupx``_vcv
zG-ark@2$RuV{8YAmkWuI$>R%cX-;qm&IM^d8onK?sqPl8fwB}r>FY#9Ou9}eNpX}P
z?((#R;lkQhu#lXRuymZ7%x~Qta7BP$8b!w2V}P{Bv_ewWkmAltQGvhV%9N$#lB}F$
zR*Fdv7Tcn}j;FgL#CqS=x0!b39e=YfvKHw|iOKNZ*<VHZRqLy~dahGHX*YIEr>4VB
zP*Eg>IKqh8D+6T?Frl)&0m!9Iha%wPCRy<HHK}0<2_WYK2Lm9Ts$HIqmfU!6x`No8
zpwnO|6}W;jPK@M@x#KvfPTyyt7X*tpM)f34qIONT<6shAelba2wJd_!3qT;}Bg$|F
z<8TP#zOwjsyR`3ziBoWr8~`FdsNq0^vT>c#k>4Z{w|d5Y7JN917I1N^QSf3k9Z*E1
zdCouLx!8Y&Cw?pH9*Ik@!C~n#nr)_(l%ce2pTawBx1qoT70(NEq)XIIaVQ_!XQ>%(
z&td7O7R0Fxu%8g2B@ZJB0H~9cC>(K&``67YQ=TC>UjG2i>00OZfW4-7TR$1cK4DRl
zp(-c|DMukW0B7s(St0QpR&YQV{<UelJh{a;L6-J4tQ88n*&+<5V0+bO(7`w)Z~k?8
zvbDN>cBnLHMvG0N5<pJb^sAk&02telDhABR^5s0$;?WdrNaloLvdK_<3vzV+A8smD
zyJ*1jQE($C17W>QH4!BvYPyG$QCeNJ0r+<G#RDP;-08(!o`{@mDlS}9jP2T)#3dSh
z5v2KQr+9=gq7&M*Yl{E_RO`z?1!wI@&}hmp@b^(E9Okoo^kJ2se@fQ+g`wyF07}fS
z=m4u59`%%Ll%i)7(V=Q28UaLSerihZgt(v&C!Ewm*MdfCG;>XC;?JO@=V719xTOk0
zm&QFi)Y;D@Iz4?U=8taP;+R3tU@7*8eg3qplM6`$1M;Gpa~q5us5A}vr`iNyjke83
zNESu90sjCh4b=C?){k{K&P53FK^)i7Gu4YD+)3Xw!jLnZedxzi*kYL}89Q@G(MYgW
zCw<0g3P#*fD@Q+{(vVTMPTbNA*dmdiT1s0W=ZY^t+PG1;q6Tq>RHbJdpK6wj%7FV&
zkkSe7QtaTQ4slG7sA#sV9gQ;LPI=mnKcS{pfJxe&L?bO6jFK~)QSnqb%>n>P-`9ap
zvf@I48~tjL><%P}$QzyLXR;1+k9v4{hH00G!5QP$s*sL91bm#})N`csQV)`H1q8O#
zPShFCIE@7XkbkW-?mJV9LWbj-X+c=r;EG1YS|a8k?gteIEwTy5)n$;zpiZ7DB3xoK
zw-p&tM9~IRq3SEil;TGx=}x-N2_RDoY=O8m6oX^QxhXu8MX`7w{vtm04zvTcAR%cv
z6-zeDqzkZF8CM?lY_iVQ;2xEg#apO22N|m!nfab70uD~xR@hO$bj#75)33;VNUOv|
zwyoP(AbM73(a|LqQny|uKg-^)uvu}Hr+^P}No7*eYHTOL$ymUsJFK$abSUrdQZ6Yi
zM$}dLnyOsqqs9YYC-bI`#+45@48500B>Eb;biJ7ql9V9;IgEPMe!>wTgt#{6aaw0g
zL20$6*1}VcO*?cV?381pt~R(0-yL6skQ5T6x$le=t8ROWqV?;-U8Pc^v`f$zt<r}I
z2?vl4p5Xo+#eD-RWx5AMi+bEjQsPuKAT71HdwwCg`U?5Cs`_qx#$-Me;7UrJLLF;E
zipK{egeYy;{zklSQxE*McH))BkDqR@OVlI@Z6UYTJU1I`Jc7YdAU?|WRtW&$5;Kuk
zzk!i25$=h)*^rw%&zECLi*HKiTJG#CJ|in-Dp5*~2?*Uo0ECQfD{c5;9dO>8aIwjD
zIU(g3uOVTTXUtQPuYW=^2-wxfr}_usUs7A2LM6^><g_i&T5Yx@ywXTH3mQ=3kfEP4
zLV*C}9m}c4HX3h5>#e$?^p2OD{{TW?BPLC@7*n?EqjE5aq+!Og5L|4d@iwqYNE_|7
zVUaqA;v$gZBf9K}6ZA}N#@udW4wNWiTAL+$c?C(_9D)XLXF7)E)K@h^YpCx9*|w)r
zyI)c=+LJL2qjvmk>RM7pbqu(Jm1D|jR_l{q1D!v9k#39?bJZ0{9u#KWaVa{Gw#iFM
z)T9p)wFD?Qm47m5@<{@NXWTlS`;-9KVf!@ThupPJXvjlsD32Jn=5msS6=VW3k`J+R
zOIGA{1-@%9NsiQ~T(`ds)hTId+@nu|<}#3=Tv9<hsX9R?Y6j)jR||3;wTBTs=xrGY
zDv*^+X(c2)m7yUe)8zoD41xjfXgjUSJ@FEtu_MHKO|f&3Lzy~^K<aXyba;R*3cx>z
zbIhsv9{vC)i~8=4Op5A$LFvwyLJ1xkl(N`q#o&2P_#EytxT{t3rsj1sFEMVBF!efC
z%QM?b^s4t6rj+l_wd(RPmR`Zin@DtIrF9u^izBEYe1-ikMsXyv)9QapWU}c?-UGQF
zVt|vS!RAh!`GML_Me5yQHduGq_c)iHl4TRwkje_)Ub5?He<|#etb>v=Qa2lhjJUe(
zeo(D8j`ch#SBVxm4&~bqQpJO6>Iiz!!<LJS&N^f*#D4;vTM1AjY?nbB0a5Ae32t=1
zQG7sDH`};cugYn-PDdIVhb5%3C&P4%1eeNONjnmpd)2W49ZPMWcD`MmipBacYV_Wu
zHr=?nAxMRCV3Xue%`#k0<RdROe3~kw@#Xuafc1kiZ4#l{FKjUDaSDHQ)YO0PDJk4G
z3#o8=6QBY%u8A&6$gb$iuYE?_m;3a~ZioK>VOwECeM-)xcsh@YkTx2WG?F&SNynPE
zPMNv2)v}}8rJ+i<F8cv3OCczJIeCW5iAcr>O4&jN!ytlvYvRv@*T&Dpp#td@*->uI
zy>FJwYe+B0aXXJO8B$76v|!{Z0~yVIukg`}H^b{ao>PnYrpo?(_X<4tURE4H-1$pO
zLbI?gBaP`ZW8%kz?G?_rVZM;~WoYX-j4k<Y-NIjp5vaDd-FFEY;Y)ESE5`VB2ZA$F
zvxe;!%N%PP4aB{E-*)YB@eo7K70Z2?iBh@zFRP4UEfsdHQp*<M)-J`d-XOI;*6qS=
z(M`1Gms*foONekN?WG813K-<Av4V41{{Y4HTdW;ZH>?@?eN0A%Wyea@mdaF?HLE)j
zkoO!NWaAj22Nr_cx+`A|?bjAMC*p5Xbk|cdtK#FO$d46%?ZiIv-+8n*z!_6$!5bY|
zHQt~2gK@N8Q4-rZw=GaFjzwkFq$Mj2$Y?m?fZM|HR<rI_QaLn^o9M~C8PxWUmuBLp
zR^%sQ%u8wTm~aH8DN=?B)>4oG{{ZH9-lw`7;o2>|OwUr@5~r<=!I>Hju}B5K0u*JH
zzO@06ubFX50Y@iEC$%%<B-``QskrF2^@^kQEw`su8Tm#gUL1rQPbzvm#j60F(s3Un
zx1Cw_tTtD|2w&{mqRD9asZC2nmOK@Oy0_eVPMGQ`$DK^D(Fx!O0pNmm=ei|Vt85sn
zp8K+G?XKYLfvqcT<vEvJ30}?TQ^V76Hpw*4#tV(#rC3b+M@+G7>8lc+QyvLVi78$Z
zSPwsq&ZpCoPD%2o03ZS*CbbZ>p4n3PASQLBewmAHakvf(t>oMqVUG>hm}Qn0G&pu1
z8Au5~@YX@^R~BKwhQ)^S9z4e4;7oB3Ct6vz{L7A{g%gFLBq_nGB>3nX5m8?WM@@A2
zf`4r|wW;TA<=E>_gjFC&aqzK_NC-}~{{R-CgpI}t*1nsa)*U46rgYntzDHEtU!3#u
zn^P{^`hF?UrrT=+!3%Y!nQgJUu-L%P*=|+i{{W)POJeQwFZOiLyeYul-h9U)*i3>%
zcA^9&Z%kTF{u6QAN;0fuH2(l5t5;k7rtc=-)3zBh-eOcXQ)FT1JWRHyb(dG14Os=(
z3Xa6AZ~zvMh!*)bXm-w>vATvEx})Xix|hnt3lt@xpxJlvWzxfnQ=278ZPfrno7evU
zXa3H%*`>D5FBb@3B!p!6%8ulR6qKZ3k>VsQ>c(`Se>G9HLM7zo=c*f+^nRPRnJ99=
z(Q)EN9G1(gTFx*J_lnY`e+X=)CYfD^={7qkk#hNKbI458!)c%r>rE*DscQUKQrF~4
zyt&+rBnmI1m__zw=ch|;N4i`SHQ92Jr&3yEYdXlrkV(K#a@o$wz=%bbW6)>XS95k$
zsS}w>173Ga5!9dEB?Ek^Wwhk^Q;;+GjZHn$9<*3`FROkbvuSkOuaXwK%0$PI%H1oJ
z2_c4*r2t!Pv@0Yhk^G>OT9-|~WVb+y%>Jih;`?l-NJ~U07j&q&@k%mMl0m|p#z4s6
zDznF-TP+qrV7JXBnT*PiyOXip2$ZA{(NOav{oTBTpdarvq~HQG2R`Dv1V-#_B6Foq
zh^T=*%?>!0LP=0z8j5#L$s;4WhXknd6G-|`r&zYhj_tbW-L1(=Sep7KyMkU&3PxKQ
z*e4&4QqBQ57y`ZVzR_Tn)9aHcyK-2I;l@L4t%%W*4p!nD1R!sp-nvK_*yjm3=fiJ{
zms=-=G{&;DWtB4?)XOPfjUb#Y=@0pwBO`>AgQZ^7N5=mEjonVgzr?qA_jjf&G?gX8
z9s>^ny1y1zft(}~Kjq$xewRk$Zqf6nP(IbF?DSYDGi6P{FG>ngKs;c%q@A|}gzh*S
zRlniIg5>L;v?jUZ&bK^SM071Jq1`;IK4mC@u>m_1&1P3y@?_q`xw4&pV+Cn&tbwV)
zp8N#)j=*-}weN%yXI?H5Zf-_mL1ivA9mbThl9EyswQiJxZ~)YOcFjZ7N!25-9VNcc
zq`nzJyWAUH-u9^{=A7}Ff*B(K0urYZqp}A2QMVa2KWDkR$5cs6w?pBC<yz6<0Izi9
zfuCB3>Fc|lKW~0xw)WQQLXqOQ>1-)rdIPdS*lpUg6#PVqV`6RIOHCQ~Ql$8K@c>XK
zAm;}aEL~`v_V>{V@e$9P>np4O05n#WK1vsWay>TZ>)xuQYIG#H7me~pKRWaB-kS2B
zWhp>$MByN5-?!43LxB$Nq?4cF?N_6cTXtEk2uLXy`qk#n+D?;<RT9wVq6sy4vc335
z`*XccQA5QSt3Uv!(yum!3<1SZtt%c{x>}tH)4eLuR<vF1%GgxfL}|@dZSn>N`KiVv
z4LsG5loaOTqUK{5qt^Jr-l6U?01DoTh)a^v0nZgny||JU?^YYMu$<)8BJ(>|q7M}|
z_BB~h>LrXT2D1L4SOsA3S_S$}vafp1@0d_l{Blh+Xh|9M-9);g2Hw>SK_ru$b5=W?
zZEI1+NT~A&o)Sf87e_SXWJ-qnn&y=dFe+7WGq@P0%oV4EoK$2&Q3GowTmF<v02^YE
zS|~Up9k{0w2_OvA+d0|^WhG}cV{!9>PUevrl>h)0BEws6`qY~yR~-Y}y$Iq+Ab>_F
z*E{-9%NgdrjP#-g;&~X&Fi?KfV$qMTDZK|fQZlK6sk7;dMvPHq2apXUwtYFI88A&9
zx%*cN4mjqD@W~?pd)JW4iN-4e&c<kxGq!o!q+37<81G4q8wne4MHa~I?N1O5kPbHE
zYGHf!KWZUEkZG3?dkUqG6|hCC2Lo#I6s~u#IFd4JjpyGzsHR;<M2ilUBYu4aCDW0;
zH5`<vfH|U;jN@ZhTPMW;ExkvI_se7sKkY?^fW|i3o<ksxJ657Iff!-x+fIMdk}c$J
z2He!*BM#`LDmcb8<GoHw&PggOPe9m$kNea-$R~ZO=JOk$FX>S;qIxEwBm~MVxLQ<j
zHmK*D18h~gWI(LpH3=>zoZ&S-idh7v$TW}uASh98vOypwhEr-Csnmj){B$6kovO)a
zb+(;KjtCpoYB*3k=B91r_^BSdaZH%$(YSO+Q^C@f9?}jrC;e+ljU}lmNolZhIUTDK
z==hj<3P2o;<W|*Y8lxvD83TM$zRJh$KR!@xlG!;RcdEjnE@P@t835G1>1s$h1wE^l
z80m1GK#+X}JQ<`*bmg)2AQp(uz>E-aSG!~jvod2fZkn$Z;t*T;ojV?RPx;b%TG*ct
z0U;UOX1jHIMYc*7r&vR2BL@q~KWZ;#iK=x)<#tY;jT+?L%3O3ZH6*wkPDbZZ0YHFw
z&0`mSpoMar<7i>Tpn?{qqab8{6@+CV@A$UItzNpS^k>*3&TihPVg!U2SaHOy2OE_E
zl%IUn8Vsv_!SvdgmV`)cUPnsG0*5+D$DbpyA46VGsBO-Uyh$Zw0JO`t%WOls8HJ@J
zswxl?^KE1CC&M6+KbRekGwD{zy+L+Gp~Xw-L3w5?XXTe7D@l~n$R2jzK+hfbJPksf
zYR?!rBHQjV7<9N@Ojyka76<VwQQ=*|1nsp2)GwtAZHrG(t`*4&54i7u0+XGBid#t|
z&5jDUI}Fqkwp!H{PM(2ove^FsZQW$FZBwKaHi^$BJGRLV@|ND!;Vrc<1f)2Ul^sdJ
z)RLQgex8?Ze%*1jxd!Z_sL5}{LfBHznM{`57zINGB`PS&ldwvXs<icQO@;Jx?qA%x
zOq8G=T%<;IE<B86rQphRk`6Gpapl}B5ragR)kwVbtJZtpOWs|Ez-~l2%7}?F+epb<
zE+{81wSu4y%1P${lUGXii4d@?>9<{_UuWCyvn^#QLgBjChWiO8PtM0)>HujW#HS?$
z9YaY5t+t+(xgzYjJu>uLf}uxTt6Ygpr6FiY0c?kW!jZVtf;8amgUyGcA-i|zSn#d)
z*jJWe$Wsv(^hCD%ZRjd#w;n;%Qlpfi%{}xeCz_Ip(qXhFS4PH+W8BoQoawo83f9zu
z4!;bb6samstQ28sP~#esLwC`ZyD0H`j>US8^c#v-CRL{G8RKhkCAmgA(&9@2Ux2!d
zl%sT&DYWTP){#^8Xx9_g_GBdtbpv)7NL)LJK#r-f-!3b`aU>0GsYxK^AhhsC!z38_
zjP#UQk*<F+1y?wd7VE1lxgJ(RLVtJ&B}-0$l!BzKB&Z_-i&S(u*5?*?V$Vo2ZO=qn
z+f0<Gf~2Z3Av%?zXi|_vWQNE&BC8yF=tn)3PM5sNkJiv^W(27gH*U_@j1tnDTHrj(
zi}`FgNef8@K__&$TOgcoYWT%+W>gEOjdpCmYiJ;;ECnURyvh*NfD|}!9s{@s8B2f+
zqzbup2TVwlW1ZAChoVQ4-O4S-Gj2PTq0p^sV{l3wNiGyPPvHTzgsCLg&$Ip@l)o9f
z-O4Mdi25vd0`4;PzEad#dBlu>rX&!R4gz!(k&3E=m(ov??G+w|_)XK-JFvFJY1GbC
zhW=J-MJ_BlhJ&oEdJh!<WjPoM82#X@PsU5l`=sP_&A>r!5b;}sDmq`skjK{X1Na-z
z;?;xbJw;r;2lT`{z2Z!jxa4tZMxceDt#M^G;PF@_l1`F^Cnxw->5N(aIrJpnsp!6<
zm2FO|q_>!Y(*eK03^c}Z<n7%=g%iS1;q6iP9Hf)*TZ&x+{{Uytix4H+EjL?)g#=qV
zO@@Sm;7hJaLd1D0IooE^7ry*=#Ru_+;mn)V3!IpZghRUY$VrMqQrps#aCz9xAo2#z
zDSPfnPYSP{x>EY@!_KIUVruGz+fV~ePjRhrmRu=b&G$Y;C?`46PI<4UQD(f{V0ARF
z4l?3Hfc&d`xz85-1-#o~Lv<1Rv@K&%{{V^N;~P^+H1sBsj|+_Ze_vRxvykGXwCI&2
zvciI;uv|-MQC0~+R+p8Xu#!i5)_UIea<TMGcjB#w;#%QJTgyXOdD(MjIM;@=zLe@^
z)nE<$A)ScCPe3luJ;{~{mo`&>ocmD2k02#UXcVbyDfH19V|)!xD+6;`_r))amrjF$
zeCeBGNQ~t9483V}nBE#trM%%wq~irCZ)6U|YF;vPLJ7}H!KBhB>>YnD4_QmFT;7ry
zxCB69E;w8vO*XfWjilr{mzUxxqH-0dSUN}$-`ZQLA8V)jYTX7&ZdL9Sc?>dMEw>6H
zJj<5&In#|^8+x}SKIY{}HE8$|@KLY!H>P^7?h$ffyPV0Aknm70NJ7b8H_6CJBpr$t
zu5&{Cc=&w=&ueneHhFP{Om_J#*H_{=`whnb0CR}92nPw>eiC*bKmo=>rwB<gRm(+N
z;Djb-y9VWRDs}@C9{DJWZEU#U@B$Fx6iyI)MI}XQIm2r>%68uRfpc#CT^7o#!WfqV
z;^pJ1UV@^ez=a+<Q5M|VmE{hlw^&JE;SwJz2r57+!L0pku7T+}T~`J#&)wA>HtQTm
z5|uOar3KgBP{v&F)Z$cj0I5L($_A~1&C^mkL!~Sq4_TiP9YWywi>$dP#aga!l_<1Q
zjo)zyTjxnu3c}o3N)nKx2)g9_nr&YcReE-O85b_5oqLGemYbv~p~w-`W47j}63j>q
ztw1a#O~z7A;I|YwwH<2|CF8Et)Auf<M2J&*f=$u86K*u&(-AHCUw9-9w56OR55fr<
zz!foTu~;s>d(+mr-8}TWLs8Q0k%@9Nyr|8iQtpyf<<jHsYAv9Gd^YtEK}M8(QM1~v
zcj)opF2iqO>4}UYMJ}mph}wY-hLV7WThzB4eO@3_i#k^3%1AR-_xplBU>Pk!==HVJ
zy)d^CbDq<!+^!C=e{{;qRMJpaj4eshbf}yKH>>e!EL-{)ta^H47j%*%G?X}*O*yN>
zLr8HskEvQqgso#E1xr?}fB^<$tDtz@MQ^${bdCE~ET0<B5;LtOF&jx3kn;1OERvUX
zDB(U*2>?{f{VyJSu)2Z;$-b!hN8b)l2zgLEM$)V`87d)eq%RttJE&{Y5(!X~*F!Dn
zg}imN9-Ml_^oYrN@TDc;9Z||(ZZC*d*jONj&H&P#&Z4ydkc473<!^7OEfV^yy(U_x
zD6r?tFyaufrD|JYO^r$bfE1R_RjB1dbyNyEf2;0&T`u!FQ@6PiWGv2;aX%!}7#?pK
zO7gVYQ@UfP3JD1sQ<0q>7gKdVQoh$n+2=@YhFVjpXXMshxM?Q}0idANbp9d<^AVR8
za1trTx)J@%X4K8<$nxK}^u5ws(p(|9tbl^<`bw4%Jcvp_N>CN0ARWmh*0Fu*Tc!U1
zrQT($YtS3;v*E*vqT}vhX>A1g+!^5OLJ0(&fTeYV7JituL7UROO53;h)Bft@>#GTl
z(hkaeAt@OrASDSICz5xgI&-OKHGONR(Kg<xa#&N7CCY^^OU*VnT8h#%Cp!%(8x4s_
ztvmflF>A?zT)qY0*@o<rBEfmLTv~;~;~cn+vO|FiBP3_akfTrZVRp}YY*{XVvfi9H
z!%Oh_1pX~J1AXz>Vz#cN>DZUcgA<}9g*2}to;*_>EU!5WR>T91^zYBE)g3#cH8v*O
z<G!$^Zu`kBB0<g$gMw9o$Ke$hK5Cg~qW=J7p8f4VB&U}pvf<S0tOm<1>_(>*y0h==
zwL^SP=%y|GJj9E5Y0m{j0);09B<G#B-ihc-b=k$RA~!Y25&WucP~2E(f=`vjD{<~V
z)o=K*2F-Qo7pBBhvQ-2nY1bKG0yzMuKv=&7YS{foDC5Un5uCTP<>^;NG7BqsBO4T_
zkJ6ZpaRGi&fx$WF>t48!>I;r6B`19$q0anwsDB+ubR-o4wn3uN6GyU5*QW|UonLJ7
z5_FvVb6EYBZj}!;XckH2=^BTvO{P*T)h%*{5;OJ9TrG1jK*`#(D_rd9IsIzcZn)_K
zCwiZf>WwB0%p@F&n__U%Jk>&jaoZ(TGR4RQ?y7Ym8Z9o*0Y8eNE;j%M=74_bfsE8F
z`<|eok@?Yb0z(&h`hoL)wM@H7$vf1Y%6_bne_E^DWa`ci)QL70(O$c~t5*EyV^r&+
z3d(>RRjzp|Bx63Jpk+rcDEsYKNRwqr_=QV5AIi9kW=|O%jad_HQq+9Ad(my$PCpGZ
zS|-(%GidVcpVFCj(yV?CYV?aiC-DB6t~+GF&WvWc=BP&mXAjzr{!>CC-mvJ{?Y9-6
z$+PLm2;5Xtmd!3?pbq~4YJ`@Kc|!UkBf=mLE`8|cVT6pS8T!=JTX-lQUFflFDg|0b
zYnirkxa`T+GtD-%_9BT=H^wvml#<qSwS6Oz-k1?@DjofEUhq?nDA2}79MXhf2Oq6X
z%?_CySg#gxoax)ONhT;GBx019nK?Mets}^W<VA?*JZFCN^KlMG8`PR%4oIilgg=wN
zIHB$!SR)a_cg6_qOItbMcd0Z*!gd>z%^`8X#?<O&Ph>*c?}}w?oNZFdjurXSYm5d?
zF;wJ><76X@jm{`il9D#S^{KaGk4lezImjSj6In><T^@{$M<+CLk^t47d($ym(hfyF
zpibDuUK16o8wknRccPkWCpf0qYjd}1kY$W})AJ`Kp(#K}-}0oE^>Q=onk&HsY(4hv
zUJRUUIjOlEQ;H+qV>}JLDmp}Bb|S1Lf_9-^iIKsm%48mjq=*A@Y93q;a-Y_&k3<8J
z?NPEIg>QkqMa<SvWkB5dedt9($=p@eRk0^NUH+9E?Xwy3(ma$EBDmZvnv}OiNO3v!
z-iZ(faineQMnizV6)8Z@{*=*}>P6$EoN=cT0#dQ)KsB#f$T1Rhgl)h%tlOg?Je8wT
zQd5rOiqo2H5o3)=*p5M}r)NwE%j=Dl;Ac6ax<*10vXyU8*L8TxA-w(1CZ~EL++L7?
z{>jeuR2dpAmDeAAJ{ZDJJ*d8+5zI9t;4e-Bi6c+mr6IL!g4$G-q$>ieJzXR`(u*oi
z2jV%~+irg$LwB=8_e(Cqh%#81>;5~5!)r^(TX`AfK!TsRrtl&>89{xu=PkO!=tv;?
zo(Lwr(KD4H=NYwIVT7Rr#gXCos4Dj^0i5r)KJ>fgx)XN+Jy8_X>GNu5LfTh+08f-{
zzBlx*FV(NA+1-ZdJ;?XV#le)zA)Ap`io}K#rAkV1_?%ct@lZWRahw{kxeDt1CtJQY
zsIysdd3WU;a7jXvHwsRjlr|wd?ZK#CoVvBucNpltNKzS!vRe6dG?ErElz^h)+#c!0
zXjjN$-x)>?ruO4HJR(`OB12CS0Z4Qw0X~6V+#SVB8gdNUj*Noo*7B`ZsWNQVYqUk3
zY{GR0L#*l^Qd5$U&`{th3C0f~;;MZ@<*%q6Y5t|XrVRUoi1FPFINOoc{5knxhnr9s
z0ZMI2SSP({(`NOBvDVhb$EV<?Mm1YqwlJ9sK;a8Q0muaYGz{&}YKBisS*#ZWac1)x
zG;^UvreV7k)|R9E8r~izNcAK7S4Ze4T$x|#t38erj>Lz4eGyU=(3aZ>n6!l-??;4`
zxU{I`mfS|%g(XzaTl5W{)g7p;b*;JEjm4odGf%T8Q;SIet(P?nF8~tS(v<?OiNLK|
zCF*NWMVHdnXEi?Fl+07nVK{{ok2({jc_a@u>N)-@RE94$($wC&xq!l)K1Wi`&NE5a
zp|!BIB0<W4B}q60cEt!b-$8$K=6<ZV%f56{rF8ZAtG&k5^KxAf<rZFHAg5Maby#t=
zc`!8s2_+znLazS+3i{5+)%K_0^%dHDD|?`+3A#Msivj%VL-Ak2*9}=G%!A<(sVi>-
zHFv*s_ruh@;{IaPt58F(Eep4)))TEKTG=5=QgCyrwT0kr5(x&edo`ASL-kB4&>M$u
zwaS*J;Jy;<%Ot{~%3W{wtQ8{!rv)cYbf*N4h)Kn4n@U|DMqaGV!H0Lb!ffTlEZ^Y1
z+FN}D>2bMiDGDvRk1n93<tt8j!2{v{0FDc`Nx8~QcxX#+FtkbtaOq!)Hd;L!)`To1
zl0u5NBL=?kfTsF`rS3L}cR2QIm8H_+f*I174io#q;iwH!AITv85UoI9kWa%O+6LJ7
z`&}eA6_#bjW5Je&SFN=+jl$3|l5nj&d;Kb!<eDUtacQ4Ke`Sc1u01bn>Y38mREgJw
zNV~D&lH$^+<&`_DxN}=hE3hPyoZx*ccelNDu*I}nTTy-Y&AAFwX(>onu;YsCbm78A
zdyMl~hxSzIxv+X>(PNa_K4vQ^NA468;xhIY(q05*Nz^pz17H)_*8K9_QMgJWOvGt&
zJRLYn9PwN!3dZL-B}cF-$<9S_MqH&Q_b-ujesBDGwc4RwtQT7w%6YqVnJH>0N|4Kp
zG*aA&t}Q?fDp*4efChXb5)RdTjq$Zid^;9vg6A#CscGqGYB*VS)*S>ftZa~_wvQ28
zkbsnp*NSWVRoE?%I(x!)EVtWs;}s|^C0{Wq)V5q-h@}tRlH-dhNf}BKwQ0)9s((OO
z+5Z69gytqT`%>hkF10D$+hE9ZOHGb5;ipp6zv5c4{1s&=@n5*DREX|?>PPMot`|Ae
z<uQJBNq9(%pAkq%BrPMdiCWN|Sli8kP**vV{yltehiVa;fd<Z)j4CXL5Cd#98g13~
z3eq&`9FPFy;+61D7lU%R$AZ+!jID<nf(p`wsV$*y6e~L;I#QwQs19>d-`U&Y)y{v#
z`SIk#dQ1|D_Q}D*2?^Y5QBLX!Ep8LObnY`w<gxPb{{YCbi=7{L{4?p8R&R%p9JaEy
zo`|V(<HJ*_4YuT!6nG^1BSB81whz5yKiWndX1Z}#_S!P87YmA7wz9PpCNdPzOW5+9
z;2{fJs~{mRfsIE^eK`)o(|tc45*lhEBE23e8%Qr|ZLz7+us7V~Z@&BF<eYo^Tf(@n
zq-QZ`Weqap3Q7yDpdq!orK!g=kHkSb5;TBxsYeJk2T7|M4&INEIu`W;_wgRo{jLZW
zZka+Lbj&oTc@l}$^D7G^sHwua3M=p7B<wc(7o~bli+hIUB0BC9+HGyYa%1j3+7jU&
zG?@z;Npa+j(1ia0ci|vm3Q#{2`~{H;A5zDX`kfKtpsluW6kk@6r3CGSYzq777#pY>
z{SfHhoA`!{G^ZjrZA`gsVJ^KXNn39^GN%^dL0Snvz=W0ga52d1p5~~JQ8K%?M>E#<
z_0OD`_In|gwwG=bOLH&N0S-I!4y7f;w2XqLk_z$?6dMRg6>gxt^h_DjdPdy>G9raJ
zn2p?qQqyS|irWaoY_tFxmgAqsqC(W8LjM40sqesu*Gb6uy30}$(Ta;(EijE9U<yhY
zD|90ykkQkeg0(2*)eofp5<<0JBQ1`za!lDRw;i|HvaL-v;Ym_b%Dhc0Z~#ak6sMAP
z0YU299V;{ro_DBj@~)F${2#HREMPnZ{>Xh$BZK?L7bZf|b&W(Q>IqVgkenx(&Zmpi
z7dt#q<n_d23KD}T$d20NmHCv^c0!PlRCWmmfs>44pJSsOc{bZ5%coCXrnHh{vv;@$
za&d)~HshWUc;O@|(!A-wNx%Z<5-(KQS!PxF_;+U<%MhE6^IssyCk0NGr@=z>fD(kQ
z!lROKHmOVM`i=T@jCAxki2^<DRl3yc=qp5ar61ZUw<P4N3?O(bc}I|fNGjb~^HggW
z3rogq3xj<@J-LVt&W8dTt<B*B4T%T^YfEDp{0=Lfu~*NCcQ-Ciiv`|Ok0C%f^D$To
z@EZi^Wh!Y&Wgz1@Nzbt6nQ47re`Xu3GVQj|)TB52LSkV<lL<v2VGb1}fN*)%qE5se
z=*Ca8G!4*p*7~mYxi-tCjY(!EgKxRQc0^RYl!lZMsSc${(sbk}ark`#O^f24vh_dY
z-h$F3xuRV6qb@5tv!4Zqq@1NAU^EfV>a7wFPugR$h<zV(eo=jDa$J~6c?)q{0DM)Y
z)hz!2k5UI<DXXteE;_Yc<UyG&fQKVRe&r!<GN0o~aZW5`f;PrB^gCxO7O9YN+r3-e
zjb)N7aNkHT$e%J}&jg&Dt4KN$GuRb<Xh`;Wj!cnrm2qh>@U*6S7N*eRw#jWPP$wtQ
zeJi&Zks(SB%9845B{^+^^GZt`=}USOr)=;Bt@)}o(%~@|C9{8&kuAVNO#C5BPIekj
zb$eo^#a@TERc3+H*DJ(l79q-u;4tPA6P;=s{{RZoG#(E1eCjKUZhSMl-Y?d-kjr>2
zWxo)%;TYAeD#lKEAd1LrZvic(yviCw%Su5F2K4^`<~xtIXrC0P!GqBa+ime$dC3VY
zd%<l1z@9-vgCRtB2A&R>ty%L0*h~pBS}l<7=_r!f$od12eT`R~ktNZk#qqy98kv(Z
z!s5c2E5YAeVLwhP9#T-JO1D8LI!MU;=t<cyt7GL01e(0rX5b{}0Pk7+PshIJKDA`F
z%2EP+z#q<}!kMVr?gIeo6={WV10HRKW)|y#!g6pb(E{WM2UjAb)s#{+Xm?E@4RtEj
z*y#kXde5U=`15Q%>P5S#N^lkSp;R@N_pZZ9^BRYlbQ_Icz3LJBzS@emsEJoyM2%VA
zia>HDCfq`aIr~&Ri8}MW1mxngoaT@mD&K!fv{5$BJT>;Fk#AF{AbqIlvCGGU_Nn+5
zu%Hf{?YF&EAv+->*^b^%(vND)0l-PCR9is)UHxi7yE)hdewDi#tfwN*Mn;3Zc~*Q3
zXsgm~8-5W@xoQ9$Bz{#nM$JZZD$1S6N8i?<<=Ki?l&2W&T8yhu9H;*P%}}##ARK@z
zno+G2akf#|x2Xg0??k3Lf_SR|w*-^0A8Hm%aD^NVzLm!<GnXVOpFWn)JXb?UJBl$Z
zA4+gV?X`PPEtyCSEac#hed*5w+j=<>y>YcVBkh7JQkoe^j}gXq2A)K2!8CA;amF{N
z*^Rd1qVf&M4H1FCz|S00sfslI6TL_xHvAfSF~%@Qed&F`a<CM}PM$cVDldFwRH|d+
zAdL2}Dlk2=ezirA+=w;C#`vM!V*?ne=OXNJwkT&~o$9M8Xk;KBj&$S?y#VV3907{5
zJsZk)-lJb*8QT>8;oAvW9@{!o#c?h9wg!$2?TX?UI<j~ARaQ*}v$2@S7#vZ-jU({V
zqce@M`O)pMk&#TY*(oqIvT=Ymu7(aWQff)K+wV>)G5!!o2kT2Lz7AQDVT|vC=}C4j
zMsZUqh^%VC_O7lXvA733*F3Tuk}A(d=gUDo7di5u%B>GVIXh(WnhkJ?BoVbi<pq^T
z;pc#8Cm<chk}BwJx!+Gd)PraQV@TSj^4Llvmh~j(N&KnkjG?84lpK#rxUR~aZaede
zXju`Q6m#1Y(VHDzlD#i-Y#=2i1dME<inGa_>y6{X+uMq(+HFf={YX!g<L_3~Fo3`W
z5OI@T_?@Gw7vza9Eo=Saew)?nqcjo{Km^rImXPzo1tXfh^yR#;66$*l*GlMiTMP%D
zMo_cI6-nyEI@@kG4yAV|>sR=)QJ{yia5K&`S@&Eymx(}cPxSu)S{JadlfMrOHwTpp
zd`6xK!AMlM8943bN#7OQ<XP6KYel|GGZ;=v#uld#3ET|lf3*+MH%Ss?rr7iMkl9H}
zOk{;6vGgfFa0sa0xLbCa#cZNX_23qi+T20+)q$xeoyPUW>f^l~cpi&SKutxzsvDbS
z>INhLpBhU`j2R#S<{HvLD#mvl58=&hx5=F;aI?QGLo?$(G`Zr$`Nrd9=Tg>$1*JLQ
ztn7Eqe328X?)Jynyg1XyLqzCX(zU4P@gYPcD;eyeMl=1Qp_uMDbz<1bXDuk1GCXIG
z8PeLb^f>wu4JJ6tq>VJ6X5!aQ%1LSY_WMj&vICwK-rH_Zi50K_8E`X|XWaeG4{+(J
zc4iVKK)6n_q-8%c(@AST#I);98_uDyImsBuHIc;l`4aJHofQ_PyJu{r)fp4k$_AY3
z0mW&?Nf=4aPaxECem~f{owm@cauXqf6A~zn%f*Zxma-5MJLv;fPUlZGr;dLHi(e+&
zU}vXovf5h+>Q5QvtHUtR?1>0K!3`H$ASoSya4#9}+faupWP5x_mZT&);PIM|axXC;
zC@b-(w!k?~dkmlE2C;sc_>Xas8R-z`-mPh6Ki;k6wzrfA;te!{vz_@V2OQ&SrO*D;
z9}BGCFd0?bsXC8XOod#eB(~dOSqf2cejAD;0u%etFbQo$U^WHIt%E9Q(>GqF==+t<
z_M64)rEXSYwJ4(D9zxKQg#HEih+154Cm<~<89M>Fr{{b!v|V8v)EKdpw1utd7RUS_
z2m!vPRFW2VI}%5*8&}3%bMbSj`hxJQr%qhDo=w9@2%6>P_Srscg{80>(}Tv8B|8r;
zC_6vJU+o1YZ(eNHHm)}dQjsQ2&N*?p#0BY2rx2v2%%~7GA!S?S5yg1&`cfxccG>n%
zPv2z3LSBl>E>M_n<eoyp7vVNlx|C1>N>zYPoTJ-gUp4;#XfBSn+xpntBijC6ETuN<
zR|xL-iHx#$3rHAJPI3Ih1D+48f7_+m3T=;w{TSqVtsM?dfSpQE&V22a44i?u)87KL
zE}iR7hF=?D{>Y0dhsu$qEV$x<3nyYm#RoXS;1HaR^ISRSlhZqrbai2ba`g6X-wh)&
zlOAV_6qKO|WpS34(E104vY>(dszA~>^AX=QZbk0bY=a%U>fD!bag@3tv=p_r+P?;r
z5w=_*I>^RUH~`}~={}o(rC=f(M5XA-W;+UaYQv#v)U)ZVX&D;|ExxNPGTZWH>K8e3
zJVz4w{o`S2OMZL<q!(~eoywJrS2i5+NNkv!W?k`>3AsCNfpdv*Okp4<{WVJqOl$zM
z+BA+Af`FxY)I7vv7|Cun)!n`wUN8MaW?i1d#HFn-I<kT^B({PQF}fN<fe0BopE=wT
zHTgOChi$spBJ~TaZP%ot;XMHU^<}a!5R<Da-K4ev+^Zzu=9lq~S4~~IYU9&zr>aUV
z5Se|pnZlcG(4?}Kk&Nn7XI8b0<0~1?anlyLDiOC-W*-6iP08}5E*p=-i++k>C7DN4
zo*`jDD?)pymqrP|2}s=5*YIPbru5fWKW}~%c;V}Wx1i5$(613CYFnfMwxo~}b$M}&
z9kW$`hnEQb94#(iVW;+$jh3RZH7nQiPI&kX5yq_qDJvP+0o!};Nc9B!=S$iqwGg6x
zy|<GsmKrN|O9kSGgGc$YS{hC=O82dkBb(484k-FwBeA5-w?{r2K5{FPS$VV+Ct8u=
zsX5^o*yksMSs(U+xWl@1g~Dy|nC`<dNr28&fJy^uQq%~_)^s?XxkA)3Gg7u6i4iO=
zt{Y!7DTkbS0oiS!D4;7i7~qoPM;rsqP(Cdim&7RMEvizG<S!XaJE#OGI?)9IDg{a-
zagqT6CjezMWsx<uBp~A>A^y()01ac!_?;5xHZmR!=0;3?E|uUczNBP@CtAFtVdMo^
z0|)A^y@7n{nfCsgu*Xz}p(N!oUSVzxK!TON`<@F21f6JU#c~i6_^Tu6D}9&Y{fQRI
zH!={@Npj-y){v)_&VoQXwI4ChlmMS8$OeA%{h~j!_d>MlBtL4nO&KAjT$)HjgdZ}>
zjtaNnAw&;P!&Jwc+|vx;)8vT-d>*lMOU_(ww>v$NX-Yf^R|)29w^B2fS!gB2D;<)7
zw)`E$=S@k!^(=I(S?;%6bCDOQ+$~xm<ty1Nve|2MozksDfUm7%KmPzXe+x&-$+Pqy
zKy?-Q8oUQ4$aqOwiP@&!Q%$sijsOWx*c;Wd$Nj3U{Z#S|*P`wJ05t{DeqB>#%5|kH
z(5gp;zX?bwK3o8Ju9zq#S4TXfk-KWmK3`6Bdunp@rF5H<qqb2BB5kgrwh&H6Vlv*P
zD4xz8ha`bi3*W>>)2)uE1=x|*;9xfFcE~tVlD4os7LF7WalQ@-&MNs6;&;QouY(MA
zeTfm{OhR6RB16bQ(g7d==52fvq%5ya`bG{1>RXM8HiI_795{y%rkZgpdH4>Xa@l#M
zVL98#>Jmml0E(kZv`Rd#q>A;nRfnnCL<?Px4VExqG}F&M#Kv^#I8svj#>9=3jAN2B
zRqLPa_${NS<+IXKZLhLg1iL9-I!dstJlmzDWB_reZuzI#lW@C6a@ix{q$gm=N|&MA
zv!*d#z@timEpNY-AY*)IG*y<*)AkuJ+^lFs3xhG#+AZ-WL~$W!gee8&sbFLH^n;wA
zO6F7K&W=1*i92<g>fN~(>s_O!t<b<K)`gX=NGA%?7JN1242)-N3<JGeBHONgLDMfm
zx<{DwH6;NKAq!ze18pG2J|G_0NImwdzfRsSQIqobPL#FU(K(`DmnJ(b>mz-dY=9NE
z<q9D4nz36gaoaz(tzAbU*={y<xitzAQj)L2kfmusz}Y%Lz!}bWdq|yGk9BWHz_HvP
z(%h*mHlG$WJt}WW&PiI)l@reDR(Q@Tr7OjqBjyc<<y;^oy$egKZ(+GQipG8F!SOLx
zwq{GXUu~_n(`zX4RUsq9ByMyvvHV<PN>9CKRvD>s?j-o7s4fjDQDH;v<tiEank^`!
zZJQUvD@C3=sebJ9hC?K*AzIxa{Yf|&?b@KeJ@qkqOHrfSgKQxjmeP!8f<Q>m8*TTi
zA{PcPO)b{mY?l$C2}?w&Iq#m?{Ob<tkC;q{12a5Cr#@7;N`NPT7q}k6v=*!l6`5B;
z_*r$e9YbOkK_33~7PktDj;%>M@m-lY8CdypR6mJo{{SICA^fOh=8=fWINXgnsfcHB
zzV!zcK}Q7bQ;@;P$KMq;z=`-ysXBM5_>#1MGrd4VNjTUHcBxqDQ8?3{{L(anPPK4S
ze~5uZe$-M^l7A|QLR2>u(vys23N#xeS9H~|S85T-gy#qKpp~d_bMKnsNhi!{k`=JD
zJ(XaZl!no8=_=-<Bco9p=O;UwpSHG=Ks4)O-IG?hAn70FR_krN933?eYiS81ziPSK
zSxEzGs#Y*WJ80X<`c#uv!MB&6YLbB!Bn`dlRk&mMe@ZkPEB^qq#*hksN*(A4!SdCe
z$oL#kQW@Bq4I0XXxB%yBf|&#YNB|F7x=Up{k_ACbWMtq}WQQt`!^%kmH4`!}$0L7Q
zw7lLosHtrQXJf^A*_ARGQS*uz1z9N{Z|hH?fO!-`895t&+Mi5vM&SNcSC{T^-5^vl
z2m+iD_uFCVMmEkk9Mg$oJAi6$C{qMXe2zAv8;%u#N9jd39AhMmQBBAGYKzJ<V2#G$
zVwM>8%}FLO#&AC>>5LBer1x3{k)9b(+w`w#{{Rh17|PBKaL2|lKTau@S~ZcYZ#e`|
zuE#r&Fa=B&FgV-vrk23Y^+@Iln;_g{{52aYHl6XGTCzI9PI1T5fRMo>7^=rT9hmYm
z05lFpJLaEeKBJ8^X^b<14G`3Dk_a2)nxgd@D)J366-0g-kY+akjwwvGI3kT{p`41#
z)DfKQLL+BzGf#+sk)A%3V+cvYig*-a8b;Wx^BnS#8X;h310%H|$fE}dCZ-hFBS`JV
zGFnd@=bDuAk#5LGA_Z@c(wG+VPU#zZ)ahU-@%dL%7!$CrXOJ-=k!}MA6tCH7Jf|kC
z4Z_a)M*h_4hqgB3(ucS@R8|*i1abOP4qEUrq*buP17Ik3p<|J^(y1JjG9hi2@S+e7
z6;hI|#kK}ie?93;=w(?0j`T|3OX(>I;BlJteKuP<AoNz0vl>QHNF$2S`a<HF41~G1
z-jzpO6)I*_owJ(0*li&(LfXJqHpgo8lhHz2d(B5}YD=J#y->QD3Uwhw;1R!`tdnCt
z+o#5G=^Nsz_X%m2G)8eaNauk-bPdvWxHl!Z+~D@49HYWf3M6;iWBF12Qymuw4v1=P
z#^R4LBsAtv8d9U?r;#Qtsn&{HVn|5}(wB7U`(vNI9n>9D7Uu~LL5L#CXca5Yf<k+g
zg{Ncx09Gm<qAjZ{g{1alH-P8tIZhGcxYw8|Y6^HK81oUh6_G`?KYft2$8Ad_<vtR1
zt2(iT4sq@)T<Z>`jN(+7mlYMZ4yP1b3r+^<)YcR91RC$crP`$2V$6}#_Jx$bmQ^84
zr7L$(&Xp~*qfqRk5PMYNCkQr*O(`@~dQ;%F`u^*zqBxN3R|ACs#$HlA2yD0xah_5~
zt}E#8guem$TGMc0i{wj-swq%H-)RUrUdl-f>rqawFhYKuRjruTo|t&7Hi&llX!182
z5?%4VRFV>(IOk4IeR&nEbjMQk)t1(j+pe+Qj^Ng{7u;pdB<`glSRo^xPAi)xX=axh
zJrs^Ljy$5a6yL)>owjuK$}B5vR$DMug)6}1gdGP~axihgHTj9vy%y_TNn`4V;uM>m
zu)6A6I+R^Y%Vl65#1NeS0L&}(>dL5BSjv$O=K^~YTP>;9W-WLa#s&wJobF0%^TXp`
zN4wIpZ#_QrH>1hvxzi;u;q$olvQbLXusGDqt+a4C8nc65pP|OLiFsN$^GW`<;D@nv
zyQ<UjksFQ!K|xDU!o6^SPNR+Y10MBpv43crPxfu;zN2XTERWT4x}E58*m&FwLl`m~
z)(S&Q)u=e5sACEPAc{}J`-P*cdU#8+y4fr+ggBR6(CB!l&NQ8YNhjAeO8C9fGcH!=
zBgJJ}wWdsEuK*~Ff-~DAKYHWzSspCkTc(Zbpz=ba(M0H;wCdl9Ry&VNbu30FmSrkh
zWl4mCDM@#K55!0WAtfY$oNS^F*c2C2bRSTBJCh%)x?yuI(j$=Q2vJEGBg~wBsXtoR
zei3{$7P~5x%Jt3OM2FH;g#r?GLEB`3+;4-%XrGMtQy$wE(c!$Qa+}gy@R64G04T52
z<ZL*}s(SBNJh|07u{{<g2)$B1oBq$A6k+^n_+e}6C8NW*M<yKRLBSGQNbvyw01AOh
z$JT@Rv(ndCQJa@-MuuRltqw9mM0AjKImX!2&cGFFImQpp-`LvcbLcOM()vO9A-i4v
z5~no6)(893K#gex<0T+x)MCEk>pM#q`#h*>Xm~HjTSS}^K?>X46Wi<SznAJTEE1G{
zKZDfAH!6Q8$Nd6}g}bTcB*%6orq0ujLt5l1ubYruTMauP9F;bf-Z)Z@>(Fti0Y~)|
z7!Yg^D%l)Qb|7Uw3oWFm`7)I_q=SK_Dp+h_6+6dz6585bpDto1I~7sVq<GFCnC&{J
zNQ$*=d_^U|qJ(1wMLd&`zNMRLHrN+<pF19H##f4f(`B}sX|Ne>FCcz31m$Yy%yf@H
z>GGjk;>hlav|R2iMrFw8mej>KsEAu(3WAhIWwaef%%8-Pq5&sK@0yRFe7N+?w(N0k
zQdAg>%xMonP)f_JIzV+G{uL;UC;{A^ytQ|;Po9?Le8f1@Au#jG(HXx1#}e=uY-%|H
zDNBb>Do%oT=Cf;oBK_CV;=d9*vTl+PjHHYtHWZzCJd#{GhTA6_k(y-<a$-tvqUF=}
z`|nYZ%var79(iR1AmJ!d+jRpc8zD;|{YTo{@gn(V_(|~o_tkefDM5?krIdq}6=OnB
z3FS&q$FKu6rF5@FvuL&@^u=|R2DGgqMB!-!j1F?7l@D@({JB2~fBZl`B+9z=qz{GG
z#eojYz@0e_os}Vah)MMcN$<W*R|lqfB$Fj9$tOl9b?eWFKNGGJ6Dsd55S=P2jLL{g
zfjRSIp5Ku4uc!VD{{UqF0D-^z0lNHV_@mV>H#Q^l(A~N%$}0}75u4VK+ioc<apmc7
zgc33WNauXt;3vbFw`&^ahR=wt2?|*$Le_QzBL|a?Yrh^o8;IRE^PU1-R435uszAz=
zj1j03Gk{1xt$KcwBpz-xrQqQ8GLnkFA+hbB10`7PcP^HYF4C@P3wF~bj}Alx>QZp(
z(iD>Bq~k{5q=Uf}M^tpAIM)Fqqs;iNMN&}>{FE#xek}zgtqqgDl>$9LtZU&7wNd)6
zY@B8rOt#Q2Em$gAfyVrh0*~IlsflA|d;uX!LKoqZm8&`c;O86f+iI`+m#KG2B)-e-
z{YiQ#admC}?8UC15jvK{;$M*(8dUb7a(XLC@aHy^5}%liKxM;_qO1fGe90}jmle5u
z9k_KLSi_%rwOgICGW#rormje73wKYMD?`9|$=nQ_x|{%|xy@wv4xF{#y5b#@I)c*L
zR_w*NtTxy<P6^xiLyE_uO?{pHlzL66e+AWkpAdijM|6`lu;PkOfvBaTGJLxX?T%`0
zF_)xMeZ3uA+;X&IJ$KdJanW$(Hz}Bkhm9^ewv<>|)5sB(p<bLIli2fF6JOfmS{BS6
zrRjTenU9PrG(du-0DR>x<Q(UHzg_F^ezWNdbCR2zDik@7As{2>U0cKecLAOA_0jY-
z^GC)%g<T5kk6q?;JI_o*Yf8);Tvi(nV2~8*$B4CW!gt$><iwO6WL8#lb5|0NW&YFM
z9{S^@t8BQ)jij*^i0q|>sN+#nK_NNY0Y@Yb_^sYA#QvD-t7B8*-kXm(NJ^CzprkaE
zf^?^CIA5VTPC(+nJbKFMZM929zSm@3sm7E^9tsd#17M-3c%Hf7RF_BmM!ZCIq_-tX
z#!^zS3KgEpPB3excy)0mvR;|?KgA2=exQQMcjj*HFar`Jt+Y0@0CEc4@dmNn0H8VB
zH9+YbG)Y&MS%D?yn$~uEDAAtYLVvAPU%kqm3&amP(pH_)!%0hc$8`FANgqnT{47e|
zWphkGP|)EFrA0({&}uWa5u#dp^~elFMX9MQ$CTS(1uT`NUbym;`JYP1Zpf0$YQ%&q
zPWVblKc>}w>T9R%NeV;XGL=RJ4YsK5wh|8`%4;^i$dHmkrRhl+(v=aB?geJJ#Lic;
zyCvnV%&7?{JM)j$nq*~r(=M%UJ)9g5QcXz1jHQ4``sSk{8~F@?!BHD#tgs@Dwg+*|
zM_b{Lp|K*ZcF0-MHlzXxiyH#~f2B&tkAdabQ_&!#Bm+`R!brfzDQLh+kKdE;O}fzo
zH8b*u8x81b5y==SKT10_5N{2;(`Y$W9Q1Gw>HR6T<A8IY(wZyeTcD^->c%iJRtsZA
zBx5_&R3s<=07j?n=*a-~JXI2)7CTA?pbcE@Bqtl@srLT>FMrakmgn@5zZFr7)w$Gw
z1x%sDY2;N>1c87-six!tk>;ad4SB$GKu93u*PVQ0ni1&<AdOh0Y-9;61MN_A5HfhF
zX%38$nv9eI-x;Y%jF|~3yk~KmjCH)^=BDH)ZR#Q6g<zWV^UB#QjKCXX>Fq=zy||}a
zY<3jtTgLdVFLzr!-vc3{2NdEO$-o&D@*Bv;1w5AZRArbcv>~=}+jH$iw$TGLrrRTt
z^rG5lSMc%eNV3>$Lv3fa^zVbe6jL!2NZ8Rr8Ne7k(<a|R?2PaS1wWM`QOMwJQ(q!a
zW4|@UK=l;KurdK`V_=b=dSS@KW~W?(+Z#~sLRJpcE=U9ejPJ(hdKK8kT#BB05gZNZ
zbwwa<DU_K-CP6ADPZYGq0OK6g!H1O{kL6qt{{X^8GMg#Ri15k#)6JzwJBmC)$p=Y3
z)TBfGYja@bVC|EQk2FhekxCI2oax8jokTN@(oF}FLcf!NWkVqSX+Uqd+K6Tzb|W-L
z$dGuYb8Qk}UKVy8_u`*sF;U|*V^GN>BLj*xh#-^Yq*7>jphn{03=OH_7#?xONTMDF
zam6|EqqR3CQlEk(!x-F9l4D^cV~%tBRfNaD;LxweAH)Z>JR4&osJQDc=}HfpkB@Fr
zzY}1C>qZs|To^~^UVZ6pqX__vSFPz@&WI`)a?pe+E7UjMq^;7G3>0MXwLtju+<Xik
zcNBWqd^h4?jE?o|N1@iqV3l0drqR7tdV<7YxH3`-vY>OhtA9tsQk_$a-10IxsK1Es
zFoIL92sr2P(sFiDX%!oLZb+5#&CH71s>XbGG^CHND)#S8v~0?VyR@$nO_eREZ6vfw
z8){fkJNwpEWuH1k{{RK1*m1VisH?_XYAMeNR^vZv`Uj=DX}6-Hd}-=zV+z)^zNHb^
zsHhS40+Th-NNJxQb^idv`}BLtybElUG6_&a%2qZQ$x=WE?fKM~!`_~~+x!<~O|!Wk
zQOcuh!|6J=0a{CeM<30D`d8K6MbH!O0ZNRZ@mTVqN%05QNhAuO-8wTu!;P^GwYG7o
z=a7`6d=QX?rw7<%``4cH$v!P;>R8j*b-fWAkd~$1Os5%W1xJhEIWdm<9Nj7-7~G8i
z09v!!y3#B;X|p(DA|jRI;q@eysBxVNDs5h>&fcc7gC64*GoO<ZOYq1owpf#(qmV#&
z2L^kPym7@yz?}oCEv&aQ#rZg-10X1_Kq*di>T^mq$;lby;-xsrZ4{3erf<C$@r|~x
zPFXH-P{)dVMqP{IJn{y7!C;`RNf{fBfakSWf3)YrrS;XmFJAOhX`6es$5LKxk*zZ$
z5;Ziv_B@aX<ecKNC;FX)Dohy;tuRT@htYD>^G8k!0vk!l0~tsg4DYp0bT7vKyXnh{
zEi>fDy4jo>5E^x1Nk|@CD`b<UD!^7h4&yb`hdxYuZ)lA1MgEo9^98!`dFTuK_x_O*
z6>iBhtxhW{VO%H>pmG2h3dX|#;{es#KgNE8>U6f+{$?YpNCqUf!j#zq@TI_Tq~{y%
zYw3Q!_zCd);`|mmucha)Yw9;z3Pgxbq@mn)T0qjQZ@NZ%8u`!S7xrQJlhZ4HOum+f
zdW#rpT)1E|;**VvkdwNPY>My5{V%O*b9^WLnavfK57QOOKiw7wJ}2~#OWGQCWz)ex
z%ZMzsl@*kflpJR%!9BgrWY2XS^RI61Pd+2<FhUzlc5>WS$;P#)Y?5<<sEyCkhKbV`
zE}M2r+W2vD64co9Nol7F2sz10+CP}V<L)Kbqy8s#mAk{c!@GXWQj&x)^Xp3>jq(^z
zlpKGDBQ$Y(Ur*_uxhba2DdoxZ#x#SvU#sFk_~+A?IBu8OwM;Oikdfgh#2P$3c_BpM
zN7Ey0<2CwXBsFsBf1X;vLYqp`NWmZ=AH(mrKBs|SmkOT)p}IyFMk+f^O<nzq+TBSB
zTTQmwn*&xiNeU-%u^(LhvGC{Pt)HmA9<8?>Tau93PK_fuJEgn<*a4h+dh9&!^-Pgp
z`94)0UXL8-uZC4Fkmh{-(TF}ANUbaw=}E$zfA^YIfq()^%9Ft=NLQe$z0O9fJZYvA
zF^poE?YJ0Ef|ggQVZ<bDq$x;3c`II3?OI)?*7e59<;GL4;%1`KSOJ8+r)6bW)HM`i
zVlbTVj%z(xZFUb%lVd9-lrV<F`=0=>8E;nQAwf7%{5Ti`U_AUeKAoL;F|Krq<;EjS
zw5F`dY+NKSEvar4G}4kn8fcxtQjmv^LCGn_N_1o?i1?|~kBKp#M5kSF4-AhGtt6Et
zJf!8-0(Ss~cBz+ba_!bwRwoh+ZAmI=andxolC4Ww?Uby9@}i<g8!gKa>F%kuUniv|
zJ-H64wKUldX*k~<v8y8<s_$JA$^QV4M?7)fO`l8N<VUnw-MGHsVbFnvYR&)~fDQq{
z&(jz?SLBDqOPYGe<Ih>$<g4Zq^XmvH$Q}|vLV|s8k)O6}^grWw#w%^JqZ1M|;^Wg=
zC8g>CNmx$GCuE%GCmWvQn*8k+M0F*l`^a?`>y4sZfMz7<RMoh>X#v8m%1~9YPvHP=
zR0Vq<>DeQZxqKdfs>LjEQGQJa;744wVpr@t!>DzpR?|peyi$BnqNKZ#z6inR8w&ad
zsw}ZDmdA^PHzCrJoG-@6T6YII7|w8>PIs@4JZp?=nxyqTr7CrCl9r!>Q*L!3Ng7r|
zXj4uMauk7`@<FRVO?+7RfzvUQY=b4a5tdX^g5H+~dBVn^KG^zJ&FHdoc`!RnSY?Ew
zp_LlvPW&shSmiX_ga%oRRJ5>!kfW=TM>*r4YO(dr*4NR!KFlc(6Fo^~sPF@REQu>~
z@E|siF(mf%8TYJ$C&a&q_ooayZ1(KPRucN(Uy9ntBzeG2c-*LQR$hg%{KB7aqb<tn
zzNVIyDrBVs<9Jc?1-r0H4&b!V)Zofi;*pdX%hG3?<(<@gm5i}RSk<yeqk0>uFTswN
zVQG7;dgb30_mEN>fo{mw;y^vY(2RaBIT;QEU!a!mn}yL`Cj+OgF~mkKaZwGIT^g1W
zl!T2bISJz=>^bJK?}UF3<9sB8E3JCA`wCxDU4MBpWXF>nwgN^HHLDFJBVmEC$I^r9
z--|Ze)PyX)TY%9~MrNTbNm7Oa{%oZQZ*2EbJ8_z}T=8jA`d37uiQRd9m#(GiNmkIL
z-z7qf9Z7AaGj5WE6^)Lk6f}dl-zV%V9qSK>_L&Sj5-dr{kdRwT%t*oVk*N7l;Xa&k
zRr{w|!RgtEW(@nNOw<sxSYJe~$=L<LsGj&FgTFOVn=<g^7bf23Tw&Ydl_9v(ZVdu8
zc67Ev*1tsVMRP`Oa<|b}1sKtqd{?u#a!dCcBiDwIw4WWhE0l1oZ>eDkNd#vFK_j*{
ztb)%D99yGoNlS}xAN)W9PuOqzR<qRa0`Qd{8lkOBq=IC+paCQeii%Es2;}Y!W>*fN
zDw<?2Y(+m5;ISezK}s3lLx~taOy;|xZBfqYGkEF5bhFW!wm#}z3Tzdn*HV#_#ucZH
z>qqIdH_}LPxE7^Kj`73Io${YVs|WfIwS4Q-7fBasEIAe+aZf8sw%lj<a4-n(#eH9H
zx7=ZL!*0xfNDTs|M~(`cPBG>x2lG3M=4z!(veL6Nz0Hd{bFM^!<0w+jTO)pU864uO
zciU`qFczlJl`DK4X0OxYH!(-RC|;bC<E^x){f8#2cN;oXv*Iw(Cyg!)=j}%$vUX;_
zv)o*Rp&){G!0l6zEsMwiXX(XQSg;=#J`hSXw}0}f_!bllc|rBspwsM2A}!XX06djw
zw_6HGBxfGgEpM`HKX<l$D#Z@Q07)qQIi%voL`--fBzXn}8q_@_WSzTI`xdYO$|!`|
z{{X|aZaN#HxM7uTr0+~SZdNiA`PIR@Nl?a}xXpFzNz;sDK9p2I%=SzUMxV}vR1$)4
zo+|Mw)q--3sy)kJs%fJU8Y_%8q%RofV^enIt57F_UHz1D2pBo^sR&TY2*Q5Ysvu~S
zgOC6t-ln1z1I9h*7*S7gQxVwXDedV@9*Jl!orO$CkfJ;BQ1O{5#sx_=sUvZXs5=N!
z+)<$3D;@TtH^zM_r&379)i-8Li<IhyPG|=oAe@8#wIeOEGBo5=M1+yg>DsySH%4xd
z+v+MA-+n4CKs=05&M4`{0V_L@c>cA+lcSz<F*e5dq5?SF(h!n3{V0~xgM+yo*O5H5
zahywIAOTOI71XC;fwef4l0KW_xn-0n>J#S4-zV0kp-0q^aY4n7R&WJat?<=>k+G`c
ze7Xu&afq46mw$ROxIOgqQgJQiKoSl<l`6%o9!3Y!y*zl^9W9mg+sgj{hcvTkIM{Pm
zrY$)B57LWGr#t8s*1bDnaVU=2)s2#UwxL|KsCj~GL|vb6!JoAXt0*U6X@A?ZC>hhX
zr61wr+J`ohKZ3M%SaaKo9d;eR4|*T%*|?eUvy;L|q#II!$rYfg!;Q)~q$<b>15PM=
z3@K4WZq`OJj`ZqnXBa3ym0d-MpWxf+PNv2&_&*_A*kMsA3ALPl98;*Z?}D6fwOxN=
zV15pJ;*^U%4oY$Kt?V`qET*R0ry5hYu%}T4Tol#7+46Qrw)M~0;DO{nTvC6xVc=EU
zw{M&pDX1z00Fp6Q1lmac98>Mu$T-uQKETQhf<z<EE2a((!mLTQf!dt?o1fw5+MC!J
zM5`EH*rDV>UNM7L18FCbOgn8TABR4a@xwzTQI~EdUcR-}TQw&tJ5`!Y5L7@$2==J?
z5u_!x5~2nxzXUkDDwP$bGa4iNr#s_*X!)%NA?s-%Z9qQ4%#0Dis|-7A+oWYiw#8HH
zLUk;RB@L(fmbGChPr8T|=yG&XXs+4sYlQh{&CWAWop%m(?bJuN#eQsuPwv=h3R{jJ
zK$L<{+OP0>H>ct$6K~feDGNSS#&_l76Y29SNn77W8&~||r24n1<JdZV*qt{4h>a}-
zDJ-_8T37tTQFZVBtA#({tzRbP@MV1?@VBX(Z7+35vvll4I0Dx$V~UWB`n1s@KSv5r
zwQP1Cr|1rtjq*0B^Dgf;Fyx6#G1*uAxdB9bWQzIH*>j)NokoFjxj!YwloHBZAU4Q9
z!rN&92p{r+T0E|rCfZqQ=(ws^D{*Hz_N2)w(nTP-ZL>_c^?YlbcXT1o45I-EkcMOF
z_tF6Sl4~-)-kEV4U49cwa|mWSA8Gybx0TET$id_5Qe8aD%*S#A1yT|bhb20nDuy;b
zbztEC0EV-!v4?rI$dDtdN@6ICx?E@_N8bTBA8*dQ%)hGScENo#jV8r6sBI0kZjo>k
z{$qcZVmnR@_6r1{Ap8DawE@*}qg%W;WZ31}+6yjdinkE8C>@j=LX-jLjFa1X%HTM}
zCS1BiR2eGC(x+hSLKU6ztb`o;l?t?3`irFBf&=zh3!SA!*Wuh?G|IF1fIuqJIQ}9@
z?NhDUN+p}M+2u@vmhSCGo?QuJ!hOU^aB`#;<Tc?Ec{#y3<Q?iZ`O!UQ#m$S~RoYBe
zyhQ^0DQ(p_;2J?8ZfQ#Kz7Xg**kcsC>(`m>wHm{DnJ(Of=z2IBN>p*>{n1L)K-)Q2
z)3r%jFVgyDO|^2f^uwqT40sZ*&Ce1`qzx^%p)HoicilwqkWE(>g~@(F`ZKQYT{PUN
z?Dc4Ax00n4DfS!kqzo-6DoTR1gPbiu5Dw(l#qj&%<T-Z7p6Zw>lM#WG$$=G5CP>Kx
zQt{z~@2rzpwEm%k)M$80mu_NGG$KkZ;ciD$gE)B&tpm)Vk(8?>gOGEYkAC5Cf*HBY
zn|HZH0WCb_)}2xr2{;}Sf^?vqfrVg>{*=;bx)DbEKE1R2Rrq6iNRJVldmb}MQX5c5
zh*EzMI6AOB>pA%2eChLJTXvSx+=pB!Y%QdrF6487HtaPW_xc*gVoqXgUk=`=vRqSb
zC2dGlW^j{}mt1izww8e0ft9HL01!Ae$$e;|!}jK_(3_GHmK$;mbvi<FLe|;GhLQdx
z<0lw689Z6}wO$U-PY6a=<oR2#l_8d1Z_NAXd2tlAp#YpK8A!k*0B@3eaaz~FDANL=
zL3y{M#%IkV%jbM!a6klrqp;hzde1t>LOPGCwFI_^@Lp0>g)KTpPBFIHv+7UYwx5O8
zz}mvnOBw-bBr9};<l~LbRB@}fy>x%5O+QZvKEdeV<CarCjDn^eW;;*{noM@XX-<Na
z5R7f9X8|}$PRA$TB!N2S)p3tz65fY8Q;w_@Etu?;Xdr1)Mlpg2+^n6>0nTauh>vQF
z1ZK-AZ3!w`Op>i?BL^c;JY*2Ae%KdFgovxPqSVaQwBbq;mk^9N0Mt&Bk_p)=PW)#)
z*Po6{gG{90`KF3xy3pgWSeo2fDp?9chC^Yr5LB%V>eYo5lC>u}0373UQQbti@7r96
zqar)ewRm78lC4R?&=jPcCzG7_BDD0mY?Ekh*<K=1;H3$`@fE%>QdB~YcSzg=xfmPP
zL)LF5(ENyY_Fqsa@YJI44U&+Y0#xP{p`G`}<Y3^M>TOb^nlY8O&yzkn-ES@1oU+{%
z@5{Dx8DU;j60?E;I7!j~$m9y3SU+jaLs8>IOoA2TXnCOEQi<JTzCu6*oR4bQzAsrD
zzT4H{LkcKCSB3~#QVyJupm3~iayxBQ&WnAx?+bhSo}SQDc3LhWASm&GliNwi`hmVP
z=z1Knju}BMll55KvPN&JKDqb_@b{oP`=-+#@6+&DRlPnFsdOaxido1mDMM|E0ByZn
z`j7hzd>fnK&4SA9s}7R16)d6L@STn^zS?=L)1qfP7U12=&woryfo;sTnF&_D<qd!U
zQ-GWn*Z9B%Aw-ZZZuOP$-W0{O$YIn(f99aiV@eR03UXTHjZ5<<P*Bb=Q<4Wh4A~;u
zZ~KQfD7~UVzwEK_FQa3k;ditl<ssCiD@)JIX(}fGEwq&_DJt6}Y<VJ{x^=8hf`X`>
zE7CTIk>jN<A?Z!I1*D8_Le8bBSl|S!Bx3@qkuT4x-6HC%QX0BG`b)`T>{^oA$Ss#r
z8*_9mDH_sFoQ^6o;d_Gg*OfD+MYJi9w|+_*qq6ZMNpoL_3RQ%R6(`r6=DJ*ydwqQD
z=Hlpq>OY7bZPZuRAYQtuaidA_-Yqe2T4Ow<`I3byLc9<FSK%9yp`I(V?RO55RE5jw
z$ZbM-7*Z|J07Hvfuuq1{R+m(80SeRx0I1uQ@w;8NJWiInJe>+ElO|zL?+#8IAz{$6
zGx$<K*qjQf$?ABpB|S1^-<D<kHm{j&=h<<=jA&j;$oI$|fB{E?o3kdPp!qhY?d~&j
zl`+d1SSVyA0umZ;NCQYnKtbmzUgYGDN)~DvxPIbqQ?yd0YhepqSu4Q*0OG4E9-v@$
z-kWOIY@Fxqexbd`X)-~7#gQX8P&_40YR1^X&IhQej-Be8EY=^k3uDjcDMM?2#zVSx
z$V+;G9^lpEr9>v%+A5c8Ght}7qBC+IdnHQ?2z3DQg|ecPo_Idupg;Ck0&~i4dRlR0
z<zF(<*Pr>oN{8I@T7990C*4A=ww7bZK_EEF6XB3K1uESq{{S!0Ri$Y{S@GSD6{P7d
zke4(cMIFwcQ^@o*lgT9M1~FB#6X7SqJA4<|l>@_0h3s9Bk~I(E!mvM6>}zwrS^APJ
z9|5GzWsM)o5|!=zD%=o#Gg18m5$LeoT*zo?r47)eY0`H5KzUEpbDYtWXWk%4b#~=V
zf)ANrv!({}J#YZe(B_*GQg-_nCC65065>8mTAvv#xCfQTeERqH9+ev9v9(|}TXjHS
zlmm~}v{iLElH!Hh*Jrwn;F)m)GBfV<a)M8}2AoB^p>Bi~vv;yPz|s*uKj-_mK>I4G
zC?-PLoyN0ec__|JO~SF`PnUYT#^^*nwVwHCxRbbT5<CY_{{T(km1pUiCElE$YEW+H
zY>^d5j~S&T5$s68`x;+w>>EXT6`YJ6CZ{6XFamT`lvqFy!%~sr3=ODp84(D!r_!Hp
z+%x!d_NlbQ0B(^>5yzztA+jkJ@Blh^q`d(+<F!qAV;H8KYicS0D)KF;!NO`S`+kyg
zIOl4%$z)*WqaS2vflQ(TxTpY=k@-;(p(Q7Skw>kJWPzUhQky{sY*FHB9g4tC+f=I&
z(g!pX1ScF~q#9VG#KCPN0*q-pXYWBF+;^fHdm9x}@c>Xj?kTmWBWmx8F}*b7*v>Kc
zrE&NGJchHdH5Dc(!60r$OD({$oQe_Q*bD<voOA~175Po*&c=aT1$=^YQ?eNf&S+s2
z9mXqzCOw?<R$$3OLH__saT{<zrh<t+rk`b{tn8hu=5tBYIgt7W+wYEP=Gsz@2IicL
z0mZAnK9xIfwE2cdaCa5JxlQOO^`_dT={ikY?Y6Q2$~UgsZK+5BC>3kBz)C;>Z;JPQ
zC#P*}c%UejzyQK(UNyHk%703eiv$g{;-%w28+mKr!+@$(M6+t(Y5gdqTJiil)cY_}
z0Lo~V;HT2N<A*~im4#ZyHWbqBKjEu+f<Kou+Mti+-|0o+4-%lOK;WX2`y+3|R?`c<
zDFz<+(kWgL@hU2_kL9M#m;?AK-eC;>5nM1wprG&?iixwQjTE719-jcyKrFwdT8ts+
zKPusc+$2`^4Mkt}K<uB=ylq22hOFi%_(dec^+=)Y2Z>t!mYjYc(z*LF9n<<%m*mIt
z@$Fs_2cZ0@dmm#_TK$wUvTLH*zv1WFtY#QHX|H^qPH|h<GDUn_d+DzT0DlkZRu7c+
zt{A7Vq3i~T6A2)3Lq7}wx@yR{hth^}0@kevZAwzM01T5wVG2}Jr9merNc^fQRjORY
zco7&|t}FNuGJoy$tHg_2EIFHtiW*1v3}CGNN!cH%_Ne*yH<*s5A-0H2;~H8dtzPO`
z-2RmD$6buA=@eINwo3~9ex{Zka1BX_%8ovYP~Bfd;D3&VyY=IeB8XOK5#un~%V}Fl
zT2Olwwf)#X;!s+@#Y|tL8A?$|QBsanPW23ndfFItV0|h1wb59|<c3W7@7~4E<uUm$
zlZ86-g=@#APj7B2(|LWHjktv?P|>)>K+cNTAwxL_{HgRb=gF^0CvFL$-(%Gj-k7n8
zooFLlTL>XQt$l@ScZjp>tn0Rj+L8uV@!eldv-YYdPO|N`>}0f7l2lFyCarEmpFUHv
zF0Xic##;(bcJ>uHzXM~@TXbBcfcx>-QoO_~!ql#L8ncS0bz4`9(pien&L!k*RHZf$
z4tC%vK>LweTqOP`B+be=8rlon*j0Y_I@;8Qwt%-%kKtP=&^^Kc-|t*mBAMBaTufR`
zj^?M-+_hZgsRweUwxj|(`O}Z{se2uSNOh!7ea1^(WUUs&4FmpmlrnMaT3VX6U6}`4
ziE|!T9%&(IKA|HzXsaJXS`(^U5hBcRkTUzL)K%(2Ml<@?BhM&ybo**(sQjnww-a);
z-|kVv4X6Ui#-e`~ol8nUJ;uMT!l9#e9fDNVLz>IcBlutMOLjt=QqD<H3XB{la1Oo3
zPqlANSXeRB8hc9sCrD;8$U;ZBAe{SVqvU)jvt6GO7i?w4BmNqYtte3bEba;3v^`R4
zni5!2*^qy=9X#Xlt(FU8&N$Xn3HN0sBg5pO3m_{P+>Z=#-mkC~aA9jdwBolGGhtE_
zCDpo=ZLrIUXdze|4PX#918TD|@DHcb>uthq;Gp1HX(5#%D($0E0^8>vQ<||#>Dw<z
z#%|X#({*w?uB0Vxs0H|m*Z>?>7TC!+Bphc>K%&yf?IS5DF3g3d?enC@W<ByuqPL2W
zVmeXbx7jF5jwu>*i~#0A*aMxb{{RviVmjQi^vhQW47L*CM8zReJVcUI^Ti7#z<-BP
zR@oypy<OF2xx)VdW>WROI{`?1Fm2?fLkR?GDGES1R&tT4TalFZ&L#8_arqJ4d^)uh
zx~R|^Y-?JMuM9MuKmeR)&Pqppsiu@&kA>f7&lXEWi)xiB>guExoKn^h6{t1Fl#PKW
zow>*ct^Ff?m1`F0*9%kWeo{$Fn^KT+yaAPNMiLI=a7Y{0vp3<3whLFz%D0^A3scT%
zcS2M_&nh`cCv_blas_2wQ5toPF&(|IGV{xIuMbK>aIeIHoPa$BKD7ztkEnlht|{V4
z_%|Mvy0s;dAt`yaINO1=f(l%0DL;n=L?~l?<n7NDqcL}n6$*M&i6zCQC>ng&I8MPm
zj!DVef30F232=zpEV$!MAt^{uQb-GMa6v!B0KhxsVAaiu-X+yWG`MaMu$GjCD6Pjq
zI$Bmo;Lb+FvC66t#VG7)&N9<4MpGg26T%@%QdSr!)U7B4D?Z0yduJ7ud{pZTbXae>
z4W^wS5TvO?Iyt}~jX5OozDeG!^6u@m8cAtL&Z6=ZwE|a!A1Fe%DFZm*V;$=j_``X9
z>v4vudZjwBoRh0Y2mpD3&@gg9!OD(!uBc*iXy$WBt6aLK<8ik+JX%Dgkn)2FN}N{X
z@TXu$aezGr{pwRJQK#7=bv3mpisFz|!)he2Ac3Hc5<$+LvPe8uk?`lHZZFME@!8C?
z0%8*!r%DuD3BfB$9KuN786@+vPT18O_&;cp+V34M#CIgTrFaW5QKuyS5wK2Fa7YQj
zPQx1terptykAIp69&xF3Xa4{QCd-(n;9TR)eYGo6j94gJPC7sscZUW-0R-$yLB^!-
za(>ThX~<05`lU_Dwy9nRooa}n6|2J~$FSH0t1Yb~2O|T^RfakVeKFLI#=1mPjwL~n
zsV^ZRE*ynN@ZC;)`{_D&$E{k!QqggZ9&CA?M;$12D?>5X!;xD;ib{}QZwfk-g>R?~
zCm?~Il+>Na$;mY1*b;8HMj^D^?yHSxpW--tD_y`%Y^6&it3Y3f!8`1bq=G=kDwiP_
zmT!xc$&q+*DpJ~H#&IfbymFAFw1N-Oe5+&4Snto@Al_EbNm;`!f~S<`A%FsaDJXLa
zI7s$E=Q^`k-NsK>i^8T!4o?LqGGt0p!-HN%>On#j4`GaYZ%+%0**P@VWs>REKM+?k
zQfGAYa+`FPn_Dpw!hjhFNpA}Z#`xnQBXLs}uAQ|_3hF!0PuykC3?w@L0L-)_FrWYl
z($lMO9D(If81(H>cHW`tGRmDrhSwqGf`x*^DocYH^D7}rBXTz%oic}Jl7&4PxQ?Yj
z>TWKT9rnt~$^QUe^-1T7jbHR79B%Ams~@|q#m9?lgoYL5*A1mK>wiO?{^_dinkDKi
z#IyTICA8p_$$6}e_8~yz9sP}5uDxq~wnuFUeqm)u^7v?H83Sx;2Pf(JRVq#Pbm<Pu
zek56oc|a>$KP=`)xXKCswM=tTjf&#aqeHZHt1vDKRneB*lF9={Qczi2i35VPjSh4C
zX;yuE)yJZxE2u6ikS%4l+F5M4&`3+D?d_jYpUbv0V(3e??&IR6wL@rtqy@M&0qL*+
zkG9pl^evL%)5=6qZfR=gQe5!$D0c@sPJYIw+MB42$BMRW_U4&|YiioTaytAXmXh*z
zPy=Sk_4M`_^r|$weEXBdrN{xmbNiP_C+Ym(l}X(0@)FV+S|me#pa>w8IN2M56}Tgg
z#*y+3+!|{09fXG5SMS?w1o&@bay0FwUx<CjYV3lfa?ifOVOySHcye&*PTG`_s-#gE
zB<D>!8E}3jI8P)mbNSQgXaV#cskn&FHytYgDIkwRGAeS>cf4AW;M?w!S!D9hiK(OY
zZCOG5sx`b0-KqA9L86r!iz62Z6A1mKWsj)x^ds`?`sv-X`3jR}+i#Eow_TenJTomm
z1D`;wlq>%LFsi(Fa6lA$Pk=cUpWGE)64I^Gp({+d&N6#zP67HJ4KSnxjfHY0=42%J
zQKh(s)ysLyXX=xY`cl_(i6KBqj+T^v;xL>Kx;Xr?K}81Nkaeg5x93bPz<bgqx#1k^
zN{~;LN#M{*lm~p{-n9gI^(P1PsOQKejO|NyT1fm^z}|s&K+kQc@iE1Ln&tv{rc8iN
zb6xp_{{Y^af<A{d(I}IF%|$L+dmX7CHVNi~MkkXb6S*AGElKa1uD2_F%^1v{Fm|Cp
zWaxb30~=Bl?yTh0dvZPL)Vv9$aRmir({3t~9RLjCn{m*1G_E#)A=H9C^p6Z<dg9fw
zHSGCwUUo%HjFE=MwkW6_Ap2CrTT#Yz?cSwhTZY+B+Z8bu-;d$1pO>cObEuIqEudf&
zRP-Bg)Coz+^`e-!okPpP%}m3IjBQVg(eI&@ens1%07sNnmJB{Np&-Qoi~+S+Au)gk
zE7<h-D58&$n2|OIPCY7CG+quV5l{#N6)7FCdsnlI9U;=Ati<-B(F|ja>DE!(6jA{6
zu9SiXV)n&0sGij)pc?UP8r)1nsu}JnwL|u)l{7sm3S@T_IE02VHsX?E)T){SkXjy;
zD8yln?MeA>K~iP0TrGjxw-gdA#?IKJ7{fWmNv!~OriuZf#K0df>t6X$&eV5?4JacX
zmAIIX`EOBO5rO1;RF8%aYEWy`P~s9eMQ<DH`vFQZBKke~CZs$O>0JzuN<2(Pn9-6p
zjHMq{uW!f?^QIqd#U!BtEhPT{3u)7zspS53GvEW-nst$+?LvT&Q<((`Nx}#DRa*5F
zoTPE+D*13=kZ>xcc|kxs5^8c(hmU4?H7IG)l@W~8Wx?p>IZ8%-sE(vlh;)&jY9*Fd
z>t|Ny-nrX4BApSg$5f|sk%WWIKCc2Y0||2~R@<NQp)PVHLQ2w<kT7yZM_6YE=ao!K
zl##f|??%l<vIk49EKzeZQdXmomHPIsPr1jlC{CL3lJlboZ#e_o6)@zBY^!rkN^xjT
zMiK`6RCVUv0^JE01vC_Zd2@mOr|C&1G)ly|7Xa~``<@C9oh`PFM+djHG|a1P;<qj^
zRH9BBYe)p2abBO+RyR_j<8G-VVvQxFpRhWQ{{VwR+q&B3l&M`i7(tE+8AcFQzxYS_
zn&ilI!|XV_A5=<|T#skCrV3NGgekNgw$GFSM%y}jsba@WnW<s~b)PPR7ParVLR7CY
z^-;i~WnSSp%3Z!BOql-w{_6>D6)n<s)}ft2yyO5kR|5kkpONsx;wAAS{Uf5@i5W>4
zn{^FaBQ}6G30#8OLO|dqxt=$d!+)Q#bv_-n`TqbVhTdO_?}m#hLr!29Buil`Qgg-%
zvPty)D(P*TEFkHC^9a-eP|{ieaq6%=^H~&oC4$u@Nx1w*=*W%}rAmSGS4V;Gg)qrv
zeV#vBA_qwH71iAq7rO7NrWIhDH%zmP`#AHi1b@t=ZA%|Yqb;re>;C|wODmH90H^-|
z!{~cd6x`N=A}PlZHDT`{2R!OXKc_vbw`dk8QTrnE1xZV*IMWomganL-6PC`BJCdXN
z;;KIlBFO2<$hIDlMXAC8D!p|~SEealvePJUNk05miFUHexR+mM?8_1sgSEUV#W$15
zSyDjHAB#29?bLL?Ke5fr>hJUWn6Fu}(y%<vBG{;*wWx)F?xBDQNYoXccS>q!;o5pp
z>ohs>l8I1KUTh@*KHEu73XogxosObN<D3d+C&G@bzB6`XtNQ-oDl>{(P*DDdFq{#n
z;4CRboj4@p9QLZ>dIzum4D@RjE{}%Q+FO#smYEU+e+i`@Hrfz00lrjLcmM%hv&5fr
z{{RPMGA@!dPO`MmzCvOQRwlCupf<F)#<UUmyufS*{A1H|wS1-V;u0*^6x!}hy%kPj
zF<x~zTg5ws<5t)h=R2QViu$EpXVeypos!nShtJAGr7RhuP+A*OK+2W^2tiQ%IV#w3
zkwyGp_zBa07W#CvA{v^O$_Z)oD3XP%d}DHOcjJE53>fP#D=U*ThCVGH2_xcETP$QS
z+v;{g76SAf4>#h-J7gWW=Lfx8Z2tfnt<s@1x5JQ}B(47dFeD_ULmUnDD?E&!VOc-L
z&Vl%e@KWOA4_Xm%h=r+2b=c}@YX_YQ&iFeVgMvHMRkr^C9_MX~b4+zKi6{!turdyC
zN9UdE*vSY+38>onvy%`0S+wO(;*Glf@%^UmA2hZa@LP&fmQrw3>Q|5n<N}nZZZb~w
zN4fRYldT$NYmTBaVXVC+r7Hv^6Oaizf^qo|rDnZb)RLe(4%cS3nRBNS8q{!v<JTLL
zzZJcIW{>QndHA6+Cs_PJcrg)^mZC*aLqS8#l`TVAR&o4XdR1|#EPR}5{`>xfb3PN|
zrFHf<KZ9Kg{{U9>+%Bw{4b~&6v?@D`S#K%Mq_{DRV2>&Cf)$Kyn%K-~R%mS{qV!1d
z$XXQSu(0ZXn@C8)a!$%4-0fE>)@Ljh0|ws>w%m^yC}<(Xl&eS@TS{_~gN=bYM{i2Z
zWx4Txs5)ljDT_KRQkqJQxlF7$*;w-a@TFJ;@<vn!{Ee&3o+Ssq&WL4sBYqIqP}m?}
ztx_T0SxOsUnXj37WlLT`3IwPTzCAXqe&f>8X86jt%x)dFJm#Nlc<04yoP-pd0iIRH
z-1e)lO7wrm$=xFN`(Y*v*A`eUtcc@69M9s=Fx#a=azP{>IjIhe_%rbXqk3XuWy`l-
zuFs*xGcr;nC(KS#j3v?vaoD77^s2@Wd|&b;vPFKPmd8t6>}@7QzM8d8MAvi&u52R&
z9mxtwSBMp{Bk8qQE<GfvuZd#a4VcRWDG=c;rn8-t29R}o?diRJ6Km)$qBeGsez$bz
zj-03^%Y45BB#m0MNLb`;<v&i<8+>t#Vpk20hv}=_hY}RrcwIyCt~hwY66tK`BVaT1
z&0aWVB~AYTCnT~*mXU-x)9_*?FD5D;PMm7g0*C%#zvoeM3?&sXWiZOY)1hceNIw4n
z_N$LkUphi7%5SZH1$DPa$j;78e&X9JAK`fg#He%s0101uiGgeQTYVwBY+K}nugn^W
z*AP~;dj_Xl*k9QysY}u|{{ZO}j!j)Z?fyzc`#rqK@TEjS*M)+N9bWlQKT;`8^ixvQ
zhStN#3Lv!Z6M{X+<b7*TN5lUBh8DJy9{lgb8&VW?u$zsh9JDmA=55AaY?PkqNIs)_
zo0-s72irpyPm4A&hLYoA;SyXRq#wet=Z3b*PZ=u5sRUM!rk(fw(WRO+Qf)mpsBR)U
z9YaL^CjS5u2=&3=A4<@y_IZqCI3v8tz|u)SlDP-#KsV4I5ra*p1-YB_be%{xyNqjt
zdG1mhaJr2D0L%8P-%ndFR*P|V_iCSRc#kqC!f~(v00~JS=Uw>l${(p5v&$KDRPJ{5
zOe<`aDJb7Ufdpf?P^T<s_E9NQ8lpU(4gCaUkLJf^B<?+T9qK~!d2f6Ml6X1}-(oqc
z*_h*M)E*dC;}y`A5$#hf4u@epxUQ%Yo)k9<9EA^JFl&MIlZ@0)?L#gJ0eOB3cK-kq
z8UFzCN&JZPppkYe+#S19n6#zTfO1AYw6wmQ^ZM0g7jg~?wj<WN`(GP{RxxZuu$*Ai
z!r}ngRZRV|f15q2V(xg)=Rg`|zxI$H#Yt7kIsAW0sH*XSvWMqGxq47J)%w;6a5r#}
zq~s8-ls$j%L%B3<h2(nGJ$IEK;x)uxRIlB5pNQ@CqGHbU>}StHIdDiP%~f}=q!Z`=
z04?YTud2><3MkfFm2pmg6(LtNox-@PQtxE%u9;QrjNy65tq@sua^gAFz@(<)Kbolu
zz8svdk9z36<AsV8YcwWjq>wdH?M_YrD#zZlX0N9phcmye8rM*qX;Ykc&1wyrA}$CR
z^8rsHdwl92oo9&ZEO4x5iEpS1j&*I#0~g<(MsgMU*FJD4ZmOh<?nXgBdLfIxum-wW
z8J5*b1t$uweDFxpc{Kpl#9#uw_^vrTYTZ@kdY7}GB{D)azj2QBD;fbCV2YBDZqvR$
zI*?`#F|{mxJ)4e*<{<iv)SPH|z$v2|hz2&J8je%iuOCj3$|oYn(tYY)GB5=&!$e|^
zX#`hPF;HrQBMelee_D-aY*DPcjjO6Ej7hYtQ3zP5<iAd((sva@5&>eBk?TOE<LgTC
zt676GDYX&anE8)-Nqar(S%*|bHm|KTx3=b)Tj$n*Q1X^D%^^z|=7%quX>az;0E8)E
z(lfs_F@N9nr1<C7utN2F*R^}lzAve-2fgbUF$%|eeI!r`d)}Ty+;3O`n|7{&w$#9P
zYF4_&9u(7~BObNy5|UM_v55I?<Qk^+8+uT!>hmK69nDj^g{^5&B9>hSCP#e)twVA_
z^r+YfQ6w)Uind(s?dnk_KSF9U6~0ri6vMX4l0n=@j<-KOAlD4Lqp5UP?yyjzJVE6&
zixfL}m=Z!%2?uJn%jo{Byf~N;x^7{Rv8fFm2w5JwRG>f}ybkn^p6P21iEl5fddk-s
z_Eb`+R>gkMm!9CMpjmIzb*TRUhMhu9bO$7~3)NAfTcXCB9j_fgf)EY?J+&Ug)`(9|
z^<Pq36LNaSwqg|ENtmQXih=Cw#VE(nnj%-l?vCk{trjkxx%j9hDSclqi3IwGoewEL
z{WLdOk6Qdyy3L%MlRX=2l_h7(D)juTv&bL)X9HvZ0K3Pv7mgiDMvIqe6Vg5$^bnQ}
z&#C2JrKsojZ%kX)J=Gsi5q=wgbcK(tOdB1B+Zd&P3oX{9V+<>;x{Jb6)#_t5wmhS_
z(!B9i3unZxs_K|avpac{37|6jP?DHTY4q^e2ub$HHC&Nsb*qz-qOc0c(%M^20#Z8+
zbLvkd<kusXJb$Hrf6e~@=(<vuZ}a;X>0M=YvbNwK7+~33U-yjkH%$Dxh{qo>rZ#rq
zmlsuG)E^J#NL|8bPha|qKshryo2V73N7uuSkfHwo-z)1?`|O5oP|BpZmA>yK!^hgG
zf%jy(x7qTr*vgzfRF7JgPgN-@ul@f3XQI%<71#Lw4QZaW{hWnA)15if7Kb;|VA+~=
z<$Yh3<2(5gQF8j%uCDEPy;0V;sWG2)Hq=om=TfqEOKr3XN|bmBN-6$<_)FJaM-X1F
zO{&La@Vs=&nATl+pXX(j6y*p102S4=&;I}uwP>9?d+956(XN{KX58D=+reJ9tq-6{
zTVQ_iZ{3uTh|Gcb#5U4^HdK`j=_8Lk)ydR<Izv2|;{KIy^R@%h{WSHi=N8v`k17S#
zX!(Y^emD}Yr2~HwLny)`BO8@Sa43J_n%u20ZxSucqTxNOb1$G4BSdsaeP}ys0S7=%
zKZu+kL&aikbxXxkIzQpGD?Q3<@1Zgz!eqB%gzB?}4--?DH}Vc2l2(j@T4`ZA#Gg)l
zLY32(b7HjKpT9nUxmSDamjYw9gbzBK)svwk1H@TU)^VgH6oOnaVp{zF06*LABY84+
z`Th+K=kVoAkmSKWRAn71*DccXq>Zu?jXDN00#Sf6bInmlSbRX}`FA$u*qEIH6QO8z
z^%OX;2P*R@=LBIR+dI{zpNKbW;|5KxA`CR7DQYKDmPx`gdDKoeIX{&uRm%S4H{-c(
zW-DzdHl?csrPY2V41W?vLQXOcrDtr{25+hTCtfGV_3W>)@%ChhZal7xk@GF6c#O8o
zZ@SUkrLqG50K7hhNyc|p#~LS1d_=h3bjM?+BtStxFUXejX+wD%r6D=W^RXj1<29b!
zZoU}Mo||+b4mgB|7lo->K{!G{8|+R_*-lTan;m9iup3R0Ek{WhaY`g86M}i)f(LJE
zhAgs-xi(WQAt>=kE8_nEK=kj#Yg*#mn+~lAP-VomkkRne0<v+E0Vf#%d2(^F{L1*}
z(bvC&_qiQ1>z*_aQtTOx3?WF=3X*mhDJMzW8Q_}z0_#4!xZD|i7RQ~AQqt6)6|dcF
zG86`ND0zGHfym8#<MA@bCg}~9`l1sqtx^(@>XIB<2b_Qj+yb-d$}{O+-=)dQS1yk;
zG+s6IvbX(`eifgI9dxd<>UFgm&0q-d+J7NWy1heK2ZW6~k`I4M{SCF4mbih7a|lrl
z0+$F5cz_NFPvSm=bL*P;$KkvbTx`rH-0MkoJ{!fxmK#uT;YAy8Hd0Q<8qUWX?cF;!
zsI4tVh^cK#X$ZhBtcO;E=15QnN{HNNVhGrfK&_f`Qqt%Uik90n`JH*zD@>+Yu{K0R
z>OvIxNRZw#tr<YZGwbV~DuZ|Uv+)uX3scZ~eW#H*3Iz|y9us68fLQ>A;Qj|PG7d9V
z74`M?FQOz!DU{YTC4MPRj187Ex`-TujE?(M9nxP?+$W{Tkz+d*u!K7u<myJ1<P*mk
z$Ee(TkUZ>}B<0<mxZ@bDq!oMkht|rs#D{3JC5h5pB3k1D)2(4Z1qRBnqo2ez1IMXN
zDV6+LlO?9@x2ThWP+HLPR>Y=AKfDD9SDhz2Xz>Wncg6%fNK#=)OzG%!$%eGDR~!Yp
z$SD9UAzoLLk1CQ>l2fNi%5Z1yvwEJ@W=B!F+zY*`H<Cl}HD#hk$|+Xq1YluU!iL&&
zimoY1uaPqFU#CRm>Aw(QKz&!clxCP~DG$rTk%rq9Je1|5{{T21_^dnPO`_G*)0>6Y
z@uc*1&PEji=C(w;HOVA?FBPCp{{YUA;2Q;CR+^uUZOG~2arvjw9IxdQaPgFnlzfba
z-cSlHr9f&)Qe13+a+Ls}D@(sYZFd_pDmJ%}HQphyZA7SUp+iz+PO_4wooPuR{L}?-
zb$~O0-wbicyZ)yOl%V(hiWD19#7~9s*^)2UyHp3xn3r2M-PK!U{{V-?Wo43mlmdrw
z=_H=F>IpiL)pqL-Q#O!F&DO@kq!jvxqlTS7T}0ITOf%CBvwrEGqmR+7-am5O#%3z%
z(pQiEAIuc3OdnPzHoZO8kxbnMnlth3zYuylUqnxklH@C`m9aL@IlufwG8S@=@kMoM
zANWIxe`ax7Zod6~e(3?_Pqq2^@W$<ZRna!KbTekY*=_R1blI+HxLee&<VKbON%c>Q
zny}jKeI3*-w-VXZwlyWy7bEM<qFWBDpWQxu1qE%XExG|a;GE`-FX5fuBMRzkQ?TyV
zHLv%#D@)%q319r=fEL2NokA2SgVXIQK3a4oD+9=*medby`g(s_F`S_I&Oe{P8jO|2
zlD$drl4NP=mp;)hGTYq9OC_?eat>Q?d!&)?RvTYZ-uiAMQSDttJ{d}IK3+oGN&282
zKc*=?2P*w+Kx!S@WM^61EWV_NTOF2><xx)nka9WVtv3T`p3!#o)0Vg2$NuoxR3{bx
z05Y2L1MW~qt~su#S8;T6w$`4?1<&Fo+Dww0{hG;sM+I{r2A}&s9Jc4?RZ*|pJ|11Q
zBj#@%F(>(6K)J%trnbT#K>q-t%9XxbdNtL$#CzkmNoha(z2zUeC!7gPf)n*|w2$#t
zdDT{H-%h;JuNK!MGc0P0Bg*7?DLvYG1h(h>Dn9tDCj5(0@L2_es%`8ZqO`Eh`qvzi
z?At@aTM16!rR1mgtmI@L6$r*^8dSzxaV|u2#EkkwtS>p#gTOfMI6mU5ur70LliIr5
zCrN`nkM88fc}%>6?6eS}^vztNb*vtz)eEkyNS`4<t*fQ9Hwxfa{95pz0zn{W&V;Cu
zyMUb0RMD-XJvS0S^M1A8ohsW_DOWz6Ex>a9(|1%~EbL&s`%M;P!p{O!a!Q)^SSx7u
zUUsN2no!|il`B+bvT#ko$xyB;<T)!f1Y|6e$@<p|%fQrsTEQk<xgG&D8@EMg0a-O1
z<HrX$$TT|N17f2>j7YBN;3Vp%9J?z{>-MPEUO3qO=vNk_zH>z&GC|9#xk}9h^~q{K
zi|f4v<G9TV<I2^5clV~CSaX*(B}5fa>${!uYCXpZCnQkLdgiqP$wgiWBxblTOVzrz
zs6}cgpU%9T_rdR<N;G1?i`u~aM`1)ads2w=9l5I7K?7|1W}Zf%H$CW4tg@o?K^}bn
z0F@BU{P2}y?^QCT9rNCnhZH-|XerTiMfF+Tf1OD|y%I1L&MJo6%h+nE2r`xy5>>Wp
zSucs3)gwh_>!~`m<oZ*l<fDuep4C!~DPcZU(a4m(3FFedoZDwSne^*%GlPmPxT1@0
zFu>Y~LqKvdUaVtBKr?Z`#S(@`r8<-faVZ{~)2N`FX|O5u;C7^O#dHHqv|;qDaZe@9
z^O_JPM}8^54;#~1C^@%>8K=_qMx|s^sB_zjHfki2@4YL;CWAtWr^jl*p~AZ1a+8g*
zTq!4N#-odKYGrPArxcS-mi=o4ZAzw=s(iLbB9L0h$E6DhHotRS1e47;rU>myODEES
zhW9+zfIQI&XFCvSVV`Oy8A#uXbpZ2AOFQvS2CzS^Ycb@VyVA3tt#d{zpd4{qW;zL_
zjGk#b*FmFTqsv2OB&jMV&=5abuU=H!Xi-Es<odXh=jcv9O0cUsa4Ht#Z+cQ!prt85
z%GR@`4UbhH=mtsb4QJ7`iZ#OVIx<wWOYn5;3vD)!u)=Dse?!PwUsv7kix>-T*4&i|
z4ui*ut0|6?>ZFyBe5n2*XuVuhXi|ldDiU}%2UCYr{{YEP4S&tMo`pPCdojN{+*l<<
zl@g<pN#v8<pIYF{E)43#tpiKC&y^8&$Z_LZ?aqEEP*LHw=l=jTWS1X4?kQftaZg&h
zd<KV^K%Ig{HmLW!aNsQ`ap)@L(eYn>vh<OHoM2KZIkDuFQB%5fL&|wh_<6|(dW@CQ
zFO;Y_$Wvo|g>7>pCFuH!K*#G<%cCfY(K<oM&eXY=9)peY?D^LHGT~vkCNw5|Gy|!{
zWAT%~`w!Hbm$CIbvLda8Gne<l9FKalU%GbV)b|8ityZ^W$&jKOX`~e`XOn<EIVXTO
zI2+NCx<jIV6hssH{^K5P%1V4jt<4hiXV@hQO64BjRkav*eS4tBIOUyd`jzIfI%PJ?
z@Sm)HBwQ5i7$H&8?}Vw66j2$qe=fWdwc~#cu%-V1hrL=QH}J>cqXa~0U1`+Js3<$3
zu@)nP`7##ML?7bBhrK>sTY@j27-inAc$JG?p$!(%6)qt^xgWx^grNZ+{5H~thw_t^
z7`csf&7M0KUbcj{Uy#xyTl#eiQ!mJALnwAqhnh5X3=o~3bRQ@}4~bKZmN`~6Z{_sA
z<MZ|_$C|mi`yd;iQguhf+e4jA@ecUikENQFq^d%jTG@6`87#zLsJS{`F_NXIr()}<
zO*wCtFZSQ|FQe>}Z5=r+_L_Pua+x<f)Rknkhui@vedS?FOG8X{5){2SNm`)p&~Kj+
zI)(#}A$L2o;V>;0*o_9-j*aw2j>>$Dw4KQV005<^lc`lJ)px6ZO4?oM&V#zQIpD(J
z+^?)3Bf)s_*oLmnIG_WM4c$r*+Hlf;c%JH&&9`1#?fsX+8-tBOE&l*ybErCi!*Q;G
z=wz_-?yKbFIc+9GdYmB&i4l0z`j&L1=_kzku-Y`R0#+AMO^?%#tD=~cnI<}r?eXZu
zI3-I#%3-#o=Tj`Jk~VeiL}e&$o3b?T!`l>opi=a8A5cbL?2rZ(ZnrBaa&P!udkSff
z<!n0W-A$S|;Z209==WL6d8iRB3)2out?FrsC~UmBMLa3EY$Z}iI~P7xzxbe3Zds<I
z<^KR=I!Y08@V~n?I}DqbOxw{J^{|OH<i7Ko))LBRvYg0uYU99#tqf}1DoWhOd@NB{
z9=1v&MuQBv=9eO}g((<YuQ-Id;)+iSI-1zzfuTE)NuW+~7sf1h7Z`fno~yhw9q8*>
zQmwWKC`+O{gz~VLRQts4xAm27lTnwBw0<8^T&1b7({^aGZxWD3&CHUprkE$yDt;?i
z^p2s$bWSm@2<MI6nvKf+G3%18=qL<FLd%{X;#vGoZ|YT%^aIkN(z}DJQ;x|~s3$5K
zvNMcn1F+y@uoaKlJ|W6#^yHaR_<i`HvC7w}VIfD~DcDq%yW&;B`LUof>n(>|1;;VC
zX*`^NF)Hn_$8(B{!vxzZvP!7AOK+%xA5$!)3M)!l2~Gy*d;!V8$DqinHP52&*Tugg
z;KG#J-au`=T2GYWI6bm;9lcMjY&LuR8SaS1Ne{Asr8Y>)bH-0@DETbIZ8}y*mr8)n
zH`@dXXemMcB;_RgG2Wf{e{iuRrHsb#3oa$lai9UGQTlm)!mLo?b!D;SCSREea0R7)
zV3UEt?l#BTwEKHdq#;DOg(b$yR5{rH0Nj4H6L)clK_T`2?I=h&86yCIe^2XFe44hz
z<d%p4i{p|(jN;mHAxnz*<PZr7J&4FDAo2}Mz`x!h&0$STLLtj2LfKkD@Y3KZDF=`O
zwPXHN_o}3hwh(S_TS3ye>^&(MJ1Hn_ML-`>f%${lq8HVkHL+3}Rxpr|t`4J>1d-T<
zf;}ov8g9xd$tq@v8trgQhNre%kcr`K?sqHD;VqHahZ%W3#RnT=phv~K_fExks_d;s
z@gqq@S&XSayOzVKTAL1_`BaqzxP<~RNcF}O(%oRhxV=juw1WMHRDP*t19DqpOKwX@
z9)*=SulkmI)iz8gPhIs#!b}Z!bWOI}mUuJSTAzlK{{TALEf4<yX!owHcw+?_M2x27
z+L5I<)7B}_lko@Oyd+A6dx+|;{TDgXv`A&LlOee3{6w;)B}KpQ8wzda%95;BXMb|Z
z9yOgZ?@}Xlte2ffN%d=?X_eCx(ses)xR!>rw30z3JGK-bYbaBViU-24j5g~H%@$kz
z=3T1Eba|IoagCDwl5=2)*EWZBGE}6vj1cP!d9n|e8^L)!MLVeNeL>Rj(bTM^4E3i@
zLwPO5y|9cHZg5;aR5$~PafiqEWz?n3QQy|zbQE$cT4y|zpz8Ey&D{PYbeB?GZ7D#q
zbz`AFGYq!V*3PG4Nz{<*Bq}q`Ek0)5NG-ew(o*3CcF%{sSrY3V);dm1PMLmdnVHhJ
zQ}Z1;b9C+dw7yTtyiUM4p}xp{2MDOSNVIgU3wJ?vyytHn4LP?Y^q*3*6<;-MY`8;#
zEgv?7ItpaKQ3`3$r8H8kc!2enOy2DZMZ#f>?8h5IoS6~8>|4B7K@87fz%c3@(xfk7
zb*Ib{wJ22FvPZAa&*;XezW)F}K0F$Ym0dj5;ZwRw=_ld$RWh|O^a*noh>kc8l{Va^
z`I_7j;K~IZyhB7@E)W%TcEj-xrlj=s=VdN+raD<twQ#qfk{Wp@pAm&Ok+=%1+Dp=U
zv!z(-6*g6Mv=*(GgU^h~7>O|V$q6ysC-|FbM|Ud2@c#g*Zt<eG)A!Ufc(F2~TDIJ^
zDKSaqQJx3HjD3)F9_Uq3H5I@5{-5BJ-$td=-5oO1Zx=YyV%@Fd@Xv%wn;y<3KrNMj
zg=rsp?aAg{3S{9rhZ*9edSoOhP3S9>w8=tFU@d7x)Tg*?rwEddU;@2S-h{Z!>F%Hh
z_JT#Ceo92>j1b9Jqw&-DcjZlS-3F@|s<KV&%KDz#mqCp+-6=*xFOADQ`A8h2+mz=c
zY>J}WZ@ov-PRWUBxUM@7EB)pYl853*{{ZI4a4Dxgr8)E>v>%ApKB2O<gE^EgJfxLI
zmcxPA@=`mt;Mrc|Vmp9qD{-fSikF|gQZ|C=qRyL#LErSI?`M0Z<`J~pIu#}YmFna-
z6kKR~vf_uMeW)wXO<e2@F>?8c)2(t`(Guwa?;0!44fKWJ6gW98xDc`3J?aQ#aCfOo
z4^dsZb@$%3Jl}^c0jaShHXe&LvBPh&e6A<Z?4+I0q@C&R#A*0FZ@k>?S2pi<d5^%H
z%8HtPI<zSn7*HLGPT-{FBpeV%)jU_zU1R;=gVQ(Ve-OhqnacP70QDyi-GYDcX_Z**
zolJ)&bW2XNH3(C(EDpF`c_~ioTt^P_TLUDZ{`mkLhX5<8%2pHtljaH;+i*wu&|&O0
z>1?^!=e=sYTi=5=F~iJ0jVne*LC0`L;EaL}K_hw%PGYB^bwh4jnCb4QjP1(O!gRz^
z2oo)!{{Z^4irAw&mKJ<7{5~4I)g67aJ!lL`YFkt7vvn#Q_5lsP6Zn!+e9gF=jcqH;
zl1Ctg5ZAzLjasrXs)s4xf6koMk&o#~JfG5mlJYiDk?ZuP98WmS6h_<cO}LDW{{TT*
z2eLKcf^&~*0o;SKyVR4(88saEBVqLBm{3)#&Bke^W49jEw@-gs8ON{){OFmTRx`k_
zAc29wq1N%_aZ6=IM<c)MTEK$SvB(tQ#&NwcoboC34mRenSfBwrZ%WbqDQF%409tto
z0B_oru#F`c=X}%bt4SN+eQU%3DJdsV$9hRCKxmtUwJ3L~i0LE(M>P*^Ms$N#IIQG?
zp||<hlb=lImYMa};oxswPIls+G1N|XuA(vT>t3=tP9Z#PP6o5upKXs(Poa-$WDr!e
zPik>2o@l=SdQ!tf(ue@VL{|z;7+dzFmU4KkU_AuWt+uT3PlkP|*4qPYRtSV7fGN}k
zZLy~ijP|FJ-?d;3fNQ!*{OR<y?M{X^;)noCYV@vnXVQ*SB9dA5til4O$sAWaBk4zY
zUeuD_J5Vqug|-cJFnbCxGo7(cC6nn`z(JBbQqsxvqeC9lu*N%9Fc*W4)z1&wk1S+T
zd=F8Y#scv6;<!<YJHXFsN|@e>fWX^fwQ!`9#TVdvQ;3Y6J|mga*bY>EJ%_zy07OOw
z1=miN!PA^>qv(<A`VY{=-qg36&=L^R2~t~60Q&o%bNUUcz~U6)$p8Q_l!1Ve=mkey
zV(M&)EZ@10bu(*9)Ixr>MwYkBm|n*{t6AzRa&oMv^Q_CLJQ%A{Bm-V|s9QTQy#PmG
z_=wMQ&TB-3B0_}Qg?QU0ve9xXPz||&E0_MImm1PoY2_6WjlasLifKA2IN7L5y+>c*
zl9HceOkzFhl}K_zV?k-eu;i7YP6Fz`3oW5OXP#H^w2jXvNZzsTm3HkdEl<8W`e4d&
z#^A+e<h3H`<6xHqZYwEfJ3tcjrHu`MKnh=!F6r@sh~PwnaBKH1Dm!x{Hl|u-m3}s1
zHm@@+D}7F{FTKjjLD5&1K1-CkB%L~mrgaZoK<OB6SvpQ47?|(w<;ZZM<+uJ96f%Xl
z4=txGws!sM!CG=_jhQ_`aa5vYPPtEYBT&@y&$_Gxfy$Pnumf}0RcHOaP5R>-raDU8
ztA)-|l!ha+q^D%0sY)tQ2|^H%RHdkdp(!d+024*G@lwOmF`T#bwe6YL7m`|^eq^X2
zNl?O=gOC)Kak8HvE#1OG8#rGy@#LP~qQ5_>cuFwR+bk~hqkRz|IP0+PeNNi)&{|OZ
z8zRT?X*ooxVDX(yvU@tQ<gK&P_lwQv<?ZQ{cD!6<Da~%Zt=SJNDqD^?v+0#B#hfGp
zr4R;bs~=Skr@KSx>nvv{PnzIjELH&2wPQE~fB*!Na1xM7AOdQ~bL$S8wMCKAT`_WE
zdy<pj-Y(J9OLFi*`B>`b<x-$Bt&8dbN5~KlB;22Riv0ekL~8ZhEAzUY9UX4IS~@Oj
zCdys8$k%?MY$zsCc}Z*KAm<8hB0|<w;lku8Ae}9Qp;hjS5ub@Wsd~aH&r9^;TXJ(v
zuR^Xe9M8*JnpYujsVdZ={o4(^=P6EYFm0c<q${T_Of>xKve;%bN>D&j!<v+(sO*F#
z0c%pu$x0NXu&Ca)z4WA*u=-=CCZ*n;u@?+y%5)oyrGO+)T2A1(r73MEbTZpX=T=kP
z<dmfyE|=%`8-;x-?f3co5R7#fRs2ipktz7;oqE4aZH5_+j}5gtvX<IqwD8+W8XHP@
zLJ~UzT6bLa6^o)QL#80eZhRXgCZ$UyI84N;&k{3adP|a@op5vV(cz%}5-P9oj#ZDQ
zU|&8d-P#L-Y;Egxy&QPY6eTiBPpM3Z@j)CJ%TV2;fUK;s)z^Dty-6fU7mlKK@ppKP
z2P4E_Jn_c?Iu#%Rzytw|3brVFapy19FXR5$lb0vOewhA$1;WEABdeu1a&dAS>01jn
z>CdTKxiVUnBI2@tn59<~{{YfGt1Rjp%-MZAHcYdb{YiaxOqKQ_hJ%G8*gG%(0KguV
zZD#1?Z-&z^Q{Er6dfxo}*#|f}Lzz(eYD|&za8*wE3^Q!$_?YvC=E?&c_>z>^KXDvX
z@<}DHi~hg#2q$~}zo-3`Lj0&#1<h##Q!~D8pLb3aKEWg(t}1fGg~z)+D+&#;lItF!
zI8gwf@SQ*pZ1)t2(VB2DwuhCG;SvY&j!)cU@}r=kUIJJ=x#DO%0&tK=(2vx6)P2iz
zR<v3Aa`hq%*VOBz`0Dc<?sSX~>T1aMQqGfcY^l`{<DM$XIlxHZdTc6%;p<9XWzAU1
zNbiIGd8t_MsGuol%z#pJ^sKTqp=AVGA$5f4jg>O-N|MM=Cj^vZ^v-D)zOH)ZB`>u4
zv^^b4KjAtv`2{CGb5)X}rdASo0B7G6G$okqElU|o4Y&X!v2A1g=&X;SeYMy%%50WH
zvl=JHaDvx;EaZ5pKjB)El>Ie!G*iAbYav4p5*?DcNm74_8ZeSR>N|Soqg+eUd?J9S
z9B_DHBa(On{Hj@?(TZX=;#@=|h=F5;r&uLJ{<SENWeN)=jNwF=!#kXKUlnaAoaUQ#
z-chwr-*1<i-^;m?=sZN9wMG14S5Za3+wb<pt~K$CeZJt8pDL3+DezXmT!}FjPu`>E
zMRk`X$zeq+NK>t-e+tvONc(|T&+Q1NM^&cs!vQH>!s8v#ld(*X0^*O>;Q0Rlrn@lC
z@k{K^dBrS??u-xPA56q&;!VcyVUDQQUaV?ARN4?YWr^tV%On2)l<|F^V$Eqk4En17
z0MtD(3M@Gej=T7A4as&%2?1_g>`et1;S`;naw-(!(1V{6DxYfvG>};88#{eN@UGQy
zV+((!F0w64Kg1zT)h;nU$&iW39-7CkQfxPiw@=^g{Xx^Rmv6k<r8h1lgbbEnZCOHq
z?lZ_c5LA<~HAkJP+v)y)W-7*<Ysx-^k<?uo)!jC}qJ0h{qk4v8XzFM&QeTnMx1x<K
zU81D<mdnUam5=zSLXe_eN)%(da$UpW?ap7qUc6~n4xN^-83H1b_kdnXTx#WPaN#OL
zp)D+<ArB`Sw5=gYUrsp!&(x8AA#`<~GhH>;ab;Vk*&GE?qD&zxlWTNy;icfytgG<`
zU1@1Lka25|($<cnx7+$YB&EE=s5dTypCLgmv>aJ3T3=TfE`_B%ks&7-OZ#;>-q(N6
z{XgV_jcN7#fAB@BD?J(1P~EyT-1`;cBG9R@fmCayvBDDK+s@%>AS*(ETW++FKqGm%
zT{#}lCPk&^lBc4qwj4>>)bf?2{{SkIQladjLG)v-^x9?g&F!fYY_g;mortu6<Y%#f
z`vqU!ya$y)`Bny%_9?M-U7F<{1)A*@P23`&8+ew^d^s#2YL4a(=uk$sl#{wtzz2{{
z#<`~c3CZzvF53}JLDHeALvfvmJ%}TN?_IcF+qx}Gb%JCi)nGMzijw4(@xYMY-!g&x
z!w!Mne};&QsmSOd1qCC0_@<^r`KHzT)TKaw5Xd#r2VDrtefz08%i;T<FDfi6rH<|j
z(3d~nt#?!oRD}#-WBHO$^JinHimgtvTKc{#_g{yiLgX;gq)h4Q3l2_>+D265bpSqb
zl#R(q4xoNC5$85X=iGXG=Ao_(OO2{(f~H$!l`1rnI9HVFZPENLpoOQ@jHJ?flc$@4
z!g|-DC(T{9B!<eAT=3`Fq?4A?@|(soNABb!30^VeUQt$CCiKd(A4-5)NFeSA;L?M<
z`teqTxfkMtfri<Q3$7KNDQzicH&CnDBMD}4{!xbCJOsJ2>7F9nQk!`TLx?2{LcvOs
zpm0*4cLWeY2e>qUV5jmP)R$Rx53=ixI?~ePZZ@)&wzQn3X;2`gK!8C9Al0kIXRnl1
zp|(1$M5y>_N*cW$_(;NAm)`>jd$3EJE|cRAT5$_W7zgQtjD7o1uxu$xBRu47#twd+
zsM|g9cE1`Vh^d`yno=i0TJ#5W{udCf{5O-6p~1M|g(RsYsZXA%jP1=wRq8TJvqs<V
z%>lfD{{Y2E{`7wk{ovoks0F&5@+fpQl9!txwxOBOC4B9wX;;ciD9}ky;%U_9_irO3
zQaIcxB&35;dD!NTvtQF`ThXP)Gc9wpqI5laONmZ&xP>3U@-j*n?ox7<C{uU13ADV*
z*pwzfnu7EhX-<J5yx`?Oz~WDpXgj4txFDTh0J_j2Kdm9v9Vc;3e8E1Lq`2_MHH~%-
z{&A5}(n-Mp=V~eEv%wSt&Vp&EP&ZUg!xS@#Rx(HBQW^q|^n$=YeXAIa@YSmxwDV1?
z(-c`@Bn)rfyb_e-7~8!AAx$7-Bbsz!JeuM_{{Ydhk^sj0dr%y4B`F`3FU23}LAd4z
z`BMszr2BhTG9<iQkULZDBP3>{VlP_17HU=+H7srU)TZbRo42$;N&PCx6*xf1A4-Y0
zwv{Cy?TVXWDFEQ)W18{(MAe+LXW0Tbd~Zuj0CG5^!&%tZd=F~&k=6*7y{W`Cj?_w7
z7^S7JYGVxsfv<QVcA`8#O6X6hqF`?TS3D4VQCRgA?K!MqZv)zvmItK}5x*4TQRpie
zWh?={Da5nwPoWv7f(Kz)7#%E(WK)5?xTk_ZrC?D;2hcq$#kAv!eMtj)X-5Z|#u-y!
z9w|N$J*m_6=DCygpks+Z9jU;P&INE0OCMUu$C8mw1digkF`m@mrbZK|917uY+MU|G
zNUQ;!8`led)aW9*09O6G)-cjpPBpA-J8)08{pl)6QnE+T0k9PL!9B6t+PQ&P10ER1
z6fCy2B#cq#9jS+sF})KM4yB4h6ri2)SP#SrN@>+4C%GF}(Oq8aY{hWxur>1!U0f3E
z*Bc-llg)DGha;mCqN5(=B+3!n)JgB2YeTejqYv7J`>5D)*eN7OhL;K#J=F4mp}HFf
z@whi1g+61H#q>u@M!2};=I9oSY*i#YTR3s|2+Cu$f1N-QRl*aMAK?nsQ3h4Xg-x1<
z+rkplOtPFb!zma;NC$+ZeaR%8l1)iLEtPU~VZRWhUEdPp!&+UDHI~?&EtdQyiwMG-
zgB830)JDi2TxG>16XgIL#uvlRqU#%jcF3%$a$YJ8x2zxx0;MTSSCt4z1xi|ul%x_!
z0=}?UM@G6VG~9JMwJ0G811eIEzz=g#y-U*YtxzAZS@i0W<+)32{{ZheAchiu@DiX2
z{{X~L3O-bzRPyAPZ=k_0muJeIS7S#?+GTWSM9EXpC!`|%dI>3V^MwTn?Sq9i0SW+y
z&J_BR9Cu*bZPH@&mr&j&vj*P+Oqmj0LlN0w1x`HT)B;e1j#Nni4|@7i>C#N)UMndf
zKg4(*^ogstd@_op_-al5;K>ds3@X_X#Qp*NvK>M59$Rf%c2d=+pZ(0TKe8?DDC^|N
z@0NMp180-a-6btAUT#eo-A{aBVQDBQ&1;6>{{ZaiAP3$b->hgKxj^wM?2GSDBUTjS
zfOFV;)`Ksp>?&V|P==D6X-Yr<3YY0+^!w<O*0;zE$+FoUZKk5UJeNy~!BY_rt6(I&
z_Bd%8b}pu;ER)Lk)`n1yHFi~QmtLdjj*Bs$4vwnpNNHTLj4IJ$cM5HfRLGhFTwioW
z4X6JAv@8tj4^DNTS6yOQZ7n$b`<#ZM!)G`SKI+07Nj-+844%U$wQLtz2dE}Uyj-UV
z*9q?~RJjfjt>;#9r7Iq!@HPVgU;$SD0EC@CZD#8ysOJST%rvS44xr&_dFCBlhZ1`!
zm@P|KJ(ji)aY(YfnHD&zMI2Nh=HALr#Y8o3wOD=_^rP!~%V}k|Q1DI+meb+}9#{CJ
zE!mTZJSJ>({{X@mOa`V}x(4ZX!nLyz`y*=3;mMA+w@4npD;@Ob{{V?oGh`zF0D6^k
zkr}q0nChM_pkXa%@U}e?goL3Z(ITdmXL0LqOZ37ckN*HoTo*OpMJ30R5wFP&JbsQ;
z=j!E`E0S8OU&0E)p#-00cj85+WxgLtbeM4Xwo6J(fH*HZq`CF;rO*7;A9ronquAuE
z;Zdy7nm_X>DMbGO<}M$oH8=4h=O*XXChvlzy&5}!j@r@U<MVXTZ-q;>+uZ~a;ky*}
z!jR!{ztPe^I!u#pa!ar9Xr!YXD~-17a^q`8rkntS=tusI7>p&RkgblBkD*bc8e}ab
zDInx`G}_uvh!UizB_l}o-2VWrBx2WNxi16O<)>Ln&I#D>&%f5BTy0GyL0l0~%4NvT
z4B({h4r);njEI^Ra#As$YJMVEhNUGbQ9LNnlcgaspKUz3r67Isf29v4YH1kH98jus
zI8;<JMxK84Ivobbe?dZWLexCIqu>21S|o7PMZN~fT0zJ1sU#ohKPsw0d2d{`5>Dc-
zuvUlSNRCHzAJ_i$i?O7rZKjhVIcSYbI7k@hCm-ilUy3a%?Pa;m)TKC?mU*ew=;V2^
z$^7FsKEk6cF#Jo@1{L^zX{ETX-Wn7ZvVDS5qxPfjZ+z{JLvEip><b)+JyNG6yx`Bi
zB#+Lz@XDam{L#;nHzh@5;g)X>-uff>x{A>vJanD}=-=;=cJ(V^ZIAwO9qPK>iHU_0
zqkxr=6c4B)Bl8tKV13@1>Igkk5g3tamdxUj{6&?A2j&t--lRI_)f=aJs`X%k<{N`G
z)Wuh2$5GbF`?jPXy%@f5UvJOyD_ms${{WxhnD|$AwpuPuboWt95v+H&qb5tr84xBk
zyu|?aKncH{kNz5zq%Rz@>!Yx%0gom#H{OAq%H!Q^@+D7)82%)_$i&Fw8=Xxy9Zoq^
ze2>LgJNGN%XEZ_zu<8=Dox*`o1QFh~#%@<mwy<0J`pYeGx~rrlM~86nH&d-4ShvkT
z%tnuvaG%AYoc{oLbKU9UoOsK}@%gixD|cv}lkqY~TiGS`3^v=D)HXI!?fN)4T=+Q%
zP07Dipp>FO9hBSL7crsy9qQ{<?Uy=scgz@9aiqm01tLmPOHQDjg7P$aC@TkSV=A`v
zS+jMeyREuCvuqEK+LqKP1)?}}TW`a0CuO#kc+4p$Wwu6t6=<iVVf57dZ&dWd1~s|q
zZM@8VWkwUtxRp5+sPYrBLXPTfNj!yPwKSchnpafI$h3T!W%eFSxKfj@G?vB^;(LM)
z=~&@NJA>>&BhR-juw6zpoz#VlQ++dVdL7#7WR}B=f+oDvt)$?M4k;*c9*Y2Mcq3!6
z+G6P$aMokG=ykQEEvG>8xY^-D`VrWI4;1PsOT8Jr_R`YA++{^Rsm^OYb<2-WT|a4C
zJRs(Uvg=PH#AZt>#<m_u4m6-Jq@Q94P)Qo4+5*(I6yyWwHmY^%(nwd7A4)CI+-(`x
zSahr(*~6<kWw%vheblikJFD>>wMtMVM|E7O3c*t^ZLJ`KhY+DhGbMi&3vi{ng!Q^n
zqBNDLC;g!b9#&l^Z=L{Q4jg#k1grPDRI-@3P3dcklOx1tTzM?2Ecg;L^7Epog0>yT
z;1U9oq2@x;m8S(FtG!#(Q7y<~`P1?qnbWr=sSw_K9z1ta0bEHMc1!K#9YX<WDRCPd
zCn?yxXo6DKg((Y4lB5!qB|wEGK;VK7;DfOptI3t5?N<3dC&buKPe>~3I1DN2X#;!o
zR@0R}kU5yz23!Mokdol@rOi|$m0+iEcLW`=^{rzi@=CFFdL!*ii2B)mm4_R32MR)m
z1b^%FqwWSZ(i@lCTP?6!Df#$l*f6CK1FepH6&<nv01Zb;*r1x$70DlJaVRHqoN-%`
zsCdaIJn!#Pc8jYX6P6ph-W{@H{7(>qeo96#nJFHa!dwIKBi#w8fYP?ujCZFJ!nUEv
z4OsNHW!vSw0&89ri0du5jOPj-<B{AE$RPKj-FTFp%}hMn?sm3bP|S;B6B%mm<b2_x
zeM$-N^Zcd1N{%p;a+AdsAn_v>r7YumY_@s92iB#&LmTO&E*aw$y})oHf;Z!OQC2wL
zYBa-9Ja0`o6+;_V<zV8$RzTkr*;(SATPpPCn(&Tx6l~OREkGUX#bHN^c@gu<Ii(1P
zIi=(p20medL%P}D2BgywL(CJ!HtcTNX?de;W&%#Ea6hG4ZSi;@3WkEw3&0-LYqqpB
zWMh8S&6;AhWQ-Qh*c;T6Foc|fDTt^9fuv%JT6r34#?3otB-YQlLs;!z@cy(|Xe)zU
z@N4OkXJ{~Px)F*bwg+lS09G*8fm|p9wL8TmgG9r6Yrs;<MSD$S9Y_?krnmv@DPtnB
zg{3sSVvq?H-BpYz?_MVqoko&ZnkGE8nrUmUEoO&XrnhDtE@N!cJY$?w1!VJF3+<fH
zFi(rWwLIGMw-h2(=}(~Iv4WDSP+mLJDPGylbgGC4b^F(K=e;wU?yPsCW*yh`uK0r#
z!C$?2FW#|-ipQ-axr%7G2YPKzfmkEO-%4gfz#xN77fyMgt`ic=MM^v3v5HS#&q^b>
zlCH;W*Upmc@_LV`Q*VgtZR*zR&%2)rGbtLJd3gR5Cx23Kk-CLzpAqk^w9BPVfB+<u
zz^v_}ZKL)W1v9QCxfeoz6KEJ)5q+_gy5RnD+E4d&44*8l7r|`lt5}@mW@0qsHn<5>
zaM^GTcu~sTa~pWB5EP@^%8}(jtIFC}hw{it8)Bk*bw4Qqu!Iqj%~`E+S&-tMlA;)B
zc&{jO5IYZYNJ#fS)i~@jbTpWWvfF-W9Wo^(hR^;RM<Gid>fIyIYCXkK?^kH3Qkx|?
z;8MOM{6ddwOU}OP5~U504gx?JN=Kjo0rVAw#rTE()b#>jMU+})C~`!(Sk#D-n;M;X
zj`~x;{v{M7ZlO@kA93+Tas4Zljdg@Zg2CzuNNr^cEy7{Jl)U)HR-xUt@(CZrqLlHf
zs69_-wq71wi;SS_?x{{V<BQ6cvT{_T+>%Kqs6AEj8uxpi;^4%3EGEzyi5*8JmQ$T6
zKC1pD9_mt!K({NYXQx}LaXV~sO>pJJmXkWM&GyNAufwKpGh^DU%!yOxzKGG8O0nWP
zwFIPRu*n%chDqY0z9L#*nu~W$^mMIu9-KnsMR@}sIQrBTDsdjKIFYB(me^15)Rl$%
zy@Jtp>Zvce^O7@SwC+Jd-EUOK$GRh^G!JD<2b9q9Z!27O(w4$PHrRHhZdjv9_A*N(
zwq+efD)G{B)bo$Kge6NPWbiU;QaxdC>04#jq$MbP&qZ{CziORWKew*ZA|^^ctgy62
z{{Z}Fed=n>Vzf)2{?uO~wJzP$ZGPB&mq00f#@3M<IUU2YRLV&1fajsC%dPB}C|wUK
zTI8xl%d4)=T_(Q2#`|%Lh)UJ}06duzm-T3RS5Kv!Tw9vk{QgO1jH4=x{Qm&q?wk0W
z)6!pY)dHlXxDrnL4DI<=@NdxId_hz_K>q-;uoi=Xuv{y8l&}8)9u@bmioP9Nu2&C<
zy)k5vhY-};+$it*cqQ}~GwtDp*3a<cuH5RW-E-8lN!2RfGEh0rOxuG9$NpE>Nt4n>
zKU80p{f!*%T4ssVV-v#>@Z)HQ2{`8<L}rMvFqu}#v0X{aN^V*Q_(o8_$Snuxlj%W7
zY(H>fbT@S(L<lcwJKz}2r}QZzr!CH|EA7Z(q$SiGjHDE+BLorL5J)4?n&w#QZIqPm
zIwK_85YvTeP66C{8bWRQldJ+q9B)<$jIj2VM@o=|6ND*QC^-Bi_x3%;JLZtYa)W}L
z161HABEIY_c}Dx~wG7)uuuw)1Z1Glypru18P6~JTsL2ryb&ZHQqT!(OD|a*$HZk6#
zN=s=fCnW7t^W&9(k`Bj=R5Q(-s4IlmJmW>6>}Jx?OysRy^O~NEE9KvK=OIBO?Wj~N
z6`>E66R-xIdU_Cz^yOp@YFr;>6uwK3&W53>5f!Rr^r|9A{{V)C;V1s3k`wHl{{S}9
zT|uMfy6I8Zskp4eipd;!apthXasL2HDhH#SYDZ979Fn2(@n8Ap$vXz4q`t4wtbOWI
z+NHT*zP})9T!vvZ;V1Z)BshM%v}&+!zpwHC0A%Eq>+Ssi0MS!L#5k~`09tSqlfsAn
z#acSLDZfVP>*H!6`L-y@S?;GIsc{$g#B^utREwcZIZ5#GjPLZTY>DYtKv?IpwFO(Q
z*C;^t0mMg;k@>0L%++uv(?qiArY6nPzdW|w1t^fxhi}D=#a#M9q0+Z;GF&qv8|^s`
ze01&e5gZtGvHmTB(t9lkJ*t47rA#-PxiGR4tZPrFtzRwuOA6%`AH6_QfYPjuzO~Vd
z9J?VoBUguIMXKE0sJh&w!Jg;J%0h6NF{Cov%{rfDgcW14Q5}s*$GE_^q4*b+EjHPx
z2#~a=C6x^5d=BLVlkSqV_oz;kxIb{{mtzoC_fE`?u)OY=Zx>P!eL@~yN1%9)!lz&P
zYrIs%aUeM68wZS&wm(|*VyUK%8NF<qcG}8%s^#WSjpM0GDEula0a}uKr0U2$!4)Lo
zZA~iOIO%ATwqEDJaQ8pqR|@+c$1Fix5}A=SI28N`6p(Y0xSGgFKE4?sf6A~whKHFs
z5>!+{Ao^AB$f;js70@`ti;W?Y9Bkl)at&23j3Hp10DbEFHaWXT@+(xxh&p{<Wg{ee
zsRKV${{YKXDY2BGa1A7iM9sQ}+!TX^imKYK39wC-65|DP?3YBPi=+go1v=VED0QNC
zM23kfZGDbXtf6ZMNxR$JP*xHM`c*3BX(~}AKc#cZPA<_@tghaybNWj9%I()xtheCY
zZC6-?wq9tRY)NSHg=B6yLejLI(rDhJw?yhl3+szSMq@qVCA;hf5uv!!R(!HrK>RV^
zAo)hkyQv?AP3C$GBwqS){+g1c36`Ms!qByxYHa!CM9KbOJaiA|L!fTpsH+>#S*MAO
zM@)uXZBgY$Q6UneFCHT*^a=nFe}zdxhUpYN%Ke$RU+#)`$nW?~EfB^Jmado1mu}TD
zD2V#xz?RZwL6EfIyuyh9tI=s8NLcK&D0)yIvq=6N9MZYgm{f0M3Sr~`Qho7Gw*(wv
zJ5=f|#EfXoKHao}HWa?-coDE8qBH4nXewxehY~-DwQQmJZau)KU9@-(siH!HPRavi
zr;x9>!TZ$P7Tz+R^m^d<u^U)Vos2Sb-3KKfmgDlR?;G1HOSb?p6P!~DwUTy7t6{f}
zG>z+q-RPPg@OTvU+k$yWp<cJ-Y@NBQi`L>!2%*($+XW_@-az)sJGS)=vHDX&+@h@D
zlk}^jmVHM$4)mm1JLx-nQ+vSRR2sIBc%u<%ByI&-k!DJF(@(W)#>FO;<~IVMZA5Yj
z`%v!NKp%&<rCuGi=Y;P-y=pt<H7Aq~1x=V~O7Ww5nugdyamQ+S+elD4e<4q|6r^r3
zP@KrgP*h=zW0OZEwG{b<2^ks`GuwKVZ6RsF0PS9WO|zY)BklxJ>xoXnltwG-N}&dl
zq?1ns`ikg4CV;^D52bfUwGrW0ygJ4P(d}Lc_M!`Tr1)pjv4+A(?Ow$>;h&{*PAC{o
z_3lM?9+kogqG1XmkV(eWvXVV(pIXKSbw`RBWaoM{#=^K9(+XyRfy#w*C$&4^9w|v-
zUX_Azutzli0NRq4jXajm6ijdvN*VO7=sl@nCV&nBj(DyfaZYK-;*q3^CJ?QuvZGF@
zW|>gM24N~Y^GPZmw3UK4rj(;b%|h0_BO<DOPj*Chm6h-+YU4CA(z1J2H}M;)TZ+TV
zTc8AjN%RyOp^{}+sPBv}d-rK=rTDg>m;4oe7vvL$8a}Bj8k9egwbTblEpu*0Wak$l
z_ncF%y1mrniaAn`xg?sebw%ywUWwC+P!6)8HhNDYOQ{7dpLW^MoOV*(s|QR>L~tEb
zhyYhUT`DV&do}ZPVm-SV!4((Q9eB@7^!ss?y$?FrmiW%&Q2o+A$U3p==e1W`yvLt1
zJZUd!jg>heWB6D?O5HzXC0~42JJ!8baO*3a$IyinA>}xvjuWWlC+G$M`cp&nno&uW
z4z=oA_gdXuN(+f+RyOZfUW)j5^|z;5g0z&&>JJKR(mxU*Az6&i{c8pF4@9x)RzHWo
z4N-FWf15@;bpW%D6Z}U$$;qvL4W=sPyD?26CBu}b0|`I@C`kGwBz>!&Ea^TaA#Lo+
z&%>z8DG6-uF`RdyuKgz(*989Lt3b|2e`?h(R|{>mId#U;bL&;dx{}8(^9>KW+SrU&
zIq*j<FoNp7$HUYA09AR7N>Zb-G?A6{{fbjFVsz^$A-g1B0eVKpQ-{Tpudj%$A4Zeu
zS>s5!Ta>mQWT^#v5G!6sR9Ym<adV_3$bD*Y#VF@1Q6)$7CWn=2wqD(C6av|mCB(AI
zQa3IwK`8xHD9YQ~G*uNTl3TWPm7d0@6dvja6?~FE{@t~%JTxD=WxMoo2hxP<s7%a7
zt_zqSEoFkzf9h|<SLU}=e)Ti)Po|r8{{SxUW^k?)6r$e{B<v$aY%tzFscTo&Mypr<
z0GJolu%#%7Y(|8?@py6DLXZAnXv@lvT0mP$`zh9bpB6r?mDFiMhwN)b*5Z&8pWX;g
zw$TT$4!Yj`st@7SuvzR~UlEsb4Yu4GJ@h8$8$bR{U+&S@8sF6Kx`3p)X=XA>^?Gz<
zpQcikexj+}D!^*r)y--krp(ic3hj%OERWW@px*?)bDio;(t2&hxoow}<j2=<cjy#d
zX#LXS)Bga|tIG^FrqWx+ahj!kG)gD3<*jN7`NSnCCtx2*%V+LNk)KBorCuVow#JpA
zNm6nOR4_sJ^fk}-EA|<63C`&NkIuT=n{&k?W4;+Zg+`gF2g-TmW}3monk!0^gMesR
zEG1i@XWFB`Ya|?N#VxCvRB%)d`%wa00J*iGDO+F;^;5b_Lrgf}RslP2RvxIH8HA#e
zGOjBnzx4u-`=e_hlh5^_vGds6xRzKGo5r=MZ<1;*P1!Q8T2Q<tz0E+qcT`B4L!M<+
zY*~*r9q9lP<AnRs_H=G$t!3&qE%u2|IO=|4=C6^CobH&&DO86D>=dBWL)3WQl|Kup
z+D}VMa67b{#8{(#%}0I3ey=xQJ;P@5w^Z|KR;M{2d)Ao^sZsi0r)}>8;!K%yxw*ga
zm9rVM{{Z@rRU?ibeg%}}-~+5WklXId3qQIfV|tmibwZ|mGrO|VQn1HTTb31_(q4r%
zIQ#e`@~ZD$^!t;Nmy^DvCvodWbbXQbdJn53s2?ob1cB^Q<ivmf09vjn!aWk3lNV{7
zHd-8GCm`ekD(lhDEU3pM>~aC@4<f4!HkiS=SO|3(3&(TnKD11$v)0Fi=!iOh67=jx
zy;?N-Ci>a*SHsSrhi_6@pC~mJ=^+-0YFe-abvC_f&#P*6moxqfnzD5x(XMiA_jH!0
z+7{rFk}#w^(u#QQJWLAta^dkYn~N+*Z3{x;rD4PlRHdA)M0ydVZP_)WJ=D`Wj@4xN
zg$?yPCioF-x&S=Lp8+XLs!;y`<21NWd#OwGHPWE?{>MAqjn}3xR|`DGpLbN2+c;VW
z4z!_#p#K2Ol1H|4+O4lC5hN}vd3CHMY&w79G@+>q`=pHf=hCyznwqC#vb7+jl#NOB
zKGmQ_@F!Z3px`cBy-xlD)&BtGte>-<)uYhOQ#{JvL&%WAi)(E`Xi()U2V+j%Ev&i|
zK09Hy-zQUP;kO6kNcP_&*pEsfhLBP;kaO)u#$hTeisl1yFp^IRSo|vc6NBtcYP}n+
zqKiJxmkA{`T)A}mPDw^{T4alI(xoM6AwZBuI3tl!vTeyyPE%ajB3cZ`E*4D+Id3D}
zP=edO$<1i@Yf1r82&($v43JY^blZzMBCP=1VT+Ze?w@fQ-j1ctw{5<yIen4;06J0@
z2lKXnoYYW)4&fsh+N_MmdMoYAh_4Oy7MEIAqSA5?<|xaxsf0`tp)?zdH3Cy8#zSF5
z>Tmvqg(v6*XFgH=jH67dqS`nE^rw+;18qO0Pa@xdFgyBF>9=piP0V!y2@t&#L^B`5
z4<SejORbb2u0cur5I<U+MZ0b*s<#YrwJV!LC?dX?L-}jJC$Y*nso@6)@YfX&1Mt%O
zjRioht{74LBvKV`18|?xr_4O7AW~NsAHzr7X5d%9XQc9;DbJC>0V&7YrHpO=015o*
z#^UD#PTrWJvWnnCCJ5t6rHCW}<<Hiv4M7L7{Hft?&IX!3;5ZfJK?~VU<a>%kwopbw
zc&k%Z%y60|HuRCe{HRzZ0)XwC93uzmL$1}6jXwVXTE0lOq#ii^YB}3}GBNoINj+%i
zo-&+mz#a(tiYb;fU=dRCAQYWJG{}sk`U>Tgju}IXKFdxiM-(TDplhMiUtwsc5RB52
zMGU7;Cc1|+Ld`*7?~GH40QRO3plQ^fYQ~{jL9P@ESQ~e)5NMchM|$CJ+MY^9D5kX-
z6t+EU+7q5B@{x`y1ORphia;7#0qso`Z^aTu+*blqS)hX!^SvcZjPXW<3gK-hwF3br
zIP|56^h#R6;+;!Fy<-6Uxf`A7VX82-?MN-2!m)w$54|T&4Lqa~%`#5>&<aOL^rRGy
zYowml<Hq%jHl))_PkMDmkgu&^3@I7T^gGB(5>FILfd@IL%e1tx%86HM!z5KYvg8<L
zcvouq&#bS^D(K|7<Hdt&ZC4phle4L8fQG;Nj;Xf(i$0a4{733^$7$TVjg5TK@dEsz
zWJE-(_m`PUkiY)`XJDxfkD+x>EB^qdpGs8ijMJj5yZp)52ksN47o<sfZ#?FATZ>Ur
zmG>l?wApV@xTf1m)xW-KiED<J)(YIyS}#Z1;x{P^Cr|`ZWp;;x*`z?W%zLG-GSY<?
zW&~TBKj9=e@&5qIhSUE5)HFv&d^8C`kuZKUk^rl>Pe+FJstjErN$`~Zp4nQ+&;C><
ztTGVXD{)d16QmQcsi&$LfJ9Bdr|t4qjVKK@;Uxb6iDw`F=v`2HpW<vwN)#J+fx>CN
zuId%0D>0jKMROl@?0<zHyHD%lRzo|du9s=ADpSQR@;9!0l*fYd1JvIWt{0guGZ|_C
zpIWc8)c1J3J=IrbtyyjqYimT4_@zooSN{NtjPU;es-&ga;@MPBg{b7@p7jTMTa)?*
zdofwbnbeUGbpHS=Fr+99eP5vk8?-#`wr7uZjo}kR{{S+1&NEjph94Ae*3PMs)iRgj
zM(I0T%j=0drB611)I^{8rN0~O4N$KgC3|TF_TxE@e+j1kLgi%mJ$aPmbtXqtUzxXz
zq~t@lHY7XtRGCqK`=4s*#|TN^!5PV|8ka=;V`^Qst98NcLnV@>Zb`>KdeJYDW6roP
z<q>Hm@|R9HAxQ*(TKI<9c9juJj1*9;g{QXSw~vQE6O^??jc|8l;RimL+x;t#)Ovm!
ziELSU>Go@Pdm`ubZ%_u(ONn9`7#x8pAM1+A`gU83{{U%iavH<W^yJ`<a#YKVN&f)%
zW2pVB=$l{4+$^`G#VIRQy$_@g6dRDzzox3r*V4B5-BWM8t*|03(;iqK!o$f^FK6i6
zt3PU<ESy?QY>~9u2l!(lw-Oq6ka5o-*4Sev=^V{Q^3p$kp6-A@ydUDn;vN407jukr
zpgMMCo>J|TK@uUjsP5!s>p>}3>YCVX;n?X)kfkALAxTnB2qV5}Cs7(I%Ejrj*!uUX
zqsm%Jw>6{n1XivM7n*Gi%Kjt)gs=Qa^ggNly#)q*MP)z(kaq-Pp>)e7@<YENVP|||
zml{&e{mmuTFi28<n^Umjm2Zuzie%hKeyWtD#ZV_3aaja{>z4((q>_}H**ccqU2LFc
zst2Y5m7N1BJK$Agkt8dFWj&^xmFHL`AnjAuTLZF7X)T{Ox2;;OeHE>3>TAoKn!VZj
zVVLeAMW6yXspQXkiAdtDvI5!B>1l?N)J<-y32m@9QjW<_)RG6@tQY>8jMd1-kA*dA
zWxH!j8-@5Fg{*(f>YZ2pRdcmP5uR1y2pVmTDoO$4#v~=5{{R@ItNw#glbA$kC`)4?
zjDDQgGHjzt%2bOelNh$7=NSB`yG^wb>{m-Vq3bslL_Q{e2Y|J#ZQnZmcE@_KTbpfo
z@c`e}r|p)=s+0l8n1ROGtHM%EGBd7>)>lcpEk0Bctacsf7dlqs6_S^l)ufc<jfnI$
zw+mEO6tuX3jPp&)ferT5)>2eP2ch(@M--YkrhMxwqKnc}q%@|M50s8`>03v^j-QzC
zPh2`~djZggP4YHIKYqbOPLKNIEqV3uQ|U^L)5(<S4<kyFaFU_UKhCyKhIW>1PDYaZ
zVQsX%E8B9~fl9~SDphr?kD@i?L@RuD5`cshq;fqCPFm!~gE^-j$&9dCkTL%N31dqC
z03qfc=@f0o#<;FK6(d)IwKtL92Os58egSD(7x@~=yB|!NX}93ZcHbj+W4NZ4M+Ae&
z_B@YcQ?Z&$sNSIi!Q?`kvz4uo!-V%+UO;~6@ao5IT2|Nsop(ZPT&SV+CP5x!<X`;5
zzuaT>sL2oxG80x-SxU7wcv$^=AN_jJ@)%AC#ZM$`F?3Ka(69(1s&XPEI7jrYD(M+I
zRg~2-d|W$_JJ*+<6m{ZcGGc?;k7_V}(&Gx<aGmjxfB0@;D<43xgZ}_gu7<$Q!iu+)
ze9{_M;e08R{{T?HKc16Z(RAzNyILdLhydJE>5QD>oK7*g!SC#8V5c0?F^?gHfyH@D
z4gjU4c;38KkxJNTYvqzp0Ml!XIK>nWPc_Bq!KIT!A~8|MbAh!swm}DKkx~IUq$!;3
zik*%7kzyuKA9^9ULqGc7jYA_mQBAUwz~Y%PUVz$)2Wl<2V2#BR+dyzB^q>z)su8ec
z#m4xjQxs#0d}BDpD<d3J^iIg>F;k8OL_WYtBCHRdb5XJo2*}5!D>GI=$!R4dU}u^I
zY=TBd{{Wh$rMH8ypjMHq1XnIaOytWNKA$e1THtfg(Ec#sisrrcudN~wrw^?<%88)@
zv)_s(kWMQYIjSKXP|a;mp;X8X1SY&eq@iQIbh<H`AUZyk?OwEy=%lI4)-bZ6-jYbh
z)Y_Ia&3jNC>sgNzT>~3e0pEJ=p;*JADaCjJN!5W|)&*k@fCVTk9jTD&abEF3tYPA?
zYl`2D(>ld@PJlV1W;pHu#RBVzN{;mV&R}m*a+XheRun#Eqt?Ci0bTP^3Rx@U)9kN2
z&<P}x6~0X=Nf@EpD(^)ElAiQHz@)E2PW7L4rSwE$WUm;_Uare1X+(Nf0r7J3xX~Ud
zCm|;^l4HeLiTqpZ@|8T6+y!GHBRrnN^Q>>F@6F7=xed2Unzy022qgai;U@||cRWRZ
z)kAe{{ZI8<PDpt^3|AE~dX<1u%2e(*<dT3f-!+@Xzq{jj(if@630ls1Q8_2`CYmac
zZrQR}klM&nmBuSb=~kV2)CQTz&pwrkbgxi0=}JHu^(MA{lbDPkss2WGH7uPE6MX6L
zT538qBgqmvcJ2GI{<LNC=$0*Sc&Zx>suII7ua-lE<H&@1{uG4&0F_s|{`OmBw$UW4
zX#@1AUdKyYE1gW?ROXr(A1M`<j!*FstzW9mR;Bes2mxA@R^o;<iaO}s*6Dqkrt$)x
zS_TH#2T1;uO`qX}m*=I~Z1|e+K_iXna!}D^J-)>!t4d^iu}U+xMk?iO_)8s&5oP9)
zwaqd%PjbOe`BnDS(h=<uK-LrG^rKjr39Dp;c%@~hnNiQawEqC5a>{R{Rx8Uh>0b`*
zPNXunQNGy#RBu`Q8@D}Tvs*-F%S=I%;KFt;JuTI!{YXn={*hWk(xa_OaX1+5Qxn~f
zF4=F$8Tm2KZ{9gjj#iVVQNr3kKfWUW0B6~jpzQm5Bo^?XF|iez$B8=6H%ic?qh%!H
zA6oq`xOCi0wY_Y-%2pJd9H+H>@9{U`1Q;wkEfC5^qvlB4e&(%<)j53=B|IuGlt6w9
z^<tlX92pM+CAQ=ON$l%tLP7rktf2j>-y%D5<M^O?9Z3$Ox=(t-EDDolxERf?O}xs&
zno!%!4pM(GD|upTFU@iGzxR3J6B`)(DRc$n?4qRq0MkNnps8$_i38@YtX<Q&xkhdG
za5;Dn4d2aesbBR~ujPYD$r#%d$4gra*3g6_Dun9aZ1el4G=-0{l7s&MQmSU;&<yGu
z5HZb9H&#;?^i=MoV1jecHCmdatdzO}`(*R^{{XE_U1Vul1fQ)}Bs;56&MTTP$#$rW
zmdX^oBoIEd^freH13c3Xx=OG=Dk9pkul;zdLL_9tuF-)6<2!RfL${%82Lo;B+uWTU
z@tTWn=9W+rRtK$7<7^5^7N}@xI$CKvj!&gYDdt*r5z@5eo%gDeI)XeHs*<$?fHHmR
zHp$fN%8@qxaZxt-0BTNg<2tPPY=izH4t)@np2CcMJCUW8BV4(>#9L%rXiyHyD3tim
z2*C`b{{YffzxvZq9Z7MA8Kxrzf~6hBXU%oY+lPyKp7~njtxGE4D3jQB9)qv|Re9d8
zcIA9R3W}11twZg#ReD>IH=2>A#k|BXp^$Nb-1_lT%-*85fa+ZtNY(!UugboBM_4?L
zq)bhC1SJH1RbZ2Tl*0v0x=x&&XOW%7bjQ;r+0QJtZBshN8;Eh0tYO69FF637GHMDJ
zP(LPHkyoq5N!SzkhqY!_yX)?_li?TQpDU?PAt$){RqDjUZKyc%JVbJb2d82x+*B5c
zHs0B%bn0Avg!to9xZl#X?wE#_VWfkMjBmbcBIz}$_uE5n6@afQx%(Q|pu6(SrKb{7
zR6B86J=h;a)!s^rsgtEYzh$>Rr%siB_(?TcnJZHF0DDn)c`y8fN<*0NoLhWvg#fYo
z>#C%gt|@p?&%If*MN-*mizCFfx;w{`hX>JVDL<+aQ&y?OU>;3Utn*Y+GB_vqkruJ;
zTPbjUi8VWCm=r)HXV)}9(R+^}OGrA7Y1dsV)S?FU9|gctH_bllxK1-c^kf5Z2~tf{
z?oAD3<AQ3<HT?5c%af_eBE0<S&a8SM+$jee(nG}x4kKcc6c4!H{Hd7_fZ^lqO$Tsy
z73Q{eB2GfIzU~OYT9h&9PJhmrE@yCmT4m=(lqh6nw3GD%2l`WG-|I;?v7)pZm%aF|
zmj(p}mw%;rx^}6yGC`r-Zfo8lJ@}x*qLdvzznv>%D1{|i+!|3LhD(_p>Cmq9s5Cx}
z#{5xj;(={oZ`jdDUi{M`SXdxZ!iFeaW7d>i82Z(s$w+*w2X58kyeB!{g)eV_X)AaH
z^HfZmJt6PTfLptBT#)|f27z0^pY))`u~HNWGz!ShPkMdVLGM90;K@0ze3VNj&!!w`
zfyFXVt_i7Cuy@Eb!lR7i74>H&g0_RsXvTIO>#2>7DbzF*_N;3jWYJ0EiX9q9dUQCS
zYGh%|9tCiME1oKQ9qWa_Xn;bNQ?(^d6W+R@4|;5;zH1ovbGB(?RUs!Hl==#BzSV+w
zd)KuAwJQld=_^WckyzavQ-d7R6yd=%g%r{ht+q4_c~2VPkg?vIBpPi*gMnLN3lHbL
zbi0ww0j!OwV5p6bD;RmlGnxtDPH{wOIL>Hi2K%b9fkSy$G)ru7X(1cti(w$*v4>-_
zeJG;Y18i5CMLM+MG)!0T$tjM!9@X<7#?`OoUjxwB)3?;+g=3so&A%BSI_!6f5>Cg8
zl$h~80QiQp`$f>MbslPv9auRB9+jwkLFu`RxjL^5kWSw9N<B3&sD9;J)5b_w5Wb$f
zy2FV`$T=9T!=kUsC8V5^a6L_A_704i<ESN-4aNp5a`<=AGQ328yQrOxaZ#I8RtCq@
zGUP~zl!U09XEh@x$eHVHDr;J`1IN8gbk|G1)KsTNoxM4$D;AHH1gK=@p-np}$f{eq
zb~FXZc}N<|ieH<2WRq4%(&IZ7AuXjUNXA76abd+R>|`fd<CB_@wA!0YVx{K<clDt+
z$pR{XZWkp8#jPZ%ILEz1#zV4WTp4YgDNF}J{{WH1EBb1wo4gf}LL5p?Hu}_570PPt
z+X-+4$V%8(r=2Au^&+_5p3a{dB2XPcTW+l6dWxNz`eQX2lIQ`bagcI&C9tFVuBshL
zIn+gYXvkY>^L;?5uA;fz9=R<ot-^)2&N1uYqPh6mWv`=Bw%j6wVQ#0)JDQE_3q&i;
zrbKY=>p|mv>W^w_?<O)^Dsd_pD9Pk{Rm#_HxX5TB^(ZJ0PHIa`GFE({*LF|Yla!}|
zQ2R##Kx00YXXxv(y7c@<Qb2y*;fmk~mexw!KV^8M@~tQ0pTie!@Rcz!^6E3IZSn6}
z4?@Fo!PLsL+yO&V2ZFU{U`YiE`zQnS&3c&RvO|5Ga!xWnl=yz2n9V-46$}ue{Kz8;
zPuvqv^%6cwYh_#GV1Jl)KXFBLwY<f(p+!1yFrYgTxA{{&Mx;)NQJhGOFh9hO$Lx0d
zissC|o+YuWm6?U@VM|sB$l9wiBoyRl-lnh4DGE_J2Q^ca{toIK*CbKXC$rB#pD5gq
zdNSOiglbhpAwf&db5boUN-zy}Mkg$p3Dnm_FjTh{fH)OTP3;f5U2$K#NzOCKsV=r`
zrz>@S`_^GP@DS&O+OVAPJu9;uEfJKcwn)EC>M7{ALkUAk&Q!H&LQ*$UlzSy3B%eW0
zom<suWtMDq#}t?m)JtTIDtT(aTgx4ktN;h_q$NFxFsbT9DU)lr1vZ7@Qr#qF(hfof
zI6&Cry0<(4D?LMSZ7vrQ;ZYk`Od&)ZnWBHR%6i`x3o6+oDm{RwY_|mX-!P++lBE5O
zQS7pmn@@`H!-WSGEnu3|i)!$Hc$H+2QU3s;S;o3DWt~ytJ9R|RaCyhAT8t)?`U{K8
zQby;|UbR!TMib+y*43#<&cmEmkJ6Te-eDn?0j)Ryk>01YH6VKxxLMyJ-OyABRyaQX
z)pfSbEGRZe4Xcg#{{Xd0L+Fs?0@BcRBxzPo=sy0UpwniZ3g9i$Qm`^SHDhmK^rnkX
zY)NZIlkoZ+#9h8&hlp)hI!7N`+!&U^n@e(10RUjub@0cg-Ap{as1%F<MgjK~d3rX5
zXj#bh6wwrVdgD0`LrHjWg^fqJIR1*NMZ|`dPNToQOx!?W3MhP#q2@_fBcJlBwB>4F
zkYhcE6-BVs5}h|BF7X+HbK{p8$F8>4pXx<OS*}3^1gr5RclN5)k<~MLfd2p<9JW>Y
zEM}x&rwR!{;NTx@(V%%W%T>umAZq^rI+8N=C(Lu2r&?TfAS9^o$F*1)C28E6S9BC6
ze1w2-p;cRyt<L`dtyreEFmQLOwbeKZ8}VE@(>gJ-8P~G2=HE5I_WuC)pq_BHyc5kM
z$34HTc}(nG60S)~+GzltND2q(oR9OS!=8upphLMEovG9~In|uh+ZiC>;XIM;UMoJd
zI#;&mYVN)>O2!EecCPCr{pfVBea$alAPP{}QWu~5Q^{WZ(2c9yQ=wb)Kq0NGenk?6
zPz|rt(I{CQ_oA4gI<~JB9CN)i7m>|*YVz|`ixNokoFbWV)26t!ZM`t#gBzNvAs~4U
z9OIGRg;Ky7Bx08(eX=t_y6H$c`%rF(H(NUU-~*B>E$K>9%5%5YyD8JS+;-Z8NGn%=
zTI8IYAt|{ZXsilhKxcY)UZJ_B$j^HF6iDVk?_9_`is5W}n&Pkq)I&#9NZ`_ZLr78E
zY|;YA<WRFv{<3@5yhGVWnJ$5h*8|DU#)v{giZ$(d=7;%;>RnN-W(W$&;+7OWc&1X|
z#`WT{*iisNRB>EWyJHm3h(_X)r$1U26U%T@N>Lr@KoedGB<?F&hKq!H(p2w!Q>tqe
zpOz$TSi=QrSJsr5QU)_h`9tYm`BDyOGaU~PTIO3k(`$~QzVwu~q0VbqLXeip+Z0P}
zp*w>?Hj|n$wCCEg07_Pric61-Qgob9?j>8C(Pjg<;nWa)D=q6@nimF~8Z(i_YHzZ#
zDmq22FPtek%_CM6==lEtss0e6Q|on6!QXAFu;0N?-vVL-pYYe(Qo2otvzCFO6VWk(
zfh>SNl|Br9NuM?J{{X?Zm<dv&56ZOun)rIm9Xey<?OL=>mSv(pbTRa)Mr_TX01=8j
zK1`)cMNSokxa{tp)f0SMQ2eUKF<K;xctd9uo^`Fg<us+Xl1Kzrn$e=|?4U{NMip=|
z26Zg#0Pj||7UCf#N=`DIf$vmnA5Kel_?dEeO14QAqFiIPn4z+8a5fdn-J;zx89E-w
ze+1<As-ITeBinHJP)d=5oZ|!?jT?HuF=%Psc?%%v--^OIr>$h%uAeB7r$dUs1A$!f
zi;-wFi%(Hr8=Z953B~wR+7E=|Y^!AtvciYjt#<o*CJ64jf&198-Oo5a5<&e_ReqD{
z`A<PYqy!`7lwM~$`4x?6`zQr{)mIsJC{UnBLs&waZ%*FIBxCGqGfK2tEtV@XVKUHV
z)SxZ0N>8?G(*<l4p~ljjE1Y`PNB-Bwb$ZZ3a&ds5cgEtKwtPTKVZ=1)CADsm$gXFm
zJz6#iv0_STTEb4Tf-4XBh48(U-kQ3@YXGQfC>vF_55y!%K}jTbAL&QL>NwXV{G7Cd
z*b;NKbmGXanGBfSSa(e!<XRHUq2|$rE7d@A_o|Ol$#x9I6ky<xM`8~mv^Y$mF$+Kr
zJf^e$vSmwJ$w<_mX}wHzgMsdh)2fz*r^jhTrFp?U{g1e+EXTnmImf+RZmu}WoI-$7
z70MJc5BeW*y;3KFl@bm;>R97tB<MpDJlH4msTQPxg%jGLS}QIN)i}gfd=gwZ+cno1
zq;t1sS@ACXrd(42XB+|QYcEslbS=e`m5gSu{{Rypm**)+Nh3RAt9EXdn{jm{wxHg2
z+Ph&W#x~A*+m#RqW%RTeVJa>=PyYb0*9Xvry3*H^laZR-IzQo?1g1oY`8~2YtqG2W
zhK2!C2_B%Pp_A3qq*5N9*3X@7T^l)YbS<(5lZ~jZp6L0^zWZuH-*7Rp74!-^PBI@u
zEu>(a9k{6o{T&cG5uqt_1aZwldZ{vx3QWg(U#8~UCM{kXl*^ogKM%cMx<b`-hjdeD
zN$`|HlYzD>(JIj%BPFFalq?(*j>m&VTSS;h5*cilW0B&tIVt{O>s5@UAsk}5FE+@g
zLrHNfN*_u<An>nhGTmgi&XJ;HDDhIS!mm!`Rb`v!j-e(oC@SnYKHplCwcS-R<j5{~
zZ>3o0Wql1=F<wYbn=O*k)JVw<?4Un~j8n5%2}vqQ&OIxZp{g5d&zl~&HQDYfMhe0A
z?^j4fRW4CNwH%Ii0;{qk;FRE1lHh`sk_ja3P?AE4-|tR>M<t+z9YAD$wJB~`1PoyD
zK&d51iaG;Ck%LSQi)Ene!S7Z^+g6O@wLn-}R-vDzT^M01Xk>P!s{>_zVh)qFR&K3p
zNx-Yk!Y`K?s)gMFv=f@-&l@j^lpC7#ICjAl8eRA8>rdYo`_6mRV{TWCJu}dcM4DYV
z-BkN-9P?4AU%eF4{l3*EG9(baxBRQRjya|Ru${NB7YN3GI$90TQdTpurG<{>giEK_
zYFb>$-;B^|fS7Ab8TLKt;H~I3-1GvUO7q15tTDCca(JQ=p_~o=v=eK_H`xCGT70PD
zfr3KwN-7w^p%SWatmA5AD@U9l=LUs!SOl7JZULOo?kK1Y`Kra7*)$IN_T+8-XeGI}
z^i+@ncIVoooK^|v)9F)_ws1~+2Y<|}>nx=GX@#jpSR2u?h7qe)^%u4XK1}-PoE@v$
z_uO+!4H9;){ISk)n)?w($;~B7{Hfx?yNc(_sO&~)u!5aL>?yUR<35yUfOCvj6&&M=
z1|!a@X-ZbD#YicG!A&H`8~JyvU`EL_LII_sIn|9cN+XU1V1g8@(!2uB!jAcD@+nDU
z8{)Bn5QT6>I+jWP8Z8V1+Mh!h?M8qA8Nl3Eybj`|iy0hOK3_bF)M7L>*wT{9hXSMo
zap_$V#S;MrGBc*SnEC!1kxYMTSdJ?&A}LXldr<0(0ph0>8`_y%Vcb@r*%Bk~_-KaO
zz}+T|OCHqnSQU~30q`<56op0hsZktKOnvsFMzSm9f$d3Eykm-&OnvvIi22}FqcRjg
z2eo*>AIthw;EX*hpDXQKiL8Z8B!T#8VkjM|TjhpxUHNg?P@`E2^e~`up4D0E3o2iZ
zr0CrHRrzcuABLhXaT2D~H1_nRlx!){iYnBIG77*NfyE7YbxDk+1q`Hjt6xz@3_7EY
zvsw37Piibz3fF<4k4p3Mz8X5Qu0|d4F8J%x3rsw9l@$K~$|?h*Iz9Q*(=p<uHkD+k
z?oBf`NWAp}h-?yq<_WEbrtG5IV@#5b1v`WfPg+?%DvG7CyPU{9EeIuPNC1oz!AazO
z>kjKqzLt>aj-_RnJfl1KdGGIvvHVi%RnA|?A!^sSPM^}V-kRyFjo#$sNyu3XP*GO-
z(kgLDZI0XN7fzdjI`kQk$t5z7G^;rCbNbbe*U;Bvb`GqmD?Ud#!pCD*Ux(cv76b%a
zpDGQhK0}Yelm5Tfv^ek!ASL!vTxA(?KyIKvi`>^N?etYiBvox43d@KBOJxfq3OV2F
zUc7W<i)zs!l@bzgPrqu@EzL0ci$tWXfTSz=hiZ0IBuZsWC<CcD+<p~Mj2B|OkD{q(
zn&RLDx(A4XimG)={7(}~cL4pxTGJKyPOY^yq<e8yKBQ<VMQJBKmC=#%iYE;-DC!cN
ztAL_-#Zm4FDhe3j_N)H@P(yLjuPsv~LwCd@LEqZ8HK5Iq(2%9RkVZGFm9oUSXbK?X
zA8L!Y+Rus=pzeP4q;%E1028O$uGXxL?9A^!4U=({99tU$o+?hw@b!jPhEW|M2;}3^
zwOMdkZ6uI0^q~;rF<U42iOoV%m6k~n@vO08HY2o@>H`?`HRA&$q>wNU1sNG-PWZSR
z<kOENC9jz|*&yv)@^)2e6`5;l*)_E~p|Qao%~_%`3gzLr)1VRt<JPF%SqglqA27pe
zDGCc1<Np9gj)W$_aft&y0>BOq;2!nSiZSetNit^t0AZ0WwI!XLZ#eZM)9X<X?nppP
zDTBe1&O#QgvONj(tDI>nbqR4x3R*zVqSNb8T}{&~mg%|M#vC8sH=q4;U2&zOnpQx{
zxHS@5l7XQvel+?YTAb+S6+Y?|f=Nlv2>nG=*=W6RZak>vrvVv4{XJ4^=pTmt7Qz^6
zTASCX1FIM!se7a^g41Pf(83Vm-yWOOw;1}+Q{pG-R$*>whCnIPw>4e8#7NXov-Hhf
zCQj{@Ijrc<oBq`j>Qq#61}b_(K#VA#y+*CHrCg1vtUZybkF8k7!}F;qYy>DJG>uKP
za!C78Fq~=EfCh<~WU~b%gM*s7L{dvYfrC|>LrFSCTH{AjTLzg3$oc9TNjR!a{nRNc
z7{++3)y0COn$Nn81+=X3jMqMHiel`|FKa1~pnKGFOXCWnua0YYpL&jE&wc%C!^zp{
zN1{mz%0_6mR0n#GLXHOXOH1^rG(G5$3)=$~?Rne$sf4KMuIs?u)}btYE5~Y3cihmS
z&p}=eWZ?SIp=>=4@H0=P-OhLewMMq+2eAg9L!&?CO5znJN;K%$`q4>VF}+1H=f2cp
z9PiB|R8cU!LvBSVxG*SSv&L~sQufYhFR*M<loji>HUU3ML2zcel>?KVg*tjUb1|7f
zV`0TcJU~wsD<c|m4Ms@PcB#E1$;jz|r6o!mZJIr#gtjq{%Zd~N4m(i^a9{!2ryFeO
z!L#h7$0K}GiD(hU6qdrcu9m`U?C71?DQnue8qaDcYEVi|69O2+kSQiGG<Jc5Nhus+
zg$Bs=MHt2@DU05ZE#|lytwyj^#2v*rBBUNDfSht^Sj`hyDULlWp`2rCEH_>#0)-mJ
zP|zHTa5gK7UY)T>`GG{+AMnSeaHHCihe7X7gP!$)V`V<vSAs`0?w@)=7_1)7jj3Tm
zna((_=hB6mhYEr1NK!{TR|b2E>b9(EJQLcK5<Mv(=W1GU+L-|E{*;`GQBWHiX?xaY
z90Yq)fP=jYoN-Q}MunP!5-W<XC0X>Z6Gp&z<Gm!;3CXU|3GQ~F<}?(7F-BuF)8oo^
ztYb0jIH4#~fZ*o7b@;P$b*Rc=Cv~T>uco@y2@%$PjeNQBRwCu0%^235#d-dzJ4apz
z*--dj(rKG7#9LuRt5TFldepk1NVC8#p<op^qzv$JwL*L+gs7H}IKtJweQI*y5B9vo
zH0UL!)uj9HT(fPLj+ul=>C2Vo<Q-*_H7`2I;O4f@llXNW-x-NcuoCIPD^T3kmN!b<
zZHqv4E$Js9j!iG9I=a^aE3nJJDGlT=IMRDlC^y-huc6<Oi3IqH^7u;JdJj+idQ>Ir
ztXj8BL2bb2%VdxKA`c_-{*^+%{7HUqPsuGfTYW0R6gkd&`+u!a*y;<N(sEXSrFe=;
zR1~A-Q5Xb!9mnNKIO{==dpAo@S1wCJL{wq6)=~*PZ?#|Tw?teUM4+83Pz7nm+3)(+
zWzroxA|ypdkb|ZDD;Vsd&1rTkd1WYq`NFY;tFpZfbHPgf#b)9aG7C>As3d@)oMXLG
zdXCDaYSN68?OGGaTznzGagB`uHX3Co1v~nIU6G2EPF`GDh+H~(*9ir@o%>WPofNkf
zkkJ|6AY!*Cx_!5Q6MObG);e9R?%q3bMmSqwc~UW0T@th`E5bjOT5Y{PVK=O!aqY0J
zMUIqcVOj>>)DKa^jcbCnl%*#X)$CHXL*2JX$}di(prokbTvY<~czL-V3Qj!Rf2~@t
z62tNqM*y5v2LAvZ^*E=o<>tKB?Va&jC*j}POMYc=nwz&yW=jPsUZJ26JDSDOcLMGs
zsm~^KpRV<8iGD0O7CS(cs05Nb8ZRB~8On`19jY|=YG~uju=*PFJuoH1Q!OneKosx&
z4GP`ihFU|PhgtwVx2dj+y+wB9<aclmalZ#V8hAp9%4B;TF>{9<0o17_qug;;OJ%vY
z8bMN0r6gd2IrghmhoDGku(FayKt2A3p`x&cW#&4;N!!}2pySa8J5)#AdIsNieVGij
z1A##7Fl$}t%PF_OLuEO_am7RQ>}F)Vj}g2mW850jVMYmMBVmE*U09=T$Vy%adz5le
za5xoBx<^`+cdZI~Q>2B8sodkEB|`?T&=azgDU4*E1w%_f1dZy`IVn1TLG<FQ^JAzI
zaktWd@%xaDgoO<AMqA2KM&t9L9Z5p+N4GTeaze&AtY*vAh1Dn|>cJIsgtaZOl0|3M
z7t#twuWG+rRD#-Ycf~wJXQsAF57Mb!MGY%ydB$;9xko7?shvcXcxuLKd1Gj3Ix%jh
zhFW=QN3VLXHtrRS_p6Umq=m0idCxUMb<Yju0k&(z%L<X-8EBY8=NJ@QFJ~UL0^FPo
zQOrx@eX1=F$eT*`=kH!p^S|6sD3Uom($e-vINOR8Erb^iYk|);Gn!+?-*a5op5lnM
z8izO7kxwPvx7$2>&?s|$_{BPxGJDcA6v-x>$s5s0a|iK#sx_7HGnyqU;}telF!Vk4
zr5AS@B7hEaOYs34aoT`vd2Wsl{`9muQk?3})gJTMJb|Cik@G4Ez*f|<=;Y28`~Iea
zanz{ep7hJIbqs^f#)U5cc0ZL(v&qW#78kxg)bnc6GI#bg2aE{XpN`>BIO3W=Cr$|;
zbp(O9t}JKXyp@ji(nWoc2EupZloP)+ps%GXn#K}EHdBsi-0w(GqRb>$Kpwl-G~oBH
z;L?QdoYz7Cr6Yq&3Ga$10zjmxIqy#P;<!g_QKJH_BL;?0^Ja{3KGZpHeT4v>Xi-Sm
zQwT{E@CUzoY5=tyjj6IuE4jxsg0V_udb|j(1&a1TG#at2R-s<hJJ$k7y?Yp}fZ5!q
zy)7p^Qzby{T}rTMfOvpKHoqvzJ68+93T4NE+O~{^7ZMLN;v5EoR_8jY#5;kotR91s
ztZkZJb3&nc=ATIv)B#-aT}ba-+c>2HD^Z1jDVLF`5GmY>;R6+g=%aOvQmD(o?O!@|
zb*Rajw*LT_@3nnD)lH>QR&kw;V7JJL7fnQe7Wu9(Qxm5PB06@~n`OU35&>7?{{T8&
z!do`B3$Lp|&Qbu})Xad=7)Wg+DC{bWer|R0=$6uO$}y~*_C2eG(v>r*bz*M)Upnfz
z?LrGnSPIA;{WI-Q-D6~!<W{7%rMg@L#XRW*U?0w|5v-Bzw$!i~{BG*=4fi|Hm&=1u
zkuyS1g&8F{1HLz++py@&A#_TeGa)l0S#6TCm2OpykM*KD8>C&0dMrw86NLpfp}L#K
z*!Lclb?K>A&k2bEy+1I{wXI*oqqaXfkwv}du5ZqprLwr_`DG;FrNoo@AL=P2oQC9)
zceO|HA~unLvYiU`){&5Y;|KGp87h?0T6qccHg`|4-)e=oIOMxhUAZ!-?;&YRjx=^r
z<qP&akEp5Gek^$=1gQb!EgIN(`W?~_x4mlBB6qS$sY$pqp<j=Y+nVzc&PrFvq1cYN
z@04Jqpzwc`@%b8-LP=6}Cy~ux4|K_7($LakCp-!%h-e2mrGNtTnm#jN;=LF|<eY?}
zDiVz87_5uq72Pmmtvc{GA9~u~Z7GhU?O!hc0BA|Cl4CcKsO`NUQS{?B1l-C3F0IJQ
zlAub78O=8~H_S2;H!3-*bP3KkDGVn$91lvgH+p52fZ!VqBiPrAID8(Wl^N$ySr-oK
zU2GGjSx_J8Q?@qzR>yJxBiPimsI1)HE;og(Nhcnnm1Km7juu^S1uGw6{+rW9K80+y
z#*U@hlDR|@oG53w#^BUrOwF`iT9*~&DTVAlpI>UQ+U~a5P5#bOe7m+!^5T^m1OUB2
zLce6oTsIC7Rjs}ArCy&yREm`d+D9YxT&ErlJo=KPZJ$xt{VGy5)d-S_4csuL5uY~M
z^dQwAtszgeJS9XLk&LB4>_??mWA#<O<mbySM}a9AK^eeT+PYRY(H?5(`wQXp#^va9
z#0vHATB;U;$OCPw2KXCkmWyk07W$n^xCLBR?8k<dKv1h;x+6ZwyNFV==Gv?C8B+Ug
zRy%^w0l*kF2_-{-oYk19uE=F2MI|PqCP6}zoMYas54H&;>}onn0ZB+1r5O~t_SNb-
z0sB)Ct<Rw9SUBRW&p}g)&UWUKVrU-T)r{SZTNqO~#anHY6p#|3#aH7XAddAb9mI?b
z(<BC2wISyJ03P*Ry|j>pXMb9ej<xC3lbVmXCFio8KYG*J0(4}(VH|?B0rnM?+??>_
zD3i8pdg?1G-%85U$i{KSV_jQgax;rsLD>3Nm!BKEI$@HuLZ)8Bekisj6OS>)S5)tY
zbdii2A&H5|%D+A<j!`Sfv^6I@9MLF|oMl{9d8xP<DnFeX%)ITIn?buKJbwAEhx3l!
z)Dv!kosRV8tZ`V>NH)BYy*`!i%}28EKhRU?btPHI%_Gprl4Z=CB~(gO0R$6K@zuE|
zjY$-Yst8K9t~f{;PB-<WC0kdVRw-)C!JT-525Ggpl%t$iT>*Rh&}(p{5UxGzN)~eE
zwuLz<2}v2_^r4j{JDh<*NOa)wLMlNUf@(ZZA1kM_VKPD6R&(h`#e5D`o+`G~jmAl-
z7}7STOThSYQ$GF+rFfK$CXk@8af<ruI;eaoS4-I1hXHlql^SFQf|BS}FJnWY1Y<Nx
z1r)%~5lO(Oh)ytRm5g@HV;sf~Y0$BZ8W~F>yp<Ii#th0xz{MnkTyGTNPDM9h=oRNQ
zoKprs<4t-{DOWTpAahI5Lz*d3q5$neMj+A1NFtpvSj&h#sB(p8IH?laam_TQy{lE4
zz=LY<UQSYX6kdbeQ)>Vn#b%N<jd+S23Q|c|G_5K0u7C!^Y6A_aBakTvQ0+u%QRGrf
zWYBbAauj=0Ye~U0U}wE3v9}Z`)<rKVHP-bLnmx$LJ*kB>P;rXEyB5(So#=#<*ig;n
z8Y#Lq1Eid172u^BSQ)Mqk-)9kEbvk9OFN1{80NdSD1)*Z{Lox=0y1$}hgL=`%~J8U
zYk0aNQ!3A;WZheC@trtEbL~*fth5~!3u7ii$X-fF=8+QL9xR9TAS(w@9)s4bwu>^_
zK|UG!;;P+T<lAbWL`KPR>?I%$39cNF;`uY866K^+dW))Cm34B<0!D{$K*93?&V8yn
z94m~7??|~2h{!tfu1M?;xv3^BP;63Fb(Dm;HPjV6r4HVM(y85H)OT5O)sU!`sO=d}
z;Ey;@BkR}?dRnwUMV5Ta6{hJ9G=!<wQjeJ^PR+EEKp8!~s0n>*3G2!jcshv2VqnL6
z7*4W5&V4hsed>*S>ZD9zyVS;(!X-2dD0FBlLU#%&JCWNILN7NPjB7+04Mm0XGQe%`
z#1Ilvq^SC2=k%p(Lw0QaJJj~qV$HT0YSk&V_zG8)6{D1I_*4kbrgKvLIeKF9(jm=l
z#$~X<PPC^^cjr&=s2t@yXN{{MwDk-&E_2$tEm9Nn&ZauhfvJ0b^I@^y3de9xJJr{q
zx}x5*9^)cP6kBjdE-aMkLbKtAGrpa%p5JPf%KpPNsMDRfGal5`xo&DxBsA$yVZc!K
zsmpYxBt=TYp+tY{HD0{Qg1F*RqUmYpf*wY5p?;?wkAKdQ21?sP8jiw}o)QvskzEmQ
zkqITlwn-@tq@?5#Mn!M|RhY8mDcueQX&a=PosLOH*{@FwtsHYzpxN?})K#dFfRbzF
z-;2*1wbf}D(t>xzZ~aL;MS|j%yli<j^H*8i3-X_JCw!Xb_4z6)CH*WnW_~U4(E5~w
zg1zu@LfsrhW&(<Gpnob<c~7cZ+|%U;6kLYHn8#Bf<ZWJROzv%TLRhXzVpDFo;2vBA
z=e~EME*FO#nyE3AG>^oQ+<)4tJxvN+JW7qIB?`_5s5IY9Ur;Xp052;c4jvD5pY%1+
z6>8BJrd(a>by^nOpD-&pSN-Zv_tp-ymZ!yDRE;Yr&-aM$kMgcJVuyN+xP>6P%Dk!^
z<FWUso99SephH7NDlMRS6b9gWRpBM%l+Zl?0ErVNA$Ikw$Y6p{N^-3V{{S$1Rcgc2
z7fa>&k{epJI0mt{Q<|K#^n1@qY4kVjq@LgQucST?^c*P<w)3%5sTk9gaA~ELbrCq~
z*{OUf>4{d099H$5=o!i9>soptthP0JQ1+X8Ajd;VLQ{d*Y-&?Vv7GH!f~7>8vLa2p
zP^h<}6p|F0vpNz+Flbj|YDvmzj8&yF&>OZ%_o&CBg)7S51zKdn&+yO=vWD^pG(ph>
zZQ&y&12hbnDcwV0H^oj!*dEmtF+&7re@e#ai_@W75nW2oa1P?UwsL#t=}t8im1#N6
zWwR1C_gy$ji8VIN511L#$f)~eyn+-(TP?9l6Zl3ckgb(_pHC%C9Z2n2H(T_>dK^)2
z7|(7i==o7waf7G~;<ApujM7<ie#d&2TxqK^Z?oi{tBLY*R8xV2P>oI80HIcYi0f?*
ztfc-ES>sc3paJ}=#mi8YBC*I?nV5DN+*F)dTDd#ss&S-)$<BDGcv3e6ZbeV^7liD#
zMtte_G+0<tax+kIA0%_WG0znu#DYiw?deF-bi)v^6b%rEHyh@LNd-EMbH&rKKdlNg
zWW;APjrpU{y@CAd3Nzzxe|nQc;P7@dl8EX-$yqt2EkL9W=hRT1Am=>SRPNc$1dQ3|
z2+8-MA5K9zr<T4+#WcJu9MgCqlW2yR3Jw9=v8dAN&)3?eC-Z?(Dr+YqqpLiBP`9Dh
zoB)p1BEyGlZBdOig=ZX5>=BN5_M*Edi2V^r_ob9-q1#ufG+Sy&=DxLT?mUJ^aZCAh
z*L*>WNqil%MvxB{h}hRg+fz$_o#};0IoVW9*kZGaZ0*f9)8vmeJE<mtklK(4&3PaW
zPZiaW4#tIj;E;E%NWn>T4)pJgF^tq9ltIpE=4NXg(M-(;zLYFv9q3<{1zFYhq^e3l
z-!;TY3Rc;m=*TUmf@q{|wrF-$sUtM_+icb_=Dj?c=x`cgYvg8{E+`&rT^g(@OHXns
zmlQFzGFDA<3OpUCIapLrD3jj2g=ys1h#*oio@fjbmxt23tvqIi)DuZBfOoBCpwMYt
zb3&o)jg2qGIHIHmhg3nP9RiZKh&vi+$Qj&<&6y!-(kbLD6N5pQ(s(u0ypg`D7G{YM
zKo#tyjpzX|W12{s0l`%jBnl*Qk=~M3?ZpV0N%a-gO2-C`qj4LathPofr7qZIK1ok{
zw7l?fB_Pu*#RYlA1s+V&d{G$|$lEOwq;XllRxuttmt9hP+47ujeT8gtbfK*y^sJAs
zp*p3L9BNp~hE4#e`l!@&H>ZDA%yRp8a%|IKZM2tPB}ylK2+y@={X|@d>f$YL$F|1Q
z<MNi+@b&xsHozZx(V6MVP_D1eO8jSJAeU5><ta~L{{U)*xb&PSm|8DAK=Z9AjVXBv
zOHJ>Rbe!b>09xTqMQrV>dNL+1R*s)+b$!z7aG4>ze2jTag_jYZ#2Y*AI41)YX>I%O
zRY7N{y0Q#d5D@5GCfP@XqMQ(vsQ&;8K{|=<YDV)P;WtCDiC3<skuhb$;E2m83w2$J
zhIMK56gA7@?T$n!Hol1I)y{GE4yNWbwWkM?PB1-kPijd?)`m-a4C$#h=rSICR_Mwx
zQS*gOyW%0I!6-n<8zn>fp464Q(X*_dDJgOzPn-h-!dOuAC@V_RIMP+I8)F1jBdI#R
z$J5sK<Mkcwh*x`@gu3U-L@G{^w}+%2J;?W|+ecP>I7^n>g`RYyJwYJLdNRvJ8Q`~#
zh#AQnW3@+hbW)M0^jlq3Xt%v%zt04cP>icqTEGb-?Su9erC1WL8HZB#LX)9jcTnL&
z-mtE$mDJ2U-O3<q=HocpoX|=TwDOXi@{%g~@W<jTJEGcd`Kl~{>1{|z$`Y*QCnJyh
z)gdJoiOMcW*XA|gup}uuNKhpEQL$Y?Y%4nvOm$Q3x6`X}vJ_U3Qla&zn_TiEAh@sL
z9M`Fd@i-;2H?JSWHvuOY?O!}~04_STgSO`#>wD`dEx#E<BN*>kA5yrK%5bFY4r_<j
zu2VZbH7Jk}BOy*W){f-yOL<I0l8EX60yDh|#nCLRsW=5$7!@6NxcJH?BQ6br{{Yw0
zr3f@UrSM7h1+G2D32me&Qi0R;tlmdTNp3<UHqww<P*QQ&cNKnvJ=HLmRB|@hp8l0>
zx3y}Y8&aE2usq(QJk=9%=uZ18Jv=1DOHYNAr~nRqJu5|n^0$kA9Bn0O*-6C)I&Lb`
zw#MmJ_#0sMBAK?wl?BHT(NeS>$MYtS88inZDVtD_DL*QNsUYB>jq&MUM*JwVw%it-
zD8V2TSdF=m{FWR_NGl_b_4Kd9_?{K9t%k`Rsj)6oC6UxiJA^g>#}t7vBg#!VB!RJ@
z7UDv-O?nh_hqmsm>13-OS`D{UcA}fyXEmt6@1XO6nu4Abl2wqOTCqNmQg;V61wATO
zQ`)r{uF95EkWPNp8sfr^NCPx{=7>?>dWm!`D+&t6c@${YDMW>AGfuF)FC>9MzavUA
z4)hYP3PIR=<km<n_%8P8O(z*uSz*d=11c4iw|+!@++G0Oikr4|I}czs_#^97CTNl*
zwp$<`)s_50UbxPLr?EA8jU(oe6oryI)pM(@-ZXa;ryz~ZEY^&f@lV93lwW-t=TYzP
zS-Ve=YjF6l($6&N-a}&@_TsSzW4P1mK}M1dcpj!LOn8z}WGNsFew8@^?N}N1s^bVC
z3?Is;Ac8TrH}6u4F9OLK!?F7e)Qb+1K<}D_VR!%z)gv9k6~4rJ(c)g{on^p4&Ud6b
z;!?A{2#3Zw?MPeMKZ><d1g1!`@lH3Q+m*OECu)RC_}fwDpG1sf%qbd7UvQspNl@n>
zdU0=@WF4v@m-3&*p8U}sI4M4A97pC5r}o_bdsmD8Pxb9V_@_VjuC45&KWbTZL^4Pk
zO3IXIbmtf*o_#=!;~lA-jQ*U|S;3QRSUBzeRO4iUjwy|2JJBtq6qB9>G#AJr9iO5>
z1aK)pC|7|b(z|B9tmzQ7C=O|owS{nL#VU}M@4XaZSk>6mN=m`}G)e||rD)9%Rf3?9
zcN^2mUMXq-cBB$BLd<z&sCj5dqq@Fmut#c8B=0~ZN};+A3U>9SqA!Lr6hWyK0o>CL
zq!F>KVlqOMG6Al{E-3D4Pc}1(b|Xm}={18yjC_RdY0~zcjSd!4FcXSp$4)jU@~qt%
zAkAR$N)76$^gc&=Et!KDCwdxYsvRxq+)_a~rd^f+{8Y)94&czBhLr)&6w2K|IMqPO
zn}M;Y=Ow85fui(fvTem@gG0Em2F8M7QkF5OntYI<lAIbHgmn@WlfLx#&FUOfa-BjB
zGhIxE{{Wh?y9#yXat#W(3I}6cTfl>bP)|BR$fdIdDpG*vkf}@KRYxuIdCeomQijHr
z+ZiH6%2wnPfm{<6t7DpuNprqw#DxKpcA%3prb;4OxF(im;BI-SpBRqy;`uuhOb1Ao
zW%9M*B$}3EWagpmXe+@YrW6Vho-3mgrDSI9&^)IUuu`6Ch1L;VLKNvBkO<nY&aEL{
z!m6EM*pUgutQ|Nz3YVyN7B(eJ!ESOc5}o-dl8PD%{qKgYDIZhGqb2k-39;j<>u_n+
zj42*3lxN#Ked={FX2m5Im<%+dk>aQfkEJsv-+CIPPr1Zalw>7Lc#8Uj*NJ=jk5ZFb
zC~tHRK-(NHz0(Hb^X}<(QzuwR&cP(|bK4bJyxJzkzDz}{rqKzP8%r^e>O!1I<x5&p
zaFBgHt44BM$W5wax0mEh-@y;W%0b`+HZfHT&CcT;D;EJV5@iC17KU48r6`}m02+xM
z_pI77i%g<5@a<Bra&H&ggG*Co31LyAwvvI9s3>o?NW!v6-mexuhqOqzEjEcwC8C0c
z60Pv|kVaB=ByN2O$l9w8u_bCvx)ce>ZZZq5Hj)$K^Q#`Gll2s_)IJ1gw_9z^Z8ZTw
ziEOPoN|JvHjy%ac9FOv)w?2mAqH03J8e2=T+}mZT2xz3G1zreH$M<Po`h8QKyH+{W
z-2>O%7k7H+X?bp2)}W%cRj4O@wykHUZ_Uqbw`C=%DY&wd+sbsWOR4RvJAyl(&aRLx
z7RRBvX_V{lu=);^zN4+4`cke)^v-cs#=H`nr)aKxE$i!tRP^)KE}?T&#=ij|jHav-
zu1>%}Mt=wZ8WCM965S;-;<gf5{yc6+)KhmBU3o>l$Q*yAb>ec0q7q5U$os{~a-%CM
zJBrBqjbTnWow9TJR+n^+xbakzk+H1K_@p$YEoX9S8Lo2KTy+#wrll=3rx4l5$=a&d
zd!y}Wl)yU-C{^m>lzfrE0IN9r)CBuG5+ect1zh8d)gu;=u1d<~m#F2frwh%MCvo%@
za_O7H^45j2RpK_BDLfSqQR`66bm}31qL7~6x%Cw(3eob6H!=&lleTbi{{ZHpUnX+V
zq}XpMb<)Gg8j-=^5$RTTtlHf9#z4{rckVqaC2{+b5}EK@aIFLPfDQ5Mio0|_SzINw
zVM=RG{#D@MdlA~Gqw-C=E|w?_eDVuOz}1~6jQdy6T`qhZdtoC3NUSEt`=(gK8cv~{
z9gS{%F0i-=2W*UwYNj>DMCY_ix}vW1K9j3B%^Bez;=vWt+D37WtEiO&YQWn&Q_U3-
zPG|&fE1E)cf(2t9S~YsqbhzLs`WhkmVT7Ejqn@~j6V62o3RYL|F<effo(ACNs}kfr
z_z;w5zpYyCPB`vBP{lw$Z1DkgWMlQLnVKOUli`hSY;pP2WA_leq$wbNwDs;Im{*3f
zF~-$aG8TslR0+Z7)}2Tsq=#HkB_}?)rmb&EZb%7qkWbc}VFA|jjOVs#$cal2=Rrv1
zn%7`x)4Fm@w517II!#z+#QCNQRGvjudRa<P7*SGc@fot>a85m`(H3BT6uMYH73CqU
z;2dOgUo3T1oiL`5P(cU1ec#v7P^K0GX~qstE938p@d9o~N->oZ1$lnA7m~Qv%=MN>
zru8iqttkL{Q0y`7Qn1>_HFh4A=T#k1R4tI8I5b;pB;`l)uO%tQkSp48xH!cdptcYm
z2PrBbQXP^L{9m0gr$TTB*ye(fCpj7Rskdi5=UoPggd7EJ{b+_|<ycy?^fg~E`L$}I
znUnO#y$T?v?6S2iX&F@%nIw$r*z>hl*m&RoPq?Fy=>tFISy_0=l72cy<bq9j$p>uU
zeQGr?g&Y%0*TxP-ERige332^E=k=taY3xRGO%srEX&{uVwM$1gY%v8n$s;sM6rDTl
zPu`<LfE;J_r;zX`ZhbSgGDk!Y(hx}AlBN1nUMa>iT*|XwP(MeOqE9u%1rx{RODV{u
zAQ80{6Ii4xM>OblCWd)hMrpRzrFN}#qflFm9jIlwNp(jZ_Muj_6UnR)q`AH`y(?Mm
zK_N;flSQ_LE0bEyNKsOaGUC>O>qLf$?kTrfDi{<D1eN%T%91Gd8%P<zp_3U&T@VVJ
z98)j2AJ`+pN$pLz%XrQyw9EE1hh-D66kCWAV=lB3Ng1XVqMi=)@|Lv?s7F*#a-&nw
z=N4T)_28ueoi)atQjYk=Da@RJDA|FYR=kXnLkOxW&ax@@8#=cpmXvMH2eT~;j*^bz
zpIe(_JWz@yt`+vCOvRIee%P&a9>h&~Nj%U7deW=|nl;jwK?<S7A^5b?JMa`ZnTl~E
zd{Ro5cPBZglEM-)Q(o5EPByGyCsd@8X{0!<T$(w>HnXtWhf-0iZqyr~i*4Qa{b}7`
ziWOY(O{vEcMgYLA$UH-KJ7Sz|qE9)X+K_Sqz%+9Y4P!W_TM_7;w>Fm04l!Q1PeVY0
zp-duL5Z~g))l=$gs*$=C!K=<{5hn<9kz{4$$zDO&b3$I4ZRn05p4Aayx`n3-S2ZDV
zn1wK)oyhdAcRon38fI10R-U7AZ?R*PSSlC=C<T9SN~DW-QS|&JIPJs<ueuTAOC`l9
zXZdru{VPhi&Yrg(Y9i9oRIMs}?tY0C8F99-#i*?<c(UCaKKA6}>&1C<g}bx16KPRd
zXXfK5J#@Pn(j3?Xp)I8Y=qd-PW4!jAX;NKbWlgIY0OQlrr&_i?i+s7MLyJ&OP)W+O
zp4DEoCPn4H9&4|*Le$%htZo3P$y+NI<f+AxbF)uzGn;MQL~1*Lf~~joN!#dXc(HAM
z3v-=@yhoT=eQ6-6EuGW<05At2)Sp#Zq{4OEn%Co`6oqIx1)y<)KQE;w#{riV_JiYE
z?rkI$j#zLUqfqVj_1d2lSCMi`$t!FJZF6R?rrHg%jV>x6NK+*$8#oYn8OR`USKAH7
zTt!D}W6uT@d`4Px3R_D4R4beg!19yys2e?{uNDe?ccVVTdKi?Sz~f~86?%Y1G04F7
zt6cj8_)NDY^ft64rAW{YS#)wo_!Kjd$A3{>DIzWSC2r0tG$QMM8>}r}Vw1aMoq?$6
zP9{TSl4(~rR~{>YDJ``jpd3LU;F=yHGaV~IAme{}zBWooul0kT8%ooP$nPzQGL(R@
zG36b`Yews-{{StNE0bA7o4X5;RE3RMDg)N1Dsp8PL=R9;T3b^AT0jSXOjLEds9Rcm
z2?PR0Njy`R*v?E#szQh=1SI+zq$M(%aJKqLP&ng}REtt3?K1v)jv7j%%SZ0h_(<ZF
zW$No}c}5Wm@Rodv<tNyRAEr8Wi==ePdEX*VNLM1RF*<*wo6)1g4ksAnW9le!j19%H
z$X#C=8^0)Ff`P%)oM*Ob^V84U<tRKEN))^uc*cIT9har;S1boom1AH*!9QA<iPLOA
zkQ;{hKpTXUus*b_B4co6{{W{UMsf6=vF*66ddk8ZjGqs%taGPhNMZDs6hYf0clwIg
ztggB8gMIN<$Gwo6403abSxE<-sioYM-uTJ-(30dI5T#=T)N1A7T1eY9UO-5lMb}u~
zyt$yHkiKdam(ab1JUl*6KDCsYniVZ?Ip1m&HsgugAom<mM@dL1AcMIypO^{$AH5I(
zNMk!Hf_aS%loOHbik5Nq6}y0n7F3vtZ9ounMFEtn#9WmYfhXMURhc%{NYtfiS36^R
zvvn24Y_{T=My}g;tm@^|YHuOT_?gBI)OjlzMUsuhEvV^zL+Ei+7JEyNI+t8<sU-gZ
z5$37zZ>99>Q$+{d9qN7;Q+%=#H7P(HxT-2>MwzHtBccEb3i6)bm1<_>*hWSUD?PH_
z)igGgsDY0AZ&qX?T7gn=fx!ZrKVoeHb$s@uL|Q@ui3Yw__?K#Rb6k?7;Uu0o8`sox
z?at0Z(4Yz9$}1xHiPCSOw+dNSFi#c9>Z8gfF`qoQ89?Bt9+{%rMv<I;lzgau3#vd*
z9E?!<2msfP&a<O$ttH1c&Ym_N_03pM6fB3fsmYR)68qWC4k{`4ijMqyQ_ni1u;0Ga
z0@g{u#!V%h^4RNTS>}spQ8@i5W?Cy50Pjbz&iiNYSqs61#!(nMWOv0JhqV9)dKtF(
z^8IPn&_d3`oYPqGw1SYF?gj-u+eDIf-_TbW9+>>-lp{#um7+4(*+%=1%A0%?0!Yto
z=&%NN*m{~`W&?Qj8`9CDtOP!F>CeznjGXVswMM4}c+T}8$U1U8C>MlJ(hmq~+nRJI
z0OpoKY-FeJOs({Tj^@6jNYvQsPI;y3NIOvB^y8eGZPyk?Q?+Xi1(w5rns39<MGnlh
zB#>24CGhIGpv;dr0oxSHnQxp^&bShGBAZrLoTn5RPBMo5Hy-q3X+g)eMXA-atm2<T
ztJsR%sMTbW*cvjEy$n~R;EpTJOi@mijDbmUXi?M^gId8yl;#d_Ysy<|PnS9GOgJ{#
zK##2@3n1qd$P{}%VgSu8T0qGZtOaC>9zZzqngb4@ZTj;<$yy1<DF_Z{bk~%|PNH#H
z^`j&*geaV*o(f1C^Gicujg2&>lof=Wnh4Kek{=q#2ZP$2LDbnK0bG76bp@S|wJE1i
zgy~So?_0=FK82Hv=DMiCTak(=7Sw#BZ(2*rDId~@?qELvr{5IoXaI9u4y=vG>0Bxx
z<c+Io80+X+LDFg_LLAP()Y^2oozB%6E#}&D%yI{<W|KD18U&Q`4lAl#iOKIq2itMH
zqkM5dyza6_4I@!QD>!B3;Y06U(pTq6{OOfLTF$Ll6jWxCkW;8_LW5*<iD89qPHT-^
z+*s6=Q&(0LrR53KDG=B|7*e_9aZ1~|K~>ufe)^``$tQ}U^+#AV&`?sp4>fd=Y(vPx
zK;EdmQP3{WDk)Y0=7wH!jUn;zi?##P{{Rspc$>Tu0py*k^9!jX-B5%f(S>eLwPa)A
zT=cqJKu}0I02;V-?TTfk=2Y`*QW7zbQ&5AN(?v3HdqkTrnKyY8%x2RXI?5Q-cR1x(
z{*@r5z`5!1)U)ab;ryxQ;WEpG=bQ;75R_y5sfQ*)Zc}L+YV;V*GL7h+(8cQ5h|m;^
zb6Sv7<_Z22>ME%|8qK`CmOtJI^AItd3>wt!%n3>eLIF9?9jayigNZStmV$K*f&~}p
zT(*Jl@zXwPzgyKl$sw3dgh*QlAdkf6Gy2mX4eav2W;*1Ck`km8NI~04&cyp2^ZC}T
z)jtenK}!4puL~r$us;ucesvYlaGfklhdHke)VQ=94U{?igUwDjnw2r$MAf2KrL56S
zyF`{$veSnsS?~Qf;QHdFrL^O0=!FTw*&0An_WuBs`+adobe+0PGN`YB<2flp$j*`2
zE7d>Jr7rLtODm4hG2i7nPExO?YVQ)F3nt96=EEKy5fLmpwdqoDa+6YVZRu(_Ndr1+
zVrNgE2gh|mOF_!E0Gf@PC&N$f#x~-SE<KfPNXdR7MK8%yLn9{@j^1O!wm7HdIx;;E
zA8OV9C*IqP>f9I#8Q;*?&0Rr#m2|kcl`TQMk(~Ff6q{jvSw7!W!;>Kq(!tUW3Ws`b
z>gAb}^9~S_p`4xbiixwqaZ5&q&JN)E*Lu3#@}M~9gh&bW-y^rusIiFViKKM`WI++F
zYQnVOf$2=!Ef<N-B}~X#a<Q-^VxztsTbA=_GZ=9R1P?Ypr=@B!<43n0QP4pNB<<@{
zYMmjIwQQZ~J1y!ELxw_z1{M@~9ja}_Om5)Yt~#;?I47EkiF3F)7#7r|j02>P(`qrR
zqROMBM_!_=I5KgXlf>6!b2JNO+Up(0Nr<-^J-|FxnPtB^(i;jVNg3X&of9?%#gs-{
zU@QPIwAGU7U~#=FYw#-s5OIolDMM+ADJP~_o<e8G@fra~9gS6PFGz^JDrDn4;;wxq
zZ(1V;-pM3)-krYLp*S$SC0t<luBviYK}jZ17`!c|Y}&GR#Spu?@sffEsTiXdWeAX+
z!jPU1dU41Qv7nGM??GQfUyw+*0`}yPGtX)@D^gNUp@Y6@gj<l>e2#1@s!NTHD#sj-
z^z{6K>_g6}#FQV>qHYn60?X<^9nv?V6w{^Btt%rtW15&|E03}RjD-`yJkYk?29heH
zrqMA0Zb(vr>^99qPWXQmEV!0azo^Y=k>H`C3y44>y0(}km4S~+bhIUzLV6@bA;m`u
zNjvSmao(S4r;xJ3$nF5GN%TWX2`WzAsvY2$7pbQkSqIcnrhr0OA?R?FMky&ze)VH$
z%qSow#HZV8HqmNZ>q#mA_p1wbkbrfZ{i%~^4Wc&M!FdS|4DHPU)>amz+t#AkBTs$m
z+a5DX000hXKB0u;kQ_o%Kq8G=rVFNgS=Y9<?e|57(WfdS7}}}Mn$o^5TcyRh>QWAp
zNvz4$V4Rxp^2Vg->rT)!%PGhiBk57kyrZ4)eW<tAaxsdJeM1KXZQN93iO6i~P^M7I
z*5DW)ojja&+|#ka3IvU+d{~?>VNs%O#@M4#6obVP352N$P{!l$MJcWQH>Ii?f!bt_
zKT25|RtXvHMK;g_KE3P9Xlons??+5JI~bhN=ou6<E%}BxsWy<1oNP^PoO4vc{30?)
z{U}L|5{-uyFX80w1}T|M4Lhf5Dgs7S=&bl<E62ZTEusL)`fXj1(FFN^w9ZO+Cu%Mf
zb;3vK+p1(Yf<;BhLU&S5!jSr(dA&M_KGc>_>nR1?k?L#cT(7~RsAReT*ysGK$$u-w
z5dQ#xq7;np&e);dXaW?2LBD~A%RxgRZ(Ktv3P0;eWo{_wD$P9FR)u+v)stqUii#Xi
z3eV?7e6T_?38u9uD;w90m0+Z()~%Rn%x4Capn%o~CyE@YZZ89RcLa<pVL@G+R3U`8
zqsG*ikn(at%@CEgJg893AcunTNg(@EPyv~B%{p8dG_Ml}@lj?NbIcC-q1O;Ml2i`W
zwqYMIQa=?q%gJ-`0ksTEL0AVkrx0|VML11s4NyXg6p{hK_MqQOr&c%|(vdxn8^a1s
zI{yHj2T9~m)9lLcK#|<ZDe*V1mt9Kqgq(`4NKMi$)Vt%P$No?-YSRu}X$_}RhHFKq
zp`@rxq`IVKDLivf%S-8VWh*58s?w#h8zWC0=vNgHLrS#$D{--T5sftINK>DkDa8|w
zK#}W;HMV?RnmY<<=*wwif30dYiPF*$sGqeCpcnW-#T4scpapMBjLKWWl%9QSH13Rm
z($lF<bdWv8M@xtEYrs9~$n)%$%X|}1<|D{>x-x<2JJzZN*fTb-4ZIa``cq4W5aBsF
zuRl6kAMnPkdyS~HIEAD4hjZ;qac@I3&@-){30gNesP<I%i-jbNRG<YaB&j&{2b%7d
z)UC*@)tNFm#@lnsq0Jy$09K@A=hlyUJ1JJ6tZ(Z^Fvv<Zl_wRFGh|}fTSy7wnJCns
zGIBlW>yxD;DAIn_8}lnr(xhPhHZ_|_$u<y%qopM(z@{9I(HQ^}+J|?>BoypD#c^)D
z!Ujg6?McZFqSy?i2+~qrNWzZ9b6k^YdQ0hXxa6frIRc+iT*aj583!Y5){AC}!l1U7
z8%{vpgfThEv<c6O*gr`0_)AH3&<Vi%Rm0@cgpD9+^r3CR3?!vE0*n#}+n@8U^%RvH
zjkdt1THBJ+npy!$hXip{hq^JFZP9Yj&+Z=C)Q{mklq*K@QlXA>O*+~gK|;VIidkb)
z*i_s~%XTJWvRp<ANamsDJr&X5pE8@+fN*}*bS<JP3Kp;3oq~__H32=VZMo&i4TWkP
zfst9`O#w-!Q18<c^NEPrI}GIW_o}VkuLXrTeK!>;)R$)8xwz^8NC#{TRZ{JLVZ^w~
z-AGn98RochRkU{FPLn=;_`!8U@3|Q`DnF%UQEkYS;cH%%m2HaDek()?oVfsOt--2q
zM_LTan`>8?C4Xv$JF>HSvr*_ihK{HRT$WO;fl0?S=U#L&6Kiy^!a{;FkbC-yyW1X*
zZiLYoNJ=*q3GoxEqsOp<qLHtP^wbiM+~96bMonk7Jsq@b(uAbogo?XBdf3wm`TqbY
z1OTN0jQZ7bz?4oYSB-#4Cw?k<`$BHAScG3n8*FP(93M*N;ZDo)wMD`#c(U#3l)^BL
zLka_p`_YCzl%vCCwWyqAbDZr^((Z_~MPl0>L?=i|Pxz`+r`VTplG>D^jX;7=;vT;A
zty?!pzjT5fj-;*B=^*0+oc$|UVdmyULRZKgt1pTKMI~+=fw}gpeX{J5lqsh&2;zc!
zp<h;M7D(O$DbhlRZ+ai8yd)=H2q*7Kv&{;Om1J>M?ytSLm3S#^kVo3P<rJWE#q1He
z%(|}uYr*s*H5)b|al%ltQJsjP*%Dkxc_~s>In6$o-$@#fr02L9s5VtAIG+`qCqZy~
zcA`K_n};xaq|q@MG6(Nc;tAtwZd5lLUy(t2eQREWRL=&Il!JlpYsN#Z<+QeNcvdNK
z+gZk>eQ<lx#V7zMryzDBf{<DQK$iLeBP4gBjAzP}l{<xZ*wug&pcFHlj>B<Gh9y4k
z3qD^`H>}K3u2!3ij_N{`tev+s!w8jcZ(5krP;fv_b6Nw|r$)+boNh8Hry(?ilr4~=
zeMJkSRgHAT!L=hVF@kr>s?52OmYktU$E8*ngNJm4gahlfSYp9!M;XR_F;!D^O{7N4
zlDN_}sd^h5<aheilq-Z;@d!(SX(I<a*PgRAHam^#10!H+vl2J^KKj^o%mAWPleTk4
z<==x+GcS&vH66y5`cZ-bBLj;0T5wY?Egpo|**_H|u{P54jHx-&ajTKFe06+DMZwf4
zZ=CU7XRL~h`Fxdx(p1Q|GO`DLXeV1Fk?YMKQZcX;!q72-D~ie{n8Qtz!8=pzw4lAe
zLtSqPKD6^$2Q(cbiD;L%U?hDg)ua=GHlVHQCkHsi8nt0u`_$x-8M<deM&xnZdP0@n
zlH;K{0APEX4N?b*f~rP|ITDOIJ!(aj4%oq};w1ofQ09+`IA=*b(?`9W*&XPfN`XiK
z0%?Z^Nd!`(Je3R*GmWWlsR$hXE2%{^MOq?dGL({kOw?PcQC<==Qu35m4FKXoQb;`1
z@@XA7lWhGST$DEGLa;N%E%}WlIv2%8$eSIO!IPDoWE70kamkjII@$?BJ!{)-rK6!F
z$ppP0MpAVF&wNxp#_JXf$wH*1Ds<riO2$v6CC3!GUJ@TmYQ{i4>MHwYg?f=4<${;l
zR-k)iccpUp8;xw3gFZogrQzkq*rMgmf59%tMzVpp2YMOnd15<9#Vc`1*e07_h=#lu
z(zJzt5ES=X7QBr))OQCMC5S329N_PoiFWnW@tjJ<zr}5Qgr6`TwP}e2$6&5H$VuGl
zNTf@g8gELHl&EmL3{!HYLZt7r*10b}+8->b3L_;d8;Uwue51wi&H%}vt+Hln%a1Um
zuXOs<j*Ea=yhR|kuELg^4bcw7)}*<qwGgE2K@}^l*V{-0f;-SsE#WSxLs~VR^!n35
zJwo1}8HU3^?sK^mRB3IR(`3LysYl|~ijKJn$wpaDLHDEMKGSVwN-NJ9rk;8CnsBf(
zQhx~)Xx2cn5^IhZ2vU8sQc;y9Xz*NYes$QhoNYy?qo`+-=}x;cJa+?|`Ms-sD9yLz
z9m{ZR_=(QCB0ELlg>0@4X=##|9L_d83i9QpwfW<b+arovO$OcQW^I|bn^9AM0yn7m
zR+eM0Q&I}C*qW4@)9yl2(g*}pD^|C>G`${DkWzO^Cx2>Mjr11V?3_z0Pzc7L1q&|M
zh4_J3_RV>z&$fWduv{JeXeo=h$P0D1z$o0O=}9TO(j7%r*mDYTpTcr|>9?O$hYLXA
zdB**!yBFdey_ZW5Fr_$yf&j?Ku1@Pfw^UlUt)&#|1w#P))iqs;kbT1N{gLL|l-Y4B
zI0Ox-_!r2s_yGj=Jc`aPy=d&BTbhBl89UT8j;fN<a}<E*10ASyNIV&~Ip8aOuz0BJ
zv(q9lU5wh7fmBYF>OQNG0l2c2Hh&54k9xZ^I&00TwY88_up*sFGId2}&wjZ{oZz`Y
zQd>%}m2wZ(t!i8s(dAYTrx>Q;An019Mo6VJ+8tR5V?qhs_M}Ng4&NYIaY1QLwy=;l
zJJd4zg4Eg+mnEeuPTbXo=hM<;M{z95N>it7RzG>@d*q33AuAXMIX&sB9Y|E)XtRr&
zUVONj=qLv`EA8~6lh%==Vlt$qOD7l@#Zq8c<RmT<+h2;_$~fCKJJY>A2Hh*ixS%+r
z_;qp(5219z+@EN<#*p-uJX8#Y4DZ^9ORhH{rNnGb#+4>}EF-~&JTs3dsD)3txZ;~s
ze6O)1WA96@>?xK6hMgKywF^173QKK`L~tlSom(1Qe1_Bqm}k8<CzqWLut`bpP{{IK
zMHPn%*~Z;^FbXY@N2ubhy+3;kWhq*)M%7Q9A>)AWka4QH5>t_gP;n;*d>XbnG){5k
zYj9<yV;Q40XE-%RvR@l>2`(oi-mMKyLcma1-nyeMNat~;NC_!Q^r+&A4j_#|LwbXn
z9DO?2SPL8A_o&ypkuXwQo`tkW3dTYEiae6+9B-n*agfSdQdF%waak8ubtlNmaw}36
zKkC~a)hpEfaG#Z0BQIL9fJI|Bx$|Ft#c@heaGVl&`c!k};=Z2CVaL0WRp$I&DkVo&
z;FDCVjFifE#SiJych7n@=)~956&28}yufaL{*_C-+V3t%NNFwv=X`gsJB+05owpc(
zd_iHE_Vp0xE}(k$svkg1O{!yvmT`q`lUC{dJ9(bsnQ67Cft;SzpWb?FsT!2yabqkk
z8NmXrjX1kxa!o#sjXg~oLQ@qlCv%gE&Hgd<18Wg2$#4{p#e-Bhtb9eb$y=`5<f$WJ
zjmNb?URdi(%%2ivsz;SXr%>j*RN~bfuk30aGkk#pC`#V|1oM%RQ$0V{Fz#1{Jst3-
z+d5ABU>d<BS^D!whSj~~rCVl=>DZlRWOqVs(iD|~Go<oqT%pj*<oY7z)mG<U1qnKv
zJ05YpSS;OIhUB5<i4CaWgq{v74Y%0-B3&bv#hKQgUd^q}4tUSnul+gE9}s$C!i$~8
zFqCqjP>@Ydqv3~_gK~*}`Pi*F9l}UBQO<t3smRw<+ZjxUA3#QY!#%4b>1q8``zZ;r
zKGRC%sRwU*(q()ZzV!{Ao8i8=+7F+>KAxXSMB{e0GrBcSpX*pozzb#IqDI&`sOz6v
z#6*N9OF?03(h1upsvQ&4^7?XWVLqQ8KRG*Kj^9eZK<U^wd3j<PDnZmi6z|lzEef$D
zd8ZXQw3x^!9)^+=HMtFqI)<&mJ*sr*gbXyY66y!xBXdf6IM9{&V5xg)2Z~oNNMxw%
zZt6fPX}||N=8A&cNiG6ctPS*<VJmsY(-XiL#sN753Vz#R3_RzE;caC(NC(ogei4|f
zqtT;iZHVbm&nO&M9Vv1+QW7#o+td1jR;C<bODBc;Q>cXVje+5XA3@C&)94$$jl_7e
z$kfjG^Ca(@ad+fHQhaEn0&sTiOgkD{RhHGA9H5QpYn9?N5jDt$R563)&h&XTqf%ti
z>KkIn3dkqgo-yLi!-&SA`qq5i;5RKUA~DGYK`K_n3Vz#tyxi{YGRs~IEhk_>8~WzA
zxSOVp!j+l|VzPwcNCf&3QFj@SMP<~XqvhNmK~PZowqc&*HzoK!3bf-H)O~6fsQRy|
z8@YHbHpj|12-KoMH2l|rc)uf&y5@SWD~uw#<6vMEifgE(OSv*t;kwe3!71D6nv97q
z_SBCR2#lefif~SSD_7~qAl&U?iLA8a>jf(EV2UwrOO2cKZjYYslG3<RV@+98h|W?~
zzvW*z^`mVycUfxz3jGazb@4ypJZR~ac!2Vf`jSaVCpiBAI{443<f#{DVyt<ZRu!J+
zxIJoo5`B@&CEyGYd;M#QX-XN!b3-*Eh;8)a8OHwrTJr^lK|To?R{sD>TVSa1?@KNW
zeX&Ytk4#p-2V`<+IuQ*4njyz>ayX#SzSuunS#T$QKJ+i-hDnQgV+X!{Dl$XG3M86+
z`2#sB;-Y0d6`lV8?L`siXPulu$ReMIaZy+SUrL2@T9#6kbL&J#W!&+#Z`hQY@?4?Z
z)Di(I`qa!+xhu_rK&s866U!iPT3=6E0$K`i9G&X;Zcm7uh;P{MxRNyEYLA_0eUN;-
zR*oVzk%BR`0XhRtAc6*Q=~l^sHQ8KwDW7G;l^r0#kPwuelDuY?%ONRRK+Ak7L(|s1
zZ;xu*=8A%V2>~M|WcH~Bt-&pY>vaSYl(sRP1Kd}nx1+nFDmA7vflevZEDeGFA?aO_
zrYaP%2s!}R4oykRvz52=t(2um9rh%1$9iRLy@2C-U0wzXcii#~8lYX2b@eAw%elJE
zvdW0)ZNJ^P>E!3I^{Z8-S0|vNMdg&48nCpWU;*nzx|fjiZK1TOOA5#%2~IXAG`3@0
z8r0!R)RiQqI!06vuhNRJ1he5Sm0$vcum{qGn`5`mU^4r@MV~T2+*1s(cSAZRfHf~j
zQz$tn-_nRnGZCseVM<yS4~P^76YHMzTT;~-MwQtTzL<c-)>jT{)Dy5um5kBuNf1h!
zao|86xunRB%%_y8I@RG3q@azDtqm)ul_xF5kmD=-YD&?9K9oA#P<#DM0)J9KkK!o{
zLf&t15_cwn{{Y%sv0{OBwuFTfj4U57trB`Ir8webnCerUtSd^1p$EeIbABS^g}vir
z1x^(JDOWYHzC8gi;w7$knT6;mRy>>jbv0?~gj@!;SAsTCP{j_%!dpvBA*hH;X+4p#
zKYsM|K8v--35fKUBODZ@DZrh7_pIEXMqGT7v8LOfDpI7Ow^Dq-(k5Q*47IImZAwb9
zoQ>!S-46ybRODF_6y`MQ3&;b$MN&WZbJU2r%U$x}^OlCsh{~HdQc3J_=|Rd-@nK0n
zv7*PgB6`y%D%x5xf|RIaRHhPJ@fD3Y+*LNu(>*a{Vm-n`&cj|p5T&|EDm-Sa(rw?p
zWhk6R6^tivywgSA&>gPF7dk7-`Bh0vdU)R%r+;o)t#Mp-M~1XCGOhAzHch75?6#PA
zEw|84vPQ@2NRw-W5ou}Qg(*PkZ11f;wAR+r1t-{3w>xK01V3wN<v+`v#R!<fE3V`#
z3;ap+%_0+QTATTce6t@)j#&uL<w#B1;d7j{CeI;BbZPNO2?x;7O||tchl)?>=xNTE
zwY-vIp$#~)PP}(D8#dh;nT^}-4k5)If|K5-T9q;*FA1@CWcgB&sYm^4q}Rn;a~HV~
zFWo6{h=#O;K4X<7L0$4VrKqs0M7nwwz{8EKwj3%thONajcKCm3nIRGzX{PbWN&RS+
zx|gPI5>TkqooJxop=rQ5<DMz>U#xc74*bo<hLWWdt>fh%dfwVz0Gnc!4g?E$bqhj6
zU;?eNMn`lQf#~iuweko%8lv?)l=U2B8)fzjR^3V}@SMPK)p@(aHrPtHwJu3{k`@#j
zR-K4A)yEZOjnOAGp3P*pCRhuQlGsn7r7S$o5YCmfp~<Snhpc*gtBA7;scrP&6fEdb
z+dCfhVQ;5ex2>|&$xBGw0!n~B^f=dJU%>iXli?M%g%pjH0iYDja-}-^M4sDXrXk32
zX>&r8fwub(y?W_36c}GOa#~wLQdFbdVy5Hf##?leO>*CIb;Z2fEg)yO^ASikIWM0W
zqaY7TwL!kyCM7CmI?|vBI&+YCKSM)au5HF|a)@DjB~G0nn$8|>!ldqqMqw7#kuEqS
z{{VKbHxvcR>kiuYg*C)9zf9ER*z>2_()F6>4cExlQlY6oP(@|;FNR%XdY<gL7v@_+
zj#5&Bt^EKsly_1Cm2_OKj@#}JrOz2M8(OiP)%loxI?O<qn`kK`DZs2nHoN!3sOh;(
zxH9`8U^yKrAgp?Td(q1H`_XJa8Xe2Uh}@Nii>{;uHc7xLJ%(|;1&-asXDd|AHOYNF
zF{dIq2?|yUK>q+btRtWx6+I$N$;Q&=N*hYglu<6uh7{VHVZ)u$ruPa5y+1A^$b3KZ
zn$o0$pqwAdr)s*k6&9btS#I9D8vg*@<~Zs#V=6ovm5bFfW0IFi)9XP@OC&u10EYU?
zHdlmuA8M^m>L_-13v!!#La<VzgZkEtrTYxjdb2@euG$WGE~|WG5mEQfvT4K=C21=3
zHIiNW=H3)b8B)-%CV_+Ws`T!<mJp)k1*f)2+|^vB)d-^-FIS7}pE8Eq&<IxJy$tHw
zl;Cyd@DYrhRR%XuOj6sFt0axa&UPd3Q%v6Hy%%50Jld44#xu1nV*G`)`X!!Suf$M-
zLSI?I-)?9L(JpEsrV!c+j-aDYTdmE^{q#l@9sdA`hfb1{?Ng|T@5q@BFto4m;b=HX
z_ohNWu|vC9r?!;7#l>wukPZe0YF^!IVoj_jYiL}6cX27)p82aw{Uvd^Js<X~Np3rd
z%2b7ZRblDAlDJ3z0CkTLNI)ea#}k2%W9vy2qVzPR<f)m}#m1pVY9xz0T6V+w0m%2;
znhE<SPh6dCtCRxD7Cerjzb<~DR;$$A8+vXc(Cl`~H0MeYsX+GQfl$zGZrkI+gC%;O
zb=<bejz|ZSy$MQz6=Ww*d^xa4d86`PE`g|PNGVbO06H>P!+R9DgTR2w9745VCvjKq
znuiRj`-RHp;AHI6Xh86rRxnQciX!U{N?G~4Y=pSkSn(F5fE0tVDbJ+`QdSzKRom%H
zVwnw?%MT?WWhgs)=hBO}^rV7IanOg6f(XvvdY^@Hj{HaLrxDw42wbL~3R=cJKgy-L
zHq&m3;-Lp5w%9tgtYoX-dNFERXcA64B3iROZ;jzUZ)qqeK2f+dMX;^6{3qc!{6YAV
z*y64dm1&0=^~6+((h!2Qwj1+oI|`@UdZpB`<;mD$HV+4E4&t=pgBIBB`qON23b;aL
z)^^W^r0<INPhAlLDx8dkwz8azr@c&EAjy=5C9J5I5rMeUq@BmyZ%(siw!=ancHoCp
zoCP-C-uzZkr=wXH8bqn@e8h<UBgI(qrh>Rxx~a*Ed4`>3yy^<ZKpuoul@cL9kfbH|
z5`wKsN)QxOeLamkalc4nA$1R_p{G_IPc@VkZJU$55HMQ47t*C8=2G5DvE@+w>P}3j
z;<Sm)v{$|gbCQ}=UaGg~N~IPH$N&J=M(6pxz3Jbbg5xeNOqNQ{q^Tn~{JU0BN$Kp(
zzbJi6`LKxyNNYdA&S;x;!G_#$_(iu9vD7)JrALmC%8bVlbplq{$yc^0$=7HW)HsIL
z+@+~G1w?JP^`?*TEVeZ?PWhFs4F?jf(~jWPcIz_Z;)`6nQ-Lnzp=lbA{j1UX*7oyC
zWxHbAQub+H894s{wMw(pN^}-xFyNAe?xNll4|>*~(+W)#nHGr-x|IY@Y37bUyU5Oe
zp{a<ryORk^tw;!YX*(3-A4+2Je16q!k$ZWIyKtItf4nFH5O~J>pY*6(Z^YJa4LG@e
zGkU>NToH~6efP*eN}7$kvH582%l6cF%I3~*L!^R3eq@o~J?JT2Lc4%0tqsX(r06Tt
z=09qW>prdOCLzU(4m<#j1<w)I%G`Ogor-;Z#V@4#f3K%DghJ_K9I3AmJ1Jqbs2#!e
z`fZ-osrX@SC9zEjpCa1qRtE%vqE<CyC)9glul*w`;RYC0{$njV;0kFd<tpqC)~ND2
zOX9ToqRP$9=w<Rmg|eL~X#i}kSkC>i&wN$K;rGHtTF#55-dvf*Bgb_QYeR$+f}D>|
zcIK%F)9jq4t3~>KwB7AqB&K}IPMiasK>aJ>KkW(d_DzER_0}D!^g?mNsqYMg=j&g6
zIEC5cr7;uYAwE?HK=SYS_Qt}nzwJY*9}VXG!ces-#3>6wI0Msuf6ANHVp(yv`&&6{
zlcV8~l7cy>kZ?u?IFzLCkAG_DXef<2<DW|K+$eu#ra0P_$7+4G1nt2T(o#UlHR7#1
z3Y3~2sRtyTImT&;E)X%D=_!yCwrF_^)tnlaF9$Y8#vXM<5ud#dyyKne!UlB?{?+X|
zlrv7HWG;n6gW|Z7cd7Wcl^iHHU9v+<gkXJrs^JRWk`j|$xS*=cqU1Bv7I0)FELNim
z(1zFw3BdxZ7P!cc>RbaDtDH&fhV-dGitP08inhVQfT}Jtk;MTrDFBp=nwoY?C0Gg@
z)Me6R4Kk%_8OGEyQrOXs&#GFw2bLtF$WoZV>RN~%=K%6{sTStMNNMKOLdx}Fc^J;>
z89vkipKE#F3NjQ>+EfRNsk5kdIUMhjYAx3uVYcGAa1Y2+p-!!~kB6ZrJ{Tvq<0Ny%
zS}%{>^_su2W>wxAirW_yq`8L>($kgaAA}LLHRD`U6}MftlaQdW&?h_g$6_iuj;@Iu
zE;DfmWwofS<;DRiJ^aU!xZb9%y=vO!s@oUB)!`+;ogicrwh89}v@1*0{^G3~eo3_B
z;BBd|I_@2}wuB=9XbL-?%2DI7B8%7JG^NC4Db#g1q^n>%k@-}2?a7lNtx{TXl}K9K
zOK1hp3?%u~t+AZ#o;En3(H{29b+IPv9W1Fn8)|uihcHOSNFZY!@sevfS3ptuC6yXn
zcF_79b@Z!s1IqpJ+PQM+#NAY<ADHBHr9cIx&6Wty9jG@Loo|g7<|n)p8!f5SqXpyb
zo<Cajx#Y)5aSB4)(sdy@DksxcQ;opS*A&XA4Y8{qfcvsTF<(n<>PT_Hj}Yer1Ji0M
z=i*3h4Y@U>ty+?Yd}5M)h6X4Z3w=@Ao`#=7m8C$2fpDZ8lc$V!;-O|iyPz^-+g?yw
zlpAmrBsNGSWFGk(4CbcpG0^F!EtWBDVTlo;+}v(ks*bk;pFmfKr9&B3*e5@wBlgdN
zr%`V3<Tl5h<!AVF{5YyqZ-=q2F<nfl65M)3#m$`R9wFITPM-x!I8$jnVFPTORMoc3
z=SX;SV8oc|@El9QDO;*=s0|1SB%CEfWoN&oDs$3HLq00)?46x_)fJzROO8lVv?Mse
z(o{ykp4DGz`O=tjUHhe_CPH4vgBjGgcl<jm>65srWxG9y&PSVMd`K%(#zLIR65s<q
zbFvTt#@QzZfm8{-Q3Vn%4r`31p-Egf^#vg+9&n+Jx!>A^lbZ}&Vu*WRRCSfY_-wZ>
zn3EPG>pCTQdt<h9>H5{L_DgOAEV%pV4dqHw@ZO+!(kOeo#?;~=TcgU5<D53oN{SK_
zx|F3HBxAXz;$I^&!W?Q1!PTNkL+LGf2?^aJZTCElv!6;aN-vD1u(v5LNfBL3YU-=1
z;>3>y1-OrEP$&VljCnxs^{LzK#j8EATrMd@WU!EbbCsx{K#(${^r#!-y=m3Y{>isR
zx?1BQ;AFiS2}0d$BO$F$Y=BQH0Y?B0vS_5)I;9MzTh}<y5`vVq9q@?@N;y_Yz~B??
zDB^@6Cs(m&l2S=J(B;3XY*&Jr5@tVbTj4rexG<>ht8e931K)~1O^m-PHO&&g-ogqH
zLVSvEAfLCmsA<+_Ww|ANOVqaL(4IzLW*9jw={?-fhBA9|M0EAfqbxSYI(pwd;>;$T
zZM8n?oJkyjRISE$#{+y-rx<uA4sXFU)s+QGY4wMjK_?*?D^cyS-)bTqv{{tmBevs>
zY^7;A!R!DuJlA3645DaZz%?J;l_yZ_4l$g6N`BXMmKkYJjcAkSL0e}yJRfB7liGr=
zfbEV&wlOM0nxhzsQo@4Pqm>+ICZcX}82RZboeh;V+QADe+02c`YGJj`W;2uIN-Il_
zD6N;U5Z};t7~Z2MrqgnemdqKgI#r<xT8LpsPELh@K?AtvmYeW1Ro2RszMknf?v72h
zbmP;ctog!9lHayZdJ_Kt(T&{YH4fp@@?+cHKfHA$6gI9v!8yULA;|HeKpqT_4m#V=
zT5TmuTDKp==KK0lFxiH@v|Eh$QbBD1>KFsf_>MsvesxX=o0BOGd@kwc;l^q_WCohY
zgq-<X2fhZzq(9j^eZl0jG@+EmMuZ610e<9tD&8eqQd)r$BWrYL2?@eek)D0&rKxW%
zpooe=+^DkOn06qZ;8CQ!gKxn{xb$VGrdFH8SK_KdKnZE)fhymDjp@r*RQ$To#OS&Z
zLGw1PSSO8-YO%X*jQDPM$~FNBQFUa6t2_WV1p50^GoZ%#CzCo0YmQD<u!0B!2T!ka
zMan4}F=Par_fOr>s4YsA+TB)?wKJh4`<(4Zyt`v7ObPD=uSNpZg#t%!erd<LYQ-&U
zjP0PdrqUJVUJz5KAc6T0tpg{gY!_w|XfTxww%7?$SIRI5)susf`c&ON(mJ)-ArIx7
zP%ztOGMPyll97TvML}O1iFbXdlBUFxrrZt&)}PGd)K@0-x**4mZ?m$q9i*K~ab%31
z!yFCHGfb<c7;UH8jU{M%gso(3m2bi08Kh~YO$?k~(dbcMvpm~Yq%v4}WTCXQLO@a9
zamM-Dl;abhb*F!5vcnCye|CZopF0fl0jmd2#A4jre>CUMNNpgup(6n&_=(Ow{`7s%
zsUwuBxwc0_6OCs=FrQ4C7LpFzDswOIu@O$rwntI|f(TkQC}iU}#}$!vx5B8se1%;_
zurUR<OK!Un03pH-=vV1lM^W5wj=b2mSyap?UC_5n$w_D{ZAB+?owy#fu8eBk(#Uc=
z6uk6l&Ie?)PwvZuK43ja@84=}E=uecm8vQBShMU_X!e-)sVRd1Aq<qQU%Nt1F_XSA
zR-3KjG96*oV5}09mgG3FTik4&_sKhPS{t`^r^aqAzA7;*N;H9yx#3=(xvKTr)nS77
z$lNWFCp_$chL-E916J5AjBa<@rR~$~e{+|D6%jg-*4HuHGf{(R*1abh)7x>)RPU1C
znGs(xXl>;_l9s%=DObO0+HH<WhLu7rzaPU!lm`)%W7wVQK6Q!|mJ-Wx64+YH%Wc-q
z4il9uV~w$lVyjlE>61>K_K%ku_%qZGNAaf!GM6Q4P(C8iT1h(*fuHoNB!7lBxOP;|
z>NKI$u%8iXKv{4B$n`vNTfA#zn2Tm(5rer{Z6VQ(3G7q>-yGCe#ICBi*xI+VVd&G;
zQtm5TOJ$s;q=AFyN_@j#0G~rhqSD(Q)R{rB^wp040Mlxj7F(!s4i=e&{6#dj(l!Uq
z27PKi4@bvj_!2IGD)9hP+Kjb?kaM;+C)%Hr@nfy6Eh6Q6_(A&~+Rm`zB!`Jtn*m63
z7|7zaE}fNRz1Si3N9_0Q%}y!9t`Zwr#@a|br_hl?SlpGSGLnaAt}4^h@3yDy@*8{Q
zj#O6BN!ELTjEvO9yQksJj$z7E#rX~DLQv5u#y85lVx(u<^g?yVCxoGuha7N(q$mTj
zb>olpp;j(0Ee}MP!>oj*X>7iR{6rIwpsn?7`R!Vtk!^S(Bw21S-1$XA`Q~FB_*(R-
ztPF($ds7WG{G~7#;kBW#;`BSLpyzDk*NU^;AqbkT7dF<Fw$Rj<Tt>-J!5&@k4R#H(
z*B)EnvXoo#)17RTgs%i+R!-wN+qNiD=@~zfrQR+woO%;oK2){<A<*E$(~nR4)M9N3
zD0XbP&NnS(E+{Jp%zuR=wmqt4QDa*eRH^qz)2S*-{$L;}vU$>krz*xa$2(MQ<~6=e
zyge}wIUU!Vc}q!INeD(Y0)du_v${`uX**Xgz`NH)YIHeoT-k;a9&^E4o(rHB!5odZ
zCZ!W*m2<Ww=3Pr^j1sWkk_t)LN#hF#98*F^Pju9Wt|naB5>VJNSp`M7`mhRCf15q`
z^u-*~Ubwc}%vm2Ub*#9{%Slp+%8FB{1B2;NaJJ~8b=a##sgWa7P@}3-g+a!=EDkov
z<BsC2A1LEZ2c$vp3O7fb4hH^Q)pFFcaYL34pSQ_&b8eybo%x^MJT@T;Ir3pD-0m^8
zSbDC{)+!A!hAi#0+a4mmeQqsfz&`*80AP`gU!_tyS`qiKbjW3<()Jc)Cmv@Jq-f6|
zr+uhaQ!3{@$kIAeGcjBRLDZ-$uRNV3ZJIDil^zr*H%mHF;xgtUxR*2xWF%nV{{R=J
zK%}wMP_HjNZDt9KqRP~lmfx8i5#JroGH`K5RJ#Os;96}(G8W5omGW$XfH+Ef40`8k
z^!V*N6=CcoD1aPJG7r<zkpcy!h97nkCb=P(iX408*1bu|1Slts_u`is`0piF=&@w7
z+B`%(^DQerTxCcr100MfZA-7y<UfA|B5mDFhns316%`8#@e`G)&ftT{`Wmuap?vd2
z4nmNE+A^lrbgPfVGtb(Ohi$Z&3TkV?Mm`FGk^)AY6p~YdoC8NH*=m<5GUB)-0#w;r
z93Q(nZ~-9q9gP(aAaW~a;nSTo^k(KI5*GVfmQ#Y19Ppqw9>miQM43M9mzydU_d!!Z
zrVEY$dt_}!Na@R6t6bP|V>2PQf*g?MgoU^{2@VtM>%9|iw@Q%X>1}R945+?b2|+<#
z2}n+o6^wTGrcrN97NfKTh&SkR{&L-LRA$&A9s}|64tx2qM)Wk+7MP|>mewZR<Gwt0
zODa!@3H(V&?~hHWyWded+B^xD1;tXHNM5E&w2W>E8OH;ELNm7Xsnac!87TSZRPLsf
zg+^^kSSrp6w44;`6dG5ctF45x?XqgyX_dr;meZGyiW0VzdDIVYtwVJ+@-$!n07O~j
zL%2K}{6Hxh#&-ck^Xv^uTDpEbYn;2a&N7@#rcm3>1eX)7CuNOXWO6-fBdD!*dpwY-
zw#m;#xDeEI!fCOoTM!OYf%WIm&~2r36;1YMm-|)bEwSTgzx=t9qPOEZrMNWk0ZR5N
z2Vi`swHrR-%+8vHXzF`Zcm7&39*=RhGQ2pBQWTVr5eJ@lJ?iF;qh?*%7W=R5II~fy
zQQUdPmg3qfz|SM`4*kYQHO2IWsvUx2?Jc?#<BL|OlpIQ2Ki&WbW0I_G-i<f2Ho75W
z^^J}Nu`@4_U6$G;7|*y8ok?*&fTbUeJ7ArN=B@E1UoWgQzMYL8-5Kn765nZ0C0IUS
zk}>JQ#?|xnhSxURd0+cQax$VOVv^g*g8IWKDd4!2k+|F*af-5hKkF`~gK&vr=(&!w
z4fQ<oCtg}yb{{}iK~hO4BXNKdNEzCj9Fx@oaq^kA#pn}xn$_y()UuqP6stm_K3Zv@
z58=|4AJ_5*rC{`DPe+~eExmnl>}gn$adk}`@8!-m`cmy+Met<JxyJpVl&KP>#9$@4
zB#ilRdmm6w^{J`%yK@L)E0PdjeJWZTTZH(DNXaS2GC<@F!1k`1Oog#-GLokw_&_1V
zge64esaWA&n-9H5O)aGjHubHiSxE?5Uv0G^dD!4DW9dOny)?`QWtkCMLyJgpjb%z&
z4u1&)bq;a$s=rs<7beOjvh=|fw)G^XwXL@rBpqrA1zMB7K;E;GqbRRE9~}PxYAX*<
z*uF1Zp!CmAHD!)jX~!cn-iE`f1gS|#*aNo7+OkaxN#oMB{{ZbB0^=XzOS0v@!|`Rq
zW!P^rwJ8cf*x;+p&tftwCvY74*Ny68mRTtDcH&9F4W-{Mk|_>kf}%IB>ECMe#3eZU
z*O`@W$;}}d8guDQtmJL^Qgvr@T?p6TA8O^B*~d7Z@{Ne!+P#CQC{uXz5$I^PThC)g
z6K&BI3uS|*-%3`UTW?w%ILx}*9ce?{R%LDqXy+Ii+cjy0b#b<|>&6D#*Q@F0s%KU_
z8nWaRhJgxD`cRUtYiTO*$=i+Uz58k6cUHoX_04YWwm7b-WJyvT;UrD^LQbN*V~S#3
z=}$C5oF_f14qTP$!c7KN=?6QGcCIP6BSQ)L2D8+YXLzWzuw(dvy&>>)HV#UPkK$G^
za0WYOr@v=1rI9Imkl{K&KsX(Tpab=;w&ELF&<V<sq+vUg-xVJ!>3M|yz`WDT{qKo@
zlpuNv_L7S1qjHvzWR9A)&p?FiH8~h7P*&grw&UMF(v>@;*pSOnU_F4+w4{No<ZqCB
zQ_&<dA+^eKL!>yYp$CNRj1O+~RA9KxsiY}MF6ALeIOo!xDE1?A8HsI`5tkV8guO%s
z6yqr)vBxJo(9+WaEW)|7&|XkNv^j-iAA}#FB#Jf1B}8FJVL>`lMv<|}_okNtE-)NZ
zZ8ml)2?J7z_4Tact42-U!R95`VQE_3eWffE4Py!*=Wq0;U^5g>b#BB_a|10cN?U1H
z0FK!n)hJ=1wu5SYL4S!m4TpWZQzitj3D5VS2ILHGN%x`0$axX(E^JwXN}6BGIG}{3
zCt8Wd{P2*FHXX_KqLB5*VplB^-D3$M0mON|j{9##n`#t-+wtAXz;DMZ^J5$1(uR)0
znjwhtlC&^Y`1T~7vPtyCZFx2P49@BV+Z}1Eq-R}0bHhs72?;7YW7~pBG+36~>+w|6
zjHSXbLbUmPN%sE$N)gy|ZeA_EN63L7jSgU_j2z_Vj<?wUR!oUAX9?}EDe%i+C9noO
zzTa9z<fX2L`WoM0v!3uFpsD8E1*8I{d6eRP5}r5Cb5+}?RJ{STv2TMFu#{;bM`WaS
z0D93=*t0nDnRY|0EE0WE5C=68CIy|_a^*L6j_OC_)v>{$In6zGR~#W${{Vt1{-Bzo
zx89V)VdJS@3mGK#$;R|VOZC~;K4LB9)sS#QfayPD>+MIP(bIEK1QqH~PIIqw>S{<?
zEzdFX<^XL+1q(+hBZEyC&Fz9&arA&3>S>odidt?<ttAUVS`n3Ce-JtKJ%%tl(5=OO
zREB5Bxw4eNb=D!R=5;O9l!B!Lo^z=^@CT`*<8+fw1V&xy?QM;I7)DP!d5;~xT4onO
ztER%VUgSR$1St>UcqHcl_5&nizG)>HCa4Ngr?IyMj#IzyjkKp$vEn+GvQ@fx8}c-P
z^rJ)83r)FA+HF27!P2GaWhY+brzJ<WH~Z0S$G1A*bx~d}tZowLkYi!M{{Yt2B5l?W
z6|};RwL6R)1Ar*g>{jbWd3Wo(t7YW3(ej>`sXy-V-zz{*C0QdFPC@rM98;Emh|JxZ
z(<aLz8>dSN4kaL@ae;zH4l|R!^`eV4t)N4TdLdrQB>9d{Vt4OQ&h%eO!j`#BMM-rB
zDp(2yY8>OSr*_vsQe70wkL)SZN>MgLQ(jsS17xftCnIC@JNwXgc2#F@Lb`?M;l&cB
z(v_?YKqM3Vpb|L8>slNb78Jaawp%ZS0EL~m9DQre+oIi^ek*d2zZ@rCmPT@&!QYde
z{RRaUIYbR6R;(zLy$m&Tf5OqNZ#>ePD<tovljR>uTSuzW>&`{JyrjC}4k3C96QgVZ
zK|ARi=Clv?W$4iS7tkGHr0GgRcE&MP3yYC0i*cmB+ersxsNf+Uhi|CG9#R6Q(1qXb
zO{fOhjIsgMl^t%WSOob`+XK>?W2kPi(#!9%r<)Hg)hMk&PI+<=2LKOBIWu}%LyWf~
zeYhPsO0p6Zka7oXeW<C4=&$cnA(P=G6q3A+S;4^1paPjDwL-EJrv4#D>ZfHvv&DJ0
zp`aAEB;##GCw|0iK8CL|SX9<gi9ul~+?<rTw{y-psHl)&GZk~JF(Jlkb9#JD2_@1t
zN`d18bHCn^V6)yJ+uDNnP&qloc!^xMPNk_OYEnr!0FW>^00HSitJ4knM`WsGa-igv
z*(eE8R)-VE@TDN~HpkMU-kg?nIO`H7A(W{_rm&Q_NhM)9!Oq;Bw#I5^Yf@7kGT6D0
zK-8AjfW1h@3C9V@-xV3u5~Rgy8+OQzsX-1rveHOEQb_WpImjF5(<X-3MqAO?OvSj#
z(Cbp#VaL>!HihLe+E)DMCmeSfr>Dn^w7S7<rvph)OUeVrSUKF1aHF{QsJ13tP!?AE
z@xg4k;Y+?!tb>vdpvb`Hq$8@+Jbdw;CQ@8R8*K>%NgyQWAaY0owZGgnyFGIWwMA*D
z2vXDn8wm*jEBs2|IKesgpb)O8i2KUAp@}h^bm?0_0HlmzBmw22E;HqY!GUWNC+K`4
zKqx410HSyH8*`2*vi0?roVWhRbxg-}INDS<3mM5O2aU+bxb&rW;44dE4O!pti?ZP*
z<&fjr7nEfj;NyS+7*$BfL{FD)Sy*#AT2o<>fsKIA{!#YcyKiaM8jTs|08E4^FF-hK
zl$?MG-z4K1?M*(^`0<;zMRk-Z<riLCx`J6FvQBr}jWB3q{njcROYAI(B|xAg5&+{V
z0~=>!+K*GR3n`e=Tu}`WT#(L6a6F^{Gmt$!sAyJkB6MZBJ>?b>w2dpme9F#9`c#Y1
zB!;r<<p6#Jl#{1U0687Vt@k#%0D(4RYH5i@*$%n^9w0gut8y@S*k?Ze)EeR4q)nXD
zF_g+TQamE1Hmm>wbra70>T)b6<vOQPEG5*B4M`XwL-}%Ye=k}ZM0=7PR5tFdH6RUo
zQ@95^WPhJD9M_f=AAl=;#^Q+YHsj7M&9fa%rQ@Jy&5^z`2>VlQ-OLEDFwKTcnG*={
z4vi~l05p#_*ubM0wkkB|B{v~@p9l{v3IVW$j3)r@HzXbC_TWY`o0A}=Dnd(P#VgEG
za85wmw?AqrxqZOt6-F-Cq2ot3-`XgYgslunQeQ(zC+HGRaq4#VqHRO0Zr1r-KJ*}%
zt&o?QX&@zp?4S}ewn6PvW2e&@9xb`ThEz)N_*+OPH~{WPYGTPQY)g!{ZMO*SA=idk
zQZQAGv!Ay#A3M-j7QW>zuAS=Gw^MXt?7(3$$AtmFml`SubrNz;Kmg-$n!2-aQcF!q
zf#Dz^Eoku2RLYb#8O91m<8Nw{op^yU#OPrm=!6oMl8|>J&Y$KVr!@h7yveeq7U(e_
zQ;!`%_fS-jNF?NgfO2@_w|XgY1-VjS9ZhMEcbb$vbf0Stv*D^kZK2XZ<5HA110!M0
z1#P#xZMB;)>fhQCwKX0)veMU-i~`q?1rIPjyB>Sgn-inBpO>EVD9T2sT`I$h0|1@&
zBB(bxH&jfTfXhvmkRB3Z!`V%za8kSw4&)Gdthqjd)fyC5Au!rLPC^_FtxX>Z3Ri%z
z0N*)YoyHHgC=R2Oc6L&tTkX;=_V!fTq$##^w(1Tt*&$k$zJG*c1DsT4t?S27!AmyQ
zqgzZ>mmPTuSb4>T94iUi8)tu|NUF`$w#&oP?|n?ll_9jGhGGGh^>BS~PwHsXUrP}*
z-H^AK9Za<{EapX~-s+aO5~zYg##8{(0ZB>JNx{x)JEv9EQsz9&`7$HNg(pNgYfgd2
z4l;441n1P8cB{Mh_)*e}EXZ@;EbAdnwpI|<a+0iVz&v_X91A1P$d;=V#U|Y$(=Fu9
zNLpSh!SaK;c-#@3issXm#J#l|Wo}jSMUMCv4)YpZSuH7(A{<b^3sQ+GUXm0DB{))5
zogjmpRhlH5KUCOl>XkIf(E?H+gqKhx6gYLHEa2oRy@}r&?hQz)G$^W!#hAv21-zLI
zw9-zMV`U?JkapPdiW2b#)RNL+Jh-WBmy*K|C@IC0xd4NYTyQDl7OueMszf`G`jY1v
zHrqY>_GEtuw#xi;2?T(ZYC2M_N*Px9!PtydzTat^Z*nSbcR!i|r3RdF(}bx+5{)2|
z6R<nvayFsajn!^kqs)tOjMQ0dYIQF(1C5ky5|oXwcNrKo-N6^fuki~`+nn%H<1$c`
z7LanDa6u%4gN*V%w8`j3zU2G28v|~%ReFU0%5bTtLX;BYgy1VV7~gCVc&Ryxb~IHn
zdzTwYQV^Hi**I76<P_sPV<M>?KWB*&)ZCb`K4NL{ye}e<y+O@eYR=hLVcMlGj9IQ0
zv>vdA#G|Pz4av@wtPnN>0O02pqa}MYkHBMh9+c`AUuD(XB{r8lGRw_vfG~IdoYZxa
z4bsnYo^1>@COIAvY3~smfvF=pcfs2{;L~jMZMsY8Ri5ftWy~c>)2S^aZ>2rK=LeDR
zML|kpt~7Hx?ECQ+wV^UlQXbMgrALJUw>#q%R@GctHFvaeQ$)uS8jXiz5BJ8CDJx4Z
z6!GD>HykG*<a3IietaotY4W!GAC)PP(hy3Pe+~juvGhCCP0`_WxuH;{w8HR!RFakI
z)14sau{(O>6pNHAv$GhNZdhtiY^13T<r?&XozBN%FnP@)dvrUFm4z}ID{xvgpsfWl
zlDD_da6!of?tQ74mY3mO%*&!<%MPIV<6$mj1CXJR4%pt9v_LjcggJI~=igEBi_EkZ
zm4ZnrCtwNfNSAnJQ6}7GHi^CqN!8|4I!XK~+aP4}NA#lBtX172UNqKj4t(?%77&D(
z>v3r-8iKM(9E@ihAEiW_L>rT>%~O&jA~@a05TJ~xk_ZQJr(vEwYAP)I>{&6;&7;n+
zzZWiLB<LkRehA|}xZ8SS{{VAxR6ARDew%bjRHCpVJhIEt&T>_t>Pa~q`{t(Rnxe6f
z7EUV8)CV1DxTuo3ua^{B!Es7Q;oOms4y=whrYtfp5M?S`_Tv@FGCUVsX_S$tI~8)I
z0CT?mxHaha#UeB27f!z&D|7}>$jX%2<$L4;JC69L?kS$t!W?->E+M?Fmk;rIch8~Y
zYAJjQ_1Wv>x7~>v)Y}poU=Zu!%YL}-#mNe5DIrK-2U2*dGyM%3Y&A7%Y8u~l_T)CT
z%34t2M*%?KYAG5Hz~ejmT&7$dacP8saZie%U1<qg!zU*ME4F=!71<Uyk>kjIGpcP$
zISL6<)O6q|9rT^)q?!U~i?m;^GTvMVIvCrZL#{fh3`}Wp(#}Xi0NH14lt=^-+-_&A
zoh3F)?xN{@THRssDl(h_N@RkqBLti%B<aY(Bamt|bmKPI=qh?bWFaNJK|xE<rEAiy
zw;A;tRf^kevn91IT2$k{8e4J24>`vkVa_W;-lS=?zey<Nl2k-4JYd|0q^K6#enCP|
ztaeT_jEv(yl~#qnujgHKDeHSIMF=Q<HeDVtS0zD7P{77>kU=1dw#GykT~lS1xY9t%
zin2#xiheX0@rqLgT3b>{DM|p!yOZgk>s=l6LhG~TFYQh6&#W%;&s5tjZO?}RhaQyk
zLl3Zke8WjPl-SOD>EE{X^2*VXo@?|=<Am$HzJt6*v&Wu^2#mc*X8}$&2cH@ILry^h
zdi?QF(~wVU^F3@78f(ebifzW2E|3YyrX-Dk9QukSU=H*ohdW>s+PsX4!yt7O;|82<
zU?&?;Za=4DcFt>veObpf$&v>=kaI&*n~wD@8aOy43=Y*94=PZ=sm2J>J66RCV`IjL
z+CVD61J;Vpl;bqhL(7n9(ZBHxtI&p#+X!lpC2B~d1t_bYX~zI4+L><z<2fJFnaD09
ziX{Fn)#Yx3pHE8ZQ3GS`Nl^sgt9lkHRDO^6Pe^GFu;D`Su?IL56o@V@l_a6{k>$>L
zskqSGg1|x$R6y@Z64cVN>pN$9_Y{6kPEWxO*>W;lN~5GD0|O-UM<LfqLf=z6af6@A
zh=1iLr~`zc3X7Q-DdF<&X-as_YNul-dJUN^OfnYKWh|*)VtFIly%IrFq2}R|(tjCI
z+?q@z&2hiqIF+P~aoUS0M#$g_F4?q>+*a(U#psb#h2sN$Xiu7XxW!J5OCxYSXp)sA
zsE3XR2^1UakjhG$Z^R>f8XYru7#F#%AQ>$jr|=^r@7pzBUA}yo#!QQwr^HcFYzzPo
zYLgc!0PB>}QgfVQjcP)k{rZ{+N$-wmxG5@-RNP&ml)Cv~#%VbyBP;$OJ*b#79F;XT
z#JsXqzL0hwS{TS#dBO<7h5!`&M$*Uo(>hS_{vbCrwAVq?J$UGL9+assIHx?M6zaeO
z+*MBOu9&mJlM+)ThZT~Blag^(ONIL8j}u8J#26<P4Q%PgZm%g<CoqL?y)PJZx*bYt
z6yAtFnnj9igsKZ~GI#t+I|{ri7mwi^T2y>21(3aosL57%cEjMrYy<<1KvbokL|BZi
zu;-c0@K1VnlXVhq(zXdUJLr<BG8F2A8-zA+PqFo(WnZMqwyC$^t~QO(gZxLfYF*)Q
zeb8ek%1%^IdXJeU_M`@7#z9CRf@xkho!C}H)lgqdI`zUaYGW!6jCh4V<wrX6u^(_E
zG?XL{DNexs>C0S})C%JxLE|7%P9gW#hn~x4k&0xrBYPq)3vyG9Or0rZEu?5{55x7Z
zPJTMrN|hyODf|GOfDbh|?XJ}GJ{m@q5HfSkN8CDEG?9S4cpE1>QB8i9X4Biz32t&5
zPb4!dTFE4Mz#aQyoQz=Gt}Z(+VUV>eDo#cQPaAfoWOP!N>R)K7ryDp<4&Rj&&qluF
zZxL^xyU94_mBQ5_o<(+DVqThiN(0CV=NYIjw~cJNDmwx*YI)^jP#=VPnutY((J`=O
zl#no@I|?H0X?i;a__CB#k({fVdXrs~`ct%ub<f};B&)=kd3p4g3eo@oARfY`Y`+g|
zR=J5>*(Oquc5T%RagD`49_Ky65ajnkR@!xQLf%xS9ZH@GDkG40syHt!%2VjJLAS!T
zKuj2jM}U<p!azP&!j+N$`kM1F9cgboxl3Da1dj|PXwozKRS7Q7eF$l%oIoc7bDBAq
zZ|uL}32F#P$=_o^q`aA1WtnqYM28!Sm7PE%DHtRUF`9{yImYEYp%D!$!tm3nWUXq)
ze@X@)RJ{c%OUP~6DpAsLy+=mt>ph}uqb-$dkh0^cSC|E3x1gZl#!^YpY&iE9BDz$|
zno80V6p}~yjY%;H^{EWEAHz|=1+;0@alX{VFNqdQgfyZv=|-{wytpbk;;iwl@-8T5
zY7}yv(2%SEDR{V#?q6kTrM(8=q$T%KmldIB83z?54m^fuxiOhV#;~l32W65!7CWBQ
zW#TjJge|WDT2<!?JBl_kZSDjaP9Yjb;P9$g++SkKT(?Aer8>7!Ddq~&5D)<Jgp<j@
zqnn)ySn!K&4B;vTJ0EP((i^q47`V&HE5^W#<W)vbRj}2;HuoYU!V1o;V4U`?E2~V=
zYpN#1*pQB?3=ry7fKZH#4%>F5GHx>v>5kGgsd~~KR_Y#=Hkb*1Dpe8Cf>I6?ov~F*
zoV&%kYNJMm+R{IZj(^g!sO_0PT12#HvXTjQC}jmo-2;8;O1E3tOV3P>9L^k0dCoW{
zg|^&fy2xf@psAs;Pj!3!sn>1}w;jhGM~*<iK?9M-DbkFEmng6pVe=zfv6h4^lc7q=
zLUYX;i-AdfPQ@wk{K!_YO8vkbR3*OobX$scWHA|?SkPCM8j@?}*+QRFsY)DHcQldL
zLrZ;zGa^@lrMMdKlmb9dB;!53C|j(S!|fM5J#8yc@XiPWf7b(Q2IqM33M0hAijEYl
z({0+8>RMZbDZ;-oCxqj*J+C5u!iQAa8Ik)wSAZ0j$U}&48c8|9^%*<TWnJTj6Fx(2
zIM@meC1WYh*f<@|^z+>>7Gq7TKu9^%SK5Vpq#9y6P#;kNc{n*3tvJSU1Z3kbh-U?=
zYhD@=t>*zz+sKjhG?}*ub1m*$V<^K>K44lX4gfKv_NlpjDxlI(!cLMis2mYOLFsup
zLlTv!DB!EmR;YFxZ%mcwtId`Ly)fi>4aoyju7v(9_RqCUyVPAa^SWPbD3gTc1A46v
zl7NX1u0SOT7)T1iKK1KMeJqJ34>IX-uym7?y=igq1e@&%yWLv(sV+r;rAH{#*eJ+S
z`wvm|rmc4OWwWnBV93;!tteKu)~tRU`|xPV4gBNmn2ZFaCkjbC`;U5-W&~KsQlt`e
z`GP>(H7eNmNQ1gsX16HIi7iQ55}dc;qX3S5>OWI$rKc{ef})@Uji>O8{t$f)6@8iW
zPOZqWB&p4b$RQ^KZRj>FcI&Hg+A`!YwK=J5g`8>~y{J7s47%BAdD!|A@*Yx@?vQ-g
zC`sSSMG16%)Z6jdFw*O*)ZsbFPuJYgeMIHPD-6Z9+w~BfI^wv-Hak=dAKPVX(7kRs
zk1<9<dm5x%e2GuNc3jpTZadL}lD1HMs9u#M9k%zT-exCJ#7Vd*G8}1MbUm_ye!c3s
z(@TwIe0=<aDZsKuvARh+k8x7>ORbruC_Nmj$5z20?MW#w{{ST;jn>ByxVGj}(u*y$
zP7o30!1wo|ZfzMMiBY9K`n4z_q>na$GH`uPDT~}>+?-`@L`Q&=k`ws7shb_K#?>G(
zA>hhRwzV&G9^iXYa#If;9!YCN%RQz~&!PgN>p*2*Fw}F%KGb6RvAd8=1Vc-W$Wp>y
zX$J#I-%;(J)B>P2p~ob;>w^HODCaxWi;K0zjM7_dG?i{bM{lKCH_<2ZD%RDRFTZX~
z2G^taiv>El?~~0#KW%9;*CNisQtPCnLh>3`HyIwHr(wBnN!HMD)e-zX^IQvn+=f|w
zm9nKP0Vn#=tx^}wkv9m}$&E&C5sTuof=aRgKj9yKC<L{6eZ@TUOKHZyNm5Eca~_~^
zjq2hTZ-B#zT0+)VLuWgmYKXdA60~V;jV09tXkV|bD~$}BdPwX8A`>Mt&>SgGky`SS
zpP=L3qpsIkv2F>QaDAmjcm~8kfC@ny9QssM-s>{ZhZ>UVFb-Ql-9S_X%k7foi4se$
zx`d2?qB4*-7@@YbGF7%+=HKGPf90Bt;uKN?j-^A(>ACi$T`m)%MO51qS0;SQ0+r&s
z`@gCG0JUeAA#Cc1iD9SZ#f;hW1T2D~{HMMuwIoiky7jq>thS&?1R(+9cRjtUe&MBB
zHr|=I&zm7hmda4#8&8O>I0W_y&m$C+N}F?@^|_ZCEAc$Z5y(cLbHP61unAXgyXhAp
zT&?pJAv(iu1v*u&8QUP#>;C`}di@A)Q)@&xs&Hybhwn4ok5N=<D%iE~Y%}HcS5HB8
z^96^4c_bmu+2;qna?RHdU6@m@MtQWfKYXMONd$V1MryG2qV?6*Wr>$*N^VmjC&ONq
z1N^|$bO>EV@a+t#cP|~4CqjWonS1h2uJrbhotBQT)76$mTr945&mjs4XsD#2c*b+=
zM}9%C&a0x`jc}67>C)qGEh|XdN`j6?KDGKiW$FtYxc>k)&X}lH6r`Z;Ro`y)@E^w*
z@gaOs>IR?@^6QkR5rKi`+~8LauHH2M27{|9tCBe1Gz;(JB<8wTf%QJLE5}*;*O1d?
z5^`fMKdoEs=})zdhS@YPhM=qfcRZSXuEQJsDmf!TaqvvRR*{^Fm1X?nd}5%OBmqYu
z>;OqQ_ol}Nwo(zIWw|4a?N5gQ?@;YLLBXQK!%sP@ts?87)jl!K1u)#82?rczgj?Cj
z-v?~d0q~QJTl!F)Bu05SlFE^*kN*HQH{%kWhQ@&sPIo(<sY;M^9rzR@D|AQRR)7E5
DoLPtM

diff --git a/contrib/clojure-package/test/test_helper.clj b/contrib/clojure-package/test/test_helper.clj
new file mode 100644
index 000000000000..1dda7871ad69
--- /dev/null
+++ b/contrib/clojure-package/test/test_helper.clj
@@ -0,0 +1,26 @@
+;; Licensed to the Apache Software Foundation (ASF) under one or more
+;; contributor license agreements.  See the NOTICE file distributed with
+;; this work for additional information regarding copyright ownership.
+;; The ASF licenses this file to You under the Apache License, Version 2.0
+;; (the "License"); you may not use this file except in compliance with
+;; the License.  You may obtain a copy of the License at
+;;
+;;    http://www.apache.org/licenses/LICENSE-2.0
+;;
+;; Unless required by applicable law or agreed to in writing, software
+;; distributed under the License is distributed on an "AS IS" BASIS,
+;; WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+;; See the License for the specific language governing permissions and
+;; limitations under the License.
+;;
+
+
+(ns test-helper
+  (:require [clojure.java.io :as io]
+            [clojure.java.shell :refer [sh]]))
+
+(def data-dir "test/test-images/")
+
+(defn load-test-images []
+  (when-not (.exists (io/file (str data-dir "Pug-Cookie.jpg")))
+    (sh "./scripts/get_test_images.sh")))

From c3f5eeabf9bf9d37ac47a4f1c8cc93c292825d2a Mon Sep 17 00:00:00 2001
From: ckt624 <ckt624@gmail.com>
Date: Tue, 13 Aug 2019 02:16:25 +0800
Subject: [PATCH 166/813] Numpy Tensordot and Dot Operator (#15820)

* Implements tensordot and dot.

* Change tests.

* Add spaces.

* Reorganize codes.

* Remove np_matrix_op.h
---
 python/mxnet/ndarray/numpy/_op.py        |  73 ++-
 python/mxnet/numpy/multiarray.py         |  59 +-
 python/mxnet/symbol/numpy/_symbol.py     |  57 +-
 src/operator/numpy/np_dot-inl.h          | 110 ++++
 src/operator/numpy/np_dot.cc             | 150 +++++
 src/operator/numpy/np_dot.cu             |  37 ++
 src/operator/numpy/np_tensordot_op-inl.h | 688 +++++++++++++++++++++++
 src/operator/numpy/np_tensordot_op.cc    | 235 ++++++++
 src/operator/numpy/np_tensordot_op.cu    |  42 ++
 tests/python/unittest/test_numpy_op.py   | 183 ++++++
 10 files changed, 1631 insertions(+), 3 deletions(-)
 create mode 100644 src/operator/numpy/np_dot-inl.h
 create mode 100644 src/operator/numpy/np_dot.cc
 create mode 100644 src/operator/numpy/np_dot.cu
 create mode 100644 src/operator/numpy/np_tensordot_op-inl.h
 create mode 100644 src/operator/numpy/np_tensordot_op.cc
 create mode 100644 src/operator/numpy/np_tensordot_op.cu

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index d7c06e76c182..caa9ba1de67c 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -25,7 +25,7 @@
 from ...context import current_context
 from . import _internal as _npi
 
-__all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power']
+__all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power', 'tensordot']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -293,3 +293,74 @@ def power(x1, x2, out=None):
         This is a scalar if both x1 and x2 are scalars.
     """
     return _ufunc_helper(x1, x2, _npi.power, _np.power, _npi.power_scalar, _npi.rpower_scalar, out)
+
+
+@set_module('mxnet.ndarray.numpy')
+def tensordot(a, b, axes=2):
+    r"""
+    tensordot(a, b, axes=2)
+    Compute tensor dot product along specified axes for arrays >= 1-D.
+    Given two tensors (arrays of dimension greater than or equal to one),
+    `a` and `b`, and an ndarray object containing two ndarray
+    objects, ``(a_axes, b_axes)``, sum the products of `a`'s and `b`'s
+    elements (components) over the axes specified by ``a_axes`` and
+    ``b_axes``. The third argument can be a single non-negative
+    integer_like scalar, ``N``; if it is such, then the last ``N``
+    dimensions of `a` and the first ``N`` dimensions of `b` are summed
+    over.
+    Parameters
+    ----------
+    a, b : ndarray, len(shape) >= 1
+        Tensors to "dot".
+    axes : int or (2,) ndarray
+        * integer_like
+        If an int N, sum over the last N axes of `a` and the first N axes
+        of `b` in order. The sizes of the corresponding axes must match.
+        * (2,) ndarray
+        Or, a list of axes to be summed over, first sequence applying to `a`,
+        second to `b`. Both elements ndarray must be of the same length.
+    See Also
+    --------
+    dot, einsum
+    Notes
+    -----
+    Three common use cases are:
+        * ``axes = 0`` : tensor product :math:`a\otimes b`
+        * ``axes = 1`` : tensor dot product :math:`a\cdot b`
+        * ``axes = 2`` : (default) tensor double contraction :math:`a:b`
+    When `axes` is integer_like, the sequence for evaluation will be: first
+    the -Nth axis in `a` and 0th axis in `b`, and the -1th axis in `a` and
+    Nth axis in `b` last.
+    When there is more than one axis to sum over - and they are not the last
+    (first) axes of `a` (`b`) - the argument `axes` should consist of
+    two sequences of the same length, with the first axis to sum over given
+    first in both sequences, the second axis second, and so forth.
+    Examples
+    --------
+    >>> a = np.arange(60.).reshape(3,4,5)
+    >>> b = np.arange(24.).reshape(4,3,2)
+    >>> c = np.tensordot(a,b, axes=([1,0],[0,1]))
+    >>> c.shape
+    (5, 2)
+    >>> c
+    array([[ 4400.,  4730.],
+           [ 4532.,  4874.],
+           [ 4664.,  5018.],
+           [ 4796.,  5162.],
+           [ 4928.,  5306.]])
+    """
+    if _np.isscalar(axes):
+        return _npi.tensordot_int_axes(a, b, axes)
+
+    if len(axes) != 2:
+        raise ValueError('Axes must consist of two arrays.')
+    a_axes_summed, b_axes_summed = axes
+    if _np.isscalar(a_axes_summed):
+        a_axes_summed = (a_axes_summed,)
+    if _np.isscalar(b_axes_summed):
+        b_axes_summed = (b_axes_summed,)
+
+    if len(a_axes_summed) != len(b_axes_summed):
+        raise ValueError('Axes length mismatch')
+
+    return _npi.tensordot(a, b, a_axes_summed, b_axes_summed)
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 9e0c52dbfd68..f4b6e733c495 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -44,7 +44,7 @@
 from ..ndarray.numpy import _internal as _npi
 
 __all__ = ['ndarray', 'empty', 'array', 'zeros', 'ones', 'add', 'subtract', 'multiply', 'divide',
-           'mod', 'power']
+           'mod', 'power', 'tensordot']
 
 
 # This function is copied from ndarray.py since pylint
@@ -1549,3 +1549,60 @@ def power(x1, x2, out=None):
         This is a scalar if both x1 and x2 are scalars.
     """
     return _mx_nd_np.power(x1, x2, out=out)
+
+
+@set_module('mxnet.numpy')
+def tensordot(a, b, axes=2):
+    r"""
+    tensordot(a, b, axes=2)
+    Compute tensor dot product along specified axes for arrays >= 1-D.
+    Given two tensors (arrays of dimension greater than or equal to one),
+    `a` and `b`, and an ndarray object containing two ndarray
+    objects, ``(a_axes, b_axes)``, sum the products of `a`'s and `b`'s
+    elements (components) over the axes specified by ``a_axes`` and
+    ``b_axes``. The third argument can be a single non-negative
+    integer_like scalar, ``N``; if it is such, then the last ``N``
+    dimensions of `a` and the first ``N`` dimensions of `b` are summed
+    over.
+    Parameters
+    ----------
+    a, b : ndarray, len(shape) >= 1
+        Tensors to "dot".
+    axes : int or (2,) ndarray
+        * integer_like
+        If an int N, sum over the last N axes of `a` and the first N axes
+        of `b` in order. The sizes of the corresponding axes must match.
+        * (2,) ndarray
+        Or, a list of axes to be summed over, first sequence applying to `a`,
+        second to `b`. Both elements ndarray must be of the same length.
+    See Also
+    --------
+    dot, einsum
+    Notes
+    -----
+    Three common use cases are:
+        * ``axes = 0`` : tensor product :math:`a\otimes b`
+        * ``axes = 1`` : tensor dot product :math:`a\cdot b`
+        * ``axes = 2`` : (default) tensor double contraction :math:`a:b`
+    When `axes` is integer_like, the sequence for evaluation will be: first
+    the -Nth axis in `a` and 0th axis in `b`, and the -1th axis in `a` and
+    Nth axis in `b` last.
+    When there is more than one axis to sum over - and they are not the last
+    (first) axes of `a` (`b`) - the argument `axes` should consist of
+    two sequences of the same length, with the first axis to sum over given
+    first in both sequences, the second axis second, and so forth.
+    Examples
+    --------
+    >>> a = np.arange(60.).reshape(3,4,5)
+    >>> b = np.arange(24.).reshape(4,3,2)
+    >>> c = np.tensordot(a,b, axes=([1,0],[0,1]))
+    >>> c.shape
+    (5, 2)
+    >>> c
+    array([[ 4400.,  4730.],
+           [ 4532.,  4874.],
+           [ 4664.,  5018.],
+           [ 4796.,  5162.],
+           [ 4928.,  5306.]])
+    """
+    return _mx_nd_np.tensordot(a, b, axes)
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 616f3066d98d..65db429e8ba7 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -28,7 +28,7 @@
 from .._internal import _set_np_symbol_class
 from . import _internal as _npi
 
-__all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power']
+__all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power', 'tensordot']
 
 
 def _num_outputs(sym):
@@ -1010,4 +1010,59 @@ def power(x1, x2, out=None):
     return _ufunc_helper(x1, x2, _npi.power, _np.power, _npi.power_scalar, _npi.rpower_scalar, out)
 
 
+@set_module('mxnet.symbol.numpy')
+def tensordot(a, b, axes=2):
+    r"""
+    tensordot(a, b, axes=2)
+    Compute tensor dot product along specified axes for arrays >= 1-D.
+    Given two tensors (arrays of dimension greater than or equal to one),
+    `a` and `b`, and an ndarray object containing two ndarray
+    objects, ``(a_axes, b_axes)``, sum the products of `a`'s and `b`'s
+    elements (components) over the axes specified by ``a_axes`` and
+    ``b_axes``. The third argument can be a single non-negative
+    integer_like scalar, ``N``; if it is such, then the last ``N``
+    dimensions of `a` and the first ``N`` dimensions of `b` are summed
+    over.
+    Parameters
+    ----------
+    a, b : _Symbol
+        Tensors to "dot".
+    axes : int or (2,) ndarray
+        * integer_like
+        If an int N, sum over the last N axes of `a` and the first N axes
+        of `b` in order. The sizes of the corresponding axes must match.
+        * (2,) array_like
+        Or, a list of axes to be summed over, first sequence applying to `a`,
+        second to `b`. Both elements array_like must be of the same length.
+    Notes
+    -----
+    Three common use cases are:
+        * ``axes = 0`` : tensor product :math:`a\otimes b`
+        * ``axes = 1`` : tensor dot product :math:`a\cdot b`
+        * ``axes = 2`` : (default) tensor double contraction :math:`a:b`
+    When `axes` is integer_like, the sequence for evaluation will be: first
+    the -Nth axis in `a` and 0th axis in `b`, and the -1th axis in `a` and
+    Nth axis in `b` last.
+    When there is more than one axis to sum over - and they are not the last
+    (first) axes of `a` (`b`) - the argument `axes` should consist of
+    two sequences of the same length, with the first axis to sum over given
+    first in both sequences, the second axis second, and so forth.
+    """
+    if _np.isscalar(axes):
+        return _npi.tensordot_int_axes(a, b, axes)
+
+    if len(axes) != 2:
+        raise ValueError('Axes must consist of two arrays.')
+    a_axes_summed, b_axes_summed = axes
+    if _np.isscalar(a_axes_summed):
+        a_axes_summed = (a_axes_summed,)
+    if _np.isscalar(b_axes_summed):
+        b_axes_summed = (b_axes_summed,)
+
+    if len(a_axes_summed) != len(b_axes_summed):
+        raise ValueError('Axes length mismatch')
+
+    return _npi.tensordot(a, b, a_axes_summed, b_axes_summed)
+
+
 _set_np_symbol_class(_Symbol)
diff --git a/src/operator/numpy/np_dot-inl.h b/src/operator/numpy/np_dot-inl.h
new file mode 100644
index 000000000000..a854777c3109
--- /dev/null
+++ b/src/operator/numpy/np_dot-inl.h
@@ -0,0 +1,110 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file np_dot-inl.h
+ * \brief Function definition of matrix numpy-compatible dot operator
+ */
+
+#ifndef MXNET_OPERATOR_NUMPY_NP_DOT_INL_H_
+#define MXNET_OPERATOR_NUMPY_NP_DOT_INL_H_
+
+#include <mxnet/operator_util.h>
+#include <vector>
+#include "../tensor/dot-inl.h"
+#include "../tensor/elemwise_binary_op.h"
+#include "../tensor/broadcast_reduce_op.h"
+#include "np_tensordot_op-inl.h"
+
+namespace mxnet {
+namespace op {
+
+template<typename xpu>
+inline void NumpyDotForward(const nnvm::NodeAttrs& attrs,
+                            const OpContext& ctx,
+                            const std::vector<TBlob>& inputs,
+                            const std::vector<OpReqType>& req,
+                            const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mxnet_op;
+
+  CHECK_EQ(inputs.size(), 2U);
+  CHECK_EQ(outputs.size(), 1U);
+
+  const TBlob& a = inputs[0];
+  const TBlob& b = inputs[1];
+  const TBlob& out = outputs[0];
+  const mxnet::TShape a_shape = a.shape_;
+  const mxnet::TShape b_shape = b.shape_;
+
+  MSHADOW_REAL_TYPE_SWITCH(out.type_flag_, DType, {
+    if (b_shape.ndim() < 3) {
+      // Case 1, 2, 3, 4, 5: a is N-D array (N >= 1) and b is vector or matrix, sum product
+      //        over the last axis of a and the first axis of b
+      TensordotIntAxesImpl<xpu>(1, ctx, a, b, out, req[0]);
+    } else {
+      // Case 3, 5.5: a is N-D array and b is M-D array (M > 2), sum product over the last axis
+      //         of a and the 2nd-to-last axis of b
+      const Tuple<int> a_axes_summed({a_shape.ndim() - 1});
+      const Tuple<int> b_axes_summed({b_shape.ndim() - 2});
+      TensordotImpl<xpu>(a_axes_summed, b_axes_summed, ctx, a, b, out, req);
+    }
+  });
+}
+
+template<typename xpu>
+inline void NumpyDotBackward(const nnvm::NodeAttrs& attrs,
+                             const OpContext& ctx,
+                             const std::vector<TBlob>& inputs,
+                             const std::vector<OpReqType>& req,
+                             const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mshadow_op;
+
+  CHECK_EQ(inputs.size(), 3U);
+  CHECK_EQ(outputs.size(), 2U);
+
+  const TBlob& ograd = inputs[0];
+  const TBlob& a = inputs[1];
+  const TBlob& b = inputs[2];
+  const TBlob& grad_a = outputs[0];
+  const TBlob& grad_b = outputs[1];
+  const mxnet::TShape a_shape = a.shape_;
+  const mxnet::TShape b_shape = b.shape_;
+
+  MSHADOW_REAL_TYPE_SWITCH(ograd.type_flag_, DType, {
+    if (b_shape.ndim() < 3) {
+      // Case 1, 2, 3, 4, 5: a is N-D array (N >= 1) and b is vector or matrix, sum product
+      //        over the last axis of a and the first axis of b
+      TensordotIntAxesBackwardImpl<xpu>(1, ctx, ograd, a, b, grad_a, grad_b, req);
+    } else {
+      // Case 3, 5.5: a is N-D array and b is M-D array (M > 2), sum product over the last axis
+      //         of a and the 2nd-to-last axis of b
+      const Tuple<int> a_axes_summed({a_shape.ndim() - 1});
+      const Tuple<int> b_axes_summed({b_shape.ndim() - 2});
+      TensordotBackwardImpl<xpu>(a_axes_summed, b_axes_summed, ctx, ograd, a, b, grad_a,
+          grad_b, req);
+    }
+  });
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_NUMPY_NP_DOT_INL_H_
diff --git a/src/operator/numpy/np_dot.cc b/src/operator/numpy/np_dot.cc
new file mode 100644
index 000000000000..627e68877998
--- /dev/null
+++ b/src/operator/numpy/np_dot.cc
@@ -0,0 +1,150 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file np_dot.cc
+ * \brief CPU Implementation of numpy-compatible dot
+ */
+
+#include "./np_dot-inl.h"
+
+namespace mxnet {
+namespace op {
+
+inline bool NumpyDotShape(const nnvm::NodeAttrs& attrs,
+                          mxnet::ShapeVector *in_attrs,
+                          mxnet::ShapeVector *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 2U);
+  CHECK_EQ(out_attrs->size(), 1U);
+
+  const mxnet::TShape& a_shape = in_attrs->at(0);
+  const mxnet::TShape& b_shape = in_attrs->at(1);
+
+  if (!ndim_is_known(a_shape) || !ndim_is_known(b_shape)) {
+    return false;
+  }
+
+  if (a_shape.ndim() == 1 && b_shape.ndim() == 1) {
+    // Case 1: both 1-D arrays, inner product of vectors
+    SHAPE_ASSIGN_CHECK(*in_attrs, 0, in_attrs->at(1));
+    SHAPE_ASSIGN_CHECK(*in_attrs, 1, in_attrs->at(0));
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, mxnet::TShape(0, 0));
+  } else if (a_shape.ndim() == 2 && b_shape.ndim() == 2) {
+    // Case 2: both 2-D arrays, matrix multiplication
+    mxnet::TShape tmp_shape(2, -1);
+    tmp_shape[1] = b_shape[0];
+    SHAPE_ASSIGN_CHECK(*in_attrs, 0, tmp_shape);
+
+    tmp_shape[0] = a_shape[1];
+    tmp_shape[1] = -1;
+    SHAPE_ASSIGN_CHECK(*in_attrs, 1, tmp_shape);
+
+    tmp_shape[0] = a_shape[0];
+    tmp_shape[1] = b_shape[1];
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, tmp_shape);
+  } else if (a_shape.ndim() == 0 || b_shape.ndim() == 0) {
+    // Case 3 + 3.5: either of them is a scalar, just scale by one of them
+    mxnet::TShape oshape = (a_shape.ndim() == 0) ? b_shape : a_shape;
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, oshape);
+  } else if (b_shape.ndim() == 1) {
+    // Case 4: a is N-D array and b is 1-D array, sum product over the last axis
+    TShape tmp_shape(a_shape.ndim(), -1);
+    tmp_shape[a_shape.ndim() - 1] = b_shape[0];
+    SHAPE_ASSIGN_CHECK(*in_attrs, 0, tmp_shape);
+
+    tmp_shape = TShape(1, -1);
+    tmp_shape[0] = a_shape[a_shape.ndim() - 1];
+    SHAPE_ASSIGN_CHECK(*in_attrs, 1, tmp_shape);
+
+    mxnet::TShape out_shape(a_shape.ndim() - 1, -1);
+    for (int i = 0; i < a_shape.ndim() - 1; ++i) {
+      out_shape[i] = a_shape[i];
+    }
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, out_shape);
+  } else {
+    // Case 5: a is N-D array and b is M-D array, sum product over the last axis
+    //         of a and the 2nd-to-last axis of b
+    TShape tmp_shape(a_shape.ndim(), -1);
+    tmp_shape[a_shape.ndim() - 1] = b_shape[b_shape.ndim() - 2];
+    SHAPE_ASSIGN_CHECK(*in_attrs, 0, tmp_shape);
+
+    tmp_shape = TShape(b_shape.ndim(), -1);
+    tmp_shape[b_shape.ndim() - 2] = a_shape[a_shape.ndim() - 1];
+    SHAPE_ASSIGN_CHECK(*in_attrs, 1, tmp_shape);
+
+    tmp_shape = TShape(a_shape.ndim() + b_shape.ndim() - 2, -1);
+    for (int i = 0; i < a_shape.ndim() - 1; ++i) {
+      tmp_shape[i] = a_shape[i];
+    }
+    for (int i = 0; i < b_shape.ndim() - 2; ++i) {
+      tmp_shape[i + a_shape.ndim() - 1] = b_shape[i];
+    }
+    tmp_shape[tmp_shape.ndim() - 1] = b_shape[b_shape.ndim() - 1];
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, tmp_shape);
+  }
+  return shape_is_known(*in_attrs) && shape_is_known(*out_attrs);
+}
+
+NNVM_REGISTER_OP(_np_dot)
+.describe(R"doc(Dot product of two arrays. Specifically,
+
+- If both a and b are 1-D arrays, it is inner product of vectors.
+
+- If both a and b are 2-D arrays, it is matrix multiplication.
+
+- If either a or b is 0-D (scalar), it is equivalent to multiply and using numpy.multiply(a, b) or a * b is preferred.
+
+- If a is an N-D array and b is a 1-D array, it is a sum product over the last axis of a and b.
+
+- If a is an N-D array and b is an M-D array (where M>=2), it is a sum product over the last axis of a and the second-to-last axis of b:
+
+  Example ::
+
+    dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])
+
+)doc" ADD_FILELINE)
+.set_num_inputs(2)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"a", "b"};
+  })
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyDotShape)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<2, 1>)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", NumpyDotForward<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_np_dot"})
+.add_argument("a", "NDArray-or-Symbol", "First input")
+.add_argument("b", "NDArray-or-Symbol", "Second input");
+
+NNVM_REGISTER_OP(_backward_np_dot)
+.set_num_inputs(3)
+.set_num_outputs(2)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", NumpyDotBackward<cpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_dot.cu b/src/operator/numpy/np_dot.cu
new file mode 100644
index 000000000000..9a9c69aa98e5
--- /dev/null
+++ b/src/operator/numpy/np_dot.cu
@@ -0,0 +1,37 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file np_dot.cu
+ * \brief GPU Implementation of numpy-compatible dot
+ */
+
+#include "./np_dot-inl.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(_np_dot)
+.set_attr<FCompute>("FCompute<gpu>", NumpyDotForward<gpu>);
+
+NNVM_REGISTER_OP(_backward_np_dot)
+.set_attr<FCompute>("FCompute<gpu>", NumpyDotBackward<gpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_tensordot_op-inl.h b/src/operator/numpy/np_tensordot_op-inl.h
new file mode 100644
index 000000000000..da3891665c4b
--- /dev/null
+++ b/src/operator/numpy/np_tensordot_op-inl.h
@@ -0,0 +1,688 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file np_tensordot_op-inl.h
+ * \brief CPU Implementation of numpy-compatible tensordot
+ */
+#ifndef MXNET_OPERATOR_NUMPY_NP_TENSORDOT_OP_INL_H_
+#define MXNET_OPERATOR_NUMPY_NP_TENSORDOT_OP_INL_H_
+
+#include <vector>
+#include "../tensor/matrix_op-inl.h"
+
+namespace mxnet {
+namespace op {
+
+using namespace mshadow;
+
+struct TensordotParam : public dmlc::Parameter<TensordotParam> {
+  mxnet::Tuple<int> a_axes_summed, b_axes_summed;
+  DMLC_DECLARE_PARAMETER(TensordotParam) {
+    DMLC_DECLARE_FIELD(a_axes_summed);
+    DMLC_DECLARE_FIELD(b_axes_summed);
+  }
+};
+
+/**
+ * deals with negative axes.
+ */
+inline void ShiftAxes(Tuple<int>* axes_summed, const int ndim) {
+  for (auto& i : *axes_summed) {
+    i = (i + ndim) % ndim;
+  }
+}
+
+/**
+ * Gets matrix dimensions of a and b after transpose and reshape.
+ */
+inline void GetMatrixDimensions(int* ad1,
+                                int* ad2,
+                                int* bd1,
+                                int* bd2,
+                                const mxnet::Tuple<int>& a_axes_remained,
+                                const mxnet::Tuple<int>& a_axes_summed,
+                                const mxnet::Tuple<int>& b_axes_remained,
+                                const mxnet::Tuple<int>& b_axes_summed,
+                                const mxnet::TShape& a_shape,
+                                const mxnet::TShape& b_shape) {
+  *ad1 = 1;
+  *ad2 = 1;
+  *bd1 = 1;
+  *bd2 = 1;
+
+  for (int i = 0; i < a_axes_remained.ndim(); i++) {
+    *ad1 *= a_shape[a_axes_remained[i]];
+  }
+  for (int i = 0; i < a_axes_summed.ndim(); i++) {
+    *ad2 *= a_shape[a_axes_summed[i]];
+  }
+  for (int i = 0; i < b_axes_summed.ndim(); i++) {
+    *bd1 *= b_shape[b_axes_summed[i]];
+  }
+  for (int i = 0; i < b_axes_remained.ndim(); i++) {
+    *bd2 *= b_shape[b_axes_remained[i]];
+  }
+}
+
+/**
+ * gets new axes of a and b after transpose and reshape.
+ */
+inline void GetReorderedAxes(const mxnet::Tuple<int>& a_axes_summed,
+                             mxnet::Tuple<int>* a_axes_remained,
+                             mxnet::Tuple<int>* a_axes,
+                             const mxnet::Tuple<int>& b_axes_summed,
+                             mxnet::Tuple<int>* b_axes_remained,
+                             mxnet::Tuple<int>* b_axes,
+                             const mxnet::TShape& a_shape,
+                             const mxnet::TShape& b_shape) {
+  std::vector<int> a_axes_remained_vector;
+  for (int i = 0; i < a_shape.ndim(); i++) {
+    a_axes_remained_vector.push_back(i);
+  }
+  for (auto& i : a_axes_summed) {
+    a_axes_remained_vector.erase(std::find(a_axes_remained_vector.begin(),
+      a_axes_remained_vector.end(), i));
+  }
+  *a_axes_remained = mxnet::Tuple<int>(a_axes_remained_vector);
+
+  std::vector<int> a_axes_vector(a_axes_remained_vector);
+  for (auto& i : a_axes_summed) {
+    a_axes_vector.push_back(i);
+  }
+  *a_axes = mxnet::Tuple<int>(a_axes_vector);
+
+  std::vector<int> b_axes_remained_vector;
+  for (int i = 0; i < b_shape.ndim(); i++) {
+    b_axes_remained_vector.push_back(i);
+  }
+  for (auto& i : b_axes_summed) {
+    b_axes_remained_vector.erase(std::find(b_axes_remained_vector.begin(),
+                                           b_axes_remained_vector.end(), i));
+  }
+  *b_axes_remained = mxnet::Tuple<int>(b_axes_remained_vector);
+
+  std::vector<int> b_axes_vector;
+  for (auto& i : b_axes_summed) {
+    b_axes_vector.push_back(i);
+  }
+  for (auto& i : b_axes_remained_vector) {
+    b_axes_vector.push_back(i);
+  }
+  *b_axes = mxnet::Tuple<int>(b_axes_vector);
+}
+
+/**
+ * gets shapes of a and b after transpose and reshape.
+ */
+inline mxnet::TShape GetReorderedShape(const mxnet::TShape& shape, const mxnet::Tuple<int>& axes) {
+  mxnet::TShape new_shape(shape);
+  for (int i = 0; i < axes.ndim(); i++) {
+    new_shape[i] = shape[axes[i]];
+  }
+  return new_shape;
+}
+
+/**
+ * gets matrix dot. Reshapes tensor a as ad1-by-ad2 matrix, tensor b as bd1-by-bd2 matrix, then 
+ * calculates matrix dot a * b and stores in tensor out.
+ */
+template<typename xpu>
+void MatrixDot(const OpContext& ctx,
+               const TBlob& a,
+               const TBlob& b,
+               const TBlob& out,
+               const OpReqType req,
+               const int ad1,
+               const int ad2,
+               const int bd1,
+               const int bd2,
+               const bool aT = false,
+               const bool bT = false) {
+  using namespace mshadow;
+  using namespace mshadow_op;
+
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+
+  MSHADOW_REAL_TYPE_SWITCH(out.type_flag_, DType, {
+    Tensor<xpu, 2, DType> a_tensor = a.get_with_shape<xpu, 2, DType>(Shape2(ad1, ad2), s);
+    Tensor<xpu, 2, DType> b_tensor = b.get_with_shape<xpu, 2, DType>(Shape2(bd1, bd2), s);
+
+    if (aT && bT) {
+      CHECK_EQ(ad1, bd2);
+      Tensor<xpu, 2, DType> out_tensor = out.get_with_shape<xpu, 2, DType>(Shape2(ad2, bd1), s);
+      ASSIGN_DISPATCH(out_tensor, req, dot(a_tensor.T(), b_tensor.T()));
+    } else if (aT && !bT) {
+      CHECK_EQ(ad1, bd1);
+      Tensor<xpu, 2, DType> out_tensor = out.get_with_shape<xpu, 2, DType>(Shape2(ad2, bd2), s);
+      ASSIGN_DISPATCH(out_tensor, req, dot(a_tensor.T(), b_tensor));
+    } else if (!aT && bT) {
+      CHECK_EQ(ad2, bd2);
+      Tensor<xpu, 2, DType> out_tensor = out.get_with_shape<xpu, 2, DType>(Shape2(ad1, bd1), s);
+      ASSIGN_DISPATCH(out_tensor, req, dot(a_tensor, b_tensor.T()));
+    } else {
+      CHECK_EQ(ad2, bd1);
+      Tensor<xpu, 2, DType> out_tensor = out.get_with_shape<xpu, 2, DType>(Shape2(ad1, bd2), s);
+      ASSIGN_DISPATCH(out_tensor, req, dot(a_tensor, b_tensor));
+    }
+  });
+}
+
+/**
+ * Scalar multiply.
+ */
+template<int req>
+struct scalar_mul_kernel {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int i, DType *out, const DType* tensor, const DType *scalar) {
+    KERNEL_ASSIGN(out[i], req, tensor[i] * scalar[0]);
+  }
+};
+
+/**
+ * Calculates tensordot.
+ */
+template<typename xpu>
+void TensordotImpl(const Tuple<int>& a_axes_summed,
+                   const Tuple<int>& b_axes_summed,
+                   const OpContext& ctx,
+                   const TBlob& a,
+                   const TBlob& b,
+                   const TBlob& out,
+                   const std::vector<OpReqType>& req) {
+  if (req[0] == kNullOp) {
+    return;
+  }
+
+  if (out.shape_.Size() == 0U) {
+    return;  // zero-size output, no need to launch kernel
+  }
+
+  const mxnet::TShape& a_shape = a.shape_;
+  const mxnet::TShape& b_shape = b.shape_;
+
+  mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+  CHECK_EQ(out.type_flag_, a.type_flag_)
+      << "Binary function only support input/output with the same type";
+  CHECK_EQ(out.type_flag_, b.type_flag_)
+      << "Binary function only support input/output with the same type";
+  CHECK(out.type_flag_ == kFloat32 || out.type_flag_ == kFloat64 ||
+       (out.type_flag_ == kFloat16 && ctx.run_ctx.ctx.dev_mask() == mshadow::gpu::kDevMask))
+      << "Tensordot only supports float32/float64 for CPU, and float16/float32/float64 for GPU";
+
+  MSHADOW_REAL_TYPE_SWITCH(out.type_flag_, DType, {
+    if (a_shape.Size() == 0U || b_shape.Size() == 0U) {
+      // 0-size input
+      if (req[0] != kAddTo) {
+        Tensor<xpu, 1, DType> out_data = out.get_with_shape<xpu, 1, DType>(
+            Shape1(out.shape_.Size()), s);
+        out_data = static_cast<DType>(0);
+      }
+    } else if (a_shape.ndim() == 0 && b_shape.ndim() == 0) {
+      // Both 0-D scalars, equivalent to multiply
+      Tensor<xpu, 1, DType> a_data = a.get_with_shape<xpu, 1, DType>(Shape1(1), s);
+      Tensor<xpu, 1, DType> b_data = b.get_with_shape<xpu, 1, DType>(Shape1(1), s);
+      Tensor<xpu, 1, DType> out_data = out.get_with_shape<xpu, 1, DType>(Shape1(1), s);
+      ASSIGN_DISPATCH(out_data, req[0], a_data * b_data);
+    } else if (a_shape.ndim() == 0 || b_shape.ndim() == 0) {
+      // Either of them is a scalar, just scale by one of them
+      const DType* tensor = (a_shape.ndim() == 0) ? b.dptr<DType>() : a.dptr<DType>();
+      const DType* scalar = (a_shape.ndim() == 0) ? a.dptr<DType>() : b.dptr<DType>();
+      MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
+        mxnet_op::Kernel<scalar_mul_kernel<Req>, xpu>::Launch(
+          s, out.Size(), out.dptr<DType>(), tensor, scalar);
+      });
+    } else {
+      // Two tensors of at least 1 dimensions.
+      Tuple<int> a_axes_remained;
+      Tuple<int> b_axes_remained;
+      Tuple<int> a_axes;
+      Tuple<int> b_axes;
+      GetReorderedAxes(a_axes_summed, &a_axes_remained, &a_axes, b_axes_summed, &b_axes_remained,
+                       &b_axes, a_shape, b_shape);
+
+      int ad1 = 1, ad2 = 1, bd1 = 1, bd2 = 1;
+      GetMatrixDimensions(&ad1, &ad2, &bd1, &bd2, a_axes_remained, a_axes_summed,
+                          b_axes_remained, b_axes_summed, a_shape, b_shape);
+
+      mxnet::TShape a_temp_shape = GetReorderedShape(a_shape, a_axes);
+      mxnet::TShape b_temp_shape = GetReorderedShape(b_shape, b_axes);
+
+      Tensor<xpu, 1, DType> workspace = ctx.requested[0].get_space_typed<xpu, 1, DType>
+        (Shape1(a.Size() + b.Size()), s);
+      DType* a_ptr = reinterpret_cast<DType*>(workspace.dptr_);
+      DType* b_ptr = reinterpret_cast<DType*>(workspace.dptr_ + a.Size());
+      TBlob a_res = TBlob(a_ptr, a_temp_shape, xpu::kDevMask);
+      TBlob b_res = TBlob(b_ptr, b_temp_shape, xpu::kDevMask);
+
+      mxnet::op::TransposeImpl<xpu>(ctx.run_ctx, a, a_res,
+                                    mxnet::TShape(a_axes.begin(), a_axes.end()));
+      mxnet::op::TransposeImpl<xpu>(ctx.run_ctx, b, b_res,
+                                    mxnet::TShape(b_axes.begin(), b_axes.end()));
+
+      MatrixDot<xpu>(ctx, a_res, b_res, out, req[0], ad1, ad2, bd1, bd2);
+    }
+  });
+}
+
+/**
+ * forward function
+ */
+template<typename xpu>
+void TensordotOpForward(const nnvm::NodeAttrs& attrs,
+                        const OpContext& ctx,
+                        const std::vector<TBlob>& inputs,
+                        const std::vector<OpReqType>& req,
+                        const std::vector<TBlob>& outputs) {
+  CHECK_EQ(inputs.size(), 2U);
+  CHECK_EQ(outputs.size(), 1U);
+  CHECK_EQ(req.size(), 1U);
+
+  const TBlob& a = inputs[0];
+  const TBlob& b = inputs[1];
+  const TBlob& out = outputs[0];
+  const mxnet::TShape a_shape = a.shape_;
+  const mxnet::TShape b_shape = b.shape_;
+
+  const TensordotParam& param = nnvm::get<TensordotParam>(attrs.parsed);
+  Tuple<int> a_axes_summed = param.a_axes_summed;
+  Tuple<int> b_axes_summed = param.b_axes_summed;
+  ShiftAxes(&a_axes_summed, a_shape.ndim());
+  ShiftAxes(&b_axes_summed, b_shape.ndim());
+
+  TensordotImpl<xpu>(a_axes_summed, b_axes_summed, ctx, a, b, out, req);
+}
+
+/**
+ * gets shapes for inverse transpose.
+ */
+inline mxnet::TShape GetReverseShape(const mxnet::Tuple<int>& shape) {
+  mxnet::TShape shape2(shape.begin(), shape.end());
+  for (int i = 0; i < shape.ndim(); i++) {
+    shape2[shape[i]] = i;
+  }
+  return shape2;
+}
+
+/**
+ * calculates tensordot derivative.
+ */
+template<typename xpu>
+void TensordotBackwardImpl(const Tuple<int>& a_axes_summed,
+                           const Tuple<int>& b_axes_summed,
+                           const OpContext& ctx,
+                           const TBlob& out_grad,
+                           const TBlob& a,
+                           const TBlob& b,
+                           const TBlob& grad_a,
+                           const TBlob& grad_b,
+                           const std::vector<OpReqType>& req) {
+  mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+
+  const mxnet::TShape& a_shape = a.shape_;
+  const mxnet::TShape& b_shape = b.shape_;
+
+  if ((a_shape.Size() == 0U) || (b_shape.Size() == 0U)) {
+    return;  // zero-size output, no need to launch kernel
+  }
+  MSHADOW_REAL_TYPE_SWITCH(out_grad.type_flag_, DType, {
+    if (a_shape.ndim() == 0 && b_shape.ndim() == 0) {
+      // Both 0-D scalars, equivalent to multiply
+      Tensor<xpu, 1, DType> out_grad_data = out_grad.get_with_shape<xpu, 1, DType>(Shape1(1), s);
+      Tensor<xpu, 1, DType> a_data = a.get_with_shape<xpu, 1, DType>(Shape1(1), s);
+      Tensor<xpu, 1, DType> b_data = b.get_with_shape<xpu, 1, DType>(Shape1(1), s);
+      Tensor<xpu, 1, DType> grad_a_data = grad_a.get_with_shape<xpu, 1, DType>(Shape1(1), s);
+      Tensor<xpu, 1, DType> grad_b_data = grad_b.get_with_shape<xpu, 1, DType>(Shape1(1), s);
+      ASSIGN_DISPATCH(grad_a_data, req[0], b_data * out_grad_data);
+      ASSIGN_DISPATCH(grad_b_data, req[1], a_data * out_grad_data);
+    } else if (a_shape.ndim() == 0 || b_shape.ndim() == 0) {
+      // Either of them is a scalar, just scale by one of them
+      const TBlob& tensor = (a_shape.ndim() == 0) ? b : a;
+      const TBlob& tensor_grad = (a_shape.ndim() == 0) ? grad_b : grad_a;
+      const TBlob& scalar = (a_shape.ndim() == 0) ? a : b;
+      const TBlob& scalar_grad = (a_shape.ndim() == 0) ? grad_a : grad_b;
+      Tensor<xpu, 1, DType> scalar_ = scalar.get_with_shape<xpu, 1, DType>(Shape1(1), s);
+      Tensor<xpu, 1, DType> scalar_grad_ = scalar_grad.get_with_shape<xpu, 1, DType>(Shape1(1), s);
+      Tensor<xpu, 1, DType> tensor_ = tensor.FlatTo1D<xpu, DType>(s);
+      Tensor<xpu, 1, DType> tensor_grad_ = tensor_grad.FlatTo1D<xpu, DType>(s);
+      Tensor<xpu, 1, DType> out_grad_ = out_grad.FlatTo1D<xpu, DType>(s);
+      const OpReqType& tensor_req = (a_shape.ndim() == 0) ? req[1] : req[0];
+      const OpReqType& scalar_req = (a_shape.ndim() == 0) ? req[0] : req[1];
+      ASSIGN_DISPATCH(tensor_grad_, tensor_req,
+                      broadcast_scalar(scalar_, tensor_grad_.shape_) * out_grad_);
+      Tensor<xpu, 1, DType> workspace =
+        ctx.requested[0].get_space_typed<xpu, 1, DType>(Shape1(out_grad.shape_.Size()), s);
+      ASSIGN_DISPATCH(workspace, kWriteTo, tensor_ * out_grad_);
+
+      ReduceAxesComputeImpl<xpu, mshadow_op::sum, true>(
+        ctx, {TBlob(workspace)}, {scalar_req}, {TBlob(scalar_grad_)}, scalar_grad_.shape_);
+    } else {
+      // Two tensors of at least 1 dimensions.
+      Tuple<int> a_axes_remained;
+      Tuple<int> b_axes_remained;
+      Tuple<int> a_axes;
+      Tuple<int> b_axes;
+      GetReorderedAxes(a_axes_summed, &a_axes_remained, &a_axes, b_axes_summed, &b_axes_remained,
+                      &b_axes, a_shape, b_shape);
+
+      int ad1 = 1, ad2 = 1, bd1 = 1, bd2 = 1;
+      GetMatrixDimensions(&ad1, &ad2, &bd1, &bd2, a_axes_remained, a_axes_summed,
+                          b_axes_remained, b_axes_summed, a_shape, b_shape);
+
+      std::vector<int> a_T_axes;
+      for (int i = 0; i < a_axes_summed.ndim(); i++) {
+        a_T_axes.push_back(a_axes_summed[i]);
+      }
+      for (int i = 0; i < a_axes_remained.ndim(); i++) {
+        a_T_axes.push_back(a_axes_remained[i]);
+      }
+      mxnet::TShape a_temp_shape(GetReorderedShape(a_shape, a_axes));
+      mxnet::TShape a_T_temp_shape(GetReorderedShape(a_shape, a_T_axes));
+
+      std::vector<int> b_T_axes;
+      for (int i = 0; i < b_axes_remained.ndim(); i++) {
+        b_T_axes.push_back(b_axes_remained[i]);
+      }
+      for (int i = 0; i < b_axes_summed.ndim(); i++) {
+        b_T_axes.push_back(b_axes_summed[i]);
+      }
+      mxnet::TShape b_temp_shape(GetReorderedShape(b_shape, b_axes));
+      mxnet::TShape b_T_temp_shape(GetReorderedShape(b_shape, b_T_axes));
+
+      Tensor<xpu, 1, DType> workspace = ctx.requested[0].get_space_typed<xpu, 1, DType>
+        (Shape1((a.Size() + b.Size()) * 2), s);
+      DType* a_ptr = reinterpret_cast<DType*>(workspace.dptr_);
+      DType* a_ptr2 = reinterpret_cast<DType*>(workspace.dptr_ + a.Size());
+      DType* b_ptr = reinterpret_cast<DType*>(workspace.dptr_ + 2 * a.Size());
+      DType* b_ptr2 = reinterpret_cast<DType*>(workspace.dptr_ + 2 * a.Size() + b.Size());
+
+      TBlob a_res = TBlob(a_ptr, a_temp_shape, xpu::kDevMask);
+      TBlob b_res = TBlob(b_ptr, b_temp_shape, xpu::kDevMask);
+      TBlob a_res2 = TBlob(a_ptr2, a_T_temp_shape, xpu::kDevMask);
+      TBlob b_res2 = TBlob(b_ptr2, b_T_temp_shape, xpu::kDevMask);
+
+      mxnet::op::TransposeImpl<xpu>(ctx.run_ctx, a, a_res2,
+                                    mxnet::TShape(a_T_axes.begin(), a_T_axes.end()));
+      mxnet::op::TransposeImpl<xpu>(ctx.run_ctx, b, b_res2,
+                                    mxnet::TShape(b_T_axes.begin(), b_T_axes.end()));
+
+      MatrixDot<xpu>(ctx, a_res2, out_grad, b_res, req[1], ad2, ad1, ad1, bd2);
+      MatrixDot<xpu>(ctx, out_grad, b_res2, a_res, req[0], ad1, bd2, bd2, bd1);
+
+      mxnet::op::TransposeImpl<xpu>(ctx.run_ctx, a_res, grad_a, GetReverseShape(a_axes));
+      mxnet::op::TransposeImpl<xpu>(ctx.run_ctx, b_res, grad_b, GetReverseShape(b_axes));
+    }
+  });
+}
+
+/**
+ * backward function.
+ */
+template<typename xpu>
+void TensordotOpBackward(const nnvm::NodeAttrs& attrs,
+                         const OpContext& ctx,
+                         const std::vector<TBlob>& inputs,
+                         const std::vector<OpReqType>& req,
+                         const std::vector<TBlob>& outputs) {
+  CHECK_EQ(inputs.size(), 3U);
+  CHECK_EQ(outputs.size(), 2U);
+  CHECK_EQ(req.size(), 2U);
+
+  const TBlob& out_grad = inputs[0];
+  const TBlob& a = inputs[1];
+  const TBlob& b = inputs[2];
+  const TBlob& grad_a = outputs[0];
+  const TBlob& grad_b = outputs[1];
+  const mxnet::TShape a_shape = a.shape_;
+  const mxnet::TShape b_shape = b.shape_;
+
+  const TensordotParam& param = nnvm::get<TensordotParam>(attrs.parsed);
+  Tuple<int> a_axes_summed = param.a_axes_summed;
+  Tuple<int> b_axes_summed = param.b_axes_summed;
+  ShiftAxes(&a_axes_summed, a_shape.ndim());
+  ShiftAxes(&b_axes_summed, b_shape.ndim());
+
+  TensordotBackwardImpl<xpu>(a_axes_summed, b_axes_summed, ctx, out_grad, a, b, grad_a,
+                             grad_b, req);
+}
+
+struct TensordotIntAxesParam : public dmlc::Parameter<TensordotIntAxesParam> {
+  int axes;
+  DMLC_DECLARE_PARAMETER(TensordotIntAxesParam) {
+    DMLC_DECLARE_FIELD(axes);
+  }
+};
+
+/**
+ * gets summed axes of a and b from parameter axes.
+ */
+inline void GetSummedAxes(mxnet::Tuple<int>* a_axes_summed_ptr,
+                          mxnet::Tuple<int>* b_axes_summed_ptr,
+                          const int axes,
+                          const mxnet::TShape& a_shape) {
+  std::vector<int> a_axes_summed_vector;
+  for (int i = 0; i < axes; i++) {
+    a_axes_summed_vector.push_back(a_shape.ndim() - axes + i);
+  }
+  *a_axes_summed_ptr = mxnet::Tuple<int>(a_axes_summed_vector);
+
+  std::vector<int> b_axes_summed_vector;
+  for (int i = 0; i < axes; i++) {
+    b_axes_summed_vector.push_back(i);
+  }
+  *b_axes_summed_ptr = mxnet::Tuple<int>(b_axes_summed_vector);
+}
+
+/**
+ * Calculates tensordot.
+ */
+template<typename xpu>
+void TensordotIntAxesImpl(const int axes,
+                          const OpContext& ctx,
+                          const TBlob& a,
+                          const TBlob& b,
+                          const TBlob& out,
+                          const OpReqType req) {
+  if (req == kNullOp) {
+    return;
+  }
+
+  if (out.shape_.Size() == 0U) {
+    return;  // zero-size output, no need to launch kernel
+  }
+
+  const mxnet::TShape& a_shape = a.shape_;
+  const mxnet::TShape& b_shape = b.shape_;
+
+  mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+  CHECK_EQ(out.type_flag_, a.type_flag_)
+      << "Binary function only support input/output with the same type";
+  CHECK_EQ(out.type_flag_, b.type_flag_)
+      << "Binary function only support input/output with the same type";
+  CHECK(out.type_flag_ == kFloat32 || out.type_flag_ == kFloat64 ||
+       (out.type_flag_ == kFloat16 && ctx.run_ctx.ctx.dev_mask() == mshadow::gpu::kDevMask))
+      << "Tensordot only supports float32/float64 for CPU, and float16/float32/float64 for GPU";
+
+  MSHADOW_REAL_TYPE_SWITCH(out.type_flag_, DType, {
+    if (a_shape.Size() == 0U || b_shape.Size() == 0U) {
+      // 0-size input
+      if (req != kAddTo) {
+        Tensor<xpu, 1, DType> out_data = out.get_with_shape<xpu, 1, DType>(
+            Shape1(out.shape_.Size()), s);
+        out_data = static_cast<DType>(0);
+      }
+    } else if (a_shape.ndim() == 0 && b_shape.ndim() == 0) {
+      // Both 0-D scalars, equivalent to multiply
+      Tensor<xpu, 1, DType> a_data = a.get_with_shape<xpu, 1, DType>(Shape1(1), s);
+      Tensor<xpu, 1, DType> b_data = b.get_with_shape<xpu, 1, DType>(Shape1(1), s);
+      Tensor<xpu, 1, DType> out_data = out.get_with_shape<xpu, 1, DType>(Shape1(1), s);
+      ASSIGN_DISPATCH(out_data, req, a_data * b_data);
+    } else if (a_shape.ndim() == 0 || b_shape.ndim() == 0) {
+      // Either of them is a scalar, just scale by one of them
+      const DType* tensor = (a_shape.ndim() == 0) ? b.dptr<DType>() : a.dptr<DType>();
+      const DType* scalar = (a_shape.ndim() == 0) ? a.dptr<DType>() : b.dptr<DType>();
+      MXNET_ASSIGN_REQ_SWITCH(req, Req, {
+        mxnet_op::Kernel<scalar_mul_kernel<Req>, xpu>::Launch(
+          s, out.Size(), out.dptr<DType>(), tensor, scalar);
+      });
+    } else {
+      // Two tensors of at least 1 dimensions.
+      Tuple<int> a_axes_summed;
+      Tuple<int> b_axes_summed;
+      GetSummedAxes(&a_axes_summed, &b_axes_summed, axes, a_shape);
+
+      Tuple<int> a_axes_remained;
+      Tuple<int> b_axes_remained;
+      Tuple<int> a_axes;
+      Tuple<int> b_axes;
+      GetReorderedAxes(a_axes_summed, &a_axes_remained, &a_axes, b_axes_summed, &b_axes_remained,
+                      &b_axes, a_shape, b_shape);
+
+      int ad1 = 1, ad2 = 1, bd1 = 1, bd2 = 1;
+      GetMatrixDimensions(&ad1, &ad2, &bd1, &bd2, a_axes_remained, a_axes_summed,
+                          b_axes_remained, b_axes_summed, a_shape, b_shape);
+      MatrixDot<xpu>(ctx, a, b, out, req, ad1, ad2, bd1, bd2);
+    }
+  });
+}
+
+/**
+ * forward function
+ */
+template<typename xpu>
+void TensordotIntAxesOpForward(const nnvm::NodeAttrs& attrs,
+                               const OpContext& ctx,
+                               const std::vector<TBlob>& inputs,
+                               const std::vector<OpReqType>& req,
+                               const std::vector<TBlob>& outputs) {
+  CHECK_EQ(inputs.size(), 2U);
+  CHECK_EQ(outputs.size(), 1U);
+  CHECK_EQ(req.size(), 1U);
+
+  const TBlob& a = inputs[0];
+  const TBlob& b = inputs[1];
+  const TBlob& out = outputs[0];
+
+  const TensordotIntAxesParam& param = nnvm::get<TensordotIntAxesParam>(attrs.parsed);
+  const int axes = param.axes;
+
+  TensordotIntAxesImpl<xpu>(axes, ctx, a, b, out, req[0]);
+}
+
+template<typename xpu>
+void TensordotIntAxesBackwardImpl(const int axes,
+                                  const OpContext& ctx,
+                                  const TBlob& out_grad,
+                                  const TBlob& a,
+                                  const TBlob& b,
+                                  const TBlob& grad_a,
+                                  const TBlob& grad_b,
+                                  const std::vector<OpReqType>& req) {
+  const mxnet::TShape& a_shape = a.shape_;
+  const mxnet::TShape& b_shape = b.shape_;
+
+  if ((a_shape.Size() == 0U) || (b_shape.Size() == 0U)) {
+    return;  // zero-size output, no need to launch kernel
+  }
+
+  mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+
+  MSHADOW_REAL_TYPE_SWITCH(out_grad.type_flag_, DType, {
+    if (a_shape.ndim() == 0 && b_shape.ndim() == 0) {
+      // Both 0-D scalars, equivalent to multiply
+      Tensor<xpu, 1, DType> out_grad_data = out_grad.get_with_shape<xpu, 1, DType>(Shape1(1), s);
+      Tensor<xpu, 1, DType> a_data = a.get_with_shape<xpu, 1, DType>(Shape1(1), s);
+      Tensor<xpu, 1, DType> b_data = b.get_with_shape<xpu, 1, DType>(Shape1(1), s);
+      Tensor<xpu, 1, DType> grad_a_data = grad_a.get_with_shape<xpu, 1, DType>(Shape1(1), s);
+      Tensor<xpu, 1, DType> grad_b_data = grad_b.get_with_shape<xpu, 1, DType>(Shape1(1), s);
+      ASSIGN_DISPATCH(grad_a_data, req[0], b_data * out_grad_data);
+      ASSIGN_DISPATCH(grad_b_data, req[1], a_data * out_grad_data);
+    } else if (a_shape.ndim() == 0 || b_shape.ndim() == 0) {
+      // Either of them is a scalar, just scale by one of them
+      const TBlob& tensor = (a_shape.ndim() == 0) ? b : a;
+      const TBlob& tensor_grad = (a_shape.ndim() == 0) ? grad_b : grad_a;
+      const TBlob& scalar = (a_shape.ndim() == 0) ? a : b;
+      const TBlob& scalar_grad = (a_shape.ndim() == 0) ? grad_a : grad_b;
+      Tensor<xpu, 1, DType> scalar_ = scalar.get_with_shape<xpu, 1, DType>(Shape1(1), s);
+      Tensor<xpu, 1, DType> scalar_grad_ = scalar_grad.get_with_shape<xpu, 1, DType>(Shape1(1), s);
+      Tensor<xpu, 1, DType> tensor_ = tensor.FlatTo1D<xpu, DType>(s);
+      Tensor<xpu, 1, DType> tensor_grad_ = tensor_grad.FlatTo1D<xpu, DType>(s);
+      Tensor<xpu, 1, DType> out_grad_ = out_grad.FlatTo1D<xpu, DType>(s);
+      const OpReqType& tensor_req = (a_shape.ndim() == 0) ? req[1] : req[0];
+      const OpReqType& scalar_req = (a_shape.ndim() == 0) ? req[0] : req[1];
+      ASSIGN_DISPATCH(tensor_grad_, tensor_req,
+                      broadcast_scalar(scalar_, tensor_grad_.shape_) * out_grad_);
+      Tensor<xpu, 1, DType> workspace =
+        ctx.requested[0].get_space_typed<xpu, 1, DType>(Shape1(out_grad.shape_.Size()), s);
+      ASSIGN_DISPATCH(workspace, kWriteTo, tensor_ * out_grad_);
+
+      ReduceAxesComputeImpl<xpu, mshadow_op::sum, true>(
+        ctx, {TBlob(workspace)}, {scalar_req}, {TBlob(scalar_grad_)}, scalar_grad_.shape_);
+    } else {
+      // Two tensors of at least 1 dimensions.
+      Tuple<int> a_axes_summed;
+      Tuple<int> b_axes_summed;
+      GetSummedAxes(&a_axes_summed, &b_axes_summed, axes, a_shape);
+
+      Tuple<int> a_axes_remained;
+      Tuple<int> b_axes_remained;
+      Tuple<int> a_axes;
+      Tuple<int> b_axes;
+      GetReorderedAxes(a_axes_summed, &a_axes_remained, &a_axes, b_axes_summed, &b_axes_remained,
+                      &b_axes, a_shape, b_shape);
+
+      int ad1 = 1, ad2 = 1, bd1 = 1, bd2 = 1;
+      GetMatrixDimensions(&ad1, &ad2, &bd1, &bd2, a_axes_remained, a_axes_summed,
+                          b_axes_remained, b_axes_summed, a_shape, b_shape);
+
+      MatrixDot<xpu>(ctx, a, out_grad, grad_b, req[1], ad1, ad2, ad1, bd2, true, false);
+      MatrixDot<xpu>(ctx, out_grad, b, grad_a, req[0], ad1, bd2, bd1, bd2, false, true);
+    }
+  });
+}
+
+/**
+ * backward function.
+ */
+template<typename xpu>
+void TensordotIntAxesOpBackward(const nnvm::NodeAttrs& attrs,
+                                const OpContext& ctx,
+                                const std::vector<TBlob>& inputs,
+                                const std::vector<OpReqType>& req,
+                                const std::vector<TBlob>& outputs) {
+  CHECK_EQ(inputs.size(), 3U);
+  CHECK_EQ(outputs.size(), 2U);
+  CHECK_EQ(req.size(), 2U);
+
+  const TBlob& out_grad = inputs[0];
+  const TBlob& a = inputs[1];
+  const TBlob& b = inputs[2];
+  const TBlob& grad_a = outputs[0];
+  const TBlob& grad_b = outputs[1];
+
+  const TensordotIntAxesParam& param = nnvm::get<TensordotIntAxesParam>(attrs.parsed);
+  const int axes = param.axes;
+
+  TensordotIntAxesBackwardImpl<xpu>(axes, ctx, out_grad, a, b, grad_a, grad_b, req);
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_NUMPY_NP_TENSORDOT_OP_INL_H_
diff --git a/src/operator/numpy/np_tensordot_op.cc b/src/operator/numpy/np_tensordot_op.cc
new file mode 100644
index 000000000000..50c1647e0264
--- /dev/null
+++ b/src/operator/numpy/np_tensordot_op.cc
@@ -0,0 +1,235 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file np_tensordot_op.cc
+ * \brief CPU Implementation of numpy-compatible tensordot
+ */
+
+#include <string>
+#include "np_tensordot_op-inl.h"
+
+namespace mxnet {
+namespace op {
+
+bool TensordotOpShape(const nnvm::NodeAttrs& attrs,
+                      mxnet::ShapeVector *in_attrs,
+                      mxnet::ShapeVector *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 2U);
+  CHECK_EQ(out_attrs->size(), 1U);
+
+  const mxnet::TShape& a_shape = in_attrs->at(0);
+  const mxnet::TShape& b_shape = in_attrs->at(1);
+
+  if (!ndim_is_known(a_shape) || !ndim_is_known(b_shape)) {
+    return false;
+  }
+
+  if (a_shape.ndim() == 0) {
+    // a is scalar
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, b_shape);
+    SHAPE_ASSIGN_CHECK(*in_attrs, 1, out_attrs->at(0));
+  } else if (b_shape.ndim() == 0) {
+    // b is scalar
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, a_shape);
+    SHAPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
+  } else {
+    // Two tensors of at least 1 dimensions.
+    const TensordotParam& param = nnvm::get<TensordotParam>(attrs.parsed);
+    Tuple<int> a_axes_summed = param.a_axes_summed;
+    Tuple<int> b_axes_summed = param.b_axes_summed;
+    ShiftAxes(&a_axes_summed, a_shape.ndim());
+    ShiftAxes(&b_axes_summed, b_shape.ndim());
+
+    Tuple<int> a_axes_remained;
+    Tuple<int> b_axes_remained;
+    Tuple<int> a_axes;
+    Tuple<int> b_axes;
+    GetReorderedAxes(a_axes_summed, &a_axes_remained, &a_axes, b_axes_summed, &b_axes_remained,
+                    &b_axes, a_shape, b_shape);
+
+    CHECK_EQ(a_axes_summed.ndim(), b_axes_summed.ndim());
+
+    mxnet::TShape out_shape(a_axes_remained.ndim() + b_axes_remained.ndim(), -1);
+    for (int i = 0; i < a_axes_remained.ndim(); i++) {
+      out_shape[i] = a_shape[a_axes_remained[i]];
+    }
+    for (int i = 0; i < b_axes_remained.ndim(); i++) {
+      out_shape[a_axes_remained.ndim() + i] = b_shape[b_axes_remained[i]];
+    }
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, out_shape);
+
+    mxnet::TShape tem_shape1(a_axes.ndim(), -1);
+    for (int i = 0; i < a_axes_remained.ndim(); i++) {
+      tem_shape1[a_axes_remained[i]] = out_shape[i];
+    }
+    for (int i = 0; i < a_axes_summed.ndim(); i++) {
+      tem_shape1[a_axes_summed[i]] = b_shape[b_axes_summed[i]];
+    }
+    SHAPE_ASSIGN_CHECK(*in_attrs, 0, tem_shape1);
+
+    mxnet::TShape tem_shape2(b_axes.ndim(), -1);
+    for (int i = 0; i < b_axes_remained.ndim(); i++) {
+      tem_shape2[b_axes_remained[i]] = out_shape[a_axes_remained.ndim() + i];
+    }
+    for (int i = 0; i < b_axes_summed.ndim(); i++) {
+      tem_shape2[b_axes_summed[i]] = a_shape[a_axes_summed[i]];
+    }
+    SHAPE_ASSIGN_CHECK(*in_attrs, 1, tem_shape2);
+  }
+
+  return shape_is_known(*in_attrs) && shape_is_known(*out_attrs);
+}
+
+DMLC_REGISTER_PARAMETER(TensordotParam);
+
+NNVM_REGISTER_OP(_npi_tensordot)
+.set_attr_parser(mxnet::op::ParamParser<TensordotParam>)
+.set_num_inputs(2)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"a", "b"};
+  })
+.set_attr<mxnet::FInferShape>("FInferShape", TensordotOpShape)
+.set_attr<nnvm::FInferType>("FInferType", mxnet::op::ElemwiseType<2, 1>)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", TensordotOpForward<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", mxnet::op::ElemwiseGradUseIn{"_backward_npi_tensordot"})
+.add_argument("a", "NDArray-or-Symbol", "First input")
+.add_argument("b", "NDArray-or-Symbol", "Second input")
+.add_arguments(TensordotParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_backward_npi_tensordot)
+.set_attr_parser(mxnet::op::ParamParser<TensordotParam>)
+.set_num_inputs(3)
+.set_num_outputs(2)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", TensordotOpBackward<cpu>);
+
+bool TensordotIntAxesOpShape(const nnvm::NodeAttrs& attrs,
+                             mxnet::ShapeVector *in_attrs,
+                             mxnet::ShapeVector *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 2U);
+  CHECK_EQ(out_attrs->size(), 1U);
+
+  const mxnet::TShape& a_shape = in_attrs->at(0);
+  const mxnet::TShape& b_shape = in_attrs->at(1);
+
+  if (!ndim_is_known(a_shape) || !ndim_is_known(b_shape)) {
+    return false;
+  }
+
+  if (a_shape.ndim() == 0) {
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, b_shape);
+    SHAPE_ASSIGN_CHECK(*in_attrs, 1, out_attrs->at(0));
+  } else if (b_shape.ndim() == 0) {
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, a_shape);
+    SHAPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
+  } else {
+    const TensordotIntAxesParam& param = nnvm::get<TensordotIntAxesParam>(attrs.parsed);
+    const int& axes = param.axes;
+
+    Tuple<int> a_axes_summed;
+    Tuple<int> b_axes_summed;
+    GetSummedAxes(&a_axes_summed, &b_axes_summed, axes, a_shape);
+
+    Tuple<int> a_axes_remained;
+    Tuple<int> b_axes_remained;
+    Tuple<int> a_axes;
+    Tuple<int> b_axes;
+    GetReorderedAxes(a_axes_summed, &a_axes_remained, &a_axes, b_axes_summed, &b_axes_remained,
+                    &b_axes, a_shape, b_shape);
+
+    CHECK_EQ(a_axes_summed.ndim(), b_axes_summed.ndim());
+
+    mxnet::TShape out_shape(a_axes_remained.ndim() + b_axes_remained.ndim(), -1);
+    for (int i = 0; i < a_axes_remained.ndim(); i++) {
+      out_shape[i] = a_shape[a_axes_remained[i]];
+    }
+    for (int i = 0; i < b_axes_remained.ndim(); i++) {
+      out_shape[a_axes_remained.ndim() + i] = b_shape[b_axes_remained[i]];
+    }
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, out_shape);
+
+    mxnet::TShape tem_shape1(a_axes.ndim(), -1);
+    for (int i = 0; i < a_axes_remained.ndim(); i++) {
+      tem_shape1[a_axes_remained[i]] = out_shape[i];
+    }
+    for (int i = 0; i < a_axes_summed.ndim(); i++) {
+      tem_shape1[a_axes_summed[i]] = b_shape[b_axes_summed[i]];
+    }
+    SHAPE_ASSIGN_CHECK(*in_attrs, 0, tem_shape1);
+
+    mxnet::TShape tem_shape2(b_axes.ndim(), -1);
+    for (int i = 0; i < b_axes_remained.ndim(); i++) {
+      tem_shape2[b_axes_remained[i]] = out_shape[a_axes_remained.ndim() + i];
+    }
+    for (int i = 0; i < b_axes_summed.ndim(); i++) {
+      tem_shape2[b_axes_summed[i]] = a_shape[a_axes_summed[i]];
+    }
+    SHAPE_ASSIGN_CHECK(*in_attrs, 1, tem_shape2);
+  }
+
+  return shape_is_known(*in_attrs) && shape_is_known(*out_attrs);
+}
+
+DMLC_REGISTER_PARAMETER(TensordotIntAxesParam);
+
+NNVM_REGISTER_OP(_npi_tensordot_int_axes)
+.set_attr_parser(mxnet::op::ParamParser<TensordotIntAxesParam>)
+.set_num_inputs(2)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"a", "b"};
+  })
+.set_attr<mxnet::FInferShape>("FInferShape", TensordotIntAxesOpShape)
+.set_attr<nnvm::FInferType>("FInferType", mxnet::op::ElemwiseType<2, 1>)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", TensordotIntAxesOpForward<cpu>)
+.set_attr<nnvm::FGradient>("FGradient",
+  mxnet::op::ElemwiseGradUseIn{"_backward_npi_tensordot_int_axes"})
+.add_argument("a", "NDArray-or-Symbol", "First input")
+.add_argument("b", "NDArray-or-Symbol", "Second input")
+.add_arguments(TensordotIntAxesParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_backward_npi_tensordot_int_axes)
+.set_attr_parser(mxnet::op::ParamParser<TensordotIntAxesParam>)
+.set_num_inputs(3)
+.set_num_outputs(2)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", TensordotIntAxesOpBackward<cpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_tensordot_op.cu b/src/operator/numpy/np_tensordot_op.cu
new file mode 100644
index 000000000000..e1d8a0b85e6e
--- /dev/null
+++ b/src/operator/numpy/np_tensordot_op.cu
@@ -0,0 +1,42 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.ø
+ */
+
+/*!
+ * \file np_tensordot_inplace.cu
+ * \brief GPU Implementation of numpy-compatible tensordot
+ */
+
+#include "np_tensordot_op-inl.h"
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(_npi_tensordot)
+.set_attr<FCompute>("FCompute<gpu>", TensordotOpForward<gpu>);
+
+NNVM_REGISTER_OP(_backward_npi_tensordot)
+.set_attr<FCompute>("FCompute<gpu>", TensordotOpBackward<gpu>);
+
+NNVM_REGISTER_OP(_npi_tensordot_int_axes)
+.set_attr<FCompute>("FCompute<gpu>", TensordotIntAxesOpForward<gpu>);
+
+NNVM_REGISTER_OP(_backward_npi_tensordot_int_axes)
+.set_attr<FCompute>("FCompute<gpu>", TensordotIntAxesOpBackward<gpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index c172336be32e..b0388d7c996c 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -30,6 +30,189 @@
 import collections
 
 
+@with_seed()
+@use_np
+def test_np_tensordot():
+    class TestTensordot(HybridBlock):
+        def __init__(self, axes):
+            super(TestTensordot, self).__init__()
+            self._axes = axes
+            
+        def hybrid_forward(self, F, a, b):
+            return F.np.tensordot(a, b, self._axes)
+
+    def tensordot_backward(a, b, axes=2):
+        if (a.ndim < 1) or (b.ndim < 1):
+            raise ValueError('An input is zero-dim')
+
+        if _np.isscalar(axes):
+            a_axes_summed = [i + a.ndim - axes for i in range(axes)]
+            b_axes_summed = [i for i in range(axes)]
+        else:
+            if len(axes) != 2:
+                raise ValueError('Axes must consist of two arrays.')
+            a_axes_summed, b_axes_summed = axes
+            if _np.isscalar(a_axes_summed):
+                a_axes_summed = a_axes_summed,
+            if _np.isscalar(b_axes_summed):
+                b_axes_summed = b_axes_summed,
+
+            for i in range(len(a_axes_summed)):
+                a_axes_summed[i] = (a_axes_summed[i] + a.ndim) % a.ndim
+
+            for i in range(len(b_axes_summed)):
+                b_axes_summed[i] = (b_axes_summed[i] + b.ndim) % b.ndim
+
+        if len(a_axes_summed) != len(b_axes_summed):
+            raise ValueError('Axes length mismatch') 
+
+        a_axes_remained = []
+        for i in range(a.ndim):
+            if not (i in a_axes_summed):
+                a_axes_remained.append(i)
+        a_axes = a_axes_remained[:] + a_axes_summed[:]
+
+        b_axes_remained = []
+        for i in range(b.ndim):
+            if not (i in b_axes_summed):
+                b_axes_remained.append(i)
+        b_axes = b_axes_summed[:] + b_axes_remained[:]
+
+        ad1 = _np.prod([a.shape[i] for i in a_axes_remained]) if len(a_axes_remained) > 0 else 1
+        ad2 = _np.prod([a.shape[i] for i in a_axes_summed]) if len(a_axes_summed) > 0 else 1
+        bd1 = _np.prod([b.shape[i] for i in b_axes_summed]) if len(b_axes_summed) > 0 else 1
+        bd2 = _np.prod([b.shape[i] for i in b_axes_remained]) if len(b_axes_remained) > 0 else 1
+
+        out_grad = _np.ones((ad1, bd2))
+
+        new_a = _np.transpose(a, a_axes)
+        new_a_shape = new_a.shape[:]
+        new_a = new_a.reshape((ad1, ad2))
+        new_b = _np.transpose(b, b_axes)
+        new_b_shape = new_b.shape[:]
+        new_b = new_b.reshape((bd1, bd2))
+
+        reverse_a_axes = [0 for i in a_axes]
+        for i in range(len(a_axes)):
+            reverse_a_axes[a_axes[i]] = i
+
+        reverse_b_axes = [0 for i in b_axes]
+        for i in range(len(b_axes)):
+            reverse_b_axes[b_axes[i]] = i
+
+        grad_b = _np.dot(new_a.T, out_grad).reshape(new_b_shape)
+        grad_b = _np.transpose(grad_b, reverse_b_axes)
+        grad_a = _np.dot(out_grad, new_b.T).reshape(new_a_shape)
+        grad_a = _np.transpose(grad_a, reverse_a_axes)
+
+        return [grad_a, grad_b]
+
+    # test non zero size input
+    tensor_shapes = [
+        ((3, 5), (5, 4), 1),  # (a_shape, b_shape, axes)
+        ((3,), (3,), 1),
+        ((3, 4, 5, 3, 2), (5, 3, 2, 1, 2), 3),
+        ((3, 5, 4, 3, 2), (2, 3, 5, 1, 2), [[1, 3, 4], [2, 1, 0]]),
+        ((3, 5, 4), (5, 4, 3), [[1, 0, 2], [0, 2, 1]]),
+        ((3, 5, 4), (5, 3, 4), [[2, 0], [-1, -2]]),
+        ((2, 2), (2, 2), 2),
+        ((3, 5, 4), (5, ), [[-2], [0]]),
+        ((3, 5, 4), (5, ), [[1], [0]]),
+        ((2,), (2, 3), 1),
+        ((3,), (3,), 0),
+        ((2,), (2, 3), 0),
+        ((3, 5, 4), (5, ), 0),
+        ((2, 3, 4), (4, 3, 2), [[], []]),
+        ((3, 0), (0, 5), 1),
+        ((3, 0), (0, 4), [[1], [0]]),
+        ((0, 3), (3, 5), 1),
+        ((0, 3), (5, 0), [[0], [1]])
+    ]
+
+    for hybridize in [True, False]:
+        for a_shape, b_shape, axes in tensor_shapes:
+            for dtype in [_np.float32, _np.float64]:
+                test_tensordot = TestTensordot(axes)
+                if hybridize:
+                    test_tensordot.hybridize()
+                a = rand_ndarray(shape = a_shape, dtype = dtype).as_np_ndarray()
+                b = rand_ndarray(shape = b_shape, dtype = dtype).as_np_ndarray()
+                a.attach_grad()
+                b.attach_grad()
+
+                np_out = _np.tensordot(a.asnumpy(), b.asnumpy(), axes)
+                with mx.autograd.record():
+                    mx_out = test_tensordot(a, b)
+                assert mx_out.shape == np_out.shape
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol = 1e-3, atol = 1e-5)
+                mx_out.backward()
+                np_backward = tensordot_backward(a.asnumpy(), b.asnumpy(), axes)
+                assert_almost_equal(a.grad.asnumpy(), np_backward[0], rtol = 1e-3, atol=1e-5)
+                assert_almost_equal(b.grad.asnumpy(), np_backward[1], rtol = 1e-3, atol=1e-5)
+
+                # Test imperative once again
+                mx_out = np.tensordot(a, b, axes)
+                np_out = _np.tensordot(a.asnumpy(), b.asnumpy(), axes)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+
+                # test numeric gradient
+                if (_np.prod(a_shape) > 0 and _np.prod(b_shape) > 0):
+                    a_sym = mx.sym.Variable("a").as_np_ndarray()
+                    b_sym = mx.sym.Variable("b").as_np_ndarray()
+                    mx_sym = mx.sym.np.tensordot(a_sym, b_sym, axes).as_nd_ndarray()
+                    check_numeric_gradient(mx_sym, [a.as_nd_ndarray(), b.as_nd_ndarray()],
+                      rtol=1e-1, atol=1e-1, dtype = dtype)
+
+
+@with_seed()
+@use_np
+def test_np_dot():
+    shapes = [
+        ((3, 0), (0, 4)),
+        ((3,), (3,)),        # Case 1
+        ((3, 4), (4, 5)),    # Case 2
+        ((), ()),            # Case 3
+        ((3, 4, 5), ()),     # Case 3.5.1
+        ((), (3, 4, 5)),     # Case 3.5.2
+        ((3, 4, 5), (5, )),  # Case 4
+        ((3, 4, 5), (5, 2)), # Case 5
+        ((5,), (5, 2)),
+        ((3, 5, 4), (5, 4, 3)),  
+        ((3, 4), (5, 4, 3)),
+        ((4,), (5, 4, 3))
+    ]
+
+    eps = 1e-3
+
+    for shape_a, shape_b in shapes:
+        np_a = _np.random.uniform(-1.0, 1.0, shape_a)
+        np_a[abs(np_a) < eps] = 2 * eps
+        np_b = _np.random.uniform(-1.0, 1.0, shape_b)
+        np_b[abs(np_b) < eps] = 2 * eps
+        a = mx.nd.array(np_a)
+        b = mx.nd.array(np_b)
+        np_res = _np.dot(np_a, np_b)
+        mx_res = np.dot(a.as_np_ndarray(), b.as_np_ndarray())
+        assert mx_res.shape == np_res.shape
+        assert_almost_equal(np_res, mx_res.asnumpy(), rtol=1e-5, atol=1e-5)
+        mx_a = mx.sym.Variable("a")
+        mx_b = mx.sym.Variable("b")
+        mx_sym = mx.sym.np.dot(mx_a.as_np_ndarray(), mx_b.as_np_ndarray()).as_nd_ndarray()
+        if (len(shape_a) > 0 and len(shape_b) > 0 and _np.prod(shape_a) > 0 and _np.prod(shape_b) > 0):
+            check_numeric_gradient(mx_sym, {"a": a, "b": b}, numeric_eps=eps, rtol=1e-2, atol=1e-3)
+
+    bad_shapes = [((4, 5), (2, 3)), ((3, 4, 5), (6, ))]
+
+    for shape_a, shape_b in bad_shapes:
+        a = mx.nd.array(random.random()) if len(shape_a) == 0 else rand_ndarray(shape_a)
+        b = mx.nd.array(random.random()) if len(shape_b) == 0 else rand_ndarray(shape_b)
+        try:
+            mx_res = np.dot(a.as_np_ndarray(), b.as_np_ndarray())
+        except mx.base.MXNetError:
+            continue
+        assert False
+
+
 @with_seed()
 @use_np
 def test_np_sum():

From bd32de48eda2e2fd2dc86e6d708eb94c8b3c6bc6 Mon Sep 17 00:00:00 2001
From: Rohit Kumar Srivastava <srivastava.141@osu.edu>
Date: Mon, 12 Aug 2019 20:51:20 -0700
Subject: [PATCH 167/813] fixing problem with existing Singleton Caching
 (#15868)

---
 python/mxnet/runtime.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/python/mxnet/runtime.py b/python/mxnet/runtime.py
index edfa24525ab9..0f7de76937c0 100644
--- a/python/mxnet/runtime.py
+++ b/python/mxnet/runtime.py
@@ -77,11 +77,9 @@ class Features(collections.OrderedDict):
     def __new__(cls):
         if cls.instance is None:
             cls.instance = super(Features, cls).__new__(cls)
+            super(Features, cls.instance).__init__([(f.name, f) for f in feature_list()])
         return cls.instance
 
-    def __init__(self):
-        super(Features, self).__init__([(f.name, f) for f in feature_list()])
-
     def __repr__(self):
         return str(list(self.values()))
 

From 795990b81b3fd40fed0dbea801dc33dfb6475a00 Mon Sep 17 00:00:00 2001
From: Jake Lee <gstu1130@gmail.com>
Date: Mon, 12 Aug 2019 20:52:43 -0700
Subject: [PATCH 168/813] numpy linspace (#15852)

---
 python/mxnet/ndarray/numpy/_op.py      | 100 ++++++++++++++++++++++++-
 python/mxnet/numpy/multiarray.py       |  88 +++++++++++++++++++++-
 python/mxnet/symbol/numpy/_symbol.py   |  75 ++++++++++++++++++-
 src/operator/tensor/init_op.cc         |   1 +
 tests/python/unittest/test_numpy_op.py |  70 +++++++++++++++++
 5 files changed, 331 insertions(+), 3 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index caa9ba1de67c..0cc2192cdcac 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -24,8 +24,9 @@
 from ...util import set_module
 from ...context import current_context
 from . import _internal as _npi
+from ..ndarray import NDArray
 
-__all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power', 'tensordot']
+__all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power', 'tensordot', 'linspace']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -364,3 +365,100 @@ def tensordot(a, b, axes=2):
         raise ValueError('Axes length mismatch')
 
     return _npi.tensordot(a, b, a_axes_summed, b_axes_summed)
+
+
+def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0, ctx=None):  # pylint: disable=too-many-arguments
+    r"""
+    Return evenly spaced numbers over a specified interval.
+
+    Returns num evenly spaced samples, calculated over the interval [start, stop].
+    The endpoint of the interval can optionally be excluded.
+
+    Parameters
+    ----------
+    start : real number
+        The starting value of the sequence.
+    stop : real number
+        The end value of the sequence, unless endpoint is set to False. In
+        that case, the sequence consists of all but the last of num + 1
+        evenly spaced samples, so that stop is excluded. Note that the step
+        size changes when endpoint is False.
+    num : int, optional
+        Number of samples to generate. Default is 50. Must be non-negative.
+    endpoint : bool, optional
+        If True, stop is the last sample. Otherwise, it is not included.
+        Default is True.
+    retstep : bool, optional
+        If True, return (samples, step), where step is the spacing between samples.
+    dtype : dtype, optional
+        The type of the output array. If dtype is not given, infer the data
+        type from the other input arguments.
+    axis : int, optional
+        The axis in the result to store the samples. Relevant only if start or
+        stop are array-like. By default (0), the samples will be along a new
+        axis inserted at the beginning. Use -1 to get an axis at the end.
+
+    Returns
+    -------
+    samples : ndarray
+        There are num equally spaced samples in the closed interval
+        `[start, stop]` or the half-open interval `[start, stop)`
+        (depending on whether endpoint is True or False).
+    step : float, optional
+        Only returned if retstep is True
+        Size of spacing between samples.
+
+
+    See Also
+    --------
+    arange : Similar to `linspace`, but uses a step size (instead of the
+             number of samples).
+
+    Examples
+    --------
+    >>> np.linspace(2.0, 3.0, num=5)
+    array([2.  , 2.25, 2.5 , 2.75, 3.  ])
+    >>> np.linspace(2.0, 3.0, num=5, endpoint=False)
+    array([2. , 2.2, 2.4, 2.6, 2.8])
+    >>> np.linspace(2.0, 3.0, num=5, retstep=True)
+    (array([2.  , 2.25, 2.5 , 2.75, 3.  ]), 0.25)
+
+    Graphical illustration:
+
+    >>> import matplotlib.pyplot as plt
+    >>> N = 8
+    >>> y = np.zeros(N)
+    >>> x1 = np.linspace(0, 10, N, endpoint=True)
+    >>> x2 = np.linspace(0, 10, N, endpoint=False)
+    >>> plt.plot(x1.asnumpy(), y.asnumpy(), 'o')
+    [<matplotlib.lines.Line2D object at 0x...>]
+    >>> plt.plot(x2.asnumpy(), (y + 0.5).asnumpy(), 'o')
+    [<matplotlib.lines.Line2D object at 0x...>]
+    >>> plt.ylim([-0.5, 1])
+    (-0.5, 1)
+    >>> plt.show()
+
+    Notes
+    -----
+
+    This function differs from the original `numpy.linspace
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html>`_ in
+    the following aspects:
+
+    - `start` and `stop` do not support list, numpy ndarray and mxnet ndarray
+    - axis could only be 0
+    - There could be an additional `ctx` argument to specify the device, e.g. the i-th
+      GPU.
+    """
+    if isinstance(start, (list, _np.ndarray, NDArray)) or \
+       isinstance(stop, (list, _np.ndarray, NDArray)):
+        raise NotImplementedError('start and stop only support int')
+    if axis != 0:
+        raise NotImplementedError("the function only support axis 0")
+    if ctx is None:
+        ctx = current_context()
+    if retstep:
+        step = (stop - start) / (num - 1)
+        return _npi.linspace(start=start, stop=stop, num=num, endpoint=endpoint, ctx=ctx, dtype=dtype), step
+    else:
+        return _npi.linspace(start=start, stop=stop, num=num, endpoint=endpoint, ctx=ctx, dtype=dtype)
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index f4b6e733c495..8a9028bf5125 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -44,7 +44,7 @@
 from ..ndarray.numpy import _internal as _npi
 
 __all__ = ['ndarray', 'empty', 'array', 'zeros', 'ones', 'add', 'subtract', 'multiply', 'divide',
-           'mod', 'power', 'tensordot']
+           'mod', 'power', 'tensordot', 'linspace']
 
 
 # This function is copied from ndarray.py since pylint
@@ -1606,3 +1606,89 @@ def tensordot(a, b, axes=2):
            [ 4928.,  5306.]])
     """
     return _mx_nd_np.tensordot(a, b, axes)
+
+
+def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0, ctx=None):  # pylint: disable=too-many-arguments
+    r"""
+    Return evenly spaced numbers over a specified interval.
+
+    Returns num evenly spaced samples, calculated over the interval [start, stop].
+    The endpoint of the interval can optionally be excluded.
+
+    Parameters
+    ----------
+    start : real number
+        The starting value of the sequence.
+    stop : real number
+        The end value of the sequence, unless endpoint is set to False. In
+        that case, the sequence consists of all but the last of num + 1
+        evenly spaced samples, so that stop is excluded. Note that the step
+        size changes when endpoint is False.
+    num : int, optional
+        Number of samples to generate. Default is 50. Must be non-negative.
+    endpoint : bool, optional
+        If True, stop is the last sample. Otherwise, it is not included.
+        Default is True.
+    retstep : bool, optional
+        If True, return (samples, step), where step is the spacing between samples.
+    dtype : dtype, optional
+        The type of the output array. If dtype is not given, infer the data
+        type from the other input arguments.
+    axis : int, optional
+        The axis in the result to store the samples. Relevant only if start or
+        stop are array-like. By default (0), the samples will be along a new
+        axis inserted at the beginning. Use -1 to get an axis at the end.
+
+    Returns
+    -------
+    samples : ndarray
+        There are num equally spaced samples in the closed interval
+        `[start, stop]` or the half-open interval `[start, stop)`
+        (depending on whether endpoint is True or False).
+    step : float, optional
+        Only returned if retstep is True
+        Size of spacing between samples.
+
+
+    See Also
+    --------
+    arange : Similar to `linspace`, but uses a step size (instead of the
+             number of samples).
+
+    Examples
+    --------
+    >>> np.linspace(2.0, 3.0, num=5)
+    array([2.  , 2.25, 2.5 , 2.75, 3.  ])
+    >>> np.linspace(2.0, 3.0, num=5, endpoint=False)
+    array([2. , 2.2, 2.4, 2.6, 2.8])
+    >>> np.linspace(2.0, 3.0, num=5, retstep=True)
+    (array([2.  , 2.25, 2.5 , 2.75, 3.  ]), 0.25)
+
+    Graphical illustration:
+
+    >>> import matplotlib.pyplot as plt
+    >>> N = 8
+    >>> y = np.zeros(N)
+    >>> x1 = np.linspace(0, 10, N, endpoint=True)
+    >>> x2 = np.linspace(0, 10, N, endpoint=False)
+    >>> plt.plot(x1.asnumpy(), y.asnumpy(), 'o')
+    [<matplotlib.lines.Line2D object at 0x...>]
+    >>> plt.plot(x2.asnumpy(), (y + 0.5).asnumpy(), 'o')
+    [<matplotlib.lines.Line2D object at 0x...>]
+    >>> plt.ylim([-0.5, 1])
+    (-0.5, 1)
+    >>> plt.show()
+
+    Notes
+    -----
+
+    This function differs from the original `numpy.linspace
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html>`_ in
+    the following aspects:
+
+    - `start` and `stop` do not support list, numpy ndarray and mxnet ndarray
+    - axis could only be 0
+    - There could be an additional `ctx` argument to specify the device, e.g. the i-th
+      GPU.
+    """
+    return _mx_nd_np.linspace(start, stop, num, endpoint, retstep, dtype, axis, ctx)
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 65db429e8ba7..d0137932b8ec 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -28,7 +28,7 @@
 from .._internal import _set_np_symbol_class
 from . import _internal as _npi
 
-__all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power', 'tensordot']
+__all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power', 'tensordot', 'linspace']
 
 
 def _num_outputs(sym):
@@ -1065,4 +1065,77 @@ def tensordot(a, b, axes=2):
     return _npi.tensordot(a, b, a_axes_summed, b_axes_summed)
 
 
+def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0, ctx=None): # pylint: disable=too-many-arguments
+    r"""
+    Return evenly spaced numbers over a specified interval.
+
+    Returns num evenly spaced samples, calculated over the interval [start, stop].
+    The endpoint of the interval can optionally be excluded.
+
+    Parameters
+    ----------
+    start : real number
+        The starting value of the sequence.
+    stop : real number
+        The end value of the sequence, unless endpoint is set to False. In
+        that case, the sequence consists of all but the last of num + 1
+        evenly spaced samples, so that stop is excluded. Note that the step
+        size changes when endpoint is False.
+    num : int, optional
+        Number of samples to generate. Default is 50. Must be non-negative.
+    endpoint : bool, optional
+        If True, stop is the last sample. Otherwise, it is not included.
+        Default is True.
+    retstep : bool, optional
+        If True, return (samples, step), where step is the spacing between samples.
+    dtype : dtype, optional
+        The type of the output array. If dtype is not given, infer the data
+        type from the other input arguments.
+    axis : int, optional
+        The axis in the result to store the samples. Relevant only if start or
+        stop are array-like. By default (0), the samples will be along a new
+        axis inserted at the beginning. Use -1 to get an axis at the end.
+
+    Returns
+    -------
+    samples : _Symbol
+        There are num equally spaced samples in the closed interval
+        `[start, stop]` or the half-open interval `[start, stop)`
+        (depending on whether endpoint is True or False).
+    step : float, optional
+        Only returned if retstep is True
+        Size of spacing between samples.
+
+
+    See Also
+    --------
+    arange : Similar to `linspace`, but uses a step size (instead of the
+             number of samples).
+
+    Notes
+    -----
+
+    This function differs from the original `numpy.linspace
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html>`_ in
+    the following aspects:
+
+    - `start` and `stop` do not support list, numpy ndarray and mxnet ndarray
+    - axis could only be 0
+    - There could be an additional `ctx` argument to specify the device, e.g. the i-th
+      GPU.
+    """
+    if isinstance(start, (list, _np.ndarray)) or \
+        isinstance(stop, (list, _np.ndarray)):
+        raise NotImplementedError('start and stop only support int')
+    if axis != 0:
+        raise NotImplementedError("the function only support axis 0")
+    if ctx is None:
+        ctx = current_context()
+    if retstep:
+        step = (stop - start) / (num - 1)
+        return (_npi.linspace(start=start, stop=stop, num=num, endpoint=endpoint, ctx=ctx, dtype=dtype), step)
+    else:
+        return _npi.linspace(start=start, stop=stop, num=num, endpoint=endpoint, ctx=ctx, dtype=dtype)
+
+
 _set_np_symbol_class(_Symbol)
diff --git a/src/operator/tensor/init_op.cc b/src/operator/tensor/init_op.cc
index 239cff798e59..172665fbbf12 100644
--- a/src/operator/tensor/init_op.cc
+++ b/src/operator/tensor/init_op.cc
@@ -138,6 +138,7 @@ Examples::
 .add_arguments(RangeLikeParam::__FIELDS__());
 
 NNVM_REGISTER_OP(_linspace)
+.add_alias("_npi_linspace")
 .describe("Return evenly spaced numbers over a specified interval. Similar to Numpy")
 .set_num_inputs(0)
 .set_num_outputs(1)
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index b0388d7c996c..12d9f779fd50 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -275,6 +275,76 @@ def is_int(dtype):
                         assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
 
 
+@with_seed()
+@use_np
+def test_np_linspace():
+    configs = [
+        (0.0, 1.0, 10),
+        (-2, 4, 30),
+        (5.234324, 8.98324, 324),
+        (2, 10, 100)
+    ]
+    exception_configs = [
+        (0, 10, -1),
+        (0, 1, 2.5)
+    ]
+    dtypes = ['int32', 'float16', 'float32', 'float64', None]
+    for config in configs:
+        for dtype in dtypes:
+            for endpoint in [False, True]:
+                for retstep in [False, True]:
+                    if isinstance(config, tuple):
+                        mx_ret = np.linspace(*config, endpoint=endpoint, retstep=retstep, dtype=dtype)
+                        np_ret = _np.linspace(*config, endpoint=endpoint, retstep=retstep, dtype=dtype)
+                    else:
+                        mx_ret = np.linspace(config, endpoint=endpoint, retstep=retstep, dtype=dtype)
+                        np_ret = _np.linspace(config, endpoint=endpoint, retstep=retstep, dtype=dtype)
+                    if retstep:
+                        assert_almost_equal(mx_ret[0].asnumpy(), np_ret[0], atol=1e-3, rtol=1e-5)
+                        same(mx_ret[1], np_ret[1])
+                    else:
+                        assert_almost_equal(mx_ret.asnumpy(), np_ret, atol=1e-3, rtol=1e-5)
+    # check for exception input
+    for config in exception_configs:
+        assertRaises(MXNetError, np.linspace, *config)
+    # check linspace equivalent to arange
+    for test_index in range(1000):
+        assert_almost_equal(mx.np.linspace(0, test_index, test_index + 1).asnumpy(), _np.arange(test_index + 1))
+    @use_np
+    class TestLinspace(HybridBlock):
+        def __init__(self, start, stop, num=50, endpoint=None, retstep=False, dtype=None, axis=0):
+            super(TestLinspace, self).__init__()
+            self._start = start
+            self._stop = stop
+            self._num = num
+            self._endpoint = endpoint
+            self._retstep = retstep
+            self._dtype = dtype
+
+        def hybrid_forward(self, F, x):
+            if self._retstep:
+                raise ValueError("linspace didn't support retstep = True inside HybridBlock")
+            else:
+                return x + F.np.linspace(self._start, self._stop, self._num, \
+                self._endpoint, self._retstep, self._dtype)
+
+    for dtype in dtypes:
+        x = np.zeros(shape=(), dtype=dtype)
+        for config in configs:
+            for hybridize in [False, True]:
+                for endpoint in [False, True]:
+                    if isinstance(config, tuple):
+                        net = TestLinspace(*config, endpoint=endpoint, dtype=dtype)
+                        np_out = _np.linspace(*config, endpoint=endpoint, dtype=dtype)
+                    else:
+                        net = TestLinspace(config, endpoint=endpoint, dtype=dtype)
+                        np_out = _np.linspace(config, endpoint=endpoint, dtype=dtype)
+                    if hybridize:
+                        net.hybridize()
+                    mx_out = net(x)
+                    assert_almost_equal(mx_out.asnumpy(), np_out, atol=1e-3, rtol=1e-5)
+
+
 @with_seed()
 @use_np
 def test_npx_slice():

From 05f3ae13a39835c4690eb5d9567f457be84189e3 Mon Sep 17 00:00:00 2001
From: Rohit Kumar Srivastava <srivastava.141@osu.edu>
Date: Mon, 12 Aug 2019 21:23:31 -0700
Subject: [PATCH 169/813] Large Index Support for Slice (#15593)

* Adding Large Index Support for slice operator

* adding changes to fix py2 related error in CI/CD

* fixing base.py

* rearrange system call and slower Feature() call

* refactoring c_api, c_symbolic_api, c_api_common

* templatizing code

* caching results of runtime features and minor refactoring

* fixing local caching in ndarray shape
---
 include/mxnet/c_api.h               | 156 ++++++++++++++++++++-
 include/mxnet/c_predict_api.h       |   4 +-
 python/mxnet/base.py                |   1 +
 python/mxnet/ndarray/ndarray.py     |  49 +++++--
 python/mxnet/symbol/symbol.py       |  75 ++++++----
 src/c_api/c_api.cc                  | 111 ++++++++++-----
 src/c_api/c_api_common.h            |  14 +-
 src/c_api/c_api_executor.cc         |  12 +-
 src/c_api/c_api_ndarray.cc          |  10 +-
 src/c_api/c_api_profile.cc          |   2 +-
 src/c_api/c_api_symbolic.cc         | 206 ++++++++++++++++++++--------
 src/imperative/imperative.cc        |   4 +-
 src/imperative/imperative_utils.h   |   2 +-
 src/operator/tensor/matrix_op-inl.h |  42 +++---
 src/operator/tensor/slice-inl.h     |   6 +-
 tests/nightly/test_large_array.py   |   1 -
 tests/nightly/test_large_vector.py  |  37 +++++
 17 files changed, 549 insertions(+), 183 deletions(-)
 create mode 100644 tests/nightly/test_large_vector.py

diff --git a/include/mxnet/c_api.h b/include/mxnet/c_api.h
index 20b2aa2d5c9b..5ab10b6b2204 100644
--- a/include/mxnet/c_api.h
+++ b/include/mxnet/c_api.h
@@ -55,7 +55,9 @@ extern "C" {
 #endif
 
 /*! \brief manually define unsigned int */
-typedef unsigned int mx_uint;
+typedef uint32_t mx_uint;
+/*! \brief manually define 64-bit int */
+typedef int64_t mx_int64;
 /*! \brief manually define float */
 typedef float mx_float;
 /*! \brief data type to store dim size */
@@ -572,6 +574,13 @@ MXNET_DLL int MXNDArrayCreateEx(const mx_uint *shape,
                               int dtype,
                               NDArrayHandle *out);
 
+MXNET_DLL int MXNDArrayCreateEx64(const mx_int64 *shape,
+                                  int ndim,
+                                  int dev_type,
+                                  int dev_id,
+                                  int delay_alloc,
+                                  int dtype,
+                                  NDArrayHandle *out);
 
 /*!
  * \brief create an empty sparse NDArray with specified shape and data type
@@ -603,6 +612,19 @@ MXNET_DLL int MXNDArrayCreateSparseEx(int storage_type,
                                       const mx_uint *aux_shape,
                                       NDArrayHandle *out);
 
+MXNET_DLL int MXNDArrayCreateSparseEx64(int storage_type,
+                                        const mx_int64 *shape,
+                                        int ndim,
+                                        int dev_type,
+                                        int dev_id,
+                                        int delay_alloc,
+                                        int dtype,
+                                        mx_uint num_aux,
+                                        int *aux_type,
+                                        int *aux_ndims,
+                                        const mx_int64 *aux_shape,
+                                        NDArrayHandle *out);
+
 /*!
  * \brief create a NDArray handle that is loaded from raw bytes.
  * \param buf the head of the raw bytes
@@ -650,6 +672,12 @@ MXNET_DLL int MXNDArrayLoad(const char* fname,
                             mx_uint *out_name_size,
                             const char*** out_names);
 
+MXNET_DLL int MXNDArrayLoad64(const char* fname,
+                              mx_int64 *out_size,
+                              NDArrayHandle** out_arr,
+                              mx_int64 *out_name_size,
+                              const char*** out_names);
+
 /*!
  * \brief Load list / dictionary of narrays from file content loaded into memory.
  * This will load a list of ndarrays in a similar
@@ -665,11 +693,18 @@ MXNET_DLL int MXNDArrayLoad(const char* fname,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXNDArrayLoadFromBuffer(const void *ndarray_buffer,
-                            size_t size,
-                            mx_uint *out_size,
-                            NDArrayHandle** out_arr,
-                            mx_uint *out_name_size,
-                            const char*** out_names);
+                                      size_t size,
+                                      mx_uint *out_size,
+                                      NDArrayHandle** out_arr,
+                                      mx_uint *out_name_size,
+                                      const char*** out_names);
+
+MXNET_DLL int MXNDArrayLoadFromBuffer64(const void *ndarray_buffer,
+                                        size_t size,
+                                        mx_int64 *out_size,
+                                        NDArrayHandle** out_arr,
+                                        mx_int64 *out_name_size,
+                                        const char*** out_names);
 
 /*!
  * \brief Perform a synchronize copy from a continugous CPU memory region.
@@ -809,6 +844,11 @@ MXNET_DLL int MXNDArrayReshape64(NDArrayHandle handle,
 MXNET_DLL int MXNDArrayGetShape(NDArrayHandle handle,
                                 mx_uint *out_dim,
                                 const mx_uint **out_pdata);
+
+MXNET_DLL int MXNDArrayGetShape64(NDArrayHandle handle,
+                                  int *out_dim,
+                                  const int64_t **out_pdata);
+
 /*!
  * \brief get the shape of the array
  * \param handle the handle to the narray
@@ -819,6 +859,11 @@ MXNET_DLL int MXNDArrayGetShape(NDArrayHandle handle,
 MXNET_DLL int MXNDArrayGetShapeEx(NDArrayHandle handle,
                                   int *out_dim,
                                   const int **out_pdata);
+
+MXNET_DLL int MXNDArrayGetShapeEx64(NDArrayHandle handle,
+                                    int *out_dim,
+                                    const mx_int64 **out_pdata);
+
 /*!
  * \brief get the content of the data in NDArray
  * \param handle the handle to the ndarray
@@ -902,6 +947,10 @@ MXNET_DLL int MXNDArrayGetAuxType(NDArrayHandle handle,
                                   mx_uint i,
                                   int *out_type);
 
+MXNET_DLL int MXNDArrayGetAuxType64(NDArrayHandle handle,
+                                    mx_int64 i,
+                                    int *out_type);
+
 /*!
  * \brief Get a deep copy of the ith aux data blob
  * in the form of an NDArray of default storage type.
@@ -911,6 +960,10 @@ MXNET_DLL int MXNDArrayGetAuxNDArray(NDArrayHandle handle,
                                      mx_uint i,
                                      NDArrayHandle *out);
 
+MXNET_DLL int MXNDArrayGetAuxNDArray64(NDArrayHandle handle,
+                                       mx_int64 i,
+                                       NDArrayHandle *out);
+
 /*!
  * \brief Get a deep copy of the data blob
  * in the form of an NDArray of default storage type.
@@ -966,6 +1019,10 @@ MXNET_DLL int MXNDArrayGetGradState(NDArrayHandle handle, int *out);
  */
 MXNET_DLL int MXListFunctions(mx_uint *out_size,
                               FunctionHandle **out_array);
+
+MXNET_DLL int MXListFunctions64(mx_int64 *out_size,
+                                FunctionHandle **out_array);
+
 /*!
  * \brief get the function handle by name
  * \param name the name of the function
@@ -1233,6 +1290,10 @@ MXNET_DLL int MXInvokeCachedOpEx(CachedOpHandle handle,
  */
 MXNET_DLL int MXListAllOpNames(mx_uint *out_size,
                                const char ***out_array);
+
+MXNET_DLL int MXListAllOpNames64(mx_int64 *out_size,
+                                 const char ***out_array);
+
 /*!
  * \brief list all the available AtomicSymbolEntry
  * \param out_size the size of returned array
@@ -1242,6 +1303,9 @@ MXNET_DLL int MXListAllOpNames(mx_uint *out_size,
 MXNET_DLL int MXSymbolListAtomicSymbolCreators(mx_uint *out_size,
                                                AtomicSymbolCreator **out_array);
 
+MXNET_DLL int MXSymbolListAtomicSymbolCreators64(mx_int64 *out_size,
+                                                 AtomicSymbolCreator **out_array);
+
 /*!
  * \brief Get the name of an atomic symbol.
  * \param creator the AtomicSymbolCreator.
@@ -1454,6 +1518,11 @@ MXNET_DLL int MXSymbolListAttrShallow(SymbolHandle symbol,
 MXNET_DLL int MXSymbolListArguments(SymbolHandle symbol,
                                     mx_uint *out_size,
                                     const char ***out_str_array);
+
+MXNET_DLL int MXSymbolListArguments64(SymbolHandle symbol,
+                                      size_t *out_size,
+                                      const char ***out_str_array);
+
 /*!
  * \brief List returns in the symbol.
  * \param symbol the symbol
@@ -1465,6 +1534,10 @@ MXNET_DLL int MXSymbolListOutputs(SymbolHandle symbol,
                                   mx_uint *out_size,
                                   const char ***out_str_array);
 
+MXNET_DLL int MXSymbolListOutputs64(SymbolHandle symbol,
+                                    size_t *out_size,
+                                    const char ***out_str_array);
+
 /*!
  * \brief Get number of outputs of the symbol.
  * \param symbol The symbol
@@ -1472,7 +1545,7 @@ MXNET_DLL int MXSymbolListOutputs(SymbolHandle symbol,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXSymbolGetNumOutputs(SymbolHandle symbol,
-                                     mx_uint *output_count);
+                                    mx_uint *output_count);
 
 /*!
  * \brief Get a symbol that contains all the internals.
@@ -1511,6 +1584,11 @@ MXNET_DLL int MXSymbolGetOutput(SymbolHandle symbol,
 MXNET_DLL int MXSymbolListAuxiliaryStates(SymbolHandle symbol,
                                           mx_uint *out_size,
                                           const char ***out_str_array);
+
+MXNET_DLL int MXSymbolListAuxiliaryStates64(SymbolHandle symbol,
+                                            size_t *out_size,
+                                            const char ***out_str_array);
+
 /*!
  * \brief Compose the symbol on other symbols.
  *
@@ -1582,6 +1660,22 @@ MXNET_DLL int MXSymbolInferShape(SymbolHandle sym,
                                  const mx_uint ***aux_shape_data,
                                  int *complete);
 
+MXNET_DLL int MXSymbolInferShape64(SymbolHandle sym,
+                                   mx_uint num_args,
+                                   const char** keys,
+                                   const mx_int64 *arg_ind_ptr,
+                                   const mx_int64 *arg_shape_data,
+                                   size_t *in_shape_size,
+                                   const int **in_shape_ndim,
+                                   const mx_int64 ***in_shape_data,
+                                   size_t *out_shape_size,
+                                   const int **out_shape_ndim,
+                                   const mx_int64 ***out_shape_data,
+                                   size_t *aux_shape_size,
+                                   const int **aux_shape_ndim,
+                                   const mx_int64 ***aux_shape_data,
+                                   int *complete);
+
 /*!
  * \brief infer shape of unknown input shapes given the known one.
  *  The shapes are packed into a CSR matrix represented by arg_ind_ptr and arg_shape_data
@@ -1619,6 +1713,23 @@ MXNET_DLL int MXSymbolInferShapeEx(SymbolHandle sym,
                                    const int **aux_shape_ndim,
                                    const int ***aux_shape_data,
                                    int *complete);
+
+MXNET_DLL int MXSymbolInferShapeEx64(SymbolHandle sym,
+                                     mx_uint num_args,
+                                     const char** keys,
+                                     const mx_int64 *arg_ind_ptr,
+                                     const mx_int64 *arg_shape_data,
+                                     size_t *in_shape_size,
+                                     const int **in_shape_ndim,
+                                     const mx_int64 ***in_shape_data,
+                                     size_t *out_shape_size,
+                                     const int **out_shape_ndim,
+                                     const mx_int64 ***out_shape_data,
+                                     size_t *aux_shape_size,
+                                     const int **aux_shape_ndim,
+                                     const mx_int64 ***aux_shape_data,
+                                     int *complete);
+
 /*!
  * \brief DEPRECATED. Use MXSymbolInferShapePartialEx instead.
  * partially infer shape of unknown input shapes given the known one.
@@ -1660,6 +1771,21 @@ MXNET_DLL int MXSymbolInferShapePartial(SymbolHandle sym,
                                         const mx_uint ***aux_shape_data,
                                         int *complete);
 
+MXNET_DLL int MXSymbolInferShapePartial64(SymbolHandle sym,
+                                          mx_uint num_args,
+                                          const char** keys,
+                                          const mx_int64 *arg_ind_ptr,
+                                          const mx_int64 *arg_shape_data,
+                                          size_t *in_shape_size,
+                                          const int **in_shape_ndim,
+                                          const mx_int64 ***in_shape_data,
+                                          size_t *out_shape_size,
+                                          const int **out_shape_ndim,
+                                          const mx_int64 ***out_shape_data,
+                                          size_t *aux_shape_size,
+                                          const int **aux_shape_ndim,
+                                          const mx_int64 ***aux_shape_data,
+                                          int *complete);
 
 /*!
  * \brief partially infer shape of unknown input shapes given the known one.
@@ -1701,6 +1827,22 @@ MXNET_DLL int MXSymbolInferShapePartialEx(SymbolHandle sym,
                                           const int ***aux_shape_data,
                                           int *complete);
 
+MXNET_DLL int MXSymbolInferShapePartialEx64(SymbolHandle sym,
+                                            mx_uint num_args,
+                                            const char** keys,
+                                            const mx_int64 *arg_ind_ptr,
+                                            const mx_int64 *arg_shape_data,
+                                            size_t *in_shape_size,
+                                            const int **in_shape_ndim,
+                                            const mx_int64 ***in_shape_data,
+                                            size_t *out_shape_size,
+                                            const int **out_shape_ndim,
+                                            const mx_int64 ***out_shape_data,
+                                            size_t *aux_shape_size,
+                                            const int **aux_shape_ndim,
+                                            const mx_int64 ***aux_shape_data,
+                                            int *complete);
+
 /*!
  * \brief infer type of unknown input types given the known one.
  *  The types are packed into a CSR matrix represented by arg_ind_ptr and arg_type_data
diff --git a/include/mxnet/c_predict_api.h b/include/mxnet/c_predict_api.h
index 18bec625f05f..c79baa4a86ff 100644
--- a/include/mxnet/c_predict_api.h
+++ b/include/mxnet/c_predict_api.h
@@ -42,7 +42,9 @@ extern "C" {
 #endif
 
 /*! \brief manually define unsigned int */
-typedef unsigned int mx_uint;
+typedef uint32_t mx_uint;
+/*! \brief manually define 64-bit int */
+typedef int64_t mx_int64;
 /*! \brief manually define float */
 typedef float mx_float;
 /*! \brief handle to Predictor */
diff --git a/python/mxnet/base.py b/python/mxnet/base.py
index 848c36fb4d89..dd5fcf0f6db9 100644
--- a/python/mxnet/base.py
+++ b/python/mxnet/base.py
@@ -215,6 +215,7 @@ def _load_lib():
 # type definitions
 mx_int = ctypes.c_int
 mx_uint = ctypes.c_uint
+mx_int64 = ctypes.c_int64
 mx_float = ctypes.c_float
 mx_float_p = ctypes.POINTER(mx_float)
 mx_real_t = _np.float32
diff --git a/python/mxnet/ndarray/ndarray.py b/python/mxnet/ndarray/ndarray.py
index 171ba0a5008c..5f03c65a2e79 100644
--- a/python/mxnet/ndarray/ndarray.py
+++ b/python/mxnet/ndarray/ndarray.py
@@ -33,11 +33,13 @@
 import warnings
 import operator
 from functools import reduce # pylint: disable=redefined-builtin
+import sys
 import numpy as np
 from ..base import _LIB, numeric_types, integer_types
 from ..base import c_str, c_array, c_array_buf, c_handle_array, mx_real_t
-from ..base import mx_uint, NDArrayHandle, check_call, DLPackHandle, mx_int
+from ..base import mx_uint, NDArrayHandle, check_call, DLPackHandle, mx_int, mx_int64
 from ..base import ctypes2buffer
+from ..runtime import Features
 from ..context import Context, current_context
 from . import _internal
 from . import op
@@ -105,6 +107,14 @@
 _NDARRAY_BASIC_INDEXING = 0
 _NDARRAY_ADVANCED_INDEXING = 1
 
+# Caching whether MXNet was built with INT64 support or not
+_INT64_TENSOR_SIZE_ENABLED = None
+
+def _int64_enabled():
+    global _INT64_TENSOR_SIZE_ENABLED
+    if _INT64_TENSOR_SIZE_ENABLED is None:
+        _INT64_TENSOR_SIZE_ENABLED = Features().is_enabled('INT64_TENSOR_SIZE')
+    return _INT64_TENSOR_SIZE_ENABLED
 
 def _new_empty_handle():
     """Returns a new empty handle.
@@ -132,14 +142,24 @@ def _new_alloc_handle(shape, ctx, delay_alloc, dtype=mx_real_t):
         A new empty `NDArray` handle.
     """
     hdl = NDArrayHandle()
-    check_call(_LIB.MXNDArrayCreateEx(
-        c_array_buf(mx_uint, native_array('I', shape)),
-        mx_uint(len(shape)),
-        ctypes.c_int(ctx.device_typeid),
-        ctypes.c_int(ctx.device_id),
-        ctypes.c_int(int(delay_alloc)),
-        ctypes.c_int(int(_DTYPE_NP_TO_MX[np.dtype(dtype).type])),
-        ctypes.byref(hdl)))
+    if sys.version_info[0] > 2 and _int64_enabled():
+        check_call(_LIB.MXNDArrayCreateEx64(
+            c_array_buf(mx_int64, native_array('q', shape)),
+            ctypes.c_int(len(shape)),
+            ctypes.c_int(ctx.device_typeid),
+            ctypes.c_int(ctx.device_id),
+            ctypes.c_int(int(delay_alloc)),
+            ctypes.c_int(int(_DTYPE_NP_TO_MX[np.dtype(dtype).type])),
+            ctypes.byref(hdl)))
+    else:
+        check_call(_LIB.MXNDArrayCreateEx(
+            c_array_buf(mx_uint, native_array('I', shape)),
+            mx_uint(len(shape)),
+            ctypes.c_int(ctx.device_typeid),
+            ctypes.c_int(ctx.device_id),
+            ctypes.c_int(int(delay_alloc)),
+            ctypes.c_int(int(_DTYPE_NP_TO_MX[np.dtype(dtype).type])),
+            ctypes.byref(hdl)))
     return hdl
 
 
@@ -2218,9 +2238,14 @@ def shape(self):
         (2L, 3L, 4L)
         """
         ndim = mx_int()
-        pdata = ctypes.POINTER(mx_int)()
-        check_call(_LIB.MXNDArrayGetShapeEx(
-            self.handle, ctypes.byref(ndim), ctypes.byref(pdata)))
+        if _int64_enabled():
+            pdata = ctypes.POINTER(mx_int64)()
+            check_call(_LIB.MXNDArrayGetShapeEx64(
+                self.handle, ctypes.byref(ndim), ctypes.byref(pdata)))
+        else:
+            pdata = ctypes.POINTER(mx_int)()
+            check_call(_LIB.MXNDArrayGetShapeEx(
+                self.handle, ctypes.byref(ndim), ctypes.byref(pdata)))
         if ndim.value == -1:
             return None
         else:
diff --git a/python/mxnet/symbol/symbol.py b/python/mxnet/symbol/symbol.py
index 542b3796a9fe..3ac44176a87b 100644
--- a/python/mxnet/symbol/symbol.py
+++ b/python/mxnet/symbol/symbol.py
@@ -29,17 +29,17 @@
 import ctypes
 import warnings
 from numbers import Number
-
+import sys
 import numpy as _numpy  # pylint: disable=relative-import
 
 from ..attribute import AttrScope
 from ..base import _LIB, numeric_types, c_array, c_array_buf, c_str, c_str_array, c_handle_array
-from ..base import mx_uint, py_str, string_types, integer_types, mx_int
+from ..base import mx_uint, py_str, string_types, integer_types, mx_int, mx_int64
 from ..base import NDArrayHandle, ExecutorHandle, SymbolHandle
 from ..base import check_call, MXNetError, NotImplementedForSymbol
 from ..context import Context, current_context
 from ..ndarray import NDArray, _DTYPE_NP_TO_MX, _DTYPE_MX_TO_NP, _GRAD_REQ_MAP
-from ..ndarray.ndarray import _STORAGE_TYPE_STR_TO_ID
+from ..ndarray.ndarray import _STORAGE_TYPE_STR_TO_ID, _int64_enabled
 from ..ndarray import _ndarray_cls
 from ..executor import Executor
 from . import _internal
@@ -1207,34 +1207,59 @@ def _infer_shape_impl(self, partial, *args, **kwargs):
             keys = c_str_array(str_keys)
         arg_shape_size = mx_uint()
         arg_shape_ndim = ctypes.POINTER(mx_int)()
-        arg_shape_data = ctypes.POINTER(ctypes.POINTER(mx_int))()
         out_shape_size = mx_uint()
         out_shape_ndim = ctypes.POINTER(mx_int)()
-        out_shape_data = ctypes.POINTER(ctypes.POINTER(mx_int))()
         aux_shape_size = mx_uint()
         aux_shape_ndim = ctypes.POINTER(mx_int)()
-        aux_shape_data = ctypes.POINTER(ctypes.POINTER(mx_int))()
         complete = ctypes.c_int()
-        if partial:
-            infer_func = _LIB.MXSymbolInferShapePartialEx
+        if sys.version_info[0] > 2 and _int64_enabled():
+            arg_shape_data = ctypes.POINTER(ctypes.POINTER(mx_int64))()
+            out_shape_data = ctypes.POINTER(ctypes.POINTER(mx_int64))()
+            aux_shape_data = ctypes.POINTER(ctypes.POINTER(mx_int64))()
+            if partial:
+                infer_func = _LIB.MXSymbolInferShapePartialEx64
+            else:
+                infer_func = _LIB.MXSymbolInferShapeEx64
+            check_call(infer_func(
+                self.handle,
+                mx_uint(len(indptr) - 1),
+                keys,
+                c_array_buf(mx_int64, array('q', indptr)),
+                c_array_buf(mx_int64, array('q', sdata)),
+                ctypes.byref(arg_shape_size),
+                ctypes.byref(arg_shape_ndim),
+                ctypes.byref(arg_shape_data),
+                ctypes.byref(out_shape_size),
+                ctypes.byref(out_shape_ndim),
+                ctypes.byref(out_shape_data),
+                ctypes.byref(aux_shape_size),
+                ctypes.byref(aux_shape_ndim),
+                ctypes.byref(aux_shape_data),
+                ctypes.byref(complete)))
         else:
-            infer_func = _LIB.MXSymbolInferShapeEx
-        check_call(infer_func(
-            self.handle,
-            mx_uint(len(indptr) - 1),
-            keys,
-            c_array_buf(mx_uint, array('I', indptr)),
-            c_array_buf(mx_int, array('i', sdata)),
-            ctypes.byref(arg_shape_size),
-            ctypes.byref(arg_shape_ndim),
-            ctypes.byref(arg_shape_data),
-            ctypes.byref(out_shape_size),
-            ctypes.byref(out_shape_ndim),
-            ctypes.byref(out_shape_data),
-            ctypes.byref(aux_shape_size),
-            ctypes.byref(aux_shape_ndim),
-            ctypes.byref(aux_shape_data),
-            ctypes.byref(complete)))
+            arg_shape_data = ctypes.POINTER(ctypes.POINTER(mx_int))()
+            out_shape_data = ctypes.POINTER(ctypes.POINTER(mx_int))()
+            aux_shape_data = ctypes.POINTER(ctypes.POINTER(mx_int))()
+            if partial:
+                infer_func = _LIB.MXSymbolInferShapePartialEx
+            else:
+                infer_func = _LIB.MXSymbolInferShapeEx
+            check_call(infer_func(
+                self.handle,
+                mx_uint(len(indptr) - 1),
+                keys,
+                c_array_buf(mx_uint, array('I', indptr)),
+                c_array_buf(mx_int, array('i', sdata)),
+                ctypes.byref(arg_shape_size),
+                ctypes.byref(arg_shape_ndim),
+                ctypes.byref(arg_shape_data),
+                ctypes.byref(out_shape_size),
+                ctypes.byref(out_shape_ndim),
+                ctypes.byref(out_shape_data),
+                ctypes.byref(aux_shape_size),
+                ctypes.byref(aux_shape_ndim),
+                ctypes.byref(aux_shape_data),
+                ctypes.byref(complete)))
         if complete.value != 0:
             arg_shapes = [tuple(arg_shape_data[i][:arg_shape_ndim[i]])
                           if arg_shape_ndim[i] >= 0 else None
diff --git a/src/c_api/c_api.cc b/src/c_api/c_api.cc
index 17648458ba22..c2b80b3f601c 100644
--- a/src/c_api/c_api.cc
+++ b/src/c_api/c_api.cc
@@ -67,7 +67,7 @@ inline int MXAPIGetFunctionRegInfo(const FunRegType *e,
                                    const char ***arg_type_infos,
                                    const char ***arg_descriptions,
                                    const char **return_type) {
-  MXAPIThreadLocalEntry *ret = MXAPIThreadLocalStore::Get();
+  MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
 
   API_BEGIN();
   *name = e->name.c_str();
@@ -189,6 +189,19 @@ int MXNDArrayCreateNone(NDArrayHandle *out) {
   API_END();
 }
 
+template<typename DataType, typename dimtype>
+void CreateNDArray(const DataType* shape,
+                   dimtype ndim,
+                   int dev_type,
+                   int dev_id,
+                   int delay_alloc,
+                   int dtype,
+                   NDArrayHandle* out) {
+  *out = new NDArray(mxnet::TShape(shape, shape + ndim),
+                     Context::Create(static_cast<Context::DeviceType>(dev_type), dev_id),
+                     delay_alloc != 0, dtype);
+}
+
 int MXNDArrayCreate(const mx_uint *shape,
                     mx_uint ndim,
                     int dev_type,
@@ -196,41 +209,48 @@ int MXNDArrayCreate(const mx_uint *shape,
                     int delay_alloc,
                     NDArrayHandle *out) {
   API_BEGIN();
-  *out = new NDArray(
-      mxnet::TShape(shape, shape + ndim),
-      Context::Create(static_cast<Context::DeviceType>(dev_type), dev_id),
-      delay_alloc != 0);
+  *out = new NDArray(mxnet::TShape(shape, shape + ndim),
+                     Context::Create(static_cast<Context::DeviceType>(dev_type), dev_id),
+                     delay_alloc != 0);
+  API_END();
+}
+
+int MXNDArrayCreateEx64(const mx_int64 *shape,
+                        int ndim,
+                        int dev_type,
+                        int dev_id,
+                        int delay_alloc,
+                        int dtype,
+                        NDArrayHandle *out) {
+  API_BEGIN();
+  CreateNDArray<mx_int64, int>(shape, ndim, dev_type, dev_id, delay_alloc, dtype, out);
   API_END();
 }
 
 int MXNDArrayCreateEx(const mx_uint *shape,
-                    mx_uint ndim,
-                    int dev_type,
-                    int dev_id,
-                    int delay_alloc,
-                    int dtype,
-                    NDArrayHandle *out) {
+                      mx_uint ndim,
+                      int dev_type,
+                      int dev_id,
+                      int delay_alloc,
+                      int dtype,
+                      NDArrayHandle *out) {
   API_BEGIN();
-  *out = new NDArray(
-      mxnet::TShape(shape, shape + ndim),
-      Context::Create(static_cast<Context::DeviceType>(dev_type), dev_id),
-      delay_alloc != 0,
-      dtype);
+  CreateNDArray<mx_uint, mx_uint>(shape, ndim, dev_type, dev_id, delay_alloc, dtype, out);
   API_END();
 }
 
 int MXNDArrayCreateSparseEx(int storage_type,
-                    const mx_uint *shape,
-                    mx_uint ndim,
-                    int dev_type,
-                    int dev_id,
-                    int delay_alloc,
-                    int dtype,
-                    mx_uint num_aux,
-                    int *aux_type,
-                    mx_uint *aux_ndims,
-                    const mx_uint *aux_shape,
-                    NDArrayHandle *out) {
+                            const mx_uint *shape,
+                            mx_uint ndim,
+                            int dev_type,
+                            int dev_id,
+                            int delay_alloc,
+                            int dtype,
+                            mx_uint num_aux,
+                            int *aux_type,
+                            mx_uint *aux_ndims,
+                            const mx_uint *aux_shape,
+                            NDArrayHandle *out) {
   API_BEGIN();
   std::vector<int> aux_types;
   mxnet::ShapeVector aux_shapes;
@@ -269,7 +289,7 @@ int MXNDArrayLoadFromRawBytes(const void *buf,
 int MXNDArraySaveRawBytes(NDArrayHandle handle,
                           size_t *out_size,
                           const char **out_buf) {
-  MXAPIThreadLocalEntry *ret = MXAPIThreadLocalStore::Get();
+  MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
   API_BEGIN();
   ret->ret_str.resize(0);
   dmlc::MemoryStringStream strm(&ret->ret_str);
@@ -365,7 +385,7 @@ int MXNDArrayLoad(const char* fname,
                   NDArrayHandle** out_arr,
                   mx_uint *out_name_size,
                   const char*** out_names) {
-  MXAPIThreadLocalEntry *ret = MXAPIThreadLocalStore::Get();
+  MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
   ret->ret_vec_str.clear();
   API_BEGIN();
   std::vector<NDArray> data;
@@ -397,7 +417,7 @@ int MXNDArrayLoadFromBuffer(const void *ndarray_buffer,
                             NDArrayHandle** out_arr,
                             mx_uint *out_name_size,
                             const char*** out_names) {
-  MXAPIThreadLocalEntry *ret = MXAPIThreadLocalStore::Get();
+  MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
   ret->ret_vec_str.clear();
   API_BEGIN();
   CHECK_NOTNULL(ndarray_buffer);
@@ -521,7 +541,7 @@ int MXNDArrayGetStorageType(NDArrayHandle handle,
 int MXNDArrayGetShape(NDArrayHandle handle,
                       mx_uint *out_dim,
                       const mx_uint **out_pdata) {
-  MXAPIThreadLocalEntry *ret = MXAPIThreadLocalStore::Get();
+  MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
   API_BEGIN();
   NDArray *arr = static_cast<NDArray*>(handle);
   if (!arr->is_none()) {
@@ -537,12 +557,10 @@ int MXNDArrayGetShape(NDArrayHandle handle,
   API_END();
 }
 
-int MXNDArrayGetShapeEx(NDArrayHandle handle,
-                        int *out_dim,
-                        const int **out_pdata) {
-  MXAPIThreadLocalEntry *ret = MXAPIThreadLocalStore::Get();
-  API_BEGIN();
-  NDArray *arr = static_cast<NDArray*>(handle);
+template<typename dtype>
+inline void GetShape(NDArrayHandle handle, const dtype** out_pdata, int* out_dim,
+                     MXAPIThreadLocalEntry<dtype>* ret) {
+  NDArray* arr = static_cast<NDArray*>(handle);
   if (!arr->is_none()) {
     mxnet::TShape s = arr->shape();
     if (!Imperative::Get()->is_np_shape()) {
@@ -550,7 +568,7 @@ int MXNDArrayGetShapeEx(NDArrayHandle handle,
     }
     *out_dim = s.ndim();
     if (s.ndim() >= 0) {
-      std::vector<int> &buffer = ret->arg_shape_buffer_ex;
+      std::vector<dtype> &buffer = ret->arg_shape_buffer_ex;
       buffer.resize(s.ndim());
       mxnet::ShapeTypeCast(s.begin(), s.end(), buffer.data());
       *out_pdata = buffer.data();
@@ -562,6 +580,23 @@ int MXNDArrayGetShapeEx(NDArrayHandle handle,
       *out_dim = 0;
     }
   }
+}
+
+int MXNDArrayGetShapeEx(NDArrayHandle handle,
+                        int *out_dim,
+                        const int **out_pdata) {
+  MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
+  API_BEGIN();
+  GetShape<int>(handle, out_pdata, out_dim, ret);
+  API_END();
+}
+
+int MXNDArrayGetShapeEx64(NDArrayHandle handle,
+                          int *out_dim,
+                          const mx_int64 **out_pdata) {
+  MXAPIThreadLocalEntry<int64_t> *ret = MXAPIThreadLocalStore<int64_t>::Get();
+  API_BEGIN();
+  GetShape<mx_int64>(handle, out_pdata, out_dim, ret);
   API_END();
 }
 
diff --git a/src/c_api/c_api_common.h b/src/c_api/c_api_common.h
index 233acc85f36b..93fcff09a8e6 100644
--- a/src/c_api/c_api_common.h
+++ b/src/c_api/c_api_common.h
@@ -57,6 +57,7 @@
 using namespace mxnet;
 
 /*! \brief entry to to easily hold returning information */
+template<typename dtype = int>
 struct MXAPIThreadLocalEntry {
   /*! \brief result holder for returning string */
   std::string ret_str;
@@ -81,11 +82,11 @@ struct MXAPIThreadLocalEntry {
   /*! \brief result holder for returning shape pointer */
   std::vector<const mx_uint*> arg_shape_data, out_shape_data, aux_shape_data;
   /*! \brief result holder for returning shape pointer */
-  std::vector<const int*> arg_shape_data_ex, out_shape_data_ex, aux_shape_data_ex;
+  std::vector<const dtype*> arg_shape_data_ex, out_shape_data_ex, aux_shape_data_ex;
   /*! \brief uint32_t buffer for returning shape pointer */
   std::vector<uint32_t> arg_shape_buffer, out_shape_buffer, aux_shape_buffer;
   /*! \brief uint32_t buffer for returning shape pointer */
-  std::vector<int> arg_shape_buffer_ex, out_shape_buffer_ex, aux_shape_buffer_ex;
+  std::vector<dtype> arg_shape_buffer_ex, out_shape_buffer_ex, aux_shape_buffer_ex;
   /*! \brief bool buffer */
   std::vector<bool> save_inputs, save_outputs;
   // DEPRECATED. Use SetupShapeArrayReturnWithBufferEx instead.
@@ -111,8 +112,8 @@ struct MXAPIThreadLocalEntry {
   inline static void SetupShapeArrayReturnWithBufferEx(
       const mxnet::ShapeVector &shapes,
       std::vector<int> *ndim,
-      std::vector<const int*> *data,
-      std::vector<int> *buffer) {
+      std::vector<const dtype*> *data,
+      std::vector<dtype> *buffer) {
     ndim->resize(shapes.size());
     data->resize(shapes.size());
     size_t size = 0;
@@ -122,7 +123,7 @@ struct MXAPIThreadLocalEntry {
       }
     }
     buffer->resize(size);
-    int *ptr = buffer->data();
+    dtype* ptr = buffer->data();
     for (size_t i = 0; i < shapes.size(); ++i) {
       ndim->at(i) = shapes[i].ndim();
       data->at(i) = ptr;
@@ -134,7 +135,8 @@ struct MXAPIThreadLocalEntry {
 };
 
 // define the threadlocal store.
-typedef dmlc::ThreadLocalStore<MXAPIThreadLocalEntry> MXAPIThreadLocalStore;
+template<typename dtype = int>
+using MXAPIThreadLocalStore = dmlc::ThreadLocalStore<MXAPIThreadLocalEntry<dtype>>;
 
 namespace mxnet {
 // copy attributes from inferred vector back to the vector of each type.
diff --git a/src/c_api/c_api_executor.cc b/src/c_api/c_api_executor.cc
index ebe3f17d7d90..31b74b503f80 100644
--- a/src/c_api/c_api_executor.cc
+++ b/src/c_api/c_api_executor.cc
@@ -32,7 +32,7 @@
 
 int MXExecutorPrint(ExecutorHandle handle, const char **out_str) {
   Executor *exec = static_cast<Executor*>(handle);
-  MXAPIThreadLocalEntry *ret = MXAPIThreadLocalStore::Get();
+  MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
   API_BEGIN();
   std::ostringstream os;
   exec->Print(os);
@@ -78,7 +78,7 @@ int MXExecutorBackwardEx(ExecutorHandle handle,
 int MXExecutorOutputs(ExecutorHandle handle,
                       mx_uint *out_size,
                       NDArrayHandle **out) {
-  MXAPIThreadLocalEntry *ret = MXAPIThreadLocalStore::Get();
+  MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
   API_BEGIN();
   Executor *exec = static_cast<Executor*>(handle);
   std::vector<NDArray> heads = exec->outputs();
@@ -252,7 +252,7 @@ int MXExecutorSimpleBind(SymbolHandle symbol_handle,
                          NDArrayHandle** aux_states,
                          ExecutorHandle shared_exec_handle,
                          ExecutorHandle* out) {
-  MXAPIThreadLocalEntry *ret = MXAPIThreadLocalStore::Get();
+  MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
   API_BEGIN();
   nnvm::Symbol *sym = static_cast<nnvm::Symbol*>(symbol_handle);
 
@@ -586,7 +586,7 @@ int MXExecutorSimpleBindEx(SymbolHandle symbol_handle,
                            NDArrayHandle** aux_states,
                            ExecutorHandle shared_exec_handle,
                            ExecutorHandle* out) {
-  MXAPIThreadLocalEntry *ret = MXAPIThreadLocalStore::Get();
+  MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
   API_BEGIN();
   nnvm::Symbol *sym = static_cast<nnvm::Symbol*>(symbol_handle);
 
@@ -870,7 +870,7 @@ int MXExecutorReshape(int partial_shaping,
                       ExecutorHandle *out) {
   Executor* new_exec = nullptr;
 
-  MXAPIThreadLocalEntry *ret = MXAPIThreadLocalStore::Get();
+  MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
   API_BEGIN();
   *out = nullptr;  // ensure we can know whether to free executor on early abort
   // create shape map for in_args and aux_states
@@ -961,7 +961,7 @@ int MXExecutorReshapeEx(int partial_shaping,
                         ExecutorHandle *out) {
   Executor* new_exec = nullptr;
 
-  MXAPIThreadLocalEntry *ret = MXAPIThreadLocalStore::Get();
+  MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
   API_BEGIN();
   *out = nullptr;  // ensure we can know whether to free executor on early abort
   // create shape map for in_args and aux_states
diff --git a/src/c_api/c_api_ndarray.cc b/src/c_api/c_api_ndarray.cc
index c9c6000e2f6f..4546659ca64e 100644
--- a/src/c_api/c_api_ndarray.cc
+++ b/src/c_api/c_api_ndarray.cc
@@ -87,7 +87,7 @@ void MXImperativeInvokeImpl(AtomicSymbolCreator creator,
                             const char **param_keys,
                             const char **param_vals) {
   const nnvm::Op* op = static_cast<nnvm::Op*>(creator);
-  MXAPIThreadLocalEntry *ret = MXAPIThreadLocalStore::Get();
+  MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
 
   nnvm::NodeAttrs attrs = imperative::ParseAttrs(op, num_inputs, num_params,
                                                  param_keys, param_vals);
@@ -138,7 +138,7 @@ int MXImperativeInvokeEx(AtomicSymbolCreator creator,
                          const char **param_keys,
                          const char **param_vals,
                          const int **out_stypes) {  // outputs storage types
-  MXAPIThreadLocalEntry *ret = MXAPIThreadLocalStore::Get();
+  MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
   API_BEGIN();
   MXImperativeInvokeImpl(creator, num_inputs, inputs, num_outputs, outputs,
                          num_params, param_keys, param_vals);
@@ -194,7 +194,7 @@ int MXInvokeCachedOp(CachedOpHandle handle,
                      NDArrayHandle *inputs,
                      int *num_outputs,
                      NDArrayHandle **outputs) {
-  MXAPIThreadLocalEntry *ret = MXAPIThreadLocalStore::Get();
+  MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
 
   API_BEGIN();
   CachedOpPtr op = *static_cast<CachedOpPtr*>(handle);
@@ -238,7 +238,7 @@ int MXInvokeCachedOpEx(CachedOpHandle handle,
                        int *num_outputs,
                        NDArrayHandle **outputs,
                        const int **out_stypes) {  // outputs storage types
-  MXAPIThreadLocalEntry *ret = MXAPIThreadLocalStore::Get();
+  MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
   int err = MXInvokeCachedOp(handle, num_inputs, inputs, num_outputs, outputs);
   if (err != 0) return err;
   API_BEGIN();
@@ -331,7 +331,7 @@ int MXAutogradBackwardEx(mx_uint num_output,
                          int is_train,
                          NDArrayHandle **grad_handles,
                          int **grad_stypes) {
-  MXAPIThreadLocalEntry *ret = MXAPIThreadLocalStore::Get();
+  MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
   API_BEGIN();
 
   std::vector<NDArray*> outputs, ograds, variables;
diff --git a/src/c_api/c_api_profile.cc b/src/c_api/c_api_profile.cc
index cec70288b126..5eb219a4e525 100644
--- a/src/c_api/c_api_profile.cc
+++ b/src/c_api/c_api_profile.cc
@@ -312,7 +312,7 @@ int MXAggregateProfileStatsPrint(const char **out_str, int reset) {
 
 int MXAggregateProfileStatsPrintEx(const char **out_str, int reset, int format, int sort_by,
                                   int ascending) {
-  MXAPIThreadLocalEntry *ret = MXAPIThreadLocalStore::Get();
+  MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
   API_BEGIN();
     CHECK_NOTNULL(out_str);
     profiler::Profiler *profiler = profiler::Profiler::Get();
diff --git a/src/c_api/c_api_symbolic.cc b/src/c_api/c_api_symbolic.cc
index 020c0d17f0d1..e8d59d90e99b 100644
--- a/src/c_api/c_api_symbolic.cc
+++ b/src/c_api/c_api_symbolic.cc
@@ -98,7 +98,7 @@ int MXSymbolGetAtomicSymbolInfo(AtomicSymbolCreator creator,
                                 const char **return_type) {
   static auto& map_key_var_args = nnvm::Op::GetAttr<std::string>("key_var_num_args");
   const Op* op = static_cast<Op*>(creator);
-  MXAPIThreadLocalEntry *ret = MXAPIThreadLocalStore::Get();
+  MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
   ret->ret_str.resize(0);
 
   if (map_key_var_args.count(op) != 0) {
@@ -203,7 +203,7 @@ int MXSymbolGetAttr(SymbolHandle symbol,
                     const char** out,
                     int* success) {
   nnvm::Symbol *s = static_cast<nnvm::Symbol*>(symbol);
-  MXAPIThreadLocalEntry *ret = MXAPIThreadLocalStore::Get();
+  MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
   API_BEGIN();
   if (s->GetAttr(key, &(ret->ret_str))) {
     *out = (ret->ret_str).c_str();
@@ -251,7 +251,7 @@ int MXSymbolListAttr(SymbolHandle symbol,
                      mx_uint *out_size,
                      const char*** out) {
   nnvm::Symbol *s = static_cast<nnvm::Symbol*>(symbol);
-  MXAPIThreadLocalEntry *ret = MXAPIThreadLocalStore::Get();
+  MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
   API_BEGIN();
   std::vector<std::tuple<std::string, std::string, std::string> > attr =
       s->ListAttrsRecursive();
@@ -281,7 +281,7 @@ int MXSymbolListAttrShallow(SymbolHandle symbol,
                             mx_uint *out_size,
                             const char*** out) {
   nnvm::Symbol *s = static_cast<nnvm::Symbol*>(symbol);
-  MXAPIThreadLocalEntry *ret = MXAPIThreadLocalStore::Get();
+  MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
   API_BEGIN();
   std::unordered_map<std::string, std::string> attr =
       s->ListAttrs(static_cast<nnvm::Symbol::ListAttrOption>(1));  // NOLINT(*)
@@ -360,7 +360,7 @@ int MXSymbolGetInputSymbols(SymbolHandle sym, SymbolHandle **input_arr, int *inp
   std::vector<nnvm::Symbol *> input_syms = mxnet::GetInputSymbols(*s);
   *input_size = input_syms.size();
 
-  MXAPIThreadLocalEntry *ret = MXAPIThreadLocalStore::Get();
+  MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
   ret->ret_handles.clear();
   ret->ret_handles.reserve(*input_size);
   for (int i = 0; i < *input_size; ++i) ret->ret_handles.push_back(input_syms[i]);
@@ -405,7 +405,7 @@ int MXSymbolCutSubgraph(SymbolHandle sym, SymbolHandle **input_symbols,
     }
     *input_size = input_syms.size();
 
-    MXAPIThreadLocalEntry *ret = MXAPIThreadLocalStore::Get();
+    MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
     ret->ret_handles.clear();
     ret->ret_handles.reserve(*input_size);
     for (int i = 0; i < *input_size; ++i) ret->ret_handles.push_back(input_syms[i]);
@@ -464,7 +464,7 @@ int MXSymbolSaveToFile(SymbolHandle symbol, const char *fname) {
 
 int MXSymbolSaveToJSON(SymbolHandle symbol, const char **out_json) {
   nnvm::Symbol *s = static_cast<nnvm::Symbol*>(symbol);
-  MXAPIThreadLocalEntry *ret = MXAPIThreadLocalStore::Get();
+  MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
   API_BEGIN();
   ret->ret_str = nnvm::pass::SaveJSON(Symbol2Graph(*s));
   *out_json = ret->ret_str.c_str();
@@ -528,7 +528,7 @@ int MXSymbolInferShape(SymbolHandle sym,
                        const mx_uint ***aux_shape_data,
                        int *complete) {
   nnvm::Symbol *s = static_cast<nnvm::Symbol*>(sym);
-  MXAPIThreadLocalEntry *ret = MXAPIThreadLocalStore::Get();
+  MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
   API_BEGIN();
   nnvm::Graph g = Symbol2Graph(*s);
   mxnet::ShapeVector arg_shapes(g.indexed_graph().input_nodes().size(), mxnet::TShape());
@@ -565,11 +565,11 @@ int MXSymbolInferShape(SymbolHandle sym,
            &(ret->arg_shapes), &(ret->out_shapes), &(ret->aux_shapes));
 
   // copy data back
-  MXAPIThreadLocalEntry::SetupShapeArrayReturnWithBuffer(ret->arg_shapes,
+  MXAPIThreadLocalEntry<>::SetupShapeArrayReturnWithBuffer(ret->arg_shapes,
       &(ret->arg_shape_ndim), &(ret->arg_shape_data), &(ret->arg_shape_buffer));
-  MXAPIThreadLocalEntry::SetupShapeArrayReturnWithBuffer(ret->out_shapes,
+  MXAPIThreadLocalEntry<>::SetupShapeArrayReturnWithBuffer(ret->out_shapes,
       &(ret->out_shape_ndim), &(ret->out_shape_data), &(ret->out_shape_buffer));
-  MXAPIThreadLocalEntry::SetupShapeArrayReturnWithBuffer(ret->aux_shapes,
+  MXAPIThreadLocalEntry<>::SetupShapeArrayReturnWithBuffer(ret->aux_shapes,
       &(ret->aux_shape_ndim), &(ret->aux_shape_data), &(ret->aux_shape_buffer));
   *in_shape_size = static_cast<mx_uint>(ret->arg_shapes.size());
   *in_shape_ndim = dmlc::BeginPtr(ret->arg_shape_ndim);
@@ -585,76 +585,149 @@ int MXSymbolInferShape(SymbolHandle sym,
   API_END();
 }
 
-int MXSymbolInferShapeEx(SymbolHandle sym,
-                         mx_uint num_args,
-                         const char** keys,
-                         const mx_uint *arg_ind_ptr,
-                         const int *arg_shape_data,
-                         mx_uint *in_shape_size,
-                         const int **in_shape_ndim,
-                         const int ***in_shape_data,
-                         mx_uint *out_shape_size,
-                         const int **out_shape_ndim,
-                         const int ***out_shape_data,
-                         mx_uint *aux_shape_size,
-                         const int **aux_shape_ndim,
-                         const int ***aux_shape_data,
-                         int *complete) {
-  nnvm::Symbol *s = static_cast<nnvm::Symbol*>(sym);
-  MXAPIThreadLocalEntry *ret = MXAPIThreadLocalStore::Get();
-  API_BEGIN();
+template<typename dtype, typename stype, typename itype>
+inline void SymbolInferShape(const char** keys,
+                             mx_uint num_args,
+                             const dtype* arg_shape_data,
+                             const itype* arg_ind_ptr,
+                             const int** in_shape_ndim,
+                             const dtype*** in_shape_data,
+                             const int** out_shape_ndim,
+                             const dtype*** out_shape_data,
+                             const int** aux_shape_ndim,
+                             const dtype*** aux_shape_data,
+                             nnvm::Symbol* s,
+                             MXAPIThreadLocalEntry<dtype>* ret,
+                             stype* in_shape_size,
+                             stype* out_shape_size,
+                             stype* aux_shape_size,
+                             int* complete) {
   nnvm::Graph g = Symbol2Graph(*s);
   mxnet::ShapeVector arg_shapes(g.indexed_graph().input_nodes().size(), mxnet::TShape());
   if (keys == nullptr && num_args != 0) {
-    std::vector<uint32_t> read_only_args = mxnet::ReadOnlyArgIndices(g.indexed_graph());
+    std::vector < uint32_t > read_only_args = mxnet::ReadOnlyArgIndices(g.indexed_graph());
     CHECK_LE(num_args, read_only_args.size());
     for (mx_uint i = 0; i < num_args; ++i) {
-      arg_shapes[read_only_args[i]] = mxnet::ShapeTypeCast(
-          arg_shape_data + arg_ind_ptr[i], arg_shape_data + arg_ind_ptr[i+1]);
+      arg_shapes[read_only_args[i]] = mxnet::ShapeTypeCast(arg_shape_data + arg_ind_ptr[i],
+                                                           arg_shape_data + arg_ind_ptr[i + 1]);
     }
   } else {
     std::unordered_map<std::string, mxnet::TShape> kwargs;
     for (mx_uint i = 0; i < num_args; ++i) {
-      kwargs[keys[i]] = mxnet::ShapeTypeCast(
-          arg_shape_data + arg_ind_ptr[i], arg_shape_data + arg_ind_ptr[i+1]);
+      kwargs[keys[i]] = mxnet::ShapeTypeCast(arg_shape_data + arg_ind_ptr[i],
+                                             arg_shape_data + arg_ind_ptr[i + 1]);
     }
     mxnet::MatchArguments(g.indexed_graph(), kwargs, &arg_shapes, "InferShape");
   }
-
   try {
     g = mxnet::exec::InferShape(std::move(g), std::move(arg_shapes), "__shape__");
-  } catch (const mxnet::op::InferShapeError &err) {
+  } catch (const mxnet::op::InferShapeError& err) {
     throw dmlc::Error(err.msg);
   }
-
   // if use legacy shape definition, need to convert numpy shape to legacy shape
   mxnet::ShapeVector shapes = g.GetAttr<mxnet::ShapeVector>("shape");
   if (!Imperative::Get()->is_np_shape()) {
     common::ConvertToLegacyShape(&shapes);
   }
-
   // copy back
-  CopyAttr(g.indexed_graph(), shapes,
-           &(ret->arg_shapes), &(ret->out_shapes), &(ret->aux_shapes));
-
+  CopyAttr(g.indexed_graph(), shapes, &(ret->arg_shapes), &(ret->out_shapes), &(ret->aux_shapes));
   // copy data back
-  MXAPIThreadLocalEntry::SetupShapeArrayReturnWithBufferEx(ret->arg_shapes,
-      &(ret->arg_shape_ndim_ex), &(ret->arg_shape_data_ex), &(ret->arg_shape_buffer_ex));
-  MXAPIThreadLocalEntry::SetupShapeArrayReturnWithBufferEx(ret->out_shapes,
-      &(ret->out_shape_ndim_ex), &(ret->out_shape_data_ex), &(ret->out_shape_buffer_ex));
-  MXAPIThreadLocalEntry::SetupShapeArrayReturnWithBufferEx(ret->aux_shapes,
-      &(ret->aux_shape_ndim_ex), &(ret->aux_shape_data_ex), &(ret->aux_shape_buffer_ex));
-  *in_shape_size = static_cast<mx_uint>(ret->arg_shapes.size());
+  MXAPIThreadLocalEntry<dtype>::SetupShapeArrayReturnWithBufferEx(ret->arg_shapes,
+                                                                  &(ret->arg_shape_ndim_ex),
+                                                                  &(ret->arg_shape_data_ex),
+                                                                  &(ret->arg_shape_buffer_ex));
+  MXAPIThreadLocalEntry<dtype>::SetupShapeArrayReturnWithBufferEx(ret->out_shapes,
+                                                                  &(ret->out_shape_ndim_ex),
+                                                                  &(ret->out_shape_data_ex),
+                                                                  &(ret->out_shape_buffer_ex));
+  MXAPIThreadLocalEntry<dtype>::SetupShapeArrayReturnWithBufferEx(ret->aux_shapes,
+                                                                  &(ret->aux_shape_ndim_ex),
+                                                                  &(ret->aux_shape_data_ex),
+                                                                  &(ret->aux_shape_buffer_ex));
+  *in_shape_size = static_cast<stype>(ret->arg_shapes.size());
   *in_shape_ndim = dmlc::BeginPtr(ret->arg_shape_ndim_ex);
   *in_shape_data = dmlc::BeginPtr(ret->arg_shape_data_ex);
-  *out_shape_size = static_cast<mx_uint>(ret->out_shapes.size());
+  *out_shape_size = static_cast<stype>(ret->out_shapes.size());
   *out_shape_ndim = dmlc::BeginPtr(ret->out_shape_ndim_ex);
   *out_shape_data = dmlc::BeginPtr(ret->out_shape_data_ex);
-  *aux_shape_size = static_cast<mx_uint>(ret->aux_shapes.size());
+  *aux_shape_size = static_cast<stype>(ret->aux_shapes.size());
   *aux_shape_ndim = dmlc::BeginPtr(ret->aux_shape_ndim_ex);
   *aux_shape_data = dmlc::BeginPtr(ret->aux_shape_data_ex);
   // mark complete
   *complete = (g.GetAttr<size_t>("shape_num_unknown_nodes") == 0);
+}
+
+int MXSymbolInferShapeEx(SymbolHandle sym,
+                         mx_uint num_args,
+                         const char** keys,
+                         const mx_uint *arg_ind_ptr,
+                         const int *arg_shape_data,
+                         mx_uint *in_shape_size,
+                         const int **in_shape_ndim,
+                         const int ***in_shape_data,
+                         mx_uint *out_shape_size,
+                         const int **out_shape_ndim,
+                         const int ***out_shape_data,
+                         mx_uint *aux_shape_size,
+                         const int **aux_shape_ndim,
+                         const int ***aux_shape_data,
+                         int *complete) {
+  nnvm::Symbol *s = static_cast<nnvm::Symbol*>(sym);
+  MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
+  API_BEGIN();
+  SymbolInferShape<int, mx_uint, mx_uint>(keys,
+                                              num_args,
+                                              arg_shape_data,
+                                              arg_ind_ptr,
+                                              in_shape_ndim,
+                                              in_shape_data,
+                                              out_shape_ndim,
+                                              out_shape_data,
+                                              aux_shape_ndim,
+                                              aux_shape_data,
+                                              s,
+                                              ret,
+                                              in_shape_size,
+                                              out_shape_size,
+                                              aux_shape_size,
+                                              complete);
+  API_END();
+}
+
+int MXSymbolInferShapeEx64(SymbolHandle sym,
+                           mx_uint num_args,
+                           const char** keys,
+                           const int64_t *arg_ind_ptr,
+                           const int64_t *arg_shape_data,
+                           size_t *in_shape_size,
+                           const int **in_shape_ndim,
+                           const int64_t ***in_shape_data,
+                           size_t *out_shape_size,
+                           const int **out_shape_ndim,
+                           const int64_t ***out_shape_data,
+                           size_t *aux_shape_size,
+                           const int **aux_shape_ndim,
+                           const int64_t ***aux_shape_data,
+                           int *complete) {
+  nnvm::Symbol *s = static_cast<nnvm::Symbol*>(sym);
+  MXAPIThreadLocalEntry<int64_t> *ret = MXAPIThreadLocalStore<int64_t>::Get();
+  API_BEGIN();
+  SymbolInferShape<int64_t, size_t, int64_t>(keys,
+                                                 num_args,
+                                                 arg_shape_data,
+                                                 arg_ind_ptr,
+                                                 in_shape_ndim,
+                                                 in_shape_data,
+                                                 out_shape_ndim,
+                                                 out_shape_data,
+                                                 aux_shape_ndim,
+                                                 aux_shape_data,
+                                                 s,
+                                                 ret,
+                                                 in_shape_size,
+                                                 out_shape_size,
+                                                 aux_shape_size,
+                                                 complete);
   API_END();
 }
 
@@ -673,7 +746,7 @@ int MXSymbolInferShapePartial(SymbolHandle sym,
                               const mx_uint **aux_shape_ndim,
                               const mx_uint ***aux_shape_data,
                               int *complete) {
-  int succ;
+  int succ = 0;
   *complete = 1;
   return MXSymbolInferShape(sym, num_args, keys,
                             arg_ind_ptr, arg_shape_data,
@@ -698,7 +771,7 @@ int MXSymbolInferShapePartialEx(SymbolHandle sym,
                                 const int **aux_shape_ndim,
                                 const int ***aux_shape_data,
                                 int *complete) {
-  int succ;
+  int succ = 0;
   *complete = 1;
   return MXSymbolInferShapeEx(sym, num_args, keys,
                               arg_ind_ptr, arg_shape_data,
@@ -708,6 +781,31 @@ int MXSymbolInferShapePartialEx(SymbolHandle sym,
                               &succ);
 }
 
+int MXSymbolInferShapePartialEx64(SymbolHandle sym,
+                                  mx_uint num_args,
+                                  const char** keys,
+                                  const int64_t *arg_ind_ptr,
+                                  const int64_t *arg_shape_data,
+                                  size_t *in_shape_size,
+                                  const int **in_shape_ndim,
+                                  const int64_t ***in_shape_data,
+                                  size_t *out_shape_size,
+                                  const int **out_shape_ndim,
+                                  const int64_t ***out_shape_data,
+                                  size_t *aux_shape_size,
+                                  const int **aux_shape_ndim,
+                                  const int64_t ***aux_shape_data,
+                                  int *complete) {
+  int succ = 0;
+  *complete = 1;
+  return MXSymbolInferShapeEx64(sym, num_args, keys,
+                                arg_ind_ptr, arg_shape_data,
+                                in_shape_size, in_shape_ndim, in_shape_data,
+                                out_shape_size, out_shape_ndim, out_shape_data,
+                                aux_shape_size, aux_shape_ndim, aux_shape_data,
+                                &succ);
+}
+
 int MXSymbolInferType(SymbolHandle sym,
                       mx_uint num_args,
                       const char** keys,
@@ -720,7 +818,7 @@ int MXSymbolInferType(SymbolHandle sym,
                       const int **aux_type_data,
                       int *complete) {
   nnvm::Symbol *s = static_cast<nnvm::Symbol*>(sym);
-  MXAPIThreadLocalEntry *ret = MXAPIThreadLocalStore::Get();
+  MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
   API_BEGIN();
   nnvm::Graph g = Symbol2Graph(*s);
   nnvm::DTypeVector arg_types(g.indexed_graph().input_nodes().size(), -1);
@@ -764,7 +862,7 @@ int MXSymbolInferTypePartial(SymbolHandle sym,
                              mx_uint *aux_type_size,
                              const int **aux_type_data,
                              int *complete) {
-  int succ;
+  int succ = 0;
   *complete = 1;
   return MXSymbolInferType(sym, num_args, keys,
                             arg_type_data,
diff --git a/src/imperative/imperative.cc b/src/imperative/imperative.cc
index c00021c44d1d..e6a177e27847 100644
--- a/src/imperative/imperative.cc
+++ b/src/imperative/imperative.cc
@@ -48,7 +48,7 @@ OpStatePtr Imperative::InvokeOp(
   using namespace imperative;
   static auto& createop = nnvm::Op::GetAttr<FCreateOpState>("FCreateOpState");
   static auto& is_layer_backward = Op::GetAttr<bool>("TIsLayerOpBackward");
-  MXAPIThreadLocalEntry *ret = MXAPIThreadLocalStore::Get();
+  MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
 
   const nnvm::Op *op = attrs.op;
 
@@ -197,7 +197,7 @@ void Imperative::RecordOp(
     const OpStatePtr& state,
     std::vector<bool>* p_save_inputs,
     std::vector<bool>* p_save_outputs) {
-  MXAPIThreadLocalEntry *local_buff = MXAPIThreadLocalStore::Get();
+  MXAPIThreadLocalEntry<> *local_buff = MXAPIThreadLocalStore<>::Get();
 
   for (auto output : outputs) {
     CHECK(AGInfo::IsNone(*output))
diff --git a/src/imperative/imperative_utils.h b/src/imperative/imperative_utils.h
index 477139fd84b8..b0476fc8491a 100644
--- a/src/imperative/imperative_utils.h
+++ b/src/imperative/imperative_utils.h
@@ -104,7 +104,7 @@ inline void SetShapeType(const Context& ctx,
   static auto& infershape = nnvm::Op::GetAttr<mxnet::FInferShape>("FInferShape");
   static auto& infertype = nnvm::Op::GetAttr<nnvm::FInferType>("FInferType");
   static auto& inferstorage = nnvm::Op::GetAttr<FInferStorageType>("FInferStorageType");
-  MXAPIThreadLocalEntry *ret = MXAPIThreadLocalStore::Get();
+  MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
   // infer shape
   mxnet::ShapeVector& in_shapes  = ret->arg_shapes;
   in_shapes.clear();
diff --git a/src/operator/tensor/matrix_op-inl.h b/src/operator/tensor/matrix_op-inl.h
index 96c86c42a6c4..611dd7287206 100644
--- a/src/operator/tensor/matrix_op-inl.h
+++ b/src/operator/tensor/matrix_op-inl.h
@@ -668,9 +668,9 @@ void SliceEx(const nnvm::NodeAttrs& attrs,
 
 template<int ndim>
 inline void GetIndexRange(const mxnet::TShape& dshape,
-                          const mxnet::Tuple<dmlc::optional<int>>& param_begin,
-                          const mxnet::Tuple<dmlc::optional<int>>& param_end,
-                          const mxnet::Tuple<dmlc::optional<int>>& param_step,
+                          const mxnet::Tuple<dmlc::optional<index_t>>& param_begin,
+                          const mxnet::Tuple<dmlc::optional<index_t>>& param_end,
+                          const mxnet::Tuple<dmlc::optional<index_t>>& param_step,
                           common::StaticArray<index_t, ndim>* begin,
                           common::StaticArray<index_t, ndim>* end,
                           common::StaticArray<index_t, ndim>* step) {
@@ -1033,8 +1033,8 @@ void SliceAssignOpForward(const nnvm::NodeAttrs& attrs,
 
 struct SliceAssignScalarParam : public dmlc::Parameter<SliceAssignScalarParam> {
   double scalar;
-  mxnet::Tuple<dmlc::optional<int>> begin, end;
-  mxnet::Tuple<dmlc::optional<int>> step;
+  mxnet::Tuple<dmlc::optional<index_t>> begin, end;
+  mxnet::Tuple<dmlc::optional<index_t>> step;
   DMLC_DECLARE_PARAMETER(SliceAssignScalarParam) {
     DMLC_DECLARE_FIELD(scalar)
     .set_default(0)
@@ -1044,7 +1044,7 @@ struct SliceAssignScalarParam : public dmlc::Parameter<SliceAssignScalarParam> {
     DMLC_DECLARE_FIELD(end)
     .describe("ending indices for the slice operation, supports negative indices.");
     DMLC_DECLARE_FIELD(step)
-    .set_default(mxnet::Tuple<dmlc::optional<int>>())
+    .set_default(mxnet::Tuple<dmlc::optional<index_t>>())
     .describe("step for the slice operation, supports negative values.");
   }
 };
@@ -1346,12 +1346,12 @@ inline bool SliceLikeShape(const nnvm::NodeAttrs& attrs,
 inline void SliceLikeInferRanges(const mxnet::TShape& dshape,
                                  const mxnet::TShape& fshape,
                                  const mxnet::Tuple<int>& axes,
-                                 mxnet::Tuple<dmlc::optional<int>>* param_begin,
-                                 mxnet::Tuple<dmlc::optional<int>>* param_end,
-                                 mxnet::Tuple<dmlc::optional<int>>* param_step) {
-  std::vector<dmlc::optional<int>> pb(dshape.ndim());
-  std::vector<dmlc::optional<int>> pe(dshape.ndim());
-  std::vector<dmlc::optional<int>> ps(dshape.ndim());
+                                 mxnet::Tuple<dmlc::optional<index_t>>* param_begin,
+                                 mxnet::Tuple<dmlc::optional<index_t>>* param_end,
+                                 mxnet::Tuple<dmlc::optional<index_t>>* param_step) {
+  std::vector<dmlc::optional<index_t>> pb(dshape.ndim());
+  std::vector<dmlc::optional<index_t>> pe(dshape.ndim());
+  std::vector<dmlc::optional<index_t>> ps(dshape.ndim());
   if (axes.ndim() == 0) {
     for (int i = 0; i < dshape.ndim(); ++i) {
       pb[i] = 0;
@@ -1375,9 +1375,9 @@ inline void SliceLikeInferRanges(const mxnet::TShape& dshape,
       ps[axis] = 1;
     }
   }
-  *param_begin = mxnet::Tuple<dmlc::optional<int>>(pb.begin(), pb.end());
-  *param_end = mxnet::Tuple<dmlc::optional<int>>(pe.begin(), pe.end());
-  *param_step = mxnet::Tuple<dmlc::optional<int>>(ps.begin(), ps.end());
+  *param_begin = mxnet::Tuple<dmlc::optional<index_t>>(pb.begin(), pb.end());
+  *param_end = mxnet::Tuple<dmlc::optional<index_t>>(pe.begin(), pe.end());
+  *param_step = mxnet::Tuple<dmlc::optional<index_t>>(ps.begin(), ps.end());
 }
 
 template<typename xpu>
@@ -1396,9 +1396,9 @@ void SliceLikeForward(const nnvm::NodeAttrs& attrs,
   const TBlob& out = outputs[0];
   const mxnet::TShape& ishape = data.shape_;
   const mxnet::TShape& from_shape = inputs[1].shape_;
-  mxnet::Tuple<dmlc::optional<int>> param_begin;
-  mxnet::Tuple<dmlc::optional<int>> param_end;
-  mxnet::Tuple<dmlc::optional<int>> param_step;
+  mxnet::Tuple<dmlc::optional<index_t>> param_begin;
+  mxnet::Tuple<dmlc::optional<index_t>> param_end;
+  mxnet::Tuple<dmlc::optional<index_t>> param_step;
   SliceLikeInferRanges(ishape, from_shape, param.axes, &param_begin, &param_end, &param_step);
 
   MXNET_NDIM_SWITCH(data.ndim(), ndim, {
@@ -1444,9 +1444,9 @@ void SliceLikeBackward(const nnvm::NodeAttrs& attrs,
 
   const mxnet::TShape& ishape = ograd.shape_;
   const mxnet::TShape& from_shape = outputs[1].shape_;
-  mxnet::Tuple<dmlc::optional<int>> param_begin;
-  mxnet::Tuple<dmlc::optional<int>> param_end;
-  mxnet::Tuple<dmlc::optional<int>> param_step;
+  mxnet::Tuple<dmlc::optional<index_t>> param_begin;
+  mxnet::Tuple<dmlc::optional<index_t>> param_end;
+  mxnet::Tuple<dmlc::optional<index_t>> param_step;
   SliceLikeInferRanges(ishape, from_shape, param.axes, &param_begin, &param_end, &param_step);
 
   MXNET_NDIM_SWITCH(ograd.ndim(), ndim, {
diff --git a/src/operator/tensor/slice-inl.h b/src/operator/tensor/slice-inl.h
index 78a2bd8c7b45..7450e466440a 100644
--- a/src/operator/tensor/slice-inl.h
+++ b/src/operator/tensor/slice-inl.h
@@ -34,15 +34,15 @@ namespace mxnet {
 namespace op {
 
 struct SliceParam : public dmlc::Parameter<SliceParam> {
-  mxnet::Tuple<dmlc::optional<int>> begin, end;
-  mxnet::Tuple<dmlc::optional<int>> step;
+  mxnet::Tuple<dmlc::optional<index_t>> begin, end;
+  mxnet::Tuple<dmlc::optional<index_t>> step;
   DMLC_DECLARE_PARAMETER(SliceParam) {
     DMLC_DECLARE_FIELD(begin)
     .describe("starting indices for the slice operation, supports negative indices.");
     DMLC_DECLARE_FIELD(end)
     .describe("ending indices for the slice operation, supports negative indices.");
     DMLC_DECLARE_FIELD(step)
-    .set_default(mxnet::Tuple<dmlc::optional<int>>())
+    .set_default(mxnet::Tuple<dmlc::optional<index_t>>())
     .describe("step for the slice operation, supports negative values.");
   }
   bool operator==(const SliceParam& other) const {
diff --git a/tests/nightly/test_large_array.py b/tests/nightly/test_large_array.py
index 0df481a01987..9dc29eb1584e 100644
--- a/tests/nightly/test_large_array.py
+++ b/tests/nightly/test_large_array.py
@@ -24,7 +24,6 @@
 # dimension constants
 MEDIUM_X = 10000
 LARGE_X = 100000000
-LARGE_Y = 50000000
 SMALL_Y = 50
 LARGE_SIZE = LARGE_X * SMALL_Y
 
diff --git a/tests/nightly/test_large_vector.py b/tests/nightly/test_large_vector.py
new file mode 100644
index 000000000000..8c030f5bc20e
--- /dev/null
+++ b/tests/nightly/test_large_vector.py
@@ -0,0 +1,37 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import numpy as np
+import mxnet as mx
+from mxnet.test_utils import rand_ndarray, assert_almost_equal, rand_coord_2d
+from mxnet import gluon, nd
+from tests.python.unittest.common import with_seed
+
+# dimension constants
+LARGE_X = 5000000000
+MEDIUM_X = 1000000000
+
+
+def test_slice():
+    a = nd.ones(LARGE_X)
+    res = nd.slice(a, begin=(LARGE_X - MEDIUM_X), end=LARGE_X)
+    assert res.shape[0] == MEDIUM_X
+
+
+if __name__ == '__main__':
+    import nose
+    nose.runmodule()

From 67daae7eff1ef06254693bc1bc30bbfc23b2cf99 Mon Sep 17 00:00:00 2001
From: Haozheng Fan <fhztc1997618@gmail.com>
Date: Tue, 13 Aug 2019 16:24:23 +0800
Subject: [PATCH 170/813] tvm infra for op attrs (#15854)

---
 contrib/tvmop/compile.py |  4 ++--
 contrib/tvmop/opdef.py   | 12 ++++++++----
 2 files changed, 10 insertions(+), 6 deletions(-)

diff --git a/contrib/tvmop/compile.py b/contrib/tvmop/compile.py
index 94274fe4142a..e6af0a276560 100644
--- a/contrib/tvmop/compile.py
+++ b/contrib/tvmop/compile.py
@@ -44,11 +44,11 @@ def get_target(device):
 
     # TODO: attach instruction features to the library, e.g., avx-512, etc.
     for operator_def in __OP_DEF__:
-        for sch, args in operator_def.invoke_all():
+        for sch, args, name in operator_def.invoke_all():
             if tvm.module.enabled(get_target(operator_def.target)):
                 func_list = func_list_llvm if operator_def.target == "cpu" else func_list_cuda
                 func_lower = tvm.lower(sch, args,
-                                       name=operator_def.get_op_name(args),
+                                       name=name,
                                        binds=operator_def.get_binds(args))
                 func_list.append(func_lower)
 
diff --git a/contrib/tvmop/opdef.py b/contrib/tvmop/opdef.py
index c65824588047..32d1832d13dd 100644
--- a/contrib/tvmop/opdef.py
+++ b/contrib/tvmop/opdef.py
@@ -56,6 +56,8 @@ def __init__(self, func, name, target, auto_broadcast, **kwargs):
         #   {"ldtype": "int32", "rdtype": "float16"},
         #   {"ldtype": "int32", "rdtype": "int16"},
         # ]
+        self.attrs = kwargs.pop('attrs', [])
+        self.attrs_valid = kwargs.pop('attrs_valid', lambda **kwargs: True)
         args = [k for k in kwargs]
         values = [kwargs[k] if isinstance(kwargs[k], (list, tuple)) else [kwargs[k]]
                   for k in args]
@@ -72,10 +74,12 @@ def __call__(self, *args, **kwargs):
 
     def invoke_all(self):
         for each_kwargs in self.arg_combination:
-            yield self.func(**each_kwargs)
-
-    def get_op_name(self, args):
-        return self.name + ''.join(["%s_%d" % (arg.dtype, len(arg.shape)) for arg in args])
+            if (self.attrs_valid(**each_kwargs)):
+                sch, args = self.func(**each_kwargs)
+                name = self.name \
+                    + ''.join(["{}_{}".format(key, each_kwargs[key]) for key in self.attrs]) \
+                    + ''.join(["%s_%d" % (arg.dtype, len(arg.shape)) for arg in args])
+                yield sch, args, name
 
     def get_binds(self, args):
         if self.auto_broadcast:

From c81535ce33a9bb167662af53181f9a42ed5b4bc4 Mon Sep 17 00:00:00 2001
From: Dick Carter <dcarter@nvidia.com>
Date: Tue, 13 Aug 2019 09:14:11 -0700
Subject: [PATCH 171/813] cuDNN support cleanup (#15812)

* Remove old cudnn support (<v7).

* Simplify conv impl selection.

* Remove comments justifying STATIC_ASSERT_CUDNN_VERSION_GE.
---
 src/common/cuda_utils.h                       |  16 +-
 src/executor/attach_op_resource_pass.cc       |   4 +-
 src/imperative/imperative_utils.h             |   4 +-
 src/operator/bilinear_sampler.cu              |   8 +-
 src/operator/cudnn_bilinear_sampler-inl.h     |   9 +-
 src/operator/cudnn_spatial_transformer-inl.h  |   9 +-
 src/operator/nn/batch_norm.cu                 |   8 +-
 src/operator/nn/convolution.cu                |  99 ++---
 src/operator/nn/cudnn/cudnn_activation-inl.h  |  37 +-
 src/operator/nn/cudnn/cudnn_algoreg-inl.h     |   4 +-
 src/operator/nn/cudnn/cudnn_batch_norm-inl.h  |  46 +--
 src/operator/nn/cudnn/cudnn_batch_norm.cc     |   4 +-
 src/operator/nn/cudnn/cudnn_batch_norm.cu     |  90 -----
 src/operator/nn/cudnn/cudnn_convolution-inl.h | 354 ++----------------
 .../nn/cudnn/cudnn_deconvolution-inl.h        | 246 +-----------
 src/operator/nn/cudnn/cudnn_pooling-inl.h     |  29 +-
 src/operator/quantization/quantized_conv.cu   |   9 +-
 .../quantization/quantized_pooling.cu         |   9 +-
 src/operator/rnn-inl.h                        | 162 ++++----
 src/operator/rnn.cc                           |   2 +-
 src/operator/spatial_transformer-inl.h        |   2 +-
 src/operator/spatial_transformer.cu           |   8 +-
 22 files changed, 195 insertions(+), 964 deletions(-)
 delete mode 100644 src/operator/nn/cudnn/cudnn_batch_norm.cu

diff --git a/src/common/cuda_utils.h b/src/common/cuda_utils.h
index acc8d5fac6df..3bb6b81a6de2 100644
--- a/src/common/cuda_utils.h
+++ b/src/common/cuda_utils.h
@@ -29,6 +29,7 @@
 #include <dmlc/parameter.h>
 #include <dmlc/optional.h>
 #include <mshadow/base.h>
+#include <mxnet/libinfo.h>
 
 /*! \brief Macros/inlines to assist CLion to parse Cuda files (*.cu, *.cuh) */
 #ifdef __JETBRAINS_IDE__
@@ -482,13 +483,10 @@ static_assert(CUDNN_PATCHLEVEL < 100 && CUDNN_MINOR < 10,
  *         want to populate.
  */
 inline int MaxForwardAlgos(cudnnHandle_t cudnn_handle) {
-#if CUDNN_MAJOR >= 7
+  STATIC_ASSERT_CUDNN_VERSION_GE(7000);
   int max_algos = 0;
   CUDNN_CALL(cudnnGetConvolutionForwardAlgorithmMaxCount(cudnn_handle, &max_algos));
   return max_algos;
-#else
-  return 10;
-#endif
 }
 
 /*!
@@ -499,13 +497,10 @@ inline int MaxForwardAlgos(cudnnHandle_t cudnn_handle) {
  *         want to populate.
  */
 inline int MaxBackwardFilterAlgos(cudnnHandle_t cudnn_handle) {
-#if CUDNN_MAJOR >= 7
+  STATIC_ASSERT_CUDNN_VERSION_GE(7000);
   int max_algos = 0;
   CUDNN_CALL(cudnnGetConvolutionBackwardFilterAlgorithmMaxCount(cudnn_handle, &max_algos));
   return max_algos;
-#else
-  return 10;
-#endif
 }
 
 /*!
@@ -516,13 +511,10 @@ inline int MaxBackwardFilterAlgos(cudnnHandle_t cudnn_handle) {
  *         want to populate.
  */
 inline int MaxBackwardDataAlgos(cudnnHandle_t cudnn_handle) {
-#if CUDNN_MAJOR >= 7
+  STATIC_ASSERT_CUDNN_VERSION_GE(7000);
   int max_algos = 0;
   CUDNN_CALL(cudnnGetConvolutionBackwardDataAlgorithmMaxCount(cudnn_handle, &max_algos));
   return max_algos;
-#else
-  return 10;
-#endif
 }
 
 #endif  // MXNET_USE_CUDNN
diff --git a/src/executor/attach_op_resource_pass.cc b/src/executor/attach_op_resource_pass.cc
index aa03a1104ede..160ba8fb8d63 100644
--- a/src/executor/attach_op_resource_pass.cc
+++ b/src/executor/attach_op_resource_pass.cc
@@ -82,12 +82,12 @@ void AttachOpResources(
             requested.push_back(ResourceManager::Get()->Request(ctx, req));
             break;
           }
-#if MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 7
+#if MXNET_USE_CUDNN == 1
           case ResourceRequest::kCuDNNDropoutDesc: {
             requested.push_back(ResourceManager::Get()->Request(ctx, req));
             break;
           }
-#endif  // MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 7
+#endif  // MXNET_USE_CUDNN == 1
           default:
             LOG(FATAL) << "resource type " << req.type << " is not yet supported";
         }
diff --git a/src/imperative/imperative_utils.h b/src/imperative/imperative_utils.h
index b0476fc8491a..21caafa124f9 100644
--- a/src/imperative/imperative_utils.h
+++ b/src/imperative/imperative_utils.h
@@ -263,12 +263,12 @@ inline void SetDependency(const nnvm::NodeAttrs& attrs,
         requested.push_back(ResourceManager::Get()->Request(ctx, req));
         write_vars.push_back(requested.back().var);
         break;
-#if MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 7
+#if MXNET_USE_CUDNN == 1
        case ResourceRequest::kCuDNNDropoutDesc:
         requested.push_back(ResourceManager::Get()->Request(ctx, req));
         write_vars.push_back(requested.back().var);
         break;
-#endif  // MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 7
+#endif  // MXNET_USE_CUDNN == 1
        default:
         LOG(FATAL) << "resource type not yet supported";
       }
diff --git a/src/operator/bilinear_sampler.cu b/src/operator/bilinear_sampler.cu
index 03734a61316b..fab1433533ce 100644
--- a/src/operator/bilinear_sampler.cu
+++ b/src/operator/bilinear_sampler.cu
@@ -27,9 +27,9 @@
 #include "./bilinear_sampler-inl.h"
 #include <algorithm>
 #include "../common/cuda_utils.h"
-#if MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 5
+#if MXNET_USE_CUDNN == 1
 #include "./cudnn_bilinear_sampler-inl.h"
-#endif  // MXNET_USE_CUDNN && CUDNN_MAJOR
+#endif  // MXNET_USE_CUDNN
 
 namespace mshadow {
 namespace cuda {
@@ -228,7 +228,7 @@ namespace op {
 template<>
 Operator* CreateOp<gpu>(BilinearSamplerParam param, int dtype) {
   Operator *op = NULL;
-#if MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 5
+#if MXNET_USE_CUDNN == 1
   MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
     if (param.cudnn_off.has_value() && param.cudnn_off.value()) {
       op = new BilinearSamplerOp<gpu, DType>(param);
@@ -240,7 +240,7 @@ Operator* CreateOp<gpu>(BilinearSamplerParam param, int dtype) {
   MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
     op = new BilinearSamplerOp<gpu, DType>(param);
   })
-#endif  // MXNET_USE_CUDNN && CUDNN_MAJOR
+#endif  // MXNET_USE_CUDNN
   return op;
 }
 
diff --git a/src/operator/cudnn_bilinear_sampler-inl.h b/src/operator/cudnn_bilinear_sampler-inl.h
index c2171e6651a6..d72257682edd 100644
--- a/src/operator/cudnn_bilinear_sampler-inl.h
+++ b/src/operator/cudnn_bilinear_sampler-inl.h
@@ -31,7 +31,8 @@
 #include "./bilinear_sampler-inl.h"
 namespace mxnet {
 namespace op {
-#if defined(__CUDACC__) && MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 5
+#if defined(__CUDACC__) && MXNET_USE_CUDNN == 1
+STATIC_ASSERT_CUDNN_VERSION_GE(5000);
 template<typename DType>
 class CuDNNBilinearSamplerOp : public Operator {
  public:
@@ -132,9 +133,7 @@ class CuDNNBilinearSamplerOp : public Operator {
                    const std::vector<TBlob> &in_data,
                    const std::vector<TBlob> &out_data) {
     using namespace mshadow;
-    #if CUDNN_MAJOR >= 5
     format_ = CUDNN_TENSOR_NCHW;
-    #endif
     CHECK_EQ(in_data.size(), 2U);
     CHECK_EQ(out_data.size(), 2U);
     if (!init_cudnn_) {
@@ -174,12 +173,10 @@ class CuDNNBilinearSamplerOp : public Operator {
   cudnnTensorDescriptor_t in_desc_;
   cudnnTensorDescriptor_t out_desc_;
   cudnnSamplerType_t sampler_;
-  #if CUDNN_MAJOR >= 5
   cudnnTensorFormat_t format_;
-  #endif
   BilinearSamplerParam param_;
 };
-#endif  // __CUDACC__ && CUDNN
+#endif  // __CUDACC__ && MXNET_USE_CUDNN
 }  // namespace op
 }  // namespace mxnet
 
diff --git a/src/operator/cudnn_spatial_transformer-inl.h b/src/operator/cudnn_spatial_transformer-inl.h
index 1d7242a83c74..2e069e515563 100644
--- a/src/operator/cudnn_spatial_transformer-inl.h
+++ b/src/operator/cudnn_spatial_transformer-inl.h
@@ -31,7 +31,8 @@
 #include "./spatial_transformer-inl.h"
 namespace mxnet {
 namespace op {
-#if defined(__CUDACC__) && MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 5
+#if defined(__CUDACC__) && MXNET_USE_CUDNN == 1
+STATIC_ASSERT_CUDNN_VERSION_GE(5000);
 template<typename DType>
 class CuDNNSpatialTransformerOp : public Operator {
  public:
@@ -145,9 +146,7 @@ class CuDNNSpatialTransformerOp : public Operator {
                    const std::vector<TBlob> &in_data,
                    const std::vector<TBlob> &out_data) {
     using namespace mshadow;
-    #if CUDNN_MAJOR >= 5
     format_ = CUDNN_TENSOR_NCHW;
-    #endif
     CHECK_EQ(in_data.size(), 2U);
     CHECK_EQ(out_data.size(), 3U);
     if (!init_cudnn_) {
@@ -189,12 +188,10 @@ class CuDNNSpatialTransformerOp : public Operator {
   cudnnTensorDescriptor_t in_desc_;
   cudnnTensorDescriptor_t out_desc_;
   cudnnSamplerType_t sampler_;
-  #if CUDNN_MAJOR >= 5
   cudnnTensorFormat_t format_;
-  #endif
   SpatialTransformerParam param_;
 };
-#endif  // __CUDACC__ && CUDNN
+#endif  // __CUDACC__ && MXNET_USE_CUDNN
 }  // namespace op
 }  // namespace mxnet
 
diff --git a/src/operator/nn/batch_norm.cu b/src/operator/nn/batch_norm.cu
index 9fb44e8fae81..be9309c8bfb1 100644
--- a/src/operator/nn/batch_norm.cu
+++ b/src/operator/nn/batch_norm.cu
@@ -35,7 +35,7 @@
 #define IS_TRAINING_FLAG      16
 #define USE_GLOBAL_STATS_FLAG 32
 
-#if MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 5
+#if MXNET_USE_CUDNN == 1
 #include "./cudnn/cudnn_batch_norm-inl.h"
 #endif
 
@@ -641,7 +641,7 @@ void BatchNormBackwardImpl(mshadow::Stream<gpu> *stream,
   MSHADOW_CUDA_POST_KERNEL_CHECK(BatchNormOp_DoBackward_gpu);
 }
 
-#if MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 4
+#if MXNET_USE_CUDNN == 1
 template<typename DType>
 static CuDNNBatchNormOp<DType> &GetCuDNNOp(const BatchNormParam& param) {
 #if DMLC_CXX11_THREAD_LOCAL
@@ -667,7 +667,7 @@ void BatchNormCompute<gpu>(const nnvm::NodeAttrs& attrs,
   mxnet::TShape shape = inputs[0].shape_;
 
   param.axis = mxnet::op::batchnorm::GetRealAxis(shape, param.axis);
-#if MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 5
+#if MXNET_USE_CUDNN == 1
   if (!param.use_global_stats && !param.cudnn_off
       && param.axis == mxnet::op::batchnorm::DEFAULT_AXIS) {
     MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
@@ -696,7 +696,7 @@ void BatchNormGradCompute<gpu>(const nnvm::NodeAttrs& attrs,
   mxnet::TShape shape = inputs[0].shape_;
 
   param.axis = mxnet::op::batchnorm::GetRealAxis(shape, param.axis);
-#if MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 5
+#if MXNET_USE_CUDNN == 1
   if (!param.use_global_stats && !param.cudnn_off
       && param.axis == mxnet::op::batchnorm::DEFAULT_AXIS) {
     MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
diff --git a/src/operator/nn/convolution.cu b/src/operator/nn/convolution.cu
index 010be8a208fb..053c3fb6c748 100644
--- a/src/operator/nn/convolution.cu
+++ b/src/operator/nn/convolution.cu
@@ -94,39 +94,8 @@ void ConvolutionCompute<gpu>(const nnvm::NodeAttrs& attrs,
   const ConvolutionParam& param = nnvm::get<ConvolutionParam>(attrs.parsed);
   int dtype = inputs[conv::kData].type_flag_;
 
-#if CUDNN_MAJOR < 5
-  if (param.layout.value() != kNCW &&
-      param.layout.value() != kNCHW &&
-      param.layout.value() != kNCDHW) {
-    // Need CuDNN > 5.0 for layout support. use MXNet implementation
-    MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
-      ConvolutionOp<gpu, DType> op;
-      op.Init(param);
-      op.Forward(ctx, inputs, req, outputs);
-    })
-    return;
-  }
-#endif
-
-#if MXNET_USE_CUDNN == 0 || CUDNN_MAJOR < 7
-  if (param.num_filter == param.num_group &&
-      param.layout.value() == mshadow::kNCHW &&
-      param.num_filter == inputs[conv::kData].shape_[1] &&
-      param.kernel.ndim() == 2 &&
-      param.dilate == mshadow::Shape2(1, 1) &&
-      dtype == mshadow::kFloat32) {
-    mxnet::ShapeVector in_shape(inputs.size());
-    mxnet::ShapeVector out_shape(1, outputs[0].shape_);
-    for (size_t i = 0; i < in_shape.size(); i++)
-      in_shape[i] = inputs[i].shape_;
-    DepthwiseConvolutionOp<float> op;
-    op.Init(param, in_shape, out_shape);
-    op.Forward(ctx, inputs, req, outputs);
-    return;
-  }
-#endif
-
 #if MXNET_USE_CUDNN == 1
+  STATIC_ASSERT_CUDNN_VERSION_GE(7000);
   // On fp16-I/O instances, use fp32 compute (i.e. pseudo-fp16).
   int compute_type = (dtype == mshadow::kFloat16) ? mshadow::kFloat32 : dtype;
 
@@ -154,6 +123,22 @@ void ConvolutionCompute<gpu>(const nnvm::NodeAttrs& attrs,
     }
   })
 #else
+  if (param.num_filter == param.num_group &&
+      param.layout.value() == mshadow::kNCHW &&
+      param.num_filter == inputs[conv::kData].shape_[1] &&
+      param.kernel.ndim() == 2 &&
+      param.dilate == mshadow::Shape2(1, 1) &&
+      dtype == mshadow::kFloat32) {
+    mxnet::ShapeVector in_shape(inputs.size());
+    mxnet::ShapeVector out_shape(1, outputs[0].shape_);
+    for (size_t i = 0; i < in_shape.size(); i++)
+      in_shape[i] = inputs[i].shape_;
+    DepthwiseConvolutionOp<float> op;
+    op.Init(param, in_shape, out_shape);
+    op.Forward(ctx, inputs, req, outputs);
+    return;
+  }
+
   MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
     ConvolutionOp<gpu, DType> op;
     op.Init(param);
@@ -174,39 +159,8 @@ void ConvolutionGradCompute<gpu>(const nnvm::NodeAttrs& attrs,
   const std::vector<TBlob> &in_grad = outputs;
   int dtype = out_grad.type_flag_;
 
-#if CUDNN_MAJOR < 5
-  if (param.layout.value() != kNCW &&
-      param.layout.value() != kNCHW &&
-      param.layout.value() != kNCDHW) {
-    // Need CuDNN > 5.0 for layout support. use MXNet implementation
-    MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
-      ConvolutionOp<gpu, DType> op;
-      op.Init(param);
-      op.Backward(ctx, std::vector<TBlob>{out_grad}, in_data, req, in_grad);
-    })
-    return;
-  }
-#endif
-#if MXNET_USE_CUDNN == 0 || CUDNN_MAJOR < 7
-  if (param.num_filter == param.num_group &&
-      param.layout.value() == mshadow::kNCHW &&
-      param.num_filter == in_data[conv::kData].shape_[1] &&
-      param.kernel.ndim() == 2 &&
-      param.dilate == mshadow::Shape2(1, 1) &&
-      dtype == mshadow::kFloat32) {
-    // The first element stores out grad.
-    mxnet::ShapeVector in_shape(in_data.size());
-    mxnet::ShapeVector out_shape(1, out_grad.shape_);
-    for (size_t i = 0; i < in_shape.size(); i++)
-      in_shape[i] = in_data[i].shape_;
-    DepthwiseConvolutionOp<float> op;
-    op.Init(param, in_shape, out_shape);
-    op.Backward(ctx, std::vector<TBlob>{out_grad}, in_data, req, in_grad);
-    return;
-  }
-#endif
-
 #if MXNET_USE_CUDNN == 1
+  STATIC_ASSERT_CUDNN_VERSION_GE(7000);
   // On fp16-I/O instances, use fp32 compute (i.e. pseudo-fp16).
   int compute_type = (dtype == mshadow::kFloat16) ? mshadow::kFloat32 : dtype;
 
@@ -234,6 +188,23 @@ void ConvolutionGradCompute<gpu>(const nnvm::NodeAttrs& attrs,
     }
   })
 #else
+  if (param.num_filter == param.num_group &&
+      param.layout.value() == mshadow::kNCHW &&
+      param.num_filter == in_data[conv::kData].shape_[1] &&
+      param.kernel.ndim() == 2 &&
+      param.dilate == mshadow::Shape2(1, 1) &&
+      dtype == mshadow::kFloat32) {
+    // The first element stores out grad.
+    mxnet::ShapeVector in_shape(in_data.size());
+    mxnet::ShapeVector out_shape(1, out_grad.shape_);
+    for (size_t i = 0; i < in_shape.size(); i++)
+      in_shape[i] = in_data[i].shape_;
+    DepthwiseConvolutionOp<float> op;
+    op.Init(param, in_shape, out_shape);
+    op.Backward(ctx, std::vector<TBlob>{out_grad}, in_data, req, in_grad);
+    return;
+  }
+
   MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
     ConvolutionOp<gpu, DType> op;
     op.Init(param);
diff --git a/src/operator/nn/cudnn/cudnn_activation-inl.h b/src/operator/nn/cudnn/cudnn_activation-inl.h
index 2c1f442808c1..186274b2f1e1 100644
--- a/src/operator/nn/cudnn/cudnn_activation-inl.h
+++ b/src/operator/nn/cudnn/cudnn_activation-inl.h
@@ -29,18 +29,19 @@
 #include <algorithm>
 #include <vector>
 #include "../activation-inl.h"
+#include "../../../common/cuda_utils.h"
 
 namespace mxnet {
 namespace op {
 template<typename DType>
 class CuDNNActivationOp {
+  STATIC_ASSERT_CUDNN_VERSION_GE(5000);
+
  public:
   CuDNNActivationOp() {
     dtype_ = mshadow::DataType<DType>::kCudnnFlag;
-    #if CUDNN_MAJOR >= 5
     nan_prop_ = CUDNN_NOT_PROPAGATE_NAN;
     CUDNN_CALL(cudnnCreateActivationDescriptor(&desc_));
-    #endif
     CUDNN_CALL(cudnnCreateTensorDescriptor(&shape_desc_));
   }
 
@@ -60,16 +61,12 @@ class CuDNNActivationOp {
         LOG(FATAL) << "Not implmented";
         break;
     }
-    #if CUDNN_MAJOR >= 5
     CUDNN_CALL(cudnnSetActivationDescriptor(desc_, mode_, nan_prop_, relu_ceil_));
-    #endif
   }
 
   ~CuDNNActivationOp() {
     CUDNN_CALL(cudnnDestroyTensorDescriptor(shape_desc_));
-    #if CUDNN_MAJOR >= 5
     CUDNN_CALL(cudnnDestroyActivationDescriptor(desc_));
-    #endif
   }
 
   void Forward(const OpContext &ctx, const TBlob &in_data,
@@ -109,16 +106,6 @@ class CuDNNActivationOp {
                                           data.shape_[1],
                                           data.shape_[2],
                                           data.shape_[3]));
-    #if CUDNN_MAJOR <= 4
-    CUDNN_CALL(cudnnActivationForward(s->dnn_handle_,
-                                      mode_,
-                                      &alpha,
-                                      shape_desc_,
-                                      data.dptr_,
-                                      &beta,
-                                      shape_desc_,
-                                      out.dptr_));
-    #elif CUDNN_MAJOR >= 5
     CUDNN_CALL(cudnnActivationForward(s->dnn_handle_,
                                      desc_,
                                      &alpha,
@@ -127,7 +114,6 @@ class CuDNNActivationOp {
                                      &beta,
                                      shape_desc_,
                                      out.dptr_));
-    #endif
   }
 
   // backward computation for cudnn activation operator. Note that for relu
@@ -177,20 +163,6 @@ class CuDNNActivationOp {
                                           data.shape_[1],
                                           data.shape_[2],
                                           data.shape_[3]));
-    #if CUDNN_MAJOR <= 4
-    CUDNN_CALL(cudnnActivationBackward(s->dnn_handle_,
-                                       mode_,
-                                       &alpha,
-                                       shape_desc_,
-                                       output_data.dptr_,
-                                       shape_desc_,
-                                       grad.dptr_,
-                                       shape_desc_,
-                                       data.dptr_,
-                                       &beta,
-                                       shape_desc_,
-                                       input_grad.dptr_));
-    #elif CUDNN_MAJOR >= 5
     CUDNN_CALL(cudnnActivationBackward(s->dnn_handle_,
                                        desc_,
                                        &alpha,
@@ -203,7 +175,6 @@ class CuDNNActivationOp {
                                        &beta,
                                        shape_desc_,
                                        input_grad.dptr_));
-    #endif
   }
 
  private:
@@ -211,11 +182,9 @@ class CuDNNActivationOp {
   cudnnActivationMode_t mode_;
   cudnnTensorDescriptor_t shape_desc_;
   ActivationParam param_;
-#if CUDNN_MAJOR >= 5
   cudnnActivationDescriptor_t desc_;
   cudnnNanPropagation_t nan_prop_;
   double relu_ceil_;
-#endif
 };  // class CuDNNActivationOp
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/nn/cudnn/cudnn_algoreg-inl.h b/src/operator/nn/cudnn/cudnn_algoreg-inl.h
index 3f2d24c5bf7e..f7e01e214719 100644
--- a/src/operator/nn/cudnn/cudnn_algoreg-inl.h
+++ b/src/operator/nn/cudnn/cudnn_algoreg-inl.h
@@ -44,6 +44,8 @@ namespace op {
  */
 template <typename CuDNNAlgoType>
 class CuDNNAlgo {
+  STATIC_ASSERT_CUDNN_VERSION_GE(7000);
+
  public:
   CuDNNAlgo() :
       algo_number_(static_cast<CuDNNAlgoType>(0)),
@@ -54,11 +56,9 @@ class CuDNNAlgo {
   }
   CuDNNAlgoType AlgoNumber() const { return algo_number_; }
   bool IsTensorCoreAlgo() const { return is_tensor_core_algo_; }
-  #if CUDNN_MAJOR >= 7
   cudnnMathType_t MathType() {
     return IsTensorCoreAlgo() ? CUDNN_TENSOR_OP_MATH : CUDNN_DEFAULT_MATH;
   }
-  #endif
  private:
   CuDNNAlgoType algo_number_;
   bool is_tensor_core_algo_;
diff --git a/src/operator/nn/cudnn/cudnn_batch_norm-inl.h b/src/operator/nn/cudnn/cudnn_batch_norm-inl.h
index 820f8504d74c..3fc91196708c 100644
--- a/src/operator/nn/cudnn/cudnn_batch_norm-inl.h
+++ b/src/operator/nn/cudnn/cudnn_batch_norm-inl.h
@@ -34,7 +34,7 @@
 
 namespace mxnet {
 namespace op {
-#if MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 4
+#if MXNET_USE_CUDNN == 1
 namespace cudnnbatchnorm {
 enum CuDNNBatchNormOpInputs {kData, kGamma, kBeta};
 enum CuDNNBatchNormOpOutputs {kOut, kMean, kInvVar};
@@ -44,6 +44,8 @@ enum CuDNNBatchNormOpAuxiliary {kMovingMean, kMovingInvVar};
 #if defined(__CUDACC__)
 template<typename DType>
 class CuDNNBatchNormOp {
+  STATIC_ASSERT_CUDNN_VERSION_GE(5000);
+
  public:
   CuDNNBatchNormOp() {
     using namespace mshadow;
@@ -182,7 +184,6 @@ class CuDNNBatchNormOp {
 
     const bool global_stats = !ctx.is_train || param_.use_global_stats;
 
-#if CUDNN_VERSION >= 4007
 #if CUDNN_VERSION >= 7002
     auto mode = CUDNN_BATCHNORM_SPATIAL_PERSISTENT;
 #else
@@ -229,45 +230,6 @@ class CuDNNBatchNormOp {
         global_stats ? nullptr : save_inv_var.dptr_));
       if (param_.fix_gamma) dgamma = 0.f;
     })
-#else  // CUDNN_VERSION < 4007
-    MSHADOW_REAL_TYPE_SWITCH(dtype_param_, DTypeParam, {
-      Tensor<gpu, 1, DTypeParam> gamma =
-        in_gamma.get_with_shape<gpu, 1, DTypeParam>(Shape1(shape_[1]), s);
-      Tensor<gpu, 1, DTypeParam> dbeta =
-        in_grad[cudnnbatchnorm::kBeta].get_with_shape<gpu, 1, DTypeParam>(Shape1(shape_[1]), s);
-      Tensor<gpu, 1, DTypeParam> dgamma =
-        in_grad[cudnnbatchnorm::kGamma].get_with_shape<gpu, 1, DTypeParam>(Shape1(shape_[1]), s);
-      Tensor<gpu, 1, DTypeParam> save_mean =
-        out_mean.get_with_shape<gpu, 1, DTypeParam>(Shape1(shape_[1]), s);
-      Tensor<gpu, 1, DTypeParam> save_inv_var =
-        out_var.get_with_shape<gpu, 1, DTypeParam>(Shape1(shape_[1]), s);
-
-      typename DataType<DType>::ScaleType a = 1.0f;
-      typename DataType<DType>::ScaleType b = 0.0f;
-      typename DataType<DType>::ScaleType b_add = 1.0f;
-      CHECK_EQ(s->dnn_handle_ownership_, mshadow::Stream<gpu>::OwnHandle);
-
-      if (param_.fix_gamma) gamma = 1.f;
-      CUDNN_CALL(cudnnBatchNormalizationBackward(s->dnn_handle_,
-                                                 CUDNN_BATCHNORM_SPATIAL,
-                                                 &a,
-                                                 &b,
-                                                 io_desc_,
-                                                 x.dptr_,
-                                                 io_desc_,
-                                                 dy.dptr_,
-                                                 io_desc_,
-                                                 dx.dptr_,
-                                                 mean_desc_,
-                                                 gamma.dptr_,
-                                                 dgamma.dptr_,
-                                                 dbeta.dptr_,
-                                                 param_.eps,
-                                                 global_stats ? nullptr : save_mean.dptr_,
-                                                 global_stats ? nullptr : save_inv_var.dptr_));
-      if (param_.fix_gamma) dgamma = 0.f;
-    })
-#endif
   }
 
  private:
@@ -303,7 +265,7 @@ class CuDNNBatchNormOp {
 };
 #endif  // defined(__CUDACC__)
 
-#endif  // MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 4
+#endif  // MXNET_USE_CUDNN == 1
 }  // namespace op
 }  // namespace mxnet
 #endif  // MXNET_OPERATOR_NN_CUDNN_CUDNN_BATCH_NORM_INL_H_
diff --git a/src/operator/nn/cudnn/cudnn_batch_norm.cc b/src/operator/nn/cudnn/cudnn_batch_norm.cc
index cb35ce170e8e..d691b785a6e6 100644
--- a/src/operator/nn/cudnn/cudnn_batch_norm.cc
+++ b/src/operator/nn/cudnn/cudnn_batch_norm.cc
@@ -30,7 +30,7 @@
 
 namespace mxnet {
 namespace op {
-#if MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 4
+#if MXNET_USE_CUDNN == 1
 
 static bool BatchNormShape(const nnvm::NodeAttrs& attrs, mxnet::ShapeVector *in_shape,
     mxnet::ShapeVector *out_shape) {
@@ -114,7 +114,7 @@ NNVM_REGISTER_OP(_backward_CuDNNBatchNorm)
 .set_attr_parser(ParamParser<BatchNormParam>)
 .set_attr<FCompute>("FCompute<cpu>", BatchNormGradCompute_CPU);
 
-#endif  // CUDNN_MAJOR >= 4
+#endif  // MXNET_USE_CUDNN
 
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/nn/cudnn/cudnn_batch_norm.cu b/src/operator/nn/cudnn/cudnn_batch_norm.cu
deleted file mode 100644
index e07cd1e6c8f6..000000000000
--- a/src/operator/nn/cudnn/cudnn_batch_norm.cu
+++ /dev/null
@@ -1,90 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- */
-
-/*!
- * Copyright (c) 2015 by Contributors
- * \file cudnn_batch_norm.cu
- * \brief
- * \author Junyuan Xie, Da Zheng
-*/
-
-#include "./cudnn_batch_norm-inl.h"
-#include <vector>
-
-namespace mxnet {
-namespace op {
-#if CUDNN_MAJOR == 4
-
-template<typename DType>
-static CuDNNBatchNormOp<DType> &GetCuDNNOp(const BatchNormParam& param) {
-#if DMLC_CXX11_THREAD_LOCAL
-  static thread_local CuDNNBatchNormOp<DType> op;
-#else
-  static MX_THREAD_LOCAL CuDNNBatchNormOp<DType> op;
-#endif
-  op.Init(param);
-  return op;
-}
-
-static void BatchNormCompute_CuDNNv4(const nnvm::NodeAttrs& attrs,
-    const OpContext& ctx, const std::vector<TBlob>& inputs,
-    const std::vector<OpReqType>& req,
-    const std::vector<TBlob>& outputs) {
-#if CUDNN_MAJOR >= 5
-  LOG(FATAL) << "CuDNNBatchNorm is merged into BatchNorm for cudnn version above v5."
-    "Use the later instead.";
-#else
-  const BatchNormParam& param = nnvm::get<BatchNormParam>(attrs.parsed);
-  CHECK_EQ(inputs.size(), 5U);
-  std::vector<TBlob> in_data(inputs.begin(), inputs.begin() + 3);
-  std::vector<TBlob> aux_states(inputs.begin() + 3, inputs.end());
-  GetCuDNNOp<float>(param).Forward(ctx, in_data, req, outputs, aux_states);
-#endif
-}
-
-static void BatchNormGradCompute_CuDNNv4(const nnvm::NodeAttrs& attrs,
-    const OpContext& ctx, const std::vector<TBlob>& inputs,
-    const std::vector<OpReqType>& req,
-    const std::vector<TBlob>& outputs) {
-#if CUDNN_MAJOR >= 5
-  LOG(FATAL) << "CuDNNBatchNorm is merged into BatchNorm for cudnn version above v5."
-    "Use the later instead.";
-#else
-  CHECK_EQ(inputs.size(), 11U);
-  const BatchNormParam& param = nnvm::get<BatchNormParam>(attrs.parsed);
-  std::vector<TBlob> out_grad(1, inputs[0]);
-  std::vector<TBlob> in_data(inputs.begin() + 3, inputs.begin() + 6);
-  std::vector<TBlob> aux_states(inputs.begin() + 6, inputs.begin() + 8);
-  std::vector<TBlob> out_data(inputs.begin() + 8, inputs.end());
-  std::vector<TBlob> in_grad(outputs.begin(), outputs.begin() + 3);
-  GetCuDNNOp<float>(param).Backward(ctx, out_grad, in_data, out_data,
-      req, in_grad, aux_states);
-#endif
-}
-
-NNVM_REGISTER_OP(CuDNNBatchNorm)
-.set_attr<FCompute>("FCompute<gpu>", BatchNormCompute_CuDNNv4);
-
-NNVM_REGISTER_OP(_backward_CuDNNBatchNorm)
-.set_attr<FCompute>("FCompute<gpu>", BatchNormGradCompute_CuDNNv4);
-
-#endif  // CUDNN_MAJOR == 4
-}  // namespace op
-}  // namespace mxnet
-
diff --git a/src/operator/nn/cudnn/cudnn_convolution-inl.h b/src/operator/nn/cudnn/cudnn_convolution-inl.h
index 679e0cd1057b..d35e41701918 100644
--- a/src/operator/nn/cudnn/cudnn_convolution-inl.h
+++ b/src/operator/nn/cudnn/cudnn_convolution-inl.h
@@ -44,6 +44,8 @@ namespace op {
  */
 template<typename DType>
 class CuDNNConvolutionOp {
+  STATIC_ASSERT_CUDNN_VERSION_GE(7000);
+
  public:
   CuDNNConvolutionOp() {
     CUDNN_CALL(cudnnCreateTensorDescriptor(&in_desc_));
@@ -75,7 +77,6 @@ class CuDNNConvolutionOp {
     // TensorCore algos only allowed on fp16-I/O convolutions if permitted by the global policy.
     cudnn_tensor_core_ = DataType<DType>::kFlag == kFloat16 && GetEnvAllowTensorCore();
 
-#if CUDNN_MAJOR >= 5
     auto effective_layout = param_.layout.value();
     switch (effective_layout) {
       // 1D convolutions will be executed as 2D convolutions with a height of 1.
@@ -88,14 +89,9 @@ class CuDNNConvolutionOp {
     MSHADOW_LAYOUT_SWITCH(effective_layout, Layout, {
       format_ = LayoutType<Layout>::kCudnnFlag;
     });
-#else
-    CHECK(param_.layout.value() == kNCW ||
-          param_.layout.value() == kNCHW ||
-          param_.layout.value() == kNCDHW) << "Need CuDNN > 5.0 for layout support";
-#endif
     // Double check to make sure this class supports the operation
     if (!Supports(param, forward_compute_type, backward_compute_type, rctx.ctx.dev_id))
-      LOG(FATAL) << "Need CuDNN >= 6.0 for dilated convolution.";
+      LOG(FATAL) << "Convolution parameters not supported by cuDNN implementation.";
 
     InitDescriptors(in_shape, out_shape,
                     cudnn_forward_compute_type, cudnn_backward_compute_type);
@@ -141,7 +137,6 @@ class CuDNNConvolutionOp {
     DType *wmat_ptr = GetNdPtr(in_data[conv::kWeight], param_.kernel.ndim() + 2, s);
     DType *out_ptr = GetNdPtr(out_data[conv::kOut], param_.kernel.ndim() + 2, s);
 
-    #if CUDNN_MAJOR >= 7
     typename DataType<DType>::ScaleType alpha = 1.0f;
     typename DataType<DType>::ScaleType beta = 0.0f;
     typename DataType<DType>::ScaleType beta_add = 1.0f;
@@ -169,48 +164,6 @@ class CuDNNConvolutionOp {
                               out_desc_,
                               out_ptr));
     }
-    #else
-    for (uint32_t g = 0; g < param_.num_group; ++g) {
-      typename DataType<DType>::ScaleType alpha = 1.0f;
-      typename DataType<DType>::ScaleType beta = 0.0f;
-      typename DataType<DType>::ScaleType beta_add = 1.0f;
-      CUDNN_CALL(cudnnConvolutionForward(s->dnn_handle_,
-                                       &alpha,
-                                       in_desc_,
-                                       data_ptr + data_offset_ * g,
-                                       filter_desc_,
-                                       wmat_ptr + weight_offset_ * g,
-                                       forward_conv_desc_,
-                                       forward_algo_.AlgoNumber(),
-                                       workspace.dptr_,
-                                       workspace_size,
-                                       req[conv::kOut] == kAddTo? &beta_add : &beta,
-                                       out_desc_,
-                                       out_ptr + out_offset_ * g));
-      if (!param_.no_bias) {
-        Tensor<gpu, 1, DType> bias = in_data[conv::kBias].get<gpu, 1, DType>(s);
-        #if CUDNN_MAJOR >= 4
-        CUDNN_CALL(cudnnAddTensor(s->dnn_handle_,
-                                &alpha,
-                                bias_desc_,
-                                bias.dptr_ + bias_offset_ * g,
-                                &beta_add,
-                                out_desc_,
-                                out_ptr + out_offset_ * g));
-        #endif
-        #if CUDNN_MAJOR == 3
-        CUDNN_CALL(cudnnAddTensor(s->dnn_handle_,
-                                CUDNN_ADD_SAME_C,
-                                &alpha,
-                                bias_desc_,
-                                bias.dptr_ + bias_offset_ * g,
-                                &beta_add,
-                                out_desc_,
-                                out_ptr + out_offset_ * g));
-        #endif
-      }
-    }
-    #endif  // CUDNN_MAJOR >= 7
   }
 
   void Backward(const OpContext &ctx,
@@ -256,7 +209,6 @@ class CuDNNConvolutionOp {
       CHECK_LE(back_workspace_byte_dgrad_, workspace_size);
       CHECK_LE(back_workspace_byte_wgrad_, workspace_size);
     }
-    #if CUDNN_MAJOR >= 7
     typename DataType<DType>::ScaleType alpha = 1.0f;
     typename DataType<DType>::ScaleType beta = 0.0f;
     typename DataType<DType>::ScaleType beta_add = 1.0f;
@@ -301,85 +253,6 @@ class CuDNNConvolutionOp {
             in_desc_,
             gdata_ptr));
     }
-    #else
-    for (uint32_t g = 0; g < param_.num_group; ++g) {
-      typename DataType<DType>::ScaleType alpha = 1.0f;
-      typename DataType<DType>::ScaleType beta = 0.0f;
-      typename DataType<DType>::ScaleType beta_add = 1.0f;
-      if (!param_.no_bias && (req[conv::kBias] != kNullOp)) {
-        Tensor<gpu, 1, DType> gbias = in_grad[conv::kBias].get<gpu, 1, DType>(s);
-        CUDNN_CALL(cudnnConvolutionBackwardBias(s->dnn_handle_,
-                                              &alpha,
-                                              out_desc_,
-                                              grad_ptr + out_offset_ * g,
-                                              req[conv::kBias] == kAddTo ? &beta_add : &beta,
-                                              bias_desc_,
-                                              gbias.dptr_ + bias_offset_ * g));
-      }
-      if (req[conv::kWeight] != kNullOp) {
-        #if CUDNN_MAJOR <= 4
-          CUDNN_CALL(cudnnConvolutionBackwardFilter_v3(s->dnn_handle_,
-               &alpha,
-               in_desc_,
-               data_ptr + data_offset_ * g,
-               out_desc_,
-               grad_ptr + out_offset_ * g,
-               back_conv_desc_w_,
-               back_algo_w_.AlgoNumber(),
-               workspace.dptr_,
-               workspace_size,
-               req[conv::kWeight] == kAddTo? &beta_add : &beta,
-               filter_desc_,
-               gwmat_ptr + weight_offset_ * g));
-        #elif CUDNN_MAJOR >= 5
-          CUDNN_CALL(cudnnConvolutionBackwardFilter(s->dnn_handle_,
-               &alpha,
-               in_desc_,
-               data_ptr + data_offset_ * g,
-               out_desc_,
-               grad_ptr + out_offset_ * g,
-               back_conv_desc_w_,
-               back_algo_w_.AlgoNumber(),
-               workspace.dptr_,
-               workspace_size,
-               req[conv::kWeight] == kAddTo? &beta_add : &beta,
-               filter_desc_,
-               gwmat_ptr + weight_offset_ * g));
-        #endif
-      }
-      if (req[conv::kData] != kNullOp) {
-        #if CUDNN_MAJOR <= 4
-          CUDNN_CALL(cudnnConvolutionBackwardData_v3(s->dnn_handle_,
-               &alpha,
-               filter_desc_,
-               wmat_ptr + weight_offset_ * g,
-               out_desc_,
-               grad_ptr + out_offset_ * g,
-               back_conv_desc_,
-               back_algo_.AlgoNumber(),
-               workspace.dptr_,
-               workspace_size,
-               req[conv::kData] == kAddTo? &beta_add : &beta,
-               in_desc_,
-               gdata_ptr + data_offset_ * g));
-        #elif CUDNN_MAJOR >= 5
-          CUDNN_CALL(cudnnConvolutionBackwardData(s->dnn_handle_,
-               &alpha,
-               filter_desc_,
-               wmat_ptr + weight_offset_ * g,
-               out_desc_,
-               grad_ptr + out_offset_ * g,
-               back_conv_desc_,
-               back_algo_.AlgoNumber(),
-               workspace.dptr_,
-               workspace_size,
-               req[conv::kData] == kAddTo? &beta_add : &beta,
-               in_desc_,
-               gdata_ptr + data_offset_ * g));
-        #endif
-      }
-    }
-    #endif  // CUDNN_MAJOR >= 7
   }
 
 /*!
@@ -407,14 +280,7 @@ class CuDNNConvolutionOp {
       return false;
     }
 
-    // The factor by which the effective filter size grows based on dilation.
-    auto filterDilationFactor = param.dilate.Size();
-
-    // The v6 kernels that backprop a dilated convolution don't handle fp16.
-    // Dilation support across all architectures only available after v6.0.20.
-    return filterDilationFactor == 1 ||
-           filterDilationFactor > 1 && (CUDNN_VERSION > 6020) &&
-           (backward_compute_type != kFloat16);
+    return true;
   }
 
  private:
@@ -443,17 +309,7 @@ class CuDNNConvolutionOp {
     mxnet::TShape wshape = in_shape[conv::kWeight];
     mxnet::TShape oshape = out_shape[conv::kOut];
     mxnet::TShape dstride, ostride;
-#if CUDNN_MAJOR <= 6
-    wshape[0] /= param_.num_group;
-#endif
 
-#if CUDNN_MAJOR <= 5
-      // As of cuDNN_v6, the unsuffixed version of cudnnSetConvolution2dDescriptor()
-      // takes an additional 'computeType' parameter to set the precision of the
-      // convolution calculation.  Supply this method signature for cuDNN versions < 6.
-#define cudnnSetConvolution2dDescriptor(cdesc, p0, p1, s0, s1, d0, d1, m, ct) \
-        cudnnSetConvolution2dDescriptor(cdesc, p0, p1, s0, s1, d0, d1, m)
-#endif
     if (param_.kernel.ndim() == 1 || param_.kernel.ndim() == 2) {
       // 1d or 2d conv
       auto pad = param_.kernel.ndim() == 2 ?
@@ -489,13 +345,6 @@ class CuDNNConvolutionOp {
                                                dilate[1],
                                                CUDNN_CROSS_CORRELATION,
                                                cudnn_backward_compute_type));
-#if CUDNN_MAJOR < 5
-      // As of cuDNN_v5, cudnnSetFilter4dDescriptor() takes a format parameter.
-      // Supply this method signature for cuDNN versions < 5.
-#define cudnnSetFilter4dDescriptor(fdesc, dt, f, w0, w1, w2, w3) \
-        cudnnSetFilter4dDescriptor(fdesc, dt, w0, w1, w2, w3)
-      CHECK_EQ(format_, CUDNN_TENSOR_NCHW) << "CuDNN V4 and earlier only supports NCHW layout";
-#endif
       if (param_.kernel.ndim() == 2) {
         wshape = ConvertLayout(wshape.get<4>(), param_.layout.value(), kNCHW);
         dstride = ConvertLayout(Strides<4>(dshape), param_.layout.value(), kNCHW);
@@ -536,7 +385,6 @@ class CuDNNConvolutionOp {
 #endif
     } else if (param_.kernel.ndim() == 3) {
       // 3d conv
-      #if CUDNN_MAJOR >= 5
       CHECK_EQ(param_.layout.value(), kNCDHW) << "CuDNN only support 3D conv with NCDHW layout";
       std::vector<int> wshape_buffer(wshape.ndim());
       CUDNN_CALL(cudnnSetFilterNdDescriptor(filter_desc_,
@@ -544,9 +392,6 @@ class CuDNNConvolutionOp {
                                           CUDNN_TENSOR_NCHW,
                                           static_cast<int>(wshape.ndim()),
                                           CastTShapeToIntPtr(wshape, &wshape_buffer)));
-      #else
-      LOG(FATAL) << "Only support CUDNN V5 for 3D convolution";
-      #endif
       CUDNN_CALL(cudnnSetConvolutionNdDescriptor(forward_conv_desc_,
                                                3,
                                                param_pad_.data(),
@@ -577,29 +422,19 @@ class CuDNNConvolutionOp {
       oshape = ConvertLayout(oshape.get<5>(), param_.layout.value(), kNCDHW);
     }
     // Set "allow tensor core" flag in convolution descriptors, if available.
-    #if CUDNN_MAJOR >= 7
-      cudnnMathType_t math_type = cudnn_tensor_core_ ? CUDNN_TENSOR_OP_MATH
-                                                    : CUDNN_DEFAULT_MATH;
-      #if CUDNN_VERSION >= 7200
-            if (GetEnvAllowTensorCore() && GetEnvAllowTensorCoreConversion() &&
-                (DataType<DType>::kFlag != kFloat16))
-              math_type = CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION;
-      #endif
-      CUDNN_CALL(cudnnSetConvolutionMathType(forward_conv_desc_, math_type));
-      CUDNN_CALL(cudnnSetConvolutionMathType(back_conv_desc_, math_type));
-      CUDNN_CALL(cudnnSetConvolutionMathType(back_conv_desc_w_, math_type));
-      CUDNN_CALL(cudnnSetConvolutionGroupCount(forward_conv_desc_, param_.num_group));
-      CUDNN_CALL(cudnnSetConvolutionGroupCount(back_conv_desc_, param_.num_group));
-      CUDNN_CALL(cudnnSetConvolutionGroupCount(back_conv_desc_w_, param_.num_group));
-    #endif
-
-  #if CUDNN_MAJOR <= 6
-    dshape[1] /= param_.num_group;
-    oshape[1] /= param_.num_group;
-  #endif
-    weight_offset_ = wshape.Size();
-    data_offset_ = dstride[1] * dshape[1];
-    out_offset_ = ostride[1] * oshape[1];
+    cudnnMathType_t math_type = cudnn_tensor_core_ ? CUDNN_TENSOR_OP_MATH
+                                                   : CUDNN_DEFAULT_MATH;
+#if CUDNN_VERSION >= 7200
+    if (GetEnvAllowTensorCore() && GetEnvAllowTensorCoreConversion() &&
+        (DataType<DType>::kFlag != kFloat16))
+      math_type = CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION;
+#endif
+    CUDNN_CALL(cudnnSetConvolutionMathType(forward_conv_desc_, math_type));
+    CUDNN_CALL(cudnnSetConvolutionMathType(back_conv_desc_, math_type));
+    CUDNN_CALL(cudnnSetConvolutionMathType(back_conv_desc_w_, math_type));
+    CUDNN_CALL(cudnnSetConvolutionGroupCount(forward_conv_desc_, param_.num_group));
+    CUDNN_CALL(cudnnSetConvolutionGroupCount(back_conv_desc_, param_.num_group));
+    CUDNN_CALL(cudnnSetConvolutionGroupCount(back_conv_desc_w_, param_.num_group));
 
     std::vector<int> dshape_buffer(dshape.ndim());
     nnvm::ShapeTypeCast(dshape.begin(), dshape.end(), dshape_buffer.data());
@@ -624,18 +459,10 @@ class CuDNNConvolutionOp {
 
     if (!param_.no_bias) {
       mxnet::TShape bias = in_shape[conv::kBias];
-      #if CUDNN_MAJOR >= 7
-      bias_offset_ = bias[0];
       std::vector<int> bias_shape = {1,
                                      static_cast<int>(bias[0]),
                                      1, 1};
-      #else
-      bias_offset_ = bias[0] / param_.num_group;
-      std::vector<int> bias_shape = {1,
-                                     static_cast<int>(bias[0] / param_.num_group),
-                                     1, 1};
-      #endif
-      std::vector<int> bias_stride = {static_cast<int>(bias_offset_), 1, 1, 1};
+      std::vector<int> bias_stride = {static_cast<int>(bias[0]), 1, 1, 1};
       if (param_.kernel.ndim() == 3) {
         bias_shape.push_back(1);
         bias_stride.push_back(1);
@@ -660,12 +487,9 @@ class CuDNNConvolutionOp {
     mshadow::Stream<gpu> *s = rctx.get_stream<gpu>();
     CHECK_EQ(s->dnn_handle_ownership_, mshadow::Stream<gpu>::OwnHandle);
     size_t workspace_byte = static_cast<size_t>(param_.workspace * sizeof(DType));
-#if CUDNN_MAJOR >= 7
-    // Starting with cuDNNv7, the algo number returned by *Get*() is not the entire
-    // story: the notion of whether the algo ran in Tensor Core mode is not known.
-    // Since we want to report the Tensor Core mode in the verbose output, we switch
-    // to using the new *Get*_v7() call.  Since the function signature of *Get*_v7() matches
-    // that of *Find*(), we can unify the find-vs-get logic by using function pointers.
+
+    // Since the function signature of *Get*_v7() matches that of *Find*(),
+    // we can unify the find-vs-get logic by using function pointers.
 
     // Forward Algorithm Find/Get() v7
     std::vector<cudnnConvolutionFwdAlgoPerf_t> fwd_results(MaxForwardAlgos(s->dnn_handle_));
@@ -727,130 +551,6 @@ class CuDNNConvolutionOp {
     AlgoFinalSelect<cudnnConvolutionBwdDataAlgoPerf_t,
                     cudnnConvolutionBwdDataAlgo_t>(bwd_data_results, "backprop-to-data",
                                                    workspace_byte, bwd, exclude_dgrad_algo_);
-#else
-    // CUDNN_MAJOR < 7
-    const int kMaxAlgos = 10;
-    int nalgo = kMaxAlgos;
-    int i = 0;
-    size_t min_memory_needs = 0;
-    // Forward Algorithm Find/Get, v6 and earlier
-    if (CUDNN_MAJOR == 6 && param_.layout.value() == mshadow::kNHWC) {
-      // In cuDNNv6, for kNHWC, only CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM is
-      // supported.  Hard-coded this since the algo find() or get() throws an FPE.
-      fwd->Set(CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM, false);
-    } else if (!param_.cudnn_tune.value()) {
-      cudnnConvolutionFwdAlgo_t fastest_fwd_algo;
-      CUDNN_CALL(cudnnGetConvolutionForwardAlgorithm(s->dnn_handle_,
-                                               in_desc_,
-                                               filter_desc_,
-                                               forward_conv_desc_,
-                                               out_desc_,
-                                               CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
-                                               workspace_byte,
-                                               &fastest_fwd_algo));
-      fwd->Set(fastest_fwd_algo, false);
-    } else {
-      cudnnConvolutionFwdAlgoPerf_t fwd_algo[kMaxAlgos];
-      CUDNN_CALL(cudnnFindConvolutionForwardAlgorithm(s->dnn_handle_,
-                                                      in_desc_,
-                                                      filter_desc_,
-                                                      forward_conv_desc_,
-                                                      out_desc_,
-                                                      kMaxAlgos,
-                                                      &nalgo,
-                                                      fwd_algo));
-      i = 0;
-      while (i < nalgo
-             && (fwd_algo[i].status != CUDNN_STATUS_SUCCESS
-                 || (param_.cudnn_tune.value() != conv::kFastest
-                     && fwd_algo[i].memory > workspace_byte))) {
-        ++i;
-        min_memory_needs =
-          (i == 0) ? fwd_algo[i].memory : std::min(min_memory_needs, fwd_algo[i].memory);
-      }
-      if (i == nalgo) {
-        LogNoSuitableAlgoAndExit(nalgo, min_memory_needs, workspace_byte, "forward");
-      } else {
-        fwd->Set(fwd_algo[i].algo, false);
-      }
-    }
-    // Backprop-to-Filter Algorithm Find/Get, v6 and earlier
-    if (!param_.cudnn_tune.value()) {
-      cudnnConvolutionBwdFilterAlgo_t fastest_bwd_filt_algo;
-      CUDNN_CALL(cudnnGetConvolutionBackwardFilterAlgorithm(s->dnn_handle_,
-                                        in_desc_,
-                                        out_desc_,
-                                        back_conv_desc_w_,
-                                        filter_desc_,
-                                        CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT,
-                                        workspace_byte,
-                                        &fastest_bwd_filt_algo));
-      flt->Set(fastest_bwd_filt_algo, false);
-    } else {
-      cudnnConvolutionBwdFilterAlgoPerf_t bwd_filter_algo[kMaxAlgos];
-      CUDNN_CALL(cudnnFindConvolutionBackwardFilterAlgorithm(s->dnn_handle_,
-                                                             in_desc_,
-                                                             out_desc_,
-                                                             back_conv_desc_w_,
-                                                             filter_desc_,
-                                                             kMaxAlgos,
-                                                             &nalgo,
-                                                             bwd_filter_algo));
-      i = 0;
-      while (i < nalgo
-             && (bwd_filter_algo[i].status != CUDNN_STATUS_SUCCESS
-                 || (param_.cudnn_tune.value() != conv::kFastest
-                     && bwd_filter_algo[i].memory > workspace_byte))) {
-        ++i;
-        min_memory_needs = (i == 0) ?
-                           bwd_filter_algo[i].memory :
-                           std::min(min_memory_needs, bwd_filter_algo[i].memory);
-      }
-      if (i == nalgo) {
-        LogNoSuitableAlgoAndExit(nalgo, min_memory_needs, workspace_byte, "backward filter");
-      } else {
-        flt->Set(bwd_filter_algo[i].algo, false);
-      }
-    }
-    // Backprop-to-Data Algorithm Get(), v6 and earlier
-    if (!param_.cudnn_tune.value()) {
-      cudnnConvolutionBwdDataAlgo_t fastest_bwd_data_algo;
-      CUDNN_CALL(cudnnGetConvolutionBackwardDataAlgorithm(s->dnn_handle_,
-                                          filter_desc_,
-                                          out_desc_,
-                                          back_conv_desc_,
-                                          in_desc_,
-                                          CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,
-                                          workspace_byte,
-                                          &fastest_bwd_data_algo));
-      bwd->Set(fastest_bwd_data_algo, false);
-    } else {
-      cudnnConvolutionBwdDataAlgoPerf_t bwd_data_algo[kMaxAlgos];
-      CUDNN_CALL(cudnnFindConvolutionBackwardDataAlgorithm(s->dnn_handle_,
-                                                           filter_desc_,
-                                                           out_desc_,
-                                                           back_conv_desc_,
-                                                           in_desc_,
-                                                           kMaxAlgos,
-                                                           &nalgo,
-                                                           bwd_data_algo));
-      i = 0;
-      while (i < nalgo
-             && (bwd_data_algo[i].status != CUDNN_STATUS_SUCCESS
-                 || (param_.cudnn_tune.value() != conv::kFastest
-                     && bwd_data_algo[i].memory > workspace_byte))) {
-        ++i;
-        min_memory_needs = (i == 0) ?
-                           bwd_data_algo[i].memory :
-                           std::min(min_memory_needs, bwd_data_algo[i].memory);
-      }
-      if (i == nalgo) {
-        LogNoSuitableAlgoAndExit(nalgo, min_memory_needs, workspace_byte, "backward data");
-      } else {
-        bwd->Set(bwd_data_algo[i].algo, false);
-      }
-    }
-#endif  // CUDNN_MAJOR < 7
 
     // Fix for issue #11241
     int cudnn_find_issue_max_features = 64 * 1024;
@@ -911,11 +611,9 @@ class CuDNNConvolutionOp {
     // *Find*() or *Get*(), but a non-Tensor-Core algo variant is the fastest,
     // we must change the descriptor to preclude Tensor Core.  Simplest is to
     // once again set the mathType in all cases.
-    #if CUDNN_MAJOR >= 7
     CUDNN_CALL(cudnnSetConvolutionMathType(forward_conv_desc_, forward_algo_.MathType()));
     CUDNN_CALL(cudnnSetConvolutionMathType(back_conv_desc_, back_algo_.MathType()));
     CUDNN_CALL(cudnnSetConvolutionMathType(back_conv_desc_w_, back_algo_w_.MathType()));
-    #endif
   }
 
   // Look over the results from *Find*() or *Get*() and pick the fastest algo given possible
@@ -931,13 +629,9 @@ class CuDNNConvolutionOp {
       const auto &result = perf_results[i];
       bool algo_exclusion = static_cast<int32_t>(result.algo) == algo_exclude;
       bool algo_is_tensor_core = false;
-      #if CUDNN_MAJOR >= 7
-        algo_is_tensor_core = result.mathType == CUDNN_TENSOR_OP_MATH;
-      #endif
+      algo_is_tensor_core = result.mathType == CUDNN_TENSOR_OP_MATH;
       if (result.status == CUDNN_STATUS_SUCCESS &&
-        #if CUDNN_MAJOR >= 7
           (!enforce_determinism || result.determinism == cudnnDeterminism_t::CUDNN_DETERMINISTIC) &&
-        #endif
           (param_.cudnn_tune.value() == conv::kLimited || result.memory <= workspace_byte) &&
           !algo_exclusion) {
         algo->Set(result.algo, algo_is_tensor_core);
@@ -1091,10 +785,6 @@ class CuDNNConvolutionOp {
   size_t back_workspace_byte_dgrad_;
   // Temp workspace size in bytes needed for Backward() wgrad (weight gradient) operation.
   size_t back_workspace_byte_wgrad_;
-  size_t data_offset_;
-  size_t out_offset_;
-  size_t weight_offset_;
-  size_t bias_offset_;
   cudnnDataType_t dtype_;
   cudnnTensorDescriptor_t in_desc_;
   cudnnTensorDescriptor_t out_desc_;
diff --git a/src/operator/nn/cudnn/cudnn_deconvolution-inl.h b/src/operator/nn/cudnn/cudnn_deconvolution-inl.h
index adb6caf1c028..ec7eec32b5b8 100644
--- a/src/operator/nn/cudnn/cudnn_deconvolution-inl.h
+++ b/src/operator/nn/cudnn/cudnn_deconvolution-inl.h
@@ -41,6 +41,8 @@ namespace op {
 
 template<typename DType>
 class CuDNNDeconvolutionOp {
+  STATIC_ASSERT_CUDNN_VERSION_GE(7000);
+
  public:
   CuDNNDeconvolutionOp() {
     CUDNN_CALL(cudnnCreateTensorDescriptor(&in_desc_));
@@ -71,7 +73,6 @@ class CuDNNDeconvolutionOp {
     // TensorCore algos only allowed on fp16-I/O deconvolutions if permitted by the global policy.
     cudnn_tensor_core_ = DataType<DType>::kFlag == kFloat16 && GetEnvAllowTensorCore();
 
-#if CUDNN_MAJOR >= 5
     auto effective_layout = param_.layout.value();
     switch (effective_layout) {
       // 1D convolutions will be executed as 2D convolutions with a height of 1.
@@ -84,14 +85,9 @@ class CuDNNDeconvolutionOp {
     MSHADOW_LAYOUT_SWITCH(effective_layout, Layout, {
         format_ = LayoutType<Layout>::kCudnnFlag;
       });
-#else
-    CHECK(param_.layout.value() == kNCW ||
-          param_.layout.value() == kNCHW ||
-          param_.layout.value() == kNCDHW) << "Need CuDNN > 5.0 for layout support";
-#endif
     // Double check to make sure this class supports the operation
     if (!Supports(param, forward_compute_type, backward_compute_type, rctx.ctx.dev_id))
-      LOG(FATAL) << "Need CuDNN >= 6.0 for dilated deconvolution.";
+      LOG(FATAL) << "Deconvolution parameters not supported by cuDNN implementation.";
 
     InitDescriptors(in_shape, out_shape,
                     cudnn_forward_compute_type, cudnn_backward_compute_type);
@@ -140,21 +136,6 @@ class CuDNNDeconvolutionOp {
     for (uint32_t g = 0; g < param_.num_group; ++g) {
       typename DataType<DType>::ScaleType alpha = 1.0f;
       typename DataType<DType>::ScaleType beta  = 0.0f;
-      #if CUDNN_MAJOR <= 4
-      CUDNN_CALL(cudnnConvolutionBackwardData_v3(s->dnn_handle_,
-                 &alpha,
-                 filter_desc_,
-                 wmat_ptr + weight_offset_ * g,
-                 in_desc_,
-                 data_ptr + data_offset_ * g,
-                 forward_conv_desc_,  // this backward algorithm used for inference
-                 back_algo_.AlgoNumber(),
-                 workspace.dptr_,
-                 workspace_size,
-                 &beta,
-                 out_desc_,
-                 out.dptr_ + out_offset_ * g));
-      #elif CUDNN_MAJOR >= 5
       CUDNN_CALL(cudnnConvolutionBackwardData(s->dnn_handle_,
                  &alpha,
                  filter_desc_,
@@ -168,11 +149,9 @@ class CuDNNDeconvolutionOp {
                  &beta,
                  out_desc_,
                  out_ptr + out_offset_ * g));
-      #endif
       if (!param_.no_bias) {
         beta = 1.0f;
         Tensor<gpu, 1, DType> bias = in_data[deconv::kBias].get<gpu, 1, DType>(s);
-#if CUDNN_MAJOR >= 4
         CUDNN_CALL(cudnnAddTensor(s->dnn_handle_,
                                   &alpha,
                                   bias_desc_,
@@ -180,17 +159,6 @@ class CuDNNDeconvolutionOp {
                                   &beta,
                                   out_desc_,
                                   out_ptr + out_offset_ * g));
-#endif
-#if CUDNN_MAJOR == 3
-        CUDNN_CALL(cudnnAddTensor(s->dnn_handle_,
-                                  CUDNN_ADD_SAME_C,
-                                  &alpha,
-                                  bias_desc_,
-                                  bias.dptr_ + bias_offset_ * g,
-                                  &beta,
-                                  out_desc_,
-                                  out_ptr + out_offset_ * g));
-#endif
       }
     }
   }
@@ -244,23 +212,7 @@ class CuDNNDeconvolutionOp {
                                                 gbias.dptr_ + bias_offset_ * g));
       }
       if (req[deconv::kWeight] != kNullOp) {
-        #if CUDNN_MAJOR <= 4
-        CUDNN_CALL(cudnnConvolutionBackwardFilter_v3(
-          s->dnn_handle_,
-          &alpha,
-          out_desc_,
-          grad_ptr + out_offset_ * g,
-          in_desc_,
-          data_ptr + data_offset_ * g,
-          back_conv_desc_,
-          back_algo_w_.AlgoNumber(),
-          workspace.dptr_,
-          workspace_size,
-          &weight_beta,
-          filter_desc_,
-          gwmat.dptr_ + weight_offset_ * g));
-        #elif CUDNN_MAJOR >= 5
-        CHECK_EQ(add_to_weight_, req[deconv::kWeight] == kAddTo);
+       CHECK_EQ(add_to_weight_, req[deconv::kWeight] == kAddTo);
         CUDNN_CALL(cudnnConvolutionBackwardFilter(
           s->dnn_handle_,
           &alpha,
@@ -275,7 +227,6 @@ class CuDNNDeconvolutionOp {
           &weight_beta,
           filter_desc_,
           gwmat_ptr + weight_offset_ * g));
-        #endif
       }
       if (req[deconv::kData] != kNullOp) {
         CUDNN_CALL(cudnnConvolutionForward(s->dnn_handle_,
@@ -323,16 +274,7 @@ class CuDNNDeconvolutionOp {
     // The factor by which the effective filter size grows based on dilation.
     auto filterDilationFactor = param.dilate.Size();
 
-    // The v6 kernels that backprop a dilated convolution don't handle fp16.
-    // Since the deconvolution "forward" kernel is really a backprop-to-data
-    // cuDNN kernel, the following logic is slightly different than that
-    // used in CuDNNConvolution::Supports().
-
-    // Dilation support across all architectures only available after v6.0.20.
-    return filterDilationFactor == 1 ||
-           filterDilationFactor > 1 && (CUDNN_VERSION > 6020) &&
-           (backward_compute_type != kFloat16) &&
-           (forward_compute_type != kFloat16);
+    return true;
   }
 
  private:
@@ -362,13 +304,6 @@ class CuDNNDeconvolutionOp {
     mxnet::TShape oshape = out_shape[deconv::kOut];
     mxnet::TShape dstride, ostride;
     wshape[0] /= param_.num_group;
-#if CUDNN_MAJOR <= 5
-      // As of cuDNN_v6, the unsuffixed version of cudnnSetConvolution2dDescriptor()
-      // takes an additional 'computeType' parameter to set the precision of the
-      // convolution calculation.  Supply this method signature for cuDNN versions < 6.
-#define cudnnSetConvolution2dDescriptor(cdesc, p0, p1, s0, s1, d0, d1, m, ct) \
-        cudnnSetConvolution2dDescriptor(cdesc, p0, p1, s0, s1, d0, d1, m)
-#endif
     if (param_.kernel.ndim() == 1 || param_.kernel.ndim() == 2) {
       // 1d or 2d conv
       index_t o_pad[2];
@@ -414,13 +349,6 @@ class CuDNNDeconvolutionOp {
                                                  dilate[1],
                                                  CUDNN_CROSS_CORRELATION,
                                                  cudnn_backward_compute_type));
-#if CUDNN_MAJOR < 5
-      // As of cuDNN_v5, cudnnSetFilter4dDescriptor() takes a format parameter.
-      // Supply this method signature for cuDNN versions < 5.
-#define cudnnSetFilter4dDescriptor(fdesc, dt, f, w0, w1, w2, w3) \
-        cudnnSetFilter4dDescriptor(fdesc, dt, w0, w1, w2, w3)
-      CHECK_EQ(format_, CUDNN_TENSOR_NCHW) << "CuDNN V4 and earlier only supports NCHW layout";
-#endif
       if (param_.kernel.ndim() == 2) {
         wshape = ConvertLayout(wshape.get<4>(), param_.layout.value(), kNCHW);
         dstride = ConvertLayout(Strides<4>(dshape), param_.layout.value(), kNCHW);
@@ -465,7 +393,6 @@ class CuDNNDeconvolutionOp {
       index_t o_adj[3];
       param_.InferPad(dshape, o_pad, o_adj);
 
-      #if CUDNN_MAJOR >= 5
       CHECK_EQ(param_.layout.value(), kNCDHW) << "CuDNN only support 3D conv with NCDHW layout";
       std::vector<int> wshape_buffer(wshape.ndim());
       CUDNN_CALL(cudnnSetFilterNdDescriptor(filter_desc_,
@@ -473,9 +400,6 @@ class CuDNNDeconvolutionOp {
                                             CUDNN_TENSOR_NCHW,
                                             static_cast<int>(wshape.ndim()),
                                             CastTShapeToIntPtr(wshape, &wshape_buffer)));
-      #else
-      LOG(FATAL) << "Only support CUDNN V5 for 3D convolution";
-      #endif
       CUDNN_CALL(cudnnSetConvolutionNdDescriptor(forward_conv_desc_,
                                                  3,
                                                  reinterpret_cast<int*>(&o_pad[0]),
@@ -506,13 +430,11 @@ class CuDNNDeconvolutionOp {
       oshape = ConvertLayout(oshape.get<5>(), param_.layout.value(), kNCDHW);
     }
     // Set "allow tensor core" flag in convolution descriptors, if available.
-#if CUDNN_MAJOR >= 7
     cudnnMathType_t math_type = cudnn_tensor_core_ ? CUDNN_TENSOR_OP_MATH
-                                                  : CUDNN_DEFAULT_MATH;
+                                                   : CUDNN_DEFAULT_MATH;
     CUDNN_CALL(cudnnSetConvolutionMathType(forward_conv_desc_, math_type));
     CUDNN_CALL(cudnnSetConvolutionMathType(back_conv_desc_, math_type));
     CUDNN_CALL(cudnnSetConvolutionMathType(back_conv_desc_w_, math_type));
-#endif
     dshape[1] /= param_.num_group;
     oshape[1] /= param_.num_group;
     weight_offset_ = wshape.Size();
@@ -566,12 +488,9 @@ class CuDNNDeconvolutionOp {
     mshadow::Stream <gpu> *s = rctx.get_stream<gpu>();
     CHECK_EQ(s->dnn_handle_ownership_, mshadow::Stream<gpu>::OwnHandle);
     size_t workspace_byte = static_cast<size_t>(param_.workspace * sizeof(DType));
-#if CUDNN_MAJOR >= 7
-    // Starting with cuDNNv7, the algo number returned by *Get*() is not the entire
-    // story: the notion of whether the algo ran in Tensor Core mode is not known.
-    // Since we want to report the Tensor Core mode in the verbose output, we switch
-    // to using the new *Get*_v7() call.  Since the function signature of *Get*_v7() matches
-    // that of *Find*(), we can unify the find-vs-get logic by using function pointers.
+
+    // Since the function signature of *Get*_v7() matches that of *Find*(),
+    // we can unify the find-vs-get logic by using function pointers.
 
     // Forward Algorithm Find/Get() v7
     std::vector<cudnnConvolutionFwdAlgoPerf_t> fwd_results(MaxForwardAlgos(s->dnn_handle_));
@@ -632,134 +551,6 @@ class CuDNNDeconvolutionOp {
     AlgoFinalSelect<cudnnConvolutionBwdDataAlgoPerf_t,
         cudnnConvolutionBwdDataAlgo_t>(bwd_data_results, "backprop-to-data",
                                        workspace_byte, bwd, exclude_dgrad_algo_);
-#else
-    // CUDNN_MAJOR < 7
-    const int kMaxAlgos = 10;
-    int nalgo = kMaxAlgos;
-    int i = 0;
-    size_t min_memory_needs = 0;
-    // Forward Algorithm Find/Get, v6 and earlier
-    if (CUDNN_MAJOR == 6 && param_.layout.value() == mshadow::kNHWC) {
-      // In cuDNNv6, for kNHWC, only CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM is
-      // supported.  Hard-coded this since the algo find() or get() throws an FPE.
-      fwd->Set(CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM, false);
-    } else if (!param_.cudnn_tune.value()) {
-      cudnnConvolutionFwdAlgo_t fastest_fwd_algo;
-      CUDNN_CALL(cudnnGetConvolutionForwardAlgorithm(s->dnn_handle_,
-                                                 out_desc_,
-                                                 filter_desc_,
-                                                 back_conv_desc_,  // fwd algo used in dgrad
-                                                 in_desc_,
-                                                 CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
-                                                 workspace_byte,
-                                                 &fastest_fwd_algo));
-      fwd->Set(fastest_fwd_algo, false);
-    } else {
-      cudnnConvolutionFwdAlgoPerf_t fwd_algo[kMaxAlgos];
-      CUDNN_CALL(cudnnFindConvolutionForwardAlgorithm(s->dnn_handle_,
-                                                    out_desc_,
-                                                    filter_desc_,
-                                                    back_conv_desc_,  // fwd algo used in dgrad
-                                                    in_desc_,
-                                                    kMaxAlgos,
-                                                    &nalgo,
-                                                    fwd_algo));
-      i = 0;
-      while (i < nalgo
-             && (fwd_algo[i].status != CUDNN_STATUS_SUCCESS
-                 || (param_.cudnn_tune.value() == deconv::kLimited
-                     && fwd_algo[i].memory > workspace_byte))) {
-        ++i;
-        min_memory_needs = (i == 0) ?
-                           fwd_algo[i].memory :
-                           std::min(min_memory_needs, fwd_algo[i].memory);
-      }
-      if (i == nalgo) {
-        LogNoSuitableAlgoAndExit(nalgo, min_memory_needs, workspace_byte,
-                                 "forward algos (for use in deconv op backprop-to-data)");
-      } else {
-        fwd->Set(fwd_algo[i].algo, false);
-      }
-    }
-    // Backprop-to-Filter Algorithm Find/Get, v6 and earlier
-    if (!param_.cudnn_tune.value()) {
-      cudnnConvolutionBwdFilterAlgo_t fastest_bwd_filt_algo;
-      CUDNN_CALL(cudnnGetConvolutionBackwardFilterAlgorithm(s->dnn_handle_,
-                                          out_desc_,
-                                          in_desc_,
-                                          back_conv_desc_,
-                                          filter_desc_,
-                                          CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT,
-                                          workspace_byte,
-                                          &fastest_bwd_filt_algo));
-      flt->Set(fastest_bwd_filt_algo, false);
-    } else {
-      cudnnConvolutionBwdFilterAlgoPerf_t bwd_filter_algo[kMaxAlgos];
-      CUDNN_CALL(cudnnFindConvolutionBackwardFilterAlgorithm(s->dnn_handle_,
-                                                             out_desc_,
-                                                             in_desc_,
-                                                             back_conv_desc_,
-                                                             filter_desc_,
-                                                             kMaxAlgos,
-                                                             &nalgo,
-                                                             bwd_filter_algo));
-      i = 0;
-      while (i < nalgo
-             && (bwd_filter_algo[i].status != CUDNN_STATUS_SUCCESS
-                 || (param_.cudnn_tune.value() == deconv::kLimited
-                     && bwd_filter_algo[i].memory > workspace_byte))) {
-        ++i;
-        min_memory_needs = (i == 0) ?
-                           bwd_filter_algo[i].memory :
-                           std::min(min_memory_needs, bwd_filter_algo[i].memory);
-      }
-      if (i == nalgo) {
-        LogNoSuitableAlgoAndExit(nalgo, min_memory_needs, workspace_byte,
-                                 "backward filter algos (for use in deconv op backprop-to-filter)");
-      } else {
-        flt->Set(bwd_filter_algo[i].algo, false);
-      }
-    }
-    // Backprop-to-Data Algorithm Get(), v6 and earlier
-    if (!param_.cudnn_tune.value()) {
-      cudnnConvolutionBwdDataAlgo_t fastest_bwd_data_algo;
-      CUDNN_CALL(cudnnGetConvolutionBackwardDataAlgorithm(s->dnn_handle_,
-                                            filter_desc_,
-                                            in_desc_,
-                                            forward_conv_desc_,  // bwd algo used for inference
-                                            out_desc_,
-                                            CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,
-                                            workspace_byte,
-                                            &fastest_bwd_data_algo));
-      bwd->Set(fastest_bwd_data_algo, false);
-    } else {
-      cudnnConvolutionBwdDataAlgoPerf_t bwd_data_algo[kMaxAlgos];
-      CUDNN_CALL(cudnnFindConvolutionBackwardDataAlgorithm(s->dnn_handle_,
-                                             filter_desc_,
-                                             in_desc_,
-                                             forward_conv_desc_,  // bwd algo used in inference
-                                             out_desc_,
-                                             kMaxAlgos,
-                                             &nalgo,
-                                             bwd_data_algo));
-      i = 0;
-      while (i < nalgo
-             && (bwd_data_algo[i].status != CUDNN_STATUS_SUCCESS
-                 || (param_.cudnn_tune.value() == deconv::kLimited
-                     && bwd_data_algo[i].memory > workspace_byte))) {
-        ++i;
-        min_memory_needs = (i == 0) ?
-                           bwd_data_algo[i].memory :
-                           std::min(min_memory_needs, bwd_data_algo[i].memory);
-      }
-      if (i == nalgo) {
-        LogNoSuitableAlgoAndExit(nalgo, min_memory_needs, workspace_byte,
-                                 "backward data algos (for use in deconv op forward inference)");
-      } else {
-        bwd->Set(bwd_data_algo[i].algo, false);
-      }
-    }
-#endif  // CUDNN_MAJOR < 7
 
     // Fix for issue #11241
     int cudnn_find_issue_max_features = 64 * 1024;
@@ -794,9 +585,9 @@ class CuDNNDeconvolutionOp {
         // DirectFree(), which makes these areas available for cudnn's subsequent cudaMalloc().
 
         // Allocate for x (or dx), w (or dw) and y (or dy).
-        ReserveElements({in_shape[conv::kData].Size(),
-                         in_shape[conv::kWeight].Size(),
-                         out_shape[conv::kOut].Size()});
+        ReserveElements({in_shape[deconv::kData].Size(),
+                         in_shape[deconv::kWeight].Size(),
+                         out_shape[deconv::kOut].Size()});
 
         // We're about to call cudnnFind so we need to quiet the system by grabbing
         // the Storage lock.  Concurrent cudaMalloc's can disrupt the accurate timing
@@ -825,7 +616,7 @@ class CuDNNDeconvolutionOp {
     // *Find*() or *Get*(), but a non-Tensor-Core algo variant is the fastest,
     // we must change the descriptor to preclude Tensor Core.  Simplest is to
     // once again set the mathType in all cases.
-    #if CUDNN_MAJOR >= 7
+
     // The next two code lines will look like they have typos, but they don't!
     // The forward_conv_desc_ is used during inference, which invokes the back_algo_.
     // Thus, the mathType of the back_algo_ should be stored in the forward_conv_desc_.
@@ -835,7 +626,6 @@ class CuDNNDeconvolutionOp {
     CUDNN_CALL(cudnnSetConvolutionMathType(forward_conv_desc_, back_algo_.MathType()));
     CUDNN_CALL(cudnnSetConvolutionMathType(back_conv_desc_, forward_algo_.MathType()));
     CUDNN_CALL(cudnnSetConvolutionMathType(back_conv_desc_w_, back_algo_w_.MathType()));
-    #endif
   }
 
   // Look over the results from *Find*() or *Get*() and pick the fastest algo given possible
@@ -850,20 +640,16 @@ class CuDNNDeconvolutionOp {
       const auto &result = perf_results[i];
       bool algo_exclusion = static_cast<int32_t>(result.algo) == algo_exclude;
       bool algo_is_tensor_core = false;
-      #if CUDNN_MAJOR >= 7
-        algo_is_tensor_core = result.mathType == CUDNN_TENSOR_OP_MATH;
-      #endif
+      algo_is_tensor_core = result.mathType == CUDNN_TENSOR_OP_MATH;
       if (result.status == CUDNN_STATUS_SUCCESS &&
-        #if CUDNN_MAJOR >= 7
           (!enforce_determinism || result.determinism == cudnnDeterminism_t::CUDNN_DETERMINISTIC) &&
-        #endif
-          (param_.cudnn_tune.value() != conv::kLimited || result.memory <= workspace_byte) &&
+          (param_.cudnn_tune.value() != deconv::kLimited || result.memory <= workspace_byte) &&
           !algo_exclusion) {
         algo->Set(result.algo, algo_is_tensor_core);
         return;
       }
     }
-    auto mode = param_.cudnn_tune.value() == conv::kOff ? " get " : " find ";
+    auto mode = param_.cudnn_tune.value() == deconv::kOff ? " get " : " find ";
     LOG(FATAL) << "Failed to" << mode << "any " << kernel_name << " deconvolution algorithm"
                << " with workspace size of " << workspace_byte << " bytes,"
                << " please consider reducing batch/model size or increasing the workspace size";
diff --git a/src/operator/nn/cudnn/cudnn_pooling-inl.h b/src/operator/nn/cudnn/cudnn_pooling-inl.h
index ada605db0ee9..f52848b4a452 100644
--- a/src/operator/nn/cudnn/cudnn_pooling-inl.h
+++ b/src/operator/nn/cudnn/cudnn_pooling-inl.h
@@ -35,6 +35,8 @@ namespace op {
 
 template<typename DType>
 class CuDNNPoolingOp {
+  STATIC_ASSERT_CUDNN_VERSION_GE(7000);
+
  public:
   CuDNNPoolingOp() {
     // TODO(xxx): fp16
@@ -48,12 +50,8 @@ class CuDNNPoolingOp {
     param_ = p;
     switch (param_.pool_type) {
       case pool_enum::kMaxPooling:
-      #if CUDNN_MAJOR >= 7
         mode_ = dmlc::GetEnv("MXNET_ENFORCE_DETERMINISM", false) ?
           CUDNN_POOLING_MAX_DETERMINISTIC : CUDNN_POOLING_MAX;
-      #else
-        mode_ = CUDNN_POOLING_MAX;
-      #endif
         break;
       case pool_enum::kAvgPooling:
         if (param_.count_include_pad.has_value() && !param_.count_include_pad.value()) {
@@ -229,10 +227,6 @@ class CuDNNPoolingOp {
 #endif
     } else if (param.kernel.ndim() == 3) {
       // 3d pooling
-#if CUDNN_MAJOR < 5
-      LogUnsupportedDim(&unsupported_dim_warning_issued, param.kernel.ndim());
-      return false;
-#endif
       if (!(layout == mshadow::kNCDHW || layout == mshadow::kNDHWC))
         return false;
     } else {
@@ -250,9 +244,7 @@ class CuDNNPoolingOp {
       const TBlob &out_data) {
     using namespace mshadow;
     bool is_supported = true;
-    #if CUDNN_MAJOR >= 5
     nan_prop_ = CUDNN_NOT_PROPAGATE_NAN;
-    #endif
     int layout = param_.GetLayout(in_data.ndim());
     if (param_.kernel.ndim() == 2) {
       // 2d pooling
@@ -290,7 +282,6 @@ class CuDNNPoolingOp {
       #if CUDNN_VERSION == 7104
       is_supported = kernel_height <= 8 && kernel_width <= 8;
       #endif
-      #if CUDNN_MAJOR >= 5
       CUDNN_CALL(cudnnSetPooling2dDescriptor(pooling_desc_,
                                              mode_,
                                              nan_prop_,
@@ -300,16 +291,6 @@ class CuDNNPoolingOp {
                                              param_.global_pool ? 0 : param_.pad[1],
                                              param_.global_pool ? 1 : param_.stride[0],
                                              param_.global_pool ? 1 : param_.stride[1]));
-      #else
-      CUDNN_CALL(cudnnSetPooling2dDescriptor(pooling_desc_,
-                                             mode_,
-                                             kernel_height,
-                                             kernel_width,
-                                             param_.global_pool ? 0 : param_.pad[0],
-                                             param_.global_pool ? 0 : param_.pad[1],
-                                             param_.global_pool ? 1 : param_.stride[0],
-                                             param_.global_pool ? 1 : param_.stride[1]));
-      #endif
     } else {
       CHECK(layout == mshadow::kNCDHW ||
             layout == mshadow::kNDHWC) << "Need 3D layout NCDHW or NDHWC.";
@@ -376,7 +357,6 @@ class CuDNNPoolingOp {
                                             static_cast<int>(oshape_ncdhw_int.size()),
                                             &oshape_ncdhw_int[0],
                                             &ostride_ncdhw_int[0]));
-      #if CUDNN_MAJOR >= 5
       CUDNN_CALL(cudnnSetPoolingNdDescriptor(pooling_desc_,
                                              mode_,
                                              nan_prop_,
@@ -384,9 +364,6 @@ class CuDNNPoolingOp {
                                              &(kernel_vec[0]),
                                              &(pad_vec[0]),
                                              &(stride_vec[0])));
-      #else
-      LOG(FATAL) << "3D pooling is only supported by CUDNN v5 and above.";
-      #endif
     }
     return is_supported;
   }
@@ -406,9 +383,7 @@ class CuDNNPoolingOp {
   cudnnTensorDescriptor_t in_desc_;
   cudnnTensorDescriptor_t out_desc_;
   cudnnPoolingDescriptor_t pooling_desc_;
-  #if CUDNN_MAJOR >= 5
   cudnnNanPropagation_t nan_prop_;
-  #endif
   PoolingParam param_;
 };  // class CuDNNPoolingOp
 }  // namespace op
diff --git a/src/operator/quantization/quantized_conv.cu b/src/operator/quantization/quantized_conv.cu
index 23c41a17ef4a..28bd43239066 100644
--- a/src/operator/quantization/quantized_conv.cu
+++ b/src/operator/quantization/quantized_conv.cu
@@ -49,7 +49,8 @@ struct QuantizedBiasAddKernel {
   }
 };
 
-#if MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 6 && CUDA_VERSION >= 8000
+#if MXNET_USE_CUDNN == 1 && CUDA_VERSION >= 8000
+STATIC_ASSERT_CUDNN_VERSION_GE(6000);
 template<typename SrcType, typename DstType, typename CmpType>
 class QuantizedCuDNNConvOp {
  public:
@@ -260,7 +261,7 @@ class QuantizedCuDNNConvOp {
   float alpha_ = 1.0f;
   float beta_ = 0.0f;
 };  // class QuantizedCuDNNConvOp
-#endif  // MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 6 && CUDA_VERSION >= 8000
+#endif  // MXNET_USE_CUDNN == 1 && CUDA_VERSION >= 8000
 
 void QuantizedConvForwardGPU(const nnvm::NodeAttrs& attrs,
                              const OpContext& ctx,
@@ -270,7 +271,7 @@ void QuantizedConvForwardGPU(const nnvm::NodeAttrs& attrs,
   const ConvolutionParam& param = nnvm::get<ConvolutionParam>(attrs.parsed);
   CHECK_EQ(param.kernel.ndim(), 2U)
     << "QuantizedConvForward<gpu> only supports 2D convolution for now";
-#if MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 6 && CUDA_VERSION >= 8000
+#if MXNET_USE_CUDNN == 1 && CUDA_VERSION >= 8000
   typedef QuantizedCuDNNConvOp<int8_t, float, int32_t> QuantizedConvOpInt8;
 #if DMLC_CXX11_THREAD_LOCAL
   static thread_local QuantizedConvOpInt8 op;
@@ -282,7 +283,7 @@ void QuantizedConvForwardGPU(const nnvm::NodeAttrs& attrs,
 #else
   LOG(FATAL) << "QuantizedConvForward<gpu> only supports cudnnConvolutionForward "
                 "with CUDNN >= 6.0 and CUDA >= 8.0";
-#endif  // MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 6 && CUDA_VERSION >= 8000
+#endif  // MXNET_USE_CUDNN == 1 && CUDA_VERSION >= 8000
 }
 
 NNVM_REGISTER_OP(_contrib_quantized_conv)
diff --git a/src/operator/quantization/quantized_pooling.cu b/src/operator/quantization/quantized_pooling.cu
index a8fba87090ab..167c08e99683 100644
--- a/src/operator/quantization/quantized_pooling.cu
+++ b/src/operator/quantization/quantized_pooling.cu
@@ -29,7 +29,8 @@
 namespace mxnet {
 namespace op {
 
-#if MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 6 && CUDA_VERSION >= 8000
+#if MXNET_USE_CUDNN == 1 && CUDA_VERSION >= 8000
+STATIC_ASSERT_CUDNN_VERSION_GE(6000);
 template<typename DType>
 class QuantizedCuDNNPoolingOp {
  public:
@@ -115,7 +116,7 @@ class QuantizedCuDNNPoolingOp {
   cudnnTensorDescriptor_t out_desc_;
   cudnnPoolingDescriptor_t pool_desc_;
 };  // class QuantizedCuDNNPoolingOp
-#endif  // MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 6 && CUDA_VERSION >= 8000
+#endif  // MXNET_USE_CUDNN == 1 && CUDA_VERSION >= 8000
 
 void QuantizedPoolingForwardGPU(const nnvm::NodeAttrs& attrs,
                                 const OpContext& ctx,
@@ -125,7 +126,7 @@ void QuantizedPoolingForwardGPU(const nnvm::NodeAttrs& attrs,
   const PoolingParam& param = nnvm::get<PoolingParam>(attrs.parsed);
   CHECK_EQ(param.kernel.ndim(), 2U)
     << "QuantizedPoolingForward<gpu> only supports 2D convolution for now";
-#if MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 6 && CUDA_VERSION >= 8000
+#if MXNET_USE_CUDNN == 1 && CUDA_VERSION >= 8000
 #if DMLC_CXX11_THREAD_LOCAL
   static thread_local QuantizedCuDNNPoolingOp<int8_t> op;
 #else
@@ -136,7 +137,7 @@ void QuantizedPoolingForwardGPU(const nnvm::NodeAttrs& attrs,
 #else
   LOG(FATAL) << "QuantizedPoolingForward<gpu> only supports cudnnPoolingForward "
                 "with CUDNN >= 6.0 and CUDA >= 8.0";
-#endif  // MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 6 && CUDA_VERSION >= 8000
+#endif  // MXNET_USE_CUDNN == 1 && CUDA_VERSION >= 8000
 }
 
 NNVM_REGISTER_OP(_contrib_quantized_pooling)
diff --git a/src/operator/rnn-inl.h b/src/operator/rnn-inl.h
index 328e28de8537..5eae413b078b 100644
--- a/src/operator/rnn-inl.h
+++ b/src/operator/rnn-inl.h
@@ -26,7 +26,9 @@
 #ifndef MXNET_OPERATOR_RNN_INL_H_
 #define MXNET_OPERATOR_RNN_INL_H_
 
-#define MXNET_USE_CUDNN_RNN MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 5
+#if MXNET_USE_CUDNN == 1
+STATIC_ASSERT_CUDNN_VERSION_GE(7000);
+#endif
 #define MXNET_USE_CUDNN_GE_7200 MXNET_USE_CUDNN == 1 && CUDNN_VERSION >= 7200
 
 #include <dmlc/logging.h>
@@ -396,7 +398,7 @@ class RNNOp {
  public:
   RNNParam param_;
   Context ctx_;
-  #if MXNET_USE_MKLDNN == 1
+#if MXNET_USE_MKLDNN == 1
   std::vector<mkldnn::memory> concat_weight_memory;
   std::vector<mkldnn::memory> concat_iter_memory;
   std::vector<primitive> rnn_forward_prim;
@@ -411,15 +413,15 @@ class RNNOp {
   bool init_mem_;
   size_t reserve_mem_size_;
   Storage::Handle mem_space_;
-  #endif
+#endif
   explicit RNNOp(RNNParam param, Context ctx) {
     this->param_ = param;
     this->ctx_ = ctx;
-    #if MXNET_USE_MKLDNN == 1
+#if MXNET_USE_MKLDNN == 1
     init_mem_ = false;
     reserve_mem_size_ = 0;
-    #endif
-    #if MXNET_USE_CUDNN_RNN
+#endif
+#if MXNET_USE_CUDNN == 1
     init_cudnn_ = false;
     dtype_ = mshadow::DataType<DType>::kCudnnFlag;
     // TensorCore algos only allowed on fp16-I/O convolutions if permitted by the global policy.
@@ -457,7 +459,7 @@ class RNNOp {
 #else
     CHECK(!param_.projection_size.has_value())
       << "Projection is only supported for LSTM with CuDNN version later than 7.1.1.";
-#endif
+#endif  // MXNET_USE_CUDNN_GE_7200
 #if MXNET_USE_CUDNN_GE_7200
     if (param_.lstm_state_clip_min.has_value()
         || param_.lstm_state_clip_max.has_value()) {
@@ -472,7 +474,7 @@ class RNNOp {
     CHECK(!param_.lstm_state_clip_min.has_value()
           && !param_.lstm_state_clip_max.has_value())
       << "State clipping is only supported for LSTM with CuDNN version later than 7.2.1.";
-#endif
+#endif  // MXNET_USE_CUDNN_GE_7200
     // RNN Direction
     direction_ = param_.bidirectional ? CUDNN_BIDIRECTIONAL : CUDNN_UNIDIRECTIONAL;
     // Create descriptors
@@ -491,17 +493,17 @@ class RNNOp {
     CUDNN_CALL(cudnnCreateRNNDescriptor(&rnn_desc_));
     CUDNN_CALL(cudnnCreateDropoutDescriptor(&dropout_desc_));
 
-    #if MXNET_USE_CUDNN_GE_7200
+#if MXNET_USE_CUDNN_GE_7200
     CUDNN_CALL(cudnnCreateRNNDataDescriptor(&x_data_desc_));
     CUDNN_CALL(cudnnCreateRNNDataDescriptor(&y_data_desc_));
     CUDNN_CALL(cudnnCreateRNNDataDescriptor(&dx_data_desc_));
     CUDNN_CALL(cudnnCreateRNNDataDescriptor(&dy_data_desc_));
-    #endif
-    #else
+#endif
+#else
     if (ctx_.dev_type == kGPU) {
       LOG(FATAL) << "RNN on GPU is only available for cuDNN at the moment.";
     }
-    #endif
+#endif  // MXNET_USE_CUDNN == 1
 
     if (ctx_.dev_type == kCPU) {
       this->init_space_ = false;
@@ -520,13 +522,13 @@ class RNNOp {
   }
 
   ~RNNOp() {
-    #if MXNET_USE_MKLDNN == 1
+#if MXNET_USE_MKLDNN == 1
     if (init_mem_) {
       Storage::Get()->Free(mem_space_);
       init_mem_ = false;
     }
-    #endif
-    #if MXNET_USE_CUDNN_RNN
+#endif  // MXNET_USE_MKLDNN
+#if MXNET_USE_CUDNN == 1
     CUDNN_CALL(cudnnDestroyTensorDescriptor(hx_desc_));
     CUDNN_CALL(cudnnDestroyTensorDescriptor(cx_desc_));
     CUDNN_CALL(cudnnDestroyTensorDescriptor(hy_desc_));
@@ -551,13 +553,13 @@ class RNNOp {
       init_cudnn_ = false;
       Storage::Get()->Free(reserve_space_);
     }
-    #if MXNET_USE_CUDNN_GE_7200
+#if MXNET_USE_CUDNN_GE_7200
     CUDNN_CALL(cudnnDestroyRNNDataDescriptor(x_data_desc_));
     CUDNN_CALL(cudnnDestroyRNNDataDescriptor(y_data_desc_));
     CUDNN_CALL(cudnnDestroyRNNDataDescriptor(dx_data_desc_));
     CUDNN_CALL(cudnnDestroyRNNDataDescriptor(dy_data_desc_));
-    #endif
-    #endif
+#endif  // MXNET_USE_CUDNN_GE_7200
+#endif  // MXNET_USE_CUDNN
 
     if (ctx_.dev_type == kCPU) {
       if (init_space_) {
@@ -671,7 +673,7 @@ class RNNOp {
     CHECK_EQ(hx.CheckContiguous(), true);
     CHECK_EQ(y.CheckContiguous(), true);
 
-#if MXNET_USE_CUDNN_RNN && defined(__CUDACC__)
+#if MXNET_USE_CUDNN == 1 && defined(__CUDACC__)
     if (!init_cudnn_) {
       Init(ctx, s, in_data, out_data);
     }
@@ -736,9 +738,7 @@ class RNNOp {
                                            sequence_length_cpu_int,
                                            reinterpret_cast<void*>(&padding_fill_)));
     }
-#endif
 
-#if MXNET_USE_CUDNN_GE_7200
     bool clip_state = param_.lstm_state_clip_min.has_value();
     bool clip_nan = param_.lstm_state_clip_nan;
     CUDNN_CALL(cudnnRNNSetClip(s->dnn_handle_,
@@ -747,7 +747,7 @@ class RNNOp {
                                clip_nan ? CUDNN_NOT_PROPAGATE_NAN : CUDNN_PROPAGATE_NAN,
                                clip_state ? param_.lstm_state_clip_min.value() : 0.0,
                                clip_state ? param_.lstm_state_clip_max.value() : 0.0));
-#endif
+#endif  // MXNET_USE_CUDNN_GE_7200
 
     if (ctx.is_train) {
 #if MXNET_USE_CUDNN_GE_7200
@@ -801,7 +801,7 @@ class RNNOp {
                                          workspace_byte_,
                                          reserve_space_.dptr,
                                          reserve_space_byte_));
-#endif
+#endif  // MXNET_USE_CUDNN_GE_7200
     } else {
 #if MXNET_USE_CUDNN_GE_7200
       CUDNN_CALL(cudnnRNNForwardInferenceEx(s->dnn_handle_,
@@ -850,9 +850,9 @@ class RNNOp {
                                           cy_ptr,
                                           temp_space.dptr_,
                                           workspace_byte_));
-#endif
+#endif  // MXNET_USE_CUDNN_GE_7200
     }
-#endif
+#endif  // MXNET_USE_CUDNN == 1 && defined(__CUDACC__)
 
     if (ctx_.dev_type == kCPU) {
       if (ctx.is_train) {
@@ -907,7 +907,7 @@ class RNNOp {
                                   param_.p,
                                   param_.mode);
       } else {
-        #if MXNET_USE_MKLDNN == 1
+#if MXNET_USE_MKLDNN == 1
         if (dmlc::GetEnv("MXNET_USE_MKLDNN_RNN", 1) && param_.mode != rnn_enum::kGru) {
           // TODO(zixuanweeei): MKLDNN GRU has precision issue. A stable one
           //   will be added to MXNet when we figure out the issue.
@@ -942,7 +942,7 @@ class RNNOp {
                                            ctx.is_train,
                                            param_.mode);
         } else {
-        #endif
+#endif  // MXNET_USE_MKLDNN == 1
           //  Before integrating MKLDNN GRU fp32 inference
           //  using below code for keep func being OK
           const size_t work_cpu_space_size =
@@ -976,9 +976,9 @@ class RNNOp {
                                      hy_ptr,
                                      cy_ptr,
                                      param_.mode);
-        #if MXNET_USE_MKLDNN == 1
+#if MXNET_USE_MKLDNN == 1
         }
-        #endif
+#endif
       }
     }
   }
@@ -1061,7 +1061,7 @@ class RNNOp {
         dcy_ptr = (out_grad[rnn_enum::kStateCellOut].get<xpu, 3, DType>(s)).dptr_;
     }
 
-    #if MXNET_USE_CUDNN_RNN && defined(__CUDACC__)
+#if MXNET_USE_CUDNN == 1 && defined(__CUDACC__)
     if (!init_cudnn_) {
       Init(ctx, s, in_data, out_data);
     }
@@ -1072,7 +1072,7 @@ class RNNOp {
         ctx.requested[rnn_enum::kTempSpace].get_space_typed<gpu, 1, DType>(
             mshadow::Shape1(temp_size), s);
 
-    #if MXNET_USE_CUDNN_GE_7200
+#if MXNET_USE_CUDNN_GE_7200
     CUDNN_CALL(cudnnRNNBackwardDataEx(s->dnn_handle_,
                                       rnn_desc_,
                                       y_data_desc_,
@@ -1117,7 +1117,7 @@ class RNNOp {
                                          dw.dptr_,
                                          reserve_space_.dptr,
                                          reserve_space_byte_));
-    #else
+#else
     CUDNN_CALL(cudnnRNNBackwardData(s->dnn_handle_,
                                     rnn_desc_,
                                     param_.seq_length_,
@@ -1160,8 +1160,8 @@ class RNNOp {
                                        dw.dptr_,
                                        reserve_space_.dptr,
                                        reserve_space_byte_));
-    #endif
-    #endif
+#endif  // MXNET_USE_CUDNN_GE_7200
+#endif  // MXNET_USE_CUDNN == 1 && defined(__CUDACC__)
 
     if (ctx_.dev_type == kCPU) {
       // allocate temp space
@@ -1230,10 +1230,8 @@ class RNNOp {
     CHECK_EQ(in_data.size(), num_inputs);
     CHECK_EQ(out_data.size(), num_outputs);
 
-    #if MXNET_USE_CUDNN_RNN && defined(__CUDACC__)
-    #if CUDNN_MAJOR >= 5
+#if MXNET_USE_CUDNN == 1 && defined(__CUDACC__)
     format_ = CUDNN_TENSOR_NCHW;
-    #endif
 
     if (!init_cudnn_) {
       init_cudnn_ = true;
@@ -1304,7 +1302,7 @@ class RNNOp {
       strideA[0] = dimA[2] * dimA[1];
       strideA[1] = dimA[2];
       strideA[2] = 1;
-      #if MXNET_USE_CUDNN_GE_7200
+#if MXNET_USE_CUDNN_GE_7200
       int dimB[3];
       int strideB[3];
       dimB[0] = param_.num_layers * (param_.bidirectional ? 2 : 1);
@@ -1314,74 +1312,74 @@ class RNNOp {
       strideB[0] = dimB[2] * dimB[1];
       strideB[1] = dimB[2];
       strideB[2] = 1;
-      #endif
-      #if MXNET_USE_CUDNN_GE_7200
+#endif  // MXNET_USE_CUDNN_GE_7200
+#if MXNET_USE_CUDNN_GE_7200
       CUDNN_CALL(cudnnSetTensorNdDescriptor(hx_desc_,
                                             dtype_,
                                             3,
                                             dimB,
                                             strideB));
-      #else
+#else
       CUDNN_CALL(cudnnSetTensorNdDescriptor(hx_desc_,
                                             dtype_,
                                             3,
                                             dimA,
                                             strideA));
-      #endif
+#endif  // MXNET_USE_CUDNN_GE_7200
       CUDNN_CALL(cudnnSetTensorNdDescriptor(cx_desc_,
                                             dtype_,
                                             3,
                                             dimA,
                                             strideA));
-      #if MXNET_USE_CUDNN_GE_7200
+#if MXNET_USE_CUDNN_GE_7200
       CUDNN_CALL(cudnnSetTensorNdDescriptor(hy_desc_,
                                             dtype_,
                                             3,
                                             dimB,
                                             strideB));
-      #else
+#else
       CUDNN_CALL(cudnnSetTensorNdDescriptor(hy_desc_,
                                             dtype_,
                                             3,
                                             dimA,
                                             strideA));
-      #endif
+#endif  // MXNET_USE_CUDNN_GE_7200
       CUDNN_CALL(cudnnSetTensorNdDescriptor(cy_desc_,
                                             dtype_,
                                             3,
                                             dimA,
                                             strideA));
-      #if MXNET_USE_CUDNN_GE_7200
+#if MXNET_USE_CUDNN_GE_7200
       CUDNN_CALL(cudnnSetTensorNdDescriptor(dhx_desc_,
                                             dtype_,
                                             3,
                                             dimB,
                                             strideB));
-      #else
+#else
       CUDNN_CALL(cudnnSetTensorNdDescriptor(dhx_desc_,
                                             dtype_,
                                             3,
                                             dimA,
                                             strideA));
-      #endif
+#endif  // MXNET_USE_CUDNN_GE_7200
       CUDNN_CALL(cudnnSetTensorNdDescriptor(dcx_desc_,
                                             dtype_,
                                             3,
                                             dimA,
                                             strideA));
-      #if MXNET_USE_CUDNN_GE_7200
+#if MXNET_USE_CUDNN_GE_7200
       CUDNN_CALL(cudnnSetTensorNdDescriptor(dhy_desc_,
                                             dtype_,
                                             3,
                                             dimB,
                                             strideB));
-      #else
+#else
       CUDNN_CALL(cudnnSetTensorNdDescriptor(dhy_desc_,
                                             dtype_,
                                             3,
                                             dimA,
                                             strideA));
-      #endif
+#endif  // MXNET_USE_CUDNN_GE_7200
       CUDNN_CALL(cudnnSetTensorNdDescriptor(dcy_desc_,
                                             dtype_,
                                             3,
@@ -1403,20 +1401,19 @@ class RNNOp {
 
       // RNN descriptors
       cudnnDataType_t dtype_with_fallback_;
-      #if CUDNN_MAJOR >= 6
       cudnnRNNAlgo_t rnn_algo = CUDNN_RNN_ALGO_STANDARD;
       // On arch's 50 and 52(Maxwell), the gpu doesn't support native fp16 compute.
       // Before cuDNN 7.5.0, when running fp16, cuDNN fallback to fp32 under the hood on Maxwell.
       // That's not the case begining from 7.5.0. Thereby adding fallback explicitly here.
-      #if __CUDA_ARCH__ < 530 && CUDNN_MAJOR >=7 && CUDNN_MINOR >= 5
+#if __CUDA_ARCH__ < 530 && CUDNN_VERSION >= 7500
       if (dtype_ == CUDNN_DATA_HALF) {
         dtype_with_fallback_ = CUDNN_DATA_FLOAT;
       } else {
         dtype_with_fallback_ = dtype_;
       }
-      #else
+#else
         dtype_with_fallback_ = dtype_;
-      #endif
+#endif
       CUDNN_CALL(cudnnSetRNNDescriptor_v6(s->dnn_handle_,
                                           rnn_desc_,
                                           param_.state_size,
@@ -1427,45 +1424,30 @@ class RNNOp {
                                           mode_,
                                           rnn_algo,
                                           dtype_with_fallback_));
-      #else
-      CUDNN_CALL(cudnnSetRNNDescriptor(rnn_desc_,
-                                       param_.state_size,
-                                       param_.num_layers,
-                                       dropout_desc_,
-                                       input_mode_,
-                                       direction_,
-                                       mode_,
-                                       dtype_));
-      #endif
-      #if CUDNN_MAJOR >= 7
-        cudnnMathType_t math_type = CUDNN_DEFAULT_MATH;
-        if (cudnn_tensor_core_ && rnn_algo == CUDNN_RNN_ALGO_STANDARD) {
-          math_type = CUDNN_TENSOR_OP_MATH;
-        }
-      #if CUDNN_VERSION >= 7200
-            if (GetEnvAllowTensorCore() && GetEnvAllowTensorCoreConversion() &&
-                (DataType<DType>::kFlag != kFloat16)) {
-              math_type = CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION;
-            }
-      #endif
-        CUDNN_CALL(cudnnSetRNNMatrixMathType(rnn_desc_, math_type));
-      #endif
-      #if MXNET_USE_CUDNN_GE_7200
+      cudnnMathType_t math_type = CUDNN_DEFAULT_MATH;
+      if (cudnn_tensor_core_ && rnn_algo == CUDNN_RNN_ALGO_STANDARD) {
+        math_type = CUDNN_TENSOR_OP_MATH;
+      }
+#if CUDNN_VERSION >= 7200
+      if (GetEnvAllowTensorCore() && GetEnvAllowTensorCoreConversion() &&
+          (DataType<DType>::kFlag != kFloat16)) {
+        math_type = CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION;
+      }
+#endif
+      CUDNN_CALL(cudnnSetRNNMatrixMathType(rnn_desc_, math_type));
+#if MXNET_USE_CUDNN_GE_7200
       if (param_.projection_size.has_value()) {
         CUDNN_CALL(cudnnSetRNNProjectionLayers(s->dnn_handle_,
                                                rnn_desc_,
                                                param_.projection_size.value(),
                                                0));
       }
-      #endif
-      // Get temp space sizes
-
-      #if MXNET_USE_CUDNN_GE_7200
       if (param_.use_sequence_length) {
         CUDNN_CALL(cudnnSetRNNPaddingMode(rnn_desc_, CUDNN_RNN_PADDED_IO_ENABLED));
       }
-      #endif
+#endif  // MXNET_USE_CUDNN_GE_7200
 
+      // Get temp space sizes
       CUDNN_CALL(cudnnGetRNNWorkspaceSize(s->dnn_handle_,
                                           rnn_desc_,
                                           param_.seq_length_,
@@ -1537,9 +1519,9 @@ class RNNOp {
       //   }
       // }
     }
-  #endif
+#endif  // MXNET_USE_CUDNN == 1 && defined(__CUDACC__)
   }
-  #if MXNET_USE_CUDNN_RNN
+#if MXNET_USE_CUDNN == 1
   cudnnDataType_t dtype_;
   bool init_cudnn_;
   cudnnRNNDescriptor_t rnn_desc_;
@@ -1552,10 +1534,10 @@ class RNNOp {
   size_t workspace_byte_, reserve_space_byte_;
   int workspace_size_;
   std::vector<cudnnTensorDescriptor_t> x_desc_vec_, y_desc_vec_, dx_desc_vec_, dy_desc_vec_;
-  #if MXNET_USE_CUDNN_GE_7200
+#if MXNET_USE_CUDNN_GE_7200
   cudnnRNNDataDescriptor_t x_data_desc_, y_data_desc_, dx_data_desc_, dy_data_desc_;
   DType padding_fill_ = 0;
-  #endif
+#endif  // MXNET_USE_CUDNN_GE_7200
   cudnnTensorDescriptor_t hx_desc_, cx_desc_;
   cudnnTensorDescriptor_t hy_desc_, cy_desc_;
   cudnnTensorDescriptor_t dhx_desc_, dcx_desc_;
@@ -1565,10 +1547,8 @@ class RNNOp {
   // Allow TensorCore algo policy
   bool cudnn_tensor_core_;
 
-  #if CUDNN_MAJOR >= 5
   cudnnTensorFormat_t format_;
-  #endif
-  #endif
+#endif  // MXNET_USE_CUDNN
   bool init_space_, temp_init_space_;
   size_t reserve_cpu_space_size_, temp_cpu_space_size_;
   Storage::Handle reserve_cpu_space_, temp_cpu_space_;
diff --git a/src/operator/rnn.cc b/src/operator/rnn.cc
index 244e39335a91..86fb1c7d1ec6 100644
--- a/src/operator/rnn.cc
+++ b/src/operator/rnn.cc
@@ -171,7 +171,7 @@ static std::vector<ResourceRequest> RNNResourceEx(const NodeAttrs& attrs, const
                                                   const DispatchMode dispatch_mode) {
   std::vector<ResourceRequest> request;
   if (dev_mask == kGPU) {
-#if MXNET_USE_CUDNN_RNN
+#if MXNET_USE_CUDNN == 1
     request.emplace_back(ResourceRequest::kTempSpace);
 
     const RNNParam& param = nnvm::get<RNNParam>(attrs.parsed);
diff --git a/src/operator/spatial_transformer-inl.h b/src/operator/spatial_transformer-inl.h
index 660d57d55bab..1a684a899d85 100644
--- a/src/operator/spatial_transformer-inl.h
+++ b/src/operator/spatial_transformer-inl.h
@@ -267,7 +267,7 @@ class SpatialTransformerProp : public OperatorProperty {
     return {ResourceRequest::kTempSpace};
   }
 
-  #if CUDNN_MAJOR >= 5
+  #if MXNET_USE_CUDNN == 1
   std::vector<ResourceRequest> BackwardResource(
       const mxnet::ShapeVector &in_shape) const override {
     return {ResourceRequest::kTempSpace};
diff --git a/src/operator/spatial_transformer.cu b/src/operator/spatial_transformer.cu
index fd330bd4ca87..4067714d426d 100644
--- a/src/operator/spatial_transformer.cu
+++ b/src/operator/spatial_transformer.cu
@@ -26,9 +26,9 @@
 
 #include "./spatial_transformer-inl.h"
 #include <algorithm>
-#if MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 5
+#if MXNET_USE_CUDNN == 1
 #include "./cudnn_spatial_transformer-inl.h"
-#endif  // MXNET_USE_CUDNN && CUDNN_MAJOR
+#endif  // MXNET_USE_CUDNN
 
 namespace mshadow {
 template<typename DType>
@@ -214,7 +214,7 @@ namespace op {
 template<>
 Operator* CreateOp<gpu>(SpatialTransformerParam param, int dtype) {
   Operator *op = NULL;
-#if MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 5
+#if MXNET_USE_CUDNN == 1
   MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
     if (param.cudnn_off.has_value() && param.cudnn_off.value()) {
       op = new SpatialTransformerOp<gpu, DType>(param);
@@ -226,7 +226,7 @@ Operator* CreateOp<gpu>(SpatialTransformerParam param, int dtype) {
   MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
     op = new SpatialTransformerOp<gpu, DType>(param);
   })
-#endif  // MXNET_USE_CUDNN && CUDNN_MAJOR
+#endif  // MXNET_USE_CUDNN
   return op;
 }
 

From 39bf4e062bac4fc23d7c8591005b99592e42ab25 Mon Sep 17 00:00:00 2001
From: reminisce <wujun.nju@gmail.com>
Date: Tue, 13 Aug 2019 11:39:41 -0700
Subject: [PATCH 172/813] Port several np ops to master (#15867)

* Add squeeze/flatten/transpose/reshape

Add more ops back

Add new files

Add unit tests

Add unit test for broadcast_arrays

Add more ops and tests

Add arange

* clean up
---
 python/mxnet/ndarray/numpy/_op.py             | 172 +++++++-
 python/mxnet/numpy/__init__.py                |   4 +
 python/mxnet/numpy/arrayprint.py              |  62 +++
 python/mxnet/numpy/function_base.py           | 115 ++++++
 python/mxnet/numpy/io.py                      |  43 ++
 python/mxnet/numpy/multiarray.py              | 157 ++++++-
 python/mxnet/numpy/stride_tricks.py           |  56 +++
 python/mxnet/symbol/numpy/_symbol.py          | 171 +++++++-
 python/mxnet/test_utils.py                    |  28 +-
 src/operator/nn/mkldnn/mkldnn_transpose.cc    |   3 +-
 src/operator/numpy/np_broadcast_reduce_op.h   |  60 +++
 .../numpy/np_broadcast_reduce_op_value.cc     |  76 ++++
 .../numpy/np_broadcast_reduce_op_value.cu     |  12 +
 src/operator/numpy/np_init_op.cc              |  49 +--
 src/operator/numpy/np_init_op.cu              |   3 +
 src/operator/numpy/np_matrix_op-inl.h         |  65 +++
 src/operator/numpy/np_matrix_op.cc            | 252 ++++++++++++
 src/operator/numpy/np_matrix_op.cu            |  41 ++
 tests/python/unittest/test_numpy_op.py        | 382 +++++++++++++++++-
 19 files changed, 1684 insertions(+), 67 deletions(-)
 create mode 100644 python/mxnet/numpy/arrayprint.py
 create mode 100644 python/mxnet/numpy/function_base.py
 create mode 100644 python/mxnet/numpy/io.py
 create mode 100644 python/mxnet/numpy/stride_tricks.py
 create mode 100644 src/operator/numpy/np_matrix_op-inl.h
 create mode 100644 src/operator/numpy/np_matrix_op.cc
 create mode 100644 src/operator/numpy/np_matrix_op.cu

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index 0cc2192cdcac..30cbc6999b88 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -26,7 +26,8 @@
 from . import _internal as _npi
 from ..ndarray import NDArray
 
-__all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power', 'tensordot', 'linspace']
+__all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power', 'tensordot',
+           'linspace', 'expand_dims', 'tile', 'arange']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -97,6 +98,58 @@ def ones(shape, dtype=_np.float32, order='C', ctx=None):
     return _npi.ones(shape=shape, ctx=ctx, dtype=dtype)
 
 
+@set_module('mxnet.ndarray.numpy')
+def arange(start, stop=None, step=1, dtype=None, ctx=None):
+    """Return evenly spaced values within a given interval.
+
+    Values are generated within the half-open interval ``[start, stop)``
+    (in other words, the interval including `start` but excluding `stop`).
+    For integer arguments the function is equivalent to the Python built-in
+    `range` function, but returns an ndarray rather than a list.
+
+    Parameters
+    ----------
+    start : number, optional
+        Start of interval. The interval includes this value.  The default
+        start value is 0.
+    stop : number
+        End of interval. The interval does not include this value, except
+        in some cases where `step` is not an integer and floating point
+        round-off affects the length of `out`.
+    step : number, optional
+        Spacing between values. For any output `out`, this is the distance
+        between two adjacent values, ``out[i+1] - out[i]``.  The default
+        step size is 1.  If `step` is specified as a position argument,
+        `start` must also be given.
+    dtype : dtype
+        The type of the output array. The default is `float32`.
+
+    Returns
+    -------
+    arange : ndarray
+        Array of evenly spaced values.
+
+        For floating point arguments, the length of the result is
+        ``ceil((stop - start)/step)``.  Because of floating point overflow,
+        this rule may result in the last element of `out` being greater
+        than `stop`.
+    """
+    if dtype is None:
+        dtype = 'float32'
+    if ctx is None:
+        ctx = current_context()
+    if stop is None:
+        stop = start
+        start = 0
+    if step is None:
+        step = 1
+    if start is None and stop is None:
+        raise ValueError('start and stop cannot be both None')
+    if step == 0:
+        raise ZeroDivisionError('step cannot be 0')
+    return _npi.arange(start=start, stop=stop, step=step, dtype=dtype, ctx=ctx)
+
+
 #pylint: disable= too-many-arguments, no-member, protected-access
 def _ufunc_helper(lhs, rhs, fn_array, fn_scalar, lfn_scalar, rfn_scalar=None, out=None):
     """ Helper function for element-wise operation.
@@ -462,3 +515,120 @@ def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis
         return _npi.linspace(start=start, stop=stop, num=num, endpoint=endpoint, ctx=ctx, dtype=dtype), step
     else:
         return _npi.linspace(start=start, stop=stop, num=num, endpoint=endpoint, ctx=ctx, dtype=dtype)
+
+
+@set_module('mxnet.ndarray.numpy')
+def expand_dims(a, axis):
+    """Expand the shape of an array.
+
+    Insert a new axis that will appear at the `axis` position in the expanded
+
+    Parameters
+    ----------
+    a : ndarray
+        Input array.
+    axis : int
+        Position in the expanded axes where the new axis is placed.
+
+    Returns
+    -------
+    res : ndarray
+        Output array. The number of dimensions is one greater than that of
+        the input array.
+    """
+    return _npi.expand_dims(a, axis)
+
+
+def _unary_func_helper(x, fn_array, fn_scalar, out=None, **kwargs):
+    """Helper function for unary operators.
+
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input of the unary operator.
+    fn_array : function
+        Function to be called if x is of ``ndarray`` type.
+    fn_scalar : function
+        Function to be called if x is a Python scalar.
+    out : ndarray
+        The buffer ndarray for storing the result of the unary function.
+
+    Returns
+    -------
+    out : mxnet.numpy.ndarray or scalar
+        Result array or scalar.
+    """
+    if isinstance(x, numeric_types):
+        return fn_scalar(x, **kwargs)
+    elif isinstance(x, NDArray):
+        return fn_array(x, out=out, **kwargs)
+    else:
+        raise TypeError('type {} not supported'.format(str(type(x))))
+
+
+@set_module('mxnet.ndarray.numpy')
+def tile(A, reps):
+    r"""
+    Construct an array by repeating A the number of times given by reps.
+
+    If `reps` has length ``d``, the result will have dimension of
+    ``max(d, A.ndim)``.
+
+    If ``A.ndim < d``, `A` is promoted to be d-dimensional by prepending new
+    axes. So a shape (3,) array is promoted to (1, 3) for 2-D replication,
+    or shape (1, 1, 3) for 3-D replication. If this is not the desired
+    behavior, promote `A` to d-dimensions manually before calling this
+    function.
+
+    If ``A.ndim > d``, `reps` is promoted to `A`.ndim by pre-pending 1's to it.
+    Thus for an `A` of shape (2, 3, 4, 5), a `reps` of (2, 2) is treated as
+    (1, 1, 2, 2).
+
+    Parameters
+    ----------
+    A : ndarray or scalar
+        An input array or a scalar to repeat.
+    reps : a single integer or tuple of integers
+        The number of repetitions of `A` along each axis.
+
+    Returns
+    -------
+    c : ndarray
+        The tiled output array.
+
+    Examples
+    --------
+    >>> a = np.array([0, 1, 2])
+    >>> np.tile(a, 2)
+    array([0., 1., 2., 0., 1., 2.])
+    >>> np.tile(a, (2, 2))
+    array([[0., 1., 2., 0., 1., 2.],
+           [0., 1., 2., 0., 1., 2.]])
+    >>> np.tile(a, (2, 1, 2))
+    array([[[0., 1., 2., 0., 1., 2.]],
+           [[0., 1., 2., 0., 1., 2.]]])
+
+    >>> b = np.array([[1, 2], [3, 4]])
+    >>> np.tile(b, 2)
+    array([[1., 2., 1., 2.],
+           [3., 4., 3., 4.]])
+    >>> np.(b, (2, 1))
+    array([[1., 2.],
+           [3., 4.],
+           [1., 2.],
+           [3., 4.]])
+
+    >>> c = np.array([1,2,3,4])
+    >>> np.tile(c,(4,1))
+    array([[1., 2., 3., 4.],
+           [1., 2., 3., 4.],
+           [1., 2., 3., 4.],
+           [1., 2., 3., 4.]])
+
+    Scalar as input:
+
+    >>> np.tile(2, 3)
+    array([2, 2, 2]) # repeating integer `2`
+
+    """
+    return _unary_func_helper(A, _npi.tile, _np.tile, reps=reps)
diff --git a/python/mxnet/numpy/__init__.py b/python/mxnet/numpy/__init__.py
index 979e8d61a5ab..1994148d14d1 100644
--- a/python/mxnet/numpy/__init__.py
+++ b/python/mxnet/numpy/__init__.py
@@ -26,5 +26,9 @@
 from . import _register
 from ._op import *  # pylint: disable=wildcard-import
 from .utils import *  # pylint: disable=wildcard-import
+from .function_base import *  # pylint: disable=wildcard-import
+from .stride_tricks import *  # pylint: disable=wildcard-import
+from .io import *  # pylint: disable=wildcard-import
+from .arrayprint import *  # pylint: disable=wildcard-import
 
 __all__ = []
diff --git a/python/mxnet/numpy/arrayprint.py b/python/mxnet/numpy/arrayprint.py
new file mode 100644
index 000000000000..9be7faf1f602
--- /dev/null
+++ b/python/mxnet/numpy/arrayprint.py
@@ -0,0 +1,62 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""ndarray print format controller."""
+
+from __future__ import absolute_import, print_function
+
+import numpy as onp
+from ..util import set_module
+
+__all__ = ['set_printoptions']
+
+
+@set_module('mxnet.numpy')
+def set_printoptions(precision=None, threshold=None, **kwarg):
+    """
+    Set printing options.
+
+    These options determine the way floating point numbers and arrays are displayed.
+
+    Parameters
+    ----------
+    precision : int or None, optional
+        Number of digits of precision for floating point output (default 8).
+        May be `None` if `floatmode` is not `fixed`, to print as many digits as
+        necessary to uniquely specify the value.
+    threshold : int, optional
+        Total number of array elements which trigger summarization
+        rather than full repr (default 1000).
+
+    Examples
+    --------
+    Floating point precision can be set:
+
+    >>> np.set_printoptions(precision=4)
+    >>> print(np.array([1.123456789]))
+    [ 1.1235]
+
+    Long arrays can be summarised:
+
+    >>> np.set_printoptions(threshold=5)
+    >>> print(np.arange(10))
+    [0. 1. 2. ... 7. 8. 9.]
+    """
+    if kwarg:
+        raise NotImplementedError('mxnet.numpy.set_printoptions only supports parameters'
+                                  ' precision and threshold for now.')
+    onp.set_printoptions(precision=precision, threshold=threshold, **kwarg)
diff --git a/python/mxnet/numpy/function_base.py b/python/mxnet/numpy/function_base.py
new file mode 100644
index 000000000000..e8e07c70a167
--- /dev/null
+++ b/python/mxnet/numpy/function_base.py
@@ -0,0 +1,115 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Numpy basic functions."""
+from __future__ import absolute_import
+
+from .stride_tricks import broadcast_arrays
+
+__all__ = ['meshgrid']
+
+
+def meshgrid(*xi, **kwargs):
+    """
+    Return coordinate matrices from coordinate vectors.
+
+    Make N-D coordinate arrays for vectorized evaluations of
+    N-D scalar/vector fields over N-D grids, given
+    one-dimensional coordinate arrays x1, x2,..., xn.
+
+    Parameters
+    ----------
+    x1, x2,..., xn : ndarrays
+        1-D arrays representing the coordinates of a grid.
+    indexing : {'xy', 'ij'}, optional
+        Cartesian ('xy', default) or matrix ('ij') indexing of output.
+        See Notes for more details.
+
+    sparse : bool, optional
+        If True a sparse grid is returned in order to conserve memory.
+        Default is False. Please note that `sparse=True` is currently
+        not supported.
+
+    copy : bool, optional
+        If False, a view into the original arrays are returned in order to
+        conserve memory.  Default is True. Please note that `copy=False`
+        is currently not supported.
+
+    Returns
+    -------
+    X1, X2,..., XN : ndarray
+        For vectors `x1`, `x2`,..., 'xn' with lengths ``Ni=len(xi)`` ,
+        return ``(N1, N2, N3,...Nn)`` shaped arrays if indexing='ij'
+        or ``(N2, N1, N3,...Nn)`` shaped arrays if indexing='xy'
+        with the elements of `xi` repeated to fill the matrix along
+        the first dimension for `x1`, the second for `x2` and so on.
+
+    Notes
+    -----
+    This function supports both indexing conventions through the indexing
+    keyword argument.  Giving the string 'ij' returns a meshgrid with
+    matrix indexing, while 'xy' returns a meshgrid with Cartesian indexing.
+    In the 2-D case with inputs of length M and N, the outputs are of shape
+    (N, M) for 'xy' indexing and (M, N) for 'ij' indexing.  In the 3-D case
+    with inputs of length M, N and P, outputs are of shape (N, M, P) for
+    'xy' indexing and (M, N, P) for 'ij' indexing.  The difference is
+    illustrated by the following code snippet::
+
+        xv, yv = np.meshgrid(x, y, sparse=False, indexing='ij')
+        for i in range(nx):
+            for j in range(ny):
+                # treat xv[i,j], yv[i,j]
+
+        xv, yv = np.meshgrid(x, y, sparse=False, indexing='xy')
+        for i in range(nx):
+            for j in range(ny):
+                # treat xv[j,i], yv[j,i]
+
+    In the 1-D and 0-D case, the indexing and sparse keywords have no effect.
+    """
+    ndim = len(xi)
+
+    copy_ = kwargs.pop('copy', True)
+    if not copy_:
+        raise NotImplementedError('copy=False is not implemented')
+    sparse = kwargs.pop('sparse', False)
+    if sparse:
+        raise NotImplementedError('sparse=False is not implemented')
+    indexing = kwargs.pop('indexing', 'xy')
+
+    if kwargs:
+        raise TypeError("meshgrid() got an unexpected keyword argument '%s'"
+                        % (list(kwargs)[0],))
+
+    if indexing not in ['xy', 'ij']:
+        raise ValueError(
+            "Valid values for `indexing` are 'xy' and 'ij'.")
+
+    s0 = (1,) * ndim
+    output = [x.reshape(s0[:i] + (-1,) + s0[i + 1:])
+              for i, x in enumerate(xi)]
+
+    if indexing == 'xy' and ndim > 1:
+        # switch first and second axis
+        output[0] = output[0].reshape(1, -1, *s0[2:])
+        output[1] = output[1].reshape(-1, 1, *s0[2:])
+
+    if not sparse:
+        # Return the full N-D matrix (not only the 1-D vector)
+        output = broadcast_arrays(*output)
+
+    return output
diff --git a/python/mxnet/numpy/io.py b/python/mxnet/numpy/io.py
new file mode 100644
index 000000000000..aece13fa1db4
--- /dev/null
+++ b/python/mxnet/numpy/io.py
@@ -0,0 +1,43 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+
+"""I/O functions for ndarrays."""
+from __future__ import absolute_import
+import numpy as onp
+from ..context import current_context
+from .multiarray import array
+
+__all__ = ['genfromtxt']
+
+
+# TODO(junwu): Add doc
+def genfromtxt(*args, **kwargs):
+    """This is a wrapper of the official NumPy's `genfromtxt` function.
+    Please refer to the documentation here
+    https://docs.scipy.org/doc/numpy/reference/generated/numpy.genfromtxt.html.
+
+    Notes
+    -----
+    This function has added an additional parameter `ctx` which allows to create
+    ndarrays on the user-specified device.
+    """
+    ctx = kwargs.pop('ctx', current_context())
+    if ctx is None:
+        ctx = current_context()
+    ret = onp.genfromtxt(*args, **kwargs)
+    return array(ret, dtype=ret.dtype, ctx=ctx)
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 8a9028bf5125..b8633699435d 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -44,7 +44,7 @@
 from ..ndarray.numpy import _internal as _npi
 
 __all__ = ['ndarray', 'empty', 'array', 'zeros', 'ones', 'add', 'subtract', 'multiply', 'divide',
-           'mod', 'power', 'tensordot', 'linspace']
+           'mod', 'power', 'tensordot', 'linspace', 'expand_dims', 'tile', 'arange']
 
 
 # This function is copied from ndarray.py since pylint
@@ -632,7 +632,10 @@ def reshape(self, *args, **kwargs):  # pylint: disable=arguments-differ
                                           ' while received {}'.format(order))
         if len(args) == 0:
             raise TypeError('reshape() takes exactly 1 argument (0 given)')
-        raise NotImplementedError
+        if len(args) == 1 and isinstance(args[0], tuple):
+            return _mx_np_op.reshape(self, newshape=args[0], order=order)
+        else:
+            return _mx_np_op.reshape(self, newshape=args, order=order)
 
     def reshape_like(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`reshape_like`.
@@ -812,7 +815,7 @@ def sign(self, *args, **kwargs):
 
     def flatten(self, order='C'):  # pylint: disable=arguments-differ
         """Return a copy of the array collapsed into one dimension."""
-        raise NotImplementedError
+        return self.reshape(-1, order=order)
 
     def shape_array(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`shape_array`.
@@ -830,7 +833,7 @@ def size_array(self, *args, **kwargs):
         """
         raise AttributeError('mxnet.numpy.ndarray object has no attribute size_array')
 
-    def expand_dims(self, *args, **kwargs):  # pylint: disable=arguments-differ
+    def expand_dims(self, *args, **kwargs):  # pylint: disable=arguments-differ,unused-argument
         """Convenience fluent method for :py:func:`expand_dims`.
 
         The arguments are the same as for :py:func:`expand_dims`, with
@@ -848,7 +851,7 @@ def tile(self, *args, **kwargs):
 
     def transpose(self, *axes):  # pylint: disable=arguments-differ
         """Permute the dimensions of an array."""
-        raise NotImplementedError
+        return _mx_np_op.transpose(self, axes=axes if len(axes) != 0 else None)
 
     def flip(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`flip`.
@@ -883,12 +886,8 @@ def diag(self, k=0, **kwargs):
         raise AttributeError('mxnet.numpy.ndarray object has no attribute diag')
 
     def sum(self, axis=None, dtype=None, out=None, keepdims=False):  # pylint: disable=arguments-differ
-        """Convenience fluent method for :py:func:`sum`.
-
-        The arguments are the same as for :py:func:`sum`, with
-        this array as data.
-        """
-        raise NotImplementedError
+        """Return the sum of the array elements over the given axis."""
+        return _mx_np_op.sum(self, axis=axis, dtype=dtype, out=out, keepdims=keepdims)
 
     def nansum(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`nansum`.
@@ -900,7 +899,7 @@ def nansum(self, *args, **kwargs):
 
     def prod(self, axis=None, dtype=None, out=None, keepdims=False):  # pylint: disable=arguments-differ
         """Return the product of the array elements over the given axis."""
-        raise NotImplementedError
+        return _mx_np_op.prod(self, axis=axis, dtype=dtype, keepdims=keepdims, out=out)
 
     def nanprod(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`nanprod`.
@@ -1244,9 +1243,8 @@ def softmin(self, *args, **kwargs):
         raise AttributeError('mxnet.numpy.ndarray object has no attribute softmin')
 
     def squeeze(self, axis=None):  # pylint: disable=arguments-differ
-        """Remove single-dimensional entries from the shape of a.
-        """
-        raise NotImplementedError
+        """Remove single-dimensional entries from the shape of a."""
+        return _mx_np_op.squeeze(self, axis=axis)
 
     def broadcast_to(self, shape):
         raise AttributeError('mxnet.numpy.ndarray object has no attribute broadcast_to')
@@ -1692,3 +1690,132 @@ def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis
       GPU.
     """
     return _mx_nd_np.linspace(start, stop, num, endpoint, retstep, dtype, axis, ctx)
+
+
+@set_module('mxnet.numpy')
+def expand_dims(a, axis):
+    """Expand the shape of an array.
+
+    Insert a new axis that will appear at the `axis` position in the expanded array shape.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input array.
+    axis : int
+        Position in the expanded axes where the new axis is placed.
+
+    Returns
+    -------
+    res : ndarray
+        Output array. The number of dimensions is one greater than that of
+        the input array.
+    """
+    return _npi.expand_dims(a, axis)
+
+
+@set_module('mxnet.numpy')
+def tile(A, reps):
+    r"""
+    Construct an array by repeating A the number of times given by reps.
+
+    If `reps` has length ``d``, the result will have dimension of
+    ``max(d, A.ndim)``.
+
+    If ``A.ndim < d``, `A` is promoted to be d-dimensional by prepending new
+    axes. So a shape (3,) array is promoted to (1, 3) for 2-D replication,
+    or shape (1, 1, 3) for 3-D replication. If this is not the desired
+    behavior, promote `A` to d-dimensions manually before calling this
+    function.
+
+    If ``A.ndim > d``, `reps` is promoted to `A`.ndim by pre-pending 1's to it.
+    Thus for an `A` of shape (2, 3, 4, 5), a `reps` of (2, 2) is treated as
+    (1, 1, 2, 2).
+
+    Parameters
+    ----------
+    A : ndarray or scalar
+        An input array or a scalar to repeat.
+    reps : a single integer or tuple of integers
+        The number of repetitions of `A` along each axis.
+
+    Returns
+    -------
+    c : ndarray
+        The tiled output array.
+
+    Examples
+    --------
+    >>> a = np.array([0, 1, 2])
+    >>> np.tile(a, 2)
+    array([0., 1., 2., 0., 1., 2.])
+    >>> np.tile(a, (2, 2))
+    array([[0., 1., 2., 0., 1., 2.],
+           [0., 1., 2., 0., 1., 2.]])
+    >>> np.tile(a, (2, 1, 2))
+    array([[[0., 1., 2., 0., 1., 2.]],
+           [[0., 1., 2., 0., 1., 2.]]])
+
+    >>> b = np.array([[1, 2], [3, 4]])
+    >>> np.tile(b, 2)
+    array([[1., 2., 1., 2.],
+           [3., 4., 3., 4.]])
+    >>> np.(b, (2, 1))
+    array([[1., 2.],
+           [3., 4.],
+           [1., 2.],
+           [3., 4.]])
+
+    >>> c = np.array([1,2,3,4])
+    >>> np.tile(c,(4,1))
+    array([[1., 2., 3., 4.],
+           [1., 2., 3., 4.],
+           [1., 2., 3., 4.],
+           [1., 2., 3., 4.]])
+
+    Scalar as input:
+
+    >>> np.tile(2, 3)
+    array([2, 2, 2]) # repeating integer `2`
+
+    """
+    return _mx_nd_np.tile(A, reps)
+
+
+@set_module('mxnet.numpy')
+def arange(start, stop=None, step=1, dtype=None, ctx=None):
+    """Return evenly spaced values within a given interval.
+
+    Values are generated within the half-open interval ``[start, stop)``
+    (in other words, the interval including `start` but excluding `stop`).
+    For integer arguments the function is equivalent to the Python built-in
+    `range` function, but returns an ndarray rather than a list.
+
+    Parameters
+    ----------
+    start : number, optional
+        Start of interval. The interval includes this value.  The default
+        start value is 0.
+    stop : number
+        End of interval. The interval does not include this value, except
+        in some cases where `step` is not an integer and floating point
+        round-off affects the length of `out`.
+    step : number, optional
+        Spacing between values. For any output `out`, this is the distance
+        between two adjacent values, ``out[i+1] - out[i]``.  The default
+        step size is 1.  If `step` is specified as a position argument,
+        `start` must also be given.
+    dtype : dtype
+        The type of the output array. The default is `float32`.
+
+    Returns
+    -------
+    arange : ndarray
+        Array of evenly spaced values.
+
+        For floating point arguments, the length of the result is
+        ``ceil((stop - start)/step)``.  Because of floating point overflow,
+        this rule may result in the last element of `out` being greater
+        than `stop`.
+    """
+    return _mx_nd_np.arange(start, stop, step, dtype, ctx)
diff --git a/python/mxnet/numpy/stride_tricks.py b/python/mxnet/numpy/stride_tricks.py
new file mode 100644
index 000000000000..1848a292e673
--- /dev/null
+++ b/python/mxnet/numpy/stride_tricks.py
@@ -0,0 +1,56 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Util functions with broadcast."""
+
+from ..ndarray.ndarray import _get_broadcast_shape
+from . import _op as _mx_np_op
+
+
+__all__ = ['broadcast_arrays']
+
+
+def _broadcast_shape(*args):
+    shape = ()
+    for arr in args:
+        shape = _get_broadcast_shape(shape, arr.shape)
+    return shape
+
+
+def broadcast_arrays(*args):
+    """
+    Broadcast any number of arrays against each other.
+
+    Parameters
+    ----------
+    `*args` : a list of ndarrays
+        The arrays to broadcast.
+
+    Returns
+    -------
+    broadcasted : list of arrays
+        These arrays are copies of the original arrays unless that all the input
+        arrays have the same shape, the input list of arrays are returned
+        instead of a list of copies.
+    """
+    shape = _broadcast_shape(*args)
+
+    if all(array.shape == shape for array in args):
+        # Common case where nothing needs to be broadcasted.
+        return args
+
+    return [_mx_np_op.broadcast_to(array, shape) for array in args]
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index d0137932b8ec..587dae3deab9 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -21,6 +21,7 @@
 from __future__ import absolute_import
 import ctypes
 import numpy as _np
+from . import _op as _mx_np_op
 from ...base import _LIB, SymbolHandle, numeric_types, mx_uint
 from ...util import check_call, set_module
 from ...context import current_context
@@ -28,7 +29,8 @@
 from .._internal import _set_np_symbol_class
 from . import _internal as _npi
 
-__all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power', 'tensordot', 'linspace']
+__all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power', 'tensordot',
+           'linspace', 'expand_dims', 'tile', 'arange']
 
 
 def _num_outputs(sym):
@@ -236,7 +238,10 @@ def reshape(self, *args, **kwargs):  # pylint: disable=arguments-differ
                                           ' while received {}'.format(order))
         if len(args) == 0:
             raise TypeError('reshape() takes exactly 1 argument (0 given)')
-        raise NotImplementedError
+        if len(args) == 1 and isinstance(args[0], tuple):
+            return _mx_np_op.reshape(self, newshape=args[0], order=order)
+        else:
+            return _mx_np_op.reshape(self, newshape=args, order=order)
 
     def argmax(self, axis=None, out=None):  # pylint: disable=arguments-differ
         raise NotImplementedError
@@ -437,7 +442,7 @@ def size_array(self, *args, **kwargs):
         """
         raise AttributeError('_Symbol object has no attribute size_array')
 
-    def expand_dims(self, *args, **kwargs):  # pylint: disable=arguments-differ
+    def expand_dims(self, *args, **kwargs):  # pylint: disable=arguments-differ,unused-argument
         """Convenience fluent method for :py:func:`expand_dims`.
 
         The arguments are the same as for :py:func:`expand_dims`, with
@@ -454,12 +459,10 @@ def tile(self, *args, **kwargs):
         raise AttributeError('_Symbol object has no attribute tile')
 
     def transpose(self, *axes):  # pylint: disable=arguments-differ
-        """Convenience fluent method for :py:func:`transpose`.
-
-        The arguments are the same as for :py:func:`transpose`, with
+        """The arguments are the same as for :py:func:`transpose`, with
         this array as data.
         """
-        raise NotImplementedError
+        return _mx_np_op.transpose(self, axes=axes if len(axes) != 0 else None)
 
     def flip(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`flip`.
@@ -494,12 +497,8 @@ def diag(self, k=0, **kwargs):
         raise AttributeError('_Symbol object has no attribute diag')
 
     def sum(self, axis=None, dtype=None, out=None, keepdims=False):  # pylint: disable=arguments-differ
-        """Convenience fluent method for :py:func:`sum`.
-
-        The arguments are the same as for :py:func:`sum`, with
-        this array as data.
-        """
-        raise NotImplementedError
+        """Return the sum of the array elements over the given axis."""
+        return _mx_np_op.sum(self, axis=axis, dtype=dtype, out=out, keepdims=keepdims)
 
     def nansum(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`nansum`.
@@ -511,7 +510,7 @@ def nansum(self, *args, **kwargs):
 
     def prod(self, axis=None, dtype=None, out=None, keepdims=False):  # pylint: disable=arguments-differ
         """Return the product of the array elements over the given axis."""
-        raise NotImplementedError
+        return _mx_np_op.prod(self, axis=axis, dtype=dtype, keepdims=keepdims, out=out)
 
     def nanprod(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`nanprod`.
@@ -850,9 +849,8 @@ def softmin(self, *args, **kwargs):
         raise AttributeError('_Symbol object has no attribute softmin')
 
     def squeeze(self, axis=None):  # pylint: disable=arguments-differ
-        """Remove single-dimensional entries from the shape of a.
-        """
-        raise NotImplementedError
+        """Remove single-dimensional entries from the shape of a."""
+        return _mx_np_op.squeeze(self, axis=axis)
 
     def broadcast_to(self, *args, **kwargs):
         raise AttributeError('_Symbol object has no attribute broadcast_to')
@@ -1124,8 +1122,7 @@ def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis
     - There could be an additional `ctx` argument to specify the device, e.g. the i-th
       GPU.
     """
-    if isinstance(start, (list, _np.ndarray)) or \
-        isinstance(stop, (list, _np.ndarray)):
+    if isinstance(start, (list, _np.ndarray)) or isinstance(stop, (list, _np.ndarray)):
         raise NotImplementedError('start and stop only support int')
     if axis != 0:
         raise NotImplementedError("the function only support axis 0")
@@ -1133,9 +1130,143 @@ def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis
         ctx = current_context()
     if retstep:
         step = (stop - start) / (num - 1)
-        return (_npi.linspace(start=start, stop=stop, num=num, endpoint=endpoint, ctx=ctx, dtype=dtype), step)
+        return _npi.linspace(start=start, stop=stop, num=num, endpoint=endpoint, ctx=ctx, dtype=dtype), step
     else:
         return _npi.linspace(start=start, stop=stop, num=num, endpoint=endpoint, ctx=ctx, dtype=dtype)
 
 
+@set_module('mxnet.symbol.numpy')
+def expand_dims(a, axis):
+    """Expand the shape of an array.
+
+    Insert a new axis that will appear at the `axis` position in the expanded
+
+    Parameters
+    ----------
+    a : _Symbol
+        Input array.
+    axis : int
+        Position in the expanded axes where the new axis is placed.
+
+    Returns
+    -------
+    res : _Symbol
+        Output array. The number of dimensions is one greater than that of
+        the input array.
+    """
+    return _npi.expand_dims(a, axis)
+
+
+def _unary_func_helper(x, fn_array, fn_scalar, out=None, **kwargs):
+    """Helper function for unary operators.
+
+    Parameters
+    ----------
+    x : _Symbol or scalar
+        Input of the unary operator.
+    fn_array : function
+        Function to be called if x is of ``_Symbol`` type.
+    fn_scalar : function
+        Function to be called if x is a Python scalar.
+    out : _Symbol
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+
+    Returns
+    -------
+    out : _Symbol or scalar
+        Result _Symbol or scalar.
+    """
+    if isinstance(x, numeric_types):
+        return fn_scalar(x, **kwargs)
+    elif isinstance(x, _Symbol):
+        return fn_array(x, out=out, **kwargs)
+    else:
+        raise TypeError('type {} not supported'.format(str(type(x))))
+
+
+@set_module('mxnet.symbol.numpy')
+def tile(A, reps):
+    r"""
+    Construct an array by repeating A the number of times given by reps.
+
+    If `reps` has length ``d``, the result will have dimension of
+    ``max(d, A.ndim)``.
+
+    If ``A.ndim < d``, `A` is promoted to be d-dimensional by prepending new
+    axes. So a shape (3,) array is promoted to (1, 3) for 2-D replication,
+    or shape (1, 1, 3) for 3-D replication. If this is not the desired
+    behavior, promote `A` to d-dimensions manually before calling this
+    function.
+
+    If ``A.ndim > d``, `reps` is promoted to `A`.ndim by pre-pending 1's to it.
+    Thus for an `A` of shape (2, 3, 4, 5), a `reps` of (2, 2) is treated as
+    (1, 1, 2, 2).
+
+    Parameters
+    ----------
+    A : _Symbol or scalar
+        An input array or a scalar to repeat.
+    reps : a single integer or tuple of integers
+        The number of repetitions of `x` along each axis.
+
+    Returns
+    -------
+    c : _Symbol
+        The tiled output array.
+    """
+    return _unary_func_helper(A, _npi.tile, _np.tile, reps=reps)
+
+
+@set_module('mxnet.symbol.numpy')
+def arange(start, stop=None, step=1, dtype=None, ctx=None):
+    """Return evenly spaced values within a given interval.
+
+    Values are generated within the half-open interval ``[start, stop)``
+    (in other words, the interval including `start` but excluding `stop`).
+    For integer arguments the function is equivalent to the Python built-in
+    `range` function, but returns an ndarray rather than a list.
+
+    Parameters
+    ----------
+    start : number, optional
+        Start of interval. The interval includes this value.  The default
+        start value is 0.
+    stop : number
+        End of interval. The interval does not include this value, except
+        in some cases where `step` is not an integer and floating point
+        round-off affects the length of `out`.
+    step : number, optional
+        Spacing between values. For any output `out`, this is the distance
+        between two adjacent values, ``out[i+1] - out[i]``.  The default
+        step size is 1.  If `step` is specified as a position argument,
+        `start` must also be given.
+    dtype : dtype
+        The type of the output array. The default is `float32`.
+
+    Returns
+    -------
+    arange : ndarray
+        Array of evenly spaced values.
+
+        For floating point arguments, the length of the result is
+        ``ceil((stop - start)/step)``.  Because of floating point overflow,
+        this rule may result in the last element of `out` being greater
+        than `stop`.
+    """
+    if dtype is None:
+        dtype = 'float32'
+    if ctx is None:
+        ctx = current_context()
+    if stop is None:
+        stop = start
+        start = 0
+    if step is None:
+        step = 1
+    if start is None and stop is None:
+        raise ValueError('start and stop cannot be both None')
+    if step == 0:
+        raise ZeroDivisionError('step cannot be 0')
+    return _npi.arange(start=start, stop=stop, step=step, dtype=dtype, ctx=ctx)
+
+
 _set_np_symbol_class(_Symbol)
diff --git a/python/mxnet/test_utils.py b/python/mxnet/test_utils.py
index 89013f4392e8..c326091dbd9f 100644
--- a/python/mxnet/test_utils.py
+++ b/python/mxnet/test_utils.py
@@ -487,13 +487,18 @@ def same(a, b):
     """
     return np.array_equal(a, b)
 
-def almost_equal(a, b, rtol=None, atol=None, equal_nan=False):
+def almost_equal(a, b, rtol=None, atol=None, equal_nan=False, use_broadcast=True):
     """Test if two numpy arrays are almost equal."""
     # pylint: disable=unexpected-keyword-arg
+    if (not use_broadcast) and a.shape != b.shape:
+        msg = npt.build_err_msg([a, b],
+                                err_msg="a.shape = {} and b.shape = {} are not equal"
+                                .format(str(a.shape), str(b.shape)))
+        raise AssertionError(msg)
     return np.allclose(a, b, rtol=get_rtol(rtol), atol=get_atol(atol), equal_nan=equal_nan)
     # pylint: enable=unexpected-keyword-arg
 
-def assert_almost_equal(a, b, rtol=None, atol=None, names=('a', 'b'), equal_nan=False):
+def assert_almost_equal(a, b, rtol=None, atol=None, names=('a', 'b'), equal_nan=False, use_broadcast=True):
     """Test that two numpy arrays are almost equal. Raise exception message if not.
 
     Parameters
@@ -505,7 +510,7 @@ def assert_almost_equal(a, b, rtol=None, atol=None, names=('a', 'b'), equal_nan=
     """
     rtol = get_rtol(rtol)
     atol = get_atol(atol)
-    if almost_equal(a, b, rtol, atol, equal_nan=equal_nan):
+    if almost_equal(a, b, rtol, atol, equal_nan=equal_nan, use_broadcast=use_broadcast):
         return
     index, rel = find_max_violation(a, b, rtol, atol)
     np.set_printoptions(threshold=4, suppress=True)
@@ -2188,3 +2193,20 @@ def __exit__(self, ptype, value, trace):
             os.environ[self._key] = self._prev_val
         else:
             del os.environ[self._key]
+
+
+def collapse_sum_like(a, shape):
+    """Given `a` as a numpy ndarray, perform reduce_sum on `a` over the axes that do not
+    exist in `shape`. Note that an ndarray with `shape` must be broadcastable to `a`."""
+    assert len(a.shape) >= len(shape)
+    if np.prod(shape) == 0 or a.size == 0:
+        return np.zeros(shape, dtype=a.dtype)
+    axes = []
+    ndim_diff = len(a.shape) - len(shape)
+    for i in range(ndim_diff):
+        axes.append(i)
+    for i, s in enumerate(shape):
+        if s != a.shape[i+ndim_diff]:
+            assert s == 1
+            axes.append(i+ndim_diff)
+    return np.sum(a, axis=tuple(axes)).reshape(shape)
diff --git a/src/operator/nn/mkldnn/mkldnn_transpose.cc b/src/operator/nn/mkldnn/mkldnn_transpose.cc
index eec19bababb7..48444feedcec 100644
--- a/src/operator/nn/mkldnn/mkldnn_transpose.cc
+++ b/src/operator/nn/mkldnn/mkldnn_transpose.cc
@@ -35,7 +35,8 @@ bool SupportMKLDNNTranspose(const TransposeParam& param,
                             const NDArray &data) {
   auto data_ndim = data.shape().ndim();
 
-  if (data_ndim > 4 || data.dtype() != mshadow::kFloat32)
+  if (data_ndim > 4 || data_ndim == 0 || data.shape().Size() == 0 ||
+      data.dtype() != mshadow::kFloat32)
     return false;
 
   return true;
diff --git a/src/operator/numpy/np_broadcast_reduce_op.h b/src/operator/numpy/np_broadcast_reduce_op.h
index c516e6b0689a..b05f4e901d13 100644
--- a/src/operator/numpy/np_broadcast_reduce_op.h
+++ b/src/operator/numpy/np_broadcast_reduce_op.h
@@ -213,6 +213,66 @@ inline void NumpyReduceAxesBackwardUseNone(const nnvm::NodeAttrs& attrs,
   }
 }
 
+template<typename xpu, typename OP, bool normalize = false>
+void NumpyReduceAxesBackwardUseInOut(const nnvm::NodeAttrs& attrs,
+                                     const OpContext& ctx,
+                                     const std::vector<TBlob>& inputs,
+                                     const std::vector<OpReqType>& req,
+                                     const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mshadow::expr;
+  const NumpyReduceAxesParam& param = nnvm::get<NumpyReduceAxesParam>(attrs.parsed);
+  TShape small;
+  if (param.keepdims) {
+    small = inputs[0].shape_;
+  } else {
+    small = NumpyReduceAxesShapeImpl(outputs[0].shape_, param.axis, true);
+  }
+  ReduceAxesBackwardUseInOutImpl<xpu, OP, normalize>(ctx, small, inputs, req, outputs);
+}
+
+template<typename xpu>
+void NumpyBroadcastToForward(const nnvm::NodeAttrs& attrs,
+                             const OpContext& ctx,
+                             const std::vector<TBlob>& inputs,
+                             const std::vector<OpReqType>& req,
+                             const std::vector<TBlob>& outputs) {
+  if (outputs[0].shape_.Size() == 0U) return;  // zero-size tensor
+  TShape expanded_ishape(outputs[0].shape_.ndim(), 1);
+  const TShape& ishape = inputs[0].shape_;
+  CHECK_LE(ishape.ndim(), expanded_ishape.ndim()) << "output ndim cannot be less than input ndim";
+  const int ndim_delta = expanded_ishape.ndim() - ishape.ndim();
+  for (int i = 0; i < ishape.ndim(); ++i) {
+    expanded_ishape[i + ndim_delta] = ishape[i];
+  }
+  BroadcastComputeImpl<xpu>(attrs, ctx, {inputs[0].reshape(expanded_ishape)},
+                            req, outputs, expanded_ishape);
+}
+
+template<typename xpu>
+void NumpyBroadcastToBackward(const nnvm::NodeAttrs& attrs,
+                              const OpContext& ctx,
+                              const std::vector<TBlob>& inputs,
+                              const std::vector<OpReqType>& req,
+                              const std::vector<TBlob>& outputs) {
+  if (inputs[0].shape_.Size() == 0U) return;  // zero-size ograd
+  TShape expanded_igrad_shape(inputs[0].shape_.ndim(), 1);
+  const TShape& igrad_shape = outputs[0].shape_;
+  CHECK_LE(igrad_shape.ndim(), expanded_igrad_shape.ndim())
+      << "output ndim cannot be less than input ndim";
+  const int ndim_delta = expanded_igrad_shape.ndim() - igrad_shape.ndim();
+  for (int i = 0; i < igrad_shape.ndim(); ++i) {
+    expanded_igrad_shape[i + ndim_delta] = igrad_shape[i];
+  }
+  if (NeedSafeAcc<true>(inputs[0].type_flag_, outputs[0].type_flag_)) {
+    ReduceAxesComputeImpl<xpu, mshadow_op::sum, true>(
+        ctx, inputs, req, {outputs[0].reshape(expanded_igrad_shape)}, expanded_igrad_shape);
+  } else {
+    ReduceAxesComputeImpl<xpu, mshadow_op::sum, false>(
+        ctx, inputs, req, {outputs[0].reshape(expanded_igrad_shape)}, expanded_igrad_shape);
+  }
+}
+
 }  // namespace op
 }  // namespace mxnet
 #endif  // MXNET_OPERATOR_NUMPY_NP_BROADCAST_REDUCE_OP_H_
diff --git a/src/operator/numpy/np_broadcast_reduce_op_value.cc b/src/operator/numpy/np_broadcast_reduce_op_value.cc
index fd227de168e1..96f0c9b350b9 100644
--- a/src/operator/numpy/np_broadcast_reduce_op_value.cc
+++ b/src/operator/numpy/np_broadcast_reduce_op_value.cc
@@ -74,5 +74,81 @@ NNVM_REGISTER_OP(_backward_np_sum)
 .set_num_inputs(1)
 .set_attr<FCompute>("FCompute<cpu>", NumpyReduceAxesBackwardUseNone<cpu>);
 
+NNVM_REGISTER_OP(_np_prod)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<NumpyReduceAxesParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyReduceAxesShape)
+.set_attr<nnvm::FInferType>("FInferType", NumpySumType)
+.add_arguments(NumpyReduceAxesParam::__FIELDS__())
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"a"};
+  })
+.add_argument("a", "NDArray-or-Symbol", "The input")
+.set_attr<FCompute>("FCompute<cpu>", NumpyReduceAxesCompute<cpu, mshadow_op::product, true>)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<nnvm::FGradient>("FGradient", ReduceGrad{"_backward_np_prod"});
+
+NNVM_REGISTER_OP(_backward_np_prod)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<NumpyReduceAxesParam>)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FCompute>("FCompute<cpu>", NumpyReduceAxesBackwardUseInOut<cpu, mshadow_op::rdiv>);
+
+bool NumpyBroadcastToShape(const nnvm::NodeAttrs& attrs,
+                           mxnet::ShapeVector *in_attrs,
+                           mxnet::ShapeVector *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  mxnet::TShape& ishape = (*in_attrs)[0];
+  if (!mxnet::shape_is_known(ishape)) return false;
+  const BroadcastToParam& param = nnvm::get<BroadcastToParam>(attrs.parsed);
+  CHECK(mxnet::shape_is_known(param.shape))
+      << "the objective shape for broadcasting array must be known";
+  CHECK_LE(ishape.ndim(), param.shape.ndim())
+      << "shape " << ishape << " is not broadcastable to " << param.shape;
+  for (int i = param.shape.ndim() - 1; i >= 0; --i) {
+    int j = i - param.shape.ndim() + ishape.ndim();
+    if (j < 0) break;
+    CHECK(ishape[j] == param.shape[i] || ishape[j] == 1)
+        << "shape " << ishape << " is not broadcastable to " << param.shape;
+  }
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, param.shape);
+  return true;
+}
+
+NNVM_REGISTER_OP(_np_broadcast_to)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"array"};
+  })
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
+.set_attr<nnvm::FGradient>("FGradient",
+  [](const nnvm::NodePtr& n,
+    const std::vector<nnvm::NodeEntry>& ograds) {
+    return MakeNonlossGradNode("_backward_np_broadcast_to", n, ograds, {}, n->attrs.dict);
+  })
+.add_argument("array", "NDArray-or-Symbol", "The input")
+.set_attr_parser(ParamParser<BroadcastToParam>)
+.add_arguments(BroadcastToParam::__FIELDS__())
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyBroadcastToShape)
+.set_attr<FCompute>("FCompute<cpu>", NumpyBroadcastToForward<cpu>);
+
+NNVM_REGISTER_OP(_backward_np_broadcast_to)
+.set_attr_parser(ParamParser<BroadcastToParam>)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FCompute>("FCompute<cpu>", NumpyBroadcastToBackward<cpu>)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  });
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/np_broadcast_reduce_op_value.cu b/src/operator/numpy/np_broadcast_reduce_op_value.cu
index 1eb773009a79..a681f0d8a162 100644
--- a/src/operator/numpy/np_broadcast_reduce_op_value.cu
+++ b/src/operator/numpy/np_broadcast_reduce_op_value.cu
@@ -32,5 +32,17 @@ NNVM_REGISTER_OP(_np_sum)
 NNVM_REGISTER_OP(_backward_np_sum)
 .set_attr<FCompute>("FCompute<gpu>", NumpyReduceAxesBackwardUseNone<gpu>);
 
+NNVM_REGISTER_OP(_np_prod)
+.set_attr<FCompute>("FCompute<gpu>", NumpyReduceAxesCompute<gpu, mshadow_op::product, true>);
+
+NNVM_REGISTER_OP(_backward_np_prod)
+.set_attr<FCompute>("FCompute<gpu>", NumpyReduceAxesBackwardUseInOut<gpu, mshadow_op::rdiv>);
+
+NNVM_REGISTER_OP(_np_broadcast_to)
+.set_attr<FCompute>("FCompute<gpu>", NumpyBroadcastToForward<gpu>);
+
+NNVM_REGISTER_OP(_backward_np_broadcast_to)
+.set_attr<FCompute>("FCompute<gpu>", NumpyBroadcastToBackward<gpu>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/np_init_op.cc b/src/operator/numpy/np_init_op.cc
index 83a44c8ae280..b85a92f97683 100644
--- a/src/operator/numpy/np_init_op.cc
+++ b/src/operator/numpy/np_init_op.cc
@@ -29,7 +29,6 @@ namespace mxnet {
 namespace op {
 
 NNVM_REGISTER_OP(_npi_zeros)
-.describe("Return a new array of given shape, type, and context, filled with zeros.")
 .set_num_inputs(0)
 .set_num_outputs(1)
 .set_attr_parser(ParamParser<InitOpParam>)
@@ -50,17 +49,6 @@ NNVM_REGISTER_OP(_npi_ones)
 .add_arguments(InitOpParam::__FIELDS__());
 
 NNVM_REGISTER_OP(_np_zeros_like)
-.describe(R"code(Return an array of zeros with the same shape and type as a given array.
-
-Examples::
-
-  x = [[ 1.,  1.,  1.],
-       [ 1.,  1.,  1.]]
-
-  zeros_like(x) = [[ 0.,  0.,  0.],
-                   [ 0.,  0.,  0.]]
-
-)code")
 .set_num_inputs(1)
 .set_num_outputs(1)
 .set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
@@ -79,17 +67,6 @@ Examples::
               "The shape and data-type of a define these same attributes of the returned array.");
 
 NNVM_REGISTER_OP(_np_ones_like)
-.describe(R"code(Return an array of ones with the same shape and type as a given array.
-
-Examples::
-
-  x = [[ 0.,  0.,  0.],
-       [ 0.,  0.,  0.]]
-
-  ones_like(x) = [[ 1.,  1.,  1.],
-                  [ 1.,  1.,  1.]]
-
-)code")
 .set_num_inputs(1)
 .set_num_outputs(1)
 .set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
@@ -107,5 +84,31 @@ Examples::
 .add_argument("a", "NDArray-or-Symbol",
               "The shape and data-type of a define these same attributes of the returned array.");
 
+bool NumpyRangeShape(const nnvm::NodeAttrs& attrs,
+                     mxnet::ShapeVector* in_shapes,
+                     mxnet::ShapeVector* out_shapes) {
+  const RangeParam& param = nnvm::get<RangeParam>(attrs.parsed);
+  CHECK_EQ(in_shapes->size(), 0U);
+  CHECK_EQ(out_shapes->size(), 1U);
+  CHECK_NE(param.step, 0) << "_npi_arange does not support step=0";
+  CHECK_EQ(param.repeat, 1) << "_npi_arange only supports repeat=1, received " << param.repeat;
+  CHECK(param.stop.has_value()) << "_npi_arange requires stop to have a value";
+  double out_size = std::ceil((param.stop.value() - param.start) / param.step);
+  if (out_size < 0) {
+    out_size = 0;
+  }
+  SHAPE_ASSIGN_CHECK(*out_shapes, 0, mxnet::TShape({static_cast<nnvm::dim_t>(out_size)}));
+  return true;
+}
+
+NNVM_REGISTER_OP(_npi_arange)
+.set_num_inputs(0)
+.set_num_outputs(1)
+.set_attr_parser(RangeParamParser)
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyRangeShape)
+.set_attr<nnvm::FInferType>("FInferType", InitType<RangeParam>)
+.set_attr<FCompute>("FCompute<cpu>", RangeCompute<cpu, RangeParam>)
+.add_arguments(RangeParam::__FIELDS__());
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/np_init_op.cu b/src/operator/numpy/np_init_op.cu
index 2eb8ed6d83b7..fe631f388a19 100644
--- a/src/operator/numpy/np_init_op.cu
+++ b/src/operator/numpy/np_init_op.cu
@@ -40,5 +40,8 @@ NNVM_REGISTER_OP(_np_zeros_like)
 NNVM_REGISTER_OP(_np_ones_like)
 .set_attr<FCompute>("FCompute<gpu>", FillCompute<gpu, 1>);
 
+NNVM_REGISTER_OP(_npi_arange)
+.set_attr<FCompute>("FCompute<gpu>", RangeCompute<gpu, RangeParam>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/np_matrix_op-inl.h b/src/operator/numpy/np_matrix_op-inl.h
new file mode 100644
index 000000000000..44a6c909c9cf
--- /dev/null
+++ b/src/operator/numpy/np_matrix_op-inl.h
@@ -0,0 +1,65 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file np_matrix_op-inl.h
+ * \brief Function definition of matrix related operators
+ */
+#ifndef MXNET_OPERATOR_NUMPY_NP_MATRIX_OP_INL_H_
+#define MXNET_OPERATOR_NUMPY_NP_MATRIX_OP_INL_H_
+
+#include <vector>
+#include "../tensor/matrix_op-inl.h"
+
+namespace mxnet {
+namespace op {
+
+struct NumpyTransposeParam : public dmlc::Parameter<NumpyTransposeParam> {
+  mxnet::TShape axes;
+  DMLC_DECLARE_PARAMETER(NumpyTransposeParam) {
+    DMLC_DECLARE_FIELD(axes).set_default(mxnet::TShape(-1, 0))
+    .describe("By default, reverse the dimensions, otherwise permute "
+              "the axes according to the values given.");
+  }
+};
+
+template<typename xpu>
+void NumpyTranspose(const nnvm::NodeAttrs& attrs,
+                    const OpContext& ctx,
+                    const std::vector<TBlob>& inputs,
+                    const std::vector<OpReqType>& req,
+                    const std::vector<TBlob>& outputs) {
+  const NumpyTransposeParam& param = nnvm::get<NumpyTransposeParam>(attrs.parsed);
+  CHECK_EQ(req[0], kWriteTo) << "Transpose does not support inplace";
+  if (ndim_is_known(param.axes)) {
+    TransposeImpl<xpu>(ctx.run_ctx, inputs[0], outputs[0], param.axes);
+  } else {
+    mxnet::TShape axes(inputs[0].ndim(), -1);
+    for (int i = 0; i < axes.ndim(); ++i) {
+      axes[i] = axes.ndim() - 1 - i;
+    }
+    TransposeImpl<xpu>(ctx.run_ctx, inputs[0], outputs[0], axes);
+  }
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_NUMPY_NP_MATRIX_OP_INL_H_
diff --git a/src/operator/numpy/np_matrix_op.cc b/src/operator/numpy/np_matrix_op.cc
new file mode 100644
index 000000000000..1f31a650e771
--- /dev/null
+++ b/src/operator/numpy/np_matrix_op.cc
@@ -0,0 +1,252 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file np_matrix_op.cc
+ * \brief CPU Implementation of numpy matrix operations
+ */
+
+#include "./np_matrix_op-inl.h"
+#include "../nn/concat-inl.h"
+
+namespace mxnet {
+namespace op {
+
+DMLC_REGISTER_PARAMETER(NumpyTransposeParam);
+
+bool NumpyTransposeShape(const nnvm::NodeAttrs& attrs,
+                         mxnet::ShapeVector *in_attrs,
+                         mxnet::ShapeVector *out_attrs) {
+  const NumpyTransposeParam& param = nnvm::get<NumpyTransposeParam>(attrs.parsed);
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  mxnet::TShape& shp = (*in_attrs)[0];
+  CHECK_LE(shp.ndim(), 6) << "Transpose support at most 6 dimensions";
+  mxnet::TShape ret(shp.ndim(), -1);
+  if (ndim_is_known(param.axes)) {
+    CHECK_EQ(shp.ndim(), param.axes.ndim());
+    for (int i = 0; i < shp.ndim(); ++i) {
+      CHECK(param.axes[i] < static_cast<int64_t>(shp.ndim()));
+      ret[i] = shp[param.axes[i]];
+    }
+  } else {
+    for (int i = 0; i < shp.ndim(); ++i) {
+      ret[i] = shp[shp.ndim()-1-i];
+    }
+  }
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, ret);
+  return shape_is_known(ret);
+}
+
+NNVM_REGISTER_OP(_np_transpose)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<NumpyTransposeParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyTransposeShape)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
+.set_attr<nnvm::FGradient>("FGradient",
+  [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
+    const NumpyTransposeParam& param = nnvm::get<NumpyTransposeParam>(n->attrs.parsed);
+    if (ndim_is_known(param.axes)) {
+      mxnet::TShape axes = mxnet::TShape(param.axes.ndim(), -1);
+      for (int i = 0; i < axes.ndim(); ++i) {
+        axes[param.axes[i]] = i;
+      }
+      std::ostringstream os;
+      os << axes;
+      return MakeNonlossGradNode("transpose", n, ograds, {}, {{"axes", os.str()}});
+    } else {
+      return MakeNonlossGradNode("transpose", n, ograds, {},
+                                 std::unordered_map<std::string, std::string>());
+    }
+  })
+.set_attr<FCompute>("FCompute<cpu>", NumpyTranspose<cpu>)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"a"};
+  })
+.add_argument("a", "NDArray-or-Symbol", "Source input")
+.add_arguments(NumpyTransposeParam::__FIELDS__());
+
+struct NumpyReshapeParam : public dmlc::Parameter<NumpyReshapeParam> {
+  mxnet::TShape newshape;
+  std::string order;
+  DMLC_DECLARE_PARAMETER(NumpyReshapeParam) {
+      DMLC_DECLARE_FIELD(newshape)
+          .describe("The new shape should be compatible with the original shape."
+                    " If an integer, then the result will be a 1-D array of that length."
+                    " One shape dimension can be -1. In this case, the value is inferred"
+                    " from the length of the array and remaining dimensions.");
+      DMLC_DECLARE_FIELD(order)
+      .set_default("C")
+      .describe("Read the elements of a using this index order, and place the elements into"
+                " the reshaped array using this index order. 'C' means to read/write the elements"
+                " using C-like index order, with the last axis index changing fastest, back to the"
+                " first axis index changing slowest. Note that currently only C-like order is"
+                " supported");
+  }
+};
+
+DMLC_REGISTER_PARAMETER(NumpyReshapeParam);
+
+bool NumpyReshapeInferShape(const mxnet::TShape& src, mxnet::TShape* dst) {
+  if (shape_is_known(src) && shape_is_known(*dst)) {
+    CHECK_EQ(src.Size(), dst->Size()) << "Cannot reshape array of size "
+                                      << src.Size() << " into shape " << *dst;
+    return true;
+  } else if (!shape_is_known(src) || !ndim_is_known(*dst)) {
+    return false;
+  } else {
+    int unknown_axis = -1;
+    dim_t known_dim_size_prod = 1;
+    for (int i = 0; i < dst->ndim(); ++i) {
+      if (!dim_size_is_known(*dst, i)) {
+        if (unknown_axis == -1) {
+          unknown_axis = i;
+        } else {
+          return false;  // more than one unknown dim
+        }
+      } else {
+        known_dim_size_prod *= (*dst)[i];
+      }
+    }
+    CHECK_NE(known_dim_size_prod, 0) << "Cannot reshape array of size "
+                                     << src.Size() << " into shape " << *dst;
+    CHECK_EQ(src.Size() % known_dim_size_prod, 0) << "Cannot reshape array of size "
+                                                  << src.Size() << " into shape " << *dst;
+    (*dst)[unknown_axis] = src.Size() / known_dim_size_prod;
+    return true;
+  }
+}
+
+bool NumpyReshapeShape(const nnvm::NodeAttrs& attrs,
+                       mxnet::ShapeVector* in_attrs,
+                       mxnet::ShapeVector* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U) << "Input: [data]";
+  CHECK_EQ(out_attrs->size(), 1U);
+  const NumpyReshapeParam& param = nnvm::get<NumpyReshapeParam>(attrs.parsed);
+  // sanity check
+  bool has_unknown_dim_size = false;
+  for (int i = 0; i < param.newshape.ndim(); ++i) {
+    if (param.newshape[i] < 0) {
+      CHECK_EQ(param.newshape[i], -1) << "The shape dimension size to inferred must be -1";
+      CHECK(!has_unknown_dim_size) << "Can only specify one unknown dimension";
+      has_unknown_dim_size = true;
+    }
+  }
+
+  mxnet::TShape target_shape = param.newshape;
+  bool success = NumpyReshapeInferShape(in_attrs->at(0), &target_shape);
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, target_shape);
+  if (!success) {
+    success = NumpyReshapeInferShape(out_attrs->at(0), &in_attrs->at(0));
+  }
+  return success;
+}
+
+NNVM_REGISTER_OP(_np_reshape)
+.describe(R"code()code" ADD_FILELINE)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<NumpyReshapeParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyReshapeShape)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_reshape"})
+.set_attr<FCompute>("FCompute<cpu>", UnaryOp::IdentityCompute<cpu>)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::pair<int, int> >{{0, 0}};
+  })
+.set_attr<nnvm::FInplaceIdentity>("FInplaceIdentity",
+  [](const NodeAttrs& attrs){
+    return std::vector<bool>{true};
+  })
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"a"};
+  })
+.add_argument("a", "NDArray-or-Symbol", "Array to be reshaped.")
+.add_arguments(NumpyReshapeParam::__FIELDS__());
+
+bool NumpySqueezeShape(const nnvm::NodeAttrs& attrs,
+                       mxnet::ShapeVector *in_attrs,
+                       mxnet::ShapeVector *out_attrs) {
+  const SqueezeParam& param = nnvm::get<SqueezeParam>(attrs.parsed);
+  CHECK_EQ(in_attrs->size(), 1U) << "Input: [a]";
+  CHECK_EQ(out_attrs->size(), 1U);
+  const mxnet::TShape& dshape = in_attrs->at(0);
+  const int dndim = dshape.ndim();
+  if (!shape_is_known(dshape)) return false;
+  mxnet::TShape oshape = dshape;
+  // special case, scalar tensor
+  if (dshape.ndim() == 0) {
+    if (param.axis.has_value()) {
+      mxnet::Tuple<int> axes = param.axis.value();
+      CHECK_EQ(axes.ndim(), 1) << "cannot specify more than one axis for a scalar tensor";
+      CHECK(axes[0] == 0 || axes[0] == -1) << "axis " << axes[0]
+                                           << " is out of bounds of array of dimension 0";
+    }
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, mxnet::TShape(0, -1));
+    return true;
+  }
+  if (param.axis.has_value()) {
+    // preprocess axis
+    mxnet::Tuple<int> axes = param.axis.value();
+    for (int i = 0; i < axes.ndim(); ++i) {
+      if (axes[i] < 0) {
+        axes[i] += dndim;
+        CHECK_GE(axes[i], 0)
+            << "axis " << axes[i] - dndim << " is out of bounds for array of dimension " << dndim;
+      }
+      CHECK_LT(axes[i], dndim)
+          << "axis " << axes[i] << " is out of bounds for array of dimension " << dndim;
+      CHECK_EQ(dshape[axes[i]], 1)
+          << "cannot select an axis to squeeze out which has size="
+          << dshape[axes[i]] << " not equal to one";
+      CHECK_NE(oshape[axes[i]], 0) << "duplicate value in axis";
+      oshape[axes[i]] = -1;
+    }
+  } else {
+    for (int i = 0; i < oshape.ndim(); ++i) {
+      if (oshape[i] == 1) oshape[i] = -1;
+    }
+  }
+  size_t oshape_size = SqueezeShapeHelper(&oshape);
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, mxnet::TShape(oshape.data(), oshape.data()+oshape_size));
+  return true;
+}
+
+NNVM_REGISTER_OP(_np_squeeze)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<SqueezeParam>)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"a"};
+  })
+.set_attr<mxnet::FInferShape>("FInferShape", NumpySqueezeShape)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
+.set_attr<FCompute>("FCompute<cpu>", UnaryOp::IdentityCompute<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_squeeze"})
+.add_argument("a", "NDArray-or-Symbol[]", "data to squeeze")
+.add_arguments(SqueezeParam::__FIELDS__());
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_matrix_op.cu b/src/operator/numpy/np_matrix_op.cu
new file mode 100644
index 000000000000..efeba8e089af
--- /dev/null
+++ b/src/operator/numpy/np_matrix_op.cu
@@ -0,0 +1,41 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file np_matrix_op.cu
+ * \brief GPU Implementation of numpy matrix operations
+ */
+#include "./np_matrix_op-inl.h"
+#include "../nn/concat-inl.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(_np_transpose)
+.set_attr<FCompute>("FCompute<gpu>", NumpyTranspose<gpu>);
+
+NNVM_REGISTER_OP(_np_reshape)
+.set_attr<FCompute>("FCompute<gpu>", UnaryOp::IdentityCompute<gpu>);
+
+NNVM_REGISTER_OP(_np_squeeze)
+.set_attr<FCompute>("FCompute<gpu>", UnaryOp::IdentityCompute<gpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 12d9f779fd50..9d130e701491 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -24,7 +24,7 @@
 from mxnet.gluon import HybridBlock
 from mxnet.base import MXNetError
 from mxnet.test_utils import same, assert_almost_equal, rand_shape_nd, rand_ndarray
-from mxnet.test_utils import check_numeric_gradient, use_np
+from mxnet.test_utils import check_numeric_gradient, use_np, collapse_sum_like
 from common import assertRaises, with_seed
 import random
 import collections
@@ -257,7 +257,7 @@ def is_int(dtype):
                             y = test_sum(x)
                         assert y.shape == expected_ret.shape
                         assert_almost_equal(y.asnumpy(), expected_ret, rtol=1e-3 if dtype == 'float16' else 1e-3,
-                                            atol=1e-5 if dtype == 'float16' else 1e-5)
+                                            atol=1e-5 if dtype == 'float16' else 1e-5, use_broadcast=False)
 
                         y.backward()
                         assert same(x.grad.asnumpy(), _np.ones(shape=x.shape, dtype=x.dtype))
@@ -272,7 +272,7 @@ def is_int(dtype):
                         # test imperative
                         mx_out = np.sum(x, axis=axis, dtype=dtype, keepdims=keepdims)
                         np_out = _np.sum(x.asnumpy(), axis=axis, dtype=acc_type[itype], keepdims=keepdims).astype(dtype)
-                        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+                        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
 
 
 @with_seed()
@@ -310,7 +310,7 @@ def test_np_linspace():
     # check linspace equivalent to arange
     for test_index in range(1000):
         assert_almost_equal(mx.np.linspace(0, test_index, test_index + 1).asnumpy(), _np.arange(test_index + 1))
-    @use_np
+
     class TestLinspace(HybridBlock):
         def __init__(self, start, stop, num=50, endpoint=None, retstep=False, dtype=None, axis=0):
             super(TestLinspace, self).__init__()
@@ -402,6 +402,380 @@ def hybrid_forward(self, F, a):
             assert same(a.grad.asnumpy(), expected_grad)
 
 
+@with_seed()
+@use_np
+def test_np_reshape():
+    class TestReshape(HybridBlock):
+        def __init__(self, newshape):
+            super(TestReshape, self).__init__()
+            self._newshape = newshape
+
+        def hybrid_forward(self, F, a):
+            return F.np.reshape(a, self._newshape)
+
+    shape_pairs = [((2, 6), (6, 2)), ((2, 6), (3, 4)), ((1, 0), (0,)), ((0, 0), (0,)), ((), (1, 1, 1))]
+    for hybridize in [True, False]:
+        for shape_pair in shape_pairs:
+            shape1, shape2 = shape_pair
+            print(shape1, shape2)
+            test_reshape = TestReshape(shape2)
+            if hybridize:
+                test_reshape.hybridize()
+            x = rand_ndarray(shape1).as_np_ndarray()
+            x.attach_grad()
+            np_out = _np.reshape(x.asnumpy(), shape2)
+            with mx.autograd.record():
+                mx_out = test_reshape(x)
+            assert mx_out.shape == np_out.shape
+            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
+            mx_out.backward()
+            np_backward = _np.ones(shape1)
+            assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=1e-3, atol=1e-5, use_broadcast=False)
+
+            mx_out = np.reshape(x, shape2)
+            np_out = _np.reshape(x.asnumpy(), shape2)
+            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
+
+
+@with_seed()
+@use_np
+def test_np_squeeze():
+    config = [((), None),
+              ((), -1),
+              ((), 0),
+              ((4, 1, 2), None),
+              ((1, 1, 1), None),
+              ((1, 0, 1, 5), 2),
+              ((1, 0, 1, 1), (-1, -4))]
+
+    class TestSqueeze(HybridBlock):
+        def __init__(self, axis):
+            super(TestSqueeze, self).__init__()
+            self._axis = axis
+
+        def hybrid_forward(self, F, x):
+            return F.np.squeeze(x, axis=self._axis)
+
+    for shape, axis in config:
+        data_np = _np.random.uniform(size=shape)
+        data_mx = np.array(data_np, dtype=data_np.dtype)
+        ret_np = _np.squeeze(data_np, axis=axis)
+        ret_mx = np.squeeze(data_mx, axis=axis)
+        assert_almost_equal(ret_mx.asnumpy(), ret_np, rtol=1e-5, atol=1e-6, use_broadcast=False)
+
+        net = TestSqueeze(axis)
+        for hybrid in [False, True]:
+            if hybrid:
+                net.hybridize()
+            data_mx.attach_grad()
+            with mx.autograd.record():
+                ret_mx = net(data_mx)
+            assert_almost_equal(ret_mx.asnumpy(), ret_np, rtol=1e-5, atol=1e-6, use_broadcast=False)
+            ret_mx.backward()
+            assert_almost_equal(data_mx.grad.asnumpy(), _np.ones_like(data_np),
+                                rtol=1e-5, atol=1e-6, use_broadcast=False)
+
+
+@with_seed()
+@use_np
+def test_np_prod():
+    class TestProd(HybridBlock):
+        def __init__(self, axis=None, dtype=None, keepdims=False):
+            super(TestProd, self).__init__()
+            self._axis = axis
+            self._dtype = dtype
+            self._keepdims = keepdims
+
+        def hybrid_forward(self, F, a, *args, **kwargs):
+            return F.np.prod(a, axis=self._axis, dtype=self._dtype, keepdims=self._keepdims)
+
+    in_data_dim = random.choice([3, 4])
+    shape = rand_shape_nd(in_data_dim, dim=3)
+    for hybridize in [False, True]:
+        for keepdims in [True, False]:
+            for axis in ([i for i in range(in_data_dim)] + [(), None]):
+                for itype in ['float32', 'float64']:
+                    for dtype in ['float32', 'float64']:
+                        # test gluon
+                        test_prod = TestProd(axis=axis, dtype=dtype, keepdims=keepdims)
+                        if hybridize:
+                            test_prod.hybridize()
+                        x = np.array(_np.random.uniform(-2.0, 2.0, size=shape), dtype=itype)
+                        x.attach_grad()
+                        print(x.grad.dtype)
+                        expected_ret = _np.prod(x.asnumpy(), axis=axis, keepdims=keepdims)
+                        expected_ret = expected_ret.astype(dtype)
+                        with mx.autograd.record():
+                            y = test_prod(x)
+                        assert y.shape == expected_ret.shape
+                        assert_almost_equal(y.asnumpy(), expected_ret, rtol=1e-3, atol=1e-5, use_broadcast=False)
+                        y.backward()
+                        # use keepdims=True so that broadcast divide can be used to calculate
+                        # grad of input
+                        expected_ret = _np.prod(x.asnumpy(), axis=axis, keepdims=True)
+                        assert_almost_equal(x.grad.asnumpy(), expected_ret / x.asnumpy(), rtol=1e-3, atol=1e-3,
+                                            use_broadcast=False)
+
+                        # test numeric
+                        if itype == 'float32' and dtype == 'float32':
+                            x_sym = mx.sym.Variable("x").as_np_ndarray()
+                            mx_sym = mx.sym.np.prod(x_sym, axis=axis, dtype=dtype, keepdims=keepdims).as_nd_ndarray()
+                            check_numeric_gradient(mx_sym, [x.as_nd_ndarray()],
+                                                   numeric_eps=1e-3, rtol=1e-3, atol=1e-4, dtype=_np.float32)
+
+                        # test imperative
+                        mx_out = np.prod(x, axis=axis, dtype=dtype, keepdims=keepdims)
+                        np_out = _np.prod(x.asnumpy(), axis=axis, keepdims=keepdims).astype(dtype)
+                        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
+
+
+@with_seed()
+@use_np
+def test_np_flatten():
+    class TestFlatten(HybridBlock):
+        def hybrid_forward(self, F, x):
+            return x.flatten()
+
+    shapes = [(), (2, 0, 1), (3, 4, 5), 6, (0,), (0, 0, 0)]
+    for shape in shapes:
+        for hybridize in [True, False]:
+            test_flatten = TestFlatten()
+            if hybridize:
+                test_flatten.hybridize()
+            a_np = _np.random.uniform(size=shape).astype('float32')
+            a_mx = np.array(a_np, dtype=a_np.dtype)
+            a_mx.attach_grad()
+            with mx.autograd.record():
+                ret = test_flatten(a_mx)
+            expected_ret = a_np.flatten()
+            assert_almost_equal(expected_ret, ret.asnumpy(), rtol=1e-5, atol=1e-6, use_broadcast=False)
+            # check gradient
+            ret.backward()
+            assert_almost_equal(a_mx.grad.asnumpy(), _np.ones_like(a_np), rtol=1e-5, atol=1e-6, use_broadcast=False)
+
+
+@with_seed()
+@use_np
+def test_np_broadcast_to():
+    class TestBroadcastTo(HybridBlock):
+        def __init__(self, dst_shape):
+            super(TestBroadcastTo, self).__init__()
+            self._dst_shape = dst_shape
+
+        def hybrid_forward(self, F, x):
+            return F.np.broadcast_to(x, self._dst_shape)
+
+    shapes = [
+        ((), (1, 2, 4, 5)),
+        ((1,), (4, 5, 6)),
+        ((1, 0), (2, 4, 0)),
+        ((1, 1), (2, 4, 0)),
+        ((4, 1), (1, 2, 3, 4, 5)),
+        ((4, 1), (1, 0, 3, 4, 5))
+    ]
+    for src_shape, dst_shape in shapes:
+        for hybridize in [True, False]:
+            test_broadcast_to = TestBroadcastTo(dst_shape)
+            if hybridize:
+                test_broadcast_to.hybridize()
+
+            a = _np.random.uniform(size=src_shape).astype(np.float32)
+            expected_ret = _np.broadcast_to(a, dst_shape)
+            a_mx = np.array(a, dtype=a.dtype)
+            a_mx.attach_grad()
+            with mx.autograd.record():
+                ret = test_broadcast_to(a_mx)
+            assert_almost_equal(ret.asnumpy(), expected_ret, rtol=1e-5, atol=1e-6, use_broadcast=False)
+            ret.backward()
+            expected_grad = collapse_sum_like(_np.ones_like(expected_ret), src_shape)
+            assert_almost_equal(a_mx.grad.asnumpy(), expected_grad, rtol=1e-5, atol=1e-6, use_broadcast=False)
+
+
+@with_seed()
+@use_np
+def test_np_transpose():
+    def np_transpose_grad(out_shape, dtype, axes=None):
+        ograd = _np.ones(out_shape, dtype=dtype)
+        if axes is None or axes == ():
+            return _np.transpose(ograd, axes)
+        np_axes = _np.array(list(axes))
+        return _np.transpose(ograd, tuple(list(_np.argsort(np_axes))))
+
+    class TestTranspose(HybridBlock):
+        def __init__(self, axes=None):
+            super(TestTranspose, self).__init__()
+            self.axes = axes
+
+        def hybrid_forward(self, F, a):
+            return F.np.transpose(a, self.axes)
+
+    for hybridize in [True, False]:
+        for dtype in [_np.int32, _np.float32]:
+            for ndim in range(7):
+                shape = rand_shape_nd(ndim, dim=5, allow_zero_size=True)
+                axeses = [None]
+                if ndim == 0:
+                    axeses += [()]
+                else:
+                    axes = [i for i in range(ndim)]
+                    axeses.append(tuple(axes))
+                    random.shuffle(axes)
+                    axeses.append(tuple(axes))
+                for axes in axeses:
+                    test_trans = TestTranspose(axes)
+                    if hybridize:
+                        test_trans.hybridize()
+                    x = rand_ndarray(shape).as_np_ndarray()
+                    x = x.astype(dtype)
+                    x.attach_grad()
+                    np_out = _np.transpose(x.asnumpy(), axes)
+                    with mx.autograd.record():
+                        mx_out = test_trans(x)
+                    assert mx_out.shape == np_out.shape
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
+                    mx_out.backward()
+                    np_backward = np_transpose_grad(np_out.shape, dtype, axes)
+                    assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=1e-3, atol=1e-5, use_broadcast=False)
+
+                    mx_out = np.transpose(x, axes)
+                    np_out = _np.transpose(x.asnumpy(), axes)
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
+
+
+@with_seed()
+@use_np
+def test_np_meshgrid():
+    nx, ny = (4, 5)
+    x = np.array(_np.linspace(0, 1, nx), dtype=np.float32)
+    y = np.array(_np.linspace(0, 1, ny), dtype=np.float32)
+    z = np.ones(())
+    xv, yv, zv = np.meshgrid(x, y, z)
+    xv_expected, yv_expected, zv_expected = _np.meshgrid(x.asnumpy(), y.asnumpy(), z.asnumpy())
+    assert same(xv.asnumpy(), xv_expected)
+    assert same(yv.asnumpy(), yv_expected)
+    assert same(zv.asnumpy(), zv_expected)
+
+
+@with_seed()
+@use_np
+def test_np_broadcast_arrays():
+    shape_config = [
+        [(), (2, 1), (1, 3), (4, 1, 1), (5, 4, 2, 3)],
+        [(0,), (), (2, 1), (1, 0), (3, 2, 1)]
+    ]
+    for shapes in shape_config:
+        arrays_np = [_np.random.randint(low=0, high=1000, size=shape, dtype=_np.int32) for shape in shapes]
+        arrays_mx = [np.array(arr, dtype=arr.dtype) for arr in arrays_np]
+        expected_rets = _np.broadcast_arrays(*arrays_np)
+        rets = np.broadcast_arrays(*arrays_mx)
+        for expected_ret, ret in zip(expected_rets, rets):
+            assert same(expected_ret, ret.asnumpy())
+
+
+@with_seed()
+@use_np
+def test_np_tile():
+    config = [
+        ((), ()),
+        ((), 0),
+        ((), (2, 0)),
+        ((), (2, 3)),
+        ((4, 2), (2,)),
+        ((4, 2), (2, 3)),
+        ((4, 2), (2, 1, 4)),
+        ((4, 2), (2, 3, 4)),
+        ((4, 2), (2, 0)),
+        ((4, 2), (2, 0, 3)),
+        ((4, 2), (2, 0, 3)),
+        ((4, 0), (2, 0, 3)),
+    ]
+
+    class TestTile(HybridBlock):
+        def __init__(self, reps):
+            super(TestTile, self).__init__()
+            self._reps = reps
+
+        def hybrid_forward(self, F, x):
+            return F.np.tile(x, reps=self._reps)
+
+    for shape, reps in config:
+        data_np = _np.random.randint(low=0, high=1000, size=shape)
+        data_mx = np.array(data_np, dtype=data_np.dtype)
+        ret_np = _np.tile(data_np, reps=reps)
+        ret_mx = np.tile(data_mx, reps=reps)
+        assert same(ret_mx.asnumpy(), ret_np)
+
+        net = TestTile(reps)
+        for hybrid in [False, True]:
+            if hybrid:
+                net.hybridize()
+            ret_mx = net(data_mx)
+            assert same(ret_mx.asnumpy(), ret_np)
+
+
+@with_seed()
+@use_np
+def test_np_arange():
+    configs = [
+        (1, 10, 2),
+        (1, 10, 4),
+        (1, -10, 4),
+        (1, -10, -2),
+        (1, -10, -4),
+        (2, 3),
+        (2, -3),
+        (-2, -3),
+        (-2, 3),
+        (4, 0, 5),
+        (-4, 0, 5),
+        (-4, 0, -5),
+        (0, 0),
+        (11, 11),
+        (0, 0, 2),
+        (0, 0, -2),
+        (0, 5, None),
+        (0, -5, None),
+        0,
+        6,
+    ]
+    dtypes = ['int32', 'float16', 'float32', 'float64', None]
+    for config in configs:
+        for dtype in dtypes:
+            if isinstance(config, tuple):
+                mx_ret = np.arange(*config, dtype=dtype)
+                np_ret = _np.arange(*config, dtype=dtype)
+            else:
+                mx_ret = np.arange(config, dtype=dtype)
+                np_ret = _np.arange(config, dtype=dtype)
+            assert same(mx_ret.asnumpy(), np_ret)
+
+    class TestRange(HybridBlock):
+        def __init__(self, start, stop=None, step=None, dtype=None):
+            super(TestRange, self).__init__()
+            self._start = start
+            self._stop = stop
+            self._step = step
+            self._dtype = dtype
+
+        def hybrid_forward(self, F, x):
+            return x + F.np.arange(self._start, self._stop, self._step, dtype=self._dtype)
+
+    for dtype in dtypes:
+        x = np.zeros(shape=(), dtype=dtype)
+        for config in configs:
+            for hybridize in [False, True]:
+                if isinstance(config, tuple):
+                    net = TestRange(*config, dtype=dtype)
+                    np_out = _np.arange(*config, dtype=dtype)
+                else:
+                    net = TestRange(config, dtype=dtype)
+                    np_out = _np.arange(config, dtype=dtype)
+                if hybridize:
+                    net.hybridize()
+                mx_out = net(x)
+                assert same(mx_out.asnumpy(), np_out)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 11ce2a229d8ecd66f665d2680b37d77e901ef355 Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Tue, 13 Aug 2019 13:37:40 -0700
Subject: [PATCH 173/813] Add large tensor support binary arithmetic (#15785)

* test rdiv

* floating_point exception handle

* add 10 other ops

* added rpow and made numpy consistent

* attempt to solve memory issue

* linting fix

* Trigger notification

* lint
---
 tests/nightly/test_large_array.py | 111 +++++++++++++++++++++++++++++-
 1 file changed, 109 insertions(+), 2 deletions(-)

diff --git a/tests/nightly/test_large_array.py b/tests/nightly/test_large_array.py
index 9dc29eb1584e..03ecdb88f84f 100644
--- a/tests/nightly/test_large_array.py
+++ b/tests/nightly/test_large_array.py
@@ -291,10 +291,10 @@ def test_unravel_index():
     assert (indices_2d.asnumpy() == np.array(original_2d_indices)).all()
 
 
-def create_2d_tensor(rows, columns):
+def create_2d_tensor(rows, columns, dtype=np.int64):
     a = np.arange(0, rows).reshape(rows, 1)
     b = np.broadcast_to(a, shape=(a.shape[0], columns))
-    return nd.array(b, dtype=np.int64)
+    return nd.array(b, dtype=dtype)
 
 
 def test_transpose():
@@ -351,6 +351,113 @@ def test_topk():
     assert l.sum() == np.sum(np.arange(0, SMALL_Y))
 
 
+def test_add():
+    a = nd.ones(shape=(LARGE_X, SMALL_Y))
+    b = nd.ones(shape=(LARGE_X, SMALL_Y))
+    c = b
+    c = c.__add__(a)
+    assert c[0][-1] == 2
+    assert c.shape == a.shape
+
+
+def test_sub():
+    a = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
+    b = nd.ones(shape=(LARGE_X, SMALL_Y))
+    c = b
+    c = c.__sub__(a)
+    assert c[0][-1] == -2
+    assert c.shape == a.shape
+
+
+def test_rsub():
+    a = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
+    b = nd.ones(shape=(LARGE_X, SMALL_Y))
+    c = b
+    c = c.__rsub__(a)
+    assert c[0][-1] == 2
+    assert c.shape == a.shape
+
+
+def test_neg():
+    a = nd.ones(shape=(LARGE_X, SMALL_Y))
+    c = a
+    c = c.__neg__()
+    assert c[0][-1] == -1
+    assert c.shape == a.shape
+
+
+def test_mul():
+    a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
+    b = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
+    c = b
+    c = c.__mul__(a)
+    assert c[0][-1] == 6
+    assert c.shape == a.shape
+
+
+def test_div():
+    a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
+    b = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
+    c = b
+    c = c.__div__(a)
+    assert c[0][-1] == 3/2
+    assert c.shape == a.shape
+
+
+def test_rdiv():
+    a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
+    b = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
+    c = b
+    c = c.__rdiv__(a)
+    assert c[0][-1] == 2/3
+    assert c.shape == a.shape
+
+
+def test_mod():
+    a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
+    b = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
+    c = b
+    c = c.__mod__(a)
+    assert c[0][-1] == 1
+    assert c.shape == a.shape
+
+
+def test_rmod():
+    a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
+    b = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
+    c = b
+    c = c.__rmod__(a)
+    assert c[0][-1] == 2
+    assert c.shape == a.shape
+
+
+def test_imod():
+    a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
+    b = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
+    c = b
+    c = c.__imod__(a)
+    assert c[0][-1] == 1
+    assert c.shape == a.shape
+
+
+def test_pow():
+    a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
+    b = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
+    c = b
+    c = c.__pow__(a)
+    assert c[0][-1] == 9
+    assert c.shape == a.shape
+
+
+def test_rpow():
+    a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
+    b = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
+    c = b
+    c = c.__rpow__(a)
+    assert c[0][-1] == 8
+    assert c.shape == a.shape
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 24a5cf060617692d5511117ea17ba9f983f5045d Mon Sep 17 00:00:00 2001
From: Przemyslaw Tredak <ptredak@nvidia.com>
Date: Tue, 13 Aug 2019 15:44:59 -0700
Subject: [PATCH 174/813] Allow operators with multiple outputs in
 get_atomic_symbol (#15740)

* Allow operators with multiple outputs in get_atomic_symbol

* Added unittest
---
 src/c_api/c_api_symbolic.cc          | 13 +++++++++----
 tests/python/unittest/test_symbol.py |  9 +++++++++
 2 files changed, 18 insertions(+), 4 deletions(-)

diff --git a/src/c_api/c_api_symbolic.cc b/src/c_api/c_api_symbolic.cc
index e8d59d90e99b..272579bce03b 100644
--- a/src/c_api/c_api_symbolic.cc
+++ b/src/c_api/c_api_symbolic.cc
@@ -1158,10 +1158,15 @@ int MXGenAtomicSymbolFromSymbol(SymbolHandle sym_handle, SymbolHandle *ret_sym_h
   nnvm::Symbol *s = new nnvm::Symbol();
   API_BEGIN();
   nnvm::Symbol *source = static_cast<nnvm::Symbol *>(sym_handle);
-  CHECK_EQ(source->outputs.size(), 1U)
-      << "Generating atomic symbol from other symbol only works for nongrouped symbol.";
-  const auto &node = source->outputs[0];
-  const auto *op = node.node->op();
+  CHECK_GE(source->outputs.size(), 1) << "Input symbol does not have outputs.";
+  const auto &node = source->outputs[0].node;
+  for (const auto &other_node : source->outputs) {
+    if (node.get() != other_node.node.get()) {
+      LOG(FATAL)
+        << "Generating atomic symbol from other symbol only works for nongrouped symbol.";
+    }
+  }
+  const auto *op = node->op();
   const auto attrs = source->ListAttrs(nnvm::Symbol::ListAttrOption::kShallow);
   *s = nnvm::Symbol::CreateFunctor(op, attrs);
   *ret_sym_handle = s;
diff --git a/tests/python/unittest/test_symbol.py b/tests/python/unittest/test_symbol.py
index 963b32493b44..28f302f9ec15 100644
--- a/tests/python/unittest/test_symbol.py
+++ b/tests/python/unittest/test_symbol.py
@@ -389,6 +389,15 @@ def test_children_same_name():
     for c in b.get_children():
         pass
 
+def test_gen_atomic_symbol_multiple_outputs():
+    data=mx.sym.Variable('data')
+    p = mx.sym.Variable('param')
+    h0 = mx.sym.Variable('h0')
+    h1 = mx.sym.Variable('h1')
+    s = mx.sym.RNN(data, p, h0, h1, state_size=10, num_layers=2, 
+                   bidirectional=True, state_outputs=True, mode='lstm')
+    atomic_sym = s._gen_atomic_symbol()
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From f32b58ecec6b1c4e44f77d8ede073484c89e05e9 Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Tue, 13 Aug 2019 20:40:01 -0700
Subject: [PATCH 175/813] numpy-compatible split upstream (#15841)

---
 python/mxnet/ndarray/numpy/_op.py      | 52 ++++++++++++++++++++++++-
 python/mxnet/numpy/multiarray.py       | 36 ++++++++++++++++-
 python/mxnet/symbol/numpy/_symbol.py   | 45 +++++++++++++++++++++-
 tests/python/unittest/test_numpy_op.py | 53 ++++++++++++++++++++++++++
 4 files changed, 183 insertions(+), 3 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index 30cbc6999b88..db4d86179e59 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -27,7 +27,7 @@
 from ..ndarray import NDArray
 
 __all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power', 'tensordot',
-           'linspace', 'expand_dims', 'tile', 'arange']
+           'linspace', 'expand_dims', 'tile', 'arange', 'split']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -420,6 +420,7 @@ def tensordot(a, b, axes=2):
     return _npi.tensordot(a, b, a_axes_summed, b_axes_summed)
 
 
+@set_module('mxnet.ndarray.numpy')
 def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0, ctx=None):  # pylint: disable=too-many-arguments
     r"""
     Return evenly spaced numbers over a specified interval.
@@ -632,3 +633,52 @@ def tile(A, reps):
 
     """
     return _unary_func_helper(A, _npi.tile, _np.tile, reps=reps)
+
+
+@set_module('mxnet.ndarray.numpy')
+def split(ary, indices_or_sections, axis=0):
+    """Split an array into multiple sub-arrays.
+    Parameters
+    ----------
+    ary : ndarray
+        Array to be divided into sub-arrays.
+    indices_or_sections : int or 1-D array
+        If `indices_or_sections` is an integer, N, the array will be divided
+        into N equal arrays along `axis`.  If such a split is not possible,
+        an error is raised.
+        If `indices_or_sections` is a 1-D array of sorted integers, the entries
+        indicate where along `axis` the array is split.  For example,
+        ``[2, 3]`` would, for ``axis=0``, result in
+          - ary[:2]
+          - ary[2:3]
+          - ary[3:]
+        If an index exceeds the dimension of the array along `axis`,
+        an empty sub-array is returned correspondingly.
+    axis : int, optional
+        The axis along which to split, default is 0.
+    Returns
+    -------
+    sub-arrays : list of ndarrays
+        A list of sub-arrays.
+    Raises
+    ------
+    ValueError
+        If `indices_or_sections` is given as an integer, but
+        a split does not result in equal division.
+    """
+    indices = []
+    axis_size = ary.shape[axis]
+    if isinstance(indices_or_sections, int):
+        sections = indices_or_sections
+        if axis_size % sections:
+            raise ValueError('array split does not result in an equal division')
+        section_size = int(axis_size / sections)
+        indices = [i * section_size for i in range(sections)]
+    elif isinstance(indices_or_sections, tuple):
+        indices = [0] + list(indices_or_sections)
+    else:
+        raise ValueError('indices_or_sections must either int or tuple of ints')
+    ret = _npi.split(ary, indices, axis, False)
+    if not isinstance(ret, list):
+        return [ret]
+    return ret
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index b8633699435d..0f05aa8da50e 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -44,7 +44,7 @@
 from ..ndarray.numpy import _internal as _npi
 
 __all__ = ['ndarray', 'empty', 'array', 'zeros', 'ones', 'add', 'subtract', 'multiply', 'divide',
-           'mod', 'power', 'tensordot', 'linspace', 'expand_dims', 'tile', 'arange']
+           'mod', 'power', 'tensordot', 'linspace', 'expand_dims', 'tile', 'arange', 'split']
 
 
 # This function is copied from ndarray.py since pylint
@@ -1606,6 +1606,7 @@ def tensordot(a, b, axes=2):
     return _mx_nd_np.tensordot(a, b, axes)
 
 
+@set_module('mxnet.numpy')
 def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0, ctx=None):  # pylint: disable=too-many-arguments
     r"""
     Return evenly spaced numbers over a specified interval.
@@ -1819,3 +1820,36 @@ def arange(start, stop=None, step=1, dtype=None, ctx=None):
         than `stop`.
     """
     return _mx_nd_np.arange(start, stop, step, dtype, ctx)
+
+
+@set_module('mxnet.numpy')
+def split(ary, indices_or_sections, axis=0):
+    """Split an array into multiple sub-arrays.
+    Parameters
+    ----------
+    ary : ndarray
+        Array to be divided into sub-arrays.
+    indices_or_sections : int or 1-D array
+        If `indices_or_sections` is an integer, N, the array will be divided
+        into N equal arrays along `axis`.  If such a split is not possible,
+        an error is raised.
+        If `indices_or_sections` is a 1-D array of sorted integers, the entries
+        indicate where along `axis` the array is split.  For example,
+        ``[2, 3]`` would, for ``axis=0``, result in
+          - ary[:2]
+          - ary[2:3]
+          - ary[3:]
+        If an index exceeds the dimension of the array along `axis`,
+        an empty sub-array is returned correspondingly.
+    axis : int, optional
+        The axis along which to split, default is 0.
+    Returns
+    -------
+    sub-arrays : list of ndarrays
+        A list of sub-arrays.
+    Raises
+    ------
+    ValueError
+        If `indices_or_sections` is given as an integer, but
+        a split does not result in equal division."""
+    return _mx_nd_np.split(ary, indices_or_sections, axis=axis)
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 587dae3deab9..bf4a6d159363 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -30,7 +30,7 @@
 from . import _internal as _npi
 
 __all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power', 'tensordot',
-           'linspace', 'expand_dims', 'tile', 'arange']
+           'linspace', 'expand_dims', 'tile', 'arange', 'split']
 
 
 def _num_outputs(sym):
@@ -1063,6 +1063,7 @@ def tensordot(a, b, axes=2):
     return _npi.tensordot(a, b, a_axes_summed, b_axes_summed)
 
 
+@set_module('mxnet.symbol.numpy')
 def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0, ctx=None): # pylint: disable=too-many-arguments
     r"""
     Return evenly spaced numbers over a specified interval.
@@ -1269,4 +1270,46 @@ def arange(start, stop=None, step=1, dtype=None, ctx=None):
     return _npi.arange(start=start, stop=stop, step=step, dtype=dtype, ctx=ctx)
 
 
+@set_module('mxnet.symbol.numpy')
+def split(ary, indices_or_sections, axis=0):
+    """Split an array into multiple sub-arrays.
+    Parameters
+    ----------
+    ary : ndarray
+        Array to be divided into sub-arrays.
+    indices_or_sections : int or 1-D array
+        If `indices_or_sections` is an integer, N, the array will be divided
+        into N equal arrays along `axis`.  If such a split is not possible,
+        an error is raised.
+        If `indices_or_sections` is a 1-D array of sorted integers, the entries
+        indicate where along `axis` the array is split.  For example,
+        ``[2, 3]`` would, for ``axis=0``, result in
+          - ary[:2]
+          - ary[2:3]
+          - ary[3:]
+        If an index exceeds the dimension of the array along `axis`,
+        an empty sub-array is returned correspondingly.
+    axis : int, optional
+        The axis along which to split, default is 0.
+    Returns
+    -------
+    sub-arrays : list of ndarrays
+        A list of sub-arrays.
+    Raises
+    ------
+    ValueError
+        If `indices_or_sections` is given as an integer, but
+        a split does not result in equal division."""
+    indices = []
+    sections = 0
+    if isinstance(indices_or_sections, int):
+        sections = indices_or_sections
+    elif isinstance(indices_or_sections, tuple):
+        indices = [0] + list(indices_or_sections)
+    else:
+        raise ValueError('indices_or_sections must either int or tuple of ints')
+    ret = _npi.split(ary, indices, axis, False, sections)
+    return ret
+
+
 _set_np_symbol_class(_Symbol)
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 9d130e701491..aba262f88042 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -776,6 +776,59 @@ def hybrid_forward(self, F, x):
                 assert same(mx_out.asnumpy(), np_out)
 
 
+@with_seed()
+@use_np
+def test_np_split():
+    class TestSplit(HybridBlock):
+        def __init__(self, indices_or_sections, axis=None):
+            super(TestSplit, self).__init__()
+            self._axis = axis
+            self._indices_or_sections = indices_or_sections
+
+        def hybrid_forward(self, F, a, *args, **kwargs):
+            return F.np.split(a, indices_or_sections=self._indices_or_sections,
+                              axis=self._axis)
+
+    def get_indices(axis_size):
+        if axis_size is 0:
+            axis_size = random.randint(3, 6)
+        samples = random.randint(1, axis_size - 1)
+        indices = sorted(random.sample([i for i in range(1, axis_size)], samples))
+        indices = tuple(indices)
+        return indices
+
+    dim = random.randint(0, 3)
+    shape = [0] + [random.randint(2, 4) for i in range(dim)]
+    for hybridize in [True, False]:
+        for axis in range(len(shape)):
+            indices = get_indices(shape[axis])
+            sections = 7 if shape[axis] is 0 else shape[axis]
+            for indices_or_sections in [indices, sections]:
+                # test gluon
+                test_split = TestSplit(axis=axis, indices_or_sections=indices_or_sections)
+                if hybridize:
+                    test_split.hybridize()
+
+                a = mx.nd.random.uniform(-1.0, 1.0, shape=shape).as_np_ndarray()
+                a.attach_grad()
+                expected_ret = _np.split(a.asnumpy(), indices_or_sections=indices_or_sections, axis=axis)
+                with mx.autograd.record():
+                    y = test_split(a)
+                assert len(y) == len(expected_ret)
+                for mx_out, np_out in zip(y, expected_ret):
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+
+                mx.autograd.backward(y)
+
+                assert_almost_equal(a.grad.asnumpy(), _np.ones(a.shape), rtol=1e-3, atol=1e-5)
+
+                # test imperative
+                mx_outs = np.split(a, indices_or_sections=indices_or_sections, axis=axis)
+                np_outs = _np.split(a.asnumpy(), indices_or_sections=indices_or_sections, axis=axis)
+                for mx_out, np_out in zip(mx_outs, np_outs):
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From b914d0a6296b5773499a1232dd8d4a3851b6a842 Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Tue, 13 Aug 2019 23:14:55 -0700
Subject: [PATCH 176/813] Large tensor support for random ops (#15783)

* random ops

* replace array with uniform

* remove dtype

* randn add

* add multinomial

* multi,randn small fix

* add negative bino

* fix memory issue - Failed to allocate CPU Memory

* Trigger notification

* linting fix

* Trigger notification
---
 tests/nightly/test_large_array.py | 83 +++++++++++++++++++++++++++++++
 1 file changed, 83 insertions(+)

diff --git a/tests/nightly/test_large_array.py b/tests/nightly/test_large_array.py
index 03ecdb88f84f..a0dfea6ad4bf 100644
--- a/tests/nightly/test_large_array.py
+++ b/tests/nightly/test_large_array.py
@@ -24,6 +24,7 @@
 # dimension constants
 MEDIUM_X = 10000
 LARGE_X = 100000000
+SMALL_X = 100
 SMALL_Y = 50
 LARGE_SIZE = LARGE_X * SMALL_Y
 
@@ -79,6 +80,88 @@ def test_ndarray_random_randint():
     assert a.__gt__(low) and a.__lt__(high)
 
 
+@with_seed()
+def test_ndarray_random_exponential():
+    scale_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_Y))
+    a = nd.random.exponential(scale=scale_array, shape=(SMALL_X, SMALL_Y))
+    assert a.shape == (MEDIUM_X, SMALL_Y, SMALL_X, SMALL_Y)
+    assert a[-1][0][0][0] >= 0
+
+
+@with_seed()
+def test_ndarray_random_gamma():
+    alpha_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_Y))
+    beta_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_Y))
+    a = nd.random.gamma(alpha=alpha_array, beta=beta_array,
+                        shape=(SMALL_X, SMALL_Y))
+    assert a.shape == (MEDIUM_X, SMALL_Y, SMALL_X, SMALL_Y)
+    assert a[-1][0][0][0] >= 0
+
+
+@with_seed()
+def test_ndarray_random_multinomial():
+    # test 1 shape dimension
+    probs = nd.random.uniform(shape=(LARGE_X, SMALL_Y))
+    a = nd.random.multinomial(probs)
+    assert a.shape == (LARGE_X,)
+    assert a[-1] >= 0
+    # test for NDArray multi-dimension shape
+    a = nd.random.multinomial(probs, shape=(SMALL_X, SMALL_Y))
+    assert a.shape == (LARGE_X, SMALL_X, SMALL_Y)
+    assert a[-1][0][0] >= 0
+    # test log_likelihood output shape
+    a = nd.random.multinomial(probs, shape=(SMALL_X, SMALL_Y), get_prob=True)
+    assert a[0].shape == (LARGE_X, SMALL_X, SMALL_Y) and a[0].shape == a[1].shape
+    assert a[-1][0][0] >= 0
+
+
+@with_seed()
+def test_ndarray_random_generalized_negative_binomial():
+    alpha_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_Y))
+    mu_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_Y))
+    a = nd.random.generalized_negative_binomial(mu=mu_array, alpha=alpha_array,
+                                                shape=(SMALL_X, SMALL_Y))
+    assert a.shape == (MEDIUM_X, SMALL_Y, SMALL_X, SMALL_Y)
+    assert a[-1][0][0][0] >= 0
+
+
+@with_seed()
+def test_ndarray_random_negative_binomial():
+    k_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_Y))
+    p_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_Y))
+    a = nd.random.negative_binomial(k=k_array, p=p_array,
+                                    shape=(SMALL_X, SMALL_Y))
+    assert a.shape == (MEDIUM_X, SMALL_Y, SMALL_X, SMALL_Y)
+    assert a[-1][0][0][0] >= 0
+
+
+@with_seed()
+def test_ndarray_random_normal():
+    scale_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_Y))
+    loc_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_Y))
+    a = nd.random.normal(loc=loc_array, scale=scale_array,
+                         shape=(SMALL_X, SMALL_Y))
+    assert a.shape == (MEDIUM_X, SMALL_Y, SMALL_X, SMALL_Y)
+    assert a[-1][0][0][0] >= 0
+
+
+@with_seed()
+def test_ndarray_random_poisson():
+    lambda_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_Y))
+    a = nd.random.poisson(lam=lambda_array, shape=(SMALL_X, SMALL_Y))
+    assert a.shape == (MEDIUM_X, SMALL_Y, SMALL_X, SMALL_Y)
+    assert a[-1][0][0][0] >= 0
+
+
+@with_seed()
+def test_ndarray_random_randn():
+    a = nd.random.randn(LARGE_X, SMALL_Y)
+    assert a.shape == (LARGE_X, SMALL_Y)
+    assert a[-1][0] >= 0
+    # TODO: Once PR for randn ndarray dtype for loc,scale param merged
+    # Add check for (x,y,m,n) where x,y shape of loc,scale and m,n input shape
+
+
 def test_ndarray_empty():
     a = nd.empty((LARGE_X, SMALL_Y))
     assert a.shape == (LARGE_X, SMALL_Y)

From 843c3ab4e55e0dfcb08de5c5f2b0c4a819978359 Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Wed, 14 Aug 2019 13:10:58 -0700
Subject: [PATCH 177/813] Add Large Tensor Support for Sequence, NN Ops 
 (#15807)

* sequence_last, sequence_reverse, sequence_mask

* working softmax_cross_entropy

* fix linting, add index_copy

* add softmax output

* add leaky relu

* add pooling

* add layernorm

* add dropout, activation, batchnorm and update layernorm

* address comments to remove some comments

* handling imports
---
 tests/nightly/test_large_array.py  | 300 ++++++++++++++++++++++++++++-
 tests/nightly/test_large_vector.py |   1 +
 2 files changed, 293 insertions(+), 8 deletions(-)

diff --git a/tests/nightly/test_large_array.py b/tests/nightly/test_large_array.py
index a0dfea6ad4bf..585748ee59b1 100644
--- a/tests/nightly/test_large_array.py
+++ b/tests/nightly/test_large_array.py
@@ -15,9 +15,11 @@
 # specific language governing permissions and limitations
 # under the License.
 
+import math
 import numpy as np
 import mxnet as mx
-from mxnet.test_utils import rand_ndarray, assert_almost_equal, rand_coord_2d
+
+from mxnet.test_utils import rand_ndarray, assert_almost_equal, rand_coord_2d, default_context
 from mxnet import gluon, nd
 from tests.python.unittest.common import with_seed
 
@@ -299,9 +301,11 @@ def test_pick():
 def test_depthtospace():
     def numpy_depth_to_space(x, blocksize):
         b, c, h, w = x.shape[0], x.shape[1], x.shape[2], x.shape[3]
-        tmp = np.reshape(x, [b, blocksize, blocksize, c // (blocksize**2), h, w])
+        tmp = np.reshape(x, [b, blocksize, blocksize, c // (blocksize**2), h,
+                         w])
         tmp = np.transpose(tmp, [0, 3, 4, 1, 5, 2])
-        y = np.reshape(tmp, [b, c // (blocksize**2), h * blocksize, w * blocksize])
+        y = np.reshape(tmp, [b, c // (blocksize**2), h * blocksize,
+                       w * blocksize])
         return y
 
     shape_inp = (LARGE_X, 8, 4, 2)
@@ -315,9 +319,11 @@ def numpy_depth_to_space(x, blocksize):
 def test_spacetodepth():
     def numpy_space_to_depth(x, blocksize):
         b, c, h, w = x.shape[0], x.shape[1], x.shape[2], x.shape[3]
-        tmp = np.reshape(x, [b, c, h // blocksize, blocksize, w // blocksize, blocksize])
+        tmp = np.reshape(x, [b, c, h // blocksize, blocksize, w // blocksize,
+                         blocksize])
         tmp = np.transpose(tmp, [0, 3, 5, 1, 2, 4])
-        y = np.reshape(tmp, [b, c * (blocksize**2), h // blocksize, w // blocksize])
+        y = np.reshape(tmp, [b, c * (blocksize**2), h // blocksize,
+                       w // blocksize])
         return y
 
     shape_inp = (LARGE_X, 2, 8, 4)
@@ -327,6 +333,7 @@ def numpy_space_to_depth(x, blocksize):
     output = mx.nd.space_to_depth(data, 2)
     assert_almost_equal(output.asnumpy(), expected, atol=1e-3, rtol=1e-3)
 
+
 @with_seed()
 def test_diag():
     a_np = np.random.random((LARGE_X, SMALL_Y)).astype(np.float32)
@@ -358,7 +365,8 @@ def test_ravel_multi_index():
     x2, y2 = rand_coord_2d((LARGE_X - 200), LARGE_X, 9, SMALL_Y)
     x3, y3 = rand_coord_2d((LARGE_X - 300), LARGE_X, 8, SMALL_Y)
     indices_2d = [[x1, x2, x3], [y1, y2, y3]]
-    idx = mx.nd.ravel_multi_index(mx.nd.array(indices_2d, dtype=np.int64), shape=(LARGE_X, SMALL_Y))
+    idx = mx.nd.ravel_multi_index(mx.nd.array(indices_2d, dtype=np.int64),
+                                  shape=(LARGE_X, SMALL_Y))
     idx_numpy = np.ravel_multi_index(indices_2d, (LARGE_X, SMALL_Y))
     assert np.sum(1 for i in range(idx.size) if idx[i] == idx_numpy[i]) == 3
 
@@ -370,7 +378,8 @@ def test_unravel_index():
     x3, y3 = rand_coord_2d((LARGE_X - 300), LARGE_X, 8, SMALL_Y)
     original_2d_indices = [[x1, x2, x3], [y1, y2, y3]]
     idx_numpy = np.ravel_multi_index(original_2d_indices, (LARGE_X, SMALL_Y))
-    indices_2d = mx.nd.unravel_index(mx.nd.array(idx_numpy, dtype=np.int64), shape=(LARGE_X, SMALL_Y))
+    indices_2d = mx.nd.unravel_index(mx.nd.array(idx_numpy, dtype=np.int64),
+                                     shape=(LARGE_X, SMALL_Y))
     assert (indices_2d.asnumpy() == np.array(original_2d_indices)).all()
 
 
@@ -427,13 +436,288 @@ def test_topk():
     b = create_2d_tensor(rows=LARGE_X, columns=SMALL_Y)
     k = nd.topk(b, k=10, axis=0, dtype=np.int64)
     assert np.sum(k.asnumpy() == (LARGE_X - 1)) == SMALL_Y
-    ind, val = mx.nd.topk(b, k=3, axis=0, dtype=np.int64, ret_typ="both", is_ascend=False)
+    ind, val = mx.nd.topk(b, k=3, axis=0, dtype=np.int64, ret_typ="both",
+                          is_ascend=False)
     assert np.all(ind == val)
     b = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
     l = nd.topk(b, k=1, axis=-1, dtype=np.int64, ret_typ="value")
     assert l.sum() == np.sum(np.arange(0, SMALL_Y))
 
 
+def test_sequence_mask():
+    # Sequence Mask input [max_sequence_length, batch_size, other_feature_dims]
+    # test with input batch_size = 2
+    a = nd.arange(0, LARGE_X * SMALL_Y * 2).reshape(LARGE_X, 2, SMALL_Y)
+
+    # test as identity operator
+    b = nd.SequenceMask(a)
+    assert b[-1][0][1] == a[-1][0][1]
+    assert b.shape == a.shape
+
+    # test with default mask
+    b = nd.SequenceMask(a, sequence_length=nd.array([1, 1]),
+                        use_sequence_length=True)
+    assert b[0][1][-1] == a[0][1][-1]  # first sequence of each batch kept
+    assert b[-1][-1][-1] != a[-1][-1][-1]  # rest sequences masked
+    assert b[-1][-1][-1] == 0
+
+    # test with mask value
+    b = nd.SequenceMask(a, sequence_length=nd.array([1, 1]),
+                        use_sequence_length=True, value=-1)
+    assert b[-1][-1][-1] == -1
+
+
+def test_sequence_reverse():
+    a = nd.arange(0, LARGE_X * SMALL_Y * 2).reshape(LARGE_X, 2, SMALL_Y)
+    # test as reverse operator
+    b = nd.SequenceReverse(a)
+    assert b[-1][0][0] == a[0][0][0]
+    assert b.shape == a.shape
+
+    # test with sequence length
+    b = nd.SequenceReverse(a, sequence_length=[2, 3])
+    assert b[1][0][0] == a[0][0][0]  # check if reversed
+    assert b[-1][0][0] == a[-1][0][0]  # check if intact
+    assert b.shape == a.shape
+
+
+def test_sequence_last():
+    a = nd.arange(0, LARGE_X * SMALL_Y * 2).reshape(LARGE_X, 2, SMALL_Y)
+
+    # test if returns last sequence
+    b = nd.SequenceLast(a)
+    assert_almost_equal(b, a[-1])  # only checks for (2,SMALL_Y) tensor
+    assert b.shape == (2, SMALL_Y)
+
+    # test with sequence length
+    # parameter sequence_length - NDArray with shape (batch_size)
+    # (2,3) indicates 2nd sequence from batch 1 and 3rd sequence from batch 2
+    b = nd.SequenceLast(a, sequence_length=mx.nd.array([2, 3]),
+                        use_sequence_length=True)
+    # check if it takes 2nd sequence from the first batch
+    assert b[0][-1] == a[1][0][-1]
+
+
+def test_softmax_cross_entropy():
+    # dtype of input data, mxnet cross entropy set explicitly to float64
+    # numpy implicitly takes care of double precision
+    batch_size = SMALL_Y
+    num_labels = LARGE_X
+    input_data = mx.nd.ones((batch_size, num_labels), dtype="float64")
+    input_label = mx.nd.zeros((batch_size,), dtype="float64")
+
+    true_softmax = np.full((batch_size, num_labels), (1 / num_labels))
+    # use 1/batch_size when softmax axis=0
+    # here 1/num_labels since softmax_cross_entropy uses default axis
+    # by default axis=1
+    np_one_hot_label = np.zeros((batch_size, num_labels))
+    np_one_hot_label[:, 0] = 1
+
+    true_softmax_cross_entropy = np.sum(-np.log(true_softmax) *
+                                        np_one_hot_label)
+    mx_softmax_cross_entropy = mx.nd.softmax_cross_entropy(input_data,
+                                                           input_label,
+                                                           dtype="float64")
+    assert_almost_equal(mx_softmax_cross_entropy.asnumpy(),
+                        true_softmax_cross_entropy, rtol=1e-3, atol=1e-5)
+
+
+def test_index_copy():
+    x = mx.nd.zeros((LARGE_X, SMALL_Y))
+    t = mx.nd.arange(1, SMALL_Y + 1).reshape((1, SMALL_Y))
+    index = mx.nd.array([LARGE_X - 1])
+
+    x = mx.nd.contrib.index_copy(x, index, t)
+    assert x[-1][-1] == t[0][-1]
+
+
+def testSoftmaxOutput():
+    x = mx.sym.Variable('x')
+    label = mx.sym.Variable('label')
+    x_nd = mx.nd.ones((LARGE_X, SMALL_Y))
+    grad_x = mx.nd.zeros((LARGE_X, SMALL_Y))
+    label_nd = mx.nd.ones((LARGE_X))
+
+    sym = mx.sym.SoftmaxOutput(data=x, label=label, ignore_label=0,
+                               use_ignore=False)
+    ex = sym.bind(ctx=default_context(), args={'x': x_nd, 'label': label_nd},
+                  args_grad={'x': grad_x})
+
+    ex.forward(is_train=True)
+    softmax_out = ex.outputs[0][0].asnumpy()
+    expected_softmax_out = (1/SMALL_Y)*mx.nd.ones((SMALL_Y)).asnumpy()
+    assert np.isclose(softmax_out, expected_softmax_out).all()
+
+    ex.backward(is_train=True)
+    grad_out = ex.grad_arrays[0][0].asnumpy()
+    k = int(label_nd[0].asscalar())
+    expected_grad_out = np.zeros((SMALL_Y,))
+    expected_grad_out[k] = -1
+    assert np.isclose(grad_out - softmax_out, expected_grad_out).all()
+
+
+# TODO: correctness of prelu (currently flaky)
+def test_leaky_relu():
+    a = -1*mx.nd.ones((LARGE_X, SMALL_Y))
+
+    def test_leaky():
+        res = mx.nd.LeakyReLU(a, act_type="leaky", slope=0.3)
+        assert res[-1][-1].asnumpy() == 0.3*a[-1][-1].asnumpy()
+
+    def test_elu():
+        res = mx.nd.LeakyReLU(a, act_type="elu", slope=0.3)
+        assert res[-1][-1].asnumpy() == 0.3*(np.exp(a[-1][-1].asnumpy())-1)
+
+    def test_selu():
+        lam = 1.0507009873554804934193349852946
+        alpha = 1.6732632423543772848170429916717
+        res = mx.nd.LeakyReLU(a, act_type="selu")
+        assert res[-1][-1].asnumpy() == (lam * alpha * (np.exp(a[-1][-1].asnumpy())-1))
+
+    def test_rrelu():
+        lower = 0.125
+        upper = 0.333999991
+        res = mx.nd.LeakyReLU(a, act_type="rrelu")
+        assert res[-1][-1].asnumpy() == (lower + upper) / 2 * a[-1][-1].asnumpy()
+
+    test_leaky()
+    test_elu()
+    test_selu()
+    test_rrelu()
+
+
+def test_pooling():
+    a = mx.nd.ones((MEDIUM_X, MEDIUM_X, SMALL_Y, SMALL_Y))
+
+    def test_avg_pooling():
+        res = mx.nd.Pooling(a, kernel=(5, 5), pool_type='avg')
+        assert res[-1][-1][-1][-1] == 1.0000001
+        assert res.shape == SMALL_Y - 5 + 1
+
+    def test_max_pooling():
+        res = mx.nd.Pooling(a, kernel=(5, 5), pool_type='max')
+        assert res[-1][-1][-1][-1] == 1.
+        assert res.shape == SMALL_Y - 5 + 1
+
+    def test_sum_pooling():
+        res = mx.nd.Pooling(a, kernel=(5, 5), pool_type='sum')
+        assert res[-1][-1][-1][-1] == 25
+        assert res.shape == SMALL_Y - 5 + 1
+
+    def test_lp_pooling():
+        res = mx.nd.Pooling(a, kernel=(5, 5), pool_type='lp', p_value=2)
+        assert res[-1][-1][-1][-1] == 5.
+        assert res.shape == SMALL_Y - 5 + 1
+
+        res = mx.nd.Pooling(a, kernel=(5, 5), pool_type='lp', p_value=1)
+        assert res[-1][-1][-1][-1] == 25.
+        assert res.shape == SMALL_Y - 5 + 1
+
+    test_avg_pooling()
+    test_max_pooling()
+    test_sum_pooling()
+    test_lp_pooling()
+
+
+def test_layer_norm():
+    dtype = np.float32
+    forward_check_eps = 1E-3
+    axis = 1
+    eps = 1E-5
+    in_shape = (LARGE_X, SMALL_Y)
+    ctx = mx.cpu()
+
+    def npy_layer_norm(data, gamma, beta, axis=1, eps=1E-5):
+        if axis < 0:
+            axis += data.ndim
+        broadcast_shape = [1 for _ in range(data.ndim)]
+        broadcast_shape[axis] = data.shape[axis]
+        mean = data.mean(axis=axis, keepdims=True).astype(dtype)
+        var = data.var(axis=axis, keepdims=True).astype(dtype)
+        std = np.sqrt(var + dtype(eps)).astype(dtype)
+        out = np.reshape(gamma, broadcast_shape) * (data - mean) / std + \
+              np.reshape(beta, broadcast_shape)
+        return out
+    data = np.random.normal(0, 1, in_shape).astype(dtype)
+    gamma = np.random.normal(0, 1, (in_shape[axis],)).astype(dtype)
+    beta = np.random.normal(0, 1, (in_shape[axis],)).astype(dtype)
+    data_s = mx.symbol.Variable('data')
+    gamma_s = mx.symbol.Variable('gamma')
+    beta_s = mx.symbol.Variable('beta')
+    out_s = mx.symbol.LayerNorm(data=data_s, gamma=gamma_s, beta=beta_s,
+                                axis=axis, eps=eps)
+    exe = out_s.simple_bind(ctx, data=in_shape)
+    exe.arg_dict['data'][:] = data
+    exe.arg_dict['gamma'][:] = gamma
+    exe.arg_dict['beta'][:] = beta
+    out_nd = exe.forward()[0]
+    out = npy_layer_norm(data, gamma, beta, axis, eps)
+    assert_almost_equal(out, out_nd.asnumpy(), forward_check_eps,
+                        forward_check_eps)
+
+# TODO: correctness of dropout
+# currently only test for dropout to work
+# since testing for correctness involves flakiness issue #14288
+def test_dropout():
+    shape = (10, 10)
+    x = mx.sym.var('data')
+    y = mx.sym.Dropout(x, p=1, cudnn_off=True)
+    exe = y.simple_bind(ctx=default_context(), data=shape)
+    exe.arg_arrays[0][:] = 1
+    out = exe.forward(is_train=True)
+    out[0].wait_to_read()
+
+
+def test_activation():
+    a = mx.nd.ones((LARGE_X, SMALL_Y))
+    test_x = -2
+    a[-1, -1] = test_x
+
+    # Hyperbolic tangent (tanh)
+    # y = (exp(x)-exp(-x))/(exp(x)+exp(-x))
+    a = mx.nd.Activation(a, act_type="tanh")
+    tanh_x = (np.exp(-2)-np.exp(2))/(np.exp(-2)+np.exp(2))
+    assert a[-1][-1] == tanh_x
+
+    # Recitified Linear Unit (relu)
+    # y = max(x,0)
+    a = mx.nd.Activation(a, act_type="relu")
+    assert a[-1][-1] == 0
+
+    # Sigmoid
+    # y = x/(1+abs(x))
+    a = mx.nd.Activation(a, act_type="sigmoid")
+    sigmoid_x = 1/(1+math.exp(-test_x))
+    assert a[-1][-1] == sigmoid_x
+
+    # Soft Sign
+    # y = 1/(1+exp(-x))
+    a = mx.nd.Activation(a, act_type="softsign")
+    softsign_x = test_x/(1+abs(test_x))
+    assert a[-1][-1] == softsign_x
+
+
+# TODO: correctness of batchnorm
+# in future, we could test if mean, var of output
+# matches target output's mean, var
+def test_batchnorm():
+    shape = (LARGE_X, SMALL_Y)
+    axis = 1  # default
+    expand_shape = [1] * len(shape)
+    expand_shape[axis] = shape[axis]
+
+    nch = shape[axis]
+    data = mx.nd.ones(shape=shape)
+    bn_gamma = mx.nd.random.uniform(shape=(nch,))
+    bn_beta = mx.nd.random.uniform(shape=(nch,))
+    bn_running_mean = mx.nd.zeros(nch)
+    bn_running_var = mx.nd.ones(nch)
+
+    output = mx.nd.BatchNorm(data, bn_gamma, bn_beta,
+                             bn_running_mean, bn_running_var)
+    output.wait_to_read()
+
+
 def test_add():
     a = nd.ones(shape=(LARGE_X, SMALL_Y))
     b = nd.ones(shape=(LARGE_X, SMALL_Y))
diff --git a/tests/nightly/test_large_vector.py b/tests/nightly/test_large_vector.py
index 8c030f5bc20e..3a66500957e0 100644
--- a/tests/nightly/test_large_vector.py
+++ b/tests/nightly/test_large_vector.py
@@ -17,6 +17,7 @@
 
 import numpy as np
 import mxnet as mx
+
 from mxnet.test_utils import rand_ndarray, assert_almost_equal, rand_coord_2d
 from mxnet import gluon, nd
 from tests.python.unittest.common import with_seed

From 3d8035b40005623d99dffef9962516abb086758b Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Wed, 14 Aug 2019 21:52:10 -0700
Subject: [PATCH 178/813] Numpy-compatible concatenate upstream (#15894)

* numpy-compatible concatenate upstream

* extend ci deadline
---
 ci/jenkins/Jenkinsfile_unix_cpu        |  2 +-
 python/mxnet/ndarray/numpy/_op.py      | 25 ++++++++++-
 python/mxnet/numpy/multiarray.py       | 26 +++++++++++-
 python/mxnet/symbol/numpy/_symbol.py   | 25 ++++++++++-
 src/operator/numpy/np_matrix_op-inl.h  |  1 +
 src/operator/numpy/np_matrix_op.cc     | 58 +++++++++++++++++++++++++-
 src/operator/numpy/np_matrix_op.cu     |  7 +++-
 tests/python/unittest/test_numpy_op.py | 51 ++++++++++++++++++++++
 8 files changed, 189 insertions(+), 6 deletions(-)

diff --git a/ci/jenkins/Jenkinsfile_unix_cpu b/ci/jenkins/Jenkinsfile_unix_cpu
index fa0942988d9c..3533a123ea86 100644
--- a/ci/jenkins/Jenkinsfile_unix_cpu
+++ b/ci/jenkins/Jenkinsfile_unix_cpu
@@ -21,7 +21,7 @@
 // See documents at https://jenkins.io/doc/book/pipeline/jenkinsfile/
 
 // timeout in minutes
-max_time = 180
+max_time = 240
 
 node('utility') {
   // Loading the utilities requires a node context unfortunately
diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index db4d86179e59..d7f3fd1ace54 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -27,7 +27,7 @@
 from ..ndarray import NDArray
 
 __all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power', 'tensordot',
-           'linspace', 'expand_dims', 'tile', 'arange', 'split']
+           'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -682,3 +682,26 @@ def split(ary, indices_or_sections, axis=0):
     if not isinstance(ret, list):
         return [ret]
     return ret
+
+
+@set_module('mxnet.ndarray.numpy')
+def concatenate(seq, axis=0, out=None):
+    """Join a sequence of arrays along an existing axis.
+    Parameters
+    ----------
+    a1, a2, ... : sequence of array_like
+        The arrays must have the same shape, except in the dimension
+        corresponding to `axis` (the first, by default).
+    axis : int, optional
+        The axis along which the arrays will be joined.  If axis is None,
+        arrays are flattened before use.  Default is 0.
+    out : ndarray, optional
+        If provided, the destination to place the result. The shape must be
+        correct, matching that of what concatenate would have returned if no
+        out argument were specified.
+    Returns
+    -------
+    res : ndarray
+        The concatenated array.
+    """
+    return _npi.concatenate(*seq, dim=axis, out=out)
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 0f05aa8da50e..8988b4eb19c9 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -44,7 +44,8 @@
 from ..ndarray.numpy import _internal as _npi
 
 __all__ = ['ndarray', 'empty', 'array', 'zeros', 'ones', 'add', 'subtract', 'multiply', 'divide',
-           'mod', 'power', 'tensordot', 'linspace', 'expand_dims', 'tile', 'arange', 'split']
+           'mod', 'power', 'tensordot', 'linspace', 'expand_dims', 'tile', 'arange', 'split',
+           'concatenate']
 
 
 # This function is copied from ndarray.py since pylint
@@ -1853,3 +1854,26 @@ def split(ary, indices_or_sections, axis=0):
         If `indices_or_sections` is given as an integer, but
         a split does not result in equal division."""
     return _mx_nd_np.split(ary, indices_or_sections, axis=axis)
+
+
+@set_module('mxnet.numpy')
+def concatenate(seq, axis=0, out=None):
+    """Join a sequence of arrays along an existing axis.
+    Parameters
+    ----------
+    a1, a2, ... : sequence of array_like
+        The arrays must have the same shape, except in the dimension
+        corresponding to `axis` (the first, by default).
+    axis : int, optional
+        The axis along which the arrays will be joined.  If axis is None,
+        arrays are flattened before use.  Default is 0.
+    out : ndarray, optional
+        If provided, the destination to place the result. The shape must be
+        correct, matching that of what concatenate would have returned if no
+        out argument were specified.
+    Returns
+    -------
+    res : ndarray
+        The concatenated array.
+    """
+    return _mx_nd_np.concatenate(seq, axis=axis, out=out)
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index bf4a6d159363..a6699d60871a 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -30,7 +30,7 @@
 from . import _internal as _npi
 
 __all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power', 'tensordot',
-           'linspace', 'expand_dims', 'tile', 'arange', 'split']
+           'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate']
 
 
 def _num_outputs(sym):
@@ -1312,4 +1312,27 @@ def split(ary, indices_or_sections, axis=0):
     return ret
 
 
+@set_module('mxnet.symbol.numpy')
+def concatenate(seq, axis=0, out=None):
+    """Join a sequence of arrays along an existing axis.
+    Parameters
+    ----------
+    a1, a2, ... : sequence of array_like
+        The arrays must have the same shape, except in the dimension
+        corresponding to `axis` (the first, by default).
+    axis : int, optional
+        The axis along which the arrays will be joined.  If axis is None,
+        arrays are flattened before use.  Default is 0.
+    out : ndarray, optional
+        If provided, the destination to place the result. The shape must be
+        correct, matching that of what concatenate would have returned if no
+        out argument were specified.
+    Returns
+    -------
+    res : ndarray
+        The concatenated array.
+    """
+    return _npi.concatenate(*seq, dim=axis, out=out)
+
+
 _set_np_symbol_class(_Symbol)
diff --git a/src/operator/numpy/np_matrix_op-inl.h b/src/operator/numpy/np_matrix_op-inl.h
index 44a6c909c9cf..6d3d9ea5ec85 100644
--- a/src/operator/numpy/np_matrix_op-inl.h
+++ b/src/operator/numpy/np_matrix_op-inl.h
@@ -27,6 +27,7 @@
 
 #include <vector>
 #include "../tensor/matrix_op-inl.h"
+#include "../nn/concat-inl.h"
 
 namespace mxnet {
 namespace op {
diff --git a/src/operator/numpy/np_matrix_op.cc b/src/operator/numpy/np_matrix_op.cc
index 1f31a650e771..73340981037d 100644
--- a/src/operator/numpy/np_matrix_op.cc
+++ b/src/operator/numpy/np_matrix_op.cc
@@ -24,7 +24,6 @@
  */
 
 #include "./np_matrix_op-inl.h"
-#include "../nn/concat-inl.h"
 
 namespace mxnet {
 namespace op {
@@ -248,5 +247,62 @@ NNVM_REGISTER_OP(_np_squeeze)
 .add_argument("a", "NDArray-or-Symbol[]", "data to squeeze")
 .add_arguments(SqueezeParam::__FIELDS__());
 
+bool ConcatShape(const nnvm::NodeAttrs& attrs,
+                 mxnet::ShapeVector *in_shape,
+                 mxnet::ShapeVector *out_shape);
+
+bool ConcatType(const nnvm::NodeAttrs& attrs,
+                std::vector<int> *in_type,
+                std::vector<int> *out_type);
+
+struct NumpyConcatGrad {
+  const char *op_name;
+  std::vector<nnvm::NodeEntry> operator()(const nnvm::NodePtr& n,
+                                          const std::vector<nnvm::NodeEntry>& ograds) const {
+    CHECK_EQ(ograds.size(), 1);
+    std::vector<nnvm::NodeEntry> heads(ograds.begin(), ograds.end());
+    return MakeGradNode(op_name, n, heads, n->attrs.dict);
+  }
+};
+
+
+NNVM_REGISTER_OP(_npi_concatenate)
+.describe(R"code(Join a sequence of arrays along an existing axis.)code" ADD_FILELINE)
+.set_num_inputs([](const NodeAttrs& attrs) {
+  const ConcatParam& params = nnvm::get<ConcatParam>(attrs.parsed);
+  return params.num_args;
+})
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<ConcatParam>)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    const ConcatParam& params = nnvm::get<ConcatParam>(attrs.parsed);
+    std::vector<std::string> ret;
+    for (int i = 0; i < params.num_args; ++i) {
+      ret.push_back(std::string("data") + std::to_string(i));
+    }
+    return ret;
+})
+.set_attr<nnvm::FListOutputNames>("FListOutputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"out"};
+})
+.set_attr<std::string>("key_var_num_args", "num_args")
+.set_attr<nnvm::FInferType>("FInferType", ConcatType)
+.set_attr<mxnet::FInferShape>("FInferShape", ConcatShape)
+.set_attr<FCompute>("FCompute<cpu>", ConcatCompute<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", NumpyConcatGrad{"_backward_np_concat"})
+.add_argument("data", "NDArray-or-Symbol[]", "List of arrays to concatenate")
+.add_arguments(ConcatParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_backward_np_concat)
+.set_num_outputs([](const NodeAttrs& attrs) {
+  const ConcatParam& params = nnvm::get<ConcatParam>(attrs.parsed);
+  return params.num_args;
+})
+.set_attr_parser(ParamParser<ConcatParam>)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FCompute>("FCompute<cpu>", ConcatGradCompute<cpu>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/np_matrix_op.cu b/src/operator/numpy/np_matrix_op.cu
index efeba8e089af..f192560f4ac9 100644
--- a/src/operator/numpy/np_matrix_op.cu
+++ b/src/operator/numpy/np_matrix_op.cu
@@ -23,7 +23,6 @@
  * \brief GPU Implementation of numpy matrix operations
  */
 #include "./np_matrix_op-inl.h"
-#include "../nn/concat-inl.h"
 
 namespace mxnet {
 namespace op {
@@ -37,5 +36,11 @@ NNVM_REGISTER_OP(_np_reshape)
 NNVM_REGISTER_OP(_np_squeeze)
 .set_attr<FCompute>("FCompute<gpu>", UnaryOp::IdentityCompute<gpu>);
 
+NNVM_REGISTER_OP(_npi_concatenate)
+.set_attr<FCompute>("FCompute<gpu>", ConcatCompute<gpu>);
+
+NNVM_REGISTER_OP(_backward_np_concat)
+.set_attr<FCompute>("FCompute<gpu>", ConcatGradCompute<gpu>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index aba262f88042..2291bcdb6d3d 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -829,6 +829,57 @@ def get_indices(axis_size):
                     assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
 
 
+@with_seed()
+@use_np
+def test_np_concat():
+    class TestConcat(HybridBlock):
+        def __init__(self, axis=None):
+            super(TestConcat, self).__init__()
+            self._axis = axis
+
+        def hybrid_forward(self, F, a, *args):
+            return F.np.concatenate([a] + list(args), axis=self._axis)
+
+    def get_new_shape(shape, axis):
+        shape_lst = list(shape)
+        shape_lst[axis] = random.randint(0, 3)
+        return tuple(shape_lst)
+
+    for shape in [(0, 0), (2, 3)]:
+        for hybridize in [True, False]:
+            for axis in range(2):
+                # test gluon
+                test_concat = TestConcat(axis=axis)
+                if hybridize:
+                    test_concat.hybridize()
+
+                a = mx.nd.random.uniform(-1.0, 1.0, shape=get_new_shape(shape, axis)).as_np_ndarray()
+                a.attach_grad()
+                b = mx.nd.random.uniform(-1.0, 1.0, shape=get_new_shape(shape, axis)).as_np_ndarray()
+                b.attach_grad()
+                c = mx.nd.random.uniform(-1.0, 1.0, shape=get_new_shape(shape, axis)).as_np_ndarray()
+                c.attach_grad()
+                d = mx.nd.random.uniform(-1.0, 1.0, shape=get_new_shape(shape, axis)).as_np_ndarray()
+                d.attach_grad()
+                expected_ret = _np.concatenate([a.asnumpy(), b.asnumpy(), c.asnumpy(), d.asnumpy()], axis=axis)
+                with mx.autograd.record():
+                    y = test_concat(a, b, c, d)
+                assert y.shape == expected_ret.shape
+                assert_almost_equal(y.asnumpy(), expected_ret, rtol=1e-3, atol=1e-5)
+
+                y.backward()
+
+                assert_almost_equal(a.grad.asnumpy(), _np.ones(a.shape), rtol=1e-3, atol=1e-5)
+                assert_almost_equal(b.grad.asnumpy(), _np.ones(b.shape), rtol=1e-3, atol=1e-5)
+                assert_almost_equal(c.grad.asnumpy(), _np.ones(c.shape), rtol=1e-3, atol=1e-5)
+                assert_almost_equal(d.grad.asnumpy(), _np.ones(d.shape), rtol=1e-3, atol=1e-5)
+
+                # test imperative
+                mx_out = np.concatenate([a, b, c, d], axis=axis)
+                np_out = _np.concatenate([a.asnumpy(), b.asnumpy(), c.asnumpy(), d.asnumpy()], axis=axis)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 78733f07447f6bfd0e947f18b52f39afbcd442ce Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Wed, 14 Aug 2019 21:52:30 -0700
Subject: [PATCH 179/813] Numpy-compatible concatenate upstream (#15894)

* numpy-compatible concatenate upstream

* extend ci deadline

From b98f2970b7b19eb5568ede42a9391d179a9177e8 Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Wed, 14 Aug 2019 21:52:52 -0700
Subject: [PATCH 180/813] Numpy-compatible concatenate upstream (#15894)

* numpy-compatible concatenate upstream

* extend ci deadline

From 9dbfc2daaa16d0e71feb9e91114bbc30a22b0324 Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Wed, 14 Aug 2019 21:53:51 -0700
Subject: [PATCH 181/813] Numpy-compatible concatenate upstream (#15894)

* numpy-compatible concatenate upstream

* extend ci deadline

From 40593c6f6c20baed98a914d14987db5438c0a5a5 Mon Sep 17 00:00:00 2001
From: Anirudh Subramanian <anirudh2290@ufl.edu>
Date: Wed, 14 Aug 2019 21:56:44 -0700
Subject: [PATCH 182/813] Fix ConcatType backward type inference (#15829)

* Fix ConcatType and add test

* Remove return false

* Change error message

* Run RNN test only when CUDNN enabled

* set default context for test_contrib_amp
---
 src/operator/nn/concat.cc            | 32 +++++++++++++++++++---------
 tests/python/gpu/test_contrib_amp.py | 19 +++++++++++++++--
 2 files changed, 39 insertions(+), 12 deletions(-)

diff --git a/src/operator/nn/concat.cc b/src/operator/nn/concat.cc
index 80469b5385eb..9e016bf884f2 100644
--- a/src/operator/nn/concat.cc
+++ b/src/operator/nn/concat.cc
@@ -144,6 +144,7 @@ bool ConcatType(const nnvm::NodeAttrs& attrs,
   const ConcatParam& param_ = nnvm::get<ConcatParam>(attrs.parsed);
   int dtype = -1;
 
+  // checks uniformity of input
   for (int i : *in_type) {
     if (dtype == -1) {
       dtype = i;
@@ -154,18 +155,29 @@ bool ConcatType(const nnvm::NodeAttrs& attrs,
     }
   }
 
-  if (dtype == -1) {
-    LOG(FATAL) << "Not enough information to infer type in Concat.";
-    return false;
-  }
-
   size_t nin = param_.num_args;
-  in_type->clear();
-  for (size_t i = 0; i < nin; ++i) in_type->push_back(dtype);
-
-  out_type->clear();
-  out_type->push_back(dtype);
 
+  // if in types are known out types are unknown
+  if (dtype != -1 && (*out_type)[0] == -1) {
+    (*out_type)[0] = dtype;
+    in_type->clear();
+    for (size_t i = 0; i < nin; ++i) {
+      in_type->push_back(dtype);
+    }
+  // if out types are known in types are unknown
+  } else if ((*out_type)[0] != -1 && dtype == -1) {
+    in_type->clear();
+    for (size_t i = 0; i < nin; ++i) {
+      in_type->push_back((*out_type)[0]);
+    }
+  // if both out_types and in_types are known, and different
+  } else if ((*out_type)[0] != -1 && dtype != -1 && ((*out_type)[0] != dtype)) {
+    std::ostringstream os;
+    os << "Type inconsistent, Provided output type = "
+       << mxnet::op::type_string((*out_type)[0]) << ','
+       << " inferred type = " << mxnet::op::type_string(dtype);
+    throw mxnet::op::InferTypeError(os.str(), 0);
+  }
   return true;
 }
 
diff --git a/tests/python/gpu/test_contrib_amp.py b/tests/python/gpu/test_contrib_amp.py
index 7927cc99160b..3daab0f7bb6a 100644
--- a/tests/python/gpu/test_contrib_amp.py
+++ b/tests/python/gpu/test_contrib_amp.py
@@ -26,11 +26,12 @@
 from nose.tools import assert_raises
 from mxnet.test_utils import set_default_context, download_model, same_symbol_structure
 from mxnet.gluon.model_zoo.vision import get_model
-from mxnet.gluon import SymbolBlock
+from mxnet.gluon import SymbolBlock, nn, rnn
 from mxnet.contrib.amp import amp
 curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
 sys.path.insert(0, os.path.join(curr_path, '../unittest'))
-from common import with_seed, teardown
+from common import with_seed, teardown, assert_raises_cudnn_not_satisfied
+set_default_context(mx.gpu(0))
 
 def test_amp_coverage():
     conditional = [item[0] for item in amp.lists.symbol.CONDITIONAL_FP32_FUNCS]
@@ -305,6 +306,20 @@ def check_amp_convert_hybrid_block():
         check_amp_convert_model()
         check_amp_convert_hybrid_block()
 
+@with_seed()
+@assert_raises_cudnn_not_satisfied(min_version='5.1.10')
+def test_amp_conversion_rnn():
+    with mx.Context(mx.gpu(0)):
+        model = nn.HybridSequential()
+        model.add(rnn.LSTM(hidden_size=10, num_layers=2, bidirectional=True))
+        model.add(nn.Dense(2))
+        model.initialize()
+        model.hybridize()
+        out = model(mx.nd.ones((2, 3, 4)))
+        new_model = amp.convert_hybrid_block(model)
+        out2 = new_model(mx.nd.ones((2, 3, 4)))
+        mx.test_utils.assert_almost_equal(out.asnumpy(), out2.asnumpy(), atol=1e-2, rtol=1e-2)
+
 
 @with_seed()
 def test_module_backward_compatibility():

From f586c0760062aeded1b98da49a48348a59dba411 Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Thu, 15 Aug 2019 13:45:30 -0700
Subject: [PATCH 183/813] [OpPerf] Profiler flag for Python, Cpp  (#15881)

* first

* flag for timing python

* delete old file

* added profiler example

* add operator name to python profile output

* add other pending files with profiler flag

* change cpp to native - for user-facing api

* fixes

* Trigger notification

* Trigger notification

* Trigger notification

* markdown update for python
---
 .../opperf/nd_operations/array_rearrange.py   |  4 +-
 .../opperf/nd_operations/binary_operators.py  |  8 +--
 .../opperf/nd_operations/gemm_operators.py    |  6 +-
 .../nd_operations/nn_activation_operators.py  |  5 +-
 .../nd_operations/nn_basic_operators.py       |  5 +-
 .../opperf/nd_operations/nn_conv_operators.py | 12 +++-
 .../nd_operations/nn_optimizer_operators.py   |  4 +-
 .../random_sampling_operators.py              |  4 +-
 .../nd_operations/reduction_operators.py      |  4 +-
 .../sorting_searching_operators.py            |  4 +-
 .../opperf/nd_operations/unary_operators.py   |  4 +-
 benchmark/opperf/opperf.py                    | 40 +++++++------
 benchmark/opperf/utils/benchmark_utils.py     | 27 ++++++---
 benchmark/opperf/utils/common_utils.py        | 38 ++++++++-----
 benchmark/opperf/utils/ndarray_utils.py       |  4 --
 benchmark/opperf/utils/profiler_utils.py      | 57 ++++++++++++++++---
 16 files changed, 154 insertions(+), 72 deletions(-)

diff --git a/benchmark/opperf/nd_operations/array_rearrange.py b/benchmark/opperf/nd_operations/array_rearrange.py
index 151127c7b49a..9b1cbfe3632d 100644
--- a/benchmark/opperf/nd_operations/array_rearrange.py
+++ b/benchmark/opperf/nd_operations/array_rearrange.py
@@ -29,7 +29,7 @@
 """
 
 
-def run_rearrange_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, runs=100):
+def run_rearrange_operators_benchmarks(ctx=mx.cpu(), dtype='float32', profiler='native', warmup=25, runs=100):
     """Runs benchmarks with the given context and precision (dtype) for all the
     rearrange operators  in MXNet.
 
@@ -53,5 +53,5 @@ def run_rearrange_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25,
     mx_rearrange_ops = get_all_rearrange_operators()
 
     # Run benchmarks
-    mx_rearrange_op_results = run_op_benchmarks(mx_rearrange_ops, dtype, ctx, warmup, runs)
+    mx_rearrange_op_results = run_op_benchmarks(mx_rearrange_ops, dtype, ctx, profiler, warmup, runs)
     return mx_rearrange_op_results
diff --git a/benchmark/opperf/nd_operations/binary_operators.py b/benchmark/opperf/nd_operations/binary_operators.py
index 1898f5dcbe04..7847e92c6088 100644
--- a/benchmark/opperf/nd_operations/binary_operators.py
+++ b/benchmark/opperf/nd_operations/binary_operators.py
@@ -38,7 +38,7 @@
     get_all_elemen_wise_binary_operators
 
 
-def run_mx_binary_broadcast_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, runs=100):
+def run_mx_binary_broadcast_operators_benchmarks(ctx=mx.cpu(), dtype='float32', profiler='native', warmup=25, runs=100):
     """Runs benchmarks with the given context and precision (dtype) for all the binary
     broadcast operators in MXNet.
 
@@ -61,11 +61,11 @@ def run_mx_binary_broadcast_operators_benchmarks(ctx=mx.cpu(), dtype='float32',
     # Fetch all Binary Broadcast Operators
     mx_binary_broadcast_ops = get_all_broadcast_binary_operators()
     # Run benchmarks
-    mx_binary_op_results = run_op_benchmarks(mx_binary_broadcast_ops, dtype, ctx, warmup, runs)
+    mx_binary_op_results = run_op_benchmarks(mx_binary_broadcast_ops, dtype, ctx, profiler, warmup, runs)
     return mx_binary_op_results
 
 
-def run_mx_binary_element_wise_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, runs=100):
+def run_mx_binary_element_wise_operators_benchmarks(ctx=mx.cpu(), dtype='float32', profiler='native', warmup=25, runs=100):
     """Runs benchmarks with the given context and precision (dtype) for all the binary
     element_wise operators in MXNet.
 
@@ -88,5 +88,5 @@ def run_mx_binary_element_wise_operators_benchmarks(ctx=mx.cpu(), dtype='float32
     # Fetch all Binary Element_wise Operators
     mx_binary_element_wise_ops = get_all_elemen_wise_binary_operators()
     # Run benchmarks
-    mx_binary_op_results = run_op_benchmarks(mx_binary_element_wise_ops, dtype, ctx, warmup, runs)
+    mx_binary_op_results = run_op_benchmarks(mx_binary_element_wise_ops, dtype, ctx, profiler, warmup, runs)
     return mx_binary_op_results
diff --git a/benchmark/opperf/nd_operations/gemm_operators.py b/benchmark/opperf/nd_operations/gemm_operators.py
index e1eaabc78954..73337538d89f 100644
--- a/benchmark/opperf/nd_operations/gemm_operators.py
+++ b/benchmark/opperf/nd_operations/gemm_operators.py
@@ -34,7 +34,7 @@
 """
 
 
-def run_gemm_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, runs=100):
+def run_gemm_operators_benchmarks(ctx=mx.cpu(), dtype='float32', profiler='native', warmup=25, runs=100):
     """Runs benchmarks with the given context and precision (dtype)for all the GEMM
     operators (dot, batch_dot) in MXNet.
 
@@ -67,7 +67,7 @@ def run_gemm_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, runs
                  "rhs": (100, 1000),
                  "transpose_a": True,
                  "transpose_b": True}],
-        warmup=warmup, runs=runs)
+        warmup=warmup, runs=runs, profiler=profiler)
 
     batch_dot_benchmark_res = run_performance_test(
         [getattr(MX_OP_MODULE, "batch_dot")], run_backward=True,
@@ -81,7 +81,7 @@ def run_gemm_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, runs
                  "rhs": (32, 100, 1000),
                  "transpose_a": True,
                  "transpose_b": True}],
-        warmup=warmup, runs=runs)
+        warmup=warmup, runs=runs, profiler=profiler)
 
     # Prepare combined results for GEMM operators
     mx_gemm_op_results = merge_map_list(dot_benchmark_res + batch_dot_benchmark_res)
diff --git a/benchmark/opperf/nd_operations/nn_activation_operators.py b/benchmark/opperf/nd_operations/nn_activation_operators.py
index ae5ec90f367e..83813fe13c2e 100644
--- a/benchmark/opperf/nd_operations/nn_activation_operators.py
+++ b/benchmark/opperf/nd_operations/nn_activation_operators.py
@@ -35,7 +35,7 @@
 """
 
 
-def run_activation_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, runs=100):
+def run_activation_operators_benchmarks(ctx=mx.cpu(), dtype='float32', profiler='native', warmup=25, runs=100):
     """Runs benchmarks with the given context and precision (dtype)for all the activation
     operators (relu, sigmoid, softmax) in MXNet.
 
@@ -60,6 +60,7 @@ def run_activation_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25
                                               run_backward=True,
                                               dtype=dtype,
                                               ctx=ctx,
+                                              profiler=profiler,
                                               inputs=[{"data": (1024, 1024), "act_type": "leaky", "slope": 0.1},
                                                       {"data": (10000, 1), "act_type": "leaky", "slope": 0.1},
                                                       {"data": (10000, 100), "act_type": "leaky", "slope": 0.1},
@@ -82,6 +83,7 @@ def run_activation_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25
                                                       run_backward=True,
                                                       dtype=dtype,
                                                       ctx=ctx,
+                                                      profiler=profiler,
                                                       inputs=[{"data": (1024, 1024), "alpha": 0.25, "beta": 0.5},
                                                               {"data": (10000, 1), "alpha": 0.25, "beta": 0.5},
                                                               {"data": (10000, 100), "alpha": 0.25, "beta": 0.5}
@@ -95,6 +97,7 @@ def run_activation_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25
                                                  run_backward=True,
                                                  dtype=dtype,
                                                  ctx=ctx,
+                                                 profiler=profiler,
                                                  inputs=[{"data": (1024, 1024), "axis": -1, "temperature": 0.5},
                                                          {"data": (10000, 1), "axis": -1, "temperature": 0.5},
                                                          {"data": (10000, 100), "axis": -1, "temperature": 0.5}
diff --git a/benchmark/opperf/nd_operations/nn_basic_operators.py b/benchmark/opperf/nd_operations/nn_basic_operators.py
index 597f7719d2cb..9a34a9a725ee 100644
--- a/benchmark/opperf/nd_operations/nn_basic_operators.py
+++ b/benchmark/opperf/nd_operations/nn_basic_operators.py
@@ -29,12 +29,13 @@
 """
 
 
-def run_nn_basic_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, runs=100):
+def run_nn_basic_operators_benchmarks(ctx=mx.cpu(), dtype='float32', profiler='native', warmup=25, runs=100):
     # FullyConnnected operator benchmarks
     fc_benchmark_res = run_performance_test([getattr(MX_OP_MODULE, "FullyConnected")],
                                             run_backward=True,
                                             dtype=dtype,
                                             ctx=ctx,
+                                            profiler=profiler,
                                             inputs=[{"data": (32, 3, 256, 256),
                                                      "num_hidden": 64,
                                                      "weight": (64, 3 * 256 * 256),
@@ -53,6 +54,7 @@ def run_nn_basic_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25,
                                                  run_backward=True,
                                                  dtype=dtype,
                                                  ctx=ctx,
+                                                 profiler=profiler,
                                                  inputs=[{"data": (32, 3, 256, 256),
                                                           "p": 0.5,
                                                           "mode": "always"},
@@ -66,6 +68,7 @@ def run_nn_basic_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25,
                                                    run_backward=True,
                                                    dtype=dtype,
                                                    ctx=ctx,
+                                                   profiler=profiler,
                                                    inputs=[{"data": (32, 3, 256, 256),
                                                             "gamma": (3,),
                                                             "beta": (3,),
diff --git a/benchmark/opperf/nd_operations/nn_conv_operators.py b/benchmark/opperf/nd_operations/nn_conv_operators.py
index 973058929b2f..5d614e9065c2 100644
--- a/benchmark/opperf/nd_operations/nn_conv_operators.py
+++ b/benchmark/opperf/nd_operations/nn_conv_operators.py
@@ -51,7 +51,7 @@
 """
 
 
-def run_pooling_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, runs=100):
+def run_pooling_operators_benchmarks(ctx=mx.cpu(), dtype='float32', profiler='native', warmup=25, runs=100):
     pool_types = ['avg', 'max', 'sum']
     global_pool_types = [0, 1]
 
@@ -65,6 +65,7 @@ def run_pooling_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, r
                                                              run_backward=True,
                                                              dtype=dtype,
                                                              ctx=ctx,
+                                                             profiler=profiler,
                                                              inputs=[{"data": pool1d_data,
                                                                       "kernel": 3,
                                                                       "pool_type": pool_type,
@@ -79,6 +80,7 @@ def run_pooling_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, r
                                                              run_backward=True,
                                                              dtype=dtype,
                                                              ctx=ctx,
+                                                             profiler=profiler,
                                                              inputs=[{"data": pool2d_data,
                                                                       "kernel": (3, 3),
                                                                       "pool_type": pool_type,
@@ -93,7 +95,7 @@ def run_pooling_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, r
     return mx_pooling_op_results
 
 
-def run_convolution_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, runs=100):
+def run_convolution_operators_benchmarks(ctx=mx.cpu(), dtype='float32', profiler='native', warmup=25, runs=100):
     # Conv1D Benchmarks
     conv1d_benchmark_res = []
     for conv_data in [(32, 3, 256), (32, 3, 64)]:
@@ -101,6 +103,7 @@ def run_convolution_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=2
                                                      run_backward=True,
                                                      dtype=dtype,
                                                      ctx=ctx,
+                                                     profiler=profiler,
                                                      inputs=[{"data": conv_data,
                                                               "weight": (64, 3, 3),
                                                               "bias": (64,),
@@ -120,6 +123,7 @@ def run_convolution_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=2
                                                      run_backward=True,
                                                      dtype=dtype,
                                                      ctx=ctx,
+                                                     profiler=profiler,
                                                      inputs=[{"data": conv_data,
                                                               "weight": (64, 3, 3, 3),
                                                               "bias": (64,),
@@ -137,7 +141,7 @@ def run_convolution_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=2
     return mx_conv_op_results
 
 
-def run_transpose_convolution_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=10, runs=50):
+def run_transpose_convolution_operators_benchmarks(ctx=mx.cpu(), profiler='native', dtype='float32', warmup=10, runs=50):
     # Conv1DTranspose Benchmarks
     conv1d_transpose_benchmark_res = []
     for conv_data in [(32, 3, 256), (32, 3, 64)]:
@@ -145,6 +149,7 @@ def run_transpose_convolution_operators_benchmarks(ctx=mx.cpu(), dtype='float32'
                                                                run_backward=True,
                                                                dtype=dtype,
                                                                ctx=ctx,
+                                                               profiler=profiler,
                                                                inputs=[{"data": conv_data,
                                                                         "weight": (3, 64, 3),
                                                                         "bias": (64,),
@@ -166,6 +171,7 @@ def run_transpose_convolution_operators_benchmarks(ctx=mx.cpu(), dtype='float32'
                                                                run_backward=True,
                                                                dtype=dtype,
                                                                ctx=ctx,
+                                                               profiler=profiler,
                                                                inputs=[{"data": conv_data,
                                                                         "weight": (3, 64, 3, 3),
                                                                         "bias": (64,),
diff --git a/benchmark/opperf/nd_operations/nn_optimizer_operators.py b/benchmark/opperf/nd_operations/nn_optimizer_operators.py
index 130ab85300dc..99e2351363b6 100644
--- a/benchmark/opperf/nd_operations/nn_optimizer_operators.py
+++ b/benchmark/opperf/nd_operations/nn_optimizer_operators.py
@@ -36,7 +36,7 @@
 """
 
 
-def run_optimizer_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, runs=100):
+def run_optimizer_operators_benchmarks(ctx=mx.cpu(), dtype='float32', profiler='native', warmup=25, runs=100):
     """Runs benchmarks with the given context and precision (dtype) for all the neural network
     optimizer update operators in MXNet.
 
@@ -60,5 +60,5 @@ def run_optimizer_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25,
     mx_optimizer_ops = get_all_optimizer_operators()
 
     # Run benchmarks
-    mx_optimizer_op_results = run_op_benchmarks(mx_optimizer_ops, dtype, ctx, warmup, runs)
+    mx_optimizer_op_results = run_op_benchmarks(mx_optimizer_ops, dtype, ctx, profiler, warmup, runs)
     return mx_optimizer_op_results
diff --git a/benchmark/opperf/nd_operations/random_sampling_operators.py b/benchmark/opperf/nd_operations/random_sampling_operators.py
index d0cf2c2fea4a..a39526ab95cd 100644
--- a/benchmark/opperf/nd_operations/random_sampling_operators.py
+++ b/benchmark/opperf/nd_operations/random_sampling_operators.py
@@ -34,7 +34,7 @@
 from benchmark.opperf.utils.op_registry_utils import get_all_random_sampling_operators
 
 
-def run_mx_random_sampling_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, runs=100):
+def run_mx_random_sampling_operators_benchmarks(ctx=mx.cpu(), dtype='float32', profiler='native', warmup=25, runs=100):
     """Runs benchmarks with the given context and precision (dtype)for all the random sampling
     operators in MXNet.
 
@@ -57,5 +57,5 @@ def run_mx_random_sampling_operators_benchmarks(ctx=mx.cpu(), dtype='float32', w
     # Fetch all Random Sampling Operators
     mx_random_sample_ops = get_all_random_sampling_operators()
     # Run benchmarks
-    mx_random_sample_op_results = run_op_benchmarks(mx_random_sample_ops, dtype, ctx, warmup, runs)
+    mx_random_sample_op_results = run_op_benchmarks(mx_random_sample_ops, dtype, ctx, profiler, warmup, runs)
     return mx_random_sample_op_results
diff --git a/benchmark/opperf/nd_operations/reduction_operators.py b/benchmark/opperf/nd_operations/reduction_operators.py
index e34debc95810..84d9d3779c39 100644
--- a/benchmark/opperf/nd_operations/reduction_operators.py
+++ b/benchmark/opperf/nd_operations/reduction_operators.py
@@ -31,7 +31,7 @@
 from benchmark.opperf.utils.benchmark_utils import run_op_benchmarks
 
 
-def run_mx_reduction_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, runs=100):
+def run_mx_reduction_operators_benchmarks(ctx=mx.cpu(), dtype='float32', profiler='native', warmup=25, runs=100):
     """Runs benchmarks with the given context and precision (dtype)for all the reduction
     operators in MXNet.
 
@@ -54,5 +54,5 @@ def run_mx_reduction_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=
     # Fetch all Reduction Operators
     mx_reduction_broadcast_ops = get_all_reduction_operators()
     # Run benchmarks
-    mx_reduction_op_results = run_op_benchmarks(mx_reduction_broadcast_ops, dtype, ctx, warmup, runs)
+    mx_reduction_op_results = run_op_benchmarks(mx_reduction_broadcast_ops, dtype, ctx, profiler, warmup, runs)
     return mx_reduction_op_results
diff --git a/benchmark/opperf/nd_operations/sorting_searching_operators.py b/benchmark/opperf/nd_operations/sorting_searching_operators.py
index ab98b3feee06..373f64ea3af8 100644
--- a/benchmark/opperf/nd_operations/sorting_searching_operators.py
+++ b/benchmark/opperf/nd_operations/sorting_searching_operators.py
@@ -29,7 +29,7 @@
 """
 
 
-def run_sorting_searching_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, runs=100):
+def run_sorting_searching_operators_benchmarks(ctx=mx.cpu(), dtype='float32', profiler='native', warmup=25, runs=100):
     """Runs benchmarks with the given context and precision (dtype)for all the sorting and searching
     operators in MXNet.
 
@@ -52,5 +52,5 @@ def run_sorting_searching_operators_benchmarks(ctx=mx.cpu(), dtype='float32', wa
     # Fetch all Random Sampling Operators
     mx_sort_search_ops = get_all_sorting_searching_operators()
     # Run benchmarks
-    mx_sort_search_op_results = run_op_benchmarks(mx_sort_search_ops, dtype, ctx, warmup, runs)
+    mx_sort_search_op_results = run_op_benchmarks(mx_sort_search_ops, dtype, ctx, profiler, warmup, runs)
     return mx_sort_search_op_results
diff --git a/benchmark/opperf/nd_operations/unary_operators.py b/benchmark/opperf/nd_operations/unary_operators.py
index 790a4c12b64b..f7a8caf62db1 100644
--- a/benchmark/opperf/nd_operations/unary_operators.py
+++ b/benchmark/opperf/nd_operations/unary_operators.py
@@ -35,7 +35,7 @@
 from benchmark.opperf.utils.benchmark_utils import run_op_benchmarks
 
 
-def run_mx_unary_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, runs=100):
+def run_mx_unary_operators_benchmarks(ctx=mx.cpu(), dtype='float32', profiler='native', warmup=25, runs=100):
     """Runs benchmarks with the given context and precision (dtype)for all the unary
     operators in MXNet.
 
@@ -58,5 +58,5 @@ def run_mx_unary_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25,
     # Fetch all Unary Operators
     mx_unary_broadcast_ops = get_all_unary_operators()
     # Run benchmarks
-    mx_unary_op_results = run_op_benchmarks(mx_unary_broadcast_ops, dtype, ctx, warmup, runs)
+    mx_unary_op_results = run_op_benchmarks(mx_unary_broadcast_ops, dtype, ctx, profiler, warmup, runs)
     return mx_unary_op_results
diff --git a/benchmark/opperf/opperf.py b/benchmark/opperf/opperf.py
index b8055d7d14ae..fd8f6672f3a7 100755
--- a/benchmark/opperf/opperf.py
+++ b/benchmark/opperf/opperf.py
@@ -47,7 +47,7 @@
     get_current_runtime_features
 
 
-def run_all_mxnet_operator_benchmarks(ctx=mx.cpu(), dtype='float32'):
+def run_all_mxnet_operator_benchmarks(ctx=mx.cpu(), dtype='float32', profiler='native'):
     """Run all the MXNet operators (NDArray) benchmarks.
 
     Returns
@@ -59,48 +59,48 @@ def run_all_mxnet_operator_benchmarks(ctx=mx.cpu(), dtype='float32'):
     # *************************MXNET TENSOR OPERATOR BENCHMARKS*****************************
 
     # Run all Unary operations benchmarks with default input values
-    mxnet_operator_benchmark_results.append(run_mx_unary_operators_benchmarks(ctx=ctx, dtype=dtype))
+    mxnet_operator_benchmark_results.append(run_mx_unary_operators_benchmarks(ctx=ctx, dtype=dtype, profiler=profiler))
 
     # Run all Binary Broadcast, element_wise operations benchmarks with default input values
     mxnet_operator_benchmark_results.append(run_mx_binary_broadcast_operators_benchmarks(ctx=ctx,
-                                                                                         dtype=dtype))
+                                                                                         dtype=dtype, profiler=profiler))
     mxnet_operator_benchmark_results.append(run_mx_binary_element_wise_operators_benchmarks(ctx=ctx,
-                                                                                            dtype=dtype))
+                                                                                            dtype=dtype, profiler=profiler))
 
     # Run all GEMM operations benchmarks with default input values
     mxnet_operator_benchmark_results.append(run_gemm_operators_benchmarks(ctx=ctx,
-                                                                          dtype=dtype))
+                                                                          dtype=dtype, profiler=profiler))
 
     # Run all Random sampling operations benchmarks with default input values
-    mxnet_operator_benchmark_results.append(run_mx_random_sampling_operators_benchmarks(ctx=ctx, dtype=dtype))
+    mxnet_operator_benchmark_results.append(run_mx_random_sampling_operators_benchmarks(ctx=ctx, dtype=dtype, profiler=profiler))
 
     # Run all Reduction operations benchmarks with default input values
-    mxnet_operator_benchmark_results.append(run_mx_reduction_operators_benchmarks(ctx=ctx, dtype=dtype))
+    mxnet_operator_benchmark_results.append(run_mx_reduction_operators_benchmarks(ctx=ctx, dtype=dtype, profiler=profiler))
 
     # Run all Sorting and Searching operations benchmarks with default input values
-    mxnet_operator_benchmark_results.append(run_sorting_searching_operators_benchmarks(ctx=ctx, dtype=dtype))
+    mxnet_operator_benchmark_results.append(run_sorting_searching_operators_benchmarks(ctx=ctx, dtype=dtype, profiler=profiler))
 
     # Run all Array Rearrange operations benchmarks with default input values
-    mxnet_operator_benchmark_results.append(run_rearrange_operators_benchmarks(ctx=ctx, dtype=dtype))
+    mxnet_operator_benchmark_results.append(run_rearrange_operators_benchmarks(ctx=ctx, dtype=dtype, profiler=profiler))
 
     # ************************ MXNET NN OPERATOR BENCHMARKS ****************************
 
     # Run all basic NN operations benchmarks with default input values
-    mxnet_operator_benchmark_results.append(run_nn_basic_operators_benchmarks(ctx=ctx, dtype=dtype))
+    mxnet_operator_benchmark_results.append(run_nn_basic_operators_benchmarks(ctx=ctx, dtype=dtype, profiler=profiler))
 
     # Run all Activation operations benchmarks with default input values
-    mxnet_operator_benchmark_results.append(run_activation_operators_benchmarks(ctx=ctx, dtype=dtype))
+    mxnet_operator_benchmark_results.append(run_activation_operators_benchmarks(ctx=ctx, dtype=dtype, profiler=profiler))
 
     # Run all Pooling operations benchmarks with default input values
-    mxnet_operator_benchmark_results.append(run_pooling_operators_benchmarks(ctx=ctx, dtype=dtype))
+    mxnet_operator_benchmark_results.append(run_pooling_operators_benchmarks(ctx=ctx, dtype=dtype, profiler=profiler))
 
     # Run all Convolution operations benchmarks with default input values
-    mxnet_operator_benchmark_results.append(run_convolution_operators_benchmarks(ctx=ctx, dtype=dtype))
+    mxnet_operator_benchmark_results.append(run_convolution_operators_benchmarks(ctx=ctx, dtype=dtype, profiler=profiler))
 
     # Run all Optimizer operations benchmarks with default input values
-    mxnet_operator_benchmark_results.append(run_optimizer_operators_benchmarks(ctx=ctx, dtype=dtype))
+    mxnet_operator_benchmark_results.append(run_optimizer_operators_benchmarks(ctx=ctx, dtype=dtype, profiler=profiler))
     # Run all Transpose Convolution operations benchmarks with default input values
-    mxnet_operator_benchmark_results.append(run_transpose_convolution_operators_benchmarks(ctx=ctx, dtype=dtype))
+    mxnet_operator_benchmark_results.append(run_transpose_convolution_operators_benchmarks(ctx=ctx, dtype=dtype, profiler=profiler))
 
     # ****************************** PREPARE FINAL RESULTS ********************************
     final_benchmark_result_map = merge_map_list(mxnet_operator_benchmark_results)
@@ -138,6 +138,11 @@ def main():
                         help='Name and path for the '
                              'output file.')
 
+    parser.add_argument('-p', '--profiler', type=str, default='native',
+                        help='Use built-in CPP profiler (native) or Python'
+                             'time module.'
+                             'Valid Inputs - native, python')
+
     args = parser.parse_args()
     logging.info("Running MXNet operator benchmarks with the following options: {args}".format(args=args))
     assert not os.path.isfile(args.output_file),\
@@ -146,11 +151,12 @@ def main():
     # 2. RUN BENCHMARKS
     ctx = _parse_mxnet_context(args.ctx)
     dtype = args.dtype
-    final_benchmark_results = run_all_mxnet_operator_benchmarks(ctx=ctx, dtype=dtype)
+    profiler = args.profiler
+    final_benchmark_results = run_all_mxnet_operator_benchmarks(ctx=ctx, dtype=dtype, profiler=profiler)
 
     # 3. PREPARE OUTPUTS
     run_time_features = get_current_runtime_features()
-    save_to_file(final_benchmark_results, args.output_file, args.output_format, run_time_features)
+    save_to_file(final_benchmark_results, args.output_file, args.output_format, run_time_features, profiler)
 
     # 4. Generate list of MXNet operators not covered in benchmarks
     ops_not_covered = get_operators_with_no_benchmark(final_benchmark_results.keys())
diff --git a/benchmark/opperf/utils/benchmark_utils.py b/benchmark/opperf/utils/benchmark_utils.py
index adf5d533ff52..f25183db15d2 100644
--- a/benchmark/opperf/utils/benchmark_utils.py
+++ b/benchmark/opperf/utils/benchmark_utils.py
@@ -24,6 +24,7 @@
 from .common_utils import merge_map_list
 from .op_registry_utils import prepare_op_inputs
 from benchmark.opperf.rules.default_params import PARAMS_OF_TYPE_NDARRAY
+from .profiler_utils import cpp_profile,python_profile
 
 
 def _prepare_op_inputs(inputs, run_backward, dtype, ctx):
@@ -44,11 +45,19 @@ def _prepare_op_inputs(inputs, run_backward, dtype, ctx):
     return kwargs_list
 
 
-def _run_nd_operator_performance_test(op, inputs, run_backward, warmup, runs, kwargs_list):
-    if run_backward:
-        benchmark_helper_func = nd_forward_backward_and_profile
+def _run_nd_operator_performance_test(op, inputs, run_backward, warmup, runs, kwargs_list, profiler):
+    if profiler == 'native':
+        if run_backward:
+            benchmark_helper_func = cpp_profile(nd_forward_backward_and_profile)
+        else:
+            benchmark_helper_func = cpp_profile(nd_forward_and_profile)
+    elif profiler == 'python':
+        if run_backward:
+            benchmark_helper_func = python_profile(nd_forward_backward_and_profile)
+        else:
+            benchmark_helper_func = python_profile(nd_forward_and_profile)
     else:
-        benchmark_helper_func = nd_forward_and_profile
+        raise ValueError("Incorrect input for profiler. Valid input - 'python' or 'native'")
 
     # Warm up, ignore the profiler output
     _, _ = benchmark_helper_func(op, warmup, **kwargs_list[0])
@@ -67,7 +76,7 @@ def _run_nd_operator_performance_test(op, inputs, run_backward, warmup, runs, kw
 
 
 def run_performance_test(ops, inputs, run_backward=True,
-                         dtype='float32', ctx=mx.cpu(),
+                         dtype='float32', ctx=mx.cpu(), profiler='native',
                          warmup=10, runs=50):
     """Run operator benchmark for given operator or list of operators, ops, with the given inputs.
 
@@ -88,6 +97,9 @@ def run_performance_test(ops, inputs, run_backward=True,
         Precision to use for input tensors. Defaults to float32. Example: 'float32', 'int64'
     ctx: mx.ctx, default mx.cpu()
         Context to use for benchmarks. Default to mx.cpu()
+    profiler: Str, default 'native'
+        Type of profiler to run benchmarks. Default to 'native'
+        Option - ['python', 'native']
     warmup: int, default 10
         Number of warmup runs
     runs: int, default 50
@@ -106,14 +118,14 @@ def run_performance_test(ops, inputs, run_backward=True,
     op_benchmark_result = []
     for op in ops:
         if hasattr(mx.nd, op.__name__):
-            benchmark_result = _run_nd_operator_performance_test(op, inputs, run_backward, warmup, runs, kwargs_list)
+            benchmark_result = _run_nd_operator_performance_test(op, inputs, run_backward, warmup, runs, kwargs_list, profiler)
         else:
             raise ValueError("Unknown NDArray operator provided to benchmark. -  ", op.__name__)
         op_benchmark_result.append(benchmark_result)
     return op_benchmark_result
 
 
-def run_op_benchmarks(ops, dtype, ctx, warmup, runs):
+def run_op_benchmarks(ops, dtype, ctx, profiler, warmup, runs):
     # For each operator, run benchmarks
     mx_op_benchmark_results = []
     for _, op_params in ops.items():
@@ -123,6 +135,7 @@ def run_op_benchmarks(ops, dtype, ctx, warmup, runs):
         cur_op_res = run_performance_test(op_params["nd_op_handle"],
                                           run_backward=op_params["has_backward"],
                                           dtype=dtype, ctx=ctx,
+                                          profiler=profiler,
                                           inputs=inputs,
                                           warmup=warmup, runs=runs)
         mx_op_benchmark_results += cur_op_res
diff --git a/benchmark/opperf/utils/common_utils.py b/benchmark/opperf/utils/common_utils.py
index 0b4fab97811a..e657702d3596 100644
--- a/benchmark/opperf/utils/common_utils.py
+++ b/benchmark/opperf/utils/common_utils.py
@@ -44,7 +44,7 @@ def merge_map_list(map_list):
     return dict(ChainMap(*map_list))
 
 
-def save_to_file(inp_dict, out_filepath, out_format='json', runtime_features=None):
+def save_to_file(inp_dict, out_filepath, out_format='json', runtime_features=None, profiler='native'):
     """Saves the given input dictionary to the given output file.
 
     By default, saves the input dictionary as JSON file. Other supported formats include:
@@ -69,7 +69,7 @@ def save_to_file(inp_dict, out_filepath, out_format='json', runtime_features=Non
     elif out_format == 'md':
         # Save as md
         with open(out_filepath, "w") as result_file:
-            result_file.write(_prepare_markdown(inp_dict, runtime_features))
+            result_file.write(_prepare_markdown(inp_dict, runtime_features, profiler))
     else:
         raise ValueError("Invalid output file format provided - '{}'. Supported - json, md".format(format))
 
@@ -90,12 +90,13 @@ def get_json(inp_dict):
     return json.dumps(inp_dict, indent=4)
 
 
-def _prepare_op_benchmark_result(op, op_bench_result):
+def _prepare_op_benchmark_result(op, op_bench_result, profiler):
     operator_name = op
     avg_forward_time = "---"
     avg_backward_time = "---"
     max_mem_usage = "---"
     inputs = "---"
+    avg_time = "---"
     for key, value in op_bench_result.items():
         if "avg_time_forward" in key:
             avg_forward_time = value
@@ -105,11 +106,18 @@ def _prepare_op_benchmark_result(op, op_bench_result):
             max_mem_usage = value
         elif "inputs" in key:
             inputs = value
-    return "| {} | {} | {} | {} | {} |".format(operator_name, avg_forward_time, avg_backward_time,
-                                               max_mem_usage, inputs)
-
-
-def _prepare_markdown(results, runtime_features=None):
+        elif "avg_time" in key:
+            avg_time = value
+    result = ""
+    if profiler == "native":
+        result = "| {} | {} | {} | {} | {} |".format(operator_name,
+                 avg_forward_time, avg_backward_time, max_mem_usage, inputs)
+    elif profiler == "python":
+        result = "| {} | {} | {} |".format(operator_name, avg_time, inputs)
+    return result
+
+
+def _prepare_markdown(results, runtime_features=None, profiler='native'):
     results_markdown = []
     if runtime_features and 'runtime_features' in runtime_features:
         results_markdown.append("# Runtime Features")
@@ -118,13 +126,17 @@ def _prepare_markdown(results, runtime_features=None):
             results_markdown.append('{}. {} : {}'.format(idx, key, value))
 
     results_markdown.append("# Benchmark Results")
-    results_markdown.append(
-        "| Operator | Avg Forward Time (ms) | Avg. Backward Time (ms) | Max Mem Usage (Storage) (Bytes)"
-        " | Inputs |")
-    results_markdown.append("| :---: | :---: | :---: | :---:| :--- |")
+    if profiler == 'native':
+        results_markdown.append(
+            "| Operator | Avg Forward Time (ms) | Avg. Backward Time (ms) | Max Mem Usage (Storage) (Bytes)"
+            " | Inputs |")
+    elif profiler == 'python':
+        results_markdown.append(
+            "| Operator | Avg Time (ms) | Inputs |")
+    results_markdown.append("| :---: | :---: | :---: |")
 
     for op, op_bench_results in sorted(results.items(), key=itemgetter(0)):
         for op_bench_result in op_bench_results:
-            results_markdown.append(_prepare_op_benchmark_result(op, op_bench_result))
+            results_markdown.append(_prepare_op_benchmark_result(op, op_bench_result, profiler))
 
     return os.linesep.join(results_markdown)
diff --git a/benchmark/opperf/utils/ndarray_utils.py b/benchmark/opperf/utils/ndarray_utils.py
index 7ed2fa107066..013be3ec580f 100644
--- a/benchmark/opperf/utils/ndarray_utils.py
+++ b/benchmark/opperf/utils/ndarray_utils.py
@@ -19,10 +19,7 @@
 import mxnet as mx
 import mxnet.ndarray as nd
 
-from .profiler_utils import profile
 
-
-@profile
 def nd_forward_backward_and_profile(op, runs, *args, **kwargs):
     """Helper function to run a given NDArray operator (op) for 'runs' number of times with
     given args and kwargs. Executes both forward and backward pass.
@@ -53,7 +50,6 @@ def nd_forward_backward_and_profile(op, runs, *args, **kwargs):
     return res
 
 
-@profile
 def nd_forward_and_profile(op, runs, *args, **kwargs):
     """Helper function to run a given NDArray operator (op) for 'runs' number of times with
     given args and kwargs. Executes ONLY forward pass.
diff --git a/benchmark/opperf/utils/profiler_utils.py b/benchmark/opperf/utils/profiler_utils.py
index 0df67a00ff26..5d4eb5576220 100644
--- a/benchmark/opperf/utils/profiler_utils.py
+++ b/benchmark/opperf/utils/profiler_utils.py
@@ -15,6 +15,7 @@
 # specific language governing permissions and limitations
 # under the License.
 
+import time
 import functools
 
 from .common_utils import merge_map_list
@@ -22,7 +23,7 @@
 
 """
 TODO: Below we are using logic of parsing the MXNet profiler output string to
-fetch the benchmark results. Note that this is a temporary solution till we add 
+fetch the benchmark results. Note that this is a temporary solution till we add
 a new utility API into MXNet profiler to get_summary(), reset(). All the below
 parsing logic should be removed once these read APIs are available in Profiler.
 
@@ -88,7 +89,7 @@ def parse_profiler_dump(operator_name, profiler_dump):
     """
     MXNet profiler output from mx.profiler.dumps() API looks like below. This function parses
     this string profiler output to fetch Memory and Compute metrics.
-    
+
     Profile Statistics.
     Note that counter items are counter values and not time units.
     Device Storage
@@ -151,7 +152,7 @@ def parse_profiler_dump(operator_name, profiler_dump):
     return merge_map_list([memory_profile, operator_profile])
 
 
-def profile(func):
+def cpp_profile(func):
     """Decorator for profiling MXNet operation.
     Uses MXNet profiler to collect metrics on memory usage and execution time
     of the operation.
@@ -163,7 +164,7 @@ def profile(func):
 
     Returns
     -------
-    res, profiler output. res being an return values from operator execution.
+    res, profiler output. res being result returned after operator execution.
     profiler output is a dictionary with summary of operation execution.
     Example output : { "add": [{"avg_time_mem_alloc_cpu/0": 207618.0469,
                                 "avg_time_forward_broadcast_add": 4.204,
@@ -176,7 +177,7 @@ def profile(func):
     """
 
     @functools.wraps(func)
-    def profile_it(*args, **kwargs):
+    def cpp_profile_it(*args, **kwargs):
         # Profile the operation
         profiler.set_config(profile_all=True, aggregate_stats=True)
         profiler.set_state('run')
@@ -186,7 +187,7 @@ def profile_it(*args, **kwargs):
         # Prepare the results
         profiler_dump = profiler.dumps(reset=True)
 
-        # args[0] is assumed to operator name, if not found check for block name.
+        # args[0] is assumed to be operator name, if not found check for block name.
         # NOTE: This parameter should be removed when we get away from parsing
         # profiler output and start using new profiler APIs - get_summary(), reset()
         if len(args) > 0:
@@ -200,4 +201,46 @@ def profile_it(*args, **kwargs):
         profiler_output = parse_profiler_dump(operator_name, profiler_dump)
         return res, profiler_output
 
-    return profile_it
+    return cpp_profile_it
+
+
+def python_profile(func):
+    """Decorator for profiling MXNet operation.
+    Uses Python's time module to collect execution time information
+    of the operation.
+
+    Parameters
+    ----------
+    func:
+        Operation to be executed and timed.
+
+    Returns
+    -------
+    res, timing output. res being result returned after operator execution.
+    profiler output is a dictionary with summary of operation execution.
+    Example output : { "add": [{"avg_time_add": 0.4053089120425284,
+                                "inputs": {
+                                    "lhs": [1024, 1024],
+                                    "rhs": [1024,1024]
+                                }]
+                     }
+    """
+
+    @functools.wraps(func)
+    def python_profile_it(*args, **kwargs):
+        start_time = time.perf_counter()    # 1
+        res = func(*args, **kwargs)
+        end_time = time.perf_counter()      # 2
+        run_time = end_time - start_time    # 3
+
+        # NOTE : same as cpp_profile_it
+        if len(args) > 0:
+            operator_name = args[0].__name__
+        elif 'block' in kwargs:
+            operator_name = kwargs['block']._op_name
+        else:
+            raise ValueError("Unable to identify operator name to extract profiler output!")
+
+        profiler_output = {'avg_time_'+str(operator_name): run_time}
+        return res, profiler_output
+    return python_profile_it

From acc786586bf662a88690b7868eeb86c3ee0f98a5 Mon Sep 17 00:00:00 2001
From: Sam Skalicky <samskalicky@gmail.com>
Date: Thu, 15 Aug 2019 18:34:04 -0700
Subject: [PATCH 184/813] added check for empty params file and unknown param
 (not arg/aux) (#15917)

* added check for empty params file and unknown param (not arg/aux)

* changed exception to warning for unknown params

* removed unnecessary MXNetError import

* added warning message is params is empty

* fixed print

* fixed formatting

* missing paren
---
 python/mxnet/model.py | 19 +++++++++++++------
 1 file changed, 13 insertions(+), 6 deletions(-)

diff --git a/python/mxnet/model.py b/python/mxnet/model.py
index aee4a8ce2b45..1ff1ee04643f 100644
--- a/python/mxnet/model.py
+++ b/python/mxnet/model.py
@@ -451,12 +451,19 @@ def load_checkpoint(prefix, epoch):
     save_dict = nd.load('%s-%04d.params' % (prefix, epoch))
     arg_params = {}
     aux_params = {}
-    for k, v in save_dict.items():
-        tp, name = k.split(':', 1)
-        if tp == 'arg':
-            arg_params[name] = v
-        if tp == 'aux':
-            aux_params[name] = v
+    #load any params in the dict, skip if params are empty
+    if not save_dict:
+        logging.warning("Params file '%s' is empty", '%s-%04d.params' % (prefix, epoch))
+    else:
+        for k, v in save_dict.items():
+            tp, name = k.split(':', 1)
+            if tp == 'arg':
+                arg_params[name] = v
+            elif tp == 'aux':
+                aux_params[name] = v
+            else:
+                logging.warning("Params file '%s' contains unknown param '%s'",
+                                '%s-%04d.params' % (prefix, epoch), k)
     return (symbol, arg_params, aux_params)
 
 from .callback import LogValidationMetricsCallback # pylint: disable=wrong-import-position

From c8f0b21271747363436d22040a035fb152dcc46c Mon Sep 17 00:00:00 2001
From: Zach Kimberg <zachary@kimberg.com>
Date: Thu, 15 Aug 2019 19:00:52 -0700
Subject: [PATCH 185/813] Remove Scala package test on build (#15915)

Various build improvements to mirror pip build
---
 ci/publish/scala/build.sh         | 2 +-
 make/maven/maven_darwin_mkl.mk    | 2 +-
 make/maven/maven_linux_cu90mkl.mk | 4 +++-
 make/maven/maven_linux_cu92mkl.mk | 4 +++-
 make/maven/maven_linux_mkl.mk     | 2 +-
 make/pip/pip_linux_cu100.mk       | 2 ++
 make/pip/pip_linux_cu100mkl.mk    | 2 ++
 make/pip/pip_linux_cu101.mk       | 2 ++
 make/pip/pip_linux_cu101mkl.mk    | 2 ++
 make/pip/pip_linux_cu91.mk        | 2 ++
 make/pip/pip_linux_cu91mkl.mk     | 2 ++
 make/pip/pip_linux_cu92.mk        | 2 ++
 make/pip/pip_linux_cu92mkl.mk     | 2 ++
 tools/dependencies/openblas.sh    | 2 +-
 tools/setup_gpu_build_tools.sh    | 9 +++++++--
 15 files changed, 33 insertions(+), 8 deletions(-)

diff --git a/ci/publish/scala/build.sh b/ci/publish/scala/build.sh
index 11386ce2114f..30ee20e732f0 100755
--- a/ci/publish/scala/build.sh
+++ b/ci/publish/scala/build.sh
@@ -28,4 +28,4 @@ set -ex
 
 # Compile tests for discovery later
 cd scala-package
-mvn -B deploy
+mvn -B deploy -DskipTests=true
diff --git a/make/maven/maven_darwin_mkl.mk b/make/maven/maven_darwin_mkl.mk
index a7f2bdb027d4..9bf3fc46ce0b 100644
--- a/make/maven/maven_darwin_mkl.mk
+++ b/make/maven/maven_darwin_mkl.mk
@@ -77,7 +77,7 @@ USE_CUDNN = 0
 # CUDA_ARCH :=
 
 # whether use cuda runtime compiling for writing kernels in native language (i.e. Python)
-USE_NVRTC = 0
+ENABLE_CUDA_RTC = 0
 
 # use openmp for parallelization
 USE_OPENMP = 0
diff --git a/make/maven/maven_linux_cu90mkl.mk b/make/maven/maven_linux_cu90mkl.mk
index 8da56d803973..00cfd5dfa31d 100644
--- a/make/maven/maven_linux_cu90mkl.mk
+++ b/make/maven/maven_linux_cu90mkl.mk
@@ -79,7 +79,9 @@ USE_NCCL = 1
 # CUDA_ARCH :=
 
 # whether use cuda runtime compiling for writing kernels in native language (i.e. Python)
-USE_NVRTC = 1
+ENABLE_CUDA_RTC = 1
+
+USE_NVTX=1
 
 # use openmp for parallelization
 USE_OPENMP = 1
diff --git a/make/maven/maven_linux_cu92mkl.mk b/make/maven/maven_linux_cu92mkl.mk
index dd3e86b8a64f..6ac920a12ff0 100644
--- a/make/maven/maven_linux_cu92mkl.mk
+++ b/make/maven/maven_linux_cu92mkl.mk
@@ -79,7 +79,9 @@ USE_NCCL = 1
 # CUDA_ARCH :=
 
 # whether use cuda runtime compiling for writing kernels in native language (i.e. Python)
-USE_NVRTC = 1
+ENABLE_CUDA_RTC = 1
+
+USE_NVTX=1
 
 # use openmp for parallelization
 USE_OPENMP = 1
diff --git a/make/maven/maven_linux_mkl.mk b/make/maven/maven_linux_mkl.mk
index 3c8534a7e2aa..10aee5f35a46 100644
--- a/make/maven/maven_linux_mkl.mk
+++ b/make/maven/maven_linux_mkl.mk
@@ -76,7 +76,7 @@ USE_CUDNN = 0
 # CUDA_ARCH :=
 
 # whether use cuda runtime compiling for writing kernels in native language (i.e. Python)
-USE_NVRTC = 0
+ENABLE_CUDA_RTC = 0
 
 # use openmp for parallelization
 USE_OPENMP = 1
diff --git a/make/pip/pip_linux_cu100.mk b/make/pip/pip_linux_cu100.mk
index a972c538e512..09a760f16b3a 100644
--- a/make/pip/pip_linux_cu100.mk
+++ b/make/pip/pip_linux_cu100.mk
@@ -81,6 +81,8 @@ USE_NCCL = 1
 # whether use cuda runtime compiling for writing kernels in native language (i.e. Python)
 ENABLE_CUDA_RTC = 1
 
+USE_NVTX=1
+
 # use openmp for parallelization
 USE_OPENMP = 1
 USE_OPERATOR_TUNING = 1
diff --git a/make/pip/pip_linux_cu100mkl.mk b/make/pip/pip_linux_cu100mkl.mk
index 1f5d99642f91..862c1f56f8ae 100644
--- a/make/pip/pip_linux_cu100mkl.mk
+++ b/make/pip/pip_linux_cu100mkl.mk
@@ -81,6 +81,8 @@ USE_NCCL = 1
 # whether use cuda runtime compiling for writing kernels in native language (i.e. Python)
 ENABLE_CUDA_RTC = 1
 
+USE_NVTX=1
+
 # use openmp for parallelization
 USE_OPENMP = 1
 USE_OPERATOR_TUNING = 1
diff --git a/make/pip/pip_linux_cu101.mk b/make/pip/pip_linux_cu101.mk
index b180d048557f..82044bdae4ec 100644
--- a/make/pip/pip_linux_cu101.mk
+++ b/make/pip/pip_linux_cu101.mk
@@ -81,6 +81,8 @@ USE_NCCL = 1
 # whether use cuda runtime compiling for writing kernels in native language (i.e. Python)
 ENABLE_CUDA_RTC = 1
 
+USE_NVTX=1
+
 # use openmp for parallelization
 USE_OPENMP = 1
 USE_OPERATOR_TUNING = 1
diff --git a/make/pip/pip_linux_cu101mkl.mk b/make/pip/pip_linux_cu101mkl.mk
index d893fd611bad..6161431454ba 100644
--- a/make/pip/pip_linux_cu101mkl.mk
+++ b/make/pip/pip_linux_cu101mkl.mk
@@ -81,6 +81,8 @@ USE_NCCL = 1
 # whether use cuda runtime compiling for writing kernels in native language (i.e. Python)
 ENABLE_CUDA_RTC = 1
 
+USE_NVTX=1
+
 # use openmp for parallelization
 USE_OPENMP = 1
 USE_OPERATOR_TUNING = 1
diff --git a/make/pip/pip_linux_cu91.mk b/make/pip/pip_linux_cu91.mk
index fb6be493cf15..b1684cf83fa8 100644
--- a/make/pip/pip_linux_cu91.mk
+++ b/make/pip/pip_linux_cu91.mk
@@ -81,6 +81,8 @@ USE_NCCL = 1
 # whether use cuda runtime compiling for writing kernels in native language (i.e. Python)
 ENABLE_CUDA_RTC = 1
 
+USE_NVTX=1
+
 # use openmp for parallelization
 USE_OPENMP = 1
 USE_OPERATOR_TUNING = 1
diff --git a/make/pip/pip_linux_cu91mkl.mk b/make/pip/pip_linux_cu91mkl.mk
index c29fcef0ce0b..b2a33d7e36c8 100644
--- a/make/pip/pip_linux_cu91mkl.mk
+++ b/make/pip/pip_linux_cu91mkl.mk
@@ -81,6 +81,8 @@ USE_NCCL = 1
 # whether use cuda runtime compiling for writing kernels in native language (i.e. Python)
 ENABLE_CUDA_RTC = 1
 
+USE_NVTX=1
+
 # use openmp for parallelization
 USE_OPENMP = 1
 USE_OPERATOR_TUNING = 1
diff --git a/make/pip/pip_linux_cu92.mk b/make/pip/pip_linux_cu92.mk
index 616ee14ccf9c..ee431d88afbf 100644
--- a/make/pip/pip_linux_cu92.mk
+++ b/make/pip/pip_linux_cu92.mk
@@ -81,6 +81,8 @@ USE_NCCL = 1
 # whether use cuda runtime compiling for writing kernels in native language (i.e. Python)
 ENABLE_CUDA_RTC = 1
 
+USE_NVTX=1
+
 # use openmp for parallelization
 USE_OPENMP = 1
 USE_OPERATOR_TUNING = 1
diff --git a/make/pip/pip_linux_cu92mkl.mk b/make/pip/pip_linux_cu92mkl.mk
index b36f97d67fe7..bbaa4bfcd772 100644
--- a/make/pip/pip_linux_cu92mkl.mk
+++ b/make/pip/pip_linux_cu92mkl.mk
@@ -81,6 +81,8 @@ USE_NCCL = 1
 # whether use cuda runtime compiling for writing kernels in native language (i.e. Python)
 ENABLE_CUDA_RTC = 1
 
+USE_NVTX=1
+
 # use openmp for parallelization
 USE_OPENMP = 1
 USE_OPERATOR_TUNING = 1
diff --git a/tools/dependencies/openblas.sh b/tools/dependencies/openblas.sh
index 9732aa214f6b..1201c8947568 100755
--- a/tools/dependencies/openblas.sh
+++ b/tools/dependencies/openblas.sh
@@ -20,7 +20,7 @@
 # This script builds the static library of openblas that can be used as dependency of mxnet.
 set +e # This script throws an error but otherwise works
 set -x
-OPENBLAS_VERSION=0.3.3
+OPENBLAS_VERSION=0.3.5
 if [[ ! -e $DEPS_PATH/lib/libopenblas.a ]]; then
     # download and build openblas
     >&2 echo "Building openblas..."
diff --git a/tools/setup_gpu_build_tools.sh b/tools/setup_gpu_build_tools.sh
index b25be6b697e1..fa5596fc66b8 100755
--- a/tools/setup_gpu_build_tools.sh
+++ b/tools/setup_gpu_build_tools.sh
@@ -73,6 +73,7 @@ fi
 if [[ $VARIANT == cu* ]]; then
     CUDA_MAJOR_VERSION=$(echo $CUDA_VERSION | tr '-' '.' | cut -d. -f1,2)
     CUDA_MAJOR_DASH=$(echo $CUDA_VERSION | tr '-' '.' | cut -d. -f1,2 | tr '.' '-')
+    CUDA_PATCH_MAJOR_DASH=$(echo $CUDA_PATCH_VERSION | tr '-' '.' | cut -d. -f1,2 | tr '.' '-')
     NVIDIA_MAJOR_VERSION=$(echo $LIBCUDA_VERSION | cut -d. -f1)
     LIBCUDA_MAJOR=$(echo $LIBCUDA_VERSION | cut -d. -f1)
     LIBCUDNN_MAJOR=$(echo $LIBCUDNN_VERSION | cut -d. -f1)
@@ -83,8 +84,8 @@ if [[ $VARIANT == cu* ]]; then
         os_id="ubuntu1604"
     fi
     export PATH=/usr/lib/binutils-2.26/bin/:${PATH}:$DEPS_PATH/usr/local/cuda-$CUDA_MAJOR_VERSION/bin
-    export CPLUS_INCLUDE_PATH=${CPLUS_INCLUDE_PATH}:$DEPS_PATH/usr/local/cuda-$CUDA_MAJOR_VERSION/include
-    export C_INCLUDE_PATH=${C_INCLUDE_PATH}:$DEPS_PATH/usr/local/cuda-$CUDA_MAJOR_VERSION/include
+    export CPLUS_INCLUDE_PATH=${CPLUS_INCLUDE_PATH}:$DEPS_PATH/usr/local/cuda-$CUDA_MAJOR_VERSION/include:$DEPS_PATH/usr/include
+    export C_INCLUDE_PATH=${C_INCLUDE_PATH}:$DEPS_PATH/usr/local/cuda-$CUDA_MAJOR_VERSION/include:$DEPS_PATH/usr/include
     export LIBRARY_PATH=${LIBRARY_PATH}:$DEPS_PATH/usr/local/cuda-$CUDA_MAJOR_VERSION/lib64:$DEPS_PATH/usr/lib/x86_64-linux-gnu:$DEPS_PATH/usr/lib/nvidia-$NVIDIA_MAJOR_VERSION
     export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:$DEPS_PATH/usr/local/cuda-$CUDA_MAJOR_VERSION/lib64:$DEPS_PATH/usr/lib/x86_64-linux-gnu:$DEPS_PATH/usr/lib/nvidia-$NVIDIA_MAJOR_VERSION
     export NVCC=$DEPS_PATH/usr/local/cuda-$CUDA_MAJOR_VERSION/bin/nvcc
@@ -108,6 +109,7 @@ if [[ $VARIANT == cu101* ]]; then
       "cuda-cusolver-dev-${CUDA_MAJOR_DASH}_${CUDA_VERSION}_amd64.deb" \
       "cuda-misc-headers-${CUDA_MAJOR_DASH}_${CUDA_VERSION}_amd64.deb" \
       "cuda-nvcc-${CUDA_MAJOR_DASH}_${CUDA_VERSION}_amd64.deb" \
+      "cuda-nvtx-${CUDA_MAJOR_DASH}_${CUDA_VERSION}_amd64.deb" \
       "libcuda1-${LIBCUDA_MAJOR}_${LIBCUDA_VERSION}_amd64.deb" \
       "nvidia-${LIBCUDA_MAJOR}_${LIBCUDA_VERSION}_amd64.deb" \
     )
@@ -132,6 +134,7 @@ elif [[ $VARIANT == cu100* ]]; then
       "cuda-cusolver-dev-${CUDA_MAJOR_DASH}_${CUDA_VERSION}_amd64.deb" \
       "cuda-misc-headers-${CUDA_MAJOR_DASH}_${CUDA_VERSION}_amd64.deb" \
       "cuda-nvcc-${CUDA_MAJOR_DASH}_${CUDA_PATCH_VERSION}_amd64.deb" \
+      "cuda-nvtx-${CUDA_MAJOR_DASH}_${CUDA_VERSION}_amd64.deb" \
       "libcuda1-${LIBCUDA_MAJOR}_${LIBCUDA_VERSION}_amd64.deb" \
       "nvidia-${LIBCUDA_MAJOR}_${LIBCUDA_VERSION}_amd64.deb" \
     )
@@ -156,6 +159,7 @@ elif [[ $VARIANT == cu92* ]]; then
       "cuda-cusolver-dev-${CUDA_MAJOR_DASH}_${CUDA_VERSION}_amd64.deb" \
       "cuda-misc-headers-${CUDA_MAJOR_DASH}_${CUDA_VERSION}_amd64.deb" \
       "cuda-nvcc-${CUDA_MAJOR_DASH}_${CUDA_PATCH_VERSION}_amd64.deb" \
+      "cuda-nvtx-${CUDA_MAJOR_DASH}_${CUDA_VERSION}_amd64.deb" \
       "libcuda1-${LIBCUDA_MAJOR}_${LIBCUDA_VERSION}_amd64.deb" \
       "nvidia-${LIBCUDA_MAJOR}_${LIBCUDA_VERSION}_amd64.deb" \
     )
@@ -180,6 +184,7 @@ elif [[ $VARIANT == cu91* ]]; then
       "cuda-cusolver-dev-${CUDA_MAJOR_DASH}_${CUDA_VERSION}_amd64.deb" \
       "cuda-misc-headers-${CUDA_MAJOR_DASH}_${CUDA_VERSION}_amd64.deb" \
       "cuda-nvcc-${CUDA_MAJOR_DASH}_9.1.85.2-1_amd64.deb" \
+      "cuda-nvtx-${CUDA_MAJOR_DASH}_${CUDA_VERSION}_amd64.deb" \
       "libcuda1-${LIBCUDA_MAJOR}_${LIBCUDA_VERSION}_amd64.deb" \
       "nvidia-${LIBCUDA_MAJOR}_${LIBCUDA_VERSION}_amd64.deb" \
     )

From 09cf75bac638f5a26de39571bfffa1fd6d7df37f Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Thu, 15 Aug 2019 23:45:07 -0700
Subject: [PATCH 186/813] Add power, exponent, log ops large tensor support
 (#15794)

* power, exponent, log ops

* lint

* Trigger notification

* Trigger notification

* Trigger notification
---
 tests/nightly/test_large_array.py | 66 +++++++++++++++++++++++++++++++
 1 file changed, 66 insertions(+)

diff --git a/tests/nightly/test_large_array.py b/tests/nightly/test_large_array.py
index 585748ee59b1..02c867720609 100644
--- a/tests/nightly/test_large_array.py
+++ b/tests/nightly/test_large_array.py
@@ -444,6 +444,72 @@ def test_topk():
     assert l.sum() == np.sum(np.arange(0, SMALL_Y))
 
 
+def test_exponent_logarithm_operators():
+    a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
+    # exponent
+    result = nd.exp(a)
+    assert result[0][-1] == 7.389056
+    assert result.shape == a.shape
+
+    # exponent minus 1
+    result = nd.expm1(a)
+    assert result[0][-1] == 6.389056
+    assert result.shape == a.shape
+
+    # log2
+    result = nd.log2(a)
+    assert result[0][-1] == 1
+    assert result.shape == a.shape
+
+    # log10
+    result = nd.log10(a)
+    assert result[0][-1] == 0.30103
+    assert result.shape == a.shape
+
+    # log1p
+    result = nd.log1p(a)
+    assert result[0][-1] == 1.0986123
+    assert result.shape == a.shape
+
+    # log
+    result = nd.log(a)
+    assert result[0][-1] == 0.6931472
+    assert result.shape == a.shape
+
+
+def test_power_operators():
+    a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
+    # sqrt
+    result = nd.sqrt(a)
+    assert result[0][-1] == 1.4142135
+    assert result.shape == a.shape
+
+    # rsqrt
+    result = nd.rsqrt(a)
+    assert result[0][-1] == 0.70710677
+    assert result.shape == a.shape
+
+    # cbrt
+    result = nd.cbrt(a)
+    assert result[0][-1] == 1.2599211
+    assert result.shape == a.shape
+
+    # rcbrt
+    result = nd.rcbrt(a)
+    assert result[0][-1] == 0.7937005
+    assert result.shape == a.shape
+
+    # square
+    result = nd.square(a)
+    assert result[0][-1] == 4
+    assert result.shape == a.shape
+
+    # reciprocal
+    result = nd.reciprocal(a)
+    assert result[0][-1] == 0.5
+    assert result.shape == a.shape
+
+
 def test_sequence_mask():
     # Sequence Mask input [max_sequence_length, batch_size, other_feature_dims]
     # test with input batch_size = 2

From 5a4c01bac9afd4e75227a0b4b1231bceffb204df Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Thu, 15 Aug 2019 23:45:44 -0700
Subject: [PATCH 187/813] [Opperf] Filter out deprecated ops (#15541)

* normal, uniform ops

* Revert "normal, uniform ops"

This reverts commit f8d6f9583da859e1f5053c2689eafae5c3031553.

* filter out deprecated ops

* Trigger notification

* additional deprecated ops

* Trigger notification

* Trigger notification

* Trigger notification
---
 benchmark/opperf/utils/op_registry_utils.py | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/benchmark/opperf/utils/op_registry_utils.py b/benchmark/opperf/utils/op_registry_utils.py
index 860b83a4dace..207895d9b1ea 100644
--- a/benchmark/opperf/utils/op_registry_utils.py
+++ b/benchmark/opperf/utils/op_registry_utils.py
@@ -37,6 +37,8 @@ def _select_ops(operator_names, filters=("_contrib", "_"), merge_op_forward_back
     By default, filter out all Contrib operators that starts with '_contrib' and internal operators that
     starts with '_'.
 
+    Note - All deprecated operators are filtered out as well.
+
     Parameters
     ----------
     operator_names: List[str]
@@ -53,6 +55,11 @@ def _select_ops(operator_names, filters=("_contrib", "_"), merge_op_forward_back
     mx_operators = {}
     operators_with_backward = []
 
+    # Filter out deprecated operators
+    filters += ("normal", "uniform", "BatchNorm_v1", "Flatten", "contrib_CTCLoss", "Pad", "Cast",
+                "Pooling_v1", "Concat", "Reshape", "Convolution_v1", "SliceChannel", "Crop",
+                "crop", "onehot_encode")
+
     if merge_op_forward_backward:
         filters += ("_backward",)
 

From a8b9728d5729b565fdf2f588d1985bb5eca7f68a Mon Sep 17 00:00:00 2001
From: Zhennan Qin <zhennan.qin@intel.com>
Date: Sat, 17 Aug 2019 13:54:27 +0800
Subject: [PATCH 188/813] Add disable attr to subgraph property (#15926)

---
 docs/tutorials/c++/subgraphAPI.md                   |  6 ++++--
 src/c_api/c_api_symbolic.cc                         |  8 ++++++++
 src/executor/graph_executor.cc                      |  9 +++++++--
 src/operator/subgraph/mkldnn/mkldnn_conv_property.h |  7 +++----
 src/operator/subgraph/mkldnn/mkldnn_fc_property.h   |  7 +++----
 src/operator/subgraph/subgraph_property.h           | 11 +++++++----
 6 files changed, 32 insertions(+), 16 deletions(-)

diff --git a/docs/tutorials/c++/subgraphAPI.md b/docs/tutorials/c++/subgraphAPI.md
index 7403e2654423..91d24bd0319a 100644
--- a/docs/tutorials/c++/subgraphAPI.md
+++ b/docs/tutorials/c++/subgraphAPI.md
@@ -105,9 +105,11 @@ class SgProperty : public SubgraphProperty {
 };
 ```
 `SetAttr` is optional and developer can define their own attributes to control property behavior.
-There're 2 built-in attributes that used by MXNet executor.
+There're some built-in attributes that used by MXNet executor.
 
-`property_name`  : std::string, name of this property.
+`property_name`  : std::string, name of this property, used for diagnose.
+
+`disable` : bool, whther to disable this property.
 
 `inference_only` : bool, apply this property only for inference. Property will be skiped when need_grad=True. Default `false` if this attribute isn't defined.
 
diff --git a/src/c_api/c_api_symbolic.cc b/src/c_api/c_api_symbolic.cc
index 272579bce03b..15b7ba56b9ee 100644
--- a/src/c_api/c_api_symbolic.cc
+++ b/src/c_api/c_api_symbolic.cc
@@ -1142,6 +1142,14 @@ int MXGenBackendSubgraph(SymbolHandle sym_handle, const char *backend_name,
   auto backend = mxnet::op::SubgraphBackendRegistry::Get()->GetSubgraphBackend(backend_name);
   const auto& subgraph_prop_list = backend->GetSubgraphProperties();
   for (auto property : subgraph_prop_list) {
+    if (property->HasAttr("disable") && property->GetAttr<bool>("disable") == true) {
+      auto full_name = property->HasAttr("property_name")
+                           ? property->GetAttr<std::string>("property_name")
+                           : std::string();
+      LOG(INFO) << "subgraph property " << full_name << " from backend " << backend_name
+                << " is disabled.";
+      continue;
+    }
     nnvm::Graph g = Symbol2Graph(*s);
     property->SetAttr("graph", g);
     g.attrs["subgraph_property"] = std::make_shared<nnvm::any>(property);
diff --git a/src/executor/graph_executor.cc b/src/executor/graph_executor.cc
index c5da0a0cf90d..7bdeac708003 100644
--- a/src/executor/graph_executor.cc
+++ b/src/executor/graph_executor.cc
@@ -1659,10 +1659,15 @@ static bool SubgraphBackendCheck(const op::SubgraphBackendPtr& backend,
 static bool SubgraphPropertyCheck(const std::string& backend_name,
                                   const op::SubgraphPropertyPtr& prop, bool need_grad,
                                   bool verbose = false) {
+  auto full_name =
+      prop->HasAttr("property_name") ? prop->GetAttr<std::string>("property_name") : std::string();
+  if (prop->HasAttr("disable") && prop->GetAttr<bool>("disable") == true) {
+    LOG(INFO) << "subgraph property " << full_name << " from backend " << backend_name
+              << " is disabled.";
+    return false;
+  }
   if (prop->HasAttr("inference_only") && prop->GetAttr<bool>("inference_only") == true) {
     if (need_grad) {
-      auto full_name = prop->HasAttr("property_name") ? prop->GetAttr<std::string>("property_name")
-                                                      : std::string();
       if (verbose) {
         LOG(INFO) << "skip partitioning graph with subgraph property " << full_name
                   << " from backend " << backend_name << " as it requires `grad_req=null`.";
diff --git a/src/operator/subgraph/mkldnn/mkldnn_conv_property.h b/src/operator/subgraph/mkldnn/mkldnn_conv_property.h
index bf278ab75718..42ea9ea67fcf 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_conv_property.h
+++ b/src/operator/subgraph/mkldnn/mkldnn_conv_property.h
@@ -177,13 +177,12 @@ class SgMKLDNNConvProperty : public SubgraphProperty {
   }
   static SubgraphPropertyPtr Create() {
     static const std::string &name = "MKLDNN convolution optimization pass";
-    if (dmlc::GetEnv("MXNET_DISABLE_MKLDNN_CONV_OPT", 0)) {
-      LOG(INFO) << name << " is disabled.";
-      return nullptr;
-    }
     auto property = std::make_shared<SgMKLDNNConvProperty>();
     property->SetAttr<std::string>("property_name", name);
     property->SetAttr<bool>("inference_only", true);
+    if (dmlc::GetEnv("MXNET_DISABLE_MKLDNN_CONV_OPT", 0)) {
+      property->SetAttr<bool>("disable", true);
+    }
     return property;
   }
   nnvm::NodePtr CreateSubgraphNode(const nnvm::Symbol &sym,
diff --git a/src/operator/subgraph/mkldnn/mkldnn_fc_property.h b/src/operator/subgraph/mkldnn/mkldnn_fc_property.h
index 28350c2f0e99..caedecc417c0 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_fc_property.h
+++ b/src/operator/subgraph/mkldnn/mkldnn_fc_property.h
@@ -132,13 +132,12 @@ class SgMKLDNNFCProperty : public SubgraphProperty {
 
   static SubgraphPropertyPtr Create() {
     static const std::string &name = "MKLDNN FullyConnected optimization pass";
-    if (dmlc::GetEnv("MXNET_DISABLE_MKLDNN_FC_OPT", 0)) {
-      LOG(INFO) << name << " is disabled.";
-      return nullptr;
-    }
     auto property = std::make_shared<SgMKLDNNFCProperty>();
     property->SetAttr<std::string>("property_name", name);
     property->SetAttr<bool>("inference_only", true);
+    if (dmlc::GetEnv("MXNET_DISABLE_MKLDNN_FC_OPT", 0)) {
+      property->SetAttr<bool>("disable", true);
+    }
     return property;
   }
 
diff --git a/src/operator/subgraph/subgraph_property.h b/src/operator/subgraph/subgraph_property.h
index e2c861ef512a..b8b125fdb5de 100644
--- a/src/operator/subgraph/subgraph_property.h
+++ b/src/operator/subgraph/subgraph_property.h
@@ -357,7 +357,7 @@ class SubgraphPropertyEntry {
 
   template<typename T>
   SubgraphPropertyEntry set_attr(const std::string& name, const T value) const {
-    entry_->SetAttr<T>(name, value);
+    if (entry_) entry_->SetAttr<T>(name, value);
     return *this;
   }
 
@@ -403,9 +403,12 @@ class SubgraphBackend {
     }
   }
 
-  SubgraphPropertyPtr& RegisterSubgraphProperty(const SubgraphPropertyPtr prop) {
-    prop_ptr_.push_back(prop);
-    return prop_ptr_.back();
+  SubgraphPropertyPtr RegisterSubgraphProperty(SubgraphPropertyPtr prop) {
+    if (prop) {
+      prop_ptr_.push_back(prop);
+      return prop_ptr_.back();
+    }
+    return prop;
   }
 
   const std::string& GetName() const { return name_; }

From 1a6fe602e0e445cc3b052b4103069b7c6f5bf012 Mon Sep 17 00:00:00 2001
From: Dick Carter <dcarter@nvidia.com>
Date: Sat, 17 Aug 2019 17:40:21 -0700
Subject: [PATCH 189/813] Refactor for windows CI 'out of heap space' errors
 (#15922)

* Speed up test_random.py:test_shuffle to get past CI timeouts.

* Fix flakey test_operator.py:test_laop_6.

* Break up broadcast_reduce_op_value.{cc,cu}

* Revert "Fix flakey test_operator.py:test_laop_6."

This reverts commit 479ba3860884402914ef7df2fc5a031fed5b0f37.

* Break up elemwise_unary_op_basic.{cc,cu}
---
 .../tensor/broadcast_reduce_minmax_value.cc   |  59 +++
 .../tensor/broadcast_reduce_minmax_value.cu   |  43 ++
 .../tensor/broadcast_reduce_norm_value.cc     | 118 ++++++
 .../tensor/broadcast_reduce_norm_value.cu     |  60 +++
 src/operator/tensor/broadcast_reduce_op.h     |  15 +
 .../tensor/broadcast_reduce_op_value.cc       | 239 -----------
 .../tensor/broadcast_reduce_op_value.cu       |  71 ----
 .../tensor/broadcast_reduce_prod_value.cc     |  59 +++
 .../tensor/broadcast_reduce_prod_value.cu     |  43 ++
 .../tensor/broadcast_reduce_sum_value.cc      | 113 ++++++
 .../tensor/broadcast_reduce_sum_value.cu      |  49 +++
 .../tensor/elemwise_unary_op_basic.cc         | 377 ------------------
 .../tensor/elemwise_unary_op_basic.cu         | 101 -----
 .../tensor/elemwise_unary_op_logexp.cc        | 230 +++++++++++
 .../tensor/elemwise_unary_op_logexp.cu        |  76 ++++
 src/operator/tensor/elemwise_unary_op_pow.cc  | 212 ++++++++++
 src/operator/tensor/elemwise_unary_op_pow.cu  |  82 ++++
 tests/python/unittest/test_random.py          |  15 +-
 18 files changed, 1167 insertions(+), 795 deletions(-)
 create mode 100644 src/operator/tensor/broadcast_reduce_minmax_value.cc
 create mode 100644 src/operator/tensor/broadcast_reduce_minmax_value.cu
 create mode 100644 src/operator/tensor/broadcast_reduce_norm_value.cc
 create mode 100644 src/operator/tensor/broadcast_reduce_norm_value.cu
 create mode 100644 src/operator/tensor/broadcast_reduce_prod_value.cc
 create mode 100644 src/operator/tensor/broadcast_reduce_prod_value.cu
 create mode 100644 src/operator/tensor/broadcast_reduce_sum_value.cc
 create mode 100644 src/operator/tensor/broadcast_reduce_sum_value.cu
 create mode 100644 src/operator/tensor/elemwise_unary_op_logexp.cc
 create mode 100644 src/operator/tensor/elemwise_unary_op_logexp.cu
 create mode 100644 src/operator/tensor/elemwise_unary_op_pow.cc
 create mode 100644 src/operator/tensor/elemwise_unary_op_pow.cu

diff --git a/src/operator/tensor/broadcast_reduce_minmax_value.cc b/src/operator/tensor/broadcast_reduce_minmax_value.cc
new file mode 100644
index 000000000000..f8bc33ba375d
--- /dev/null
+++ b/src/operator/tensor/broadcast_reduce_minmax_value.cc
@@ -0,0 +1,59 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2016 by Contributors
+ * \file broadcast_reduce_minmax_value.cc
+ * \brief CPU Implementation of broadcast and reduce min and max functions based on value.
+ */
+#include "./broadcast_reduce_op.h"
+
+namespace mxnet {
+namespace op {
+
+MXNET_OPERATOR_REGISTER_MINMAX_REDUCE(max)
+.add_alias("max_axis")
+.describe(get_reduce_axes_description("max", __LINE__))
+.set_attr<FCompute>("FCompute<cpu>", ReduceAxesCompute<cpu, mshadow::red::maximum>)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<nnvm::FGradient>("FGradient", ReduceGrad{"_backward_max"});
+
+MXNET_OPERATOR_REGISTER_REDUCE_BACKWARD(_backward_max)
+.set_num_inputs(3)
+.set_attr<FCompute>("FCompute<cpu>", ReduceAxesBackwardUseInOut<cpu, mshadow_op::eq>);
+
+MXNET_OPERATOR_REGISTER_MINMAX_REDUCE(min)
+.add_alias("min_axis")
+.describe(get_reduce_axes_description("min", __LINE__))
+.set_attr<FCompute>("FCompute<cpu>", ReduceAxesCompute<cpu, mshadow::red::minimum>)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<nnvm::FGradient>("FGradient", ReduceGrad{"_backward_min"});
+
+MXNET_OPERATOR_REGISTER_REDUCE_BACKWARD(_backward_min)
+.set_num_inputs(3)
+.set_attr<FCompute>("FCompute<cpu>", ReduceAxesBackwardUseInOut<cpu, mshadow_op::eq>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/tensor/broadcast_reduce_minmax_value.cu b/src/operator/tensor/broadcast_reduce_minmax_value.cu
new file mode 100644
index 000000000000..baf79feb5c60
--- /dev/null
+++ b/src/operator/tensor/broadcast_reduce_minmax_value.cu
@@ -0,0 +1,43 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2016 by Contributors
+ * \file broadcast_reduce_minmax_value.cu
+ * \brief GPU Implementation of broadcast and reduce min and max functions based on value.
+ */
+#include "./broadcast_reduce_op.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(max)
+.set_attr<FCompute>("FCompute<gpu>", ReduceAxesCompute<gpu, mshadow::red::maximum>);
+
+NNVM_REGISTER_OP(_backward_max)
+.set_attr<FCompute>("FCompute<gpu>", ReduceAxesBackwardUseInOut<gpu, mshadow_op::eq>);
+
+NNVM_REGISTER_OP(min)
+.set_attr<FCompute>("FCompute<gpu>", ReduceAxesCompute<gpu, mshadow::red::minimum>);
+
+NNVM_REGISTER_OP(_backward_min)
+.set_attr<FCompute>("FCompute<gpu>", ReduceAxesBackwardUseInOut<gpu, mshadow_op::eq>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/tensor/broadcast_reduce_norm_value.cc b/src/operator/tensor/broadcast_reduce_norm_value.cc
new file mode 100644
index 000000000000..63a05b4980fc
--- /dev/null
+++ b/src/operator/tensor/broadcast_reduce_norm_value.cc
@@ -0,0 +1,118 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2016 by Contributors
+ * \file broadcast_reduce_norm_value.cc
+ * \brief CPU Implementation of broadcast and reduce norm functions based on value.
+ */
+#include "./broadcast_reduce_op.h"
+
+namespace mxnet {
+namespace op {
+DMLC_REGISTER_PARAMETER(NormParam);
+
+template<>
+void L2NormComputeEx<cpu>(const nnvm::NodeAttrs& attrs,
+                          const OpContext& ctx,
+                          const std::vector<NDArray>& inputs,
+                          const std::vector<OpReqType>& req,
+                          const std::vector<NDArray>& outputs) {
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 1U);
+  CHECK_EQ(req.size(), 1U);
+  const NormParam& param = nnvm::get<NormParam>(attrs.parsed);
+  mshadow::Stream<cpu>* s = ctx.get_stream<cpu>();
+  const NDArrayStorageType istype = inputs[0].storage_type();
+  const mxnet::TShape axis = param.axis.has_value() ? param.axis.value() : mxnet::TShape();
+  if ((istype == kRowSparseStorage || istype == kCSRStorage) && axis.ndim() == 0 &&
+       param.ord == 2) {
+    // l2 norm on the entire array
+    L2NormComputeSparseImpl<cpu>(s, inputs[0], req[0], outputs[0].data());
+  } else if (istype == kCSRStorage && axis.ndim() == 1 && (axis[0] == 0 || axis[0] == 1) &&
+             !param.keepdims && param.ord == 2) {
+    // l2 norm on a particular axis
+    NDArray output = outputs[0];
+    ReduceCsrImpl<cpu, sq_sum, false>(s, ctx, inputs[0], req[0], &output, axis);
+    CHECK_EQ(outputs[0].storage_type(), kDefaultStorage);
+    SqRootForL2<cpu>(ctx, req[0], outputs[0].data());
+  } else {
+    LogUnimplementedOp(attrs, ctx, inputs, req, outputs);
+  }
+}
+
+NNVM_REGISTER_OP(norm)
+MXNET_ADD_SPARSE_OP_ALIAS(norm)
+.describe(R"code(Computes the norm on an NDArray.
+
+This operator computes the norm on an NDArray with the specified axis, depending
+on the value of the ord parameter. By default, it computes the L2 norm on the entire
+array. Currently only ord=2 supports sparse ndarrays.
+
+Examples::
+
+  x = [[[1, 2],
+        [3, 4]],
+       [[2, 2],
+        [5, 6]]]
+
+  norm(x, ord=2, axis=1) = [[3.1622777 4.472136 ]
+                            [5.3851647 6.3245554]]
+
+  norm(x, ord=1, axis=1) = [[4., 6.],
+                            [7., 8.]]
+
+  rsp = x.cast_storage('row_sparse')
+
+  norm(rsp) = [5.47722578]
+
+  csr = x.cast_storage('csr')
+
+  norm(csr) = [5.47722578]
+
+)code" ADD_FILELINE)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<NormParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", NormShape)
+.set_attr<nnvm::FInferType>("FInferType", NormType)
+.set_attr<FInferStorageType>("FInferStorageType", LpNormStorageType)
+.set_attr<nnvm::FGradient>("FGradient", ReduceGrad{ "_backward_norm" })
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", LpNormCompute<cpu>)
+.set_attr<FComputeEx>("FComputeEx<cpu>", L2NormComputeEx<cpu>)
+.add_argument("data", "NDArray-or-Symbol", "The input")
+.add_arguments(NormParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_backward_norm)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<NormParam>)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", LpNormGradCompute<cpu>);
+
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/tensor/broadcast_reduce_norm_value.cu b/src/operator/tensor/broadcast_reduce_norm_value.cu
new file mode 100644
index 000000000000..188c93e61221
--- /dev/null
+++ b/src/operator/tensor/broadcast_reduce_norm_value.cu
@@ -0,0 +1,60 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2016 by Contributors
+ * \file broadcast_reduce_norm_value.cu
+ * \brief GPU Implementation of broadcast and reduce norm functions based on value.
+ */
+#include "./broadcast_reduce_op.h"
+
+namespace mxnet {
+namespace op {
+
+template<>
+void L2NormComputeEx<gpu>(const nnvm::NodeAttrs& attrs,
+                          const OpContext& ctx,
+                          const std::vector<NDArray>& inputs,
+                          const std::vector<OpReqType>& req,
+                          const std::vector<NDArray>& outputs) {
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 1U);
+  CHECK_EQ(req.size(), 1U);
+  const NormParam& param = nnvm::get<NormParam>(attrs.parsed);
+  mshadow::Stream<gpu>* s = ctx.get_stream<gpu>();
+  const NDArrayStorageType istype = inputs[0].storage_type();
+  const mxnet::TShape axis = param.axis.has_value() ? param.axis.value() : mxnet::TShape();
+  if ((istype == kRowSparseStorage || istype == kCSRStorage) && axis.ndim() == 0 &&
+       param.ord == 2) {
+    // l2 norm on the entire array
+    L2NormComputeSparseImpl<gpu>(s, inputs[0], req[0], outputs[0].data());
+  } else {
+    LogUnimplementedOp(attrs, ctx, inputs, req, outputs);
+  }
+}
+
+NNVM_REGISTER_OP(norm)
+.set_attr<FCompute>("FCompute<gpu>", LpNormCompute<gpu>)
+.set_attr<FComputeEx>("FComputeEx<gpu>", L2NormComputeEx<gpu>);
+
+NNVM_REGISTER_OP(_backward_norm)
+.set_attr<FCompute>("FCompute<gpu>", LpNormGradCompute<gpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/tensor/broadcast_reduce_op.h b/src/operator/tensor/broadcast_reduce_op.h
index 27edf0195a1c..2e40b87a4b6d 100644
--- a/src/operator/tensor/broadcast_reduce_op.h
+++ b/src/operator/tensor/broadcast_reduce_op.h
@@ -26,6 +26,7 @@
 #define MXNET_OPERATOR_TENSOR_BROADCAST_REDUCE_OP_H_
 
 #include <mxnet/operator_util.h>
+#include <string>
 #include <vector>
 #include <utility>
 #include <algorithm>
@@ -1580,6 +1581,20 @@ void PickOpBackward(const nnvm::NodeAttrs& attrs,
   });
 }
 
+inline std::string get_reduce_axes_description(const std::string& op_name, int line) {
+  std::string doc = R"code(Computes the __op__ of array elements over given axes.
+
+Defined in )code";
+  doc += std::string(__FILE__) + std::string(":L") + std::to_string(line);
+  size_t pos = 0;
+  std::string holder("__op__");
+  while ((pos = doc.find(holder, pos)) != std::string::npos) {
+    doc.replace(pos, holder.length(), op_name);
+    pos += op_name.length();
+  }
+  return doc;
+}
+
 #define MXNET_OPERATOR_REGISTER_REDUCE_AXIS(name)               \
   NNVM_REGISTER_OP(name)                                        \
   .set_num_inputs(1)                                            \
diff --git a/src/operator/tensor/broadcast_reduce_op_value.cc b/src/operator/tensor/broadcast_reduce_op_value.cc
index 7e8a7f05659b..43bfc729329a 100644
--- a/src/operator/tensor/broadcast_reduce_op_value.cc
+++ b/src/operator/tensor/broadcast_reduce_op_value.cc
@@ -27,193 +27,11 @@
 namespace mxnet {
 namespace op {
 DMLC_REGISTER_PARAMETER(ReduceAxesParam);
-DMLC_REGISTER_PARAMETER(NormParam);
 DMLC_REGISTER_PARAMETER(ReduceAxisParam);
 DMLC_REGISTER_PARAMETER(BroadcastAxesParam);
 DMLC_REGISTER_PARAMETER(BroadcastToParam);
 DMLC_REGISTER_PARAMETER(BroadcastLikeParam);
 
-inline std::string get_reduce_axes_description(const std::string& op_name, int line) {
-  std::string doc = R"code(Computes the __op__ of array elements over given axes.
-
-Defined in )code";
-  doc += std::string(__FILE__) + std::string(":L") + std::to_string(line);
-  size_t pos = 0;
-  std::string holder("__op__");
-  while ((pos = doc.find(holder, pos)) != std::string::npos) {
-    doc.replace(pos, holder.length(), op_name);
-    pos += op_name.length();
-  }
-  return doc;
-}
-
-template<>
-void L2NormComputeEx<cpu>(const nnvm::NodeAttrs& attrs,
-                          const OpContext& ctx,
-                          const std::vector<NDArray>& inputs,
-                          const std::vector<OpReqType>& req,
-                          const std::vector<NDArray>& outputs) {
-  CHECK_EQ(inputs.size(), 1U);
-  CHECK_EQ(outputs.size(), 1U);
-  CHECK_EQ(req.size(), 1U);
-  const NormParam& param = nnvm::get<NormParam>(attrs.parsed);
-  mshadow::Stream<cpu>* s = ctx.get_stream<cpu>();
-  const NDArrayStorageType istype = inputs[0].storage_type();
-  const mxnet::TShape axis = param.axis.has_value() ? param.axis.value() : mxnet::TShape();
-  if ((istype == kRowSparseStorage || istype == kCSRStorage) && axis.ndim() == 0 &&
-       param.ord == 2) {
-    // l2 norm on the entire array
-    L2NormComputeSparseImpl<cpu>(s, inputs[0], req[0], outputs[0].data());
-  } else if (istype == kCSRStorage && axis.ndim() == 1 && (axis[0] == 0 || axis[0] == 1) &&
-             !param.keepdims && param.ord == 2) {
-    // l2 norm on a particular axis
-    NDArray output = outputs[0];
-    ReduceCsrImpl<cpu, sq_sum, false>(s, ctx, inputs[0], req[0], &output, axis);
-    CHECK_EQ(outputs[0].storage_type(), kDefaultStorage);
-    SqRootForL2<cpu>(ctx, req[0], outputs[0].data());
-  } else {
-    LogUnimplementedOp(attrs, ctx, inputs, req, outputs);
-  }
-}
-
-MXNET_OPERATOR_REGISTER_REDUCE(sum)
-MXNET_ADD_SPARSE_OP_ALIAS(sum)
-.add_alias("sum_axis")
-.describe(R"code(Computes the sum of array elements over given axes.
-
-.. Note::
-
-  `sum` and `sum_axis` are equivalent.
-  For ndarray of csr storage type summation along axis 0 and axis 1 is supported.
-  Setting keepdims or exclude to True will cause a fallback to dense operator.
-
-Example::
-
-  data = [[[1, 2], [2, 3], [1, 3]],
-          [[1, 4], [4, 3], [5, 2]],
-          [[7, 1], [7, 2], [7, 3]]]
-
-  sum(data, axis=1)
-  [[  4.   8.]
-   [ 10.   9.]
-   [ 21.   6.]]
-
-  sum(data, axis=[1,2])
-  [ 12.  19.  27.]
-
-  data = [[1, 2, 0],
-          [3, 0, 1],
-          [4, 1, 0]]
-
-  csr = cast_storage(data, 'csr')
-
-  sum(csr, axis=0)
-  [ 8.  3.  1.]
-
-  sum(csr, axis=1)
-  [ 3.  4.  5.]
-
-)code" ADD_FILELINE)
-.set_attr<FCompute>("FCompute<cpu>", ReduceAxesCompute<cpu, mshadow::red::sum>)
-.set_attr<FComputeEx>("FComputeEx<cpu>", ReduceAxesOpForwardEx<cpu, mshadow::red::sum>)
-.set_attr<FInferStorageType>("FInferStorageType", ReduceAxesOpForwardStorage)
-.set_attr<FResourceRequest>("FResourceRequest",
-  [](const NodeAttrs& attrs) {
-    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
-  })
-.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_sum"});
-
-MXNET_OPERATOR_REGISTER_REDUCE_BACKWARD(_backward_sum)
-.set_num_inputs(1)
-.set_attr<FCompute>("FCompute<cpu>", ReduceAxesBackwardUseNone<cpu>);
-
-MXNET_OPERATOR_REGISTER_REDUCE(mean)
-MXNET_ADD_SPARSE_OP_ALIAS(mean)
-.describe(get_reduce_axes_description("mean", __LINE__))
-.set_attr<FCompute>("FCompute<cpu>", ReduceAxesCompute<cpu, mshadow::red::sum, true>)
-.set_attr<FComputeEx>("FComputeEx<cpu>", ReduceAxesOpForwardEx<cpu, mshadow::red::sum, true>)
-.set_attr<FInferStorageType>("FInferStorageType", ReduceAxesOpForwardStorage)
-.set_attr<FResourceRequest>("FResourceRequest",
-  [](const NodeAttrs& attrs) {
-    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
-  })
-.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_mean"});
-
-MXNET_OPERATOR_REGISTER_REDUCE_BACKWARD(_backward_mean)
-.set_num_inputs(1)
-.set_attr<FCompute>("FCompute<cpu>", ReduceAxesBackwardUseNone<cpu, true>);
-
-MXNET_OPERATOR_REGISTER_REDUCE(prod)
-.describe(get_reduce_axes_description("product", __LINE__))
-.set_attr<FCompute>("FCompute<cpu>", ReduceAxesCompute<cpu, mshadow_op::product>)
-.set_attr<FResourceRequest>("FResourceRequest",
-  [](const NodeAttrs& attrs) {
-    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
-  })
-.set_attr<nnvm::FGradient>("FGradient", ReduceGrad{ "_backward_prod" });
-
-MXNET_OPERATOR_REGISTER_REDUCE_BACKWARD(_backward_prod)
-.set_num_inputs(3)
-.set_attr<FCompute>("FCompute<cpu>", ReduceAxesBackwardUseInOut< cpu, mshadow_op::rdiv>);
-
-MXNET_OPERATOR_REGISTER_REDUCE(nansum)
-.describe(R"code(Computes the sum of array elements over given axes treating Not a Numbers (``NaN``) as zero.
-
-)code" ADD_FILELINE)
-.set_attr<FCompute>("FCompute<cpu>", ReduceAxesCompute<cpu, mshadow_op::nansum>)
-.set_attr<FResourceRequest>("FResourceRequest",
-  [](const NodeAttrs& attrs) {
-    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
-  })
-.set_attr<nnvm::FGradient>("FGradient", ReduceGrad{ "_backward_nansum" });
-
-MXNET_OPERATOR_REGISTER_REDUCE_BACKWARD(_backward_nansum)
-.set_num_inputs(3)
-.set_attr<FCompute>("FCompute<cpu>", ReduceAxesBackwardUseInOut<cpu, mshadow_op::nansum_grad>);
-
-MXNET_OPERATOR_REGISTER_REDUCE(nanprod)
-.describe(R"code(Computes the product of array elements over given axes treating Not a Numbers (``NaN``) as one.
-
-)code" ADD_FILELINE)
-.set_attr<FCompute>("FCompute<cpu>", ReduceAxesCompute<cpu, mshadow_op::nanprod>)
-.set_attr<FResourceRequest>("FResourceRequest",
-  [](const NodeAttrs& attrs) {
-    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
-  })
-.set_attr<nnvm::FGradient>("FGradient", ReduceGrad{ "_backward_nanprod" });
-
-MXNET_OPERATOR_REGISTER_REDUCE_BACKWARD(_backward_nanprod)
-.set_num_inputs(3)
-.set_attr<FCompute>("FCompute<cpu>", ReduceAxesBackwardUseInOut<cpu, mshadow_op::nanprod_grad>);
-
-MXNET_OPERATOR_REGISTER_MINMAX_REDUCE(max)
-.add_alias("max_axis")
-.describe(get_reduce_axes_description("max", __LINE__))
-.set_attr<FCompute>("FCompute<cpu>", ReduceAxesCompute<cpu, mshadow::red::maximum>)
-.set_attr<FResourceRequest>("FResourceRequest",
-  [](const NodeAttrs& attrs) {
-    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
-  })
-.set_attr<nnvm::FGradient>("FGradient", ReduceGrad{"_backward_max"});
-
-MXNET_OPERATOR_REGISTER_REDUCE_BACKWARD(_backward_max)
-.set_num_inputs(3)
-.set_attr<FCompute>("FCompute<cpu>", ReduceAxesBackwardUseInOut<cpu, mshadow_op::eq>);
-
-MXNET_OPERATOR_REGISTER_MINMAX_REDUCE(min)
-.add_alias("min_axis")
-.describe(get_reduce_axes_description("min", __LINE__))
-.set_attr<FCompute>("FCompute<cpu>", ReduceAxesCompute<cpu, mshadow::red::minimum>)
-.set_attr<FResourceRequest>("FResourceRequest",
-  [](const NodeAttrs& attrs) {
-    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
-  })
-.set_attr<nnvm::FGradient>("FGradient", ReduceGrad{"_backward_min"});
-
-MXNET_OPERATOR_REGISTER_REDUCE_BACKWARD(_backward_min)
-.set_num_inputs(3)
-.set_attr<FCompute>("FCompute<cpu>", ReduceAxesBackwardUseInOut<cpu, mshadow_op::eq>);
-
 MXNET_OPERATOR_REGISTER_BROADCAST(broadcast_axis)
 .add_alias("broadcast_axes")
 .describe(R"code(Broadcasts the input array over particular axes.
@@ -320,62 +138,5 @@ For example::
 .set_attr<mxnet::FInferShape>("FInferShape", BroadcastLikeShape)
 .set_attr<FCompute>("FCompute<cpu>", BroadcastCompute<cpu>);
 
-NNVM_REGISTER_OP(norm)
-MXNET_ADD_SPARSE_OP_ALIAS(norm)
-.describe(R"code(Computes the norm on an NDArray.
-
-This operator computes the norm on an NDArray with the specified axis, depending
-on the value of the ord parameter. By default, it computes the L2 norm on the entire
-array. Currently only ord=2 supports sparse ndarrays.
-
-Examples::
-
-  x = [[[1, 2],
-        [3, 4]],
-       [[2, 2],
-        [5, 6]]]
-
-  norm(x, ord=2, axis=1) = [[3.1622777 4.472136 ]
-                            [5.3851647 6.3245554]]
-
-  norm(x, ord=1, axis=1) = [[4., 6.],
-                            [7., 8.]]
-
-  rsp = x.cast_storage('row_sparse')
-
-  norm(rsp) = [5.47722578]
-
-  csr = x.cast_storage('csr')
-
-  norm(csr) = [5.47722578]
-
-)code" ADD_FILELINE)
-.set_num_inputs(1)
-.set_num_outputs(1)
-.set_attr_parser(ParamParser<NormParam>)
-.set_attr<mxnet::FInferShape>("FInferShape", NormShape)
-.set_attr<nnvm::FInferType>("FInferType", NormType)
-.set_attr<FInferStorageType>("FInferStorageType", LpNormStorageType)
-.set_attr<nnvm::FGradient>("FGradient", ReduceGrad{ "_backward_norm" })
-.set_attr<FResourceRequest>("FResourceRequest",
-  [](const NodeAttrs& attrs) {
-    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
-  })
-.set_attr<FCompute>("FCompute<cpu>", LpNormCompute<cpu>)
-.set_attr<FComputeEx>("FComputeEx<cpu>", L2NormComputeEx<cpu>)
-.add_argument("data", "NDArray-or-Symbol", "The input")
-.add_arguments(NormParam::__FIELDS__());
-
-NNVM_REGISTER_OP(_backward_norm)
-.set_num_outputs(1)
-.set_attr_parser(ParamParser<NormParam>)
-.set_attr<nnvm::TIsBackward>("TIsBackward", true)
-.set_attr<FResourceRequest>("FResourceRequest",
-  [](const NodeAttrs& attrs) {
-    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
-  })
-.set_attr<FCompute>("FCompute<cpu>", LpNormGradCompute<cpu>);
-
-
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/tensor/broadcast_reduce_op_value.cu b/src/operator/tensor/broadcast_reduce_op_value.cu
index 2d91c5074496..35b3c0272db8 100644
--- a/src/operator/tensor/broadcast_reduce_op_value.cu
+++ b/src/operator/tensor/broadcast_reduce_op_value.cu
@@ -27,70 +27,6 @@
 namespace mxnet {
 namespace op {
 
-template<>
-void L2NormComputeEx<gpu>(const nnvm::NodeAttrs& attrs,
-                          const OpContext& ctx,
-                          const std::vector<NDArray>& inputs,
-                          const std::vector<OpReqType>& req,
-                          const std::vector<NDArray>& outputs) {
-  CHECK_EQ(inputs.size(), 1U);
-  CHECK_EQ(outputs.size(), 1U);
-  CHECK_EQ(req.size(), 1U);
-  const NormParam& param = nnvm::get<NormParam>(attrs.parsed);
-  mshadow::Stream<gpu>* s = ctx.get_stream<gpu>();
-  const NDArrayStorageType istype = inputs[0].storage_type();
-  const mxnet::TShape axis = param.axis.has_value() ? param.axis.value() : mxnet::TShape();
-  if ((istype == kRowSparseStorage || istype == kCSRStorage) && axis.ndim() == 0 &&
-       param.ord == 2) {
-    // l2 norm on the entire array
-    L2NormComputeSparseImpl<gpu>(s, inputs[0], req[0], outputs[0].data());
-  } else {
-    LogUnimplementedOp(attrs, ctx, inputs, req, outputs);
-  }
-}
-
-NNVM_REGISTER_OP(sum)
-.set_attr<FCompute>("FCompute<gpu>", ReduceAxesCompute<gpu, mshadow::red::sum>);
-
-NNVM_REGISTER_OP(_backward_sum)
-.set_attr<FCompute>("FCompute<gpu>", ReduceAxesBackwardUseNone<gpu>);
-
-NNVM_REGISTER_OP(mean)
-.set_attr<FCompute>("FCompute<gpu>", ReduceAxesCompute<gpu, mshadow::red::sum, true>);
-
-NNVM_REGISTER_OP(_backward_mean)
-.set_attr<FCompute>("FCompute<gpu>", ReduceAxesBackwardUseNone<gpu, true>);
-
-NNVM_REGISTER_OP(prod)
-.set_attr<FCompute>("FCompute<gpu>", ReduceAxesCompute<gpu, mshadow_op::product>);
-
-NNVM_REGISTER_OP(_backward_prod)
-.set_attr<FCompute>("FCompute<gpu>", ReduceAxesBackwardUseInOut<gpu, mshadow_op::rdiv>);
-
-NNVM_REGISTER_OP(nansum)
-.set_attr<FCompute>("FCompute<gpu>", ReduceAxesCompute<gpu, mshadow_op::nansum>);
-
-NNVM_REGISTER_OP(_backward_nansum)
-.set_attr<FCompute>("FCompute<gpu>", ReduceAxesBackwardUseInOut<gpu, mshadow_op::nansum_grad>);
-
-NNVM_REGISTER_OP(nanprod)
-.set_attr<FCompute>("FCompute<gpu>", ReduceAxesCompute<gpu, mshadow_op::nanprod>);
-
-NNVM_REGISTER_OP(_backward_nanprod)
-.set_attr<FCompute>("FCompute<gpu>", ReduceAxesBackwardUseInOut<gpu, mshadow_op::nanprod_grad>);
-
-NNVM_REGISTER_OP(max)
-.set_attr<FCompute>("FCompute<gpu>", ReduceAxesCompute<gpu, mshadow::red::maximum>);
-
-NNVM_REGISTER_OP(_backward_max)
-.set_attr<FCompute>("FCompute<gpu>", ReduceAxesBackwardUseInOut<gpu, mshadow_op::eq>);
-
-NNVM_REGISTER_OP(min)
-.set_attr<FCompute>("FCompute<gpu>", ReduceAxesCompute<gpu, mshadow::red::minimum>);
-
-NNVM_REGISTER_OP(_backward_min)
-.set_attr<FCompute>("FCompute<gpu>", ReduceAxesBackwardUseInOut<gpu, mshadow_op::eq>);
-
 NNVM_REGISTER_OP(broadcast_axis)
 .set_attr<FCompute>("FCompute<gpu>", BroadcastCompute<gpu>);
 
@@ -103,12 +39,5 @@ NNVM_REGISTER_OP(broadcast_like)
 NNVM_REGISTER_OP(_broadcast_backward)
 .set_attr<FCompute>("FCompute<gpu>", ReduceAxesCompute<gpu, mshadow::red::sum>);
 
-NNVM_REGISTER_OP(norm)
-.set_attr<FCompute>("FCompute<gpu>", LpNormCompute<gpu>)
-.set_attr<FComputeEx>("FComputeEx<gpu>", L2NormComputeEx<gpu>);
-
-NNVM_REGISTER_OP(_backward_norm)
-.set_attr<FCompute>("FCompute<gpu>", LpNormGradCompute<gpu>);
-
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/tensor/broadcast_reduce_prod_value.cc b/src/operator/tensor/broadcast_reduce_prod_value.cc
new file mode 100644
index 000000000000..4778865bf11d
--- /dev/null
+++ b/src/operator/tensor/broadcast_reduce_prod_value.cc
@@ -0,0 +1,59 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2016 by Contributors
+ * \file broadcast_reduce_prod_value.cc
+ * \brief CPU Implementation of broadcast and reduce of prod functions based on value.
+ */
+#include "./broadcast_reduce_op.h"
+
+namespace mxnet {
+namespace op {
+
+MXNET_OPERATOR_REGISTER_REDUCE(prod)
+.describe(get_reduce_axes_description("product", __LINE__))
+.set_attr<FCompute>("FCompute<cpu>", ReduceAxesCompute<cpu, mshadow_op::product>)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<nnvm::FGradient>("FGradient", ReduceGrad{ "_backward_prod" });
+
+MXNET_OPERATOR_REGISTER_REDUCE_BACKWARD(_backward_prod)
+.set_num_inputs(3)
+.set_attr<FCompute>("FCompute<cpu>", ReduceAxesBackwardUseInOut< cpu, mshadow_op::rdiv>);
+
+MXNET_OPERATOR_REGISTER_REDUCE(nanprod)
+.describe(R"code(Computes the product of array elements over given axes treating Not a Numbers (``NaN``) as one.
+
+)code" ADD_FILELINE)
+.set_attr<FCompute>("FCompute<cpu>", ReduceAxesCompute<cpu, mshadow_op::nanprod>)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<nnvm::FGradient>("FGradient", ReduceGrad{ "_backward_nanprod" });
+
+MXNET_OPERATOR_REGISTER_REDUCE_BACKWARD(_backward_nanprod)
+.set_num_inputs(3)
+.set_attr<FCompute>("FCompute<cpu>", ReduceAxesBackwardUseInOut<cpu, mshadow_op::nanprod_grad>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/tensor/broadcast_reduce_prod_value.cu b/src/operator/tensor/broadcast_reduce_prod_value.cu
new file mode 100644
index 000000000000..5731de308064
--- /dev/null
+++ b/src/operator/tensor/broadcast_reduce_prod_value.cu
@@ -0,0 +1,43 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2016 by Contributors
+ * \file broadcast_reduce_prod_value.cu
+ * \brief GPU Implementation of broadcast and reduce prod functions based on value.
+ */
+#include "./broadcast_reduce_op.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(prod)
+.set_attr<FCompute>("FCompute<gpu>", ReduceAxesCompute<gpu, mshadow_op::product>);
+
+NNVM_REGISTER_OP(_backward_prod)
+.set_attr<FCompute>("FCompute<gpu>", ReduceAxesBackwardUseInOut<gpu, mshadow_op::rdiv>);
+
+NNVM_REGISTER_OP(nanprod)
+.set_attr<FCompute>("FCompute<gpu>", ReduceAxesCompute<gpu, mshadow_op::nanprod>);
+
+NNVM_REGISTER_OP(_backward_nanprod)
+.set_attr<FCompute>("FCompute<gpu>", ReduceAxesBackwardUseInOut<gpu, mshadow_op::nanprod_grad>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/tensor/broadcast_reduce_sum_value.cc b/src/operator/tensor/broadcast_reduce_sum_value.cc
new file mode 100644
index 000000000000..c5c9f5cb48e4
--- /dev/null
+++ b/src/operator/tensor/broadcast_reduce_sum_value.cc
@@ -0,0 +1,113 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2016 by Contributors
+ * \file broadcast_reduce_sum_value.cc
+ * \brief CPU Implementation of broadcast and reduce sum (and related) functions based on value.
+ */
+#include "./broadcast_reduce_op.h"
+
+namespace mxnet {
+namespace op {
+
+MXNET_OPERATOR_REGISTER_REDUCE(sum)
+MXNET_ADD_SPARSE_OP_ALIAS(sum)
+.add_alias("sum_axis")
+.describe(R"code(Computes the sum of array elements over given axes.
+
+.. Note::
+
+  `sum` and `sum_axis` are equivalent.
+  For ndarray of csr storage type summation along axis 0 and axis 1 is supported.
+  Setting keepdims or exclude to True will cause a fallback to dense operator.
+
+Example::
+
+  data = [[[1, 2], [2, 3], [1, 3]],
+          [[1, 4], [4, 3], [5, 2]],
+          [[7, 1], [7, 2], [7, 3]]]
+
+  sum(data, axis=1)
+  [[  4.   8.]
+   [ 10.   9.]
+   [ 21.   6.]]
+
+  sum(data, axis=[1,2])
+  [ 12.  19.  27.]
+
+  data = [[1, 2, 0],
+          [3, 0, 1],
+          [4, 1, 0]]
+
+  csr = cast_storage(data, 'csr')
+
+  sum(csr, axis=0)
+  [ 8.  3.  1.]
+
+  sum(csr, axis=1)
+  [ 3.  4.  5.]
+
+)code" ADD_FILELINE)
+.set_attr<FCompute>("FCompute<cpu>", ReduceAxesCompute<cpu, mshadow::red::sum>)
+.set_attr<FComputeEx>("FComputeEx<cpu>", ReduceAxesOpForwardEx<cpu, mshadow::red::sum>)
+.set_attr<FInferStorageType>("FInferStorageType", ReduceAxesOpForwardStorage)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_sum"});
+
+MXNET_OPERATOR_REGISTER_REDUCE_BACKWARD(_backward_sum)
+.set_num_inputs(1)
+.set_attr<FCompute>("FCompute<cpu>", ReduceAxesBackwardUseNone<cpu>);
+
+MXNET_OPERATOR_REGISTER_REDUCE(mean)
+MXNET_ADD_SPARSE_OP_ALIAS(mean)
+.describe(get_reduce_axes_description("mean", __LINE__))
+.set_attr<FCompute>("FCompute<cpu>", ReduceAxesCompute<cpu, mshadow::red::sum, true>)
+.set_attr<FComputeEx>("FComputeEx<cpu>", ReduceAxesOpForwardEx<cpu, mshadow::red::sum, true>)
+.set_attr<FInferStorageType>("FInferStorageType", ReduceAxesOpForwardStorage)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_mean"});
+
+MXNET_OPERATOR_REGISTER_REDUCE_BACKWARD(_backward_mean)
+.set_num_inputs(1)
+.set_attr<FCompute>("FCompute<cpu>", ReduceAxesBackwardUseNone<cpu, true>);
+
+MXNET_OPERATOR_REGISTER_REDUCE(nansum)
+.describe(R"code(Computes the sum of array elements over given axes treating Not a Numbers (``NaN``) as zero.
+
+)code" ADD_FILELINE)
+.set_attr<FCompute>("FCompute<cpu>", ReduceAxesCompute<cpu, mshadow_op::nansum>)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<nnvm::FGradient>("FGradient", ReduceGrad{ "_backward_nansum" });
+
+MXNET_OPERATOR_REGISTER_REDUCE_BACKWARD(_backward_nansum)
+.set_num_inputs(3)
+.set_attr<FCompute>("FCompute<cpu>", ReduceAxesBackwardUseInOut<cpu, mshadow_op::nansum_grad>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/tensor/broadcast_reduce_sum_value.cu b/src/operator/tensor/broadcast_reduce_sum_value.cu
new file mode 100644
index 000000000000..2385d36f35b0
--- /dev/null
+++ b/src/operator/tensor/broadcast_reduce_sum_value.cu
@@ -0,0 +1,49 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2016 by Contributors
+ * \file broadcast_reduce_sum_value.cu
+ * \brief GPU Implementation of broadcast and reduce sum (and related) functions based on value.
+ */
+#include "./broadcast_reduce_op.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(sum)
+.set_attr<FCompute>("FCompute<gpu>", ReduceAxesCompute<gpu, mshadow::red::sum>);
+
+NNVM_REGISTER_OP(_backward_sum)
+.set_attr<FCompute>("FCompute<gpu>", ReduceAxesBackwardUseNone<gpu>);
+
+NNVM_REGISTER_OP(mean)
+.set_attr<FCompute>("FCompute<gpu>", ReduceAxesCompute<gpu, mshadow::red::sum, true>);
+
+NNVM_REGISTER_OP(_backward_mean)
+.set_attr<FCompute>("FCompute<gpu>", ReduceAxesBackwardUseNone<gpu, true>);
+
+NNVM_REGISTER_OP(nansum)
+.set_attr<FCompute>("FCompute<gpu>", ReduceAxesCompute<gpu, mshadow_op::nansum>);
+
+NNVM_REGISTER_OP(_backward_nansum)
+.set_attr<FCompute>("FCompute<gpu>", ReduceAxesBackwardUseInOut<gpu, mshadow_op::nansum_grad>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/tensor/elemwise_unary_op_basic.cc b/src/operator/tensor/elemwise_unary_op_basic.cc
index c293ee6e22bd..117cfa96518a 100644
--- a/src/operator/tensor/elemwise_unary_op_basic.cc
+++ b/src/operator/tensor/elemwise_unary_op_basic.cc
@@ -703,55 +703,6 @@ The storage type of ``negative`` output depends upon the input storage type:
 )code")
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"negative"});
 
-// reciprocal
-MXNET_OPERATOR_REGISTER_UNARY(reciprocal)
-.describe(R"code(Returns the reciprocal of the argument, element-wise.
-
-Calculates 1/x.
-
-Example::
-
-    reciprocal([-2, 1, 3, 1.6, 0.2]) = [-0.5, 1.0, 0.33333334, 0.625, 5.0]
-
-)code" ADD_FILELINE)
-.set_attr<FCompute>("FCompute<cpu>", UnaryOp::Compute<cpu, mshadow_op::reciprocal>)
-.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_reciprocal"});
-
-MXNET_OPERATOR_REGISTER_BINARY(_backward_reciprocal)
-.set_attr<FCompute>("FCompute<cpu>",
-  ElemwiseBinaryOp::Compute<cpu, unary_bwd<mshadow_op::reciprocal_grad> >)
-.set_attr<nnvm::FGradient>("FGradient",
-  [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
-    // ograds[0]: dL/dxgrad
-    // inputs[0]: dL/dy
-    // inputs[1]: x
-    // f(x) = y = 1/x
-    // f'(x) = -1/x^2
-    // f''(x) = 2/x^3 = -2 * (f'(x) * f(x))
-
-    const std::unordered_map<std::string, std::string> args = {{"scalar", "-2.0"}};
-
-    auto dydx_mul_dldy = nnvm::NodeEntry{n};  // f'(x) * head_grads
-    auto dydx = MakeNode("elemwise_div", n->attrs.name + "_dydx",
-                         {dydx_mul_dldy, n->inputs[0]}, nullptr, &n);
-    auto fx = MakeNode("reciprocal", n->attrs.name + "_fx",
-                       {n->inputs[1]}, nullptr, &n);
-
-    auto d2ydx2_mid = MakeNode("elemwise_mul", n->attrs.name + "_d2ydx2_mid",
-                               {dydx_mul_dldy, nnvm::NodeEntry{fx}}, nullptr, &n);
-
-    auto d2ydx2 = MakeNode("_mul_scalar", n->attrs.name + "_d2ydx2",
-                           {nnvm::NodeEntry{d2ydx2_mid}}, &args, &n);
-
-    std::vector<nnvm::NodeEntry> ret;
-
-    ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "_backward_grad_grad",
-                             {ograds[0], nnvm::NodeEntry{dydx}}, nullptr, &n));
-    ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "_backward_grad_grad_inp",
-                             {ograds[0], nnvm::NodeEntry{d2ydx2}}, nullptr, &n));
-    return ret;
-});
-
 // abs
 MXNET_OPERATOR_REGISTER_UNARY_WITH_RSP_CSR(abs, cpu, mshadow_op::abs)
 .describe(R"code(Returns element-wise absolute value of the input.
@@ -923,116 +874,6 @@ The storage type of ``fix`` output depends upon the input storage type:
 )code" ADD_FILELINE)
 .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes);
 
-// square
-#if MSHADOW_USE_MKL == 1
-MXNET_MKL_OPERATOR_REGISTER_UNARY_WITH_RSP_CSR(square, cpu, mshadow_op::square, mkl_func::square)
-.describe(R"code(Returns element-wise squared value of the input.
-
-.. math::
-   square(x) = x^2
-
-Example::
-
-   square([2, 3, 4]) = [4, 9, 16]
-
-The storage type of ``square`` output depends upon the input storage type:
-
-   - square(default) = default
-   - square(row_sparse) = row_sparse
-   - square(csr) = csr
-
-)code" ADD_FILELINE)
-.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_square"});
-#else
-MXNET_OPERATOR_REGISTER_UNARY_WITH_RSP_CSR(square, cpu, mshadow_op::square)
-.describe(R"code(Returns element-wise squared value of the input.
-
-.. math::
-   square(x) = x^2
-
-Example::
-
-   square([2, 3, 4]) = [4, 9, 16]
-
-The storage type of ``square`` output depends upon the input storage type:
-
-   - square(default) = default
-   - square(row_sparse) = row_sparse
-   - square(csr) = csr
-
-)code" ADD_FILELINE)
-.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_square"});
-#endif
-
-MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU(_backward_square,
-                                               unary_bwd<mshadow_op::square_grad>);
-
-// sqrt
-MXNET_OPERATOR_REGISTER_UNARY_WITH_RSP_CSR(sqrt, cpu, mshadow_op::square_root)
-.describe(R"code(Returns element-wise square-root value of the input.
-
-.. math::
-   \textrm{sqrt}(x) = \sqrt{x}
-
-Example::
-
-   sqrt([4, 9, 16]) = [2, 3, 4]
-
-The storage type of ``sqrt`` output depends upon the input storage type:
-
-   - sqrt(default) = default
-   - sqrt(row_sparse) = row_sparse
-   - sqrt(csr) = csr
-
-)code" ADD_FILELINE)
-.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{"_backward_sqrt"});
-
-MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_sqrt,
-                                                  unary_bwd<mshadow_op::square_root_grad>);
-
-// rsqrt
-MXNET_OPERATOR_REGISTER_UNARY_WITH_SPARSE_DR(rsqrt, cpu, mshadow_op::reciprocal_square_root)
-MXNET_ADD_SPARSE_OP_ALIAS(rsqrt)
-.describe(R"code(Returns element-wise inverse square-root value of the input.
-
-.. math::
-   rsqrt(x) = 1/\sqrt{x}
-
-Example::
-
-   rsqrt([4,9,16]) = [0.5, 0.33333334, 0.25]
-
-The storage type of ``rsqrt`` output is always dense
-
-)code" ADD_FILELINE)
-.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_rsqrt"});
-
-MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(
-  _backward_rsqrt, unary_bwd<mshadow_op::reciprocal_square_root_grad>);
-
-// cbrt
-MXNET_OPERATOR_REGISTER_UNARY_WITH_RSP_CSR(cbrt, cpu, mshadow_op::cube_root)
-.describe(R"code(Returns element-wise cube-root value of the input.
-
-.. math::
-   cbrt(x) = \sqrt[3]{x}
-
-Example::
-
-   cbrt([1, 8, -125]) = [1, 2, -5]
-
-The storage type of ``cbrt`` output depends upon the input storage type:
-
-   - cbrt(default) = default
-   - cbrt(row_sparse) = row_sparse
-   - cbrt(csr) = csr
-
-)code" ADD_FILELINE)
-.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{"_backward_cbrt"});
-
-MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_cbrt,
-                                                  unary_bwd<mshadow_op::cube_root_grad>);
-
 // erf
 MXNET_OPERATOR_REGISTER_UNARY(erf)
 .describe(R"code(Returns element-wise gauss error function of the input.
@@ -1070,224 +911,6 @@ MXNET_OPERATOR_REGISTER_BINARY(_backward_erfinv)
 .set_attr<FCompute>("FCompute<cpu>",
                     ElemwiseBinaryOp::Compute<cpu, unary_bwd<mshadow_op::erfinv_grad>>);
 
-// rcbrt
-MXNET_OPERATOR_REGISTER_UNARY(rcbrt)
-.describe(R"code(Returns element-wise inverse cube-root value of the input.
-
-.. math::
-   rcbrt(x) = 1/\sqrt[3]{x}
-
-Example::
-
-   rcbrt([1,8,-125]) = [1.0, 0.5, -0.2]
-
-)code" ADD_FILELINE)
-.set_attr<FCompute>("FCompute<cpu>", UnaryOp::Compute<cpu, mshadow_op::reciprocal_cube_root>)
-.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_rcbrt"});
-
-MXNET_OPERATOR_REGISTER_BINARY(_backward_rcbrt)
-.set_attr<FCompute>("FCompute<cpu>",
-                    ElemwiseBinaryOp::Compute<cpu,
-                      unary_bwd<mshadow_op::reciprocal_cube_root_grad>>);
-
-// exp
-#if MSHADOW_USE_MKL == 1
-MXNET_MKL_OPERATOR_REGISTER_UNARY_WITH_SPARSE_DR(exp, cpu, mshadow_op::exp, mkl_func::exp)
-MXNET_ADD_SPARSE_OP_ALIAS(exp)
-.describe(R"code(Returns element-wise exponential value of the input.
-
-.. math::
-   exp(x) = e^x \approx 2.718^x
-
-Example::
-
-   exp([0, 1, 2]) = [1., 2.71828175, 7.38905621]
-
-The storage type of ``exp`` output is always dense
-
-)code" ADD_FILELINE)
-.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{"_mul"});
-#else
-MXNET_OPERATOR_REGISTER_UNARY_WITH_SPARSE_DR(exp, cpu, mshadow_op::exp)
-MXNET_ADD_SPARSE_OP_ALIAS(exp)
-.describe(R"code(Returns element-wise exponential value of the input.
-
-.. math::
-   exp(x) = e^x \approx 2.718^x
-
-Example::
-
-   exp([0, 1, 2]) = [1., 2.71828175, 7.38905621]
-
-The storage type of ``exp`` output is always dense
-
-)code" ADD_FILELINE)
-.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{"_mul"});
-#endif
-
-// log
-MXNET_OPERATOR_REGISTER_UNARY(log)
-MXNET_ADD_SPARSE_OP_ALIAS(log)
-.describe(R"code(Returns element-wise Natural logarithmic value of the input.
-
-The natural logarithm is logarithm in base *e*, so that ``log(exp(x)) = x``
-
-The storage type of ``log`` output is always dense
-
-)code" ADD_FILELINE)
-#if MSHADOW_USE_MKL == 1
-.set_attr<FCompute>("FCompute<cpu>", UnaryOp::MKL_Compute<mshadow_op::log, mkl_func::log>)
-#else
-.set_attr<FCompute>("FCompute<cpu>", UnaryOp::Compute<cpu, mshadow_op::log>)
-#endif    // MSHADOW_USE_MKL == 1
-.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_log"});
-
-// log10
-MXNET_OPERATOR_REGISTER_UNARY_WITH_SPARSE_DR(log10, cpu, mshadow_op::log10)
-MXNET_ADD_SPARSE_OP_ALIAS(log10)
-.describe(R"code(Returns element-wise Base-10 logarithmic value of the input.
-
-``10**log10(x) = x``
-
-The storage type of ``log10`` output is always dense
-
-)code" ADD_FILELINE)
-.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_log10"});
-
-// log2
-MXNET_OPERATOR_REGISTER_UNARY_WITH_SPARSE_DR(log2, cpu, mshadow_op::log2)
-MXNET_ADD_SPARSE_OP_ALIAS(log2)
-.describe(R"code(Returns element-wise Base-2 logarithmic value of the input.
-
-``2**log2(x) = x``
-
-The storage type of ``log2`` output is always dense
-
-)code" ADD_FILELINE)
-.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_log2"});
-
-MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_log,
-                                                  unary_bwd<mshadow_op::log_grad>)
-.set_attr<nnvm::FGradient>("FGradient",
-  [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
-    // ograds[0]: dL/dxgrad
-    // inputs[0]: dL/dy
-    // inputs[1]: x
-    // f(x) = y = log(x)
-    // f'(x) = 1/x
-    // f''(x) = -1 * (f'(x) * f'(x))
-    auto dydx_mul_dldy = nnvm::NodeEntry{n};  // f'(x) * head_grads
-    auto dlogx = MakeNode("reciprocal", n->attrs.name + "_dlogx",
-                            {n->inputs[1]}, nullptr, &n);
-    auto d2ydx2_mid = MakeNode("elemwise_mul", n->attrs.name + "_d2ydx2_mid",
-                            {dydx_mul_dldy, nnvm::NodeEntry{dlogx}}, nullptr, &n);
-    auto d2ydx2 = MakeNode("negative", n->attrs.name + "_d2ydx2",
-                        {nnvm::NodeEntry{d2ydx2_mid}}, nullptr, &n);
-
-    std::vector<nnvm::NodeEntry> ret;
-
-    ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "_backward_grad_grad",
-                             {ograds[0], nnvm::NodeEntry{dlogx}}, nullptr, &n));
-    ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "_backward_grad_grad_inp",
-                             {ograds[0], nnvm::NodeEntry{d2ydx2}}, nullptr, &n));
-    return ret;
-  });
-
-MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_log10,
-                                                  unary_bwd<mshadow_op::log10_grad>)
-.set_attr<nnvm::FGradient>("FGradient",
-  [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
-    // ograds[0]: dL/dxgrad
-    // inputs[0]: dL/dy
-    // inputs[1]: x
-    // f(x) = y = log10(x)
-    // f'(x) = 1 / (log(10) * x)
-    // f''(x) = -1 * (f'(x) * 1/x)
-    auto dydx_mul_dldy = nnvm::NodeEntry{n};  // f'(x) * head_grads
-    auto dydx = MakeNode("elemwise_div", n->attrs.name + "_dydx",
-                            {n->inputs[0]}, nullptr, &n);
-    auto dlogx = MakeNode("reciprocal", n->attrs.name + "_dlogx",
-                            {n->inputs[1]}, nullptr, &n);
-    auto d2ydx2_mid = MakeNode("elemwise_mul", n->attrs.name + "_d2ydx2_mid",
-                            {dydx_mul_dldy, nnvm::NodeEntry{dlogx}}, nullptr, &n);
-    auto d2ydx2 = MakeNode("negative", n->attrs.name + "_d2ydx2",
-                        {nnvm::NodeEntry{d2ydx2_mid}}, nullptr, &n);
-
-    std::vector<nnvm::NodeEntry> ret;
-
-    ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "_backward_grad_grad",
-                             {ograds[0], nnvm::NodeEntry{dydx}}, nullptr, &n));
-    ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "_backward_grad_grad_inp",
-                             {ograds[0], nnvm::NodeEntry{d2ydx2}}, nullptr, &n));
-    return ret;
-  });
-
-MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_log2,
-                                                  unary_bwd<mshadow_op::log2_grad>)
-.set_attr<nnvm::FGradient>("FGradient",
-  [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
-    // ograds[0]: dL/dxgrad
-    // inputs[0]: dL/dy
-    // inputs[1]: x
-    // f(x) = y = log2(x)
-    // f'(x) = 1 / (log(2) * x)
-    // f''(x) = -1 * (f'(x) * 1/x)
-    auto dydx_mul_dldy = nnvm::NodeEntry{n};  // f'(x) * head_grads
-    auto dydx = MakeNode("elemwise_div", n->attrs.name + "_dydx",
-                            {n->inputs[0]}, nullptr, &n);
-    auto dlogx = MakeNode("reciprocal", n->attrs.name + "_dlogx",
-                            {n->inputs[1]}, nullptr, &n);
-    auto d2ydx2_mid = MakeNode("elemwise_mul", n->attrs.name + "_d2ydx2_mid",
-                            {dydx_mul_dldy, nnvm::NodeEntry{dlogx}}, nullptr, &n);
-    auto d2ydx2 = MakeNode("negative", n->attrs.name + "_d2ydx2",
-                        {nnvm::NodeEntry{d2ydx2_mid}}, nullptr, &n);
-
-    std::vector<nnvm::NodeEntry> ret;
-
-    ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "_backward_grad_grad",
-                             {ograds[0], nnvm::NodeEntry{dydx}}, nullptr, &n));
-    ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "_backward_grad_grad_inp",
-                             {ograds[0], nnvm::NodeEntry{d2ydx2}}, nullptr, &n));
-    return ret;
-  });
-
-// log1p
-MXNET_OPERATOR_REGISTER_UNARY_WITH_RSP_CSR(log1p, cpu, mshadow_op::log1p)
-.describe(R"code(Returns element-wise ``log(1 + x)`` value of the input.
-
-This function is more accurate than ``log(1 + x)``  for small ``x`` so that
-:math:`1+x\approx 1`
-
-The storage type of ``log1p`` output depends upon the input storage type:
-
-   - log1p(default) = default
-   - log1p(row_sparse) = row_sparse
-   - log1p(csr) = csr
-
-)code" ADD_FILELINE)
-.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_log1p"});
-
-MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_log1p,
-                                                  unary_bwd<mshadow_op::log1p_grad>);
-
-// expm1
-MXNET_OPERATOR_REGISTER_UNARY_WITH_RSP_CSR(expm1, cpu, mshadow_op::expm1)
-.describe(R"code(Returns ``exp(x) - 1`` computed element-wise on the input.
-
-This function provides greater precision than ``exp(x) - 1`` for small values of ``x``.
-
-The storage type of ``expm1`` output depends upon the input storage type:
-
-   - expm1(default) = default
-   - expm1(row_sparse) = row_sparse
-   - expm1(csr) = csr
-
-)code" ADD_FILELINE)
-.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_expm1"});
-
-MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_expm1, unary_bwd<mshadow_op::exp>);
-
-
 // gamma
 MXNET_OPERATOR_REGISTER_UNARY_WITH_SPARSE_DR(gamma, cpu, mshadow_op::gamma)
 MXNET_ADD_SPARSE_OP_ALIAS(gamma)
diff --git a/src/operator/tensor/elemwise_unary_op_basic.cu b/src/operator/tensor/elemwise_unary_op_basic.cu
index 642cb0e6e48b..e5b60b1726e6 100644
--- a/src/operator/tensor/elemwise_unary_op_basic.cu
+++ b/src/operator/tensor/elemwise_unary_op_basic.cu
@@ -154,14 +154,6 @@ NNVM_REGISTER_OP(negative)
 .set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, mshadow_op::negation>)
 .set_attr<FComputeEx>("FComputeEx<gpu>", UnaryOp::ComputeEx<gpu, mshadow_op::negation>);
 
-// reciprocal
-NNVM_REGISTER_OP(reciprocal)
-.set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, mshadow_op::reciprocal>);
-
-NNVM_REGISTER_OP(_backward_reciprocal)
-.set_attr<FCompute>("FCompute<gpu>", ElemwiseBinaryOp::Compute<
-  gpu, unary_bwd<mshadow_op::reciprocal_grad> >);
-
 // abs
 NNVM_REGISTER_OP(abs)
 .set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, mshadow_op::abs>)
@@ -204,104 +196,11 @@ NNVM_REGISTER_OP(rint)
 .set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, mshadow_op::rint>)
 .set_attr<FComputeEx>("FComputeEx<gpu>", UnaryOp::ComputeEx<gpu, mshadow_op::rint>);
 
-
 // fix
 NNVM_REGISTER_OP(fix)
 .set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, mshadow_op::fix>)
 .set_attr<FComputeEx>("FComputeEx<gpu>", UnaryOp::ComputeEx<gpu, mshadow_op::fix>);
 
-
-// square
-NNVM_REGISTER_OP(square)
-.set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, mshadow_op::square>)
-.set_attr<FComputeEx>("FComputeEx<gpu>", UnaryOp::ComputeEx<gpu, mshadow_op::square>);
-
-NNVM_REGISTER_OP(_backward_square)
-.set_attr<FCompute>("FCompute<gpu>", ElemwiseBinaryOp::Compute<
-  gpu, unary_bwd<mshadow_op::square_grad> >);
-
-// sqrt
-NNVM_REGISTER_OP(sqrt)
-.set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, mshadow_op::square_root>)
-.set_attr<FComputeEx>("FComputeEx<gpu>", UnaryOp::ComputeEx<gpu, mshadow_op::square_root>);
-
-
-NNVM_REGISTER_OP(_backward_sqrt)
-.set_attr<FCompute>("FCompute<gpu>", ElemwiseBinaryOp::Compute<
-  gpu, unary_bwd<mshadow_op::square_root_grad> >);
-
-// rsqrt
-NNVM_REGISTER_OP(rsqrt)
-.set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, mshadow_op::reciprocal_square_root>);
-
-NNVM_REGISTER_OP(_backward_rsqrt)
-.set_attr<FCompute>("FCompute<gpu>",
-  ElemwiseBinaryOp::Compute<gpu, unary_bwd<mshadow_op::reciprocal_square_root_grad> >);
-
-// cbrt
-NNVM_REGISTER_OP(cbrt)
-.set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, mshadow_op::cube_root>)
-.set_attr<FComputeEx>("FComputeEx<gpu>", UnaryOp::ComputeEx<gpu, mshadow_op::cube_root>);
-
-
-NNVM_REGISTER_OP(_backward_cbrt)
-.set_attr<FCompute>("FCompute<gpu>", ElemwiseBinaryOp::Compute<
-  gpu, unary_bwd<mshadow_op::cube_root_grad> >);
-
-// rcbrt
-NNVM_REGISTER_OP(rcbrt)
-.set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, mshadow_op::reciprocal_cube_root>);
-
-NNVM_REGISTER_OP(_backward_rcbrt)
-.set_attr<FCompute>("FCompute<gpu>",
-  ElemwiseBinaryOp::Compute<gpu, unary_bwd<mshadow_op::reciprocal_cube_root_grad> >);
-
-// exp
-NNVM_REGISTER_OP(exp)
-.set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, mshadow_op::exp>);
-
-// log
-NNVM_REGISTER_OP(log)
-.set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, mshadow_op::log>);
-
-// log10
-NNVM_REGISTER_OP(log10)
-.set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, mshadow_op::log10>);
-
-// log2
-NNVM_REGISTER_OP(log2)
-.set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, mshadow_op::log2>);
-
-NNVM_REGISTER_OP(_backward_log)
-.set_attr<FCompute>("FCompute<gpu>", ElemwiseBinaryOp::Compute<
-  gpu, unary_bwd<mshadow_op::log_grad> >);
-
-NNVM_REGISTER_OP(_backward_log10)
-.set_attr<FCompute>("FCompute<gpu>", ElemwiseBinaryOp::Compute<
-  gpu, unary_bwd<mshadow_op::log10_grad> >);
-
-NNVM_REGISTER_OP(_backward_log2)
-.set_attr<FCompute>("FCompute<gpu>", ElemwiseBinaryOp::Compute<
-  gpu, unary_bwd<mshadow_op::log2_grad> >);
-
-// log1p
-NNVM_REGISTER_OP(log1p)
-.set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, mshadow_op::log1p>)
-.set_attr<FComputeEx>("FComputeEx<gpu>", UnaryOp::ComputeEx<gpu, mshadow_op::log1p>);
-
-NNVM_REGISTER_OP(_backward_log1p)
-.set_attr<FCompute>("FCompute<gpu>", ElemwiseBinaryOp::Compute<
-  gpu, unary_bwd<mshadow_op::log1p_grad> >);
-
-// expm1
-NNVM_REGISTER_OP(expm1)
-.set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, mshadow_op::expm1>)
-.set_attr<FComputeEx>("FComputeEx<gpu>", UnaryOp::ComputeEx<gpu, mshadow_op::expm1>);
-
-NNVM_REGISTER_OP(_backward_expm1)
-.set_attr<FCompute>("FCompute<gpu>", ElemwiseBinaryOp::Compute<
-  gpu, unary_bwd<mshadow_op::exp> >);
-
 // gamma
 NNVM_REGISTER_OP(gamma)
 .set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, mshadow_op::gamma>);
diff --git a/src/operator/tensor/elemwise_unary_op_logexp.cc b/src/operator/tensor/elemwise_unary_op_logexp.cc
new file mode 100644
index 000000000000..65394826276f
--- /dev/null
+++ b/src/operator/tensor/elemwise_unary_op_logexp.cc
@@ -0,0 +1,230 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file elemwise_unary_op_logexp.cc
+ * \brief CPU Implementation of elementwise log and exp function.
+ */
+#include <mxnet/base.h>
+#include "elemwise_unary_op.h"
+#include "./elemwise_binary_op-inl.h"
+#include "../nn/mkldnn/mkldnn_ops-inl.h"
+
+namespace mxnet {
+namespace op {
+
+// exp
+#if MSHADOW_USE_MKL == 1
+MXNET_MKL_OPERATOR_REGISTER_UNARY_WITH_SPARSE_DR(exp, cpu, mshadow_op::exp, mkl_func::exp)
+MXNET_ADD_SPARSE_OP_ALIAS(exp)
+.describe(R"code(Returns element-wise exponential value of the input.
+
+.. math::
+   exp(x) = e^x \approx 2.718^x
+
+Example::
+
+   exp([0, 1, 2]) = [1., 2.71828175, 7.38905621]
+
+The storage type of ``exp`` output is always dense
+
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{"_mul"});
+#else
+MXNET_OPERATOR_REGISTER_UNARY_WITH_SPARSE_DR(exp, cpu, mshadow_op::exp)
+MXNET_ADD_SPARSE_OP_ALIAS(exp)
+.describe(R"code(Returns element-wise exponential value of the input.
+
+.. math::
+   exp(x) = e^x \approx 2.718^x
+
+Example::
+
+   exp([0, 1, 2]) = [1., 2.71828175, 7.38905621]
+
+The storage type of ``exp`` output is always dense
+
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{"_mul"});
+#endif
+
+// log
+MXNET_OPERATOR_REGISTER_UNARY(log)
+MXNET_ADD_SPARSE_OP_ALIAS(log)
+.describe(R"code(Returns element-wise Natural logarithmic value of the input.
+
+The natural logarithm is logarithm in base *e*, so that ``log(exp(x)) = x``
+
+The storage type of ``log`` output is always dense
+
+)code" ADD_FILELINE)
+#if MSHADOW_USE_MKL == 1
+.set_attr<FCompute>("FCompute<cpu>", UnaryOp::MKL_Compute<mshadow_op::log, mkl_func::log>)
+#else
+.set_attr<FCompute>("FCompute<cpu>", UnaryOp::Compute<cpu, mshadow_op::log>)
+#endif    // MSHADOW_USE_MKL == 1
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_log"});
+
+// log10
+MXNET_OPERATOR_REGISTER_UNARY_WITH_SPARSE_DR(log10, cpu, mshadow_op::log10)
+MXNET_ADD_SPARSE_OP_ALIAS(log10)
+.describe(R"code(Returns element-wise Base-10 logarithmic value of the input.
+
+``10**log10(x) = x``
+
+The storage type of ``log10`` output is always dense
+
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_log10"});
+
+// log2
+MXNET_OPERATOR_REGISTER_UNARY_WITH_SPARSE_DR(log2, cpu, mshadow_op::log2)
+MXNET_ADD_SPARSE_OP_ALIAS(log2)
+.describe(R"code(Returns element-wise Base-2 logarithmic value of the input.
+
+``2**log2(x) = x``
+
+The storage type of ``log2`` output is always dense
+
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_log2"});
+
+MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_log,
+                                                  unary_bwd<mshadow_op::log_grad>)
+.set_attr<nnvm::FGradient>("FGradient",
+  [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
+    // ograds[0]: dL/dxgrad
+    // inputs[0]: dL/dy
+    // inputs[1]: x
+    // f(x) = y = log(x)
+    // f'(x) = 1/x
+    // f''(x) = -1 * (f'(x) * f'(x))
+    auto dydx_mul_dldy = nnvm::NodeEntry{n};  // f'(x) * head_grads
+    auto dlogx = MakeNode("reciprocal", n->attrs.name + "_dlogx",
+                            {n->inputs[1]}, nullptr, &n);
+    auto d2ydx2_mid = MakeNode("elemwise_mul", n->attrs.name + "_d2ydx2_mid",
+                            {dydx_mul_dldy, nnvm::NodeEntry{dlogx}}, nullptr, &n);
+    auto d2ydx2 = MakeNode("negative", n->attrs.name + "_d2ydx2",
+                        {nnvm::NodeEntry{d2ydx2_mid}}, nullptr, &n);
+
+    std::vector<nnvm::NodeEntry> ret;
+
+    ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "_backward_grad_grad",
+                             {ograds[0], nnvm::NodeEntry{dlogx}}, nullptr, &n));
+    ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "_backward_grad_grad_inp",
+                             {ograds[0], nnvm::NodeEntry{d2ydx2}}, nullptr, &n));
+    return ret;
+  });
+
+MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_log10,
+                                                  unary_bwd<mshadow_op::log10_grad>)
+.set_attr<nnvm::FGradient>("FGradient",
+  [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
+    // ograds[0]: dL/dxgrad
+    // inputs[0]: dL/dy
+    // inputs[1]: x
+    // f(x) = y = log10(x)
+    // f'(x) = 1 / (log(10) * x)
+    // f''(x) = -1 * (f'(x) * 1/x)
+    auto dydx_mul_dldy = nnvm::NodeEntry{n};  // f'(x) * head_grads
+    auto dydx = MakeNode("elemwise_div", n->attrs.name + "_dydx",
+                            {n->inputs[0]}, nullptr, &n);
+    auto dlogx = MakeNode("reciprocal", n->attrs.name + "_dlogx",
+                            {n->inputs[1]}, nullptr, &n);
+    auto d2ydx2_mid = MakeNode("elemwise_mul", n->attrs.name + "_d2ydx2_mid",
+                            {dydx_mul_dldy, nnvm::NodeEntry{dlogx}}, nullptr, &n);
+    auto d2ydx2 = MakeNode("negative", n->attrs.name + "_d2ydx2",
+                        {nnvm::NodeEntry{d2ydx2_mid}}, nullptr, &n);
+
+    std::vector<nnvm::NodeEntry> ret;
+
+    ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "_backward_grad_grad",
+                             {ograds[0], nnvm::NodeEntry{dydx}}, nullptr, &n));
+    ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "_backward_grad_grad_inp",
+                             {ograds[0], nnvm::NodeEntry{d2ydx2}}, nullptr, &n));
+    return ret;
+  });
+
+MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_log2,
+                                                  unary_bwd<mshadow_op::log2_grad>)
+.set_attr<nnvm::FGradient>("FGradient",
+  [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
+    // ograds[0]: dL/dxgrad
+    // inputs[0]: dL/dy
+    // inputs[1]: x
+    // f(x) = y = log2(x)
+    // f'(x) = 1 / (log(2) * x)
+    // f''(x) = -1 * (f'(x) * 1/x)
+    auto dydx_mul_dldy = nnvm::NodeEntry{n};  // f'(x) * head_grads
+    auto dydx = MakeNode("elemwise_div", n->attrs.name + "_dydx",
+                            {n->inputs[0]}, nullptr, &n);
+    auto dlogx = MakeNode("reciprocal", n->attrs.name + "_dlogx",
+                            {n->inputs[1]}, nullptr, &n);
+    auto d2ydx2_mid = MakeNode("elemwise_mul", n->attrs.name + "_d2ydx2_mid",
+                            {dydx_mul_dldy, nnvm::NodeEntry{dlogx}}, nullptr, &n);
+    auto d2ydx2 = MakeNode("negative", n->attrs.name + "_d2ydx2",
+                        {nnvm::NodeEntry{d2ydx2_mid}}, nullptr, &n);
+
+    std::vector<nnvm::NodeEntry> ret;
+
+    ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "_backward_grad_grad",
+                             {ograds[0], nnvm::NodeEntry{dydx}}, nullptr, &n));
+    ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "_backward_grad_grad_inp",
+                             {ograds[0], nnvm::NodeEntry{d2ydx2}}, nullptr, &n));
+    return ret;
+  });
+
+// log1p
+MXNET_OPERATOR_REGISTER_UNARY_WITH_RSP_CSR(log1p, cpu, mshadow_op::log1p)
+.describe(R"code(Returns element-wise ``log(1 + x)`` value of the input.
+
+This function is more accurate than ``log(1 + x)``  for small ``x`` so that
+:math:`1+x\approx 1`
+
+The storage type of ``log1p`` output depends upon the input storage type:
+
+   - log1p(default) = default
+   - log1p(row_sparse) = row_sparse
+   - log1p(csr) = csr
+
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_log1p"});
+
+MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_log1p,
+                                                  unary_bwd<mshadow_op::log1p_grad>);
+
+// expm1
+MXNET_OPERATOR_REGISTER_UNARY_WITH_RSP_CSR(expm1, cpu, mshadow_op::expm1)
+.describe(R"code(Returns ``exp(x) - 1`` computed element-wise on the input.
+
+This function provides greater precision than ``exp(x) - 1`` for small values of ``x``.
+
+The storage type of ``expm1`` output depends upon the input storage type:
+
+   - expm1(default) = default
+   - expm1(row_sparse) = row_sparse
+   - expm1(csr) = csr
+
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_expm1"});
+
+MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_expm1, unary_bwd<mshadow_op::exp>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/tensor/elemwise_unary_op_logexp.cu b/src/operator/tensor/elemwise_unary_op_logexp.cu
new file mode 100644
index 000000000000..febc1914feb7
--- /dev/null
+++ b/src/operator/tensor/elemwise_unary_op_logexp.cu
@@ -0,0 +1,76 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file elemwise_unary_op_logexp.cu
+ * \brief GPU Implementation of unary log and exp functions.
+ */
+#include "./elemwise_binary_op.h"
+
+namespace mxnet {
+namespace op {
+
+// exp
+NNVM_REGISTER_OP(exp)
+.set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, mshadow_op::exp>);
+
+// log
+NNVM_REGISTER_OP(log)
+.set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, mshadow_op::log>);
+
+// log10
+NNVM_REGISTER_OP(log10)
+.set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, mshadow_op::log10>);
+
+// log2
+NNVM_REGISTER_OP(log2)
+.set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, mshadow_op::log2>);
+
+NNVM_REGISTER_OP(_backward_log)
+.set_attr<FCompute>("FCompute<gpu>", ElemwiseBinaryOp::Compute<
+  gpu, unary_bwd<mshadow_op::log_grad> >);
+
+NNVM_REGISTER_OP(_backward_log10)
+.set_attr<FCompute>("FCompute<gpu>", ElemwiseBinaryOp::Compute<
+  gpu, unary_bwd<mshadow_op::log10_grad> >);
+
+NNVM_REGISTER_OP(_backward_log2)
+.set_attr<FCompute>("FCompute<gpu>", ElemwiseBinaryOp::Compute<
+  gpu, unary_bwd<mshadow_op::log2_grad> >);
+
+// log1p
+NNVM_REGISTER_OP(log1p)
+.set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, mshadow_op::log1p>)
+.set_attr<FComputeEx>("FComputeEx<gpu>", UnaryOp::ComputeEx<gpu, mshadow_op::log1p>);
+
+NNVM_REGISTER_OP(_backward_log1p)
+.set_attr<FCompute>("FCompute<gpu>", ElemwiseBinaryOp::Compute<
+  gpu, unary_bwd<mshadow_op::log1p_grad> >);
+
+// expm1
+NNVM_REGISTER_OP(expm1)
+.set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, mshadow_op::expm1>)
+.set_attr<FComputeEx>("FComputeEx<gpu>", UnaryOp::ComputeEx<gpu, mshadow_op::expm1>);
+
+NNVM_REGISTER_OP(_backward_expm1)
+.set_attr<FCompute>("FCompute<gpu>", ElemwiseBinaryOp::Compute<
+  gpu, unary_bwd<mshadow_op::exp> >);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/tensor/elemwise_unary_op_pow.cc b/src/operator/tensor/elemwise_unary_op_pow.cc
new file mode 100644
index 000000000000..f22dabc7201a
--- /dev/null
+++ b/src/operator/tensor/elemwise_unary_op_pow.cc
@@ -0,0 +1,212 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file elemwise_unary_op_pow.cc
+ * \brief CPU Implementation of elementwise power (x^k for fixed k) function.
+ */
+#include <mxnet/base.h>
+#include "elemwise_unary_op.h"
+#include "./elemwise_binary_op-inl.h"
+#include "../nn/mkldnn/mkldnn_ops-inl.h"
+
+namespace mxnet {
+namespace op {
+
+// reciprocal
+MXNET_OPERATOR_REGISTER_UNARY(reciprocal)
+.describe(R"code(Returns the reciprocal of the argument, element-wise.
+
+Calculates 1/x.
+
+Example::
+
+    reciprocal([-2, 1, 3, 1.6, 0.2]) = [-0.5, 1.0, 0.33333334, 0.625, 5.0]
+
+)code" ADD_FILELINE)
+.set_attr<FCompute>("FCompute<cpu>", UnaryOp::Compute<cpu, mshadow_op::reciprocal>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_reciprocal"});
+
+MXNET_OPERATOR_REGISTER_BINARY(_backward_reciprocal)
+.set_attr<FCompute>("FCompute<cpu>",
+  ElemwiseBinaryOp::Compute<cpu, unary_bwd<mshadow_op::reciprocal_grad> >)
+.set_attr<nnvm::FGradient>("FGradient",
+  [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
+    // ograds[0]: dL/dxgrad
+    // inputs[0]: dL/dy
+    // inputs[1]: x
+    // f(x) = y = 1/x
+    // f'(x) = -1/x^2
+    // f''(x) = 2/x^3 = -2 * (f'(x) * f(x))
+
+    const std::unordered_map<std::string, std::string> args = {{"scalar", "-2.0"}};
+
+    auto dydx_mul_dldy = nnvm::NodeEntry{n};  // f'(x) * head_grads
+    auto dydx = MakeNode("elemwise_div", n->attrs.name + "_dydx",
+                         {dydx_mul_dldy, n->inputs[0]}, nullptr, &n);
+    auto fx = MakeNode("reciprocal", n->attrs.name + "_fx",
+                       {n->inputs[1]}, nullptr, &n);
+
+    auto d2ydx2_mid = MakeNode("elemwise_mul", n->attrs.name + "_d2ydx2_mid",
+                               {dydx_mul_dldy, nnvm::NodeEntry{fx}}, nullptr, &n);
+
+    auto d2ydx2 = MakeNode("_mul_scalar", n->attrs.name + "_d2ydx2",
+                           {nnvm::NodeEntry{d2ydx2_mid}}, &args, &n);
+
+    std::vector<nnvm::NodeEntry> ret;
+
+    ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "_backward_grad_grad",
+                             {ograds[0], nnvm::NodeEntry{dydx}}, nullptr, &n));
+    ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "_backward_grad_grad_inp",
+                             {ograds[0], nnvm::NodeEntry{d2ydx2}}, nullptr, &n));
+    return ret;
+});
+
+// square
+#if MSHADOW_USE_MKL == 1
+MXNET_MKL_OPERATOR_REGISTER_UNARY_WITH_RSP_CSR(square, cpu, mshadow_op::square, mkl_func::square)
+.describe(R"code(Returns element-wise squared value of the input.
+
+.. math::
+   square(x) = x^2
+
+Example::
+
+   square([2, 3, 4]) = [4, 9, 16]
+
+The storage type of ``square`` output depends upon the input storage type:
+
+   - square(default) = default
+   - square(row_sparse) = row_sparse
+   - square(csr) = csr
+
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_square"});
+#else
+MXNET_OPERATOR_REGISTER_UNARY_WITH_RSP_CSR(square, cpu, mshadow_op::square)
+.describe(R"code(Returns element-wise squared value of the input.
+
+.. math::
+   square(x) = x^2
+
+Example::
+
+   square([2, 3, 4]) = [4, 9, 16]
+
+The storage type of ``square`` output depends upon the input storage type:
+
+   - square(default) = default
+   - square(row_sparse) = row_sparse
+   - square(csr) = csr
+
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_square"});
+#endif
+
+MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU(_backward_square,
+                                               unary_bwd<mshadow_op::square_grad>);
+
+// sqrt
+MXNET_OPERATOR_REGISTER_UNARY_WITH_RSP_CSR(sqrt, cpu, mshadow_op::square_root)
+.describe(R"code(Returns element-wise square-root value of the input.
+
+.. math::
+   \textrm{sqrt}(x) = \sqrt{x}
+
+Example::
+
+   sqrt([4, 9, 16]) = [2, 3, 4]
+
+The storage type of ``sqrt`` output depends upon the input storage type:
+
+   - sqrt(default) = default
+   - sqrt(row_sparse) = row_sparse
+   - sqrt(csr) = csr
+
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{"_backward_sqrt"});
+
+MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_sqrt,
+                                                  unary_bwd<mshadow_op::square_root_grad>);
+
+// rsqrt
+MXNET_OPERATOR_REGISTER_UNARY_WITH_SPARSE_DR(rsqrt, cpu, mshadow_op::reciprocal_square_root)
+MXNET_ADD_SPARSE_OP_ALIAS(rsqrt)
+.describe(R"code(Returns element-wise inverse square-root value of the input.
+
+.. math::
+   rsqrt(x) = 1/\sqrt{x}
+
+Example::
+
+   rsqrt([4,9,16]) = [0.5, 0.33333334, 0.25]
+
+The storage type of ``rsqrt`` output is always dense
+
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_rsqrt"});
+
+MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(
+  _backward_rsqrt, unary_bwd<mshadow_op::reciprocal_square_root_grad>);
+
+// cbrt
+MXNET_OPERATOR_REGISTER_UNARY_WITH_RSP_CSR(cbrt, cpu, mshadow_op::cube_root)
+.describe(R"code(Returns element-wise cube-root value of the input.
+
+.. math::
+   cbrt(x) = \sqrt[3]{x}
+
+Example::
+
+   cbrt([1, 8, -125]) = [1, 2, -5]
+
+The storage type of ``cbrt`` output depends upon the input storage type:
+
+   - cbrt(default) = default
+   - cbrt(row_sparse) = row_sparse
+   - cbrt(csr) = csr
+
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{"_backward_cbrt"});
+
+MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_cbrt,
+                                                  unary_bwd<mshadow_op::cube_root_grad>);
+
+// rcbrt
+MXNET_OPERATOR_REGISTER_UNARY(rcbrt)
+.describe(R"code(Returns element-wise inverse cube-root value of the input.
+
+.. math::
+   rcbrt(x) = 1/\sqrt[3]{x}
+
+Example::
+
+   rcbrt([1,8,-125]) = [1.0, 0.5, -0.2]
+
+)code" ADD_FILELINE)
+.set_attr<FCompute>("FCompute<cpu>", UnaryOp::Compute<cpu, mshadow_op::reciprocal_cube_root>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_rcbrt"});
+
+MXNET_OPERATOR_REGISTER_BINARY(_backward_rcbrt)
+.set_attr<FCompute>("FCompute<cpu>",
+                    ElemwiseBinaryOp::Compute<cpu,
+                      unary_bwd<mshadow_op::reciprocal_cube_root_grad>>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/tensor/elemwise_unary_op_pow.cu b/src/operator/tensor/elemwise_unary_op_pow.cu
new file mode 100644
index 000000000000..4dbdf349cdb0
--- /dev/null
+++ b/src/operator/tensor/elemwise_unary_op_pow.cu
@@ -0,0 +1,82 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file elemwise_unary_op_pow.cu
+ * \brief GPU Implementation of power (x^k for fixed k) functions.
+ */
+#include "./elemwise_binary_op.h"
+
+namespace mxnet {
+namespace op {
+
+// reciprocal
+NNVM_REGISTER_OP(reciprocal)
+.set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, mshadow_op::reciprocal>);
+
+NNVM_REGISTER_OP(_backward_reciprocal)
+.set_attr<FCompute>("FCompute<gpu>", ElemwiseBinaryOp::Compute<
+  gpu, unary_bwd<mshadow_op::reciprocal_grad> >);
+
+// square
+NNVM_REGISTER_OP(square)
+.set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, mshadow_op::square>)
+.set_attr<FComputeEx>("FComputeEx<gpu>", UnaryOp::ComputeEx<gpu, mshadow_op::square>);
+
+NNVM_REGISTER_OP(_backward_square)
+.set_attr<FCompute>("FCompute<gpu>", ElemwiseBinaryOp::Compute<
+  gpu, unary_bwd<mshadow_op::square_grad> >);
+
+// sqrt
+NNVM_REGISTER_OP(sqrt)
+.set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, mshadow_op::square_root>)
+.set_attr<FComputeEx>("FComputeEx<gpu>", UnaryOp::ComputeEx<gpu, mshadow_op::square_root>);
+
+NNVM_REGISTER_OP(_backward_sqrt)
+.set_attr<FCompute>("FCompute<gpu>", ElemwiseBinaryOp::Compute<
+  gpu, unary_bwd<mshadow_op::square_root_grad> >);
+
+// rsqrt
+NNVM_REGISTER_OP(rsqrt)
+.set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, mshadow_op::reciprocal_square_root>);
+
+NNVM_REGISTER_OP(_backward_rsqrt)
+.set_attr<FCompute>("FCompute<gpu>",
+  ElemwiseBinaryOp::Compute<gpu, unary_bwd<mshadow_op::reciprocal_square_root_grad> >);
+
+// cbrt
+NNVM_REGISTER_OP(cbrt)
+.set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, mshadow_op::cube_root>)
+.set_attr<FComputeEx>("FComputeEx<gpu>", UnaryOp::ComputeEx<gpu, mshadow_op::cube_root>);
+
+
+NNVM_REGISTER_OP(_backward_cbrt)
+.set_attr<FCompute>("FCompute<gpu>", ElemwiseBinaryOp::Compute<
+  gpu, unary_bwd<mshadow_op::cube_root_grad> >);
+
+// rcbrt
+NNVM_REGISTER_OP(rcbrt)
+.set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, mshadow_op::reciprocal_cube_root>);
+
+NNVM_REGISTER_OP(_backward_rcbrt)
+.set_attr<FCompute>("FCompute<gpu>",
+  ElemwiseBinaryOp::Compute<gpu, unary_bwd<mshadow_op::reciprocal_cube_root_grad> >);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/tests/python/unittest/test_random.py b/tests/python/unittest/test_random.py
index 8fdca619a5c8..720c25d2711e 100644
--- a/tests/python/unittest/test_random.py
+++ b/tests/python/unittest/test_random.py
@@ -893,14 +893,15 @@ def compute_expected_prob():
 def test_shuffle():
     def check_first_axis_shuffle(arr):
         stride = int(arr.size / arr.shape[0])
-        column0 = arr.reshape((arr.size,))[::stride].sort()
+        column0 = arr.reshape((arr.size,))[::stride]
         seq = mx.nd.arange(0, arr.size - stride + 1, stride, ctx=arr.context)
-        assert (column0 == seq).prod() == 1
-        for i in range(arr.shape[0]):
-            subarr = arr[i].reshape((arr[i].size,))
-            start = subarr[0].asscalar()
-            seq = mx.nd.arange(start, start + stride, ctx=arr.context)
-            assert (subarr == seq).prod() == 1
+        assert (column0.sort() == seq).prod() == 1
+        # Check for ascending flattened-row sequences for 2D or greater inputs.
+        if stride > 1:
+            ascending_seq = mx.nd.arange(0, stride, ctx=arr.context)
+            equalized_columns = arr.reshape((arr.shape[0], stride)) - ascending_seq
+            column0_2d = column0.reshape((arr.shape[0],1))
+            assert (column0_2d == equalized_columns).prod() == 1
 
     # This tests that the shuffling is along the first axis with `repeat1` number of shufflings
     # and the outcomes are uniformly distributed with `repeat2` number of shufflings.

From 8eb0f6139d6af2d7dabed961424409022c59910d Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Sun, 18 Aug 2019 09:21:42 -0700
Subject: [PATCH 190/813] Numpy-compatible stack upstream (#15842)

---
 python/mxnet/ndarray/numpy/_op.py      | 29 ++++++++++++++-
 python/mxnet/numpy/multiarray.py       | 23 +++++++++++-
 python/mxnet/symbol/numpy/_symbol.py   | 29 ++++++++++++++-
 src/operator/numpy/np_matrix_op.cc     | 41 +++++++++++++++++++++
 src/operator/numpy/np_matrix_op.cu     |  4 ++
 tests/python/unittest/test_numpy_op.py | 51 ++++++++++++++++++++++++++
 6 files changed, 174 insertions(+), 3 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index d7f3fd1ace54..a129f738c566 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -27,7 +27,7 @@
 from ..ndarray import NDArray
 
 __all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power', 'tensordot',
-           'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate']
+           'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -705,3 +705,30 @@ def concatenate(seq, axis=0, out=None):
         The concatenated array.
     """
     return _npi.concatenate(*seq, dim=axis, out=out)
+
+
+@set_module('mxnet.ndarray.numpy')
+def stack(arrays, axis=0, out=None):
+    """Join a sequence of arrays along a new axis.
+        The axis parameter specifies the index of the new axis in the dimensions of the result.
+        For example, if `axis=0` it will be the first dimension and if `axis=-1` it will be the last dimension.
+    Parameters
+    ----------
+    arrays : sequence of array_like
+        Each array must have the same shape.
+    axis : int, optional
+        The axis in the result array along which the input arrays are stacked.
+    out : ndarray, optional
+        If provided, the destination to place the result. The shape must be correct,
+        matching that of what stack would have returned if no out argument were specified.
+    Returns
+    -------
+    stacked : ndarray
+        The stacked array has one more dimension than the input arrays."""
+    def get_list(arrays):
+        if not hasattr(arrays, '__getitem__') and hasattr(arrays, '__iter__'):
+            raise ValueError("expected iterable for arrays but got {}".format(type(arrays)))
+        return [arr for arr in arrays]
+
+    arrays = get_list(arrays)
+    return _npi.stack(*arrays, axis=axis, out=out)
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 8988b4eb19c9..316d88ae0a81 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -45,7 +45,7 @@
 
 __all__ = ['ndarray', 'empty', 'array', 'zeros', 'ones', 'add', 'subtract', 'multiply', 'divide',
            'mod', 'power', 'tensordot', 'linspace', 'expand_dims', 'tile', 'arange', 'split',
-           'concatenate']
+           'concatenate', 'stack']
 
 
 # This function is copied from ndarray.py since pylint
@@ -1877,3 +1877,24 @@ def concatenate(seq, axis=0, out=None):
         The concatenated array.
     """
     return _mx_nd_np.concatenate(seq, axis=axis, out=out)
+
+
+@set_module('mxnet.numpy')
+def stack(arrays, axis=0, out=None):
+    """Join a sequence of arrays along a new axis.
+        The axis parameter specifies the index of the new axis in the dimensions of the result.
+        For example, if `axis=0` it will be the first dimension and if `axis=-1` it will be the last dimension.
+    Parameters
+    ----------
+    arrays : sequence of array_like
+        Each array must have the same shape.
+    axis : int, optional
+        The axis in the result array along which the input arrays are stacked.
+    out : ndarray, optional
+        If provided, the destination to place the result. The shape must be correct,
+        matching that of what stack would have returned if no out argument were specified.
+    Returns
+    -------
+    stacked : ndarray
+        The stacked array has one more dimension than the input arrays."""
+    return _mx_nd_np.stack(arrays, axis=axis, out=out)
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index a6699d60871a..5537e6305b83 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -30,7 +30,7 @@
 from . import _internal as _npi
 
 __all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power', 'tensordot',
-           'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate']
+           'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack']
 
 
 def _num_outputs(sym):
@@ -1335,4 +1335,31 @@ def concatenate(seq, axis=0, out=None):
     return _npi.concatenate(*seq, dim=axis, out=out)
 
 
+@set_module('mxnet.symbol.numpy')
+def stack(arrays, axis=0, out=None):
+    """Join a sequence of arrays along a new axis.
+        The axis parameter specifies the index of the new axis in the dimensions of the result.
+        For example, if `axis=0` it will be the first dimension and if `axis=-1` it will be the last dimension.
+    Parameters
+    ----------
+    arrays : sequence of array_like
+        Each array must have the same shape.
+    axis : int, optional
+        The axis in the result array along which the input arrays are stacked.
+    out : ndarray, optional
+        If provided, the destination to place the result. The shape must be correct,
+        matching that of what stack would have returned if no out argument were specified.
+    Returns
+    -------
+    stacked : ndarray
+        The stacked array has one more dimension than the input arrays."""
+    def get_list(arrays):
+        if not hasattr(arrays, '__getitem__') and hasattr(arrays, '__iter__'):
+            raise ValueError("expected iterable for arrays but got {}".format(type(arrays)))
+        return [arr for arr in arrays]
+
+    arrays = get_list(arrays)
+    return _npi.stack(*arrays, axis=axis, out=out)
+
+
 _set_np_symbol_class(_Symbol)
diff --git a/src/operator/numpy/np_matrix_op.cc b/src/operator/numpy/np_matrix_op.cc
index 73340981037d..5ad6c8908017 100644
--- a/src/operator/numpy/np_matrix_op.cc
+++ b/src/operator/numpy/np_matrix_op.cc
@@ -23,6 +23,7 @@
  * \brief CPU Implementation of numpy matrix operations
  */
 
+#include <vector>
 #include "./np_matrix_op-inl.h"
 
 namespace mxnet {
@@ -304,5 +305,45 @@ NNVM_REGISTER_OP(_backward_np_concat)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
 .set_attr<FCompute>("FCompute<cpu>", ConcatGradCompute<cpu>);
 
+NNVM_REGISTER_OP(_npi_stack)
+.describe(R"code(Join a sequence of arrays along a new axis.
+
+The axis parameter specifies the index of the new axis in the dimensions of the
+result. For example, if axis=0 it will be the first dimension and if axis=-1 it
+will be the last dimension.
+
+Examples::
+
+  x = [1, 2]
+  y = [3, 4]
+
+  stack(x, y) = [[1, 2],
+                 [3, 4]]
+  stack(x, y, axis=1) = [[1, 3],
+                         [2, 4]]
+)code")
+.set_num_inputs([](const nnvm::NodeAttrs& attrs) {
+    const StackParam& param = dmlc::get<StackParam>(attrs.parsed);
+    return static_cast<uint32_t>(param.num_args);
+  })
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<StackParam>)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    uint32_t num_args = dmlc::get<StackParam>(attrs.parsed).num_args;
+    std::vector<std::string> ret;
+    for (uint32_t i = 0; i < num_args; ++i) {
+      ret.push_back(std::string("arg") + std::to_string(i));
+    }
+    return ret;
+  })
+.set_attr<std::string>("key_var_num_args", "num_args")
+.set_attr<mxnet::FInferShape>("FInferShape", StackOpShape)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<-1, 1>)
+.set_attr<FCompute>("FCompute<cpu>", StackOpForward<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_stack"})
+.add_argument("data", "NDArray-or-Symbol[]", "List of arrays to stack")
+.add_arguments(StackParam::__FIELDS__());
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/np_matrix_op.cu b/src/operator/numpy/np_matrix_op.cu
index f192560f4ac9..4ba527deca09 100644
--- a/src/operator/numpy/np_matrix_op.cu
+++ b/src/operator/numpy/np_matrix_op.cu
@@ -22,6 +22,7 @@
  * \file np_matrix_op.cu
  * \brief GPU Implementation of numpy matrix operations
  */
+
 #include "./np_matrix_op-inl.h"
 
 namespace mxnet {
@@ -42,5 +43,8 @@ NNVM_REGISTER_OP(_npi_concatenate)
 NNVM_REGISTER_OP(_backward_np_concat)
 .set_attr<FCompute>("FCompute<gpu>", ConcatGradCompute<gpu>);
 
+NNVM_REGISTER_OP(_npi_stack)
+.set_attr<FCompute>("FCompute<gpu>", StackOpForward<gpu>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 2291bcdb6d3d..6a1a6a6dfd5d 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -864,6 +864,7 @@ def get_new_shape(shape, axis):
                 expected_ret = _np.concatenate([a.asnumpy(), b.asnumpy(), c.asnumpy(), d.asnumpy()], axis=axis)
                 with mx.autograd.record():
                     y = test_concat(a, b, c, d)
+
                 assert y.shape == expected_ret.shape
                 assert_almost_equal(y.asnumpy(), expected_ret, rtol=1e-3, atol=1e-5)
 
@@ -880,6 +881,56 @@ def get_new_shape(shape, axis):
                 assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
 
 
+@with_seed()
+@use_np
+def test_np_stack():
+    class TestStack(HybridBlock):
+        def __init__(self, axis=None):
+            super(TestStack, self).__init__()
+            self._axis = axis
+
+        def hybrid_forward(self, F, a, *args):
+            return F.np.stack([a] + list(args), axis=self._axis)
+
+    a, b, c, d = mx.sym.Variable("a"), mx.sym.Variable("b"), mx.sym.Variable("c"), mx.sym.Variable("d")
+    ret = mx.sym.np.stack([a.as_np_ndarray(), b.as_np_ndarray(), c.as_np_ndarray(), d.as_np_ndarray()])
+    assert type(ret) == mx.sym.np._Symbol
+
+    for shape in [(0, 0), (2, 3)]:
+        for hybridize in [True, False]:
+            for axis in range(2):
+                test_stack = TestStack(axis=axis)
+                if hybridize:
+                    test_stack.hybridize()
+                np_a = _np.random.uniform(-1.0, 1.0, shape).astype(_np.float32)
+                np_b = _np.random.uniform(-1.0, 1.0, shape).astype(_np.float32)
+                np_c = _np.random.uniform(-1.0, 1.0, shape).astype(_np.float32)
+                np_d = _np.random.uniform(-1.0, 1.0, shape).astype(_np.float32)
+
+                mx_a = np.array(np_a)
+                mx_a.attach_grad()
+                mx_b = np.array(np_b)
+                mx_b.attach_grad()
+                mx_c = np.array(np_c)
+                mx_c.attach_grad()
+                mx_d = np.array(np_d)
+                mx_d.attach_grad()
+                expected_ret = _np.stack([np_a, np_b, np_c, np_d], axis=axis)
+                with mx.autograd.record():
+                    y = test_stack(mx_a, mx_b, mx_c, mx_d)
+
+                y.backward()
+
+                assert_almost_equal(mx_a.grad.asnumpy(), _np.ones(shape), rtol=1e-3, atol=1e-5)
+                assert_almost_equal(mx_b.grad.asnumpy(), _np.ones(shape), rtol=1e-3, atol=1e-5)
+                assert_almost_equal(mx_c.grad.asnumpy(), _np.ones(shape), rtol=1e-3, atol=1e-5)
+                assert_almost_equal(mx_d.grad.asnumpy(), _np.ones(shape), rtol=1e-3, atol=1e-5)
+
+                np_out = _np.stack([np_a, np_b, np_c, np_d], axis=axis)
+                mx_out = np.stack([mx_a, mx_b, mx_c, mx_d], axis=axis)
+                assert same(mx_out.asnumpy(), np_out)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From bcbdc1cc797064fafc0a46a3dd00d446a7c60357 Mon Sep 17 00:00:00 2001
From: Pedro Larroy <pedro.larroy.lists@gmail.com>
Date: Sun, 18 Aug 2019 18:02:00 -0700
Subject: [PATCH 191/813] Re-enable flaky test_prelu (#15777)

Fixes #12885
---
 tests/python/unittest/test_operator.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 1991b16be317..e28fe7869f20 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -794,7 +794,6 @@ def fleaky_relu_grad(grad, x, y, act_type, slope=0.25):
 # NOTE(haojin2): Skipping the numeric check tests for float16 data type due to precision issues,
 # the analytical checks are still performed on each and every data type to verify the correctness.
 @with_seed()
-@unittest.skip("Flaky test tracked by https://github.com/apache/incubator-mxnet/issues/12885")
 def test_prelu():
     def fprelu(x, gamma):
         pos_indices = x > 0

From 64a0502d625bf63ddccdf551d24cbf3b5cb47c10 Mon Sep 17 00:00:00 2001
From: Haozheng Fan <fhztc1997618@gmail.com>
Date: Mon, 19 Aug 2019 14:00:44 +0800
Subject: [PATCH 192/813] declare explicitly the tblob default assign operator
 and copy constructor (#15937)

---
 include/mxnet/tensor_blob.h | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)

diff --git a/include/mxnet/tensor_blob.h b/include/mxnet/tensor_blob.h
index a08dab1b1b74..91d91a3f1d2d 100755
--- a/include/mxnet/tensor_blob.h
+++ b/include/mxnet/tensor_blob.h
@@ -148,6 +148,13 @@ class TBlob {
   TBlob(const mshadow::Tensor<Device, dim, DType> &src) {  // NOLINT(*)
     *this = src;
   }
+  /*!
+   * \brief constructor from TBlob (copy constructor)
+   * \param src source TBlob
+   */
+  TBlob(const TBlob &src): dptr_(src.dptr_), shape_(src.shape_), type_flag_(src.type_flag_) {
+    this->SetDLTensor(src.dev_mask(), src.dev_id());
+  }
   /*!
    * \brief assignment from tensor
    * \param src source tensor
@@ -164,6 +171,18 @@ class TBlob {
     SetDLTensor(Device::kDevMask, -1);
     return *this;
   }
+  /*!
+   * \brief assignment from TBlob (copy assignment)
+   * \param src source TBlob
+   * \return reference of self
+   */
+  inline TBlob &operator=(const TBlob &src) {
+    dptr_ = src.dptr_;
+    shape_ = src.shape_;
+    type_flag_ = src.type_flag_;
+    SetDLTensor(src.dev_mask(), src.dev_id());
+    return *this;
+  }
   /*!
    * \return whether the tensor's memory is continuous
    */

From c50bfe23017ffe93b91ecddc5f8fb51c2693035a Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Mon, 19 Aug 2019 02:09:22 -0700
Subject: [PATCH 193/813] [OpPerf] Handle positional arguments (#15761)

* handle args

* intermediate, error with getting 2 values for data param for other ops

* handle args

* None type issue

* try

* indent fix

* lint fix

* Trigger notification

* Trigger notification bcoz validation/edge passed but shows pending
---
 benchmark/opperf/rules/default_params.py  | 10 ++++--
 benchmark/opperf/utils/benchmark_utils.py | 38 ++++++++++++++++-------
 benchmark/opperf/utils/ndarray_utils.py   | 10 ++++--
 3 files changed, 43 insertions(+), 15 deletions(-)

diff --git a/benchmark/opperf/rules/default_params.py b/benchmark/opperf/rules/default_params.py
index 8f3973244aa2..3b00a324314f 100644
--- a/benchmark/opperf/rules/default_params.py
+++ b/benchmark/opperf/rules/default_params.py
@@ -25,6 +25,10 @@
 
 """Default Input Tensor shapes to use for benchmarking"""
 
+# For operators like concat, ElementWiseSum, squeeze, stack
+# argument data is passed as variable arg (*args)
+DEFAULT_ARGS = [(1024, 1024)]
+
 # For Unary operators like abs, arccos, arcsin etc..
 DEFAULT_DATA = [(1024, 1024), (10000, 1), (10000, 100)]
 
@@ -146,7 +150,8 @@
                    "data_4d": DEFAULT_DATA_4d,
                    "dim1": DEFAULT_DIM_1,
                    "dim2": DEFAULT_DIM_2,
-                   "block_size": DEFAULT_BLOCK_SIZE}
+                   "block_size": DEFAULT_BLOCK_SIZE,
+                   "args": DEFAULT_ARGS}
 
 
 # These are names of MXNet operator parameters that is of type NDArray.
@@ -157,4 +162,5 @@
 PARAMS_OF_TYPE_NDARRAY = ["lhs", "rhs", "data", "base", "exp", "sample",
                           "mu", "sigma", "lam", "alpha", "beta", "gamma", "k", "p",
                           "low", "high", "weight", "bias", "moving_mean", "moving_var",
-                          "weight", "weight32", "grad", "mean", "var", "mom", "n", "d", "v", "z", "g", "delta"]
+                          "weight", "weight32", "grad", "mean", "var", "mom", "n", "d",
+                          "v", "z", "g", "delta", "args"]
diff --git a/benchmark/opperf/utils/benchmark_utils.py b/benchmark/opperf/utils/benchmark_utils.py
index f25183db15d2..adede8cfb062 100644
--- a/benchmark/opperf/utils/benchmark_utils.py
+++ b/benchmark/opperf/utils/benchmark_utils.py
@@ -29,11 +29,17 @@
 
 def _prepare_op_inputs(inputs, run_backward, dtype, ctx):
     kwargs_list = []
+    args_list = []
 
     for inp in inputs:
         kwargs = {}
         for key, value in inp.items():
-            if key in PARAMS_OF_TYPE_NDARRAY:
+            if key in PARAMS_OF_TYPE_NDARRAY and key=='args':
+                args_list.append(get_mx_ndarray(ctx=ctx, in_tensor=value,
+                                                dtype=dtype,
+                                                initializer=nd.normal,
+                                                attach_grad=run_backward))
+            elif key in PARAMS_OF_TYPE_NDARRAY:
                 kwargs[key] = get_mx_ndarray(ctx=ctx, in_tensor=value,
                                              dtype=dtype,
                                              initializer=nd.normal,
@@ -41,8 +47,7 @@ def _prepare_op_inputs(inputs, run_backward, dtype, ctx):
             else:
                 kwargs[key] = value
         kwargs_list.append(kwargs)
-
-    return kwargs_list
+    return args_list, kwargs_list
 
 
 def _run_nd_operator_performance_test(op, inputs, run_backward, warmup, runs, kwargs_list, profiler):
@@ -60,17 +65,28 @@ def _run_nd_operator_performance_test(op, inputs, run_backward, warmup, runs, kw
         raise ValueError("Incorrect input for profiler. Valid input - 'python' or 'native'")
 
     # Warm up, ignore the profiler output
-    _, _ = benchmark_helper_func(op, warmup, **kwargs_list[0])
+    if not args_list:
+        _, _ = benchmark_helper_func(op, warmup, [], **kwargs_list[0])
+    else:    
+        _, _ = benchmark_helper_func(op, warmup, args_list[0], **kwargs_list[0])
 
     # Run Benchmarks
     op_benchmark_result = {op.__name__: []}
     logging.info("Begin Benchmark - {name}".format(name=op.__name__))
-    for idx, kwargs in enumerate(kwargs_list):
-        _, profiler_output = benchmark_helper_func(op, runs, **kwargs)
+    if not args_list:
+        for idx, kwargs in enumerate(kwargs_list):
+            _, profiler_output = benchmark_helper_func(op, runs, [], **kwargs)
+
+            # Add inputs used for profiling this operator into result
+            profiler_output["inputs"] = inputs[idx]
+            op_benchmark_result[op.__name__].append(profiler_output)
+    else:
+        for idx, (args,kwargs) in enumerate(zip(args_list,kwargs_list)):
+            _, profiler_output = benchmark_helper_func(op, runs, args, **kwargs)
 
-        # Add inputs used for profiling this operator into result
-        profiler_output["inputs"] = inputs[idx]
-        op_benchmark_result[op.__name__].append(profiler_output)
+            # Add inputs used for profiling this operator into result
+            profiler_output["inputs"] = inputs[idx]
+            op_benchmark_result[op.__name__].append(profiler_output)
     logging.info("Complete Benchmark - {name}".format(name=op.__name__))
     return op_benchmark_result
 
@@ -110,7 +126,7 @@ def run_performance_test(ops, inputs, run_backward=True,
     List of dictionary of benchmark results. key -> name of the operator, Value is benchmark results.
 
     """
-    kwargs_list = _prepare_op_inputs(inputs, run_backward, dtype, ctx)
+    args_list, kwargs_list = _prepare_op_inputs(inputs, run_backward, dtype, ctx)
 
     if not isinstance(ops, list):
         ops = [ops]
@@ -118,7 +134,7 @@ def run_performance_test(ops, inputs, run_backward=True,
     op_benchmark_result = []
     for op in ops:
         if hasattr(mx.nd, op.__name__):
-            benchmark_result = _run_nd_operator_performance_test(op, inputs, run_backward, warmup, runs, kwargs_list, profiler)
+            benchmark_result = _run_nd_operator_performance_test(op, inputs, run_backward, warmup, runs, args_list, kwargs_list, profiler)
         else:
             raise ValueError("Unknown NDArray operator provided to benchmark. -  ", op.__name__)
         op_benchmark_result.append(benchmark_result)
diff --git a/benchmark/opperf/utils/ndarray_utils.py b/benchmark/opperf/utils/ndarray_utils.py
index 013be3ec580f..7d9cd5256a2e 100644
--- a/benchmark/opperf/utils/ndarray_utils.py
+++ b/benchmark/opperf/utils/ndarray_utils.py
@@ -44,7 +44,10 @@ def nd_forward_backward_and_profile(op, runs, *args, **kwargs):
     """
     for _ in range(runs):
         with mx.autograd.record():
-            res = op(*args, **kwargs)
+            if not isinstance(args[0],nd.NDArray):
+                res = op(**kwargs)
+            else:
+                res = op(*args, **kwargs)
         res.backward()
         nd.waitall()
     return res
@@ -72,7 +75,10 @@ def nd_forward_and_profile(op, runs, *args, **kwargs):
     any results from NDArray operation execution
     """
     for _ in range(runs):
-        res = op(*args, **kwargs)
+        if not isinstance(args[0],nd.NDArray):
+            res = op(**kwargs)
+        else:
+            res = op(*args, **kwargs)
         nd.waitall()
     return res
 

From 92d231967e6a96fbf6a87207eb47644cc8d97c93 Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Mon, 19 Aug 2019 14:29:42 -0700
Subject: [PATCH 194/813] [OpPerf] Take care of 4d param  (#15736)

* take care of 4d

* Trigger notification

* Trigger notification

* Trigger notification

* Trigger notification

* Trigger notification

* Trigger notification

* Trigger notification

* Trigger notification

* Trigger notification bcoz validation/edge passed but shows pending
---
 benchmark/opperf/rules/default_params.py    | 5 +++--
 benchmark/opperf/utils/benchmark_utils.py   | 4 ++--
 benchmark/opperf/utils/op_registry_utils.py | 7 +++++--
 3 files changed, 10 insertions(+), 6 deletions(-)

diff --git a/benchmark/opperf/rules/default_params.py b/benchmark/opperf/rules/default_params.py
index 3b00a324314f..69b28c6535cc 100644
--- a/benchmark/opperf/rules/default_params.py
+++ b/benchmark/opperf/rules/default_params.py
@@ -97,10 +97,11 @@
 # Hence below we append 4d to mark the difference.
 # For depth_to_space, dimension 3 needs to be a multiple of 'block' and 1 should be a multiple of `block^2`
 DEFAULT_DATA_4d = [(1, 4, 2, 4), (10,25,10,100)]
-DEFAULT_DIM_1 = [0, 1, 2, 3]
-DEFAULT_DIM_2 = [1, 2, 3, 0]
 DEFAULT_BLOCK_SIZE = [2, 5]
 
+# For swapaxis operator
+DEFAULT_DIM_1 = [0]
+DEFAULT_DIM_2 = [1]
 
 # Default Inputs. MXNet Op Param Name to Default Input mapping
 DEFAULTS_INPUTS = {"data": DEFAULT_DATA,
diff --git a/benchmark/opperf/utils/benchmark_utils.py b/benchmark/opperf/utils/benchmark_utils.py
index adede8cfb062..c530f9bf6177 100644
--- a/benchmark/opperf/utils/benchmark_utils.py
+++ b/benchmark/opperf/utils/benchmark_utils.py
@@ -144,9 +144,9 @@ def run_performance_test(ops, inputs, run_backward=True,
 def run_op_benchmarks(ops, dtype, ctx, profiler, warmup, runs):
     # For each operator, run benchmarks
     mx_op_benchmark_results = []
-    for _, op_params in ops.items():
+    for op, op_params in ops.items():
         # Prepare inputs for the operator
-        inputs = prepare_op_inputs(op_params)
+        inputs = prepare_op_inputs(op, op_params)
         # Run benchmarks
         cur_op_res = run_performance_test(op_params["nd_op_handle"],
                                           run_backward=op_params["has_backward"],
diff --git a/benchmark/opperf/utils/op_registry_utils.py b/benchmark/opperf/utils/op_registry_utils.py
index 207895d9b1ea..2c0940e3520b 100644
--- a/benchmark/opperf/utils/op_registry_utils.py
+++ b/benchmark/opperf/utils/op_registry_utils.py
@@ -113,16 +113,19 @@ def prepare_op_inputs(arg_params, arg_values):
     return inputs
 
 
-def prepare_op_inputs(arg_params):
+def prepare_op_inputs(op, arg_params):
     inputs = []
 
+    # 4d tensor is needed only by following two ops
+    ops_4d = ['depth_to_space','space_to_depth']
+
     # Prepare op to default input mapping
     arg_values = {}
     for arg_name, arg_type in zip(arg_params["params"]["arg_names"],
                                   arg_params["params"]["arg_types"]):
         if "NDArray" in arg_type and arg_name + "_nd" in DEFAULTS_INPUTS:
             arg_values[arg_name] = DEFAULTS_INPUTS[arg_name + "_nd"]
-        elif "NDArray" in arg_type and arg_name + "_4d" in DEFAULTS_INPUTS:
+        elif "NDArray" in arg_type and op in ops_4d and arg_name + "_4d" in DEFAULTS_INPUTS:
             arg_values[arg_name] = DEFAULTS_INPUTS[arg_name + "_4d"]
         elif arg_name in DEFAULTS_INPUTS:
             arg_values[arg_name] = DEFAULTS_INPUTS[arg_name]

From 3b7f8886e701374df79ab621af344abf5fce1448 Mon Sep 17 00:00:00 2001
From: Rohit Kumar Srivastava <srivastava.141@osu.edu>
Date: Mon, 19 Aug 2019 15:31:05 -0700
Subject: [PATCH 195/813] removing unnecessary int64 C apis that were added to
 support Large Tensors and Vectors (#15944)

---
 include/mxnet/c_api.h | 21 ---------------------
 1 file changed, 21 deletions(-)

diff --git a/include/mxnet/c_api.h b/include/mxnet/c_api.h
index 5ab10b6b2204..14209640d77d 100644
--- a/include/mxnet/c_api.h
+++ b/include/mxnet/c_api.h
@@ -1020,9 +1020,6 @@ MXNET_DLL int MXNDArrayGetGradState(NDArrayHandle handle, int *out);
 MXNET_DLL int MXListFunctions(mx_uint *out_size,
                               FunctionHandle **out_array);
 
-MXNET_DLL int MXListFunctions64(mx_int64 *out_size,
-                                FunctionHandle **out_array);
-
 /*!
  * \brief get the function handle by name
  * \param name the name of the function
@@ -1291,9 +1288,6 @@ MXNET_DLL int MXInvokeCachedOpEx(CachedOpHandle handle,
 MXNET_DLL int MXListAllOpNames(mx_uint *out_size,
                                const char ***out_array);
 
-MXNET_DLL int MXListAllOpNames64(mx_int64 *out_size,
-                                 const char ***out_array);
-
 /*!
  * \brief list all the available AtomicSymbolEntry
  * \param out_size the size of returned array
@@ -1303,9 +1297,6 @@ MXNET_DLL int MXListAllOpNames64(mx_int64 *out_size,
 MXNET_DLL int MXSymbolListAtomicSymbolCreators(mx_uint *out_size,
                                                AtomicSymbolCreator **out_array);
 
-MXNET_DLL int MXSymbolListAtomicSymbolCreators64(mx_int64 *out_size,
-                                                 AtomicSymbolCreator **out_array);
-
 /*!
  * \brief Get the name of an atomic symbol.
  * \param creator the AtomicSymbolCreator.
@@ -1519,10 +1510,6 @@ MXNET_DLL int MXSymbolListArguments(SymbolHandle symbol,
                                     mx_uint *out_size,
                                     const char ***out_str_array);
 
-MXNET_DLL int MXSymbolListArguments64(SymbolHandle symbol,
-                                      size_t *out_size,
-                                      const char ***out_str_array);
-
 /*!
  * \brief List returns in the symbol.
  * \param symbol the symbol
@@ -1534,10 +1521,6 @@ MXNET_DLL int MXSymbolListOutputs(SymbolHandle symbol,
                                   mx_uint *out_size,
                                   const char ***out_str_array);
 
-MXNET_DLL int MXSymbolListOutputs64(SymbolHandle symbol,
-                                    size_t *out_size,
-                                    const char ***out_str_array);
-
 /*!
  * \brief Get number of outputs of the symbol.
  * \param symbol The symbol
@@ -1585,10 +1568,6 @@ MXNET_DLL int MXSymbolListAuxiliaryStates(SymbolHandle symbol,
                                           mx_uint *out_size,
                                           const char ***out_str_array);
 
-MXNET_DLL int MXSymbolListAuxiliaryStates64(SymbolHandle symbol,
-                                            size_t *out_size,
-                                            const char ***out_str_array);
-
 /*!
  * \brief Compose the symbol on other symbols.
  *

From 3dfb19a9804c589a6089453f01c7b491d6bda014 Mon Sep 17 00:00:00 2001
From: Zixuan Wei <zixuan.wei@intel.com>
Date: Tue, 20 Aug 2019 09:59:55 +0800
Subject: [PATCH 196/813] Discard needless test cases in
 `test_convolution_independent_gradients` (#15939)

* Drop needless num_bases in test of conv

* Trigger CI

* Re-trigger CI
---
 tests/python/unittest/test_operator.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index e28fe7869f20..c360db9f01a5 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -2009,7 +2009,7 @@ def test_convolution_independent_gradients():
     reqs = ["null", "write", "add"]
     var_names = ["x", "w", "b"]
     dims = [1, 2]
-    num_bases = [1, 16, 64]
+    num_bases = [1, 8]
     kernel_xs = [3, 5]
     stride_xs = [1, 2]
     pad_xs = [0, 1]

From 5656a18c266455831f596fa6e30dd7955a874a7d Mon Sep 17 00:00:00 2001
From: Rohit Kumar Srivastava <srivastava.141@osu.edu>
Date: Mon, 19 Aug 2019 23:08:40 -0700
Subject: [PATCH 197/813] creating ndarray directly using mxnet ndarray
 primitives to reduce memory footprint of tests for topk, sort and argsort
 (#15900)

---
 tests/nightly/test_large_array.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/tests/nightly/test_large_array.py b/tests/nightly/test_large_array.py
index 02c867720609..c9d48b77a283 100644
--- a/tests/nightly/test_large_array.py
+++ b/tests/nightly/test_large_array.py
@@ -31,6 +31,12 @@
 LARGE_SIZE = LARGE_X * SMALL_Y
 
 
+def create_2d_tensor(rows, columns, dtype=np.int64):
+    a = nd.arange(0, rows, dtype=dtype).reshape(rows, 1)
+    b = nd.broadcast_to(a, shape=(a.shape[0], columns))
+    return nd.array(b, dtype=dtype)
+
+
 def test_gluon_embedding():
     m = gluon.nn.Embedding(SMALL_Y, MEDIUM_X)
     m.initialize()
@@ -383,12 +389,6 @@ def test_unravel_index():
     assert (indices_2d.asnumpy() == np.array(original_2d_indices)).all()
 
 
-def create_2d_tensor(rows, columns, dtype=np.int64):
-    a = np.arange(0, rows).reshape(rows, 1)
-    b = np.broadcast_to(a, shape=(a.shape[0], columns))
-    return nd.array(b, dtype=dtype)
-
-
 def test_transpose():
     b = create_2d_tensor(rows=LARGE_X, columns=SMALL_Y)
     t = b.T

From cd397a3125b9e7a77466820ef07de724ab1ed0c5 Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Mon, 19 Aug 2019 23:13:39 -0700
Subject: [PATCH 198/813] Benchmark doc fix (#15769)

* Update pre-req for opperf

* Update README.md

* correct command to import binary broadcast

* no such op called nd.sub, it is nd.subtract

* Trigger notification

* Trigger notification
---
 benchmark/opperf/README.md | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/benchmark/opperf/README.md b/benchmark/opperf/README.md
index 7e708d3cbe1d..6e628dfe40a8 100644
--- a/benchmark/opperf/README.md
+++ b/benchmark/opperf/README.md
@@ -47,7 +47,10 @@ Hence, in this utility, we will build the functionality to allow users and devel
 
 ## Prerequisites
 
-Make sure to build the flavor of MXNet, for example - with/without MKL, with CUDA 9 or 10.1 etc., on which you would like to measure operator performance. Finally, you need to add path to your cloned MXNet repository to the PYTHONPATH.
+Provided you have MXNet installed (any version >= 1.5.1), all you need to use opperf utility is to add path to your cloned MXNet repository to the PYTHONPATH.
+
+Note: 
+To install MXNet, refer [Installing MXNet page](https://mxnet.incubator.apache.org/versions/master/install/index.html)
 
 ```
 export PYTHONPATH=$PYTHONPATH:/path/to/incubator-mxnet/
@@ -75,7 +78,7 @@ For example, you want to run benchmarks for all NDArray Broadcast Binary Operato
 
 ```
 #!/usr/bin/python
-from benchmark.opperf.nd_operations.binary_broadcast_operators import run_mx_binary_broadcast_operators_benchmarks
+from benchmark.opperf.nd_operations.binary_operators import run_mx_binary_broadcast_operators_benchmarks
 
 # Run all Binary Broadcast operations benchmarks with default input values
 print(run_mx_binary_broadcast_operators_benchmarks())
@@ -136,7 +139,7 @@ from mxnet import nd
 
 from benchmark.opperf.utils.benchmark_utils import run_performance_test
 
-add_res = run_performance_test([nd.add, nd.sub], run_backward=True, dtype='float32', ctx=mx.cpu(),
+add_res = run_performance_test([nd.add, nd.subtract], run_backward=True, dtype='float32', ctx=mx.cpu(),
                                inputs=[{"lhs": (1024, 1024),
                                         "rhs": (1024, 1024)}],
                                warmup=10, runs=25)

From 308e4acc1acd34d8492154a0076edd507e4908f2 Mon Sep 17 00:00:00 2001
From: Rohit Kumar Srivastava <srivastava.141@osu.edu>
Date: Tue, 20 Aug 2019 14:33:14 -0700
Subject: [PATCH 199/813] Adding tests to verify support for Large Tensors in
 additional Ops along with new C_Apis supporting 64bit indexing (#15895)

---
 include/mxnet/c_api.h             |  10 +
 python/mxnet/ndarray/ndarray.py   |  32 ++-
 src/c_api/c_api.cc                |  33 +++-
 src/operator/tensor/dot-inl.h     |   2 +-
 tests/nightly/test_large_array.py | 316 +++++++++++++++++++++++++++++-
 5 files changed, 378 insertions(+), 15 deletions(-)

diff --git a/include/mxnet/c_api.h b/include/mxnet/c_api.h
index 14209640d77d..f8274ce1a226 100644
--- a/include/mxnet/c_api.h
+++ b/include/mxnet/c_api.h
@@ -734,6 +734,7 @@ MXNET_DLL int MXNDArraySyncCopyFromCPU(NDArrayHandle handle,
 MXNET_DLL int MXNDArraySyncCopyToCPU(NDArrayHandle handle,
                                      void *data,
                                      size_t size);
+
 /*!
  * \brief Copy src.data() to dst.data() if i = -1, else dst.aux_data(i) if i >= 0
  * This function blocks. Do not use it in performance critical code.
@@ -790,6 +791,11 @@ MXNET_DLL int MXNDArraySlice(NDArrayHandle handle,
                              mx_uint slice_end,
                              NDArrayHandle *out);
 
+MXNET_DLL int MXNDArraySlice64(NDArrayHandle handle,
+                               int64_t slice_begin,
+                               int64_t slice_end,
+                               NDArrayHandle *out);
+
 /*!
  * \brief Index the NDArray along axis 0.
  * \param handle the handle to the NDArray
@@ -801,6 +807,10 @@ MXNET_DLL int MXNDArrayAt(NDArrayHandle handle,
                           mx_uint idx,
                           NDArrayHandle *out);
 
+MXNET_DLL int MXNDArrayAt64(NDArrayHandle handle,
+                            int64_t idx,
+                            NDArrayHandle *out);
+
 /*!
  * \brief get the storage type of the array
  */
diff --git a/python/mxnet/ndarray/ndarray.py b/python/mxnet/ndarray/ndarray.py
index 5f03c65a2e79..612017cdaff7 100644
--- a/python/mxnet/ndarray/ndarray.py
+++ b/python/mxnet/ndarray/ndarray.py
@@ -932,14 +932,24 @@ def _get_nd_basic_indexing(self, key):
             )
             handle = NDArrayHandle()
             flat_self = self.reshape(-1)
-            check_call(
-                _LIB.MXNDArraySlice(
-                    flat_self.handle,
-                    mx_uint(flat_begin),
-                    mx_uint(flat_end),
-                    ctypes.byref(handle),
+            if sys.version_info[0] > 2 and _int64_enabled():
+                check_call(
+                    _LIB.MXNDArraySlice64(
+                        flat_self.handle,
+                        ctypes.c_int64(flat_begin),
+                        ctypes.c_int64(flat_end),
+                        ctypes.byref(handle),
+                    )
+                )
+            else:
+                check_call(
+                    _LIB.MXNDArraySlice(
+                        flat_self.handle,
+                        ctypes.c_uint32(flat_begin),
+                        ctypes.c_uint32(flat_end),
+                        ctypes.byref(handle),
+                    )
                 )
-            )
             sliced_shape = self._basic_indexing_sliced_shape(slc_key, self.shape)
             sliced = NDArray(handle=handle, writable=self.writable).reshape(sliced_shape)
         else:
@@ -1235,8 +1245,12 @@ def _at(self, idx):
             if idx < 0:
                 raise IndexError('index %d is out of bounds for axis 0 with size %d'
                                  % (idx-length, length))
-        check_call(_LIB.MXNDArrayAt(
-            self.handle, mx_uint(idx), ctypes.byref(handle)))
+        if sys.version_info[0] > 2 and _int64_enabled():
+            check_call(_LIB.MXNDArrayAt64(
+                self.handle, ctypes.c_int64(idx), ctypes.byref(handle)))
+        else:
+            check_call(_LIB.MXNDArrayAt(
+                self.handle, ctypes.c_uint32(idx), ctypes.byref(handle)))
         return self.__class__(handle=handle, writable=self.writable)
 
     def reshape(self, *shape, **kwargs):
diff --git a/src/c_api/c_api.cc b/src/c_api/c_api.cc
index c2b80b3f601c..f6b67d3cb437 100644
--- a/src/c_api/c_api.cc
+++ b/src/c_api/c_api.cc
@@ -451,20 +451,35 @@ int MXNDArrayFree(NDArrayHandle handle) {
   API_END();
 }
 
+template<typename dtype>
+void SliceArray(NDArrayHandle handle, dtype slice_begin, dtype slice_end, NDArray* ptr,
+                NDArrayHandle* out) {
+  *ptr = static_cast<NDArray*>(handle)->SliceWithRecord(slice_begin, slice_end);
+  *out = ptr;
+}
+
 int MXNDArraySlice(NDArrayHandle handle,
                    mx_uint slice_begin,
                    mx_uint slice_end,
                    NDArrayHandle *out) {
   NDArray *ptr = new NDArray();
   API_BEGIN();
-  *ptr = static_cast<NDArray*>(handle)->SliceWithRecord(
-      slice_begin, slice_end);
-  *out = ptr;
+  SliceArray<uint32_t>(handle, slice_begin, slice_end, ptr, out);
+  API_END_HANDLE_ERROR(delete ptr);
+}
+
+int MXNDArraySlice64(NDArrayHandle handle,
+                     int64_t slice_begin,
+                     int64_t slice_end,
+                     NDArrayHandle *out) {
+  NDArray *ptr = new NDArray();
+  API_BEGIN();
+  SliceArray<int64_t>(handle, slice_begin, slice_end, ptr, out);
   API_END_HANDLE_ERROR(delete ptr);
 }
 
 int MXNDArrayAt(NDArrayHandle handle,
-                mx_uint idx,
+                uint32_t idx,
                 NDArrayHandle *out) {
   NDArray *ptr = new NDArray();
   API_BEGIN();
@@ -473,6 +488,16 @@ int MXNDArrayAt(NDArrayHandle handle,
   API_END_HANDLE_ERROR(delete ptr);
 }
 
+int MXNDArrayAt64(NDArrayHandle handle,
+                  int64_t idx,
+                  NDArrayHandle *out) {
+  NDArray *ptr = new NDArray();
+  API_BEGIN();
+  *ptr = static_cast<NDArray*>(handle)->AtWithRecord(idx);
+  *out = ptr;
+  API_END_HANDLE_ERROR(delete ptr);
+}
+
 MXNET_DLL int MXNDArrayReshape(NDArrayHandle handle,
                                int ndim,
                                int *dims,
diff --git a/src/operator/tensor/dot-inl.h b/src/operator/tensor/dot-inl.h
index 77e8e36bbef8..96c869f40d40 100644
--- a/src/operator/tensor/dot-inl.h
+++ b/src/operator/tensor/dot-inl.h
@@ -91,7 +91,7 @@ void DotForward_(const nnvm::NodeAttrs& attrs,
                 inputs[0].get<xpu, 1, DType>(s),
                 inputs[1].get<xpu, 1, DType>(s));
     } else {
-      int ma, na, mb, nb, m, n;
+      index_t ma, na, mb, nb, m, n;
       if (param.transpose_a) {
         ma = inputs[0].size(0);
         na = inputs[0].Size()/ma;
diff --git a/tests/nightly/test_large_array.py b/tests/nightly/test_large_array.py
index c9d48b77a283..bd452fb75f6c 100644
--- a/tests/nightly/test_large_array.py
+++ b/tests/nightly/test_large_array.py
@@ -19,7 +19,7 @@
 import numpy as np
 import mxnet as mx
 
-from mxnet.test_utils import rand_ndarray, assert_almost_equal, rand_coord_2d, default_context
+from mxnet.test_utils import rand_ndarray, assert_almost_equal, rand_coord_2d, default_context, check_symbolic_forward
 from mxnet import gluon, nd
 from tests.python.unittest.common import with_seed
 
@@ -891,6 +891,320 @@ def test_rpow():
     assert c.shape == a.shape
 
 
+def test_shape():
+    b = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
+    mx.nd.waitall()
+    assert b.shape == (SMALL_Y, LARGE_X)
+
+
+def test_size():
+    b = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
+    mx.nd.waitall()
+    assert b.size == LARGE_SIZE
+
+
+def test_copy():
+    a = nd.ones((SMALL_Y, LARGE_X))
+    b = a.copy()
+    nd.waitall()
+    assert b.shape == a.shape
+    assert b.size == LARGE_SIZE
+
+
+def test_copy_to():
+    a = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
+    b = nd.array(np.zeros((SMALL_Y, LARGE_X)))
+    c = a.copyto(b)
+    assert c is b
+    print(b)
+    assert b[0][-1] == LARGE_X-1
+
+
+def test_zeros_like():
+    a = nd.array(np.ones((SMALL_Y, LARGE_X)))
+    b = nd.zeros_like(a)
+    assert b[-1][-1] == 0
+    assert b.shape == a.shape
+
+
+def test_ones_like():
+    a = nd.array(np.zeros((SMALL_Y, LARGE_X)))
+    b = nd.ones_like(a)
+    assert b[-1][-1] == 1
+    assert b.shape == a.shape
+
+
+def test_reshape_like():
+    a = nd.array(np.zeros((SMALL_Y, LARGE_X)))
+    b = nd.array(np.zeros((SMALL_Y//2, LARGE_X*2)))
+    c = nd.reshape_like(a, b)
+    assert c.shape == (SMALL_Y//2, LARGE_X*2)
+
+
+def test_flatten():
+    a = create_2d_tensor(rows=LARGE_X, columns=SMALL_Y).reshape((LARGE_X//2, 2, SMALL_Y))
+    b = nd.flatten(a)
+    assert b[-1][-1] == (LARGE_X-1)
+    assert b[-1][0] == (LARGE_X-2)
+    assert b.shape == (LARGE_X//2, SMALL_Y*2)
+
+
+def test_expand_dims():
+    a = nd.array(np.ones((SMALL_Y, LARGE_X)))
+    b = nd.expand_dims(a, axis=1)
+    nd.waitall()
+    assert b.shape == (SMALL_Y, 1, LARGE_X)
+
+
+def test_concat():
+    a = nd.array(np.ones((SMALL_Y, LARGE_X)))
+    b = nd.array(np.zeros((SMALL_Y, LARGE_X)))
+    c = nd.concat(a,b, dim=0)
+    assert c.shape == (b.shape[0]*2, LARGE_X)
+
+
+def test_stack():
+    a = nd.array(np.ones((SMALL_Y, LARGE_X)))
+    b = nd.array(np.zeros((SMALL_Y, LARGE_X)))
+    c = nd.stack(a,b, axis=1)
+    assert c.shape == (b.shape[0], 2, LARGE_X)
+
+
+def test_broadcast_axes():
+    a = create_2d_tensor(rows=1, columns=LARGE_X)
+    b = nd.broadcast_axis(a, axis=[0], size=2)
+    assert b.shape == (a.shape[0]*2, a.shape[1])
+
+
+def test_sum():
+    a = nd.array(np.ones((SMALL_Y, LARGE_X)))
+    b = nd.sum(a, axis=1)
+    assert b.shape[0] == SMALL_Y
+
+
+def test_prod():
+    a = nd.array(np.ones((SMALL_Y, LARGE_X)))
+    b = nd.prod(a, axis=1)
+    assert b.shape[0] == SMALL_Y
+
+
+def test_mean():
+    a = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
+    b = nd.mean(a, axis=0)
+    assert b[0] == (SMALL_Y/2-1)
+
+
+def test_min():
+    a = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
+    b = nd.min(a, axis=0)
+    assert b[0] == 0
+    assert b[-1] == 0
+
+
+def test_max():
+    a = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
+    b = nd.max(a, axis=0)
+    assert b[0] == (SMALL_Y-1)
+    assert b[-1] == (SMALL_Y-1)
+
+
+def test_norm():
+    a = np.array(np.full((1, LARGE_X), 3))
+    b = np.array(np.full((1, LARGE_X), 4))
+    c = nd.array(np.concatenate((a,b), axis=0))
+    d = nd.norm(c, ord=2, axis=0)
+    e = nd.norm(c, ord=1, axis=0)
+    assert d.shape[0] == LARGE_X
+    assert e.shape[0] == LARGE_X
+    assert d[-1] == 5
+    assert e[-1] == 7
+
+
+def test_argmax():
+    a = np.ones((SMALL_Y, LARGE_X))
+    b = np.zeros((SMALL_Y, LARGE_X))
+    c = nd.array(np.concatenate((a,b), axis=0))
+    d = nd.argmax(c, axis=0)
+    assert d.shape[0] == LARGE_X
+    assert d[-1] == d[0] == 0
+
+
+def test_relu():
+    def frelu(x):
+        return np.maximum(x, 0.0)
+    def frelu_grad(x):
+        return 1.0 * (x > 0.0)
+    shape = (SMALL_Y, LARGE_X)
+    x = mx.symbol.Variable("x")
+    y = mx.sym.relu(x)
+    xa = np.random.uniform(low=-1.0,high=1.0,size=shape)
+    eps = 1e-4
+    xa[abs(xa) < eps] = 1.0
+    ya = frelu(xa)
+    ga = frelu_grad(xa)
+    check_symbolic_forward(y, [xa], [ya])
+
+
+def test_sigmoid():
+    def fsigmoid(a):
+        return np.divide(1.0, (1.0 + np.exp(-a)))
+    shape = (SMALL_Y, LARGE_X)
+    x = mx.symbol.Variable("x")
+    y = mx.sym.sigmoid(x)
+    xa = np.random.uniform(low=-1.0,high=1.0,size=shape)
+    ya = fsigmoid(xa)
+    check_symbolic_forward(y, [xa], [ya])
+
+
+def np_softmax(x, axis=-1, temperature=1.0):
+    x = x - np.max(x, axis=axis, keepdims=True)
+    x = np.exp(x/temperature)
+    x /= np.sum(x, axis=axis, keepdims=True)
+    return x
+
+
+def test_log_softmax():
+    ndim = 2
+    shape = (SMALL_Y, LARGE_X)
+    axis = np.random.randint(0, ndim)
+    data = np.random.uniform(-2, 2, size=shape)
+    sym = mx.sym.log_softmax(axis=axis-ndim)
+    check_symbolic_forward(sym, [data], [np.log(np_softmax(data, axis=axis)+1e-20)])
+
+
+def test_iadd():
+    a = nd.array(np.ones((SMALL_Y, LARGE_X)))
+    b = nd.array(np.ones((SMALL_Y, LARGE_X)))
+    c = b
+    c += a
+    assert c.shape == a.shape
+    assert c[0][-1] == 2
+
+
+def test_isub():
+    a = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
+    b = nd.array(np.ones((SMALL_Y, LARGE_X)))
+    c = a
+    c -= b
+    assert c.shape == a.shape
+    assert c[0][-1] == 2
+
+
+def test_imul():
+    a = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
+    b = nd.array(np.ones((SMALL_Y, LARGE_X)))
+    c = b
+    c *= a
+    assert c.shape == a.shape
+    assert c[0][-1] == 3
+
+
+def test_idiv():
+    a = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 4)))
+    b = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 2)))
+    c = a
+    c /= b
+    assert c.shape == a.shape
+    assert c[0][-1] == 2
+
+
+def test_imod():
+    a = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
+    b = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 2)))
+    c = a
+    c %= b
+    assert c.shape == a.shape
+    assert c[0][-1] == 1
+
+
+def test_eq():
+    a = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
+    b = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
+    c = (a == b)
+    assert np.sum(c[0].asnumpy() == 1).all()
+
+
+def test_neq():
+    a = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 2)))
+    b = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
+    c = (a != b)
+    assert np.sum(c[0].asnumpy() == 1).all()
+
+
+def test_lt():
+    a = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 2)))
+    b = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
+    d = (a <= b)
+    assert np.sum(d[0].asnumpy() == 1).all()
+
+
+def test_lte():
+    a = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 2)))
+    b = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
+    c = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 2)))
+    d = (a <= b)
+    e = (a <= c)
+    assert np.sum(d[0].asnumpy() == 1).all()
+    assert np.sum(e[0].asnumpy() == 1).all()
+
+
+def test_gt():
+    a = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
+    b = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 2)))
+    d = (a >= b)
+    assert np.sum(d[0].asnumpy() == 1).all()
+
+
+def test_gte():
+    a = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
+    b = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 2)))
+    c = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
+    d = (a >= b)
+    e = (a >= c)
+    assert np.sum(d[0].asnumpy() == 1).all()
+    assert np.sum(e[0].asnumpy() == 1).all()
+
+
+def test_slice_like():
+    a = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
+    b = nd.array(np.ones((SMALL_Y//2, LARGE_X//2)))
+    c = nd.slice_like(a, b)
+    d = nd.slice_like(a, b, axes=(0))
+    e = nd.slice_like(a, b, axes=(-1))
+    assert c.shape == b.shape
+    assert d.shape[0] == b.shape[0]
+    assert e.shape[-1] == b.shape[-1]
+    assert c[0][-1] == 0
+    assert d[-1][0] == (SMALL_Y//2-1)
+    assert e[-1][-1] == (SMALL_Y-1)
+
+
+def test_slice_axis():
+    a = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
+    c = nd.slice_axis(a, axis=0, begin=0, end=SMALL_Y//2)
+    d = nd.slice_axis(a, axis=1, begin=0, end=LARGE_X//2)
+    assert c.shape[0] == a.shape[0]//2
+    assert d.shape[1] == a.shape[1]//2
+    assert c[-1][0] == (SMALL_Y//2-1)
+    assert d[-1][-1] == (SMALL_Y-1)
+
+
+def test_one_hot():
+    a = nd.array(np.zeros(SMALL_Y))
+    a[0] = 1
+    a[-1] = 1
+    b = nd.one_hot(a, LARGE_X)
+    b[0][1] == 1
+    b[-1][1] == 1
+
+
+def test_full():
+    a = nd.full((SMALL_Y, LARGE_X), 3)
+    assert a.shape == (SMALL_Y, LARGE_X)
+    assert a[SMALL_Y//2][LARGE_X//2] == 3
+    assert a[-1][-1] == 3
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From d225074c53b39ebf109056c8135cca5a2ee6926e Mon Sep 17 00:00:00 2001
From: Wang Jiajun <wangjiajun.phy@gmail.com>
Date: Tue, 20 Aug 2019 19:26:47 -0500
Subject: [PATCH 200/813] fix naive engine for multi-threaded inference
 (#15574)

* fix naive engine

* remove thread local

* trigger CI
---
 src/engine/naive_engine.cc | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/engine/naive_engine.cc b/src/engine/naive_engine.cc
index 92b38a8ea368..a7a9e992db7b 100644
--- a/src/engine/naive_engine.cc
+++ b/src/engine/naive_engine.cc
@@ -155,9 +155,9 @@ class NaiveEngine final : public Engine {
                  int priority = 0,
                  const char* opr_name = nullptr,
                  bool wait = false) override {
+    bool req_completed = false;
     CallbackOnComplete callback = CreateCallback(
-        NaiveEngine::OnComplete, nullptr);
-    this->req_completed_ = false;
+        NaiveEngine::OnComplete, &req_completed);
     profiler::Profiler *profiler = profiler::Profiler::Get();
     auto opr_deleter = [this](NaiveOpr* p) {
       this->DeleteOperator(p);
@@ -202,7 +202,7 @@ class NaiveEngine final : public Engine {
     for (auto var : mutable_vars) {
       ++var->version_;
     }
-    CHECK(this->req_completed_)
+    CHECK(req_completed)
         << "NaiveEngine only support synchronize Push so far";
     if (profiling) {
       opr->opr_profile->stop();
@@ -235,10 +235,9 @@ class NaiveEngine final : public Engine {
   // callback to oncomplete
   static void OnComplete(Engine *engine, void *param,
                          const dmlc::Error* error) {
-    static_cast<NaiveEngine*>(engine)->req_completed_ = true;
+    bool *req_completed = static_cast<bool*>(param);
+    *req_completed = true;
   }
-  // whether action is completed
-  bool req_completed_;
   /*! \brief whether it is during shutdown phase*/
   std::atomic<bool> shutdown_phase_{false};
   // CPU stream
@@ -261,5 +260,6 @@ class NaiveEngine final : public Engine {
 Engine *CreateNaiveEngine() {
   return new NaiveEngine();
 }
+
 }  // namespace engine
 }  // namespace mxnet

From 0b5526b9c09546a02314647aa82f72aa3e93e96d Mon Sep 17 00:00:00 2001
From: Xinyu Chen <xinyu1.chen@intel.com>
Date: Wed, 21 Aug 2019 13:59:57 +0800
Subject: [PATCH 201/813] [Quantization]support exclude operators while
 quantization (#15910)

* skip unregistered op and add exclude_op_names

* wrap a function

* add testcase for excluded op

* skip test for native cpu since all op will be excluded

* trigger

* trigger again

* revert check inner node op name

* test subgraph

* trigger

* trigger
---
 docs/tutorials/mkldnn/mkldnn_quantization.md  |   8 +-
 example/quantization/imagenet_gen_qsym.py     |  13 +-
 include/mxnet/c_api.h                         |  14 +-
 python/mxnet/contrib/quantization.py          | 130 ++++++++----------
 src/c_api/c_api_symbolic.cc                   |  12 +-
 .../quantization/quantize_graph_pass.cc       |  68 ++++++---
 tests/python/mkl/test_subgraph.py             |   5 +-
 .../python/quantization/test_quantization.py  |  20 ++-
 8 files changed, 152 insertions(+), 118 deletions(-)

diff --git a/docs/tutorials/mkldnn/mkldnn_quantization.md b/docs/tutorials/mkldnn/mkldnn_quantization.md
index 459bf2a17d40..a5547c02790c 100644
--- a/docs/tutorials/mkldnn/mkldnn_quantization.md
+++ b/docs/tutorials/mkldnn/mkldnn_quantization.md
@@ -99,7 +99,7 @@ calib_layer = None
 quantized_dtype = 'auto'
 logger.info('Quantizing FP32 model Resnet18-V1')
 qsym, qarg_params, aux_params, collector = quantize_graph(sym=sym, arg_params=arg_params, aux_params=aux_params,
-                                                          excluded_sym_names=excluded_names,
+                                                          ctx=mx.cpu(), excluded_sym_names=excluded_names,
                                                           calib_mode=calib_mode, calib_layer=calib_layer,
                                                           quantized_dtype=quantized_dtype, logger=logger)
 # (optional) visualize quantized model
@@ -151,9 +151,9 @@ calib_layer = None
 quantized_dtype = 'auto'
 logger.info('Quantizing FP32 model resnet18-V1')
 cqsym, cqarg_params, aux_params, collector = quantize_graph(sym=sym, arg_params=arg_params, aux_params=aux_params,
-                                                          excluded_sym_names=excluded_names,
-                                                          calib_mode=calib_mode, calib_layer=calib_layer,
-                                                          quantized_dtype=quantized_dtype, logger=logger)
+                                                            ctx=mx.cpu(), excluded_sym_names=excluded_names,
+                                                            calib_mode=calib_mode, calib_layer=calib_layer,
+                                                            quantized_dtype=quantized_dtype, logger=logger)
 
 # download imagenet validation dataset
 mx.test_utils.download('http://data.mxnet.io/data/val_256_q90.rec', 'dataset.rec')
diff --git a/example/quantization/imagenet_gen_qsym.py b/example/quantization/imagenet_gen_qsym.py
index 41713c3c3f51..27a1d7a45a68 100644
--- a/example/quantization/imagenet_gen_qsym.py
+++ b/example/quantization/imagenet_gen_qsym.py
@@ -138,6 +138,7 @@ def save_params(fname, arg_params, aux_params, logger=None):
 
     exclude_first_conv = args.exclude_first_conv
     excluded_sym_names = []
+    excluded_op_names = []
     if args.model == 'imagenet1k-resnet-152':
         rgb_mean = '0,0,0'
         if args.ctx == 'gpu':
@@ -155,16 +156,6 @@ def save_params(fname, arg_params, aux_params, logger=None):
         if args.ctx == 'gpu':
             calib_layer = lambda name: name.endswith('_output') and (name.find('conv') != -1
                                                                      or name.find('fc') != -1)
-            excluded_sym_names += ['ch_concat_3a_chconcat',
-                                   'ch_concat_3b_chconcat',
-                                   'ch_concat_3c_chconcat',
-                                   'ch_concat_4a_chconcat',
-                                   'ch_concat_4b_chconcat',
-                                   'ch_concat_4c_chconcat',
-                                   'ch_concat_4d_chconcat',
-                                   'ch_concat_4e_chconcat',
-                                   'ch_concat_5a_chconcat',
-                                   'ch_concat_5b_chconcat']
         else:
             calib_layer = lambda name: name.endswith('_output') and (name.find('conv') != -1)
             excluded_sym_names += ['flatten', 'fc1']
@@ -187,6 +178,7 @@ def save_params(fname, arg_params, aux_params, logger=None):
         logger.info('Quantizing FP32 model %s' % args.model)
         qsym, qarg_params, aux_params = quantize_model(sym=sym, arg_params=arg_params, aux_params=aux_params,
                                                        ctx=ctx, excluded_sym_names=excluded_sym_names,
+                                                       excluded_op_names=excluded_op_names,
                                                        calib_mode=calib_mode, quantized_dtype=args.quantized_dtype,
                                                        logger=logger)
         sym_name = '%s-symbol.json' % (prefix + '-quantized')
@@ -208,6 +200,7 @@ def save_params(fname, arg_params, aux_params, logger=None):
 
         cqsym, qarg_params, aux_params = quantize_model(sym=sym, arg_params=arg_params, aux_params=aux_params,
                                                         ctx=ctx, excluded_sym_names=excluded_sym_names,
+                                                        excluded_op_names=excluded_op_names,
                                                         calib_mode=calib_mode, calib_data=data,
                                                         num_calib_examples=num_calib_batches * batch_size,
                                                         calib_layer=calib_layer, quantized_dtype=args.quantized_dtype,
diff --git a/include/mxnet/c_api.h b/include/mxnet/c_api.h
index f8274ce1a226..d4f756f5333c 100644
--- a/include/mxnet/c_api.h
+++ b/include/mxnet/c_api.h
@@ -1898,16 +1898,22 @@ MXNET_DLL int MXSymbolInferTypePartial(SymbolHandle sym,
  * \brief Convert a symbol into a quantized symbol where FP32 operators are replaced with INT8
  * \param sym_handle symbol to be converted
  * \param ret_sym_handle quantized symbol result
- * \param num_excluded_symbols number of layers excluded from being quantized in the input symbol
- * \param excluded_symbols op names to be excluded from being quantized
+ * \param dev_type device type
+ * \param num_excluded_sym_names number of layers excluded from being quantized in the input symbol
+ * \param excluded_sym_names node names to be excluded from being quantized
+ * \param num_excluded_op_names number of operators excluded from being quantized in the input symbol
+ * \param excluded_op_names operator names to be excluded from being quantized
  * \param num_offline number of parameters that are quantized offline
  * \param offline_params array of c strings representing the names of params quantized offline
  * \param quantized_dtype the quantized destination type for input data
  * \param calib_quantize **Deprecated**. quantize op will always be calibrated if could
  */
 MXNET_DLL int MXQuantizeSymbol(SymbolHandle sym_handle, SymbolHandle *ret_sym_handle,
-                               const mx_uint num_excluded_symbols,
-                               const char **excluded_symbols,
+                               const int* dev_type,
+                               const mx_uint num_excluded_sym_names,
+                               const char **excluded_sym_names,
+                               const mx_uint num_excluded_op_names,
+                               const char **excluded_op_names,
                                const mx_uint num_offline, const char **offline_params,
                                const char *quantized_dtype, const bool calib_quantize);
 
diff --git a/python/mxnet/contrib/quantization.py b/python/mxnet/contrib/quantization.py
index 262cc075bc89..c34e934bfa02 100644
--- a/python/mxnet/contrib/quantization.py
+++ b/python/mxnet/contrib/quantization.py
@@ -83,7 +83,8 @@ def _quantize_params(qsym, params, th_dict):
                 quantized_params[name] = ndarray.array([th_dict[output][1]])
     return quantized_params
 
-def _quantize_symbol(sym, excluded_symbols=None, offline_params=None, quantized_dtype='int8'):
+def _quantize_symbol(sym, ctx, excluded_symbols=None, excluded_operators=None,
+                     offline_params=None, quantized_dtype='int8'):
     """Given a symbol object representing a neural network of data type FP32,
     quantize it into a INT8 network.
 
@@ -91,9 +92,14 @@ def _quantize_symbol(sym, excluded_symbols=None, offline_params=None, quantized_
     ----------
     sym : Symbol
         FP32 neural network symbol.
-    excluded_sym_names : list of strings
+    ctx : Context
+        Defines the device that users want to run quantized symbol.
+    excluded_symbols : list of strings
         A list of strings representing the names of the symbols that users want to excluding
         from being quantized.
+    excluded_operators : list of strings
+        A list of strings representing the names of the operators that users want to excluding
+        from being quantized.
     offline_params : list of strs
         Names of the parameters that users want to quantize offline. It's always recommended to
         quantize parameters offline so that quantizing parameters during the inference can be
@@ -108,6 +114,13 @@ def _quantize_symbol(sym, excluded_symbols=None, offline_params=None, quantized_
     else:
         excluded_symbols = []
 
+    num_excluded_ops = 0
+    if excluded_operators is not None:
+        assert isinstance(excluded_operators, list)
+        num_excluded_ops = len(excluded_operators)
+    else:
+        excluded_operators = []
+
     num_offline = 0
     offline = []
     if offline_params is not None:
@@ -118,8 +131,11 @@ def _quantize_symbol(sym, excluded_symbols=None, offline_params=None, quantized_
     out = SymbolHandle()
     check_call(_LIB.MXQuantizeSymbol(sym.handle,
                                      ctypes.byref(out),
+                                     ctypes.byref(ctypes.c_int(ctx.device_typeid)),
                                      mx_uint(num_excluded_symbols),
                                      c_str_array(excluded_symbols),
+                                     mx_uint(num_excluded_ops),
+                                     c_str_array(excluded_operators),
                                      mx_uint(num_offline),
                                      c_array(ctypes.c_char_p, offline),
                                      c_str(quantized_dtype),
@@ -462,7 +478,7 @@ def _as_data_iter(calib_data):
 
 def quantize_model(sym, arg_params, aux_params,
                    data_names=('data',), label_names=('softmax_label',),
-                   ctx=cpu(), excluded_sym_names=None, calib_mode='entropy',
+                   ctx=cpu(), excluded_sym_names=None, excluded_op_names=None, calib_mode='entropy',
                    calib_data=None, num_calib_examples=None, calib_layer=None,
                    quantized_dtype='int8', logger=logging):
     """User-level API for generating a quantized model from a FP32 model w/ or w/o calibration.
@@ -494,6 +510,9 @@ def quantize_model(sym, arg_params, aux_params,
     excluded_sym_names : list of strings
         A list of strings representing the names of the symbols that users want to excluding
         from being quantized.
+    excluded_op_names : list of strings
+        A list of strings representing the names of the operators that users want to excluding
+        from being quantized.
     calib_mode : str
         If calib_mode='none', no calibration will be used and the thresholds for
         requantization after the corresponding layers will be calculated at runtime by
@@ -534,11 +553,19 @@ def quantize_model(sym, arg_params, aux_params,
                          ' the names of the symbols that will not be quantized,'
                          ' while received type %s' % str(type(excluded_sym_names)))
 
+    if excluded_op_names is None:
+        excluded_op_names = []
+    if not isinstance(excluded_op_names, list):
+        raise ValueError('excluded_op_names must be a list of strings representing'
+                         ' the names of the operators that will not be quantized,'
+                         ' while received type %s' % str(type(excluded_op_names)))
+
     logger.info('Quantizing symbol')
     if quantized_dtype not in ('int8', 'uint8', 'auto'):
         raise ValueError('unknown quantized_dtype %s received,'
                          ' expected `int8`, `uint8` or `auto`' % quantized_dtype)
-    qsym = _quantize_symbol(sym, excluded_symbols=excluded_sym_names,
+    qsym = _quantize_symbol(sym, ctx, excluded_symbols=excluded_sym_names,
+                            excluded_operators=excluded_op_names,
                             offline_params=list(arg_params.keys()),
                             quantized_dtype=quantized_dtype)
 
@@ -586,9 +613,9 @@ def quantize_model(sym, arg_params, aux_params,
 
 def quantize_model_mkldnn(sym, arg_params, aux_params,
                           data_names=('data',), label_names=('softmax_label',),
-                          ctx=cpu(), excluded_sym_names=None, calib_mode='entropy',
-                          calib_data=None, num_calib_examples=None, calib_layer=None,
-                          quantized_dtype='int8', logger=logging):
+                          ctx=cpu(), excluded_sym_names=None, excluded_op_names=None,
+                          calib_mode='entropy', calib_data=None, num_calib_examples=None,
+                          calib_layer=None, quantized_dtype='int8', logger=logging):
     """User-level API for generating a fusion + quantized model from a FP32 model
     w/ or w/o calibration with Intel MKL-DNN.
     The backend quantized operators are only enabled for Linux systems. Please do not run
@@ -596,50 +623,7 @@ def quantize_model_mkldnn(sym, arg_params, aux_params,
 
     Parameters
     ----------
-    sym : str or Symbol
-        Defines the structure of a neural network for FP32 data types.
-    arg_params : dict
-        Dictionary of name to `NDArray`.
-    aux_params : dict
-        Dictionary of name to `NDArray`.
-    data_names : a list of strs
-        Data names required for creating a Module object to run forward propagation on the
-        calibration dataset.
-    label_names : a list of strs
-        Label names required for creating a Module object to run forward propagation on the
-        calibration dataset.
-    ctx : Context
-        Defines the device that users want to run forward propagation on the calibration
-        dataset for collecting layer output statistics. Currently, only supports single context.
-    excluded_sym_names : list of strings
-        A list of strings representing the names of the symbols that users want to excluding
-        from being quantized.
-    calib_mode : str
-        If calib_mode='none', no calibration will be used and the thresholds for
-        requantization after the corresponding layers will be calculated at runtime by
-        calling min and max operators. The quantized models generated in this
-        mode are normally 10-20% slower than those with calibrations during inference.
-        If calib_mode='naive', the min and max values of the layer outputs from a calibration
-        dataset will be directly taken as the thresholds for quantization.
-        If calib_mode='entropy' (default mode), the thresholds for quantization will be
-        derived such that the KL divergence between the distributions of FP32 layer outputs and
-        quantized layer outputs is minimized based upon the calibration dataset.
-    calib_data : DataIter
-        A data iterator initialized by the calibration dataset.
-    num_calib_examples : int or None
-        The maximum number of examples that user would like to use for calibration. If not provided,
-        the whole calibration dataset will be used.
-    calib_layer : function
-        Given a layer's output name in string, return True or False for deciding whether to
-        calibrate this layer. If yes, the statistics of the layer's output will be collected;
-        otherwise, no information of the layer's output will be collected. If not provided,
-        all the layers' outputs that need requantization will be collected.
-    quantized_dtype : str
-        The quantized destination type for input data. Currently support 'int8'
-        , 'uint8' and 'auto'. 'auto' means automatically select output type according to calibration result.
-        Default value is 'int8'.
-    logger : Object
-        A logging object for printing information during the process of quantization.
+    same with quantize_model
 
     Returns
     -------
@@ -656,6 +640,7 @@ def quantize_model_mkldnn(sym, arg_params, aux_params,
     qsym, qarg_params, aux_params = quantize_model(sym=sym, arg_params=arg_params, aux_params=aux_params,
                                                    data_names=data_names, label_names=label_names,
                                                    ctx=ctx, excluded_sym_names=excluded_sym_names,
+                                                   excluded_op_names=excluded_op_names,
                                                    calib_mode=calib_mode, calib_data=calib_data,
                                                    num_calib_examples=num_calib_examples, calib_layer=calib_layer,
                                                    quantized_dtype=quantized_dtype, logger=logger)
@@ -664,22 +649,20 @@ def quantize_model_mkldnn(sym, arg_params, aux_params,
 
     return qsym, qarg_params, aux_params
 
-def quantize_graph(sym, arg_params, aux_params,
-                   excluded_sym_names=None, calib_mode='entropy',
+def quantize_graph(sym, arg_params, aux_params, ctx=cpu(),
+                   excluded_sym_names=None, excluded_op_names=None, calib_mode='entropy',
                    calib_layer=None, quantized_dtype='int8', logger=logging):
     """User-level API for generating a quantized model from a FP32 model w/o calibration
     and a collector for naive or entropy calibration.
     The backend quantized operators are only enabled for Linux systems. Please do not run
     inference using the quantized models on Windows for now.
-    The quantization implementation adopts the TensorFlow's approach:
-    https://www.tensorflow.org/performance/quantization.
-    The calibration implementation borrows the idea of Nvidia's 8-bit Inference with TensorRT:
-    http://on-demand.gputechconf.com/gtc/2017/presentation/s7310-8-bit-inference-with-tensorrt.pdf
-    and adapts the method to MXNet.
     Parameters
     ----------
     sym : str or Symbol
         Defines the structure of a neural network for FP32 data types.
+    ctx : Context
+        Defines the device that users want to run forward propagation on the calibration
+        dataset for collecting layer output statistics. Currently, only supports single context.
     arg_params : dict
         Dictionary of name to `NDArray`.
     aux_params : dict
@@ -687,6 +670,8 @@ def quantize_graph(sym, arg_params, aux_params,
     excluded_sym_names : list of strings
         A list of strings representing the names of the symbols that users want to excluding
         from being quantized.
+    excluded_op_names : list of strings
+        A list of strings representing the names of the operators that users want to excluding
     calib_mode : str
         If calib_mode='none', no calibration will be used and the thresholds for
         requantization after the corresponding layers will be calculated at runtime by
@@ -720,12 +705,14 @@ def quantize_graph(sym, arg_params, aux_params,
         raise ValueError('excluded_sym_names must be a list of strings representing'
                          ' the names of the symbols that will not be quantized,'
                          ' while received type %s' % str(type(excluded_sym_names)))
-
+    if not isinstance(ctx, Context):
+        raise ValueError('currently only supports single ctx, while received %s' % str(ctx))
     logger.info('Quantizing graph')
     if quantized_dtype not in ('int8', 'uint8', 'auto'):
         raise ValueError('unknown quantized_dtype %s received,'
                          ' expected `int8`, `uint8` or `auto`' % quantized_dtype)
-    qsym = _quantize_symbol(sym, excluded_symbols=excluded_sym_names,
+    qsym = _quantize_symbol(sym, ctx, excluded_symbols=excluded_sym_names,
+                            excluded_operators=excluded_op_names,
                             offline_params=list(arg_params.keys()),
                             quantized_dtype=quantized_dtype)
 
@@ -758,11 +745,6 @@ def calib_graph(qsym, arg_params, aux_params, collector,
     """User-level API for calibrating a quantized model using a filled collector.
     The backend quantized operators are only enabled for Linux systems. Please do not run
     inference using the quantized models on Windows for now.
-    The quantization implementation adopts the TensorFlow's approach:
-    https://www.tensorflow.org/performance/quantization.
-    The calibration implementation borrows the idea of Nvidia's 8-bit Inference with TensorRT:
-    http://on-demand.gputechconf.com/gtc/2017/presentation/s7310-8-bit-inference-with-tensorrt.pdf
-    and adapts the method to MXNet.
     Parameters
     ----------
     qsym : str or Symbol
@@ -821,16 +803,13 @@ def calib_graph(qsym, arg_params, aux_params, collector,
 
     return qsym, qarg_params, aux_params
 
-def quantize_net(network, quantized_dtype='auto', exclude_layers=None, exclude_layers_match=None, calib_data=None,
-                 data_shapes=None, calib_mode='none', num_calib_examples=None, ctx=cpu(), logger=logging):
+def quantize_net(network, quantized_dtype='auto',
+                 exclude_layers=None, exclude_layers_match=None, exclude_operators=None,
+                 calib_data=None, data_shapes=None, calib_mode='none',
+                 num_calib_examples=None, ctx=cpu(), logger=logging):
     """User-level API for Gluon users to generate a quantized SymbolBlock from a FP32 HybridBlock w/ or w/o calibration.
     The backend quantized operators are only enabled for Linux systems. Please do not run
     inference using the quantized models on Windows for now.
-    The quantization implementation adopts the TensorFlow's approach:
-    https://www.tensorflow.org/performance/quantization.
-    The calibration implementation borrows the idea of Nvidia's 8-bit Inference with TensorRT:
-    http://on-demand.gputechconf.com/gtc/2017/presentation/s7310-8-bit-inference-with-tensorrt.pdf
-    and adapts the method to MXNet.
 
     Parameters
     ----------
@@ -845,6 +824,8 @@ def quantize_net(network, quantized_dtype='auto', exclude_layers=None, exclude_l
     exclude_layers_match : list of strings
         A list of strings wildcard matching the names of the symbols that users want to excluding
         from being quantized.
+    exclude_operators : list of strings
+        A list of strings representing the names of the operators that users want to excluding
     calib_data : mx.io.DataIter or gluon.DataLoader
         A iterable data loading object.
     data_shapes : list
@@ -930,6 +911,8 @@ def __exit__(self, exc_type, exc_value, traceback):
         exclude_layers = []
     if exclude_layers_match is None:
         exclude_layers_match = []
+    if exclude_operators is None:
+        exclude_operators = []
     for name_match in exclude_layers_match:
         for layers in list(symnet.get_internals()):
             if layers.name.find(name_match) != -1:
@@ -940,7 +923,8 @@ def __exit__(self, exc_type, exc_value, traceback):
         symnet = symnet.get_backend_symbol('MKLDNN_QUANTIZE')
 
     qsym, qarg_params, aux_params, collector = quantize_graph(
-        sym=symnet, arg_params=args, aux_params=auxs, excluded_sym_names=exclude_layers,
+        sym=symnet, arg_params=args, aux_params=auxs, ctx=ctx,
+        excluded_sym_names=exclude_layers, excluded_op_names=exclude_operators,
         calib_mode=calib_mode, calib_layer=None, quantized_dtype=quantized_dtype, logger=logger)
 
     if calib_mode is not None and calib_mode != 'none':
diff --git a/src/c_api/c_api_symbolic.cc b/src/c_api/c_api_symbolic.cc
index 15b7ba56b9ee..be0cad648015 100644
--- a/src/c_api/c_api_symbolic.cc
+++ b/src/c_api/c_api_symbolic.cc
@@ -880,6 +880,9 @@ int MXSymbolGrad(SymbolHandle sym, mx_uint num_wrt, const char** wrt, SymbolHand
 
 int MXQuantizeSymbol(SymbolHandle sym_handle,
                      SymbolHandle *ret_sym_handle,
+                     const int* dev_type,
+                     const mx_uint num_excluded_sym_names,
+                     const char **excluded_sym_names,
                      const mx_uint num_excluded_op_names,
                      const char **excluded_op_names,
                      const mx_uint num_offline,
@@ -890,9 +893,14 @@ int MXQuantizeSymbol(SymbolHandle sym_handle,
   API_BEGIN();
   nnvm::Symbol *sym = static_cast<nnvm::Symbol*>(sym_handle);
   nnvm::Graph g = Symbol2Graph(*sym);
+  int target_dev = *dev_type;
   std::unordered_set<std::string> excluded_node_names;
+  for (size_t i = 0; i < num_excluded_sym_names; ++i) {
+    excluded_node_names.emplace(excluded_sym_names[i]);
+  }
+  std::unordered_set<std::string> excluded_op;
   for (size_t i = 0; i < num_excluded_op_names; ++i) {
-    excluded_node_names.emplace(excluded_op_names[i]);
+    excluded_op.emplace(excluded_op_names[i]);
   }
   std::unordered_set<std::string> offline;
   for (size_t i = 0; i < num_offline; ++i) {
@@ -900,8 +908,10 @@ int MXQuantizeSymbol(SymbolHandle sym_handle,
   }
   std::string quantized_type(quantized_dtype);
   g.attrs["excluded_nodes"] = std::make_shared<nnvm::any>(std::move(excluded_node_names));
+  g.attrs["excluded_ops"] = std::make_shared<nnvm::any>(std::move(excluded_op));
   g.attrs["offline_params"] = std::make_shared<nnvm::any>(std::move(offline));
   g.attrs["quantized_dtype"] = std::make_shared<nnvm::any>(std::move(quantized_type));
+  g.attrs["target_ctx"] = std::make_shared<nnvm::any>(target_dev);
   g = ApplyPass(std::move(g), "QuantizeGraph");
   s->outputs = g.outputs;
   *ret_sym_handle = s;
diff --git a/src/operator/quantization/quantize_graph_pass.cc b/src/operator/quantization/quantize_graph_pass.cc
index 31e3539b8a84..e3139913332d 100644
--- a/src/operator/quantization/quantize_graph_pass.cc
+++ b/src/operator/quantization/quantize_graph_pass.cc
@@ -102,7 +102,24 @@ std::vector<NodeEntry> OfflineParams(std::vector<NodeEntry>&& outputs,
   return outputs;
 }
 
-inline NodePtr NeedQuantize(NodePtr node, const std::unordered_set<std::string>& excluded_nodes) {
+// To check if a node is registered with a computation function on a target device.
+bool isRegistered(NodePtr node, const int& dev_type) {
+  const auto& op = node->op();
+  Context ctx = Context::Create(static_cast<Context::DeviceType>(dev_type), 0);
+  FCompute fcompute = common::GetFCompute<FCompute>(op, "FCompute", ctx);
+  FComputeEx fcomp_ex = common::GetFCompute<FComputeEx>(op, "FComputeEx", ctx);
+  FStatefulCompute fcomputestateful =
+      common::GetFCompute<FStatefulCompute>(op, "FStatefulCompute", ctx);
+  FStatefulComputeEx fcomputestateful_ex =
+      common::GetFCompute<FStatefulComputeEx>(op, "FStatefulComputeEx", ctx);
+  return (fcompute != nullptr || fcomp_ex != nullptr ||
+          fcomputestateful != nullptr || fcomputestateful_ex != nullptr);
+}
+
+inline NodePtr NeedQuantize(
+    NodePtr node, const std::unordered_set<std::string>& excluded_nodes,
+    const std::unordered_set<std::string>& excluded_ops,
+    const int& dev_type) {
   std::unordered_map<NodePtr, NodePtr> quantized_node;
   static auto& quantized_op_map = Op::GetAttr<mxnet::FQuantizedOp>("FQuantizedOp");
   static auto& fexec_type = nnvm::Op::GetAttr<FExecType>("FExecType");
@@ -110,20 +127,31 @@ inline NodePtr NeedQuantize(NodePtr node, const std::unordered_set<std::string>&
 
   if (op && quantized_op_map.count(op)) {
     bool need = true;
-    if (excluded_nodes.count(node->attrs.name)) {
+    // If the quantized node is not registered with a computation function, the node
+    // will be excluded automatically.
+    auto q_ptr = quantized_op_map[node->op()];
+    auto qnode = q_ptr(node->attrs);
+    if (!isRegistered(qnode, dev_type)) {
+      LOG(INFO) << "Neither FCompute nor FComputeEx registered, " << node->op()->name
+                << " is excluded automatically.";
       need = false;
-    } else if (!node->attrs.subgraphs.empty()) {
-      ExecType exec_type = fexec_type.count(op) ? fexec_type[op](node->attrs) : ExecType::kSync;
-      if (exec_type != ExecType::kSubgraphExec) {
-        // This is a fused subgraph node, try to match inner node.
-        CHECK_EQ(node->attrs.subgraphs.size(), 1);
-        auto subgraph_sym = node->attrs.subgraphs[0];
-        DFSVisit(subgraph_sym->outputs, [&](const nnvm::NodePtr& n) {
-          if (n->is_variable()) return;
-          if (excluded_nodes.count(n->attrs.name)) {
-            need = false;
-          }
-        });
+    } else {
+      if (excluded_nodes.count(node->attrs.name) ||
+          excluded_ops.count(node->op()->name)) {
+        need = false;
+      } else if (!node->attrs.subgraphs.empty()) {
+        ExecType exec_type = fexec_type.count(op) ? fexec_type[op](node->attrs) : ExecType::kSync;
+        if (exec_type != ExecType::kSubgraphExec) {
+          // This is a fused subgraph node, try to match inner node.
+          CHECK_EQ(node->attrs.subgraphs.size(), 1);
+          auto subgraph_sym = node->attrs.subgraphs[0];
+          DFSVisit(subgraph_sym->outputs, [&](const nnvm::NodePtr& n) {
+            if (n->is_variable()) return;
+            if (excluded_nodes.count(n->attrs.name)) {
+              need = false;
+            }
+          });
+        }
       }
     }
 
@@ -147,8 +175,10 @@ Graph QuantizeGraph(Graph &&src) {
   static const auto& need_requantize_map = Op::GetAttr<mxnet::FNeedRequantize>("FNeedRequantize");
   static const auto& avoid_quantize_input_map =
       Op::GetAttr<mxnet::FAvoidQuantizeInput>("FAvoidQuantizeInput");
+  const auto dev_type = src.GetAttr<int>("target_ctx");
   const auto offline_params = src.GetAttr<std::unordered_set<std::string>>("offline_params");
   const auto excluded_nodes = src.GetAttr<std::unordered_set<std::string>>("excluded_nodes");
+  const auto excluded_ops = src.GetAttr<std::unordered_set<std::string>>("excluded_ops");
   const auto quantized_dtype = src.GetAttr<std::string>("quantized_dtype");
 
   // mirror_map stores the mapping from the currently visited graph to the newly created quantized
@@ -160,7 +190,7 @@ Graph QuantizeGraph(Graph &&src) {
     NodePtr new_node = Node::Create();
     // If the currently visited node needs quantization, insert a quantize op node before the
     // current node and replace the current node with the quantized version in the new graph.
-    auto tmp_node = NeedQuantize(node, excluded_nodes);
+    auto tmp_node = NeedQuantize(node, excluded_nodes, excluded_ops, dev_type);
     if (tmp_node) {
       new_node = tmp_node;
 
@@ -178,7 +208,7 @@ Graph QuantizeGraph(Graph &&src) {
         if (avoid_quantize_input_map.count(node->op()) &&
             avoid_quantize_input_map[node->op()](node->attrs, i)) {
           new_node->inputs.emplace_back(mirror_entry);
-        } else if (!NeedQuantize(e.node, excluded_nodes)) {
+        } else if (!NeedQuantize(e.node, excluded_nodes, excluded_ops, dev_type)) {
           if (mirror_entry_map.count(e)) {
             new_node->inputs.emplace_back(mirror_entry_map[e]);
           } else {
@@ -231,7 +261,7 @@ Graph QuantizeGraph(Graph &&src) {
           // skip non-quantized input
           continue;
         }
-        if (NeedQuantize(e.node, excluded_nodes)) {
+        if (NeedQuantize(e.node, excluded_nodes, excluded_ops, dev_type)) {
           // here we calculate the output number (exclude min/max, in order to
           // calculate min/max index from mirror node) based on assumption that
           // there is only 1min and 1max output from mirror node (which is
@@ -283,7 +313,7 @@ Graph QuantizeGraph(Graph &&src) {
         NodeEntry mirror_entry = NodeEntry{
           mirror_node, e.index, e.version};
         // if input node is quantized operator, add dequantize node
-        if (NeedQuantize(e.node, excluded_nodes) &&
+        if (NeedQuantize(e.node, excluded_nodes, excluded_ops, dev_type) &&
             (mirror_node->op() != Op::Get("_contrib_dequantize"))) {
           // here we calculate the output number (exclude min/max, in order to
           // calculate min/max index from mirror node) based on assumption that
@@ -314,7 +344,7 @@ Graph QuantizeGraph(Graph &&src) {
 
   std::vector<NodeEntry> outputs;
   for (const auto& e : src.outputs) {
-    if (NeedQuantize(e.node, excluded_nodes)) {
+    if (NeedQuantize(e.node, excluded_nodes, excluded_ops, dev_type)) {
       // Only insert dequantize for those Ops supports quantize and not excluded.
       NodePtr mirror_node = mirror_map.at(e.node.get());
       NodeEntry mirror_entry = NodeEntry{mirror_node, e.index, e.version};
diff --git a/tests/python/mkl/test_subgraph.py b/tests/python/mkl/test_subgraph.py
index 563fff1a6aa1..f7d03a3519cc 100644
--- a/tests/python/mkl/test_subgraph.py
+++ b/tests/python/mkl/test_subgraph.py
@@ -150,9 +150,9 @@ def check_quantize(sym, data_shape, out_type, name='conv',
   ref_out = mod.get_outputs()
 
   excluded_sym_names = []
+  excluded_op_names = []
   if mx.current_context() == mx.cpu() and gluon_forward == True:
-    excluded_sym_names += ['sg_mkldnn_fully_connected_0']
-    excluded_sym_names += ['fc_softmax']
+    excluded_op_names += ['_sg_mkldnn_fully_connected']
 
   calib_data = CalibIter(batch, data_shape, 1)
 
@@ -161,6 +161,7 @@ def check_quantize(sym, data_shape, out_type, name='conv',
                                                                    aux_params=aux_params,
                                                                    ctx=mx.current_context(),
                                                                    excluded_sym_names=excluded_sym_names,
+                                                                   excluded_op_names=excluded_op_names,
                                                                    quantized_dtype=out_type,
                                                                    calib_mode='naive',
                                                                    calib_data=calib_data,
diff --git a/tests/python/quantization/test_quantization.py b/tests/python/quantization/test_quantization.py
index 0cf38b7375a8..a1c23fb23208 100644
--- a/tests/python/quantization/test_quantization.py
+++ b/tests/python/quantization/test_quantization.py
@@ -678,6 +678,10 @@ def check_quantized_bn(data_shape, qdtype):
 
 @with_seed()
 def test_quantize_params():
+    if is_test_for_native_cpu():
+        print('skipped testing quantized_params for native cpu since it is not supported yet')
+        return
+
     data = mx.sym.Variable('data')
     conv = mx.sym.Convolution(data, kernel=(1, 1), num_filter=2048, name='conv')
     sym = mx.sym.BatchNorm(data=conv, eps=2e-05, fix_gamma=False, momentum=0.9, use_global_stats=False, name='bn')
@@ -686,7 +690,7 @@ def test_quantize_params():
     params = {}
     for name in offline_params:
         params[name] = mx.nd.uniform(shape=(2, 2))
-    qsym = mx.contrib.quant._quantize_symbol(sym, offline_params=offline_params)
+    qsym = mx.contrib.quant._quantize_symbol(sym, ctx=mx.current_context(), offline_params=offline_params)
     qparams = mx.contrib.quant._quantize_params(qsym, params, th_dict = {})
     param_names = params.keys()
     qparam_names = qparams.keys()
@@ -938,13 +942,13 @@ def check_qsym_forward(qsym, qarg_params, qaux_params, data_shape, label_shape=N
             arg_params, aux_params = mod.get_params()
 
             excluded_sym_names = []
+            excluded_op_names = []
             # sym3/sym4 doesn't have such layers
             if name not in ['sym3', 'sym4']:
                 excluded_names = []
                 if mx.current_context() == mx.cpu():
-                   excluded_names += ['fc', 'conv1']
-                if mx.current_context() == mx.gpu():
-                   excluded_names += ['sum0', 'relu0', 'relu1']
+                   excluded_op_names += ['FullyConnected']
+                   excluded_names += ['conv1']
                 excluded_names += ['concat']
 
                 optional_names = ['pool0']
@@ -959,6 +963,7 @@ def check_qsym_forward(qsym, qarg_params, qaux_params, data_shape, label_shape=N
                                                                              arg_params=arg_params,
                                                                              aux_params=aux_params,
                                                                              excluded_sym_names=excluded_sym_names,
+                                                                             excluded_op_names=excluded_op_names,
                                                                              ctx=mx.current_context(),
                                                                              quantized_dtype=qdtype,
                                                                              calib_mode='none')
@@ -973,6 +978,7 @@ def check_qsym_forward(qsym, qarg_params, qaux_params, data_shape, label_shape=N
                                                                              arg_params=arg_params,
                                                                              aux_params=aux_params,
                                                                              excluded_sym_names=excluded_sym_names,
+                                                                             excluded_op_names=excluded_op_names,
                                                                              ctx=mx.current_context(),
                                                                              quantized_dtype=qdtype,
                                                                              calib_mode='naive',
@@ -1037,10 +1043,14 @@ def check_quantize_net(qdtype):
 
 @with_seed()
 def test_quantize_sym_with_calib():
+    if is_test_for_native_cpu():
+        print('skipped testing quantized_pooling for native cpu since it is not supported yet')
+        return
+
     sym = get_fp32_sym()
     offline_params = [name for name in sym.list_arguments()
                       if not name.startswith('data') and not name.endswith('label')]
-    qsym = mx.contrib.quant._quantize_symbol(sym, offline_params=offline_params)
+    qsym = mx.contrib.quant._quantize_symbol(sym, ctx=mx.current_context(), offline_params=offline_params)
     requantize_op_names = ['requantize_conv', 'requantize_fc']
     th_dict = {'conv_output': (np.random.uniform(low=100.0, high=200.0), np.random.uniform(low=100.0, high=200.0)),
                'fc_output': (np.random.uniform(low=100.0, high=200.0), np.random.uniform(low=100.0, high=200.0))}

From 521e925b8c9417532667bcd97cfa9aac664056b4 Mon Sep 17 00:00:00 2001
From: Xi Wang <xidulu@gmail.com>
Date: Thu, 22 Aug 2019 05:53:48 +0800
Subject: [PATCH 202/813] [Numpy] Numpy behavior random.uniform() (#15858)

* numpy behavior uniform sampling implemented

* improve code style according to review comment

* remove rebundant namespace
---
 python/mxnet/ndarray/numpy/random.py        |  62 +++++-
 python/mxnet/numpy/random.py                |  37 +++-
 python/mxnet/symbol/numpy/random.py         |  62 +++++-
 src/operator/numpy/random/dist_common.h     | 177 +++++++++++++++++
 src/operator/numpy/random/np_uniform_op.cc  |  63 ++++++
 src/operator/numpy/random/np_uniform_op.cu  |  35 ++++
 src/operator/numpy/random/np_uniform_op.h   | 207 ++++++++++++++++++++
 tests/python/unittest/test_numpy_ndarray.py |  36 ++++
 8 files changed, 676 insertions(+), 3 deletions(-)
 create mode 100644 src/operator/numpy/random/dist_common.h
 create mode 100644 src/operator/numpy/random/np_uniform_op.cc
 create mode 100644 src/operator/numpy/random/np_uniform_op.cu
 create mode 100644 src/operator/numpy/random/np_uniform_op.h

diff --git a/python/mxnet/ndarray/numpy/random.py b/python/mxnet/ndarray/numpy/random.py
index 339fb1e77920..be918615bfd9 100644
--- a/python/mxnet/ndarray/numpy/random.py
+++ b/python/mxnet/ndarray/numpy/random.py
@@ -17,5 +17,65 @@
 
 """Namespace for operators used in Gluon dispatched by F=ndarray."""
 from __future__ import absolute_import
+from ...context import current_context
+from . import _internal as _npi
 
-__all__ = []
+__all__ = ['uniform']
+
+
+def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
+    """Draw samples from a uniform distribution.
+
+    Samples are uniformly distributed over the half-open interval
+    ``[low, high)`` (includes low, but excludes high).  In other words,
+    any value within the given interval is equally likely to be drawn
+    by `uniform`.
+
+    Parameters
+    ----------
+    low : float, ndarray, optional
+        Lower boundary of the output interval.  All values generated will be
+        greater than or equal to low.  The default value is 0.
+    high : float, ndarray, optional
+        Upper boundary of the output interval.  All values generated will be
+        less than high.  The default value is 1.0.
+    size : int or tuple of ints, optional
+        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k`` samples are drawn.  If size is ``None`` (default),
+        a scalar tensor containing a single value is returned if
+        ``low`` and ``high`` are both scalars.
+    dtype : {'float16', 'float32', 'float64'}, optional
+        Data type of output samples. Default is 'float32'
+    ctx : Context, optional
+        Device context of output. Default is current context.
+
+    Returns
+    -------
+    out : ndarray
+        Drawn samples from the parameterized uniform distribution.
+    """
+    from ...numpy import ndarray as np_ndarray
+    input_type = (isinstance(low, np_ndarray), isinstance(high, np_ndarray))
+    if dtype is None:
+        dtype = 'float32'
+    if ctx is None:
+        ctx = current_context()
+    if out is not None:
+        size = out.shape
+    if size == ():
+        size = None
+    if input_type == (True, True):
+        return _npi.uniform(low, high, low=None, high=None, size=size,
+                            ctx=ctx, dtype=dtype, out=out)
+    elif input_type == (False, True):
+        return _npi.uniform(high, low=low, high=None, size=size,
+                            ctx=ctx, dtype=dtype, out=out)
+    elif input_type == (True, False):
+        return _npi.uniform(low, low=None, high=high, size=size,
+                            ctx=ctx, dtype=dtype, out=out)
+    else:
+        return _npi.uniform(low=low, high=high, size=size,
+                            ctx=ctx, dtype=dtype, out=out)
+
+    raise ValueError(
+        "Distribution parameters must be either mxnet.numpy.ndarray or numbers")
diff --git a/python/mxnet/numpy/random.py b/python/mxnet/numpy/random.py
index c4109378e146..f85936345b7f 100644
--- a/python/mxnet/numpy/random.py
+++ b/python/mxnet/numpy/random.py
@@ -18,5 +18,40 @@
 """Namespace for ops used in imperative programming."""
 
 from __future__ import absolute_import
+from ..ndarray import numpy as _mx_nd_np
 
-__all__ = []
+__all__ = ['uniform']
+
+
+def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
+    """Draw samples from a uniform distribution.
+
+    Samples are uniformly distributed over the half-open interval
+    ``[low, high)`` (includes low, but excludes high).  In other words,
+    any value within the given interval is equally likely to be drawn
+    by `uniform`.
+
+    Parameters
+    ----------
+    low : float, ndarray, optional
+        Lower boundary of the output interval.  All values generated will be
+        greater than or equal to low.  The default value is 0.
+    high : float, ndarray, optional
+        Upper boundary of the output interval.  All values generated will be
+        less than high.  The default value is 1.0.
+    size : int or tuple of ints, optional
+        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k`` samples are drawn.  If size is ``None`` (default),
+        a scalar tensor containing a single value is returned if
+        ``low`` and ``high`` are both scalars.
+    dtype : {'float16', 'float32', 'float64'}, optional
+        Data type of output samples. Default is 'float32'
+    ctx : Context, optional
+        Device context of output. Default is current context.
+
+    Returns
+    -------
+    out : ndarray
+        Drawn samples from the parameterized uniform distribution.
+    """
+    return _mx_nd_np.random.uniform(low, high, size=size, ctx=ctx, dtype=dtype, out=out)
diff --git a/python/mxnet/symbol/numpy/random.py b/python/mxnet/symbol/numpy/random.py
index 28cfd0f3806a..338a5e28be4e 100644
--- a/python/mxnet/symbol/numpy/random.py
+++ b/python/mxnet/symbol/numpy/random.py
@@ -18,5 +18,65 @@
 """Namespace for operators used in Gluon dispatched by F=symbol."""
 
 from __future__ import absolute_import
+from ...context import current_context
+from . import _internal as _npi
 
-__all__ = []
+__all__ = ['uniform']
+
+
+def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
+    """Draw samples from a uniform distribution.
+
+    Samples are uniformly distributed over the half-open interval
+    ``[low, high)`` (includes low, but excludes high).  In other words,
+    any value within the given interval is equally likely to be drawn
+    by `uniform`.
+
+    Parameters
+    ----------
+    low : float, ndarray, optional
+        Lower boundary of the output interval.  All values generated will be
+        greater than or equal to low.  The default value is 0.
+    high : float, ndarray, optional
+        Upper boundary of the output interval.  All values generated will be
+        less than high.  The default value is 1.0.
+    size : int or tuple of ints, optional
+        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k`` samples are drawn.  If size is ``None`` (default),
+        a scalar tensor containing a single value is returned if
+        ``low`` and ``high`` are both scalars.
+    dtype : {'float16', 'float32', 'float64'}, optional
+        Data type of output samples. Default is 'float32'
+    ctx : Context, optional
+        Device context of output. Default is current context.
+
+    Returns
+    -------
+    out : ndarray
+        Drawn samples from the parameterized uniform distribution.
+    """
+    from ._symbol import _Symbol as np_symbol
+    input_type = (isinstance(low, np_symbol), isinstance(high, np_symbol))
+    if dtype is None:
+        dtype = 'float32'
+    if ctx is None:
+        ctx = current_context()
+    if out is not None:
+        size = out.shape
+    if size == ():
+        size = None
+    if input_type == (True, True):
+        return _npi.uniform(low, high, low=None, high=None, size=size,
+                            ctx=ctx, dtype=dtype, out=out)
+    elif input_type == (False, True):
+        return _npi.uniform(high, low=low, high=None, size=size,
+                            ctx=ctx, dtype=dtype, out=out)
+    elif input_type == (True, False):
+        return _npi.uniform(low, low=None, high=high, size=size,
+                            ctx=ctx, dtype=dtype, out=out)
+    else:
+        return _npi.uniform(low=low, high=high, size=size,
+                            ctx=ctx, dtype=dtype, out=out)
+
+    raise ValueError(
+        "Distribution parameters must be either mxnet.numpy.ndarray or numbers")
diff --git a/src/operator/numpy/random/dist_common.h b/src/operator/numpy/random/dist_common.h
new file mode 100644
index 000000000000..42d7507f7e74
--- /dev/null
+++ b/src/operator/numpy/random/dist_common.h
@@ -0,0 +1,177 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2015 by Contributors
+ * \file dist_common.h
+ * \brief Function definition of common functions for distributions
+ * \with two parameters.
+ */
+
+#ifndef MXNET_OPERATOR_NUMPY_RANDOM_DIST_COMMON_H_
+#define MXNET_OPERATOR_NUMPY_RANDOM_DIST_COMMON_H_
+
+#include <mshadow/base.h>
+#include <mxnet/operator_util.h>
+#include <algorithm>
+#include <string>
+#include <vector>
+#include "../../elemwise_op_common.h"
+#include "../../mshadow_op.h"
+#include "../../mxnet_op.h"
+#include "../../operator_common.h"
+#include "../../tensor/elemwise_binary_broadcast_op.h"
+
+namespace mxnet {
+namespace op {
+
+inline int FillShape(const mxnet::TShape &lshape, const mxnet::TShape &rshape,
+                     const mxnet::TShape &oshape, mxnet::TShape *new_lshape,
+                     mxnet::TShape *new_rshape, mxnet::TShape *new_oshape) {
+  const int odim = std::max(oshape.ndim(), broadcast::MAX_DIM);
+  *new_lshape = mxnet::TShape(odim, 1);
+  *new_rshape = mxnet::TShape(odim, 1);
+  *new_oshape = mxnet::TShape(odim, 1);
+  int bl = oshape.ndim() - lshape.ndim();
+  int br = oshape.ndim() - rshape.ndim();
+  int j = 0, lprod = 1, rprod = 1, oprod = 1;
+  for (int i = 0; i < oshape.ndim(); ++i) {
+    int l = 1;
+    int r = 1;
+    int o = oshape[i];
+    if (i >= bl) l = lshape[i - bl];
+    if (i >= br) r = rshape[i - br];
+    if ((lprod != rprod || lprod != oprod || l != r || l != o) &&
+        (lprod * l > 1 || rprod * r > 1 || oprod * o > 1)) {
+      (*new_lshape)[j] = lprod;
+      (*new_rshape)[j] = rprod;
+      (*new_oshape)[j] = oprod;
+      lprod = rprod = oprod = 1;
+      ++j;
+    }
+    lprod *= l;
+    rprod *= r;
+    oprod *= o;
+  }
+  if (lprod > 1 || rprod > 1 || oprod > 1) {
+    (*new_lshape)[j] = lprod;
+    (*new_rshape)[j] = rprod;
+    (*new_oshape)[j] = oprod;
+    ++j;
+  }
+  if (j <= broadcast::MAX_DIM) {
+    BROADCAST_NDIM_SWITCH(j, NDim, {
+      new_lshape->assign(new_lshape->begin(), new_lshape->begin() + NDim);
+      new_rshape->assign(new_rshape->begin(), new_rshape->begin() + NDim);
+      new_oshape->assign(new_oshape->begin(), new_oshape->begin() + NDim);
+    });
+  } else {
+    LOG(FATAL) << "Too many broadcast dimensions with operands " << lshape
+               << " " << rshape;
+  }
+  return j;
+}
+
+inline void CheckBroadcastable(const mxnet::TShape &from,
+                               const mxnet::TShape &to) {
+  const int bl = to.ndim() - from.ndim();
+  const int br = 0;
+  for (int i = 0; i < to.ndim(); ++i) {
+    int l = 1, r = 1;
+    if (i >= bl) l = from[i - bl];
+    if (i >= br) r = to[i - br];
+    if (!mxnet::dim_size_is_known(l) || !mxnet::dim_size_is_known(r)) continue;
+    if (l != r) {
+      // Make it compatible with NumPy.
+      // For example, (2, 3) cannot broadcast to (2, 0, 3), but (1, 3) can
+      // broadcast to (2, 0, 3).
+      CHECK(l == 1 || r == 1)
+          << "operands could not be broadcast together with shapes " << from
+          << " " << to;
+    }
+  }
+}
+
+inline void InferBroadcastShape(const mxnet::TShape &lhs,
+                                const mxnet::TShape &rhs,
+                                mxnet::TShape *out_ptr) {
+  mxnet::TShape &out = (*out_ptr);
+  const int bl = out.ndim() - lhs.ndim();
+  const int br = out.ndim() - rhs.ndim();
+  for (int i = 0; i < out.ndim(); ++i) {
+    int l = 1, r = 1;
+    if (i >= bl) l = lhs[i - bl];
+    if (i >= br) r = rhs[i - br];
+    if (!mxnet::dim_size_is_known(l) || !mxnet::dim_size_is_known(r)) continue;
+    if (l != r) {
+      // Make it compatible with NumPy.
+      // For example, (2, 3) cannot broadcast to (2, 0, 3), but (1, 3) can
+      // broadcast to (2, 0, 3).
+      CHECK(l == 1 || r == 1)
+          << "operands could not be broadcast together with shapes " << lhs
+          << " " << rhs;
+      out[i] = (l == 1 ? r : l);
+    } else {
+      out[i] = l;
+    }
+  }
+}
+
+template <typename DistParam>
+inline bool TwoparamsDistOpShape(const nnvm::NodeAttrs &attrs,
+                                 std::vector<TShape> *in_attrs,
+                                 std::vector<TShape> *out_attrs) {
+  const DistParam &param = nnvm::get<DistParam>(attrs.parsed);
+  CHECK_EQ(out_attrs->size(), 1U);
+  if (param.size.has_value()) {
+    // Size declared.
+    std::vector<dim_t> oshape_vec;
+    const mxnet::Tuple<int> &size = param.size.value();
+    for (int i = 0; i < size.ndim(); ++i) {
+      oshape_vec.emplace_back(size[i]);
+    }
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, TShape(oshape_vec));
+    for (size_t input_idx = 0; input_idx < in_attrs->size(); input_idx++) {
+      CheckBroadcastable((*in_attrs)[input_idx], (*out_attrs)[0]);
+    }
+  } else {
+    // Size undeclared.
+    if (in_attrs->size() == 2U) {
+      // Both params from ndarray.
+      mxnet::TShape &low = (*in_attrs)[0];
+      mxnet::TShape &high = (*in_attrs)[1];
+      mxnet::TShape out(std::max(low.ndim(), high.ndim()), -1);
+      InferBroadcastShape(low, high, &out);
+      SHAPE_ASSIGN_CHECK(*out_attrs, 0, out);
+    } else if (in_attrs->size() == 1U) {
+      // One param from ndarray.
+      SHAPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0))
+    } else if (in_attrs->size() == 0) {
+      // Two scalar case.
+      SHAPE_ASSIGN_CHECK(*out_attrs, 0, TShape(0, -1))
+      return true;
+    }
+  }
+  return out_attrs->at(0).ndim() != 0U;
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_NUMPY_RANDOM_DIST_COMMON_H_ */
diff --git a/src/operator/numpy/random/np_uniform_op.cc b/src/operator/numpy/random/np_uniform_op.cc
new file mode 100644
index 000000000000..394626d07596
--- /dev/null
+++ b/src/operator/numpy/random/np_uniform_op.cc
@@ -0,0 +1,63 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_uniform_op.cc
+ * \brief Operator for numpy sampling from uniform distributions
+ */
+#include "./np_uniform_op.h"
+
+namespace mxnet {
+namespace op {
+
+DMLC_REGISTER_PARAMETER(NumpyUniformParam);
+
+NNVM_REGISTER_OP(_npi_uniform)
+.describe("numpy behavior uniform")
+.set_num_inputs(
+  [](const nnvm::NodeAttrs& attrs) {
+    const NumpyUniformParam& param = nnvm::get<NumpyUniformParam>(attrs.parsed);
+    int num_inputs = 2;
+    if (param.low.has_value()) num_inputs -= 1;
+    if (param.high.has_value()) num_inputs -= 1;
+    return num_inputs;
+  }
+)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"input1", "input2"};
+  })
+.set_attr_parser(ParamParser<NumpyUniformParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", TwoparamsDistOpShape<NumpyUniformParam>)
+.set_attr<nnvm::FInferType>("FInferType", NumpyUniformOpType)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const nnvm::NodeAttrs& attrs) {
+      return std::vector<ResourceRequest>{
+        ResourceRequest::kRandom, ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", NumpyUniformForward<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.add_argument("input1", "NDArray-or-Symbol", "Source input")
+.add_argument("input2", "NDArray-or-Symbol", "Source input")
+.add_arguments(NumpyUniformParam::__FIELDS__());
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/random/np_uniform_op.cu b/src/operator/numpy/random/np_uniform_op.cu
new file mode 100644
index 000000000000..d997bc57d3be
--- /dev/null
+++ b/src/operator/numpy/random/np_uniform_op.cu
@@ -0,0 +1,35 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_uniform_op.cu
+ * \brief Operator for numpy sampling from uniform distributions
+ */
+
+#include "./np_uniform_op.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(_npi_uniform)
+.set_attr<FCompute>("FCompute<gpu>", NumpyUniformForward<gpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/random/np_uniform_op.h b/src/operator/numpy/random/np_uniform_op.h
new file mode 100644
index 000000000000..580dc5d05eaa
--- /dev/null
+++ b/src/operator/numpy/random/np_uniform_op.h
@@ -0,0 +1,207 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_uniform_op.h
+ * \brief Operator for numpy sampling from uniform distributions
+ */
+#ifndef MXNET_OPERATOR_NUMPY_RANDOM_NP_UNIFORM_OP_H_
+#define MXNET_OPERATOR_NUMPY_RANDOM_NP_UNIFORM_OP_H_
+
+#include <mshadow/base.h>
+#include <mxnet/operator_util.h>
+#include <algorithm>
+#include <string>
+#include <vector>
+#include "../../elemwise_op_common.h"
+#include "../../mshadow_op.h"
+#include "../../mxnet_op.h"
+#include "../../operator_common.h"
+#include "../../tensor/elemwise_binary_broadcast_op.h"
+#include "./dist_common.h"
+
+namespace mxnet {
+namespace op {
+
+struct NumpyUniformParam : public dmlc::Parameter<NumpyUniformParam> {
+  dmlc::optional<float> low;
+  dmlc::optional<float> high;
+  std::string ctx;
+  int dtype;
+  dmlc::optional<mxnet::Tuple<int>> size;
+  DMLC_DECLARE_PARAMETER(NumpyUniformParam) {
+    DMLC_DECLARE_FIELD(low);
+    DMLC_DECLARE_FIELD(high);
+    DMLC_DECLARE_FIELD(size)
+        .set_default(dmlc::optional<mxnet::Tuple<int>>())
+        .describe(
+            "Output shape. If the given shape is, "
+            "e.g., (m, n, k), then m * n * k samples are drawn. "
+            "Default is None, in which case a single value is returned.");
+    DMLC_DECLARE_FIELD(ctx).set_default("cpu").describe(
+        "Context of output, in format [cpu|gpu|cpu_pinned](n)."
+        " Only used for imperative calls.");
+    DMLC_DECLARE_FIELD(dtype)
+        .add_enum("float32", mshadow::kFloat32)
+        .add_enum("float64", mshadow::kFloat64)
+        .add_enum("float16", mshadow::kFloat16)
+        .set_default(mshadow::kFloat32)
+        .describe(
+            "DType of the output in case this can't be inferred. "
+            "Defaults to float32 if not defined (dtype=None).");
+  }
+};
+
+inline bool NumpyUniformOpType(const nnvm::NodeAttrs &attrs,
+                               std::vector<int> *in_attrs,
+                               std::vector<int> *out_attrs) {
+  const NumpyUniformParam &param = nnvm::get<NumpyUniformParam>(attrs.parsed);
+  int otype = param.dtype;
+  if (otype != -1) {
+    (*out_attrs)[0] = otype;
+  } else {
+    (*out_attrs)[0] = mshadow::kFloat32;
+  }
+  return true;
+}
+
+namespace mxnet_op {
+template <int ndim, typename IType, typename OType>
+struct uniform_kernel {
+  MSHADOW_XINLINE static void Map(index_t i, const Shape<ndim> &lstride,
+                                  const Shape<ndim> &hstride,
+                                  const Shape<ndim> &oshape, IType *low,
+                                  IType *high, float *uniform, OType *out) {
+    Shape<ndim> coord = unravel(i, oshape);
+    auto lidx = static_cast<index_t>(dot(coord, lstride));
+    auto hidx = static_cast<index_t>(dot(coord, hstride));
+    IType low_value = low[lidx];
+    IType high_value = high[hidx];
+    out[i] = low_value + uniform[i] * (high_value - low_value);
+  }
+};
+
+template <int ndim, typename IType, typename OType>
+struct uniform_one_scalar_kernel {
+  MSHADOW_XINLINE static void Map(index_t i, int scalar_pos,
+                                  const Shape<ndim> &stride,
+                                  const Shape<ndim> &oshape, IType *array,
+                                  float scalar, float *uniform, OType *out) {
+    Shape<ndim> coord = unravel(i, oshape);
+    auto idx = static_cast<index_t>(dot(coord, stride));
+    IType low_value;
+    IType high_value;
+    if (scalar_pos == 0) {
+      low_value = scalar;
+      high_value = array[idx];
+    } else {
+      low_value = array[idx];
+      high_value = scalar;
+    }
+    out[i] = low_value + uniform[i] * (high_value - low_value);
+  }
+};
+
+template <typename OType>
+struct uniform_two_scalar_kernel {
+  MSHADOW_XINLINE static void Map(index_t i, float low, float high,
+                                  float *uniform, OType *out) {
+    out[i] = low + uniform[i] * (high - low);
+  }
+};
+}  // namespace mxnet_op
+
+template <typename xpu>
+void NumpyUniformForward(const nnvm::NodeAttrs &attrs,
+                         const OpContext &ctx,
+                         const std::vector<TBlob> &inputs,
+                         const std::vector<OpReqType> &req,
+                         const std::vector<TBlob> &outputs) {
+  using namespace mshadow;
+  using namespace mxnet_op;
+  const NumpyUniformParam &param = nnvm::get<NumpyUniformParam>(attrs.parsed);
+  CHECK_EQ(outputs.size(), 1);
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+
+  // Generate base random number.
+  Random<xpu, float> *prnd = ctx.requested[0].get_random<xpu, float>(s);
+  Tensor<xpu, 1, float> uniform_tensor =
+      ctx.requested[1].get_space_typed<xpu, 1, float>(Shape1(outputs[0].Size()),
+                                                      s);
+  prnd->SampleUniform(&uniform_tensor, 0, 1);
+  mxnet::TShape new_lshape, new_hshape, new_oshape;
+
+  // [scalar scalar] case
+  if (inputs.size() == 0U) {
+    MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, OType, {
+      Kernel<uniform_two_scalar_kernel<OType>, xpu>::Launch(
+          s, outputs[0].Size(), param.low.value(), param.high.value(),
+          uniform_tensor.dptr_, outputs[0].dptr<OType>());
+    });
+  } else if (inputs.size() == 1U) {
+    // [scalar tensor], [tensor scalar] case
+    int ndim = FillShape(inputs[0].shape_, inputs[0].shape_, outputs[0].shape_,
+                         &new_lshape, &new_lshape, &new_oshape);
+    int scalar_pos;
+    float scalar_value;
+    // int type_flag = param.t;
+    if (param.low.has_value()) {
+      scalar_pos = 0;
+      scalar_value = param.low.value();
+    } else {
+      scalar_pos = 1;
+      scalar_value = param.high.value();
+    }
+    MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, IType, {
+      MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, OType, {
+        BROADCAST_NDIM_SWITCH(ndim, NDim, {
+          Shape<NDim> oshape = new_oshape.get<NDim>();
+          Shape<NDim> stride = calc_stride(new_lshape.get<NDim>());
+          Kernel<uniform_one_scalar_kernel<NDim, IType, OType>, xpu>::Launch(
+              s, outputs[0].Size(), scalar_pos, stride, oshape,
+              inputs[0].dptr<IType>(), scalar_value, uniform_tensor.dptr_,
+              outputs[0].dptr<OType>());
+        });
+      });
+    });
+  } else if (inputs.size() == 2U) {
+    // [tensor tensor] case
+    int ndim = FillShape(inputs[0].shape_, inputs[1].shape_, outputs[0].shape_,
+                         &new_lshape, &new_hshape, &new_oshape);
+    MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, IType, {
+      MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, OType, {
+        BROADCAST_NDIM_SWITCH(ndim, NDim, {
+          Shape<NDim> oshape = new_oshape.get<NDim>();
+          Shape<NDim> lstride = calc_stride(new_lshape.get<NDim>());
+          Shape<NDim> hstride = calc_stride(new_hshape.get<NDim>());
+          Kernel<uniform_kernel<NDim, IType, OType>, xpu>::Launch(
+              s, outputs[0].Size(), lstride, hstride, oshape,
+              inputs[0].dptr<IType>(), inputs[1].dptr<IType>(),
+              uniform_tensor.dptr_, outputs[0].dptr<OType>());
+        });
+      });
+    });
+  }
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_NUMPY_RANDOM_NP_UNIFORM_OP_H_
diff --git a/tests/python/unittest/test_numpy_ndarray.py b/tests/python/unittest/test_numpy_ndarray.py
index 6024ac9b4acd..7fa05206ac09 100644
--- a/tests/python/unittest/test_numpy_ndarray.py
+++ b/tests/python/unittest/test_numpy_ndarray.py
@@ -25,6 +25,8 @@
 from mxnet.gluon import HybridBlock
 from mxnet.test_utils import same, assert_almost_equal, rand_shape_nd, rand_ndarray, retry, assert_exception, use_np
 from common import with_seed, TemporaryDirectory
+from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf
+import scipy.stats as ss
 
 
 @with_seed()
@@ -667,6 +669,40 @@ def test_np_save_load_ndarrays():
             assert _np.array_equal(v.asnumpy(), arr_dict[k].asnumpy())
 
 
+@retry(5)
+@with_seed()
+@use_np
+def test_np_uniform():
+    types = [None, "float32", "float64"]
+    ctx = mx.context.current_context()
+    samples = 1000000
+    # Generation test
+    trials = 8
+    num_buckets = 5
+    for dtype in types:
+        for low, high in [(-100.0, -98.0), (99.0, 101.0)]:
+            scale = high - low
+            buckets, probs = gen_buckets_probs_with_ppf(lambda x: ss.uniform.ppf(x, loc=low, scale=scale), num_buckets)
+            buckets = np.array(buckets, dtype=dtype).tolist()
+            probs = [(buckets[i][1] - buckets[i][0])/scale for i in range(num_buckets)]
+            generator_mx_np = lambda x: mx.np.random.uniform(low, high, size=x, ctx=ctx, dtype=dtype).asnumpy()
+            verify_generator(generator=generator_mx_np, buckets=buckets, probs=probs, nsamples=samples, nrepeat=trials)
+
+    # Broadcasting test
+    params = [
+        (1.0, mx.np.ones((4,4)) + 2.0),
+        (mx.np.zeros((4,4)) + 1, 2.0),
+        (mx.np.zeros((1,4)), mx.np.ones((4,4)) + mx.np.array([1, 2, 3, 4])),
+        (mx.np.array([1, 2, 3, 4]), mx.np.ones((2,4,4)) * 5)
+    ]
+    for dtype in types:
+        for low, high in params:
+            expect_mean = (low + high) / 2
+            expanded_size = (samples,) + expect_mean.shape
+            uniform_samples = mx.np.random.uniform(low, high, size=expanded_size, dtype=dtype)
+            mx.test_utils.assert_almost_equal(uniform_samples.asnumpy().mean(0), expect_mean.asnumpy(), rtol=0.20, atol=1e-1)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From bdeb7bc5a0433df350ee212c31158f819de22a60 Mon Sep 17 00:00:00 2001
From: Przemyslaw Tredak <ptredak@nvidia.com>
Date: Wed, 21 Aug 2019 16:00:21 -0700
Subject: [PATCH 203/813] Softmax optimization for GPU (#15545)

* Softmax optimization

* Fix lint

* Unifying softmax with length with regular softmax

* Fix lint

* Fixes from review

* Making less templated kernels

* Unifying softmaxgrad and softmaxwithlengthgrad

* Better gradient of softmax

* Dividing softmax.cc into multiple files

* Fix

* Trigger CI

* Moving get_rows_per_block to common place

* Fix

* Fix lint

* Actually fix lint
---
 src/common/cuda_utils.cc       |  90 ++++
 src/common/cuda_utils.h        |  50 ++-
 src/operator/mxnet_op.h        |  30 ++
 src/operator/nn/log_softmax.cc |  78 ++++
 src/operator/nn/log_softmax.cu |  39 ++
 src/operator/nn/softmax-inl.h  | 723 ++++++++++++++++++---------------
 src/operator/nn/softmax.cc     |  99 -----
 src/operator/nn/softmax.cu     |  18 +-
 src/operator/nn/softmin.cc     |  89 ++++
 src/operator/nn/softmin.cu     |  39 ++
 10 files changed, 818 insertions(+), 437 deletions(-)
 create mode 100644 src/common/cuda_utils.cc
 create mode 100644 src/operator/nn/log_softmax.cc
 create mode 100644 src/operator/nn/log_softmax.cu
 create mode 100644 src/operator/nn/softmin.cc
 create mode 100644 src/operator/nn/softmin.cu

diff --git a/src/common/cuda_utils.cc b/src/common/cuda_utils.cc
new file mode 100644
index 000000000000..f38b2f8b5490
--- /dev/null
+++ b/src/common/cuda_utils.cc
@@ -0,0 +1,90 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2017 by Contributors
+ * \file cuda_utils.cc
+ * \brief Common CUDA utilities.
+ */
+
+#include <mxnet/base.h>
+#include <mshadow/base.h>
+
+#include <algorithm>
+
+#include "cuda_utils.h"
+
+#if MXNET_USE_CUDA
+
+namespace mxnet {
+namespace common {
+namespace cuda {
+
+namespace {
+  bool IsPower2(size_t N) {
+    return ((N & (N - 1)) == 0) && N != 0;
+  }
+
+  size_t RoundToPower2(size_t N) {
+    size_t ret = 1;
+    size_t copyN = N;
+    while (N >= 2) {
+      ret *= 2;
+      N /= 2;
+    }
+    if (ret < copyN) {
+      ret *= 2;
+    }
+    return ret;
+  }
+}  // namespace
+
+int get_load_type(size_t N) {
+  using namespace mshadow;
+  if (N % 8 == 0) {
+    return kFloat64;
+  } else if (N % 4 == 0) {
+    return kFloat32;
+  } else if (N % 2 == 0) {
+    return kFloat16;
+  } else {
+    return kUint8;
+  }
+}
+
+int get_rows_per_block(size_t row_size, int num_threads_per_block) {
+  const int warp_size = 32;
+  CHECK(IsPower2(num_threads_per_block))
+    << "Number of threads in a block must be power of 2 to use get_rows_per_block function";
+  // How many read instructions should 1 thread at least do
+  const int read_instructions = 2;
+  const int desired_num_threads_per_row = (row_size + read_instructions - 1) / read_instructions;
+  int desired_num_warps_per_row = (desired_num_threads_per_row + warp_size - 1) / warp_size;
+  int actual_num_warps_per_row = std::min(desired_num_warps_per_row,
+                                          num_threads_per_block / warp_size);
+  // actual number of warps needs to be power of 2
+  actual_num_warps_per_row = RoundToPower2(desired_num_warps_per_row);
+  return num_threads_per_block / (warp_size * actual_num_warps_per_row);
+}
+
+}  // namespace cuda
+}  // namespace common
+}  // namespace mxnet
+
+#endif  // MXNET_USE_CUDA
diff --git a/src/common/cuda_utils.h b/src/common/cuda_utils.h
index 3bb6b81a6de2..dd98a0e6c966 100644
--- a/src/common/cuda_utils.h
+++ b/src/common/cuda_utils.h
@@ -20,7 +20,7 @@
 /*!
  * Copyright (c) 2015 by Contributors
  * \file cuda_utils.h
- * \brief CUDA debugging utilities.
+ * \brief Common CUDA utilities.
  */
 #ifndef MXNET_COMMON_CUDA_UTILS_H_
 #define MXNET_COMMON_CUDA_UTILS_H_
@@ -327,6 +327,28 @@ class DeviceStore {
   bool restore_;
 };
 
+/*!
+ * \brief Get the largest datatype suitable to read
+ *         requested number of bytes.
+ *
+ *  \input Number of bytes to be read
+ *  \return mshadow representation of type that could
+ *          be used for reading
+ */
+int get_load_type(size_t N);
+
+/*!
+ * \brief Determine how many rows in a 2D matrix should a block
+ *        of threads handle based on the row size and the number
+ *        of threads in a block.
+ * \param row_size Size of the row expressed in the number of reads required to fully
+ *                 load it. For example, if the row has N elements, but  each thread
+ *                 reads 2 elements with a single read, row_size should be N / 2.
+ * \param num_threads_per_block Number of threads in a block.
+ * \return the number of rows that should be handled by a single block.
+ */
+int get_rows_per_block(size_t row_size, int num_threads_per_block);
+
 }  // namespace cuda
 }  // namespace common
 }  // namespace mxnet
@@ -542,7 +564,7 @@ static inline  __device__  void atomicAdd(double *address, double val) {
 // Overload atomicAdd for half precision
 // Taken from:
 // https://github.com/torch/cutorch/blob/master/lib/THC/THCAtomics.cuh
-#if defined(__CUDA_ARCH__)
+#ifdef __CUDACC__
 static inline __device__ void atomicAdd(mshadow::half::half_t *address,
                                         mshadow::half::half_t val) {
   unsigned int *address_as_ui =
@@ -607,6 +629,28 @@ __device__ inline DType ldg(const DType* address) {
     return *address;
 #endif
 }
-#endif
+
+template <typename OP, typename T>
+__device__ inline T warp_reduce(T value, OP redfun) {
+  value = redfun(value, __shfl_down_sync(0xffffffff, value, 16));
+  value = redfun(value, __shfl_down_sync(0xffffffff, value, 8));
+  value = redfun(value, __shfl_down_sync(0xffffffff, value, 4));
+  value = redfun(value, __shfl_down_sync(0xffffffff, value, 2));
+  value = redfun(value, __shfl_down_sync(0xffffffff, value, 1));
+  return value;
+}
+
+template <typename OP>
+__device__ inline mshadow::half::half_t warp_reduce(mshadow::half::half_t value, OP redfun) {
+  float v = static_cast<float>(value);
+  v = redfun(v, __shfl_down_sync(0xffffffff, v, 16));
+  v = redfun(v, __shfl_down_sync(0xffffffff, v, 8));
+  v = redfun(v, __shfl_down_sync(0xffffffff, v, 4));
+  v = redfun(v, __shfl_down_sync(0xffffffff, v, 2));
+  v = redfun(v, __shfl_down_sync(0xffffffff, v, 1));
+  return mshadow::half::half_t(v);
+}
+
+#endif  // __CUDACC__
 
 #endif  // MXNET_COMMON_CUDA_UTILS_H_
diff --git a/src/operator/mxnet_op.h b/src/operator/mxnet_op.h
index 52788f697f11..ae98952bb7bf 100644
--- a/src/operator/mxnet_op.h
+++ b/src/operator/mxnet_op.h
@@ -414,6 +414,36 @@ inline int get_num_threads<cpu>(const int N) {
     LOG(FATAL) << "Unknown type enum " << type;            \
   }
 
+#define MXNET_LOAD_TYPE_SWITCH(type, DType, ...)           \
+  switch (type) {                                          \
+  case mshadow::kFloat32:                                  \
+    {                                                      \
+      typedef float DType;                                 \
+      {__VA_ARGS__}                                        \
+    }                                                      \
+    break;                                                 \
+  case mshadow::kFloat64:                                  \
+    {                                                      \
+      typedef double DType;                                \
+      {__VA_ARGS__}                                        \
+    }                                                      \
+    break;                                                 \
+  case mshadow::kFloat16:                                  \
+    {                                                      \
+      typedef mshadow::half::half_t DType;                 \
+      {__VA_ARGS__}                                        \
+    }                                                      \
+    break;                                                 \
+  case mshadow::kUint8:                                    \
+    {                                                      \
+      typedef uint8_t DType;                               \
+      {__VA_ARGS__}                                        \
+    }                                                      \
+    break;                                                 \
+  default:                                                 \
+    LOG(FATAL) << "Invalid loading enum type " << type;    \
+  }
+
 /*!
  * \brief assign the val to out according
  * to request in Kernel::Launch
diff --git a/src/operator/nn/log_softmax.cc b/src/operator/nn/log_softmax.cc
new file mode 100644
index 000000000000..73db44805c8e
--- /dev/null
+++ b/src/operator/nn/log_softmax.cc
@@ -0,0 +1,78 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2017 by Contributors
+ * \file log_softmax.cc
+ * \brief CPU Implementation of log_softmax
+ */
+#include "./softmax-inl.h"
+#include "../tensor/elemwise_unary_op.h"
+#include "../tensor/elemwise_binary_op.h"
+#include "../operator_common.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(log_softmax)
+.add_alias("_npx_log_softmax")
+.describe(R"code(Computes the log softmax of the input.
+This is equivalent to computing softmax followed by log.
+
+Examples::
+
+  >>> x = mx.nd.array([1, 2, .1])
+  >>> mx.nd.log_softmax(x).asnumpy()
+  array([-1.41702998, -0.41702995, -2.31702995], dtype=float32)
+
+  >>> x = mx.nd.array( [[1, 2, .1],[.1, 2, 1]] )
+  >>> mx.nd.log_softmax(x, axis=0).asnumpy()
+  array([[-0.34115392, -0.69314718, -1.24115396],
+         [-1.24115396, -0.69314718, -0.34115392]], dtype=float32)
+
+
+)code")
+.set_attr_parser(ParamParser<SoftmaxParam>)
+.set_attr<FCompute>("FCompute<cpu>", SoftmaxCompute<cpu, mxnet_op::log_softmax_fwd>)
+.set_attr<nnvm::FGradient>("FGradient", SoftmaxFGradient{"_backward_log_softmax"})
+.set_attr<nnvm::FInferType>("FInferType", SoftmaxOpType)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs){
+    return std::vector<std::pair<int, int> >{{0, 0}};
+  })
+.add_argument("data", "NDArray-or-Symbol", "The input array.")
+.add_arguments(SoftmaxParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_backward_log_softmax)
+.set_num_inputs(SoftmaxGradOpNumInputs)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames", SoftmaxGradOpInputNames)
+.set_attr<mxnet::FInferShape>("FInferShape", SoftmaxGradOpShape)
+.set_attr<nnvm::FInferType>("FInferType", SoftmaxGradOpType)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption", SoftmaxGradOpInplaceOption)
+.add_argument("args", "NDArray-or-Symbol[]", "Positional input arguments")
+.set_attr_parser(ParamParser<SoftmaxParam>)
+.set_attr<FCompute>("FCompute<cpu>", SoftmaxGradCompute<cpu, mshadow_op::left,
+                                                        mxnet_op::log_softmax_bwd>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/nn/log_softmax.cu b/src/operator/nn/log_softmax.cu
new file mode 100644
index 000000000000..8bff2777f7b9
--- /dev/null
+++ b/src/operator/nn/log_softmax.cu
@@ -0,0 +1,39 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2017 by Contributors
+ * \file log_softmax.cu
+ * \brief GPU Implementation of log_softmax
+ */
+#include "./softmax-inl.h"
+#include "../tensor/elemwise_unary_op.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(log_softmax)
+.set_attr<FCompute>("FCompute<gpu>", SoftmaxCompute<gpu, mxnet_op::log_softmax_fwd>);
+
+NNVM_REGISTER_OP(_backward_log_softmax)
+.set_attr<FCompute>("FCompute<gpu>", SoftmaxGradCompute<gpu, mshadow_op::left,
+                                                        mxnet_op::log_softmax_bwd>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/nn/softmax-inl.h b/src/operator/nn/softmax-inl.h
index 2c82d839e5ed..73e0ccce4328 100644
--- a/src/operator/nn/softmax-inl.h
+++ b/src/operator/nn/softmax-inl.h
@@ -29,10 +29,12 @@
 #include <string>
 #include <utility>
 #include <vector>
+#include <type_traits>
 
 #include "../mxnet_op.h"
 #include "../operator_common.h"
 #include "../tensor/broadcast_reduce_op.h"
+#include "../../common/cuda_utils.h"
 
 namespace mxnet {
 namespace op {
@@ -64,8 +66,9 @@ struct log_softmax_fwd {
 };
 
 
-template<typename OP, bool negate, typename AType, typename DType, typename OType, int ndim>
-inline void Softmax(Stream<cpu> *s, DType *in, OType *out,
+template<typename OP, bool negate, typename AType, typename DType, typename OType,
+         typename IType, int ndim>
+inline void Softmax(Stream<cpu> *s, DType *in, OType *out, IType *length,
                     Shape<ndim> shape, int axis, const DType temperature) {
   index_t M = shape[axis];
   index_t N = shape.Size()/M;
@@ -74,100 +77,89 @@ inline void Softmax(Stream<cpu> *s, DType *in, OType *out,
   sshape[axis] = 1;
   index_t sa = stride[axis];
 
-  #pragma omp parallel for
-  for (index_t i = 0; i < N; ++i) {
-    index_t base = unravel_dot(i, sshape, stride);
-
-    DType mmax = negate ? -in[base] : in[base];
-    DType val;
-    for (index_t j = 1; j < M; ++j) {
-      val = negate ? -in[base + j*sa] : in[base + j*sa];
-      if (mmax < val) mmax = val;
-    }
-
-    AType sum = AType(0);
-    DType in_val;
-    // By default temperature is 1.0.
-    // Adding a branch here to save the CPU 'divide-by-1' computation at runtime
-    if (temperature == 1.0) {
-      for (index_t j = 0; j < M; ++j) {
-        in_val = negate ? -in[base + j*sa] : in[base + j*sa];
-        sum += std::exp(in_val - mmax);
-      }
-
-      for (index_t j = 0; j < M; ++j) {
-        in_val = negate ? -in[base + j*sa] : in[base + j*sa];
-        out[base + j*sa] = OP::Map(in_val - mmax, sum);
-      }
-    } else {
-      for (index_t j = 0; j < M; ++j) {
-        in_val = negate ? -in[base + j*sa] : in[base + j*sa];
-        sum += std::exp((in_val - mmax)/temperature);
-      }
+  if (length == nullptr) {
+    #pragma omp parallel for
+    for (index_t i = 0; i < N; ++i) {
+      index_t base = unravel_dot(i, sshape, stride);
 
-      for (index_t j = 0; j < M; ++j) {
-        in_val = negate ? -in[base + j*sa] : in[base + j*sa];
-        out[base + j*sa] = OP::Map((in_val - mmax)/temperature, sum);
+      DType mmax = negate ? -in[base] : in[base];
+      DType val;
+      for (index_t j = 1; j < M; ++j) {
+        val = negate ? -in[base + j*sa] : in[base + j*sa];
+        if (mmax < val) mmax = val;
       }
-    }
-  }
-}
-
-template<typename OP, bool negate, typename AType, typename DType, typename OType,
-         typename IType, int ndim>
-inline void SoftmaxWithLength(Stream<cpu> *s, DType *in, OType *out, IType *length,
-                              Shape<ndim> shape, int axis, const DType temperature) {
-  index_t M = shape[axis];
-  index_t N = shape.Size()/M;
-  Shape<ndim> stride = calc_stride(shape);
-  Shape<ndim> sshape = shape;
-  sshape[axis] = 1;
-  index_t sa = stride[axis];
 
-  #pragma omp parallel for
-  for (index_t i = 0; i < N; ++i) {
-    index_t len = static_cast<index_t>(length[i]);
-    index_t base = unravel_dot(i, sshape, stride);
+      AType sum = AType(0);
+      DType in_val;
+      // By default temperature is 1.0.
+      // Adding a branch here to save the CPU 'divide-by-1' computation at runtime
+      if (temperature == 1.0) {
+        for (index_t j = 0; j < M; ++j) {
+          in_val = negate ? -in[base + j*sa] : in[base + j*sa];
+          sum += std::exp(in_val - mmax);
+        }
 
-    DType mmax = negate ? -in[base] : in[base];
-    DType val;
-    for (index_t j = 1; j < len; ++j) {
-      val = negate ? -in[base + j*sa] : in[base + j*sa];
-      if (mmax < val) mmax = val;
-    }
-    for (index_t j = len; j < M; ++j) {
-      out[base + j*sa] = OType(0.0f);
-    }
+        for (index_t j = 0; j < M; ++j) {
+          in_val = negate ? -in[base + j*sa] : in[base + j*sa];
+          out[base + j*sa] = OP::Map(in_val - mmax, sum);
+        }
+      } else {
+        for (index_t j = 0; j < M; ++j) {
+          in_val = negate ? -in[base + j*sa] : in[base + j*sa];
+          sum += std::exp((in_val - mmax)/temperature);
+        }
 
-    AType sum = AType(0);
-    DType in_val;
-    // By default temperature is 1.0.
-    // Adding a branch here to save the CPU 'divide-by-1' computation at runtime
-    if (temperature == 1.0) {
-      for (index_t j = 0; j < len; ++j) {
-        in_val = negate ? -in[base + j*sa] : in[base + j*sa];
-        sum += std::exp(in_val - mmax);
+        for (index_t j = 0; j < M; ++j) {
+          in_val = negate ? -in[base + j*sa] : in[base + j*sa];
+          out[base + j*sa] = OP::Map((in_val - mmax)/temperature, sum);
+        }
       }
-
-      for (index_t j = 0; j < len; ++j) {
-        in_val = negate ? -in[base + j*sa] : in[base + j*sa];
-        out[base + j*sa] = OP::Map(in_val - mmax, sum);
+    }
+  } else {
+    #pragma omp parallel for
+    for (index_t i = 0; i < N; ++i) {
+      index_t len = static_cast<index_t>(length[i]);
+      index_t base = unravel_dot(i, sshape, stride);
+
+      DType mmax = negate ? -in[base] : in[base];
+      DType val;
+      for (index_t j = 1; j < len; ++j) {
+        val = negate ? -in[base + j*sa] : in[base + j*sa];
+        if (mmax < val) mmax = val;
       }
-    } else {
-      for (index_t j = 0; j < len; ++j) {
-        in_val = negate ? -in[base + j*sa] : in[base + j*sa];
-        sum += std::exp((in_val - mmax)/temperature);
+      for (index_t j = len; j < M; ++j) {
+        out[base + j*sa] = OType(0.0f);
       }
 
-      for (index_t j = 0; j < len; ++j) {
-        in_val = negate ? -in[base + j*sa] : in[base + j*sa];
-        out[base + j*sa] = OP::Map((in_val - mmax)/temperature, sum);
+      AType sum = AType(0);
+      DType in_val;
+      // By default temperature is 1.0.
+      // Adding a branch here to save the CPU 'divide-by-1' computation at runtime
+      if (temperature == 1.0) {
+        for (index_t j = 0; j < len; ++j) {
+          in_val = negate ? -in[base + j*sa] : in[base + j*sa];
+          sum += std::exp(in_val - mmax);
+        }
+
+        for (index_t j = 0; j < len; ++j) {
+          in_val = negate ? -in[base + j*sa] : in[base + j*sa];
+          out[base + j*sa] = OP::Map(in_val - mmax, sum);
+        }
+      } else {
+        for (index_t j = 0; j < len; ++j) {
+          in_val = negate ? -in[base + j*sa] : in[base + j*sa];
+          sum += std::exp((in_val - mmax)/temperature);
+        }
+
+        for (index_t j = 0; j < len; ++j) {
+          in_val = negate ? -in[base + j*sa] : in[base + j*sa];
+          out[base + j*sa] = OP::Map((in_val - mmax)/temperature, sum);
+        }
       }
     }
   }
 }
 
-
 struct softmax_bwd {
   template<typename DType, typename AType>
   MSHADOW_XINLINE static AType Map(DType ograd, DType out, AType sum) {
@@ -188,12 +180,11 @@ struct log_softmax_bwd {
   }
 };
 
-
 template<typename OP1, typename OP2, int Req, bool negate,
-         typename AType, typename DType, typename OType, int ndim>
+         typename AType, typename DType, typename OType, typename IType, int ndim>
 inline void SoftmaxGrad(Stream<cpu> *s, OType *out, OType *ograd,
-                        DType *igrad, Shape<ndim> shape, int axis,
-                        const DType temperature) {
+                        DType *igrad, IType *length, Shape<ndim> shape,
+                        int axis, const DType temperature) {
   index_t M = shape[axis];
   index_t N = shape.Size()/M;
   Shape<ndim> stride = calc_stride(shape);
@@ -201,76 +192,65 @@ inline void SoftmaxGrad(Stream<cpu> *s, OType *out, OType *ograd,
   sshape[axis] = 1;
   index_t sa = stride[axis];
 
-  #pragma omp parallel for
-  for (index_t i = 0; i < N; ++i) {
-    index_t base = unravel_dot(i, sshape, stride);
-
-    AType sum = AType(0);
-    for (index_t j = 0; j < M; ++j) {
-      sum += OP1::Map(ograd[base + j*sa], out[base + j*sa]);
-    }
+  if (length != nullptr) {
+    #pragma omp parallel for
+    for (index_t i = 0; i < N; ++i) {
+      index_t base = unravel_dot(i, sshape, stride);
+      index_t len = static_cast<index_t>(length[i]);
 
-    // By default temperature is 1.0.
-    // Adding a branch here to save the CPU 'divide-by-1' computation at runtime
-    DType final_result;
-    if (temperature == 1.0) {
-      for (index_t j = 0; j < M; ++j) {
-        final_result = negate ?
-                       -OP2::Map(ograd[base + j*sa], out[base + j*sa], sum) :
-                       OP2::Map(ograd[base + j*sa], out[base + j*sa], sum);
-        KERNEL_ASSIGN(igrad[base + j*sa], Req, final_result);
-      }
-    } else {
-      for (index_t j = 0; j < M; ++j) {
-        final_result = negate ?
-                       -OP2::Map(ograd[base + j*sa], out[base + j*sa], sum) / temperature :
-                       OP2::Map(ograd[base + j*sa], out[base + j*sa], sum) / temperature;
-        KERNEL_ASSIGN(igrad[base + j*sa], Req, final_result);
+      AType sum = AType(0);
+      for (index_t j = 0; j < len; ++j) {
+        sum += OP1::Map(ograd[base + j*sa], out[base + j*sa]);
       }
-    }
-  }
-}
-
-template<typename OP1, typename OP2, int Req, bool negate,
-         typename AType, typename DType, typename OType, typename IType, int ndim>
-inline void SoftmaxWithLengthGrad(Stream<cpu> *s, OType *out, OType *ograd,
-                                  DType *igrad, IType *length, Shape<ndim> shape,
-                                  int axis, const DType temperature) {
-  index_t M = shape[axis];
-  index_t N = shape.Size()/M;
-  Shape<ndim> stride = calc_stride(shape);
-  Shape<ndim> sshape = shape;
-  sshape[axis] = 1;
-  index_t sa = stride[axis];
-
-  #pragma omp parallel for
-  for (index_t i = 0; i < N; ++i) {
-    index_t base = unravel_dot(i, sshape, stride);
-    index_t len = static_cast<index_t>(length[i]);
 
-    AType sum = AType(0);
-    for (index_t j = 0; j < len; ++j) {
-      sum += OP1::Map(ograd[base + j*sa], out[base + j*sa]);
+      // By default temperature is 1.0.
+      // Adding a branch here to save the CPU 'divide-by-1' computation at runtime
+      DType final_result;
+      if (temperature == 1.0) {
+        for (index_t j = 0; j < M; ++j) {
+          final_result = negate ?
+                         -OP2::Map(ograd[base + j*sa], out[base + j*sa], sum) :
+                         OP2::Map(ograd[base + j*sa], out[base + j*sa], sum);
+          final_result = (j < len) ? final_result : DType(0.0f);
+          KERNEL_ASSIGN(igrad[base + j*sa], Req, final_result);
+        }
+      } else {
+        for (index_t j = 0; j < M; ++j) {
+          final_result = negate ?
+                         -OP2::Map(ograd[base + j*sa], out[base + j*sa], sum) / temperature :
+                         OP2::Map(ograd[base + j*sa], out[base + j*sa], sum) / temperature;
+          final_result = (j < len) ? final_result : DType(0.0f);
+          KERNEL_ASSIGN(igrad[base + j*sa], Req, final_result);
+        }
+      }
     }
+  } else {
+    #pragma omp parallel for
+    for (index_t i = 0; i < N; ++i) {
+      index_t base = unravel_dot(i, sshape, stride);
 
-    // By default temperature is 1.0.
-    // Adding a branch here to save the CPU 'divide-by-1' computation at runtime
-    DType final_result;
-    if (temperature == 1.0) {
+      AType sum = AType(0);
       for (index_t j = 0; j < M; ++j) {
-        final_result = negate ?
-                       -OP2::Map(ograd[base + j*sa], out[base + j*sa], sum) :
-                       OP2::Map(ograd[base + j*sa], out[base + j*sa], sum);
-        final_result = (j < len) ? final_result : DType(0.0f);
-        KERNEL_ASSIGN(igrad[base + j*sa], Req, final_result);
+        sum += OP1::Map(ograd[base + j*sa], out[base + j*sa]);
       }
-    } else {
-      for (index_t j = 0; j < M; ++j) {
-        final_result = negate ?
-                       -OP2::Map(ograd[base + j*sa], out[base + j*sa], sum) / temperature :
-                       OP2::Map(ograd[base + j*sa], out[base + j*sa], sum) / temperature;
-        final_result = (j < len) ? final_result : DType(0.0f);
-        KERNEL_ASSIGN(igrad[base + j*sa], Req, final_result);
+
+      // By default temperature is 1.0.
+      // Adding a branch here to save the CPU 'divide-by-1' computation at runtime
+      DType final_result;
+      if (temperature == 1.0) {
+        for (index_t j = 0; j < M; ++j) {
+          final_result = negate ?
+                         -OP2::Map(ograd[base + j*sa], out[base + j*sa], sum) :
+                         OP2::Map(ograd[base + j*sa], out[base + j*sa], sum);
+          KERNEL_ASSIGN(igrad[base + j*sa], Req, final_result);
+        }
+      } else {
+        for (index_t j = 0; j < M; ++j) {
+          final_result = negate ?
+                         -OP2::Map(ograd[base + j*sa], out[base + j*sa], sum) / temperature :
+                         OP2::Map(ograd[base + j*sa], out[base + j*sa], sum) / temperature;
+          KERNEL_ASSIGN(igrad[base + j*sa], Req, final_result);
+        }
       }
     }
   }
@@ -279,18 +259,19 @@ inline void SoftmaxWithLengthGrad(Stream<cpu> *s, OType *out, OType *ograd,
 
 #ifdef __CUDACC__
 template<int x_bits, typename OP, bool negate, typename AType, int ndim,
-         typename DType, typename OType>
-__global__ void softmax_compute_kernel(DType *in, OType *out, index_t M, int axis,
-                                       Shape<ndim> sshape, Shape<ndim> stride,
-                                       const double temperature) {
+         typename DType, typename OType, typename IType>
+__global__ void softmax_compute_kernel(DType *in, OType *out, IType *length,
+                                       index_t M, int axis, Shape<ndim> sshape,
+                                       Shape<ndim> stride, const double temperature) {
   const unsigned x_size = 1 << x_bits;
   __shared__ AType smem[x_size];
   index_t sa = stride[axis];
   index_t base = unravel_dot(blockIdx.x, sshape, stride);
   index_t x = threadIdx.x;
+  const index_t len = length == nullptr ? M : static_cast<index_t>(length[blockIdx.x]);
 
   red::maximum::SetInitValue(smem[x]);
-  for (index_t i = x; i < M; i += x_size) {
+  for (index_t i = x; i < len; i += x_size) {
     smem[x] = ::max(smem[x], negate ? -in[base + i*sa] : in[base + i*sa]);
   }
   __syncthreads();
@@ -301,7 +282,7 @@ __global__ void softmax_compute_kernel(DType *in, OType *out, index_t M, int axi
 
   red::sum::SetInitValue(smem[x]);
   DType val;
-  for (index_t i = x; i < M; i += x_size) {
+  for (index_t i = x; i < len; i += x_size) {
     val = negate ? -in[base + i*sa]:in[base + i*sa];
     smem[x] += static_cast<AType>(expf((val - smax) / static_cast<AType>(temperature)));
   }
@@ -313,71 +294,110 @@ __global__ void softmax_compute_kernel(DType *in, OType *out, index_t M, int axi
 
   for (index_t i = x; i < M; i += x_size) {
     val = negate ? -in[base + i*sa] : in[base + i*sa];
-    out[base + i*sa] = OP::Map((val - smax)/static_cast<DType>(temperature), ssum);
+    out[base + i*sa] =
+      (i < len) ? OType(OP::Map((val - smax)/static_cast<DType>(temperature), ssum)) : OType(0.0f);
   }
 }
 
-template<typename OP, bool negate, typename AType, typename DType, typename OType, int ndim>
-inline void Softmax(Stream<gpu> *s, DType *in, OType *out,
-                    Shape<ndim> shape, int axis, const double temperature) {
-  const int x_bits = 7;
-  const int x_size = 1 << x_bits;
-  index_t M = shape[axis];
-  index_t N = shape.Size()/M;
-  Shape<ndim> stride = calc_stride(shape);
-  Shape<ndim> sshape = shape;
-  sshape[axis] = 1;
-
-  softmax_compute_kernel<x_bits, OP, negate, AType, ndim>
-    <<<N, x_size, 0, mshadow::Stream<gpu>::GetStream(s)>>>(
-      in, out, M, axis, sshape, stride, temperature);
-  MSHADOW_CUDA_POST_KERNEL_CHECK(softmax_compute_kernel);
-}
+const int softmax_threads_per_block = 512;
+
+template<typename OP, bool negate, typename AType, typename LType,
+  typename DType, typename OType, typename IType>
+__global__ void softmax_stride1_compute_kernel(const DType *in, OType *out, IType *length,
+                                               const index_t M, const double temperature,
+                                               const int rows_per_block, const index_t total_rows) {
+  __shared__ AType scratch[softmax_threads_per_block];
+  __shared__ LType persistent_storage[20 * 1024 / sizeof(LType)];
+  const int warp_size = 32;
+  const int threads_per_row = softmax_threads_per_block / rows_per_block;
+  const int my_local_row = threadIdx.x / threads_per_row;
+  const int my_row = blockIdx.x * rows_per_block + my_local_row;
+  if (my_row >= total_rows) return;
+  const int my_id = threadIdx.x % threads_per_row;
+  const int entries_per_load = sizeof(LType)/sizeof(DType);
+  const index_t len = length == nullptr ? M : static_cast<index_t>(length[my_row]);
+  // Due to usage of MSHADOW_TYPE_SWITCH macro we are generating
+  // kernels where sizeof(LType) may be less than sizeof(DType),
+  // resulting in entries_per_load being 0.
+  // This is not a valid combination and is being checked against
+  // in the launcher code. This switch here is just to silence
+  // the division by zero warning generated for such invalid cases.
+  const int row_length = entries_per_load > 0 ? M / entries_per_load : 0;
+
+  const LType* in_aligned = reinterpret_cast<const LType*>(in);
+  size_t base = my_row * row_length;
+
+  for (index_t i = my_id; i < row_length; i += threads_per_row) {
+    persistent_storage[my_local_row * row_length + i] = in_aligned[base + i];
+  }
+  DType * row = reinterpret_cast<DType *>(persistent_storage + my_local_row * row_length);
+  __syncthreads();
 
-template<int x_bits, typename OP, bool negate, typename AType, int ndim,
-         typename DType, typename OType, typename IType>
-__global__ void softmax_with_length_kernel(DType *in, OType *out, IType *length,
-                                           index_t M, int axis, Shape<ndim> sshape,
-                                           Shape<ndim> stride, const double temperature) {
-  const unsigned x_size = 1 << x_bits;
-  __shared__ AType smem[x_size];
-  index_t sa = stride[axis];
-  index_t base = unravel_dot(blockIdx.x, sshape, stride);
-  index_t x = threadIdx.x;
-  index_t len = static_cast<index_t>(length[blockIdx.x]);
+  DType my_max_value;
+  red::maximum::SetInitValue(my_max_value);
 
-  red::maximum::SetInitValue(smem[x]);
-  for (index_t i = x; i < len; i += x_size) {
-    smem[x] = ::max(smem[x], negate ? -in[base + i*sa] : in[base + i*sa]);
+  for (index_t i = my_id; i < len; i += threads_per_row) {
+    my_max_value = ::max(my_max_value, negate ? -row[i] : row[i]);
   }
+  scratch[threadIdx.x] = my_max_value;
   __syncthreads();
-  cuda::Reduce1D<red::maximum, x_bits>(smem);
+  for (int size = threads_per_row / 2; size >= warp_size; size /= 2) {
+    if (my_id < size) {
+      scratch[threadIdx.x] = ::max(scratch[threadIdx.x], scratch[threadIdx.x + size]);
+    }
+    __syncthreads();
+  }
+  if (my_id < warp_size) {
+    AType my_value = warp_reduce(scratch[threadIdx.x],
+                                 [](AType x, AType y) { return ::max(x, y); });
+    scratch[threadIdx.x] = my_value;
+  }
   __syncthreads();
-  DType smax = smem[0];
+  DType smax = scratch[threadIdx.x - threadIdx.x % threads_per_row];
   __syncthreads();
 
-  red::sum::SetInitValue(smem[x]);
-  DType val;
-  for (index_t i = x; i < len; i += x_size) {
-    val = negate ? -in[base + i*sa]:in[base + i*sa];
-    smem[x] += static_cast<AType>(expf((val - smax) / static_cast<AType>(temperature)));
+  AType my_sum;
+  red::sum::SetInitValue(my_sum);
+
+  for (index_t i = my_id; i < len; i += threads_per_row) {
+    const DType val = negate ? -row[i] : row[i];
+    my_sum += static_cast<AType>(expf((val - smax) / static_cast<AType>(temperature)));
   }
+  scratch[threadIdx.x] = my_sum;
   __syncthreads();
-  cuda::Reduce1D<red::sum, x_bits>(smem);
+  for (int size = threads_per_row / 2; size >= warp_size; size /= 2) {
+    if (my_id < size) {
+      scratch[threadIdx.x] += scratch[threadIdx.x + size];
+    }
+    __syncthreads();
+  }
+  if (my_id < warp_size) {
+    AType my_value = warp_reduce(scratch[threadIdx.x],
+                                 [](AType x, AType y) { return x + y;});
+    scratch[threadIdx.x] = my_value;
+  }
   __syncthreads();
-  AType ssum = smem[0];
+
+  AType ssum = scratch[threadIdx.x - threadIdx.x % threads_per_row];
   __syncthreads();
 
-  for (index_t i = x; i < M; i += x_size) {
-    val = negate ? -in[base + i*sa] : in[base + i*sa];
-    out[base + i*sa] =
-      (i < len) ? OType(OP::Map((val - smax)/static_cast<DType>(temperature), ssum)) : OType(0.0f);
+  for (index_t i = my_id; i < M; i += threads_per_row) {
+    const DType val = negate ? -row[i] : row[i];
+    row[i] = (i < len) ? DType(OP::Map((val - smax)/static_cast<DType>(temperature), ssum)) :
+                         DType(0.0f);
+  }
+  __syncthreads();
+
+  LType* out_aligned = reinterpret_cast<LType*>(out);
+
+  for (index_t i = my_id; i < row_length; i += threads_per_row) {
+    out_aligned[base + i] = persistent_storage[my_local_row * row_length + i];
   }
 }
 
 template<typename OP, bool negate, typename AType, typename DType, typename OType,
          typename IType, int ndim>
-inline void SoftmaxWithLength(Stream<gpu> *s, DType *in, OType *out, IType *length,
+inline void Softmax(Stream<gpu> *s, DType *in, OType *out, IType *length,
                     Shape<ndim> shape, int axis, const double temperature) {
   const int x_bits = 7;
   const int x_size = 1 << x_bits;
@@ -387,76 +407,124 @@ inline void SoftmaxWithLength(Stream<gpu> *s, DType *in, OType *out, IType *leng
   Shape<ndim> sshape = shape;
   sshape[axis] = 1;
 
-  softmax_with_length_kernel<x_bits, OP, negate, AType, ndim>
-    <<<N, x_size, 0, mshadow::Stream<gpu>::GetStream(s)>>>(
-      in, out, length, M, axis, sshape, stride, temperature);
-  MSHADOW_CUDA_POST_KERNEL_CHECK(softmax_compute_kernel);
+  const size_t DSize = sizeof(DType);
+  // Using 20 kB of shared memory for persistent storage in the optimized case
+  const size_t max_opt_M = 20 * 1024 / DSize;
+  if (stride[axis] == 1 &&
+      static_cast<size_t>(M) <= max_opt_M &&
+      std::is_same<DType, OType>::value) {
+    int ltype = mxnet::common::cuda::get_load_type(M * sizeof(DType));
+    MXNET_LOAD_TYPE_SWITCH(ltype, LType, {
+      int rows_per_block = mxnet::common::cuda::get_rows_per_block(M *
+                                                                   sizeof(DType) / sizeof(LType),
+                                                                   softmax_threads_per_block);
+      int nblocks = (N + rows_per_block - 1) / rows_per_block;
+      CHECK_LE(sizeof(DType), sizeof(LType));
+      softmax_stride1_compute_kernel<OP, negate, AType, LType>
+        <<<nblocks, softmax_threads_per_block, 0, mshadow::Stream<gpu>::GetStream(s)>>>(
+          in, out, length, M, temperature, rows_per_block, N);
+    });
+    MSHADOW_CUDA_POST_KERNEL_CHECK(softmax_stride1_compute_kernel);
+  } else {
+    softmax_compute_kernel<x_bits, OP, negate, AType, ndim>
+      <<<N, x_size, 0, mshadow::Stream<gpu>::GetStream(s)>>>(
+        in, out, length, M, axis, sshape, stride, temperature);
+    MSHADOW_CUDA_POST_KERNEL_CHECK(softmax_compute_kernel);
+  }
 }
 
+template<typename OP1, typename OP2, int Req, bool negate, typename AType, typename LType,
+  typename DType, typename OType, typename IType>
+__global__ void softmax_stride1_grad_kernel(const OType *out, const OType *ograd,
+                                            DType *igrad, const IType *length,
+                                            const index_t M,
+                                            const double temperature,
+                                            const int rows_per_block,
+                                            const index_t total_rows) {
+  __shared__ AType scratch[softmax_threads_per_block];
+  __shared__ LType persistent_storage[20 * 1024 / sizeof(LType)];
+  const int warp_size = 32;
+  const int threads_per_row = softmax_threads_per_block / rows_per_block;
+  const int my_local_row = threadIdx.x / threads_per_row;
+  const int my_row = blockIdx.x * rows_per_block + my_local_row;
+  if (my_row >= total_rows) return;
+  const int my_id = threadIdx.x % threads_per_row;
+  const int entries_per_load = sizeof(LType)/sizeof(DType);
+  const index_t len = length == nullptr ? M : static_cast<index_t>(length[my_row]);
+  // Due to usage of MSHADOW_TYPE_SWITCH macro we are generating
+  // kernels where sizeof(LType) may be less than sizeof(DType),
+  // resulting in entries_per_load being 0.
+  // This is not a valid combination and is being checked against
+  // in the launcher code. This switch here is just to silence
+  // the division by zero warning generated for such invalid cases.
+  const int row_length = entries_per_load > 0 ? M / entries_per_load : 0;
+
+  const LType* out_aligned = reinterpret_cast<const LType*>(out);
+  const LType* ograd_aligned = reinterpret_cast<const LType*>(ograd);
+  size_t base = my_row * row_length;
+
+  for (index_t i = my_id; i < row_length; i += threads_per_row) {
+    persistent_storage[my_local_row * row_length * 2 + i] = out_aligned[base + i];
+    persistent_storage[my_local_row * row_length * 2 + row_length + i] = ograd_aligned[base + i];
+  }
+  DType * row = reinterpret_cast<DType *>(persistent_storage + my_local_row * row_length * 2);
+  __syncthreads();
 
-template<int x_bits, typename OP1, typename OP2, int Req, bool negate, typename AType, int ndim,
-         typename DType, typename OType>
-__global__ void softmax_gradient_kernel(OType *out, OType *ograd, DType *igrad,
-                                        index_t M, int axis, Shape<ndim> sshape,
-                                        Shape<ndim> stride, const double temperature) {
-  const unsigned x_size = 1 << x_bits;
-  __shared__ AType smem[x_size];
-  index_t sa = stride[axis];
-  index_t base = unravel_dot(blockIdx.x, sshape, stride);
-  index_t x = threadIdx.x;
+  AType my_sum_value;
+  red::sum::SetInitValue(my_sum_value);
 
-  red::sum::SetInitValue(smem[x]);
-  for (index_t i = x; i < M; i += x_size) {
-    smem[x] += OP1::Map(ograd[base + i*sa], out[base + i*sa]);
+  for (index_t i = my_id; i < len; i += threads_per_row) {
+    my_sum_value += OP1::Map(row[i + M], row[i]);
   }
+  scratch[threadIdx.x] = my_sum_value;
   __syncthreads();
-  cuda::Reduce1D<red::sum, x_bits>(smem);
+  for (int size = threads_per_row / 2; size >= warp_size; size /= 2) {
+    if (my_id < size) {
+      scratch[threadIdx.x] = scratch[threadIdx.x] + scratch[threadIdx.x + size];
+    }
+    __syncthreads();
+  }
+  if (my_id < warp_size) {
+    AType my_value = warp_reduce(scratch[threadIdx.x],
+                                 [](AType x, AType y) { return x + y; });
+    scratch[threadIdx.x] = my_value;
+  }
   __syncthreads();
-  AType ssum = smem[0];
+  AType ssum = scratch[threadIdx.x - threadIdx.x % threads_per_row];
   __syncthreads();
 
-  DType final_result;
-  for (index_t i = x; i < M; i += x_size) {
-    final_result =
+  for (index_t i = my_id; i < M; i += threads_per_row) {
+    const DType val =
       negate ?
-      -OP2::Map(ograd[base + i*sa], out[base + i*sa], ssum) :
-      OP2::Map(ograd[base + i*sa], out[base + i*sa], ssum);
-    KERNEL_ASSIGN(igrad[base + i*sa], Req, final_result / static_cast<DType>(temperature));
+      -OP2::Map(row[i + M], row[i], ssum) :
+      OP2::Map(row[i + M], row[i], ssum);
+    row[i] = (i < len) ? DType(val / static_cast<DType>(temperature)) :
+                         DType(0.0f);
+    if (Req == kAddTo) {
+      row[i] += igrad[my_row * M + i];
+    }
   }
-}
-
+  __syncthreads();
 
-template<typename OP1, typename OP2, int Req, bool negate, typename AType, int ndim,
-         typename DType, typename OType>
-inline void SoftmaxGrad(Stream<gpu> *s, OType *out, OType *ograd,
-                        DType *igrad, Shape<ndim> shape, int axis,
-                        const double temperature) {
-  const int x_bits = 7;
-  const int x_size = 1 << x_bits;
-  index_t M = shape[axis];
-  index_t N = shape.Size()/M;
-  Shape<ndim> stride = calc_stride(shape);
-  Shape<ndim> sshape = shape;
-  sshape[axis] = 1;
+  LType* igrad_aligned = reinterpret_cast<LType*>(igrad);
 
-  softmax_gradient_kernel<x_bits, OP1, OP2, Req, negate, AType, ndim>
-    <<<N, x_size, 0, mshadow::Stream<gpu>::GetStream(s)>>>(
-      out, ograd, igrad, M, axis, sshape, stride, temperature);
-  MSHADOW_CUDA_POST_KERNEL_CHECK(softmax_gradient_kernel);
+  for (index_t i = my_id; i < row_length; i += threads_per_row) {
+    igrad_aligned[base + i] = persistent_storage[my_local_row * row_length * 2 + i];
+  }
 }
 
 template<int x_bits, typename OP1, typename OP2, int Req, bool negate, typename AType, int ndim,
          typename DType, typename OType, typename IType>
-__global__ void softmax_with_length_grad_kernel(OType *out, OType *ograd, DType *igrad,
-                                                IType *length, index_t M, int axis,
-                                                Shape<ndim> sshape, Shape<ndim> stride,
-                                                const double temperature) {
+__global__ void softmax_grad_kernel(OType *out, OType *ograd, DType *igrad,
+                                    const IType *length, index_t M, int axis,
+                                    Shape<ndim> sshape, Shape<ndim> stride,
+                                    const double temperature) {
   const unsigned x_size = 1 << x_bits;
   __shared__ AType smem[x_size];
   index_t sa = stride[axis];
   index_t base = unravel_dot(blockIdx.x, sshape, stride);
   index_t x = threadIdx.x;
-  index_t len = static_cast<index_t>(length[blockIdx.x]);
+  index_t len = length != nullptr ? static_cast<index_t>(length[blockIdx.x]) : M;
 
   red::sum::SetInitValue(smem[x]);
   for (index_t i = x; i < len; i += x_size) {
@@ -479,12 +547,11 @@ __global__ void softmax_with_length_grad_kernel(OType *out, OType *ograd, DType
   }
 }
 
-
 template<typename OP1, typename OP2, int Req, bool negate, typename AType, int ndim,
          typename DType, typename OType, typename IType>
-inline void SoftmaxWithLengthGrad(Stream<gpu> *s, OType *out, OType *ograd,
-                                  DType *igrad, IType *length, Shape<ndim> shape, int axis,
-                                  const double temperature) {
+inline void SoftmaxGrad(Stream<gpu> *s, OType *out, OType *ograd,
+                        DType *igrad, IType *length, Shape<ndim> shape, int axis,
+                        const double temperature) {
   const int x_bits = 7;
   const int x_size = 1 << x_bits;
   index_t M = shape[axis];
@@ -493,10 +560,32 @@ inline void SoftmaxWithLengthGrad(Stream<gpu> *s, OType *out, OType *ograd,
   Shape<ndim> sshape = shape;
   sshape[axis] = 1;
 
-  softmax_with_length_grad_kernel<x_bits, OP1, OP2, Req, negate, AType, ndim>
-    <<<N, x_size, 0, mshadow::Stream<gpu>::GetStream(s)>>>(
-      out, ograd, igrad, length, M, axis, sshape, stride, temperature);
-  MSHADOW_CUDA_POST_KERNEL_CHECK(softmax_with_length_grad_kernel);
+  const size_t DSize = sizeof(DType);
+  // Using 20 kB of shared memory for persistent storage in the optimized case
+  // Need to store both out and ograd, so M can be only half compared to
+  // forward pass.
+  const size_t max_opt_M = 20 * 1024 / DSize / 2;
+  if (stride[axis] == 1 &&
+      static_cast<size_t>(M) <= max_opt_M &&
+      std::is_same<DType, OType>::value) {
+    int ltype = mxnet::common::cuda::get_load_type(M * sizeof(DType));
+    MXNET_LOAD_TYPE_SWITCH(ltype, LType, {
+      int rows_per_block = mxnet::common::cuda::get_rows_per_block(M *
+                                                                   sizeof(DType) / sizeof(LType),
+                                                                   softmax_threads_per_block);
+      int nblocks = (N + rows_per_block - 1) / rows_per_block;
+      CHECK_LE(sizeof(DType), sizeof(LType));
+      softmax_stride1_grad_kernel<OP1, OP2, Req, negate, AType, LType>
+        <<<nblocks, softmax_threads_per_block, 0, mshadow::Stream<gpu>::GetStream(s)>>>(
+          out, ograd, igrad, length, M, temperature, rows_per_block, N);
+    });
+    MSHADOW_CUDA_POST_KERNEL_CHECK(softmax_stride1_grad_kernel);
+  } else {
+    softmax_grad_kernel<x_bits, OP1, OP2, Req, negate, AType, ndim>
+      <<<N, x_size, 0, mshadow::Stream<gpu>::GetStream(s)>>>(
+        out, ograd, igrad, length, M, axis, sshape, stride, temperature);
+    MSHADOW_CUDA_POST_KERNEL_CHECK(softmax_grad_kernel);
+  }
 }
 #endif
 
@@ -564,8 +653,9 @@ static inline bool SoftmaxOpShape(const nnvm::NodeAttrs& attrs,
     mxnet::TShape& dshape = in_attrs->at(0);
     mxnet::TShape tmp_shape((dshape.ndim() == 1) ? 1U : dshape.ndim() - 1, 1);
     int j = 0;
+    int axis = param.axis != -1 ? param.axis : dshape.ndim() - 1;
     for (int i = 0; i < dshape.ndim(); ++i) {
-      if (i != param.axis) {
+      if (i != axis) {
         tmp_shape[j++] = dshape[i];
       }
     }
@@ -694,47 +784,43 @@ void SoftmaxCompute(const nnvm::NodeAttrs& attrs,
 
   MXNET_REAL_ACC_TYPE_SWITCH(inputs[0].type_flag_, DType, AType, {
     MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, OType, {
-      if (!param.use_length.value()) {
-        if (safe_acc) {
-          if (shape.ndim() == 2) {
-            Softmax<OP, negate, AType>(
-                ctx.get_stream<xpu>(), inputs[0].dptr<DType>(),
-                outputs[0].dptr<OType>(), shape.get<2>(), axis,
-                static_cast<DType>(temperature));
-          } else {
-            Softmax<OP, negate, AType>(
-                ctx.get_stream<xpu>(), inputs[0].dptr<DType>(),
-                outputs[0].dptr<OType>(), shape.get<3>(), axis,
-                static_cast<DType>(temperature));
-          }
-        } else {
-          if (shape.ndim() == 2) {
-            Softmax<OP, negate, DType>(
-                ctx.get_stream<xpu>(), inputs[0].dptr<DType>(),
-                outputs[0].dptr<OType>(), shape.get<2>(), axis,
-                static_cast<DType>(temperature));
-          } else {
-            Softmax<OP, negate, DType>(
-                ctx.get_stream<xpu>(), inputs[0].dptr<DType>(),
-                outputs[0].dptr<OType>(), shape.get<3>(), axis,
-                static_cast<DType>(temperature));
+      int type = kInt32;
+      if (param.use_length.value()) {
+        CHECK(inputs.size() > 1)
+          << "Mask needs to be provided when using softmax with use_length=True.";
+        type = inputs[1].type_flag_;
+      }
+      MXNET_INT_TYPE_SWITCH(type, IType, {
+          IType* mask_ptr = nullptr;
+          if (param.use_length.value()) {
+            mask_ptr = inputs[1].dptr<IType>();
           }
-        }
-      } else {
-        MXNET_INT_TYPE_SWITCH(inputs[1].type_flag_, IType, {
-          if (shape.ndim() == 2) {
-            SoftmaxWithLength<OP, negate, AType>(
-              ctx.get_stream<xpu>(), inputs[0].dptr<DType>(),
-              outputs[0].dptr<OType>(), inputs[1].dptr<IType>(),
-              shape.get<2>(), axis, static_cast<DType>(temperature));
+          if (safe_acc) {
+            if (shape.ndim() == 2) {
+              Softmax<OP, negate, AType>(
+                  ctx.get_stream<xpu>(), inputs[0].dptr<DType>(),
+                  outputs[0].dptr<OType>(), mask_ptr, shape.get<2>(),
+                  axis, static_cast<DType>(temperature));
+            } else {
+              Softmax<OP, negate, AType>(
+                  ctx.get_stream<xpu>(), inputs[0].dptr<DType>(),
+                  outputs[0].dptr<OType>(), mask_ptr, shape.get<3>(),
+                  axis, static_cast<DType>(temperature));
+            }
           } else {
-            SoftmaxWithLength<OP, negate, AType>(
-              ctx.get_stream<xpu>(), inputs[0].dptr<DType>(),
-              outputs[0].dptr<OType>(), inputs[1].dptr<IType>(),
-              shape.get<3>(), axis, static_cast<DType>(temperature));
+            if (shape.ndim() == 2) {
+              Softmax<OP, negate, DType>(
+                  ctx.get_stream<xpu>(), inputs[0].dptr<DType>(),
+                  outputs[0].dptr<OType>(), mask_ptr, shape.get<2>(),
+                  axis, static_cast<DType>(temperature));
+            } else {
+              Softmax<OP, negate, DType>(
+                  ctx.get_stream<xpu>(), inputs[0].dptr<DType>(),
+                  outputs[0].dptr<OType>(), mask_ptr, shape.get<3>(),
+                  axis, static_cast<DType>(temperature));
+            }
           }
-        });
-      }
+      });
     });
   });
 }
@@ -747,7 +833,16 @@ void SoftmaxGradCompute(const nnvm::NodeAttrs& attrs,
                         const std::vector<OpReqType>& req,
                         const std::vector<TBlob>& outputs) {
   using namespace mxnet_op;
+  if (softmax_use_length(attrs)) {
+    MXNET_INT_TYPE_SWITCH(inputs[2].type_flag_, IType, {
+      if (req[1] != kNullOp) {
+        mxnet_op::Kernel<mxnet_op::set_zero, xpu>::Launch(
+          ctx.get_stream<xpu>(), outputs[1].Size(), outputs[1].dptr<IType>());
+      }
+    });
+  }
   if (req[0] == kNullOp) return;
+  const int itype = softmax_use_length(attrs) ? inputs[2].type_flag_ : kInt32;
   const SoftmaxParam& param = nnvm::get<SoftmaxParam>(attrs.parsed);
   int axis = CheckAxis(param.axis, inputs[0].ndim());
   const double temperature = param.temperature.has_value() ?
@@ -761,51 +856,41 @@ void SoftmaxGradCompute(const nnvm::NodeAttrs& attrs,
   MXNET_REAL_ACC_TYPE_SWITCH(inputs[0].type_flag_, OType, AType, {
     MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, DType, {
       MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
-        if (!softmax_use_length(attrs)) {
+        MXNET_INT_TYPE_SWITCH(itype, IType, {
+          IType * length_ptr = nullptr;
+          if (softmax_use_length(attrs)) {
+            length_ptr = inputs[2].dptr<IType>();
+          }
           if (safe_acc) {
             if (shape.ndim() == 2) {
               SoftmaxGrad<OP1, OP2, Req, negate, AType>(
                   ctx.get_stream<xpu>(), inputs[out_idx].dptr<OType>(),
                   inputs[0].dptr<OType>(), outputs[0].dptr<DType>(),
-                  shape.get<2>(), axis, static_cast<DType>(temperature));
+                  length_ptr, shape.get<2>(), axis,
+                  static_cast<DType>(temperature));
             } else {
               SoftmaxGrad<OP1, OP2, Req, negate, AType>(
                   ctx.get_stream<xpu>(), inputs[out_idx].dptr<OType>(),
                   inputs[0].dptr<OType>(), outputs[0].dptr<DType>(),
-                  shape.get<3>(), axis, static_cast<DType>(temperature));
+                  length_ptr, shape.get<3>(), axis,
+                  static_cast<DType>(temperature));
             }
           } else {
             if (shape.ndim() == 2) {
               SoftmaxGrad<OP1, OP2, Req, negate, DType>(
                   ctx.get_stream<xpu>(), inputs[out_idx].dptr<OType>(),
                   inputs[0].dptr<OType>(), outputs[0].dptr<DType>(),
-                  shape.get<2>(), axis, static_cast<DType>(temperature));
+                  length_ptr, shape.get<2>(), axis,
+                  static_cast<DType>(temperature));
             } else {
               SoftmaxGrad<OP1, OP2, Req, negate, DType>(
                   ctx.get_stream<xpu>(), inputs[out_idx].dptr<OType>(),
                   inputs[0].dptr<OType>(), outputs[0].dptr<DType>(),
-                  shape.get<3>(), axis, static_cast<DType>(temperature));
+                  length_ptr, shape.get<3>(), axis,
+                  static_cast<DType>(temperature));
             }
           }
-        } else {
-          MXNET_INT_TYPE_SWITCH(inputs[2].type_flag_, IType, {
-            if (req[1] != kNullOp) {
-              mxnet_op::Kernel<mxnet_op::set_zero, xpu>::Launch(
-                ctx.get_stream<xpu>(), outputs[1].Size(), outputs[1].dptr<IType>());
-            }
-            if (shape.ndim() == 2) {
-              SoftmaxWithLengthGrad<OP1, OP2, Req, negate, AType>(
-                ctx.get_stream<xpu>(), inputs[out_idx].dptr<OType>(),
-                inputs[0].dptr<OType>(), outputs[0].dptr<DType>(),
-                inputs[2].dptr<IType>(), shape.get<2>(), axis, static_cast<DType>(temperature));
-            } else {
-              SoftmaxWithLengthGrad<OP1, OP2, Req, negate, AType>(
-                ctx.get_stream<xpu>(), inputs[out_idx].dptr<OType>(),
-                inputs[0].dptr<OType>(), outputs[0].dptr<DType>(),
-                inputs[2].dptr<IType>(), shape.get<3>(), axis, static_cast<DType>(temperature));
-            }
-          });
-        }
+        });
       });
     });
   });
diff --git a/src/operator/nn/softmax.cc b/src/operator/nn/softmax.cc
index b9a31ccc9313..ce19dda23d87 100644
--- a/src/operator/nn/softmax.cc
+++ b/src/operator/nn/softmax.cc
@@ -150,104 +150,5 @@ NNVM_REGISTER_OP(_backward_softmax)
 .set_attr<FCompute>("FCompute<cpu>", SoftmaxGradCompute<cpu, op::mshadow_op::mul,
                                                         mxnet_op::softmax_bwd>);
 
-NNVM_REGISTER_OP(softmin)
-.describe(R"code(Applies the softmin function.
-
-The resulting array contains elements in the range (0,1) and the elements along the given axis sum
-up to 1.
-
-.. math::
-   softmin(\mathbf{z/t})_j = \frac{e^{-z_j/t}}{\sum_{k=1}^K e^{-z_k/t}}
-
-for :math:`j = 1, ..., K`
-
-t is the temperature parameter in softmax function. By default, t equals 1.0
-
-Example::
-
-  x = [[ 1.  2.  3.]
-       [ 3.  2.  1.]]
-
-  softmin(x,axis=0) = [[ 0.88079703,  0.5,  0.11920292],
-                       [ 0.11920292,  0.5,  0.88079703]]
-
-  softmin(x,axis=1) = [[ 0.66524094,  0.24472848,  0.09003057],
-                       [ 0.09003057,  0.24472848,  0.66524094]]
-
-)code" ADD_FILELINE)
-.set_attr_parser(ParamParser<SoftmaxParam>)
-.set_attr<nnvm::FListOutputNames>("FListOutputNames",
-    [](const NodeAttrs& attrs) {
-    return std::vector<std::string>{"output"};
-})
-.set_attr<FCompute>("FCompute<cpu>", SoftmaxCompute<cpu, mxnet_op::softmax_fwd, true>)
-.set_attr<nnvm::FGradient>("FGradient", SoftmaxFGradient{"_backward_softmin"})
-.set_attr<nnvm::FInferType>("FInferType", SoftmaxOpType)
-.set_num_inputs(1)
-.set_num_outputs(1)
-.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
-.set_attr<nnvm::FInplaceOption>("FInplaceOption",
-  [](const NodeAttrs& attrs){
-    return std::vector<std::pair<int, int> >{{0, 0}};
-  })
-.add_argument("data", "NDArray-or-Symbol", "The input array.")
-.add_arguments(SoftmaxParam::__FIELDS__());
-
-NNVM_REGISTER_OP(_backward_softmin)
-.set_num_inputs(SoftmaxGradOpNumInputs)
-.set_num_outputs(1)
-.set_attr<nnvm::FListInputNames>("FListInputNames", SoftmaxGradOpInputNames)
-.set_attr<mxnet::FInferShape>("FInferShape", SoftmaxGradOpShape)
-.set_attr<nnvm::FInferType>("FInferType", SoftmaxGradOpType)
-.set_attr<nnvm::FInplaceOption>("FInplaceOption", SoftmaxGradOpInplaceOption)
-.add_argument("args", "NDArray-or-Symbol[]", "Positional input arguments")
-.set_attr_parser(ParamParser<SoftmaxParam>)
-.set_attr<FCompute>("FCompute<cpu>", SoftmaxGradCompute<cpu, op::mshadow_op::mul,
-                                                        mxnet_op::softmax_bwd, true>);
-
-NNVM_REGISTER_OP(log_softmax)
-.add_alias("_npx_log_softmax")
-.describe(R"code(Computes the log softmax of the input.
-This is equivalent to computing softmax followed by log.
-
-Examples::
-
-  >>> x = mx.nd.array([1, 2, .1])
-  >>> mx.nd.log_softmax(x).asnumpy()
-  array([-1.41702998, -0.41702995, -2.31702995], dtype=float32)
-
-  >>> x = mx.nd.array( [[1, 2, .1],[.1, 2, 1]] )
-  >>> mx.nd.log_softmax(x, axis=0).asnumpy()
-  array([[-0.34115392, -0.69314718, -1.24115396],
-         [-1.24115396, -0.69314718, -0.34115392]], dtype=float32)
-
-
-)code")
-.set_attr_parser(ParamParser<SoftmaxParam>)
-.set_attr<FCompute>("FCompute<cpu>", SoftmaxCompute<cpu, mxnet_op::log_softmax_fwd>)
-.set_attr<nnvm::FGradient>("FGradient", SoftmaxFGradient{"_backward_log_softmax"})
-.set_attr<nnvm::FInferType>("FInferType", SoftmaxOpType)
-.set_num_inputs(1)
-.set_num_outputs(1)
-.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
-.set_attr<nnvm::FInplaceOption>("FInplaceOption",
-  [](const NodeAttrs& attrs){
-    return std::vector<std::pair<int, int> >{{0, 0}};
-  })
-.add_argument("data", "NDArray-or-Symbol", "The input array.")
-.add_arguments(SoftmaxParam::__FIELDS__());
-
-NNVM_REGISTER_OP(_backward_log_softmax)
-.set_num_inputs(SoftmaxGradOpNumInputs)
-.set_num_outputs(1)
-.set_attr<nnvm::FListInputNames>("FListInputNames", SoftmaxGradOpInputNames)
-.set_attr<mxnet::FInferShape>("FInferShape", SoftmaxGradOpShape)
-.set_attr<nnvm::FInferType>("FInferType", SoftmaxGradOpType)
-.set_attr<nnvm::FInplaceOption>("FInplaceOption", SoftmaxGradOpInplaceOption)
-.add_argument("args", "NDArray-or-Symbol[]", "Positional input arguments")
-.set_attr_parser(ParamParser<SoftmaxParam>)
-.set_attr<FCompute>("FCompute<cpu>", SoftmaxGradCompute<cpu, mshadow_op::left,
-                                                        mxnet_op::log_softmax_bwd>);
-
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/nn/softmax.cu b/src/operator/nn/softmax.cu
index 254e726d5e26..d5762cf0217f 100644
--- a/src/operator/nn/softmax.cu
+++ b/src/operator/nn/softmax.cu
@@ -19,8 +19,8 @@
 
 /*!
  * Copyright (c) 2017 by Contributors
- * \file softmax.cc
- * \brief CPU Implementation of softmax
+ * \file softmax.cu
+ * \brief GPU Implementation of softmax
  */
 #include "./softmax-inl.h"
 #include "../tensor/elemwise_unary_op.h"
@@ -35,19 +35,5 @@ NNVM_REGISTER_OP(_backward_softmax)
 .set_attr<FCompute>("FCompute<gpu>", SoftmaxGradCompute<gpu, op::mshadow_op::mul,
                                                         mxnet_op::softmax_bwd>);
 
-NNVM_REGISTER_OP(softmin)
-.set_attr<FCompute>("FCompute<gpu>", SoftmaxCompute<gpu, mxnet_op::softmax_fwd, true>);
-
-NNVM_REGISTER_OP(_backward_softmin)
-.set_attr<FCompute>("FCompute<gpu>", SoftmaxGradCompute<gpu, op::mshadow_op::mul,
-                                                        mxnet_op::softmax_bwd, true>);
-
-NNVM_REGISTER_OP(log_softmax)
-.set_attr<FCompute>("FCompute<gpu>", SoftmaxCompute<gpu, mxnet_op::log_softmax_fwd>);
-
-NNVM_REGISTER_OP(_backward_log_softmax)
-.set_attr<FCompute>("FCompute<gpu>", SoftmaxGradCompute<gpu, mshadow_op::left,
-                                                        mxnet_op::log_softmax_bwd>);
-
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/nn/softmin.cc b/src/operator/nn/softmin.cc
new file mode 100644
index 000000000000..0522c8c9b120
--- /dev/null
+++ b/src/operator/nn/softmin.cc
@@ -0,0 +1,89 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2017 by Contributors
+ * \file softmax.cc
+ * \brief CPU Implementation of softmin
+ */
+#include "./softmax-inl.h"
+#include "../tensor/elemwise_unary_op.h"
+#include "../tensor/elemwise_binary_op.h"
+#include "../operator_common.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(softmin)
+.describe(R"code(Applies the softmin function.
+
+The resulting array contains elements in the range (0,1) and the elements along the given axis sum
+up to 1.
+
+.. math::
+   softmin(\mathbf{z/t})_j = \frac{e^{-z_j/t}}{\sum_{k=1}^K e^{-z_k/t}}
+
+for :math:`j = 1, ..., K`
+
+t is the temperature parameter in softmax function. By default, t equals 1.0
+
+Example::
+
+  x = [[ 1.  2.  3.]
+       [ 3.  2.  1.]]
+
+  softmin(x,axis=0) = [[ 0.88079703,  0.5,  0.11920292],
+                       [ 0.11920292,  0.5,  0.88079703]]
+
+  softmin(x,axis=1) = [[ 0.66524094,  0.24472848,  0.09003057],
+                       [ 0.09003057,  0.24472848,  0.66524094]]
+
+)code" ADD_FILELINE)
+.set_attr_parser(ParamParser<SoftmaxParam>)
+.set_attr<nnvm::FListOutputNames>("FListOutputNames",
+    [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"output"};
+})
+.set_attr<FCompute>("FCompute<cpu>", SoftmaxCompute<cpu, mxnet_op::softmax_fwd, true>)
+.set_attr<nnvm::FGradient>("FGradient", SoftmaxFGradient{"_backward_softmin"})
+.set_attr<nnvm::FInferType>("FInferType", SoftmaxOpType)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs){
+    return std::vector<std::pair<int, int> >{{0, 0}};
+  })
+.add_argument("data", "NDArray-or-Symbol", "The input array.")
+.add_arguments(SoftmaxParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_backward_softmin)
+.set_num_inputs(SoftmaxGradOpNumInputs)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames", SoftmaxGradOpInputNames)
+.set_attr<mxnet::FInferShape>("FInferShape", SoftmaxGradOpShape)
+.set_attr<nnvm::FInferType>("FInferType", SoftmaxGradOpType)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption", SoftmaxGradOpInplaceOption)
+.add_argument("args", "NDArray-or-Symbol[]", "Positional input arguments")
+.set_attr_parser(ParamParser<SoftmaxParam>)
+.set_attr<FCompute>("FCompute<cpu>", SoftmaxGradCompute<cpu, op::mshadow_op::mul,
+                                                        mxnet_op::softmax_bwd, true>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/nn/softmin.cu b/src/operator/nn/softmin.cu
new file mode 100644
index 000000000000..d00d0bdad231
--- /dev/null
+++ b/src/operator/nn/softmin.cu
@@ -0,0 +1,39 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2017 by Contributors
+ * \file softmin.cu
+ * \brief GPU Implementation of softmin
+ */
+#include "./softmax-inl.h"
+#include "../tensor/elemwise_unary_op.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(softmin)
+.set_attr<FCompute>("FCompute<gpu>", SoftmaxCompute<gpu, mxnet_op::softmax_fwd, true>);
+
+NNVM_REGISTER_OP(_backward_softmin)
+.set_attr<FCompute>("FCompute<gpu>", SoftmaxGradCompute<gpu, op::mshadow_op::mul,
+                                                        mxnet_op::softmax_bwd, true>);
+
+}  // namespace op
+}  // namespace mxnet

From 681cfc4205a568d3bfc35da1a601489abdd44074 Mon Sep 17 00:00:00 2001
From: Jake Lee <gstu1130@gmail.com>
Date: Wed, 21 Aug 2019 16:20:21 -0700
Subject: [PATCH 204/813] [Dependency Update] [Doc] move the general
 prerequisite software to the top (#15896)

* move the general prerequisite software to the top

* retrigger CI
---
 tools/dependencies/README.md | 36 ++++++++++++++++++------------------
 1 file changed, 18 insertions(+), 18 deletions(-)

diff --git a/tools/dependencies/README.md b/tools/dependencies/README.md
index db35aabfe133..0f10d368c3bf 100644
--- a/tools/dependencies/README.md
+++ b/tools/dependencies/README.md
@@ -76,18 +76,8 @@ The dependencies could be categorized by several groups: BLAS libraries, CPU-bas
 
 ## How to update them?
 
-### MKL, MKLDNN
-
-@pengzhao-intel (https://github.com/apache/incubator-mxnet/commits?author=pengzhao-intel) and his team are tracking and updating these versions. Kudos to them!
-
-### CUDA, cuDNN, NCCL
-#### 1. Environment Setup
-We will install all the prerequsite software.
-We demonstrate with CUDA10/cuDNN7.5/NCCL 2.4.2.
-You might want to change these versions to suit your needs.
-
+#### 0. Prerequisite Software
 ```
-# Take Ubuntu 16.04 for example.
 sudo apt update
 sudo apt-get install -y git \
     cmake \
@@ -109,9 +99,21 @@ sudo apt-get install -y git \
     pandoc \
     python3-pip \
     automake \
-    pkg-config \
-    openjdk-8-jdk
-    
+    pkg-config
+```
+
+### MKL, MKLDNN
+
+@pengzhao-intel (https://github.com/apache/incubator-mxnet/commits?author=pengzhao-intel) and his team are tracking and updating these versions. Kudos to them!
+
+### CUDA, cuDNN, NCCL
+
+#### 1. Environment Setup
+We will install all the prerequsite software.
+We demonstrate with CUDA10/cuDNN7.5/NCCL 2.4.2.
+You might want to change these versions to suit your needs.
+
+```    
 # CUDA installation 
 # Take CUDA 10 for example, please follow the instructions on https://developer.nvidia.com/cuda-downloads
 # Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 410.48?
@@ -272,8 +274,7 @@ sudo apt-get install -y git \
     pandoc \
     python3-pip \
     automake \
-    pkg-config \
-    openjdk-8-jdk
+    pkg-config
 ```
 2. Build PyPi package
 ```
@@ -322,8 +323,7 @@ sudo apt-get install -y git \
     pandoc \
     python3-pip \
     automake \
-    pkg-config \
-    openjdk-8-jdk
+    pkg-config
 ```
 2. Build PyPi package
 ```

From 434f185d877d81b9e15c4e2a371b25e72e912986 Mon Sep 17 00:00:00 2001
From: ciyong <ciyong.chen@intel.com>
Date: Thu, 22 Aug 2019 08:20:03 +0800
Subject: [PATCH 205/813] [MKLDNN]Support fullyconnected and element-wise ops
 fusion (#15950)

* enable fc fusion with more eltwise ops

* refactor codes

* Add test cases for fc fusion pattern

* cleanup codes

* remove a_max limitation for bounded_relu
---
 src/operator/nn/mkldnn/mkldnn_base-inl.h      |  7 ++
 .../nn/mkldnn/mkldnn_convolution-inl.h        | 11 +--
 .../nn/mkldnn/mkldnn_fully_connected-inl.h    |  8 +-
 .../nn/mkldnn/mkldnn_fully_connected.cc       | 10 +-
 src/operator/subgraph/mkldnn/mkldnn_fc-inl.h  | 83 ++++++++++++++++
 src/operator/subgraph/mkldnn/mkldnn_fc.cc     | 29 +++++-
 .../subgraph/mkldnn/mkldnn_fc_property.h      | 95 ++++++++++++-------
 tests/python/mkl/test_subgraph.py             | 43 +++++++--
 8 files changed, 223 insertions(+), 63 deletions(-)
 create mode 100644 src/operator/subgraph/mkldnn/mkldnn_fc-inl.h

diff --git a/src/operator/nn/mkldnn/mkldnn_base-inl.h b/src/operator/nn/mkldnn/mkldnn_base-inl.h
index e01b7b14082b..5db9d6e5defc 100644
--- a/src/operator/nn/mkldnn/mkldnn_base-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_base-inl.h
@@ -620,6 +620,13 @@ bool MKLDNNStorageType(const nnvm::NodeAttrs &attrs,
 #define MKLDNN_OPCHECK_COPY_RESULT(outputs, indice) \
     if (debug) check.CopyResult(outputs, indice);
 
+struct MKLDNNPostEltwiseParam {
+  mkldnn::algorithm alg = mkldnn::algorithm::algorithm_undef;
+  float scale = 1.f;
+  float alpha = 0.f;
+  float beta = 1.f;
+};
+
 }  // namespace mxnet
 #endif
 #endif  // MXNET_OPERATOR_NN_MKLDNN_MKLDNN_BASE_INL_H_
diff --git a/src/operator/nn/mkldnn/mkldnn_convolution-inl.h b/src/operator/nn/mkldnn/mkldnn_convolution-inl.h
index b4289e524999..880b9d19cd81 100644
--- a/src/operator/nn/mkldnn/mkldnn_convolution-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_convolution-inl.h
@@ -70,20 +70,13 @@ struct MKLDNNConvParam : public dmlc::Parameter<MKLDNNConvParam> {
   }
 };
 
-struct MKLDNNPostActParam {
-  mkldnn::algorithm alg = mkldnn::algorithm::algorithm_undef;
-  float scale = 1.f;
-  float alpha = 0.f;
-  float beta = 1.f;
-};
-
 struct MKLDNNConvFullParam {
   ConvolutionParam conv_param;
   MKLDNNConvParam mkldnn_param;
   float sum_scale = 1.f;
   std::vector<float> requantize_scales;
-  MKLDNNPostActParam act_param;
-  MKLDNNPostActParam postsum_act_param;
+  MKLDNNPostEltwiseParam act_param;
+  MKLDNNPostEltwiseParam postsum_act_param;
 };
 
 mkldnn::convolution_forward::primitive_desc GetConvFwdImpl(const MKLDNNConvFullParam &param,
diff --git a/src/operator/nn/mkldnn/mkldnn_fully_connected-inl.h b/src/operator/nn/mkldnn/mkldnn_fully_connected-inl.h
index c08371489fed..fddaedc2459d 100644
--- a/src/operator/nn/mkldnn/mkldnn_fully_connected-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_fully_connected-inl.h
@@ -40,7 +40,7 @@ namespace op {
 struct MKLDNNFCParam: public dmlc::Parameter<MKLDNNFCParam> {
   bool quantized;
   bool enable_float_output;
-  bool with_relu;
+  bool with_eltwise;
   dmlc::optional<float> min_calib_range;  // min float value calculated from calibration dataset
   dmlc::optional<float> max_calib_range;  // max float value calculated from calibration dataset
 
@@ -49,8 +49,8 @@ struct MKLDNNFCParam: public dmlc::Parameter<MKLDNNFCParam> {
     .describe("Whether it's a quantized FullyConnected operator");
     DMLC_DECLARE_FIELD(enable_float_output).set_default(false)
     .describe("Whether to enable float32 output");
-    DMLC_DECLARE_FIELD(with_relu).set_default(false)
-    .describe("Whether there's a post relu after FullyConnected operator");
+    DMLC_DECLARE_FIELD(with_eltwise).set_default(false)
+    .describe("Whether there's a post elemwise after FullyConnected operator");
     DMLC_DECLARE_FIELD(min_calib_range)
     .set_default(dmlc::optional<float>())
     .describe("The minimum scalar value in the form of float32 obtained "
@@ -67,6 +67,7 @@ struct MKLDNNFCParam: public dmlc::Parameter<MKLDNNFCParam> {
 struct MKLDNNFCFullParam {
   FullyConnectedParam default_param;
   MKLDNNFCParam mkldnn_param;
+  MKLDNNPostEltwiseParam eltwise_param;
   std::vector<float> output_scales = {0.0};
   std::vector<float> requantize_scales = {0.0};
 };
@@ -86,7 +87,6 @@ class MKLDNNFullyConnectedForward {
                               const mkldnn::memory::desc &out_md)
       : fwd_pd(GetFCFwdImpl(full_param, is_train, data, weight, bias, out_md)) {}
 
-
   void SetNewMem(const mkldnn::memory &data, const mkldnn::memory &weight,
                  const mkldnn::memory *bias, const mkldnn::memory &output);
 
diff --git a/src/operator/nn/mkldnn/mkldnn_fully_connected.cc b/src/operator/nn/mkldnn/mkldnn_fully_connected.cc
index 1dfd2a95f338..fbe37e227cd1 100644
--- a/src/operator/nn/mkldnn/mkldnn_fully_connected.cc
+++ b/src/operator/nn/mkldnn/mkldnn_fully_connected.cc
@@ -44,11 +44,11 @@ mkldnn::inner_product_forward::primitive_desc GetFCFwdImpl(
 
   mkldnn::primitive_attr attr;
   mkldnn::post_ops ops;
-  if (full_param.mkldnn_param.with_relu) {
-    const float scale = 1.0f;
-    const float alpha = 0.0f;
-    const float beta = 1.0f;
-    ops.append_eltwise(scale, eltwise_relu, alpha, beta);
+  if (full_param.mkldnn_param.with_eltwise) {
+    ops.append_eltwise(full_param.eltwise_param.scale,
+                       full_param.eltwise_param.alg,
+                       full_param.eltwise_param.alpha,
+                       full_param.eltwise_param.beta);
   }
   attr.set_post_ops(ops);
 
diff --git a/src/operator/subgraph/mkldnn/mkldnn_fc-inl.h b/src/operator/subgraph/mkldnn/mkldnn_fc-inl.h
new file mode 100644
index 000000000000..cb355dab9abe
--- /dev/null
+++ b/src/operator/subgraph/mkldnn/mkldnn_fc-inl.h
@@ -0,0 +1,83 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+#ifndef MXNET_OPERATOR_SUBGRAPH_MKLDNN_MKLDNN_FC_INL_H_
+#define MXNET_OPERATOR_SUBGRAPH_MKLDNN_MKLDNN_FC_INL_H_
+#if MXNET_USE_MKLDNN == 1
+
+#include <string>
+#include <utility>
+#include <vector>
+#include "mkldnn.hpp"
+#include "../../nn/mkldnn/mkldnn_fully_connected-inl.h"
+
+namespace mxnet {
+namespace op {
+
+static inline bool SupportMKLDNNFCEltwiseFusion(const std::string op_name) {
+  if (op_name == "Activation" ||
+      op_name == "square" ||
+      op_name == "sqrt" ||
+      op_name == "exp" ||
+      op_name == "abs" ||
+      op_name == "clip") {
+    return true;
+  } else {
+    return false;
+  }
+}
+
+static inline mkldnn::algorithm GetMKLDNNEltwiseAlgo(const std::string op_name) {
+  if (op_name == "square")
+    return mkldnn::algorithm::eltwise_square;
+  else if (op_name == "sqrt")
+    return mkldnn::algorithm::eltwise_sqrt;
+  else if (op_name == "exp")
+    return mkldnn::algorithm::eltwise_exp;
+  else if (op_name == "abs")
+    return mkldnn::algorithm::eltwise_abs;
+  else
+    LOG(FATAL) << "Unsupported eltwise fusion op: " << op_name;
+
+  return mkldnn::algorithm::algorithm_undef;
+}
+
+static inline bool IsOutputUint8(const MKLDNNFCFullParam& full_param) {
+  auto alg = full_param.eltwise_param.alg;
+  // TODO(ciyong): some alg doesn't support int8 so far.
+  if (full_param.mkldnn_param.with_eltwise &&
+      (alg == mkldnn::algorithm::eltwise_relu ||
+       alg == mkldnn::algorithm::eltwise_logistic ||
+       alg == mkldnn::algorithm::eltwise_soft_relu ||
+       alg == mkldnn::algorithm::eltwise_bounded_relu ||
+       alg == mkldnn::algorithm::eltwise_square ||
+       alg == mkldnn::algorithm::eltwise_sqrt ||
+       alg == mkldnn::algorithm::eltwise_exp ||
+       alg == mkldnn::algorithm::eltwise_abs)) {
+    return true;
+  }
+
+  return false;
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_USE_MKLDNN == 1
+#endif  // MXNET_OPERATOR_SUBGRAPH_MKLDNN_MKLDNN_FC_INL_H_
diff --git a/src/operator/subgraph/mkldnn/mkldnn_fc.cc b/src/operator/subgraph/mkldnn/mkldnn_fc.cc
index f345a18c18a6..22432193dcf9 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_fc.cc
+++ b/src/operator/subgraph/mkldnn/mkldnn_fc.cc
@@ -33,7 +33,10 @@
 #include "../../nn/mkldnn/mkldnn_base-inl.h"
 #include "../../nn/mkldnn/mkldnn_ops-inl.h"
 #include "../../nn/mkldnn/mkldnn_fully_connected-inl.h"
+#include "../../nn/mkldnn/mkldnn_act-inl.h"
+#include "../../tensor/matrix_op-inl.h"
 #include "../../quantization/quantization_utils.h"
+#include "mkldnn_fc-inl.h"
 
 namespace mxnet {
 namespace op {
@@ -143,7 +146,7 @@ void SgMKLDNNFCOp::Forward(const OpContext &ctx,
       auto data_range = (data.dtype() == mshadow::kInt8) ? kInt8Range : kUint8Range;
       float data_scale  = data_range / MaxAbs(cached_min_data_, cached_max_data_);
       float weight_scale = kInt8Range / MaxAbs(cached_min_weight_, cached_max_weight_);
-      float quantized_out_range = mkldnn_param.with_relu ? kUint8Range : kInt8Range;
+      float quantized_out_range = IsOutputUint8(full_param_)? kUint8Range : kInt8Range;
 
       if (has_bias) {
         NDArray bias = in_data[fullc::kBias];
@@ -205,6 +208,13 @@ void SgMKLDNNFCOp::Forward(const OpContext &ctx,
 }
 
 static void SgMKLDNNFCParamParser(nnvm::NodeAttrs *attrs) {
+  // For backward compatible, with_relu->with_eltwise
+  auto legacy = attrs->dict.find("with_relu");
+  if (legacy != attrs->dict.end()) {
+    attrs->dict["with_eltwise"] = attrs->dict["with_relu"];
+    attrs->dict.erase(legacy);
+  }
+
   MKLDNNFCFullParam full_param;
   try {
     full_param.mkldnn_param.Init(attrs->dict);
@@ -222,10 +232,21 @@ static void SgMKLDNNFCParamParser(nnvm::NodeAttrs *attrs) {
   auto subgraph_sym = attrs->subgraphs[0];
   DFSVisit(subgraph_sym->outputs, [&](const nnvm::NodePtr &node) {
     if (node->is_variable()) return;
-    auto &node_name = node->op()->name;
-    if (node_name == "FullyConnected") {
+    auto &op_name = node->op()->name;
+    if (op_name == "FullyConnected") {
       full_param.default_param =
           nnvm::get<FullyConnectedParam>(node->attrs.parsed);
+    } else if (SupportMKLDNNFCEltwiseFusion(op_name)) {
+      if (op_name == "Activation") {
+        const ActivationParam act_param = nnvm::get<ActivationParam>(node->attrs.parsed);
+        full_param.eltwise_param.alg = GetMKLDNNActAlgo(act_param);
+      } else if (op_name == "clip") {
+        const ClipParam clip_param = nnvm::get<ClipParam>(node->attrs.parsed);
+        full_param.eltwise_param.alg = mkldnn::algorithm::eltwise_bounded_relu;
+        full_param.eltwise_param.alpha = clip_param.a_max;
+      } else {
+        full_param.eltwise_param.alg = GetMKLDNNEltwiseAlgo(op_name);
+      }
     }
   });
   attrs->parsed = std::move(full_param);
@@ -326,7 +347,7 @@ static bool SgMKLDNNFCInferType(const nnvm::NodeAttrs &attrs,
     } else {
       if (full_param.mkldnn_param.min_calib_range.has_value() &&
           full_param.mkldnn_param.max_calib_range.has_value()) {
-        if (full_param.mkldnn_param.with_relu) {
+        if (IsOutputUint8(full_param)) {
           TYPE_ASSIGN_CHECK(*out_types, 0, mshadow::kUint8);
         } else {
           TYPE_ASSIGN_CHECK(*out_types, 0, mshadow::kInt8);
diff --git a/src/operator/subgraph/mkldnn/mkldnn_fc_property.h b/src/operator/subgraph/mkldnn/mkldnn_fc_property.h
index caedecc417c0..17d30d272ad1 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_fc_property.h
+++ b/src/operator/subgraph/mkldnn/mkldnn_fc_property.h
@@ -31,7 +31,9 @@
 #include <string>
 #include <vector>
 #include "../common.h"
+#include "../../tensor/matrix_op-inl.h"
 #include "../subgraph_property.h"
+#include "mkldnn_fc-inl.h"
 
 namespace mxnet {
 namespace op {
@@ -46,18 +48,21 @@ class SgMKLDNNFCSelector : public SubgraphSelector {
   };
 
  private:
-  bool disable_fc_relu;
-  SelectStatus status;
-  std::vector<const nnvm::Node *> matched_list;
+  bool disable_fc_eltwise_;
+  bool quantized_;
+  SelectStatus status_;
+  std::vector<const nnvm::Node *> matched_list_;
 
  public:
-  explicit SgMKLDNNFCSelector(const bool dis_fc_relu) : disable_fc_relu(dis_fc_relu) {}
+  explicit SgMKLDNNFCSelector(const bool dis_fc_eltwise, bool quantized) :
+      disable_fc_eltwise_(dis_fc_eltwise),
+      quantized_(quantized) {}
 
   bool Select(const nnvm::Node &n) override {
     if (n.op() == Op::Get("FullyConnected")) {
-      status = disable_fc_relu ? kSuccess : kStart;
-      matched_list.clear();
-      matched_list.push_back(&n);
+      status_ = disable_fc_eltwise_ ? kSuccess : kStart;
+      matched_list_.clear();
+      matched_list_.push_back(&n);
       return true;
     }
     return false;
@@ -68,44 +73,70 @@ class SgMKLDNNFCSelector : public SubgraphSelector {
   }
 
   bool SelectOutput(const nnvm::Node &n, const nnvm::Node &new_node) override {
-    if (status == kFail || status == kSuccess || new_node.is_variable())
+    if (status_ == kFail || status_ == kSuccess || new_node.is_variable())
       return false;
 
     // If n isn't the last matched node, then we encoutered a internal
     // branch, we should pop out the node behind n and stop fusion.
-    if (matched_list.back() != &n) {
-      if (std::find(matched_list.begin(), matched_list.end(), &n) !=
-        matched_list.end()) {
-        while (matched_list.back() != &n) {
-          matched_list.pop_back();
+    if (matched_list_.back() != &n) {
+      if (std::find(matched_list_.begin(), matched_list_.end(), &n) !=
+        matched_list_.end()) {
+        while (matched_list_.back() != &n) {
+          matched_list_.pop_back();
         }
       }
 
-      status = kSuccess;
+      status_ = kSuccess;
       return false;
     }
 
-    switch (status) {
+    switch (status_) {
       case kStart:
-        if (new_node.op() == Op::Get("Activation") &&
-            new_node.attrs.dict.at("act_type") == "relu") {
-          matched_list.push_back(&new_node);
-          status = kSuccess;
+        // Currently, For INT8 FC fusion, only supports relu/bounded_relu(clip)/abs.
+        if (new_node.op() == Op::Get("Activation")) {
+          const ActivationParam &param = nnvm::get<ActivationParam>(new_node.attrs.parsed);
+          if ((quantized_ && SupportQuantizedMKLDNNAct(param)) ||
+              (!quantized_ && SupportMKLDNNAct(param))) {
+            matched_list_.push_back(&new_node);
+            status_ = kSuccess;
+            return true;
+          }
+        }
+        if (!quantized_ && (new_node.op() == Op::Get("square") ||
+            new_node.op() == Op::Get("sqrt") ||
+            new_node.op() == Op::Get("exp"))) {
+          matched_list_.push_back(&new_node);
+          status_ = kSuccess;
+          return true;
+        }
+        if (new_node.op() == Op::Get("abs")) {
+          matched_list_.push_back(&new_node);
+          status_ = kSuccess;
           return true;
         }
+        if (new_node.op() == Op::Get("clip")) {
+          const ClipParam &param = nnvm::get<ClipParam>(new_node.attrs.parsed);
+          if (param.a_min == 0.f) {
+            matched_list_.push_back(&new_node);
+            status_ = kSuccess;
+            return true;
+          }
+          status_ = kSuccess;
+          return false;
+        }
       default:
-        status = kSuccess;
+        status_ = kSuccess;
         return false;
     }
   }
 
   std::vector<nnvm::Node *> Filter(
       const std::vector<nnvm::Node *> &candidates) override {
-    if (status == kFail) {
+    if (status_ == kFail) {
       return std::vector<nnvm::Node *>(0);
     } else {
       std::vector<nnvm::Node *> ret;
-      for (auto i : matched_list) {
+      for (auto i : matched_list_) {
         auto non_const_i = const_cast<nnvm::Node *>(i);
         if (std::find(candidates.begin(), candidates.end(), non_const_i) !=
             candidates.end()) {
@@ -117,9 +148,9 @@ class SgMKLDNNFCSelector : public SubgraphSelector {
   }
 
   void Reset() override {
-    CHECK_GE(matched_list.size(), 1);
-    auto new_selector = SgMKLDNNFCSelector(disable_fc_relu);
-    new_selector.Select(*matched_list[0]);
+    CHECK_GE(matched_list_.size(), 1);
+    auto new_selector = SgMKLDNNFCSelector(disable_fc_eltwise_, quantized_);
+    new_selector.Select(*matched_list_[0]);
     *this = new_selector;
   }
 };
@@ -127,7 +158,7 @@ class SgMKLDNNFCSelector : public SubgraphSelector {
 class SgMKLDNNFCProperty : public SubgraphProperty {
  public:
   SgMKLDNNFCProperty() {
-    disable_fc_relu = dmlc::GetEnv("MXNET_DISABLE_MKLDNN_FUSE_FC_RELU", false);
+    disable_fc_eltwise_ = dmlc::GetEnv("MXNET_DISABLE_MKLDNN_FUSE_FC_ELTWISE", false);
   }
 
   static SubgraphPropertyPtr Create() {
@@ -155,10 +186,9 @@ class SgMKLDNNFCProperty : public SubgraphProperty {
       auto &sub_name = node->op()->name;
       if (sub_name == "FullyConnected") {
         node_name << "fully_connected_";
-      } else if ((sub_name == "Activation") &&
-                 (node->attrs.dict.at("act_type") == "relu")) {
-          node_name << "relu_";
-          n->attrs.dict["with_relu"] = "True";
+      } else if (SupportMKLDNNFCEltwiseFusion(sub_name)) {
+          node_name << "eltwise_";
+          n->attrs.dict["with_eltwise"] = "True";
       }
     });
     node_name << std::to_string(subgraph_id);
@@ -171,7 +201,8 @@ class SgMKLDNNFCProperty : public SubgraphProperty {
   }
 
   SubgraphSelectorPtr CreateSubgraphSelector() const override {
-    auto selector = std::make_shared<SgMKLDNNFCSelector>(disable_fc_relu);
+    bool quantized = HasAttr("quantize") ? GetAttr<bool>("quantize") : false;
+    auto selector = std::make_shared<SgMKLDNNFCSelector>(disable_fc_eltwise_, quantized);
     return selector;
   }
 
@@ -186,7 +217,7 @@ class SgMKLDNNFCProperty : public SubgraphProperty {
   }
 
  private:
-  bool disable_fc_relu;
+  bool disable_fc_eltwise_;
 };
 
 }  // namespace op
diff --git a/tests/python/mkl/test_subgraph.py b/tests/python/mkl/test_subgraph.py
index f7d03a3519cc..eba77f2c8710 100644
--- a/tests/python/mkl/test_subgraph.py
+++ b/tests/python/mkl/test_subgraph.py
@@ -49,6 +49,8 @@
 }
 
 DATA_SHAPE=[(64, 4, 10, 10), (4, 3, 24, 24), (1, 16, 32, 32)]
+fc_post_ops_list=['relu', 'sigmoid', 'tanh', 'softrelu',
+                  'square', 'square_root', 'abs', 'exp', 'bounded_relu']
 
 def check_qsym_calibrated(qsym, out_type, name='conv'):
   quantized_op_name = 'quantized_' + name
@@ -237,6 +239,12 @@ def check_quantize_whole_model(out_type):
 @with_seed()
 def check_fusion(sym, data_shape, attrs_dict, check_fp32_fusion=True, check_quantization=True, out_types=['uint8', 'int8', 'auto']):
   if check_fp32_fusion:
+    data_min = -1.0
+    data_max = 1.0
+    if ''.join(sym.get_internals().list_outputs()).find('sqrt'):
+      check_quantization = False
+      data_min = 0
+
     sym_sg = sym.get_backend_symbol(SG_PASS_NAME)
     for name, attrs in attrs_dict.items():
       if name in config:
@@ -252,9 +260,8 @@ def check_fusion(sym, data_shape, attrs_dict, check_fp32_fusion=True, check_quan
               for attr_name, attr_value in attrs.items():
                 assert v[attr_name].lower() == attr_value.lower()
           assert found
-
     arg_shapes, _, aux_shapes = sym.infer_shape()
-    arg_array = [mx.nd.random.uniform(-1.0, 1.0, shape=shape) for shape in arg_shapes]
+    arg_array = [mx.nd.random.uniform(data_min, data_max, shape=shape) for shape in arg_shapes]
     aux_array = [mx.nd.random.uniform(shape=shape) for shape in aux_shapes]
     exe = sym.bind(ctx=mx.current_context(), args=arg_array, aux_states=aux_array, grad_req='null')
     exe.forward()
@@ -626,13 +633,28 @@ def single_fc(no_bias, data_shape, flatten=True):
                                 no_bias=no_bias, flatten=flatten)
   return fc, attr
 
-def fc_relu(no_bias, data_shape, flatten=True):
-  attr = {'fc': {'with_relu': 'true'}}
+# fc + eltwise fusion case
+def fc_eltwise(no_bias, data_shape, flatten=True, alg='relu'):
+  assert alg in fc_post_ops_list
+
+  attr = {'fc': {'with_eltwise': 'true'}}
   data, weight = head_symbol(data_shape)
   fc = mx.symbol.FullyConnected(name='fc', data=data, weight=weight, num_hidden=64,
                                 no_bias=no_bias, flatten=flatten)
-  relu = mx.symbol.Activation(data=fc, name='relu', act_type="relu")
-  return relu, attr
+  if alg in ['relu', 'sigmoid', 'tanh', 'softrelu']:
+    sym = mx.symbol.Activation(data=fc, name='act', act_type=alg)
+  elif alg == 'square':
+    sym = mx.symbol.square(data=fc, name='square')
+  elif alg == 'square_root':
+    sym = mx.symbol.sqrt(data=fc, name='sqrt')
+  elif alg == 'abs':
+    sym = mx.symbol.abs(data=fc, name='abs')
+  elif alg == 'exp':
+    sym = mx.symbol.exp(data=fc, name='exp')
+  else:
+    sym = mx.symbol.clip(data=fc, name='bounded_relu', a_min=0, a_max=1.0)
+
+  return sym, attr
 
 # fc + relu can't be fusion case
 # eg.1
@@ -806,9 +828,12 @@ def test_single_fc():
 
 
 @with_seed()
-def test_fc_relu():
-  for dshape, no_bias, flatten in itertools.product(DATA_SHAPE, [True, False], [True, False]):
-    syms, attrs = fc_relu(no_bias, dshape, flatten)
+def test_fc_eltwise():
+  for dshape, no_bias, flatten, alg in itertools.product(DATA_SHAPE,
+                                                        [True, False],
+                                                        [True, False],
+                                                        fc_post_ops_list):
+    syms, attrs = fc_eltwise(no_bias, dshape, flatten, alg)
     if flatten is True:
       check_fusion(syms, dshape, attrs, check_quantization=True)
     else:

From 9023256cca74f17d3cd81a534433925726686779 Mon Sep 17 00:00:00 2001
From: Haozheng Fan <fhztc1997618@gmail.com>
Date: Thu, 22 Aug 2019 15:38:24 +0800
Subject: [PATCH 206/813] Tvm broadcast backward (#15938)

* tvm broadcast backward

* dispatch by req

* pad for broadcast to a larger dim
---
 contrib/tvmop/__init__.py            |  1 +
 contrib/tvmop/basic/ufunc.py         | 54 ++++++++++++++-
 contrib/tvmop/utils.py               | 29 ++++++++
 src/operator/contrib/tvmop/ufunc.cc  | 99 +++++++++++++++++++++++++---
 tests/python/unittest/test_tvm_op.py | 49 ++++++++++++--
 5 files changed, 215 insertions(+), 17 deletions(-)

diff --git a/contrib/tvmop/__init__.py b/contrib/tvmop/__init__.py
index 31189d499b5a..1234ee7d31f1 100644
--- a/contrib/tvmop/__init__.py
+++ b/contrib/tvmop/__init__.py
@@ -18,5 +18,6 @@
 # coding: utf-8
 from .opdef import defop
 from .utils import AllTypes, RealTypes
+from .utils import assign_by_req, reduce_axes
 
 from . import basic
diff --git a/contrib/tvmop/basic/ufunc.py b/contrib/tvmop/basic/ufunc.py
index 0419e5fd2ca9..6bb102ccf7e3 100644
--- a/contrib/tvmop/basic/ufunc.py
+++ b/contrib/tvmop/basic/ufunc.py
@@ -18,6 +18,7 @@
 # coding: utf-8
 import tvm
 from .. import defop, AllTypes
+from .. import assign_by_req, reduce_axes
 
 def compute_add(dtype, ndim):
     A = tvm.placeholder([tvm.var() for _ in range(ndim)], name='A', dtype=dtype)
@@ -27,8 +28,9 @@ def compute_add(dtype, ndim):
     s = tvm.create_schedule(C.op)
     return s, A, B, C
 
+
 @defop(name="vadd", target="cpu", auto_broadcast=True,
-       dtype=AllTypes, ndim=list(range(1, 6)))
+       dtype=AllTypes, ndim=[5])
 def vadd(dtype, ndim):
     s, A, B, C = compute_add(dtype, ndim)
     axes = [axis for axis in C.op.axis]
@@ -37,8 +39,9 @@ def vadd(dtype, ndim):
 
     return s, [A, B, C]
 
+
 @defop(name="cuda_vadd", target="cuda", auto_broadcast=True,
-       dtype=["float32", "float64"], ndim=list(range(1, 6)))
+       dtype=["float32", "float64"], ndim=[5])
 def vadd_gpu(dtype, ndim):
     s, A, B, C = compute_add(dtype, ndim)
     s = tvm.create_schedule(C.op)
@@ -48,3 +51,50 @@ def vadd_gpu(dtype, ndim):
     s[C].bind(bx, tvm.thread_axis("blockIdx.x"))
     s[C].bind(tx, tvm.thread_axis("threadIdx.x"))
     return s, [A, B, C]
+
+
+def compute_backward_vadd(dtype, ndim, reduce1st, req):
+    # The backward of broadcast op is basically a reduction on broadcast axes.
+    # We label the reduce axes as 1 and other axes as 0, and they form a bit string.
+    # Each bit string correponds to a kernel, so the number of kernels is as many as `2^n`
+    # To reduce it, the bit string is compressed by combining consecutive 0s or 1s.
+    # In this way, the number of bit string (the number of kernels) is reduced to `2 * n`
+    # They compressed bit string is stored in `axes`. And `reduce1st` represents the first bit
+    # of the compressed bit string. Credit to @junrushao1994 and @yzhliu.
+    axes = ([reduce1st, 1 - reduce1st] * ndim)[:ndim]
+    X = tvm.placeholder([tvm.var() for _ in range(ndim)], name='X', dtype=dtype)
+    reducer = tvm.comm_reducer(lambda x, y: x + y,
+        lambda t: tvm.const(0, dtype=t), name="sum")
+    ret = reduce_axes(X, axes, reducer)
+    in_grad_a, in_grad = assign_by_req(ret, req)
+    s = tvm.create_schedule(in_grad.op)
+    return s, X, in_grad_a, in_grad, [ret, in_grad]
+
+
+@defop(name="backward_vadd", target="cpu", dtype=AllTypes, 
+       ndim=[5], reduce1st=[0, 1],
+       req=["kWriteTo", "kAddTo"], attrs=["reduce1st", "req"])
+def backward_vadd(dtype, ndim, reduce1st, req):
+    s, X, in_grad_a, in_grad, c_list = compute_backward_vadd(dtype, ndim, reduce1st, req)
+    for t in c_list:
+        axes = [axis for axis in t.op.axis]
+        fused = s[t].fuse(*axes)
+        s[t].parallel(fused)
+    return s, [X, in_grad_a, in_grad]
+
+
+@defop(name="cuda_backward_vadd", target="gpu", dtype=["float32", "float64"],
+       ndim=[5], reduce1st=[0, 1],
+       req=["kWriteTo", "kAddTo"], attrs=["reduce1st", "req"])
+def backward_vadd_gpu(dtype, ndim, reduce1st, req):
+    s, X, in_grad_a, in_grad, c_list = compute_backward_vadd(dtype, ndim, reduce1st, req)
+    num_thread = 64
+    for t in c_list:
+        block_x = tvm.thread_axis("blockIdx.x")
+        thread_x = tvm.thread_axis("threadIdx.x")
+        axes = [axis for axis in t.op.axis]
+        fused = s[t].fuse(*axes)
+        bx, tx = s[t].split(fused, factor=num_thread)
+        s[t].bind(bx, block_x)
+        s[t].bind(tx, thread_x)
+    return s, [X, in_grad_a, in_grad]
diff --git a/contrib/tvmop/utils.py b/contrib/tvmop/utils.py
index 0b2416b4f3ae..329dce2148d9 100644
--- a/contrib/tvmop/utils.py
+++ b/contrib/tvmop/utils.py
@@ -16,5 +16,34 @@
 # under the License.
 
 # coding: utf-8
+import tvm
+
 AllTypes = ["float32", "float64", "float16", "uint8", "int8", "int32", "int64"]
 RealTypes = ["float32", "float64", "float16"]
+
+def assign_by_req(a, req):
+    b = tvm.placeholder(a.shape, name='assign_by_req_b', dtype=a.dtype)
+    if (req == "kAddTo"):
+        c = tvm.compute(a.shape, lambda *idx: a[idx] + b[idx])
+    else:
+        c = tvm.compute(a.shape, lambda *idx: a[idx])
+    return b, c
+
+
+def reduce_axes(X, axes, reducer):
+    def get_index(idx, ridx):
+        j = 0
+        k = 0
+        ret = []
+        for val in axes:
+            ret.append(idx[j] if val == 0 else ridx[k])
+            j += (val == 0)
+            k += (val != 0)
+        return tuple(ret)
+    
+    ishape = X.shape
+    odim = (len(ishape) + 1 - axes[0]) // 2
+    oshape = [tvm.var() for _ in range(odim)]
+    ridx = [tvm.reduce_axis((0, ishape[i])) for (i, val) in enumerate(axes) if val == 1]
+    ret = tvm.compute(oshape, lambda *idx: reducer(X[get_index(idx, ridx)], axis=ridx), name='ret')
+    return ret
diff --git a/src/operator/contrib/tvmop/ufunc.cc b/src/operator/contrib/tvmop/ufunc.cc
index 3475a211cfec..7dc28bd0f419 100644
--- a/src/operator/contrib/tvmop/ufunc.cc
+++ b/src/operator/contrib/tvmop/ufunc.cc
@@ -28,6 +28,7 @@
 #include <tvm/runtime/registry.h>
 #include <tvm/runtime/c_runtime_api.h>
 #include <mxnet/base.h>
+#include <string>
 #include "../../tensor/elemwise_binary_broadcast_op.h"
 #include "../../tvmop/op_module.h"
 #include "../../tensor/elemwise_binary_op.h"
@@ -37,16 +38,82 @@ namespace op {
 
 static constexpr char func_vadd_cpu[] = "vadd";
 static constexpr char func_vadd_gpu[] = "cuda_vadd";
+static constexpr char func_bakcward_vadd_cpu[] = "backward_vadd";
+static constexpr char func_bakcward_vadd_gpu[] = "cuda_backward_vadd";
+static constexpr int max_dim = 5;
+
+TBlob padding(const TBlob& tblob, const int max_dim) {
+  TShape tshape(max_dim, 1);
+  int ndim = tblob.shape_.ndim();
+  for (int i = max_dim - ndim; i < max_dim; ++i) {
+    tshape[i] = tblob.size(i - max_dim + ndim);
+  }
+  return tblob.reshape(tshape);
+}
 
 template<const char* func>
-void TVMBroadcastCompute(const nnvm::NodeAttrs& attrs,
-                         const mxnet::OpContext& ctx,
-                         const std::vector<TBlob>& inputs,
-                         const std::vector<OpReqType>& req,
-                         const std::vector<TBlob>& outputs) {
+void TVMBinaryCompute(const nnvm::NodeAttrs& attrs,
+                      const mxnet::OpContext& ctx,
+                      const std::vector<TBlob>& inputs,
+                      const std::vector<OpReqType>& req,
+                      const std::vector<TBlob>& outputs) {
   CHECK_EQ(inputs.size(), 2U);
   CHECK_EQ(outputs.size(), 1U);
-  tvm::runtime::TVMOpModule::Get()->Call(func, ctx, {inputs[0], inputs[1], outputs[0]});
+  TBlob idata[2], odata;
+  for (int k = 0; k < 2; ++k) {
+    idata[k] = padding(inputs[k], max_dim);
+  }
+  odata = padding(outputs[0], max_dim);
+  tvm::runtime::TVMOpModule::Get()->Call(func, ctx, {idata[0], idata[1], odata});
+}
+
+template<const char* func>
+void TVMBinaryBackwardComputeUseNone(const nnvm::NodeAttrs& attrs,
+                                     const mxnet::OpContext& ctx,
+                                     const std::vector<TBlob>& inputs,
+                                     const std::vector<OpReqType>& req,
+                                     const std::vector<TBlob>& outputs) {
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 2U);
+  int ndim = inputs[0].shape_.ndim();
+  for (int k = 0; k < 2; ++k) {
+    // dispatch by backward
+    std::vector<int> ov, iv;
+    TBlob ograd = padding(inputs[0], ndim), igrad = padding(outputs[k], ndim);
+    int flag;
+    if (ograd.size(0) != igrad.size(0)) {
+      flag = 1;
+    } else {
+      flag = 0;
+    }
+    for (int i = 0; i < ndim; ++i) {
+      if (i == 0 || (ograd.size(i) != igrad.size(i)) != (ograd.size(i - 1) != igrad.size(i - 1))) {
+        ov.push_back(ograd.size(i));
+      } else {
+        ov.back() *= ograd.size(i);
+      }
+    }
+    for (int i = ov.size(); i < max_dim; ++i) {
+      ov.push_back(1);
+    }
+    for (int i = flag; i < ov.size(); i += 2) {
+      iv.push_back(ov[i]);
+    }
+    TShape oshape(ov.begin(), ov.end()), ishape(iv.begin(), iv.end());
+    TBlob ograd_tvm(ograd.reshape(oshape));
+    TBlob igrad_tvm(igrad.reshape(ishape));
+    std::string funcname = std::string(func) + "reduce1st_" + std::to_string(flag);
+    // dispatch by req
+    funcname += "req_";
+    MXNET_ASSIGN_REQ_SWITCH(req[k], req_type, {
+      if (req_type == kWriteTo) {
+        funcname += "kWriteTo";
+      } else {
+        funcname += "kAddTo";
+      }
+    })
+    tvm::runtime::TVMOpModule::Get()->Call(funcname, ctx, {ograd_tvm, igrad_tvm, igrad_tvm});
+  }
 }
 
 NNVM_REGISTER_OP(_contrib_tvm_vadd)
@@ -54,12 +121,28 @@ NNVM_REGISTER_OP(_contrib_tvm_vadd)
     .set_num_outputs(1)
     .add_argument("a", "NDArray-or-Symbol", "first input")
     .add_argument("b", "NDArray-or-Symbol", "second input")
+    .set_attr<nnvm::FListInputNames>("FListInputNames",
+      [](const NodeAttrs& attrs) {
+        return std::vector<std::string>{"a", "b"};
+      })
     .set_attr<mxnet::FInferShape>("FInferShape", BinaryBroadcastShape)
     .set_attr<nnvm::FInferType>("FInferType", mxnet::op::ElemwiseType<2, 1>)
 #if MXNET_USE_CUDA
-    .set_attr<mxnet::FCompute>("FCompute<gpu>", mxnet::op::TVMBroadcastCompute<func_vadd_gpu>)
+    .set_attr<mxnet::FCompute>("FCompute<gpu>", mxnet::op::TVMBinaryCompute<func_vadd_gpu>)
+#endif  // MXNET_USE_CUDA
+    .set_attr<mxnet::FCompute>("FCompute<cpu>", mxnet::op::TVMBinaryCompute<func_vadd_cpu>)
+    .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_contrib_tvm_vadd"});
+
+NNVM_REGISTER_OP(_backward_contrib_tvm_vadd)
+    .set_num_inputs(1)
+    .set_num_outputs(2)
+    .set_attr<nnvm::TIsBackward>("TIsBackward", true)
+#if MXNET_USE_CUDA
+    .set_attr<mxnet::FCompute>("FCompute<gpu>",
+                               mxnet::op::TVMBinaryBackwardComputeUseNone<func_bakcward_vadd_gpu>)
 #endif  // MXNET_USE_CUDA
-    .set_attr<mxnet::FCompute>("FCompute<cpu>", mxnet::op::TVMBroadcastCompute<func_vadd_cpu>);
+    .set_attr<mxnet::FCompute>("FCompute<cpu>",
+                               mxnet::op::TVMBinaryBackwardComputeUseNone<func_bakcward_vadd_cpu>);
 
 }  // namespace op
 }  // namespace mxnet
diff --git a/tests/python/unittest/test_tvm_op.py b/tests/python/unittest/test_tvm_op.py
index 3ab2a25bc20e..9c1f567ad31a 100644
--- a/tests/python/unittest/test_tvm_op.py
+++ b/tests/python/unittest/test_tvm_op.py
@@ -16,6 +16,7 @@
 # under the License.
 
 import mxnet as mx
+import numpy as _np
 from mxnet.test_utils import same, rand_shape_nd
 from mxnet.runtime import Features
 from common import with_seed
@@ -25,13 +26,47 @@
 @with_seed()
 def test_tvm_broadcast_add():
     if _features.is_enabled("TVM_OP"):
-        a_shape = rand_shape_nd(4)
-        b_shape = (1,) + a_shape[1:2] + (1, 1)
-        a = mx.nd.normal(shape=a_shape)
-        b = mx.nd.normal(shape=b_shape)
-        c = mx.nd.contrib.tvm_vadd(a, b)
-        c_np = a.asnumpy() + b.asnumpy()
-        assert same(c.asnumpy(), c_np)
+        configs = [
+            [[5, 6, 7, 8, 9], [1]],
+            [[6, 4, 5, 2, 1], [6, 1, 5, 1, 1]],
+            [[3, 5, 6], [1, 6]],
+            [[3, 5, 6], [5, 1]],
+            [[3, 5, 6], [5, 6]],
+            [[4, 3, 2, 1], [2, 1]],
+            [[4, 3, 2, 2], [4, 1, 1, 2]],
+            [[6, 6], [6, 6]],
+        ]
+        for config in configs:
+            a_shape = config[0]
+            b_shape = config[1]
+            a = mx.nd.normal(shape=a_shape)
+            b = mx.nd.normal(shape=b_shape)
+            a.attach_grad()
+            b.attach_grad()
+            with mx.autograd.record():
+                c = mx.nd.contrib.tvm_vadd(a, b)
+            c_np = a.asnumpy() + b.asnumpy()
+            assert same(c.asnumpy(), c_np)
+            # test backward
+            c.backward()
+            expected_grad_a = _np.ones_like(a.asnumpy()) * c_np.size / a.asnumpy().size
+            expected_grad_b = _np.ones_like(b.asnumpy()) * c_np.size / b.asnumpy().size
+            assert same(a.grad.asnumpy(), expected_grad_a)
+            assert same(b.grad.asnumpy(), expected_grad_b)
+            # test kAddTo request
+            a = mx.nd.normal(shape=a_shape)
+            b = mx.nd.normal(shape=b_shape)
+            a.attach_grad()
+            b.attach_grad()
+            with mx.autograd.record():
+                c = mx.nd.contrib.tvm_vadd(a, b)
+                d = mx.nd.contrib.tvm_vadd(a, b)
+            mx.autograd.backward([c, d])
+            expected_grad_a = 2 * _np.ones_like(a.asnumpy()) * c.size / a.size
+            expected_grad_b = 2 * _np.ones_like(b.asnumpy()) * c.size / b.size
+            assert same(a.grad.asnumpy(), expected_grad_a)
+            assert same(b.grad.asnumpy(), expected_grad_b)
+
 
 if __name__ == '__main__':
     import nose

From fa16a9e269713100a3def29aa1c88a1d174d21c6 Mon Sep 17 00:00:00 2001
From: Jake Lee <gstu1130@gmail.com>
Date: Thu, 22 Aug 2019 14:35:10 -0700
Subject: [PATCH 207/813] Disable test coverage for Clang MKLDNN (#15977)

* disable test coverage

* disable the job
---
 ci/jenkins/Jenkinsfile_clang | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ci/jenkins/Jenkinsfile_clang b/ci/jenkins/Jenkinsfile_clang
index 029c7208107b..c0af50dc3cee 100644
--- a/ci/jenkins/Jenkinsfile_clang
+++ b/ci/jenkins/Jenkinsfile_clang
@@ -37,8 +37,8 @@ core_logic: {
     custom_steps.compile_unix_clang_3_9_cpu(),
     custom_steps.compile_unix_clang_6_cpu(),
     custom_steps.compile_unix_clang_tidy_cpu(),
-    custom_steps.compile_unix_clang_3_9_mkldnn_cpu(),
-    custom_steps.compile_unix_clang_6_mkldnn_cpu()
+    custom_steps.compile_unix_clang_3_9_mkldnn_cpu()
+    // custom_steps.compile_unix_clang_6_mkldnn_cpu()
   ]) 
 }
 ,

From fade1591b7f7f152ebe154d5fb1a5904fd679f57 Mon Sep 17 00:00:00 2001
From: Przemyslaw Tredak <ptredak@nvidia.com>
Date: Thu, 22 Aug 2019 20:53:10 -0700
Subject: [PATCH 208/813] Fix get_rows_per_block (#15979)

---
 src/common/cuda_utils.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/common/cuda_utils.cc b/src/common/cuda_utils.cc
index f38b2f8b5490..893b34e6ff29 100644
--- a/src/common/cuda_utils.cc
+++ b/src/common/cuda_utils.cc
@@ -79,7 +79,7 @@ int get_rows_per_block(size_t row_size, int num_threads_per_block) {
   int actual_num_warps_per_row = std::min(desired_num_warps_per_row,
                                           num_threads_per_block / warp_size);
   // actual number of warps needs to be power of 2
-  actual_num_warps_per_row = RoundToPower2(desired_num_warps_per_row);
+  actual_num_warps_per_row = RoundToPower2(actual_num_warps_per_row);
   return num_threads_per_block / (warp_size * actual_num_warps_per_row);
 }
 

From 73a692e252fa5625bf002366653f31f46b1751e8 Mon Sep 17 00:00:00 2001
From: Lin Yuan <apeforest@gmail.com>
Date: Thu, 22 Aug 2019 22:02:59 -0700
Subject: [PATCH 209/813] Fix a memory misalignment in topk operator (#15948)

* fix alignment

* use correct type for shape index

* clean up unnecessary space in topk

* fix lint

* add additional temp space

* address reviewer comment

* fix incorrect nidex type
---
 3rdparty/mshadow/mshadow/tensor.h     |  6 +++---
 src/operator/tensor/ordering_op-inl.h | 24 ++++++++++++------------
 2 files changed, 15 insertions(+), 15 deletions(-)

diff --git a/3rdparty/mshadow/mshadow/tensor.h b/3rdparty/mshadow/mshadow/tensor.h
index 0d662621aa4d..ad29e751a050 100755
--- a/3rdparty/mshadow/mshadow/tensor.h
+++ b/3rdparty/mshadow/mshadow/tensor.h
@@ -69,7 +69,7 @@ struct Shape {
    * \param idx dimension index
    * \return the corresponding dimension size
    */
-  MSHADOW_XINLINE index_t &operator[](index_t idx) {
+  MSHADOW_XINLINE index_t &operator[](int idx) {
     return shape_[idx];
   }
   /*!
@@ -77,7 +77,7 @@ struct Shape {
    * \param idx dimension index
    * \return the corresponding dimension size
    */
-  MSHADOW_XINLINE const index_t &operator[](index_t idx) const {
+  MSHADOW_XINLINE const index_t &operator[](int idx) const {
     return shape_[idx];
   }
   /*!
@@ -484,7 +484,7 @@ struct Tensor: public TRValue<Tensor<Device, dimension, DType>,
    * \param idx the dimension count from the highest dimensin
    * \return the size
    */
-  MSHADOW_XINLINE index_t size(index_t idx) const {
+  MSHADOW_XINLINE index_t size(int idx) const {
     return shape_[idx];
   }
   /*!
diff --git a/src/operator/tensor/ordering_op-inl.h b/src/operator/tensor/ordering_op-inl.h
index 880acf1f4cae..b36d79acfc7b 100644
--- a/src/operator/tensor/ordering_op-inl.h
+++ b/src/operator/tensor/ordering_op-inl.h
@@ -404,7 +404,7 @@ void TopKImpl(const RunContext &ctx,
   bool do_transpose = false;
   bool is_ascend = false;
   index_t k = 0;
-  size_t alignment = std::max(sizeof(DType), sizeof(int));
+  size_t alignment = std::max(sizeof(DType), sizeof(index_t));
   mxnet::TShape target_shape;
   ParseTopKParam(src.shape_, param,
                  &target_shape, &batch_size, &element_num, &axis, &k, &do_transpose, &is_ascend);
@@ -414,30 +414,30 @@ void TopKImpl(const RunContext &ctx,
     << element_num << ", but the selected index_t can only represent "
     << mxnet::common::MaxIntegerValue<index_t>() << " elements";
   Tensor<xpu, 3, DType> dat = src.FlatTo3D<xpu, DType>(axis, axis, s);
-  size_t temp_size = 0;
-  // Temp space needed by the gpu-based full sorts.
-  temp_size = std::max(temp_size,
-    mxnet::op::SortByKeyWorkspaceSize<int, int, xpu>(src.Size()));
-  temp_size = std::max(temp_size,
-    mxnet::op::SortByKeyWorkspaceSize<int, DType, xpu>(src.Size()));
+  // Temp space needed by the full sorts.
+  size_t temp_size = std::max(
+      mxnet::op::SortByKeyWorkspaceSize<index_t, DType, xpu>(src.Size()),
+      mxnet::op::SortByKeyWorkspaceSize<DType, index_t, xpu>(src.Size()));
+
   temp_size = std::max(temp_size,
-    mxnet::op::SortByKeyWorkspaceSize<DType, int, xpu>(src.Size()));
+      mxnet::op::SortByKeyWorkspaceSize<index_t, index_t, xpu>(src.Size()));
   // Additional temp space for gpu full sorts for batch ids.
   temp_size += PadBytes(sizeof(index_t) * src.Size(), alignment);
   // Temp space for cpu sorts.
-  temp_size = std::max(temp_size, static_cast<size_t>(sizeof(DType) * src.Size()));
+  temp_size = std::max(temp_size, sizeof(DType) * src.Size());
+
   size_t workspace_size = temp_size + PadBytes(sizeof(DType) * src.Size(), alignment)
                                     + PadBytes(sizeof(index_t) * src.Size(), alignment);
   if (param.ret_typ == topk_enum::kReturnMask) {
-    workspace_size += PadBytes(sizeof(int) * batch_size * k, alignment);
+    workspace_size += PadBytes(sizeof(index_t) * batch_size * k, alignment);
   }
   workspace = resource.get_space_typed<xpu, 1, char>(Shape1(workspace_size), s);
   char* workspace_curr_ptr = workspace.dptr_;
   sorted_dat = Tensor<xpu, 1, DType>(reinterpret_cast<DType*>(workspace_curr_ptr),
-                                      Shape1(src.Size()), s);  // contain sorted dat
+      Shape1(src.Size()), s);  // contain sorted dat
   workspace_curr_ptr += PadBytes(sizeof(DType) * src.Size(), alignment);
   indices = Tensor<xpu, 1, index_t>(reinterpret_cast<index_t*>(workspace_curr_ptr),
-                                Shape1(src.Size()), s);  // indices in the original matrix
+      Shape1(src.Size()), s);  // indices in the original matrix
   workspace_curr_ptr += PadBytes(sizeof(index_t) * src.Size(), alignment);
 
   if (param.ret_typ == topk_enum::kReturnMask) {

From d8c2d85e2c3b8ab23f342f95ffe5866485b6c0c0 Mon Sep 17 00:00:00 2001
From: YixinBao <yixin.bao@intel.com>
Date: Fri, 23 Aug 2019 14:11:32 +0800
Subject: [PATCH 210/813] update support MKLDNN BN conditions (#15870)

* update support MKLDNN BN conditions

* add bn test case for channel size = 8

* fix bn gradient with use_global_stats

* rm useless comments

* fix mkldnn bn output when use_global_stats

* fix lint

* retrigger ci

* retrigger ci again

* retrigger ci again 2

* retrigger ci again 3

* retrigger ci again 4
---
 src/operator/nn/batch_norm.cc                 | 10 ++++-----
 .../nn/mkldnn/mkldnn_batch_norm-inl.h         | 21 ++++++++++---------
 tests/python/unittest/test_operator.py        |  4 ++--
 3 files changed, 17 insertions(+), 18 deletions(-)

diff --git a/src/operator/nn/batch_norm.cc b/src/operator/nn/batch_norm.cc
index 6382d46d272d..3214e3b9b9ac 100644
--- a/src/operator/nn/batch_norm.cc
+++ b/src/operator/nn/batch_norm.cc
@@ -384,7 +384,6 @@ static inline bool SupportMKLDNNBN(const NDArray &input, const BatchNormParam &p
   mxnet::TShape shape = input.shape();
   return SupportMKLDNN(input) && shape.ndim() == 4
       && param.axis == mxnet::op::batchnorm::DEFAULT_AXIS
-      && shape[param.axis] % 8 == 0
       && !mxnet::op::batchnorm::disable_mkl;
 }
 
@@ -395,8 +394,8 @@ void BatchNormComputeExCPU(const nnvm::NodeAttrs &attrs,
                            const std::vector<NDArray> &outputs) {
   CHECK_EQ(inputs.size(), 5U);
   const BatchNormParam &param = nnvm::get<BatchNormParam>(attrs.parsed);
-  // MKLDNN batchnorm only works well on the special MKLDNN layout.
-  if (SupportMKLDNNBN(inputs[0], param) && inputs[0].IsMKLDNNData()) {
+
+  if (SupportMKLDNNBN(inputs[0], param)) {
     std::vector<NDArray> in_data(inputs.begin(), inputs.begin() + batchnorm::kInMovingMean);
     std::vector<NDArray> aux_states(inputs.begin() + batchnorm::kInMovingMean, inputs.end());
 
@@ -419,9 +418,8 @@ void BatchNormGradComputeExCPU(const nnvm::NodeAttrs &attrs,
   const BatchNormParam &param = nnvm::get<BatchNormParam>(attrs.parsed);
 
   mxnet::TShape shape = inputs[0].shape();
-  // MKLDNN batchnorm only works well on the special MKLDNN layout.
-  if (SupportMKLDNNBN(inputs[0], param)
-      && (inputs[3].IsMKLDNNData() || inputs[0].IsMKLDNNData())) {
+
+  if (SupportMKLDNNBN(inputs[0], param)) {
     std::vector<NDArray> out_grad(1);
     std::vector<NDArray> out_data(3);
     std::vector<NDArray> in_data(3);
diff --git a/src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h b/src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h
index f294153ecc24..2d2bf2c64596 100644
--- a/src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h
@@ -51,7 +51,7 @@ using mkldnn::forward_inference;
 
 inline static unsigned _GetFlags(const std::vector<NDArray> &in_data,
                                  const std::vector<NDArray> &aux_states,
-                                 const BatchNormParam &param, bool is_train) {
+                                 const BatchNormParam &param, bool is_train_and_not_global_stats) {
   unsigned flags = 0U;
   if (in_data.size() == 3U) {
     flags |= use_scale_shift;
@@ -59,7 +59,7 @@ inline static unsigned _GetFlags(const std::vector<NDArray> &in_data,
 
   // aux_states[0]: inMean
   // aux_states[1]: inVariance
-  if (aux_states.size() == 2U && !is_train) {
+  if (aux_states.size() == 2U && !is_train_and_not_global_stats) {
     flags |= use_global_stats;
   }
   return flags;
@@ -107,13 +107,13 @@ class MKLDNNBNForward {
   std::shared_ptr<const mkldnn::memory> mean_m;
   std::shared_ptr<const mkldnn::memory> var_m;
   std::shared_ptr<mkldnn::batch_normalization_forward> fwd;
-  bool is_train;
+  bool is_train_and_not_global_stats;
   t_bn_f_pdesc pd;
 
  public:
-  MKLDNNBNForward(const t_bn_f_pdesc &_pd, bool is_train): pd(_pd) {
+  MKLDNNBNForward(const t_bn_f_pdesc &_pd, bool is_train_and_not_global_stats): pd(_pd) {
     weight_m.reset(new mkldnn::memory(pd.weights_primitive_desc()));
-    this->is_train = is_train;
+    this->is_train_and_not_global_stats = is_train_and_not_global_stats;
   }
 
   const mkldnn::memory &GetWeight() const {
@@ -161,7 +161,7 @@ class MKLDNNBNForward {
     }
 
     if (fwd == nullptr) {
-      if (!is_train)
+      if (!is_train_and_not_global_stats)
         fwd.reset(new mkldnn::batch_normalization_forward(
                 pd, *data_m, mkldnn::primitive::at(*mean_m),
                 mkldnn::primitive::at(*var_m), *weight_m, *out_m));
@@ -194,13 +194,14 @@ static MKLDNNBNForward &GetBNForward(const BatchNormParam& param,
 #endif
   MKLDNNBNSignature key(param);
   key.AddSign(ctx.is_train);
+  key.AddSign(param.use_global_stats);
   key.AddSign(in_data);
 
   auto it = fwds.find(key);
   if (it == fwds.end()) {
     auto fwd_pd = _GetFwd(*in_data.GetMKLDNNData(), ctx.is_train,
                           (DType) param.eps, flags);
-    MKLDNNBNForward fwd(fwd_pd, ctx.is_train);
+    MKLDNNBNForward fwd(fwd_pd, ctx.is_train && !param.use_global_stats);
     it = AddToCache(&fwds, key, fwd);
   }
   return it->second;
@@ -213,7 +214,7 @@ void MKLDNNBatchNormForward(const OpContext &ctx, const BatchNormParam &param,
                             const std::vector<NDArray>   &out_data,
                             const std::vector<NDArray>   &aux_states) {
   TmpMemMgr::Get()->Init(ctx.requested[batchnorm::kTempSpace]);
-  unsigned flags      = _GetFlags(in_data, aux_states, param, ctx.is_train);
+  unsigned flags = _GetFlags(in_data, aux_states, param, ctx.is_train && !param.use_global_stats);
   const NDArray &data = in_data[batchnorm::kData];
 
   auto &fwd = GetBNForward<DType>(param, ctx, data, flags);
@@ -253,7 +254,7 @@ void MKLDNNBatchNormForward(const OpContext &ctx, const BatchNormParam &param,
       }
     }
 
-    if (!ctx.is_train) {
+    if (!ctx.is_train || param.use_global_stats) {
       DType* omean    = out_data[batchnorm::kMean].data().dptr<DType>();
       DType* ovar     = out_data[batchnorm::kVar].data().dptr<DType>();
       DType* inmean   = aux_states[batchnorm::kMovingMean].data().dptr<DType>();
@@ -378,7 +379,7 @@ void MKLDNNBatchNormBackward(const OpContext &ctx, const BatchNormParam &param,
   CHECK_EQ(in_data.size(), 3U);
   CHECK_EQ(out_data.size(), 3U);
   CHECK_EQ(in_grad.size(), 3U);
-  unsigned flags = _GetFlags(in_data, aux_states, param, ctx.is_train);
+  unsigned flags = _GetFlags(in_data, aux_states, param, ctx.is_train && !param.use_global_stats);
 
   const NDArray &data         = in_data[batchnorm::kData];
   const NDArray &diff         = out_grad[batchnorm::kOut];
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index c360db9f01a5..466cee823029 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -1626,7 +1626,7 @@ def test_bilinear_upsampling():
 @with_seed()
 def test_batchnorm_training():
     def check_batchnorm_training(stype):
-        for shape in [(2, 3), (2, 3, 2, 2)]:
+        for shape in [(2, 3), (2, 3, 2, 2), (2, 8, 2, 2)]:
             data_tmp = np.random.normal(-0.1, 0.1, size=shape)
             s = shape[1],
             gamma = np.ones(s)
@@ -1821,7 +1821,7 @@ def _test_batchnorm_impl(op, shape, axis, cudnn_off, output_mean_var):
                 bn_beta.grad.asnumpy(), db.asnumpy(), atol=atol, rtol=rtol)
 
     for op in [mx.nd.BatchNorm, mx.nd.contrib.SyncBatchNorm]:
-        for shape in [(24, 2), (24, 3, 4), (24, 4, 4, 4), (24, 5, 6, 4, 4)]:
+        for shape in [(24, 2), (24, 3, 4), (24, 4, 4, 4), (24, 8, 4, 4), (24, 5, 6, 4, 4)]:
             for axis in range(len(shape)):
                 for cudnn_off in [False, True]:
                     for output_mean_var in [False, True]:

From 717d88384a4c06abd59e97a56491f1e56ac7e80d Mon Sep 17 00:00:00 2001
From: Leonard Lausen <lausen@amazon.com>
Date: Fri, 23 Aug 2019 08:46:19 +0000
Subject: [PATCH 211/813] Remove requests_failed_to_import handling

We already required requests package. If a user is missing it nevertheless, fail
with a standard error message instead of some hard to understand error that
requests_failed_to_import object does not have a get method..

Example https://github.com/dmlc/gluon-nlp/issues/892
---
 python/mxnet/gluon/utils.py | 7 +------
 1 file changed, 1 insertion(+), 6 deletions(-)

diff --git a/python/mxnet/gluon/utils.py b/python/mxnet/gluon/utils.py
index f12f15e7d41a..deb5dd765ff7 100644
--- a/python/mxnet/gluon/utils.py
+++ b/python/mxnet/gluon/utils.py
@@ -28,12 +28,7 @@
 import warnings
 import collections
 import weakref
-try:
-    import requests
-except ImportError:
-    class requests_failed_to_import(object):
-        pass
-    requests = requests_failed_to_import
+import requests
 
 import numpy as np
 

From 8d0b895ce427c97d5c75a2579e0b9306e1beeb27 Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn@amazon.com>
Date: Fri, 23 Aug 2019 11:52:36 -0700
Subject: [PATCH 212/813] np elemwise unary ops upstream (#15831)

---
 python/mxnet/ndarray/numpy/_op.py             | 1175 +++++++++++++++-
 python/mxnet/numpy/multiarray.py              | 1194 ++++++++++++++++-
 python/mxnet/symbol/numpy/_symbol.py          |  970 ++++++++++++-
 .../numpy/np_elemwise_unary_op_basic.cc       |  366 +++++
 .../numpy/np_elemwise_unary_op_basic.cu       |  110 ++
 tests/python/unittest/test_numpy_op.py        |  149 ++
 6 files changed, 3958 insertions(+), 6 deletions(-)
 create mode 100644 src/operator/numpy/np_elemwise_unary_op_basic.cc
 create mode 100644 src/operator/numpy/np_elemwise_unary_op_basic.cu

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index a129f738c566..04b3b19bcf2e 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -16,6 +16,7 @@
 # specific language governing permissions and limitations
 # under the License.
 
+# pylint: disable=unused-argument
 """Namespace for numpy operators used in Gluon dispatched by F=ndarray."""
 
 from __future__ import absolute_import
@@ -26,7 +27,11 @@
 from . import _internal as _npi
 from ..ndarray import NDArray
 
-__all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power', 'tensordot',
+__all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power', 'sin',
+           'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs', 'absolute',
+           'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2', 'log1p',
+           'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
+           'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack']
 
 
@@ -567,6 +572,1174 @@ def _unary_func_helper(x, fn_array, fn_scalar, out=None, **kwargs):
         raise TypeError('type {} not supported'.format(str(type(x))))
 
 
+@set_module('mxnet.ndarray.numpy')
+def sin(x, out=None, **kwargs):
+    r"""Trigonometric sine, element-wise.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Angle, in radians (:math:`2 \pi` rad equals 360 degrees).
+    out : ndarray or None
+        A location into which the result is stored. If provided, it
+        must have a shape that the inputs broadcast to. If not provided
+        or None, a freshly-allocated array is returned. The dtype of the
+        output is the same as that of the input if the input is an ndarray.
+    Returns
+    -------
+    y : ndarray or scalar
+        The sine of each element of x. This is a scalar if `x` is a scalar.
+    Notes
+    ----
+    This function only supports input type of float.
+    """
+    return _unary_func_helper(x, _npi.sin, _np.sin, out=out, **kwargs)
+
+@set_module('mxnet.ndarray.numpy')
+def cos(x, out=None, **kwargs):
+    r"""Cosine, element-wise.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Angle, in radians (:math:`2 \pi` rad equals 360 degrees).
+    out : ndarray or None
+        A location into which the result is stored. If provided, it
+        must have a shape that the inputs broadcast to. If not provided
+        or None, a freshly-allocated array is returned. The dtype of the
+        output is the same as that of the input if the input is an ndarray.
+    Returns
+    -------
+    y : ndarray or scalar
+        The corresponding cosine values. This is a scalar if x is a scalar.
+    Notes
+    ----
+    This function only supports input type of float.
+    """
+    return _unary_func_helper(x, _npi.cos, _np.cos, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+def sinh(x, out=None, **kwargs):
+    """Hyperbolic sine, element-wise.
+    Equivalent to ``1/2 * (np.exp(x) - np.exp(-x))`` or ``-1j * np.sin(1j*x)``.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input array or scalar.
+    out : ndarray or None
+        A location into which the result is stored. If provided, it
+        must have a shape that the inputs broadcast to. If not provided
+        or None, a freshly-allocated array is returned. The dtype of the
+        output is the same as that of the input if the input is an ndarray.
+    Returns
+    -------
+    y : ndarray or scalar
+        The corresponding hyperbolic sine values. This is a scalar if `x` is a scalar.
+    Notes
+    ----
+    This function only supports input type of float.
+    """
+    return _unary_func_helper(x, _npi.sinh, _np.sinh, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+def cosh(x, out=None, **kwargs):
+    """Hyperbolic cosine, element-wise.
+    Equivalent to ``1/2 * (np.exp(x) + np.exp(-x))`` and ``np.cos(1j*x)``.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input array or scalar.
+    out : ndarray or None
+        A location into which the result is stored. If provided, it
+        must have a shape that the inputs broadcast to. If not provided
+        or None, a freshly-allocated array is returned. The dtype of the
+        output is the same as that of the input if the input is an ndarray.
+    Returns
+    -------
+    y : ndarray or scalar
+        The corresponding hyperbolic cosine values. This is a scalar if `x` is a scalar.
+    Notes
+    ----
+    This function only supports input type of float.
+    """
+    return _unary_func_helper(x, _npi.cosh, _np.cosh, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+def tanh(x, out=None, **kwargs):
+    """
+    Compute hyperbolic tangent element-wise.
+    Equivalent to ``np.sinh(x)/np.cosh(x)``.
+    Parameters
+    ----------
+    x : ndarray or scalar.
+        Input array.
+    out : ndarray or None
+        A location into which the result is stored. If provided, it
+        must have a shape that the inputs fill into. If not provided
+        or None, a freshly-allocated array is returned. The dtype of the
+        output and input must be the same.
+    Returns
+    -------
+    y : ndarray or scalar
+       The corresponding hyperbolic tangent values.
+    Notes
+    -----
+    If `out` is provided, the function writes the result into it,
+    and returns a reference to `out`.  (See Examples)
+    - input x does not support complex computation (like imaginary number)
+    >>> np.tanh(np.pi*1j)
+    TypeError: type <type 'complex'> not supported
+    Examples
+    --------
+    >>> np.tanh(np.array[0, np.pi]))
+    array([0.       , 0.9962721])
+    >>> np.tanh(np.pi)
+    0.99627207622075
+    >>> # Example of providing the optional output parameter illustrating
+    >>> # that what is returned is a reference to said parameter
+    >>> out1 = np.array(1)
+    >>> out2 = np.tanh(np.array(0.1), out1)
+    >>> out2 is out1
+    True
+    >>> # Example of ValueError due to provision of shape mis-matched `out`
+    >>> np.tanh(np.zeros((3,3)),np.zeros((2,2)))
+    mxnet.base.MXNetError:
+    [07:17:36] ../src/ndarray/./../operator/tensor/../elemwise_op_common.h:135:
+    Check failed: assign(&dattr, vec.at(i)): Incompatible attr in node
+    at 0-th output: expected [3,3], got [2,2]
+    """
+    return _unary_func_helper(x, _npi.tanh, _np.tanh, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+def log10(x, out=None, **kwargs):
+    """Return the base 10 logarithm of the input array, element-wise.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input array or scalar.
+    out : ndarray or None
+        A location into which t'absolute', he result is stored. If provided, it
+        must have a shape that the inputs broadcast to. If not provided
+        or None, a freshly-allocated array is returned. The dtype of the
+        output is the same as that of the input if the input is an ndarray.
+    Returns
+    -------
+    y : ndarray or scalar
+        The logarithm to the base 10 of `x`, element-wise. NaNs are
+        returned where x is negative. This is a scalar if `x` is a scalar.
+    Notes
+    ----
+    This function only supports input type of float.
+    """
+    return _unary_func_helper(x, _npi.log10, _np.log10, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+def sqrt(x, out=None, **kwargs):
+    """
+    Return the non-negative square-root of an array, element-wise.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        The values whose square-roots are required.
+    out : ndarray, or None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned.
+    Returns
+    -------
+    y : ndarray or scalar
+        An array of the same shape as `x`, containing the positive
+        square-root of each element in `x`. This is a scalar if `x` is a scalar.
+    Notes
+    ----
+    This function only supports input type of float.
+    """
+    return _unary_func_helper(x, _npi.sqrt, _np.sqrt, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+def cbrt(x, out=None, **kwargs):
+    r"""
+    Return the cube-root of an array, element-wise.
+    Parameters
+    ----------
+    x : ndarray
+        The values whose cube-roots are required.
+    out : ndarray, optional
+        A location into which the result is stored. If provided, it must have a shape that the
+        inputs broadcast to. If not provided or None, a freshly-allocated array is returned.
+        A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
+    Returns
+    ----------
+    y : ndarray
+        An array of the same shape as x, containing the cube cube-root of each element in x.
+        If out was provided, y is a reference to it. This is a scalar if x is a scalar.
+    Examples
+    ----------
+    >>> np.cbrt([1,8,27])
+    array([ 1.,  2.,  3.])
+    """
+    return _unary_func_helper(x, _npi.cbrt, _np.cbrt, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+def abs(x, out=None, **kwargs):
+    r"""abs(x, out=None, **kwargs)
+    Calculate the absolute value element-wise.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input array.
+    out : ndarray or None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned.
+    Returns
+    -------
+    absolute : ndarray
+        An ndarray containing the absolute value of
+        each element in `x`. This is a scalar if `x` is a scalar.
+    Examples
+    --------
+    >>> x = np.array([-1.2, 1.2])
+    >>> np.abs(x)
+    array([1.2, 1.2])
+    """
+    return _unary_func_helper(x, _npi.abs, _np.abs, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+def absolute(x, out=None, **kwargs):
+    r"""
+    Calculate the absolute value element-wise.
+    np.abs is a shorthand for this function.
+    Parameters
+    ----------
+    x : ndarray
+        Input array.
+    out : ndarray, optional
+        A location into which the result is stored. If provided, it must have a shape
+        that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned.
+        A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
+    Returns
+    ----------
+    absolute : ndarray
+        An ndarray containing the absolute value of each element in x.
+    Examples
+    ----------
+    >>> x = np.array([-1.2, 1.2])
+    >>> np.absolute(x)
+    array([ 1.2,  1.2])
+    """
+    return _unary_func_helper(x, _npi.absolute, _np.absolute, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+def sign(x, out=None, **kwargs):
+    r"""
+    sign(x, out=None)
+    Returns an element-wise indication of the sign of a number.
+    The `sign` function returns ``-1 if x < 0, 0 if x==0, 1 if x > 0``. Only supports real number.
+    Parameters
+    ----------
+    x : ndarray or a scalar
+        Input values.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+        If provided, it must have the same shape and dtype as input ndarray.
+        If not provided or `None`, a freshly-allocated array is returned.
+    Returns
+    -------
+    y : ndarray
+        The sign of `x`.
+        This is a scalar if `x` is a scalar.
+    Note
+    -------
+    - Only supports real number as input elements.
+    - Input type does not support Python native iterables(list, tuple, ...).
+    - ``out`` param: cannot perform auto broadcasting. ``out`` ndarray's shape must be the same as the expected output.
+    - ``out`` param: cannot perform auto type cast. ``out`` ndarray's dtype must be the same as the expected output.
+    - ``out`` param does not support scalar input case.
+    Examples
+    --------
+    >>> a = np.array([-5., 4.5])
+    >>> np.sign(a)
+    array([-1.,  1.])
+    Scalars as input:
+    >>> np.sign(4.0)
+    1.0
+    >>> np.sign(0)
+    0
+    Use ``out`` parameter:
+    >>> b = np.zeros((2, ))
+    >>> np.sign(a, out=b)
+    array([-1.,  1.])
+    >>> b
+    array([-1.,  1.])
+    """
+    return _unary_func_helper(x, _npi.sign, _np.sign, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+def exp(x, out=None, **kwargs):
+    r"""exp(x, out=None, **kwargs)
+    Calculate the exponential of all elements in the input array.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input values.
+    out : ndarray or None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned.
+    Returns
+    -------
+    out : ndarray or scalar
+        Output array, element-wise exponential of `x`.
+        This is a scalar if `x` is a scalar.
+    Examples
+    --------
+    >>> np.exp(1)
+    2.718281828459045
+    >>> x = np.array([-1, 1, -2, 2])
+    >>> np.exp(x)
+    array([0.36787945, 2.7182817 , 0.13533528, 7.389056  ])
+    """
+    return _unary_func_helper(x, _npi.exp, _np.exp, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+def expm1(x, out=None, **kwargs):
+    r"""expm1(x, out=None, **kwargs)
+    Calculate `exp(x) - 1` of all elements in the input array.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input values.
+    out : ndarray or None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned.
+    Returns
+    -------
+    out : ndarray or scalar
+        Output array, element-wise exponential minus one: `out = exp(x) - 1`.
+        This is a scalar if `x` is a scalar.
+    Examples
+    --------
+    >>> np.expm1(1)
+    1.718281828459045
+    >>> x = np.array([-1, 1, -2, 2])
+    >>> np.expm1(x)
+    array([-0.63212056,  1.71828183, -0.86466472,  6.3890561])
+    """
+    return _unary_func_helper(x, _npi.expm1, _np.expm1, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+def arcsin(x, out=None, **kwargs):
+    r"""
+    arcsin(x, out=None)
+    Inverse sine, element-wise.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        `y`-coordinate on the unit circle.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+        If provided, it must have the same shape as the input.
+        If not provided or None, a freshly-allocated array is returned.
+    Returns
+    -------
+    angle : ndarray or scalar
+        Output array is same shape and type as x. This is a scalar if x is a scalar.
+        The inverse sine of each element in `x`, in radians and in the
+        closed interval ``[-pi/2, pi/2]``.
+    Examples
+    --------
+    >>> np.arcsin(1)     # pi/2
+    1.5707963267948966
+    >>> np.arcsin(-1)    # -pi/2
+    -1.5707963267948966
+    >>> np.arcsin(0)
+    0.0
+    Notes
+    -----
+    `arcsin` is a multivalued function: for each `x` there are infinitely
+    many numbers `z` such that :math:`sin(z) = x`.  The convention is to
+    return the angle `z` whose real part lies in [-pi/2, pi/2].
+    For real-valued input data types, *arcsin* always returns real output.
+    For each value that cannot be expressed as a real number or infinity,
+    it yields ``nan`` and sets the `invalid` floating point error flag.
+    The inverse sine is also known as `asin` or sin^{-1}.
+    The output `ndarray` has the same `ctx` as the input `ndarray`.
+    This function differs from the original `numpy.arcsin
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.arcsin.html>`_ in
+    the following aspects:
+    - Only support ndarray or scalar now.
+    - `where` argument is not supported.
+    - Complex input is not supported.
+    References
+    ----------
+    Abramowitz, M. and Stegun, I. A., *Handbook of Mathematical Functions*,
+    10th printing, New York: Dover, 1964, pp. 79ff.
+    http://www.math.sfu.ca/~cbm/aands/
+    """
+    return _unary_func_helper(x, _npi.arcsin, _np.arcsin, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+def arccos(x, out=None, **kwargs):
+    r"""
+    Trigonometric inverse cosine, element-wise.
+    The inverse of cos so that, if y = cos(x), then x = arccos(y).
+    Parameters
+    ----------
+    x : ndarray
+        x-coordinate on the unit circle. For real arguments, the domain is [-1, 1].
+    out : ndarray, optional
+        A location into which the result is stored. If provided, it must have a shape that
+        the inputs broadcast to. If not provided or None, a freshly-allocated array is returned.
+        A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
+    Returns
+    ----------
+    angle : ndarray
+        The angle of the ray intersecting the unit circle at the given x-coordinate in radians [0, pi].
+        This is a scalar if x is a scalar.
+    See also
+    ----------
+    cos, arctan, arcsin
+    Notes
+    ----------
+    arccos is a multivalued function: for each x there are infinitely many numbers z such that
+    cos(z) = x. The convention is to return the angle z whose real part lies in [0, pi].
+    For real-valued input data types, arccos always returns real output.
+    For each value that cannot be expressed as a real number or infinity, it yields nan and sets
+    the invalid floating point error flag.
+    The inverse cos is also known as acos or cos^-1.
+    Examples
+    ----------
+    We expect the arccos of 1 to be 0, and of -1 to be pi:
+    >>> np.arccos([1, -1])
+    array([ 0.        ,  3.14159265])
+    """
+    return _unary_func_helper(x, _npi.arccos, _np.arccos, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+def arctan(x, out=None, **kwargs):
+    r"""arctan(x, out=None, **kwargs)
+    Trigonometric inverse tangent, element-wise.
+    The inverse of tan, so that if ``y = tan(x)`` then ``x = arctan(y)``.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input values.
+    out : ndarray or None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned.
+    Returns
+    -------
+    out : ndarray or scalar
+        Out has the same shape as `x`. It lies is in
+        ``[-pi/2, pi/2]`` (``arctan(+/-inf)`` returns ``+/-pi/2``).
+        This is a scalar if `x` is a scalar.
+    Notes
+    -----
+    `arctan` is a multi-valued function: for each `x` there are infinitely
+    many numbers `z` such that tan(`z`) = `x`.  The convention is to return
+    the angle `z` whose real part lies in [-pi/2, pi/2].
+    For real-valued input data types, `arctan` always returns real output.
+    For each value that cannot be expressed as a real number or infinity,
+    it yields ``nan`` and sets the `invalid` floating point error flag.
+    For complex-valued input, we do not have support for them yet.
+    The inverse tangent is also known as `atan` or tan^{-1}.
+    Examples
+    --------
+    We expect the arctan of 0 to be 0, and of 1 to be pi/4:
+    >>> x = np.array([0, 1])
+    >>> np.arctan(x)
+    array([0.       , 0.7853982])
+    >>> np.pi/4
+    0.7853981633974483
+    """
+    return _unary_func_helper(x, _npi.arctan, _np.arctan, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+def log(x, out=None, **kwargs):
+    """
+    log(x, out=None)
+    Natural logarithm, element-wise.
+    The natural logarithm `log` is the inverse of the exponential function,
+    so that `log(exp(x)) = x`. The natural logarithm is logarithm in base
+    `e`.
+    Parameters
+    ----------
+    x : ndarray
+        Input value. Elements must be of real value.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+        If provided, it must have the same shape and dtype as input ndarray.
+        If not provided or `None`, a freshly-allocated array is returned.
+    Returns
+    -------
+    y : ndarray
+        The natural logarithm of `x`, element-wise.
+        This is a scalar if `x` is a scalar.
+    Notes
+    -----
+     Currently only supports data of real values and ``inf`` as input. Returns data of real value, ``inf``, ``-inf`` and
+    ``nan`` according to the input.
+    This function differs from the original `numpy.log
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.log.html>`_ in
+    the following aspects:
+    - Does not support complex number for now
+    - Input type does not support Python native iterables(list, tuple, ...).
+    - ``out`` param: cannot perform auto broadcasting. ``out`` ndarray's shape must be the same as the expected output.
+    - ``out`` param: cannot perform auto type cast. ``out`` ndarray's dtype must be the same as the expected output.
+    - ``out`` param does not support scalar input case.
+    Examples
+    --------
+    >>> a = np.array([1, np.exp(1), np.exp(2), 0], dtype=np.float64)
+    >>> np.log(a)
+    array([  0.,   1.,   2., -inf], dtype=float64)
+    Due to internal calculation mechanism, using default float32 dtype may cause some special behavior:
+    >>> a = np.array([1, np.exp(1), np.exp(2), 0], dtype=np.float32)
+    >>> np.log(a)
+    array([  0.,  0.99999994,   2., -inf])
+    Scalar calculation:
+    >>> np.log(1)
+    0.0
+    """
+    return _unary_func_helper(x, _npi.log, _np.log, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+def degrees(x, out=None, **kwargs):
+    """
+    degrees(x, out=None)
+    Convert angles from radians to degrees.
+    Parameters
+    ----------
+    x : ndarray
+        Input value. Elements must be of real value.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+        If provided, it must have the same shape and dtype as input ndarray.
+        If not provided or `None`, a freshly-allocated array is returned.
+    Returns
+    -------
+    y : ndarray
+        The corresponding degree values; if `out` was supplied this is a
+        reference to it.
+        This is a scalar if `x` is a scalar.
+    Notes
+    -------
+    This function differs from the original `numpy.degrees
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.degrees.html>`_ in
+    the following aspects:
+    - Input type does not support Python native iterables(list, tuple, ...). Only ndarray is supported.
+    - ``out`` param: cannot perform auto broadcasting. ``out`` ndarray's shape must be the same as the expected output.
+    - ``out`` param: cannot perform auto type cast. ``out`` ndarray's dtype must be the same as the expected output.
+    - ``out`` param does not support scalar input case.
+    Examples
+    --------
+    Convert a radian array to degrees
+    >>> rad = np.arange(12.) * np.pi / 6
+    >>> np.degrees(rad)
+    array([  0.,  30.,  60.,  90., 120., 150., 180., 210., 240., 270., 300., 330.])
+    Use specified ``out`` ndarray:
+    >>> out = np.zeros((rad.shape))
+    >>> np.degrees(rad, out)
+    array([  0.,  30.,  60.,  90., 120., 150., 180., 210., 240., 270., 300., 330.])
+    >>> out
+    array([  0.,  30.,  60.,  90., 120., 150., 180., 210., 240., 270., 300., 330.])
+    """
+    return _unary_func_helper(x, _npi.degrees, _np.degrees, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+def rint(x, out=None, **kwargs):
+    """
+    Round elements of the array to the nearest integer.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input array.
+    out : ndarray or None
+        A location into which the result is stored.
+        If provided, it must have the same shape and type as the input.
+        If not provided or None, a freshly-allocated array is returned.
+    Returns
+    -------
+    out : ndarray or scalar
+        Output array is same shape and type as x. This is a scalar if x is a scalar.
+    Notes
+    -----
+    This function differs from the original `numpy.rint
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.rint.html>`_ in
+    the following way(s):
+    - only ndarray or scalar is accpted as valid input, tuple of ndarray is not supported
+    - broadcasting to `out` of different shape is currently not supported
+    - when input is plain python numerics, the result will not be stored in the `out` param
+    Examples
+    --------
+    >>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
+    >>> np.rint(a)
+    array([-2., -2., -0.,  0.,  1.,  2.,  2.])
+    """
+    return _unary_func_helper(x, _npi.rint, _np.rint, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+def log2(x, out=None, **kwargs):
+    """
+    Base-2 logarithm of x.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input values.
+    out : ndarray or None
+        A location into which the result is stored.
+        If provided, it must have the same shape and type as the input.
+        If not provided or None, a freshly-allocated array is returned.
+    Returns
+    -------
+    y : ndarray
+        The logarithm base two of `x`, element-wise.
+        This is a scalar if `x` is a scalar.
+    Notes
+    -----
+    This function differs from the original `numpy.log2
+    <https://www.google.com/search?q=numpy+log2>`_ in
+    the following way(s):
+    - only ndarray or scalar is accpted as valid input, tuple of ndarray is not supported
+    - broadcasting to `out` of different shape is currently not supported
+    - when input is plain python numerics, the result will not be stored in the `out` param
+    Examples
+    --------
+    >>> x = np.array([0, 1, 2, 2**4])
+    >>> np.log2(x)
+    array([-inf,   0.,   1.,   4.])
+    """
+    return _unary_func_helper(x, _npi.log2, _np.log2, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+def log1p(x, out=None, **kwargs):
+    """
+    Return the natural logarithm of one plus the input array, element-wise.
+    Calculates ``log(1 + x)``.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input array.
+    out : ndarray or None
+        A location into which the result is stored. If provided, it
+        must have a shape that the inputs fill into. If not provided
+        or None, a freshly-allocated array is returned. The dtype of the
+        output and input must be the same.
+    Returns
+    -------
+    y : ndarray or scalar
+        Natural logarithm of 1 + x, element-wise. This is a scalar
+        if x is a scalar.
+    Notes
+    -----
+    For real-valued input, `log1p` is accurate also for `x` so small
+    that `1 + x == 1` in floating-point accuracy.
+    Logarithm is a multivalued function: for each `x` there is an infinite
+    number of `z` such that `exp(z) = 1 + x`. The convention is to return
+    the `z` whose imaginary part lies in `[-pi, pi]`.
+    For real-valued input data types, `log1p` always returns real output.
+    For each value that cannot be expressed as a real number or infinity,
+    it yields ``nan`` and sets the `invalid` floating point error flag.
+    cannot support complex-valued input.
+    Examples
+    --------
+    >>> np.log1p(1e-99)
+    1e-99
+    >>> a = np.array([3, 4, 5])
+    >>> np.log1p(a)
+    array([1.3862944, 1.609438 , 1.7917595])
+    """
+    return _unary_func_helper(x, _npi.log1p, _np.log1p, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+def radians(x, out=None, **kwargs):
+    """
+    Convert angles from degrees to radians.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input array in degrees.
+    out : ndarray or None
+        A location into which the result is stored.
+        If provided, it must have the same shape and type as the input.
+        If not provided or None, a freshly-allocated array is returned.
+    Returns
+    -------
+    y : ndarray
+        The corresponding radian values. This is a scalar if x is a scalar.
+    Notes
+    -----
+    This function differs from the original `numpy.radians
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.radians.html>`_ in
+    the following way(s):
+    - only ndarray or scalar is accpted as valid input, tuple of ndarray is not supported
+    - broadcasting to `out` of different shape is currently not supported
+    - when input is plain python numerics, the result will not be stored in the `out` param
+    Examples
+    --------
+    >>> deg = np.arange(12.) * 30.
+    >>> np.radians(deg)
+    array([0.       , 0.5235988, 1.0471976, 1.5707964, 2.0943952, 2.6179938,
+           3.1415927, 3.6651914, 4.1887903, 4.712389 , 5.2359877, 5.7595863],
+           dtype=float32)
+    """
+    return _unary_func_helper(x, _npi.radians, _np.radians, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+def reciprocal(x, out=None, **kwargs):
+    r"""
+    reciprocal(x, out=None)
+    Return the reciprocal of the argument, element-wise.
+    Calculates ``1/x``.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        The values whose reciprocals are required.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+        If provided, it must have the same shape as the input.
+        If not provided or None, a freshly-allocated array is returned.
+    Returns
+    -------
+    y : ndarray or scalar
+        Output array is same shape and type as x. This is a scalar if x is a scalar.
+    Examples
+    --------
+    >>> np.reciprocal(2.)
+    0.5
+    >>> x = np.array([1, 2., 3.33])
+    >>> np.reciprocal(x)
+    array([1.       , 0.5      , 0.3003003])
+    Notes
+    -----
+    .. note::
+        This function is not designed to work with integers.
+    For integer arguments with absolute value larger than 1 the result is
+    always zero because of the way Python handles integer division.  For
+    integer zero the result is an overflow.
+    The output `ndarray` has the same `ctx` as the input `ndarray`.
+    This function differs from the original `numpy.reciprocal
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.reciprocal.html>`_ in
+    the following aspects:
+    - Only support ndarray and scalar now.
+    - `where` argument is not supported.
+    """
+    return _unary_func_helper(x, _npi.reciprocal, _np.reciprocal, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+def square(x, out=None, **kwargs):
+    r"""
+    square(x, out=None)
+    Return the element-wise square of the input.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        The values whose squares are required.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+        If provided, it must have the same shape as the input.
+        If not provided or None, a freshly-allocated array is returned.
+    Returns
+    -------
+    y : ndarray or scalar
+        Output array is same shape and type as x. This is a scalar if x is a scalar.
+    Examples
+    --------
+    >>> np.square(2.)
+    4.0
+    >>> x = np.array([1, 2., -1])
+    >>> np.square(x)
+    array([1., 4., 1.])
+    Notes
+    -----
+    The output `ndarray` has the same `ctx` as the input `ndarray`.
+    This function differs from the original `numpy.square
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.square.html>`_ in
+    the following aspects:
+    - Only support ndarray and scalar now.
+    - `where` argument is not supported.
+    - Complex input is not supported.
+    """
+    return _unary_func_helper(x, _npi.square, _np.square, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+def negative(x, out=None, where=True, **kwargs):
+    r"""
+    negative(x, out=None, where=True)
+    Numerical negative, element-wise.
+    Parameters:
+    ------------
+    x : ndarray or scalar
+        Input array.
+    out : ndarray, None, or tuple of ndarray and None, optional
+          A location into which the result is stored.
+    where : ndarray, optional
+            Values of True indicate to calculate the ufunc at that position,
+            values of False indicate to leave the value in the output alone.
+    Returns:
+    ---------
+    y : ndarray or scalar
+        Returned array or scalar: y = -x. This is a scalar if x is a scalar.
+    Examples:
+    ---------
+    >>> np.negative(1)
+    -1
+    """
+    return _unary_func_helper(x, _npi.negative, _np.negative, out=out)
+
+
+@set_module('mxnet.ndarray.numpy')
+def fix(x, out=None):
+    r"""
+    Round an array of floats element-wise to nearest integer towards zero.
+    The rounded values are returned as floats.
+
+    Parameters:
+    ----------
+    x : ndarray
+        An array of floats to be rounded
+    out : ndarray, optional
+        Output array
+    Returns:
+    -------
+    y : ndarray of floats
+    Examples
+    ---------
+    >>> np.fix(3.14)
+    3
+    """
+    return _unary_func_helper(x, _npi.fix, _np.fix, out=out)
+
+
+@set_module('mxnet.ndarray.numpy')
+def tan(x, out=None, where=True, **kwargs):
+    r"""
+    tan(x, out=None, where=True)
+    Compute tangent element-wise.
+    Equivalent to np.sin(x)/np.cos(x) element-wise.
+
+    Parameters:
+    ----------
+    x : array_like
+        Input array.
+    out : ndarray, None, or tuple of ndarray and None, optional
+          A location into which the result is stored. If provided,
+          it must have a shape that the inputs broadcast to. If not provided or None,
+          a freshly-allocated array is returned. A tuple (possible only as a keyword argument)
+          must have length equal to the number of outputs.
+    where : ndarray, optional
+            Values of True indicate to calculate the ufunc at that position,
+            values of False indicate to leave the value in the output alone.
+    Returns:
+    -------
+    y : ndarray
+    The corresponding tangent values. This is a scalar if x is a scalar.
+    Examples:
+    >>> np.tan(0.5)
+    0.5463024898437905
+    """
+
+    return _unary_func_helper(x, _npi.tan, _np.tan, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+def ceil(x, out=None, **kwargs):
+    r"""
+    Return the ceiling of the input, element-wise.
+    The ceil of the ndarray `x` is the smallest integer `i`, such that
+    `i >= x`.  It is often denoted as :math:`\lceil x \rceil`.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input array.
+    out : ndarray or None
+        A location into which the result is stored. If provided, it
+        must have a same shape that the inputs fill into. If not provided
+        or None, a freshly-allocated array is returned. The dtype of the
+        output and input must be the same.
+    Returns
+    -------
+    y : ndarray or scalar
+        The ceiling of each element in `x`, with `float` dtype.
+        This is a scalar if `x` is a scalar.
+    Examples
+    --------
+    >>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
+    >>> np.ceil(a)
+    array([-1., -1., -0.,  1.,  2.,  2.,  2.])
+    >>> #if you use parameter out, x and out must be ndarray. if not, you will get an error!
+    >>> a = np.array(1)
+    >>> np.ceil(np.array(3.5), a)
+    array(4.)
+    >>> a
+    array(4.)
+    """
+    return _unary_func_helper(x, _npi.ceil, _np.ceil, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+def floor(x, out=None, **kwargs):
+    r"""
+    Return the floor of the input, element-wise.
+    The floor of the ndarray `x` is the largest integer `i`, such that
+    `i <= x`.  It is often denoted as :math:`\lfloor x \rfloor`.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input array.
+    out : ndarray or None
+        A location into which the result is stored. If provided, it
+        must have a same shape that the inputs fill into. If not provided
+        or None, a freshly-allocated array is returned. The dtype of the
+        output and input must be the same.
+    Returns
+    -------
+    y : ndarray or scalar
+        The floor of each element in `x`, with `float` dtype.
+        This is a scalar if `x` is a scalar.
+    Examples
+    --------
+    >>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
+    >>> np.floor(a)
+    array([-2., -2., -1.,  0.,  1.,  1.,  2.])
+    >>> #if you use parameter out, x and out must be ndarray. if not, you will get an error!
+    >>> a = np.array(1)
+    >>> np.floor(np.array(3.5), a)
+    array(3.)
+    >>> a
+    array(3.)
+    """
+    return _unary_func_helper(x, _npi.floor, _np.floor, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+def trunc(x, out=None, **kwargs):
+    r"""
+    trunc(x, out=None)
+    Return the truncated value of the input, element-wise.
+    The truncated value of the scalar `x` is the nearest integer `i` which
+    is closer to zero than `x` is. In short, the fractional part of the
+    signed number `x` is discarded.
+
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input data.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+
+    Returns
+    -------
+    y : ndarray or scalar
+        The truncated value of each element in `x`.
+        This is a scalar if `x` is a scalar.
+    Notes
+    -----
+    This function differs from the original numpy.trunc in the following aspects:
+        - Do not support `where`, a parameter in numpy which indicates where to calculate.
+        - Cannot cast type automatically. Dtype of `out` must be same as the expected one.
+        - Cannot broadcast automatically. Shape of `out` must be same as the expected one.
+        - If `x` is plain python numeric, the result won't be stored in out.
+
+    Examples
+    --------
+    >>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
+    >>> np.trunc(a)
+    array([-1., -1., -0.,  0.,  1.,  1.,  2.])
+    """
+    return _unary_func_helper(x, _npi.trunc, _np.trunc, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+def logical_not(x, out=None, **kwargs):
+    r"""
+    logical_not(x, out=None)
+    Compute the truth value of NOT x element-wise.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Logical NOT is applied to the elements of `x`.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+
+    Returns
+    -------
+    y : bool or ndarray of bool
+        Boolean result with the same shape as `x` of the NOT operation
+        on elements of `x`.
+        This is a scalar if `x` is a scalar.
+    Notes
+    -----
+    This function differs from the original numpy.logical_not in the following aspects:
+        - Do not support `where`, a parameter in numpy which indicates where to calculate.
+        - Cannot cast type automatically. Dtype of `out` must be same as the expected one.
+        - Cannot broadcast automatically. Shape of `out` must be same as the expected one.
+        - If `x` is plain python numeric, the result won't be stored in out.
+    Examples
+    --------
+    >>> x= np.array([True, False, 0, 1])
+    >>> np.logical_not(x)
+    array([0., 1., 1., 0.])
+
+    >>> x = np.arange(5)
+    >>> np.logical_not(x<3)
+    array([0., 0., 0., 1., 1.])
+    """
+    return _unary_func_helper(x, _npi.logical_not, _np.logical_not, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+def arcsinh(x, out=None, **kwargs):
+    r"""
+    arcsinh(x, out=None)
+    Inverse hyperbolic sine, element-wise.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input array.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+
+    Returns
+    -------
+    arcsinh : ndarray
+        Array of the same shape as `x`.
+        This is a scalar if `x` is a scalar.
+    Notes
+    -----
+    `arcsinh` is a multivalued function: for each `x` there are infinitely
+    many numbers `z` such that `sinh(z) = x`.
+
+    For real-valued input data types, `arcsinh` always returns real output.
+    For each value that cannot be expressed as a real number or infinity, it
+    yields ``nan`` and sets the `invalid` floating point error flag.
+
+    This function differs from the original numpy.arcsinh in the following aspects:
+        - Do not support `where`, a parameter in numpy which indicates where to calculate.
+        - Do not support complex-valued input.
+        - Cannot cast type automatically. DType of `out` must be same as the expected one.
+        - Cannot broadcast automatically. Shape of `out` must be same as the expected one.
+        - If `x` is plain python numeric, the result won't be stored in out.
+    Examples
+    --------
+    >>> a = np.array([3.2, 5.0])
+    >>> np.arcsinh(a)
+    array([1.8309381, 2.2924316])
+    >>> np.arcsinh(1)
+    0.0
+    """
+    return _unary_func_helper(x, _npi.arcsinh, _np.arcsinh, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+def arccosh(x, out=None, **kwargs):
+    r"""
+    arccosh(x, out=None)
+    Inverse hyperbolic cosine, element-wise.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input array.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+
+    Returns
+    -------
+    arccosh : ndarray
+        Array of the same shape as `x`.
+        This is a scalar if `x` is a scalar.
+    Notes
+    -----
+    `arccosh` is a multivalued function: for each `x` there are infinitely
+    many numbers `z` such that `cosh(z) = x`.
+
+    For real-valued input data types, `arccosh` always returns real output.
+    For each value that cannot be expressed as a real number or infinity, it
+    yields ``nan`` and sets the `invalid` floating point error flag.
+
+    This function differs from the original numpy.arccosh in the following aspects:
+        - Do not support `where`, a parameter in numpy which indicates where to calculate.
+        - Do not support complex-valued input.
+        - Cannot cast type automatically. Dtype of `out` must be same as the expected one.
+        - Cannot broadcast automatically. Shape of `out` must be same as the expected one.
+        - If `x` is plain python numeric, the result won't be stored in out.
+    Examples
+    --------
+    >>> a = np.array([3.2, 5.0])
+    >>> np.arccosh(a)
+    array([1.8309381, 2.2924316])
+    >>> np.arccosh(1)
+    0.0
+    """
+    return _unary_func_helper(x, _npi.arccosh, _np.arccosh, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+def arctanh(x, out=None, **kwargs):
+    r"""
+    arctanh(x, out=None)
+    Inverse hyperbolic tangent, element-wise.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input array.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+
+    Returns
+    -------
+    arctanh : ndarray
+        Array of the same shape as `x`.
+        This is a scalar if `x` is a scalar.
+    Notes
+    -----
+    `arctanh` is a multivalued function: for each `x` there are infinitely
+    many numbers `z` such that `tanh(z) = x`.
+
+    For real-valued input data types, `arctanh` always returns real output.
+    For each value that cannot be expressed as a real number or infinity, it
+    yields ``nan`` and sets the `invalid` floating point error flag.
+
+    This function differs from the original numpy.arctanh in the following aspects:
+        - Do not support `where`, a parameter in numpy which indicates where to calculate.
+        - Do not support complex-valued input.
+        - Cannot cast type automatically. Dtype of `out` must be same as the expected one.
+        - Cannot broadcast automatically. Shape of `out` must be same as the expected one.
+        - If `x` is plain python numeric, the result won't be stored in out.
+    Examples
+    --------
+    >>> a = np.array([0.0, -0.5])
+    >>> np.arctanh(a)
+    array([0., -0.54930615])
+    >>> np.arctanh(0.0)
+    0.0
+    """
+    return _unary_func_helper(x, _npi.arctanh, _np.arctanh, out=out, **kwargs)
+
+
 @set_module('mxnet.ndarray.numpy')
 def tile(A, reps):
     r"""
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 316d88ae0a81..a47a9c01b7c4 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -17,7 +17,7 @@
 # specific language governing permissions and limitations
 # under the License.
 
-# pylint: disable=too-many-lines
+# pylint: disable=too-many-lines, unused-argument
 """numpy ndarray and util functions."""
 
 from __future__ import absolute_import
@@ -44,8 +44,12 @@
 from ..ndarray.numpy import _internal as _npi
 
 __all__ = ['ndarray', 'empty', 'array', 'zeros', 'ones', 'add', 'subtract', 'multiply', 'divide',
-           'mod', 'power', 'tensordot', 'linspace', 'expand_dims', 'tile', 'arange', 'split',
-           'concatenate', 'stack']
+           'mod', 'power', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt',
+           'abs', 'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log',
+           'degrees', 'log2', 'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative',
+           'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh',
+           'tensordot', 'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate',
+           'stack']
 
 
 # This function is copied from ndarray.py since pylint
@@ -1550,6 +1554,1190 @@ def power(x1, x2, out=None):
     return _mx_nd_np.power(x1, x2, out=out)
 
 
+@set_module('mxnet.numpy')
+def sin(x, out=None, **kwargs):
+    r"""Trigonometric sine, element-wise.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Angle, in radians (:math:`2 \pi` rad equals 360 degrees).
+    out : ndarray or None
+        A location into which the result is stored. If provided, it
+        must have a shape that the inputs broadcast to. If not provided
+        or None, a freshly-allocated array is returned. The dtype of the
+        output is the same as that of the input if the input is an ndarray.
+    Returns
+    -------
+    y : ndarray or scalar
+        The sine of each element of x. This is a scalar if `x` is a scalar.
+    Notes
+    ----
+    This function only supports input type of float.
+    """
+    return _mx_nd_np.sin(x, out=out, **kwargs)
+
+
+@set_module('mxnet.numpy')
+def cos(x, out=None, **kwargs):
+    r"""Cosine, element-wise.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Angle, in radians (:math:`2 \pi` rad equals 360 degrees).
+    out : ndarray or None
+        A location into which the result is stored. If provided, it
+        must have a shape that the inputs broadcast to. If not provided
+        or None, a freshly-allocated array is returned. The dtype of the
+        output is the same as that of the input if the input is an ndarray.
+    Returns
+    -------
+    y : ndarray or scalar
+        The corresponding cosine values. This is a scalar if x is a scalar.
+    Notes
+    ----
+    This function only supports input type of float.
+    """
+    return _mx_nd_np.cos(x, out=out, **kwargs)
+
+
+def sinh(x, out=None, **kwargs):
+    """Hyperbolic sine, element-wise.
+    Equivalent to ``1/2 * (np.exp(x) - np.exp(-x))`` or ``-1j * np.sin(1j*x)``.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input array or scalar.
+    out : ndarray or None
+        A location into which the result is stored. If provided, it
+        must have a shape that the inputs broadcast to. If not provided
+        or None, a freshly-allocated array is returned. The dtype of the
+        output is the same as that of the input if the input is an ndarray.
+    Returns
+    -------
+    y : ndarray or scalar
+        The corresponding hyperbolic sine values. This is a scalar if `x` is a scalar.
+    Notes
+    ----
+    This function only supports input type of float.
+    """
+    return _mx_nd_np.sinh(x, out=out, **kwargs)
+
+
+@set_module('mxnet.numpy')
+def cosh(x, out=None, **kwargs):
+    """Hyperbolic cosine, element-wise.
+    Equivalent to ``1/2 * (np.exp(x) + np.exp(-x))`` and ``np.cos(1j*x)``.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input array or scalar.
+    out : ndarray or None
+        A location into which the result is stored. If provided, it
+        must have a shape that the inputs broadcast to. If not provided
+        or None, a freshly-allocated array is returned. The dtype of the
+        output is the same as that of the input if the input is an ndarray.
+    Returns
+    -------
+    y : ndarray or scalar
+        The corresponding hyperbolic cosine values. This is a scalar if `x` is a scalar.
+    Notes
+    ----
+    This function only supports input type of float.
+    """
+    return _mx_nd_np.cosh(x, out=out, **kwargs)
+
+
+@set_module('mxnet.numpy')
+def tanh(x, out=None, **kwargs):
+    """
+    Compute hyperbolic tangent element-wise.
+    Equivalent to ``np.sinh(x)/np.cosh(x)``.
+    Parameters
+    ----------
+    x : ndarray or scalar.
+        Input array.
+    out : ndarray or None
+        A location into which the result is stored. If provided, it
+        must have a shape that the inputs fill into. If not provided
+        or None, a freshly-allocated array is returned. The dtype of the
+        output and input must be the same.
+    Returns
+    ----------
+    y : ndarray or scalar
+       The corresponding hyperbolic tangent values.
+    Notes
+    -----
+    If `out` is provided, the function writes the result into it,
+    and returns a reference to `out`.  (See Examples)
+    - input x does not support complex computation (like imaginary number)
+    >>> np.tanh(np.pi*1j)
+    TypeError: type <type 'complex'> not supported
+    Examples
+    --------
+    >>> np.tanh(np.array[0, np.pi]))
+    array([0.       , 0.9962721])
+    >>> np.tanh(np.pi)
+    0.99627207622075
+    >>> # Example of providing the optional output parameter illustrating
+    >>> # that what is returned is a reference to said parameter
+    >>> out1 = np.array(1)
+    >>> out2 = np.tanh(np.array(0.1), out1)
+    >>> out2 is out1
+    True
+    >>> # Example of ValueError due to provision of shape mis-matched `out`
+    >>> np.tanh(np.zeros((3,3)),np.zeros((2,2)))
+    mxnet.base.MXNetError:
+    [07:17:36] ../src/ndarray/./../operator/tensor/../elemwise_op_common.h:135:
+    Check failed: assign(&dattr, vec.at(i)): Incompatible attr in node
+    at 0-th output: expected [3,3], got [2,2]
+    """
+    return _mx_nd_np.tanh(x, out=out, **kwargs)
+
+
+@set_module('mxnet.numpy')
+def log10(x, out=None, **kwargs):
+    """Return the base 10 logarithm of the input array, element-wise.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input array or scalar.
+    out : ndarray or None
+        A location into which the result is stored. If provided, it
+        must have a shape that the inputs broadcast to. If not provided
+        or None, a freshly-allocated array is returned. The dtype of the
+        output is the same as that of the input if the input is an ndarray.
+    Returns
+    -------
+    y : ndarray or scalar
+        The logarithm to the base 10 of `x`, element-wise. NaNs are
+        returned where x is negative. This is a scalar if `x` is a scalar.
+    Notes
+    ----
+    This function only supports input type of float.
+    """
+    return _mx_nd_np.log10(x, out=out, **kwargs)
+
+
+@set_module('mxnet.numpy')
+def sqrt(x, out=None, **kwargs):
+    """
+    Return the non-negative square-root of an array, element-wise.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        The values whose square-roots are required.
+    out : ndarray, or None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned.
+    Returns
+    -------
+    y : ndarray or scalar
+        An array of the same shape as `x`, containing the positive
+        square-root of each element in `x`. This is a scalar if `x` is a scalar.
+    Notes
+    ----
+    This function only supports input type of float.
+    """
+    return _mx_nd_np.sqrt(x, out=out, **kwargs)
+
+
+@set_module('mxnet.numpy')
+def cbrt(x, out=None, **kwargs):
+    """
+    Return the cube-root of an array, element-wise.
+    Parameters
+    ----------
+    x : ndarray
+        The values whose cube-roots are required.
+    out : ndarray, optional
+        A location into which the result is stored. If provided, it must have a shape that the
+        inputs broadcast to. If not provided or None, a freshly-allocated array is returned.
+        A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
+    Returns
+    ----------
+    y : ndarray
+        An array of the same shape as x, containing the cube cube-root of each element in x.
+        If out was provided, y is a reference to it. This is a scalar if x is a scalar.
+    Examples
+    ----------
+    >>> np.cbrt([1,8,27])
+    array([ 1.,  2.,  3.])
+    """
+    return _mx_nd_np.cbrt(x, out=out, **kwargs)
+
+
+@set_module('mxnet.numpy')
+def abs(x, out=None, **kwargs):
+    r"""abs(x, out=None, **kwargs)
+    Calculate the absolute value element-wise.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input array.
+    out : ndarray or None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned.
+    Returns
+    -------
+    absolute : ndarray
+        An ndarray containing the absolute value of
+        each element in `x`. This is a scalar if `x` is a scalar.
+    Examples
+    --------
+    >>> x = np.array([-1.2, 1.2])
+    >>> np.abs(x)
+    array([1.2, 1.2])
+    """
+    return _mx_nd_np.abs(x, out=out, **kwargs)
+
+
+@set_module('mxnet.numpy')
+def absolute(x, out=None, **kwargs):
+    """
+    Calculate the absolute value element-wise.
+    np.abs is a shorthand for this function.
+    Parameters
+    ----------
+    x : ndarray
+        Input array.
+    out : ndarray, optional
+        A location into which the result is stored. If provided, it must have a shape
+        that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned.
+        A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
+    Returns
+    ----------
+    absolute : ndarray
+        An ndarray containing the absolute value of each element in x.
+    Examples
+    ----------
+    >>> x = np.array([-1.2, 1.2])
+    >>> np.absolute(x)
+    array([ 1.2,  1.2])
+    """
+    return _mx_nd_np.absolute(x, out=out, **kwargs)
+
+
+@set_module('mxnet.numpy')
+def exp(x, out=None, **kwargs):
+    r"""exp(x, out=None, **kwargs)
+    Calculate the exponential of all elements in the input array.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input values.
+    out : ndarray or None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned.
+    Returns
+    -------
+    out : ndarray or scalar
+        Output array, element-wise exponential of `x`.
+        This is a scalar if `x` is a scalar.
+    Examples
+    --------
+    >>> np.exp(1)
+    2.718281828459045
+    >>> x = np.array([-1, 1, -2, 2])
+    >>> np.exp(x)
+    array([0.36787945, 2.7182817 , 0.13533528, 7.389056  ])
+    """
+    return _mx_nd_np.exp(x, out=out, **kwargs)
+
+
+@set_module('mxnet.numpy')
+def expm1(x, out=None, **kwargs):
+    r"""expm1(x, out=None, **kwargs)
+    Calculate `exp(x) - 1` for all elements in the array.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input values.
+    out : ndarray or None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned.
+    Returns
+    -------
+    out : ndarray or scalar
+        Output array, element-wise exponential minus one: `out = exp(x) - 1`.
+        This is a scalar if `x` is a scalar.
+    Examples
+    --------
+    >>> np.expm1(1)
+    1.718281828459045
+    >>> x = np.array([-1, 1, -2, 2])
+    >>> np.exp(x)
+    array([-0.63212056,  1.71828183, -0.86466472,  6.3890561])
+    """
+    return _mx_nd_np.expm1(x, out=out, **kwargs)
+
+
+@set_module('mxnet.numpy')
+def arcsin(x, out=None, **kwargs):
+    r"""
+    arcsin(x, out=None)
+    Inverse sine, element-wise.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        `y`-coordinate on the unit circle.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+        If provided, it must have the same shape as the input.
+        If not provided or None, a freshly-allocated array is returned.
+    Returns
+    -------
+    angle : ndarray or scalar
+        Output array is same shape and type as x. This is a scalar if x is a scalar.
+        The inverse sine of each element in `x`, in radians and in the
+        closed interval ``[-pi/2, pi/2]``.
+    Examples
+    --------
+    >>> np.arcsin(1)     # pi/2
+    1.5707963267948966
+    >>> np.arcsin(-1)    # -pi/2
+    -1.5707963267948966
+    >>> np.arcsin(0)
+    0.0
+    Notes
+    -----
+    `arcsin` is a multivalued function: for each `x` there are infinitely
+    many numbers `z` such that :math:`sin(z) = x`.  The convention is to
+    return the angle `z` whose real part lies in [-pi/2, pi/2].
+    For real-valued input data types, *arcsin* always returns real output.
+    For each value that cannot be expressed as a real number or infinity,
+    it yields ``nan`` and sets the `invalid` floating point error flag.
+    The inverse sine is also known as `asin` or sin^{-1}.
+    The output `ndarray` has the same `ctx` as the input `ndarray`.
+    This function differs from the original `numpy.arcsin
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.arcsin.html>`_ in
+    the following aspects:
+    - Only support ndarray or scalar now.
+    - `where` argument is not supported.
+    - Complex input is not supported.
+    References
+    ----------
+    Abramowitz, M. and Stegun, I. A., *Handbook of Mathematical Functions*,
+    10th printing, New York: Dover, 1964, pp. 79ff.
+    http://www.math.sfu.ca/~cbm/aands/
+    """
+    return _mx_nd_np.arcsin(x, out=out, **kwargs)
+
+
+@set_module('mxnet.numpy')
+def arccos(x, out=None, **kwargs):
+    """
+    Trigonometric inverse cosine, element-wise.
+    The inverse of cos so that, if y = cos(x), then x = arccos(y).
+    Parameters
+    ----------
+    x : ndarray
+        x-coordinate on the unit circle. For real arguments, the domain is [-1, 1].
+    out : ndarray, optional
+        A location into which the result is stored. If provided, it must have a shape that
+        the inputs broadcast to. If not provided or None, a freshly-allocated array is returned.
+        A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
+    Returns
+    ----------
+    angle : ndarray
+        The angle of the ray intersecting the unit circle at the given x-coordinate in radians [0, pi].
+        This is a scalar if x is a scalar.
+    See also
+    ----------
+    cos, arctan, arcsin
+    Notes
+    ----------
+    arccos is a multivalued function: for each x there are infinitely many numbers z such that
+    cos(z) = x. The convention is to return the angle z whose real part lies in [0, pi].
+    For real-valued input data types, arccos always returns real output.
+    For each value that cannot be expressed as a real number or infinity, it yields nan and sets
+    the invalid floating point error flag.
+    The inverse cos is also known as acos or cos^-1.
+    Examples
+    ----------
+    We expect the arccos of 1 to be 0, and of -1 to be pi:
+    >>> np.arccos([1, -1])
+    array([ 0.        ,  3.14159265])
+    """
+    return _mx_nd_np.arccos(x, out=out, **kwargs)
+
+
+@set_module('mxnet.numpy')
+def arctan(x, out=None, **kwargs):
+    r"""arctan(x, out=None, **kwargs)
+    Trigonometric inverse tangent, element-wise.
+    The inverse of tan, so that if ``y = tan(x)`` then ``x = arctan(y)``.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input values.
+    out : ndarray or None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned.
+    Returns
+    -------
+    out : ndarray or scalar
+        Out has the same shape as `x`. It lies is in
+        ``[-pi/2, pi/2]`` (``arctan(+/-inf)`` returns ``+/-pi/2``).
+        This is a scalar if `x` is a scalar.
+    Notes
+    -----
+    `arctan` is a multi-valued function: for each `x` there are infinitely
+    many numbers `z` such that tan(`z`) = `x`.  The convention is to return
+    the angle `z` whose real part lies in [-pi/2, pi/2].
+    For real-valued input data types, `arctan` always returns real output.
+    For each value that cannot be expressed as a real number or infinity,
+    it yields ``nan`` and sets the `invalid` floating point error flag.
+    For complex-valued input, we do not have support for them yet.
+    The inverse tangent is also known as `atan` or tan^{-1}.
+    Examples
+    --------
+    We expect the arctan of 0 to be 0, and of 1 to be pi/4:
+    >>> x = np.array([0, 1])
+    >>> np.arctan(x)
+    array([0.       , 0.7853982])
+    >>> np.pi/4
+    0.7853981633974483
+    """
+    return _mx_nd_np.arctan(x, out=out, **kwargs)
+
+
+@set_module('mxnet.numpy')
+def sign(x, out=None):
+    """
+    sign(x, out=None)
+    Returns an element-wise indication of the sign of a number.
+    The `sign` function returns ``-1 if x < 0, 0 if x==0, 1 if x > 0``. Only supports real number.
+    Parameters
+    ----------
+    x : ndarray or a scalar
+        Input values.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+        If provided, it must have the same shape and dtype as input ndarray.
+        If not provided or `None`, a freshly-allocated array is returned.
+    Returns
+    -------
+    y : ndarray
+        The sign of `x`.
+        This is a scalar if `x` is a scalar.
+    Note
+    -------
+    - Only supports real number as input elements.
+    - Input type does not support Python native iterables(list, tuple, ...).
+    - ``out`` param: cannot perform auto broadcasting. ``out`` ndarray's shape must be the same as the expected output.
+    - ``out`` param: cannot perform auto type cast. ``out`` ndarray's dtype must be the same as the expected output.
+    - ``out`` param does not support scalar input case.
+    Examples
+    --------
+    >>> a = np.array([-5., 4.5])
+    >>> np.sign(a)
+    array([-1.,  1.])
+    Scalars as input:
+    >>> np.sign(4.0)
+    1.0
+    >>> np.sign(0)
+    0
+    Use ``out`` parameter:
+    >>> b = np.zeros((2, ))
+    >>> np.sign(a, out=b)
+    array([-1.,  1.])
+    >>> b
+    array([-1.,  1.])
+    """
+    return _mx_nd_np.sign(x, out=out)
+
+
+@set_module('mxnet.numpy')
+def log(x, out=None, **kwargs):
+    """
+    log(x, out=None)
+    Natural logarithm, element-wise.
+    The natural logarithm `log` is the inverse of the exponential function,
+    so that `log(exp(x)) = x`. The natural logarithm is logarithm in base
+    `e`.
+    Parameters
+    ----------
+    x : ndarray
+        Input value. Elements must be of real value.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+        If provided, it must have the same shape and dtype as input ndarray.
+        If not provided or `None`, a freshly-allocated array is returned.
+    Returns
+    -------
+    y : ndarray
+        The natural logarithm of `x`, element-wise.
+        This is a scalar if `x` is a scalar.
+    Notes
+    -----
+    Currently only supports data of real values and ``inf`` as input. Returns data of real value, ``inf``, ``-inf`` and
+    ``nan`` according to the input.
+    This function differs from the original `numpy.log
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.log.html>`_ in
+    the following aspects:
+    - Does not support complex number for now
+    - Input type does not support Python native iterables(list, tuple, ...).
+    - ``out`` param: cannot perform auto broadcasting. ``out`` ndarray's shape must be the same as the expected output.
+    - ``out`` param: cannot perform auto type cast. ``out`` ndarray's dtype must be the same as the expected output.
+    - ``out`` param does not support scalar input case.
+    Examples
+    --------
+    >>> a = np.array([1, np.exp(1), np.exp(2), 0], dtype=np.float64)
+    >>> np.log(a)
+    array([  0.,   1.,   2., -inf], dtype=float64)
+    Due to internal calculation mechanism, using default float32 dtype may cause some special behavior:
+    >>> a = np.array([1, np.exp(1), np.exp(2), 0])
+    >>> np.log(a)
+    array([  0.,  0.99999994,   2., -inf])
+    Scalar calculation:
+    >>> np.log(1)
+    0.0
+    """
+    return _mx_nd_np.log(x, out=out, **kwargs)
+
+
+@set_module('mxnet.numpy')
+def rint(x, out=None, **kwargs):
+    """
+    Round elements of the array to the nearest integer.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input array.
+    out : ndarray or None
+        A location into which the result is stored.
+        If provided, it must have the same shape and type as the input.
+        If not provided or None, a freshly-allocated array is returned.
+    Returns
+    -------
+    out : ndarray or scalar
+        Output array is same shape and type as x. This is a scalar if x is a scalar.
+    Notes
+    -----
+    This function differs from the original `numpy.rint
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.rint.html>`_ in
+    the following way(s):
+    - only ndarray or scalar is accpted as valid input, tuple of ndarray is not supported
+    - broadcasting to `out` of different shape is currently not supported
+    - when input is plain python numerics, the result will not be stored in the `out` param
+    Examples
+    --------
+    >>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
+    >>> np.rint(a)
+    array([-2., -2., -0.,  0.,  1.,  2.,  2.])
+    """
+    return _mx_nd_np.rint(x, out=out, **kwargs)
+
+
+@set_module('mxnet.numpy')
+def log2(x, out=None, **kwargs):
+    """
+    Base-2 logarithm of x.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input values.
+    out : ndarray or None
+        A location into which the result is stored.
+        If provided, it must have the same shape and type as the input.
+        If not provided or None, a freshly-allocated array is returned.
+    Returns
+    -------
+    y : ndarray
+        The logarithm base two of `x`, element-wise.
+        This is a scalar if `x` is a scalar.
+    Notes
+    -----
+    This function differs from the original `numpy.log2
+    <https://www.google.com/search?q=numpy+log2>`_ in
+    the following way(s):
+    - only ndarray or scalar is accpted as valid input, tuple of ndarray is not supported
+    - broadcasting to `out` of different shape is currently not supported
+    - when input is plain python numerics, the result will not be stored in the `out` param
+    Examples
+    --------
+    >>> x = np.array([0, 1, 2, 2**4])
+    >>> np.log2(x)
+    array([-inf,   0.,   1.,   4.])
+    """
+    return _mx_nd_np.log2(x, out=out, **kwargs)
+
+
+@set_module('mxnet.numpy')
+def log1p(x, out=None, **kwargs):
+    """
+    Return the natural logarithm of one plus the input array, element-wise.
+    Calculates ``log(1 + x)``.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input array.
+    out : ndarray or None
+        A location into which the result is stored. If provided, it
+        must have a shape that the inputs fill into. If not provided
+        or None, a freshly-allocated array is returned. The dtype of the
+        output and input must be the same.
+    Returns
+    -------
+    y : ndarray or scalar
+        Natural logarithm of 1 + x, element-wise. This is a scalar
+        if x is a scalar.
+    Notes
+    -----
+    For real-valued input, `log1p` is accurate also for `x` so small
+    that `1 + x == 1` in floating-point accuracy.
+    Logarithm is a multivalued function: for each `x` there is an infinite
+    number of `z` such that `exp(z) = 1 + x`. The convention is to return
+    the `z` whose imaginary part lies in `[-pi, pi]`.
+    For real-valued input data types, `log1p` always returns real output.
+    For each value that cannot be expressed as a real number or infinity,
+    it yields ``nan`` and sets the `invalid` floating point error flag.
+    cannot support complex-valued input.
+    Examples
+    --------
+    >>> np.log1p(1e-99)
+    1e-99
+    >>> a = np.array([3, 4, 5])
+    >>> np.log1p(a)
+    array([1.3862944, 1.609438 , 1.7917595])
+    """
+    return _mx_nd_np.log1p(x, out=out, **kwargs)
+
+
+@set_module('mxnet.numpy')
+def degrees(x, out=None, **kwargs):
+    """
+    degrees(x, out=None)
+    Convert angles from radians to degrees.
+    Parameters
+    ----------
+    x : ndarray
+        Input value. Elements must be of real value.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+        If provided, it must have the same shape and dtype as input ndarray.
+        If not provided or `None`, a freshly-allocated array is returned.
+    Returns
+    -------
+    y : ndarray
+        The corresponding degree values; if `out` was supplied this is a
+        reference to it.
+        This is a scalar if `x` is a scalar.
+    Notes
+    -------
+    This function differs from the original `numpy.degrees
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.degrees.html>`_ in
+    the following aspects:
+    - Input type does not support Python native iterables(list, tuple, ...). Only ndarray is supported.
+    - ``out`` param: cannot perform auto broadcasting. ``out`` ndarray's shape must be the same as the expected output.
+    - ``out`` param: cannot perform auto type cast. ``out`` ndarray's dtype must be the same as the expected output.
+    - ``out`` param does not support scalar input case.
+    Examples
+    --------
+    Convert a radian array to degrees
+    >>> rad = np.arange(12.) * np.pi / 6
+    >>> np.degrees(rad)
+    array([  0.,  30.,  60.,  90., 120., 150., 180., 210., 240., 270., 300., 330.])
+    Use specified ``out`` ndarray:
+    >>> out = np.zeros((rad.shape))
+    >>> np.degrees(rad, out)
+    array([  0.,  30.,  60.,  90., 120., 150., 180., 210., 240., 270., 300., 330.])
+    >>> out
+    array([  0.,  30.,  60.,  90., 120., 150., 180., 210., 240., 270., 300., 330.])
+    """
+    return _mx_nd_np.degrees(x, out=out, **kwargs)
+
+
+@set_module('mxnet.numpy')
+def radians(x, out=None, **kwargs):
+    """
+    Convert angles from degrees to radians.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input array in degrees.
+    out : ndarray or None
+        A location into which the result is stored.
+        If provided, it must have the same shape and type as the input.
+        If not provided or None, a freshly-allocated array is returned.
+    Returns
+    -------
+    y : ndarray
+        The corresponding radian values. This is a scalar if x is a scalar.
+    Notes
+    -----
+    This function differs from the original `numpy.radians
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.radians.html>`_ in
+    the following way(s):
+    - only ndarray or scalar is accpted as valid input, tuple of ndarray is not supported
+    - broadcasting to `out` of different shape is currently not supported
+    - when input is plain python numerics, the result will not be stored in the `out` param
+    Examples
+    --------
+    >>> deg = np.arange(12.) * 30.
+    >>> np.radians(deg)
+    array([0.       , 0.5235988, 1.0471976, 1.5707964, 2.0943952, 2.6179938,
+           3.1415927, 3.6651914, 4.1887903, 4.712389 , 5.2359877, 5.7595863],
+           dtype=float32)
+    """
+    return _mx_nd_np.radians(x, out=out, **kwargs)
+
+
+@set_module('mxnet.numpy')
+def reciprocal(x, out=None, **kwargs):
+    r"""
+    reciprocal(x, out=None)
+    Return the reciprocal of the argument, element-wise.
+    Calculates ``1/x``.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        The values whose reciprocals are required.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+        If provided, it must have the same shape as the input.
+        If not provided or None, a freshly-allocated array is returned.
+    Returns
+    -------
+    y : ndarray or scalar
+        Output array is same shape and type as x. This is a scalar if x is a scalar.
+    Examples
+    --------
+    >>> np.reciprocal(2.)
+    0.5
+    >>> x = np.array([1, 2., 3.33])
+    >>> np.reciprocal(x)
+    array([1.       , 0.5      , 0.3003003])
+    Notes
+    -----
+    .. note::
+        This function is not designed to work with integers.
+    For integer arguments with absolute value larger than 1 the result is
+    always zero because of the way Python handles integer division.  For
+    integer zero the result is an overflow.
+    The output `ndarray` has the same `ctx` as the input `ndarray`.
+    This function differs from the original `numpy.reciprocal
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.reciprocal.html>`_ in
+    the following aspects:
+    - Only support ndarray and scalar now.
+    - `where` argument is not supported.
+    """
+    return _mx_nd_np.reciprocal(x, out=out, **kwargs)
+
+
+@set_module('mxnet.numpy')
+def square(x, out=None, **kwargs):
+    r"""
+    square(x, out=None)
+    Return the element-wise square of the input.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        The values whose squares are required.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+        If provided, it must have the same shape as the input.
+        If not provided or None, a freshly-allocated array is returned.
+    Returns
+    -------
+    y : ndarray or scalar
+        Output array is same shape and type as x. This is a scalar if x is a scalar.
+    Examples
+    --------
+    >>> np.square(2.)
+    4.0
+    >>> x = np.array([1, 2., -1])
+    >>> np.square(x)
+    array([1., 4., 1.])
+    Notes
+    -----
+    The output `ndarray` has the same `ctx` as the input `ndarray`.
+    This function differs from the original `numpy.square
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.square.html>`_ in
+    the following aspects:
+    - Only support ndarray and scalar now.
+    - `where` argument is not supported.
+    - Complex input is not supported.
+    """
+    return _mx_nd_np.square(x, out=out, **kwargs)
+
+
+@set_module('mxnet.numpy')
+def negative(x, out=None, where=True, **kwargs):
+    r"""
+    negative(x, out=None, where=True)
+    Numerical negative, element-wise.
+    Parameters:
+    ------------
+    x : ndarray or scalar
+        Input array.
+    out : ndarray, None, or tuple of ndarray and None, optional
+          A location into which the result is stored.
+          If provided, it must have a shape that the inputs broadcast to.
+          If not provided or None, a freshly-allocated array is returned.
+          A tuple (possible only as a keyword argument) must have length
+          equal to the number of outputs.
+    where : ndarray, optional
+            Values of True indicate to calculate the ufunc at that position,
+            values of False indicate to leave the value in the output alone.
+    Returns:
+    -------
+    y : ndarray or scalar
+        Returned array or scalar: y = -x. This is a scalar if x is a scalar.
+    Examples:
+    --------
+    >>> np.negative(1)
+    -1
+    """
+    return _mx_nd_np.negative(x, out=out)
+
+
+@set_module('mxnet.numpy')
+def fix(x, out=None):
+    """
+    Round an array of floats element-wise to nearest integer towards zero.
+    The rounded values are returned as floats.
+
+    Parameters:
+    ----------
+    x : ndarray
+        An array of floats to be rounded
+    out : ndarray, optional
+        Output array
+    Returns:
+    -------
+    y : ndarray or scalar
+    Returned array or scalar: y = -x. This is a scalar if x is a scalar.ndarray of floats
+    Examples:
+    ---------
+    >>> np.fix(3.14)
+    3
+    """
+    return _mx_nd_np.fix(x, out=out)
+
+
+@set_module('mxnet.numpy')
+def tan(x, out=None, where=True, **kwargs):
+    r"""
+    tan(x, out=None, where=True)
+    Compute tangent element-wise.
+    Equivalent to np.sin(x)/np.cos(x) element-wise.
+
+    Parameters:
+    ----------
+    x : ndarray
+        Input array.
+    out : ndarray or none, optional
+          A location into which the result is stored. If provided,
+          it must have a shape that the inputs broadcast to. If not provided or None,
+          a freshly-allocated array is returned. A tuple (possible only as a keyword argument)
+          must have length equal to the number of outputs.
+    where : ndarray, optional
+            Values of True indicate to calculate the ufunc at that position,
+            values of False indicate to leave the value in the output alone.
+    Returns:
+    -------
+    y : ndarray
+    The corresponding tangent values. This is a scalar if x is a scalar.
+    Examples:
+    ---------
+    >>> np.tan(0.5)
+    0.5463024898437905
+    """
+
+    return _mx_nd_np.tan(x, out=out, **kwargs)
+
+
+@set_module('mxnet.numpy')
+def ceil(x, out=None, **kwargs):
+    r"""
+    Return the ceiling of the input, element-wise.
+    The ceil of the ndarray `x` is the smallest integer `i`, such that
+    `i >= x`.  It is often denoted as :math:`\lceil x \rceil`.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input array.
+    out : ndarray or None
+        A location into which the result is stored. If provided, it
+        must have a shape that the inputs fill into. If not provided
+        or None, a freshly-allocated array is returned. The dtype of the
+        output and input must be the same.
+    Returns
+    -------
+    y : ndarray or scalar
+        The ceiling of each element in `x`, with `float` dtype.
+        This is a scalar if `x` is a scalar.
+    Examples
+    --------
+    >>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
+    >>> np.ceil(a)
+    array([-1., -1., -0.,  1.,  2.,  2.,  2.])
+    >>> #if you use parameter out, x and out must be ndarray. if not, you will get an error!
+    >>> a = np.array(1)
+    >>> np.ceil(np.array(3.5), a)
+    array(4.)
+    >>> a
+    array(4.)
+    """
+    return _mx_nd_np.ceil(x, out=out, **kwargs)
+
+
+@set_module('mxnet.numpy')
+def floor(x, out=None, **kwargs):
+    r"""
+    Return the floor of the input, element-wise.
+    The ceil of the ndarray `x` is the largest integer `i`, such that
+    `i <= x`.  It is often denoted as :math:`\lfloor x \rfloor`.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input array.
+    out : ndarray or None
+        A location into which the result is stored. If provided, it
+        must have a shape that the inputs fill into. If not provided
+        or None, a freshly-allocated array is returned. The dtype of the
+        output and input must be the same.
+    Returns
+    -------
+    y : ndarray or scalar
+        The floor of each element in `x`, with `float` dtype.
+        This is a scalar if `x` is a scalar.
+    Examples
+    --------
+    >>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
+    >>> np.floor(a)
+    array([-2., -2., -1.,  0.,  1.,  1.,  2.])
+    >>> #if you use parameter out, x and out must be ndarray. if not, you will get an error!
+    >>> a = np.array(1)
+    >>> np.floor(np.array(3.5), a)
+    array(3.)
+    >>> a
+    array(3.)
+    """
+    return _mx_nd_np.floor(x, out=out, **kwargs)
+
+
+@set_module('mxnet.numpy')
+def trunc(x, out=None, **kwargs):
+    r"""
+    trunc(x, out=None)
+    Return the truncated value of the input, element-wise.
+    The truncated value of the scalar `x` is the nearest integer `i` which
+    is closer to zero than `x` is. In short, the fractional part of the
+    signed number `x` is discarded.
+
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input data.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+
+    Returns
+    -------
+    y : ndarray or scalar
+        The truncated value of each element in `x`.
+        This is a scalar if `x` is a scalar.
+    Notes
+    -----
+    This function differs from the original numpy.trunc in the following aspects:
+        - Do not support `where`, a parameter in numpy which indicates where to calculate.
+        - Cannot cast type automatically. Dtype of `out` must be same as the expected one.
+        - Cannot broadcast automatically. Shape of `out` must be same as the expected one.
+        - If `x` is plain python numeric, the result won't be stored in out.
+
+    Examples
+    --------
+    >>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
+    >>> np.trunc(a)
+    array([-1., -1., -0.,  0.,  1.,  1.,  2.])
+    """
+    return _mx_nd_np.trunc(x, out=out, **kwargs)
+
+
+@set_module('mxnet.numpy')
+def logical_not(x, out=None, **kwargs):
+    r"""
+    logical_not(x, out=None)
+    Compute the truth value of NOT x element-wise.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Logical NOT is applied to the elements of `x`.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+
+    Returns
+    -------
+    y : bool or ndarray of bool
+        Boolean result with the same shape as `x` of the NOT operation
+        on elements of `x`.
+        This is a scalar if `x` is a scalar.
+    Notes
+    -----
+    This function differs from the original numpy.logical_not in the following aspects:
+        - Do not support `where`, a parameter in numpy which indicates where to calculate.
+        - Cannot cast type automatically. Dtype of `out` must be same as the expected one.
+        - Cannot broadcast automatically. Shape of `out` must be same as the expected one.
+        - If `x` is plain python numeric, the result won't be stored in out.
+
+    Examples
+    --------
+    >>> x= np.array([True, False, 0, 1])
+    >>> np.logical_not(x)
+    array([0., 1., 1., 0.])
+
+    >>> x = np.arange(5)
+    >>> np.logical_not(x<3)
+    array([0., 0., 0., 1., 1.])
+    """
+    return _mx_nd_np.logical_not(x, out=out, **kwargs)
+
+
+@set_module('mxnet.numpy')
+def arcsinh(x, out=None, **kwargs):
+    r"""
+    arcsinh(x, out=None)
+    Inverse hyperbolic cosine, element-wise.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input array.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+
+    Returns
+    -------
+    arcsinh : ndarray
+        Array of the same shape as `x`.
+        This is a scalar if `x` is a scalar.
+
+    Notes
+    -----
+    `arcsinh` is a multivalued function: for each `x` there are infinitely
+    many numbers `z` such that `sinh(z) = x`.
+
+    For real-valued input data types, `arcsinh` always returns real output.
+    For each value that cannot be expressed as a real number or infinity, it
+    yields ``nan`` and sets the `invalid` floating point error flag.
+
+    This function differs from the original numpy.arcsinh in the following aspects:
+        - Do not support `where`, a parameter in numpy which indicates where to calculate.
+        - Do not support complex-valued input.
+        - Cannot cast type automatically. DType of `out` must be same as the expected one.
+        - Cannot broadcast automatically. Shape of `out` must be same as the expected one.
+        - If `x` is plain python numeric, the result won't be stored in out.
+
+    Examples
+    --------
+    >>> a = np.array([3.2, 5.0])
+    >>> np.arcsinh(a)
+    array([1.8309381, 2.2924316])
+
+    >>> np.arcsinh(1)
+    0.0
+    """
+    return _mx_nd_np.arcsinh(x, out=out, **kwargs)
+
+
+@set_module('mxnet.numpy')
+def arccosh(x, out=None, **kwargs):
+    r"""
+    arccosh(x, out=None)
+    Inverse hyperbolic cosine, element-wise.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input array.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+
+    Returns
+    -------
+    arccosh : ndarray
+        Array of the same shape as `x`.
+        This is a scalar if `x` is a scalar.
+
+    Notes
+    -----
+    `arccosh` is a multivalued function: for each `x` there are infinitely
+    many numbers `z` such that `cosh(z) = x`.
+
+    For real-valued input data types, `arccosh` always returns real output.
+    For each value that cannot be expressed as a real number or infinity, it
+    yields ``nan`` and sets the `invalid` floating point error flag.
+
+    This function differs from the original numpy.arccosh in the following aspects:
+        - Do not support `where`, a parameter in numpy which indicates where to calculate.
+        - Do not support complex-valued input.
+        - Cannot cast type automatically. Dtype of `out` must be same as the expected one.
+        - Cannot broadcast automatically. Shape of `out` must be same as the expected one.
+        - If `x` is plain python numeric, the result won't be stored in out.
+
+    Examples
+    --------
+    >>> a = np.array([3.2, 5.0])
+    >>> np.arccosh(a)
+    array([1.8309381, 2.2924316])
+
+    >>> np.arccosh(1)
+    0.0
+    """
+    return _mx_nd_np.arccosh(x, out=out, **kwargs)
+
+
+@set_module('mxnet.numpy')
+def arctanh(x, out=None, **kwargs):
+    r"""
+    arctanh(x, out=None)
+    Inverse hyperbolic tangent, element-wise.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input array.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+
+    Returns
+    -------
+    arctanh : ndarray
+        Array of the same shape as `x`.
+        This is a scalar if `x` is a scalar.
+
+    Notes
+    -----
+    `arctanh` is a multivalued function: for each `x` there are infinitely
+    many numbers `z` such that `tanh(z) = x`.
+
+    For real-valued input data types, `arctanh` always returns real output.
+    For each value that cannot be expressed as a real number or infinity, it
+    yields ``nan`` and sets the `invalid` floating point error flag.
+
+    This function differs from the original numpy.arctanh in the following aspects:
+        - Do not support `where`, a parameter in numpy which indicates where to calculate.
+        - Do not support complex-valued input.
+        - Cannot cast type automatically. Dtype of `out` must be same as the expected one.
+        - Cannot broadcast automatically. Shape of `out` must be same as the expected one.
+        - If `x` is plain python numeric, the result won't be stored in out.
+
+    Examples
+    --------
+    >>> a = np.array([0.0, -0.5])
+    >>> np.arctanh(a)
+    array([0., -0.54930615])
+
+    >>> np.arctanh(1)
+    0.0
+    """
+    return _mx_nd_np.arctanh(x, out=out, **kwargs)
+
+
 @set_module('mxnet.numpy')
 def tensordot(a, b, axes=2):
     r"""
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 5537e6305b83..251a8a1b8e56 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -15,7 +15,7 @@
 # specific language governing permissions and limitations
 # under the License.
 
-# pylint: disable=too-many-lines
+# pylint: disable=too-many-lines, unused-argument
 """numpy namespace for operators used in Gluon APIs dispatched by F=symbol module."""
 
 from __future__ import absolute_import
@@ -29,7 +29,11 @@
 from .._internal import _set_np_symbol_class
 from . import _internal as _npi
 
-__all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power', 'tensordot',
+__all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power', 'sin', 'cos',
+           'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs', 'absolute', 'exp',
+           'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2', 'log1p',
+           'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
+           'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack']
 
 
@@ -1185,6 +1189,968 @@ def _unary_func_helper(x, fn_array, fn_scalar, out=None, **kwargs):
         raise TypeError('type {} not supported'.format(str(type(x))))
 
 
+@set_module('mxnet.symbol.numpy')
+def sin(x, out=None, **kwargs):
+    r"""Trigonometric sine, element-wise.
+    Parameters
+    ----------
+    x : _Symbol or scalar
+        Angle, in radians (:math:`2 \pi` rad equals 360 degrees).
+    out : _Symbol or None
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+    Returns
+    -------
+    y : _Symbol
+        The sine of each element of x.
+        This is a scalar if `x` is a scalar.
+    Notes
+    ----
+    This function only supports input type of float.
+    """
+    return _unary_func_helper(x, _npi.sin, _np.sin, out=out, **kwargs)
+
+
+@set_module('mxnet.symbol.numpy')
+def cos(x, out=None, **kwargs):
+    r"""Cosine, element-wise.
+    Parameters
+    ----------
+    x : _Symbol or scalar
+        Angle, in radians (:math:`2 \pi` rad equals 360 degrees).
+    out : _Symbol or None
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+    Returns
+    -------
+    y : _Symbol
+        The corresponding cosine values. This is a scalar if x is a scalar.
+    Notes
+    ----
+    This function only supports input type of float.
+    """
+    return _unary_func_helper(x, _npi.cos, _np.cos, out=out, **kwargs)
+
+
+@set_module('mxnet.symbol.numpy')
+def sinh(x, out=None, **kwargs):
+    """Hyperbolic sine, element-wise.
+    Equivalent to ``1/2 * (np.exp(x) - np.exp(-x))`` or ``-1j * np.sin(1j*x)``.
+    Parameters
+    ----------
+    x : _Symbol or scalar
+        Input array or scalar.
+    out : _Symbol or None
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+    Returns
+    -------
+    y : _Symbol or scalar
+        The corresponding hyperbolic sine values. This is a scalar if `x` is a scalar.
+    Notes
+    ----
+    This function only supports input type of float.
+    """
+    return _unary_func_helper(x, _npi.sinh, _np.sinh, out=out, **kwargs)
+
+
+@set_module('mxnet.symbol.numpy')
+def cosh(x, out=None, **kwargs):
+    """Hyperbolic cosine, element-wise.
+    Equivalent to ``1/2 * (np.exp(x) + np.exp(-x))`` and ``np.cos(1j*x)``.
+    Parameters
+    ----------
+    x : _Symbol or scalar
+        Input array or scalar.
+    out : ndarray or None
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+    Returns
+    -------
+    y : _Symbol or scalar
+        The corresponding hyperbolic cosine values. This is a scalar if `x` is a scalar.
+    Notes
+    ----
+    This function only supports input type of float.
+    """
+    return _unary_func_helper(x, _npi.cosh, _np.cosh, out=out, **kwargs)
+
+
+@set_module('mxnet.symbol.numpy')
+def tanh(x, out=None, **kwargs):
+    """
+    Compute hyperbolic tangent element-wise.
+    Equivalent to ``np.sinh(x)/np.cosh(x)``.
+    Parameters
+    ----------
+    x : _Symbol
+        Input array.
+    out : _Symbol or None
+          Dummy parameter to keep the consistency with the ndarray counterpart.
+    Returns
+    -------
+    y : _Symbol
+        The corresponding hyperbolic tangent values.
+    Notes
+    -----
+    If `out` is provided, the function writes the result into it,
+    and returns a reference to `out`.  (See Examples)
+    - input x does not support complex computation (like imaginary number)
+    >>> np.tanh(np.pi*1j)
+    TypeError: type <type 'complex'> not supported
+    Examples
+    --------
+    >>> np.tanh(np.array[0, np.pi]))
+    array([0.       , 0.9962721])
+    >>> np.tanh(np.pi)
+    0.99627207622075
+    >>> # Example of providing the optional output parameter illustrating
+    >>> # that what is returned is a reference to said parameter
+    >>> out1 = np.array(1)
+    >>> out2 = np.tanh(np.array(0.1), out1)
+    >>> out2 is out1
+    True
+    >>> # Example of ValueError due to provision of shape mis-matched `out`
+    >>> np.tanh(np.zeros((3,3)),np.zeros((2,2)))
+    mxnet.base.MXNetError:
+    [07:17:36] ../src/ndarray/./../operator/tensor/../elemwise_op_common.h:135:
+    Check failed: assign(&dattr, vec.at(i)): Incompatible attr in node
+    at 0-th output: expected [3,3], got [2,2]
+    """
+    return _unary_func_helper(x, _npi.tanh, _np.tanh, out=out, **kwargs)
+
+
+@set_module('mxnet.symbol.numpy')
+def log10(x, out=None, **kwargs):
+    """Return the base 10 logarithm of the input array, element-wise.
+    Parameters
+    ----------
+    x : _Symbol or scalar
+        Input array or scalar.
+    out : _Symbol or None
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+    Returns
+    -------
+    y : _Symbol or scalar
+        The logarithm to the base 10 of `x`, element-wise. NaNs are
+        returned where x is negative. This is a scalar if `x` is a scalar.
+    Notes
+    ----
+    This function only supports input type of float.
+    """
+    return _unary_func_helper(x, _npi.log10, _np.log10, out=out, **kwargs)
+
+
+@set_module('mxnet.symbol.numpy')
+def sqrt(x, out=None, **kwargs):
+    """
+    Return the non-negative square-root of an array, element-wise.
+    Parameters
+    ----------
+    x : _Symbol or scalar
+        The values whose square-roots are required.
+    out : _Symbol, or None, optional
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+    Returns
+    -------
+    y : _Symbol or scalar
+        An array of the same shape as `x`, containing the positive
+        square-root of each element in `x`. This is a scalar if `x` is a scalar.
+    Notes
+    ----
+    This function only supports input type of float.
+    """
+    return _unary_func_helper(x, _npi.sqrt, _np.sqrt, out=out, **kwargs)
+
+
+@set_module('mxnet.symbol.numpy')
+def cbrt(x, out=None, **kwargs):
+    r"""
+    Return the cube-root of an array, element-wise.
+    Parameters
+    ----------
+    x : _Symbol
+        The values whose cube-roots are required.
+    out : _Symbol or None
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+    Returns
+    ----------
+    y : _Symbol
+        An array of the same shape as x, containing the cube cube-root of each element in x.
+        If out was provided, y is a reference to it. This is a scalar if x is a scalar.
+    """
+    return _unary_func_helper(x, _npi.cbrt, _np.cbrt, out=out, **kwargs)
+
+
+@set_module('mxnet.symbol.numpy')
+def abs(x, out=None, **kwargs):
+    r"""abs(x, out=None, **kwargs)
+    Calculate the absolute value element-wise.
+    Parameters
+    ----------
+    x : _Symbol or scalar
+        Input array.
+    out : _Symbol or None
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+    Returns
+    -------
+    absolute : _Symbol
+        An ndarray containing the absolute value of
+        each element in `x`. This is a scalar if `x` is a scalar.
+    """
+    return _unary_func_helper(x, _npi.abs, _np.abs, out=out, **kwargs)
+
+
+@set_module('mxnet.symbol.numpy')
+def absolute(x, out=None, **kwargs):
+    r"""
+    Calculate the absolute value element-wise.
+    np.abs is a shorthand for this function.
+    Parameters
+    ----------
+    x : _Symbol
+        Input array.
+    out : _Symbol or None
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+    Returns
+    ----------
+    absolute : _Symbol
+        An ndarray containing the absolute value of each element in x.
+    """
+    return _unary_func_helper(x, _npi.absolute, _np.absolute, out=out, **kwargs)
+
+
+@set_module('mxnet.symbol.numpy')
+def sign(x, out=None, **kwargs):
+    r"""
+    sign(x, out=None)
+    Returns an element-wise indication of the sign of a number.
+    The `sign` function returns ``-1 if x < 0, 0 if x==0, 1 if x > 0``. Only supports real number.
+    Parameters
+    ----------
+    x : _Symbol or a scalar
+        Input values.
+    out : _Symbol or None, optional
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+    Returns
+    -------
+    y : _Symbol
+        The sign of `x`.
+        This is a scalar if `x` is a scalar.
+    Note
+    -------
+    - Only supports real number as input elements.
+    - Input type does not support Python native iterables(list, tuple, ...)
+    - ``out`` param: cannot perform auto broadcasting. ``out`` symbol's shape must be the same as the expected output.
+    - ``out`` param: cannot perform auto type cast. ``out`` symbol's dtype must be the same as the expected output.
+    - ``out`` param does not support scalar input case.
+    """
+    return _unary_func_helper(x, _npi.sign, _np.sign, out=out, **kwargs)
+
+
+@set_module('mxnet.symbol.numpy')
+def exp(x, out=None, **kwargs):
+    r"""exp(x, out=None, **kwargs)
+    Calculate the exponential of all elements in the input array.
+    Parameters
+    ----------
+    x : _Symbol or scalar
+        Input values.
+    out : _Symbol or None
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+    Returns
+    -------
+    out : _Symbol
+        Output array, element-wise exponential of `x`.
+        This is a scalar if `x` is a scalar.
+    """
+    return _unary_func_helper(x, _npi.exp, _np.exp, out=out, **kwargs)
+
+
+@set_module('mxnet.symbol.numpy')
+def expm1(x, out=None, **kwargs):
+    r"""expm1(x, out=None, **kwargs)
+    Calculate `exp(x) - 1` for all elements in the array.
+    Parameters
+    ----------
+    x : _Symbol or scalar
+        Input values.
+    out : _Symbol or None
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+    Returns
+    -------
+    out : _Symbol
+        Output array, .
+        This is a scalar if `x` is a scalar.
+    """
+    return _unary_func_helper(x, _npi.expm1, _np.expm1, out=out, **kwargs)
+
+
+@set_module('mxnet.symbol.numpy')
+def arcsin(x, out=None, **kwargs):
+    r"""
+    arcsin(x, out=None)
+    Inverse sine, element-wise.
+    Parameters
+    ----------
+    x : _Symbol or scalar
+        The values whose reciprocals are required.
+    out : _Symbol, or None, optional
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+    Returns
+    -------
+    angle : _Symbol or scalar
+        Output array is same shape and type as x. This is a scalar if x is a scalar.
+    Notes
+    -----
+    `arcsin` is a multivalued function: for each `x` there are infinitely
+    many numbers `z` such that :math:`sin(z) = x`.  The convention is to
+    return the angle `z` whose real part lies in [-pi/2, pi/2].
+    For real-valued input data types, *arcsin* always returns real output.
+    For each value that cannot be expressed as a real number or infinity,
+    it yields ``nan`` and sets the `invalid` floating point error flag.
+    The inverse sine is also known as `asin` or sin^{-1}.
+    The output `symbol` has the same `ctx` as the input `symbol`.
+    This function differs from the original `numpy.arcsin
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.arcsin.html>`_ in
+    the following aspects:
+    - Only support _Symbol or scalar now.
+    - `where` argument is not supported.
+    - Complex input is not supported.
+    References
+    ----------
+    Abramowitz, M. and Stegun, I. A., *Handbook of Mathematical Functions*,
+    10th printing, New York: Dover, 1964, pp. 79ff.
+    http://www.math.sfu.ca/~cbm/aands/
+    """
+    return _unary_func_helper(x, _npi.arcsin, _np.arcsin, out=out, **kwargs)
+
+
+@set_module('mxnet.symbol.numpy')
+def arccos(x, out=None, **kwargs):
+    r"""
+    Trigonometric inverse cosine, element-wise.
+    The inverse of cos so that, if y = cos(x), then x = arccos(y).
+    Parameters
+    ----------
+    x : _Symbol
+        x-coordinate on the unit circle. For real arguments, the domain is [-1, 1].
+    out : _Symbol or None
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+    Returns
+    ----------
+    angle : _Symbol
+        The angle of the ray intersecting the unit circle at the given x-coordinate in radians [0, pi].
+        This is a scalar if x is a scalar.
+    See also
+    ----------
+    cos, arctan, arcsin
+    Notes
+    ----------
+    arccos is a multivalued function: for each x there are infinitely many numbers z such that
+    cos(z) = x. The convention is to return the angle z whose real part lies in [0, pi].
+    For real-valued input data types, arccos always returns real output.
+    For each value that cannot be expressed as a real number or infinity, it yields nan and sets
+    the invalid floating point error flag.
+    The inverse cos is also known as acos or cos^-1.
+    """
+    return _unary_func_helper(x, _npi.arccos, _np.arccos, out=out, **kwargs)
+
+
+@set_module('mxnet.symbol.numpy')
+def arctan(x, out=None, **kwargs):
+    r"""arctan(x, out=None, **kwargs)
+    Trigonometric inverse tangent, element-wise.
+    The inverse of tan, so that if ``y = tan(x)`` then ``x = arctan(y)``.
+    Parameters
+    ----------
+    x : _Symbol or scalar
+        Input values.
+    out : _Symbol or None
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+    Returns
+    -------
+    out : _Symbol
+        Out has the same shape as `x`. It lies is in
+        ``[-pi/2, pi/2]`` (``arctan(+/-inf)`` returns ``+/-pi/2``).
+        This is a scalar if `x` is a scalar.
+    Notes
+    -----
+    `arctan` is a multi-valued function: for each `x` there are infinitely
+    many numbers `z` such that tan(`z`) = `x`.  The convention is to return
+    the angle `z` whose real part lies in [-pi/2, pi/2].
+    For real-valued input data types, `arctan` always returns real output.
+    For each value that cannot be expressed as a real number or infinity,
+    it yields ``nan`` and sets the `invalid` floating point error flag.
+    For complex-valued input, we do not have support for them yet.
+    The inverse tangent is also known as `atan` or tan^{-1}.
+    """
+    return _unary_func_helper(x, _npi.arctan, _np.arctan, out=out, **kwargs)
+
+
+@set_module('mxnet.symbol.numpy')
+def log(x, out=None, **kwargs):
+    """
+    log(x, out=None)
+    Natural logarithm, element-wise.
+    The natural logarithm `log` is the inverse of the exponential function,
+    so that `log(exp(x)) = x`. The natural logarithm is logarithm in base
+    `e`.
+    Parameters
+    ----------
+    x : _Symbol
+        Input value. Elements must be of real value.
+    out : _Symbol or None, optional
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+    Returns
+    -------
+    y : _Symbol
+        The natural logarithm of `x`, element-wise.
+        This is a scalar if `x` is a scalar.
+    Notes
+    -----
+     Currently only supports data of real values and ``inf`` as input. Returns data of real value, ``inf``, ``-inf`` and
+    ``nan`` according to the input.
+    This function differs from the original `numpy.log
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.log.html>`_ in
+    the following aspects:
+    - Does not support complex number for now
+    - Input type does not support Python native iterables(list, tuple, ...). Only ndarray is supported.
+    - ``out`` param: cannot perform auto braodcasting. ``out`` symbol's shape must be the same as the expected output.
+    - ``out`` param: cannot perform auto type cast. ``out`` symbol's dtype must be the same as the expected output.
+    - ``out`` param does not support scalar input case.
+    """
+    return _unary_func_helper(x, _npi.log, _np.log, out=out, **kwargs)
+
+
+@set_module('mxnet.symbol.numpy')
+def degrees(x, out=None, **kwargs):
+    """
+    degrees(x, out=None)
+    Convert angles from radians to degrees.
+    Parameters
+    ----------
+    x : _Symbol
+        Input value. Elements must be of real value.
+    out : _Symbol or None, optional
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+    Returns
+    -------
+    y : _Symbol of floats
+        The corresponding degree values; if `out` was supplied this is a
+        reference to it.
+        This is a scalar if `x` is a scalar.
+    Notes
+    -------
+    This function differs from the original `numpy.degrees
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.degrees.html>`_ in
+    the following aspects:
+    - Input type does not support Python native iterables(list, tuple, ...). Only ndarray is supported.
+    - ``out`` param: cannot perform auto broadcasting. ``out`` symbol's shape must be the same as the expected output.
+    - ``out`` param: cannot perform auto type cast. ``out`` symbol's dtype must be the same as the expected output.
+    - ``out`` param does not support scalar input case.
+    """
+    return _unary_func_helper(x, _npi.degrees, _np.degrees, out=out, **kwargs)
+
+
+def rint(x, out=None, **kwargs):
+    """
+    Round elements of the array to the nearest integer.
+    Parameters
+    ----------
+    x : _Symbol or scalar
+        Input array.
+    out : _Symbol or None
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+    Returns
+    -------
+    out : _Symbol or scalar
+        Output array is same shape and type as x. This is a scalar if x is a scalar.
+    Notes
+    -----
+    This function differs from the original `numpy.rint
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.rint.html>`_ in
+    the following way(s):
+    - only _Symbol or scalar is accpted as valid input, tuple of _Symbol is not supported
+     - broadcasting to `out` of different shape is currently not supported
+    - when input is plain python numerics, the result will not be stored in the `out` param
+    """
+    return _unary_func_helper(x, _npi.rint, _np.rint, out=out, **kwargs)
+
+
+@set_module('mxnet.symbol.numpy')
+def log2(x, out=None, **kwargs):
+    """
+    Base-2 logarithm of x.
+    Parameters
+    ----------
+    x : _Symbol
+        Input values.
+    out : ndarray or None
+        A location into which the result is stored.
+        If provided, it must have the same shape and type as the input.
+        If not provided or None, a freshly-allocated array is returned.
+    Returns
+    -------
+    y : _Symbol
+        The logarithm base two of `x`, element-wise.
+        This is a scalar if `x` is a scalar.
+    Notes
+    -----
+    This function differs from the original `numpy.log2
+    <https://www.google.com/search?q=numpy+log2>`_ in
+    the following way(s):
+    - only ndarray or scalar is accpted as valid input, tuple of ndarray is not supported
+    - broadcasting to `out` of different shape is currently not supported
+    - when input is plain python numerics, the result will not be stored in the `out` param
+    """
+    return _unary_func_helper(x, _npi.log2, _np.log2, out=out, **kwargs)
+
+
+@set_module('mxnet.symbol.numpy')
+def log1p(x, out=None, **kwargs):
+    """
+    Return the natural logarithm of one plus the input array, element-wise.
+    Calculates ``log(1 + x)``.
+    Parameters
+    ----------
+    x : _Symbol or scalar
+        Input array.
+    out : _Symbol or None
+          Dummy parameter to keep the consistency with the ndarray counterpart.
+    Returns
+    -------
+    y : _Symbol or scalar
+        Natural logarithm of 1 + x, element-wise. This is a scalar
+        if x is a scalar.
+    Notes
+    -----
+    For real-valued input, `log1p` is accurate also for `x` so small
+    that `1 + x == 1` in floating-point accuracy.
+    Logarithm is a multivalued function: for each `x` there is an infinite
+    number of `z` such that `exp(z) = 1 + x`. The convention is to return
+    the `z` whose imaginary part lies in `[-pi, pi]`.
+    For real-valued input data types, `log1p` always returns real output.
+    For each value that cannot be expressed as a real number or infinity,
+    it yields ``nan`` and sets the `invalid` floating point error flag.
+    cannot support complex-valued input.
+    Examples
+    --------
+    >>> np.log1p(1e-99)
+    1e-99
+    >>> a = np.array([3, 4, 5])
+    >>> np.log1p(a)
+    array([1.3862944, 1.609438 , 1.7917595])
+    """
+    return _unary_func_helper(x, _npi.log1p, _np.log1p, out=out, **kwargs)
+
+
+@set_module('mxnet.symbol.numpy')
+def radians(x, out=None, **kwargs):
+    """
+    Convert angles from degrees to radians.
+    Parameters
+    ----------
+    x : _Symbol or scalar
+        Input array in degrees.
+    out : _Symbol or None
+       Dummy parameter to keep the consistency with the ndarray counterpart.
+    Returns
+    -------
+    y : _Symbol
+        The corresponding radian values. This is a scalar if x is a scalar.
+    Notes
+    -----
+    This function differs from the original `numpy.radians
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.radians.html>`_ in
+    the following way(s):
+    - only _Symbol or scalar is accpted as valid input, tuple of _Symbol is not supported
+    - broadcasting to `out` of different shape is currently not supported
+    - when input is plain python numerics, the result will not be stored in the `out` param
+    Examples
+    --------
+    >>> deg = np.arange(12.) * 30.
+    >>> np.radians(deg)
+    array([0.       , 0.5235988, 1.0471976, 1.5707964, 2.0943952, 2.6179938,
+           3.1415927, 3.6651914, 4.1887903, 4.712389 , 5.2359877, 5.7595863],
+           dtype=float32)
+    """
+    return _unary_func_helper(x, _npi.radians, _np.radians, out=out, **kwargs)
+
+
+@set_module('mxnet.symbol.numpy')
+def reciprocal(x, out=None, **kwargs):
+    r"""
+    reciprocal(x, out=None)
+    Return the reciprocal of the argument, element-wise.
+    Calculates ``1/x``.
+    Parameters
+    ----------
+    x : _Symbol or scalar
+        The values whose reciprocals are required.
+    out : _Symbol, or None, optional
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+    Returns
+    -------
+    y : _Symbol or scalar
+        Output array is same shape and type as x. This is a scalar if x is a scalar.
+    Notes
+    -----
+    .. note::
+        This function is not designed to work with integers.
+    For integer arguments with absolute value larger than 1 the result is
+    always zero because of the way Python handles integer division.  For
+    integer zero the result is an overflow.
+    The output `symbol` has the same `ctx` as the input `symbol`.
+    This function differs from the original `numpy.reciprocal
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.reciprocal.html>`_ in
+    the following aspects:
+    - Only support _Symbol and scalar now.
+    - `where` argument is not supported.
+    """
+    return _unary_func_helper(x, _npi.reciprocal, _np.reciprocal, out=out, **kwargs)
+
+
+@set_module('mxnet.symbol.numpy')
+def square(x, out=None, **kwargs):
+    r"""
+    square(x, out=None)
+    Return the element-wise square of the input.
+    Parameters
+    ----------
+    x : _Symbol or scalar
+        The values whose reciprocals are required.
+    out : _Symbol, or None, optional
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+    Returns
+    -------
+    y : _Symbol or scalar
+        Output array is same shape and type as x. This is a scalar if x is a scalar.
+    Notes
+    -----
+    The output `symbol` has the same `ctx` as the input `symbol`.
+    This function differs from the original `numpy.square
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.square.html>`_ in
+    the following aspects:
+    - Only support _Symbol and scalar now.
+    - `where` argument is not supported.
+    """
+    return _unary_func_helper(x, _npi.square, _np.square, out=out, **kwargs)
+
+
+@set_module('mxnet.symbol.numpy')
+def negative(x, out=None, where=True, **kwargs):
+    r"""
+    negative(x, out=None, where=True)
+    Numerical negative, element-wise.
+    Parameters:
+    ------------
+    x : _Symbol or scalar
+        Input array.
+    out : _Symbol or None, optional
+          A location into which the result is stored.
+          If provided, it must have a shape that the inputs broadcast to.
+          If not provided or None, a freshly-allocated array is returned.
+          A tuple (possible only as a keyword argument) must have length
+          equal to the number of outputs.
+    where : _Symbol or scalar, optional
+            Values of True indicate to calculate the ufunc at that position,
+            values of False indicate to leave the value in the output alone.
+    Returns:
+    -------
+    y : _Symbol or scalar
+        Returned array or scalar: y = -x. This is a scalar if x is a scalar.
+    Examples:
+    ---------
+    >>> np.negative(1)
+    -1
+    """
+    return _unary_func_helper(x, _npi.negative, _np.negative, out=out)
+
+
+@set_module('mxnet.symbol.numpy')
+def fix(x, out=None):
+    """
+    Round to nearest integer towards zero.
+
+    Round an array of floats element-wise to nearest integer towards zero. The rounded values are returned as floats.
+
+    Parameters:
+    ----------
+    x : _Symbol or scalar
+        An array of floats to be rounded
+    out : _Symbol or scalar, optional
+          Output array
+    Returns:
+    ---------
+    y : _Symbol or scalar
+    Examples:
+    ----------
+    >>> np.fix(3.14)
+    3
+    """
+    return _unary_func_helper(x, _npi.fix, _np.fix, out=out)
+
+
+@set_module('mxnet.symbol.numpy')
+def tan(x, out=None, where=True, **kwargs):
+    r"""
+    tan(x, out=None, where=True)
+    Compute tangent element-wise.
+    Equivalent to np.sin(x)/np.cos(x) element-wise.
+
+    Parameters:
+    ----------
+    x : _Symbol or scalar
+        Input array.
+    out : _Symbol or scalar or None.
+        A location into which the result is stored. If provided,
+        it must have a shape that the inputs broadcast to. If not provided or None,
+        a freshly-allocated array is returned. A tuple (possible only as a keyword argument)
+        must have length equal to the number of outputs.
+    where : array_like, optional
+            Values of True indicate to calculate the ufunc at that position,
+            values of False indicate to leave the value in the output alone.
+    Returns:
+    -------
+    y : _Symbol or scalar
+        The corresponding tangent values. This is a scalar if x is a scalar.
+    """
+
+    return _unary_func_helper(x, _npi.tan, _np.tan, out=out, **kwargs)
+
+
+@set_module('mxnet.symbol.numpy')
+def ceil(x, out=None, **kwargs):
+    r"""
+    Return the ceiling of the input, element-wise.
+    The ceil of the ndarray `x` is the smallest integer `i`, such that
+    `i >= x`.  It is often denoted as :math:`\lceil x \rceil`.
+    Parameters
+    ----------
+    x : _Symbol or scalar
+        Input array.
+    out : _Symbol or None
+          Dummy parameter to keep the consistency with the ndarray counterpart.
+    Returns
+    -------
+    y :
+        _Symbol or scalar
+        The ceiling of each element in `x`, with `float` dtype.
+        This is a scalar if `x` is a scalar.
+    Examples
+    --------
+    >>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
+    >>> np.ceil(a)
+    array([-1., -1., -0.,  1.,  2.,  2.,  2.])
+    >>> #if you use parameter out, x and out must be ndarray. if not, you will get an error!
+    >>> a = np.array(1)
+    >>> np.ceil(np.array(3.5), a)
+    array(4.)
+    >>> a
+    array(4.)
+    """
+    return _unary_func_helper(x, _npi.ceil, _np.ceil, out=out, **kwargs)
+
+
+@set_module('mxnet.symbol.numpy')
+def floor(x, out=None, **kwargs):
+    r"""
+    Return the floor of the input, element-wise.
+    The floor of the ndarray `x` is the largest integer `i`, such that
+    `i <= x`.  It is often denoted as :math:`\lfloor x \rfloor`.
+    Parameters
+    ----------
+    x : _Symbol or scalar
+        Input array.
+    out : _Symbol or None
+          Dummy parameter to keep the consistency with the ndarray counterpart.
+    Returns
+    -------
+    y :
+        _Symbol or scalar
+        The floor of each element in `x`, with `float` dtype.
+        This is a scalar if `x` is a scalar.
+    Examples
+    --------
+    >>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
+    >>> np.floor(a)
+    array([-2., -2., -1.,  0.,  1.,  1.,  2.])
+    >>> #if you use parameter out, x and out must be ndarray. if not, you will get an error!
+    >>> a = np.array(1)
+    >>> np.floor(np.array(3.5), a)
+    array(3.)
+    >>> a
+    array(3.)
+    """
+    return _unary_func_helper(x, _npi.floor, _np.floor, out=out, **kwargs)
+
+
+@set_module('mxnet.symbol.numpy')
+def trunc(x, out=None, **kwargs):
+    r"""
+    trunc(x, out=None)
+    Return the truncated value of the input, element-wise.
+    The truncated value of the scalar `x` is the nearest integer `i` which
+    is closer to zero than `x` is. In short, the fractional part of the
+    signed number `x` is discarded.
+
+    Parameters
+    ----------
+    x : _Symbol or scalar
+        Input data.
+    out : _Symbol or None, optional
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+
+    Returns
+    -------
+    y : _Symbol or scalar
+        The truncated value of each element in `x`.
+        This is a scalar if `x` is a scalar.
+    Notes
+    -----
+    This function differs from the original numpy.trunc in the following aspects:
+        - Do not support `where`, a parameter in numpy which indicates where to calculate.
+        - Cannot cast type automatically. Dtype of `out` must be same as the expected one.
+        - Cannot broadcast automatically. Shape of `out` must be same as the expected one.
+        - If `x` is plain python numeric, the result won't be stored in out.
+    """
+    return _unary_func_helper(x, _npi.trunc, _np.trunc, out=out, **kwargs)
+
+
+@set_module('mxnet.symbol.numpy')
+def logical_not(x, out=None, **kwargs):
+    r"""
+    logical_not(x, out=None)
+    Compute the truth value of NOT x element-wise.
+    Parameters
+    ----------
+    x : _Symbol or scalar
+        Logical NOT is applied to the elements of `x`.
+    out : _Symbol or None, optional
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+
+    Returns
+    -------
+    y : bool or _Symbol
+        Boolean result with the same shape as `x` of the NOT operation
+        on elements of `x`.
+        This is a scalar if `x` is a scalar.
+    Notes
+    -----
+    This function differs from the original numpy.logical_not in the following aspects:
+        - Do not support `where`, a parameter in numpy which indicates where to calculate.
+        - Cannot cast type automatically. Dtype of `out` must be same as the expected one.
+        - Cannot broadcast automatically. Shape of `out` must be same as the expected one.
+        - If `x` is plain python numeric, the result won't be stored in out.
+    """
+    return _unary_func_helper(x, _npi.logical_not, _np.logical_not, out=out, **kwargs)
+
+
+@set_module('mxnet.symbol.numpy')
+def arcsinh(x, out=None, **kwargs):
+    r"""
+    arcsinh(x, out=None)
+    Inverse hyperbolic sine, element-wise.
+    Parameters
+    ----------
+    x : _Symbol or scalar
+        Input array.
+    out : _Symbol or None, optional
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+
+    Returns
+    -------
+    arcsinh : _Symbol
+        Array of the same shape as `x`.
+        This is a scalar if `x` is a scalar.
+    Notes
+    -----
+    `arcsinh` is a multivalued function: for each `x` there are infinitely
+    many numbers `z` such that `sinh(z) = x`.
+
+    For real-valued input data types, `arcsinh` always returns real output.
+    For each value that cannot be expressed as a real number or infinity, it
+    yields ``nan`` and sets the `invalid` floating point error flag.
+
+    This function differs from the original numpy.arcsinh in the following aspects:
+        - Do not support `where`, a parameter in numpy which indicates where to calculate.
+        - Do not support complex-valued input.
+        - Cannot cast type automatically. DType of `out` must be same as the expected one.
+        - Cannot broadcast automatically. Shape of `out` must be same as the expected one.
+        - If `x` is plain python numeric, the result won't be stored in out.
+    """
+    return _unary_func_helper(x, _npi.arcsinh, _np.arcsinh, out=out, **kwargs)
+
+
+@set_module('mxnet.symbol.numpy')
+def arccosh(x, out=None, **kwargs):
+    r"""
+    arccosh(x, out=None)
+    Inverse hyperbolic cosine, element-wise.
+    Parameters
+    ----------
+    x : _Symbol or scalar
+        Input array.
+    out : _Symbol or None, optional
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+
+    Returns
+    -------
+    arccosh : _Symbol
+        Array of the same shape as `x`.
+        This is a scalar if `x` is a scalar.
+    Notes
+    -----
+    `arccosh` is a multivalued function: for each `x` there are infinitely
+    many numbers `z` such that `cosh(z) = x`.
+
+    For real-valued input data types, `arccosh` always returns real output.
+    For each value that cannot be expressed as a real number or infinity, it
+    yields ``nan`` and sets the `invalid` floating point error flag.
+
+    This function differs from the original numpy.arccosh in the following aspects:
+        - Do not support `where`, a parameter in numpy which indicates where to calculate.
+        - Do not support complex-valued input.
+        - Cannot cast type automatically. Dtype of `out` must be same as the expected one.
+        - Cannot broadcast automatically. Shape of `out` must be same as the expected one.
+        - If `x` is plain python numeric, the result won't be stored in out.
+    """
+    return _unary_func_helper(x, _npi.arccosh, _np.arccosh, out=out, **kwargs)
+
+
+@set_module('mxnet.symbol.numpy')
+def arctanh(x, out=None, **kwargs):
+    r"""
+    arctanh(x, out=None)
+    Inverse hyperbolic tangent, element-wise.
+    Parameters
+    ----------
+    x : _Symbol or scalar
+        Input array.
+    out : _Symbol or None, optional
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+
+    Returns
+    -------
+    arctanh : _Symbol
+        Array of the same shape as `x`.
+        This is a scalar if `x` is a scalar.
+    Notes
+    -----
+    `arctanh` is a multivalued function: for each `x` there are infinitely
+    many numbers `z` such that `tanh(z) = x`.
+
+    For real-valued input data types, `arctanh` always returns real output.
+    For each value that cannot be expressed as a real number or infinity, it
+    yields ``nan`` and sets the `invalid` floating point error flag.
+
+    This function differs from the original numpy.arctanh in the following aspects:
+        - Do not support `where`, a parameter in numpy which indicates where to calculate.
+        - Do not support complex-valued input.
+        - Cannot cast type automatically. Dtype of `out` must be same as the expected one.
+        - Cannot broadcast automatically. Shape of `out` must be same as the expected one.
+        - If `x` is plain python numeric, the result won't be stored in out.
+    """
+    return _unary_func_helper(x, _npi.arctanh, _np.arctanh, out=out, **kwargs)
+
+
 @set_module('mxnet.symbol.numpy')
 def tile(A, reps):
     r"""
diff --git a/src/operator/numpy/np_elemwise_unary_op_basic.cc b/src/operator/numpy/np_elemwise_unary_op_basic.cc
new file mode 100644
index 000000000000..b5764a7fbb4b
--- /dev/null
+++ b/src/operator/numpy/np_elemwise_unary_op_basic.cc
@@ -0,0 +1,366 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file np_elemwise_unary_op_basic.cc
+ * \brief CPU Implementation of numpy elementwise unary function.
+ */
+#include <mxnet/base.h>
+#include "../tensor/elemwise_unary_op.h"
+
+namespace mxnet {
+namespace op {
+
+MXNET_OPERATOR_REGISTER_UNARY(_npx_relu)
+.describe(R"code(Computes rectified linear activation.
+.. math::
+   max(features, 0)
+)code" ADD_FILELINE)
+.set_attr<FCompute>("FCompute<cpu>", UnaryOp::Compute<cpu, mshadow_op::relu>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{"_backward_relu"});
+
+MXNET_OPERATOR_REGISTER_UNARY(_npx_sigmoid)
+.describe(R"code(Computes sigmoid of x element-wise.
+.. math::
+   y = 1 / (1 + exp(-x))
+)code" ADD_FILELINE)
+.set_attr<FCompute>("FCompute<cpu>", UnaryOp::Compute<cpu, mshadow_op::sigmoid>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{"_backward_sigmoid"});
+
+NNVM_REGISTER_OP(_np_copy)
+.describe(R"code(Return an array copy of the given object.)code" ADD_FILELINE)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs){
+    return std::vector<std::pair<int, int> >{{0, 0}};
+  })
+.set_attr<FCompute>("FCompute<cpu>", UnaryOp::IdentityCompute<cpu>)
+.set_attr<nnvm::FInplaceIdentity>("FInplaceIdentity",
+  [](const NodeAttrs& attrs){
+    return std::vector<bool>{true};
+  })
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_copy"})
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"a"};
+  })
+.add_argument("a", "NDArray-or-Symbol", "The input");
+
+#define MXNET_OPERATOR_REGISTER_NUMPY_UNARY(__name$, __input_name$, __kernel$)          \
+NNVM_REGISTER_OP(__name$)                                                               \
+.set_num_inputs(1)                                                                      \
+.set_num_outputs(1)                                                                     \
+.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)                       \
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)                           \
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",                                       \
+  [](const NodeAttrs& attrs){                                                           \
+    return std::vector<std::pair<int, int> >{{0, 0}};                                   \
+  })                                                                                    \
+.set_attr<nnvm::FListInputNames>("FListInputNames",                                     \
+  [](const NodeAttrs& attrs) {                                                          \
+    return std::vector<std::string>{__input_name$};                                     \
+  })                                                                                    \
+.set_attr<FCompute>("FCompute<cpu>", UnaryOp::Compute<cpu, __kernel$>)                  \
+.add_argument(__input_name$, "NDArray-or-Symbol", "The input array.")
+
+// negative
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_negative, "x", mshadow_op::negation)
+.describe(R"code(Numerical negative, element-wise.
+Example::
+    negative([1.,  -1.]) = [-1.,  1.]
+)code")
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"negative"});
+
+// reciprocal
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_reciprocal, "x", mshadow_op::reciprocal)
+.describe(R"code(Return the reciprocal of the argument, element-wise.
+Example::
+    reciprocal([-2, 1, 3, 1.6, 0.2]) = [-0.5, 1.0, 0.33333334, 0.625, 5.0]
+)code")
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_reciprocal"});
+
+// abs
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_absolute, "x", mshadow_op::abs)
+.add_alias("_npi_abs")
+.describe(R"code(Returns element-wise absolute value of the input.
+Example::
+   absolute([-2, 0, 3]) = [2, 0, 3]
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_abs"});
+
+// sign
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_sign, "x", mshadow_op::sign)
+.describe(R"code(Returns an element-wise indication of the sign of a number.
+The sign function returns -1 if x < 0, 0 if x==0, 1 if x > 0.
+Example::
+   sign([-2, 0, 3]) = [-1, 0, 1]
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_sign"});
+
+// rint
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_rint, "x", mshadow_op::rint)
+.describe(R"code(Round elements of the array to the nearest integer.
+Example::
+   rint([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]) = [-2., -2., -0.,  0.,  2.,  2.,  2.]
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes);
+
+// ceil
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_ceil, "x", mshadow_op::ceil)
+.describe(R"code(Return the ceiling of the input, element-wise.
+The ceil of the scalar x is the smallest integer i, such that i >= x.
+Example::
+   ceil([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]) = [-1., -1., -0.,  1.,  2.,  2.,  2.]
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes);
+
+// floor
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_floor, "x", mshadow_op::floor)
+.describe(R"code(Return the floor of the input, element-wise.
+The floor of the scalar x is the largest integer i, such that i <= x.
+Example::
+   floor([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]) = [-2., -2., -1.,  0.,  1.,  1.,  2.]
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes);
+
+// trunc
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_trunc, "x", mshadow_op::trunc)
+.describe(R"code(Return the truncated value of the input, element-wise.
+The truncated value of the scalar x is the nearest integer i which is closer to
+zero than x is. In short, the fractional part of the signed number x is discarded.
+Example::
+   trunc([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]) = [-1., -1., -0.,  0.,  1.,  1.,  2.]
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes);
+
+// fix
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_fix, "x", mshadow_op::fix)
+.describe(R"code(Round to nearest integer towards zero.
+Round an array of floats element-wise to nearest integer towards zero.
+The rounded values are returned as floats.
+Example::
+   fix([-2.1, -1.9, 1.9, 2.1]) = [-2., -1.,  1., 2.]
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes);
+
+// square
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_square, "x", mshadow_op::square)
+.describe(R"code(Return the element-wise square of the input.
+Example::
+   square([2, 3, 4]) = [4, 9, 16]
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_square"});
+
+// sqrt
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_sqrt, "x", mshadow_op::square_root)
+.describe(R"code(Return the non-negative square-root of an array, element-wise.
+Example::
+   sqrt([4, 9, 16]) = [2, 3, 4]
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{"_backward_sqrt"});
+
+// cbrt
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_cbrt, "x", mshadow_op::cube_root)
+.describe(R"code(Return the cube-root of an array, element-wise.
+Example::
+   cbrt([1, 8, -125]) = [1, 2, -5]
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{"_backward_cbrt"});
+
+// exp
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_exp, "x", mshadow_op::exp)
+.describe(R"code(Calculate the exponential of all elements in the input array.
+Example::
+   exp([0, 1, 2]) = [1., 2.71828175, 7.38905621]
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{"_mul"});
+
+// log
+NNVM_REGISTER_OP(_npi_log)
+.describe(R"code(Returns element-wise Natural logarithmic value of the input.
+The natural logarithm is logarithm in base *e*, so that ``log(exp(x)) = x``
+)code" ADD_FILELINE)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs){
+    return std::vector<std::pair<int, int> >{{0, 0}};
+  })
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"x"};
+  })
+.set_attr<FCompute>("FCompute<cpu>", UnaryOp::Compute<cpu, mshadow_op::log>)
+.add_argument("x", "NDArray-or-Symbol", "The input array.")
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_log"});
+
+// log10
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_log10, "x", mshadow_op::log10)
+.describe(R"code(Returns element-wise Base-10 logarithmic value of the input.
+``10**log10(x) = x``
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_log10"});
+
+// log2
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_log2, "x", mshadow_op::log2)
+.describe(R"code(Returns element-wise Base-2 logarithmic value of the input.
+``2**log2(x) = x``
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_log2"});
+
+// log1p
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_log1p, "x", mshadow_op::log1p)
+.describe(R"code(Return the natural logarithm of one plus the input array, element-wise.
+Calculates ``log(1 + x)``.
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_log1p"});
+
+// expm1
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_expm1, "x", mshadow_op::expm1)
+.describe(R"code(Calculate ``exp(x) - 1`` for all elements in the array.)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_expm1"});
+
+
+// logical_not
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_logical_not, "x", mshadow_op::nt)
+.describe(R"code(Compute the truth value of NOT x element-wise.
+Example::
+  logical_not([-2., 0., 1.]) = [0., 1., 0.]
+)code")
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes);
+
+// sin
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_sin, "x", mshadow_op::sin)
+.describe(R"code(Trigonometric sine, element-wise.
+.. math::
+   sin([0, \pi/4, \pi/2]) = [0, 0.707, 1]
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{ "_backward_sin" });
+
+// cos
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_cos, "x", mshadow_op::cos)
+.describe(R"code(Computes the element-wise cosine of the input array.
+.. math::
+   cos([0, \pi/4, \pi/2]) = [1, 0.707, 0]
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_cos"});
+
+// tan
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_tan, "x", mshadow_op::tan)
+.describe(R"code(Computes the element-wise tangent of the input array.
+.. math::
+   tan([0, \pi/4, \pi/2]) = [0, 1, -inf]
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{ "_backward_tan" });
+
+// arcsin
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_arcsin, "x", mshadow_op::arcsin)
+.describe(R"code(Returns element-wise inverse sine of the input array.
+.. math::
+   arcsin([-1, -.707, 0, .707, 1]) = [-\pi/2, -\pi/4, 0, \pi/4, \pi/2]
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{ "_backward_arcsin" });
+
+// arccos
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_arccos, "x", mshadow_op::arccos)
+.describe(R"code(Returns element-wise inverse cosine of the input array.
+The input should be in range `[-1, 1]`.
+The output is in the closed interval :math:`[0, \pi]`
+.. math::
+   arccos([-1, -.707, 0, .707, 1]) = [\pi, 3\pi/4, \pi/2, \pi/4, 0]
+The storage type of ``arccos`` output is always dense
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{ "_backward_arccos" });
+
+// arctan
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_arctan, "x", mshadow_op::arctan)
+.describe(R"code(Returns element-wise inverse tangent of the input array.
+.. math::
+   arctan([-1, 0, 1]) = [-\pi/4, 0, \pi/4]
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{ "_backward_arctan" });
+
+// degrees
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_degrees, "x", mshadow_op::degrees)
+.describe(R"code(Converts each element of the input array from radians to degrees.
+.. math::
+   degrees([0, \pi/2, \pi, 3\pi/2, 2\pi]) = [0, 90, 180, 270, 360]
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{ "_backward_degrees" });
+
+// radians
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_radians, "x", mshadow_op::radians)
+.describe(R"code(Converts each element of the input array from degrees to radians.
+.. math::
+   radians([0, 90, 180, 270, 360]) = [0, \pi/2, \pi, 3\pi/2, 2\pi]
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{ "_backward_radians" });
+
+// sinh
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_sinh, "x", mshadow_op::sinh)
+.describe(R"code(Returns the hyperbolic sine of the input array, computed element-wise.
+.. math::
+   sinh(x) = 0.5\times(exp(x) - exp(-x))
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{ "_backward_sinh" });
+
+// cosh
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_cosh, "x", mshadow_op::cosh)
+.describe(R"code(Returns the hyperbolic cosine  of the input array, computed element-wise.
+.. math::
+   cosh(x) = 0.5\times(exp(x) + exp(-x))
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{ "_backward_cosh" });
+
+// tanh
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_tanh, "x", mshadow_op::tanh)
+.describe(R"code(Returns the hyperbolic tangent of the input array, computed element-wise.
+.. math::
+   tanh(x) = sinh(x) / cosh(x)
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{ "_backward_tanh" });
+
+// arcsinh
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_arcsinh, "x", mshadow_op::arcsinh)
+.describe(R"code(Returns the element-wise inverse hyperbolic sine of the input array, \
+computed element-wise.
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{ "_backward_arcsinh" });
+
+// arccosh
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_arccosh, "x", mshadow_op::arccosh)
+.describe(R"code(Returns the element-wise inverse hyperbolic cosine of the input array, \
+computed element-wise.
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{ "_backward_arccosh" });
+
+// arctanh
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_arctanh, "x", mshadow_op::arctanh)
+.describe(R"code(Returns the element-wise inverse hyperbolic tangent of the input array, \
+computed element-wise.
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{ "_backward_arctanh" });
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_elemwise_unary_op_basic.cu b/src/operator/numpy/np_elemwise_unary_op_basic.cu
new file mode 100644
index 000000000000..cb8bb771b27d
--- /dev/null
+++ b/src/operator/numpy/np_elemwise_unary_op_basic.cu
@@ -0,0 +1,110 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file np_elemwise_unary_op_basic.cu
+ * \brief GPU Implementation of numpy unary functions.
+ */
+#include "../tensor/elemwise_binary_op.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(_npx_relu)
+.set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, mshadow_op::relu>);
+
+NNVM_REGISTER_OP(_npx_sigmoid)
+.set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, mshadow_op::sigmoid>);
+
+NNVM_REGISTER_OP(_np_copy)
+.set_attr<FCompute>("FCompute<gpu>", UnaryOp::IdentityCompute<gpu>);
+
+#define MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(__name$, __kernel$)     \
+NNVM_REGISTER_OP(__name$)                                               \
+.set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, __kernel$>)  \
+
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_negative, mshadow_op::negation);
+
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_reciprocal, mshadow_op::reciprocal);
+
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_absolute, mshadow_op::abs);
+
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_sign, mshadow_op::sign);
+
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_rint, mshadow_op::rint);
+
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_ceil, mshadow_op::ceil);
+
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_floor, mshadow_op::floor);
+
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_trunc, mshadow_op::trunc);
+
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_fix, mshadow_op::fix);
+
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_square, mshadow_op::square);
+
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_sqrt, mshadow_op::square_root);
+
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_cbrt, mshadow_op::cube_root);
+
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_exp, mshadow_op::exp);
+
+NNVM_REGISTER_OP(_npi_log)
+.set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, mshadow_op::log>);
+
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_log10, mshadow_op::log10);
+
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_log2, mshadow_op::log2);
+
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_log1p, mshadow_op::log1p);
+
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_expm1, mshadow_op::expm1);
+
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_logical_not, mshadow_op::nt);
+
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_sin, mshadow_op::sin);
+
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_cos, mshadow_op::cos);
+
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_tan, mshadow_op::tan);
+
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_arcsin, mshadow_op::arcsin);
+
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_arccos, mshadow_op::arccos);
+
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_arctan, mshadow_op::arctan);
+
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_degrees, mshadow_op::degrees);
+
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_radians, mshadow_op::radians);
+
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_sinh, mshadow_op::sinh);
+
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_cosh, mshadow_op::cosh);
+
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_tanh, mshadow_op::tanh);
+
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_arcsinh, mshadow_op::arcsinh);
+
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_arccosh, mshadow_op::arccosh);
+
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_arctanh, mshadow_op::arctanh);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 6a1a6a6dfd5d..399cdead6177 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -713,6 +713,155 @@ def hybrid_forward(self, F, x):
             assert same(ret_mx.asnumpy(), ret_np)
 
 
+@with_seed()
+@use_np
+def test_np_unary_funcs():
+    def check_unary_func(func, ref_grad, shape, low, high):
+        class TestUnary(HybridBlock):
+            def __init__(self, func):
+                super(TestUnary, self).__init__()
+                self._func = func
+
+            def hybrid_forward(self, F, a, *args, **kwargs):
+                print(self._func)
+                return getattr(F.np, self._func)(a)
+
+        np_func = getattr(_np, func)
+        mx_func = TestUnary(func)
+        np_test_data = _np.random.uniform(low, high, shape).astype(_np.float32)
+        mx_test_data = mx.numpy.array(np_test_data)
+        for hybridize in [True, False]:
+            if hybridize:
+                mx_func.hybridize()
+            if ref_grad:
+                mx_test_data.attach_grad()
+            np_out = np_func(np_test_data)
+            with mx.autograd.record():
+                y = mx_func(mx_test_data)
+            assert y.shape == np_out.shape
+            assert_almost_equal(y.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+
+            if ref_grad:
+                y.backward()
+                assert_almost_equal(mx_test_data.grad.asnumpy(), ref_grad(np_test_data), rtol=1e-5, atol=1e-6, equal_nan=True)
+
+    funcs = {
+        'absolute' : (lambda x: -1. * (x < 0) + (x > 0), -1.0, 1.0),
+        'cbrt' : (lambda x: 1. / (3. * _np.cbrt(x) ** 2), -1.0, 1.0),
+        'ceil' : (None, -10.0, 10.0),
+        'exp' : (lambda x: _np.exp(x), -1.0, 1.0),
+        'expm1' : (lambda x: _np.exp(x), -1.0, 1.0),
+        'fix' : (None, -10.0, 10.0),
+        'floor' : (None, -10.0, 10.0),
+        'log' : (lambda x: 1.0 / x, 0.1, 5.0),
+        'log10' : (lambda x: 1.0 / (x * _np.log(10)), 0.1, 10.0),
+        'log1p' : (lambda x: 1.0 / (1.0 + x), -0.9, 5.0),
+        'log2' : (lambda x: 1.0 / (x * _np.log(2)), 0.1, 2.0),
+        'logical_not' : (None, -1.0, 1.0),
+        'negative' : (lambda x: -1. * _np.ones(x.shape), -1.0, 1.0),
+        'reciprocal' : (lambda x: -1. / (x ** 2), 0.01, 1.0),
+        'rint' : (None, -5.0, 5.0),
+        'sign' : (None, -1.0, 1.0),
+        'sqrt' : (lambda x: 0.5 / _np.sqrt(x), 0.001, 10.0),
+        'square' : (lambda x: 2.0 * x, -1.0, 1.0),
+        'trunc' : (None, -5.0, 5.0),
+        'sin' : (lambda x: _np.cos(x), -1.0, 1.0),
+        'cos' : (lambda x: -_np.sin(x), -1.0, 1.0),
+        'tan' : (lambda x: _np.tan(x) ** 2 + 1.0, -1.0, 1.0),
+        'arcsin' : (lambda x: 1. / (1. - x ** 2) ** (1. / 2.), -1.0, 1.0),
+        'arccos' : (lambda x: -1. / (1. - x ** 2.) ** (1. / 2.), -1.0, 1.0),
+        'arctan' : (lambda x: 1. / (x ** 2. + 1.), -1.0, 1.0),
+        'degrees' : (lambda x: 180. / _np.pi * _np.ones(x.shape), -1.0, 1.0),
+        'radians' : (lambda x: _np.pi / 180. * _np.ones(x.shape), -1.0, 1.0),
+        'sinh' : (lambda x: _np.cosh(x), -1.0, 1.0),
+        'cosh' : (lambda x: _np.sinh(x), -1.0, 1.0),
+        'tanh' : (lambda x: 1. - _np.tanh(x) ** 2, -1.0, 1.0),
+        'arcsinh' : (lambda x: 1./(x**2 + 1.)**(1./2.), -1.0, 1.0),
+        'arccosh' : (lambda x: 1./(x**2 - 1.)**(1./2.), 2.0, 5.0),
+        'arctanh' : (lambda x: -1./(x**2 - 1.), -0.99, 0.99)
+    }
+    ndim = random.choice([2, 3, 4])
+    shape = random.choice([rand_shape_nd(ndim, dim=3), (1, 0, 2)])
+    for shape in [rand_shape_nd(ndim, dim=3), (1, 0, 2)]:
+        for func, func_data in funcs.items():
+            ref_grad, low, high = func_data
+            check_unary_func(func, ref_grad, shape, low, high)
+
+
+@with_seed()
+@use_np
+def test_npx_relu():
+    def np_relu(x):
+        return _np.maximum(x, 0.0)
+    def np_relu_grad(x):
+        return 1.0 * (x > 0.0)
+
+    class TestReLU(HybridBlock):
+        def __init__(self):
+            super(TestReLU, self).__init__()
+
+        def hybrid_forward(self, F, a):
+            return F.npx.relu(a)
+
+    shapes = [(), (2, 3, 4), (2, 0, 3), (1, 0, 0)]
+    for hybridize in [True, False]:
+        for shape in shapes:
+            test_relu = TestReLU()
+            if hybridize:
+                test_relu.hybridize()
+            x = rand_ndarray(shape).as_np_ndarray()
+            x.attach_grad()
+            np_out = np_relu(x.asnumpy())
+            with mx.autograd.record():
+                mx_out = test_relu(x)
+            assert mx_out.shape == np_out.shape
+            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+            mx_out.backward()
+            np_backward = np_relu_grad(x.asnumpy())
+            assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=1e-3, atol=1e-5)
+
+            mx_out = npx.relu(x)
+            np_out = np_relu(x.asnumpy())
+            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+
+
+@with_seed()
+@use_np
+def test_npx_sigmoid():
+    def np_sigmoid(x):
+        return _np.divide(1.0, (1.0 + _np.exp(-x)))
+    def np_sigmoid_grad(ya):
+        return ya * (1 - ya)
+
+    class TestSigmoid(HybridBlock):
+        def __init__(self):
+            super(TestSigmoid, self).__init__()
+
+        def hybrid_forward(self, F, a):
+            return F.npx.sigmoid(a)
+
+    shapes = [(), (2, 3, 4), (2, 0, 3), (1, 0, 0)]
+    for hybridize in [True, False]:
+        for shape in shapes:
+            test_sigmoid = TestSigmoid()
+            if hybridize:
+                test_sigmoid.hybridize()
+            x = rand_ndarray(shape).as_np_ndarray()
+            x.attach_grad()
+            np_out = np_sigmoid(x.asnumpy())
+            with mx.autograd.record():
+                mx_out = test_sigmoid(x)
+            assert mx_out.shape == np_out.shape
+            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+            mx_out.backward()
+            np_backward = np_sigmoid_grad(np_out)
+            assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=1e-3, atol=1e-5)
+
+            mx_out = npx.sigmoid(x)
+            np_out = np_sigmoid(x.asnumpy())
+            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+
+
 @with_seed()
 @use_np
 def test_np_arange():

From 1eb1925ce20f17c60d1a4c02a0bea0a72dd51885 Mon Sep 17 00:00:00 2001
From: Lai Wei <royweilai@gmail.com>
Date: Fri, 23 Aug 2019 12:00:31 -0700
Subject: [PATCH 213/813] remove Julia cat image for license issue (#15964)

* remove cat image

* trigger ci
---
 .../Prediction with Pre-trained Model.ipynb    |   4 ++--
 .../imagenet/ijulia-pretrained-predict/cat.png | Bin 123126 -> 0 bytes
 2 files changed, 2 insertions(+), 2 deletions(-)
 delete mode 100644 julia/examples/imagenet/ijulia-pretrained-predict/cat.png

diff --git a/julia/examples/imagenet/ijulia-pretrained-predict/Prediction with Pre-trained Model.ipynb b/julia/examples/imagenet/ijulia-pretrained-predict/Prediction with Pre-trained Model.ipynb
index 3ef8040c30f6..878a3851e77e 100644
--- a/julia/examples/imagenet/ijulia-pretrained-predict/Prediction with Pre-trained Model.ipynb	
+++ b/julia/examples/imagenet/ijulia-pretrained-predict/Prediction with Pre-trained Model.ipynb	
@@ -17,7 +17,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let us first load and display the demo image (try to use other images you like). You will need to install `Images.jl` and `Colors.jl` to load the image."
+    "Let us first load and display the demo image (download it from [here](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/python/predict_image/cat.jpg) or try to use other images you like). You will need to install `Images.jl` and `Colors.jl` to load the image."
    ]
   },
   {
@@ -46,7 +46,7 @@
    ],
    "source": [
     "using Images, Colors, ImageMagick\n",
-    "img = load(\"cat.png\")"
+    "img = load(\"cat.jpg\")"
    ]
   },
   {
diff --git a/julia/examples/imagenet/ijulia-pretrained-predict/cat.png b/julia/examples/imagenet/ijulia-pretrained-predict/cat.png
deleted file mode 100644
index 5f681ec7e99cfbca148cb89547f53f13e0eeffc8..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 123126
zcmWifcRX9)8^=*vv_))MMT|zORlD|z6_k+JqqSNisM@<N6|qMUX|0+e_Nd)fZK+m>
zO??$b#il5Je&_yi?l^y(*FE?7oX_(<_r#ez*1gHd%Sc5<byHstVoF6teR)bvMNe~C
zkyz9uFDu$-8b%saRJF<1|2?I<ycTfMGc}^33K6BEii)P9I=Q?R^_z;y{~;CC`V%TD
z#cV1nE}wU;Fy+e!R~-#>AygOtKk_?D(=S_6=|eQ&LDPn3R`nt_X?Na+=e1RH07AE<
zfB9??Y)AxrZrhLF)GXhPSlBBC3#fTq3K?_;_eE9KO7&esQk<oDo!NySOQBix@C^A=
z&-az{OO%{fcn;?QL)TQkmdh<`tqkcat^TpBoju$0{yB1H9Ep#@<MF~&V33t&HJFO3
zpA}F8dX26Xp@O!a9!N=vNU4b3=6GPTktkjRA8&{o9nSGbH0m_2lN(@PhYiQAJv!t$
zA#g}enMLKs`b?8OId*SZSy|Wa!A1a|s|g#Rz*Pzl#-zN4{3VYi8F<?nQRm}<^-vw3
zTTZuDQ-;Anh@s#OPH|~%b{BDJ%`r30uhB#kAp7GSR*I?=Of|c<3g_*Ca0}8d2A6sv
zrPc}(M-x~x#?t6n`9z#((@fP1@p_5AfEl$_Jdi=)Z9Nq%nT8u6E});52pSmHgwWP0
z+7vU{<JD!-$*z+Oasy9uvQC%Q^2yH$2x@&uW=WeaC)I{g<~MUHI7j`%oqvnnB+}kq
zY|zR2$=~td@Of*?SoJXgS0#;XvpS;5@LKxQH5kuazRUn-449@bRq-4znqsQYN|$Dx
ziV!KW!m}eyui_Mp@aY4!ne(9}IkPrSb}OJC+-q&Zc4XPZHrG*k<zUS>!uDkbZK^g0
zV1QddD~grN0>Md@4Apz@#d*bQnU-B$(29|B<6>(+9pZ{+MMRZtCPRlSWvApu49nH2
zYk;v16^z;(FRq&o{0MD$uu?c^WbR?G<#WBhKf%wN5SM`!L5asCK!QHg*>@(Qhxh66
z9k;nssIv7qY4C|qiHy)F%?eHPx2uysc@Xe2hV;S|C{YtZ?PpwLnF<oQH;Dn#rTWnH
zTi+W^ce);!{B!d68>|&3<Do3TgcXFiB8H)H1zr)qYxJZ?Q-l`8DNrmfj4q-Fwxj8&
zHK^2Eua~>BP|@oP(8DziA;R^(b~bXKbl$#1lqKkJY;7N~k~9%I)*ocAmxeZyOI*^*
z8G^D`aHqF6>ptIevYQBA54X=zsgUw`q-*~rwFV81Pa5qXeu8F$SaXOy`7{?lKr18Q
zDTifg9MvrP8V%<}3s|umvm-$44D5P<S1eRO2a1FGXDCEC4TVSTI}UQEQVQK&k@zR$
zdJs`+O=3H1nJ}a%J4K2^G-_;K91GzY7#to8X^GeCF$Q;^OQ=%;X#{|HMwV0=>9jjp
zK7zE9QNqQHr*|P>^eyGW%5rRDNm&`*{_WtPo)f0wDt$_7ZR9E%`cOy*qD`w`1Eirk
z-d!r^F)_)O{nN-W;E0LB#?b0Qu3o3r#y}AHtlEWOX}!0xIoeD#;Zg`m;&#8_avU0b
z@|`|OR#z_ps;dDh5HEyW-#+^#RbeR(6ss{Dz~j~3ehpbr6%+=wxYscXQ*+it{w@@Y
z#&eilbpx6L1c6b`wb8YN8hS2QsUc|#-JAUie{QcP?TL6(9+NWmhTnaul=lc9jR<`7
zE{RZ%CGIH`?^{l6oP>2uHkG76*JMBTvQPTG>pN&ICVf{oPtSiwo6ks%5|4^bGtCTL
z%1>iNX^7sZ;if^jb1<0DMqR;ha8U8ypo*TxiP=oZm0PCKy%Gkj;rcigKH5pkIhz-A
zr5E8pW?7`MF4LvaimAJzQfTU0%MbK%*<ZxO+3N%*9cVk>oz5C(qf%xszRjNUYt(|r
zovT^zNE>`g)hkM|US=O)rvVOAnWPN!#;LnW?u@d~Q;9GPfXZsPF@XKqg+uNSWd9U)
z!8DN=s91qmaRc^QN->@)`sx768E7DdHW7f|RM*zl5If$r<{AK_o_KO0n#eP@L{mCH
z(GTG8ami7qkE@fuLBP~FM=&lMH6GDchW5E|!kCJsYW_&*7f+*`k&ydTZvqlII*mcl
zg6KTdUDF6-w9e0;Yh`4YfUneMJ$qhk@$e_L#EZY?onAhZ(u3Rkl;Cjbq~_ReBizcP
zdGg$xTc=eC>8X9@(k4vw4cyyW-!Mg6f-7-)wz<<NmIPF62~Ak%Z`z3Ux1~wS5?ai}
z2y3Xv(1>K{iLepB3a5$9fuJQQh5>a9eG#4)5Bw5e=q^_-BE=y2z73@(c$8^8Nfg&J
z$RG{%ZPUZub%0hqyZGVZv9kvN(>Q2dYX>_gAnMncm;GE0JDLh?A`*(F(*RbWchkw~
zvr6zf$PKGC4O0vV{(?fvXAXqZB*MpOAjPlrT}4zFP3==<Rx*pD78R}8T(sbsTy#)s
zAz#nb{AVqz+%^alC|}sHocOi>fqG^}83Gf>883V<22sqDKu=RBz|lYKh|eC65W6A{
z{|0;Z!<H$wGyncnA_rq3+Ir>9oyMCBRW>o3#Crm*kvfI$>|)e(5a&1znf5x`6ubar
z9Bicr5R`fvr#>DO{KQ&Mz{XpQ4y1>GIZ0AyWMoU3$`tEUFFr_f8E5CnC+WHg*$m0{
za)W-oJyJVg59v^?jy;~7G%mH6Vmb>7T;Fq!Un_tWWZ>(@@q^b^^e2iPn#-%rR;=K1
z2kDPHW<q$fVmD7B^s>K3geTFAXJCf&^@Q9s<5Dt2-JOd$HFz0>3a}svL0TA9c{&>I
z>MVRzT0Tnq0H{u*2RDyf^Q*xZo<t}>t%7#$HZ}|xPX|5ChjXlm3G}FQHh3rvZ+^dN
zrAd*!D}ALWtFBi5A#FBRxPm^^P1(|9d|K&7i5?(zo66Em)MTo`0wFm76co*m%Xi)?
zlX~aIEtG=82o_??8vL%MU@4tP)^n}zeub%i_})fLx4O5<;>05IpCsfci>r{AUIZ*n
zElt3yw~r<G4bG>tgDRoy2K8@Ix06FYGAMmCOn8DUGD+*y&OAYFuB$e@V;3`l3`bjw
zU@AqeD~GeNnW6H`FvL;6VJa<LkK;uk6)mI5I5;F)Zmr`@q-<1Nc1aL|$^^rT$9D_r
zJ8e84Z{9|I&+sf;nKkueEC|fc@hbb&9lK}d8R%Q@d6tQ)dfIRPu%SGQ9f%k!yb6?N
z6CwODS#e<vE@p}y@FW{gGn=gipZ-gueu{$pfQ)8<3O<M#;PKJ!CPem3Q}E|-Q^y)w
zdU_(QD3Gxzv_u{17$hnSrUsy8;N&4QjOM6&&iFM_2T;R8ujaC^$FD&t;$LVV3XQWG
zjg^o~gV%=US%s+t5BnOM3s1A#)Cz7yfv7)#e$F@4QNbCl2y8d*r%LJy0JCM>qVBRz
z-nxMYr-XK=!^U`V9uklhj=gp&(KJECm$SB-@0CYp#(x(dZp}q{C=v^e(jK;2jDiP`
zO|Mv#u@@^lwCb~@7>1}9Sn1L945C2pCG5YXh<iKpB{;&g_4jhVLdQq%m6`c*RUQch
z)YW_wJdjZ@PLmEw0|LULQu0%%*<r#rP+h!CyLY`AS{y>GFyUT=#<am3g7Ia;ct-|w
ziX&$bQHOP!)*6;eOSn-6LzxWpqi3N#qrzj<I@X_QJ1}zUgCsT$DPS)*(poBfF3gh>
zVFZC!sp_bb-hL~ri_vO=URNaU>ikjgKLdkJL5Yd~6$;X_JjZL^X9R)FS!q*wqRaFs
zPI$Nu2Q=mhoeABmhaNM%$7$&qG}<sR))ZRxA~Xi2cTiIIyX>Z*kflu;%c81@%Sw`L
zIWHRMXlGP>q-y2iUV;Yz04%}O2u5tDgZJ!)mkWm_k3OtyfT@Z_)DhZvr!K#d!9C)@
zpRaS7`wduphy{Y+umZtNGoo}0SQkw)eV$;#-NiXzCPRirClQ2r6!>URy}WD@6V3j>
z3S(79FJR>fYGr<vg2&kN8oEFc<|t?hwB!nlQ=T~LY5E<g*SnX)d$(;4obg@8G5PSf
zLh<h6fs#fvK8#}!Lpqi}AIyX^wDm=4L?%NG)&0>x)Tc5ix{%g6BOWgOp$1$O?YzA_
zQ3bwwFTNK7ey@m0+lK~v7J^TIr2Jy}@=)+Dloj`NTv8vYTK<lb+}zwudaAFNfV7(^
z;VZ{xZ7NFn%*l1xR&S76?W<P(Z6B;Xt1tULc7p@)bOgqcRS|TD5}Mwss#7IK1FI{e
zBHsQnnGhP7kx$8|il&20>VWD~JvgA?b~r&^Mw0YexTeH-w4tI5D)};>oeHQ-YBmWN
z9XPX&I~%5Sw6fESS^@B;w@`%uyGcXQd+CX8j!@GK01P86#(JgLidxS_VZ0v<$#51(
zCr_(_YU+#%?OZ5BpHC{IT@Ht2zZnB4-7tG>KeZ<X>oVO_7|p=Rf*LH-Fr^W7kMfx|
zt`0~Wlaj>CNHS{dhN<SAg~YuSrGr^jzNL2&1*3Xcl;-c40z?r1m2jlNd0p<i9B;~u
z&novl^Pbm>JQC-h_T=xdCl4Ji1v<<vMD89OEN!WU^D9Kf`P8s-U_}Q--GONk6T*!r
z+ORrMzagi7jih@d0Iqk$5j6;>hnsVz&cTNnO%m~-06|L*KwKejG-6&4qc96l9V{-W
z)#5gD%fu)7NtBisVeYy-k5i8?jQ?rko91rKeW!2pzi0NVGPcg~wJk{-0TE$YFJ5$2
zwFDzwtsyzMCI(bL;0w};nrTKe<-<E`&O#0`tMRCZym9!?gVPnkan7>KoD`sTc^O!X
zdh)gN_{#5>-UeFt3}yW#>&vkwqzbpU6DtYeO8<`Dke13@O&ggrC%X}1hAg%GF8xg9
z+Bf6Otv90R6RcQ1kGWxg<YW^mE%wclI_71lKW)X8j-2&}OK>SL=7}a~6ne{faP8sL
zXlDAMD5-MVxr4mNv$5Bd>tQouzmNPVY<o}IluA(A<B#+grdN7aoa$_&(bJPQ($e&F
z67`SNopbc>n)%1?3dG0VpwN3>5svE+!t0sMNtQ|R%0l1${v{KrKy-QM+ZGxgrX*p}
z?tT=zdeB$BHLpylb+r2hl=&6&f=>M1)e>0oSagp1=Bbi^v=*YA7Yl59^B7CZs98{Q
zt=N*4-n>Iy5c+#w<j-GS(|S-ULaFx?zLJ~T*y5n0*2KVBvWza@nlXnmjzJA`rx2Xe
z?x(Z;DmpXQx*8{*RQs)DwRC71=$|-raC|I9E{#vVCjF*m4|*>!nfJN23mo>Y%*`6z
zKTC`B_)u|tI=l*DaZH0}GF}&2)O@Nh`pKsNg=qEuETzW){BS$wNxm}xRX60TwB~7M
z<{>gXyl*H7gUdQ}nA~YZ0^V?o6w_svI{%OfOQA~9V<c(CtO=#RVs*+3^>$~!mtaU3
zb#o8T2}{^JZYNyvgQi0JQT>&0Ck8#^Bb0uRC~2xF6t`PbB&#Bhw6@-OSMtXI`BLSm
zUD<f!=XX)d+T%%XD@J#=rC+!YAR`(cfTJl7RxhTOTH;A9%9WDkt<xfH&l}H+h0%@p
zc-p^_G%OV`F=50+JL;aL!?{6EaKy*t==7&49?I_@J6r^I9&L4Yo&Qz4=ulH>KmQ%-
za7y7nU)##7=C?KUVZ-4J2BT_VW9pnSc%UPuOhfKoVNJJZPD&Orjy`IzNVALwrZ1@R
z2N9ST@4m%W94AIo=$AYU@Q7~92r`EjzvV7|W#3T9p=(Z!{9QPlxi(w?6jOQFD&s~l
z!W+@ra|7zCDsNzd=fc9Rn@dag_Fe{Iq8sCL?lzm2uJpp}Jb^{Stz{zB;fI=;8R=J_
z+_O6xZ^|(jtZ0|AM6;?_ux7m0iDpHx<|eJPivpdUe~X7pfgm<!??Z8I*z$>9bKH%i
za9mn=ZrCl~d0{P60eWEwNVa!7+z`cRuJQcIDhg7RrsK)OnVP{qZX9u94?!Sk$<DdR
z99I}0#y%BW2?VD?gv!)EA88x0ZjO~`mp&N!Y(3$&HUu2<Z2CQm;7Gv(K_KFAf4F?A
z+*-r%!$vZ^2dghY8m2|;!>B)vK|rQS>YUkd;{gGk#4<qbO@5&-i#B#&O&ioD@Z~bK
z`0&BfPoMZo#T#?HGvB4L%9~y66Z^a$$8P+cEm`C0+^JXyJ|k?NS^U@YaO8L`Ts5+#
z@VBfXJVV_Y7cCN9#)zPYYOs$9i|PEJ9fQNm>blEkVR?C;TrqW9D*A%O_EgXBvl{X%
z=p-}_8klB_GcFZ3Ofabp%^%`l_kgIi_g$QwG3>fq<Y%OT%7HQ7^1@1d>ds%kh_FX$
zk(POR-)8m>c7yEX7n(}Hlw~BOS{mHGq4&0Gu0*b}V5A2bs}Qrovp*IzwIXJXaB)r0
zWGGO=f6U`%1F;A->%pr%?E!+V4aeV0raW`YYkF178MPbA(FH~XydL{)!~2|;<s8HP
zJ|pTb+O7$+ukvX!nzBa1-w3;Veknn^lV0R-lB&=4Z^2AoYnr*4;a14ckgo8X0#a-O
zjc5#_PR&*e<zyl$(Q)Kuz?Z3oBa9IESLF_tc3c&23nBlmEW+&LREc{9jXfvM(@Ge*
zIGGHp6lhHmHv(p*kIvAGrWGlVV&`D6?Q5!<@DEo^-r(%)&5=#(R-~%|d(@j=Kh1q?
z$j;6M(|-QrE0dxPn~U4|f6c0=<+x-oFSU?=>uVF8=eI7l<<BkDP8&z|_2(ncx5v30
zBF+xvyZ$vZD{*&gVrFLb@n7K7)LOE?V#13Gb<n8Eu~X;Skx%F8e~3O&2r7C(Ha#(u
zves(xHST5ZnKGF5G${IsgHxt(An-@h&op;<1xi@kph!6VNou**=*if3lk0?9*&$8H
z&FYQd@Q_DpVISKs)_2zCNC5|azXA%P@YB|AnrUfY1j9`?V>=DaCK!NP+R`i_i7dS<
z?<h<+d!j<elOngAKrxl0mEv!;ec>$k!CE?0XOl_t23s#Z*klQ*P(fV?FmU@W@H4)D
zo9ZsJ@QvcDTA!PFJuu0`yL$Ol{q`~4TKS^WkXB2s&B8>2S=XU-X><j&o}3ehMm4zQ
zln&$=b>Oh5t0~&3N2!&AKspYgFV;%8Lu}T4ER(L0-mHx!1z2X1d|Nq?;t7D6CRxnP
z%+gMScxg*uu^Ud@ipJez4YP=~0s$T91!<ZbEvU;e+(IwEeym*GIPkUW^RE<YHjFJA
z>@+|)&4hZH`d14)(S+lm_;Low{`ZrDfz228W!38|6P<gvB3HR0HyZDF*@uVi{g62O
zGIB<q*it=vA9-l(WA|pL^=TTDfRrDr;4l=fZ(b~CnvsnzVjOT=-z%s9h%?e@(qJ(C
zdX&jX&U!zGPZ!Z#RPbN+F6m{WN+3J-I7j&BRE%K^TS`8c0UaEL;Vz4&r!6eUyM8Hg
zba$@=-dApxl~~<82@3zWzWaAPUClxp2Cp-W*6|QQI{a6A<d#FBcrDD|p{%&oc|lxV
zT<yZ)F(FI6zGI?k7z|xacCGY>vN0NMab@LCbw18d7J`D^;Z4E0A#CY#2bFq7IjA^2
zS+gS)Y8Xwctv{rv|0>=h0en>X%2wqyk6X%sb)34G>mPTcQz?B5BtE^->+Jh}dLEY0
zIDZ#`K%mv(UuhlX4!qnQ2_t4((o-gMjw~XEX^Ule)jc0;>vR{udxgg1hk0YJ7`bNG
zcPXYKgy!zgncbtMu6Cv6<qR=;&gMD^FK#YCST*~<{hhw5?CAg3eAUCwqRY#vqiS_=
zW(B)E!!1nAxEvZIb~ePE!!r<saS(iuyD_6g$7h|iHo59Gv>5cf&*AuwMI^<es|IrM
zFZp77mn-s9ZNw7S`HzKD!iABt?YD&s`2>ytp_k3%SO=SXgAqgOw13QNXv?Duj1~Ds
zafrj7#x!lfKtBq?0Cr&1WTXS5G)1M^VqQZKl<4o8>ITGAgcX)?@qDK=3Y6&BBwCZE
zQ)U8B=uHFCo0sPd6cyW4Sq%eTlpn1g{96=Hw({`OqIVNV7kd)xWWT@lKpoDIHj)=2
zLPB3!hp63tEI(8+m6X~)h`e(3H`4argI`TqbP@u5Iryuo;bQlubM!*qKKm{bs9gN$
z?ZdB59-`r1o8L`Mjfy{17}c|`q>AXlH9vsgo^q!l?1g4wJTV}Lz^_wq2n1?k<cAGO
zCpY+S5F3VMn%%GjT<7th&I4D8d(_e%?wNE?SuylrLtL8Z6lA4${*xv(Sp8`bZ6Qx%
zoZ-7ld8Nj+$x1BVXYlp!F(w0Cj=xRjR7f*9q*N}5JPB0kYf3^ssT7iyl5Y0Ml~@Y1
z<ClwUC6nsaye7Xbg-tz}IGFOd)filKK(;P@vJNi612xTxQDyWoE*t`7;&`#)@ESIr
zy!kFKeTjdG-WR>bMhnO55*NFH7d>5n_teg}F8)OReWP~zX5skU;aq;<nBs7Hu<)->
zZI9@EwwQO`tQHn_u{L8|GOwl_{=<5KAsC-u3%M<r!_qjg;*wM1+9B2=(GQ<KPt%28
zRTreu)@0N$hjYk~U~3w|uR0D9QWd_{H)oH^WBLuNzdwofFGk3kYG;d%epMIC$iT}n
zYKMq1A_`=K=9&(smfFXy{Q&!@L{`T{UU$>ibz^cw(#`>~Vj}QJzI5WK*?YB{xac$E
zM<dHxdo|3-F}^m~<vFs9O5ld1^i!&;!L>1ehW9H0`8g-w_r)C(4NZtphC+VByTj?&
z?+KnIU;(McT8=d#A>E|b1mv78jVz<pnv#6#D0{D`Gh3sl-S8xn98u>Uw!Hq+^ZG+A
z(oD6iSx#JzyP0fp84DO=nu@WqVsa(hGZ$#0%jCA{7Wr&k(O7h2cbMY-hej*HJ$M7o
z4HhE35p^j`82^4@jj1L;ZE>5!CA%Jvb!hRr6L2u8Eadl6slsbiXPKV=4i`EKDr$Im
zea^ZZ|BdmUfeAssE`K^O0KSQQD*MoKeYtEWae_bc>_f6ca@)c`a@XDmwUh3JQ~8Uz
z?vvc4WuLGIq37pZT^A$gOKl7b$2J#34kws}fA25;Tx>5%M1~#h+~v>ZhcY0ibF$0y
z*6cl_zm(6ct>~1NKvTsF(R3sE$&0xdc3P9kxJVwQZlAo@?su<tHfYU;K7|9~Cd;+z
z(#N&3Y2*qpbp;&Oo6$1taV9Wip;3%*A!DlEGrC@dzMkgzRYkn>DmT4Aldw5}^9s82
zo&7>r_{$L4xpu`nUdDZGGfNfaM3~F8h`T|;tYhigGaX@I2zVSp|N1`lyQJ9Vs*PGc
z=Ch#Cp93dE;rI4{XxZ8xPj?bS;e5zeeI{*z%eU8UcMY5ulwCycF~Uqa#jH$W3C=Kh
zeofN6wY10F<X2aZcz3R~*ClYxr*<X5qvxraDb|i)VA*@MyD)+4%a~kyp8m37)b<Sm
zm$gi^rUs|37mdQVJ(-bGoD%uEeiYEvu2DoCqi#|T4p-b)Jw+X~o^U%QZus-fD9Y_f
zHkA6)c+N51edlG!g~tRSq>(_^{5X6Z{*_|`IRtO&+0^N9OX&kbDu7)2P}?^FP_y>X
zh{vL0XV3f6aCl!JmF*+8?{+x|Jti5Ibhfs1VR@10#<QOe7ZZW<%?tlxx=#1h4kpe?
z7wZ?hkH9(bbh38qq_8KVC<ivgo7o}}FDoP*_5Srem|mZpjw0OfQzb17!J_>t1tNm~
zSpCatGBRSp`{D*8$Ir(puPt8<ux6I0+UI1Pg)r)+qF;kv;US_bqbj5pyDt+%hZ(^L
zor2q@G;uIaR|5LMed2nJ_)+!3e8hg^gtwPNS4&=2i!ni1@HI_JXRi;c(F*BmVC*1)
zXIVp6(F>N4MvciUa`*Rk#=ks3Qo^xJu}(H2N{^A5ozFMYWudXM(zr7E%GB;|-5Vn?
zkgOKI2o4Xf4G1^W9&1u4GgX=elM#krFAYHY{6Y1}>(>qQ*7C3)YdR^fEAbG9&x3>e
zv|#FM1_3aAZHRVHe*>TQZtc(wQ-Tz0p_zBbfs6e~gFrz(>T#=~TpUFcHSC9|b&(oJ
zcDx(8^o1KD5?MnBA$+%(<LBDlQ&ou}%7@Euhcp`Wae5hbYu^~Ohi1p!;nMG)6Rkan
zIZa+*uqFrEJdTovm%%ggRC50POSw50arig!GR2a(*x5Qi8evW@T_R)_PA%7A-Edjm
zJBQ(w6KWS0M{}L$^5;J!BDYo}FY+$(B0TAd#auZxf+F;9Q#eSKeM_@#ap-cmh(O96
zKvu;mH%3X8UgFBvB<8`+rF1G4yH8&pi_9GH3^+nw4-Vo5TrOURxuMxj1$$hDLEoAr
zl&}i$<-4{Ih-J4qt~4gT(DwZK_`XvC`}ZgtC-3`Go})7}ZKoF13+-pW$16^gRYO7*
z-@{`DD`~(&y-i2}qOl=>WNh>-ZG(HsdM$-l?x1^9`TZml;eLnA=~%M-T@U=t%}>is
zM%UPkknA7)?ulXYX;^WgaAA^DP>{Jx(Q5z)&gpAYFeC$bg+s(h9rY^l`{)BXtPug5
zQUr}d<z$!ZZxqJ1-LcX6vxm?II#O8yIIISsMWN9at_W9i9qkpLNULffR0}aNn<LBd
zs=w}btG_7XZh8r$*;Pi(quLQMz(?C$RpoZ2yb0v5Ca=W}nT@#$Z5f-@MNp~I<~2(;
zUO0-Thn_(q!<<nYM6{MIUU!tUVPiwe9gzdAz1Rdzqr6;2Ursa-a1)cNr@!jE&ZZU~
zU2N|$Z$*Z^42uEd9`d((fADnA!3QYb$y2S|**?ZK_j#ZHx1iR4aB<vrQIx~CVui@Z
zyry~gBj?-DSkB@6VMVlncD=LR#HKRvbq~~8`%kq*&5{~|W38~Tq;^6)gFb-g>L(i5
z6OMaU&eZuP>NVN<mJ%f<4YkfF6Do=Yyzn*#1;E5OSIXiKm$gF7{}|l(=<NMP6X-XJ
zJH0Fu)m!!*$G<<2h?`-L*b4?<qb8dO#vApq_0L(JZZ++z8mu=JJ5(_?6dT9N@w=#g
zlWKI8yZ;i0GeRoL;-XF2Ez{-9aOg&IK?j6N)Iauz{c{)=bH{XBuP`hK!YT4{qSx0#
z1j^<ChRQa2k^N#<ST)A^BFu%XLiQ5H@KV@RY;Nuh_af736Y`En$ySsF<gKnE*eUW_
zozCY1P2_0}KY%*J$Bk6WpTaxoMk6E=FHYuk;=46C;jf~mrmCvuS>ZjQapY1r)7Byj
z_`Dge_RddL^KL<Ow^)tqsrN<?(XOBMJG>~+&9zXpklbM9F(*nQ=LF0x`nz8to_;NP
zZoX&heR5VEaXOK!c2Q$DVl>_Ex6wO15S<U3K&r^#ycggeBWi!SV&$9F4w}zD@c)~S
z+_|fEvUj-B_|QMAObYcp{hQf_j@fY>=ql#6$7P2qEZ`k_S1sEf-3)_`a5T(p5{Wa(
z^WsD8((K(TY(;<OSP$)P6h&#m0%{E5>_&m^y;U*A(a<=VSFRx<)C6@7FxI|%t|F(b
z@o}L}67<#gdqU+k1C)i?+mB;Uo4r*}PD<qiue}Ei&f}iFUFGhyug>y6IB3<H#%HE3
zy8+uW`vpt~k|L}daM%ZZEut9xOp>QvqdaOf$B*Pip5id`7GYJNnr_W8{kQ;7n-c#+
zBSkYk-C#)Ax~?t9!2v7y(z-ujEe%MMP;zI5qxG~Aw5cbJd7XgTVP9r`y%FCN*(!@J
z`#!m_TNJ>I4?ji_!@5XG1z<G*NTkDe#zoc1eMNJ~Z0=)*kdi)szGv0>{VUyl0ahcO
z@5ARFWbG}tzqP2WsyuDOb-yq#ClRIiTEs{1KGpigp@}dXAKr!Z#?O{!m(xg^ZG=~^
zXnK$hArS3yqoJ?X^2n*yx}AV^v-*}9htrdh+};;TYJAS%=)*S<gpRjS3f0V5@1zuY
zqS@PE`;FSU#lm^d!oMkp6WqmA<W6M7a~q`w=);FUT@`UeGY?k~{1v<K+{+oqj*s3u
zzAS7%BW~(%K5O|@BHt`uP4Ql6k2pIao^5U-rCrFa$YbE}Rs3NsH~WI@EJFM-xzGhu
z!Px*UW6XDc=EW5=n3gbz9`rS4G#>KHag`;JhqJNE>NR?+mR{3m6(r&raU*%YGh`2M
zTyjPyI@PqXx4gUm*QZmtw+R~KUdj_=R9peM+BD40pY)NDy8conrG{ot3PxgGa&zcU
zl^f@uTrP%cL)4v2p$6h{GG@N~B!_5LtCo7>(7X^EOHU$GpNqn9bD(@5?${?g)Z2C1
zFwmVk;D_iZ@GHGdN6M&iSKvfWcIp$I=q!5eU#<2MmxFk=`)6jo(<P{7sg<A_w$QuD
zhhh}8@<|(eKbDr9)wk^Tv8^TU1<#UVSWq;5Sb~3BRaWVcf8y`~>%>szhR)xoZm~GI
z#?ISCBjeBZd&xFWoCyLLWY;!pnjqs$HAz_Tag4iA4v2uI%zOZ+O1;81h#?%;6^+T(
zL^dzxX0@}GQitl&)sZgGeBw(i6qD?q%cTozEILI&Z`d!~Rvp+2==itX96{lav%T23
zI8^)mlyyx(&SZGMWW+4gYC5g~>MCgaE$f9!@-|nCEPh+lwETTU1(7Ls`Cy|Bhtu+w
zGbU}ib#^EW$BLv*Y{=FJP&sq`URG7va!1sBN~5x><eKiO!akm|qI!bp`SwHO^Vswl
z<ucGtaWlA??ll>SaQ93e;~5>oQh2@7T)S%6zkxn)hscK8Ok@a7UADU!sjQ}=q8jnY
z;m)i?iYHl+-dGHKI*W7{o9XASs1r5-kzTCDx{?}hGZPya9ZT$R`nYjJ0Zok5oP3Bo
zFe*FEw9F1s$EH3!?CJGT3?mM3o3pNWvA1rXKV9^(%sjqiAEismL|3Rje@pc!c(f8b
ztvBKQ@87-J!jOjzjorkskJu6|k93z1h}Q(t#>tb1i9ceFJZYJPv}Jjis+lGNRkw0>
zX>|~C&Yf@XY~~fx?a0r3wYztGy!OwB&uL~>ccpB*;LEXJO;v}!_tZ*5RYy9|iYe#|
z^{akVZKCGpe=^mTcE<LnZmhC+lN6=lY|U&v4_V@rc(DfdsM^?I!~0Y|;(W8Zhq-#C
zlQ7|iEq;1;-DvJ)ZDr@!%2(Z;d>9rS5p;N(tfmlo+}EdiG2OMdbuqMX+;z6Y99g!v
z$vNUqD8H>}6a(V8*Z!x8EKBx)bAJ4w+(zsDObV{A-@HUz8$a7zdvr8VQWZOI9r{@k
z?cZtJ>mT~U^z(K9B)ZP*Nj*kt3Fv1~ej$D8==;A#t&tJ{_Q6V{Ii}d|4IIh0y``lk
z{OshMGEUrFEDOiylAKE|kip?$2P+5is%QHL8}i|6WW87Tzo^pKzN0NQ<;$Yd9(l*1
z05XZMgQnzkFPh}|u2<F)cv3NRi<jV74z0(jB^yUJr2YMzY~OHDwwNn9SXT%&h!%h&
zo|lBzwhYZeFw+f29S5#p(L&*)OkEJ+h}<fRD|J(~^x$h0x)w8+sad}8Z^N5aQYyzN
zFt8Md>#iFf@_jTvKR5RvcB#@Gmu$&*%l(#wvYN!#s;PN530@3PU&ocPc!W*X+dC`R
z%9Q>UFDR8`Bza`DG}hx$Vc5+hus-t9w;|^eHa|Q$<G)c2j|oc^Ekkwl^^csUZ1iFX
zj_go3pRH?D;D&q}StuK-l?Og))EMU;Fp&0d|D0Ieao)E1l$SXV_s!MX<m%7I>8l&9
z;>}ArGXV<Fx?P{WlDv}GSpKfyvlkJ67uROG9x+^o=2j#nRzyMz!YnuPo))5n0^j;X
zzwkEUqt|WGwq0}R75X9*(I@v0mM0h5Tf5$8#{KT7u<|(bu`HSI<(?P{nW~~Nl<8OW
zzZd0fV}4U3_T?~+i=4wfX%<?pF?0CevwW)_bL6O7uGjp0mr7@6M^~_a<VUsJE4Zbd
zM+<z+dz*8fW}3UinNxZGOK7&)*Ehoxp3Uu|c^smurfjd)9vO5*>2pwtc+pV>qO^t&
zI1g;2qa0FaR&tZGkO@Dn7ZKA2KPTZfm?9=*6svj6pfzmZORK$lUqqApg7a397Pujv
z5sWU^d~CEzZpC8#%c}@tJ*`&tcxbYrG5kZ5C_#TC=jS#TNLTH2c|zTY<kzks9^2hM
zyeGxawCSffH#?t~RO*7Ql$4U|3h`pBV1ajhBG-|gYQmx(iHQzL-S4xyiR_)R(3Mp$
zl%a8of6-~5_bqGkD)?RiT~URLLjuiv1g#-D#ntAoOIyMqD`fOqMZ$NmUN+*|E8#Sr
zmP8JQFOWdD^#_X<<N693x7%xWR(w8`4K43>7)Lfm>MN_F<nWqkJ&xaA9kofMweco}
z2JO1hEpO(Gkc;yd*p<^6i-mtZ4yPowzjF&0q=l}$q#@_ghX!$vGY%cMHKu@l0d%I&
z%a0x|bv-Iavre-Enh_{3UrI`RObFaeZ=8obG9nlu=g1o&d^0Ei-2sLY(x-1)LKzb!
zLcC*(4*5Wbnjna$W-TK-5^u1vx8}3Aez18N*k@;Jo11566~#u+TguinR$KU!hAvI>
z>H2w%_#Z{FpE-buJ<?Jv^T^g6)%Mffu;9t2)uE82aRxl^b=9WfeQw0^>W*Kh?E?#c
zm!>_`{tft6JwC;g<m`%PXDrtf3J{x&x0P{tC!eBitSdr?ppkQNQJ;!NYnK0dtY~Uh
zFdaXUo#U7#L3#Mb&EuwN?#y<D(805aGeY^}y|733hhAVy)_g3P`nshg_LQ6FLp=!c
zxI;QgUuRd1V%Y*#O;{O<1-63O^OSEU?HoRde|~GPP_ew>b?fQ2Phgj=`4QjD)jBXA
zjpXz!<b%7#$}@9Shmnnd@=;iS$|9XLhh?D)kHT9vYifFBsaa~lqJU5aJU$1!MmI7H
zlU);EW?Mbo4Bbl>*-!@3%SqSkm`QMrR!0P$x+?Nn=Mhh~H;#|HW%z)*eR&rjmlh9>
z&JP1G6;17Y<UIM$M}>BslE>E=2i|{4Ec(lhN}LoCZ#k-q5<Qe=xW8kXOt|H>wUyk5
zE6L_UBHfMZ!}bsvWEo$oAJ6KU+%m3m<N);aOfvfCoF2f6Ma*XrO0+CMs7ciP=~IOV
zeWaO7KT4r6S0|CeJ}M03i+GHtX8e>Q_kUEl$VsXgc2p!KNH#G$grEPVEOvJ<b??Ql
zmL9DRk^DpLt2g{Rl2WOpbe&^#L;j*S(<b)n9kgox_WCPr^^<&Gvb;g#X&7ON04%Ih
zGi0TyHCDH`)lRAo4X8E#tSMwfEAi>E2u9}KpSm`?&p3&Y`HrOlE}T37Ey_W@fsY%_
zJL3-r;t9?3rNQ<q!3uYnhU&!kp5_paw8tS(qG74sWnNuaTmJ8BRe}HnNMAG3uJCP$
z=-X~r((|d#6@Kj!l^wLk^iB32Sf6_NCLo&`rj8|lJ=R)iZvwn%vG3&s5!s7ad9AP}
z*=Xu*nBR{2RyykMws<)$@MtHE-sc{ZGoW5N+r{FzEb=VN`@&Tv>``CHbGrnmA-lO4
zUr`_$DD>Y>w}pJEwox_i)V$DFnePt2>c!tf%I3(w73Pa=hl?K$=NsJ{76hXVBR9u!
zUNP*a^*b%cofBwhF@x)dzz<+UF)<#6-tBJU+6Va~18EP!(N(Ps?@l|ihtzn2s3%$U
zJ!idD!)CZWG}ij%BLReXNrPv4cH?_ioq_ka4AE1{M@w#<!Dj<AB;8BL`<MvD74Sgd
z|Ij~}Nfq77!R<fRGIsr1COK!8jzd%&zD?}!?C(?Ny0e5X^Dplilwb90Jx(cSoA9=i
z$+zBs30{kl`%=;d7DuQb%w~Ca$pTBfkLO})obEb9$SX6PLeeIUHe1=K8itIvm))H~
zlQ4j^Nw>5LYd>@(KdKdX+TPBLHxYvgn>P64@Pl93Ta3}u1is|h?!`%Kwj7i*bjwZ&
zp?FpfG<a*@GgtAk!Y<%LER<cU-jNM+?DOc3H}lxoO58STcd`j-cTY}?`Kh*JLai4U
zm2|87v7BRmjr$&c|I?(D!Ovm4VVaa_g1(e)ub$2xpu~Cr%BX~nf{ZJ^wyiIOv~uf7
z-EwfKKJIAF3qziJDl1>Bc5Sk2myl~#1<qZeWAvtYKbRO=^s6A8{nS<aGe@D!*Zc~J
zFj<-1I{$OA=WxE?w{XmSadvUA_3!hA-F>&~i$KGtwI)T6{NJFP5@7emU%vve3*Cd4
zB?(u2Cj0;UT`{6|{`-9Y1mR<Q#z4ayTSWZ2A~m5=!Z0W<@F8rb3acldp<Uz^?uo%R
z;CU(ET^IAe<VRCfMO3f$%f%i3?rV}Ex!$T%R3Y!Rc3NfL@~%GHZglHRj!>zdsca!H
zhn%G@F-wdvPxMK2p6sAGD3aixV&akHf?|1A5A&9>2f=)-&)0=IqBN3Qw@=2I=NIPZ
znUZ$iWs8mz47KCMpL1y8_WyVgZX)k$h~4@TAW@^Kt%CptM79&~E<ndekIGq|o8eZi
zEXO>pL9W`=N(1Kk#C2C}C2bqMC86?Z&NPu&aG(vLU8gt>gB@NfRSIR^PyQNTdXuU>
zDB5{^`^5oGkhh%t{x@=#sC5U22zj&pc(Q39VV=w@^u}q<Q5f;I+R^$6IsfCTd5okp
zA6B!vV7!7+43wEE{PKzCIz`mF2RV0mj~|Hjl8bkPp)*tZMG3DIhm6$LxwgJ7oKWsp
z-w*c~?@8ck$Z>t+lW=>c7(dLkr)(TcYO-^&JqvM@<70~LE3NKrU8_|4SAAZ+a29*<
zck3Tv;Uw~6BT_QFmFlGn#YY;AivM&F!+w=_)~Pu<o%I843Wt&lW153%*iPR<9^pl9
zA(%0BxNzT!`L}ItqTr|zJCD7P@6{rj8AY;x&~T><q;e@ZMdF;k)dn-wsk+VG-N9m{
z#H!GKKM`GKG`m%Sco#uk-C1xKu=eW1eFctakHq-gEjiVdShe5_UCrE@Fv~oXsD5sz
zuN<bo2UD@!spd#Hlcz^(8`K(6;uLy&>%R)pn$OXi+mmcA=@Y4VD&DB;ePbMe&Nz@%
zz(rkt8T5*bG$zeCD1m@i2CE3EwC_%76^wf3e2bmRDZuFraBom_NYh24cW`C5j*hQH
zIpc~XrdQ=g=1y*uBxjD;>Nb=gv>4CGjMRsgfg>FA4N^5LJ^T*IghnyDxT@^dI}K*e
z&aK`ybU^ox-j4~jNY&q2<K2xOWeN13>S)#wsV-P;%VkQD02T#XUdpi{*$=$nYkS}D
z%!ENNL@!o$PQRwmQPCe~vqelIh}tfXPuLSn#mFr{n3Q?mvpsx2;aPp%H?gnP6%zqW
zv3ItrMn>wj|N4iAc@HK#bTEt~!;dI)%$<je$ITH-kvmEkD_yk;R_ja|ADoB$iT6Pb
z&YE!mA;J6*xTs#@BCy8ZNa3ja=ilvr<IHLe$rLYytj8bgs~Z7hZ?Y#@G9J&tSMjFU
zfIPBfZz~gjW0h0FK^Q^S2CHk=KTQmLl>vo)3UIucefG~U_+1_dnw6Z(T&>FAoK>!%
zlgOOBB{4E$>&hv-H$FEvldNWyw3ocJ6KHmKc!H>=E|uE0BmAq~9=5eO+jK)$b?Box
zt#Z(uIiJY)*Y`ZbclpnHvd(0NhV$E?KMPyAUM0Ljz2D_(y9)}`*?f>gHm%+LVUrc{
zDSGbYmQAKytM?tm3sndHyuS9ywU4v6nPfs!g*z-KJk@is=lj)>MHy6;Q)`E=_DST^
zG3z`8?t6$%VlC5%|C`e$q>%J*1`F){N?ZwxLF4?&QkZ4Qk_=zgvhcL_4Sb1pLDrs&
zU0zs_?a;(#aOK3yw?ct^oX!XeCp`=Rb9yd<xZ!0OXOuF0|DST0ftV0pD*7${3{|{J
zS)Xb9hUA37kmQx%+^phew{<xT(k*YkGrWFE`N68exMIMK2l_J%R>wfbKg8%(@p|lN
z5&yG|v`*;2W`%{|SJ%KbhYL#J`LDjNqvK<(yyVsC*EY+O|4qf;u~u-0dvbqhx;b%E
zXy_ie8)TNX_T8n*)pjKY{ng!_B~jB=<2l|Hn(CJOXmcM(A~VMpAC(wo;kwUuTyvB0
zg>O}7F41RvH1;*^7|2=pdQHh0<)M-MHI-o&4D-z0h3e#s&7Y?m%X6_3dAYgs9gz_s
zn)R(-OUJuD!6)04dz@;gf25JS$8G#7mC44pnRik+Oz2`{=na%#Po(-S$SG8B6MUGw
zavsCo&0oRN43&0+NQjo&G@*N=l5%s%FHOjJp8^m<{izTfQi;Xx&!5hg&f9Esc;vyI
zw=mFtGDNYF%cazM&DS<_*(aM*s9U8&Kv+9+WkPM*&g1{aCaJzwKdw>k$8ff6ljPgv
z`%N!8yk;hwP?fJ`H;sr5Mu~O-US!`#cd}e^SLXwH-{!Cgg%LJf+H7;1JjPsS);Ih;
zvpjmEo{I$v@%`~kW-Tn>{_J02NY203E9>Z_C@rO8WL=U_dovlT4YAH3kL5gPODE%e
zS2Y`56o~<z0i=@7<0idR^5Xn_bq_0Rx9%S~m81k#2_F3POo4D#u<HBAbiz66I1sS6
zX?jc|Yjx4sJCedRe|zElTi4%Jn-OC8a@l>0YQNTrN%RD&_W0Ashw}Yiej+swkgSqF
z$ln=snmM~a_v6u(yfOVybVGS#3)W~lpdik)!01%Q#+!VqY|P#ioK})PUc{^Znae|+
zgLM!M+<$_!qU@iqz|SZuorf)9XV+j`cNUJX(aKyW`{%apmnGn*N_PJ)wn_YFy>K=y
z(VpC=7IIPQkTldn^P3j0%wqC~H$aM2Ober;(z~~P9o(S9AwH-vZMzBDdzyAf3<ZU|
zX#WKl0vfA4qOlJQ1p+DZ#m3>3%X}|u%}3d2&zV@c(O^iL&0HE&`b^KzFs5&9v2hdq
zAh^};q~LA<YlmP*oK;cA7sF8LIo%uDgS_MJ35Em1h7vbEUn)??z|X2gc0{J$L|jt5
z1Ijw{P2A^kdBb$F`<2G3tRa^+Rj0NL?@N!0thjKF5;c(0`xMyRGiL(kxh2Kef8$Ts
z3GZ;!DFwg8lZsUuaZ*^L$cfWXf035?QN+kYvtPd8_r7D`g=+ABYm2qHBMR;KhDUO#
zK9~p3#b5Fl7bk%1n-6?m-t`!Nl#GlC?IGv*%y=c0uI|N_E}VVHJI8dL)-RluUi@Lc
zIQENtQxT%W*xP^bN7k`YLC&fg@NG9U&Ms4os2T7r$kd|1IV~eLc69?)z9EHEe9N#=
zb>u3u3G`GLGy~RyrXgZZUr`O^#<9n!<B*65RkfR+EmQr=Zy0YeR6KlI|0a89PVwUR
zKR)lghQsXD@%3DJt?$fzi~SqqXzew&7O&s_>S4#rs|z7P^I>7fi@NiP<bQQ>=25gF
z;1>s+<NY9S4%N|j?={M!@}*!j1Mfo2dILJ#^FBcho?$_B*v?aDi%-&)J+s&X{9thI
zs+*KuNV8#9X4}!QJ`+|Uti}HE7Qd>pg9OgI(l!KF!efQ=*522uDDQJI`pn(^&gm`-
z=sHJ!rgcg62AGw{ws}MD0cC@8BGqG=6A&i(X8K@-3V-BwW1)%KH|H6`WiWt`#&k36
zpzm@cB5!TgMyq{wF1GD`n4*<o381)f(|7oW*nKI3@aqiDf;xylDk6C0^Fc4b_bOna
zl~5mx#in6ZH%F0zW8-FvQJ);K{_$vKazdcRCUU0fK=GvA^B`kG&VO80qi5RC*ZG8-
zf4=K<J-+%9gDv~!mbi7;CdEDoH+3V`%??*bY?nsvOD~*Lyf26g=N~S4`Lq4i|5mzn
zt0u~Bz8FV3-`Gi21?p<R30!MyVD*-SaPueTaXNwzCZtVot<qBuH^1~UGcR?9a(H$y
z<-=KA8(32do2y*^oAS;5<evUM?^hUrf8#CeVNd&T^R#U9P9yVq#pYkR(mj{Z-El~{
z>zAPeyyE>>EgkWP8eVIq5zo0oXLCm)a*Ge<MgLo@3vNvn&9RRW#~?py!eC$YfS75U
zXP!UvR|VYc8mv+gm|QURoC?;cJgU;^9*rI+mDs@LpCCu`a144)UFsh637Ex9dJm$7
zdrbE7slQV-asRDx2U`vWHh}ixOG{uxm;OFF@6}Zra&&Jp>=<D)y59M6Dm-CzkF>XZ
z9HyeCqNc2(mN?`i5e0so*YH7x@@p1Q(Gbwy#!*9GeNzPC+fv1B%>Q7uK6#OHhr7b!
z^(i}tI!g>U3}6Zo5PFsQYLeBy7I7oFmMtSdSHoR$$Y!abLGy{TNd1>#kRFSKxzO^W
zrrk+xyxiy8yX#sF(WgP3p7~AMv*DE%1zJ)SKt{5cap}^|?~UmaF<@gH`V=+Z7xE$*
zFZsBdNkt+rY_%<mIcwo;FLJMw3<!=mJm7sXKl}2?Kfd2?RhtH#Ryq8>OrPB?BaS_}
zZ20ySru6y7BQmXtq_!3`#8zS^fbW%Xfrz0dEsJ35H0Pb9?Wdm_3`d#zKUO}wA1vFI
zIMaRm7<YWK)qd8}+#XC0>r%BMKUFRKEsoe2wejf5pm%MUqFs#JJM_C)+3f!}$N!Ic
zVsMXM;@UlsVY)MeDF=(|*C&;k7G&$jt2$5q=uRg$Z~k?!ebc~0sE~bXRxjEbD){7<
z(A!%|)QEb~ORQGTSk%~3@yZ%m7c3=<Pt$?9y(~=|B>$<o&D2-y54iuK$rbz?pY9Jk
zz;AH73PV_L!*ZPtWSeu01{oE4S0)aQTXQ8Q_JYG(6IM5KM8R^;D7-P1X_G3-!j+eM
zW!r5jC335lQ}^B;&0l{7UXY94TFF7o-Ur5Ok5_6!(>S@YEJh1<3n{Q#52+3*X=Rv*
zQn$ko0Rs=95-FUyv;4p}MxZz4{#d9GcoRKjrgAtlCmEjmee{~;=beAtzKPBJ9oDT8
zJKg+iAsq^HjZI&h?7vg&yE=nUcP9UP)Vq8<w?o`u-kQ&oV4gYncXHU<XD+xpwB+0J
zuY2JnIbwC;UvpL;b6)bK&hdAvQjd3Y^3EoZB5LvIC=R$}h>K}klE0SK)gyjzcXi9a
z_ZEK!IvM#OJ3%wxgVl|7%f17@m#90$hWDb-cy=cS{3t*I@;h|>(cFD*`2jb#q24Z)
z@U4Z@6A40(?ti?J2dW60dPC3JI}KI$RSbDOxy;EZ&rH&+dv{-8-uy^&WJ~yBZ!U3;
zO+>UU_4>fv!-@b!^rpIugY{b7`|=-(nQz>x9Tgi$QPVREsQ!J_)6xF(G&IdeC5-Ib
zWI1uPlv}-*QUbP&M)O*Mw0Awy>H`Z!HjSI@<u5r}84tn%<b88}#MZ?8e=%|nZF^%6
zWYE4xm(a6nVxtL}HMAVklHkurQKIBY3`i>J5~3KB8AF^U307nM{X~fmO{tgbh-+b;
z5Lbv!Qhx&K3x)smzgvl#>b?Ek^tw#Hp4?0r@AhcEG37R^1mKNxEjcx4KNZ)*Z$hb0
z<VLT-+R6ifLT6zepprbr@DRm5xyv^w`UORt*t~>kXBBNDs;6rbsu%xu_d6L@mor2z
zXWYH-^J-^g`RTCNms%n2T{2WTM4a23GQM`p*tpcVE41O*;e3AU;+V?;`jg^!?QmD%
z>!Co-+>2Znu#Oqj*P<0r?lczCd9uvw;rYxqFHfj>X{|>5TJb>DJa4UC^AK-x^2CVR
z#echDJKX=D6;>sI($N|{mJ`#rhAVHT0_3G6&Yq`49<3-{{G91ppPE;TRR3r@=M1)H
z+X&}ef1r6qOmog30Tj*DaK;<ug`L$$9`|gW&9`-dUV$SEe^;hCItc(?Wf*4KAgwHT
zp6$Hse<(QhaPDw9aq5*jSQI(mcXN)gJ4=ea(qpO~=(KIeeOKpU;KBIg%uszi4Ddey
z+CU}0qbRa~+AWeYWr#q!sx|?{#QH3)0SShRWR!mUS3ZWYb*i!Uz#>NEaE~!!U03%p
zd~Dn1!y_VMl<%zUEC#9kD`9KGJy033LOc=MSTDY9<8ob}&+B#DwsqaMZ4J-N!97(X
zxqFuEve)ZKABeSTz#TpiQFLtX0IK#}7E~1#6sqv01C}g_odQW3h3@X|!*lgfg|Urw
zJwL4L;?pm_`fRBfm&-aTQ@X&TDDuURzF3y!XMXOdK0JK+<-4!e@Q9IdHM8UKe%(AX
zu1`;yp~BF1eteLUoZ5DN(7GJ$?$f{aYyb4W{3n0%!$19*uYE7wAI=|U$kZm){mJZ+
zGCO5I$=x?1*L4lA?Yic;#_;qMqBsIupO@Ry{bUW?hk?z<K<d-u_4_~g(dS=&?!#lm
z^V8Gya?ZGVL<Png5o1kX!!4@QV(K?n{o9Ane(R_HM!WjM;|I+`R9g<9h)J6d%9c`d
zn?yvrWv*RiYL@9F0J~nEpRVVRKK|N&_rrhn2Vea8(q;nMteD_H1)*Z%*y9HgPB^d;
zn{q&hMXq|n>7$!|E4tu#I83c2vY@)_?1kGH;nlU1eUJ7rim=+$tTodD133wM<N&=-
zX00`wL_l^kg*q&y8O4wgF%?o9ODmJetPvheTBa*?Pm0Uz?w;weHcQ`&PDW*>nN5c!
z36yRe2&6{@RW1dSvY-@3$x1S*&*!r=Va0CZm^C(E$F`+!8BqbD?$$AT_>zw#HL9Vm
zmGDt71r><hLyEemp6;R`?lz^Y5gSTT)u?Ei5=e*0h}gCfQEJL{tt%#T_Ng?F2fl4{
zaC6WrYlN^vwu-tt9{%D_|8T6&pMCZV8Glxtr`ucYJp+IGXMeC>{cE3m9OD^ThdyQH
z;pWZb(}$05C-=NuF&@7^;~W3;fA1f?dwihR<>}nC1w_r~nnOU(@JwMOA}ednvF9A-
zJQ-0@Xp>Py<vQ`O#<s5Ox?Z}>;_BRx=@}{=KyGVZu8;Yy&V$C1FQ>zN|LXPq{rzk<
zkSoC)T^f{!p}+C!v;Xl&Kfk^o@1HLK1G-EzRHYe`SrFrFizcX&EVwF#RDmYTumOnG
z6*%^}wdFY9U1A%)PC}Zj;X$IHR$|PR(!eEn@zt~%_uz!(c4-F{&8b;40TOI`@S<3c
zs0>kQyO$VHO+?jdkI^V=O*1oxuaRp!+ms~l<fzsu+7yrqu~JQBj%{E~uY$$6G8qxE
zjf#vh)*RLsvBpGF)N81xsv_#FrImXe4T-jk-O)4^Nnf3fo;gM~<)%(*F?Y9oDP)XH
z&m1m$7z_W(9+V|isQ{`;y};cpL4=^JumvJyL>ZEidwEGCYp80X_>+V=K@W3euv%Fw
zNt9DnDn_jClCI@L!F3Hb<j^wz^!DxDeE7@r4>Q==S6n=uv72e~baV5?R}bs>{@1?#
z+2>!q8zET9E^l7F2IP8K57Xi4>3vKS10ODrtnvQ+`!}y%1Gzpt&7Xhy5B|Y_@cEzp
z+5i6k^`9*F%M$i(dz#cz*hOniP*qrKz&XYktF2?5w9iYoR+VEq@%;S0S*i6t%<F0R
zH6n(LibPga4%v$6+DorFwh!;$8?7v#&RcBRrJY5Z>XoVCYx0-A_RU{@^~ukiK05pJ
zyYqu5yB?-4kXBVOythTs6r_XFfXW<HDZt%T8yMH^I?pXBKI-;(dw05P(^RX7sN<RN
z#!8hk3M+j`i4vO^WD?2281(sM)2VS9)%W4EN>#fiFi<KO=OH1g4M2_|W>pXoZPV1W
zHS;PaeLS9h1*hElvt*a6No&*Gg(c`+sK|;T#$@COEpL5XR{*LlV=Yuu+Wo6r5sWc1
z(`c14qJR|HN(6wJsA@)4Ya&v)M#QL$eVdmEL=+J5lYmt~QB}#vjLj1$fkZ`-Y{tlh
zW+t<0x6V%@OH@Zzg#ePiWg=;ns0vj@1fZoUFOibP8ik9dL=`nrgFy;{n~O?>=s>FY
zR09KG#I|1AEM01>PnYM1OcbNwVk(vHUL~s2n>RO?^ZM}gczb&~Z(}+hPlqFb%jNR=
z_07%Qtv%fQ_y>Rb(d#z~T+jaS;d!xXzPr0*@kf7h`|Y3m&ENQ+{HOoo|6#}5KY2JW
zH^;6q(xrGi&qeugUb8$QERpoL^8xcrthYD!HU;=W7prR4D9=%0NUX8?x*=I<5v~of
zo-?n3LXl+yS$kB(`q*(u$o)t6@BGVo^}qG4U;lgG{FOV_ho{G<>$L|(${JMK9^TgG
zF0}o&Qw14_Xvm_F9OFvS*4uLT3XktwL~iT*%cW8jT$X|d(sB>O#9)?|cp54)nJGE;
zxI}3On_pSGYqQBr1#HWZ7LYJly8GBNDa9^ouxQ3crI^U^xJd6KybC?9lpD%vcK`_I
z^TV~zi_PgU2u)Pv1zwjX?i8U>m_0Re2OxpU)W7-L-#Z-Vd7Ae?rJqDWk|<Set(lpK
zsJ^r$StX(h-M#!oHG$ZjQupyRQG51pZMpNXUc$6Wz&)dnRk{}o6n68Q5(+C~7w%OS
zG9!w>9y}-@RK-e-K#Dj?ZG>o8!AL1<LE9XI5-d_E6qQzCEF|*0uG?k(dq4H--?@AJ
z!!Lilt%8WEK^m)~%EVX%IW0$&eC^Xu-@JKyT{pjO?>@YX+}7>#;ra3FpM3iM{TGi9
z4{zSQZmn0w<$66FXR|)m=fnNo?c0wJfAMc0&QD)ieKZ}qnF4ox{`hpWxu4fKwkgU9
zMda;q`Sj}_uY9tjfAsdo+L)JqxS6iFl#Ip{T8X%?+Xn1nK_A!a_38P+V+f^0toM^0
zwz0*wX;$}=*xr5lgTMcsU-`$s{2On>o}bQZ4x%K{+eA>MC4^?v;Yey^w5CRGW0T5?
z!96cg_7o^(@M(M0uKoV?KmEb)eAzF@>A1UwQzc3w1B}W*DU|8%Bg$KCs8!OVH@SOz
zTJEjQ(k9`g?9@pFs!bx?BUH6pGiamDeL5ZbvfSLvx27Vy$8?0UWM>X#?s}TtOpi=a
zqwY(z6r~u+ip&a#NTlyR9ZawoG4xme{&#wB%QDZ?44@DcfTdzDr(gHP)t@LWDk}q7
zsK`p+oeHXICn)SBrv1xUVC^!01$S*TtF%%n251Qjd$QL{^{8S=MzBzxm5EAZRfbHa
z%_v1lQMWE4mXtKwgw`7gYge-%ObInXv7jRnl?GJSsF<by#<zdzvp1i<KVKi-KUYu$
zV{=vW7_6M9mX(E4wJ*N-(jz?l{o^BrU%va|!&g6keE-$u;o)b0{uloEkN)8C>BBrv
zlscauBVw5kx6^4oKOVpLbMt!rZ~ow4zPWw#R_tiYo5SmGAMZ{&J+519`PoOG#26pm
zeR%u&{_5v9cgK(49?#D|{LZHzy?XU(QfX6c^#sA5X-}oEHLkS>C6Ks|@pQg0%P3V@
z(6`6C^Yb-5-rn8K+vVxUKmP4s{=5I%zxCTUnp>{d%bJ@{(~i2Uh&hC!)*DKz$`pi3
z<eq5?mWYC^$gZk_qCB48&+~Hl`lrA1@Q3|onlKM21tx;ZZET*EsF131CMs1+Wgs@P
zayi)P=3vLN17x91Y`bfeHjhle)Qmc}KJ|87mb>F|nWwp*%sOhf<rHLPF?XhaBzKSE
zYA7?Y-mP^jM8u%%Ii9Mj3W>x^c1I=xx#bwE{@UOFDHSzQGc(m@8c{xiFWC#)tf}c9
zmknV=A|wl58MWurMMZdKX2l-BzWY}&Rcx1k?*RkUJ-IR=Dh1hPkGpb<m7Y~vFCL7n
zlFAAXpjw~U6Y8YUq<4sFHNv7V6dj_XCgg^Kl<pQqEFk6#N@cB@ZX)sZS0DYIzxFq8
zIQ!}pUbky(+h5sK5uB&v&8ydk!%d%-Hcx$??_R&6$?g5C`&X~SE7tM;-G_nt=*_D?
z{5QXs@XAo&cv?clRA$8Tt#8Bp!(aU2T=V@h-SxvyfBdztPsfLcbJ_H^&mW(T;=Ddx
zzxDM`ud!Zn`S!;j^<LWRqnrEFeY?a%ZQE`b>S@;vbm7IHKwx8yZH()-S!)EstHPK0
z_IzGh_3F6j<?`3Q^S%GwKll$%j;}5cUp_si$2`s37DR^%ZDMOTceg_L=1Xr%&4{Lr
zO_E8Jim*gYrm2hC^Y-Z5c4)`CTmG^>jDl(_YcJXGBAK;C4EQK-DM$-%IZ#)HOEW#p
z3mRmuerRG;5mBTe$nCmmand%^4!s}Sw5X~`*J)26ELLW1=`J;Dgb(+vD%bTw2Bjv`
zc~a$~CSXSG7QZxQ6U{`vh!8zIa%4pfU+v{&zoe4WtT&A?uu`E4t=cnc0l=tTX6H5N
z_2Q*o5Qb#M00he2(sKV_QMq#*sHFx$K?O8~L?axEYO3%^Fday(J=|airouC`O~y&f
zpgPoynqn*^TkK)(0wc4ef{l^{BccK-U5b`fE}jBWlV&Ep9b0RZ6sXdI6`+E>FQ>!t
zbUI0!lN`ej$D1Nzi)or3KRn#u-@pC%)7%E6zW--`@vFb`OJD!SxBl$U{^ZsDX=-*j
z91cgWHJ+c&cOSlgcl*(AeERL*`|7KN%{RwyFQ-OCj_Kj??vqcy`0&o&d~|QiqV4wW
za&3=?f!nv=+%!IY_0134kM3@7d?V|zaV&b+E{FGz?|N`B0l$oEWSu4vZI72Ly~=St
zUuNT$>y6-l_V4}nZ~W?CpVsRa4<9aLbsu8=8bc@mLIBfwp0_bnc$j<JPgz!Sx93d}
zVFJi$>YHCHO1*kpY&rGb%O=0MIe7SU#c04V&Za|k>lFf_XF6kqZ!8ZGZE9*gB%ymu
zOb*!_5nt;{QC1nyn=06W%Jg!=RZ6<auBfhZ-^#DU;htn|+ZGWb7P+n0E*NF#M;6LG
zn`u><wbN-*ZQJuDBf?3`j6u@&MaJIxtLjU{BucG!ZOx{v2n34m&MZdkUVxsy8&g6v
zBHefF+8`+3-Dstf$*g_RQzDWmnvr5IQi#kE`4YK7Rv~xh`tA-(NNY_RQbse=rrNwT
zT-jzx%>qj)TEa{rO|nqoTJ-E=Rg5&YEy_9OF2}>GPe1<5HXUtbgXzS45J3*~uF_0Q
zZJy@SVcWLb+uQJZK0iJ^JoIkw-hK7OkAL{-Ctv&IlaK!G_s^H7_0Rs|&%gUKKlkDB
z;dof4xow%(r>C3wkfPi3I(_oVN8kA7<M}ysS#FnGFERV6H?MEz!@7?7=CyadE=Qf_
zx9bSLdV72Oq0ZIxo9)wkZ`P6XyI1YiSD$||x1+S%KO2K{^65NAwALpxlQNy_8c!`u
zkGFlEC;P_Rul?Ph{p-K|>wjmvK0dD>)^V-E)cVVeN0!ynPuhECrib)SK}3jD>k`{Q
z<kV!W+ZJ_P4(qx`4rHmCmRHqub7N1Zh1Jh%U#)A0B5Gwl0YL5^345Zo0<{aiY06GB
z%i?fv6G~;vR6&jrxlJ-tib_^_4uMjTwGyo&Af+0tO|e0s7TZ`Ep@wz6M)`GH<>BG>
zbUDPb_<^ljQ)q!2wBun`ZLZB^LOpw|>*l>3tg5O|2~brnkh>KlN!9j3n^vvzuHDN8
zkQB|GU5;>%LII^a6Jb~4LKIcV%7hoRQuej48Oy+kRt3dMM3snDWC(=eRU|VKCFH(@
zOwptbDy4@u*;J|r6QHR@X}RtpP(f;y#TJts9Jy`d>{}*o@8;9t*yaAySD*E!QAKIf
zvWFlDV-MSaubzoh#{K<$#K@@o`<vDMv@D0?^cR2rZy(;j7qg~woBNmV-+d##{jJZw
zdwG7my*-6rpT^~T_qSbL3gmG<zB+ZqG#zak<6I+q;M2R?>m}Lc>{po%H+Q!`diS_`
zeEXZ<`u@{jJYUyu;Wkdw(+__f%QwaQ-RZ{CZ|^>Re0;pD>o>pt(f5D!{`KvvQ@2>R
zo73wS_=68Wx@+^-Z*J~Q{)4~s_x|QLe|Eb*uIG1GY|%RZe?<N1uWsF%mxtZm7-P=)
zbgSF_+n=5Sswk=`o6bN0$1w&8r9pBWCGyAgi6j3V$(JZ1N+QKd90(>LV}S{sp$e!D
zs_L9mr%r#~{@r`+)jiFebBw#mhq({=zNEFJHJ|y6ao@k+bxE=5$nGRXq+4xvkcxz<
zTPg)2Of{jn=^~$ju;yXm4saT01ZAOMRGq^-NmrwlzN|zspl_?Gnz?WbL}pIv&AlxJ
zd@wh2!eH(K0YD|O6Eq++#HNNo;MSU3s7RwGfn8aFM}&ccHgiDXjNlELdaYV#P}j+!
znl>|=n?8(yAS85uyt#}gTRdBJW$8P)uc%LER<c0VlDe8S7Jaz6Orti#uJ?`r)CHj9
z%!x24parm101O0Kj`7va5m2a@Kr+*CtKr@jw|N**l9It=No-<}nlmd#TDqLb6brB{
zoQNtZ5V$bTNa_R@fs9}@a}Fa2iV!1`Aaf4R-XUX%Q&nz}m>J_u%%Hi|fS#z=aFWC#
z^K@Kq%G=MM?uMP7BI;q1N$H$w3RZTgjf@<oIZ%#ueD&#v1hL(2caI)z&o)wft^V@m
z%MU(y|M3g|_@hriHN57OrfK@g$DjOxKlpS1@}K?V^IiAWi)V+2)8Vu~-}X4};r9D8
zwB7n_Hf*girD>XnZq==ZbVjPJ>8e}}s)ubZMkS?ub@f%(T1v6#;QZ%5AMZXpe*BfI
z7yE~Cuki=2UclT=)-JB{qs?~Jt&UAozdjwt({#K#@82EdkAC%+-oJQO>%%zjrv`Nl
zA+UO)Wek#7x_+8#@+hmGDwtLgAy`r*0D;V86<V_pCx~dCiWnF>7|KJvd(278h6uKV
zQ=jBia$0*mm^5NW6(ULOv(+#{4YmMaP9jDSMw9{>ToEap4K$3b5dn^&;T3Wrnng5G
zW-G`Xewx}L!sb@zT8}oiXyG$BcnzIxF=tO20C`GtP%uZrU5D9;AvqXKZH@@%pk$fZ
zs31&cCb#>slxC45yGas47D2>tHd_!V5N<@$mMof?`@;4>1WAb$*kfV<FeBoW(&Acz
zh^7lu+5i`9E_+}!Cjtlt0!~mUH8qS#iR;o>0@pX)E@lbL#tBqh3gN<#VORuNL`WP!
z%E;)1kz3Pn%#^%hoh{rSU0pnT{OIaz+oeIa!(})0OcFDpj;&!yI*mor@i5moS#ttr
z#^dog&8O>^rCDrt+tq6I`t|E>wQ9|5p3K|z@n)4*x1W7__x!~l{}X@m2jBV4byqIe
z>+k*K2N|E1evR>pn}c+#b-B5@8Nugy+I3~S8W1sX1wk3hY0j5Je{*wVb=q!L$l1(u
zH*7A?F7EuQT|R&G<Q3oj-p3z4zqsgz{@dUE-aF6V@=?#vAI<2u`|<tr%VhQWvqxWj
z^p$_>OMmEi`h5TJfKX-|!(g1puInH|x{_C$c%zO651>@YkUdJ@wbnpqu%>7h;1NuW
z0gFLhUEAW|Lkf4vOsh;bLnsIaY9yneu647k`J@p&$^;e!oFq#UBX>6xb60~Pqi|4e
zZs7{%Eabru#&|?XnZd;nCz!HAwP*&ld26jTYq3Z<tA+z2nFctrW7Re`KsqHkh;+=%
zY|%o;hNjp>&Y3b<Pi?l88H4!pYv+LqzJPetA>Kel1Kh#g7he$(N4T4Ncx&ov&D6{z
z$imHy5RypBS(r;Meb*I9Z*0VjNU*3gW9iXgMif{AVZ_L|xMy@3vO21Tqb-^@6azs_
zD9K5Tm>oUXfSd@CoX{BoL0r*{2)h8*iRamdoL@YC{Ot14X0={-!>;TuHs?>uS<<Lx
z$7vddm1NdB87+v(e(2Zhv-5tnUhmG=XOG;hPBlC}{K-e>=jU&|_2TnSKP{<Hz@+oD
zCzlToH!q$)yIQYaeg5%KIwEn^%>3}`Gi)=a#3^@ue{*wpcYoV=-EOyQtxfYh^sB>h
zZ0`3DclY<#j4^a&ZZ(%ua)LS~!b1G~`CF^9mgbk6RXd(G>*4L~S&y_`Z&#95tF<IK
zXL)P=?EPn7N#!ulx8tc&PU>v)+^^Se<`$`}rrKI-g+)?gBqV0ZOTk=-RMl;st(oaE
z7x`(Hsx9e4uwaz!`n*0}eIH(htAo|asy6_0QvhQEW)LB7)oMgzaKzwv^Q?kVG;kzW
zZ*v{1HEm1d4I`GUAh<aLo7>zbgVwapUMpA&)v$mdB5UF1ZV<Law!<AT5JSwMsiu^N
z)gU4SKlRuylAY$$IGQ%C+GgYJdn=J(6b3M>5kY|z=wWVN>ujbF0tgY-T3h};UwGcJ
zI3A*TBgQ1l#hG)?9p}Q*rHq;0q#eQmfY^x^zv!}DK#M>oCU7*6MeZ$2?#<l|l`<j|
z=3sJ3gn|nN9}JOV!T$N;By?czAVepH$uq%jy?J(Zb$PxU)<f3~c_^E*f-$CtX;`n<
zo6UGQlu{g)^gIzK3((d$Nm&gj>HOmC^6KjR;t{%k=bPW`*4^3FWj%}+j~;J!{aN4d
zy8Q9=XSrlN?Oz}EoAnvMq_sZWJ>;B*VNmsw%F9<@9QKFJcDp@0pXU1d<rm{L-X9*0
zhr9jZ)=k}<nAL5)SslmY%j=sKx<1>L-Oeq##C`7A`N`EeA_e$5=goTEcSAu*4HM@V
zFJ52?tr(3Jy^ggIw${L-?}u@!bJNmwxhq8IbBj7lO1WQ|R#ydFE@1?L05ML}IF70n
zm)SF60F^X6e)iVOwSIU1QO@G6HJuPlgb=W{*|ZU|kPw$J2UXAjqlkozhRgPpkU&A-
zER~k)H6Rg)@Dd+D4|Q)5vra9V;Ub4N2M336HxEP!N3UeMY>dJi5c1+N0`OeAoV##b
z;Qp;OZ8{z&o!WFfP2)Jv#;dn`rVtQ-XkI;Fsm`0JYE!N5hCyzw)>@k|=uIBQ+|Aw~
zJ{XuVi4;kxOUoWRiR6?d2@sp1`@*xwH`*5B@I~If%nKX{Gbdb@Vi*V*0ggrj#KZ+z
zAcP2W1dtOtL=YIFrvMC8^8jRA$oSSAakXAQdvbn$HuPOrhBc(fJ%YjM)&6XIadv(d
zP51kg<T5?%?{5wdcdzH^aJ+wgxP3LAP7iml?r(1T^)C0r*>-!j-Q3?ly!*lXMzGzi
zHk*z$HtQY|4SmrzdbQJd0`~FvFjcQjFVA-4Sa0uc9v=2(wUW|XU~Mx}+`PV?jz<7X
zr5uk_$wRAE5(mPomoJZ}!{wu^)vD80A8zk1&d)euP9+N+#v^*1?{=%cpC^6&@^;#f
z&%gBkZo8iDr+GSgC^U4_I=N)zlr$ho=}MRTf(YzEu~=mcJ(93wY1(QtCW-KI9JQ&r
zGA;Q4U;uMWWp(w=TR*_-A00myp{BEf5iZTvKnC@yHoJN=H4O+976N1x00COuZWLk5
zz80KVgop%e$zU(sAG2uciZG)YS+Fk*=ENk73}(S@VG4^c%*<TFjY606olVnRs|scK
zsdcb5o5xzm>TWvM=`_}<^6+e6VPOJr2XlZo-8S>q>YHmopt)*ou$+mOH1xuoL-YlD
z511thGc)Cs6X%?|lq3w72=H=RhrlJ=?M~|E0Z4!m?gl^%L<C3%U=fQHI#7@WKp@!N
z5HzzfpaPP+L_;w4@BqUIS7heo90*P*iJx9wKDxLVR;ygHFa^&v!%+K2=Z}M7v+DbP
z#YEGrC}K994)=tSnWn>0bprJTV|u&4Pkp~mL&@^a?|=XP<;$yKb!>-`lnw{4)8@(3
zXa*sZS)E7FW~L?4>2&<`)6cBw`PHLvS|ZpWuw8F#(%^i3yEo$Xu=3D_g8y*;&<)*N
z&tDu5$Nj_p?)Hwt0ig8VJWn?-zqmNNFo>q}@pyXm`u^GTXYalLKBzwIZ#@*8$7v1+
z?mHBk%u*TpJS5<S5k}3CkYz0_9L?0)TmjrI=K0j>)Y=lH6(rJDGpCfqRJmVmUOfBe
z;U^!>pJBMF&a;N9syUcOGZ+coqRwrulUai>TK2<>MFuHgG1_^A;*xYG@Wg^Z;2@L{
zG03?fG6Vq<F3bft#ATM`8or?5A{=2UnHT^Fkwk#1S+!Oz0tAr4=BnDX)>h}aPSsVX
zahm6uho?Qdq$Es^5MgfSh`!X)YE`ueuyC)fMl7sJ^Z;F4FhIWG?jsg~MG`5cbg4^O
zmisU-J}U(C0AMCT<Yq105S$PfmiTh54pbnEAP)>!mfjJl$cCQ5#G9dE#L~Bj#r;Sa
zh!hU)A#b!3Ow4^p7iYuy`9)cG)Df}x41HaF<>EaN>Q~(xX|+P6bUf|%54XYsDXBv4
zhE#@>GdPT=gRDwz4d&YB<HeKnv&UC2zxBQCll9mRr|a8?<Ke~I@7?c@V(>5?!`kEX
zvvC@0odDwI?!n!Gu-0~VerDhio+Y17lL60arON`rB$LjMuCDfX_q*L$$;0t<`sm{i
zApnuqL(j~geEbP`lw1zS<EhRG<;mlx?|t<HGT7hWRx~iu8mBt-L&vFewJs0t9O~c*
z%t$Fq7S2>kF5MeixawT1xkA`9P4hgeYECJ@A+ReYn2Gpo_h|jzJOA?2-~Pevk5FS8
zr|EPwovTh&XLEO*!km}_(5khn^VoC-R{#sw>Hrv|5UP!k3$eKg3ZW1HA*ILx0i;Vw
ziUEX?IRc#^BEUS%F?3O*5*mf^!lhV@Er>`UjN%?<Ey9@vBV5f?>oi+yb*^)rPt%@P
z&sKG|M3xA^)y+cPbXKsYHqT(b;N;9L0GI4Tn6(A49?Sb5K~2LWrBq7p(jw-d1#*wF
z2n!t`!ot*DAwo@^JP2ZO^8rQhl9dw=R#%JaA&3cB83Z*8imI9dx<vEP1%jwc)s%pR
z&}>d5nG0rublR-))2pY$D$TkN6gqNuOdni6aja9F=W)D09_mS_sXknPvA?<X@SImD
zUB4OD>$5{^Ii<@-m*<=H(~GlUBl4RMKYj6)x61kI`oo{}0f7u6X((5hk6*w1cy13}
z?sDoH!0|YHv$<L7WPds!N53A1p}!cm$NT-m@#M_?x?6Xpq<sDQdiUt6wwVzK(|&)P
z>iEMSeDB?N-+BD(@ib4bZmti91CVsXYEoVLG#6JF4P$Pt&Qk)ZvyJoImw`x5<B6#-
zB|;+PlDb?5mIRK-+$9cT?p7?YOgI>Vxztuqr&HHu5uWBLNh+yxNsjPj_mrMJ`p4h;
zXWxJMeFo9V%(S&}u5)eGT6n9=s~8LsQUuS_6t%`|VQ7$%2twS0mY@L!GO?o@E?sa6
zX9gm64*>);1X2ihAVly8GgkvOYmO%5ohzz(xU(>#5fTO=FhIncYB(|BJkM=z;MV3@
zRcjm7XI?)Y%$o(GlPk2QrfSW+VJI$2w)i&}5Xa_0NM`O!7zkns=$ku4q?Egq7e_lU
z!luL`LQFuY=4PQwEmFZ0n}%A18!{jw#F7$Xk3fq0rvGlVB-1^tg#dz)HFRqX=!WRH
zXw=@QzyzEvF?M7fb4q#GY_{t}t(<PBE4X-X^S0LMINe`=`g$JQcs$(S?Z^34=i_ud
zji-})wbojV)!EsXzWlCYd~yB7ZYaC;s$@pAR*x?~{`8%n{mTBcn|6PQAqUXy7cU=Q
zKE6BR)zDG0mk$r;yNfJxdv`leqk914-Pz+M4sv;Qb$fr~X064rU2Xb)Ivws$`_<5E
zwTrVS*S9w)p>rKihj-t94_(@<cXxOD{X-TZ#+-$bwN|sq>+HZ_e%kM++K>sDX077X
zP!b?X282K}LID;41aJrR5CmdQUFk|Ti>ft3MF@!NTm@m(6(TV*CVBSsy|&)`wO{=k
zpWnV*;mV>yr8=9bs#XsT^Z<8<rn6aqGfM)Cn8^)|XTf6V?sLER%==D&h)5743>g;h
zkfR1V1t4Wab2I=z1we9)a3$*t_Dt-KXo{GEfPerCVp=fkD3nu|B!gM28qwydYPA42
zRrfF(dHZ}tNZMLxxQ8}x&BDkXOu^j{B_d31sUwCi$Z2S*0pY%6nt>c3mbEeEk{6Vj
zh)9yfAJfzql8LKnb63^5X>$(@Ty%;H8fj5gAsDsh(L&<QBkmEQfsWysf~~3602hFU
z2m+awPA~|dFh(b(A3!=D*1MdOsmVxB@{{+k-b0O-x7QE*{qf;Ajz?>?w%W#NIvrax
zZ3Z60&^a}*`Mvi)c>es^=U;rdKU^<^GDduT_j!AD_l=+Zx!?Wq4^bO|-+u8@EiN8E
z9}lm)By;o9b!vWhb9;O9D(A$Qw`W_TlvAb@_qPwsStMKNx-W<OLm7rra^J5{<NWIO
z?&9L?)6YJA^7P5&<EwEz9u5zu(>RWkfg@ovlbm<k?KGYkDMQ)s_xr=1MMQElWf5>#
za*l}L0j6MvDdz}}=Exx|>TGU8RB{1|rlVHKf>o=Bl`av9Z~^3g)nB~*&h_E;um0En
z+1<RS3U!`ao#xgK)4{9(n6{?YrdriZD_BEZIK57$-n0fVy0RdVXOS$Dg=IOi5Mlrz
zs_Wv21Vo?)VT(7M83C|0OF)IUp=1^d1E9GhArb<}8|OHtzU#W7=Y^0T0q$X5=V{5=
z&vjO-JUlJnV6}NwGBCF#&#+7(5on=L#!qDozKnm|o7>XMvW3{V1e(nuB8j{?{1Y)Q
zVLM!wXb};PVXkUk-DeLCcXKdAhQNPw5p3$=3{L0>fo=ru7yvF|ZdToz>jI8MSrA2t
zLco$3Ga^a`PPi&*yIOI{Rmb6YaUtiIyIp3ve|=}_2z0tXJiLBgtLjv>&8jUd%o+>6
zc<YG>e)P#tt{z{$^ZvV^efrVe&C7WlQ|c1<XCHm|aJql*gLgjp=%bFs+kBk&SC6*y
zq&cr3%uNsb`~B(Wu)pu}jyq<d^Rsi0XtlL@KAjF{yIq~fE|tUaVY}OM9!^s=Z4h2c
zdj0zD>hTkgNTs{IyS;w-Mb`~;osyKqX>pQUL|`25Zm;id&d<*Q=`@`JAt<qQfhip3
zI+|60C|%dQHET2^%*iOYAb6bi$K!ENggLV?gX1)vkWHj7WmUZ<Dpwa5-CJ+{&JTX?
zum8jU@_3qbwCOk>PRFU$httDpJS@nYsnuEMR$EiE%Ar1=rs<$&4T!+ewGj~{$_WZW
zDXDM}c|viI0CQ}MKf%2PC?UHoc};Tz!OT&7={}pQK?I>8P!cH1xg3CmQ_3Z$l$gm0
z;7v|v$$dhARjaM>>gg(C=|y>%BY?e$b9w|Kky*IK;+i#EBF~_X5WGOy7X6fi`vQH9
zV4_7>okWP7k;6gF5W$+eMr&#<yhWQ-tD0$85Yn4qor4=Jhpt%mXBHHQ9^hb#9u&S@
zJ_?YgW!uJ-(i?IN5;HMo0>)MDheEl4Yk9Z-najMXr-$>iO{?x^fN}Tw_5JOgS3qF(
zX_}`9Ky&~oT{=I%>^lDNqaV4!lgH2Mbg1(*&LbGKXrF%k<BQ9S^{{?@eZ6~hu|M7=
z?sgZCv`yNoP_E;C(&IeVS$Vx%vE<EqeL9^?J-m(kL+(pXX*!M7TGw^qaGK`66l)a?
zht+0%u~Ab={oVa(n&-rL8V`N9!lkz8VD59PcXzM*?K;f&#{(xWDKkmam`+m*^rqoJ
zAg*oFleU=|2|2*r8j|Odn%1dSMh>*YX`JV|NZxKQ0$_i--<@wZn^iu4^qmiX^q>Fz
zzq8+;>SXuFhvR9U#;R7Qd79>#h=5UD!QCw&vH&NL`OwDWY(~Ij<|5b+oY+gkB(X@{
z3KwwHm<;qyBr_^TnEP^n#2_Xi;|L%?i^Ud5f=E9#X9R(%G7AwSkqBd8nA?KP@xbMS
zRn5$`DzC1Fuw{ya3k`#u7Uy6DMxa@|Q2{tWFgS%RxdQ?VCL%xtfEH%4TsK4{<^1Mx
zWDH<H(nS)W(W-0JXie3|X_~9W@_Ap%1O%!IfB?D84HnXs1}u7K3-cfrq+ljVoO>+2
zi?B$Mge1z$$U%gJ%$cc_u1J>)%){5uzW$}hZ`bRGX_~O3+LRsd?r+tbH>$09)RfXZ
zRbfh9*RR%?2|Y6UXP<sl=XpDvncMB{OQA#rlH}7bUOsvH3_MELm0^XkN+sXlTqDG4
z7{+lL4`ZSZnO5tw)v$&TB68C@H?0#1ccr*lZ8hgk)ehsqYVEtOs`i_8%1MY%r}5Rx
zYYZF5eeP3Nx@nx48Brimr@7mnbKv3T&fPetB1xMzYwOLX41HLHIRK7_V{2Mc5%YRH
z%=?2*O5s}PoRW|+(dkrQ-F#8<s_%A4b~^15`Q5jlGf8^#;=lT}U;Z!t$^VzBKt*eF
z9haj;i<m7G339j8&5$W&T4hKQEYTCtG|kP-g;}icvFlRd0BVSkG7AboI9s#IBtXFw
zf=r2#9Nj<x6~KKVU$J6f5g-6`at<&wKqO=$mLyF4<_f&5cb7@OLO8Uhb2Sf;03!%r
z*qnf_5x7+0W%;&}L=os-i6JE+76>H*1V=<`0*DK_KioVhjNDNpS}e{d2dE3$g$act
z;c{$uVqc&t;p_^E;b<fXh-Ml-dl&%%1QCHjI1?aBARCLPB$CmYQxaxIgor>)B7zct
zL81{dw`tbxJjV)f5Mqa6yFMN7Yi(JQvUj;_!)AguJybXIs+-js$T{Z#Xl+*WOt=}+
z-OU$X=eOT}&SI~xUv0PB`EU~HKK}UQciw*c<tLxL{lR-|rA=*Y8UWrjELyWV&E3#v
zf_Xf(HuwE1!j9tsv39*MrO$6)I?gNWh1aHWu*0xfZ`K1K&y%(~PN%!j(`kQ5g4^|4
zo93ZxFV9W!czD>ZhKBhxo(K(CAn0(Ka_O%w&q-FNlY%6noG6*I*ILq+6M|0I>OAeW
zO=e!0x10W9-rwzOmv_TzSgnR>I_~%T58io~C1VU&(s#c19h+iUuN)e9pAxa7)fq&&
z<Efo!;!=h{5~9L{!E<;iY1OZ4ZMUbrGaOIKIfgX?Dh5*o5g-^RBlkpX9;g8c4SZ4W
zA}4{saFIfhAV5+=O9*c0gaQ<9&D|vxRYeRI(q_ES;gK+qP{LaTdJD8bL_tId#V|yS
zg|<oJ5O8uxOdJ@Dh^|fy-~nvH^oBYe<QB^u9v2)Dpf+!_hDNBH86pAdQd!8H8tU?;
zk#iS;edk`ol)%8*LqSJCmN500X+e7vQVM_wMI>kl$kCNlO2SOUzKjr%oSBlOl!(QH
z3<@(ZuQxhPBii)(`udXA#~3lgpy^?1O>0Vtq+4yc*);BPoU0_>Y}Vsc(W@D^aWsm6
z5RN(@zWDsp%SV@Hc7J!f8M^KI{O<1lWyz19zj*n{XJ7vEm*)wbk#cd!(^x6dbZk`9
z{_tAElax~4o~uS!&Uu=~FFv~tjS!t%HLqsgl>#V|)8X)ts3Z1id>D@ph@5lrMoiML
z*TE9*+q2Eh_3N?eZZ&K#&PIc#v0iVaU#rG+Jk;idour&g->=UT!tz@ZZxr_$ha`E|
zUAeWX>HYoD9M3mTQgQ&BX9bX9-Otl;{pi{4-OJzjtzYF1Q-^54IfGd9#()&XG{9n-
zPwL15o3sL~s!T-4xFF}rqs`R~58`KA8!`hDAzEuJKnY<ECIB}OMkXM4wZ#@p6okPD
z8CEFjAQTu#f(W6;7>dm_%m~oTn4O8tEFraU(|{#^Vb)qh3`XSj<qC8;PX>e|a6l}c
zIzmK5Hw%9Qt_mQ=ym<KlIh-Td7Ks7Cl9@w95gAG+j0Gut8K)9BgrPOkiYZAZp+Io0
z&E{$z7;e#8w4|+}0|gTTBZ^Q&0J$&+2MS82+yNz*!bya9(R6$6sp}*cP8qlW3wE*!
zQA8;Odg85KKEJr=SdPchFa+^5&XZ}aM{Qc`41k->`ZVuZ2*NVUYTLEiYOSqJgh7Pk
zc>3(~&$pYab+<AwL3-=?TgUNK=T^FIe|YJYSKFuV27WRy4@#0*N`O7=Z!DsemAgS8
zdQ9VVINq&>B57bQhr|6?r}g?QCu-Wp<G5L^PsfKJ|KPh?y&pO$tD#?^h$E>5A>LlU
zYOU=yYcuQm^;Fw>HLQlAY9uV{;cUCv<T7N=Q?27Tw{ad%hjD+HPlstb>8zmYYNv6&
zxw!#__3jMGq51J)e}8u?C0#wfP>c187k}@U|H=R9@BPi~)q0RbnMjPqNf4QkGZHbI
zldFcsL_~e+!3mAo*qu&oI@P&NA>crkT~<;IEign9p$8cV#sY+r2ntS;1Q87vEg(fo
zxj-fa2x1UIKp`ZD#R_P#U_`=cK{deAO{+ScrfHh%+*;EJ13+To-J?wecr?UdSmcuS
zQzVgvEwLX82Lcj8L?jHv3@ATE^iY6D1O_k>qNJ2ckuHhj;&S>aJI*N#BOKgP5=Lt3
z(>$4mRYql30~Y`xmbCanC>)6)0RksRg0z7D6LJC(gcKM=?74%|mDHuwMNSbZ0wX0w
zOwpv1RA6`{PuG+IoX8LNdu>L-O?8@^hk}EtCE@e)^IRwusy1gqVG3`p&4iecvv482
zzI(Om*H^pC`BVeq+0$pY*LUaVkAn1cs8^5P$wRumdD)jvge4VK-|o(DZ*IWTH0kBz
z^RDaaF#;N3II2jxzuTuSZ`SKo$=2-taO;Zee)!_^k6wQE$!@b*Z??##ls<QPwuVSH
zPoeXAHPmq=q_Ng&t@Il;OeHaQX_XDlT~FgN(A;gDCT*4oiNfDFhTwFZ4yV0^ulh47
zqE%1F!+1JC#P)2v*>tHJhRY{^?eG1qU-{^_*6Z!cg$g>D5D+i@sXzu!V(1`^I&kyC
zx#P7PvoU(iCqGTq>J-dijH*&FWuid9Dv1aQAcD*=%p*VvW7#VSL6#&TJ*^R785acr
zELs(!FxLnVTRh}Kq_u^*H!!og*_(sOlCfk;NtluQtK?zkhQWv#kpdVYxy6za07L>t
zA^@PJ%+3G=h>U>$2IYVZ<iLz1#EE2iD-Z#21i1-f0)W8kVd^d<%(=NW3vF)I!-5zI
zJ#tXNR9F%L00b~%5CI_)F(*!lOGHGF03aeIPMK3zq)(I(1ekJARGfq)`8v<}Nq+iF
z9%n2b?pDXc{^4+lh&HRb6LOtT03O~-AN{~XpAjnpAnNIK%OVI7Fq>QHdt#Wz6OuoB
z@#0kLcs#uS-j^J7J)9G}>V(7zQ#;MWW{_@mn(O*<quM$YMkw3!NL}vrv_F}vNSVje
z;c(oncct%v^4;r)<HI!<Bux)DUyOBsasGtM+HGE~*WGY_nx?Kxr_)3TwN*Emb)Ky4
z&d$SOp2m3|)!T7ASS6865>Z>0f{Z#(Za&%QX4Vt~$MH1Jr|oL1kXR5nPNxI7_q)yR
z;%vxib@e!1J^I`K=pTIRv)>z5kjaC97dvzkbEAZm0!1+yWpEO8N2i1-Dn?ap)tc9_
zjjCvrMMiW7$drI2&>{$hoR(BHx?4^;1rZ<yA|Qb<3qaBU3`ZbX4kLiNSYO`cI*5TW
z$lJp8)Cj0eVX2u}LkdpxCZ{u8tRmi|dLk^`5HMgN_8>ThBQp^a6NQJ+q6db>4uAk2
zivkXWP?$+DW#Pn3B2oab&KdzC2>{&|x-B9yGJ9aH&E}?6W68aBKpDA^q>!bWi3k`F
zK)6IE0Ew7`c-cKrI3WTMU<%0~$&r+k<dhgZbSZb_Ev?V3XJH51-`-EB5tGzuYV)k7
z*5;f*$hu4bP1{1>M`nS*(`jGws&vI6a$X?`a^9V7!+d{VA3u3E)x&r?K+2DHPsTc#
zdDT|unL?ew0C$&HB55ASQi^&dfai~%9uBuRcXuh}B;49Gj&*zSNOHNkx%<h7-(QJ2
zb*r<p{oTzx&)sI1WwpCJTkkGzZ%#x$*HNTXophe6Rd-%(E>&ZHf3G^%b`+*EZ2SJa
znYk(&-oL&--rbMKW2=)kv*w|7uC3OXTL57{47=T0t=6#8_obBWYLiRae&vJf+lPPr
zuYUEVy=H1D3jjqp3vgoO00tJoguwxXhTsTbh92asb5&R32#D$$8sfx6u8j#PTjCTB
z4uMR>2?;n6vC#5A1|~#zMB)gFU{DK34+ujKX!gb{>=9vvL17Wg;^+;Z0dNs!hc92b
zB+SAB%K$zQ!XVo6o`x_4Kwtnw0BvqI2IQQ@1H*wV61Z>}Ee|Jzg9`x?p)d+A;~<Tu
z-kOR)N(qA9Qg|RVM1&B6D#R&K*JHD2;mB{)1A>SI9AxIUj6dJpN)Ql$2|V5yryWBB
zIZPV{=8}<s$Otpj2FOnH;eI@RGRHc1d6n9cyjjWvl2NAcNDLXID_w2mrhvmVpK7f{
zWZnp+?^lv&=+~;<(C<=89`i74IQ-`I%O8CIThE`r<$<l)Tu<BW`NP8lu{_*d@4Dp8
zQ%8Ny<LsA@-<|G1fBEXRlroKbpJTmV&*Ld^0E!WH>lHeF^5gG@Pr3VG*j%i3TSy&A
z->-J-ew%Xs<kMH-Qc_Dy$NfWXhb$#i=a{BxGM|{7sjSY<0Y!r5X^+S{*W2s;;o(7u
z-2%eZoC8c%(IoYmCHF%&@Hn52^J#OjTWz=Pc)~C#J4pQ($Gh%oc(OmA=fPdu{7~mW
z2H^+`4}8P7bZ*TE-7qTn%oA}!Az~DZmD5<`q!H#h+>u&9^$@k3vo%YMnS~ILF#wtd
zQXn9asJS(DAPP4lMgT|ma0^E?_1PC?=o=KLnqmZz5MW>|3QZ(ivU-bNh&PW0L<(EB
z#|$B67+|rq+lkx_AXo?qAvnl=x#>cLnFs!q)=vSzNpemJ1(A_pF#((T;<-UwK7{5L
z2t<Lc)>dmoAfaObEhxAJ&K;D<0m7X9|E~&slb~h*V2{uxu<C$Bm_iEWPFR>QSh52(
zLk?GshiUGyzbF?&&aI9fmWi2IaxOV<*K0t_S-QSYDW%ehBoAoSQj%^}5TMqRWMFnc
z9ruUQap}89S5J_b2`?@#OWG<zNeG~3fe!nJdt%sa&PwWnurGtR)2E;O@Z!lcF6*0{
z8^X|fJUr}oyT_aDSz`Y5!w>g2Up#;N-52kFtsho{pv}|na>I#I>TYgtSu(S?@!)l;
z?UZuqR@>=VRmZs<-KsU~%hn^^KAaGxFUd`h$4R(5zqnL)un11B4NRLlrUanExLR*c
z^Td63etCfiCfE<@>gw|R+4CQN{_=M|``zz;_M_|5eV;iAxG-f-R3gY63_>yiJEjP7
z5^*p<cZZC9CKL(*qA-YN9u}!$V3(0n%83gj2flFy8#5xJYXfG8pO#>nn*|z1fHk+~
z%`glNeA2nq=5HPmkMJ-z4R0P1gcyzySnGu3l88A6B9hA+m|-|_;F7-ph~R~ain!3<
z-CZas7H>+$0BVbVZ?Ob;umqAY^YUZvz6^Gj1Vl4)MK3}8<_P2tZW`u{%q*Nk+&#ct
zApkW3!O#H!jKKrL5y=B1%n@u3B?CftQdJfV0Lo-F=FtH}SRm9LRRcUJdJ|BubF+gU
zR(&_m^E8iV+nuBg5mhHau^P+?xwcUm6ZqKmDZ)z{HW#axFF&uNo^6KJYSp^l%%bTR
z_n+*x=R+EvJbv!P#~M0aM+BO$@-X4iB5JhFs`F{m)+BeFQlE=Ehz!i9`@_TWP@g`{
zkFTzN^5G9(zWR8*JG;1i@%YijXgc5TS7qfTROe|vZMSC-b$|P69A^L;wi_V6zrO{W
zo6l}YI1K$p1IGLN)DO9o+S)YLq1)`XmoH!4+}$5uJbyY`ef5)j)Ry}p_uc9E5YGF%
zqZH`4AVRac!65~de%OorlOO!6Z@v89b-RY1b=F==NUGHhwaxQFuLA@TF;e6J$IKki
zTC+-1#w3CQNz`>n&7wuB4G|0h-N`H@Aqg*cN(h2LLICq6OmDuVbRsYWpqXlQ4`q}H
zn`{PihT;_AMgZurlnxdekNZ#2XA8#=01*vL+=3Vx1JRLy1kghpgd#G5M+7tlWS2yU
zXs)PM-NFfZ*?c?lV&r8(3Uk9C0(Bw^L^7+bTEj5&#qk6Hq25|mw?J4}|G2aekQQ<%
z1V94_a`V<aT$k5A5kydH8We!6fNGA|!lZOmM_^V$rX&{Tki3~d5eA=)B@=_IYt@U3
zOAaJ|Q}j0lk(B#xnoer1)zJ-*qU4@gj>n^Le*47-w>Nk5<SF@f``EQ^R;$a)-SzeL
z^<f%zn=gOk_ucL5-ODzOuWOsw>FVlgiaD6n$JMZ@Ee;Q#)zP$JF7WK?;s+mJPtDFR
zubMYtn%aa(p1k$`*~O)+_k9^}5B+L=s$-qv{PN=U-R<?~KiKsB<>jN0?&fxH^}tZa
z`4|yro5w7rn$BZgt+!`q7ZDbiHmfxSU*Ej??A2%6i~g;zy!GJ^e~bZKI!ZE)r+HFZ
zbvemqkmJM6Ve+%9N9Pw$S!>_--`n5*;QsTM_TV|T(T|L$HcJq9&=fHX8VDkyM<}AW
zx(I;>W6C7aYGk0CgcMv-=$i7}%v^<35=B_6!Av4R!r{@(GAE)G)*#T$RUIOQ!4j9$
zYF2e_5l#Uf=mh9KFA1~=#AOBwVYX0fu{C98Nkq&Z;o#s^ggHV3oJdHJcu`piFxFY)
zr>}V7L=Zs6EMy+dH4sx~v4F+d%qb`$mc_7zlLI4J5MdWFwN@8mD02`IsMT7hR@D>$
zmW?kW{4@)P3rIc8wOYgizH&egBKPLtLA02cz+J;Dpw={vW(ZEE1(_f`#IQ9pf^9jw
z+&t~OO^=3JP3M#btu0hFG!bZ3Ktu2_ClpS2c6Qn3$=$oFv*TX(57X{!ouoUCr#2o}
zXM?&wzI+joKmO#$AAkO%&H3)Fx4%3cj)%KzKt9gnj!^Q>!@7QxFt>KBwSM`%uRVJ5
z*28@NaJosUtXG$P&yOBoFeir?)|;nKUtC>XRPZ7>(Wte8QyKcxJl)=Yk;>}n+h4A#
zAAj<yaOyg76Y2UyX&&3*bO-FYtmk=tb^Qs-YP~w!w;2rPlU-e$z5T)4^E}tF_QR@b
zy}!Sk_7AB`1&c?F(>!bIw_O-O*KfBMf%AB{+v|O>XO?P}NQ694TT@_S;J{^c(;y(s
zg$aoy7i=vB6rv=|7@1W$q@YAF8Kj_I96itocy3C<5{MWQ;aHK9kXYC}S6={Y5ui3}
zvldpZHbW-_HgJMK3oZ$PYpno)g6QCC0E?Z(7hF}C2ZUsnuH*h95ekrSa$;o`L}W%l
zYI<^mjGQ~k9dc4)B%FncDm0x5AW>d~shE+|;<#bq48Q=8k|Zo@;HIV;&7uN8^4gAd
zJWXw$W~ej38x~44(1sBl7-kv`V?u9On`cmR2G72f`yE0VEy5HG(EyYoQh*^M0%HUk
zxHhofm8Lj}cJFl0AMKv>!zxQci&|@#2?A$^zz8?Bxt>%TA?8vz$!fI{&Jp?i(W9;_
z^YOSEhDVPcOTXUj&ez)=kY7B0EO~W(eRKQj^*Gn{Dy!?=?OkX+wmGQFCG{N=rH9+w
zW+bc9_cWiz({U`E0pRBL)!F%WDZ*Y|KG|MgvhZrXnUAOY!{;|QH-f8RxVyhSyEwag
z^i+*LfBD&Vor$0VBw1;|G|$KJzF+6A-<Hz5Vkw!0%_58-EcAH!<dTNr_KVkt*AK5>
z-?%sMS;%rXIF)D<T5!o=jSSxY>hBxgdkd=n`j3C@n|GfINUK`itQu-GGqYs@YRHZY
zPX?Wkh@4B#LGng^h+Q7KM4dO)(H&fAKTg#H5|XeIxdd?n3`j&_3!%-!gE0OLnj{iB
zX?4@^xj_&vVMz@40Hl;eSfBQd!vFw)07*naRNNtv5CId3NI2x&6CtWCos$UXfw_}a
zKL~|$@jwiG!x$tagBgWV>gL%&EM;QBoH-D}N!U)uYQ|X5ts){rn4xMENp6505!u4x
zLvj&{Tq3|~o9gIQ1u(46^Q>;fkP>^CT1BO33MPOF4b?2dB7%X`7|DSef^!MVT$`Js
zwh~~>Z07Sca+28zRYTLvRYD+XS@nZIdCZ%)A3Z<s*KIz5+B8jov0issXlgY9fhS|&
zBoWR8DJ257wiq$h+Pu5GdinqrLF&HNyRTneKg{vy>>{Pyf-#t2`0AIh&d;_V{^Zkd
ze*1fr^c!FLKytpB_F0BLq;#n%vG~-gNbb6IfX{R5a(8p{npvJbeNSgZoS!~@W{QlY
zuBu8z;klGu>bqkdce~w|yO($O*l(Ucd+X-<v-^kRFsxhD>2%*(JKJ4JqT}(1Fy&mv
zu@P4hvzT+2HW%A@I`Oe<^Z4r37tPyzvN}EtLzhbr5MHZ?^tl_yW_Ic}D@adq=cEDv
zQ_QVtC_3ZPZS-gmBti?8p#dP0V{Kl0?Jx-jP&h{jD2BI~0bL>9{96MB3BW^ug#fF0
zfCVutIU{-lQx=v45y;vi8n~ky24wSwBn08Ai7BNdoG_dMBaoTg&E5LaA*LyU`_(cP
z#T3%Xu<3G6BAJI_VDZ*wh>YlzSs*u^dCG{WQ8`KJAZ2g}a5iri&RD{rg{73h!3|AA
zwe<rN8?pe9Sx}gyB&p9iTdfaw4-XIH=#bpaDnyt70lI5-YmPn|ge*X54_a>gi5LUD
zHA>0}hb+?ban^bAaMy~eMhtqw8QiMc6jp_XNaU@~2J&RszJK=K<KYUeP2=9%$<>&-
zwYJ-w3FqnIfEY_-A*EDGX-$PQ1|4<Gj9_|y*gNH`r_Xa1@FtSmczpHp(;t2J;n8mO
z*1PX~<LiI0tS|oVKlodpfA-<ee&Y|6G9VN#?2+f@SsV~K;cBx!9mcnxzx{jP`KGq{
z?Be2l`?$8#@$m5Y(X)s9lbNmZP^VcS!24mn4y&bf>tQpV+UDX(w_e|U{>gNl`cmrY
z;dDB2iEiivv1ttJRh?$Fah^<a$6~9^uJ5zE^<6(5j??~_5GJ*H+H(Ns&1|8EyOL2k
zhjU)_&z`<3(gy<MoUpIcc&M{$t@Aj#qC2$Ksv}qy00(3=H$brP=Fzo^OdLR-L`aAj
z%-sSA8HfQO3?KtKxgaq_gc%^ZFREk@5F=m;4{#w5S8$CmFm*;E5?6=7ET!x6a`cBK
z{}~7#084H_z^vm43IO7qQP4<PKv<>pISZ%arATY3Ma5)pK*&;nCX8TU&mA$NGk{W<
z0MB04Ei;iLI3T)$GhpOgn3sYKE-;`$I15Kl)jP$Q>-}+mI?mM*Bmpm<vS#cAYU4}*
z2Bbt72tpKrZb1<dE{vOX?l%N%AiUmm^C?dE(dKHig-Qc(t*ue$F-|C*Y*#~RmF9=0
z^>msT70^^Ch*;+{!F=<@?Rr>AqWim}ySM6n-!tcwx~1pYmBCf(JcS;Ll*-VKcKdLD
zcm3-A^{c!6%~YEmj_cLx{ONmt>`(tYfB7%|_y6ku?f+ccZ~em0{?e&Q_vrF?yi1d<
zIY-2>DFK>@Plsb*oS&Z`4)?-c5?T+dFfSbwr+KoR!Ay|5sUE_bq+zbQeSEdrUYzb<
z=OU}k_TlDws`r#-yV`W!a6GhBI;#rcjFAaNlJqN1$u;IW38gwmou}rOSSroy_0?*7
z`TFiPhixt{fQk}aUR*qWa(0?u_3%6l@Ah{OuOA*<p;m3Rw&ty27-%3vog7Jmn!*$d
zA(Lb>P11UK(-d3^zJZ;AvS2Vo7I$yp>_Mi01T4sCP1S)}01*Q*K+PdYohb|m?ag>b
z5eacIwPQFFr+@%-cPD{}5T;xbV<1771U!r>51oTg=4Q>2B^Tr}<&HSX!)cC55raqr
zh%>Ob8$louuy|lts)Q>M)aGFj?CzmV9XUoM;RplN008u^>xrl&S@ju2Y8bJTh1zHg
z(Hs&>aSpX+k_a^@gI8O0(%}$h%#0)f6&cG~R_9EmMa1RVwrN0hUXzzR9;dmgPIH#7
zmmble`p~U+W&Pgn?D6iZ%bSzdE~?LsQM!JhlnJSsy?pt3zgi<eo#&i0b8;h=oH%No
z%uOg$;nwCh9$rt!SFdmDX*}%5AO7IePp@CMaG|nW(eUzjUR_`R`9JsX|K-2<SN_`H
z{LeQ(`r^gAU%5Ms9dVZ2T0^AqI9)uuIO_iH{-Iy@^)PRDXRD!0iMQJ=5m9Cm;hZqV
z&3<kHbDQ@2{l!(jxOlwV?Z)|(#1QQ6=2b7T8g|n4K!rJVL&~|oxp~#~2^}#x3WPPS
z*~7qLg41DtyxZTOJ-M){rta)~_xO06Z9cwu`$Y>ShKom!cH8p$c;A2V#qeUp{_;5C
z-OXz)Tt~G@wau+HB4B}VB1|X}G`-=L1Y+arwH9{C0N`K&Ktv!xnh1g!DH8$;6A=Wy
zk*Z{1L`DUV0ED)r&;T6R%-yRixEF*Zi`)kdV4*Q15Sq5|kj3bN0T3dRCAHRQVU`pj
z!Fg5+P<Jqvej_9^WypndvpN|9Fa@%E6G@>E0D_1SO6ZOTTmu}TivaMT06-_qEQn-)
zp%y@1%Fy>&0!zth=p^Sg;;!#{$;NfRf4F(Q2d<nP(a3=`1*hIx)aB)}EFd(<7UvUW
z+B_*`+bBbyheT!nFiQ$XXdL3?5CXI+-MaLK;;4*~P@<*jSQ8wM$q;%_6;1$zh^ND`
zPNS>d-Q5kjBcf6YEHzFyV(z;P0NP-hp=mr^A1D{d=O2Ff`rE(zqleMdR<#|+cG8>E
z#XxuWpWom8%|G|={@FkNOMmjWzw^Dfzx1`!{s?`3dcM86e_7}D;?W~JHVDH^+32XP
zRPWAHKV*ftJij^~_oeGmFe3Jc`_OipDxq{;9yaT0?QnlR9&T?w|NLyXlFW%aBAN`Q
zYNhX5vs7{^DL`gBwrMOJu0})%t{REtt+$>|)h9hZe*Er!uj6#sovkT}Dm=Qn$T{uj
zahoo8vNFA)oVu<vvo_jvnxnbTHct~~8A|3Zg218?2?Z<IGRj6y)uVc_&*VTTB*_91
zBhVqylH?=-VKxRJ1XOoTAV8?05Fn6Lof!jy2mxcsIsg(vLTt<AwdBkK<`WW95CEDr
z;zc@SlJfFi7QvDNF+4?cBZQz38Hgn%w%kLm1P6^^Ctxy`-~`r|c!dK8U}R1hiD*`6
zB7n%?80vwHnT$tDS=|trIEb86Sq($ZS#nA#F>Xrfo&AefaI6oL&)$iM2$@vXiry=&
z2mo3DAR`w8Yi0zoI`8wD4?F6*LJn*(C6+WBB*CDj3CntY-sO@~8Yrot;_=n>jULBG
z+s(!4WXB#ao^GTU5)E(1eLanJn%q^}DTd8<y*r+!fCiyMUNU$hA}pCOVOgJ!r~AA5
z{SUwYlh0ng|CO&jUk#^+>9D`O-oGAaJ?*F8{LvR5-rnu&fBmQa^dEci-m^*PXHTBj
zcDk$WRBhJ}W(IB)P^384^{`4Qsaxq*hTsv~-KB;G@N{ac;q2zsXAk@F@#CxA?sBfC
zZaH@<!?R~!3b83Fn4rfR(H|b#T-!XayFNTvBr&TtuP5xJS!7HpXJiU6iuUB`<4-=i
zJseujxf^7Rc5!)jb$)I<sY;(9I_A8&|NMLZ>N~$O)zAidGPi~b7*vEA*ybifI5~qb
zB*g}T0f=IX&?1E~nRzfU6+jbR;D3oJF*=%}vJ@m^Vemjlf5YNLSg>h^rV|92p*j*d
zp(D9hMTR12q5imgN|}Ho0)R~o09tEJAxk7?M70JYl86~;88b=B!OTF_S*Yj85Jnv8
z?oyluh!bL1SQ7T-yT@hNASZ`rVF8c`Ah?ng$s81meyp{*NoMI%N~IT(R8pT)*XM4A
zu~|LY-QBS=A&``fP_@IsN+TXHuL+Uc=tK;_b2CqnyBIdOS~F3Q0)YvK6d_Kao&ZT>
zbGhnDj|2k>Q*Ka3z8O~C`TEJDuFr&1gAN0^0km4jd7L96q5=Bpbm+RS`S`HEX4jb{
zQT9+|Ak3SKXV2dH(&f|j-}$@$@RQG9|KUIKZ<W=+gn!~M{i(9O{D1%Tzy0gK^3Td;
z-EJR$`&<8d|M0E1-+kxFJI@{t`>%d=+tKAmFF#~L^qHtPh-fng+}|D6U3Y%5%Ux%_
zFbU=~tN}@_m#(#ko7;!Oqi0W_zj%S%-#?7&%U$1ZfxFxfr~L!fW9oWnc6hiW0<AuD
z84+v4Rmc5MP@JtrMua4hD3{WuRX>pMtJmYK$9@<H*sMM}JAZPyRv#&1l@drqB7pv*
zPd<Hhb2uLN^J&D&u8>k%uQvT^z3od=J;Fmk<B(2x)Y&5=7Wd<u`Z=*FDG&e@p_-hP
zO-%p^MUycZf~TLhz5*d8!yrHo4@3+NXl5SG6Ce{gqAmgcMKkJdKefdZL~{!Oj6ipr
zCKFL12~iO-4`ntBM0PiE;1}OmOEMM{Mn-D0Gch_5k_WqkH!THL=P3`A2oQ*fh&}b<
zVH`Pd%AMpPbp!W301{eMJ=A#)$Q}1xpNC;nN>@7WdUAvSLE=-}BhO4B2+U2o1tUb8
z9q-O<z0OpKQebDpdDpE=Um*8Zdd7(A4WWSKaB^&p6ENnk>-)T3ulv61H>*4_q?q)C
zV4GoBi}bQ8+$Dr?br02G0H`{8BkCwfYgL=aaqk|}aXOt&jv8Q_v#WQ%_KWAwzVg}a
z>DPbvcQ0N%Jv%%5wO{+~k3YZrPyW;Y-Q&j>fA#<RpZ)HSe)Rh0<&#I}U;4@izx&<q
zbHtb4djI_D$!5D5)*Xb}@ep-f?|O5ZkH_QTw(D1Y=_RL&-K83H!u58AtKH;%?)!0U
zN1NXL(%a7;KWnOYclWAsy1(1+Z!o9hX<X-?LXZlljtGXXFIhlJb<Mq~Y5+=dw}56{
z>7=ZO^)PI=C2w+GuQtQ_VyC9s=Ckwdlc$ecwCW8bQs&+HTf_SwpdbI?-~YS+@Ynvu
zUQa{ns38P(tFkWrs;t+mZZll=X*<On)N1I-TObNZ0A-MbIEW-@>jbMV38e_MATu6o
zp28$y%EClJfka>`CISLs01-&)VBrviK}^U(Ko|h%Oe9DqLKq+{B_*Qj5rj&i<S7Cy
zstER-NM1n9PG}LJ=B8o6Lfk|O3o{{R9EPEerU3y<)BvPIE&$+(Iuwvatqc~;`++70
zQl@MvlMqXULJJf(J{`49r(VW+oM6{-?kR~OaOS=r(l`#ox<%LI02W~=LTQ&^qd7|+
zI!ey9t5d2v=N`a^08QPI#T)>dyHt0vVsjl;YU&HKLDDkts`LzKjMb+#_l3c!86~$M
z(hMoW+ceiYgLz^au$W?CyS=|-W}+bzSDMrgM(eAKN6+8;eXGYG98|vj?Qgr*Cr_WY
z)5G=M=imC)5C6hn`0r}?w|?*2nfS9$zWDUxkN-#i!~gJWKl4XE{Pd&S+lTGt1t%7i
zzT|tiHl7G@D58|M+q3n0VD+?K&uuKFq*c$uImoWCFnXo^*S_@S>Gt-!zx#V%eD>kP
z;fprc`C%sFZr%6W?t?FV<LR?^jhr*H3TDc~iuU&&EdrWbVy3jV(=7pqIStM^W$Htl
zMmW%JSa)xy{n<Wcp6dY-`@A-<?w*IjC4cd~?|=8Z-+X$p{>tT-KDpih@b-I2V$-dY
zkg#)b=HQ%rUybI%$|maMOql=xS~F+ub4E($;iQ1>5I~qRb#FGf$dVx}+!xX%;+t4M
zU<4>25=ktjbp~)i2TlkSh{>1e8#GP`NJ6f@SaQLqrman*>xLyN1tXw`VgPumQ=8|L
zFsGE6$d{R8w@R%pvQ%aW$>sr6j1Vy(QP*|N0?mblD^CXImN<8Ul!ZhD5RC*7>gjNt
zZrA;Kz1oc9zTc)W2@r@z04YP?uTC{vK+X)7C<Kuq(P}6jREDnW@{vv*tXcZ-rshdF
z$--dlr>X%HXQl*9OoemFnI*9#B#$73EXbD7jU@t_hMR{WYt^O`T1<0Og3Yj+{8Zn7
z^RT4u)MC9}mpnXw_THnnKIqnu17Lr9^U)`tUtT<U_t~?5{!f4T;jm9b_fP-nKQ*M?
zZnK~6|IDBL^Z(=j$$$8(|LkAvw#Pc&|M0VqU%Yq*8Ym%vB!sE+%BgStkidC9o=VwJ
zWKNzsk1mz<W5%xed_2~-p1*T{{pmmY2mjUeXGd%0RCykGYDSLRyOYS>rysq1`^#_r
zzHfYOd-c}59R#5srkt=bosN@h3+5bM+FhRJiE~QG9VS<+Z3GLT)OFpk=6<+P^=X<)
z?wdwc%d3>uCHD6*J$&tJUs+#Xe(#Hy$Kx><iK>N%Bt!rybIKTqS*Sr4f!TC|5JE&F
zR|v2u77M^H16J^*QKjYzZ_K%i=7HT22?+>%iI6z}5G7uaw_rfZVyFO0ivSpaS|CtJ
zLdnJmUSq*G&uvs~?rwo|t0iF&AO=&I&jjerthx}92qA?B5<$+KvbDyH5aC22!YNTn
zKv0Q$6hH@HD1}*sIhabO6}TaS5v0hjZ*=m<yVLE>q3iOn#<c2JL#M4VW35VTITK6H
z(PtvWgwBjYDf*NtW9fP~0<Y$Rlsp=`Wnm#UHZXB^WEG>7BOzmUNtOtZ8uNf2kjz*J
zd4Rl?q1L@p<_2Npb*l5RU|O%&z;M`)8ck~hfTlCB48xjpdi>~O=q}e6mz(qR(?Q$m
zVV=jwZ#}<$b$fSvG<U>u@n{_pp>}(``_q5^-}{gL%3pbY_4xnrzyCiP%Kd5VS3`%)
z0p48oIHVNNl|hfkJsKgfC)%7}qP5fM`s`}QtBp3ZN~!N`(%<;yU%k0*AHCGkV*(OB
zORMc^e|Y(D+Q{Z2;?v*zvEDt{&&Q*8-ag)sr-!?M=3G+ol(K3&sr7vs601%?aheYZ
z-1j{)aZa(sD*+;6wHa!y5HPIz$IsuA-5Jz&7;o<P55NAs-~H_RHf^>~FW$Ppd0nS{
zKb)tOI8o^s0CJ_-Ar=A(w89AqTs7QXl|+P5Py{iO^W>o5O^pZ%2|Pt0vqli6h*-*4
z?uHoR*&RHb1tlS|BO7LMH9#av3p7aBIE)#A9h-vFJU2B!P7vrCtud3c;7_##BU4Z$
z0Wju_oCPsJ*+MeoJOLmYfm6=Xb-5H%n5zOhA$6$)@hmBb(vUkwFqf1GzyNTW6{97n
zwtqctw}<<EDXS@^3Ea%WA=DJflaN4{Krk|L2B0LIFl`3HOtsCO<*Ecg^8`dr;AX&(
z6kIZ!QiL%B_W{WWi6bC^3xpfcED3<ALxFHfWQOkEyfJ`?c=I~VSRfanM!}BZI3xra
zhJnBlvD*#P=+9rQ>U7FkIO%>m>||55jmJtcAW+~aA`tTb`I~>^Klxw$$G`CNKlkYI
z)8GE>Z~lQVy<1j;5R+lf9lAY!bipEb_YZxyou`weezV&-VIq-8;+;`$x!LKoF8w#Z
z{ms|cryu-y{Qlj8oOh2dAMY+Mbbi=8KZlUp>+#z^`tjGUE_U<gcfR}aR}I(0N^&wF
zHgBdxL@tIPB3+ho)TUAOxE@xSIIu+cA|`1%yW4c~z~G_Z?RNe8GC2L@2jBW9zxgk|
z{ppX!{q3f#y8ev7!RPz=0Wp}7&;-#L`qG*E?3FPP*eVG_qAg+tQ-XwuNLErW6b@9-
z<~p@n)f*s0b%Tb&Z*;wmrsxKU1Swb$!%M=P5eW#%T^pGDVjqPhEM&rj-aMQ*P!WLK
zfHB;`gFF<`!O$HM2`N~D7$nI+0>lo%W(aSTD;5AA83_9obpvyT@T`vLND;!A2L!;B
z8NCIBU}0enoNKLGv4u9IMB*{-+x`7<y*+?O->;fYRVM{-_rxN&LJJfM5X2q<2%U(C
z8FZ0l%<AER2!a@>3c-RfV0eJD(U6wwa<n3tDTT&N<P^YwfTS3aU<KP^IcrETfI0C(
zK=tMeJwD~k-3~oOq>lHG9$j&!LiY6OQ=n8BE_SQSizmPP{g3|4ANv#Uy!-x-KKl6K
z{+9S$QXcC#46BcS_?`dh|NMXbrAN>A4>z~Bujc99-R_(h^cmN)-Qx%x+acu)9>@Lu
z>hV*-&IyIc5NN$YN@CHk`+D6z`S9~!`K{mk{N%sz=l<wdzV_AUyRZJWzxlWSn}7cI
zbUgh#fAmj&<M)4k|JIZ5|JJwqN<X)GaynSo84=iAZJu&Ufu2%dz}$73Ey>EY2^I7_
z)g~D_DiCOHSuk?)7Q&e_=AoDM#q|32SAOlk`B&fnoxJH^Ts}TEo*<CZc5`Xi4#(Fh
z>oyIilu%k??^35zWy}LH2bfo2Cz1jsLn0y~!mwc&q))9P<@3#?`{^{!$7!nODxttm
z0pUPMog`E6NTo2NW{Jd*SeT;f%#iDZtp<4#L4<kXR53~sB1&hGPRE%zKw5wzIkB24
zf`p}rLd+?P46AJ}V?+xl_vP3lDfMP^AW|YJRC+)Pap54L2&)j>r4<TzhE~Zf7#&Gb
zeb#2`(P(NF6Q&-$rfDDd_j?SA2qYU%qk}gbBTzVrpjB0al!{r4D4{(i6Eein-NPJG
zB<>{<3fSb7QW!GKD3+OXDkN+m&?roUDicUfF6I_s0ggJiRbm2W2@Y<Wf;A#^t~O5q
zak{Ui$F9%JzTWg68bF_Z{8>KRA-$EPdvtd33qSJ*|M@@vtuH?N{QWP#|LJERo=)F+
zef!!<GSkbm?H~C4@0FN;@ZpcY_k$mT>aN?3r`9fD)BAXsnex<hvra-GKCSw`&e~t)
zwAtw0{r2%QSa)XCXIJNsKK}UI-~a4~pYOl=hksV*_V51b|KR48{@xEhzkWSVr^8?W
z8-MfPyZ>W9|3`lD>%a61KmM(6er4Xg|K%?agB=cE7>odbBm^oz(=-LfYCV`4pqDZb
zB?}jKSIfD$nujA$&RkL|eV>O5*sNdv_`Bn@e}29_hRgZ3-Oh{W!P4Nc+3VcmP(xFQ
zU_)$Ka+V}5Q$&}%23Or@9D3gNB7O3d5(*N*rt9VsnEH*$Z2B;r?(X*Ec$nrxn^lMt
zi*U}8&?LCzg2JAYLQD)ZQwXIQ2oi@vAR{OPhgZswq-zS`EL}=K9%={zlMp7slyI&y
z03w!@62ZVf^T!{H0C8c;0M@G38m+3EMKEz%4gIhYUe3-K+#?|&u2Wx_0X!1qg+@ng
zrga>vHf_$wJ-E?!yGw<EqjY_e<XX|Aj-$58V>VQloFxO0DF~;o+a)2SU=j#w?ZHf!
zWPc_vnKI_3HR+MYHdXYlq&}}w>WBm*gMl!WL69rR6fp~>m26kkv4_sC;oxzaYcq58
zd7jO+UvJi{Gj{}}uIq`V&6S86v=4vuJ#CKt#ayTNA73_&zyB|P`RS93M~^R8{SeV|
z7w^6E;unAZXaD%0_+!H9U;p-ZzWF=9`{LP?Kl>;C$m88+x85XTpHJgFb|nv8N#Mu(
zTNXLLI>*&I4Xbf~DC-^M4F;BOb$oUAoo{{fzxYSL{-v+}?Bk2__x`~@{=fX+|JGms
zTYvrg-~TOB84rj1`|DQQYPZ^4oagdns`cw%|GJ;FgUQ;xx`MXQI-ib=(69Q^cc;^d
z1w~|Pqr(K@feZ*NL?|8r5Xe3}c`H5tz)NcPch`s454T@DJnUDi&CqvUpnyw{kzH{H
ziN4FF1fvmqqSV|cm>Ip#0%RTfstiojb+qlvs!#n&QpzbODVg(-rKH5U&)Z>F@=B6~
zqiZD0T`oh;t6{V5hxJg_>u$9wEI|p95CH<r;chOq&WRx<32cjkOHc#_ol5Em6A?qy
z)K$qw6bq!MbRCf(Bq<#+8M<@IME!UqghuF?d7czNm^raSkf|?*Cl<<?$Urh<Np%K6
z4G;4=`B9ZjIdl=c_&DuX+o#+0y2s2F41IEMh_h*jvQ0(JoKR3EJE(Un&PdGE2-|}F
z&8b0vI}tHEf*WzsS`mY=6U`~QoK_^1C;&@TPeKe9LPCtvLZ_;8jntU~V`!SDG3KdO
z7si|zX%iF;e7#*0L?&VM5WatXeR;Kga(?;Yk3NjFS`8%)oB!Tl{F939*MI#tKlqt1
zzxAc}*O%u*=CiZ2l!x#A==wK)?OVU|uYTpl^XGs0KlpE7o~_2yL#r3BuWxlamP|v(
z>P;gk=M>nb6>}fvoRUK7S6iDL#(H>l_b-0)TR;Bn<{Q7bZS7`0-T&q{f1`A(v+ZvG
z`qiKR_y5v=`oI03{crwP|I6=w>-*>H=i94`hnv^0?jN45hTBiRINpEQj$`#P5*L=z
z4Qi*>&MtRX(=;89w}4I|K)^|eh!>7`(`NIkD`~TavLWzc7`g_VZrycVbyvyvj%)5_
zkA`t-YIyHn33{M=iY(}i?k)Nbhn#yr>@x<q2aA);LRk8eMVL}Z&z`+WYnZ%m9ZJ`y
zzF)1jbw5R`fGy{g1#^<FXHFAvFrE=JqmIYwUMm`gqVwt4AT)Pa>uFeXpNFtKLxjVy
zI?FjX+qL*2<D_vCsZ)Rh_T6@}5D^jaL@XrLa$9xdaSz1oDpZ&`6=!jGkp+9g8V&~0
z2{V9DXFRKhMOCkW4G94IR*{fM+B6<6wwp(fE>)FDNFo?!*DzpD4B~iFs}T%L9<#%V
zB7+yi0G=c<MkWG70`~}V3yUC1W)Tj8lv3$)FXG;us8a-DPn{qJMl}LM4V_w}2Lou4
zJd4e3K2b0Whk0vr#=>bf50<1hYxPhMx6{qT_O12PtNxSk{_DwUb@k}?|I8oykN(o1
zed}`j8{hio-dws)6+ismhto8D{`p-}{Ez<!f8iH@@fZ5M8E;>|yneObl&kYCfs2rt
zCC;s$O36dlDLB|1hq+&EVCcz}2=70A`NL0N{<Gisoy*J1zDqAZe@HHmF3wd7NSf!v
z*T4S3zx{9j;-C66|E>Sm|MUOxOMk@NY_5L0Km5M8o<3Z|)TZ(NdTOV<UXKnehhcqr
zfn}73d2Z9`VAgiq%S7dLyd#Xw`aI{Y<RpzS_YrQOsCsvQlg9aKSY6w=zj?LlaJ9QQ
z+H@Ezv-DlADun7-6Rj8UauV;eC*+(9QWBg5J2NAm+yPP;L02TD0c6vmxI?SCYM#vz
zX8<TEYd)g21=&D=xl5cqWdUr2ft=A>Dz&z3Zd#*NuQei^Qjie1mBJJ$ObJ$(=TC%a
zp4W{ErIY&YX}$y3)~XcTY_~!LUFu5em>FkX^<CSd*>Rj2LSjZDV-h9`uS7s1P2tUv
z2L+LG7FDf!L?RMS9a=L$<HVtQadEag+pJbyYv$@XNreePg^-aFa!9t&a1bGfYNkM>
zrV^C|l60;uU`~>)y1O89_|n7&L}u{JlBINP%LG$66H)2NV}_->$t1|*G#@nv$el>h
zK$sf9@i1ze*Xu1S69=fND+{Xm@o*Bx<5+KgaQymLzOw1FXteqM*M9StA78!u2fy*?
zgLnV<_4UKe-Tr=m;*0HSD4SJ(wj0iOt1qti`@^`~tjN<`Pjf{EJnavgp;xV{nmGay
zN|~l<=sE+WdB*WDp8V$K_SMbtWQ7RS{OtTm>es-pI!3mZWcSa1^|wC!^b2u#{^GHU
zO{ZBb2?gfUd+&WP-`~|^TjBcde$NEketkThj?-t``~BtRxp0S&+N|z}3a75?DM;N?
zDMMOI0MrnzHa*naFIE@b;Q`9tF1vDsY1X^jIvu@H?pMrd%2q&QCOyl&r_|~(*48mm
zNgc`LN|^;Scu*2(I?>$Ct5Q}aNsdTPoJ?d6I?Z9glIkp)0L>%NbR-PsU_ll!pMjMT
zn>hnARzmjxcXET)T0nG#02`ondEMs?!KO$LSglp3p4m3rN9kB_{~BzZ>St%$l>i#2
zA}I?ccg3#YHWSR(ceHPfBvNK#M1(?O>WG?TMc8FYIcGIQ^wOarnQLp66HAwJ&XsJn
zTfOt%+x;-4A^>2-ES?-YE{x8C$jA#t0uX=zOtn&G^V+N>MqKDHK#Ud)VMdS$fl#_4
zQzLJc1uzVNkux|l6{exf;2z-<N_YEkoa$+n)+<iQKm^e#4;^Vl#A>yYl0DcX0ZL$N
zRYkaTX_}@?Xs6@%zx(~Kf8%GLbbU$ulh0m%^26_*ZZDr)Ts_+4cOE?&)~k8OX_}Dy
zbi9A{`JSNL6*(TRL&x#ZV3(^xn=21%cfY&2-93M_-kwdss`G3rRo7YS{muQI9p~Dm
z^6c`-cfR<^{loQFUOa#Iy=UM1@W=I_eIJjXKKku{{p-K*^FR0ATkrhrAN+dGfDy;T
ze7RcpPda>k@%CFE<gAC=`?<PsOfw*dU=Trs5s4|xdLpFU72=}q;%?1K=D>)mV8N|k
zbmcmip}U;t7~}MCc$i|3>AV9D(vD?}ZXZobN;LOr+YJ{%2d4?6rJ^VfscWM#Mpp{6
zx=mypIAF(FSg?aMjI9Zr5+HJ+amJVt+#3=ov4T;~nFWX&1H>W(W{SoHl2{YVv3Xb^
zxT^zV%3^+^<g0QSn$%831hLmTC0QX%L?DFg)oL8^czobL^k=S0*Ok&ENkq=PB@llj
zh}E2Nz0KXg$QGzUD!hhBiMye!17OzK0t6$a;^D0YF%p3}LeTTKt{y*n(sf-*9RiCm
z3up@n%7r*FFlo%;ihyQbTZ>Sp65%AuNdc?*WL^mciI&wN;$qfe4~y{C&~>>4Mqn@%
z;a+If=g659)zoQ@99`1XRccke8V8#BtZmko`>tDU@=&DoLL!`!NYf@Mvt+AUy1ZWZ
zhvUO~Gn~eG=(@)jTXIDtZ8%R=5^gTepT6_uZne8VoaW<cRb-kEuU>wBJl#Ee_INst
zH?MDY+g)Z^4@u`K95>tZ-1X!A%`{D$^|oxw{&4u{2cHF(-R@kY{*!<FFNgKzm%e)0
zXF5IjXV*U&r`Bc!KVDwsFMs`Oj~>0axP1Eh_07**UjE^q`~ADOFJhWt8aW4-q8d=g
zq2Hv!5CIU{<~r3>BxRJGO4=}GPRn8wOX=u*!{=9Rn(8M%oQ@9$GWpzWatI<8;qB(4
z6lMw*tgMdKt$5S-S<bq0-miPk1mP%3Bt#Mpb#6@RF(#4Rb=_*6R$IzLH*{UsiC|zg
zXeljlP9Q`KXPyySL@=%Z4do=s!drL{u&Y=NQwWvuWDwL3sdOQ^mC{R6PAb9(Nu;<V
z38qBEOkx)IKt{wEQtKp(z#Whfb4tuCjEUm1AGWJuoaVd4W0wFBr%5v><}GMSoSBol
z2LeVyh;GmjXidxvN?UDpx4C$9@uW|MxglsNy~k(<>WvcS+$lOTq0JE^1A5fC-eGJl
zpUlFJhTJ$+j~PHC#FY^kfq@JmT0?D)2n;Ap>W#=V3j+}ZP)`vM+*-?=Gj9mGp7Z%`
z=z^qL%hPPNcekeQRPq3TI;pAw&I@<X!peF8qiOW6xBJ7x_Uw@x6y}R{PV4mSt#<%B
zPPt#7mu_?S!|zRpJAji}Jw99?_g{SN>tElL^y3ddJ=?Bxk$^Vtr)X7EnQOy(n+T`l
z;o<I8>Vnjxf<p|C*Z-YA|7Y$WuK&*8|CL|g?0)W-zVRpjT*5T|<fBh5>`U+e+*iN$
z!R4c??b-9!FYmS!{h>efrS-D`x-;Ltp842I_L4az*$oyx9;-+`jk7tNU2ccf761W4
zQdcrlrV6kwnMeW(cx~4=?Qjt8ck7L+(sbM=nfl$)+E`B$)KkKlyr#&jE)U%xk4UhT
zl=Eu6-rkkloBg%NjKO`!P<0w>E{zi>b;9hLH1T8xY?M=>-q7dcsV~AvC2`HrrZk5l
zI>xPogCE#|$pA+SB4(793s2&?AYsRh$d%>)N7JA6TGM3teb|~JB8R>AnV#vLYOZ2+
zvzs%!n-nQg9MqH{0lu*X+Y$^Lf*||K-@^BTFAN*7KZ0SvfFMD(A&ZbK%95HQd#vuR
zs&{_IGwi)HGa}aDi(NdQ;GC10F|74pzaJ1bg6!5?e?257dWl3ttd@dY60=M80*SJW
zLvb<hX5mgLbIQdV5G2~)<`!eprgQLnk`;*(5(E<q<s0UtejJJbkG0mIuC}rGI8kfh
z9wpI!%rjHUfz%Nl*@Wo)cx--7!2lDJuw>Ou_0+67w7|BtH4kS*uGMJm$<@rv6x~!&
z1d_0Y(*;q7k%$n~tO6JWfjA)wab^;k3R!3#01teZ^Uhbz$uOX`1%w!Q$b)BBbf6?c
zNs6i5e>hpK7<PTNn?`whtiZ##JG_2#zx4R*<4?A8O+vJ^cb|WL`|RcY({fz?#XO9?
zcaBY_;rw(uuglHNm2T_1brI~urrS9X`nrlp(|$T1`*zMb1Gu;K-7mj-cz@~(?()=@
z_oU1J=HLA{fBebKfA(Mg$shc4{_OjA|D%8V-}>tFFF^Y3;pN?n&r-5K`oo{mhxNCA
z@`IZnmfiQh;{g499yr#zEl-a_rZn9>t!Hc)#J%e2{z-;$-d!<`rc1ZZ9g}cL1dN`C
zln=44X?-x%Xb627wX)<2<&n?5#Zpa#B#F9B*sWkG`>9NlIpai>#spJ70Oj}ZJ_i7#
zUP{(&+gb<Wd6(1#j0psQYgY+}ph1#}pmcyJIhTZIRc$B04S^WZ6&)b40ji@BlVo%C
zlxY~!1{~2ZNFHRe&<O3ifgclzWbU;CcCfbDBXZ3{xC-D{g!r%g>n{Ot$OVBYIHg%|
z3=q_wdDu<kR5CyyHc2{;)0Fpv>^Nl-6k%p-_NdX^3^5buhz*f<^KLAZCCNyXxOEPU
zX4}KlJGE^;KO-7AaZbs-sy|sY0PoG*Bj+(C0`!1NBo-GPc?Lj$3$+26SVSBl5vSo-
z88-nQ%2b9cqM-;S;U4w8yj$D*s;Aahi_O=*E@y&h>xSq#<(x|%269U*9{TY3Xa>_T
zC&G1E9v@EU<z7+{vS}>K)~gT0^!ClS@7}!q@cx_k@7})t_N(*5H_Pb*LTEE<eOXrp
zT)ltu_VwfOIPRvLSR%lTa%Rax9=7dF6hP2>V`Q@Qr$75y-5#Ia?Q&XsJr0aN{LV+;
zeKn-6Z{M8nPY?V3L6DYnYftg@S6|L%zx~6{e)G3}@`K;{b?kco=Fk0`&zJjm52uHE
zIzd8_AtIZ51PaSI4rZ-eQ>~JLK^(vXO8Atg{PLqX9PHh<c6#bfAJ?O;PXtgn1;MJ=
zRJ(91T-lT(Wk;e;w43)sx|;GVi3w#MuctgNOMQBJ2jPSq&>dEA9MTBmzKj!31rtFu
zh;Re9&EjnK1n%LeUEPp?RKZ;XnraV#h|MoH_i&7h4KfpEk4C9-+DMdzkOIlvLbZ46
z$EV}g9$3_TQ9B9^xn#P;CsFt>|En(%eHdnD+1gUlZX9oVe*(r`xz42w)6ATE^hiGE
zeaasZmz;$;M_{)Hi{&DEM092*hKMdyN-n9i%;Gk+zM7v;r}MHNNpPADk}?6aP*Byr
zxG9jDQg{-PVa$#d006pKySOU>i3pfDr$or^4$z3P(7?i+K!lkyK?w&Z16K9(X?y?J
z-fnhm8=59WERqu=fY#N{>(aG)w_bbI*0vf7B;u&5D>!!R7x!B(IhQ;Q<N34<)A-_}
zPoB=(u-{=S!|nhg4%`ivbM@+{x^(Z}Wb0wprZim5H`6%p_Im|sj(nLo4v4n&b3HvC
zSyJ~}*N3^VB+`^__Sd_+JG1rWA^-F{FUNI}twDvGBs1AZ^YGVx`h(y2n}6v$zxF+7
zrXRjMef_7$Z-4RO_3P8Rcw&HIH|{9Sk^mqG3Ja~<nUMConK4sN`)Q^kJWliN3%uG_
zb>5x`I+#R&+9DA^SOJyH0Gv}~Msz`>G&v>hCqRCF`~93tkO`N0982Dhd2(&FF78Iu
zG1Od3!C~H0*{2}`IDsRo0f5z5&HAyett+Xv=#gm1JP4Dc*0#7Ehe4TpM4-=zATlx*
zByeaEfeaoh(h-v@*~M}iYypUzWMDxAASyX?5<*k}6l4g5?&n0=ygi-2WEh5gt5zd$
z8V5?0@-?P@T!CoEL;(rFb54M^tjZXfvEky*5Js#lid2C|;$mR6f7n`O#;WVq)|9V^
z&pGAV++7kMQo8Sc?}oWBQs>mnfr2k??6s?^vj<avu`uME38l5>E+R6F>`}M2ItG#<
z3HgDcSagq0ghIrSOP+G%S{tx0&5}je1sszMw)NJldJtpnOYc@qh5Ty1y1se&9G@K@
zj`t6zlveBOt7jj7@x>RX)9Is+KKc6Vug5uk{K+fR_Hh5M*YkcJPLHSXZClsyc|Ywk
z?@OMu>}A|{AnzMvm<2GS+S`X?UCv>^1E-?XJh!&pQtYqB^?ZNz@yFl)t-tlfzxeOl
z>u>)0U;RW|;=FeX%IW5Ie|>%JLdW-?zx(``Pp`j-^Md9<KIBB@kT`Wy^#%YrXBpCm
z4{t3r&{H0EbLpmA)Z2119j@`^r?@-tn{T7JfIvpIT7e>h7&Fo?C8i47S%?ecsh^zj
zhUX`gx99e>KEAmA7?mYC7iJ%C=9lyL_aC0#eew10oz9Pmcqm=%zPAtN&+9t64N$Ur
zCkVjo0=A7^&q+{tY}DNlA{D+)oKwuF<+b)FmOx6x3_%W_hcJVk4P{7byj88|nhvS;
zN<@U+pdukgsA|c%LsP4!8AS+zg!pg#!%qVhsWUPFYd?p_ZhB@Kl5;6ctVjmr5Dsv$
zL^BZtcH-9T(QE?>(`s>175%DgS#lOi;f{o@ZCm?*)FFbI=6Rp<fW!_eoRC`gdxuJ7
zK**ehG6tEuF_?SXEYuN+ITLY8BAil61cE@q4XFn9z{=vxC4wB3NCD5a9jz&QkzPtn
zQ=UmZE1QL6hSqi4)YaX<{PFRAIo-P|290Hk-iMM&$SlH;DTs0FYE8fX_6rvK;SYbX
zoX+cVEQ0eO^DrO)<;;}3!D(Gnirc&E=dV7wx%)_2x&|X>l9Xo2<$QkV>k^?(VFZMw
z@Q?`2^zP+L$^zQ*i|04r{SlZs=myr?+q>Jt_4RDNY|H(ZfBq-$KL4}FH(%<9w-8-N
zsTXgS+UazE(uE1jz=BD&19}+>VHSv#3y2PRIPCB8i;vpf3r9{*N2*7!t!_26Vgv+5
zSbz^H3jhQ<B0#W1;*2t2$hjR?f81YPC7CZ8@SKUFx#ltN=IItfk)N26rGo&9<eWHX
zM`P*)(A7e%Z2^YSG}8#0w4>Pt3xdKTR4}qzG+hD6jRLybhA81Qlz|C_m~gl-dCWb6
z5hWZVAY2h7!pt}3l+(zRn3;d)?|*^-j1k^)66VzU1{g!SCSr3BR3bO6O*ez4>VOC-
zx}6jji}MAnPLRSPzyQSq5nE2#F0*R+T37&;DFsA8&1o3Q9wQ?JG7tw~n4bayF$qsh
zMXd$72llSodP>B?Ib~)-L_!u4H!%;Ql~S-|00T6l5-#Bg&{b=62TQwreN$dd>6XHT
zQ=-g()_d2@+8W-Lx*ngFR_g`jTV(8A*JWKE-`CU9wryR{k53P$r}O)F_f<Dy9)|Jz
z-}}*v+x_wQdcU7vzIqPf?~f<W>3C}A>eFtQ(y+gJetq|8rulGv$Rz`2N@E_2vw0GZ
zsM`}lO2ZuiY^Qf61G4UhX}I1$y?@Wk^7!VL6ZNY!XF~LPS{^>U`?fxQ`1%(=-_B1=
zu-hH-{<dm6-M`t6PsFq<6G91+luAm&)A8xU<3m?%-7t);*R@f?yZwvt_9fh2VHT-d
zJiLo~qM+V;U5|BLv{wMjxpdd9wje;}h=_phu`z8DfYPB6oJj&{JkViemOQ02nrYWg
z%w;HmfJwoEJq>w>IUxcEt0mX)78C_c+j^>}1wDiB+=;jm*`>F`fbK28yDDDL&z_hj
zW(rUYBS>Kdk<`35C(20%rc6N0$;^o&=yVZ{A&lXP|Hj{cfq+C|aPp{#6ye)eB^73{
z=q!*qT3^?`g!Qf$O8e%~)D{EFX>vnA6D|l$keLb407-Z%NwVxj#$lLK8r7S|k;F@y
zm=h5rI}-s!x0Q$j0-cG-$PI!J<l?F)zzbuZgdjnL8@rJtA-OOx5Fr8_Lc&XULbTAf
zuC=$C5XrEfHv*0D0B|>LYu(PvwzSsPW!cu&T00$A4{m11^9f7N!fS0$r*kmpaa^me
zOUvVMxPEnW`&=80yJ_4_8q$np+~;w>RMYSe$FpfV%=6o~U%&q9^ZVDoKx^Zea~Y%z
zBn+G|?>6(THo-n+39xm20D%4BCUKhf*L-(Nd5ZhT*MIiMA3l7$AEmF`)8mIs)ayn_
zcQ<#0aXLP{e*5+P;|C_1<}2ak?y5$pX_#BZoOyQ`0wsy$1PN0bX*cYyUwo3EKL-d{
zpWyt6eM2+rPKa(>-6~*E#Kqh^Vgf>U<G=z5%qw<ErEo%EThCkl0Li8@k9^=fxCsQ*
ze(HK+!7`N|fzc5{I0t74w&vj&rqRvK0J>{EolaeihyelAC6|i|x2;EaC1iC6^a#O7
zh$M-#Fp+4DuFb;!B@F%2*2zplm=Tc}keYclH-aGu`L}=f6-lSm0Nf)0E!@Mn^*$s)
zai9*?x~<Kssr6o)RwPAovmO>q34;PmXe=@$EC~xkL7Jv<FPI`6qKFjE5NNe;ZaS1H
z4++sd0?E5q)iYA_a3Tx{b#z09aCLM>DTGuKmOLUPzmQj`1OQP-*0>OMLnAnBw2K@Z
zy=%ATu9#pqmm4hETeIFNLf54&8yI-&+u8yFfl8TDnSt?e*w5F8rK<_&`M_laWX=V1
z$}${o?>_qU;~)OyCx(n^B$<Ey`PaR}_1%l>+m9UM!_(=DU%dU|i$4c_e@GVn?YnQk
ze)rYa$9G>nz5nvz?Q82DOuJ>kToR25hk>ZbG~VPw#G90>a=3A_LH*&~`?L4f^|-9N
z`H*B(hd??VpMLpgf3$7KtKIe8_4PR43FmPff-a^|gYY5=qjg;wu#_aijD-^&4%g$&
zEneN3dRmU&&frV|+qSt^1UCzF3)5Vrpb)_79l=XrAOfKP49(a$3>%)B->Wq#Gs+0=
zW>MSOEIcam`C>2k@D`VqZj4CS6q|;6nAgiBvoG4$tu9ShX2b{}U|GYD+I!nTA)K~Q
zBus#D2^frJ4kuGpgVt4KU>cdzILu6iAQK6oU*awv-Q59!(D`@%@k^2>$s!120Mf1%
zJat76ErpT5O{>+eHNv{}ZX0m(XyF<d;4H|@nL&!o!pRH}$0Sph;s&a<q$#DOzSVvX
zYQ)SqK|ru;2Q>|79zDDgCL$pb3;`#;2$3KmlVAu%N|~v2>j(gm5CWqUnt_FfhXN$<
zAw0Ra-kO@KZWLuqFPUVBVayXXM|I&82<K&`fc?#_q>^&V(+CjLIH<SN@f1W^IKbD|
zPTOY9UOfBg<tHD#`1qqwKmPu-n|Ax%)y?hg-OKNP|A#0r9&Qw9ZLP>~TKk*V?@sSe
zkFS?`nx-)y@83PWecjIQb9MwfJ$(J{!#6w}4u^w;0>a^X_w4SLhcu<+WW#)w4tI+~
zM&W>EU8mjMt507c1h_vueu!xEJnhCi;Y&_FHC=06kTa)@fUUJ^wTB_3VHgp}ni0b7
z)%EUh7<YTVzK%4o8El&^t7#|Gww~8*5vC-Jp+x9v<X(sfq4gEO6Uhj}oOX!f0#Ck#
zEK#4jlJ(NIQ>}GtHK29f+}DU|u4w+N{nv@PAVkyE!owR7o=;EO7VxIFHGSIj5wQa;
z2rHnvH?z>L<|hV<U?yhfQbyu*K}lmE2_q2`4y8=<OvK><fB+<k%v-<~;o;7l`S1SY
zSAfO@oCrePD6q%n#llD_QWA4mw<ACUwBDe5H`wUHHwjEw5+_Ds62MHv0j}!kCPZ|Z
z24YB=JaB7A$!CDxJ8&uh5TNRtY9)3;LEu0VN#MYM0pREaC<H<#<Uoj&dT*v3Ex9X3
za54{b^Iw^1Y+z)wZr;?+VJa|Z+$TvnWiUWDW=3E%9}{YVr)8~Mb9BcJ0IhXo9Oii}
z`#@T@t#zIEH}loUoaaxz_vx?y^w)Od)w-_tkB{Bpv~11%?$yWBe7(Q9NolyAuG27n
z{^d7MZ_mH?dw;M!9bddUm@Ro4^SnQ8`0dwEwJxWM)Aem%PS5u9;jlBS97Y6;GVh+#
z?uK4GqZhA+=bvT~Tb8ZgfBEGvA3nTE(r>PIdQ#}oyc+LZLI8&8Chu?NtE>6&{BZkX
zoNv-N&)0X?H!lNHYt5{wo;lIYbTj6`t%C^gfZJ-P*RA#F6~l*IBHVRL$POfwXej$K
zsP(Gn0mC&A#4`oKifhO4P^ZV%nwxK1Z(Uo{ZCh(q?LDGvZy3Zu+RQOCj^SN#3w2k}
zsJ&V}E!I|mCMm+L`6)n?gfJnZqcH;X7GVJ30Yo_>rIZ6Lz?esX3zC;p%0n55(Om;n
zWQYLwMywtN5KNPx1d|{u0u!5RKoEc;W|E~gBoYV#>_h=Th90i2JtVv5aKXe<sP=}C
zvmCmvgjHBnb=l7IxCeBLTAKy&IF2N@Yn)bnSUZXBm>G=Sjj%c(A+tn;i=^hwt%o@?
zGYkJp2h+`-x|%5|KM-XX00ls7tpl1!iXe>$=ZMe_j;qLrr3=HY_ie^0C)U=Nx<qd&
z=R|y!ug0MWj|j5A9;e;)T&{bsn;{DC4+j7l4^y7T^TTP(0a!Z0cyp_dr>|e%Pt)$*
z&%TgSa?U^c(NFicx6>7W#`!P*wZHoLbZ33J9^u{Nc{jkbt1FV5|LQ;gy~x8~__g2o
z;g5ea?dh)QN1wgOBcC32r|l$;!|ki|>0d-1wPsh$n8)WoEMLB@$D=mZ!1wR_ybrs&
z3-_#RJ#UZq$1Lf^%Y%$N1B7ryDf<KKq^d0_?QUj5s0HB_W!j2NH+xJ2_6_uG+gdwp
zwI;AE?0_C5A_#_!&;#5SaCAKI*dmy0vxi=}Gg35G%aTp<g7+tv9Q9#)O_A9L?ZM*H
zH0I>X`q-R;MSJbrYoH5gO3=|lLYu}4Q4?njDWgWL7|o(~TbVOUg~;NQ)rx@T764#d
z&VXcKE0C9gvE_))*_^vIFf(KfXb|EMiJ|1&D{bw>fAe>5h+UWwDT2&EwYwvcFf*e4
zYWzwepu0i0AaV~A&H&OHU=T5yf*T`2&N&=CI$*Dxs`l=++v>OgYD&X?{B+E>=6zcq
z&3jzrt^sC^*%1MQFQ6|70stf@g2cc~hz8grK-E2>wRSPw2RMZHz6MZuK^VfwYzdF%
zM~4lefiXH05R(uN`|F$C)&A<{<8gjI?{~X-ob$f6ZT0ix)9Lu|;dpv~+ScA$_okqy
z<@n7vU%mPATTr{&Pr0OVn39yP0m#pvy_A%{^Mmi-zIyrexPDldwOV2PwZHh&+WVjW
z!N0g2XdbWX+SOx!_u?mi`S1Sr-}$$1p1&Ms8TRngpMEx7U*mjzT06<X=nBeCu3p6S
z9OZz3ZY$=Hoa%DIz*ei|bUr>F-+py`|27PwKlW|WaP{h~o*q6tJw3Md<XWw_x;#ZY
zxh@ECUQSAFcNllWojGD5b&Vd$Nf1D2%!vTh4BWt*nHq3}L72Kbm}<iaFgI6J&E5>1
z0hwCJ5(sJ>QM-9<XRQqYk&Hl?2M?=T?G}XHy}4?rMmJYC+j=`%YaBpf;lx}-vIrn<
zK;68FgsO&9AZ^`<Ga@3fnntYV%|anam@cPhLGtF-Y5<ybB<B=>J>{%swLS`jm@0xW
zGXyd#b5b|J-dea5;;E3Rs)xFRN53G)YOUcH1#!cgg=PR+*7w<l%Z>p6!bA5pbb)Tf
zW649wJHf+{p^sQsU+WeeA}*K^M~KD=!aV{36$w1LS<NL~G~GZ1M%GcSwc1UkV*^T@
zNWDV{d9nyjYSA1k3uT1Uc|)8IyF!r4ZvW}C9~Pa?=VL_Zc{@IQ`}qF#>Gb||Y-Jd3
zKYo65d*_`VA0PdAd^$cX=WRY*0m~rs&CON7)1s&EfBb6OHi|UOhtIzEeG0m|dr@}7
z_4Nz*5&!8gepc7>ZhzH!{|mqV7vKDAfB*OY$^RoQxu2HI!}-*{_u2EZ8z0Yq&V_#P
z(We=Rr)#{uz1rUth2^v%4G0inTm}GH@1N?!o8{r{pML!n^dr{$_n*JXoTtOJlp$f1
zt7liYxw)z~Lf=orX*oxadJd&HOfx8K>zdPW*zGyVe%kM@CdI;$t!=%Yz%0y18QXGN
zx7EEv7zZ(Biw1!KpuJsKJ>UTl<eY^f@s$Lit5I)=CG81L+9DW?7#Z<`6rG1R8(tsA
zV@D|oei}+dP_$~dXsIBGQCrpC)TU~$&>%{!YOSEdC~8z2Be9Cwt5wv9T{}kAUT@x$
zKOpDi=G^-o&+~a~rM%FoqfKr)>fiYL2bfi`iKp*v+S%dpA!v=cuh+APjwSC`QL=AP
zjNiIH8;gADmL%gPnv~6-9Iei)c6+)gB>~<aLhS`NA*)x?OePG?bqVQ`3@m7P2K6oe
z7xf<TK#fxT!NFW7Vze!^UNehI{$H~ctmIp(VjiITLl_wzvkHv_bE*p>+91VejP%(B
zSzS1n<y-jVLTXWvpcXo}0+o!KXH=BrQ2cbu6{g|9*ou$~3Q$ki$-)PzNxzqL<TtQn
zE+Y!~Asa&vChRHR&BlSJqeE-tYpe^~`uqHfLeDXV#km@Ph!RW3WKz4|>B%*pu|M;<
zJ}r77XKOxZ%eF`R+uK#PZ=$(&$i#8!xs<504I=;L1>f0TNuB<X@`=-tJMJfQy2)fE
z&w-g-{07bPE-C&MJ~E>IoOIdKqQ-T(ba|@ZIFei$68sLW1obI+?K)(GL3y10`)(N=
zCB7IeJ1`QdJZsPG+x!AX+wXQysp9;Crm~NCxLsMfeEhwn>okb)AXZIuZ}G3wOtw^s
z+cykHg8I+;Dc-wgK?TJ<oZ-RY_g~M4DIX<{OFa0J?Wk+$hGi4;vi{ItGGd(NG5a+S
z`LxFjt5Qp6B%}TUu4VZ+Cc|Iq<G`b@^t>vsO1}Br^~oW;J-k55!(AMt_E_GGIN1w+
zpJXV%F7y0(V2~{h6wwn+Be8JLz-#WM{}LQ)e3bo)UkvB}zEyfj)~oBu^<(<IulK!*
z&!5yvC#XNR6%ST4_WaSv(8bJ=fGY(&(S}IvQ9_`DupP25m}3M0@s70F_XNs_gxgbE
z--r^U*MiV}hdirsl}_}~VFyDQW?721s%;<)Q&(B64cWTOT$GU)rs>I2U!gOuErQ9$
zf~2T_6~GC^(zvPbbgWeTJ=l*+(W$1Rw+R;G4@Gopbf)Qsfr-N=Q?&<jd-9FJyAyq`
z1?0}_z?P^Wn`HNL*H;k}EB3l@^SDPee~;D|w_lTXgriP`l}{Iy!-FqJWqVsr4liD|
z{GGa-93_{JB}W}ePy+TYS1A59Lc~dXi*H2OcsZpoGt;q-B$u@4S>>3Q*Y0yZvEE<X
z{p}tV9zHp?fAr6!JmU1=VtwoT$l_vMm2%_J57wxi%*e&9Mc4R^nyLRhbGkf9%_r+q
z<J&V$5qks2Yo5Xt|1}B-)6*}&v>46DoSUzEcvV=;Sob{Rl@@SydL)=Qm%7TF^=lzm
zkq`p?E6b}IHkd$bt;D)`aWLvz&xiu`>Ti>S74wGPUHxP(tDVuel;6ty=`N{$39gD#
zDC$rZjCNYCsA~mo05t$CrAG9EzEv|Gq*fpt>}<z|RAs@~;19pJ(MZBgY)1?Ff8D1O
zOi6si4S#RRz~I7W4;z{=uJO(M?Z?eTmXbEE^`_zxByr+jl~H5V1L6UjwRdI-uefC1
zp(NGq95r^LFMrO4#2P>n4OE>}5zj_g0G8z9>F|oCM@oTSkv{-&svikCu?mkW*6~4f
zXm9r)=z(v)bZ?vdcnu;A{HhCSrplbjwH%)T7w#m-NcHpf^Upa28nV0>&EiK3sOq(1
z5)$w47jIFJunc(#N#C&&4`r0|XI9{|=c`rWe|HbtrrH;Y<J-;NWVe3D_kJ_Hy^jeu
zY2ywn>fD0-PtT6ePwS#iz9{#lE1&**HG5PZwZAwN8LC)Ua6Z3%9(D2Ra%$+}A8YgJ
zhyTCI7gG%xr}0%$2VZ6{w=dWD`cK>czi-G>NZGTBbHfFOKq$g_$22x7pV{W)op~BI
z3H_3IIlh_ZkW8a850BdqPG($JDi~Q2;u)IJC3|d@&I_b>Rs;8Y)pCma3S?4*K82Ft
zG$|t@La4p1djli#EQ|71*2)E}b&K8<*yHP(4Q`UVwO)c74}IJm28a+rWM!>{9G)Fh
zdhq`YB&mLlChG^<c$t3^pB%I#eX325^J{)#zo|=cpNVWP+j9a@%*Dmm`8i@b7FMr#
zBlM;*79(SK8Kf!Q7?LQ)4uBg#-n{}nmZ8qnc_|$unH`el@zo;tevPjgKH0lNwbWOy
zVJRuK@ry-gH;{(y1?q@a5TBnbj=zJj(~xnviTriH?BZtUt8|d{S$${!S#H!RQR_x0
zEl7-cj-@=LY5=b=4_ZRLw;}z<4+my{D$g0KtnlV6dn6GlvP2<F;LCHSzPls^NBcRa
zxc|s*s4F_W+Q#)WwGuedR2Fak(R+*KBh@mLlYSMQUj$0G?VsDKwBybCxjzulBap~;
zN5(7S^k^}~runR&yCvlO`{FLsNMOWyc>j<%6QZI5?~g<ayz4*B&G@@gaM>1mzNR0!
zN0AVI7CQT)d_EypaJhMSc^u`hSa3NudmeT<IeS^B&2`zMd_2^0a#kL+y*=9!b~Htq
z=AQ2`%%1+qw5{1`Z~x#Ms>s`AZ<R@2+x<M|rI1&}GJbg2B}qwGnfz+zH5-2XM={yC
zB}%?-W^-+~*)2-(LFu=UiLrp&?TXt4FNy8pze$ZpvMWCnV2K8`_=CVCr>nuZL*lNl
zN{##W##0WhcX82eOF!^l&_{nI2Z+A5V_(z&h>ruOe$5X|DylspOm|+&E*c2>-OL7C
z%NcA;HH->i1}IR9eKA%u7mP_N@~XQGrIxTWIdrtvudvqW7I>nzK4(LL=G3y9>NiCX
zQ$@GSieUu*XgTRht5shISrdwC@H3OeO%2!i!AWC`_CNz5$INS7c6(#lXE)%0H&WiH
zA0pcG+X7ZiY>?CFRiJ6O+X|wG_NHp9{sXjE3wB5C)+y6n>!2hiAd%=%(nD}~E7Pi(
z{Erz&l}+3qvq#2<KpIw=@AZu*RKEwV;co+KM?K0p2bf7zKd3&gxnIC^!5Ud$tSYdE
z!RH~-y`Jt9)%A1%2DExG+EK)q9-$dj*LPKD#U#$2rz^e3(UDs@Y~QRs&^CO3g)jf2
z-YLtd$Et=0t<V;OGlu?=9Ii0n>cqJPpPrm|QEY<yYe5#Xk;i=nlq>h@+ls!E=X~c&
z+vgjXk{{3HF83+!<WrxE#E^b9t`vQ8)J4k?MFjUj|Du56TRa=Tn6!=9+d5Z#vlqHm
z+2<A#s%TabQQo~YKO;NPe!k{sIk`qrE1i#De$CS#dT_C|b?o0!iI*<H=Quschw69z
z`MUo3WY?beXLI57olwhS=5Ofbg%=Gl8TxL?o5EK=_D!7DeoEm4ED=ev8HT(@gSyc`
z3w&ijxjtL<^~1e+@58^QIr+zu6U7EkZwjzJXKu*Le-M5&JXkVz(}30IH^+3SZC$fb
zFFgWSlqB>J=GhN5`rw9<5{Ste7k7GYF7u({VB}|A=w?OAF`;`YIRj~QWoS9C6st>B
z@1_patY=nMw`(o3$OLN`13r~;>cIzT-@WH_;qpY)n+<Gr@`L-os=+X@yU3nWT0k2n
zUq(n9BO1p~B@1xnq}Y1)*~Da+_KJ3XrZwU<vaY7zBT6K6wh1hlzhk^AI`7>1OSZqY
zKh;2xhk2L=kUBo|u*r*1L26V1$pblVq&z`r?N|hhg#1DTjctJ%#dOHRtuMb*M`Hz7
z%@(}P%&4{J*}f%r7-`tQFG2|~xyn(>^Yim6o6CiAxhNDY!Q~&nFoT?C5XZvj9g+UA
z>z%dut7^d2Mg6V*&6TK&mF<=Yd7jERMNqQx(V9=>zr5|Ui|zA7<ulhy+2@fbYr+Ey
z(-a9i>t#^-F~#w8qI{u$nRhwCclo1$=&&7OnYp)1fm9fg#Z=px@IkYUHtQ!RxjxM&
ze^&IjXPYh#tW^cmW~?h_Ni%P7R8M#qIC4sMPqwm+vpXY}KL`0VRraZJ=|CeqShR&?
zJ_cO__Rz6W#(rC{xu$%U5c(0s3?GZ}O~|iLZgTAbHy!niio>(24oB|%VO$m^3-___
zAfB9um+7zU2ixeg(!Rk=3=_ENL+fgvD5(WMrt{q&MMNjO5|Itik(Dgk$}?a~7AZ1S
zT#t7No&4nUEfplK!;+e=IiKXz<Wt5g)iy9Hzzxq5kDi~$W<3G;xO;Qb-(&ecO5z4T
zGc)yK%37X@RWSC|cBEBb{qsPT8I~9V<7j@CiIx%+%2-0rdjq~jt!;;|1GHYK2Q&&l
z1>D^O(C|D=6SozxMEC=c_JfpZ|0K~J7K2L1U}B=dZ%HJC>39*|-98D`4?$s~;eb|o
zZ2<d~?SB4NaFaPXutw5JfQ43-4aU-i-b_Xzm@UEptRb9Fd+6<kyp_d6j{fE<-@Co7
z)6CdM1+0dTBN&0t<7%(}WNGIzE+=s)pC4&Da?LayuTM#|1nTlBMUA@i9h{mKL_Q0@
zcQG*(8EEr{I5l-nnxF1=JPNvOin<K9y8J-#pkF8-&0fSu9i1v$>h*c&L-~|>Qc}jp
z&Gr<<`b7Os*88e1CZaCGD7w6nipq%c$@O_j<<r$gX};tYW9y+Y4?Q>K6qk*~MFC-P
zXYOUv#d>Ive1;oOajEX}FxMM;z_IUZjbp2p9?iE?YI!WZZiWZ%u7#iHogUhbcOCL=
z*%m;z0?g3@({)|~KG{3IK@=Fm`7+s6`9-b=C?P(m?|i6_aPstCK6Hq+D^Ss0nc;UW
z5B|=3lo+O4OpQWh8)|FAu?Ya^Ew%$JKY8)AqPKWOTff0>{xhw^^JN}9v(dkTl&x6-
zcs>Hl7qJa)1(z0mq7;^N=IFrLIV1a*u=n*iOa+G3v0?I|*<5Kr%FfI$*;+a{+eqpN
zRFVqqfy{ayP|kV%eS%<Hht)Kg%b4C_gFtnU|F7-ksj{zT+%Iy{nu;KZt1*Q3r>=Y<
z2K)lG{2w)TVnFHaeL%o-H1sz}23};smXM>FRdz6jm%)HuN!o;RB0b+OTk2`a$Aa0A
zw06KWVvd~V!_3bN2wLiAfPXqgUF{TfWpI*^W_P(+0cc;2?;?9IeJ(8ErT6t3SBD@o
z&w*PpiL6gbmz;9{Ydq~?jfzkxh}=z7R{eW^F)={ii?2%=Hw!Wgs-xW6`)k2Daeg@^
ziv1}iUWCwt!;Af>(>}`U-g04k@!@i`<+A1MZ~1uC8AB9hjdJO0uD8`Jwn8KlC7O@k
zj~(n#tZ$b)tPjasC(FlQi06$u>C1j%A*e-4fuCL2PrI_XySw{MDU~|yKkU!nIc0%d
z(r6tA4xaew(Ccy82<!xcZ|wHT-}wt(jnvxs(>mMmU3F+DA7y#C^fdNk-$;B(iSUN7
zMy4V^6Eerx<wnCyikt+76_Ph5^XpCfNyq@Mb^qwdJ|gt*FwO}6G)PM|6Uxp8S4Z0l
zHd2*YhtaUIRzxUN>W|)4Bef;l6igO}uD@L&#Hla5*KkSABHgFX>ipv+ndnu%@#t#q
zbFOEFFpfgtlO#GR#v$`7#yMST;;(iPS@@~iye3P;UpoXL{J*m084$i};{6LrW6=;!
zPPo*#mHb;c9Y4GD<)HErRWyKb44-(6Mln7lNyWk0kzP$9sp?ms(yGD_4JrWB#B5V6
z8U=3EZS$P$A5aAgF;iv5K6P;MF0+0K5xbj+0I|h^s03lv2o{*H4>AWMQRlAl>^Tx0
zjRk;A%X0|rT~!bKk`51*`Qm?gjpNOQS$&NP2R1zspv{Nhi^U@EDg6gkK0W$E*;m^}
z?I%u^w@03c@id>DY!Ih1;eU^ZE<a_wWB%S+>Af}PPa24yJ)-n<2j{t$N6P0dm${c?
z$|m^P)9s7ns>{hL<p&R>7pLmB9~@I6nOe@Y@6=WVmhZ_@CI`$U^`(R514<BV=BI<y
zKkM!@WbIz*<PSMb#7kcaNqNX-M^fQ$stj6~6D#TA4>f0FuiU7;uQ>I~=uJpY1$5jw
z<aD{OWN(BNsl2v3bTQX*zTP)}O1wrqHz=0gWF52I4v^C&PMOt7*z)E_)euAT6RORa
z{tc)<eJwAslI;(W^|0^FX5^t#he((yA!P3Oa=J7qqIFiav;83cs&QyV4zQ@Afq1Lh
z)YnP2)`wKjLF!7e0->&2)?pr&9=T;zeT4l*0`GmC;avxSY-=YQmfJ3KJSg84DT}uP
z3}@A-dyyHrXtam~Ib_iDVoipPCi{EW#Q?3cSA?CYIe-rKE;m%)v+12pUGA{MmuOYu
z90ZdyOT$nuJtT__kBx5nO%}Zv<fnBYV|dvuH~`J$OpBo1NxUZk0O6)#F~I62dmc?z
z<8mlBdO0^%%D=cD+cD;4S_i262+m<k1UN{7KGffGbm)Z+Ti}xQDZpv??JDIn|Am2!
zc7{8$FA+{#?mFYQ0SW1?{Yxt&i#9CE=kq>I7t2%5=027AQW;(q+cPa8`)7;Tqdn!*
z59!~j%$Jsz1FdiRANQn0p8tDxCULnIwL2BHQD@V1vZ;I-b!n!2&ZMkRP<I}AxL0s_
z9Mrr=>gxJHlp0ag7rjOe$C4BWPZbd0rnQ5!t&{JGQ-u8VxyY#c=4P&9LsN;6(KYuJ
zpxWTbW8@Q*b55Mwq=JF?_pjN9$G!$1_J~zn)HI)<Q)}nBRV_lT=iYS>BKMYa3uvBk
z#oM>cT9bkc{ltcBnj49ceqpB$p}SgyllpS%+jni<xEvzN4*%b7goK9%J>Vl%1LE;B
z>(1q4zO=epMm_KZAmt<IFV2{N{7sep%F3x`;1^IbTMfqkg+AB-#^^dfO-@3ApNx4}
zKm2g`==Ag0m-!css4Fhf)wcLpUq|kzgMcjmE1SCLC4eh_aum#!)=u63O`+8Tms6>f
zv&ilT`%8C{h{D8*fca&pL2!n9m*m;=NPxWiq5I>sq}EqX8d4?XU+zBiy^LA>u*a}Q
z16|c@zok@UL-)JFehdT+>R*CdTUM;!z-Cj*)J1vJat9hxQ2|cIxEMa=dP=`FASY81
zDLn#US6LQIO-L2JycrquA9cajsjx(IX2{tASz<Yx#$BEP_-z;u7MLrZQG}P^j;<Jt
zAMBeR_pr8{R)z15Ud~m_ZrS4h1c!%vZv0(1aI83|6l3AL3l02$Z~OaK>2szu3nB~0
zE|-QP4;KC;+FtI@9t}|-MC1CAe?QEe?(ffM7F?c9d?5x{>z7c5a3vX3Pb`O~j%D>n
zBwv#5v9leGdao2a=XAQMnlcYLHZ<L5js?<mi_XhuY_xl~6bQYZB3f!Fdb<KsrJ2=i
zl2fz{;@Iq2eVVxg)q4k!A#&K-{Ko}KQFrx1!b0DpB}uGA{gy+=hS0lz{GuY}RwO)c
z!nAtvg{3hIS@Mke4+r<xE?ypMOv=j^Q87{hq*A3mCpn{ph#?-MCRB{nCZ?nPR8*Gb
zQv`rWwmpFDivh&vtt(|J@a{8A$dIJ#k)DJqE3;0ZLM8O9qCO#8+`|k3{;=p$Hbb;j
zJgU+(-U{%k*5Bg=oLMcckP?o{5xO_}!Q7MO)`u)>>5(>hwoMY93JBu|>qmE9_O=|8
z0ACI8S3e6VM9U;7s1(+IdnCssq~QY6(-o}Ogft3-xbQ%{#1xn)2?8K08M(SK51+RW
zMd&+K1a(x4Gtz<A5bIa1*;S>#SZVY;0dl%G<7e8_V%cxBVSrc&W}b6iKV?I+PPs9s
za<(ZVqMl0z9HU;}I{0mr5VrRb1$_$?E#aRI`tv=d`SRN3QsSkzg0gO-+Er`4+V#HN
z?U?r5|6kTeA8^vCDA6{fx|7Z0zOiwy4a(T1<;Wvy(e{ir>hYD$!#ZevWeOk;l9Hmo
zC;K^-c<9|1TIdUx+s0Q|O!ch{ssDT$j;mS@k-NtK#VhI#{XfwXKd8@EWl6~7_OctU
z%Wsk#C|YFoLQ)-|ks&LjRjH%e7c{Hh*~O~da^LDT>2@_M59l`NAf9+$&(NBiOZ4qu
z46i@^IIZ~MJYWtdR4V86Y2(pyc5!)<P#D^q#U+;|p&oJsTJ7)9MfamRrav{@cL1H2
z$)y01g`8LnbgK+H*PU+d<P~{&^lUa0B2$#~t{aF7P#7J<W{GMdD?G;OCLVMA?CcSD
zsqA>Mgk=6j^GHU*82}RuG^W+Ty&->|F#N5aCDebdo&J|aQCSL=aAWtBo2q*U>M<lp
zl?x8)W@dcV)fu(H7nLOPu&$U1fk?sdi=Zbs+8jx;07F?nHZ60OrB@V2rR0?q9Jc#5
zCmYNKQQZps3rJdc!xfJ!4&muk$dnd<bMxzC-t&U#;NTD_N}h*W%)(X)KHmWXY8wJY
z-)2vYxHU9fY=(v&e_v6~tqRZA{L1xB06}}(gp?&doYC7F^FcFSk0Nc@4nN;7_@r;E
zl-q23hORc(AKCGYir7CP*9~puXD=LobsssOcQMx+k^N}Tc5$*HVbgRLtRO*AWj;6^
z4C3S44vHrohr{2Gtl|7sR1Z6K0dL*sYOl-0gsyt?_-mE%iUEE#lBTDB%_O{Cs=3ax
zH#$1{Wr`v++-N61tG~C&DkO~5-NW_CXZTIN9;j`+{}y)eGPEyfh<9PFTta^L=&UH7
z`cuv_t*9;OjnQApA&Cc!YUJA~W{ZjX(~Q4h(_j7B6eZ3tnud|BPhJB|ELq8U7Pboh
zoC%0DT+v(K>|&$141cc944pyq(HwWZ5ASj-RRS9>y(*yPl`O1HdDi?d(ds`_M>gq~
z_3;&e=+yQe$EjL6Q4~Slf4f0G4QwNM5p74M*Ma3hSZ89JaYJ*tud?%D1gAJ(LASB7
zN0tHP|3LoDNmt>2=|Lc7DU5<&R)Z)ZWJB{JZ}wadW35VsKhV)kN*nDl3H!PQ#%d`h
z0`+<lX<&(ScD4L9g?(oTzBrS=v`>_{Y5Ct?|9S;1o#96zdq~=BvuZs>%k05~02X`T
zY`a&2=v018#23ONAs!&(1X$YcO~*!Pe%JZ_)<mXl__u^U156i=0m1yU6^M`&X1v12
z)A`R6Qn~R{`E*HmX0rW!eCR+z>9?KCFn(cxB+R<K9lKXsky6rSu`xi#@6&e!O1(oL
z%pT7Cp9>d$JSw4l{_oZ4DQkRtWgVfikKm2WoaEH7!?06PnB<OcyhtyWDn{LS)N9I(
zUSw!<b$XF3%7^l2muqw=8WGd{71F3ZPD2X|mtJzi$-h+T7kDiO%IfTq4Ugy;E4eoN
zvg7>1a%pX+hqdXRD)Zt%^%S|vcDY@pQ))!AB%wBruse@feC_?2x8Nwejoj06yl`Hp
ztmzQ=c&laq_uz`=ssiig@&LinS5Gh@mHo1;|LME6Amcks$i&qErfq(N*t_sg8w}QY
zbOtcA4%O2n0Mx4gq->I4R5vE^{i$c!bvVWc&0+`Fx~c>N@PO=Tb=}=<P6_zQx=@RC
z0q_gRu*MV7lq)RIshaG-AZn*V$tvh|e^uWAbth&vrzH2L!<kUaW1ou;+w^l|W8RkA
z18VV#R@xO?!=|KY2=!IB?hyS{SezIQTf@8I)&)2eB8FvNAN@x4n*>h38SB`?D3#@m
zTXB;IUc2+T7zr?(w(C}C6a4S}RYPWjCSpTthkW*Zlv-W-a!An8!oEcGQFR!<NLz*u
zoLbd=0I1zNLf4!gto$dFv9e$`^3WyfaCGR>bMPa?`r1@k>)F{v2~nDFR(Z9yb|y6>
zD8f$5gXF!nHPdnlOP^`FJm^xs*rh~i<{EKzeI6rBk!PF2d}cri*r$VKp;SU3KRpGl
zuLNf2Pfep1tfy*OK2y@$EA|l6`{-PJNPeduGTkko)yyoQye(aDC4&m~M@oqQZgoG7
z`t{HJ5X{2BtqsZ_SQ_<n;C5hDBqUuh#=O&BVg$Me<^4I3wD?$`=}2Rwb<e)ryzaHN
zcJ|n*ZTMc-Ws;EDzxdN}b8+>^)h3yzy^LuTC14C(t62o?UNJ@Be`f$kA_q%BZMCL?
z+Cu)^79e==V+Q|P7t1WL{09N&k?BdJsh^ES9KcdJHSc+JRy!CxeG3d`cA36kAHP3L
zZy_;1$7vI9-`&rGUhVhxp0uATBhb@Q%M|VCBDLgOr_rLhkM_Pd23D|89Pq5oA3&B>
zP5Xp9rmw|xfhzMLfOW1q0{}4Sj52~~B(ZI6Sm>|#cXVz>FaO{PMbo9{lm=osE^c}D
zrE0c;pLg1eE0)@hfkElMcYGB7h(!Y+pKR_t!ixE+$@42n!xlzIw=U)*$hTJ%3`G@-
zGvuiZuk_>ds3Q917=5x_;GBc1@z*4jLoeqo+w5>Jek6C%gx}lklJIUhB}=rB_c8NS
z22Y`#YwhRl<z`ibeLsmde3c3I`9Zc3`}>s2I<KUpBqTqDQeMk2Sr-R_`kBG%9oF&E
zsQ~tOJ2l>B@ia(`=0=_|fk?E>^v9?pp}iNrsCL{_eir~rRLbrq7d-Wk*`MH4o$qj8
zEJ*+M=ii1}wDHdY`^^f4=L5nj3V@uu!ulaE(ybVmZ#`&i8xhTKep&cK)Z~>o`7C2u
z^c_yQaomP<0yC$*O|fGRnoGl^8JVax>QAdQ^puJ+1cmJ3g-lCOHy&**1^(y{5RHE&
zga@OJa%$RFYDd{`bk{iYg!*{xIp1NR8K2}#0d^r$B#~DngBZFezcE|C5{-+Fw_Qeu
zd0=&M3Ev!vRtHu(N{fV8TiIY)FiYkR)KHp6Zsa%2WRyh1byRmT@5<>7tsJg>Wa*sj
z2CWe+?BtoW&ZYK+NZI$Qy^pq{{;jwk7_*9`|2@i`3Ra`Vd%N(eK-g(v-Ll*4DQpQ7
z{(53I9s#UZ)mA63JG}ighjV`VY@g+>(*xi&RpV{q`PYf(YdIm;w6SUNG2g!3&{7<3
z2Z`+dcdXc}xZIOtJv8*0H9}#KMi|F1nX*>v)v)B`6?S=&tNV^0Bbz+3`!%WkYr+3d
z?+=+o85u~viI0!Bhfo07$KlN|3-KN=01~4UFY<>2T;z0vpOZ6?$st`Z^IjZKOB-or
z>8!;qmF%w_Ys9r|j{M~%LL)V9Vcb*9uL`B&Sr*5U?g3PdnG4}>%Z2Y|(JvE7YUoX$
zo<~krdcw&NZ(C&H@8k@Gw{qO+_{qUTgZNsC@1mjcT;1dRV6>c;yxZ=<$xjQo5@S*$
z*g)%&U_his4yt3$Z7*Ok?s76Oj{d7-i@rMg1D+mgwgF29(R%#*qLffQEb_GvM~mV2
z!soqt6~_7%e=`-x1g;&k+$h`Xod0^8MH0;p^Mxn+x`O$&Mbn4`O9bXi;JW3^*lHe?
z^m~9jn-x9tKpT1CJqr1}L5ltIK>6g`_em!EttOAYq9nDR1nhsJZQp*8&}p%34q%)%
zr<S@PmF(A4r*<@o!<)Xz($g}4;TfZ`zmF2kL3xW&zL7q7p(bQDwCP_@n{Rpfw>OyB
zW!!;atP~biC`ni1QcoY~vQ$4DFD<3=tT4lgR*;%rf?5E>>>TyG$*F&}S&3H@N~<ms
zvgDP(vVZ*L+Ax+f4^i`lxG>PBP-4C0d}cmQTvSsIwW`?d=^@EGs5J85j7Nn_90VTj
z=lR+Qkc!oTiZLfJvX>|(DS%Q_g|4dIbguSAbW_Y^%}MurVvS~MZz{Yt`g#j~Fr$Qv
z{Z?_9A75CpQ40>-Mu|R`gTYm=bjc#BN?TntX*+7hOZ*ji>q@?_H%wd82q-ouE(IR4
zE4N5&w;YJ+snI-cIsS68V>-k424B%7>>_K&DgC8l3<jUK&x-TYE<O4#$M(ZC4Vds4
zY4k$J4U0H^WI0RVqGQi?6fUb)^g2x0cbYk9&;Kh<K)c1@>Seb}oYy9K6F{Ui$@njU
zn=hvykA~Ixy8g|Ok~Fyk0KD7b5bw{{nikbHVN{gDDtMblOSVntr2>C5xADTUY#5|f
zr7Xps!FN8~>Tap@>qjao>uIN0$7bf6=<L$C`&Ux>1-ddG7lNqeWo2BGGpML7W)%t1
zKKL9J<fJ>96c)d?CcA`l_GLht7-w)i0r6weM!DsO_BeL3(Dq)cj+Bht!%~>A<@#q1
zFlU`z4koTYgDngtZ)Gz2B*{Q}ThXDVMa!*4%=L>uBFnzcSh*$kw)aX()z7}u!$X#)
zs$AobG4}z+gMXeqSTRB~t}V1I;Sy2WS`;yogpQpk4P2_v`4(v29)hG*(B`6fo6)a}
z3x;&QOLF8YOcD#pIZQ7(_Py;)jRYs438U%$R^m2*fT_o5ApSa)l#vo&4_rP(gDUn4
z^2vZgyu#YCE@YoH)jeJ+_|EIzW(wa-_p)>IewW1Vx!RIQyx-~0OE2L}>b*Vk8O6sD
zrHd+vSUFu;d_|D9ANMXp%MbYXIBGK{jE#ACP<5yGO4bzG;{=@r18G%dy~<2)Lw@()
z*B~DZ9FxqtB(^gd1aH9L%T}!@%OE=AvL^2UL&qSF8}DgbK@QyWp3BRet)lEkG?KKw
z6va<5@{MkTSK3IjfSZ2`8n9sxCh>@Su-4rFNL3Gb8zvB^$n4Tmw)A)VZ0k@_JQo`#
zBH(zG1<Ry3rV)rchR)<UjfB|Pgaj@sUSBm}kp2W*b%5%Jp$Dlg`)$^%TY_x9%sZq=
zO<4NxCilmKY)$Mvisbel9FWoyLRQj)Gxt{gwg^%$Ldc&}zoyIV+NGr;ky%0B-p!wB
z=Ml1RzUtQqYtJZ@@Kk5t0WL~@<Oixcw-F6YEa5QL%(V+Bs9oOL&LE>uPVMc+hKDI{
z7Z|X$t$d^|tCz9&*1f3?i$J<?cmBi+Yt2cr2vA>fg=>X~Eq1#tnf#Spwx`#176EL6
z=M1zPaG&Drk6wRgpJ;7UT=|hlctrJUlE24-#zaMK_-fW^4O?%h)w!F$&&J8c$!JOK
zBc=Q*<zsQ9PXq_9`q5zl5F<<1P)lWBd>YN)-|s8*efqMTbM!WfC(}7NZ@3r}k&#Ne
zB7O_FFDQ*d{?{1{$R`zs_1*)&u;xZoNOgHU`TQg}P-ZS;f-WV(%akbv@;v0~c(K=n
zo@rfm-$!jhy)EDQ1=>4_&}dPHw=x0*0c0BgJi_pkaj?y`J2V=}l1aY!ox-iHCoFR+
zx@fAm=ww7c%W6N)h<+8zbV-gnQ)AWyvaL>vR!jEB#_3|`wy;0t)=jD3<9l&GDS15_
z$-aXAJ6QU9bp$Q^v+9NgSb7n_&mjU(@2t#qYE+mu{E9G{m3)?Jjnoo-x!7UwxYvZD
zmQVf5|D%5NwBo_X+DWsUnoC}koO?(&I4LWZiH#~-eGd?$@;5nHS30&~&As{Hky>5h
zU6J@iuGxX0;+L^pg=(0fjrpyIL`?|#I-)hQ&T*b{tx^<4@5fGde3hA>6WPJ&QG&Fw
zwgNnmczkg|1$ZSWGer)>_Tyr^5&>7U6&QuxOfv)0#4x`m1HVCvGsOF%1}8TEE+-sb
z75Za8xt<MsvsbDj5i&bysoZqvn-KosLB)zOx8^9zXY-0Xfs{UVYA7m!b8%i<x+9X0
zs1~#{6qTL!(>wU+Wavu!zWt0Yc<$f4iV-~$POrhqywk5BY>zd%0*1rbqlwjMO`q~w
z4M2w^GkeMhrg3~(wx6e^mz8!l`1to=%9JrVLcu3Nqhp$Eb=uQt>IKTLd6T3{vmmr;
zihbcIwAR7#qzx07z_j@1Wv`P1VIxoA=>~UnB`0`ZGzN}(38d8mC|<LV&H#h4_tHzv
z-o*VTUH|`X0Guui2L1}Yn<6MA<OC0p0|goFStzxt-K%!$rD3-|$pE@r*5zcq!e$HT
zEW~tD@tXsO+tZn28J@Www(C)kzGJ0!?{v<JyOmUvTh&|bnr`w}ep(w2n+>&HKlckk
z?)ICi<=Jt&w(;04Ez!fSbkAso3Dr01IW9y~hiz24b_jWHRG3RXW;W@R_4e5C9@Wjo
z?>FM<0ANOJ>hEE&8*U|Ndv+zG)9-9&UQL?~+*i}TXQl1^u*B`gdmJ4=ujJ41o*Cs3
z<LnVh84$3^e`&GVsr=Pr>sw80kuBTS_#n2i8DnkyBe;77<NNys$$T-65P533_K|i5
z#R4}T2&1m0acVl|v^KWfz9247?3_Zd+qwAeUAJ-A(w!d6<0W$R4EpJtc!6GumSlFz
zsjq(ZmT_%yv?qy{8a-|D*r?at-TPm8qEG5uRWlk@DnYy6(j`GLq!zWLA~3=D-k}Ze
z6(P{QiGz#a%i-i@PN@Dlrm|v^kT+VcF?E*q-S|yLYNGTW>;qN+?8on^`Uoh)*%4y`
zuQ_;kJ*UPEX7|K{5GWX+qDtp$rjaUpAMp7P?nQ=_ri=gRR{04{x{3k$_2shiQJN|{
zKP@RV=mpT3l{M&2wd5E4feWQUPnr`FcB1mezQ8V5tld<&x(G<663E0kzlFuf?(*m{
zXRv2dYHAnuVql@5OP<4Tay~g|{PftHdqu?s!vj<>L{>&@?7j`Ok@KI{b_s;}YQ;K~
zT>rNmFd~^U!voNoR;yrI5&Yo(-Nf+g6x%S#t(EuVb9Nnja3k}<>U#_8J7}Z)(2>dY
z)OZScH29vj_STF=U&Ai~_(x=mrXZLfE_0vffMQ9@Dn4Fc=LTqtVkMblvEq+dgYt?=
zf2x03T<?;C_j|U?R`vCEWa`BP1pr-a^jz)l<)w)I91R%p6Y1M69la9c%wvwFGWA%t
zb8?#OFPI6B2$Mf*&+Iy7ZQmm<42UVN@F-|(@;z|US4{DVI$jwbBJvj`S1e)d)^BA3
z;3+#gyc~v0F$%5(6H6m_Iy{>(U9c0z;)DUn39zbY+=%tXu+)y8L`H-@e()*qXC>i#
z*+Df=U9;#x#c6Tk{DM6&@B3AQpOSB~6|xc@^FF$P$^a_t<$tzA$=1C2syedey*z2@
zL83|c^LP6V5~m=eGH4zw;*4N+0I;NtKHLAOf?qS5>TNkED)&+YZc7p@H+cU;l*Y&0
zr*})i<K6Q_UgK!EvFxv6HM1m%D)sO%?TKlWlb8b7-0WL=tT>g+`&1kQ_+7qvYAH=H
z+MCcqAd!8pTqXM5l!iPZciBTQfu?nneCz|>M%J;P5bG=y`0T3nMsY>XVa0~In!-ST
zsdro_YO7xU>rCFa!1TD^9x0+%$K$VA#tVvJi^s}q17lkKCe>22zhsn?Dw&UW*R+u7
z*fKWo7ue6p7J>IzdFYUtSv)Z6aEl~d5PmQDmAj5sRtTiG=>8l3<_vJyw#$u<bf>zh
z>};%{7JOlA)o*@%MX)&1Ji$NP2-;Me?gn8fZ*oej2~(V5)g3DzV{h<jT0Xx6os9_I
z^O}5kw4Cd_`q|lgs{0A+_A_zCt*Ig9A-PklK)#vC$ln7bNAu)yIXSA}kPMUvF7Dl#
zPPC!`Tt=W~DViTl4Er(z2EllM2H255TmoSJvi0`G_&R;Q<_D3dHp-raoS(~|I#)yR
z`1Q{ljMm?X26O7`or+84V)wP!z%1M|j4s8#Op+=RPV7i(nmLzws^yvX_UAB!Amq8i
z%UB2ySY+iymxSFyQj4t)xL1%o4j&Ttw$Ix6uH3*tpK#h$#NWIJlmdqYZx=7y&+Byv
zCXmn&@E1G^6~pruky9f{W5}kB8NSMs^jnq#q$SVMf7^LTfh#tS>fJ=PacJP7wFalr
zGHt(y68~DNhOf6}ig>~o)vP>;yqZeRH>B0Ze`B8x%Rb+uW0FY>v>qRAvZz|M5p-r5
zSy$TrOR@?S^WRPbwtm>BAATG>*Q8F3wue52hMK`_>vD{9ic1e8&0B(qdB8*OvNuMP
z<YiXAErGc|eVG6hX5_1YstXm+h2p9cRLFETuXQGo1GS9AL-JisZ#OVg0tS;hVlu6Z
zm=*pw@oK+O)Jtfsa1<xLs~0d}v>fIJOO(Wyux_s}GCT<&oC|tBgg&IW-kJs%vyhrQ
z1Zdv|Lqmg&V6tsQ$Uey|bUGgj?JT8edk1!R!Bp#G2}axYWBnJ-IlKsb)ka(9i})}f
zdR=(77P=-Qp%bj2BA(^{Z_LtbG98u1C7&lSI0{`q;J5|)k0<*#M?bfQ;CrJZBLm)e
zIF=fV6)W9OrfzM$rk@H|F?6P7gxxVMFRPsFf1R35^O;En-jg6M{;D;VHVK8m-Y5h#
z1^Eo*l;#QuUYK2cJAB0v>;0ob#mw$~E4meEv|)W86aBdqq@^X`$au}E3*ZAWu@nQb
zU^^J6`7t|a6sflCPAe5y3pD(Rq5R#=e+X^!dOOnOmt6fm$p$)U_Pw?6dlIZdpAqhT
z#kEFutfovd2!!$Da^ix~X^nTJpFh?gj)5iZ_+Gt|AD`AL`ee>8m5Uf{EI;lwT01()
z(dOmPl$4admnzs-Tp`EX#mn?^F^jG4(SYJgxWB(+u}h@FdHH&+SG#0}kx}o6>>KHS
zm0-ONB5BLW9}+hot_c1ipFH9XjpJVlpRkl?Ok_4^)9Dp0q6H9-VPdN<L=AsGUb4FX
zD=vXJ(F%iD->0uk;X4_*e!ux8sZT8~t@LT!X-RNo)0FM^b^(s+gH`>M<1mS#sur)R
z7PjQBQ@PmV57+pVA1LCIHC5p^;lDyDkGef)Pz%r0v_Km=W+X|PT?epaWRU(mJ15X<
zrKrHH4RM1F+FPBw+_cFs$$rDqt2_>DO$DpzPhf{Q4JxodmE@-bU>K-X0UrYrUx%<`
z418NWKd%26urwQGK|}2`6-3{Or~^Ro)Nie59#J;d1TN}UDV}?eM=d;_Balli`zet)
z$`&kME`ZdgBAfHvEqFU{&1sayMRrNbP_#xT14JO+A#+<kukc_%W+1M8>&c^$67sGj
z-St<8gOjSaKvY>c9Cey;<wh@#B|gQXeJhbzxgUH_N&Yu%?>M~d9B6Ot5yYUo`CUz{
zO)MwWtG7SXRFu(4k3%a4l=?wX+fhs4n&Gm>*yvqtS!vo>Ej?g#B{T_>0aQm%JTJD-
zVKr;}X<<sH9Gdjz;b|XpA@L8Tw5hjp*AZ=(<YgiDHH1vvMWaNowj1<{q{qGEDM&^>
zljkuSEXwGUV}~w3(r%dCrU>u-dqjm??6L0(VNc0(N`Truc5}K;fP0^4w6xu`uzOKc
zZ-Q1z>d+&y91Cq6JT4;-ZKtv<Mp<<DChw91ZJvKDQi0Ai@~zB|mMD92Gk4<94=IV}
z{hlsZTsjhvRwyW1BAb*>F9?uC7;Fy{4#&pSiFB`W1o%N&aJJ~hNuq-MpQ2GmCGW9>
zk<=hHu6Sg+B$}}Aa=i_b{ESOWc-mcERt|-wo%=y$I0^$kCodpW{iSa>bOMCq6tS9Q
zLKgMgE20iq5I@+e*uRx`_{z~QZi<LWQ&|N8uPj@R8^l}et-nixK)q)OK}9=oSzpNo
zNfx!XMlZs|1X%p5*nX#Hw#(nmV(73E;BDQVk934`YS>S^cm32TEbp}GyRoeJ>+dP)
z5*A+Mda<gZzWY7u)+v2;akm&BKXc*8VeA58P?fqFI-+}(C=Ec%(P#EVT2@6CQCF2w
z|BYMKZ}~Bsoq3=o#TWtm$3DwQW?1S^gIs~4cNrN*K3=3Z<>fT+90h>qRN(y2?ZeZV
z)3s;Zac=oRbrct&>>HzLHB+6Q5m_w7r|UnsRos-=EopVnvE3jJ=bygh0wol<-Vkg{
z?~_jgJPu<zlx3cCsbFcJ8}qb${+pQS-8YiEzh>WZJw$g*FXotIamwf7-@z8QFTNy<
zYhtpq>pS+ZN%h2eeAk8<12TF}v}bW`AUNPl0m~RCfSU4^yp6SSBI!o703$Z8F3}>-
znXoC{>u|#TxCCFoQ@!@cF6a8x{LKCEpMOegO9=acl{VM!>(n4v-*y|xyTa#j%QzSQ
z$<R+zob$AvgYH?<?R1wVx0nFm#(XVD#eQ)BK|k7`^Z!A}rp$aOj#iqi!2^-fF65@{
z{S0X~d5d7lB<5EEn=dJVq^2QB>~{eUd)nTz3h}K<QTyPmgG%rl#>5?@tdV#&OZE-8
z{9%#hL&UWVUbg?ZG4xk$a>te{?%>BZH^G?-asdv>y0Pps4i2vAp$Tj(YVxJGJtByn
zwztXqBPU~SQ|s}!IUQge^Na}bR7QF+H>Yf%AH*4=p~|q0#K9PUwu;hx5r7CeJ@ZE!
z27bsGtECK2_m07zbb5`X-|3FL<_0)&X?We+X2EjUqlR<X3@JZOi_D18&)SNv#SzJ*
zy~e5S!x35QL6xamu%Bp1UG7RqiTj@7;Y@8n?ux=n5{7uL_c+cT1k!5L1to87)Fe^h
zPeu-a&8<>qHN8nraF6;{itrxR*?K1Gmc#aJ#A)#5@+kSRc)mEKA@pKhvUO}MmvnJK
zCVqdj5fXa3e@$C)>6`Z5a($l`s3hNPln4!cV&s%{L2>KG60H80?1z1CXr@+~Z#}Yk
zydpN}9Y~Za<CT+3%bA6<s*@iIGMOWpR&9v<RT6q)uyI;a(lzoS2F&PityZiZHm)11
z{&y!Fnon~QT08O~M@Tz68)BSlM&{n?urKxaRZLIC&0^>QP=Wq%d@kwqv}Fg#jshGe
z4*uZ40$`vRMU^}g<BfqkM-pda{XezyCABf>PK7^n4+2>_fcRxdY;02y#tmmHO{Tpk
z+=}};^3;Y(LZAnwf|uw>$~qbQr0F$PTZw&L2zCb8`w3Eca;<dGP5VQ?Y_g>ob6G33
zj*~CW7bPqeZ|~zLC;aYg_QPeJvdwW5pjT?IH$|D~1k>!b;mJU(cw7uJiQ?7>N2QO-
z4u7_+;_mDV(W22OX79;o#AIY0f6b#3EwR?7sDb?IlqEKf!|8Y2a(sMz4&&Xd<-Tx!
zbGiF)&pRlqM2QWyAg@;zshD5vc8{m>X>*5AaLa6g-b!Ue-7M>(@0wj=w|5<r!Hw(^
z-t?vPx{AA$oBM&{w7-1X#-nV9tiIz$j0M~mHkCidYMZBbsD(^P6c<i>m0m?|ynX{X
zOHgWQq7e9L-jM8nj}qLr=L74Tz=M=#2s?bbs1(E8@N8qiO5~-&N(X7vNKQp2$jr=M
z`YFTpJ;{EvHnntGoQQBSvsjrsUg~jups78DYf&J)7hFc39vi7u`bz%VY<Vtb&Ze)`
zyFXvNq_AQyH>0~)?i#OOxiVB^<%K6!iMCr+3IN)2?`}j}b^8ZXYoLEUY8<{6C*{XN
zLnBEPp^>!kL0#=eixXD#0GyC~Qle<w;@Hc}AD2fypQZ%ZK%6gy15_cv-LDmRde}2Z
zw7s2n+KExgd|5`%oGw<?q2jjX$VXInKYF^N_O}7@Vdmav4>_~9y`lhkNrzx5s+c(%
z!;An^jG1uuI9gNIO`C#siMoU6Y?^!TX$}O<jT{#G>AoSSY5K#UsX~(SeqiainTm8N
z3S$5mIE+_fi?--$O^*}ZHi8;M59Na5%UzQ(lpz6N&pzl*hF{LI8;|nzY(ZuKB+e~o
z<(RTCe&ei=r?-{w-#{et#@V+hXDIR%=)v7`mVqy@e%#*P-k{H8IsVWtu#|}@^68+$
zip~o?3jRgaDNQz6jZIw5UK<-6ZlTvjxhSX*LtXKYZZ&w7u#VLpukRdf-TGy0#<YKK
zHQ47UO7zRGvTY2%T)xiei!M1my%vA{_W(xv6>e-~Z9WaKQm$qz_v~u5*A(ap#ML1V
z=#bMn-^T9RmeD~<GoX!CH-f(h%$Xq5ZEJ>Q9bg#4$8YJ?dqs^EB>Eh9fxp};GGQ*(
zQw<)@{ct)1qwKDE_nYyg;DDgu+5#KsGSQ5(1dKd<As-uU31gxeW0C;pS!GFn;=e)7
zXp9I-%vgI`e1m!(uBc`3Y|mRuu(6_6AvJXC#r)iBPh-$x)OOVQHlP^Fq!Coj^iPoc
z0J?lD2D8%PhEjX?wZhWtj{X+`8uPc{Z2HRUk4?b#3zg-eGWbgTx)xCrQ$__$U!jK6
zAfY7@T6EK~=WV5Z3OafmNf1G`x&SX$bgLlR(#m0#YvAr*BrN&XpNC#5(dl&7T&CTo
z<2Jc;RH?Mj?|%lqxRWQu7s#jYtelw;-Q{<){iFN(jAfrFxv~;WxqH+9P{@-TH7x|r
zwEr)cFD^&^xjua5r4<TY9^pO!foD^*T|bs>t<!U(5zLqzz*QQp$0I=P_gn$71-G*k
zQeg<vYN=k{Jz<#^{y3#i-$()A*p}+sft&d0*~5^7h}+Jj+Pv)M^8=zAx4K}Q{cJf(
z9;%OB+Un1E+OI&c1R5fzl{$S>OlNgW`<Vevvzo!4F34^SQZ`8;u45B9xCCFY?js;J
zBJNWX8oGW~+?H6$oaI?j08zo!4bur;Qs3Lxb{2i2!tN<O@gbkzi#TP(&eg6c_)J|~
z{JpZ(4vH83T>Tc8oHCf9=2ST5UOHe!;-vzkvmJrnZ;Ok2yAibLelaQd`*v@e>3agy
z1a}75Gn+cE5sT1@tF;c}p~Tvat%vVkxxz)lyzqQ1B_Y9~@=D724-}Oz+$p`A+8iiG
zrRp5cgGpC22-x9-pa6`ZEILxOId<4Wa~#EAC||^H_~0RH+LzQ+s+(o@X3vtDzi|g^
z!sY;HEfhWZtJI3;m@o)F=9=%!fp<0Cpup8=k!sp!)r3lq&u>P42dE(vR75ls5EAaZ
z!`NK&ks0?Y#EvbaC^lankp%q%%%>9B)um+-LVosM&iX5&%fjjOK$p*AqwlZFOd2CU
zfgO-#;h_}A0sng8{hKmXM0mO9K>%Ozk6uqFrE#fdNTHx2H?XJf5oX)KN}l?R8uC<A
zVxAF#1-n3t4<MiX+Z~Z~K?3<dZD?;!1$sQBkgAn?70M5AUjki=Q}~oW&S(~(n7bBt
zlSdPz_Gi?9{a?MVgGtKN*ww8?`O3bBcme7h_74F<$TU0rn<n{SpQ7>WtIfC;izW*X
zJk2kiYMVpO?3W5lo=y)RUI!7XJ^K#Gas}?Q6r6sD;)6AtF+#pxN_#4COR76lqTmY<
ze80XNZlt?*9_n3`a}Pak)Q`h%85{Z{5NRm3Ih#~J@+9D1Om0JfI>MN4yz(`Tsf-$v
zTBY}PUEk@`X7YHGkIU^pbDez_M!kH{mj5TRtnJS4?zZp6$G7|4%B;vgBs?SDB~52I
za&qZjwGxekiP|Fo?+h&GDg3%w&rARS4bVcWn5+(~rTS&X_{G3*Mvxljx5K}#cI15%
zJwm_XRdmhoioUFix^xYw!h7-l`oZ<zTS&$u@6#>g7mj+gIQ6%sL8e~gQ}j*>z*H<o
zEi@Fus%qdaC9UWEu|p5AB!zN8L#Sa2HzDs+r07+iIU^oDOAw&KQ|5iL%9iaJZGPdX
zc|Y?(ol8q6YMrLs`*8YVh$Ex~%-A1D{*NI2;=003sjoA&dMrR@!P5PildE|L_;PQB
zhpLR&P@nDXOqgn6z$`Kg1qdbuF`gYA;r{xz06HN9;SzOI<dekh%bjQ8H^cbh$QM#d
z4<0PzMspf!Yx!8Z+UJMM8tgr+G3wo$hox)x&E&O3PN8ZjQPSS%TynC@43SMHtf?`|
zx=O;QAnIe^tzS)<O;vJi`OEE3kJ(U0IGx-7@c=cnM$SYOKlO#^hC@=qHb&N-G}+Eb
zRE|E>DV=*m4YwO~Ya+#sq{YwS$C?U5mLXHkSwUVA)|nBr79M4JSjtFIs#vrrK|R#W
zpULpCMp}|yktsPZ!LgqQ<7Ag&xsmJW*Wea1>odM_LLlR(fo|P0=&e7}#eTi|j**Ia
zohkYyE60Ai4M&lr8Hvm~@6o1b9zZm5avBGJyOU?7qJrZQf<4_Rey+>qx~+SKW_f9%
zan{q#pd-$X#sSf&d<6Qi*K&GzIXz9Zsq9maQ1@Z6#Iv|FhhU9?a4PD+RZFk1FU=Xv
zwRck%WqGj<h5TB2Zb+Evoev-l2IQgObBylc4#)D$K)(sqS##wJ>{7p7$6I%u(oD&?
zkYE;`cpe(z`0A|<uMmc3K_+B=j1(UV!}y+7Wt}vw(2E&Fzo6v>BDA05H<x*v_#yH=
zsVw{lFI0P)d`|Dg=^_fEE=aq<o)sBC-ADLSh6*0I%LoSvr==Px7DyCrwo5AF9$-LJ
z)tf>X`5{dg`y0k`0bihw+<yz#;qNlatXD$3EB0q7tAVyh>*w6nmF2`P$Tly){?Vw{
z6!PO?de+;_x`N2!nXlKr5Xi3lCr9M}QFQL{O#OcxpZmyVF4fYoxs*k*DA!z<kX)mX
zd*ss0ZNyx2o03b(J*7g?=quaYh9Z<($QH99w^%H>Ecf4jkNvg3&K~DH_Brp*=XH5T
zQHrI7IhPvm_*lN8qLPK+iSQB!lF^B~=~d`wp)F<1`qkZwVl!a|K?2^g-JwC7nsWFH
z0;&UGQ>xi&|I6wMDRB#UNmG-!g*nevq^N|qwX=OU^w;Z&fiaLwvNWZ-A~K|I4qdSy
z5qDTmZlez_&hL)|SGQa=*B7*dqnQU~RqEY7!1G;*R11?0&(F)-F|@(sos3WidQnCa
zC*dJ4kAzk#+iCOpvFQyaSABHLHKgs2hj4R;kjyA2^y^=jhFOXOZ|WXK9j@*&RJfbA
zDQy?yv}o7#QuS>da(H}jc0a~;D(RFFG`>i|W&h6}tl~8O>}N#fkJj(au$;FD*aJH#
zugSWTqhs^S-MGngiStd3l8usQOgQbnyC<Lqm*Ne(1nofhqsQ*OiD9sh+_U`vR{ETq
zRHSN)lw&&qQ{s@gUXi9JxM_8f^j@A4KJy~c^axLoTV`lskHgs@VaVt`a&5v!PeP^y
z!+j{US4?c^@p_w<-*q-cq~lz{!|J!oot>R)|3E&C?sWxy(U2<uNSg6xmiYTn>C3Mv
z$9zn>C_*n}vadLj>A_W_@b35M-Aft;;cd6hRpf<2QtwT%v1t*`!OXw4<Vr=I>^Qa6
z2RM^@;@LFS(#lQ+V0b?TOLKZ{PNvt^8GSa!N5K*jqFYvI`qwNJd0iOp9hgF~1wTKx
z6-@}+x2i*#OZj&N^dCsTgf}Ii_~*0X$3K@MGGTPFX$beX{VXFzULX!Cq0|L6F^0a!
znG37G7G0x6pir((qjZ0TyMChm9o_et@vgxIg-Vs>8#3ZD!g(%Br2Q~*Jozr#=tAPF
zPEftA_pw!zb7XAIU_b7ZU1E&K@Vg4|b%GY)ld9Loc%bUEnYq+>DA>d*Q*_Vfuu6=4
zmVCTxfe}QrbWV$nEi|0lP?h2V`b7L;M$!r3`s$E~yD~|lJ$KMK_Jdxww3^6N>mP+U
z<>vpK!-H_bi3`)+m1#y@U>z>?7hxe`wn3MtK=yf~0SC(Z(22GisEe<g@`=B1tXl&e
zVsV9_mXP26eG|iBk8@L{E{gkbW%R(ZivGgmrGLR1ts<;+A#&NH5$8=EWvad;#&ZO-
zC*PhQex4HU)RxN%^gk619x9;LmW8=fI9n^b&C#VlGo9gsucG&7(&cX2rc{MCdxWlD
zE}C5f06aX}AfgWVqu(a!?<D&+!;ClbMw(H0$}aRx-hG_8YzqAyL0UfD95cnnVD&t1
zz8|Yu7PZbD+4$$>dPA#Ehdcc3D&1xL+s@i{=-CR@8bY;`18{bx(S30BJw13-8<N&Q
z5Cu^8k7+~7%D8))oiKL<b6;`!tC7DmmYdIF=>x^d8NA%WkfL!24gf6r4EzF`6izgi
z2l2iDDOW(hbv(T9^&-^%OJB3<JbuY#tD+`V8KJ1?B}$z~IoHkpD@<!;X(jRV5h|E}
zw-=c<i}M=BdTB*EfKM`p{->%Hk)DHtnbtVfVO!Ta!Ya}PD%c6tv9&5)MJUdh!_tgG
zfr$P+wSyI&=G9F~L{vI)y->~|ZtqF@HqT>q!Nw*weJhY$R87+ScB$$5PO$^9^ZU_9
zK`Tcz0M|#Q)g^LEqS5{Ljk@%}t^xp409wXq6m%2P3X^}qAvJM#eYmOpTJlP3zVCQc
zRIdYrM059mM|{%$`2~McxiG*d33m>d<g)<CJoYIMx~Tb*ZzYSDXJPc;k3jMygIT{B
z98^~|`j)zrz`=medQE3PkJk~EJOUUd{_f4`d0qM3@Ng2#Sx{zZ==i6jw<%LY9&ECK
zfx*1~27~pje@dk<qd%6O5B|Kz&Ml`>3t(caZM9cb2&BkEsMYA}<%LI(Z(j(G<g*1g
z{4a|6pqVR?r%v;$(ON(fy&=W#T*vdtYMF{T(x4_%Tk^VkCJaZceL%lEn5?62Uq`0u
z%2eikc>RN(kGh0=9SSkj9}Ap#7<~j8=<YJSFoB{4<IbAB#d?t9>PYbg<)GJ^P)RQy
z(4tZ2T$lB9O0Cnm0{dg<wQ{mz$Y;@4bx7>>t>_iB)>OG(^Q431Pt`9&1?$ashvecB
z3r{FnFJLD3Lz%mMD}=iRp`~K!RXLx<CvzjFCSJB1777|UXxx5~oAbN5Ry0gnG}nS&
zWFmx^hlM~Nz~vOKz_Lm?7|wAv@_PemV%fcY|4)s_;j$cLL2UgGXx28!l}*kIbF1rk
z)KzmF`Dpm3Iza5G%Y!D;$j(?yxP#G!DV}6Ni&JtXyUaB_^--hXDMeFFiGn$R2m}lf
zydE&Me=jDmQc*u^2){Z1_h(zW5nOhweuV%9_=kU}#hyL+s<Lp{o23d#P62s?KKmnE
zQE@?f{#eV==3DKp?HWZ;L{ESLt`{A@PPMuyXI&lH*2)Mz&dl#dEasY=sc=8H=&`@d
z3G=M(dV$mQ+uF#t&;_Mwqf3hNG~#@zI0u*lgcU{yZN`u}T~-owJ=2?Cv%27mjOFdH
zi(`2sGD&jb@35D9%**rX?<n<c?d01H7@Zc9a&CuP6!#EZ<?%&@T*h)t6Eaud@ASC|
zlp4ylGTP{vKqewmEiom9Or;!=<P37KMHp|A-iC#VuyGJZUErAUDVMq%QA3EyYewXg
z3X%#BZGobbk5+BD`FOc`*V~fe<;jt@EDV)V`qO<G|GQ?4y^lA!pp1{i|A3_Kt@0ho
zLw?%cYT^`N?Dgpw)VTDPu7`FDU|g8^?_s5e2G8Qx)Z;vH10~#zY`?4NuDxB})6+Pa
zYJr=?;@gmQNMWiQ@>3p9m60jV#~Ptda@ppr(;gMt@~MmqpO=nL9sV6%*Xrq<)f$qM
z%d#O6Vxtq980UOjB2dS8opDcjA0@wTBGMhX0)kZh`B3~qwLfu{^kyDUD<4bXyY~*S
zjRfsXK7nLjNS41%;*qa^O^Fnbj@ctdiNN?RsPis6XK*PTvVCPM{o;qqFXbOBk6ka<
zxe8U3w^)*zhnt-r!Qbs4ISCmtZMZ9w-4kFws;OV4FY&3ql)Y(&1in}8qt+Q}tgWw;
zbVOX~`M%NEgu^Wwi?j4Zg87ll6AT8!1URZm|6o*+SGuk#v#QgSH?q6C9mR3_r;FLl
zYBwjhP$zIY72jh}1PKkvjNUfZY+(>86Pz)?atrj;R34+hV}ni5N55kUrBAqmsVDi1
z>?tm4q^S3iv4y5(RHlwPv|}bj!otlJr#}a)Xk2lHe-?lK+<-tKVYt#?njvq0vxISJ
zj8cN=r1{C^L31S78zhK>!d`0*uKSpCS>)zk293W{qh57&`VpmCGIsyh<NJkmePM0x
zb)*V^rj^V)Eu%GeTHDyfQ!YsFWItCfy|0hS(jFO|)$-AOO0@r!$Nx!6JJ56^psXyb
zD!XTtWUBpGu(74C;+>nDNpI1!$GYIe;xUGgxv9C}tB`IfNDd$kF$cb;SG<<6i%wV?
zL~znIhL>VB7NUDQ_4N%d#a#up&bn_scaJ~v?c3**Z;Og0v$K9N5}T7H=c~!gG=9SG
z%t2n6BkEJ*N7JKa!_AC8@J_2jxl+YhC}-FwoHCLTaN(6$6*~E~`z4{MP3B7A0z?Lu
zHRG&}@?D{X=YdmXT=Y&7!E&i(Vfu3cqB=D(n}mGnvzJA`!R;R45Tc16xwhsbTUvEC
zwj=Jv?@=bb=WL!EeAeFE7#*sstE(JDa0a?w9m(95r}ZtamYquxzsYKS@)AB)>-tko
zq1DyZ1${adA-c~3_v5I;^z_oAKQUwpPs=6QeG<cJx9zOF*<94cN9r(chmbLMij$Lx
zcUFZ~S1l&ge=a}Fi5=DwOnvxT{n0G3pp>5*k`}wDm*bfSR-D)08gUI8)d!l$W=Dfb
zD1kqs`{&#gJ`vHFDXxxN1(+qbB%}Mh87aVNiXQm<f5nj+W*Mpxr{yYAVBWZCBQIe@
zFun}E*cTHwl^-}UsT({Or-B_n1Gh6zc^4ck4bLYg#O+7RX7|iW+Nl!GhX~)#O<UuF
z3iYX5ZL8mj1=|NkIuN9)f(gx$w$r_Xuay<)flN$}R}Po1lz;rxgQS=c3=^}Mu%57e
zMV`mOY?ZTlDYahL6015Lo)8z^a;qRXk{s{*(>ToN)K~c;fGOAqDC%C*gk=UT)g()v
zvAA)!q*mhejCg~#Jx+(=gT2np<8@ubfqSt(bg&0fl_x*zxmkRE@!z$eA2FA|&+ph1
zDoP9C|8mk5uDVc{+q#WDiDz8JY|Q4A6@`CwzzHhXKha99tR46$7*n&`mX8V|++8H8
z;aQzt@dtla!Z>~4&ntO2fQ9YFArFJ>QGKiAKY#vhaC~|lc|T>~`S!v1T4wP`B98N1
zJrZ4Ns^4z;Hhosqx${9!<Nl$F-4PR@M|-RwOt>@0eq^GY?Lx9Y&P!zw*eF*c^@^LM
z3X1o{R-6SqRguE=4g8Lfbx4Tq|3&R%>dWa^U&a*oP`=KD+$9i3KH8Nm<Z()Ct5<!G
zWTd8PE(8?Q$s9n_f58)vl+HiIy|=Z>z1*M`Wdl=q%%iSk6!yznC=rGfP2%<e=5PZw
z`r^JSe&R$3&5R7tzVK@nDh;(FZak+V%r=l#bJLa*e>>*44%QdzAXQ#q?(h|99&V$r
zqoD?4F2jkbX@vuvf+D9A*j~{TdPtBS&#RYkd54wi1qqX=3#`GEq!J?ef~hiQ@o+og
za4~(22KA|FaTZIWhxYfd>(F)7KBv7HJr=1>#~zql=mX7A&P~^})_JWj|AXk(OHV9b
zf!%*~k-Az*5lp?+FPM{z?Bah1N6KGJ3?2ka&N+wWb>;a0YZn&Q|M)j+2Lxf?dI**V
zK!FQ)zalQbd`X7cK1eToj?b<zlC1=v%C*yetM}2!NHq9VrWEL5bFy3-T>bJ1!}x!z
z5wZ6Y+8tG@I$t^6tmUjy1)ANXoeJTFm7|?Y<;1e}X;CUOwWy9tYd*($o@*~S^ag{z
zDI%T_sb()S%Tmc@_lp5Qxq;b;V%mJ{i5|qf=Q6v0rT$hL1n+q4mkTjY5DC9Jex@;S
zs$Vk&_*E6rZSgEWF1)5zN!@G)#S$BmvoLZ)@CrRv1X;qzt_PiknfG&~lWPKk1)FJr
zs@Z^2GgJ5cfw4Mf4B2g9Wl$UT0y08A39ipf_mLLM5;nqFn8-JY)r-^d*ZpP4HOFfB
zMJout&ZOF_YC*1gcR`PnDsTuSw}cSSDH9`@w`iWv9DBW>4sCd~3wBB9!?8Yut}$e!
zuxGQ`WL#t*;~{0FuA)uvo)C^w+C?}1NS7-0IrI70$|VVyPNTeX3=04BW$j+wAvHm}
zdsZ`(?nw_`-lfrO3YY`>WU?C&|Gj{@Q}p<xvKR7Q3N%(tZK|)-8p5X$ZttL|C2(1R
zvmag_3;}q)kX7W?=I{nSaH@E?uJhgmg*_1UPZW(q-#G>nBi|Jba;tU2g0jMpPZ-6~
zr)v?`pLj+?d*yseshA-5b#-UHQ;nusWBOgtQi7I$@n3b(E6YDE)6;vriiz`;)^djN
zt))XOv<J4`k>*0shZMc)N=)p6nB2o+1#{7Tn^n)_^EzN%pt%rj(M}^IGNS1g@^*10
zn@tLkUM*_EVt@2scLkO|jnR76Bn`8@ZPLI9RKdyiipvcDM<Y%)jDfvxTrs)<z1>wQ
z+K=e^ojKt`yIOOp%Gld}wAAPEw~zGFG1iLv1;<gsJ9Cis)&$~DVOrnz%W7D+@_lQW
zhJulnV2-!g6l7ur&x*DhRhKi&6q~Zlw0@>Aub-~z38c8xxeb`}LL%ze#)tS|H7fhL
zhkL^A&L%U=?W(iu*~a_#yg?tq^5u=^?XQbf9(|S)ZWT>_Hs$V7Is)R{rtc>;HsWj~
zMD{oTZpP6N(pNO<97|qd&JLVu*<j>O{ukvBdymVr7ZwC0ioe(<f2^mHZybC$WVRb^
zfECWSa<F1`tmjh)3w?cJ1bjgsmAZ~@cB^)}G7HG^%u32|%MgJ_1o=rPYk$B#w>RUL
zUnZZBs(i7KA}U%PoOS<{GW{;=;n=ZIew)Cj-0$B5y<KZoN+`7_lUVbVz(=x{^yWa&
z?K#GKc4aVPAidK8XVYb8!aG+$qLOb-x(uh<UXP<knT1tfWB>4ayk<S32eHZfxl3cN
z?49RG6H^HXYn&S5w^5tfatS&M+ne)M0c8ubd;d26Y_H$(R%ddiB{3RNJ-uqNC)xla
z+QYyOHW4CQ+$sb36FJWq%qPyelZm1MgvI-aVx1j{%=*Nj8j%w`{{f#8+uP#(BEsHa
zADM7BsvW-~V%#B93V>YZ6nj@wz)BU`i(D2iB&?)IVe)&x{btPRq(bkn&cjn`EId29
z^xA$sepWjj)K{N}p%F#IfyV5!Jt2jQC=W_pE|k6CB7NtJLO6i?kDpnW;0b_ygcd6J
z!Rm_%m!;~!+td8t^rWbGG}YHNzy8b9WJN30j-Oq(f4fHE9fyH#dPxX}W=62#vxJAD
z>l-P(gec_17N30M^6p>CZZQust25&E>-ys#x+es$o5PD8Smeq?JK>Th&=qSg@wdl6
zPl#k37H|XvC8RgYoI1a=wbe2~Av~L^Yj2@Y@$J`ZbQYV(iSG1gIP8>OHFx=2;?G1+
zsJkh2;h3_(3{2kt!b4;A?sFzTx{Uj6d&MVPf&_v*J>3XfC8jHFXChf{ZyVxA<Ad<7
zOetj~9Mh5=kNuC~Xb-p|f1(=^F5hD%snhP>_ItCrj_H2XsLQIR*)v~r!e7b<Z2y4q
zdw*qre<yXa(yO`6!&PNnpE#18E;bdrPOm?^&Y9+)w*@&B^lk4P9ITCMweK(WOq}78
z&FUFlf~CY(ITI*@i{X|!W{A<lGZN{$#)n}|nkXG%G@7M&e!xM6KnNCfr0S$Mqb;~G
zoF%|(quHH^$+-Iow@MC{@Bb9+PAf~dN&yOe8-`Y_smr<9kIJV^ySu&uKl^s;(s=<d
zp;>L7d*i_<7Uu_vBpvL{AIUA{M7OpW?D_zzQ;CjMj}Q1U^!<~lDkIn<uFIHEhpyWL
zAl)n;j!rzoE(=f;2Sju*P--;@>*(5wsb$U~i0jHtI-&>IAa$OHHwZP=@(K~as=P<a
zlo;XMh$yrn&ur%&vO9{KGwiv!{&6CGu6~7`gGwanA|!aU)nQLso@jAh=N6WLn`gnU
znrsr$p0c2S9{W=Vyh;5X*Ie9&NNU)oe3zIDU4yN2P`Rx3e_Nf;-_<0<9nxGAm;-^E
zgm+A$6FzXP96GI^2|JRQ4l;cJ1%rJOL7IqDUvNo@*eRKo-D!Tpj{8IIyYtO?R|zBK
zIxr~canX$$en3{{@Gxbi^(?J9xZ@emiF-8{&I?_>A@B#Jq2Up{NL?Ya_~^FgGFJ0>
zHa^7}FRLl_znS>EkL*N8CmZOs=&E!bF&?|U)#C|30FAhyj8;QaY53nOvpv~vySuwB
zB~>|DRj(q|*Rg$To12&^YEMtP-sF0G#_ff}9gg`JC0Pl&?Tm(>GP#vs!yCD2PjU}}
z@@wK-D5Cu?*zsUwB9QVF%(}#NgT&x8F^NTMMqMb#nbnlIufQt{vb**qWmo<s`;^4b
zd-rxevyrWCwe%hRLHImlnTVG#6@KhocnvEMgqf0`S<@a`N2DPvrNfY?PJ08{+L}1e
zUPW=R`YGIaj?ATQ;Q>k;=DV*Nsp1@=)nkaj0t+TR>*z8vawV-^*1Ax8nQ$iJ7rEr^
z1+JXHNHYB#Rco$H0na25>kH_XoN=D@%t&4WgJ(o>XPxtZX?AVwDWp+FO36qz4e=R<
zyH|(7Co%}@hC9^ffFBJ{!pCCT+R&H)yh~NDmvrS~I=U>o{a|5<b@);3u&wtW$?zc3
zb&QMR>qe<(5!hqztU5y>e>DfXki75#9mKTKov$V)r3x)IE+}8jmDF!~n6cg8=H*o4
z2{5j(Wb>F$L_;8{vij+FH?JT<{un7TD9+J`hmb|qO!uaId6uXlC-te~E(-0^QY_Mm
zFg^twoy{@1NxV9KJm{>YiSUGO>YugQ^x4@ed)my5o%a4hI_JL_yEFzIrdMs78#CJ4
zfD!<!bcGLqX??l=cWj|)WLMaY=vOXUA3`Bl$G75`WS++2GFfXHait=2H04)8pHj~o
z3LAs+9Vn_@e2DQ$8dbl`jHVsC?Y9sL*=%$0vcr2m5C-P`wNfOERiFKQn_fc6`(S>P
z@<?#l&6O%jX$dS!OYfX99V7of%U4v@<nPMHt1{d00n_m8j_WQfc-<c}A)H;LN+NL~
z1!mK0JYd27;T|XgbN%SYnd#AvS)i1W_H9Hu#<{)BqrH~JViGnOEytCVm<=W1SJ5ho
z9kP}caZ@qOG+Vvz_JpX*nG%-HhV60ZoD_TQYXQ0-u0C}cC11h--K~km&+ig=c2&A%
zgwgPpg}<OQ(_yz-*I1pT0oJrV*nB<$y2FWXuC}i15*ZCAt5d$U?f-koGB|j?IHOY`
zbz57c{mocy-%0VrtJJpO%LCR9z+_}m*w<OG%&UvX&zP(96@N&XHaW{{U*$CRvDr(j
znp~LF@}Crdofg=y2nXhH6@ePxo;*%s9F+-5MZMj1X-!sn!*H1@*uM%Yo`qzP^(2it
z+3cEE5;ZQD2D3k%2!MRJN8PL!?<KC1Xc%x+v~KR)kVm{b>9S&LTWp+R`?b-TQLU-u
z-IW+UNOoUa9{x4>xvbon<5+j7bkAD~nSJo*k8Kbt?zmw8?^Sl;9Tv)sN|~T1025)H
zGTppDw~r!LJ+KDGIW*emL3;XZAM@erk%qH^1co>dRHXA}Aj&a4AO|dLHGrGd9+Vds
zkD+5ffL?D7xb<vn&ILFZz!t$(_+aDjl#v$es!?^Bf${>!FwWyN=3K_<>yY7o;|fiC
zU{>3O+FVdM_EAPU4gN^d>|+OryHSCY&Xws%%)PFc4lw5&^q;}uq%w3Tnras@m(T-o
zwtnJo21xNoig>R4nfAwhn;-^b=o3T~#RWy+)bSe$8(WTJ>ktTm@3k_w6lE89UaT;e
ztADvuD}ym;va6Qr7?w@xfA%s3WGc*XsS&#SdZ1eC;8g;H$G4IzPyB63EfY`q85r2|
z{_vw#!p_Xh4e}<Vb<CYK?^26r2b6M!&|M$Q=7}NY&pAL8{fxY?k49NPxIT95)T!G-
zu&O728lE~?QiF}YW6`LmbG>AzN2+#aNw)#qkIHepM?<s86vkvJ5kuuC8>4AXQ?>hK
zpLKW1u2=n#25LPX`(LF$T!=5r=ikO&#_}B9*jc&FBiM}=H5DIKC<pqpvOgbXcsQ@A
zH1jcuQ~4*-X)!jXhSm|mOl_f}HXv6NZ+`t7qKjC6_WN5)2JcvzeKS*I6^L({J|=Dn
z@2l@FTm{`S2_eO`H$^o_ui8ISF6a~aD5$#;$hm{3h2Wy5vIM63Yeo#~QHqIq3gYu5
zwngB5_}ASwy^U>kG}bMjrk(|_bb-AFQ1bgOh?y#46ShsdDsGUREMbp3H9u>DuBvff
zdws;18>|VJ%iO$@FP63yKKp;mNK^*E_(CNVYnj2!rxRv)qqD-6t7NU;r}%XiK?3K;
zk3N;J0(C2RHLs|AfTStF^f3G@Lio=4zxG=)nvVtW<fkY|cc;GjRs|kU?3s0tj<0j{
zdkqB@QGM}vXj0&H)MYfjhO31?<o3tHdzMS_E0N*MebL`DhT8?@QV(ISQGbGKD=wzK
z<acRW4I2DQukt97NQ5`Asn4hKpWCW}#EU;joS!Y!vF7UNFzPs3t{uaAYMWbgrs*=W
zciZdhb7W=T%B=<hBSZH{Lm<u#WL%`A4R(pD@i#NpO@84mRyi_2mab0qy#KBLVvD#q
z%$DixzlsmEn{0^m56vkJm}=vH+_Qb0qA7M__~=9^SW?st->|tpWC$9Wo$Xm%w86cv
zCDjfLq?<;5K+)3>b<A@R(>yO}Z!5sRe;e=5m6*RF$Y!T=Ua++=q+cz8k;|#X1Xu>(
z)U#f-v@{W)*`C0V>8*js1anKLHy70eZU0-~;Jao3iM-06@T_Ru26oB<faHbpuTRye
z;Kqd&yP^#mJ9n9V>0(1^8<7X}nR`-21uOa4=`$gXZtYtx1-{G*wMA0x{=Iw4*GT)3
zG5TD8oAH4nT<Lkh40tF`{^bN2!^`Uh<<H{80ABF966%w+u0V_K835CWla$TnK*4+c
zU%u5O{RMEvsQpr(>(sPKdcwoAxr%Z_xdIU9TQ)BaRxPI6m_Cq~l?*}w|NfcT)q#ko
zaK*S5<=PSB@Xbxm1hVrLiEg7bBM7(fY%f>J-<%#PNK(Js@V4Ax2|6f>SN`-V)BDw*
z8+3@|-{k1CM>5W@)8_HtQFOJdyKw&7S3VDv=WxMJ56aKsu9ZXvHP;+9;d=u#gJi8c
zSe|~5=$VbZ=PBFXmN4Fj>2=Nta^Uv8?Wq;?$v6l1OsQE?5Qqt>J&U4ct(xYHf;oi=
zh#n9@&lT*$Abp@X?Y1q|6DnLzra{Ebcin@`;fuU4Ry`~Gb}Md0LYwL5tY*aiFA2sT
zMzzI8Z`0%s!dWygjw)1&TS}kW`$LbK@<3Ay*d&j`nL2tdZ^`m(rzy+jYnj8sR7;#C
zsmy`5gMrN)?zdEtK#mbe*sTlZ%A}z2T6jic=};YM{i@n-XXrWF#1{EGidN8hPr4iO
zvVnqSC7zOpEB*WxdFANV?&I<R;|{Fs$qt`>yO+^ZS0CBgVw&h26UgYy5_7wrv=7Us
zG_cD|fyTmdb+q+PA5YGialbgwjvLk#C6J?C2~swD+|j!r%8L{B3gNx|Ge^8ArN4nB
z3OIH?lz+W1p1ws6$~8iq!oXc=JA^0JOX`1MHa%Y2ycxk0vE?sTr=NN2$HvAVd}g;7
zSM`A8d$dJOt(U^0RcIF%1p8fv-6LqV1Hr0TRLe3WMoZvRVtn0~llBjjwATv7l7aO}
zdZsyf|DGR+O>u=j@DbMdkd_kcG{JBF!r^Rl!9nD9z&KisW!Tz!Fkk7>fqikTtH{=*
z;M?$cZd6d$8~nMxc*C|Nyr-Y8JaT46dw&LG2rA;0o=fIIZF*Z$j`m)6yT8b#?41K!
zPfRJ)<`chuDp1ImRLhx(&tJQ*lMYIQL3|1YeL}ccv@jKVq=zL+RiAf|TjDQehY=hd
z^PA3_X8(@G=Xe#b!)eMFNYKt&7i-@xY2U6noLW+kTVIp36AC?0OIUmE4VEh_JN%12
zSZ3{$4UMPk)ap#Xrl*g_7AEY_Sy?@jPbMwa@GA?lrs@t+;GW@0M=btA_X|Rco`i^3
zVlZc>r{ya_L`P{fYQgh_Lhk<cx5d<zG_ph(4wgAwBwU)qm8dN*uRi(YGX8hJb=M2&
z6o?C+aKrawUfB4xG5ccz9{H%kw2qr~4_BrE6VmeX;i!RkE3~jun}zpN6tKNDPbfg$
zODM|otF}pTDUKHiD!nCdsU^?@c$}v^mu${m6W5-rSd_hd5Krc479%x}!F-%b)(Ue&
z(dT7<NtvUurQ9u0QroLPq0-<9@H(5L`)vGEUqIL(EVJ}0aS+=4%HsNb5YWAkF@a}J
zFyn9N19Ujfwvx9zu;Q8&%Ms_ot|B;}F%n>Q;o#2UPPT+dt`|@z%u1YPb28H&{QmiJ
z8$O7PU!?u@ty#>%&RKI02-y|!m-Xn(113muDl#|$)^{w=gd0I;K9+Zud{=cwJHaP^
z;2m35aihC{mI~tK)-~_Y+=_+(ZkF(zj`ChpE{}0=SgE87-<FUrM|ulNdS!Av1qtHt
zWaof*+A$u_Dk%7hgt*!5EoK-EqceE=ZYRhbYlljg%IaApkI{VF%MxU>(zE5-cbB3J
z?^d{-BN1+K^ugIMdZm8c&YyvVgNN<fm-^RZbuSsj9V~?l&2Tt&SS+^v^c-+I#&xmt
zqizEwyUKL@jIzW>ndgRG<tw}TN+0NsY~}^1s17o}e(D^RWb`jY_rz9~zSXPf<DT3P
zxKr34viL<~(YIk-pDsP3x~f$yXtvDOmzm~NRoz*ca0yR@i6yOV9hfc5YQ9W-`6^`u
zYn==_S5WbtJSNejDI*%`aSECSQA#nssr^kIDwO*s$3=ms;I~&jBk|enZT=reJe6#O
zJu5)mrawfYx(haU+VG3b@o%pRl-|okJX$vu=HdF!7PN%XXqo>qqQ1B$m6nrU>fjez
zE}TU>u=ZE|<^m^R=xU?_d-kf+ge6e)#X{gsT5aG$?M-RS6~H@H37^C%>@&Bqf9?AP
zQykr>z`?#oqUc0thp~Kw^!lHWdl4uSDqtYU*VM~MLxDd-|1~(Jp9}Dcx4bdmrCC1o
z27PM;(2<!NuKV2^JB~ETOGl0QU2a+Rbz~(%qV+Q+A3uVFV3L<J7I>km!bWZnO|?XV
zmaueggrwbT&9eKYVftCo()L2&z#3O)?56UkE`-1_do|^W@{Hv?8;B3P+r*f=`Yh?1
zqsSS_N3&~n2McQMQ|@JDZ3Cf@@R3>)b7hy6u)W<^;AOQwqi$7Y&&IfM%J&aW6E^xb
zBlXMdg$Pa#&$^okr{rfNCP`;`znT(jDKS=)?_kX}foiYgoIN_muog*V62sM8!uKb1
zPU014k~tlJdT5NCKVbrlnh<)@sNiJ|f9h0ATG!8prT993TZ_;|F(+JAFppr3ohY{f
zaivBf@r*ysC|nMw`NUU+iE_g-5u*5`T?TWevVL9|jxu57r5JWQ!tAzZV!!;hyQJwG
zh7@~%_}QwQ6d$dza!of^a)Isw{}<CF|1qy8STk!juPW#gHtLwkA5@%HYqwxh$FRLs
zik*stHx*N-cI~QVfK@okcFV=pjS>{Vrw(`%FjYgW#9tqKz?(Qn*L4zlkTQK&q*?bx
z!{DCS;V!G}dHdn&=#&?v#GMQ32?@8%a45DJoekC9^1}v@ecnEW!hrJgumJ}NW5+jR
z;^W5V0D6oA58B!~L%b!@$m<K1gC969O30s&bik0z+)X^a7t8~udrb%B`+*Wa{l5)7
z*rH4ZITs#%@suq80#_68;FYa4Lcs6$v92*D@%rXDD`Q3E%U-{upvsvf-HUYl;x9cN
z_bHZVdCdR>7OZRNd4db2Pi}A7rfqxUA#I+?QH1+vMyt>iJa6vkQsdJ&ec_4Uti81<
zt}3~NooD9%HkL-)(7qnxOCj2y3jw7!J48CtXA-R^T3RYWE=;>FNAdm5%_>G8iY(j-
zvOh=W$~hss05e}E7X|`zbBa1O`>o2y{DRquGV(>^MEtcrud<-C_@$YUhTz3@S0Kda
zRz6<_g$%I~LsT-V36In6R`wBz?&nyGE|$U;L9{+R-W9Efc%4_OI-irq)1&B>7OJ_@
zc*FdJn83PdV&0v*@gD8cIzmu|n;!WX)_h#MdvG0XMF`>}UELYR{Cw0~USUC9*oQ`m
zn@Yw3F{70O(c$hoX8;?NR08qgN4{vW+^}O&zKU187RamE3qNgxg4q0lRMZrm7YfSZ
z(cxBjrLoRBoWeRn=MoOkhX<u)&*j=rjwUCwcD>sKnM)gx?Yvn{(Gwx9_=htDQ-$<A
zMP8VEgn(ymS6S;@y{NlL`AkTrqM4Yo1FMl#F@dVv#W)Ak#$#1h>Vxl$b}#pfJ4<N_
zT6pqAUUj=<E-xHxc9+tE<<pe1?_`K~yeCb#0tLC!x>m{gcxqd1iOGOYg?<hWDF`d#
z={eC_y{TPUxR6wZIE|d3G}qqY-P_sx+;H&oc*4J>S+%ydgMTGc2lGp79G%e1xqz`3
zqRl<6&cV~@%Vu?L%+E9D!w%-v5}d!bsrKdY%je`Ec>$!j(<$K2XLG#2@YlTt;Z1Kr
ztGn?nx56GXbbShKdQePr1TSl%GT>B-eN+f;hFyP5D^o|_WCdR%F0TQLW>);hlv%I7
zq1#KRbk2u0DWFS=Q&Ku~PHAYY2%Speyh-z*_2>FlVvZn6FlU)EX7pxCv&8VsA4r-w
zGAH*t`NsV$=jpXqgb)F(q7#=Dqpq_QANvW<R<<>lQRA2m^2$A~cowbN+Jt*BIHz6{
z?>bJ<JJM@(P37L5`pRj%&iCM&aRz}$S3^(5>+%-38`@OTq?f2?J1pWj-0C<P#q?7I
zrdR<P=13DiTwGl>U1~dFFDl{uV)id&7n#jG_&i<B=+PFLP5BD9hGj9`DO+jfuTB(k
zJf0&*Hq%}}>~8Ui=#FK9;OE7;1m(f5Vq`Xj;}a*dT-)=B>`LGEfT>(Qqe8_{C{LdF
zEx?x-ku)FS1(C#bc%}OJN=0XNRo^KBmqpM+XU7kI?u0j+wT({)Tx5DoEvrh;i%Um5
zNi)8D|76q^YYF9zk)1ZK4a)BJ%Gt2Jg%XxGcxaJbP#|X*yBAp|JH;3(plxa!a%!3R
zFI6su+}l|}AG)}Cc(jXlhUm&LrlxFq1dNvPH8=Rf-lN=3z*B!<PluxE_$p{+#TfoK
z_QBc;lUn-AEB*)dm_l+=jg8OUYahq$QpX2apHKo%rEY>#2I#Bub}ackhvfmfF>))8
zM!d8Lt0=$l5OI@5YnBH*j^MjI9&}@Ubj{N-=q<ueagufpA^5PU1SUDBed-Ihlzm#y
zqU@{WGap{u)F`njJ-v~|H9<?ANDFo;q1O`fHa5KUOU*88sN_b?=UESSPMo_Ib>IIh
zLCrcf`DuNmfPyV~Y|HP(#1EBC_eob);Hkv_YQ`vlyAb<KA7D9Y7uX2nrn?PdQv>Zz
z=m9`N;`c3bfiO>;c)ttYjfQDiOITME>)9T6yg!G3=D|n+{78q0#K=Bb!sS>%AT1o)
zp~MXy1>UeL;qthbpK_9Ufz?iS16)b3RJ5VZ>K0aCxNyM!fsSVR>-<Ok1OS?XTvjeO
z02l|28%(6-JN|Rv`@-;ryph5*C|8n^Be|CLGL1gU*rhmyPX!^CZR8Aa$dEp9T<fmc
zq>nH#zNOYPC%WTQ(QF%<HgSHvP(WRkz^bsCRSj))<Ddj2Mzp44)r=`9nY37gcF9?e
z*?)e1erBdYm4t5FK9#n&$-F9@G*{ag3^bSzHIE*z6Xy4Okv2EsTBBEd$*PA$sw12m
zvdOL>Hj}(c0q-`KS4G=c%x3_{ug4rtc?;dQf-9NuBST!N%w{)E!(sH93I<ga(1y|j
znCD5&uHEy;liS+d(Nz68lpC|zpOt7JBj$#o<rlsL6}S{}i*xx1%S(6*_k(<hUO&?X
zyw?In^!UopwUaR1FO$gRXePw?{R(sI;BV8)oo@?sj|xNGgC0rrz$PhF2KIw*I*I?`
zNAA`$cSZ4pW;9-lB*etO3wnDQTS0ld>F0+p`Ihsk_q=>!G;Czzp)-#u{}rR(fi8t1
zy&2rfD5<+(`z%%5n}*uWShuZ(y%ocp*}YzfOC6mf5#H&z`rySX;lGf!GBv`{m&Y1~
zY(c`Res1_TPRQp!OO>ziT9@C{VBzqAN>a*RqnD}>i|`(w$Dufl&6aP^1aZdP=Z!8@
zo6~>L-&35nC=o0|bQMBQdya8ZJcHNxfiR-0%b2_I>a-aAQeimNwmBGe18~I7zCZeQ
z)Jy*F{TE3CU+Bg)few{<I!P4!Vk3Y?R1R1sv{ZCd-K~~malRqGGwX3^k;$2!;|6B~
zz*jVOIp25oBE9nUh1X;M{o9*aRC}JPB$iBEC8PV!zt+r_e8CWVx7!Tjd^TG|&9Nw#
z!ip`<iO9z&`F;dY@&n+<N^@rCuRu3UnON;a3^f?8#0~fZ>v0OgU--Nx>!9!w?2uXh
zeI@2nfql!e=C##>gDrculA!!J@f?+A|Mg;41|Xk#R5)x92(`n>Ai^ZB@bHM5Lj-%N
zCxcS40upD=MEYG)gYYQvMyWi~PVc!QAA`k4d(=|fw0^;4BB+5kp2jt`C8}L$1$%&8
zhr^f9fD!^Bk3qCWx7Nnc-ru#o_=b<_`WD+x4}3hUt?w#eVrswe;FfD$MQ|V>#T$wP
zEdPK-0FS;ta>C&uvX+Bzrcnq#^kX^bhxBn&c4@(8+*{T)wv$JygY|4Zo)$wV)l-k?
zHvWed^Mam&wxi>?5PY0E_rBVP<9^kmia?jE7ovJ~9{I?cPD_P%l%^+jfMNh)Bx{+%
z-u~|ypqmZYpys;;ZLB2I9Q8RbT(nB{4>+7p-H{iG>*-vFCmi9Y-?}7)A8^XO^+KV+
zSVFn5C?=?_rpDFLzxTg!cLJ|YljZG?)zcil!DS10RjfHPEPj8_7tDcfKQz>q<>=&7
zc5|~;HUXB=g(Tm%1UVZU_*}M}!QN_=VfE7J`Y1{#h{ub<NJG4(xc6zx6s#O81UBcp
z;c%@4U$=99N}rb^-6vI&0%=^AK68Wt0>#ubg10j?6FZ(T6}&`mI5~M-l!#ZVvhc30
zXgc}dqtPENT%Lpf5h5==N0U-<992@=s;@b5R=Xeh8bg{WSjRM-V-nA7Oh`{2ff%Wl
zazNAiT~dFIUuttlt+;L9(>&%{#~^{Sw1q>+p&FnqnDm{;aLx!<#qML)qX}coc$Ba?
z`3IB2(28^2tN8a46*PY<h$>3<baho(i8))x+HEG)2i;g1dsnPeFhKBmkO2oATQ4!K
z@rCbm8>PHE`vmTs{?+J<!i$Jsd?NU(=vysLAF?3xGrKm2iH8hBEV1p$cW+ePLv;xO
zz9)r+`;pECw26ots9%>V;a>rn)^oiOBYUY6ykC?e18EFakWU1&qWH0i6{6pC5O&^7
z{(<&ubxNC~t5J1;aA=NtVUvsLuMU5ASEaPl+nN=6PX%u_u<#yFbrpc1REGthsE9_i
z^6^v^zRMKzqi?xhb+%DS%Y<UxqS4?;p7Dn)jGDqDyYq!t3OKBRs?$jCmyCaXLxz?)
zthRlFu9>i=3#cKv=iLF^K!6kRLe^+(EO>EmYyKf-fA#njs~~86IiSpA%6)M=$Kjq~
zJ9JH7&XLrMTcEP*ew|G-F5F_*@rRVkaeN}=9NQ1N*GyHM`V6kkHmiz>iP3jZ^suif
z)<$kE)q$+cQiobXElJ6;l{VLZ-Tbfm27`o((lYwaAflA+d-dzwj)-+VhpG#g4y;L(
zHT}^y*tI$l<eR~zRMC%wEtW1qcphmyXjK1;)69Kus#sYo69!s&ubOC{<lJQypmsj1
zr}OOvw_+Xk@wh1(YRcn5e!<H6O4ybY(Vn5!M=T`p_)|FbnaurG)^6Z*M9(jU%YH(+
z*IHW2Una}Ha|>E2p^atKIB?elR#+@1!Y<ZVNN+d^TJa}Of8{z=X?G9Osc`rzVe6{x
z<q`c<iY_2?dzc>SY8v}%fI2_YTrP=74Y_TwC8}u7+l}Px)H723lwQ9p+-f!kL}-4l
z9fm))&Bp<{hJ_2c|M^>2v{&EnO$0gIGY>${@dwvt);<s|<<!Z%tfaI?ZSgbu+#|Jc
zkUZ|2cO;NzU0FA(N{YExrDW*6KkbJ|`(t<v{W}&Y+@LEpZJpnk%?*Y@x@SubF{Y#m
zPQu{Wu^~7kpB(iFp>JTIRK%$P)4%-jn~PkSwa*XgQ5jjoxNj6S7E5fsk&1B(wiyyL
zf6t<gcFt<b<!`#V@-h47Re!^MHJjTG6LUv%NVKQdO{E7~f9sjSeMLBRm6T6i_!}W&
z5(B~+_Ljfi*ItElumxU_W?hX3ydf^6I9gq24*1LWdc{*4jII3zN>!(@l~tHCrGjAo
z9lPF9^y*|}&}x~_py>iM>i$(rg>;;bLj4IlQR`=lFM)FzpXOLvYRB7O{rt-BR<0GM
z(l^p^(kOuNpBjy}$d;2i6Mpw770;;M4@1?}ExPM;U&)=T{`JNdz4=L*TlGFWV94z)
z0J^Y{Nw*}$;Ym(4I>!Vwc&MVy$y#QwkG&9>(H0H2F0%I)w+K!`2<H|(J8VhV6o9`E
z5w+kX+kAvoxdw`BxVPQ0D059ACEg4><jXO$?@GMkAf$<AMQ~YoSOeyg<eL^<#W}#a
z3H<l<XS!mm{GRW(YH^xb3bV<P-a<%=d!F)+EQ)1<z)1R%l&8ro{wcZE4O2%RlleB8
zbmCU0lRM=vMMSh`X>CnWV#~ejv&hx;g{<#@mf{oI4yVsULDr14emU7H4$`}Uy}n(=
z{M)R?87;n%P&U!=+LvT((v`Pkbh32}WDQqou4qi@9iG8{d3!=|fl6~_X^e`oV)qZM
zF`6?~-z;GE6+F*Q|L-ZvjUk$@{Fp?z%PgR^A-)jLMLC{>lAn8sjs6ZM<OLL8f0~i5
z-<8*&SD4+CyiE5yUPona#ymoFE5{%50dP3@Z{H#KRpBU$^|%mnT?=OA@|T&l8BY{7
z7&sHzhC%1u)a@>n$+IxYjQSV^K0lw4I?!(iwa$F^-3pwf&KF3go<w>I@+bBx|E{KP
za+HpCy*d`SvG6c9Zg+bV4Gu9Epx(TAxHtQEhlml;xg@ri;|==M`*xGFt#lOO%Ej?{
z`-ywK?f{u{zcNP6zr+{%FuieEww6lBj*F>(LI*jgQSpZnJF8ytrPdBv_cOZ!Cx@ko
z_Jey>hofrgGj___&<t3hm4qpjRYAXAelgf3L-~)9dSzkG8}=CLxL4YBS7xJvS;WPP
zJTv3E3b6kL1%a859N^*#@vJIWo@G<@s;C)q^v^x8!LMf|)F*(>Bb*Pujrx$}w;*bj
zh`jlc;zu4^4H`S@Vwy>7{!QcN^)a~CIa*c4lC^n$zHIV6QOW#NHkFAd%4#=8aYKa@
zPZ?{-NydFeP(}t>9_;;o2=F~oq|_j8=U{C+S-N?9KFpvjjT7^Y-{0O}U!3B+;n%(T
zlUHc8-I}d@Q(P`53`^f#iL^W0vmMgZs@8WQ^`8FgA;UvnCxCuM)AxaQlIqe$Dmf2<
z91e$s5z_v`KXU71^TI;K?l`v~JS+t@rI<3ibUiR`dk2F`<x2Lf`3y<(lFGm-LQJHP
zwowj{?xxjCsflDXi9PG}V0Q8-d5P#onwOpwe>B;0(c#gOdwsUPwd!w>GK1?Gz~SfG
zlTd%=Nf<8kSK_C0MbSt~HL3;ANqMS~>Pqql%k|1gC!0CIh5-76O`RGw49=gv)CreS
zI4=l8Xg9bNzr6~z?lsA@(oWHT9e_*MdtG4U%EF>$f}$K(hDV{JYGpQuzi1u@$=OqT
zGg>xN38mSiP>IBw&)5{|NG)?EESi?jq3E^mU_?W~CxBxz2z66j=I>p`)>s4I4JNq)
zk`|#UBM6)%sEF&O*$3jE;(Wk#aAugU`ZYqpy0oIs6P<<$N(BtypiH1paF=W)DiM!z
zZLqGH#YPv8^rz46!LF_nsPPp)WXhvyE=&04qbqJt!S>@1oKXXlx69<JHiUKknQ-f>
zr4-AFi+_muc#6Dr;Gh&Zd0X~=#XZ~0{O`yOLOrwau25HY$5Lkx+FRPgV+xCgXIIUx
zb@qs{SZWEI)I$sGzpWbyrHip~`(e@h#p(A-tH3|5E7^mZ%ZBh{ov}bwYTpI3=$QB)
zBpS8O<393+O{hPC-^{^B5N7Fl>LYV=?_LZP`4gJb|89bf(jd8L0en(uD}j|sJkV}^
zSsO8<*d|6WH7X^((8g|r$XZ>+S>0Xs1C83cco0{%Io&X3N>ik;@#EZAZis@5#NQC`
z`2z%b*sGLM2P>WmZGO|2_r4VxO@cFdhH>CVr`z@d@|s8?i(z4eW#;1{QeSp~+6}7c
z{hXeSzS%#ZOxxt-*k~;IX^`WU*Jzs|;J0@*HFis6`O4n#xg7b+TSl<qLUl1t@7)(j
z=SKi#e}Rz_y|Dq}WS0yMhL`^J^>B)9IwOD<lPw-hVlb^fpESP}+47qdG({OHJ={H<
z(K`jT6nUptC6$xt3~9WeMkvt%G;_Za(cu&F>CN>SuW{kh2z%*{C7b2u@&5$AK~xr+
zU4Q4K2ml0VIhYhg>%;%fZ~3B00lM9w6FOfzC#LduicN80!jB96rES*tZciR{S;t3@
z`q5m1nN6F|I9^|smWxw;7&6LpN&QmN5uPgb((d%CP6y$JS`X5`72wymn%@uQ^YaP=
za9#lUAm!M?b(rTbde-{J%$g~4YjZyQ!P?r!zd!#%v<=?uh#Bt9yHgCepCm{vaHysS
zpa!|_Os@1&ES_>kJfHlI7Dn<@UyCGLn--@1D``LITOk@4#Hwous9^)*oWDw#7FGsK
zUG$5=CNL?NKN0c9jf77rxc7B8%>Gcqfc+miH8jd81V>CsWdLNsiOt;nnFwE$vZuz=
z^H;xGRaORes>ko|Y(|nnM|ujoXNkW_1!@(y=rFNP^(@$Y&Z`f$nGUkaNWo7gh!5qk
z2y=xYIoOapx$eeaetFIcz-VTTu~n{gX_rL8VhkXo>QPXwww3ED@q6k+(f%dJRy?Mr
zYH8Mw8wf^R#sy&-@4X5BM@3DIU`OnIz8huMu0AUAftVC3*dX}4^3w$JC3~`^<if!p
zK#~p|-$N^B5n(b3dmB4DF>b+EojJ}<CHa1zpN9Kd&v6Qg(%z`)Z>c;ljvQ$!u&2Zc
z{)GF6^ubcVFD-CRI}?3rKg2UIg5R6T;X?AJLb;OHqGfMJx{Z;8qSU*Q2CpMPUzqXn
z?R#AkqO)4OFta-!?Sq5kzKYq^>v4rhFN8{uxiIg1&rc$E)VsUIJrI+y&z!TaCR5tp
zl@tk9MVH!ihT~6uJI0G`S|YC2cm!QOf^X_F5a|RhYR*9gK1uE8{fggt_>0&&b*_NM
zi3z8R$rYxYu$Mht;Lu;4cauH#-MkK#m^NZn>1c!4^?Bw{qEQyFEdfP~)O%j#6`>yW
zfsx<0JKK4^FEaj;#=80(!?NEVyZiU=hvcEzNs&o%LH$Yz%mu$p`5Na|*U7PAzX=3>
zAM>_`i}pgiBVM|&HzTj!e{K-_b@n=;DLU(f;L&&N7@I$&iTwGCHV$eiH%g={4*H^Q
zcVxhrJ_#68=j31w^}Cm#2oaqsSXC!-t*i9iImKkLE1r2!z*j1seH8zScZf$k(dniz
zxoC`5P$x3o8P)zcOO17BI<)TOkFyqlO){%)v4Bmx-?bVyrS+lhH_OqL>F(;~l<)TT
zM2^Bq&axhU@h8UwrC4YFi(|lt+1ScSn5D0NlfJtf={iWI?X*z;-tZV~-`@G#GQZz?
zcDMQcZYSuuUyK3o!Rf=<(*h=WwJ%;!=w45iUp&p^J6&(y@#*6ioP~B!Qic0bddv-4
zrYew6qGIHk(j17BjDQ$IWGhFPh!ZGsHD;zWPYfYy{=<d-;4JgJ%@ZT<zfdVjN*gs^
zgVZagIvFR1^eMA-XuoAjKJ`Ok1YDNB5z47l4rh|T9|gF6?28^}er^CHElGUA7p!kH
zi8P`Qhd~KQG#4_q?+(=u(MRxKPm^t6qdcg-u=UZ=Q1Gdh+UoSqShD-a-I<U{Qhd{`
zu>OR<QHTGA4*wd$hSxhCXl&Yi^Y^f}Rz<<l!rDc)mpOGEQ)VXKimJgR+`D)EW^;aC
zTHexL8?Uvh@z`RgF`i0!+(kUjF+hV}rugGvqVQ}TF%Xh|ZA_div8ch(zQuit7*~=d
zHXET@vEkBBzP{<rfxS2-0Wz*%P!Cii=f{zy)XYcNLbqW;_S2<&xw#A5c3D-B9xwgM
zZ5-0CdwuxlZ+H==O7(oBur)uXqS>x+cP(Q<;Q|`%p~m85nT8BYJrbI4G0^C`f|a-)
z6x~5s$BIPTouFQgZmB(P%BTK`F%eHAVaP86s0;4TELrq{C3cZ)<YMK7*YoruZS4nu
z2P;-elH7w#MjdsN8MD0-y-nvNtuYDCTdt^+4Oud+Vis9Ni?>IUwx93y*$~DSUXZaq
zuLdK;>np`iWG{tGU%5EB%b4-D1>5tym2q>FXE*W?1qFw>$k@yFA!6!}mZA&GI9la#
z#e_=<&VBhI$D}!7nc=)3^4VQ#?Toe!#uc0^2pGg9sQ3<xc3HpqA4lin$khM;arav;
zBh{+KTq;W0LPl~=?$;2@J;Iv%eeQQ9mvSeUaw&``TdcX?%C#_M#z*eW{Wf#`p5O03
zU}xu?y<e~A^YNIF+&Dsn6^Tv{s$};G*yr#X%^rp32#us=pM?LBzxB5AOQmxJVe<BS
z7T%A`6MoXiO${$`AG)3d3UF!16#PXLe^HehZCJI@7-6fq7(ct5XtIa>isut<QR~5v
zTbcz0pH772c8^RSAD&iBA1%m}aVH1k5p)0S?7~9`-$1>y9ovYf4VT`1MRFf)cvWMT
zvE4a7hGYO#02<JbE-)0vqv6v1P`Af4w%!p3(Z#i_@~Bud!!irUET}gm=^3lG)fbd>
z*;G}B2=hqObZgk9?x}<MqbZWi$2(BbOrlVaB*0FnQea;xC%V1h&&_A*5{%{+(0E~h
z#Ok>WZo`u``p?2hVV38E53=BCmMd3WM+L8P#_1$&?4>Xi3eBYDY;Wxbo~{o?{DGYO
z1+o!C5r^ZoePp}i{c)gKaY5pe+7P}L0Qfu^TwJ}$`V5Z$)uaGB%UIX=rN(3B!++f%
z?sox#due+$mn)#~jQXsAvl4&v->kOc9oPLaR(<5P%k}9Dpa;TCZvwoai^1)|u_AX)
z<xh{0W?hDVKZF})nxDxu>5=7KMl9s?(Cfc>?k3lur$gkJConwerc3r$dfh7=?Y~|$
z{%l-w|83zz1;@+k1>+~p)hh2zTh)NIGn6^9u>nm4z0{CeUHFn6&c=j3IoQ+i9QO|4
zR%(176a*>qRkrm!Bxer^Y#+^x%y@B2o~LB({;Hh1ZIF=`BbM&)hB`Ff5+WUO<{M*D
zJ|hDw!xXcFp%H|`8~(w=2JRFXp!d4x$i9;5b4Gx{?GJ5y`>+{}%~mp;l?^-Ec;bGz
zHQ!eozMHW0+nT;b|C}4P4<Otg^7gZ=fz%O&DJJSw4H=3lvpDI*wc?(Ul1FE}brn_t
z-pidp4WEac1?rE)UeqC<&5$sG$m|K8Vh#MYsNoX%)v_<PAbuxB{T>LrJnLUUc0xYj
zMn=Wia*j@3?FO@pyA*@#V?pZL3S9`l5fI0)*`NYnapsc>heS8cdXJoPC!mRQN$6Yd
z`UOEI&Qh)@i7@h~@6=Ql1K<B<VBrVh!C~}Os;7n!AhBKqtn;g=V>D&j9piZzU^R$p
zBoZTl;=NjdzLnx@9tZHRR_!^dsI%8wCGOOVn}10WxVIn|d!-ndB?1*6Fy;4+P4srz
ztifRompD8wtu*G6EPtRxIY#%6;a^7~=F{1T%><8ImCv%$)U4zw_wPoJx6)yuzDi23
zI%1-p)Jiw364x3k6)s&Y7FaQvd>80RDGv63$M$%leJ$bWTc)i)<p*KnmhN?RcAVh?
zG*;c^Q_bPKcnl%;ZUb%=Lv0xIX$+UdRcs2<qZodg6{^fF1MHHiEb9Ia3=$7@_><K~
zNBs%2(<hY6c4DOM3N)=f8~g+u;5uoN`IZZU2?<a!u}k9~0|uR%5)TR+{;h5)FEF+O
zwJKs<PTKO6d_-udEv}1Ov$KD}?W^q+@R8vYI+ka9_!!bmC-;tbHkKj<M4{il5qTwZ
z6qv?HX`y+kl^-YrH~2Y$VQNUA`Ad;6wpaP!&ZYKj3psz&w(Pamvyu1|O>7(PF6`Mm
zP^ZSkc%JxN6k*IXAKC-%r`3D3E2VrE6GZoN^H%n12I?vJmbNDTrz^;03f|7x7AZ}V
zC<A>AMCo=UrG5^ML70Xxl^^e21vwRjOkeR$EShNjQbea-bZ-+M8u1FYQE4fNARO(_
zQm3tJN1!iOzfBF|S8>_YdU=?*><)rA7-E*vsSRxxGzIxxgT%x2zOY$LHmW`OcsZ(Z
zyyjgz*Vx5-vBX&m6ma-LnfU@RNNkmk>b0xUAXa7x2j;nm-DeTHAek64)8E%NjbI5N
zy1v-s$=e|xwVl>%Me3s;vBH_+p-%Q{u(2)^I4phwZMk$_RXwuOpPb)bW<%R6!^yA5
z^Nx)|N@DIoEsNVfnA4nZ0u;jq!-?Wumz05--Ggn5z1hu?w1;yg^5$oA3t#RoAe9Yt
z+M6c_J?&~1J;Geiz3YJ_fV(vl;8%Py*ihT&KnkDYroL|-iB#~VZmo*@mYI9ATS_w_
zqI_(A$a1%Tsd;G?5fqY<HkqC#ECeV8_h*5m7}c2wT*=VGRr&IHmD9!3Y*@th@}YO3
znJ(UWpd;g#SL38+X;qU3Xkzc}gGa9vDl0kG58I<!;hkV7Dd$x9s3`>615lKl(lUNQ
zgH@9xe(Gzx+R(sk%+LR&lzI#f^zWhNE!9aAsGI+8b;Edi!11z$Pr-?uqf8+qzmTjO
z)Lj=FC;g=rF0S)&98rjPK&};?==Shwnsz7TVc(md{dctL!VmvI<9FJQUbxs?OS4yr
zAkm7B_bfOeztBL;{&?r$<ZrL&68BhW?QkAwsHdYRc;Bm_2msJ~hleA=5j?`(8xEJ6
zF-qb03?HifV2ElZG}K^F4TRkVQq?*XQ3{XMRLaw+0ulkhJ#g2&I-WrSqM6fs6#)GF
zWK^1GSUY+K^y1jEfxW7nyF_v#%fuCiA2}&QJu0dK3vI;(o<-*7;^_}z($3VdtLiMW
z;!src9Vgy5@)NpY$Si-N6uy?j0I}PR^nNYNd$x{wmGWbGXJ%%<<oQJ9yUU+Jn&?Lu
z`3o|C_V7fa$($f>b=?YZKTF)dV^Hysx6X4lq;YC05cPE6$Hd!tKg1lYv4J-~o}|MF
zDX}0hZUyak38soAbq-@6MLc`{{4<I>MMuDQVtgL=WPuKady<8sAq%sf;rryC#e|W~
z8I_}m6XA#*s)`@2g-&f*@;_W7uL~O*sg>^HYN&`5B8p>XYJ>y+@grjzM_Ol2gZzej
z)1u$?oj~YAFVTR&tWHQ8eJ%Aru==Gub$Ru4w;URBnK)G8{l_9DJ<$dL#k%yDVjCs-
zC9LN%w))|L?2ZBK>Ps#zQ|rP#wxoGrSyggj=ZZ}U%AU#7UJg{JsS8-J;`#Y8iS1%%
zneX`A9S81h&1+Lv&RjO(fR&BorN_s|U(NUv3Tf0PtRA+2$>|z*FZ_ph;?T&B!;hhg
z9f#zKksZH0L*MYhBayg8halzTY2bez3CfAtu}0p+TA@jKtUe9rmb4%~;2uQMvzy@r
zBDyaC`Hxi1Pnu%x$hqqBcM0wTWih)3(i-VLU5hxhbH|p+UjG)@1Bs+42E=*{RM17b
zCl4yW-k!(q=y5YjDR!2RHC_A8@H36^6Fv_yMCVip=$Ya{@rc3~rY=LlFsQ+-y+sq6
zk1us3s33t(-w!3&)MTF%;b7C6Lb<s+DYNNd$H@Udr#+v6|M47zhX$RjS+*F;u5tl5
zE}vkcmY@uBqIp$r-YZ9@F5qT^yR_~Hp1q0Dx|`i28&RB9>#pm=9-{DHVh8_~GxK5h
z2-Q03ibZ(vRjm;E!J2ji&FExb<w*DxXlu}T5JwB1r*}DCbp(O=r1<#(#>(5|dqeD^
znw{a-TTL|E9SSC&lWE=%#(OynK}$ABPnX*QvG2V!MV%g%Dh+bAX2d$Jueo#}T&YCd
z^vOm(9l`Vlm$F1Oncf3)Fg_@4cvrdYr#|-!qy4>j{V>l7#M!XhG;ZpV-G{OY0F+*b
z0hIA8n1^Pl1{_;t|J;I<z~UPj2k-$*v@Uz>kcS68^qM96CD1(s^L&a@`VI>XqgPI5
zah=XcpB$;2?5Sj#=0@|gEw~WnL6@9TxQi!rz!G;pe;ivADNl37R;|i8e5ye;lB{vW
zEQ_>z><eCTk0g^d--lF<{aEYU4JG5L0gJB@iZBg`#5E&kRuPe>49qh4i5sUwr)$%Z
zWb$e#<+eR<wd%{eV*MO7pBP*(`K&jL&NnrWHnTnb^yFq!lH8;Bt?vo=7mQ+<^LdG8
z_AXG<vspP#>s>@qW>%v{QMrZ?MT2(6E9G#7ERx{2>|fkE_g#CGN((9^ukM5szDfCe
ztNtTUPnIvVoa`@Ket0x_a^!h>bhIa=@DVA`!mw)ol~FL=V*=-FS-TMRU)tZ|CV{oq
z$Hc7gD%;^6Zv-Pq^bFi@3CmHomck~P=!PV%l092m3WnU}dN#<y;otO7Ih|3Us0Y@l
z-P56w2)Xa|x9D$ks;;U(bx9(oHdRVnGeQ|VlGB}JFA4Aa(=Tc_r}4#HoaIKd*)zNP
zy{}bzRyAQRsV+6M7TO#0z2^hh=gQu5w@**DtrHx$MWBu^>Z;;F=C|hC2D1E7Hb$v@
zYg+mO^~{swo837Y`Y!f04B&0h-~Uonqoo!G)yp!b4(qYP%Hg^S_k~hx_Vxm_-hqFQ
zZNaiVwh?&>GR(zd(F9QAuBrPVGi!J-J>-57XA}tM&nJ>H>*_IejtGo^K?xbAx{M$W
z)!zmW+Rt{nhSgEee0lYzghjuN(tjzSY-;$0-);2ac2BBZO<(qWA9YvHj%?tm8d+Rc
zJHgLAR%yL%i*J(F1LT1IWO=hUgpYUiA`iofXla*wh@7gQLhrka`@5q%UC&u}VMnd|
znlmk4jIG&lGUiD9N=s)+k^<2z@Wd~?*pDl#XYAc@PqHla`VD0h@myUJEQJTo_eRF2
zox`VaRT^as90clu{fF-c*5gEro2>uUWNabrx{sFDxb3Y#>8)*_<j~(@IoLkG5pAD8
zSPUGB7|3%c7@j@ZlWt+26^=OC!ad=^*`VC%h_YUYbMrrXkJ0J=rIv3-725Ege#69m
zis1R+9THbb>I<+!MN_q%Rm%C&T!moIG*=&=1qbEMDNj3EM0J(M><kRvgAL_G_yjFY
zfKA#c?(8Bx^&#us?bKBqn(VDxq!Pv>nPfb2>~}!w?+^Q9!4eu8IzgjQB98m)Ru2bz
zXAzn8GA8S5s&F0y0+YbQ>qe3wN6QK@jd<w;2Vku#brE3^bu(}KP6-(UdYKbSo#^rU
z>BU&nI%Oj>n{3`eQSwxR5*U%k9|@z`ra#c5Zx@9Z2&VuQyn{XOM%$v4^_Jj>rpZCx
zo&B8@NKS5UdMrO{D}NU0E4IyrW3(l-GeIY%4*xR?kIVAf?{;ndWcFw5JgiuczOy;Y
zv+KA2QYHK(&=apB+|zrFibi@O<KF!q4Z*(^6X_(56Xm9*uJ?)|#Z<p57<m6^H+Z*~
z8A=%9>+W^=K+M-#8Ai*5ohj6B8|GK=5@)xR4`5&)LnCoHnc}Q|w}>Rtm%CS)PCS7W
zz4_rm2;HwS+?#Uo3Z;10gR7Lr6@~G%&_yE2DDtoxsKH78s*5=6F2_G?whgn3ujvy`
zoq<2OQVQPJCBp1}P_f?>KDOKTtp-$8x?4}?ar8TcN9;S~@EIUw&}?K}VZ1lMtWx0P
zTeY|?E!?^q6r!RMcJ|i?%E=LZJ>qDerqL<Fb`f?(GVyxk@`fyAnp1Oi0@?F7MQ5v3
zR*1Kv!1T{;Du7r0a#h{@2G_Z_Rx%O~#LY;}`5!-`4;5ck`2XpKW67*0fQytrM&n16
zM9OT*3h;u*F7Ji}hlVN@6x!Ba5IY0EsOsyl?hi0pWWK&9z~mYx<X(ySE)nwxV$57|
zt)e0<rN;S+#kn?~$Xh_DK64VFXYKCZd;?`}s&ebqVPvgCF}Dl8&x>OIO}Qtj7ewFM
zs+c~`o)w&KIrNt_-wnv&1H{E{;K!zk9f*Q0M3kJ!Vnz~EMaGDjud(ZghUQR<@s0iG
zDFJ_A+)@v{paC0$o#;Qx;Rl;T(u<1!COjkdH-)nTns=Qk9uu_~4Vj<T1TnpOL+ild
z@}`F7v8J1XJz?bOtW}w@F8Ae6@6r^&f)`dJiEEYjwb_;|Ud^<=Zi{>Aw_0|d%}GFv
zyG@7V$+~`2Sgp%s$RC`I-1h0_>A_24bs4oDyJ8Lo8Q-&@MlLa!<fQ4R=UzFdkM)N+
z3RS9c#4r<XeukeF&B<+4lx2awad{yWS*)f}ZcVfGM>d)B%+Q<0gjJ4$?ib~v3*$#N
zz4HJD?MO}!*+=a!M6}6pTVzX9A|=e`c4R??nPHD?DR%l~XC&g=lM@8DKs@Mr5g+Is
zI~U)ON~sr<hFUFtfM?9BQ1}hR#O!ZD`|~WEx!y;%0b{`{tlyb2{epD_^+1}l4OT-a
z!9gFpPQ<xgGSw$G1S>+ffLU$*?apMjY-*-e^YI#nd1~)QfB&yM5di?}I}+fz(~U^;
zLU)UF%jTV}1HlkZ93q;3TOp{Q764nBK&io#NYvfXf@kM}mirrRNQK5Nax+DfgCFxz
zf6ZQBFo!Xe90-s>{b&~Co}%y;n_45I1iD{8y)=?m7rPwPDQgOr&>XZ{$W73=WHjr?
zP3I6qRZ%k9yr%2Sz}JO-UzE^~yskm=_%tQ%VtU%c7qMT#7lE&_;|Wb@UXh;id_ZfG
z3AvU8?{D8!-d$Ja{qO!4!`O7~SOvb%_F9@RR$>hZF$#6L8BI8gocG3}1?oA8zBeD!
zeochiE%D4Lm>J#?=T6oTFsBYo+8{CvkgO+`r<B8AS<XlX?lxf3O5sdnbhMNoFR0h{
z6Z3)yEDB<3+h2RDURAArtd<$WA;TIGgh*BZygut?d6Hd_oHhg28F0}X6_$PF9#1pG
z?eXLJ)heeXkt^kPO3j!&AEDXb4O4s0KPrO3z9}NS6T}_$PKdFO;uWEMG;$SSKtI~P
z&jX1*GXd*f-Var59ui54EpHmT+T9DvQ!h@w_4<svI;MJnhRs}y?0??Iv@5Rl-0=sp
zam<>*zL0lTR*G6i%W@Qo=TB$+)DWJNs?cRrr1Ig$*Mzk4;4G!6je2x!zaUU7@k#)T
zeXRh46=IiKMbotz(jJG%Kjap9oy^w%y1%Zf2de3ZtRYhATMHg=pkMf{nj9LGkyevU
z#bFbIUpP#T|1EZjmq-uFdHr$Rn&&9Q3Rod&OjpI2a(E+jlOxyWVq?7v%9F%;(hT|5
z7e~x~m=!KqV6Gb~oH<Kit0LMwi8WeR%%8T^$#7L496kT!lG9Or%hCS9B6(FHP6tqc
zE>-e!{GnV`<yMG|jEsFAPxM}Qw@u_=)KU6@*cfe+G`UJG)*YrG&Cv@3zmuS!^$q!<
zxbm=j`Au>SyrPA?%9Fb>=?shtub(b-W+bv0)3YOdkN&KGg?|>AdPW3^YCTUGN8db-
zr<I{xhmrjd(R|Sk&_3X>yCmjqiR3d22@fx<FuOK7rTgdSyEmVV1(MRuIMhaTrc@S$
ztK&o<G6v+W7oUW?X}K+7K?nm4O&_@@eG_Pvet^O$HCa90blG25`PHmns4A`LqS;ej
zYCRse5D7P3mzxVzNa+XV7UNV4)y4a%xN+jN!*iD>mqDDyffu}ZTQ82N&O+IvA0e4h
ziD|w9bG5Zaw6J?`3&lIZd<v=L=AJYm!y&uoP$_@mcz9>dFPKn@wqrb=d}mHj{5csw
zmWbzr&A3I3#}~L_N0EH+y!Y1ET{xK&dZpmkWxN6fCU9jLXwR@l3aA)04J_kRdJ;%4
zGnvX2cr+5REC0RVY8UC&*Q7LIR9c$%NaN&H*wBd5$EMCSQ+=92s<{5shL}|afwM3@
z!2nNpRt;HZGzPkdfZoeh6yO#6I(!mMty;HpgTRbF5sBZL3-En?^F4iAl|P=FEpT>a
z-euS1hHC}BS?cWXOrCoYf7$ez8Yjr<hqq(azve*cuvH6A(X&>l;W!W_%06*s&X6)$
zz2~#(ZtsGP)%1=BU17*k=#;qAjp&5V%ChNjw5GPCR5nOPpAonO7GW=l`d)k~NdG-b
z3V9(_Cq5@dWDZKFY*J{`$Me7p*Y0Q!-*UW1cPMxraz;PU0j9P-abOz{lVyd@MGgu%
zf?hf1RA_KiXghINJA58C8aWJ1Re|tM&$pJXU^D0Ue8|}d1K+%Y$6*g);$nLsM-GSn
z>+*kU<Sl>G{2PglG4{NXlsL|p`EJ*dK#Y`c2s)`&GajJOlAC`o*UZw1cRMGx%RIVM
zA(inui+XKMaXDHVPYp%_yYb)xp1GOLe6kM#i(hX(T-#pE-kyJ*5E>c;0KN(q+?4W4
zV0fJHVhl%>yY5F|xOYZ$l%y#>?_eX(-rEtWKN00*&*{<`EdWm2tIu^ut>UC2AEPLG
z6xz9;G=Dw^?Wo(?MZcxu!P?c9(Su7<*j-)GU|<DfxqA7=&M$FeR9H^8132y~*Mn{l
zlMEps086$e@uVTN<$W7EMN-zI^~v*9;{vHeg%3@E`K#rN_XaKE606%r*{Q-{lPfCW
z!G|M85eLh&5huV*oNS*akT5tGWI>3_$-Pso=QT5@Y9cNUm5t%G7Zr)u$S8UL5p&BN
z<1e!inYd7C1BWH0J=<FHShGRbHn`QQ>po7<*@EO=hQEo-FM~lABzq8@5OJIZOD1ZW
z>&IV@?-atvvh&QShV`ikn)J!R^fwGmndPH1f#QKUaM0&2BOM<T?2a~$J(K2leGDTK
zQGZm9cixQ4lw8;hn-(+v67a6^2A2AdKh>>YBzLW>g?Gdc={X?HpWXrgF3UR<n*?>8
zuBJTB1_rG7ku;E~i2jK0U{Co4Xzgx&+(qv>=v0#;8zhL&|IBMG<u8MlWxe+w><G@L
zv0dJ9DGczKXtBldLY&gU9O|A>Mun#=q-$WN5)qL^gzLvw45nNz7JC7qN>4oPR3hm6
ztA~NW2gipeD;fZg>3Iun$7En?qY5p>R^1Sb6=!g<rR)X(Mm`DAx;tVoK0}j2#Imgw
zBhRXW-&9%wpm%IpMhKqRoFk(C!~+j-1dsQ|Bdl;?MD+G7*w?+h$um(g_BZM#_6sgm
zqtcns90vFTNGOmH-y2D7hIH3>$}7muU=UaOIdew9xSO|$&<TI`FQa|`=p~hI<a0lv
zBW5r+@N>PH=?ljZ6{BgD=9A-yQ_s_)Q<@O3>dEHnh<{^)MaW}m`~rR0s_h!DO589}
zelpih^GmB1`kJWlldxKU=fvdF;p)%1xpH!rj@566lg>VzQ|;GRo_v;QfXx@txRiLM
z$E{dUg46q{jscN2n6C3wNN8ck0a*Tor_b&@T4l0FS@A~?8)K;GbARnZBldw!Okc~%
zn%&8+-SNWm@iLXXS|6(5Y4@Yv>(iAR>}|HOzi)ljkq-NlZ9}<&oc0<?@as)NjX0L=
zyplNYTFiu{1;`zFWASX%De1&P?#c;VL%F_0@-pK7W?xZlw&k_AzKY4~O!o=a_+Yd!
zWDgtRpd7@s`YHwzpTO8vg{S0=zq|bM<w&oX#UpEt*Yw_4fzFymJ<1Jx9&Z7Cwy7JP
z=}dJ2es|gK>eB}GIZE!0;jS{V5Sod$fG8kv`oqEEkaR~zPq1-}%POd1s{c!clC6j3
zS6X|&cqba!-`c-NEEFpI0^rgvbLzQdO=9G?Ns6MdPwzjpH;<F@tSrqLth35i3<#bL
z#LzHv_UkRT<>ejEn*+KUNc?ZAt$5n5i@~?>ljT%_5At#{{FM#7BjA`1cQvmHh+h+D
ze-P{PLxERBghOUvb`%6miq%4wkRh~}-uptfgC_woiwq-C2Os9!-JUy?FB3e&52a5x
zX?7=erz58o2MJ!EUfKj8_sA*AO|`0CR@u7ueP41$581ykj_ZN`e#H~>Wimg8o&C;d
zmtt&Y^SDQ{hN_yZcuKJ9Bh9CxffVGei6q2B?ws!q1#B-a#<J?X#h#BWZxtwC3~V`C
z*tGP3_tox4&>{}7#-hn=*5rwEo$muB!czNNj@HJf??;>-Z9q&P|0p|jneZeBG{2Sh
z#Mhr=Rk0f%0+#8g9YxAv`y;qog9gDB);D_R_jt4C>zA5hUbCp<UR?Y<hQcn!H|(4n
z1F%5J8X>AEt$QVGml&O`2FU^bE?VNsLh+vByqXphsE)Ut)(un0D*3kCQcMEks!%A)
zwazb9wI!F|V|bp6JagoeQh2c&|0tcL+AAu>{IzmV&?hBI62@IaB)c#ea56e_0}!7A
z4UW``#qD6^xDR5Gqd&tGVsMWES2OM}_eF`f#=czgRgDFTKp_Djb_b=Rp-47E$?^gp
zPYQRAWVg0dqQ}Bv^ZMbSRN`o*?d_Kew*l;6c9HcTVZ)00Y>PmesO|)A_sE$engM8%
zq<>q(OY&i`v$DGu{KqppiT#+5@Yjm&Ziy2FW_zfEV5x+IBL@Ut3TaP%-A1?@H>DKx
z;%t@JBh5Xq1niQNu|0!d@2q5nu$}Tzh4AT`MfhaHlRYw-96W+{TkHb@s<lM`#sfHA
zI)O~h%y@Thb;OQdv94W-?Cln1`;DPw0hK&+>J5%HhI|XvtDGX5;x}yYrucfs8*q?5
zP-<1y2@()ujmdShyAq<yc=bLcJa=2e3Qr0UF@(e{%h|gd_n%ABDa{W%@}VC_?5FzX
zagA1NdrSBA(XGGa;i;y|Cp$%#)+$t#GqsBBZcE##jEB7~pOwAp7dgRtuGPJ+YR~;a
zy2Dl7>IU|ehg-KxzP{06zy5N*i5Px7`tMuE{_6G!{Aq!ZVMRqBE~x7AVzB6UP806C
z3`=O=voKd;044GP2KzWxz*OW~${(wck$T!#RjRuE+>Lan?kfnd?lrb%n!xYe)@lJZ
zIp9Z1KgRL{7UM}lKKp`3Iz0*iJBDBgE10*wleNfx_Ui%TO=#8q5Cy*AgRa17S|#4+
zEr5wAp#iPHdysN(vb>1n1%@Reo)og4=<1?bTG<f5-zYztV`ZMb`(kwRAKHeHm7__R
z-s|g7avHzpTK3Lr?2^-6YGkxMG4T8pK3wU3Hh{Z2|8A1J8&IWxakTZGBkP&&Xu~_*
zj0qhGukv^j2B^~Xcp>FAV`@EdH-ygeRwK8ayMo(0R@OXjo>d<QF6ok&V)Nnhe1&`S
zbKLkP>)(AL&EJ%-v|zGNT{;<Nn76Tb8E*~D>PCvax{u-gHv1{=26>?Lht03dbs*Ss
zb%T>W$cC!6WhI-Fr#!lvGRuEEA?im)3y`;5w0^6Nm&@;>+)5@64vQcguY-P9>r|oU
zw4*0@f}ng+PJ$50FH@&|Kyqh)`BW|4`7h8}-hsX1>(HK{23zfIh0OTc%^&#?+7!8&
z>+@-Dz21pL4~FvJG4Ui3&U<4nNF<}ZaM7>UyWUSizXc~qx}mdcBUX-)oF%E(Qe``L
z20DOR1YM8*J%B_qc-ir5m-On3o~K@9;<bhe9Pti&(ywn-(F~f5*9#<~=R9oP%{<}n
z6!ZMMUVL{H8UqkOME|d!Kn&FZW#z4iVi`sa8Dn*0{|CryvQo0^r?qFCZdO@2?4p%u
z8&ChNnfZ?FzNY?`0&{<2%{bX=s%4r+TmjOn`Q;x!#)BR;xL*)Y|L_l}#%XY@1rcvE
z{I^ES#4rq7YjjSnu7<7=#p)`t<9Kgvg=eUcyaj4At_Ikmez0;()~{<2@@4X+7Mdxx
z5LdfQ1?}5P?ot=pwTXKfrW_^HxULZHTR#{P(9DS%l8hLb1%Kt=^_zTxF8n`}%g>2l
zcKJAVOad02r?bK;(#Sst%{%CIGH;jt`!H!#DZS|Uucr|m=_a3-LU=1#Etq(Kn&$N4
z5{U(Yo0uqF+UC|s66w6WprG%!QFQ^Q%%{JZ8m&DKo3kfKgVxWvCGUL1i;F@7Q1USg
zpBQS$Y*J(k75whV$fl)uZ^UM8j|b1&VMotyZiQ5MnS|s#qnfSl^vTh3&;MWap47u;
zBQp-Bm7f~bq+4Fee~M-9h;c`~(U#W`{jSsX_D^z>x3&%<>8W@p!)w=Mc-i;Ff;Pp-
zyyz(KqHL!hxEt~?^3!uFbyucXAxh)SFMirQZb`^EO6jq=7B8e{bJOccIW<)a{JkRM
z4!4H(YoEYjTUx_yXE)5PiC0e&FAj5F{Dje9QnfS-eE7H5NwAw#=jIt_4Zk(Oeectx
z<yF;>adDdMFhk@Y<j45^tpjt-_yRooX4w<ZaqxRk(a@r)u5MDIb(KqbrR02YA!fZy
zFwjVn;(<5)aXeW|t*oS7Xzjn9-RDD42>OG!*t4a|uTNd)N0S}$@Vo8Yl3m`~95(74
z1?jR6T&}@B8}`>__3f^jkv(}c8QMMo3&CcQ4>0Rmju#w^acb&9do`%#=*TD#*Uk+?
z+kxSy$%U`|MK1QL*)^YJN9;?V&djdc)f7>i!oo0(wpDWHQ=(!ej}|Vp94~CbS#?C@
zY-$eA*b_Q#w(5*Aq*;NOMFUpLNhmA;I?-=yqIIJKDFu@T1Lh_2>p<?{?$tdI=T8KK
zkN=cz%k$%v$BAds7}Zf28xPl9`=<eJ$L66-DvvL)v7p>vxiK)TDjJySx;0;E4n6P*
zzSj~ON`GnBlA2`-(e1zytjoX^v$D8+OO9c&g-c$aZQRv`IH4&8Z4AlUg5O@I%kh6g
ztaEqXQgma3vKQ;xPK4C-)j_6B@+4bS?uVTyhBq}wdi_xD4E#^%fio9>^C~g_3shmj
zNr*4S+f&}*OV^|XG`-hUeiieFMx|p(x|$APzKUSpr<Iyt<f_W_R?;0`D|jt+-NlM{
z%gS9hu4$;UeCi_fC7Al`FL=9u%_FSwZQoQ;Vc{UPap`vaTtFCR)k7rqUT$NCkIK#>
zb>24M5gycje-Qo|JlniJPR(5Jd>-j+wKid@IY~PXAi!k;SBO>a9Is79WzI{Jue#hF
zAiagc9{94{`TNzw;9wl=WcX0?h1>5R<EBHO9-;2*o-Hv9K~Cw~7<OcAf4)3ECH`>E
zgae2B_I5g6(x^jwlf@kxr|oBXiK%bJdDGt$-{cpD0p`6%5!GP>9qIV<Ky)^3e{*xj
z3nAJxdlF2sl~&mcy38wk2Z4ojAG(14F7uujXB+*C34EaC5MAj`X%xGOaQA-6QDC-C
z;nC2Amw2@;-M|rETg<k9zth#}<oW#fyEM9NspMi^c5ei|md)=xxBgEo$+|&IhGp=`
zcULi`!wXTUp6tO&9MmwBOiH_ZOV8;$yktAeQ6K93*)bXVl>Z(hgRv+tcbb_C7pZM*
z@?-RRlRP`?$M{!oMPCK0i$7CIc2NCCTn!_Sk3Se<!@F^9!<-em_MvwcchdNR^9<pG
z6+N>Z9lk%r9?D^<&Q~K<xkg`l{D)ho(tX@8tT5VnSBy}Q%iUk-o7CWC=Q9Y%)~th)
zzPDiNZ|w$VFmn{Us{HeI#{q)1VR~8!KB1?lZx;=DUzIs;4V-aP`?r8oYKM|=g}2X$
zvsK8tXdS7XWK=P-8gRMi=argqV4+>eJxX#%xmW<lY}?xxT-X>ipJdI4DpwRp<HxP>
z9$~%onf<Tpc#E3i)twE7L@AN@-=hiMc@>O`lQow7Noou?>p)s+VuB1V>gV}H%9UQk
zN<>58owB^!oA9A^*pT;+nu#CRK<d5T+S=M`t_4sd5PR8KnYR(ZkqX#;OhbvG#N!#G
z)18@BKGi%&sBs{mYd<C|K2-B-cP1P!&#v1}PjN>ou=TPHcsZrGaHp(I5pWX+Q~3dC
z9=)@wTvEJkX-s7xpbVq=IPF7>sDc)Y1;^XRxy8uqS*;nyvF&Nys+=}YG!kcE!aXxk
z`x0~W$#)>QKrycQ7AB)~K%6BoRlpRoNDm??lc<a2hPmfXkm;iEi~9%lPqZ7>>T-nI
zSzgC!f(0;;%!}BjC`pTkIPLOIOts5I??n?6P0>6njq9x2hB&D{a^HB6Ut;hxzKU5|
zf+xvG&y7J!@HRi+==ZRB=n{onBD&shmb$t-Kkth|`k^6jjJZe$Y@dXTG=SgHkQ|>+
z;LH+JGIPz-33rb~nAKLnwp9AWAn~m4gGc;UBmr3#&f{m1h@5Qt#&F1k1n?G_jJ(yw
zkPFaHq}$<TGeU%jgDv{z4vc#Qhc;RlN^-r#Hw$Ya0_;5BLE8LQWk_m-JNA^E>1^dQ
zNXAi|O02m=?|e@DQr}STx(?Fd`2OnqsJ@f_e^TDu(AcTVv9P9WQ|lDT!l~5Y>HM3I
z#AQ_1d0hp}ASrMjw}P}dZAV?qbS@oMWGzh$j9*gYznCb(We>G))t)_4Iqf;!PvEly
z!aGtL4h`=-Flb=SwK*dpYH*0QQ3>0xqv&SEOjR>YyYR$#Yq~tVgQti<r|zRlYSMpS
z{d!MS^5e?{u%nP;DM+nt9cglfBMk~upbFi!cqc+{ols<aSls3#<a=*o5{akoiSXR8
z^|xcx*8MPbg+Cs`%*Fuv>u(j3k^{SXjW!#aL3g!nXcXXxOIU7P`2>(E33up9fTzv%
zcp?0*70h)G9uxTE-gfr<(6AnFg5ab#`*kgTg?E>EGc11NLYQX_EBDWa$h>k}dfXu!
zw_&jMS(DWR)8Zb%SW#eD#`2m-3Ol?c89#Bf3J7Cvm3$I-%%mr0;=RVZX)3O-O?p=<
zSjR8stFHfZTXmiZAf_YYdg%AwnM5<Q*Cbv^I%j=36sq(%kkj23vl<M6xc?bUSSH@w
zA?0-h6`<Vac7j4e!orSE4n1j8z`Iq}fFXX#<h54@+_u(|zwttZE+FFmMkMl^a5#{0
zU@_6Dh=g1`+BY%`$>c4cEM*^lzo@4(s$3TGs3B{>{bG*;xEoD2<*U49sG(``S&;Xs
zIaB?!@-#kE1Dks@Z#iE{%+NRKGju{w4x-y3<hSlLK9lerb}f0dEzeX)31<9t-3fm9
zxi+t7Q8@e<f4UAFT@UxSgtuCKg>*-@=f7p|y|fE8`j9xO))GqpVoUnz#NoD%wJmlN
zHc<CO*qSoMxyC*E`6^NS+jM&F!r+?C<giJT+2t+CH??SLxiRK?YL3{Qn_23H2tFp%
z`{An<Q=!C6A-Nv!{y(aZ!lqkZyw2{%dMG%}{bVo?5i-j_q{wiD)3@9*sWX0%9wjeh
z-l?R-M_$vk0^hBmsYHhtYK-sC-m^yK|5C^aSjmdTT!@8t^Vl|hTgXs_F(q;+C@PID
zn35QtbxfSI;3_L5Qz0FK6%HI=3108kPhR52JH&`E`pWpY$jir1vZ%KBN}@mX9%zBl
zP~9!@Ni~eD^xy@fw7<=J#ogPP-ZH8#rJ$!m2@_V?2Gc(jvu}2`S;gvTimFMn4p(fB
zQue67zC|^7B}ja&5Pbh%{nR5l6KyDdd;WOjFxxpD-2a?%fq$7eZoO;!llixv_d^*p
zc3?ZSHayGJF&WUE_fNV5%CT*wY}36kJKW`|qy%xcDFfzh1dsE2R6B0+2ky@2PH21V
zr-BxyeUFWe9o0xzWi2+S=DQj3wmYh~-X@t2GC-4;y?RIBLwthlC>D|?%OtXPB=qmn
zz^F+O*g>B+*5KplxJuYjk8~~DNxt35!s?y_@hu8T+@qF#J6&^7x+ZhA)dz`33Gc(6
zS&T7PjH3x_YeL;tNtP}gJFZN`=UOOj$|LQD59VH8@}0N;dz#Alkyd^s?=_RgmE4$}
zmEouuzLB(2z-rQMnqMNpuvDDoNv@PKi8U5DU9c-9DlmnhR+9BA6^A-<?j(dFg!aOS
z)#H_VZVk-WbwpbITh7a^lAE%0G3Y*AxX?l<qPcJL5&0zGczJaTcMkpU<HHXnU;frG
zWCG6GN3aX-;yIVKf97E>xI-`oF1Fa`Wi=0w*!TsH)?`u6L&T4ck)t8Qo))XY@ooA`
zTv=cf<y$oo1w+dmyiZP03P2lDTv=<>wDx`~?UJ<N-Wjrf8gdLS&t94p@j*Co){*av
zC7Q&d#dZemdq+dOJ#}8gnA7}+B>6<vsdAs#RkeQO^P3!Fv1`-ehc;FI7>vf!h+iTc
zfT<5SDGLezXik9AIBSCoJ@LqO3*A=&1AaE=MeoF+?UFbcY;A2rP-|02??4tabZSoz
z<gBS#t@HFd?G(3ya?_VFQ@1bNlB*+ll$VjZ&c%s7T#yj-;f8a6&w1>n1SzM(Ai|Q#
zY}J)^QvkHSHa0HqVs4tIfSx#iKKnceh{AO5_1@{a$`R%CfEE#WGrRZOo{yDh?IJ#5
zb5p+MNcq9#p5R-D731R;Zv6?6(ke^*6{<ympaC2vs#CGTs~gGNwC7$1u%N!mf2g`h
z5WU8Iy^yJRjfWfZ=u;WLMkzmg2He1G1EN?r(cICbZqu*plwA3n>s#~G;3B$3?qs*6
zfLrqEU+sVK#UmNrf`vs!iK0qHLWY%iY&F@sX8iN=4Rws$B9%%%*ll{UNZ;St`L8!&
znGXDIi#_yU@;~~){Pv>owC$IIvKoSUYjnd}&6kPEN>@&YqT;l>-$?b?%XtE4-B})u
zl=Eu2ayOu)KVh)uX9qQul^_u^+q9NY8o&q~IJNq_TwxTUG_CB;L-qGE6?MpSS%?C7
zv0v`i5Tc=4d<x}h4_W1zyrj%A*O`m|a{`t|l05D;gXuCQ&z>>r35sjqYRFV9P0uBf
zf6*>oWS(iVHNN}-N%aObC?><TF8YRqhfHNTlWP?g6K3VzPXbO3PLA<wzcGaDFb)$8
zu$W5MEJYE8{X~KJ)|za{dIJDu^)p9%oTDsCp1I5jS)hjm-y7)Zm1>WDqIcDc#d(K>
zgBz|H?+K{ucW8?+_;Lw2Cbqv;U*&PX><A<vGT7x{FiRu=n+oW2Q@m1?4r0b6-)77a
z5${e0_MDrkMbe8!wu7f@MJFAL(kgeC*qZRln)Ctb2rzpJfAv96k*&wL4b%sk@pW&a
z82Uys@(la4fKt)F>YA!+)dMIphR>d7!kYFX?%5Bg#6LQbzlq3)795G`@T(6>K)Gq>
zKIENiAnlR!>*5{MlZj;in|M89C~4l5?s<~Wo)X;;*HZPBn1wD~b;;x7Blam7z)R8C
z3H-$E#acZqcJ~Aq-t@#a!5cC{&<H;olRjB+J6UiDhb(@~*bQk!AC5__)`lQ~j7O8X
zxIpBBH=!*&=DC<d{Ot#20ks1$8ZY?NJ3>&TSHs{}@V)X*uy!aOO_jWV^p_B^w+5j9
zicbHHw;WhbN6@ktx3{)-hXzO%oCP)_#n-;;*O?&wr!lp}p0u-kl7`|ekrRH?C`y#v
z(?MS)HV#3y3VtbpNI%DRK{MWu*&f(|Sq_H9zVX(rlVh_N4&)0Ph0L{ww;tp!GM8}m
z$r=vv4W$QJ)k@vh1&;uAHv#tC3VLXxc9w??O8&FIO_FOb=VX@ia;3(gCXnHI$biD`
zO@T@>+FdIL3@Vh^T*g9jT+~QSDv<J8X}f4+EKhn$Pb2;cR>1K;uW`$l950L$)mVP!
z#(_Wu>8<*vg}e^~$*90q@EdC+{<3wBuuwFJWp9?#X=@@KtfT6AI<tO4Io%gN8J|Ag
z;cJPYQ=ExX*tN_08v9+e5rlnVG$H)p=wQUCh}N#^Xy0AR&=ugX;i$P{%WxCU5BFtu
zEsr$Bs?`dPeW%>-92B$f@AQ+F4rVXo5WJY9#L?QS%?TY%p{SY*B&2UaB+7xw8O|mw
z*q5S#A3R-Y$&F7`I3pwKKPMyu5!P6)Rk+Wh++dyKJ>6H|?6&P;i$mMs!m6kZZx%hL
zPetRFy$|-rRgU&%Hr^7d$-CRx)BeN-*|*bs(sut=f4MiNxFO=+2d(J(+X?n=Mb4zp
z$AS)AE`Bay2^P$wks$VICTXpKGQ#cfuHfm3zP<|CSL+e`1BX2wN*kd;1wHHeHH-5`
z{_^V<DE?ka<9@aFCsk-?@=G}<5eFfj;e#5Y+u3yyu%;06tEsw2Bj?$K#AC(HdSd`1
zh~sKZZ$0oB!v_}%2?HfZmo-vc*H0jfs06WLO=3zInE?%B$Uk4~B1N^<$+%>vYksT$
z)@lNtu~P;@2ZOE7%af_`stqJ-09P2Y-p|5dbF(R5LC;=OzGy3hIfPuDxlPWt&h`e6
z`@dc6a1g@zN0BN-&LntrM@4r|-QwVroVuXMY!95K*|^&t=frA2n=FGUqfQ{kB(<l>
zT1l{(95^rfR&1Q>$-(mK^l?SRDNXpK4~?YaoNo4XY)}Ypg|drJIqYnK8iQYWu+5Kn
z-d?}!Zh6tnmp*f$<XNw9$jGo<bFo~-RyL#%+^C(hbCPgQhZlm3!~kQByXT*3axpTe
zzxsQd_{a95z80@cCtP%7!W~VNGMIjR20alty^lZr_uua;JfB{w>WcnESavqA9)b#y
z%g!xL?=MG}s8Jh84Q~0Ze|LQb%tFIYu8nN0(SbU}X2`vW5c*~xRfX4z&uOi-&wrc}
zubHrv0N1~BJ^{Z##VM%vrgm&d-T4=Q4YAM<v}W?@rSHI6in>jD`8C@pqQAYATf&r2
z8*u@>d~S#WK$<A+UV}S#fBXtT#4INPIwzX$XuXH7V$JNQMCDCb+{F^jHCsWVX2z@7
z5YOTU8TJ&(SmWy5Da@ab_$I)Vybv5767(YWsfRnq(tn~eu%es?kB9Gfh2~saC6H5^
z&FnZ`$)1`VS~d+ChR`w~9=%&%edaUhFAhzl)$l|CYHpzLTlp4zrMiKHysplms>LtN
zT;g#U;^sfn%nufMH4;0=o0rg5$&{<PY;BG){L@6(g|;O<j@B0CiVQ+1c?q(Jr$7>j
zE7ba2k=J}lj)l2rJ0p|-ak!<4hX*5-3+;ft1wB9^JD?dLrUjSMhJv+k3qX3vP2b)Q
z)h<rm*fr2dgo~$7Vi`Eiv?k0lKw<}x;Eep3#`lDEXScH8E&xHo{T-wb3)Zk-cXgS%
za#=fYnD+y3vETX`v;Sbw>(IOElYSIkqRru+1r484jlj}X-^CToy4Z+b{UxZR={mv0
zGDhtqtGp0K$+hlm?DsqN_5IUoKvD=CA~Px{vnq2zmrl04nu!7?x2S;RP%Rj;7`sn7
zEXHrK$>};i><ODYu;1z0zy;#AHX=n*_<R4ZZqv=UbStOK&G~!~aqL_m)kF_WLfCX0
zB)WxIcv^8vp9U&89n(ki(?DcTj~MK^F5G+pH$7H6?w?uQuy%XI7L{W-eg8N!oX+m}
z*0E;wpU@XZlS|iE%qb=Oqf>{rtvpGOE(B_zS)vrVV+VzNLWHzLMfD~3=Z|z*UYEMR
zlH?oVHf*q~lV_2&naq#mWcHcRJ7>X)XbH<T<b!luxU&*$h78EE2D6AMk8Cp9KysG^
z23&U;^)jE`si(Ud`V7DQonA^re$wL1(efe|WLw;2Q5H%q^U#N>gp2Wg^H{9;(N7$$
zza`K!1-zH<{A1>4)fpHC5#2$B!a^_}Jo4Bo;evBNv$cqQb~=RN=AsGm!M4SW%IQAg
z^r&arX3#F$(>szjGK*kUe{gfEoqBM9Bd!v?&)*nyg_~%nv(ym))IA)k3pF+T*YC_%
z>EEC0EeLs-ey5c`{mJ8u$pcb#zV!=9@$R)Tog`-MmBH6qydtr%S2?lq2$t_IUX{B(
z$nnQ3t{YV#(M}!s!}RlYcXy0Kt_K8MzZU52Bil?-*8CQ?&zoj~&3$nG%y!BHVsJ+X
zyi2>~bavXe6VbcdBulC7tD30Ed$N$Y>z%iuRxR;&(%o+3jyRonQ}~4)^yqFg=5*&i
zV{>(%&;3m2s$rCwF2wbB<6w`%hDpC+OkgJ-`DXuf!9oN*^>lCgbR^<*b(s*j&Nrge
zX+Jx&y#ER>o%396ok6y<<YSrF!5YO*n%o0^f`G?qPxUj$s~_5u211fGc^~J0zKLHr
zSHeDU_MtJ8i~8mQORPs<eHD^pGc<S>1UM3u{x56vCwqH)+qWuzmkN?G5q~Y1s!`kZ
zgX;frxBK2z8WT}r1)m>sp*w7-&wrW74AGTj@bYBK4-9PSdtfftRpLGXFXZjfSG;;X
zwvTSA>yR>7L$s5wb-539`zSb~&AuupAYkHV@Qyzn7fxtq3{utY03q)AwU<_|6n3{!
zYVm!xS%UF!-kKNO*QzB<41Tyg0MZ;=slckY<=E~dVTi0!_G#YfQqsAXK*#4G00TVa
zDH&qvF5WY<9iat$CdT<Y3j!TMd4JO$A)lIT5VPQ0BeU@MBzrDp5mT;<a(lS!V=8?Y
zMSbv!H^B`YZ%c0SbkP!cAle1vp7qW%d#c6l_B6@kxr=k7+vkaw{Ab@mjoE^=bDwx&
zvsrJ(HD!LaVvs1-VJf*rekpM=Iq~ay>wgMb`OFCo=dONo%Q^RNJ^Tm*yMLuHiW+?E
zOU}-;JlFk$lP<z{KSa*%yya)k`(8$W5&i3_Do0y{mItxn1e<08%g+=B275gnO|?fS
zXEX3MP1ENoqVe`OJhPpZ3`R!#Zwn%>S>tY%r)dGf=Z&qIb(P~(JKJu%qko5id@Z6t
zeBz<$^x&CZ+k=}LTo-B03Hu(rkq74DO_^zq(qsAY{pUFje_>4$8S7s9$L?C-Y;#SG
zMi|-Kl2>|r`#Znh!hnJg5B_nq(08RFE87)CfHdh{p40E_syXk(>!MsSAKu85_24fs
zZZ1updUe>lyYB|Doj%Gfx5xd#-P`og?B-mnbsH={BY$HFA&`>03CvKAJEF{gQpfRt
z`y+IF?f%3_v+V?~7%v!0IcSCfY-5f({Ewew)nD?|M=J*cR@nR_PCNL*<U)l~hK#6a
zVlz=v99u@ap{9Aqfz9HBrE+Mh_i+7x<F!R9q4dpN>iE1C(a2A`Lv_@KAphZ3HEK-Y
zZ(8^Af24*_LuOn@exZPlSRyJa&i7JJaG{ToBGgsd|6#G@e1~jBhNdHjNKb|j>=|Wc
zcPpfsc&jfrt~{7mME#AwO)WW-b<z8hDQ|j0C@8~&ZAB_xb*b};uH?%aYy*cznu5`_
zY5f?6lqUyByV1!|D*C!ytGeFn>Q|(#E?7fKat1R`Lax?)S7;R^^7H-V>xMmLHFgo9
z{!hR+pyw%PTa@}{r_L!oEuE^hca)uUPyJcMtJEA3PNU%07oK6sdU?i4`po|wcEoJ`
zr=<Y7H%gM_)%bX+0iyjfJ<v>2VK|Vl$Fi!{Zip>{rC3Dx{RUhf$%>Jk>^pd-Kkb*{
z;Xi1Esk&mj-8xl4+H?0*Go0?%NO<BgzZ*h$SFc+-+)Be5!9Q#OACJ$krdMv<BDT`C
z=J((xkwAj#g3dw<Ft-#gmTBMIVTp&AX9(O$?*zXmTvof}2=R&C**e&Dwxaz*VOPsr
zZ!pu4xB7%YdrgeYx?=XK=Icvs)}BNWX5SFS`vtrez3E@8@p_c7!e@awi|;jLJXs2K
zhI#n^+zMu5lktBj>K?ZJE2T%lVtu#i$L{WD73Jefn!Af64>2D+cGzSn17OEN&p2GY
zy+X>9c~pS7=<aNA7<e&)jz2wUYOXxbJ`V%M*Hcfv@u$NuRm2PaX@uf%A+yA?N1Ol;
z8B!iB)D4YY(2j)mifA?m_Ap8<LY(>)WPF7WVfzkq=*th<H-orE7=~^1$mV9c8Y!BO
zP^%OMFvHrLH_3n3zMWZr(kSo~W-N5gVbqm8^89ivwSrAhEUAj=Bc{<KF2u)p+x>-M
zDU6GrCT={}ueB5f@Ybcn5A!2th9hVdDkp1vr*1ZQu!byK_N@R>fQals>iV*dN3|s~
zBXdSzQTAi+@cGT5lp#;#wH20IV<RJCs(xQOf}U);PjtOME0l7lUya9OwQ29Aq|rze
zBN^y!&CgSOWtXD?a)ZxWI;V!+kB8z9_|6NJAN8TTh~Ida*$j%m6X!ycgGtr@o=kZd
zLKL(T(xSY2MeeDiZkZZyQ0bXXS5hB(?#>)L+0!ZOrw+($&*tDC@NRLJtdWAh;%aFk
zi<8!YYlg2QQyxBdfBSd{Njbz;tJ;#vD_Op->V1@Z6?0P(;VsZo#CA0Us>JP9xJa6)
z*uL%nN=GC|Z3LVMJc#|=Sx!0^bx}}vRJASLxtKF}Vn;numC{;N*i7F#kbL)0eHEWv
z(QE_NcLJi8nlb5-YiP0(pK>y|>(Rx#QUe))VpF3`ZVqmwacS&_tih1c`Xc?2UJLQ9
z9#&$~`kvkNBe_Ize*3MOxnlc?K3ik*ZSiebc}Bq#mZV}@d1WAR1d&o1LZGbu{V=<U
zFfc>CRp`vYtZQcYo-ZL3Z%ld1$AFzU)DR$MM4lCgnx-QvF~|2jzZLQQhhc$Pdp3;Q
zC4Kcv_H!UhEQ(R)k-R2L_g@#Bcm`B`Me3?iPYYlLIBYrYi#YeGXkihL$E!3?LBzEf
zpV<Tl6)eEI;}sz-p$*jfKd!B}+aTl+%yECjiQoOxwT_6BjZMDwR6g(C25CaUV#T+;
zGv<CP9t3122q3!WZn=@EMztzY_{65?o(fOpBo2ccfiEen8~#6z&c%`G@Bib&Xe36;
z$YpcOBn-JH<`NQ_$R+m>%RTqp7mMZAa=%0_p{X!JlUzpbePqf)?$tt?ODy`mzrXzl
z?{m)1<@I_#pAT7mAVs{O#kGO5s<#uTKRPctu)lIpzS~q;_J*ZJ4uMhq;?HS@p620#
zA;hz6hiNnkz1x>BOAmMhInVsomtUL{>|Dyaba-^{P<0$=fH4u()5DhobRK5m#k`E=
z>@raG$vP+_3t&3=Tsx<njF!D!l=H7Zkku0%?b4ztwxV1B{{kxTaU!XEAcPkk-X0%!
zSg;tZRg&%TIsNI~yHen@RTE;L*jgfH+t?jV7eJuN1&3I5#wSwI(XWj8n|a6kLUi=T
zYA^FIwMJiX=rBe+&_=1gMg?0+9IG#0I-z^FBs8_wsEyGiG>*7BIk~j7#GGU;ZB-C|
z%b6R?q>?^21vK)^CEm1~rZ)vRW2X%AhohY%?ppUo=}8O#-+wnKX#fVGhPR?dqQA7R
z#2?rWMY2V2HrBJcPTCMKjB)0v>K06^Qv)6zGbSL=7Jjztkqr*N+KcZ785sYLxuAPu
zVsCL;eR1b_jHH@EFt5H|P2o^0jUB)7t)|~!Q~m64r&)OOK=i>;np;=G!5m{f#9^+q
zYae<vL9fw?kmKStGl*x-%)~pl1q6Ncn9|5;i;j+C{po12p5}KOmh`JE&if*>9HI;w
z_V)nN%53Kw&33l7#R9bYkkt#!oh#?;o>5{GFb$Ody4EwN|NDeHd1)CHwHG+o(B<0Y
z{^wV3<UFkjCzF-+_Tb>)q(G&Fi?C+hnJ3Jy7i}vH72af2Xr8=uIxd8$9bwQNS&M*t
z$z#Am19m3Y_yxbtGWn6!exlDCbg6nOYL9TxBv7|Nk^xAZz1YzLNxYba+>p)x*y8`f
z7+hv&cd8p0(GZ<rUXVLUS_+ZtJY?e>cAfxE1@Q8?q~Lr1jBd#@96f`vimw90d45hP
zt)2UiBTEg&;coHyMD4(bH^D{mTSuLPTYt8BnfOwpmlSCO21{_&qoZNcBiQINoDKL3
zXA@vfA(zkfWG@n#iTJjarhGE-zoIg!S|O^D&i7mOBQW&st8D?!)8X@bPJ2#_jz&hu
z+_A*$FO)h#wPtq$iaJP$be@Y9&SAA{hS{xCRGD4Hox12v7w5};#7g{YKuVX{i3Bfq
z4yoXv4qqq@YRz9xlR$ohr<`?K>iR<M{(}l0mqkc$owo-Kiog4JF12gA@ruOkc?qEU
zLSe0Hi3fe@D!Ftyc@oj(qH*KS7b6)^hEcspUKc6!e9|L+5;I~)YiD{Z8I#W}pV^hQ
zG^@Hg4b4=w$~2%U(XF)!oxqc(vNUty{fz%lzKaAnm(=ov?S#5`N8k_LiI#6=A5M5E
z2PiuXM;(TW_b`^W?~sjPH4A{eAU6ZsbYb?%fz^X@$$_<{U_Dt88H8LG^2x2Q2FR87
zto0p+wXGGIHtyy&;1v^p&^708d6h)0Pc$w_-3q>8=eI8CE{0wYq)4|%8-b;ULSUD5
z-_CwHl>=>W{m<znpHyz@JuR`&v6_y?bAtWQiR+|>gOyNoM4HysG`0#&dhF&z@KDy3
zeKGK7ZiIFYvf#(g`h_MHYsI8Nwl~p|o_FPP{%jvzBs3>CAzy_C+$^Fsm0;uJ`O++X
zCK`>WWGbnl&dQATz|J=RAE=t>vclfhO=V@}dJAI|kVba=kw&C&PPA6(Du>EXR##)}
z=B&#sg}A%BYd@4ah~&<lpFG6u=I7_L?0<DCigi18dZ}(7d92cAN=3XfLF$=mmC}`+
zZ0?(-NSqn+KC~xWRjS^f1jOT+I_^c#e{3c7s-JDN&N}=-y7Q8RkZ3-Zw}tO~`r@m<
z6|yhTKixaag}yNOc7H>;t26=L-4=ex9Nvsz{RSAZF<n0y)QD~*hbg)YQ=et@bdpU~
zh}!BUdRlyOPGD{*M$R5;=d)S=F^4TDMc$FJPTy#W+-Iic2OzBnP^c+0KiDI1j|2*K
zSBhQ5?yS~sgfahr``xONX#*8OOZP2k_oc3Wu`KZ{Kh@oCrCN8gN~h}vvp}2soT^iQ
z^@ZDKP#*we>CAdMtd;B%=$3{Qw`MkNfMH8tgvO`&Nv_rdL7`)$^^LClaK5MK+9TJ+
zH{S7nf_)x2_Fz)aRjDofoT4Lns>4Y13x(E^CoVwJF#NGWz7SMe9mG-sF!TByC$)$q
zX!q`}w>}{{%J@ug_aK0?6_bIY>%Hl15}{WNVedX*;8HPBI@FL3lDC-P{U@h{CMd^<
z#P)6M3+%FVW<|xTr;QgJuDuKo3Rm;ti-sTwOFsbuV1Qc&1cBMCzEhmn6jWWP%KX0l
zscS#Jq_T6z)kzsSTT3c28R`mjMyUnM^vq#D(^vj}XH4gZlu$!Pw0Gw6z1uT-H~H8=
z*Ec}{>`!9x<0A+afkMO0L*GvqI`HsN5{uGhDT9}Hdpn0qz7RvxlG8BN@2>5*Q(T@o
zsUl|lJNMg80LL{M;~)gY;`!B~qn%mSc5u|Mxv}v5@3lZ!(NP7loZVXLHL0OkYp9KX
z9!KoO&#ypb&y$QH9SlCSb_D6!edz%eX<0@b^AP)w{UQIKm(!$9yPvO5oQ?T7F)Ol-
z#HsWNF(F?AbwR<|u={3D5Z6`tR<$foIxCQZScL;mFMM$E5`A=xZQy4{ehuPwi6>z{
zq@W4!uvJg?o4s?MQ8|kuP@)?i_r4COey*8{7Hc`CDeUnCJTcxjJ<zL&gnhdhwod6x
z?tH;;Vw7Rf$Jnn}a+cP4FRZOyX8mqeJdQrgK1quSunTIVhqenYiZgClm@9_yT560j
z1KO7hzI=EWIu@X<`;&1bij*z636fI5!IEQPsWzmJJ=eDHQ9=@8EebzlqmMQmM6)jk
zBkcp%oYnYelhl3D*pF-yk|_M+<op-YZTWzDZHAmugwRPO+k?8FpFFg@aicBnv)G;4
z<De?y*xHL&m$eXdU*k@EPB5Fn;>!T~{=K<9Q6mW|*xmRZKVz1%7t#44P911T16J+t
z63bOY%uwW~U!`T+VH+&Bowa|DpC|3HF={AxSQo>UydQazk)ODJCufEI2%(e2_};{_
z^=QArf`y}<$Ion^K55UV7+d+1Cvg>5KCYZ_@32`b;fPu8s(-H~YODhHvCT!dG4@qf
ze)hoT+zI}{SA_NTgN0V*pJu$a#+goSqP(B(qd(ClPDQwU%0{pabC+8|1U<90Gbc+j
zMNFZ=X#cCTV&$Hma!H;9Vl)r8(D@L{5R4)~8f611h6d6s4L@-pDsKfVTCC=%taJx{
z(PfGkLVT#kLa;$@w8Wo?xXJpy5q%ZHw{Nt9FtOzU{(oSDr9W1>uVWz|vw`lejKI=a
zXGR0qz|t6O*{zUFB8NL;R%ap@t9*OfDy9g*U=9_5?}1Eplb)_N==b%VD>4_w2kh;f
zLyX{U)GPETpYVrD$P*-T%|$D7+-y}bb{Z3_Wnq<qQ5v1oKm7h{^abIVzq9}FsBWu2
zMW-9eFJyDbzHRxZ%v1U0(cS$?fDPr=dWrzZ%=<>@t?b?Q5%9aX@N-u3Y^8~**^_t8
zQXaPgc~GsOL_BY=fjmXnPFhM$8f_eT+iXM#qqAJ%+%VkqB~a6@bj{J&0vYLUN%%f@
zK16dBgqsC&-!ZLF_Qmn1Rg~!fEM-1FCwB8N{>C{w<38?A5e@ylMxK!C5(Su+u6Dci
z&I_hb$UmRWDuycw4pzPT+*v!2wh+~R?@w>qTj>)C2Y>k~td8CLU8&iYF+u$<mnaTB
z#;2nbH!M4^0W3Bz9kEG7#-BGy`6Cas*XvkpY!794tQQ4y`Z;uhvpouGwt^+q+;*#9
z%RY)O^CC<$!s$OSSaaE`L4vx}jOf#4vCSWzrz^NvU6CdRYz;n?hKwgt=Z8as-cjYH
zJh?!72wYAfcl)jc`bqEe(zB~Vi(P@W0JXMvN%H6qV6S{D!FTCjz&8VY{;;>Gs&pRa
z>9t3^N~@>Qul?A*NvT@qxRL|z=hR2A33;L;D+bcm<gX8Nx%+7Jy?Nn`ZJ@_=QsRJE
zl_tjeZ*Ple$HqPZ^ZU37i|Fbo^j9&jFzlrphpSIa3u87}F~du&BsNd05FbJO&qEN$
zMSPwKn?0P6_EhE-{`OZZgOjCmwcnc#7>t$4czW2_xV1IH0Q<1e1a0vdDii(HkM9BJ
z@on{Gc|))J7LoGrv@(&##$W@Vw@-m{+Ne$UqiAdZa3w7ACm~m2UnjgsulGmpNXW8x
zmzD~I@gP1rU8$pJNDZMzn0jM?a;Z4{-0-@m&j<wP6Qc|`f2E!!3+>qp&7O{ei=5s3
zm`s4Y!9Gwu{Iz}b>%|eju@F+|GUD1dnZMgnQTK}YZ5IOoseI#--{0_aGGFGz0XBPW
zM{GLknwcS2VJNJVJP<U8FyPzQDo@o*h2E?n%bDSMBN0L^^%NnM6h%c?=V?2Wv^k#F
z4H_TIv<YdbwVME_h&K0eeLWNi{4yFs&T<$UpUw%Ozr29<?RwhNQS`VY<*5qx1!a#O
z6Sns;wUfK<DmIK<l=h4^dZEU~vB>1?Mde|p-<aQ@w1mI>({zSjmt>ke`m2CSsWQ+u
z_V%}tAFL}>Jtkuqw!Xo0N&1(U1+rB63d4XG(XYN70t_c#$Y6?DVz^7nEsh@)Hy~wU
zN418?{IeUOO*om@*_t+K&Q2sz)$^KmFq7(aXA0Zw_nb&Ht!0mr_E#M{amN9pb)QG7
z*31@etYw9E|Cn%{SauID$_U<TJji$2vxb<(Zw(3_4HDF@-O6l%^Nt952?@A+ZW*F0
zqd=;NlRe!=OV^zVsDXvIW$vC_0_K8RTvGm|c3sdH=|4~voDg~tKnGeNrx~ZR$<whC
z_XfHq<0Q{W38drOT)7|q3fAErSb7ypmz)mwHjL~nZg(wiRr+KYd&LoU;(g>i>62fS
z=x=84!oJ_pk-EM<f$35sF2gO5xb8BA7|3oK?4F&Ogv?($TwLEz3uiTG^%>}y*&A%Y
z*dymtJ#;KD17~EPjL!%x7nj{#O|~c3U%*mmnesfXo<SsTj_63KTfyJR*nkGdvy}+`
z+*|Y0jGd038A(^ZdX;TDl(DiGO3!=y&|B*iG9b<Mz~w)m-IA%&(diy+%EOL_RkcTN
zW@aY;n6td^h36<}s~Pi{6-Lvshkv$gr@i$hTCrD4zl4;fVuq|Q9SWV~2vG~DNfh%0
zfp#`lL0>6J(VM5e#A@nXgWSJ1Mi)qwm#y`FBr6TiMqVM*eW7{3ZLJ~v4%Tzj6sUna
z4KMZD@-#hPlQ$Y^uBQcNq2P(Mz#9Cs85)+89HI}+mWq=5Z;>=>o{RsS>c#ieIsCFO
z8y^=Q3N4BqXd<mO`-wDHtwq-QbtPa);oK#m2JR|tjp4KPZO&C<p7R~j<JPtksjy}n
z_%WHLxhqKH<!*+(dd$mm1ZXd0LRV${ix&a8w)9=d=yl5D>voj+utjgj#%TtF(NOrn
zZ0!48o&H|+Os~O;&Ygg>mGIDl%i-r%_$t05-Wx>`K{QHoilt$bQR2Yl-dnAsgZ=H<
z-tT@A{9#4cWN_J-bnR-W_Jhb<CQ@oDk@A!s*O*V`xG!^)al>5E%`SS&VzRbXFfG9c
z9Tm>g_4TRUC_G-?JD=5Maeq`CcPkO<aDA#XjA$I`;6!gFVS(cTT~BtxQZA@*^gx01
zpo0DQgBR21GOCCh0Yv(GE74_V?!k7pC*No8hZBw;PYL80qoo9cy{Vl_!XOA|qW`Sp
z8}bY(?d$YT&0MHN8(IAvN`hlxX1D1}Dk5tMCUrb5;Kh>GYRO()5J?bKIsf!OSAV24
zKJ=gKBviKFC!Rtwo)x&_H*$w(uCr6O+oP;7xsa`!zW?{04%aG8=jn1mTlkAv3{Uk(
zv6HnBikb&xD^PlqTdL^^uVdxeWE9pf;9}an;7k}-qA_U0;CAr8O}2Ucg#BNW*25h%
z`yX>+7ImGvDnN~CQhnAaTFn;gAchF+kN*o;PLIy9!sGMd3Ps^+kwG;u@Q5>MgkYTb
zI)J_s>Nq_$vcWmgJK6g;N_6$Rgq(oQtq@NOBWwUHHM(pwjVZymQj+EYTP;7i;_Vpt
zB438>zDbqy+p~;ZDNX@_!c3&swI8`5VoYYQ=DcJAYkeUtNTLj2U2wUpe_Rc8whcn+
zu_Z=xgjzo5bVB09XKe?y<rtJZow2rL8G#S7izSysRTzY_M?@d1TN{bbeAqUNFJ7!p
zN+C=x)Ar&v+0~QlTkB&RNMwu#dDX}A-KoHQ&cT#H^{Zpo0sNJKz-9y*t@7`)c2OtC
zHjr)FP@l=XyZP++tB!T@vq-6-UIQ6JD(vKk@iXNcg_D#~J`jp(uTA|%3mQJAu6flb
zLQmf^&1;jQIP4*c^tIy*lS0GSrx0^-IrRuGo)4`T@GZ1f%zdpvElEi)(HFqPm#>Ko
zy6GpdJ*lltL$0_IOt1|4YFh8s`uc^lA{hO*U9bAq2E6DD`@QT$qaT4}TC{#+SKZ9v
zdllwvwqMX?iA9gy+DK?-t|`O4BPWvi6T+UFnfUc99wK4fv$OuUc3jfdB58SYAZwaA
z993Xlds)q4dPje(93yHg$Iev)jo$(Iy^|q&QpfDVS%utCyo?!%+}X(j8bw-E*rxJs
zYrw|bZ0Oj)QclVn*RgOqIWlrw`xvp)#%p!$|9`AAo;?2Nb3jqfd-Q<MF%K0Ze?9Fy
zfjqfblEzvGD6FFi4O1t&+Y6@B{vOfS+(wx;IK$^B3&Pt^R(v6S3oz_*3^<XytZ@?G
z{t)S%nYudj!}X2udY;2a5L?Sm+zZ}Jup%xnheH4&0>VQpi$yAn{dfusXJ>gK=cy17
zc|G`kfU~Qc8&<h?(yyq7<}~Y!=LPZ1Ih`~;HX9ReXOHj^KvZ^m$ySCG#b3OdE{Zk_
zabLv@g;J*GeyfNB^|S?*W$qk-e{G%6Zh`Na%4gkegL~%yj#g`cw(+ggIlX_?m){Q!
zjr6LtV8mO<f={bF?%sDjC18Z7I|+5-RX9!4QLeOPkdcIEfrA_<__~2(-taKvMu3mc
zpFb*m6&g-2)|OPxw}n9fT~bnaY!D^#-7$;rt)+~P>cPQRVYg>${eU=L{{Vo^NQ;wB
zgd=6%4O~7`783~>;oqd3p1Shu1enMP5rKk069TFG-_zdyY54x#hJLiWqV_~&5ZEFq
z`;Re0+8hQJD5{x7FN~@6l7KX;TU%jX(Jc9$jrmb;HU^-@8~lG=AFiR~VUh>==M%<9
zNPCzet;}yP&oh8L`e_Cb=S_$z7m0W~{1x<dPLTt2j64?^WPA<VHrK~y_(448MYZBu
z8_VPMWyjz1M;ZSzbv2u_f+QP<>0$#289Cbzm7FI=pCeO+*aBwr=g#T8j%`6Wyyr+Y
zulfGJxI{m@Lc#25Di{*z{%vEL{tz0L2}93bxCY{i4BNv5(}&{3r7{w2Z`e&yh~X}?
zF6{lV|5V2Oo$Fo(L>CS8R;<f%a=auz)?K$vY^9__a(|Kn$#^r12Zc$<hbdKNznVv2
z6ES=A^$1NDqoRR(u(Zip%&yZFB#kxeU*Fo9d>>*{Oq;9Mm_(EdUa^|g;u~0c*Rta?
z%i4*F(<*BnJ*6Whm}4eSo+}5|ek>!Fo_(*R=gsIe$sf2He(ehi%CE6A=8DWw$r_-q
z`>axKWL%@E_I(&>T~|GU;z<EGjK6;WuJ%8BOJ=j$A)u`4+nDYpJ-vca{Gj5VJbO2{
z)1E}_Sy&fme_;BXVD}$YLB2dU^e<{>Mg9eAaj;v}N**2jIldhqzu%egFUWc&QSI$t
zAPwr^qf_kOMj_%3-F_~;Q-68s(Ipp}_pGpoMl)ka%<J#^iz6RBpoB;QFs91|GL|3o
z?>W%IITCvp;AjK^m+8)E+NNdW*#?mFY$Eh7qWC8F9OVS>syAK0&{HOn5=glKGN^<Q
z(VkMEmvc%%YvC?Q!LP57x>&n6;x_4BcgQV2MkemGdH$E9{e(kG$AeCsn4P`yJ<&&F
zrwDpJ<IKl6j-ZJ%QZ+<RLcP(ZxR6f$x$dUhkqZR83>&vt#p$WJ!uF3pHkeTmgwX6J
z6bE?cgXpaSfCvbd!i$tx>%DmM)<^Xdb7WfAHp%Z)rOZ3-B72p$ZRP%?NVfOmidIu`
z^A<?=F~m>uCix4c6<V831(=Z2t!G{ra(tzTH!HT$6x2v<!(A>SB`L<%=F4{r-gVE?
zybb#jBiyV>{^VAKFUaqdiW5@ygd@2@B$1=2Q%8tT-AWB+HThcO-}VaAHIP0NHumpt
z(9vpfP}lcSTDX8e`Hz@i5!I;yxtxvtP|~BtM}AK4xMGQ1^Mle)aa><#wGzd0x<CHb
z%2hc&fT+zj9hp)1dg2c(II-&+$?eixS}S<BrSbc|K-w{oDF|#NV-OD2ru^HPKVr1e
zBlQ0PjB~epOb&q)M(L>Ct>8|VE6CG+LH$K}aq|M^pTzUM_y6v!T<it#?%|YTW@+@&
z6Zgpb;~kA{^mSL>6}Y>SRHPlGBR?QRs+t@Jc#?s*9N)cQ>wqcIb#|P$+xWC*Te)m`
zOSjetQkL^f&6bifWjeEJy1yHwp7!Tn%-v0%sW)w=)h(T20s_tD!|$t{$VNBLq{991
z+(jCJv`U;1rh^V^cDQmwGvu%^Iw;SU{cI#)!`8TQ9O)$#dd&{<6T;}&oqNz-5rXs5
zQE7&dhIfvnPy4pDZiw&Bu^MYgYFfGHqRzQE3(E+HUJ&pj&0a{^Tiu_te<3c;YQ1nQ
zKn|U_dRq_!NrF8|(tWl&rwiSldDCEa?FKXgzUPXS&h{wrpN$RJu!1iUsU=vK;vITN
zqDL+-p?VJd)X%Ro-PlgHdF0)-$Xj5}j~+#`P1E)kjuz|vN-xSKGY29!=ko3M0t`tD
zs4b!HXVdYU`_i5_wn?jcw90f{05qJF+t~~Axs07H5ShJxQ8ILs(w@Nl({)Mz!@`pj
zcMfws*~@I`oAC#6jA3Pd>j^QDR4oxsW6;@wOgMzgpf>XD{?wi%Eq{3EJ_H0$S5Zq8
zPqncNiv8SrMs0lHat)l#sUe?1x3`}HFc=S#oL;O_ev&P$5$U)9`rI5~^Pr2Lj?fX0
z0jziI+m8y@S*wj{y@tjyK!;!<_!}kR@F1Kn9QhcAW3Ozhsn(G4CkGBUlI}2pPz{-d
zKpt~4nP%xd9|<x@PD|rprl2<|^#>h|d&bL+qLfGPH%MU99~8i=QpAte<K;NUt%xwm
zWE0pdx^s*(*Z^!Sz>VjPnhj($&6_VWw^V8;^6ao@QV}?~+1|3afCnddR9jjnx2>KO
zIa&VVxwmpP5tEI?Wg9(THIN^Zf=Qi(n;Hu;=frp`XkCxVZBk~&-v$lM2ET|u-~+Zx
zt!l9izl{VJrwW9L8e;?ZsZ;P*L0Z!nolnGDbY&bKq+QjUy^1V<;g;{sTHSJ<2x!PZ
zroBs>Y4x;XY6Vt<JcindLp6e!CM66+BU01kS0en5*uaO`OL6;^{!)l6jweo$=H{`F
zjPbBZMD4xyu4+)?tV2MX-f~TxSemPDh`UtfMC{3m8gk%Hl%DCrkMk05FYL=1_#Y1M
z)MlxYN(m}=8p^7ptSWB1MQY!QG&a6_7krl;42nJ25FWVnSKpbwN7<1wXJ4={=HOkz
zqXfdN5Y*e58=2l$y&+52oTgZ4$12#OdMb=bA%wNRA=m63SVIt0n3wDHA#k2fGooFl
zp;dQwB)rA=!p6)=aYw(m0FDHiytMVF!R_d`)<g6ELW;Zy-7Sp`v6!iF%DRk^CG(!8
zpIPrMA9fr;=xLQYe(*3L@Tzhf7Q1e>-DNF!htB>Lu`D)ebypX-GscMw*my$rEX`9i
zIo?FpmKJi_jCQ-QIbo;cl&hclkysrhF*8E|I%5ucNec9`M9D^e0mQd^y6`+m2aU4J
zGU|NmLOOM;NB)Tg2(^ZL4z!~<(%|1R4eWFitK3)3WR?mTt<s)iUfI_z+HWdV6QTdz
zgN0e)<$EHf+6(7>b8epZrDQGd&b_}#mEUl(H}6ig`LE4-vvQ(iE{{HS73>uNJm^5U
z;n0l$^X<9Wi&XFVLbUt$bosFha@^WOt?T&k`HKTWvd=iOPac(YokSQ&{_f)<?L?R1
z@wTq5bo;<)t?dAlJ~5-uc_orA;)T0i1dm%|;Ab;<s3kdHNG;^rvuB*yz0AmTBS+VR
z4oci%N1k1+dG>$M!Yw8FU=Y|eDD&QQ<l1*X5^zTh8;d>fmY;!#ZmElx&Bvwi<ujbI
zCU)2$OV!o7$AB<71oqol2AP^RY0JyD;Jd}#iux-e4p=lc6r^e=zaO&phMQU<gkOEo
z8)t@?8XIh9qL%e6|Cc!6HqXZt^ST!wrB`Z&qGA9N__Oc?z|eKD^APWZ3D+x?s6kHt
zd;4tf+pF`#|Mj*;4!8>Imkp29|NNoU)cGV2a$6?2CkbT!F;URq+uk=xvr7ijqZbw&
zo)q1(y>ngoT(iQu+Lo*F&B(u><FUc6k2mDy1d?Jm^|2(1ei}6ArsC62<uBD+N>(c6
zRj5u3=Kh<%H>eH}Lw5y{J(Nr;|ES{`=C5ss)AymCj$VckTMKA>W|N}?*V>_0CW>l_
zM1XM<+SBBKJ4GvHI($Z#cuQV2)uSO-Cr6kBRIB~K^1~0NEFo#G300nj-*X7Gyh%A6
zgi=cF0U24M5_43OP!E`5$<|1oM*9_>o@Jl{VNg(#C5}TSSE7N6$KW}T0r1~=LT;s9
zvuIX)T40G3NR~FkxtHeBir`<LAA?$$+L(pAu#5R_dL|>lV|E}eceuj^>p7i=pkSJy
z1sjOwRB*56H>be~eL>un>gOV>AqAF}@_j<23sCUpk`~__3wAiFR4Wuqs)h>L8V65n
zBAsy8c%w=P&8tVNt!)t!mx<ZO_}E@N<(Qb-=YswqGz{gc=<UDu;dED*^{ug1`tMHM
zusyg;2XypjH|<h7>_-zw-7wP-4FW)miEvSIWVgyOfnS!hZ=&~ii%~LK&du|e29|H^
zn0lL7JWz9;_9ob1<Mi9+Sl`}g#@x|mXFT_GbPQJdr{}I5etuXLpx&Bc52MW<2q0RV
z>=@zelueeBY;bru*)^y%tasw-Bo0a?s#1$<_Wyj3PSCp3MsH|n020T6(N_B1WJyr)
z8VS9qTRy&|g|XOr3*`D8E*b<0OV+HYdh?=hi<sms3xXDNHAavepg%C!8sQq)gAh`P
zeDm*lxPu~Z3ylh^y$VM5KYJK5#<`iF;fMd=x0G_3zz!}A&(QD`yNo6ApO#8g*;a}Y
zh9?sQ$2ga6LS6-Th9ncAxL43*_a6BQf}n*bdyFcL1xwnY>GFeQps|}JYRL~Qe+xa*
zm~j+_KO35=pYtSO*dYszjDyxB(6u3AE=>npx@UMM1!|R%aVC)%N_|k-Vy}Haxa2@c
zJY53n@c7K8oCSvc*UL+S1u#SCR~+0}1-x0Zgpsq93%J=r)7re5{!)A`@aI8Y5X+;C
z3;Ph&xIR;{SyHIk@J4aD9;2`Z=hWo|r_@RE6ebm<R@6dP3(*Ax=7_8>AqB*p_>T2?
z=FLckqMF9_sgAkX+C_E+K6vdlG`6^Quj5h}K_K}pYhd1AcYbc3XH3A;>=_tthD;`o
zh;5tUETJ5bswW3b0b>jE?62yhF=d3Z?JvjH)C%(fX(-luROM~~JiA+qmw<VHUy!|O
zIhpyP_8Ym(=ojGwaWVG^2f`H$igxuWboxZbxg$B(a#5y`Q|Nld@uoM8H+Et;1j<Ig
z?5=BF)z<-WL(I5=)nMOn0RR`8AzeD+AZ3cXXU0Uld~qnmNc&6tDX1B>;M;6ZwW}zn
zw_<gl00<RQ=mkRH^i3cYVW!xJ&^m@7NQIXJl!&o>Q$f_hvby%>PFhqG@wt`qgvhYq
zfO&6AMa*=?rWXYByI)fb{pPL_TECfMl#T#{LF^!}ztPL-IiaS=s|e90NF~S^f-vhd
z8H5?ZB8=83Ze8auj)TyHW(24gk5sVIwR8fCsWlpN>$-e@UZfDZV69y;hVbg^-ye&D
zHSn;)<E={I2VcM3^(1r58NSf$p?@~TJ*3gfxI>TvrdP~9-%<DCHG(}?qq-}JNY8Mg
zZHf{z-f1tDWUv9ei@R|28aqhfq{A%*+jLBNI|0ZPudBKzD*E*)<`#^LH)&9&g6Qr}
zdKKtHcy;HB%^+CwLtAhAb2kN>(MJtG66>$xIL&~-g3tDZdC9U%QGV0(QDHz$S{s@0
zlhx=Na8u6?EoILMRWVNTeYVby&He&PO!wq~T)eObd+Hu)=SICnDlMIUw6*ly_vDbp
zC=<+mlVpGMrM30==!A4K?CD%5Z0RRCoUf`SaSzykII5p3UO_hBUp(zq;`@~BnElfO
zw#(}6DL==kmv07AQ=l!huK2Cr-~UAEZ5Tls!pU_s2=Jyy+<EulVWIHHMP|Fx>&xYw
zesDmXory~RdT*0etzbKrCxA!E5oK_ey|)KMyh@&H(tdn}TP9#OUVFQ!WlH0+!`g%U
z@^#Nq?|!xlO=bhZ43dzKD+hn~Uo=$OP2>iFsz!z?TdmsG3$GP=i&Se>K|Cvoil|VS
z*Tu2k=PA|%LreUg4J2nR9ZG<fnd)9W+2?-#^@Xm@4!<93(Z&#)F_jxif6T{0Rf@xe
zS7Ys6n<<-0N)zQVA-6lI&S$G-Aa^vJ-y<vK^DTHRAe{%Q<4m!t?d?Co2fwneb$Y1c
z8k&T>E`jjvPM@`$8KqkmbtVJLY>PM_$Aw-He#gTUrb1Qe_jZ-}N?^j3_|gMyPnFmj
zidedr-KqPbviI(D40)p?5Oz8F(NzbIbhhQD8d&upVTViB%(h|(Vn?@b>o>7EF3HQs
zm&?u;^po3&U;{3=YjlqIcjwY!t;UGaw8JK(Cs0EB9Ml*D$JKEJAN_LpVlE$+Sdd+P
zLO<r>E(djcb+@vf2Lv=RCe?rLv0%ME%?meiFi(QTul0!DY8iqkwloa)h~SC6NG)*p
zCjfe^&s~a^X<eJb3JC-WuMwD=B3an=u8ok(q-dL)#Hk=s)pGwRiB=RghCGg_Sze0{
z9?M$7Lwlk;+`PuLx!;E&JQk0GDTy~h#tcPpIrp=%_Wsgjlw5o8yK$!8_{m-zf`H5^
zL4!_nja7#A!mc3Ec%&@keeQOi_h<IgrrDKDgbW%!fD0=NVT1F25Wb(AD~*!}B3srR
z+b`*zPk91GtMYN<po(O@Dl_PlWIKY@;l~oEo|j=`BD&+!YrI~$QtK?6;)=#A$((Xv
zOzxIyhisgVs9J5dN<R@p^2G+wTVq-s?FEy`We0U?5rTx{sZcZI>!xRj?zyTlzv2Uh
zD5SB=osYsMjwEVtZA=m3;aXajieb%v717B=Pf5e%u~Z`b9So#(j2~eI;Xq5HAxonj
z+iZd*5JYpcKJ0}rZ&Xf()NqF0UR)XkIOXxb{nbX;OutIYf>p0Wy3{_LLvZ^Lq=i#I
z701Kia7Os~LiH3oZ>4(jWoWkqM-HZ7=273B*pQvOF=cDfKUl=%<KGYIk@;<r=PZC?
zVRiwCeIi)pUc$(bxS*hcq6x76Q;D|PnN0Poc6R2EC3g?g(%O*P$#yP5o`f}~Be%^U
z!Baget`}x#hJDB}#?!BTIa`G~+%yx|)~ka1%IX57qga(xOudQ3#9qpt{&0HJ^T(L6
z_QR#g+JUT$IlTGSLI2^(MlzHZKH83Dc|74&>+vGMD=vjdCGJTe&@j7PCNX)Zygcdl
z(>)Xp>;WhEm~`QG4G4l@rdGJoyvA9eUtOS<>nlr*dMPTMh)PG<4B4cQIb*}Ycr>qI
zGE8w>8VBj??u5O$iu3@Tv}`VX%8!bXt9nurOoeg4ONxE5;bm(W2m#SuzHoj`tXgQ`
zHFQ9FfKql4_Xy*e?2H``P|JL}l^G&}E?Rr_W7y777A)*jJZNWu#4SHhg-f*%6M`tx
z*<Y*HUcEwnuQ-7)$-b=wU4qD#drr0juOS21MxoKC$KryDkBscIZ+idTogycijuX~K
z_a`LEJ6}n=8^D(9U(O-G&o%K$%SlY`sFHg;NU7X@oRJv9W-vNPvAdwIguTE!H{=V0
z_FBY>ZN(9Xhs#}Yd|WJKquXZeBOIw*fGId`Y4(zfZ$|i$Jk~V2Kt~>Br~VoSkprcg
zNrA=8v-`J#$1+9_GcT-HO!+>om3WsHDcO8eaQ6a8rWyyg{GgVfzOJ*Fh~R=#wJ5LO
zSR(uImGrEdGhEqqi=)E~cK}cs2}w2Qw~K3Y^#Y+iWvUI}JY()uA_l>hu1Z|I?Ykyc
z8}4fFXm2$s^wcd~x%xVw#quv=YF+4^IJF<qMmNN##z&w8s8R%+8g-DlWpMxA;Q?8J
z7}2^(jSfC}7wj<>J-=sxM4mz@))>EjGW_$g_h&(q*LP1V6{^KMCYdXtA!96<;V(n$
zu(HQ?uHem-W<>P6{l=LP)6-D*BDn@%TsE;#Na2w-DDld|Bew!={ZygE=6A=gUgJT0
z{|rrqE*^g!kg~~=@M_799%?ei6PBWMN~?$noZrFo6BnqTp1Ry#^m{)aUI0ZyxZf4~
zQMuKSQr>{{CR=Y@xbZ{Joh!)xf|~bS<#{*rs?YO!JxWv?8ykDU%!S|8TW3tI`n#xE
zwiM=!4271i09Am{cPiL}noa6EO`}M{yXW<bKCvO~b7RW$TH^CvECk^MMAW7de1=6G
zMS*w)%vHD`s7mVXfYg}B=MX7vvcdxFW7<3qzTPg)0#)H|l^G%*W$mntuB=?dRVitt
zF&|z3anK)^=Z%{v_)l+KfBL#Yh1ON*nMr~#Z(^00OpXfLte2Q(a8u2c(Z$;0$2?|~
zoZSRf40Pq^Dj+IPWE+E^ps(8-u?g{CD6j8(vkBr=AY74*6NuXdXANw<ucV`@lb}aC
z({94v`|FZj+8bHPz6~9>aA{yE?s9A+xx^?yk9xPWQRV59R%QwxH_8-lDPU}9Qnp4E
zT>HX|PtYknh&SWrsYtJMvQotPQC!0eAEIiR`_Tz4O3&>Znnr{j-nR?j4Ai==IeQVP
zB49;)nHiF#fs`Hk*0b77116!DaE&2u`pH>y$B6LiOQ2t>n(S$b-?!IqwVY6@40bV-
zy`P1Y{nSd@+vuNlfmA3l>Tf<=8UzpANwRtL;StKzg4ct~^VghUGA0>p%i#60bdJ<7
zZ?-&TBH-0RJI|j=o6#PCVT*hge)0N9q_Q2DK`TqntSzcs8#DR^uc?=BQcT0EP^r=W
zEIzL{;IZ=^Rrie;CC4jz!Jd7kVTW3hyQ5nZ7D(xb90pLidIbyZM+zq`>lKtWO&o0j
z9es%)HqPZmu@ZUxJ$6zf?I>znxOexUsWEcS!={eOd^dJN4$5J=@AIvw@uD{9q9sBK
z`;?rA%7m!Jpdk)xaGnx0Rkk+qng!&2y&bt;8ecrRy3v2!@{d*~chBsu&iE-gJrfxd
zA6vY@HN+`j2>K?mVEFHZAPl6668X7`Qw1T61B+63b^`bp>GN?M30pl*m&ThVY8f|P
z+{e%eXcEXcV#A8XAEJ6|qWGZ&H{9Tb@(X?KfV){C*(K6;w@UncOHfnwgXu5eRhKBN
z@B90`>UH&Bs4#6`BF41O6YojVq$YlAs&pq5Mwg8c-9bplAQ%AXjd)<jV8xd`yiieZ
zkH*8|AZ$HVM8Vupt2B=yat)Ec_ZQIZdfVR75yM<PvGy0pu>!ve2eDaXYZy)BK$l6<
z1OZUx)NwbMA%q(-BZ@sQecj@F+()1Pe1@vn-yMUhP~n^qPsde@EkB8(Rx;BVS(|MA
zbfaVA$B&Li`**Pjt^DWf#S!zw2B#Ir#`Gq6E7n@b4|pHkqvw{n83A71r?rDxglhQ?
zE0|+1j;kSB8nH-kppQjY%1?(TTj!|Mt7amK9%{_VO5XitRqO`?fdc5N2qk4@)O)MT
zwPe@RWD^_3;y=Vhlqmy=>wET%6$0Z5aG8%VvGGV6OwadrUH)<KyYl((<>cJQL#k9W
zyD6lsnzeT!mhrp(w9L2Eli<-$+OrZ8qK+-lmD5gZQj5A3W)M@jpiN2drwh!g%U+hh
zJ~$oT)mt`-j|@Kn`oS{w9&s*~H^L8Fs>+4FLK77FGwSnHHD@?C=@l8M%;wj&$`-D-
zNx&xl8pd%enLFnh!R%Rv-n&~nY-f)Z;I5$)<I*j?ZRdfbunI8H?OF3nu2y6zQNIQ7
zTAitIPJEzUZfo#>JAiVbNVV{8mZ2&w{?9hyq>y8(+7HiQVAPwM47e#rf*8a3uEUUT
zXWv}_O~mG-<(s@kpwtlcu}GKLxLD?osdG@<Nr{r<$fW4j_1m?67Tls<Gp!e>D~^tN
z{ab(jZp>OElj`e-n!s>zzO)s0Gvpc)-~%q))j6?rn^fv+o9E4g;LH+%qObKJPiNcw
zefyV@P*;V6+=t!_j-fI2oNH}CFi%L%g(3rE<h$~?chTt~VlhMCB0B5p)5G4$UjdEM
zB#LdPOJOG&Q!}0J52?gwm7g{u(CN?~t%pcX!w0C{sDr9evvw#NPB0=g(~jSKc|CG$
zUvGRYdhM^2S{u!Y^)C&Fv0zrpXGlFtS1Xg}f+|!D)hk?2h5kn~0B2Onrzfk)X=QNV
zCJFoD$u!s`fA9W1KBee2)~icpd}&KZK;+2SKuB3>hJXQbYJEL}#nM%R^x&X;Hi-~8
z1Zu2s(!|9AX+$W&AdzM{PYe=8ioz0f)Ou2i;k@W&C3Z6vpO3Y-DbtV6+`Yux+~y%V
zkY#P{nCIeh(@D5Qtwarsz~PTOP8WlLm+i2SEI`LXi*+JX6IJZn(~<Xunkx$kw6T!7
z>xH&CM(+_7DoHp9h~Um?&V7xIEgdCTq-MF=TWigq<={O&4s_JBkaC1)*W0g0%YoAu
zS`uRZZLB}31(!%19M-lCis(wrOxF3uS{mmFOGJUBP5$ghZ<gj`WMNFb8x}RVH~hzG
z=c)&Zn8GUDdEF3%tkkFe;PB}E)tzF+N2pY9mO0)G8SXOOx~?~lsh_GZTRocXESJ>R
zhgxoIxCUxClXkYE)Tx+sNCo7bp0X#w+S+=uiW`A+!V<!`WT0XYiDdl!L3|ir-?p!O
zU<HURTOJ{w)aZw>XlP=$iP>ZTFRA~WoQ3*xy_9gz8B;s`SM$8rwf=4&)%H%FTwDCY
zU(PrF_V16W-9^tWpZ50l{a?Q}w!Zgz{4-bSd4^ANXlO4X896b&PO6V4XmC42bwOX7
znyw;u<Jt(m8D{R$hLYR}1Q+#_+#@GZEBU8r><K6i#*H8j5l|nFA^MueoIajEZgCZA
zryxwIB@&uVt*=IIq{bJGs&_`0oh3aUg57{F`hCM#;5<|+qQzLP^g!k75euY%&WZr~
zY<*veiDYogD088gb<FP0=G@2TbrIQ{jyytr?R#bL@mJM%L8v4l!Uxb(1_N9DG5+o5
zfD%>yk5U5-jc<C+)(4iRYOe{(b{BNx_HVt3Y`UJi)t5_D==)}5#{FAy&4deT4FU}!
z5<xPMo9q(oyi!(BDX;<iQ+|F)$%Lb=H2y5>wLpnZHD7t<^rijfw}%m;I*!Yedb}?A
zn2t|BRq~l1<74(Nf)5Ulh_8Mg9qGRdj@h`<(f&5p1a_%3vzE!02oyfDDN!K<Fzr$+
zGz6j5uQl9M!4O$d-f+un@K4bDyL;*1Mp}8oY0KLe`BS*qaM`Qo{9c;8v%hQ65SQum
zH?)w^HbNQPLlK=ukkaurzm1|AM~%Y<J3FEKU(>?`yd1R5@T%pBP|Wh@z&2@EWpqi2
z+X(+jvm^u_)eAF`tZbW9{{V^*rvF&%abo@bJEHxzqa$=IZm*Xg(Fb(?!|hUpZqP?#
zD1l=Gj`S>6?R#D&E(@fIpF~>xZT)*u(|eR#dN}BlTB6nD_U<}eak!O!9&HTypzaG<
z@M(4gc~z@|63IRYNBQd0Q(u~FN^9PWs0{#$cZ@>O8YX(PysWIo@Ao@ZYLlW7MET1M
zI7w3j{mWN)3dUC5k~gJgO_<OduLe|4{aPcyIa7YYi@oQQ2FuUMQB6%QIZcVn>nunP
zaosWR3L)0weLIM|TD3u=_;6_)0I6-fJ$R8|J~iW8|D^DtnunOpDZ)dgGt|$fiBQK>
z6zH0qDtitsMs#P<#hiNpFFmLtqdBd4bK=`y70a|kzh=5=He8PPI8FeU?QSMN<5RrM
zak<Z0lm`UqO#~$xSaA0h2pWex;Nn2B3AHOo%q;xdXT8PF<oU`X=B~mGE*1KSW~?op
zRs1j-)8-n)Y-*f7mpm9lk=XuA4?61In@{+s=>}kb*C}p~i|cP5e;2sXI5xVl|6Oa8
z1Lx66`4kYOQG5aoMSoPk8qCM$AhZG$mu}eEg?lNt|M@-qq9csC-Zmeq^jHv$GIp#H
z^b(WSA-GJ3J7KG@mz^UZWyR3yyeWGUJwTbsH{dQ*PZ$k?*;-(3<#HpN-!y{cCe=q`
zjJb&oqOyqysSj!1!rFe~UY7dcHA7396WCqu@p7`4RJ%+;gzXw7AETsy9%E5xPL=nr
z`rh$m4a(jR3ccGr+t}2z!#w)#(L7TF?rbddm45d1<}F7qDX`Va<f;*PAFYpQi8uB3
zL|u0-Q9-w$A$6}W#M_jnLendsGiJ0qtA4~_UGP-x`4+r8kabZOB858Fq(^;5>L+*_
z)4Pg0LSEJ0ho(SXc;n_dKyWUI49KC-*HJFOukpn+Qub+St6cRHSUA-b#8x$tf`>~*
zmHFZ)(1*`5LW{U?&>FK)zZ~{!afdhl{XA-LV}r}&t1pRkzNDqPp9d#F2mSDaX*j65
z6*l1UNJ^WNv&FTteB2Yq$P9dSeFpK#WTs%*;+r>6SuOUW+^|f+Q2i3_mA_Ic2tJpk
z()abE-gb);GQ5aP5JLX6sV@k^K?HT<{;rlEY6j2`mxBH&4LA9_a0sV*1jnCF$DUEZ
z+=-aJapMNabWQKpwUAepD-mJCw*i1I1H}c)FBGj^E#9;ua`QNr341%PL~l?SEUUVo
zi;Eq8R>>$=As^b_-d^WAuqPbtNtR0ly=c7QZO*1nb+_~+^s0Ceh7<sCpvyFXw<?el
z{DQG~*lOV&85tSwq8#r)5U37KHfeNdug|aZ6DdaE(DG=c8B&lJInI2e<iEM3a`o0v
z`u(ocrR5}ZeK>^Mm>4>?-`^iWb~=wrP3wIJZkr3#uI^R2X0f@kp`-+S+0Tx^t(y}w
z|Ep(txlEf>$njELoe%v!XdSn#dqSJ>gT-0SIVakT4TaHfGkMt@y8N)~s%@y&D928}
zk7Lnj`IB$tEs%$L<2dM$b}AlYT5^TkpY-%Z_0Y!+sYGJ3n(5_o_`3|vGx+a|p$}19
z+~#tIb`IG54yo#!@k+!0#a`k~`QM{c(361RCH%A8h79_HwX5g2-o3v5B`r}91XMP-
z??c(F)Y3){HtsvPfXr4DtmUgA+QafWOM3eiuV_Y4JNwky+AuYZS*Vw&t(pEGAps|$
zMJ;N!w|CiEi8)BBukS3sdrPy1Pfhl5S)2^YFcG4lVuo}QZrK?#lQ-~$^$>5ezfTj8
z!@-YD7$&Mz9e=0I{<`baVdTHrw+D+GyOQyHd!Q=+wz)g4K#xXc&9jq|og3CjD9mE5
zDGqx{AZ3N^;u6B25`VDttCw9q&a?*T^18UJA1RKx;x)Y;EylF1%00D8GBq_d1r`7g
z0{8vmQPFI5K5<a6-%pom4+846i>_$cZ`7dWm-)9Jolc@5hS6niuC6|gJ_JL&Vi`w9
ztOW!(G4GGIKX0|QnEa$Xuezja?C$l1KvX{)Si+4UgcYqo(WO9n(J{|vq-t4MSUB2I
z@8ZQ1lK`{J4vG6lS5h8kv0~S5(lsFOdOZ^K%ZHl?sGB!`5{C}Df{IQjsuvYvoP_$D
z9MLpI2SW%OA`!KIAjdVJeHo%6Cttm!Vq}p2=`LxV-R*ec!=<kzM8VA0a(<Sjf}x0q
zjO@hTMYLI+ce4gGl@-z&vsi+_Q5!c?{hB{N*Sv7H#ZMH15EgO=3!FE|6Xr4eZ|`vN
z)z%u;bUOUFWB6won3tzI%*)~_iYvM0wwC=)O+Zm=KxF3$P<Lk{yhicuo9M%%!`Kl{
z8Lb7dlvs-)<j*ayrYqHF9sYoJ2YiZnLGX_E{fR0yU_-O#<neJ^mv!8=gw;=i=uE&1
zdvEThD-@rcF{72kP1x#>$qzxb9p0*4Uu8!XYGn|T4WKv(d=m}1m@9-}V{^DY2I-D2
znLqq<xo;sNvTjt}k8~?OJX}_b69iR+^}bJ2P=<qPI<U!4dmseSV4JT`<sr}{<u-&M
zo|bd|u`2dGygnVC=M7*SqswX>F;;>)7dMM*a@rLi<K!J0?n6<@)eS)RAKIfjf5r<9
zvGB&dgh_i@T6BqRU*hhX^t{ma$xvA-<I3}~9=se5g2>BYs9$xmHkyxbEv5<$K*!gb
z6)ZrYeq(OO)qZq}w#5Gozrk9@+45BSlxX5D(kL*o8oa%2^YO9aA+KfwFQYSAl(5x}
zm5iMjZ&IOL2?k9Bf{HF)h8Wkp*}D?d0~*pZf_#5eO{{?cp|ufVA+I_Y?>KGCQ^!EY
zNa39h`sN<LWESyPlOpF*0R2EJU*0d^0I2>4cFw9&iyC)Oia9)UCTKHyDY=ZBU8V5?
z*OiYwbpi4V(G_|8oQ6&J7Oy<Cu{e*Zs6K;((iq{F_@Z5vBDsm~>;_ozxNe95T3`QS
z4N*xsa`+fv+QfuBwrkj_(B8Bj&{TF4U|;=Ag}UQtGa>)2tAF}RA|qZy|MT5bg_RzI
zRP=hh{qtE3sK+6zL&};Io99sPhMREQgt+qIHw-RpfXnjoszuiB`euUL4eeoM6+4Ix
z#raP6B8G?UF&pURvsqgUxB<RYLPG!VMAkq_=-5!<2qec^^3qKg7fSr0j_hmW*JU%A
zD0Tzbj^23K?}d;9c=ZTV5G{r-NYfrxeP2h`7{2a!YOQs>Lx~EMWF*!)8mplR%(j2k
zWdo6tbEBrQ0Y0RG_}_q77(3Q4wc|uBXk_H=Wukx$iMZE0!Qb1z(yCywcGHoD!1ZU3
zJ3WLNLR?&3so#Kk{eIU8%w=LHh0BX|%JWuw9siR=u5uu)hWHP#IiIQQg?fM}uZS2c
z`J`v!2hnAZho0`m6p_et(b(CA;F2{*l?a{EyaH!zfbQMh0ZG8~K4CqV24(N@3kh8=
zQ5IrwPNw2-VXBD{x<pLOy?bD8+aba$noi=Ay)NuAOLvzS_lfDco1W}uNV{8{CVwWG
zwv*(zc`S)U&RCv~%|eK>9&jsE^QsG=n{FzdWCGRF=8%l*Jx<DYfzKre1V9EMPT*<c
zf3PLAw+mqi4RKUL;AnnpuTg4VoLn!<*I<WRQC(eKl&*GQ8IO;T=L^$e_mT}1BJ|sE
z*^0yH^!YgEfLETIn_Ilq5f6g;EKVyc;1VydN2>V~5a^{=U2vYZZ;56Cz&f+ByC22{
zno=Jf7_bJOJi?fvN_pU3I`iEAT;Zv=hmmhj=EfcFOv5YOUAk4M5U=!XcaR<kMqOSr
z)`r7Oat#jNU{fZv_vi_KBEhWnRkY^S;3NOYDeI-l$=XVJ=sU358m}>)+n-!mQ|I)U
zIC(fJVdEKh7Xbokb`JLwPJ1NkImqCEyMYA~OQOl)zIoJZFgk@_=wH+u+12&3L=MmH
z)+ns<OX`d94FH|{|5>@^aLduV+uOim^1V1N>UGEENwSv(RHnI=ss7lT8v&fp?<JoI
zDgU*??y&z<>M`|wj*{#DR#MCc1}&rWg!6Baosc=^g(rHS1Xv=<I!$%nw;MpN4)m_9
ztn~NyqqS*HX<xGjS~olIvkFi3mrE$ZanFE`4`1&SvN!Nx13?megwmj=bJe^?qW}F`
za4CCi;SKHS?Ck7c2riQI;;sJZq{-_ABq9L8m5wWpazc(CW-p1M-?2zz%lCn8YBM2Y
zDQzy($j4rUVbvSat$KyX>i=u#+T)q*|MzHW4%NgrL(zoC{KQs-(r6);Lrs!$$T>MR
za+ol!Ma`l#r<|TVMmZKChY>Pm8MRR4xL6D;!yM;#?~nbr*S-7n`CRYoeO=d^{#b2a
zvJ>anm0`CsKl!&mA7y#BS@2mwUK8@<3>;(%f2gjW^HS+bp{zsD0;9SN*%$FY?!dsn
zw^gnQ2#VAK{h>>VLY%6vRC~704G&ffv187x4CdITUE|jWu5O;eJVZ@8S%N#rIvvOR
z-tHbU29TEHvs6^Ub1p~x3`#*+qvUZVG~Y}VlZn6MANP0T3RXHVvT4LU#LsnC_AWc`
z=g?mu?TPem3Dm<Q)*w<gs6!freVRViT$rFPVI$$@>3IU7ixp3~5W8SO?9mPj{iQtI
z$@Axw&w(&x7k<c79vsZG8>#0QhMrUvbV_o`P#?KYqz)b0F*t12caGvl2_tT%wQa7d
z7MwshUl}k66S$1b6>dFiOW0fm(&b;ZlwC<VJ+<%{7}xOgP?m~rh7LvJ|CVn4G_)rP
z{Z52dx<iDrmlQkRLfW$$@rYE}kEiW7xZMqyhi+x+#I;Auqc+GZZe%F=2JI^}jx;ef
z)d4%vCoD=nG+f2ME{LzI8qD2@F^P6BrBP7Ykhex~%ZzeN1`5o}OK%(;zFSJWj6Eio
zCtL>(2y_E8^Bt1$HtypwL3EnZNG~U$?W+IvGkZ$4vYeey@!%g8X5#?*SdYV`a<2{<
zaluYY0|vx5zcWe$uUY!$MSwJT-nFg<CskcD@z|^!8TeaQ!RqGr<n8Zn^HI6S;2*v#
zV@x7BW_{RK*y#rsS$WowM^L@q#}}saa+kN>5|d!I4>#T@q{GQGgt*u(K*FDQ$T2`W
z#%65O2H{6j-?s<+ccDCJ`<eLfwj-rSIQcSvfXUcgUq~C@>91dyx`QCEDg2hG%$J5A
zc~O;Lt?x^+;6IHJZw%y`t72>saI$=2;BQLNyYC*>N)ksadbpo3)*9_cR33sryD=)t
zX$W1=Q8^?o5r>sEgC=aRc#465v2lUAx_GWSfsxI3qDX^rGPbT151Dq;v@h3znV?Wf
z_^(?Th@4#@HFRrtwyxE|260nWui;$*Aip)V7mO2+K-U!w^s5%D!V1!oMPcLdE7Q5L
z4e8{)!#7LYb`sK{jcsk+53dE4evt%OXy9;_k#aW}!yfLQHi$m^a|g(Ir-PJs-J#+$
z{WymC4y6yw>Zz(s81Lut-*8f8bRin;MRm@^v)!rkUQ62}>iWjlv!Q^Z%K;)Sk;eUK
z+Sb-K9vo#ZUgLI))GO*ROGO`nXtN3<i$};NyFND=-I(WfB9HJF3qZUDX)RIlAb%zH
zh5E6K7>O!rIBL-9(#?>w7{t9~$HKAJcv(=zkkQ!~rwo+TLwR_fB)NTx0Dk_47#sVX
zM`(7fJQ-Bt8y<?UxLP79ClC5pk3|@?k7$j@JO9c!A)6^zR&<+v|7|9z*ZO=|xpO3X
zgob;{qdn1k##$`WJI*Fh-wY|FK7mT24v>@sMoN^EQZ;@v#)DnTdmZ+=8!x8|6I7U&
z%8N9?$5gMAFM|H8_Xk5E(5<!3J_`2C+_}4<&u7xcx5P1M3z|3OiB|`N34!}Eg!&e+
z8A26*Fa3M=k5jn;bm$a*$BccHW%699Vm}CWCh=MjcW-pP9=|c9G>~R=c5oLJq=>Nc
znCag@=0p2H+Qe{vzsV`^L~rkCtLdO$<NGSo7pa%+zvVy{U@tiwws8vc>c_rb;n^n@
z*TQt@ge%8&F?VDM5r043by9SLQ1K&%DX=cn2I8qWVYm6uF?S_D)06jKmj4E_ZEu_0
ztmF~g!UdUkU!$U)C+o4Qs;csgQ@R=^-1>x5(KUvX^vlQET7N&1$FOwX*y#hb`}dvR
z6(cf|RZ~`W(0R9O$RJQcDt`bfO5#`mfEbdVjI|MCViOjdQg36W$zaooN{^ej9{jo!
z7=_YizP!)8Zd^RcE!~5dt}$B~J855gvrh?@0<8&#WwEquKEn9%WXOVk6=3#e*<iO#
zHej#Pv!DD{*lj`wNhd@`H<u3q{A4#5MK3P3)v-l%!Q!J{(r&@;J|`rsY)H|s_8q0i
zxl}cKLEveq(Vss*KvGMArDSLAFE^IqPpz}}yu4n7Rlq{v-gfpJ8^`i8Nsvl_QY|{^
z3y|ZtkH@?|BvRrQs~1+6*|`Tdup31JBJjYwDq}DZ5bPWkPR1YFvycxjEt%b1-ES`b
zJNrkGs!!3iR)2P$1|`95P*M&#&~#LZVzS8@b#tw^7ZCD#4$1$?qTw+nhqX$wvQD#S
z&C*aNf_S1?@~`rhvWcawxDWd<k`l!bwxv$kYjoj>-HbusWAfgpSlD@}{m5bB-xSZ~
z&{O$r7Z>A{s{0Z8zW$ZT;28`PQhZsL)l^j*k!?L9MO*}~X)HrqakkwCp{#H%h)HmD
zw)<o?7m3c_Xf3RZ(%+imdsYptAfsrvRW*`yG?#~n+qp1qLR&RI_60H~Og^=<KQex2
zX_{5^?o_^mU~IeMa=|MNaKj-$t^vR{fIsemwIk(NRE7>UtMhfgy0jN-^mzo@Ucmy<
z7qLd%672BJ><vN%I&{!3eW@;TM1omiOXP)n0T`oEWs>60Lv9uPYqtj?^CJ>W<KU-K
zr7ZE^xKrA1-?{1C1<W@-R9w;VNq0Ayw1HD+iyw2Y@tj}L%B~ps%>RiqQxky_jl;$u
zaNvYvYlho@A?Q#A^>dW|Q)lEDv~WVEH&vq8tWWE|JXDhW`;XoIb?1iaMaCinj}{Z7
zO8m(O?~rI55c?q#j01Ye@>6s$$gT1g!W85CMg0ME=7%=~1q{#Sx!E!A=7QGWnggt)
zxjo0kapjhCR?ve7YLUhw02tcWU65uET0%7(^uJ>cqKnf6W5ciZz|d&(+#fgxNPwgh
z`Qnkp02wA&;s_b4i^L+oLnW>AE-Ax5l3SXZ?#t!(ozH$B%4lqBhLg?WHh|2e){P~8
zE<Xs1clSq;624JUJ>ZP&vvTqhpzS}SqGxIkd>g7;o8E0Zlh{%F`+w9gBwiqY;WoNp
zKm4kU7&9eVNxr;R-H@f!CotDxgSwJCPjtW3k@1*0@_{pJ=;(AwQMP$7FpEbZi#er^
zR8-j3WZV+`ww5U`taVGqX=A%#{31C8i1Ei}TvwO=?@Q_8+fq4&&rHJH@KJbi*-*$x
ztUw?LMKT9tt-F8;EH=Z+KAhq{-U+yGLATbrES%;S{`B=}C4kET7LVcjh0T-9Y#u!#
z^w1{$kg~HTb8Tt*LXz!JB<RBfgf#pK?l=a$t5cKwgAgWR!~JG(T<n@QxCTsay5^FS
zo7GSN)DfaFSvEriz2W08=v4BIldza;a=^a%Rd|+4e}bX&{wb#o2cLwKIg>yj?NPEv
zYaD_eH)ftRf$*aqwbtU9Nlpao%=KJjv3bJv;B%$#dW0=yx@-c(;kFv4KXwUy9Pu!J
z-$LtlA5b#8>P!1`4E-8^VUi3z$K6i$uCK4-n@7X-^G{I!>#}puW#`0gEN{(DPAavn
z;bv+ARWM6tF615nJA1~jSjqi6%LW*jz~`&Its$GgIld)ng$1;a%r`ELmNc^k^%r}Z
zb8|}sNE-}gAe0ZFT@~}{vW<alS&P4Bv%gyE2DL!xqOtQ26(rf49Py92N0ML-OECIO
zf?khNM_hf_g2_LHu|Z?Rtzy~~DOQl|bt->f?&xmIKR-MNfVR*Z{Ydxd#nfta{1$Jg
zNao0ytt~V+*j-6GCTW1>cI@>1Pd9ll3o4<yQg>rwHNePENO`MPY=YzwwL(!ID#(tS
zaJIUv#Re|kTDFlkH{4U`U6A4Th6y|FK!%1szYS>mQj>Q*d~#7bL{WDa0&X%`{<E>)
zaIc>n$Z+}j<UOAyPV_hse=8D%g&MGm4r<7v3LKNIid(5-Xop!J5ENAg+=lFnSb|`j
zaaY6v{NF%$mer-vu`wA$Zb=UFX#_A#Qz$R?+~Mt9nHlJhx=?kmF4ik}hY;aS%{ZRB
z0q@WO7p|D3a=kGO=ul4$4X2-JjcT7(iA5hSv8|>UIVobU4*6k`R_{Oxg^vZu(9PD6
z5f+P8q*IZW0;56G4}d|6^k3<nU{Ips3;ZFMa-;ukO?H;H3QT!WI)3rK5cXb?I|aWl
zjc5B|A3s|~9%Flzc5}>fn@9MYo4E_2y|72p1rDWjI2w&^CGI5D{y3X$!i!`aBzb|E
z_%ZWE;+nYIb89JOL}>`cq#dEZ<u}2$M_2MAUhq|Xc(n8td8j`5cP^mQ_a{rYKSn)#
zLMQ+(hmu7TgV9?Xo*kQb#O{dozxF%JvzL12Qw&<)%byCN?muXGCYo&(q+xUOIM<}!
zn*U0@$NNm5PXt=h27Z)dSdlWo%2gsbtDcy7aJBLZ2{@bNWRY7q=B9Q;#sw&+KS=wx
zXWVuQ7$PU0S`Bv|V8)goK3W`ozxM2u1?m|t_1RwpHpkEPAV~<vGY=;8nDxO;K<|%}
zviu%NZvqap;ga>K0e(vFDkpglHn8J&zgl^?aB6WM=KJGbV6UXE5H}l$DJZeHSpb!+
z_H0W<srCJ19M~~?tBzeWH4;YL*%q0Hv*C99vcX|*s`FEG^ZX_;&Ci=LdhxTVr{sMA
zoPVsDUtNu|6_@dw0#!iJ|Ga;swh(vFF5Y+X&Y6BBe0yn1QU5gs(jP9jyXED<+wWfX
zY;z)RACw=CjQ5yv&D?u#Kk{X{M!QWQ1IgTIRl=oq{`vC<Xn%HQC-!?$g=V6$lB?!u
z@tcqQ=Fx$Q^Ma!RU#-Rs7YZxXOhNCv-&F*b1`ezD0kGyqW<<NGi;Exl#exbJ@ec+u
zofpXtKMSj-|H8iNoL$>m`({)by|}oTDvPq`1RQ=M680-t8$@4+I=xPf(5373EXA1V
zAN(KT1q3b<8N|PiT`eD=picG)4fldOyV$rHSH_rrz2pVJoiX2;9NoPw?%Mu8-d;M{
zyCy>hPJ&cuR+U%i=(1<mf0lrKNG>i}_MEFB7qpDabrT`-`{2|{ina-FzQU6M473XY
zh&B}e3wvyCf>A0b38}2(0)^Jl{%2*BUjJzylX=zberT|YBl>EgoP3S4HA1WRIRC$2
zxR2Beu6}d9{Mfs1B#>98wXZ8}i+{c4lAup29yflJzktdi<TZs}U5GlwX{wS=YELIy
zjPH*hSE$jU1lwG?y$_VFeR%55n-N?1aD9Ha^wAHnVGob_&}!fn*lgQuCN_^%%3}r}
zlzkqVx$Im{OBYSXp?ig9cSc{$nkQ_<#779T+BN`-Bi;8hG-)qLlcmYc?Jdz8q#ngw
z98Z%@{FCFD7#bQ1)lf&rxxxMo83Eq8f<E5J2*8DCXniJ}v_FSkyKQo5H;_<qpmq80
z<QZ2|CfT=pCYY3SJ_cQALU(pXk)8=HpUm8W9Kqik;;U$`inllM+0ja>AUJPpmQy<z
zd))(C#In<ZLVTwOjF^{IjqMd)m@prLCdiUnm31NGbs?H$GSo|wib6~mhg?$3i%_q)
z>~{8~{j(lR=gS;JpfyH=b+!uNcFxWRF#c!4*~E30C-6TB1re_fa-|&}yh(tYC<pY^
z{0xz)j9hM2y%y^W*CazN5nv^aNvga1*w&v;N2oT0Gx?{*g5SLjgdeX&H5Z@P|Gxk#
z%IaO&74~Ox(XG3qj{k|&pz}Vkw8I}RB7u=R`|Tm{J_@(-FWXa~zuK29Qcn;sv4Exb
zlv<j7M0hPQ`aRt>)4~Gw(QP01Ww)1e^(RU(&FxR2QILKyMEM`wG|nF;snpUSp@sZ>
z-?3hY0!v4+T}LAigWpxv6Rc^UrFgjT99wYB=)l0_vUsu-5Efz*p`h0v@gxeoqfz;e
z{xQtQdl31W!Ca{PLxd&FD<^|%Vy)Cu6Fe+lpVid72W_w?Mnpu!<9`_Lh5w2P9SLkb
z_)|I8@2iOwjL~-YOj^{u$b9P$r();N#!3)qYJ8`gxc0Xjhy~kl0Y;z@|CW31JTBNB
zz)9IO#^_j11b2)?t9vpC_)~|)3vHVXq^D&%VO5?jO+<T5wbBHYxst$>xxE77&gRZ?
zp_m(%AXKd+3`Nqu4%}K<UY=cC1xTo_KJ&JSF&}cV9w5AVfBM~M!p5?#&OKo3n)I}Q
zbbEb(&f;sB^HJByZ)#OAum7x3n##&3C>i+IYlVQ4B>PJ4<GOutoeT;Br3)onJHml(
zqp<@Shxz^Y4rJ_f2orCEfCVH4PPjqXKp*4{xc$J=(`l)a56Q55-H;doj*{Z^4+z=*
z^6@@|j24Os<9NX~L%#`uFejPXdjGG?>*u8pml9MANf7dTsRr#2APt+b(`?^Mw7lA)
zi!dLyl+6v;4EL`NU6&F!YlHJIb-IAav^tnx&LTx*-dJjP((S2v*V?2BcI3z*U(f=1
zEzzj6J+K#BKkM6@YxH=ZVtC{UA9EBU`vB)#mK7@XevY;CFH~1mkTM`C#k@jU<L_C_
z4f_{ZtD9N--W*o+aaS~0Tm%OwYJlMy{j>KhJbh^gaxmXGWN)0CTWGW=BQ`QJvT<ZF
zG&D>)k*SP*nR~2bZM1JZerHsK@gqlMge|S?<sP_ohL4E-Xv)Ls4fNg(pzekqkaNyF
ztyE#eCMb2pGOEgOWp;?V(fM#X^~>gVflTX<g}fobc*4!xd+Ah)cXM;pAe&=R8yt<_
zg^U+YD@kbj^s-#4^{cC_nBDk6>$^M<Syw4!B=7lA=KS2;sb?5JZ})|gOO6pr3Nbrt
ztzoCNw0tV~O*K{eQA*?SqR~y!em_^~_Uh_t6jNZ{l3&uQ<I5<^GiKUVdooOndyU8A
zIvb1KCU(|6#j==>LLvH;fysD<%gQak%Dtd>&`rDsbqKISRR@HWxbA|UaQ1g)n|MAf
zWc5fZd^V9qC@US%WWR$IDSk~hNmJ1#A%{baF}8MB>paRed}#gzbe}K)*+)3Tx2l_G
z?4PVvuM-uZD2RF{TR6{XKz~roBC9fE#-oL12Ft}Y6(nKy4XiZyh*TO%gQ`fV^!0zc
z8`_ev(sip&5{b0V0&8Nt#vR!N@(oC<UJ&ptk9j@z&6&X#9%}zxaRIoupd@6MqR+U9
zYv^~p6MBh!BBR{w9E?t;eFiH*Fc90!Y1q;7$C2W>)mSy2pWFk4tH23+p+!?mQ(~fG
zI?te83iEIw7TEctYF(X3LfkP$y)%H0@$~5Fu+qTiNgiKG7wboZqnh2_hpIgou9e1q
zIjZ~?(a7lP)YO4pzkZr6cM}Yyi<((kBYY)8gQAmT%KI<gPJo@ZLvrsNN`|)3I!w#M
zALtBf-_8Mk3{GLS_){$)O;uqd{?C)cTG#U<N_1Gs$&QG%^70fVgHSj{-Mn>le(uLy
zSnGD@>JmvhiVzw2*)Vl`y348{YjDjmZU4E`fJ4{eoOyKEI5B2ey`?r<WKd<`S&%D?
zY96kN=J)Dq79RK!Y>X)07Fz(;2Msd)33$pS%)<#gQhy&h`qNDouBN7zTFQt<vo15L
zx^00z?9Tpe<hOGBr$a!g8@AJk(k^~^yo>%2;Toi_rq;5@&Za{u@!!)8Q3diz@*e{8
z2Fs7VFk@bJHo_p8mXQ1swHu$`**%m&!01?iWJkqkCk*zbi-D(&))9bB&M0>cQZ;+=
z?ej()`ULZU9oUH~uT>FpDu|?b;E2u%Tj|Gee^(F1^#iYKM}fyV9S5}rh*V*X9)eDu
zSh>Y8Sw4a7DeIwpHZ9D$#i-)PK7h_Pp~*MqZ&pe@+PDFikzVuqoq@~2F@H~lnCJ1G
zR~(7kLHZ})|GOuR`Ru$~Uhb0wKhq4A5rDI`R;JVNIo?I&qlwDX=&L+Eo*^?PG?$aG
zAEnFBFpAyT$^+dT@ybiu+}!kAKa4>RYEbJ?*cq20Kv&W$oX`6aYMfWwT?$|vc}rvz
zke7yRt*WL3Z%&C*JhwLt07kN>tZXRKxys~O-Xj{0g61BQ|60H<tw*&liuUAWwQWt6
zCxWsJOa8Ub#gmbxd9t5S9qeRUqT&IJZ+;r=Qwt`Wso3N-L2<$&JK%{WriFc_KR(0G
zBECm8pUwUNLy=%70=NmpxShqr8gJd46JNXar0w5NLo#oU*Tcz>_W*#+_$b~86MyPm
zNvRoz>dwVFXOxf5%!#{H0T&xgVe0-fw=O6PJFp_vZYXu5k|>;E#nvHemKJB!G~SMe
zdbA}}(sU?uGh(ZPX+VSXOnG|<*t%G`{PCU1w4J}|;ukuyU<swUom~>&#<x2#+e{Gf
Nv9)rrtis$N{XbuLeG32p


From cba7c4e360d843d523b29be7fb52fa220fcafa92 Mon Sep 17 00:00:00 2001
From: Anirudh <anirudhkrec@gmail.com>
Date: Fri, 23 Aug 2019 12:43:25 -0700
Subject: [PATCH 214/813] Add fp16 support for topk (#15560)

* fp16 for topk

* indentation and mem alloc

* fix cuda code

* fix cuda sort routine for half precision

* remove ambiguous half type
---
 src/operator/tensor/ordering_op-inl.h  | 22 +++------
 src/operator/tensor/sort_op-inl.cuh    | 68 +++++++++++++++++---------
 tests/python/unittest/test_operator.py | 64 ++++++++++++++----------
 3 files changed, 92 insertions(+), 62 deletions(-)

diff --git a/src/operator/tensor/ordering_op-inl.h b/src/operator/tensor/ordering_op-inl.h
index b36d79acfc7b..0ccbe410a1e1 100644
--- a/src/operator/tensor/ordering_op-inl.h
+++ b/src/operator/tensor/ordering_op-inl.h
@@ -377,7 +377,6 @@ MSHADOW_FORCE_INLINE void TopKSort(const Tensor<gpu, 1, DType>& dat,
    * \param resource temporary resource handler
    * \param src the Source blob
    * \param ret the destination blobs
-   * \param k the K elements to keep
    * \param param the topk parameters
    * \tparam xpu the device type.
    * \tparam DType type of the output value/mask.
@@ -563,13 +562,13 @@ void TopK(const nnvm::NodeAttrs& attrs,
           const std::vector<TBlob>& outputs) {
   const TopKParam& param = nnvm::get<TopKParam>(attrs.parsed);
   if (param.ret_typ == topk_enum::kReturnIndices || param.ret_typ == topk_enum::kReturnBoth) {
-    MXNET_NO_FLOAT16_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, DType, {
       MSHADOW_TYPE_SWITCH(param.dtype, IDType, {
         TopKImpl<xpu, DType, IDType>(ctx.run_ctx, ctx.requested[0], req, inputs[0], outputs, param);
       })
     });
   } else {
-    MXNET_NO_FLOAT16_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, DType, {
       TopKImpl<xpu, DType, index_t>(ctx.run_ctx, ctx.requested[0], req, inputs[0], outputs, param);
     });
   }
@@ -695,13 +694,13 @@ void TopKBackward_(const nnvm::NodeAttrs& attrs,
                    const std::vector<TBlob>& outputs) {
   const TopKParam& param = nnvm::get<TopKParam>(attrs.parsed);
   if (param.ret_typ == topk_enum::kReturnBoth) {
-    MXNET_NO_FLOAT16_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, DType, {
       MSHADOW_TYPE_SWITCH(param.dtype, IDType, {
         TopKBackwardImpl<xpu, DType, IDType>(ctx, inputs, req, outputs, param);
       });
     });
   } else if (param.ret_typ == topk_enum::kReturnValue) {
-    MXNET_NO_FLOAT16_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, DType, {
       TopKBackwardImpl<xpu, DType, index_t>(ctx, inputs, req, outputs, param);
     });
   } else {
@@ -732,7 +731,6 @@ inline bool TopKType(const nnvm::NodeAttrs& attrs,
                      std::vector<int> *in_attrs,
                      std::vector<int> *out_attrs) {
   const TopKParam& param = nnvm::get<TopKParam>(attrs.parsed);
-  int data_type = -1;
   size_t in_size = in_attrs->size();
   size_t out_size = out_attrs->size();
   CHECK_EQ(in_size, 1);
@@ -756,15 +754,9 @@ inline bool TopKType(const nnvm::NodeAttrs& attrs,
     CHECK(type_assign(&(*out_attrs)[0], param.dtype))
             << "Failed to set the type of ret_indices.";
   } else {
-    CHECK(type_assign(&data_type, (*in_attrs)[0])) << "Incompatible dtype of input, in_attrs[0]="
-                                                   << (*in_attrs)[0];
-    CHECK(type_assign(&data_type, (*out_attrs)[0])) << "Incompatible dtype of output, out_attrs[0]="
-                                                    << (*out_attrs)[0];
-    CHECK(type_assign(&(*in_attrs)[0], data_type)) << "Incompatible dtype of input, in_attrs[0]="
-                                                   << (*in_attrs)[0];
-    CHECK(type_assign(&(*out_attrs)[0], data_type)) << "Incompatible dtype of output, out_attrs[0]="
-                                                    << (*out_attrs)[0];
-    if (data_type == -1) return false;
+    TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
+    TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
+    return out_attrs->at(0) != -1;
   }
   return true;
 }
diff --git a/src/operator/tensor/sort_op-inl.cuh b/src/operator/tensor/sort_op-inl.cuh
index f0caee4f5cb6..b20b466d9c2b 100644
--- a/src/operator/tensor/sort_op-inl.cuh
+++ b/src/operator/tensor/sort_op-inl.cuh
@@ -60,24 +60,48 @@ struct greater_half
   typedef T second_argument_type;
   typedef bool result_type;
   __host__ __device__ bool operator()(const T &lhs, const T &rhs) const {
-    return static_cast<mshadow::half::half_t>(lhs) < static_cast<mshadow::half::half_t>(rhs);
+    return static_cast<mshadow::half::half_t>(lhs) > static_cast<mshadow::half::half_t>(rhs);
   }
 };
 }
 
+#ifndef SORT_WITH_THRUST
+template <typename KDType, typename VDType>
+inline void WorkspaceSize4KeysAndValues(
+  const size_t num_keys, size_t *pKeys_bytes, size_t *pValues_bytes) {
+  const size_t alignment = std::max(sizeof(KDType), sizeof(VDType));
+  *pKeys_bytes = PadBytes(num_keys * sizeof(KDType), alignment);
+  *pValues_bytes = PadBytes(num_keys * sizeof(VDType), alignment);
+}
+
+template <typename KDType, typename VDType>
+inline typename std::enable_if<!std::is_same<KDType, mshadow::half::half_t>::value, size_t>::type
+SortPairsWorkspaceSize(const size_t num_keys) {
+  size_t sortpairs_bytes = 0;
+  cub::DeviceRadixSort::SortPairs<KDType, VDType>(NULL, sortpairs_bytes,
+    NULL, NULL, NULL, NULL, num_keys);
+  return sortpairs_bytes;
+}
+
+template <typename KDType, typename VDType>
+inline typename std::enable_if<std::is_same<KDType, mshadow::half::half_t>::value, size_t>::type
+SortPairsWorkspaceSize(const size_t num_keys) {
+  size_t sortpairs_bytes = 0;
+  cub::DeviceRadixSort::SortPairs<__half, VDType>(NULL, sortpairs_bytes,
+    NULL, NULL, NULL, NULL, num_keys);
+  return sortpairs_bytes;
+}
+#endif
+
 template <typename KDType, typename VDType, typename xpu>
 inline typename std::enable_if<std::is_same<xpu, gpu>::value, size_t>::type
 SortByKeyWorkspaceSize(const size_t num_keys) {
 #ifdef SORT_WITH_THRUST
   return 0;
 #else
-  size_t sortpairs_bytes = 0;
-  cub::DeviceRadixSort::SortPairs<KDType, VDType>(NULL, sortpairs_bytes,
-      NULL, NULL, NULL, NULL, num_keys);
-  size_t alignment = std::max(sizeof(KDType), sizeof(VDType));
-  size_t keys_bytes = PadBytes(num_keys*sizeof(KDType), alignment);
-  size_t values_bytes = PadBytes(num_keys*sizeof(VDType), alignment);
-  return (keys_bytes + values_bytes + sortpairs_bytes);
+  size_t keys_bytes, values_bytes;
+  WorkspaceSize4KeysAndValues<KDType, VDType>(num_keys, &keys_bytes, &values_bytes);
+  return keys_bytes + values_bytes + SortPairsWorkspaceSize<KDType, VDType>(num_keys);
 #endif
 }
 
@@ -142,11 +166,11 @@ SortByKeyImpl(mshadow::Tensor<gpu, 1, KDType> keys,
     if (is_ascend) {
       thrust::stable_sort_by_key(
         thrust::cuda::par.on(stream),
-        key_iter, key_iter + keys.size(0), value_iter, thrust::less<KDType>());
+        key_iter, key_iter + keys.size(0), value_iter.get(), thrust::less<KDType>());
     } else {
       thrust::stable_sort_by_key(
         thrust::cuda::par.on(stream),
-        key_iter, key_iter + keys.size(0), value_iter, thrust::greater<KDType>());
+        key_iter, key_iter + keys.size(0), value_iter.get(), thrust::greater<KDType>());
     }
 #ifndef SORT_WITH_THRUST
   }
@@ -169,8 +193,8 @@ SortByKeyImpl(mshadow::Tensor<gpu, 1, KDType> keys,
 #if CUDA_VERSION >= 9000
   cudaStream_t stream = mshadow::Stream<gpu>::GetStream(keys.stream_);
   thrust::device_ptr<KDType> key_iter = thrust::device_pointer_cast(keys.dptr_);
-  thrust::device_ptr<half> value_iter = thrust::device_pointer_cast(
-    reinterpret_cast<half*>(values.dptr_));
+  thrust::device_ptr<__half> value_iter = thrust::device_pointer_cast(
+    reinterpret_cast<__half*>(values.dptr_));
   if (is_ascend) {
     thrust::stable_sort_by_key(
       thrust::cuda::par.on(stream),
@@ -197,17 +221,17 @@ SortByKeyImpl(mshadow::Tensor<gpu, 1, KDType> keys,
   CHECK_EQ(values.CheckContiguous(), true);
 #if CUDA_VERSION >= 9000
   cudaStream_t stream = mshadow::Stream<gpu>::GetStream(keys.stream_);
-  thrust::device_ptr<half> key_iter = thrust::device_pointer_cast(
-    reinterpret_cast<half*>(keys.dptr_));
+  thrust::device_ptr<__half> key_iter = thrust::device_pointer_cast(
+    reinterpret_cast<__half*>(keys.dptr_));
   thrust::device_ptr<VDType> value_iter = thrust::device_pointer_cast(values.dptr_);
   if (is_ascend) {
     thrust::stable_sort_by_key(
       thrust::cuda::par.on(stream),
-      key_iter, key_iter + (keys.size(0)), value_iter, cuda::less_half<half>());
+      key_iter, key_iter + (keys.size(0)), value_iter.get(), cuda::less_half<__half>());
   } else {
     thrust::stable_sort_by_key(
       thrust::cuda::par.on(stream),
-      key_iter, key_iter + (keys.size(0)), value_iter, cuda::greater_half<half>());
+      key_iter, key_iter + (keys.size(0)), value_iter.get(), cuda::greater_half<__half>());
   }
   MSHADOW_CUDA_POST_KERNEL_CHECK(SortByKey);
 #else
@@ -227,18 +251,18 @@ SortByKeyImpl(mshadow::Tensor<gpu, 1, KDType> keys,
   CHECK_EQ(values.CheckContiguous(), true);
 #if CUDA_VERSION >= 9000
   cudaStream_t stream = mshadow::Stream<gpu>::GetStream(keys.stream_);
-  thrust::device_ptr<half> key_iter = thrust::device_pointer_cast(
-    reinterpret_cast<half*>(keys.dptr_));
-  thrust::device_ptr<half> value_iter = thrust::device_pointer_cast(
-    reinterpret_cast<half*>(values.dptr_));
+  thrust::device_ptr<__half> key_iter = thrust::device_pointer_cast(
+    reinterpret_cast<__half*>(keys.dptr_));
+  thrust::device_ptr<__half> value_iter = thrust::device_pointer_cast(
+    reinterpret_cast<__half*>(values.dptr_));
   if (is_ascend) {
     thrust::stable_sort_by_key(
       thrust::cuda::par.on(stream),
-      key_iter, key_iter + (keys.size(0)), value_iter, cuda::less_half<half>());
+      key_iter, key_iter + (keys.size(0)), value_iter.get(), cuda::less_half<__half>());
   } else {
     thrust::stable_sort_by_key(
       thrust::cuda::par.on(stream),
-      key_iter, key_iter + (keys.size(0)), value_iter, cuda::greater_half<half>());
+      key_iter, key_iter + (keys.size(0)), value_iter.get(), cuda::greater_half<__half>());
   }
   MSHADOW_CUDA_POST_KERNEL_CHECK(SortByKey);
 #else
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 466cee823029..4de7d16e6f61 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -4304,15 +4304,6 @@ def get_large_matrix():
 
     large_matrix_npy = get_large_matrix()
 
-    for axis in [1, 3, None]:
-        K = [1, 3, 5, 7] if axis is None else [1, 3, 5]
-        for k in K:
-            for is_ascend in [True, False]:
-                b = mx.sym.topk(a, axis=axis, is_ascend=is_ascend, ret_typ="value", k=k)
-                out_npy = gt_topk(dat=a_npy, axis=axis, ret_typ="value", k=k, is_ascend=is_ascend)
-                check_numeric_gradient(b, location={'a': a_npy}, numeric_eps=1e-2, ctx=ctx)
-                check_symbolic_forward(b, location={'a': a_npy}, expected=[out_npy])
-
     for axis in [1, 3, None]:
         for is_ascend in [True, False]:
             b = mx.sym.sort(a, axis=axis, is_ascend=is_ascend)
@@ -4332,22 +4323,6 @@ def get_large_matrix():
                                              ret_typ="indices", k=5,
                                              is_ascend=is_ascend)])
 
-    b = mx.sym.topk(a, axis=3, is_ascend=is_ascend, ret_typ="indices", k=3)
-    check_symbolic_backward(sym=b, location={'a': a_npy},
-                            out_grads=[np.random.normal(size=(5, 5, 5, 3))],
-                            expected=[np.zeros((5, 5, 5, 5))])
-    check_symbolic_forward(b, location={'a': a_npy},
-                           expected=[gt_topk(dat=a_npy, axis=3, ret_typ="indices", k=3,
-                                             is_ascend=False)])
-
-    b = mx.sym.topk(a, axis=1, is_ascend=True, ret_typ="mask", k=3)
-    check_symbolic_backward(sym=b, location={'a': a_npy},
-                            out_grads=[np.random.normal(size=(5, 5, 5, 5))],
-                            expected=[np.zeros((5, 5, 5, 5))])
-    check_symbolic_forward(b, location={'a': a_npy},
-                           expected=[gt_topk(dat=a_npy, axis=1, ret_typ="mask", k=3,
-                                             is_ascend=True)])
-
     b = mx.sym.argsort(a, axis=1, is_ascend=False)
     check_symbolic_backward(sym=b, location={'a': a_npy},
                             out_grads=[np.random.normal(size=(5, 5, 5, 5))],
@@ -4372,6 +4347,45 @@ def get_large_matrix():
                            expected=[gt_topk(dat=a_npy, axis=1, ret_typ="indices", k=1,
                                              is_ascend=True)])
 
+    for dtype in [np.float16, np.float32, np.float64]:
+        dshape = (5, 5, 5, 5)
+        a_npy = np.arange(np.prod(dshape)).astype(dtype)
+        np.random.shuffle(a_npy)
+        a_npy = a_npy.reshape(dshape)
+        a = mx.sym.Variable('a')
+        for axis in [1, 3, None]:
+            K = [1, 3, 5, 7] if axis is None else [1, 3, 5]
+            for k in K:
+                for is_ascend in [True, False]:
+                    b = mx.sym.topk(a, axis=axis, is_ascend=is_ascend, ret_typ="value", k=k)
+                    out_npy = gt_topk(dat=a_npy, axis=axis, ret_typ="value", k=k, is_ascend=is_ascend)
+                    check_numeric_gradient(b, location={'a': a_npy}, numeric_eps=1e-2, ctx=ctx)
+                    check_symbolic_forward(b, location={'a': a_npy}, expected=[out_npy])
+
+        b = mx.sym.topk(a, axis=1, is_ascend=is_ascend, ret_typ="indices", k=5)
+        check_symbolic_backward(sym=b, location={'a': large_matrix_npy},
+                out_grads=[np.random.normal(size=(100, 5))],
+                expected=[np.zeros((100, 300096))])
+        check_symbolic_forward(b, location={'a': large_matrix_npy},
+                expected=[gt_topk(dat=large_matrix_npy, axis=1,
+                    ret_typ="indices", k=5, is_ascend=is_ascend)])
+
+        b = mx.sym.topk(a, axis=3, is_ascend=is_ascend, ret_typ="indices", k=3)
+        check_symbolic_backward(sym=b, location={'a': a_npy},
+                out_grads=[np.random.normal(size=(5, 5, 5, 3))],
+                expected=[np.zeros((5, 5, 5, 5))])
+        check_symbolic_forward(b, location={'a': a_npy},
+                expected=[gt_topk(dat=a_npy, axis=3, ret_typ="indices", k=3,
+                    is_ascend=False)])
+
+        b = mx.sym.topk(a, axis=1, is_ascend=True, ret_typ="mask", k=3)
+        check_symbolic_backward(sym=b, location={'a': a_npy},
+                out_grads=[np.random.normal(size=(5, 5, 5, 5))],
+                expected=[np.zeros((5, 5, 5, 5))])
+        check_symbolic_forward(b, location={'a': a_npy},
+                expected=[gt_topk(dat=a_npy, axis=1, ret_typ="mask", k=3,
+                    is_ascend=True)])
+
 
 @with_seed()
 def test_blockgrad():

From 77a633f972a2096807b5df099f94e808d88df0a1 Mon Sep 17 00:00:00 2001
From: Zach Kimberg <kimbergz@amazon.com>
Date: Fri, 23 Aug 2019 22:41:12 -0700
Subject: [PATCH 215/813] Fix Nightly Maven GPU (#15989)

---
 make/maven/maven_darwin_mkl.mk    | 2 +-
 make/maven/maven_linux_cu90mkl.mk | 3 +--
 make/maven/maven_linux_cu92mkl.mk | 3 +--
 make/maven/maven_linux_mkl.mk     | 2 +-
 4 files changed, 4 insertions(+), 6 deletions(-)

diff --git a/make/maven/maven_darwin_mkl.mk b/make/maven/maven_darwin_mkl.mk
index 9bf3fc46ce0b..a7f2bdb027d4 100644
--- a/make/maven/maven_darwin_mkl.mk
+++ b/make/maven/maven_darwin_mkl.mk
@@ -77,7 +77,7 @@ USE_CUDNN = 0
 # CUDA_ARCH :=
 
 # whether use cuda runtime compiling for writing kernels in native language (i.e. Python)
-ENABLE_CUDA_RTC = 0
+USE_NVRTC = 0
 
 # use openmp for parallelization
 USE_OPENMP = 0
diff --git a/make/maven/maven_linux_cu90mkl.mk b/make/maven/maven_linux_cu90mkl.mk
index 00cfd5dfa31d..e9ba46509973 100644
--- a/make/maven/maven_linux_cu90mkl.mk
+++ b/make/maven/maven_linux_cu90mkl.mk
@@ -79,9 +79,8 @@ USE_NCCL = 1
 # CUDA_ARCH :=
 
 # whether use cuda runtime compiling for writing kernels in native language (i.e. Python)
-ENABLE_CUDA_RTC = 1
-
 USE_NVTX=1
+USE_NVRTC = 1
 
 # use openmp for parallelization
 USE_OPENMP = 1
diff --git a/make/maven/maven_linux_cu92mkl.mk b/make/maven/maven_linux_cu92mkl.mk
index 6ac920a12ff0..caa1c59c01d5 100644
--- a/make/maven/maven_linux_cu92mkl.mk
+++ b/make/maven/maven_linux_cu92mkl.mk
@@ -79,9 +79,8 @@ USE_NCCL = 1
 # CUDA_ARCH :=
 
 # whether use cuda runtime compiling for writing kernels in native language (i.e. Python)
-ENABLE_CUDA_RTC = 1
-
 USE_NVTX=1
+USE_NVRTC = 1
 
 # use openmp for parallelization
 USE_OPENMP = 1
diff --git a/make/maven/maven_linux_mkl.mk b/make/maven/maven_linux_mkl.mk
index 10aee5f35a46..3c8534a7e2aa 100644
--- a/make/maven/maven_linux_mkl.mk
+++ b/make/maven/maven_linux_mkl.mk
@@ -76,7 +76,7 @@ USE_CUDNN = 0
 # CUDA_ARCH :=
 
 # whether use cuda runtime compiling for writing kernels in native language (i.e. Python)
-ENABLE_CUDA_RTC = 0
+USE_NVRTC = 0
 
 # use openmp for parallelization
 USE_OPENMP = 1

From 2e4242e0e5635c128a6eaa49f2227c7da9a456d6 Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Sat, 24 Aug 2019 18:40:11 -0700
Subject: [PATCH 216/813] Add symbol api for randn and fix shape issue for
 randn ndarray and symbol api (#15772)

* symbol api for randn, fix shape issue for randn ndarray and symbol api

* address comments; add unit test for symbol and check for NDArray

* shape issue

* address comments - unit test, doc fix, etc

* remove prints, test randn for symbol and multidistribution

* Trigger notification bcoz data.mxnet.io failed

* removed stray file

* Trigger notification bcoz R/cpu failed

* add to symbol doc
---
 docs/api/python/symbol/symbol.md     |  1 +
 python/mxnet/ndarray/random.py       |  4 ++--
 python/mxnet/symbol/random.py        | 32 +++++++++++++++++++++++++++-
 tests/python/unittest/test_random.py | 20 +++++++++++------
 4 files changed, 48 insertions(+), 9 deletions(-)

diff --git a/docs/api/python/symbol/symbol.md b/docs/api/python/symbol/symbol.md
index fea746bb02f4..264f69093709 100644
--- a/docs/api/python/symbol/symbol.md
+++ b/docs/api/python/symbol/symbol.md
@@ -612,6 +612,7 @@ Composite multiple symbols into a new one by an operator.
     random.normal
     random.poisson
     random.randint
+    random.randn
     random.shuffle
     random.uniform
     mxnet.random.seed
diff --git a/python/mxnet/ndarray/random.py b/python/mxnet/ndarray/random.py
index f19c1e03202f..b0683b439c2a 100644
--- a/python/mxnet/ndarray/random.py
+++ b/python/mxnet/ndarray/random.py
@@ -220,8 +220,8 @@ def randn(*shape, **kwargs):
     dtype = kwargs.pop('dtype', _Null)
     ctx = kwargs.pop('ctx', None)
     out = kwargs.pop('out', None)
-    assert isinstance(loc, (int, float))
-    assert isinstance(scale, (int, float))
+    assert isinstance(loc, (int, float, NDArray))
+    assert isinstance(scale, (int, float, NDArray))
     return _random_helper(_internal._random_normal, _internal._sample_normal,
                           [loc, scale], shape, dtype, ctx, out, kwargs)
 
diff --git a/python/mxnet/symbol/random.py b/python/mxnet/symbol/random.py
index 4bdfe7045625..b2ff104ff0f3 100644
--- a/python/mxnet/symbol/random.py
+++ b/python/mxnet/symbol/random.py
@@ -22,7 +22,7 @@
 from .symbol import Symbol
 
 
-__all__ = ['uniform', 'normal', 'poisson', 'exponential', 'gamma', 'multinomial',
+__all__ = ['uniform', 'normal', 'randn', 'poisson', 'exponential', 'gamma', 'multinomial',
            'negative_binomial', 'generalized_negative_binomial', 'shuffle', 'randint']
 
 
@@ -113,6 +113,36 @@ def normal(loc=0, scale=1, shape=_Null, dtype=_Null, **kwargs):
                           [loc, scale], shape, dtype, kwargs)
 
 
+def randn(*shape, **kwargs):
+    """Draw random samples from a normal (Gaussian) distribution.
+
+    Samples are distributed according to a normal distribution parametrized
+    by *loc* (mean) and *scale* (standard deviation).
+
+
+    Parameters
+    ----------
+    loc : float or Symbol, optional
+        Mean (centre) of the distribution.
+    scale : float or Symbol, optional
+        Standard deviation (spread or width) of the distribution.
+    shape : int or tuple of ints
+        The number of samples to draw. If shape is, e.g., `(m, n)` and `loc` and
+        `scale` are scalars, output shape will be `(m, n)`. If `loc` and `scale`
+        are NDArrays with shape, e.g., `(x, y)`, then output will have shape
+        `(x, y, m, n)`, where `m*n` samples are drawn for each `[loc, scale)` pair.
+    dtype : {'float16', 'float32', 'float64'}, optional
+        Data type of output samples. Default is 'float32'
+    """
+    loc = kwargs.pop('loc', 0)
+    scale = kwargs.pop('scale', 1)
+    dtype = kwargs.pop('dtype', _Null)
+    assert isinstance(loc, (int, float, Symbol))
+    assert isinstance(scale, (int, float, Symbol))
+    return _random_helper(_internal._random_normal, _internal._sample_normal,
+                          [loc, scale], shape, dtype, kwargs)
+
+
 def poisson(lam=1, shape=_Null, dtype=_Null, **kwargs):
     """Draw random samples from a Poisson distribution.
 
diff --git a/tests/python/unittest/test_random.py b/tests/python/unittest/test_random.py
index 720c25d2711e..fe276685bfe3 100644
--- a/tests/python/unittest/test_random.py
+++ b/tests/python/unittest/test_random.py
@@ -61,8 +61,10 @@ def check_with_device(device, dtype):
         },
         {
             'name': 'randn',
+            'symbol': mx.sym.random.randn,
             'ndop': mx.nd.random.randn,
             'params': { 'loc': 10.0, 'scale': 0.5 },
+            'inputs': [ ('loc',[ [ 0.0, 2.5 ], [ -9.75, -7.0 ] ]) , ('scale',[ [ 1.0, 3.7 ], [ 4.2, 1.5 ] ]) ],
             'checks': [
                 ('mean', lambda x, params: np.mean(x.astype(np.float64) - params['loc']),  tol),
                 ('std',  lambda x, params: np.std(x.astype(np.float64)) - params['scale'], tol)
@@ -250,6 +252,9 @@ def check_with_device(device, dtype):
 
         params = {'shape': shape, 'dtype': dtype, 'ctx': device}
         params.update({k : mx.nd.array(v, ctx=device, dtype=dtype) for k, v in symbdic['inputs']})
+        if name == 'randn':
+            params.pop('shape')  # randn does not accept shape param
+            args = shape
         mx.random.seed(128)
         ret1 = ndop(*args, **params).asnumpy()
         mx.random.seed(128)
@@ -263,14 +268,12 @@ def check_with_device(device, dtype):
                     err = np.abs(check_func(ret2[i,j], stats))
                     assert err < tol, "%f vs %f: symbolic test: %s check for `%s` did not pass" % (err, tol, check_name, name)
 
-        if 'symbol' not in symbdic: continue  # randn does not have symbol
-
         # check symbolic
         symbol = symbdic['symbol']
         X = mx.sym.Variable("X")
         params = symbdic['params'].copy()
         params.update(shape=shape, dtype=dtype)
-        if name.endswith('_like'):
+        if name.endswith('_like') or name == 'randn':
             params['data'] = mx.sym.ones(params.pop('shape'))
         Y = symbol(**params) + X
         x = mx.nd.zeros(shape, dtype=dtype, ctx=device)
@@ -298,7 +301,12 @@ def check_with_device(device, dtype):
         single_param = len(symbdic['inputs']) == 1
         v1 = mx.sym.Variable('v1')
         v2 = mx.sym.Variable('v2')
-        Y = symbol(v1,**params) if single_param else symbol(v1,v2,**params)
+        if name == 'randn':
+            params.pop('shape')  # randn does not accept shape param
+            args=shape
+            Y = symbol(v1, **params) if single_param else symbol(*args, loc=v1, scale=v2,**params)
+        else:
+            Y = symbol(v1,**params) if single_param else symbol(v1,v2,**params)
         bindings = { 'v1' : mx.nd.array(symbdic['inputs'][0][1]) }
         if not single_param :
             bindings.update({ 'v2' : mx.nd.array(symbdic['inputs'][1][1]) })
@@ -315,9 +323,10 @@ def check_with_device(device, dtype):
                 for check_name, check_func, tol in symbdic['checks']:
                     assert np.abs(check_func(samples, params)) < tol, "symbolic test: %s check for `%s` did not pass" % (check_name, name)
 
+        if 'pdfsymbol' not in symbdic: continue  # randn not tested for pdf
+
         # check pdfs with only a subset of the generated samples
         un1 = np.resize(un1, (un1.shape[0], un1.shape[1], pdfshape[0], pdfshape[1]))
-        print(name)
         symbol  = symbdic['pdfsymbol']
         pdffunc = symbdic['pdffunc']
         v0 = mx.sym.Variable('v0')
@@ -355,7 +364,6 @@ def check_with_device(device, dtype):
                 check_symbolic_forward(test_pdf, [un1, p1, p2], [res], atol=forw_atol, rtol=forw_rtol, dtype=dtype)
                 if dtype == np.float64:
                   grad_nodes = ['v1', 'v2'] if symbdic['discrete'] else ['v0', 'v1', 'v2']
-                  print(backw_rtol)
                   check_numeric_gradient(test_pdf, [un1, p1, p2], grad_nodes=grad_nodes, atol=backw_atol, rtol=backw_rtol, dtype=dtype)
 
 @with_seed(1000)

From 3b7e48454bf157f6ab98e32a8d6506251bbc410b Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Sun, 25 Aug 2019 01:40:04 -0700
Subject: [PATCH 217/813] Decouple dtype from shape for Random multinomial
 (#15980)

* change int to index t and remove the check

* fix lint

* Trigger notification

* Trigger notification coz of laop crash
---
 src/operator/random/sample_multinomial_op.h | 7 +------
 1 file changed, 1 insertion(+), 6 deletions(-)

diff --git a/src/operator/random/sample_multinomial_op.h b/src/operator/random/sample_multinomial_op.h
index 5a0b9bb21acb..e5ecadaf0c24 100644
--- a/src/operator/random/sample_multinomial_op.h
+++ b/src/operator/random/sample_multinomial_op.h
@@ -70,11 +70,6 @@ inline bool SampleMultinomialOpShape(const nnvm::NodeAttrs& attrs,
   const mxnet::TShape& ishape = (*in_attrs)[0];
   if (!ndim_is_known(ishape)) return false;
 
-  MSHADOW_TYPE_SWITCH(param.dtype, DType, {
-    CHECK_LE(ishape[ishape.ndim() - 1], mxnet::common::MaxIntegerValue<DType>())
-    << "'dtype' does not have a sufficient precision to represent the indices of the input array.";
-  });
-
   if (ishape.ndim() == 1) {
     if (param.shape.ndim() > 0) {
       SHAPE_ASSIGN_CHECK(*out_attrs, 0, param.shape);
@@ -121,7 +116,7 @@ inline bool SampleMultinomialOpType(const nnvm::NodeAttrs& attrs,
 
 struct SampleMultinomialKernel {
   template<typename DType, typename IType>
-  MSHADOW_XINLINE static void Map(int i, index_t K, index_t M,
+  MSHADOW_XINLINE static void Map(index_t i, index_t K, index_t M,
                                   DType* dist, float* uniform, float* cum_table,
                                   IType* out, DType* prob) {
     double acc = 0.0;

From c7a8a788bd2f7525794473c4d2f2bab4ad3dee59 Mon Sep 17 00:00:00 2001
From: Tao Lv <tao.a.lv@intel.com>
Date: Mon, 26 Aug 2019 07:37:39 +0800
Subject: [PATCH 218/813] Fix dtype inference in arange_like operator (#15930)

* fix dtype in arange_like operator

* add unit test
---
 src/operator/tensor/init_op.cc        |  2 +-
 src/operator/tensor/init_op.h         |  4 ----
 tests/python/gpu/test_operator_gpu.py | 16 ++++++++++++++++
 3 files changed, 17 insertions(+), 5 deletions(-)

diff --git a/src/operator/tensor/init_op.cc b/src/operator/tensor/init_op.cc
index 172665fbbf12..4e8900be24ca 100644
--- a/src/operator/tensor/init_op.cc
+++ b/src/operator/tensor/init_op.cc
@@ -129,7 +129,7 @@ Examples::
 .set_num_outputs(1)
 .set_attr_parser(ParamParser<RangeLikeParam>)
 .set_attr<mxnet::FInferShape>("FInferShape", RangeLikeShape)
-.set_attr<nnvm::FInferType>("FInferType", InitType<RangeLikeParam, 1>)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
 .set_attr<nnvm::FIgnoreInputs>("FIgnoreInputs",
     [](const NodeAttrs& attrs) { return std::vector<uint32_t>(1, 0); })
 .set_attr<FCompute>("FCompute<cpu>", RangeCompute<cpu, RangeLikeParam>)
diff --git a/src/operator/tensor/init_op.h b/src/operator/tensor/init_op.h
index 51c84363489a..f3c405d7103c 100644
--- a/src/operator/tensor/init_op.h
+++ b/src/operator/tensor/init_op.h
@@ -179,7 +179,6 @@ struct RangeLikeParam : public dmlc::Parameter<RangeLikeParam> {
   double step;
   int repeat;
   std::string ctx;
-  int dtype;
   dmlc::optional<int> axis;
 
   DMLC_DECLARE_PARAMETER(RangeLikeParam) {
@@ -197,9 +196,6 @@ struct RangeLikeParam : public dmlc::Parameter<RangeLikeParam> {
     .set_default("")
     .describe("Context of output, in format [cpu|gpu|cpu_pinned](n)."
               "Only used for imperative calls.");
-    DMLC_DECLARE_FIELD(dtype).set_default(mshadow::kFloat32)
-    MXNET_ADD_ALL_TYPES
-    .describe("Target data type.");
     DMLC_DECLARE_FIELD(axis)
     .set_default(dmlc::optional<int>())
     .describe("Arange elements according to the size of a certain axis of input array."
diff --git a/tests/python/gpu/test_operator_gpu.py b/tests/python/gpu/test_operator_gpu.py
index f8d8b4496afc..db550e4254b7 100644
--- a/tests/python/gpu/test_operator_gpu.py
+++ b/tests/python/gpu/test_operator_gpu.py
@@ -2331,6 +2331,22 @@ def test_math():
             for op in ops:
                 run_math(op, shape, dtype, check_value=check_value)
 
+@with_seed()
+def test_arange_like_dtype():
+    dtypes = [np.float16, np.float32, np.float64]
+
+    for t in dtypes:
+        x = mx.sym.Variable('x', dtype=t)
+        y = mx.sym.reshape(x, shape=(0, 0, -1))
+        z = mx.sym.contrib.arange_like(y, axis=-1)
+    
+        mod = z.simple_bind(ctx=mx.gpu(0), x=(3, 4, 5, 6), grad_req='null')
+        mod.arg_arrays[0][:] = np.random.normal(size=mod.arg_arrays[0].shape).astype(t)
+        out = mod.forward(is_train=False)
+        for v in out:
+            assert v.dtype == t
+    
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 5042d2ad9fe6d98da7ff81775d23717604f8aa93 Mon Sep 17 00:00:00 2001
From: ciyong <ciyong.chen@intel.com>
Date: Mon, 26 Aug 2019 10:30:08 +0800
Subject: [PATCH 219/813] [MKLDNN] Fix out of bound access of req vector
 (#16000)

* remove unnecessary creation of req vector

* retrigger CI
---
 src/operator/subgraph/mkldnn/mkldnn_conv.cc | 5 +----
 src/operator/subgraph/mkldnn/mkldnn_fc.cc   | 5 +----
 2 files changed, 2 insertions(+), 8 deletions(-)

diff --git a/src/operator/subgraph/mkldnn/mkldnn_conv.cc b/src/operator/subgraph/mkldnn/mkldnn_conv.cc
index b7776d648e18..be2716f139ed 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_conv.cc
+++ b/src/operator/subgraph/mkldnn/mkldnn_conv.cc
@@ -426,15 +426,12 @@ void SgMKLDNNConvOperator::Forward(const OpContext &ctx,
     MKLDNNStream::Get()->Submit();
   } else {
     std::vector<NDArray> new_inputs;
-    std::vector<OpReqType> new_req;
     if (has_bias) {
       new_inputs = {data, cached_weight_, cached_bias_};
-      new_req = {req[in_data], req[in_weight], req[in_bias]};
     } else {
       new_inputs = {data, cached_weight_};
-      new_req = {req[in_data], req[in_weight]};
     }
-    MKLDNNConvolutionForwardFullFeature(full_conv_param, ctx, fwd_.get(), new_inputs, new_req,
+    MKLDNNConvolutionForwardFullFeature(full_conv_param, ctx, fwd_.get(), new_inputs, req,
                                         {output});
   }
 
diff --git a/src/operator/subgraph/mkldnn/mkldnn_fc.cc b/src/operator/subgraph/mkldnn/mkldnn_fc.cc
index 22432193dcf9..6ebbb79d2571 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_fc.cc
+++ b/src/operator/subgraph/mkldnn/mkldnn_fc.cc
@@ -188,16 +188,13 @@ void SgMKLDNNFCOp::Forward(const OpContext &ctx,
     initialized_ = true;
   }
   std::vector<NDArray> new_inputs;
-  std::vector<OpReqType> new_req;
   if (has_bias) {
     new_inputs = {data, weight, cached_bias_};
-    new_req = {req[fullc::kData], req[fullc::kWeight], req[fullc::kBias]};
   } else {
     new_inputs = {data, weight};
-    new_req = {req[fullc::kData], req[fullc::kWeight]};
   }
 
-  MKLDNNFCForwardFullFeature(full_param_, ctx, fwd_.get(), new_inputs, new_req, out_data);
+  MKLDNNFCForwardFullFeature(full_param_, ctx, fwd_.get(), new_inputs, req, out_data);
 
   if (mkldnn_param.quantized && !mkldnn_param.enable_float_output) {
     float *min_output_ptr = out_data[quantized_fullc::kOutMin].data().dptr<float>();

From 7d8c2ecf086c15ef86aaba9f5dd0cfedb382748e Mon Sep 17 00:00:00 2001
From: Marco de Abreu <marcoabreu@users.noreply.github.com>
Date: Sun, 25 Aug 2019 19:49:14 -0700
Subject: [PATCH 220/813] Disable laop_6 (#15976)

---
 tests/python/unittest/test_operator.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 4de7d16e6f61..ceee51a3e503 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -6851,6 +6851,7 @@ def test_laop_5():
 
 # Tests for linalg.inverse
 @with_seed()
+@unittest.skip("Test crashes https://github.com/apache/incubator-mxnet/issues/15975")
 def test_laop_6():
     dtype = np.float64
     rtol_fw = 1e-7

From d8b6e47b91a737d29c0589bda8640aee69d8599b Mon Sep 17 00:00:00 2001
From: Marco de Abreu <marcoabreu@users.noreply.github.com>
Date: Sun, 25 Aug 2019 19:49:39 -0700
Subject: [PATCH 221/813] Update CODEOWNERS. (#15972)

* Update CODEOWNERS

* retrigger ci

* Update CODEOWNERS
---
 CODEOWNERS | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/CODEOWNERS b/CODEOWNERS
index 74caea2c4cc7..90744501ec71 100644
--- a/CODEOWNERS
+++ b/CODEOWNERS
@@ -47,7 +47,8 @@ CMakeLists.txt    @szha @pllarroy
 
 # MXNet CI
 dev_menu.py         @pllarroy
-/ci/                @pllarroy
+/ci/                @pllarroy @marcoabreu 
+/docker/            @marcoabreu
 /tests/ci_build/    @marcoabreu
 Jenkinsfile         @marcoabreu
 .travis.yml         @marcoabreu

From 9410cc4ff6b26723beb95cef39001b245392b840 Mon Sep 17 00:00:00 2001
From: YixinBao <yixin.bao@intel.com>
Date: Mon, 26 Aug 2019 20:56:23 +0800
Subject: [PATCH 222/813] add uint8 bn mkldnn implementation (#16003)

* add uint8 bn mkldnn implementation

* update test case for uint8 bn

* fix lint

* update test with gpu

* add comment for quantization

* fix quantized_bn test

* fix quantize_model_with_forward test
---
 .../quantization/imagenet_gen_qsym_mkldnn.py  |  3 +-
 .../nn/mkldnn/mkldnn_batch_norm-inl.h         | 11 ++---
 .../mkldnn/mkldnn_quantized_batch_norm.cc     | 23 ++++++++-
 .../quantization/quantized_batch_norm.cc      |  6 +++
 .../python/quantization/test_quantization.py  | 49 +++++++++----------
 5 files changed, 58 insertions(+), 34 deletions(-)

diff --git a/example/quantization/imagenet_gen_qsym_mkldnn.py b/example/quantization/imagenet_gen_qsym_mkldnn.py
index 302a04449885..6c87f58b63e2 100644
--- a/example/quantization/imagenet_gen_qsym_mkldnn.py
+++ b/example/quantization/imagenet_gen_qsym_mkldnn.py
@@ -216,7 +216,8 @@ def save_params(fname, arg_params, aux_params, logger=None):
             if exclude_first_conv:
                 excluded_sym_names += ['resnetv10_conv0_fwd']
         elif args.model.find('resnet') != -1 and args.model.find('v2') != -1:
-            excluded_sym_names += ['resnetv20_flatten0_flatten0']
+            # resnetv20_stage1_batchnorm0_fwd is excluded for the sake of accuracy
+            excluded_sym_names += ['resnetv20_flatten0_flatten0', 'resnetv20_stage1_batchnorm0_fwd']
             if exclude_first_conv:
                 excluded_sym_names += ['resnetv20_conv0_fwd']
         elif args.model.find('vgg') != -1:
diff --git a/src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h b/src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h
index 2d2bf2c64596..510ca29d7f91 100644
--- a/src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h
@@ -132,14 +132,13 @@ class MKLDNNBNForward {
     return *var_m;
   }
 
-  void SetDataHandle(const NDArray &data, const mkldnn::memory *mean,
+  void SetDataHandle(const mkldnn::memory *data, const mkldnn::memory *mean,
                      const mkldnn::memory *var, const mkldnn::memory *out) {
-    auto _data = data.GetMKLDNNData();
     if (data_m) {
-      data_m->set_data_handle(_data->get_data_handle());
+      data_m->set_data_handle(data->get_data_handle());
     } else {
-      data_m.reset(new mkldnn::memory(_data->get_primitive_desc(),
-                                      _data->get_data_handle()));
+      data_m.reset(new mkldnn::memory(data->get_primitive_desc(),
+                                      data->get_data_handle()));
     }
     if (out_m) {
       out_m->set_data_handle(out->get_data_handle());
@@ -175,7 +174,7 @@ class MKLDNNBNForward {
 
   void SetDataHandle(const NDArray &data, const NDArray &mean,
                      const NDArray &var, const mkldnn::memory &out) {
-    SetDataHandle(data, mean.GetMKLDNNData(), var.GetMKLDNNData(), &out);
+    SetDataHandle(data.GetMKLDNNData(), mean.GetMKLDNNData(), var.GetMKLDNNData(), &out);
   }
 
   const mkldnn::batch_normalization_forward &GetFwd() const {
diff --git a/src/operator/quantization/mkldnn/mkldnn_quantized_batch_norm.cc b/src/operator/quantization/mkldnn/mkldnn_quantized_batch_norm.cc
index df5e48744f2d..429a80e6b186 100644
--- a/src/operator/quantization/mkldnn/mkldnn_quantized_batch_norm.cc
+++ b/src/operator/quantization/mkldnn/mkldnn_quantized_batch_norm.cc
@@ -40,6 +40,27 @@ static void MKLDNNQuantizedBatchNormForward(const nnvm::NodeAttrs &attrs, const
   TmpMemMgr::Get()->Init(ctx.requested[batchnorm::kTempSpace]);
   const BatchNormParam &param = nnvm::get<BatchNormParam>(attrs.parsed);
   const NDArray &data = in_data[quantized_batchnorm::kData];
+  auto data_mem = data.GetMKLDNNData();
+
+  // reorder if data type = uint8
+  if (in_data[quantized_batchnorm::kData].dtype() == mshadow::kUint8) {
+    auto u8_pd = data_mem->get_primitive_desc();
+    auto u8_md = u8_pd.desc();
+    mkldnn::memory::desc s8_md(
+        mkldnn::memory::dims(u8_md.data.dims, u8_md.data.dims + u8_md.data.ndims),
+        mkldnn::memory::data_type::s8, static_cast<mkldnn::memory::format>(u8_md.data.format));
+    auto s8_pd = mkldnn::memory::primitive_desc(s8_md, CpuEngine::Get()->get_engine());
+    auto data_reorder_mem = TmpMemMgr::Get()->Alloc(s8_pd);
+
+    std::vector<float> reorder_scale;
+    reorder_scale = {static_cast<float>(kInt8Range) / kUint8Range};
+    primitive_attr reorder_attr;
+    reorder_attr.set_int_output_round_mode(round_mode::round_nearest);
+    reorder_attr.set_output_scales(0, reorder_scale);
+    const auto reorder_pd = mkldnn::reorder::primitive_desc(u8_pd, s8_pd, reorder_attr);
+    MKLDNNStream::Get()->RegisterPrim(mkldnn::reorder(reorder_pd, *data_mem, *data_reorder_mem));
+    data_mem = data_reorder_mem;
+  }
   const size_t channelAxis = static_cast<size_t>(
       param.axis < 0 ? static_cast<int>(data.shape().ndim()) + param.axis : param.axis);
   const int channel_count = data.shape()[channelAxis];
@@ -92,7 +113,7 @@ static void MKLDNNQuantizedBatchNormForward(const nnvm::NodeAttrs &attrs, const
 
   auto out_mem = CreateMKLDNNMem(outputs[batchnorm::kOut],
       fwd.GetPd().dst_primitive_desc(), req[batchnorm::kOut], &data);
-  fwd.SetDataHandle(data, rescaled_mean_mem, rescaled_var_mem, out_mem.second);
+  fwd.SetDataHandle(data_mem, rescaled_mean_mem, rescaled_var_mem, out_mem.second);
 
   MKLDNNStream::Get()->RegisterPrim(fwd.GetFwd());
   MKLDNNStream::Get()->Submit();
diff --git a/src/operator/quantization/quantized_batch_norm.cc b/src/operator/quantization/quantized_batch_norm.cc
index 3187826fe996..3c46e1b8bd5c 100644
--- a/src/operator/quantization/quantized_batch_norm.cc
+++ b/src/operator/quantization/quantized_batch_norm.cc
@@ -67,7 +67,13 @@ bool QuantizedBatchNormType(const nnvm::NodeAttrs& attrs, std::vector<int>* in_t
   CHECK_EQ(in_type->size(), 7U);
   CHECK_EQ(out_type->size(), 3U);
 
+#if MXNET_USE_MKLDNN == 1
+  CHECK(in_type->at(0) == mshadow::kInt8 || in_type->at(0) == mshadow::kUint8)
+      << "QuantizedBatchNorm with MKLDNN backend only supports int8/uint8 input, while "
+      << in_type->at(0) << " is given.";
+#else
   TYPE_ASSIGN_CHECK(*in_type, 0, mshadow::kInt8);
+#endif
   for (size_t i = 1; i < 7; ++i) {
     TYPE_ASSIGN_CHECK(*in_type, i, mshadow::kFloat32);
   }
diff --git a/tests/python/quantization/test_quantization.py b/tests/python/quantization/test_quantization.py
index a1c23fb23208..31bc1638b010 100644
--- a/tests/python/quantization/test_quantization.py
+++ b/tests/python/quantization/test_quantization.py
@@ -607,19 +607,21 @@ def get_mean_var(data):
         return mean, var
 
     def check_quantized_bn(data_shape, qdtype):
-        if qdtype == 'uint8':
-            print('skipped testing quantize_bn for uint8 since it is not supported yet')
-            return
-        elif is_test_for_native_cpu():
+        if is_test_for_native_cpu():
             print('skipped testing quantize_bn for native cpu since it is not supported yet')
             return
         elif is_test_for_gpu():
             print('skipped testing quantize_bn for gpu since it is not supported yet')
             return
 
-        # qdtype = int8
-        data_low = -127.0
-        data_high = 127.0
+        # qdtype = uint8
+        if qdtype == 'uint8':
+            data_low = 0.0
+            data_high = 127.0
+        else:
+            data_low = -127.0
+            data_high = 127.0
+        # output type = int8
         quantized_range = 127.0
         # run fp32 bn
         data_sym = mx.sym.Variable(name='data', shape=data_shape, dtype='float32')
@@ -639,9 +641,6 @@ def check_quantized_bn(data_shape, qdtype):
         bn_fp32_exe.arg_dict[arg_names[2]][:] = beta
         bn_fp32_exe.aux_dict[aux_names[0]][:] = moving_mean
         bn_fp32_exe.aux_dict[aux_names[1]][:] = moving_var
-        min_data = mx.nd.min(data)
-        max_data = mx.nd.max(data)
-        data_range = mx.nd.maximum(mx.nd.abs(min_data), mx.nd.abs(max_data))
 
         output= bn_fp32_exe.forward()[0]
 
@@ -654,11 +653,12 @@ def check_quantized_bn(data_shape, qdtype):
 
         calib_data = NDArrayIter(data=data, batch_size=data_shape[0])
         calib_data = DummyIter(calib_data)
+        # quantize bn with quantized_type = int8: MKLDNN BN only support int8 output
         qsym, qarg_params, qaux_params = mx.contrib.quant.quantize_model(sym=bn_fp32,
                                                                              arg_params=arg_params,
                                                                              aux_params=bn_fp32_exe.aux_dict,
                                                                              ctx=mx.current_context(),
-                                                                             quantized_dtype=qdtype,
+                                                                             quantized_dtype='int8',
                                                                              calib_mode='naive',
                                                                              calib_data=calib_data,
                                                                              num_calib_examples=20)
@@ -668,13 +668,14 @@ def check_quantized_bn(data_shape, qdtype):
         mod.set_params(qarg_params, qaux_params)
         batch = mx.io.DataBatch([data], [])
         mod.forward(batch, is_train=False)
-        output_int8_to_fp32= mod.get_outputs()[0]
+        output_int8_to_fp32 = mod.get_outputs()[0]
 
-        assert_almost_equal(output.asnumpy(), output_int8_to_fp32.asnumpy(), rtol=1e-1, atol=3)
+        assert_almost_equal(output.asnumpy(), output_int8_to_fp32.asnumpy(), rtol=1e-1, atol=4)
 
-    check_quantized_bn((32, 512, 4, 4), 'int8')
-    check_quantized_bn((32, 1024, 8, 8), 'int8')
-    check_quantized_bn((32, 3, 224, 224), 'int8')
+    for qdtype in ['int8', 'uint8']:
+      check_quantized_bn((32, 512, 4, 4), qdtype)
+      check_quantized_bn((32, 1024, 8, 8), qdtype)
+      check_quantized_bn((32, 3, 224, 224), qdtype)
 
 @with_seed()
 def test_quantize_params():
@@ -918,16 +919,12 @@ def check_qsym_forward(qsym, qarg_params, qaux_params, data_shape, label_shape=N
         lshape_list.append(None)
 
         for s, dshape, lshape, name in zip(sym_list, dshape_list, lshape_list, name_list):
-            if qdtype == 'int8' and is_test_for_mkldnn() and name in ['sym1', 'sym2', 'sym3']:
-              print('skipped testing test_quantize_model_with_forward for mkldnn cpu int8 since it is not supported yet')
-              continue
-            elif qdtype == 'uint8' and is_test_for_mkldnn() and name in ['sym1']:
-              print('skipping test_quantize_model_with_forward for mkldnn cpu uint8 since it is not supported yet')
-              continue
-            elif qdtype == 'int8' and is_test_for_gpu() and name in ['sym1']:
-              print('skipped testing test_quantize_model_with_forward for gpu int8 since it is not supported yet')
-              continue
-
+            if qdtype == 'int8' and name in ['sym1','sym2','sym3']:
+                print('mkldnn_quantized_conv op only supports uint8 as input type, skip test with int8.')
+                continue
+            if qdtype == 'uint8' and name in ['sym1']:
+                print('mkldnn_quantized_bn doesn\'t support calib_mode=None')
+                continue
             if lshape is None:
                 mod = Module(symbol=s, label_names=None)
                 mod.bind(for_training=False,

From 72c180c304cdc00196fab568728ef6b23661dab7 Mon Sep 17 00:00:00 2001
From: Vandana Kannan <vandanavk@users.noreply.github.com>
Date: Mon, 26 Aug 2019 10:45:15 -0700
Subject: [PATCH 223/813] Correct ONNX documentation (#15914)

---
 CONTRIBUTORS.md                                   | 1 +
 docs/api/python/contrib/onnx.md                   | 2 +-
 python/mxnet/contrib/onnx/mx2onnx/export_model.py | 2 +-
 3 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md
index 750a22a806d1..86c20cbc3d87 100644
--- a/CONTRIBUTORS.md
+++ b/CONTRIBUTORS.md
@@ -245,6 +245,7 @@ List of Contributors
 * [Rohit Srivastava](https://github.com/access2rohit)
 * [Caner Turkmen](https://github.com/canerturkmen)
 * [Disi A](https://github.com/adis300)
+* [Vandana Kannan](https://github.com/vandanavk)
 
 Label Bot
 ---------
diff --git a/docs/api/python/contrib/onnx.md b/docs/api/python/contrib/onnx.md
index 5691fb022959..7a1655c74afb 100644
--- a/docs/api/python/contrib/onnx.md
+++ b/docs/api/python/contrib/onnx.md
@@ -30,7 +30,7 @@ With ONNX format support for MXNet, developers can build and train models with a
 ```
 
 ### Installation Instructions
-- To use this module developers need to **install ONNX**, which requires the protobuf compiler to be installed separately. Please follow the [instructions to install ONNX and its dependencies](https://github.com/onnx/onnx#installation). **MXNet currently supports ONNX v1.2.1**. Once installed, you can go through the tutorials on how to use this module.
+- To use this module developers need to **install ONNX**, which requires the protobuf compiler to be installed separately. Please follow the [instructions to install ONNX and its dependencies](https://github.com/onnx/onnx#installation). **MXNet currently supports ONNX v1.3**. Once installed, you can go through the tutorials on how to use this module.
 
 
 This document describes all the ONNX-MXNet APIs.
diff --git a/python/mxnet/contrib/onnx/mx2onnx/export_model.py b/python/mxnet/contrib/onnx/mx2onnx/export_model.py
index f5e4c3b69e15..158c24b08fc6 100644
--- a/python/mxnet/contrib/onnx/mx2onnx/export_model.py
+++ b/python/mxnet/contrib/onnx/mx2onnx/export_model.py
@@ -37,7 +37,7 @@ def export_model(sym, params, input_shape, input_type=np.float32,
     """Exports the MXNet model file, passed as a parameter, into ONNX model.
     Accepts both symbol,parameter objects as well as json and params filepaths as input.
     Operator support and coverage -
-    https://cwiki.apache.org/confluence/display/MXNET/MXNet-ONNX+Integration
+    https://cwiki.apache.org/confluence/display/MXNET/ONNX+Operator+Coverage
 
     Parameters
     ----------

From acc074f2b8a425196d0d1c1f66022ab1738ad607 Mon Sep 17 00:00:00 2001
From: Anirudh Subramanian <anirudh2290@ufl.edu>
Date: Mon, 26 Aug 2019 11:24:54 -0700
Subject: [PATCH 224/813] Add AMP Conversion support for BucketingModule
 (#15528)

* Add AMP Conversion support for bucketing module

* Add bucketing module changes

* Add load_aprams

* Add bucketing module conversion

* Fix unused arg
---
 docs/tutorials/amp/amp_tutorial.md           |   2 +-
 example/rnn/bucketing/README.md              |   6 +
 example/rnn/bucketing/cudnn_rnn_bucketing.py |  19 ++-
 python/mxnet/contrib/amp/amp.py              |  64 +++++++
 python/mxnet/model.py                        |  33 ++--
 python/mxnet/module/bucketing_module.py      | 170 ++++++++++++++++++-
 python/mxnet/module/module.py                |   2 +-
 tests/python/gpu/test_contrib_amp.py         |  35 ++++
 tests/python/train/test_bucketing.py         |  50 +++---
 tests/python/unittest/test_module.py         |  71 ++++++++
 10 files changed, 405 insertions(+), 47 deletions(-)

diff --git a/docs/tutorials/amp/amp_tutorial.md b/docs/tutorials/amp/amp_tutorial.md
index 9da0505e9ff6..2b747c6c82f6 100644
--- a/docs/tutorials/amp/amp_tutorial.md
+++ b/docs/tutorials/amp/amp_tutorial.md
@@ -258,6 +258,7 @@ To do inference with mixed precision for a trained model in FP32, you can use th
 Below, we demonstrate for a gluon model and a symbolic model:
 - Conversion from FP32 model to mixed precision model.
 - Run inference on the mixed precision model.
+- For AMP conversion of bucketing module please refer to [example/rnn/bucketing/README.md](https://github.com/apache/incubator-mxnet/blob/master/example/rnn/bucketing/README.md).
 
 ```python
 with mx.Context(mx.gpu(0)):
@@ -336,7 +337,6 @@ with mx.Context(mx.gpu(0)):
     mod.save_checkpoint("amp_tutorial_model", 0, remove_amp_cast=False)
 ```
 
-
 ## Current limitations of AMP
 
 - AMP's dynamic loss scaling currently supports only Gluon trainer with `update_on_kvstore=False` option set
diff --git a/example/rnn/bucketing/README.md b/example/rnn/bucketing/README.md
index 707370af5a96..d44b23e69b23 100644
--- a/example/rnn/bucketing/README.md
+++ b/example/rnn/bucketing/README.md
@@ -55,6 +55,12 @@ You can check this improved [Gluon implementation](http://gluon-nlp.mxnet.io/mod
 
       $ python3 [cudnn_rnn_bucketing.py](cudnn_rnn_bucketing.py) --gpus 0,1,2,3
 
+- To run the mixed precision inference for the trained model, you should use the `--dtype`.
+
+  This uses AMP conversion API for bucketing module to convert to a mixed precision module.
+
+    $ python [cudnn_rnn_bucketing.py](cudnn_rnn_bucketing.py) --gpus 0 --model-prefix saved_rnn_model --load-epoch 12 --test --dtype float16
+
 
 ### Performance Note:
 
diff --git a/example/rnn/bucketing/cudnn_rnn_bucketing.py b/example/rnn/bucketing/cudnn_rnn_bucketing.py
index 66d5a55c02cb..38275ae3dfb8 100644
--- a/example/rnn/bucketing/cudnn_rnn_bucketing.py
+++ b/example/rnn/bucketing/cudnn_rnn_bucketing.py
@@ -18,6 +18,7 @@
 import numpy as np
 import mxnet as mx
 import argparse
+from mxnet.contrib.amp import amp
 
 parser = argparse.ArgumentParser(description="Train RNN on Sherlock Holmes",
                                  formatter_class=argparse.ArgumentDefaultsHelpFormatter)
@@ -67,6 +68,10 @@
                     help='dropout probability (1.0 - keep probability)')
 parser.add_argument('--rnntype', type=str, default='lstm',
                     help='rnn type: gru, lstm, rnn_tanh and rnn_relu are supported')
+parser.add_argument('--dtype', type=str, default='float32',
+                    help='if float16 is provided AMP convert model'
+                         'is used to convert model to mixed precision model'
+                         'before running inference')
 
 #buckets = [32]
 buckets = [10, 20, 30, 40, 50, 60]
@@ -234,12 +239,20 @@ def sym_gen(seq_len):
         context             = contexts)
     model.bind(data_val.provide_data, data_val.provide_label, for_training=False)
 
-    # note here we load using SequentialRNNCell instead of FusedRNNCell.
     _, arg_params, aux_params = mx.rnn.load_rnn_checkpoint(stack, args.model_prefix, args.load_epoch)
     model.set_params(arg_params, aux_params)
 
-    model.score(data_val, mx.metric.Perplexity(invalid_label),
-                batch_end_callback=mx.callback.Speedometer(args.batch_size, 5))
+    if args.dtype == "float32":
+        model.set_params(arg_params, aux_params)
+        model.score(data_val, mx.metric.Perplexity(invalid_label),
+                    batch_end_callback=mx.callback.Speedometer(args.batch_size, 5))
+    else:
+        assert args.dtype == "float16", "Only float32 and float16 are supported currently"
+        model = amp.convert_bucketing_module(model, target_dtype="float16")
+        model.bind(data_val.provide_data, data_val.provide_label,
+                   for_training=False)
+        model.score(data_val, mx.metric.Perplexity(invalid_label),
+                    batch_end_callback=mx.callback.Speedometer(args.batch_size, 5))
 
 if __name__ == '__main__':
     import logging
diff --git a/python/mxnet/contrib/amp/amp.py b/python/mxnet/contrib/amp/amp.py
index ef2f7209d946..746a9a7f6d68 100755
--- a/python/mxnet/contrib/amp/amp.py
+++ b/python/mxnet/contrib/amp/amp.py
@@ -32,6 +32,7 @@
 from ... import symbol
 from ...context import gpu
 from ...symbol import Symbol
+from ...module import BucketingModule
 from ...symbol import contrib as symbol_contrib
 from ... import ndarray
 from ...ndarray import NDArray, _DTYPE_NP_TO_MX, _DTYPE_MX_TO_NP
@@ -672,6 +673,69 @@ def convert_hybrid_block(block, target_dtype="float16", target_dtype_ops=None,
     ret.collect_params().load_dict(arg_dict, ctx=ctx)
     return ret
 
+def convert_bucketing_module(bucketing_mod, target_dtype="float16", target_dtype_ops=None,
+                             fp32_ops=None, conditional_fp32_ops=None,
+                             excluded_sym_names=None, cast_optional_params=False):
+    """Given a bucketing module cast the symbols associated with the BucketingModule
+    and params if cast_optional_params is set.
+    bucketing_mod : BucketingModule instance
+    target_dtype : str
+        Currently only supports float16. The target dtype indicates to add cast layers
+        when possible so that lower precision computation can be leveraged.
+    target_dtype_ops : list of strs
+        Override the list of operator names casted to target_dtype.
+        If None, uses the framework's default list to be casted to target dtype.
+    fp32_ops : list of strs
+        Override the lists of operator names casted to FP32.
+        If None, uses the framework's default list to be casted to FP32.
+    widest_dtype_ops : list of strs
+        A list of op names provided by user which should run in widest precision among its inputs.
+        If None, uses the framework's default list of widest_precision_ops.
+    conditional_fp32_ops : list of (string, string, list of string)
+        Override the list of operators to be casted to FP32.
+        The format of the list is
+        (name of the function, name of the parameter,
+         list of values of the parameter that make the operator to be casted to
+        fp32)
+    excluded_sym_names : list of strs
+        A list of strings that represent the names of symbols that users want to exclude
+        from being executed in lower precision.
+    cast_optional_params : bool, default False
+        Whether to cast the arg_params and aux_params that don't require to be in FP16
+        because of a cast layer following it, but will reduce the computation and memory
+        overhead of the model if casted.
+    """
+    assert isinstance(bucketing_mod, BucketingModule), "module should be instance of bucketing module"
+    assert len(bucketing_mod._buckets) > 0, "Bucketing Module should not be empty"
+
+    sym_dict = {}
+    assert bucketing_mod.params_initialized, \
+        "bucketing_mod params should be initialized for mixed precision conversion"
+    arg_params, aux_params = bucketing_mod._curr_module._arg_params, bucketing_mod._curr_module._aux_params
+    for key, val in bucketing_mod._buckets.items():
+        sym_dict[key], result_arg_params, result_aux_params = convert_model(val._symbol,
+                                                                            arg_params,
+                                                                            aux_params,
+                                                                            target_dtype=target_dtype,
+                                                                            target_dtype_ops=target_dtype_ops,
+                                                                            fp32_ops=fp32_ops,
+                                                                            conditional_fp32_ops=conditional_fp32_ops,
+                                                                            excluded_sym_names=excluded_sym_names,
+                                                                            cast_optional_params=cast_optional_params)
+    result_mod = BucketingModule.load_dict(sym_dict,
+                                           sym_gen=bucketing_mod._sym_gen,
+                                           arg_params=result_arg_params,
+                                           aux_params=result_aux_params,
+                                           default_bucket_key=bucketing_mod._default_bucket_key,
+                                           logger=bucketing_mod.logger,
+                                           context=bucketing_mod._context,
+                                           work_load_list=bucketing_mod._work_load_list,
+                                           fixed_param_names=bucketing_mod._fixed_param_names,
+                                           state_names=bucketing_mod._state_names,
+                                           group2ctxs=bucketing_mod._group2ctxs,
+                                           compression_params=bucketing_mod._compression_params)
+    return result_mod
+
 def list_fp16_ops():
     """Get the default list of FP16 ops for AMP
     """
diff --git a/python/mxnet/model.py b/python/mxnet/model.py
index 1ff1ee04643f..9ec66e8cf286 100644
--- a/python/mxnet/model.py
+++ b/python/mxnet/model.py
@@ -423,6 +423,22 @@ def save_checkpoint(prefix, epoch, symbol, arg_params, aux_params, remove_amp_ca
     logging.info('Saved checkpoint to \"%s\"', param_name)
 
 
+def load_params(prefix, epoch):
+    """Load params from a file
+    """
+    save_dict = nd.load("%s-%04d.params" % (prefix, epoch))
+    arg_params = {}
+    aux_params = {}
+    if not save_dict:
+        logging.warning("Params file '%s' is empty", '%s-%04d.params' % (prefix, epoch))
+    for k, v in save_dict.items():
+        tp, name = k.split(":", 1)
+        if tp == "arg":
+            arg_params[name] = v
+        if tp == "aux":
+            aux_params[name] = v
+    return (arg_params, aux_params)
+
 def load_checkpoint(prefix, epoch):
     """Load model checkpoint from file.
 
@@ -448,22 +464,7 @@ def load_checkpoint(prefix, epoch):
     - Parameters will be loaded from ``prefix-epoch.params``.
     """
     symbol = sym.load('%s-symbol.json' % prefix)
-    save_dict = nd.load('%s-%04d.params' % (prefix, epoch))
-    arg_params = {}
-    aux_params = {}
-    #load any params in the dict, skip if params are empty
-    if not save_dict:
-        logging.warning("Params file '%s' is empty", '%s-%04d.params' % (prefix, epoch))
-    else:
-        for k, v in save_dict.items():
-            tp, name = k.split(':', 1)
-            if tp == 'arg':
-                arg_params[name] = v
-            elif tp == 'aux':
-                aux_params[name] = v
-            else:
-                logging.warning("Params file '%s' contains unknown param '%s'",
-                                '%s-%04d.params' % (prefix, epoch), k)
+    arg_params, aux_params = load_params(prefix, epoch)
     return (symbol, arg_params, aux_params)
 
 from .callback import LogValidationMetricsCallback # pylint: disable=wrong-import-position
diff --git a/python/mxnet/module/bucketing_module.py b/python/mxnet/module/bucketing_module.py
index 66c666659d0b..dcf2ad7b8e1e 100644
--- a/python/mxnet/module/bucketing_module.py
+++ b/python/mxnet/module/bucketing_module.py
@@ -24,13 +24,17 @@
 
 import logging
 import warnings
+import numpy as np
 
 from .. import context as ctx
 
 from ..initializer import Uniform
+from .. import ndarray as nd
+from .. import symbol as sym
 
 from .base_module import BaseModule, _check_input_names
 from .module import Module
+from ..model import load_params
 from ..name import NameManager
 
 class BucketingModule(BaseModule):
@@ -170,7 +174,7 @@ def get_params(self):
         `(arg_params, aux_params)`
             A pair of dictionaries each mapping parameter names to NDArray values.
         """
-        assert self.binded and self.params_initialized
+        assert self.params_initialized
         self._curr_module._params_dirty = self._params_dirty
         params = self._curr_module.get_params()
         self._params_dirty = False
@@ -335,12 +339,16 @@ def bind(self, data_shapes, label_shapes=None, for_training=True,
         self._grad_req = grad_req
 
         symbol, data_names, label_names = self._call_sym_gen(self._default_bucket_key)
-        module = Module(symbol, data_names, label_names, logger=self.logger,
-                        context=self._context, work_load_list=self._work_load_list,
-                        fixed_param_names=self._fixed_param_names,
-                        state_names=self._state_names,
-                        group2ctxs=self._group2ctxs,
-                        compression_params=self._compression_params)
+        module = None
+        if not self._default_bucket_key in self._buckets:
+            module = Module(symbol, data_names, label_names, logger=self.logger,
+                            context=self._context, work_load_list=self._work_load_list,
+                            fixed_param_names=self._fixed_param_names,
+                            state_names=self._state_names,
+                            group2ctxs=self._group2ctxs,
+                            compression_params=self._compression_params)
+        else:
+            module = self._buckets[self._default_bucket_key]
         module.bind(data_shapes, label_shapes, for_training, inputs_need_grad,
                     force_rebind=False, shared_module=None, grad_req=self._grad_req)
         self._curr_module = module
@@ -380,6 +388,13 @@ def switch_bucket(self, bucket_key, data_shapes, label_shapes=None):
             if self._monitor is not None:
                 module.install_monitor(self._monitor)
             self._buckets[bucket_key] = module
+        else:
+            module = self._buckets[bucket_key]
+            if not module.binded:
+                module.bind(data_shapes, label_shapes, self._curr_module.for_training,
+                            self._curr_module.inputs_need_grad,
+                            force_rebind=False, shared_module=self._buckets[self._default_bucket_key],
+                            grad_req=self._grad_req)
 
         self._curr_module = self._buckets[bucket_key]
         self._curr_bucket_key = bucket_key
@@ -544,3 +559,144 @@ def install_monitor(self, mon):
         self._monitor = mon
         for mod in self._buckets.values():
             mod.install_monitor(mon)
+
+    def save_checkpoint(self, prefix, epoch, remove_amp_cast=False):
+        """Saves current progress to checkpoint for all buckets in BucketingModule
+        Use `mx.callback.module_checkpoint` as `epoch_end_callback` to save during training.
+
+        Parameters
+        ----------
+        prefix : str
+            The file prefix to checkpoint to.
+        epoch : int
+            The current epoch number.
+        """
+
+        assert len(self._buckets) > 0, "Empty BucketingModule cannot be saved"
+        param_name = "%s-%04d.params" % (prefix, epoch)
+        self.save_params(param_name)
+        for bucket_key in self._buckets:
+            symbol, _, _ = self._sym_gen(bucket_key)
+            symbol.save("%s-%s-symbol.json" % (prefix, bucket_key), remove_amp_cast=remove_amp_cast)
+        nd.save("%s.buckets" % (prefix), nd.array(list(self._buckets.keys()), dtype=np.int32))
+
+    @staticmethod
+    def load(prefix, epoch, sym_gen=None, default_bucket_key=None, **kwargs):
+        """Creates a model from previously saved checkpoint.
+
+        Parameters
+        ----------
+        prefix : str
+            path prefix of saved model files. You should have
+            "prefix-symbol.json", "prefix-xxxx.params", and
+            optionally "prefix-xxxx.states", where xxxx is the
+            epoch number.
+        epoch : int
+            epoch to load.
+        sym_gen : function
+            A function when called with a bucket key, returns a triple
+            ``(symbol, data_names, label_names)``.
+            provide sym_gen which was used when saving bucketing module.
+        logger : Logger
+            Default is `logging`.
+        context : Context or list of Context
+            Default is ``cpu()``.
+        work_load_list : list of number
+            Default ``None``, indicating uniform workload.
+        fixed_param_names: list of str
+            Default ``None``, indicating no network parameters are fixed.
+        state_names : list of str
+            States are similar to data and label, but not provided by data iterator.
+            Instead they are initialized to 0 and can be set by set_states()
+        group2ctxs : dict of str to context or list of context,
+                     or list of dict of str to context
+            Default is `None`. Mapping the `ctx_group` attribute to the context assignment.
+        compression_params : dict
+            Specifies type of gradient compression and additional arguments depending
+            on the type of compression being used. For example, 2bit compression requires a threshold.
+            Arguments would then be {'type':'2bit', 'threshold':0.5}
+            See mxnet.KVStore.set_gradient_compression method for more details on gradient compression.
+        """
+        assert sym_gen is not None, \
+            "sym_gen is required for loading BucketingModule"
+        assert default_bucket_key is not None, \
+            "default_bucket_key is required for loading BucketingModule"
+        buckets = nd.load("%s.buckets" % prefix)
+        buckets = list(buckets[0].asnumpy().astype('int32'))
+        bucketing_mod = BucketingModule(sym_gen, default_bucket_key, **kwargs)
+        for bucket_key in buckets:
+            _, data_names, label_names = sym_gen(bucket_key)
+            symbol = sym.load("%s-%s-symbol.json" % (prefix, bucket_key))
+            bucketing_mod._buckets[bucket_key] = Module(symbol, data_names, label_names, **kwargs)
+            if bucket_key == default_bucket_key:
+                bucketing_mod._curr_module = bucketing_mod._buckets[bucket_key]
+        arg_params, aux_params = load_params(prefix, epoch)
+        bucketing_mod._curr_module._arg_params = arg_params
+        bucketing_mod._curr_module._aux_params = aux_params
+        bucketing_mod._curr_module.params_initialized = True
+        bucketing_mod.params_initialized = True
+        return bucketing_mod
+
+    @staticmethod
+    def load_dict(sym_dict=None, sym_gen=None, default_bucket_key=None, arg_params=None,
+                  aux_params=None, **kwargs):
+        """Creates a model from a dict mapping bucket_key to symbols and shared arg_params
+        and aux_params.
+
+        Parameters
+        ----------
+        sym_dict : dict mapping bucket_key to symbol
+            Dict mapping bucket key to symbol
+        sym_gen : function
+            A function when called with a bucket key, returns a triple
+            ``(symbol, data_names, label_names)``.
+            provide sym_gen which was used when saving bucketing module.
+        default_bucket_key : str (or any python object)
+            The key for the default bucket.
+        arg_params : dict
+            Required for loading the BucketingModule.
+            Dict of name to parameter ndarrays.
+        aux_params : dict
+            Required for loading the BucketingModule.
+            Dict of name to auxiliary state ndarrays.
+        logger : Logger
+            Default is `logging`.
+        context : Context or list of Context
+            Default is ``cpu()``.
+        work_load_list : list of number
+            Default ``None``, indicating uniform workload.
+        fixed_param_names: list of str
+            Default ``None``, indicating no network parameters are fixed.
+        state_names : list of str
+            States are similar to data and label, but not provided by data iterator.
+            Instead they are initialized to 0 and can be set by set_states()
+        group2ctxs : dict of str to context or list of context,
+                     or list of dict of str to context
+            Default is `None`. Mapping the `ctx_group` attribute to the context assignment.
+        compression_params : dict
+            Specifies type of gradient compression and additional arguments depending
+            on the type of compression being used. For example, 2bit compression requires a threshold.
+            Arguments would then be {'type':'2bit', 'threshold':0.5}
+            See mxnet.KVStore.set_gradient_compression method for more details on gradient compression.
+        """
+
+        assert sym_dict is not None, \
+            "sym_dict needs to be provided for BucketingModule.load_dict"
+        assert arg_params is not None, \
+            "arg_params need to be provided for BucketingModule.load_dict"
+        assert aux_params is not None, \
+            "aux_params need to be provided for BucketingModule.load_dict"
+        assert default_bucket_key is not None, \
+            "default_bucket_key needs to be provided for BucketingModule.load_dict"
+
+        bucketing_mod = BucketingModule(sym_gen, default_bucket_key, **kwargs)
+        for bucket_key, loaded_sym in sym_dict.items():
+            _, data_names, label_names = sym_gen(default_bucket_key)
+            bucketing_mod._buckets[bucket_key] = Module(loaded_sym, data_names, label_names, **kwargs)
+            if bucket_key == default_bucket_key:
+                bucketing_mod._curr_module = bucketing_mod._buckets[bucket_key]
+        bucketing_mod._curr_module._arg_params = arg_params
+        bucketing_mod._curr_module._aux_params = aux_params
+        bucketing_mod._curr_module.params_initialized = True
+        bucketing_mod.params_initialized = True
+        return bucketing_mod
diff --git a/python/mxnet/module/module.py b/python/mxnet/module/module.py
index c1867282e215..3ba141e94f62 100644
--- a/python/mxnet/module/module.py
+++ b/python/mxnet/module/module.py
@@ -250,7 +250,7 @@ def get_params(self):
         `(arg_params, aux_params)`
             A pair of dictionaries each mapping parameter names to NDArray values.
         """
-        assert self.binded and self.params_initialized
+        assert self.params_initialized
 
         if self._params_dirty:
             self._sync_params_from_devices()
diff --git a/tests/python/gpu/test_contrib_amp.py b/tests/python/gpu/test_contrib_amp.py
index 3daab0f7bb6a..401bfcad3494 100644
--- a/tests/python/gpu/test_contrib_amp.py
+++ b/tests/python/gpu/test_contrib_amp.py
@@ -19,6 +19,7 @@
 import sys
 import mxnet as mx
 import numpy as np
+from random import randint
 import warnings
 import collections
 import ctypes
@@ -31,6 +32,8 @@
 curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
 sys.path.insert(0, os.path.join(curr_path, '../unittest'))
 from common import with_seed, teardown, assert_raises_cudnn_not_satisfied
+sys.path.insert(0, os.path.join(curr_path, '../train'))
+from test_bucketing import train_model
 set_default_context(mx.gpu(0))
 
 def test_amp_coverage():
@@ -301,10 +304,42 @@ def check_amp_convert_hybrid_block():
         params = converted_model.collect_params()
         assert params["stage2_unit1_conv2_weight"].dtype == np.float16
 
+
+    def check_amp_convert_bucketing_module():
+        model = train_model(context=mx.current_context())
+        result_model = amp.convert_bucketing_module(model)
+        val_sent = []
+        batch_size = 128
+        invalid_label = -1
+        num_sentence = 1000
+        buckets = [5, 10, 20, 30, 40]
+        len_vocab = 50
+
+        for _ in range(num_sentence):
+            len_sentence = randint(6, max(buckets)-1) # leave out the two last buckets empty
+            val_sentence = []
+            for _ in range(len_sentence):
+                val_sentence.append(randint(1, len_vocab))
+            val_sent.append(val_sentence)
+
+        data_val =  mx.rnn.BucketSentenceIter(val_sent, batch_size, buckets=buckets,
+                                     invalid_label=invalid_label)
+        result_model.bind(data_val.provide_data, data_val.provide_label, for_training=False)
+        result_model.score(data_val, mx.metric.Perplexity(invalid_label),
+                           batch_end_callback=mx.callback.Speedometer(batch_size, 1))
+
+        # AMP conversion with cast_optional_params set to true
+        result_model = amp.convert_bucketing_module(model, cast_optional_params=True)
+        result_model.bind(data_val.provide_data, data_val.provide_label, for_training=False)
+        result_model.score(data_val, mx.metric.Perplexity(invalid_label),
+                           batch_end_callback=mx.callback.Speedometer(batch_size, 1))
+
+
     with mx.Context(mx.gpu(0)):
         check_amp_convert_symbol()
         check_amp_convert_model()
         check_amp_convert_hybrid_block()
+        check_amp_convert_bucketing_module()
 
 @with_seed()
 @assert_raises_cudnn_not_satisfied(min_version='5.1.10')
diff --git a/tests/python/train/test_bucketing.py b/tests/python/train/test_bucketing.py
index 882c4a4a513d..a233e46e0992 100644
--- a/tests/python/train/test_bucketing.py
+++ b/tests/python/train/test_bucketing.py
@@ -20,9 +20,32 @@
 import mxnet as mx
 import random
 from random import randint
+from mxnet.contrib.amp import amp
 
 
-def test_bucket_module():
+def prepare_bucketing_data(buckets, len_vocab, batch_size, invalid_label, num_sentence):
+    train_sent = []
+    val_sent = []
+
+    for _ in range(num_sentence):
+        len_sentence = randint(6, max(buckets)-1) # leave out the two last buckets empty
+        train_sentence = []
+        val_sentence = []
+        for _ in range(len_sentence):
+            train_sentence.append(randint(1, len_vocab))
+            val_sentence.append(randint(1, len_vocab))
+        train_sent.append(train_sentence)
+        val_sent.append(val_sentence)
+
+    data_train = mx.rnn.BucketSentenceIter(train_sent, batch_size, buckets=buckets,
+                                   invalid_label=invalid_label)
+    data_val =  mx.rnn.BucketSentenceIter(val_sent, batch_size, buckets=buckets,
+                                 invalid_label=invalid_label)
+
+    return (data_train, data_val)
+
+
+def train_model(context=mx.cpu()):
     import logging
     head = '%(asctime)-15s %(message)s'
     logging.basicConfig(level=logging.DEBUG, format=head)
@@ -41,23 +64,7 @@ def test_bucket_module():
     invalid_label = -1
     num_sentence = 1000
 
-    train_sent = []
-    val_sent = []
-
-    for _ in range(num_sentence):
-        len_sentence = randint(6, max(buckets)-1) # leave out the two last buckets empty
-        train_sentence = []
-        val_sentence = []
-        for _ in range(len_sentence):
-            train_sentence.append(randint(1, len_vocab))
-            val_sentence.append(randint(1, len_vocab))
-        train_sent.append(train_sentence)
-        val_sent.append(val_sentence)
-
-    data_train = mx.rnn.BucketSentenceIter(train_sent, batch_size, buckets=buckets,
-                                   invalid_label=invalid_label)
-    data_val =  mx.rnn.BucketSentenceIter(val_sent, batch_size, buckets=buckets,
-                                 invalid_label=invalid_label)
+    data_train, data_val = prepare_bucketing_data(buckets, len_vocab, batch_size, invalid_label, num_sentence)
 
     stack = mx.rnn.SequentialRNNCell()
     for i in range(num_layers):
@@ -80,7 +87,7 @@ def sym_gen(seq_len):
 
         return loss, ('data',), ('softmax_label',)
 
-    contexts = mx.cpu(0)
+    contexts = context
 
     model = mx.mod.BucketingModule(
         sym_gen=sym_gen,
@@ -101,9 +108,14 @@ def sym_gen(seq_len):
         num_epoch=num_epochs,
         batch_end_callback=mx.callback.Speedometer(batch_size, 50))
     logging.info('Finished fit...')
+    return model
+
+
+def test_bucket_module():
     # This test forecasts random sequence of words to check bucketing.
     # We cannot guarantee the accuracy of such an impossible task, and comments out the following line.
     # assert model.score(data_val, mx.metric.MSE())[0][1] < 350, "High mean square error."
+    model = train_model()
 
 
 if __name__ == "__main__":
diff --git a/tests/python/unittest/test_module.py b/tests/python/unittest/test_module.py
index c82afdfe033a..b82933126d67 100644
--- a/tests/python/unittest/test_module.py
+++ b/tests/python/unittest/test_module.py
@@ -15,6 +15,7 @@
 # specific language governing permissions and limitations
 # under the License.
 
+import os
 import mxnet as mx
 import mxnet.ndarray as nd
 from mxnet.test_utils import *
@@ -23,6 +24,9 @@
 from mxnet.module.executor_group import DataParallelExecutorGroup
 from common import setup_module, with_seed, assertRaises, teardown
 from collections import namedtuple
+curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
+sys.path.insert(0, os.path.join(curr_path, "../train"))
+from test_bucketing import train_model, prepare_bucketing_data
 
 
 @with_seed()
@@ -216,6 +220,73 @@ def dict_equ(a, b):
     os.putenv('MXNET_UPDATE_ON_KVSTORE', previous_update_on_kvstore)
 
 
+@with_seed()
+def test_bucketing_save_load():
+    previous_update_on_kvstore = os.getenv('MXNET_UPDATE_ON_KVSTORE', "1")
+    os.putenv('MXNET_UPDATE_ON_KVSTORE', '1')
+    def dict_equ(a, b):
+        assert set(a) == set(b)
+        for k in a:
+            assert (a[k].asnumpy() == b[k].asnumpy()).all()
+
+
+    len_vocab = 50
+    num_embed = 25
+    num_epochs = 5
+    batch_size = 128
+    num_layers = 2
+    num_hidden = 25
+    buckets = [5, 10, 20, 30, 40]
+    invalid_label = -1
+    num_sentence=1000
+
+    stack = mx.rnn.SequentialRNNCell()
+    for i in range(num_layers):
+        stack.add(mx.rnn.LSTMCell(num_hidden=num_hidden, prefix='lstm_l%d_' % i))
+
+    def sym_gen(seq_len):
+        data = mx.sym.Variable('data')
+        label = mx.sym.Variable('softmax_label')
+        embed = mx.sym.Embedding(data=data, input_dim=len_vocab,
+                                 output_dim=num_embed, name='embed')
+        stack.reset()
+        outputs, states = stack.unroll(seq_len, inputs=embed, merge_outputs=True)
+
+        pred = mx.sym.Reshape(outputs, shape=(-1, num_hidden))
+        pred = mx.sym.FullyConnected(data=pred, num_hidden=len_vocab, name='pred')
+
+        label = mx.sym.Reshape(label, shape=(-1,))
+        loss = mx.sym.SoftmaxOutput(data=pred, label=label, name='softmax')
+
+        return loss, ('data',), ('softmax_label',)
+
+    model = train_model(context=mx.current_context())
+    model.save_checkpoint("test", 0)
+    data_train, data_val = prepare_bucketing_data(buckets, len_vocab, batch_size, invalid_label, num_sentence)
+    mod2 = mx.mod.BucketingModule.load('test', 0, sym_gen=sym_gen,
+                                       default_bucket_key=data_train.default_bucket_key)
+
+    mod2.bind(data_shapes=data_train.provide_data,
+              label_shapes=data_train.provide_label)
+
+    for bucket_key in model._buckets.keys():
+        dict_equ(model._buckets[model._default_bucket_key].get_params()[0],
+                 mod2._buckets[mod2._default_bucket_key].get_params()[0])
+    mod2.fit(
+        train_data=data_train,
+        eval_data=data_val,
+        eval_metric=mx.metric.Perplexity(invalid_label), # Use Perplexity for multiclass classification.
+        kvstore='device',
+        optimizer='sgd',
+        optimizer_params={'learning_rate': 0.01,
+                          'momentum': 0,
+                          'wd': 0.00001},
+        initializer=mx.init.Xavier(factor_type="in", magnitude=2.34),
+        num_epoch=num_epochs,
+        batch_end_callback=mx.callback.Speedometer(batch_size, 50))
+    os.putenv('MXNET_UPDATE_ON_KVSTORE', previous_update_on_kvstore)
+
+
 @with_seed()
 def test_module_reshape():
     data = mx.sym.Variable('data')

From ab602142d97681401526e2278effcdff43bec31a Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Mon, 26 Aug 2019 15:16:53 -0700
Subject: [PATCH 225/813] Add Median,p50,p99 to python profiler (#15953)

* median,avg,p90,p99

unused var remove

add parameter

missing arg

add mean, median, p90,p99 to markdown

markdown

* Trigger notification coz of gpu windows fail

* Trigger notification

* Trigger notification

* add _time
---
 benchmark/opperf/utils/benchmark_utils.py |  2 +-
 benchmark/opperf/utils/common_utils.py    | 17 ++++++++++---
 benchmark/opperf/utils/profiler_utils.py  | 29 +++++++++++++++++++----
 3 files changed, 39 insertions(+), 9 deletions(-)

diff --git a/benchmark/opperf/utils/benchmark_utils.py b/benchmark/opperf/utils/benchmark_utils.py
index c530f9bf6177..a6ee38bf9f65 100644
--- a/benchmark/opperf/utils/benchmark_utils.py
+++ b/benchmark/opperf/utils/benchmark_utils.py
@@ -50,7 +50,7 @@ def _prepare_op_inputs(inputs, run_backward, dtype, ctx):
     return args_list, kwargs_list
 
 
-def _run_nd_operator_performance_test(op, inputs, run_backward, warmup, runs, kwargs_list, profiler):
+def _run_nd_operator_performance_test(op, inputs, run_backward, warmup, runs, args_list, kwargs_list, profiler):
     if profiler == 'native':
         if run_backward:
             benchmark_helper_func = cpp_profile(nd_forward_backward_and_profile)
diff --git a/benchmark/opperf/utils/common_utils.py b/benchmark/opperf/utils/common_utils.py
index e657702d3596..fa0331f3468e 100644
--- a/benchmark/opperf/utils/common_utils.py
+++ b/benchmark/opperf/utils/common_utils.py
@@ -97,6 +97,10 @@ def _prepare_op_benchmark_result(op, op_bench_result, profiler):
     max_mem_usage = "---"
     inputs = "---"
     avg_time = "---"
+    p50_time = "---"
+    p90_time = "---"
+    p99_time = "---"
+
     for key, value in op_bench_result.items():
         if "avg_time_forward" in key:
             avg_forward_time = value
@@ -108,12 +112,19 @@ def _prepare_op_benchmark_result(op, op_bench_result, profiler):
             inputs = value
         elif "avg_time" in key:
             avg_time = value
+        elif "p50_time" in key:
+            p50_time = value
+        elif "p90_time" in key:
+            p90_time = value
+        elif "p99_time" in key:
+            p99_time = value
+
     result = ""
     if profiler == "native":
         result = "| {} | {} | {} | {} | {} |".format(operator_name,
                  avg_forward_time, avg_backward_time, max_mem_usage, inputs)
     elif profiler == "python":
-        result = "| {} | {} | {} |".format(operator_name, avg_time, inputs)
+        result = "| {} | {} | {} | {} | {} | {} |".format(operator_name, avg_time, p50_time, p90_time, p99_time, inputs)
     return result
 
 
@@ -132,8 +143,8 @@ def _prepare_markdown(results, runtime_features=None, profiler='native'):
             " | Inputs |")
     elif profiler == 'python':
         results_markdown.append(
-            "| Operator | Avg Time (ms) | Inputs |")
-    results_markdown.append("| :---: | :---: | :---: |")
+            "| Operator | Avg Time (ms) | P50 Time (ms) | P90 Time (ms) | P99 Time (ms) | Inputs |")
+    results_markdown.append("| :---: | :---: | :---: | :---: | :---: | :---: |")
 
     for op, op_bench_results in sorted(results.items(), key=itemgetter(0)):
         for op_bench_result in op_bench_results:
diff --git a/benchmark/opperf/utils/profiler_utils.py b/benchmark/opperf/utils/profiler_utils.py
index 5d4eb5576220..21e2606ab94e 100644
--- a/benchmark/opperf/utils/profiler_utils.py
+++ b/benchmark/opperf/utils/profiler_utils.py
@@ -17,6 +17,7 @@
 
 import time
 import functools
+import numpy as np
 
 from .common_utils import merge_map_list
 from mxnet import profiler
@@ -219,6 +220,9 @@ def python_profile(func):
     res, timing output. res being result returned after operator execution.
     profiler output is a dictionary with summary of operation execution.
     Example output : { "add": [{"avg_time_add": 0.4053089120425284,
+                                'p50_time_add': 16.761042876169086,
+                                'p90_time_add': 18.081666342914108,
+                                'p99_time_add': 19.060144051909447,
                                 "inputs": {
                                     "lhs": [1024, 1024],
                                     "rhs": [1024,1024]
@@ -228,10 +232,16 @@ def python_profile(func):
 
     @functools.wraps(func)
     def python_profile_it(*args, **kwargs):
-        start_time = time.perf_counter()    # 1
-        res = func(*args, **kwargs)
-        end_time = time.perf_counter()      # 2
-        run_time = end_time - start_time    # 3
+        runs = args[1]
+        modified_args = (args[0], 1, args[2])
+        times = []
+
+        for _ in range(runs):
+            start_time = time.perf_counter()    # 1
+            res = func(*modified_args, **kwargs)
+            end_time = time.perf_counter()      # 2
+            run_time = (end_time - start_time)*1000    # 3
+            times.append(run_time)
 
         # NOTE : same as cpp_profile_it
         if len(args) > 0:
@@ -241,6 +251,15 @@ def python_profile_it(*args, **kwargs):
         else:
             raise ValueError("Unable to identify operator name to extract profiler output!")
 
-        profiler_output = {'avg_time_'+str(operator_name): run_time}
+        avg_run_time = np.mean(times)
+        p50_run_time = np.percentile(times, 50)
+        p90_run_time = np.percentile(times, 90)
+        p99_run_time = np.percentile(times, 99)
+
+        profiler_output = {'avg_time_'+str(operator_name): avg_run_time,
+                           'p50_time_'+str(operator_name): p50_run_time,
+                           'p90_time_'+str(operator_name): p90_run_time,
+                           'p99_time_'+str(operator_name): p99_run_time,
+                           }
         return res, profiler_output
     return python_profile_it

From 79ed678a0fd4553d53a62ff7cc554d2994009374 Mon Sep 17 00:00:00 2001
From: Xi Wang <xidulu@gmail.com>
Date: Tue, 27 Aug 2019 14:14:54 +0800
Subject: [PATCH 226/813] [Numpy] random.randint() implemented (#15956)

* randint completed

* doc fixed

* tiny bugs fixed

* Update runtime_functions.sh

* change CI config back
---
 python/mxnet/ndarray/numpy/random.py   | 66 +++++++++++++++++++++++++-
 python/mxnet/numpy/random.py           | 55 ++++++++++++++++++++-
 python/mxnet/symbol/numpy/random.py    | 66 +++++++++++++++++++++++++-
 src/operator/random/sample_op.cc       |  1 +
 tests/python/unittest/test_numpy_op.py | 46 ++++++++++++++++++
 5 files changed, 231 insertions(+), 3 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/random.py b/python/mxnet/ndarray/numpy/random.py
index be918615bfd9..71707d41c8e8 100644
--- a/python/mxnet/ndarray/numpy/random.py
+++ b/python/mxnet/ndarray/numpy/random.py
@@ -20,7 +20,71 @@
 from ...context import current_context
 from . import _internal as _npi
 
-__all__ = ['uniform']
+
+__all__ = ['randint', 'uniform']
+
+
+def randint(low, high=None, size=None, dtype=None, **kwargs):
+    """Return random integers from `low` (inclusive) to `high` (exclusive).
+
+    Return random integers from the "discrete uniform" distribution of
+    the specified dtype in the "half-open" interval [`low`, `high`). If
+    `high` is None (the default), then results are from [0, `low`).
+
+    Parameters
+    ----------
+    low : int
+        Lowest (signed) integer to be drawn from the distribution (unless
+        ``high=None``, in which case this parameter is one above the
+        *highest* such integer).
+    high : int, optional
+        If provided, one above the largest (signed) integer to be drawn
+        from the distribution (see above for behavior if ``high=None``).
+    size : int or tuple of ints, optional
+        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k`` samples are drawn.  Default is None, in which case a
+        single value is returned.
+    dtype : dtype, optional
+        Desired dtype of the result. All dtypes are determined by their
+        name, i.e., 'int64', 'int', etc, so byteorder is not available
+        and a specific precision may have different C types depending
+        on the platform. The default value is 'np.int'.
+    ctx : Context, optional
+        Device context of output. Default is current context.
+    out : ndarray, optional
+        The output ndarray (default is `None`).
+
+    Returns
+    -------
+    out : ndarray of ints
+        `size`-shaped array of random integers from the appropriate
+        distribution, or a single such random int if `size` not provided.
+
+    Examples
+    --------
+    >>> np.random.randint(2, size=10)
+    array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])
+    >>> np.random.randint(1, size=10)
+    array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
+
+    Generate a 2 x 4 array of ints between 0 and 4, inclusive:
+
+    >>> np.random.randint(5, size=(2, 4))
+    array([[4, 0, 2, 1],
+        [3, 2, 2, 0]])
+    """
+    ctx = kwargs.pop('ctx', None)
+    out = kwargs.pop('out', None)
+    if dtype is None:
+        dtype = 'int'
+    if ctx is None:
+        ctx = current_context()
+    if size is None:
+        size = 1
+    if high is None:
+        high = low
+        low = 0
+    return _npi.random_randint(low, high, shape=size, dtype=dtype, ctx=ctx, out=out)
 
 
 def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
diff --git a/python/mxnet/numpy/random.py b/python/mxnet/numpy/random.py
index f85936345b7f..f0fd43eb0e70 100644
--- a/python/mxnet/numpy/random.py
+++ b/python/mxnet/numpy/random.py
@@ -20,7 +20,60 @@
 from __future__ import absolute_import
 from ..ndarray import numpy as _mx_nd_np
 
-__all__ = ['uniform']
+
+__all__ = ["randint", "uniform"]
+
+
+def randint(low, high=None, size=None, dtype=None, **kwargs):
+    """Return random integers from `low` (inclusive) to `high` (exclusive).
+
+    Return random integers from the "discrete uniform" distribution of
+    the specified dtype in the "half-open" interval [`low`, `high`). If
+    `high` is None (the default), then results are from [0, `low`).
+
+    Parameters
+    ----------
+    low : int
+        Lowest (signed) integer to be drawn from the distribution (unless
+        ``high=None``, in which case this parameter is one above the
+        *highest* such integer).
+    high : int, optional
+        If provided, one above the largest (signed) integer to be drawn
+        from the distribution (see above for behavior if ``high=None``).
+    size : int or tuple of ints, optional
+        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k`` samples are drawn.  Default is None, in which case a
+        single value is returned.
+    dtype : dtype, optional
+        Desired dtype of the result. All dtypes are determined by their
+        name, i.e., 'int64', 'int', etc, so byteorder is not available
+        and a specific precision may have different C types depending
+        on the platform. The default value is 'np.int'.
+    ctx : Context, optional
+        Device context of output. Default is current context.
+    out : ndarray, optional
+        The output ndarray (default is `None`).
+
+    Returns
+    -------
+    out : ndarray of ints
+        `size`-shaped array of random integers from the appropriate
+        distribution, or a single such random int if `size` not provided.
+
+    Examples
+    --------
+    >>> np.random.randint(2, size=10)
+    array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])
+    >>> np.random.randint(1, size=10)
+    array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
+
+    Generate a 2 x 4 array of ints between 0 and 4, inclusive:
+
+    >>> np.random.randint(5, size=(2, 4))
+    array([[4, 0, 2, 1],
+        [3, 2, 2, 0]])
+    """
+    return _mx_nd_np.random.randint(low, high, size, dtype, **kwargs)
 
 
 def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
diff --git a/python/mxnet/symbol/numpy/random.py b/python/mxnet/symbol/numpy/random.py
index 338a5e28be4e..86d0ba3095e1 100644
--- a/python/mxnet/symbol/numpy/random.py
+++ b/python/mxnet/symbol/numpy/random.py
@@ -21,7 +21,71 @@
 from ...context import current_context
 from . import _internal as _npi
 
-__all__ = ['uniform']
+
+__all__ = ['randint', 'uniform']
+
+
+def randint(low, high=None, size=None, dtype=None, **kwargs):
+    """Return random integers from `low` (inclusive) to `high` (exclusive).
+
+    Return random integers from the "discrete uniform" distribution of
+    the specified dtype in the "half-open" interval [`low`, `high`). If
+    `high` is None (the default), then results are from [0, `low`).
+
+    Parameters
+    ----------
+    low : int
+        Lowest (signed) integer to be drawn from the distribution (unless
+        ``high=None``, in which case this parameter is one above the
+        *highest* such integer).
+    high : int, optional
+        If provided, one above the largest (signed) integer to be drawn
+        from the distribution (see above for behavior if ``high=None``).
+    size : int or tuple of ints, optional
+        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k`` samples are drawn.  Default is None, in which case a
+        single value is returned.
+    dtype : dtype, optional
+        Desired dtype of the result. All dtypes are determined by their
+        name, i.e., 'int64', 'int', etc, so byteorder is not available
+        and a specific precision may have different C types depending
+        on the platform. The default value is 'np.int'.
+    ctx : Context, optional
+        Device context of output. Default is current context.
+    out : symbol, optional
+        The output symbol (default is `None`).
+
+    Returns
+    -------
+    out : symbol
+        `size`-shaped array of random integers from the appropriate
+        distribution, or a single such random int if `size` not provided.
+
+    Examples
+    --------
+    >>> np.random.randint(2, size=10)
+    array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])
+    >>> np.random.randint(1, size=10)
+    array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
+
+    Generate a 2 x 4 array of ints between 0 and 4, inclusive:
+
+    >>> np.random.randint(5, size=(2, 4))
+    array([[4, 0, 2, 1],
+        [3, 2, 2, 0]])
+    """
+    ctx = kwargs.pop('ctx', None)
+    out = kwargs.pop('out', None)
+    if dtype is None:
+        dtype = 'int'
+    if ctx is None:
+        ctx = current_context()
+    if size is None:
+        size = 1
+    if high is None:
+        high = low
+        low = 0
+    return _npi.random_randint(low, high, shape=size, dtype=dtype, ctx=ctx, out=out)
 
 
 def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
diff --git a/src/operator/random/sample_op.cc b/src/operator/random/sample_op.cc
index 543146257ddf..d5b5e288258c 100644
--- a/src/operator/random/sample_op.cc
+++ b/src/operator/random/sample_op.cc
@@ -182,6 +182,7 @@ Example::
 
 MXNET_OPERATOR_REGISTER_SAMPLE(_random_randint, SampleRandIntParam)
 .add_alias("random_randint")
+.add_alias("_npi_random_randint")
 .describe(R"code(Draw random samples from a discrete uniform distribution.
 
 Samples are uniformly distributed over the half-open interval *[low, high)*
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 399cdead6177..0a1a4fb2b9b1 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -28,6 +28,8 @@
 from common import assertRaises, with_seed
 import random
 import collections
+import scipy.stats as ss
+from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf, retry
 
 
 @with_seed()
@@ -1080,6 +1082,50 @@ def hybrid_forward(self, F, a, *args):
                 assert same(mx_out.asnumpy(), np_out)
 
 
+@with_seed()
+@use_np
+def test_np_randint():
+    ctx = mx.context.current_context()
+    # test shapes
+    params = [
+        (0, 10),
+        (5, None)
+    ]
+    shapes = [
+        (3, 3),
+        (3, 4),
+        (0, 0),
+        (3, 3, 3),
+        (0, 0, 0),
+        (2, 2, 4, 3),
+        (2, 2, 4, 3),
+        (2, 0, 3, 0),
+        (2, 0, 2, 3)
+    ]
+    for shape in shapes:
+        for (low, high) in params:
+            data_mx = np.random.randint(low, high, size=shape)
+            assert data_mx.shape == shape
+
+    # test generator
+    for dtype in ['int32', 'int64']:
+        for low, high in [(50000000, 50001000),(-50000100,-50000000),(-500,199)]:
+            scale = high - low
+            buckets, probs = gen_buckets_probs_with_ppf(lambda x: ss.uniform.ppf(x, loc=low, scale=scale), 5)
+            # Quantize bucket boundaries to reflect the actual dtype and adjust probs accordingly
+            buckets = _np.array(buckets, dtype=dtype).tolist()
+            probs = [(buckets[i][1] - buckets[i][0]) / float(scale) for i in range(5)]
+            generator_mx = lambda x: np.random.randint(low, high, size=x, dtype=dtype, ctx=ctx).asnumpy()
+            verify_generator(generator=generator_mx, buckets=buckets, probs=probs, nrepeat=100)
+            # Scipy uses alpha = 0.01 for testing discrete distribution generator but we are using default alpha=0.05 (higher threshold ensures robustness)
+            # Refer - https://github.com/scipy/scipy/blob/9f12af697763fb5f9767d5cb1280ce62456a3974/scipy/stats/tests/test_discrete_basic.py#L45
+            generator_mx_same_seed = \
+                lambda x: _np.concatenate(
+                    [np.random.randint(low, high, size=x // 10, dtype=dtype, ctx=ctx).asnumpy()
+                        for _ in range(10)])
+            verify_generator(generator=generator_mx_same_seed, buckets=buckets, probs=probs, nrepeat=100)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 0e71fbdafcdb50194bb4a1e767ced3c60ea021bb Mon Sep 17 00:00:00 2001
From: Rohit Kumar Srivastava <srivastava.141@osu.edu>
Date: Mon, 26 Aug 2019 23:53:47 -0700
Subject: [PATCH 227/813] Added tests to verify Large Vector Support for
 initial set of ops  (#15943)

* Adding tests to verify support for Large Tensors in additional Ops along with new C_Apis supporting 64bit indexing

* removing skipped tests

* enabling Large Index support for slice and softmax

* removing tests not required for vector testing
---
 python/mxnet/test_utils.py          |  11 +++
 src/operator/softmax_output-inl.h   |  18 ++--
 src/operator/tensor/matrix_op-inl.h |   6 +-
 tests/nightly/test_large_array.py   |   8 +-
 tests/nightly/test_large_vector.py  | 139 +++++++++++++++++++++++++++-
 5 files changed, 162 insertions(+), 20 deletions(-)

diff --git a/python/mxnet/test_utils.py b/python/mxnet/test_utils.py
index c326091dbd9f..bb730fd3a007 100644
--- a/python/mxnet/test_utils.py
+++ b/python/mxnet/test_utils.py
@@ -262,6 +262,17 @@ def assign_each2(input1, input2, function):
 
     return output
 
+# For testing Large Tensors having total size > 2^32 elements
+def create_2d_tensor(rows, columns, dtype=np.int64):
+    a = mx.nd.arange(0, rows, dtype=dtype).reshape(rows, 1)
+    b = mx.nd.broadcast_to(a, shape=(a.shape[0], columns))
+    return b
+
+# For testing Large Vectors having total size > 2^32 elements
+def create_vector(size, dtype=np.int64):
+    a = mx.nd.arange(0, size, dtype=dtype)
+    return a
+
 def rand_sparse_ndarray(shape, stype, density=None, dtype=None, distribution=None,
                         data_init=None, rsp_indices=None, modifier_func=None,
                         shuffle_csr_indices=False, ctx=None):
diff --git a/src/operator/softmax_output-inl.h b/src/operator/softmax_output-inl.h
index 80ab40ef6c50..db8676c028e4 100644
--- a/src/operator/softmax_output-inl.h
+++ b/src/operator/softmax_output-inl.h
@@ -117,9 +117,9 @@ class SoftmaxOutputOp : public Operator {
     CHECK_EQ(out_data.size(), 1U) << "SoftmaxOutput Output: [output]";
     Stream<xpu> *s = ctx.get_stream<xpu>();
     if (param_.multi_output) {
-      int n = in_data[softmaxout_enum::kData].size(0);
-      int k = in_data[softmaxout_enum::kData].size(1);
-      Shape<3> s3 = Shape3(n, k, static_cast<int>(in_data[softmaxout_enum::kData].Size()/n/k));
+      index_t n = in_data[softmaxout_enum::kData].size(0);
+      index_t k = in_data[softmaxout_enum::kData].size(1);
+      Shape<3> s3 = Shape3(n, k, static_cast<index_t>(in_data[softmaxout_enum::kData].Size()/n/k));
       Tensor<xpu, 3, DType> data =
           in_data[softmaxout_enum::kData].get_with_shape<xpu, 3, DType>(s3, s);
       Tensor<xpu, 3, DType> out =
@@ -131,8 +131,8 @@ class SoftmaxOutputOp : public Operator {
         Tensor<xpu, 2, DType> out = out_data[softmaxout_enum::kOut].FlatTo2D<xpu, DType>(s);
         Softmax(out, data);
       } else {
-        int n = in_data[softmaxout_enum::kData].size(0);
-        int k = in_data[softmaxout_enum::kData].Size()/n;
+        index_t n = in_data[softmaxout_enum::kData].size(0);
+        index_t k = in_data[softmaxout_enum::kData].Size()/n;
         Shape<2> s2 = Shape2(n, k);
         Tensor<xpu, 2, DType> data =
             in_data[softmaxout_enum::kData].get_with_shape<xpu, 2, DType>(s2, s);
@@ -171,9 +171,9 @@ class SoftmaxOutputOp : public Operator {
         grad = (out - label) * scalar<DType>(param_.grad_scale);
       }
     } else if (param_.multi_output) {
-      int n = out_data[softmaxout_enum::kOut].size(0);
-      int k = out_data[softmaxout_enum::kOut].size(1);
-      Shape<3> s3 = Shape3(n, k, static_cast<int>(out_data[softmaxout_enum::kOut].Size()/n/k));
+      index_t n = out_data[softmaxout_enum::kOut].size(0);
+      index_t k = out_data[softmaxout_enum::kOut].size(1);
+      Shape<3> s3 = Shape3(n, k, static_cast<index_t>(out_data[softmaxout_enum::kOut].Size()/n/k));
       Shape<2> s2 = Shape2(s3[0], s3[2]);
       Tensor<xpu, 2, DType> label =
           in_data[softmaxout_enum::kLabel].get_with_shape<xpu, 2, DType>(s2, s);
@@ -224,7 +224,7 @@ class SoftmaxOutputOp : public Operator {
 //        Tensor<xpu, 2, DType> out = out_data[softmaxout_enum::kOut].FlatTo2D<xpu, DType>(s);
 //        Tensor<xpu, 2, DType> grad = in_grad[softmaxout_enum::kData].FlatTo2D<xpu, DType>(s);
       } else {
-        int n = out_data[softmaxout_enum::kOut].size(0);
+        index_t n = out_data[softmaxout_enum::kOut].size(0);
         data_shape = Shape2(n, out_data[softmaxout_enum::kOut].Size()/n);
       }
       Tensor<xpu, 1, DType> label = in_data[softmaxout_enum::kLabel].get_with_shape<xpu, 1, DType>(
diff --git a/src/operator/tensor/matrix_op-inl.h b/src/operator/tensor/matrix_op-inl.h
index 611dd7287206..58a535353e10 100644
--- a/src/operator/tensor/matrix_op-inl.h
+++ b/src/operator/tensor/matrix_op-inl.h
@@ -732,8 +732,8 @@ inline void GetIndexRange(const mxnet::TShape& dshape,
 }
 
 inline void SetSliceOpOutputDimSize(const mxnet::TShape& dshape,
-                                    const index_t i, const int b,
-                                    const int e, const int s,
+                                    const index_t i, const index_t b,
+                                    const index_t e, const index_t s,
                                     mxnet::TShape* oshape) {
   if (!mxnet::dim_size_is_known(dshape, i)) {
     (*oshape)[i] = -1;
@@ -765,7 +765,7 @@ inline bool SliceOpShape(const nnvm::NodeAttrs& attrs,
     common::StaticArray<index_t, ndim> begin, end, step;
     GetIndexRange(dshape, param.begin, param.end, param.step, &begin, &end, &step);
     for (int i = 0; i < param.begin.ndim(); ++i) {
-      const int b = begin[i], e = end[i], s = step[i];
+      const index_t b = begin[i], e = end[i], s = step[i];
       SetSliceOpOutputDimSize(dshape, i, b, e, s, &oshape);
     }
   })
diff --git a/tests/nightly/test_large_array.py b/tests/nightly/test_large_array.py
index bd452fb75f6c..cdacce91ab6e 100644
--- a/tests/nightly/test_large_array.py
+++ b/tests/nightly/test_large_array.py
@@ -19,7 +19,7 @@
 import numpy as np
 import mxnet as mx
 
-from mxnet.test_utils import rand_ndarray, assert_almost_equal, rand_coord_2d, default_context, check_symbolic_forward
+from mxnet.test_utils import rand_ndarray, assert_almost_equal, rand_coord_2d, default_context, check_symbolic_forward, create_2d_tensor
 from mxnet import gluon, nd
 from tests.python.unittest.common import with_seed
 
@@ -31,12 +31,6 @@
 LARGE_SIZE = LARGE_X * SMALL_Y
 
 
-def create_2d_tensor(rows, columns, dtype=np.int64):
-    a = nd.arange(0, rows, dtype=dtype).reshape(rows, 1)
-    b = nd.broadcast_to(a, shape=(a.shape[0], columns))
-    return nd.array(b, dtype=dtype)
-
-
 def test_gluon_embedding():
     m = gluon.nn.Embedding(SMALL_Y, MEDIUM_X)
     m.initialize()
diff --git a/tests/nightly/test_large_vector.py b/tests/nightly/test_large_vector.py
index 3a66500957e0..64bfa8a1d3e9 100644
--- a/tests/nightly/test_large_vector.py
+++ b/tests/nightly/test_large_vector.py
@@ -18,7 +18,7 @@
 import numpy as np
 import mxnet as mx
 
-from mxnet.test_utils import rand_ndarray, assert_almost_equal, rand_coord_2d
+from mxnet.test_utils import rand_ndarray, assert_almost_equal, rand_coord_2d, create_vector
 from mxnet import gluon, nd
 from tests.python.unittest.common import with_seed
 
@@ -30,9 +30,146 @@
 def test_slice():
     a = nd.ones(LARGE_X)
     res = nd.slice(a, begin=(LARGE_X - MEDIUM_X), end=LARGE_X)
+    assert a[0] == 1
     assert res.shape[0] == MEDIUM_X
 
 
+def test_ndarray_zeros():
+    a = nd.zeros(shape=LARGE_X)
+    assert a[-1] == 0
+    assert a.shape == (LARGE_X,)
+    assert a.size == LARGE_X
+
+
+def test_ndarray_ones():
+    a = nd.ones(shape=LARGE_X)
+    assert a[-1] == 1
+    assert nd.sum(a).asnumpy() == LARGE_X
+
+
+@with_seed()
+def test_ndarray_random_uniform():
+    a = nd.random.uniform(shape=LARGE_X)
+    assert a[-1] != 0
+
+
+@with_seed()
+def test_ndarray_random_randint():
+    a = nd.random.randint(100, 10000, shape=LARGE_X)
+    assert a.shape == (LARGE_X,)
+    # check if randint can generate value greater than 2**32 (large)
+    low_large_value = 2**32
+    high_large_value = 2**34
+    a = nd.random.randint(low_large_value, high_large_value, dtype=np.int64)
+    low = mx.nd.array([low_large_value], dtype='int64')
+    high = mx.nd.array([high_large_value], dtype='int64')
+    assert a > low  and a < high
+
+
+def test_ndarray_empty():
+    a = nd.empty(LARGE_X)
+    assert a.shape == (LARGE_X,)
+
+
+def test_elementwise():
+    a = nd.ones(shape=LARGE_X)
+    b = nd.ones(shape=LARGE_X)
+    res = a + b
+    assert res[-1].asnumpy() == 2
+    res = a + 1
+    assert res[-1].asnumpy() == 2
+    res = nd.sqrt(a + 8)
+    assert res[-1].asnumpy() == 3
+
+
+def test_reduce():
+    a = nd.ones(shape=(LARGE_X, 1))
+    assert nd.sum(a).asnumpy() == a.shape[0] * a.shape[1]
+
+
+def test_clip():
+    a = create_vector(LARGE_X)
+    res = nd.clip(a, a_min=100, a_max=1000)
+    assert np.sum(res[-1].asnumpy() == 1000) == 1
+
+
+def test_argmin():
+    a = create_vector(LARGE_X, dtype=np.float32)
+    assert a[0] == 0
+    idx = mx.nd.argmin(a, axis=0)
+    assert idx[0] == 0
+    assert idx.shape[0] == 1
+
+
+def test_take():
+    a = nd.ones(shape=LARGE_X)
+    idx = nd.arange(LARGE_X - 1000, LARGE_X)
+    res = nd.take(a, idx)
+    assert np.sum(res.asnumpy() == 1) == res.shape[0]
+
+
+def test_slice_assign():
+    a = nd.ones(shape=LARGE_X)
+    a[LARGE_X-1:LARGE_X] = 1000
+    assert np.sum(a[-1].asnumpy() == 1000) == 1
+
+
+def test_expand_dims():
+    a = nd.ones(shape=LARGE_X)
+    res = nd.expand_dims(a, axis=0)
+    assert res[0][0] == 1
+    assert res.shape == (1, a.shape[0])
+
+
+def test_squeeze():
+    a = nd.ones(shape=LARGE_X)
+    data = nd.expand_dims(a, axis=0)
+    res = nd.squeeze(data)
+    assert a[0] == res[0]
+    assert res.shape == a.shape
+
+
+def test_broadcast_div():
+    a = nd.ones(shape=LARGE_X)
+    b = nd.ones(shape=LARGE_X) * 2
+    res = a / b
+    assert np.sum(res.asnumpy() == 0.5) == a.shape[0]
+
+
+def test_Dense(ctx=mx.cpu(0)):
+    data = mx.nd.ones(shape=LARGE_X)
+    linear = gluon.nn.Dense(2)
+    linear.initialize(ctx=ctx)
+    res = linear(data)
+    res.wait_to_read()
+    assert res.shape == (LARGE_X, 2)
+
+
+def test_argsort():
+    b = create_vector(size=LARGE_X)
+    s = nd.argsort(b, axis=0, is_ascend=False, dtype=np.int64)
+    mx.nd.waitall()
+    assert (s[0].asnumpy() == (LARGE_X - 1)).all()
+
+
+def test_sort():
+    b = create_vector(size=LARGE_X)
+    s = nd.sort(b, axis=0, is_ascend=False)
+    assert np.sum(s[-1].asnumpy() == 0).all()
+    s = nd.sort(b, is_ascend=True)
+    assert np.sum(s[0].asnumpy() == 0).all()
+
+
+def test_topk():
+    b = create_vector(size=LARGE_X)
+    ind = nd.topk(b, k=10, axis=0, dtype=np.int64)
+    assert np.sum(ind.asnumpy() == (LARGE_X - 1)) == 1
+    ind, val = mx.nd.topk(b, k=3, axis=0, dtype=np.int64, ret_typ="both", is_ascend=False)
+    assert np.all(ind == val)
+    val = nd.topk(b, k=1, axis=0, dtype=np.int64, ret_typ="value")
+    assert val.sum() == (LARGE_X - 1)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 8df946978d68713237fd520605adc22729d241aa Mon Sep 17 00:00:00 2001
From: Zoey Xinyi Ge <xinyige@umich.edu>
Date: Wed, 28 Aug 2019 03:44:32 +0800
Subject: [PATCH 228/813] Refines NDArray indexing and adds numpy ndarray
 indexing [READY FOR REVIEW] (#15942)

* Completed NDArray indexing fix and numpy ndarray indexing.

* Style and test fixed

* Retrigger CI
---
 3rdparty/mshadow/mshadow/extension/slice.h  |   4 +-
 python/mxnet/ndarray/ndarray.py             | 421 +++++++++++++------
 python/mxnet/ndarray/numpy/_op.py           |  57 ++-
 python/mxnet/numpy/multiarray.py            | 423 ++++++++++++++++---
 python/mxnet/symbol/numpy/_symbol.py        |  55 +++
 python/mxnet/test_utils.py                  |   1 -
 src/c_api/c_api.cc                          |   2 -
 src/ndarray/ndarray.cc                      |   1 +
 src/operator/tensor/indexing_op.cc          |   2 +
 src/operator/tensor/init_op.cc              |   1 +
 src/operator/tensor/matrix_op-inl.h         |  28 +-
 src/operator/tensor/matrix_op.cc            |   3 +
 tests/python/unittest/test_ndarray.py       |  44 +-
 tests/python/unittest/test_numpy_ndarray.py | 425 +++++++++++---------
 14 files changed, 1094 insertions(+), 373 deletions(-)

diff --git a/3rdparty/mshadow/mshadow/extension/slice.h b/3rdparty/mshadow/mshadow/extension/slice.h
index cb2eff4548aa..d0c266284639 100644
--- a/3rdparty/mshadow/mshadow/extension/slice.h
+++ b/3rdparty/mshadow/mshadow/extension/slice.h
@@ -33,8 +33,8 @@ struct SliceExp : public TRValue<SliceExp<SrcExp,
       : src_(src), ch_begin_(begin) {
     shape_ = ShapeCheck<srcdim, SrcExp>::Check(src_);
     ch_old_ = shape_[dimslice];
-    CHECK(begin < shape_[dimslice] && end <= shape_[dimslice])
-        << "The slice went out of range";
+    CHECK(begin <= shape_[dimslice] && end <= shape_[dimslice])
+        << "The slice went out of range. ";
     shape_[dimslice] = end - begin;
   }
   template<typename E, int etype>
diff --git a/python/mxnet/ndarray/ndarray.py b/python/mxnet/ndarray/ndarray.py
index 612017cdaff7..6c2bb8078922 100644
--- a/python/mxnet/ndarray/ndarray.py
+++ b/python/mxnet/ndarray/ndarray.py
@@ -51,7 +51,8 @@
            "logical_xor", "maximum", "minimum", "moveaxis", "modulo", "multiply", "not_equal",
            "onehot_encode", "power", "subtract", "true_divide", "waitall", "_new_empty_handle",
            "histogram", "split_v2", "to_dlpack_for_read", "to_dlpack_for_write", "from_dlpack",
-           "from_numpy"]
+           "from_numpy", "zeros", "indexing_key_expand_implicit_axes", "get_indexing_dispatch_code",
+           "get_oshape_of_gather_nd_op"]
 
 _STORAGE_TYPE_UNDEFINED = -1
 _STORAGE_TYPE_DEFAULT = 0
@@ -480,40 +481,61 @@ def __setitem__(self, key, value):
         array([[ 6.,  5.,  5.],
                [ 6.,  0.,  4.]], dtype=float32)
         """
-        if self.ndim == 0 and key == ():
-            _internal._full(shape=self.shape, value=float(value), ctx=self.context,
-                            dtype=self.dtype, out=self)
-            return
-        key = _indexing_key_expand_implicit_axes(key, self.shape)
-        slc_key = tuple(idx for idx in key if idx is not None)
+        if self.ndim == 0:
+            if not isinstance(key, (tuple, py_slice)):
+                raise IndexError('scalar tensor can only accept `()` and `:` as index')
+            if isinstance(key, tuple) and len(key) != 0:
+                raise IndexError('scalar tensor can only accept `()` and `:` as index')
+            if isinstance(value, numeric_types):
+                self._full(value)
+            elif isinstance(value, NDArray) and value.size == 1:
+                if value.shape != self.shape:
+                    value = value.reshape(self.shape)
+                value.copyto(self)
+            elif isinstance(value, (np.ndarray, np.generic)) and value.size == 1:
+                if isinstance(value, np.generic) or value.shape != self.shape:
+                    value = value.reshape(self.shape)
+                self._sync_copyfrom(value)
+            else:
+                raise ValueError('setting an array element with a sequence.')
 
-        if len(slc_key) < self.ndim:
-            raise RuntimeError(
-                'too few indices after normalization: expected `ndim` ({}) '
-                'but got {}. This is a bug, please report it!'
-                ''.format(self.ndim, len(slc_key))
-            )
-        if len(slc_key) > self.ndim:
-            raise IndexError(
-                'too many indices ({}) for array with {} dimensions'
-                ''.format(len(slc_key), self.ndim)
-            )
+        elif self.size == 0:
+            return
 
-        indexing_dispatch_code = _get_indexing_dispatch_code(slc_key)
-        if indexing_dispatch_code == _NDARRAY_BASIC_INDEXING:
-            self._set_nd_basic_indexing(slc_key, value)
-        elif indexing_dispatch_code == _NDARRAY_ADVANCED_INDEXING:
-            self._set_nd_advanced_indexing(slc_key, value)
         else:
-            raise ValueError(
-                'Indexing NDArray with index {} of type {} is not supported'
-                ''.format(key, type(key))
-            )
+            key = indexing_key_expand_implicit_axes(key, self.shape)
+            slc_key = tuple(idx for idx in key if idx is not None)
+
+            if len(slc_key) < self.ndim:
+                raise RuntimeError(
+                    'too few indices after normalization: expected `ndim` ({}) '
+                    'but got {}. This is a bug, please report it!'
+                    ''.format(self.ndim, len(slc_key))
+                )
+            if len(slc_key) > self.ndim:
+                raise IndexError(
+                    'too many indices ({}) for array with {} dimensions'
+                    ''.format(len(slc_key), self.ndim)
+                )
 
-    def __getitem__(self, key):
+            indexing_dispatch_code = get_indexing_dispatch_code(slc_key)
+            if indexing_dispatch_code == _NDARRAY_BASIC_INDEXING:
+                self._set_nd_basic_indexing(key, value)
+            elif indexing_dispatch_code == _NDARRAY_ADVANCED_INDEXING:
+                self._set_nd_advanced_indexing(key, value)
+            else:
+                raise ValueError(
+                    'Indexing NDArray with index {} of type {} is not supported'
+                    ''.format(key, type(key))
+                )
+
+    def __getitem__(self, key):  # pylint: disable=too-many-return-statements
         """x.__getitem__(i) <=> x[i]
 
-        Returns the subarray ``self[key]``.
+        Returns a sliced view of this array if the elements fetched are contiguous in memory;
+        otherwise, returns a newly created NDArray.
+        This functions supports advanced indexing defined in the following reference with
+        some restrictions.
 
         For basic indexing, i.e., if ``key`` consists only of integers,
         ``slice``, ``Ellipsis`` (``...``) and ``None``, a mutable view is
@@ -644,13 +666,41 @@ def __getitem__(self, key):
         array([[[4., 5.],
                 [6., 7.]]], dtype=float32)
         """
-        if self.ndim == 0 and key == ():
+        ndim = self.ndim
+        shape = self.shape
+
+        if ndim == 0 and (key == () or key == slice(None, None, None)):
+            return self
+
+        # Handle simple cases for higher speed
+        if isinstance(key, tuple) and len(key) == 0:
             return self
-        key = _indexing_key_expand_implicit_axes(key, self.shape)
+        if isinstance(key, tuple) and len(key) == ndim\
+                and all(isinstance(idx, integer_types) for idx in key):
+            out = self
+            for idx in key:
+                out = out[idx]
+            return out
+        if isinstance(key, integer_types):
+            if key > shape[0] - 1:
+                raise IndexError(
+                    'index {} is out of bounds for axis 0 with size {}'.format(
+                        key, shape[0]))
+            return self._at(key)
+        elif isinstance(key, py_slice):
+            if (key.step is None or key.step == 1):
+                if  key.start is not None or key.stop is not None:
+                    return self._slice(key.start, key.stop)
+                else:
+                    return self
+            elif key.step == 0:
+                raise ValueError("slice step cannot be zero")
+
+        key = indexing_key_expand_implicit_axes(key, self.shape)
         if len(key) == 0:
             raise ValueError('indexing key cannot be an empty tuple')
 
-        indexing_dispatch_code = _get_indexing_dispatch_code(key)
+        indexing_dispatch_code = get_indexing_dispatch_code(key)
         if indexing_dispatch_code == _NDARRAY_BASIC_INDEXING:
             return self._get_nd_basic_indexing(key)
         elif indexing_dispatch_code == _NDARRAY_ADVANCED_INDEXING:
@@ -658,18 +708,17 @@ def __getitem__(self, key):
         else:
             raise RuntimeError
 
-    def _prepare_value_nd(self, value, new_axes, bcast_shape):
+    def _prepare_value_nd(self, value, bcast_shape, squeeze_axes=None):
         """Return a broadcast `NDArray` with same context and dtype as ``self``.
-
-        Before broadcasting, ``new_axes`` of length 1 will be added to
-        ``value``. This is done in contrast to blindly reshaping based on
-        ``bcast_shape``, since the latter would silently ignore wrongly shaped
-        ``value`` arrays, e.g. ``nd.zeros((2, 3))[:, :1] = nd.ones(2)``.
+        For setting item, The returned `ndarray` is squeezed according to squeeze_axes since the
+        value_nd is assigned to not yet expanded space in original array.
+        `value`: numeric types or array like.
+        `bcast_shape`: a shape tuple.
+        `squeeze_axes`: a sequence of axes to squeeze in the value array.
         """
         if isinstance(value, numeric_types):
             value_nd = full(bcast_shape, value, ctx=self.context, dtype=self.dtype)
-            new_axes = []  # ignore for scalar
-        elif isinstance(value, NDArray):
+        elif type(value) == self.__class__:  # pylint: disable=unidiomatic-typecheck
             value_nd = value.as_in_context(self.context)
             if value_nd.dtype != self.dtype:
                 value_nd = value_nd.astype(self.dtype)
@@ -677,18 +726,20 @@ def _prepare_value_nd(self, value, new_axes, bcast_shape):
             try:
                 value_nd = array(value, ctx=self.context, dtype=self.dtype)
             except:
-                raise TypeError('NDArray does not support assignment with non-array-like '
-                                'object {} of type {}'.format(value, type(value)))
+                raise TypeError('{} does not support assignment with non-array-like '
+                                'object {} of type {}'.format(self.__class__, value, type(value)))
 
-        # First reshape `value_nd` to a new shape that incorporates existing
-        # axes, new axes and broadcasting axes in the right way.
-        tmp_shape = _shape_for_bcast(
-            value_nd.shape, target_ndim=len(bcast_shape), new_axes=new_axes
-        )
-        value_nd = value_nd.reshape(tmp_shape)
+        # For setitem, if there is None in indices, we need to squeeze the assigned value_nd
+        # since None is also ignored in slicing the  original array.
+        if squeeze_axes and value_nd.ndim > len(bcast_shape):
+            squeeze_axes = tuple([ax for ax in squeeze_axes if ax < len(value_nd.shape)])
+            value_nd = value_nd.squeeze(axis=tuple(squeeze_axes))
 
         if value_nd.shape != bcast_shape:
-            value_nd = value_nd.broadcast_to(bcast_shape)
+            if value_nd.size == 0:
+                value_nd = value_nd.reshape(bcast_shape)
+            else:
+                value_nd = value_nd.broadcast_to(bcast_shape)
         return value_nd
 
     # pylint: disable=invalid-name
@@ -723,24 +774,46 @@ def _basic_indexing_key_int_to_slice(idcs):
     # pylint: enable=invalid-name
 
     @staticmethod
-    def _new_axes_after_basic_indexing(axes, key_nd):
-        """Return indices of ``axes`` after slicing with ``key_nd``.
+    def _new_axes_after_basic_indexing(axes, key):
+        """Return indices of ``axes`` after slicing with ``key``.
 
         This function is used to calculate the positions where new axes should
         end up after indexing, taking into account the removal of axes by
         integer indexing.
 
-        The ``key_nd`` sequence should contain slices and integers only, no
-        ``None`` entries.
+        The ``key`` sequence should be the exapanded key including slices, integer types
+        and ``None``.
         """
-        steps = [0] + [0 if isinstance(idx, integer_types) else 1
-                       for idx in key_nd]
+        steps = [0] + [0 if isinstance(idx, integer_types) else 1 for idx in key]
         cum_steps = np.cumsum(steps)
-        axes_in_bounds = [ax for ax in axes if ax < len(cum_steps)]
-        axes_out_of_bounds = [ax for ax in axes if ax >= len(cum_steps)]
-        axes_after = tuple(cum_steps[axes_in_bounds])
-        oob_offsets = [ax - len(key_nd) for ax in axes_out_of_bounds]
-        axes_after += tuple(cum_steps[-1] + offset for offset in oob_offsets)
+        axes_after = tuple(cum_steps[axes])
+        return axes_after
+
+    @staticmethod
+    def _new_axes_after_advanced_indexing(key, adv_axs, bcast_adv_ndim, adv_are_adjacent):  # pylint: disable=invalid-name
+        """
+        Return indices of ``axes`` after slicing with ``key_nd``.
+
+        This function is used to calculate the positions where new axes should
+        end up after indexing, taking into account the removal of axes by
+        integer indexing.
+
+        The ``key`` sequence should be the exapanded key including slices, array like objects,
+        integer types and ``None``.
+        ``adv_axes`` is the sequence of indices of advanced axes.
+        ``bcast_adv_ndim`` is the number of dimensions of advanced indexing subspace.
+        ``adv_are_adjacent`` is a boolean value. Value being True means all advanced indicies are adjacent.
+
+        Note: integer indices are also considered advanced indices here.
+        """
+        new_axes = [ax for ax in range(len(key)) if key[ax] is None]
+        adv_axs_set = set(adv_axs)
+        if not adv_are_adjacent:
+            steps = [bcast_adv_ndim] + [0 if ax in adv_axs_set else 1 for ax in range(len(key))]
+        else:
+            steps = [0] + [0 if ax in adv_axs_set else 1 for ax in range(len(key))]
+        cum_steps = np.cumsum(steps)
+        axes_after = tuple(cum_steps[new_axes])
         return axes_after
 
     # pylint: disable=invalid-name
@@ -807,15 +880,24 @@ def _basic_indexing_contiguous_flat_begin_end(slc_key, shape):
     def _set_nd_basic_indexing(self, key, value):
         """This function indexes ``self`` with a tuple of ``slice`` objects only."""
         for idx in key:
-            if not isinstance(idx, (py_slice, integer_types)):
+            if idx is not None and not isinstance(idx, (py_slice, integer_types)):
                 raise RuntimeError(
                     '`key` may only contain `slice` or integer objects in the '
                     'basic implementation, got object of type {}. '
                     'This is a bug, please report it!'
                     ''.format(type(idx)))
+        key_nd = tuple(idx for idx in key if idx is not None)
         int_axes = [
-            ax for ax in range(len(key)) if isinstance(key[ax], integer_types)
+            ax for ax in range(len(key_nd)) if isinstance(key_nd[ax], integer_types)
         ]
+
+        # Check bounds for integer axes
+        for ax in int_axes:  # pylint: disable=invalid-name
+            if not -self.shape[ax] <= key_nd[ax] < self.shape[ax]:
+                raise IndexError(
+                    'index {} is out of bounds for axis {} with size {}'
+                    ''.format(key_nd[ax], ax, self.shape[ax]))
+
         begin, end, step = self._basic_indexing_key_to_begin_end_step(
             key, self.shape, keep_none=False
         )
@@ -828,10 +910,12 @@ def _set_nd_basic_indexing(self, key, value):
         begin, end, step = self._basic_indexing_key_to_begin_end_step(
             key, self.shape, keep_none=True
         )
+        none_axes = [ax for ax in range(len(key)) if key[ax] is None]
+        new_axes = self._new_axes_after_basic_indexing(none_axes, key)
 
         if can_assign_directly:
             # Easy case, overwrite whole array.
-            if isinstance(value, NDArray):
+            if type(value) == self.__class__:  # pylint: disable=unidiomatic-typecheck
                 if value.handle is not self.handle:
                     # Need to do this before `broadcast_to`.
                     tmp_shape = _shape_for_bcast(
@@ -844,17 +928,13 @@ def _set_nd_basic_indexing(self, key, value):
                     value.copyto(self)
 
             elif isinstance(value, numeric_types):
-                _internal._full(
-                    shape=self.shape, value=float(value), ctx=self.context,
-                    dtype=self.dtype, out=self
-                )
+                self._full(value)
 
             elif isinstance(value, (np.ndarray, np.generic)):
                 tmp_shape = _shape_for_bcast(
                     value.shape, target_ndim=self.ndim, new_axes=int_axes
                 )
                 value = value.reshape(tmp_shape)
-
                 if isinstance(value, np.generic) or value.shape != self.shape:
                     value = np.broadcast_to(value, self.shape)
                 self._sync_copyfrom(value)
@@ -862,20 +942,27 @@ def _set_nd_basic_indexing(self, key, value):
             else:
                 # Other array-like
                 value_nd = self._prepare_value_nd(
-                    value, new_axes=int_axes, bcast_shape=self.shape
+                    value, bcast_shape=self.shape
                 )
                 value_nd.copyto(self)
 
         elif isinstance(value, numeric_types):
-            _internal._slice_assign_scalar(
-                self, float(value), begin, end, step, out=self
-            )
+            self.slice_assign_scalar(float(value), begin, end, step)
 
         else:
+            # drop the axis of indexed_shape corresponding to int axes
+            bcast_shape = []
+            for i, size in enumerate(indexed_shape):
+                if i not in int_axes:
+                    bcast_shape.append(size)
+            if bcast_shape == []:
+                bcast_shape = [1]
+            bcast_shape = tuple(bcast_shape)
             value_nd = self._prepare_value_nd(
-                value, new_axes=int_axes, bcast_shape=indexed_shape
+                value, bcast_shape=bcast_shape, squeeze_axes=new_axes
             )
-            _internal._slice_assign(self, value_nd, begin, end, step, out=self)
+            value_nd = value_nd.reshape(indexed_shape)
+            self.slice_assign(value_nd, begin, end, step)
 
     def _get_nd_basic_indexing(self, key):
         """This function indexes ``self`` with a tuple of `slice` objects only."""
@@ -891,10 +978,12 @@ def _get_nd_basic_indexing(self, key):
                 'too many indices ({}) for array with {} dimensions'
                 ''.format(len(key_nd), self.ndim)
             )
-
-        none_axes = [ax for ax in range(len(key)) if key[ax] is None]  # pylint: disable=invalid-name
         slc_key, int_axes = self._basic_indexing_key_int_to_slice(key_nd)
-        new_axes = self._new_axes_after_basic_indexing(none_axes, key_nd)
+        none_axes = [ax for ax in range(len(key)) if key[ax] is None]
+        if none_axes:
+            new_axes = self._new_axes_after_basic_indexing(none_axes, key)
+        else:
+            new_axes = []
 
         # Check bounds for integer axes
         for ax in int_axes:  # pylint: disable=invalid-name
@@ -903,27 +992,11 @@ def _get_nd_basic_indexing(self, key):
                     'index {} is out of bounds for axis {} with size {}'
                     ''.format(key_nd[ax], ax, self.shape[ax]))
 
-        # Make sure we don't accidentally have advanced indexing or
-        # unsupported entries.
-        for idx in slc_key:
-            if not isinstance(idx, py_slice):
-                raise RuntimeError(
-                    'found object of type {} instead of `slice`. '
-                    'This is a bug, please report it!'
-                    ''.format(type(idx)))
-
         # Convert to begin, end and step, and return immediately if the slice
         # is empty
         begin, end, step = self._basic_indexing_key_to_begin_end_step(
             slc_key, self.shape, keep_none=False
         )
-        # Pylint is wrong about this
-        # pylint: disable=bad-continuation
-        if any(
-            b >= e and s > 0 or b <= e and s < 0 for b, e, s in zip(begin, end, step)
-        ):
-            return array([], self.context, self.dtype)
-        # pylint: enable=bad-continuation
 
         if self._basic_indexing_slice_is_contiguous(slc_key, self.shape):
             # Create a shared-memory view by using low-level flat slicing
@@ -967,7 +1040,6 @@ def _get_nd_basic_indexing(self, key):
         if final_shape == []:
             # Override for single element indexing
             final_shape = [1]
-
         return sliced.reshape(final_shape)
 
     @staticmethod
@@ -1056,7 +1128,10 @@ def _drop_slice_none_at_end(key):
         return tuple(key)
 
     def _get_index_nd(self, key):
-        """Return an index array for use in `scatter_nd` and `gather_nd`."""
+        """
+        Return an index array for use in `scatter_nd` and `gather_nd`,
+        and a list of positions of new_axes in ouptut shape.
+        """
         key_nd = tuple(idx for idx in key if idx is not None)
         if len(key_nd) < self.ndim:
             raise RuntimeError(
@@ -1125,6 +1200,14 @@ def _get_index_nd(self, key):
         bcast_idcs_permut_short = self._broadcast_advanced_indices(
             converted_idcs_short, block_axes=block_axs_nd
         )
+
+        # Get the ndim of advanced indexing subspace
+        converted_advanced_idcs = [
+            self._advanced_index_to_array(idx, ax_len, self.context)
+            for idx, ax_len in zip(adv_idcs_nd, [self.shape[ax] for ax in adv_axs_nd])
+        ]
+        bcast_advanced_shape = _broadcast_shapes(converted_advanced_idcs)
+
         # Undo the permutation to restore the original order
         bcast_idcs_short = [
             bcast_idcs_permut_short[ax]
@@ -1132,21 +1215,38 @@ def _get_index_nd(self, key):
             if axs_nd_permut[ax] not in dropped_axs
         ]
 
-        return op.stack(*bcast_idcs_short)
+        # Calculate where the newaxes are inserted after advanced indexing
+        new_axes_positions = self._new_axes_after_advanced_indexing(key, adv_axs,\
+                                len(bcast_advanced_shape), adv_idcs_are_adjacent)
+
+                                # if any array is numpy.ndarray, stack in numpy ndarray class.
+        for idcs in bcast_idcs_short:
+            if type(idcs) != NDArray:  # pylint: disable=unidiomatic-typecheck
+                return bcast_idcs_short, new_axes_positions
+
+        return op.stack(*bcast_idcs_short), new_axes_positions
 
     def _set_nd_advanced_indexing(self, key, value):
         """This function is called by __setitem__ when key is an advanced index."""
-        indices = self._get_index_nd(key)
-        vshape = _get_oshape_of_gather_nd_op(self.shape, indices.shape)
-        value_nd = self._prepare_value_nd(value, new_axes=[], bcast_shape=vshape)
-        _internal._scatter_set_nd(
-            lhs=self, rhs=value_nd, indices=indices, shape=self.shape, out=self
-        )
+        indices, new_axes = self._get_index_nd(key)
+        vshape = get_oshape_of_gather_nd_op(self.shape, indices.shape)
+        value_nd = self._prepare_value_nd(value, bcast_shape=vshape, squeeze_axes=new_axes)
+        self._scatter_set_nd(value_nd, indices)
 
     def _get_nd_advanced_indexing(self, key):
         """Get item when key is a tuple of any objects of the following types:
         NDArray, np.ndarray, list, tuple, slice, and integer."""
-        return op.gather_nd(self, self._get_index_nd(key))
+        slc_key, new_axes = self._get_index_nd(key)
+        sliced = op.gather_nd(self, slc_key)
+
+        # Reshape due to `None` entries in `key`.
+        if new_axes:
+            final_shape = [sliced.shape[i] for i in range(sliced.ndim)]
+            for ax in new_axes:  # pylint: disable=invalid-name
+                final_shape.insert(ax, 1)
+            return sliced.reshape(final_shape)
+        else:
+            return sliced
 
     def _sync_copyfrom(self, source_array):
         """Performs a synchronized copy from the `source_array` to the current array.
@@ -2522,6 +2622,88 @@ def copy(self):
         """
         return self.copyto(self.context)
 
+    def slice_assign_scalar(self, value, begin, end, step):
+        """
+        Assign the scalar to a cropped subset of this NDArray. Value will broadcast to the shape of the cropped shape
+        and will be cast to the same dtype of the NDArray.
+
+        Parameters
+        ----------
+        value: numeric value
+            Value and this NDArray should be of the same data type.
+            The shape of rhs should be the same as the cropped shape of this NDArray.
+        begin: tuple of begin indices
+        end: tuple of end indices
+        step: tuple of step lenghths
+
+        Returns
+        -------
+        This NDArray.
+
+        Examples
+        --------
+        >>> from mxnet import nd
+        >>> x = nd.ones((2, 2, 2))
+        >>> y = x.slice_assign_scalar(0, (0, 0, None), (1, 1, None), (None, None, None))
+        >>> y
+        [[[0. 0.]
+        [1. 1.]]
+
+        [[1. 1.]
+        [1. 1.]]]
+        <NDArray 2x2x2 @cpu(0)>
+        >>> x
+        [[[0. 0.]
+        [1. 1.]]
+
+        [[1. 1.]
+        [1. 1.]]]
+        <NDArray 2x2x2 @cpu(0)>
+
+        """
+        return _internal._slice_assign_scalar(self, value, begin=begin, end=end, step=step, out=self)
+
+    def slice_assign(self, rhs, begin, end, step):
+        """
+        Assign the rhs to a cropped subset of this NDarray in place.
+        Returns the view of this NDArray.
+
+        Parameters
+        ----------
+        rhs: NDArray.
+            rhs and this NDArray should be of the same data type, and on the same device.
+            The shape of rhs should be the same as the cropped shape of this NDArray.
+        begin: tuple of begin indices
+        end: tuple of end indices
+        step: tuple of step lenghths
+
+        Returns
+        -------
+        This NDArray.
+
+        Examples
+        --------
+        >>> x = nd.ones((2, 2, 2))
+        >>> assigned = nd.zeros((1, 1, 2))
+        >>> y = x.slice_assign(assigned, (0, 0, None), (1, 1, None), (None, None, None))
+        >>> y
+        [[[0. 0.]
+        [1. 1.]]
+
+        [[1. 1.]
+        [1. 1.]]]
+        <NDArray 2x2x2 @cpu(0)>
+        >>> x
+        [[[0. 0.]
+        [1. 1.]]
+
+        [[1. 1.]
+        [1. 1.]]]
+        <NDArray 2x2x2 @cpu(0)>
+        """
+        return _internal._slice_assign(self, rhs, begin=begin, end=end, step=step, out=self)
+
+
     def as_in_context(self, context):
         """Returns an array on the target device with the same value as this array.
 
@@ -2692,20 +2874,32 @@ def to_dlpack_for_write(self):
         """
         return to_dlpack_for_write(self)
 
+    def _full(self, value):
+        """
+        This is added as an NDArray class method in order to support polymorphism in NDArray and numpy.ndarray indexing
+        """
+        return _internal._full(self.shape, value=value, ctx=self.context, dtype=self.dtype, out=self)
+
+    def _scatter_set_nd(self, value_nd, indices):
+        """
+        This is added as an NDArray class method in order to support polymorphism in NDArray and numpy.ndarray indexing
+        """
+        return _internal._scatter_set_nd(
+            lhs=self, rhs=value_nd, indices=indices, shape=self.shape, out=self
+        )
 
-def _indexing_key_expand_implicit_axes(key, shape):
+def indexing_key_expand_implicit_axes(key, shape):
     """Make implicit axes explicit by adding ``slice(None)``.
-
     Examples
     --------
     >>> shape = (3, 4, 5)
-    >>> _indexing_key_expand_implicit_axes(np.s_[2, 1, 1], shape)
+    >>> indexing_key_expand_implicit_axes(np.s_[2, 1, 1], shape)
     (2, 1, 1)
-    >>> _indexing_key_expand_implicit_axes(np.s_[0], shape)
+    >>> indexing_key_expand_implicit_axes(np.s_[0], shape)
     (0, slice(None, None, None), slice(None, None, None))
-    >>> _indexing_key_expand_implicit_axes(np.s_[0, ...], shape)  # equivalent
+    >>> indexing_key_expand_implicit_axes(np.s_[0, ...], shape)  # equivalent
     (0, slice(None, None, None), slice(None, None, None))
-    >>> _indexing_key_expand_implicit_axes(np.s_[:2, None, 0, ...], shape)
+    >>> indexing_key_expand_implicit_axes(np.s_[:2, None, 0, ...], shape)
     (slice(None, 2, None), None, 0, slice(None, None, None))
     """
     if not isinstance(key, tuple):
@@ -2794,7 +2988,7 @@ def _is_advanced_index(idx):
         raise RuntimeError('illegal index type {}'.format(type(idx)))
 
 
-def _get_indexing_dispatch_code(key):
+def get_indexing_dispatch_code(key):
     """Returns a dispatch code for calling basic or advanced indexing functions."""
     assert isinstance(key, tuple)
 
@@ -2854,7 +3048,7 @@ def _get_index_range(start, stop, length, step=1):
     return start, stop, step
 
 
-def _get_oshape_of_gather_nd_op(dshape, ishape):
+def get_oshape_of_gather_nd_op(dshape, ishape):
     """Given data and index shapes, get the output `NDArray` shape.
     This basically implements the infer shape logic of op gather_nd."""
     assert len(dshape) > 0 and len(ishape) > 0
@@ -2865,8 +3059,9 @@ def _get_oshape_of_gather_nd_op(dshape, ishape):
 
 
 def _get_dim_size(start, stop, step):
-    """Given start, stop, and stop, calculate the number of elements
-    of this slice."""
+    """Given start, stop, and step, calculate the number of elements
+    of this slice.
+    """
     assert step != 0
     if stop == start:
         return 0
diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index 04b3b19bcf2e..f0785a76818e 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -27,7 +27,7 @@
 from . import _internal as _npi
 from ..ndarray import NDArray
 
-__all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power', 'sin',
+__all__ = ['zeros', 'ones', 'full', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power', 'sin',
            'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs', 'absolute',
            'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2', 'log1p',
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
@@ -103,6 +103,61 @@ def ones(shape, dtype=_np.float32, order='C', ctx=None):
     return _npi.ones(shape=shape, ctx=ctx, dtype=dtype)
 
 
+@set_module('mxnet.ndarray.numpy')
+def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):  # pylint: disable=too-many-arguments
+    """
+    Return a new array of given shape and type, filled with `fill_value`.
+    Parameters
+    ----------
+    shape : int or sequence of ints
+        Shape of the new array, e.g., ``(2, 3)`` or ``2``.
+    fill_value : scalar
+        Fill value.
+    dtype : data-type, optional
+        The desired data-type for the array. The default, `None`, means
+        `np.array(fill_value).dtype`.
+    order : {'C'}, optional
+        Whether to store multidimensional data in C- or Fortran-contiguous
+        (row- or column-wise) order in memory. Currently only supports C order.
+    ctx: to specify the device, e.g. the i-th GPU.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+        If provided, it must have the same shape and dtype as input ndarray.
+        If not provided or `None`, a freshly-allocated array is returned.
+    Returns
+    -------
+    out : ndarray
+        Array of `fill_value` with the given shape, dtype, and order.
+    Notes
+    -----
+    This function differs from the original `numpy.full
+    https://docs.scipy.org/doc/numpy/reference/generated/numpy.full.html`_ in
+    the following way(s):
+    - Have an additional `ctx` argument to specify the device
+    - Have an additional `out` argument
+    - Currently does not support `order` selection
+    See Also
+    --------
+    empty : Return a new uninitialized array.
+    ones : Return a new array setting values to one.
+    zeros : Return a new array setting values to zero.
+    Examples
+    --------
+    >>> np.full((2, 2), 10)
+    array([[10., 10.],
+           [10., 10.]])
+    >>> np.full((2, 2), 2, dtype=np.int32, ctx=mx.cpu(0))
+    array([[2, 2],
+           [2, 2]], dtype=int32)
+    """
+    if order != 'C':
+        raise NotImplementedError
+    if ctx is None:
+        ctx = current_context()
+    dtype = _np.float32 if dtype is None else dtype
+    return _npi.full(shape=shape, value=fill_value, ctx=ctx, dtype=dtype, out=out)
+
+
 @set_module('mxnet.ndarray.numpy')
 def arange(start, stop=None, step=1, dtype=None, ctx=None):
     """Return evenly spaced values within a given interval.
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index a47a9c01b7c4..5e7129226e34 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -34,16 +34,19 @@
 import warnings
 import numpy as _np
 from ..ndarray import NDArray, _DTYPE_NP_TO_MX, _GRAD_REQ_MAP
+from ..ndarray import indexing_key_expand_implicit_axes, get_indexing_dispatch_code,\
+                      get_oshape_of_gather_nd_op
 from ..ndarray._internal import _set_np_ndarray_class
 from . import _op as _mx_np_op
 from ..base import check_call, _LIB, NDArrayHandle
 from ..base import mx_real_t, c_array_buf, mx_uint, numeric_types, integer_types
+from ..context import Context
 from ..util import _sanity_check_params, set_module
 from ..context import current_context
 from ..ndarray import numpy as _mx_nd_np
 from ..ndarray.numpy import _internal as _npi
 
-__all__ = ['ndarray', 'empty', 'array', 'zeros', 'ones', 'add', 'subtract', 'multiply', 'divide',
+__all__ = ['ndarray', 'empty', 'array', 'zeros', 'ones', 'full', 'add', 'subtract', 'multiply', 'divide',
            'mod', 'power', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt',
            'abs', 'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log',
            'degrees', 'log2', 'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative',
@@ -51,6 +54,10 @@
            'tensordot', 'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate',
            'stack']
 
+# Return code for dispatching indexing function call
+_NDARRAY_UNSUPPORTED_INDEXING = -1
+_NDARRAY_BASIC_INDEXING = 0
+_NDARRAY_ADVANCED_INDEXING = 1
 
 # This function is copied from ndarray.py since pylint
 # keeps giving false alarm error of undefined-all-variable
@@ -101,80 +108,234 @@ def _get_index(idx):
 
 @set_module('mxnet.numpy')  # pylint: disable=invalid-name
 class ndarray(NDArray):
-    """An array object represents a multidimensional, homogeneous array of fixed-size items.
+    """
+    An array object represents a multidimensional, homogeneous array of fixed-size items.
     An associated data-type object describes the format of each element in the array
     (its byte-order, how many bytes it occupies in memory, whether it is an integer, a
     floating point number, or something else, etc.). Arrays should be constructed using
-    `array`, `zeros` or `empty`. Currently, only c-contiguous arrays are supported."""
+    `array`, `zeros` or `empty`. Currently, only c-contiguous arrays are supported.
+    """
+    def _get_np_basic_indexing(self, key):
+        """
+        This function indexes ``self`` with a tuple of `slice` objects only.
+        """
+        key_nd = tuple(idx for idx in key if idx is not None)
+        if len(key_nd) < self.ndim:
+            raise RuntimeError(
+                'too few indices after normalization: expected `ndim` ({}) '
+                'but got {}. This is a bug, please report it!'
+                ''.format(self.ndim, len(key_nd))
+            )
+        if len(key_nd) > self.ndim:
+            raise IndexError(
+                'too many indices ({}) for array with {} dimensions'
+                ''.format(len(key_nd), self.ndim)
+            )
+
+        none_axes = [ax for ax in range(len(key)) if key[ax] is None]  # pylint: disable=invalid-name
+        slc_key, int_axes = self._basic_indexing_key_int_to_slice(key_nd)
+        new_axes = self._new_axes_after_basic_indexing(none_axes, key)
+
+        # Check bounds for integer axes
+        for ax in int_axes:  # pylint: disable=invalid-name
+            if not -self.shape[ax] <= key_nd[ax] < self.shape[ax]:
+                raise IndexError(
+                    'index {} is out of bounds for axis {} with size {}'
+                    ''.format(key_nd[ax], ax, self.shape[ax]))
+
+        if self._basic_indexing_slice_is_contiguous(slc_key, self.shape):
+            # Create a shared-memory view by using low-level flat slicing
+            flat_begin, flat_end = self._basic_indexing_contiguous_flat_begin_end(
+                slc_key, self.shape
+            )
+            handle = NDArrayHandle()
+            flat_self = self.reshape_view(-1)
+            check_call(
+                _LIB.MXNDArraySlice(
+                    flat_self.handle,
+                    mx_uint(flat_begin),
+                    mx_uint(flat_end),
+                    ctypes.byref(handle),
+                )
+            )
+            sliced_shape = self._basic_indexing_sliced_shape(slc_key, self.shape)
+            sliced = self.__class__(handle=handle, writable=self.writable)
+            if 0 in sliced_shape:
+                sliced = sliced.reshape(sliced_shape)
+            else:
+                sliced = sliced.reshape_view(sliced_shape)
+
+        else:
+            begin, end, step = self._basic_indexing_key_to_begin_end_step(
+                slc_key, self.shape, keep_none=True
+            )
+            sliced = _npi.slice(self, begin, end, step)
+
+        # Reshape to final shape due to integer and `None` entries in `key`.
+        final_shape = [sliced.shape[i] for i in range(sliced.ndim) if i not in int_axes]
+        for ax in new_axes:  # pylint: disable=invalid-name
+            final_shape.insert(ax, 1)
+
+        if sliced.size == 0:
+            return sliced.reshape(tuple(final_shape))
+        else:
+            return sliced.reshape_view(tuple(final_shape))
+
+    def _get_np_advanced_indexing(self, key):
+        idcs, new_axes = self._get_index_nd(key)
+        if type(idcs) == NDArray:  # pylint: disable=unidiomatic-typecheck
+            idcs = idcs.as_np_ndarray()
+        else:
+            idcs = _npi.stack(*[i if isinstance(i, self.__class__) else i.as_np_ndarray() for i in idcs])
+        sliced = _npi.gather_nd(self, idcs)
+        # Reshape due to `None` entries in `key`.
+        if new_axes:
+            final_shape = [sliced.shape[i] for i in range(sliced.ndim)]
+            for ax in new_axes:  # pylint: disable=invalid-name
+                final_shape.insert(ax, 1)
+            return sliced.reshape(tuple(final_shape))
+        else:
+            return sliced
+
+    def _set_np_advanced_indexing(self, key, value):
+        """This function is called by __setitem__ when key is an advanced index."""
+        idcs, new_axes = self._get_index_nd(key)
+        if type(idcs) == NDArray:  # pylint: disable=unidiomatic-typecheck
+            idcs = idcs.as_np_ndarray()
+        else:
+            idcs = _npi.stack(*[i if isinstance(i, self.__class__) else i.as_np_ndarray() for i in idcs])
+        vshape = get_oshape_of_gather_nd_op(self.shape, idcs.shape)
+        value_nd = self._prepare_value_nd(value, bcast_shape=vshape, squeeze_axes=new_axes)
+        self._scatter_set_nd(value_nd, idcs)
 
     # pylint: disable=too-many-return-statements
     def __getitem__(self, key):
-        # TODO(junwu): calling base class __getitem__ is a temp solution
+        """
+        Overriding the method in NDArray class in a numpy fashion.
+        Calling numpy ndarray's _get_np_basic_indexing(key) and _get_np_advanced_indexing(key).
+        """
         ndim = self.ndim
         shape = self.shape
         if ndim == 0:
             if key != ():
                 raise IndexError('scalar tensor can only accept `()` as index')
+        # Handle simple cases for higher speed
         if isinstance(key, tuple) and len(key) == 0:
             return self
-        elif isinstance(key, tuple) and len(key) == ndim\
+        if isinstance(key, tuple) and len(key) == ndim\
                 and all(isinstance(idx, integer_types) for idx in key):
             out = self
             for idx in key:
                 out = out[idx]
             return out
-        elif isinstance(key, integer_types):
+        if isinstance(key, integer_types):
             if key > shape[0] - 1:
                 raise IndexError(
                     'index {} is out of bounds for axis 0 with size {}'.format(
                         key, shape[0]))
             return self._at(key)
         elif isinstance(key, py_slice):
-            if key.step is not None and key.step != 1:
-                if key.step == 0:
-                    raise ValueError("slice step cannot be zero")
-                return self.as_nd_ndarray().__getitem__(key).as_np_ndarray()
-            elif key.start is not None or key.stop is not None:
-                return self._slice(key.start, key.stop)
-            else:
-                return self
-
-        if isinstance(key, ndarray):
-            key = key.as_nd_ndarray()
-        elif isinstance(key, tuple):
-            key = [_get_index(idx) for idx in key]
-            key = tuple(key)
-        elif isinstance(key, list):
-            key = [_get_index(idx) for idx in key]
-        elif sys.version_info[0] > 2 and isinstance(key, range):
-            key = _get_index(key)
-        return self.as_nd_ndarray().__getitem__(key).as_np_ndarray()
-    # pylint: enable=too-many-return-statements
+            if (key.step is None or key.step == 1):
+                if  key.start is not None or key.stop is not None:
+                    return self._slice(key.start, key.stop)
+                else:
+                    return self
+            elif key.step == 0:
+                raise ValueError("slice step cannot be zero")
+
+        key = indexing_key_expand_implicit_axes(key, self.shape)
+        indexing_dispatch_code = get_indexing_dispatch_code(key)
+        if indexing_dispatch_code == _NDARRAY_BASIC_INDEXING:
+            return self._get_np_basic_indexing(key)
+        elif indexing_dispatch_code == _NDARRAY_ADVANCED_INDEXING:
+            return self._get_np_advanced_indexing(key)
+        else:
+            raise RuntimeError
 
     def __setitem__(self, key, value):
-        # TODO(junwu): calling base class __setitem__ is a temp solution
+        """
+        x.__setitem__(i, y) <=> x[i]=y
+        Sets ``self[key]`` to ``value``.
+
+        Overriding the method in NDArray class in a numpy fashion.
+        """
         if isinstance(value, NDArray) and not isinstance(value, ndarray):
             raise TypeError('Cannot assign mx.nd.NDArray to mxnet.numpy.ndarray')
         if self.ndim == 0:
             if not isinstance(key, tuple) or len(key) != 0:
                 raise IndexError('scalar tensor can only accept `()` as index')
-        if isinstance(value, ndarray):
-            value = value.as_nd_ndarray()
-        # TODO(junwu): Better handling of this situation
-        if isinstance(key, tuple) and len(key) == 0:
-            self.as_nd_ndarray().__setitem__(key, value)
-            return
-
-        if isinstance(key, ndarray):
-            key = key.as_nd_ndarray()
-        elif isinstance(key, tuple):
-            key = [_get_index(idx) for idx in key]
-            key = tuple(key)
-        elif isinstance(key, list):
-            key = [_get_index(idx) for idx in key]
-        elif sys.version_info[0] > 2 and isinstance(key, range):
-            key = _get_index(key)
-        self.as_nd_ndarray().__setitem__(key, value)
+            if isinstance(value, numeric_types):
+                self.full(value)
+            elif isinstance(value, ndarray) and value.size == 1:
+                if value.shape != self.shape:
+                    value = value.reshape(self.shape)
+                value.copyto(self)
+            elif isinstance(value, (_np.ndarray, _np.generic)) and value.size == 1:
+                if isinstance(value, _np.generic) or value.shape != self.shape:
+                    value = value.reshape(self.shape)
+                self._sync_copyfrom(value)
+            else:
+                raise ValueError('setting an array element with a sequence.')
+        else:
+            key = indexing_key_expand_implicit_axes(key, self.shape)
+            slc_key = tuple(idx for idx in key if idx is not None)
+            if len(slc_key) < self.ndim:
+                raise RuntimeError(
+                    'too few indices after normalization: expected `ndim` ({}) '
+                    'but got {}. This is a bug, please report it!'
+                    ''.format(self.ndim, len(slc_key))
+                )
+            if len(slc_key) > self.ndim and self.ndim != 0:
+                raise IndexError(
+                    'too many indices ({}) for array with {} dimensions'
+                    ''.format(len(slc_key), self.ndim)
+                )
+            indexing_dispatch_code = get_indexing_dispatch_code(slc_key)
+            if indexing_dispatch_code == _NDARRAY_BASIC_INDEXING:
+                self._set_nd_basic_indexing(key, value)  # function is inheritated from NDArray class
+            elif indexing_dispatch_code == _NDARRAY_ADVANCED_INDEXING:
+                self._set_np_advanced_indexing(key, value)
+            else:
+                raise ValueError(
+                    'Indexing NDArray with index {} of type {} is not supported'
+                    ''.format(key, type(key))
+                )
+
+    def _prepare_value_nd(self, value, bcast_shape, squeeze_axes=None):
+        """Return a broadcast `ndarray` with same context and dtype as ``self``.
+        For setting item, The returned `ndarray` is squeezed according to squeeze_axes since the
+        value_nd is assigned to not yet expanded space in original array.
+        `value`: numeric types or array like.
+        `bcast_shape`: a shape tuple.
+        `squeeze_axes`: a sequence of axes to squeeze in the value array.
+        Note: mxnet.numpy.ndarray not support NDArray as assigned value.
+        """
+        if isinstance(value, numeric_types):
+            value_nd = full(bcast_shape, value, ctx=self.context, dtype=self.dtype)
+        elif isinstance(value, self.__class__):
+            value_nd = value.as_in_context(self.context)
+            if value_nd.dtype != self.dtype:
+                value_nd = value_nd.astype(self.dtype)
+        else:
+            try:
+                value_nd = array(value, ctx=self.context, dtype=self.dtype)
+            except:
+                raise TypeError('mxnet.np.ndarray does not support assignment with non-array-like '
+                                'object {} of type {}'.format(value, type(value)))
+
+        # For advanced indexing setitem, if there is None in indices, we need to squeeze the
+        # assigned value_nd since None is also ignored in slicing the original array.
+        if squeeze_axes and value_nd.ndim > len(bcast_shape):
+            squeeze_axes = tuple([ax for ax in squeeze_axes if ax < len(value_nd.shape)])
+            value_nd = value_nd.squeeze(axis=tuple(squeeze_axes))
+
+        if value_nd.shape != bcast_shape:
+            if value_nd.size == 0:
+                value_nd = value_nd.reshape(bcast_shape)
+            else:
+                value_nd = value_nd.broadcast_to(bcast_shape)
+        return value_nd
+
 
     def __add__(self, other):
         """x.__add__(y) <=> x + y"""
@@ -550,7 +711,7 @@ def copyto(self, other):
         ``self.shape`` should be the same. This function copies the value from
         ``self`` to ``other``.
 
-        If ``other`` is a context, a new ``NDArray`` will be first created on
+        If ``other`` is a context, a new ``np.ndarray`` will be first created on
         the target context, and the value of ``self`` is copied.
 
         Parameters
@@ -560,14 +721,14 @@ def copyto(self, other):
 
         Returns
         -------
-        ndarray
+        out: ndarray
             The copied array. If ``other`` is an ``ndarray``, then the return value
             and ``other`` will point to the same ``ndarray``.
 
         Examples
         --------
         >>> x = np.ones((2,3))
-        >>> y = np.zeros((2,3), mx.gpu(0))
+        >>> y = np.zeros((2,3), ctx=mx.gpu(0))
         >>> z = x.copyto(y)
         >>> z is y
         True
@@ -576,8 +737,15 @@ def copyto(self, other):
                [ 1.,  1.,  1.]], dtype=float32)
         """
         if isinstance(other, ndarray):
-            other = other.as_nd_ndarray()
-        return self.as_nd_ndarray().copyto(other).as_np_ndarray()
+            if other.handle is self.handle:
+                warnings.warn('You are attempting to copy an array to itself', RuntimeWarning)
+                return False
+            return _npi.copyto(self, out=other)
+        elif isinstance(other, Context):
+            hret = ndarray(_new_alloc_handle(self.shape, other, True, self.dtype))
+            return _npi.copyto(self, out=hret)
+        else:
+            raise TypeError('copyto does not support type ' + str(type(other)))
 
     def asscalar(self):
         raise AttributeError('mxnet.numpy.ndarray object has no attribute asscalar')
@@ -650,6 +818,12 @@ def reshape_like(self, *args, **kwargs):
         """
         raise AttributeError('mxnet.numpy.ndarray object has no attribute reshape_like')
 
+    def reshape_view(self, *shape, **kwargs):
+        """Returns a **view** of this array with a new shape without altering any data.
+        Inheritated from NDArray.reshape.
+        """
+        return super(ndarray, self).reshape(*shape, **kwargs)
+
     def zeros_like(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`zeros_like`.
 
@@ -732,6 +906,82 @@ def slice_like(self, *args, **kwargs):
         """
         raise AttributeError('mxnet.numpy.ndarray object has no attribute slice_like')
 
+    def slice_assign_scalar(self, value, begin, end, step):
+        """
+        Assign the scalar to a cropped subset of this ndarray. Value will broadcast to the shape of the cropped shape
+        and will be cast to the same dtype of the ndarray.
+
+        Parameters
+        ----------
+        value: numeric value
+            Value and this ndarray should be of the same data type.
+            The shape of rhs should be the same as the cropped shape of this ndarray.
+        begin: tuple of begin indices
+        end: tuple of end indices
+        step: tuple of step lenghths
+
+        Returns
+        -------
+        This ndarray.
+
+        Examples
+        --------
+        >>> x = np.ones((2, 2, 2))
+        >>> y = x.slice_assign_scalar(0, (0, 0, None), (1, 1, None), (None, None, None))
+        >>> y
+        array([[[0., 0.],
+                [1., 1.]],
+
+               [[1., 1.],
+                [1., 1.]]])
+        >>> x
+        array([[[0., 0.],
+                [1., 1.]],
+
+               [[1., 1.],
+                [1., 1.]]])
+        """
+        return _npi.slice_assign_scalar(self, value, begin=begin, end=end, step=step, out=self)
+
+    def slice_assign(self, rhs, begin, end, step):
+        """
+        Assign the rhs to a cropped subset of this ndarray in place.
+        Returns the view of this ndarray.
+
+        Parameters
+        ----------
+        rhs: ndarray.
+            rhs and this NDArray should be of the same data type, and on the same device.
+            The shape of rhs should be the same as the cropped shape of this ndarray.
+        begin: tuple of begin indices
+        end: tuple of end indices
+        step: tuple of step lenghths
+
+        Returns
+        -------
+        out : ndarray
+            This ndarray.
+
+        Examples
+        --------
+        >>> x = np.ones((2, 2, 2))
+        >>> assigned = np.zeros((1, 1, 2))
+        >>> y = x.slice_assign(assigned, (0, 0, None), (1, 1, None), (None, None, None))
+        >>> y
+        array([[[0., 0.],
+                [1., 1.]],
+
+               [[1., 1.],
+                [1., 1.]]])
+        >>> x
+        array([[[0., 0.],
+                [1., 1.]],
+
+               [[1., 1.],
+                [1., 1.]]])
+        """
+        return _npi.slice_assign(self, rhs, begin=begin, end=end, step=step, out=self)
+
     def take(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`take`.
 
@@ -816,7 +1066,7 @@ def sign(self, *args, **kwargs):
         The arguments are the same as for :py:func:`sign`, with
         this array as data.
         """
-        raise AttributeError('mxnet.numpy.ndarray object has no attribute abs')
+        raise AttributeError('mxnet.numpy.ndarray object has no attribute sign')
 
     def flatten(self, order='C'):  # pylint: disable=arguments-differ
         """Return a copy of the array collapsed into one dimension."""
@@ -1252,11 +1502,26 @@ def squeeze(self, axis=None):  # pylint: disable=arguments-differ
         return _mx_np_op.squeeze(self, axis=axis)
 
     def broadcast_to(self, shape):
-        raise AttributeError('mxnet.numpy.ndarray object has no attribute broadcast_to')
+        return _mx_np_op.broadcast_to(self, shape)
 
     def broadcast_like(self, other):
         raise AttributeError('mxnet.numpy.ndarray object has no attribute broadcast_like')
 
+    def _full(self, value):
+        """
+        Currently for internal use only. Implemented for __setitem__.
+        Assign to self an array of self's same shape and type, filled with value.
+        """
+        return _mx_nd_np.full(self.shape, value, ctx=self.context, dtype=self.dtype, out=self)
+
+    def _scatter_set_nd(self, value_nd, indices):
+        """
+        This is added as an ndarray class method in order to support polymorphism in NDArray and numpy.ndarray indexing
+        """
+        return _npi.scatter_set_nd(
+            lhs=self, rhs=value_nd, indices=indices, shape=self.shape, out=self
+        )
+
     @property
     def shape(self):
         return super(ndarray, self).shape
@@ -1408,6 +1673,62 @@ def ones(shape, dtype=_np.float32, order='C', ctx=None):
     return _mx_nd_np.ones(shape, dtype, order, ctx)
 
 
+@set_module('mxnet.numpy')
+def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):  # pylint: disable=too-many-arguments
+    """
+    Return a new array of given shape and type, filled with `fill_value`.
+
+    Parameters
+    ----------
+    shape : int or sequence of ints
+        Shape of the new array, e.g., ``(2, 3)`` or ``2``.
+    fill_value : scalar
+        Fill value.
+    dtype : data-type, optional
+        The desired data-type for the array. The default, `None`, means
+        `np.array(fill_value).dtype`.
+    order : {'C'}, optional
+        Whether to store multidimensional data in C- or Fortran-contiguous
+        (row- or column-wise) order in memory. Currently only supports C order.
+    ctx: to specify the device, e.g. the i-th GPU.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+        If provided, it must have the same shape and dtype as input ndarray.
+        If not provided or `None`, a freshly-allocated array is returned.
+
+    Returns
+    -------
+    out : ndarray
+        Array of `fill_value` with the given shape, dtype, and order.
+
+    Notes
+    -----
+    This function differs from the original `numpy.full
+    https://docs.scipy.org/doc/numpy/reference/generated/numpy.full.html`_ in
+    the following way(s):
+
+    - Has an additional `ctx` argument to specify the device
+    - Has an additional `out` argument
+    - Currently does not support `order` selection
+
+    See Also
+    --------
+    empty : Return a new uninitialized array.
+    ones : Return a new array setting values to one.
+    zeros : Return a new array setting values to zero.
+
+    Examples
+    --------
+    >>> np.full((2, 2), 10)
+    array([[10., 10.],
+           [10., 10.]])
+    >>> np.full((2, 2), 2, dtype=np.int32, ctx=mx.cpu(0))
+    array([[2, 2],
+           [2, 2]], dtype=int32)
+    """
+    return _mx_nd_np.full(shape, fill_value, order=order, ctx=ctx, dtype=dtype, out=out)
+
+
 @set_module('mxnet.numpy')
 def add(x1, x2, out=None):
     """Add arguments element-wise.
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 251a8a1b8e56..ff20cabdb748 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -931,6 +931,61 @@ def ones(shape, dtype=_np.float32, order='C', ctx=None):
     return _npi.ones(shape=shape, ctx=ctx, dtype=dtype)
 
 
+@set_module('mxnet.symbol.numpy')
+def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):  # pylint: disable=too-many-arguments
+    """
+    Return a new array of given shape and type, filled with `fill_value`.
+    Parameters
+    ----------
+    shape : int or sequence of ints
+        Shape of the new array, e.g., ``(2, 3)`` or ``2``.
+    fill_value : scalar
+        Fill value.
+    dtype : data-type, optional
+        The desired data-type for the array. The default, `None`, means
+        `np.array(fill_value).dtype`.
+    order : {'C'}, optional
+        Whether to store multidimensional data in C- or Fortran-contiguous
+        (row- or column-wise) order in memory. Currently only supports C order.
+    ctx: to specify the device, e.g. the i-th GPU.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+        If provided, it must have the same shape and dtype as input ndarray.
+        If not provided or `None`, a freshly-allocated array is returned.
+    Returns
+    -------
+    out : ndarray
+        Array of `fill_value` with the given shape, dtype, and order.
+    Notes
+    -----
+    This function differs from the original `numpy.full
+    https://docs.scipy.org/doc/numpy/reference/generated/numpy.full.html`_ in
+    the following way(s):
+    - Have an additional `ctx` argument to specify the device
+    - Have an additional `out` argument
+    - Currently does not support `order` selection
+    See Also
+    --------
+    empty : Return a new uninitialized array.
+    ones : Return a new array setting values to one.
+    zeros : Return a new array setting values to zero.
+    Examples
+    --------
+    >>> np.full((2, 2), 10)
+    array([[10., 10.],
+           [10., 10.]])
+    >>> np.full((2, 2), 2, dtype=np.int32, ctx=mx.cpu(0))
+    array([[2, 2],
+           [2, 2]], dtype=int32)
+    """
+    if order != 'C':
+        raise NotImplementedError
+    if ctx is None:
+        ctx = current_context()
+    dtype = _np.float32 if dtype is None else dtype
+    return _npi.full(shape=shape, value=fill_value, ctx=ctx, dtype=dtype, out=out)
+
+
 #pylint: disable= too-many-arguments, no-member, protected-access
 def _ufunc_helper(lhs, rhs, fn_array, fn_scalar, lfn_scalar, rfn_scalar=None, out=None):
     """ Helper function for element-wise operation.
diff --git a/python/mxnet/test_utils.py b/python/mxnet/test_utils.py
index bb730fd3a007..4533729e2c15 100644
--- a/python/mxnet/test_utils.py
+++ b/python/mxnet/test_utils.py
@@ -1090,7 +1090,6 @@ def check_symbolic_forward(sym, location, expected, rtol=1E-4, atol=None,
 
     executor = sym.bind(ctx=ctx, args=location, args_grad=args_grad_data, aux_states=aux_states)
     for g in executor.grad_arrays:
-        print(g.shape)
         if g.ndim == 0:
             g[()] = 0
         else:
diff --git a/src/c_api/c_api.cc b/src/c_api/c_api.cc
index f6b67d3cb437..71f459d49185 100644
--- a/src/c_api/c_api.cc
+++ b/src/c_api/c_api.cc
@@ -543,8 +543,6 @@ MXNET_DLL int MXNDArrayReshape64(NDArrayHandle handle,
   API_BEGIN();
   NDArray *arr = static_cast<NDArray*>(handle);
   mxnet::Tuple<dim_t> shape(dims, dims+ndim);
-  CHECK_GT(arr->shape().Size(), 0) << "Source ndarray's shape is undefined. Input shape: "
-    << arr->shape();
   mxnet::TShape new_shape = mxnet::op::InferReshapeShape(shape, arr->shape(), reverse);
   *ptr = arr->ReshapeWithRecord(new_shape);
   *out = ptr;
diff --git a/src/ndarray/ndarray.cc b/src/ndarray/ndarray.cc
index 7fca6aa3f733..cc21dd242a2d 100644
--- a/src/ndarray/ndarray.cc
+++ b/src/ndarray/ndarray.cc
@@ -2110,6 +2110,7 @@ void CopyFromToSimple(
 // copy function is special
 // that we need to remove kAcceptEmptyMutateTarget from it
 NNVM_REGISTER_OP(_copyto)
+.add_alias("_npi_copyto")
 .set_num_inputs(1)
 .set_num_outputs(1)
 .set_attr<mxnet::FInferShape>("FInferShape", op::ElemwiseShape<1, 1>)
diff --git a/src/operator/tensor/indexing_op.cc b/src/operator/tensor/indexing_op.cc
index ad4e54db54f1..21aefc5b2fd4 100644
--- a/src/operator/tensor/indexing_op.cc
+++ b/src/operator/tensor/indexing_op.cc
@@ -815,6 +815,7 @@ Examples::
 
 
 NNVM_REGISTER_OP(gather_nd)
+.add_alias("_npi_gather_nd")
 .describe(R"code(Gather elements or slices from `data` and store to a tensor whose
 shape is defined by `indices`.
 
@@ -1008,6 +1009,7 @@ Examples::
 .add_arguments(ScatterNDParam::__FIELDS__());
 
 NNVM_REGISTER_OP(_scatter_set_nd)
+.add_alias("_npi_scatter_set_nd")
 .describe(R"code(This operator has the same functionality as scatter_nd
 except that it does not reset the elements not indexed by the input
 index `NDArray` in the input data `NDArray`. output should be explicitly
diff --git a/src/operator/tensor/init_op.cc b/src/operator/tensor/init_op.cc
index 4e8900be24ca..7f60bf4e2246 100644
--- a/src/operator/tensor/init_op.cc
+++ b/src/operator/tensor/init_op.cc
@@ -82,6 +82,7 @@ NNVM_REGISTER_OP(_ones)
 .add_arguments(InitOpParam::__FIELDS__());
 
 NNVM_REGISTER_OP(_full)
+.add_alias("_npi_full")
   .describe("fill target with a scalar value")
   .set_num_inputs(0)
   .set_num_outputs(1)
diff --git a/src/operator/tensor/matrix_op-inl.h b/src/operator/tensor/matrix_op-inl.h
index 58a535353e10..0d66907ad6cd 100644
--- a/src/operator/tensor/matrix_op-inl.h
+++ b/src/operator/tensor/matrix_op-inl.h
@@ -667,13 +667,15 @@ void SliceEx(const nnvm::NodeAttrs& attrs,
 }
 
 template<int ndim>
-inline void GetIndexRange(const mxnet::TShape& dshape,
+inline bool GetIndexRange(const mxnet::TShape& dshape,
                           const mxnet::Tuple<dmlc::optional<index_t>>& param_begin,
                           const mxnet::Tuple<dmlc::optional<index_t>>& param_end,
                           const mxnet::Tuple<dmlc::optional<index_t>>& param_step,
                           common::StaticArray<index_t, ndim>* begin,
                           common::StaticArray<index_t, ndim>* end,
                           common::StaticArray<index_t, ndim>* step) {
+  // Function returns false if output is zero-sized, true otherwise.
+  bool zero_size_shape = false;
   CHECK_NE(dshape.ndim(), 0U);
   CHECK_LE(param_begin.ndim(), dshape.ndim())
     << "Slicing axis exceeds data dimensions";
@@ -722,6 +724,10 @@ inline void GetIndexRange(const mxnet::TShape& dshape,
     (*begin)[i] = b;
     (*end)[i] = e;
     (*step)[i] = s;
+    // checking begin==end
+    if (b == e) {
+      zero_size_shape = true;
+    }
   }
 
   for (int i = param_begin.ndim(); i < dshape.ndim(); ++i) {
@@ -729,6 +735,8 @@ inline void GetIndexRange(const mxnet::TShape& dshape,
     (*end)[i] = dshape[i];
     (*step)[i] = 1;
   }
+
+  return zero_size_shape;
 }
 
 inline void SetSliceOpOutputDimSize(const mxnet::TShape& dshape,
@@ -973,7 +981,7 @@ inline bool SliceAssignOpShape(const nnvm::NodeAttrs& attrs,
   CHECK_EQ(in_attrs->size(), 2U);
   CHECK_EQ(out_attrs->size(), 1U);
   const mxnet::TShape& dshape = (*in_attrs)[0];
-  if (dshape.ndim() == 0U || dshape.Size() == 0U) return false;
+  if (!mxnet::ndim_is_known(dshape)) return false;
   mxnet::TShape vshape = dshape;  // vshape is the value shape on the right hand side
   const SliceParam& param = nnvm::get<SliceParam>(attrs.parsed);
   MXNET_NDIM_SWITCH(dshape.ndim(), ndim, {
@@ -1016,7 +1024,11 @@ void SliceAssignOpForward(const nnvm::NodeAttrs& attrs,
   const SliceParam& param = nnvm::get<SliceParam>(attrs.parsed);
   MXNET_NDIM_SWITCH(data.ndim(), ndim, {
     common::StaticArray<index_t, ndim> begin, end, step;
-    GetIndexRange(data.shape_, param.begin, param.end, param.step, &begin, &end, &step);
+    bool zero_size_shape = GetIndexRange(data.shape_, param.begin, param.end, param.step,
+                                        &begin, &end, &step);
+    if (zero_size_shape) {
+      return;  // slice_assign of zero-sized subspace needs no operation.
+    }
     MSHADOW_TYPE_SWITCH(out.type_flag_, DType, {
       MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
         int num_threads = val.shape_.FlatTo2D()[0];
@@ -1117,7 +1129,11 @@ void SliceAssignScalarOpForward(const nnvm::NodeAttrs& attrs,
   const SliceAssignScalarParam& param = nnvm::get<SliceAssignScalarParam>(attrs.parsed);
   MXNET_NDIM_SWITCH(data.ndim(), ndim, {
     common::StaticArray<index_t, ndim> begin, end, step;
-    GetIndexRange(data.shape_, param.begin, param.end, param.step, &begin, &end, &step);
+    bool zero_size_shape = GetIndexRange(data.shape_, param.begin, param.end, param.step,
+                                        &begin, &end, &step);
+    if (zero_size_shape) {
+      return;  // slice_assign of zero-sized subspaced needs no operation.
+    }
     for (index_t i = 0; i < param.begin.ndim(); ++i) {
       const int b = begin[i], e = end[i], s = step[i];
       SetSliceOpOutputDimSize(data.shape_, i, b, e, s, &vshape);
@@ -1250,6 +1266,9 @@ void SliceAxisGrad_(const nnvm::NodeAttrs& attrs,
                 const std::vector<TBlob>& inputs,
                 const std::vector<OpReqType>& req,
                 const std::vector<TBlob>& outputs) {
+  if (outputs[0].shape_.Size() == 0) {
+    return;
+  }
   const SliceAxisParam& param = nnvm::get<SliceAxisParam>(attrs.parsed);
   using namespace mshadow::op;
   using namespace mshadow::expr;
@@ -1258,7 +1277,6 @@ void SliceAxisGrad_(const nnvm::NodeAttrs& attrs,
   index_t begin, end;
   GetSliceAxisParams(param, outputs[0].shape_, &axis, &begin, &end);
   int ndim = outputs[0].shape_.ndim();
-
   if (axis + 1 == ndim) {
     MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
         mshadow::Tensor<xpu, 2, DType> ograd =
diff --git a/src/operator/tensor/matrix_op.cc b/src/operator/tensor/matrix_op.cc
index f02a38ac07c4..d35c364fba47 100644
--- a/src/operator/tensor/matrix_op.cc
+++ b/src/operator/tensor/matrix_op.cc
@@ -507,6 +507,7 @@ Example::
                                                             [1.,  3.]]
 )code" ADD_FILELINE)
 .add_alias("_npx_slice")
+.add_alias("_npi_slice")
 .set_attr_parser(ParamParser<SliceParam>)
 .set_attr<mxnet::FInferShape>("FInferShape", SliceOpShape)
 .set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
@@ -531,6 +532,7 @@ NNVM_REGISTER_OP(_backward_slice)
 
 NNVM_REGISTER_OP(_slice_assign)
 .add_alias("_crop_assign")
+.add_alias("_npi_slice_assign")
 .MXNET_DESCRIBE("Assign the rhs to a cropped subset of lhs.\n\n"
 "Requirements\n"
 "------------\n"
@@ -556,6 +558,7 @@ NNVM_REGISTER_OP(_slice_assign)
 
 NNVM_REGISTER_OP(_slice_assign_scalar)
 .add_alias("_crop_assign_scalar")
+.add_alias("_npi_slice_assign_scalar")
 .MXNET_DESCRIBE("(Assign the scalar to a cropped subset of the input.\n\n"
 "Requirements\n"
 "------------\n"
diff --git a/tests/python/unittest/test_ndarray.py b/tests/python/unittest/test_ndarray.py
index 87d3a41fd5bf..e15240c30dd3 100644
--- a/tests/python/unittest/test_ndarray.py
+++ b/tests/python/unittest/test_ndarray.py
@@ -141,12 +141,6 @@ def test_ndarray_setitem():
     x_np[:, -3:-1, -2:-1] = 1
     assert same(x.asnumpy(), x_np)
 
-    # Scalar array, no assignment allowed
-    with mx.np_shape():
-        x = mx.nd.zeros(())
-        with assert_raises(IndexError):
-            x[:] = 1
-
     # Assignments for empty axes
     for trivial_shape in [(1,), (1, 1), (1, 1, 1)]:
         x = mx.nd.zeros(trivial_shape)
@@ -1387,7 +1381,6 @@ def assert_same(np_array, np_index, mx_array, mx_index, mx_value, np_value=None)
             except Exception as e:
                 print('Failed with index = {}, value.shape = {}'.format(mx_index, mx_value.shape))
                 raise e
-
             assert same(np_array, mx_array.asnumpy())
 
         np_index = index
@@ -1476,9 +1469,12 @@ def convert(num):
     np_array = np.arange(np.prod(shape), dtype='int32').reshape(shape)
     # index_list is a list of tuples. The tuple's first element is the index, the second one is a boolean value
     # indicating whether we should expect the result as a scalar compared to numpy.
-    index_list = [(0, False), (np.int32(0), False), (np.int64(0), False),
+    index_list = [# Basic indexing
+                  # Single int as index
+                  (0, False), (np.int32(0), False), (np.int64(0), False),
                   (5, False), (np.int32(5), False), (np.int64(5), False),
                   (-1, False), (np.int32(-1), False), (np.int64(-1), False),
+                  # Slicing as index
                   (slice(5), False), (np_int(slice(5), np.int32), False), (np_int(slice(5), np.int64), False),
                   (slice(1, 5), False), (np_int(slice(1, 5), np.int32), False), (np_int(slice(1, 5), np.int64), False),
                   (slice(1, 5, 2), False), (np_int(slice(1, 5, 2), np.int32), False),
@@ -1499,6 +1495,7 @@ def convert(num):
                   (np_int(slice(None, None, -1)), False), (np_int(slice(None, None, -1), np.int64), False),
                   (slice(None, None, -2), False),
                   (np_int(slice(None, None, -2), np.int32), False), (np_int(slice(None, None, -2), np.int64), False),
+                  # slice(None) as indices
                   ((slice(None), slice(None), 1, 8), False),
                   ((slice(None), slice(None), -1, 8), False),
                   ((slice(None), slice(None), 1, -8), False),
@@ -1511,6 +1508,7 @@ def convert(num):
                   ((slice(None), 2, slice(1, 5), 1), False),
                   (np_int((slice(None), 2, slice(1, 5), 1)), False),
                   (np_int((slice(None), 2, slice(1, 5), 1), np.int64), False),
+                  # Multiple ints as indices
                   ((1, 2, 3), False),
                   (np_int((1, 2, 3)), False),
                   (np_int((1, 2, 3), np.int64), False),
@@ -1535,6 +1533,19 @@ def convert(num):
                   ((slice(1, 8, 2), 1, slice(3, 8), 2), False),
                   (np_int((slice(1, 8, 2), 1, slice(3, 8), 2)), False),
                   (np_int((slice(1, 8, 2), 1, slice(3, 8), 2), np.int64), False),
+                  # Test Ellipsis ('...')
+                  ((1, Ellipsis, -1), False),
+                  ((slice(2), Ellipsis, None, 0), False),
+                  # Test basic indexing with newaxis
+                  (None, False),
+                  ((1, None, -2, 3, -4), False),
+                  ((1, slice(2, 5), None), False), 
+                  ((slice(None), slice(1, 4), None, slice(2, 3)), False), 
+                  ((slice(1, 3), slice(1, 3), slice(1, 3), slice(1, 3), None), False), 
+                  ((slice(1, 3), slice(1, 3), None, slice(1, 3), slice(1, 3)), False), 
+                  ((None, slice(1, 2), 3, None), False),
+                  ((1, None, 2, 3, None, None, 4), False),
+                  # Advanced indexing
                   ([1], False), ([1, 2], False), ([2, 1, 3], False), ([7, 5, 0, 3, 6, 2, 1], False),
                   (np.array([6, 3], dtype=np.int32), False),
                   (np.array([[3, 4], [0, 6]], dtype=np.int32), False),
@@ -1571,16 +1582,15 @@ def convert(num):
                   (([[[[1]]]], [[2], [12]], slice(0, 3), slice(None)), False),
                   (([1, 2], slice(3, 5), [2, 3], [3, 4]), False),
                   (([1, 2], slice(3, 5), (2, 3), [3, 4]), False),
-                  ((1, Ellipsis, -1), False),
-                  ((slice(2), Ellipsis, None, 0), False),
-                  (None, False),
-                  ((1, None, -2, 3, -4), False),
-                  # TODO(zoeygxy): Support None in advanced indexing
-                  # (([1, 2], slice(3, 5), None, None, [3, 4]), False),
-                  # ((slice(None), slice(3, 5), None, None, [2, 3], [3, 4]), False),
-                  # ((slice(None), slice(3, 5), None, [2, 3], None, [3, 4]), False),
-                  # ((None, slice(None), slice(3, 5), [2, 3], None, [3, 4]), False),
+                  # Advanced indexing with None
+                  (([1, 2], slice(3, 5), None, None, [3, 4]), False),
+                  ((slice(None), slice(3, 5), None, None, [2, 3], [3, 4]), False),
+                  ((slice(None), slice(3, 5), None, [2, 3], None, [3, 4]), False),
+                  ((None, slice(None), slice(3, 5), [2, 3], None, [3, 4]), False),
+                  ((None, slice(None), None, slice(3, 5), [2, 3], None, [3, 4]), False),
+                  (([2, 3, 4], None, [3, 4, 6], None, slice(1, 2), None, [1, 2, 3]), False),
     ]
+
     for index in index_list:
         test_getitem(np_array, index[0], index[1])
         test_setitem(np_array, index[0], index[1])
diff --git a/tests/python/unittest/test_numpy_ndarray.py b/tests/python/unittest/test_numpy_ndarray.py
index 7fa05206ac09..6dd7b43cd82c 100644
--- a/tests/python/unittest/test_numpy_ndarray.py
+++ b/tests/python/unittest/test_numpy_ndarray.py
@@ -377,26 +377,54 @@ def test_np_ndarray_copy():
 @with_seed()
 @use_np
 def test_np_ndarray_indexing():
+    """
+    Test all indexing.
+    """
+    def np_int(index, int_type=np.int32):
+        """
+        Helper function for testing indexing that converts slices to slices of ints or None, and tuples to 
+        tuples of ints or None.
+        """
+        def convert(num):
+            if num is None:
+                return num
+            else:
+                return int_type(num)
+
+        if isinstance(index, slice):
+            return slice(convert(index.start), convert(index.stop), convert(index.step))
+        elif isinstance(index, tuple):  # tuple of slices and integers
+            ret = []
+            for elem in index:
+                if isinstance(elem, slice):
+                    ret.append(slice(convert(elem.start), convert(elem.stop), convert(elem.step)))
+                else:
+                    ret.append(convert(elem))
+            return tuple(ret)
+        else:
+            assert False
+
+    # Copied from test_ndarray.py. Under construction. 
     def test_getitem(np_array, index):
-        """`is_scalar` indicates whether we should expect a scalar for the result.
-        If so, the indexed array of NDArray should call asscalar to compare
-        with numpy's indexed array."""
         np_index = index
+        if type(index) == mx.nd.NDArray:  # use of NDArray is prohibited
+            assert False
         if isinstance(index, np.ndarray):
             np_index = index.asnumpy()
         if isinstance(index, tuple):
-            np_index = []
-            for idx in index:
-                if isinstance(idx, np.ndarray):
-                    np_index.append(idx.asnumpy())
-                else:
-                    np_index.append(idx)
-            np_index = tuple(np_index)
-
+            np_index = tuple([
+                idx.asnumpy() if isinstance(idx, mx.nd.NDArray) else idx
+                for idx in index]
+            )
         np_indexed_array = np_array[np_index]
-        mx_array = np.array(np_array, dtype=np_array.dtype)
-        mx_indexed_array = mx_array[index].asnumpy()
-        assert same(np_indexed_array, mx_indexed_array), 'Failed with index=%s' % str(index)
+        mx_np_array = np.array(np_array, dtype=np_array.dtype)
+        try:
+            mx_indexed_array = mx_np_array[index]
+        except Exception as e:
+            print('Failed with index = {}'.format(index))
+            raise e
+        mx_indexed_array = mx_indexed_array.asnumpy()
+        assert same(np_indexed_array, mx_indexed_array), 'Failed with index = {}'.format(index)
 
     def test_setitem(np_array, index):
         def assert_same(np_array, np_index, mx_array, mx_index, mx_value, np_value=None):
@@ -406,10 +434,15 @@ def assert_same(np_array, np_index, mx_array, mx_index, mx_value, np_value=None)
                 np_array[np_index] = mx_value.asnumpy()
             else:
                 np_array[np_index] = mx_value
-            mx_array[mx_index] = mx_value
+            try:
+                mx_array[mx_index] = mx_value
+            except Exception as e:
+                print('Failed with index = {}, value.shape = {}'.format(mx_index, mx_value.shape))
+                raise e
+
             assert same(np_array, mx_array.asnumpy())
 
-        np_index = index
+        np_index = index  # keep this native numpy type
         if isinstance(index, np.ndarray):
             np_index = index.asnumpy()
         if isinstance(index, tuple):
@@ -421,29 +454,33 @@ def assert_same(np_array, np_index, mx_array, mx_index, mx_value, np_value=None)
                     np_index.append(idx)
             np_index = tuple(np_index)
 
-        mx_array = np.array(np_array, dtype=np_array.dtype)
-        np_array = mx_array.asnumpy()
+        mx_array = np.array(np_array, dtype=np_array.dtype)  # mxnet.np.ndarray
+        np_array = mx_array.asnumpy()  # native numpy array
         indexed_array_shape = np_array[np_index].shape
         np_indexed_array = _np.random.randint(low=-10000, high=0, size=indexed_array_shape)
-        # test value is a numpy array without broadcast
+        # test value is a native numpy array without broadcast
         assert_same(np_array, np_index, mx_array, index, np_indexed_array)
+        # test value is a mxnet numpy array without broadcast
+        assert_same(np_array, np_index, mx_array, index, np.array(np_indexed_array))
         # test value is an numeric_type
         assert_same(np_array, np_index, mx_array, index, _np.random.randint(low=-10000, high=0))
         if len(indexed_array_shape) > 1:
-            # test ndarray with broadcast
-            assert_same(np_array, np_index, mx_array, index,
-                        _np.random.uniform(low=-10000, high=0, size=(indexed_array_shape[-1],)))
-            # test numpy array with broadcast
-            assert_same(np_array, np_index, mx_array, index,
-                        _np.random.randint(low=-10000, high=0, size=(indexed_array_shape[-1],)))
+            np_value = _np.random.randint(low=-10000, high=0, size=(indexed_array_shape[-1],))
+            # test mxnet ndarray with broadcast
+            assert_same(np_array, np_index, mx_array, index, np.array(np_value))
+            # test native numpy array with broadcast
+            assert_same(np_array, np_index, mx_array, index, np_value)
             # test list with broadcast
             assert_same(np_array, np_index, mx_array, index,
                         [_np.random.randint(low=-10000, high=0)] * indexed_array_shape[-1])
 
     def test_getitem_autograd(np_array, index):
+        """
+        np_array: native numpy array.
+        """
         x = np.array(np_array, dtype=np_array.dtype)
         x.attach_grad()
-        with autograd.record():
+        with mx.autograd.record():
             y = x[index]
         y.backward()
         value = np.ones_like(y)
@@ -452,177 +489,203 @@ def test_getitem_autograd(np_array, index):
         assert same(x_grad.asnumpy(), x.grad.asnumpy())
 
     def test_setitem_autograd(np_array, index):
+        """
+        np_array: native numpy array.
+        """
         x = np.array(np_array, dtype=np_array.dtype)
         out_shape = x[index].shape
         y = np.array(_np.random.uniform(size=out_shape))
         y.attach_grad()
         try:
-            with autograd.record():
+            with mx.autograd.record():
                 x[index] = y
-                assert False  # should not reach here
+                x.backward()
+                y_grad = np.ones_like(y)
+                assert same(y_grad.asnumpy(), y.grad.asnumpy())
         except mx.base.MXNetError as err:
             assert str(err).find('Inplace operations (+=, -=, x[:]=, etc) are not supported when recording with') != -1
 
-    def np_int(index, int_type=_np.int32):
-        def convert(num):
-            if num is None:
-                return num
-            else:
-                return int_type(num)
-
-        if isinstance(index, slice):
-            return slice(convert(index.start), convert(index.stop), convert(index.step))
-        elif isinstance(index, tuple):  # tuple of slices and integers
-            ret = []
-            for elem in index:
-                if isinstance(elem, slice):
-                    ret.append(slice(convert(elem.start), convert(elem.stop), convert(elem.step)))
-                else:
-                    ret.append(convert(elem))
-            return tuple(ret)
-        else:
-            assert False
-
     shape = (8, 16, 9, 9)
-    np_array = _np.arange(_np.prod(shape), dtype='int32').reshape(shape)
+    np_array = _np.arange(_np.prod(_np.array(shape)), dtype='int32').reshape(shape)  # native np array
+    
+    # Test sliced output being ndarray:
     index_list = [
-        (),
-        0,
-        _np.int32(0),
-        _np.int64(0),
-        5,
-        _np.int32(5),
-        _np.int64(5),
-        -1,
-        _np.int32(-1),
-        _np.int64(-1),
-        slice(5),
-        np_int(slice(5), _np.int32),
-        np_int(slice(5), _np.int64),
-        slice(1, 5),
-        np_int(slice(1, 5), _np.int32),
-        np_int(slice(1, 5), _np.int64),
-        slice(1, 5, 2),
-        np_int(slice(1, 5, 2), _np.int32),
-        np_int(slice(1, 5, 2), _np.int64),
-        slice(7, 0, -1),
-        np_int(slice(7, 0, -1)),
-        np_int(slice(7, 0, -1), _np.int64),
-        slice(None, 6),
-        np_int(slice(None, 6)),
-        np_int(slice(None, 6), _np.int64),
-        slice(None, 6, 3),
-        np_int(slice(None, 6, 3)),
-        np_int(slice(None, 6, 3), _np.int64),
-        slice(1, None),
-        np_int(slice(1, None)),
-        np_int(slice(1, None), _np.int64),
-        slice(1, None, 3),
-        np_int(slice(1, None, 3)),
-        np_int(slice(1, None, 3), _np.int64),
-        slice(None, None, 2),
-        np_int(slice(None, None, 2)),
-        np_int(slice(None, None, 2), _np.int64),
-        slice(None, None, -1),
-        np_int(slice(None, None, -1)),
-        np_int(slice(None, None, -1), _np.int64),
-        slice(None, None, -2),
-        np_int(slice(None, None, -2), _np.int32),
-        np_int(slice(None, None, -2), _np.int64),
-        (slice(None), slice(None), 1, 8),
-        (slice(None), slice(None), -1, 8),
-        (slice(None), slice(None), 1, -8),
-        (slice(None), slice(None), -1, -8),
-        np_int((slice(None), slice(None), 1, 8)),
-        np_int((slice(None), slice(None), 1, 8), _np.int64),
-        (slice(None), slice(None), 1, 8),
-        np_int((slice(None), slice(None), -1, -8)),
-        np_int((slice(None), slice(None), -1, -8), _np.int64),
-        (slice(None), 2, slice(1, 5), 1),
-        np_int((slice(None), 2, slice(1, 5), 1)),
-        np_int((slice(None), 2, slice(1, 5), 1), _np.int64),
-        (1, 2, 3),
-        np_int((1, 2, 3)),
-        np_int((1, 2, 3), _np.int64),
-        (-1, -2, -3),
-        np_int((-1, -2, -3)),
-        np_int((-1, -2, -3), _np.int64),
-        (1, 2, 3, 4),
-        np_int((1, 2, 3, 4)),
-        np_int((1, 2, 3, 4), _np.int64),
-        (-4, -3, -2, -1),
-        np_int((-4, -3, -2, -1)),
-        np_int((-4, -3, -2, -1), _np.int64),
-        (slice(None, None, -1), 2, slice(1, 5), 1),
-        np_int((slice(None, None, -1), 2, slice(1, 5), 1)),
-        np_int((slice(None, None, -1), 2, slice(1, 5), 1), _np.int64),
-        (slice(None, None, -1), 2, slice(1, 7, 2), 1),
-        np_int((slice(None, None, -1), 2, slice(1, 7, 2), 1)),
-        np_int((slice(None, None, -1), 2, slice(1, 7, 2), 1), _np.int64),
-        (slice(1, 8, 2), slice(14, 2, -2), slice(3, 8), slice(0, 7, 3)),
-        np_int((slice(1, 8, 2), slice(14, 2, -2), slice(3, 8), slice(0, 7, 3))),
-        np_int((slice(1, 8, 2), slice(14, 2, -2), slice(3, 8), slice(0, 7, 3)), _np.int64),
-        (slice(1, 8, 2), 1, slice(3, 8), 2),
-        np_int((slice(1, 8, 2), 1, slice(3, 8), 2)),
-        np_int((slice(1, 8, 2), 1, slice(3, 8), 2), _np.int64),
-        [1],
-        [1, 2],
-        [2, 1, 3],
-        [7, 5, 0, 3, 6, 2, 1],
-        _np.array([6, 3], dtype=_np.int32),
-        _np.array([[3, 4], [0, 6]], dtype=_np.int32),
-        _np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=_np.int32),
-        _np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=_np.int64),
-        _np.array([[2], [0], [1]], dtype=_np.int32),
-        _np.array([[2], [0], [1]], dtype=_np.int64),
-        np.array([4, 7], dtype=_np.int32),
-        np.array([4, 7], dtype=_np.int64),
-        np.array([[3, 6], [2, 1]], dtype=_np.int32),
-        np.array([[3, 6], [2, 1]], dtype=_np.int64),
-        np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=_np.int32),
-        np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=_np.int64),
-        (1, [2, 3]),
-        (1, [2, 3], _np.array([[3], [0]], dtype=_np.int32)),
-        (1, [2, 3]),
-        (1, [2, 3], _np.array([[3], [0]], dtype=_np.int64)),
-        (1, [2], _np.array([[5], [3]], dtype=_np.int32), slice(None)),
-        (1, [2], _np.array([[5], [3]], dtype=_np.int64), slice(None)),
-        (1, [2, 3], _np.array([[6], [0]], dtype=_np.int32), slice(2, 5)),
-        (1, [2, 3], _np.array([[6], [0]], dtype=_np.int64), slice(2, 5)),
-        (1, [2, 3], _np.array([[4], [7]], dtype=_np.int32), slice(2, 5, 2)),
-        (1, [2, 3], _np.array([[4], [7]], dtype=_np.int64), slice(2, 5, 2)),
-        (1, [2], _np.array([[3]], dtype=_np.int32), slice(None, None, -1)),
-        (1, [2], _np.array([[3]], dtype=_np.int64), slice(None, None, -1)),
-        (1, [2], _np.array([[3]], dtype=_np.int32), np.array([[5, 7], [2, 4]], dtype=_np.int64)),
-        (1, [2], np.array([[4]], dtype=_np.int32), np.array([[1, 3], [5, 7]], dtype='int64')),
-        [0],
-        [0, 1],
-        [1, 2, 3],
-        [2, 0, 5, 6],
-        ([1, 1], [2, 3]),
-        ([1], [4], [5]),
-        ([1], [4], [5], [6]),
-        ([[1]], [[2]]),
-        ([[1]], [[2]], [[3]], [[4]]),
-        (slice(0, 2), [[1], [6]], slice(0, 2), slice(0, 5, 2)),
-        ([[[[1]]]], [[1]], slice(0, 3), [1, 5]),
-        ([[[[1]]]], 3, slice(0, 3), [1, 3]),
-        ([[[[1]]]], 3, slice(0, 3), 0),
-        ([[[[1]]]], [[2], [12]], slice(0, 3), slice(None)),
-        ([1, 2], slice(3, 5), [2, 3], [3, 4]),
-        ([1, 2], slice(3, 5), (2, 3), [3, 4]),
-        range(4),
-        range(3, 0, -1),
-        (range(4,), [1]),
-        # slice(0, 0) does not support output zero-size tensor yet
+                # Basic indexing
+                # Single int as index
+                0,
+                np.int32(0),
+                np.int64(0),
+                5,
+                np.int32(5),
+                np.int64(5),
+                -1, 
+                np.int32(-1), 
+                np.int64(-1),
+                # Slicing as index
+                slice(5),
+                np_int(slice(5), np.int32),
+                np_int(slice(5), np.int64),
+                slice(1, 5),
+                np_int(slice(1, 5), np.int32),
+                np_int(slice(1, 5), np.int64),
+                slice(1, 5, 2),
+                np_int(slice(1, 5, 2), np.int32),
+                np_int(slice(1, 5, 2), np.int64),
+                slice(7, 0, -1),
+                np_int(slice(7, 0, -1)),
+                np_int(slice(7, 0, -1), np.int64),
+                slice(None, 6),
+                np_int(slice(None, 6)),
+                np_int(slice(None, 6), np.int64),
+                slice(None, 6, 3),
+                np_int(slice(None, 6, 3)),
+                np_int(slice(None, 6, 3), np.int64),
+                slice(1, None),
+                np_int(slice(1, None)),
+                np_int(slice(1, None), np.int64),
+                slice(1, None, 3),
+                np_int(slice(1, None, 3)),
+                np_int(slice(1, None, 3), np.int64),
+                slice(None, None, 2),
+                np_int(slice(None, None, 2)),
+                np_int(slice(None, None, 2), np.int64),
+                slice(None, None, -1),
+                np_int(slice(None, None, -1)),
+                np_int(slice(None, None, -1), np.int64),
+                slice(None, None, -2),
+                np_int(slice(None, None, -2), np.int32),
+                np_int(slice(None, None, -2), np.int64),
+                # Multiple ints as indices
+                (1, 2, 3),
+                np_int((1, 2, 3)),
+                np_int((1, 2, 3), np.int64),
+                (-1, -2, -3),
+                np_int((-1, -2, -3)),
+                np_int((-1, -2, -3), np.int64),
+                (1, 2, 3, 4),
+                np_int((1, 2, 3, 4)),
+                np_int((1, 2, 3, 4), np.int64),
+                (-4, -3, -2, -1),
+                np_int((-4, -3, -2, -1)),
+                np_int((-4, -3, -2, -1), np.int64),
+                # slice(None) as indices
+                (slice(None), slice(None), 1, 8),
+                (slice(None), slice(None), -1, 8),
+                (slice(None), slice(None), 1, -8),
+                (slice(None), slice(None), -1, -8),
+                np_int((slice(None), slice(None), 1, 8)),
+                np_int((slice(None), slice(None), 1, 8), np.int64),
+                (slice(None), slice(None), 1, 8),
+                np_int((slice(None), slice(None), -1, -8)),
+                np_int((slice(None), slice(None), -1, -8), np.int64),
+                (slice(None), 2, slice(1, 5), 1),
+                np_int((slice(None), 2, slice(1, 5), 1)),
+                np_int((slice(None), 2, slice(1, 5), 1), np.int64),
+                # Mixture of ints and slices as indices
+                (slice(None, None, -1), 2, slice(1, 5), 1),
+                np_int((slice(None, None, -1), 2, slice(1, 5), 1)),
+                np_int((slice(None, None, -1), 2, slice(1, 5), 1), np.int64),
+                (slice(None, None, -1), 2, slice(1, 7, 2), 1),
+                np_int((slice(None, None, -1), 2, slice(1, 7, 2), 1)),
+                np_int((slice(None, None, -1), 2, slice(1, 7, 2), 1), np.int64),
+                (slice(1, 8, 2), slice(14, 2, -2), slice(3, 8), slice(0, 7, 3)),
+                np_int((slice(1, 8, 2), slice(14, 2, -2), slice(3, 8), slice(0, 7, 3))),  
+                np_int((slice(1, 8, 2), slice(14, 2, -2), slice(3, 8), slice(0, 7, 3)), np.int64),  
+                (slice(1, 8, 2), 1, slice(3, 8), 2),  
+                np_int((slice(1, 8, 2), 1, slice(3, 8), 2)),  
+                np_int((slice(1, 8, 2), 1, slice(3, 8), 2), np.int64),  
+                # Test Ellipsis ('...')
+                (1, Ellipsis, -1),  
+                (slice(2), Ellipsis, None, 0),  
+                # Test newaxis
+                None,  
+                (1, None, -2, 3, -4),  
+                (1, slice(2, 5), None),   
+                (slice(None), slice(1, 4), None, slice(2, 3)),   
+                (slice(1, 3), slice(1, 3), slice(1, 3), slice(1, 3), None),   
+                (slice(1, 3), slice(1, 3), None, slice(1, 3), slice(1, 3)),   
+                (None, slice(1, 2), 3, None),  
+                (1, None, 2, 3, None, None, 4),  
+                # Advanced indexing
+                ([1, 2], slice(3, 5), None, None, [3, 4]),  
+                (slice(None), slice(3, 5), None, None, [2, 3], [3, 4]),  
+                (slice(None), slice(3, 5), None, [2, 3], None, [3, 4]),  
+                (None, slice(None), slice(3, 5), [2, 3], None, [3, 4]),  
+                [1],   
+                [1, 2],   
+                [2, 1, 3],  
+                [7, 5, 0, 3, 6, 2, 1],  
+                np.array([6, 3], dtype=np.int32),  
+                np.array([[3, 4], [0, 6]], dtype=np.int32),  
+                np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=np.int32),  
+                np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=np.int64),  
+                np.array([[2], [0], [1]], dtype=np.int32),  
+                np.array([[2], [0], [1]], dtype=np.int64),  
+                np.array([4, 7], dtype=np.int32),  
+                np.array([4, 7], dtype=np.int64),  
+                np.array([[3, 6], [2, 1]], dtype=np.int32),  
+                np.array([[3, 6], [2, 1]], dtype=np.int64),  
+                np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=np.int32),  
+                np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=np.int64),  
+                (1, [2, 3]),   
+                (1, [2, 3], np.array([[3], [0]], dtype=np.int32)),  
+                (1, [2, 3]),   
+                (1, [2, 3], np.array([[3], [0]], dtype=np.int64)),  
+                (1, [2], np.array([[5], [3]], dtype=np.int32), slice(None)),  
+                (1, [2], np.array([[5], [3]], dtype=np.int64), slice(None)),  
+                (1, [2, 3], np.array([[6], [0]], dtype=np.int32), slice(2, 5)),  
+                (1, [2, 3], np.array([[6], [0]], dtype=np.int64), slice(2, 5)),  
+                (1, [2, 3], np.array([[4], [7]], dtype=np.int32), slice(2, 5, 2)),  
+                (1, [2, 3], np.array([[4], [7]], dtype=np.int64), slice(2, 5, 2)),  
+                (1, [2], np.array([[3]], dtype=np.int32), slice(None, None, -1)),  
+                (1, [2], np.array([[3]], dtype=np.int64), slice(None, None, -1)),  
+                (1, [2], np.array([[3]], dtype=np.int32), np.array([[5, 7], [2, 4]], dtype=np.int64)),  
+                (1, [2], np.array([[4]], dtype=np.int32), np.array([[1, 3], [5, 7]], dtype='int64')),  
+                [0],   
+                [0, 1],   
+                [1, 2, 3],   
+                [2, 0, 5, 6],
+                ([1, 1], [2, 3]),
+                ([1], [4], [5]),
+                ([1], [4], [5], [6]),  
+                ([[1]], [[2]]),   
+                ([[1]], [[2]], [[3]], [[4]]),  
+                (slice(0, 2), [[1], [6]], slice(0, 2), slice(0, 5, 2)),  
+                ([[[[1]]]], [[1]], slice(0, 3), [1, 5]),  
+                ([[[[1]]]], 3, slice(0, 3), [1, 3]),  
+                ([[[[1]]]], 3, slice(0, 3), 0),  
+                ([[[[1]]]], [[2], [12]], slice(0, 3), slice(None)),  
+                ([1, 2], slice(3, 5), [2, 3], [3, 4]),  
+                ([1, 2], slice(3, 5), (2, 3), [3, 4]),   
     ]
     for index in index_list:
         test_getitem(np_array, index)
         test_setitem(np_array, index)
         test_getitem_autograd(np_array, index)
-        if not isinstance(index, tuple) or len(index) != 0:
-            # When index = (), this is same a[()] = b is equivalent to b.copyto(a)
-            # which should have no problem to do autograd
+        test_setitem_autograd(np_array, index)
+    
+    # Test indexing to zero-size tensors
+    index_list = [
+        (slice(0, 0), slice(0, 0), 1, 2),  
+        (slice(0, 0), slice(0, 0), slice(0, 0), slice(0, 0)),  
+    ]
+    for index in index_list:
+        test_getitem(np_array, index)
+        test_setitem(np_array, index)
+        test_getitem_autograd(np_array, index)
+        test_setitem_autograd(np_array, index)
+    
+    # test zero-size tensors get and setitem
+    shapes_indices = [
+                        ((0), [slice(None, None, None)]),
+                        ((3, 0), [2, (slice(None, None, None)), (slice(None, None, None), None)]),
+    ]
+    for shape, indices in shapes_indices:
+        for index in indices:
+            np_array = np.zeros(shape)
+            test_getitem(np_array, index)
+            test_setitem(np_array, index)
+            test_getitem_autograd(np_array, index)
             test_setitem_autograd(np_array, index)
 
 

From b2c0cbc8ea4defa0a8b53fb628a9679accca281a Mon Sep 17 00:00:00 2001
From: Pedro Larroy <pedro.larroy.lists@gmail.com>
Date: Wed, 28 Aug 2019 14:27:37 -0700
Subject: [PATCH 229/813] Windows cmake flags cleanup (#16013)

---
 ci/build_windows.py     | 165 ++++++++++++++++++++--------------------
 cmake/cmake_options.yml |   1 -
 2 files changed, 83 insertions(+), 83 deletions(-)

diff --git a/ci/build_windows.py b/ci/build_windows.py
index 7ec24395e22e..4673bd535e3e 100755
--- a/ci/build_windows.py
+++ b/ci/build_windows.py
@@ -51,88 +51,89 @@ class BuildFlavour(Enum):
 
 
 CMAKE_FLAGS = {
-    'WIN_CPU': ('-DUSE_CUDA=0 '
-                '-DUSE_CUDNN=0 '
-                '-DUSE_NVRTC=0 '
-                '-DUSE_OPENCV=1 '
-                '-DUSE_OPENMP=1 '
-                '-DUSE_PROFILER=1 '
-                '-DUSE_BLAS=open '
-                '-DUSE_LAPACK=1 '
-                '-DUSE_DIST_KVSTORE=0 '
-                '-DBUILD_CPP_EXAMPLES=1 '
-                '-DUSE_MKL_IF_AVAILABLE=0 '
-                '-DCMAKE_BUILD_TYPE=Release')
-
-    , 'WIN_CPU_MKLDNN': ('-DUSE_CUDA=0 '
-                         '-DUSE_CUDNN=0 '
-                         '-DUSE_NVRTC=0 '
-                         '-DUSE_OPENCV=1 '
-                         '-DUSE_OPENMP=1 '
-                         '-DUSE_PROFILER=1 '
-                         '-DUSE_BLAS=open '
-                         '-DUSE_LAPACK=1 '
-                         '-DUSE_DIST_KVSTORE=0 '
-                         '-DUSE_MKL_IF_AVAILABLE=1 '
-                         '-DUSE_MKLDNN=1 '
-                         '-DCMAKE_BUILD_TYPE=Release')
-
-    , 'WIN_CPU_MKLDNN_MKL': ('-DUSE_CUDA=0 '
-                         '-DUSE_CUDNN=0 '
-                         '-DUSE_NVRTC=0 '
-                         '-DUSE_OPENCV=1 '
-                         '-DUSE_OPENMP=1 '
-                         '-DUSE_PROFILER=1 '
-                         '-DUSE_BLAS=mkl '
-                         '-DUSE_LAPACK=1 '
-                         '-DUSE_DIST_KVSTORE=0 '
-                         '-DUSE_MKL_IF_AVAILABLE=1 '
-                         '-DUSE_MKLDNN=1 '
-                         '-DCMAKE_BUILD_TYPE=Release')
-
-    , 'WIN_CPU_MKL': ('-DUSE_CUDA=0 '
-                         '-DUSE_CUDNN=0 '
-                         '-DUSE_NVRTC=0 '
-                         '-DUSE_OPENCV=1 '
-                         '-DUSE_OPENMP=1 '
-                         '-DUSE_PROFILER=1 '
-                         '-DUSE_BLAS=mkl '
-                         '-DUSE_LAPACK=1 '
-                         '-DUSE_DIST_KVSTORE=0 '
-                         '-DUSE_MKL_IF_AVAILABLE=1 '
-                         '-DUSE_MKLDNN=0 '
-                         '-DCMAKE_BUILD_TYPE=Release')
-    , 'WIN_GPU': ('-DUSE_CUDA=1 '
-                  '-DUSE_CUDNN=1 '
-                  '-DUSE_NVRTC=1 '
-                  '-DUSE_OPENCV=1  '
-                  '-DUSE_OPENMP=1 '
-                  '-DUSE_PROFILER=1 '
-                  '-DUSE_BLAS=open '
-                  '-DUSE_LAPACK=1 '
-                  '-DUSE_DIST_KVSTORE=0 '
-                  '-DCUDA_ARCH_NAME=Manual '
-                  '-DCUDA_ARCH_BIN=52 '
-                  '-DCUDA_ARCH_PTX=52 '
-                  '-DCMAKE_CXX_FLAGS="/FS /MD /O2 /Ob2" '
-                  '-DUSE_MKL_IF_AVAILABLE=0 '
-                  '-DCMAKE_BUILD_TYPE=Release')
-
-    , 'WIN_GPU_MKLDNN': ('-DUSE_CUDA=1 '
-                         '-DUSE_CUDNN=1 '
-                         '-DUSE_NVRTC=1 '
-                         '-DUSE_OPENCV=1 '
-                         '-DUSE_OPENMP=1 '
-                         '-DUSE_PROFILER=1 '
-                         '-DUSE_BLAS=open '
-                         '-DUSE_LAPACK=1 '
-                         '-DUSE_DIST_KVSTORE=0 '
-                         '-DCUDA_ARCH_NAME=Manual '
-                         '-DCUDA_ARCH_BIN=52 '
-                         '-DCUDA_ARCH_PTX=52 '
-                         '-DUSE_MKLDNN=1 '
-                         '-DCMAKE_CXX_FLAGS="/FS /MD /O2 /Ob2" '
-                         '-DCMAKE_BUILD_TYPE=Release')
+    'WIN_CPU': (
+        '-DUSE_CUDA=OFF '
+        '-DUSE_CUDNN=OFF '
+        '-DUSE_NVRTC=OFF '
+        '-DUSE_OPENCV=ON '
+        '-DUSE_OPENMP=ON '
+        '-DUSE_BLAS=open '
+        '-DUSE_LAPACK=ON '
+        '-DUSE_DIST_KVSTORE=OFF '
+        '-DBUILD_CPP_EXAMPLES=ON '
+        '-DUSE_MKL_IF_AVAILABLE=OFF '
+        '-DCMAKE_BUILD_TYPE=Release')
+
+    , 'WIN_CPU_MKLDNN': (
+        '-DUSE_CUDA=OFF '
+        '-DUSE_CUDNN=OFF '
+        '-DUSE_NVRTC=OFF '
+        '-DUSE_OPENCV=ON '
+        '-DUSE_OPENMP=ON '
+        '-DUSE_BLAS=open '
+        '-DUSE_LAPACK=ON '
+        '-DUSE_DIST_KVSTORE=OFF '
+        '-DUSE_MKL_IF_AVAILABLE=ON '
+        '-DUSE_MKLDNN=ON '
+        '-DCMAKE_BUILD_TYPE=Release')
+
+    , 'WIN_CPU_MKLDNN_MKL': (
+        '-DUSE_CUDA=OFF '
+        '-DUSE_CUDNN=OFF '
+        '-DUSE_NVRTC=OFF '
+        '-DUSE_OPENCV=ON '
+        '-DUSE_OPENMP=ON '
+        '-DUSE_BLAS=mkl '
+        '-DUSE_LAPACK=ON '
+        '-DUSE_DIST_KVSTORE=OFF '
+        '-DUSE_MKL_IF_AVAILABLE=ON '
+        '-DUSE_MKLDNN=ON '
+        '-DCMAKE_BUILD_TYPE=Release')
+
+    , 'WIN_CPU_MKL': (
+        '-DUSE_CUDA=OFF '
+        '-DUSE_CUDNN=OFF '
+        '-DUSE_NVRTC=OFF '
+        '-DUSE_OPENCV=ON '
+        '-DUSE_OPENMP=ON '
+        '-DUSE_BLAS=mkl '
+        '-DUSE_LAPACK=ON '
+        '-DUSE_DIST_KVSTORE=OFF '
+        '-DUSE_MKL_IF_AVAILABLE=ON '
+        '-DUSE_MKLDNN=OFF '
+        '-DCMAKE_BUILD_TYPE=Release')
+
+    , 'WIN_GPU': (
+        '-DUSE_CUDA=ON '
+        '-DUSE_CUDNN=ON '
+        '-DUSE_NVRTC=ON '
+        '-DUSE_OPENCV=ON  '
+        '-DUSE_OPENMP=ON '
+        '-DUSE_BLAS=open '
+        '-DUSE_LAPACK=ON '
+        '-DUSE_DIST_KVSTORE=OFF '
+        '-DCUDA_ARCH_NAME=Manual '
+        '-DCUDA_ARCH_BIN=52 '
+        '-DCUDA_ARCH_PTX=52 '
+        '-DCMAKE_CXX_FLAGS="/FS /MD /O2 /Ob2" '
+        '-DUSE_MKL_IF_AVAILABLE=OFF '
+        '-DCMAKE_BUILD_TYPE=Release')
+
+    , 'WIN_GPU_MKLDNN': (
+        '-DUSE_CUDA=ON '
+        '-DUSE_CUDNN=ON '
+        '-DUSE_NVRTC=ON '
+        '-DUSE_OPENCV=ON '
+        '-DUSE_OPENMP=ON '
+        '-DUSE_BLAS=open '
+        '-DUSE_LAPACK=ON '
+        '-DUSE_DIST_KVSTORE=OFF '
+        '-DCUDA_ARCH_NAME=Manual '
+        '-DCUDA_ARCH_BIN=52 '
+        '-DCUDA_ARCH_PTX=52 '
+        '-DUSE_MKLDNN=ON '
+        '-DCMAKE_CXX_FLAGS="/FS /MD /O2 /Ob2" '
+        '-DCMAKE_BUILD_TYPE=Release')
 
 }
 
diff --git a/cmake/cmake_options.yml b/cmake/cmake_options.yml
index a4323feb92d4..81138241dfc6 100644
--- a/cmake/cmake_options.yml
+++ b/cmake/cmake_options.yml
@@ -31,7 +31,6 @@ USE_MKLDNN: "ON" # Use MKLDNN variant of MKL (if MKL found) IF USE_MKL_IF_AVAILA
 USE_OPERATOR_TUNING: "ON" # Enable auto-tuning of operators IF NOT MSVC
 USE_GPERFTOOLS: "ON" # Build with GPerfTools support (if found)
 USE_JEMALLOC: "ON" # Build with Jemalloc support
-USE_PROFILER: "ON" # Build with Profiler support
 USE_DIST_KVSTORE: "OFF" # Build with DIST_KVSTORE support
 USE_PLUGINS_WARPCTC: "OFF" # Use WARPCTC Plugins
 USE_PLUGIN_CAFFE: "OFF" # Use Caffe Plugin

From 9b906a532c34c8eebd1dc997f92b07f37bda457d Mon Sep 17 00:00:00 2001
From: Pedro Larroy <pedro.larroy.lists@gmail.com>
Date: Wed, 28 Aug 2019 14:45:05 -0700
Subject: [PATCH 230/813] Improve diagnose.py to display environment variables
 (#15715)

---
 tools/diagnose.py | 53 ++++++++++++++++++++++++++++++++---------------
 1 file changed, 36 insertions(+), 17 deletions(-)

diff --git a/tools/diagnose.py b/tools/diagnose.py
index f3026e635844..9cd5792a2f1a 100755
--- a/tools/diagnose.py
+++ b/tools/diagnose.py
@@ -30,23 +30,6 @@
     from urllib2 import urlopen
 import argparse
 
-def parse_args():
-    """Parse arguments."""
-    parser = argparse.ArgumentParser(
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
-        description='Diagnose script for checking the current system.')
-    choices = ['python', 'pip', 'mxnet', 'os', 'hardware', 'network']
-    for choice in choices:
-        parser.add_argument('--' + choice, default=1, type=int,
-                            help='Diagnose {}.'.format(choice))
-    parser.add_argument('--region', default='', type=str,
-                        help="Additional sites in which region(s) to test. \
-                        Specify 'cn' for example to test mirror sites in China.")
-    parser.add_argument('--timeout', default=10, type=int,
-                        help="Connection test timeout threshold, 0 to disable.")
-    args = parser.parse_args()
-    return args
-
 URLS = {
     'MXNet': 'https://github.com/apache/incubator-mxnet',
     'Gluon Tutorial(en)': 'http://gluon.mxnet.io',
@@ -55,6 +38,7 @@ def parse_args():
     'PYPI': 'https://pypi.python.org/pypi/pip',
     'Conda': 'https://repo.continuum.io/pkgs/free/',
 }
+
 REGIONAL_URLS = {
     'cn': {
         'PYPI(douban)': 'https://pypi.douban.com/',
@@ -81,6 +65,7 @@ def test_connection(name, url, timeout=10):
     load_elapsed = time.time() - start
     print("Timing for {}: {}, DNS: {:.4f} sec, LOAD: {:.4f} sec.".format(name, url, dns_elapsed, load_elapsed))
 
+
 def check_python():
     print('----------Python Info----------')
     print('Version      :', platform.python_version())
@@ -97,11 +82,13 @@ def check_pip():
     except ImportError:
         print('No corresponding pip install for current python.')
 
+
 def get_build_features_str():
     import mxnet.runtime
     features = mxnet.runtime.Features()
     return '\n'.join(map(str, list(features.values())))
 
+
 def check_mxnet():
     print('----------MXNet Info-----------')
     try:
@@ -131,6 +118,7 @@ def check_mxnet():
             print("This is very likely due to missing missing or incompatible library files.")
         print(traceback.format_exc())
 
+
 def check_os():
     print('----------System Info----------')
     print('Platform     :', platform.platform())
@@ -139,6 +127,7 @@ def check_os():
     print('release      :', platform.release())
     print('version      :', platform.version())
 
+
 def check_hardware():
     print('----------Hardware Info----------')
     print('machine      :', platform.machine())
@@ -154,6 +143,7 @@ def check_hardware():
     elif sys.platform.startswith('win32'):
         subprocess.call(['wmic', 'cpu', 'get', 'name'])
 
+
 def check_network(args):
     print('----------Network Test----------')
     if args.timeout > 0:
@@ -171,6 +161,32 @@ def check_network(args):
     for name, url in URLS.items():
         test_connection(name, url, args.timeout)
 
+
+def check_environment():
+    print('----------Environment----------')
+    for k,v in os.environ.items():
+        if k.startswith('MXNET_') or k.startswith('OMP_') or k.startswith('KMP_') or k == 'CC' or k == 'CXX':
+            print('{}="{}"'.format(k,v))
+
+
+def parse_args():
+    """Parse arguments."""
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+        description='Diagnose script for checking the current system.')
+    choices = ['python', 'pip', 'mxnet', 'os', 'hardware', 'network', 'environment']
+    for choice in choices:
+        parser.add_argument('--' + choice, default=1, type=int,
+                            help='Diagnose {}.'.format(choice))
+    parser.add_argument('--region', default='', type=str,
+                        help="Additional sites in which region(s) to test. \
+                        Specify 'cn' for example to test mirror sites in China.")
+    parser.add_argument('--timeout', default=10, type=int,
+                        help="Connection test timeout threshold, 0 to disable.")
+    args = parser.parse_args()
+    return args
+
+
 if __name__ == '__main__':
     args = parse_args()
     if args.python:
@@ -190,3 +206,6 @@ def check_network(args):
 
     if args.network:
         check_network(args)
+
+    if args.environment:
+        check_environment()

From 649429d055e8ac89b2a45929ff2344959768a6e3 Mon Sep 17 00:00:00 2001
From: Anirudh Subramanian <anirudh2290@ufl.edu>
Date: Wed, 28 Aug 2019 19:37:11 -0700
Subject: [PATCH 231/813] Disable flaky test in test_amp_conversion (#16031)

---
 tests/python/gpu/test_contrib_amp.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/tests/python/gpu/test_contrib_amp.py b/tests/python/gpu/test_contrib_amp.py
index 401bfcad3494..c49fa9b49865 100644
--- a/tests/python/gpu/test_contrib_amp.py
+++ b/tests/python/gpu/test_contrib_amp.py
@@ -329,10 +329,13 @@ def check_amp_convert_bucketing_module():
                            batch_end_callback=mx.callback.Speedometer(batch_size, 1))
 
         # AMP conversion with cast_optional_params set to true
+        # Flaky test when cast_optional_params set to True : https://github.com/apache/incubator-mxnet/issues/16030
+        '''
         result_model = amp.convert_bucketing_module(model, cast_optional_params=True)
         result_model.bind(data_val.provide_data, data_val.provide_label, for_training=False)
         result_model.score(data_val, mx.metric.Perplexity(invalid_label),
                            batch_end_callback=mx.callback.Speedometer(batch_size, 1))
+        '''
 
 
     with mx.Context(mx.gpu(0)):

From 3f7b6ee57865b79634c82a8f58e3551fc95e4dda Mon Sep 17 00:00:00 2001
From: Zhennan Qin <zhennan.qin@intel.com>
Date: Thu, 29 Aug 2019 11:39:33 +0800
Subject: [PATCH 232/813] Improve quantization flow (#15961)

* Add mkldnn imlementation for quantized flatten and smart quantize mode

Change-Id: Id9d7504890852faf4e84fdcd66585d1fd78beeb2

* Add calibrate op

Change-Id: I4c82f64dbef501d2560f7ffee93991119a66a5ee

* Fix merge

Change-Id: I068df3d4f3309bc9b950a3b869da9407282c8577

* Fix lint

Change-Id: Ia38369d31c33d0f76a671275910729dfce693950

* Run CI

* Run CI

* Update test_quantization.py

* Run CI

* Fix CI

Change-Id: I7479327db5ebc7c57b7bd810a67d2b765c820534

* Fix CI

Change-Id: I4273938cb972c12b8f43dbd95c736a7d32df040e

* Fix CI

Change-Id: I80b47bd1d95520a7cd78cacbbc1a85fe0900123d

* Fix CI

Change-Id: I56542470010e7bc403f62dc8a8991c2fb58d229e

* Fix CI

Change-Id: If8482fe4da2f3d627dd3cbac8795e021a09a441f

* Fix GPU

Change-Id: Ic239cbf7aa3d111f2895badd1cac196fce6a1b86
---
 example/quantization/README.md                |  23 +-
 example/quantization/imagenet_gen_qsym.py     |  17 +-
 .../quantization/imagenet_gen_qsym_mkldnn.py  |   3 +-
 example/ssd/quantization.py                   |  11 +-
 include/mxnet/c_api.h                         |   7 +-
 include/mxnet/op_attr_types.h                 |  30 ++
 python/mxnet/contrib/quantization.py          | 285 +++++++---------
 src/c_api/c_api_symbolic.cc                   |  17 +-
 src/common/utils.h                            |  10 +
 src/executor/graph_executor.cc                |  15 +-
 src/operator/nn/mkldnn/mkldnn_flatten-inl.h   |  48 +++
 src/operator/nn/mkldnn/mkldnn_flatten.cc      |  10 +-
 src/operator/quantization/calibrate-inl.h     |  48 +++
 src/operator/quantization/calibrate.cc        | 215 ++++++++++++
 .../mkldnn/mkldnn_quantize_v2-inl.h           |   6 +-
 .../mkldnn/mkldnn_quantized_elemwise_add.cc   |   4 +-
 .../mkldnn/mkldnn_quantized_flatten.cc        |  61 ++++
 .../quantization/quantization_utils.h         |   4 +-
 .../quantization/quantize_graph_pass.cc       | 315 +++++++++++-------
 src/operator/quantization/quantize_v2.cc      |   3 +
 .../quantization/quantized_batch_norm.cc      |   3 +
 src/operator/quantization/quantized_conv.cc   |   3 +
 .../quantization/quantized_fully_connected.cc |   3 +
 src/operator/quantization/requantize.cc       |   3 +
 src/operator/subgraph/mkldnn/mkldnn_conv.cc   |   3 +
 src/operator/subgraph/mkldnn/mkldnn_fc.cc     |   3 +
 tests/python/mkl/test_subgraph.py             |   9 +-
 .../python/quantization/test_quantization.py  |  53 ++-
 tests/python/unittest/test_operator.py        |   5 +-
 29 files changed, 839 insertions(+), 378 deletions(-)
 create mode 100644 src/operator/nn/mkldnn/mkldnn_flatten-inl.h
 create mode 100644 src/operator/quantization/calibrate-inl.h
 create mode 100644 src/operator/quantization/calibrate.cc
 create mode 100644 src/operator/quantization/mkldnn/mkldnn_quantized_flatten.cc

diff --git a/example/quantization/README.md b/example/quantization/README.md
index 1ae58fbb3a69..2215a13a97d5 100644
--- a/example/quantization/README.md
+++ b/example/quantization/README.md
@@ -86,21 +86,20 @@ Use the following command to install [Gluon-CV](https://gluon-cv.mxnet.io/):
 pip install gluoncv
 ```
 
-Below are some quantization demos. These models have been tested on Linux systems.
+The following models have been tested on Linux systems. Accuracy is collected on Intel XEON Cascade Lake CPU. For CPU with Skylake Lake or eariler architecture, the accuracy may not be the same.
 
 | Model | Source | Dataset | FP32 Accuracy (top-1/top-5)| INT8 Accuracy (top-1/top-5)|
 |:---|:---|---|:---:|:---:|
-| [ResNet18-V1](#3)  | [Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)  | [Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)  |70.15%/89.38%|69.92%/89.26%|
-| [ResNet50-V1](#3)  | [Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)  | [Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)  | 76.34%/93.13%  |  75.91%/92.95% |
-| [ResNet50-V1b](#3)  | [Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)  | [Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)  | 76.82%/93.38% |  76.39%/93.24% |
-| [ResNet101-V1](#3)  | [Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)  | [Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)  | 77.33%/93.59%  | 77.05%/93.43%  |
-|[Squeezenet 1.0](#4)|[Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|56.98%/79.20%|52.98%/77.21%|
-|[MobileNet 1.0](#5)|[Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|72.23%/90.64%|72.03%/90.42%|
-|[MobileNetV2 1.0](#6)|[Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|70.27%/89.62%|69.70%/89.26%|
-|[Inception V3](#7)|[Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|77.76%/93.83% |77.87%/93.78% |
-|[ResNet152-V2](#8)|[MXNet ModelZoo](http://data.mxnet.io/models/imagenet/resnet/152-layers/)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|76.65%/93.07%|76.36%/92.89%|
-|[Inception-BN](#9)|[MXNet ModelZoo](http://data.mxnet.io/models/imagenet/inception-bn/)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|72.28%/90.63%|72.20%/90.56%|
-| [SSD-VGG16](#10) | [example/ssd](https://github.com/apache/incubator-mxnet/tree/master/example/ssd)  | VOC2007/2012  | 0.8366 mAP  | 0.8364 mAP  |
+| [ResNet18-V1](#3)  | [Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)  | [Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)  |70.15%/89.38%|69.92%/89.30%|
+| [ResNet50-V1](#3)  | [Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)  | [Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)  | 76.34%/93.13%  |  76.06%/92.99% |
+| [ResNet101-V1](#3)  | [Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)  | [Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)  | 77.33%/93.59%  | 77.07%/93.47%  |
+|[Squeezenet 1.0](#4)|[Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|56.98%/79.20%|56.79%/79.47%|
+|[MobileNet 1.0](#5)|[Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|72.23%/90.64%|72.06%/90.53%|
+|[MobileNetV2 1.0](#6)|[Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|70.27%/89.62%|69.82%/89.35%|
+|[Inception V3](#7)|[Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|77.76%/93.83% |78.05%/93.91% |
+|[ResNet152-V2](#8)|[MXNet ModelZoo](http://data.mxnet.io/models/imagenet/resnet/152-layers/)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|76.65%/93.07%|76.19%/92.88%|
+|[Inception-BN](#9)|[MXNet ModelZoo](http://data.mxnet.io/models/imagenet/inception-bn/)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|72.28%/90.63%|72.02%/90.53%|
+| [SSD-VGG16](#10) | [example/ssd](https://github.com/apache/incubator-mxnet/tree/master/example/ssd)  | VOC2007/2012  | 0.8366 mAP  | 0.8357 mAP  |
 | [SSD-VGG16](#10) | [example/ssd](https://github.com/apache/incubator-mxnet/tree/master/example/ssd)  | COCO2014  | 0.2552 mAP  | 0.253 mAP  |
 
 <h3 id='3'>ResNetV1</h3>
diff --git a/example/quantization/imagenet_gen_qsym.py b/example/quantization/imagenet_gen_qsym.py
index 27a1d7a45a68..ec4c809a79d0 100644
--- a/example/quantization/imagenet_gen_qsym.py
+++ b/example/quantization/imagenet_gen_qsym.py
@@ -141,23 +141,12 @@ def save_params(fname, arg_params, aux_params, logger=None):
     excluded_op_names = []
     if args.model == 'imagenet1k-resnet-152':
         rgb_mean = '0,0,0'
-        if args.ctx == 'gpu':
-            calib_layer = lambda name: name.endswith('_output') and (name.find('conv') != -1
-                                                                     or name.find('sc') != -1
-                                                                     or name.find('fc') != -1)
-        else:
-            calib_layer = lambda name: name.endswith('_output') and (name.find('conv') != -1
-                                                                     or name.find('sc') != -1)
-            excluded_sym_names += ['flatten0', 'fc1']
+        excluded_sym_names += ['flatten0', 'fc1']
         if exclude_first_conv:
             excluded_sym_names += ['conv0']
     elif args.model == 'imagenet1k-inception-bn':
         rgb_mean = '123.68,116.779,103.939'
-        if args.ctx == 'gpu':
-            calib_layer = lambda name: name.endswith('_output') and (name.find('conv') != -1
-                                                                     or name.find('fc') != -1)
-        else:
-            calib_layer = lambda name: name.endswith('_output') and (name.find('conv') != -1)
+        if args.ctx == 'cpu':
             excluded_sym_names += ['flatten', 'fc1']
         if exclude_first_conv:
             excluded_sym_names += ['conv_1']
@@ -203,7 +192,7 @@ def save_params(fname, arg_params, aux_params, logger=None):
                                                         excluded_op_names=excluded_op_names,
                                                         calib_mode=calib_mode, calib_data=data,
                                                         num_calib_examples=num_calib_batches * batch_size,
-                                                        calib_layer=calib_layer, quantized_dtype=args.quantized_dtype,
+                                                        quantized_dtype=args.quantized_dtype,
                                                         logger=logger)
         if calib_mode == 'entropy':
             suffix = '-quantized-%dbatches-entropy' % num_calib_batches
diff --git a/example/quantization/imagenet_gen_qsym_mkldnn.py b/example/quantization/imagenet_gen_qsym_mkldnn.py
index 6c87f58b63e2..e24fb780a9d8 100644
--- a/example/quantization/imagenet_gen_qsym_mkldnn.py
+++ b/example/quantization/imagenet_gen_qsym_mkldnn.py
@@ -193,7 +193,6 @@ def save_params(fname, arg_params, aux_params, logger=None):
     # get image shape
     image_shape = args.image_shape
 
-    calib_layer = lambda name: name.endswith('_output') or name == "data"
     exclude_first_conv = args.exclude_first_conv
     if args.quantized_dtype == "uint8":
         logger.info('quantized dtype is set to uint8, will exclude first conv.')
@@ -295,7 +294,7 @@ def save_params(fname, arg_params, aux_params, logger=None):
                                                               ctx=ctx, excluded_sym_names=excluded_sym_names,
                                                               calib_mode=calib_mode, calib_data=data,
                                                               num_calib_examples=num_calib_batches * batch_size,
-                                                              calib_layer=calib_layer, quantized_dtype=args.quantized_dtype,
+                                                              quantized_dtype=args.quantized_dtype,
                                                               label_names=(label_name,), logger=logger)
         if calib_mode == 'entropy':
             suffix = '-quantized-%dbatches-entropy' % num_calib_batches
diff --git a/example/ssd/quantization.py b/example/ssd/quantization.py
index d50935499240..9801a7dc0076 100644
--- a/example/ssd/quantization.py
+++ b/example/ssd/quantization.py
@@ -115,19 +115,10 @@ def save_params(fname, arg_params, aux_params, logger=None):
     # get image shape
     image_shape = '3,300,300'
 
-    def calib_layer(name): return not (name.endswith('_data') or
-                                       name.endswith('_weight') or
-                                       name.endswith('_bias') or
-                                       name.endswith('_workspace'))
     # Quantization layer configs
     exclude_first_conv = args.exclude_first_conv
     excluded_sym_names = []
     rgb_mean = '123,117,104'
-    for i in range(1,19):
-        excluded_sym_names += ['flatten'+str(i)]
-    excluded_sym_names += ['multibox_loc_pred',
-                           'concat0',
-                           'concat1']
     if exclude_first_conv:
         excluded_sym_names += ['conv1_1']
 
@@ -159,7 +150,7 @@ def calib_layer(name): return not (name.endswith('_data') or
                                                         ctx=ctx, excluded_sym_names=excluded_sym_names,
                                                         calib_mode=calib_mode, calib_data=eval_iter,
                                                         num_calib_examples=num_calib_batches * batch_size,
-                                                        calib_layer=calib_layer, quantized_dtype=args.quantized_dtype,
+                                                        quantized_dtype=args.quantized_dtype,
                                                         label_names=(label_name,), logger=logger)
         sym_name = '%s-symbol.json' % ('./model/cqssd_vgg16_reduced_300')
         param_name = '%s-%04d.params' % ('./model/cqssd_vgg16_reduced_300', epoch)
diff --git a/include/mxnet/c_api.h b/include/mxnet/c_api.h
index d4f756f5333c..e0ed316a1bdc 100644
--- a/include/mxnet/c_api.h
+++ b/include/mxnet/c_api.h
@@ -1907,6 +1907,9 @@ MXNET_DLL int MXSymbolInferTypePartial(SymbolHandle sym,
  * \param offline_params array of c strings representing the names of params quantized offline
  * \param quantized_dtype the quantized destination type for input data
  * \param calib_quantize **Deprecated**. quantize op will always be calibrated if could
+ * \param quantize_mode quantize mode to be used in quantize pass
+ * \param out_num_calib_names return the number of nodes to be calibrated
+ * \param out_calib_names return the node names to be calibrated
  */
 MXNET_DLL int MXQuantizeSymbol(SymbolHandle sym_handle, SymbolHandle *ret_sym_handle,
                                const int* dev_type,
@@ -1915,7 +1918,9 @@ MXNET_DLL int MXQuantizeSymbol(SymbolHandle sym_handle, SymbolHandle *ret_sym_ha
                                const mx_uint num_excluded_op_names,
                                const char **excluded_op_names,
                                const mx_uint num_offline, const char **offline_params,
-                               const char *quantized_dtype, const bool calib_quantize);
+                               const char *quantized_dtype, const bool calib_quantize,
+                               const char *quantize_mode, mx_uint* out_num_calib_names,
+                               const char ***out_calib_names);
 
 /*!
  * \brief Convert a symbol into a mixed precision symbol with cast operators for target dtype casting
diff --git a/include/mxnet/op_attr_types.h b/include/mxnet/op_attr_types.h
index 889b5028a460..75d843c98bd2 100644
--- a/include/mxnet/op_attr_types.h
+++ b/include/mxnet/op_attr_types.h
@@ -131,6 +131,16 @@ enum class DispatchMode {
   kVariable,
 };
 
+/*! \brief the quantization type of the operator */
+enum class QuantizeType {
+  // This operator doesn't support quantization
+  kNone = 0,
+  // This operator can get huge benefit from quantization, thus must be quantized
+  kMust,
+  // This operator support quantization, but will be decided depending on the connection
+  kSupport,
+};
+
 /*!
  * \brief Operator state. This is a pointer type, its content is mutable
  *  even if OpStatePtr is const.
@@ -297,6 +307,12 @@ using FInferStorageType = std::function<bool (const NodeAttrs& attrs,
                                               std::vector<int>* in_attrs,
                                               std::vector<int>* out_attrs)>;
 
+/*!
+ * \brief Register a quantized node creation function based on the attrs of the node
+ * \note Register under "FQuantizedOp" for non-quantized operators
+ */
+using FQuantizable = std::function<QuantizeType (const NodeAttrs& attrs)>;
+
 /*!
  * \brief Register a quantized node creation function based on the attrs of the node
  * \note Register under "FQuantizedOp" for non-quantized operators
@@ -319,6 +335,20 @@ using FNeedRequantize = std::function<bool (const NodeAttrs& attrs)>;
 using FAvoidQuantizeInput = std::function<bool (const NodeAttrs& attrs,
                                                 size_t index)>;
 
+/*!
+ * \brief Register a function to determine if the input of a quantized operator
+ * needs to be calibrated. This is usually used for the quantized operators
+ * which need calibration on its input.
+ */
+using FNeedCalibrateInput = std::function<std::vector<int> (const NodeAttrs& attrs)>;
+
+/*!
+ * \brief Register a function to determine if the output of a quantized operator
+ * needs to be calibrated. This is usually used for the quantized operators
+ * which need calibration on its output.
+ */
+using FNeedCalibrateOutput = std::function<std::vector<int> (const NodeAttrs& attrs)>;
+
 }  // namespace mxnet
 
 #endif  // MXNET_OP_ATTR_TYPES_H_
diff --git a/python/mxnet/contrib/quantization.py b/python/mxnet/contrib/quantization.py
index c34e934bfa02..ef24bddf9bd1 100644
--- a/python/mxnet/contrib/quantization.py
+++ b/python/mxnet/contrib/quantization.py
@@ -84,7 +84,7 @@ def _quantize_params(qsym, params, th_dict):
     return quantized_params
 
 def _quantize_symbol(sym, ctx, excluded_symbols=None, excluded_operators=None,
-                     offline_params=None, quantized_dtype='int8'):
+                     offline_params=None, quantized_dtype='int8', quantize_mode='smart'):
     """Given a symbol object representing a neural network of data type FP32,
     quantize it into a INT8 network.
 
@@ -106,6 +106,9 @@ def _quantize_symbol(sym, ctx, excluded_symbols=None, excluded_operators=None,
         avoided.
     quantized_dtype: str
         The quantized destination type for input data.
+    quantize_mode: str
+        The mode that quantization pass to apply.
+
     """
     num_excluded_symbols = 0
     if excluded_symbols is not None:
@@ -129,6 +132,8 @@ def _quantize_symbol(sym, ctx, excluded_symbols=None, excluded_operators=None,
             offline.append(c_str(k))
 
     out = SymbolHandle()
+    size = mx_uint()
+    calib_str = ctypes.POINTER(ctypes.c_char_p)()
     check_call(_LIB.MXQuantizeSymbol(sym.handle,
                                      ctypes.byref(out),
                                      ctypes.byref(ctypes.c_int(ctx.device_typeid)),
@@ -139,47 +144,75 @@ def _quantize_symbol(sym, ctx, excluded_symbols=None, excluded_operators=None,
                                      mx_uint(num_offline),
                                      c_array(ctypes.c_char_p, offline),
                                      c_str(quantized_dtype),
-                                     ctypes.c_bool(True)))
-    return Symbol(out)
-
-
-class _LayerOutputCollector(object):
-    """Saves layer output NDArray in a dict with layer names as keys and lists of NDArrays as
-    values. The collected NDArrays will be used for calculating the optimal thresholds for
+                                     ctypes.c_bool(True),
+                                     c_str(quantize_mode),
+                                     ctypes.byref(size),
+                                     ctypes.byref(calib_str)))
+    calib_layer = []
+    calib_layer = [py_str(calib_str[i]) for i in range(size.value)]
+    return Symbol(out), calib_layer
+
+def combine_histogram(old_hist, arr, new_min, new_max, new_th):
+    """ Collect layer histogram for arr and combine it with old histogram.
+    """
+    (old_hist, old_hist_edges, old_min, old_max, old_th) = old_hist
+    if new_th <= old_th:
+        hist, _ = np.histogram(arr, bins=len(old_hist), range=(-old_th, old_th))
+        return (old_hist + hist, old_hist_edges, min(old_min, new_min), max(old_max, new_max), old_th)
+    else:
+        # Need to generate new histogram with new_th
+        old_num_bins = len(old_hist)
+        old_step = 2 * old_th / old_num_bins
+        half_increased_bins = int((new_th - old_th) // old_step + 1)
+        new_num_bins = half_increased_bins * 2 + old_num_bins
+        new_th = half_increased_bins * old_step + old_th
+        hist, hist_edges = np.histogram(arr, bins=new_num_bins, range=(-new_th, new_th))
+        hist[half_increased_bins:new_num_bins - half_increased_bins] += old_hist
+        return (hist, hist_edges, min(old_min, new_min), max(old_max, new_max), new_th)
+
+class _LayerHistogramCollector(object):
+    """Saves layer histogram in a dict with layer names as keys and lists of NDArrays as
+    values. The collected histogram will be used for calculating the optimal thresholds for
     quantization using KL divergence.
     """
-    def __init__(self, include_layer=None, logger=None):
-        self.nd_dict = {}
+    def __init__(self, num_bins=8001, include_layer=None, logger=None):
+        self.hist_dict = {}
+        self.num_bins = num_bins
         self.include_layer = include_layer
         self.logger = logger
 
     def collect(self, name, arr):
         """Callback function for collecting layer output NDArrays."""
         name = py_str(name)
-        if self.include_layer is not None and not self.include_layer(name):
+        if name not in self.include_layer:
             return
         handle = ctypes.cast(arr, NDArrayHandle)
-        arr = NDArray(handle, writable=False).copyto(cpu())
+        arr = NDArray(handle, writable=False).copyto(cpu()).asnumpy()
         if self.logger is not None:
-            self.logger.info("Collecting layer %s output of shape %s" % (name, arr.shape))
-        if name in self.nd_dict:
-            self.nd_dict[name].append(arr)
+            self.logger.info("Collecting layer %s histogram of shape %s" % (name, arr.shape))
+        min_range = np.min(arr)
+        max_range = np.max(arr)
+        th = max(abs(min_range), abs(max_range))
+        if name in self.hist_dict:
+            self.hist_dict[name] = combine_histogram(self.hist_dict[name], arr, min_range, max_range, th)
         else:
-            self.nd_dict[name] = [arr]
+            hist, hist_edges = np.histogram(arr, bins=self.num_bins, range=(-th, th))
+            self.hist_dict[name] = (hist, hist_edges, min_range, max_range, th)
 
 class _LayerOutputMinMaxCollector(object):
     """Saves layer output min and max values in a dict with layer names as keys.
     The collected min and max values will be directly used as thresholds for quantization.
     """
-    def __init__(self, include_layer=None, logger=None):
+    def __init__(self, quantized_dtype, include_layer=None, logger=None):
         self.min_max_dict = {}
+        self.quantized_dtype = quantized_dtype
         self.include_layer = include_layer
         self.logger = logger
 
     def collect(self, name, arr):
         """Callback function for collecting min and max values from an NDArray."""
         name = py_str(name)
-        if self.include_layer is not None and not self.include_layer(name):
+        if name not in self.include_layer:
             return
         handle = ctypes.cast(arr, NDArrayHandle)
         arr = NDArray(handle, writable=False)
@@ -239,21 +272,23 @@ def _collect_layer_statistics(mod, data, collector, max_num_examples=None, logge
     return num_examples
 
 
-def _collect_layer_output_min_max(mod, data, include_layer=None,
+def _collect_layer_output_min_max(mod, data, quantized_dtype, include_layer=None,
                                   max_num_examples=None, logger=None):
     """Collect min and max values from layer outputs and save them in
     a dictionary mapped by layer names.
     """
-    collector = _LayerOutputMinMaxCollector(include_layer=include_layer, logger=logger)
+    collector = _LayerOutputMinMaxCollector(quantized_dtype=quantized_dtype,
+                                            include_layer=include_layer, logger=logger)
     num_examples = _collect_layer_statistics(mod, data, collector, max_num_examples, logger)
     return collector.min_max_dict, num_examples
 
 
-def _collect_layer_outputs(mod, data, include_layer=None, max_num_examples=None, logger=None):
+def _collect_layer_histogram(mod, data, include_layer=None,
+                             max_num_examples=None, logger=None):
     """Collect layer outputs and save them in a dictionary mapped by layer names."""
-    collector = _LayerOutputCollector(include_layer=include_layer, logger=logger)
+    collector = _LayerHistogramCollector(include_layer=include_layer, logger=logger)
     num_examples = _collect_layer_statistics(mod, data, collector, max_num_examples, logger)
-    return collector.nd_dict, num_examples
+    return collector.hist_dict, num_examples
 
 
 def _smooth_distribution(p, eps=0.0001):
@@ -277,126 +312,55 @@ def _smooth_distribution(p, eps=0.0001):
 
 
 # pylint: disable=line-too-long
-def _get_optimal_threshold(arr, quantized_dtype, num_bins=8001, num_quantized_bins=255):
+def _get_optimal_threshold(hist_data, quantized_dtype, num_quantized_bins=255):
     """Given a dataset, find the optimal threshold for quantizing it.
     The reference distribution is `q`, and the candidate distribution is `p`.
     `q` is a truncated version of the original distribution.
 
     Ref: http://on-demand.gputechconf.com/gtc/2017/presentation/s7310-8-bit-inference-with-tensorrt.pdf
     """
-    if isinstance(arr, NDArray):
-        arr = arr.asnumpy()
-    elif isinstance(arr, list):
-        assert len(arr) != 0
-        for i, nd in enumerate(arr):
-            if isinstance(nd, NDArray):
-                arr[i] = nd.asnumpy()
-            elif not isinstance(nd, np.ndarray):
-                raise TypeError('get_optimal_threshold only supports input type of NDArray,'
-                                ' list of np.ndarrays or NDArrays, and np.ndarray,'
-                                ' while received type=%s' % (str(type(nd))))
-        arr = np.concatenate(arr)
-    elif not isinstance(arr, np.ndarray):
-        raise TypeError('get_optimal_threshold only supports input type of NDArray,'
-                        ' list of NDArrays and np.ndarray,'
-                        ' while received type=%s' % (str(type(arr))))
-    min_val = np.min(arr)
-    max_val = np.max(arr)
-    th = max(abs(min_val), abs(max_val))
-
+    (hist, hist_edges, min_val, max_val, _) = hist_data
+    num_bins = len(hist)
+    assert (num_bins % 2 == 1)
     if min_val >= 0 and quantized_dtype in ['auto', 'uint8']:
         # We need to move negative bins to positive bins to fit uint8 range.
         num_quantized_bins = num_quantized_bins * 2 + 1
-
-    hist, hist_edges = np.histogram(arr, bins=num_bins, range=(-th, th))
-    zero_bin_idx = num_bins // 2
-    num_half_quantized_bins = num_quantized_bins // 2
-
-    thresholds = np.zeros(num_bins // 2 + 1 - num_quantized_bins // 2)
-    divergence = np.zeros_like(thresholds)
-    quantized_bins = np.zeros(num_quantized_bins, dtype=np.int32)
-    # i means the number of bins on half axis excluding the zero bin.
-    for i in range(num_quantized_bins // 2,
-                   num_bins // 2 + 1):
-        p_bin_idx_start = zero_bin_idx - i
-        p_bin_idx_stop = zero_bin_idx + i + 1
-        thresholds[i - num_half_quantized_bins] = hist_edges[p_bin_idx_stop]
-        sliced_nd_hist = hist[p_bin_idx_start:p_bin_idx_stop]
-
-        # generate reference distribution p
-        p = sliced_nd_hist.copy()
-        assert p.size % 2 == 1
-        assert p.size >= num_quantized_bins
-        # put left outlier count in p[0]
-        left_outlier_count = np.sum(hist[0:p_bin_idx_start])
-        p[0] += left_outlier_count
-        # put right outlier count in p[-1]
-        right_outlier_count = np.sum(hist[p_bin_idx_stop:])
-        p[-1] += right_outlier_count
-        # is_nonzeros[k] indicates whether hist[k] is nonzero
-        is_nonzeros = (p != 0).astype(np.int32)
-
-        # calculate how many bins should be merged to generate quantized distribution q
-        num_merged_bins = sliced_nd_hist.size // num_quantized_bins
-        # merge hist into num_quantized_bins bins
-        for j in range(num_quantized_bins):
-            start = j * num_merged_bins
-            stop = start + num_merged_bins
-            quantized_bins[j] = sliced_nd_hist[start:stop].sum()
-        quantized_bins[-1] += sliced_nd_hist[num_quantized_bins * num_merged_bins:].sum()
-        # expand quantized_bins into p.size bins
-        q = np.zeros(sliced_nd_hist.size, dtype=np.float32)
-        for j in range(num_quantized_bins):
-            start = j * num_merged_bins
-            if j == num_quantized_bins - 1:
-                stop = len(is_nonzeros)
-            else:
-                stop = start + num_merged_bins
-            norm = is_nonzeros[start:stop].sum()
-            if norm != 0:
-                q[start:stop] = float(quantized_bins[j]) / float(norm)
-        q[p == 0] = 0
-        p = _smooth_distribution(p)
-        # There is a chance that q is an invalid probability distribution.
-        try:
-            q = _smooth_distribution(q)
-        except ValueError:
-            divergence[i - num_half_quantized_bins] = float("inf")
-        divergence[i - num_half_quantized_bins] = stats.entropy(p, q)
-
-    min_divergence_idx = np.argmin(divergence)
-    min_divergence = divergence[min_divergence_idx]
-    opt_th = thresholds[min_divergence_idx]
-    return min_val, max_val, min_divergence, opt_th
+    hist = ndarray.array(hist, ctx=cpu())
+    hist_edges = ndarray.array(hist_edges, ctx=cpu())
+    threshold, divergence = ndarray.contrib.calibrate_entropy(hist=hist,
+                                                              hist_edges=hist_edges,
+                                                              num_quantized_bins=num_quantized_bins)
+    threshold = threshold.asnumpy()
+    divergence = divergence.asnumpy()
+    return min_val, max_val, threshold, divergence
 # pylint: enable=line-too-long
 
-
-def _get_optimal_thresholds(nd_dict, quantized_dtype, num_bins=8001, num_quantized_bins=255, logger=None):
+def _get_optimal_thresholds(hist_dict, quantized_dtype, num_quantized_bins=255, logger=None):
     """Given a ndarray dict, find the optimal threshold for quantizing each value of the key."""
     if stats is None:
         raise ImportError('scipy.stats is required for running entropy mode of calculating'
                           ' the optimal thresholds for quantizing FP32 ndarrays into int8.'
                           ' Please check if the scipy python bindings are installed.')
-    assert isinstance(nd_dict, dict)
+    assert isinstance(hist_dict, dict)
     if logger is not None:
         logger.info('Calculating optimal thresholds for quantization using KL divergence'
-                    ' with num_bins=%d and num_quantized_bins=%d' % (num_bins, num_quantized_bins))
+                    ' with num_quantized_bins=%d' % num_quantized_bins)
     th_dict = {}
-    # copy nd_dict keys since the keys() only returns a view in python3
-    layer_names = list(nd_dict.keys())
+    # copy hist_dict keys since the keys() only returns a view in python3
+    layer_names = list(hist_dict.keys())
     for name in layer_names:
-        assert name in nd_dict
-        min_val, max_val, min_divergence, opt_th = \
-            _get_optimal_threshold(nd_dict[name], quantized_dtype, num_bins=num_bins,
+        assert name in hist_dict
+        min_val, max_val, th, divergence = \
+            _get_optimal_threshold(hist_dict[name], quantized_dtype,
                                    num_quantized_bins=num_quantized_bins)
-        del nd_dict[name]  # release the memory of ndarray
-        if min_val < 0:
-            th_dict[name] = (-opt_th, opt_th)
+        if min_val >= 0 and quantized_dtype in ['auto', 'uint8']:
+            th_dict[name] = (0, th)
         else:
-            th_dict[name] = (0, opt_th)
+            th_dict[name] = (-th, th)
+        del hist_dict[name]  # release the memory
         if logger is not None:
-            logger.info('layer=%s, min_val=%f, max_val=%f, min_divergence=%f, optimal_threshold=%f'
-                        % (name, min_val, max_val, min_divergence, opt_th))
+            logger.info('layer=%s, min_val=%f, max_val=%f, th=%f, divergence=%f'
+                        % (name, min_val, max_val, th, divergence))
     return th_dict
 
 
@@ -479,8 +443,8 @@ def _as_data_iter(calib_data):
 def quantize_model(sym, arg_params, aux_params,
                    data_names=('data',), label_names=('softmax_label',),
                    ctx=cpu(), excluded_sym_names=None, excluded_op_names=None, calib_mode='entropy',
-                   calib_data=None, num_calib_examples=None, calib_layer=None,
-                   quantized_dtype='int8', logger=logging):
+                   calib_data=None, num_calib_examples=None,
+                   quantized_dtype='int8', quantize_mode='smart', logger=logging):
     """User-level API for generating a quantized model from a FP32 model w/ or w/o calibration.
     The backend quantized operators are only enabled for Linux systems. Please do not run
     inference using the quantized models on Windows for now.
@@ -528,15 +492,14 @@ def quantize_model(sym, arg_params, aux_params,
     num_calib_examples : int or None
         The maximum number of examples that user would like to use for calibration. If not provided,
         the whole calibration dataset will be used.
-    calib_layer : function
-        Given a layer's output name in string, return True or False for deciding whether to
-        calibrate this layer. If yes, the statistics of the layer's output will be collected;
-        otherwise, no information of the layer's output will be collected. If not provided,
-        all the layers' outputs that need requantization will be collected.
     quantized_dtype : str
-        The quantized destination type for input data. Currently support 'int8'
-        , 'uint8' and 'auto'. 'auto' means automatically select output type according to calibration result.
+        The quantized destination type for input data. Currently support 'int8', 'uint8' and 'auto'.
+        'auto' means automatically select output type according to calibration result.
         Default value is 'int8'.
+    quantize_mode : str
+        The mode that quantization pass to apply. Support 'full' and 'smart'.
+        'full' means quantize all operator if possible.
+        'smart' means quantization pass will smartly choice which operator should be quantized.
     logger : Object
         A logging object for printing information during the process of quantization.
 
@@ -564,11 +527,11 @@ def quantize_model(sym, arg_params, aux_params,
     if quantized_dtype not in ('int8', 'uint8', 'auto'):
         raise ValueError('unknown quantized_dtype %s received,'
                          ' expected `int8`, `uint8` or `auto`' % quantized_dtype)
-    qsym = _quantize_symbol(sym, ctx, excluded_symbols=excluded_sym_names,
-                            excluded_operators=excluded_op_names,
-                            offline_params=list(arg_params.keys()),
-                            quantized_dtype=quantized_dtype)
-
+    qsym, calib_layer = _quantize_symbol(sym, ctx, excluded_symbols=excluded_sym_names,
+                                         excluded_operators=excluded_op_names,
+                                         offline_params=list(
+                                             arg_params.keys()),
+                                         quantized_dtype=quantized_dtype, quantize_mode=quantize_mode)
     th_dict = {}
     if calib_mode is not None and calib_mode != 'none':
         if not isinstance(ctx, Context):
@@ -587,23 +550,22 @@ def quantize_model(sym, arg_params, aux_params,
             mod.bind(for_training=False, data_shapes=calib_data.provide_data)
         mod.set_params(arg_params, aux_params)
         if calib_mode == 'entropy':
-            nd_dict, num_examples = _collect_layer_outputs(mod, calib_data,
-                                                           include_layer=calib_layer,
-                                                           max_num_examples=num_calib_examples,
-                                                           logger=logger)
+            hist_dict, num_examples = _collect_layer_histogram(mod, calib_data,
+                                                               include_layer=calib_layer,
+                                                               max_num_examples=num_calib_examples,
+                                                               logger=logger)
             logger.info('Collected layer outputs from FP32 model using %d examples' % num_examples)
             logger.info('Calculating optimal thresholds for quantization')
-            th_dict = _get_optimal_thresholds(nd_dict, quantized_dtype, logger=logger)
+            th_dict = _get_optimal_thresholds(hist_dict, quantized_dtype, logger=logger)
         elif calib_mode == 'naive':
             th_dict, num_examples = _collect_layer_output_min_max(
-                mod, calib_data, include_layer=calib_layer, max_num_examples=num_calib_examples,
+                mod, calib_data, quantized_dtype, include_layer=calib_layer, max_num_examples=num_calib_examples,
                 logger=logger)
             logger.info('Collected layer output min/max values from FP32 model using %d examples'
                         % num_examples)
         else:
             raise ValueError('unknown calibration mode %s received,'
                              ' expected `none`, `naive`, or `entropy`' % calib_mode)
-        logger.info('Calibrating quantized symbol')
         qsym = _calibrate_quantized_sym(qsym, th_dict)
 
     logger.info('Quantizing parameters')
@@ -615,7 +577,7 @@ def quantize_model_mkldnn(sym, arg_params, aux_params,
                           data_names=('data',), label_names=('softmax_label',),
                           ctx=cpu(), excluded_sym_names=None, excluded_op_names=None,
                           calib_mode='entropy', calib_data=None, num_calib_examples=None,
-                          calib_layer=None, quantized_dtype='int8', logger=logging):
+                          quantized_dtype='int8', logger=logging):
     """User-level API for generating a fusion + quantized model from a FP32 model
     w/ or w/o calibration with Intel MKL-DNN.
     The backend quantized operators are only enabled for Linux systems. Please do not run
@@ -642,7 +604,7 @@ def quantize_model_mkldnn(sym, arg_params, aux_params,
                                                    ctx=ctx, excluded_sym_names=excluded_sym_names,
                                                    excluded_op_names=excluded_op_names,
                                                    calib_mode=calib_mode, calib_data=calib_data,
-                                                   num_calib_examples=num_calib_examples, calib_layer=calib_layer,
+                                                   num_calib_examples=num_calib_examples,
                                                    quantized_dtype=quantized_dtype, logger=logger)
 
     qsym = qsym.get_backend_symbol('MKLDNN_QUANTIZE')
@@ -651,7 +613,7 @@ def quantize_model_mkldnn(sym, arg_params, aux_params,
 
 def quantize_graph(sym, arg_params, aux_params, ctx=cpu(),
                    excluded_sym_names=None, excluded_op_names=None, calib_mode='entropy',
-                   calib_layer=None, quantized_dtype='int8', logger=logging):
+                   quantized_dtype='int8', logger=logging):
     """User-level API for generating a quantized model from a FP32 model w/o calibration
     and a collector for naive or entropy calibration.
     The backend quantized operators are only enabled for Linux systems. Please do not run
@@ -682,11 +644,6 @@ def quantize_graph(sym, arg_params, aux_params, ctx=cpu(),
         If calib_mode='entropy' (default mode), the thresholds for quantization will be
         derived such that the KL divergence between the distributions of FP32 layer outputs and
         quantized layer outputs is minimized based upon the calibration dataset.
-    calib_layer : function
-        Given a layer's output name in string, return True or False for deciding whether to
-        calibrate this layer. If yes, the statistics of the layer's output will be collected;
-        otherwise, no information of the layer's output will be collected. If not provided,
-        all the layers' outputs that need requantization will be collected.
     quantized_dtype : str
         The quantized destination type for input data. Currently support 'int8'
         , 'uint8' and 'auto'. 'auto' means automatically select output type according to calibration result.
@@ -711,22 +668,23 @@ def quantize_graph(sym, arg_params, aux_params, ctx=cpu(),
     if quantized_dtype not in ('int8', 'uint8', 'auto'):
         raise ValueError('unknown quantized_dtype %s received,'
                          ' expected `int8`, `uint8` or `auto`' % quantized_dtype)
-    qsym = _quantize_symbol(sym, ctx, excluded_symbols=excluded_sym_names,
-                            excluded_operators=excluded_op_names,
-                            offline_params=list(arg_params.keys()),
-                            quantized_dtype=quantized_dtype)
+    qsym, calib_layer = _quantize_symbol(sym, ctx, excluded_symbols=excluded_sym_names,
+                                         excluded_operators=excluded_op_names,
+                                         offline_params=list(
+                                             arg_params.keys()),
+                                         quantized_dtype=quantized_dtype)
 
     th_dict = {}
     collector = None
     if calib_mode is not None and calib_mode != 'none':
         if calib_mode == 'entropy':
-            collector = _LayerOutputCollector(
+            collector = _LayerHistogramCollector(
                 include_layer=calib_layer, logger=logger)
             logger.info(
                 'Create a layer output collector for entropy calibration.')
         elif calib_mode == 'naive':
-            collector = _LayerOutputMinMaxCollector(
-                include_layer=calib_layer, logger=logger)
+            collector = _LayerOutputMinMaxCollector(quantized_dtype=quantized_dtype,
+                                                    include_layer=calib_layer, logger=logger)
             logger.info(
                 'Create a layer output minmax collector for naive calibration')
         else:
@@ -765,11 +723,6 @@ def calib_graph(qsym, arg_params, aux_params, collector,
         If calib_mode='entropy' (default mode), the thresholds for quantization will be
         derived such that the KL divergence between the distributions of FP32 layer outputs and
         quantized layer outputs is minimized based upon the calibration dataset.
-    calib_layer : function
-        Given a layer's output name in string, return True or False for deciding whether to
-        calibrate this layer. If yes, the statistics of the layer's output will be collected;
-        otherwise, no information of the layer's output will be collected. If not provided,
-        all the layers' outputs that need requantization will be collected.
     quantized_dtype : str
         The quantized destination type for input data. Currently support 'int8'
         , 'uint8' and 'auto'. 'auto' means automatically select output type according to calibration result.
@@ -787,13 +740,12 @@ def calib_graph(qsym, arg_params, aux_params, collector,
         if calib_mode == 'entropy':
             logger.info('Calculating optimal thresholds for quantization')
             th_dict = _get_optimal_thresholds(
-                collector.nd_dict, quantized_dtype, logger=logger)
+                collector.hist_dict, quantized_dtype, logger=logger)
         elif calib_mode == 'naive':
             th_dict = collector.min_max_dict
         else:
             raise ValueError('unknown calibration mode %s received,'
                              ' expected `none`, `naive`, or `entropy`' % calib_mode)
-        logger.info('Calibrating quantized symbol')
         qsym = _calibrate_quantized_sym(qsym, th_dict)
     else:
         raise ValueError('please set calibration mode to naive or entropy.')
@@ -840,11 +792,6 @@ def quantize_net(network, quantized_dtype='auto',
         If calib_mode='entropy' (default mode), the thresholds for quantization will be
         derived such that the KL divergence between the distributions of FP32 layer outputs and
         quantized layer outputs is minimized based upon the calibration dataset.
-    calib_layer : function
-        Given a layer's output name in string, return True or False for deciding whether to
-        calibrate this layer. If yes, the statistics of the layer's output will be collected;
-        otherwise, no information of the layer's output will be collected. If not provided,
-        all the layers' outputs that need requantization will be collected.
     num_calib_examples : int or None
         The maximum number of examples that user would like to use for calibration. If not provided,
         the whole calibration dataset will be used.
@@ -925,7 +872,7 @@ def __exit__(self, exc_type, exc_value, traceback):
     qsym, qarg_params, aux_params, collector = quantize_graph(
         sym=symnet, arg_params=args, aux_params=auxs, ctx=ctx,
         excluded_sym_names=exclude_layers, excluded_op_names=exclude_operators,
-        calib_mode=calib_mode, calib_layer=None, quantized_dtype=quantized_dtype, logger=logger)
+        calib_mode=calib_mode, quantized_dtype=quantized_dtype, logger=logger)
 
     if calib_mode is not None and calib_mode != 'none':
         if not isinstance(ctx, Context):
diff --git a/src/c_api/c_api_symbolic.cc b/src/c_api/c_api_symbolic.cc
index be0cad648015..40391082e1b5 100644
--- a/src/c_api/c_api_symbolic.cc
+++ b/src/c_api/c_api_symbolic.cc
@@ -888,7 +888,10 @@ int MXQuantizeSymbol(SymbolHandle sym_handle,
                      const mx_uint num_offline,
                      const char **offline_params,
                      const char *quantized_dtype,
-                     const bool calib_quantize) {
+                     const bool calib_quantize,
+                     const char *quantize_mode,
+                     mx_uint* out_num_calib_names,
+                     const char ***out_calib_names) {
   nnvm::Symbol *s = new nnvm::Symbol();
   API_BEGIN();
   nnvm::Symbol *sym = static_cast<nnvm::Symbol*>(sym_handle);
@@ -907,12 +910,24 @@ int MXQuantizeSymbol(SymbolHandle sym_handle,
     offline.emplace(offline_params[i]);
   }
   std::string quantized_type(quantized_dtype);
+  std::string quantized_mode(quantize_mode);
   g.attrs["excluded_nodes"] = std::make_shared<nnvm::any>(std::move(excluded_node_names));
   g.attrs["excluded_ops"] = std::make_shared<nnvm::any>(std::move(excluded_op));
   g.attrs["offline_params"] = std::make_shared<nnvm::any>(std::move(offline));
   g.attrs["quantized_dtype"] = std::make_shared<nnvm::any>(std::move(quantized_type));
   g.attrs["target_ctx"] = std::make_shared<nnvm::any>(target_dev);
+  g.attrs["quantize_mode"] = std::make_shared<nnvm::any>(std::move(quantized_mode));
   g = ApplyPass(std::move(g), "QuantizeGraph");
+  const auto& calib_nodes = g.GetAttr<std::vector<std::string>>("calib_nodes");
+  MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
+  ret->ret_vec_str = std::move(calib_nodes);
+  *out_num_calib_names = ret->ret_vec_str.size();
+  ret->ret_vec_charp.clear();
+  ret->ret_vec_charp.reserve(ret->ret_vec_str.size());
+  for (const auto &str : ret->ret_vec_str) {
+    ret->ret_vec_charp.push_back(str.c_str());
+  }
+  *out_calib_names = dmlc::BeginPtr(ret->ret_vec_charp);
   s->outputs = g.outputs;
   *ret_sym_handle = s;
   API_END_HANDLE_ERROR(delete s);
diff --git a/src/common/utils.h b/src/common/utils.h
index 9dad5df84fd2..6edfa3c6bb56 100644
--- a/src/common/utils.h
+++ b/src/common/utils.h
@@ -28,6 +28,7 @@
 #include <dmlc/logging.h>
 #include <dmlc/omp.h>
 #include <nnvm/graph.h>
+#include <nnvm/node.h>
 #include <mxnet/engine.h>
 #include <mxnet/ndarray.h>
 #include <mxnet/op_attr_types.h>
@@ -804,6 +805,15 @@ inline void ConvertToLegacyShape(mxnet::ShapeVector* shapes) {
   }
 }
 
+/*!
+ * \brief This is function can return the output names of a NodeEntry.
+ */
+static inline std::string GetOutputName(const nnvm::NodeEntry& e) {
+  nnvm::Symbol sym;
+  sym.outputs.push_back(e);
+  return sym.ListOutputNames()[0];
+}
+
 }  // namespace common
 }  // namespace mxnet
 #endif  // MXNET_COMMON_UTILS_H_
diff --git a/src/executor/graph_executor.cc b/src/executor/graph_executor.cc
index 7bdeac708003..d92253266f35 100644
--- a/src/executor/graph_executor.cc
+++ b/src/executor/graph_executor.cc
@@ -1370,24 +1370,13 @@ void GraphExecutor::ExecuteMonInputCallback(size_t nid) {
 }
 
 void GraphExecutor::ExecuteMonOutputCallback(size_t nid) {
-  static const auto& flist_outputs =
-      nnvm::Op::GetAttr<nnvm::FListOutputNames>("FListOutputNames");
   const auto& idx = graph_.indexed_graph();
-  std::vector<std::string> output_names;
   OpNode& opnode = op_nodes_[nid];
-  const auto& inode = idx[nid];
   const auto& node = idx[nid].source;
-  if (flist_outputs.count(node->op())) {
-    output_names = flist_outputs[node->op()](node->attrs);
-  } else {
-    for (size_t i = 0; i < node->num_outputs(); ++i) {
-      output_names.emplace_back(std::to_string(i));
-    }
-  }
-  CHECK_EQ(opnode.exec->out_array.size(), output_names.size());
   for (size_t i = 0; i < opnode.exec->out_array.size(); ++i) {
     NDArray *cpy = new NDArray(opnode.exec->out_array[i]);
-    std::string name = inode.source->attrs.name + "_" + output_names[i];
+    nnvm::NodePtr node_ptr = std::make_shared<nnvm::Node>(*node);
+    std::string name = GetOutputName({node_ptr, static_cast<uint32_t >(i), 0});
     this->monitor_callback_(name.c_str(), reinterpret_cast<void*>(cpy));
   }
 }
diff --git a/src/operator/nn/mkldnn/mkldnn_flatten-inl.h b/src/operator/nn/mkldnn/mkldnn_flatten-inl.h
new file mode 100644
index 000000000000..ae890d8f3d91
--- /dev/null
+++ b/src/operator/nn/mkldnn/mkldnn_flatten-inl.h
@@ -0,0 +1,48 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file mkldnn_flatten-inl.h
+ * \brief Implement flatten operator by using mkldnn reorder primitive
+ * \author Wuxun Zhang
+ */
+
+#ifndef MXNET_OPERATOR_NN_MKLDNN_MKLDNN_FLATTEN_INL_H_
+#define MXNET_OPERATOR_NN_MKLDNN_MKLDNN_FLATTEN_INL_H_
+#if MXNET_USE_MKLDNN == 1
+
+#include "mkldnn_reshape-inl.h"
+
+namespace mxnet {
+namespace op {
+
+class MKLDNNFlattenFwd : public MKLDNNReshapeFwd {
+ public:
+  explicit MKLDNNFlattenFwd(const OpReqType &req, const NDArray &input, const NDArray &output)
+      : MKLDNNReshapeFwd(req, input, output) {}
+};
+
+void MKLDNNFlattenForward(const nnvm::NodeAttrs &attrs, const OpContext &ctx, const NDArray &input,
+                          const OpReqType &req, const NDArray &output);
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_USE_MKLDNN == 1
+#endif  // MXNET_OPERATOR_NN_MKLDNN_MKLDNN_FLATTEN_INL_H_
diff --git a/src/operator/nn/mkldnn/mkldnn_flatten.cc b/src/operator/nn/mkldnn/mkldnn_flatten.cc
index fdc02f960009..4090eb026cfc 100644
--- a/src/operator/nn/mkldnn/mkldnn_flatten.cc
+++ b/src/operator/nn/mkldnn/mkldnn_flatten.cc
@@ -25,19 +25,11 @@
 
 #if MXNET_USE_MKLDNN == 1
 
-#include "mkldnn_reshape-inl.h"
+#include "mkldnn_flatten-inl.h"
 
 namespace mxnet {
 namespace op {
 
-class MKLDNNFlattenFwd : public MKLDNNReshapeFwd {
- public:
-  explicit MKLDNNFlattenFwd(const OpReqType &req,
-                            const NDArray &input,
-                            const NDArray &output)
-    : MKLDNNReshapeFwd(req, input, output) {}
-};
-
 static MKLDNNFlattenFwd &GetFlattenForward(const OpReqType &req,
                                            const NDArray &input,
                                            const NDArray &output) {
diff --git a/src/operator/quantization/calibrate-inl.h b/src/operator/quantization/calibrate-inl.h
new file mode 100644
index 000000000000..ac7d04403be1
--- /dev/null
+++ b/src/operator/quantization/calibrate-inl.h
@@ -0,0 +1,48 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file calibraite-inl.h
+ * \brief Implementation of calibrate operator
+ */
+#ifndef MXNET_OPERATOR_QUANTIZATION_CALIBRATE_INL_H_
+#define MXNET_OPERATOR_QUANTIZATION_CALIBRATE_INL_H_
+
+#include <limits>
+#include <vector>
+#include "../mxnet_op.h"
+#include "./quantization_utils.h"
+
+namespace mxnet {
+namespace op {
+
+struct CalibrateEntropyParam : public dmlc::Parameter<CalibrateEntropyParam> {
+  int num_quantized_bins;
+  DMLC_DECLARE_PARAMETER(CalibrateEntropyParam) {
+    DMLC_DECLARE_FIELD(num_quantized_bins)
+      .set_default(255)
+      .describe(
+          "The number of quantized bins.");
+  }
+};
+
+}  // namespace op
+}  // namespace mxnet
+#endif  // MXNET_OPERATOR_QUANTIZATION_CALIBRATE_INL_H_
diff --git a/src/operator/quantization/calibrate.cc b/src/operator/quantization/calibrate.cc
new file mode 100644
index 000000000000..3852eab979f8
--- /dev/null
+++ b/src/operator/quantization/calibrate.cc
@@ -0,0 +1,215 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file calibrate.cc
+ * \brief
+ */
+
+#include <numeric>
+#include "./calibrate-inl.h"
+
+namespace mxnet {
+namespace op {
+
+DMLC_REGISTER_PARAMETER(CalibrateEntropyParam);
+
+// Given a discrete distribution (may have not been normalized to 1),
+// smooth it by replacing zeros with eps multiplied by a scaling factor and taking the
+// corresponding amount off the non-zero values.
+std::vector<float> SmoothDistribution(const std::vector<float>& p, const float eps = 0.0001) {
+  std::vector<size_t> is_zeros(p.size());
+  std::vector<size_t> is_nonzeros(p.size());
+  {
+    auto it = p.begin();
+    std::generate(is_zeros.begin(), is_zeros.end(),
+                  [&it]() { return static_cast<size_t>(*(it++) == 0.f); });
+  }
+  {
+    auto it = p.begin();
+    std::generate(is_nonzeros.begin(), is_nonzeros.end(),
+                  [&it]() { return static_cast<size_t>(*(it++) != 0.f); });
+  }
+
+  size_t n_zeros = std::accumulate(is_zeros.begin(), is_zeros.end(), 0);
+  size_t n_nonzeros = p.size() - n_zeros;
+  if (!n_nonzeros) {
+    // The discrete probability distribution is malformed. All entries are 0.
+    return std::vector<float>();
+  }
+  float eps1 = eps * static_cast<float>(n_zeros) / static_cast<float>(n_nonzeros);
+  if (eps1 >= 1.0) return std::vector<float>();
+  auto ret = p;
+  for (size_t i = 0; i < p.size(); i++) {
+    ret[i] += eps * is_zeros[i] - eps1 * is_nonzeros[i];
+  }
+  return ret;
+}
+
+static float ComputeEntropy(std::vector<float>* p_ptr, std::vector<float>* q_ptr) {
+  std::vector<float>& p = *p_ptr;
+  std::vector<float>& q = *q_ptr;
+  CHECK_EQ(p.size(), q.size());
+  float p_sum = std::accumulate(p.begin(), p.end(), 0.f);
+  float q_sum = std::accumulate(q.begin(), q.end(), 0.f);
+  for (auto& it : p) {
+    it = it / p_sum;
+  }
+
+  for (auto& it : q) {
+    it = it / q_sum;
+  }
+  float ret = 0;
+  for (size_t i = 0; i < p.size(); i++) {
+    CHECK(p[i] > 0 && q[i] > 0);
+    if (p[i] && q[i]) ret += p[i] * std::log(p[i] / q[i]);
+  }
+  return ret;
+}
+
+void CalibrateComputeCPU(const nnvm::NodeAttrs& attrs, const OpContext& ctx,
+                         const std::vector<TBlob>& inputs, const std::vector<OpReqType>& req,
+                         const std::vector<TBlob>& outputs) {
+  const auto& param = nnvm::get<CalibrateEntropyParam>(attrs.parsed);
+  const auto& hist = inputs[0];
+  const auto& hist_ptr = hist.dptr<float>();
+  const auto& hist_edges = inputs[1];
+  const auto& hist_edges_ptr = hist_edges.dptr<float>();
+  float* const out_threshold = outputs[0].dptr<float>();
+  float* const out_divergence = outputs[1].dptr<float>();
+  const auto num_bins = hist.Size();
+  CHECK_EQ(num_bins + 1, hist_edges.Size());
+  int num_quantized_bins = param.num_quantized_bins;
+
+  const int zero_bin_idx = num_bins / 2;
+  const int num_half_quantized_bins = num_quantized_bins / 2;
+  std::vector<float> thresholds(num_bins / 2 + 1 - num_quantized_bins / 2, 0.f);
+  std::vector<float> divergence(thresholds.size(), 0.f);
+  #pragma omp parallel for num_threads(engine::OpenMP::Get()->GetRecommendedOMPThreadCount())
+  for (index_t i = num_quantized_bins / 2; i <= zero_bin_idx; i++) {
+    const size_t p_bin_idx_start = zero_bin_idx - i;
+    const size_t p_bin_idx_stop = zero_bin_idx + i + 1;
+    thresholds[i - num_half_quantized_bins] = hist_edges_ptr[p_bin_idx_stop];
+
+    std::vector<size_t> sliced_nd_hist(p_bin_idx_stop - p_bin_idx_start);
+    std::vector<float> p(p_bin_idx_stop - p_bin_idx_start);
+    p[0] = 0;
+    p.back() = 0;
+    for (size_t j = 0; j < num_bins; j++) {
+      if (j <= p_bin_idx_start) {
+        p[0] += hist_ptr[j];
+      } else if (j >= p_bin_idx_stop) {
+        p.back() += hist_ptr[j];
+      } else {
+        sliced_nd_hist[j - p_bin_idx_start] = hist_ptr[j];
+        p[j - p_bin_idx_start] = hist_ptr[j];
+      }
+    }
+    // calculate how many bins should be merged to generate quantized distribution q
+    const auto num_merged_bins = sliced_nd_hist.size() / num_quantized_bins;
+    // merge hist into num_quantized_bins bins
+    std::vector<float> quantized_bins(num_quantized_bins, 0);
+    for (index_t j = 0; j < num_quantized_bins; j++) {
+      const int start = j * num_merged_bins;
+      const int stop = (j + 1) * num_merged_bins;
+      quantized_bins[j] =
+          std::accumulate(sliced_nd_hist.begin() + start, sliced_nd_hist.begin() + stop, 0);
+    }
+    quantized_bins.back() += std::accumulate(
+        sliced_nd_hist.begin() + static_cast<int>(num_quantized_bins * num_merged_bins),
+        sliced_nd_hist.end(), 0);
+    // expand quantized_bins into p.size bins
+    std::vector<float> q(sliced_nd_hist.size(), 0);
+    for (index_t j = 0; j < num_quantized_bins; j++) {
+      const int start = j * num_merged_bins;
+      const int stop = (j == num_quantized_bins - 1) ? q.size() : ((j + 1) * num_merged_bins);
+      int norm = std::count_if(sliced_nd_hist.begin() + start, sliced_nd_hist.begin() + stop,
+                               [](size_t i) { return i != 0; });
+      if (norm) {
+        for (index_t k = start; k < stop; k++) {
+          if (p[k]) q[k] = quantized_bins[j] / norm;
+        }
+      }
+    }
+    p = SmoothDistribution(p);
+    q = SmoothDistribution(q);
+
+    if (!q.size()) {
+      divergence[i - num_half_quantized_bins] = std::numeric_limits<float>::infinity();
+    } else {
+      divergence[i - num_half_quantized_bins] = ComputeEntropy(&p, &q);
+    }
+  }
+
+  size_t min_divergence_idx = 0;
+  float min_divergence = mshadow::red::limits::MaxValue<float>();
+  for (size_t i = 0; i < divergence.size(); i++) {
+    if (divergence[i] < min_divergence) {
+      min_divergence = divergence[i];
+      min_divergence_idx = i;
+    }
+  }
+  *out_divergence = min_divergence;
+  *out_threshold = thresholds[min_divergence_idx];
+}
+
+static inline bool CalibrateShape(const nnvm::NodeAttrs& attrs, std::vector<TShape>* in_attrs,
+                                  std::vector<TShape>* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 2U);
+  CHECK_EQ(out_attrs->size(), 2U);
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, TShape(1, 1));
+  SHAPE_ASSIGN_CHECK(*out_attrs, 1, TShape(1, 1));
+  return (!shape_is_none(in_attrs->at(0))) && (!shape_is_none(in_attrs->at(1)));
+}
+
+static inline bool CalibrateType(const nnvm::NodeAttrs& attrs, std::vector<int>* in_attrs,
+                                 std::vector<int>* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 2U);
+  CHECK_EQ(out_attrs->size(), 2U);
+  CHECK(in_attrs->at(0) == mshadow::kFloat32);
+  TYPE_ASSIGN_CHECK(*in_attrs, 1, mshadow::kFloat32);
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::kFloat32);
+  TYPE_ASSIGN_CHECK(*out_attrs, 1, mshadow::kFloat32);
+  return true;
+}
+
+NNVM_REGISTER_OP(_contrib_calibrate_entropy)
+.describe(R"code(Provide calibrated min/max for input histogram.
+
+.. Note::
+    This operator only supports forward propagation. DO NOT use it in training.)code" ADD_FILELINE)
+.set_attr_parser(ParamParser<CalibrateEntropyParam>)
+.set_num_inputs(2)
+.set_num_outputs(2)
+.set_attr<nnvm::FListInputNames>("FListInputNames", [](const NodeAttrs& attrs) {
+  return std::vector<std::string>{"hist", "hist_edges"};
+})
+.set_attr<nnvm::FListOutputNames>("FListOutputNames", [](const NodeAttrs& attrs) {
+  return std::vector<std::string>{"threshold", "divergence"};
+})
+.set_attr<mxnet::FInferShape>("FInferShape", CalibrateShape)
+.set_attr<nnvm::FInferType>("FInferType", CalibrateType)
+.set_attr<FCompute>("FCompute<cpu>", CalibrateComputeCPU)
+.add_argument("hist", "NDArray-or-Symbol", "A ndarray/symbol of type `float32`")
+.add_argument("hist_edges", "NDArray-or-Symbol", "A ndarray/symbol of type `float32`")
+.add_arguments(CalibrateEntropyParam::__FIELDS__());
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/quantization/mkldnn/mkldnn_quantize_v2-inl.h b/src/operator/quantization/mkldnn/mkldnn_quantize_v2-inl.h
index bd1b47e4c2de..7cdce8e32bc8 100644
--- a/src/operator/quantization/mkldnn/mkldnn_quantize_v2-inl.h
+++ b/src/operator/quantization/mkldnn/mkldnn_quantize_v2-inl.h
@@ -68,10 +68,10 @@ void SgMKLDNNQuantizeOperator::Forward(const OpContext &ctx, const std::vector<N
     } else {
       if (inputs[0].dtype() == mshadow::kUint8) {
         *outputs[1].data().dptr<float>() = 0;
-        *outputs[2].data().dptr<float>() = 255;
+        *outputs[2].data().dptr<float>() = kUint8Range;
       } else {
-        *outputs[1].data().dptr<float>() = -127;
-        *outputs[2].data().dptr<float>() = 127;
+        *outputs[1].data().dptr<float>() = -kInt8Range;
+        *outputs[2].data().dptr<float>() = kInt8Range;
       }
     }
     if (req[0] != kWriteInplace) {
diff --git a/src/operator/quantization/mkldnn/mkldnn_quantized_elemwise_add.cc b/src/operator/quantization/mkldnn/mkldnn_quantized_elemwise_add.cc
index 05da99207651..2be6b2baca63 100644
--- a/src/operator/quantization/mkldnn/mkldnn_quantized_elemwise_add.cc
+++ b/src/operator/quantization/mkldnn/mkldnn_quantized_elemwise_add.cc
@@ -60,7 +60,7 @@ static void MKLDNNQuantizedElemwiseAddForward(const nnvm::NodeAttrs& attrs, cons
   auto dataB_mem  = in_data[quantized_elemwise_add_enum::kDataB].GetMKLDNNData();
   const bool is_dataA_int8 = (in_data[quantized_elemwise_add_enum::kDataA].dtype()
                               == mshadow::kInt8);
-  const size_t dataA_range = is_dataA_int8 ? kInt8Range : kUint8Range;
+  const float dataA_range = is_dataA_int8 ? kInt8Range : kUint8Range;
 
   const float A_scale = GetScale(in_data[quantized_elemwise_add_enum::kDataA],
                                  dataA_min,
@@ -72,7 +72,7 @@ static void MKLDNNQuantizedElemwiseAddForward(const nnvm::NodeAttrs& attrs, cons
   mkldnn::memory *rescaled_mem;
 
   // output default set as int32
-  size_t output_data_range = kInt32Range;
+  float output_data_range = kInt32Range;
   auto output_data_type = mkldnn::memory::s32;
   // dataA && dataB are uint8
   if (out_data[quantized_elemwise_add_enum::kOut].dtype() == mshadow::kInt8) {
diff --git a/src/operator/quantization/mkldnn/mkldnn_quantized_flatten.cc b/src/operator/quantization/mkldnn/mkldnn_quantized_flatten.cc
new file mode 100644
index 000000000000..31da936915e6
--- /dev/null
+++ b/src/operator/quantization/mkldnn/mkldnn_quantized_flatten.cc
@@ -0,0 +1,61 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2018 by Contributors
+ * \file quantized_flatten.cc
+ * \brief
+ */
+
+#if MXNET_USE_MKLDNN == 1
+#include "../../nn/mkldnn/mkldnn_flatten-inl.h"
+#include "../quantization_utils.h"
+
+namespace mxnet {
+namespace op {
+
+inline static bool FlattenStorageType(const nnvm::NodeAttrs& attrs, const int dev_mask,
+                                     DispatchMode* dispatch_mode, std::vector<int>* in_attrs,
+                                     std::vector<int>* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 3U);
+  CHECK_EQ(out_attrs->size(), 3U);
+  return MKLDNNStorageType(attrs, dev_mask, true, dispatch_mode, in_attrs, out_attrs);
+}
+
+static void MKLDNNQuantizedFlattenForward(const nnvm::NodeAttrs& attrs, const OpContext& ctx,
+                                          const std::vector<NDArray>& inputs,
+                                          const std::vector<OpReqType>& req,
+                                          const std::vector<NDArray>& outputs) {
+  MKLDNNFlattenForward(attrs, ctx, inputs[0], req[0], outputs[0]);
+  outputs[1].data().dptr<float>()[0] = inputs[1].data().dptr<float>()[0];
+  outputs[2].data().dptr<float>()[0] = inputs[2].data().dptr<float>()[0];
+}
+
+NNVM_REGISTER_OP(_contrib_quantized_flatten)
+.set_attr<FInferStorageType>("FInferStorageType", FlattenStorageType)
+.set_attr<FComputeEx>("FComputeEx<cpu>", MKLDNNQuantizedFlattenForward)
+.set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& n) {
+  return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+})
+.set_attr<bool>("TIsMKLDNN", true);
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_USE_MKLDNN == 1
diff --git a/src/operator/quantization/quantization_utils.h b/src/operator/quantization/quantization_utils.h
index e7f7ccdf13b7..857841d0db1b 100644
--- a/src/operator/quantization/quantization_utils.h
+++ b/src/operator/quantization/quantization_utils.h
@@ -32,8 +32,8 @@
 namespace mxnet {
 namespace op {
 
-static const size_t kUint8Range = 255;
-static const size_t kInt8Range = 127;
+static const float kUint8Range = 255.5;
+static const float kInt8Range = 127.5;
 static const size_t kInt32Range = 0x7fffffff;
 
 template<typename T>
diff --git a/src/operator/quantization/quantize_graph_pass.cc b/src/operator/quantization/quantize_graph_pass.cc
index e3139913332d..182f6339308a 100644
--- a/src/operator/quantization/quantize_graph_pass.cc
+++ b/src/operator/quantization/quantize_graph_pass.cc
@@ -22,11 +22,16 @@
  * \file quantization.cc
  * \brief
  */
+
+#include <mxnet/op_attr_types.h>
 #include <nnvm/graph.h>
 #include <nnvm/pass.h>
-#include <mxnet/op_attr_types.h>
+#include <queue>
+#include <unordered_map>
 #include <unordered_set>
+#include <vector>
 #include "quantize_v2-inl.h"
+#include "../../common/utils.h"
 
 namespace mxnet {
 namespace op {
@@ -37,7 +42,7 @@ using nnvm::NodePtr;
 using nnvm::NodeEntry;
 using nnvm::Graph;
 
-inline size_t GetNumOutputs(NodePtr node) {
+static inline size_t GetNumOutputs(NodePtr node) {
   // Get NumOutputs, check if current node has NumVisibleOutputs function, if yes, return
   // num_visible_outputs
   size_t num_outputs = node->num_outputs();
@@ -116,17 +121,19 @@ bool isRegistered(NodePtr node, const int& dev_type) {
           fcomputestateful != nullptr || fcomputestateful_ex != nullptr);
 }
 
-inline NodePtr NeedQuantize(
-    NodePtr node, const std::unordered_set<std::string>& excluded_nodes,
-    const std::unordered_set<std::string>& excluded_ops,
-    const int& dev_type) {
+inline QuantizeType NeedQuantize(NodePtr node,
+                                 const std::unordered_set<std::string>& excluded_nodes,
+                                 const std::unordered_set<std::string>& excluded_ops,
+                                 const int& dev_type,
+                                 std::unordered_map<NodePtr, NodePtr>* quantized_node_map) {
   std::unordered_map<NodePtr, NodePtr> quantized_node;
+  static auto& quantizable_map = Op::GetAttr<mxnet::FQuantizable>("FQuantizable");
   static auto& quantized_op_map = Op::GetAttr<mxnet::FQuantizedOp>("FQuantizedOp");
   static auto& fexec_type = nnvm::Op::GetAttr<FExecType>("FExecType");
   const auto& op = node->op();
-
+  bool need = false;
   if (op && quantized_op_map.count(op)) {
-    bool need = true;
+    need = true;
     // If the quantized node is not registered with a computation function, the node
     // will be excluded automatically.
     auto q_ptr = quantized_op_map[node->op()];
@@ -154,20 +161,101 @@ inline NodePtr NeedQuantize(
         }
       }
     }
-
     if (need) {
-      auto n_ptr = quantized_op_map[node->op()];
-      auto tmp_node = n_ptr(node->attrs);
-      if (tmp_node->op()) {
-        quantized_node[node] = tmp_node;
+      auto quantized_node = quantized_op_map[op](node->attrs);
+      if (!quantized_node->op()) need = false;
+      if (need) {
+        quantized_node_map->insert(std::make_pair(node, quantized_node));
+      }
+      if (quantizable_map.count(op)) {
+        return quantizable_map[op](node->attrs);
       } else {
-        quantized_node[node] = nullptr;
+        return QuantizeType::kSupport;
       }
-    } else {
-      quantized_node[node] = nullptr;
     }
   }
-  return quantized_node[node];
+  CHECK(!need);
+  return QuantizeType::kNone;
+}
+
+enum quantize_bit {
+  kFromInput = 1,
+  kFromOutput = 2,
+};
+
+static void MarkQuantizedNodes(const Graph& src,
+                               std::unordered_map<NodePtr, NodePtr>* quantized_node_map) {
+  const auto excluded_nodes = src.GetAttr<std::unordered_set<std::string>>("excluded_nodes");
+  const auto excluded_ops = src.GetAttr<std::unordered_set<std::string>>("excluded_ops");
+  const auto quantize_mode = src.GetAttr<std::string>("quantize_mode");
+  const auto dev_type = src.GetAttr<int>("target_ctx");
+
+  std::unordered_map<NodePtr, std::vector<NodePtr>> node_output_map;
+  std::unordered_set<NodePtr> must_quantize_nodes;
+  std::unordered_map<NodePtr, int> support_quantize_nodes;
+  // Build node_output_map, must_quantize_nodes and support_quantize_nodes;
+  DFSVisit(src.outputs, [&](const NodePtr& node) {
+    auto quantize_type =
+        NeedQuantize(node, excluded_nodes, excluded_ops, dev_type, quantized_node_map);
+    if (quantize_type == QuantizeType::kMust) {
+      must_quantize_nodes.insert(node);
+    } else if (quantize_type == QuantizeType::kSupport) {
+      support_quantize_nodes[node] = 0;
+    }
+    for (size_t i = 0; i < node->inputs.size(); ++i) {
+      node_output_map[node->inputs[i].node].push_back(node);
+    }
+  });
+
+  if (quantize_mode == "full") {
+    return;
+  } else if (quantize_mode == "smart") {
+    // Mark quantized nodes from input
+    std::queue<NodePtr> task_queue;
+    for (const auto& node : must_quantize_nodes) {
+      task_queue.push(node);
+    }
+    while (!task_queue.empty()) {
+      const auto& node = task_queue.front();
+      task_queue.pop();
+      for (size_t i = 0; i < node->inputs.size(); ++i) {
+        const auto& input = node->inputs[i].node;
+        auto it = support_quantize_nodes.find(input);
+        if (it != support_quantize_nodes.end()) {
+          it->second = it->second | kFromInput;
+          task_queue.push(input);
+        }
+      }
+    }
+
+    // Mark quantized nodes from output
+    for (const auto& node : must_quantize_nodes) {
+      task_queue.push(node);
+    }
+    while (!task_queue.empty()) {
+      const auto& node = task_queue.front();
+      task_queue.pop();
+      const auto& outputs = node_output_map[node];
+      for (size_t i = 0; i < outputs.size(); ++i) {
+        const auto& output = outputs[i];
+        auto it = support_quantize_nodes.find(output);
+        if (it != support_quantize_nodes.end()) {
+          it->second = it->second | kFromOutput;
+          task_queue.push(output);
+        }
+      }
+    }
+
+    // Summarize the result
+    for (const auto& node : support_quantize_nodes) {
+      CHECK(quantized_node_map->count(node.first));
+      if (node.second != (kFromInput | kFromOutput)) {
+        quantized_node_map->erase(node.first);
+      }
+    }
+  } else {
+    LOG(FATAL) << "unrecognized quantize mode: " << quantize_mode;
+  }
 }
 
 Graph QuantizeGraph(Graph &&src) {
@@ -175,24 +263,25 @@ Graph QuantizeGraph(Graph &&src) {
   static const auto& need_requantize_map = Op::GetAttr<mxnet::FNeedRequantize>("FNeedRequantize");
   static const auto& avoid_quantize_input_map =
       Op::GetAttr<mxnet::FAvoidQuantizeInput>("FAvoidQuantizeInput");
-  const auto dev_type = src.GetAttr<int>("target_ctx");
   const auto offline_params = src.GetAttr<std::unordered_set<std::string>>("offline_params");
-  const auto excluded_nodes = src.GetAttr<std::unordered_set<std::string>>("excluded_nodes");
-  const auto excluded_ops = src.GetAttr<std::unordered_set<std::string>>("excluded_ops");
   const auto quantized_dtype = src.GetAttr<std::string>("quantized_dtype");
 
+  std::unordered_map<NodePtr, NodePtr> quantized_node_map;
+  MarkQuantizedNodes(src, &quantized_node_map);
+
   // mirror_map stores the mapping from the currently visited graph to the newly created quantized
   // graph. Key is the currently visited graph's node pointer, and value is a copied node of the key
   // node. The existing key's value may be updated with the newly created quantize/dequantize op.
   std::unordered_map<Node*, NodePtr> mirror_map;
+  std::unordered_map<NodePtr, NodePtr> reverse_mirror_map;
   nnvm::NodeEntryMap<NodeEntry> mirror_entry_map;
   DFSVisit(src.outputs, [&](const NodePtr& node) {
     NodePtr new_node = Node::Create();
     // If the currently visited node needs quantization, insert a quantize op node before the
     // current node and replace the current node with the quantized version in the new graph.
-    auto tmp_node = NeedQuantize(node, excluded_nodes, excluded_ops, dev_type);
-    if (tmp_node) {
-      new_node = tmp_node;
+    if (quantized_node_map.count(node)) {
+      std::cout << node->attrs.name << " is quantized." << std::endl;
+      new_node = quantized_node_map[node];
 
       // add data into quantized op input
       for (size_t i = 0; i < node->inputs.size(); ++i) {
@@ -208,7 +297,7 @@ Graph QuantizeGraph(Graph &&src) {
         if (avoid_quantize_input_map.count(node->op()) &&
             avoid_quantize_input_map[node->op()](node->attrs, i)) {
           new_node->inputs.emplace_back(mirror_entry);
-        } else if (!NeedQuantize(e.node, excluded_nodes, excluded_ops, dev_type)) {
+        } else if (!quantized_node_map.count(e.node)) {
           if (mirror_entry_map.count(e)) {
             new_node->inputs.emplace_back(mirror_entry_map[e]);
           } else {
@@ -261,7 +350,7 @@ Graph QuantizeGraph(Graph &&src) {
           // skip non-quantized input
           continue;
         }
-        if (NeedQuantize(e.node, excluded_nodes, excluded_ops, dev_type)) {
+        if (quantized_node_map.count(e.node)) {
           // here we calculate the output number (exclude min/max, in order to
           // calculate min/max index from mirror node) based on assumption that
           // there is only 1min and 1max output from mirror node (which is
@@ -306,6 +395,7 @@ Graph QuantizeGraph(Graph &&src) {
       // (e.g., a quantized_conv2d node), and insert a dequantize op node in the new graph if there
       // are any. Otherwise, simply add a copy of the current node's entry to the inputs of
       // the new_node.
+      if (!node->is_variable()) std::cout << node->attrs.name << " is NOT quantized." << std::endl;
       *new_node = *node;
       new_node->inputs.clear();
       for (const auto& e : node->inputs) {
@@ -313,7 +403,7 @@ Graph QuantizeGraph(Graph &&src) {
         NodeEntry mirror_entry = NodeEntry{
           mirror_node, e.index, e.version};
         // if input node is quantized operator, add dequantize node
-        if (NeedQuantize(e.node, excluded_nodes, excluded_ops, dev_type) &&
+        if (quantized_node_map.count(e.node) &&
             (mirror_node->op() != Op::Get("_contrib_dequantize"))) {
           // here we calculate the output number (exclude min/max, in order to
           // calculate min/max index from mirror node) based on assumption that
@@ -330,7 +420,8 @@ Graph QuantizeGraph(Graph &&src) {
           dequantize_node->op()->attr_parser(&(dequantize_node->attrs));
 
           new_node->inputs.emplace_back(dequantize_node, 0, 0);
-          mirror_map[e.node.get()] = std::move(dequantize_node);
+          mirror_map[e.node.get()] = dequantize_node;
+          reverse_mirror_map[dequantize_node] = e.node;
         } else if (mirror_entry_map.count(e)) {
           new_node->inputs.emplace_back(
               mirror_entry_map[e].node->inputs[0].node, e.index, e.version);
@@ -339,12 +430,13 @@ Graph QuantizeGraph(Graph &&src) {
         }
       }
     }
-    mirror_map[node.get()] = std::move(new_node);
+    mirror_map[node.get()] = new_node;
+    reverse_mirror_map[new_node] = node;
   });
 
   std::vector<NodeEntry> outputs;
   for (const auto& e : src.outputs) {
-    if (NeedQuantize(e.node, excluded_nodes, excluded_ops, dev_type)) {
+    if (quantized_node_map.count(e.node)) {
       // Only insert dequantize for those Ops supports quantize and not excluded.
       NodePtr mirror_node = mirror_map.at(e.node.get());
       NodeEntry mirror_entry = NodeEntry{mirror_node, e.index, e.version};
@@ -372,103 +464,89 @@ Graph QuantizeGraph(Graph &&src) {
 
   Graph ret;
   ret.outputs = std::move(outputs);
-  return ret;
-}
 
-Graph SetCalibTableToQuantizedGraph(Graph&& g) {
-  static const auto& flist_outputs =
-    nnvm::Op::GetAttr<nnvm::FListOutputNames>("FListOutputNames");
-  static const auto& need_requantize_map =
-    nnvm::Op::GetAttr<mxnet::FNeedRequantize>("FNeedRequantize");
-  const auto& calib_table =
-    g.GetAttr<std::unordered_map<std::string, std::pair<float, float>>>("calib_table");
-  DFSVisit(g.outputs, [&](const NodePtr& node) {
-    // If the current op is requantize
-    // find the thresholds from the calibration table with the key equal
-    // to the current op's input node name, e.g. a quantized_conv2d node.
-    if (node->op() == Op::Get("_contrib_requantize")) {
-      NodePtr quantized_op_node = node->inputs[0].node;
-      CHECK(quantized_op_node->op() != nullptr) << quantized_op_node->attrs.name
-                                                << " must be an quantized op node";
-      CHECK(need_requantize_map.count(quantized_op_node->op()) > 0
-          && need_requantize_map[quantized_op_node->op()](quantized_op_node->attrs))
-          << quantized_op_node->attrs.name << " op must register FNeedRequantize attr"
-                                              " and the attr func should return true";
-      const std::string prefix = "quantized_";
-      CHECK(std::equal(prefix.begin(), prefix.end(), quantized_op_node->attrs.name.begin()))
-          << "an quantized op should start with `quantized_`";
-
-      std::string out_data_name = quantized_op_node->attrs.name.substr(prefix.size()) + "_";
-      auto list_output_names_func = flist_outputs.get(quantized_op_node->op(), nullptr);
-      // Here it's assumed that the quantized_op node only produces three outputs:
-      // out_data, min_range, and max_range. So we want to get the pre-calculated min_calib_range
-      // and max_calib_range from the calibration table for out_data. Here we create the output
-      // data name same as its constructed in GraphExecutor::ExecuteMonCallback.
-      if (list_output_names_func != nullptr) {
-        std::vector<std::string> names = list_output_names_func(quantized_op_node->attrs);
-        CHECK_EQ(names.size(), 3U) << "ListOutputNames is expected to return three string for"
-                                      " quantized operators";
-        out_data_name += names[0];
-      } else {
-        out_data_name += "0";
-      }
-      const auto calib_table_iter = calib_table.find(out_data_name);
-      if (calib_table_iter != calib_table.end()) {
-        node->attrs.dict["min_calib_range"] = std::to_string(calib_table_iter->second.first);
-        node->attrs.dict["max_calib_range"] = std::to_string(calib_table_iter->second.second);
-        node->op()->attr_parser(&(node->attrs));
-      }
-    } else if (node->op() == Op::Get("_contrib_quantize_v2")) {
-      NodePtr float_op_node = node->inputs[0].node;
-      auto float_op_idx = node->inputs[0].index;
-      std::string out_data_name = float_op_node->attrs.name;
-      if (float_op_node->op()) {
-        auto list_output_names_func = flist_outputs.get(float_op_node->op(), nullptr);
-        // We want to get the pre-calculated min_range and max_range from the calibration table for
-        // out_data. Here we create the output data name same as its constructed in
-        // GraphExecutor::ExecuteMonCallback.
-        if (list_output_names_func != nullptr) {
-          std::vector<std::string> names = list_output_names_func(float_op_node->attrs);
-          out_data_name += "_" + names[float_op_idx];
+  static const auto& need_calib_input_map =
+      Op::GetAttr<mxnet::FNeedCalibrateInput>("FNeedCalibrateInput");
+  static const auto& need_calib_output_map =
+      Op::GetAttr<mxnet::FNeedCalibrateOutput>("FNeedCalibrateOutput");
+  std::vector<std::string> calib_nodes;
+  DFSVisit(ret.outputs, [&](const NodePtr& node) {
+    if (need_calib_input_map.count(node->op())) {
+      const auto calib_idx = need_calib_input_map[node->op()](node->attrs);
+      for (const auto &idx : calib_idx) {
+        if (reverse_mirror_map.count(node)) {
+          calib_nodes.push_back(common::GetOutputName(
+              {reverse_mirror_map[node], node->inputs[idx].index, node->inputs[idx].version}));
         } else {
-          out_data_name += "_" + std::to_string(float_op_idx);
-        }
-      }
-      const auto calib_table_iter = calib_table.find(out_data_name);
-      if (calib_table_iter != calib_table.end()) {
-        node->attrs.dict["min_calib_range"] = std::to_string(calib_table_iter->second.first);
-        node->attrs.dict["max_calib_range"] = std::to_string(calib_table_iter->second.second);
-        node->op()->attr_parser(&(node->attrs));
-        const QuantizeV2Param& param = nnvm::get<QuantizeV2Param>(node->attrs.parsed);
-        if (param.out_type == QuantizeOutType::kUint8 &&
-            param.min_calib_range.value() < 0.0f) {
-          LOG(WARNING) << "Calibration statistics indicates that node `" << node->attrs.name
-                       << "` has negative input, consider use `auto` or `int8` as out_type";
+          const auto& e = node->inputs[idx];
+          if (e.node->is_variable()) {
+            calib_nodes.push_back(e.node->attrs.name);
+          } else {
+            if (reverse_mirror_map.count(e.node)) {
+              const auto& fp32_in_node = reverse_mirror_map.at(e.node);
+              calib_nodes.push_back(common::GetOutputName({fp32_in_node, e.index, e.version}));
+            } else {
+              LOG(FATAL) << "Can't find calibration node for " << node->attrs.name;
+            }
+          }
         }
       }
-    } else if (node->op() == Op::Get("_contrib_quantized_batch_norm")) {
-      auto quantized_op_idx = node->inputs[0].index;
-      const std::string prefix = "quantized_";
-      std::string out_data_name = node->attrs.name.substr(prefix.size());
-      if (node->op()) {
-        auto list_output_names_func = flist_outputs.get(node->op(), nullptr);
-        // We want to get the pre-calculated min_range and max_range from the calibration table for
-        // out_data. Here we create the output data name same as its constructed in
-        // GraphExecutor::ExecuteMonCallback.
-        if (list_output_names_func != nullptr) {
-          std::vector<std::string> names = list_output_names_func(node->attrs);
-          out_data_name += "_" + names[quantized_op_idx];
+    } else if (need_calib_output_map.count(node->op())) {
+      const auto calib_idx = need_calib_output_map[node->op()](node->attrs);
+      for (const auto& idx : calib_idx) {
+        if (reverse_mirror_map.count(node)) {
+          calib_nodes.push_back(
+              common::GetOutputName({reverse_mirror_map[node], static_cast<uint32_t>(idx), 0}));
         } else {
-          out_data_name += "_" + std::to_string(quantized_op_idx);
+          calib_nodes.push_back(common::GetOutputName({node, static_cast<uint32_t>(idx), 0}));
         }
       }
+    }
+  });
+  ret.attrs["calib_nodes"] = std::make_shared<dmlc::any>(std::move(calib_nodes));
+  return ret;
+}
 
-      const auto calib_table_iter = calib_table.find(out_data_name);
-      if (calib_table_iter != calib_table.end()) {
-        node->attrs.dict["min_calib_range"] = std::to_string(calib_table_iter->second.first);
-        node->attrs.dict["max_calib_range"] = std::to_string(calib_table_iter->second.second);
-        node->op()->attr_parser(&(node->attrs));
-      }
+static inline void SetCalibTableForEntry(
+    const NodeEntry& e, const NodePtr& node,
+    const std::unordered_map<std::string, std::pair<float, float>>& calib_table) {
+  std::string out_data_name = common::GetOutputName(e);
+  const std::string prefix = "quantized_";
+  if (e.node->attrs.name.rfind(prefix, 0) == 0) {
+    out_data_name = out_data_name.substr(prefix.size());
+  }
+  const auto calib_table_iter = calib_table.find(out_data_name);
+  if (calib_table_iter != calib_table.end()) {
+    std::cout << "Set calibration result to " << node->attrs.name
+              << " : min=" << calib_table_iter->second.first
+              << " max=" << calib_table_iter->second.second << std::endl;
+    node->attrs.dict["min_calib_range"] = std::to_string(calib_table_iter->second.first);
+    node->attrs.dict["max_calib_range"] = std::to_string(calib_table_iter->second.second);
+    if (node->op() && node->op()->attr_parser) node->op()->attr_parser(&(node->attrs));
+  } else {
+    std::cout << "Can't find calibration result for " << node->attrs.name << std::endl;
+  }
+}
+
+Graph SetCalibTableToQuantizedGraph(Graph&& g) {
+  const auto& calib_table =
+      g.GetAttr<std::unordered_map<std::string, std::pair<float, float>>>("calib_table");
+  static const auto& need_calib_input_map =
+      Op::GetAttr<mxnet::FNeedCalibrateInput>("FNeedCalibrateInput");
+  static const auto& need_calib_output_map =
+      Op::GetAttr<mxnet::FNeedCalibrateOutput>("FNeedCalibrateOutput");
+  std::cout << "Set calibration result to quantized symbol." << std::endl;
+  DFSVisit(g.outputs, [&](const NodePtr& node) {
+    if (need_calib_input_map.count(node->op())) {
+      const auto calib_idx = need_calib_input_map[node->op()](node->attrs);
+      CHECK_EQ(calib_idx.size(), 1);
+      const auto& idx = calib_idx[0];
+      SetCalibTableForEntry(node->inputs[idx], node, calib_table);
+    } else if (need_calib_output_map.count(node->op())) {
+      const auto calib_idx = need_calib_output_map[node->op()](node->attrs);
+      CHECK_EQ(calib_idx.size(), 1);
+      const auto& idx = calib_idx[0];
+      SetCalibTableForEntry({node, static_cast<uint32_t>(idx), 0}, node, calib_table);
     }
   });
   return g;
@@ -477,6 +555,7 @@ Graph SetCalibTableToQuantizedGraph(Graph&& g) {
 NNVM_REGISTER_PASS(QuantizeGraph)
 .describe("")
 .set_body(QuantizeGraph)
+.provide_graph_attr("calib_nodes")
 .set_change_graph(true);
 
 NNVM_REGISTER_PASS(SetCalibTableToQuantizedGraph)
diff --git a/src/operator/quantization/quantize_v2.cc b/src/operator/quantization/quantize_v2.cc
index e9017a58a82c..9a30386723be 100644
--- a/src/operator/quantization/quantize_v2.cc
+++ b/src/operator/quantization/quantize_v2.cc
@@ -114,6 +114,9 @@ If min_calib_range isn't presented, the output type will be int8.
 .set_attr<nnvm::FInplaceIdentity>("FInplaceIdentity", [](const NodeAttrs& attrs){
   return std::vector<bool>{true};
 })
+.set_attr<FNeedCalibrateInput>("FNeedCalibrateInput", [](const NodeAttrs& attrs){
+  return std::vector<int>{0};
+})
 .set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& attrs) {
   const QuantizeV2Param &param = nnvm::get<QuantizeV2Param>(attrs.parsed);
   if (param.min_calib_range.has_value() && param.max_calib_range.has_value()) {
diff --git a/src/operator/quantization/quantized_batch_norm.cc b/src/operator/quantization/quantized_batch_norm.cc
index 3c46e1b8bd5c..285c34d3a67f 100644
--- a/src/operator/quantization/quantized_batch_norm.cc
+++ b/src/operator/quantization/quantized_batch_norm.cc
@@ -112,6 +112,9 @@ the float32 data into int8.
 .set_attr<nnvm::FInferType>("FInferType", QuantizedBatchNormType)
 .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
 .set_attr<FNeedRequantize>("FNeedRequantize", [](const NodeAttrs& attrs) { return false; })
+.set_attr<FNeedCalibrateInput>("FNeedCalibrateOutput", [](const NodeAttrs& attrs){
+  return std::vector<int>{0};
+})
 .add_argument("data", "NDArray-or-Symbol", "Input data.")
 .add_argument("gamma", "NDArray-or-Symbol", "gamma.")
 .add_argument("beta", "NDArray-or-Symbol", "beta.")
diff --git a/src/operator/quantization/quantized_conv.cc b/src/operator/quantization/quantized_conv.cc
index aa3f5ce1ad61..9d774ddf24f1 100644
--- a/src/operator/quantization/quantized_conv.cc
+++ b/src/operator/quantization/quantized_conv.cc
@@ -180,6 +180,9 @@ and max thresholds representing the threholds for quantizing the float32 output
 .add_arguments(ConvolutionParam::__FIELDS__());
 
 NNVM_REGISTER_OP(Convolution)
+.set_attr<FQuantizable>("FQuantizable", [](const NodeAttrs& attrs) {
+    return QuantizeType::kMust;
+})
 .set_attr<FQuantizedOp>("FQuantizedOp", [](const NodeAttrs& attrs) {
     nnvm::NodePtr node = nnvm::Node::Create();
     node->attrs.op = Op::Get("_contrib_quantized_conv");
diff --git a/src/operator/quantization/quantized_fully_connected.cc b/src/operator/quantization/quantized_fully_connected.cc
index 23790ca78b3d..4c9d9d2f8095 100644
--- a/src/operator/quantization/quantized_fully_connected.cc
+++ b/src/operator/quantization/quantized_fully_connected.cc
@@ -361,6 +361,9 @@ and max thresholds representing the threholds for quantizing the float32 output
 .add_arguments(FullyConnectedParam::__FIELDS__());
 
 NNVM_REGISTER_OP(FullyConnected)
+.set_attr<FQuantizable>("FQuantizable", [](const NodeAttrs& attrs) {
+    return QuantizeType::kMust;
+})
 .set_attr<FQuantizedOp>("FQuantizedOp", [](const NodeAttrs& attrs) {
     nnvm::NodePtr node = nnvm::Node::Create();
     node->attrs.op = Op::Get("_contrib_quantized_fully_connected");
diff --git a/src/operator/quantization/requantize.cc b/src/operator/quantization/requantize.cc
index 43682383b0d6..9ee299cf4ae9 100644
--- a/src/operator/quantization/requantize.cc
+++ b/src/operator/quantization/requantize.cc
@@ -77,6 +77,9 @@ inference accuracy.
 #else
 .set_attr<FCompute>("FCompute<cpu>", RequantizeForward<cpu>)
 #endif
+.set_attr<FNeedCalibrateInput>("FNeedCalibrateInput", [](const NodeAttrs& attrs){
+  return std::vector<int>{0};
+})
 .set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& attrs) {
     const RequantizeParam& param =
       nnvm::get<RequantizeParam>(attrs.parsed);
diff --git a/src/operator/subgraph/mkldnn/mkldnn_conv.cc b/src/operator/subgraph/mkldnn/mkldnn_conv.cc
index be2716f139ed..d9bfa02b8820 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_conv.cc
+++ b/src/operator/subgraph/mkldnn/mkldnn_conv.cc
@@ -789,6 +789,9 @@ NNVM_REGISTER_OP(_sg_mkldnn_conv)
                                 DefaultSubgraphOpMutableInputs)
 .set_attr<std::string>("key_var_num_args", "num_args")
 .set_attr<nnvm::FInplaceOption>("FInplaceOption", SgMKLDNNConvInplaceOption)
+.set_attr<FQuantizable>("FQuantizable", [](const NodeAttrs& attrs) {
+    return QuantizeType::kMust;
+})
 .set_attr<FQuantizedOp>("FQuantizedOp", SgMKLDNNConvQuantizedOp)
 .set_attr<FNeedRequantize>("FNeedRequantize", [](const NodeAttrs& attrs) { return true; })
 .set_attr<FAvoidQuantizeInput>("FAvoidQuantizeInput", SgMKLDNNAvoidQuantizeInput);
diff --git a/src/operator/subgraph/mkldnn/mkldnn_fc.cc b/src/operator/subgraph/mkldnn/mkldnn_fc.cc
index 6ebbb79d2571..a4f0bfa32ade 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_fc.cc
+++ b/src/operator/subgraph/mkldnn/mkldnn_fc.cc
@@ -457,6 +457,9 @@ NNVM_REGISTER_OP(_sg_mkldnn_fully_connected)
 .set_attr<nnvm::FMutateInputs>("FMutateInputs",
                                DefaultSubgraphOpMutableInputs)
 .set_attr<std::string>("key_var_num_args", "num_args")
+.set_attr<FQuantizable>("FQuantizable", [](const NodeAttrs& attrs) {
+    return QuantizeType::kMust;
+})
 .set_attr<FQuantizedOp>("FQuantizedOp", SgMKLDNNFCQuantizedOp)
 .set_attr<FNeedRequantize>("FNeedRequantize", [](const NodeAttrs& attrs) { return true; });
 
diff --git a/tests/python/mkl/test_subgraph.py b/tests/python/mkl/test_subgraph.py
index eba77f2c8710..00e2e824c6f5 100644
--- a/tests/python/mkl/test_subgraph.py
+++ b/tests/python/mkl/test_subgraph.py
@@ -167,9 +167,9 @@ def check_quantize(sym, data_shape, out_type, name='conv',
                                                                    quantized_dtype=out_type,
                                                                    calib_mode='naive',
                                                                    calib_data=calib_data,
-                                                                   calib_layer=None,
                                                                    label_names=None,
-                                                                   num_calib_examples=1)
+                                                                   num_calib_examples=1,
+                                                                   quantize_mode='full')
   qsym = qsym.get_backend_symbol(QUANTIZE_SG_PASS_NAME)
   if check_calibration:
     check_qsym_calibrated(qsym, out_type, name=name)
@@ -218,7 +218,6 @@ def check_quantize_whole_model(out_type):
     calib_data = mx.nd.random.uniform(shape=data_shape)
     calib_data = mx.io.NDArrayIter(data=calib_data)
     calib_data = DummyIter(calib_data)
-    calib_layer = lambda name: name.endswith('_output')
     qsym, qarg_params, qaux_params = mx.contrib.quant.quantize_model(sym=sym_sg,
                                                                      arg_params=arg_params,
                                                                      aux_params=aux_params,
@@ -227,9 +226,9 @@ def check_quantize_whole_model(out_type):
                                                                      quantized_dtype=out_type,
                                                                      calib_mode='naive',
                                                                      calib_data=calib_data,
-                                                                     calib_layer=calib_layer,
                                                                      label_names=None,
-                                                                     num_calib_examples=1)
+                                                                     num_calib_examples=1,
+                                                                     quantize_mode='full')
     qsym = qsym.get_backend_symbol('MKLDNN_QUANTIZE')
     check_qsym_forward(qsym, qarg_params, qaux_params, data_shape)
 
diff --git a/tests/python/quantization/test_quantization.py b/tests/python/quantization/test_quantization.py
index 31bc1638b010..d753b514302e 100644
--- a/tests/python/quantization/test_quantization.py
+++ b/tests/python/quantization/test_quantization.py
@@ -80,7 +80,7 @@ def baseline_dequantization(qdata, real_range, qdata_np):
     def test_nd_array_dequantization(qdata, min_range, max_range, expected_result):
         data = mx.nd.contrib.dequantize(qdata, min_range, max_range, out_type='float32')
         assert data.dtype == np.float32
-        assert_almost_equal(data.asnumpy(), expected_result)
+        assert_almost_equal(data.asnumpy(), expected_result, atol = 1)
 
     def test_symbolic_api_dequantization(qdata, min_range, max_range, expected_result):
         sym_data = mx.sym.Variable('data')
@@ -92,9 +92,9 @@ def test_symbolic_api_dequantization(qdata, min_range, max_range, expected_resul
                            args={'data':qdata, 'min_range':min_range, 'max_range':max_range})
         data = out.forward()[0]
         assert data.dtype == np.float32
-        assert_almost_equal(data.asnumpy(), expected_result)
+        assert_almost_equal(data.asnumpy(), expected_result, atol = 1)
 
-    real_range = 402.3347
+    real_range = 128
     shape = rand_shape_nd(4)
     qdata_np = np.random.uniform(low=-127, high=127, size=shape).astype(dtype=np.int8)
     qdata, min_range, max_range = get_test_data(real_range, qdata_np)
@@ -691,7 +691,8 @@ def test_quantize_params():
     params = {}
     for name in offline_params:
         params[name] = mx.nd.uniform(shape=(2, 2))
-    qsym = mx.contrib.quant._quantize_symbol(sym, ctx=mx.current_context(), offline_params=offline_params)
+    qsym, _ = mx.contrib.quant._quantize_symbol(sym, ctx=mx.current_context(),
+                                                offline_params=offline_params, quantize_mode='full')
     qparams = mx.contrib.quant._quantize_params(qsym, params, th_dict = {})
     param_names = params.keys()
     qparam_names = qparams.keys()
@@ -807,7 +808,8 @@ def check_qsym_qdtype(qsym, qdtype):
                                                                              aux_params=aux_params,
                                                                              ctx=mx.current_context(),
                                                                              quantized_dtype=qdtype,
-                                                                             calib_mode='none')
+                                                                             calib_mode='none',
+                                                                             quantize_mode='full')
             check_params(arg_params, qarg_params, qsym)
             check_params(aux_params, qaux_params)
 
@@ -821,7 +823,8 @@ def check_qsym_qdtype(qsym, qdtype):
                                                                              quantized_dtype=qdtype,
                                                                              calib_mode='naive',
                                                                              calib_data=calib_data,
-                                                                             num_calib_examples=20)
+                                                                             num_calib_examples=20,
+                                                                             quantize_mode='full')
             check_params(arg_params, qarg_params, qsym)
             check_params(aux_params, qaux_params)
             check_qsym_calibrated(qsym)
@@ -963,7 +966,8 @@ def check_qsym_forward(qsym, qarg_params, qaux_params, data_shape, label_shape=N
                                                                              excluded_op_names=excluded_op_names,
                                                                              ctx=mx.current_context(),
                                                                              quantized_dtype=qdtype,
-                                                                             calib_mode='none')
+                                                                             calib_mode='none',
+                                                                             quantize_mode='full')
             check_params(arg_params, qarg_params, qsym)
             check_params(aux_params, qaux_params)
             check_qsym_forward(qsym, qarg_params, qaux_params, dshape, lshape)
@@ -980,7 +984,8 @@ def check_qsym_forward(qsym, qarg_params, qaux_params, data_shape, label_shape=N
                                                                              quantized_dtype=qdtype,
                                                                              calib_mode='naive',
                                                                              calib_data=calib_data,
-                                                                             num_calib_examples=20)
+                                                                             num_calib_examples=20,
+                                                                             quantize_mode='full')
             check_params(arg_params, qarg_params, qsym)
             check_params(aux_params, qaux_params)
             check_qsym_calibrated(qsym)
@@ -1047,7 +1052,8 @@ def test_quantize_sym_with_calib():
     sym = get_fp32_sym()
     offline_params = [name for name in sym.list_arguments()
                       if not name.startswith('data') and not name.endswith('label')]
-    qsym = mx.contrib.quant._quantize_symbol(sym, ctx=mx.current_context(), offline_params=offline_params)
+    qsym, _ = mx.contrib.quant._quantize_symbol(sym, ctx=mx.current_context(),
+                                             offline_params=offline_params, quantize_mode='full')
     requantize_op_names = ['requantize_conv', 'requantize_fc']
     th_dict = {'conv_output': (np.random.uniform(low=100.0, high=200.0), np.random.uniform(low=100.0, high=200.0)),
                'fc_output': (np.random.uniform(low=100.0, high=200.0), np.random.uniform(low=100.0, high=200.0))}
@@ -1080,11 +1086,22 @@ def test_optimal_threshold_adversarial_case():
     # The worst case for the optimal_threshold function is when the values are concentrated
     # at one edge: [0, 0, ..., 1000]. (histogram)
     # We want to make sure that the optimal threshold in this case is the max.
-    arr = np.array([2] * 1000)
+    hist = []
+    hist_edges = []
+    min_val = -2
+    max_val = 2
+    for i in range(0, 998):
+        hist.append(0)
+    for i in range(0, 999):
+        hist_edges.append((max_val - min_val) / 999 * i + min_val)
+    hist.append(1000)
+    hist_edges.append(max_val)
+    hist_data = (hist, hist_edges, min_val, max_val, max_val)
     for dtype in ['uint8', 'int8', 'auto']:
-        res = mx.contrib.quant._get_optimal_threshold(arr, dtype, num_quantized_bins=5)
+        res = mx.contrib.quant._get_optimal_threshold(hist_data, dtype, num_quantized_bins=5)
         # The threshold should be 2.
-        assert res[3] - 2 < 1e-5
+        print (res)
+        assert abs(res[2] - 2) < 1e-5
 
 
 @with_seed()
@@ -1097,9 +1114,15 @@ def get_threshold(nd):
         return mx.nd.maximum(mx.nd.abs(min_nd), mx.nd.abs(max_nd)).asnumpy()
 
     for dtype in ['uint8', 'int8', 'auto']:
-        nd_dict = {'layer1': mx.nd.uniform(low=-10.532, high=11.3432, shape=(8, 3, 23, 23), dtype=np.float64)}
-        expected_threshold = get_threshold(nd_dict['layer1'])
-        th_dict = mx.contrib.quant._get_optimal_thresholds(nd_dict, dtype)
+        nd = mx.nd.uniform(low=-10.532, high=11.3432, shape=(8, 3, 23, 23), dtype=np.float64)
+        expected_threshold = get_threshold(nd)
+        arr = nd.asnumpy()
+        min_range = np.min(arr)
+        max_range = np.max(arr)
+        th = max(abs(min_range), abs(max_range))
+        hist, hist_edges = np.histogram(arr, bins=8001, range=(-th, th))
+        hist_dict = {'layer1' : (hist, hist_edges, min_range, max_range, th)}
+        th_dict = mx.contrib.quant._get_optimal_thresholds(hist_dict, dtype)
         assert 'layer1' in th_dict
         assert_almost_equal(np.array([th_dict['layer1'][1]]), expected_threshold, rtol=1e-2, atol=1e-4)
 
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index ceee51a3e503..f864003f7c60 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -5571,9 +5571,10 @@ def test_quantization_op():
 
     qa_real = mx.nd.array([[18, 75], [77, 109]])
     a_real  = mx.nd.array([[0.14173228, 0.5905512], [0.6062992, 0.8582677]])
-
+    print(a_.asnumpy())
+    print(a_real.asnumpy())
     assert same(qa.asnumpy(), qa_real.asnumpy())
-    assert same(a_.asnumpy(),  a_real.asnumpy())
+    assert_almost_equal(a_.asnumpy(),  a_real.asnumpy(), rtol=1e-2)
 
 @with_seed()
 def test_index_copy():

From 2d86c7088d4cf2b485227a6b87dd9f84f7797ba5 Mon Sep 17 00:00:00 2001
From: reminisce <wujun.nju@gmail.com>
Date: Wed, 28 Aug 2019 23:31:12 -0700
Subject: [PATCH 233/813] Port ops from np branch (#16018)

* Add maximum, minimum, swapaxes, argmax, clip in python

* Add backend

* Fix pylint

* Add unit test decorators back

* Fix gpu compile

* Add np.random.normal and npx.seed

* Expose seed through npx.random

* Add rtol atol in seed testing
---
 python/mxnet/ndarray/numpy/_op.py             | 187 +++++++++++++-
 python/mxnet/ndarray/numpy/random.py          |  52 +++-
 python/mxnet/numpy/multiarray.py              | 187 +++++++++++++-
 python/mxnet/numpy/random.py                  |  38 ++-
 python/mxnet/numpy_extension/__init__.py      |   1 +
 python/mxnet/numpy_extension/random.py        |  74 ++++++
 python/mxnet/symbol/numpy/_symbol.py          | 131 +++++++++-
 python/mxnet/symbol/numpy/random.py           |  52 +++-
 .../numpy/np_broadcast_reduce_op_index.cc     |  61 +++++
 .../numpy/np_broadcast_reduce_op_index.cu     |  34 +++
 .../numpy/np_elemwise_broadcast_op.cc         |  74 ------
 .../numpy/np_elemwise_broadcast_op.cu         |  12 -
 .../tensor/elemwise_binary_broadcast_op.h     |   4 +
 .../elemwise_binary_broadcast_op_extended.cc  |   2 +
 .../elemwise_binary_scalar_op_extended.cc     |   6 +-
 tests/python/unittest/test_numpy_op.py        | 239 ++++++++++++++++++
 16 files changed, 1050 insertions(+), 104 deletions(-)
 create mode 100644 python/mxnet/numpy_extension/random.py
 create mode 100644 src/operator/numpy/np_broadcast_reduce_op_index.cc
 create mode 100644 src/operator/numpy/np_broadcast_reduce_op_index.cu

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index f0785a76818e..37f48f70c336 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -32,7 +32,8 @@
            'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2', 'log1p',
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
-           'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack']
+           'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack',
+           'maximum', 'minimum', 'swapaxes', 'clip', 'argmax']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -1960,3 +1961,187 @@ def get_list(arrays):
 
     arrays = get_list(arrays)
     return _npi.stack(*arrays, axis=axis, out=out)
+
+
+@set_module('mxnet.ndarray.numpy')
+def maximum(x1, x2, out=None):
+    """Returns element-wise maximum of the input arrays with broadcasting.
+
+    Parameters
+    ----------
+    x1, x2 : scalar or mxnet.numpy.ndarray
+        The arrays holding the elements to be compared. They must have the same shape,
+        or shapes that can be broadcast to a single shape.
+
+    Returns
+    -------
+    out : mxnet.numpy.ndarray or scalar
+        The maximum of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars."""
+    return _ufunc_helper(x1, x2, _npi.maximum, _np.maximum, _npi.maximum_scalar, None, out)
+
+
+@set_module('mxnet.ndarray.numpy')
+def minimum(x1, x2, out=None):
+    """Returns element-wise minimum of the input arrays with broadcasting.
+
+    Parameters
+    ----------
+    x1, x2 : scalar or mxnet.numpy.ndarray
+        The arrays holding the elements to be compared. They must have the same shape,
+        or shapes that can be broadcast to a single shape.
+
+    Returns
+    -------
+    out : mxnet.numpy.ndarray or scalar
+        The minimum of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars."""
+    return _ufunc_helper(x1, x2, _npi.minimum, _np.minimum, _npi.minimum_scalar, None, out)
+
+
+@set_module('mxnet.ndarray.numpy')
+def swapaxes(a, axis1, axis2):
+    """Interchange two axes of an array.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input array.
+    axis1 : int
+        First axis.
+    axis2 : int
+        Second axis.
+
+    Returns
+    -------
+    a_swapped : ndarray
+        Swapped array. This is always a copy of the input array.
+    """
+    return _npi.swapaxes(a, dim1=axis1, dim2=axis2)
+
+
+@set_module('mxnet.ndarray.numpy')
+def clip(a, a_min, a_max, out=None):
+    """clip(a, a_min, a_max, out=None)
+
+    Clip (limit) the values in an array.
+    Given an interval, values outside the interval are clipped to
+    the interval edges.  For example, if an interval of ``[0, 1]``
+    is specified, values smaller than 0 become 0, and values larger
+    than 1 become 1.
+
+    Parameters
+    ----------
+    a : ndarray
+        Array containing elements to clip.
+    a_min : scalar or `None`
+        Minimum value. If `None`, clipping is not performed on lower
+        interval edge. Not more than one of `a_min` and `a_max` may be
+        `None`.
+    a_max : scalar or `None`
+        Maximum value. If `None`, clipping is not performed on upper
+        interval edge. Not more than one of `a_min` and `a_max` may be
+        `None`.
+    out : ndarray, optional
+        The results will be placed in this array. It may be the input
+        array for in-place clipping.  `out` must be of the right shape
+        to hold the output.  Its type is preserved.
+
+    Returns
+    -------
+    clipped_array : ndarray
+        An array with the elements of `a`, but where values
+        < `a_min` are replaced with `a_min`, and those > `a_max`
+        with `a_max`.
+
+    Notes
+    -----
+    array_like `a_min` and `a_max` are not supported.
+
+    Examples
+    --------
+    >>> a = np.arange(10)
+    >>> np.clip(a, 1, 8)
+    array([1., 1., 2., 3., 4., 5., 6., 7., 8., 8.], dtype=float32)
+    >>> a
+    array([0., 1., 2., 3., 4., 5., 6., 7., 8., 9.], dtype=float32)
+    >>> np.clip(a, 3, 6, out=a)
+    array([3., 3., 3., 3., 4., 5., 6., 6., 6., 6.], dtype=float32)
+    """
+    if a_min is None and a_max is None:
+        raise ValueError('array_clip: must set either max or min')
+    if a_min is None:
+        a_min = float('-inf')
+    if a_max is None:
+        a_max = float('inf')
+    return _npi.clip(a, a_min, a_max, out=out)
+
+
+@set_module('mxnet.ndarray.numpy')
+def argmax(a, axis=None, out=None):
+    r"""
+    argmax(a, axis=None, out=None)
+
+    Returns the indices of the maximum values along an axis.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input array. Only support ndarrays of dtype `float16`, `float32`, and `float64`.
+    axis : int, optional
+        By default, the index is into the flattened array, otherwise
+        along the specified axis.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+        If provided, it must have the same shape and dtype as input ndarray.
+        If not provided or `None`, a freshly-allocated array is returned.
+
+    Returns
+    -------
+    index_array : ndarray of indices whose dtype is same as the input ndarray.
+        Array of indices into the array. It has the same shape as `a.shape`
+        with the dimension along `axis` removed.
+
+    Notes
+    -----
+    In case of multiple occurrences of the maximum values, the indices
+    corresponding to the first occurrence are returned.
+
+    This function differs from the original `numpy.argmax
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmax.html>`_ in
+    the following aspects:
+
+    - Input type does not support Python native iterables(list, tuple, ...).
+    - Output has dtype that is same as the input ndarray.
+    - ``out`` param: cannot perform auto broadcasting. ``out`` ndarray's shape must be the same as the expected output.
+    - ``out`` param: cannot perform auto type cast. ``out`` ndarray's dtype must be the same as the expected output.
+    - ``out`` param does not support scalar input case.
+
+    Examples
+    --------
+    >>> a = np.arange(6).reshape(2,3) + 10
+    >>> a
+    array([[10., 11., 12.],
+           [13., 14., 15.]])
+    >>> np.argmax(a)
+    array(5.)
+    >>> np.argmax(a, axis=0)
+    array([1., 1., 1.])
+    >>> np.argmax(a, axis=1)
+    array([2., 2.])
+
+    >>> b = np.arange(6)
+    >>> b[1] = 5
+    >>> b
+    array([0., 5., 2., 3., 4., 5.])
+    >>> np.argmax(b)  # Only the first occurrence is returned.
+    array(1.)
+
+    Specify ``out`` ndarray:
+
+    >>> a = np.arange(6).reshape(2,3) + 10
+    >>> b = np.zeros((2,))
+    >>> np.argmax(a, axis=1, out=b)
+    array([2., 2.])
+    >>> b
+    array([2., 2.])
+    """
+    return _npi.argmax(a, axis=axis, keepdims=False, out=out)
diff --git a/python/mxnet/ndarray/numpy/random.py b/python/mxnet/ndarray/numpy/random.py
index 71707d41c8e8..d892ccdaca73 100644
--- a/python/mxnet/ndarray/numpy/random.py
+++ b/python/mxnet/ndarray/numpy/random.py
@@ -19,9 +19,10 @@
 from __future__ import absolute_import
 from ...context import current_context
 from . import _internal as _npi
+from ...base import numeric_types
 
 
-__all__ = ['randint', 'uniform']
+__all__ = ['randint', 'uniform', 'normal']
 
 
 def randint(low, high=None, size=None, dtype=None, **kwargs):
@@ -141,5 +142,50 @@ def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
         return _npi.uniform(low=low, high=high, size=size,
                             ctx=ctx, dtype=dtype, out=out)
 
-    raise ValueError(
-        "Distribution parameters must be either mxnet.numpy.ndarray or numbers")
+
+def normal(loc=0.0, scale=1.0, size=None, **kwargs):
+    """Draw random samples from a normal (Gaussian) distribution.
+
+    Samples are distributed according to a normal distribution parametrized
+    by *loc* (mean) and *scale* (standard deviation).
+
+
+    Parameters
+    ----------
+    loc : float, optional
+        Mean (centre) of the distribution.
+    scale : float, optional
+        Standard deviation (spread or "width") of the distribution.
+    size : int or tuple of ints, optional
+        Output shape. If the given shape is, e.g., `(m, n, k)`, then `m * n * k`
+        samples are drawn. If size is `None` (default), a scalar tensor containing
+        a single value is returned if loc and scale are both scalars.
+    dtype : {'float16', 'float32', 'float64'}, optional
+        Data type of output samples. Default is 'float32'
+    ctx : Context, optional
+        Device context of output. Default is current context.
+    out : ``ndarray``, optional
+        Store output to an existing ``ndarray``.
+
+    Returns
+    -------
+    out : ndarray
+        Drawn samples from the parameterized normal distribution.
+
+    Notes
+    -----
+    This function currently does not support ``loc`` and ``scale`` as ndarrays.
+    """
+    dtype = kwargs.pop('dtype', None)
+    if dtype is None:
+        dtype = 'float32'
+    ctx = kwargs.pop('ctx', None)
+    if ctx is None:
+        ctx = current_context()
+    out = kwargs.pop('out', None)
+    if size is None and out is None:
+        size = ()
+    if (not isinstance(loc, numeric_types)) or (not isinstance(scale, numeric_types)):
+        raise NotImplementedError('np.random.normal only supports loc and scale of '
+                                  'numeric types for now')
+    return _npi.random_normal(loc, scale, shape=size, dtype=dtype, ctx=ctx, out=out, **kwargs)
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 5e7129226e34..749d560d58d7 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -52,7 +52,7 @@
            'degrees', 'log2', 'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative',
            'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh',
            'tensordot', 'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate',
-           'stack']
+           'stack', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -751,7 +751,9 @@ def asscalar(self):
         raise AttributeError('mxnet.numpy.ndarray object has no attribute asscalar')
 
     def argmax(self, axis=None, out=None):  # pylint: disable=arguments-differ
-        raise NotImplementedError
+        """Return indices of the maximum values along the given axis.
+        Refer to `mxnet.numpy.argmax` for full documentation."""
+        return argmax(self, axis, out)
 
     def as_in_context(self, context):
         """Returns an array on the target device with the same value as this array.
@@ -864,7 +866,7 @@ def swapaxes(self, axis1, axis2):  # pylint: disable=arguments-differ
         """Return a copy of the array with axis1 and axis2 interchanged.
         Refer to `mxnet.numpy.swapaxes` for full documentation.
         """
-        raise NotImplementedError
+        return swapaxes(self, axis1, axis2)
 
     def split(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`split`.
@@ -1050,7 +1052,7 @@ def clip(self, min=None, max=None, out=None):  # pylint: disable=arguments-diffe
         """Return an array whose values are limited to [min, max].
         One of max or min must be given.
         """
-        raise NotImplementedError
+        return clip(self, min, max, out=out)
 
     def abs(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`abs`.
@@ -3407,3 +3409,180 @@ def stack(arrays, axis=0, out=None):
     stacked : ndarray
         The stacked array has one more dimension than the input arrays."""
     return _mx_nd_np.stack(arrays, axis=axis, out=out)
+
+
+@set_module('mxnet.numpy')
+def maximum(x1, x2, out=None):
+    """Returns element-wise maximum of the input arrays with broadcasting.
+
+    Parameters
+    ----------
+    x1, x2 : scalar or mxnet.numpy.ndarray
+        The arrays holding the elements to be compared. They must have the same shape,
+        or shapes that can be broadcast to a single shape.
+
+    Returns
+    -------
+    out : mxnet.numpy.ndarray or scalar
+        The maximum of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars."""
+    return _mx_nd_np.maximum(x1, x2, out=out)
+
+
+@set_module('mxnet.numpy')
+def minimum(x1, x2, out=None):
+    """Returns element-wise minimum of the input arrays with broadcasting.
+
+    Parameters
+    ----------
+    x1, x2 : scalar or mxnet.numpy.ndarray
+        The arrays holding the elements to be compared. They must have the same shape,
+        or shapes that can be broadcast to a single shape.
+
+    Returns
+    -------
+    out : mxnet.numpy.ndarray or scalar
+        The minimum of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars."""
+    return _mx_nd_np.minimum(x1, x2, out=out)
+
+
+@set_module('mxnet.numpy')
+def swapaxes(a, axis1, axis2):
+    """Interchange two axes of an array.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input array.
+    axis1 : int
+        First axis.
+    axis2 : int
+        Second axis.
+
+    Returns
+    -------
+    a_swapped : ndarray
+        Swapped array. This is always a copy of the input array.
+    """
+    return _npi.swapaxes(a, dim1=axis1, dim2=axis2)
+
+
+@set_module('mxnet.numpy')
+def clip(a, a_min, a_max, out=None):
+    """clip(a, a_min, a_max, out=None)
+
+    Clip (limit) the values in an array.
+    Given an interval, values outside the interval are clipped to
+    the interval edges.  For example, if an interval of ``[0, 1]``
+    is specified, values smaller than 0 become 0, and values larger
+    than 1 become 1.
+
+    Parameters
+    ----------
+    a : ndarray
+        Array containing elements to clip.
+    a_min : scalar or `None`
+        Minimum value. If `None`, clipping is not performed on lower
+        interval edge. Not more than one of `a_min` and `a_max` may be
+        `None`.
+    a_max : scalar or `None`
+        Maximum value. If `None`, clipping is not performed on upper
+        interval edge. Not more than one of `a_min` and `a_max` may be
+        `None`.
+    out : ndarray, optional
+        The results will be placed in this array. It may be the input
+        array for in-place clipping.  `out` must be of the right shape
+        to hold the output.  Its type is preserved.
+
+    Returns
+    -------
+    clipped_array : ndarray
+        An array with the elements of `a`, but where values
+        < `a_min` are replaced with `a_min`, and those > `a_max`
+        with `a_max`.
+
+    Notes
+    -----
+    array_like `a_min` and `a_max` are not supported.
+
+    Examples
+    --------
+    >>> a = np.arange(10)
+    >>> np.clip(a, 1, 8)
+    array([1., 1., 2., 3., 4., 5., 6., 7., 8., 8.], dtype=float32)
+    >>> a
+    array([0., 1., 2., 3., 4., 5., 6., 7., 8., 9.], dtype=float32)
+    >>> np.clip(a, 3, 6, out=a)
+    array([3., 3., 3., 3., 4., 5., 6., 6., 6., 6.], dtype=float32)
+    """
+    return _mx_nd_np.clip(a, a_min, a_max, out=out)
+
+
+@set_module('mxnet.numpy')
+def argmax(a, axis=None, out=None):
+    r"""
+    argmax(a, axis=None, out=None)
+
+    Returns the indices of the maximum values along an axis.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input array. Only support ndarrays of dtype `float16`, `float32`, and `float64`.
+    axis : int, optional
+        By default, the index is into the flattened array, otherwise
+        along the specified axis.
+    out : ndarray or None, optional
+        If provided, the result will be inserted into this array. It should
+        be of the appropriate shape and dtype.
+
+    Returns
+    -------
+    index_array : ndarray of indices whose dtype is same as the input ndarray.
+        Array of indices into the array. It has the same shape as `a.shape`
+        with the dimension along `axis` removed.
+
+    Notes
+    -----
+    In case of multiple occurrences of the maximum values, the indices
+    corresponding to the first occurrence are returned.
+
+    This function differs from the original `numpy.argmax
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmax.html>`_ in
+    the following aspects:
+
+    - Input type does not support Python native iterables(list, tuple, ...).
+    - Output has dtype that is same as the input ndarray.
+    - ``out`` param: cannot perform auto broadcasting. ``out`` ndarray's shape must be the same as the expected output.
+    - ``out`` param: cannot perform auto type cast. ``out`` ndarray's dtype must be the same as the expected output.
+    - ``out`` param does not support scalar input case.
+
+    Examples
+    --------
+    >>> a = np.arange(6).reshape(2,3) + 10
+    >>> a
+    array([[10., 11., 12.],
+           [13., 14., 15.]])
+    >>> np.argmax(a)
+    array(5.)
+    >>> np.argmax(a, axis=0)
+    array([1., 1., 1.])
+    >>> np.argmax(a, axis=1)
+    array([2., 2.])
+
+    >>> b = np.arange(6)
+    >>> b[1] = 5
+    >>> b
+    array([0., 5., 2., 3., 4., 5.])
+    >>> np.argmax(b)  # Only the first occurrence is returned.
+    array(1.)
+
+    Specify ``out`` ndarray:
+
+    >>> a = np.arange(6).reshape(2,3) + 10
+    >>> b = np.zeros((2,))
+    >>> np.argmax(a, axis=1, out=b)
+    array([2., 2.])
+    >>> b
+    array([2., 2.])
+    """
+    return _mx_nd_np.argmax(a, axis, out)
diff --git a/python/mxnet/numpy/random.py b/python/mxnet/numpy/random.py
index f0fd43eb0e70..dc6107476f81 100644
--- a/python/mxnet/numpy/random.py
+++ b/python/mxnet/numpy/random.py
@@ -21,7 +21,7 @@
 from ..ndarray import numpy as _mx_nd_np
 
 
-__all__ = ["randint", "uniform"]
+__all__ = ["randint", "uniform", "normal"]
 
 
 def randint(low, high=None, size=None, dtype=None, **kwargs):
@@ -108,3 +108,39 @@ def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
         Drawn samples from the parameterized uniform distribution.
     """
     return _mx_nd_np.random.uniform(low, high, size=size, ctx=ctx, dtype=dtype, out=out)
+
+
+def normal(loc=0.0, scale=1.0, size=None, **kwargs):
+    """Draw random samples from a normal (Gaussian) distribution.
+
+    Samples are distributed according to a normal distribution parametrized
+    by *loc* (mean) and *scale* (standard deviation).
+
+
+    Parameters
+    ----------
+    loc : float, optional
+        Mean (centre) of the distribution.
+    scale : float, optional
+        Standard deviation (spread or "width") of the distribution.
+    size : int or tuple of ints, optional
+        Output shape. If the given shape is, e.g., `(m, n, k)`, then `m * n * k`
+        samples are drawn. If size is `None` (default), a scalar tensor containing
+        a single value is returned if loc and scale are both scalars.
+    dtype : {'float16', 'float32', 'float64'}, optional
+        Data type of output samples. Default is 'float32'
+    ctx : Context, optional
+        Device context of output. Default is current context.
+    out : ``ndarray``, optional
+        Store output to an existing ``ndarray``.
+
+    Returns
+    -------
+    out : ndarray
+        Drawn samples from the parameterized normal distribution.
+
+    Notes
+    -----
+    This function currently does not support ``loc`` and ``scale`` as ndarrays.
+    """
+    return _mx_nd_np.random.normal(loc, scale, size, **kwargs)
diff --git a/python/mxnet/numpy_extension/__init__.py b/python/mxnet/numpy_extension/__init__.py
index d71d65f08de2..5a19d3dc4e2f 100644
--- a/python/mxnet/numpy_extension/__init__.py
+++ b/python/mxnet/numpy_extension/__init__.py
@@ -28,5 +28,6 @@
 from ..util import is_np_shape, is_np_array, set_np, reset_np
 from ..ndarray import waitall
 from .utils import *  # pylint: disable=wildcard-import
+from . import random  # pylint: disable=wildcard-import
 
 __all__ = []
diff --git a/python/mxnet/numpy_extension/random.py b/python/mxnet/numpy_extension/random.py
new file mode 100644
index 000000000000..5aa58a0cc69d
--- /dev/null
+++ b/python/mxnet/numpy_extension/random.py
@@ -0,0 +1,74 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Namespace for ops used in imperative programming."""
+
+from __future__ import absolute_import
+from .. import random as _mx_rand
+
+
+__all__ = ['seed']
+
+
+def seed(seed, ctx='all'):  # pylint: disable=redefined-outer-name
+    """Seeds the random number generators in MXNet.
+
+    This affects the behavior of modules in MXNet that uses random number generators,
+    like the dropout operator and `ndarray`'s random sampling operators.
+
+    Parameters
+    ----------
+    seed : int
+        The random number seed.
+
+    ctx : Context
+        The device context of the generator. The default is "all" which means seeding random
+        number generators of all devices.
+
+    Notes
+    -----
+    Random number generators in MXNet are device specific.
+    `npx.random.seed(seed)` sets the state of each generator using `seed` and the
+    device id. Therefore, random numbers generated from different devices can be different
+    even if they are seeded using the same seed.
+
+    To produce identical random number sequences independent of the device id,
+    set optional `ctx` argument. This produces the same sequence of random numbers independent
+    of the device id, but the sequence can be different on different kind of devices as MXNet's
+    random number generators for CPU and GPU use different algorithms.
+
+    Example
+    -------
+    >>> from mxnet import np, npx
+    >>> npx.set_np()
+    >>> npx.random.seed(0)
+    >>> np.random.uniform()
+    array(0.5488135)
+    >>> npx.random.seed(128)
+    >>> np.random.uniform()
+    array(0.03812965)
+    >>> npx.random.seed(128)
+    >>> np.random.uniform()
+    array(0.03812965)
+    >>> npx.random.seed(128)
+    >>> np.random.uniform(ctx=npx.gpu(0))
+    array(0.9894903, ctx=gpu(0))
+    >>> npx.random.seed(128)
+    >>> np.random.uniform(ctx=npx.gpu(0))
+    array(0.9894903, ctx=gpu(0))
+    """
+    _mx_rand.seed(seed_state=seed, ctx=ctx)
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index ff20cabdb748..b1d36c57c048 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -34,7 +34,8 @@
            'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2', 'log1p',
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
-           'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack']
+           'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack',
+           'maximum', 'minimum', 'swapaxes', 'clip', 'argmax']
 
 
 def _num_outputs(sym):
@@ -248,7 +249,9 @@ def reshape(self, *args, **kwargs):  # pylint: disable=arguments-differ
             return _mx_np_op.reshape(self, newshape=args, order=order)
 
     def argmax(self, axis=None, out=None):  # pylint: disable=arguments-differ
-        raise NotImplementedError
+        """Return indices of the maximum values along the given axis.
+        Refer to `mxnet.numpy.argmax` for full documentation."""
+        return argmax(self, axis, out)
 
     def reshape_like(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`reshape_like`.
@@ -298,7 +301,7 @@ def swapaxes(self, axis1, axis2):  # pylint: disable=arguments-differ
         """Return a copy of the array with axis1 and axis2 interchanged.
         Refer to `mxnet.numpy.swapaxes` for full documentation.
         """
-        raise NotImplementedError
+        return swapaxes(self, axis1, axis2)
 
     def split(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`split`.
@@ -408,7 +411,7 @@ def clip(self, min=None, max=None, out=None):  # pylint: disable=arguments-diffe
         """Return an array whose values are limited to [min, max].
         One of max or min must be given.
         """
-        raise NotImplementedError
+        return clip(self, min, max, out=out)
 
     def abs(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`abs`.
@@ -2383,4 +2386,124 @@ def get_list(arrays):
     return _npi.stack(*arrays, axis=axis, out=out)
 
 
+@set_module('mxnet.symbol.numpy')
+def maximum(x1, x2, out=None):
+    return _ufunc_helper(x1, x2, _npi.maximum, _np.maximum, _npi.maximum_scalar, None, out)
+
+
+@set_module('mxnet.symbol.numpy')
+def minimum(x1, x2, out=None):
+    return _ufunc_helper(x1, x2, _npi.minimum, _np.minimum, _npi.minimum_scalar, None, out)
+
+
+@set_module('mxnet.symbol.numpy')
+def clip(a, a_min, a_max, out=None):
+    """clip(a, a_min, a_max, out=None)
+
+    Clip (limit) the values in an array.
+    Given an interval, values outside the interval are clipped to
+    the interval edges.  For example, if an interval of ``[0, 1]``
+    is specified, values smaller than 0 become 0, and values larger
+    than 1 become 1.
+
+    Parameters
+    ----------
+    a : _Symbol
+        Array containing elements to clip.
+    a_min : scalar or `None`
+        Minimum value. If `None`, clipping is not performed on lower
+        interval edge. Not more than one of `a_min` and `a_max` may be
+        `None`.
+    a_max : scalar or `None`
+        Maximum value. If `None`, clipping is not performed on upper
+        interval edge. Not more than one of `a_min` and `a_max` may be
+        `None`.
+    out : _Symbol or `None`
+        The results will be placed in this array. It may be the input
+        array for in-place clipping.  `out` must be of the right shape
+        to hold the output.  Its type is preserved.
+
+    Returns
+    -------
+    clipped_array : _Symbol
+        An array with the elements of `a`, but where values
+        < `a_min` are replaced with `a_min`, and those > `a_max`
+        with `a_max`.
+
+    Notes
+    -----
+    array_like `a_min` and `a_max` are not supported.
+    """
+    if a_min is None and a_max is None:
+        raise ValueError('array_clip: must set either max or min')
+    if a_min is None:
+        a_min = float('-inf')
+    if a_max is None:
+        a_max = float('inf')
+    return _npi.clip(a, a_min, a_max, out=out)
+
+
+@set_module('mxnet.symbol.numpy')
+def swapaxes(a, axis1, axis2):
+    """Interchange two axes of an array.
+
+    Parameters
+    ----------
+    a : _Symbol
+        Input array.
+    axis1 : int
+        First axis.
+    axis2 : int
+        Second axis.
+
+    Returns
+    -------
+    a_swapped : _Symbol
+        Swapped array symbol.
+    """
+    return _npi.swapaxes(a, dim1=axis1, dim2=axis2)
+
+
+@set_module('mxnet.symbol.numpy')
+def argmax(a, axis=None, out=None):
+    r"""
+    argmax(a, axis=None, out=None)
+
+    Returns the indices of the maximum values along an axis.
+
+    Parameters
+    ----------
+    a : _Symbol
+        Input array. Only support dtype `float16`, `float32`, and `float64`.
+    axis : int, optional
+        By default, the index is into the flattened array, otherwise
+        along the specified axis.
+    out : _Symbol or None, optional
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+
+    Returns
+    -------
+    index_array : _Symbol of indices whose dtype is same as the input ndarray.
+        Array of indices into the array. It has the same shape as `a.shape`
+        with the dimension along `axis` removed.
+
+    Notes
+    -----
+    In case of multiple occurrences of the maximum values, the indices
+    corresponding to the first occurrence are returned.
+
+    This function differs from the original `numpy.argmax
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmax.html>`_ in
+    the following aspects:
+
+    - Input type does not support Python native iterables(list, tuple, ...).
+    - Output has dtype that is same as the input ndarray.
+    - ``out`` param: cannot perform auto broadcasting. ``out`` symbol's shape must be the same as the expected output.
+    - ``out`` param: cannot perform auto type cast. ``out`` symnbol's dtype must be the same as the expected output.
+    - ``out`` param does not support scalar input case.
+
+    """
+    return _npi.argmax(a, axis=axis, keepdims=False, out=out)
+
+
 _set_np_symbol_class(_Symbol)
diff --git a/python/mxnet/symbol/numpy/random.py b/python/mxnet/symbol/numpy/random.py
index 86d0ba3095e1..c5b8e1dc4906 100644
--- a/python/mxnet/symbol/numpy/random.py
+++ b/python/mxnet/symbol/numpy/random.py
@@ -20,9 +20,10 @@
 from __future__ import absolute_import
 from ...context import current_context
 from . import _internal as _npi
+from ...base import numeric_types
 
 
-__all__ = ['randint', 'uniform']
+__all__ = ['randint', 'uniform', 'normal']
 
 
 def randint(low, high=None, size=None, dtype=None, **kwargs):
@@ -142,5 +143,50 @@ def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
         return _npi.uniform(low=low, high=high, size=size,
                             ctx=ctx, dtype=dtype, out=out)
 
-    raise ValueError(
-        "Distribution parameters must be either mxnet.numpy.ndarray or numbers")
+
+def normal(loc=0.0, scale=1.0, size=None, **kwargs):
+    """Draw random samples from a normal (Gaussian) distribution.
+
+    Samples are distributed according to a normal distribution parametrized
+    by *loc* (mean) and *scale* (standard deviation).
+
+
+    Parameters
+    ----------
+    loc : float, optional
+        Mean (centre) of the distribution.
+    scale : float, optional
+        Standard deviation (spread or "width") of the distribution.
+    size : int or tuple of ints, optional
+        Output shape. If the given shape is, e.g., `(m, n, k)`, then `m * n * k`
+        samples are drawn. If size is `None` (default), a scalar tensor containing
+        a single value is returned if loc and scale are both scalars.
+    dtype : {'float16', 'float32', 'float64'}, optional
+        Data type of output samples. Default is 'float32'
+    ctx : Context, optional
+        Device context of output. Default is current context.
+    out : ``ndarray``, optional
+        Store output to an existing ``ndarray``.
+
+    Returns
+    -------
+    out : _Symbol (symbol representing `mxnet.numpy.ndarray` in computational graphs)
+        Drawn samples from the parameterized normal distribution.
+
+    Notes
+    -----
+    This function currently does not support ``loc`` and ``scale`` as `_Symbol`s.
+    """
+    dtype = kwargs.pop('dtype', None)
+    if dtype is None:
+        dtype = 'float32'
+    ctx = kwargs.pop('ctx', None)
+    if ctx is None:
+        ctx = current_context()
+    out = kwargs.pop('out', None)
+    if size is None and out is None:
+        size = ()
+    if (not isinstance(loc, numeric_types)) or (not isinstance(scale, numeric_types)):
+        raise NotImplementedError('np.random.normal only supports loc and scale of '
+                                  'numeric types for now')
+    return _npi.random_normal(loc, scale, shape=size, dtype=dtype, ctx=ctx, out=out, **kwargs)
diff --git a/src/operator/numpy/np_broadcast_reduce_op_index.cc b/src/operator/numpy/np_broadcast_reduce_op_index.cc
new file mode 100644
index 000000000000..bd6915cc9b27
--- /dev/null
+++ b/src/operator/numpy/np_broadcast_reduce_op_index.cc
@@ -0,0 +1,61 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file np_broadcast_reduce_op_index.cc
+ * \brief CPU Implementation of broadcast and reduce functions based on index.
+ */
+#include "./np_broadcast_reduce_op.h"
+
+namespace mxnet {
+namespace op {
+
+bool NumpyReduceAxisShape(const nnvm::NodeAttrs& attrs,
+                          std::vector<TShape> *in_attrs,
+                          std::vector<TShape> *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  if (!shape_is_known(in_attrs->at(0))) {
+    return false;
+  }
+  const ReduceAxisParam& param = nnvm::get<ReduceAxisParam>(attrs.parsed);
+  dmlc::optional<mxnet::Tuple<int>> axes;
+  if (param.axis.has_value()) {
+    mxnet::Tuple<int> t({param.axis.value()});
+    axes = dmlc::optional<mxnet::Tuple<int>>(t);
+  }
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0,
+                     NumpyReduceAxesShapeImpl((*in_attrs)[0], axes, param.keepdims));
+  return shape_is_known(out_attrs->at(0));
+}
+
+NNVM_REGISTER_OP(_npi_argmax)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<ReduceAxisParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyReduceAxisShape)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
+.add_argument("data", "NDArray-or-Symbol", "The input")
+.set_attr<FCompute>("FCompute<cpu>", SearchAxisCompute<cpu, mshadow::red::maximum>)
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.add_arguments(ReduceAxisParam::__FIELDS__());
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_broadcast_reduce_op_index.cu b/src/operator/numpy/np_broadcast_reduce_op_index.cu
new file mode 100644
index 000000000000..a07baa9c070c
--- /dev/null
+++ b/src/operator/numpy/np_broadcast_reduce_op_index.cu
@@ -0,0 +1,34 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file np_broadcast_reduce_op_index.cu
+ * \brief GPU Implementation of broadcast and reduce functions based on index.
+ */
+#include "./np_broadcast_reduce_op.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(_npi_argmax)
+.set_attr<FCompute>("FCompute<gpu>", SearchAxisCompute<gpu, mshadow::red::maximum>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_elemwise_broadcast_op.cc b/src/operator/numpy/np_elemwise_broadcast_op.cc
index c36423dff9fd..697657d84dd5 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op.cc
+++ b/src/operator/numpy/np_elemwise_broadcast_op.cc
@@ -57,96 +57,22 @@ bool NumpyBinaryScalarType(const nnvm::NodeAttrs& attrs,
 
 
 MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_add)
-.describe(R"code(Add arguments element-wise with broadcasting if necessary.
-
-Example::
-
-   x = [[ 1.,  1.,  1.],
-        [ 1.,  1.,  1.]]
-
-   y = [[ 0.],
-        [ 1.]]
-
-   add(x, y) = [[ 1.,  1.,  1.],
-                [ 2.,  2.,  2.]]
-
-)code" ADD_FILELINE)
 .set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, op::mshadow_op::plus>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_broadcast_add"});
 
 MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_subtract)
-.describe(R"code(Subtract arguments element-wise with broadcasting if necessary.
-
-Example::
-
-   x = [[ 1.,  1.,  1.],
-        [ 1.,  1.,  1.]]
-
-   y = [[ 0.],
-        [ 1.]]
-
-   subtract(x, y) = [[ 1.,  1.,  1.],
-                     [ 0.,  0.,  0.]]
-
-)code" ADD_FILELINE)
 .set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, op::mshadow_op::minus>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_broadcast_sub"});
 
 MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_multiply)
-.describe(R"code(Multiply arguments with broadcasting if necessary.
-
-Example::
-
-   x = [[ 1.,  1.,  1.],
-        [ 1.,  1.,  1.]]
-
-   y = [[ 0.],
-        [ 1.]]
-
-   multiply(x, y) = [[ 0.,  0.,  0.],
-                     [ 1.,  1.,  1.]]
-
-)code" ADD_FILELINE)
 .set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, op::mshadow_op::mul>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_broadcast_mul"});
 
 MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_mod)
-.describe(R"code(Return element-wise remainder of division.
-It is equivalent to the Python modulus operator``x1 % x2`` and has the same sign as the divisor x2.
-
-Example::
-
-   x = [[ 8.,  8.,  8.],
-        [ 8.,  8.,  8.]]
-
-   y = [[ 2.],
-        [ 3.]]
-
-   mod(x, y) = [[ 0.,  0.,  0.],
-                [ 2.,  2.,  2.]]
-
-)code" ADD_FILELINE)
 .set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, mshadow_op::mod>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_broadcast_mod"});
 
 MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_power)
-.describe(R"code(First array elements raised to powers from second array, element-wise.
-
-Raise each base in x1 to the positionally-corresponding power in x2. x1 and x2 must be
-broadcastable to the same shape.
-
-Example::
-
-   x = [[ 1.,  1.,  1.],
-        [ 1.,  1.,  1.]]
-
-   y = [[ 0.],
-        [ 1.]]
-
-   power(x, y) = [[ 2.,  2.,  2.],
-                  [ 4.,  4.,  4.]]
-
-)code" ADD_FILELINE)
 .set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, mshadow_op::power>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_broadcast_power"});
 
diff --git a/src/operator/numpy/np_elemwise_broadcast_op.cu b/src/operator/numpy/np_elemwise_broadcast_op.cu
index c858b3a4987a..ac8def2af2c2 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op.cu
+++ b/src/operator/numpy/np_elemwise_broadcast_op.cu
@@ -42,12 +42,6 @@ NNVM_REGISTER_OP(_npi_mod)
 NNVM_REGISTER_OP(_npi_power)
 .set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, mshadow_op::power>);
 
-NNVM_REGISTER_OP(_npi_maximum)
-.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, mshadow_op::maximum>);
-
-NNVM_REGISTER_OP(_npi_minimum)
-.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, mshadow_op::minimum>);
-
 NNVM_REGISTER_OP(_npi_add_scalar)
 .set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, op::mshadow_op::plus>);
 
@@ -72,11 +66,5 @@ NNVM_REGISTER_OP(_npi_power_scalar)
 NNVM_REGISTER_OP(_npi_rpower_scalar)
 .set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::rpower>);
 
-NNVM_REGISTER_OP(_npi_maximum_scalar)
-.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::maximum>);
-
-NNVM_REGISTER_OP(_npi_minimum_scalar)
-.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::minimum>);
-
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/tensor/elemwise_binary_broadcast_op.h b/src/operator/tensor/elemwise_binary_broadcast_op.h
index 8a81bbc1c475..29c476d06d1c 100644
--- a/src/operator/tensor/elemwise_binary_broadcast_op.h
+++ b/src/operator/tensor/elemwise_binary_broadcast_op.h
@@ -599,6 +599,10 @@ void BinaryBroadcastBackwardUseIn(const nnvm::NodeAttrs& attrs,
                                   const std::vector<TBlob>& inputs,
                                   const std::vector<OpReqType>& req,
                                   const std::vector<TBlob>& outputs) {
+  // skip kernel launch for zero-size tensors
+  if (inputs[0].shape_.Size() == 0U) {
+    return;
+  }
   mxnet::TShape new_lshape, new_rshape, new_oshape;
   const bool need_bc = BinaryBroadcastShapeCompact(outputs[0].shape_,
                                                    outputs[1].shape_, inputs[0].shape_,
diff --git a/src/operator/tensor/elemwise_binary_broadcast_op_extended.cc b/src/operator/tensor/elemwise_binary_broadcast_op_extended.cc
index 842007eb497a..9e52b3197dcb 100644
--- a/src/operator/tensor/elemwise_binary_broadcast_op_extended.cc
+++ b/src/operator/tensor/elemwise_binary_broadcast_op_extended.cc
@@ -62,6 +62,7 @@ NNVM_REGISTER_OP(_backward_broadcast_power)
                                                               mshadow_op::power_rgrad>);
 
 MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(broadcast_maximum)
+.add_alias("_npi_maximum")
 .describe(R"code(Returns element-wise maximum of the input arrays with broadcasting.
 
 This function compares two input arrays and returns a new array having the element-wise maxima.
@@ -97,6 +98,7 @@ NNVM_REGISTER_OP(_backward_broadcast_maximum)
                                                               mshadow_op::lt>);
 
 MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(broadcast_minimum)
+.add_alias("_npi_minimum")
 .describe(R"code(Returns element-wise minimum of the input arrays with broadcasting.
 
 This function compares two input arrays and returns a new array having the element-wise minima.
diff --git a/src/operator/tensor/elemwise_binary_scalar_op_extended.cc b/src/operator/tensor/elemwise_binary_scalar_op_extended.cc
index 3a687c2aa062..ba6ebccc5f01 100644
--- a/src/operator/tensor/elemwise_binary_scalar_op_extended.cc
+++ b/src/operator/tensor/elemwise_binary_scalar_op_extended.cc
@@ -31,7 +31,8 @@ namespace op {
 MXNET_OPERATOR_REGISTER_BINARY_SCALAR(_maximum_scalar)
 .set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::maximum>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_maximum_scalar"})
-.add_alias("_MaximumScalar");
+.add_alias("_MaximumScalar")
+.add_alias("_npi_maximum_scalar");
 
 MXNET_OPERATOR_REGISTER_BINARY(_backward_maximum_scalar)
 .add_argument("scalar", "float", "scalar value")
@@ -41,7 +42,8 @@ MXNET_OPERATOR_REGISTER_BINARY(_backward_maximum_scalar)
 MXNET_OPERATOR_REGISTER_BINARY_SCALAR(_minimum_scalar)
 .set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::minimum>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_minimum_scalar"})
-.add_alias("_MinimumScalar");
+.add_alias("_MinimumScalar")
+.add_alias("_npi_minimum_scalar");
 
 MXNET_OPERATOR_REGISTER_BINARY(_backward_minimum_scalar)
 .add_argument("scalar", "float", "scalar value")
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 0a1a4fb2b9b1..c468a6b7a0fa 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -1126,6 +1126,245 @@ def test_np_randint():
             verify_generator(generator=generator_mx_same_seed, buckets=buckets, probs=probs, nrepeat=100)
 
 
+@with_seed()
+@use_np
+def test_np_minimum_maximum():
+    def check_symbol_output_type(op_name):
+        x1, x2 = mx.sym.var('x1').as_np_ndarray(), mx.sym.var('x2').as_np_ndarray()
+        ret = getattr(mx.sym.np, op_name)(x1, x2)
+        assert type(ret) == mx.sym.np._Symbol
+
+    def check_comp_op(op_name, x1, x2):
+        mx_out = getattr(np, op_name)(x1, x2)
+        if isinstance(x1, np.ndarray) or isinstance(x2, np.ndarray):
+            assert type(mx_out) == np.ndarray
+        np_out = getattr(_np, op_name)(x1.asnumpy() if isinstance(x1, np.ndarray) else x1,
+                                       x2.asnumpy() if isinstance(x2, np.ndarray) else x2)
+        assert same(mx_out.asnumpy() if isinstance(mx_out, np.ndarray) else mx_out, np_out)
+
+    op_names = ['minimum', 'maximum']
+    for op_name in op_names:
+        check_symbol_output_type(op_name)
+        check_comp_op(op_name, np.random.uniform(size=(2, 1)), np.random.uniform(size=(5, 1, 4)))
+        check_comp_op(op_name, np.random.uniform(size=(2, 0)), np.random.uniform(size=(5, 1, 1)))
+        check_comp_op(op_name, np.random.uniform(), np.random.uniform(size=(5, 1, 4)))
+        check_comp_op(op_name, _np.random.uniform(), np.random.uniform(size=(2, 3)))
+        check_comp_op(op_name, np.random.uniform(size=(2, 3)), _np.random.uniform())
+
+
+@with_seed()
+@use_np
+def test_np_swapaxes():
+    config = [((0, 1, 2), 0, 1),
+              ((0, 1, 2), -1, -2),
+              ((4, 5, 6, 7), 2, 3),
+              ((4, 5, 6, 7), -2, -3)]
+
+    class TestSwapaxes(HybridBlock):
+        def __init__(self, axis1, axis2):
+            super(TestSwapaxes, self).__init__()
+            self._axis1 = axis1
+            self._axis2 = axis2
+
+        def hybrid_forward(self, F, x):
+            return F.np.swapaxes(x, self._axis1, self._axis2)
+
+    for shape, axis1, axis2 in config:
+        data_np = _np.random.uniform(size=shape)
+        data_mx = np.array(data_np, dtype=data_np.dtype)
+        ret_np = _np.swapaxes(data_np, axis1=axis1, axis2=axis2)
+        ret_mx = np.swapaxes(data_mx, axis1=axis1, axis2=axis2)
+        assert same(ret_mx.asnumpy(), ret_np)
+
+        net = TestSwapaxes(axis1, axis2)
+        for hybrid in [False, True]:
+            if hybrid:
+                net.hybridize()
+            ret_mx = net(data_mx)
+            assert same(ret_mx.asnumpy(), ret_np)
+
+
+@with_seed()
+@use_np
+def test_np_argmax():
+    workloads = [
+        ((), 0, False),
+        ((), -1, False),
+        ((), 1, True),
+        ((5, 3), None, False),
+        ((5, 3), -1, False),
+        ((5, 3), 1, False),
+        ((5, 3), 3, True),
+        ((5, 0, 3), 0, False),
+        ((5, 0, 3), -1, False),
+        ((5, 0, 3), None, True),
+        ((5, 0, 3), 1, True),
+    ]
+    dtypes = ['float16', 'float32', 'float64']
+
+    class TestArgMax(HybridBlock):
+        def __init__(self, axis=None):
+            super(TestArgMax, self).__init__()
+            self._axis = axis
+
+        def hybrid_forward(self, F, x):
+            return F.np.argmax(x, self._axis)
+
+    for shape, axis, throw_exception in workloads:
+        for dtype in dtypes:
+            a = np.random.uniform(size=shape, dtype=dtype)
+            if throw_exception:
+                # Cannot use assert_exception because sometimes the main thread
+                # proceeds to `assert False` before the exception is thrown
+                # in the worker thread. Have to use mx.nd.waitall() here
+                # to block the main thread.
+                try:
+                    np.argmax(a, axis)
+                    mx.nd.waitall()
+                    assert False
+                except mx.MXNetError:
+                    pass
+            else:
+                mx_ret = np.argmax(a, axis=axis)
+                np_ret = _np.argmax(a.asnumpy(), axis=axis)
+                assert same(mx_ret.asnumpy(), np_ret)
+
+            for hybridize in [False, True]:
+                net = TestArgMax(axis)
+                if hybridize:
+                    net.hybridize()
+                if throw_exception:
+                    try:
+                        net(a)
+                        mx.nd.waitall()
+                        assert False
+                    except mx.MXNetError:
+                        pass
+                else:
+                    mx_ret = net(a)
+                    assert same(mx_ret.asnumpy(), np_ret)
+
+
+@with_seed()
+@use_np
+def test_np_clip():
+    workloads = [
+        ((), None, None, True),
+        ((), None, 1, False),
+        ((), -1, 1, False),
+        ((), -1, None, False),
+        ((5, 3), None, 0.1, False),
+        ((5, 3), -0.1, None, False),
+        ((5, 3), -0.1, 0.1, False),
+        ((5, 3), 0, 0, False),
+        ((5, 0, 3), 0, None, False),
+        ((5, 0, 3), None, -1, False),
+        ((5, 0, 3), -1, 0, False),
+    ]
+    dtypes = ['float32', 'float64']
+
+    class TestClip(HybridBlock):
+        def __init__(self, a_min=None, a_max=None):
+            super(TestClip, self).__init__()
+            self._a_min = a_min
+            self._a_max = a_max
+
+        def hybrid_forward(self, F, x):
+            return x.clip(self._a_min, self._a_max)
+
+    for shape, a_min, a_max, throw_exception in workloads:
+        for dtype in dtypes:
+            a = np.random.uniform(size=shape, dtype=dtype)
+            if throw_exception:
+                # Cannot use assert_exception because sometimes the main thread
+                # proceeds to `assert False` before the exception is thrown
+                # in the worker thread. Have to use mx.nd.waitall() here
+                # to block the main thread.
+                try:
+                    a.clip(min=a_min, max=a_max)
+                    mx.nd.waitall()
+                    assert False
+                except:
+                    pass
+            else:
+                mx_ret = a.clip(min=a_min, max=a_max)
+                np_ret = a.asnumpy().clip(min=a_min, max=a_max)
+                assert_almost_equal(mx_ret.asnumpy(), np_ret, atol=1e-4, rtol=1e-3, use_broadcast=False)
+
+            for hybridize in [False, True]:
+                net = TestClip(a_min, a_max)
+                if hybridize:
+                    net.hybridize()
+                if throw_exception:
+                    try:
+                        net(a)
+                        mx.nd.waitall()
+                        assert False
+                    except:
+                        pass
+                else:
+                    mx_ret = net(a)
+                    assert_almost_equal(mx_ret.asnumpy(), np_ret, atol=1e-4, rtol=1e-3, use_broadcast=False)
+
+
+@with_seed()
+@use_np
+def test_np_random():
+    shapes = [(), (1,), (2, 3), (4, 0, 5), 6, (7, 8), None]
+    dtypes = ['float16', 'float32', 'float64']
+    op_names = ['uniform', 'normal']
+    op_names = ['normal']
+    for shape in shapes:
+        for dtype in dtypes:
+            for op_name in op_names:
+                print('-------------------------------')
+                print(op_name)
+                print(shape)
+                print(dtype)
+                op = getattr(np.random, op_name, None)
+                assert op is not None
+                out = op(size=shape, dtype=dtype)
+                expected_shape = shape
+                if not isinstance(shape, tuple):
+                    expected_shape = () if shape is None else (shape,)
+                assert out.shape == expected_shape
+
+    class TestRandom(HybridBlock):
+        def __init__(self, shape, op_name):
+            super(TestRandom, self).__init__()
+            self._shape = shape
+            self._op_name = op_name
+
+        def hybrid_forward(self, F, x):
+            op = getattr(F.np.random, self._op_name, None)
+            assert op is not None
+            return x + op(size=shape)
+
+    x = np.ones(())
+    for op_name in op_names:
+        for shape in shapes:
+            for hybridize in [False, True]:
+                net = TestRandom(shape, op_name)
+                if hybridize:
+                    net.hybridize()
+                out = net(x)
+                expected_shape = shape
+                if not isinstance(shape, tuple):
+                    expected_shape = () if shape is None else (shape,)
+                assert out.shape == expected_shape
+
+
+@with_seed()
+@use_np
+def test_random_seed():
+    for seed in [234, 594, 7240, 20394]:
+        ret = []
+        for _ in range(2):
+            npx.random.seed(seed=seed)
+            ret.append(np.random.uniform(size=(2, 3)))
+        assert_almost_equal(ret[0].asnumpy(), ret[1].asnumpy(), rtol=1e-4, atol=1e-5, use_broadcast=False)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 196d1f4b034e00a49e0798bc64a3d680e8a50f6c Mon Sep 17 00:00:00 2001
From: Robert Stone <talby@trap.mtview.ca.us>
Date: Thu, 29 Aug 2019 10:13:45 -0700
Subject: [PATCH 234/813] [MXNET-1399] multiclass-mcc metric enhancements
 (#14874)

* multiclass-mcc metric enhancements

 * Rename metric from "PCC" to "mMCC" because though the math is derived
   from Pearson CC, it's utility is as a multiclass extension of Mathews CC.
 * Harden mx.metric.mMCC.update to more variations of input format, similar to
   mx.metric.Accuracy.update.

* Harden mx.metric.PCC.update to more variations of input format, similar to mx.metric.Accuracy.update.

* Enhance testcases for mx.metric.PCC.
---
 python/mxnet/metric.py               | 6 +++++-
 tests/python/unittest/test_metric.py | 7 +++++++
 2 files changed, 12 insertions(+), 1 deletion(-)

diff --git a/python/mxnet/metric.py b/python/mxnet/metric.py
index 4bfd1b36bf89..07ec2ef4d61d 100644
--- a/python/mxnet/metric.py
+++ b/python/mxnet/metric.py
@@ -1572,7 +1572,11 @@ def update(self, labels, preds):
         # update the confusion matrix
         for label, pred in zip(labels, preds):
             label = label.astype('int32', copy=False).asnumpy()
-            pred = pred.asnumpy().argmax(axis=1)
+            pred = pred.asnumpy()
+            if pred.shape != label.shape:
+                pred = pred.argmax(axis=1)
+            else:
+                pred = pred.astype('int32', copy=False)
             n = max(pred.max(), label.max())
             if n >= self.k:
                 self._grow(n + 1 - self.k)
diff --git a/tests/python/unittest/test_metric.py b/tests/python/unittest/test_metric.py
index a70ffdb88987..0ae8aeaa697f 100644
--- a/tests/python/unittest/test_metric.py
+++ b/tests/python/unittest/test_metric.py
@@ -304,6 +304,13 @@ def test_pcc():
     _, pear = met_pear.get()
     np.testing.assert_almost_equal(pcc, pear)
 
+    # pcc should also accept pred as scalar rather than softmax vector
+    # like acc does
+    met_pcc.reset()
+    met_pcc.update(labels, [p.argmax(axis=1) for p in preds])
+    _, chk = met_pcc.get()
+    np.testing.assert_almost_equal(pcc, chk)
+
     # check multiclass case against reference implementation
     CM = [
         [ 23, 13,  3 ],

From b7cca015553d707cd1c4ce292826d7311309419c Mon Sep 17 00:00:00 2001
From: Vandana Kannan <vandanavk@users.noreply.github.com>
Date: Thu, 29 Aug 2019 14:06:18 -0700
Subject: [PATCH 235/813] [MXNET-895] ONNX import/export: TopK (#13627)

* ONNX import: TopK

* ONNX export: TopK

* Add test for TopK

* Rebasing

* Fix a comment and rebase

* Re-trigger CI

* Re-trigger CI
---
 .../contrib/onnx/mx2onnx/_op_translations.py  | 33 +++++++++++++++++++
 .../contrib/onnx/onnx2mx/_import_helper.py    |  5 +--
 .../contrib/onnx/onnx2mx/_op_translations.py  |  8 +++++
 tests/python-pytest/onnx/test_cases.py        |  3 +-
 4 files changed, 46 insertions(+), 3 deletions(-)

diff --git a/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py b/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
index 35f4ff451cdb..8fdc5944fb91 100644
--- a/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
+++ b/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
@@ -2047,3 +2047,36 @@ def convert_broadcast_to(node, **kwargs):
     )
 
     return [tensor_node, expand_node]
+
+
+@mx_op.register("topk")
+def convert_topk(node, **kwargs):
+    """Map MXNet's topk operator attributes to onnx's TopK operator
+    and return the created node.
+    """
+    name, input_nodes, attrs = get_inputs(node, kwargs)
+
+    axis = int(attrs.get('axis', '-1'))
+    k = int(attrs.get('k', '1'))
+    ret_type = attrs.get('ret_typ')
+    dtype = attrs.get('dtype')
+    outputs = [name + '_output0']
+
+    if ret_type and ret_type == 'both':
+        if dtype and dtype == 'int64':
+            outputs.append(name + '_output1')
+        else:
+            raise NotImplementedError("ONNX expects indices to be of type int64")
+    else:
+        raise NotImplementedError("ONNX expects both value and indices as output")
+
+    topk_node = onnx.helper.make_node(
+        "TopK",
+        input_nodes,
+        outputs,
+        axis=axis,
+        k=k,
+        name=name
+    )
+
+    return [topk_node]
diff --git a/python/mxnet/contrib/onnx/onnx2mx/_import_helper.py b/python/mxnet/contrib/onnx/onnx2mx/_import_helper.py
index cf95bfef09a3..bd48d26ef6ba 100644
--- a/python/mxnet/contrib/onnx/onnx2mx/_import_helper.py
+++ b/python/mxnet/contrib/onnx/onnx2mx/_import_helper.py
@@ -23,7 +23,7 @@
 from ._op_translations import tanh, arccos, arcsin, arctan, _cos, _sin, _tan
 from ._op_translations import softplus, shape, gather, lp_pooling, size
 from ._op_translations import ceil, floor, hardsigmoid, global_lppooling
-from ._op_translations import concat, hardmax
+from ._op_translations import concat, hardmax, topk
 from ._op_translations import leaky_relu, _elu, _prelu, _selu, softmax, fully_connected
 from ._op_translations import global_avgpooling, global_maxpooling, linalg_gemm
 from ._op_translations import sigmoid, pad, relu, matrix_multiplication, batch_norm
@@ -147,5 +147,6 @@
     'DepthToSpace'      : depthtospace,
     'SpaceToDepth'      : spacetodepth,
     'Hardmax'           : hardmax,
-    'LpNormalization'   : lpnormalization
+    'LpNormalization'   : lpnormalization,
+    'TopK'              : topk
 }
diff --git a/python/mxnet/contrib/onnx/onnx2mx/_op_translations.py b/python/mxnet/contrib/onnx/onnx2mx/_op_translations.py
index 734b438581a5..627181d6ae21 100644
--- a/python/mxnet/contrib/onnx/onnx2mx/_op_translations.py
+++ b/python/mxnet/contrib/onnx/onnx2mx/_op_translations.py
@@ -779,3 +779,11 @@ def lpnormalization(attrs, inputs, proto_obj):
     axis = int(attrs.get("axis", -1))
     new_attrs.update(axis=axis)
     return 'norm', new_attrs, inputs
+
+
+def topk(attrs, inputs, proto_obj):
+    """Returns the top k elements in an input array along the given axis."""
+    new_attrs = translation_utils._add_extra_attributes(attrs,
+                                                        {'ret_typ': 'both',
+                                                         'dtype': 'int64'})
+    return 'topk', new_attrs, inputs
diff --git a/tests/python-pytest/onnx/test_cases.py b/tests/python-pytest/onnx/test_cases.py
index 89b60d15e84f..eb6b71896e28 100644
--- a/tests/python-pytest/onnx/test_cases.py
+++ b/tests/python-pytest/onnx/test_cases.py
@@ -78,7 +78,8 @@
              'test_max_',
              'test_softplus',
              'test_reduce_',
-             'test_split_equal'
+             'test_split_equal',
+             'test_top_k'
              ],
     'import': ['test_gather',
                'test_softsign',

From 61f3dbc8daa5e29010447c4d6b929908302cfcd3 Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Fri, 30 Aug 2019 05:40:57 +0800
Subject: [PATCH 236/813] numpy-compatible cumsum upstream (#15924)

---
 python/mxnet/_numpy_op_doc.py          |  51 +++++++
 src/operator/numpy/np_cumsum-inl.h     | 188 +++++++++++++++++++++++++
 src/operator/numpy/np_cumsum.cc        |  94 +++++++++++++
 src/operator/numpy/np_cumsum.cu        |  37 +++++
 tests/python/unittest/test_numpy_op.py |  43 +++++-
 5 files changed, 412 insertions(+), 1 deletion(-)
 create mode 100644 src/operator/numpy/np_cumsum-inl.h
 create mode 100644 src/operator/numpy/np_cumsum.cc
 create mode 100644 src/operator/numpy/np_cumsum.cu

diff --git a/python/mxnet/_numpy_op_doc.py b/python/mxnet/_numpy_op_doc.py
index 5543ebc8e8c9..4e5ea74c2771 100644
--- a/python/mxnet/_numpy_op_doc.py
+++ b/python/mxnet/_numpy_op_doc.py
@@ -52,3 +52,54 @@ def _np_zeros_like(a):
         Array of zeros with the same shape and type as `a`.
     """
     pass
+
+
+def _np_cumsum(a, axis=None, dtype=None, out=None):
+    """Return the cumulative sum of the elements along a given axis.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array.
+    axis : int, optional
+        Axis along which the cumulative sum is computed. The default
+        (None) is to compute the cumsum over the flattened array.
+    dtype : dtype, optional
+        Type of the returned array and of the accumulator in which the
+        elements are summed.  If `dtype` is not specified, it defaults
+        to the dtype of `a`, unless `a` has an integer dtype with a
+        precision less than that of the default platform integer.  In
+        that case, the default platform integer is used.
+    out : ndarray, optional
+        Alternative output array in which to place the result. It must
+        have the same shape and buffer length as the expected output
+        but the type will be cast if necessary. See `doc.ufuncs`
+        (Section "Output arguments") for more details.
+
+    Returns
+    -------
+    cumsum_along_axis : ndarray.
+        A new array holding the result is returned unless `out` is
+        specified, in which case a reference to `out` is returned. The
+        result has the same size as `a`, and the same shape as `a` if
+        `axis` is not None or `a` is a 1-d array.
+
+    Examples
+    --------
+    >>> a = np.array([[1,2,3], [4,5,6]])
+    >>> a
+    array([[1, 2, 3],
+           [4, 5, 6]])
+    >>> np.cumsum(a)
+    array([ 1,  3,  6, 10, 15, 21])
+    >>> np.cumsum(a, dtype=float)     # specifies type of output value(s)
+    array([  1.,   3.,   6.,  10.,  15.,  21.])
+    >>> np.cumsum(a,axis=0)      # sum over rows for each of the 3 columns
+    array([[1, 2, 3],
+           [5, 7, 9]])
+    >>> np.cumsum(a,axis=1)      # sum over columns for each of the 2 rows
+    array([[ 1,  3,  6],
+           [ 4,  9, 15]])
+
+    """
+    pass
diff --git a/src/operator/numpy/np_cumsum-inl.h b/src/operator/numpy/np_cumsum-inl.h
new file mode 100644
index 000000000000..6c6b56d46e76
--- /dev/null
+++ b/src/operator/numpy/np_cumsum-inl.h
@@ -0,0 +1,188 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file np_cumsum-inl.h
+ * \brief Function definition of numpy-compatible cumsum operator
+ */
+
+#ifndef MXNET_OPERATOR_NUMPY_NP_CUMSUM_INL_H_
+#define MXNET_OPERATOR_NUMPY_NP_CUMSUM_INL_H_
+
+#include <mxnet/base.h>
+#include <mxnet/operator_util.h>
+#include <vector>
+#include "../mxnet_op.h"
+#include "../operator_common.h"
+#include "../elemwise_op_common.h"
+
+namespace mxnet {
+namespace op {
+
+struct CumsumParam : public dmlc::Parameter<CumsumParam> {
+  dmlc::optional<int> axis;
+  dmlc::optional<int> dtype;
+  DMLC_DECLARE_PARAMETER(CumsumParam) {
+    DMLC_DECLARE_FIELD(axis)
+      .set_default(dmlc::optional<int>())
+      .describe("Axis along which the cumulative sum is computed."
+        " The default (None) is to compute the cumsum over the flattened array.");
+    DMLC_DECLARE_FIELD(dtype)
+      .add_enum("float16", mshadow::kFloat16)
+      .add_enum("float32", mshadow::kFloat32)
+      .add_enum("float64", mshadow::kFloat64)
+      .add_enum("int8", mshadow::kInt8)
+      .add_enum("int32", mshadow::kInt32)
+      .add_enum("int64", mshadow::kInt64)
+      .set_default(dmlc::optional<int>())
+      .describe("Type of the returned array and of the accumulator in which the elements"
+                " are summed. If dtype is not specified, it defaults to the dtype of a,"
+                " unless a has an integer dtype with a precision less than that of the"
+                " default platform integer. In that case, the default platform integer is used.");
+  }
+};
+
+struct cumsum_forward {
+  template<typename IType, typename OType>
+  MSHADOW_XINLINE static void Map(int i,
+                                  OType *out,
+                                  const IType *in,
+                                  const int middle,
+                                  const int trailing) {
+    int left = i / trailing, right = i % trailing;
+    int offset = left * middle * trailing + right;
+    const IType *lane_in = in + offset;
+    OType *lane_out = out + offset;
+    lane_out[0] = OType(lane_in[0]);
+    for (int j = 1; j < middle; ++j) {
+      lane_out[j * trailing] = lane_out[(j - 1) * trailing] + OType(lane_in[j * trailing]);
+    }
+  }
+};
+
+template<typename xpu>
+void CumsumForwardImpl(const OpContext& ctx,
+                       const TBlob& in,
+                       const TBlob& out,
+                       const dmlc::optional<int>& axis) {
+  using namespace mshadow;
+  using namespace mxnet_op;
+
+  CHECK(!axis.has_value() ||
+        ((axis.value() >= -out.shape_.ndim()) && axis.value() < out.shape_.ndim()))
+    << "axis value " << axis.value() << " out of range";
+
+  int middle = axis.has_value() ? out.shape_[axis.value()] : out.Size();
+  if (middle == 0 || out.Size() == 0) return;
+  int trailing = 1;
+  if (axis.has_value()) {
+    for (int i = axis.value() + 1; i < out.shape_.ndim(); ++i) {
+      trailing *= out.shape_[i];
+    }
+  }
+
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  MSHADOW_TYPE_SWITCH(in.type_flag_, IType, {
+    MSHADOW_TYPE_SWITCH(out.type_flag_, OType, {
+      Kernel<cumsum_forward, xpu>::Launch(
+        s, out.Size() / middle, out.dptr<OType>(),
+        in.dptr<IType>(), middle, trailing);
+    });
+  });
+}
+
+template<typename xpu>
+void CumsumForward(const nnvm::NodeAttrs& attrs,
+                   const OpContext& ctx,
+                   const std::vector<TBlob>& inputs,
+                   const std::vector<OpReqType>& req,
+                   const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mxnet_op;
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(req.size(), 1U);
+  CHECK_EQ(outputs.size(), 1U);
+  const CumsumParam &param = nnvm::get<CumsumParam>(attrs.parsed);
+
+  CumsumForwardImpl<xpu>(ctx, inputs[0], outputs[0], param.axis);
+}
+
+struct cumsum_backward {
+  template<typename IType, typename OType>
+  MSHADOW_XINLINE static void Map(int i,
+                                  IType *igrad,
+                                  const OType *ograd,
+                                  const int middle,
+                                  const int trailing) {
+    int left = i / trailing, right = i % trailing;
+    int offset = left * middle * trailing + right;
+    const OType *lane_ograd = ograd + offset;
+    IType *lane_igrad = igrad + offset;
+    lane_igrad[(middle - 1) * trailing] = IType(lane_ograd[(middle - 1) * trailing]);
+    for (int j = middle - 2; j >= 0; --j) {
+      lane_igrad[j * trailing] = lane_igrad[(j + 1) * trailing] + IType(lane_ograd[j * trailing]);
+    }
+  }
+};
+
+template<typename xpu>
+void CumsumBackwardImpl(const OpContext& ctx,
+                        const TBlob& ograd,
+                        const TBlob& igrad,
+                        const dmlc::optional<int>& axis) {
+  using namespace mshadow;
+  using namespace mxnet_op;
+  int middle = axis.has_value() ? igrad.shape_[axis.value()] : igrad.Size();
+  if (middle == 0 || igrad.Size() == 0) return;
+  int trailing = 1;
+  if (axis.has_value()) {
+    for (int i = axis.value() + 1; i < igrad.shape_.ndim(); ++i) {
+      trailing *= igrad.shape_[i];
+    }
+  }
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  MSHADOW_TYPE_SWITCH(igrad.type_flag_, IType, {
+    MSHADOW_TYPE_SWITCH(ograd.type_flag_, OType, {
+      Kernel<cumsum_backward, xpu>::Launch(
+        s, igrad.Size() / middle, igrad.dptr<IType>(),
+        ograd.dptr<OType>(), middle, trailing);
+    });
+  });
+}
+
+template<typename xpu>
+void CumsumBackward(const nnvm::NodeAttrs& attrs,
+                    const OpContext& ctx,
+                    const std::vector<TBlob>& inputs,
+                    const std::vector<OpReqType>& req,
+                    const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mxnet_op;
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(req.size(), 1U);
+  CHECK_EQ(outputs.size(), 1U);
+  const CumsumParam &param = nnvm::get<CumsumParam>(attrs.parsed);
+
+  CumsumBackwardImpl<xpu>(ctx, inputs[0], outputs[0], param.axis);
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_NUMPY_NP_CUMSUM_INL_H_
diff --git a/src/operator/numpy/np_cumsum.cc b/src/operator/numpy/np_cumsum.cc
new file mode 100644
index 000000000000..0ddbf521186c
--- /dev/null
+++ b/src/operator/numpy/np_cumsum.cc
@@ -0,0 +1,94 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file np_cumsum.cc
+ * \brief CPU implementation of numpy-compatible cumsum operator
+ */
+
+#include "./np_cumsum-inl.h"
+
+namespace mxnet {
+namespace op {
+
+inline bool CumsumShape(const nnvm::NodeAttrs& attrs,
+                        mxnet::ShapeVector *in_attrs,
+                        mxnet::ShapeVector *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  const CumsumParam &param = nnvm::get<CumsumParam>(attrs.parsed);
+
+  if (param.axis.has_value()) {
+    return ElemwiseShape<1, 1>(attrs, in_attrs, out_attrs);
+  } else {
+    TShape out_shape(1, in_attrs->at(0).Size());
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, out_shape);
+    return shape_is_known(out_attrs->at(0));
+  }
+}
+
+inline bool CumsumType(const nnvm::NodeAttrs& attrs,
+                       std::vector<int> *in_attrs,
+                       std::vector<int> *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  const CumsumParam &param = nnvm::get<CumsumParam>(attrs.parsed);
+
+  if (param.dtype.has_value()) {
+    TYPE_ASSIGN_CHECK(*out_attrs, 0, param.dtype.value());
+  } else {
+    TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
+    TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
+  }
+
+  return out_attrs->at(0) != -1 && in_attrs->at(0) != -1;
+}
+
+DMLC_REGISTER_PARAMETER(CumsumParam);
+
+NNVM_REGISTER_OP(_np_cumsum)
+.add_alias("cumsum")
+.describe(R"code(Return the cumulative sum of the elements along a given axis.)code" ADD_FILELINE)
+.set_attr_parser(ParamParser<CumsumParam>)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"a"};
+  })
+.set_attr<mxnet::FInferShape>("FInferShape", CumsumShape)
+.set_attr<nnvm::FInferType>("FInferType", CumsumType)
+.set_attr<FCompute>("FCompute<cpu>", CumsumForward<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_np_cumsum"})
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::pair<int, int> >{{0, 0}};
+  })
+.add_argument("a", "NDArray-or-Symbol", "Input ndarray")
+.add_arguments(CumsumParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_backward_np_cumsum)
+.set_attr_parser(ParamParser<CumsumParam>)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FCompute>("FCompute<cpu>", CumsumBackward<cpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_cumsum.cu b/src/operator/numpy/np_cumsum.cu
new file mode 100644
index 000000000000..cc574ebf72c5
--- /dev/null
+++ b/src/operator/numpy/np_cumsum.cu
@@ -0,0 +1,37 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file np_cumsum.cu
+ * \brief GPU implementation of numpy-compatible cumsum operator
+ */
+
+#include "./np_cumsum-inl.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(_np_cumsum)
+.set_attr<FCompute>("FCompute<gpu>", CumsumForward<gpu>);
+
+NNVM_REGISTER_OP(_backward_np_cumsum)
+.set_attr<FCompute>("FCompute<gpu>", CumsumBackward<gpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index c468a6b7a0fa..b8b9ca75a173 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -39,7 +39,7 @@ class TestTensordot(HybridBlock):
         def __init__(self, axes):
             super(TestTensordot, self).__init__()
             self._axes = axes
-            
+
         def hybrid_forward(self, F, a, b):
             return F.np.tensordot(a, b, self._axes)
 
@@ -1365,6 +1365,47 @@ def test_random_seed():
         assert_almost_equal(ret[0].asnumpy(), ret[1].asnumpy(), rtol=1e-4, atol=1e-5, use_broadcast=False)
 
 
+@with_seed()
+@use_np
+def test_np_cumsum():
+    def np_cumsum_backward(ograd, axis=None, dtype=None):
+        return _np.flip(_np.cumsum(_np.flip(ograd, axis=axis), axis=axis, dtype=dtype), axis=axis)
+
+    class TestCumsum(HybridBlock):
+        def __init__(self, axis=None, dtype=None):
+            super(TestCumsum, self).__init__()
+            self._axis = axis
+            self._dtype = dtype
+
+        def hybrid_forward(self, F, a):
+            return F.np.cumsum(a, axis=self._axis, dtype=self._dtype)
+
+    shapes = [(2, 3, 4), (2, 0, 3), ()]
+    for hybridize in [True, False]:
+        for shape in shapes:
+            for axis in [None] + [i for i in range(0, len(shape))]:
+                for otype in [None, _np.float32, _np.float64]:
+                    test_cumsum = TestCumsum(axis=axis, dtype=otype)
+                    if hybridize:
+                        test_cumsum.hybridize()
+                    for itype in [_np.float16, _np.float32, _np.float64]:
+                        x = rand_ndarray(shape).astype(itype).as_np_ndarray()
+                        x.attach_grad()
+                        np_out = _np.cumsum(x.asnumpy(), axis=axis, dtype=otype)
+                        with mx.autograd.record():
+                            mx_out = test_cumsum(x)
+                        assert mx_out.shape == np_out.shape
+                        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+                        mx_out.backward()
+                        np_backward = np_cumsum_backward(_np.ones(np_out.shape, dtype=otype),
+                                                         axis=axis, dtype=otype).reshape(x.shape)
+                        assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=1e-3, atol=1e-5)
+
+                        mx_out = np.cumsum(x, axis=axis, dtype=otype)
+                        np_out = _np.cumsum(x.asnumpy(), axis=axis, dtype=otype)
+                        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 5d0d335077d52d3d01646d2873278f6f162fb79e Mon Sep 17 00:00:00 2001
From: RuilinZhuIntel <45059088+RuilinZhuIntel@users.noreply.github.com>
Date: Fri, 30 Aug 2019 08:29:33 +0800
Subject: [PATCH 237/813] Update README.md (#16035)

Added mxnet 1.5.0 release hyper link and description into What's new in readme.md
---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index f3e524c79540..849a54f3c60a 100644
--- a/README.md
+++ b/README.md
@@ -50,6 +50,7 @@ How to Contribute
 
 What's New
 ----------
+* [Version 1.5.0 Release](https://github.com/apache/incubator-mxnet/releases/tag/1.5.0) - MXNet 1.5.0 Release.
 * [Version 1.4.1 Release](https://github.com/apache/incubator-mxnet/releases/tag/1.4.1) - MXNet 1.4.1 Patch Release.
 * [Version 1.4.0 Release](https://github.com/apache/incubator-mxnet/releases/tag/1.4.0) - MXNet 1.4.0 Release.
 * [Version 1.3.1 Release](https://github.com/apache/incubator-mxnet/releases/tag/1.3.1) - MXNet 1.3.1 Patch Release.

From 36455b2ab0aafd9f6b569827dcf3fc0b79c5dd74 Mon Sep 17 00:00:00 2001
From: YixinBao <yixin.bao@intel.com>
Date: Fri, 30 Aug 2019 08:49:32 +0800
Subject: [PATCH 238/813] Add RROIAlign (#16017)

* add RROIAlign

* add test case for RROIAlign

* move rroi align op to contrib

* skip gpu test for rroi align

* add omp parallel

* fix lint

* update omp with num_threads

* fix extra copy
---
 src/operator/contrib/rroi_align-inl.h  |  67 +++++
 src/operator/contrib/rroi_align.cc     | 326 +++++++++++++++++++++++++
 tests/python/unittest/test_operator.py | 137 +++++++++++
 3 files changed, 530 insertions(+)
 create mode 100644 src/operator/contrib/rroi_align-inl.h
 create mode 100644 src/operator/contrib/rroi_align.cc

diff --git a/src/operator/contrib/rroi_align-inl.h b/src/operator/contrib/rroi_align-inl.h
new file mode 100644
index 000000000000..084ead78dbc7
--- /dev/null
+++ b/src/operator/contrib/rroi_align-inl.h
@@ -0,0 +1,67 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file rroi_align-inl.h
+ * \brief rroi align operator and symbol
+ * \author Yixin Bao
+*/
+#ifndef MXNET_OPERATOR_CONTRIB_RROI_ALIGN_INL_H_
+#define MXNET_OPERATOR_CONTRIB_RROI_ALIGN_INL_H_
+
+#include <dmlc/logging.h>
+#include <dmlc/parameter.h>
+#include <mxnet/operator.h>
+#include <map>
+#include <vector>
+#include <string>
+#include <utility>
+#include "../mshadow_op.h"
+#include "../operator_common.h"
+
+namespace mxnet {
+namespace op {
+
+// Declare enumeration of input order to make code more intuitive.
+// These enums are only visible within this header
+namespace rroialign {
+enum RROIAlignOpInputs{kData, kBox};
+enum RROIAlignOpOutputs {kOut};
+}  // rroialign
+
+struct RROIAlignParam : public dmlc::Parameter<RROIAlignParam> {
+  mxnet::TShape pooled_size;
+  float spatial_scale;
+  int sampling_ratio;
+  DMLC_DECLARE_PARAMETER(RROIAlignParam) {
+    DMLC_DECLARE_FIELD(pooled_size)
+    .set_expect_ndim(2).enforce_nonzero()
+    .describe("RROI align output shape (h,w) ");
+    DMLC_DECLARE_FIELD(spatial_scale).set_range(0.0, 1.0)
+    .describe("Ratio of input feature map height (or width) to raw image height (or width). "
+    "Equals the reciprocal of total stride in convolutional layers");
+    DMLC_DECLARE_FIELD(sampling_ratio).set_default(-1)
+    .describe("Optional sampling ratio of RROI align, using adaptive size by default.");
+  }
+};
+
+}  // namespace op
+}  // namespace mxnet
+#endif  // MXNET_OPERATOR_CONTRIB_RROI_ALIGN_INL_H_
diff --git a/src/operator/contrib/rroi_align.cc b/src/operator/contrib/rroi_align.cc
new file mode 100644
index 000000000000..14690d6270d2
--- /dev/null
+++ b/src/operator/contrib/rroi_align.cc
@@ -0,0 +1,326 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file rroi_align.cc
+ * \brief rroi align operator
+ * \author Yixin Bao
+ * Forward pass adapted from Caffe2
+ * link: https://github.com/pytorch/pytorch/blob/master/caffe2/operators/roi_align_rotated_op.cc
+ */
+#include "./rroi_align-inl.h"
+#include <mshadow/tensor.h>
+#include "math.h"
+
+using std::max;
+using std::min;
+using std::floor;
+using std::ceil;
+
+namespace mxnet {
+namespace op {
+
+template <typename DType>
+struct position_for_bilinear_interpolate {
+  // 4 positions and corresponding weights for
+  // computing bilinear interpolation
+  int pos1, pos2, pos3, pos4;
+  DType w1, w2, w3, w4;
+};
+
+template <typename DType>
+void pre_calc_for_bilinear_interpolate(
+    const int height, const int width, const int pooled_height, const int pooled_width,
+    const int iy_upper, const int ix_upper, DType roi_start_h, DType roi_start_w,
+    DType bin_size_h, DType bin_size_w, int roi_bin_grid_h, int roi_bin_grid_w,
+    DType roi_center_h, DType roi_center_w, DType theta,
+    std::vector<position_for_bilinear_interpolate<DType>> *pre_calc) {
+  int pre_calc_index = 0;
+  DType cosTheta = cos(theta);
+  DType sinTheta = sin(theta);
+  for (int ph = 0; ph < pooled_height; ph++) {
+    for (int pw = 0; pw < pooled_width; pw++) {
+      // calc bin grid position (xx,yy)
+      for (int iy = 0; iy < iy_upper; iy++) {
+        const DType yy = roi_start_h + ph * bin_size_h +
+            static_cast<DType>(iy + .5f) * bin_size_h /
+                static_cast<DType>(roi_bin_grid_h);  // e.g., 0.5, 1.5
+        for (int ix = 0; ix < ix_upper; ix++) {
+          const DType xx = roi_start_w + pw * bin_size_w +
+              static_cast<DType>(ix + .5f) * bin_size_w /
+                  static_cast<DType>(roi_bin_grid_w);
+
+          // Rotate by theta around the center and translate
+          DType x = xx * cosTheta + yy * sinTheta + roi_center_w;
+          DType y = yy * cosTheta - xx * sinTheta + roi_center_h;
+
+          // deal with: inverse elements are out of feature map boundary
+          if (y < -1.0 || y > height || x < -1.0 || x > width) {
+            // empty
+            position_for_bilinear_interpolate<DType> &pc = (*pre_calc)[pre_calc_index];
+            pc.pos1 = 0;
+            pc.pos2 = 0;
+            pc.pos3 = 0;
+            pc.pos4 = 0;
+            pc.w1 = 0;
+            pc.w2 = 0;
+            pc.w3 = 0;
+            pc.w4 = 0;
+            pre_calc_index += 1;
+            continue;
+          }
+          if (y <= 0) {
+            y = 0;
+          }
+          if (x <= 0) {
+            x = 0;
+          }
+
+          // calc 4 points for interpolation
+          int y_low = static_cast<int>(y);
+          int x_low = static_cast<int>(x);
+          int y_high;
+          int x_high;
+          if (y_low >= height - 1) {
+            y_high = y_low = height - 1;
+            y = (DType)y_low;
+          } else {
+            y_high = y_low + 1;
+          }
+          if (x_low >= width - 1) {
+            x_high = x_low = width - 1;
+            x = (DType)x_low;
+          } else {
+            x_high = x_low + 1;
+          }
+          DType ly = y - y_low;
+          DType lx = x - x_low;
+          DType hy = 1. - ly, hx = 1. - lx;
+          DType w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx;
+
+          // Save weights and indices
+          position_for_bilinear_interpolate<DType> &pc = (*pre_calc)[pre_calc_index];
+          pc.pos1 = y_low * width + x_low;
+          pc.pos2 = y_low * width + x_high;
+          pc.pos3 = y_high * width + x_low;
+          pc.pos4 = y_high * width + x_high;
+          pc.w1 = w1;
+          pc.w2 = w2;
+          pc.w3 = w3;
+          pc.w4 = w4;
+          pre_calc_index += 1;
+        }
+      }
+    }
+  }
+}
+
+template <typename DType>
+inline void RROIAlignForward(const OpContext &ctx, const RROIAlignParam &param,
+                             const std::vector<TBlob> &in_data, const std::vector<OpReqType> &req,
+                             const std::vector<TBlob> &out_data) {
+  // data: [batch_size, c, h, w]
+  const TBlob &data = in_data[rroialign::kData];
+  const TBlob &bbox = in_data[rroialign::kBox];
+  const DType *bottom_data = data.dptr<DType>();
+  const int channels_ = data.size(1);
+  const int height_ = data.size(2);
+  const int width_ = data.size(3);
+  const index_t data_size_c = height_ * width_;
+  const index_t data_size = channels_ * data_size_c;
+
+  // bbox: [num_rois, 6] (6: [batch_index, x, y, w, h, theta])
+  const DType *bottom_rois = bbox.dptr<DType>();
+  const int num_rois = bbox.size(0);
+  const float spatial_scale_ = param.spatial_scale;
+  const int sampling_ratio_ = param.sampling_ratio;
+
+  // out: [num_rois, c, pooled_h, pooled_w]
+  const TBlob &out = out_data[rroialign::kOut];
+  DType *top_data = out.dptr<DType>();
+  const int pooled_height_ = out.size(2);
+  const int pooled_width_ = out.size(3);
+  const index_t out_size_c = pooled_height_ * pooled_width_;
+  const index_t out_size = channels_ * out_size_c;
+
+  // (n, c, ph, pw) is an element in the pooled output
+  // can be parallelized using omp
+#pragma omp parallel for num_threads(engine::OpenMP::Get()->GetRecommendedOMPThreadCount())
+  for (int n = 0; n < num_rois; ++n) {
+    // Increment ROI data pointer
+    const DType *bottom_rois_n = bottom_rois + n * bbox.size(1);
+    DType *top_data_n = top_data + n * out_size;
+    int roi_batch_ind = static_cast<int>(bottom_rois_n[0]);
+    DType roi_center_w = bottom_rois_n[1] * spatial_scale_;
+    DType roi_center_h = bottom_rois_n[2] * spatial_scale_;
+    DType roi_width = bottom_rois_n[3] * spatial_scale_;
+    DType roi_height = bottom_rois_n[4] * spatial_scale_;
+    DType roi_theta = bottom_rois_n[5] * M_PI / 180.0;
+
+    // force malformed ROIs to be 1 * 1
+    roi_width = max(roi_width, (DType) 1.);
+    roi_height = max(roi_height, (DType) 1.);
+    // roi_start_h and roi_start_w are computed wrt the center of RoI (x, y).
+    // Appropriate translation needs to be applied after.
+    DType roi_start_h = -roi_height / 2.0;
+    DType roi_start_w = -roi_width / 2.0;
+
+    const DType bin_size_h = static_cast<DType>(roi_height) / static_cast<DType>(pooled_height_);
+    const DType bin_size_w = static_cast<DType>(roi_width) / static_cast<DType>(pooled_width_);
+    // We use roi_bin_grid to sample the grid and mimic integral,
+    // e.g. roi_bin_grid = 2, means sample 2*2=4 points in each bin
+    int roi_bin_grid_h =
+        (sampling_ratio_ > 0) ? sampling_ratio_ : ceil(roi_height / pooled_height_);
+    int roi_bin_grid_w = (sampling_ratio_ > 0) ? sampling_ratio_ : ceil(roi_width / pooled_width_);
+    const DType bin_points_count = roi_bin_grid_h * roi_bin_grid_w;  // e.g. = 4
+
+    // We want to precalculate indices and weights shared by all channels,
+    // this is the key point of optimization.
+    std::vector<position_for_bilinear_interpolate<DType>> pre_calc(roi_bin_grid_h * roi_bin_grid_w *
+                                                                   pooled_width_ * pooled_height_);
+
+    pre_calc_for_bilinear_interpolate(height_, width_, pooled_height_, pooled_width_,
+                                      roi_bin_grid_h, roi_bin_grid_w, roi_start_h, roi_start_w,
+                                      bin_size_h, bin_size_w, roi_bin_grid_h, roi_bin_grid_w,
+                                      roi_center_h, roi_center_w, roi_theta, &pre_calc);
+
+    for (int c = 0; c < channels_; ++c) {
+      const DType *offset_bottom_data = bottom_data + roi_batch_ind * data_size + c * data_size_c;
+      int pre_calc_index = 0;
+
+      for (int ph = 0; ph < pooled_height_; ph++) {
+        for (int pw = 0; pw < pooled_width_; pw++) {
+          DType output_val = 0.;
+          for (int iy = 0; iy < roi_bin_grid_h; iy++) {
+            for (int ix = 0; ix < roi_bin_grid_w; ix++) {
+              position_for_bilinear_interpolate<DType> pc = pre_calc[pre_calc_index];
+              output_val +=
+                  pc.w1 * offset_bottom_data[pc.pos1] + pc.w2 * offset_bottom_data[pc.pos2] +
+                  pc.w3 * offset_bottom_data[pc.pos3] + pc.w4 * offset_bottom_data[pc.pos4];
+
+              pre_calc_index += 1;
+            }
+          }
+          output_val /= bin_points_count;  // avg pooling for bin grid
+          int index = c * pooled_height_ * pooled_width_ + ph * pooled_width_ + pw;
+          top_data_n[index] = output_val;
+        }   // for pw
+      }   // for ph
+    }   // for c
+  }   // for n
+}
+
+template<typename xpu>
+void RROIAlignForwardCompute(const nnvm::NodeAttrs& attrs,
+                      const OpContext& ctx, const std::vector<TBlob>& in_data,
+                      const std::vector<OpReqType>& req,
+                      const std::vector<TBlob>& out_data) {
+  const RROIAlignParam& param = nnvm::get<RROIAlignParam>(attrs.parsed);
+  CHECK_EQ(in_data.size(), 2);
+  CHECK_EQ(out_data.size(), 1);
+  CHECK_EQ(out_data[rroialign::kOut].shape_[0], in_data[rroialign::kBox].shape_[0]);
+
+  MSHADOW_REAL_TYPE_SWITCH(in_data[0].type_flag_, DType, {
+    RROIAlignForward<DType>(ctx, param, in_data, req, out_data);
+  })
+}
+
+template<typename xpu>
+void RROIAlignBackwardCompute(const nnvm::NodeAttrs& attrs,
+                      const OpContext& ctx, const std::vector<TBlob>& in_data,
+                      const std::vector<OpReqType>& req,
+                      const std::vector<TBlob>& out_data) {
+  LOG(FATAL) << "RROIAlign: Backward is not supported.";
+}
+
+DMLC_REGISTER_PARAMETER(RROIAlignParam);
+
+NNVM_REGISTER_OP(_contrib_RROIAlign)
+.describe(R"code(Performs Rotated ROI Align on the input array.
+
+This operator takes a 4D feature map as an input array and region proposals as `rois`,
+then align the feature map over sub-regions of input and produces a fixed-sized output array.
+
+Different from ROI Align, RROI Align uses rotated rois, which is suitable for text detection.
+RRoIAlign computes the value of each sampling point by bilinear interpolation from the nearby
+grid points on the rotated feature map. No quantization is performed on any coordinates
+involved in the RoI, its bins, or the sampling points. Bilinear interpolation is used to
+compute the exact values of the input features at four regularly sampled locations in
+each RoI bin. Then the feature map can be aggregated by avgpooling.
+
+References
+----------
+
+Ma, Jianqi, et al. "Arbitrary-Oriented Scene Text Detection via Rotation Proposals."
+IEEE Transactions on Multimedia, 2018.
+
+)code" ADD_FILELINE)
+.set_num_inputs(2)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+    [](const NodeAttrs& attrs) {
+  return std::vector<std::string>{"data", "rois"};
+})
+.set_attr<nnvm::FListOutputNames>("FListOutputNames",
+    [](const NodeAttrs& attrs) {
+  return std::vector<std::string>{"output"};
+})
+.set_attr_parser(ParamParser<RROIAlignParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", [](const nnvm::NodeAttrs& attrs,
+      mxnet::ShapeVector *in_shape, mxnet::ShapeVector *out_shape){
+  using namespace mshadow;
+  const RROIAlignParam& param = nnvm::get<RROIAlignParam>(attrs.parsed);
+  CHECK_EQ(in_shape->size(), 2U) << "Input:[data, rois]";
+  // data: [batch_size, c, h, w]
+  mxnet::TShape dshape = in_shape->at(rroialign::kData);
+  CHECK_EQ(dshape.ndim(), 4U) << "data should be a 4D tensor";
+  // bbox: [num_rois, 6]
+  mxnet::TShape bshape = in_shape->at(rroialign::kBox);
+  CHECK_EQ(bshape.ndim(), 2U) << "bbox should be a 2D tensor of shape [batch, 6]";
+  CHECK_EQ(bshape[1], 6U) << "bbox should be a 2D tensor of shape [batch, 6]";
+  // out: [num_rois, c, pooled_h, pooled_w]
+  out_shape->clear();
+  out_shape->push_back(Shape4(bshape[0], dshape[1], param.pooled_size[0], param.pooled_size[1]));
+  return true;
+})
+.set_attr<nnvm::FInferType>("FInferType", [](const nnvm::NodeAttrs& attrs,
+      std::vector<int> *in_type, std::vector<int> *out_type) {
+  CHECK_EQ(in_type->size(), 2U);
+  int dtype = (*in_type)[0];
+  CHECK_EQ(dtype, (*in_type)[1]);
+  CHECK_NE(dtype, -1) << "Input must have specified type";
+
+  out_type->clear();
+  out_type->push_back(dtype);
+  return true;
+})
+.set_attr<FCompute>("FCompute<cpu>", RROIAlignForwardCompute<cpu>)
+.add_argument("data", "NDArray-or-Symbol", "Input data to the pooling operator, a 4D Feature maps")
+.add_argument("rois", "NDArray-or-Symbol", "Bounding box coordinates, a 2D array")
+.add_arguments(RROIAlignParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_backward_RROIAlign)
+.set_num_outputs(2)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr_parser(ParamParser<RROIAlignParam>)
+.set_attr<FCompute>("FCompute<cpu>", RROIAlignBackwardCompute<cpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index f864003f7c60..39ae0a02f0fe 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -8410,6 +8410,143 @@ def test_roi_align_autograd(sampling_ratio=0):
     test_roi_align_value(position_sensitive=True)
     test_roi_align_autograd()
 
+@with_seed()
+def test_op_rroi_align():
+    T = np.float32
+
+    def assert_same_dtype(dtype_a, dtype_b):
+        '''
+        Assert whether the two data type are the same
+        Parameters
+        ----------
+        dtype_a, dtype_b: type
+            Input data types to compare
+        '''
+        assert dtype_a == dtype_b,\
+            TypeError('Unmatched data types: %s vs %s' % (dtype_a, dtype_b))
+
+    def bilinear_interpolate(bottom, height, width, y, x):
+        if y < -1.0 or y > height or x < -1.0 or x > width:
+            return T(0.0)
+        x = T(max(0.0, x))
+        y = T(max(0.0, y))
+        x_low = int(x)
+        y_low = int(y)
+        if x_low >= width - 1:
+            x_low = x_high = width - 1
+            x = T(x_low)
+        else:
+            x_high = x_low + 1
+        if y_low >= height - 1:
+            y_low = y_high = height - 1
+            y = T(y_low)
+        else:
+            y_high = y_low + 1
+        ly = y - T(y_low)
+        lx = x - T(x_low)
+        hy = T(1.0) - ly
+        hx = T(1.0) - lx
+        v1 = bottom[y_low, x_low]
+        v2 = bottom[y_low, x_high]
+        v3 = bottom[y_high, x_low]
+        v4 = bottom[y_high, x_high]
+        w1 = hy * hx
+        w2 = hy * lx
+        w3 = ly * hx
+        w4 = ly * lx
+        assert_same_dtype(w1.dtype, T)
+        assert_same_dtype(w2.dtype, T)
+        assert_same_dtype(w3.dtype, T)
+        assert_same_dtype(w4.dtype, T)
+        val = w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4
+        assert_same_dtype(val.dtype, T)
+
+        return val
+
+    def rroialign_forward(data, rois, pooled_size, spatial_scale, sampling_ratio):
+        N, C, H, W = data.shape
+        R = rois.shape[0]
+        PH, PW = pooled_size
+        assert rois.ndim == 2,\
+            ValueError(
+                'The ndim of rois should be 2 rather than %d' % rois.ndim)
+        assert rois.shape[1] == 6,\
+            ValueError(
+                'The length of the axis 1 of rois should be 6 rather than %d' % rois.shape[1])
+        assert_same_dtype(data.dtype, T)
+        assert_same_dtype(rois.dtype, T)
+
+        out = np.zeros((R, C, PH, PW), dtype=T)
+
+        for r in range(R):
+            batch_ind = int(rois[r, 0])
+            roi_center_w, roi_center_h, roi_w, roi_h = rois[r, 1:5] * T(spatial_scale)
+            roi_theta = T(rois[r,5]  * np.pi / 180.0)
+            roi_w = T(max(roi_w, 1.0))
+            roi_h = T(max(roi_h, 1.0))
+            bin_h = roi_h / T(PH)
+            bin_w = roi_w / T(PW)
+            bdata = data[batch_ind]
+            if sampling_ratio > 0:
+                roi_bin_grid_h = roi_bin_grid_w = sampling_ratio
+            else:
+                roi_bin_grid_h = int(np.ceil(roi_h / T(PH)))
+                roi_bin_grid_w = int(np.ceil(roi_w / T(PW)))
+            count = T(roi_bin_grid_h * roi_bin_grid_w)
+            roi_start_h = T(-roi_h / 2.0)
+            roi_start_w = T(-roi_w / 2.0)
+            for c in range(C):
+                for ph in range(PH):
+                    for pw in range(PW):
+                        val = T(0.0)
+                        for iy in range(roi_bin_grid_h):
+                            yy = roi_start_h + T(ph) * bin_h + (T(iy) + T(0.5)) * \
+                                bin_h / T(roi_bin_grid_h)
+                            for ix in range(roi_bin_grid_w):
+                                xx = roi_start_w + T(pw) * bin_w + (T(ix) + T(0.5)) * \
+                                    bin_w / T(roi_bin_grid_w)
+                                x = xx * np.cos(roi_theta, dtype=T) + yy * np.sin(roi_theta, dtype=T) + roi_center_w
+                                y = yy * np.cos(roi_theta, dtype=T) - xx * np.sin(roi_theta, dtype=T) + roi_center_h
+                                v = bilinear_interpolate(
+                                    bdata[c], H, W, y, x)
+                                assert_same_dtype(v.dtype, T)
+                                val += v
+
+                        out[r, c, ph, pw] = val / count
+        assert_same_dtype(out.dtype, T)
+
+        return out
+
+    def test_rroi_align_value(sampling_ratio=-1):
+        ctx = default_context()
+        if ctx.device_type == 'gpu':
+            print('skipped testing rroi align for gpu since it is not supported yet')
+            return
+
+        dtype = np.float32
+        dlen = 224
+        N, C, H, W = 5, 3, 16, 16
+        R = 7
+        pooled_size = (3, 4)
+        spatial_scale = H * 1.0 / dlen
+        data = mx.nd.array(
+            np.arange(N * C * W * H).reshape((N, C, H, W)), ctx=ctx, dtype=dtype)
+        center_xy = mx.nd.random.uniform(0, dlen, (R, 2), ctx=ctx, dtype=dtype)
+        wh = mx.nd.random.uniform(0, dlen, (R, 2), ctx=ctx, dtype=dtype)
+        theta = mx.nd.random.uniform(0, 180, (R,1), ctx=ctx, dtype=dtype)
+        batch_ind = mx.nd.array(np.random.randint(0, N, size=(R, 1)), ctx=ctx)
+        pos = mx.nd.concat(center_xy, wh, theta, dim=1)
+        rois = mx.nd.concat(batch_ind, pos, dim=1)
+
+        output = mx.nd.contrib.RROIAlign(data, rois, pooled_size=pooled_size,
+                                        spatial_scale=spatial_scale, sampling_ratio=sampling_ratio)
+        real_output = rroialign_forward(data.asnumpy(), rois.asnumpy(), pooled_size,
+                                        spatial_scale, sampling_ratio)
+
+        assert_almost_equal(output.asnumpy(), real_output, atol=1e-3)
+
+    test_rroi_align_value()
+    test_rroi_align_value(sampling_ratio=2)
 
 @with_seed()
 def test_diag():

From 35d943e3b219714a7dec82623939951e498569df Mon Sep 17 00:00:00 2001
From: Per Goncalves da Silva <perdasilva@gmail.com>
Date: Thu, 23 May 2019 12:38:58 +0200
Subject: [PATCH 239/813] Updates git_init Jenkins utility function to support
 checking out a particular commit id

---
 ci/Jenkinsfile_utils.groovy | 17 ++++++++++++++---
 1 file changed, 14 insertions(+), 3 deletions(-)

diff --git a/ci/Jenkinsfile_utils.groovy b/ci/Jenkinsfile_utils.groovy
index b8117f39377a..14822b4cca36 100644
--- a/ci/Jenkinsfile_utils.groovy
+++ b/ci/Jenkinsfile_utils.groovy
@@ -18,7 +18,12 @@
 // under the License.
 
 // initialize source codes
-def init_git() {
+def init_git(git_sha = '') {
+
+  git_sha = git_sha ? git_sha.trim() : ''
+
+  echo "Checking out git_sha: ${git_sha}"
+
   deleteDir()
   retry(5) {
     try {
@@ -26,6 +31,11 @@ def init_git() {
       // retries as this will increase the amount of requests and worsen the throttling
       timeout(time: 15, unit: 'MINUTES') {
         checkout scm
+
+        if (git_sha) {
+          sh "git checkout ${git_sha}"
+        }
+
         sh 'git clean -xdff'
         sh 'git reset --hard'
         sh 'git submodule update --init --recursive'
@@ -80,8 +90,8 @@ return 0
 }
 
 // unpack libraries saved before
-def unpack_and_init(name, libs, include_gcov_data = false) {
-  init_git()
+def unpack_and_init(name, libs, include_gcov_data = false, git_sha = '') {
+  init_git(git_sha)
   unstash name
   sh returnStatus: true, script: """
 set +e
@@ -295,4 +305,5 @@ def main_wrapper(args) {
     }
   }
 }
+
 return this

From 15974981fbf2962c6cf45860f77f1dfd6551a937 Mon Sep 17 00:00:00 2001
From: Per Goncalves da Silva <perdasilva@gmail.com>
Date: Thu, 23 May 2019 12:41:49 +0200
Subject: [PATCH 240/813] Adds artifact repository scripts

---
 ci/cd/utils/artifact_repository.md      |  88 ++++
 ci/cd/utils/artifact_repository.py      | 623 ++++++++++++++++++++++++
 ci/cd/utils/requirements.txt            |   2 +
 ci/cd/utils/test_artifact_repository.py | 494 +++++++++++++++++++
 4 files changed, 1207 insertions(+)
 create mode 100644 ci/cd/utils/artifact_repository.md
 create mode 100755 ci/cd/utils/artifact_repository.py
 create mode 100644 ci/cd/utils/requirements.txt
 create mode 100644 ci/cd/utils/test_artifact_repository.py

diff --git a/ci/cd/utils/artifact_repository.md b/ci/cd/utils/artifact_repository.md
new file mode 100644
index 000000000000..1dc902511cbe
--- /dev/null
+++ b/ci/cd/utils/artifact_repository.md
@@ -0,0 +1,88 @@
+# Artifact Repository - Pushing and Pulling libmxnet
+
+The artifact repository is an S3 bucket accessible only to restricted Jenkins nodes. It is used to store compiled MXNet artifacts that can be used by downstream CD pipelines to package the compiled libraries for different delivery channels (e.g. DockerHub, PyPI, Maven, etc.). The S3 object keys for the files being posted will be prefixed with the following distinguishing characteristics of the binary: branch, commit id, operating system, variant and dependency linking strategy (static or dynamic). For instance, s3://bucket/73b29fa90d3eac0b1fae403b7583fdd1529942dc/ubuntu16.04/cu92mkl/static/libmxnet.so
+
+An MXNet artifact is defined as the following set of files:
+
+* The compiled libmxnet.so
+* License files for dependencies that required their licenses to be shipped with the binary
+* Dependencies that should be shipped together with the binary. For instance, for packaging the python wheel files, some dependencies that cannot be statically linked to the library need to also be included, see here (https://github.com/apache/incubator-mxnet/blob/master/tools/pip/setup.py#L142).
+
+The artifact_repository.py script automates the upload and download of the specified files with the appropriate S3 object keys by taking explicitly set, or automatically derived, values for the different characteristics of the artifact.
+
+### Determining Artifact Characteristics
+
+An mxnet compiled library, or artifact for our purposes, is identified by the following distinguishing characteristics, which when not explicitly stated, will be (as much as possible) ascertained from the environment by the artifact_repository.py script: commit id, variant, operating system, and library type.
+
+**Commit Id**
+
+Manually configured through the --git-sha argument. 
+
+If not set, derived by:
+
+1. Using the values of the MXNET_SHA environment variable, which are set during the bootstrap process for *CD* Jenkins pipelines; otherwise
+2. Using the values of the GIT_COMMIT environment variable, which are set automatically by Jenkins in the *CI* pipelines; otherwise
+3. Using the output of git rev-parse HEAD for the commit id; otherwise
+4. Fail with error
+
+**Operating System**
+
+Manually configured through the --os argument.
+
+If not set, derived through the value of sys.platform (https://docs.python.org/3/library/sys.html#sys.platform). That is:
+
+* if, linux*, extract the ID and VERSION_ID from /etc/*release, and return a concatenated string of these values, eg. ubuntu16.04, centos7, etc.
+* otherwise, return the value given by sys.platform, eg. win32, darwin, etc.
+
+**Variant**
+
+Manually configured through the --variant argument. The current variants are: cpu, mkl, cu80, cu80mk, cu90, cu90mkl, cu92, cu92mkl, cu100, cu100mkl.
+
+As long as the tool is being run from the MXNet code base, the runtime feature detection tool (https://github.com/larroy/mxnet/blob/dd432b7f241c9da2c96bcb877c2dc84e6a1f74d4/docs/api/python/libinfo/libinfo.md) can be used to detect whether the library has been compiled with MKL (library has MKLDNN feature enabled) and/or CUDA support (compiled with CUDA feature enabled).
+
+If it has been compiled with CUDA support, the output of /usr/local/cuda/bin/nvcc --version can be mined for the exact CUDA version (eg. 8.0, 9.0, etc.).
+
+By knowing which features are enabled on the binary, and if necessary, which CUDA version is installed on the machine, the value for the variant argument can be calculated. Eg. if MKL and CUDA features are enabled, and nvcc reports cuda version 10, then the variant would be cu100mkl. If neither MKL nor CUDA features are enabled, the variant would be cpu. 
+
+**Dependency Linking**
+
+The library dependencies can be either statically or dynamically linked. This property will need to be manually set by user through either the `--static` or `--dynamic` arguments. There is no foolproof and programmatic way (that I could find) that can easily discern whether the library dependencies are statically or dynamically linked.
+
+### Uploading an Artifact
+
+The user must specify the path to the libmxnet.so, any license files, and any dependencies. The latter two are optional.
+ 
+Example:
+
+`./artifact_repository.py --push --static --libmxnet /path/to/libmxnet.so --licenses path/to/license1.txt /path/to/other_licenses/*.txt --dependencies /path/to/dependencies/*.so`
+
+`./artifact_repository.py --push --dynamic --libmxnet /path/to/libmxnet.so`
+
+NOTE: There is nothing stopping the user from uploading licenses and dependencies for dynamically linked libraries.
+
+### Downloading An Artifact
+
+The user must specify the directory to which the artifact should be downloaded. The user will also need to specify the variant, since different variants can work with the host operating system.
+
+Example:
+
+`./artifact_repository.py --pull --static --variant=cu90mkl ./dist`
+
+This would result in the following directory structure:
+
+```
+dist
+  |-----> libmxnet.so
+  |-----> libmxnet.meta
+  |-----> licenses
+             |-----> MKL_LICENSE.txt
+             |-----> CUP_LICENSE.txt
+             |-----> ...
+  |-----> dependencies
+             |-----> libxxx.so
+             |-----> libyyy.so
+             |-----> ...
+```
+
+The libmxnet.meta file will include the characteristics of the artifact (ie. library type, variant, git commit id, etc.) in a “property” file format.
+
diff --git a/ci/cd/utils/artifact_repository.py b/ci/cd/utils/artifact_repository.py
new file mode 100755
index 000000000000..43657118b169
--- /dev/null
+++ b/ci/cd/utils/artifact_repository.py
@@ -0,0 +1,623 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""
+Tool for uploading artifacts to the artifact repository
+"""
+
+__author__ = 'Per Goncalves da Silva'
+__version__ = '0.1'
+
+import argparse
+import ctypes
+import glob
+import logging
+import os
+import re
+import sys
+from itertools import chain
+from subprocess import CalledProcessError, check_output
+from typing import Dict, List, Optional
+
+import boto3
+import botocore.exceptions
+import yaml
+
+s3 = boto3.client('s3')
+logger = logging.getLogger(__name__)
+
+
+def config_logging():
+    """
+    Configures default logging settings
+    """
+    logging.root.setLevel(logging.WARNING)
+    logger.setLevel(logging.INFO)
+    logging.basicConfig(format='%(levelname)s: %(message)s')
+
+
+def s3_upload(bucket: str, s3_key_prefix: str, paths: List[str]):
+    """
+    Uploads a list of files to an S3 bucket with a particular S3 key prefix
+    :param bucket: The name of the S3 bucket
+    :param s3_key_prefix: The key prefix to apply to each of the files
+    :param paths: A list of paths to files
+    """
+    for path in paths:
+        s3_key = "{}/{}".format(s3_key_prefix, os.path.basename(path))
+        logger.info('Uploading {}'.format(path))
+        logger.debug("Uploading {} to s3://{}/{}".format(path, bucket, s3_key))
+        with open(path, 'rb') as data:
+            s3.upload_fileobj(Fileobj=data, Key=s3_key, Bucket=bucket)
+
+
+def write_libmxnet_meta(args: argparse.Namespace, destination: str):
+    """
+    Writes a file called libmxnet.meta in the 'destination' folder that contains
+    the libmxnet library information (commit id, type, etc.).
+    :param args: A Namespace object containing the library
+    :param destination: The folder in which to place the libmxnet.meta
+    """
+    with open(os.path.join(destination, 'libmxnet.meta'), 'w') as fp:
+        fp.write(yaml.dump({
+            "variant": args.variant,
+            "os": args.os,
+            "commit_id": args.git_sha,
+            "dependency_linking": args.lib_type,
+        }))
+
+
+def try_s3_download(bucket, s3_key_prefix, destination) -> bool:
+    """
+    Downloads a list of files to an S3 bucket with a particular S3 key prefix to 'destination'
+    :param bucket: The name of the S3 bucket
+    :param s3_key_prefix: The key prefix to apply to each of the files
+    :param destination the path to which to download the files
+    :return False if not artifacts were found, True otherwise
+    """
+    response = s3.list_objects_v2(Bucket=bucket, Prefix=s3_key_prefix)
+    if not response:
+        raise RuntimeError('Error listing S3 objects')
+
+    if response.get('KeyCount') is None:
+        logger.debug('Invalid S3 list objects response format')
+        logger.debug(response)
+        raise RuntimeError('Invalid response format.')
+
+    key_count = response.get('KeyCount')
+    if key_count == 0:
+        logger.debug('No artifacts found')
+        return False
+
+    if not response.get('Contents'):
+        logger.debug('Invalid S3 list objects response format')
+        logger.debug(response)
+        raise RuntimeError('Invalid response format.')
+
+    for obj in response.get('Contents'):
+        key = obj['Key']
+
+        # extract file path with any subdirectories and remove the leading file separator
+        output_path = os.path.join(destination, key[len(s3_key_prefix):].lstrip(os.sep))
+        os.makedirs(os.path.dirname(output_path), exist_ok=True)
+        logger.info('Downloading {}'.format(output_path))
+        logger.debug("Downloading s3://{}/{} to {}".format(bucket, key, output_path))
+        with open(output_path, 'wb') as fp:
+            s3.download_fileobj(Fileobj=fp, Key=key, Bucket=bucket)
+
+    return True
+
+
+def get_commit_id_from_cmd() -> Optional[str]:
+    """
+    Returns the output of 'git rev-parse HEAD'
+    :return: A commit id, or None if the command fails
+    """
+    try:
+        logger.debug('Executing "git rev-parse HEAD"')
+        commit_id = check_output("git rev-parse HEAD".split(" ")).decode('UTF-8').strip()
+        logger.debug('Found commit id: {}'.format(commit_id))
+        return commit_id
+    except CalledProcessError as e:
+        logger.debug('Error getting commit id:')
+        logger.debug(format(e))
+        return None
+
+
+def probe_commit_id() -> str:
+    """
+    Probes the system in an attempt to ascertain the mxnet commit id
+    :return: The commit id, or None if not found
+    """
+    logger.debug('Probing for commit id')
+    commit_id = os.environ.get('MXNET_SHA')
+    if not commit_id:
+        logger.debug('MXNET_SHA environment variable not set. Trying GIT_COMMIT')
+        commit_id = os.environ.get('GIT_COMMIT')
+    if not commit_id:
+        logger.debug('GIT_COMMIT environment variable not set. Trying git command')
+        commit_id = get_commit_id_from_cmd()
+    if not commit_id:
+        logger.debug('Could not determine git commit id')
+    else:
+        logger.debug('Commit id is: {}'.format(commit_id))
+    return commit_id
+
+
+def get_linux_os_release_properties() -> Optional[Dict[str, str]]:
+    """
+    Makes a dictionary out of /etc/os-release
+    :return: A dictionary of os release properties
+    """
+    logger.debug('Extracting operating system properties from /etc/os-release')
+    if not os.path.isfile('/etc/os-release'):
+        logger.debug('Error: /etc/os-release not found')
+        return None
+
+    try:
+        with open('/etc/os-release', 'r') as fp:
+            # removes empty spaces and quotation marks from line
+            property_tuple_list = [line.strip().replace('"', '').split('=') for line in fp if line.strip()]
+            return {key: value for (key, value) in property_tuple_list}
+    except Exception as e:
+        logger.debug('Error parsing /etc/os-release')
+        logger.debug(e)
+        return None
+
+
+def get_linux_distribution_and_version() -> Optional[str]:
+    """
+    Returns the linux distribution and version by taking
+    the values of ID and VERSION_ID from /etc/os-release and
+    concatenating them. Eg. centos7, ubuntu16.04, etc.
+    :return: The linux distribution and version string, or None if not found.
+    """
+    logger.debug('Getting linux distribution and version')
+    os_properties = get_linux_os_release_properties()
+    if os_properties:
+        logger.debug('os properties: {}'.format(os_properties))
+        distribution = os_properties['ID']
+        version = os_properties['VERSION_ID']
+        return "{}{}".format(distribution, version)
+
+    logger.debug('Error getting linux distribution and version. Could not determine os properties.')
+    return None
+
+
+def probe_operating_system() -> str:
+    """
+    Probes the system to determine the operating system.
+    :return: The name of the operating system, e.g. win32, darwin, ubuntu16.04, centos7, etc.
+    """
+    logger.debug('Determining operating system')
+    operating_system = sys.platform
+    logger.debug('Found platform: {}'.format(operating_system))
+    if operating_system.startswith('linux'):
+        operating_system = get_linux_distribution_and_version()
+
+    logger.debug('Operating system is {}'.format(operating_system))
+    return operating_system
+
+
+def get_libmxnet_features(libmxnet_path: str) -> Optional[Dict[str, bool]]:
+    """
+    Returns a string -> boolean dictionary mapping feature name
+    to whether it is enabled or not
+    :param libmxnet_path: path to the libmxnet library
+    :return: dictionary of features to whether they are enabled
+    """
+    logger.debug('Getting feature dictionary from {}'.format(libmxnet_path))
+
+    class Feature(ctypes.Structure):
+        _fields_ = [("_name", ctypes.c_char_p), ("enabled", ctypes.c_bool)]
+
+        @property
+        def name(self):
+            return self._name.decode()
+
+    try:
+        libmxnet = ctypes.CDLL(libmxnet_path, ctypes.RTLD_LOCAL)
+    except Exception as e:
+        logger.error('Error loading {}. '
+                     'Please check check path to libmxnet library is correct.'.format(libmxnet_path))
+        logger.error(e)
+        return None
+
+    libmxnet.MXGetLastError.restype = ctypes.c_char_p
+    feature_array = ctypes.POINTER(Feature)()
+    feature_array_size = ctypes.c_size_t()
+    if libmxnet.MXLibInfoFeatures(ctypes.byref(feature_array), ctypes.byref(feature_array_size)) != 0:
+        logger.error('Could not determine features from {}. '
+                     'Please specify the variant manually using the "--variant" argument.'.format(libmxnet_path))
+        return None
+    features = {feature_array[i].name: feature_array[i].enabled for i in range(feature_array_size.value)}
+    logger.debug('Found features: {}'.format(features))
+    return features
+
+
+def get_cuda_version() -> Optional[str]:
+    """
+    Returns the major and minor cuda version without the '.'
+    eg. 10.0 => 100, 9.2 => 92, etc.
+    :return: CUDA version
+    """
+    logger.debug('Determining cuda version')
+
+    try:
+        logger.debug('Executing "nvcc -V"')
+        nvcc_version = check_output("nvcc -V".split(" ")).decode('UTF-8').strip()
+    except CalledProcessError as e:
+        logger.error('Error getting nvcc version')
+        logger.error(e)
+        return None
+
+    logger.debug('Extracting cuda version from {}'.format(nvcc_version))
+    # eg. "Cuda compilation tools, release 10.0, V10.0.130"
+    match = re.search(r' ([0-9]+.[0-9]+)', nvcc_version)
+    if match:
+        cuda_version = match.group(1).replace('.', '')
+        logger.debug('Found cuda version: {}'.format(cuda_version))
+        return cuda_version
+
+    logger.debug('Could not determine cuda version from "{}"'.format(nvcc_version))
+    return None
+
+
+def probe_cpu_variant(mxnet_features: Dict[str, bool]) -> str:
+    """
+    Returns the mxnet cpu targeted variant depending on which mxnet features are enabled
+    :param mxnet_features: An mxnet feature dictionary of feature to boolean (True = enabled)
+    :return: Either cpu, or mkl as the variant
+    """
+    logger.debug('Determining cpu variant')
+    if mxnet_features['MKLDNN']:
+        logger.debug('variant is: mkl')
+        return 'mkl'
+
+    logger.debug('variant is: cpu')
+    return 'cpu'
+
+
+def probe_gpu_variant(mxnet_features: Dict[str, bool]) -> Optional[str]:
+    """
+    Returns the mxnet gpu variant depending on which mxnet features are enabled
+    :param mxnet_features: An mxnet feature dictionary of feature to boolean (True = enabled)
+    :return: The mxnet gpu variant, eg. cu90, cu90mkl, etc.
+    :raises RuntimeError is the CUDA feature is not enabled in the library
+    """
+    if not mxnet_features['CUDA']:
+        raise RuntimeError('Cannot determine gpu variant. CUDA feature is disabled.')
+
+    cuda_version = get_cuda_version()
+    if cuda_version:
+        variant = 'cu{}'.format(cuda_version)
+        if mxnet_features['MKLDNN']:
+            variant = '{}mkl'.format(variant)
+        logger.debug('variant is: {}'.format(variant))
+        return variant
+    logger.debug('Error determining mxnet variant: Could not retrieve cuda version')
+    return None
+
+
+def probe_mxnet_variant(limxnet_path: str) -> Optional[str]:
+    """
+    Probes the libmxnet library and environment to determine
+    the mxnet variant, eg. cpu, mkl, cu90, cu90mkl, etc.
+    :return:
+    """
+    logger.debug('Probing for mxnet variant')
+    features = get_libmxnet_features(limxnet_path)
+    if not features:
+        logger.debug('Error: could not determine variant. Features could not be extracted from libmxnet')
+        return None
+
+    if features['CUDA']:
+        return probe_gpu_variant(features)
+    return probe_cpu_variant(features)
+
+
+def probe_artifact_repository_bucket() -> Optional[str]:
+    """
+    Probes environment variables in search of artifact repository bucket
+    :return: string containing the artifact repository bucket name
+    """
+    logger.debug('Probing for artifact repository bucket name')
+    bucket = os.environ.get('ARTIFACT_REPOSITORY_BUCKET')
+    if not bucket:
+        logger.debug('ARTIFACT_REPOSITORY_BUCKET environment variable not found')
+    return bucket
+
+
+def probe(args: argparse.Namespace) -> argparse.Namespace:
+    """
+    Probes the system to set any arguments that weren't manually set.
+    Modifies the input Namespace object with the probed parameters.
+    :param args: The namespace object given by argparse.parse()
+    """
+    logger.debug('Trying to auto-determine arguments from environment')
+    if not args.git_sha:
+        commit_id = probe_commit_id()
+        if not commit_id:
+            logger.error('Could not determine commit id. '
+                         'Please set it manually with --git-sha, or ensure you are in a cloned '
+                         'mxnet repository directory')
+            sys.exit(1)
+        args.git_sha = commit_id
+
+    if not args.variant:
+        variant = probe_mxnet_variant(args.libmxnet)
+        if not variant:
+            logger.error('Could not determine mxnet variant. Please set it manually with --variant')
+            sys.exit(1)
+        args.variant = variant
+
+    if not args.os:
+        operating_system = probe_operating_system()
+        if not operating_system:
+            logger.error('Could not determine operating system. Please set it manually with --os')
+            sys.exit(1)
+        args.os = operating_system
+
+    if not args.bucket:
+        artifact_repo_bucket = probe_artifact_repository_bucket()
+        if not artifact_repo_bucket:
+            logger.error('Could not determine artifact repository bucket. Please set it manually with --bucket')
+            sys.exit(1)
+        args.bucket = artifact_repo_bucket
+
+    return args
+
+
+def get_s3_key_prefix(args: argparse.Namespace, subdir: str = '') -> str:
+    """
+    Returns the S3 key prefix given the arguments namespace
+    :param args: The arguments passed in by the user or derived by the script
+    :param subdir: An optional subdirectory in which to store the files. Post-pended to the end of the prefix.
+    :return: A string containing the S3 key prefix to be used to uploading and downloading files to the artifact repository
+    """
+    prefix = "{git_sha}/{lib_type}/{os}/{variant}/".format(**vars(args))
+    if subdir:
+        return "{}{}/".format(prefix, subdir)
+    return prefix
+
+
+def push_artifact(args: argparse.Namespace):
+    """
+    Pushes the artifact to the artifact repository
+    :param args: The arguments passed in to this script by the user
+    :return 0 for success, non-zero for failure
+    """
+
+    args = probe(args)
+
+    logger.info('Pushing artifact with: ')
+    logger.info('COMMIT ID   : {}'.format(args.git_sha))
+    logger.info('OS          : {}'.format(args.os))
+    logger.info('VARIANT     : {}'.format(args.variant))
+    logger.info("LIBMXNET    : {}".format(args.libmxnet))
+    logger.info("LICENSES    : {}".format(args.licenses))
+    logger.info("DEPENDENCIES: {}".format(args.dependencies))
+    logger.info("")
+
+    # Upload mxnet
+    try:
+        logger.info('Uploading libmxnet library...')
+        s3_upload(args.bucket, get_s3_key_prefix(args), [args.libmxnet])
+        logger.info("")
+
+        # Upload licenses, if necessary
+        if args.licenses:
+            logger.info('Uploading licenses...')
+            s3_upload(args.bucket, get_s3_key_prefix(args, subdir='licenses'), args.licenses)
+            logger.info("")
+
+        # Upload dependencies, if necessary
+        if args.dependencies:
+            logger.info('Uploading dependencies...')
+            s3_upload(args.bucket, get_s3_key_prefix(args, subdir='dependencies'), args.dependencies)
+            logger.info("")
+    except botocore.exceptions.BotoCoreError as e:
+        logger.error('Error uploading artifact')
+        logger.error(e)
+        return 1
+
+    logger.info('Successfully pushed artifact')
+    return 0
+
+
+def pull_artifact(args: argparse.Namespace):
+    """
+    Pulls the artifact from the artifact repository
+    :param args: The arguments passed in to this script by the user
+    :return 0 for success, 1 for unexpected failure, 2 for no artifact found failure
+    """
+    if not args.variant:
+        logger.warning('''variant not set. Using 'cpu' by default.''')
+        args.variant = 'cpu'
+
+    args = probe(args)
+
+    logger.info('Pulling artifact with: ')
+    logger.info('COMMIT ID   : {}'.format(args.git_sha))
+    logger.info('OS          : {}'.format(args.os))
+    logger.info('VARIANT     : {}'.format(args.variant))
+    logger.info('To directory: {}'.format(args.destination))
+
+    try:
+        if not try_s3_download(args.bucket, get_s3_key_prefix(args), args.destination):
+            logging.error('No artifacts found for this configuration.')
+            return 2
+        write_libmxnet_meta(args=args, destination=args.destination)
+    except botocore.exceptions.BotoCoreError as e:
+        logger.error('Error downloading artifact')
+        logger.error(e)
+        return 1
+
+    logger.info('Successfully pulled artifact')
+    return 0
+
+
+def is_file(path: str) -> str:
+    """
+    Returns true or false if path points to an existing file
+    :param path: A path to a file
+    :return: True if file exists and is a file, False otherwise
+    :raises FileNotFoundError if file does not exist
+    """
+    if not os.path.exists(path):
+        raise FileNotFoundError('''File '{}' not found'''.format(path))
+    return os.path.isfile(path)
+
+
+def sanitize_path_array(paths: List[str]) -> List[str]:
+    """
+    Expands supplied paths and removes empty or non-file entries.
+    :param paths: A list of paths
+    :return: A sanitized list of paths
+    :raises FileNotFoundError if a file does not exist
+    """
+    expanded_paths = list(chain(*[glob.glob(path.strip()) for path in paths if path.strip() != '']))
+    return [path.strip() for path in expanded_paths if path.strip() != '' and is_file(path)]
+
+
+def main() -> int:
+    config_logging()
+
+    logger.info("MXNet-CD Artifact Repository Tool")
+
+    parser = argparse.ArgumentParser(description="Utility for uploading and downloading MXNet artifacts")
+
+    parser.add_argument("--push",
+                        help="Upload artifact to repository",
+                        required=False,
+                        action='store_true')
+
+    parser.add_argument("--pull",
+                        help="Download artifact from repository",
+                        required=False,
+                        action='store_true')
+
+    parser.add_argument("--libmxnet",
+                        help="Path to libmxnet library",
+                        required=False,
+                        type=str)
+
+    parser.add_argument("--licenses",
+                        help="Paths to license files",
+                        required=False,
+                        nargs=argparse.ZERO_OR_MORE,
+                        default=[])
+
+    parser.add_argument("--dependencies",
+                        help="Paths to dependencies",
+                        required=False,
+                        nargs=argparse.ZERO_OR_MORE,
+                        default=[])
+
+    parser.add_argument("--os",
+                        help="Target operating system",
+                        type=str)
+
+    parser.add_argument("--git-sha",
+                        help="MXNet repository commit id",
+                        required=False,
+                        type=str)
+
+    parser.add_argument("--variant",
+                        help="MXNet binary variant. Eg. cpu, mkl, cu90, cu100mkl, etc.",
+                        required=False,
+                        type=str)
+
+    parser.add_argument("--static",
+                        help="libmxnet library static dependency linking",
+                        required=False,
+                        action='store_true')
+
+    parser.add_argument("--dynamic",
+                        help="libmxnet library with dynamic dependency linking",
+                        required=False,
+                        action='store_true')
+
+    parser.add_argument('--bucket',
+                        help="S3 bucket to store files",
+                        type=str,
+                        required=False)
+
+    parser.add_argument('--destination',
+                        help="Destination for downloaded library and supporting files",
+                        type=str,
+                        default=os.getcwd(),
+                        required=False)
+
+    parser.add_argument('--verbose',
+                        help='Verbose',
+                        action='store_true',
+                        default=False)
+
+    parser.add_argument('--debug',
+                        help='Debug mode',
+                        action='store_true',
+                        default=False)
+
+    args = parser.parse_args()
+
+    if args.verbose:
+        logger.setLevel(logging.DEBUG)
+
+    if args.debug:
+        # Set debug level on root logger (ie. for all other loggers)
+        logging.getLogger().setLevel(logging.DEBUG)
+
+    if not args.push and not args.pull:
+        logger.info('''Mode not specified. Using 'push' by default.''')
+        args.push = True
+
+    if not args.static and not args.dynamic:
+        logger.info('''Library type not specified. Using 'dynamic' by default.''')
+        args.dynamic = True
+
+    # libmxnet argument is required for push mode
+    if args.push and not args.libmxnet:
+        logger.error('Path to libmxnet library must be specified when in push mode. '
+                     'Please specify it with --libmxnet.')
+        return 1
+
+    # store string version of library type - used to facilitate S3 object prefix calculation
+    args.lib_type = 'static' if args.static else 'dynamic'
+
+    # sanitize license and dependency arrays
+    # Remove empty or directory entries
+    args.licenses = sanitize_path_array(args.licenses)
+    args.dependencies = sanitize_path_array(args.dependencies)
+
+    # expand destination path
+    args.destination = os.path.abspath(args.destination)
+
+    if args.push:
+        return push_artifact(args)
+
+    elif args.pull:
+        return pull_artifact(args)
+
+    return 0
+
+
+if __name__ == '__main__':
+    sys.exit(main())
diff --git a/ci/cd/utils/requirements.txt b/ci/cd/utils/requirements.txt
new file mode 100644
index 000000000000..4ecbff9c00cb
--- /dev/null
+++ b/ci/cd/utils/requirements.txt
@@ -0,0 +1,2 @@
+boto3==1.9.114
+PyYAML==5.1
diff --git a/ci/cd/utils/test_artifact_repository.py b/ci/cd/utils/test_artifact_repository.py
new file mode 100644
index 000000000000..13a559379901
--- /dev/null
+++ b/ci/cd/utils/test_artifact_repository.py
@@ -0,0 +1,494 @@
+import unittest
+from unittest.mock import MagicMock, mock_open, patch
+
+from artifact_repository import *
+
+
+class TestArtifactRepositoryTool(unittest.TestCase):
+
+    @staticmethod
+    def create_argparse_namespace(libmxnet_path: Optional[str] = 'path_to_libmxnet',
+                                  git_sha: Optional[str] = 'abc123',
+                                  variant: Optional[str] = 'cpu',
+                                  operating_system: Optional[str] = 'linux',
+                                  lib_type: Optional[str] = 'static',
+                                  bucket: Optional[str] = 's3bucket',
+                                  licenses: Optional[List[str]] = [],
+                                  dependencies: Optional[List[str]] = []) -> argparse.Namespace:
+        """
+        Returns a namespace object containing the script's arguments with sample or specified values
+        """
+        ns = argparse.Namespace()
+        ns.libmxnet = libmxnet_path
+        ns.git_sha = git_sha
+        ns.variant = variant
+        ns.os = operating_system
+        ns.lib_type = lib_type
+        ns.bucket = bucket
+        ns.licenses = licenses
+        ns.dependencies = dependencies
+
+        return ns
+
+    @patch('artifact_repository.check_output')
+    def test_get_commit_id_from_cmd_returns_none_on_fail(self, mock):
+        """
+        Tests get_commit_id_from_cmd returns None if the command fails
+        """
+        not_sucessful = 255
+        mock.side_effect = CalledProcessError(cmd="some command", returncode=not_sucessful)
+        self.assertIsNone(get_commit_id_from_cmd())
+
+    def test_probe_commit_id_mxnet_sha(self):
+        """
+        Tests the value of MXNET_SHA env var is returned
+        """
+        with patch.dict('os.environ', {'MXNET_SHA': 'abcd1234'}):
+            self.assertEqual(probe_commit_id(), 'abcd1234')
+
+    def test_probe_commit_id_git_commit(self):
+        """
+        Tests the value of GIT_COMMIT env. var is returned
+        if MXNET_SHA env var is not present
+        """
+        with patch.dict('os.environ', {'GIT_COMMIT': 'abcd1234'}):
+            self.assertEqual(probe_commit_id(), 'abcd1234')
+
+        with patch.dict('os.environ', {'MXNET_SHA': 'efgh5678', 'GIT_COMMIT': 'abcd1234'}):
+            self.assertEqual(probe_commit_id(), 'efgh5678')
+
+    @patch('artifact_repository.get_commit_id_from_cmd')
+    def test_probe_commit_id_git_cmd(self, mock):
+        """
+        Tests the git commit id from the git command is returned
+        if neither MXNET_SHA nor GIT_COMMIT env vars are set
+        """
+        mock.return_value = 'abcd1234'
+        self.assertEqual(probe_commit_id(), 'abcd1234')
+
+    def test_get_linux_os_release_properties(self):
+        properties = """
+        KEY=value
+        KEY2=value2
+        KEY3=value3
+        """
+        mock = mock_open(read_data=properties)
+        with(patch('artifact_repository.os.path')) as path_mock:
+            path_mock.is_file.return_value = True
+            with patch('artifact_repository.open', mock, create=True):
+                properties = get_linux_os_release_properties()
+        self.assertEqual(properties['KEY3'], 'value3')
+
+    def test_get_linux_os_release_properties_with_quotes(self):
+        """
+        Tests quote marks are removed from values
+        """
+        properties = """
+        KEY="value"
+        """
+        mock = mock_open(read_data=properties)
+        with(patch('artifact_repository.os.path')) as path_mock:
+            path_mock.is_file.return_value = True
+            with patch('artifact_repository.open', mock, create=True):
+                properties = get_linux_os_release_properties()
+        self.assertEqual(properties['KEY'], 'value')
+
+    @patch('artifact_repository.sys')
+    def test_probe_operating_system_windows(self, mock):
+        mock.platform = 'win32'
+        self.assertEqual(probe_operating_system(), 'win32')
+
+    @patch('artifact_repository.sys')
+    def test_probe_operating_system_darwin(self, mock):
+        mock.platform = 'darwin'
+        self.assertEqual(probe_operating_system(), 'darwin')
+
+    @patch('artifact_repository.sys')
+    @patch('artifact_repository.get_linux_os_release_properties')
+    def test_probe_operating_system_linux(self, mock_props, mock_sys):
+        mock_props.return_value = {'ID': 'ubuntu', 'VERSION_ID': '16.04'}
+
+        mock_sys.platform = 'linux'
+        self.assertEqual(probe_operating_system(), 'ubuntu16.04')
+
+        # sys.platform can return linux or linux2
+        mock_sys.platform = 'linux2'
+        self.assertEqual(probe_operating_system(), 'ubuntu16.04')
+
+    @patch('artifact_repository.check_output')
+    def test_get_cuda_version(self, mock):
+        """
+        Tests correct cuda version with the right format is returned
+        :return:
+        """
+        mock.return_value = b'Cuda compilation tools, release 10.0, V10.0.130'
+        cuda_version = get_cuda_version()
+        self.assertEqual(cuda_version, '100')
+
+        mock.return_value = b'Cuda compilation tools, release 9.2, V9.2.148'
+        cuda_version = get_cuda_version()
+        self.assertEqual(cuda_version, '92')
+
+    @patch('artifact_repository.check_output')
+    def test_get_cuda_version_not_found(self, mock):
+        """
+        Tests None is returned there's an error retrieving the cuda version
+        :return:
+        """
+        not_sucessful = 255
+        mock.side_effect = CalledProcessError(cmd="nvidia version command", returncode=not_sucessful)
+        self.assertIsNone(get_cuda_version())
+
+    @patch('artifact_repository.get_libmxnet_features')
+    def test_probe_variant_cpu(self, mock_features):
+        """
+        Tests 'cpu' is returned if MKLDNN and CUDA features are OFF
+        """
+        mock_features.return_value = {'MKLDNN': False, 'CUDA': False}
+        self.assertEqual(probe_mxnet_variant('libmxnet.so'), 'cpu')
+
+    @patch('artifact_repository.get_libmxnet_features')
+    def test_probe_variant_mkl(self, mock_features):
+        """
+        Tests 'mkl' is returned if MKLDNN is ON and CUDA is OFF
+        """
+        mock_features.return_value = {'MKLDNN': True, 'CUDA': False}
+        self.assertEqual(probe_mxnet_variant('libmxnet.so'), 'mkl')
+
+    @patch('artifact_repository.get_libmxnet_features')
+    @patch('artifact_repository.get_cuda_version')
+    def test_probe_variant_cuda(self, mock_cuda_version, mock_features):
+        """
+        Tests 'cu100' is returned if MKLDNN is OFF and CUDA is ON and CUDA version is 10.0
+        """
+        mock_features.return_value = {'MKLDNN': False, 'CUDA': True}
+        mock_cuda_version.return_value = '100'
+        self.assertEqual(probe_mxnet_variant('libmxnet.so'), 'cu100')
+
+    @patch('artifact_repository.get_libmxnet_features')
+    @patch('artifact_repository.get_cuda_version')
+    def test_probe_variant_cuda_mkl(self, mock_cuda_version, mock_features):
+        """
+        Tests 'cu100mkl' is returned if MKLDNN is ON and CUDA is ON and CUDA version is 10.0
+        """
+        mock_features.return_value = {'MKLDNN': True, 'CUDA': True}
+        mock_cuda_version.return_value = '100'
+        self.assertEqual(probe_mxnet_variant('libmxnet.so'), 'cu100mkl')
+
+    @patch('artifact_repository.get_libmxnet_features')
+    def test_probe_variant_cuda_returns_none_on_no_features(self, mock_features):
+        """
+        Tests None is returned if the mxnet features could not be extracted from the libmxnet.so file
+        """
+        mock_features.return_value = None
+        self.assertIsNone(probe_mxnet_variant('libmxnet.so'))
+
+    @patch('artifact_repository.get_libmxnet_features')
+    @patch('artifact_repository.get_cuda_version')
+    def test_probe_variant_cuda_mkl(self, mock_cuda_version, mock_features):
+        """
+        Tests None is returned if CUDA feature is ON but cuda version could not be determined
+        """
+        mock_features.return_value = {'MKLDNN': True, 'CUDA': True}
+        mock_cuda_version.return_value = None
+        self.assertIsNone(probe_mxnet_variant('libmxnet.so'))
+
+    def test_probe_artifact_repository_bucket(self):
+        """
+        Tests artiact repository bucket is retrieved from environment variable ARTIFACT_REPOSITORY_BUCKET
+        """
+        with patch.dict('os.environ', {'ARTIFACT_REPOSITORY_BUCKET': 'some bucket'}):
+            self.assertEqual(probe_artifact_repository_bucket(), 'some bucket')
+
+    @patch('artifact_repository.probe_commit_id')
+    def test_probe_no_commit_id(self, mock):
+        """
+        Tests commit id gets probed if not set by user
+        """
+        fake_args = TestArtifactRepositoryTool.create_argparse_namespace(git_sha=None)
+        mock.return_value = 'deadbeef'
+        probe(fake_args)
+        mock.assert_called_once()
+        self.assertEqual(fake_args.git_sha, 'deadbeef')
+
+    @patch('artifact_repository.probe_commit_id')
+    def test_probe_no_commit_id_failed(self, mock):
+        """
+        Tests script will exit if commid id probe fails
+        """
+        fake_args = TestArtifactRepositoryTool.create_argparse_namespace(git_sha=None)
+        mock.return_value = None
+        with self.assertRaises(SystemExit):
+            probe(fake_args)
+
+
+    @patch('artifact_repository.probe_operating_system')
+    def test_probe_no_operating_system(self, mock):
+        """
+        Tests operating system gets probed if not set by user
+        """
+        fake_args = TestArtifactRepositoryTool.create_argparse_namespace(operating_system=None)
+        mock.return_value = 'be/os'
+        probe(fake_args)
+        mock.assert_called_once()
+        self.assertEqual(fake_args.os, 'be/os')
+
+    @patch('artifact_repository.probe_operating_system')
+    def test_probe_no_operating_system_failed(self, mock):
+        """
+        Tests script will exit if operating system probe fails
+        """
+        fake_args = TestArtifactRepositoryTool.create_argparse_namespace(operating_system=None)
+        mock.return_value = None
+        with self.assertRaises(SystemExit):
+            probe(fake_args)
+
+    @patch('artifact_repository.probe_mxnet_variant')
+    def test_probe_no_variant(self, mock):
+        """
+        Tests mxnet variant gets probed if not set by user
+        """
+        fake_args = TestArtifactRepositoryTool.create_argparse_namespace(variant=None)
+        mock.return_value = 'cpu90mkl'
+        probe(fake_args)
+        mock.assert_called_once()
+        self.assertEqual(fake_args.variant, 'cpu90mkl')
+
+    @patch('artifact_repository.probe_mxnet_variant')
+    def test_probe_no_mxnet_variant_failed(self, mock):
+        """
+        Tests script will exit if mxnet variant probe fails
+        """
+        fake_args = TestArtifactRepositoryTool.create_argparse_namespace(variant=None)
+        mock.return_value = None
+        with self.assertRaises(SystemExit):
+            probe(fake_args)
+
+    @patch('artifact_repository.probe_artifact_repository_bucket')
+    def test_probe_no_bucket(self, mock):
+        """
+        Tests artifact repository bucket gets probed if not set by user
+        """
+        fake_args = TestArtifactRepositoryTool.create_argparse_namespace(bucket=None)
+        mock.return_value = 'bucket'
+        probe(fake_args)
+        mock.assert_called_once()
+        self.assertEqual(fake_args.bucket, 'bucket')
+
+    @patch('artifact_repository.probe_artifact_repository_bucket')
+    def test_probe_no_bucket_failed(self, mock):
+        """
+        Tests script will exit if bucket probe fails
+        """
+        fake_args = TestArtifactRepositoryTool.create_argparse_namespace(bucket=None)
+        mock.return_value = None
+        with self.assertRaises(SystemExit):
+            probe(fake_args)
+
+    def test_get_s3_key_prefix(self):
+        """
+        Tests S3 key prefix is properly formated
+        """
+        fake_args = TestArtifactRepositoryTool.create_argparse_namespace(git_sha="abc123",
+                                                                         operating_system='linux',
+                                                                         variant='cpu',
+                                                                         lib_type='static')
+
+        self.assertEqual(get_s3_key_prefix(fake_args), 'abc123/static/linux/cpu/')
+
+    def test_get_s3_key_prefix_with_subdir(self):
+        """
+        Tests S3 key prefix with sub-directory is properly formated
+        """
+        fake_args = TestArtifactRepositoryTool.create_argparse_namespace(git_sha="abc123",
+                                                                         operating_system='linux',
+                                                                         variant='cpu',
+                                                                         lib_type='static')
+
+        self.assertEqual(get_s3_key_prefix(fake_args, subdir='subdir'), 'abc123/static/linux/cpu/subdir/')
+
+    @patch('artifact_repository.s3')
+    def test_try_s3_download_fails_on_bad_response(self, mock_s3):
+        """
+        Tests RuntimeError is thrown if the response is malformed
+        """
+        key_prefix = 'some/key/prefix'
+        mock_s3.list_objects_v2.return_value = {
+            'something': 'not quite right'
+        }
+
+        with self.assertRaises(RuntimeError):
+            try_s3_download(bucket='bucket', s3_key_prefix=key_prefix, destination='')
+
+    @patch('artifact_repository.s3')
+    def test_try_s3_download_returns_false_on_no_keys(self, mock_s3):
+        """
+        Tests False is returned when there are no keys for the prefix. Ie. no artifact to download
+        """
+        key_prefix = 'some/key/prefix'
+        mock_s3.list_objects_v2.return_value = {
+            'KeyCount': 0
+        }
+        self.assertFalse(try_s3_download(bucket='bucket', s3_key_prefix=key_prefix, destination=''))
+
+    @patch('artifact_repository.os.makedirs', autospec=True)
+    @patch('artifact_repository.s3')
+    def test_try_s3_download_with_destination(self, mock_s3, mock_makedirs):
+        """
+        Tests files are downloaded to the right destinations when destination parameter is not empty
+        """
+        key_prefix = 'some/key/prefix'
+        s3_keys = [
+            {'Key': '{}/file.txt'.format(key_prefix)},
+            {'Key': '{}/subdir/other.txt'.format(key_prefix)},
+            {'Key': '{}/another/sub/dir/f.txt'.format(key_prefix)}
+        ]
+
+        mock_s3.list_objects_v2.return_value = {
+            'Contents': s3_keys,
+            'KeyCount': 3
+        }
+
+        mock_s3.download_fileobj = MagicMock()
+        mock_fopen = mock_open()
+
+        with patch('artifact_repository.open', mock_fopen, create=True):
+            dest = os.path.join('dest', 'ination')
+            self.assertTrue(try_s3_download(bucket='bucket', s3_key_prefix=key_prefix, destination=dest))
+
+            # Assert directories are created
+            mock_makedirs.assert_has_calls([
+                unittest.mock.call(dest, exist_ok=True),
+                unittest.mock.call(os.path.join(dest, 'subdir'), exist_ok=True),
+                unittest.mock.call(os.path.join(dest, 'another', 'sub', 'dir'), exist_ok=True)
+            ])
+
+            # Assert files are downloaded
+            mock_s3.download_fileobj.assert_has_calls([
+                unittest.mock.call(Bucket='bucket', Fileobj=unittest.mock.ANY, Key=s3_keys[0]['Key']),
+                unittest.mock.call(Bucket='bucket', Fileobj=unittest.mock.ANY, Key=s3_keys[1]['Key']),
+                unittest.mock.call(Bucket='bucket', Fileobj=unittest.mock.ANY, Key=s3_keys[2]['Key']),
+            ])
+
+    @patch('artifact_repository.os.makedirs', autospec=True)
+    @patch('artifact_repository.s3')
+    def test_try_s3_download(self, mock_s3, mock_makedirs):
+        """
+        Tests files are downloaded to the right destinations when destination parameter is empty
+        """
+        key_prefix = 'some/key/prefix'
+        s3_keys = [
+            {'Key': '{}/file.txt'.format(key_prefix)},
+            {'Key': '{}/subdir/other.txt'.format(key_prefix)},
+            {'Key': '{}/another/sub/dir/f.txt'.format(key_prefix)}
+        ]
+
+        mock_s3.list_objects_v2.return_value = {
+            'Contents': s3_keys,
+            'KeyCount': 3
+        }
+
+        mock_s3.download_fileobj = MagicMock()
+        mock_fopen = mock_open()
+
+        with patch('artifact_repository.open', mock_fopen, create=True):
+            dest = ''
+            self.assertTrue(try_s3_download(bucket='bucket', s3_key_prefix=key_prefix, destination=dest))
+
+            # Assert directories are created
+            mock_makedirs.assert_has_calls([
+                unittest.mock.call(dest, exist_ok=True),
+                unittest.mock.call(os.path.join(dest, 'subdir'), exist_ok=True),
+                unittest.mock.call(os.path.join(dest, 'another', 'sub', 'dir'), exist_ok=True)
+            ])
+
+            # Assert files are downloaded
+            mock_s3.download_fileobj.assert_has_calls([
+                unittest.mock.call(Bucket='bucket', Fileobj=unittest.mock.ANY, Key=s3_keys[0]['Key']),
+                unittest.mock.call(Bucket='bucket', Fileobj=unittest.mock.ANY, Key=s3_keys[1]['Key']),
+                unittest.mock.call(Bucket='bucket', Fileobj=unittest.mock.ANY, Key=s3_keys[2]['Key']),
+            ])
+
+    @patch('artifact_repository.s3')
+    def test_s3_upload(self, mock_s3):
+        """
+        Tests files are uploaded using the supplied s3_key_prefix
+        """
+        key_prefix = 'some/key/prefix'
+        paths = [
+            os.path.join('mainfile.txt'),
+            os.path.join('some/dir/file.txt'),
+            os.path.join('some/other/dir/another.txt'),
+        ]
+
+        mock_s3.upload_fileobj = MagicMock()
+        mock_fopen = mock_open(read_data=b'some data')
+
+        with patch('artifact_repository.open', mock_fopen, create=True):
+            s3_upload(bucket='bucket', s3_key_prefix=key_prefix, paths=paths)
+            mock_s3.upload_fileobj.assert_has_calls([
+                unittest.mock.call(Fileobj=unittest.mock.ANY, Key='some/key/prefix/mainfile.txt', Bucket='bucket'),
+                unittest.mock.call(Fileobj=unittest.mock.ANY, Key='some/key/prefix/file.txt', Bucket='bucket'),
+                unittest.mock.call(Fileobj=unittest.mock.ANY, Key='some/key/prefix/another.txt', Bucket='bucket'),
+            ])
+
+    @patch('artifact_repository.os.path.isfile')
+    @patch('artifact_repository.os.path.exists')
+    def test_is_file_is_file(self, mock_exists, mock_isfile):
+        """
+        Tests is file returns True when path exists and is a file
+        """
+        mock_exists.return_value = True
+        mock_isfile.return_value = True
+        self.assertTrue(is_file('some/path'))
+
+    @patch('artifact_repository.os.path.isfile')
+    @patch('artifact_repository.os.path.exists')
+    def test_is_file_not_file(self, mock_exists, mock_isfile):
+        """
+        Tests is file returns False when path exists and is _not_ a file
+        """
+        mock_exists.return_value = True
+        mock_isfile.return_value = False
+        self.assertFalse(is_file('some/path'))
+
+    @patch('artifact_repository.os.path.exists')
+    def test_is_file_not_found(self, mock_exists):
+        """
+        Tests FileNotFound error thrown if file not found
+        """
+        mock_exists.return_value = False
+        with self.assertRaises(FileNotFoundError) as ctx:
+            is_file('some/path')
+        self.assertEqual(str(ctx.exception), 'File \'{}\' not found'.format('some/path'))
+
+    def test_sanitize_path_array_empty_paths(self):
+        """
+        Tests empty paths are removed
+        """
+        self.assertListEqual(sanitize_path_array([' ', '\t', '     \n']), [])
+
+    @patch('artifact_repository.is_file')
+    @patch('artifact_repository.glob', autospec=True)
+    def test_sanitize_path_array_directories(self, mock_glob, mock_isfile):
+        """
+        Tests directory paths are removed
+        """
+        mock_isfile.side_effect = [False, True, False]
+        mock_glob.glob = lambda x: [x]
+        self.assertListEqual(sanitize_path_array(['dir1', 'file', 'dir2']), ['file'])
+
+    def test_write_libmxnet_meta(self):
+        """
+        Tests libmxnet.meta is properly written out
+        """
+        mock_fopen = mock_open()
+        with patch('artifact_repository.open', mock_fopen, create=True):
+            fake_args = TestArtifactRepositoryTool.create_argparse_namespace(git_sha='abcd1234',
+                                                                             variant='gpu',
+                                                                             operating_system='lunix',
+                                                                             lib_type='stynamic')
+            write_libmxnet_meta(args=fake_args, destination='dest')
+            mock_fopen.assert_called_once_with(os.path.join('dest', 'libmxnet.meta'), 'w')
+            mock_fopen().write.called_with(
+                'commit_id: abcd1234\ndependency_linking: stynamic\nos: lunix\nvariant: gpu\n')

From 87207c5fd2eaf41ec5e0afdc68a0eb261143ced3 Mon Sep 17 00:00:00 2001
From: Per Goncalves da Silva <perdasilva@gmail.com>
Date: Thu, 23 May 2019 12:44:57 +0200
Subject: [PATCH 241/813] Adds CD pipeline framework

---
 ci/cd/Jenkinsfile_cd_pipeline  |  61 ++++++++++++++++++
 ci/cd/Jenkinsfile_release_job  | 103 ++++++++++++++++++++++++++++++
 ci/cd/Jenkinsfile_utils.groovy | 110 +++++++++++++++++++++++++++++++++
 3 files changed, 274 insertions(+)
 create mode 100644 ci/cd/Jenkinsfile_cd_pipeline
 create mode 100644 ci/cd/Jenkinsfile_release_job
 create mode 100644 ci/cd/Jenkinsfile_utils.groovy

diff --git a/ci/cd/Jenkinsfile_cd_pipeline b/ci/cd/Jenkinsfile_cd_pipeline
new file mode 100644
index 000000000000..a6a65f22895e
--- /dev/null
+++ b/ci/cd/Jenkinsfile_cd_pipeline
@@ -0,0 +1,61 @@
+// -*- mode: groovy -*-
+
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+//
+// Jenkins pipeline
+// See documents at https://jenkins.io/doc/book/pipeline/jenkinsfile/
+
+// MXNet Continuous Delivery Pipeline
+// Orchestrates the release of the different artifact by calling downstream release jobs.
+
+pipeline {
+  agent {
+    label 'restricted-utility'
+  }
+
+  parameters {
+    // Release parameters
+    string(defaultValue: "", description: 'Optional commit id to use for release', name: "MXNET_SHA")
+    booleanParam(defaultValue: false, description: 'Whether this is a release build or not', name: "RELEASE_BUILD")
+    string(defaultValue: "cpu,mkl,cu80,cu80mkl,cu90,cu90mkl,cu92,cu92mkl,cu100,cu100mkl", description: "Comma separated list of variants", name: "MXNET_VARIANTS")
+  }
+
+  stages {
+    stage("Init") {
+      steps {
+        script {
+          cd_utils = load('ci/cd/Jenkinsfile_utils.groovy')
+
+          // The address of the release job on Jenkins
+          cd_utils.set_release_job_name('restricted-mxnet-cd/mxnet-cd-release-job')
+
+          // initialize the MXNET_SHA variable to the current GIT_COMMIT if not set
+          env.MXNET_SHA = env.MXNET_SHA ?: env.GIT_COMMIT
+        }
+      }
+    }
+
+    stage("MXNet Release") {
+      steps {
+        script {
+          // Add CD Pipeline
+        }
+      }
+    }
+  }
+}
diff --git a/ci/cd/Jenkinsfile_release_job b/ci/cd/Jenkinsfile_release_job
new file mode 100644
index 000000000000..6b9d26b3ab4c
--- /dev/null
+++ b/ci/cd/Jenkinsfile_release_job
@@ -0,0 +1,103 @@
+// -*- mode: groovy -*-
+
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+//
+// Jenkins pipeline
+// See documents at https://jenkins.io/doc/book/pipeline/jenkinsfile/
+
+// This job executes a "generic" (CD) release job.
+// The pipeline to be executed is defined by the "RELEASE_JOB_TYPE" pipeline parameter.
+// This is the path (relative to the CD directory) to a directory containing the following file groovy script
+// `Jenkins_pipeline.groovy`. 
+// See `cd` directory README for more details.
+
+// timeout of each step in minutes
+max_time = 180
+
+pipeline {
+  agent {
+    label 'restricted-utility'
+  }
+
+  parameters {
+    // Release parameters
+    string(defaultValue: "", description: 'Optional commit id within specified branch to use for build', name: "MXNET_SHA")
+    string(defaultValue: "Generic release job", description: "Optional Job name", name: "RELEASE_JOB_NAME")
+    string(defaultValue: "", description: "Job Type e.g. binary_release/static, python/docker, runtime_images, etc.", name: 'RELEASE_JOB_TYPE')
+    string(defaultValue: "cpu,mkl,cu80,cu80mkl,cu90,cu90mkl,cu92,cu92mkl,cu100,cu100mkl", description: "Comma separated list of variants", name: "MXNET_VARIANTS")
+    booleanParam(defaultValue: false, description: 'Whether this is a release build or not', name: "RELEASE_BUILD")
+  }
+
+  stages {
+    stage("Init") {
+      steps {
+        script{
+          cd_utils = load('ci/cd/Jenkinsfile_utils.groovy')
+          ci_utils = load('ci/Jenkinsfile_utils.groovy')
+          ci_utils.assign_node_labels(
+            utility: 'restricted-utility',
+            linux_cpu: 'restricted-mxnetlinux-cpu',
+            linux_gpu: 'restricted-mxnetlinux-gpu',
+            linux_gpu_p3: 'restricted-mxnetlinux-gpu-p3',
+            windows_cpu: 'restricted-mxnetwindows-cpu',
+            windows_gpu: 'restricted-mxnetwindows-gpu'
+          )
+
+          // initialize the MXNET_SHA variable to the current GIT_COMMIT if not set
+          env.MXNET_SHA = env.MXNET_SHA ?: env.GIT_COMMIT
+          echo "MXNET_SHA: ${env.MXNET_SHA}"
+
+          echo """\
+            |Job Name: ${params.RELEASE_JOB_NAME}
+            |Job Type: ${params.RELEASE_JOB_TYPE}
+            |Release Build: ${params.RELEASE_BUILD}
+            |Commit Id: ${params.MXNET_SHA}
+            |Variants: ${MXNET_VARIANTS}""".stripMargin()
+        }
+      }
+    }
+    stage("Release Job") {
+      steps {
+        script {
+          
+          // Convert mxnet variants to a list
+          def mxnet_variants = params.MXNET_VARIANTS.split(',').inject([]) { list, item ->
+            list << item.trim()
+          }
+
+          // Load script for the supplied job type
+          def custom_steps = load("ci/cd/${params.RELEASE_JOB_TYPE}/Jenkins_pipeline.groovy")
+
+          // Extract the pipelines for the variants
+          def pipelines = [:]
+          
+          for (variant in mxnet_variants) {
+            pipelines << ["${variant}": custom_steps.get_pipeline(variant)]
+          }
+
+          // Execute them in parallel
+          // The build result will be set to:
+          //  - SUCCESS if all pipelines succeed
+          //  - UNSTABLE if some (but not all) pipelines fail
+          //  - FAILURE if all pipelines fail
+          cd_utils.error_checked_parallel(pipelines)
+        }
+      }
+    }
+  }
+}
diff --git a/ci/cd/Jenkinsfile_utils.groovy b/ci/cd/Jenkinsfile_utils.groovy
new file mode 100644
index 000000000000..704727b68a88
--- /dev/null
+++ b/ci/cd/Jenkinsfile_utils.groovy
@@ -0,0 +1,110 @@
+// -*- mode: groovy -*-
+
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+// Sets the name of the release job in Jenkins.
+// That is the release job that has been created in Jenkins based
+// on the Jenkinsfile_release_job definition.
+def set_release_job_name(release_job) {
+  RELEASE_JOB = release_job
+}
+
+// Triggers a downstream jenkins job responsible for building, testing
+// and publishing all the variants for a particular 'job_type'.
+// The 'job_type' should be the name of the directory that contains the 
+// 'Jenkins_pipeline.groovy' file and has the pipeline definition for the 
+// artifact (docker image, binary, pypi or maven package, etc.) that should
+// be published.
+def trigger_release_job(job_name, job_type, mxnet_variants) {
+  def run = build(
+    job: RELEASE_JOB, 
+    parameters: [
+      string(name: 'MXNET_SHA', value: "${env.MXNET_SHA}"),
+      string(name: 'RELEASE_JOB_NAME', value: "${job_name}"),
+      string(name: 'RELEASE_JOB_TYPE', value: "${job_type}"),
+      string(name: 'RELEASE_JOB_TYPE', value: "${job_type}"),
+      string(name: 'MXNET_VARIANTS', value: "${mxnet_variants}"),
+      booleanParam(name: 'RELEASE_BUILD', value: "${env.RELEASE_BUILD}")
+    ],
+    // If propagate is true, any result other than successful will
+    // mark this call as failure (inc. unstable).
+    // https://jenkins.io/doc/pipeline/steps/pipeline-build-step
+    propagate: false)
+
+  def result = run.getResult()
+
+  // In case the underlying release job is unstable,
+  // e.g. one or more (but not all) the variants failed, or;
+  // it is aborted (e.g. one of steps timed out),
+  // continue with the pipeline and try to post as many releases as possible
+  // but mark it as unstable
+  if (result == "UNSTABLE" || result == "ABORTED") {
+    currentBuild.result = "UNSTABLE" 
+  }
+
+  // Throw an exception on failure, because this would mean the whole
+  // pipeline failed (i.e. for every variant)
+  if (result == "FAILURE") {
+    error "Downstream job: ${job_name} failed"
+  }
+}
+
+// Wraps variant pipeline with error catching and
+// job status setting code
+// If there's an error in one of the pipelines, set status to UNSTABLE
+// If all pipelines fail, set to FAILURE
+// This is to be used in conjunction with the error_checked_parallel
+def wrap_variant_pipeline_fn(variant_pipeline, total_num_pipelines) {
+  // do not add def - seems to affect the scope
+  count = 0
+  return {
+    try {
+      variant_pipeline()
+    } catch (ex) {
+      count++
+      currentBuild.result = "UNSTABLE"
+
+      if (count == total_num_pipelines) {
+        currentBuild.result = "FAILURE"
+        throw ex
+      }
+    }
+  }
+}
+
+// Takes a map of key -> closure values to be executed in parallel.
+// The outcome of the execution of each parallel step will affect
+// the result (SUCCESS, FAILURE, ABORTED, UNSTABLE) of the overall job.
+// If all steps fail or are aborted, the job will be set to failed.
+// If some steps fail or are aborted, the job will be set to unstable. 
+def error_checked_parallel(variant_pipelines) {
+  pipelines = variant_pipelines.inject([:]) { mp, key, value ->
+    mp << ["${key}": wrap_variant_pipeline_fn(value, variant_pipelines.size())]
+  }
+  parallel pipelines
+}
+
+// pushes artifact to repository
+def push_artifact(libmxnet_path, variant, is_dynamic = true, license_paths = '', dependency_paths = '') {
+  if(license_paths == null) license_paths = ''
+  if(dependency_paths == null) dependency_paths = ''
+  def libtype = is_dynamic ? 'dynamic' : 'static'
+  sh "./ci/cd/utils/artifact_repository.py --push --verbose --${libtype} --variant ${variant} --libmxnet ${libmxnet_path} --licenses ${license_paths} --dependencies ${dependency_paths}"
+}
+
+return this

From b73b8d489a92dc0c97ef16a921d2d5e67e96675a Mon Sep 17 00:00:00 2001
From: Per Goncalves da Silva <perdasilva@gmail.com>
Date: Thu, 23 May 2019 12:48:44 +0200
Subject: [PATCH 242/813] Adds static libmxnet release pipeline

---
 ci/cd/mxnet_lib/mxnet_lib_pipeline.groovy     | 169 ++++++++++++++++++
 .../mxnet_lib/static/Jenkins_pipeline.groovy  |  56 ++++++
 ci/docker/runtime_functions.sh                |  54 ++++++
 3 files changed, 279 insertions(+)
 create mode 100644 ci/cd/mxnet_lib/mxnet_lib_pipeline.groovy
 create mode 100644 ci/cd/mxnet_lib/static/Jenkins_pipeline.groovy

diff --git a/ci/cd/mxnet_lib/mxnet_lib_pipeline.groovy b/ci/cd/mxnet_lib/mxnet_lib_pipeline.groovy
new file mode 100644
index 000000000000..ba6df6c38734
--- /dev/null
+++ b/ci/cd/mxnet_lib/mxnet_lib_pipeline.groovy
@@ -0,0 +1,169 @@
+// -*- mode: groovy -*-
+
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+//
+// Jenkins pipeline
+// See documents at https://jenkins.io/doc/book/pipeline/jenkinsfile/
+
+// NOTE: 
+// ci_utils is loaded by the originating Jenkins job, e.g. jenkins/Jenkinsfile_release_job
+// cd_utils is loaded by the originating Jenkins job, e.g. jenkins/Jenkinsfile_release_job
+
+def get_pipeline(mxnet_variant, build_fn) {
+  return {
+    stage("${mxnet_variant}") {
+      stage('Build') {
+        timeout(time: max_time, unit: 'MINUTES') {
+          build_fn(mxnet_variant)
+        }
+      }
+
+      stage('Test') {
+        def tests = [:]
+        tests["${mxnet_variant}: Python 2"] = {
+          stage("${mxnet_variant}: Python 2") {
+            timeout(time: max_time, unit: 'MINUTES') {
+              unittest_py2(mxnet_variant)
+            }
+          }
+        }
+        tests["${mxnet_variant}: Python 3"] = {
+          stage("${mxnet_variant}: Python 3") {
+            timeout(time: max_time, unit: 'MINUTES') {
+              unittest_py3(mxnet_variant)
+            }
+          }
+        }
+
+        // Add quantization tests for all cu variants except cu80
+        if (mxnet_variant.startsWith('cu') && !mxnet_variant.startsWith('cu80')) {
+          tests["${mxnet_variant}: Quantization Python 3"] = {
+            stage("${mxnet_variant}: Quantization Python 3") {
+              timeout(time: max_time, unit: 'MINUTES') {
+                test_gpu_quantization_py3(mxnet_variant)
+              }
+            }
+          }
+          tests["${mxnet_variant}: Quantization Python 2"] = {
+            stage("${mxnet_variant}: Python 2") {
+              timeout(time: max_time, unit: 'MINUTES') {
+                test_gpu_quantization_py2(mxnet_variant)
+              }
+            }
+          }
+        }
+        
+        parallel tests
+      }
+
+      stage('Push') {
+        timeout(time: max_time, unit: 'MINUTES') {
+          push(mxnet_variant)
+        }
+      }
+    }
+  }
+}
+
+// Returns a string of comma separated resources to be stashed b/w stages
+// E.g. the libmxnet library and any other dependencies
+def get_stash(mxnet_variant) {
+  def deps = mxnet_variant.endsWith('mkl') ? mx_mkldnn_deps : mx_deps
+  return "${libmxnet}, ${licenses}, ${deps}"
+}
+
+// Returns the (Docker) environment for the given variant
+// The environment corresponds to the docker files in the 'docker' directory
+def get_environment(mxnet_variant) {
+  if (mxnet_variant.startsWith("cu")) {
+    // Remove 'mkl' suffix from variant to properly format test environment
+    return "ubuntu_gpu_${mxnet_variant.replace('mkl', '')}"
+  }
+  return "ubuntu_cpu"
+}
+
+// Returns the variant appropriate jenkins node test in which
+// to run a step
+def get_jenkins_node_label(mxnet_variant) {
+  if (mxnet_variant.startsWith('cu')) {
+    return NODE_LINUX_GPU
+  }
+  return NODE_LINUX_CPU
+}
+
+// Runs unit tests using python 3
+def unittest_py3(mxnet_variant) {
+  def node_label = get_jenkins_node_label(mxnet_variant)
+
+  node(node_label) {
+    ws("workspace/${workspace_name}/${mxnet_variant}/${env.BUILD_NUMBER}") {
+      def image = get_environment(mxnet_variant)
+      def use_nvidia_docker = mxnet_variant.startsWith('cu')
+      ci_utils.unpack_and_init("mxnet_${mxnet_variant}", get_stash(mxnet_variant), false, env.MXNET_SHA)
+      ci_utils.docker_run(image, "cd_unittest_ubuntu ${mxnet_variant} python3", use_nvidia_docker)
+    }
+  }
+}
+
+// Runs unit tests using python 2
+def unittest_py2(mxnet_variant) {
+  def node_label = get_jenkins_node_label(mxnet_variant)
+  node(node_label) {
+    ws("workspace/${workspace_name}/${mxnet_variant}/${env.BUILD_NUMBER}") {
+      def image = get_environment(mxnet_variant)
+      def use_nvidia_docker = mxnet_variant.startsWith('cu')
+      ci_utils.unpack_and_init("mxnet_${mxnet_variant}", get_stash(mxnet_variant), false, env.MXNET_SHA)
+      ci_utils.docker_run(image, "cd_unittest_ubuntu ${mxnet_variant} python", use_nvidia_docker)
+    }
+  }
+}
+
+// Tests quantization in P3 instance using Python 2
+def test_gpu_quantization_py2(mxnet_variant) {
+  node(NODE_LINUX_GPU_P3) {
+    ws("workspace/${workspace_name}/${mxnet_variant}/${env.BUILD_NUMBER}") {
+      def image = get_environment(mxnet_variant)
+      ci_utils.unpack_and_init("mxnet_${mxnet_variant}", get_stash(mxnet_variant), false, env.MXNET_SHA)
+      ci_utils.docker_run(image, "unittest_ubuntu_python2_quantization_gpu", true)
+    }
+  }
+}
+
+// Tests quantization in P3 instance using Python 3
+def test_gpu_quantization_py3(mxnet_variant) {
+  node(NODE_LINUX_GPU_P3) {
+    ws("workspace/${workspace_name}/${mxnet_variant}/${env.BUILD_NUMBER}") {
+      def image = get_environment(mxnet_variant)
+      ci_utils.unpack_and_init("mxnet_${mxnet_variant}", get_stash(mxnet_variant), false, env.MXNET_SHA)
+      ci_utils.docker_run(image, "unittest_ubuntu_python3_quantization_gpu", true)
+    }
+  }
+}
+
+// Pushes artifact to artifact repository
+def push(mxnet_variant) {
+  node(NODE_LINUX_CPU) {
+    ws("workspace/${workspace_name}/${mxnet_variant}/${env.BUILD_NUMBER}") {
+      def deps = (mxnet_variant.endsWith('mkl')? mx_mkldnn_deps : mx_deps).replaceAll(',', '')
+      ci_utils.unpack_and_init("mxnet_${mxnet_variant}", get_stash(mxnet_variant), false, env.MXNET_SHA)
+      cd_utils.push_artifact(libmxnet, mxnet_variant, is_dynamic_binary, licenses, deps)
+    }
+  }
+}
+
+return this
diff --git a/ci/cd/mxnet_lib/static/Jenkins_pipeline.groovy b/ci/cd/mxnet_lib/static/Jenkins_pipeline.groovy
new file mode 100644
index 000000000000..4f11f0d28502
--- /dev/null
+++ b/ci/cd/mxnet_lib/static/Jenkins_pipeline.groovy
@@ -0,0 +1,56 @@
+// -*- mode: groovy -*-
+
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+//
+// Jenkins pipeline
+// See documents at https://jenkins.io/doc/book/pipeline/jenkinsfile/
+
+// NOTE: ci_utils is loaded by the originating Jenkins job, e.g. jenkins/Jenkinsfile_release_job
+
+// libmxnet location
+libmxnet = 'lib/libmxnet.so'
+
+// licenses
+licenses = 'licenses/*'
+
+// libmxnet dependencies
+mx_deps = 'lib/libgfortran.so.3, lib/libquadmath.so.0'
+mx_mkldnn_deps = 'lib/libgfortran.so.3, lib/libquadmath.so.0, lib/libiomp5.so, lib/libmkldnn.so.0, lib/libmklml_intel.so, 3rdparty/mkldnn/build/install/include/mkldnn_version.h'
+
+// settings
+workspace_name = 'static_binary'
+is_dynamic_binary = false
+
+libmxnet_pipeline = load('ci/cd/mxnet_lib/mxnet_lib_pipeline.groovy')
+
+// Builds the static binary for the specified mxnet variant
+def build(mxnet_variant) {
+  node(NODE_LINUX_CPU) {
+    ws("workspace/${workspace_name}/${mxnet_variant}/${env.BUILD_NUMBER}") {
+      ci_utils.init_git(env.MXNET_SHA)
+      ci_utils.docker_run('publish.ubuntu1404_cpu', "build_static_libmxnet ${mxnet_variant}", false)
+      ci_utils.pack_lib("mxnet_${mxnet_variant}", libmxnet_pipeline.get_stash(mxnet_variant))
+    }
+  }
+}
+
+def get_pipeline(mxnet_variant) {
+  return libmxnet_pipeline.get_pipeline(mxnet_variant, this.&build)
+}
+
+return this
diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index 2518f4c7c64f..553646502961 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -833,6 +833,50 @@ sanity_check() {
     nosetests-3.4 tests/tutorials/test_sanity_tutorials.py
 }
 
+# Tests libmxnet
+# Parameters:
+# $1 -> mxnet_variant: The variant of the libmxnet.so library
+# $2 -> python_cmd: The python command to use to execute the tests, python or python3
+cd_unittest_ubuntu() {
+    set -ex
+    export PYTHONPATH=./python/
+    export MXNET_MKLDNN_DEBUG=1  # Ignored if not present
+    export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
+
+    local mxnet_variant=${1:?"This function requires a mxnet variant as the first argument"}
+    local python_cmd=${2:?"This function requires a python command as the first argument"}
+
+    local nose_cmd="nosetests-3.4"
+
+    if [[ ${python_cmd} = "python" ]]; then
+        nose_cmd="nosetests-2.7"
+    fi
+
+    $nose_cmd $NOSE_TIMER_ARGUMENTS --verbose tests/python/unittest
+    $nose_cmd $NOSE_TIMER_ARGUMENTS --verbose tests/python/quantization 
+
+    # https://github.com/apache/incubator-mxnet/issues/11801
+    # if [[ ${mxnet_variant} = "cpu" ]] || [[ ${mxnet_variant} = "mkl" ]]; then
+        # integrationtest_ubuntu_cpu_dist_kvstore
+    # fi
+
+    if [[ ${mxnet_variant} = cu* ]]; then
+        $nose_cmd $NOSE_TIMER_ARGUMENTS --verbose tests/python/gpu
+
+        # Adding these here as CI doesn't test all CUDA environments
+        $python_cmd example/image-classification/test_score.py
+        integrationtest_ubuntu_gpu_dist_kvstore
+    fi
+
+    if [[ ${mxnet_variant} = *mkl ]]; then
+        # skipping python 2 testing
+        # https://github.com/apache/incubator-mxnet/issues/14675
+        if [[ ${python_cmd} = "python3" ]]; then
+            $nose_cmd $NOSE_TIMER_ARGUMENTS --verbose tests/python/mkl
+        fi
+    fi
+}
+
 unittest_ubuntu_python2_cpu_cython() {
     set -ex
     export PYTHONPATH=./python/
@@ -1171,6 +1215,7 @@ integrationtest_ubuntu_gpu_dist_kvstore() {
     cd tests/nightly/
     ../../tools/launch.py -n 7 --launcher local python dist_device_sync_kvstore.py
     ../../tools/launch.py -n 7 --launcher local python dist_sync_kvstore.py --type=init_gpu
+    cd ../../
 }
 
 test_ubuntu_cpu_python2() {
@@ -1442,6 +1487,15 @@ deploy_docs() {
     popd
 }
 
+build_static_libmxnet() {
+    set -ex
+    pushd .
+    local mxnet_variant=${1:?"This function requires a python command as the first argument"}
+    build_ccache_wrappers
+    source tools/staticbuild/build.sh ${mxnet_variant} pip
+    popd
+}
+
 build_static_scala_mkl() {
     set -ex
     pushd .

From 0ed97f1437fa9720237993b9d6b7d633a709cfc2 Mon Sep 17 00:00:00 2001
From: Per Goncalves da Silva <perdasilva@gmail.com>
Date: Thu, 23 May 2019 12:49:28 +0200
Subject: [PATCH 243/813] Updates CD pipeline

---
 ci/cd/Jenkinsfile_cd_pipeline | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/ci/cd/Jenkinsfile_cd_pipeline b/ci/cd/Jenkinsfile_cd_pipeline
index a6a65f22895e..416d8b2c0442 100644
--- a/ci/cd/Jenkinsfile_cd_pipeline
+++ b/ci/cd/Jenkinsfile_cd_pipeline
@@ -53,7 +53,15 @@ pipeline {
     stage("MXNet Release") {
       steps {
         script {
-          // Add CD Pipeline
+          cd_utils.error_checked_parallel([
+
+            "Static libmxnet based Release": {
+              stage("Build") {
+                cd_utils.trigger_release_job("Build static libmxnet", "mxnet_lib/static", params.MXNET_VARIANTS)    
+              }
+            }
+            
+          ])
         }
       }
     }

From 5fe1516600a669cdcea7bd686a6b1f9bee4603c8 Mon Sep 17 00:00:00 2001
From: Per Goncalves da Silva <perdasilva@gmail.com>
Date: Thu, 23 May 2019 12:50:02 +0200
Subject: [PATCH 244/813] Adds documentation

---
 ci/cd/README.md | 171 ++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 171 insertions(+)
 create mode 100644 ci/cd/README.md

diff --git a/ci/cd/README.md b/ci/cd/README.md
new file mode 100644
index 000000000000..637b5939d7e8
--- /dev/null
+++ b/ci/cd/README.md
@@ -0,0 +1,171 @@
+# MXNet Continuous Delivery
+
+## Introduction
+
+MXNet aims to support a variety of frontends, e.g. Python, Java, Perl, R, etc. as well as environments (Windows, Linux, Mac, with or without GPU, with or without MKL support, etc.). This package contains a small continuous delivery (CD) framework used to automate the delivery nightly and release builds across our delivery channels.
+
+<!-- TODO: Add links to the actual jobs, once this is live on PROD -->
+
+The CD process is driven by the [CD pipeline job](Jenkinsfile_cd_pipeline), which orchestrates the order in which the artifacts are delivered. For instance, first publish the libmxnet library before publishing the pip package. It does this by triggering the [release job](Jenkinsfile_release_job) with a specific set of parameters for each delivery channel. The release job executes the specific release pipeline for a delivery channel across all MXNet *variants*.
+
+A variant is a specific environment or features for which MXNet is compiled. For instance CPU, GPU with CUDA v10.0, CUDA v9.0 with MKL support, etc. 
+
+Currently, 10 variants are supported:
+
+* *cpu*: CPU
+* *mkl*: CPU w/ MKL
+* *cu80*: CUDA 8.0
+* *cu80mkl*: CUDA 8.0 w/ MKL-DNN
+* *cu90*: CUDA 9.0
+* *cu90mkl*: CUDA 9.0 w/ MKL-DNN
+* *cu92*: CUDA 9.2
+* *cu92mkl*: CUDA 9.2 w/ MKL-DNN
+* *cu100*: CUDA 10
+* *cu100mkl*: CUDA 10 w/ MKL-DNN
+
+*For more on variants, see [here](https://github.com/apache/incubator-mxnet/issues/8671)*
+
+## Framework Components
+
+### CD Pipeline Job
+
+The [CD pipeline job](Jenkinsfile_cd_pipeline) take three parameters:
+
+ * **MXNET_SHA**: Commit ID from which to run the CD process (Optional). Defaults to the HEAD commit ID.
+ * **RELEASE_BUILD**: Flags the run as a *release build*. The underlying jobs can then use this environment variable to disambiguate between nightly and release builds. Defaults to *false*.
+ * **MXNET_VARIANTS**: A comma separated list of variants to build. Defaults to *all* variants.
+
+This job defines and executes the CD pipeline. For example, first publish the MXNet library, then, in parallel, execute the python and maven releases. Every step of the pipeline executes a trigger for a release job](Jenkinsfile_release_job).
+
+### Release Job
+
+The [release job](Jenkinsfile_release_job) takes five parameters:
+
+ * **MXNET_SHA**: Commit ID from which to run the CD process (Optional). Defaults to the HEAD commit ID.
+ * **RELEASE_BUILD**: Flags the run as a *release build*. The underlying jobs can then use this environment variable to disambiguate between nightly and release builds. Defaults to *false*.
+ * **MXNET_VARIANTS**: A comma separated list of variants to build. Defaults to *all* variants.
+ * **RELEASE\_JOB\_NAME**: A name for this release job (Optional). Defaults to "Generic release job". It is used for debug output purposes.
+ * **RELEASE\_JOB\_TYPE**: Defines the release pipeline you want to execute.
+
+The release job executes, in parallel, the release pipeline for each of the variants (**MXNET_VARIANTS**) for the job type (**RELEASE\_JOB\_TYPE**). The job type the path to a directory (relative to the `cd` directory) that includes a `Jenkins_pipeline.groovy` file ([e.g.](mxnet_lib/static/Jenkins_pipeline.groovy)).
+
+### Release Pipelines: Jenkins_pipeline.groovy
+
+This file defines the release pipeline for a particular release channel. It defines a function `get_pipeline(mxnet_variant)`, which returns a closure with the pipeline to be executed. For instance:
+
+```
+def get_pipeline(mxnet_variant) {
+  return {
+    stage("${mxnet_variant}") {
+      stage("Build") {
+        timeout(time: max_time, unit: 'MINUTES') {
+          build(mxnet_variant)
+        }
+      }
+      stage("Test") {
+        timeout(time: max_time, unit: 'MINUTES') {
+          test(mxnet_variant)
+        }
+      }
+      stage("Publish") {
+        timeout(time: max_time, unit: 'MINUTES') {
+          publish(mxnet_variant)
+        }
+      }
+    }
+  }
+}
+
+def build(mxnet_variant) {
+  node(UBUNTU_CPU) {
+    ...
+  }
+}
+...
+```
+
+## Binary Releases
+
+The "first mile" of the CD process is posting the mxnet binaries to the [artifact repository](utils/artifact_repository.md). Once this step is complete, the pipelines for the different release channels (PyPI, Maven, etc.) can begin from the compiled binary, and focus solely on packaging it, testing the package, and posting it to the particular distribution channel.
+
+<!-- TODO: Once all the artifact repository Jenkins utility functions are in, list them here -->
+
+## Adding New Release Pipelines
+
+1. Create a directory under `cd` which represents your release channel, e.g. `python/pypi`.
+2. Add a `Jenkins_pipeline.groovy` there with a `get_pipeline(mxnet_variant)` function that describes your pipeline.
+3. Add a call to your pipeline to the [CD pipeline job](Jenkinsfile_cd_pipeline).
+
+#### General Guidelines:
+
+##### Timeout
+
+We shouldn't set global timeouts for the pipelines. Rather, the `step` being executed should be rapped with a `timeout` function (as in the pipeline example above). The `max_time` is a global variable set at the [release job](Jenkinsfile_release_job) level. 
+
+##### Node of execution
+
+Ensure that either your steps, or the whole pipeline are wrapped in a `node` call. The jobs execute in an `utility` node. If you don't wrap your pipeline, or its individual steps, in a `node` call, this will lead to problems.
+
+Examples of the two approaches:
+
+<!-- TODO: Add links to examples once the all pipelines are in -->
+
+**Whole pipeline**
+
+The release pipeline is executed on a single node, depending on the variant building released.
+This approach is fine, as long as the stages that don't need specialized hardware (e.g. compilation, packaging, publishing), are short lived.
+
+```
+def get_pipeline(mxnet_variant) {
+  def node_type = mxnet_variant.startsWith('cu') ? NODE_LINUX_GPU : NODE_LINUX_CPU
+
+  return {
+    node (node_type) {
+      stage("${mxnet_variant}") {
+        stage("Build") {
+          ...
+        }
+        stage("Test") {
+          ...
+        }
+        ...
+      }
+    }
+  }
+}
+```
+
+**Per step**
+
+Use this approach in cases where you have long running stages that don't depend on specialized/expensive hardware.
+
+```
+def get_pipeline(mxnet_variant) {
+  return {
+    stage("${mxnet_variant}") {
+      stage("Build") {
+        ...
+      }
+      ...
+    }
+  }
+}
+
+def build(mxnet_variant) {
+  node(UBUNTU_CPU) {
+    ...
+  }
+}
+
+def test(mxnet_variant) {
+  def node_type = mxnet_variant.startsWith('cu') ? NODE_LINUX_GPU : NODE_LINUX_CPU
+  node(node_type) {
+    ...
+  }
+}
+```
+
+##### MXNET_SHA
+
+Be mindful of this parameter as it defines the state of the repository that you are building. When checking out the project to execute your step, use `ci_utils.init_git(env.MXNET_SHA)`. You should ensure that what is being built is consistent across the variants.
+

From 1196c150edb62d12d14d244472fe16f9f698c6dd Mon Sep 17 00:00:00 2001
From: Per Goncalves da Silva <perdasilva@gmail.com>
Date: Thu, 23 May 2019 14:52:58 +0200
Subject: [PATCH 245/813] Updates kvstore functions to use pushd and popd

---
 ci/docker/runtime_functions.sh | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index 553646502961..5c45c379b5f0 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -1178,6 +1178,7 @@ integrationtest_ubuntu_gpu_cpp_package() {
 
 integrationtest_ubuntu_cpu_dist_kvstore() {
     set -ex
+    pushd .
     export PYTHONPATH=./python/
     export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
     export MXNET_USE_OPERATOR_TUNING=0
@@ -1191,6 +1192,7 @@ integrationtest_ubuntu_cpu_dist_kvstore() {
     ../../tools/launch.py -n 7 --launcher local python dist_sync_kvstore.py --type=compressed_cpu
     ../../tools/launch.py -n 7 --launcher local python dist_sync_kvstore.py --type=compressed_cpu --no-multiprecision
     ../../tools/launch.py -n 3 --launcher local python test_server_profiling.py
+    popd
 }
 
 integrationtest_ubuntu_cpu_scala() {
@@ -1210,12 +1212,13 @@ integrationtest_ubuntu_gpu_scala() {
 
 integrationtest_ubuntu_gpu_dist_kvstore() {
     set -ex
+    pushd .
     export PYTHONPATH=./python/
     export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
     cd tests/nightly/
     ../../tools/launch.py -n 7 --launcher local python dist_device_sync_kvstore.py
     ../../tools/launch.py -n 7 --launcher local python dist_sync_kvstore.py --type=init_gpu
-    cd ../../
+    popd
 }
 
 test_ubuntu_cpu_python2() {

From 23a7a584601e2836d50b1d00b574fc1ea16435e5 Mon Sep 17 00:00:00 2001
From: Per Goncalves da Silva <perdasilva@gmail.com>
Date: Thu, 23 May 2019 15:09:49 +0200
Subject: [PATCH 246/813] Throws exceptions instead o magic numbers

---
 ci/cd/utils/artifact_repository.py      | 17 +++++++----------
 ci/cd/utils/test_artifact_repository.py |  5 +++--
 2 files changed, 10 insertions(+), 12 deletions(-)

diff --git a/ci/cd/utils/artifact_repository.py b/ci/cd/utils/artifact_repository.py
index 43657118b169..bf7975d1db1a 100755
--- a/ci/cd/utils/artifact_repository.py
+++ b/ci/cd/utils/artifact_repository.py
@@ -312,8 +312,8 @@ def probe_gpu_variant(mxnet_features: Dict[str, bool]) -> Optional[str]:
             variant = '{}mkl'.format(variant)
         logger.debug('variant is: {}'.format(variant))
         return variant
-    logger.debug('Error determining mxnet variant: Could not retrieve cuda version')
-    return None
+
+    raise RuntimeError('Error determining mxnet variant: Could not retrieve cuda version')
 
 
 def probe_mxnet_variant(limxnet_path: str) -> Optional[str]:
@@ -436,10 +436,9 @@ def push_artifact(args: argparse.Namespace):
     except botocore.exceptions.BotoCoreError as e:
         logger.error('Error uploading artifact')
         logger.error(e)
-        return 1
+        raise e
 
     logger.info('Successfully pushed artifact')
-    return 0
 
 
 def pull_artifact(args: argparse.Namespace):
@@ -462,16 +461,14 @@ def pull_artifact(args: argparse.Namespace):
 
     try:
         if not try_s3_download(args.bucket, get_s3_key_prefix(args), args.destination):
-            logging.error('No artifacts found for this configuration.')
-            return 2
+            raise RuntimeError('No artifacts found for this configuration.')
         write_libmxnet_meta(args=args, destination=args.destination)
     except botocore.exceptions.BotoCoreError as e:
         logger.error('Error downloading artifact')
         logger.error(e)
-        return 1
+        raise e
 
     logger.info('Successfully pulled artifact')
-    return 0
 
 
 def is_file(path: str) -> str:
@@ -611,10 +608,10 @@ def main() -> int:
     args.destination = os.path.abspath(args.destination)
 
     if args.push:
-        return push_artifact(args)
+        push_artifact(args)
 
     elif args.pull:
-        return pull_artifact(args)
+        pull_artifact(args)
 
     return 0
 
diff --git a/ci/cd/utils/test_artifact_repository.py b/ci/cd/utils/test_artifact_repository.py
index 13a559379901..b587704ba297 100644
--- a/ci/cd/utils/test_artifact_repository.py
+++ b/ci/cd/utils/test_artifact_repository.py
@@ -187,11 +187,12 @@ def test_probe_variant_cuda_returns_none_on_no_features(self, mock_features):
     @patch('artifact_repository.get_cuda_version')
     def test_probe_variant_cuda_mkl(self, mock_cuda_version, mock_features):
         """
-        Tests None is returned if CUDA feature is ON but cuda version could not be determined
+        Tests exception is raised if CUDA feature is ON but cuda version could not be determined
         """
         mock_features.return_value = {'MKLDNN': True, 'CUDA': True}
         mock_cuda_version.return_value = None
-        self.assertIsNone(probe_mxnet_variant('libmxnet.so'))
+        with self.assertRaises(RuntimeError):
+            probe_mxnet_variant('libmxnet.so')
 
     def test_probe_artifact_repository_bucket(self):
         """

From e539370642c800ce3a8d445fbb557f0bed0d6052 Mon Sep 17 00:00:00 2001
From: Per Goncalves da Silva <perdasilva@gmail.com>
Date: Thu, 23 May 2019 15:25:47 +0200
Subject: [PATCH 247/813] Updates artifact repository cli to use --libtype
 instead of --static or --dynamic

---
 ci/cd/Jenkinsfile_utils.groovy                |  6 +--
 ci/cd/mxnet_lib/mxnet_lib_pipeline.groovy     | 12 +++---
 .../mxnet_lib/static/Jenkins_pipeline.groovy  |  8 ++--
 ci/cd/utils/artifact_repository.py            | 37 ++++++++-----------
 ci/cd/utils/test_artifact_repository.py       | 10 ++---
 5 files changed, 33 insertions(+), 40 deletions(-)

diff --git a/ci/cd/Jenkinsfile_utils.groovy b/ci/cd/Jenkinsfile_utils.groovy
index 704727b68a88..f3ea34fb08c9 100644
--- a/ci/cd/Jenkinsfile_utils.groovy
+++ b/ci/cd/Jenkinsfile_utils.groovy
@@ -100,11 +100,11 @@ def error_checked_parallel(variant_pipelines) {
 }
 
 // pushes artifact to repository
-def push_artifact(libmxnet_path, variant, is_dynamic = true, license_paths = '', dependency_paths = '') {
+def push_artifact(libmxnet_path, variant, libtype, license_paths = '', dependency_paths = '') {
   if(license_paths == null) license_paths = ''
   if(dependency_paths == null) dependency_paths = ''
-  def libtype = is_dynamic ? 'dynamic' : 'static'
-  sh "./ci/cd/utils/artifact_repository.py --push --verbose --${libtype} --variant ${variant} --libmxnet ${libmxnet_path} --licenses ${license_paths} --dependencies ${dependency_paths}"
+
+  sh "./ci/cd/utils/artifact_repository.py --push --verbose --libtype ${libtype} --variant ${variant} --libmxnet ${libmxnet_path} --licenses ${license_paths} --dependencies ${dependency_paths}"
 }
 
 return this
diff --git a/ci/cd/mxnet_lib/mxnet_lib_pipeline.groovy b/ci/cd/mxnet_lib/mxnet_lib_pipeline.groovy
index ba6df6c38734..9ac232d571b4 100644
--- a/ci/cd/mxnet_lib/mxnet_lib_pipeline.groovy
+++ b/ci/cd/mxnet_lib/mxnet_lib_pipeline.groovy
@@ -111,7 +111,7 @@ def unittest_py3(mxnet_variant) {
   def node_label = get_jenkins_node_label(mxnet_variant)
 
   node(node_label) {
-    ws("workspace/${workspace_name}/${mxnet_variant}/${env.BUILD_NUMBER}") {
+    ws("workspace/mxnet_${libtype}/${mxnet_variant}/${env.BUILD_NUMBER}") {
       def image = get_environment(mxnet_variant)
       def use_nvidia_docker = mxnet_variant.startsWith('cu')
       ci_utils.unpack_and_init("mxnet_${mxnet_variant}", get_stash(mxnet_variant), false, env.MXNET_SHA)
@@ -124,7 +124,7 @@ def unittest_py3(mxnet_variant) {
 def unittest_py2(mxnet_variant) {
   def node_label = get_jenkins_node_label(mxnet_variant)
   node(node_label) {
-    ws("workspace/${workspace_name}/${mxnet_variant}/${env.BUILD_NUMBER}") {
+    ws("workspace/mxnet_${libtype}/${mxnet_variant}/${env.BUILD_NUMBER}") {
       def image = get_environment(mxnet_variant)
       def use_nvidia_docker = mxnet_variant.startsWith('cu')
       ci_utils.unpack_and_init("mxnet_${mxnet_variant}", get_stash(mxnet_variant), false, env.MXNET_SHA)
@@ -136,7 +136,7 @@ def unittest_py2(mxnet_variant) {
 // Tests quantization in P3 instance using Python 2
 def test_gpu_quantization_py2(mxnet_variant) {
   node(NODE_LINUX_GPU_P3) {
-    ws("workspace/${workspace_name}/${mxnet_variant}/${env.BUILD_NUMBER}") {
+    ws("workspace/mxnet_${libtype}/${mxnet_variant}/${env.BUILD_NUMBER}") {
       def image = get_environment(mxnet_variant)
       ci_utils.unpack_and_init("mxnet_${mxnet_variant}", get_stash(mxnet_variant), false, env.MXNET_SHA)
       ci_utils.docker_run(image, "unittest_ubuntu_python2_quantization_gpu", true)
@@ -147,7 +147,7 @@ def test_gpu_quantization_py2(mxnet_variant) {
 // Tests quantization in P3 instance using Python 3
 def test_gpu_quantization_py3(mxnet_variant) {
   node(NODE_LINUX_GPU_P3) {
-    ws("workspace/${workspace_name}/${mxnet_variant}/${env.BUILD_NUMBER}") {
+    ws("workspace/mxnet_${libtype}/${mxnet_variant}/${env.BUILD_NUMBER}") {
       def image = get_environment(mxnet_variant)
       ci_utils.unpack_and_init("mxnet_${mxnet_variant}", get_stash(mxnet_variant), false, env.MXNET_SHA)
       ci_utils.docker_run(image, "unittest_ubuntu_python3_quantization_gpu", true)
@@ -158,10 +158,10 @@ def test_gpu_quantization_py3(mxnet_variant) {
 // Pushes artifact to artifact repository
 def push(mxnet_variant) {
   node(NODE_LINUX_CPU) {
-    ws("workspace/${workspace_name}/${mxnet_variant}/${env.BUILD_NUMBER}") {
+    ws("workspace/mxnet_${libtype}/${mxnet_variant}/${env.BUILD_NUMBER}") {
       def deps = (mxnet_variant.endsWith('mkl')? mx_mkldnn_deps : mx_deps).replaceAll(',', '')
       ci_utils.unpack_and_init("mxnet_${mxnet_variant}", get_stash(mxnet_variant), false, env.MXNET_SHA)
-      cd_utils.push_artifact(libmxnet, mxnet_variant, is_dynamic_binary, licenses, deps)
+      cd_utils.push_artifact(libmxnet, mxnet_variant, libtype, licenses, deps)
     }
   }
 }
diff --git a/ci/cd/mxnet_lib/static/Jenkins_pipeline.groovy b/ci/cd/mxnet_lib/static/Jenkins_pipeline.groovy
index 4f11f0d28502..5545e2e552f5 100644
--- a/ci/cd/mxnet_lib/static/Jenkins_pipeline.groovy
+++ b/ci/cd/mxnet_lib/static/Jenkins_pipeline.groovy
@@ -32,16 +32,16 @@ licenses = 'licenses/*'
 mx_deps = 'lib/libgfortran.so.3, lib/libquadmath.so.0'
 mx_mkldnn_deps = 'lib/libgfortran.so.3, lib/libquadmath.so.0, lib/libiomp5.so, lib/libmkldnn.so.0, lib/libmklml_intel.so, 3rdparty/mkldnn/build/install/include/mkldnn_version.h'
 
-// settings
-workspace_name = 'static_binary'
-is_dynamic_binary = false
+// library type
+// either static or dynamic - depending on how it links to its dependencies
+libtype = 'static'
 
 libmxnet_pipeline = load('ci/cd/mxnet_lib/mxnet_lib_pipeline.groovy')
 
 // Builds the static binary for the specified mxnet variant
 def build(mxnet_variant) {
   node(NODE_LINUX_CPU) {
-    ws("workspace/${workspace_name}/${mxnet_variant}/${env.BUILD_NUMBER}") {
+    ws("workspace/mxnet_${libtype}/${mxnet_variant}/${env.BUILD_NUMBER}") {
       ci_utils.init_git(env.MXNET_SHA)
       ci_utils.docker_run('publish.ubuntu1404_cpu', "build_static_libmxnet ${mxnet_variant}", false)
       ci_utils.pack_lib("mxnet_${mxnet_variant}", libmxnet_pipeline.get_stash(mxnet_variant))
diff --git a/ci/cd/utils/artifact_repository.py b/ci/cd/utils/artifact_repository.py
index bf7975d1db1a..7afdc108bec0 100755
--- a/ci/cd/utils/artifact_repository.py
+++ b/ci/cd/utils/artifact_repository.py
@@ -80,7 +80,7 @@ def write_libmxnet_meta(args: argparse.Namespace, destination: str):
             "variant": args.variant,
             "os": args.os,
             "commit_id": args.git_sha,
-            "dependency_linking": args.lib_type,
+            "dependency_linking": args.libtype,
         }))
 
 
@@ -392,7 +392,7 @@ def get_s3_key_prefix(args: argparse.Namespace, subdir: str = '') -> str:
     :param subdir: An optional subdirectory in which to store the files. Post-pended to the end of the prefix.
     :return: A string containing the S3 key prefix to be used to uploading and downloading files to the artifact repository
     """
-    prefix = "{git_sha}/{lib_type}/{os}/{variant}/".format(**vars(args))
+    prefix = "{git_sha}/{libtype}/{os}/{variant}/".format(**vars(args))
     if subdir:
         return "{}{}/".format(prefix, subdir)
     return prefix
@@ -542,15 +542,11 @@ def main() -> int:
                         required=False,
                         type=str)
 
-    parser.add_argument("--static",
-                        help="libmxnet library static dependency linking",
-                        required=False,
-                        action='store_true')
-
-    parser.add_argument("--dynamic",
-                        help="libmxnet library with dynamic dependency linking",
-                        required=False,
-                        action='store_true')
+    parser.add_argument("--libtype",
+                        help="libmxnet dependency linking type",
+                        choices=['static', 'dynamic'],
+                        default='dynamic',
+                        required=False)
 
     parser.add_argument('--bucket',
                         help="S3 bucket to store files",
@@ -586,19 +582,12 @@ def main() -> int:
         logger.info('''Mode not specified. Using 'push' by default.''')
         args.push = True
 
-    if not args.static and not args.dynamic:
-        logger.info('''Library type not specified. Using 'dynamic' by default.''')
-        args.dynamic = True
-
     # libmxnet argument is required for push mode
     if args.push and not args.libmxnet:
         logger.error('Path to libmxnet library must be specified when in push mode. '
                      'Please specify it with --libmxnet.')
         return 1
 
-    # store string version of library type - used to facilitate S3 object prefix calculation
-    args.lib_type = 'static' if args.static else 'dynamic'
-
     # sanitize license and dependency arrays
     # Remove empty or directory entries
     args.licenses = sanitize_path_array(args.licenses)
@@ -607,11 +596,15 @@ def main() -> int:
     # expand destination path
     args.destination = os.path.abspath(args.destination)
 
-    if args.push:
-        push_artifact(args)
+    try:
+        if args.push:
+            push_artifact(args)
 
-    elif args.pull:
-        pull_artifact(args)
+        elif args.pull:
+            pull_artifact(args)
+    except RuntimeError as err:
+        logger.error(err)
+        return 1
 
     return 0
 
diff --git a/ci/cd/utils/test_artifact_repository.py b/ci/cd/utils/test_artifact_repository.py
index b587704ba297..948352768659 100644
--- a/ci/cd/utils/test_artifact_repository.py
+++ b/ci/cd/utils/test_artifact_repository.py
@@ -11,7 +11,7 @@ def create_argparse_namespace(libmxnet_path: Optional[str] = 'path_to_libmxnet',
                                   git_sha: Optional[str] = 'abc123',
                                   variant: Optional[str] = 'cpu',
                                   operating_system: Optional[str] = 'linux',
-                                  lib_type: Optional[str] = 'static',
+                                  libtype: Optional[str] = 'static',
                                   bucket: Optional[str] = 's3bucket',
                                   licenses: Optional[List[str]] = [],
                                   dependencies: Optional[List[str]] = []) -> argparse.Namespace:
@@ -23,7 +23,7 @@ def create_argparse_namespace(libmxnet_path: Optional[str] = 'path_to_libmxnet',
         ns.git_sha = git_sha
         ns.variant = variant
         ns.os = operating_system
-        ns.lib_type = lib_type
+        ns.libtype = libtype
         ns.bucket = bucket
         ns.licenses = licenses
         ns.dependencies = dependencies
@@ -293,7 +293,7 @@ def test_get_s3_key_prefix(self):
         fake_args = TestArtifactRepositoryTool.create_argparse_namespace(git_sha="abc123",
                                                                          operating_system='linux',
                                                                          variant='cpu',
-                                                                         lib_type='static')
+                                                                         libtype='static')
 
         self.assertEqual(get_s3_key_prefix(fake_args), 'abc123/static/linux/cpu/')
 
@@ -304,7 +304,7 @@ def test_get_s3_key_prefix_with_subdir(self):
         fake_args = TestArtifactRepositoryTool.create_argparse_namespace(git_sha="abc123",
                                                                          operating_system='linux',
                                                                          variant='cpu',
-                                                                         lib_type='static')
+                                                                         libtype='static')
 
         self.assertEqual(get_s3_key_prefix(fake_args, subdir='subdir'), 'abc123/static/linux/cpu/subdir/')
 
@@ -488,7 +488,7 @@ def test_write_libmxnet_meta(self):
             fake_args = TestArtifactRepositoryTool.create_argparse_namespace(git_sha='abcd1234',
                                                                              variant='gpu',
                                                                              operating_system='lunix',
-                                                                             lib_type='stynamic')
+                                                                             libtype='stynamic')
             write_libmxnet_meta(args=fake_args, destination='dest')
             mock_fopen.assert_called_once_with(os.path.join('dest', 'libmxnet.meta'), 'w')
             mock_fopen().write.called_with(

From fff8c825f0f8177c2a9172c239dcbf8c81368c1a Mon Sep 17 00:00:00 2001
From: Per Goncalves da Silva <perdasilva@gmail.com>
Date: Thu, 23 May 2019 15:27:58 +0200
Subject: [PATCH 248/813] Clarifies ci_utils and cd_utils origin remark

---
 ci/cd/mxnet_lib/mxnet_lib_pipeline.groovy      | 5 ++---
 ci/cd/mxnet_lib/static/Jenkins_pipeline.groovy | 3 ++-
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/ci/cd/mxnet_lib/mxnet_lib_pipeline.groovy b/ci/cd/mxnet_lib/mxnet_lib_pipeline.groovy
index 9ac232d571b4..2e9c1b51efeb 100644
--- a/ci/cd/mxnet_lib/mxnet_lib_pipeline.groovy
+++ b/ci/cd/mxnet_lib/mxnet_lib_pipeline.groovy
@@ -20,9 +20,8 @@
 // Jenkins pipeline
 // See documents at https://jenkins.io/doc/book/pipeline/jenkinsfile/
 
-// NOTE: 
-// ci_utils is loaded by the originating Jenkins job, e.g. jenkins/Jenkinsfile_release_job
-// cd_utils is loaded by the originating Jenkins job, e.g. jenkins/Jenkinsfile_release_job
+// To avoid confusion, please note:
+// ci_utils and cd_utils are loaded by the originating Jenkins job, e.g. jenkins/Jenkinsfile_release_job
 
 def get_pipeline(mxnet_variant, build_fn) {
   return {
diff --git a/ci/cd/mxnet_lib/static/Jenkins_pipeline.groovy b/ci/cd/mxnet_lib/static/Jenkins_pipeline.groovy
index 5545e2e552f5..b5152603a02d 100644
--- a/ci/cd/mxnet_lib/static/Jenkins_pipeline.groovy
+++ b/ci/cd/mxnet_lib/static/Jenkins_pipeline.groovy
@@ -20,7 +20,8 @@
 // Jenkins pipeline
 // See documents at https://jenkins.io/doc/book/pipeline/jenkinsfile/
 
-// NOTE: ci_utils is loaded by the originating Jenkins job, e.g. jenkins/Jenkinsfile_release_job
+// To avoid confusion, please note:
+// ci_utils is loaded by the originating Jenkins job, e.g. jenkins/Jenkinsfile_release_job
 
 // libmxnet location
 libmxnet = 'lib/libmxnet.so'

From 0570892ecdc3182274f3dbbdb15658d64200c11d Mon Sep 17 00:00:00 2001
From: Per Goncalves da Silva <perdasilva@gmail.com>
Date: Thu, 23 May 2019 15:29:04 +0200
Subject: [PATCH 249/813] Adds clarifying note on why ubuntu 14.04 is being
 used for compilation

---
 ci/cd/mxnet_lib/static/Jenkins_pipeline.groovy | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/ci/cd/mxnet_lib/static/Jenkins_pipeline.groovy b/ci/cd/mxnet_lib/static/Jenkins_pipeline.groovy
index b5152603a02d..6c4658674aaa 100644
--- a/ci/cd/mxnet_lib/static/Jenkins_pipeline.groovy
+++ b/ci/cd/mxnet_lib/static/Jenkins_pipeline.groovy
@@ -44,6 +44,8 @@ def build(mxnet_variant) {
   node(NODE_LINUX_CPU) {
     ws("workspace/mxnet_${libtype}/${mxnet_variant}/${env.BUILD_NUMBER}") {
       ci_utils.init_git(env.MXNET_SHA)
+      // Compiling in Ubuntu14.04 due to glibc issues. 
+      // This should be updates once we have clarity on this issue.
       ci_utils.docker_run('publish.ubuntu1404_cpu', "build_static_libmxnet ${mxnet_variant}", false)
       ci_utils.pack_lib("mxnet_${mxnet_variant}", libmxnet_pipeline.get_stash(mxnet_variant))
     }

From 2b12c5931c758cd2965fbaaaa50354427f52a210 Mon Sep 17 00:00:00 2001
From: Per Goncalves da Silva <perdasilva@gmail.com>
Date: Thu, 23 May 2019 15:42:31 +0200
Subject: [PATCH 250/813] Removes MXNET_SHA

---
 ci/Jenkinsfile_utils.groovy                    | 10 ----------
 ci/cd/Jenkinsfile_cd_pipeline                  |  4 ----
 ci/cd/Jenkinsfile_release_job                  | 14 +++++---------
 ci/cd/Jenkinsfile_utils.groovy                 |  1 -
 ci/cd/README.md                                |  7 -------
 ci/cd/mxnet_lib/mxnet_lib_pipeline.groovy      | 10 +++++-----
 ci/cd/mxnet_lib/static/Jenkins_pipeline.groovy |  2 +-
 7 files changed, 11 insertions(+), 37 deletions(-)

diff --git a/ci/Jenkinsfile_utils.groovy b/ci/Jenkinsfile_utils.groovy
index 14822b4cca36..a5fe0c8a04b8 100644
--- a/ci/Jenkinsfile_utils.groovy
+++ b/ci/Jenkinsfile_utils.groovy
@@ -19,11 +19,6 @@
 
 // initialize source codes
 def init_git(git_sha = '') {
-
-  git_sha = git_sha ? git_sha.trim() : ''
-
-  echo "Checking out git_sha: ${git_sha}"
-
   deleteDir()
   retry(5) {
     try {
@@ -31,11 +26,6 @@ def init_git(git_sha = '') {
       // retries as this will increase the amount of requests and worsen the throttling
       timeout(time: 15, unit: 'MINUTES') {
         checkout scm
-
-        if (git_sha) {
-          sh "git checkout ${git_sha}"
-        }
-
         sh 'git clean -xdff'
         sh 'git reset --hard'
         sh 'git submodule update --init --recursive'
diff --git a/ci/cd/Jenkinsfile_cd_pipeline b/ci/cd/Jenkinsfile_cd_pipeline
index 416d8b2c0442..49afc8489c00 100644
--- a/ci/cd/Jenkinsfile_cd_pipeline
+++ b/ci/cd/Jenkinsfile_cd_pipeline
@@ -30,7 +30,6 @@ pipeline {
 
   parameters {
     // Release parameters
-    string(defaultValue: "", description: 'Optional commit id to use for release', name: "MXNET_SHA")
     booleanParam(defaultValue: false, description: 'Whether this is a release build or not', name: "RELEASE_BUILD")
     string(defaultValue: "cpu,mkl,cu80,cu80mkl,cu90,cu90mkl,cu92,cu92mkl,cu100,cu100mkl", description: "Comma separated list of variants", name: "MXNET_VARIANTS")
   }
@@ -43,9 +42,6 @@ pipeline {
 
           // The address of the release job on Jenkins
           cd_utils.set_release_job_name('restricted-mxnet-cd/mxnet-cd-release-job')
-
-          // initialize the MXNET_SHA variable to the current GIT_COMMIT if not set
-          env.MXNET_SHA = env.MXNET_SHA ?: env.GIT_COMMIT
         }
       }
     }
diff --git a/ci/cd/Jenkinsfile_release_job b/ci/cd/Jenkinsfile_release_job
index 6b9d26b3ab4c..aeff647cd4ed 100644
--- a/ci/cd/Jenkinsfile_release_job
+++ b/ci/cd/Jenkinsfile_release_job
@@ -36,7 +36,6 @@ pipeline {
 
   parameters {
     // Release parameters
-    string(defaultValue: "", description: 'Optional commit id within specified branch to use for build', name: "MXNET_SHA")
     string(defaultValue: "Generic release job", description: "Optional Job name", name: "RELEASE_JOB_NAME")
     string(defaultValue: "", description: "Job Type e.g. binary_release/static, python/docker, runtime_images, etc.", name: 'RELEASE_JOB_TYPE')
     string(defaultValue: "cpu,mkl,cu80,cu80mkl,cu90,cu90mkl,cu92,cu92mkl,cu100,cu100mkl", description: "Comma separated list of variants", name: "MXNET_VARIANTS")
@@ -58,16 +57,13 @@ pipeline {
             windows_gpu: 'restricted-mxnetwindows-gpu'
           )
 
-          // initialize the MXNET_SHA variable to the current GIT_COMMIT if not set
-          env.MXNET_SHA = env.MXNET_SHA ?: env.GIT_COMMIT
-          echo "MXNET_SHA: ${env.MXNET_SHA}"
-
           echo """\
-            |Job Name: ${params.RELEASE_JOB_NAME}
-            |Job Type: ${params.RELEASE_JOB_TYPE}
+            |Job Name: ${env.RELEASE_JOB_NAME}
+            |Job Type: ${env.RELEASE_JOB_TYPE}
             |Release Build: ${params.RELEASE_BUILD}
-            |Commit Id: ${params.MXNET_SHA}
-            |Variants: ${MXNET_VARIANTS}""".stripMargin()
+            |Commit Id: ${env.GIT_COMMIT}
+            |Branch: ${env.GIT_BRANCH}
+            |Variants: ${env.MXNET_VARIANTS}""".stripMargin()
         }
       }
     }
diff --git a/ci/cd/Jenkinsfile_utils.groovy b/ci/cd/Jenkinsfile_utils.groovy
index f3ea34fb08c9..56ba801e4cac 100644
--- a/ci/cd/Jenkinsfile_utils.groovy
+++ b/ci/cd/Jenkinsfile_utils.groovy
@@ -34,7 +34,6 @@ def trigger_release_job(job_name, job_type, mxnet_variants) {
   def run = build(
     job: RELEASE_JOB, 
     parameters: [
-      string(name: 'MXNET_SHA', value: "${env.MXNET_SHA}"),
       string(name: 'RELEASE_JOB_NAME', value: "${job_name}"),
       string(name: 'RELEASE_JOB_TYPE', value: "${job_type}"),
       string(name: 'RELEASE_JOB_TYPE', value: "${job_type}"),
diff --git a/ci/cd/README.md b/ci/cd/README.md
index 637b5939d7e8..93aff8c5693f 100644
--- a/ci/cd/README.md
+++ b/ci/cd/README.md
@@ -31,7 +31,6 @@ Currently, 10 variants are supported:
 
 The [CD pipeline job](Jenkinsfile_cd_pipeline) take three parameters:
 
- * **MXNET_SHA**: Commit ID from which to run the CD process (Optional). Defaults to the HEAD commit ID.
  * **RELEASE_BUILD**: Flags the run as a *release build*. The underlying jobs can then use this environment variable to disambiguate between nightly and release builds. Defaults to *false*.
  * **MXNET_VARIANTS**: A comma separated list of variants to build. Defaults to *all* variants.
 
@@ -41,7 +40,6 @@ This job defines and executes the CD pipeline. For example, first publish the MX
 
 The [release job](Jenkinsfile_release_job) takes five parameters:
 
- * **MXNET_SHA**: Commit ID from which to run the CD process (Optional). Defaults to the HEAD commit ID.
  * **RELEASE_BUILD**: Flags the run as a *release build*. The underlying jobs can then use this environment variable to disambiguate between nightly and release builds. Defaults to *false*.
  * **MXNET_VARIANTS**: A comma separated list of variants to build. Defaults to *all* variants.
  * **RELEASE\_JOB\_NAME**: A name for this release job (Optional). Defaults to "Generic release job". It is used for debug output purposes.
@@ -164,8 +162,3 @@ def test(mxnet_variant) {
   }
 }
 ```
-
-##### MXNET_SHA
-
-Be mindful of this parameter as it defines the state of the repository that you are building. When checking out the project to execute your step, use `ci_utils.init_git(env.MXNET_SHA)`. You should ensure that what is being built is consistent across the variants.
-
diff --git a/ci/cd/mxnet_lib/mxnet_lib_pipeline.groovy b/ci/cd/mxnet_lib/mxnet_lib_pipeline.groovy
index 2e9c1b51efeb..c31ce1302690 100644
--- a/ci/cd/mxnet_lib/mxnet_lib_pipeline.groovy
+++ b/ci/cd/mxnet_lib/mxnet_lib_pipeline.groovy
@@ -113,7 +113,7 @@ def unittest_py3(mxnet_variant) {
     ws("workspace/mxnet_${libtype}/${mxnet_variant}/${env.BUILD_NUMBER}") {
       def image = get_environment(mxnet_variant)
       def use_nvidia_docker = mxnet_variant.startsWith('cu')
-      ci_utils.unpack_and_init("mxnet_${mxnet_variant}", get_stash(mxnet_variant), false, env.MXNET_SHA)
+      ci_utils.unpack_and_init("mxnet_${mxnet_variant}", get_stash(mxnet_variant), false)
       ci_utils.docker_run(image, "cd_unittest_ubuntu ${mxnet_variant} python3", use_nvidia_docker)
     }
   }
@@ -126,7 +126,7 @@ def unittest_py2(mxnet_variant) {
     ws("workspace/mxnet_${libtype}/${mxnet_variant}/${env.BUILD_NUMBER}") {
       def image = get_environment(mxnet_variant)
       def use_nvidia_docker = mxnet_variant.startsWith('cu')
-      ci_utils.unpack_and_init("mxnet_${mxnet_variant}", get_stash(mxnet_variant), false, env.MXNET_SHA)
+      ci_utils.unpack_and_init("mxnet_${mxnet_variant}", get_stash(mxnet_variant), false)
       ci_utils.docker_run(image, "cd_unittest_ubuntu ${mxnet_variant} python", use_nvidia_docker)
     }
   }
@@ -137,7 +137,7 @@ def test_gpu_quantization_py2(mxnet_variant) {
   node(NODE_LINUX_GPU_P3) {
     ws("workspace/mxnet_${libtype}/${mxnet_variant}/${env.BUILD_NUMBER}") {
       def image = get_environment(mxnet_variant)
-      ci_utils.unpack_and_init("mxnet_${mxnet_variant}", get_stash(mxnet_variant), false, env.MXNET_SHA)
+      ci_utils.unpack_and_init("mxnet_${mxnet_variant}", get_stash(mxnet_variant), false)
       ci_utils.docker_run(image, "unittest_ubuntu_python2_quantization_gpu", true)
     }
   }
@@ -148,7 +148,7 @@ def test_gpu_quantization_py3(mxnet_variant) {
   node(NODE_LINUX_GPU_P3) {
     ws("workspace/mxnet_${libtype}/${mxnet_variant}/${env.BUILD_NUMBER}") {
       def image = get_environment(mxnet_variant)
-      ci_utils.unpack_and_init("mxnet_${mxnet_variant}", get_stash(mxnet_variant), false, env.MXNET_SHA)
+      ci_utils.unpack_and_init("mxnet_${mxnet_variant}", get_stash(mxnet_variant), false)
       ci_utils.docker_run(image, "unittest_ubuntu_python3_quantization_gpu", true)
     }
   }
@@ -159,7 +159,7 @@ def push(mxnet_variant) {
   node(NODE_LINUX_CPU) {
     ws("workspace/mxnet_${libtype}/${mxnet_variant}/${env.BUILD_NUMBER}") {
       def deps = (mxnet_variant.endsWith('mkl')? mx_mkldnn_deps : mx_deps).replaceAll(',', '')
-      ci_utils.unpack_and_init("mxnet_${mxnet_variant}", get_stash(mxnet_variant), false, env.MXNET_SHA)
+      ci_utils.unpack_and_init("mxnet_${mxnet_variant}", get_stash(mxnet_variant), false)
       cd_utils.push_artifact(libmxnet, mxnet_variant, libtype, licenses, deps)
     }
   }
diff --git a/ci/cd/mxnet_lib/static/Jenkins_pipeline.groovy b/ci/cd/mxnet_lib/static/Jenkins_pipeline.groovy
index 6c4658674aaa..7d0fce6a552c 100644
--- a/ci/cd/mxnet_lib/static/Jenkins_pipeline.groovy
+++ b/ci/cd/mxnet_lib/static/Jenkins_pipeline.groovy
@@ -43,7 +43,7 @@ libmxnet_pipeline = load('ci/cd/mxnet_lib/mxnet_lib_pipeline.groovy')
 def build(mxnet_variant) {
   node(NODE_LINUX_CPU) {
     ws("workspace/mxnet_${libtype}/${mxnet_variant}/${env.BUILD_NUMBER}") {
-      ci_utils.init_git(env.MXNET_SHA)
+      ci_utils.init_git()
       // Compiling in Ubuntu14.04 due to glibc issues. 
       // This should be updates once we have clarity on this issue.
       ci_utils.docker_run('publish.ubuntu1404_cpu', "build_static_libmxnet ${mxnet_variant}", false)

From a8c0fe82166793b13b7a2fa8dd137f32ff79f7c9 Mon Sep 17 00:00:00 2001
From: Per Goncalves da Silva <perdasilva@gmail.com>
Date: Thu, 23 May 2019 17:15:33 +0200
Subject: [PATCH 251/813] Removes set_release_job_name

---
 ci/cd/Jenkinsfile_cd_pipeline  | 3 ---
 ci/cd/Jenkinsfile_utils.groovy | 8 +-------
 2 files changed, 1 insertion(+), 10 deletions(-)

diff --git a/ci/cd/Jenkinsfile_cd_pipeline b/ci/cd/Jenkinsfile_cd_pipeline
index 49afc8489c00..2ec8c9befad0 100644
--- a/ci/cd/Jenkinsfile_cd_pipeline
+++ b/ci/cd/Jenkinsfile_cd_pipeline
@@ -39,9 +39,6 @@ pipeline {
       steps {
         script {
           cd_utils = load('ci/cd/Jenkinsfile_utils.groovy')
-
-          // The address of the release job on Jenkins
-          cd_utils.set_release_job_name('restricted-mxnet-cd/mxnet-cd-release-job')
         }
       }
     }
diff --git a/ci/cd/Jenkinsfile_utils.groovy b/ci/cd/Jenkinsfile_utils.groovy
index 56ba801e4cac..21dccef95db4 100644
--- a/ci/cd/Jenkinsfile_utils.groovy
+++ b/ci/cd/Jenkinsfile_utils.groovy
@@ -17,12 +17,7 @@
 // specific language governing permissions and limitations
 // under the License.
 
-// Sets the name of the release job in Jenkins.
-// That is the release job that has been created in Jenkins based
-// on the Jenkinsfile_release_job definition.
-def set_release_job_name(release_job) {
-  RELEASE_JOB = release_job
-}
+RELEASE_JOB = "restricted-mxnet-cd/mxnet-cd-release-job"
 
 // Triggers a downstream jenkins job responsible for building, testing
 // and publishing all the variants for a particular 'job_type'.
@@ -36,7 +31,6 @@ def trigger_release_job(job_name, job_type, mxnet_variants) {
     parameters: [
       string(name: 'RELEASE_JOB_NAME', value: "${job_name}"),
       string(name: 'RELEASE_JOB_TYPE', value: "${job_type}"),
-      string(name: 'RELEASE_JOB_TYPE', value: "${job_type}"),
       string(name: 'MXNET_VARIANTS', value: "${mxnet_variants}"),
       booleanParam(name: 'RELEASE_BUILD', value: "${env.RELEASE_BUILD}")
     ],

From 5cb26fd24d52a498d4fb49a544f3cc8f6ed3e0eb Mon Sep 17 00:00:00 2001
From: Per Goncalves da Silva <perdasilva@gmail.com>
Date: Thu, 23 May 2019 18:50:04 +0200
Subject: [PATCH 252/813] Adds license headers

---
 ci/cd/README.md                         | 17 +++++++++++++++++
 ci/cd/utils/artifact_repository.md      | 17 +++++++++++++++++
 ci/cd/utils/test_artifact_repository.py | 19 +++++++++++++++++++
 3 files changed, 53 insertions(+)

diff --git a/ci/cd/README.md b/ci/cd/README.md
index 93aff8c5693f..fd978c1d819e 100644
--- a/ci/cd/README.md
+++ b/ci/cd/README.md
@@ -1,3 +1,20 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
 # MXNet Continuous Delivery
 
 ## Introduction
diff --git a/ci/cd/utils/artifact_repository.md b/ci/cd/utils/artifact_repository.md
index 1dc902511cbe..4cf88428e433 100644
--- a/ci/cd/utils/artifact_repository.md
+++ b/ci/cd/utils/artifact_repository.md
@@ -1,3 +1,20 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
 # Artifact Repository - Pushing and Pulling libmxnet
 
 The artifact repository is an S3 bucket accessible only to restricted Jenkins nodes. It is used to store compiled MXNet artifacts that can be used by downstream CD pipelines to package the compiled libraries for different delivery channels (e.g. DockerHub, PyPI, Maven, etc.). The S3 object keys for the files being posted will be prefixed with the following distinguishing characteristics of the binary: branch, commit id, operating system, variant and dependency linking strategy (static or dynamic). For instance, s3://bucket/73b29fa90d3eac0b1fae403b7583fdd1529942dc/ubuntu16.04/cu92mkl/static/libmxnet.so
diff --git a/ci/cd/utils/test_artifact_repository.py b/ci/cd/utils/test_artifact_repository.py
index 948352768659..0f719d10dabb 100644
--- a/ci/cd/utils/test_artifact_repository.py
+++ b/ci/cd/utils/test_artifact_repository.py
@@ -1,3 +1,22 @@
+# -*- coding: utf-8 -*-
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
 import unittest
 from unittest.mock import MagicMock, mock_open, patch
 

From 98cdf30040e4709cf5993a66961d729d92bb9492 Mon Sep 17 00:00:00 2001
From: Per Goncalves da Silva <perdasilva@gmail.com>
Date: Sat, 25 May 2019 11:33:39 +0200
Subject: [PATCH 253/813] Updates artifact repository to expect licenses

---
 ci/cd/utils/artifact_repository.py      | 12 +++---
 ci/cd/utils/test_artifact_repository.py | 16 ++++++++
 ci/docker/runtime_functions.sh          |  9 +++++
 ci/jenkins/Jenkins_steps.groovy         | 11 ++++++
 ci/jenkins/Jenkinsfile_tools            | 49 +++++++++++++++++++++++++
 5 files changed, 92 insertions(+), 5 deletions(-)
 create mode 100644 ci/jenkins/Jenkinsfile_tools

diff --git a/ci/cd/utils/artifact_repository.py b/ci/cd/utils/artifact_repository.py
index 7afdc108bec0..432a402e4dea 100755
--- a/ci/cd/utils/artifact_repository.py
+++ b/ci/cd/utils/artifact_repository.py
@@ -416,17 +416,19 @@ def push_artifact(args: argparse.Namespace):
     logger.info("DEPENDENCIES: {}".format(args.dependencies))
     logger.info("")
 
+    if not args.licenses:
+        raise RuntimeError('No licenses defined. Please submit the licenses to be shipped with the binary.')
+
     # Upload mxnet
     try:
         logger.info('Uploading libmxnet library...')
         s3_upload(args.bucket, get_s3_key_prefix(args), [args.libmxnet])
         logger.info("")
 
-        # Upload licenses, if necessary
-        if args.licenses:
-            logger.info('Uploading licenses...')
-            s3_upload(args.bucket, get_s3_key_prefix(args, subdir='licenses'), args.licenses)
-            logger.info("")
+        # Upload licenses
+        logger.info('Uploading licenses...')
+        s3_upload(args.bucket, get_s3_key_prefix(args, subdir='licenses'), args.licenses)
+        logger.info("")
 
         # Upload dependencies, if necessary
         if args.dependencies:
diff --git a/ci/cd/utils/test_artifact_repository.py b/ci/cd/utils/test_artifact_repository.py
index 0f719d10dabb..c8ab70aecec2 100644
--- a/ci/cd/utils/test_artifact_repository.py
+++ b/ci/cd/utils/test_artifact_repository.py
@@ -512,3 +512,19 @@ def test_write_libmxnet_meta(self):
             mock_fopen.assert_called_once_with(os.path.join('dest', 'libmxnet.meta'), 'w')
             mock_fopen().write.called_with(
                 'commit_id: abcd1234\ndependency_linking: stynamic\nos: lunix\nvariant: gpu\n')
+
+    def test_push_artifact_throws_no_license_error(self):
+        """
+        Tests push artifact throwns error if no licenses are defined
+        """
+        args = TestArtifactRepositoryTool.create_argparse_namespace(licenses=[])
+        with self.assertRaises(RuntimeError) as ctx:
+            push_artifact(args)
+        self.assertEqual(str(ctx.exception),
+                          "No licenses defined. Please submit the licenses to be shipped with the binary.")
+
+        args = args = TestArtifactRepositoryTool.create_argparse_namespace(licenses=None)
+        with self.assertRaises(RuntimeError) as ctx:
+            push_artifact(args)
+        self.assertEqual(str(ctx.exception),
+                          "No licenses defined. Please submit the licenses to be shipped with the binary.")
diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index 5c45c379b5f0..8819bb7f398f 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -1556,6 +1556,15 @@ broken_link_checker() {
     ./tests/nightly/broken_link_checker_test/broken_link_checker.sh
 }
 
+# artifact repository unit tets
+test_artifact_repository() {
+    set -ex
+    pushd .
+    cd ci/cd/utils/
+    pytest test_artifact_repository.py
+    popd
+}
+
 ##############################################################
 # MAIN
 #
diff --git a/ci/jenkins/Jenkins_steps.groovy b/ci/jenkins/Jenkins_steps.groovy
index f653e0c5cb63..4075842313ae 100644
--- a/ci/jenkins/Jenkins_steps.groovy
+++ b/ci/jenkins/Jenkins_steps.groovy
@@ -1422,4 +1422,15 @@ def sanity_rat_license() {
     }]
 }
 
+def test_artifact_repository() {
+    return ['Test Artifact Repository Client': {
+      node(NODE_LINUX_CPU) {
+        ws('workspace/artifact-repo-client') {
+          utils.init_git()
+          utils.docker_run('ubuntu_cpu', 'test_artifact_repository', false)
+        }
+      }
+    }]
+}
+
 return this
diff --git a/ci/jenkins/Jenkinsfile_tools b/ci/jenkins/Jenkinsfile_tools
new file mode 100644
index 000000000000..9af63aae1b2c
--- /dev/null
+++ b/ci/jenkins/Jenkinsfile_tools
@@ -0,0 +1,49 @@
+// -*- mode: groovy -*-
+
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+//
+// Jenkins pipeline
+// See documents at https://jenkins.io/doc/book/pipeline/jenkinsfile/
+
+// A place to add tests scripts for supporting tools
+
+// timeout in minutes
+max_time = 180
+
+node('utility') {
+  // Loading the utilities requires a node context unfortunately
+  checkout scm
+  utils = load('ci/Jenkinsfile_utils.groovy')
+  custom_steps = load('ci/jenkins/Jenkins_steps.groovy')
+}
+utils.assign_node_labels(utility: 'utility', linux_cpu: 'mxnetlinux-cpu', linux_gpu: 'mxnetlinux-gpu', linux_gpu_p3: 'mxnetlinux-gpu-p3', windows_cpu: 'mxnetwindows-cpu', windows_gpu: 'mxnetwindows-gpu')
+
+utils.main_wrapper(
+core_logic: {
+  utils.parallel_stage('Tooling Tests', [
+    custom_steps.test_artifact_repository(),
+  ])    
+}
+,
+failure_handler: {
+  // Only send email if master or release branches failed
+  if (currentBuild.result == "FAILURE" && (env.BRANCH_NAME == "master" || env.BRANCH_NAME.startsWith("v"))) {
+    emailext body: 'Build for MXNet branch ${BRANCH_NAME} has broken. Please view the build at ${BUILD_URL}', replyTo: '${EMAIL}', subject: '[BUILD FAILED] Branch ${BRANCH_NAME} build ${BUILD_NUMBER}', to: '${EMAIL}'
+  }
+}
+)

From 3027296b38cc055e9748c604869b3f1aed3c4172 Mon Sep 17 00:00:00 2001
From: Per Goncalves da Silva <perdasilva@gmail.com>
Date: Sat, 25 May 2019 11:50:45 +0200
Subject: [PATCH 254/813] Moves ci/cd to cd directory

---
 {ci/cd => cd}/Jenkinsfile_cd_pipeline                  | 2 +-
 {ci/cd => cd}/Jenkinsfile_release_job                  | 4 ++--
 {ci/cd => cd}/Jenkinsfile_utils.groovy                 | 2 +-
 {ci/cd => cd}/README.md                                | 0
 {ci/cd => cd}/mxnet_lib/mxnet_lib_pipeline.groovy      | 0
 {ci/cd => cd}/mxnet_lib/static/Jenkins_pipeline.groovy | 2 +-
 {ci/cd => cd}/utils/artifact_repository.md             | 0
 {ci/cd => cd}/utils/artifact_repository.py             | 0
 {ci/cd => cd}/utils/requirements.txt                   | 0
 {ci/cd => cd}/utils/test_artifact_repository.py        | 0
 ci/docker/runtime_functions.sh                         | 2 +-
 11 files changed, 6 insertions(+), 6 deletions(-)
 rename {ci/cd => cd}/Jenkinsfile_cd_pipeline (96%)
 rename {ci/cd => cd}/Jenkinsfile_release_job (95%)
 rename {ci/cd => cd}/Jenkinsfile_utils.groovy (95%)
 rename {ci/cd => cd}/README.md (100%)
 rename {ci/cd => cd}/mxnet_lib/mxnet_lib_pipeline.groovy (100%)
 rename {ci/cd => cd}/mxnet_lib/static/Jenkins_pipeline.groovy (96%)
 rename {ci/cd => cd}/utils/artifact_repository.md (100%)
 rename {ci/cd => cd}/utils/artifact_repository.py (100%)
 rename {ci/cd => cd}/utils/requirements.txt (100%)
 rename {ci/cd => cd}/utils/test_artifact_repository.py (100%)

diff --git a/ci/cd/Jenkinsfile_cd_pipeline b/cd/Jenkinsfile_cd_pipeline
similarity index 96%
rename from ci/cd/Jenkinsfile_cd_pipeline
rename to cd/Jenkinsfile_cd_pipeline
index 2ec8c9befad0..7ac309f521f8 100644
--- a/ci/cd/Jenkinsfile_cd_pipeline
+++ b/cd/Jenkinsfile_cd_pipeline
@@ -38,7 +38,7 @@ pipeline {
     stage("Init") {
       steps {
         script {
-          cd_utils = load('ci/cd/Jenkinsfile_utils.groovy')
+          cd_utils = load('cd/Jenkinsfile_utils.groovy')
         }
       }
     }
diff --git a/ci/cd/Jenkinsfile_release_job b/cd/Jenkinsfile_release_job
similarity index 95%
rename from ci/cd/Jenkinsfile_release_job
rename to cd/Jenkinsfile_release_job
index aeff647cd4ed..7fee573d2487 100644
--- a/ci/cd/Jenkinsfile_release_job
+++ b/cd/Jenkinsfile_release_job
@@ -46,7 +46,7 @@ pipeline {
     stage("Init") {
       steps {
         script{
-          cd_utils = load('ci/cd/Jenkinsfile_utils.groovy')
+          cd_utils = load('cd/Jenkinsfile_utils.groovy')
           ci_utils = load('ci/Jenkinsfile_utils.groovy')
           ci_utils.assign_node_labels(
             utility: 'restricted-utility',
@@ -77,7 +77,7 @@ pipeline {
           }
 
           // Load script for the supplied job type
-          def custom_steps = load("ci/cd/${params.RELEASE_JOB_TYPE}/Jenkins_pipeline.groovy")
+          def custom_steps = load("cd/${params.RELEASE_JOB_TYPE}/Jenkins_pipeline.groovy")
 
           // Extract the pipelines for the variants
           def pipelines = [:]
diff --git a/ci/cd/Jenkinsfile_utils.groovy b/cd/Jenkinsfile_utils.groovy
similarity index 95%
rename from ci/cd/Jenkinsfile_utils.groovy
rename to cd/Jenkinsfile_utils.groovy
index 21dccef95db4..f1274b17fb85 100644
--- a/ci/cd/Jenkinsfile_utils.groovy
+++ b/cd/Jenkinsfile_utils.groovy
@@ -97,7 +97,7 @@ def push_artifact(libmxnet_path, variant, libtype, license_paths = '', dependenc
   if(license_paths == null) license_paths = ''
   if(dependency_paths == null) dependency_paths = ''
 
-  sh "./ci/cd/utils/artifact_repository.py --push --verbose --libtype ${libtype} --variant ${variant} --libmxnet ${libmxnet_path} --licenses ${license_paths} --dependencies ${dependency_paths}"
+  sh "./cd/utils/artifact_repository.py --push --verbose --libtype ${libtype} --variant ${variant} --libmxnet ${libmxnet_path} --licenses ${license_paths} --dependencies ${dependency_paths}"
 }
 
 return this
diff --git a/ci/cd/README.md b/cd/README.md
similarity index 100%
rename from ci/cd/README.md
rename to cd/README.md
diff --git a/ci/cd/mxnet_lib/mxnet_lib_pipeline.groovy b/cd/mxnet_lib/mxnet_lib_pipeline.groovy
similarity index 100%
rename from ci/cd/mxnet_lib/mxnet_lib_pipeline.groovy
rename to cd/mxnet_lib/mxnet_lib_pipeline.groovy
diff --git a/ci/cd/mxnet_lib/static/Jenkins_pipeline.groovy b/cd/mxnet_lib/static/Jenkins_pipeline.groovy
similarity index 96%
rename from ci/cd/mxnet_lib/static/Jenkins_pipeline.groovy
rename to cd/mxnet_lib/static/Jenkins_pipeline.groovy
index 7d0fce6a552c..39d0bb1c6c70 100644
--- a/ci/cd/mxnet_lib/static/Jenkins_pipeline.groovy
+++ b/cd/mxnet_lib/static/Jenkins_pipeline.groovy
@@ -37,7 +37,7 @@ mx_mkldnn_deps = 'lib/libgfortran.so.3, lib/libquadmath.so.0, lib/libiomp5.so, l
 // either static or dynamic - depending on how it links to its dependencies
 libtype = 'static'
 
-libmxnet_pipeline = load('ci/cd/mxnet_lib/mxnet_lib_pipeline.groovy')
+libmxnet_pipeline = load('cd/mxnet_lib/mxnet_lib_pipeline.groovy')
 
 // Builds the static binary for the specified mxnet variant
 def build(mxnet_variant) {
diff --git a/ci/cd/utils/artifact_repository.md b/cd/utils/artifact_repository.md
similarity index 100%
rename from ci/cd/utils/artifact_repository.md
rename to cd/utils/artifact_repository.md
diff --git a/ci/cd/utils/artifact_repository.py b/cd/utils/artifact_repository.py
similarity index 100%
rename from ci/cd/utils/artifact_repository.py
rename to cd/utils/artifact_repository.py
diff --git a/ci/cd/utils/requirements.txt b/cd/utils/requirements.txt
similarity index 100%
rename from ci/cd/utils/requirements.txt
rename to cd/utils/requirements.txt
diff --git a/ci/cd/utils/test_artifact_repository.py b/cd/utils/test_artifact_repository.py
similarity index 100%
rename from ci/cd/utils/test_artifact_repository.py
rename to cd/utils/test_artifact_repository.py
diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index 8819bb7f398f..f2db08a452d8 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -1560,7 +1560,7 @@ broken_link_checker() {
 test_artifact_repository() {
     set -ex
     pushd .
-    cd ci/cd/utils/
+    cd cd/utils/
     pytest test_artifact_repository.py
     popd
 }

From f6d0fc275fc3f75b16c8dbe2046cab75357ae765 Mon Sep 17 00:00:00 2001
From: Per Goncalves da Silva <perdasilva@gmail.com>
Date: Sun, 26 May 2019 07:32:23 +0200
Subject: [PATCH 255/813] Takes downstream job name from environment

---
 cd/Jenkinsfile_utils.groovy | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/cd/Jenkinsfile_utils.groovy b/cd/Jenkinsfile_utils.groovy
index f1274b17fb85..46901b3a1577 100644
--- a/cd/Jenkinsfile_utils.groovy
+++ b/cd/Jenkinsfile_utils.groovy
@@ -17,8 +17,6 @@
 // specific language governing permissions and limitations
 // under the License.
 
-RELEASE_JOB = "restricted-mxnet-cd/mxnet-cd-release-job"
-
 // Triggers a downstream jenkins job responsible for building, testing
 // and publishing all the variants for a particular 'job_type'.
 // The 'job_type' should be the name of the directory that contains the 
@@ -27,7 +25,7 @@ RELEASE_JOB = "restricted-mxnet-cd/mxnet-cd-release-job"
 // be published.
 def trigger_release_job(job_name, job_type, mxnet_variants) {
   def run = build(
-    job: RELEASE_JOB, 
+    job: env.CD_RELEASE_JOB_NAME, 
     parameters: [
       string(name: 'RELEASE_JOB_NAME', value: "${job_name}"),
       string(name: 'RELEASE_JOB_TYPE', value: "${job_type}"),

From 8241c5296ff9838c2024d60be5dee54494844309 Mon Sep 17 00:00:00 2001
From: Per Goncalves da Silva <perdasilva@gmail.com>
Date: Sun, 26 May 2019 07:36:41 +0200
Subject: [PATCH 256/813] Updates order of parameters

---
 cd/Jenkinsfile_cd_pipeline | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cd/Jenkinsfile_cd_pipeline b/cd/Jenkinsfile_cd_pipeline
index 7ac309f521f8..85b06cb09bb0 100644
--- a/cd/Jenkinsfile_cd_pipeline
+++ b/cd/Jenkinsfile_cd_pipeline
@@ -30,8 +30,8 @@ pipeline {
 
   parameters {
     // Release parameters
-    booleanParam(defaultValue: false, description: 'Whether this is a release build or not', name: "RELEASE_BUILD")
     string(defaultValue: "cpu,mkl,cu80,cu80mkl,cu90,cu90mkl,cu92,cu92mkl,cu100,cu100mkl", description: "Comma separated list of variants", name: "MXNET_VARIANTS")
+    booleanParam(defaultValue: false, description: 'Whether this is a release build or not', name: "RELEASE_BUILD")
   }
 
   stages {

From 749492fb7f646a6b2055193b69f855939bc94bd5 Mon Sep 17 00:00:00 2001
From: Per Goncalves da Silva <perdasilva@gmail.com>
Date: Sun, 26 May 2019 07:41:40 +0200
Subject: [PATCH 257/813] Updates job type parameter to dropdown

---
 cd/Jenkinsfile_release_job | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cd/Jenkinsfile_release_job b/cd/Jenkinsfile_release_job
index 7fee573d2487..92ea72fca9c4 100644
--- a/cd/Jenkinsfile_release_job
+++ b/cd/Jenkinsfile_release_job
@@ -37,7 +37,7 @@ pipeline {
   parameters {
     // Release parameters
     string(defaultValue: "Generic release job", description: "Optional Job name", name: "RELEASE_JOB_NAME")
-    string(defaultValue: "", description: "Job Type e.g. binary_release/static, python/docker, runtime_images, etc.", name: 'RELEASE_JOB_TYPE')
+    choice(choices: ["mxnet_lib/static"], description: "Pipeline to build", name: "RELEASE_JOB_TYPE")
     string(defaultValue: "cpu,mkl,cu80,cu80mkl,cu90,cu90mkl,cu92,cu92mkl,cu100,cu100mkl", description: "Comma separated list of variants", name: "MXNET_VARIANTS")
     booleanParam(defaultValue: false, description: 'Whether this is a release build or not', name: "RELEASE_BUILD")
   }

From 759e76e440aad81a0af35df3f017b56ac78d906c Mon Sep 17 00:00:00 2001
From: Per Goncalves da Silva <perdasilva@gmail.com>
Date: Fri, 5 Jul 2019 09:15:03 +0200
Subject: [PATCH 258/813] Adds libmxnet feature extraction code comments

---
 cd/utils/artifact_repository.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/cd/utils/artifact_repository.py b/cd/utils/artifact_repository.py
index 432a402e4dea..4ef34c901242 100755
--- a/cd/utils/artifact_repository.py
+++ b/cd/utils/artifact_repository.py
@@ -232,6 +232,10 @@ class Feature(ctypes.Structure):
         def name(self):
             return self._name.decode()
 
+    # we are not using the mxnet python bindings here because we cannot assume
+    # they are present and in the python path, or that they would point to the
+    # specified libmxnet.so. Therefore, we load the libmxnet.so library independently
+    # to extract its features
     try:
         libmxnet = ctypes.CDLL(libmxnet_path, ctypes.RTLD_LOCAL)
     except Exception as e:

From 36ac85a1eca291e1503b20b5de175190155b03a8 Mon Sep 17 00:00:00 2001
From: Per Goncalves da Silva <perdasilva@gmail.com>
Date: Mon, 29 Jul 2019 15:09:44 +0200
Subject: [PATCH 259/813] Removes ccache setup from static build

---
 ci/docker/runtime_functions.sh | 1 -
 1 file changed, 1 deletion(-)

diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index f2db08a452d8..baba0a032110 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -1494,7 +1494,6 @@ build_static_libmxnet() {
     set -ex
     pushd .
     local mxnet_variant=${1:?"This function requires a python command as the first argument"}
-    build_ccache_wrappers
     source tools/staticbuild/build.sh ${mxnet_variant} pip
     popd
 }

From 65928b1936d5d43f237b0f9f9e43115861faf4c0 Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Fri, 30 Aug 2019 12:43:28 +0800
Subject: [PATCH 260/813] NumPy-compatible infrastructure on Gluon (#16024)

* numpy infra residual part

* Add test

* Fix sanity

* Fix flaky test
---
 python/mxnet/contrib/text/embedding.py        |  29 +++--
 python/mxnet/gluon/data/dataloader.py         |  34 ++++--
 python/mxnet/gluon/data/vision/datasets.py    |  12 +-
 python/mxnet/gluon/data/vision/transforms.py  |  25 ++++
 python/mxnet/gluon/loss.py                    |  78 +++++++++---
 python/mxnet/gluon/model_zoo/vision/resnet.py |  19 +--
 python/mxnet/gluon/nn/activations.py          |   7 +-
 python/mxnet/gluon/nn/basic_layers.py         |  27 +++--
 python/mxnet/gluon/nn/conv_layers.py          |  54 +++++++--
 python/mxnet/gluon/rnn/rnn_layer.py           |  31 +++--
 python/mxnet/gluon/utils.py                   |  29 +++--
 python/mxnet/image/detection.py               |  17 ++-
 python/mxnet/image/image.py                   |  42 +++++--
 python/mxnet/initializer.py                   |  15 ++-
 python/mxnet/numpy/multiarray.py              |   4 +-
 python/mxnet/symbol/numpy/_symbol.py          |   4 +-
 tests/python/unittest/test_numpy_gluon.py     | 113 ++++++++++++++++++
 tests/python/unittest/test_numpy_op.py        |   2 +-
 18 files changed, 436 insertions(+), 106 deletions(-)
 create mode 100644 tests/python/unittest/test_numpy_gluon.py

diff --git a/python/mxnet/contrib/text/embedding.py b/python/mxnet/contrib/text/embedding.py
index 9d529db7d846..979ba2afbeb5 100644
--- a/python/mxnet/contrib/text/embedding.py
+++ b/python/mxnet/contrib/text/embedding.py
@@ -35,6 +35,9 @@
 from ... import ndarray as nd
 from ... import registry
 from ... import base
+from ...util import is_np_array
+from ... import numpy as _mx_np
+from ... import numpy_extension as _mx_npx
 
 
 def register(embedding_cls):
@@ -295,12 +298,15 @@ def _load_embedding(self, pretrained_file_path, elem_delim, init_unknown_vec, en
                     tokens.add(token)
 
         self._vec_len = vec_len
-        self._idx_to_vec = nd.array(all_elems).reshape((-1, self.vec_len))
+        array_fn = _mx_np.array if is_np_array() else nd.array
+        self._idx_to_vec = array_fn(all_elems).reshape((-1, self.vec_len))
 
         if loaded_unknown_vec is None:
-            self._idx_to_vec[C.UNKNOWN_IDX] = init_unknown_vec(shape=self.vec_len)
+            init_val = init_unknown_vec(shape=self.vec_len)
+            self._idx_to_vec[C.UNKNOWN_IDX] =\
+                init_val.as_np_ndarray() if is_np_array() else init_val
         else:
-            self._idx_to_vec[C.UNKNOWN_IDX] = nd.array(loaded_unknown_vec)
+            self._idx_to_vec[C.UNKNOWN_IDX] = array_fn(loaded_unknown_vec)
 
     def _index_tokens_from_vocabulary(self, vocabulary):
         self._token_to_idx = vocabulary.token_to_idx.copy() \
@@ -328,7 +334,8 @@ def _set_idx_to_vec_by_embeddings(self, token_embeddings, vocab_len, vocab_idx_t
         """
 
         new_vec_len = sum(embed.vec_len for embed in token_embeddings)
-        new_idx_to_vec = nd.zeros(shape=(vocab_len, new_vec_len))
+        zeros_fn = _mx_np.zeros if is_np_array() else nd.zeros
+        new_idx_to_vec = zeros_fn(shape=(vocab_len, new_vec_len))
 
         col_start = 0
         # Concatenate all the embedding vectors in token_embeddings.
@@ -397,7 +404,13 @@ def get_vecs_by_tokens(self, tokens, lower_case_backup=False):
                        else self.token_to_idx.get(token.lower(), C.UNKNOWN_IDX)
                        for token in tokens]
 
-        vecs = nd.Embedding(nd.array(indices), self.idx_to_vec, self.idx_to_vec.shape[0],
+        if is_np_array():
+            embedding_fn = _mx_npx.embedding
+            array_fn = _mx_np.array
+        else:
+            embedding_fn = nd.Embedding
+            array_fn = nd.array
+        vecs = embedding_fn(array_fn(indices), self.idx_to_vec, self.idx_to_vec.shape[0],
                             self.idx_to_vec.shape[1])
 
         return vecs[0] if to_reduce else vecs
@@ -425,7 +438,8 @@ def update_token_vectors(self, tokens, new_vectors):
             if not isinstance(tokens, list):
                 tokens = [tokens]
             if len(new_vectors.shape) == 1:
-                new_vectors = new_vectors.expand_dims(0)
+                expand_dims_fn = _mx_np.expand_dims if is_np_array() else nd.expand_dims
+                new_vectors = expand_dims_fn(new_vectors, axis=0)
 
         else:
             assert isinstance(new_vectors, nd.NDArray) and len(new_vectors.shape) == 2, \
@@ -444,7 +458,8 @@ def update_token_vectors(self, tokens, new_vectors):
                                  '`unknown_token` %s in `tokens`. This is to avoid unintended '
                                  'updates.' % (token, self.idx_to_token[C.UNKNOWN_IDX]))
 
-        self._idx_to_vec[nd.array(indices)] = new_vectors
+        array_fn = _mx_np.array if is_np_array() else nd.array
+        self._idx_to_vec[array_fn(indices)] = new_vectors
 
     @classmethod
     def _check_pretrained_file_names(cls, pretrained_file_name):
diff --git a/python/mxnet/gluon/data/dataloader.py b/python/mxnet/gluon/data/dataloader.py
index 4dfa94f72c50..9f0939ec7f37 100644
--- a/python/mxnet/gluon/data/dataloader.py
+++ b/python/mxnet/gluon/data/dataloader.py
@@ -18,6 +18,7 @@
 # coding: utf-8
 # pylint: disable=ungrouped-imports
 """Dataset generator."""
+from __future__ import absolute_import
 __all__ = ['DataLoader']
 
 import pickle
@@ -37,6 +38,8 @@
 
 from . import sampler as _sampler
 from ... import nd, context
+from ...util import is_np_shape, is_np_array, set_np
+from ... import numpy as _mx_np  # pylint: disable=reimported
 
 if sys.platform == 'darwin' or sys.platform == 'win32':
     def rebuild_ndarray(*args):
@@ -128,27 +131,33 @@ def __init__(self, *args, **kwargs):
 def default_batchify_fn(data):
     """Collate data into batch."""
     if isinstance(data[0], nd.NDArray):
-        return nd.stack(*data)
+        return _mx_np.stack(data) if is_np_array() else nd.stack(*data)
     elif isinstance(data[0], tuple):
         data = zip(*data)
         return [default_batchify_fn(i) for i in data]
     else:
         data = np.asarray(data)
-        return nd.array(data, dtype=data.dtype)
+        array_fn = _mx_np.array if is_np_array() else nd.array
+        return array_fn(data, dtype=data.dtype)
 
 
 def default_mp_batchify_fn(data):
     """Collate data into batch. Use shared memory for stacking."""
     if isinstance(data[0], nd.NDArray):
-        out = nd.empty((len(data),) + data[0].shape, dtype=data[0].dtype,
+        empty_fn = _mx_np.empty if is_np_array() else nd.empty
+        out = empty_fn((len(data),) + data[0].shape, dtype=data[0].dtype,
                        ctx=context.Context('cpu_shared', 0))
-        return nd.stack(*data, out=out)
+        if is_np_array():
+            return _mx_np.stack(data, out=out)
+        else:
+            return nd.stack(*data, out=out)
     elif isinstance(data[0], tuple):
         data = zip(*data)
         return [default_mp_batchify_fn(i) for i in data]
     else:
         data = np.asarray(data)
-        return nd.array(data, dtype=data.dtype,
+        array_fn = _mx_np.array if is_np_array() else nd.array
+        return array_fn(data, dtype=data.dtype,
                         ctx=context.Context('cpu_shared', 0))
 
 
@@ -384,14 +393,20 @@ def __len__(self):
         return len(self._batch_sampler)
 
 
+def _thread_worker_initializer(active_shape, active_array):
+    """Initializer for ThreadPool."""
+    set_np(shape=active_shape, array=active_array)
+
+
 _worker_dataset = None
-def _worker_initializer(dataset):
+def _worker_initializer(dataset, active_shape, active_array):
     """Initialier for processing pool."""
     # global dataset is per-process based and only available in worker processes
     # this is only necessary to handle MXIndexedRecordIO because otherwise dataset
     # can be passed as argument
     global _worker_dataset
     _worker_dataset = dataset
+    set_np(shape=active_shape, array=active_array)
 
 def _worker_fn(samples, batchify_fn, dataset=None):
     """Function for processing data in worker process."""
@@ -558,10 +573,13 @@ def __init__(self, dataset, batch_size=None, shuffle=False, sampler=None,
         self._prefetch = max(0, int(prefetch) if prefetch is not None else 2 * self._num_workers)
         if self._num_workers > 0:
             if self._thread_pool:
-                self._worker_pool = ThreadPool(self._num_workers)
+                self._worker_pool = ThreadPool(self._num_workers,
+                                               initializer=_thread_worker_initializer,
+                                               initargs=(is_np_shape(), is_np_array()))
             else:
                 self._worker_pool = multiprocessing.Pool(
-                    self._num_workers, initializer=_worker_initializer, initargs=[self._dataset])
+                    self._num_workers, initializer=_worker_initializer,
+                    initargs=[self._dataset, is_np_shape(), is_np_array()])
         if batchify_fn is None:
             if num_workers > 0:
                 self._batchify_fn = default_mp_batchify_fn
diff --git a/python/mxnet/gluon/data/vision/datasets.py b/python/mxnet/gluon/data/vision/datasets.py
index 12ef7e16ef49..bdcaff52a042 100644
--- a/python/mxnet/gluon/data/vision/datasets.py
+++ b/python/mxnet/gluon/data/vision/datasets.py
@@ -31,6 +31,8 @@
 from .. import dataset
 from ...utils import download, check_sha1, _get_repo_file_url
 from .... import nd, image, recordio, base
+from .... import numpy as _mx_np  # pylint: disable=reimported
+from ....util import is_np_array
 
 
 class MNIST(dataset._DownloadedDataset):
@@ -81,13 +83,16 @@ def _get_data(self):
         with gzip.open(label_file, 'rb') as fin:
             struct.unpack(">II", fin.read(8))
             label = np.frombuffer(fin.read(), dtype=np.uint8).astype(np.int32)
+            if is_np_array():
+                label = _mx_np.array(label, dtype=label.dtype)
 
         with gzip.open(data_file, 'rb') as fin:
             struct.unpack(">IIII", fin.read(16))
             data = np.frombuffer(fin.read(), dtype=np.uint8)
             data = data.reshape(len(label), 28, 28, 1)
 
-        self._data = nd.array(data, dtype=data.dtype)
+        array_fn = _mx_np.array if is_np_array() else nd.array
+        self._data = array_fn(data, dtype=data.dtype)
         self._label = label
 
 
@@ -183,8 +188,9 @@ def _get_data(self):
         data = np.concatenate(data)
         label = np.concatenate(label)
 
-        self._data = nd.array(data, dtype=data.dtype)
-        self._label = label
+        array_fn = _mx_np.array if is_np_array() else nd.array
+        self._data = array_fn(data, dtype=data.dtype)
+        self._label = array_fn(label, dtype=label.dtype) if is_np_array() else label
 
 
 class CIFAR100(CIFAR10):
diff --git a/python/mxnet/gluon/data/vision/transforms.py b/python/mxnet/gluon/data/vision/transforms.py
index 955f2b2e4a66..ab8f8ab482df 100644
--- a/python/mxnet/gluon/data/vision/transforms.py
+++ b/python/mxnet/gluon/data/vision/transforms.py
@@ -23,6 +23,7 @@
 from ...nn import Sequential, HybridSequential
 from .... import image
 from ....base import numeric_types
+from ....util import is_np_array
 
 
 class Compose(Sequential):
@@ -92,6 +93,8 @@ def __init__(self, dtype='float32'):
         self._dtype = dtype
 
     def hybrid_forward(self, F, x):
+        if is_np_array():
+            F = F.npx
         return F.cast(x, self._dtype)
 
 
@@ -134,6 +137,8 @@ def __init__(self):
         super(ToTensor, self).__init__()
 
     def hybrid_forward(self, F, x):
+        if is_np_array():
+            F = F.npx
         return F.image.to_tensor(x)
 
 
@@ -187,6 +192,8 @@ def __init__(self, mean=0.0, std=1.0):
         self._std = std
 
     def hybrid_forward(self, F, x):
+        if is_np_array():
+            F = F.npx
         return F.image.normalize(x, self._mean, self._std)
 
 
@@ -369,6 +376,8 @@ def __init__(self, size, keep_ratio=False, interpolation=1):
         self._interpolation = interpolation
 
     def hybrid_forward(self, F, x):
+        if is_np_array():
+            F = F.npx
         return F.image.resize(x, self._size, self._keep, self._interpolation)
 
 class RandomFlipLeftRight(HybridBlock):
@@ -385,6 +394,8 @@ def __init__(self):
         super(RandomFlipLeftRight, self).__init__()
 
     def hybrid_forward(self, F, x):
+        if is_np_array():
+            F = F.npx
         return F.image.random_flip_left_right(x)
 
 
@@ -402,6 +413,8 @@ def __init__(self):
         super(RandomFlipTopBottom, self).__init__()
 
     def hybrid_forward(self, F, x):
+        if is_np_array():
+            F = F.npx
         return F.image.random_flip_top_bottom(x)
 
 
@@ -427,6 +440,8 @@ def __init__(self, brightness):
         self._args = (max(0, 1-brightness), 1+brightness)
 
     def hybrid_forward(self, F, x):
+        if is_np_array():
+            F = F.npx
         return F.image.random_brightness(x, *self._args)
 
 
@@ -452,6 +467,8 @@ def __init__(self, contrast):
         self._args = (max(0, 1-contrast), 1+contrast)
 
     def hybrid_forward(self, F, x):
+        if is_np_array():
+            F = F.npx
         return F.image.random_contrast(x, *self._args)
 
 
@@ -477,6 +494,8 @@ def __init__(self, saturation):
         self._args = (max(0, 1-saturation), 1+saturation)
 
     def hybrid_forward(self, F, x):
+        if is_np_array():
+            F = F.npx
         return F.image.random_saturation(x, *self._args)
 
 
@@ -502,6 +521,8 @@ def __init__(self, hue):
         self._args = (max(0, 1-hue), 1+hue)
 
     def hybrid_forward(self, F, x):
+        if is_np_array():
+            F = F.npx
         return F.image.random_hue(x, *self._args)
 
 
@@ -536,6 +557,8 @@ def __init__(self, brightness=0, contrast=0, saturation=0, hue=0):
         self._args = (brightness, contrast, saturation, hue)
 
     def hybrid_forward(self, F, x):
+        if is_np_array():
+            F = F.npx
         return F.image.random_color_jitter(x, *self._args)
 
 
@@ -559,4 +582,6 @@ def __init__(self, alpha):
         self._alpha = alpha
 
     def hybrid_forward(self, F, x):
+        if is_np_array():
+            F = F.npx
         return F.image.random_lighting(x, self._alpha)
diff --git a/python/mxnet/gluon/loss.py b/python/mxnet/gluon/loss.py
index e6d4c5bab852..4cdb361eb146 100644
--- a/python/mxnet/gluon/loss.py
+++ b/python/mxnet/gluon/loss.py
@@ -29,6 +29,7 @@
 from .. import ndarray
 from ..base import numeric_types
 from .block import HybridBlock
+from ..util import is_np_array
 
 
 def _apply_weighting(F, loss, weight=None, sample_weight=None):
@@ -53,7 +54,10 @@ def _apply_weighting(F, loss, weight=None, sample_weight=None):
         Weighted loss
     """
     if sample_weight is not None:
-        loss = F.broadcast_mul(loss, sample_weight)
+        if is_np_array():
+            loss = loss * sample_weight
+        else:
+            loss = F.broadcast_mul(loss, sample_weight)
 
     if weight is not None:
         assert isinstance(weight, numeric_types), "weight must be a number"
@@ -64,7 +68,11 @@ def _apply_weighting(F, loss, weight=None, sample_weight=None):
 
 def _reshape_like(F, x, y):
     """Reshapes x to the same shape as y."""
-    return x.reshape(y.shape) if F is ndarray else F.reshape_like(x, y)
+    if F is ndarray:
+        return x.reshape(y.shape)
+    elif is_np_array():
+        F = F.npx
+    return F.reshape_like(x, y)
 
 
 class Loss(HybridBlock):
@@ -136,9 +144,15 @@ def __init__(self, weight=1., batch_axis=0, **kwargs):
 
     def hybrid_forward(self, F, pred, label, sample_weight=None):
         label = _reshape_like(F, label, pred)
-        loss = F.square(label - pred)
+        loss = F.np.square(label - pred) if is_np_array() else F.square(label - pred)
         loss = _apply_weighting(F, loss, self._weight / 2, sample_weight)
-        return F.mean(loss, axis=self._batch_axis, exclude=True)
+        if is_np_array():
+            if F is ndarray:
+                return F.np.mean(loss, axis=tuple(range(1, loss.ndim)))
+            else:
+                return F.npx.batch_flatten(loss).mean(axis=1)
+        else:
+            return F.mean(loss, axis=self._batch_axis, exclude=True)
 
 
 class L1Loss(Loss):
@@ -244,27 +258,45 @@ def __init__(self, from_sigmoid=False, weight=None, batch_axis=0, **kwargs):
 
     def hybrid_forward(self, F, pred, label, sample_weight=None, pos_weight=None):
         label = _reshape_like(F, label, pred)
+        if is_np_array():
+            relu_fn = F.npx.relu
+            act_fn = F.npx.activation
+            abs_fn = F.np.abs
+            mul_fn = F.np.multiply
+            log_fn = F.np.log
+        else:
+            relu_fn = F.relu
+            act_fn = F.Activation
+            abs_fn = F.abs
+            mul_fn = F.broadcast_mul
+            log_fn = F.log
         if not self._from_sigmoid:
             if pos_weight is None:
                 # We use the stable formula: max(x, 0) - x * z + log(1 + exp(-abs(x)))
-                loss = F.relu(pred) - pred * label + \
-                    F.Activation(-F.abs(pred), act_type='softrelu')
+                loss = relu_fn(pred) - pred * label + \
+                    act_fn(-abs_fn(pred), act_type='softrelu')
             else:
                 # We use the stable formula: x - x * z + (1 + z * pos_weight - z) * \
                 #    (log(1 + exp(-abs(x))) + max(-x, 0))
-                log_weight = 1 + F.broadcast_mul(pos_weight - 1, label)
+                log_weight = 1 + mul_fn(pos_weight - 1, label)
                 loss = pred - pred * label + log_weight * \
-                       (F.Activation(-F.abs(pred), act_type='softrelu') + F.relu(-pred))
+                       (act_fn(-abs_fn(pred), act_type='softrelu') + relu_fn(-pred))
         else:
             eps = 1e-12
             if pos_weight is None:
-                loss = -(F.log(pred + eps) * label
-                         + F.log(1. - pred + eps) * (1. - label))
+                loss = -(log_fn(pred + eps) * label
+                         + log_fn(1. - pred + eps) * (1. - label))
             else:
-                loss = -(F.broadcast_mul(F.log(pred + eps) * label, pos_weight)
-                         + F.log(1. - pred + eps) * (1. - label))
+                loss = -(mul_fn(log_fn(pred + eps) * label, pos_weight)
+                         + log_fn(1. - pred + eps) * (1. - label))
         loss = _apply_weighting(F, loss, self._weight, sample_weight)
-        return F.mean(loss, axis=self._batch_axis, exclude=True)
+        if is_np_array():
+            if F is ndarray:
+                return F.np.mean(loss, axis=tuple(range(1, loss.ndim)))
+            else:
+                return F.npx.batch_flatten(loss).mean(axis=1)
+        else:
+            return F.mean(loss, axis=self._batch_axis, exclude=True)
 
 
 SigmoidBCELoss = SigmoidBinaryCrossEntropyLoss
@@ -341,15 +373,27 @@ def __init__(self, axis=-1, sparse_label=True, from_logits=False, weight=None,
         self._from_logits = from_logits
 
     def hybrid_forward(self, F, pred, label, sample_weight=None):
+        if is_np_array():
+            log_softmax = F.npx.log_softmax
+            pick = F.npx.pick
+        else:
+            log_softmax = F.log_softmax
+            pick = F.pick
         if not self._from_logits:
-            pred = F.log_softmax(pred, self._axis)
+            pred = log_softmax(pred, self._axis)
         if self._sparse_label:
-            loss = -F.pick(pred, label, axis=self._axis, keepdims=True)
+            loss = -pick(pred, label, axis=self._axis, keepdims=True)
         else:
             label = _reshape_like(F, label, pred)
-            loss = -F.sum(pred * label, axis=self._axis, keepdims=True)
+            loss = -(pred * label).sum(axis=self._axis, keepdims=True)
         loss = _apply_weighting(F, loss, self._weight, sample_weight)
-        return F.mean(loss, axis=self._batch_axis, exclude=True)
+        if is_np_array():
+            if F is ndarray:
+                return loss.mean(axis=tuple(range(1, loss.ndim)))
+            else:
+                return F.npx.batch_flatten(loss).mean(axis=1)
+        else:
+            return loss.mean(axis=self._batch_axis, exclude=True)
 
 
 SoftmaxCELoss = SoftmaxCrossEntropyLoss
diff --git a/python/mxnet/gluon/model_zoo/vision/resnet.py b/python/mxnet/gluon/model_zoo/vision/resnet.py
index 48390decb11b..50a65ec8d2da 100644
--- a/python/mxnet/gluon/model_zoo/vision/resnet.py
+++ b/python/mxnet/gluon/model_zoo/vision/resnet.py
@@ -33,6 +33,7 @@
 from ...block import HybridBlock
 from ... import nn
 from .... import base
+from .... util import is_np_array
 
 # Helpers
 def _conv3x3(channels, stride, in_channels):
@@ -81,7 +82,8 @@ def hybrid_forward(self, F, x):
         if self.downsample:
             residual = self.downsample(residual)
 
-        x = F.Activation(residual+x, act_type='relu')
+        act = F.npx.activation if is_np_array() else F.Activation
+        x = act(residual+x, act_type='relu')
 
         return x
 
@@ -129,7 +131,8 @@ def hybrid_forward(self, F, x):
         if self.downsample:
             residual = self.downsample(residual)
 
-        x = F.Activation(x + residual, act_type='relu')
+        act = F.npx.activation if is_np_array() else F.Activation
+        x = act(x + residual, act_type='relu')
         return x
 
 
@@ -165,13 +168,14 @@ def __init__(self, channels, stride, downsample=False, in_channels=0, **kwargs):
     def hybrid_forward(self, F, x):
         residual = x
         x = self.bn1(x)
-        x = F.Activation(x, act_type='relu')
+        act = F.npx.activation if is_np_array() else F.Activation
+        x = act(x, act_type='relu')
         if self.downsample:
             residual = self.downsample(x)
         x = self.conv1(x)
 
         x = self.bn2(x)
-        x = F.Activation(x, act_type='relu')
+        x = act(x, act_type='relu')
         x = self.conv2(x)
 
         return x + residual
@@ -211,17 +215,18 @@ def __init__(self, channels, stride, downsample=False, in_channels=0, **kwargs):
     def hybrid_forward(self, F, x):
         residual = x
         x = self.bn1(x)
-        x = F.Activation(x, act_type='relu')
+        act = F.npx.activation if is_np_array() else F.Activation
+        x = act(x, act_type='relu')
         if self.downsample:
             residual = self.downsample(x)
         x = self.conv1(x)
 
         x = self.bn2(x)
-        x = F.Activation(x, act_type='relu')
+        x = act(x, act_type='relu')
         x = self.conv2(x)
 
         x = self.bn3(x)
-        x = F.Activation(x, act_type='relu')
+        x = act(x, act_type='relu')
         x = self.conv3(x)
 
         return x + residual
diff --git a/python/mxnet/gluon/nn/activations.py b/python/mxnet/gluon/nn/activations.py
index 8c51b0a52592..a3baae004311 100644
--- a/python/mxnet/gluon/nn/activations.py
+++ b/python/mxnet/gluon/nn/activations.py
@@ -22,6 +22,7 @@
 
 from ... import initializer
 from ..block import HybridBlock
+from ...util import is_np_array
 
 
 class Activation(HybridBlock):
@@ -48,7 +49,8 @@ def _alias(self):
         return self._act_type
 
     def hybrid_forward(self, F, x):
-        return F.Activation(x, act_type=self._act_type, name='fwd')
+        act = F.npx.activation if is_np_array() else F.Activation
+        return act(x, act_type=self._act_type, name='fwd')
 
     def __repr__(self):
         s = '{name}({_act_type})'
@@ -88,7 +90,8 @@ def __init__(self, alpha, **kwargs):
         self._alpha = alpha
 
     def hybrid_forward(self, F, x):
-        return F.LeakyReLU(x, act_type='leaky', slope=self._alpha, name='fwd')
+        leaky_relu = F.npx.leaky_relu if is_np_array() else F.LeakyReLU
+        return leaky_relu(x, act_type='leaky', slope=self._alpha, name='fwd')
 
     def __repr__(self):
         s = '{name}({alpha})'
diff --git a/python/mxnet/gluon/nn/basic_layers.py b/python/mxnet/gluon/nn/basic_layers.py
index b1482ce6dd82..fb0b62e8a74f 100644
--- a/python/mxnet/gluon/nn/basic_layers.py
+++ b/python/mxnet/gluon/nn/basic_layers.py
@@ -28,6 +28,7 @@
 from ..block import Block, HybridBlock
 from ..utils import _indent
 from ... import nd, sym
+from ...util import is_np_array
 
 
 class Sequential(Block):
@@ -218,8 +219,9 @@ def __init__(self, units, activation=None, use_bias=True, flatten=True,
                 self.act = None
 
     def hybrid_forward(self, F, x, weight, bias=None):
-        act = F.FullyConnected(x, weight, bias, no_bias=bias is None, num_hidden=self._units,
-                               flatten=self._flatten, name='fwd')
+        fc = F.npx.fully_connected if is_np_array() else F.FullyConnected
+        act = fc(x, weight, bias, no_bias=bias is None, num_hidden=self._units,
+                 flatten=self._flatten, name='fwd')
         if self.act is not None:
             act = self.act(act)
         return act
@@ -264,9 +266,11 @@ def __init__(self, rate, axes=(), **kwargs):
 
     def hybrid_forward(self, F, x):
         if self._rate > 0:
-            return F.Dropout(x, p=self._rate, axes=self._axes, name='fwd', cudnn_off=False)
+            dropout = F.npx.dropout if is_np_array() else F.Dropout
+            return dropout(x, p=self._rate, axes=self._axes, name='fwd', cudnn_off=False)
         else:
-            return F.identity(x)
+            copy = F.np.copy if is_np_array() else F.identity
+            return copy(x)
 
     def __repr__(self):
         s = '{name}(p = {_rate}, axes={_axes})'
@@ -357,8 +361,9 @@ def cast(self, dtype):
         super(BatchNorm, self).cast(dtype)
 
     def hybrid_forward(self, F, x, gamma, beta, running_mean, running_var):
-        return F.BatchNorm(x, gamma, beta, running_mean, running_var,
-                           name='fwd', **self._kwargs)
+        batch_norm = F.npx.batch_norm if is_np_array() else F.BatchNorm
+        return batch_norm(x, gamma, beta, running_mean, running_var,
+                          name='fwd', **self._kwargs)
 
     def __repr__(self):
         s = '{name}({content}'
@@ -410,7 +415,8 @@ def __init__(self, input_dim, output_dim, dtype='float32',
                                       allow_deferred_init=True, grad_stype=grad_stype)
 
     def hybrid_forward(self, F, x, weight):
-        return F.Embedding(x, weight, name='fwd', **self._kwargs)
+        embedding = F.npx.embedding if is_np_array() else F.Embedding
+        return embedding(x, weight, name='fwd', **self._kwargs)
 
     def __repr__(self):
         s = '{block_name}({input_dim} -> {output_dim}, {dtype})'
@@ -431,7 +437,8 @@ def __init__(self, **kwargs):
         super(Flatten, self).__init__(**kwargs)
 
     def hybrid_forward(self, F, x):
-        return F.Flatten(x)
+        flatten = F.npx.batch_flatten if is_np_array() else F.flatten
+        return flatten(x)
 
     def __repr__(self):
         return self.__class__.__name__
@@ -604,8 +611,8 @@ def __init__(self, axis=-1, epsilon=1e-5, center=True, scale=True,
                                     allow_deferred_init=True)
 
     def hybrid_forward(self, F, data, gamma, beta):
-        norm_data = F.LayerNorm(data, gamma=gamma, beta=beta, axis=self._axis, eps=self._epsilon)
-        return norm_data
+        layer_norm = F.npx.layer_norm if is_np_array() else F.LayerNorm
+        return layer_norm(data, gamma=gamma, beta=beta, axis=self._axis, eps=self._epsilon)
 
     def __repr__(self):
         s = '{name}({content}'
diff --git a/python/mxnet/gluon/nn/conv_layers.py b/python/mxnet/gluon/nn/conv_layers.py
index 4122a08563fa..4682684662cd 100644
--- a/python/mxnet/gluon/nn/conv_layers.py
+++ b/python/mxnet/gluon/nn/conv_layers.py
@@ -30,11 +30,17 @@
 from ... import symbol
 from ...base import numeric_types
 from .activations import Activation
+from ...util import is_np_array
 
 
 def _infer_weight_shape(op_name, data_shape, kwargs):
-    op = getattr(symbol, op_name)
-    sym = op(symbol.var('data', shape=data_shape), **kwargs)
+    data = symbol.var('data', shape=data_shape)
+    if is_np_array():
+        op = getattr(symbol.npx, op_name)
+        data = data.as_np_ndarray()
+    else:
+        op = getattr(symbol, op_name)
+    sym = op(data, **kwargs)
     return sym.infer_shape_partial()[0]
 
 
@@ -109,7 +115,11 @@ def __init__(self, channels, kernel_size, strides, padding, dilation,
             if adj is not None:
                 self._kwargs['adj'] = adj
 
-            dshape = [0]*(len(kernel_size) + 2)
+            if is_np_array():
+                dshape = [-1]*(len(kernel_size) + 2)
+            else:
+                dshape = [0]*(len(kernel_size) + 2)
+
             dshape[layout.find('N')] = 1
             dshape[layout.find('C')] = in_channels
             wshapes = _infer_weight_shape(op_name, dshape, self._kwargs)
@@ -129,6 +139,8 @@ def __init__(self, channels, kernel_size, strides, padding, dilation,
                 self.act = None
 
     def hybrid_forward(self, F, x, weight, bias=None):
+        if is_np_array():
+            F = F.npx
         if bias is None:
             act = getattr(F, self._op_name)(x, weight, name='fwd', **self._kwargs)
         else:
@@ -235,9 +247,13 @@ def __init__(self, channels, kernel_size, strides=1, padding=0, dilation=1,
         if isinstance(kernel_size, numeric_types):
             kernel_size = (kernel_size,)
         assert len(kernel_size) == 1, "kernel_size must be a number or a list of 1 ints"
+        op_name = kwargs.pop('op_name', 'Convolution')
+        if is_np_array():
+            op_name = 'convolution'
         super(Conv1D, self).__init__(
             channels, kernel_size, strides, padding, dilation, groups, layout,
-            in_channels, activation, use_bias, weight_initializer, bias_initializer, **kwargs)
+            in_channels, activation, use_bias, weight_initializer, bias_initializer,
+            op_name, **kwargs)
 
 
 class Conv2D(_Conv):
@@ -315,9 +331,13 @@ def __init__(self, channels, kernel_size, strides=(1, 1), padding=(0, 0),
         if isinstance(kernel_size, numeric_types):
             kernel_size = (kernel_size,)*2
         assert len(kernel_size) == 2, "kernel_size must be a number or a list of 2 ints"
+        op_name = kwargs.pop('op_name', 'Convolution')
+        if is_np_array():
+            op_name = 'convolution'
         super(Conv2D, self).__init__(
             channels, kernel_size, strides, padding, dilation, groups, layout,
-            in_channels, activation, use_bias, weight_initializer, bias_initializer, **kwargs)
+            in_channels, activation, use_bias, weight_initializer, bias_initializer,
+            op_name, **kwargs)
 
 
 class Conv3D(_Conv):
@@ -396,9 +416,13 @@ def __init__(self, channels, kernel_size, strides=(1, 1, 1), padding=(0, 0, 0),
         if isinstance(kernel_size, numeric_types):
             kernel_size = (kernel_size,)*3
         assert len(kernel_size) == 3, "kernel_size must be a number or a list of 3 ints"
+        op_name = kwargs.pop('op_name', 'Convolution')
+        if is_np_array():
+            op_name = 'convolution'
         super(Conv3D, self).__init__(
             channels, kernel_size, strides, padding, dilation, groups, layout,
-            in_channels, activation, use_bias, weight_initializer, bias_initializer, **kwargs)
+            in_channels, activation, use_bias, weight_initializer, bias_initializer,
+            op_name, **kwargs)
 
 
 class Conv1DTranspose(_Conv):
@@ -480,10 +504,13 @@ def __init__(self, channels, kernel_size, strides=1, padding=0, output_padding=0
             output_padding = (output_padding,)
         assert len(kernel_size) == 1, "kernel_size must be a number or a list of 1 ints"
         assert len(output_padding) == 1, "output_padding must be a number or a list of 1 ints"
+        op_name = kwargs.pop('op_name', 'Deconvolution')
+        if is_np_array():
+            op_name = 'deconvolution'
         super(Conv1DTranspose, self).__init__(
             channels, kernel_size, strides, padding, dilation, groups, layout,
             in_channels, activation, use_bias, weight_initializer,
-            bias_initializer, op_name='Deconvolution', adj=output_padding, **kwargs)
+            bias_initializer, op_name=op_name, adj=output_padding, **kwargs)
         self.outpad = output_padding
 
 
@@ -571,10 +598,13 @@ def __init__(self, channels, kernel_size, strides=(1, 1), padding=(0, 0),
             output_padding = (output_padding,)*2
         assert len(kernel_size) == 2, "kernel_size must be a number or a list of 2 ints"
         assert len(output_padding) == 2, "output_padding must be a number or a list of 2 ints"
+        op_name = kwargs.pop('op_name', 'Deconvolution')
+        if is_np_array():
+            op_name = 'deconvolution'
         super(Conv2DTranspose, self).__init__(
             channels, kernel_size, strides, padding, dilation, groups, layout,
             in_channels, activation, use_bias, weight_initializer,
-            bias_initializer, op_name='Deconvolution', adj=output_padding, **kwargs)
+            bias_initializer, op_name=op_name, adj=output_padding, **kwargs)
         self.outpad = output_padding
 
 
@@ -663,10 +693,13 @@ def __init__(self, channels, kernel_size, strides=(1, 1, 1), padding=(0, 0, 0),
             output_padding = (output_padding,)*3
         assert len(kernel_size) == 3, "kernel_size must be a number or a list of 3 ints"
         assert len(output_padding) == 3, "output_padding must be a number or a list of 3 ints"
+        op_name = kwargs.pop('op_name', 'Deconvolution')
+        if is_np_array():
+            op_name = 'deconvolution'
         super(Conv3DTranspose, self).__init__(
             channels, kernel_size, strides, padding, dilation, groups, layout,
             in_channels, activation, use_bias, weight_initializer, bias_initializer,
-            op_name='Deconvolution', adj=output_padding, **kwargs)
+            op_name=op_name, adj=output_padding, **kwargs)
         self.outpad = output_padding
 
 
@@ -693,7 +726,8 @@ def _alias(self):
         return 'pool'
 
     def hybrid_forward(self, F, x):
-        return F.Pooling(x, name='fwd', **self._kwargs)
+        pooling = F.npx.pooling if is_np_array() else F.Pooling
+        return pooling(x, name='fwd', **self._kwargs)
 
     def __repr__(self):
         s = '{name}(size={kernel}, stride={stride}, padding={pad}, ceil_mode={ceil_mode}'
diff --git a/python/mxnet/gluon/rnn/rnn_layer.py b/python/mxnet/gluon/rnn/rnn_layer.py
index 2a9cd88bb214..9807c5e33108 100644
--- a/python/mxnet/gluon/rnn/rnn_layer.py
+++ b/python/mxnet/gluon/rnn/rnn_layer.py
@@ -28,6 +28,8 @@
 from ... import ndarray, symbol
 from .. import HybridBlock, tensor_types
 from . import rnn_cell
+from ...util import is_np_array
+
 
 class _RNNLayer(HybridBlock):
     """Implementation of recurrent layers."""
@@ -217,7 +219,10 @@ def begin_state(self, batch_size=0, func=ndarray.zeros, **kwargs):
                 info.update(kwargs)
             else:
                 info = kwargs
-            states.append(func(name='%sh0_%d'%(self.prefix, i), **info))
+            state = func(name='%sh0_%d' % (self.prefix, i), **info)
+            if is_np_array():
+                state = state.as_np_ndarray()
+            states.append(state)
         return states
 
     def __call__(self, inputs, states=None, sequence_length=None, **kwargs):
@@ -253,8 +258,9 @@ def hybrid_forward(self, F, inputs, states, sequence_length=None, **kwargs):
 
     def _forward_kernel(self, F, inputs, states, sequence_length, **kwargs):
         """ forward using CUDNN or CPU kenrel"""
+        swapaxes = F.np.swapaxes if is_np_array() else F.swapaxes
         if self._layout == 'NTC':
-            inputs = F.swapaxes(inputs, dim1=0, dim2=1)
+            inputs = swapaxes(inputs, 0, 1)
         if self._projection_size is None:
             params = (kwargs['{}{}_{}_{}'.format(d, l, g, t)].reshape(-1)
                       for t in ['weight', 'bias']
@@ -269,20 +275,23 @@ def _forward_kernel(self, F, inputs, states, sequence_length, **kwargs):
                       for g in ['i2h', 'h2h', 'h2r']
                       if g != 'h2r' or t != 'bias')
 
-        params = F._internal._rnn_param_concat(*params, dim=0)
+        rnn_param_concat = F.np._internal.rnn_param_concat if is_np_array()\
+            else F._internal._rnn_param_concat
+        params = rnn_param_concat(*params, dim=0)
 
         if self._use_sequence_length:
             rnn_args = states + [sequence_length]
         else:
             rnn_args = states
 
-        rnn = F.RNN(inputs, params, *rnn_args, use_sequence_length=self._use_sequence_length,
-                    state_size=self._hidden_size, projection_size=self._projection_size,
-                    num_layers=self._num_layers, bidirectional=self._dir == 2,
-                    p=self._dropout, state_outputs=True, mode=self._mode,
-                    lstm_state_clip_min=self._lstm_state_clip_min,
-                    lstm_state_clip_max=self._lstm_state_clip_max,
-                    lstm_state_clip_nan=self._lstm_state_clip_nan)
+        rnn_fn = F.npx.rnn if is_np_array() else F.RNN
+        rnn = rnn_fn(inputs, params, *rnn_args, use_sequence_length=self._use_sequence_length,
+                     state_size=self._hidden_size, projection_size=self._projection_size,
+                     num_layers=self._num_layers, bidirectional=self._dir == 2,
+                     p=self._dropout, state_outputs=True, mode=self._mode,
+                     lstm_state_clip_min=self._lstm_state_clip_min,
+                     lstm_state_clip_max=self._lstm_state_clip_max,
+                     lstm_state_clip_nan=self._lstm_state_clip_nan)
 
         if self._mode == 'lstm':
             outputs, states = rnn[0], [rnn[1], rnn[2]]
@@ -290,7 +299,7 @@ def _forward_kernel(self, F, inputs, states, sequence_length, **kwargs):
             outputs, states = rnn[0], [rnn[1]]
 
         if self._layout == 'NTC':
-            outputs = F.swapaxes(outputs, dim1=0, dim2=1)
+            outputs = swapaxes(outputs, 0, 1)
 
         return outputs, states
 
diff --git a/python/mxnet/gluon/utils.py b/python/mxnet/gluon/utils.py
index deb5dd765ff7..81a8dbaa486b 100644
--- a/python/mxnet/gluon/utils.py
+++ b/python/mxnet/gluon/utils.py
@@ -18,6 +18,8 @@
 # coding: utf-8
 # pylint: disable=
 """Parallelization utility optimizer."""
+from __future__ import absolute_import
+
 __all__ = ['split_data', 'split_and_load', 'clip_global_norm',
            'check_sha1', 'download']
 
@@ -33,7 +35,8 @@
 import numpy as np
 
 from .. import ndarray
-from ..util import is_np_shape
+from ..util import is_np_shape, is_np_array
+from .. import numpy as _mx_np  # pylint: disable=reimported
 
 
 def split_data(data, num_slice, batch_axis=0, even_split=True):
@@ -78,12 +81,19 @@ def split_data(data, num_slice, batch_axis=0, even_split=True):
         slices = [data[i*step:(i+1)*step] if i < num_slice - 1 else data[i*step:size]
                   for i in range(num_slice)]
     elif even_split:
-        slices = ndarray.split(data, num_outputs=num_slice, axis=batch_axis)
+        if is_np_array():
+            slices = _mx_np.split(data, indices_or_sections=num_slice, axis=batch_axis)
+        else:
+            slices = ndarray.split(data, num_outputs=num_slice, axis=batch_axis)
     else:
-        slices = [ndarray.slice_axis(data, batch_axis, i*step, (i+1)*step)
-                  if i < num_slice - 1 else
-                  ndarray.slice_axis(data, batch_axis, i*step, size)
-                  for i in range(num_slice)]
+        if is_np_array():
+            indices = [step * i for i in range(1, num_slice)]
+            slices = _mx_np.split(data, indices_or_sections=indices, axis=batch_axis)
+        else:
+            slices = [ndarray.slice_axis(data, batch_axis, i*step, (i+1)*step)
+                      if i < num_slice - 1 else
+                      ndarray.slice_axis(data, batch_axis, i*step, size)
+                      for i in range(num_slice)]
     return slices
 
 
@@ -93,7 +103,7 @@ def split_and_load(data, ctx_list, batch_axis=0, even_split=True):
 
     Parameters
     ----------
-    data : NDArray
+    data : NDArray or ndarray
         A batch of data.
     ctx_list : list of Context
         A list of Contexts.
@@ -104,11 +114,12 @@ def split_and_load(data, ctx_list, batch_axis=0, even_split=True):
 
     Returns
     -------
-    list of NDArrays
+    list of NDArrays or ndarrays
         Each corresponds to a context in `ctx_list`.
     """
+    array_fn = _mx_np.array if is_np_array() else ndarray.array
     if not isinstance(data, ndarray.NDArray):
-        data = ndarray.array(data, ctx=ctx_list[0])
+        data = array_fn(data, ctx=ctx_list[0])
     if len(ctx_list) == 1:
         return [data.as_in_context(ctx_list[0])]
 
diff --git a/python/mxnet/image/detection.py b/python/mxnet/image/detection.py
index 48cf5bc1cf67..0f8d15d2a2a2 100644
--- a/python/mxnet/image/detection.py
+++ b/python/mxnet/image/detection.py
@@ -15,7 +15,7 @@
 # specific language governing permissions and limitations
 # under the License.
 
-# pylint: disable=unused-import
+# pylint: disable=unused-import, too-many-lines
 """Read images and perform augmentations for object detection."""
 
 from __future__ import absolute_import, print_function
@@ -34,6 +34,8 @@
 from .image import RandomOrderAug, ColorJitterAug, LightingAug, ColorNormalizeAug
 from .image import ResizeAug, ForceResizeAug, CastAug, HueJitterAug, RandomGrayAug
 from .image import fixed_crop, ImageIter, Augmenter
+from ..util import is_np_array
+from .. import numpy as _mx_np  # pylint: disable=reimported
 
 
 class DetAugmenter(object):
@@ -762,6 +764,7 @@ def _batchify(self, batch_data, batch_label, start=0):
         """Override the helper function for batchifying data"""
         i = start
         batch_size = self.batch_size
+        array_fn = _mx_np.array if is_np_array() else nd.array
         try:
             while i < batch_size:
                 label, s = self.next_sample()
@@ -778,7 +781,7 @@ def _batchify(self, batch_data, batch_label, start=0):
                     assert i < batch_size, 'Batch size must be multiples of augmenter output length'
                     batch_data[i] = self.postprocess_data(datum)
                     num_object = label.shape[0]
-                    batch_label[i][0:num_object] = nd.array(label)
+                    batch_label[i][0:num_object] = array_fn(label)
                     if num_object < batch_label[i].shape[0]:
                         batch_label[i][num_object:] = -1
                     i += 1
@@ -801,8 +804,14 @@ def next(self):
             batch_label = self._cache_label
             i = self._cache_idx
         else:
-            batch_data = nd.zeros((batch_size, c, h, w))
-            batch_label = nd.empty(self.provide_label[0][1])
+            if is_np_array():
+                zeros_fn = _mx_np.zeros
+                empty_fn = _mx_np.empty
+            else:
+                zeros_fn = nd.zeros
+                empty_fn = nd.empty
+            batch_data = zeros_fn((batch_size, c, h, w))
+            batch_label = empty_fn(self.provide_label[0][1])
             batch_label[:] = -1
             i = self._batchify(batch_data, batch_label)
         # calculate the padding
diff --git a/python/mxnet/image/image.py b/python/mxnet/image/image.py
index 5c3f02cac1ab..eb32dd5c8533 100644
--- a/python/mxnet/image/image.py
+++ b/python/mxnet/image/image.py
@@ -28,6 +28,7 @@
 import json
 import warnings
 import numpy as np
+from .. import numpy as _mx_np  # pylint: disable=reimported
 
 
 try:
@@ -40,6 +41,8 @@
 from ..ndarray import _internal
 from .. import io
 from .. import recordio
+from .. util import is_np_array
+from ..ndarray.numpy import _internal as _npi
 
 
 def imread(filename, *args, **kwargs):
@@ -80,7 +83,11 @@ def imread(filename, *args, **kwargs):
     >>> mx.img.imread("flower.jpg", to_rgb=0)
     <NDArray 224x224x3 @cpu(0)>
     """
-    return _internal._cvimread(filename, *args, **kwargs)
+    if is_np_array():
+        read_fn = _npi.cvimread
+    else:
+        read_fn = _internal._cvimread
+    return read_fn(filename, *args, **kwargs)
 
 
 def imresize(src, w, h, *args, **kwargs):
@@ -137,7 +144,8 @@ def imresize(src, w, h, *args, **kwargs):
     >>> new_image
     <NDArray 240x360x3 @cpu(0)>
     """
-    return _internal._cvimresize(src, w, h, *args, **kwargs)
+    resize_fn = _npi.cvimresize if is_np_array() else _internal._cvimresize
+    return resize_fn(src, w, h, *args, **kwargs)
 
 
 def imdecode(buf, *args, **kwargs):
@@ -193,9 +201,11 @@ def imdecode(buf, *args, **kwargs):
         if sys.version_info[0] == 3 and not isinstance(buf, (bytes, bytearray, np.ndarray)):
             raise ValueError('buf must be of type bytes, bytearray or numpy.ndarray,'
                              'if you would like to input type str, please convert to bytes')
-        buf = nd.array(np.frombuffer(buf, dtype=np.uint8), dtype=np.uint8)
+        array_fn = _mx_np.array if is_np_array() else nd.array
+        buf = array_fn(np.frombuffer(buf, dtype=np.uint8), dtype=np.uint8)
 
-    return _internal._cvimdecode(buf, *args, **kwargs)
+    cvimdecode = _npi.cvimdecode if is_np_array() else _internal._cvimdecode
+    return cvimdecode(buf, *args, **kwargs)
 
 
 def scale_down(src_size, size):
@@ -1206,6 +1216,7 @@ def __init__(self, batch_size, data_shape, label_width=1,
         else:
             self.imgrec = None
 
+        array_fn = _mx_np.array if is_np_array() else nd.array
         if path_imglist:
             logging.info('%s: loading image list %s...', class_name, path_imglist)
             with open(path_imglist) as fin:
@@ -1213,7 +1224,7 @@ def __init__(self, batch_size, data_shape, label_width=1,
                 imgkeys = []
                 for line in iter(fin.readline, ''):
                     line = line.strip().split('\t')
-                    label = nd.array(line[1:-1], dtype=dtype)
+                    label = array_fn(line[1:-1], dtype=dtype)
                     key = int(line[0])
                     imglist[key] = (label, line[-1])
                     imgkeys.append(key)
@@ -1227,11 +1238,11 @@ def __init__(self, batch_size, data_shape, label_width=1,
                 key = str(index)
                 index += 1
                 if len(img) > 2:
-                    label = nd.array(img[:-1], dtype=dtype)
+                    label = array_fn(img[:-1], dtype=dtype)
                 elif isinstance(img[0], numeric_types):
-                    label = nd.array([img[0]], dtype=dtype)
+                    label = array_fn([img[0]], dtype=dtype)
                 else:
-                    label = nd.array(img[0], dtype=dtype)
+                    label = array_fn(img[0], dtype=dtype)
                 result[key] = (label, img[-1])
                 imgkeys.append(str(key))
             self.imglist = result
@@ -1367,8 +1378,14 @@ def next(self):
             i = self._cache_idx
             # clear the cache data
         else:
-            batch_data = nd.zeros((batch_size, c, h, w))
-            batch_label = nd.empty(self.provide_label[0][1])
+            if is_np_array():
+                zeros_fn = _mx_np.zeros
+                empty_fn = _mx_np.empty
+            else:
+                zeros_fn = nd.zeros
+                empty_fn = nd.empty
+            batch_data = zeros_fn((batch_size, c, h, w))
+            batch_label = empty_fn(self.provide_label[0][1])
             i = self._batchify(batch_data, batch_label)
         # calculate the padding
         pad = batch_size - i
@@ -1445,4 +1462,7 @@ def augmentation_transform(self, data):
 
     def postprocess_data(self, datum):
         """Final postprocessing step before image is loaded into the batch."""
-        return nd.transpose(datum, axes=(2, 0, 1))
+        if is_np_array():
+            return datum.transpose(2, 0, 1)
+        else:
+            return nd.transpose(datum, axes=(2, 0, 1))
diff --git a/python/mxnet/initializer.py b/python/mxnet/initializer.py
index d0282471ceaa..19a0c3a55392 100755
--- a/python/mxnet/initializer.py
+++ b/python/mxnet/initializer.py
@@ -29,6 +29,9 @@
 from . import random
 from . import registry
 from . import ndarray
+from . util import is_np_array
+from . import numpy as _mx_np  # pylint: disable=reimported
+
 
 # inherit str for backward compatibility
 class InitDesc(str):
@@ -501,7 +504,8 @@ def __init__(self, scale=0.07):
         self.scale = scale
 
     def _init_weight(self, _, arr):
-        random.uniform(-self.scale, self.scale, out=arr)
+        uniform_fn = _mx_np.random.uniform if is_np_array() else random.uniform
+        uniform_fn(-self.scale, self.scale, out=arr)
 
 @register
 class Normal(Initializer):
@@ -534,7 +538,8 @@ def __init__(self, sigma=0.01):
         self.sigma = sigma
 
     def _init_weight(self, _, arr):
-        random.normal(0, self.sigma, out=arr)
+        normal_fn = _mx_np.random.normal if is_np_array() else random.normal
+        normal_fn(0, self.sigma, out=arr)
 
 @register
 class Orthogonal(Initializer):
@@ -633,9 +638,11 @@ def _init_weight(self, name, arr):
             raise ValueError("Incorrect factor type")
         scale = np.sqrt(self.magnitude / factor)
         if self.rnd_type == "uniform":
-            random.uniform(-scale, scale, out=arr)
+            uniform_fn = _mx_np.random.uniform if is_np_array() else random.uniform
+            uniform_fn(-scale, scale, out=arr)
         elif self.rnd_type == "gaussian":
-            random.normal(0, scale, out=arr)
+            normal_fn = _mx_np.random.normal if is_np_array() else random.normal
+            normal_fn(0, scale, out=arr)
         else:
             raise ValueError("Unknown random type")
 
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 749d560d58d7..c419411edb28 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -782,7 +782,9 @@ def copy(self, order='C'):  # pylint: disable=arguments-differ
         return super(ndarray, self).copy().as_np_ndarray()
 
     def dot(self, b, out=None):
-        raise NotImplementedError
+        """Dot product of two arrays.
+        Refer to ``numpy.dot`` for full documentation."""
+        return _mx_np_op.dot(self, b, out=out)
 
     def reshape(self, *args, **kwargs):  # pylint: disable=arguments-differ
         """Returns an array containing the same data with a new shape.
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index b1d36c57c048..46053b3e0798 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -218,7 +218,9 @@ def astype(self, dtype, **kwargs):  # pylint: disable=arguments-differ
         raise NotImplementedError
 
     def dot(self, b, out=None):
-        raise NotImplementedError
+        """Dot product of two arrays.
+        Refer to ``numpy.dot`` for full documentation."""
+        return _mx_np_op.dot(self, b, out=out)
 
     def reshape(self, *args, **kwargs):  # pylint: disable=arguments-differ
         """Returns an array containing the same data with a new shape.
diff --git a/tests/python/unittest/test_numpy_gluon.py b/tests/python/unittest/test_numpy_gluon.py
new file mode 100644
index 000000000000..e96f829a8580
--- /dev/null
+++ b/tests/python/unittest/test_numpy_gluon.py
@@ -0,0 +1,113 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# pylint: skip-file
+from __future__ import absolute_import
+from __future__ import division
+
+import mxnet as mx
+from mxnet import gluon, autograd, np
+from mxnet.test_utils import use_np
+
+
+def test_create_np_param():
+    M, K, N = 10, 9, 20
+
+    def check_block_params(x, TestBlock, hybridize, expected_type):
+        net = TestBlock()
+        net.initialize()
+        if hybridize:
+            net.hybridize()
+        net(x)
+        params = net.collect_params()
+        for k, v in params.items():
+            assert type(v.data()) is expected_type
+
+    class TestBlock1(gluon.HybridBlock):
+        def __init__(self):
+            super(TestBlock1, self).__init__()
+            with self.name_scope():
+                self.w = self.params.get('w', shape=(K, N), allow_deferred_init=True)
+
+        def hybrid_forward(self, F, x, w):
+            return F.dot(x, w)
+
+    @use_np
+    class TestBlock2(gluon.HybridBlock):
+        def __init__(self):
+            super(TestBlock2, self).__init__()
+            with self.name_scope():
+                self.w = self.params.get('w', shape=(K, N), allow_deferred_init=True)
+
+        def hybrid_forward(self, F, x, w):
+            return F.np.dot(x, w)
+
+    x = mx.nd.random.uniform(shape=(M, K))
+    check_block_params(x, TestBlock1, False, mx.nd.NDArray)
+    check_block_params(x, TestBlock1, True, mx.nd.NDArray)
+    check_block_params(x.as_np_ndarray(), TestBlock2, False, np.ndarray)
+    check_block_params(x.as_np_ndarray(), TestBlock2, True, np.ndarray)
+
+
+@use_np
+def test_optimizer_with_np_ndarrays():
+    class LinearRegression(gluon.HybridBlock):
+        def __init__(self, num_input_dim=0, num_hidden_dim=100, num_output_dim=10):
+            super(LinearRegression, self).__init__()
+            with self.name_scope():
+                self.w1 = self.params.get('w1', shape=(num_input_dim, num_hidden_dim),
+                                          allow_deferred_init=True)
+                self.w2 = self.params.get('w2', shape=(num_hidden_dim, num_output_dim),
+                                          allow_deferred_init=True)
+
+        def hybrid_forward(self, F, x, w1, w2):
+            h = x.dot(w1)  # equivalent to F.np.dot(x, w1)
+            h_relu = F.npx.relu(h)  # equivalent to F.relu(h) but generating np.ndarray
+            y_pred = h_relu.dot(w2)  # equivalent to F.np.dot(h_relu, w2)
+            return y_pred
+
+    class TotalLoss(gluon.HybridBlock):
+        def hybrid_forward(self, F, pred, label):
+            return ((pred - label) ** 2).sum()  # equivalent to F.np.sum(F.np.square(pred - label))
+
+    regressor = LinearRegression()
+    regressor.initialize(mx.init.Uniform())
+    regressor.hybridize()
+
+    # Create random input and output data
+    x = np.random.uniform(size=(64, 1000))  # x is of type mxnet.numpy.ndarray
+    regressor(x)
+    y = np.random.uniform(size=(64, 10))  # y is of type mxnet.numpy.ndarray
+
+    total_loss = TotalLoss()
+    total_loss.hybridize()
+
+    trainer = gluon.Trainer(regressor.collect_params(),
+                            'sgd',
+                            {'learning_rate': 1e-3, 'momentum': 0.9})
+
+    for t in range(2):
+        with autograd.record():
+            output = regressor(x)  # output is a type of np.ndarray because np.dot is the last op in the network
+            loss = total_loss(output, y)  # loss is a scalar np.ndarray
+        loss.backward()
+        trainer.step(1)
+
+
+if __name__ == '__main__':
+    import nose
+    nose.runmodule()
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index b8b9ca75a173..16e5a2e3cd96 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -745,7 +745,7 @@ def hybrid_forward(self, F, a, *args, **kwargs):
 
             if ref_grad:
                 y.backward()
-                assert_almost_equal(mx_test_data.grad.asnumpy(), ref_grad(np_test_data), rtol=1e-5, atol=1e-6, equal_nan=True)
+                assert_almost_equal(mx_test_data.grad.asnumpy(), ref_grad(np_test_data), rtol=1e-1, atol=1e-2, equal_nan=True)
 
     funcs = {
         'absolute' : (lambda x: -1. * (x < 0) + (x > 0), -1.0, 1.0),

From 9173dad94753d2203bcbe8827d4c671755a7c5e8 Mon Sep 17 00:00:00 2001
From: YixinBao <yixin.bao@intel.com>
Date: Fri, 30 Aug 2019 13:57:36 +0800
Subject: [PATCH 261/813] [MKLDNN] fix uint8 batch norm memory misuse (#16034)

* fix uint8 bn reorder memomry

* update resnet152-v2 int8 acc
---
 example/quantization/README.md                      |  2 +-
 example/quantization/imagenet_gen_qsym_mkldnn.py    |  3 ++-
 src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h      | 13 ++++++++++---
 .../mkldnn/mkldnn_quantized_batch_norm.cc           |  2 +-
 4 files changed, 14 insertions(+), 6 deletions(-)

diff --git a/example/quantization/README.md b/example/quantization/README.md
index 2215a13a97d5..90c111ac1a43 100644
--- a/example/quantization/README.md
+++ b/example/quantization/README.md
@@ -97,7 +97,7 @@ The following models have been tested on Linux systems. Accuracy is collected on
 |[MobileNet 1.0](#5)|[Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|72.23%/90.64%|72.06%/90.53%|
 |[MobileNetV2 1.0](#6)|[Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|70.27%/89.62%|69.82%/89.35%|
 |[Inception V3](#7)|[Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|77.76%/93.83% |78.05%/93.91% |
-|[ResNet152-V2](#8)|[MXNet ModelZoo](http://data.mxnet.io/models/imagenet/resnet/152-layers/)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|76.65%/93.07%|76.19%/92.88%|
+|[ResNet152-V2](#8)|[MXNet ModelZoo](http://data.mxnet.io/models/imagenet/resnet/152-layers/)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|76.65%/93.07%|76.25%/92.89%|
 |[Inception-BN](#9)|[MXNet ModelZoo](http://data.mxnet.io/models/imagenet/inception-bn/)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|72.28%/90.63%|72.02%/90.53%|
 | [SSD-VGG16](#10) | [example/ssd](https://github.com/apache/incubator-mxnet/tree/master/example/ssd)  | VOC2007/2012  | 0.8366 mAP  | 0.8357 mAP  |
 | [SSD-VGG16](#10) | [example/ssd](https://github.com/apache/incubator-mxnet/tree/master/example/ssd)  | COCO2014  | 0.2552 mAP  | 0.253 mAP  |
diff --git a/example/quantization/imagenet_gen_qsym_mkldnn.py b/example/quantization/imagenet_gen_qsym_mkldnn.py
index e24fb780a9d8..cc99ef0d01bc 100644
--- a/example/quantization/imagenet_gen_qsym_mkldnn.py
+++ b/example/quantization/imagenet_gen_qsym_mkldnn.py
@@ -202,7 +202,8 @@ def save_params(fname, arg_params, aux_params, logger=None):
         if args.model == 'imagenet1k-resnet-152':
             rgb_mean = '0,0,0'
             rgb_std = '1,1,1'
-            excluded_sym_names += ['flatten0']
+            # stage1_unit1_bn1 & stage4_unit1_bn1 is excluded for the sake of accuracy
+            excluded_sym_names += ['flatten0', 'stage1_unit1_bn1', 'stage4_unit1_bn1']
             if exclude_first_conv:
                 excluded_sym_names += ['conv0']
         elif args.model == 'imagenet1k-inception-bn':
diff --git a/src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h b/src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h
index 510ca29d7f91..61de08fdde23 100644
--- a/src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h
@@ -184,7 +184,7 @@ class MKLDNNBNForward {
 
 template<typename DType>
 static MKLDNNBNForward &GetBNForward(const BatchNormParam& param,
-                                     const OpContext &ctx, const NDArray &in_data,
+                                     const OpContext &ctx, const mkldnn::memory *data_mem,
                                      unsigned flags) {
 #if DMLC_CXX11_THREAD_LOCAL
   static thread_local std::unordered_map<MKLDNNBNSignature, MKLDNNBNForward, OpHash> fwds;
@@ -194,11 +194,11 @@ static MKLDNNBNForward &GetBNForward(const BatchNormParam& param,
   MKLDNNBNSignature key(param);
   key.AddSign(ctx.is_train);
   key.AddSign(param.use_global_stats);
-  key.AddSign(in_data);
+  key.AddSign(*data_mem);
 
   auto it = fwds.find(key);
   if (it == fwds.end()) {
-    auto fwd_pd = _GetFwd(*in_data.GetMKLDNNData(), ctx.is_train,
+    auto fwd_pd = _GetFwd(*data_mem, ctx.is_train,
                           (DType) param.eps, flags);
     MKLDNNBNForward fwd(fwd_pd, ctx.is_train && !param.use_global_stats);
     it = AddToCache(&fwds, key, fwd);
@@ -206,6 +206,13 @@ static MKLDNNBNForward &GetBNForward(const BatchNormParam& param,
   return it->second;
 }
 
+template<typename DType>
+static MKLDNNBNForward &GetBNForward(const BatchNormParam& param,
+                                     const OpContext &ctx, const NDArray &in_data,
+                                     unsigned flags) {
+  return GetBNForward<DType>(param, ctx, in_data.GetMKLDNNData(), flags);
+}
+
 template <typename DType>
 void MKLDNNBatchNormForward(const OpContext &ctx, const BatchNormParam &param,
                             const std::vector<NDArray>   &in_data,
diff --git a/src/operator/quantization/mkldnn/mkldnn_quantized_batch_norm.cc b/src/operator/quantization/mkldnn/mkldnn_quantized_batch_norm.cc
index 429a80e6b186..9dd86bd4dd4c 100644
--- a/src/operator/quantization/mkldnn/mkldnn_quantized_batch_norm.cc
+++ b/src/operator/quantization/mkldnn/mkldnn_quantized_batch_norm.cc
@@ -80,7 +80,7 @@ static void MKLDNNQuantizedBatchNormForward(const nnvm::NodeAttrs &attrs, const
   const float max_abs_output = std::max(std::abs(*min_output_ptr), std::abs(*max_output_ptr));
 
   unsigned flags = mkldnn::use_global_stats | mkldnn::use_scale_shift;
-  auto &fwd = GetBNForward<float>(param, ctx, data, flags);
+  auto &fwd = GetBNForward<float>(param, ctx, data_mem, flags);
   const mkldnn::memory &weight_mem = fwd.GetWeight();
   CHECK_EQ(weight_mem.get_primitive_desc().get_size(), channel_count * sizeof(float) * 2);
   float *weight_buf = reinterpret_cast<float *>(weight_mem.get_data_handle());

From 47f8ceb8a0767e8fa028975d4ea77bd94384dd95 Mon Sep 17 00:00:00 2001
From: Jake Lee <gstu1130@gmail.com>
Date: Fri, 30 Aug 2019 07:49:09 -0700
Subject: [PATCH 262/813] Disable test coverage of C++ codebase on CI  (#15981)

* disable test coverage on ci

* address the comment

* retrigger CI
---
 CMakeLists.txt                  |   2 +
 Makefile                        |   2 +
 ci/docker/runtime_functions.sh  |  27 +-------
 ci/jenkins/Jenkins_steps.groovy | 108 ++++++++++++++++----------------
 ci/jenkins/Jenkinsfile_clang    |   4 +-
 include/mxnet/tuple.h           |  10 +++
 6 files changed, 72 insertions(+), 81 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 976c736f5f35..33529747f44c 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -233,6 +233,8 @@ if(USE_TENSORRT)
           ${ONNX_PROTO_LIBRARY} ${ONNX_LIBRARY} ${PROTOBUF_LIBRARY})
 endif()
 
+# please note that when you enable this, you might run into an linker not being able to work properly due to large code injection.
+# you can find more information here https://github.com/apache/incubator-mxnet/issues/15971 
 if(ENABLE_TESTCOVERAGE)
   message(STATUS "Compiling with test coverage support enabled. This will result in additional files being written to your source directory!")
   find_program( GCOV_PATH gcov )
diff --git a/Makefile b/Makefile
index a14c29452f6e..4d823d0ac284 100644
--- a/Makefile
+++ b/Makefile
@@ -109,6 +109,8 @@ endif
 CFLAGS += -I$(TPARTYDIR)/mshadow/ -I$(TPARTYDIR)/dmlc-core/include -fPIC -I$(NNVM_PATH)/include -I$(DLPACK_PATH)/include -I$(TPARTYDIR)/tvm/include -Iinclude $(MSHADOW_CFLAGS)
 LDFLAGS = -pthread -ldl $(MSHADOW_LDFLAGS) $(DMLC_LDFLAGS)
 
+# please note that when you enable this, you might run into an linker not being able to work properly due to large code injection.
+# you can find more information here https://github.com/apache/incubator-mxnet/issues/15971
 ifeq ($(ENABLE_TESTCOVERAGE), 1)
         CFLAGS += --coverage
         LDFLAGS += --coverage
diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index baba0a032110..4dc5566d887a 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -299,7 +299,6 @@ build_centos7_cpu() {
     make \
         DEV=1 \
         USE_LAPACK=1 \
-        ENABLE_TESTCOVERAGE=1 \
         USE_LAPACK_PATH=/usr/lib64/liblapack.so \
         USE_BLAS=openblas \
         USE_MKLDNN=0 \
@@ -314,7 +313,6 @@ build_amzn_linux_cpu() {
     cmake \
         -DCMAKE_CXX_COMPILER_LAUNCHER=ccache \
         -DCMAKE_C_COMPILER_LAUNCHER=ccache \
-        -DENABLE_TESTCOVERAGE=ON \
         -DUSE_CUDA=OFF\
         -DUSE_OPENCV=ON\
         -DUSE_OPENMP=ON\
@@ -335,7 +333,6 @@ build_centos7_mkldnn() {
     build_ccache_wrappers
     make \
         DEV=1 \
-        ENABLE_TESTCOVERAGE=1 \
         USE_LAPACK=1 \
         USE_LAPACK_PATH=/usr/lib64/liblapack.so \
         USE_BLAS=openblas \
@@ -350,7 +347,6 @@ build_centos7_gpu() {
     build_ccache_wrappers
     make \
         DEV=1                                     \
-        ENABLE_TESTCOVERAGE=1                     \
         USE_LAPACK=1                              \
         USE_LAPACK_PATH=/usr/lib64/liblapack.so   \
         USE_BLAS=openblas                         \
@@ -374,7 +370,6 @@ build_ubuntu_cpu_openblas() {
     build_ccache_wrappers
     make \
         DEV=1                         \
-        ENABLE_TESTCOVERAGE=1         \
         USE_CPP_PACKAGE=1             \
         USE_BLAS=openblas             \
         USE_MKLDNN=0                  \
@@ -392,7 +387,6 @@ build_ubuntu_cpu_mkl() {
     export CXX="ccache g++"
     make \
         DEV=1                         \
-        ENABLE_TESTCOVERAGE=1         \
         USE_CPP_PACKAGE=1             \
         USE_BLAS=mkl                  \
         USE_MKLDNN=0                  \
@@ -410,7 +404,6 @@ build_ubuntu_cpu_cmake_debug() {
     cmake \
         -DCMAKE_CXX_COMPILER_LAUNCHER=ccache \
         -DCMAKE_C_COMPILER_LAUNCHER=ccache \
-        -DENABLE_TESTCOVERAGE=ON \
         -DUSE_CUDA=OFF \
         -DUSE_MKL_IF_AVAILABLE=OFF \
         -DUSE_OPENMP=OFF \
@@ -461,7 +454,6 @@ build_ubuntu_cpu_clang39() {
     export CC=clang-3.9
     build_ccache_wrappers
     make \
-        ENABLE_TESTCOVERAGE=1         \
         USE_CPP_PACKAGE=1             \
         USE_BLAS=openblas             \
         USE_MKLDNN=0                  \
@@ -479,7 +471,6 @@ build_ubuntu_cpu_clang60() {
     build_ccache_wrappers
 
     make  \
-        ENABLE_TESTCOVERAGE=1         \
         USE_CPP_PACKAGE=1             \
         USE_BLAS=openblas             \
         USE_MKLDNN=0                  \
@@ -525,7 +516,6 @@ build_ubuntu_cpu_clang39_mkldnn() {
     build_ccache_wrappers
 
     make \
-        ENABLE_TESTCOVERAGE=1         \
         USE_CPP_PACKAGE=1             \
         USE_BLAS=openblas             \
         USE_OPENMP=0                  \
@@ -542,7 +532,6 @@ build_ubuntu_cpu_clang60_mkldnn() {
     build_ccache_wrappers
 
     make \
-        ENABLE_TESTCOVERAGE=1         \
         USE_CPP_PACKAGE=1             \
         USE_BLAS=openblas             \
         USE_OPENMP=1                  \
@@ -557,7 +546,6 @@ build_ubuntu_cpu_mkldnn() {
 
     make  \
         DEV=1                         \
-        ENABLE_TESTCOVERAGE=1         \
         USE_CPP_PACKAGE=1             \
         USE_BLAS=openblas             \
         USE_SIGNAL_HANDLER=1          \
@@ -571,7 +559,6 @@ build_ubuntu_cpu_mkldnn_mkl() {
 
     make  \
         DEV=1                         \
-        ENABLE_TESTCOVERAGE=1         \
         USE_CPP_PACKAGE=1             \
         USE_BLAS=mkl                  \
         USE_SIGNAL_HANDLER=1          \
@@ -635,7 +622,6 @@ build_ubuntu_gpu_tensorrt() {
           -DUSE_OPENMP=0                          \
           -DUSE_MKLDNN=0                          \
           -DUSE_MKL_IF_AVAILABLE=OFF              \
-          -DENABLE_TESTCOVERAGE=ON                \
           -DCUDA_ARCH_NAME=Manual                 \
           -DCUDA_ARCH_BIN=$CI_CMAKE_CUDA_ARCH_BIN \
           -G Ninja                                \
@@ -651,7 +637,6 @@ build_ubuntu_gpu_mkldnn() {
 
     make  \
         DEV=1                                     \
-        ENABLE_TESTCOVERAGE=1                     \
         USE_CPP_PACKAGE=1                         \
         USE_BLAS=openblas                         \
         USE_CUDA=1                                \
@@ -669,7 +654,6 @@ build_ubuntu_gpu_mkldnn_nocudnn() {
 
     make  \
         DEV=1                                     \
-        ENABLE_TESTCOVERAGE=1                     \
         USE_BLAS=openblas                         \
         USE_CUDA=1                                \
         USE_CUDA_PATH=/usr/local/cuda             \
@@ -684,7 +668,6 @@ build_ubuntu_gpu_cuda101_cudnn7() {
     build_ccache_wrappers
     make \
         DEV=1                                     \
-        ENABLE_TESTCOVERAGE=1                     \
         USE_BLAS=openblas                         \
         USE_MKLDNN=0                              \
         USE_CUDA=1                                \
@@ -706,8 +689,7 @@ build_ubuntu_amalgamation() {
     build_ccache_wrappers
     make -C amalgamation/ clean
     make -C amalgamation/     \
-        USE_BLAS=openblas     \
-        ENABLE_TESTCOVERAGE=1
+        USE_BLAS=openblas
 }
 
 build_ubuntu_amalgamation_min() {
@@ -717,8 +699,7 @@ build_ubuntu_amalgamation_min() {
     make -C amalgamation/ clean
     make -C amalgamation/     \
         USE_BLAS=openblas     \
-        MIN=1                 \
-        ENABLE_TESTCOVERAGE=1
+        MIN=1
 }
 
 build_ubuntu_gpu_cmake_mkldnn() {
@@ -730,7 +711,6 @@ build_ubuntu_gpu_cmake_mkldnn() {
         -DCMAKE_C_COMPILER_LAUNCHER=ccache      \
         -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache   \
         -DUSE_SIGNAL_HANDLER=ON                 \
-        -DENABLE_TESTCOVERAGE=ON                \
         -DUSE_CUDA=1                            \
         -DUSE_CUDNN=1                           \
         -DUSE_MKLML_MKL=1                       \
@@ -755,7 +735,6 @@ build_ubuntu_gpu_cmake() {
         -DCMAKE_C_COMPILER_LAUNCHER=ccache      \
         -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache   \
         -DUSE_SIGNAL_HANDLER=ON                 \
-        -DENABLE_TESTCOVERAGE=ON                \
         -DUSE_CUDA=ON                           \
         -DUSE_CUDNN=ON                          \
         -DUSE_MKL_IF_AVAILABLE=OFF              \
@@ -781,7 +760,6 @@ build_ubuntu_cpu_large_tensor() {
         -DCMAKE_C_COMPILER_LAUNCHER=ccache      \
         -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache   \
         -DUSE_SIGNAL_HANDLER=ON                 \
-        -DENABLE_TESTCOVERAGE=ON                \
         -DUSE_CUDA=OFF                          \
         -DUSE_CUDNN=OFF                         \
         -DUSE_MKLDNN=OFF                        \
@@ -802,7 +780,6 @@ build_ubuntu_gpu_large_tensor() {
         -DCMAKE_C_COMPILER_LAUNCHER=ccache      \
         -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache   \
         -DUSE_SIGNAL_HANDLER=ON                 \
-        -DENABLE_TESTCOVERAGE=ON                \
         -DUSE_CUDA=ON                           \
         -DUSE_CUDNN=ON                          \
         -DUSE_MKL_IF_AVAILABLE=OFF              \
diff --git a/ci/jenkins/Jenkins_steps.groovy b/ci/jenkins/Jenkins_steps.groovy
index 4075842313ae..a376aa00ea71 100644
--- a/ci/jenkins/Jenkins_steps.groovy
+++ b/ci/jenkins/Jenkins_steps.groovy
@@ -112,7 +112,7 @@ def compile_unix_cpu_openblas() {
           timeout(time: max_time, unit: 'MINUTES') {
             utils.init_git()
             utils.docker_run('ubuntu_cpu', 'build_ubuntu_cpu_openblas', false)
-            utils.pack_lib('cpu', mx_lib_cython, true)
+            utils.pack_lib('cpu', mx_lib_cython)
           }
         }
       }
@@ -126,7 +126,7 @@ def compile_unix_openblas_debug_cpu() {
           timeout(time: max_time, unit: 'MINUTES') {
             utils.init_git()
             utils.docker_run('ubuntu_cpu', 'build_ubuntu_cpu_cmake_debug', false)
-            utils.pack_lib('cpu_debug', mx_cmake_lib_debug, true)
+            utils.pack_lib('cpu_debug', mx_cmake_lib_debug)
           }
         }
       }
@@ -140,7 +140,7 @@ def compile_unix_int64_cpu() {
           timeout(time: max_time, unit: 'MINUTES') {
             utils.init_git()
             utils.docker_run('ubuntu_cpu', 'build_ubuntu_cpu_large_tensor', false)
-            utils.pack_lib('ubuntu_cpu_int64', mx_cmake_lib, true)
+            utils.pack_lib('ubuntu_cpu_int64', mx_cmake_lib)
           }
         }
       }
@@ -154,7 +154,7 @@ def compile_unix_int64_gpu() {
           timeout(time: max_time, unit: 'MINUTES') {
             utils.init_git()
             utils.docker_run('ubuntu_gpu_cu101', 'build_ubuntu_gpu_large_tensor', false)
-            utils.pack_lib('ubuntu_gpu_int64', mx_cmake_lib, true)
+            utils.pack_lib('ubuntu_gpu_int64', mx_cmake_lib)
           }
         }
       }
@@ -168,7 +168,7 @@ def compile_unix_mkl_cpu() {
           timeout(time: max_time, unit: 'MINUTES') {
             utils.init_git()
             utils.docker_run('ubuntu_cpu', 'build_ubuntu_cpu_mkl', false)
-            utils.pack_lib('cpu_mkl', mx_mkldnn_lib, true)
+            utils.pack_lib('cpu_mkl', mx_mkldnn_lib)
           }
         }
       }
@@ -182,7 +182,7 @@ def compile_unix_mkldnn_cpu() {
           timeout(time: max_time, unit: 'MINUTES') {
             utils.init_git()
             utils.docker_run('ubuntu_cpu', 'build_ubuntu_cpu_mkldnn', false)
-            utils.pack_lib('mkldnn_cpu', mx_mkldnn_lib, true)
+            utils.pack_lib('mkldnn_cpu', mx_mkldnn_lib)
           }
         }
       }
@@ -196,7 +196,7 @@ def compile_unix_mkldnn_mkl_cpu() {
           timeout(time: max_time, unit: 'MINUTES') {
             utils.init_git()
             utils.docker_run('ubuntu_cpu', 'build_ubuntu_cpu_mkldnn_mkl', false)
-            utils.pack_lib('mkldnn_mkl_cpu', mx_mkldnn_lib, true)
+            utils.pack_lib('mkldnn_mkl_cpu', mx_mkldnn_lib)
           }
         }
       }
@@ -210,7 +210,7 @@ def compile_unix_mkldnn_gpu() {
           timeout(time: max_time, unit: 'MINUTES') {
             utils.init_git()
             utils.docker_run('ubuntu_build_cuda', 'build_ubuntu_gpu_mkldnn', false)
-            utils.pack_lib('mkldnn_gpu', mx_mkldnn_lib, true)
+            utils.pack_lib('mkldnn_gpu', mx_mkldnn_lib)
           }
         }
       }
@@ -224,7 +224,7 @@ def compile_unix_mkldnn_nocudnn_gpu() {
            timeout(time: max_time, unit: 'MINUTES') {
              utils.init_git()
              utils.docker_run('ubuntu_build_cuda', 'build_ubuntu_gpu_mkldnn_nocudnn', false)
-             utils.pack_lib('mkldnn_gpu_nocudnn', mx_mkldnn_lib, true)
+             utils.pack_lib('mkldnn_gpu_nocudnn', mx_mkldnn_lib)
            }
          }
        }
@@ -238,7 +238,7 @@ def compile_unix_full_gpu() {
           timeout(time: max_time, unit: 'MINUTES') {
             utils.init_git()
             utils.docker_run('ubuntu_build_cuda', 'build_ubuntu_gpu_cuda101_cudnn7', false)
-            utils.pack_lib('gpu', mx_lib_cpp_examples, true)
+            utils.pack_lib('gpu', mx_lib_cpp_examples)
           }
         }
       }
@@ -252,7 +252,7 @@ def compile_unix_cmake_mkldnn_gpu() {
           timeout(time: max_time, unit: 'MINUTES') {
             utils.init_git()
             utils.docker_run('ubuntu_gpu_cu101', 'build_ubuntu_gpu_cmake_mkldnn', false)
-            utils.pack_lib('cmake_mkldnn_gpu', mx_cmake_mkldnn_lib, true)
+            utils.pack_lib('cmake_mkldnn_gpu', mx_cmake_mkldnn_lib)
           }
         }
       }
@@ -266,7 +266,7 @@ def compile_unix_cmake_gpu() {
           timeout(time: max_time, unit: 'MINUTES') {
             utils.init_git()
             utils.docker_run('ubuntu_gpu_cu101', 'build_ubuntu_gpu_cmake', false)
-            utils.pack_lib('cmake_gpu', mx_cmake_lib_cython, true)
+            utils.pack_lib('cmake_gpu', mx_cmake_lib_cython)
           }
         }
       }
@@ -280,7 +280,7 @@ def compile_unix_tensorrt_gpu() {
           timeout(time: max_time, unit: 'MINUTES') {
             utils.init_git()
             utils.docker_run('ubuntu_gpu_tensorrt', 'build_ubuntu_gpu_tensorrt', false)
-            utils.pack_lib('tensorrt', mx_tensorrt_lib, true)
+            utils.pack_lib('tensorrt', mx_tensorrt_lib)
           }
         }
       }
@@ -294,7 +294,7 @@ def compile_centos7_cpu() {
           timeout(time: max_time, unit: 'MINUTES') {
             utils.init_git()
             utils.docker_run('centos7_cpu', 'build_centos7_cpu', false)
-            utils.pack_lib('centos7_cpu', mx_lib, true)
+            utils.pack_lib('centos7_cpu', mx_lib)
           }
         }
       }
@@ -308,7 +308,7 @@ def compile_centos7_cpu_mkldnn() {
           timeout(time: max_time, unit: 'MINUTES') {
             utils.init_git()
             utils.docker_run('centos7_cpu', 'build_centos7_mkldnn', false)
-            utils.pack_lib('centos7_mkldnn', mx_mkldnn_lib, true)
+            utils.pack_lib('centos7_mkldnn', mx_mkldnn_lib)
           }
         }
       }
@@ -322,7 +322,7 @@ def compile_centos7_gpu() {
           timeout(time: max_time, unit: 'MINUTES') {
             utils.init_git()
             utils.docker_run('centos7_gpu', 'build_centos7_gpu', false)
-            utils.pack_lib('centos7_gpu', mx_lib, true)
+            utils.pack_lib('centos7_gpu', mx_lib)
           }
         }
       }
@@ -375,7 +375,7 @@ def compile_unix_clang_3_9_mkldnn_cpu() {
           timeout(time: max_time, unit: 'MINUTES') {
             utils.init_git()
             utils.docker_run('ubuntu_cpu', 'build_ubuntu_cpu_clang39_mkldnn', false)
-            utils.pack_lib('mkldnn_cpu_clang3', mx_mkldnn_lib, true)
+            utils.pack_lib('mkldnn_cpu_clang3', mx_mkldnn_lib)
           }
         }
       }
@@ -389,7 +389,7 @@ def compile_unix_clang_6_mkldnn_cpu() {
           timeout(time: max_time, unit: 'MINUTES') {
             utils.init_git()
             utils.docker_run('ubuntu_cpu', 'build_ubuntu_cpu_clang60_mkldnn', false)
-            utils.pack_lib('mkldnn_cpu_clang6', mx_mkldnn_lib, true)
+            utils.pack_lib('mkldnn_cpu_clang6', mx_mkldnn_lib)
           }
         }
       }
@@ -643,7 +643,7 @@ def test_unix_python2_cpu() {
       node(NODE_LINUX_CPU) {
         ws('workspace/ut-python2-cpu') {
           try {
-            utils.unpack_and_init('cpu', mx_lib_cython, true)
+            utils.unpack_and_init('cpu', mx_lib_cython)
             python2_ut_cython('ubuntu_cpu')
             utils.publish_test_coverage()
           } finally {
@@ -661,7 +661,7 @@ def test_unix_python2_gpu() {
       node(NODE_LINUX_GPU) {
         ws('workspace/ut-python2-gpu') {
           try {
-            utils.unpack_and_init('gpu', mx_lib, true)
+            utils.unpack_and_init('gpu', mx_lib)
             python2_gpu_ut('ubuntu_gpu_cu101')
             utils.publish_test_coverage()
           } finally {
@@ -678,7 +678,7 @@ def test_unix_python2_quantize_gpu() {
         ws('workspace/ut-python2-quantize-gpu') {
           timeout(time: max_time, unit: 'MINUTES') {
             try {
-              utils.unpack_and_init('gpu', mx_lib, true)
+              utils.unpack_and_init('gpu', mx_lib)
               utils.docker_run('ubuntu_gpu_cu101', 'unittest_ubuntu_python2_quantization_gpu', true)
               utils.publish_test_coverage()
             } finally {
@@ -695,7 +695,7 @@ def test_unix_python2_mkldnn_gpu() {
       node(NODE_LINUX_GPU) {
         ws('workspace/ut-python2-mkldnn-gpu') {
           try {
-            utils.unpack_and_init('mkldnn_gpu', mx_mkldnn_lib, true)
+            utils.unpack_and_init('mkldnn_gpu', mx_mkldnn_lib)
             python2_gpu_ut('ubuntu_gpu_cu101')
             utils.publish_test_coverage()
           } finally {
@@ -711,7 +711,7 @@ def test_unix_python3_cpu() {
       node(NODE_LINUX_CPU) {
         ws('workspace/ut-python3-cpu') {
           try {
-            utils.unpack_and_init('cpu', mx_lib, true)
+            utils.unpack_and_init('cpu', mx_lib)
             python3_ut('ubuntu_cpu')
             utils.publish_test_coverage()
           } finally {
@@ -728,7 +728,7 @@ def test_unix_python3_mkl_cpu() {
       node(NODE_LINUX_CPU) {
         ws('workspace/ut-python3-cpu') {
           try {
-            utils.unpack_and_init('cpu_mkl', mx_lib, true)
+            utils.unpack_and_init('cpu_mkl', mx_lib)
             python3_ut('ubuntu_cpu')
             utils.publish_test_coverage()
           } finally {
@@ -745,7 +745,7 @@ def test_unix_python3_gpu() {
       node(NODE_LINUX_GPU) {
         ws('workspace/ut-python3-gpu') {
           try {
-            utils.unpack_and_init('gpu', mx_lib_cython, true)
+            utils.unpack_and_init('gpu', mx_lib_cython)
             python3_gpu_ut_cython('ubuntu_gpu_cu101')
             utils.publish_test_coverage()
           } finally {
@@ -762,7 +762,7 @@ def test_unix_python3_quantize_gpu() {
         ws('workspace/ut-python3-quantize-gpu') {
           timeout(time: max_time, unit: 'MINUTES') {
             try {
-              utils.unpack_and_init('gpu', mx_lib, true)
+              utils.unpack_and_init('gpu', mx_lib)
               utils.docker_run('ubuntu_gpu_cu101', 'unittest_ubuntu_python3_quantization_gpu', true)
               utils.publish_test_coverage()
             } finally {
@@ -779,7 +779,7 @@ def test_unix_python3_debug_cpu() {
       node(NODE_LINUX_CPU) {
         ws('workspace/ut-python3-cpu-debug') {
           try {
-            utils.unpack_and_init('cpu_debug', mx_cmake_lib_debug, true)
+            utils.unpack_and_init('cpu_debug', mx_cmake_lib_debug)
             python3_ut('ubuntu_cpu')
           } finally {
             utils.collect_test_results_unix('nosetests_unittest.xml', 'nosetests_python3_cpu_debug_unittest.xml')
@@ -795,7 +795,7 @@ def test_unix_python2_mkldnn_cpu() {
       node(NODE_LINUX_CPU) {
         ws('workspace/ut-python2-mkldnn-cpu') {
           try {
-            utils.unpack_and_init('mkldnn_cpu', mx_mkldnn_lib, true)
+            utils.unpack_and_init('mkldnn_cpu', mx_mkldnn_lib)
             python2_ut('ubuntu_cpu')
             utils.publish_test_coverage()
           } finally {
@@ -813,7 +813,7 @@ def test_unix_python3_mkldnn_cpu() {
       node(NODE_LINUX_CPU) {
         ws('workspace/ut-python3-mkldnn-cpu') {
           try {
-            utils.unpack_and_init('mkldnn_cpu', mx_mkldnn_lib, true)
+            utils.unpack_and_init('mkldnn_cpu', mx_mkldnn_lib)
             python3_ut_mkldnn('ubuntu_cpu')
             utils.publish_test_coverage()
           } finally {
@@ -830,7 +830,7 @@ def test_unix_python3_mkldnn_mkl_cpu() {
       node(NODE_LINUX_CPU) {
         ws('workspace/ut-python3-mkldnn-mkl-cpu') {
           try {
-            utils.unpack_and_init('mkldnn_mkl_cpu', mx_mkldnn_lib, true)
+            utils.unpack_and_init('mkldnn_mkl_cpu', mx_mkldnn_lib)
             python3_ut_mkldnn('ubuntu_cpu')
             utils.publish_test_coverage()
           } finally {
@@ -847,7 +847,7 @@ def test_unix_python3_mkldnn_gpu() {
       node(NODE_LINUX_GPU) {
         ws('workspace/ut-python3-mkldnn-gpu') {
           try {
-            utils.unpack_and_init('mkldnn_gpu', mx_mkldnn_lib, true)
+            utils.unpack_and_init('mkldnn_gpu', mx_mkldnn_lib)
             python3_gpu_ut('ubuntu_gpu_cu101')
             utils.publish_test_coverage()
           } finally {
@@ -863,7 +863,7 @@ def test_unix_python3_mkldnn_nocudnn_gpu() {
       node(NODE_LINUX_GPU) {
         ws('workspace/ut-python3-mkldnn-gpu-nocudnn') {
           try {
-            utils.unpack_and_init('mkldnn_gpu_nocudnn', mx_mkldnn_lib, true)
+            utils.unpack_and_init('mkldnn_gpu_nocudnn', mx_mkldnn_lib)
             python3_gpu_ut_nocudnn('ubuntu_gpu_cu101')
             utils.publish_test_coverage()
           } finally {
@@ -880,7 +880,7 @@ def test_unix_python3_tensorrt_gpu() {
         ws('workspace/build-tensorrt') {
           timeout(time: max_time, unit: 'MINUTES') {
             try {
-              utils.unpack_and_init('tensorrt', mx_tensorrt_lib, true)
+              utils.unpack_and_init('tensorrt', mx_tensorrt_lib)
               utils.docker_run('ubuntu_gpu_tensorrt', 'unittest_ubuntu_tensorrt_gpu', true)
               utils.publish_test_coverage()
             } finally {
@@ -897,7 +897,7 @@ def test_unix_python3_integration_gpu() {
       node(NODE_LINUX_GPU) {
         ws('workspace/it-python-gpu') {
           timeout(time: max_time, unit: 'MINUTES') {
-            utils.unpack_and_init('gpu', mx_lib, true)
+            utils.unpack_and_init('gpu', mx_lib)
             utils.docker_run('ubuntu_gpu_cu101', 'integrationtest_ubuntu_gpu_python', true)
             utils.publish_test_coverage()
           }
@@ -926,7 +926,7 @@ def test_unix_cpp_package_gpu() {
       node(NODE_LINUX_GPU) {
         ws('workspace/it-cpp-package') {
           timeout(time: max_time, unit: 'MINUTES') {
-            utils.unpack_and_init('gpu', mx_lib_cpp_examples, true)
+            utils.unpack_and_init('gpu', mx_lib_cpp_examples)
             utils.docker_run('ubuntu_gpu_cu101', 'integrationtest_ubuntu_gpu_cpp_package', true)
             utils.publish_test_coverage()
           }
@@ -940,7 +940,7 @@ def test_unix_scala_cpu() {
       node(NODE_LINUX_CPU) {
         ws('workspace/ut-scala-cpu') {
           timeout(time: max_time, unit: 'MINUTES') {
-            utils.unpack_and_init('cpu', mx_lib, true)
+            utils.unpack_and_init('cpu', mx_lib)
             utils.docker_run('ubuntu_cpu', 'integrationtest_ubuntu_cpu_scala', false)
             utils.publish_test_coverage()
           }
@@ -954,7 +954,7 @@ def test_unix_scala_mkldnn_cpu(){
       node(NODE_LINUX_CPU) {
         ws('workspace/ut-scala-mkldnn-cpu') {
           timeout(time: max_time, unit: 'MINUTES') {
-            utils.unpack_and_init('mkldnn_cpu', mx_mkldnn_lib, true)
+            utils.unpack_and_init('mkldnn_cpu', mx_mkldnn_lib)
             utils.docker_run('ubuntu_cpu', 'integrationtest_ubuntu_cpu_scala', false)
             utils.publish_test_coverage()
           }
@@ -968,7 +968,7 @@ def test_unix_scala_gpu() {
       node(NODE_LINUX_GPU) {
         ws('workspace/ut-scala-gpu') {
           timeout(time: max_time, unit: 'MINUTES') {
-            utils.unpack_and_init('gpu', mx_lib, true)
+            utils.unpack_and_init('gpu', mx_lib)
             utils.docker_run('ubuntu_gpu_cu101', 'integrationtest_ubuntu_gpu_scala', true)
             utils.publish_test_coverage()
           }
@@ -982,7 +982,7 @@ def test_unix_clojure_cpu() {
       node(NODE_LINUX_CPU) {
         ws('workspace/ut-clojure-cpu') {
           timeout(time: max_time, unit: 'MINUTES') {
-            utils.unpack_and_init('cpu', mx_lib, true)
+            utils.unpack_and_init('cpu', mx_lib)
             utils.docker_run('ubuntu_cpu', 'unittest_ubuntu_cpu_clojure', false)
             utils.publish_test_coverage()
           }
@@ -996,7 +996,7 @@ def test_unix_clojure_integration_cpu() {
       node(NODE_LINUX_CPU) {
         ws('workspace/ut-clojure-integration-cpu') {
           timeout(time: max_time, unit: 'MINUTES') {
-            utils.unpack_and_init('cpu', mx_lib, true)
+            utils.unpack_and_init('cpu', mx_lib)
             utils.docker_run('ubuntu_cpu', 'unittest_ubuntu_cpu_clojure_integration', false)
           }
         }
@@ -1009,7 +1009,7 @@ def test_unix_r_cpu() {
       node(NODE_LINUX_CPU) {
         ws('workspace/ut-r-cpu') {
           timeout(time: max_time, unit: 'MINUTES') {
-            utils.unpack_and_init('cpu', mx_lib, true)
+            utils.unpack_and_init('cpu', mx_lib)
             utils.docker_run('ubuntu_cpu', 'unittest_ubuntu_cpu_R', false)
             utils.publish_test_coverage()
           }
@@ -1023,7 +1023,7 @@ def test_unix_r_mkldnn_cpu() {
       node(NODE_LINUX_CPU) {
         ws('workspace/ut-r-mkldnn-cpu') {
           timeout(time: max_time, unit: 'MINUTES') {
-            utils.unpack_and_init('mkldnn_cpu', mx_mkldnn_lib, true)
+            utils.unpack_and_init('mkldnn_cpu', mx_mkldnn_lib)
             utils.docker_run('ubuntu_cpu', 'unittest_ubuntu_minimal_R', false)
             utils.publish_test_coverage()
           }
@@ -1037,7 +1037,7 @@ def test_unix_perl_cpu() {
       node(NODE_LINUX_CPU) {
         ws('workspace/ut-perl-cpu') {
           timeout(time: max_time, unit: 'MINUTES') {
-            utils.unpack_and_init('cpu', mx_lib, true)
+            utils.unpack_and_init('cpu', mx_lib)
             utils.docker_run('ubuntu_cpu', 'unittest_ubuntu_cpugpu_perl', false)
             utils.publish_test_coverage()
           }
@@ -1051,7 +1051,7 @@ def test_unix_cpp_gpu() {
       node(NODE_LINUX_GPU) {
         ws('workspace/ut-cpp-gpu') {
           timeout(time: max_time, unit: 'MINUTES') {
-            utils.unpack_and_init('cmake_gpu', mx_cmake_lib, true)
+            utils.unpack_and_init('cmake_gpu', mx_cmake_lib)
             utils.docker_run('ubuntu_gpu_cu101', 'unittest_cpp', true)
             utils.publish_test_coverage()
           }
@@ -1065,7 +1065,7 @@ def test_unix_cpp_mkldnn_gpu() {
       node(NODE_LINUX_GPU) {
         ws('workspace/ut-cpp-mkldnn-gpu') {
           timeout(time: max_time, unit: 'MINUTES') {
-            utils.unpack_and_init('cmake_mkldnn_gpu', mx_cmake_mkldnn_lib, true)
+            utils.unpack_and_init('cmake_mkldnn_gpu', mx_cmake_mkldnn_lib)
             utils.docker_run('ubuntu_gpu_cu101', 'unittest_cpp', true)
             utils.publish_test_coverage()
           }
@@ -1079,7 +1079,7 @@ def test_unix_cpp_cpu() {
       node(NODE_LINUX_CPU) {
         ws('workspace/ut-cpp-cpu') {
           timeout(time: max_time, unit: 'MINUTES') {
-            utils.unpack_and_init('cpu_debug', mx_cmake_lib_debug, true)
+            utils.unpack_and_init('cpu_debug', mx_cmake_lib_debug)
             utils.docker_run('ubuntu_cpu', 'unittest_cpp', false)
             utils.publish_test_coverage()
           }
@@ -1093,7 +1093,7 @@ def test_unix_perl_gpu() {
       node(NODE_LINUX_GPU) {
         ws('workspace/ut-perl-gpu') {
           timeout(time: max_time, unit: 'MINUTES') {
-            utils.unpack_and_init('gpu', mx_lib, true)
+            utils.unpack_and_init('gpu', mx_lib)
             utils.docker_run('ubuntu_gpu_cu101', 'unittest_ubuntu_cpugpu_perl', true)
             utils.publish_test_coverage()
           }
@@ -1107,7 +1107,7 @@ def test_unix_r_gpu() {
       node(NODE_LINUX_GPU) {
         ws('workspace/ut-r-gpu') {
           timeout(time: max_time, unit: 'MINUTES') {
-            utils.unpack_and_init('gpu', mx_lib, true)
+            utils.unpack_and_init('gpu', mx_lib)
             utils.docker_run('ubuntu_gpu_cu101', 'unittest_ubuntu_gpu_R', true)
             utils.publish_test_coverage()
           }
@@ -1147,7 +1147,7 @@ def test_unix_onnx_cpu() {
       node(NODE_LINUX_CPU) {
         ws('workspace/it-onnx-cpu') {
           timeout(time: max_time, unit: 'MINUTES') {
-            utils.unpack_and_init('cpu', mx_lib, true)
+            utils.unpack_and_init('cpu', mx_lib)
             utils.docker_run('ubuntu_cpu', 'integrationtest_ubuntu_cpu_onnx', false)
             utils.publish_test_coverage()
           }
@@ -1161,7 +1161,7 @@ def test_unix_distributed_kvstore_cpu() {
       node(NODE_LINUX_CPU) {
         ws('workspace/it-dist-kvstore') {
           timeout(time: max_time, unit: 'MINUTES') {
-            utils.unpack_and_init('cpu', mx_lib, true)
+            utils.unpack_and_init('cpu', mx_lib)
             utils.docker_run('ubuntu_cpu', 'integrationtest_ubuntu_cpu_dist_kvstore', false)
             utils.publish_test_coverage()
           }
@@ -1175,7 +1175,7 @@ def test_unix_distributed_kvstore_gpu() {
       node(NODE_LINUX_GPU) {
         ws('workspace/it-dist-kvstore') {
           timeout(time: max_time, unit: 'MINUTES') {
-            utils.unpack_and_init('gpu', mx_lib, true)
+            utils.unpack_and_init('gpu', mx_lib)
             utils.docker_run('ubuntu_gpu_cu101', 'integrationtest_ubuntu_gpu_dist_kvstore', true)
             utils.publish_test_coverage()
           }
@@ -1190,7 +1190,7 @@ def test_centos7_python3_cpu() {
         ws('workspace/build-centos7-cpu') {
           timeout(time: max_time, unit: 'MINUTES') {
             try {
-              utils.unpack_and_init('centos7_cpu', mx_lib, true)
+              utils.unpack_and_init('centos7_cpu', mx_lib)
               utils.docker_run('centos7_cpu', 'unittest_centos7_cpu', false)
               utils.publish_test_coverage()
             } finally {
@@ -1209,7 +1209,7 @@ def test_centos7_python3_gpu() {
         ws('workspace/build-centos7-gpu') {
           timeout(time: max_time, unit: 'MINUTES') {
             try {
-              utils.unpack_and_init('centos7_gpu', mx_lib, true)
+              utils.unpack_and_init('centos7_gpu', mx_lib)
               utils.docker_run('centos7_gpu', 'unittest_centos7_gpu', true)
               utils.publish_test_coverage()
             } finally {
@@ -1226,7 +1226,7 @@ def test_centos7_scala_cpu() {
       node(NODE_LINUX_CPU) {
         ws('workspace/ut-scala-centos7-cpu') {
           timeout(time: max_time, unit: 'MINUTES') {
-            utils.unpack_and_init('centos7_cpu', mx_lib, true)
+            utils.unpack_and_init('centos7_cpu', mx_lib)
             utils.docker_run('centos7_cpu', 'unittest_centos7_cpu_scala', false)
             utils.publish_test_coverage()
           }
diff --git a/ci/jenkins/Jenkinsfile_clang b/ci/jenkins/Jenkinsfile_clang
index c0af50dc3cee..029c7208107b 100644
--- a/ci/jenkins/Jenkinsfile_clang
+++ b/ci/jenkins/Jenkinsfile_clang
@@ -37,8 +37,8 @@ core_logic: {
     custom_steps.compile_unix_clang_3_9_cpu(),
     custom_steps.compile_unix_clang_6_cpu(),
     custom_steps.compile_unix_clang_tidy_cpu(),
-    custom_steps.compile_unix_clang_3_9_mkldnn_cpu()
-    // custom_steps.compile_unix_clang_6_mkldnn_cpu()
+    custom_steps.compile_unix_clang_3_9_mkldnn_cpu(),
+    custom_steps.compile_unix_clang_6_mkldnn_cpu()
   ]) 
 }
 ,
diff --git a/include/mxnet/tuple.h b/include/mxnet/tuple.h
index f018c8faabea..51f860995e74 100644
--- a/include/mxnet/tuple.h
+++ b/include/mxnet/tuple.h
@@ -199,7 +199,12 @@ class Tuple {
    * \return the corresponding dimension size
    */
   inline ValueType& operator[](int i) {
+    // it fixes the false alarm of assuming signed overflow does not occur
+    // when assuming that (X - c) > X is always false [-Werror=strict-overflow]
+    #pragma GCC diagnostic push
+    #pragma GCC diagnostic ignored "-Wstrict-overflow"
     CHECK(i >= 0 && i < ndim()) << "index = " << i << " must be in range [0, " << ndim() << ")";
+    #pragma GCC diagnostic pop
     return begin()[i];
   }
   /*!
@@ -208,7 +213,12 @@ class Tuple {
    * \return the corresponding dimension size
    */
   inline const ValueType& operator[](int i) const {
+    // it fixes the false alarm of assuming signed overflow does not occur
+    // when assuming that (X - c) > X is always false [-Werror=strict-overflow]
+    #pragma GCC diagnostic push
+    #pragma GCC diagnostic ignored "-Wstrict-overflow"
     CHECK(i >= 0 && i < ndim()) << "index = " << i << " must be in range [0, " << ndim() << ")";
+    #pragma GCC diagnostic pop
     return begin()[i];
   }
   /*!

From 54d27cb75034eb1068338410911e3202339c6b34 Mon Sep 17 00:00:00 2001
From: reminisce <wujun.nju@gmail.com>
Date: Fri, 30 Aug 2019 11:04:08 -0700
Subject: [PATCH 263/813] [OP] Support range as advanced index for ndarrays
 (#16047)

---
 python/mxnet/ndarray/ndarray.py             |  12 +-
 python/mxnet/numpy/multiarray.py            |   4 +-
 tests/python/unittest/test_numpy_ndarray.py | 303 ++++++++++----------
 3 files changed, 161 insertions(+), 158 deletions(-)

diff --git a/python/mxnet/ndarray/ndarray.py b/python/mxnet/ndarray/ndarray.py
index 6c2bb8078922..59306e21e6f5 100644
--- a/python/mxnet/ndarray/ndarray.py
+++ b/python/mxnet/ndarray/ndarray.py
@@ -1053,17 +1053,15 @@ def _advanced_index_to_array(idx, ax_len, ctx):
             if idx.dtype != idx_dtype:
                 idx = idx.astype(idx_dtype)
             return idx.as_in_context(ctx)
-
         elif isinstance(idx, (np.ndarray, list, tuple)):
             return array(idx, ctx, idx_dtype)
-
         elif isinstance(idx, integer_types):
             return array([idx], ctx, idx_dtype)
-
         elif isinstance(idx, py_slice):
             start, stop, step = idx.indices(ax_len)
             return arange(start, stop, step, ctx=ctx, dtype=idx_dtype)
-
+        elif sys.version_info[0] > 2 and isinstance(idx, range):
+            return arange(idx.start, idx.stop, idx.step, ctx=ctx, dtype=idx_dtype)
         else:
             raise RuntimeError('illegal index type {}'.format(type(idx)))
 
@@ -2888,6 +2886,7 @@ def _scatter_set_nd(self, value_nd, indices):
             lhs=self, rhs=value_nd, indices=indices, shape=self.shape, out=self
         )
 
+
 def indexing_key_expand_implicit_axes(key, shape):
     """Make implicit axes explicit by adding ``slice(None)``.
     Examples
@@ -2984,6 +2983,8 @@ def _is_advanced_index(idx):
         return True
     elif isinstance(idx, py_slice) or idx is None:
         return False
+    elif sys.version_info[0] > 2 and isinstance(idx, range):
+        return True
     else:
         raise RuntimeError('illegal index type {}'.format(type(idx)))
 
@@ -2995,7 +2996,8 @@ def get_indexing_dispatch_code(key):
     for idx in key:
         if isinstance(idx, (NDArray, np.ndarray, list, tuple)):
             return _NDARRAY_ADVANCED_INDEXING
-
+        elif sys.version_info[0] > 2 and isinstance(idx, range):
+            return _NDARRAY_ADVANCED_INDEXING
         elif not (isinstance(idx, (py_slice, integer_types)) or idx is None):
             raise ValueError(
                 'NDArray does not support slicing with key {} of type {}.'
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index c419411edb28..83688774f069 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -235,8 +235,8 @@ def __getitem__(self, key):
                         key, shape[0]))
             return self._at(key)
         elif isinstance(key, py_slice):
-            if (key.step is None or key.step == 1):
-                if  key.start is not None or key.stop is not None:
+            if key.step is None or key.step == 1:
+                if key.start is not None or key.stop is not None:
                     return self._slice(key.start, key.stop)
                 else:
                     return self
diff --git a/tests/python/unittest/test_numpy_ndarray.py b/tests/python/unittest/test_numpy_ndarray.py
index 6dd7b43cd82c..fafa5a827c2f 100644
--- a/tests/python/unittest/test_numpy_ndarray.py
+++ b/tests/python/unittest/test_numpy_ndarray.py
@@ -377,9 +377,6 @@ def test_np_ndarray_copy():
 @with_seed()
 @use_np
 def test_np_ndarray_indexing():
-    """
-    Test all indexing.
-    """
     def np_int(index, int_type=np.int32):
         """
         Helper function for testing indexing that converts slices to slices of ints or None, and tuples to 
@@ -507,156 +504,160 @@ def test_setitem_autograd(np_array, index):
 
     shape = (8, 16, 9, 9)
     np_array = _np.arange(_np.prod(_np.array(shape)), dtype='int32').reshape(shape)  # native np array
-    
+
     # Test sliced output being ndarray:
     index_list = [
-                # Basic indexing
-                # Single int as index
-                0,
-                np.int32(0),
-                np.int64(0),
-                5,
-                np.int32(5),
-                np.int64(5),
-                -1, 
-                np.int32(-1), 
-                np.int64(-1),
-                # Slicing as index
-                slice(5),
-                np_int(slice(5), np.int32),
-                np_int(slice(5), np.int64),
-                slice(1, 5),
-                np_int(slice(1, 5), np.int32),
-                np_int(slice(1, 5), np.int64),
-                slice(1, 5, 2),
-                np_int(slice(1, 5, 2), np.int32),
-                np_int(slice(1, 5, 2), np.int64),
-                slice(7, 0, -1),
-                np_int(slice(7, 0, -1)),
-                np_int(slice(7, 0, -1), np.int64),
-                slice(None, 6),
-                np_int(slice(None, 6)),
-                np_int(slice(None, 6), np.int64),
-                slice(None, 6, 3),
-                np_int(slice(None, 6, 3)),
-                np_int(slice(None, 6, 3), np.int64),
-                slice(1, None),
-                np_int(slice(1, None)),
-                np_int(slice(1, None), np.int64),
-                slice(1, None, 3),
-                np_int(slice(1, None, 3)),
-                np_int(slice(1, None, 3), np.int64),
-                slice(None, None, 2),
-                np_int(slice(None, None, 2)),
-                np_int(slice(None, None, 2), np.int64),
-                slice(None, None, -1),
-                np_int(slice(None, None, -1)),
-                np_int(slice(None, None, -1), np.int64),
-                slice(None, None, -2),
-                np_int(slice(None, None, -2), np.int32),
-                np_int(slice(None, None, -2), np.int64),
-                # Multiple ints as indices
-                (1, 2, 3),
-                np_int((1, 2, 3)),
-                np_int((1, 2, 3), np.int64),
-                (-1, -2, -3),
-                np_int((-1, -2, -3)),
-                np_int((-1, -2, -3), np.int64),
-                (1, 2, 3, 4),
-                np_int((1, 2, 3, 4)),
-                np_int((1, 2, 3, 4), np.int64),
-                (-4, -3, -2, -1),
-                np_int((-4, -3, -2, -1)),
-                np_int((-4, -3, -2, -1), np.int64),
-                # slice(None) as indices
-                (slice(None), slice(None), 1, 8),
-                (slice(None), slice(None), -1, 8),
-                (slice(None), slice(None), 1, -8),
-                (slice(None), slice(None), -1, -8),
-                np_int((slice(None), slice(None), 1, 8)),
-                np_int((slice(None), slice(None), 1, 8), np.int64),
-                (slice(None), slice(None), 1, 8),
-                np_int((slice(None), slice(None), -1, -8)),
-                np_int((slice(None), slice(None), -1, -8), np.int64),
-                (slice(None), 2, slice(1, 5), 1),
-                np_int((slice(None), 2, slice(1, 5), 1)),
-                np_int((slice(None), 2, slice(1, 5), 1), np.int64),
-                # Mixture of ints and slices as indices
-                (slice(None, None, -1), 2, slice(1, 5), 1),
-                np_int((slice(None, None, -1), 2, slice(1, 5), 1)),
-                np_int((slice(None, None, -1), 2, slice(1, 5), 1), np.int64),
-                (slice(None, None, -1), 2, slice(1, 7, 2), 1),
-                np_int((slice(None, None, -1), 2, slice(1, 7, 2), 1)),
-                np_int((slice(None, None, -1), 2, slice(1, 7, 2), 1), np.int64),
-                (slice(1, 8, 2), slice(14, 2, -2), slice(3, 8), slice(0, 7, 3)),
-                np_int((slice(1, 8, 2), slice(14, 2, -2), slice(3, 8), slice(0, 7, 3))),  
-                np_int((slice(1, 8, 2), slice(14, 2, -2), slice(3, 8), slice(0, 7, 3)), np.int64),  
-                (slice(1, 8, 2), 1, slice(3, 8), 2),  
-                np_int((slice(1, 8, 2), 1, slice(3, 8), 2)),  
-                np_int((slice(1, 8, 2), 1, slice(3, 8), 2), np.int64),  
-                # Test Ellipsis ('...')
-                (1, Ellipsis, -1),  
-                (slice(2), Ellipsis, None, 0),  
-                # Test newaxis
-                None,  
-                (1, None, -2, 3, -4),  
-                (1, slice(2, 5), None),   
-                (slice(None), slice(1, 4), None, slice(2, 3)),   
-                (slice(1, 3), slice(1, 3), slice(1, 3), slice(1, 3), None),   
-                (slice(1, 3), slice(1, 3), None, slice(1, 3), slice(1, 3)),   
-                (None, slice(1, 2), 3, None),  
-                (1, None, 2, 3, None, None, 4),  
-                # Advanced indexing
-                ([1, 2], slice(3, 5), None, None, [3, 4]),  
-                (slice(None), slice(3, 5), None, None, [2, 3], [3, 4]),  
-                (slice(None), slice(3, 5), None, [2, 3], None, [3, 4]),  
-                (None, slice(None), slice(3, 5), [2, 3], None, [3, 4]),  
-                [1],   
-                [1, 2],   
-                [2, 1, 3],  
-                [7, 5, 0, 3, 6, 2, 1],  
-                np.array([6, 3], dtype=np.int32),  
-                np.array([[3, 4], [0, 6]], dtype=np.int32),  
-                np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=np.int32),  
-                np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=np.int64),  
-                np.array([[2], [0], [1]], dtype=np.int32),  
-                np.array([[2], [0], [1]], dtype=np.int64),  
-                np.array([4, 7], dtype=np.int32),  
-                np.array([4, 7], dtype=np.int64),  
-                np.array([[3, 6], [2, 1]], dtype=np.int32),  
-                np.array([[3, 6], [2, 1]], dtype=np.int64),  
-                np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=np.int32),  
-                np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=np.int64),  
-                (1, [2, 3]),   
-                (1, [2, 3], np.array([[3], [0]], dtype=np.int32)),  
-                (1, [2, 3]),   
-                (1, [2, 3], np.array([[3], [0]], dtype=np.int64)),  
-                (1, [2], np.array([[5], [3]], dtype=np.int32), slice(None)),  
-                (1, [2], np.array([[5], [3]], dtype=np.int64), slice(None)),  
-                (1, [2, 3], np.array([[6], [0]], dtype=np.int32), slice(2, 5)),  
-                (1, [2, 3], np.array([[6], [0]], dtype=np.int64), slice(2, 5)),  
-                (1, [2, 3], np.array([[4], [7]], dtype=np.int32), slice(2, 5, 2)),  
-                (1, [2, 3], np.array([[4], [7]], dtype=np.int64), slice(2, 5, 2)),  
-                (1, [2], np.array([[3]], dtype=np.int32), slice(None, None, -1)),  
-                (1, [2], np.array([[3]], dtype=np.int64), slice(None, None, -1)),  
-                (1, [2], np.array([[3]], dtype=np.int32), np.array([[5, 7], [2, 4]], dtype=np.int64)),  
-                (1, [2], np.array([[4]], dtype=np.int32), np.array([[1, 3], [5, 7]], dtype='int64')),  
-                [0],   
-                [0, 1],   
-                [1, 2, 3],   
-                [2, 0, 5, 6],
-                ([1, 1], [2, 3]),
-                ([1], [4], [5]),
-                ([1], [4], [5], [6]),  
-                ([[1]], [[2]]),   
-                ([[1]], [[2]], [[3]], [[4]]),  
-                (slice(0, 2), [[1], [6]], slice(0, 2), slice(0, 5, 2)),  
-                ([[[[1]]]], [[1]], slice(0, 3), [1, 5]),  
-                ([[[[1]]]], 3, slice(0, 3), [1, 3]),  
-                ([[[[1]]]], 3, slice(0, 3), 0),  
-                ([[[[1]]]], [[2], [12]], slice(0, 3), slice(None)),  
-                ([1, 2], slice(3, 5), [2, 3], [3, 4]),  
-                ([1, 2], slice(3, 5), (2, 3), [3, 4]),   
+        (),
+        # Basic indexing
+        # Single int as index
+        0,
+        np.int32(0),
+        np.int64(0),
+        5,
+        np.int32(5),
+        np.int64(5),
+        -1,
+        np.int32(-1),
+        np.int64(-1),
+        # Slicing as index
+        slice(5),
+        np_int(slice(5), np.int32),
+        np_int(slice(5), np.int64),
+        slice(1, 5),
+        np_int(slice(1, 5), np.int32),
+        np_int(slice(1, 5), np.int64),
+        slice(1, 5, 2),
+        np_int(slice(1, 5, 2), np.int32),
+        np_int(slice(1, 5, 2), np.int64),
+        slice(7, 0, -1),
+        np_int(slice(7, 0, -1)),
+        np_int(slice(7, 0, -1), np.int64),
+        slice(None, 6),
+        np_int(slice(None, 6)),
+        np_int(slice(None, 6), np.int64),
+        slice(None, 6, 3),
+        np_int(slice(None, 6, 3)),
+        np_int(slice(None, 6, 3), np.int64),
+        slice(1, None),
+        np_int(slice(1, None)),
+        np_int(slice(1, None), np.int64),
+        slice(1, None, 3),
+        np_int(slice(1, None, 3)),
+        np_int(slice(1, None, 3), np.int64),
+        slice(None, None, 2),
+        np_int(slice(None, None, 2)),
+        np_int(slice(None, None, 2), np.int64),
+        slice(None, None, -1),
+        np_int(slice(None, None, -1)),
+        np_int(slice(None, None, -1), np.int64),
+        slice(None, None, -2),
+        np_int(slice(None, None, -2), np.int32),
+        np_int(slice(None, None, -2), np.int64),
+        # Multiple ints as indices
+        (1, 2, 3),
+        np_int((1, 2, 3)),
+        np_int((1, 2, 3), np.int64),
+        (-1, -2, -3),
+        np_int((-1, -2, -3)),
+        np_int((-1, -2, -3), np.int64),
+        (1, 2, 3, 4),
+        np_int((1, 2, 3, 4)),
+        np_int((1, 2, 3, 4), np.int64),
+        (-4, -3, -2, -1),
+        np_int((-4, -3, -2, -1)),
+        np_int((-4, -3, -2, -1), np.int64),
+        # slice(None) as indices
+        (slice(None), slice(None), 1, 8),
+        (slice(None), slice(None), -1, 8),
+        (slice(None), slice(None), 1, -8),
+        (slice(None), slice(None), -1, -8),
+        np_int((slice(None), slice(None), 1, 8)),
+        np_int((slice(None), slice(None), 1, 8), np.int64),
+        (slice(None), slice(None), 1, 8),
+        np_int((slice(None), slice(None), -1, -8)),
+        np_int((slice(None), slice(None), -1, -8), np.int64),
+        (slice(None), 2, slice(1, 5), 1),
+        np_int((slice(None), 2, slice(1, 5), 1)),
+        np_int((slice(None), 2, slice(1, 5), 1), np.int64),
+        # Mixture of ints and slices as indices
+        (slice(None, None, -1), 2, slice(1, 5), 1),
+        np_int((slice(None, None, -1), 2, slice(1, 5), 1)),
+        np_int((slice(None, None, -1), 2, slice(1, 5), 1), np.int64),
+        (slice(None, None, -1), 2, slice(1, 7, 2), 1),
+        np_int((slice(None, None, -1), 2, slice(1, 7, 2), 1)),
+        np_int((slice(None, None, -1), 2, slice(1, 7, 2), 1), np.int64),
+        (slice(1, 8, 2), slice(14, 2, -2), slice(3, 8), slice(0, 7, 3)),
+        np_int((slice(1, 8, 2), slice(14, 2, -2), slice(3, 8), slice(0, 7, 3))),
+        np_int((slice(1, 8, 2), slice(14, 2, -2), slice(3, 8), slice(0, 7, 3)), np.int64),
+        (slice(1, 8, 2), 1, slice(3, 8), 2),
+        np_int((slice(1, 8, 2), 1, slice(3, 8), 2)),
+        np_int((slice(1, 8, 2), 1, slice(3, 8), 2), np.int64),
+        # Test Ellipsis ('...')
+        (1, Ellipsis, -1),
+        (slice(2), Ellipsis, None, 0),
+        # Test newaxis
+        None,
+        (1, None, -2, 3, -4),
+        (1, slice(2, 5), None),
+        (slice(None), slice(1, 4), None, slice(2, 3)),
+        (slice(1, 3), slice(1, 3), slice(1, 3), slice(1, 3), None),
+        (slice(1, 3), slice(1, 3), None, slice(1, 3), slice(1, 3)),
+        (None, slice(1, 2), 3, None),
+        (1, None, 2, 3, None, None, 4),
+        # Advanced indexing
+        ([1, 2], slice(3, 5), None, None, [3, 4]),
+        (slice(None), slice(3, 5), None, None, [2, 3], [3, 4]),
+        (slice(None), slice(3, 5), None, [2, 3], None, [3, 4]),
+        (None, slice(None), slice(3, 5), [2, 3], None, [3, 4]),
+        [1],
+        [1, 2],
+        [2, 1, 3],
+        [7, 5, 0, 3, 6, 2, 1],
+        np.array([6, 3], dtype=np.int32),
+        np.array([[3, 4], [0, 6]], dtype=np.int32),
+        np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=np.int32),
+        np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=np.int64),
+        np.array([[2], [0], [1]], dtype=np.int32),
+        np.array([[2], [0], [1]], dtype=np.int64),
+        np.array([4, 7], dtype=np.int32),
+        np.array([4, 7], dtype=np.int64),
+        np.array([[3, 6], [2, 1]], dtype=np.int32),
+        np.array([[3, 6], [2, 1]], dtype=np.int64),
+        np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=np.int32),
+        np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=np.int64),
+        (1, [2, 3]),
+        (1, [2, 3], np.array([[3], [0]], dtype=np.int32)),
+        (1, [2, 3]),
+        (1, [2, 3], np.array([[3], [0]], dtype=np.int64)),
+        (1, [2], np.array([[5], [3]], dtype=np.int32), slice(None)),
+        (1, [2], np.array([[5], [3]], dtype=np.int64), slice(None)),
+        (1, [2, 3], np.array([[6], [0]], dtype=np.int32), slice(2, 5)),
+        (1, [2, 3], np.array([[6], [0]], dtype=np.int64), slice(2, 5)),
+        (1, [2, 3], np.array([[4], [7]], dtype=np.int32), slice(2, 5, 2)),
+        (1, [2, 3], np.array([[4], [7]], dtype=np.int64), slice(2, 5, 2)),
+        (1, [2], np.array([[3]], dtype=np.int32), slice(None, None, -1)),
+        (1, [2], np.array([[3]], dtype=np.int64), slice(None, None, -1)),
+        (1, [2], np.array([[3]], dtype=np.int32), np.array([[5, 7], [2, 4]], dtype=np.int64)),
+        (1, [2], np.array([[4]], dtype=np.int32), np.array([[1, 3], [5, 7]], dtype='int64')),
+        [0],
+        [0, 1],
+        [1, 2, 3],
+        [2, 0, 5, 6],
+        ([1, 1], [2, 3]),
+        ([1], [4], [5]),
+        ([1], [4], [5], [6]),
+        ([[1]], [[2]]),
+        ([[1]], [[2]], [[3]], [[4]]),
+        (slice(0, 2), [[1], [6]], slice(0, 2), slice(0, 5, 2)),
+        ([[[[1]]]], [[1]], slice(0, 3), [1, 5]),
+        ([[[[1]]]], 3, slice(0, 3), [1, 3]),
+        ([[[[1]]]], 3, slice(0, 3), 0),
+        ([[[[1]]]], [[2], [12]], slice(0, 3), slice(None)),
+        ([1, 2], slice(3, 5), [2, 3], [3, 4]),
+        ([1, 2], slice(3, 5), (2, 3), [3, 4]),
+        range(4),
+        range(3, 0, -1),
+        (range(4,), [1]),
     ]
     for index in index_list:
         test_getitem(np_array, index)

From d80510a3aeb37e999b37a794973fd4377236c0aa Mon Sep 17 00:00:00 2001
From: Rohit Kumar Srivastava <srivastava.141@osu.edu>
Date: Fri, 30 Aug 2019 15:13:54 -0700
Subject: [PATCH 264/813] Added more tests for Large Indices (#15960)

* Adding tests to verify support for Large Tensors in additional Ops along with new C_Apis supporting 64bit indexing

* removing skipped tests

* removing tests not required for vector testing

* Adding more tests for Large Indices and adding support for Large Indices in one_hot operator

* Re-Trigger build
---
 src/ndarray/ndarray_function.cc     |   2 +-
 src/operator/tensor/indexing_op.h   |  18 +--
 src/operator/tensor/matrix_op-inl.h |   4 +-
 tests/nightly/test_large_vector.py  | 222 +++++++++++++++++++++++++++-
 4 files changed, 233 insertions(+), 13 deletions(-)

diff --git a/src/ndarray/ndarray_function.cc b/src/ndarray/ndarray_function.cc
index 335856356534..1a699b12d76d 100644
--- a/src/ndarray/ndarray_function.cc
+++ b/src/ndarray/ndarray_function.cc
@@ -38,7 +38,7 @@ void Copy<cpu, cpu>(const TBlob &from, TBlob *to,
                     RunContext ctx) {
   MSHADOW_TYPE_SWITCH(to->type_flag_, DType, {
     if (to->type_flag_ == from.type_flag_) {
-      const index_t size = from.Size();
+      const index_t size = static_cast<index_t>(from.Size());
       CHECK_EQ(size, to->Size()) << "copying size mismatch, from: " << size * sizeof(DType)
                << " bytes, to: " << to->Size() * sizeof(DType) << " bytes.";
       common::ParallelCopy(to->dptr<DType>(), from.dptr<DType>(), size);
diff --git a/src/operator/tensor/indexing_op.h b/src/operator/tensor/indexing_op.h
index 84b6a65dd29e..7dfc77d25b27 100644
--- a/src/operator/tensor/indexing_op.h
+++ b/src/operator/tensor/indexing_op.h
@@ -1133,7 +1133,7 @@ void BatchTakeOpForward(const nnvm::NodeAttrs& attrs,
  * \brief The parameters of the one_hot operator.
  */
 struct OneHotParam : public dmlc::Parameter<OneHotParam> {
-  int depth;
+  index_t depth;
   double on_value;
   double off_value;
   int axis;
@@ -1153,7 +1153,7 @@ struct OneHotParam : public dmlc::Parameter<OneHotParam> {
   }
 };
 
-inline void GetOneHotParams(const OneHotParam& param, int* depth, double* on_value,
+inline void GetOneHotParams(const OneHotParam& param, index_t* depth, double* on_value,
                             double* off_value, int* dtype) {
   *depth = param.depth;
   CHECK_GE(*depth, 0) << "Dimension size, depth, must be a non-negative integer";
@@ -1172,7 +1172,7 @@ inline bool OneHotOpShape(const nnvm::NodeAttrs& attrs,
   const mxnet::TShape& ishape = (*in_attrs)[0];
   if (!shape_is_known(ishape)) return false;
 
-  int depth = 0;
+  index_t depth = 0;
   double on_value = 1.0;
   double off_value = 0.0;
   int dtype = mshadow::kFloat32;
@@ -1193,7 +1193,7 @@ inline bool OneHotOpType(const nnvm::NodeAttrs& attrs,
   CHECK_EQ(in_attrs->size(), 1U);
   CHECK_EQ(out_attrs->size(), 1U);
   CHECK_NE((*in_attrs)[0], -1) << "Index type must be set for one_hot operator";
-  int depth = 0;
+  index_t depth = 0;
   double on_value = 1.0;
   double off_value = 0.0;
   int dtype = -1;
@@ -1207,10 +1207,10 @@ inline bool OneHotOpType(const nnvm::NodeAttrs& attrs,
 template<int req>
 struct one_hot {
   template<typename DType, typename IType>
-  MSHADOW_XINLINE static void Map(int i, DType* out, const IType* indices,
-                                  int depth, DType on_value) {
-    int offset = i * depth;
-    int j = static_cast<int>(indices[i]);
+  MSHADOW_XINLINE static void Map(index_t i, DType* out, const IType* indices,
+                                  index_t depth, DType on_value) {
+    index_t offset = i * depth;
+    index_t j = static_cast<index_t>(indices[i]);
     if (j >= 0 && j < depth) {
       KERNEL_ASSIGN(out[offset+j], req, on_value);
     }
@@ -1229,7 +1229,7 @@ void OneHotOpForward(const nnvm::NodeAttrs& attrs,
   // The following line is needed to guard the situation when
   // an output array is empty on GPU. In that case, out.dptr() = 0x0
   if (outputs[0].Size() == 0) return;
-  int depth = 0;
+  index_t depth = 0;
   double on_value = 1.0;
   double off_value = 0.0;
   int dtype = mshadow::kFloat32;
diff --git a/src/operator/tensor/matrix_op-inl.h b/src/operator/tensor/matrix_op-inl.h
index 0d66907ad6cd..fa5f93cefc1a 100644
--- a/src/operator/tensor/matrix_op-inl.h
+++ b/src/operator/tensor/matrix_op-inl.h
@@ -1148,8 +1148,8 @@ void SliceAssignScalarOpForward(const nnvm::NodeAttrs& attrs,
 
 struct SliceAxisParam : public dmlc::Parameter<SliceAxisParam> {
   int axis;
-  int begin;
-  dmlc::optional<int> end;
+  index_t begin;
+  dmlc::optional<index_t> end;
   DMLC_DECLARE_PARAMETER(SliceAxisParam) {
     DMLC_DECLARE_FIELD(axis)
       .describe("Axis along which to be sliced, supports negative indexes.");
diff --git a/tests/nightly/test_large_vector.py b/tests/nightly/test_large_vector.py
index 64bfa8a1d3e9..4e1b48c8d047 100644
--- a/tests/nightly/test_large_vector.py
+++ b/tests/nightly/test_large_vector.py
@@ -148,7 +148,6 @@ def test_Dense(ctx=mx.cpu(0)):
 def test_argsort():
     b = create_vector(size=LARGE_X)
     s = nd.argsort(b, axis=0, is_ascend=False, dtype=np.int64)
-    mx.nd.waitall()
     assert (s[0].asnumpy() == (LARGE_X - 1)).all()
 
 
@@ -170,6 +169,227 @@ def test_topk():
     assert val.sum() == (LARGE_X - 1)
 
 
+def test_shape():
+    b = create_vector(size=LARGE_X)
+    #explicit wait_to_read()
+    assert b[0] == 0
+    assert b.shape[0] == LARGE_X
+
+
+def test_size():
+    b = create_vector(size=LARGE_X)
+    #explicit wait_to_read()
+    assert b[0] == 0
+    assert b.size == LARGE_X
+
+
+def test_copy():
+    a = nd.ones(LARGE_X)
+    b = a.copy()
+    assert a[0] == b[0]
+    assert b.shape == a.shape
+    assert b.size == LARGE_X
+
+
+def test_copy_to():
+    a = create_vector(size=LARGE_X)
+    # keeping dtype same as input uses parallel copy which is much faster
+    b = nd.zeros(LARGE_X, dtype=np.int64)
+    c = a.copyto(b)
+    assert c is b
+    assert b[-1] == LARGE_X-1
+    assert b[0] == 0
+
+
+def test_zeros_like():
+    a = nd.ones(LARGE_X)
+    b = nd.zeros_like(a)
+    assert b[-1] == 0
+    assert b.shape == a.shape
+
+
+def test_ones_like():
+    a = nd.zeros(LARGE_X)
+    b = nd.ones_like(a)
+    assert b[-1] == 1
+    assert b.shape == a.shape
+
+
+def test_concat():
+    a = nd.ones(LARGE_X)
+    b = nd.zeros(LARGE_X)
+    c = nd.concat(a,b, dim=0)
+    assert c[0][0] == 1
+    assert c[-1][-1] == 0
+    assert c.shape[0] == (2 * LARGE_X)
+
+
+def test_sum():
+    a = nd.ones(LARGE_X)
+    b = nd.sum(a, axis=0)
+    assert b[0] == LARGE_X
+
+
+def test_prod():
+    a = nd.ones(LARGE_X)
+    b = nd.prod(a, axis=0)
+    assert b[0] == 1
+
+
+def test_min():
+    a = create_vector(size=LARGE_X)
+    b = nd.min(a, axis=0)
+    assert b[0] == 0
+    assert b[-1] == 0
+
+
+def test_max():
+    a = create_vector(size=LARGE_X)
+    b = nd.max(a, axis=0)
+    assert b[0] == (LARGE_X - 1)
+
+
+def test_argmax():
+    a = nd.ones(LARGE_X)
+    b = nd.zeros(LARGE_X)
+    c = nd.concat(a, b, dim=0)
+    d = nd.argmax(c, axis=0)
+    assert c.shape[0] == (2 * LARGE_X)
+    assert d == 0
+
+
+def np_softmax(x, axis=-1, temperature=1.0):
+    x = x - np.max(x, axis=axis, keepdims=True)
+    x = np.exp(x/temperature)
+    x /= np.sum(x, axis=axis, keepdims=True)
+    return x
+
+
+def test_iadd():
+    a = nd.ones(LARGE_X)
+    b = nd.ones(LARGE_X)
+    c = b
+    c += a
+    assert c.shape == a.shape
+    assert c[-1] == 2
+
+
+def test_isub():
+    a = nd.full(LARGE_X, 3)
+    b = nd.ones(LARGE_X)
+    c = a
+    c -= b
+    assert c.shape == a.shape
+    assert c[-1] == 2
+
+
+def test_imul():
+    a = nd.full(LARGE_X, 3)
+    b = nd.ones(LARGE_X)
+    c = b
+    c *= a
+    assert c.shape == a.shape
+    assert c[-1] == 3
+
+
+def test_idiv():
+    a = nd.full(LARGE_X, 4)
+    b = nd.full(LARGE_X, 2)
+    c = a
+    c /= b
+    assert c.shape == a.shape
+    assert c[-1] == 2
+
+
+def test_imod():
+    a = nd.full(LARGE_X, 3)
+    b = nd.full(LARGE_X, 2)
+    c = a
+    c %= b
+    assert c.shape == a.shape
+    assert c[0][-1] == 1
+
+
+def test_eq():
+    a = nd.full(LARGE_X, 3)
+    b = nd.full(LARGE_X, 3)
+    c = (a == b)
+    assert np.sum(c[0].asnumpy() == 1).all()
+
+
+def test_neq():
+    a = nd.full(LARGE_X, 2)
+    b = nd.full(LARGE_X, 3)
+    c = (a != b)
+    assert np.sum(c[0].asnumpy() == 1).all()
+
+
+def test_lt():
+    a = nd.full(LARGE_X, 2)
+    b = nd.full(LARGE_X, 3)
+    d = (a <= b)
+    assert np.sum(d[0].asnumpy() == 1).all()
+
+
+def test_lte():
+    a = nd.full(LARGE_X, 2)
+    b = nd.full(LARGE_X, 3)
+    c = nd.full(LARGE_X, 2)
+    d = (a <= b)
+    assert np.sum(d[0].asnumpy() == 1).all()
+    d = (a <= c)
+    assert np.sum(d[0].asnumpy() == 1).all()
+
+
+def test_gt():
+    a = nd.full(LARGE_X, 3)
+    b = nd.full(LARGE_X, 2)
+    d = (a > b)
+    assert np.sum(d[0].asnumpy() == 1).all()
+
+
+def test_gte():
+    a = nd.full(LARGE_X, 3)
+    b = nd.full(LARGE_X, 2)
+    c = nd.full(LARGE_X, 3)
+    d = (a >= b)
+    assert np.sum(d[0].asnumpy() == 1).all()
+    d = (a >= c)
+    assert np.sum(d[0].asnumpy() == 1).all()
+
+
+def test_slice_like():
+    a = create_vector(size=LARGE_X)
+    b = nd.ones(LARGE_X//2)
+    c = nd.slice_like(a, b)
+    assert c.shape == b.shape
+    assert c[0] == 0
+    assert c[-1] == (LARGE_X//2-1)
+
+
+def test_slice_axis():
+    a = create_vector(size=LARGE_X)
+    c = nd.slice_axis(a, axis=0, begin=0, end=LARGE_X//2)
+    assert c.shape[0] == a.shape[0]//2
+    assert c[-1][0] == (LARGE_X//2-1)
+
+
+def test_full():
+    a = nd.full(LARGE_X, 3)
+    assert a.shape[0] == LARGE_X
+    assert a[LARGE_X//2] == 3
+    assert a[-1] == 3
+
+
+def test_one_hot():
+    a = nd.zeros(10)
+    a[0] = 1
+    a[-1] = 1
+    b = nd.one_hot(a, LARGE_X)
+    assert b[0][1] == 1
+    assert b[-1][1] == 1
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From aab4ded78da85356397ddad3f905063ec3bf3326 Mon Sep 17 00:00:00 2001
From: Jake Lee <gstu1130@gmail.com>
Date: Fri, 30 Aug 2019 20:45:54 -0700
Subject: [PATCH 265/813] Numpy compatible max min (#16046)

* numpy compatible max min

* retrigger CI
---
 src/operator/numpy/np_broadcast_reduce_op.h   |  93 ++++++++++++++
 .../numpy/np_broadcast_reduce_op_value.cc     |  66 ++++++++++
 .../numpy/np_broadcast_reduce_op_value.cu     |  12 ++
 tests/python/unittest/test_numpy_op.py        | 113 ++++++++++++++++++
 4 files changed, 284 insertions(+)

diff --git a/src/operator/numpy/np_broadcast_reduce_op.h b/src/operator/numpy/np_broadcast_reduce_op.h
index b05f4e901d13..305f1c775cd5 100644
--- a/src/operator/numpy/np_broadcast_reduce_op.h
+++ b/src/operator/numpy/np_broadcast_reduce_op.h
@@ -64,6 +64,24 @@ struct NumpyReduceAxesParam : public dmlc::Parameter<NumpyReduceAxesParam> {
   }
 };
 
+struct NumpyReduceAxesNoDTypeParam : public dmlc::Parameter<NumpyReduceAxesNoDTypeParam> {
+  dmlc::optional<mxnet::Tuple<int>> axis;
+  bool keepdims;
+  dmlc::optional<double> initial;
+  DMLC_DECLARE_PARAMETER(NumpyReduceAxesNoDTypeParam) {
+    DMLC_DECLARE_FIELD(axis)
+      .set_default(dmlc::optional<mxnet::Tuple<int>>())
+      .describe("Axis or axes along which a sum is performed. The default, axis=None, will sum "
+                "all of the elements of the input array. If axis is negative it counts from the "
+                "last to the first axis.");
+    DMLC_DECLARE_FIELD(keepdims).set_default(false)
+      .describe("If this is set to `True`, the reduced axes are left "
+                "in the result as dimension with size one.");
+    DMLC_DECLARE_FIELD(initial).set_default(dmlc::optional<double>())
+      .describe("Starting value for the sum.");
+  }
+};
+
 inline TShape NumpyReduceAxesShapeImpl(const TShape& ishape,
                                        const dmlc::optional<mxnet::Tuple<int>>& axis,
                                        bool keepdims) {
@@ -152,6 +170,39 @@ inline bool NumpyReduceAxesShape(const nnvm::NodeAttrs& attrs,
   return shape_is_known(out_attrs->at(0));
 }
 
+inline bool NumpyReduceAxesNoDTypeShape(const nnvm::NodeAttrs& attrs,
+                                        std::vector<TShape> *in_attrs,
+                                        std::vector<TShape> *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  if (!shape_is_known(in_attrs->at(0))) {
+    return false;
+  }
+  const NumpyReduceAxesNoDTypeParam& param = nnvm::get<NumpyReduceAxesNoDTypeParam>(attrs.parsed);
+  // check the case where the reduction axis should not be zero
+  bool is_all_reducded_axes_not_zero = true;
+  const TShape& ishape = (*in_attrs)[0];
+  if (param.axis.has_value()) {
+    const mxnet::Tuple<int>& axes = param.axis.value();
+    for (int i = 0; i < axes.ndim(); ++i) {
+      if (ishape[axes[i]] == 0) {
+        is_all_reducded_axes_not_zero = false;
+        break;
+      }
+    }
+  } else {
+    if (ishape.Size() == 0) {
+      // global reduction should excuted only when input have size more than 0
+      is_all_reducded_axes_not_zero = false;
+    }
+  }
+  CHECK(is_all_reducded_axes_not_zero)
+    << "zero-size array to reduction operation maximum which has no identity";
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0,
+                     NumpyReduceAxesShapeImpl((*in_attrs)[0], param.axis, param.keepdims));
+  return shape_is_known(out_attrs->at(0));
+}
+
 template<bool safe_acc_hint = false>
 inline bool NeedSafeAcc(int itype, int otype) {
   bool rule = (itype != otype) || (itype != mshadow::kFloat32 && itype != mshadow::kFloat64);
@@ -186,6 +237,30 @@ void NumpyReduceAxesCompute(const nnvm::NodeAttrs& attrs,
   }
 }
 
+template<typename xpu, typename reducer, typename OP = op::mshadow_op::identity>
+void NumpyReduceAxesNoDTypeCompute(const nnvm::NodeAttrs& attrs,
+                                   const OpContext& ctx,
+                                   const std::vector<TBlob>& inputs,
+                                   const std::vector<OpReqType>& req,
+                                   const std::vector<TBlob>& outputs) {
+  const NumpyReduceAxesNoDTypeParam& param = nnvm::get<NumpyReduceAxesNoDTypeParam>(attrs.parsed);
+  if (param.initial.has_value()) {
+    LOG(FATAL) << "initial is not supported yet";
+  }
+  if (inputs[0].shape_.Size() == 0U || outputs[0].shape_.Size() == 0U) return;  // zero-size tensor
+  if (param.axis.has_value() && param.axis.value().ndim() == 0) {
+    UnaryOp::IdentityCompute<xpu>(attrs, ctx, inputs, req, outputs);
+  }
+  TShape small;
+  if (param.keepdims) {
+    small = outputs[0].shape_;
+  } else {
+    small = NumpyReduceAxesShapeImpl(inputs[0].shape_, param.axis, true);
+  }
+  ReduceAxesComputeImpl<xpu, reducer, false, false, OP>(ctx, inputs, req, outputs, small);
+}
+
+
 template<typename xpu, bool normalize = false>
 inline void NumpyReduceAxesBackwardUseNone(const nnvm::NodeAttrs& attrs,
                                            const OpContext& ctx,
@@ -273,6 +348,24 @@ void NumpyBroadcastToBackward(const nnvm::NodeAttrs& attrs,
   }
 }
 
+template<typename xpu, typename OP>
+void NumpyReduceAxesNoDTypeBackward(const nnvm::NodeAttrs& attrs,
+                                    const OpContext& ctx,
+                                    const std::vector<TBlob>& inputs,
+                                    const std::vector<OpReqType>& req,
+                                    const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mshadow::expr;
+  const NumpyReduceAxesNoDTypeParam& param = nnvm::get<NumpyReduceAxesNoDTypeParam>(attrs.parsed);
+  TShape small;
+  if (param.keepdims) {
+    small = inputs[0].shape_;
+  } else {
+    small = NumpyReduceAxesShapeImpl(outputs[0].shape_, param.axis, true);
+  }
+  ReduceAxesBackwardUseInOutImpl<xpu, OP, false>(ctx, small, inputs, req, outputs);
+}
+
 }  // namespace op
 }  // namespace mxnet
 #endif  // MXNET_OPERATOR_NUMPY_NP_BROADCAST_REDUCE_OP_H_
diff --git a/src/operator/numpy/np_broadcast_reduce_op_value.cc b/src/operator/numpy/np_broadcast_reduce_op_value.cc
index 96f0c9b350b9..bccd3af8b2cf 100644
--- a/src/operator/numpy/np_broadcast_reduce_op_value.cc
+++ b/src/operator/numpy/np_broadcast_reduce_op_value.cc
@@ -29,6 +29,7 @@ namespace mxnet {
 namespace op {
 
 DMLC_REGISTER_PARAMETER(NumpyReduceAxesParam);
+DMLC_REGISTER_PARAMETER(NumpyReduceAxesNoDTypeParam);
 
 inline bool NumpySumType(const nnvm::NodeAttrs& attrs,
                          std::vector<int> *in_attrs,
@@ -74,6 +75,71 @@ NNVM_REGISTER_OP(_backward_np_sum)
 .set_num_inputs(1)
 .set_attr<FCompute>("FCompute<cpu>", NumpyReduceAxesBackwardUseNone<cpu>);
 
+inline bool NumpyReduceAxesNoDTypeType(const nnvm::NodeAttrs& attrs,
+                         std::vector<int> *in_attrs,
+                         std::vector<int> *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
+  TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
+
+  return out_attrs->at(0) != -1 && in_attrs->at(0) != -1;
+}
+
+NNVM_REGISTER_OP(_np_max)
+.describe(R"code()code" ADD_FILELINE)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<NumpyReduceAxesNoDTypeParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyReduceAxesNoDTypeShape)
+.set_attr<nnvm::FInferType>("FInferType", NumpyReduceAxesNoDTypeType)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"a"};
+  })
+.add_argument("a", "NDArray-or-Symbol", "The input")
+.add_arguments(NumpyReduceAxesNoDTypeParam::__FIELDS__())
+.set_attr<FCompute>("FCompute<cpu>", NumpyReduceAxesNoDTypeCompute<cpu, mshadow::red::maximum>)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<nnvm::FGradient>("FGradient", ReduceGrad{"_backward_np_max"});
+
+NNVM_REGISTER_OP(_backward_np_max)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<NumpyReduceAxesNoDTypeParam>)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_num_inputs(3)
+.set_attr<FCompute>("FCompute<cpu>", NumpyReduceAxesNoDTypeBackward<cpu, mshadow_op::eq>);
+
+NNVM_REGISTER_OP(_np_min)
+.describe(R"code()code" ADD_FILELINE)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<NumpyReduceAxesNoDTypeParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyReduceAxesNoDTypeShape)
+.set_attr<nnvm::FInferType>("FInferType", NumpyReduceAxesNoDTypeType)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+[](const NodeAttrs& attrs) {
+return std::vector<std::string>{"a"};
+})
+.add_argument("a", "NDArray-or-Symbol", "The input")
+.add_arguments(NumpyReduceAxesNoDTypeParam::__FIELDS__())
+.set_attr<FCompute>("FCompute<cpu>", NumpyReduceAxesNoDTypeCompute<cpu, mshadow::red::minimum>)
+.set_attr<FResourceRequest>("FResourceRequest",
+[](const NodeAttrs& attrs) {
+return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+})
+.set_attr<nnvm::FGradient>("FGradient", ReduceGrad{"_backward_np_min"});
+
+NNVM_REGISTER_OP(_backward_np_min)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<NumpyReduceAxesNoDTypeParam>)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_num_inputs(3)
+.set_attr<FCompute>("FCompute<cpu>", NumpyReduceAxesNoDTypeBackward<cpu, mshadow_op::eq>);
+
 NNVM_REGISTER_OP(_np_prod)
 .set_num_inputs(1)
 .set_num_outputs(1)
diff --git a/src/operator/numpy/np_broadcast_reduce_op_value.cu b/src/operator/numpy/np_broadcast_reduce_op_value.cu
index a681f0d8a162..d1d33cc6d8b0 100644
--- a/src/operator/numpy/np_broadcast_reduce_op_value.cu
+++ b/src/operator/numpy/np_broadcast_reduce_op_value.cu
@@ -32,6 +32,18 @@ NNVM_REGISTER_OP(_np_sum)
 NNVM_REGISTER_OP(_backward_np_sum)
 .set_attr<FCompute>("FCompute<gpu>", NumpyReduceAxesBackwardUseNone<gpu>);
 
+NNVM_REGISTER_OP(_np_max)
+.set_attr<FCompute>("FCompute<gpu>", NumpyReduceAxesNoDTypeCompute<gpu, mshadow::red::maximum>);
+
+NNVM_REGISTER_OP(_backward_np_max)
+.set_attr<FCompute>("FCompute<gpu>", NumpyReduceAxesNoDTypeBackward<gpu, mshadow_op::eq>);
+
+NNVM_REGISTER_OP(_np_min)
+.set_attr<FCompute>("FCompute<gpu>", NumpyReduceAxesNoDTypeCompute<gpu, mshadow::red::minimum>);
+
+NNVM_REGISTER_OP(_backward_np_min)
+.set_attr<FCompute>("FCompute<gpu>", NumpyReduceAxesNoDTypeBackward<gpu, mshadow_op::eq>);
+
 NNVM_REGISTER_OP(_np_prod)
 .set_attr<FCompute>("FCompute<gpu>", NumpyReduceAxesCompute<gpu, mshadow_op::product, true>);
 
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 16e5a2e3cd96..49b5b645d604 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -277,6 +277,119 @@ def is_int(dtype):
                         assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
 
 
+@with_seed()
+@use_np
+def test_np_max_min():
+    class TestMax(HybridBlock):
+        def __init__(self, axis=None, keepdims=False):
+            super(TestMax, self).__init__()
+            self._axis = axis
+            self._keepdims = keepdims
+
+        def hybrid_forward(self, F, a, *args, **kwargs):
+            return F.np.max(a, axis=self._axis, keepdims=self._keepdims)
+
+    class TestMin(HybridBlock):
+        def __init__(self, axis=None, keepdims=False):
+            super(TestMin, self).__init__()
+            self._axis = axis
+            self._keepdims = keepdims
+
+        def hybrid_forward(self, F, a, *args, **kwargs):
+            return F.np.min(a, axis=self._axis, keepdims=self._keepdims)
+
+    def is_int(dtype):
+        return 'int' == dtype
+
+    def get_grad(axis, func_name):
+        index = -1 if func_name == 'max' else 0
+        if axis == ():
+            return _np.ones((2,3,4,5))
+        else:
+            temp = _np.zeros((2,3,4,5))
+            if axis == 0:
+                temp[index,:,:,:] = 1
+                return temp
+            elif axis == 1:
+                temp[:,index,:,:] = 1
+                return temp
+            elif axis == 2:
+                temp[:,:,index,:] = 1
+                return temp
+            elif axis == 3:
+                temp[:,:,:,index] = 1
+                return temp
+            elif not axis:
+                temp[index,index,index,index] = 1
+                return temp
+            raise ValueError('axis should be int or None or ()')
+
+    def _test_np_exception(func, shape, dim):
+        x = _np.random.uniform(-1.0, 1.0, shape)
+        x = mx.nd.array(x).as_np_ndarray()
+        if func == 'max':
+            out = mx.np.max(x)
+        else:
+            out = mx.np.min(x)
+        assert out.ndim == dim, 'dimension mismatch, output.ndim={}, dim={}'.format(output.ndim, dim)
+
+    in_data_dim = random.choice([2, 3, 4])
+    shape = rand_shape_nd(in_data_dim, dim=3)
+    for func in ['max', 'min']:
+        for hybridize in [False, True]:
+            for keepdims in [True, False]:
+                for axis in ([i for i in range(in_data_dim)] + [(), None]):
+                    for itype in ['float16', 'float32', 'float64', 'int']:
+                        # test gluon
+                        if func == 'max':
+                            test_gluon = TestMax(axis=axis, keepdims=keepdims)
+                        else:
+                            test_gluon = TestMin(axis=axis, keepdims=keepdims)
+                        if hybridize:
+                            test_gluon.hybridize()
+                        if is_int(itype):
+                            x = mx.nd.arange(120).reshape((2, 3, 4, 5))
+                            x = mx.nd.array(x)
+                        else:
+                            x = mx.nd.random.uniform(-1.0, 1.0, shape=shape, dtype=itype)
+                        x = x.as_np_ndarray()
+                        x.attach_grad()
+                        if func == 'max':
+                            expected_ret = _np.amax(x.asnumpy(), axis=axis, keepdims=keepdims)
+                        else:
+                            expected_ret = _np.amin(x.asnumpy(), axis=axis, keepdims=keepdims)
+                        with mx.autograd.record():
+                            y = test_gluon(x)
+                        assert y.shape == expected_ret.shape
+                        assert_almost_equal(y.asnumpy(), expected_ret, rtol=1e-3 if itype == 'float16' else 1e-3,
+                                            atol=1e-5 if itype == 'float16' else 1e-5)
+                        y.backward()
+                        # only check the gradient with hardcoded input
+                        if is_int(itype):
+                            assert same(x.grad.asnumpy(), get_grad(axis, func)), \
+                                'x={}\ny={}\nx.grad={}\nnumpy={}'.format(x.asnumpy(), y.asnumpy(), x.grad.asnumpy(), get_grad(axis))
+
+                        # test imperative
+                        if func == 'max':
+                            mx_out = np.max(x, axis=axis, keepdims=keepdims)
+                            np_out = _np.amax(x.asnumpy(), axis=axis, keepdims=keepdims)
+                        else:
+                            mx_out = np.min(x, axis=axis, keepdims=keepdims)
+                            np_out = _np.amin(x.asnumpy(), axis=axis, keepdims=keepdims)
+                        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+
+    # test zero and zero dim
+    shapes = [(), (0), (2, 0), (0, 2, 1)]
+    exceptions = [False, True, True, True]
+    dims = [0] * len(shapes)
+    for func in ['max', 'min']:
+        for shape, exception, dim in zip(shapes, exceptions, dims):
+            if exception:
+                assertRaises(MXNetError, _test_np_exception, func, shape, dim)
+            else:
+                _test_np_exception(func, shape, dim)
+
+
 @with_seed()
 @use_np
 def test_np_linspace():

From 6997691921f66889d5796dbacd82bdf4fbbcf38e Mon Sep 17 00:00:00 2001
From: Haibin Lin <linhaibin.eric@gmail.com>
Date: Sun, 1 Sep 2019 12:15:07 -0700
Subject: [PATCH 266/813] [Dev] update ps-lite dependency (#15936)

* update ps-lite

* Revert "update ps-lite"

This reverts commit dfaece914842a8923892f7ed190ebb271c9d68e8.

* update ps-lite dependency
---
 3rdparty/ps-lite | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/3rdparty/ps-lite b/3rdparty/ps-lite
index 8a763892a973..2c8ed25384fc 160000
--- a/3rdparty/ps-lite
+++ b/3rdparty/ps-lite
@@ -1 +1 @@
-Subproject commit 8a763892a973afc1acd3d4b469d05bb338a83a6e
+Subproject commit 2c8ed25384fc0ea00a70f081f8308170146d8f25

From 36bab1c39db4088ee61fb3a8f733bd33b3b34de3 Mon Sep 17 00:00:00 2001
From: Carin Meier <cmeier@gigasquidsoftware.com>
Date: Sun, 1 Sep 2019 15:26:10 -0400
Subject: [PATCH 267/813] Fix flaky clojure profile test (#16058)

* Fix flaky clojure profile test

* disable profiler test
---
 .../examples/profiler/test/core_test.clj      |   3 +-
 .../test/profile-matmul-20iter.json.ref       | 271 ------------------
 contrib/clojure-package/integration-tests.sh  |   2 +-
 3 files changed, 2 insertions(+), 274 deletions(-)
 delete mode 100644 contrib/clojure-package/examples/profiler/test/profile-matmul-20iter.json.ref

diff --git a/contrib/clojure-package/examples/profiler/test/core_test.clj b/contrib/clojure-package/examples/profiler/test/core_test.clj
index 1173f0755bbd..9f03c94713b9 100644
--- a/contrib/clojure-package/examples/profiler/test/core_test.clj
+++ b/contrib/clojure-package/examples/profiler/test/core_test.clj
@@ -27,5 +27,4 @@
 (deftest run-profiler
 	(profiler/run)
 	(let [new-file (clojure.java.io/as-file profiler/profiler-name)]
-		(is (.exists new-file))
-		(is (> 10000 (- (System/currentTimeMillis) (.lastModified new-file))))))
\ No newline at end of file
+		(is (.exists new-file))))
diff --git a/contrib/clojure-package/examples/profiler/test/profile-matmul-20iter.json.ref b/contrib/clojure-package/examples/profiler/test/profile-matmul-20iter.json.ref
deleted file mode 100644
index d6baa42114cd..000000000000
--- a/contrib/clojure-package/examples/profiler/test/profile-matmul-20iter.json.ref
+++ /dev/null
@@ -1,271 +0,0 @@
-{
-    "traceEvents": [
-        {
-            "ph": "M",
-            "args": {
-                "name": "cpu/0"
-            },
-            "pid": 0,
-            "name": "process_name"
-        },
-        {
-            "ph": "M",
-            "args": {
-                "name": "cpu/1"
-            },
-            "pid": 1,
-            "name": "process_name"
-        },
-        {
-            "ph": "M",
-            "args": {
-                "name": "cpu/2"
-            },
-            "pid": 2,
-            "name": "process_name"
-        },
-        {
-            "ph": "M",
-            "args": {
-                "name": "cpu/3"
-            },
-            "pid": 3,
-            "name": "process_name"
-        },
-        {
-            "ph": "M",
-            "args": {
-                "name": "cpu pinned/"
-            },
-            "pid": 4,
-            "name": "process_name"
-        },
-        {
-            "ph": "M",
-            "args": {
-                "name": "cpu shared/"
-            },
-            "pid": 5,
-            "name": "process_name"
-        },        {
-            "ph": "M",
-            "args": {
-                "name": "MXNET_C_API"
-            },
-            "pid": 13841910479334118176,
-            "name": "process_name"
-        },
-
-    {
-        "name": "MXNet C API Calls",
-        "cat": "MXNET_C_API",
-        "ph": "C",
-        "ts": 51195258331,
-        "args": { "MXNet C API Calls": 1 },
-        "pid": 13841910479334118176,
-        "tid": 6902988396839073221
-    }
-,
-    {
-        "name": "MXNet C API Concurrency",
-        "cat": "MXNET_C_API",
-        "ph": "C",
-        "ts": 51195258338,
-        "args": { "MXNet C API Concurrency": 1 },
-        "pid": 13841910479334118176,
-        "tid": 6902988396839073221
-    }
-,
-    {
-        "name": "MXExecutorForward",
-        "cat": "MXNET_C_API",
-        "ph": "b",
-        "ts": 51195258348,
-        "id": 6902988396839073221,
-        "pid": 13841910479334118176,
-        "tid": 6902988396839073221
-    }
-,
-    {
-        "name": "MXExecutorForward",
-        "cat": "MXNET_C_API",
-        "ph": "e",
-        "ts": 51195258357,
-        "id": 6902988396839073221,
-        "pid": 13841910479334118176,
-        "tid": 6902988396839073221
-    }
-,
-    {
-        "name": "MXNet C API Concurrency",
-        "cat": "MXNET_C_API",
-        "ph": "C",
-        "ts": 51195258358,
-        "args": { "MXNet C API Concurrency": 0 },
-        "pid": 13841910479334118176,
-        "tid": 6902988396839073221
-    }
-,        {
-            "ph": "M",
-            "args": {
-                "name": "Device Storage"
-            },
-            "pid": 13545698322897290393,
-            "name": "process_name"
-        },
-
-    {
-        "name": "Memory: cpu/0",
-        "cat": "Device Storage",
-        "ph": "C",
-        "ts": 51195543378,
-        "args": { "Memory: cpu/0": 8 },
-        "pid": 13545698322897290393,
-        "tid": 5603937861270119161
-    }
-,
-    {
-        "name": "MXNet C API Calls",
-        "cat": "MXNET_C_API",
-        "ph": "C",
-        "ts": 51195258559,
-        "args": { "MXNet C API Calls": 2 },
-        "pid": 13841910479334118176,
-        "tid": 6902988396839073221
-    }
-,
-    {
-        "name": "Memory: cpu/0",
-        "cat": "Device Storage",
-        "ph": "C",
-        "ts": 51195857697,
-        "args": { "Memory: cpu/0": 67108872 },
-        "pid": 13545698322897290393,
-        "tid": 5603937861270119161
-    }
-,
-    {
-        "name": "MXNet C API Concurrency",
-        "cat": "MXNET_C_API",
-        "ph": "C",
-        "ts": 51195258560,
-        "args": { "MXNet C API Concurrency": 1 },
-        "pid": 13841910479334118176,
-        "tid": 6902988396839073221
-    }
-,
-
-    {
-        "name": "[dot]",
-        "cat": "operator",
-        "ph": "B",
-        "ts": 51195857671,
-        "pid": 0,
-        "tid": 5603937861270119161
-    }
-,
-    {
-        "name": "[dot]",
-        "cat": "operator",
-        "ph": "E",
-        "ts": 51196931353,
-        "pid": 0,
-        "tid": 5603937861270119161
-    }
-,
-
-    {
-        "name": "WaitForVar",
-        "cat": "operator",
-        "ph": "B",
-        "ts": 51196931369,
-        "pid": 0,
-        "tid": 5603937861270119161
-    }
-,
-    {
-        "name": "WaitForVar",
-        "cat": "operator",
-        "ph": "E",
-        "ts": 51196931376,
-        "pid": 0,
-        "tid": 5603937861270119161
-    }
-,        {
-            "ph": "M",
-            "args": {
-                "name": "operator"
-            },
-            "pid": 10847949044720084585,
-            "name": "process_name"
-        },
-
-    {
-        "name": "[dot]",
-        "cat": "operator",
-        "ph": "b",
-        "ts": 51195857671,
-        "id": 5603937861270119161,
-        "pid": 10847949044720084585,
-        "tid": 5603937861270119161
-    }
-,
-    {
-        "name": "[dot]",
-        "cat": "operator",
-        "ph": "e",
-        "ts": 51196931350,
-        "id": 5603937861270119161,
-        "pid": 10847949044720084585,
-        "tid": 5603937861270119161
-    }
-,
-    {
-        "name": "MXNDArrayWaitToRead",
-        "cat": "MXNET_C_API",
-        "ph": "b",
-        "ts": 51195258561,
-        "id": 6902988396839073221,
-        "pid": 13841910479334118176,
-        "tid": 6902988396839073221
-    }
-,
-    {
-        "name": "MXNDArrayWaitToRead",
-        "cat": "MXNET_C_API",
-        "ph": "e",
-        "ts": 51196931386,
-        "id": 6902988396839073221,
-        "pid": 13841910479334118176,
-        "tid": 6902988396839073221
-    }
-,
-    {
-        "name": "WaitForVar",
-        "cat": "operator",
-        "ph": "b",
-        "ts": 51196931369,
-        "id": 5603937861270119161,
-        "pid": 10847949044720084585,
-        "tid": 5603937861270119161
-    }
-,
-    {
-        "name": "WaitForVar",
-        "cat": "operator",
-        "ph": "e",
-        "ts": 51196931376,
-        "id": 5603937861270119161,
-        "pid": 10847949044720084585,
-        "tid": 5603937861270119161
-    }
-,
-    {
-        "name": "MXNet C API Concurrency",
-        "cat": "MXNET_C_API",
-        "ph": "C",
-        "ts": 51196931391,
-        "args": { "MXNet C API Concurrency": 0 },
-        "pid": 13841910479334118176,
-        "tid": 6902988396839073221
-    }
diff --git a/contrib/clojure-package/integration-tests.sh b/contrib/clojure-package/integration-tests.sh
index 3df9ba9787b5..39cea6536c83 100755
--- a/contrib/clojure-package/integration-tests.sh
+++ b/contrib/clojure-package/integration-tests.sh
@@ -26,7 +26,7 @@ lein install
 # then run through the examples 
 EXAMPLES_HOME=${MXNET_HOME}/contrib/clojure-package/examples
 # use AWK pattern for blacklisting
-TEST_CASES=`find ${EXAMPLES_HOME} -name test | awk '!/dontselect1|cnn-text-classification|gan|neural-style|pre-trained-models/'`
+TEST_CASES=`find ${EXAMPLES_HOME} -name test | awk '!/dontselect1|cnn-text-classification|gan|neural-style|pre-trained-models|profiler/'`
 for i in $TEST_CASES ; do
  cd ${i} && lein test
 done

From d5670fffaa7058fe4284243737eb77aae64945f4 Mon Sep 17 00:00:00 2001
From: "Shi, guangyong" <35016185+gyshi@users.noreply.github.com>
Date: Mon, 2 Sep 2019 17:08:12 +0800
Subject: [PATCH 268/813] fix test_pick test time  is too long (#16066)

---
 tests/python/unittest/test_operator.py | 69 +++++++++++++-------------
 1 file changed, 34 insertions(+), 35 deletions(-)

diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 39ae0a02f0fe..de02727e24c8 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -5260,42 +5260,41 @@ def np_softmax_with_length(data, length):
 @with_seed()
 def test_pick():
     def test_pick_helper(index_type=np.int32):
-        for _ in range(100):
-            for mode in ['clip', 'wrap']:
-                ndim = np.random.randint(1, 5)
-                bshape = np.random.randint(1, 10, size=ndim)
-                axis = np.random.randint(0, ndim)
-                sshape = bshape.copy()
-                sshape[axis] = 1
-                data = np.random.uniform(-1, 1, size=bshape)
-
-                if mode == 'wrap':
-                    index = np.random.randint(-2*bshape[axis], 2*bshape[axis], size=sshape)
-                else:
-                    index = np.random.randint(0, bshape[axis], size=sshape)
-                exp = []
-                for i in range(ndim):
-                    if i == axis:
-                        if mode == 'wrap':
-                            exp.append(index % bshape[axis])
-                        else:
-                            exp.append(index)
+        for mode in ['clip', 'wrap']:
+            ndim = np.random.randint(1, 5)
+            bshape = np.random.randint(1, 10, size=ndim)
+            axis = np.random.randint(0, ndim)
+            sshape = bshape.copy()
+            sshape[axis] = 1
+            data = np.random.uniform(-1, 1, size=bshape)
+
+            if mode == 'wrap':
+                index = np.random.randint(-2*bshape[axis], 2*bshape[axis], size=sshape)
+            else:
+                index = np.random.randint(0, bshape[axis], size=sshape)
+            exp = []
+            for i in range(ndim):
+                if i == axis:
+                    if mode == 'wrap':
+                        exp.append(index % bshape[axis])
                     else:
-                        ishape = [1 for _ in range(ndim)]
-                        ishape[i] = bshape[i]
-                        exp.append(np.arange(bshape[i]).reshape(ishape))
-                expected = data[exp]
-                data = mx.nd.array(data, dtype='float32')
-                index = mx.nd.array(index, dtype=index_type)
-                out = mx.nd.pick(data, index, axis=axis, keepdims=True, mode=mode)
-                assert_almost_equal(out.asnumpy(), expected)
-
-                data_holder = data
-                index_holder = index
-                data = mx.sym.Variable('data')
-                index = mx.sym.Variable('index')
-                sym = mx.sym.pick(data, index, axis=axis, keepdims=True, mode=mode)
-                check_numeric_gradient(sym, [data_holder, index_holder], grad_nodes=['data'])
+                        exp.append(index)
+                else:
+                    ishape = [1 for _ in range(ndim)]
+                    ishape[i] = bshape[i]
+                    exp.append(np.arange(bshape[i]).reshape(ishape))
+            expected = data[exp]
+            data = mx.nd.array(data, dtype='float32')
+            index = mx.nd.array(index, dtype=index_type)
+            out = mx.nd.pick(data, index, axis=axis, keepdims=True, mode=mode)
+            assert_almost_equal(out.asnumpy(), expected)
+
+            data_holder = data
+            index_holder = index
+            data = mx.sym.Variable('data')
+            index = mx.sym.Variable('index')
+            sym = mx.sym.pick(data, index, axis=axis, keepdims=True, mode=mode)
+            check_numeric_gradient(sym, [data_holder, index_holder], grad_nodes=['data'])
 
     test_pick_helper(np.int32)
     test_pick_helper(np.float32)

From 56999399a44467cbd1d0fbdf9cbba8b46ac53956 Mon Sep 17 00:00:00 2001
From: kshitij12345 <kshitijkalambarkar@gmail.com>
Date: Mon, 2 Sep 2019 19:47:50 +0530
Subject: [PATCH 269/813] [fix] Support nullop in `transpose` (#15865)

* support nullop transpose

* add relevant test

* retrigger CI

* update error message

* retrigger CI

* retrigger CI

* retrigger CI

* retrigger CI

* retrigger CI

* retrigger CI
---
 src/operator/tensor/matrix_op-inl.h  |  5 ++++-
 src/operator/tensor/matrix_op.cc     |  5 ++++-
 tests/python/unittest/test_symbol.py | 16 ++++++++++++++++
 3 files changed, 24 insertions(+), 2 deletions(-)

diff --git a/src/operator/tensor/matrix_op-inl.h b/src/operator/tensor/matrix_op-inl.h
index fa5f93cefc1a..5fcb718eab71 100644
--- a/src/operator/tensor/matrix_op-inl.h
+++ b/src/operator/tensor/matrix_op-inl.h
@@ -330,8 +330,11 @@ void Transpose(const nnvm::NodeAttrs& attrs,
                const std::vector<TBlob>& inputs,
                const std::vector<OpReqType>& req,
                const std::vector<TBlob>& outputs) {
+  if (req[0] == kNullOp) {
+    return;
+  }
   const TransposeParam& param = nnvm::get<TransposeParam>(attrs.parsed);
-  CHECK_EQ(req[0], kWriteTo) << "Transpose does not support inplace";
+  CHECK_EQ(req[0], kWriteTo) << "Transpose does not support kWriteInplace and kAddTo";
   if (param.axes.ndim() == 0) {
     mxnet::TShape axes(inputs[0].ndim(), -1);
     for (int i = 0; i < axes.ndim(); ++i) {
diff --git a/src/operator/tensor/matrix_op.cc b/src/operator/tensor/matrix_op.cc
index d35c364fba47..9697744d03d3 100644
--- a/src/operator/tensor/matrix_op.cc
+++ b/src/operator/tensor/matrix_op.cc
@@ -322,8 +322,11 @@ static void TransposeComputeExCPU(const nnvm::NodeAttrs& attrs,
                                   const std::vector<NDArray>& inputs,
                                   const std::vector<OpReqType>& req,
                                   const std::vector<NDArray>& outputs) {
+  if (req[0] == kNullOp) {
+    return;
+  }
   const TransposeParam& param = nnvm::get<TransposeParam>(attrs.parsed);
-  CHECK_EQ(req[0], kWriteTo) << "Transpose does not support inplace";
+  CHECK_EQ(req[0], kWriteTo) << "Transpose does not support kWriteInplace and kAddTo";
   CHECK_EQ(inputs.size(), 1U);
   CHECK_EQ(outputs.size(), 1U);
 
diff --git a/tests/python/unittest/test_symbol.py b/tests/python/unittest/test_symbol.py
index 28f302f9ec15..ae770ed0644f 100644
--- a/tests/python/unittest/test_symbol.py
+++ b/tests/python/unittest/test_symbol.py
@@ -389,6 +389,22 @@ def test_children_same_name():
     for c in b.get_children():
         pass
 
+
+def test_transpose_nullop():
+    for dim in range(1, 7):
+        a = mx.sym.Variable('a')
+        b = mx.sym.transpose(a, axes=tuple(np.random.permutation(dim)))
+        c = mx.sym.zeros_like(b)
+
+        shape = rand_shape_nd(dim)
+        nd_a = mx.nd.random.normal(shape=shape)
+        c_out = c.eval(ctx=mx.cpu(), a=nd_a)
+        b_out = b.eval(ctx=mx.cpu(), a=nd_a)
+
+        assert mx.test_utils.same(c_out[0].asnumpy(),
+                                  np.zeros_like(b_out[0].asnumpy()))
+
+
 def test_gen_atomic_symbol_multiple_outputs():
     data=mx.sym.Variable('data')
     p = mx.sym.Variable('param')

From 5def003a589e5b34538ed98eabda18567f4be315 Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Mon, 2 Sep 2019 23:31:43 +0800
Subject: [PATCH 270/813] NumPy-compatible Mean, Std and Var (#16014)

* NumPy-compatible mean upstream

* NumPy-compatible std and var

* fix sanity and flakiness of tests
---
 python/mxnet/ndarray/numpy/_op.py             | 197 ++++++++++++++++-
 python/mxnet/numpy/multiarray.py              | 199 +++++++++++++++++-
 python/mxnet/symbol/numpy/_symbol.py          | 167 ++++++++++++++-
 src/operator/numpy/np_broadcast_reduce_op.h   | 135 +++++++++++-
 .../numpy/np_broadcast_reduce_op_value.cc     | 142 +++++++++++++
 .../numpy/np_broadcast_reduce_op_value.cu     |  12 ++
 tests/python/unittest/test_numpy_op.py        | 132 +++++++++++-
 7 files changed, 972 insertions(+), 12 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index 37f48f70c336..dc6089bd5e3e 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -32,8 +32,8 @@
            'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2', 'log1p',
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
-           'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack',
-           'maximum', 'minimum', 'swapaxes', 'clip', 'argmax']
+           'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'mean',
+           'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -2145,3 +2145,196 @@ def argmax(a, axis=None, out=None):
     array([2., 2.])
     """
     return _npi.argmax(a, axis=axis, keepdims=False, out=out)
+
+
+@set_module('mxnet.ndarray.numpy')
+def mean(a, axis=None, dtype=None, out=None, keepdims=False):  # pylint: disable=arguments-differ
+    """
+    mean(a, axis=None, dtype=None, out=None, keepdims=None)
+    Compute the arithmetic mean along the specified axis.
+    Returns the average of the array elements.
+    The average is taken over the flattened array by default, otherwise over the specified axis.
+    Parameters
+    ----------
+    a : ndarray
+        ndarray containing numbers whose mean is desired.
+    axis : None or int or tuple of ints, optional
+        Axis or axes along which the means are computed. The default is to compute the mean of the flattened array.
+        If this is a tuple of ints, a mean is performed over multiple axes,
+        instead of a single axis or all the axes as before.
+    dtype : data-type, optional
+        Type to use in computing the mean. For integer inputs, the default is float32;
+        for floating point inputs, it is the same as the input dtype.
+    out : ndarray, optional
+        Alternate output array in which to place the result. The default is None; if provided,
+        it must have the same shape and type as the expected output
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left in the result
+        as dimensions with size one. With this option, the result will broadcast correctly
+        against the input array.
+        If the default value is passed, then keepdims will not be passed through to the mean
+        method of sub-classes of ndarray, however any non-default value will be. If the sub-class
+        method does not implement keepdims any exceptions will be raised.
+    Returns
+    -------
+    m : ndarray, see dtype parameter above
+        If out=None, returns a new array containing the mean values,
+        otherwise a reference to the output array is returned.
+    Notes
+    -----
+    This function differs from the original `numpy.mean
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.mean.html>`_ in
+    the following way(s):
+    - only ndarray is accepted as valid input, python iterables or scalar is not supported
+    - default data type for integer input is float32
+    Examples
+    --------
+    >>> a = np.array([[1, 2], [3, 4]])
+    >>> np.mean(a)
+    array(2.5)
+    >>> a = np.zeros((2, 512*512), dtype=np.float32)
+    >>> a[0,:] = 1.0
+    >>> a[1,:] = 0.1
+    >>> np.mean(a)
+    array(0.55)
+    >>> np.mean(a, dtype=np.float64)
+    array(0.55)
+    """
+    return _npi.mean(a, axis=axis, dtype=dtype, keepdims=keepdims, out=out)
+
+
+@set_module('mxnet.ndarray.numpy')
+def std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):  # pylint: disable=too-many-arguments
+    """
+    Compute the standard deviation along the specified axis.
+    Returns the standard deviation, a measure of the spread of a distribution,
+    of the array elements. The standard deviation is computed for the
+    flattened array by default, otherwise over the specified axis.
+
+    Parameters
+    ----------
+    a : array_like
+        Calculate the standard deviation of these values.
+    axis : None or int or tuple of ints, optional
+        Axis or axes along which the standard deviation is computed. The
+        default is to compute the standard deviation of the flattened array.
+        .. versionadded:: 1.7.0
+        If this is a tuple of ints, a standard deviation is performed over
+        multiple axes, instead of a single axis or all the axes as before.
+    dtype : dtype, optional
+        Type to use in computing the standard deviation. For arrays of
+        integer type the default is float64, for arrays of float types it is
+        the same as the array type.
+    out : ndarray, optional
+        Alternative output array in which to place the result. It must have
+        the same shape as the expected output but the type (of the calculated
+        values) will be cast if necessary.
+    ddof : int, optional
+        Means Delta Degrees of Freedom.  The divisor used in calculations
+        is ``N - ddof``, where ``N`` represents the number of elements.
+        By default `ddof` is zero.
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left
+        in the result as dimensions with size one. With this option,
+        the result will broadcast correctly against the input array.
+        If the default value is passed, then `keepdims` will not be
+        passed through to the `std` method of sub-classes of
+        `ndarray`, however any non-default value will be.  If the
+        sub-class' method does not implement `keepdims` any
+        exceptions will be raised.
+
+    Returns
+    -------
+    standard_deviation : ndarray, see dtype parameter above.
+        If `out` is None, return a new array containing the standard deviation,
+        otherwise return a reference to the output array.
+
+    Examples
+    --------
+    >>> a = np.array([[1, 2], [3, 4]])
+    >>> np.std(a)
+    1.1180339887498949 # may vary
+    >>> np.std(a, axis=0)
+    array([1.,  1.])
+    >>> np.std(a, axis=1)
+    array([0.5,  0.5])
+    In single precision, std() can be inaccurate:
+    >>> a = np.zeros((2, 512*512), dtype=np.float32)
+    >>> a[0, :] = 1.0
+    >>> a[1, :] = 0.1
+    >>> np.std(a)
+    array(0.45)
+    >>> np.std(a, dtype=np.float64)
+    array(0.45, dtype=float64)
+    """
+    return _npi.std(a, axis=axis, dtype=dtype, ddof=ddof, keepdims=keepdims, out=out)
+
+
+@set_module('mxnet.ndarray.numpy')
+def var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):  # pylint: disable=too-many-arguments
+    """
+    Compute the variance along the specified axis.
+    Returns the variance of the array elements, a measure of the spread of a
+    distribution.  The variance is computed for the flattened array by
+    default, otherwise over the specified axis.
+
+    Parameters
+    ----------
+    a : array_like
+        Array containing numbers whose variance is desired.  If `a` is not an
+        array, a conversion is attempted.
+    axis : None or int or tuple of ints, optional
+        Axis or axes along which the variance is computed.  The default is to
+        compute the variance of the flattened array.
+        .. versionadded:: 1.7.0
+        If this is a tuple of ints, a variance is performed over multiple axes,
+        instead of a single axis or all the axes as before.
+    dtype : data-type, optional
+        Type to use in computing the variance.  For arrays of integer type
+        the default is `float32`; for arrays of float types it is the same as
+        the array type.
+    out : ndarray, optional
+        Alternate output array in which to place the result.  It must have
+        the same shape as the expected output, but the type is cast if
+        necessary.
+    ddof : int, optional
+        "Delta Degrees of Freedom": the divisor used in the calculation is
+        ``N - ddof``, where ``N`` represents the number of elements. By
+        default `ddof` is zero.
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left
+        in the result as dimensions with size one. With this option,
+        the result will broadcast correctly against the input array.
+        If the default value is passed, then `keepdims` will not be
+        passed through to the `var` method of sub-classes of
+        `ndarray`, however any non-default value will be.  If the
+        sub-class' method does not implement `keepdims` any
+        exceptions will be raised.
+
+    Returns
+    -------
+    variance : ndarray, see dtype parameter above
+        If ``out=None``, returns a new array containing the variance;
+        otherwise, a reference to the output array is returned.
+
+    Examples
+    --------
+    >>> a = np.array([[1, 2], [3, 4]])
+    >>> np.var(a)
+    array(1.25)
+    >>> np.var(a, axis=0)
+    array([1.,  1.])
+    >>> np.var(a, axis=1)
+    array([0.25,  0.25])
+
+    >>> a = np.zeros((2, 512*512), dtype=np.float32)
+    >>> a[0, :] = 1.0
+    >>> a[1, :] = 0.1
+    >>> np.var(a)
+    array(0.2025)
+    >>> np.var(a, dtype=np.float64)
+    array(0.2025, dtype=float64)
+    >>> ((1-0.55)**2 + (0.1-0.55)**2)/2
+    0.2025
+    """
+    return _npi.var(a, axis=axis, dtype=dtype, ddof=ddof, keepdims=keepdims, out=out)
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 83688774f069..d59eddc3d139 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -52,7 +52,7 @@
            'degrees', 'log2', 'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative',
            'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh',
            'tensordot', 'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate',
-           'stack', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax']
+           'stack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -1172,11 +1172,9 @@ def mean(self, axis=None, dtype=None, out=None, keepdims=False):  # pylint: disa
         """Returns the average of the array elements along given axis."""
         raise NotImplementedError
 
-    # TODO(junwu): Use mxnet std op instead of onp.std
     def std(self, axis=None, dtype=None, out=None, ddof=0, keepdims=False):  # pylint: disable=arguments-differ
         """Returns the standard deviation of the array elements along given axis."""
-        ret_np = self.asnumpy().std(axis=axis, dtype=dtype, out=out, ddof=ddof, keepdims=keepdims)
-        return array(ret_np, dtype=ret_np.dtype, ctx=self.context)
+        return _mx_np_op.std(self, axis=axis, dtype=dtype, ddof=ddof, keepdims=keepdims, out=out)
 
     def cumsum(self, axis=None, dtype=None, out=None):
         """Return the cumulative sum of the elements along the given axis."""
@@ -3588,3 +3586,196 @@ def argmax(a, axis=None, out=None):
     array([2., 2.])
     """
     return _mx_nd_np.argmax(a, axis, out)
+
+
+@set_module('mxnet.numpy')
+def mean(a, axis=None, dtype=None, out=None, keepdims=False):  # pylint: disable=arguments-differ
+    """
+    mean(a, axis=None, dtype=None, out=None, keepdims=None)
+    Compute the arithmetic mean along the specified axis.
+    Returns the average of the array elements.
+    The average is taken over the flattened array by default, otherwise over the specified axis.
+    Parameters
+    ----------
+    a : ndarray
+        ndarray containing numbers whose mean is desired.
+    axis : None or int or tuple of ints, optional
+        Axis or axes along which the means are computed. The default is to compute the mean of the flattened array.
+        If this is a tuple of ints, a mean is performed over multiple axes,
+        instead of a single axis or all the axes as before.
+    dtype : data-type, optional
+        Type to use in computing the mean. For integer inputs, the default is float32;
+        for floating point inputs, it is the same as the input dtype.
+    out : ndarray, optional
+        Alternate output array in which to place the result. The default is None; if provided,
+        it must have the same shape and type as the expected output.
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left in the result
+        as dimensions with size one. With this option, the result will broadcast correctly
+        against the input array.
+        If the default value is passed, then keepdims will not be passed through to the mean
+        method of sub-classes of ndarray, however any non-default value will be. If the sub-class
+        method does not implement keepdims any exceptions will be raised.
+    Returns
+    -------
+    m : ndarray, see dtype parameter above
+        If out=None, returns a new array containing the mean values,
+        otherwise a reference to the output array is returned.
+    Notes
+    -----
+    This function differs from the original `numpy.mean
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.mean.html>`_ in
+    the following way(s):
+    - only ndarray is accepted as valid input, python iterables or scalar is not supported
+    - default data type for integer input is float32
+    Examples
+    --------
+    >>> a = np.array([[1, 2], [3, 4]])
+    >>> np.mean(a)
+    array(2.5)
+    >>> a = np.zeros((2, 512*512), dtype=np.float32)
+    >>> a[0,:] = 1.0
+    >>> a[1,:] = 0.1
+    >>> np.mean(a)
+    array(0.55)
+    >>> np.mean(a, dtype=np.float64)
+    array(0.55)
+    """
+    return _npi.mean(a, axis=axis, dtype=dtype, keepdims=keepdims, out=out)
+
+
+@set_module('mxnet.numpy')
+def std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=None):
+    """
+    Compute the standard deviation along the specified axis.
+    Returns the standard deviation, a measure of the spread of a distribution,
+    of the array elements. The standard deviation is computed for the
+    flattened array by default, otherwise over the specified axis.
+
+    Parameters
+    ----------
+    a : array_like
+        Calculate the standard deviation of these values.
+    axis : None or int or tuple of ints, optional
+        Axis or axes along which the standard deviation is computed. The
+        default is to compute the standard deviation of the flattened array.
+        .. versionadded:: 1.7.0
+        If this is a tuple of ints, a standard deviation is performed over
+        multiple axes, instead of a single axis or all the axes as before.
+    dtype : dtype, optional
+        Type to use in computing the standard deviation. For arrays of
+        integer type the default is float64, for arrays of float types it is
+        the same as the array type.
+    out : ndarray, optional
+        Alternative output array in which to place the result. It must have
+        the same shape as the expected output but the type (of the calculated
+        values) will be cast if necessary.
+    ddof : int, optional
+        Means Delta Degrees of Freedom.  The divisor used in calculations
+        is ``N - ddof``, where ``N`` represents the number of elements.
+        By default `ddof` is zero.
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left
+        in the result as dimensions with size one. With this option,
+        the result will broadcast correctly against the input array.
+        If the default value is passed, then `keepdims` will not be
+        passed through to the `std` method of sub-classes of
+        `ndarray`, however any non-default value will be.  If the
+        sub-class' method does not implement `keepdims` any
+        exceptions will be raised.
+
+    Returns
+    -------
+    standard_deviation : ndarray, see dtype parameter above.
+        If `out` is None, return a new array containing the standard deviation,
+        otherwise return a reference to the output array.
+
+    Examples
+    --------
+    >>> a = np.array([[1, 2], [3, 4]])
+    >>> np.std(a)
+    1.1180339887498949 # may vary
+    >>> np.std(a, axis=0)
+    array([1.,  1.])
+    >>> np.std(a, axis=1)
+    array([0.5,  0.5])
+    In single precision, std() can be inaccurate:
+    >>> a = np.zeros((2, 512*512), dtype=np.float32)
+    >>> a[0, :] = 1.0
+    >>> a[1, :] = 0.1
+    >>> np.std(a)
+    array(0.45)
+    >>> np.std(a, dtype=np.float64)
+    array(0.45, dtype=float64)
+    """
+    return _npi.std(a, axis=axis, dtype=dtype, ddof=ddof, keepdims=keepdims, out=out)
+
+
+@set_module('mxnet.numpy')
+def var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=None):
+    """
+    Compute the variance along the specified axis.
+    Returns the variance of the array elements, a measure of the spread of a
+    distribution.  The variance is computed for the flattened array by
+    default, otherwise over the specified axis.
+
+    Parameters
+    ----------
+    a : array_like
+        Array containing numbers whose variance is desired.  If `a` is not an
+        array, a conversion is attempted.
+    axis : None or int or tuple of ints, optional
+        Axis or axes along which the variance is computed.  The default is to
+        compute the variance of the flattened array.
+        .. versionadded:: 1.7.0
+        If this is a tuple of ints, a variance is performed over multiple axes,
+        instead of a single axis or all the axes as before.
+    dtype : data-type, optional
+        Type to use in computing the variance.  For arrays of integer type
+        the default is `float32`; for arrays of float types it is the same as
+        the array type.
+    out : ndarray, optional
+        Alternate output array in which to place the result.  It must have
+        the same shape as the expected output, but the type is cast if
+        necessary.
+    ddof : int, optional
+        "Delta Degrees of Freedom": the divisor used in the calculation is
+        ``N - ddof``, where ``N`` represents the number of elements. By
+        default `ddof` is zero.
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left
+        in the result as dimensions with size one. With this option,
+        the result will broadcast correctly against the input array.
+        If the default value is passed, then `keepdims` will not be
+        passed through to the `var` method of sub-classes of
+        `ndarray`, however any non-default value will be.  If the
+        sub-class' method does not implement `keepdims` any
+        exceptions will be raised.
+
+    Returns
+    -------
+    variance : ndarray, see dtype parameter above
+        If ``out=None``, returns a new array containing the variance;
+        otherwise, a reference to the output array is returned.
+
+    Examples
+    --------
+    >>> a = np.array([[1, 2], [3, 4]])
+    >>> np.var(a)
+    array(1.25)
+    >>> np.var(a, axis=0)
+    array([1.,  1.])
+    >>> np.var(a, axis=1)
+    array([0.25,  0.25])
+
+    >>> a = np.zeros((2, 512*512), dtype=np.float32)
+    >>> a[0, :] = 1.0
+    >>> a[1, :] = 0.1
+    >>> np.var(a)
+    array(0.2025)
+    >>> np.var(a, dtype=np.float64)
+    array(0.2025, dtype=float64)
+    >>> ((1-0.55)**2 + (0.1-0.55)**2)/2
+    0.2025
+    """
+    return _npi.var(a, axis=axis, dtype=dtype, ddof=ddof, keepdims=keepdims, out=out)
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 46053b3e0798..e752c5801bb8 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -34,8 +34,8 @@
            'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2', 'log1p',
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
-           'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack',
-           'maximum', 'minimum', 'swapaxes', 'clip', 'argmax']
+           'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'mean',
+           'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var']
 
 
 def _num_outputs(sym):
@@ -535,7 +535,7 @@ def mean(self, axis=None, dtype=None, out=None, keepdims=False):  # pylint: disa
         The arguments are the same as for :py:func:`mean`, with
         this array as data.
         """
-        raise NotImplementedError
+        return _npi.mean(self, axis=axis, dtype=dtype, keepdims=keepdims, out=out)
 
     def cumsum(self, axis=None, dtype=None, out=None):
         """Return the cumulative sum of the elements along the given axis."""
@@ -2508,4 +2508,165 @@ def argmax(a, axis=None, out=None):
     return _npi.argmax(a, axis=axis, keepdims=False, out=out)
 
 
+@set_module('mxnet.symbol.numpy')
+def mean(a, axis=None, dtype=None, out=None, keepdims=False):  # pylint: disable=arguments-differ
+    """
+    mean(a, axis=None, dtype=None, out=None, keepdims=None)
+
+    Compute the arithmetic mean along the specified axis.
+    Returns the average of the array elements.
+    The average is taken over the flattened array by default, otherwise over the specified axis.
+
+    Parameters
+    ----------
+    a : `_Symbol`
+        _Symbol containing numbers whose mean is desired.
+    axis : None or int or tuple of ints, optional
+        Axis or axes along which the means are computed. The default is to compute the mean of the flattened array.
+        If this is a tuple of ints, a mean is performed over multiple axes,
+        instead of a single axis or all the axes as before.
+    dtype : data-type, optional
+        Type to use in computing the mean. For integer inputs, the default is float32;
+        for floating point inputs, it is the same as the input dtype.
+    out : _Symbol, optional
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left in the result
+        as dimensions with size one. With this option, the result will broadcast correctly
+        against the input array.
+        If the default value is passed, then keepdims will not be passed through to the mean
+        method of sub-classes of _Symbol, however any non-default value will be. If the sub-class
+        method does not implement keepdims any exceptions will be raised.
+
+    Returns
+    -------
+    m : _Symbol, see dtype parameter above
+        If out=None, returns a new array containing the mean values,
+        otherwise a reference to the output array is returned.
+
+    Notes
+    -----
+    This function differs from the original `numpy.mean
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.mean.html>`_ in
+    the following way(s):
+
+    - only _Symbol is accepted as valid input, python iterables or scalar is not supported
+    - default data type for integer input is float32
+
+    Examples
+    --------
+    >>> a = np.array([[1, 2], [3, 4]])
+    >>> np.mean(a)
+    array(2.5)
+    >>> a = np.zeros((2, 512*512), dtype=np.float32)
+    >>> a[0,:] = 1.0
+    >>> a[1,:] = 0.1
+    >>> np.mean(a)
+    array(0.55)
+    >>> np.mean(a, dtype=np.float64)
+    array(0.55)
+    """
+    return _npi.mean(a, axis=axis, dtype=dtype, keepdims=keepdims, out=out)
+
+
+@set_module('mxnet.symbol.numpy')
+def std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):  # pylint: disable=too-many-arguments
+    """
+    Compute the standard deviation along the specified axis.
+
+    Returns the standard deviation, a measure of the spread of a distribution,
+    of the array elements. The standard deviation is computed for the
+    flattened array by default, otherwise over the specified axis.
+
+    Parameters
+    ----------
+    a : `_Symbol`
+        _Symbol containing numbers whose standard deviation is desired.
+    axis : None or int or tuple of ints, optional
+        Axis or axes along which the standard deviations are computed.
+        The default is to compute the standard deviation of the flattened array.
+        If this is a tuple of ints, computation is performed over multiple axes,
+        instead of a single axis or all the axes as before.
+    dtype : data-type, optional
+        Type to use in computing the standard deviation. For integer inputs, the default is float32;
+        for floating point inputs, it is the same as the input dtype.
+    out : _Symbol, optional
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left in the result
+        as dimensions with size one. With this option, the result will broadcast correctly
+        against the input array.
+        If the default value is passed, then keepdims will not be passed through to the mean
+        method of sub-classes of _Symbol, however any non-default value will be. If the sub-class
+        method does not implement keepdims any exceptions will be raised.
+
+    Returns
+    -------
+    m : _Symbol, see dtype parameter above
+        If out=None, returns a new array containing the standard deviation values,
+        otherwise a reference to the output array is returned.
+
+    Notes
+    -----
+    This function differs from the original `numpy.std
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.mean.html>`_ in
+    the following way(s):
+
+    - only _Symbol is accepted as valid input, python iterables or scalar is not supported
+    - default output data type for integer input is float32
+
+    """
+    return _npi.std(a, axis=axis, dtype=dtype, ddof=ddof, keepdims=keepdims, out=out)
+
+
+@set_module('mxnet.symbol.numpy')
+def var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):  # pylint: disable=too-many-arguments
+    """
+    Compute the variance along the specified axis.
+
+    Returns the variance of the array elements, a measure of the spread of a
+    distribution.  The variance is computed for the flattened array by
+    default, otherwise over the specified axis.
+
+    Parameters
+    ----------
+    a : `_Symbol`
+        _Symbol containing numbers whose variance is desired.
+    axis : None or int or tuple of ints, optional
+        Axis or axes along which the variance is computed.
+        The default is to compute the variance of the flattened array.
+        If this is a tuple of ints, computation is performed over multiple axes,
+        instead of a single axis or all the axes as before.
+    dtype : data-type, optional
+        Type to use in computing the variance. For integer inputs, the default is float32;
+        for floating point inputs, it is the same as the input dtype.
+    out : _Symbol, optional
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left in the result
+        as dimensions with size one. With this option, the result will broadcast correctly
+        against the input array.
+        If the default value is passed, then keepdims will not be passed through to the mean
+        method of sub-classes of _Symbol, however any non-default value will be. If the sub-class
+        method does not implement keepdims any exceptions will be raised.
+
+    Returns
+    -------
+    m : _Symbol, see dtype parameter above
+        If out=None, returns a new array containing the variance values,
+        otherwise a reference to the output array is returned.
+
+    Notes
+    -----
+    This function differs from the original `numpy.var
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.mean.html>`_ in
+    the following way(s):
+
+    - only _Symbol is accepted as valid input, python iterables or scalar is not supported
+    - default output data type for integer input is float32
+
+    """
+    return _npi.var(a, axis=axis, dtype=dtype, ddof=ddof, keepdims=keepdims, out=out)
+
+
 _set_np_symbol_class(_Symbol)
diff --git a/src/operator/numpy/np_broadcast_reduce_op.h b/src/operator/numpy/np_broadcast_reduce_op.h
index 305f1c775cd5..4734d6cc666f 100644
--- a/src/operator/numpy/np_broadcast_reduce_op.h
+++ b/src/operator/numpy/np_broadcast_reduce_op.h
@@ -27,6 +27,7 @@
 
 #include <algorithm>
 #include <vector>
+#include "../nn/moments-inl.h"
 #include "../tensor/broadcast_reduce_op.h"
 
 namespace mxnet {
@@ -282,7 +283,6 @@ inline void NumpyReduceAxesBackwardUseNone(const nnvm::NodeAttrs& attrs,
     Stream<xpu> *s = ctx.get_stream<xpu>();
     MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, IType, {
       Tensor<xpu, 1, IType> igrad = outputs[0].FlatTo1D<xpu, IType>(s);
-      printf("output size: %lu input_size: %lu\n", outputs[0].Size(), inputs[0].Size());
       igrad /= scalar<IType>(outputs[0].Size()/inputs[0].Size());
     });
   }
@@ -306,6 +306,139 @@ void NumpyReduceAxesBackwardUseInOut(const nnvm::NodeAttrs& attrs,
   ReduceAxesBackwardUseInOutImpl<xpu, OP, normalize>(ctx, small, inputs, req, outputs);
 }
 
+struct NumpyMomentsParam : public dmlc::Parameter<NumpyMomentsParam> {
+  dmlc::optional<mxnet::Tuple<int>> axis;
+  dmlc::optional<int> dtype;
+  bool keepdims;
+  int ddof;
+  DMLC_DECLARE_PARAMETER(NumpyMomentsParam) {
+    DMLC_DECLARE_FIELD(axis)
+      .set_default(dmlc::optional<mxnet::Tuple<int>>())
+      .describe("Axis or axes along which a sum is performed. The default, axis=None, will sum "
+                "all of the elements of the input array. If axis is negative it counts from the "
+                "last to the first axis.");
+    DMLC_DECLARE_FIELD(dtype)
+      .add_enum("float16", mshadow::kFloat16)
+      .add_enum("float32", mshadow::kFloat32)
+      .add_enum("float64", mshadow::kFloat64)
+      .add_enum("int8", mshadow::kInt8)
+      .add_enum("int32", mshadow::kInt32)
+      .add_enum("int64", mshadow::kInt64)
+      .set_default(dmlc::optional<int>())
+      .describe("The type of the returned array and of the accumulator in which the elements are "
+                "summed. The dtype of a is used by default unless a has an integer dtype of less "
+                "precision than the default platform integer. In that case, if a is signed then "
+                "the platform integer is used while if a is unsigned then an unsigned integer of "
+                "the same precision as the platform integer is used.");
+    DMLC_DECLARE_FIELD(ddof).set_default(0)
+      .describe("Starting value for the sum.");
+    DMLC_DECLARE_FIELD(keepdims).set_default(false)
+      .describe("If this is set to `True`, the reduced axes are left "
+                "in the result as dimension with size one.");
+  }
+};
+
+template<typename xpu, typename reducer, bool safe_acc, bool normalize = false,
+         typename OP = op::mshadow_op::identity>
+void ReduceAxesComputeWithWorkspaceImpl(const OpContext& ctx,
+                                        const std::vector<TBlob>& inputs,
+                                        const std::vector<OpReqType>& req,
+                                        const std::vector<TBlob>& outputs,
+                                        const mshadow::Tensor<xpu, 1, char>& workspace,
+                                        const mxnet::TShape& src_shape,
+                                        const mxnet::TShape& dst_shape,
+                                        const int ddof = 0) {
+  using namespace mshadow;
+  using namespace mshadow::expr;
+
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, OType, {
+      const TBlob in_data = inputs[0].reshape(src_shape);
+      const TBlob out_data = outputs[0].reshape(dst_shape);
+      BROADCAST_NDIM_SWITCH(dst_shape.ndim(), NDim, {
+        broadcast::Reduce<reducer, NDim, DType, OP, safe_acc>(
+            s, out_data, req[0], workspace, in_data);
+        if (normalize) {
+          auto out = out_data.FlatTo2D<xpu, OType>(s);
+          out /= scalar<OType>(src_shape.Size()/dst_shape.Size() - ddof);
+        }
+      });
+    });
+  });
+}
+
+template<typename xpu, bool sqrt>
+void NumpyMomentsForward(const nnvm::NodeAttrs& attrs,
+                         const OpContext& ctx,
+                         const std::vector<TBlob>& inputs,
+                         const std::vector<OpReqType>& req,
+                         const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mshadow::expr;
+  using namespace mshadow_op;
+  using namespace mxnet_op;
+
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(req.size(), 2U);
+  CHECK_EQ(outputs.size(), 2U);
+
+  const NumpyMomentsParam& param = nnvm::get<NumpyMomentsParam>(attrs.parsed);
+
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+
+  const TBlob& data = inputs[0];
+  const TBlob& moment = outputs[0];
+  const TBlob& mean = outputs[1];
+
+  mxnet::TShape small;
+  if (param.keepdims) {
+    small = moment.shape_;
+  } else {
+    small = NumpyReduceAxesShapeImpl(data.shape_, param.axis, true);
+  }
+
+  mxnet::TShape src_shape, dst_shape;
+  BroadcastReduceShapeCompact(data.shape_, small, &src_shape, &dst_shape);
+
+  MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, OType, {
+      // Get workspace and temp space for data - mean
+      size_t workspace_size = 0;
+      BROADCAST_NDIM_SWITCH(dst_shape.ndim(), NDim, {
+        workspace_size = broadcast::ReduceWorkspaceSize<NDim, DType>(
+          s, dst_shape, req[0], src_shape);;
+      });
+      size_t temp_data_size = data.shape_.Size() * sizeof(DType);
+      size_t temp_mem_size = temp_data_size + workspace_size;
+      Tensor<xpu, 1, char> temp_mem =
+        ctx.requested[0].get_space_typed<xpu, 1, char>(Shape1(temp_mem_size), s);
+      DType *temp_data_ptr = reinterpret_cast<DType*>(temp_mem.dptr_);
+      char *workspace_ptr = temp_mem.dptr_ + temp_data_size;
+      Tensor<xpu, 1, char> workspace(workspace_ptr, Shape1(workspace_size), s);
+      // Compute mean
+      ReduceAxesComputeWithWorkspaceImpl<xpu, mshadow_op::sum, true, true>(
+        ctx, inputs, {kWriteTo}, {mean}, workspace, src_shape, dst_shape);
+      // Compute data - mean
+      Shape<6> data_shape, mean_shape;
+      for (int i = 0; i < 6; ++i) {
+        data_shape[i] = (i < data.shape_.ndim()) ? data.shape_[i] : 1;
+        mean_shape[i] = (i < small.ndim()) ? small[i] : 1;
+      }
+      Kernel<VarBroadcastKernel, xpu>::Launch(s, data_shape.Size(), temp_data_ptr,
+        data.dptr<DType>(), mean.dptr<DType>(), data_shape, mean_shape);
+      Tensor<xpu, 1, DType> temp_data_tensor(temp_data_ptr, Shape1(data.shape_.Size()), s);
+      TBlob temp_data_blob = TBlob(temp_data_tensor).reshape(data.shape_);
+      ReduceAxesComputeWithWorkspaceImpl<xpu, mshadow_op::sum, true, true>(
+        ctx, {temp_data_blob}, {req[0]}, {moment}, workspace, src_shape, dst_shape, param.ddof);
+      if (sqrt) {
+        Tensor<xpu, 1, OType> moment_tensor = moment.FlatTo1D<xpu, OType>(s);
+        moment_tensor = F<mshadow_op::square_root>(moment_tensor);
+      }
+    });
+  });
+}
+
 template<typename xpu>
 void NumpyBroadcastToForward(const nnvm::NodeAttrs& attrs,
                              const OpContext& ctx,
diff --git a/src/operator/numpy/np_broadcast_reduce_op_value.cc b/src/operator/numpy/np_broadcast_reduce_op_value.cc
index bccd3af8b2cf..774bc11f5de8 100644
--- a/src/operator/numpy/np_broadcast_reduce_op_value.cc
+++ b/src/operator/numpy/np_broadcast_reduce_op_value.cc
@@ -30,6 +30,7 @@ namespace op {
 
 DMLC_REGISTER_PARAMETER(NumpyReduceAxesParam);
 DMLC_REGISTER_PARAMETER(NumpyReduceAxesNoDTypeParam);
+DMLC_REGISTER_PARAMETER(NumpyMomentsParam);
 
 inline bool NumpySumType(const nnvm::NodeAttrs& attrs,
                          std::vector<int> *in_attrs,
@@ -166,6 +167,147 @@ NNVM_REGISTER_OP(_backward_np_prod)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
 .set_attr<FCompute>("FCompute<cpu>", NumpyReduceAxesBackwardUseInOut<cpu, mshadow_op::rdiv>);
 
+inline bool IsIntType(const int dtype) {
+  return (dtype == mshadow::kUint8 ||
+          dtype == mshadow::kInt32 ||
+          dtype == mshadow::kInt8 ||
+          dtype == mshadow::kInt64);
+}
+
+inline bool NumpyMeanType(const nnvm::NodeAttrs& attrs,
+                          std::vector<int> *in_attrs,
+                          std::vector<int> *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  const NumpyReduceAxesParam &param = nnvm::get<NumpyReduceAxesParam>(attrs.parsed);
+
+  if (param.dtype.has_value()) {
+    if (IsIntType(in_attrs->at(0)) && !IsIntType(param.dtype.value())) {
+      LOG(FATAL) << "Output cannot be float type when input is integer type for now";
+    }
+    TYPE_ASSIGN_CHECK(*out_attrs, 0, param.dtype.value());
+  } else {
+    TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
+    TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
+  }
+
+  return out_attrs->at(0) != -1 && in_attrs->at(0) != -1;
+}
+
+NNVM_REGISTER_OP(_npi_mean)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<NumpyReduceAxesParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyReduceAxesShape)
+.set_attr<nnvm::FInferType>("FInferType", NumpyMeanType)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"a"};
+  })
+.add_argument("a", "NDArray-or-Symbol", "The input")
+.add_arguments(NumpyReduceAxesParam::__FIELDS__())
+.set_attr<FCompute>("FCompute<cpu>", NumpyReduceAxesCompute<cpu, mshadow_op::sum, true, true>)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_np_mean"});
+
+NNVM_REGISTER_OP(_backward_np_mean)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<NumpyReduceAxesParam>)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_num_inputs(1)
+.set_attr<FCompute>("FCompute<cpu>", NumpyReduceAxesBackwardUseNone<cpu, true>);
+
+inline bool NumpyMomentsShape(const nnvm::NodeAttrs& attrs,
+                              std::vector<TShape> *in_attrs,
+                              std::vector<TShape> *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 2U);
+  if (!shape_is_known(in_attrs->at(0))) {
+    return false;
+  }
+  const NumpyMomentsParam& param = nnvm::get<NumpyMomentsParam>(attrs.parsed);
+  mxnet::TShape out_shape = NumpyReduceAxesShapeImpl((*in_attrs)[0], param.axis, param.keepdims);
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, out_shape);
+  SHAPE_ASSIGN_CHECK(*out_attrs, 1, out_shape);
+
+  return shape_is_known(out_attrs->at(0)) && shape_is_known(out_attrs->at(1));
+}
+
+inline bool NumpyMomentsType(const nnvm::NodeAttrs& attrs,
+                             std::vector<int> *in_attrs,
+                             std::vector<int> *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 2U);
+  const NumpyMomentsParam &param = nnvm::get<NumpyMomentsParam>(attrs.parsed);
+
+  if (param.dtype.has_value()) {
+    TYPE_ASSIGN_CHECK(*out_attrs, 0, param.dtype.value());
+  } else {
+    TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
+    TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
+  }
+  TYPE_ASSIGN_CHECK(*out_attrs, 1, in_attrs->at(0));
+
+  return out_attrs->at(0) != -1 && in_attrs->at(0) != -1;
+}
+
+NNVM_REGISTER_OP(_npi_std)
+.set_num_inputs(1)
+.set_num_outputs(2)
+.set_attr_parser(ParamParser<NumpyMomentsParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyMomentsShape)
+.set_attr<nnvm::FInferType>("FInferType", NumpyMomentsType)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"a"};
+  })
+.set_attr<nnvm::FListOutputNames>("FListOutputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"std", "mean"};
+  })
+.set_attr<nnvm::FNumVisibleOutputs>("FNumVisibleOutputs",
+  [](const NodeAttrs& attrs) {
+    return 1;
+  })
+.add_argument("a", "NDArray-or-Symbol", "The input")
+.add_arguments(NumpyMomentsParam::__FIELDS__())
+.set_attr<FCompute>("FCompute<cpu>", NumpyMomentsForward<cpu, true>)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes);
+
+NNVM_REGISTER_OP(_npi_var)
+.set_num_inputs(1)
+.set_num_outputs(2)
+.set_attr_parser(ParamParser<NumpyMomentsParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyMomentsShape)
+.set_attr<nnvm::FInferType>("FInferType", NumpyMomentsType)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"a"};
+  })
+.set_attr<nnvm::FListOutputNames>("FListOutputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"var", "mean"};
+  })
+.set_attr<nnvm::FNumVisibleOutputs>("FNumVisibleOutputs",
+  [](const NodeAttrs& attrs) {
+    return 1;
+  })
+.add_argument("a", "NDArray-or-Symbol", "The input")
+.add_arguments(NumpyMomentsParam::__FIELDS__())
+.set_attr<FCompute>("FCompute<cpu>", NumpyMomentsForward<cpu, false>)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes);
+
 bool NumpyBroadcastToShape(const nnvm::NodeAttrs& attrs,
                            mxnet::ShapeVector *in_attrs,
                            mxnet::ShapeVector *out_attrs) {
diff --git a/src/operator/numpy/np_broadcast_reduce_op_value.cu b/src/operator/numpy/np_broadcast_reduce_op_value.cu
index d1d33cc6d8b0..53e78787d47d 100644
--- a/src/operator/numpy/np_broadcast_reduce_op_value.cu
+++ b/src/operator/numpy/np_broadcast_reduce_op_value.cu
@@ -50,6 +50,18 @@ NNVM_REGISTER_OP(_np_prod)
 NNVM_REGISTER_OP(_backward_np_prod)
 .set_attr<FCompute>("FCompute<gpu>", NumpyReduceAxesBackwardUseInOut<gpu, mshadow_op::rdiv>);
 
+NNVM_REGISTER_OP(_npi_mean)
+.set_attr<FCompute>("FCompute<gpu>", NumpyReduceAxesCompute<gpu, mshadow_op::sum, true, true>);
+
+NNVM_REGISTER_OP(_backward_np_mean)
+.set_attr<FCompute>("FCompute<gpu>", NumpyReduceAxesBackwardUseNone<gpu, true>);
+
+NNVM_REGISTER_OP(_npi_std)
+.set_attr<FCompute>("FCompute<gpu>", NumpyMomentsForward<gpu, true>);
+
+NNVM_REGISTER_OP(_npi_var)
+.set_attr<FCompute>("FCompute<gpu>", NumpyMomentsForward<gpu, false>);
+
 NNVM_REGISTER_OP(_np_broadcast_to)
 .set_attr<FCompute>("FCompute<gpu>", NumpyBroadcastToForward<gpu>);
 
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 49b5b645d604..c137b30f89c3 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -66,7 +66,7 @@ def tensordot_backward(a, b, axes=2):
                 b_axes_summed[i] = (b_axes_summed[i] + b.ndim) % b.ndim
 
         if len(a_axes_summed) != len(b_axes_summed):
-            raise ValueError('Axes length mismatch') 
+            raise ValueError('Axes length mismatch')
 
         a_axes_remained = []
         for i in range(a.ndim):
@@ -179,7 +179,7 @@ def test_np_dot():
         ((3, 4, 5), (5, )),  # Case 4
         ((3, 4, 5), (5, 2)), # Case 5
         ((5,), (5, 2)),
-        ((3, 5, 4), (5, 4, 3)),  
+        ((3, 5, 4), (5, 4, 3)),
         ((3, 4), (5, 4, 3)),
         ((4,), (5, 4, 3))
     ]
@@ -390,6 +390,134 @@ def _test_np_exception(func, shape, dim):
                 _test_np_exception(func, shape, dim)
 
 
+@with_seed()
+@use_np
+def test_np_mean():
+    class TestMean(HybridBlock):
+        def __init__(self, axis=None, dtype=None, keepdims=False):
+            super(TestMean, self).__init__()
+            self._axis = axis
+            self._dtype = dtype
+            self._keepdims = keepdims
+
+        def hybrid_forward(self, F, a, *args, **kwargs):
+            return F.np.mean(a, axis=self._axis, dtype=self._dtype, keepdims=self._keepdims)
+
+    def is_int(dtype):
+        return 'int' in dtype
+
+    in_data_dim = random.choice([2, 3, 4])
+    shape = rand_shape_nd(in_data_dim, dim=3)
+    acc_type = {'float16': 'float32', 'float32': 'float64', 'float64': 'float64',
+                'int8': 'int32', 'int32': 'int64', 'int64': 'int64'}
+    for hybridize in [False, True]:
+        for keepdims in [True, False]:
+            for axis in ([i for i in range(in_data_dim)] + [(), None]):
+                for itype in ['float16', 'float32', 'float64']:
+                    for dtype in ['float16', 'float32', 'float64']:
+                        if is_int(dtype) and not is_int(itype):
+                            continue
+                        # test gluon
+                        test_mean = TestMean(axis=axis, dtype=dtype, keepdims=keepdims)
+                        if hybridize:
+                            test_mean.hybridize()
+                        if is_int(itype):
+                            x = _np.random.randint(-128, 128, shape, dtype=itype)
+                            x = mx.nd.array(x, dtype=itype)
+                        else:
+                            x = mx.nd.random.uniform(-1.0, 1.0, shape=shape, dtype=itype)
+                        x = x.as_np_ndarray()
+                        x.attach_grad()
+
+                        expected_ret = _np.mean(x.asnumpy(), axis=axis, dtype=acc_type[itype], keepdims=keepdims)
+                        expected_ret = expected_ret.astype(dtype)
+                        with mx.autograd.record():
+                            y = test_mean(x)
+                        assert y.shape == expected_ret.shape
+                        assert_almost_equal(y.asnumpy(), expected_ret, rtol=1e-3 if dtype == 'float16' else 1e-3,
+                                            atol=1e-5 if dtype == 'float16' else 1e-5)
+
+                        y.backward()
+                        N = x.size / y.size
+                        assert same(x.grad.asnumpy(), _np.ones(shape=x.shape, dtype=x.dtype) / N)
+
+                        # test numeric
+                        if itype == 'float32' and dtype == 'float32':
+                            x_sym = mx.sym.Variable("x").as_np_ndarray()
+                            mx_sym = mx.sym.np.mean(x_sym, axis=axis, dtype=dtype, keepdims=keepdims).as_nd_ndarray()
+                            check_numeric_gradient(mx_sym, [x.as_nd_ndarray()],
+                                                   numeric_eps=1e-3, rtol=1e-3, atol=1e-4, dtype=_np.float32)
+
+                        # test imperative
+                        mx_out = np.mean(x, axis=axis, dtype=dtype, keepdims=keepdims)
+                        np_out = _np.mean(x.asnumpy(), axis=axis, dtype=acc_type[itype], keepdims=keepdims).astype(dtype)
+                        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+
+
+@with_seed()
+@use_np
+def test_np_moment():
+    class TestMoment(HybridBlock):
+        def __init__(self, name, axis=None, dtype=None, keepdims=False, ddof=0):
+            super(TestMoment, self).__init__()
+            self._name = name
+            self._axis = axis
+            self._dtype = dtype
+            self._keepdims = keepdims
+            self._ddof = ddof
+
+        def hybrid_forward(self, F, a, *args, **kwargs):
+            return getattr(F.np, self._name)(a, axis=self._axis, dtype=self._dtype, keepdims=self._keepdims, ddof=self._ddof)
+
+    def is_int(dtype):
+        return 'int' in dtype
+
+    def legalize_shape(shape):
+        shape_ = list(shape)
+        for i in range(len(shape_)):
+            shape_[i] += 1
+        return tuple(shape_)
+
+    in_data_dim = random.choice([2, 3, 4])
+    shape = rand_shape_nd(in_data_dim, dim=3)
+    shape = legalize_shape(shape)
+    acc_type = {'float16': 'float32', 'float32': 'float64', 'float64': 'float64',
+                'int8': 'float64', 'int32': 'float64', 'int64': 'float64'}
+
+    for name in ['var', 'std']:
+        for hybridize in [False, True]:
+            for ddof in [0, 1]:
+                for keepdims in [True, False]:
+                    for axis in ([i for i in range(in_data_dim)] + [(), None]):
+                        for itype in ['float16', 'float32', 'float64', 'int8', 'int32', 'int64']:
+                            for dtype in ['float16', 'float32', 'float64']:
+                                if is_int(dtype) and not is_int(itype) or is_int(itype) and is_int(dtype):
+                                    continue
+                                atol = 3e-4 if itype == 'float16' or dtype == 'float16' else 1e-5
+                                rtol = 1e-2 if itype == 'float16' or dtype == 'float16' else 1e-3
+                                # test gluon
+                                test_moment = TestMoment(name, axis=axis, dtype=dtype, keepdims=keepdims, ddof=ddof)
+                                if hybridize:
+                                    test_moment.hybridize()
+                                if is_int(itype):
+                                    x = _np.random.randint(-16, 16, shape, dtype=itype)
+                                    x = mx.nd.array(x)
+                                else:
+                                    x = mx.nd.random.uniform(-1.0, 1.0, shape=shape, dtype=itype)
+                                x = x.as_np_ndarray()
+                                x.attach_grad()
+                                expected_ret = getattr(_np, name)(x.asnumpy(), axis=axis, dtype=acc_type[itype], keepdims=keepdims, ddof=ddof)
+                                expected_ret = expected_ret.astype(dtype)
+                                y = test_moment(x)
+                                assert y.shape == expected_ret.shape
+                                assert_almost_equal(y.asnumpy(), expected_ret, rtol=rtol, atol=atol, use_broadcast=False, equal_nan=True)
+
+                                # test imperative
+                                mx_out = getattr(np, name)(x, axis=axis, dtype=dtype, keepdims=keepdims, ddof=ddof)
+                                np_out = getattr(_np, name)(x.asnumpy(), axis=axis, dtype=acc_type[itype], keepdims=keepdims, ddof=ddof).astype(dtype)
+                                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol, use_broadcast=False, equal_nan=True)
+
+
 @with_seed()
 @use_np
 def test_np_linspace():

From 1abf05b20289670c91e359353e91e0dac6b5a5b9 Mon Sep 17 00:00:00 2001
From: Zhaoqi Zhu <zhaoqizh@usc.edu>
Date: Mon, 2 Sep 2019 10:29:14 -0700
Subject: [PATCH 271/813] adding "total" (total time) to profiler aggregate
 stats sorting criteria (#16055)

* Update aggregate_stats.h

* add 'total' to sorting criteria
---
 include/mxnet/c_api.h                  |  2 +-
 python/mxnet/profiler.py               | 11 ++++++-----
 src/profiler/aggregate_stats.cc        |  3 +++
 src/profiler/aggregate_stats.h         |  2 +-
 tests/python/unittest/test_profiler.py | 13 ++++++++-----
 5 files changed, 19 insertions(+), 12 deletions(-)

diff --git a/include/mxnet/c_api.h b/include/mxnet/c_api.h
index e0ed316a1bdc..657b32452cd3 100644
--- a/include/mxnet/c_api.h
+++ b/include/mxnet/c_api.h
@@ -345,7 +345,7 @@ MXNET_DLL int MXAggregateProfileStatsPrint(const char **out_str, int reset);
  * \param out_str will receive a pointer to the output string
  * \param reset clear the aggregate stats after printing
  * \param format whether to return in tabular or json format
- * \param sort_by sort by avg, min, max, or count
+ * \param sort_by sort by total, avg, min, max, or count
  * \param ascending whether to sort ascendingly
  * \return 0 when success, -1 when failure happens.
  * \note
diff --git a/python/mxnet/profiler.py b/python/mxnet/profiler.py
index 373a11592f86..7dbc060ed60f 100644
--- a/python/mxnet/profiler.py
+++ b/python/mxnet/profiler.py
@@ -148,7 +148,7 @@ def dump_profile():
     dump(True)
 
 
-def dumps(reset=False, format='table', sort_by='avg', ascending=False):
+def dumps(reset=False, format='table', sort_by='total', ascending=False):
     """Return a printable string of aggregate profile stats.
 
     Parameters
@@ -160,9 +160,9 @@ def dumps(reset=False, format='table', sort_by='avg', ascending=False):
         can take 'table' or 'json'
         defaults to 'table'
     sort_by: string
-        can take 'avg', 'min', 'max', or 'count'
+        can take 'total', 'avg', 'min', 'max', or 'count'
         by which stat to sort the entries in each category
-        defaults to 'avg'
+        defaults to 'total'
     ascending: boolean
         whether to sort ascendingly
         defaults to False
@@ -170,12 +170,13 @@ def dumps(reset=False, format='table', sort_by='avg', ascending=False):
     debug_str = ctypes.c_char_p()
     reset_to_int = {False: 0, True: 1}
     format_to_int = {'table': 0, 'json': 1}
-    sort_by_to_int = {'avg': 0, 'min': 1, 'max': 2, 'count': 3}
+    sort_by_to_int = {'total': 0, 'avg': 1, 'min': 2, 'max': 3, 'count': 4}
     asc_to_int = {False: 0, True: 1}
     assert format in format_to_int.keys(),\
             "Invalid value provided for format: {0}. Support: 'table', 'json'".format(format)
     assert sort_by in sort_by_to_int.keys(),\
-            "Invalid value provided for sort_by: {0}. Support: 'avg', 'min', 'max', 'count'"\
+            "Invalid value provided for sort_by: {0}.\
+             Support: 'total', 'avg', 'min', 'max', 'count'"\
             .format(sort_by)
     assert  ascending in asc_to_int.keys(),\
             "Invalid value provided for ascending: {0}. Support: False, True".format(ascending)
diff --git a/src/profiler/aggregate_stats.cc b/src/profiler/aggregate_stats.cc
index 98ac5e96b761..86791ebf3074 100644
--- a/src/profiler/aggregate_stats.cc
+++ b/src/profiler/aggregate_stats.cc
@@ -56,6 +56,9 @@ inline std::priority_queue<pi>
     const AggregateStats::StatData& data = iter.second;
     double value = 0;
     switch (static_cast<AggregateStats::SortBy>(sort_by)) {
+      case AggregateStats::SortBy::Total:
+        value = data.total_aggregate_;
+        break;
       case AggregateStats::SortBy::Avg:
         if (data.type_ == AggregateStats::StatData::kCounter)
           value = (data.max_aggregate_ - data.min_aggregate_) / 2;
diff --git a/src/profiler/aggregate_stats.h b/src/profiler/aggregate_stats.h
index b9634250b82a..fe7521adc544 100644
--- a/src/profiler/aggregate_stats.h
+++ b/src/profiler/aggregate_stats.h
@@ -74,7 +74,7 @@ class AggregateStats {
   void clear();
   /* !\brief by which stat to sort */
   enum class SortBy {
-    Avg, Min, Max, Count
+    Total, Avg, Min, Max, Count
   };
 
  private:
diff --git a/tests/python/unittest/test_profiler.py b/tests/python/unittest/test_profiler.py
index d04f390f219b..e5fdd2b1798a 100644
--- a/tests/python/unittest/test_profiler.py
+++ b/tests/python/unittest/test_profiler.py
@@ -243,11 +243,12 @@ def test_aggregate_stats_valid_json_return():
     debug_str = profiler.dumps(format = 'json')
     assert(len(debug_str) > 0)
     target_dict = json.loads(debug_str)
-    assert "Memory" in target_dict and "Time" in target_dict and "Unit" in target_dict
+    assert 'Memory' in target_dict and 'Time' in target_dict and 'Unit' in target_dict
     profiler.set_state('stop')
 
 def test_aggregate_stats_sorting():
-    sort_by_options = {'avg': "Avg", 'min': "Min", 'max': "Max", 'count': "Count"}
+    sort_by_options = {'total': 'Total', 'avg': 'Avg', 'min': 'Min',\
+        'max': 'Max', 'count': 'Count'}
     ascending_options = [False, True]
     def check_ascending(lst, asc):
         assert(lst == sorted(lst, reverse = not asc))
@@ -258,9 +259,11 @@ def check_sorting(debug_str, sort_by, ascending):
         for domain_name, domain in target_dict['Time'].items():
             lst = [item[sort_by_options[sort_by]] for item_name, item in domain.items()]
             check_ascending(lst, ascending)
-        for domain_name, domain in target_dict['Memory'].items():
-            lst = [item[sort_by_options[sort_by]] for item_name, item in domain.items()]
-            check_ascending(lst, ascending)
+        # Memory items do not have stat 'Total' 
+        if sort_by != 'total':
+            for domain_name, domain in target_dict['Memory'].items():
+                lst = [item[sort_by_options[sort_by]] for item_name, item in domain.items()]
+                check_ascending(lst, ascending)
 
     file_name = 'test_aggregate_stats_sorting.json'
     enable_profiler(file_name, True, True, True)

From a8ba6d9fbb24634a0b4547ea269a08704e320578 Mon Sep 17 00:00:00 2001
From: Xinyu Chen <xinyu1.chen@intel.com>
Date: Tue, 3 Sep 2019 01:47:20 +0800
Subject: [PATCH 272/813] fix flaky test (#16074)

---
 tests/python/quantization/test_quantization.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/python/quantization/test_quantization.py b/tests/python/quantization/test_quantization.py
index d753b514302e..0a9fcda56a64 100644
--- a/tests/python/quantization/test_quantization.py
+++ b/tests/python/quantization/test_quantization.py
@@ -265,7 +265,7 @@ def check_quantized_conv(data_shape, kernel, num_filter, pad, stride, no_bias, q
         qoutput, min_range, max_range = conv_exe_int8.forward()
 
         if no_bias:
-            assert_almost_equal(output.asnumpy(), qoutput.asnumpy())
+            assert_almost_equal(output.asnumpy(), qoutput.asnumpy(), atol = 1)
         else:
             # with adding bias, accuracy loss should not be greater than one
             diff = mx.nd.abs(output - qoutput.astype(output.dtype))

From 692f3c45238a055b2348ddc90ad6b8b9f3e857de Mon Sep 17 00:00:00 2001
From: Manu Seth <22492939+mseth10@users.noreply.github.com>
Date: Mon, 2 Sep 2019 17:37:42 -0700
Subject: [PATCH 273/813] Graph Partition API (#15886)

* API to trigger partitioning

* pre- and post-partition functions for subgraph property

* adding infer shape type before partition

* modifying pre/post-partition declaration

* adding support for infer shape type before partition

* passing kwargs down to pre/post partition funcitons

* move InferForwardAttrs to common/

* Addressing github comments

* refactoring to enable infer shape/type without storage type

* check if subgraph rejected by subgraph property

* adding description

* setting graph attribute context from args

* adding unit test for optimize_for with default backend

* fixing args access

* removing options_map from PostPartition

* addressing PR comment

* adding logs about status of subgraph node creation

* allowing partial infer shapes

* added context argument back to optimize_for and removed args context validation

* fixed spacing and dev_type

* fixing lint

* reorganized args list to optimize_for

* fixing spacing

* dereferencing dev_type
---
 include/mxnet/c_api.h                     |  21 ++++
 python/mxnet/symbol/symbol.py             |  58 ++++++++++
 src/c_api/c_api_symbolic.cc               |  64 +++++++++++
 src/operator/subgraph/build_subgraph.cc   |  47 ++++----
 src/operator/subgraph/subgraph_property.h |   6 ++
 tests/python/unittest/test_subgraph_op.py | 126 ++++++++++++++++++++++
 6 files changed, 302 insertions(+), 20 deletions(-)

diff --git a/include/mxnet/c_api.h b/include/mxnet/c_api.h
index 657b32452cd3..02a583a7655d 100644
--- a/include/mxnet/c_api.h
+++ b/include/mxnet/c_api.h
@@ -2002,6 +2002,27 @@ MXNET_DLL int MXGenBackendSubgraph(SymbolHandle sym_handle, const char *backend,
  * \param ret_sym_handle returned atomic symbol
  */
 MXNET_DLL int MXGenAtomicSymbolFromSymbol(SymbolHandle sym_handle, SymbolHandle *ret_sym_handle);
+/*!
+ * \brief Partitions symbol for given backend, potentially creating subgraphs
+ * \param sym_handle symbol to be partitioned
+ * \param dev_type context device type
+ * \param backend_name backend name
+ * \param ret_sym_handle partitioned symbol returned
+ * \param len number of args
+ * \param in_args_handle args array
+ * \param num_options number of key value pairs
+ * \param keys keys for options
+ * \param vals values corresponding to keys
+ */
+MXNET_DLL int MXOptimizeForBackend(SymbolHandle sym_handle,
+                                   const char* backend_name,
+                                   const int dev_type,
+                                   SymbolHandle* ret_sym_handle,
+                                   const mx_uint len,
+                                   NDArrayHandle* in_args_handle,
+                                   const mx_uint num_options,
+                                   const char** keys,
+                                   const char** vals);
 
 
 //--------------------------------------------
diff --git a/python/mxnet/symbol/symbol.py b/python/mxnet/symbol/symbol.py
index 3ac44176a87b..b8e8db57188c 100644
--- a/python/mxnet/symbol/symbol.py
+++ b/python/mxnet/symbol/symbol.py
@@ -1437,6 +1437,64 @@ def _gen_atomic_symbol(self):
         return Symbol(handle)
 
 
+    def optimize_for(self, backend, args=None, ctx=None, **kwargs):
+        """Partitions current symbol and optimizes it for a given backend,
+        returns new partitioned symbol.
+
+        Parameters
+        ----------
+        backend : str
+            The name of backend, as registered in `SubgraphBackendRegistry`
+
+        args : list of NDArray or dict of str to NDArray, optional
+            Input arguments to the symbol, required to infer shapes/types before partitioning
+
+            - If type is a list of `NDArray`, the order is the same as that of `list_arguments()`.
+            - If type is a dict of str to `NDArray`, then it maps the name of arguments
+              to the corresponding `NDArray`.
+
+        ctx : Context, optional
+            Device context, used to infer stypes
+
+        kwargs : optional arguments
+            Passed on to `PrePartition` and `PostPartition` functions of `SubgraphProperty`
+
+        Returns
+        -------
+        out : SymbolHandle
+            The created symbol for target backend.
+        """
+        out = SymbolHandle()
+        assert isinstance(backend, str)
+
+        if args is None:
+            args = []
+            args_handle = c_array(NDArrayHandle, [])
+        else:
+            listed_arguments = self.list_arguments()
+            args_handle, args = self._get_ndarray_inputs('args', args, listed_arguments, False)
+
+        if ctx is None:
+            ctx = current_context()
+        assert isinstance(ctx, Context)
+
+        key_list = []
+        val_list = []
+        for key, val in kwargs.items():
+            key_list.append(key)
+            val_list.append(str(val))
+        check_call(_LIB.MXOptimizeForBackend(self.handle,
+                                             c_str(backend),
+                                             ctypes.c_int(ctx.device_typeid),
+                                             ctypes.byref(out),
+                                             mx_uint(len(args)),
+                                             args_handle,
+                                             mx_uint(len(key_list)),
+                                             c_str_array(key_list),
+                                             c_str_array(val_list)))
+        return Symbol(out)
+
+
     # pylint: disable=too-many-locals
     def simple_bind(self, ctx, grad_req='write', type_dict=None, stype_dict=None,
                     group2ctx=None, shared_arg_names=None, shared_exec=None,
diff --git a/src/c_api/c_api_symbolic.cc b/src/c_api/c_api_symbolic.cc
index 40391082e1b5..e6a84ceb2348 100644
--- a/src/c_api/c_api_symbolic.cc
+++ b/src/c_api/c_api_symbolic.cc
@@ -30,6 +30,7 @@
 #include "nnvm/pass_functions.h"
 #include "nnvm/symbolic.h"
 #include "./c_api_common.h"
+#include "../common/exec_utils.h"
 #include "../operator/operator_common.h"
 #include "../executor/exec_pass.h"
 #include "../operator/subgraph/subgraph_property.h"
@@ -1214,3 +1215,66 @@ int MXShallowCopySymbol(SymbolHandle src, SymbolHandle* out) {
   *out = out_sym;
   API_END_HANDLE_ERROR(delete out_sym);
 }
+
+int MXOptimizeForBackend(SymbolHandle sym_handle,
+                         const char* backend_name,
+                         const int dev_type,
+                         SymbolHandle* ret_sym_handle,
+                         const mx_uint len,
+                         NDArrayHandle* in_args_handle,
+                         const mx_uint num_options,
+                         const char** keys,
+                         const char** vals) {
+  nnvm::Symbol *s = new nnvm::Symbol();
+  API_BEGIN();
+  nnvm::Symbol *sym = static_cast<nnvm::Symbol *>(sym_handle);
+  *s = sym->Copy();
+  nnvm::Graph g = Symbol2Graph(*s);
+  if (len) {
+    NDArray **in_args_ptr = reinterpret_cast<NDArray**>(in_args_handle);
+    Context default_ctx = Context::Create(static_cast<Context::DeviceType>(dev_type), 0);
+    mxnet::ShapeVector arg_shapes(len);
+    nnvm::DTypeVector arg_dtypes(len);
+    StorageTypeVector arg_stypes(len);
+    for (mx_uint i = 0; i < len; i++) {
+      const auto &in_arg = *(in_args_ptr[i]);
+      arg_shapes[i] = in_arg.shape();
+      arg_dtypes[i] = in_arg.dtype();
+      arg_stypes[i] = in_arg.storage_type();
+    }
+    const auto& indexed_graph = g.indexed_graph();
+    const auto num_forward_inputs = indexed_graph.input_nodes().size();
+    g.attrs["context"] = std::make_shared<nnvm::any>(
+        exec::ContextVector(indexed_graph.num_nodes(), default_ctx));
+    // infer shapes
+    g = exec::InferShape(std::move(g), std::move(arg_shapes), "__shape__");
+    // infer dtypes
+    g = exec::InferType(std::move(g), std::move(arg_dtypes), "__dtype__");
+    if (g.GetAttr<size_t>("dtype_num_unknown_nodes") != 0U) {
+      common::HandleInferTypeError(num_forward_inputs, indexed_graph,
+                                   g.GetAttr<nnvm::DTypeVector>("dtype"));
+    }
+    // infer stypes
+    g = exec::InferStorageType(std::move(g), std::move(arg_stypes), "__storage_type__");
+    if (g.GetAttr<size_t>("storage_type_num_unknown_nodes") != 0U) {
+      common::HandleInferStorageTypeError(num_forward_inputs, indexed_graph,
+                                          g.GetAttr<StorageTypeVector>("storage_type"));
+    }
+  }
+  std::vector<std::pair<std::string, std::string>> options_map;
+  for (mx_uint i = 0; i < num_options; ++i) {
+    options_map.emplace_back(keys[i], vals[i]);
+  }
+  const auto backend = mxnet::op::SubgraphBackendRegistry::Get()->GetSubgraphBackend(backend_name);
+  const auto& subgraph_prop_list = backend->GetSubgraphProperties();
+  for (auto property : subgraph_prop_list) {
+    property->PrePartition(g, options_map);
+    g.attrs["subgraph_property"] = std::make_shared<nnvm::any>(property);
+    g = ApplyPass(std::move(g), "BuildSubgraph");
+    g.attrs.erase("subgraph_property");
+    property->PostPartition(g);
+  }
+  s->outputs = g.outputs;
+  *ret_sym_handle = s;
+  API_END_HANDLE_ERROR(delete s);
+}
diff --git a/src/operator/subgraph/build_subgraph.cc b/src/operator/subgraph/build_subgraph.cc
index 0420e2248077..8a33115feb8b 100644
--- a/src/operator/subgraph/build_subgraph.cc
+++ b/src/operator/subgraph/build_subgraph.cc
@@ -572,30 +572,37 @@ void CreateSubgraphNode(nnvm::Graph* g,
   }
   const SubgraphPropertyPtr& subg_prop = g->GetAttr<SubgraphPropertyPtr>("subgraph_property");
   nnvm::NodePtr n = subg_prop->CreateSubgraphNode(sym, subgraph_selector, subgraph_id);
-
-  // Connect the external nodes to the subgraph node.
-  subg_prop->ConnectSubgraphOutputs(n, &output_entries);
-  subg_prop->ConnectSubgraphInputs(n, &input_entries, &orig_input_entries);
-
-  const auto& indexed_graph = g->indexed_graph();
-  for (size_t i = 0; i < n->inputs.size(); ++i) {
-    auto& e = n->inputs[i];
-    // update entry_top_order_map with newly created orig_input_entries
-    auto it = entry_top_order_map->find(input_entries[i]);
-    CHECK(it != entry_top_order_map->end());
-    entry_top_order_map->emplace(&e, it->second);
-    // update input entries' source simple nodes' outputs map
-    nnvm::Node* node = e.node.get();
-    if (indexed_graph.exist(node)) {
-      const auto nid = indexed_graph.node_id(node);
-      BiDirectedNode* sn = simple_nodes[nid].get();
-      for (BiDirectedNode* dest_node : subgraph_nodes) {
-        sn->outputs.erase(dest_node->node);
+  // CreateSubgraphNode returns NULL if subgraph property determines that subgraph is sub-optimal
+  // In that case, subgraph node is not created and graph is not modified
+  if (n) {
+    // Connect the external nodes to the subgraph node.
+    subg_prop->ConnectSubgraphOutputs(n, &output_entries);
+    subg_prop->ConnectSubgraphInputs(n, &input_entries, &orig_input_entries);
+
+    const auto& indexed_graph = g->indexed_graph();
+    for (size_t i = 0; i < n->inputs.size(); ++i) {
+      auto& e = n->inputs[i];
+      // update entry_top_order_map with newly created orig_input_entries
+      auto it = entry_top_order_map->find(input_entries[i]);
+      CHECK(it != entry_top_order_map->end());
+      entry_top_order_map->emplace(&e, it->second);
+      // update input entries' source simple nodes' outputs map
+      nnvm::Node* node = e.node.get();
+      if (indexed_graph.exist(node)) {
+        const auto nid = indexed_graph.node_id(node);
+        BiDirectedNode* sn = simple_nodes[nid].get();
+        for (BiDirectedNode* dest_node : subgraph_nodes) {
+          sn->outputs.erase(dest_node->node);
+        }
+        sn->outputs[n.get()].push_back(i);
       }
-      sn->outputs[n.get()].push_back(i);
     }
   }
 #if DEBUG_SUBGRAPH
+  if (n)
+    LOG(INFO) << "Subgraph node created and output_entries updated.";
+  else
+    LOG(INFO) << "Subgraph node not created, output_entries not updated.";
   PrintNodeEntries(output_entries);
 #endif
 }
diff --git a/src/operator/subgraph/subgraph_property.h b/src/operator/subgraph/subgraph_property.h
index b8b125fdb5de..fd1dc2827ffd 100644
--- a/src/operator/subgraph/subgraph_property.h
+++ b/src/operator/subgraph/subgraph_property.h
@@ -26,6 +26,7 @@
 #include <unordered_map>
 #include <vector>
 #include <string>
+#include <utility>
 
 namespace mxnet {
 namespace op {
@@ -221,6 +222,11 @@ class SubgraphProperty {
     return nullptr;
   }
 
+  virtual void PrePartition(const nnvm::Graph& g,
+    const std::vector<std::pair<std::string, std::string>>& options_map) {}
+
+  virtual void PostPartition(const nnvm::Graph& g) {}
+
   virtual SubgraphSelectorV2Ptr CreateSubgraphSelectorV2() const {
     auto v1_ptr = CreateSubgraphSelector();
     return std::make_shared<SubgraphSelectorV2Bridge>(v1_ptr);
diff --git a/tests/python/unittest/test_subgraph_op.py b/tests/python/unittest/test_subgraph_op.py
index f3c5dfd4e091..f2d75658aafd 100644
--- a/tests/python/unittest/test_subgraph_op.py
+++ b/tests/python/unittest/test_subgraph_op.py
@@ -146,11 +146,137 @@ def get_executor(sym, subgraph_backend=None, op_names=None, original_exec=None):
         for i in range(len(outputs1)):
             assert_almost_equal((outputs1[i] - outputs2[i]).abs().sum().asnumpy(), np.zeros(shape=(1,)))
 
+    def set_random_inputs(exe1, input_names):
+        """Sets random values to exe1's args and auxs"""
+        for name in input_names:
+            if name in exe1.arg_dict:
+                exe1.arg_dict[name][:] = mx.nd.random.uniform(shape=exe1.arg_dict[name].shape)
+            else:
+                assert name in exe1.aux_dict
+                exe1.aux_dict[name][:] = mx.nd.random.uniform(shape=exe1.aux_dict[name].shape)
+
+    def copy_inputs_between_executors(exe1, exe2, input_names):
+        """Copies values of args and auxs from exe1 to exe2"""
+        for name in input_names:
+            if name in exe2.arg_dict:
+                exe2.arg_dict[name][:] = exe1.arg_dict[name]
+            else:
+                assert name in exe2.aux_dict
+                exe2.aux_dict[name][:] = exe1.aux_dict[name]
+
+    def _check_subgraph_exe5(sym, subgraph_backend, op_names):
+        """Call optimize_for to trigger graph partitioning without infer shapes/types before,
+        then simple_bind and compare results of the partitioned sym and the original sym."""
+        # simple_bind
+        exe1 = sym.simple_bind(ctx=mx.current_context(), grad_req='null')
+        input_names = sym.list_inputs()
+        set_random_inputs(exe1, input_names)
+        exe1.forward()
+
+        # partition before simple_bind
+        check_call(_LIB.MXSetSubgraphPropertyOpNames(c_str(subgraph_backend), mx_uint(len(op_names)),
+                                                     c_str_array(op_names)))
+        part_sym = sym.optimize_for(subgraph_backend)
+        check_call(_LIB.MXRemoveSubgraphPropertyOpNames(c_str(subgraph_backend)))
+
+        exe2 = part_sym.simple_bind(ctx=mx.current_context(), grad_req='null')
+        copy_inputs_between_executors(exe1, exe2, input_names)
+        exe2.forward()
+
+        # compare outputs
+        outputs1 = exe1.outputs
+        outputs2 = exe2.outputs
+        assert len(outputs1) == len(outputs2)
+        for i in range(len(outputs1)):
+            assert_almost_equal((outputs1[i] - outputs2[i]).abs().sum().asnumpy(), np.zeros(shape=(1,)))
+
+    def _check_subgraph_exe6(sym, subgraph_backend, op_names):
+        """Call optimize_for to trigger graph partitioning without infer shapes/types before,
+        then simple_bind and compare results of the partitioned sym and the original sym."""
+        # simple_bind
+        exe1 = sym.simple_bind(ctx=mx.current_context(), grad_req='null')
+        input_names = sym.list_inputs()
+        set_random_inputs(exe1, input_names)
+        exe1.forward()
+
+        # infer shape/type before partition before simple_bind
+        check_call(_LIB.MXSetSubgraphPropertyOpNames(c_str(subgraph_backend), mx_uint(len(op_names)),
+                                                     c_str_array(op_names)))
+        part_sym = sym.optimize_for(subgraph_backend, exe1.arg_dict)
+        check_call(_LIB.MXRemoveSubgraphPropertyOpNames(c_str(subgraph_backend)))
+
+        exe2 = part_sym.simple_bind(ctx=mx.current_context(), grad_req='null')
+        copy_inputs_between_executors(exe1, exe2, input_names)
+        exe2.forward()
+
+        # compare outputs
+        outputs1 = exe1.outputs
+        outputs2 = exe2.outputs
+        assert len(outputs1) == len(outputs2)
+        for i in range(len(outputs1)):
+            assert_almost_equal((outputs1[i] - outputs2[i]).abs().sum().asnumpy(), np.zeros(shape=(1,)))
+
+    def _check_subgraph_exe7(sym, subgraph_backend, op_names):
+        """Call optimize_for to trigger graph partitioning without infer shapes/types before,
+        then bind and compare results of the partitioned sym and the original sym."""
+        # bind
+        arg_shapes, _, aux_shapes = sym.infer_shape()
+        arg_array = [mx.nd.random.uniform(shape=shape) for shape in arg_shapes]
+        aux_array = [mx.nd.random.uniform(shape=shape) for shape in aux_shapes]
+        exe1 = sym.bind(ctx=mx.current_context(), args=arg_array, aux_states=aux_array, grad_req='null')
+        exe1.forward()
+
+        # partition before bind
+        check_call(_LIB.MXSetSubgraphPropertyOpNames(c_str(subgraph_backend), mx_uint(len(op_names)),
+                                                     c_str_array(op_names)))
+        part_sym = sym.optimize_for(subgraph_backend)
+        check_call(_LIB.MXRemoveSubgraphPropertyOpNames(c_str(subgraph_backend)))
+
+        exe2 = part_sym.bind(ctx=mx.current_context(), args=arg_array, aux_states=aux_array, grad_req='null')
+        exe2.forward()
+
+        # compare outputs
+        outputs1 = exe1.outputs
+        outputs2 = exe2.outputs
+        assert len(outputs1) == len(outputs2)
+        for i in range(len(outputs1)):
+            assert_almost_equal((outputs1[i] - outputs2[i]).abs().sum().asnumpy(), np.zeros(shape=(1,)))
+
+    def _check_subgraph_exe8(sym, subgraph_backend, op_names):
+        """Call optimize_for to infer shapes, types and dtypes followed by graph partitioning,
+        then bind and compare results of the partitioned sym and the original sym."""
+        # bind
+        arg_shapes, _, aux_shapes = sym.infer_shape()
+        arg_array = [mx.nd.random.uniform(shape=shape) for shape in arg_shapes]
+        aux_array = [mx.nd.random.uniform(shape=shape) for shape in aux_shapes]
+        exe1 = sym.bind(ctx=mx.current_context(), args=arg_array, aux_states=aux_array, grad_req='null')
+        exe1.forward()
+
+        # infer shape/type before partition before bind
+        check_call(_LIB.MXSetSubgraphPropertyOpNames(c_str(subgraph_backend), mx_uint(len(op_names)),
+                                                     c_str_array(op_names)))
+        part_sym = sym.optimize_for(subgraph_backend, arg_array)
+        check_call(_LIB.MXRemoveSubgraphPropertyOpNames(c_str(subgraph_backend)))
+
+        exe2 = part_sym.bind(ctx=mx.current_context(), args=arg_array, aux_states=aux_array, grad_req='null')
+        exe2.forward()
+
+        # compare outputs
+        outputs1 = exe1.outputs
+        outputs2 = exe2.outputs
+        assert len(outputs1) == len(outputs2)
+        for i in range(len(outputs1)):
+            assert_almost_equal((outputs1[i] - outputs2[i]).abs().sum().asnumpy(), np.zeros(shape=(1,)))
+
     def check_subgraph_exe(sym, subgraph_backend, op_names):
         _check_subgraph_exe1(sym, subgraph_backend, op_names)
         _check_subgraph_exe2(sym, subgraph_backend, op_names)
         _check_subgraph_exe3(sym, subgraph_backend, op_names)
         _check_subgraph_exe4(sym, subgraph_backend, op_names)
+        _check_subgraph_exe5(sym, subgraph_backend, op_names)
+        _check_subgraph_exe6(sym, subgraph_backend, op_names)
+        _check_subgraph_exe7(sym, subgraph_backend, op_names)
+        _check_subgraph_exe8(sym, subgraph_backend, op_names)
 
     def test_network_structure_1(subgraph_backend):
         data1 = mx.sym.var('data1', shape=(2, 3, 10, 10))

From 767e3f19348c661c8c9680dacaae40cf171707f4 Mon Sep 17 00:00:00 2001
From: reminisce <wujun.nju@gmail.com>
Date: Mon, 2 Sep 2019 20:32:11 -0700
Subject: [PATCH 274/813] Add fluent methods mean, std, var for ndarray
 (#16077)

* Add fluent methods mean, std, var for ndarray

* Fix sanity
---
 python/mxnet/numpy/multiarray.py       |  8 ++++++--
 python/mxnet/symbol/numpy/_symbol.py   | 14 +++++++++-----
 tests/python/unittest/test_numpy_op.py |  4 ++--
 3 files changed, 17 insertions(+), 9 deletions(-)

diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index d59eddc3d139..11ea0858cccd 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -1170,11 +1170,15 @@ def nanprod(self, *args, **kwargs):
 
     def mean(self, axis=None, dtype=None, out=None, keepdims=False):  # pylint: disable=arguments-differ
         """Returns the average of the array elements along given axis."""
-        raise NotImplementedError
+        return mean(self, axis=axis, dtype=dtype, out=out, keepdims=keepdims)
 
     def std(self, axis=None, dtype=None, out=None, ddof=0, keepdims=False):  # pylint: disable=arguments-differ
         """Returns the standard deviation of the array elements along given axis."""
-        return _mx_np_op.std(self, axis=axis, dtype=dtype, ddof=ddof, keepdims=keepdims, out=out)
+        return std(self, axis=axis, dtype=dtype, ddof=ddof, keepdims=keepdims, out=out)
+
+    def var(self, axis=None, dtype=None, out=None, ddof=0, keepdims=None):  # pylint: disable=arguments-differ
+        """Returns the variance of the array elements, along given axis."""
+        return var(self, axis=axis, dtype=dtype, out=out, ddof=ddof, keepdims=keepdims)
 
     def cumsum(self, axis=None, dtype=None, out=None):
         """Return the cumulative sum of the elements along the given axis."""
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index e752c5801bb8..38e9086bb281 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -530,12 +530,16 @@ def nanprod(self, *args, **kwargs):
         raise AttributeError('_Symbol object has no attribute nanprod')
 
     def mean(self, axis=None, dtype=None, out=None, keepdims=False):  # pylint: disable=arguments-differ
-        """Convenience fluent method for :py:func:`mean`.
+        """Returns the average of the array elements along given axis."""
+        return mean(self, axis=axis, dtype=dtype, out=out, keepdims=keepdims)
 
-        The arguments are the same as for :py:func:`mean`, with
-        this array as data.
-        """
-        return _npi.mean(self, axis=axis, dtype=dtype, keepdims=keepdims, out=out)
+    def std(self, axis=None, dtype=None, out=None, ddof=0, keepdims=False):  # pylint: disable=arguments-differ,too-many-arguments
+        """Returns the standard deviation of the array elements along given axis."""
+        return std(self, axis=axis, dtype=dtype, ddof=ddof, keepdims=keepdims, out=out)
+
+    def var(self, axis=None, dtype=None, out=None, ddof=0, keepdims=None):  # pylint: disable=arguments-differ,too-many-arguments
+        """Returns the variance of the array elements, along given axis."""
+        return var(self, axis=axis, dtype=dtype, out=out, ddof=ddof, keepdims=keepdims)
 
     def cumsum(self, axis=None, dtype=None, out=None):
         """Return the cumulative sum of the elements along the given axis."""
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index c137b30f89c3..51aa9054451c 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -401,7 +401,7 @@ def __init__(self, axis=None, dtype=None, keepdims=False):
             self._keepdims = keepdims
 
         def hybrid_forward(self, F, a, *args, **kwargs):
-            return F.np.mean(a, axis=self._axis, dtype=self._dtype, keepdims=self._keepdims)
+            return a.mean(axis=self._axis, dtype=self._dtype, keepdims=self._keepdims)
 
     def is_int(dtype):
         return 'int' in dtype
@@ -467,7 +467,7 @@ def __init__(self, name, axis=None, dtype=None, keepdims=False, ddof=0):
             self._ddof = ddof
 
         def hybrid_forward(self, F, a, *args, **kwargs):
-            return getattr(F.np, self._name)(a, axis=self._axis, dtype=self._dtype, keepdims=self._keepdims, ddof=self._ddof)
+            return getattr(a, self._name)(axis=self._axis, dtype=self._dtype, keepdims=self._keepdims, ddof=self._ddof)
 
     def is_int(dtype):
         return 'int' in dtype

From f1950984a016a55173b10ece4dd99f873dc6875f Mon Sep 17 00:00:00 2001
From: "Shi, guangyong" <35016185+gyshi@users.noreply.github.com>
Date: Tue, 3 Sep 2019 13:04:36 +0800
Subject: [PATCH 275/813] fix some test files test time is too long (#16067)

---
 tests/python/unittest/test_operator.py        | 4 ++--
 tests/python/unittest/test_sparse_operator.py | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index de02727e24c8..e4617afd112a 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -3704,7 +3704,7 @@ def l2norm(input_data, axis=0, keepdims=True):
 
     ctx = default_context()
     data = mx.symbol.Variable('data')
-    in_data_dim = random_sample([4,5,6], 1)[0]
+    in_data_dim = random_sample([2,3,4], 1)[0]
     in_shape = rand_shape_nd(in_data_dim, dim=5)
     epsilon = 1e-3
     acc_type = {np.float16: np.float32, np.float32: np.float32, np.float64: np.float64,
@@ -6807,7 +6807,7 @@ def test_laop_5():
     # tests for diagonal and triangular matrix extraction and generation
     data = mx.symbol.Variable('data')
     # test complete range of small matrices to cover corner cases
-    for n in range(1, 10):
+    for n in range(1, 5):
         # test batched and non-batched processing
         for b in range(3):
             shape = (n, n) if b == 0 else (b, n, n)
diff --git a/tests/python/unittest/test_sparse_operator.py b/tests/python/unittest/test_sparse_operator.py
index 05175bb435f2..91194c562a57 100644
--- a/tests/python/unittest/test_sparse_operator.py
+++ b/tests/python/unittest/test_sparse_operator.py
@@ -494,10 +494,10 @@ def check_elemwise_binary_ops(lhs_stype, rhs_stype, shape,
 
         for ii in range(1):
             # Run defaults
-            check_elemwise_binary_ops('default', 'default', rand_shape_2d())
+            check_elemwise_binary_ops('default', 'default', rand_shape_2d(5, 5))
 
             # Try different densities
-            shape = rand_shape_2d()
+            shape = rand_shape_2d(5, 5)
             for lhs_density in [0.0, random.uniform(0, 1), 1.0]:
                 for rhs_density in [0.0, random.uniform(0, 1), 1.0]:
                     for ograd_density in [0.0, random.uniform(0, 1), 1.0]:

From 4c72d2775c97eac143a8587c1a22c9893d8da126 Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Tue, 3 Sep 2019 14:11:27 +0800
Subject: [PATCH 276/813] new raise mode for nd.take and fix backward for wrap
 mode (#15887)

---
 .../mshadow/mshadow/cuda/tensor_gpu-inl.cuh   | 26 ++++++++++++++----
 3rdparty/mshadow/mshadow/tensor.h             |  4 +--
 3rdparty/mshadow/mshadow/tensor_cpu-inl.h     | 11 +++++---
 3rdparty/mshadow/mshadow/tensor_gpu-inl.h     |  4 +--
 src/operator/tensor/indexing_op.cc            | 15 ++++++++---
 src/operator/tensor/indexing_op.cu            | 18 ++++++++++---
 src/operator/tensor/indexing_op.h             | 27 +++++++++++++------
 tests/python/unittest/test_ndarray.py         |  2 +-
 tests/python/unittest/test_operator.py        | 25 ++++++++++++++---
 9 files changed, 102 insertions(+), 30 deletions(-)

diff --git a/3rdparty/mshadow/mshadow/cuda/tensor_gpu-inl.cuh b/3rdparty/mshadow/mshadow/cuda/tensor_gpu-inl.cuh
index 72e4b7eb9ee9..3ebe83cff146 100755
--- a/3rdparty/mshadow/mshadow/cuda/tensor_gpu-inl.cuh
+++ b/3rdparty/mshadow/mshadow/cuda/tensor_gpu-inl.cuh
@@ -596,7 +596,8 @@ inline void SoftmaxGrad(const Tensor<gpu, 3, DType> &dst,
   MSHADOW_CUDA_POST_KERNEL_CHECK(Softmax3DGradKernel);
 }
 
-template<int x_bits, typename DType, typename DstPlan, typename SrcPlan1, typename SrcPlan2>
+template<bool clip, int x_bits, typename DType, typename DstPlan,
+         typename SrcPlan1, typename SrcPlan2>
 __global__ void AddTakeGradKernel(DstPlan dst,
                                   SrcPlan1 index, SrcPlan2 src,
                                   index_t ymax, index_t xmax, const int K) {
@@ -606,8 +607,13 @@ __global__ void AddTakeGradKernel(DstPlan dst,
   for (unsigned y = 0; y < ymax; ++y) {
     if (threadIdx.x == 0) {
       ptr = index.Eval(0, y);
-      if (ptr <= 0) ptr = 0;
-      else if (ptr >= K) ptr = K - 1;
+      if (clip) {
+        if (ptr <= 0) ptr = 0;
+        else if (ptr >= K) ptr = K - 1;
+      } else {
+        ptr %= K;
+        if (ptr < 0) ptr += K;
+      }
     }
     __syncthreads();
     if (xindex < xmax) {
@@ -671,7 +677,7 @@ __global__ void AddTakeGradLargeBatchKernel(DType* dst,
   }
 }
 
-template<typename IndexType, typename DType>
+template<bool clip = true, typename IndexType, typename DType>
 inline void AddTakeGrad(Tensor<gpu, 2, DType> dst,
                         const Tensor<gpu, 1, IndexType>& index,
                         const Tensor<gpu, 2, DType> &src) {
@@ -688,13 +694,23 @@ inline void AddTakeGrad(Tensor<gpu, 2, DType> dst,
   cudaStream_t stream = Stream<gpu>::GetStream(dst.stream_);
   const int K = dst.shape_[0];
 
-  AddTakeGradKernel<kUnitBits, DType>
+  if (clip) {
+    AddTakeGradKernel<true, kUnitBits, DType>
+      <<<dimGrid, dimBlock, 0, stream>>>
+      (expr::MakePlan(dst),
+       expr::MakePlan(index),
+       expr::MakePlan(src),
+       src.size(0),
+       src.size(1), K);
+  } else {
+    AddTakeGradKernel<false, kUnitBits, DType>
       <<<dimGrid, dimBlock, 0, stream>>>
       (expr::MakePlan(dst),
        expr::MakePlan(index),
        expr::MakePlan(src),
        src.size(0),
        src.size(1), K);
+  }
   MSHADOW_CUDA_POST_KERNEL_CHECK(AddTakeGradKernel);
 }
 
diff --git a/3rdparty/mshadow/mshadow/tensor.h b/3rdparty/mshadow/mshadow/tensor.h
index ad29e751a050..0add5a87d63b 100755
--- a/3rdparty/mshadow/mshadow/tensor.h
+++ b/3rdparty/mshadow/mshadow/tensor.h
@@ -817,7 +817,7 @@ inline void SoftmaxGrad(const Tensor<gpu, 2, DType> &dst,
  * \param index index to take
  * \param src source output
  */
-template<typename IndexType, typename DType>
+template<bool clip = true, typename IndexType, typename DType>
 inline void AddTakeGrad(Tensor<cpu, 2, DType> dst,
                         const Tensor<cpu, 1, IndexType>& index,
                         const Tensor<cpu, 2, DType> &src);
@@ -829,7 +829,7 @@ inline void AddTakeGrad(Tensor<cpu, 2, DType> dst,
  * \param index index to take
  * \param src source output
  */
-template<typename IndexType, typename DType>
+template<bool clip = true, typename IndexType, typename DType>
 inline void AddTakeGrad(Tensor<gpu, 2, DType> dst,
                         const Tensor<gpu, 1, IndexType>& index,
                         const Tensor<gpu, 2, DType> &src);
diff --git a/3rdparty/mshadow/mshadow/tensor_cpu-inl.h b/3rdparty/mshadow/mshadow/tensor_cpu-inl.h
index ab5f9a68df14..b7ae77fe56a4 100755
--- a/3rdparty/mshadow/mshadow/tensor_cpu-inl.h
+++ b/3rdparty/mshadow/mshadow/tensor_cpu-inl.h
@@ -494,15 +494,20 @@ inline void Softmax(Tensor<cpu, 3, DType> dst,
   }
 }
 
-template<typename IndexType, typename DType>
+template<bool clip, typename IndexType, typename DType>
 inline void AddTakeGrad(Tensor<cpu, 2, DType> dst,
                         const Tensor<cpu, 1, IndexType>& index,
                         const Tensor<cpu, 2, DType> &src) {
   const int K = dst.shape_[0];
   for (index_t y = 0; y < index.size(0); ++y) {
     int j = index[y];
-    if (j <= 0) j = 0;
-    else if (j >= K) j = K - 1;
+    if (clip) {
+      if (j <= 0) j = 0;
+      else if (j >= K) j = K - 1;
+    } else {
+      j %= K;
+      if (j < 0) j += K;
+    }
     dst[j] += src[y];
   }
 }
diff --git a/3rdparty/mshadow/mshadow/tensor_gpu-inl.h b/3rdparty/mshadow/mshadow/tensor_gpu-inl.h
index 94fdb0527e72..009e5d6530ff 100755
--- a/3rdparty/mshadow/mshadow/tensor_gpu-inl.h
+++ b/3rdparty/mshadow/mshadow/tensor_gpu-inl.h
@@ -213,11 +213,11 @@ inline void SoftmaxGrad(const Tensor<gpu, 3, DType> &dst,
   cuda::SoftmaxGrad(dst, src, label, ignore_label);
 }
 
-template<typename IndexType, typename DType>
+template<bool clip, typename IndexType, typename DType>
 inline void AddTakeGrad(Tensor<gpu, 2, DType> dst,
                         const Tensor<gpu, 1, IndexType>& index,
                         const Tensor<gpu, 2, DType> &src) {
-  cuda::AddTakeGrad(dst, index, src);
+  cuda::AddTakeGrad<clip, IndexType, DType>(dst, index, src);
 }
 
 template<typename IndexType, typename DType>
diff --git a/src/operator/tensor/indexing_op.cc b/src/operator/tensor/indexing_op.cc
index 21aefc5b2fd4..147205505e24 100644
--- a/src/operator/tensor/indexing_op.cc
+++ b/src/operator/tensor/indexing_op.cc
@@ -291,8 +291,17 @@ void TakeOpForward<cpu>(const nnvm::NodeAttrs& attrs,
   Stream<cpu> *s = ctx.get_stream<cpu>();
   const int actual_axis = param.axis + ((param.axis < 0) ? arrshape.ndim() : 0);
 
-  MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {  // output data type
-    MSHADOW_TYPE_SWITCH(inputs[1].type_flag_, IType, {  // index data type
+  MSHADOW_TYPE_SWITCH(outputs[take_::kOut].type_flag_, DType, {  // output data type
+    MSHADOW_TYPE_SWITCH(inputs[take_::kIdx].type_flag_, IType, {  // index data type
+      if (param.mode == take_::kRaise) {
+        IType min = 0;
+        IType max = static_cast<IType>(arrshape[actual_axis] - 1);
+        // check with single thread is faster since data is small
+        IType* idx_ptr = inputs[take_::kIdx].dptr<IType>();
+        size_t idx_size = idxshape.Size();
+        bool is_valid = CheckIndexOutOfBound(idx_ptr, idx_size, min, max);
+        CHECK(is_valid) << "take operator contains indices out of bound";
+      }
       if (actual_axis == 0) {
         if (param.mode == take_::kClip) {
           Kernel<TakeCPU<true>, cpu>::Launch(s, idxshape.Size(),
@@ -326,7 +335,7 @@ void TakeOpForward<cpu>(const nnvm::NodeAttrs& attrs,
                                           in_strides, out_strides, arrshape.ndim(),
                                           oshape.ndim(), idxshape.ndim(),
                                           arrshape[actual_axis], actual_axis);
-        } else if (param.mode == take_::kWrap) {
+        } else {
           Kernel<Take<false>, cpu>::Launch(s, oshape.Size(),
                                            outputs[take_::kOut].dptr<DType>(),
                                            inputs[take_::kArr].dptr<DType>(),
diff --git a/src/operator/tensor/indexing_op.cu b/src/operator/tensor/indexing_op.cu
index 94fe377ebbc7..9a46d894ee22 100644
--- a/src/operator/tensor/indexing_op.cu
+++ b/src/operator/tensor/indexing_op.cu
@@ -482,8 +482,20 @@ void TakeOpForward<gpu>(const nnvm::NodeAttrs& attrs,
   Stream<gpu> *s = ctx.get_stream<gpu>();
   const int actual_axis = param.axis + ((param.axis < 0) ? arrshape.ndim() : 0);
 
-  MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {  // output data type
-    MSHADOW_TYPE_SWITCH(inputs[1].type_flag_, IType, {  // index data type
+  MSHADOW_TYPE_SWITCH(outputs[take_::kOut].type_flag_, DType, {  // output data type
+    MSHADOW_TYPE_SWITCH(inputs[take_::kIdx].type_flag_, IType, {  // index data type
+      if (param.mode == take_::kRaise) {
+        // check out-of-bound indices
+        IType min = 0;
+        IType max = static_cast<IType>(arrshape[actual_axis] - 1);
+        IType* idx_ptr = inputs[take_::kIdx].dptr<IType>();
+        size_t idx_size = idxshape.Size();
+        Tensor<gpu, 1, char> workspace =
+          ctx.requested[0].get_space_typed<gpu, 1, char>(Shape1(1), s);
+        char* is_valid_ptr = reinterpret_cast<char*>(workspace.dptr_);
+        bool is_valid = CheckIndexOutOfBound(s, idx_ptr, idx_size, min, max, is_valid_ptr);
+        CHECK(is_valid) << "Take indices contains indices out of bound";
+      }
       if (actual_axis == 0) {
         if (param.mode == take_::kClip) {
           Kernel<TakeGPU<true>, gpu>::Launch(s, oshape.Size(),
@@ -516,7 +528,7 @@ void TakeOpForward<gpu>(const nnvm::NodeAttrs& attrs,
                                           inputs[take_::kIdx].dptr<IType>(),
                                           in_strides, out_strides, arrshape.ndim(), oshape.ndim(),
                                           idxshape.ndim(), arrshape[actual_axis], actual_axis);
-        } else if (param.mode == take_::kWrap) {
+        } else {
           Kernel<Take<false>, gpu>::Launch(s, oshape.Size(),
                                            outputs[take_::kOut].dptr<DType>(),
                                            inputs[take_::kArr].dptr<DType>(),
diff --git a/src/operator/tensor/indexing_op.h b/src/operator/tensor/indexing_op.h
index 7dfc77d25b27..161acae0ebf2 100644
--- a/src/operator/tensor/indexing_op.h
+++ b/src/operator/tensor/indexing_op.h
@@ -684,9 +684,6 @@ inline bool TakeOpShape(const nnvm::NodeAttrs& attrs,
   const mxnet::TShape &idxshape = (*in_attrs)[take_::kIdx];
   if (!shape_is_known(idxshape)) return false;
   const TakeParam& param = nnvm::get<TakeParam>(attrs.parsed);
-  if (param.mode == take_::kRaise) {
-    LOG(FATAL) << "Raise is not supported for the time being...";
-  }
   CHECK(param.axis >= -1 * arrshape.ndim() && param.axis < arrshape.ndim())
     << "Axis should be in the range of [-r, r-1] where r is the rank of input tensor";
 
@@ -813,14 +810,15 @@ struct TakeGradGeneralKernel {
                                   const IType* src_indptr, const IType* original_idx,
                                   mshadow::Shape<10> in_strides, mshadow::Shape<10> out_strides,
                                   const int in_ndims, const int out_ndims, const int idx_ndims,
-                                  const int axis) {
+                                  const int axis, const int K) {
     const int in_head_index = (axis == 0) ? 0 : tid / in_strides[axis - 1];
     const int in_rest_index = (axis == 0) ? tid : tid % in_strides[axis - 1];
     const int in_mid_index = in_rest_index / in_strides[axis];
     const int in_tail_index = (axis == in_ndims - 1) ?
                               0 : (in_rest_index % in_strides[axis]);
     for (IType i = src_indptr[in_mid_index]; i < src_indptr[in_mid_index + 1]; ++i) {
-      const int out_mid_index = original_idx[i];
+      int out_mid_index = original_idx[i];
+      out_mid_index = (out_mid_index < 0) ? out_mid_index + K : out_mid_index;
       int target = in_tail_index + out_mid_index * in_strides[axis];
       target += (axis == 0) ? 0 : in_head_index * out_strides[axis - 1];
       arr_grad[tid] += ograd[target];
@@ -894,7 +892,7 @@ void TakeOpBackwardImpl(mshadow::Stream<cpu>* s,
       Kernel<TakeGradGeneralKernel, cpu>::Launch(
         s, arrshape.Size(), arr.dptr<DType>(), ograd.dptr<DType>(), src_indptr_ptr,
         original_idx_ptr, in_strides, out_strides,
-        arrshape.ndim(), oshape.ndim(), idxshape.ndim(), axis);
+        arrshape.ndim(), oshape.ndim(), idxshape.ndim(), axis, static_cast<int>(arrshape[axis]));
     });
   });
 }
@@ -968,6 +966,15 @@ void TakeOpBackwardImpl(mshadow::Stream<gpu>* s,
     int num_bits = common::ilog2ui(static_cast<unsigned int>(idxshape.Size()) - 1);
     Tensor<gpu, 1, int> sorted_idx(sorted_idx_ptr, Shape1(idxshape.Size()), s);
     SortByKey(sorted_idx, original_idx, true, &temp_storage, 0, num_bits);
+    cub::DeviceHistogram::HistogramEven(temp_storage_ptr,
+                                        temp_storage_bytes,
+                                        sorted_idx_ptr,
+                                        src_indptr_ptr,
+                                        static_cast<int>(arrshape[axis] + 1),
+                                        0,
+                                        static_cast<int>(arrshape[axis] + 1),
+                                        static_cast<int>(idxshape.Size()),
+                                        mshadow::Stream<gpu>::GetStream(s));
     cub::DeviceScan::ExclusiveSum(temp_storage_ptr,
                                   temp_storage_bytes,
                                   src_indptr_ptr,
@@ -989,7 +996,7 @@ void TakeOpBackwardImpl(mshadow::Stream<gpu>* s,
       Kernel<TakeGradGeneralKernel, gpu>::Launch(
         s, arrshape.Size(), arr.dptr<DType>(), ograd.dptr<DType>(),
         src_indptr_ptr, original_idx_ptr, in_strides, out_strides,
-        arrshape.ndim(), oshape.ndim(), idxshape.ndim(), axis);
+        arrshape.ndim(), oshape.ndim(), idxshape.ndim(), axis, static_cast<int>(arrshape[axis]));
     });
   });
 }
@@ -1044,7 +1051,11 @@ void TakeOpBackward(const nnvm::NodeAttrs& attrs,
           if (req[take_::kArr] == kWriteTo) {
             grad_in = scalar<DType>(0.0f);
           }
-          AddTakeGrad(grad_in, idx, grad_out);
+          if (param.mode == take_::kClip) {
+            AddTakeGrad(grad_in, idx, grad_out);
+          } else {
+            AddTakeGrad<false>(grad_in, idx, grad_out);
+          }
         } else {
           LOG(FATAL) << "wrong req";
         }
diff --git a/tests/python/unittest/test_ndarray.py b/tests/python/unittest/test_ndarray.py
index e15240c30dd3..c1f0ef05f979 100644
--- a/tests/python/unittest/test_ndarray.py
+++ b/tests/python/unittest/test_ndarray.py
@@ -1136,7 +1136,7 @@ def test_ndarray_lesser_equal():
 
 
 @with_seed()
-def test_take():
+def test_ndarray_take():
     for data_ndim in range(2, 5):
         for idx_ndim in range(1, 4):
             data_shape = ()
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index e4617afd112a..f0e16b2729a2 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -4426,7 +4426,7 @@ def grad_helper(grad_in, axis, idx):
         else:
             raise ValueError("axis %d is not supported..." % axis)
 
-    def check_output_n_grad(data_shape, idx_shape, axis, mode):
+    def check_output_n_grad(data_shape, idx_shape, axis, mode, out_of_range=True):
         data = mx.sym.Variable('a')
         idx = mx.sym.Variable('indices')
         idx = mx.sym.BlockGrad(idx)
@@ -4434,7 +4434,13 @@ def check_output_n_grad(data_shape, idx_shape, axis, mode):
         exe = result.simple_bind(default_context(), a=data_shape,
                                  indices=idx_shape, axis=axis, mode=mode)
         data_real = np.random.normal(size=data_shape).astype('float32')
-        idx_real = np.random.randint(low=0, high=data_shape[axis], size=idx_shape)
+        if out_of_range:
+            idx_real = np.random.randint(low=-data_shape[axis], high=data_shape[axis], size=idx_shape)
+            if mode == 'raise':
+                idx_real[idx_real == 0] = 1
+                idx_real *= data_shape[axis]
+        else:
+            idx_real = np.random.randint(low=0, high=data_shape[axis], size=idx_shape)
         if axis < 0:
             axis += len(data_shape)
 
@@ -4444,9 +4450,20 @@ def check_output_n_grad(data_shape, idx_shape, axis, mode):
         exe.arg_dict['a'][:] = mx.nd.array(data_real)
         exe.arg_dict['indices'][:] = mx.nd.array(idx_real)
         exe.forward(is_train=True)
+        if out_of_range and mode == 'raise':
+            try:
+                mx_out = exe.outputs[0].asnumpy()
+            except MXNetError as e:
+                return
+            else:
+                # Did not raise exception
+                assert False, "did not raise %s" % MXNetError.__name__
+
         assert_almost_equal(exe.outputs[0].asnumpy(), np.take(data_real, idx_real, axis=axis, mode=mode))
 
         for i in np.nditer(idx_real):
+            if mode == 'clip':
+                i = np.clip(i, 0, data_shape[axis])
             grad_helper(grad_in, axis, i)
 
         exe.backward([mx.nd.array(grad_out)])
@@ -4477,7 +4494,7 @@ def check_autograd_req():
         x.backward()
         assert_almost_equal(np.ones(sc.grad.shape), sc.grad.asnumpy())
 
-    for mode in ['clip', 'wrap']:
+    for mode in ['clip', 'wrap', 'raise']:
         for data_ndim in range(1, 5):
             for idx_ndim in range(1, 4):
                 for axis in range(-data_ndim, data_ndim):
@@ -4487,6 +4504,8 @@ def check_autograd_req():
                     idx_shape = ()
                     for _ in range(idx_ndim):
                         idx_shape += (np.random.randint(low=1, high=5), )
+                    if mode == 'raise':
+                        check_output_n_grad(data_shape, idx_shape, axis, 'raise', False)
                     check_output_n_grad(data_shape, idx_shape, axis, mode)
 
     check_autograd_req()

From 5b301c654670d2abd274ecb9216c38446442c4cc Mon Sep 17 00:00:00 2001
From: Rohit Kumar Srivastava <srivastava.141@osu.edu>
Date: Mon, 2 Sep 2019 23:13:44 -0700
Subject: [PATCH 277/813] Typedef cleanup (#15899)

* change mx_uint->int32_t

* changing mx_int64 -> int64_t

* change mx_float -> float

* maintining typedefs for other language bindings

* Re-Trigger build
---
 include/mxnet/c_api.h         | 427 +++++++++++++++++-----------------
 include/mxnet/c_api_test.h    |   4 +-
 include/mxnet/c_predict_api.h |  64 +++--
 include/mxnet/imperative.h    |   2 +-
 src/c_api/c_api.cc            | 168 ++++++-------
 src/c_api/c_api_common.h      |   8 +-
 src/c_api/c_api_executor.cc   | 128 +++++-----
 src/c_api/c_api_ndarray.cc    |  22 +-
 src/c_api/c_api_symbolic.cc   | 168 ++++++-------
 src/c_api/c_api_test.cc       |   4 +-
 src/c_api/c_predict_api.cc    |  86 +++----
 src/imperative/imperative.cc  |   2 +-
 12 files changed, 540 insertions(+), 543 deletions(-)

diff --git a/include/mxnet/c_api.h b/include/mxnet/c_api.h
index 02a583a7655d..e5719cb36447 100644
--- a/include/mxnet/c_api.h
+++ b/include/mxnet/c_api.h
@@ -56,8 +56,6 @@ extern "C" {
 
 /*! \brief manually define unsigned int */
 typedef uint32_t mx_uint;
-/*! \brief manually define 64-bit int */
-typedef int64_t mx_int64;
 /*! \brief manually define float */
 typedef float mx_float;
 /*! \brief data type to store dim size */
@@ -547,8 +545,8 @@ MXNET_DLL int MXNDArrayCreateNone(NDArrayHandle *out);
  * \param out the returning handle
  * \return 0 when success, -1 when failure happens
  */
-MXNET_DLL int MXNDArrayCreate(const mx_uint *shape,
-                              mx_uint ndim,
+MXNET_DLL int MXNDArrayCreate(const uint32_t *shape,
+                              uint32_t ndim,
                               int dev_type,
                               int dev_id,
                               int delay_alloc,
@@ -566,15 +564,15 @@ MXNET_DLL int MXNDArrayCreate(const mx_uint *shape,
  * \param out the returning handle
  * \return 0 when success, -1 when failure happens
  */
-MXNET_DLL int MXNDArrayCreateEx(const mx_uint *shape,
-                              mx_uint ndim,
+MXNET_DLL int MXNDArrayCreateEx(const uint32_t *shape,
+                              uint32_t ndim,
                               int dev_type,
                               int dev_id,
                               int delay_alloc,
                               int dtype,
                               NDArrayHandle *out);
 
-MXNET_DLL int MXNDArrayCreateEx64(const mx_int64 *shape,
+MXNET_DLL int MXNDArrayCreateEx64(const int64_t *shape,
                                   int ndim,
                                   int dev_type,
                                   int dev_id,
@@ -600,29 +598,29 @@ MXNET_DLL int MXNDArrayCreateEx64(const mx_int64 *shape,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXNDArrayCreateSparseEx(int storage_type,
-                                      const mx_uint *shape,
-                                      mx_uint ndim,
+                                      const uint32_t *shape,
+                                      uint32_t ndim,
                                       int dev_type,
                                       int dev_id,
                                       int delay_alloc,
                                       int dtype,
-                                      mx_uint num_aux,
+                                      uint32_t num_aux,
                                       int *aux_type,
-                                      mx_uint *aux_ndims,
-                                      const mx_uint *aux_shape,
+                                      uint32_t *aux_ndims,
+                                      const uint32_t *aux_shape,
                                       NDArrayHandle *out);
 
 MXNET_DLL int MXNDArrayCreateSparseEx64(int storage_type,
-                                        const mx_int64 *shape,
+                                        const int64_t *shape,
                                         int ndim,
                                         int dev_type,
                                         int dev_id,
                                         int delay_alloc,
                                         int dtype,
-                                        mx_uint num_aux,
+                                        uint32_t num_aux,
                                         int *aux_type,
                                         int *aux_ndims,
-                                        const mx_int64 *aux_shape,
+                                        const int64_t *aux_shape,
                                         NDArrayHandle *out);
 
 /*!
@@ -654,7 +652,7 @@ MXNET_DLL int MXNDArraySaveRawBytes(NDArrayHandle handle,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXNDArraySave(const char* fname,
-                            mx_uint num_args,
+                            uint32_t num_args,
                             NDArrayHandle* args,
                             const char** keys);
 /*!
@@ -667,15 +665,15 @@ MXNET_DLL int MXNDArraySave(const char* fname,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXNDArrayLoad(const char* fname,
-                            mx_uint *out_size,
+                            uint32_t *out_size,
                             NDArrayHandle** out_arr,
-                            mx_uint *out_name_size,
+                            uint32_t *out_name_size,
                             const char*** out_names);
 
 MXNET_DLL int MXNDArrayLoad64(const char* fname,
-                              mx_int64 *out_size,
+                              int64_t *out_size,
                               NDArrayHandle** out_arr,
-                              mx_int64 *out_name_size,
+                              int64_t *out_name_size,
                               const char*** out_names);
 
 /*!
@@ -694,16 +692,16 @@ MXNET_DLL int MXNDArrayLoad64(const char* fname,
  */
 MXNET_DLL int MXNDArrayLoadFromBuffer(const void *ndarray_buffer,
                                       size_t size,
-                                      mx_uint *out_size,
+                                      uint32_t *out_size,
                                       NDArrayHandle** out_arr,
-                                      mx_uint *out_name_size,
+                                      uint32_t *out_name_size,
                                       const char*** out_names);
 
 MXNET_DLL int MXNDArrayLoadFromBuffer64(const void *ndarray_buffer,
                                         size_t size,
-                                        mx_int64 *out_size,
+                                        int64_t *out_size,
                                         NDArrayHandle** out_arr,
-                                        mx_int64 *out_name_size,
+                                        int64_t *out_name_size,
                                         const char*** out_names);
 
 /*!
@@ -787,8 +785,8 @@ MXNET_DLL int MXNDArrayFree(NDArrayHandle handle);
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXNDArraySlice(NDArrayHandle handle,
-                             mx_uint slice_begin,
-                             mx_uint slice_end,
+                             uint32_t slice_begin,
+                             uint32_t slice_end,
                              NDArrayHandle *out);
 
 MXNET_DLL int MXNDArraySlice64(NDArrayHandle handle,
@@ -804,7 +802,7 @@ MXNET_DLL int MXNDArraySlice64(NDArrayHandle handle,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXNDArrayAt(NDArrayHandle handle,
-                          mx_uint idx,
+                          uint32_t idx,
                           NDArrayHandle *out);
 
 MXNET_DLL int MXNDArrayAt64(NDArrayHandle handle,
@@ -852,8 +850,8 @@ MXNET_DLL int MXNDArrayReshape64(NDArrayHandle handle,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXNDArrayGetShape(NDArrayHandle handle,
-                                mx_uint *out_dim,
-                                const mx_uint **out_pdata);
+                                uint32_t *out_dim,
+                                const uint32_t **out_pdata);
 
 MXNET_DLL int MXNDArrayGetShape64(NDArrayHandle handle,
                                   int *out_dim,
@@ -872,7 +870,7 @@ MXNET_DLL int MXNDArrayGetShapeEx(NDArrayHandle handle,
 
 MXNET_DLL int MXNDArrayGetShapeEx64(NDArrayHandle handle,
                                     int *out_dim,
-                                    const mx_int64 **out_pdata);
+                                    const int64_t **out_pdata);
 
 /*!
  * \brief get the content of the data in NDArray
@@ -954,11 +952,11 @@ MXNET_DLL int MXNDArrayGetDType(NDArrayHandle handle,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXNDArrayGetAuxType(NDArrayHandle handle,
-                                  mx_uint i,
+                                  uint32_t i,
                                   int *out_type);
 
 MXNET_DLL int MXNDArrayGetAuxType64(NDArrayHandle handle,
-                                    mx_int64 i,
+                                    int64_t i,
                                     int *out_type);
 
 /*!
@@ -967,11 +965,11 @@ MXNET_DLL int MXNDArrayGetAuxType64(NDArrayHandle handle,
  * This function blocks. Do not use it in performance critical code.
  */
 MXNET_DLL int MXNDArrayGetAuxNDArray(NDArrayHandle handle,
-                                     mx_uint i,
+                                     uint32_t i,
                                      NDArrayHandle *out);
 
 MXNET_DLL int MXNDArrayGetAuxNDArray64(NDArrayHandle handle,
-                                       mx_int64 i,
+                                       int64_t i,
                                        NDArrayHandle *out);
 
 /*!
@@ -1027,7 +1025,7 @@ MXNET_DLL int MXNDArrayGetGradState(NDArrayHandle handle, int *out);
  * \param out_array the output function array
  * \return 0 when success, -1 when failure happens
  */
-MXNET_DLL int MXListFunctions(mx_uint *out_size,
+MXNET_DLL int MXListFunctions(uint32_t *out_size,
                               FunctionHandle **out_array);
 
 /*!
@@ -1053,7 +1051,7 @@ MXNET_DLL int MXGetFunction(const char *name,
 MXNET_DLL int MXFuncGetInfo(FunctionHandle fun,
                             const char **name,
                             const char **description,
-                            mx_uint *num_args,
+                            uint32_t *num_args,
                             const char ***arg_names,
                             const char ***arg_type_infos,
                             const char ***arg_descriptions,
@@ -1069,9 +1067,9 @@ MXNET_DLL int MXFuncGetInfo(FunctionHandle fun,
  * \sa MXFuncInvoke
  */
 MXNET_DLL int MXFuncDescribe(FunctionHandle fun,
-                             mx_uint *num_use_vars,
-                             mx_uint *num_scalars,
-                             mx_uint *num_mutate_vars,
+                             uint32_t *num_use_vars,
+                             uint32_t *num_scalars,
+                             uint32_t *num_mutate_vars,
                              int *type_mask);
 /*!
  * \brief invoke a function, the array size of passed in arguments
@@ -1085,7 +1083,7 @@ MXNET_DLL int MXFuncDescribe(FunctionHandle fun,
  */
 MXNET_DLL int MXFuncInvoke(FunctionHandle fun,
                            NDArrayHandle *use_vars,
-                           mx_float *scalar_args,
+                           float *scalar_args,
                            NDArrayHandle *mutate_vars);
 /*!
  * \brief invoke a function, the array size of passed in arguments
@@ -1102,7 +1100,7 @@ MXNET_DLL int MXFuncInvoke(FunctionHandle fun,
  */
 MXNET_DLL int MXFuncInvokeEx(FunctionHandle fun,
                              NDArrayHandle *use_vars,
-                             mx_float *scalar_args,
+                             float *scalar_args,
                              NDArrayHandle *mutate_vars,
                              int num_params,
                              char **param_keys,
@@ -1194,9 +1192,9 @@ MXNET_DLL int MXSetIsNumpyShape(int is_np_shape, int* prev);
  * \param var_handles variable NDArrays
  * \return 0 when success, -1 when failure happens
  */
-MXNET_DLL int MXAutogradMarkVariables(mx_uint num_var,
+MXNET_DLL int MXAutogradMarkVariables(uint32_t num_var,
                                       NDArrayHandle *var_handles,
-                                      mx_uint *reqs_array,
+                                      uint32_t *reqs_array,
                                       NDArrayHandle *grad_handles);
 /*!
  * \brief compute the gradient of outputs w.r.t variabels
@@ -1204,7 +1202,7 @@ MXNET_DLL int MXAutogradMarkVariables(mx_uint num_var,
  * \param output_handles output NDArrays
  * \return 0 when success, -1 when failure happens
  */
-MXNET_DLL int MXAutogradComputeGradient(mx_uint num_output,
+MXNET_DLL int MXAutogradComputeGradient(uint32_t num_output,
                                         NDArrayHandle* output_handles);
 /*!
  * \brief compute the gradient of outputs w.r.t variabels
@@ -1214,7 +1212,7 @@ MXNET_DLL int MXAutogradComputeGradient(mx_uint num_output,
  * \param retain_graph whether to keep the graph after backward
  * \return 0 when success, -1 when failure happens
  */
-MXNET_DLL int MXAutogradBackward(mx_uint num_output,
+MXNET_DLL int MXAutogradBackward(uint32_t num_output,
                                  NDArrayHandle* output_handles,
                                  NDArrayHandle* ograd_handles,
                                  int retain_graph);
@@ -1229,10 +1227,10 @@ MXNET_DLL int MXAutogradBackward(mx_uint num_output,
  * \param is_train whether to do backward for training or inference
  * \return 0 when success, -1 when failure happens
  */
-MXNET_DLL int MXAutogradBackwardEx(mx_uint num_output,
+MXNET_DLL int MXAutogradBackwardEx(uint32_t num_output,
                                    NDArrayHandle *output_handles,
                                    NDArrayHandle *ograd_handles,
-                                   mx_uint num_variables,
+                                   uint32_t num_variables,
                                    NDArrayHandle *var_handles,
                                    int retain_graph,
                                    int create_graph,
@@ -1295,7 +1293,7 @@ MXNET_DLL int MXInvokeCachedOpEx(CachedOpHandle handle,
  * \param out_array the output operator name array.
  * \return 0 when success, -1 when failure happens
  */
-MXNET_DLL int MXListAllOpNames(mx_uint *out_size,
+MXNET_DLL int MXListAllOpNames(uint32_t *out_size,
                                const char ***out_array);
 
 /*!
@@ -1304,7 +1302,7 @@ MXNET_DLL int MXListAllOpNames(mx_uint *out_size,
  * \param out_array the output AtomicSymbolCreator array
  * \return 0 when success, -1 when failure happens
  */
-MXNET_DLL int MXSymbolListAtomicSymbolCreators(mx_uint *out_size,
+MXNET_DLL int MXSymbolListAtomicSymbolCreators(uint32_t *out_size,
                                                AtomicSymbolCreator **out_array);
 
 /*!
@@ -1356,7 +1354,7 @@ MXNET_DLL int MXSymbolCutSubgraph(SymbolHandle sym, SymbolHandle **inputs,
 MXNET_DLL int MXSymbolGetAtomicSymbolInfo(AtomicSymbolCreator creator,
                                           const char **name,
                                           const char **description,
-                                          mx_uint *num_args,
+                                          uint32_t *num_args,
                                           const char ***arg_names,
                                           const char ***arg_type_infos,
                                           const char ***arg_descriptions,
@@ -1372,7 +1370,7 @@ MXNET_DLL int MXSymbolGetAtomicSymbolInfo(AtomicSymbolCreator creator,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXSymbolCreateAtomicSymbol(AtomicSymbolCreator creator,
-                                         mx_uint num_param,
+                                         uint32_t num_param,
                                          const char **keys,
                                          const char **vals,
                                          SymbolHandle *out);
@@ -1390,7 +1388,7 @@ MXNET_DLL int MXSymbolCreateVariable(const char *name, SymbolHandle *out);
  * \param out pointer to the created symbol handle
  * \return 0 when success, -1 when failure happens
  */
-MXNET_DLL int MXSymbolCreateGroup(mx_uint num_symbols,
+MXNET_DLL int MXSymbolCreateGroup(uint32_t num_symbols,
                                   SymbolHandle *symbols,
                                   SymbolHandle *out);
 /*!
@@ -1497,7 +1495,7 @@ MXNET_DLL int MXSymbolSetAttr(SymbolHandle symbol,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXSymbolListAttr(SymbolHandle symbol,
-                               mx_uint *out_size,
+                               uint32_t *out_size,
                                const char*** out);
 /*!
  * \brief Get all attributes from symbol, excluding descendents.
@@ -1507,7 +1505,7 @@ MXNET_DLL int MXSymbolListAttr(SymbolHandle symbol,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXSymbolListAttrShallow(SymbolHandle symbol,
-                                      mx_uint *out_size,
+                                      uint32_t *out_size,
                                       const char*** out);
 /*!
  * \brief List arguments in the symbol.
@@ -1517,7 +1515,7 @@ MXNET_DLL int MXSymbolListAttrShallow(SymbolHandle symbol,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXSymbolListArguments(SymbolHandle symbol,
-                                    mx_uint *out_size,
+                                    uint32_t *out_size,
                                     const char ***out_str_array);
 
 /*!
@@ -1528,7 +1526,7 @@ MXNET_DLL int MXSymbolListArguments(SymbolHandle symbol,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXSymbolListOutputs(SymbolHandle symbol,
-                                  mx_uint *out_size,
+                                  uint32_t *out_size,
                                   const char ***out_str_array);
 
 /*!
@@ -1538,7 +1536,7 @@ MXNET_DLL int MXSymbolListOutputs(SymbolHandle symbol,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXSymbolGetNumOutputs(SymbolHandle symbol,
-                                    mx_uint *output_count);
+                                    uint32_t *output_count);
 
 /*!
  * \brief Get a symbol that contains all the internals.
@@ -1564,7 +1562,7 @@ MXNET_DLL int MXSymbolGetChildren(SymbolHandle symbol,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXSymbolGetOutput(SymbolHandle symbol,
-                                mx_uint index,
+                                uint32_t index,
                                 SymbolHandle *out);
 
 /*!
@@ -1575,7 +1573,7 @@ MXNET_DLL int MXSymbolGetOutput(SymbolHandle symbol,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXSymbolListAuxiliaryStates(SymbolHandle symbol,
-                                          mx_uint *out_size,
+                                          uint32_t *out_size,
                                           const char ***out_str_array);
 
 /*!
@@ -1594,7 +1592,7 @@ MXNET_DLL int MXSymbolListAuxiliaryStates(SymbolHandle symbol,
  */
 MXNET_DLL int MXSymbolCompose(SymbolHandle sym,
                               const char *name,
-                              mx_uint num_args,
+                              uint32_t num_args,
                               const char** keys,
                               SymbolHandle* args);
 /*!
@@ -1607,7 +1605,7 @@ MXNET_DLL int MXSymbolCompose(SymbolHandle sym,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXSymbolGrad(SymbolHandle sym,
-                           mx_uint num_wrt,
+                           uint32_t num_wrt,
                            const char** wrt,
                            SymbolHandle* out);
 /*!
@@ -1634,35 +1632,35 @@ MXNET_DLL int MXSymbolGrad(SymbolHandle sym,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXSymbolInferShape(SymbolHandle sym,
-                                 mx_uint num_args,
+                                 uint32_t num_args,
                                  const char** keys,
-                                 const mx_uint *arg_ind_ptr,
-                                 const mx_uint *arg_shape_data,
-                                 mx_uint *in_shape_size,
-                                 const mx_uint **in_shape_ndim,
-                                 const mx_uint ***in_shape_data,
-                                 mx_uint *out_shape_size,
-                                 const mx_uint **out_shape_ndim,
-                                 const mx_uint ***out_shape_data,
-                                 mx_uint *aux_shape_size,
-                                 const mx_uint **aux_shape_ndim,
-                                 const mx_uint ***aux_shape_data,
+                                 const uint32_t *arg_ind_ptr,
+                                 const uint32_t *arg_shape_data,
+                                 uint32_t *in_shape_size,
+                                 const uint32_t **in_shape_ndim,
+                                 const uint32_t ***in_shape_data,
+                                 uint32_t *out_shape_size,
+                                 const uint32_t **out_shape_ndim,
+                                 const uint32_t ***out_shape_data,
+                                 uint32_t *aux_shape_size,
+                                 const uint32_t **aux_shape_ndim,
+                                 const uint32_t ***aux_shape_data,
                                  int *complete);
 
 MXNET_DLL int MXSymbolInferShape64(SymbolHandle sym,
-                                   mx_uint num_args,
+                                   uint32_t num_args,
                                    const char** keys,
-                                   const mx_int64 *arg_ind_ptr,
-                                   const mx_int64 *arg_shape_data,
+                                   const int64_t *arg_ind_ptr,
+                                   const int64_t *arg_shape_data,
                                    size_t *in_shape_size,
                                    const int **in_shape_ndim,
-                                   const mx_int64 ***in_shape_data,
+                                   const int64_t ***in_shape_data,
                                    size_t *out_shape_size,
                                    const int **out_shape_ndim,
-                                   const mx_int64 ***out_shape_data,
+                                   const int64_t ***out_shape_data,
                                    size_t *aux_shape_size,
                                    const int **aux_shape_ndim,
-                                   const mx_int64 ***aux_shape_data,
+                                   const int64_t ***aux_shape_data,
                                    int *complete);
 
 /*!
@@ -1688,35 +1686,35 @@ MXNET_DLL int MXSymbolInferShape64(SymbolHandle sym,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXSymbolInferShapeEx(SymbolHandle sym,
-                                   mx_uint num_args,
+                                   uint32_t num_args,
                                    const char** keys,
-                                   const mx_uint *arg_ind_ptr,
+                                   const uint32_t *arg_ind_ptr,
                                    const int *arg_shape_data,
-                                   mx_uint *in_shape_size,
+                                   uint32_t *in_shape_size,
                                    const int **in_shape_ndim,
                                    const int ***in_shape_data,
-                                   mx_uint *out_shape_size,
+                                   uint32_t *out_shape_size,
                                    const int **out_shape_ndim,
                                    const int ***out_shape_data,
-                                   mx_uint *aux_shape_size,
+                                   uint32_t *aux_shape_size,
                                    const int **aux_shape_ndim,
                                    const int ***aux_shape_data,
                                    int *complete);
 
 MXNET_DLL int MXSymbolInferShapeEx64(SymbolHandle sym,
-                                     mx_uint num_args,
+                                     uint32_t num_args,
                                      const char** keys,
-                                     const mx_int64 *arg_ind_ptr,
-                                     const mx_int64 *arg_shape_data,
+                                     const int64_t *arg_ind_ptr,
+                                     const int64_t *arg_shape_data,
                                      size_t *in_shape_size,
                                      const int **in_shape_ndim,
-                                     const mx_int64 ***in_shape_data,
+                                     const int64_t ***in_shape_data,
                                      size_t *out_shape_size,
                                      const int **out_shape_ndim,
-                                     const mx_int64 ***out_shape_data,
+                                     const int64_t ***out_shape_data,
                                      size_t *aux_shape_size,
                                      const int **aux_shape_ndim,
-                                     const mx_int64 ***aux_shape_data,
+                                     const int64_t ***aux_shape_data,
                                      int *complete);
 
 /*!
@@ -1745,35 +1743,35 @@ MXNET_DLL int MXSymbolInferShapeEx64(SymbolHandle sym,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXSymbolInferShapePartial(SymbolHandle sym,
-                                        mx_uint num_args,
+                                        uint32_t num_args,
                                         const char** keys,
-                                        const mx_uint *arg_ind_ptr,
-                                        const mx_uint *arg_shape_data,
-                                        mx_uint *in_shape_size,
-                                        const mx_uint **in_shape_ndim,
-                                        const mx_uint ***in_shape_data,
-                                        mx_uint *out_shape_size,
-                                        const mx_uint **out_shape_ndim,
-                                        const mx_uint ***out_shape_data,
-                                        mx_uint *aux_shape_size,
-                                        const mx_uint **aux_shape_ndim,
-                                        const mx_uint ***aux_shape_data,
+                                        const uint32_t *arg_ind_ptr,
+                                        const uint32_t *arg_shape_data,
+                                        uint32_t *in_shape_size,
+                                        const uint32_t **in_shape_ndim,
+                                        const uint32_t ***in_shape_data,
+                                        uint32_t *out_shape_size,
+                                        const uint32_t **out_shape_ndim,
+                                        const uint32_t ***out_shape_data,
+                                        uint32_t *aux_shape_size,
+                                        const uint32_t **aux_shape_ndim,
+                                        const uint32_t ***aux_shape_data,
                                         int *complete);
 
 MXNET_DLL int MXSymbolInferShapePartial64(SymbolHandle sym,
-                                          mx_uint num_args,
+                                          uint32_t num_args,
                                           const char** keys,
-                                          const mx_int64 *arg_ind_ptr,
-                                          const mx_int64 *arg_shape_data,
+                                          const int64_t *arg_ind_ptr,
+                                          const int64_t *arg_shape_data,
                                           size_t *in_shape_size,
                                           const int **in_shape_ndim,
-                                          const mx_int64 ***in_shape_data,
+                                          const int64_t ***in_shape_data,
                                           size_t *out_shape_size,
                                           const int **out_shape_ndim,
-                                          const mx_int64 ***out_shape_data,
+                                          const int64_t ***out_shape_data,
                                           size_t *aux_shape_size,
                                           const int **aux_shape_ndim,
-                                          const mx_int64 ***aux_shape_data,
+                                          const int64_t ***aux_shape_data,
                                           int *complete);
 
 /*!
@@ -1801,35 +1799,35 @@ MXNET_DLL int MXSymbolInferShapePartial64(SymbolHandle sym,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXSymbolInferShapePartialEx(SymbolHandle sym,
-                                          mx_uint num_args,
+                                          uint32_t num_args,
                                           const char** keys,
-                                          const mx_uint *arg_ind_ptr,
+                                          const uint32_t *arg_ind_ptr,
                                           const int *arg_shape_data,
-                                          mx_uint *in_shape_size,
+                                          uint32_t *in_shape_size,
                                           const int **in_shape_ndim,
                                           const int ***in_shape_data,
-                                          mx_uint *out_shape_size,
+                                          uint32_t *out_shape_size,
                                           const int **out_shape_ndim,
                                           const int ***out_shape_data,
-                                          mx_uint *aux_shape_size,
+                                          uint32_t *aux_shape_size,
                                           const int **aux_shape_ndim,
                                           const int ***aux_shape_data,
                                           int *complete);
 
 MXNET_DLL int MXSymbolInferShapePartialEx64(SymbolHandle sym,
-                                            mx_uint num_args,
+                                            uint32_t num_args,
                                             const char** keys,
-                                            const mx_int64 *arg_ind_ptr,
-                                            const mx_int64 *arg_shape_data,
+                                            const int64_t *arg_ind_ptr,
+                                            const int64_t *arg_shape_data,
                                             size_t *in_shape_size,
                                             const int **in_shape_ndim,
-                                            const mx_int64 ***in_shape_data,
+                                            const int64_t ***in_shape_data,
                                             size_t *out_shape_size,
                                             const int **out_shape_ndim,
-                                            const mx_int64 ***out_shape_data,
+                                            const int64_t ***out_shape_data,
                                             size_t *aux_shape_size,
                                             const int **aux_shape_ndim,
-                                            const mx_int64 ***aux_shape_data,
+                                            const int64_t ***aux_shape_data,
                                             int *complete);
 
 /*!
@@ -1851,14 +1849,14 @@ MXNET_DLL int MXSymbolInferShapePartialEx64(SymbolHandle sym,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXSymbolInferType(SymbolHandle sym,
-                                mx_uint num_args,
+                                uint32_t num_args,
                                 const char** keys,
                                 const int *arg_type_data,
-                                mx_uint *in_type_size,
+                                uint32_t *in_type_size,
                                 const int **in_type_data,
-                                mx_uint *out_type_size,
+                                uint32_t *out_type_size,
                                 const int **out_type_data,
-                                mx_uint *aux_type_size,
+                                uint32_t *aux_type_size,
                                 const int **aux_type_data,
                                 int *complete);
 
@@ -1883,14 +1881,14 @@ MXNET_DLL int MXSymbolInferType(SymbolHandle sym,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXSymbolInferTypePartial(SymbolHandle sym,
-                                       mx_uint num_args,
+                                       uint32_t num_args,
                                        const char** keys,
                                        const int *arg_type_data,
-                                       mx_uint *in_type_size,
+                                       uint32_t *in_type_size,
                                        const int **in_type_data,
-                                       mx_uint *out_type_size,
+                                       uint32_t *out_type_size,
                                        const int **out_type_data,
-                                       mx_uint *aux_type_size,
+                                       uint32_t *aux_type_size,
                                        const int **aux_type_data,
                                        int *complete);
 
@@ -1911,15 +1909,16 @@ MXNET_DLL int MXSymbolInferTypePartial(SymbolHandle sym,
  * \param out_num_calib_names return the number of nodes to be calibrated
  * \param out_calib_names return the node names to be calibrated
  */
-MXNET_DLL int MXQuantizeSymbol(SymbolHandle sym_handle, SymbolHandle *ret_sym_handle,
+MXNET_DLL int MXQuantizeSymbol(SymbolHandle sym_handle,
+                               SymbolHandle *ret_sym_handle,
                                const int* dev_type,
-                               const mx_uint num_excluded_sym_names,
+                               const uint32_t num_excluded_sym_names,
                                const char **excluded_sym_names,
-                               const mx_uint num_excluded_op_names,
+                               const uint32_t num_excluded_op_names,
                                const char **excluded_op_names,
-                               const mx_uint num_offline, const char **offline_params,
+                               const uint32_t num_offline, const char **offline_params,
                                const char *quantized_dtype, const bool calib_quantize,
-                               const char *quantize_mode, mx_uint* out_num_calib_names,
+                               const char *quantize_mode, uint32_t* out_num_calib_names,
                                const char ***out_calib_names);
 
 /*!
@@ -1950,18 +1949,18 @@ MXNET_DLL int MXQuantizeSymbol(SymbolHandle sym_handle, SymbolHandle *ret_sym_ha
  */
 MXNET_DLL int MXReducePrecisionSymbol(SymbolHandle sym_handle,
                                       SymbolHandle *ret_sym_handle,
-                                      mx_uint num_args,
+                                      uint32_t num_args,
                                       const int* arg_type_data,
-                                      mx_uint num_ind_ptr,
+                                      uint32_t num_ind_ptr,
                                       const int* ind_ptr,
                                       const int* target_dtype,
                                       const int cast_optional_params,
-                                      const mx_uint num_target_dtype_op_names,
-                                      const mx_uint num_fp32_op_names,
-                                      const mx_uint num_widest_dtype_op_names,
-                                      const mx_uint num_conditional_fp32_op_names,
-                                      const mx_uint num_excluded_symbols,
-                                      const mx_uint num_model_params,
+                                      const uint32_t num_target_dtype_op_names,
+                                      const uint32_t num_fp32_op_names,
+                                      const uint32_t num_widest_dtype_op_names,
+                                      const uint32_t num_conditional_fp32_op_names,
+                                      const uint32_t num_excluded_symbols,
+                                      const uint32_t num_model_params,
                                       const char **target_dtype_op_names,
                                       const char **fp32_op_names,
                                       const char **widest_dtype_op_names,
@@ -1981,7 +1980,7 @@ MXNET_DLL int MXReducePrecisionSymbol(SymbolHandle sym_handle,
  * \param ret_sym_handle returned symbol
  */
 MXNET_DLL int MXSetCalibTableToQuantizedSymbol(SymbolHandle qsym_handle,
-                                               const mx_uint num_layers,
+                                               const uint32_t num_layers,
                                                const char** layer_names,
                                                const float* low_quantiles,
                                                const float* high_quantiles,
@@ -2059,7 +2058,7 @@ MXNET_DLL int MXExecutorForward(ExecutorHandle handle, int is_train);
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXExecutorBackward(ExecutorHandle handle,
-                                 mx_uint len,
+                                 uint32_t len,
                                  NDArrayHandle *head_grads);
 /*!
  * \brief Excecutor run backward
@@ -2072,7 +2071,7 @@ MXNET_DLL int MXExecutorBackward(ExecutorHandle handle,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXExecutorBackwardEx(ExecutorHandle handle,
-                                   mx_uint len,
+                                   uint32_t len,
                                    NDArrayHandle *head_grads,
                                    int is_train);
 /*!
@@ -2084,7 +2083,7 @@ MXNET_DLL int MXExecutorBackwardEx(ExecutorHandle handle,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXExecutorOutputs(ExecutorHandle handle,
-                                mx_uint *out_size,
+                                uint32_t *out_size,
                                 NDArrayHandle **out);
 
 /*!
@@ -2105,11 +2104,11 @@ MXNET_DLL int MXExecutorOutputs(ExecutorHandle handle,
 MXNET_DLL int MXExecutorBind(SymbolHandle symbol_handle,
                              int dev_type,
                              int dev_id,
-                             mx_uint len,
+                             uint32_t len,
                              NDArrayHandle *in_args,
                              NDArrayHandle *arg_grad_store,
-                             mx_uint *grad_req_type,
-                             mx_uint aux_states_len,
+                             uint32_t *grad_req_type,
+                             uint32_t aux_states_len,
                              NDArrayHandle *aux_states,
                              ExecutorHandle *out);
 /*!
@@ -2136,15 +2135,15 @@ MXNET_DLL int MXExecutorBind(SymbolHandle symbol_handle,
 MXNET_DLL int MXExecutorBindX(SymbolHandle symbol_handle,
                               int dev_type,
                               int dev_id,
-                              mx_uint num_map_keys,
+                              uint32_t num_map_keys,
                               const char** map_keys,
                               const int* map_dev_types,
                               const int* map_dev_ids,
-                              mx_uint len,
+                              uint32_t len,
                               NDArrayHandle *in_args,
                               NDArrayHandle *arg_grad_store,
-                              mx_uint *grad_req_type,
-                              mx_uint aux_states_len,
+                              uint32_t *grad_req_type,
+                              uint32_t aux_states_len,
                               NDArrayHandle *aux_states,
                               ExecutorHandle *out);
 /*!
@@ -2172,15 +2171,15 @@ MXNET_DLL int MXExecutorBindX(SymbolHandle symbol_handle,
 MXNET_DLL int MXExecutorBindEX(SymbolHandle symbol_handle,
                                int dev_type,
                                int dev_id,
-                               mx_uint num_map_keys,
+                               uint32_t num_map_keys,
                                const char** map_keys,
                                const int* map_dev_types,
                                const int* map_dev_ids,
-                               mx_uint len,
+                               uint32_t len,
                                NDArrayHandle *in_args,
                                NDArrayHandle *arg_grad_store,
-                               mx_uint *grad_req_type,
-                               mx_uint aux_states_len,
+                               uint32_t *grad_req_type,
+                               uint32_t aux_states_len,
                                NDArrayHandle *aux_states,
                                ExecutorHandle shared_exec,
                                ExecutorHandle *out);
@@ -2189,34 +2188,34 @@ MXNET_DLL int MXExecutorBindEX(SymbolHandle symbol_handle,
 MXNET_DLL int MXExecutorSimpleBind(SymbolHandle symbol_handle,
                                    int dev_type,
                                    int dev_id,
-                                   const mx_uint num_g2c_keys,
+                                   const uint32_t num_g2c_keys,
                                    const char** g2c_keys,
                                    const int* g2c_dev_types,
                                    const int* g2c_dev_ids,
-                                   const mx_uint provided_grad_req_list_len,
+                                   const uint32_t provided_grad_req_list_len,
                                    const char** provided_grad_req_names,
                                    const char** provided_grad_req_types,
-                                   const mx_uint num_provided_arg_shapes,
+                                   const uint32_t num_provided_arg_shapes,
                                    const char** provided_arg_shape_names,
-                                   const mx_uint* provided_arg_shape_data,
-                                   const mx_uint* provided_arg_shape_idx,
-                                   const mx_uint num_provided_arg_dtypes,
+                                   const uint32_t* provided_arg_shape_data,
+                                   const uint32_t* provided_arg_shape_idx,
+                                   const uint32_t num_provided_arg_dtypes,
                                    const char** provided_arg_dtype_names,
                                    const int* provided_arg_dtypes,
-                                   const mx_uint num_provided_arg_stypes,
+                                   const uint32_t num_provided_arg_stypes,
                                    const char** provided_arg_stype_names,
                                    const int* provided_arg_stypes,
-                                   const mx_uint num_shared_arg_names,
+                                   const uint32_t num_shared_arg_names,
                                    const char** shared_arg_name_list,
                                    int* shared_buffer_len,
                                    const char** shared_buffer_name_list,
                                    NDArrayHandle* shared_buffer_handle_list,
                                    const char*** updated_shared_buffer_name_list,
                                    NDArrayHandle** updated_shared_buffer_handle_list,
-                                   mx_uint* num_in_args,
+                                   uint32_t* num_in_args,
                                    NDArrayHandle** in_args,
                                    NDArrayHandle** arg_grads,
-                                   mx_uint* num_aux_states,
+                                   uint32_t* num_aux_states,
                                    NDArrayHandle** aux_states,
                                    ExecutorHandle shared_exec_handle,
                                    ExecutorHandle* out);
@@ -2225,34 +2224,34 @@ MXNET_DLL int MXExecutorSimpleBind(SymbolHandle symbol_handle,
 MXNET_DLL int MXExecutorSimpleBindEx(SymbolHandle symbol_handle,
                                      int dev_type,
                                      int dev_id,
-                                     const mx_uint num_g2c_keys,
+                                     const uint32_t num_g2c_keys,
                                      const char** g2c_keys,
                                      const int* g2c_dev_types,
                                      const int* g2c_dev_ids,
-                                     const mx_uint provided_grad_req_list_len,
+                                     const uint32_t provided_grad_req_list_len,
                                      const char** provided_grad_req_names,
                                      const char** provided_grad_req_types,
-                                     const mx_uint num_provided_arg_shapes,
+                                     const uint32_t num_provided_arg_shapes,
                                      const char** provided_arg_shape_names,
                                      const int* provided_arg_shape_data,
-                                     const mx_uint* provided_arg_shape_idx,
-                                     const mx_uint num_provided_arg_dtypes,
+                                     const uint32_t* provided_arg_shape_idx,
+                                     const uint32_t num_provided_arg_dtypes,
                                      const char** provided_arg_dtype_names,
                                      const int* provided_arg_dtypes,
-                                     const mx_uint num_provided_arg_stypes,
+                                     const uint32_t num_provided_arg_stypes,
                                      const char** provided_arg_stype_names,
                                      const int* provided_arg_stypes,
-                                     const mx_uint num_shared_arg_names,
+                                     const uint32_t num_shared_arg_names,
                                      const char** shared_arg_name_list,
                                      int* shared_buffer_len,
                                      const char** shared_buffer_name_list,
                                      NDArrayHandle* shared_buffer_handle_list,
                                      const char*** updated_shared_buffer_name_list,
                                      NDArrayHandle** updated_shared_buffer_handle_list,
-                                     mx_uint* num_in_args,
+                                     uint32_t* num_in_args,
                                      NDArrayHandle** in_args,
                                      NDArrayHandle** arg_grads,
-                                     mx_uint* num_aux_states,
+                                     uint32_t* num_aux_states,
                                      NDArrayHandle** aux_states,
                                      ExecutorHandle shared_exec_handle,
                                      ExecutorHandle* out);
@@ -2282,18 +2281,18 @@ MXNET_DLL int MXExecutorReshape(int partial_shaping,
                                 int allow_up_sizing,
                                 int dev_type,
                                 int dev_id,
-                                mx_uint num_map_keys,
+                                uint32_t num_map_keys,
                                 const char** map_keys,
                                 const int* map_dev_types,
                                 const int* map_dev_ids,
-                                const mx_uint num_provided_arg_shapes,
+                                const uint32_t num_provided_arg_shapes,
                                 const char** provided_arg_shape_names,
-                                const mx_uint* provided_arg_shape_data,
-                                const mx_uint* provided_arg_shape_idx,
-                                mx_uint* num_in_args,
+                                const uint32_t* provided_arg_shape_data,
+                                const uint32_t* provided_arg_shape_idx,
+                                uint32_t* num_in_args,
                                 NDArrayHandle** in_args,
                                 NDArrayHandle** arg_grads,
-                                mx_uint* num_aux_states,
+                                uint32_t* num_aux_states,
                                 NDArrayHandle** aux_states,
                                 ExecutorHandle shared_exec,
                                 ExecutorHandle *out);
@@ -2322,18 +2321,18 @@ MXNET_DLL int MXExecutorReshapeEx(int partial_shaping,
                                   int allow_up_sizing,
                                   int dev_type,
                                   int dev_id,
-                                  mx_uint num_map_keys,
+                                  uint32_t num_map_keys,
                                   const char** map_keys,
                                   const int* map_dev_types,
                                   const int* map_dev_ids,
-                                  const mx_uint num_provided_arg_shapes,
+                                  const uint32_t num_provided_arg_shapes,
                                   const char** provided_arg_shape_names,
                                   const int* provided_arg_shape_data,
-                                  const mx_uint* provided_arg_shape_idx,
-                                  mx_uint* num_in_args,
+                                  const uint32_t* provided_arg_shape_idx,
+                                  uint32_t* num_in_args,
                                   NDArrayHandle** in_args,
                                   NDArrayHandle** arg_grads,
-                                  mx_uint* num_aux_states,
+                                  uint32_t* num_aux_states,
                                   NDArrayHandle** aux_states,
                                   ExecutorHandle shared_exec,
                                   ExecutorHandle *out);
@@ -2366,7 +2365,7 @@ MXNET_DLL int MXExecutorSetMonitorCallbackEX(ExecutorHandle handle,
  * \param out_array the output iteratos entries
  * \return 0 when success, -1 when failure happens
  */
-MXNET_DLL int MXListDataIters(mx_uint *out_size,
+MXNET_DLL int MXListDataIters(uint32_t *out_size,
                               DataIterCreator **out_array);
 /*!
  * \brief Init an iterator, init with parameters
@@ -2379,7 +2378,7 @@ MXNET_DLL int MXListDataIters(mx_uint *out_size,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXDataIterCreateIter(DataIterCreator handle,
-                                   mx_uint num_param,
+                                   uint32_t num_param,
                                    const char **keys,
                                    const char **vals,
                                    DataIterHandle *out);
@@ -2397,7 +2396,7 @@ MXNET_DLL int MXDataIterCreateIter(DataIterCreator handle,
 MXNET_DLL int MXDataIterGetIterInfo(DataIterCreator creator,
                                     const char **name,
                                     const char **description,
-                                    mx_uint *num_args,
+                                    uint32_t *num_args,
                                     const char ***arg_names,
                                     const char ***arg_type_infos,
                                     const char ***arg_descriptions);
@@ -2466,7 +2465,7 @@ MXNET_DLL int MXDataIterGetLabel(DataIterHandle handle,
  * \param keys environment keys
  * \param vals environment values
  */
-MXNET_DLL int MXInitPSEnv(mx_uint num_vars,
+MXNET_DLL int MXInitPSEnv(uint32_t num_vars,
                           const char **keys,
                           const char **vals);
 
@@ -2488,7 +2487,7 @@ MXNET_DLL int MXKVStoreCreate(const char *type,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXKVStoreSetGradientCompression(KVStoreHandle handle,
-                                              mx_uint num_params,
+                                              uint32_t num_params,
                                               const char** keys,
                                               const char** vals);
 
@@ -2507,7 +2506,7 @@ MXNET_DLL int MXKVStoreFree(KVStoreHandle handle);
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXKVStoreInit(KVStoreHandle handle,
-                            mx_uint num,
+                            uint32_t num,
                             const int* keys,
                             NDArrayHandle* vals);
 
@@ -2520,7 +2519,7 @@ MXNET_DLL int MXKVStoreInit(KVStoreHandle handle,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXKVStoreInitEx(KVStoreHandle handle,
-                              mx_uint num,
+                              uint32_t num,
                               const char** keys,
                               NDArrayHandle* vals);
 
@@ -2534,7 +2533,7 @@ MXNET_DLL int MXKVStoreInitEx(KVStoreHandle handle,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXKVStorePush(KVStoreHandle handle,
-                            mx_uint num,
+                            uint32_t num,
                             const int* keys,
                             NDArrayHandle* vals,
                             int priority);
@@ -2548,7 +2547,7 @@ MXNET_DLL int MXKVStorePush(KVStoreHandle handle,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXKVStorePushEx(KVStoreHandle handle,
-                              mx_uint num,
+                              uint32_t num,
                               const char** keys,
                               NDArrayHandle* vals,
                               int priority);
@@ -2563,7 +2562,7 @@ MXNET_DLL int MXKVStorePushEx(KVStoreHandle handle,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXKVStorePullWithSparse(KVStoreHandle handle,
-                                      mx_uint num,
+                                      uint32_t num,
                                       const int* keys,
                                       NDArrayHandle* vals,
                                       int priority,
@@ -2579,7 +2578,7 @@ MXNET_DLL int MXKVStorePullWithSparse(KVStoreHandle handle,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXKVStorePullWithSparseEx(KVStoreHandle handle,
-                                        mx_uint num,
+                                        uint32_t num,
                                         const char** keys,
                                         NDArrayHandle* vals,
                                         int priority,
@@ -2594,7 +2593,7 @@ MXNET_DLL int MXKVStorePullWithSparseEx(KVStoreHandle handle,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXKVStorePull(KVStoreHandle handle,
-                            mx_uint num,
+                            uint32_t num,
                             const int* keys,
                             NDArrayHandle* vals,
                             int priority);
@@ -2608,7 +2607,7 @@ MXNET_DLL int MXKVStorePull(KVStoreHandle handle,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXKVStorePullEx(KVStoreHandle handle,
-                              mx_uint num,
+                              uint32_t num,
                               const char** keys,
                               NDArrayHandle* vals,
                               int priority);
@@ -2626,7 +2625,7 @@ MXNET_DLL int MXKVStorePullEx(KVStoreHandle handle,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXKVStorePullRowSparse(KVStoreHandle handle,
-                                     mx_uint num,
+                                     uint32_t num,
                                      const int* keys,
                                      NDArrayHandle* vals,
                                      const NDArrayHandle* row_ids,
@@ -2644,7 +2643,7 @@ MXNET_DLL int MXKVStorePullRowSparse(KVStoreHandle handle,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXKVStorePullRowSparseEx(KVStoreHandle handle,
-                                       mx_uint num,
+                                       uint32_t num,
                                        const char** keys,
                                        NDArrayHandle* vals,
                                        const NDArrayHandle* row_ids,
@@ -2895,7 +2894,7 @@ MXNET_DLL int MXRecordIOReaderTell(RecordIOHandle handle, size_t *pos);
 /**
  * \brief Create a MXRtc object
 */
-MXNET_DLL int MXRtcCreate(char* name, mx_uint num_input, mx_uint num_output,
+MXNET_DLL int MXRtcCreate(char* name, uint32_t num_input, uint32_t num_output,
                           char** input_names, char** output_names,
                           NDArrayHandle* inputs, NDArrayHandle* outputs,
                           char* kernel, RtcHandle *out);
@@ -2903,14 +2902,14 @@ MXNET_DLL int MXRtcCreate(char* name, mx_uint num_input, mx_uint num_output,
 /**
  * \brief Run cuda kernel
 */
-MXNET_DLL int MXRtcPush(RtcHandle handle, mx_uint num_input, mx_uint num_output,
+MXNET_DLL int MXRtcPush(RtcHandle handle, uint32_t num_input, uint32_t num_output,
                         NDArrayHandle* inputs, NDArrayHandle* outputs,
-                        mx_uint gridDimX,
-                        mx_uint gridDimY,
-                        mx_uint gridDimZ,
-                        mx_uint blockDimX,
-                        mx_uint blockDimY,
-                        mx_uint blockDimZ);
+                        uint32_t gridDimX,
+                        uint32_t gridDimY,
+                        uint32_t gridDimZ,
+                        uint32_t blockDimX,
+                        uint32_t blockDimY,
+                        uint32_t blockDimZ);
 
 /**
  * \brief Delete a MXRtc object
@@ -2982,10 +2981,10 @@ MXNET_DLL int MXRtcCudaKernelFree(CudaKernelHandle handle);
  * \param shared_mem size of dynamically allocated shared memory
  */
 MXNET_DLL int MXRtcCudaKernelCall(CudaKernelHandle handle, int dev_id, void** args,
-                                  mx_uint grid_dim_x, mx_uint grid_dim_y,
-                                  mx_uint grid_dim_z, mx_uint block_dim_x,
-                                  mx_uint block_dim_y, mx_uint block_dim_z,
-                                  mx_uint shared_mem);
+                                  uint32_t grid_dim_x, uint32_t grid_dim_y,
+                                  uint32_t grid_dim_z, uint32_t block_dim_x,
+                                  uint32_t block_dim_y, uint32_t block_dim_z,
+                                  uint32_t shared_mem);
 /*!
  * \brief Get shared memory handle from NDArray
  * \param handle NDArray handle.
@@ -3004,8 +3003,8 @@ MXNET_DLL int MXNDArrayGetSharedMemHandle(NDArrayHandle handle, int* shared_pid,
  * \param dtype data type of NDArray
  * \param out constructed NDArray
  */
-MXNET_DLL int MXNDArrayCreateFromSharedMem(int shared_pid, int shared_id, const mx_uint *shape,
-                                           mx_uint ndim, int dtype, NDArrayHandle *out);
+MXNET_DLL int MXNDArrayCreateFromSharedMem(int shared_pid, int shared_id, const uint32_t *shape,
+                                           uint32_t ndim, int dtype, NDArrayHandle *out);
 
 /*!
  * \brief Release all unreferenced memory from the devices storage managers memory pool
diff --git a/include/mxnet/c_api_test.h b/include/mxnet/c_api_test.h
index ce2670e9a6f6..8d397d42e5c1 100644
--- a/include/mxnet/c_api_test.h
+++ b/include/mxnet/c_api_test.h
@@ -40,7 +40,7 @@ extern "C" {
  */
 MXNET_DLL int MXBuildSubgraphByOpNames(SymbolHandle sym_handle,
                                         const char* prop_name,
-                                        const mx_uint num_ops,
+                                        const uint32_t num_ops,
                                         const char** op_names,
                                         SymbolHandle* ret_sym_handle);
 
@@ -50,7 +50,7 @@ MXNET_DLL int MXBuildSubgraphByOpNames(SymbolHandle sym_handle,
  * the predefined one. This is only for the purpose of testing.
  */
 MXNET_DLL int MXSetSubgraphPropertyOpNames(const char* prop_name,
-                                           const mx_uint num_ops,
+                                           const uint32_t num_ops,
                                            const char** op_names);
 
 /*!
diff --git a/include/mxnet/c_predict_api.h b/include/mxnet/c_predict_api.h
index c79baa4a86ff..7769664a9783 100644
--- a/include/mxnet/c_predict_api.h
+++ b/include/mxnet/c_predict_api.h
@@ -43,8 +43,6 @@ extern "C" {
 
 /*! \brief manually define unsigned int */
 typedef uint32_t mx_uint;
-/*! \brief manually define 64-bit int */
-typedef int64_t mx_int64;
 /*! \brief manually define float */
 typedef float mx_float;
 /*! \brief handle to Predictor */
@@ -87,10 +85,10 @@ MXNET_DLL int MXPredCreate(const char* symbol_json_str,
                            const void* param_bytes,
                            int param_size,
                            int dev_type, int dev_id,
-                           mx_uint num_input_nodes,
+                           uint32_t num_input_nodes,
                            const char** input_keys,
-                           const mx_uint* input_shape_indptr,
-                           const mx_uint* input_shape_data,
+                           const uint32_t* input_shape_indptr,
+                           const uint32_t* input_shape_data,
                            PredictorHandle* out);
 
 /*!
@@ -122,11 +120,11 @@ MXNET_DLL int MXPredCreateEx(const char* symbol_json_str,
                              const void* param_bytes,
                              int param_size,
                              int dev_type, int dev_id,
-                             const mx_uint num_input_nodes,
+                             const uint32_t num_input_nodes,
                              const char** input_keys,
-                             const mx_uint* input_shape_indptr,
-                             const mx_uint* input_shape_data,
-                             const mx_uint num_provided_arg_dtypes,
+                             const uint32_t* input_shape_indptr,
+                             const uint32_t* input_shape_data,
+                             const uint32_t num_provided_arg_dtypes,
                              const char** provided_arg_dtype_names,
                              const int* provided_arg_dtypes,
                              PredictorHandle* out);
@@ -158,11 +156,11 @@ MXNET_DLL int MXPredCreatePartialOut(const char* symbol_json_str,
                                      const void* param_bytes,
                                      int param_size,
                                      int dev_type, int dev_id,
-                                     mx_uint num_input_nodes,
+                                     uint32_t num_input_nodes,
                                      const char** input_keys,
-                                     const mx_uint* input_shape_indptr,
-                                     const mx_uint* input_shape_data,
-                                     mx_uint num_output_nodes,
+                                     const uint32_t* input_shape_indptr,
+                                     const uint32_t* input_shape_data,
+                                     uint32_t num_output_nodes,
                                      const char** output_keys,
                                      PredictorHandle* out);
 
@@ -191,10 +189,10 @@ MXNET_DLL int MXPredCreateMultiThread(const char* symbol_json_str,
                                       const void* param_bytes,
                                       int param_size,
                                       int dev_type, int dev_id,
-                                      mx_uint num_input_nodes,
+                                      uint32_t num_input_nodes,
                                       const char** input_keys,
-                                      const mx_uint* input_shape_indptr,
-                                      const mx_uint* input_shape_data,
+                                      const uint32_t* input_shape_indptr,
+                                      const uint32_t* input_shape_data,
                                       int num_threads,
                                       PredictorHandle* out);
 
@@ -213,10 +211,10 @@ MXNET_DLL int MXPredCreateMultiThread(const char* symbol_json_str,
  * \param out The reshaped predictor handle.
  * \return 0 when success, -1 when failure.
  */
-MXNET_DLL int MXPredReshape(mx_uint num_input_nodes,
+MXNET_DLL int MXPredReshape(uint32_t num_input_nodes,
                   const char** input_keys,
-                  const mx_uint* input_shape_indptr,
-                  const mx_uint* input_shape_data,
+                  const uint32_t* input_shape_indptr,
+                  const uint32_t* input_shape_data,
                   PredictorHandle handle,
                   PredictorHandle* out);
 /*!
@@ -229,9 +227,9 @@ MXNET_DLL int MXPredReshape(mx_uint num_input_nodes,
  * \return 0 when success, -1 when failure.
  */
 MXNET_DLL int MXPredGetOutputShape(PredictorHandle handle,
-                                   mx_uint index,
-                                   mx_uint** shape_data,
-                                   mx_uint* shape_ndim);
+                                   uint32_t index,
+                                   uint32_t** shape_data,
+                                   uint32_t* shape_ndim);
 
 /*!
  * \brief Get the dtype of output node.
@@ -241,7 +239,7 @@ MXNET_DLL int MXPredGetOutputShape(PredictorHandle handle,
  * \param out_dtype The dtype of the output node
  */
 MXNET_DLL int MXPredGetOutputType(PredictorHandle handle,
-                                  mx_uint out_index,
+                                  uint32_t out_index,
                                   int* out_dtype);
 
 /*!
@@ -255,8 +253,8 @@ MXNET_DLL int MXPredGetOutputType(PredictorHandle handle,
  */
 MXNET_DLL int MXPredSetInput(PredictorHandle handle,
                              const char* key,
-                             const mx_float* data,
-                             mx_uint size);
+                             const float* data,
+                             uint32_t size);
 /*!
  * \brief Run a forward pass to get the output.
  * \param handle The handle of the predictor.
@@ -289,9 +287,9 @@ MXNET_DLL int MXPredPartialForward(PredictorHandle handle, int step, int* step_l
  * \return 0 when success, -1 when failure.
  */
 MXNET_DLL int MXPredGetOutput(PredictorHandle handle,
-                              mx_uint index,
-                              mx_float* data,
-                              mx_uint size);
+                              uint32_t index,
+                              float* data,
+                              uint32_t size);
 /*!
  * \brief Free a predictor handle.
  * \param handle The handle of the predictor.
@@ -310,7 +308,7 @@ MXNET_DLL int MXPredFree(PredictorHandle handle);
 MXNET_DLL int MXNDListCreate(const char* nd_file_bytes,
                              int nd_file_size,
                              NDListHandle *out,
-                             mx_uint* out_length);
+                             uint32_t* out_length);
 /*!
  * \brief Get an element from list
  * \param handle The handle to the NDArray
@@ -322,11 +320,11 @@ MXNET_DLL int MXNDListCreate(const char* nd_file_bytes,
  * \return 0 when success, -1 when failure.
  */
 MXNET_DLL int MXNDListGet(NDListHandle handle,
-                          mx_uint index,
+                          uint32_t index,
                           const char** out_key,
-                          const mx_float** out_data,
-                          const mx_uint** out_shape,
-                          mx_uint* out_ndim);
+                          const float** out_data,
+                          const uint32_t** out_shape,
+                          uint32_t* out_ndim);
 
 /*!
  * \brief set a call back to notify the completion of operation and allow for
diff --git a/include/mxnet/imperative.h b/include/mxnet/imperative.h
index a86cc085a34b..c588565012a5 100644
--- a/include/mxnet/imperative.h
+++ b/include/mxnet/imperative.h
@@ -129,7 +129,7 @@ class Imperative {
                       OpStatePtr state = OpStatePtr());
   /*! \brief mark variables for computing gradients. */
   void MarkVariables(const std::vector<NDArray*>& variables,
-                     const std::vector<mx_uint>& grad_reqs,
+                     const std::vector<uint32_t>& grad_reqs,
                      const std::vector<NDArray*>& gradients);
   /*! \brief compute the gradient of outputs w.r.t variables. */
   std::vector<NDArray*> Backward(const std::vector<NDArray*>& outputs,
diff --git a/src/c_api/c_api.cc b/src/c_api/c_api.cc
index 71f459d49185..f3e0ba8f5c26 100644
--- a/src/c_api/c_api.cc
+++ b/src/c_api/c_api.cc
@@ -62,7 +62,7 @@ template<typename FunRegType>
 inline int MXAPIGetFunctionRegInfo(const FunRegType *e,
                                    const char **name,
                                    const char **description,
-                                   mx_uint *num_args,
+                                   uint32_t *num_args,
                                    const char ***arg_names,
                                    const char ***arg_type_infos,
                                    const char ***arg_descriptions,
@@ -72,7 +72,7 @@ inline int MXAPIGetFunctionRegInfo(const FunRegType *e,
   API_BEGIN();
   *name = e->name.c_str();
   *description = e->description.c_str();
-  *num_args = static_cast<mx_uint>(e->arguments.size());
+  *num_args = static_cast<uint32_t>(e->arguments.size());
   if (return_type) *return_type = e->return_type.c_str();
   ret->ret_vec_charp.clear();
   for (size_t i = 0; i < e->arguments.size(); ++i) {
@@ -202,8 +202,8 @@ void CreateNDArray(const DataType* shape,
                      delay_alloc != 0, dtype);
 }
 
-int MXNDArrayCreate(const mx_uint *shape,
-                    mx_uint ndim,
+int MXNDArrayCreate(const uint32_t *shape,
+                    uint32_t ndim,
                     int dev_type,
                     int dev_id,
                     int delay_alloc,
@@ -215,7 +215,7 @@ int MXNDArrayCreate(const mx_uint *shape,
   API_END();
 }
 
-int MXNDArrayCreateEx64(const mx_int64 *shape,
+int MXNDArrayCreateEx64(const int64_t *shape,
                         int ndim,
                         int dev_type,
                         int dev_id,
@@ -223,33 +223,33 @@ int MXNDArrayCreateEx64(const mx_int64 *shape,
                         int dtype,
                         NDArrayHandle *out) {
   API_BEGIN();
-  CreateNDArray<mx_int64, int>(shape, ndim, dev_type, dev_id, delay_alloc, dtype, out);
+  CreateNDArray<int64_t, int>(shape, ndim, dev_type, dev_id, delay_alloc, dtype, out);
   API_END();
 }
 
-int MXNDArrayCreateEx(const mx_uint *shape,
-                      mx_uint ndim,
+int MXNDArrayCreateEx(const uint32_t *shape,
+                      uint32_t ndim,
                       int dev_type,
                       int dev_id,
                       int delay_alloc,
                       int dtype,
                       NDArrayHandle *out) {
   API_BEGIN();
-  CreateNDArray<mx_uint, mx_uint>(shape, ndim, dev_type, dev_id, delay_alloc, dtype, out);
+  CreateNDArray<uint32_t, uint32_t>(shape, ndim, dev_type, dev_id, delay_alloc, dtype, out);
   API_END();
 }
 
 int MXNDArrayCreateSparseEx(int storage_type,
-                            const mx_uint *shape,
-                            mx_uint ndim,
+                            const uint32_t *shape,
+                            uint32_t ndim,
                             int dev_type,
                             int dev_id,
                             int delay_alloc,
                             int dtype,
-                            mx_uint num_aux,
+                            uint32_t num_aux,
                             int *aux_type,
-                            mx_uint *aux_ndims,
-                            const mx_uint *aux_shape,
+                            uint32_t *aux_ndims,
+                            const uint32_t *aux_shape,
                             NDArrayHandle *out) {
   API_BEGIN();
   std::vector<int> aux_types;
@@ -358,18 +358,18 @@ int MXNDArrayWaitAll() {
 }
 
 int MXNDArraySave(const char* fname,
-                  mx_uint num_args,
+                  uint32_t num_args,
                   NDArrayHandle* args,
                   const char** keys) {
   API_BEGIN();
   std::vector<NDArray> data(num_args);
   std::vector<std::string> names;
-  for (mx_uint i = 0; i < num_args; ++i) {
+  for (uint32_t i = 0; i < num_args; ++i) {
     data[i] = *static_cast<NDArray*>(args[i]);
   }
   if (keys != nullptr) {
     names.resize(num_args);
-    for (mx_uint i = 0; i < num_args; ++i) {
+    for (uint32_t i = 0; i < num_args; ++i) {
       names[i] = keys[i];
     }
   }
@@ -381,9 +381,9 @@ int MXNDArraySave(const char* fname,
 }
 
 int MXNDArrayLoad(const char* fname,
-                  mx_uint *out_size,
+                  uint32_t *out_size,
                   NDArrayHandle** out_arr,
-                  mx_uint *out_name_size,
+                  uint32_t *out_name_size,
                   const char*** out_names) {
   MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
   ret->ret_vec_str.clear();
@@ -404,18 +404,18 @@ int MXNDArrayLoad(const char* fname,
   for (size_t i = 0; i < names.size(); ++i) {
     ret->ret_vec_charp[i] = names[i].c_str();
   }
-  *out_size = static_cast<mx_uint>(data.size());
+  *out_size = static_cast<uint32_t>(data.size());
   *out_arr = dmlc::BeginPtr(ret->ret_handles);
-  *out_name_size = static_cast<mx_uint>(names.size());
+  *out_name_size = static_cast<uint32_t>(names.size());
   *out_names = dmlc::BeginPtr(ret->ret_vec_charp);
   API_END();
 }
 
 int MXNDArrayLoadFromBuffer(const void *ndarray_buffer,
                             size_t size,
-                            mx_uint *out_size,
+                            uint32_t *out_size,
                             NDArrayHandle** out_arr,
-                            mx_uint *out_name_size,
+                            uint32_t *out_name_size,
                             const char*** out_names) {
   MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
   ret->ret_vec_str.clear();
@@ -438,9 +438,9 @@ int MXNDArrayLoadFromBuffer(const void *ndarray_buffer,
   for (size_t i = 0; i < names.size(); ++i) {
     ret->ret_vec_charp[i] = names[i].c_str();
   }
-  *out_size = static_cast<mx_uint>(data.size());
+  *out_size = static_cast<uint32_t>(data.size());
   *out_arr = dmlc::BeginPtr(ret->ret_handles);
-  *out_name_size = static_cast<mx_uint>(names.size());
+  *out_name_size = static_cast<uint32_t>(names.size());
   *out_names = dmlc::BeginPtr(ret->ret_vec_charp);
   API_END();
 }
@@ -459,8 +459,8 @@ void SliceArray(NDArrayHandle handle, dtype slice_begin, dtype slice_end, NDArra
 }
 
 int MXNDArraySlice(NDArrayHandle handle,
-                   mx_uint slice_begin,
-                   mx_uint slice_end,
+                   uint32_t slice_begin,
+                   uint32_t slice_end,
                    NDArrayHandle *out) {
   NDArray *ptr = new NDArray();
   API_BEGIN();
@@ -562,8 +562,8 @@ int MXNDArrayGetStorageType(NDArrayHandle handle,
 }
 
 int MXNDArrayGetShape(NDArrayHandle handle,
-                      mx_uint *out_dim,
-                      const mx_uint **out_pdata) {
+                      uint32_t *out_dim,
+                      const uint32_t **out_pdata) {
   MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
   API_BEGIN();
   NDArray *arr = static_cast<NDArray*>(handle);
@@ -616,10 +616,10 @@ int MXNDArrayGetShapeEx(NDArrayHandle handle,
 
 int MXNDArrayGetShapeEx64(NDArrayHandle handle,
                           int *out_dim,
-                          const mx_int64 **out_pdata) {
+                          const int64_t **out_pdata) {
   MXAPIThreadLocalEntry<int64_t> *ret = MXAPIThreadLocalStore<int64_t>::Get();
   API_BEGIN();
-  GetShape<mx_int64>(handle, out_pdata, out_dim, ret);
+  GetShape<int64_t>(handle, out_pdata, out_dim, ret);
   API_END();
 }
 
@@ -680,7 +680,7 @@ int MXNDArrayGetDType(NDArrayHandle handle,
 }
 
 int MXNDArrayGetAuxType(NDArrayHandle handle,
-                        mx_uint i,
+                        uint32_t i,
                         int *out_type) {
   API_BEGIN();
   NDArray *arr = static_cast<NDArray*>(handle);
@@ -694,7 +694,7 @@ int MXNDArrayGetAuxType(NDArrayHandle handle,
  * This function blocks. Do not use it in performance critical code.
  */
 int MXNDArrayGetAuxNDArray(NDArrayHandle handle,
-                           mx_uint i,
+                           uint32_t i,
                            NDArrayHandle *out) {
   API_BEGIN();
   NDArray *arr = static_cast<NDArray*>(handle);
@@ -765,11 +765,11 @@ int MXNDArrayGetGradState(NDArrayHandle handle, int *out) {
   API_END();
 }
 
-int MXListFunctions(mx_uint *out_size,
+int MXListFunctions(uint32_t *out_size,
                     FunctionHandle **out_array) {
   API_BEGIN();
   auto &vec = dmlc::Registry<NDArrayFunctionReg>::List();
-  *out_size = static_cast<mx_uint>(vec.size());
+  *out_size = static_cast<uint32_t>(vec.size());
   *out_array = (FunctionHandle*)(dmlc::BeginPtr(vec));  //  NOLINT(*)
   API_END();
 }
@@ -784,7 +784,7 @@ int MXGetFunction(const char *name,
 int MXFuncGetInfo(FunctionHandle fun,
                   const char **name,
                   const char **description,
-                  mx_uint *num_args,
+                  uint32_t *num_args,
                   const char ***arg_names,
                   const char ***arg_type_infos,
                   const char ***arg_descriptions,
@@ -796,9 +796,9 @@ int MXFuncGetInfo(FunctionHandle fun,
 }
 
 int MXFuncDescribe(FunctionHandle fun,
-                   mx_uint *num_use_vars,
-                   mx_uint *num_scalars,
-                   mx_uint *num_mutate_vars,
+                   uint32_t *num_use_vars,
+                   uint32_t *num_scalars,
+                   uint32_t *num_mutate_vars,
                    int *type_mask) {
   API_BEGIN();
   auto *f = static_cast<const NDArrayFunctionReg*>(fun);
@@ -811,7 +811,7 @@ int MXFuncDescribe(FunctionHandle fun,
 
 int MXFuncInvoke(FunctionHandle fun,
                  NDArrayHandle *use_vars,
-                 mx_float *scalar_args,
+                 float *scalar_args,
                  NDArrayHandle *mutate_vars) {
   API_BEGIN();
   auto *f = static_cast<const NDArrayFunctionReg*>(fun);
@@ -826,7 +826,7 @@ int MXFuncInvoke(FunctionHandle fun,
 
 int MXFuncInvokeEx(FunctionHandle fun,
                  NDArrayHandle *use_vars,
-                 mx_float *scalar_args,
+                 float *scalar_args,
                  NDArrayHandle *mutate_vars,
                  int num_params,
                  char **param_keys,
@@ -845,11 +845,11 @@ int MXFuncInvokeEx(FunctionHandle fun,
 //--------------------------------------------
 // Part 5: IO Interface
 //--------------------------------------------
-int MXListDataIters(mx_uint *out_size,
+int MXListDataIters(uint32_t *out_size,
                     DataIterCreator **out_array) {
   API_BEGIN();
   auto &vec = dmlc::Registry<DataIteratorReg>::List();
-  *out_size = static_cast<mx_uint>(vec.size());
+  *out_size = static_cast<uint32_t>(vec.size());
   *out_array = (DataIterCreator*)(dmlc::BeginPtr(vec));  //  NOLINT(*)
   API_END();
 }
@@ -857,7 +857,7 @@ int MXListDataIters(mx_uint *out_size,
 int MXDataIterGetIterInfo(DataIterCreator creator,
                           const char **name,
                           const char **description,
-                          mx_uint *num_args,
+                          uint32_t *num_args,
                           const char ***arg_names,
                           const char ***arg_type_infos,
                           const char ***arg_descriptions) {
@@ -868,7 +868,7 @@ int MXDataIterGetIterInfo(DataIterCreator creator,
 }
 
 int MXDataIterCreateIter(DataIterCreator creator,
-                         mx_uint num_param,
+                         uint32_t num_param,
                          const char **keys,
                          const char **vals,
                          DataIterHandle *out) {
@@ -877,7 +877,7 @@ int MXDataIterCreateIter(DataIterCreator creator,
   DataIteratorReg *e = static_cast<DataIteratorReg *>(creator);
   iter = e->body();
   std::vector<std::pair<std::string, std::string> > kwargs;
-  for (mx_uint i = 0; i < num_param; ++i) {
+  for (uint32_t i = 0; i < num_param; ++i) {
     kwargs.push_back({std::string(keys[i]), std::string(vals[i])});
   }
   iter->Init(kwargs);
@@ -950,11 +950,11 @@ int MXKVStoreCreate(const char *type,
   API_END();
 }
 
-int MXKVStoreSetGradientCompression(KVStoreHandle handle, mx_uint num_params,
+int MXKVStoreSetGradientCompression(KVStoreHandle handle, uint32_t num_params,
                                     const char** keys, const char** vals) {
   API_BEGIN();
   std::vector<std::pair<std::string, std::string> > params;
-  for (mx_uint i = 0; i < num_params; ++i) {
+  for (uint32_t i = 0; i < num_params; ++i) {
     std::pair<std::string, std::string> p;
     p.first = keys[i];
     p.second = vals[i];
@@ -971,13 +971,13 @@ int MXKVStoreFree(KVStoreHandle handle) {
 }
 
 int MXKVStoreInit(KVStoreHandle handle,
-                  mx_uint num,
+                  uint32_t num,
                   const int* keys,
                   NDArrayHandle* vals) {
   API_BEGIN();
   std::vector<int> v_keys(num);
   std::vector<NDArray> v_vals(num);
-  for (mx_uint i = 0; i < num; ++i) {
+  for (uint32_t i = 0; i < num; ++i) {
     v_keys[i] = keys[i];
     v_vals[i] = *static_cast<NDArray*>(vals[i]);
   }
@@ -986,13 +986,13 @@ int MXKVStoreInit(KVStoreHandle handle,
 }
 
 int MXKVStoreInitEx(KVStoreHandle handle,
-                  mx_uint num,
+                  uint32_t num,
                   const char** keys,
                   NDArrayHandle* vals) {
   API_BEGIN();
   std::vector<std::string> v_keys(num);
   std::vector<NDArray> v_vals(num);
-  for (mx_uint i = 0; i < num; ++i) {
+  for (uint32_t i = 0; i < num; ++i) {
     v_keys[i] = keys[i];
     v_vals[i] = *static_cast<NDArray*>(vals[i]);
   }
@@ -1001,14 +1001,14 @@ int MXKVStoreInitEx(KVStoreHandle handle,
 }
 
 int MXKVStorePush(KVStoreHandle handle,
-                  mx_uint num,
+                  uint32_t num,
                   const int* keys,
                   NDArrayHandle* vals,
                   int priority) {
   API_BEGIN();
   std::vector<int> v_keys(num);
   std::vector<NDArray> v_vals(num);
-  for (mx_uint i = 0; i < num; ++i) {
+  for (uint32_t i = 0; i < num; ++i) {
     v_keys[i] = keys[i];
     v_vals[i] = *static_cast<NDArray*>(vals[i]);
   }
@@ -1017,14 +1017,14 @@ int MXKVStorePush(KVStoreHandle handle,
 }
 
 int MXKVStorePushEx(KVStoreHandle handle,
-                  mx_uint num,
+                  uint32_t num,
                   const char** keys,
                   NDArrayHandle* vals,
                   int priority) {
   API_BEGIN();
   std::vector<std::string> v_keys(num);
   std::vector<NDArray> v_vals(num);
-  for (mx_uint i = 0; i < num; ++i) {
+  for (uint32_t i = 0; i < num; ++i) {
     v_keys[i] = keys[i];
     v_vals[i] = *static_cast<NDArray*>(vals[i]);
   }
@@ -1033,14 +1033,14 @@ int MXKVStorePushEx(KVStoreHandle handle,
 }
 
 int MXKVStorePull(KVStoreHandle handle,
-                  mx_uint num,
+                  uint32_t num,
                   const int* keys,
                   NDArrayHandle* vals,
                   int priority) {
   API_BEGIN();
   std::vector<int> v_keys(num);
   std::vector<NDArray*> v_vals(num);
-  for (mx_uint i = 0; i < num; ++i) {
+  for (uint32_t i = 0; i < num; ++i) {
     v_keys[i] = keys[i];
     v_vals[i] = static_cast<NDArray*>(vals[i]);
   }
@@ -1049,14 +1049,14 @@ int MXKVStorePull(KVStoreHandle handle,
 }
 
 int MXKVStorePullEx(KVStoreHandle handle,
-                    mx_uint num,
+                    uint32_t num,
                     const char** keys,
                     NDArrayHandle* vals,
                     int priority) {
   API_BEGIN();
   std::vector<std::string> v_keys(num);
   std::vector<NDArray*> v_vals(num);
-  for (mx_uint i = 0; i < num; ++i) {
+  for (uint32_t i = 0; i < num; ++i) {
     v_keys[i] = keys[i];
     v_vals[i] = static_cast<NDArray*>(vals[i]);
   }
@@ -1065,7 +1065,7 @@ int MXKVStorePullEx(KVStoreHandle handle,
 }
 
 int MXKVStorePullWithSparse(KVStoreHandle handle,
-                            mx_uint num,
+                            uint32_t num,
                             const int* keys,
                             NDArrayHandle* vals,
                             int priority,
@@ -1073,7 +1073,7 @@ int MXKVStorePullWithSparse(KVStoreHandle handle,
   API_BEGIN();
   std::vector<int> v_keys(num);
   std::vector<NDArray*> v_vals(num);
-  for (mx_uint i = 0; i < num; ++i) {
+  for (uint32_t i = 0; i < num; ++i) {
     v_keys[i] = keys[i];
     v_vals[i] = static_cast<NDArray*>(vals[i]);
   }
@@ -1082,7 +1082,7 @@ int MXKVStorePullWithSparse(KVStoreHandle handle,
 }
 
 int MXKVStorePullWithSparseEx(KVStoreHandle handle,
-                              mx_uint num,
+                              uint32_t num,
                               const char** keys,
                               NDArrayHandle* vals,
                               int priority,
@@ -1090,7 +1090,7 @@ int MXKVStorePullWithSparseEx(KVStoreHandle handle,
   API_BEGIN();
   std::vector<std::string> v_keys(num);
   std::vector<NDArray*> v_vals(num);
-  for (mx_uint i = 0; i < num; ++i) {
+  for (uint32_t i = 0; i < num; ++i) {
     v_keys[i] = keys[i];
     v_vals[i] = static_cast<NDArray*>(vals[i]);
   }
@@ -1099,7 +1099,7 @@ int MXKVStorePullWithSparseEx(KVStoreHandle handle,
 }
 
 int MXKVStorePullRowSparse(KVStoreHandle handle,
-                           mx_uint num,
+                           uint32_t num,
                            const int* keys,
                            NDArrayHandle* vals,
                            const NDArrayHandle* row_ids,
@@ -1107,7 +1107,7 @@ int MXKVStorePullRowSparse(KVStoreHandle handle,
   API_BEGIN();
   std::vector<int> v_keys(num);
   std::vector<std::pair<NDArray*, NDArray>> v_val_rowids(num);
-  for (mx_uint i = 0; i < num; ++i) {
+  for (uint32_t i = 0; i < num; ++i) {
     v_keys[i] = keys[i];
     v_val_rowids[i] = std::make_pair(static_cast<NDArray*>(vals[i]),
                                      *static_cast<NDArray*>(row_ids[i]));
@@ -1117,7 +1117,7 @@ int MXKVStorePullRowSparse(KVStoreHandle handle,
 }
 
 int MXKVStorePullRowSparseEx(KVStoreHandle handle,
-                             mx_uint num,
+                             uint32_t num,
                              const char** keys,
                              NDArrayHandle* vals,
                              const NDArrayHandle* row_ids,
@@ -1125,7 +1125,7 @@ int MXKVStorePullRowSparseEx(KVStoreHandle handle,
   API_BEGIN();
   std::vector<std::string> v_keys(num);
   std::vector<std::pair<NDArray*, NDArray>> v_val_rowids(num);
-  for (mx_uint i = 0; i < num; ++i) {
+  for (uint32_t i = 0; i < num; ++i) {
     v_keys[i] = keys[i];
     v_val_rowids[i] = std::make_pair(static_cast<NDArray*>(vals[i]),
                                      *static_cast<NDArray*>(row_ids[i]));
@@ -1206,12 +1206,12 @@ int MXKVStoreSetBarrierBeforeExit(KVStoreHandle handle,
   API_END();
 }
 
-int MXInitPSEnv(mx_uint num_vars,
+int MXInitPSEnv(uint32_t num_vars,
                 const char **keys,
                 const char **vals) {
   API_BEGIN();
   std::unordered_map<std::string, std::string> kwargs;
-  for (mx_uint i = 0; i < num_vars; ++i) {
+  for (uint32_t i = 0; i < num_vars; ++i) {
     kwargs[std::string(keys[i])] = std::string(vals[i]);
   }
   KVStore::InitPSEnv(kwargs);
@@ -1376,7 +1376,7 @@ int MXRecordIOReaderTell(RecordIOHandle handle, size_t *pos) {
   API_END();
 }
 
-int MXRtcCreate(char* name, mx_uint num_input, mx_uint num_output,
+int MXRtcCreate(char* name, uint32_t num_input, uint32_t num_output,
                 char** input_names, char** output_names,
                 NDArrayHandle* inputs, NDArrayHandle* outputs,
                 char* kernel, RtcHandle *out) {
@@ -1385,14 +1385,14 @@ int MXRtcCreate(char* name, mx_uint num_input, mx_uint num_output,
   API_END();
 }
 
-int MXRtcPush(RtcHandle handle, mx_uint num_input, mx_uint num_output,
+int MXRtcPush(RtcHandle handle, uint32_t num_input, uint32_t num_output,
               NDArrayHandle* inputs, NDArrayHandle* outputs,
-              mx_uint gridDimX,
-              mx_uint gridDimY,
-              mx_uint gridDimZ,
-              mx_uint blockDimX,
-              mx_uint blockDimY,
-              mx_uint blockDimZ) {
+              uint32_t gridDimX,
+              uint32_t gridDimY,
+              uint32_t gridDimZ,
+              uint32_t blockDimX,
+              uint32_t blockDimY,
+              uint32_t blockDimZ) {
   API_BEGIN();
   LOG(FATAL) << "Old rtc API is deprecated. Please use CudaModule";
   API_END();
@@ -1468,10 +1468,10 @@ int MXRtcCudaKernelFree(CudaKernelHandle handle) {
 }
 
 int MXRtcCudaKernelCall(CudaKernelHandle handle, int dev_id, void** args,
-                        mx_uint grid_dim_x, mx_uint grid_dim_y,
-                        mx_uint grid_dim_z, mx_uint block_dim_x,
-                        mx_uint block_dim_y, mx_uint block_dim_z,
-                        mx_uint shared_mem) {
+                        uint32_t grid_dim_x, uint32_t grid_dim_y,
+                        uint32_t grid_dim_z, uint32_t block_dim_x,
+                        uint32_t block_dim_y, uint32_t block_dim_z,
+                        uint32_t shared_mem) {
   API_BEGIN();
 #if MXNET_USE_CUDA && MXNET_ENABLE_CUDA_RTC
   auto kernel = reinterpret_cast<std::shared_ptr<rtc::CudaModule::Kernel>*>(handle);
@@ -1514,8 +1514,8 @@ int MXNDArrayGetSharedMemHandle(NDArrayHandle handle, int* shared_pid, int* shar
   API_END();
 }
 
-int MXNDArrayCreateFromSharedMem(int shared_pid, int shared_id, const mx_uint *shape,
-                                 mx_uint ndim, int dtype, NDArrayHandle *out) {
+int MXNDArrayCreateFromSharedMem(int shared_pid, int shared_id, const uint32_t *shape,
+                                 uint32_t ndim, int dtype, NDArrayHandle *out) {
   API_BEGIN();
   *out = new NDArray(shared_pid, shared_id, mxnet::TShape(shape, shape + ndim), dtype);
   API_END();
diff --git a/src/c_api/c_api_common.h b/src/c_api/c_api_common.h
index 93fcff09a8e6..b88a087de617 100644
--- a/src/c_api/c_api_common.h
+++ b/src/c_api/c_api_common.h
@@ -76,11 +76,11 @@ struct MXAPIThreadLocalEntry {
   /*! \brief result holder for returning storage types */
   std::vector<int> arg_storage_types, out_storage_types, aux_storage_types;
   /*! \brief result holder for returning shape dimensions */
-  std::vector<mx_uint> arg_shape_ndim, out_shape_ndim, aux_shape_ndim;
+  std::vector<uint32_t> arg_shape_ndim, out_shape_ndim, aux_shape_ndim;
   /*! \brief result holder for returning shape dimensions */
   std::vector<int> arg_shape_ndim_ex, out_shape_ndim_ex, aux_shape_ndim_ex;
   /*! \brief result holder for returning shape pointer */
-  std::vector<const mx_uint*> arg_shape_data, out_shape_data, aux_shape_data;
+  std::vector<const uint32_t*> arg_shape_data, out_shape_data, aux_shape_data;
   /*! \brief result holder for returning shape pointer */
   std::vector<const dtype*> arg_shape_data_ex, out_shape_data_ex, aux_shape_data_ex;
   /*! \brief uint32_t buffer for returning shape pointer */
@@ -93,8 +93,8 @@ struct MXAPIThreadLocalEntry {
   // helper function to setup return value of shape array
   inline static void SetupShapeArrayReturnWithBuffer(
       const mxnet::ShapeVector &shapes,
-      std::vector<mx_uint> *ndim,
-      std::vector<const mx_uint*> *data,
+      std::vector<uint32_t> *ndim,
+      std::vector<const uint32_t*> *data,
       std::vector<uint32_t> *buffer) {
     ndim->resize(shapes.size());
     data->resize(shapes.size());
diff --git a/src/c_api/c_api_executor.cc b/src/c_api/c_api_executor.cc
index 31b74b503f80..ff85b4fd62fa 100644
--- a/src/c_api/c_api_executor.cc
+++ b/src/c_api/c_api_executor.cc
@@ -55,20 +55,20 @@ int MXExecutorForward(ExecutorHandle handle, int is_train) {
 }
 
 int MXExecutorBackward(ExecutorHandle handle,
-                       mx_uint len,
+                       uint32_t len,
                        NDArrayHandle *head_grads) {
   return MXExecutorBackwardEx(handle, len, head_grads, true);
 }
 
 int MXExecutorBackwardEx(ExecutorHandle handle,
-                         mx_uint len,
+                         uint32_t len,
                          NDArrayHandle *head_grads,
                          int is_train) {
   API_BEGIN();
   Executor *exec = static_cast<Executor*>(handle);
   std::vector<NDArray> ndarrays;
   NDArray **args_ptr = reinterpret_cast<NDArray**>(head_grads);
-  for (mx_uint i = 0; i < len; ++i) {
+  for (uint32_t i = 0; i < len; ++i) {
     ndarrays.push_back(*args_ptr[i]);
   }
   exec->Backward(ndarrays, is_train);
@@ -76,7 +76,7 @@ int MXExecutorBackwardEx(ExecutorHandle handle,
 }
 
 int MXExecutorOutputs(ExecutorHandle handle,
-                      mx_uint *out_size,
+                      uint32_t *out_size,
                       NDArrayHandle **out) {
   MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
   API_BEGIN();
@@ -96,11 +96,11 @@ int MXExecutorOutputs(ExecutorHandle handle,
 int MXExecutorBind(SymbolHandle symbol_handle,
                    int dev_type,
                    int dev_id,
-                   mx_uint len,
+                   uint32_t len,
                    NDArrayHandle *in_args,
                    NDArrayHandle *arg_grad_store,
-                   mx_uint *grad_req_type,
-                   mx_uint aux_states_len,
+                   uint32_t *grad_req_type,
+                   uint32_t aux_states_len,
                    NDArrayHandle *aux_states,
                    ExecutorHandle *out) {
   return MXExecutorBindX(symbol_handle,
@@ -113,15 +113,15 @@ int MXExecutorBind(SymbolHandle symbol_handle,
 int MXExecutorBindX(SymbolHandle symbol_handle,
                     int dev_type,
                     int dev_id,
-                    mx_uint num_map_keys,
+                    uint32_t num_map_keys,
                     const char** map_keys,
                     const int* map_dev_types,
                     const int* map_dev_ids,
-                    mx_uint len,
+                    uint32_t len,
                     NDArrayHandle *in_args,
                     NDArrayHandle *arg_grad_store,
-                    mx_uint *grad_req_type,
-                    mx_uint aux_states_len,
+                    uint32_t *grad_req_type,
+                    uint32_t aux_states_len,
                     NDArrayHandle *aux_states,
                     ExecutorHandle *out) {
   return MXExecutorBindEX(symbol_handle,
@@ -135,15 +135,15 @@ int MXExecutorBindX(SymbolHandle symbol_handle,
 int MXExecutorBindEX(SymbolHandle symbol_handle,
                      int dev_type,
                      int dev_id,
-                     mx_uint num_map_keys,
+                     uint32_t num_map_keys,
                      const char** map_keys,
                      const int* map_dev_types,
                      const int* map_dev_ids,
-                     mx_uint len,
+                     uint32_t len,
                      NDArrayHandle *in_args,
                      NDArrayHandle *arg_grad_store,
-                     mx_uint *grad_req_type,
-                     mx_uint aux_states_len,
+                     uint32_t *grad_req_type,
+                     uint32_t aux_states_len,
                      NDArrayHandle *aux_states,
                      ExecutorHandle shared_exec,
                      ExecutorHandle *out) {
@@ -151,7 +151,7 @@ int MXExecutorBindEX(SymbolHandle symbol_handle,
   nnvm::Symbol *symb = static_cast<nnvm::Symbol*>(symbol_handle);
   Context ctx = Context::Create(static_cast<Context::DeviceType>(dev_type), dev_id);
   std::map<std::string, Context> ctx_map;
-  for (mx_uint i = 0; i < num_map_keys; ++i) {
+  for (uint32_t i = 0; i < num_map_keys; ++i) {
     ctx_map[std::string(map_keys[i])] = Context::Create(
         static_cast<Context::DeviceType>(map_dev_types[i]), map_dev_ids[i]);
   }
@@ -162,7 +162,7 @@ int MXExecutorBindEX(SymbolHandle symbol_handle,
   std::vector<NDArray> arg_grad_vec;
   std::vector<OpReqType> grad_req_vec;
   std::vector<NDArray> aux_states_vec;
-  for (mx_uint i = 0; i < len; ++i) {
+  for (uint32_t i = 0; i < len; ++i) {
     in_args_vec.push_back(*(in_args_ptr[i]));
     if (arg_grad_ptr[i] == nullptr) {
       arg_grad_vec.emplace_back();
@@ -172,7 +172,7 @@ int MXExecutorBindEX(SymbolHandle symbol_handle,
       grad_req_vec.push_back(static_cast<OpReqType>(grad_req_type[i]));
     }
   }
-  for (mx_uint i = 0; i < aux_states_len; ++i) {
+  for (uint32_t i = 0; i < aux_states_len; ++i) {
     aux_states_vec.push_back(*(aux_states_ptr[i]));
   }
   *out = Executor::Bind(*symb, ctx, ctx_map, in_args_vec,
@@ -221,34 +221,34 @@ int MXExecutorBindEX(SymbolHandle symbol_handle,
 int MXExecutorSimpleBind(SymbolHandle symbol_handle,
                          int dev_type,
                          int dev_id,
-                         const mx_uint num_g2c_keys,
+                         const uint32_t num_g2c_keys,
                          const char** g2c_keys,
                          const int* g2c_dev_types,
                          const int* g2c_dev_ids,
-                         const mx_uint provided_grad_req_list_len,
+                         const uint32_t provided_grad_req_list_len,
                          const char** provided_grad_req_names,
                          const char** provided_grad_req_types,
-                         const mx_uint num_provided_arg_shapes,
+                         const uint32_t num_provided_arg_shapes,
                          const char** provided_arg_shape_names,
-                         const mx_uint* provided_arg_shape_data,
-                         const mx_uint* provided_arg_shape_idx,
-                         const mx_uint num_provided_arg_dtypes,
+                         const uint32_t* provided_arg_shape_data,
+                         const uint32_t* provided_arg_shape_idx,
+                         const uint32_t num_provided_arg_dtypes,
                          const char** provided_arg_dtype_names,
                          const int* provided_arg_dtypes,
-                         const mx_uint num_provided_arg_stypes,
+                         const uint32_t num_provided_arg_stypes,
                          const char** provided_arg_stype_names,
                          const int* provided_arg_stypes,
-                         const mx_uint num_shared_arg_names,
+                         const uint32_t num_shared_arg_names,
                          const char** shared_arg_name_list,
                          int* shared_buffer_len,
                          const char** shared_buffer_name_list,
                          NDArrayHandle* shared_buffer_handle_list,
                          const char*** updated_shared_buffer_name_list,
                          NDArrayHandle** updated_shared_buffer_handle_list,
-                         mx_uint* num_in_args,
+                         uint32_t* num_in_args,
                          NDArrayHandle** in_args,
                          NDArrayHandle** arg_grads,
-                         mx_uint* num_aux_states,
+                         uint32_t* num_aux_states,
                          NDArrayHandle** aux_states,
                          ExecutorHandle shared_exec_handle,
                          ExecutorHandle* out) {
@@ -283,7 +283,7 @@ int MXExecutorSimpleBind(SymbolHandle symbol_handle,
   } else {  // use user input type_dict
     // create dtype map for in_args and aux_states
     arg_dtype_map.reserve(num_provided_arg_dtypes);
-    for (mx_uint i = 0; i < num_provided_arg_dtypes; ++i) {
+    for (uint32_t i = 0; i < num_provided_arg_dtypes; ++i) {
       arg_dtype_map[provided_arg_dtype_names[i]] = provided_arg_dtypes[i];
     }
   }
@@ -300,7 +300,7 @@ int MXExecutorSimpleBind(SymbolHandle symbol_handle,
   } else {  // use user input type_dict
     // create stype map for in_args and aux_states
     arg_stype_map.reserve(num_provided_arg_stypes);
-    for (mx_uint i = 0; i < num_provided_arg_stypes; ++i) {
+    for (uint32_t i = 0; i < num_provided_arg_stypes; ++i) {
       arg_stype_map[provided_arg_stype_names[i]] = provided_arg_stypes[i];
     }
   }
@@ -312,7 +312,7 @@ int MXExecutorSimpleBind(SymbolHandle symbol_handle,
   std::vector<Context> in_arg_ctx_vec(in_arg_names.size(), ctx);
   std::vector<Context> aux_state_ctx_vec(aux_state_names.size(), ctx);
   if (nullptr != g2c_keys) {  // use user input group2ctx dict
-    for (mx_uint i = 0; i < num_g2c_keys; ++i) {
+    for (uint32_t i = 0; i < num_g2c_keys; ++i) {
       ctx_map[g2c_keys[i]] = Context::Create(
           static_cast<Context::DeviceType>(g2c_dev_types[i]), g2c_dev_ids[i]);
     }
@@ -370,7 +370,7 @@ int MXExecutorSimpleBind(SymbolHandle symbol_handle,
       && nullptr != provided_grad_req_types) {  // dict, grad_req=['lhs': 'null', 'rhs': 'write']
     grad_req_type = "dict";
     provided_grad_req_map.reserve(provided_grad_req_list_len);
-    for (mx_uint i = 0; i < provided_grad_req_list_len; ++i) {
+    for (uint32_t i = 0; i < provided_grad_req_list_len; ++i) {
       CHECK_EQ(req_map.count(provided_grad_req_types[i]), 1U)
         << "grad_req=" << provided_grad_req_types[i] << " is not a valid input in simple_bind; "
         "only \'null\', \'write\', and \'add\' are supported";
@@ -408,7 +408,7 @@ int MXExecutorSimpleBind(SymbolHandle symbol_handle,
 
   // create shape map for in_args and aux_states
   std::unordered_map<std::string, mxnet::TShape> arg_shape_map(num_provided_arg_shapes);
-  for (mx_uint i = 0; i < num_provided_arg_shapes; ++i) {
+  for (uint32_t i = 0; i < num_provided_arg_shapes; ++i) {
     auto p = arg_shape_map.emplace(provided_arg_shape_names[i],
         mxnet::TShape(provided_arg_shape_data+provided_arg_shape_idx[i],
           provided_arg_shape_data+provided_arg_shape_idx[i+1]));
@@ -423,7 +423,7 @@ int MXExecutorSimpleBind(SymbolHandle symbol_handle,
 
   // create para name set for sharing data array memory
   std::unordered_set<std::string> shared_arg_name_set(num_shared_arg_names);
-  for (mx_uint i = 0; i < num_shared_arg_names; ++i) {
+  for (uint32_t i = 0; i < num_shared_arg_names; ++i) {
     shared_arg_name_set.insert(shared_arg_name_list[i]);
   }
 
@@ -555,34 +555,34 @@ int MXExecutorSimpleBind(SymbolHandle symbol_handle,
 int MXExecutorSimpleBindEx(SymbolHandle symbol_handle,
                            int dev_type,
                            int dev_id,
-                           const mx_uint num_g2c_keys,
+                           const uint32_t num_g2c_keys,
                            const char** g2c_keys,
                            const int* g2c_dev_types,
                            const int* g2c_dev_ids,
-                           const mx_uint provided_grad_req_list_len,
+                           const uint32_t provided_grad_req_list_len,
                            const char** provided_grad_req_names,
                            const char** provided_grad_req_types,
-                           const mx_uint num_provided_arg_shapes,
+                           const uint32_t num_provided_arg_shapes,
                            const char** provided_arg_shape_names,
                            const int* provided_arg_shape_data,
-                           const mx_uint* provided_arg_shape_idx,
-                           const mx_uint num_provided_arg_dtypes,
+                           const uint32_t* provided_arg_shape_idx,
+                           const uint32_t num_provided_arg_dtypes,
                            const char** provided_arg_dtype_names,
                            const int* provided_arg_dtypes,
-                           const mx_uint num_provided_arg_stypes,
+                           const uint32_t num_provided_arg_stypes,
                            const char** provided_arg_stype_names,
                            const int* provided_arg_stypes,
-                           const mx_uint num_shared_arg_names,
+                           const uint32_t num_shared_arg_names,
                            const char** shared_arg_name_list,
                            int* shared_buffer_len,
                            const char** shared_buffer_name_list,
                            NDArrayHandle* shared_buffer_handle_list,
                            const char*** updated_shared_buffer_name_list,
                            NDArrayHandle** updated_shared_buffer_handle_list,
-                           mx_uint* num_in_args,
+                           uint32_t* num_in_args,
                            NDArrayHandle** in_args,
                            NDArrayHandle** arg_grads,
-                           mx_uint* num_aux_states,
+                           uint32_t* num_aux_states,
                            NDArrayHandle** aux_states,
                            ExecutorHandle shared_exec_handle,
                            ExecutorHandle* out) {
@@ -617,7 +617,7 @@ int MXExecutorSimpleBindEx(SymbolHandle symbol_handle,
   } else {  // use user input type_dict
     // create dtype map for in_args and aux_states
     arg_dtype_map.reserve(num_provided_arg_dtypes);
-    for (mx_uint i = 0; i < num_provided_arg_dtypes; ++i) {
+    for (uint32_t i = 0; i < num_provided_arg_dtypes; ++i) {
       arg_dtype_map[provided_arg_dtype_names[i]] = provided_arg_dtypes[i];
     }
   }
@@ -634,7 +634,7 @@ int MXExecutorSimpleBindEx(SymbolHandle symbol_handle,
   } else {  // use user input type_dict
     // create stype map for in_args and aux_states
     arg_stype_map.reserve(num_provided_arg_stypes);
-    for (mx_uint i = 0; i < num_provided_arg_stypes; ++i) {
+    for (uint32_t i = 0; i < num_provided_arg_stypes; ++i) {
       arg_stype_map[provided_arg_stype_names[i]] = provided_arg_stypes[i];
     }
   }
@@ -646,7 +646,7 @@ int MXExecutorSimpleBindEx(SymbolHandle symbol_handle,
   std::vector<Context> in_arg_ctx_vec(in_arg_names.size(), ctx);
   std::vector<Context> aux_state_ctx_vec(aux_state_names.size(), ctx);
   if (nullptr != g2c_keys) {  // use user input group2ctx dict
-    for (mx_uint i = 0; i < num_g2c_keys; ++i) {
+    for (uint32_t i = 0; i < num_g2c_keys; ++i) {
       ctx_map[g2c_keys[i]] = Context::Create(
           static_cast<Context::DeviceType>(g2c_dev_types[i]), g2c_dev_ids[i]);
     }
@@ -704,7 +704,7 @@ int MXExecutorSimpleBindEx(SymbolHandle symbol_handle,
       && nullptr != provided_grad_req_types) {  // dict, grad_req=['lhs': 'null', 'rhs': 'write']
     grad_req_type = "dict";
     provided_grad_req_map.reserve(provided_grad_req_list_len);
-    for (mx_uint i = 0; i < provided_grad_req_list_len; ++i) {
+    for (uint32_t i = 0; i < provided_grad_req_list_len; ++i) {
       CHECK_EQ(req_map.count(provided_grad_req_types[i]), 1U)
         << "grad_req=" << provided_grad_req_types[i] << " is not a valid input in simple_bind; "
         "only \'null\', \'write\', and \'add\' are supported";
@@ -742,7 +742,7 @@ int MXExecutorSimpleBindEx(SymbolHandle symbol_handle,
 
   // create shape map for in_args and aux_states
   std::unordered_map<std::string, mxnet::TShape> arg_shape_map(num_provided_arg_shapes);
-  for (mx_uint i = 0; i < num_provided_arg_shapes; ++i) {
+  for (uint32_t i = 0; i < num_provided_arg_shapes; ++i) {
     auto p = arg_shape_map.emplace(provided_arg_shape_names[i],
         mxnet::TShape(provided_arg_shape_data+provided_arg_shape_idx[i],
           provided_arg_shape_data+provided_arg_shape_idx[i+1]));
@@ -757,7 +757,7 @@ int MXExecutorSimpleBindEx(SymbolHandle symbol_handle,
 
   // create para name set for sharing data array memory
   std::unordered_set<std::string> shared_arg_name_set(num_shared_arg_names);
-  for (mx_uint i = 0; i < num_shared_arg_names; ++i) {
+  for (uint32_t i = 0; i < num_shared_arg_names; ++i) {
     shared_arg_name_set.insert(shared_arg_name_list[i]);
   }
 
@@ -853,18 +853,18 @@ int MXExecutorReshape(int partial_shaping,
                       int allow_up_sizing,
                       int dev_type,
                       int dev_id,
-                      mx_uint num_map_keys,
+                      uint32_t num_map_keys,
                       const char** map_keys,
                       const int* map_dev_types,
                       const int* map_dev_ids,
-                      const mx_uint num_provided_arg_shapes,
+                      const uint32_t num_provided_arg_shapes,
                       const char** provided_arg_shape_names,
-                      const mx_uint* provided_arg_shape_data,
-                      const mx_uint* provided_arg_shape_idx,
-                      mx_uint* num_in_args,
+                      const uint32_t* provided_arg_shape_data,
+                      const uint32_t* provided_arg_shape_idx,
+                      uint32_t* num_in_args,
                       NDArrayHandle** in_args,
                       NDArrayHandle** arg_grads,
-                      mx_uint* num_aux_states,
+                      uint32_t* num_aux_states,
                       NDArrayHandle** aux_states,
                       ExecutorHandle shared_exec,
                       ExecutorHandle *out) {
@@ -875,7 +875,7 @@ int MXExecutorReshape(int partial_shaping,
   *out = nullptr;  // ensure we can know whether to free executor on early abort
   // create shape map for in_args and aux_states
   std::unordered_map<std::string, mxnet::TShape> kwargs(num_provided_arg_shapes);
-  for (mx_uint i = 0; i < num_provided_arg_shapes; ++i) {
+  for (uint32_t i = 0; i < num_provided_arg_shapes; ++i) {
     auto p = kwargs.emplace(provided_arg_shape_names[i],
         mxnet::TShape(provided_arg_shape_data+provided_arg_shape_idx[i],
           provided_arg_shape_data+provided_arg_shape_idx[i+1]));
@@ -885,7 +885,7 @@ int MXExecutorReshape(int partial_shaping,
 
   Context ctx = Context::Create(static_cast<Context::DeviceType>(dev_type), dev_id);
   std::map<std::string, Context> ctx_map;
-  for (mx_uint i = 0; i < num_map_keys; ++i) {
+  for (uint32_t i = 0; i < num_map_keys; ++i) {
     ctx_map[std::string(map_keys[i])] = Context::Create(
         static_cast<Context::DeviceType>(map_dev_types[i]), map_dev_ids[i]);
   }
@@ -944,18 +944,18 @@ int MXExecutorReshapeEx(int partial_shaping,
                         int allow_up_sizing,
                         int dev_type,
                         int dev_id,
-                        mx_uint num_map_keys,
+                        uint32_t num_map_keys,
                         const char** map_keys,
                         const int* map_dev_types,
                         const int* map_dev_ids,
-                        const mx_uint num_provided_arg_shapes,
+                        const uint32_t num_provided_arg_shapes,
                         const char** provided_arg_shape_names,
                         const int* provided_arg_shape_data,
-                        const mx_uint* provided_arg_shape_idx,
-                        mx_uint* num_in_args,
+                        const uint32_t* provided_arg_shape_idx,
+                        uint32_t* num_in_args,
                         NDArrayHandle** in_args,
                         NDArrayHandle** arg_grads,
-                        mx_uint* num_aux_states,
+                        uint32_t* num_aux_states,
                         NDArrayHandle** aux_states,
                         ExecutorHandle shared_exec,
                         ExecutorHandle *out) {
@@ -966,7 +966,7 @@ int MXExecutorReshapeEx(int partial_shaping,
   *out = nullptr;  // ensure we can know whether to free executor on early abort
   // create shape map for in_args and aux_states
   std::unordered_map<std::string, mxnet::TShape> kwargs(num_provided_arg_shapes);
-  for (mx_uint i = 0; i < num_provided_arg_shapes; ++i) {
+  for (uint32_t i = 0; i < num_provided_arg_shapes; ++i) {
     auto p = kwargs.emplace(provided_arg_shape_names[i],
         mxnet::TShape(provided_arg_shape_data+provided_arg_shape_idx[i],
           provided_arg_shape_data+provided_arg_shape_idx[i+1]));
@@ -976,7 +976,7 @@ int MXExecutorReshapeEx(int partial_shaping,
 
   Context ctx = Context::Create(static_cast<Context::DeviceType>(dev_type), dev_id);
   std::map<std::string, Context> ctx_map;
-  for (mx_uint i = 0; i < num_map_keys; ++i) {
+  for (uint32_t i = 0; i < num_map_keys; ++i) {
     ctx_map[std::string(map_keys[i])] = Context::Create(
         static_cast<Context::DeviceType>(map_dev_types[i]), map_dev_ids[i]);
   }
diff --git a/src/c_api/c_api_ndarray.cc b/src/c_api/c_api_ndarray.cc
index 4546659ca64e..a80e171c8d36 100644
--- a/src/c_api/c_api_ndarray.cc
+++ b/src/c_api/c_api_ndarray.cc
@@ -288,17 +288,17 @@ int MXSetIsNumpyShape(int is_np_shape, int* prev) {
   API_END();
 }
 
-int MXAutogradMarkVariables(mx_uint num_var,
+int MXAutogradMarkVariables(uint32_t num_var,
                             NDArrayHandle *var_handles,
-                            mx_uint *reqs_array,
+                            uint32_t *reqs_array,
                             NDArrayHandle *grad_handles) {
   API_BEGIN();
   std::vector<NDArray*> variables, gradients;
-  std::vector<mx_uint> grad_reqs;
+  std::vector<uint32_t> grad_reqs;
   variables.reserve(num_var);
   gradients.reserve(num_var);
   grad_reqs.reserve(num_var);
-  for (mx_uint i = 0; i < num_var; ++i) {
+  for (uint32_t i = 0; i < num_var; ++i) {
     variables.emplace_back(static_cast<NDArray*>(var_handles[i]));
     gradients.emplace_back(static_cast<NDArray*>(grad_handles[i]));
     grad_reqs.emplace_back(reqs_array[i]);
@@ -307,12 +307,12 @@ int MXAutogradMarkVariables(mx_uint num_var,
   API_END();
 }
 
-int MXAutogradComputeGradient(mx_uint num_output,
+int MXAutogradComputeGradient(uint32_t num_output,
                               NDArrayHandle *output_handles) {
   return MXAutogradBackward(num_output, output_handles, nullptr, 0);
 }
 
-int MXAutogradBackward(mx_uint num_output,
+int MXAutogradBackward(uint32_t num_output,
                        NDArrayHandle *output_handles,
                        NDArrayHandle *ograd_handles,
                        int retain_graph) {
@@ -321,10 +321,10 @@ int MXAutogradBackward(mx_uint num_output,
                               nullptr, nullptr);
 }
 
-int MXAutogradBackwardEx(mx_uint num_output,
+int MXAutogradBackwardEx(uint32_t num_output,
                          NDArrayHandle *output_handles,
                          NDArrayHandle *ograd_handles,
-                         mx_uint num_variables,
+                         uint32_t num_variables,
                          NDArrayHandle *var_handles,
                          int retain_graph,
                          int create_graph,
@@ -336,12 +336,12 @@ int MXAutogradBackwardEx(mx_uint num_output,
 
   std::vector<NDArray*> outputs, ograds, variables;
   outputs.reserve(num_output);
-  for (mx_uint i = 0; i < num_output; ++i) {
+  for (uint32_t i = 0; i < num_output; ++i) {
     outputs.emplace_back(reinterpret_cast<NDArray*>(output_handles[i]));
   }
 
   ograds.reserve(num_output);
-  for (mx_uint i = 0; i < num_output; ++i) {
+  for (uint32_t i = 0; i < num_output; ++i) {
     if (ograd_handles != nullptr) {
       ograds.emplace_back(reinterpret_cast<NDArray*>(ograd_handles[i]));
     } else {
@@ -350,7 +350,7 @@ int MXAutogradBackwardEx(mx_uint num_output,
   }
 
   variables.reserve(num_variables);
-  for (mx_uint i = 0; i < num_variables; ++i) {
+  for (uint32_t i = 0; i < num_variables; ++i) {
     variables.emplace_back(reinterpret_cast<NDArray*>(var_handles[i]));
   }
 
diff --git a/src/c_api/c_api_symbolic.cc b/src/c_api/c_api_symbolic.cc
index e6a84ceb2348..fa7736659fee 100644
--- a/src/c_api/c_api_symbolic.cc
+++ b/src/c_api/c_api_symbolic.cc
@@ -81,7 +81,7 @@ int MXListAllOpNames(nn_uint *out_size,
   return NNListAllOpNames(out_size, out_array);
 }
 
-int MXSymbolListAtomicSymbolCreators(mx_uint *out_size,
+int MXSymbolListAtomicSymbolCreators(uint32_t *out_size,
                                      AtomicSymbolCreator **out_array) {
   mxnet::op::RegisterLegacyOpProp();
   mxnet::op::RegisterLegacyNDFunc();
@@ -91,7 +91,7 @@ int MXSymbolListAtomicSymbolCreators(mx_uint *out_size,
 int MXSymbolGetAtomicSymbolInfo(AtomicSymbolCreator creator,
                                 const char **name,
                                 const char **description,
-                                mx_uint *num_args,
+                                uint32_t *num_args,
                                 const char ***arg_names,
                                 const char ***arg_type_infos,
                                 const char ***arg_descriptions,
@@ -114,7 +114,7 @@ int MXSymbolGetAtomicSymbolInfo(AtomicSymbolCreator creator,
 }
 
 int MXSymbolCreateAtomicSymbol(AtomicSymbolCreator creator,
-                               mx_uint num_param,
+                               uint32_t num_param,
                                const char **keys,
                                const char **vals,
                                SymbolHandle *out) {
@@ -151,14 +151,14 @@ int MXSymbolCreateVariable(const char *name, SymbolHandle *out) {
   return NNSymbolCreateVariable(name, out);
 }
 
-int MXSymbolCreateGroup(mx_uint num_symbols,
+int MXSymbolCreateGroup(uint32_t num_symbols,
                         SymbolHandle *symbols,
                         SymbolHandle *out) {
   return NNSymbolCreateGroup(num_symbols, symbols, out);
 }
 
 int MXSymbolGetOutput(SymbolHandle symbol,
-                      mx_uint index,
+                      uint32_t index,
                       SymbolHandle *out) {
   return NNSymbolGetOutput(symbol, index, out);
 }
@@ -249,7 +249,7 @@ int MXSymbolSetAttr(SymbolHandle symbol,
 }
 
 int MXSymbolListAttr(SymbolHandle symbol,
-                     mx_uint *out_size,
+                     uint32_t *out_size,
                      const char*** out) {
   nnvm::Symbol *s = static_cast<nnvm::Symbol*>(symbol);
   MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
@@ -279,7 +279,7 @@ int MXSymbolListAttr(SymbolHandle symbol,
 }
 
 int MXSymbolListAttrShallow(SymbolHandle symbol,
-                            mx_uint *out_size,
+                            uint32_t *out_size,
                             const char*** out) {
   nnvm::Symbol *s = static_cast<nnvm::Symbol*>(symbol);
   MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
@@ -308,19 +308,19 @@ int MXSymbolListAttrShallow(SymbolHandle symbol,
 }
 
 int MXSymbolListOutputs(SymbolHandle symbol,
-                        mx_uint *out_size,
+                        uint32_t *out_size,
                         const char ***out_str_array) {
   return NNSymbolListOutputNames(symbol, out_size, out_str_array);
 }
 
 int MXSymbolGetNumOutputs(SymbolHandle symbol,
-                           mx_uint *output_count) {
+                           uint32_t *output_count) {
   return NNSymbolGetNumOutputs(symbol, output_count);
 }
 
 int MXSymbolCompose(SymbolHandle sym,
                     const char *name,
-                    mx_uint num_args,
+                    uint32_t num_args,
                     const char** keys,
                     SymbolHandle* args) {
   return NNSymbolCompose(sym, name, num_args, keys, args);
@@ -328,13 +328,13 @@ int MXSymbolCompose(SymbolHandle sym,
 
 // adapter functions that re-implements the functions.
 int MXSymbolListArguments(SymbolHandle symbol,
-                          mx_uint *out_size,
+                          uint32_t *out_size,
                           const char ***out_str_array) {
   return NNSymbolListInputNames(symbol, 1, out_size, out_str_array);
 }
 
 int MXSymbolListAuxiliaryStates(SymbolHandle symbol,
-                                mx_uint *out_size,
+                                uint32_t *out_size,
                                 const char ***out_str_array) {
   return NNSymbolListInputNames(symbol, 2, out_size, out_str_array);
 }
@@ -514,19 +514,19 @@ void MatchArguments(
 }  // namespace mxnet
 
 int MXSymbolInferShape(SymbolHandle sym,
-                       mx_uint num_args,
+                       uint32_t num_args,
                        const char** keys,
-                       const mx_uint *arg_ind_ptr,
-                       const mx_uint *arg_shape_data,
-                       mx_uint *in_shape_size,
-                       const mx_uint **in_shape_ndim,
-                       const mx_uint ***in_shape_data,
-                       mx_uint *out_shape_size,
-                       const mx_uint **out_shape_ndim,
-                       const mx_uint ***out_shape_data,
-                       mx_uint *aux_shape_size,
-                       const mx_uint **aux_shape_ndim,
-                       const mx_uint ***aux_shape_data,
+                       const uint32_t *arg_ind_ptr,
+                       const uint32_t *arg_shape_data,
+                       uint32_t *in_shape_size,
+                       const uint32_t **in_shape_ndim,
+                       const uint32_t ***in_shape_data,
+                       uint32_t *out_shape_size,
+                       const uint32_t **out_shape_ndim,
+                       const uint32_t ***out_shape_data,
+                       uint32_t *aux_shape_size,
+                       const uint32_t **aux_shape_ndim,
+                       const uint32_t ***aux_shape_data,
                        int *complete) {
   nnvm::Symbol *s = static_cast<nnvm::Symbol*>(sym);
   MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
@@ -536,13 +536,13 @@ int MXSymbolInferShape(SymbolHandle sym,
   if (keys == nullptr && num_args != 0) {
     std::vector<uint32_t> read_only_args = mxnet::ReadOnlyArgIndices(g.indexed_graph());
     CHECK_LE(num_args, read_only_args.size());
-    for (mx_uint i = 0; i < num_args; ++i) {
+    for (uint32_t i = 0; i < num_args; ++i) {
       arg_shapes[read_only_args[i]] = mxnet::ShapeTypeCast(
           arg_shape_data + arg_ind_ptr[i], arg_shape_data + arg_ind_ptr[i+1]);
     }
   } else {
     std::unordered_map<std::string, mxnet::TShape> kwargs;
-    for (mx_uint i = 0; i < num_args; ++i) {
+    for (uint32_t i = 0; i < num_args; ++i) {
       kwargs[keys[i]] = mxnet::ShapeTypeCast(
           arg_shape_data + arg_ind_ptr[i], arg_shape_data + arg_ind_ptr[i+1]);
     }
@@ -572,13 +572,13 @@ int MXSymbolInferShape(SymbolHandle sym,
       &(ret->out_shape_ndim), &(ret->out_shape_data), &(ret->out_shape_buffer));
   MXAPIThreadLocalEntry<>::SetupShapeArrayReturnWithBuffer(ret->aux_shapes,
       &(ret->aux_shape_ndim), &(ret->aux_shape_data), &(ret->aux_shape_buffer));
-  *in_shape_size = static_cast<mx_uint>(ret->arg_shapes.size());
+  *in_shape_size = static_cast<uint32_t>(ret->arg_shapes.size());
   *in_shape_ndim = dmlc::BeginPtr(ret->arg_shape_ndim);
   *in_shape_data = dmlc::BeginPtr(ret->arg_shape_data);
-  *out_shape_size = static_cast<mx_uint>(ret->out_shapes.size());
+  *out_shape_size = static_cast<uint32_t>(ret->out_shapes.size());
   *out_shape_ndim = dmlc::BeginPtr(ret->out_shape_ndim);
   *out_shape_data = dmlc::BeginPtr(ret->out_shape_data);
-  *aux_shape_size = static_cast<mx_uint>(ret->aux_shapes.size());
+  *aux_shape_size = static_cast<uint32_t>(ret->aux_shapes.size());
   *aux_shape_ndim = dmlc::BeginPtr(ret->aux_shape_ndim);
   *aux_shape_data = dmlc::BeginPtr(ret->aux_shape_data);
   // mark complete
@@ -588,7 +588,7 @@ int MXSymbolInferShape(SymbolHandle sym,
 
 template<typename dtype, typename stype, typename itype>
 inline void SymbolInferShape(const char** keys,
-                             mx_uint num_args,
+                             uint32_t num_args,
                              const dtype* arg_shape_data,
                              const itype* arg_ind_ptr,
                              const int** in_shape_ndim,
@@ -608,13 +608,13 @@ inline void SymbolInferShape(const char** keys,
   if (keys == nullptr && num_args != 0) {
     std::vector < uint32_t > read_only_args = mxnet::ReadOnlyArgIndices(g.indexed_graph());
     CHECK_LE(num_args, read_only_args.size());
-    for (mx_uint i = 0; i < num_args; ++i) {
+    for (uint32_t i = 0; i < num_args; ++i) {
       arg_shapes[read_only_args[i]] = mxnet::ShapeTypeCast(arg_shape_data + arg_ind_ptr[i],
                                                            arg_shape_data + arg_ind_ptr[i + 1]);
     }
   } else {
     std::unordered_map<std::string, mxnet::TShape> kwargs;
-    for (mx_uint i = 0; i < num_args; ++i) {
+    for (uint32_t i = 0; i < num_args; ++i) {
       kwargs[keys[i]] = mxnet::ShapeTypeCast(arg_shape_data + arg_ind_ptr[i],
                                              arg_shape_data + arg_ind_ptr[i + 1]);
     }
@@ -659,24 +659,24 @@ inline void SymbolInferShape(const char** keys,
 }
 
 int MXSymbolInferShapeEx(SymbolHandle sym,
-                         mx_uint num_args,
+                         uint32_t num_args,
                          const char** keys,
-                         const mx_uint *arg_ind_ptr,
+                         const uint32_t *arg_ind_ptr,
                          const int *arg_shape_data,
-                         mx_uint *in_shape_size,
+                         uint32_t *in_shape_size,
                          const int **in_shape_ndim,
                          const int ***in_shape_data,
-                         mx_uint *out_shape_size,
+                         uint32_t *out_shape_size,
                          const int **out_shape_ndim,
                          const int ***out_shape_data,
-                         mx_uint *aux_shape_size,
+                         uint32_t *aux_shape_size,
                          const int **aux_shape_ndim,
                          const int ***aux_shape_data,
                          int *complete) {
   nnvm::Symbol *s = static_cast<nnvm::Symbol*>(sym);
   MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
   API_BEGIN();
-  SymbolInferShape<int, mx_uint, mx_uint>(keys,
+  SymbolInferShape<int, uint32_t, uint32_t>(keys,
                                               num_args,
                                               arg_shape_data,
                                               arg_ind_ptr,
@@ -696,7 +696,7 @@ int MXSymbolInferShapeEx(SymbolHandle sym,
 }
 
 int MXSymbolInferShapeEx64(SymbolHandle sym,
-                           mx_uint num_args,
+                           uint32_t num_args,
                            const char** keys,
                            const int64_t *arg_ind_ptr,
                            const int64_t *arg_shape_data,
@@ -733,19 +733,19 @@ int MXSymbolInferShapeEx64(SymbolHandle sym,
 }
 
 int MXSymbolInferShapePartial(SymbolHandle sym,
-                              mx_uint num_args,
+                              uint32_t num_args,
                               const char** keys,
-                              const mx_uint *arg_ind_ptr,
-                              const mx_uint *arg_shape_data,
-                              mx_uint *in_shape_size,
-                              const mx_uint **in_shape_ndim,
-                              const mx_uint ***in_shape_data,
-                              mx_uint *out_shape_size,
-                              const mx_uint **out_shape_ndim,
-                              const mx_uint ***out_shape_data,
-                              mx_uint *aux_shape_size,
-                              const mx_uint **aux_shape_ndim,
-                              const mx_uint ***aux_shape_data,
+                              const uint32_t *arg_ind_ptr,
+                              const uint32_t *arg_shape_data,
+                              uint32_t *in_shape_size,
+                              const uint32_t **in_shape_ndim,
+                              const uint32_t ***in_shape_data,
+                              uint32_t *out_shape_size,
+                              const uint32_t **out_shape_ndim,
+                              const uint32_t ***out_shape_data,
+                              uint32_t *aux_shape_size,
+                              const uint32_t **aux_shape_ndim,
+                              const uint32_t ***aux_shape_data,
                               int *complete) {
   int succ = 0;
   *complete = 1;
@@ -758,17 +758,17 @@ int MXSymbolInferShapePartial(SymbolHandle sym,
 }
 
 int MXSymbolInferShapePartialEx(SymbolHandle sym,
-                                mx_uint num_args,
+                                uint32_t num_args,
                                 const char** keys,
-                                const mx_uint *arg_ind_ptr,
+                                const uint32_t *arg_ind_ptr,
                                 const int *arg_shape_data,
-                                mx_uint *in_shape_size,
+                                uint32_t *in_shape_size,
                                 const int **in_shape_ndim,
                                 const int ***in_shape_data,
-                                mx_uint *out_shape_size,
+                                uint32_t *out_shape_size,
                                 const int **out_shape_ndim,
                                 const int ***out_shape_data,
-                                mx_uint *aux_shape_size,
+                                uint32_t *aux_shape_size,
                                 const int **aux_shape_ndim,
                                 const int ***aux_shape_data,
                                 int *complete) {
@@ -783,7 +783,7 @@ int MXSymbolInferShapePartialEx(SymbolHandle sym,
 }
 
 int MXSymbolInferShapePartialEx64(SymbolHandle sym,
-                                  mx_uint num_args,
+                                  uint32_t num_args,
                                   const char** keys,
                                   const int64_t *arg_ind_ptr,
                                   const int64_t *arg_shape_data,
@@ -808,14 +808,14 @@ int MXSymbolInferShapePartialEx64(SymbolHandle sym,
 }
 
 int MXSymbolInferType(SymbolHandle sym,
-                      mx_uint num_args,
+                      uint32_t num_args,
                       const char** keys,
                       const int *arg_type_data,
-                      mx_uint *in_type_size,
+                      uint32_t *in_type_size,
                       const int **in_type_data,
-                      mx_uint *out_type_size,
+                      uint32_t *out_type_size,
                       const int **out_type_data,
-                      mx_uint *aux_type_size,
+                      uint32_t *aux_type_size,
                       const int **aux_type_data,
                       int *complete) {
   nnvm::Symbol *s = static_cast<nnvm::Symbol*>(sym);
@@ -826,12 +826,12 @@ int MXSymbolInferType(SymbolHandle sym,
   if (keys == nullptr && num_args != 0) {
     std::vector<uint32_t> read_only_args = mxnet::ReadOnlyArgIndices(g.indexed_graph());
     CHECK_LE(num_args, read_only_args.size());
-    for (mx_uint i = 0; i < num_args; ++i) {
+    for (uint32_t i = 0; i < num_args; ++i) {
       arg_types[read_only_args[i]] = arg_type_data[i];
     }
   } else {
     std::unordered_map<std::string, int> kwargs;
-    for (mx_uint i = 0; i < num_args; ++i) {
+    for (uint32_t i = 0; i < num_args; ++i) {
       kwargs[keys[i]] = arg_type_data[i];
     }
     mxnet::MatchArguments(g.indexed_graph(), kwargs, &arg_types, "InferType");
@@ -842,25 +842,25 @@ int MXSymbolInferType(SymbolHandle sym,
   CopyAttr(g.indexed_graph(), g.GetAttr<nnvm::DTypeVector>("dtype"),
            &(ret->arg_types), &(ret->out_types), &(ret->aux_types));
 
-  *in_type_size = static_cast<mx_uint>(ret->arg_types.size());
+  *in_type_size = static_cast<uint32_t>(ret->arg_types.size());
   *in_type_data = dmlc::BeginPtr(ret->arg_types);
-  *out_type_size = static_cast<mx_uint>(ret->out_types.size());
+  *out_type_size = static_cast<uint32_t>(ret->out_types.size());
   *out_type_data = dmlc::BeginPtr(ret->out_types);
-  *aux_type_size = static_cast<mx_uint>(ret->aux_types.size());
+  *aux_type_size = static_cast<uint32_t>(ret->aux_types.size());
   *aux_type_data = dmlc::BeginPtr(ret->aux_types);
   *complete = (g.GetAttr<size_t>("dtype_num_unknown_nodes") == 0);
   API_END();
 }
 
 int MXSymbolInferTypePartial(SymbolHandle sym,
-                             mx_uint num_args,
+                             uint32_t num_args,
                              const char** keys,
                              const int *arg_type_data,
-                             mx_uint *in_type_size,
+                             uint32_t *in_type_size,
                              const int **in_type_data,
-                             mx_uint *out_type_size,
+                             uint32_t *out_type_size,
                              const int **out_type_data,
-                             mx_uint *aux_type_size,
+                             uint32_t *aux_type_size,
                              const int **aux_type_data,
                              int *complete) {
   int succ = 0;
@@ -873,7 +873,7 @@ int MXSymbolInferTypePartial(SymbolHandle sym,
                             &succ);
 }
 
-int MXSymbolGrad(SymbolHandle sym, mx_uint num_wrt, const char** wrt, SymbolHandle* out) {
+int MXSymbolGrad(SymbolHandle sym, uint32_t num_wrt, const char** wrt, SymbolHandle* out) {
   API_BEGIN();
   LOG(FATAL) << "not implemented";
   API_END();
@@ -882,11 +882,11 @@ int MXSymbolGrad(SymbolHandle sym, mx_uint num_wrt, const char** wrt, SymbolHand
 int MXQuantizeSymbol(SymbolHandle sym_handle,
                      SymbolHandle *ret_sym_handle,
                      const int* dev_type,
-                     const mx_uint num_excluded_sym_names,
+                     const uint32_t num_excluded_sym_names,
                      const char **excluded_sym_names,
-                     const mx_uint num_excluded_op_names,
+                     const uint32_t num_excluded_op_names,
                      const char **excluded_op_names,
-                     const mx_uint num_offline,
+                     const uint32_t num_offline,
                      const char **offline_params,
                      const char *quantized_dtype,
                      const bool calib_quantize,
@@ -1021,18 +1021,18 @@ static void _UpdateSymDTypeAttrs(
 
 int MXReducePrecisionSymbol(SymbolHandle sym_handle,
                             SymbolHandle *ret_sym_handle,
-                            mx_uint num_args,
+                            uint32_t num_args,
                             const int *arg_type_data,
-                            mx_uint num_ind_ptr,
+                            uint32_t num_ind_ptr,
                             const int* ind_ptr,
                             const int* target_dtype,
                             const int cast_optional_params,
-                            const mx_uint num_target_dtype_op_names,
-                            const mx_uint num_fp32_op_names,
-                            const mx_uint num_widest_dtype_op_names,
-                            const mx_uint num_conditional_fp32_op_names,
-                            const mx_uint num_excluded_symbols,
-                            const mx_uint num_model_params,
+                            const uint32_t num_target_dtype_op_names,
+                            const uint32_t num_fp32_op_names,
+                            const uint32_t num_widest_dtype_op_names,
+                            const uint32_t num_conditional_fp32_op_names,
+                            const uint32_t num_excluded_symbols,
+                            const uint32_t num_model_params,
                             const char **target_dtype_op_names,
                             const char **fp32_op_names,
                             const char **widest_dtype_op_names,
@@ -1085,7 +1085,7 @@ int MXReducePrecisionSymbol(SymbolHandle sym_handle,
   std::unordered_map<std::string, int> kwargs;
   std::unordered_map<std::string, int> node_name_dtype_map, node_without_dtype_map;
   nnvm::DTypeVector arg_types(g.indexed_graph().input_nodes().size(), -1);
-  for (mx_uint i = 0; i < num_args; ++i) {
+  for (uint32_t i = 0; i < num_args; ++i) {
     kwargs[arg_names[i]] = arg_type_data[i];
     node_name_dtype_map[arg_names[i]] = arg_type_data[i];
   }
@@ -1139,7 +1139,7 @@ int MXReducePrecisionSymbol(SymbolHandle sym_handle,
 }
 
 int MXSetCalibTableToQuantizedSymbol(SymbolHandle qsym_handle,
-                                     const mx_uint num_layers,
+                                     const uint32_t num_layers,
                                      const char** layer_names,
                                      const float* min_ranges,
                                      const float* max_ranges,
diff --git a/src/c_api/c_api_test.cc b/src/c_api/c_api_test.cc
index a1f12fad60f0..8d4e432e4f7d 100644
--- a/src/c_api/c_api_test.cc
+++ b/src/c_api/c_api_test.cc
@@ -29,7 +29,7 @@
 
 int MXBuildSubgraphByOpNames(SymbolHandle sym_handle,
                               const char* prop_name,
-                              const mx_uint num_ops,
+                              const uint32_t num_ops,
                               const char** op_names,
                               SymbolHandle* ret_sym_handle) {
   nnvm::Symbol* s = new nnvm::Symbol();
@@ -62,7 +62,7 @@ int MXBuildSubgraphByOpNames(SymbolHandle sym_handle,
 }
 
 int MXSetSubgraphPropertyOpNames(const char* prop_name,
-                                 const mx_uint num_ops,
+                                 const uint32_t num_ops,
                                  const char** op_names) {
   API_BEGIN();
   std::unordered_set<std::string> op_name_set;
diff --git a/src/c_api/c_predict_api.cc b/src/c_api/c_predict_api.cc
index b371fd044dc5..efbe63207a71 100644
--- a/src/c_api/c_predict_api.cc
+++ b/src/c_api/c_predict_api.cc
@@ -67,7 +67,7 @@ struct MXAPINDList {
   mxnet::ShapeVector shapes;
   std::vector<uint32_t> shapes_buffer;
   std::vector<size_t> indptr;
-  std::vector<mx_float> data;
+  std::vector<float> data;
 };
 
 inline void _CreateExecutor(PredictorHandle pred_hnd) {
@@ -91,16 +91,16 @@ int _CreatePartialOut(const char* symbol_json_str,
                       const void* param_bytes,
                       int param_size,
                       int dev_type, int dev_id,
-                      const mx_uint num_input_nodes,
+                      const uint32_t num_input_nodes,
                       const char** input_keys,
-                      const mx_uint* input_shape_indptr,
-                      const mx_uint* input_shape_data,
-                      mx_uint num_output_nodes,
+                      const uint32_t* input_shape_indptr,
+                      const uint32_t* input_shape_data,
+                      uint32_t num_output_nodes,
                       const char** output_keys,
                       // This is used for parallel inference.
                       int num_threads,
                       bool lazy,
-                      const mx_uint num_provided_arg_dtypes,
+                      const uint32_t num_provided_arg_dtypes,
                       const char** provided_arg_dtype_names,
                       const int* provided_arg_dtypes,
                       PredictorHandle* out) {
@@ -110,7 +110,7 @@ int _CreatePartialOut(const char* symbol_json_str,
   Symbol sym;
   // make sure symbols are registered
   {
-  mx_uint outSize;
+  uint32_t outSize;
   const char **outArray;
   MXListAllOpNames(&outSize, &outArray);
   }
@@ -125,7 +125,7 @@ int _CreatePartialOut(const char* symbol_json_str,
     Symbol internal = sym.GetInternals();
     std::vector<std::string> all_out = internal.ListOutputNames();
     std::vector<Symbol> out_syms(num_output_nodes);
-    for (mx_uint i = 0; i < num_output_nodes; ++i) {
+    for (uint32_t i = 0; i < num_output_nodes; ++i) {
       std::string out_key(output_keys[i]);
       out_key += "_output";
       for (size_t j = 0; j < all_out.size(); ++j) {
@@ -176,7 +176,7 @@ int _CreatePartialOut(const char* symbol_json_str,
     }
 
     if (num_provided_arg_dtypes > 0) {
-      for (mx_uint i = 0; i < num_provided_arg_dtypes; ++i) {
+      for (uint32_t i = 0; i < num_provided_arg_dtypes; ++i) {
         if (aux_types.count(provided_arg_dtype_names[i]) == 0 &&
             arg_types.count(provided_arg_dtype_names[i]) == 0) {
           arg_types[provided_arg_dtype_names[i]] = provided_arg_dtypes[i];
@@ -187,7 +187,7 @@ int _CreatePartialOut(const char* symbol_json_str,
 
   // shape inference and bind
   std::unordered_map<std::string, mxnet::TShape> known_shape;
-  for (mx_uint i = 0; i < num_input_nodes; ++i) {
+  for (uint32_t i = 0; i < num_input_nodes; ++i) {
     known_shape[std::string(input_keys[i])] =
         mxnet::TShape(input_shape_data + input_shape_indptr[i],
                input_shape_data + input_shape_indptr[i + 1]);
@@ -309,11 +309,11 @@ int MXPredCreatePartialOut(const char* symbol_json_str,
                            const void* param_bytes,
                            int param_size,
                            int dev_type, int dev_id,
-                           mx_uint num_input_nodes,
+                           uint32_t num_input_nodes,
                            const char** input_keys,
-                           const mx_uint* input_shape_indptr,
-                           const mx_uint* input_shape_data,
-                           mx_uint num_output_nodes,
+                           const uint32_t* input_shape_indptr,
+                           const uint32_t* input_shape_data,
+                           uint32_t num_output_nodes,
                            const char** output_keys,
                            PredictorHandle* out) {
   return _CreatePartialOut(
@@ -339,10 +339,10 @@ int MXPredCreate(const char* symbol_json_str,
                  const void* param_bytes,
                  int param_size,
                  int dev_type, int dev_id,
-                 mx_uint num_input_nodes,
+                 uint32_t num_input_nodes,
                  const char** input_keys,
-                 const mx_uint* input_shape_indptr,
-                 const mx_uint* input_shape_data,
+                 const uint32_t* input_shape_indptr,
+                 const uint32_t* input_shape_data,
                  PredictorHandle* out) {
   return _CreatePartialOut(
       symbol_json_str,
@@ -368,11 +368,11 @@ int MXPredCreateEx(const char* symbol_json_str,
                    const void* param_bytes,
                    int param_size,
                    int dev_type, int dev_id,
-                   mx_uint num_input_nodes,
+                   uint32_t num_input_nodes,
                    const char** input_keys,
-                   const mx_uint* input_shape_indptr,
-                   const mx_uint* input_shape_data,
-                   const mx_uint num_provided_arg_dtypes,
+                   const uint32_t* input_shape_indptr,
+                   const uint32_t* input_shape_data,
+                   const uint32_t num_provided_arg_dtypes,
                    const char** provided_arg_dtype_names,
                    const int* provided_arg_dtypes,
                    PredictorHandle* out) {
@@ -400,10 +400,10 @@ int MXPredCreateMultiThread(const char* symbol_json_str,
                             const void* param_bytes,
                             int param_size,
                             int dev_type, int dev_id,
-                            mx_uint num_input_nodes,
+                            uint32_t num_input_nodes,
                             const char** input_keys,
-                            const mx_uint* input_shape_indptr,
-                            const mx_uint* input_shape_data,
+                            const uint32_t* input_shape_indptr,
+                            const uint32_t* input_shape_data,
                             // This is used for paralle inference.
                             int num_threads,
                             PredictorHandle* out) {
@@ -434,10 +434,10 @@ int MXPredCreateMultiThread(const char* symbol_json_str,
       out);
 }
 
-int MXPredReshape(mx_uint num_input_nodes,
+int MXPredReshape(uint32_t num_input_nodes,
                   const char** input_keys,
-                  const mx_uint* input_shape_indptr,
-                  const mx_uint* input_shape_data,
+                  const uint32_t* input_shape_indptr,
+                  const uint32_t* input_shape_data,
                   PredictorHandle handle,
                   PredictorHandle* out) {
   _CreateExecutor(handle);
@@ -447,7 +447,7 @@ int MXPredReshape(mx_uint num_input_nodes,
   API_BEGIN();
   // shape inference
   std::unordered_map<std::string, mxnet::TShape> new_shape;
-  for (mx_uint i = 0; i < num_input_nodes; ++i) {
+  for (uint32_t i = 0; i < num_input_nodes; ++i) {
     new_shape[std::string(input_keys[i])] =
         mxnet::TShape(input_shape_data + input_shape_indptr[i],
             input_shape_data + input_shape_indptr[i + 1]);
@@ -526,9 +526,9 @@ int MXPredReshape(mx_uint num_input_nodes,
 }
 
 int MXPredGetOutputShape(PredictorHandle handle,
-                         mx_uint out_index,
-                         mx_uint** shape_data,
-                         mx_uint* shape_ndim) {
+                         uint32_t out_index,
+                         uint32_t** shape_data,
+                         uint32_t* shape_ndim) {
   MXAPIPredictor* p = static_cast<MXAPIPredictor*>(handle);
   API_BEGIN();
   CHECK_LT(out_index, p->out_arrays.size())
@@ -544,7 +544,7 @@ int MXPredGetOutputShape(PredictorHandle handle,
 }
 
 int MXPredGetOutputType(PredictorHandle handle,
-                        mx_uint out_index,
+                        uint32_t out_index,
                         int* out_dtype) {
   MXAPIPredictor* p = static_cast<MXAPIPredictor*>(handle);
   API_BEGIN();
@@ -560,8 +560,8 @@ int MXPredGetOutputType(PredictorHandle handle,
 
 int MXPredSetInput(PredictorHandle handle,
                    const char* key,
-                   const mx_float* data,
-                   mx_uint size) {
+                   const float* data,
+                   uint32_t size) {
   MXAPIPredictor* p = static_cast<MXAPIPredictor*>(handle);
   API_BEGIN();
   auto it = p->key2arg.find(key);
@@ -590,9 +590,9 @@ int MXPredPartialForward(PredictorHandle handle, int step, int* step_left) {
 }
 
 int MXPredGetOutput(PredictorHandle handle,
-                    mx_uint index,
-                    mx_float* data,
-                    mx_uint size) {
+                    uint32_t index,
+                    float* data,
+                    uint32_t size) {
   MXAPIPredictor* p = static_cast<MXAPIPredictor*>(handle);
   API_BEGIN();
   CHECK_LT(index, p->out_arrays.size())
@@ -611,7 +611,7 @@ int MXPredFree(PredictorHandle handle) {
 int MXNDListCreate(const char* nd_file_bytes,
                    int nd_file_size,
                    NDListHandle *out,
-                   mx_uint* out_length) {
+                   uint32_t* out_length) {
   MXAPINDList* ret = new MXAPINDList();
   API_BEGIN();
   std::vector<NDArray> arrays;
@@ -633,16 +633,16 @@ int MXNDListCreate(const char* nd_file_bytes,
     ret->indptr.push_back(begin + size);
   }
   *out = ret;
-  *out_length = static_cast<mx_uint>(arrays.size());
+  *out_length = static_cast<uint32_t>(arrays.size());
   API_END();
 }
 
 int MXNDListGet(NDListHandle handle,
-                mx_uint index,
+                uint32_t index,
                 const char** out_key,
-                const mx_float** out_data,
-                const mx_uint** out_shape,
-                mx_uint* out_ndim) {
+                const float** out_data,
+                const uint32_t** out_shape,
+                uint32_t* out_ndim) {
   MXAPINDList* p = static_cast<MXAPINDList*>(handle);
   API_BEGIN();
   CHECK_LT(index, p->shapes.size())
diff --git a/src/imperative/imperative.cc b/src/imperative/imperative.cc
index e6a177e27847..b2ffd1096b2b 100644
--- a/src/imperative/imperative.cc
+++ b/src/imperative/imperative.cc
@@ -122,7 +122,7 @@ OpStatePtr Imperative::Invoke(
 
 void Imperative::MarkVariables(
     const std::vector<NDArray*>& variables,
-    const std::vector<mx_uint>& grad_reqs,
+    const std::vector<uint32_t>& grad_reqs,
     const std::vector<NDArray*>& gradients) {
   for (uint32_t i = 0; i < variables.size(); ++i) {
     std::string str_c(std::to_string(variable_count_++));

From 9b9326ff94b303e010a4e3e9b97232e5aa0c9da3 Mon Sep 17 00:00:00 2001
From: Tao Lv <tao.a.lv@intel.com>
Date: Wed, 4 Sep 2019 10:16:15 +0800
Subject: [PATCH 278/813] add KEY for Tao Lv (#16081)

---
 KEYS | 58 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 58 insertions(+)

diff --git a/KEYS b/KEYS
index ff503f7efb7e..51675754d62c 100644
--- a/KEYS
+++ b/KEYS
@@ -748,3 +748,61 @@ ihUvH40rKlgxbV3+KS/Ew88D2tJ2JGCACx2yzS3trw+oUXugoaAQiileXQfu47SI
 dM5GPiAt2UECZ9v4WthkRGygnoPiL/4IyDFsS9yDX7mOBSycVmT5R+VXmOs=
 =z8mO
 -----END PGP PUBLIC KEY BLOCK-----
+pub   4096R/79B0F222 2019-09-03
+uid                  Tao Lv (CODE SIGNING KEY) <taolv@apache.org>
+sig 3        79B0F222 2019-09-03  Tao Lv (CODE SIGNING KEY) <taolv@apache.org>
+sub   4096R/E1308559 2019-09-03
+sig          79B0F222 2019-09-03  Tao Lv (CODE SIGNING KEY) <taolv@apache.org>
+
+-----BEGIN PGP PUBLIC KEY BLOCK-----
+Version: GnuPG v2.0.22 (GNU/Linux)
+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+=hhsZ
+-----END PGP PUBLIC KEY BLOCK-----

From 65e37ca88227099bd6b858a420e58a7f4c222c47 Mon Sep 17 00:00:00 2001
From: Jake Lee <gstu1130@gmail.com>
Date: Tue, 3 Sep 2019 21:21:16 -0700
Subject: [PATCH 279/813] numpy multinomial op (#15878)

* numpy multinomial op

* address the comment

* retrigger CI

* retrigger CI
---
 python/mxnet/ndarray/numpy/random.py          |  54 +++++
 python/mxnet/numpy/random.py                  |  36 ++++
 .../numpy/random/np_multinomial_op.cc         |  61 ++++++
 .../numpy/random/np_multinomial_op.cu         |  34 +++
 src/operator/numpy/random/np_multinomial_op.h | 193 ++++++++++++++++++
 tests/python/unittest/test_numpy_ndarray.py   |  50 +++++
 6 files changed, 428 insertions(+)
 create mode 100644 src/operator/numpy/random/np_multinomial_op.cc
 create mode 100644 src/operator/numpy/random/np_multinomial_op.cu
 create mode 100644 src/operator/numpy/random/np_multinomial_op.h

diff --git a/python/mxnet/ndarray/numpy/random.py b/python/mxnet/ndarray/numpy/random.py
index d892ccdaca73..9ea2ef0f5405 100644
--- a/python/mxnet/ndarray/numpy/random.py
+++ b/python/mxnet/ndarray/numpy/random.py
@@ -17,8 +17,10 @@
 
 """Namespace for operators used in Gluon dispatched by F=ndarray."""
 from __future__ import absolute_import
+import numpy as np
 from ...context import current_context
 from . import _internal as _npi
+from ..ndarray import NDArray
 from ...base import numeric_types
 
 
@@ -189,3 +191,55 @@ def normal(loc=0.0, scale=1.0, size=None, **kwargs):
         raise NotImplementedError('np.random.normal only supports loc and scale of '
                                   'numeric types for now')
     return _npi.random_normal(loc, scale, shape=size, dtype=dtype, ctx=ctx, out=out, **kwargs)
+
+
+def multinomial(n, pvals, size=None):
+    """multinomial(n, pvals, size=None)
+
+    Draw samples from a multinomial distribution.
+
+    The multinomial distribution is a multivariate generalisation of the binomial distribution.
+    Take an experiment with one of ``p`` possible outcomes. An example of such an experiment is throwing a dice,
+    where the outcome can be 1 through 6. Each sample drawn from the distribution represents n such experiments.
+    Its values, ``X_i = [X_0, X_1, ..., X_p]``, represent the number of times the outcome was ``i``.
+
+    Parameters
+    ----------
+    n : int
+        Number of experiments.
+    pvals : sequence of floats, length p
+        Probabilities of each of the p different outcomes. These should sum to 1.
+    size : int or tuple of ints, optional
+        Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples
+        are drawn. Default is None, in which case a single value is returned.
+
+    Returns
+    -------
+    out : ndarray
+        The drawn samples, of shape size, if that was provided. If not, the shape is ``(N,)``.
+        In other words, each entry ``out[i,j,...,:]`` is an N-dimensional value drawn from the distribution.
+
+    Examples
+    --------
+    Throw a dice 1000 times, and 1000 times again:
+
+    >>> np.random.multinomial(1000, [1/6.]*6, size=2)
+    array([[164, 161, 179, 158, 150, 188],
+           [178, 162, 177, 143, 163, 177]])
+
+    A loaded die is more likely to land on number 6:
+
+    >>> np.random.multinomial(100, [1/7.]*5 + [2/7.])
+    array([19, 14, 12, 11, 21, 23])
+
+    >>> np.random.multinomial(100, [1.0 / 3, 2.0 / 3])
+    array([32, 68])
+    """
+    if isinstance(pvals, NDArray):
+        return _npi.multinomial(pvals, pvals=None, n=n, size=size)
+    else:
+        if isinstance(pvals, np.ndarray):
+            raise ValueError('numpy ndarray is not supported!')
+        if any(isinstance(i, list) for i in pvals):
+            raise ValueError('object too deep for desired array')
+        return _npi.multinomial(n=n, pvals=pvals, size=size)
diff --git a/python/mxnet/numpy/random.py b/python/mxnet/numpy/random.py
index dc6107476f81..bd534f0b6d97 100644
--- a/python/mxnet/numpy/random.py
+++ b/python/mxnet/numpy/random.py
@@ -144,3 +144,39 @@ def normal(loc=0.0, scale=1.0, size=None, **kwargs):
     This function currently does not support ``loc`` and ``scale`` as ndarrays.
     """
     return _mx_nd_np.random.normal(loc, scale, size, **kwargs)
+
+
+def multinomial(n, pvals, size=None, **kwargs):
+    """multinomial(n, pvals, size=None)
+    Draw samples from a multinomial distribution.
+    The multinomial distribution is a multivariate generalisation of the binomial distribution.
+    Take an experiment with one of ``p`` possible outcomes. An example of such an experiment is throwing a dice,
+    where the outcome can be 1 through 6. Each sample drawn from the distribution represents n such experiments.
+    Its values, ``X_i = [X_0, X_1, ..., X_p]``, represent the number of times the outcome was ``i``.
+    Parameters
+    ----------
+    n : int
+        Number of experiments.
+    pvals : sequence of floats, length p
+        Probabilities of each of the p different outcomes. These should sum to 1.
+    size : int or tuple of ints, optional
+        Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples
+        are drawn. Default is None, in which case a single value is returned.
+    Returns
+    -------
+    out : ndarray
+        The drawn samples, of shape size, if that was provided. If not, the shape is ``(N,)``.
+        In other words, each entry ``out[i,j,...,:]`` is an N-dimensional value drawn from the distribution.
+    Examples
+    --------
+    Throw a dice 1000 times, and 1000 times again:
+    >>> np.random.multinomial(1000, [1/6.]*6, size=2)
+    array([[164, 161, 179, 158, 150, 188],
+           [178, 162, 177, 143, 163, 177]])
+    A loaded die is more likely to land on number 6:
+    >>> np.random.multinomial(100, [1/7.]*5 + [2/7.])
+    array([19, 14, 12, 11, 21, 23])
+    >>> np.random.multinomial(100, [1.0 / 3, 2.0 / 3])
+    array([32, 68])
+    """
+    return _mx_nd_np.random.multinomial(n, pvals, size, **kwargs)
diff --git a/src/operator/numpy/random/np_multinomial_op.cc b/src/operator/numpy/random/np_multinomial_op.cc
new file mode 100644
index 000000000000..bf4f88c591cf
--- /dev/null
+++ b/src/operator/numpy/random/np_multinomial_op.cc
@@ -0,0 +1,61 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_multinomial_op.h
+ * \brief Operator for numpy sampling from multinomial distributions
+ */
+#include "./np_multinomial_op.h"
+
+namespace mxnet {
+namespace op {
+
+DMLC_REGISTER_PARAMETER(NumpyMultinomialParam);
+
+NNVM_REGISTER_OP(_npi_multinomial)
+.describe(R"code(Draw samples from a multinomial distribution. "
+"The multinomial distribution is a multivariate generalisation of the binomial distribution. "
+"Take an experiment with one of p possible outcomes. "
+"An example of such an experiment is throwing a dice, where the outcome can be 1 through 6. "
+"Each sample drawn from the distribution represents n such experiments. "
+"Its values, X_i = [X_0, X_1, ..., X_p], represent the number of times the outcome was i.
+)code")
+.set_num_inputs(
+  [](const nnvm::NodeAttrs& attrs) {
+    const NumpyMultinomialParam& param = nnvm::get<NumpyMultinomialParam>(attrs.parsed);
+    return param.pvals.has_value() ? 0U : 1U;
+  }
+)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<NumpyMultinomialParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyMultinomialOpShape)
+.set_attr<nnvm::FInferType>("FInferType", NumpyMultinomialOpType)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const nnvm::NodeAttrs& attrs) {
+      return std::vector<ResourceRequest>{
+        ResourceRequest::kRandom, ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", NumpyMultinomialForward<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.add_argument("a", "NDArray-or-Symbol", "Source input")
+.add_arguments(NumpyMultinomialParam::__FIELDS__());
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/random/np_multinomial_op.cu b/src/operator/numpy/random/np_multinomial_op.cu
new file mode 100644
index 000000000000..a80926024735
--- /dev/null
+++ b/src/operator/numpy/random/np_multinomial_op.cu
@@ -0,0 +1,34 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_multinomial_op.cu
+ * \brief Operator for numpy sampling from multinomial distributions
+ */
+#include "./np_multinomial_op.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(_npi_multinomial)
+.set_attr<FCompute>("FCompute<gpu>", NumpyMultinomialForward<gpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/random/np_multinomial_op.h b/src/operator/numpy/random/np_multinomial_op.h
new file mode 100644
index 000000000000..7115f2761202
--- /dev/null
+++ b/src/operator/numpy/random/np_multinomial_op.h
@@ -0,0 +1,193 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_multinomial_op.h
+ * \brief Operator for sampling from multinomial distributions
+ */
+#ifndef MXNET_OPERATOR_NUMPY_RANDOM_NP_MULTINOMIAL_OP_H_
+#define MXNET_OPERATOR_NUMPY_RANDOM_NP_MULTINOMIAL_OP_H_
+
+#include <mxnet/operator_util.h>
+#include <vector>
+#include "../../mshadow_op.h"
+#include "../../mxnet_op.h"
+#include "../../operator_common.h"
+#include "../../elemwise_op_common.h"
+
+namespace mxnet {
+namespace op {
+
+struct NumpyMultinomialParam : public dmlc::Parameter<NumpyMultinomialParam> {
+  int n;
+  dmlc::optional<mxnet::Tuple<double>> pvals;
+  dmlc::optional<mxnet::Tuple<int>> size;
+  DMLC_DECLARE_PARAMETER(NumpyMultinomialParam) {
+    DMLC_DECLARE_FIELD(n)
+      .describe("Number of experiments.");
+    DMLC_DECLARE_FIELD(pvals)
+      .set_default(dmlc::optional<mxnet::Tuple<double>>())
+      .describe("Probabilities of each of the p different outcomes. "
+      "These should sum to 1 (however, the last element is always assumed to "
+      "account for the remaining probability, as long as sum(pvals[:-1]) <= 1)"
+      "Note that this is for internal usage only. "
+      "This operator will only have either input mx.ndarray or this list of pvals");
+    DMLC_DECLARE_FIELD(size)
+      .set_default(dmlc::optional<mxnet::Tuple<int>>())
+      .describe("Output shape. If the given shape is, "
+      "e.g., (m, n, k), then m * n * k samples are drawn. "
+      "Default is None, in which case a single value is returned.");
+  }
+};
+
+inline bool NumpyMultinomialOpShape(const nnvm::NodeAttrs& attrs,
+                                     std::vector<TShape> *in_attrs,
+                                     std::vector<TShape> *out_attrs) {
+  const NumpyMultinomialParam& param = nnvm::get<NumpyMultinomialParam>(attrs.parsed);
+  CHECK_EQ(out_attrs->size(), 1U);
+
+  std::vector<dim_t> oshape_vec;
+  dim_t pvals_length;
+  if (param.pvals.has_value()) {
+    CHECK_EQ(in_attrs->size(), 0U);
+    pvals_length = param.pvals.value().ndim();
+  } else {
+    // pvals is from input ndarray
+    CHECK_EQ(in_attrs->size(), 1U);
+    const TShape& ishape = (*in_attrs)[0];
+    // check the input shape is only one dimension
+    CHECK_EQ(ishape.ndim(), 1U)
+      << "object too deep for desired array";
+    pvals_length = ishape[0];
+  }
+  if (param.size.has_value()) {
+    const mxnet::Tuple<int>& size = param.size.value();
+    for (int i = 0; i < size.ndim(); ++i) {
+      oshape_vec.emplace_back(size[i]);
+    }
+  }
+  oshape_vec.emplace_back(pvals_length);
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, TShape(oshape_vec));
+  return out_attrs->at(0).ndim() != 0U;;
+}
+
+inline bool NumpyMultinomialOpType(const nnvm::NodeAttrs& attrs,
+                                    std::vector<int>* in_attrs,
+                                    std::vector<int>* out_attrs) {
+  const NumpyMultinomialParam& param = nnvm::get<NumpyMultinomialParam>(attrs.parsed);
+  CHECK_EQ(in_attrs->size(), (param.pvals.has_value()) ? 0U : 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+
+  (*out_attrs)[0] = mshadow::kInt64;
+  return true;
+}
+
+struct multinomial_kernel {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int i,
+                                  const int num_exp,
+                                  const int prob_length,
+                                  DType* pvals,
+                                  double* uniform,
+                                  int64_t* out) {
+    for (int j = 0; j < num_exp; ++j) {
+      DType loc = static_cast<DType>(uniform[i * num_exp + j]);
+      DType acc = 0.0;
+      bool found = false;
+      for (int k = 0; k < prob_length; ++k) {
+        acc += pvals[k];
+        if (acc > loc) {
+          found = true;
+          out[i * prob_length + k] += 1;
+          break;
+        }
+      }
+      if (!found) {
+        out[i * prob_length + (prob_length - 1)] += 1;
+      }
+    }
+  }
+};
+
+template<typename xpu>
+void NumpyMultinomialForward(const nnvm::NodeAttrs& attrs,
+                              const OpContext& ctx,
+                              const std::vector<TBlob>& inputs,
+                              const std::vector<OpReqType>& req,
+                              const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mxnet_op;
+  const NumpyMultinomialParam& param = nnvm::get<NumpyMultinomialParam>(attrs.parsed);
+  CHECK_EQ(outputs.size(), 1U);
+  CHECK_EQ(inputs.size(), (param.pvals.has_value()) ? 0U : 1U);
+
+  int prob_length = (param.pvals.has_value())
+    ? param.pvals.value().ndim() : inputs[0].shape_[0];
+  // if intput is [] or size contains 0 dimension
+  if (prob_length == 0U || outputs[0].shape_.Size() == 0) return;
+  int num_output = outputs[0].Size() / prob_length;
+  int num_exp = param.n;
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  Random<xpu, double> *prnd = ctx.requested[0].get_random<xpu, double>(s);
+  size_t temp_space_ = (param.pvals.has_value())
+                      ? num_output * param.n + prob_length : num_output * param.n;
+  Tensor<xpu, 1, double> temp_tensor =
+      ctx.requested[1].get_space_typed<xpu, 1, double>(Shape1(temp_space_), s);
+
+  prnd->SampleUniform(&temp_tensor, 0, 1);
+  // set zero for the outputs
+  Kernel<set_zero, xpu>::Launch(s, outputs[0].Size(), outputs[0].dptr<int64_t>());
+  if (param.pvals.has_value()) {
+    // create a tensor to copy the param.pvals tuple to avoid
+    // error: calling a __host__ function from a __host__ __device__ function is not allowed
+    // reuse the uniform temp space to create pval tensor
+    double* pvals_ = temp_tensor.dptr_ + num_output * param.n;
+    // check if sum of input(pvals) > 1.0
+    double sum = 0.0;
+    for (int i = 0; i < prob_length; ++i) {
+        sum += param.pvals.value()[i];
+        // copy the tuple to data for later kernel usage
+        pvals_[i] = param.pvals.value()[i];
+        CHECK_LE(sum, 1.0)
+          << "sum(pvals[:-1]) > 1.0";
+    }
+    Kernel<multinomial_kernel, xpu>::Launch(
+      s, num_output, num_exp, prob_length, pvals_, temp_tensor.dptr_, outputs[0].dptr<int64_t>());
+  } else {
+    MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+      // check if sum of input(pvals) > 1.0
+      DType sum = DType(0);
+      DType* input = inputs[0].dptr<DType>();
+      for (int i = 0; i < prob_length; ++i) {
+        sum += input[i];
+        CHECK_LE(sum, 1.0)
+          << "sum(pvals[:-1]) > 1.0";
+      }
+      Kernel<multinomial_kernel, xpu>::Launch(
+        s, num_output, num_exp, prob_length,
+        inputs[0].dptr<DType>(), temp_tensor.dptr_, outputs[0].dptr<int64_t>());
+    });
+  }
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_NUMPY_RANDOM_NP_MULTINOMIAL_OP_H_
diff --git a/tests/python/unittest/test_numpy_ndarray.py b/tests/python/unittest/test_numpy_ndarray.py
index fafa5a827c2f..883060466836 100644
--- a/tests/python/unittest/test_numpy_ndarray.py
+++ b/tests/python/unittest/test_numpy_ndarray.py
@@ -767,6 +767,56 @@ def test_np_uniform():
             mx.test_utils.assert_almost_equal(uniform_samples.asnumpy().mean(0), expect_mean.asnumpy(), rtol=0.20, atol=1e-1)
 
 
+@retry(5)
+@with_seed()
+@use_np
+def test_np_multinomial():
+    pvals_list = [[0.0, 0.1, 0.2, 0.3, 0.4], [0.4, 0.3, 0.2, 0.1, 0.0]]
+    sizes = [None, (), (3,), (2, 5, 7), (4, 9)]
+    experiements = 10000
+    for have_size in [False, True]:
+        for pvals in pvals_list:
+            if have_size:
+                for size in sizes:
+                    freq = mx.np.random.multinomial(experiements, pvals, size=size).asnumpy() / _np.float32(experiements)
+                    # for those cases that didn't need reshape
+                    if size in [None, ()]:
+                        mx.test_utils.assert_almost_equal(freq, pvals, rtol=0.20, atol=1e-1)
+                    else:
+                        # check the shape
+                        assert freq.shape == size + (len(pvals),), 'freq.shape={}, size + (len(pvals))={}'.format(freq.shape, size + (len(pvals)))
+                        freq = freq.reshape((-1, len(pvals)))
+                        # check the value for each row
+                        for i in range(freq.shape[0]):
+                            mx.test_utils.assert_almost_equal(freq[i, :], pvals, rtol=0.20, atol=1e-1)
+            else:
+                freq = mx.np.random.multinomial(experiements, pvals).asnumpy() / _np.float32(experiements)
+                mx.test_utils.assert_almost_equal(freq, pvals, rtol=0.20, atol=1e-1)
+    # check the zero dimension
+    sizes = [(0), (0, 2), (4, 0, 2), (3, 0, 1, 2, 0)]
+    for pvals in pvals_list:
+        for size in sizes:
+            freq = mx.np.random.multinomial(experiements, pvals, size=size).asnumpy()
+            assert freq.size == 0
+    # check [] as pvals
+    for pvals in [[], ()]:
+        freq = mx.np.random.multinomial(experiements, pvals).asnumpy()
+        assert freq.size == 0
+        for size in sizes:
+            freq = mx.np.random.multinomial(experiements, pvals, size=size).asnumpy()
+            assert freq.size == 0
+    # test small experiment for github issue
+    # https://github.com/apache/incubator-mxnet/issues/15383
+    small_exp, total_exp = 20, 10000
+    for pvals in pvals_list:
+        x = np.random.multinomial(small_exp, pvals)
+        for i in range(total_exp // small_exp):
+            x = x + np.random.multinomial(20, pvals)
+    freq = (x.asnumpy() / _np.float32(total_exp)).reshape((-1, len(pvals)))
+    for i in range(freq.shape[0]):
+        mx.test_utils.assert_almost_equal(freq[i, :], pvals, rtol=0.20, atol=1e-1)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 07b44709847f2a4f6975bb2e0ce6d42798bc5213 Mon Sep 17 00:00:00 2001
From: Zixuan Wei <zixuan.wei@intel.com>
Date: Wed, 4 Sep 2019 14:08:29 +0800
Subject: [PATCH 280/813] MKL-DNN RNN checks NDArray version (#16071)

* MKL-DNN RNN checks NDArray version

* Add UT

* Use default_context()
---
 src/operator/rnn-inl.h                 | 79 +++++++-------------------
 src/operator/rnn.cc                    | 14 +++--
 tests/python/unittest/test_operator.py | 39 +++++++++++++
 3 files changed, 68 insertions(+), 64 deletions(-)

diff --git a/src/operator/rnn-inl.h b/src/operator/rnn-inl.h
index 5eae413b078b..5652960baa43 100644
--- a/src/operator/rnn-inl.h
+++ b/src/operator/rnn-inl.h
@@ -409,10 +409,11 @@ class RNNOp {
   std::vector<mkldnn::memory> bias_memory;
   std::vector<mkldnn::memory> y_memory;
   std::vector<mkldnn::memory> hcy_memory;
+  size_t weights_version;
   bool has_cache;
   bool init_mem_;
   size_t reserve_mem_size_;
-  Storage::Handle mem_space_;
+  NDArray mem_space_;
 #endif
   explicit RNNOp(RNNParam param, Context ctx) {
     this->param_ = param;
@@ -522,12 +523,6 @@ class RNNOp {
   }
 
   ~RNNOp() {
-#if MXNET_USE_MKLDNN == 1
-    if (init_mem_) {
-      Storage::Get()->Free(mem_space_);
-      init_mem_ = false;
-    }
-#endif  // MXNET_USE_MKLDNN
 #if MXNET_USE_CUDNN == 1
     CUDNN_CALL(cudnnDestroyTensorDescriptor(hx_desc_));
     CUDNN_CALL(cudnnDestroyTensorDescriptor(cx_desc_));
@@ -560,17 +555,6 @@ class RNNOp {
     CUDNN_CALL(cudnnDestroyRNNDataDescriptor(dy_data_desc_));
 #endif  // MXNET_USE_CUDNN_GE_7200
 #endif  // MXNET_USE_CUDNN
-
-    if (ctx_.dev_type == kCPU) {
-      if (init_space_) {
-        Storage::Get()->Free(reserve_cpu_space_);
-        init_space_ = false;
-      }
-      if (temp_init_space_) {
-        Storage::Get()->Free(temp_cpu_space_);
-        temp_init_space_ = false;
-      }
-    }
   }
 
   void Forward(const OpContext &ctx, const std::vector<TBlob> &in_data,
@@ -855,37 +839,30 @@ class RNNOp {
 #endif  // MXNET_USE_CUDNN == 1 && defined(__CUDACC__)
 
     if (ctx_.dev_type == kCPU) {
+      // allocate temp space
+      const size_t work_cpu_space_size = GetRNNWorkspaceSize(param_.seq_length_, param_.batch_size_,
+          param_.state_size, direction, param_.mode);
+      if (!temp_init_space_ || temp_cpu_space_size_ < work_cpu_space_size) {
+        temp_cpu_space_size_ = work_cpu_space_size;
+        temp_cpu_space_ = NDArray(TShape({static_cast<dim_t>(temp_cpu_space_size_)}), ctx_,
+            false, in_data[rnn_enum::kData].type_flag_);
+        temp_init_space_ = true;
+      }
+      DType* work_cpu_space = static_cast<DType*>(temp_cpu_space_.data().dptr_);
+
       if (ctx.is_train) {
-        // allocate temp space
-        const size_t work_cpu_space_size =
-            GetRNNWorkspaceSize(param_.seq_length_, param_.batch_size_,
-                              param_.state_size, direction, param_.mode);
-        if (temp_init_space_ && temp_cpu_space_size_ < work_cpu_space_size) {
-            Storage::Get()->Free(temp_cpu_space_);
-            temp_init_space_ = false;
-        }
-        if (!temp_init_space_) {
-          temp_cpu_space_ = Storage::Get()->Alloc
-              (work_cpu_space_size * sizeof(DType), Context::CPU());
-          temp_cpu_space_size_ = work_cpu_space_size;
-          temp_init_space_ = true;
-        }
-        DType* work_cpu_space = static_cast<DType*>(temp_cpu_space_.dptr);
+        // allocate reserve space
 
         const size_t r_size = GetRNNReserveSpaceSize(param_.num_layers, direction,
                                                      param_.seq_length_, param_.batch_size_,
                                                      param_.state_size, param_.mode);
-        if (init_space_ && reserve_cpu_space_size_ < r_size) {
-          Storage::Get()->Free(reserve_cpu_space_);
-          init_space_ = false;
-        }
-        if (!init_space_) {
-          reserve_cpu_space_ = Storage::Get()->Alloc(r_size * sizeof(DType), Context::CPU());
+        if (!init_space_ || reserve_cpu_space_size_ < r_size) {
           reserve_cpu_space_size_ = r_size;
+          reserve_cpu_space_ = NDArray(TShape({static_cast<dim_t>(reserve_cpu_space_size_)}), ctx_,
+              false, in_data[rnn_enum::kData].type_flag_);
           init_space_ = true;
         }
-
-        DType* reserve_space_ptr = static_cast<DType*>(reserve_cpu_space_.dptr);
+        DType* reserve_space_ptr = static_cast<DType*>(reserve_cpu_space_.data().dptr_);
 
         RNNForwardTraining<DType>(work_cpu_space,
                                   reserve_space_ptr,
@@ -945,20 +922,6 @@ class RNNOp {
 #endif  // MXNET_USE_MKLDNN == 1
           //  Before integrating MKLDNN GRU fp32 inference
           //  using below code for keep func being OK
-          const size_t work_cpu_space_size =
-              GetRNNWorkspaceSize(param_.seq_length_, param_.batch_size_,
-                                  param_.state_size, direction, param_.mode);
-          if (temp_init_space_ && temp_cpu_space_size_ < work_cpu_space_size) {
-            Storage::Get()->Free(temp_cpu_space_);
-            temp_init_space_ = false;
-          }
-          if (!temp_init_space_) {
-            temp_cpu_space_ = Storage::Get()->Alloc
-                (work_cpu_space_size * sizeof(DType), Context::CPU());
-            temp_cpu_space_size_ = work_cpu_space_size;
-            temp_init_space_ = true;
-          }
-          DType* work_cpu_space = static_cast<DType*>(temp_cpu_space_.dptr);
           RNNForwardInference<DType>(work_cpu_space,
                                      param_.state_outputs,
                                      param_.num_layers,
@@ -1171,7 +1134,7 @@ class RNNOp {
       if (!temp_init_space_ || temp_cpu_space_size_ != work_cpu_space_size) {
         LOG(FATAL) << "Check temp init error";
       }
-      DType* work_cpu_space = static_cast<DType*>(temp_cpu_space_.dptr);
+      DType* work_cpu_space = static_cast<DType*>(temp_cpu_space_.data().dptr_);
       size_t r_size = GetRNNReserveSpaceSize(param_.num_layers, direction,
                                              param_.seq_length_, param_.batch_size_,
                                              param_.state_size, param_.mode);
@@ -1180,7 +1143,7 @@ class RNNOp {
         LOG(FATAL) << "Check forward init error";
       }
 
-      DType* reserve_space_ptr = static_cast<DType*>(reserve_cpu_space_.dptr);
+      DType* reserve_space_ptr = static_cast<DType*>(reserve_cpu_space_.data().dptr_);
       RNNBackward<DType>(work_cpu_space,
                          reserve_space_ptr,
                          param_.num_layers,
@@ -1551,7 +1514,7 @@ class RNNOp {
 #endif  // MXNET_USE_CUDNN
   bool init_space_, temp_init_space_;
   size_t reserve_cpu_space_size_, temp_cpu_space_size_;
-  Storage::Handle reserve_cpu_space_, temp_cpu_space_;
+  NDArray reserve_cpu_space_, temp_cpu_space_;
 };  //  class RNNOp
 
 static OpStatePtr CreateRNNState(const nnvm::NodeAttrs &attrs,
diff --git a/src/operator/rnn.cc b/src/operator/rnn.cc
index 86fb1c7d1ec6..fd016d6819b9 100644
--- a/src/operator/rnn.cc
+++ b/src/operator/rnn.cc
@@ -270,22 +270,24 @@ static void RNNStatefulComputeCPU(const OpStatePtr& state_ptr,
 
       const size_t r_size = GetMKLDNNRNNCacheMemorySize(L, D, T, N, I, H, param.mode);
       if (op.init_mem_ && op.reserve_mem_size_ < r_size) {
-        Storage::Get()->Free(op.mem_space_);
         op.init_mem_ = false;
       }
+      const size_t weights_version = inputs[rnn_enum::kParams].version();
       if (!op.init_mem_) {
-        op.mem_space_ = Storage::Get()->Alloc(
-            r_size * sizeof(DType),
-            Context::CPU());
+        op.mem_space_ = NDArray(TShape({static_cast<dim_t>(r_size)}), op.ctx_, false, dtype);
         op.reserve_mem_size_ = r_size;
         op.init_mem_ = true;
         op.has_cache = false;
+        // Assign weights_version
+        op.weights_version = weights_version;
       }
-      if (op.has_cache && op.x_memory.size() == 0) {
+      // Check if NDArray was changed.
+      if (op.weights_version != weights_version) {
         op.has_cache = false;
+        op.weights_version = weights_version;
       }
 
-      DType* workptr = static_cast<DType*>(op.mem_space_.dptr);
+      DType* workptr = static_cast<DType*>(op.mem_space_.data().dptr_);
       mkldnn::memory::dims src_layer_tz_0 = {T, N, I};
       mkldnn::memory::dims src_layer_tz = {T, N, D * H};
       mkldnn::memory::dims dst_layer_tz = {T, N, D * H};
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index f0e16b2729a2..bcf618f2784a 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -75,6 +75,45 @@ def check_rnn_consistency(cell1, cell2, T, N, I, H, grad_req, rtol=1e-2, atol=1e
         assert(mod2.get_input_grads()[0] == None)
 
 
+@with_seed()
+@assert_raises_cudnn_not_satisfied(min_version='5.1.10')
+def test_rnn_with_new_param():
+    rnn_modes = ['rnn_relu', 'rnn_tanh', 'gru', 'lstm']
+    ngates_ = [1, 1, 3, 4]
+    num_layers, input_size, seq_len, batch_size, state_size = 3, 128, 5, 64, 8
+    for bidirectional in [False, True]:
+        directions = 2 if bidirectional else 1
+        for mode, ngates in zip(rnn_modes, ngates_):
+            first_layer_size = (input_size * state_size + state_size * state_size + state_size * 2) * ngates
+            rest_layer_size = (state_size * directions * state_size + state_size * state_size + state_size * 2) \
+                 * ngates * (num_layers - 1)
+            param_size = (first_layer_size + rest_layer_size) * directions
+            sym = mx.sym.RNN(mode=mode, num_layers=num_layers, bidirectional=bidirectional,
+                state_outputs=False, state_size=state_size, name='rnn')
+
+            bind_dict = {
+                'rnn_data': mx.ndarray.random.uniform(low=-1, high=1, shape=(seq_len, batch_size, input_size)),
+                'rnn_parameters': mx.ndarray.random.uniform(low=-1, high=1, shape=(param_size)),
+                'rnn_state': mx.ndarray.zeros(shape=(num_layers * directions, batch_size, state_size))
+            }
+            if mode == 'lstm':
+                bind_dict['rnn_state_cell'] = mx.ndarray.zeros(
+                    shape=(num_layers * directions, batch_size, state_size))
+
+            ex = sym.bind(default_context(), bind_dict)
+            ex.forward(is_train=True)
+            ex01 = ex.output_dict['rnn_output'].asnumpy()
+            ex.forward(is_train=False)
+            ex02 = ex.output_dict['rnn_output'].asnumpy()
+            assert_allclose(ex01, ex02, rtol=1e-2, atol=1e-4)
+            bind_dict['rnn_parameters'] = mx.ndarray.random.uniform(low=-1, high=1, shape=(param_size))
+            ex.copy_params_from(bind_dict)
+            ex.forward(is_train=True)
+            ex03 = ex.output_dict['rnn_output'].asnumpy()
+            ex.forward(is_train=False)
+            ex04 = ex.output_dict['rnn_output'].asnumpy()
+            assert_allclose(ex03, ex04, rtol=1e-2, atol=1e-4)
+
 
 @with_seed()
 @assert_raises_cudnn_not_satisfied(min_version='5.1.10')

From c742ef10321ea43cc8624662ceb1eda28843cf14 Mon Sep 17 00:00:00 2001
From: "Shi, guangyong" <35016185+gyshi@users.noreply.github.com>
Date: Wed, 4 Sep 2019 14:42:48 +0800
Subject: [PATCH 281/813] add numpy operator remainder (#16080)

---
 python/mxnet/ndarray/numpy/_op.py    | 29 ++++++++++++++++++++++++++--
 python/mxnet/numpy/multiarray.py     | 27 +++++++++++++++++++++++++-
 python/mxnet/symbol/numpy/_symbol.py |  9 +++++++--
 3 files changed, 60 insertions(+), 5 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index dc6089bd5e3e..964d533b2387 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -27,8 +27,8 @@
 from . import _internal as _npi
 from ..ndarray import NDArray
 
-__all__ = ['zeros', 'ones', 'full', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power', 'sin',
-           'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs', 'absolute',
+__all__ = ['zeros', 'ones', 'full', 'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power',
+           'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs', 'absolute',
            'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2', 'log1p',
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
@@ -384,6 +384,31 @@ def mod(x1, x2, out=None):
     return _ufunc_helper(x1, x2, _npi.mod, _np.mod, _npi.mod_scalar, _npi.rmod_scalar, out)
 
 
+@set_module('mxnet.ndarray.numpy')
+def remainder(x1, x2, out=None):
+    """Return element-wise remainder of division.
+
+    Parameters
+    ----------
+    x1 : ndarray or scalar
+        Dividend array.
+
+    x2 : ndarray or scalar
+        Divisor array.
+
+    out : ndarray
+        A location into which the result is stored. If provided, it must have a shape
+        that the inputs broadcast to. If not provided or None, a freshly-allocated array
+        is returned.
+
+    Returns
+    -------
+    out : ndarray or scalar
+        This is a scalar if both x1 and x2 are scalars.
+    """
+    return _ufunc_helper(x1, x2, _npi.mod, _np.mod, _npi.mod_scalar, _npi.rmod_scalar, out)
+
+
 @set_module('mxnet.ndarray.numpy')
 def power(x1, x2, out=None):
     """First array elements raised to powers from second array, element-wise.
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 11ea0858cccd..7d6e81a2d4a5 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -47,7 +47,7 @@
 from ..ndarray.numpy import _internal as _npi
 
 __all__ = ['ndarray', 'empty', 'array', 'zeros', 'ones', 'full', 'add', 'subtract', 'multiply', 'divide',
-           'mod', 'power', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt',
+           'mod', 'remainder', 'power', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt',
            'abs', 'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log',
            'degrees', 'log2', 'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative',
            'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh',
@@ -1855,6 +1855,31 @@ def mod(x1, x2, out=None):
     return _mx_nd_np.mod(x1, x2, out=out)
 
 
+@set_module('mxnet.numpy')
+def remainder(x1, x2, out=None):
+    """Return element-wise remainder of division.
+
+    Parameters
+    ----------
+    x1 : ndarray or scalar
+        Dividend array.
+
+    x2 : ndarray or scalar
+        Divisor array.
+
+    out : ndarray
+        A location into which the result is stored. If provided, it must have a shape
+        that the inputs broadcast to. If not provided or None, a freshly-allocated array
+        is returned.
+
+    Returns
+    -------
+    out : ndarray or scalar
+        This is a scalar if both x1 and x2 are scalars.
+    """
+    return _mx_nd_np.remainder(x1, x2, out=out)
+
+
 @set_module('mxnet.numpy')
 def power(x1, x2, out=None):
     """First array elements raised to powers from second array, element-wise.
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 38e9086bb281..ec4f6a4dd741 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -29,8 +29,8 @@
 from .._internal import _set_np_symbol_class
 from . import _internal as _npi
 
-__all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power', 'sin', 'cos',
-           'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs', 'absolute', 'exp',
+__all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power', 'sin',
+           'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs', 'absolute', 'exp',
            'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2', 'log1p',
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
@@ -1071,6 +1071,11 @@ def mod(x1, x2, out=None):
     return _ufunc_helper(x1, x2, _npi.mod, _np.mod, _npi.mod_scalar, _npi.rmod_scalar, out)
 
 
+@set_module('mxnet.symbol.numpy')
+def remainder(x1, x2, out=None):
+    return _ufunc_helper(x1, x2, _npi.mod, _np.mod, _npi.mod_scalar, _npi.rmod_scalar, out)
+
+
 @set_module('mxnet.symbol.numpy')
 def power(x1, x2, out=None):
     return _ufunc_helper(x1, x2, _npi.power, _np.power, _npi.power_scalar, _npi.rpower_scalar, out)

From 4333a7ba7f1776877b165012830d3e1a5599685a Mon Sep 17 00:00:00 2001
From: Alexander Chalk <contact@alexchalk.net>
Date: Wed, 4 Sep 2019 07:13:43 -0400
Subject: [PATCH 282/813] Update readme and project.clj comment (#16084)

---
 contrib/clojure-package/README.md   | 8 ++++----
 contrib/clojure-package/project.clj | 4 ++--
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/contrib/clojure-package/README.md b/contrib/clojure-package/README.md
index c8971d327805..d181ced5e957 100644
--- a/contrib/clojure-package/README.md
+++ b/contrib/clojure-package/README.md
@@ -133,7 +133,7 @@ After making this change and running `lein deps`, you should be able to run exam
 
 ### Option 2: Clojure package from Source, Scala Package from Jar
 
-With this option, you will install a Git revision of the Clojure package source and a [Scala package jar from Maven](https://search.maven.org/search?q=g:org.apache.mxnet) with native dependencies baked in.
+With this option, you will install a Git revision of the Clojure package source and a [Scala package jar from Maven](https://search.maven.org/search?q=g:org.apache.mxnet) with native dependencies baked in. Pick this if you want to make changes to the Clojure bindings but don't want to build the shared library or the Scala package jar yourself.
 
 - Install additional dependencies as described in [the corresponding section for Option 1](#installing-additional-dependencies),
 
@@ -172,7 +172,7 @@ In that case, however, breakage can happen at any point, for instance when the S
 
 ### Option 3: Everything from Source
 
-With this option, you will compile the core MXNet C++ package and jars for both Scala and Clojure language bindings from source. If you intend to make changes to the code in any of the parts, or if you simply want the latest and greatest features, this choice is for you.
+With this option, you will compile the core MXNet C++ package and jars for both Scala and Clojure language bindings from source. If you intend to make changes to the C++ or Scala code, or if you simply want the latest and greatest features, this choice is for you.
 
 The first step is to recursively clone the MXNet repository and checkout the desired version, (example 1.4.1). You should use the latest [version](https://search.maven.org/search?q=clojure-mxnet)), and clone into the `~/mxnet` directory:
 
@@ -207,8 +207,8 @@ The outcome of this step will be a shared library `lib/libmxnet.so` that is used
   ```
 
 #### Building the Clojure jar
- 
-- Enter the `contrib/clojure` directory and edit the `project.clj` file. Add the Scala jar that was just created and installed, e.g., `[org.apache.mxnet/mxnet-full_2.11-osx-x86_64-cpu "latest-version-SNAPSHOT"]`, to the `:dependencies`.
+
+- Enter the `contrib/clojure` directory.
 - Run `lein test`. All the tests should run without an error.
 - Run `lein install` to build and install the Clojure jar locally.
 
diff --git a/contrib/clojure-package/project.clj b/contrib/clojure-package/project.clj
index 3ad0cf6ead46..718fbf6a28fa 100644
--- a/contrib/clojure-package/project.clj
+++ b/contrib/clojure-package/project.clj
@@ -28,7 +28,7 @@
                  ;[org.apache.mxnet/mxnet-full_2.11-linux-x86_64-cpu "<insert-snapshot-version>"]
                  ;[org.apache.mxnet/mxnet-full_2.11-linux-x86_64-gpu "<insert-snapshot-version"]
 
-                 ;;; CI
+                 ;;; CI / Local Build
                  [org.apache.mxnet/mxnet-full_2.11 "INTERNAL"]
 
                  [org.clojure/tools.logging "0.4.0"]
@@ -44,4 +44,4 @@
   :repositories [["staging" {:url "https://repository.apache.org/content/repositories/staging"                  :snapshots true
                              :update :always}]
                  ["snapshots" {:url "https://repository.apache.org/content/repositories/snapshots"               :snapshots true
-                              :update :always}]])
+                               :update :always}]])

From 6122dfc763cff08dc34409b38f3eb94dc2986390 Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Wed, 4 Sep 2019 10:48:17 -0700
Subject: [PATCH 283/813] Add Large tensor vector test cases (#15941)

* add random ops

* add shuffle to test large array

* shape evaluation after value check

* add log, exponent, power ops

* fix sequence reverse issue in test_large_array and add sequence ops to test_large_vector

* add binary arithmetic

* fix lint, minor mistakes in large_array; add nn op to tensor

* Trigger notification coz of test_operator.test_laop_6 error

* Trigger notification coz of test_operator.test_laop_6 error

* Trigger notification bcoz R failures

* address comments

* normal distribution assert statement fix; randint dtype check

* correct layernorm and shuffle

* layer norm numpy flaky hence removed, dropout shape fix

* comment not working ops

* fix multi

* Trigger notification

* fix seq reverse, uncomment seq mask as it works

* index fix and uncomment test

* index fix

* seq_reverse index fix

* uncomment seq reverse test and handle static typecasts

* removing commented ops

* resolve merge conflict

* teardown, lint, remove redundant functions

* fix shape assertions and randint low,high

* remove waits, add teardown to large_array, change randint assert in large array
---
 src/operator/sequence_last-inl.h    |  24 +-
 src/operator/sequence_reverse-inl.h |  14 +-
 tests/nightly/test_large_array.py   |  67 +++---
 tests/nightly/test_large_vector.py  | 332 +++++++++++++++++++++++++++-
 4 files changed, 388 insertions(+), 49 deletions(-)

diff --git a/src/operator/sequence_last-inl.h b/src/operator/sequence_last-inl.h
index 4c42934f1618..3c3c8b0cd49e 100644
--- a/src/operator/sequence_last-inl.h
+++ b/src/operator/sequence_last-inl.h
@@ -66,24 +66,24 @@ struct SequenceLastParam : public dmlc::Parameter<SequenceLastParam> {
 template <int req>
 struct SequenceLastKernel {
   template <typename DType, typename IType>
-  MSHADOW_XINLINE static void Map(int i, DType *out, const DType *in,
-                                  const IType *idx, int offset1, int offset2,
+  MSHADOW_XINLINE static void Map(index_t i, DType *out, const DType *in,
+                                  const IType *idx, index_t offset1, index_t offset2,
                                   mshadow::Shape<2> oshape) {
     const auto opos = mxnet_op::unravel(i, oshape);
-    const int seqpos = static_cast<int>(idx[opos[0]]) - 1;
-    const int ipos = seqpos * offset1 + opos[0] * offset2 + opos[1];
+    const index_t seqpos = static_cast<index_t>(idx[opos[0]]) - 1;
+    const index_t ipos = seqpos * offset1 + opos[0] * offset2 + opos[1];
     KERNEL_ASSIGN(out[i], req, in[ipos]);
   }
 };
 
 struct SequenceLastGradKernel {
   template <typename DType, typename IType>
-  MSHADOW_XINLINE static void Map(int i, DType *in_grad, const DType *out_grad,
-                                  const IType *idx, int offset1, int offset2,
+  MSHADOW_XINLINE static void Map(index_t i, DType *in_grad, const DType *out_grad,
+                                  const IType *idx, index_t offset1, index_t offset2,
                                   mshadow::Shape<2> oshape) {
     const auto opos = mxnet_op::unravel(i, oshape);
-    const int seqpos = static_cast<int>(idx[opos[0]]) - 1;
-    const int ipos = seqpos * offset1 + opos[0] * offset2 + opos[1];
+    const index_t seqpos = static_cast<index_t>(idx[opos[0]]) - 1;
+    const index_t ipos = seqpos * offset1 + opos[0] * offset2 + opos[1];
     in_grad[ipos] += out_grad[i];
   }
 };
@@ -103,8 +103,8 @@ class SequenceLastOp : public Operator {
     int axis = param_.axis;
     int out_size = out.size(0) * out.size(1);
     int max_seq_len = data.size(axis);
-    int offset1 = axis ? out.size(1) : out_size;
-    int offset2 = axis ? (max_seq_len * out.size(1)) : out.size(1);
+    index_t offset1 = axis ? out.size(1) : out_size;
+    index_t offset2 = axis ? (max_seq_len * out.size(1)) : out.size(1);
 
     MXNET_ASSIGN_REQ_SWITCH(req, req_type, {
       mxnet_op::Kernel<SequenceLastKernel<req_type>, xpu>::Launch(
@@ -126,8 +126,8 @@ class SequenceLastOp : public Operator {
     int out_size = batch * rest;
 
     int max_seq_len = in_grad.size(axis);
-    int offset1 = axis ? rest : out_size;
-    int offset2 = axis ? (max_seq_len * rest) : rest;
+    index_t offset1 = axis ? rest : out_size;
+    index_t offset2 = axis ? (max_seq_len * rest) : rest;
 
     mxnet_op::Kernel<SequenceLastGradKernel, xpu>::Launch(
         s, out_size, in_grad.dptr_, out_grad.dptr_, indices.dptr_, offset1,
diff --git a/src/operator/sequence_reverse-inl.h b/src/operator/sequence_reverse-inl.h
index 8e2362f76dd2..e857c6ab9af4 100644
--- a/src/operator/sequence_reverse-inl.h
+++ b/src/operator/sequence_reverse-inl.h
@@ -67,30 +67,30 @@ struct SequenceReverseParam : public dmlc::Parameter<SequenceReverseParam> {
 template <OpReqType req>
 struct ReverseKernel {
   template <typename DType, typename IType>
-  MSHADOW_XINLINE static void Map(const int i, DType *const out_data,
+  MSHADOW_XINLINE static void Map(const index_t i, DType *const out_data,
                                   const DType *const in_data,
                                   const index_t max_seq_len,
                                   const index_t batch_size,
                                   const index_t other_dim, const index_t numel,
                                   const IType *const indices) {
     const index_t batch = i / (max_seq_len * other_dim);
-    const int id = (i / other_dim) % max_seq_len;
+    const index_t id = (i / other_dim) % max_seq_len;
     const index_t j = i % other_dim;
     const index_t num_seq =
         indices ? static_cast<index_t>(indices[batch]) : max_seq_len;
     const index_t padded_periods = max_seq_len - num_seq;
     // padded part
-    if (padded_periods > 0 && id < static_cast<int>(padded_periods)) {
-      const int padded_in_offset =
+    if (padded_periods > 0 && id < padded_periods) {
+      const index_t padded_in_offset =
           (id + num_seq) * batch_size * other_dim + batch * other_dim;
 
       KERNEL_ASSIGN(out_data[padded_in_offset + j], req,
                     in_data[padded_in_offset + j]);
     }
     // unpadded part
-    if (id < static_cast<int>(num_seq)) {
-      const int in_offset = id * batch_size * other_dim + batch * other_dim;
-      const int out_offset =
+    if (id < num_seq) {
+      const index_t in_offset = id * batch_size * other_dim + batch * other_dim;
+      const index_t out_offset =
           numel - (id + 1 + padded_periods) * batch_size * other_dim +
           batch * other_dim;
 
diff --git a/tests/nightly/test_large_array.py b/tests/nightly/test_large_array.py
index cdacce91ab6e..7622b76a3120 100644
--- a/tests/nightly/test_large_array.py
+++ b/tests/nightly/test_large_array.py
@@ -21,7 +21,7 @@
 
 from mxnet.test_utils import rand_ndarray, assert_almost_equal, rand_coord_2d, default_context, check_symbolic_forward, create_2d_tensor
 from mxnet import gluon, nd
-from tests.python.unittest.common import with_seed
+from tests.python.unittest.common import with_seed, teardown
 
 # dimension constants
 MEDIUM_X = 10000
@@ -56,10 +56,8 @@ def test_ndarray_ones():
 def test_ndarray_convert():
     a = nd.zeros(shape=(LARGE_X, SMALL_Y))
     b = a.astype(np.int32)
-    b.wait_to_read()
     assert b.dtype == np.int32
     b = a.tostype('row_sparse')
-    b.wait_to_read()
     assert isinstance(b, mx.nd.sparse.RowSparseNDArray)
 
 
@@ -79,15 +77,16 @@ def test_ndarray_random_randint():
     a = nd.random.randint(low_large_value, high_large_value, dtype=np.int64)
     low = mx.nd.array([low_large_value], dtype='int64')
     high = mx.nd.array([high_large_value], dtype='int64')
-    assert a.__gt__(low) and a.__lt__(high)
+    assert a >= low and a < high
+    assert a[-1][0].dtype == np.int64
 
 
 @with_seed()
 def test_ndarray_random_exponential():
     scale_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_Y))
     a = nd.random.exponential(scale=scale_array, shape=(SMALL_X, SMALL_Y))
-    assert a.shape == (MEDIUM_X, SMALL_Y, SMALL_X, SMALL_Y)
     assert a[-1][0][0][0] >= 0
+    assert a.shape == (MEDIUM_X, SMALL_Y, SMALL_X, SMALL_Y)
 
 
 @with_seed()
@@ -96,8 +95,8 @@ def test_ndarray_random_gamma():
     beta_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_Y))
     a = nd.random.gamma(alpha=alpha_array, beta=beta_array,
                         shape=(SMALL_X, SMALL_Y))
-    assert a.shape == (MEDIUM_X, SMALL_Y, SMALL_X, SMALL_Y)
     assert a[-1][0][0][0] >= 0
+    assert a.shape == (MEDIUM_X, SMALL_Y, SMALL_X, SMALL_Y)
 
 
 @with_seed()
@@ -105,16 +104,16 @@ def test_ndarray_random_multinomial():
     # test 1 shape dimension
     probs = nd.random.uniform(shape=(LARGE_X, SMALL_Y))
     a = nd.random.multinomial(probs)
-    assert a.shape == (LARGE_X,)
     assert a[-1] >= 0
+    assert a.shape == (LARGE_X,)
     # test for NDArray multi-dimension shape
     a = nd.random.multinomial(probs, shape=(SMALL_X, SMALL_Y))
-    assert a.shape == (LARGE_X, SMALL_X, SMALL_Y)
     assert a[-1][0][0] >= 0
+    assert a.shape == (LARGE_X, SMALL_X, SMALL_Y)
     # test log_likelihood output shape
     a = nd.random.multinomial(probs, shape=(SMALL_X, SMALL_Y), get_prob=True)
-    assert a[0].shape == (LARGE_X, SMALL_X, SMALL_Y) and a[0].shape == a[1].shape
     assert a[-1][0][0] >= 0
+    assert a[0].shape == (LARGE_X, SMALL_X, SMALL_Y) and a[0].shape == a[1].shape
 
 
 @with_seed()
@@ -123,8 +122,8 @@ def test_ndarray_random_generalized_negative_binomial():
     mu_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_Y))
     a = nd.random.generalized_negative_binomial(mu=mu_array, alpha=alpha_array,
                                                 shape=(SMALL_X, SMALL_Y))
-    assert a.shape == (MEDIUM_X, SMALL_Y, SMALL_X, SMALL_Y)
     assert a[-1][0][0][0] >= 0
+    assert a.shape == (MEDIUM_X, SMALL_Y, SMALL_X, SMALL_Y)
 
 
 @with_seed()
@@ -133,8 +132,8 @@ def test_ndarray_random_negative_binomial():
     p_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_Y))
     a = nd.random.negative_binomial(k=k_array, p=p_array,
                                     shape=(SMALL_X, SMALL_Y))
-    assert a.shape == (MEDIUM_X, SMALL_Y, SMALL_X, SMALL_Y)
     assert a[-1][0][0][0] >= 0
+    assert a.shape == (MEDIUM_X, SMALL_Y, SMALL_X, SMALL_Y)
 
 
 @with_seed()
@@ -144,26 +143,40 @@ def test_ndarray_random_normal():
     a = nd.random.normal(loc=loc_array, scale=scale_array,
                          shape=(SMALL_X, SMALL_Y))
     assert a.shape == (MEDIUM_X, SMALL_Y, SMALL_X, SMALL_Y)
-    assert a[-1][0][0][0] >= 0
 
 
 @with_seed()
 def test_ndarray_random_poisson():
     lambda_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_Y))
     a = nd.random.poisson(lam=lambda_array, shape=(SMALL_X, SMALL_Y))
-    assert a.shape == (MEDIUM_X, SMALL_Y, SMALL_X, SMALL_Y)
     assert a[-1][0][0][0] >= 0
+    assert a.shape == (MEDIUM_X, SMALL_Y, SMALL_X, SMALL_Y)
 
 
 @with_seed()
 def test_ndarray_random_randn():
     a = nd.random.randn(LARGE_X, SMALL_Y)
     assert a.shape == (LARGE_X, SMALL_Y)
-    assert a[-1][0] >= 0
-    # TODO: Once PR for randn ndarray dtype for loc,scale param merged
+    # TODO: Once PR #15772 for randn ndarray dtype for loc,scale param merged
     # Add check for (x,y,m,n) where x,y shape of loc,scale and m,n input shape
 
 
+@with_seed()
+def test_ndarray_random_shuffle():
+    a = nd.ones(shape=(LARGE_X, SMALL_Y))
+    a[-1] == 3  # assign 3 to entire last row
+    a = nd.random.shuffle(a)
+    # slice first column from shuffled array
+    # pass LARGE_X values to numpy instead of LARGE_X*SMALL_Y
+    # could have assigned to last column (so as to pass SMALL_Y)
+    # but shuffle operation is performed along first axis
+    unique_a = np.unique(a[:, 0].asnumpy())
+    assert len(unique_a) == 2  # only 2 unique values
+    assert unique_a[0] == 1  # first unique value is 1
+    assert unique_a[1] == 3  # second unique value is 3
+    assert a.shape[0] == (LARGE_X, SMALL_Y)
+
+
 def test_ndarray_empty():
     a = nd.empty((LARGE_X, SMALL_Y))
     assert a.shape == (LARGE_X, SMALL_Y)
@@ -277,7 +290,6 @@ def test_Dense(ctx=mx.cpu(0)):
     linear = gluon.nn.Dense(100)
     linear.initialize(ctx=ctx)
     res = linear(data)
-    res.wait_to_read()
     assert res.shape == (50000000, 100)
 
 
@@ -386,22 +398,22 @@ def test_unravel_index():
 def test_transpose():
     b = create_2d_tensor(rows=LARGE_X, columns=SMALL_Y)
     t = b.T
-    assert t.shape == (SMALL_Y, LARGE_X)
     assert np.sum(t[:, -1].asnumpy() == (LARGE_X - 1)) == b.shape[1]
+    assert t.shape == (SMALL_Y, LARGE_X)
 
 
 def test_swapaxes():
     b = create_2d_tensor(rows=LARGE_X, columns=SMALL_Y)
     t = nd.swapaxes(b, dim1=0, dim2=1)
-    assert t.shape == (SMALL_Y, LARGE_X)
     assert np.sum(t[:, -1].asnumpy() == (LARGE_X - 1)) == b.shape[1]
+    assert t.shape == (SMALL_Y, LARGE_X)
 
 
 def test_flip():
     b = create_2d_tensor(rows=LARGE_X, columns=SMALL_Y)
     t = nd.flip(b, axis=0)
-    assert t.shape == (LARGE_X, SMALL_Y)
     assert np.sum(t[-1, :].asnumpy() == 0) == b.shape[1]
+    assert t.shape == (LARGE_X, SMALL_Y)
 
 
 def test_softmax():
@@ -535,7 +547,9 @@ def test_sequence_reverse():
     assert b.shape == a.shape
 
     # test with sequence length
-    b = nd.SequenceReverse(a, sequence_length=[2, 3])
+    # 2 rows of batch 1 and 3 rows of batch 2 reversed
+    b = nd.SequenceReverse(a, sequence_length=nd.array([2, 3]),
+                           use_sequence_length=True)
     assert b[1][0][0] == a[0][0][0]  # check if reversed
     assert b[-1][0][0] == a[-1][0][0]  # check if intact
     assert b.shape == a.shape
@@ -546,7 +560,7 @@ def test_sequence_last():
 
     # test if returns last sequence
     b = nd.SequenceLast(a)
-    assert_almost_equal(b, a[-1])  # only checks for (2,SMALL_Y) tensor
+    assert_almost_equal(b.asnumpy(), a[-1].asnumpy())  # only checks for (2,SMALL_Y) tensor
     assert b.shape == (2, SMALL_Y)
 
     # test with sequence length
@@ -715,17 +729,18 @@ def npy_layer_norm(data, gamma, beta, axis=1, eps=1E-5):
     assert_almost_equal(out, out_nd.asnumpy(), forward_check_eps,
                         forward_check_eps)
 
+
 # TODO: correctness of dropout
 # currently only test for dropout to work
 # since testing for correctness involves flakiness issue #14288
 def test_dropout():
-    shape = (10, 10)
+    shape = (LARGE_X, SMALL_Y)
     x = mx.sym.var('data')
     y = mx.sym.Dropout(x, p=1, cudnn_off=True)
     exe = y.simple_bind(ctx=default_context(), data=shape)
     exe.arg_arrays[0][:] = 1
     out = exe.forward(is_train=True)
-    out[0].wait_to_read()
+    assert out.shape == out.shape
 
 
 def test_activation():
@@ -736,7 +751,7 @@ def test_activation():
     # Hyperbolic tangent (tanh)
     # y = (exp(x)-exp(-x))/(exp(x)+exp(-x))
     a = mx.nd.Activation(a, act_type="tanh")
-    tanh_x = (np.exp(-2)-np.exp(2))/(np.exp(-2)+np.exp(2))
+    tanh_x = (np.exp(test_x)-np.exp(-test_x))/(np.exp(test_x)+np.exp(-test_x))
     assert a[-1][-1] == tanh_x
 
     # Recitified Linear Unit (relu)
@@ -763,8 +778,6 @@ def test_activation():
 def test_batchnorm():
     shape = (LARGE_X, SMALL_Y)
     axis = 1  # default
-    expand_shape = [1] * len(shape)
-    expand_shape[axis] = shape[axis]
 
     nch = shape[axis]
     data = mx.nd.ones(shape=shape)
@@ -775,7 +788,7 @@ def test_batchnorm():
 
     output = mx.nd.BatchNorm(data, bn_gamma, bn_beta,
                              bn_running_mean, bn_running_var)
-    output.wait_to_read()
+    assert output.shape == shape
 
 
 def test_add():
diff --git a/tests/nightly/test_large_vector.py b/tests/nightly/test_large_vector.py
index 4e1b48c8d047..34cc368d18fe 100644
--- a/tests/nightly/test_large_vector.py
+++ b/tests/nightly/test_large_vector.py
@@ -15,12 +15,13 @@
 # specific language governing permissions and limitations
 # under the License.
 
+import math
 import numpy as np
 import mxnet as mx
 
 from mxnet.test_utils import rand_ndarray, assert_almost_equal, rand_coord_2d, create_vector
 from mxnet import gluon, nd
-from tests.python.unittest.common import with_seed
+from tests.python.unittest.common import with_seed, teardown
 
 # dimension constants
 LARGE_X = 5000000000
@@ -63,7 +64,7 @@ def test_ndarray_random_randint():
     a = nd.random.randint(low_large_value, high_large_value, dtype=np.int64)
     low = mx.nd.array([low_large_value], dtype='int64')
     high = mx.nd.array([high_large_value], dtype='int64')
-    assert a > low  and a < high
+    assert a >= low and a < high
 
 
 def test_ndarray_empty():
@@ -141,7 +142,6 @@ def test_Dense(ctx=mx.cpu(0)):
     linear = gluon.nn.Dense(2)
     linear.initialize(ctx=ctx)
     res = linear(data)
-    res.wait_to_read()
     assert res.shape == (LARGE_X, 2)
 
 
@@ -169,6 +169,332 @@ def test_topk():
     assert val.sum() == (LARGE_X - 1)
 
 
+@with_seed()
+def test_ndarray_random_exponential():
+    a = nd.random.exponential(shape=LARGE_X)
+    assert a[-1] >= 0.
+    assert a.shape[0] == LARGE_X
+
+
+@with_seed()
+def test_ndarray_random_gamma():
+    a = nd.random.gamma(shape=LARGE_X)
+    assert a[-1] >= 0.
+    assert a.shape[0] == LARGE_X
+
+
+@with_seed()
+def test_ndarray_random_generalized_negative_binomial():
+    a = nd.random.generalized_negative_binomial(shape=LARGE_X)
+    assert a[-1] >= 0.
+    assert a.shape[0] == LARGE_X
+
+
+@with_seed()
+def test_ndarray_random_multinomial():
+    a = nd.random.multinomial(nd.random.uniform(shape=LARGE_X))
+    assert a[-1] >= 0.
+    assert a.shape[0] == 1
+
+
+@with_seed()
+def test_ndarray_random_negative_binomial():
+    a = nd.random.negative_binomial(shape=LARGE_X)
+    assert a[-1] >= 0.
+    assert a.shape[0] == LARGE_X
+
+
+@with_seed()
+def test_ndarray_random_normal():
+    a = nd.random.normal(shape=LARGE_X)
+    assert a.shape[0] == LARGE_X
+
+
+@with_seed()
+def test_ndarray_random_poisson():
+    a = nd.random.poisson(shape=LARGE_X)
+    assert a[-1] >= 0.
+    assert a.shape[0] == LARGE_X
+
+
+@with_seed()
+def test_ndarray_random_randn():
+    a = nd.random.randn(LARGE_X)
+    assert a.shape[0] == LARGE_X
+
+
+@with_seed()
+def test_ndarray_random_shuffle():
+    a = nd.ones(shape=LARGE_X)
+    a[-1] = 3
+    a = nd.random.shuffle(a)
+    unique_a = np.unique(a.asnumpy())
+    assert len(unique_a) == 2  # only 2 unique values
+    assert unique_a[0] == 1  # first unique value is 1
+    assert unique_a[1] == 3  # second unique value is 3
+    assert a.shape[0] == LARGE_X
+
+
+def test_exponent_logarithm_operators():
+    a = 2*nd.ones(shape=LARGE_X)
+    # exponent
+    result = nd.exp(a)
+    assert result[-1] == 7.389056
+    assert result.shape == a.shape
+
+    # exponent minus 1
+    result = nd.expm1(a)
+    assert result[-1] == 6.389056
+    assert result.shape == a.shape
+
+    # log2
+    result = nd.log2(a)
+    assert result[-1] == 1
+    assert result.shape == a.shape
+
+    # log10
+    result = nd.log10(a)
+    assert result[-1] == 0.30103
+    assert result.shape == a.shape
+
+    # log1p
+    result = nd.log1p(a)
+    assert result[-1] == 1.0986123
+    assert result.shape == a.shape
+
+    # log
+    result = nd.log(a)
+    assert result[-1] == 0.6931472
+    assert result.shape == a.shape
+
+
+def test_power_operators():
+    a = 2*nd.ones(shape=LARGE_X)
+    # sqrt
+    result = nd.sqrt(a)
+    assert result[-1] == 1.4142135
+    assert result.shape == a.shape
+
+    # rsqrt
+    result = nd.rsqrt(a)
+    assert result[-1] == 0.70710677
+    assert result.shape == a.shape
+
+    # cbrt
+    result = nd.cbrt(a)
+    assert result[-1] == 1.2599211
+    assert result.shape == a.shape
+
+    # rcbrt
+    result = nd.rcbrt(a)
+    assert result[-1] == 0.7937005
+    assert result.shape == a.shape
+
+    # square
+    result = nd.square(a)
+    assert result[-1] == 4
+    assert result.shape == a.shape
+
+    # reciprocal
+    result = nd.reciprocal(a)
+    assert result[-1] == 0.5
+    assert result.shape == a.shape
+
+
+def test_sequence_mask():
+    # Sequence Mask input [max_sequence_length, batch_size]
+    # test with input batch_size = 2
+    a = nd.arange(0, LARGE_X * 2).reshape(LARGE_X, 2)
+
+    # test as identity operator
+    b = nd.SequenceMask(a)
+    assert b[-1][0] == a[-1][0]
+    assert b.shape == a.shape
+
+    # test with default mask
+    b = nd.SequenceMask(a, sequence_length=nd.array([1, 1]),
+                        use_sequence_length=True)
+    assert b[0][1] == a[0][1]  # first sequence of each batch kept
+    assert b[-1][-1] != a[-1][-1]  # rest sequences masked
+    assert b[-1][-1] == 0
+
+    # test with mask value
+    b = nd.SequenceMask(a, sequence_length=nd.array([1, 1]),
+                        use_sequence_length=True, value=-1)
+    assert b[-1][-1] == -1
+
+
+def test_sequence_reverse():
+    a = nd.arange(0, LARGE_X * 2).reshape(LARGE_X, 2)
+    # test as reverse operator
+    b = nd.SequenceReverse(a)
+    assert b[-1][0] == a[0][0]
+    assert b.shape == a.shape
+
+    # test with sequence length
+    b = nd.SequenceReverse(a, sequence_length=nd.array([2, 3]),
+                           use_sequence_length=True)
+    assert b[1][0] == a[0][0]  # check if reversed
+    assert b[-1][0] == a[-1][0]  # check if intact
+    assert b.shape == a.shape
+
+
+def test_sequence_last():
+    a = nd.arange(0, LARGE_X * 2).reshape(LARGE_X, 2)
+
+    # test if returns last sequence
+    b = nd.SequenceLast(a)
+    assert_almost_equal(b.asnumpy(), a[-1].asnumpy())
+    assert b.shape == (2,)
+
+    # test with sequence length
+    # parameter sequence_length - NDArray with shape (batch_size)
+    # (2,3) indicates 2nd sequence from batch 1 and 3rd sequence from batch 2
+    b = nd.SequenceLast(a, sequence_length=mx.nd.array([2, 3]),
+                        use_sequence_length=True)
+    # check if it takes 2nd sequence from the first batch
+    assert b[0] == a[1][0]
+
+
+# TODO: correctness of layernorm
+# numpy implementation for large vector is flaky
+def test_layer_norm():
+    axis = 0
+    eps = 1E-5
+    in_shape = LARGE_X
+
+    data = nd.random.normal(0, 1, in_shape)
+    gamma = nd.random.normal(0, 1, in_shape)
+    beta = nd.random.normal(0, 1, in_shape)
+    mx_out = nd.LayerNorm(data, gamma, beta, axis, eps)
+    assert mx_out.shape == (in_shape,)
+
+
+# TODO: correctness of batchnorm
+# in future, we could test if mean, var of output
+# matches target output's mean, var
+def test_batchnorm():
+    shape = LARGE_X
+    axis = 0  # since vector
+
+    data = mx.nd.ones(shape=shape)
+    bn_gamma = mx.nd.random.uniform(shape=shape)
+    bn_beta = mx.nd.random.uniform(shape=shape)
+    bn_running_mean = mx.nd.zeros(shape)
+    bn_running_var = mx.nd.ones(shape)
+
+    output = mx.nd.BatchNorm(data, bn_gamma, bn_beta,
+                             bn_running_mean, bn_running_var, axis=axis)
+    assert output.shape == (shape,)
+
+
+def test_add():
+    a = nd.ones(shape=LARGE_X)
+    b = nd.ones(shape=LARGE_X)
+    c = b
+    c = c.__add__(a)
+    assert c[-1] == 2
+    assert c.shape == a.shape
+
+
+def test_sub():
+    a = 3*nd.ones(shape=LARGE_X)
+    b = nd.ones(shape=LARGE_X)
+    c = b
+    c = c.__sub__(a)
+    assert c[-1] == -2
+    assert c.shape == a.shape
+
+
+def test_rsub():
+    a = 3*nd.ones(shape=LARGE_X)
+    b = nd.ones(shape=LARGE_X)
+    c = b
+    c = c.__rsub__(a)
+    assert c[-1] == 2
+    assert c.shape == a.shape
+
+
+def test_neg():
+    a = nd.ones(shape=LARGE_X)
+    c = a
+    c = c.__neg__()
+    assert c[-1] == -1
+    assert c.shape == a.shape
+
+
+def test_mul():
+    a = 2*nd.ones(shape=LARGE_X)
+    b = 3*nd.ones(shape=LARGE_X)
+    c = b
+    c = c.__mul__(a)
+    assert c[-1] == 6
+    assert c.shape == a.shape
+
+
+def test_div():
+    a = 2*nd.ones(shape=LARGE_X)
+    b = 3*nd.ones(shape=LARGE_X)
+    c = b
+    c = c.__div__(a)
+    assert c[-1] == 3/2
+    assert c.shape == a.shape
+
+
+def test_rdiv():
+    a = 2*nd.ones(shape=LARGE_X)
+    b = 3*nd.ones(shape=LARGE_X)
+    c = b
+    c = c.__rdiv__(a)
+    assert c[-1] == 2/3
+    assert c.shape == a.shape
+
+
+def test_mod():
+    a = 2*nd.ones(shape=LARGE_X)
+    b = 3*nd.ones(shape=LARGE_X)
+    c = b
+    c = c.__mod__(a)
+    assert c[-1] == 1
+    assert c.shape == a.shape
+
+
+def test_rmod():
+    a = 2*nd.ones(shape=LARGE_X)
+    b = 3*nd.ones(shape=LARGE_X)
+    c = b
+    c = c.__rmod__(a)
+    assert c[-1] == 2
+    assert c.shape == a.shape
+
+
+def test_imod():
+    a = 2*nd.ones(shape=LARGE_X)
+    b = 3*nd.ones(shape=LARGE_X)
+    c = b
+    c = c.__imod__(a)
+    assert c[-1] == 1
+    assert c.shape == a.shape
+
+
+def test_pow():
+    a = 2*nd.ones(shape=LARGE_X)
+    b = 3*nd.ones(shape=LARGE_X)
+    c = b
+    c = c.__pow__(a)
+    assert c[-1] == 9
+    assert c.shape == a.shape
+
+
+def test_rpow():
+    a = 2*nd.ones(shape=LARGE_X)
+    b = 3*nd.ones(shape=LARGE_X)
+    c = b
+    c = c.__rpow__(a)
+    assert c[-1] == 8
+    assert c.shape == a.shape
+
+
 def test_shape():
     b = create_vector(size=LARGE_X)
     #explicit wait_to_read()

From bc90e20a061d910750c2ee0a1948bc7c5278fd2d Mon Sep 17 00:00:00 2001
From: Chongruo Wu <6445892+chongruo@users.noreply.github.com>
Date: Thu, 5 Sep 2019 12:26:30 +0800
Subject: [PATCH 284/813] typo in docs (#16094)

* typo for NCCL

* retrigger CI
---
 docs/install/build_from_source.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/install/build_from_source.md b/docs/install/build_from_source.md
index 6fbfa247c037..8101dd3a4ac1 100644
--- a/docs/install/build_from_source.md
+++ b/docs/install/build_from_source.md
@@ -197,7 +197,7 @@ There is a configuration file for make,
 
 ``` bash
 echo "USE_NCCL=1" >> make/config.mk
-echo "USE_NCCP_PATH=path-to-nccl-installation-folder" >> make/config.mk
+echo "USE_NCCL_PATH=path-to-nccl-installation-folder" >> make/config.mk
 cp make/config.mk .
 ```
 - Run make command

From c6a92d96810ec048871af1731555e8c3e0f950e8 Mon Sep 17 00:00:00 2001
From: Lin Yuan <apeforest@gmail.com>
Date: Wed, 4 Sep 2019 21:33:52 -0700
Subject: [PATCH 285/813] remove 'foo' and other print msg from test (#16088)

* remove 'foo' and other print msg from test

* address review comment

* re-trigger CI
---
 tests/python/unittest/test_profiler.py | 87 +++++++++++++++-----------
 1 file changed, 49 insertions(+), 38 deletions(-)

diff --git a/tests/python/unittest/test_profiler.py b/tests/python/unittest/test_profiler.py
index e5fdd2b1798a..d58c08b5f26e 100644
--- a/tests/python/unittest/test_profiler.py
+++ b/tests/python/unittest/test_profiler.py
@@ -16,15 +16,16 @@
 # under the License.
 
 from __future__ import print_function
-import logging
-import mxnet as mx
-from mxnet import profiler
 import time
 import os
 import json
+import unittest
 from collections import OrderedDict
+
+import mxnet as mx
+from mxnet import profiler
 from common import run_in_spawned_process
-import unittest
+
 
 def enable_profiler(profile_filename, run=True, continuous_dump=False, aggregate_stats=False):
     profiler.set_config(profile_symbolic=True,
@@ -35,9 +36,8 @@ def enable_profiler(profile_filename, run=True, continuous_dump=False, aggregate
                         continuous_dump=continuous_dump,
                         aggregate_stats=aggregate_stats
                         )
-    print('profile file save to {}'.format(profile_filename))
     if run is True:
-      profiler.set_state('run')
+        profiler.set_state('run')
 
 
 def test_profiler():
@@ -59,9 +59,7 @@ def test_profiler():
     a.copyto(executor.arg_dict['A'])
     b.copyto(executor.arg_dict['B'])
 
-    print("execution begin")
     for i in range(iter_num):
-        print("Iteration {}/{}".format(i + 1, iter_num))
         if i == begin_profiling_iter:
             t0 = time.clock()
             profiler.set_state('run')
@@ -71,10 +69,8 @@ def test_profiler():
         executor.forward()
         c = executor.outputs[0]
         c.wait_to_read()
-    print("execution end")
+
     duration = t1 - t0
-    print('duration: {0}s'.format(duration))
-    print('          {0}ms/operator'.format(duration*1000/iter_num))
     profiler.dump(True)
     profiler.set_state('stop')
 
@@ -82,7 +78,6 @@ def test_profiler():
 def test_profile_create_domain():
     enable_profiler('test_profile_create_domain.json')
     domain = profiler.Domain(name='PythonDomain')
-    print("Domain created: {}".format(str(domain)))
     profiler.set_state('stop')
 
 
@@ -90,33 +85,34 @@ def test_profile_create_domain_dept():
     profiler.set_config(profile_symbolic=True, filename='test_profile_create_domain_dept.json')
     profiler.set_state('run')
     domain = profiler.Domain(name='PythonDomain')
-    print("Domain created: {}".format(str(domain)))
-    profiler.dump_profile()
+    profiler.dump()
     profiler.set_state('stop')
 
+
 def test_profile_task():
     def makeParams():
         objects = tuple('foo' for _ in range(50))
         template = ''.join('{%d}' % i for i in range(len(objects)))
         return template, objects
 
-    def doLog():
+    def get_log():
         template, objects = makeParams()
-        for _ in range(100000):
-            logging.info(template.format(*objects))
+        logs = []
+        for _ in range(10):
+            logs.append(template.format(*objects))
+        return logs
 
-    logging.basicConfig()
     enable_profiler('test_profile_task.json')
     python_domain = profiler.Domain('PythonDomain::test_profile_task')
     task = profiler.Task(python_domain, "test_profile_task")
     task.start()
     start = time.time()
     var = mx.nd.ones((1000, 500))
-    doLog()
+    assert len(get_log()) == 10
     var.asnumpy()
     stop = time.time()
     task.stop()
-    print('run took: %.3f' % (stop - start))
+    assert stop > start
     profiler.set_state('stop')
 
 
@@ -126,23 +122,24 @@ def makeParams():
         template = ''.join('{%d}' % i for i in range(len(objects)))
         return template, objects
 
-    def doLog():
+    def get_log():
         template, objects = makeParams()
+        logs = []
         for _ in range(100000):
-            logging.info(template.format(*objects))
+            logs.append(template.format(*objects))
+        return logs
 
-    logging.basicConfig()
     enable_profiler('test_profile_frame.json')
     python_domain = profiler.Domain('PythonDomain::test_profile_frame')
     frame = profiler.Frame(python_domain, "test_profile_frame")
     frame.start()
     start = time.time()
     var = mx.nd.ones((1000, 500))
-    doLog()
+    assert len(get_log()) == 100000
     var.asnumpy()
     stop = time.time()
     frame.stop()
-    print('run took: %.3f' % (stop - start))
+    assert stop > start
     profiler.set_state('stop')
 
 
@@ -152,23 +149,24 @@ def makeParams():
         template = ''.join('{%d}' % i for i in range(len(objects)))
         return template, objects
 
-    def doLog():
+    def get_log():
         template, objects = makeParams()
+        logs = []
         for _ in range(100000):
-            logging.info(template.format(*objects))
+            logs.append(template.format(*objects))
+        return logs
 
-    logging.basicConfig()
     if do_enable_profiler is True:
         enable_profiler('test_profile_event.json')
     event = profiler.Event("test_profile_event")
     event.start()
     start = time.time()
     var = mx.nd.ones((1000, 500))
-    doLog()
+    assert len(get_log()) == 100000
     var.asnumpy()
     stop = time.time()
     event.stop()
-    print('run took: %.3f' % (stop - start))
+    assert stop > start
     if do_enable_profiler is True:
         profiler.set_state('stop')
 
@@ -191,15 +189,17 @@ def makeParams():
         template = ''.join('{%d}' % i for i in range(len(objects)))
         return template, objects
 
-    def doLog(counter):
+    def get_log(counter):
         template, objects = makeParams()
         range_size = 100000
+        logs = []
         for i in range(range_size):
             if i <= range_size / 2:
                 counter += 1
             else:
                 counter -= 1
-            logging.info(template.format(*objects))
+            logs.append(template.format(*objects))
+        return logs
 
     if do_enable_profiler is True:
         enable_profiler('test_profile_counter.json')
@@ -208,9 +208,11 @@ def doLog(counter):
     counter.set_value(5)
     counter += 1
     start = time.time()
-    doLog(counter)
+    log = get_log(counter)
+    assert len(log) == 100000
+    assert log[0].count('foo') == 50
     stop = time.time()
-    print('run took: %.3f' % (stop - start))
+    assert stop > start
     if do_enable_profiler is True:
         profiler.set_state('stop')
 
@@ -222,7 +224,6 @@ def test_continuous_profile_and_instant_marker():
     last_file_size = 0
     for i in range(5):
         profiler.Marker(python_domain, "StartIteration-" + str(i)).mark('process')
-        print("{}...".format(i))
         test_profile_event(False)
         test_profile_counter(False)
         profiler.dump(False)
@@ -233,9 +234,9 @@ def test_continuous_profile_and_instant_marker():
     profiler.dump(False)
     debug_str = profiler.dumps()
     assert(len(debug_str) > 0)
-    print(debug_str)
     profiler.set_state('stop')
 
+
 def test_aggregate_stats_valid_json_return():
     file_name = 'test_aggregate_stats_json_return.json'
     enable_profiler(file_name, True, True, True)
@@ -246,10 +247,12 @@ def test_aggregate_stats_valid_json_return():
     assert 'Memory' in target_dict and 'Time' in target_dict and 'Unit' in target_dict
     profiler.set_state('stop')
 
+
 def test_aggregate_stats_sorting():
     sort_by_options = {'total': 'Total', 'avg': 'Avg', 'min': 'Min',\
         'max': 'Max', 'count': 'Count'}
     ascending_options = [False, True]
+
     def check_ascending(lst, asc):
         assert(lst == sorted(lst, reverse = not asc))
 
@@ -274,6 +277,7 @@ def check_sorting(debug_str, sort_by, ascending):
             check_sorting(debug_str, sb, asc)
     profiler.set_state('stop')
 
+
 def test_aggregate_duplication():
     file_name = 'test_aggregate_duplication.json'
     enable_profiler(profile_filename=file_name, run=True, continuous_dump=True, \
@@ -297,7 +301,8 @@ def test_aggregate_duplication():
     assert target_dict['Time']['operator']['sqrt']['Count'] == 1
     assert target_dict['Time']['operator']['_plus_scalar']['Count'] == 2
     profiler.set_state('stop')
-    
+
+
 def test_custom_operator_profiling(seed=None, file_name=None):
     class Sigmoid(mx.operator.CustomOp):
         def forward(self, is_train, req, in_data, out_data, aux):
@@ -354,6 +359,7 @@ def create_operator(self, ctx, in_shapes, in_dtypes):
         and 'MySigmoid::_zeros' in target_dict['Time']['Custom Operator']
     profiler.set_state('stop')
 
+
 def check_custom_operator_profiling_multiple_custom_ops_output(debug_str):
     target_dict = json.loads(debug_str)
     assert 'Time' in target_dict and 'Custom Operator' in target_dict['Time'] \
@@ -362,6 +368,7 @@ def check_custom_operator_profiling_multiple_custom_ops_output(debug_str):
         and 'MyAdd1::_plus_scalar' in target_dict['Time']['Custom Operator'] \
         and 'MyAdd2::_plus_scalar' in target_dict['Time']['Custom Operator']
 
+
 def custom_operator_profiling_multiple_custom_ops(seed, mode, file_name):
     class MyAdd(mx.operator.CustomOp):
         def forward(self, is_train, req, in_data, out_data, aux):        
@@ -427,15 +434,18 @@ def create_operator(self, ctx, shapes, dtypes):
     debug_str = profiler.dumps(format='json')
     check_custom_operator_profiling_multiple_custom_ops_output(debug_str)
     profiler.set_state('stop')
-    
+
+
 def test_custom_operator_profiling_multiple_custom_ops_symbolic():
     custom_operator_profiling_multiple_custom_ops(None, 'symbolic', \
             'test_custom_operator_profiling_multiple_custom_ops_symbolic.json')
 
+
 def test_custom_operator_profiling_multiple_custom_ops_imperative():
     custom_operator_profiling_multiple_custom_ops(None, 'imperative', \
             'test_custom_operator_profiling_multiple_custom_ops_imperative.json')
 
+
 @unittest.skip("Flaky test https://github.com/apache/incubator-mxnet/issues/15406")
 def test_custom_operator_profiling_naive_engine():
     # run the three tests above using Naive Engine
@@ -449,6 +459,7 @@ def test_custom_operator_profiling_naive_engine():
             {'MXNET_ENGINE_TYPE' : "NaiveEngine"}, 'symbolic', \
             'test_custom_operator_profiling_multiple_custom_ops_symbolic_naive.json')
 
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From b7071c4c9c1183c2b6fcbe5e26bf96f04760693d Mon Sep 17 00:00:00 2001
From: Yizhi Liu <liuyizhi@apache.org>
Date: Thu, 5 Sep 2019 14:39:38 +0800
Subject: [PATCH 286/813] Enable tvm_op for ci (#15889)

* enable tvm_op for ci

* specify python3 bin

* move rpath to top

* move tvm op dep forward

* add ldd debug info

* add libtvm_runtime.so to mx_lib_cython

* add ldd debug for py3

* fix libtvm_runtime path for cmake

* cp libtvm_runtime.so when make rpkg

* add libtvm_runtime.so to scala-pkg

* add python3 to cmake in unix-gpu build

* hack: add cuda to ld_path in cmake

* add LD_LIBRARY_PATH into cmake tvm op

* add /usr/local/cuda/compat to Dockerfile.build.ubuntu_gpu_cu101 LD_LIBRARY_PATH

* remove unused codes

* remove USE_TVM_OP from build_ubuntu_cpu_large_tensor
---
 CMakeLists.txt                                |  2 +-
 Makefile                                      | 37 +++++++++++--------
 ci/docker/Dockerfile.build.ubuntu_gpu_cu101   |  1 +
 ci/docker/install/ubuntu_python.sh            |  2 +-
 ci/docker/runtime_functions.sh                | 15 ++++++++
 ci/jenkins/Jenkins_steps.groovy               | 20 +++++-----
 .../assembly/src/main/assembly/assembly.xml   |  1 +
 .../mxnet/util/NativeLibraryLoader.scala      |  1 +
 src/operator/contrib/tvmop/ufunc.cc           |  2 +-
 tests/python/unittest/test_tvm_op.py          |  1 -
 10 files changed, 53 insertions(+), 29 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 33529747f44c..b6faf639a70e 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -761,7 +761,7 @@ if(USE_TVM_OP)
   add_custom_command(TARGET mxnet POST_BUILD
     COMMAND ${CMAKE_COMMAND} -E env
           PYTHONPATH="${CMAKE_CURRENT_SOURCE_DIR}/3rdparty/tvm/python:${CMAKE_CURRENT_SOURCE_DIR}/3rdparty/tvm/topi/python:${CMAKE_CURRENT_SOURCE_DIR}/contrib"
-          LD_LIBRARY_PATH="${CMAKE_CURRENT_BINARY_DIR}/3rdparty/tvm"
+          LD_LIBRARY_PATH=${CMAKE_CURRENT_BINARY_DIR}:${CMAKE_CURRENT_BINARY_DIR}/3rdparty/tvm:$ENV{LD_LIBRARY_PATH}
           ${Python3_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/contrib/tvmop/compile.py -o${CMAKE_CURRENT_BINARY_DIR}/libtvmop.so
     )
 endif()
diff --git a/Makefile b/Makefile
index 4d823d0ac284..fcef885bee0a 100644
--- a/Makefile
+++ b/Makefile
@@ -463,6 +463,20 @@ OBJ = $(patsubst %.cc, build/%.o, $(SRC))
 CUSRC = $(wildcard src/*/*/*/*.cu src/*/*/*.cu src/*/*.cu src/*.cu)
 CUOBJ = $(patsubst %.cu, build/%_gpu.o, $(CUSRC))
 
+ifeq ($(USE_TVM_OP), 1)
+LIB_DEP += lib/libtvm_runtime.so lib/libtvmop.so
+CFLAGS += -I$(TVM_PATH)/include -DMXNET_USE_TVM_OP=1
+LDFLAGS += -L$(ROOTDIR)/lib -ltvm_runtime -Wl,-rpath,'$${ORIGIN}'
+
+TVM_USE_CUDA := OFF
+ifeq ($(USE_CUDA), 1)
+	TVM_USE_CUDA := ON
+	ifneq ($(USE_CUDA_PATH), NONE)
+		TVM_USE_CUDA := $(USE_CUDA_PATH)
+	endif
+endif
+endif
+
 # extra operators
 ifneq ($(EXTRA_OPERATORS),)
 	EXTRA_SRC = $(wildcard $(patsubst %, %/*.cc, $(EXTRA_OPERATORS)) $(patsubst %, %/*/*.cc, $(EXTRA_OPERATORS)))
@@ -600,18 +614,6 @@ $(DMLC_CORE)/libdmlc.a: DMLCCORE
 DMLCCORE:
 	+ cd $(DMLC_CORE); $(MAKE) libdmlc.a USE_SSE=$(USE_SSE) config=$(ROOTDIR)/$(config); cd $(ROOTDIR)
 
-ifeq ($(USE_TVM_OP), 1)
-LIB_DEP += lib/libtvm_runtime.so lib/libtvmop.so
-CFLAGS += -I$(TVM_PATH)/include -DMXNET_USE_TVM_OP=1
-LDFLAGS += -L$(TVM_PATH)/build -ltvm_runtime
-
-TVM_USE_CUDA := OFF
-ifeq ($(USE_CUDA), 1)
-	TVM_USE_CUDA := ON
-	ifneq ($(USE_CUDA_PATH), NONE)
-		TVM_USE_CUDA := $(USE_CUDA_PATH)
-	endif
-endif
 lib/libtvm_runtime.so:
 	echo "Compile TVM"
 	[ -e $(LLVM_PATH)/bin/llvm-config ] || sh $(ROOTDIR)/contrib/tvmop/prepare_tvm.sh; \
@@ -619,15 +621,16 @@ lib/libtvm_runtime.so:
 	cmake -DUSE_LLVM="$(LLVM_PATH)/bin/llvm-config" \
 		  -DUSE_SORT=OFF -DUSE_CUDA=$(TVM_USE_CUDA) -DUSE_CUDNN=OFF ..; \
 	$(MAKE) VERBOSE=1; \
+	mkdir -p $(ROOTDIR)/lib; \
 	cp $(TVM_PATH)/build/libtvm_runtime.so $(ROOTDIR)/lib/libtvm_runtime.so; \
+	ls $(ROOTDIR)/lib; \
 	cd $(ROOTDIR)
 
 lib/libtvmop.so: lib/libtvm_runtime.so $(wildcard contrib/tvmop/*/*.py contrib/tvmop/*.py)
 	echo "Compile TVM operators"
-	PYTHONPATH=$(TVM_PATH)/python:$(TVM_PATH)/topi/python:$(ROOTDIR)/contrib:$PYTHONPATH \
-		LD_LIBRARY_PATH=lib \
+	PYTHONPATH=$(TVM_PATH)/python:$(TVM_PATH)/topi/python:$(ROOTDIR)/contrib \
+		LD_LIBRARY_PATH=$(ROOTDIR)/lib \
 	    python3 $(ROOTDIR)/contrib/tvmop/compile.py -o $(ROOTDIR)/lib/libtvmop.so
-endif
 
 NNVM_INC = $(wildcard $(NNVM_PATH)/include/*/*.h)
 NNVM_SRC = $(wildcard $(NNVM_PATH)/src/*/*/*.cc $(NNVM_PATH)/src/*/*.cc $(NNVM_PATH)/src/*.cc)
@@ -703,6 +706,10 @@ rpkg:
 		cp -rf lib/libmklml_intel.so R-package/inst/libs; \
 	fi
 
+	if [ -e "lib/libtvm_runtime.so" ]; then \
+		cp -rf lib/libtvm_runtime.so R-package/inst/libs; \
+	fi
+
 	mkdir -p R-package/inst/include
 	cp -rl include/* R-package/inst/include
 	Rscript -e "if(!require(devtools)){install.packages('devtools', repo = 'https://cloud.r-project.org/')}"
diff --git a/ci/docker/Dockerfile.build.ubuntu_gpu_cu101 b/ci/docker/Dockerfile.build.ubuntu_gpu_cu101
index 32f0a0a8d862..21a938f8f84a 100644
--- a/ci/docker/Dockerfile.build.ubuntu_gpu_cu101
+++ b/ci/docker/Dockerfile.build.ubuntu_gpu_cu101
@@ -80,3 +80,4 @@ RUN /work/ubuntu_adduser.sh
 COPY runtime_functions.sh /work/
 
 WORKDIR /work/mxnet
+ENV LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda/compat
diff --git a/ci/docker/install/ubuntu_python.sh b/ci/docker/install/ubuntu_python.sh
index 2ca0cceec515..b8626d3037d5 100755
--- a/ci/docker/install/ubuntu_python.sh
+++ b/ci/docker/install/ubuntu_python.sh
@@ -31,4 +31,4 @@ python3 get-pip.py
 python2 get-pip.py
 
 pip2 install nose cpplint==1.3.0 'numpy>1.16.0,<2.0.0' nose-timer 'requests<2.19.0,>=2.18.4' h5py==2.8.0rc1 scipy==1.0.1 boto3 Cython==0.29.7
-pip3 install nose cpplint==1.3.0 pylint==2.3.1 'numpy>1.16.0,<2.0.0' nose-timer 'requests<2.19.0,>=2.18.4' h5py==2.8.0rc1 scipy==1.0.1 boto3 Cython==0.29.7
+pip3 install nose cpplint==1.3.0 pylint==2.3.1 'numpy>1.16.0,<2.0.0' nose-timer 'requests<2.19.0,>=2.18.4' h5py==2.8.0rc1 scipy==1.0.1 boto3 Cython==0.29.7 decorator
diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index 4dc5566d887a..689d525a5cce 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -370,6 +370,7 @@ build_ubuntu_cpu_openblas() {
     build_ccache_wrappers
     make \
         DEV=1                         \
+        USE_TVM_OP=1                  \
         USE_CPP_PACKAGE=1             \
         USE_BLAS=openblas             \
         USE_MKLDNN=0                  \
@@ -389,6 +390,7 @@ build_ubuntu_cpu_mkl() {
         DEV=1                         \
         USE_CPP_PACKAGE=1             \
         USE_BLAS=mkl                  \
+        USE_TVM_OP=1                  \
         USE_MKLDNN=0                  \
         USE_INTEL_PATH=/opt/intel     \
         USE_DIST_KVSTORE=1            \
@@ -405,6 +407,8 @@ build_ubuntu_cpu_cmake_debug() {
         -DCMAKE_CXX_COMPILER_LAUNCHER=ccache \
         -DCMAKE_C_COMPILER_LAUNCHER=ccache \
         -DUSE_CUDA=OFF \
+        -DUSE_TVM_OP=ON \
+        -DPython3_EXECUTABLE=/usr/bin/python3 \
         -DUSE_MKL_IF_AVAILABLE=OFF \
         -DUSE_OPENMP=OFF \
         -DUSE_OPENCV=ON \
@@ -547,6 +551,7 @@ build_ubuntu_cpu_mkldnn() {
     make  \
         DEV=1                         \
         USE_CPP_PACKAGE=1             \
+        USE_TVM_OP=1                  \
         USE_BLAS=openblas             \
         USE_SIGNAL_HANDLER=1          \
         -j$(nproc)
@@ -560,6 +565,7 @@ build_ubuntu_cpu_mkldnn_mkl() {
     make  \
         DEV=1                         \
         USE_CPP_PACKAGE=1             \
+        USE_TVM_OP=1                  \
         USE_BLAS=mkl                  \
         USE_SIGNAL_HANDLER=1          \
         -j$(nproc)
@@ -642,6 +648,7 @@ build_ubuntu_gpu_mkldnn() {
         USE_CUDA=1                                \
         USE_CUDA_PATH=/usr/local/cuda             \
         USE_CUDNN=1                               \
+        USE_TVM_OP=1                              \
         CUDA_ARCH="$CI_CUDA_COMPUTE_CAPABILITIES" \
         USE_SIGNAL_HANDLER=1                      \
         -j$(nproc)
@@ -658,6 +665,7 @@ build_ubuntu_gpu_mkldnn_nocudnn() {
         USE_CUDA=1                                \
         USE_CUDA_PATH=/usr/local/cuda             \
         USE_CUDNN=0                               \
+        USE_TVM_OP=1                              \
         CUDA_ARCH="$CI_CUDA_COMPUTE_CAPABILITIES" \
         USE_SIGNAL_HANDLER=1                      \
         -j$(nproc)
@@ -673,6 +681,7 @@ build_ubuntu_gpu_cuda101_cudnn7() {
         USE_CUDA=1                                \
         USE_CUDA_PATH=/usr/local/cuda             \
         USE_CUDNN=1                               \
+        USE_TVM_OP=1                              \
         USE_CPP_PACKAGE=1                         \
         USE_DIST_KVSTORE=1                        \
         CUDA_ARCH="$CI_CUDA_COMPUTE_CAPABILITIES" \
@@ -713,6 +722,8 @@ build_ubuntu_gpu_cmake_mkldnn() {
         -DUSE_SIGNAL_HANDLER=ON                 \
         -DUSE_CUDA=1                            \
         -DUSE_CUDNN=1                           \
+        -DUSE_TVM_OP=1                          \
+        -DPython3_EXECUTABLE=/usr/bin/python3   \
         -DUSE_MKLML_MKL=1                       \
         -DCMAKE_BUILD_TYPE=Release              \
         -DCUDA_ARCH_NAME=Manual                 \
@@ -737,6 +748,8 @@ build_ubuntu_gpu_cmake() {
         -DUSE_SIGNAL_HANDLER=ON                 \
         -DUSE_CUDA=ON                           \
         -DUSE_CUDNN=ON                          \
+        -DUSE_TVM_OP=ON                         \
+        -DPython3_EXECUTABLE=/usr/bin/python3   \
         -DUSE_MKL_IF_AVAILABLE=OFF              \
         -DUSE_MKLML_MKL=OFF                     \
         -DUSE_MKLDNN=OFF                        \
@@ -782,6 +795,8 @@ build_ubuntu_gpu_large_tensor() {
         -DUSE_SIGNAL_HANDLER=ON                 \
         -DUSE_CUDA=ON                           \
         -DUSE_CUDNN=ON                          \
+        -DUSE_TVM_OP=ON                         \
+        -DPython3_EXECUTABLE=/usr/bin/python3   \
         -DUSE_MKL_IF_AVAILABLE=OFF              \
         -DUSE_MKLML_MKL=OFF                     \
         -DUSE_MKLDNN=OFF                        \
diff --git a/ci/jenkins/Jenkins_steps.groovy b/ci/jenkins/Jenkins_steps.groovy
index a376aa00ea71..eda1d349c9e1 100644
--- a/ci/jenkins/Jenkins_steps.groovy
+++ b/ci/jenkins/Jenkins_steps.groovy
@@ -23,22 +23,22 @@
 utils = load('ci/Jenkinsfile_utils.groovy')
 
 // mxnet libraries
-mx_lib = 'lib/libmxnet.so, lib/libmxnet.a, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a'
-mx_lib_cython = 'lib/libmxnet.so, lib/libmxnet.a, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
+mx_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a'
+mx_lib_cython = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
 
 // Python wheels
 mx_pip = 'build/*.whl'
 
 // mxnet cmake libraries, in cmake builds we do not produce a libnvvm static library by default.
-mx_cmake_lib = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so'
-mx_cmake_lib_cython = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
+mx_cmake_lib = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so'
+mx_cmake_lib_cython = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
 // mxnet cmake libraries, in cmake builds we do not produce a libnvvm static library by default.
-mx_cmake_lib_debug = 'build/libmxnet.so, build/libmxnet.a, build/libsample_lib.so, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests'
-mx_cmake_mkldnn_lib = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so, build/3rdparty/mkldnn/src/libmkldnn.so.0'
-mx_mkldnn_lib = 'lib/libmxnet.so, lib/libmxnet.a, libsample_lib.so, lib/libiomp5.so, lib/libmkldnn.so.0, lib/libmklml_intel.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a'
-mx_tensorrt_lib = 'build/libmxnet.so, lib/libnvonnxparser_runtime.so.0, lib/libnvonnxparser.so.0, lib/libonnx_proto.so, lib/libonnx.so'
-mx_lib_cpp_examples = 'lib/libmxnet.so, lib/libmxnet.a, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, 3rdparty/ps-lite/build/libps.a, deps/lib/libprotobuf-lite.a, deps/lib/libzmq.a, build/cpp-package/example/*, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
-mx_lib_cpp_examples_cpu = 'build/libmxnet.so, build/cpp-package/example/*'
+mx_cmake_lib_debug = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/libsample_lib.so, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests'
+mx_cmake_mkldnn_lib = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so, build/3rdparty/mkldnn/src/libmkldnn.so.0'
+mx_mkldnn_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, libsample_lib.so, lib/libiomp5.so, lib/libmkldnn.so.0, lib/libmklml_intel.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a'
+mx_tensorrt_lib = 'build/libmxnet.so, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, lib/libnvonnxparser_runtime.so.0, lib/libnvonnxparser.so.0, lib/libonnx_proto.so, lib/libonnx.so'
+mx_lib_cpp_examples = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, 3rdparty/ps-lite/build/libps.a, deps/lib/libprotobuf-lite.a, deps/lib/libzmq.a, build/cpp-package/example/*, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
+mx_lib_cpp_examples_cpu = 'build/libmxnet.so, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/cpp-package/example/*'
 
 // Python unittest for CPU
 // Python 2
diff --git a/scala-package/assembly/src/main/assembly/assembly.xml b/scala-package/assembly/src/main/assembly/assembly.xml
index b463ac12a6d6..bcc5408cd3db 100644
--- a/scala-package/assembly/src/main/assembly/assembly.xml
+++ b/scala-package/assembly/src/main/assembly/assembly.xml
@@ -54,6 +54,7 @@
       <directory>${MXNET_DIR}/lib</directory>
       <includes>
         <include>libmxnet.so</include>
+        <include>libtvm_runtime.so</include>
         <include>libgfortran.so.3</include>
         <include>libquadmath.so.0</include>
         <include>libiomp5.so</include>
diff --git a/scala-package/core/src/main/scala/org/apache/mxnet/util/NativeLibraryLoader.scala b/scala-package/core/src/main/scala/org/apache/mxnet/util/NativeLibraryLoader.scala
index 1e6d9c4f9fce..9609ba25da40 100644
--- a/scala-package/core/src/main/scala/org/apache/mxnet/util/NativeLibraryLoader.scala
+++ b/scala-package/core/src/main/scala/org/apache/mxnet/util/NativeLibraryLoader.scala
@@ -86,6 +86,7 @@ private[mxnet] object NativeLibraryLoader {
     logger.debug(s"Attempting to load $loadLibname")
     val libFileInJar = libPathInJar + loadLibname
     saveLibraryToTemp("libmxnet.so", "/lib/native/libmxnet.so", true)
+    saveLibraryToTemp("libtvm_runtime.so", "/lib/native/libtvm_runtime.so", false)
     saveLibraryToTemp("libgfortran.so.3", "/lib/native/libgfortran.so.3", false)
     saveLibraryToTemp("libquadmath.so.0", "/lib/native/libquadmath.so.0", false)
     saveLibraryToTemp("libiomp5.so", "/lib/native/libiomp5.so", false)
diff --git a/src/operator/contrib/tvmop/ufunc.cc b/src/operator/contrib/tvmop/ufunc.cc
index 7dc28bd0f419..89a90c022845 100644
--- a/src/operator/contrib/tvmop/ufunc.cc
+++ b/src/operator/contrib/tvmop/ufunc.cc
@@ -96,7 +96,7 @@ void TVMBinaryBackwardComputeUseNone(const nnvm::NodeAttrs& attrs,
     for (int i = ov.size(); i < max_dim; ++i) {
       ov.push_back(1);
     }
-    for (int i = flag; i < ov.size(); i += 2) {
+    for (uint32_t i = flag; i < ov.size(); i += 2) {
       iv.push_back(ov[i]);
     }
     TShape oshape(ov.begin(), ov.end()), ishape(iv.begin(), iv.end());
diff --git a/tests/python/unittest/test_tvm_op.py b/tests/python/unittest/test_tvm_op.py
index 9c1f567ad31a..e325edcf4d75 100644
--- a/tests/python/unittest/test_tvm_op.py
+++ b/tests/python/unittest/test_tvm_op.py
@@ -67,7 +67,6 @@ def test_tvm_broadcast_add():
             assert same(a.grad.asnumpy(), expected_grad_a)
             assert same(b.grad.asnumpy(), expected_grad_b)
 
-
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From d0fa8c0192a4be26d9c514fed1ceadeec00c1f25 Mon Sep 17 00:00:00 2001
From: Lin Yuan <apeforest@gmail.com>
Date: Thu, 5 Sep 2019 02:02:50 -0700
Subject: [PATCH 287/813] Test large vector mean operator and fix a few bugs
 (#16079)

* add test_mean and fix a few potential bugs

* address reviewer commnet

* fix and refactor test

* fix topk test

* address reviewer comment
---
 src/common/tensor_inspector.h              |  1 -
 src/operator/mshadow_op.h                  |  2 +-
 src/operator/tensor/broadcast_reduce-inl.h |  4 +-
 tests/nightly/test_large_vector.py         | 95 ++++++++++++----------
 4 files changed, 55 insertions(+), 47 deletions(-)

diff --git a/src/common/tensor_inspector.h b/src/common/tensor_inspector.h
index 2df94b7fc04f..d1ff595f5c91 100644
--- a/src/common/tensor_inspector.h
+++ b/src/common/tensor_inspector.h
@@ -33,7 +33,6 @@
 #include <vector>
 #include <fstream>
 #include "../../3rdparty/mshadow/mshadow/base.h"
-#include "../../tests/cpp/include/test_util.h"
 
 namespace mxnet {
 
diff --git a/src/operator/mshadow_op.h b/src/operator/mshadow_op.h
index ab53e7733066..616192e4af57 100644
--- a/src/operator/mshadow_op.h
+++ b/src/operator/mshadow_op.h
@@ -98,7 +98,7 @@ MXNET_UNARY_MATH_OP(identity_grad, 1);
 
 struct identity_with_cast {
   template<typename DTypeIn, typename DTypeOut>
-  MSHADOW_XINLINE static void Map(int i, DTypeOut *out, DTypeIn *in) {
+  MSHADOW_XINLINE static void Map(index_t i, DTypeOut *out, DTypeIn *in) {
     out[i] = DTypeOut(in[i]);
   }
 };
diff --git a/src/operator/tensor/broadcast_reduce-inl.h b/src/operator/tensor/broadcast_reduce-inl.h
index d107e89806aa..415059ac12a0 100644
--- a/src/operator/tensor/broadcast_reduce-inl.h
+++ b/src/operator/tensor/broadcast_reduce-inl.h
@@ -98,8 +98,10 @@ MSHADOW_XINLINE int diff(const Shape<ndim>& small, const Shape<ndim>& big, Shape
     mdim += small[i] != big[i];
     (*dims)[i] = (*stride)[i] = 1;
   }
+
+  index_t s = 1;
   #pragma unroll
-  for (int i = ndim-1, j = mdim, s = 1; i >= 0; --i) {
+  for (int i = ndim - 1, j = mdim; i >= 0; --i) {
     if (small[i] != big[i]) {
       --j;
       (*stride)[j] = s;
diff --git a/tests/nightly/test_large_vector.py b/tests/nightly/test_large_vector.py
index 34cc368d18fe..b59a4462d922 100644
--- a/tests/nightly/test_large_vector.py
+++ b/tests/nightly/test_large_vector.py
@@ -24,15 +24,15 @@
 from tests.python.unittest.common import with_seed, teardown
 
 # dimension constants
-LARGE_X = 5000000000
+LARGE_X = 4300000000
 MEDIUM_X = 1000000000
 
 
 def test_slice():
     a = nd.ones(LARGE_X)
     res = nd.slice(a, begin=(LARGE_X - MEDIUM_X), end=LARGE_X)
-    assert a[0] == 1
     assert res.shape[0] == MEDIUM_X
+    assert res[0] == 1
 
 
 def test_ndarray_zeros():
@@ -45,7 +45,7 @@ def test_ndarray_zeros():
 def test_ndarray_ones():
     a = nd.ones(shape=LARGE_X)
     assert a[-1] == 1
-    assert nd.sum(a).asnumpy() == LARGE_X
+    assert nd.sum(a) == LARGE_X
 
 
 @with_seed()
@@ -56,15 +56,12 @@ def test_ndarray_random_uniform():
 
 @with_seed()
 def test_ndarray_random_randint():
-    a = nd.random.randint(100, 10000, shape=LARGE_X)
-    assert a.shape == (LARGE_X,)
     # check if randint can generate value greater than 2**32 (large)
-    low_large_value = 2**32
-    high_large_value = 2**34
-    a = nd.random.randint(low_large_value, high_large_value, dtype=np.int64)
-    low = mx.nd.array([low_large_value], dtype='int64')
-    high = mx.nd.array([high_large_value], dtype='int64')
-    assert a >= low and a < high
+    low = 2**32
+    high = 2**34
+    a = nd.random.randint(low, high, dtype=np.int64, shape=LARGE_X).asnumpy()
+    assert a.shape == (LARGE_X,)
+    assert (a >= low).all()  and (a < high).all()
 
 
 def test_ndarray_empty():
@@ -83,15 +80,10 @@ def test_elementwise():
     assert res[-1].asnumpy() == 3
 
 
-def test_reduce():
-    a = nd.ones(shape=(LARGE_X, 1))
-    assert nd.sum(a).asnumpy() == a.shape[0] * a.shape[1]
-
-
 def test_clip():
     a = create_vector(LARGE_X)
     res = nd.clip(a, a_min=100, a_max=1000)
-    assert np.sum(res[-1].asnumpy() == 1000) == 1
+    assert res[-1] == 1000
 
 
 def test_argmin():
@@ -146,27 +138,41 @@ def test_Dense(ctx=mx.cpu(0)):
 
 
 def test_argsort():
-    b = create_vector(size=LARGE_X)
-    s = nd.argsort(b, axis=0, is_ascend=False, dtype=np.int64)
-    assert (s[0].asnumpy() == (LARGE_X - 1)).all()
+    a = create_vector(size=LARGE_X)
+    s = nd.argsort(a, axis=0, is_ascend=False, dtype=np.int64)
+    assert s[0] == (LARGE_X - 1)
 
 
 def test_sort():
-    b = create_vector(size=LARGE_X)
-    s = nd.sort(b, axis=0, is_ascend=False)
-    assert np.sum(s[-1].asnumpy() == 0).all()
-    s = nd.sort(b, is_ascend=True)
-    assert np.sum(s[0].asnumpy() == 0).all()
+    a = create_vector(size=LARGE_X)
+
+    def test_descend(x):
+        s = nd.sort(x, axis=0, is_ascend=False)
+        assert s[-1] == 0
+
+    def test_ascend(x):
+        s = nd.sort(x, is_ascend=True)
+        assert s[0] == 0
+
+    test_descend(a)
+    test_ascend(a)
 
 
 def test_topk():
-    b = create_vector(size=LARGE_X)
-    ind = nd.topk(b, k=10, axis=0, dtype=np.int64)
-    assert np.sum(ind.asnumpy() == (LARGE_X - 1)) == 1
-    ind, val = mx.nd.topk(b, k=3, axis=0, dtype=np.int64, ret_typ="both", is_ascend=False)
+    a = create_vector(size=LARGE_X)
+    ind = nd.topk(a, k=10, axis=0, dtype=np.int64)
+    for i in range(10):
+        assert ind[i] == (LARGE_X - i - 1)
+    ind, val = mx.nd.topk(a, k=3, axis=0, dtype=np.int64, ret_typ="both", is_ascend=False)
     assert np.all(ind == val)
-    val = nd.topk(b, k=1, axis=0, dtype=np.int64, ret_typ="value")
-    assert val.sum() == (LARGE_X - 1)
+    val = nd.topk(a, k=1, axis=0, dtype=np.int64, ret_typ="value")
+    assert val == (LARGE_X - 1)
+
+    
+def test_mean():
+    a = nd.arange(-LARGE_X // 2, LARGE_X // 2 + 1, dtype=np.int64)
+    b = nd.mean(a, axis=0)
+    assert b == 0
 
 
 @with_seed()
@@ -640,21 +646,21 @@ def test_eq():
     a = nd.full(LARGE_X, 3)
     b = nd.full(LARGE_X, 3)
     c = (a == b)
-    assert np.sum(c[0].asnumpy() == 1).all()
+    assert (c.asnumpy() == 1).all()
 
 
 def test_neq():
     a = nd.full(LARGE_X, 2)
     b = nd.full(LARGE_X, 3)
     c = (a != b)
-    assert np.sum(c[0].asnumpy() == 1).all()
+    assert (c.asnumpy() == 1).all()
 
 
 def test_lt():
     a = nd.full(LARGE_X, 2)
     b = nd.full(LARGE_X, 3)
     d = (a <= b)
-    assert np.sum(d[0].asnumpy() == 1).all()
+    assert (d.asnumpy() == 1).all()
 
 
 def test_lte():
@@ -662,16 +668,16 @@ def test_lte():
     b = nd.full(LARGE_X, 3)
     c = nd.full(LARGE_X, 2)
     d = (a <= b)
-    assert np.sum(d[0].asnumpy() == 1).all()
+    assert (d.asnumpy() == 1).all()
     d = (a <= c)
-    assert np.sum(d[0].asnumpy() == 1).all()
+    assert (d.asnumpy() == 1).all()
 
 
 def test_gt():
     a = nd.full(LARGE_X, 3)
     b = nd.full(LARGE_X, 2)
     d = (a > b)
-    assert np.sum(d[0].asnumpy() == 1).all()
+    assert (d.asnumpy() == 1).all()
 
 
 def test_gte():
@@ -679,9 +685,9 @@ def test_gte():
     b = nd.full(LARGE_X, 2)
     c = nd.full(LARGE_X, 3)
     d = (a >= b)
-    assert np.sum(d[0].asnumpy() == 1).all()
+    assert (d.asnumpy() == 1).all()
     d = (a >= c)
-    assert np.sum(d[0].asnumpy() == 1).all()
+    assert (d.asnumpy() == 1).all()
 
 
 def test_slice_like():
@@ -690,20 +696,21 @@ def test_slice_like():
     c = nd.slice_like(a, b)
     assert c.shape == b.shape
     assert c[0] == 0
-    assert c[-1] == (LARGE_X//2-1)
+    assert c[-1] == (LARGE_X // 2 - 1)
 
 
 def test_slice_axis():
     a = create_vector(size=LARGE_X)
-    c = nd.slice_axis(a, axis=0, begin=0, end=LARGE_X//2)
-    assert c.shape[0] == a.shape[0]//2
-    assert c[-1][0] == (LARGE_X//2-1)
+    med = LARGE_X // 2
+    c = nd.slice_axis(a, axis=0, begin=0, end=med)
+    assert c.shape[0] == a.shape[0] // 2
+    assert c[-1][0] == (med - 1)
 
 
 def test_full():
     a = nd.full(LARGE_X, 3)
     assert a.shape[0] == LARGE_X
-    assert a[LARGE_X//2] == 3
+    assert a[LARGE_X // 2] == 3
     assert a[-1] == 3
 
 

From d60be31df6b05385cd8adc4b8b26b34a33e7693c Mon Sep 17 00:00:00 2001
From: kshitij12345 <kshitijkalambarkar@gmail.com>
Date: Fri, 6 Sep 2019 00:03:43 +0530
Subject: [PATCH 288/813] Fix gradient tensor mutate in
 `{adam/ftrl/rmprop/rmspropalex}_update`. (#15768)

* update code to fix #15759

* add relevant test

* re-add the removed conditional dispatch

* fix grad mutate for ftrl_update

* add test for ftrl_update

* fix grad mutate for rmspropalex_update

* add test for rmspropalex_update

* use KERNEL_ASSIGN in RMSPropAlexUpdateKernel.

* fix grad mutate for rmsprop_update

* add test for rmsprop_update

* add more optimizers for mutation test

* retrigger CI

* retrigger CI

* retrigger CI

* retrigger CI

* address comments.

* refactor code.

* retrigger CI

* retrigger CI

* retrigger CI
---
 src/operator/optimizer_op-inl.h       | 279 ++++++++++++++------------
 tests/python/unittest/test_ndarray.py |  67 ++++++-
 2 files changed, 216 insertions(+), 130 deletions(-)

diff --git a/src/operator/optimizer_op-inl.h b/src/operator/optimizer_op-inl.h
index 50637a8e7b42..3c03f8061d87 100644
--- a/src/operator/optimizer_op-inl.h
+++ b/src/operator/optimizer_op-inl.h
@@ -1293,15 +1293,37 @@ struct AdamParam : public dmlc::Parameter<AdamParam> {
   }
 };
 
+struct AdamUpdateKernel {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int i, DType* out_data,
+    DType* mean_data, DType* var_data, const DType* weight_data, const DType* grad_data,
+    const DType clip_gradient, const DType rescale_grad,
+    const DType beta1, const DType beta2,
+    const DType lr, const DType wd,
+    const DType epsilon, const OpReqType req) {
+    using namespace mshadow_op;
+
+    DType grad_rescaled = grad_data[i] * rescale_grad + weight_data[i] * wd;
+    if (clip_gradient >= 0.f) {
+      grad_rescaled = clip::Map(grad_rescaled, clip_gradient);
+    }
+
+    mean_data[i] = beta1 * mean_data[i] + (1.f - beta1) * grad_rescaled;
+    var_data[i] = beta2 * var_data[i] +
+                        (1.f - beta2) * grad_rescaled * grad_rescaled;
+
+    KERNEL_ASSIGN(out_data[i], req, weight_data[i] - lr * mean_data[i] /
+                  (square_root::Map(var_data[i]) + epsilon));
+  }
+};
+
 template<typename xpu>
 inline void AdamUpdate(const nnvm::NodeAttrs& attrs,
                        const OpContext &ctx,
                        const std::vector<TBlob> &inputs,
                        const std::vector<OpReqType> &req,
                        const std::vector<TBlob> &outputs) {
-  using namespace mshadow;
-  using namespace mshadow::expr;
-  using namespace mshadow_op;
+  using namespace mxnet_op;
   const AdamParam& param = nnvm::get<AdamParam>(attrs.parsed);
   Stream<xpu>* s = ctx.get_stream<xpu>();
   MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
@@ -1311,22 +1333,12 @@ inline void AdamUpdate(const nnvm::NodeAttrs& attrs,
     Tensor<xpu, 2, DType> var = inputs[3].FlatTo2D<xpu, DType>(s);
     Tensor<xpu, 2, DType> out = outputs[0].FlatTo2D<xpu, DType>(s);
 
-    grad = scalar<DType>(param.rescale_grad) * grad +
-      scalar<DType>(param.wd) * weight;
-
-    if (param.clip_gradient >= 0.0f) {
-      mean = scalar<DType>(param.beta1)*mean + scalar<DType>(1.f-param.beta1) *
-          F<clip>(grad, DType(param.clip_gradient));
-      var = scalar<DType>(param.beta2)*var + scalar<DType>(1.f-param.beta2)*F<square>(
-          F<clip>(grad, DType(param.clip_gradient)));
-    } else {
-      mean = scalar<DType>(param.beta1)*mean + scalar<DType>(1.f-param.beta1) * grad;
-      var = scalar<DType>(param.beta2)*var + scalar<DType>(1.f-param.beta2) * F<square>(grad);
-    }
-    Assign(out, req[0],
-           weight -
-           scalar<DType>(param.lr) * mean /
-           (F<square_root>(var) + scalar<DType>(param.epsilon)));
+    Kernel<AdamUpdateKernel, xpu>::Launch(s, weight.shape_.Size(),
+          out.dptr_, mean.dptr_, var.dptr_, weight.dptr_, grad.dptr_,
+          static_cast<DType>(param.clip_gradient), static_cast<DType>(param.rescale_grad),
+          static_cast<DType>(param.beta1), static_cast<DType>(param.beta2),
+          static_cast<DType>(param.lr), static_cast<DType>(param.wd),
+          static_cast<DType>(param.epsilon), req[0]);
   });
 }
 
@@ -1596,57 +1608,64 @@ struct RMSPropAlexParam : public dmlc::Parameter<RMSPropAlexParam> {
   }
 };
 
+struct RMSPropAlexUpdateKernel {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int i, DType* out_data,
+    DType* state_n_data, DType* state_g_data, DType* delta_data,
+    const DType* weight_data, const DType* grad_data,
+    const DType clip_gradient, const DType rescale_grad,
+    const DType gamma1, const DType gamma2,
+    const DType lr, const DType wd,
+    const DType clip_weights, const DType epsilon,
+    const OpReqType req) {
+    using namespace mshadow_op;
+
+    DType grad_rescaled = rescale_grad * grad_data[i] + wd * weight_data[i];
+    if (clip_gradient >= 0.0f) {
+      grad_rescaled = clip::Map(grad_rescaled, clip_gradient);
+    }
+
+    state_n_data[i] = (1.f - gamma1) * grad_rescaled * grad_rescaled +
+                      gamma1 * state_n_data[i];
+    state_g_data[i] = (1.f - gamma1) * grad_rescaled +
+                      gamma1 * state_g_data[i];
+    delta_data[i] = gamma2 * delta_data[i] -
+                    (lr * (grad_rescaled) /
+                      (square_root::Map(state_n_data[i] -
+                                        state_g_data[i] * state_g_data[i] + epsilon)));
+
+    if (clip_weights >= 0.0f) {
+      const DType clipped_weight = clip::Map(weight_data[i] + delta_data[i], clip_weights);
+      KERNEL_ASSIGN(out_data[i], req, clipped_weight);
+    } else {
+      KERNEL_ASSIGN(out_data[i], req, weight_data[i] + delta_data[i]);
+    }
+  }
+};
+
 template <typename xpu>
 inline void RMSPropAlexUpdate(const nnvm::NodeAttrs &attrs,
                               const OpContext &ctx,
                               const std::vector<TBlob> &inputs,
                               const std::vector<OpReqType> &req,
                               const std::vector<TBlob> &outputs) {
-  using namespace mshadow;
-  using namespace mshadow::expr;
-  using namespace mshadow_op;
+  using namespace mxnet_op;
   const RMSPropAlexParam &param = nnvm::get<RMSPropAlexParam>(attrs.parsed);
   Stream<xpu> *s = ctx.get_stream<xpu>();
   MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
-    Tensor<xpu, 2, DType> weight = inputs[0].FlatTo2D<xpu, DType>(s);
-    Tensor<xpu, 2, DType> grad = inputs[1].FlatTo2D<xpu, DType>(s);
-    Tensor<xpu, 2, DType> state_n = inputs[2].FlatTo2D<xpu, DType>(s);
-    Tensor<xpu, 2, DType> state_g = inputs[3].FlatTo2D<xpu, DType>(s);
-    Tensor<xpu, 2, DType> delta = inputs[4].FlatTo2D<xpu, DType>(s);
-    Tensor<xpu, 2, DType> out = outputs[0].FlatTo2D<xpu, DType>(s);
-
-    grad = scalar<DType>(param.rescale_grad) * grad +
-           scalar<DType>(param.wd) * weight;
-
-    if (param.clip_gradient >= 0.0f) {
-      state_n = scalar<DType>(1.f - param.gamma1) *
-                    F<clip>(grad, DType(param.clip_gradient)) *
-                    F<clip>(grad, DType(param.clip_gradient)) +
-                scalar<DType>(param.gamma1) * state_n;
-      state_g = scalar<DType>(1.f - param.gamma1) *
-                    F<clip>(grad, DType(param.clip_gradient)) +
-                scalar<DType>(param.gamma1) * state_g;
-      delta = scalar<DType>(param.gamma2) * delta -
-              scalar<DType>(param.lr) *
-                  (F<clip>(grad, DType(param.clip_gradient)) /
-                   (F<square_root>(state_n - state_g * state_g +
-                                   scalar<DType>(param.epsilon))));
-    } else {
-      state_n = scalar<DType>(1.f - param.gamma1) * (grad * grad) +
-                scalar<DType>(param.gamma1) * state_n;
-      state_g = scalar<DType>(1.f - param.gamma1) * grad +
-                scalar<DType>(param.gamma1) * state_g;
-      delta = scalar<DType>(param.gamma2) * delta -
-              scalar<DType>(param.lr) *
-                  (grad / (F<square_root>(state_n - state_g * state_g +
-                                          scalar<DType>(param.epsilon))));
-    }
+    DType* weight_data = inputs[0].dptr<DType>();
+    DType* grad_data = inputs[1].dptr<DType>();
+    DType* state_n_data = inputs[2].dptr<DType>();
+    DType* state_g_data = inputs[3].dptr<DType>();
+    DType* delta_data = inputs[4].dptr<DType>();
+    DType* out_data = outputs[0].dptr<DType>();
 
-    if (param.clip_weights >= 0.0f) {
-      Assign(out, req[0], F<clip>(weight + delta, DType(param.clip_weights)));
-    } else {
-      Assign(out, req[0], weight + delta);
-    }
+    Kernel<RMSPropAlexUpdateKernel, xpu>::Launch(s, inputs[0].shape_.Size(),
+      out_data, state_n_data, state_g_data, delta_data, weight_data, grad_data,
+      static_cast<DType>(param.clip_gradient), static_cast<DType>(param.rescale_grad),
+      static_cast<DType>(param.gamma1), static_cast<DType>(param.gamma2),
+      static_cast<DType>(param.lr), static_cast<DType>(param.wd),
+      static_cast<DType>(param.clip_weights), static_cast<DType>(param.epsilon), req[0]);
   });
 }
 
@@ -1688,64 +1707,52 @@ struct RMSPropParam : public dmlc::Parameter<RMSPropParam> {
   }
 };
 
+struct RMSPropUpdateKernel {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int i,
+    DType* out_data, DType* state_n_data,
+    const DType* weight_data, const DType* grad_data,
+    const DType clip_gradient, const DType rescale_grad,
+    const DType gamma1, const DType lr, const DType wd,
+    const DType clip_weights, const DType epsilon,
+    const OpReqType req) {
+    using namespace mshadow_op;
+
+    DType grad_rescaled = rescale_grad * grad_data[i] + wd * weight_data[i];
+    if (clip_gradient >= 0.0f) {
+      grad_rescaled = clip::Map(grad_rescaled, clip_gradient);
+    }
+
+    state_n_data[i] = (1.f - gamma1) * (grad_rescaled * grad_rescaled) + gamma1 * state_n_data[i];
+
+    DType weight = weight_data[i] -
+                   lr * (grad_rescaled / square_root::Map(state_n_data[i] + epsilon));
+    if (clip_weights >= 0.0f) {
+      weight = clip::Map(weight, clip_weights);
+    }
+    KERNEL_ASSIGN(out_data[i], req, weight);
+  }
+};
+
 template <typename xpu>
 inline void RMSPropUpdate(const nnvm::NodeAttrs &attrs, const OpContext &ctx,
                           const std::vector<TBlob> &inputs,
                           const std::vector<OpReqType> &req,
                           const std::vector<TBlob> &outputs) {
-  using namespace mshadow;
-  using namespace mshadow::expr;
-  using namespace mshadow_op;
+  using namespace mxnet_op;
   const RMSPropParam &param = nnvm::get<RMSPropParam>(attrs.parsed);
   Stream<xpu> *s = ctx.get_stream<xpu>();
   MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
-    Tensor<xpu, 2, DType> weight = inputs[0].FlatTo2D<xpu, DType>(s);
-    Tensor<xpu, 2, DType> grad = inputs[1].FlatTo2D<xpu, DType>(s);
-    Tensor<xpu, 2, DType> state_n = inputs[2].FlatTo2D<xpu, DType>(s);
-    Tensor<xpu, 2, DType> out = outputs[0].FlatTo2D<xpu, DType>(s);
+    DType* weight_data = inputs[0].dptr<DType>();
+    DType* grad_data = inputs[1].dptr<DType>();
+    DType* state_n_data = inputs[2].dptr<DType>();
+    DType* out_data = outputs[0].dptr<DType>();
 
-    grad = scalar<DType>(param.rescale_grad) * grad +
-           scalar<DType>(param.wd) * weight;
-
-    if (param.clip_gradient >= 0.0f) {
-      state_n = scalar<DType>(1.f - param.gamma1) *
-                    F<clip>(grad, DType(param.clip_gradient)) *
-                    F<clip>(grad, DType(param.clip_gradient)) +
-                scalar<DType>(param.gamma1) * state_n;
-      if (param.clip_weights >= 0.0f) {
-        Assign(out, req[0],
-               F<clip>(weight -
-                       scalar<DType>(param.lr) *
-                           (F<clip>(grad, DType(param.clip_gradient)) /
-                            (F<square_root>(state_n +
-                                            scalar<DType>(param.epsilon)))),
-                       DType(param.clip_weights)));
-      } else {
-        Assign(out, req[0], weight -
-                            scalar<DType>(param.lr) *
-                              (F<clip>(grad, DType(param.clip_gradient)) /
-                               (F<square_root>(state_n +
-                                               scalar<DType>(param.epsilon)))));
-      }
-    } else {
-      state_n = scalar<DType>(1.f - param.gamma1) * (grad * grad) +
-                scalar<DType>(param.gamma1) * state_n;
-      if (param.clip_weights >= 0.0f) {
-        Assign(out, req[0],
-               F<clip>(weight -
-                       scalar<DType>(param.lr) *
-                           (grad /
-                            (F<square_root>(state_n +
-                                            scalar<DType>(param.epsilon)))),
-                       DType(param.clip_weights)));
-      } else {
-        Assign(out, req[0], weight -
-                            scalar<DType>(param.lr) *
-                              (grad /
-                               (F<square_root>(state_n +
-                                               scalar<DType>(param.epsilon)))));
-      }
-    }
+    Kernel<RMSPropUpdateKernel, xpu>::Launch(s, inputs[0].shape_.Size(),
+      out_data, state_n_data, weight_data, grad_data,
+      static_cast<DType>(param.clip_gradient), static_cast<DType>(param.rescale_grad),
+      static_cast<DType>(param.gamma1), static_cast<DType>(param.lr), static_cast<DType>(param.wd),
+      static_cast<DType>(param.clip_weights), static_cast<DType>(param.epsilon), req[0]);
   });
 }
 
@@ -1781,15 +1788,41 @@ struct FtrlParam : public dmlc::Parameter<FtrlParam> {
   }
 };
 
+struct FtrlUpdateKernel {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int i, DType* out_data,
+    DType* n_data, DType* z_data, const DType* weight_data, const DType* grad_data,
+    const DType clip_gradient, const DType rescale_grad,
+    const DType beta, const DType lamda1,
+    const DType lr, const DType wd,
+    const OpReqType req) {
+    using namespace mshadow_op;
+
+    DType grad_rescaled = grad_data[i] * rescale_grad;
+    if (clip_gradient >= 0.0f) {
+      grad_rescaled = clip::Map(grad_rescaled, clip_gradient);
+    }
+
+    z_data[i] += grad_rescaled - (square_root::Map(n_data[i] +
+                      square::Map(grad_rescaled)) - square_root::Map(n_data[i])) *
+                      weight_data[i] / lr;
+    n_data[i] += square::Map(grad_rescaled);
+
+    KERNEL_ASSIGN(out_data[i], req,
+                  (sign::Map(z_data[i]) * lamda1 - z_data[i]) /
+                  ((beta + square_root::Map(n_data[i])) / lr + wd) *
+                  gt::Map(abs::Map(z_data[i]), lamda1));
+  }
+};
+
 template<typename xpu>
 inline void FtrlUpdate(const nnvm::NodeAttrs& attrs,
                        const OpContext &ctx,
                        const std::vector<TBlob> &inputs,
                        const std::vector<OpReqType> &req,
                        const std::vector<TBlob> &outputs) {
-  using namespace mshadow;
-  using namespace mshadow::expr;
-  using namespace mshadow_op;
+  using namespace mxnet_op;
+
   const FtrlParam& param = nnvm::get<FtrlParam>(attrs.parsed);
   Stream<xpu>* s = ctx.get_stream<xpu>();
   MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
@@ -1799,23 +1832,11 @@ inline void FtrlUpdate(const nnvm::NodeAttrs& attrs,
     Tensor<xpu, 2, DType> n = inputs[3].FlatTo2D<xpu, DType>(s);
     Tensor<xpu, 2, DType> out = outputs[0].FlatTo2D<xpu, DType>(s);
 
-    grad = scalar<DType>(param.rescale_grad) * grad;
-
-    if (param.clip_gradient >= 0.0f) {
-      z += F<clip>(grad, DType(param.clip_gradient)) - (F<square_root>(n +
-           F<square>(F<clip>(grad, DType(param.clip_gradient)))) - F<square_root>(n)) *
-           weight / scalar<DType>(param.lr);
-      n += F<square>(F<clip>(grad, DType(param.clip_gradient)));
-    } else {
-      z += grad - (F<square_root>(n + F<square>(grad)) - F<square_root>(n)) *
-           weight / scalar<DType>(param.lr);
-      n += F<square>(grad);
-    }
-    Assign(out, req[0],
-           (F<sign>(z) * scalar<DType>(param.lamda1) - z) /
-           ((scalar<DType>(param.beta) + F<square_root>(n)) /
-           scalar<DType>(param.lr) + scalar<DType>(param.wd)) *
-           F<gt>(F<abs>(z), scalar<DType>(param.lamda1)));
+    Kernel<FtrlUpdateKernel, xpu>::Launch(s, weight.shape_.Size(),
+      out.dptr_, n.dptr_, z.dptr_, weight.dptr_, grad.dptr_,
+      static_cast<DType>(param.clip_gradient), static_cast<DType>(param.rescale_grad),
+      static_cast<DType>(param.beta), static_cast<DType>(param.lamda1),
+      static_cast<DType>(param.lr), static_cast<DType>(param.wd), req[0]);
   });
 }
 
diff --git a/tests/python/unittest/test_ndarray.py b/tests/python/unittest/test_ndarray.py
index c1f0ef05f979..a05b3eae0e44 100644
--- a/tests/python/unittest/test_ndarray.py
+++ b/tests/python/unittest/test_ndarray.py
@@ -28,7 +28,7 @@
 from mxnet.test_utils import default_context
 from mxnet.test_utils import np_reduce
 from mxnet.test_utils import same
-from mxnet.test_utils import random_sample, rand_shape_nd
+from mxnet.test_utils import random_sample, rand_shape_nd, random_arrays
 from mxnet import runtime
 from numpy.testing import assert_allclose
 import mxnet.autograd
@@ -1887,6 +1887,71 @@ def check_save_load(save_is_np_shape, load_is_np_shape, shapes, save_throw_excep
     check_save_load(True, True, [(2, 0, 1), (0,), (), (), (0, 4), (), (3, 0, 0, 0), (2, 1), (0, 5, 0)], False, False)
 
 
+@with_seed()
+def test_update_ops_mutation():
+    def assert_mutate(x, y, op):
+            np.testing.assert_raises(
+                AssertionError, np.testing.assert_allclose, x, y)
+
+    def assert_unchanged(x, y, op):
+            np.testing.assert_allclose(x, y)
+
+    def test_op(op, num_inputs, mutated_inputs, **kwargs):
+        for dim in range(1, 7):
+            shape = rand_shape_nd(dim)
+            shapes = (shape,) * num_inputs
+
+            # Generate Arrays
+            arrays = tuple(map(mx.nd.array, random_arrays(*shapes)))
+
+            # Arrays before update
+            pre_arrays = tuple(map(
+                lambda x: x.asnumpy(), arrays))
+
+            # Operate
+            # weight -> arrays[0]
+            op(*arrays, out=arrays[0], **kwargs)
+
+            # Arrays post update
+            post_arrays = tuple(map(
+                lambda x: x.asnumpy(), arrays))
+
+            for idx, (pre_array, post_array) in \
+                    enumerate(zip(pre_arrays, post_arrays)):
+                if idx in mutated_inputs:
+                    assert_mutate(pre_array, post_array, op)
+                else:
+                    assert_unchanged(pre_array, post_array, op)
+
+    test_op(mx.nd.signsgd_update, 2, [0], **
+            {'rescale_grad': 0.1, 'lr': 0.01, 'wd': 1e-3,
+             'clip_gradient': 1e-3})
+    test_op(mx.nd.signum_update, 3, [0, 2], **
+            {'rescale_grad': 0.1, 'lr': 0.01, 'wd': 1e-3,
+             'momentum': 1e-3, 'clip_gradient': 1e-3,
+             'wd_lh': 1e-3})
+    test_op(mx.nd.sgd_update, 2, [0], **
+            {'rescale_grad': 0.1, 'lr': 0.01, 'wd': 1e-3,
+             'clip_gradient': 1e-3})
+    test_op(mx.nd.sgd_mom_update, 3, [0, 2], **
+            {'rescale_grad': 0.1, 'lr': 0.01, 'wd': 1e-3,
+             'momentum': 0.01, 'clip_gradient': 1e-3})
+    test_op(mx.nd.nag_mom_update, 3, [0, 2], **
+            {'rescale_grad': 0.1, 'lr': 0.01, 'wd': 1e-3,
+             'momentum': 0.01, 'clip_gradient': 1e-3})
+    test_op(mx.nd.ftml_update, 5, [0, 2, 3, 4], **
+            {'t': 3, 'rescale_grad': 0.1, 'lr': 0.01, 'wd': 1e-3,
+             'clip_grad': 1e-3})
+    test_op(mx.nd.ftrl_update, 4, [0, 2, 3], **
+            {'rescale_grad': 0.1, 'lr': 0.01, 'wd': 1e-3})
+    test_op(mx.nd.adam_update, 4, [0, 2, 3], **
+            {'rescale_grad': 0.1, 'lr': 0.01, 'wd': 1e-3})
+    test_op(mx.nd.rmspropalex_update, 5, [0, 2, 3, 4], **
+            {'rescale_grad': 0.1, 'lr': 0.01, 'wd': 1e-3})
+    test_op(mx.nd.rmsprop_update, 3, [0, 2], **
+            {'rescale_grad': 0.1, 'lr': 0.01, 'wd': 1e-3})
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 7f57e8e10c504fe7a463ba695321b11c6dd4912d Mon Sep 17 00:00:00 2001
From: Thomas Delteil <tdelteil@amazon.com>
Date: Thu, 5 Sep 2019 18:23:46 -0700
Subject: [PATCH 289/813] [WIP] New Website: New Docs [1/3] (#15884)

* Adding new website files

Co-authored-by: Thomas Delteil <thomas.delteil1@gmail.com>
Co-authored-by: Aaron Markham <markhama@amazon.com>

* add license header

* fixing links and url + css

* fixing tutorials

* exclude python_docs folder when building prod site

This allows things to coexist until a following PR deletes this file.

* license header fixes

* exclude static site from license header check

* exclude static site and sphinx theme

* exclude static_site and cpp_docs from sphinx

* add licenses and excludes
---
 docs/conf.py                                  |    2 +-
 docs/cpp_docs/Doxyfile                        | 2370 ++++++++++++++++
 docs/cpp_docs/Makefile                        |   26 +
 docs/python_docs/README.md                    |   24 +
 .../_static/apache_incubator_logo.png         |  Bin 0 -> 16552 bytes
 docs/python_docs/_static/google_analytics.js  |   26 +
 .../_static/minima-social-icons.svg           |   33 +
 docs/python_docs/_static/mxnet-icon.png       |  Bin 0 -> 2741 bytes
 docs/python_docs/_static/mxnet.css            |  199 ++
 docs/python_docs/_static/mxnet_logo.png       |  Bin 0 -> 22390 bytes
 docs/python_docs/environment.yml              |   38 +
 docs/python_docs/python/.gitignore            |   20 +
 docs/python_docs/python/Makefile              |   57 +
 docs/python_docs/python/Makefile_sphinx       |  216 ++
 docs/python_docs/python/README.md             |  130 +
 .../python_docs/python/api/advanced/index.rst |   74 +
 .../python/api/advanced/mxnet.engine.rst      |   34 +
 .../python/api/advanced/mxnet.executor.rst    |   34 +
 .../api/advanced/mxnet.executor_manager.rst   |   38 +
 .../api/advanced/mxnet.kvstore_server.rst     |   36 +
 .../python/api/advanced/mxnet.rtc.rst         |   36 +
 .../python/api/advanced/mxnet.test_utils.rst  |   91 +
 .../python/api/advanced/mxnet.util.rst        |   31 +
 .../python/api/gluon-related/index.rst        |  111 +
 .../api/gluon-related/mxnet.autograd.rst      |   38 +
 .../api/gluon-related/mxnet.context.rst       |   33 +
 .../python/api/gluon-related/mxnet.image.rst  |   99 +
 .../api/gluon-related/mxnet.initializer.rst   |   58 +
 .../python/api/gluon-related/mxnet.io.rst     |   52 +
 .../gluon-related/mxnet.kvstore.KVStore.rst   |   61 +
 .../gluon-related/mxnet.kvstore.create.rst    |   23 +
 .../api/gluon-related/mxnet.kvstore.rst       |   27 +
 .../api/gluon-related/mxnet.lr_scheduler.rst  |   31 +
 .../python/api/gluon-related/mxnet.metric.rst |   57 +
 .../api/gluon-related/mxnet.optimizer.rst     |   55 +
 .../api/gluon-related/mxnet.profiler.rst      |   54 +
 .../python/api/gluon-related/mxnet.random.rst |   26 +
 .../api/gluon-related/mxnet.recordio.rst      |   43 +
 docs/python_docs/python/api/gluon/index.rst   |  156 ++
 .../python/api/gluon/mxnet.gluon.Constant.rst |   23 +
 .../api/gluon/mxnet.gluon.HybridBlock.rst     |   40 +
 .../api/gluon/mxnet.gluon.ParameterDict.rst   |   79 +
 .../api/gluon/mxnet.gluon.SymbolBlock.rst     |   28 +
 .../python/api/gluon/mxnet.gluon.Trainer.rst  |   51 +
 .../python/api/gluon/mxnet.gluon.contrib.rst  |  173 ++
 .../python/api/gluon/mxnet.gluon.data.rst     |   50 +
 .../api/gluon/mxnet.gluon.data.vision.rst     |   58 +
 .../python/api/gluon/mxnet.gluon.loss.rst     |   40 +
 .../api/gluon/mxnet.gluon.model_zoo.rst       |  167 ++
 .../python/api/gluon/mxnet.gluon.nn.Block.rst |   86 +
 .../api/gluon/mxnet.gluon.nn.HybridBlock.rst  |   66 +
 .../api/gluon/mxnet.gluon.nn.SymbolBlock.rst  |   67 +
 .../api/gluon/mxnet.gluon.parameter.rst       |   68 +
 .../python/api/gluon/mxnet.gluon.utils.rst    |   31 +
 docs/python_docs/python/api/gluon/nn.rst      |  156 ++
 docs/python_docs/python/api/gluon/rnn.rst     |   68 +
 docs/python_docs/python/api/index.rst         |   77 +
 docs/python_docs/python/api/ndarray/index.rst |  124 +
 .../api/ndarray/mxnet.ndarray.NDArray.rst     |  310 +++
 .../mxnet.ndarray.sparse.CSRNDArray.rst       |  203 ++
 .../mxnet.ndarray.sparse.RowSparseNDArray.rst |  183 ++
 .../python/api/ndarray/routines.rst           |  461 ++++
 .../python/api/ndarray/sparse_routines.rst    |  200 ++
 .../python/api/symbol-related/index.rst       |   53 +
 .../api/symbol-related/mxnet.callback.rst     |   45 +
 .../python/api/symbol-related/mxnet.model.rst |   45 +
 .../api/symbol-related/mxnet.module.rst       |   35 +
 .../api/symbol-related/mxnet.monitor.rst      |   35 +
 .../symbol-related/mxnet.visualization.rst    |   35 +
 docs/python_docs/python/api/symbol/index.rst  |   65 +
 .../python/api/symbol/mxnet.symbol.Symbol.rst |  335 +++
 .../python/api/symbol/mxnet.symbol.linalg.rst |   49 +
 docs/python_docs/python/index.rst             |   52 +
 docs/python_docs/python/scripts/conf.py       |  243 ++
 docs/python_docs/python/scripts/md2ipynb.py   |   47 +
 .../python_docs/python/scripts/process_rst.py |   86 +
 .../python/tutorials/deploy/export/export.rst |   21 +
 .../python/tutorials/deploy/export/index.rst  |   42 +
 .../python/tutorials/deploy/index.rst         |  123 +
 .../python/tutorials/deploy/inference/cpp.rst |   21 +
 .../tutorials/deploy/inference/index.rst      |   57 +
 .../tutorials/deploy/inference/scala.rst      |   21 +
 .../tutorials/deploy/run-on-aws/ami.png       |  Bin 0 -> 124310 bytes
 .../tutorials/deploy/run-on-aws/aws.png       |  Bin 0 -> 178726 bytes
 .../tutorials/deploy/run-on-aws/cloud.rst     |  106 +
 .../tutorials/deploy/run-on-aws/connect.png   |  Bin 0 -> 85927 bytes
 .../tutorials/deploy/run-on-aws/disk.png      |  Bin 0 -> 165192 bytes
 .../tutorials/deploy/run-on-aws/ec2.png       |  Bin 0 -> 283254 bytes
 .../tutorials/deploy/run-on-aws/index.rst     |   55 +
 .../tutorials/deploy/run-on-aws/keypair.png   |  Bin 0 -> 193217 bytes
 .../tutorials/deploy/run-on-aws/launching.png |  Bin 0 -> 62956 bytes
 .../tutorials/deploy/run-on-aws/limits.png    |  Bin 0 -> 79889 bytes
 .../deploy/run-on-aws/login_screen.png        |  Bin 0 -> 474538 bytes
 .../tutorials/deploy/run-on-aws/mxnet.png     |  Bin 0 -> 32270 bytes
 .../python/tutorials/deploy/run-on-aws/os.png |  Bin 0 -> 210080 bytes
 .../tutorials/deploy/run-on-aws/p2x.png       |  Bin 0 -> 77498 bytes
 .../tutorials/deploy/run-on-aws/sagemaker.png |  Bin 0 -> 117671 bytes
 .../tutorials/deploy/run-on-aws/ssh.png       |  Bin 0 -> 376297 bytes
 .../tutorials/deploy/run-on-aws/use_ec2.rst   |   74 +
 .../deploy/run-on-aws/use_sagemaker.rst       |   91 +
 .../python/tutorials/extend/custom_layer.md   |  266 ++
 .../python/tutorials/extend/customop.md       |  236 ++
 .../python/tutorials/extend/index.rst         |   55 +
 .../getting-started/crash-course/1-ndarray.md |  127 +
 .../getting-started/crash-course/2-nn.md      |  142 +
 .../crash-course/3-autograd.md                |   99 +
 .../getting-started/crash-course/4-train.md   |  176 ++
 .../getting-started/crash-course/5-predict.md |  161 ++
 .../crash-course/6-use_gpus.md                |  146 +
 .../getting-started/crash-course/index.rst    |   52 +
 .../tutorials/getting-started/index.rst       |   48 +
 .../getting-started/to-mxnet/index.rst        |   44 +
 .../getting-started/to-mxnet/pytorch.md       |  463 ++++
 docs/python_docs/python/tutorials/index.rst   |  175 ++
 .../tutorials/packages/autograd/autograd.md   |  272 ++
 .../packages/gluon/activations/activations.md |  276 ++
 .../packages/gluon/activations/images/elu.png |  Bin 0 -> 10707 bytes
 .../gluon/activations/images/leakyrelu.png    |  Bin 0 -> 10769 bytes
 .../gluon/activations/images/prelu.png        |  Bin 0 -> 10202 bytes
 .../gluon/activations/images/relu.png         |  Bin 0 -> 9581 bytes
 .../gluon/activations/images/selu.png         |  Bin 0 -> 11342 bytes
 .../gluon/activations/images/sigmoid.png      |  Bin 0 -> 12763 bytes
 .../gluon/activations/images/softrelu.png     |  Bin 0 -> 11210 bytes
 .../gluon/activations/images/softsign.png     |  Bin 0 -> 15627 bytes
 .../gluon/activations/images/swish.png        |  Bin 0 -> 11634 bytes
 .../gluon/activations/images/tanh.png         |  Bin 0 -> 13530 bytes
 .../tutorials/packages/gluon/blocks.svg       |  514 ++++
 .../tutorials/packages/gluon/custom-layer.md  |  127 +
 .../packages/gluon/custom-loss/custom-loss.md |  232 ++
 .../custom-loss/images/contrastive_loss.jpeg  |  Bin 0 -> 32367 bytes
 .../gluon/custom-loss/images/inuktitut_1.png  |  Bin 0 -> 2031 bytes
 .../gluon/custom-loss/images/inuktitut_2.png  |  Bin 0 -> 2217 bytes
 .../packages/gluon/custom_layer_beginners.md  |  247 ++
 .../tutorials/packages/gluon/customop.md      |  227 ++
 .../python/tutorials/packages/gluon/data.md   |  141 +
 .../packages/gluon/fit_api_tutorial.md        |  269 ++
 .../tutorials/packages/gluon/hybridize.md     |  306 +++
 .../packages/gluon/image-augmentation.md      |  150 ++
 .../tutorials/packages/gluon/image/index.rst  |  105 +
 .../tutorials/packages/gluon/image/mnist.md   |  350 +++
 .../packages/gluon/image/pretrained_models.md |  245 ++
 .../python/tutorials/packages/gluon/index.rst |  276 ++
 .../python/tutorials/packages/gluon/init.md   |  322 +++
 .../packages/gluon/loss/ctc_loss.png          |  Bin 0 -> 14513 bytes
 .../packages/gluon/loss/kl_divergence.md      |  138 +
 .../tutorials/packages/gluon/loss/loss.md     |  308 +++
 .../packages/gluon/loss/triplet_loss.png      |  Bin 0 -> 13670 bytes
 .../python/tutorials/packages/gluon/naming.md |  209 ++
 .../python/tutorials/packages/gluon/nn.md     |  313 +++
 .../gluon/normalization/imgs/NCHW_BN.png      |  Bin 0 -> 45584 bytes
 .../gluon/normalization/imgs/NCHW_IN.png      |  Bin 0 -> 46218 bytes
 .../gluon/normalization/imgs/NCHW_LN.png      |  Bin 0 -> 45487 bytes
 .../gluon/normalization/imgs/NTC_BN.png       |  Bin 0 -> 37519 bytes
 .../gluon/normalization/imgs/NTC_IN.png       |  Bin 0 -> 38694 bytes
 .../gluon/normalization/imgs/NTC_LN.png       |  Bin 0 -> 38531 bytes
 .../imgs/data_normalization.jpeg              |  Bin 0 -> 58978 bytes
 .../gluon/normalization/normalization.md      |  274 ++
 .../tutorials/packages/gluon/parameters.md    |  243 ++
 .../tutorials/packages/gluon/text/gnmt.rst    |  489 ++++
 .../tutorials/packages/gluon/text/index.rst   |  116 +
 .../packages/gluon/text/transformer.rst       |  607 +++++
 .../tutorials/packages/gluon/trainer.md       |  168 ++
 .../python/tutorials/packages/index.rst       |  140 +
 .../tutorials/packages/kvstore/index.rst      |   44 +
 .../tutorials/packages/module/index.rst       |   52 +
 .../packages/ndarray/01-ndarray-intro.md      |  176 ++
 .../packages/ndarray/02-ndarray-operations.md |  260 ++
 .../packages/ndarray/03-ndarray-contexts.md   |   89 +
 .../tutorials/packages/ndarray/index.rst      |   88 +
 .../python/tutorials/packages/onnx/index.rst  |   51 +
 .../images/momentum_sgd_animation.gif         |  Bin 0 -> 45436 bytes
 .../images/nesterov_momentum_animation.gif    |  Bin 0 -> 38179 bytes
 .../optimizer/images/sgd_animation.gif        |  Bin 0 -> 44001 bytes
 .../tutorials/packages/optimizer/optimizer.md |  429 +++
 .../tutorials/packages/symbol/index.rst       |   41 +
 .../python/tutorials/packages/viz/index.rst   |   38 +
 .../performance/backend/MKLDNN_README.md      |  291 ++
 .../tutorials/performance/backend/index.rst   |   52 +
 .../tutorials/performance/backend/mkl-dnn.rst |   33 +
 .../backend/mkldnn_quantization.md            |  258 ++
 .../tutorials/performance/backend/profiler.md |  333 +++
 .../tutorials/performance/backend/tvm.rst     |   21 +
 .../performance/compression/index.rst         |   59 +
 .../performance/compression/int8.rst          |   21 +
 .../python/tutorials/performance/index.rst    |  130 +
 docs/python_docs/themes/.babelrc              |    3 +
 docs/python_docs/themes/.circleci/config.yml  |   37 +
 docs/python_docs/themes/.gitignore            |   13 +
 docs/python_docs/themes/.sassrc               |    5 +
 docs/python_docs/themes/mx-theme/LICENSE      |   21 +
 docs/python_docs/themes/mx-theme/MANIFEST.in  |    1 +
 docs/python_docs/themes/mx-theme/README.md    |   65 +
 .../themes/mx-theme/mxtheme/__init__.py       |   13 +
 .../themes/mx-theme/mxtheme/card.py           |   42 +
 .../themes/mx-theme/mxtheme/drawer.html       |   17 +
 .../themes/mx-theme/mxtheme/footer.html       |   46 +
 .../themes/mx-theme/mxtheme/header.html       |   45 +
 .../mx-theme/mxtheme/header_search.html       |   17 +
 .../mx-theme/mxtheme/header_sourcelink.html   |   18 +
 .../themes/mx-theme/mxtheme/header_top.html   |   25 +
 .../themes/mx-theme/mxtheme/layout.html       |   52 +
 .../themes/mx-theme/mxtheme/localtoc.html     |    8 +
 .../themes/mx-theme/mxtheme/relations.html    |   20 +
 .../themes/mx-theme/mxtheme/search.html       |   56 +
 .../static/font/Roboto/Roboto-Regular.eot     |  Bin 0 -> 56856 bytes
 .../static/font/Roboto/Roboto-Regular.ttf     |  Bin 0 -> 126072 bytes
 .../static/font/Roboto/Roboto-Regular.woff    |  Bin 0 -> 65388 bytes
 .../static/font/Roboto/Roboto-Regular.woff2   |  Bin 0 -> 49248 bytes
 .../mxtheme/static/fontawesome/all.css        |    5 +
 .../themes/mx-theme/mxtheme/static/fonts.css  |  390 +++
 .../iconfont/MaterialIcons-Regular.eot        |  Bin 0 -> 143258 bytes
 .../iconfont/MaterialIcons-Regular.svg        | 2373 +++++++++++++++++
 .../iconfont/MaterialIcons-Regular.ttf        |  Bin 0 -> 128180 bytes
 .../iconfont/MaterialIcons-Regular.woff       |  Bin 0 -> 57620 bytes
 .../iconfont/MaterialIcons-Regular.woff2      |  Bin 0 -> 44300 bytes
 .../material.blue-deep_orange.min.css         |    8 +
 .../static/sphinx_materialdesign_theme.css    |    2 +
 .../sphinx_materialdesign_theme.css.map       |    1 +
 .../static/sphinx_materialdesign_theme.js     |  705 +++++
 .../static/sphinx_materialdesign_theme.js.map |    1 +
 .../themes/mx-theme/mxtheme/theme.conf        |   20 +
 docs/python_docs/themes/mx-theme/setup.py     |   19 +
 .../themes/mx-theme/src/js/adjust-height.js   |   63 +
 .../themes/mx-theme/src/js/scrollspy.js       |  111 +
 .../src/js/sphinx_materialdesign_theme.js     |  174 ++
 .../themes/mx-theme/src/scss/_root.scss       |  115 +
 .../themes/mx-theme/src/scss/_variables.scss  |   42 +
 .../src/scss/admonitions/_admonitions.scss    |  101 +
 .../src/scss/blockquote/_blockquote.scss      |   18 +
 .../themes/mx-theme/src/scss/card/_card.scss  |   48 +
 .../themes/mx-theme/src/scss/code/_code.scss  |  134 +
 .../src/scss/downloadlink/_downloadlink.scss  |   14 +
 .../mx-theme/src/scss/drawer/_drawer.scss     |   55 +
 .../src/scss/fonts/_material-icons.scss       |   29 +
 .../mx-theme/src/scss/footer/_footer.scss     |  173 ++
 .../mx-theme/src/scss/grid/_simplegrid.scss   |  207 ++
 .../mx-theme/src/scss/header/_header.scss     |  182 ++
 .../src/scss/headerings/_headerings.scss      |   92 +
 .../mx-theme/src/scss/layout/_layout.scss     |   85 +
 .../mx-theme/src/scss/lists/_lists.scss       |   19 +
 .../mx-theme/src/scss/search/_search.scss     |   33 +
 .../src/scss/sphinx_materialdesign_theme.scss |   28 +
 .../mx-theme/src/scss/tables/_tables.scss     |   30 +
 .../mx-theme/src/scss/toc/_globaltoc.scss     |   78 +
 .../mx-theme/src/scss/toc/_localtoc.scss      |   40 +
 .../mx-theme/src/scss/toc/_toctree.scss       |   38 +
 docs/static_site/.gitignore                   |    3 +
 docs/static_site/.nojekyll                    |    1 +
 docs/static_site/Makefile                     |   26 +
 docs/static_site/README.md                    |   42 +
 docs/static_site/src/.gitignore               |    1 +
 docs/static_site/src/.htaccess                |   24 +
 docs/static_site/src/.nojekyll                |    0
 docs/static_site/src/404.html                 |   24 +
 docs/static_site/src/Gemfile                  |   52 +
 docs/static_site/src/Gemfile.lock             |   84 +
 docs/static_site/src/_config.yml              |   62 +
 docs/static_site/src/_config_beta.yml         |   60 +
 docs/static_site/src/_config_prod.yml         |   59 +
 .../src/_includes/disqus_comments.html        |   20 +
 docs/static_site/src/_includes/footer.html    |   51 +
 .../src/_includes/get_started/cloud/cpu.md    |    8 +
 .../src/_includes/get_started/cloud/gpu.md    |   32 +
 .../get_started/devices/nvidia-jetson.md      |    4 +
 .../get_started/devices/raspberry_pi.md       |  254 ++
 .../_includes/get_started/get_started.html    |  474 ++++
 .../src/_includes/get_started/gpu_snippet.md  |   11 +
 .../get_started/linux/clojure/cpu.md          |   15 +
 .../get_started/linux/clojure/gpu.md          |   15 +
 .../_includes/get_started/linux/cpp/cpp.md    |    4 +
 .../_includes/get_started/linux/java/cpu.md   |   17 +
 .../_includes/get_started/linux/java/gpu.md   |   17 +
 .../linux/julia/build-from-source.md          |    2 +
 .../_includes/get_started/linux/julia/pkg.md  |   10 +
 .../_includes/get_started/linux/perl/perl.md  |    1 +
 .../linux/python/cpu/build-from-source.md     |    1 +
 .../get_started/linux/python/cpu/docker.md    |   43 +
 .../get_started/linux/python/cpu/pip.md       |  121 +
 .../linux/python/gpu/build-from-source.md     |    1 +
 .../get_started/linux/python/gpu/docker.md    |   48 +
 .../get_started/linux/python/gpu/pip.md       |   74 +
 .../src/_includes/get_started/linux/r/cpu.md  |    9 +
 .../src/_includes/get_started/linux/r/gpu.md  |   16 +
 .../_includes/get_started/linux/scala/cpu.md  |   14 +
 .../_includes/get_started/linux/scala/gpu.md  |   16 +
 .../get_started/macos/clojure/cpu.md          |   17 +
 .../get_started/macos/clojure/gpu.md          |    1 +
 .../_includes/get_started/macos/cpp/cpp.md    |    3 +
 .../_includes/get_started/macos/java/cpu.md   |   16 +
 .../_includes/get_started/macos/java/gpu.md   |    1 +
 .../macos/julia/build-from-source.md          |    1 +
 .../_includes/get_started/macos/julia/pkg.md  |   10 +
 .../_includes/get_started/macos/perl/perl.md  |    1 +
 .../macos/python/cpu/build-from-source.md     |    2 +
 .../get_started/macos/python/cpu/docker.md    |   35 +
 .../get_started/macos/python/cpu/pip.md       |   73 +
 .../macos/python/gpu/build-from-source.md     |    2 +
 .../macos/python/gpu/pip_docker.md            |    1 +
 .../src/_includes/get_started/macos/r/cpu.md  |   28 +
 .../src/_includes/get_started/macos/r/gpu.md  |    1 +
 .../_includes/get_started/macos/scala/cpu.md  |   14 +
 .../_includes/get_started/macos/scala/gpu.md  |    1 +
 .../src/_includes/get_started/pip_snippet.md  |   14 +
 .../get_started/windows/clojure/clojure.md    |    1 +
 .../_includes/get_started/windows/cpp/cpp.md  |    3 +
 .../get_started/windows/java/java.md          |    1 +
 .../windows/julia/build-from-source.md        |    1 +
 .../get_started/windows/julia/pkg.md          |   10 +
 .../get_started/windows/perl/perl.md          |    1 +
 .../windows/python/cpu/build-from-source.md   |    1 +
 .../get_started/windows/python/cpu/docker.md  |   34 +
 .../get_started/windows/python/cpu/pip.md     |   73 +
 .../windows/python/gpu/build-from-source.md   |    1 +
 .../get_started/windows/python/gpu/pip.md     |   74 +
 .../_includes/get_started/windows/r/cpu.md    |   15 +
 .../_includes/get_started/windows/r/gpu.md    |   16 +
 .../get_started/windows/scala/scala.md        |    1 +
 .../src/_includes/google-analytics.html       |   12 +
 docs/static_site/src/_includes/head.html      |   18 +
 docs/static_site/src/_includes/header.html    |   49 +
 .../src/_includes/icon-github.html            |    1 +
 .../static_site/src/_includes/icon-github.svg |    1 +
 .../src/_includes/icon-twitter.html           |    1 +
 .../src/_includes/icon-twitter.svg            |    1 +
 docs/static_site/src/_includes/social.html    |   14 +
 docs/static_site/src/_layouts/default.html    |   18 +
 docs/static_site/src/_layouts/home.html       |  177 ++
 docs/static_site/src/_layouts/page.html       |   25 +
 docs/static_site/src/_layouts/page_api.html   |   52 +
 .../src/_layouts/page_category.html           |   18 +
 .../src/_layouts/page_landing_tutorials.html  |   13 +
 docs/static_site/src/_layouts/post.html       |   29 +
 docs/static_site/src/_plugins/markdowner.rb   |   30 +
 docs/static_site/src/_sass/minima.scss        |   64 +
 docs/static_site/src/_sass/minima/_base.scss  |  278 ++
 docs/static_site/src/_sass/minima/_blog.scss  |   41 +
 docs/static_site/src/_sass/minima/_docs.scss  |   79 +
 .../src/_sass/minima/_ecosystem.scss          |    9 +
 .../src/_sass/minima/_features.scss           |   23 +
 .../src/_sass/minima/_getting_started.scss    |  178 ++
 docs/static_site/src/_sass/minima/_home.scss  |  181 ++
 .../static_site/src/_sass/minima/_layout.scss |  350 +++
 .../_sass/minima/_syntax-highlighting.scss    |  322 +++
 .../src/_sass/minima/colorful.scss            |  370 +++
 .../src/_sass/minima/simple-grid.scss         |  207 ++
 docs/static_site/src/assets/img/Github.svg    |    1 +
 docs/static_site/src/assets/img/R_logo.svg    |   14 +
 docs/static_site/src/assets/img/algorithm.svg |    1 +
 .../src/assets/img/algorithmv1.svg            |    1 +
 .../src/assets/img/apache_incubator_logo.png  |  Bin 0 -> 16552 bytes
 docs/static_site/src/assets/img/api.svg       |    1 +
 .../assets/img/artificial-intelligence.svg    |   20 +
 docs/static_site/src/assets/img/book.svg      |    1 +
 .../static_site/src/assets/img/bottom-min.jpg |  Bin 0 -> 952968 bytes
 docs/static_site/src/assets/img/chip.svg      |    1 +
 docs/static_site/src/assets/img/circuit.svg   |   66 +
 docs/static_site/src/assets/img/clipboard.svg |   47 +
 .../src/assets/img/clojure_logo.svg           |   50 +
 .../static_site/src/assets/img/coach_logo.png |  Bin 0 -> 41911 bytes
 docs/static_site/src/assets/img/compass.svg   |   47 +
 docs/static_site/src/assets/img/cpp_logo.svg  |   25 +
 docs/static_site/src/assets/img/front.jpg     |  Bin 0 -> 280416 bytes
 docs/static_site/src/assets/img/horovod.png   |  Bin 0 -> 50211 bytes
 docs/static_site/src/assets/img/java_logo.svg |   13 +
 .../static_site/src/assets/img/julia_logo.svg |   10 +
 docs/static_site/src/assets/img/keras.png     |  Bin 0 -> 13784 bytes
 .../static_site/src/assets/img/line-graph.svg |  107 +
 docs/static_site/src/assets/img/logos.png     |  Bin 0 -> 493103 bytes
 docs/static_site/src/assets/img/mxfusion.png  |  Bin 0 -> 21013 bytes
 .../img/mxnet-background-compressed.jpeg      |  Bin 0 -> 86747 bytes
 .../static_site/src/assets/img/mxnet_logo.png |  Bin 0 -> 22390 bytes
 docs/static_site/src/assets/img/mxnet_m.png   |  Bin 0 -> 3463 bytes
 docs/static_site/src/assets/img/octocat.png   |  Bin 0 -> 32590 bytes
 docs/static_site/src/assets/img/perl_logo.svg |    1 +
 .../src/assets/img/programming.svg            |   55 +
 .../src/assets/img/programmingv1.svg          |  109 +
 .../src/assets/img/python_logo.svg            |  269 ++
 .../static_site/src/assets/img/scala_logo.svg |    1 +
 docs/static_site/src/assets/img/search.svg    |    1 +
 .../src/assets/img/slack-logo-icon.svg        |   33 +
 .../src/assets/img/tensorly_logo.png          |  Bin 0 -> 100093 bytes
 docs/static_site/src/assets/img/textbook.svg  |    1 +
 docs/static_site/src/assets/img/tvm.png       |  Bin 0 -> 4808 bytes
 .../src/assets/img/video-tutorial.svg         |    2 +
 docs/static_site/src/assets/img/visual.svg    |    1 +
 docs/static_site/src/assets/img/xfer.png      |  Bin 0 -> 8932 bytes
 docs/static_site/src/assets/js/clipboard.js   |  818 ++++++
 docs/static_site/src/assets/js/copycode.js    |   97 +
 docs/static_site/src/assets/js/options.js     |  120 +
 docs/static_site/src/assets/main.scss         |    8 +
 .../src/assets/minima-social-icons.svg        |   33 +
 docs/static_site/src/index.html               |   54 +
 docs/static_site/src/pages/api/api.html       |  191 ++
 .../api/architecture/exception_handling.md    |  130 +
 .../api/architecture/note_data_loading.md     |  267 ++
 .../src/pages/api/architecture/note_engine.md |  391 +++
 .../src/pages/api/architecture/note_memory.md |  351 +++
 .../src/pages/api/architecture/overview.md    |  874 ++++++
 .../pages/api/architecture/program_model.md   |  629 +++++
 .../pages/api/clojure/docs/tutorials/index.md |   23 +
 .../api/clojure/docs/tutorials/kvstore.md     |  109 +
 .../api/clojure/docs/tutorials/module.md      |  259 ++
 .../api/clojure/docs/tutorials/ndarray.md     |  143 +
 .../api/clojure/docs/tutorials/symbol.md      |  156 ++
 .../docs/tutorials/symbol_in_pictures.md      |  101 +
 .../src/pages/api/clojure/index.md            |   47 +
 .../pages/api/cpp/docs/tutorials/basics.md    |  222 ++
 .../src/pages/api/cpp/docs/tutorials/index.md |   22 +
 .../tutorials/mxnet_cpp_inference_tutorial.md |  293 ++
 .../api/cpp/docs/tutorials/subgraphAPI.md     |  175 ++
 docs/static_site/src/pages/api/cpp/index.md   |   61 +
 .../src/pages/api/faq/add_op_in_backend.md    |  692 +++++
 docs/static_site/src/pages/api/faq/caffe.md   |  208 ++
 docs/static_site/src/pages/api/faq/cloud.md   |  208 ++
 .../src/pages/api/faq/distributed_training.md |  332 +++
 docs/static_site/src/pages/api/faq/env_var.md |  354 +++
 docs/static_site/src/pages/api/faq/float16.md |  256 ++
 .../src/pages/api/faq/gradient_compression.md |  132 +
 .../src/pages/api/faq/model_parallel_lstm.md  |   90 +
 docs/static_site/src/pages/api/faq/new_op.md  |  400 +++
 docs/static_site/src/pages/api/faq/nnpack.md  |  162 ++
 docs/static_site/src/pages/api/faq/perf.md    |  315 +++
 .../static_site/src/pages/api/faq/recordio.md |  109 +
 .../src/pages/api/faq/s3_integration.md       |  125 +
 .../static_site/src/pages/api/faq/security.md |   49 +
 .../src/pages/api/faq/smart_device.md         |  120 +
 .../src/pages/api/faq/visualize_graph.md      |   88 +
 .../src/pages/api/faq/why_mxnet.md            |  207 ++
 .../pages/api/java/docs/tutorials/index.md    |   22 +
 .../docs/tutorials/mxnet_java_on_intellij.md  |  188 ++
 .../api/java/docs/tutorials/ssd_inference.md  |  210 ++
 docs/static_site/src/pages/api/java/index.md  |   41 +
 docs/static_site/src/pages/api/julia/index.md |   39 +
 .../pages/api/perl/docs/tutorials/index.md    |   23 +
 .../src/pages/api/perl/docs/tutorials/io.md   |  136 +
 .../pages/api/perl/docs/tutorials/kvstore.md  |  134 +
 .../pages/api/perl/docs/tutorials/module.md   |   70 +
 .../pages/api/perl/docs/tutorials/ndarray.md  |   66 +
 .../pages/api/perl/docs/tutorials/symbol.md   |  151 ++
 docs/static_site/src/pages/api/perl/index.md  |   91 +
 .../static_site/src/pages/api/python/index.md |   44 +
 .../api/r/docs/tutorials/callback_function.md |  278 ++
 .../api/r/docs/tutorials/char_rnn_model.md    |  317 +++
 ...assify_real_image_with_pretrained_model.md |  208 ++
 .../api/r/docs/tutorials/custom_iterator.md   |  227 ++
 .../r/docs/tutorials/custom_loss_function.md  |  231 ++
 .../tutorials/five_minutes_neural_network.md  |  341 +++
 .../src/pages/api/r/docs/tutorials/index.md   |   23 +
 .../api/r/docs/tutorials/mnist_competition.md |  363 +++
 .../api/r/docs/tutorials/multi_dim_lstm.md    |  327 +++
 .../src/pages/api/r/docs/tutorials/ndarray.md |  231 ++
 .../src/pages/api/r/docs/tutorials/symbol.md  |  154 ++
 docs/static_site/src/pages/api/r/index.md     |   52 +
 .../api/scala/docs/tutorials/char_lstm.md     |  533 ++++
 .../pages/api/scala/docs/tutorials/index.md   |   22 +
 .../pages/api/scala/docs/tutorials/infer.md   |   66 +
 .../src/pages/api/scala/docs/tutorials/io.md  |  189 ++
 .../pages/api/scala/docs/tutorials/kvstore.md |  129 +
 .../pages/api/scala/docs/tutorials/mnist.md   |  141 +
 .../pages/api/scala/docs/tutorials/model.md   |  142 +
 .../pages/api/scala/docs/tutorials/module.md  |  162 ++
 .../docs/tutorials/mxnet_scala_on_intellij.md |  456 ++++
 .../pages/api/scala/docs/tutorials/ndarray.md |  181 ++
 .../pages/api/scala/docs/tutorials/symbol.md  |  155 ++
 .../docs/tutorials/symbol_in_pictures.md      |  100 +
 docs/static_site/src/pages/api/scala/index.md |   59 +
 docs/static_site/src/pages/blog.html          |   38 +
 .../src/pages/community/contribute.md         |  192 ++
 docs/static_site/src/pages/ecosystem.html     |  133 +
 docs/static_site/src/pages/features.html      |  106 +
 .../pages/get_started/build_from_source.md    |  329 +++
 .../src/pages/get_started/c_plus_plus.md      |   55 +
 .../src/pages/get_started/centos_setup.md     |  115 +
 .../src/pages/get_started/download.md         |   83 +
 .../src/pages/get_started/index.html          |   33 +
 .../src/pages/get_started/java_setup.md       |  135 +
 .../src/pages/get_started/jetson_setup.md     |  243 ++
 .../src/pages/get_started/osx_setup.md        |  275 ++
 .../src/pages/get_started/scala_setup.md      |  165 ++
 .../src/pages/get_started/ubuntu_setup.md     |  561 ++++
 .../src/pages/get_started/validate_mxnet.md   |  226 ++
 .../src/pages/get_started/windows_setup.md    |  490 ++++
 .../apache_rat_license_check/rat-excludes     |   29 +-
 tools/license_header.py                       |    7 +
 484 files changed, 51184 insertions(+), 3 deletions(-)
 create mode 100644 docs/cpp_docs/Doxyfile
 create mode 100644 docs/cpp_docs/Makefile
 create mode 100644 docs/python_docs/README.md
 create mode 100644 docs/python_docs/_static/apache_incubator_logo.png
 create mode 100644 docs/python_docs/_static/google_analytics.js
 create mode 100644 docs/python_docs/_static/minima-social-icons.svg
 create mode 100644 docs/python_docs/_static/mxnet-icon.png
 create mode 100644 docs/python_docs/_static/mxnet.css
 create mode 100644 docs/python_docs/_static/mxnet_logo.png
 create mode 100644 docs/python_docs/environment.yml
 create mode 100644 docs/python_docs/python/.gitignore
 create mode 100644 docs/python_docs/python/Makefile
 create mode 100644 docs/python_docs/python/Makefile_sphinx
 create mode 100644 docs/python_docs/python/README.md
 create mode 100644 docs/python_docs/python/api/advanced/index.rst
 create mode 100644 docs/python_docs/python/api/advanced/mxnet.engine.rst
 create mode 100644 docs/python_docs/python/api/advanced/mxnet.executor.rst
 create mode 100644 docs/python_docs/python/api/advanced/mxnet.executor_manager.rst
 create mode 100644 docs/python_docs/python/api/advanced/mxnet.kvstore_server.rst
 create mode 100644 docs/python_docs/python/api/advanced/mxnet.rtc.rst
 create mode 100644 docs/python_docs/python/api/advanced/mxnet.test_utils.rst
 create mode 100644 docs/python_docs/python/api/advanced/mxnet.util.rst
 create mode 100644 docs/python_docs/python/api/gluon-related/index.rst
 create mode 100644 docs/python_docs/python/api/gluon-related/mxnet.autograd.rst
 create mode 100644 docs/python_docs/python/api/gluon-related/mxnet.context.rst
 create mode 100644 docs/python_docs/python/api/gluon-related/mxnet.image.rst
 create mode 100644 docs/python_docs/python/api/gluon-related/mxnet.initializer.rst
 create mode 100644 docs/python_docs/python/api/gluon-related/mxnet.io.rst
 create mode 100644 docs/python_docs/python/api/gluon-related/mxnet.kvstore.KVStore.rst
 create mode 100644 docs/python_docs/python/api/gluon-related/mxnet.kvstore.create.rst
 create mode 100644 docs/python_docs/python/api/gluon-related/mxnet.kvstore.rst
 create mode 100644 docs/python_docs/python/api/gluon-related/mxnet.lr_scheduler.rst
 create mode 100644 docs/python_docs/python/api/gluon-related/mxnet.metric.rst
 create mode 100644 docs/python_docs/python/api/gluon-related/mxnet.optimizer.rst
 create mode 100644 docs/python_docs/python/api/gluon-related/mxnet.profiler.rst
 create mode 100644 docs/python_docs/python/api/gluon-related/mxnet.random.rst
 create mode 100644 docs/python_docs/python/api/gluon-related/mxnet.recordio.rst
 create mode 100644 docs/python_docs/python/api/gluon/index.rst
 create mode 100644 docs/python_docs/python/api/gluon/mxnet.gluon.Constant.rst
 create mode 100644 docs/python_docs/python/api/gluon/mxnet.gluon.HybridBlock.rst
 create mode 100644 docs/python_docs/python/api/gluon/mxnet.gluon.ParameterDict.rst
 create mode 100644 docs/python_docs/python/api/gluon/mxnet.gluon.SymbolBlock.rst
 create mode 100644 docs/python_docs/python/api/gluon/mxnet.gluon.Trainer.rst
 create mode 100644 docs/python_docs/python/api/gluon/mxnet.gluon.contrib.rst
 create mode 100644 docs/python_docs/python/api/gluon/mxnet.gluon.data.rst
 create mode 100644 docs/python_docs/python/api/gluon/mxnet.gluon.data.vision.rst
 create mode 100644 docs/python_docs/python/api/gluon/mxnet.gluon.loss.rst
 create mode 100644 docs/python_docs/python/api/gluon/mxnet.gluon.model_zoo.rst
 create mode 100644 docs/python_docs/python/api/gluon/mxnet.gluon.nn.Block.rst
 create mode 100644 docs/python_docs/python/api/gluon/mxnet.gluon.nn.HybridBlock.rst
 create mode 100644 docs/python_docs/python/api/gluon/mxnet.gluon.nn.SymbolBlock.rst
 create mode 100644 docs/python_docs/python/api/gluon/mxnet.gluon.parameter.rst
 create mode 100644 docs/python_docs/python/api/gluon/mxnet.gluon.utils.rst
 create mode 100644 docs/python_docs/python/api/gluon/nn.rst
 create mode 100644 docs/python_docs/python/api/gluon/rnn.rst
 create mode 100644 docs/python_docs/python/api/index.rst
 create mode 100644 docs/python_docs/python/api/ndarray/index.rst
 create mode 100644 docs/python_docs/python/api/ndarray/mxnet.ndarray.NDArray.rst
 create mode 100644 docs/python_docs/python/api/ndarray/mxnet.ndarray.sparse.CSRNDArray.rst
 create mode 100644 docs/python_docs/python/api/ndarray/mxnet.ndarray.sparse.RowSparseNDArray.rst
 create mode 100644 docs/python_docs/python/api/ndarray/routines.rst
 create mode 100644 docs/python_docs/python/api/ndarray/sparse_routines.rst
 create mode 100644 docs/python_docs/python/api/symbol-related/index.rst
 create mode 100644 docs/python_docs/python/api/symbol-related/mxnet.callback.rst
 create mode 100644 docs/python_docs/python/api/symbol-related/mxnet.model.rst
 create mode 100644 docs/python_docs/python/api/symbol-related/mxnet.module.rst
 create mode 100644 docs/python_docs/python/api/symbol-related/mxnet.monitor.rst
 create mode 100644 docs/python_docs/python/api/symbol-related/mxnet.visualization.rst
 create mode 100644 docs/python_docs/python/api/symbol/index.rst
 create mode 100644 docs/python_docs/python/api/symbol/mxnet.symbol.Symbol.rst
 create mode 100644 docs/python_docs/python/api/symbol/mxnet.symbol.linalg.rst
 create mode 100644 docs/python_docs/python/index.rst
 create mode 100644 docs/python_docs/python/scripts/conf.py
 create mode 100644 docs/python_docs/python/scripts/md2ipynb.py
 create mode 100644 docs/python_docs/python/scripts/process_rst.py
 create mode 100644 docs/python_docs/python/tutorials/deploy/export/export.rst
 create mode 100644 docs/python_docs/python/tutorials/deploy/export/index.rst
 create mode 100644 docs/python_docs/python/tutorials/deploy/index.rst
 create mode 100644 docs/python_docs/python/tutorials/deploy/inference/cpp.rst
 create mode 100644 docs/python_docs/python/tutorials/deploy/inference/index.rst
 create mode 100644 docs/python_docs/python/tutorials/deploy/inference/scala.rst
 create mode 100644 docs/python_docs/python/tutorials/deploy/run-on-aws/ami.png
 create mode 100644 docs/python_docs/python/tutorials/deploy/run-on-aws/aws.png
 create mode 100644 docs/python_docs/python/tutorials/deploy/run-on-aws/cloud.rst
 create mode 100644 docs/python_docs/python/tutorials/deploy/run-on-aws/connect.png
 create mode 100644 docs/python_docs/python/tutorials/deploy/run-on-aws/disk.png
 create mode 100644 docs/python_docs/python/tutorials/deploy/run-on-aws/ec2.png
 create mode 100644 docs/python_docs/python/tutorials/deploy/run-on-aws/index.rst
 create mode 100644 docs/python_docs/python/tutorials/deploy/run-on-aws/keypair.png
 create mode 100644 docs/python_docs/python/tutorials/deploy/run-on-aws/launching.png
 create mode 100644 docs/python_docs/python/tutorials/deploy/run-on-aws/limits.png
 create mode 100644 docs/python_docs/python/tutorials/deploy/run-on-aws/login_screen.png
 create mode 100644 docs/python_docs/python/tutorials/deploy/run-on-aws/mxnet.png
 create mode 100644 docs/python_docs/python/tutorials/deploy/run-on-aws/os.png
 create mode 100644 docs/python_docs/python/tutorials/deploy/run-on-aws/p2x.png
 create mode 100644 docs/python_docs/python/tutorials/deploy/run-on-aws/sagemaker.png
 create mode 100644 docs/python_docs/python/tutorials/deploy/run-on-aws/ssh.png
 create mode 100644 docs/python_docs/python/tutorials/deploy/run-on-aws/use_ec2.rst
 create mode 100644 docs/python_docs/python/tutorials/deploy/run-on-aws/use_sagemaker.rst
 create mode 100644 docs/python_docs/python/tutorials/extend/custom_layer.md
 create mode 100644 docs/python_docs/python/tutorials/extend/customop.md
 create mode 100644 docs/python_docs/python/tutorials/extend/index.rst
 create mode 100644 docs/python_docs/python/tutorials/getting-started/crash-course/1-ndarray.md
 create mode 100644 docs/python_docs/python/tutorials/getting-started/crash-course/2-nn.md
 create mode 100644 docs/python_docs/python/tutorials/getting-started/crash-course/3-autograd.md
 create mode 100644 docs/python_docs/python/tutorials/getting-started/crash-course/4-train.md
 create mode 100644 docs/python_docs/python/tutorials/getting-started/crash-course/5-predict.md
 create mode 100644 docs/python_docs/python/tutorials/getting-started/crash-course/6-use_gpus.md
 create mode 100644 docs/python_docs/python/tutorials/getting-started/crash-course/index.rst
 create mode 100644 docs/python_docs/python/tutorials/getting-started/index.rst
 create mode 100644 docs/python_docs/python/tutorials/getting-started/to-mxnet/index.rst
 create mode 100644 docs/python_docs/python/tutorials/getting-started/to-mxnet/pytorch.md
 create mode 100644 docs/python_docs/python/tutorials/index.rst
 create mode 100644 docs/python_docs/python/tutorials/packages/autograd/autograd.md
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/activations/activations.md
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/activations/images/elu.png
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/activations/images/leakyrelu.png
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/activations/images/prelu.png
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/activations/images/relu.png
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/activations/images/selu.png
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/activations/images/sigmoid.png
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/activations/images/softrelu.png
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/activations/images/softsign.png
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/activations/images/swish.png
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/activations/images/tanh.png
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/blocks.svg
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/custom-layer.md
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/custom-loss/custom-loss.md
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/custom-loss/images/contrastive_loss.jpeg
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/custom-loss/images/inuktitut_1.png
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/custom-loss/images/inuktitut_2.png
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/custom_layer_beginners.md
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/customop.md
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/data.md
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/fit_api_tutorial.md
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/hybridize.md
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/image-augmentation.md
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/image/index.rst
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/image/mnist.md
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/image/pretrained_models.md
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/index.rst
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/init.md
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/loss/ctc_loss.png
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/loss/kl_divergence.md
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/loss/loss.md
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/loss/triplet_loss.png
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/naming.md
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/nn.md
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/normalization/imgs/NCHW_BN.png
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/normalization/imgs/NCHW_IN.png
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/normalization/imgs/NCHW_LN.png
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/normalization/imgs/NTC_BN.png
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/normalization/imgs/NTC_IN.png
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/normalization/imgs/NTC_LN.png
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/normalization/imgs/data_normalization.jpeg
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/normalization/normalization.md
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/parameters.md
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/text/gnmt.rst
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/text/index.rst
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/text/transformer.rst
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/trainer.md
 create mode 100644 docs/python_docs/python/tutorials/packages/index.rst
 create mode 100644 docs/python_docs/python/tutorials/packages/kvstore/index.rst
 create mode 100644 docs/python_docs/python/tutorials/packages/module/index.rst
 create mode 100644 docs/python_docs/python/tutorials/packages/ndarray/01-ndarray-intro.md
 create mode 100644 docs/python_docs/python/tutorials/packages/ndarray/02-ndarray-operations.md
 create mode 100644 docs/python_docs/python/tutorials/packages/ndarray/03-ndarray-contexts.md
 create mode 100644 docs/python_docs/python/tutorials/packages/ndarray/index.rst
 create mode 100644 docs/python_docs/python/tutorials/packages/onnx/index.rst
 create mode 100644 docs/python_docs/python/tutorials/packages/optimizer/images/momentum_sgd_animation.gif
 create mode 100644 docs/python_docs/python/tutorials/packages/optimizer/images/nesterov_momentum_animation.gif
 create mode 100644 docs/python_docs/python/tutorials/packages/optimizer/images/sgd_animation.gif
 create mode 100644 docs/python_docs/python/tutorials/packages/optimizer/optimizer.md
 create mode 100644 docs/python_docs/python/tutorials/packages/symbol/index.rst
 create mode 100644 docs/python_docs/python/tutorials/packages/viz/index.rst
 create mode 100644 docs/python_docs/python/tutorials/performance/backend/MKLDNN_README.md
 create mode 100644 docs/python_docs/python/tutorials/performance/backend/index.rst
 create mode 100644 docs/python_docs/python/tutorials/performance/backend/mkl-dnn.rst
 create mode 100644 docs/python_docs/python/tutorials/performance/backend/mkldnn_quantization.md
 create mode 100644 docs/python_docs/python/tutorials/performance/backend/profiler.md
 create mode 100644 docs/python_docs/python/tutorials/performance/backend/tvm.rst
 create mode 100644 docs/python_docs/python/tutorials/performance/compression/index.rst
 create mode 100644 docs/python_docs/python/tutorials/performance/compression/int8.rst
 create mode 100644 docs/python_docs/python/tutorials/performance/index.rst
 create mode 100644 docs/python_docs/themes/.babelrc
 create mode 100644 docs/python_docs/themes/.circleci/config.yml
 create mode 100644 docs/python_docs/themes/.gitignore
 create mode 100644 docs/python_docs/themes/.sassrc
 create mode 100644 docs/python_docs/themes/mx-theme/LICENSE
 create mode 100644 docs/python_docs/themes/mx-theme/MANIFEST.in
 create mode 100644 docs/python_docs/themes/mx-theme/README.md
 create mode 100644 docs/python_docs/themes/mx-theme/mxtheme/__init__.py
 create mode 100644 docs/python_docs/themes/mx-theme/mxtheme/card.py
 create mode 100644 docs/python_docs/themes/mx-theme/mxtheme/drawer.html
 create mode 100644 docs/python_docs/themes/mx-theme/mxtheme/footer.html
 create mode 100644 docs/python_docs/themes/mx-theme/mxtheme/header.html
 create mode 100644 docs/python_docs/themes/mx-theme/mxtheme/header_search.html
 create mode 100644 docs/python_docs/themes/mx-theme/mxtheme/header_sourcelink.html
 create mode 100644 docs/python_docs/themes/mx-theme/mxtheme/header_top.html
 create mode 100644 docs/python_docs/themes/mx-theme/mxtheme/layout.html
 create mode 100644 docs/python_docs/themes/mx-theme/mxtheme/localtoc.html
 create mode 100644 docs/python_docs/themes/mx-theme/mxtheme/relations.html
 create mode 100644 docs/python_docs/themes/mx-theme/mxtheme/search.html
 create mode 100644 docs/python_docs/themes/mx-theme/mxtheme/static/font/Roboto/Roboto-Regular.eot
 create mode 100644 docs/python_docs/themes/mx-theme/mxtheme/static/font/Roboto/Roboto-Regular.ttf
 create mode 100644 docs/python_docs/themes/mx-theme/mxtheme/static/font/Roboto/Roboto-Regular.woff
 create mode 100644 docs/python_docs/themes/mx-theme/mxtheme/static/font/Roboto/Roboto-Regular.woff2
 create mode 100644 docs/python_docs/themes/mx-theme/mxtheme/static/fontawesome/all.css
 create mode 100644 docs/python_docs/themes/mx-theme/mxtheme/static/fonts.css
 create mode 100644 docs/python_docs/themes/mx-theme/mxtheme/static/material-design-icons-3.0.1/iconfont/MaterialIcons-Regular.eot
 create mode 100644 docs/python_docs/themes/mx-theme/mxtheme/static/material-design-icons-3.0.1/iconfont/MaterialIcons-Regular.svg
 create mode 100644 docs/python_docs/themes/mx-theme/mxtheme/static/material-design-icons-3.0.1/iconfont/MaterialIcons-Regular.ttf
 create mode 100644 docs/python_docs/themes/mx-theme/mxtheme/static/material-design-icons-3.0.1/iconfont/MaterialIcons-Regular.woff
 create mode 100644 docs/python_docs/themes/mx-theme/mxtheme/static/material-design-icons-3.0.1/iconfont/MaterialIcons-Regular.woff2
 create mode 100644 docs/python_docs/themes/mx-theme/mxtheme/static/material-design-lite-1.3.0/material.blue-deep_orange.min.css
 create mode 100644 docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.css
 create mode 100644 docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.css.map
 create mode 100644 docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.js
 create mode 100644 docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.js.map
 create mode 100644 docs/python_docs/themes/mx-theme/mxtheme/theme.conf
 create mode 100644 docs/python_docs/themes/mx-theme/setup.py
 create mode 100644 docs/python_docs/themes/mx-theme/src/js/adjust-height.js
 create mode 100644 docs/python_docs/themes/mx-theme/src/js/scrollspy.js
 create mode 100644 docs/python_docs/themes/mx-theme/src/js/sphinx_materialdesign_theme.js
 create mode 100644 docs/python_docs/themes/mx-theme/src/scss/_root.scss
 create mode 100644 docs/python_docs/themes/mx-theme/src/scss/_variables.scss
 create mode 100644 docs/python_docs/themes/mx-theme/src/scss/admonitions/_admonitions.scss
 create mode 100644 docs/python_docs/themes/mx-theme/src/scss/blockquote/_blockquote.scss
 create mode 100644 docs/python_docs/themes/mx-theme/src/scss/card/_card.scss
 create mode 100644 docs/python_docs/themes/mx-theme/src/scss/code/_code.scss
 create mode 100644 docs/python_docs/themes/mx-theme/src/scss/downloadlink/_downloadlink.scss
 create mode 100644 docs/python_docs/themes/mx-theme/src/scss/drawer/_drawer.scss
 create mode 100644 docs/python_docs/themes/mx-theme/src/scss/fonts/_material-icons.scss
 create mode 100644 docs/python_docs/themes/mx-theme/src/scss/footer/_footer.scss
 create mode 100644 docs/python_docs/themes/mx-theme/src/scss/grid/_simplegrid.scss
 create mode 100644 docs/python_docs/themes/mx-theme/src/scss/header/_header.scss
 create mode 100644 docs/python_docs/themes/mx-theme/src/scss/headerings/_headerings.scss
 create mode 100644 docs/python_docs/themes/mx-theme/src/scss/layout/_layout.scss
 create mode 100644 docs/python_docs/themes/mx-theme/src/scss/lists/_lists.scss
 create mode 100644 docs/python_docs/themes/mx-theme/src/scss/search/_search.scss
 create mode 100644 docs/python_docs/themes/mx-theme/src/scss/sphinx_materialdesign_theme.scss
 create mode 100644 docs/python_docs/themes/mx-theme/src/scss/tables/_tables.scss
 create mode 100644 docs/python_docs/themes/mx-theme/src/scss/toc/_globaltoc.scss
 create mode 100644 docs/python_docs/themes/mx-theme/src/scss/toc/_localtoc.scss
 create mode 100644 docs/python_docs/themes/mx-theme/src/scss/toc/_toctree.scss
 create mode 100644 docs/static_site/.gitignore
 create mode 100644 docs/static_site/.nojekyll
 create mode 100644 docs/static_site/Makefile
 create mode 100644 docs/static_site/README.md
 create mode 100644 docs/static_site/src/.gitignore
 create mode 100644 docs/static_site/src/.htaccess
 create mode 100644 docs/static_site/src/.nojekyll
 create mode 100644 docs/static_site/src/404.html
 create mode 100644 docs/static_site/src/Gemfile
 create mode 100644 docs/static_site/src/Gemfile.lock
 create mode 100644 docs/static_site/src/_config.yml
 create mode 100644 docs/static_site/src/_config_beta.yml
 create mode 100644 docs/static_site/src/_config_prod.yml
 create mode 100644 docs/static_site/src/_includes/disqus_comments.html
 create mode 100644 docs/static_site/src/_includes/footer.html
 create mode 100644 docs/static_site/src/_includes/get_started/cloud/cpu.md
 create mode 100644 docs/static_site/src/_includes/get_started/cloud/gpu.md
 create mode 100644 docs/static_site/src/_includes/get_started/devices/nvidia-jetson.md
 create mode 100644 docs/static_site/src/_includes/get_started/devices/raspberry_pi.md
 create mode 100644 docs/static_site/src/_includes/get_started/get_started.html
 create mode 100644 docs/static_site/src/_includes/get_started/gpu_snippet.md
 create mode 100644 docs/static_site/src/_includes/get_started/linux/clojure/cpu.md
 create mode 100644 docs/static_site/src/_includes/get_started/linux/clojure/gpu.md
 create mode 100644 docs/static_site/src/_includes/get_started/linux/cpp/cpp.md
 create mode 100644 docs/static_site/src/_includes/get_started/linux/java/cpu.md
 create mode 100644 docs/static_site/src/_includes/get_started/linux/java/gpu.md
 create mode 100644 docs/static_site/src/_includes/get_started/linux/julia/build-from-source.md
 create mode 100644 docs/static_site/src/_includes/get_started/linux/julia/pkg.md
 create mode 100644 docs/static_site/src/_includes/get_started/linux/perl/perl.md
 create mode 100644 docs/static_site/src/_includes/get_started/linux/python/cpu/build-from-source.md
 create mode 100644 docs/static_site/src/_includes/get_started/linux/python/cpu/docker.md
 create mode 100644 docs/static_site/src/_includes/get_started/linux/python/cpu/pip.md
 create mode 100644 docs/static_site/src/_includes/get_started/linux/python/gpu/build-from-source.md
 create mode 100644 docs/static_site/src/_includes/get_started/linux/python/gpu/docker.md
 create mode 100644 docs/static_site/src/_includes/get_started/linux/python/gpu/pip.md
 create mode 100644 docs/static_site/src/_includes/get_started/linux/r/cpu.md
 create mode 100644 docs/static_site/src/_includes/get_started/linux/r/gpu.md
 create mode 100644 docs/static_site/src/_includes/get_started/linux/scala/cpu.md
 create mode 100644 docs/static_site/src/_includes/get_started/linux/scala/gpu.md
 create mode 100644 docs/static_site/src/_includes/get_started/macos/clojure/cpu.md
 create mode 100644 docs/static_site/src/_includes/get_started/macos/clojure/gpu.md
 create mode 100644 docs/static_site/src/_includes/get_started/macos/cpp/cpp.md
 create mode 100644 docs/static_site/src/_includes/get_started/macos/java/cpu.md
 create mode 100644 docs/static_site/src/_includes/get_started/macos/java/gpu.md
 create mode 100644 docs/static_site/src/_includes/get_started/macos/julia/build-from-source.md
 create mode 100644 docs/static_site/src/_includes/get_started/macos/julia/pkg.md
 create mode 100644 docs/static_site/src/_includes/get_started/macos/perl/perl.md
 create mode 100644 docs/static_site/src/_includes/get_started/macos/python/cpu/build-from-source.md
 create mode 100644 docs/static_site/src/_includes/get_started/macos/python/cpu/docker.md
 create mode 100644 docs/static_site/src/_includes/get_started/macos/python/cpu/pip.md
 create mode 100644 docs/static_site/src/_includes/get_started/macos/python/gpu/build-from-source.md
 create mode 100644 docs/static_site/src/_includes/get_started/macos/python/gpu/pip_docker.md
 create mode 100644 docs/static_site/src/_includes/get_started/macos/r/cpu.md
 create mode 100644 docs/static_site/src/_includes/get_started/macos/r/gpu.md
 create mode 100644 docs/static_site/src/_includes/get_started/macos/scala/cpu.md
 create mode 100644 docs/static_site/src/_includes/get_started/macos/scala/gpu.md
 create mode 100644 docs/static_site/src/_includes/get_started/pip_snippet.md
 create mode 100644 docs/static_site/src/_includes/get_started/windows/clojure/clojure.md
 create mode 100644 docs/static_site/src/_includes/get_started/windows/cpp/cpp.md
 create mode 100644 docs/static_site/src/_includes/get_started/windows/java/java.md
 create mode 100644 docs/static_site/src/_includes/get_started/windows/julia/build-from-source.md
 create mode 100644 docs/static_site/src/_includes/get_started/windows/julia/pkg.md
 create mode 100644 docs/static_site/src/_includes/get_started/windows/perl/perl.md
 create mode 100644 docs/static_site/src/_includes/get_started/windows/python/cpu/build-from-source.md
 create mode 100644 docs/static_site/src/_includes/get_started/windows/python/cpu/docker.md
 create mode 100644 docs/static_site/src/_includes/get_started/windows/python/cpu/pip.md
 create mode 100644 docs/static_site/src/_includes/get_started/windows/python/gpu/build-from-source.md
 create mode 100644 docs/static_site/src/_includes/get_started/windows/python/gpu/pip.md
 create mode 100644 docs/static_site/src/_includes/get_started/windows/r/cpu.md
 create mode 100644 docs/static_site/src/_includes/get_started/windows/r/gpu.md
 create mode 100644 docs/static_site/src/_includes/get_started/windows/scala/scala.md
 create mode 100644 docs/static_site/src/_includes/google-analytics.html
 create mode 100644 docs/static_site/src/_includes/head.html
 create mode 100644 docs/static_site/src/_includes/header.html
 create mode 100644 docs/static_site/src/_includes/icon-github.html
 create mode 100644 docs/static_site/src/_includes/icon-github.svg
 create mode 100644 docs/static_site/src/_includes/icon-twitter.html
 create mode 100644 docs/static_site/src/_includes/icon-twitter.svg
 create mode 100644 docs/static_site/src/_includes/social.html
 create mode 100644 docs/static_site/src/_layouts/default.html
 create mode 100644 docs/static_site/src/_layouts/home.html
 create mode 100644 docs/static_site/src/_layouts/page.html
 create mode 100644 docs/static_site/src/_layouts/page_api.html
 create mode 100644 docs/static_site/src/_layouts/page_category.html
 create mode 100644 docs/static_site/src/_layouts/page_landing_tutorials.html
 create mode 100644 docs/static_site/src/_layouts/post.html
 create mode 100644 docs/static_site/src/_plugins/markdowner.rb
 create mode 100644 docs/static_site/src/_sass/minima.scss
 create mode 100644 docs/static_site/src/_sass/minima/_base.scss
 create mode 100644 docs/static_site/src/_sass/minima/_blog.scss
 create mode 100644 docs/static_site/src/_sass/minima/_docs.scss
 create mode 100644 docs/static_site/src/_sass/minima/_ecosystem.scss
 create mode 100644 docs/static_site/src/_sass/minima/_features.scss
 create mode 100644 docs/static_site/src/_sass/minima/_getting_started.scss
 create mode 100644 docs/static_site/src/_sass/minima/_home.scss
 create mode 100644 docs/static_site/src/_sass/minima/_layout.scss
 create mode 100644 docs/static_site/src/_sass/minima/_syntax-highlighting.scss
 create mode 100644 docs/static_site/src/_sass/minima/colorful.scss
 create mode 100644 docs/static_site/src/_sass/minima/simple-grid.scss
 create mode 100644 docs/static_site/src/assets/img/Github.svg
 create mode 100644 docs/static_site/src/assets/img/R_logo.svg
 create mode 100644 docs/static_site/src/assets/img/algorithm.svg
 create mode 100644 docs/static_site/src/assets/img/algorithmv1.svg
 create mode 100644 docs/static_site/src/assets/img/apache_incubator_logo.png
 create mode 100644 docs/static_site/src/assets/img/api.svg
 create mode 100644 docs/static_site/src/assets/img/artificial-intelligence.svg
 create mode 100644 docs/static_site/src/assets/img/book.svg
 create mode 100644 docs/static_site/src/assets/img/bottom-min.jpg
 create mode 100644 docs/static_site/src/assets/img/chip.svg
 create mode 100644 docs/static_site/src/assets/img/circuit.svg
 create mode 100644 docs/static_site/src/assets/img/clipboard.svg
 create mode 100644 docs/static_site/src/assets/img/clojure_logo.svg
 create mode 100644 docs/static_site/src/assets/img/coach_logo.png
 create mode 100644 docs/static_site/src/assets/img/compass.svg
 create mode 100644 docs/static_site/src/assets/img/cpp_logo.svg
 create mode 100644 docs/static_site/src/assets/img/front.jpg
 create mode 100644 docs/static_site/src/assets/img/horovod.png
 create mode 100755 docs/static_site/src/assets/img/java_logo.svg
 create mode 100644 docs/static_site/src/assets/img/julia_logo.svg
 create mode 100644 docs/static_site/src/assets/img/keras.png
 create mode 100644 docs/static_site/src/assets/img/line-graph.svg
 create mode 100644 docs/static_site/src/assets/img/logos.png
 create mode 100644 docs/static_site/src/assets/img/mxfusion.png
 create mode 100644 docs/static_site/src/assets/img/mxnet-background-compressed.jpeg
 create mode 100644 docs/static_site/src/assets/img/mxnet_logo.png
 create mode 100644 docs/static_site/src/assets/img/mxnet_m.png
 create mode 100644 docs/static_site/src/assets/img/octocat.png
 create mode 100644 docs/static_site/src/assets/img/perl_logo.svg
 create mode 100644 docs/static_site/src/assets/img/programming.svg
 create mode 100644 docs/static_site/src/assets/img/programmingv1.svg
 create mode 100644 docs/static_site/src/assets/img/python_logo.svg
 create mode 100644 docs/static_site/src/assets/img/scala_logo.svg
 create mode 100644 docs/static_site/src/assets/img/search.svg
 create mode 100755 docs/static_site/src/assets/img/slack-logo-icon.svg
 create mode 100644 docs/static_site/src/assets/img/tensorly_logo.png
 create mode 100644 docs/static_site/src/assets/img/textbook.svg
 create mode 100644 docs/static_site/src/assets/img/tvm.png
 create mode 100644 docs/static_site/src/assets/img/video-tutorial.svg
 create mode 100644 docs/static_site/src/assets/img/visual.svg
 create mode 100644 docs/static_site/src/assets/img/xfer.png
 create mode 100644 docs/static_site/src/assets/js/clipboard.js
 create mode 100644 docs/static_site/src/assets/js/copycode.js
 create mode 100644 docs/static_site/src/assets/js/options.js
 create mode 100644 docs/static_site/src/assets/main.scss
 create mode 100644 docs/static_site/src/assets/minima-social-icons.svg
 create mode 100644 docs/static_site/src/index.html
 create mode 100644 docs/static_site/src/pages/api/api.html
 create mode 100644 docs/static_site/src/pages/api/architecture/exception_handling.md
 create mode 100644 docs/static_site/src/pages/api/architecture/note_data_loading.md
 create mode 100644 docs/static_site/src/pages/api/architecture/note_engine.md
 create mode 100644 docs/static_site/src/pages/api/architecture/note_memory.md
 create mode 100644 docs/static_site/src/pages/api/architecture/overview.md
 create mode 100644 docs/static_site/src/pages/api/architecture/program_model.md
 create mode 100644 docs/static_site/src/pages/api/clojure/docs/tutorials/index.md
 create mode 100644 docs/static_site/src/pages/api/clojure/docs/tutorials/kvstore.md
 create mode 100644 docs/static_site/src/pages/api/clojure/docs/tutorials/module.md
 create mode 100644 docs/static_site/src/pages/api/clojure/docs/tutorials/ndarray.md
 create mode 100644 docs/static_site/src/pages/api/clojure/docs/tutorials/symbol.md
 create mode 100644 docs/static_site/src/pages/api/clojure/docs/tutorials/symbol_in_pictures.md
 create mode 100644 docs/static_site/src/pages/api/clojure/index.md
 create mode 100644 docs/static_site/src/pages/api/cpp/docs/tutorials/basics.md
 create mode 100644 docs/static_site/src/pages/api/cpp/docs/tutorials/index.md
 create mode 100644 docs/static_site/src/pages/api/cpp/docs/tutorials/mxnet_cpp_inference_tutorial.md
 create mode 100644 docs/static_site/src/pages/api/cpp/docs/tutorials/subgraphAPI.md
 create mode 100644 docs/static_site/src/pages/api/cpp/index.md
 create mode 100644 docs/static_site/src/pages/api/faq/add_op_in_backend.md
 create mode 100644 docs/static_site/src/pages/api/faq/caffe.md
 create mode 100644 docs/static_site/src/pages/api/faq/cloud.md
 create mode 100644 docs/static_site/src/pages/api/faq/distributed_training.md
 create mode 100644 docs/static_site/src/pages/api/faq/env_var.md
 create mode 100644 docs/static_site/src/pages/api/faq/float16.md
 create mode 100644 docs/static_site/src/pages/api/faq/gradient_compression.md
 create mode 100644 docs/static_site/src/pages/api/faq/model_parallel_lstm.md
 create mode 100644 docs/static_site/src/pages/api/faq/new_op.md
 create mode 100644 docs/static_site/src/pages/api/faq/nnpack.md
 create mode 100644 docs/static_site/src/pages/api/faq/perf.md
 create mode 100644 docs/static_site/src/pages/api/faq/recordio.md
 create mode 100644 docs/static_site/src/pages/api/faq/s3_integration.md
 create mode 100644 docs/static_site/src/pages/api/faq/security.md
 create mode 100644 docs/static_site/src/pages/api/faq/smart_device.md
 create mode 100644 docs/static_site/src/pages/api/faq/visualize_graph.md
 create mode 100644 docs/static_site/src/pages/api/faq/why_mxnet.md
 create mode 100644 docs/static_site/src/pages/api/java/docs/tutorials/index.md
 create mode 100644 docs/static_site/src/pages/api/java/docs/tutorials/mxnet_java_on_intellij.md
 create mode 100644 docs/static_site/src/pages/api/java/docs/tutorials/ssd_inference.md
 create mode 100644 docs/static_site/src/pages/api/java/index.md
 create mode 100644 docs/static_site/src/pages/api/julia/index.md
 create mode 100644 docs/static_site/src/pages/api/perl/docs/tutorials/index.md
 create mode 100644 docs/static_site/src/pages/api/perl/docs/tutorials/io.md
 create mode 100644 docs/static_site/src/pages/api/perl/docs/tutorials/kvstore.md
 create mode 100644 docs/static_site/src/pages/api/perl/docs/tutorials/module.md
 create mode 100644 docs/static_site/src/pages/api/perl/docs/tutorials/ndarray.md
 create mode 100644 docs/static_site/src/pages/api/perl/docs/tutorials/symbol.md
 create mode 100644 docs/static_site/src/pages/api/perl/index.md
 create mode 100644 docs/static_site/src/pages/api/python/index.md
 create mode 100644 docs/static_site/src/pages/api/r/docs/tutorials/callback_function.md
 create mode 100644 docs/static_site/src/pages/api/r/docs/tutorials/char_rnn_model.md
 create mode 100644 docs/static_site/src/pages/api/r/docs/tutorials/classify_real_image_with_pretrained_model.md
 create mode 100644 docs/static_site/src/pages/api/r/docs/tutorials/custom_iterator.md
 create mode 100644 docs/static_site/src/pages/api/r/docs/tutorials/custom_loss_function.md
 create mode 100644 docs/static_site/src/pages/api/r/docs/tutorials/five_minutes_neural_network.md
 create mode 100644 docs/static_site/src/pages/api/r/docs/tutorials/index.md
 create mode 100644 docs/static_site/src/pages/api/r/docs/tutorials/mnist_competition.md
 create mode 100644 docs/static_site/src/pages/api/r/docs/tutorials/multi_dim_lstm.md
 create mode 100644 docs/static_site/src/pages/api/r/docs/tutorials/ndarray.md
 create mode 100644 docs/static_site/src/pages/api/r/docs/tutorials/symbol.md
 create mode 100644 docs/static_site/src/pages/api/r/index.md
 create mode 100644 docs/static_site/src/pages/api/scala/docs/tutorials/char_lstm.md
 create mode 100644 docs/static_site/src/pages/api/scala/docs/tutorials/index.md
 create mode 100644 docs/static_site/src/pages/api/scala/docs/tutorials/infer.md
 create mode 100644 docs/static_site/src/pages/api/scala/docs/tutorials/io.md
 create mode 100644 docs/static_site/src/pages/api/scala/docs/tutorials/kvstore.md
 create mode 100644 docs/static_site/src/pages/api/scala/docs/tutorials/mnist.md
 create mode 100644 docs/static_site/src/pages/api/scala/docs/tutorials/model.md
 create mode 100644 docs/static_site/src/pages/api/scala/docs/tutorials/module.md
 create mode 100644 docs/static_site/src/pages/api/scala/docs/tutorials/mxnet_scala_on_intellij.md
 create mode 100644 docs/static_site/src/pages/api/scala/docs/tutorials/ndarray.md
 create mode 100644 docs/static_site/src/pages/api/scala/docs/tutorials/symbol.md
 create mode 100644 docs/static_site/src/pages/api/scala/docs/tutorials/symbol_in_pictures.md
 create mode 100644 docs/static_site/src/pages/api/scala/index.md
 create mode 100644 docs/static_site/src/pages/blog.html
 create mode 100644 docs/static_site/src/pages/community/contribute.md
 create mode 100644 docs/static_site/src/pages/ecosystem.html
 create mode 100644 docs/static_site/src/pages/features.html
 create mode 100644 docs/static_site/src/pages/get_started/build_from_source.md
 create mode 100644 docs/static_site/src/pages/get_started/c_plus_plus.md
 create mode 100644 docs/static_site/src/pages/get_started/centos_setup.md
 create mode 100644 docs/static_site/src/pages/get_started/download.md
 create mode 100644 docs/static_site/src/pages/get_started/index.html
 create mode 100644 docs/static_site/src/pages/get_started/java_setup.md
 create mode 100644 docs/static_site/src/pages/get_started/jetson_setup.md
 create mode 100644 docs/static_site/src/pages/get_started/osx_setup.md
 create mode 100644 docs/static_site/src/pages/get_started/scala_setup.md
 create mode 100644 docs/static_site/src/pages/get_started/ubuntu_setup.md
 create mode 100644 docs/static_site/src/pages/get_started/validate_mxnet.md
 create mode 100644 docs/static_site/src/pages/get_started/windows_setup.md

diff --git a/docs/conf.py b/docs/conf.py
index 3b5c38dd23bc..d2a0ef0283f8 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -107,7 +107,7 @@
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
-exclude_patterns = ['3rdparty', 'api/python/model.md', 'build_version_doc', 'README.md', 'tutorial_utils', 'virtualenv']
+exclude_patterns = ['3rdparty', 'api/python/model.md', 'build_version_doc', 'cpp_docs', 'python_docs', 'README.md', 'static_site', 'tutorial_utils', 'virtualenv']
 
 
 # The reST default role (used for this markup: `text`) to use for all documents.
diff --git a/docs/cpp_docs/Doxyfile b/docs/cpp_docs/Doxyfile
new file mode 100644
index 000000000000..463641a6bdab
--- /dev/null
+++ b/docs/cpp_docs/Doxyfile
@@ -0,0 +1,2370 @@
+# Doxyfile 1.8.8
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# This file describes the settings to be used by the documentation system
+# doxygen (www.doxygen.org) for a project.
+#
+# All text after a double hash (##) is considered a comment and is placed in
+# front of the TAG it is preceding.
+#
+# All text after a single hash (#) is considered a comment and will be ignored.
+# The format is:
+# TAG = value [value, ...]
+# For lists, items can also be appended using:
+# TAG += value [value, ...]
+# Values that contain spaces should be placed between quotes (\" \").
+
+#---------------------------------------------------------------------------
+# Project related configuration options
+#---------------------------------------------------------------------------
+
+# This tag specifies the encoding used for all characters in the config file
+# that follow. The default is UTF-8 which is also the encoding used for all text
+# before the first occurrence of this tag. Doxygen uses libiconv (or the iconv
+# built into libc) for the transcoding. See http://www.gnu.org/software/libiconv
+# for the list of possible encodings.
+# The default value is: UTF-8.
+
+DOXYFILE_ENCODING      = UTF-8
+
+# The PROJECT_NAME tag is a single word (or a sequence of words surrounded by
+# double-quotes, unless you are using Doxywizard) that should identify the
+# project for which the documentation is generated. This name is used in the
+# title of most generated pages and in a few other places.
+# The default value is: My Project.
+
+PROJECT_NAME           = "mxnet"
+
+# The PROJECT_NUMBER tag can be used to enter a project or revision number. This
+# could be handy for archiving the generated documentation or if some version
+# control system is used.
+
+PROJECT_NUMBER         =
+
+# Using the PROJECT_BRIEF tag one can provide an optional one line description
+# for a project that appears at the top of each page and should give viewer a
+# quick idea about the purpose of the project. Keep the description short.
+
+PROJECT_BRIEF          =
+
+# With the PROJECT_LOGO tag one can specify an logo or icon that is included in
+# the documentation. The maximum height of the logo should not exceed 55 pixels
+# and the maximum width should not exceed 200 pixels. Doxygen will copy the logo
+# to the output directory.
+
+PROJECT_LOGO           =
+
+# The OUTPUT_DIRECTORY tag is used to specify the (relative or absolute) path
+# into which the generated documentation will be written. If a relative path is
+# entered, it will be relative to the location where doxygen was started. If
+# left blank the current directory will be used.
+
+OUTPUT_DIRECTORY       = build/html
+
+# If the CREATE_SUBDIRS tag is set to YES, then doxygen will create 4096 sub-
+# directories (in 2 levels) under the output directory of each output format and
+# will distribute the generated files over these directories. Enabling this
+# option can be useful when feeding doxygen a huge amount of source files, where
+# putting all generated files in the same directory would otherwise causes
+# performance problems for the file system.
+# The default value is: NO.
+
+CREATE_SUBDIRS         = NO
+
+# If the ALLOW_UNICODE_NAMES tag is set to YES, doxygen will allow non-ASCII
+# characters to appear in the names of generated files. If set to NO, non-ASCII
+# characters will be escaped, for example _xE3_x81_x84 will be used for Unicode
+# U+3044.
+# The default value is: NO.
+
+#ALLOW_UNICODE_NAMES    = NO
+
+# The OUTPUT_LANGUAGE tag is used to specify the language in which all
+# documentation generated by doxygen is written. Doxygen will use this
+# information to generate all constant output in the proper language.
+# Possible values are: Afrikaans, Arabic, Armenian, Brazilian, Catalan, Chinese,
+# Chinese-Traditional, Croatian, Czech, Danish, Dutch, English (United States),
+# Esperanto, Farsi (Persian), Finnish, French, German, Greek, Hungarian,
+# Indonesian, Italian, Japanese, Japanese-en (Japanese with English messages),
+# Korean, Korean-en (Korean with English messages), Latvian, Lithuanian,
+# Macedonian, Norwegian, Persian (Farsi), Polish, Portuguese, Romanian, Russian,
+# Serbian, Serbian-Cyrillic, Slovak, Slovene, Spanish, Swedish, Turkish,
+# Ukrainian and Vietnamese.
+# The default value is: English.
+
+OUTPUT_LANGUAGE        = English
+
+# If the BRIEF_MEMBER_DESC tag is set to YES doxygen will include brief member
+# descriptions after the members that are listed in the file and class
+# documentation (similar to Javadoc). Set to NO to disable this.
+# The default value is: YES.
+
+BRIEF_MEMBER_DESC      = YES
+
+# If the REPEAT_BRIEF tag is set to YES doxygen will prepend the brief
+# description of a member or function before the detailed description
+#
+# Note: If both HIDE_UNDOC_MEMBERS and BRIEF_MEMBER_DESC are set to NO, the
+# brief descriptions will be completely suppressed.
+# The default value is: YES.
+
+REPEAT_BRIEF           = YES
+
+# This tag implements a quasi-intelligent brief description abbreviator that is
+# used to form the text in various listings. Each string in this list, if found
+# as the leading text of the brief description, will be stripped from the text
+# and the result, after processing the whole list, is used as the annotated
+# text. Otherwise, the brief description is used as-is. If left blank, the
+# following values are used ($name is automatically replaced with the name of
+# the entity):The $name class, The $name widget, The $name file, is, provides,
+# specifies, contains, represents, a, an and the.
+
+ABBREVIATE_BRIEF       =
+
+# If the ALWAYS_DETAILED_SEC and REPEAT_BRIEF tags are both set to YES then
+# doxygen will generate a detailed section even if there is only a brief
+# description.
+# The default value is: NO.
+
+ALWAYS_DETAILED_SEC    = NO
+
+# If the INLINE_INHERITED_MEMB tag is set to YES, doxygen will show all
+# inherited members of a class in the documentation of that class as if those
+# members were ordinary class members. Constructors, destructors and assignment
+# operators of the base classes will not be shown.
+# The default value is: NO.
+
+INLINE_INHERITED_MEMB  = NO
+
+# If the FULL_PATH_NAMES tag is set to YES doxygen will prepend the full path
+# before files name in the file list and in the header files. If set to NO the
+# shortest path that makes the file name unique will be used
+# The default value is: YES.
+
+FULL_PATH_NAMES        = YES
+
+# The STRIP_FROM_PATH tag can be used to strip a user-defined part of the path.
+# Stripping is only done if one of the specified strings matches the left-hand
+# part of the path. The tag can be used to show relative paths in the file list.
+# If left blank the directory from which doxygen is run is used as the path to
+# strip.
+#
+# Note that you can specify absolute paths here, but also relative paths, which
+# will be relative from the directory where doxygen is started.
+# This tag requires that the tag FULL_PATH_NAMES is set to YES.
+
+STRIP_FROM_PATH        =
+
+# The STRIP_FROM_INC_PATH tag can be used to strip a user-defined part of the
+# path mentioned in the documentation of a class, which tells the reader which
+# header file to include in order to use a class. If left blank only the name of
+# the header file containing the class definition is used. Otherwise one should
+# specify the list of include paths that are normally passed to the compiler
+# using the -I flag.
+
+STRIP_FROM_INC_PATH    =
+
+# If the SHORT_NAMES tag is set to YES, doxygen will generate much shorter (but
+# less readable) file names. This can be useful is your file systems doesn't
+# support long names like on DOS, Mac, or CD-ROM.
+# The default value is: NO.
+
+SHORT_NAMES            = NO
+
+# If the JAVADOC_AUTOBRIEF tag is set to YES then doxygen will interpret the
+# first line (until the first dot) of a Javadoc-style comment as the brief
+# description. If set to NO, the Javadoc-style will behave just like regular Qt-
+# style comments (thus requiring an explicit @brief command for a brief
+# description.)
+# The default value is: NO.
+
+JAVADOC_AUTOBRIEF      = NO
+
+# If the QT_AUTOBRIEF tag is set to YES then doxygen will interpret the first
+# line (until the first dot) of a Qt-style comment as the brief description. If
+# set to NO, the Qt-style will behave just like regular Qt-style comments (thus
+# requiring an explicit \brief command for a brief description.)
+# The default value is: NO.
+
+QT_AUTOBRIEF           = NO
+
+# The MULTILINE_CPP_IS_BRIEF tag can be set to YES to make doxygen treat a
+# multi-line C++ special comment block (i.e. a block of //! or /// comments) as
+# a brief description. This used to be the default behavior. The new default is
+# to treat a multi-line C++ comment block as a detailed description. Set this
+# tag to YES if you prefer the old behavior instead.
+#
+# Note that setting this tag to YES also means that rational rose comments are
+# not recognized any more.
+# The default value is: NO.
+
+MULTILINE_CPP_IS_BRIEF = NO
+
+# If the INHERIT_DOCS tag is set to YES then an undocumented member inherits the
+# documentation from any documented member that it re-implements.
+# The default value is: YES.
+
+INHERIT_DOCS           = YES
+
+# If the SEPARATE_MEMBER_PAGES tag is set to YES, then doxygen will produce a
+# new page for each member. If set to NO, the documentation of a member will be
+# part of the file/class/namespace that contains it.
+# The default value is: NO.
+
+SEPARATE_MEMBER_PAGES  = NO
+
+# The TAB_SIZE tag can be used to set the number of spaces in a tab. Doxygen
+# uses this value to replace tabs by spaces in code fragments.
+# Minimum value: 1, maximum value: 16, default value: 4.
+
+TAB_SIZE               = 8
+
+# This tag can be used to specify a number of aliases that act as commands in
+# the documentation. An alias has the form:
+# name=value
+# For example adding
+# "sideeffect=@par Side Effects:\n"
+# will allow you to put the command \sideeffect (or @sideeffect) in the
+# documentation, which will result in a user-defined paragraph with heading
+# "Side Effects:". You can put \n's in the value part of an alias to insert
+# newlines.
+
+ALIASES                =
+
+# This tag can be used to specify a number of word-keyword mappings (TCL only).
+# A mapping has the form "name=value". For example adding "class=itcl::class"
+# will allow you to use the command class in the itcl::class meaning.
+
+TCL_SUBST              =
+
+# Set the OPTIMIZE_OUTPUT_FOR_C tag to YES if your project consists of C sources
+# only. Doxygen will then generate output that is more tailored for C. For
+# instance, some of the names that are used will be different. The list of all
+# members will be omitted, etc.
+# The default value is: NO.
+
+OPTIMIZE_OUTPUT_FOR_C  = NO
+
+# Set the OPTIMIZE_OUTPUT_JAVA tag to YES if your project consists of Java or
+# Python sources only. Doxygen will then generate output that is more tailored
+# for that language. For instance, namespaces will be presented as packages,
+# qualified scopes will look different, etc.
+# The default value is: NO.
+
+OPTIMIZE_OUTPUT_JAVA   = NO
+
+# Set the OPTIMIZE_FOR_FORTRAN tag to YES if your project consists of Fortran
+# sources. Doxygen will then generate output that is tailored for Fortran.
+# The default value is: NO.
+
+OPTIMIZE_FOR_FORTRAN   = NO
+
+# Set the OPTIMIZE_OUTPUT_VHDL tag to YES if your project consists of VHDL
+# sources. Doxygen will then generate output that is tailored for VHDL.
+# The default value is: NO.
+
+OPTIMIZE_OUTPUT_VHDL   = NO
+
+# Doxygen selects the parser to use depending on the extension of the files it
+# parses. With this tag you can assign which parser to use for a given
+# extension. Doxygen has a built-in mapping, but you can override or extend it
+# using this tag. The format is ext=language, where ext is a file extension, and
+# language is one of the parsers supported by doxygen: IDL, Java, Javascript,
+# C#, C, C++, D, PHP, Objective-C, Python, Fortran (fixed format Fortran:
+# FortranFixed, free formatted Fortran: FortranFree, unknown formatted Fortran:
+# Fortran. In the later case the parser tries to guess whether the code is fixed
+# or free formatted code, this is the default for Fortran type files), VHDL. For
+# instance to make doxygen treat .inc files as Fortran files (default is PHP),
+# and .f files as C (default is Fortran), use: inc=Fortran f=C.
+#
+# Note For files without extension you can use no_extension as a placeholder.
+#
+# Note that for custom extensions you also need to set FILE_PATTERNS otherwise
+# the files are not read by doxygen.
+
+EXTENSION_MAPPING      =
+
+# If the MARKDOWN_SUPPORT tag is enabled then doxygen pre-processes all comments
+# according to the Markdown format, which allows for more readable
+# documentation. See http://daringfireball.net/projects/markdown/ for details.
+# The output of markdown processing is further processed by doxygen, so you can
+# mix doxygen, HTML, and XML commands with Markdown formatting. Disable only in
+# case of backward compatibilities issues.
+# The default value is: YES.
+
+#MARKDOWN_SUPPORT       = YES
+
+# When enabled doxygen tries to link words that correspond to documented
+# classes, or namespaces to their corresponding documentation. Such a link can
+# be prevented in individual cases by by putting a % sign in front of the word
+# or globally by setting AUTOLINK_SUPPORT to NO.
+# The default value is: YES.
+
+#AUTOLINK_SUPPORT       = YES
+
+# If you use STL classes (i.e. std::string, std::vector, etc.) but do not want
+# to include (a tag file for) the STL sources as input, then you should set this
+# tag to YES in order to let doxygen match functions declarations and
+# definitions whose arguments contain STL classes (e.g. func(std::string);
+# versus func(std::string) {}). This also make the inheritance and collaboration
+# diagrams that involve STL classes more complete and accurate.
+# The default value is: NO.
+
+BUILTIN_STL_SUPPORT    = NO
+
+# If you use Microsoft's C++/CLI language, you should set this option to YES to
+# enable parsing support.
+# The default value is: NO.
+
+CPP_CLI_SUPPORT        = NO
+
+# Set the SIP_SUPPORT tag to YES if your project consists of sip (see:
+# http://www.riverbankcomputing.co.uk/software/sip/intro) sources only. Doxygen
+# will parse them like normal C++ but will assume all classes use public instead
+# of private inheritance when no explicit protection keyword is present.
+# The default value is: NO.
+
+SIP_SUPPORT            = NO
+
+# For Microsoft's IDL there are propget and propput attributes to indicate
+# getter and setter methods for a property. Setting this option to YES will make
+# doxygen to replace the get and set methods by a property in the documentation.
+# This will only work if the methods are indeed getting or setting a simple
+# type. If this is not the case, or you want to show the methods anyway, you
+# should set this option to NO.
+# The default value is: YES.
+
+IDL_PROPERTY_SUPPORT   = YES
+
+# If member grouping is used in the documentation and the DISTRIBUTE_GROUP_DOC
+# tag is set to YES, then doxygen will reuse the documentation of the first
+# member in the group (if any) for the other members of the group. By default
+# all members of a group must be documented explicitly.
+# The default value is: NO.
+
+DISTRIBUTE_GROUP_DOC   = NO
+
+# Set the SUBGROUPING tag to YES to allow class member groups of the same type
+# (for instance a group of public functions) to be put as a subgroup of that
+# type (e.g. under the Public Functions section). Set it to NO to prevent
+# subgrouping. Alternatively, this can be done per class using the
+# \nosubgrouping command.
+# The default value is: YES.
+
+SUBGROUPING            = YES
+
+# When the INLINE_GROUPED_CLASSES tag is set to YES, classes, structs and unions
+# are shown inside the group in which they are included (e.g. using \ingroup)
+# instead of on a separate page (for HTML and Man pages) or section (for LaTeX
+# and RTF).
+#
+# Note that this feature does not work in combination with
+# SEPARATE_MEMBER_PAGES.
+# The default value is: NO.
+
+INLINE_GROUPED_CLASSES = NO
+
+# When the INLINE_SIMPLE_STRUCTS tag is set to YES, structs, classes, and unions
+# with only public data fields or simple typedef fields will be shown inline in
+# the documentation of the scope in which they are defined (i.e. file,
+# namespace, or group documentation), provided this scope is documented. If set
+# to NO, structs, classes, and unions are shown on a separate page (for HTML and
+# Man pages) or section (for LaTeX and RTF).
+# The default value is: NO.
+
+INLINE_SIMPLE_STRUCTS  = NO
+
+# When TYPEDEF_HIDES_STRUCT tag is enabled, a typedef of a struct, union, or
+# enum is documented as struct, union, or enum with the name of the typedef. So
+# typedef struct TypeS {} TypeT, will appear in the documentation as a struct
+# with name TypeT. When disabled the typedef will appear as a member of a file,
+# namespace, or class. And the struct will be named TypeS. This can typically be
+# useful for C code in case the coding convention dictates that all compound
+# types are typedef'ed and only the typedef is referenced, never the tag name.
+# The default value is: NO.
+
+TYPEDEF_HIDES_STRUCT   = NO
+
+# The size of the symbol lookup cache can be set using LOOKUP_CACHE_SIZE. This
+# cache is used to resolve symbols given their name and scope. Since this can be
+# an expensive process and often the same symbol appears multiple times in the
+# code, doxygen keeps a cache of pre-resolved symbols. If the cache is too small
+# doxygen will become slower. If the cache is too large, memory is wasted. The
+# cache size is given by this formula: 2^(16+LOOKUP_CACHE_SIZE). The valid range
+# is 0..9, the default is 0, corresponding to a cache size of 2^16=65536
+# symbols. At the end of a run doxygen will report the cache usage and suggest
+# the optimal cache size from a speed point of view.
+# Minimum value: 0, maximum value: 9, default value: 0.
+
+LOOKUP_CACHE_SIZE      = 0
+
+#---------------------------------------------------------------------------
+# Build related configuration options
+#---------------------------------------------------------------------------
+
+# If the EXTRACT_ALL tag is set to YES doxygen will assume all entities in
+# documentation are documented, even if no documentation was available. Private
+# class members and static file members will be hidden unless the
+# EXTRACT_PRIVATE respectively EXTRACT_STATIC tags are set to YES.
+# Note: This will also disable the warnings about undocumented members that are
+# normally produced when WARNINGS is set to YES.
+# The default value is: NO.
+
+EXTRACT_ALL            = YES
+
+# If the EXTRACT_PRIVATE tag is set to YES all private members of a class will
+# be included in the documentation.
+# The default value is: NO.
+
+EXTRACT_PRIVATE        = NO
+
+# If the EXTRACT_PACKAGE tag is set to YES all members with package or internal
+# scope will be included in the documentation.
+# The default value is: NO.
+
+#EXTRACT_PACKAGE        = NO
+
+# If the EXTRACT_STATIC tag is set to YES all static members of a file will be
+# included in the documentation.
+# The default value is: NO.
+
+EXTRACT_STATIC         = NO
+
+# If the EXTRACT_LOCAL_CLASSES tag is set to YES classes (and structs) defined
+# locally in source files will be included in the documentation. If set to NO
+# only classes defined in header files are included. Does not have any effect
+# for Java sources.
+# The default value is: YES.
+
+EXTRACT_LOCAL_CLASSES  = YES
+
+# This flag is only useful for Objective-C code. When set to YES local methods,
+# which are defined in the implementation section but not in the interface are
+# included in the documentation. If set to NO only methods in the interface are
+# included.
+# The default value is: NO.
+
+EXTRACT_LOCAL_METHODS  = NO
+
+# If this flag is set to YES, the members of anonymous namespaces will be
+# extracted and appear in the documentation as a namespace called
+# 'anonymous_namespace{file}', where file will be replaced with the base name of
+# the file that contains the anonymous namespace. By default anonymous namespace
+# are hidden.
+# The default value is: NO.
+
+EXTRACT_ANON_NSPACES   = NO
+
+# If the HIDE_UNDOC_MEMBERS tag is set to YES, doxygen will hide all
+# undocumented members inside documented classes or files. If set to NO these
+# members will be included in the various overviews, but no documentation
+# section is generated. This option has no effect if EXTRACT_ALL is enabled.
+# The default value is: NO.
+
+HIDE_UNDOC_MEMBERS     = NO
+
+# If the HIDE_UNDOC_CLASSES tag is set to YES, doxygen will hide all
+# undocumented classes that are normally visible in the class hierarchy. If set
+# to NO these classes will be included in the various overviews. This option has
+# no effect if EXTRACT_ALL is enabled.
+# The default value is: NO.
+
+HIDE_UNDOC_CLASSES     = NO
+
+# If the HIDE_FRIEND_COMPOUNDS tag is set to YES, doxygen will hide all friend
+# (class|struct|union) declarations. If set to NO these declarations will be
+# included in the documentation.
+# The default value is: NO.
+
+HIDE_FRIEND_COMPOUNDS  = NO
+
+# If the HIDE_IN_BODY_DOCS tag is set to YES, doxygen will hide any
+# documentation blocks found inside the body of a function. If set to NO these
+# blocks will be appended to the function's detailed documentation block.
+# The default value is: NO.
+
+HIDE_IN_BODY_DOCS      = NO
+
+# The INTERNAL_DOCS tag determines if documentation that is typed after a
+# \internal command is included. If the tag is set to NO then the documentation
+# will be excluded. Set it to YES to include the internal documentation.
+# The default value is: NO.
+
+INTERNAL_DOCS          = NO
+
+# If the CASE_SENSE_NAMES tag is set to NO then doxygen will only generate file
+# names in lower-case letters. If set to YES upper-case letters are also
+# allowed. This is useful if you have classes or files whose names only differ
+# in case and if your file system supports case sensitive file names. Windows
+# and Mac users are advised to set this option to NO.
+# The default value is: system dependent.
+
+CASE_SENSE_NAMES       = YES
+
+# If the HIDE_SCOPE_NAMES tag is set to NO then doxygen will show members with
+# their full class and namespace scopes in the documentation. If set to YES the
+# scope will be hidden.
+# The default value is: NO.
+
+HIDE_SCOPE_NAMES       = NO
+
+# If the SHOW_INCLUDE_FILES tag is set to YES then doxygen will put a list of
+# the files that are included by a file in the documentation of that file.
+# The default value is: YES.
+
+SHOW_INCLUDE_FILES     = YES
+
+# If the SHOW_GROUPED_MEMB_INC tag is set to YES then Doxygen will add for each
+# grouped member an include statement to the documentation, telling the reader
+# which file to include in order to use the member.
+# The default value is: NO.
+
+#SHOW_GROUPED_MEMB_INC  = NO
+
+# If the FORCE_LOCAL_INCLUDES tag is set to YES then doxygen will list include
+# files with double quotes in the documentation rather than with sharp brackets.
+# The default value is: NO.
+
+FORCE_LOCAL_INCLUDES   = NO
+
+# If the INLINE_INFO tag is set to YES then a tag [inline] is inserted in the
+# documentation for inline members.
+# The default value is: YES.
+
+INLINE_INFO            = YES
+
+# If the SORT_MEMBER_DOCS tag is set to YES then doxygen will sort the
+# (detailed) documentation of file and class members alphabetically by member
+# name. If set to NO the members will appear in declaration order.
+# The default value is: YES.
+
+SORT_MEMBER_DOCS       = YES
+
+# If the SORT_BRIEF_DOCS tag is set to YES then doxygen will sort the brief
+# descriptions of file, namespace and class members alphabetically by member
+# name. If set to NO the members will appear in declaration order. Note that
+# this will also influence the order of the classes in the class list.
+# The default value is: NO.
+
+SORT_BRIEF_DOCS        = NO
+
+# If the SORT_MEMBERS_CTORS_1ST tag is set to YES then doxygen will sort the
+# (brief and detailed) documentation of class members so that constructors and
+# destructors are listed first. If set to NO the constructors will appear in the
+# respective orders defined by SORT_BRIEF_DOCS and SORT_MEMBER_DOCS.
+# Note: If SORT_BRIEF_DOCS is set to NO this option is ignored for sorting brief
+# member documentation.
+# Note: If SORT_MEMBER_DOCS is set to NO this option is ignored for sorting
+# detailed member documentation.
+# The default value is: NO.
+
+SORT_MEMBERS_CTORS_1ST = NO
+
+# If the SORT_GROUP_NAMES tag is set to YES then doxygen will sort the hierarchy
+# of group names into alphabetical order. If set to NO the group names will
+# appear in their defined order.
+# The default value is: NO.
+
+SORT_GROUP_NAMES       = NO
+
+# If the SORT_BY_SCOPE_NAME tag is set to YES, the class list will be sorted by
+# fully-qualified names, including namespaces. If set to NO, the class list will
+# be sorted only by class name, not including the namespace part.
+# Note: This option is not very useful if HIDE_SCOPE_NAMES is set to YES.
+# Note: This option applies only to the class list, not to the alphabetical
+# list.
+# The default value is: NO.
+
+SORT_BY_SCOPE_NAME     = NO
+
+# If the STRICT_PROTO_MATCHING option is enabled and doxygen fails to do proper
+# type resolution of all parameters of a function it will reject a match between
+# the prototype and the implementation of a member function even if there is
+# only one candidate or it is obvious which candidate to choose by doing a
+# simple string match. By disabling STRICT_PROTO_MATCHING doxygen will still
+# accept a match between prototype and implementation in such cases.
+# The default value is: NO.
+
+STRICT_PROTO_MATCHING  = NO
+
+# The GENERATE_TODOLIST tag can be used to enable ( YES) or disable ( NO) the
+# todo list. This list is created by putting \todo commands in the
+# documentation.
+# The default value is: YES.
+
+GENERATE_TODOLIST      = YES
+
+# The GENERATE_TESTLIST tag can be used to enable ( YES) or disable ( NO) the
+# test list. This list is created by putting \test commands in the
+# documentation.
+# The default value is: YES.
+
+GENERATE_TESTLIST      = YES
+
+# The GENERATE_BUGLIST tag can be used to enable ( YES) or disable ( NO) the bug
+# list. This list is created by putting \bug commands in the documentation.
+# The default value is: YES.
+
+GENERATE_BUGLIST       = YES
+
+# The GENERATE_DEPRECATEDLIST tag can be used to enable ( YES) or disable ( NO)
+# the deprecated list. This list is created by putting \deprecated commands in
+# the documentation.
+# The default value is: YES.
+
+GENERATE_DEPRECATEDLIST= YES
+
+# The ENABLED_SECTIONS tag can be used to enable conditional documentation
+# sections, marked by \if <section_label> ... \endif and \cond <section_label>
+# ... \endcond blocks.
+
+ENABLED_SECTIONS       =
+
+# The MAX_INITIALIZER_LINES tag determines the maximum number of lines that the
+# initial value of a variable or macro / define can have for it to appear in the
+# documentation. If the initializer consists of more lines than specified here
+# it will be hidden. Use a value of 0 to hide initializers completely. The
+# appearance of the value of individual variables and macros / defines can be
+# controlled using \showinitializer or \hideinitializer command in the
+# documentation regardless of this setting.
+# Minimum value: 0, maximum value: 10000, default value: 30.
+
+MAX_INITIALIZER_LINES  = 30
+
+# Set the SHOW_USED_FILES tag to NO to disable the list of files generated at
+# the bottom of the documentation of classes and structs. If set to YES the list
+# will mention the files that were used to generate the documentation.
+# The default value is: YES.
+
+SHOW_USED_FILES        = YES
+
+# Set the SHOW_FILES tag to NO to disable the generation of the Files page. This
+# will remove the Files entry from the Quick Index and from the Folder Tree View
+# (if specified).
+# The default value is: YES.
+
+SHOW_FILES             = YES
+
+# Set the SHOW_NAMESPACES tag to NO to disable the generation of the Namespaces
+# page. This will remove the Namespaces entry from the Quick Index and from the
+# Folder Tree View (if specified).
+# The default value is: YES.
+
+SHOW_NAMESPACES        = YES
+
+# The FILE_VERSION_FILTER tag can be used to specify a program or script that
+# doxygen should invoke to get the current version for each file (typically from
+# the version control system). Doxygen will invoke the program by executing (via
+# popen()) the command command input-file, where command is the value of the
+# FILE_VERSION_FILTER tag, and input-file is the name of an input file provided
+# by doxygen. Whatever the program writes to standard output is used as the file
+# version. For an example see the documentation.
+
+FILE_VERSION_FILTER    =
+
+# The LAYOUT_FILE tag can be used to specify a layout file which will be parsed
+# by doxygen. The layout file controls the global structure of the generated
+# output files in an output format independent way. To create the layout file
+# that represents doxygen's defaults, run doxygen with the -l option. You can
+# optionally specify a file name after the option, if omitted DoxygenLayout.xml
+# will be used as the name of the layout file.
+#
+# Note that if you run doxygen from a directory containing a file called
+# DoxygenLayout.xml, doxygen will parse it automatically even if the LAYOUT_FILE
+# tag is left empty.
+
+LAYOUT_FILE            =
+
+# The CITE_BIB_FILES tag can be used to specify one or more bib files containing
+# the reference definitions. This must be a list of .bib files. The .bib
+# extension is automatically appended if omitted. This requires the bibtex tool
+# to be installed. See also http://en.wikipedia.org/wiki/BibTeX for more info.
+# For LaTeX the style of the bibliography can be controlled using
+# LATEX_BIB_STYLE. To use this feature you need bibtex and perl available in the
+# search path. See also \cite for info how to create references.
+
+CITE_BIB_FILES         =
+
+#---------------------------------------------------------------------------
+# Configuration options related to warning and progress messages
+#---------------------------------------------------------------------------
+
+# The QUIET tag can be used to turn on/off the messages that are generated to
+# standard output by doxygen. If QUIET is set to YES this implies that the
+# messages are off.
+# The default value is: NO.
+
+QUIET                  = NO
+
+# The WARNINGS tag can be used to turn on/off the warning messages that are
+# generated to standard error ( stderr) by doxygen. If WARNINGS is set to YES
+# this implies that the warnings are on.
+#
+# Tip: Turn warnings on while writing the documentation.
+# The default value is: YES.
+
+WARNINGS               = YES
+
+# If the WARN_IF_UNDOCUMENTED tag is set to YES, then doxygen will generate
+# warnings for undocumented members. If EXTRACT_ALL is set to YES then this flag
+# will automatically be disabled.
+# The default value is: YES.
+
+WARN_IF_UNDOCUMENTED   = YES
+
+# If the WARN_IF_DOC_ERROR tag is set to YES, doxygen will generate warnings for
+# potential errors in the documentation, such as not documenting some parameters
+# in a documented function, or documenting parameters that don't exist or using
+# markup commands wrongly.
+# The default value is: YES.
+
+WARN_IF_DOC_ERROR      = YES
+
+# This WARN_NO_PARAMDOC option can be enabled to get warnings for functions that
+# are documented, but have no documentation for their parameters or return
+# value. If set to NO doxygen will only warn about wrong or incomplete parameter
+# documentation, but not about the absence of documentation.
+# The default value is: NO.
+
+WARN_NO_PARAMDOC       = YES
+
+# The WARN_FORMAT tag determines the format of the warning messages that doxygen
+# can produce. The string should contain the $file, $line, and $text tags, which
+# will be replaced by the file and line number from which the warning originated
+# and the warning text. Optionally the format may contain $version, which will
+# be replaced by the version of the file (if it could be obtained via
+# FILE_VERSION_FILTER)
+# The default value is: $file:$line: $text.
+
+WARN_FORMAT            = "$file:$line: $text"
+
+# The WARN_LOGFILE tag can be used to specify a file to which warning and error
+# messages should be written. If left blank the output is written to standard
+# error (stderr).
+
+WARN_LOGFILE           =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the input files
+#---------------------------------------------------------------------------
+
+# The INPUT tag is used to specify the files and/or directories that contain
+# documented source files. You may enter file names like myfile.cpp or
+# directories like /usr/src/myproject. Separate the files or directories with
+# spaces.
+# Note: If this tag is empty the current directory is searched.
+
+INPUT                  = ../../include ../../src/common ../../cpp-package/include/mxnet-cpp
+
+# This tag can be used to specify the character encoding of the source files
+# that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses
+# libiconv (or the iconv built into libc) for the transcoding. See the libiconv
+# documentation (see: http://www.gnu.org/software/libiconv) for the list of
+# possible encodings.
+# The default value is: UTF-8.
+
+INPUT_ENCODING         = UTF-8
+
+# If the value of the INPUT tag contains directories, you can use the
+# FILE_PATTERNS tag to specify one or more wildcard patterns (like *.cpp and
+# *.h) to filter out the source-files in the directories. If left blank the
+# following patterns are tested:*.c, *.cc, *.cxx, *.cpp, *.c++, *.java, *.ii,
+# *.ixx, *.ipp, *.i++, *.inl, *.idl, *.ddl, *.odl, *.h, *.hh, *.hxx, *.hpp,
+# *.h++, *.cs, *.d, *.php, *.php4, *.php5, *.phtml, *.inc, *.m, *.markdown,
+# *.md, *.mm, *.dox, *.py, *.f90, *.f, *.for, *.tcl, *.vhd, *.vhdl, *.ucf,
+# *.qsf, *.as and *.js.
+
+FILE_PATTERNS          = *.h
+
+# The RECURSIVE tag can be used to specify whether or not subdirectories should
+# be searched for input files as well.
+# The default value is: NO.
+
+RECURSIVE              = YES
+
+# The EXCLUDE tag can be used to specify files and/or directories that should be
+# excluded from the INPUT source files. This way you can easily exclude a
+# subdirectory from a directory tree whose root is specified with the INPUT tag.
+#
+# Note that relative paths are relative to the directory from which doxygen is
+# run.
+
+EXCLUDE                = 3rdparty
+
+# The EXCLUDE_SYMLINKS tag can be used to select whether or not files or
+# directories that are symbolic links (a Unix file system feature) are excluded
+# from the input.
+# The default value is: NO.
+
+EXCLUDE_SYMLINKS       = NO
+
+# If the value of the INPUT tag contains directories, you can use the
+# EXCLUDE_PATTERNS tag to specify one or more wildcard patterns to exclude
+# certain files from those directories.
+#
+# Note that the wildcards are matched against the file with absolute path, so to
+# exclude all test directories for example use the pattern */test/*
+
+EXCLUDE_PATTERNS       = */test/* \
+                         logging.h
+
+# The EXCLUDE_SYMBOLS tag can be used to specify one or more symbol names
+# (namespaces, classes, functions, etc.) that should be excluded from the
+# output. The symbol name can be a fully qualified name, a word, or if the
+# wildcard * is used, a substring. Examples: ANamespace, AClass,
+# AClass::ANamespace, ANamespace::*Test
+#
+# Note that the wildcards are matched against the file with absolute path, so to
+# exclude all test directories use the pattern */test/*
+
+EXCLUDE_SYMBOLS        =
+
+# The EXAMPLE_PATH tag can be used to specify one or more files or directories
+# that contain example code fragments that are included (see the \include
+# command).
+
+EXAMPLE_PATH           =
+
+# If the value of the EXAMPLE_PATH tag contains directories, you can use the
+# EXAMPLE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp and
+# *.h) to filter out the source-files in the directories. If left blank all
+# files are included.
+
+EXAMPLE_PATTERNS       =
+
+# If the EXAMPLE_RECURSIVE tag is set to YES then subdirectories will be
+# searched for input files to be used with the \include or \dontinclude commands
+# irrespective of the value of the RECURSIVE tag.
+# The default value is: NO.
+
+EXAMPLE_RECURSIVE      = NO
+
+# The IMAGE_PATH tag can be used to specify one or more files or directories
+# that contain images that are to be included in the documentation (see the
+# \image command).
+
+IMAGE_PATH             =
+
+# The INPUT_FILTER tag can be used to specify a program that doxygen should
+# invoke to filter for each input file. Doxygen will invoke the filter program
+# by executing (via popen()) the command:
+#
+# <filter> <input-file>
+#
+# where <filter> is the value of the INPUT_FILTER tag, and <input-file> is the
+# name of an input file. Doxygen will then use the output that the filter
+# program writes to standard output. If FILTER_PATTERNS is specified, this tag
+# will be ignored.
+#
+# Note that the filter must not add or remove lines; it is applied before the
+# code is scanned, but not when the output code is generated. If lines are added
+# or removed, the anchors will not be placed correctly.
+
+INPUT_FILTER           =
+
+# The FILTER_PATTERNS tag can be used to specify filters on a per file pattern
+# basis. Doxygen will compare the file name with each pattern and apply the
+# filter if there is a match. The filters are a list of the form: pattern=filter
+# (like *.cpp=my_cpp_filter). See INPUT_FILTER for further information on how
+# filters are used. If the FILTER_PATTERNS tag is empty or if none of the
+# patterns match the file name, INPUT_FILTER is applied.
+
+FILTER_PATTERNS        =
+
+# If the FILTER_SOURCE_FILES tag is set to YES, the input filter (if set using
+# INPUT_FILTER ) will also be used to filter the input files that are used for
+# producing the source files to browse (i.e. when SOURCE_BROWSER is set to YES).
+# The default value is: NO.
+
+FILTER_SOURCE_FILES    = NO
+
+# The FILTER_SOURCE_PATTERNS tag can be used to specify source filters per file
+# pattern. A pattern will override the setting for FILTER_PATTERN (if any) and
+# it is also possible to disable source filtering for a specific pattern using
+# *.ext= (so without naming a filter).
+# This tag requires that the tag FILTER_SOURCE_FILES is set to YES.
+
+FILTER_SOURCE_PATTERNS =
+
+# If the USE_MDFILE_AS_MAINPAGE tag refers to the name of a markdown file that
+# is part of the input, its contents will be placed on the main page
+# (index.html). This can be useful if you have a project on for instance GitHub
+# and want to reuse the introduction page also for the doxygen output.
+
+#USE_MDFILE_AS_MAINPAGE =
+
+#---------------------------------------------------------------------------
+# Configuration options related to source browsing
+#---------------------------------------------------------------------------
+
+# If the SOURCE_BROWSER tag is set to YES then a list of source files will be
+# generated. Documented entities will be cross-referenced with these sources.
+#
+# Note: To get rid of all source code in the generated output, make sure that
+# also VERBATIM_HEADERS is set to NO.
+# The default value is: NO.
+
+SOURCE_BROWSER         = NO
+
+# Setting the INLINE_SOURCES tag to YES will include the body of functions,
+# classes and enums directly into the documentation.
+# The default value is: NO.
+
+INLINE_SOURCES         = NO
+
+# Setting the STRIP_CODE_COMMENTS tag to YES will instruct doxygen to hide any
+# special comment blocks from generated source code fragments. Normal C, C++ and
+# Fortran comments will always remain visible.
+# The default value is: YES.
+
+STRIP_CODE_COMMENTS    = YES
+
+# If the REFERENCED_BY_RELATION tag is set to YES then for each documented
+# function all documented functions referencing it will be listed.
+# The default value is: NO.
+
+REFERENCED_BY_RELATION = NO
+
+# If the REFERENCES_RELATION tag is set to YES then for each documented function
+# all documented entities called/used by that function will be listed.
+# The default value is: NO.
+
+REFERENCES_RELATION    = NO
+
+# If the REFERENCES_LINK_SOURCE tag is set to YES and SOURCE_BROWSER tag is set
+# to YES, then the hyperlinks from functions in REFERENCES_RELATION and
+# REFERENCED_BY_RELATION lists will link to the source code. Otherwise they will
+# link to the documentation.
+# The default value is: YES.
+
+REFERENCES_LINK_SOURCE = YES
+
+# If SOURCE_TOOLTIPS is enabled (the default) then hovering a hyperlink in the
+# source code will show a tooltip with additional information such as prototype,
+# brief description and links to the definition and documentation. Since this
+# will make the HTML file larger and loading of large files a bit slower, you
+# can opt to disable this feature.
+# The default value is: YES.
+# This tag requires that the tag SOURCE_BROWSER is set to YES.
+
+#SOURCE_TOOLTIPS        = YES
+
+# If the USE_HTAGS tag is set to YES then the references to source code will
+# point to the HTML generated by the htags(1) tool instead of doxygen built-in
+# source browser. The htags tool is part of GNU's global source tagging system
+# (see http://www.gnu.org/software/global/global.html). You will need version
+# 4.8.6 or higher.
+#
+# To use it do the following:
+# - Install the latest version of global
+# - Enable SOURCE_BROWSER and USE_HTAGS in the config file
+# - Make sure the INPUT points to the root of the source tree
+# - Run doxygen as normal
+#
+# Doxygen will invoke htags (and that will in turn invoke gtags), so these
+# tools must be available from the command line (i.e. in the search path).
+#
+# The result: instead of the source browser generated by doxygen, the links to
+# source code will now point to the output of htags.
+# The default value is: NO.
+# This tag requires that the tag SOURCE_BROWSER is set to YES.
+
+USE_HTAGS              = NO
+
+# If the VERBATIM_HEADERS tag is set the YES then doxygen will generate a
+# verbatim copy of the header file for each class for which an include is
+# specified. Set to NO to disable this.
+# See also: Section \class.
+# The default value is: YES.
+
+VERBATIM_HEADERS       = YES
+
+# If the CLANG_ASSISTED_PARSING tag is set to YES, then doxygen will use the
+# clang parser (see: http://clang.llvm.org/) for more accurate parsing at the
+# cost of reduced performance. This can be particularly helpful with template
+# rich C++ code for which doxygen's built-in parser lacks the necessary type
+# information.
+# Note: The availability of this option depends on whether or not doxygen was
+# compiled with the --with-libclang option.
+# The default value is: NO.
+
+#CLANG_ASSISTED_PARSING = NO
+
+# If clang assisted parsing is enabled you can provide the compiler with command
+# line options that you would normally use when invoking the compiler. Note that
+# the include paths will already be set by doxygen for the files and directories
+# specified with INPUT and INCLUDE_PATH.
+# This tag requires that the tag CLANG_ASSISTED_PARSING is set to YES.
+
+#CLANG_OPTIONS          =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the alphabetical class index
+#---------------------------------------------------------------------------
+
+# If the ALPHABETICAL_INDEX tag is set to YES, an alphabetical index of all
+# compounds will be generated. Enable this if the project contains a lot of
+# classes, structs, unions or interfaces.
+# The default value is: YES.
+
+ALPHABETICAL_INDEX     = YES
+
+# The COLS_IN_ALPHA_INDEX tag can be used to specify the number of columns in
+# which the alphabetical index list will be split.
+# Minimum value: 1, maximum value: 20, default value: 5.
+# This tag requires that the tag ALPHABETICAL_INDEX is set to YES.
+
+COLS_IN_ALPHA_INDEX    = 5
+
+# In case all classes in a project start with a common prefix, all classes will
+# be put under the same header in the alphabetical index. The IGNORE_PREFIX tag
+# can be used to specify a prefix (or a list of prefixes) that should be ignored
+# while generating the index headers.
+# This tag requires that the tag ALPHABETICAL_INDEX is set to YES.
+
+IGNORE_PREFIX          =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the HTML output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_HTML tag is set to YES doxygen will generate HTML output
+# The default value is: YES.
+
+GENERATE_HTML          = YES
+
+# The HTML_OUTPUT tag is used to specify where the HTML docs will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it.
+# The default directory is: html.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_OUTPUT            = html
+
+# The HTML_FILE_EXTENSION tag can be used to specify the file extension for each
+# generated HTML page (for example: .htm, .php, .asp).
+# The default value is: .html.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_FILE_EXTENSION    = .html
+
+# The HTML_HEADER tag can be used to specify a user-defined HTML header file for
+# each generated HTML page. If the tag is left blank doxygen will generate a
+# standard header.
+#
+# To get valid HTML the header file that includes any scripts and style sheets
+# that doxygen needs, which is dependent on the configuration options used (e.g.
+# the setting GENERATE_TREEVIEW). It is highly recommended to start with a
+# default header using
+# doxygen -w html new_header.html new_footer.html new_stylesheet.css
+# YourConfigFile
+# and then modify the file new_header.html. See also section "Doxygen usage"
+# for information on how to generate the default header that doxygen normally
+# uses.
+# Note: The header is subject to change so you typically have to regenerate the
+# default header when upgrading to a newer version of doxygen. For a description
+# of the possible markers and block names see the documentation.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_HEADER            =
+
+# The HTML_FOOTER tag can be used to specify a user-defined HTML footer for each
+# generated HTML page. If the tag is left blank doxygen will generate a standard
+# footer. See HTML_HEADER for more information on how to generate a default
+# footer and what special commands can be used inside the footer. See also
+# section "Doxygen usage" for information on how to generate the default footer
+# that doxygen normally uses.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_FOOTER            =
+
+# The HTML_STYLESHEET tag can be used to specify a user-defined cascading style
+# sheet that is used by each HTML page. It can be used to fine-tune the look of
+# the HTML output. If left blank doxygen will generate a default style sheet.
+# See also section "Doxygen usage" for information on how to generate the style
+# sheet that doxygen normally uses.
+# Note: It is recommended to use HTML_EXTRA_STYLESHEET instead of this tag, as
+# it is more robust and this tag (HTML_STYLESHEET) will in the future become
+# obsolete.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_STYLESHEET        =
+
+# The HTML_EXTRA_STYLESHEET tag can be used to specify additional user-defined
+# cascading style sheets that are included after the standard style sheets
+# created by doxygen. Using this option one can overrule certain style aspects.
+# This is preferred over using HTML_STYLESHEET since it does not replace the
+# standard style sheet and is therefor more robust against future updates.
+# Doxygen will copy the style sheet files to the output directory.
+# Note: The order of the extra stylesheet files is of importance (e.g. the last
+# stylesheet in the list overrules the setting of the previous ones in the
+# list). For an example see the documentation.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+#HTML_EXTRA_STYLESHEET  =
+
+# The HTML_EXTRA_FILES tag can be used to specify one or more extra images or
+# other source files which should be copied to the HTML output directory. Note
+# that these files will be copied to the base HTML output directory. Use the
+# $relpath^ marker in the HTML_HEADER and/or HTML_FOOTER files to load these
+# files. In the HTML_STYLESHEET file, use the file name only. Also note that the
+# files will be copied as-is; there are no commands or markers available.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_EXTRA_FILES       =
+
+# The HTML_COLORSTYLE_HUE tag controls the color of the HTML output. Doxygen
+# will adjust the colors in the stylesheet and background images according to
+# this color. Hue is specified as an angle on a colorwheel, see
+# http://en.wikipedia.org/wiki/Hue for more information. For instance the value
+# 0 represents red, 60 is yellow, 120 is green, 180 is cyan, 240 is blue, 300
+# purple, and 360 is red again.
+# Minimum value: 0, maximum value: 359, default value: 220.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_COLORSTYLE_HUE    = 220
+
+# The HTML_COLORSTYLE_SAT tag controls the purity (or saturation) of the colors
+# in the HTML output. For a value of 0 the output will use grayscales only. A
+# value of 255 will produce the most vivid colors.
+# Minimum value: 0, maximum value: 255, default value: 100.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_COLORSTYLE_SAT    = 100
+
+# The HTML_COLORSTYLE_GAMMA tag controls the gamma correction applied to the
+# luminance component of the colors in the HTML output. Values below 100
+# gradually make the output lighter, whereas values above 100 make the output
+# darker. The value divided by 100 is the actual gamma applied, so 80 represents
+# a gamma of 0.8, The value 220 represents a gamma of 2.2, and 100 does not
+# change the gamma.
+# Minimum value: 40, maximum value: 240, default value: 80.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_COLORSTYLE_GAMMA  = 80
+
+# If the HTML_TIMESTAMP tag is set to YES then the footer of each generated HTML
+# page will contain the date and time when the page was generated. Setting this
+# to NO can help when comparing the output of multiple runs.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_TIMESTAMP         = YES
+
+# If the HTML_DYNAMIC_SECTIONS tag is set to YES then the generated HTML
+# documentation will contain sections that can be hidden and shown after the
+# page has loaded.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_DYNAMIC_SECTIONS  = NO
+
+# With HTML_INDEX_NUM_ENTRIES one can control the preferred number of entries
+# shown in the various tree structured indices initially; the user can expand
+# and collapse entries dynamically later on. Doxygen will expand the tree to
+# such a level that at most the specified number of entries are visible (unless
+# a fully collapsed tree already exceeds this amount). So setting the number of
+# entries 1 will produce a full collapsed tree by default. 0 is a special value
+# representing an infinite number of entries and will result in a full expanded
+# tree by default.
+# Minimum value: 0, maximum value: 9999, default value: 100.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+#HTML_INDEX_NUM_ENTRIES = 100
+
+# If the GENERATE_DOCSET tag is set to YES, additional index files will be
+# generated that can be used as input for Apple's Xcode 3 integrated development
+# environment (see: http://developer.apple.com/tools/xcode/), introduced with
+# OSX 10.5 (Leopard). To create a documentation set, doxygen will generate a
+# Makefile in the HTML output directory. Running make will produce the docset in
+# that directory and running make install will install the docset in
+# ~/Library/Developer/Shared/Documentation/DocSets so that Xcode will find it at
+# startup. See http://developer.apple.com/tools/creatingdocsetswithdoxygen.html
+# for more information.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+GENERATE_DOCSET        = NO
+
+# This tag determines the name of the docset feed. A documentation feed provides
+# an umbrella under which multiple documentation sets from a single provider
+# (such as a company or product suite) can be grouped.
+# The default value is: Doxygen generated docs.
+# This tag requires that the tag GENERATE_DOCSET is set to YES.
+
+DOCSET_FEEDNAME        = "Doxygen generated docs"
+
+# This tag specifies a string that should uniquely identify the documentation
+# set bundle. This should be a reverse domain-name style string, e.g.
+# com.mycompany.MyDocSet. Doxygen will append .docset to the name.
+# The default value is: org.doxygen.Project.
+# This tag requires that the tag GENERATE_DOCSET is set to YES.
+
+DOCSET_BUNDLE_ID       = org.doxygen.Project
+
+# The DOCSET_PUBLISHER_ID tag specifies a string that should uniquely identify
+# the documentation publisher. This should be a reverse domain-name style
+# string, e.g. com.mycompany.MyDocSet.documentation.
+# The default value is: org.doxygen.Publisher.
+# This tag requires that the tag GENERATE_DOCSET is set to YES.
+
+DOCSET_PUBLISHER_ID    = org.doxygen.Publisher
+
+# The DOCSET_PUBLISHER_NAME tag identifies the documentation publisher.
+# The default value is: Publisher.
+# This tag requires that the tag GENERATE_DOCSET is set to YES.
+
+DOCSET_PUBLISHER_NAME  = Publisher
+
+# If the GENERATE_HTMLHELP tag is set to YES then doxygen generates three
+# additional HTML index files: index.hhp, index.hhc, and index.hhk. The
+# index.hhp is a project file that can be read by Microsoft's HTML Help Workshop
+# (see: http://www.microsoft.com/en-us/download/details.aspx?id=21138) on
+# Windows.
+#
+# The HTML Help Workshop contains a compiler that can convert all HTML output
+# generated by doxygen into a single compiled HTML file (.chm). Compiled HTML
+# files are now used as the Windows 98 help format, and will replace the old
+# Windows help format (.hlp) on all Windows platforms in the future. Compressed
+# HTML files also contain an index, a table of contents, and you can search for
+# words in the documentation. The HTML workshop also contains a viewer for
+# compressed HTML files.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+GENERATE_HTMLHELP      = NO
+
+# The CHM_FILE tag can be used to specify the file name of the resulting .chm
+# file. You can add a path in front of the file if the result should not be
+# written to the html output directory.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+CHM_FILE               =
+
+# The HHC_LOCATION tag can be used to specify the location (absolute path
+# including file name) of the HTML help compiler ( hhc.exe). If non-empty
+# doxygen will try to run the HTML help compiler on the generated index.hhp.
+# The file has to be specified with full path.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+HHC_LOCATION           =
+
+# The GENERATE_CHI flag controls if a separate .chi index file is generated (
+# YES) or that it should be included in the master .chm file ( NO).
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+GENERATE_CHI           = NO
+
+# The CHM_INDEX_ENCODING is used to encode HtmlHelp index ( hhk), content ( hhc)
+# and project file content.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+CHM_INDEX_ENCODING     =
+
+# The BINARY_TOC flag controls whether a binary table of contents is generated (
+# YES) or a normal table of contents ( NO) in the .chm file. Furthermore it
+# enables the Previous and Next buttons.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+BINARY_TOC             = NO
+
+# The TOC_EXPAND flag can be set to YES to add extra items for group members to
+# the table of contents of the HTML help documentation and to the tree view.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+TOC_EXPAND             = NO
+
+# If the GENERATE_QHP tag is set to YES and both QHP_NAMESPACE and
+# QHP_VIRTUAL_FOLDER are set, an additional index file will be generated that
+# can be used as input for Qt's qhelpgenerator to generate a Qt Compressed Help
+# (.qch) of the generated HTML documentation.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+GENERATE_QHP           = NO
+
+# If the QHG_LOCATION tag is specified, the QCH_FILE tag can be used to specify
+# the file name of the resulting .qch file. The path specified is relative to
+# the HTML output folder.
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QCH_FILE               =
+
+# The QHP_NAMESPACE tag specifies the namespace to use when generating Qt Help
+# Project output. For more information please see Qt Help Project / Namespace
+# (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#namespace).
+# The default value is: org.doxygen.Project.
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHP_NAMESPACE          = org.doxygen.Project
+
+# The QHP_VIRTUAL_FOLDER tag specifies the namespace to use when generating Qt
+# Help Project output. For more information please see Qt Help Project / Virtual
+# Folders (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#virtual-
+# folders).
+# The default value is: doc.
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHP_VIRTUAL_FOLDER     = doc
+
+# If the QHP_CUST_FILTER_NAME tag is set, it specifies the name of a custom
+# filter to add. For more information please see Qt Help Project / Custom
+# Filters (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#custom-
+# filters).
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHP_CUST_FILTER_NAME   =
+
+# The QHP_CUST_FILTER_ATTRS tag specifies the list of the attributes of the
+# custom filter to add. For more information please see Qt Help Project / Custom
+# Filters (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#custom-
+# filters).
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHP_CUST_FILTER_ATTRS  =
+
+# The QHP_SECT_FILTER_ATTRS tag specifies the list of the attributes this
+# project's filter section matches. Qt Help Project / Filter Attributes (see:
+# http://qt-project.org/doc/qt-4.8/qthelpproject.html#filter-attributes).
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHP_SECT_FILTER_ATTRS  =
+
+# The QHG_LOCATION tag can be used to specify the location of Qt's
+# qhelpgenerator. If non-empty doxygen will try to run qhelpgenerator on the
+# generated .qhp file.
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHG_LOCATION           =
+
+# If the GENERATE_ECLIPSEHELP tag is set to YES, additional index files will be
+# generated, together with the HTML files, they form an Eclipse help plugin. To
+# install this plugin and make it available under the help contents menu in
+# Eclipse, the contents of the directory containing the HTML and XML files needs
+# to be copied into the plugins directory of eclipse. The name of the directory
+# within the plugins directory should be the same as the ECLIPSE_DOC_ID value.
+# After copying Eclipse needs to be restarted before the help appears.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+GENERATE_ECLIPSEHELP   = NO
+
+# A unique identifier for the Eclipse help plugin. When installing the plugin
+# the directory name containing the HTML and XML files should also have this
+# name. Each documentation set should have its own identifier.
+# The default value is: org.doxygen.Project.
+# This tag requires that the tag GENERATE_ECLIPSEHELP is set to YES.
+
+ECLIPSE_DOC_ID         = org.doxygen.Project
+
+# If you want full control over the layout of the generated HTML pages it might
+# be necessary to disable the index and replace it with your own. The
+# DISABLE_INDEX tag can be used to turn on/off the condensed index (tabs) at top
+# of each HTML page. A value of NO enables the index and the value YES disables
+# it. Since the tabs in the index contain the same information as the navigation
+# tree, you can set this option to YES if you also set GENERATE_TREEVIEW to YES.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+DISABLE_INDEX          = NO
+
+# The GENERATE_TREEVIEW tag is used to specify whether a tree-like index
+# structure should be generated to display hierarchical information. If the tag
+# value is set to YES, a side panel will be generated containing a tree-like
+# index structure (just like the one that is generated for HTML Help). For this
+# to work a browser that supports JavaScript, DHTML, CSS and frames is required
+# (i.e. any modern browser). Windows users are probably better off using the
+# HTML help feature. Via custom stylesheets (see HTML_EXTRA_STYLESHEET) one can
+# further fine-tune the look of the index. As an example, the default style
+# sheet generated by doxygen has an example that shows how to put an image at
+# the root of the tree instead of the PROJECT_NAME. Since the tree basically has
+# the same information as the tab index, you could consider setting
+# DISABLE_INDEX to YES when enabling this option.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+GENERATE_TREEVIEW      = NO
+
+# The ENUM_VALUES_PER_LINE tag can be used to set the number of enum values that
+# doxygen will group on one line in the generated HTML documentation.
+#
+# Note that a value of 0 will completely suppress the enum values from appearing
+# in the overview section.
+# Minimum value: 0, maximum value: 20, default value: 4.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+ENUM_VALUES_PER_LINE   = 4
+
+# If the treeview is enabled (see GENERATE_TREEVIEW) then this tag can be used
+# to set the initial width (in pixels) of the frame in which the tree is shown.
+# Minimum value: 0, maximum value: 1500, default value: 250.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+TREEVIEW_WIDTH         = 250
+
+# When the EXT_LINKS_IN_WINDOW option is set to YES doxygen will open links to
+# external symbols imported via tag files in a separate window.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+EXT_LINKS_IN_WINDOW    = NO
+
+# Use this tag to change the font size of LaTeX formulas included as images in
+# the HTML documentation. When you change the font size after a successful
+# doxygen run you need to manually remove any form_*.png images from the HTML
+# output directory to force them to be regenerated.
+# Minimum value: 8, maximum value: 50, default value: 10.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+FORMULA_FONTSIZE       = 10
+
+# Use the FORMULA_TRANPARENT tag to determine whether or not the images
+# generated for formulas are transparent PNGs. Transparent PNGs are not
+# supported properly for IE 6.0, but are supported on all modern browsers.
+#
+# Note that when changing this option you need to delete any form_*.png files in
+# the HTML output directory before the changes have effect.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+FORMULA_TRANSPARENT    = YES
+
+# Enable the USE_MATHJAX option to render LaTeX formulas using MathJax (see
+# http://www.mathjax.org) which uses client side Javascript for the rendering
+# instead of using prerendered bitmaps. Use this if you do not have LaTeX
+# installed or if you want to formulas look prettier in the HTML output. When
+# enabled you may also need to install MathJax separately and configure the path
+# to it using the MATHJAX_RELPATH option.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+USE_MATHJAX            = NO
+
+# When MathJax is enabled you can set the default output format to be used for
+# the MathJax output. See the MathJax site (see:
+# http://docs.mathjax.org/en/latest/output.html) for more details.
+# Possible values are: HTML-CSS (which is slower, but has the best
+# compatibility), NativeMML (i.e. MathML) and SVG.
+# The default value is: HTML-CSS.
+# This tag requires that the tag USE_MATHJAX is set to YES.
+
+#MATHJAX_FORMAT         = HTML-CSS
+
+# When MathJax is enabled you need to specify the location relative to the HTML
+# output directory using the MATHJAX_RELPATH option. The destination directory
+# should contain the MathJax.js script. For instance, if the mathjax directory
+# is located at the same level as the HTML output directory, then
+# MATHJAX_RELPATH should be ../mathjax. The default value points to the MathJax
+# Content Delivery Network so you can quickly see the result without installing
+# MathJax. However, it is strongly recommended to install a local copy of
+# MathJax from http://www.mathjax.org before deployment.
+# The default value is: http://cdn.mathjax.org/mathjax/latest.
+# This tag requires that the tag USE_MATHJAX is set to YES.
+
+MATHJAX_RELPATH        = http://www.mathjax.org/mathjax
+
+# The MATHJAX_EXTENSIONS tag can be used to specify one or more MathJax
+# extension names that should be enabled during MathJax rendering. For example
+# MATHJAX_EXTENSIONS = TeX/AMSmath TeX/AMSsymbols
+# This tag requires that the tag USE_MATHJAX is set to YES.
+
+MATHJAX_EXTENSIONS     =
+
+# The MATHJAX_CODEFILE tag can be used to specify a file with javascript pieces
+# of code that will be used on startup of the MathJax code. See the MathJax site
+# (see: http://docs.mathjax.org/en/latest/output.html) for more details. For an
+# example see the documentation.
+# This tag requires that the tag USE_MATHJAX is set to YES.
+
+#MATHJAX_CODEFILE       =
+
+# When the SEARCHENGINE tag is enabled doxygen will generate a search box for
+# the HTML output. The underlying search engine uses javascript and DHTML and
+# should work on any modern browser. Note that when using HTML help
+# (GENERATE_HTMLHELP), Qt help (GENERATE_QHP), or docsets (GENERATE_DOCSET)
+# there is already a search function so this one should typically be disabled.
+# For large projects the javascript based search engine can be slow, then
+# enabling SERVER_BASED_SEARCH may provide a better solution. It is possible to
+# search using the keyboard; to jump to the search box use <access key> + S
+# (what the <access key> is depends on the OS and browser, but it is typically
+# <CTRL>, <ALT>/<option>, or both). Inside the search box use the <cursor down
+# key> to jump into the search results window, the results can be navigated
+# using the <cursor keys>. Press <Enter> to select an item or <escape> to cancel
+# the search. The filter options can be selected when the cursor is inside the
+# search box by pressing <Shift>+<cursor down>. Also here use the <cursor keys>
+# to select a filter and <Enter> or <escape> to activate or cancel the filter
+# option.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+SEARCHENGINE           = YES
+
+# When the SERVER_BASED_SEARCH tag is enabled the search engine will be
+# implemented using a web server instead of a web client using Javascript. There
+# are two flavors of web server based searching depending on the EXTERNAL_SEARCH
+# setting. When disabled, doxygen will generate a PHP script for searching and
+# an index file used by the script. When EXTERNAL_SEARCH is enabled the indexing
+# and searching needs to be provided by external tools. See the section
+# "External Indexing and Searching" for details.
+# The default value is: NO.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+SERVER_BASED_SEARCH    = NO
+
+# When EXTERNAL_SEARCH tag is enabled doxygen will no longer generate the PHP
+# script for searching. Instead the search results are written to an XML file
+# which needs to be processed by an external indexer. Doxygen will invoke an
+# external search engine pointed to by the SEARCHENGINE_URL option to obtain the
+# search results.
+#
+# Doxygen ships with an example indexer ( doxyindexer) and search engine
+# (doxysearch.cgi) which are based on the open source search engine library
+# Xapian (see: http://xapian.org/).
+#
+# See the section "External Indexing and Searching" for details.
+# The default value is: NO.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+#EXTERNAL_SEARCH        = NO
+
+# The SEARCHENGINE_URL should point to a search engine hosted by a web server
+# which will return the search results when EXTERNAL_SEARCH is enabled.
+#
+# Doxygen ships with an example indexer ( doxyindexer) and search engine
+# (doxysearch.cgi) which are based on the open source search engine library
+# Xapian (see: http://xapian.org/). See the section "External Indexing and
+# Searching" for details.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+#SEARCHENGINE_URL       =
+
+# When SERVER_BASED_SEARCH and EXTERNAL_SEARCH are both enabled the unindexed
+# search data is written to a file for indexing by an external tool. With the
+# SEARCHDATA_FILE tag the name of this file can be specified.
+# The default file is: searchdata.xml.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+#SEARCHDATA_FILE        = searchdata.xml
+
+# When SERVER_BASED_SEARCH and EXTERNAL_SEARCH are both enabled the
+# EXTERNAL_SEARCH_ID tag can be used as an identifier for the project. This is
+# useful in combination with EXTRA_SEARCH_MAPPINGS to search through multiple
+# projects and redirect the results back to the right project.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+#EXTERNAL_SEARCH_ID     =
+
+# The EXTRA_SEARCH_MAPPINGS tag can be used to enable searching through doxygen
+# projects other than the one defined by this configuration file, but that are
+# all added to the same external search index. Each project needs to have a
+# unique id set via EXTERNAL_SEARCH_ID. The search mapping then maps the id of
+# to a relative location where the documentation can be found. The format is:
+# EXTRA_SEARCH_MAPPINGS = tagname1=loc1 tagname2=loc2 ...
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+#EXTRA_SEARCH_MAPPINGS  =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the LaTeX output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_LATEX tag is set to YES doxygen will generate LaTeX output.
+# The default value is: YES.
+
+GENERATE_LATEX         = YES
+
+# The LATEX_OUTPUT tag is used to specify where the LaTeX docs will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it.
+# The default directory is: latex.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_OUTPUT           = latex
+
+# The LATEX_CMD_NAME tag can be used to specify the LaTeX command name to be
+# invoked.
+#
+# Note that when enabling USE_PDFLATEX this option is only used for generating
+# bitmaps for formulas in the HTML output, but not in the Makefile that is
+# written to the output directory.
+# The default file is: latex.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_CMD_NAME         = latex
+
+# The MAKEINDEX_CMD_NAME tag can be used to specify the command name to generate
+# index for LaTeX.
+# The default file is: makeindex.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+MAKEINDEX_CMD_NAME     = makeindex
+
+# If the COMPACT_LATEX tag is set to YES doxygen generates more compact LaTeX
+# documents. This may be useful for small projects and may help to save some
+# trees in general.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+COMPACT_LATEX          = NO
+
+# The PAPER_TYPE tag can be used to set the paper type that is used by the
+# printer.
+# Possible values are: a4 (210 x 297 mm), letter (8.5 x 11 inches), legal (8.5 x
+# 14 inches) and executive (7.25 x 10.5 inches).
+# The default value is: a4.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+PAPER_TYPE             = a4
+
+# The EXTRA_PACKAGES tag can be used to specify one or more LaTeX package names
+# that should be included in the LaTeX output. To get the times font for
+# instance you can specify
+# EXTRA_PACKAGES=times
+# If left blank no extra packages will be included.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+EXTRA_PACKAGES         =
+
+# The LATEX_HEADER tag can be used to specify a personal LaTeX header for the
+# generated LaTeX document. The header should contain everything until the first
+# chapter. If it is left blank doxygen will generate a standard header. See
+# section "Doxygen usage" for information on how to let doxygen write the
+# default header to a separate file.
+#
+# Note: Only use a user-defined header if you know what you are doing! The
+# following commands have a special meaning inside the header: $title,
+# $datetime, $date, $doxygenversion, $projectname, $projectnumber,
+# $projectbrief, $projectlogo. Doxygen will replace $title with the empy string,
+# for the replacement values of the other commands the user is refered to
+# HTML_HEADER.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_HEADER           =
+
+# The LATEX_FOOTER tag can be used to specify a personal LaTeX footer for the
+# generated LaTeX document. The footer should contain everything after the last
+# chapter. If it is left blank doxygen will generate a standard footer. See
+# LATEX_HEADER for more information on how to generate a default footer and what
+# special commands can be used inside the footer.
+#
+# Note: Only use a user-defined footer if you know what you are doing!
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_FOOTER           =
+
+# The LATEX_EXTRA_FILES tag can be used to specify one or more extra images or
+# other source files which should be copied to the LATEX_OUTPUT output
+# directory. Note that the files will be copied as-is; there are no commands or
+# markers available.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+#LATEX_EXTRA_FILES      =
+
+# If the PDF_HYPERLINKS tag is set to YES, the LaTeX that is generated is
+# prepared for conversion to PDF (using ps2pdf or pdflatex). The PDF file will
+# contain links (just like the HTML output) instead of page references. This
+# makes the output suitable for online browsing using a PDF viewer.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+PDF_HYPERLINKS         = YES
+
+# If the USE_PDFLATEX tag is set to YES, doxygen will use pdflatex to generate
+# the PDF file directly from the LaTeX files. Set this option to YES to get a
+# higher quality PDF documentation.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+USE_PDFLATEX           = YES
+
+# If the LATEX_BATCHMODE tag is set to YES, doxygen will add the \batchmode
+# command to the generated LaTeX files. This will instruct LaTeX to keep running
+# if errors occur, instead of asking the user for help. This option is also used
+# when generating formulas in HTML.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_BATCHMODE        = NO
+
+# If the LATEX_HIDE_INDICES tag is set to YES then doxygen will not include the
+# index chapters (such as File Index, Compound Index, etc.) in the output.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_HIDE_INDICES     = NO
+
+# If the LATEX_SOURCE_CODE tag is set to YES then doxygen will include source
+# code with syntax highlighting in the LaTeX output.
+#
+# Note that which sources are shown also depends on other settings such as
+# SOURCE_BROWSER.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_SOURCE_CODE      = NO
+
+# The LATEX_BIB_STYLE tag can be used to specify the style to use for the
+# bibliography, e.g. plainnat, or ieeetr. See
+# http://en.wikipedia.org/wiki/BibTeX and \cite for more info.
+# The default value is: plain.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_BIB_STYLE        = plain
+
+#---------------------------------------------------------------------------
+# Configuration options related to the RTF output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_RTF tag is set to YES doxygen will generate RTF output. The
+# RTF output is optimized for Word 97 and may not look too pretty with other RTF
+# readers/editors.
+# The default value is: NO.
+
+GENERATE_RTF           = NO
+
+# The RTF_OUTPUT tag is used to specify where the RTF docs will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it.
+# The default directory is: rtf.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+RTF_OUTPUT             = rtf
+
+# If the COMPACT_RTF tag is set to YES doxygen generates more compact RTF
+# documents. This may be useful for small projects and may help to save some
+# trees in general.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+COMPACT_RTF            = NO
+
+# If the RTF_HYPERLINKS tag is set to YES, the RTF that is generated will
+# contain hyperlink fields. The RTF file will contain links (just like the HTML
+# output) instead of page references. This makes the output suitable for online
+# browsing using Word or some other Word compatible readers that support those
+# fields.
+#
+# Note: WordPad (write) and others do not support links.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+RTF_HYPERLINKS         = NO
+
+# Load stylesheet definitions from file. Syntax is similar to doxygen's config
+# file, i.e. a series of assignments. You only have to provide replacements,
+# missing definitions are set to their default value.
+#
+# See also section "Doxygen usage" for information on how to generate the
+# default style sheet that doxygen normally uses.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+RTF_STYLESHEET_FILE    =
+
+# Set optional variables used in the generation of an RTF document. Syntax is
+# similar to doxygen's config file. A template extensions file can be generated
+# using doxygen -e rtf extensionFile.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+RTF_EXTENSIONS_FILE    =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the man page output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_MAN tag is set to YES doxygen will generate man pages for
+# classes and files.
+# The default value is: NO.
+
+GENERATE_MAN           = NO
+
+# The MAN_OUTPUT tag is used to specify where the man pages will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it. A directory man3 will be created inside the directory specified by
+# MAN_OUTPUT.
+# The default directory is: man.
+# This tag requires that the tag GENERATE_MAN is set to YES.
+
+MAN_OUTPUT             = man
+
+# The MAN_EXTENSION tag determines the extension that is added to the generated
+# man pages. In case the manual section does not start with a number, the number
+# 3 is prepended. The dot (.) at the beginning of the MAN_EXTENSION tag is
+# optional.
+# The default value is: .3.
+# This tag requires that the tag GENERATE_MAN is set to YES.
+
+MAN_EXTENSION          = .3
+
+# The MAN_SUBDIR tag determines the name of the directory created within
+# MAN_OUTPUT in which the man pages are placed. If defaults to man followed by
+# MAN_EXTENSION with the initial . removed.
+# This tag requires that the tag GENERATE_MAN is set to YES.
+
+#MAN_SUBDIR             =
+
+# If the MAN_LINKS tag is set to YES and doxygen generates man output, then it
+# will generate one additional man file for each entity documented in the real
+# man page(s). These additional files only source the real man page, but without
+# them the man command would be unable to find the correct page.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_MAN is set to YES.
+
+MAN_LINKS              = NO
+
+#---------------------------------------------------------------------------
+# Configuration options related to the XML output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_XML tag is set to YES doxygen will generate an XML file that
+# captures the structure of the code including all documentation.
+# The default value is: NO.
+
+GENERATE_XML           = YES
+
+# The XML_OUTPUT tag is used to specify where the XML pages will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it.
+# The default directory is: xml.
+# This tag requires that the tag GENERATE_XML is set to YES.
+
+XML_OUTPUT             = xml
+
+# If the XML_PROGRAMLISTING tag is set to YES doxygen will dump the program
+# listings (including syntax highlighting and cross-referencing information) to
+# the XML output. Note that enabling this will significantly increase the size
+# of the XML output.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_XML is set to YES.
+
+XML_PROGRAMLISTING     = YES
+
+#---------------------------------------------------------------------------
+# Configuration options related to the DOCBOOK output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_DOCBOOK tag is set to YES doxygen will generate Docbook files
+# that can be used to generate PDF.
+# The default value is: NO.
+
+#GENERATE_DOCBOOK       = NO
+
+# The DOCBOOK_OUTPUT tag is used to specify where the Docbook pages will be put.
+# If a relative path is entered the value of OUTPUT_DIRECTORY will be put in
+# front of it.
+# The default directory is: docbook.
+# This tag requires that the tag GENERATE_DOCBOOK is set to YES.
+
+#DOCBOOK_OUTPUT         = docbook
+
+# If the DOCBOOK_PROGRAMLISTING tag is set to YES doxygen will include the
+# program listings (including syntax highlighting and cross-referencing
+# information) to the DOCBOOK output. Note that enabling this will significantly
+# increase the size of the DOCBOOK output.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_DOCBOOK is set to YES.
+
+#DOCBOOK_PROGRAMLISTING = NO
+
+#---------------------------------------------------------------------------
+# Configuration options for the AutoGen Definitions output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_AUTOGEN_DEF tag is set to YES doxygen will generate an AutoGen
+# Definitions (see http://autogen.sf.net) file that captures the structure of
+# the code including all documentation. Note that this feature is still
+# experimental and incomplete at the moment.
+# The default value is: NO.
+
+GENERATE_AUTOGEN_DEF   = NO
+
+#---------------------------------------------------------------------------
+# Configuration options related to the Perl module output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_PERLMOD tag is set to YES doxygen will generate a Perl module
+# file that captures the structure of the code including all documentation.
+#
+# Note that this feature is still experimental and incomplete at the moment.
+# The default value is: NO.
+
+GENERATE_PERLMOD       = NO
+
+# If the PERLMOD_LATEX tag is set to YES doxygen will generate the necessary
+# Makefile rules, Perl scripts and LaTeX code to be able to generate PDF and DVI
+# output from the Perl module output.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_PERLMOD is set to YES.
+
+PERLMOD_LATEX          = NO
+
+# If the PERLMOD_PRETTY tag is set to YES the Perl module output will be nicely
+# formatted so it can be parsed by a human reader. This is useful if you want to
+# understand what is going on. On the other hand, if this tag is set to NO the
+# size of the Perl module output will be much smaller and Perl will parse it
+# just the same.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_PERLMOD is set to YES.
+
+PERLMOD_PRETTY         = YES
+
+# The names of the make variables in the generated doxyrules.make file are
+# prefixed with the string contained in PERLMOD_MAKEVAR_PREFIX. This is useful
+# so different doxyrules.make files included by the same Makefile don't
+# overwrite each other's variables.
+# This tag requires that the tag GENERATE_PERLMOD is set to YES.
+
+PERLMOD_MAKEVAR_PREFIX =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the preprocessor
+#---------------------------------------------------------------------------
+
+# If the ENABLE_PREPROCESSING tag is set to YES doxygen will evaluate all
+# C-preprocessor directives found in the sources and include files.
+# The default value is: YES.
+
+ENABLE_PREPROCESSING   = YES
+
+# If the MACRO_EXPANSION tag is set to YES doxygen will expand all macro names
+# in the source code. If set to NO only conditional compilation will be
+# performed. Macro expansion can be done in a controlled way by setting
+# EXPAND_ONLY_PREDEF to YES.
+# The default value is: NO.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+MACRO_EXPANSION        = NO
+
+# If the EXPAND_ONLY_PREDEF and MACRO_EXPANSION tags are both set to YES then
+# the macro expansion is limited to the macros specified with the PREDEFINED and
+# EXPAND_AS_DEFINED tags.
+# The default value is: NO.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+EXPAND_ONLY_PREDEF     = NO
+
+# If the SEARCH_INCLUDES tag is set to YES the includes files in the
+# INCLUDE_PATH will be searched if a #include is found.
+# The default value is: YES.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+SEARCH_INCLUDES        = YES
+
+# The INCLUDE_PATH tag can be used to specify one or more directories that
+# contain include files that are not input files but should be processed by the
+# preprocessor.
+# This tag requires that the tag SEARCH_INCLUDES is set to YES.
+
+INCLUDE_PATH           =
+
+# You can use the INCLUDE_FILE_PATTERNS tag to specify one or more wildcard
+# patterns (like *.h and *.hpp) to filter out the header-files in the
+# directories. If left blank, the patterns specified with FILE_PATTERNS will be
+# used.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+INCLUDE_FILE_PATTERNS  =
+
+# The PREDEFINED tag can be used to specify one or more macro names that are
+# defined before the preprocessor is started (similar to the -D option of e.g.
+# gcc). The argument of the tag is a list of macros of the form: name or
+# name=definition (no spaces). If the definition and the "=" are omitted, "=1"
+# is assumed. To prevent a macro definition from being undefined via #undef or
+# recursively expanded use the := operator instead of the = operator.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+PREDEFINED             = MXNET_USE_CUDA DMLC_USE_CXX11
+
+# If the MACRO_EXPANSION and EXPAND_ONLY_PREDEF tags are set to YES then this
+# tag can be used to specify a list of macro names that should be expanded. The
+# macro definition that is found in the sources will be used. Use the PREDEFINED
+# tag if you want to use a different macro definition that overrules the
+# definition found in the source code.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+EXPAND_AS_DEFINED      =
+
+# If the SKIP_FUNCTION_MACROS tag is set to YES then doxygen's preprocessor will
+# remove all references to function-like macros that are alone on a line, have
+# an all uppercase name, and do not end with a semicolon. Such function macros
+# are typically used for boiler-plate code, and will confuse the parser if not
+# removed.
+# The default value is: YES.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+SKIP_FUNCTION_MACROS   = YES
+
+#---------------------------------------------------------------------------
+# Configuration options related to external references
+#---------------------------------------------------------------------------
+
+# The TAGFILES tag can be used to specify one or more tag files. For each tag
+# file the location of the external documentation should be added. The format of
+# a tag file without this location is as follows:
+# TAGFILES = file1 file2 ...
+# Adding location for the tag files is done as follows:
+# TAGFILES = file1=loc1 "file2 = loc2" ...
+# where loc1 and loc2 can be relative or absolute paths or URLs. See the
+# section "Linking to external documentation" for more information about the use
+# of tag files.
+# Note: Each tag file must have a unique name (where the name does NOT include
+# the path). If a tag file is not located in the directory in which doxygen is
+# run, you must also specify the path to the tagfile here.
+
+TAGFILES               =
+
+# When a file name is specified after GENERATE_TAGFILE, doxygen will create a
+# tag file that is based on the input files it reads. See section "Linking to
+# external documentation" for more information about the usage of tag files.
+
+GENERATE_TAGFILE       =
+
+# If the ALLEXTERNALS tag is set to YES all external class will be listed in the
+# class index. If set to NO only the inherited external classes will be listed.
+# The default value is: NO.
+
+ALLEXTERNALS           = NO
+
+# If the EXTERNAL_GROUPS tag is set to YES all external groups will be listed in
+# the modules index. If set to NO, only the current project's groups will be
+# listed.
+# The default value is: YES.
+
+EXTERNAL_GROUPS        = YES
+
+# If the EXTERNAL_PAGES tag is set to YES all external pages will be listed in
+# the related pages index. If set to NO, only the current project's pages will
+# be listed.
+# The default value is: YES.
+
+#EXTERNAL_PAGES         = YES
+
+# The PERL_PATH should be the absolute path and name of the perl script
+# interpreter (i.e. the result of 'which perl').
+# The default file (with absolute path) is: /usr/bin/perl.
+
+PERL_PATH              = /usr/bin/perl
+
+#---------------------------------------------------------------------------
+# Configuration options related to the dot tool
+#---------------------------------------------------------------------------
+
+# If the CLASS_DIAGRAMS tag is set to YES doxygen will generate a class diagram
+# (in HTML and LaTeX) for classes with base or super classes. Setting the tag to
+# NO turns the diagrams off. Note that this option also works with HAVE_DOT
+# disabled, but it is recommended to install and use dot, since it yields more
+# powerful graphs.
+# The default value is: YES.
+
+CLASS_DIAGRAMS         = YES
+
+# You can define message sequence charts within doxygen comments using the \msc
+# command. Doxygen will then run the mscgen tool (see:
+# http://www.mcternan.me.uk/mscgen/)) to produce the chart and insert it in the
+# documentation. The MSCGEN_PATH tag allows you to specify the directory where
+# the mscgen tool resides. If left empty the tool is assumed to be found in the
+# default search path.
+
+MSCGEN_PATH            =
+
+# You can include diagrams made with dia in doxygen documentation. Doxygen will
+# then run dia to produce the diagram and insert it in the documentation. The
+# DIA_PATH tag allows you to specify the directory where the dia binary resides.
+# If left empty dia is assumed to be found in the default search path.
+
+#DIA_PATH               =
+
+# If set to YES, the inheritance and collaboration graphs will hide inheritance
+# and usage relations if the target is undocumented or is not a class.
+# The default value is: YES.
+
+HIDE_UNDOC_RELATIONS   = YES
+
+# If you set the HAVE_DOT tag to YES then doxygen will assume the dot tool is
+# available from the path. This tool is part of Graphviz (see:
+# http://www.graphviz.org/), a graph visualization toolkit from AT&T and Lucent
+# Bell Labs. The other options in this section have no effect if this option is
+# set to NO
+# The default value is: YES.
+
+HAVE_DOT               = YES
+
+# The DOT_NUM_THREADS specifies the number of dot invocations doxygen is allowed
+# to run in parallel. When set to 0 doxygen will base this on the number of
+# processors available in the system. You can set it explicitly to a value
+# larger than 0 to get control over the balance between CPU load and processing
+# speed.
+# Minimum value: 0, maximum value: 32, default value: 0.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_NUM_THREADS        = 0
+
+# When you want a differently looking font in the dot files that doxygen
+# generates you can specify the font name using DOT_FONTNAME. You need to make
+# sure dot is able to find the font, which can be done by putting it in a
+# standard location or by setting the DOTFONTPATH environment variable or by
+# setting DOT_FONTPATH to the directory containing the font.
+# The default value is: Helvetica.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_FONTNAME           = Helvetica
+
+# The DOT_FONTSIZE tag can be used to set the size (in points) of the font of
+# dot graphs.
+# Minimum value: 4, maximum value: 24, default value: 10.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_FONTSIZE           = 10
+
+# By default doxygen will tell dot to use the default font as specified with
+# DOT_FONTNAME. If you specify a different font using DOT_FONTNAME you can set
+# the path where dot can find it using this tag.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_FONTPATH           =
+
+# If the CLASS_GRAPH tag is set to YES then doxygen will generate a graph for
+# each documented class showing the direct and indirect inheritance relations.
+# Setting this tag to YES will force the CLASS_DIAGRAMS tag to NO.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+CLASS_GRAPH            = YES
+
+# If the COLLABORATION_GRAPH tag is set to YES then doxygen will generate a
+# graph for each documented class showing the direct and indirect implementation
+# dependencies (inheritance, containment, and class references variables) of the
+# class with other documented classes.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+COLLABORATION_GRAPH    = YES
+
+# If the GROUP_GRAPHS tag is set to YES then doxygen will generate a graph for
+# groups, showing the direct groups dependencies.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+GROUP_GRAPHS           = YES
+
+# If the UML_LOOK tag is set to YES doxygen will generate inheritance and
+# collaboration diagrams in a style similar to the OMG's Unified Modeling
+# Language.
+# The default value is: NO.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+UML_LOOK               = YES
+
+# If the UML_LOOK tag is enabled, the fields and methods are shown inside the
+# class node. If there are many fields or methods and many nodes the graph may
+# become too big to be useful. The UML_LIMIT_NUM_FIELDS threshold limits the
+# number of items for each type to make the size more manageable. Set this to 0
+# for no limit. Note that the threshold may be exceeded by 50% before the limit
+# is enforced. So when you set the threshold to 10, up to 15 fields may appear,
+# but if the number exceeds 15, the total amount of fields shown is limited to
+# 10.
+# Minimum value: 0, maximum value: 100, default value: 10.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+#UML_LIMIT_NUM_FIELDS   = 10
+
+# If the TEMPLATE_RELATIONS tag is set to YES then the inheritance and
+# collaboration graphs will show the relations between templates and their
+# instances.
+# The default value is: NO.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+TEMPLATE_RELATIONS     = NO
+
+# If the INCLUDE_GRAPH, ENABLE_PREPROCESSING and SEARCH_INCLUDES tags are set to
+# YES then doxygen will generate a graph for each documented file showing the
+# direct and indirect include dependencies of the file with other documented
+# files.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+INCLUDE_GRAPH          = YES
+
+# If the INCLUDED_BY_GRAPH, ENABLE_PREPROCESSING and SEARCH_INCLUDES tags are
+# set to YES then doxygen will generate a graph for each documented file showing
+# the direct and indirect include dependencies of the file with other documented
+# files.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+INCLUDED_BY_GRAPH      = YES
+
+# If the CALL_GRAPH tag is set to YES then doxygen will generate a call
+# dependency graph for every global function or class method.
+#
+# Note that enabling this option will significantly increase the time of a run.
+# So in most cases it will be better to enable call graphs for selected
+# functions only using the \callgraph command.
+# The default value is: NO.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+CALL_GRAPH             = NO
+
+# If the CALLER_GRAPH tag is set to YES then doxygen will generate a caller
+# dependency graph for every global function or class method.
+#
+# Note that enabling this option will significantly increase the time of a run.
+# So in most cases it will be better to enable caller graphs for selected
+# functions only using the \callergraph command.
+# The default value is: NO.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+CALLER_GRAPH           = NO
+
+# If the GRAPHICAL_HIERARCHY tag is set to YES then doxygen will graphical
+# hierarchy of all classes instead of a textual one.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+GRAPHICAL_HIERARCHY    = YES
+
+# If the DIRECTORY_GRAPH tag is set to YES then doxygen will show the
+# dependencies a directory has on other directories in a graphical way. The
+# dependency relations are determined by the #include relations between the
+# files in the directories.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DIRECTORY_GRAPH        = YES
+
+# The DOT_IMAGE_FORMAT tag can be used to set the image format of the images
+# generated by dot.
+# Note: If you choose svg you need to set HTML_FILE_EXTENSION to xhtml in order
+# to make the SVG files visible in IE 9+ (other browsers do not have this
+# requirement).
+# Possible values are: png, png:cairo, png:cairo:cairo, png:cairo:gd, png:gd,
+# png:gd:gd, jpg, jpg:cairo, jpg:cairo:gd, jpg:gd, jpg:gd:gd, gif, gif:cairo,
+# gif:cairo:gd, gif:gd, gif:gd:gd and svg.
+# The default value is: png.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_IMAGE_FORMAT       = png
+
+# If DOT_IMAGE_FORMAT is set to svg, then this option can be set to YES to
+# enable generation of interactive SVG images that allow zooming and panning.
+#
+# Note that this requires a modern browser other than Internet Explorer. Tested
+# and working are Firefox, Chrome, Safari, and Opera.
+# Note: For IE 9+ you need to set HTML_FILE_EXTENSION to xhtml in order to make
+# the SVG files visible. Older versions of IE do not have SVG support.
+# The default value is: NO.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+INTERACTIVE_SVG        = NO
+
+# The DOT_PATH tag can be used to specify the path where the dot tool can be
+# found. If left blank, it is assumed the dot tool can be found in the path.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_PATH               =
+
+# The DOTFILE_DIRS tag can be used to specify one or more directories that
+# contain dot files that are included in the documentation (see the \dotfile
+# command).
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOTFILE_DIRS           =
+
+# The MSCFILE_DIRS tag can be used to specify one or more directories that
+# contain msc files that are included in the documentation (see the \mscfile
+# command).
+
+MSCFILE_DIRS           =
+
+# The DIAFILE_DIRS tag can be used to specify one or more directories that
+# contain dia files that are included in the documentation (see the \diafile
+# command).
+
+#DIAFILE_DIRS           =
+
+# When using plantuml, the PLANTUML_JAR_PATH tag should be used to specify the
+# path where java can find the plantuml.jar file. If left blank, it is assumed
+# PlantUML is not used or called during a preprocessing step. Doxygen will
+# generate a warning when it encounters a \startuml command in this case and
+# will not generate output for the diagram.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+#PLANTUML_JAR_PATH      =
+
+# The DOT_GRAPH_MAX_NODES tag can be used to set the maximum number of nodes
+# that will be shown in the graph. If the number of nodes in a graph becomes
+# larger than this value, doxygen will truncate the graph, which is visualized
+# by representing a node as a red box. Note that doxygen if the number of direct
+# children of the root node in a graph is already larger than
+# DOT_GRAPH_MAX_NODES then the graph will not be shown at all. Also note that
+# the size of a graph can be further restricted by MAX_DOT_GRAPH_DEPTH.
+# Minimum value: 0, maximum value: 10000, default value: 50.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_GRAPH_MAX_NODES    = 50
+
+# The MAX_DOT_GRAPH_DEPTH tag can be used to set the maximum depth of the graphs
+# generated by dot. A depth value of 3 means that only nodes reachable from the
+# root by following a path via at most 3 edges will be shown. Nodes that lay
+# further from the root node will be omitted. Note that setting this option to 1
+# or 2 may greatly reduce the computation time needed for large code bases. Also
+# note that the size of a graph can be further restricted by
+# DOT_GRAPH_MAX_NODES. Using a depth of 0 means no depth restriction.
+# Minimum value: 0, maximum value: 1000, default value: 0.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+MAX_DOT_GRAPH_DEPTH    = 0
+
+# Set the DOT_TRANSPARENT tag to YES to generate images with a transparent
+# background. This is disabled by default, because dot on Windows does not seem
+# to support this out of the box.
+#
+# Warning: Depending on the platform used, enabling this option may lead to
+# badly anti-aliased labels on the edges of a graph (i.e. they become hard to
+# read).
+# The default value is: NO.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_TRANSPARENT        = NO
+
+# Set the DOT_MULTI_TARGETS tag to YES allow dot to generate multiple output
+# files in one run (i.e. multiple -o and -T options on the command line). This
+# makes dot run faster, but since only newer versions of dot (>1.8.10) support
+# this, this feature is disabled by default.
+# The default value is: NO.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_MULTI_TARGETS      = YES
+
+# If the GENERATE_LEGEND tag is set to YES doxygen will generate a legend page
+# explaining the meaning of the various boxes and arrows in the dot generated
+# graphs.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+GENERATE_LEGEND        = YES
+
+# If the DOT_CLEANUP tag is set to YES doxygen will remove the intermediate dot
+# files that are used to generate the various graphs.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_CLEANUP            = YES
\ No newline at end of file
diff --git a/docs/cpp_docs/Makefile b/docs/cpp_docs/Makefile
new file mode 100644
index 000000000000..c3496b7086ef
--- /dev/null
+++ b/docs/cpp_docs/Makefile
@@ -0,0 +1,26 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+all: html
+
+html:
+	mkdir -p build/html
+	doxygen Doxyfile
+
+
+clean:
+	rm -rf build
diff --git a/docs/python_docs/README.md b/docs/python_docs/README.md
new file mode 100644
index 000000000000..8b4c13b0de43
--- /dev/null
+++ b/docs/python_docs/README.md
@@ -0,0 +1,24 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# README
+
+Preview at https://mxnet-beta.staged.apache.org/
+
+## [Building the Docs and Website](https://cwiki.apache.org/confluence/display/MXNET/Building+the+New+Website)
+
+## [Technical details for the building the Python microsite](python/README.md)
\ No newline at end of file
diff --git a/docs/python_docs/_static/apache_incubator_logo.png b/docs/python_docs/_static/apache_incubator_logo.png
new file mode 100644
index 0000000000000000000000000000000000000000..96804c4a55a72bec853b3ae85bca0635207cb79f
GIT binary patch
literal 16552
zcmbt+^;?u*@b*KO$gY%#w1`WIq;xIP-7V-6Qqmm?$RdqPcPvOO9nvZwT>?uip>zmH
zH+<L6_kG`g;QeKvy{^6XoHJ+UoSC`jo+naELy?^H0Vx0g<jP7=9RR>N!hR1TCd7XJ
z{J0zf0L*|gR94R?b0^C?bFS<7>tbtyS7i%rRZPsc>6g(s;~ASmNB6{}RYGL!NZy5w
zvg8rTk$~PXA1#RA=M$bRqNjb^{Us+b5Sexw6#Qq)c`v}i-WxgJ_NqN6PO3QRg@DWH
zqc%t-aQ_Zr=(m@A|38nsI#3WnY7@w9<%8GK#U;^jJ^|%PSo0>EjF5p$xhBua?A!GF
zH9cI<4|@Hhc=G?7&59XnlQhpxErvR?x)bO=buM^l*<;Jy3w*gri|cz<y!hn8Spq4u
zME7rbAhCIsJl&l^^5X%2jg_v<g|aLvB>?#PD7SVGc5L5p8t8937Cf$5kUaiRk`Gmz
zG^AOEL&lbEf!`0!F28NWu6LP4v^m6F9>TQRh8^Q!JimnX4iEN(CoQiJU0RQZcnr7}
zUuC4B&st)suX`SJJO3MoSANQ1qa1m0R+_QV!ZmE~ZFb`LP1|-pv_P|&+Tp{H$@1#)
zVZ>Nn#BeU%X;v3c?PXz3r}p|HS7Xz`L#reag-31w$(lC?!^)X1DJR*nL-m%{u%5B*
zLU5U|Sn{RQ>8U_1N>j7x-0gZtef`~`yZW|lT~grJ6w{CG^nrA-6aOg(DxadaaS=sd
z`BV2c!1dx;({738fcf5))3%8(FJIop(H8wi#=uQ#(0!5S4Go8uj+_Mdw*UR7T8i$*
z)^WjNE<xPRjNey_vW11q?zZj_p2y*ngq5l7)t4u(BeL_h+x|Uozq&jgxXxyHI_lN=
zrssb*a)X~Vi?YnGEQKN<$6;}CTKw^p<gtAdW<El3vMAkSf9EI=bJX_vVeMD3zbKSK
zcSyK)z5PF9lIQgx2_M#YRew3{M`k)TK#*8}INr1DM<jjE7ZWADI2;ITIelkF1^`uq
z^|6^PcMXm#sXQ_NEgB!WLM=8q!c>$^cgpY?Nv#|iU*2gG^PO>fIJ)k);1^%x;M0;J
zn~>UEA^^xmmE(2H;$AwQ>F2%v&x~0pgGd?39w`=U?Ua*`CpCSZJF~s)&6$D}@)H4|
z)xeZ#MRlg=K(Cy1s=@jRcY5{J?i?3GCDH#5LT^%j%xDt~#&o(*zV_b4galyb6u#<~
zS~>*3)E9j5^QFvOvG+x+VbB*a@Thn(E>#q_jBtdfNR2Qwug+>MVQ`KTcn}V94+D{R
zC-;qBKN*5}H&)O3zMfY=jsH0b`7r&gmPiJI@5=&f(sU9C-R>}={cjKA(xr_Mr{+4Z
z`&dKaMXXCjC2{SiP{W8c@x#{}60i9HypYa87q%swF%^TKd;lQez!IkKXr&U8s`Dli
zW-N);3rUNFJAcy2HdXqTF9SJdW3A{O94|yHfB*a`%^}rS`^~K}WVPCA!c+;NQ&#hY
zQ7Mi8&;!%ZxckR!6XK!MaCtT@I(KwE3w+JD?o>DdTb465@UJIH$52T|XpZby%bhlv
zcdStF#o+)U)4%kq**cg=tG66)e9a?cZ&!9JCJx(6>e0bTIu`UF_>gzOL-JiL3%00Z
zh))0IM~H5<mP+7DV|46{MbF@)uz2lWJ?7^-E>BeJ2PKS%pD_U;@N`f$f3zQ&+6J42
zjSUO5xWly%mDf_qbZDZIhSx<nWlW?#Es>buYYlT@hOK*`WNPmE`DcE9D9!``nFNv(
zWfAT4MLtMeu=<Wqbeag)2X1o`$f{6$Kx$bOkLpAr*Wtvv|3`M!YUcc%v@~%}cUCv^
zscTt?XAb%z0Cn79HkDOcuThwy+Lmh!nRK$fI4C?*mRG??c@7T@6ENhpP!9XY+swJW
zpDGQUjvkwK=sL|40`bq)z0SQn`*ohJN`F`Eioqd=!~1E28OyRdlGBAVDb0|C#gE3Y
zTuJ$M*x2(TwMl^(y!ZACX4noiMhJf{SuafljzDU7V)o|1$O-*qnEBlAx4-r@p?6su
zT1HZv;y~64PjG2Id{*3ufg7!QtCK9XslFg#amxS?zfYvg8=pHpnOt1-!*#$n0IIP^
zp$3s83WR*#=;#%*KJl~oVtjF4GJkZWsKL`<W+e;F@wprf5D;(x0G6%?Qv|^~#4P<l
z?uWE-#h3G59={>(|9(hbkP=vDeBVU&yLnfAv)>HjDgJsUO}6X4K%9#7<1ix)Xhy`N
zqP$){cV&+kr1_&IH|w7Fp6;BJQsRCBn$A6^;5Qioj7|m4(J6r`rxVW%$kT0i{{G!u
zu)Y3W?LB_5*Ry8o0ttbeB6lpm4o3Loq&0nBAS7U&y6mtZ*8VYwN*<io{$`J=RK^6W
z5LZX*>Uzl$e;W*p;xIn~jcGJE-9FnnygyJg+>fl7*ig4aWsKi7T;VGC7Ox>o<+rpM
z%27r0^${>7_J_fLn~awe+OoTS*+EcvC=C6Uptu;yHtainS#CMMXMQ(tfmL^RhwtzL
z0N`iVvt*c7#{iI=Uhxi@%>t+Hc?BV>#m0^*Bo44MC&M~to16UmZq%gE0O_3qnJ$+@
zl&*?#9-@>z-xdD++2X~W4<4+B8$2#o=Rs{tEuO|?FWH%cMsH6do7Dl}CC&S~iN!_l
z*kG&R^8Ln#6b2nH06=4K6SXMz8_BoD!;1O<mxD941==`g#W3d+gwvFk$wHqZcXABW
zjK7D$W2s9B#96l{?5M$0kaE57PS1H07fZ;oU#h2R@+3XDILx^C8vv@7n<A6Ke7oMN
zpnQj4)sT0!G17=%M~WDVCZHq}I<L$4UOFbVQj0F}O26%uIvQ8)ATQgM`#y*YDuhn(
zMlrD7a5x2Pv8--=EW9jYvrcJc5|bz1k8fNY%TjW<4r|ne){l}`JGF2C0N8$@hE#y~
z+5Lk@{Nclee~J_-0x9bNH15h{te?c|fwnj=SOAY!RnWk0Mn^DzG^fJQDAVefVDqIw
zgFVK-&&A2j$DHhI8bje2ZwY1S=JtnmYL(B<GL6PXO7vNkH2$(GzJ-l(@cChSIfwbg
z6W4Je8o~Q2(Y;iD1N4ShQS%pWk4f_VI-6Gg&2_c5-#$((H?B#}4Zd1r`6C>DuTI9<
z>tr&2qynXEdUR<kommo4@@=qF!Vu+AP~idEU#q(grcr6Y&R6B7De{+6?$5k29qPDv
zK?=;n6J{1Jr!upZeE&yt(>JXrtlArI7S_cdOSLSY781nMXa$W1lS)(f&00715#IY+
zsvxGhb6Ik$@U6=0cJJMS)KK6r8Oh6IdH$rQ?5V!#jXBN}lVDbp?1o#r2{%uo)F!TT
zYHw13%H^10U2vJyZh0o6Mo5sI)l1dE8#zIFgS_+MC(FI?g`>eH3O|FL{h^@X#)oyA
z95;sHWLzA>oK^`hnD6B#Ys|t^)R`z>%;)?4H51)UR!zf6#n~vR-6JhnkPCss004^6
zJ}>Mj1n+<Tyt-8i(socxNV_$~T2x4y=_~ni$W>tky1AZPz?%$Dbjs7T;}gtS(T3B3
z5Ah(6>uBxNxdY0R^X#D#V21;1q5YwNVyj!hs2k4!v$$Hsxde5sL1_6KLqfcs%pknC
z^?3L)>H?QdOcsBcZp%KZ5;?4f;vqDdka2O)&%Ae8ozof?SC=#V3EyU4T0bsC6to4h
zKpT6N?2OU9)&qaJSbtj<I%sju9Dg55?+P2*TXdCjz+ncJ#`>OVX)hE!8ISkdEu2q<
zau(?xIBY3=5}p)Td~Oswz1<YBO>5BFE%hrjE7P_o@D8qwX3M&dKZa`rr>uPDc55jF
zp1mi^wnP-P8V)kW`&vFxPx;G`YFB`Guyv5EyLT`aWSu_3&aTdRRn!=qkGRRB(HTF$
z1!NqqJ1SD2aWQllli(4vHh)UHTrmX;^zfAVKiVBP>9_;%v=dCpY|)vpAm<=VqH-~-
z7Pq~|nvdH34v0a3(O9s;o@)CaiYJ~@xN9n%!9Q+g28v>-Z~^F+I5E(45^JgV@N|pj
zcis2=)!$AwuJg2=IKbOq?Myusfv%XhTX$(${Yb$Ss)2XPQ`2VDRa0inshVknMc3~b
zF$B?u?CxU<rT@$XFzub1y-NPf3$5so_2$qC9jRa(kc9FtxD!OJRv6y!7s9p#6#0~2
z^5m|C-R@d)!-fmld9M8)#pBzMsL@n4gwz9VFPENQA5J)Zw^x4qM<i|s&jg4EOwMM7
z>m)KgFRGMv*NhXgTf3r;7h-xZ!iNTrfv@xGX_RAfE|w10R>D(75uxn)*WZ(eUY;ez
zd#^JKVompF%v2TOOWdld`BglJ>9tQ+l^+Q$t~wtV5`fqPe_^jjLLm9;qrCGb0Y<zv
zEsO8d4rh1F_Eu~qmc_llcQ=M>yQG4~4(%^f)=lC-xM{v^T49_xI=@Xa0)#nMe{{#|
ztuQEp%`MXK0Olo=g`kG<pv_YRD8EcmOYrpv7T=MGdQY;{CZd&F(-l;ly!M9Q@1tkx
zIg&xq#S&bnqPH$q6B%5)?<*VM=&zUnlt8iBh7y@peQm_se3rUs>l5MNW<)(fxucf5
z<-^K#%~|&H=1u@ip@!*Tjo>t4lCTL9r^-{oXMr}2*hFcpHKqFEdlb+nMxe#EW#n}F
zxyBVYf2-<07h8KK0wofl;BB~LmEgxjm8C`HcRkQ(kuX^J0c7gZ=)LoM`D41r*Yk^R
zwokgZHTr3di)J$tzmo?#-%wmepDjG_>}M+n8)1H_weFWzZ4J}1UbxKOIGm$Xk|{0?
z@i>%0V`?@F4-)+x460X26VN46kX0U0bhWty%>4P`LxJ0;P90non-7AzSDdI6hmIv_
zvPArlQ&0E2`!KJ{>qVZJc(5C2l%FKBkbve+c65TnmHbDDKUkzZt^RJZUVLFmD6-K0
zTLNSf0(_RZj>wySHQLIzHT-Eb#u#qrOH1)Mu~FO1Th<}41i_zTOfTJhpp=CJz<U+C
zjH<&S;D2!*o~#WccUUUL=yA*v+>Q(yEphpB<%Mg#ch<3>mfOz5GD2S7B5Z(e=eOQ4
z00->qwVMERpK;jAW7(l|Zo$Pj<rWS&Co@a_8Yqm2bkHEZ)uj0wX6prBE_OLX1F^gi
zc#5KSCAxXw63Lc7G}so!6Y&j%Xr&V4NH4u79Z-YEg^iEeZjYcAQXW?EXG={5X^Glf
z&nL(JMjtDYF(=I#ktnJ8Kh=wxJ9w2dn^vFnhnxG-3SR`ZL2Gj@Iw~fnN$1V}Q#c|`
zKiPDfiU=Gy>HFzJ1+IWgcKg~8%dbgx<e!>^WwR;u#<Py%)SCNuX5v%iTzpO=$360q
z(R4B=j8Z*}Uv{Ib@COLI(H!DsFllnt3fL<*a&rzIjA@cYu`ur2cE8_$`7k{nEe5I<
z(U$DCcn@a|zU|OP7zYdU9m=@mzzs-PmgL|ybv)VpXZ<z~S=leT)IS~$-C;@0zSbgU
z;alX9d|+j$j(UUfa+rNNP;>WLfrq>yi*5KY^vgjtmrEF8%y@OUXMwKhrLnvTdckv!
zOGY{8?FOG5QOu6;W1Ld2VUUX8hh}F=%*jMXtNm$L&#hQSR#ARA*)#XDF@sbH@A!36
zTGOM@jr;EtvcuTR#m3!>y;mnnH=oVpF#mCM69y-R>DT4?C)CQhq={Jtd+D-S;eKA&
zLu_|aaXa|7`>iv-v9EZUm2N!++4kD1s&B4lhk3ULuFKrchEg*5|Lx@V7n0-r8hdyg
zrHB<=>!-C_S^TyWLmM&6M;eE(@k|WKni_X%omopyPwLSMS@i?dm<Gk-rX?$*UB^KQ
zeBdGUPhv+@UR|GY-c;L7Z<-oV4!k+J2-J4B(e3P)s`t)05ahxseDo-^wJ>_oq<C?h
zD$wLB-TEbk*wASv#j3dF+wduSCAFZxgH_w7p>PAg%QT(BH5DS5)<kS3i^$KZVI(5Y
zCX!OlG1I`?kTDYNBd*hbf1M7Z4e<;I<xd`VyJjY8ZoJ+wSt%U#d}TheRzi8gOfG}3
zRekNkNMc&$Xym)s6z5x__7102&82*ns<C(_5+s8Mu#42c_S$%wIla-A4NJOXy?iC%
zN!b>#@V5%ni_dxj*pW}ByMGT6N@!2!4SO#A=Yo7Jzdk$X<e7(RFiTe}U-5pF0%vHl
z(q%O8)nq&PwP^~vd8VdZwQZzgdx1KtTZ3FtCzM55qr}-j(sv4})_3`k%fCFDe9=zj
zvkpvl**`9O>}sgR-{VNsg}i8e6{dW+$fn6eLReZ!K2M-}LU&QHQTtoXLf8qPPiEX<
z%G@yU4@2IAUnmk*lQr0&^_qJD>0Hvm?;jINl@9?G;epINNuB;)HV@zar^jPtw6prk
z{+|FP$Zh{%FiSpt7)X<O>CK+B&F;|Pr30$6!O8z^#GvQ*hMm}y`^|iVSEj79QiWR&
zK>Iglyn~Rc!Pdn+NqL?tSA5`W2$}Px)9Q)8beih&c|N`2v5CiUa+I++N>fq%n*9mS
z=R4&zc7%Wo`i>F*uMZ=HVLTb)w*6jQE|)`!0AN&mk}s7K)F4c(4cAtctx*7vt-^ii
zWKsreCbMcnVFX~F7hN|1V6si1)6W7Hp7MM82KDrljs=~SX+lALyM~JIxAb8F_a9%G
zDz=BKocvz_Zwum|TC2TG8;;z{TG))C@_R|AQm6O)F!;eYe4vI~yM6Pn0!i-o{>WRm
z8|T_y*By^#kptlmXdw@~#^hL3(54zNp=Y_gXmVl{`a%<@#a5Cs#G?LfZt1nJ9o0^u
zJoHu-v&gGE6QE3?^mB6A`!@YALKKkc>VL?*gQU_2GUQm1tsnqOM0RF?KDz$|xyJ9#
zggqH@Q+jLRUt8tnm>t3`F8}Tr%Dpi+IoW6=<|;E#U`b`-+HE?LuB&PAZmy;W)uH0N
zwGL3cj`#q=%~F>bVHc^$l2f(JQ!{cQV9YUfNS|}rqpn@|=`JfHSxGk`H1naSKd{43
zkVKo?Rt1VbXyI*OCa__q5+2ug@Sopo#Hv)Vusua*%N*s22e!9)%=*)0`vr1B(SmNE
z<^u_f0hQraD_kB<_e~-UM3%b~sK060j<0-c{(Zsir>gO-d$P3qZ4*>kbw4}e_9orz
z!BEPD2;;eYXIOEprnj`}i!xOw2E`=?ZcN)mX&0`M;;D7n+-2*)Co3BqllFk$AOmOb
zSTZN{sA`VFd~4hLSH@^(o&Gn_x-if^Fb@Ql1Tz+=GHn1oquUVU=wD<nZm!kY*j=*x
zP-7j2aQjoe_uG5@r=3*Qc2~;0e>YDd&gV&TRA-S+4>fMf^ohu-z~Y#pB<IpAFJF>e
ziQi2>H`!|yU!YU};eJ7ruaTpsL1#9q2Fyly2&bwU4sDY97x0xUfs^#UbasF6Vx67-
zaCTxbLiGWtaBe!0*Qq>rUTMM9G%j(vH*3*K!|UO_Ma@~BpFjR#Q-90Yxttmf&Wkm}
zCq6e=|3U95uur6a4n7TEp6@36;IqA=yy!lbE{hWSQ%2*|KTqFRrg!eap7WqjQ8}Q1
zVG&4jthHa#<xs?=|F=98c6r;<Szm~da>WKQi_4pz!?y0|w*-7$%6Qt^5hyk>Sv-RI
z-fHd7<*h=Hu8pgNL`dKO$qLO<REw|IQ&JNJaEGt<gl&qETy$~&aN4{mkWrpUpb#uV
zm`&gtj7?E7dr&fK^>pFw@|NpNwR)3(@*jqzfbCg7lZJW&5n&uw3;n%Czg?s24}%|S
zEVv|9FOyxQ<~aIK{u6im(HFTKepstWmU-J@I{`!1_Jw|%VcYRs^$rRxNn6)of8w#a
zaN|QX)iCknkqqM0B(3TAs$}k@Dx$UABhp2BmTl|*jKo$Y=W=zijgnNDeRbNF4cGY`
zdlfK7_Y}#sb;4_>%=TP`DZG)s-aFAry)kC3I$v?+CU4br?=o!tMM|oGljFqBKly%7
zDgH9r<ryz_`ThXFmkF5u%fyN(VOSB{U$b9y>@z7e+ZBh-Qd%F!SG@nUe`eCzUer_+
zd9;1M9B2FLe$pStn$xcY<pg-SZ`X!q`TpP9uu^useFEST17){g&u<LFI)92gH@~}A
zF|Pr}^GFO}H;*ia_yU0AHvZdLS18VEL1Q*3X2q#DJCGJk0{WV}ZFo|+_Tde8oY&66
zKiIZ|7gxPWMC01ce-!u%+^@7l=qGuC2M7RQV#28HDl%f^jqQwv)AmMugKLk_QQh&u
zk$Wx%J*AAN=h$|rsvS&WH4{7UGOgL?Q2`9;ay;mK{;xz3rotR<A?<pQZGqpw*s`;)
zHX-LL7<98)QD$7(IN+MyP*UecI$K%LXsYQ_rrK4uRW2164C(SNnRCAFktDk;g8cZ|
z#??7^o#tz9!}tP<^#CJqJ5F$$3>5S1aCUHx2xxZyY#GYJZ9Qm~q#}yRfDdb_%%c#^
zmJm-I0SdLOGTcKo*rQQ||8<w9mu)!Et0fs!Y?x2e|7s?f>CD}2zP<B-&#Z~>wv`&E
z|DHr6qb3&{a35Mj-wEhrcF(Qr7IR|`T*cdJmLf4Xguv+0>WVqp*kkbg$Y5t~;Ak);
ztO_Uh9$4hA(TF}!#Z))sU-*iI<%CiW%FrlYgzyt%50!@wjh?u?VCm9qaIysJFGG)n
zS$7FX7akYLe?Y0@{0fPkYOc6MI$<(a$69xu597g&V&S1cF72Z|YkVLoO*&qOF-7s(
zAq>7Ie<1n$8oim9x&?Brvt$^QK&0gm0J6{lxE%@pfLQX&H5=dwr5u8U<sYC7a^+_Y
zLhN`)-a}3t+A~F_Wu(qr#?ChENMo@;jn~Nf1!IUOARxeMz+%nRdv)`I1uCKcc<>CJ
z3|f+a4q}{)A;)mU!IOl-<1Zjw5GF>bQuX%hV0nG^OJjrAd)lncS!p8u#dmEg6uZI!
zK60=NxpH0f*}6Q(y~RIYP9@e;6ha9ANB-5$1%tTs+0O*yF)@>idtdY!WO^R1+qXcv
z=Z1}VC2xKGeD|Kpm8m=kI3DUi&A=+(ue#mti_l{sYlGu*hLhfv8#xtf*H1xBexXtu
zvTipo(%Yiah)7w`&odJH6&OnIK#vSrx+bk)q_1q$p(4niZ*AP?o8{m+0Us6iFHZ?W
zR?i#C>f4o6&QIS$x9WnB^*1auQBHRilr&2be*HD&o%~6z4{a5=X|_K(&__a-dVD0L
zB&N1Ip$?MDDougBQ}@QkfWOSFvsS%{{cheU#K98ZmMUAR#5xM3ty(J3ZaC_2FdQ*#
zV(R>zXPi0Y&lk3`Qqr2B_N7RU5yuoERP7_~W}ZxkX43~+J)0+jOh<b64PJ&E#E4|+
z!S|L}mV`XZ391UL|A-|NI>q4H>^|q*D_851^#~-{!FiO=h7$tXXR<1~rjk>FtP()F
zZyY`<g6w<rRqVU1t7CA!YOAo<p*tdL+B9aRCo0vja%5@J+ZSBxFgswgEeS9L8t{l4
zb{7kn>!_SJP6*Vy7#-8#Lb*Tb?2-U#P#&m<&VuI!^-Cnrl<N{HzKo}57HI6$47>kr
zB38Ezg<Pp~Vybbarq_Lj6^x_}LN)VmG9-P@)LIrp2*S^J8S`6hdB7oFb(8ZtC8R9z
za7M0%hv)hASi9(9{!T~4g3r2?5xYG2u{1_Uj`%l*<qp|EjkQjHirLhVub((Qd?P+r
zKt6VP<yEC_{HZh56Qm9E5<V7#s!#AMvd~uX>}x%Q-&gFqe19jKAH8fC5?Pp`<w`(o
zroBL%U%=)gD5#ku>Q|p2S{yT2qCPeiy?7`(zk=WxFR;D@2Z(nsoOq)SpGp;g7TFlI
zEzEmN0CqEa1Su-Tgp+wG0eUoLJ&-`ejV$NXdsS`ZD9Os1TEj)tecybx!CW4iF!F$e
z1y^w>y4xD>(4I5SA&SH_StwsC#yEMBtoiLa$E4_M)(0sA$FfCp$mP;hLL4)TF@!4H
z%+jDGpV=h(^e+|P6Q%T}&nKj_0%IS^>BOQ;?(H|2Z8#P9jHYJ3(bQdIH5k*YR<<g}
z-~fCU#yN;^E%C@2xkgK3tk%1eSxn&@YEOQu0^l~HFE5|A$+3m~HI0bFheDBhg?r_W
zJHiipd?k8{n)P~~BI7@n7^T7QLaZ-+-@$VP+||)j=hUM-k#6L~q@d@FE|4zbz?tQd
zn+hS_bZ6DVpXpXZx^ws-`nut`;SsV3zZ?vDLbu?oE}^w}WdKcoER>u?UFe>8_Xiif
ziTQ3ak8#WB4sAT|xcuY%TJZjf^}sv0z(*c=JwErb_fL$I^(*S7_&zo#=@?@uv8K7W
zHf@$dmq$6~1HEdUc4J-=fEYIND)U-^+npVFqEhbaFeiHJ`odoEzK0F`OtBC*Dc-gG
zmU)qJ@rOaPsffJo_eDW5lh+CdFp7@Yv7?ULiKA%RFN&rq(Pr^GHdf<=gthqW|Eg)t
zWL2SOeFN?4$7+&<RSW>%CJoe^Y?}Z+BmfO5ivg~T0Dz#>rU0h#!KZ`hGeyXxqp<;c
z(=#niPWUfH#^*LsepX}vqnrVJur(HDJSl1+4kb>|uTy8~Qe%CPb|D?HGnzmx2e)?+
z>lqY+KWE9fs6&(3wBv#o6}mLVxa6T3lw%fhaMeP@yBshJR1!K)cW_n4s^D;4f^aL#
zT@9pe#*7**^FXa&3M-OMD$ij&18**Ci@)ghC`1IlB&3V07Yq=F5uv9a>{A0F1jums
zs<1*FK>bSOTu?aoc0@ZkZa9Ab;DttHQAWC(ADss^sQNv8SD{OnbmBFdf|j*CUfmZP
zF|#VNN9g@uF2LP;audokO@W$3x5ip3lDtTX?|bBGP(&2Jrg_=aBh758F86Pq^Yl)b
z8+YL;I~0YM%C!JvTqSK`tIk#>wzBjoBP^>SxE4kbn>l~iCYvp+cZ-Q8uE#LS1ym=R
zbJ!#y;!wdIrHw>48><+DR~8hyAn@k|Kn0G{aO~@`JZqZ@8aYAyBpyK2w}bESCF+=7
zr=L9ZsJ{?l`hgSStgL4zGa3TGU_&!{*<{TnCH<3+sEM_hWr`bCf-r8)gg9SLa(z{^
z1NUze*Ky#VOx>Cn$c{gj2$m|BodxwU_^VDVTB*a0j0FnwC$-&G6{=UBP{Dop1uFF&
zMa4qX@Sc)^w8;Qs2&^8&T4rxBP!p@>3q1T(VkSlN_1xqK4ZY27#*UCqKkf4V*#*FC
zHg$ZRL(C#aF812kV0gP04ma60qxR_gKKRbo8g54qfer7;4Ai_~jv6KgQ+LM}u*ty*
zl3_6#O`U{^mJSTl{=J5Il+{|evSj$`57RYw(?`{Z^gqg!PBgb=T$CAJa=rXz&tb9H
z>T^Du{oZf~5&Y&Az>3oa)Yx{gx1C^$?JD%kNoJzNJ4|N3>_&pDt*T)gaP&oUu5R*R
zD<kWgpqmB}n27+$tJm=O`C=qs`&Q@`#*3okZaRWruoCO&9fDw2D`O^$^vM!PKp?Kq
z8qLyPRC4?fB-=_?h<Jva5ND~Ud))qGOb-54wX2=f1kr8UI##p&C4orZ)o}7ur)%2P
zBYUoD9=#V5$mqJY<?r_~NC>uIMelbg)boNT`0;0jZj<F+(*|8NV^Xy~4~x_$6%}9E
zuQigmP`d$A2rO;8d9kowJ55tbOa30~Y=@T9xx$H?c_SG3V8cln)qN4psh0qj3)nGy
zJlO*VKwB*oU=*{8WWQFmZ#;8;v~w2xzK>InVIBq>gJ#P)RXA)=*DaaZr%Jknp7ggm
z>1D6A5$-3jwU^zlUGh8}Dj5s#n>iZuI}n(dC@-Dy8t{IzcwLpNUHDU*YuoSB3xEgU
zBK=fdp~sxhlwU5r0XKyRN%|zss8<qoH|(fmOSs5-DPv-A7bf3`MvHwMe8^Vc{y=ol
zY_;*k9^;h+vTk22)yRHlqe6%}CX($}u<xVQ+ia>Z_1M0QWwy4H{SiRzXWY?FCb_!x
zc&<0Fcsn><oIg-+AqWwJGF>HPYd1doQ^w)(u)AgF#eoysWrFv-+vvLZp0qf+gKuV1
zOn$Or3lCw2TXlJ~M?)bXQzc{ueW(mgp_R#uo^IIL=2e0w*ml-B`G+A6ZbG`FkfTv=
zva;F3Ni9BAe=4qbz8-qmla^WH_}>yCtFb{ao!^LpAE%<#PW}%)mZP0+*pB)S2sK3?
zn`FAZj6GWYS<~tjqM|#uqlj@b9ocCNu>GB}$CN20w)gya*U8Ly=I><5dYe(h@6VN-
zA`5(b+_Fvi+w`Tz?13AocX%~!rVv=_`33VTXusw&tS?N5bD2z7--d>@-GbP+89-7=
z&&9omTHN`CdK=rw6xY*uSf&Y~wmUD@W6AG0PjWRs<$wm{gATB6{xXRrzm1GV_1Rxb
z`Nl$PjF%wuqjbuIOKOoyqe;yu1V1fcQb^xi54qUVW@~pWF`q21tv@)r`;HL5xEk4S
zZ&zw@n}2w7zy2ys>v(8V4fp<|zJu>X4UzFU0BGVZ_muryYjH3VVS1Sad7awN{luQ*
zP$wi!fS1T-d9v#96@oyZ)(LZ~A)T!Bh@bFI4fl4l+IkDnzJS-@MC8VE2XCMzK;Y*y
z_lmI#6~^H3*w3OgnomISc6Jpuu_vobg6y1Bz!MxN-E|0;KRYvW1?NwD8*Opicg%}b
zr|mzRJ^nj2%Vn$bM6^P5A55xCX&rFnrYqtN3?E$M=Mms?da`n=tnhV=C83+`z(aS=
zS;EeQ$9+-9J?9Np#^@@w^(39L^CnEgjzeuhy%Z-!^~aAnEihXP2JHfb-_!APN8E7_
zp#W;J*<`N%r6E#$!AX%5xL?1yz$<@;lgX(c15@U<MYRF2;GL#%lCcKQRKF=^89f8x
z=$6x*O9hBG=w7{Npdj%}j2Bd5LC4Wou8aP<4wT?B$_OoI&G%<bhBy==jC(YAK{jF8
zI8&a6=bmWLex};`wU$cTw&9P4oynpcuxrtFGoARy!7CZ5nk0Um{`c$UGF^w){UGhe
zC_DfJ%~LIb`ucuq)Mz`y(22s{k&v1uZ_P1RsOO!|c?_;x5OkDd#<J8yi+{BR;;0bc
zcO5QKFmE9Ml&)&*;BND&gn-iw?~gz4PtDj0CqcwuqRS?$!v3u9g9!_&n@^Ay+<K-T
z%KQ49MqD#jfv*itwtwL4_aZAn`?Z;mRI&TWE9KOh_ItKk%CpCPr~+n`#mq>t*>Jb}
z1?1Sw$diDSO2DpVXmLNkTHSxYt)TNwt3WxUq`@P+ZXezC&3;MW@~I;FU>)(DVs$Z2
z4_c6!l6qrt(I5X<dU5-<PjOea2!0}~O$QgI-5DH&i{KWQ%%7iQZ9g|t83jJme*opb
z<b5Oum*mgVR0tKIdS}G#b(aW86p%%kV#D+u!$$OMWY2QO8e5LemT(9EzE40*`=%Ux
z1_yTtVLkdO`U&!}jhH4>;V|*<p{nhgc6eep1s0_5_mHB$jFTY^?6MG-*oKU_SO##s
zA|42cJl<{63k_RA`+iN$E<j)Ub(hZ+coHe(%>BGZ6FB-FQnOb}9UtmG&HCdh=C@bd
zquZU#p~}CYB?liC<B%<mBwN~!hSP<zg2xi&daX7dvV`p=^TaNf$Lp*y+)-8WwJADO
zfh^7lv-YRLh8lJlXZrPVqx(D(;RZA+@v(NObsMp#VNW8>9zZ?3LinRX(X>i=6igkH
zWeZQf<TbRr9QHR$$<p^GryqY%+uQI$)9}4`k4v&I)kZTloGo3$)9&AM(C5_1u{$Sg
zRI|}u*W8564?@prTNJ<k`kQFM&@rus#SqVmRZQ|_Q}he&?E9i53Ey2}^ogrkb1uk{
z1YjqIsBSr5&GUKjWY~EHr79%nfZiT!54o~&D!44r7t8!M6+8%T2HCC@{w!KKDC6-2
z1#2^(QiCD{_AB82yH~ZR3okdk7dE`RTP*|3@!XJ87oF_`RPFkqAvG3~$*9cJfxm?k
zHJ)toDA<cEw}}|lj<ojfxRG|(0KFPQ_G+I^=5A&|EPi+K6nZ%YKA{HhJ!Z8$m4MEr
z!7JVat}$$n4!Yz&e9x^nP$|($CcG|+Nz!?j1M6{Z$7+=^&IKSiD5eP2d!L|2H6&z6
zobYT7`s{Gz4XOhGUUWHJ_uI5Mf{pSjYbU2klnwbp>??z*jHUKJ-UJ4Z!B-kg8XB7l
zd^7a57vk+)2gDgQorwb*?~9K)x%P8d243<M#EPHAetu2fpGd*6he`{_4cQtJ?!yEY
zae)On#8@rvC{Hz7kHIVoKMX2CcwEdETr)F2<?<cAm2Tv=X@6!Hh&54x4t|sE=<O2r
z4~yl-W7!ZJ#y{>-l^2np)8Hn!j?SEkHf1{z2)9G-bTMA~CS0zggchgkM3IXupWDNA
z*tnALG&PJoEZNy*enIM)s<nP#rBLh=!9l)@jpP2#t;KWqVtQV&Z@e;hy`_t8FY}^W
zXJUd{c2ZqAKZ`HX9<jCA#jYQGNDCw{{|I+A9l&A1@3OA`l%yjXLzSd+d&o7OR%Y_}
zUL^DdnIklSKjY$~vf`_0jVI~!PXzY1bstqCjiB7}T|Oa0{hq=y5FvVSE$Mqo?2dLU
ze-SZz_lq<v?6E&uQJ3F@d|ky@-4m6ZY)}?6s!K|rkZy=RJ|1ov{brjb`{<rC9ik`W
z!>!F!z9PQK`TWrkeKPv49xENQZ7<8El#yc^X$;7kUBFf_cc1ugVlPR@u__A`V%xz_
zDoMdTz&XNh+v<eD*of^lS5(UCa(HH^Z?=MP5jyQb6WG}8X}<>}m`!u7{&5(Hu0W38
z{Z-!UJ_4IHX6-$(ME#({tRU}=<81x&LPg8Vz=jwCS*3~4h6ji5QlgQsLel#49Bz#|
z-u8)Q6&pHXPTCK*LJOU!15tkHPfv4}c!=&?4DBlHy`&;z>1s4_Sj-iWBUW{|-qX-n
z6_T4W5qT~46v<6eHYHRgINk?1q=|kTq}HCmfO}lm#A6Rzm!Cf+oVgd-<sZAO=8i>9
z;_K3YJ|SI{=dT36_;Ryk7|dzzBizysCE#U}c7EWv%|i%Jedfr+O0#mac?Safft2bP
zR(i(5$}wdt_BLA|2yu#?UElTI4cs01%G@5fci78X=@h*7%y8)H?N&B0SJCqh&=T+&
z)jU>pn9>Ph4v{TvTL<G41NWOdzciv}tmaI$`%=iI+y0(BQ#QtVkr^<d-kn^Wsstet
zsJ$om+AIa3bCV6fiGkB~`r3qomZX8d#Z_BekAi5<f;wy~_}p)CXYRIP!&8mHV%#3r
z?AUJW2pdi|BsWL?P$)j5GHkVfVj<dyJ9%8M9l=jXC-%9)Nikc1?G9+)8TjI$z8(eh
z`7wxho&KUL@#vBx-yigBnOoy=%@)BC9sggBvYHx;(2%gQS0m<uBRoyQb95b7f5(l0
z5Ej$uY&(7ZiTYD%&DaU9i_>$Yv;EH3qh42rz>(9Aoy5*g1ealZ+xotAqo5$#$KkR~
zgweQ8%?0@$#Hz({58OdgYYsu~2^OKn>h3~WBDcEx2DdLVpQZ(DRvZ1{!}?!@_G9Q#
zAYo`2K}@$by-b9Rh_N%>J{c`WpZFfXh-2!E$Uqk2U{0?09u?~NHSlCWF%9o^$tVt>
z(Q^7TP?G)fH>FGVR>Si=f@aIB&P?@%rsISG9Dp!}{#yG2(cEY`);N{_gW59?>u2@|
zaz$694H-d|6@w7O>(qEO+R$6_f;K79;WC3)lqT#+8tuzx`?^&2tGMdLAHorC9xNEL
zC}L{AAA{~D+lf&X5%BgsL1{cCXaI{uq+4ghPO0?(2){!k%d&Uw2}sVP{;Rp1N)rRH
z!f;o@A-sLz3%T@`sNGjHa^fML(yM~v1qpUb771ZVyQzrv;M?W35i^hNHcXaVueviQ
zO$&|Fu6@$AACGgX%T~hf@EXhzvjT71qp#i;X17w1x*VdQa5mFlGz|sHY)4i*DW<Q-
ztOxWsiCNY_WAZrtdCSlDA|{<doljWBAmt~`Vk#xk3*08#%HfIZ^jI|Oe%@eZfez>M
zSly%8a3+<Ky{mrE*xm4W_DM8d(DsN*N%5hRqJz06G)Bra_im*QXB~I0C{OEN;E&(I
zr!R)$%zYP8<XqeGQ4hW6oM;(Nj>U5xo=t~0N*FB^um3dY&Z@cB$ThH?L7(wP&%vvH
zMc8ic#ix_oo~Arc-UC^V%o>s5<^yFHFRPU?q6*in%_&mW?5~6El0S{O8$N9}+X6BR
z=slC|7Q9~lMD44qJ0)gQD6*EJnwpwu-||=0(|g~Y1@5GZE)%OewEbAub};8hk5n6#
zuL!)K!6qVY@8L`|ay0+wo;yJ>(3nU>3zJU&18DidOn+$M>jrKIN!I=iZ7?E}>LOP0
zkL$?S1o@8r_Z<?R^YT*>EO&sAI-d2!NQ%2}N?j_9>su=<dji(IU<&fzwfE6=rMOmI
zjF|SnEga6XQrvV0tDi78-X$3yF^o*3-oFikB{r9^s`yCEf~)YM*6H#t3v^-NNir-8
zTxb^W=o<b3&i{d+-l-)$4KD(IRDeWwPZ7}rDsEX=4?5POK>Vz{c6m_3OvPAm;Lq>|
zY17Xnopvj%(?~E);zGW;PR>b9>lAD_X0ubVf!={NaM^mNym~5qtmZ=Q|9yNf>!`DM
zF@{2(82|<vvfOi;?70$kyw!%S^$|i}x_;89?F9`#OJN9JXJ@B0x9_8nb~@NMkGr1y
z<Wf8^ELAqmpTsX4I=r%^I7e5pt#QnS<p{)Qdsf(?wrho+eA2ln0=&#mW9E==G`VRG
z$+I}ajA#t}t>mEe=Tw_VjfN_cl<%JVvV{2*PV=9=gA-jORdYLp=x|n2HKdK2ELs~I
zXm+@1H~HASPrD@IPH7y{8n$#^p`j1@vdL&Fy@#?|^c$9zk9jNj^6b}UaNzOD7%QWL
zGE*MeyDoTF@@n?}nWy;fendgVo!a>T-`zp398a7OAb0O^fs|?R2tzhcgBmg#>&x(o
zGw&t;PI1D#`_8KZDa+T|g03h*%ECa#o;p~>Sn6Z9^{YCxufLC=h{y=EE4rg``MKpf
zMnlgixxPR%VNYD#4&!BEZ%_b$!_i_t@1&f+hf`t|5oyFdFe?Y4_9Ie&cvd2Qigfr^
z+bo2ERO`_LcnEpcX6c-UgDq61@Aynl7bn-25b<YogQ?qe$_moSQ~$4jzl~R39{q0j
z($~+j?b8^<MXoygAGLdvGc{`e2xtvo>y}>pJ6h8eg&q%)srFg=OH^U}D)zO!e;wML
zbdm<_L|KSsQj&fr7af34KUg#&W~tHM!iKrSWeEPt*!tz?w8<$cm3xM~G*K}_#aW+U
zu6ZI6bmnYGMA?wkmx@c}kSzr&dN8b&2;Mt&QZQ|lO#Q^82Fr=$LP`;rnIk7&57cPf
z)u92uvZ~ME(dloOwZSIOP@7@V*UlECk`|0E&3|vAPWBNyDWBSB0+$9T<;@Qx4G}^X
zmt%pU!V6AiDIT2BcYf@2@p3SnjqqXmQf59C?Ft_^dB+`IEmNGWTgF1Rwe{<c-kFm_
zR-41!bmhA=>(Db_;8i-DwjF1Brdd*ha>R?Y%@Hu;V2bHSlw$_6tA}>>_Gye!-2nnJ
z|KN<qQ6X#s3X!RvL<cxwhR^&VNI4O)Bblz~hDYY?qj$hN4-3uS5VG&Zx^YUp$sKM&
zH*d(cUHu#?N*Xvn@AsFqnpi84nmtTy+f`~#BKfk~iebFA>i4khxp9PJg^8Yb$grgX
z>I6ABr)FPHiv$uK=TO?vpI5y`x5n&B-?+Fm7CFz`MieXVw8{6OdXmJjM<(nYg1<};
zl?-#b17x}xivFx@-EOAv|0qFC=ZGad6Q@2N(^ioLLoJ|JP{`!HuFw6hnY3_te}BI)
zu}-5CrrjaQIMI*@3vxQ>W<|@W$Pth6-r*CVIBNM(Q_d**krsc-<;b8v>j?!kV9|5_
zY3-M=dNc_u>FaV*KnAMlfXcuL39a}JL9P*!aO(s2Ot#BHsRlipMowu--n6)%1g!@P
zxcqn@g9A8X+;(#Urc5R!O9N0$p$9YcpD0?cDMTbae+m`n)Z9#yOEC&sGg#jC$o*<~
z)PLK)Qyq1WTwMqk@T-{gz9C?`^3JjzNcz5)L%R{l%$fv=c?6#S05|3+-MC}3n_yx%
z5^jc`ac6|^3Gmfsq#J+Lvq5Hx^_e)ZBkv%b6Vp5!cD_`YYC?0^mQDxf@PHr65XCcf
zO>ZccVgxfzv2~9@`$<Dq2OPvkAT^(Lh*{if%9!)Bz}4oCcs509p7top884ugJwd7U
zL0r%2K7^+Gn!!(tb;@%c(~$Kn(B9S#MW1%A45U5{`-}%DsY_GfhIG1Z>VXq7mi7E!
zcbLZiox)Ow>lj!Y2DW#-2O@-7n|Djf8YKh9KW&8=$Cte-j+S;94(3=Csw$UY)JXJ)
zqGV)r%~jP)p142$QPz_qSXq+SM5yDsGU9WCj((O)o(7h~0ul41)DCHB5m+a@?&hRY
zG97QbV2eb&GB%Ki@?w8I1I7}|9{^s;PNKh`4?o2QJqKo$mXxdt)5l(6a1TXT_Q##w
zsgrQ<M{GG8o4i>5J<s7pIyZ20)l5uUSz%5<$ccwoW{HivIcJ`V#k1>iZIwyWWc@m-
z3I|ppO1jsQf%gD}EDd-?PT=>`$|nv!E6Xi}y$3aq+2gnADJOgO-mZ<P((sbO>OSgl
zk^}Vv2Im@a)bD@jAGbYS-0V8{<l4GSl(DfdAzN&5@zLL7q!V~WcO~29&Crvk3vc*G
zVu5cen@^#JUZnZ+xf?tk2+&p$>anh_C!4XE#?qbe?z8&wj0+9k(diFX;LxX!U2e_V
zY=s%KQwK{>Cd0^eRnBe7qe!PbgE<voyRy*siXdR7*q3~gqf&7%9oZ=5Kbd?#uKo*9
zRZ~&2uIztfe=%oFc6B9pE&GUoSe2HCDY2vQyn1P!UA}VmFnV)<cB%rUBRV_cvyz^5
zd54j&WxB?b*1CGS*<_n}yWfh1bO*ikd{__DfM-nP4JCd?K{^4(7ErNf#!mf~-88jn
z{IfSU2RH)TtWoAC*m+*LA@P7GNHJ9&vqY>XU<Y4YV$od{b1dNZ<4Hs}A=pw+W$1{D
zBP`xz+y345kY@QLUGvSay*<Cp^y=()M(%P+S*;`Ci-FRfFD&wQMiVOtaiZiO?63DH
zHjz!;Vk(Mn>)t5HTdEkJ=A5pOW9~9F{#NWpl!@Q-P^V-5f>L}8de%@x8;swDE$}ht
z_%LBH%S;$b<E{DWA0uuW6T5X*7O~nOZ}U^d4l?kV$hwRXF^kP5U57G6lM5kX6#~B{
z01}Xf${K~=*|4=sT)J#(`hSl+6^J4MN+9vGXlMj^=J`>fI2w1rt1X1jmeF;!FypEJ
zuVJ(8Xzbnh2E^x&9yyOYabH;#&u%`;mmE(2eJ_{R@8qNRu37NLV>Z^PkRyi(IPUxr
zD6zw5tNUpknyy&doqbh`4Pbud^V6}4yG#e-`6hK!jiJZ*1(bfeA<)#wljcE>D`V)L
z+WQSo6eAvOI;Ah4^-5kz5bJzXBu)PFh`SI0gVwoI(OtlmD8~|l79O#yXuTSc8S#2;
z_1EuoyJ;f{;(hwCeXQzf+t|fnT)Y28AJNs$K+ZbPupbo_SCj|@RP{#RJy$EwO2^W4
z>XfO+LhN^mY4#VDYS06a9|gnNvU-lhXvd-$UZDTEc@{vZnlP^je=dN%M(A?wK97&+
z^h+|AOiK?MYTrvRP?03-6O2swGGdhMiKS;_o2iO*IE#mI!LIJ^hXFPgTr^R~^3X-d
zH=XxsGoJH=J+8YrKmsHHrL*PRae)mIAbL1>`SmAJsvr}>?{e?qd#aED9Oeo{KL)J?
z(H|Hr0FB9Y$?RsbE3x*r5o53Lp&*!?yXim|a{cCVztQsitdVuHPX;&}sP7QB&k6PN
zA!a8eeO&W)Vd?nBrSZ__8PLji+|Ed?znVbc_xGjEez$+lUhtv)RPoRI{MdZ6_IkRJ
zH{frgWP!7^B|;|$%R`BehGOorggF@-FyqOij>qHcVt4=8#LK}0#hVSoVwRuB@gLea
zJdcrsr(tg=5$%3#4<f?k3G!iT$*0c2+<$d)dGND|jmVkY`UPEwYs-O}3-3Y7Y`3OY
zQ=S?1=ucdnoL+G%cWo>NTr<5pOErn}(wel1tFeRzX$TB`UYVrh!hdMDu`;7nzh43~
zj#%SyylaQ@)>E0M=qn#Zv9ee{Ow3m13Vkh#2WY7bS=o>PU4wUlcn~X=Ob@bt2Q$ul
zecn%mI*tL}im@70Y*t&_Fz4IY1WnYG_tw7QL`q>J9v0E;>YuM(^SQrjpt%aNLrDWb
z$IN<aPM)PKl^F1qHs`A9kSVrh&D@5@VzBezz^Ai==@N66P!OTM-_l;4*JN`$q+v_|
z{QZj&iMcQ6@zPH8wfL*e@33t%j}3(!lu1KPu+GBD-rHSNu!MC+V8hS_f%QhRhj2IT
ztezj#$BTqLMSJ+AQm21|%C#@&lb}yXf&mEu=WT6Di)@eW*=4W$hsi09$h?^nvER7^
z?ayz14YZ$~3>eeNpXnPUP1$Qa$U~WFFFxfI4bHwEAPkl~sEI6&x$T^z9xymQO(y%`
z+`2&P9q@Ka>_r;=&$nVTDh#yI{2~Y`^HpC?2&u4~=6bg+$IW6#h5yyXEcNmA&swVJ
z8ILwijpu>4!O@FOwY1_byYTsuocg!BOUJy;yAzbBlCOJ+IuZ8$ii>~t{{&WM-cCkX
z?rvT`!38)=`?q7S#}k4vd(&;s6t$B7HC5Mqa|yGc3g30z(T|(E__Iu@;mJUKiZ1>n
z_bl`367MExFw%eC%-Gz**7zp@@MX7#XfNPq%v*EOIYr&FX;V6Q(4h?>*tb@cTsoC=
z^Q)`nc7Jrp0z=%#)Lm29vfPf5IA6~gmJrVI`p1}iNgSGJumJ!=>XH2sj6R307#H8&
z(xZfGfH3lXY1x3Gh1X6W$6n3l+DP#JX5D@<gZmCX4nWz_RLp8Yb69_|-J742LLT@h
z+mJW)?^chZAX@175sIf$;CFrSM&#vO!1tL53k%{-@&AN-uXR8=U(nv()Z<VY;I|Tb
z+O}Wp>rNA=M4GHuw|!?@`f#<eV*cfMO*4+abMUSl>kkHR#=|Yt>6L4Tn&)s%tzB2N
z{h{#XMHC(8_N=qDTA+Anv9P+L+R$$v=wr%T46z`KlYn(>doKoKdi+jGjPLz66#Cbw
z&f?G<EvIR_HNn)_wkKU_adw<i+i20?I4OezxO4<xkJh7i1@?Yj^WL2F(2*(P0Y`hL
zwHs9hUQK01im;uJurk8IoJODPrrKaSQUA3OdVA!~k#?gkGCQc;dv6O#AI&IsyrFy2
zWHqq!1A0%X>%V4bzKuju$t>7_0r{65G;M{E|H~ZT;;iaL>GX6H&c=uSHh41ruq>%r
zXK}RGLjpe1U;U*A&3Jcb_P;G_VosZ-0{wY8SG=G7CxBsdtK4369LH_dc4W_DYO=*A
z92UCcM}he5zh8uQJZ$VHWO8n~-e~+l&h)&^uyPA2B+2POZYbJ|eLaBTzxM)=J+1z)
zxBPsJhq}4PdvTcVbl_?Ic3s``)t_>c1w}OtE{j_VcvD*f9Ysafu<BKT|K{bsDtJr(
mpRN4YD*fLO|5?tvTcXBS(BBL1l%26t0Lls)&<eSi@Baq}Cr0f6

literal 0
HcmV?d00001

diff --git a/docs/python_docs/_static/google_analytics.js b/docs/python_docs/_static/google_analytics.js
new file mode 100644
index 000000000000..5a8f6a243fb8
--- /dev/null
+++ b/docs/python_docs/_static/google_analytics.js
@@ -0,0 +1,26 @@
+ /*!
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
+  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+  })(window,document,'script','https://www.google-analytics.com/analytics.js','ga');
+
+  ga('create', 'UA-96378503-11', 'auto');
+  ga('send', 'pageview');
diff --git a/docs/python_docs/_static/minima-social-icons.svg b/docs/python_docs/_static/minima-social-icons.svg
new file mode 100644
index 000000000000..fa7399fe2509
--- /dev/null
+++ b/docs/python_docs/_static/minima-social-icons.svg
@@ -0,0 +1,33 @@
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
+
+<symbol id="dribbble" fill-rule="evenodd" clip-rule="evenodd" stroke-linejoin="round" stroke-miterlimit="1.414"><path d="M8 16c-4.41 0-8-3.59-8-8s3.59-8 8-8 8 3.59 8 8-3.59 8-8 8zm6.747-6.905c-.234-.074-2.115-.635-4.257-.292.894 2.456 1.258 4.456 1.328 4.872 1.533-1.037 2.624-2.68 2.93-4.58zM10.67 14.3c-.102-.6-.5-2.688-1.46-5.18l-.044.014C5.312 10.477 3.93 13.15 3.806 13.4c1.158.905 2.614 1.444 4.194 1.444.947 0 1.85-.194 2.67-.543zm-7.747-1.72c.155-.266 2.03-3.37 5.555-4.51.09-.03.18-.056.27-.08-.173-.39-.36-.778-.555-1.16-3.413 1.02-6.723.977-7.023.97l-.003.208c0 1.755.665 3.358 1.756 4.57zM1.31 6.61c.307.005 3.122.017 6.318-.832-1.132-2.012-2.353-3.705-2.533-3.952-1.912.902-3.34 2.664-3.784 4.785zM6.4 1.368c.188.253 1.43 1.943 2.548 4 2.43-.91 3.46-2.293 3.582-2.468C11.323 1.827 9.736 1.176 8 1.176c-.55 0-1.087.066-1.6.19zm6.89 2.322c-.145.194-1.29 1.662-3.816 2.694.16.325.31.656.453.99.05.117.1.235.147.352 2.274-.286 4.533.172 4.758.22-.015-1.613-.59-3.094-1.543-4.257z"/></symbol>
+
+<symbol id="facebook" fill-rule="evenodd" clip-rule="evenodd" stroke-linejoin="round" stroke-miterlimit="1.414"><path d="M15.117 0H.883C.395 0 0 .395 0 .883v14.234c0 .488.395.883.883.883h7.663V9.804H6.46V7.39h2.086V5.607c0-2.066 1.262-3.19 3.106-3.19.883 0 1.642.064 1.863.094v2.16h-1.28c-1 0-1.195.48-1.195 1.18v1.54h2.39l-.31 2.42h-2.08V16h4.077c.488 0 .883-.395.883-.883V.883C16 .395 15.605 0 15.117 0" fill-rule="nonzero"/></symbol>
+
+<symbol id="flickr" fill-rule="evenodd" clip-rule="evenodd" stroke-linejoin="round" stroke-miterlimit="1.414"><path d="M0 8c0 2.05 1.662 3.71 3.71 3.71 2.05 0 3.713-1.66 3.713-3.71S5.76 4.29 3.71 4.29C1.663 4.29 0 5.95 0 8zm8.577 0c0 2.05 1.662 3.71 3.712 3.71C14.33 11.71 16 10.05 16 8s-1.662-3.71-3.71-3.71c-2.05 0-3.713 1.66-3.713 3.71z"/></symbol>
+
+<symbol id="github" fill-rule="evenodd" clip-rule="evenodd" stroke-linejoin="round" stroke-miterlimit="1.414"><path d="M8 0C3.58 0 0 3.582 0 8c0 3.535 2.292 6.533 5.47 7.59.4.075.547-.172.547-.385 0-.19-.007-.693-.01-1.36-2.226.483-2.695-1.073-2.695-1.073-.364-.924-.89-1.17-.89-1.17-.725-.496.056-.486.056-.486.803.056 1.225.824 1.225.824.714 1.223 1.873.87 2.33.665.072-.517.278-.87.507-1.07-1.777-.2-3.644-.888-3.644-3.953 0-.873.31-1.587.823-2.147-.09-.202-.36-1.015.07-2.117 0 0 .67-.215 2.2.82.64-.178 1.32-.266 2-.27.68.004 1.36.092 2 .27 1.52-1.035 2.19-.82 2.19-.82.43 1.102.16 1.915.08 2.117.51.56.82 1.274.82 2.147 0 3.073-1.87 3.75-3.65 3.947.28.24.54.73.54 1.48 0 1.07-.01 1.93-.01 2.19 0 .21.14.46.55.38C13.71 14.53 16 11.53 16 8c0-4.418-3.582-8-8-8"/></symbol>
+
+<symbol id="googleplus" fill-rule="evenodd" clip-rule="evenodd" stroke-linejoin="round" stroke-miterlimit="1.414"><path d="M5.09 7.273v1.745h2.89c-.116.75-.873 2.197-2.887 2.197-1.737 0-3.155-1.44-3.155-3.215S3.353 4.785 5.09 4.785c.99 0 1.652.422 2.03.786l1.382-1.33c-.887-.83-2.037-1.33-3.41-1.33C2.275 2.91 0 5.19 0 8s2.276 5.09 5.09 5.09c2.94 0 4.888-2.065 4.888-4.974 0-.334-.036-.59-.08-.843H5.09zm10.91 0h-1.455V5.818H13.09v1.455h-1.454v1.454h1.455v1.455h1.46V8.727H16"/></symbol>
+
+<symbol id="instagram" fill-rule="evenodd" clip-rule="evenodd" stroke-linejoin="round" stroke-miterlimit="1.414"><path d="M8 0C5.827 0 5.555.01 4.702.048 3.85.088 3.27.222 2.76.42c-.526.204-.973.478-1.417.923-.445.444-.72.89-.923 1.417-.198.51-.333 1.09-.372 1.942C.008 5.555 0 5.827 0 8s.01 2.445.048 3.298c.04.852.174 1.433.372 1.942.204.526.478.973.923 1.417.444.445.89.72 1.417.923.51.198 1.09.333 1.942.372.853.04 1.125.048 3.298.048s2.445-.01 3.298-.048c.852-.04 1.433-.174 1.942-.372.526-.204.973-.478 1.417-.923.445-.444.72-.89.923-1.417.198-.51.333-1.09.372-1.942.04-.853.048-1.125.048-3.298s-.01-2.445-.048-3.298c-.04-.852-.174-1.433-.372-1.942-.204-.526-.478-.973-.923-1.417-.444-.445-.89-.72-1.417-.923-.51-.198-1.09-.333-1.942-.372C10.445.008 10.173 0 8 0zm0 1.44c2.136 0 2.39.01 3.233.048.78.036 1.203.166 1.485.276.374.145.64.318.92.598.28.28.453.546.598.92.11.282.24.705.276 1.485.038.844.047 1.097.047 3.233s-.01 2.39-.05 3.233c-.04.78-.17 1.203-.28 1.485-.15.374-.32.64-.6.92-.28.28-.55.453-.92.598-.28.11-.71.24-1.49.276-.85.038-1.1.047-3.24.047s-2.39-.01-3.24-.05c-.78-.04-1.21-.17-1.49-.28-.38-.15-.64-.32-.92-.6-.28-.28-.46-.55-.6-.92-.11-.28-.24-.71-.28-1.49-.03-.84-.04-1.1-.04-3.23s.01-2.39.04-3.24c.04-.78.17-1.21.28-1.49.14-.38.32-.64.6-.92.28-.28.54-.46.92-.6.28-.11.7-.24 1.48-.28.85-.03 1.1-.04 3.24-.04zm0 2.452c-2.27 0-4.108 1.84-4.108 4.108 0 2.27 1.84 4.108 4.108 4.108 2.27 0 4.108-1.84 4.108-4.108 0-2.27-1.84-4.108-4.108-4.108zm0 6.775c-1.473 0-2.667-1.194-2.667-2.667 0-1.473 1.194-2.667 2.667-2.667 1.473 0 2.667 1.194 2.667 2.667 0 1.473-1.194 2.667-2.667 2.667zm5.23-6.937c0 .53-.43.96-.96.96s-.96-.43-.96-.96.43-.96.96-.96.96.43.96.96z"/></symbol>
+
+<symbol id="linkedin" fill-rule="evenodd" clip-rule="evenodd" stroke-linejoin="round" stroke-miterlimit="1.414"><path d="M13.632 13.635h-2.37V9.922c0-.886-.018-2.025-1.234-2.025-1.235 0-1.424.964-1.424 1.96v3.778h-2.37V6H8.51v1.04h.03c.318-.6 1.092-1.233 2.247-1.233 2.4 0 2.845 1.58 2.845 3.637v4.188zM3.558 4.955c-.762 0-1.376-.617-1.376-1.377 0-.758.614-1.375 1.376-1.375.76 0 1.376.617 1.376 1.375 0 .76-.617 1.377-1.376 1.377zm1.188 8.68H2.37V6h2.376v7.635zM14.816 0H1.18C.528 0 0 .516 0 1.153v13.694C0 15.484.528 16 1.18 16h13.635c.652 0 1.185-.516 1.185-1.153V1.153C16 .516 15.467 0 14.815 0z" fill-rule="nonzero"/></symbol>
+
+<symbol id="pinterest" fill-rule="evenodd" clip-rule="evenodd" stroke-linejoin="round" stroke-miterlimit="1.414"><path d="M8 0C3.582 0 0 3.582 0 8c0 3.39 2.108 6.285 5.084 7.45-.07-.633-.133-1.604.028-2.295.146-.625.938-3.977.938-3.977s-.24-.48-.24-1.188c0-1.11.646-1.943 1.448-1.943.683 0 1.012.513 1.012 1.127 0 .687-.436 1.713-.662 2.664-.19.797.4 1.445 1.185 1.445 1.42 0 2.514-1.498 2.514-3.662 0-1.91-1.376-3.25-3.342-3.25-2.276 0-3.61 1.71-3.61 3.47 0 .69.263 1.43.593 1.83.066.08.075.15.057.23-.06.25-.196.8-.223.91-.035.15-.115.18-.268.11C3.516 10.46 2.89 9 2.89 7.82c0-2.52 1.834-4.84 5.287-4.84 2.774 0 4.932 1.98 4.932 4.62 0 2.76-1.74 4.98-4.16 4.98-.81 0-1.57-.42-1.84-.92l-.5 1.9c-.18.698-.67 1.57-1 2.1.75.23 1.54.357 2.37.357 4.41 0 8-3.58 8-8s-3.59-8-8-8z" fill-rule="nonzero"/></symbol>
+
+<symbol id="rss" fill-rule="evenodd" clip-rule="evenodd" stroke-linejoin="round" stroke-miterlimit="1.414"><path d="M12.8 16C12.8 8.978 7.022 3.2 0 3.2V0c8.777 0 16 7.223 16 16h-3.2zM2.194 11.61c1.21 0 2.195.985 2.195 2.196 0 1.21-.99 2.194-2.2 2.194C.98 16 0 15.017 0 13.806c0-1.21.983-2.195 2.194-2.195zM10.606 16h-3.11c0-4.113-3.383-7.497-7.496-7.497v-3.11c5.818 0 10.606 4.79 10.606 10.607z"/></symbol>
+
+<symbol id="stackoverflow" class="svg-icon" xmlns="http://www.w3.org/2000/svg" fill-rule="evenodd" clip-rule="evenodd" stroke-linejoin="round" stroke-miterlimit="1.414"><path d="M12.658 14.577v-4.27h1.423V16H1.23v-5.693h1.42v4.27h10.006zm-8.583-1.423h7.16V11.73h-7.16v1.424zm.173-3.235l6.987 1.46.3-1.38L4.55 8.54l-.302 1.38zm.906-3.37l6.47 3.02.602-1.3-6.47-3.02-.602 1.29zm1.81-3.19l5.478 4.57.906-1.08L7.87 2.28l-.9 1.078zM10.502 0L9.338.863l4.27 5.735 1.164-.862L10.5 0z"/></symbol>
+
+<symbol id="twitter" fill-rule="evenodd" clip-rule="evenodd" stroke-linejoin="round" stroke-miterlimit="1.414"><path d="M16 3.038c-.59.26-1.22.437-1.885.517.677-.407 1.198-1.05 1.443-1.816-.634.37-1.337.64-2.085.79-.598-.64-1.45-1.04-2.396-1.04-1.812 0-3.282 1.47-3.282 3.28 0 .26.03.51.085.75-2.728-.13-5.147-1.44-6.766-3.42C.83 2.58.67 3.14.67 3.75c0 1.14.58 2.143 1.46 2.732-.538-.017-1.045-.165-1.487-.41v.04c0 1.59 1.13 2.918 2.633 3.22-.276.074-.566.114-.865.114-.21 0-.41-.02-.61-.058.42 1.304 1.63 2.253 3.07 2.28-1.12.88-2.54 1.404-4.07 1.404-.26 0-.52-.015-.78-.045 1.46.93 3.18 1.474 5.04 1.474 6.04 0 9.34-5 9.34-9.33 0-.14 0-.28-.01-.42.64-.46 1.2-1.04 1.64-1.7z" fill-rule="nonzero"/></symbol>
+
+<symbol id="youtube" fill-rule="evenodd" clip-rule="evenodd" stroke-linejoin="round" stroke-miterlimit="1.414"><path d="M0 7.345c0-1.294.16-2.59.16-2.59s.156-1.1.636-1.587c.608-.637 1.408-.617 1.764-.684C3.84 2.36 8 2.324 8 2.324s3.362.004 5.6.166c.314.038.996.04 1.604.678.48.486.636 1.588.636 1.588S16 6.05 16 7.346v1.258c0 1.296-.16 2.59-.16 2.59s-.156 1.102-.636 1.588c-.608.638-1.29.64-1.604.678-2.238.162-5.6.166-5.6.166s-4.16-.037-5.44-.16c-.356-.067-1.156-.047-1.764-.684-.48-.487-.636-1.587-.636-1.587S0 9.9 0 8.605v-1.26zm6.348 2.73V5.58l4.323 2.255-4.32 2.24z"/></symbol>
+
+<symbol id="mastodon" fill-rule="evenodd" clip-rule="evenodd" stroke-linejoin="round" stroke-miterlimit="1.414">
+  <path transform="scale(0.07)" d="M211.80734 139.0875c-3.18125 16.36625-28.4925 34.2775-57.5625 37.74875-15.15875 1.80875-30.08375 3.47125-45.99875 2.74125-26.0275-1.1925-46.565-6.2125-46.565-6.2125 0 2.53375.15625 4.94625.46875 7.2025 3.38375 25.68625 25.47 27.225 46.39125 27.9425 21.11625.7225 39.91875-5.20625 39.91875-5.20625l.8675 19.09s-14.77 7.93125-41.08125 9.39c-14.50875.7975-32.52375-.365-53.50625-5.91875C9.23234 213.82 1.40609 165.31125.20859 116.09125c-.365-14.61375-.14-28.39375-.14-39.91875 0-50.33 32.97625-65.0825 32.97625-65.0825C49.67234 3.45375 78.20359.2425 107.86484 0h.72875c29.66125.2425 58.21125 3.45375 74.8375 11.09 0 0 32.975 14.7525 32.975 65.0825 0 0 .41375 37.13375-4.59875 62.915"/>
+  <path transform="scale(0.07)" fill="#FFF" d="M177.50984 80.077v60.94125h-24.14375v-59.15c0-12.46875-5.24625-18.7975-15.74-18.7975-11.6025 0-17.4175 7.5075-17.4175 22.3525v32.37625H96.20734V85.42325c0-14.845-5.81625-22.3525-17.41875-22.3525-10.49375 0-15.74 6.32875-15.74 18.7975v59.15H38.90484V80.077c0-12.455 3.17125-22.3525 9.54125-29.675 6.56875-7.3225 15.17125-11.07625 25.85-11.07625 12.355 0 21.71125 4.74875 27.8975 14.2475l6.01375 10.08125 6.015-10.08125c6.185-9.49875 15.54125-14.2475 27.8975-14.2475 10.6775 0 19.28 3.75375 25.85 11.07625 6.36875 7.3225 9.54 17.22 9.54 29.675"/>
+</symbol>
+
+
+</svg>
diff --git a/docs/python_docs/_static/mxnet-icon.png b/docs/python_docs/_static/mxnet-icon.png
new file mode 100644
index 0000000000000000000000000000000000000000..9e02cbcb609beb9b0ef961d733f34c449e2ddd1f
GIT binary patch
literal 2741
zcmY*bc{~)_`<AjBvRylaWQiG+ZJHVTGBUDeDN}>74#S|a%h-~o5wc_{QL?2h;YyYa
z6-H!F3?fl>F<kSddw=)${hrU~ob#UdJn!?o&-v>lSz4F?*hSbG7#IKuQ$y>cUHf>l
zG9RreLb35j+lfGH6Mcs2ezB#a*wM?@A;<w~4spf#g3xX_7Yr!G*Z&B`zyJ+_9EHA^
zAT%(<7wZ=Y34uxfMnI0@<1|<r_!|=B1Cw?@S^^DlcnnY-bOEFy4QB@efl$1gJH*=1
z_@DBl6HMAOD99fI1``Mb5aB!shxY)hYHMqQRn)+0YRX3l<-kzCAasbbU!ct2B>%@_
zhzWGXd-(@>;rxKdyl59(a1cyd`dH}S@pqp=Uhe;|<QMplts{fr;~TIlNCo^a_faYI
zI19Oq_re@0AM?Xip}&#;llw;p3O*M9uV(&s`a64M70wO?|NCrkcJmH7M+OEqc7&mx
zttZpeRStjKVc|o{IF@&Vk<Xlw?MxQQ!A4$7w=W@1AWq2bdz}qw#JXP5Z21#gj-hZj
zVp8~$`EAYubjgsKjjxewu#;jY&wD>vZVBs$RxE6|+xIftG)}#|`#GFeB>FRKb>Z`$
z1lPjD^SR;ubO@b5*?%L-XkdQHTf|0|ydg9Y1XPOQagj?=z46wdYz3`r@dx1|=!zXr
zXqmoL>{4U-9V<eFfhyDYOkpk7n5J*h#CVOCwizJ?FXIP)!8FFET1Gy)w)fwA|M1qe
zU9-6h#%OhBnhaKb$oj*AnX`OUw&lit<gyrYp`2QJovoy{8O>E%a_>Y0_k)-5I1nDN
zHN_d%GK;cb(~NLY$BUdcUBu<pK65@)?ytn8Yie6^;7={C*H+&ll1*WToP&--jl8K1
zE(VBn4GmTruSL@Lq;X&H>spt1x8WiTsq_J%D<pDmd0?GV36X@rX5E#(C;q$>nq5_g
zJ`x{MPp`@Obz+QAsU?240PNqa6l~8wTaL9r>up3<3*OM|-yL75HZ}}n(nT#Of+3zG
z%?dsDnxDA6Iko?SWmPRut>fi#oYi9M9b&X-mtN|r2rio2PPP>nDY2;McFSBV0AQ0^
zBShSyb>rHdmrydDR%8kZ>*k^7JfvlHO`pW!BWh2z{>vu4U49|*+!Ys5qxJ0v8Oa*r
zrWq#(*=!r|3;cUx2#GHzdt62#-8%JpTg!+)NZ#G@x@pO`dQo%BNm4yV^{IgY6(J54
zD$F0WVTWv&edb2C&!Fy>TaMvaeE?*A;nj-e`ee&Yk)U<*lku_POT#oiBb)UBT-vB8
zX(n~yQaNHxTSkU0KU9HKl+K#;A`-cE%1vu3;N;Ed&nHa42jDVMr<zH5e&l6l6AQHn
zR~NCj6Z^DB6;B1z@_)5e-{;!>JO@zcsCr6%zoP2qM<oJHAM_Ruc*8Gfp2U6<D_L~c
zW!d=oY!1CmRO&%q8fa#}zuNXta60(n%E9G}EzOIZtu}x4c~J}a(7^$Jl$>39lIf!3
z1=U4`F|IW>s9(GFBXNQl70*O2)Nf_94n=;bfF~J~%^tmEIvXu;mr}&8er^MB!g~s;
zavmVjonCAm#2+6lJ2caUY0|${E)l&UNI%ocdZ^!<)T>nOlh+>1PbvtIkh`CuZBT0R
zN<98L78j$*jbe;`9!e|4zPk{zTWF3^+w9+4!M{>d;x5?UT2>yWZ)j&o$-+fk{IyzX
zmT;(w43dZGjBpy&|I6?om)zu^cH2=D6ON~G&QR>VmV8mgPJs->ueo*PVi}+tM&EDz
z0jVSGJ;JVwLz<i%wZSLvqbdI0Udu$J7AFPO^z2R+b+2P^slD0hc87$*dt^**ZqiyG
zB8h!6c$OU-P!!wm_r`irrlj}+i`j;aUG2>I&4vI{E|d#|Qnz5*rV_Jh?&@d`;s~)r
z;hKu!Tfr>3<d^&(r|-Tp7{6NdjRD{O<uaW^d^A!zFoZL_Gj~*T1!ds>CW`KM8mKw*
z{Pp45qW;zX`M}R9YqEWysR&>aq~h9(vR&Wv7#ZOr$%qqQrYDkMAx>HsLLW*NY_d9+
zom<_B^|hp@3vo&c6ll`IG(IJHOTHid^f)Xc=1+yUCX2wpHiUd@$H)p?Ty0Ff@5cJ~
z)LVs*o&}taZW|s<sTLkHq2A<6lb0e`uds9B+qS|sjlS(r>nGxNB|HqT(t}Z2jCHip
z*W?{17~1iyb|x_&#P4)tqq9xNKak5LqKp4y61bT<FzzFro+juQJgSg*g|);=*fc_Z
z+IuSbsnGSDA6>np^$%v7+}-(Jr06lqrsROFc*4b<nY9vT6{13g6GpZ1Z2@qaynAWt
zT>%-v$(j1zhoU;V?`DzMwLD>~%Z-<>KejEdk`d_|(2Tl6!z}p#csHf0>ZtM3tyZ~`
z+6jlg+>RW!(%ra%<ebS$fhf__Ryl8Ddd|do%sW2Un7JvGKfZr(z`<^sP*k(+w_Trq
zVLenzVMM|l_f2`LULi!5_v1X<3-oH7*bQb>g<)ofh<mb}VSA}qZ*6vEvu@j!gft?{
zHYX?W97ZgHa0LRL&5<^bUt@4bBCNS)4(6Z0I>|<p18}a@yrmkKpKj)fJFE3<0XJvB
zGm2l-B3_#l!pzcM8GxQ#EJTq2P>(akOF8a{PFpse;BntmArgoVY<>a?`pV;~PYuQc
zIRs*?w6z<nZO~;k&q{>v6$E@8k<q)klwvCnei5}Z9k8N?od`ecN&mpIsq!d`xALmO
znmO~E`RU^R>XICUNnsw}z-PO$<?=0`^)oBVTqIYkLCF^X@GFxS3l`5u#TQh~D~5;|
zsB|=zlfu@Fi)9aum*HAfnB4mm!uKJei)r<p=w2Qp{XE;!>(bWs-lAgF`7t_nj<Lz4
zdt@5{@_a^nk+VR&&K1{DPWxM#D-F|+W*Qa69#j0tSc;G9Xw3YZvKw)*SoUUFGar?S
z4??@lgs-sdx`6Mx_?Z2sJ%E-Vz06MJ9Ol6D^K328A%}qY_Q`jp_*c;YIC-Mx$Gyir
zzl`3z<0A&yPc3&*hAK|W(^DrMfQ60prtqq{OnC<>)9PKSlL^Pp5^sLtZ12mSNz-*!
z=o|=qkYTl%0Bnlc*cUREXfF<!{nWF$w!VCgB9P}7-W)k75Ax`Xk~=}O&zxhavoZ*A
zN>H;8r7rmMI5NH~P^$FH>y{4}OwOg|bH%o>ylPf&<a3x{GEc0~7>mqGGkBre5Rv~F
z=e^hIOh##&J%fHw)RiduTF6O6r)X(v9G?1B0`P>-tb`8~Z4(8v2b`|+hdS0FbDEOG
z=9O^=895ZWs>Ms1FW*j6`kWd-8LB-r)Hi23dXHE;(%1S)OvQA#t2mqcNzYDO?O4Tu
zXe9l4X}QZh<$>phMiij@rd>Z>3BL(eZdEfK9^RmZO=czMTAXd?UVA$GOG?Ki%V=$-
zjcuNh{H3-twLmg6oAbQ{%oAu3qgBD}#ygQr*U-bSH{PWSb_}XsExMI$dl7`vdY{Xp
tLk+75;yqVo=$`1D6<<^=RuOxN&JtGnHIUmroay+3i7>J-tk%Cl{11_x|84*P

literal 0
HcmV?d00001

diff --git a/docs/python_docs/_static/mxnet.css b/docs/python_docs/_static/mxnet.css
new file mode 100644
index 000000000000..7f6380bf6733
--- /dev/null
+++ b/docs/python_docs/_static/mxnet.css
@@ -0,0 +1,199 @@
+/* THOMAS EDITS */
+
+.logo {
+    width: 120px !important;
+    padding: 21px !important;
+    margin: auto !important;    
+}
+
+@media only screen and (min-width: 1025px) {
+    .mdl-navigation.breadcrumb {
+        margin-left: 100px !important;
+    }
+    .mdl-layout__drawer {
+        width: 300px !important;
+    }
+}
+
+.mdl-layout__container {
+    visibility: hidden;
+}
+
+.document .page-content {
+    padding: 0 10% !important;
+}
+
+.mdl-layout__header--waterfall.is-casting-shadow {
+    box-shadow: none !important;
+}
+
+.mdl-layout__header.is-compact {
+    max-height: inherit !important;
+}
+
+.mx-card {
+    box-shadow: none;
+    transition: box-shadow 0.3s linear;
+}
+
+.mx-card.head-card {
+    width: 100% !important;
+}
+
+.mx-card-title {
+    font-weight: 300 !important
+}
+
+.mx-card:hover .mx-card-title {
+    color: orangered !important;
+}
+
+.mdl-card:hover {
+    box-shadow: inset 0 -2px 0 0 #048ccc !important;
+}
+
+.mdl-layout__header-row {
+    height: 84px !important;
+}
+
+.mdl-shadow--2dp {
+    box-shadow: none !important;
+}
+
+
+div.mdl-layout__header-row.header-links {
+    display: none !important;
+    visibility: none !important;
+    background-color: #fafafa !important;
+}
+
+.header_links {
+    display: none !important;
+    visibility: none !important;
+    background-color: #fafafa !important;
+}
+
+
+.mdl-layout__header {
+    background-color: rgb(4,140,204);
+}
+.mdl-layout-title {
+    background-color: rgb(4,140,204);
+}
+
+
+.mdl-layout__drawer {
+    box-shadow: none !important;
+    border: none !important;
+}
+
+.pagenation {
+    visibility: hidden !important;
+}
+
+footer.mdl-mini-footer {
+    width: 100%;
+    padding-left: 150px;
+    background-color: #424242 !important;
+}
+
+    
+/* END OF THOMAS EDITS */
+
+
+body {
+    font-family: 'Roboto', sans-serif;
+}
+
+p {
+    font-size: 16px;
+    /*     font-weight: 400; */
+    line-height: 1.5em;
+    margin: 16px 0;
+}
+
+.sidebar {
+    float: right;
+    display: block;
+    width: 30%;
+    padding: 0 20px;
+    margin: 0 20px;
+    background-color: #eee;
+    border-radius: 8px;
+
+}
+
+@media (max-width: 500px) {
+    .sidebar {
+        float: none;
+        width: 100%;
+        padding: 0 10px;
+        margin: 0 10px;
+        width: 80%;
+    }
+}
+
+.sidebar .sidebar-title {
+    text-align: center;
+    display: block;
+    margin-bottom: 0px;
+    display: none;
+}
+
+.align-center {
+    text-align: center;
+    display: block;
+    /* float: right; */
+    margin: auto;
+}
+
+/* API section */
+
+.mx-api .section .hidden-section {
+    display: none;
+}
+
+.mx-api h3.mdl-color-text--primary {
+    /* display: none; */
+
+    /* border-top-style: solid; */
+    /* border-color: #ccc; */
+    /* border-top-width: 1px; */
+    padding: 1em 0 0 0;
+    margin: 2em 0 0 0;
+    height: 0;
+}
+
+/* .section .viewcode-link { */
+/*     padding-left: 2em; */
+/*     font-size: 80%; */
+/* } */
+
+.section .class dt {
+    padding-bottom: 1em;
+}
+
+.install {
+    max-width: 800px;
+}
+.install .title {
+    display: inline-block;
+    min-width: 100px;
+    text-transform: uppercase;
+    font-size: 90%;
+    color: #555;
+}
+
+.install .option {
+    margin: 5px;
+}
+
+@media (max-width: 650px) {
+    .install .option, .install .title {
+        width: 90%;
+    }
+}
+
+.install .title {
+    margin-top: 1em;
+}
\ No newline at end of file
diff --git a/docs/python_docs/_static/mxnet_logo.png b/docs/python_docs/_static/mxnet_logo.png
new file mode 100644
index 0000000000000000000000000000000000000000..be8ad9ab9ccdfcc03b8f32e1d66e661fd97ae5fb
GIT binary patch
literal 22390
zcmY(q19&FE(kL8l%#Cf^wr$%s-#8mP+1R#iY&N#-<c*Dule>G)cfb4pQ_swFR~Nc!
zx_g?DN(zz)usE<FARq|RQer9~AfO3<!;#RCf1h9|4zzz?5SGI7!XO|G@o?|P-~Psl
zOr=!hK|s7IKtKXRKtNvqq5@AqK-`%?K+cRnKzPzYKro%MJC*tVCcrpJX}f}eFp&Pc
zLA^=@J^pIYwpP<}(~_6tF?F<OFg9~EF=z0yclt{W0>bCT^EYU3?q*EnWpC%;%HzdP
z@*fJGzu|vqMiQd`kht0MlW55+5s5mwm=m!xurV-^2*45%5%Ia0S@5WcN&FA{-xxoM
zm7ALr4<n<erze9aD}$qpB_lI8H#Z{_3nL2){a*@tSAc_?u@}9AE9rlQ{9id@=B}nL
z)=qBLjt)fs<Qkhey1Vg{ko;5hzt?~B>1J*5|1>$c{*PII4P^Y6!pO|P#Q49m|FZJ^
z!}2J)SeyUV{7=3BGv9wG|9`mu(Zk31Px=2RGyk>pKiI#f3c&I){;%5vVC4oVwLw6H
zK%~Wl)x1D2bD+A-#2dbRT&E|Fy4%fAz=;^f!BI^h!M_jU5el##9Ut%y_L&U#|E7~+
z;S*Q>DJG1Zh?hDQG{rsgb-l5`zQO7_kkP^VG~MIo=OF)ds;aNAzooylm+k7)?g0Jv
z=kmP323rT=3FUOIXv9FaoE&W?*Nkx-e8zA86o9!Un<fqZ54Lo`haekdH8{9pR{O8V
zY!*}6(rECHhp;cyKl!zjQO3idi~vcZW2dAa6bNJpZl(xs+ECxI9nR{zl7Aylfo$QD
z;xT4p4LHLhAtPcUqKGVLuIfhYjW}PRIiNdW)S}j6FkmvEGN22P>y@yyQPY8>M6wPP
zco~!&(u^iB{Dk}mo#l|7+2Dymt>~gb3(#RcmFs&(dng0HLz+U8Lb>3x#YjPp8)e$1
zo<nYo_kssTLZT0u?sUz5!hGzfv(#CMWf58bgM3-5d|9ppqlk}c14y2}mg0xR`8(H;
z5tw)s0)|23WbhbXD_yyzGo+lk`bLlmP(&c{-cG0|^mS6TExLC?^(a!^<c4iW_9^Fo
z#j&#+4L1Ob;S|7M%);TahnY%xq@G>&ZbdZkChtu9$5v(f?`K=Ezr4@w6vCN2L~e$-
z2kT`qtEC*!KQolvSIcKbohWXkp>BjJsiknFa8A5&%2(1kYD#}O8znQ>9zkT!=an@t
zgw!E7&PRZDZi$?U8q^n^Ss)H&!%oNCnHoi(D-KktmHAwTote2b4w)m4JCrf~H*Y2E
zv&s@2N#D(sF1!1A5%pdB{i1I0_98`U5$sZeP&h>Go_JUXQHXGfX;IBsca1(J_7p=`
zMy)S)C7j6k-D6SZ64?I1EIHfkcSJGB#XO{R(4|Ku>+9G7PdIG=R=Tsqm4*urg`smq
zcPq=Sdki!eZHL%sr5WmJrDqU0Ilb__&_AfM$zQ;ah_N8VR+@%q$_Gc!CVmgRktzUX
z_0Ff|ip?&z517hug0eKZVHEGBe@}VXC4`G}HxZ+j`|A-Y94mGNw@mQF#0LpAi3MIL
z{X80=uiEo_KAFW8kpk&9By3xkT_7p5<@Y)nFPC$Y(}T9fYAwNH@_)R4xycdS+QAcz
zizf|OlKuM(S7zoPk3;H^qJFiz#9wA3rZJArDhpS18k7ut>AnI$h>Hmy5(qxTv0?rd
zI~^Hte4G5=)$4-#C0>Ho`v(K@FaS2pU2=YS3r`(qr8=8_8MrcQM&4T0_o{)q(Zq|N
zweQP0`>iQuy8jXI6Ou<09REltU0SjV(cra9>iLI~Urd={M#y=YZD6pG`VZ7^4ygWW
z-d*9*TH$(>|IPG?90HqPDPz%!=%{dqy|$ki`ezmLF^c2fAk;)oMbb1KF8(gxEP5xm
z!L#P+wJ0p0$c3{L%_IGwY!l+Ym9~U!R!x*lG;POQIXfQ9U-mhS2wKs=m?`UOPz0b)
zZ=@TIE}cj841}FYLDeSL-#7DmLC*Xi-kidqQf_2y?(E=z-#X5@)vpCN$i%F+@J03u
zRGIldAhlxiW!Iw|?R!My4jfLCZ4Cs((*Ccpdf@UIAFQIo^)TpLPZ&#4*TQ>zTJF0-
zKkpO1vw(GqSf0cww(C*m^ulZvnhrTL{69VfPa+npTjLi%<(#?5Hr+7{ZL2AcL8+IA
zr;z0dBL3arI&hv<vCf&{2dXq8wmI29Pn{?su(`oijRJ14Xc!^ECUfl>cPNYVI@yuW
z?<QP`xK1BJG`7y<u#^6N%CjY*-*{4q_s_i$-ipXk{O@d{wQZp?$I<TI4cVhm#L*l1
zZmr3ge+&GL+g#v9kRT<(Be{Xy^%@S;`6rGS^KT^$dttkFv_7?t9Tcv?uv(7N$j(ei
zldVr7g~J}Rh>b#=U^l`4R6P)cT{{S_q|E#+L^2Mos4t<C`)~T!OK7Fw+nJd~7W=D_
zzMA-f@k;a9&uVhsJXIF%rSN~-Sw-+hDBrywNJ~FNN$@J3q4(q)G4I97@;NByYiml3
z%9%{C=(p)c03h9Y3EAPA;;I2wf_UFL{rlF9l6d+7B42J8#;UT4lmD#MQ2=iQ=iBbn
z+?)G;B^Y|d<&|#j)P7YrE08nTH=Ry780Y0v%Y?dkDfA;@TZsCTI%OF5NQjSix}<o=
z!loZ5r}e#f-K2#F35jVo$XC+FVlUG3UtONC?)ONwkZMd~l^O}=7Mjx4a_3i}{T&i$
zAT>0j@(w10Np_sLAS(H_&u4dZgWfWB>O$fMc`k)duSmoAuK~Vi?>GGdLHxl*98pv6
z6Sa)w&W((IY>1XzkXiHWDS8X7@f1aIBPINSBlOe^dgIqJ)nI2#Ef=qN?Do??2{P9K
zhsh@fAtKSOD_r3$*J}Af$SlE$z}Lk02W+p}XfDn+9X1VhK~fM$e`Nj2i)hQ->a0eE
zJx_<R%B?hbPJR$*!DMDzY2&y~5_qEU_!lV^`VIb`un2rA{_x+zoy6Wh`GfqdK;%=M
z!LjP#W<eBziu7+Y#<?6g%v}ZJiuGGm{jzh9eda>Q<QLjk2pJ%UiQj(dhQgXtln41z
zrVJ3gK2G4EwCGvufti3s_V<6-+zdHs9D}5??*2P;`a(#Jr3xwc0uZ$Sg--S>XmNnl
zOjbCFT_~#_7Ia!O3tx1DI#+|(fc%uwI{(c^$aD*Dcv4BW%fYLJ6*~Z%sL#D5;W)C7
z;&5lK(H)&|@PtJe&N|Q)X}Z*^DSFZ4W2xaeTx~3}S&5Wf`6nb!HD~})t@AWDm7x%n
z)cxsaWsqP7Uc@*n(s1~j7kB8IoeP1ANHn-Yh^M<2`)l13joOZkuJ*j>kK&-Ka?qyJ
z88x5<d)E&Qtu+@jyJyd4JAHmdvDUmeaO)^ZT3V@2<#@<_j@OX&8*OXaDgyRWK51}$
z32=_J<CshsGu3!(8Xjb9yw=*)O0ZrcJTfy!e*0i47<~vU+VvR6-S@KD&o=L&)^LWu
z4)$CLw8Q&)tfg#aFmeoK*5P5h4XFmIk-nr~++VGr!90}`8PO+wWS(o<<Bqc?3ppk-
zl|ckaRdwViaDK}v0(HfxQ`YTO&bg3@3^FZnCHKm-74j?G_tFSx(6&!oRx77;Z@Jz5
zsw%5xT~Xx2;w)txM&e^R#`IGaa(Q{W2fJ6K$)*zPrAzWl+8O9C^;TLAf(YVr12PSN
zGNXkzp60}#dE2wYLc&)eufHAGEZ8odw$Ye{YCi@?7n=63<|{X==3fY161|M48df8W
znL<`RYlzk^S-A6lN@+U#sI>j$tB_UpV`9m0pG$yk3zk;)d+EbmY?9X(AAy_e_6qlF
z`eQn2bl*y|!x8!^82BBh-!LNa3auyP^(8zzL3+iK)=SVClto+LS0sb5sG!EynWW?k
z-3EC)`CSA3)s8r3f+w5hhr3PVir6!8JxY_{wkUq#f%qhQB4+i}qU3i#P$p5D`_JRb
zK-Uo89~7#W>|V$ml=UPvkTn#BOe*w6rA)bgZ?YT|@EIG8YWjmS*)m&S5#?*+k!N-a
zzcDM^a%B#kUYv*(spT=_Q17%|TTcK3i+Et2>kOOKzUOFvJ8wk3a?c4)aSy?!w@FOn
zXg}Miy@UF^aB0qh8lBvZ_@2{n0_Cw>HB$nnmanOowo@8YU7`Q1URjy@8yW=)y(pTL
zv!pz?zU9)WU?Lsf6;gMtG#{H<aF4JY;Vqmmw792e(<im{LYD|ejV{q^YIJ;mTogg^
zYf-7(9tm{H702z>eC*Y8sa8+_BK%Xrb<#V^6A&3^mqRaWnPyn&M^i#vcV#-u1pks!
zI*<LN8Uh7t2}=osgxt8%Z`lj_I!>`?vq!RrIv_b@*)WTX_xxLSWjpZHUsi}L3EN}(
z%Ycec`x%V*(PC(hK#0>-Z5bc74t<-3-%fcHJH)fTbNXEG)IIolF09!+h?Om@bH)Q*
zanzrwcxj(+bbtn~%PcNrLT+~0Um9zEa6%9eD&&g@J}jVbJQ2h>>(el;Q<vflHhXxa
zKa0+TRyDg#!xzI~OQr8^x!h!l%YLlwWr;}<G7k{ok0V;~A3@!z;8x&WmpKHdx$bgP
z2qow3yn31WMVnpY`#vJdgHuUIU+%z$MFnSO@oYK<$T1sBtFgV>(^|dvOHBZ^dsJ=S
z0tt9lK3Y-V{wPf{nU;C-R^w86!kUCXs}X?=APwXYL6Qx{lu2dRu%JHb3{UEJPLCV)
zslFj2mPlUXR4BsGqS*Hd4M1*07>~jGXbVA2#A`<PTjpicS6q5L1qD>MV9my2%h#0&
z8Tn1lUu1496M|U_v@13k!ZJob3>5sS<r4=dt=yn$C_+#~$)(OFgE*noe#LWd^4=<c
z#v@LGs%k1E4|+R^85^#P70y-pu_hF_%Rw9^3G=cPzE2O%eW25h-eNqV&}MUh*k<zE
zqs{w3q0Qo)p$#xX&}NAQ>6drD)m4VO-q(i9TU>`LSlE}vWD<zWUv0SklDw}i8%hm6
zg3KGI@AId#CCj5d1=pqB`(B~V;oPCkZ-u}T_;|js5R11s5{pmM5%%PHK(=xtY&UiU
z(wSaXvVye_Vf2c$7kRduMdm0Cj{Nv7d*mr32PQAE8dHkmys2|?CIq{iE5Tx*g65jD
zG->}w@>Oi;Kt<;hV}W0Id3tlt%A{|6GTnX6WO~884E7vn)DgnK@lZ3d17yKqjqi7!
za7Xxicjw3(lQ9v(0V{U%a|(uLJw4#@Tm8PN_{j7aZzyUJ->q0A5AaPzELZ?!AbohA
zj*X|*G&t6vm-@Z;p|QxIhbph}I7g7fD=Y!O;B+83BplWfrpE3ptb6FPxVX{Coryo0
zoX+)tc2Z^hSCu>XFY3XC3Kcr}O+@S-+iTsc)as2Z$j8f5GhAFSxy81t%>%?-rq!P3
zi~~MJc;|{pZe2bDU1N{<1ux$<3eTMZgeync*7m%bwSt#&3KAEbms(_5=^CTix-_4$
z(cY`Wir}r=8LZQ|be_q_^CVl;ajLe$NbMnK);o&(LUr9u(P+-j0J`-P2S1yvF6ZEj
zCl6}6YLESHfjGS$veiI?t4ZdME$M}@_G8)FU<$;?>0k~Jt8kuQ>)B1n7K~|du5uHs
zn-~tviA+-|Weykk-kO5MRnac>9Gk1Pvyf`s>pJs(wh*~zN*N#kO&DjQlh{a2_K&Dy
zlG#-*|3BB7u%@Er6L{s0=_tS+dJRmhIZx=aL#p?>yc}9c#ou%i;oizfB5r*8&*Xcy
z>dSpqg4;z2HMp-9KIgQpYr^J7makEH1=)G1CT5^jjP4K>uJEI<2*gh{Us(8^rPI#-
zU1f*e3i-r-oj1554|mF^`+2Y*MFUZyN$6BFpZMVKpEYt4W@Vcy?zBndd2#~%XX#y)
zpR)W|Nzs$(&vcbk6N2a2jtIW(h=^i8D@V6j{QG>ZV!O_MA@F$5Kd;hnk;2Lw5ogdV
zV2Lv0Kjr_zuHFF(_`IiggGV-@jQHF5=W$-pE<3t<^7RgXT+O*(9Zxiy){D9?$Zd}`
z#NuymAfPLHL%3vNp&{kz4**=4R_eC)YSWxOpK9EXr*QVC1w4N6n$0sAN@!4$Y!Msy
zhu)n0M7Zh<*XSagTH7Vis%n_AA3J>g<D?q?=<!-BRXZe4wG{@@*C^kOAzjv|)wuLw
z@@Y|nuF>I0z&zixcLJlsjDU=OcbwhPH~J}x;>H$(QkBpYk{vp$JmFv-Ib2*!h=nAc
z1(|GY6pYw05rp7<*u>RL;o0H4G@tm}OG}F$>B(LXMTbweSB9+=6;CCI&W5+vTF{Zs
z-LJG(klrjB+z5Vyt5%bM7WIP!E(vD+5Q|th`W>}NkoH1}vhdC+)7Ks+{DXaOBP5VJ
z)P<Mwfwo=;SK07fbrF|e#c=_O*y;ndM=0RO4dmFP|JX65^@)V@`AlaH%<j}6=*?x-
z>?;^>aU=JJHR9aL8Yn`W^_CR{CkZoY5U1(&1DR>)P+cOH0!gC_Z*~TswYW`}mK44y
zofX>_6#gCL<Go1{iBqNN`U6B$=HUmRXc}ht=(1}I0jb()r_;0K?OaA4F{9<_`V^61
zb>k^YzHtyY**&yIh%bDA@O{Z?$)Wh}K^>L9)2FWMW)I<N0FbrCPATtJ=QSN7r$FYx
zGO_I7s=3oN>30GuAJtY~_jrCvfpB>lR$h<))3WGLr<9?2qFKF~Z7jr`u<8{)3B33y
z{(AFV;Gr$$>Z}8Ii_<`0iQi{f$^!7ng|Z4-x|>#B^D?693H(|0IscNlS{%0X`KiqA
zqYn1G4ifCCmzts-6}d&q_(I3ntf9HD-G2s6@7&4pZWE+M;5F#dqG>pc3t05+kPKT2
z<8u*|wl+lR&By{9XCG0PLAt4IakwEz3d?w}er4KDt9r+jjSHYErW?1%DTg<^QBb`N
zM*r$aP-pLlsE93k^mGx#GFe|*--j8xMi!vbBiW>a;PTBQ;gfkDKyK?)?geVHAtgs|
zCk9b=`IEm$NHf|!8tdA+lbobCv4M0PcstKz!!{=WlpIqB1^jaH`!yf^JC=$WG9Bb1
z!=;W+RfE_LsRni0_?rqswbhAC;`t|pM(;j3FM%*akv)R*!JatoCZ~h;1w&5NS_k}t
z7dJEfgZJ1&Iyby|5tvn1whl5lWfQ}wpa!1=;`IT+R_{p9-4omvkcShn;GEQhs{Grh
z2!R*;?pie?Tecpg<7E&1syi#KvrDt;8vX)^h{iamO`J(C20`+OF*PABQtLf<C-kV9
zfYjj@(3pLekK(swf9}zFtan@7b^pk^*#H%bp<b((?HJonR6p&Nx$jxw+61et<nk5-
zq!H>h2~_mqsk;>wUMyC_o_#njgJ+rr^>t*%+Sz~Dw}mHYzD<uXALG<^9xk+XGrmjT
zwln%o=r?4JW?tuw-Nw=62+x&!SOVM2m_6MRnBn9PO{K8rMvuEN9PWmUeeinLNYfwe
zVeI3Wexv*IfNZQ-+-(TGMrs4$h|dY94?_W^P}huR2`TyF+!`_EJS*hnLSoErBfq!}
zbjdRNyCS;378fgWe^v7IEIG@r7<6CJ`-b{35_(w;8a?-PyFUElNG?_t8|0`YuTGas
z4tm1Mt5pf#1AZ0jEy7Ej9YSqF&q5M7=DF4$3v4?n0IGcTG?U);?%IRqk{Wxw2DU}@
zz!nv_N=RQxQjHuU9s&jTyM#NQpJi}mvum>FPicw&m-WxqAL&JP>aVH|sBkaz6`m-4
z{mW2RQ{#^kx^FsAXL@D^b-7nlEA;NBZuuXM;qeHp{w)f-p}%i*t?!_5-KD-3HZVF|
zziMk7?Nz6+mL>Tu^RgKAA^H+&RRD9HgO5`&;v_n-v*9T%TJjPBylS*RC0nnYXTE-e
zaavjldhR|G(_xkb!J^X*@ugX07?g1I5y2D9n<UarPhK303=X);e^)?nY;F};B={}G
zO)>Kk<=U?(`K?`&yLiZ`G0~&r28EcF{du<9G^8n%4;}q}#T?WLY0VT$c#Sq>B7`=y
z8=OfKdJuS|;?hTP?XB-G%_Sajn@*KY!KcI}715h7F7da^#U3=aJ7d1+PpEX|2+M4-
z_^nriuqyBREJ#9Fnuw9lCMDWirR*4PhoM`Gs=<aOdHg5B;7Z;j6`u;XKc(0RmhG9I
zg2zK(%!wiRlAn&>r%EV5$Uq<Krvd>DD1+v^$(&xz&BQ<(>>N5Dj4Y@(GvxqkJlx>1
zMCfQ>EiI@yc!(klw_*{Y&fBUVIRNJs0~K?A;#8<j2Fx!t653@>5qgdsm8cdWVuO9p
z0vbU|2%8edQpcpjDW_F^vy}hJzj~8l2nNVb@#h+tJ~oq<Qe4UuYpWAQ_%tzRMC22>
zvy9)hRl>>WM%XEOpiLVG4)x?RVV?6C7O%OYh0Z4D#s$Z^&J}jqId!p9YFaYhz$Un#
zd@K#fwTzg<0}OfYv=464iSj!y3XP?+E~7;b;?*;1H!bA^T=IF2p=ND@>Nvxzt(z?6
z+@qUikP$=Dit%$HeiNA1SXQZ^cK~T0PDqRH?!1jiC#X!BBITicy{d`cN;3#Z5WLY0
zM^oflfk-t@b@@AotXoh5kk}vlsVf}cM-BRr&z5>LgFLUPRU;rPrLNSZSu~&%_)Dyp
zH>6GjKyDc<jG-!<+jr8J*%v`lIFhgbw?9qCJ}X@`oh<)eH-H=Rg*J`CfawUC8m=TD
zKL;sh*}0ydWf%{W>{wrmgwZSW>_FV4{fu^^yP_TF+q+RN*RcI0t{)&^*6iTm+hKt<
zY~QH0LL`ygiGZwtN<bXK_*w!V=Ae<!XPf3JRZ1w!(d(9A{%lGAdm*xnJ3dmTT|~-C
zVtpa`S(sxz?UkR1PxX$OdSD_^!VmQ1<mOZ7shdEQKjK^ii2~!8%D#8t3$1WR#ZPvO
z;Pq_f@Hc1<5y1rdt2aa@N6=V4ZcOYGOomuSc9i$c9rbYPhGfVwOo4Ez4M<MQZ5i4K
zbE8)vWn8d-kSY!Z9yuPZ*b0$fhCm80xU#v@Bm+4J<uO<t8f`yiMzh}o)<s7XWjkve
zUX7&EQ>S{CM@OhP$@wyHV-taPk+KEIB7Te|OIB#T?5(+*L!F?v4RG3ap36J#{Ob0s
z$>M8_cQlXTgY<4x8wz^B<8DTGVfhpiWQbvf#@3%E)PSjQ_vOVHoG0(iA}w=G$lg2P
z0qHw3q}}vU$jlW)5=NR&%WASK^4YmQ?!Z+)7JVRFap4XP9p~(ySQ4f0n4zELNEck?
ztdj_0N|0N4`!f$171-&rJ9O#7N{FVri+9L)#v_7yeWc{*R6VYBXXJVYF(kZNfi`P6
z;ud$ti@+9E{r0HFdI<U5d0eSo;n!CF74wE~{7ut9fd~&Demh2Db$81CWls3>yxI+m
zfSXE@>YYFI9d=G1u?_)skLpu`>(7Ml{5kEdltTv$e`znj4i>kW^B?tiS_UcB;H$W#
zC5{@o0fC*Kp#z4n@o2*=CoHJ5>UO}OgLkXZp=WkUP{T8um3#EBq8mtJ$Or1Nkoy~=
zj4;|CO&|3)<RWfrRdyD%aVpzu1Lq;d=(zmkOHe<P?!l+Mci+JOPQt}BEve3N1QL|P
zdB3IIr0h4S@s0lWVR>lyneTJ>jc#!7nX=dIIy&lUg+HcsX~9h>7mF(TkMQ;k9Jkif
zqmq`gpm3P^xGD<SglZ5GqwNrUC?{rl_L2-()sOLkxfoOiu?i6UT-aF6XRz-O&?doC
zl;y~Tf9C#7yIRIQIKS=jhd<e^pAzVG|MEMp1ztU|RHB-ZX-2`}&={FzjED3-jn}V_
zAJ}rm2h_SZNj$n&rSxjRk5p8=Mmp_r=$}juAY2}7bDBo*TB!wi25y&})MR{R(syl6
zMRRu1KC+?455;M&tCEZetIe}KDM4G(C_r88_<Gg&EGe6Ag(T{PQbn@HkEA)cIX+J%
zUHwAn+T7Ct@R5awZ6+lr)-(b5M5;$o_(=F%1G6{^^9T@6E+_Px9m3xV5C`*C+b3di
z7dPy`e_#Mh(x`PXPI7d`n26mL`uG}$@p)~VSCZBd!8JJ?BXOB;qm%Q31-@J|t+1T{
zN}UdGk|<-Q5olOa7J`OzAG~OvYF_zF92T9`uog~+^a5oOl*7}(`}WzHTTRF>XiqHP
zG4o~am|c13W-~VROxUWg1LSY-laVrvMR=NTiqBsuV9ZY%c_KLK1aE^eI}77Z&QVJI
zlx}fDCl#nko6Ajrza|PcM#nBbMPa7^N`z<dgYH>%=h2~><(uT2bl*BhFos+9Uo1m+
z&_2760Zro+Ds`+yg2F3Y$Vu9Be}rd?m|u!X<h-e7)+ese!zG$TIH#5Loe4hByeakM
z^e%N?y!I!uN6XMGfnjvbfXxvTjb$h)pR~Jo8D9su2WI`T_7iJ3FSKAqQHm|2A&}(4
zi7o0c-UwY9sPOdbF7d<Wk^p9PV2ujiBoYbjgH_4YVOamXi>7!V4PEh}!T#@xn7IkZ
zzf(t9e2enW2YgQ--?FyQxKT7n&@<3)w@b7+rh{4IueaL!{e-6vuvrGP>+HT?!gK!s
z*kzsa?W&_fwYLelCiB{?J8^25BVAOfCYIN;x(PVHiqdYKJ7^!xNK=gZ&?QARa-p>L
z4M*H4+hcFlJ;^!|jkXv8Ej*{MkGzka(>1`6tZl6m#?8(h2&Uy4dpwVjJ<m{zHVcx%
zI+aCVfjjgCm5p{+^y{y$bC<H4m;^f4!!nZa>>b>KHU-pB4`>n)@ibpL*InJVml~Xa
z2cqlMeebjhLyC{T>lIu_c_CO$963@s!#JBFibejru`O3$^0%5a*X$zC#Fg0W;<RAk
zgYbkCzP-#3V!J)GG+iX9ClGkGDjYZ8h?@k=I7sb*RAYV;&6vo_0x^IgY-+a_kpz7G
z7s(RQPhRwGTC-(E*G#*p)NkiY7oko1w+%wp-lcI3q%W<tjA}10YqqnLYgDM-$i2yq
zP<O{sao9&$q0F%iRbUSwRmc=<DFl;&#TfOXu3iB=X2k?t2`>G9uNXqQ2i?q{?%eTs
zxQ2|`Qiiw2kH29<Jj^8Id36C%7$B#!3o}liZQnlB{Al2K=Gs8VjQoE1kXx^t(c~Bb
zux&9MMWq2Eudq^UT>y&>NwCKYWR1&38Y<2Mb%)y<M^)1s?i&J5-@v!sThx-uT!+M%
zbFR7Brm7%9@o~X~CoTxht&UZ{i)r=Do+oEoAfMGEHkP%;lM9Jvw_V}e=_yo~YJA3_
zMGXE0<Rh&c!3`EqeZ0@*9O{-kV-6kh19Z(ItIh?syIMD%Q+(ojUc^o9H@Rl_s1nZ6
znJ$amE?GvJeLjNJCM<G)(vbV+6whwNSx2ueH_th1s25LdC!Rkhg_ik*(+U)0Cjl4e
ztXpMB*hd=W0;6I`O(<5)L&W>__#>ZNq`00F84Vs4xJH)Zy-ZF8m#o3Bpp|B=GyT4L
zx16w=u+9YV>D7V6`B`%41{D6Jy9hhg@M6)4x8OpzGc<&()`2`Kn$KCkUW%=z07P#J
z6SSEYK`ZW5mhx1C4~8{zNj_oFg5=F>B(FhGj}&pgy>iC6G~4C8jbffq&KjX02XJ9R
zKWlE0uQb~A*=`^ND9vO;7)~E6>T&W7gDdujTCwtTw*-(G``ppni8hRg?X{=1pyoIA
zWHC7_`~~47wa-oYWWx5_R9)tF2)+m1#*L^}_%4T4H^(DDG;GTN$1;}*1X5Q9ewtU-
zwvx*R6c7TtVNiqxfhOO;@(-gX`P7M2{QGKtUu;VJX2X7Hi(jggu|(Bj!~v`&7z{7y
z+C4n|bZrMFrd&N_ErmZ8FL9KZMy|GImf7dEDPkWI2>PY^9NbfOp#-CB=rRd5S$foU
z9y%*}$$t(>#<^k&xQ6@*^yn)1S(e!#VKV;EWB72teC;;zIKR($rFBs|d+=LwRGE+N
zm`magOxA2t(C697GZb%KS~A8R95_iPA)S%n+;(Wv@({O{v{_8-w<&TO`Ko@tHJY?B
ztkU693AeI)L`K=k34BYI6NGjYC!p<NR?1+1wXxhMB6*DmYZb~E+Ae#}7`PO%(B(?6
z7HTrW%B6ize2&cVB;mLjzeBOV;1i%5xCdu`FGq60%8QS@{yOka=)k}pTgDdBEwGH?
z^KM*Yw{9kOKe+iKTe|dw_1l7;Ent$p-d(_b2%=26w()|V)lcot<$AAohX(5a{TaRT
zChJ!*GlAAk!4%|3zI`&Xo@LKxd!9)lbyx+W@fk1Hq!3X{a~q!f8Bt^z3dQuoBG8dM
z{Z`YG;}<%mHJQ8_-mLk@Nby5s#tC1fYRUJyNkryW(#OgVVgI~(pAn9uBYvk@?00c^
z7Li_^o5e{q$A!nSpl$Knx^>fLHH@h7B?239leZ`WNFNW$MM(|2hD7ph6lpU{nw(ug
ztU4IIHcSG5l*L-SS!k!`by^;7s{`pQM$?i2t|ikL804zkWZm}m>!RVRsV*0|gQgjN
z<MOL3S+01`4pc*dkl!kO^3q@jt6emT4(f2cCdTC@ZM0!8!Nth9W7B@#RnBD$ik6E>
z=^1n*tW!jUNy$G;;`Utpby{fJsG`*7Acn6z=jujhk29p1e7d4quG130^!-``W11br
z6(?jA)K}}Bfdov^3-wwoVk18jYj%E=$en`i*tu(;vPvMn!l0UKamt%Xb#jv$%--=9
zv=)6H5wP_$9c4A1Ff*~0M7unk+iM*IV8uEhV-)u8kD`wW&3az={oo`w&$qy}WDB_@
zjW+yG(;oqG_k&Fo9oc9tx7c!bkz#(m(QQg4kNP8rv@iNuy-yNC>a02MK_l#C+lRvi
zex4nyD*Afh86+MKW9t3$*<8`qNXsRwosr=+8kQiI>4!Q*m!RJT(NpVfX8%$>jSl4;
zlx^3^AN#JYFTUQySd*`L5_Xef`}OXPkHe7LD}73N&yNQ;oN3sg%CVBTgSI#1_l|`_
zp+hQws9(gHioCABnyTfVP0sBFyrY4}grqike>11XQyi7EpsNZNJPxKm&?$qZQ4=Yj
zM)&kN1Xj>F&rERJi%Heil#MR7@tbADH4>PkD-OobNzSZp)$jKHL*GxjWRP{Xqiv@T
z2=p85TT~uR9COjbv1fJ8&0h$VhKGn<YxJZ4ILs6{&^5J*S7LUNGR8V+^~L^_k@%w0
zw?p)-b|8nbl}9s-Ovzh)=RR~?kc@uL+=Qa}XpaFzX|cUGF8`T=+qI<L8;^m?nWxF1
ztY%lC1uwFc3@Apdm7K7uQ*xb2HqK!@z8$iRbt3eNjA<5di%7J9tb?2+OQqu9+Wqv~
zR5pbgPqv1Ww#e)Ww8=CjvqNu0;53MbXc95{#-szRn{8bh8i{qb>~X4OBxX#zviw2x
znLG#nXq~?8(5Au8A<vMuW64}Vz5wa>W}DX2>39lcNmB?f0s2}HRrut8`wfIj+&9GR
zV}!~rHg_k%LeRz5D;D;q30Aj`S<PpM$%#Hs4N!?jpNJ0)<VJv#KA0Q3MmC<M%~i)}
zY$nMk>^uhB%Tmu<s!YSbps8@EZw-*a!+>A_<X9fc=&ta?w6KW0=?Hqo&U3%zZ&luw
zBA!6DXXMQuEzd~Xu-&wC%{=GRE*Mv^?7uO>LZqC?-defo)LdpU3jRqA4_yr7O7Mjd
z_^B`84Y?t~SIaA_U>4UJKAkHt`q7mw`85hayQ}jJ%~OWD6H}EOw?E0e$3Pr)tpMJ;
z?$8llCpvxhXFc7!8-0%b4(n*^v8vR-JzM%!k#el|iI``XS2L0@G<g5Lt8y2Jv4;U%
zqYqz2tUvp;ujJ2EJFmIPDK!mG+(+0Sl~)!ch@#Wdh=_4_01_xmKEr&YzIngI{@zWh
zl$~+P81iiRw`kFf^+*ExB*mJyegV=6xJrG>&Nvhw?~uThMm$7nZGjq0vLpsAZY=A=
z>%1n)uF0(`OYeP4txJ*^Zx9GfiqG}&>=L>Cl|)QEbZRs}(A3l}n>X91c#W;}!8ASn
zwy9-r>TRj=?>zd`f1VvK)yr!Lg=-?)4UjMPa%VMA88`iohE%tpv9)t=8k>T3fc2(7
zm|r2FH^glRacoWLwPW+f*yRDob{hwAxJxrRKJ#jvLX6<cv}x9m1WE1*)0<I{4G)bh
z;t+HKE@g7&6-}E=?2~GSv;LXko%qll>{Ras-f<}bZ?ExDtbz%H8A}SN#$@4e0mN&i
zQFQ?!Dl1r+<LNcezyV>A|2jj|Ee#pP-qVSoIps0%OJ&PJt;dQst3q|DQ}}(5K@gyY
zkEIQ8zqhv>CQaGV7+KOFL^J=$XoAsD2`mBD;=I%LG`yx`u4>TGE(3!K^S<9T+jf5=
zz$ZwbJy5$FRPxsQ1qrPtoo2tr>b@)Qz?eQQ?C<mL#VPv*2R_<v3bda1fM1^Q{Y3Ww
zpjG*DuXEvdgh?~>-c76!F!DL8cs0}?VLp?za;x84d|~y}Q^8SYxcPrqPL9cxN!cnS
zKebWJ&Zj#NZOwLSxgR~A%^&DzWgX7Xqc_W_i&{3jc#X<^hi}b`+#$L>NYwQb)V0=x
z%>c<*s8^D{!khbuh_LDkzR#!Og6$l&AE<){RcC!O1~AAD8QQ+Z21T7idbUIX7YvfE
zX(LV1$_SnG-Mz%v6bHS7EQ^%Qhw9n%(m(foO7=~YrTQQnkH(bSM(fyz$;M3Mx3qP@
z#V41aW{Lh}ERBfZEA$zDWgU>u9NKA~b{0(U9$dZ{<4%qlvEntluy%MQ<xti#@Yy8u
zP6r@xa#rFHrQ%j2aUF$OT}3w7;n>Dgz}{ujNYJ>O{L1k#D4)8R+G;U*wAf1$(bYBs
zIw%~6jyq9jm_b`mKi43KkI;O}J>vUA<DWYISut>R$j6kw_;*Zt7VL@Q<+cuA^Ai`A
z7iYYN!@}-zm1(!ZA<$dL@DYaGf7G-p`DHULuARexj2&%o$IfCIJ!=WH!-7LC9noUA
zAHs<UM5cB8CFC_A27y?s<0F6e@6O7sjSkAyw7G*eN55?@PN^?%jN@NEh_QjaE(D+Q
z9+ycOW+~5^da8?y@uyuH2alg4xVfC$jwS4BwKsQWrI1!JH2XFiApKlJe#E5}ybPj7
z3TC~KxC)0l#ES13zv_mwaI#_?F_2o}yiQD`kO^94gg>_in$#)2!hdl(qasvCohkJ*
zo47r*jUf~qj)PYT+*XGd$OfKUBp+mGzAEE3`dQaurt76NrpM(2vhyg~0;(}xiO_Vd
zsj>uM%0%ddt{9+>gjPF2Zf;VKP|v=YdvvriP}QEHFBPBDJ_L%=?&30kO^1N6b3${8
zVxPPyo45}*)8z5reyvOE+F;`G4)=8haa|e!#D<<q@7B~(auA7|Kz}N!$28j;JJ**r
zq~(3HI8e(S&x?%M>Dl*59sN`+2d|h^fLN0gy;i}?@7rgaL_xVLS)wI9o+F1UJyV9)
z(A3cVnvh?bYlVuo#&x%UbwKO$ex4<?S3!+UZ>+W}6z;m;sz61%lX7l)4H|3KH^-xe
zNcc7HClX0`Xw1SC=X2J?*It-!e1Om@59Nx{`<<p{fd^g{-{2~1B<rAYwGMCu8h35W
z?vfhM(XimXoM^R#5jKwa@G`ulT=dqFglM`?H&LRl+ynKRD?As!eEyRB(cr6+B6Z0f
zg*5;IE<W<)6LvQG$4pA}6#`t;h!N*g+&Q~{CZyG-zPG%%F)({HxOxyf&_Zj?0T4a@
z66@tdDuSQ9tltkm&7yu1iCC%D#j62+Xy!7pOtZVej!FM!>j$x}I-$aT4SAU?u7IoQ
z;T9-H;d5VX%Vde$T!cHxSq8WB(fBA#2Wq$ijJ-gb#JWJt!y$kiPvviV?neEYLe3uq
z?zfG-2mJj+$T1Mmo-p7f77H=M@MY>(B?p7Rz?*CmI*i{L1M)}?MY%tr{C>>xz(tGr
z<20NLtW%gh{|~&9$V8L4^E99ThI~$VWg663j=16r{AGkzo)uK|dQ&ArMNtaAqJ#7d
zm;@BkK@z#4k2Mu%^nKv88D8nDOWY9N`usYrWH|5|A1B&x7&3UB-gH4Y?Az^O=Gx3`
z*$mVPud<Mb>-8A!YcRNRmn}O==z;{$pw|AOvb;cKWoA-`_6iQ+dKQPO+U^}yIVwQi
zIalnZJt4>vu|S7DAzGsW9hx?NhZR}6EXD@D^X4P0O*;B~fP$^r8sorjsX)*)8Aeyx
zax_fX_?ttOlXw>f(=J!wA;Ju#=!g9<!}6!b4xw#Yp#7kAl2U5VqmFhjp8TI`b3p64
z1agr+-?$|KndoK&XXZpI5%`QoYMwjwC(Mv`WH$XE@uCgY=x<c*3sTo<lPPf(&yb^5
z|8uL!vkadwK@ONDb27#vwBMVRMvV<`qP|)6P4u!;odZFE9To0VYqaL|f5Iu^G{>91
z4DQ*j{p<oVsE^o<nn)0BvzFn1t!~VJ^w317%vAM2ti_0To9q$lTq4gn()Ja0wE2M7
zU16}*$m>We+sLxww5(I$u1?+n+`84<!V;p(LQ-LCYB6XkY(b`jvGJ-BD8T%BA-B-r
z6MsZQj%)Gmth=hAtaN&#H*&P$GDr!-JbZf<TOj621chz;JIEIM)+;oc`YkxRR~sQ=
z!K#m(plT?3#Pj2;BQi#bUEtLJ+$MjyS1Q0rqEGJ`V^5D5s*<l@Wizn|%mze4(A^aH
z3!9+-tcx`t@<5M@AUHWA8(~>P#i^hep|5hqn^P&lyJf4iV}q<&QtBM_axam(4xT^0
zV4n|5>KMkpuM49ns2d9^W;lM(w#Mr&8S~iZ)6j{A|JQ;dIM%_)5~c=?-ZFaSP9$os
zH@)(<Gn-_ed-M{Xb0_0UZJ8Ce=NDqI2nCZMN-p+#c#vXfy7azOqZ;CI5b9hS%LixE
zbKjY#s)UqgLVmPOphwNO>OOci3Ifn0)TcpPpsH-fWdRU~^y-}kVc{re2zV{|Wpue}
z)Ou;YQfSiBrh_o4XlQk&!s9}~6_E$~cliQI*p^rqom4h8*g5;#F$bQEK`o0xZP4jg
zUFU!XJHdB$VQPf(k43vrypXR_5UsvZef2MM7GvG}&kgIPCbB3Ub|a`nc)t7~A7TWv
zl%yKwK0t%bWqd4kFR@-GEstLT3~H3#RCM;pOiI*!zjXS2zQ?Wzu@K?Z*(?**sGz=A
z=r~UJeZHJN<%teXJ`zZa|Lojd!x?m5<BjjUA}U+|uG!0=u)-6hE8cPs&M7UgzN<z@
z7<8k)o}KiT(s1Ivk-KxojavaXl+G~TN*%hOj#mjX5q}k3tNkM#a8Jn3$%Ul6eqx}L
z2#~5yRm?F^aEwn%WV<t_T(&cbBQ!R-%3(R3n4xsJDl=uVMZ*}774n`_1_<djqMZ+`
zAaNnU^2#2`3K^OPA92Y^_}Xhw*@Fu4gqxzK(m}V<dftUlME(>rzc<&^AZev9J%p&K
z1Z_c2{S8U=(N7dDPMjw%MiCm*-EH$8qX~<f4+@D9>?I2Bh2AhazbvlJ^U$~itP;mY
zz{VXmGsWtMo<$}{=Z-gwy2cG{xbI&z-E>6nl?FO0<p#mNbe?Jth>L9Gsx;FmVNysY
zJ6GPkiTKRbSMer>HN(7~kn|t4TjVt^=Cro;vIp~oecF9w6(fQ_ntn+EJl&1pS&ZZv
ztZp33*tCPsXQM9G;fpw69;NJSmS%gAxsmYtfddO69R1X&$G>0?t_K#_R#(FLbVZ*-
z=4UYOs`>aD2_hES<e7a_9%VZ@C{-p*2h3z=0XY3Zs{!cE{;KjgwolqNp1sfTT3^%p
z?z45Tta;1A1icjNv;zu>WpXX@hUN~oL_j>^y8~q5eg4UySt%iK+9e?k?lqkEjk7!T
zatGYG4KlS+y*mv2(bpe4Ecr=PH8vwOWAtQE;rx{whSFT|D{xcL{X5wseQ1K};PxrN
zIrjcC>1T&!`l!B@vcqhImqw7d0jn9*y7-^c@6X9mOPp|d8im%Lrfn+bm1BjiCg`tb
z`4j7eHTHQ0zNn(x3QJjML8N;<WWTH3VfH!M%PX^_{J&~Cl;YosbT9@@``OD)Ocsa~
zrj+$%;a0L~(vG88>}J>O7(e5kfUc<>rg1_A-0>@sY#s#FWLf||8s8qCLzRglTO=7#
z2J$yao`8a9OBc^~kF-nI@Zv^LU5z8LXRia@*zdg<ipCSxL<IRyP^Z=L8mP-B&xR!7
z-}ciDTi!1_>->Qenp|Sf?t5vWWiavI@6rKnpc_aA-)#m1O%mE>`wO>lsT9K9i}L3(
zgS{cDDHVf^hCzz(o<hM2t1)rNTJLswB{ZQ8$zyV$!XNA;r_g&9;!JvhbMBxgxnJIO
z-!nS&i8`sH`Y4fqEyRdR(O}QcOd0Fb5ZDf$<Amzp+Pm9U{Y1ZHh%DH;)V1c50-cT1
zUe6XVzWfjw`Xci8UxG2z=fq8qP@PsD_=<ov#q}t7-69DcebB!;)lnAI-fuJ~N|n8J
zC0~scA*dQAx=);_vO+ehNC>9s#ZD>-64O9XlMub|7frvw<d|U~TnnfpHyK^p<M_c*
zws1d6=2zgLB*}WrtR&||P=~gkUXBI6kv(EE<V+B+OQ4?yF?ZOvP5T20whUSk!-Q-%
ztAttmGF^EuAhBD;_d5R~p*CMuD7DigGHQ|NP*9v#kxNv{u#nI^6tgN8kepQp$1CF*
zL%F-u(f`D2cS*EY0IEs-fGB2M1@_8fI_TvCNX(z8Azp2*x2Ky>64N~|X=tbqdv`ah
z2=XDiOc1fbta5d2t|Ck)Rf@Q8m(iLARL}Ca)(|xFEmhL%Op7`x*h%J)n&}p493b%{
z#xQ=}wZ0nfrx~y%v*@686KmkoC;GN6QjwyP1U>~>ylFwbmn$~dm<;^prK&>^&par+
z-Uq2zndzY9nSRDGj=}i_o;d#N)hXB$^0Mbw>~>H&7@k@~4hWkR^68{2%N8)*^0kIk
zmZBG#z1$;X;v9LvgLV>YQq@okSFBZAtrW+KXm#AQp5=yQmo}tCgMbe>6!ap9Wf!&a
z5>l8V4CrpqY*$IPgmw0^3*AF&1be6!qkwtYIZcXj)PT&9TcP)HqRy$*U+o<{1@A*q
z@vJ@7TnZGh_dB<X7()8(;Mb$``DZ(z>hDHXx%wszhPX@6y_`LRBCvxUD*qv0yEfPl
z)-aHdh1-uCsfc0gx9erRCVvNG1bEm+G`+?CVz{3cH8vWZb~J%mE!<G+lTjBR`Eqzz
zhrghal*6h*>D*0^gMO7R*nhvvh0>LD8_n!X#T_?JeM-3NlOO#O;VjFyqR6*1GvN(V
z%!NntozgNlnU@xu;y`n5;c2XH-{f*Y%{Wi?(rq`kX4ZGcTa*7>sfdk-fv|v%KHc!E
zVR@Fct4=P9T!R7^&Bi*2cDA_FW9XbJJm8Vl7t=sD5r}O;?kF_PeQ6KK<&5}a1g-2u
zCnt5)-@kYfP1jy-7~z|8ZaMdmtzD%fXZc0lAOLD@>q;~k17abaK?IMZ0V$dSdVI@b
ze8Gn$gk_Q1Q)aunNu~3=a>1885|}|I`_)JF3({segPL!a=mT#p4%LOiL}AuKQ^tWt
zE54b@*0^Qf5~v$hyWMy;wITu8!fY?UQ9s^IhHPeU`^xT@ND2Dl30f_3QnkDzZ38XF
zGH@Lbq8jqRh%1X>SlK)gDn!~^H;J?Qh1PJc2Q&5Y7{zAJQq*)k-MkTm_9uU!s?xnO
zv74q3?1!KGwF$c_-Aq@;H?))d-2oZ%^Km>WP~i$V$b#9gbKTHv>iVG@mA{6ZNLrN7
z%<X;@Sz#;vaY-p`34SJOO4GfSMHduwy6t9lG0V>g0+j3vlH@&KCdYh4aDvLM)YrDb
zo3%1;K%l^BnUn`<UFpdH4c7}2S?0?E4Pn2|OxMf`W2f?aH7;ML6IS{lF!wJJ2UipY
zjEbKv(Ync*#G#VoKjq>GE92?mpK1*D8e55Czz-%`8H6%gS~W4eX~S9QdPx~Bq_@0&
zbp4=%cTGn3_t9d0xuZdrY#VJe4E$(_DE?z*fMLxQWoc~oeKDOL?wl|7xF#=iGYev)
z?trh#o3!~OJE7wcLL@l2&Dt3c9xEGU_>U0qrRaw2uh~jj6<554!0sTMyUD_m^G1Pu
z-`n3x#t(vZ{TA-d9ZTsW;O}y`ZyCsC?V`l-&-?E<VEf`*A7>KI+uyjjm?XmJcDo{=
zxwC;Eko_xynD&!(X3_QotYDh2!6}9nL`A}mGw>l;=N@C&>zM+Q8=q+j{u0>Q@Jx@!
ze~@nK;`0~<><s67wcW2-F~1TFr2>Korw>eFKY3?~vu>Y&r#QH8;8)S9_NyN8;9lRf
zs%H_~Y=OM}>1d(MdB64ute2;4EAky7)GeC!zo5m6M>0fR{sii$;CH!tM9kY5w^};N
z**97+c9EGIb?A0;LVh|^lix#hOhtv9C6m^0Z=I9>gd;bfSI-ajI?nxIpp^BC3L%Sj
zzw26pa)Qndcoq=WJ3#;`uyUW^MZ{&AY<BogKxvjh(t8`s3D|w4B@P6o|11u^rF7J$
zN+lmH>hCL;5`bh-j8d|wJ)r&xnt<tj$S><v<3&*DeFcfmI19Q`SOHCbM}#_l&<$zC
zYaW&0Qgw%lAjBy)Qmr?QGT_)Xen!fM|6OB|^u>fKRsCsj*%9{^Y?~Uz9;0`0=m|gc
zIO<8x4E{apF``=gJPxb*TjN&svp5;V==`O;XLT~*^~&mYjoj?}K0Zu$tZ@f2PQem5
zlu=T-u}q)VOYir)d4=97^g<*-XC<$oJ33T)K!G(x`sV$Rov~4R$ybq2Qc#l+(kMl7
z?#*MVqVod>tE4j1aNeCGZghWYWg8R;O`dp!&=o(8!O|tdFktHl|7yAghq1>eYR;cD
zZ3bC90WJEFeXc6p$7Kt#&CjxyurhRrlcbjP3<xWy;jp=vyGpZ76aJm1LE`t{eFHxt
znj6gc4S7}~R+@LtPEpUE3-r96mr@yyYEZ6cfe91mg?NTri(f%YV<L)A0q35bfA`P3
z6i}VvRO&EzX2)T1L^#55Rv(y3V-aPmW3lW)!WdG&2cIyaDqyS!G8+V?AW7Y`;@z&H
z+_L0iM47(`SNje%Cd9KD0z#3hvO^1u&eY+QEbnHI716dyw4Wcw(!T>!@i8d^BKbxu
zH{L9u8Bqehq|rl#hDxUs4+G@;uD|DfVZ5%@d1WEiWKZ(Rj?whaXhaI4HmVfz>U2bD
ze6yOHlOxtmEraBiZ8cPP2m<+_wsG+lc7CP33_Bg2-#!v6jrdgjc`6tB;7kGAE1!bF
zyU%ee<oMWMAne*X?}*x)_XalngR>hpal))i4*0IMBD5xu+@-%zcTB!rm2zI2urNM3
ze&V)d+U+$PFdM!@u5>~fWir4^)QZ#6JJi$hDU$I)kUL;4)2(hL@e8e>Sfsxmw_hxD
zt>j?)$ua#}z-dK>(^$!|O`({#U`(|+c2-t0+6W!dYDK_v!?vtzEgWwbhFlXGJpvNk
z049GMiZBsK<>Mt9t7rK78}DiDZKZQ8*rzK+g}+8W4)^r?ki0sle!E{Cudx>4Q#2?3
zAw?X{Nwj2bw%N^S%ARBL3w5z(u2OtBW{qdK;Y)GI_VMs&Lk)&1F15MVi!#5wkMlj}
zwTmH_Z^+vqHB@JiAGRWQ#DvHwK_R`e;-W&SxJc@s$vCdnrV)HfDnGIQ4A?wUJ_E1f
zSt0_aR14yqtje2d$R<{T#Z{8xO)O;loN@DS%HPQ_bArmcM{zYnx!k>aZlVW1MQAnW
zG6Ro7`X1k2q6{hDvz%&?U!L>M75^RuA1gDvCL4|3*1B6z9{ziJAs+oRi)p3eJdL?_
z!*BmdcdtjpY@JMxgnJ-HL{nYJrHMSO2#aD+Dx1nTCn21K3hv`a3y3#F4Q^ADb94eL
z$N#C}zN6Xx{y%_I#GbK7DzrvWik4W7J)$T@QPifjYSpflqK(?CLhZe4i%p3gdv7Hj
z_O9~Nem}qe-{+q9`*q&;o_p?nJs%gWTTUs2tt6Ei7Pc|yz>{*<ccP}kcfy$BR|MU@
z{yBcRd7iXKg^@QE3+~Sl>`W#fVH~DP?bA+oqM_^UtG>ugH_H~CO1W%uW@gq#AcM_`
zzxG*O2pckrDufS8kUbJ)Vaf5K8DYY$^>*SE?By@cfSv2;PUT$x#y^rh6=PB3VHAHC
z1wO9BlMx}4=1Eh}5z*7tx8<*8e;>OkCi=B>*U;if2QH<F&Tb$F2EV@+fX<3HR(QT~
znnQ@-`d9K}&k<=?>!r~oj6n=iMS~RWFn{mOrYU?;diOc*;^YSo@ZLT&qO6uYh2FH?
z9W7p^B#?Za0__gi=ow$wz(RTH;@7r{&+1YZdT$%uv^t##lP|a!{&EH0nI`kD$$FUv
z6R1wU|E#;@&a@pB$G0V~1tkfm!56ZqbVTUx&q{eB8G}B}_Y6oVjnvicFkwc~?S_xR
z-)=#QXeRM_Xof=rjN!Z+psy$HajRU3BgM>(G0Ir~Hnftu3xlQqS|BMk3IL!M$!ILO
z2(;q1VxI|AAU4#<mxzCMr-Y?0vI((7#S|Cb)U)y<KkQ|w^spX#xa#Y`{yDx4qM`XR
zjt^pPrn%F*5L|!)ipp2K(C5<RE6o_yRNI<Kt_w}I(18=i_nw56shcUO(yOMxy6r$=
zr7cO?LCq?&gbBdfNnS=hsSC{&yg@C(e=o-+8)%(}R1DK3Y$sJ9IJ-|(`dLra9YoIX
z$mrOAVq?lvQQqHauzIWj)Jq!M<~;~0qU02aSV{?6;8!AU7rzPmYepSI*MAdC;#!u<
z1-MuawCSD;ShAClf#dn_J`zpERLs|OImKr!H`-Oq3$CO6|4^N-R1fX=xj$@ZWb<kS
z8~eQIbp>fs##FNEtccQTZO@M{w)oUbf17q+JP$8@Et~=!jJhq?PMM&{I&qU_f;PCQ
z7gz%Px5{Omqv}TM;H^<{PCM^~cD}1lHJJ;|e)pyN`72#j$PN74&B5~WCN#sQ?d&E|
z<~#VOmzH~xHvU}$ZJZ74vsq)MxeFyBzhFgGCu_xW_4sOEor9nA<~xp^3?jA#Rgm#k
zG8A}m>M?*ZNy+=k&}7-2GgC$2*LdyY-SYdhPuc<#HT(ArS)YhBk?b}$sm$mf1y1Fy
zzKeDnF@fdLb-*I{Q-EX8tFtU_IvOU}SI5+kHXK+K;8ZR88Ln7pCg6vEsM4*ZTej^%
zn;6qy*Bp6#Zpiu16Fde_TOOW%;SsqPGwTs-#;k<rmT)Vc&Dem$!;dD2`Z7q+ci>6V
zH6naJ_c_hEy>R^a#uHs-W|o~#hm{YVD^Lm1_cWuQvuRU^if}PaTn>=2@E&L*3iwVd
z!kFK9gseVn5iq7(J5l}$yrqDo?V;`JJ?h`c{e^se{c>jh2^McVT58;oiRApb_Bbm1
z%!>T+?YY^dH(7D6#R;F4nWnU3(ipDbh@N)YBWo>M-UFaTM`t@=;8??)3w5VPT-g)+
zgU@2|#MEzby4-x_UP?1&Xrk0-hY`BMVd3AG^7!^xuZyzNQ)lDqpvaq>$?&8-EhYbC
z^`9e)1*E)%43XpfC+Fc*r}Ga2Y9~XvKh%HGY|VRB*YqV&ws*1WF@;x&$FNMdOPsf3
zLQ1V9^&Mq*`<Tg&r_is&bvs@zZ8-k@=ir~4fLG5J4nDE$%!;Xg^Hh@K^x`wT4oLNz
zmKDhJw229QO61?dL%oe-gx~A0nmS4qxXje^=&6Yp>coZqZa5zsVY<2-9@e@Mmd?Ip
zUpsyOW&G{deNG9l?x1IKtVk{<QWv^gl@B;~0umIYsCptDY-yE05g&An#IQYOU?GVO
z)iNS2gy3-;-EZ#_(Q?sdPsO)Z(Q3k5oZZl=xdR)4ki!hbY>&SWi$V6Sd~;;1a{}yn
z*YI#RaS=tcqd9x1mFy?YZ6Z}W8-Y(8)}i7Pr>R)9`F=9rX;0@g3AX+?b}ca5#0Pe7
zBg<($FoKo{j^A{bx{3Gg;AaoYm*#S~H{>I6rP^w8ZI)x6VjcC({l7!|yCPA%N=!8+
z$MVYXkjdWfOh6;qqO{u0Y5ieS8Z3_DvbEk>FbPkBZy)8_MQ`h&yDC?&t1{uVusMZ{
z$%(J}1kJ4f!F~T>AKyKC-MIUzO~yfUa(O(Pd%lBva#!u%;knPMY@$s*=)tn-USMQ;
zJbP6z1+K+NyXC7E^lhW;SHABZzA!eH76{TsM;7mLLaAh{HyHa~J5QbW-7D)Z?#Sne
za~OA?HkV=(tEp$>TyHhFOyVoSJdIM_57@|Fy6KU}gHFbdF~tc^jT_A;*7^PhovK2U
z=)$k^%U?}?J9r0D6DVNJsTZ*>f<~MJ>tB(V+(#aE*Yr)A%~}R-t{H6z=HG7=6zpvl
z8-ru!p13h4r#D`G|I*hd|8T1w&fw;JXt7#=sMDAT8qgNxD`}8{cRlZj&Qg5#{Oxuo
zz7<FXF1Ir)(yFwtt#R)PYOE?r&2s{Q)F<Yh-85Qyo@Q_YwYse(&V?p~sGp~$a(8&Z
zn5DA>JpTA-XC4fGO~ZSh;}LPQlcw3EXZi*Z>q@l9m}n+`h#gAocpt3k)ZRcJR|oz=
zXgl@8nlRW1VuvuGCD_;157NibJCO=m9)q!4XRdE~c8gEV8NVU}s*t(iHF+5wW)fO{
z9<sH{N~X1>Mtud{`i~4iW3z-Tz7%M)$RRzf0w~+<z*s_5ptD?vI%}saj`IpkXquhx
zFxO<9>9chB)Hc>T!qI6Yl}P|NuUe~167>}T$Q`;R<$ZTs%}4`danKW5N^e@V)bKDp
zDceQq6GlbaNJhZCL|e6{3S(_!G@E4R&S&MYtZGFP@c2)P&{_1&N%veGD*HT<WxwHZ
zsneRcy#_vabQLff_Wgy_7R9DN=7<e}<iB>M+y97{IKb5&ZPl|}auyFc{h}}rQbAgt
zs|yfM4A6Y4ARhs`fKE?0XEHZ<@Z48;!0-esL~d#b{ZR09Q{V+g^zqL|ZGgZuRh4a2
z-8UK@sFz%OIEB)!yhQ(`LM41z*?l<)&GUSLVINK9*=$krnM5_!nik(R`qYztT6?`>
zo)x^e5EIu2oBb+CM?9(V;S^%{`mH~>DNx?)Q-f8f++@6$&aFuu7}$M-g@Ie6$vK-K
z2&Yog%kLsaDJw8@^`wRc%ciwgRR=l{S*;v@iqDwXfytBP3oh=Ur9MSg^qNjG5lwPW
z)Y^}f8E+O2(fdjK^`Af(VCW?8R7q_r1~1_5Y@<s}-htaPo>aeObgO|;l&U;m=sRH6
z+k=Q-Ib_R4KX&<LRQu|ttih*NFs*(GpLM^@wLYz70xNGA)3v~5YX8<H;Z{d~=*g~*
zPHGi!^>*PO^;>Ae2v9!B2OC3O<riuDDytIRzkT_z4>oCpy*Qh;K8JL4T|Pg3%}H;c
zu8L)MoWOrzM}GPeuyYz-`kPsvTTvsqBM&_!#aRJBMG1~hQQGlPSBi4Mb2b%u#0lQw
zviCN7$LO4sWhPd^NyHwDwjmha*%@SQ50y(~U!!sJwE{<QT|SQ)muZ?c#ZBtVh)j%z
zwM@Qhkl<jD($JvQoMDUK&4p7;Ew)4qFKUhA;1Ho^*k|ST`q~3bO&&9)*%BE5;v;6c
zZwoVvZXEd27RM$PN=|<sIl57Qbn>nTApK4CTrx)aiKU=k0!pY@>RibC*rv@U_lIBN
zp_!>(8xSGPC}${Xsy3flp&|O+OZi!T^BWQ$`ZH~ai`WE&IzvWo#y+4^4oGM!k{`QH
zi$G6Af-;0<RQd>r<HKYIMRaaj5n@t#?|MdjXC%4iN6X9XJlPJu=vXcviaC3=IQr*u
zK8x=gcW0^s^KL=nck0p9@bSRka8HpIBb9_Hdmf4yf<}1=OHn~(F@AK_FlcPPEcv<B
z#)>4leT{!0VIisvVigR6Z088oy%bT};@SSGghL(z#lBA-O!~D&Z<ySj%`aR4dGH3$
zG26AT1ZbW~gWGey5OJ7IGiUz3T%@{qvaDA{^mQnieJs@|M2NJHW5fEZ*Bd)BBdzEj
zQ6i}e{Fx%1;>A}Ce0zK5)H-IS7fkDt%gq!|1EB1m@5*R;SGvA)4RNiB=`ABBVPRV$
z<?GgQW?EyPB1tB}GQ2wm?VIP0hI)&2`lo`6^ax0^eQMt3EY#H`!84Zu-PkA7Sqe9K
z3w_6SgH^z0!IBS63-l}caK)CbL^bP5#Tn+WvVYC!OPuXz-*>yO9BYMc7SExQHnet-
zsub$7D#|%hgSgQX08%$NjPptp{k=>zr?+4Avy}Jx5F7X)lT|<0f2TF88<!%}B+dyx
z*}89b2~xAWPVn%~noE#ON;|qmlAfQFYx6tnXDPHVvNWA?yZ8kS1NlUUufV;fwkR{y
zvz?~+W8<o!0SxMasb|ivZ&!I`u9;HTPqyBW`!+V&dR=IOjvXgpwNS0uFD*1)!tHf<
zn-KIJS%fq$Ta7x^v)o^jGN>t$J?(B3fyNVn%sIVHtEr?>-=O~TB8J7G$AnHwuXR{Z
z8g5mH^D63K5jhMK)@2iZ!uDoc-kcE%d^`rrd5{_CJlxF#W*&PF4AvEmpjMgGZlX`}
z(8xQe)ioX^x)4G3SaIQ=T!2eS>OA;)s#SfNibO!_(&pQV`7CmX3w<^H9h!&C{=$VK
z2s@$>@D_V6H-2pH-g{*(J>n0E$*S|des|MrPpLx>U-%4kbKXcI^;0mBcali-cm#@!
zKmvqjl;Bn%`819>)G<yD%_J`|Y2J`fuC?IWoP$`SotL=$-;Gu%%+VBtm1p9&QS!^*
zW*Ro&HFk1ZU<lI|{R^7aWB?qY(<-7Y%-6N*2zG@4F`CLhPuzq{DMJ%RHCQu;9FIFp
zaxp$Nx<Bq1{gcZ2%VCAlwuM)O=3rvD1erOfW(f^{)`#FSIp@R$-u~c9WlX%GgXm}v
zt7v4J_{|u6&9~(HUf4@oZ(_Tz=`3BJAv|!hFwC{bt-1G;#H&mSY1Oi+sNV4+rrahT
zN^yAt1`aj8=rY0CG|)8e;^t125*ikHmS^uS{^D16TVe~8K9&b>2<y4_Nqnzn4Avtg
zI3C6x!BKeFxIK?RBjf9;HRa3Lb`tE-%sumk!U+Vcx98~5>T^xmyqJINl_`Od0jHx)
z6<J4QsV+he1WBx`Hg5KWp0@ko-6p@u-D{RaqqN3c{*pk_4`MLEVWQ~OSp18Ya$zzD
z$LJYGbu|rna{Hh|N)yvSBPTDX82)s>8rb?B+M5D>01Wn77e_n2<=c>kpdtqu=N_u_
zfFcVO#b3;SkSGzqO8<6J8Dgy;_n+AYW-Y`7>ts{JULPS8<kY<4U6CSCTK+8Mb`<gM
z?q1)H)s(FempL8xUtcKAJ~K&3(A<XQPHW4E*+rMeiu56#-!BKRANts$M>Y6%dX56E
zypq&&WCi~}eim!tY;f4vpMnsZlrxz<UWHFBuSRw7fnNq$SCIWn53eKU-d#s&{?1oC
z9E50w{ROsa?KAHYbFFt<l#Y~~8PZ>S@rkpP8rLjK8l%E)HkA`R%OX?n3&PnoS^gnr
z_Jx3#as-iZY7?Wt9~%4SxTQ1%v)I`uc|CD?;DaEsUiH0H+~)xc)MKO9^RDUteK(fX
zKAAwCS?np<x!II{ry*%PFqTdV&vL87Yb|X9_7e$7SNDVw<Z{IUGKN|0Pa*Pv56Kk2
z{EQ0Vsc#uY*=!Av!SGRb%I@KHYRDRLMaKTahRmm&!|n%vm8vmVGY1KptE=ac_1n-r
zR<IVKT6hCgR{UVv<Ne3;wr`~}bwsKe84WLU^MMt)!MVZJto9KE!G0S`XT(R6u>**`
z-o8ARs)SKaSn@Xgq8X3ou%d?AKQL~e)jowl{-`g~(Gc~k^n+reH(GoM87ewQo2lsc
z^fYa3KeaR6VG~Ogm0$X|nx3yRv#1F9LxB4#TKm)x9Ga6uxWNK%+$v8V{D2VkCR5sj
zWbbwNH7D0rtQox$?LRb3>N&x$K(Z24cSq%UnLv*=aPc%O2eE$@(D;)-pbanz{?%PK
zsR4FLao@2R=60gaM*ic?ILiOxW-qyVgR%1hp#|Hm-1J+wb!E*-a(C@Ek92SnQ`lcz
z-99p0ren^RaAWtciQc(>e_v9a;4v1~RbOezKh%y{sTw-S+-XrdD}!~Newj&9R}ta6
zLpDm<RCT?z@7ZI{iVAn4=4nwTgJb`e?&CB}&ye%A=Z4ayp})G`<x?Va#^DX)psH>W
zuM`>jxsY}v;*TA}Qs4NlNTF2o)%`yziEOmF?%u3EGDG5{6AWsla2EONcj6B3uZ=tQ
zJK+tCDIM02u5o7vV9&ZQRO3W;D!r=1+#E@8@W0|1s}gX?txta^D7f34GT%0RjA^>c
z-t&<cdPx3Os#MLJi?Ww?`gphL3Vr0xaS;>P&<h<K$5v&B{Hu{raWK^z&U|8u9Ax&}
z5VhA=UEphzFjc(*!cRLt)dpYBkOx2Y3EpGt+=UBE)8`kI>L}cZQMtiMldu9PjW}0t
z$IGyS$iZ06iDTlhEs|bxeD=tpF=@wvq2z_L&$^yQ`Ido#=x4qwZ6x{n#Q*&?2dvvq
zi7n^&SJkK{TIxxcxllafE0v4V90(zBGpPaCx;A(n@0UNp!FlIE*rTNN6xCZLqC^hO
zWdEb!?0qO}5@_!|%xucwqC7Ay8%K;p);IuI&aIGYQbO;$Zt}Ysq#n^+m%9lio05$2
zg}&8^c>_lbrbn@Vr(5PL`^-G{h$2luV(Nu6%TtQ`0I!G;AVM-e`G&3hO4A0!VyW(W
zG4cSZz<?#rEuBk^Rdan|IZ5>v*Ly7<L;aBdu*2jr7+i`*oNxWTqa*HTY_Ltwsyt4N
zp(7H-C1W6|@r<HQq3UR+Xu@P`=81PNV+vD+Na^6@`vv~XoIT%3i;P8)-mD+!_!__e
z^uuk)UlG#l*`E;Z4z(}6zzT6!QB>j}V+N=H&DmuPZ&QCTG-(DuAt!1as|QuJ@GxQM
zP`_04{<7zVkv6md-Ki+kyxcN9kkGyJch);(lE2K{<WMUe7jakp7O-DT$hyc@yrQ%E
zq>~##OkP0NOF~S>MrzWHbSgsJFq)_2yFfWe<3q@x;@#9kCH}bok&r|WMsKQxGgO(~
zu+@j5oXSJ=5%x1zth^{e6kR7N*=s&-gd{sV{4Ln-UY*p7#IDqCFK`JNdnYTHDzw*;
ziJS?s$De3|hA4o%!PEjE11+NJ*rfl~y^>e$6t9!ZRbBb^8TER;AYjq!B3r><z>k8k
zTP;9$u1ojQYAVh>u1Bz?8re<#*uZ~4XfvP*LBQ{v+n1&DsKlxn>DN8ZPN~&XAqs(}
zSk8`l{?DR(gAHPt-qHR1{+L9o9i{R7RFW#%yT{^Vh<mbRi=`3H_P@ub+pmdF)IV14
TU!|e`>bC02T1w>#FGBtg^F;L5

literal 0
HcmV?d00001

diff --git a/docs/python_docs/environment.yml b/docs/python_docs/environment.yml
new file mode 100644
index 000000000000..a3f2e2eca4a2
--- /dev/null
+++ b/docs/python_docs/environment.yml
@@ -0,0 +1,38 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+name: mxnet-docs
+channels:
+- conda-forge
+dependencies:
+- conda>=4.6.13
+- pip
+- python
+- jupyter
+- sphinx
+- matplotlib
+- notebook
+- pip:
+  # using nbconvert master until v5.5 comes out
+  - git+https://github.com/jupyter/nbconvert@master
+  - nbsphinx>=0.4.2
+  - recommonmark
+  - notedown
+  - pypandoc
+  - breathe
+  - mock
+  - awscli
diff --git a/docs/python_docs/python/.gitignore b/docs/python_docs/python/.gitignore
new file mode 100644
index 000000000000..de209aeeab4c
--- /dev/null
+++ b/docs/python_docs/python/.gitignore
@@ -0,0 +1,20 @@
+_build/
+build/**/*.rst
+build/**/*.ipynb
+build/**/*.md
+__pycache__
+/build.sh
+_autogen
+*.ndarray
+*.pickle
+/365px-Golden_Retriever_medium-to-light-coat.jpg
+/net.params
+/synset.txt
+*.pt
+**/raw/*-ubyte
+dogcat
+*.tar.gz
+*.jpg
+*checkpoint.md
+*.ipynb_checkpoints*
+*.json
diff --git a/docs/python_docs/python/Makefile b/docs/python_docs/python/Makefile
new file mode 100644
index 000000000000..8bbb10cee521
--- /dev/null
+++ b/docs/python_docs/python/Makefile
@@ -0,0 +1,57 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+all: html
+
+# markdown files that will not be evaluated, simply copy to build/
+PURE_MARKDOWN =  ./README.md
+# markdown files that will be evaluated and then saved as ipynb files
+IPYNB_MARKDOWN = $(shell find . -not -path "./build/*" -not -path "*.ipynb_checkpoints*" -name "*.md" | sort -h)
+# RST files will be simply coped to build/
+RST = $(shell find . -not -path "./build/*" -not -path "*.ipynb_checkpoints*" -name "*.rst")
+
+OBJ = $(patsubst %.rst, build/%.rst, $(RST)) \
+	$(patsubst %.md, build/%.md, $(PURE_MARKDOWN)) \
+	$(patsubst %.md, build/%.ipynb, \
+		$(filter-out $(PURE_MARKDOWN), $(IPYNB_MARKDOWN)))
+
+build/%.ipynb: %.md
+	@mkdir -p $(@D)
+	python scripts/md2ipynb.py $< $@
+
+
+build/%.rst: %.rst
+	@mkdir -p $(@D)
+	# python scripts/process_rst.py $< $@
+	cp $< $@
+
+build/%: %
+	@mkdir -p $(@D)
+	@cp -r $< $@
+
+
+html: $(OBJ)
+	mkdir -p build
+	cp Makefile_sphinx build/Makefile
+	sphinx-autogen build/api/*.rst build/api/*/*.rst   -t build/_templates/
+	# make -C build linkcheck doctest html
+	make -C build html
+	sed -i.bak 's/33\,150\,243/23\,141\,201/g'  build/_build/html/_static/material-design-lite-1.3.0/material.blue-deep_orange.min.css
+
+
+clean:
+	rm -rf build
diff --git a/docs/python_docs/python/Makefile_sphinx b/docs/python_docs/python/Makefile_sphinx
new file mode 100644
index 000000000000..12fb7fcb8982
--- /dev/null
+++ b/docs/python_docs/python/Makefile_sphinx
@@ -0,0 +1,216 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# Makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line.
+SPHINXOPTS    = -j 32 -c ../scripts
+SPHINXBUILD   = sphinx-build
+PAPER         =
+BUILDDIR      = _build
+
+# User-friendly check for sphinx-build
+ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1)
+$(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/)
+endif
+
+# Internal variables.
+PAPEROPT_a4     = -D latex_paper_size=a4
+PAPEROPT_letter = -D latex_paper_size=letter
+ALLSPHINXOPTS   = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .
+# the i18n builder cannot share the environment and doctrees with the others
+I18NSPHINXOPTS  = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .
+
+.PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest coverage gettext
+
+all: html
+
+help:
+	@echo "Please use \`make <target>' where <target> is one of"
+	@echo "  html       to make standalone HTML files"
+	@echo "  dirhtml    to make HTML files named index.html in directories"
+	@echo "  singlehtml to make a single large HTML file"
+	@echo "  pickle     to make pickle files"
+	@echo "  json       to make JSON files"
+	@echo "  htmlhelp   to make HTML files and a HTML help project"
+	@echo "  qthelp     to make HTML files and a qthelp project"
+	@echo "  applehelp  to make an Apple Help Book"
+	@echo "  devhelp    to make HTML files and a Devhelp project"
+	@echo "  epub       to make an epub"
+	@echo "  latex      to make LaTeX files, you can set PAPER=a4 or PAPER=letter"
+	@echo "  latexpdf   to make LaTeX files and run them through pdflatex"
+	@echo "  latexpdfja to make LaTeX files and run them through platex/dvipdfmx"
+	@echo "  text       to make text files"
+	@echo "  man        to make manual pages"
+	@echo "  texinfo    to make Texinfo files"
+	@echo "  info       to make Texinfo files and run them through makeinfo"
+	@echo "  gettext    to make PO message catalogs"
+	@echo "  changes    to make an overview of all changed/added/deprecated items"
+	@echo "  xml        to make Docutils-native XML files"
+	@echo "  pseudoxml  to make pseudoxml-XML files for display purposes"
+	@echo "  linkcheck  to check all external links for integrity"
+	@echo "  doctest    to run all doctests embedded in the documentation (if enabled)"
+	@echo "  coverage   to run coverage check of the documentation (if enabled)"
+
+clean:
+	rm -rf $(BUILDDIR)/*
+
+livehtml:
+	sphinx-autobuild --ignore "web-data/*" -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
+
+html:
+	export BUILD_VER=$(BUILD_VER)
+	@echo "Env var set for BUILD_VER: $(BUILD_VER)"
+	$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
+	@echo
+	@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
+
+dirhtml:
+	$(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml
+	@echo
+	@echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml."
+
+singlehtml:
+	$(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml
+	@echo
+	@echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml."
+
+pickle:
+	$(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle
+	@echo
+	@echo "Build finished; now you can process the pickle files."
+
+json:
+	$(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json
+	@echo
+	@echo "Build finished; now you can process the JSON files."
+
+htmlhelp:
+	$(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp
+	@echo
+	@echo "Build finished; now you can run HTML Help Workshop with the" \
+	      ".hhp project file in $(BUILDDIR)/htmlhelp."
+
+qthelp:
+	$(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp
+	@echo
+	@echo "Build finished; now you can run "qcollectiongenerator" with the" \
+	      ".qhcp project file in $(BUILDDIR)/qthelp, like this:"
+	@echo "# qcollectiongenerator $(BUILDDIR)/qthelp/zongyanwei.qhcp"
+	@echo "To view the help file:"
+	@echo "# assistant -collectionFile $(BUILDDIR)/qthelp/zongyanwei.qhc"
+
+applehelp:
+	$(SPHINXBUILD) -b applehelp $(ALLSPHINXOPTS) $(BUILDDIR)/applehelp
+	@echo
+	@echo "Build finished. The help book is in $(BUILDDIR)/applehelp."
+	@echo "N.B. You won't be able to view it unless you put it in" \
+	      "~/Library/Documentation/Help or install it in your application" \
+	      "bundle."
+
+devhelp:
+	$(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp
+	@echo
+	@echo "Build finished."
+	@echo "To view the help file:"
+	@echo "# mkdir -p $$HOME/.local/share/devhelp/zongyanwei"
+	@echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/zongyanwei"
+	@echo "# devhelp"
+
+epub:
+	$(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub
+	@echo
+	@echo "Build finished. The epub file is in $(BUILDDIR)/epub."
+
+latex:
+	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
+	@echo
+	@echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex."
+	@echo "Run \`make' in that directory to run these through (pdf)latex" \
+	      "(use \`make latexpdf' here to do that automatically)."
+
+latexpdf:
+	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
+	@echo "Running LaTeX files through pdflatex..."
+	$(MAKE) -C $(BUILDDIR)/latex all-pdf
+	@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
+
+latexpdfja:
+	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
+	@echo "Running LaTeX files through platex and dvipdfmx..."
+	$(MAKE) -C $(BUILDDIR)/latex all-pdf-ja
+	@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
+
+text:
+	$(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text
+	@echo
+	@echo "Build finished. The text files are in $(BUILDDIR)/text."
+
+man:
+	$(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man
+	@echo
+	@echo "Build finished. The manual pages are in $(BUILDDIR)/man."
+
+texinfo:
+	$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
+	@echo
+	@echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo."
+	@echo "Run \`make' in that directory to run these through makeinfo" \
+	      "(use \`make info' here to do that automatically)."
+
+info:
+	$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
+	@echo "Running Texinfo files through makeinfo..."
+	make -C $(BUILDDIR)/texinfo info
+	@echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo."
+
+gettext:
+	$(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale
+	@echo
+	@echo "Build finished. The message catalogs are in $(BUILDDIR)/locale."
+
+changes:
+	$(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes
+	@echo
+	@echo "The overview file is in $(BUILDDIR)/changes."
+
+linkcheck:
+	$(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck
+	@echo
+	@echo "Link check complete; look for any errors in the above output " \
+	      "or in $(BUILDDIR)/linkcheck/output.txt."
+
+doctest:
+	$(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest
+	@echo "Testing of doctests in the sources finished, look at the " \
+	      "results in $(BUILDDIR)/doctest/output.txt."
+
+coverage:
+	$(SPHINXBUILD) -b coverage $(ALLSPHINXOPTS) $(BUILDDIR)/coverage
+	@echo "Testing of coverage in the sources finished, look at the " \
+	      "results in $(BUILDDIR)/coverage/python.txt."
+
+xml:
+	$(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml
+	@echo
+	@echo "Build finished. The XML files are in $(BUILDDIR)/xml."
+
+pseudoxml:
+	$(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml
+	@echo
+	@echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml."
diff --git a/docs/python_docs/python/README.md b/docs/python_docs/python/README.md
new file mode 100644
index 000000000000..4c52c5d9a50d
--- /dev/null
+++ b/docs/python_docs/python/README.md
@@ -0,0 +1,130 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Python binding docs
+
+The following guide will help you build a local version of the Python API website,
+so that you may work on and test any contributions.
+
+It is recommended that you read the MXNet developer wiki's info on [building the website & docs](https://cwiki.apache.org/confluence/display/MXNET/Building+the+New+Website) as that includes info on how to test and build the site using Docker. The following information should only be used if you can't use Docker or if you're trying to run the site locally.
+
+## Setup
+
+The default configuration requires a GPU and CUDA 9.2 and expects Ubuntu.
+However, you may setup the website on macOS or Windows with or without a GPU.
+
+### Prerequisites
+
+* [Conda >= 4.6.13](https://www.anaconda.com/distribution/#download-section) (install to PATH)
+
+You can update Conda with the following:
+
+```
+conda update conda
+```
+
+### GPU setup
+To run the full build, including tests of all tutorials,
+**you will need at least two GPUs**.
+Distributed training is a key feature of MXNet,
+so multiple GPUs are required for running through every tutorial.
+* [CUDA 9.2](https://developer.nvidia.com/cuda-downloads)
+
+### CPU-only setup
+In the `environment.yml` file:
+* Change `mxnet-cu92` to `mxnet`.
+
+### macOS setup
+In the `environment.yml` file:
+* Change `mxnet-cu92` to `mxnet`. (There is no CUDA package for mac anyway.)
+* Change `pytorch-cpu` to `pytorch`.
+* Change `torchvision-cpu` to `torchvision`.
+
+### Windows Setup
+If you have a GPU and have installed CUDA 9.2 you can leave the MXNet dependency alone.
+Otherwise, in the `environment.yml` file:
+* Change `mxnet-cu92` to `mxnet`.
+
+Install recommended software:
+* [git bash](https://gitforwindows.org/)
+* Be sure to install `Conda` in `PATH`
+* Install `make` from a `git bash` terminal with Admin rights
+    - [Install chocolatey](https://chocolatey.org/install)
+    - Use `choco to install make`
+* Restart terminals after installations to make sure PATH is set.
+    - The `choco`, `make`, and `conda` commands should work in `git bash`.
+
+### Conda environment setup
+Run the following commands from the project root (`python-docs`) to setup the environment.
+
+```bash
+conda env create -f environment.yml
+source activate mxnet-docs
+```
+
+## Build the docs
+
+* Change directories to `python-docs/python`.
+
+To build without GPUs and without testing the notebooks (faster):
+
+```bash
+make EVAL=0
+```
+
+To build with testing the notebooks (requires GPU):
+
+```bash
+make
+```
+
+The build docs will be available at `build/_build/html`.
+
+Each build may take a few minutes even without evaluation. To accelerate it, we can use one of the following ways:
+
+1. open `build/conf.py`, add the folders you want to skip into `exclude_patterns`, such as `exclude_patterns = ['templates', 'api', 'develop', 'blog']`.
+2. move the files into a different folder, such as `mv api /tmp/`, and then `make clean`.
+
+## Check results
+
+To run a server to see the website:
+
+1. Start a http server: `cd build/_build/html; python -m http.server`
+2. For viewing a remote machine, ssh to your machine with port forwarding: `ssh -L8000:localhost:8000 your_machine`
+3. Open http://localhost:8000 in your local machine
+
+## Run tutorials
+
+In addition to view the built html pages, you can run the Jupyter notebook from a remote machine.
+1. Install `notedown` plugin: `pip install https://github.com/mli/notedown/tarball/master` in remote server
+2. Start Jupyter notebook `jupyter notebook --NotebookApp.contents_manager_class='notedown.NotedownContentsManager'` in remote server
+3. ssh to your machine with port forwarding: `ssh -L8888:localhost:8888 your_machine`
+4. Open http://localhost:8888 in your local machine and run the md files directly
+
+Optionally, one can run the following to launch the notedown plugin automatically when starting jupyter notebook.
+1. Generate the jupyter configure file `~/.jupyter/jupyter_notebook_config.py` if it
+is not existing by run `jupyter notebook --generate-config`
+2. Add `c.NotebookApp.contents_manager_class = 'notedown.NotedownContentsManager'` to `~/.jupyter/jupyter_notebook_config.py`
+3. Simply run `jupyter notebook`
+
+## Troubleshooting
+Dependencies and the setup steps for this website are changing often. Here are some troubleshooting tips.
+
+* You might need to update the environment for the latest modules.
+```bash
+conda env update -f environment.yml
+```
diff --git a/docs/python_docs/python/api/advanced/index.rst b/docs/python_docs/python/api/advanced/index.rst
new file mode 100644
index 000000000000..1af3690624ea
--- /dev/null
+++ b/docs/python_docs/python/api/advanced/index.rst
@@ -0,0 +1,74 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Advanced modules
+=================
+
+.. container:: cards
+
+   .. card::
+      :title: mxnet.executor
+      :link: mxnet.executor.html
+
+      Managing symbolic graph execution.
+
+   .. card::
+      :title: mxnet.kvstore_server
+      :link: mxnet.kvstore_server.html
+
+      
+
+   .. card::
+      :title: mxnet.engine
+      :link: mxnet.engine.html
+
+      
+
+   .. card::
+      :title: mxnet.executor_manager
+      :link: mxnet.executor_manager.html
+
+      
+
+   .. card::
+      :title: mxnet.rtc
+      :link: mxnet.rtc.html
+
+      Tools for compiling and running CUDA code from the python frontend.
+
+   .. card::
+      :title: mxnet.test_utils
+      :link: mxnet.test_utils.html
+
+      Tools for using and testing MXNet.
+
+   .. card::
+      :title: mxnet.util
+      :link: mxnet.util.html
+
+      
+
+.. toctree::
+   :hidden:
+
+   mxnet.executor
+   mxnet.kvstore_server
+   mxnet.engine
+   mxnet.executor_manager
+   mxnet.rtc
+   mxnet.test_utils
+   mxnet.util
diff --git a/docs/python_docs/python/api/advanced/mxnet.engine.rst b/docs/python_docs/python/api/advanced/mxnet.engine.rst
new file mode 100644
index 000000000000..17b31c5ae315
--- /dev/null
+++ b/docs/python_docs/python/api/advanced/mxnet.engine.rst
@@ -0,0 +1,34 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.engine
+============
+
+.. automodule:: mxnet.engine
+
+
+
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree: _autogen
+
+      bulk
+      set_bulk_size
+
+
+
diff --git a/docs/python_docs/python/api/advanced/mxnet.executor.rst b/docs/python_docs/python/api/advanced/mxnet.executor.rst
new file mode 100644
index 000000000000..a84f10f4f852
--- /dev/null
+++ b/docs/python_docs/python/api/advanced/mxnet.executor.rst
@@ -0,0 +1,34 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.executor
+==============
+
+.. automodule:: mxnet.executor
+
+
+
+
+
+
+
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree: _autogen
+
+      Executor
diff --git a/docs/python_docs/python/api/advanced/mxnet.executor_manager.rst b/docs/python_docs/python/api/advanced/mxnet.executor_manager.rst
new file mode 100644
index 000000000000..30476b6e3da0
--- /dev/null
+++ b/docs/python_docs/python/api/advanced/mxnet.executor_manager.rst
@@ -0,0 +1,38 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.executor\_manager
+=======================
+
+.. automodule:: mxnet.executor_manager
+
+
+
+
+
+
+
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree: _autogen
+
+      DataParallelExecutorGroup
+      DataParallelExecutorManager
+
+
+
diff --git a/docs/python_docs/python/api/advanced/mxnet.kvstore_server.rst b/docs/python_docs/python/api/advanced/mxnet.kvstore_server.rst
new file mode 100644
index 000000000000..6ea8e0d2f296
--- /dev/null
+++ b/docs/python_docs/python/api/advanced/mxnet.kvstore_server.rst
@@ -0,0 +1,36 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.kvstore\_server
+=====================
+
+.. automodule:: mxnet.kvstore_server
+
+
+
+
+
+
+
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree: _autogen
+
+      KVStoreServer
+
+
diff --git a/docs/python_docs/python/api/advanced/mxnet.rtc.rst b/docs/python_docs/python/api/advanced/mxnet.rtc.rst
new file mode 100644
index 000000000000..b6ab2c16b745
--- /dev/null
+++ b/docs/python_docs/python/api/advanced/mxnet.rtc.rst
@@ -0,0 +1,36 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.rtc
+=========
+
+.. automodule:: mxnet.rtc
+
+
+
+
+
+
+
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree: _autogen
+
+      CudaKernel
+      CudaModule
+
diff --git a/docs/python_docs/python/api/advanced/mxnet.test_utils.rst b/docs/python_docs/python/api/advanced/mxnet.test_utils.rst
new file mode 100644
index 000000000000..65392d3fc432
--- /dev/null
+++ b/docs/python_docs/python/api/advanced/mxnet.test_utils.rst
@@ -0,0 +1,91 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.test\_utils
+=================
+
+.. automodule:: mxnet.test_utils
+
+
+
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree: _autogen
+
+      almost_equal
+      almost_equal_ignore_nan
+      assert_almost_equal
+      assert_almost_equal_ignore_nan
+      assert_exception
+      assign_each
+      assign_each2
+      check_consistency
+      check_numeric_gradient
+      check_speed
+      check_symbolic_backward
+      check_symbolic_forward
+      chi_square_check
+      create_sparse_array
+      create_sparse_array_zd
+      default_context
+      default_dtype
+      discard_stderr
+      download
+      find_max_violation
+      gen_buckets_probs_with_ppf
+      get_atol
+      get_bz2_data
+      get_cifar10
+      get_im2rec_path
+      get_mnist
+      get_mnist_iterator
+      get_mnist_pkl
+      get_mnist_ubyte
+      get_rtol
+      get_zip_data
+      list_gpus
+      mean_check
+      np_reduce
+      numeric_grad
+      rand_ndarray
+      rand_shape_2d
+      rand_shape_3d
+      rand_shape_nd
+      rand_sparse_ndarray
+      random_arrays
+      random_sample
+      retry
+      same
+      same_array
+      set_default_context
+      set_env_var
+      shuffle_csr_column_indices
+      simple_forward
+      var_check
+      verify_generator
+
+
+
+
+
+   .. rubric:: Classes
+
+   .. autosummary::
+
+      DummyIter
+
diff --git a/docs/python_docs/python/api/advanced/mxnet.util.rst b/docs/python_docs/python/api/advanced/mxnet.util.rst
new file mode 100644
index 000000000000..c9d3f5eeb8a4
--- /dev/null
+++ b/docs/python_docs/python/api/advanced/mxnet.util.rst
@@ -0,0 +1,31 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.util
+==========
+
+.. automodule:: mxnet.util
+
+
+
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree: _autogen
+
+      makedirs
+
diff --git a/docs/python_docs/python/api/gluon-related/index.rst b/docs/python_docs/python/api/gluon-related/index.rst
new file mode 100644
index 000000000000..0f02f239db8b
--- /dev/null
+++ b/docs/python_docs/python/api/gluon-related/index.rst
@@ -0,0 +1,111 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Gluon related modules
+======================
+
+.. container:: cards
+
+   .. card::
+      :title: mxnet.autograd
+      :link: mxnet.autograd.html
+
+      Functions for Automatic differentiation.
+
+   .. card::
+      :title: mxnet.image
+      :link: mxnet.image.html
+
+      Image iterators and image augmentation functions.
+
+   .. card::
+      :title: mxnet.io
+      :link: mxnet.io.html
+
+      Data iterators for common data formats and utility functions.
+
+   .. card::
+      :title: mxnet.recordio
+      :link: mxnet.recordio.html
+
+      Read and write for the RecordIO data format.
+
+   .. card::
+      :title: mxnet.kvstore
+      :link: mxnet.kvstore.KVStore.html
+
+      Key value store interface of MXNet for parameter synchronization.
+
+   .. card::
+      :title: mxnet.optimizer
+      :link: mxnet.optimizer.html
+
+      Functions for applying an optimizer on weights.
+   
+   .. card::
+      :title: mxnet.random
+      :link: mxnet.random.html
+
+      Imperative random distribution generator functions.
+
+   .. card::
+      :title: mxnet.profiler
+      :link: mxnet.profiler.html
+
+      Profiler setting methods.
+
+   .. card::
+      :title: mxnet.context
+      :link: mxnet.context.html
+
+      CPU and GPU context information.
+
+   .. card::
+      :title: mxnet.initializer
+      :link: mxnet.initializer.html
+
+      Default behaviors to initialize parameters.
+
+   .. card::
+      :title: mxnet.lr_scheduler
+      :link: mxnet.lr_scheduler.html
+
+      Scheduling the learning rate.
+
+   .. card::
+      :title: mxnet.metric
+      :link: mxnet.metric.html
+
+      Metrics to evaluate the performance of a learned model.
+
+
+
+.. toctree::
+   :hidden:
+
+   mxnet.autograd
+   mxnet.image
+   mxnet.io
+   mxnet.recordio
+   mxnet.kvstore
+   mxnet.optimizer
+   mxnet.random
+   mxnet.profiler
+   mxnet.context
+   mxnet.initializer
+   mxnet.lr_scheduler
+   mxnet.metric
diff --git a/docs/python_docs/python/api/gluon-related/mxnet.autograd.rst b/docs/python_docs/python/api/gluon-related/mxnet.autograd.rst
new file mode 100644
index 000000000000..ee37e5e89f07
--- /dev/null
+++ b/docs/python_docs/python/api/gluon-related/mxnet.autograd.rst
@@ -0,0 +1,38 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.autograd
+===================
+
+.. automodule:: mxnet.autograd
+
+.. autosummary::
+   :toctree: _autogen
+
+   backward
+   get_symbol
+   grad
+   is_recording
+   is_training
+   mark_variables
+   pause
+   predict_mode
+   record
+   set_recording
+   set_training
+   train_mode
+   Function
diff --git a/docs/python_docs/python/api/gluon-related/mxnet.context.rst b/docs/python_docs/python/api/gluon-related/mxnet.context.rst
new file mode 100644
index 000000000000..12e51834c7d3
--- /dev/null
+++ b/docs/python_docs/python/api/gluon-related/mxnet.context.rst
@@ -0,0 +1,33 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.context
+=============
+
+.. automodule:: mxnet.context
+
+
+
+.. autosummary::
+   :toctree: _autogen
+
+   cpu
+   cpu_pinned
+   current_context
+   gpu
+   num_gpus
+   Context
diff --git a/docs/python_docs/python/api/gluon-related/mxnet.image.rst b/docs/python_docs/python/api/gluon-related/mxnet.image.rst
new file mode 100644
index 000000000000..84d2095f470d
--- /dev/null
+++ b/docs/python_docs/python/api/gluon-related/mxnet.image.rst
@@ -0,0 +1,99 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.image
+===========
+
+.. automodule:: mxnet.image
+
+
+Image processing functions
+--------------------------
+
+.. autosummary::
+   :toctree: _autogen
+
+   imdecode
+   scale_down
+   resize_short
+   fixed_crop
+   random_crop
+   center_crop
+   color_normalize
+   random_size_crop
+
+Image classifiction
+-------------------
+
+Iterators
+^^^^^^^^^^
+
+.. autosummary::
+   :toctree: _autogen
+
+   ImageIter
+
+Augmentations
+^^^^^^^^^^^^^
+
+.. autosummary::
+   :toctree: _autogen
+
+   CreateAugmenter
+   Augmenter
+   SequentialAug
+   RandomOrderAug
+   ResizeAug
+   ForceResizeAug
+   RandomCropAug
+   RandomSizedCropAug
+   CenterCropAug
+   BrightnessJitterAug
+   ContrastJitterAug
+   SaturationJitterAug
+   HueJitterAug
+   ColorJitterAug
+   LightingAug
+   ColorNormalizeAug
+   RandomGrayAug
+   HorizontalFlipAug
+   CastAug
+
+Object detection
+----------------
+
+Iterators
+^^^^^^^^^
+
+.. autosummary::
+   :toctree: _autogen
+
+   ImageDetIter
+
+
+Augmentations
+^^^^^^^^^^^^^
+
+.. autosummary::
+   :toctree: _autogen
+
+   CreateDetAugmenter
+   DetBorrowAug
+   DetRandomSelectAug
+   DetHorizontalFlipAug
+   DetRandomCropAug
+   DetRandomPadAug
diff --git a/docs/python_docs/python/api/gluon-related/mxnet.initializer.rst b/docs/python_docs/python/api/gluon-related/mxnet.initializer.rst
new file mode 100644
index 000000000000..bb2480cf58de
--- /dev/null
+++ b/docs/python_docs/python/api/gluon-related/mxnet.initializer.rst
@@ -0,0 +1,58 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.initializer
+=================
+
+.. automodule:: mxnet.initializer
+
+
+
+
+Initialization methods
+-----------------------
+
+
+.. autosummary::
+   :toctree: _autogen/
+
+   Bilinear
+   Constant
+   FusedRNN
+   InitDesc
+   Initializer
+   LSTMBias
+   Load
+   MSRAPrelu
+   Mixed
+   Normal
+   One
+   Orthogonal
+   Uniform
+   Xavier
+   Zero
+
+
+
+
+Helper functions
+----------------
+
+.. autosummary::
+   :toctree: _autogen/
+
+   register
diff --git a/docs/python_docs/python/api/gluon-related/mxnet.io.rst b/docs/python_docs/python/api/gluon-related/mxnet.io.rst
new file mode 100644
index 000000000000..8b0cdf653ddd
--- /dev/null
+++ b/docs/python_docs/python/api/gluon-related/mxnet.io.rst
@@ -0,0 +1,52 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.io
+========
+
+.. automodule:: mxnet.io
+
+
+Iterators
+---------
+
+.. autosummary::
+   :toctree: _autogen
+
+   NDArrayIter
+   CSVIter
+   LibSVMIter
+   MNISTIter
+   ImageDetRecordIter
+   ImageRecordIter
+   ImageRecordIter_v1
+   ImageRecordUInt8Iter
+   ImageRecordUInt8Iter_v1
+
+
+Helper classes and functions
+----------------------------
+
+.. autosummary::
+   :toctree: _autogen
+
+   DataBatch
+   DataDesc
+   DataIter
+   MXDataIter
+   PrefetchingIter
+   ResizeIter
diff --git a/docs/python_docs/python/api/gluon-related/mxnet.kvstore.KVStore.rst b/docs/python_docs/python/api/gluon-related/mxnet.kvstore.KVStore.rst
new file mode 100644
index 000000000000..aa5ebb391115
--- /dev/null
+++ b/docs/python_docs/python/api/gluon-related/mxnet.kvstore.KVStore.rst
@@ -0,0 +1,61 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+KVStore
+=======
+
+.. currentmodule:: mxnet.kvstore
+
+.. autoclass:: KVStore
+
+
+Init, push and pull
+-------------------
+
+.. autosummary::
+   :toctree: _autogen
+
+   KVStore.init
+   KVStore.pull
+   KVStore.push
+   KVStore.row_sparse_pull
+
+Optimizer
+-------------------
+
+.. autosummary::
+   :toctree: _autogen
+
+   KVStore.load_optimizer_states
+   KVStore.save_optimizer_states
+   KVStore.set_gradient_compression
+   KVStore.set_optimizer
+
+
+
+
+
+Attributes
+----------
+
+.. autosummary::
+   :toctree: _autogen
+
+   KVStore.num_workers
+   KVStore.rank
+   KVStore.type
+
diff --git a/docs/python_docs/python/api/gluon-related/mxnet.kvstore.create.rst b/docs/python_docs/python/api/gluon-related/mxnet.kvstore.create.rst
new file mode 100644
index 000000000000..1a317221b17b
--- /dev/null
+++ b/docs/python_docs/python/api/gluon-related/mxnet.kvstore.create.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+create
+======
+
+.. currentmodule:: mxnet.kvstore
+
+.. autofunction:: create
diff --git a/docs/python_docs/python/api/gluon-related/mxnet.kvstore.rst b/docs/python_docs/python/api/gluon-related/mxnet.kvstore.rst
new file mode 100644
index 000000000000..815335215575
--- /dev/null
+++ b/docs/python_docs/python/api/gluon-related/mxnet.kvstore.rst
@@ -0,0 +1,27 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.kvstore
+=============
+
+.. automodule:: mxnet.kvstore
+
+   .. autosummary::
+      :toctree: .
+
+      create
+      KVStore
diff --git a/docs/python_docs/python/api/gluon-related/mxnet.lr_scheduler.rst b/docs/python_docs/python/api/gluon-related/mxnet.lr_scheduler.rst
new file mode 100644
index 000000000000..b4707ae34f7e
--- /dev/null
+++ b/docs/python_docs/python/api/gluon-related/mxnet.lr_scheduler.rst
@@ -0,0 +1,31 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.lr\_scheduler
+===================
+
+.. automodule:: mxnet.lr_scheduler
+
+.. autosummary::
+   :toctree: _autogen
+
+   LRScheduler
+   FactorScheduler
+   MultiFactorScheduler
+   PolyScheduler
+   CosineScheduler
+
diff --git a/docs/python_docs/python/api/gluon-related/mxnet.metric.rst b/docs/python_docs/python/api/gluon-related/mxnet.metric.rst
new file mode 100644
index 000000000000..cb11c9597590
--- /dev/null
+++ b/docs/python_docs/python/api/gluon-related/mxnet.metric.rst
@@ -0,0 +1,57 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.metric
+============
+
+.. automodule:: mxnet.metric
+
+
+Metrics
+-------
+
+.. autosummary::
+   :toctree: _autogen
+
+   Accuracy
+   Caffe
+   CompositeEvalMetric
+   CrossEntropy
+   CustomMetric
+   EvalMetric
+   F1
+   Loss
+   MAE
+   MCC
+   MSE
+   NegativeLogLikelihood
+   PearsonCorrelation
+   Perplexity
+   RMSE
+   TopKAccuracy
+   Torch
+
+
+Helper functions
+-----------------
+
+.. autosummary::
+   :toctree: _autogen
+
+   check_label_shapes
+   create
+   np
diff --git a/docs/python_docs/python/api/gluon-related/mxnet.optimizer.rst b/docs/python_docs/python/api/gluon-related/mxnet.optimizer.rst
new file mode 100644
index 000000000000..dc92ed24b3c5
--- /dev/null
+++ b/docs/python_docs/python/api/gluon-related/mxnet.optimizer.rst
@@ -0,0 +1,55 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.optimizer
+===============
+
+.. automodule:: mxnet.optimizer
+
+
+
+Optimization methods
+--------------------
+
+.. autosummary::
+   :toctree: _autogen
+
+   AdaDelta
+   AdaGrad
+   Adam
+   Adamax
+   DCASGD
+   FTML
+   Ftrl
+   LBSGD
+   NAG
+   Nadam
+   Optimizer
+   RMSProp
+   SGD
+   SGLD
+   Signum
+   Updater
+
+Helper functions
+----------------
+
+.. autosummary::
+
+   create
+   get_updater
+   register
diff --git a/docs/python_docs/python/api/gluon-related/mxnet.profiler.rst b/docs/python_docs/python/api/gluon-related/mxnet.profiler.rst
new file mode 100644
index 000000000000..9307220a57e3
--- /dev/null
+++ b/docs/python_docs/python/api/gluon-related/mxnet.profiler.rst
@@ -0,0 +1,54 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.profiler
+==============
+
+.. automodule:: mxnet.profiler
+
+
+Profiling system control
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+.. autosummary::
+   :toctree: _autogen
+
+   set_config
+   set_kvstore_handle
+   set_state
+   profiler_set_config
+   profiler_set_state
+   pause
+   resume
+   dump
+   dump_profile
+   dumps
+
+
+Profiling Objects
+~~~~~~~~~~~~~~~~~
+
+.. autosummary::
+   :toctree: _autogen
+
+   Counter
+   Domain
+   Event
+   Frame
+   Marker
+   Task
diff --git a/docs/python_docs/python/api/gluon-related/mxnet.random.rst b/docs/python_docs/python/api/gluon-related/mxnet.random.rst
new file mode 100644
index 000000000000..348b48c62cd8
--- /dev/null
+++ b/docs/python_docs/python/api/gluon-related/mxnet.random.rst
@@ -0,0 +1,26 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.random
+============
+
+.. automodule:: mxnet.random
+
+.. autosummary::
+   :toctree: _autogen
+
+   seed
diff --git a/docs/python_docs/python/api/gluon-related/mxnet.recordio.rst b/docs/python_docs/python/api/gluon-related/mxnet.recordio.rst
new file mode 100644
index 000000000000..1fa6de759ffc
--- /dev/null
+++ b/docs/python_docs/python/api/gluon-related/mxnet.recordio.rst
@@ -0,0 +1,43 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.recordio
+==============
+
+.. automodule:: mxnet.recordio
+
+Iterators
+---------
+
+.. autosummary::
+   :toctree: _autogen
+
+   MXIndexedRecordIO
+   MXRecordIO
+
+Read and write RecordIO files
+------------------------------------------
+
+.. autosummary::
+   :toctree: _autogen
+
+   IRHeader
+   pack
+   pack_img
+   unpack
+   unpack_img
+
diff --git a/docs/python_docs/python/api/gluon/index.rst b/docs/python_docs/python/api/gluon/index.rst
new file mode 100644
index 000000000000..e2c8aa99961c
--- /dev/null
+++ b/docs/python_docs/python/api/gluon/index.rst
@@ -0,0 +1,156 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.gluon
+=======================
+
+The Gluon library in Apache MXNet provides a clear, concise, and simple API for deep learning.
+It makes it easy to prototype, build, and train deep learning models without sacrificing training speed.
+
+Example
+-------
+
+The following example shows how you might create a simple neural network with three layers:
+one input layer, one hidden layer, and one output layer.
+
+.. code-block:: python
+
+   net = gluon.nn.Sequential()
+   # When instantiated, Sequential stores a chain of neural network layers.
+   # Once presented with data, Sequential executes each layer in turn, using
+   # the output of one layer as the input for the next
+   with net.name_scope():
+       net.add(gluon.nn.Dense(256, activation="relu")) # 1st layer (256 nodes)
+       net.add(gluon.nn.Dense(256, activation="relu")) # 2nd hidden layer
+       net.add(gluon.nn.Dense(num_outputs))
+
+
+Tutorials
+---------
+
+.. container:: cards
+
+   .. card::
+      :title: Gluon Guide
+      :link: ../../guide/packages/gluon/
+
+      The Gluon guide. Start here!
+
+   .. card::
+      :title: Gluon-CV Toolkit
+      :link: https://gluon-cv.mxnet.io/
+
+      A Gluon add-on module for computer vision.
+
+   .. card::
+      :title: Gluon-NLP Toolkit
+      :link: https://gluon-nlp.mxnet.io/
+
+      A Gluon add-on module for natural language processing.
+
+
+APIs and Packages
+-----------------
+
+Core Modules
+~~~~~~~~~~~~
+
+.. container:: cards
+
+   .. card::
+      :title: gluon.nn
+      :link: nn.html
+
+      Neural network components.
+
+   .. card::
+      :title: gluon.rnn
+      :link: rnn.html 
+
+      Recurrent neural network components.
+
+Training
+~~~~~~~~
+
+.. container:: cards
+
+   .. card::
+      :title: gluon.loss
+      :link: mxnet.gluon.loss.html
+
+      Loss functions for training neural networks.
+
+   .. card::
+      :title: gluon.parameter
+      :link: mxnet.gluon.parameter.html
+
+      Parameter getting and setting functions.
+
+   .. card::
+      :title: gluon.Trainer
+      :link: mxnet.gluon.trainer.html
+
+      Functions for applying an optimizer on a set of parameters.
+
+Data
+~~~~
+
+.. container:: cards
+
+   .. card::
+      :title: gluon.data
+      :link: mxnet.gluon.data.html
+
+      Dataset utilities.
+
+   .. card::
+      :title: gluon.data.vision
+      :link: mxnet.gluon.data.vision.html
+
+      Image dataset utilities.
+
+   .. card::
+      :title: gluon.model_zoo.vision
+      :link: mxnet.gluon.model_zoo.vision.html
+
+      A module for loading pre-trained neural network models.
+
+
+Utilities
+~~~~~~~~~
+
+.. container:: cards
+
+   .. card::
+      :title: gluon.utils
+      :link: mxnet.gluon.utils.html
+
+      A variety of utilities for training.
+
+.. toctree::
+   :hidden:
+   :maxdepth: 2
+
+   nn
+   rnn
+   mxnet.gluon.loss
+   mxnet.gluon.parameter
+   mxnet.gluon.Trainer
+   mxnet.gluon.data
+   mxnet.gluon.data.vision
+   mxnet.gluon.model_zoo
+   mxnet.gluon.utils
diff --git a/docs/python_docs/python/api/gluon/mxnet.gluon.Constant.rst b/docs/python_docs/python/api/gluon/mxnet.gluon.Constant.rst
new file mode 100644
index 000000000000..2db732f6872f
--- /dev/null
+++ b/docs/python_docs/python/api/gluon/mxnet.gluon.Constant.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Constant
+========
+
+.. currentmodule:: mxnet.gluon
+
+.. autoclass:: Constant
diff --git a/docs/python_docs/python/api/gluon/mxnet.gluon.HybridBlock.rst b/docs/python_docs/python/api/gluon/mxnet.gluon.HybridBlock.rst
new file mode 100644
index 000000000000..ebba455d2f23
--- /dev/null
+++ b/docs/python_docs/python/api/gluon/mxnet.gluon.HybridBlock.rst
@@ -0,0 +1,40 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+HybridBlock
+===========
+
+.. currentmodule:: mxnet.gluon.nn
+
+.. autoclass:: HybridBlock
+   :members:
+   :inherited-members:
+
+   HybridBlock inherits all methods and attributes from
+   :py:class:`mxnet.gluon.nn.Block`. It adds the following additional methods:
+
+   .. rubric:: Symbol related methods
+
+   .. autosummary::
+      :toctree: _autogen
+
+      HybridBlock.hybridize
+      HybridBlock.hybrid_forward
+      HybridBlock.export
+      HybridBlock.infer_shape
+      HybridBlock.infer_type
+
diff --git a/docs/python_docs/python/api/gluon/mxnet.gluon.ParameterDict.rst b/docs/python_docs/python/api/gluon/mxnet.gluon.ParameterDict.rst
new file mode 100644
index 000000000000..3750ae8ad9c5
--- /dev/null
+++ b/docs/python_docs/python/api/gluon/mxnet.gluon.ParameterDict.rst
@@ -0,0 +1,79 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+ParameterDict
+=============
+
+.. currentmodule:: mxnet.gluon
+
+.. autoclass:: ParameterDict
+
+Load and save parameters
+--------------------------
+
+.. autosummary::
+   :toctree: _autogen
+
+   ParameterDict.load
+   ParameterDict.save
+
+Get a particular parameter
+--------------------------
+
+.. autosummary::
+   :toctree: _autogen
+
+   ParameterDict.get
+   ParameterDict.get_constant
+
+Get (name, paramter) pairs
+--------------------------
+
+.. autosummary::
+   :toctree: _autogen
+
+   ParameterDict.items
+   ParameterDict.keys
+   ParameterDict.values
+
+Update parameters
+--------------------------
+
+.. autosummary::
+   :toctree: _autogen
+
+   ParameterDict.initialize
+   ParameterDict.setattr
+   ParameterDict.update
+
+
+Set devices contexts and gradients
+---------------------------------------
+
+.. autosummary::
+   :toctree: _autogen
+
+   ParameterDict.reset_ctx
+   ParameterDict.zero_grad
+
+Attributes
+---------------------------------------
+
+
+.. autosummary::
+
+   ParameterDict.prefix
diff --git a/docs/python_docs/python/api/gluon/mxnet.gluon.SymbolBlock.rst b/docs/python_docs/python/api/gluon/mxnet.gluon.SymbolBlock.rst
new file mode 100644
index 000000000000..161e7a111b18
--- /dev/null
+++ b/docs/python_docs/python/api/gluon/mxnet.gluon.SymbolBlock.rst
@@ -0,0 +1,28 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+SymbolBlock
+===========
+
+.. currentmodule:: mxnet.gluon.nn
+
+.. autoclass:: SymbolBlock
+   :members:
+   :inherited-members:
+
+   Besides the contruction method, SymbolBlock can be used as same as
+   :py:class:`mxnet.gluon.nn.HybridBlock`.
diff --git a/docs/python_docs/python/api/gluon/mxnet.gluon.Trainer.rst b/docs/python_docs/python/api/gluon/mxnet.gluon.Trainer.rst
new file mode 100644
index 000000000000..ff122c8481f1
--- /dev/null
+++ b/docs/python_docs/python/api/gluon/mxnet.gluon.Trainer.rst
@@ -0,0 +1,51 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Trainer
+===========
+
+.. currentmodule:: mxnet.gluon
+
+.. autoclass:: Trainer
+
+Updating parameters
+-------------------
+
+.. autosummary::
+   :toctree: _autogen
+
+   Trainer.step
+   Trainer.allreduce_grads
+   Trainer.update
+
+Trainer States
+--------------
+
+.. autosummary::
+   :toctree: _autogen
+
+   Trainer.load_states
+   Trainer.save_states
+
+Learning rate
+--------------
+
+.. autosummary::
+   :toctree: _autogen
+
+   Trainer.learning_rate
+   Trainer.set_learning_rate
diff --git a/docs/python_docs/python/api/gluon/mxnet.gluon.contrib.rst b/docs/python_docs/python/api/gluon/mxnet.gluon.contrib.rst
new file mode 100644
index 000000000000..848da81bf110
--- /dev/null
+++ b/docs/python_docs/python/api/gluon/mxnet.gluon.contrib.rst
@@ -0,0 +1,173 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+contrib
+=======
+
+This document lists the contrib APIs in Gluon:
+
+
+.. autosummary::
+    :nosignatures:
+
+    mxnet.gluon.contrib
+
+
+The `Gluon Contrib` API, defined in the `gluon.contrib` package, provides
+many useful experimental APIs for new features.
+This is a place for the community to try out the new features,
+so that feature contributors can receive feedback.
+
+
+.. warning:: This package contains experimental APIs and may change in the near future.
+
+
+In the rest of this document, we list routines provided by the `gluon.contrib` package.
+
+Neural Network
+--------------
+
+
+.. currentmodule:: mxnet.gluon.contrib.nn
+
+.. autosummary::
+    :nosignatures:
+
+    Concurrent
+    HybridConcurrent
+    Identity
+    SparseEmbedding
+    SyncBatchNorm
+    PixelShuffle1D
+    PixelShuffle2D
+    PixelShuffle3D
+
+
+Convolutional Neural Network
+----------------------------
+
+.. currentmodule:: mxnet.gluon.contrib.cnn
+
+.. autosummary::
+    :nosignatures:
+
+    DeformableConvolution
+
+
+Recurrent Neural Network
+------------------------
+
+
+.. currentmodule:: mxnet.gluon.contrib.rnn
+
+.. autosummary::
+    :nosignatures:
+
+    VariationalDropoutCell
+    Conv1DRNNCell
+    Conv2DRNNCell
+    Conv3DRNNCell
+    Conv1DLSTMCell
+    Conv2DLSTMCell
+    Conv3DLSTMCell
+    Conv1DGRUCell
+    Conv2DGRUCell
+    Conv3DGRUCell
+    LSTMPCell
+
+
+Data
+----
+
+
+.. currentmodule:: mxnet.gluon.contrib.data
+
+.. autosummary::
+    :nosignatures:
+    IntervalSampler
+```
+
+Text Dataset
+------------
+
+.. currentmodule:: mxnet.gluon.contrib.data.text
+
+.. autosummary::
+    :nosignatures:
+
+    WikiText2
+    WikiText103
+
+
+Estimator
+---------
+
+.. currentmodule:: mxnet.gluon.contrib.estimator
+
+.. autosummary::
+    :nosignatures:
+
+    Estimator
+
+
+Event Handler
+-------------
+
+.. currentmodule:: mxnet.gluon.contrib.estimator
+
+.. autosummary::
+    :nosignatures:
+
+    StoppingHandler
+    MetricHandler
+    ValidationHandler
+    LoggingHandler
+    CheckpointHandler
+    EarlyStoppingHandler
+
+
+API Reference
+-------------
+
+
+.. automodule:: mxnet.gluon.contrib
+    :members:
+    :imported-members:
+
+.. automodule:: mxnet.gluon.contrib.nn
+    :members:
+    :imported-members:
+
+.. automodule:: mxnet.gluon.contrib.cnn
+    :members:
+    :imported-members:
+
+.. automodule:: mxnet.gluon.contrib.rnn
+    :members:
+    :imported-members:
+
+.. automodule:: mxnet.gluon.contrib.data
+    :members:
+    :imported-members:
+
+.. automodule:: mxnet.gluon.contrib.data.text
+    :members:
+    :imported-members:
+
+.. automodule:: mxnet.gluon.contrib.estimator
+    :members:
+    :imported-members:
\ No newline at end of file
diff --git a/docs/python_docs/python/api/gluon/mxnet.gluon.data.rst b/docs/python_docs/python/api/gluon/mxnet.gluon.data.rst
new file mode 100644
index 000000000000..aa4f985d75f8
--- /dev/null
+++ b/docs/python_docs/python/api/gluon/mxnet.gluon.data.rst
@@ -0,0 +1,50 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+data
+====
+
+.. automodule:: mxnet.gluon.data
+
+Datasets
+--------
+
+.. autosummary::
+   :toctree: _autogen
+
+   Dataset
+   ArrayDataset
+   RecordFileDataset
+
+Sampling examples
+------------------
+
+.. autosummary::
+   :toctree: _autogen
+
+   Sampler
+   SequentialSampler
+   RandomSampler
+   BatchSampler
+
+Loading examples
+----------------
+
+.. autosummary::
+   :toctree: _autogen
+
+   DataLoader
\ No newline at end of file
diff --git a/docs/python_docs/python/api/gluon/mxnet.gluon.data.vision.rst b/docs/python_docs/python/api/gluon/mxnet.gluon.data.vision.rst
new file mode 100644
index 000000000000..062a894b48f4
--- /dev/null
+++ b/docs/python_docs/python/api/gluon/mxnet.gluon.data.vision.rst
@@ -0,0 +1,58 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+data.vision
+============
+
+.. automodule:: mxnet.gluon.data.vision
+
+Datasets
+^^^^^^^^
+
+.. autosummary::
+   :toctree: _autogen
+
+   datasets.MNIST
+   datasets.FashionMNIST
+   datasets.CIFAR10
+   datasets.CIFAR100
+   datasets.ImageRecordDataset
+   datasets.ImageFolderDataset
+
+
+Data transformations
+^^^^^^^^^^^^^^^^^^^^
+
+
+.. autosummary::
+   :toctree: _autogen
+
+   transforms.Compose
+   transforms.Cast
+   transforms.ToTensor
+   transforms.Normalize
+   transforms.RandomResizedCrop
+   transforms.CenterCrop
+   transforms.Resize
+   transforms.RandomFlipLeftRight
+   transforms.RandomFlipTopBottom
+   transforms.RandomBrightness
+   transforms.RandomContrast
+   transforms.RandomSaturation
+   transforms.RandomHue
+   transforms.RandomColorJitter
+   transforms.RandomLighting
diff --git a/docs/python_docs/python/api/gluon/mxnet.gluon.loss.rst b/docs/python_docs/python/api/gluon/mxnet.gluon.loss.rst
new file mode 100644
index 000000000000..e18b86da7006
--- /dev/null
+++ b/docs/python_docs/python/api/gluon/mxnet.gluon.loss.rst
@@ -0,0 +1,40 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+``loss``
+=================================
+
+Gluon provides pre-defined loss functions in the :py:mod:`mxnet.gluon.parameter`
+module.
+
+.. automodule:: mxnet.gluon.loss
+
+.. autosummary::
+   :toctree: _autogen
+
+   Loss
+   L2Loss
+   L1Loss
+   SigmoidBinaryCrossEntropyLoss
+   SoftmaxCrossEntropyLoss
+   KLDivLoss
+   HuberLoss
+   HingeLoss
+   SquaredHingeLoss
+   LogisticLoss
+   TripletLoss
+   CTCLoss
diff --git a/docs/python_docs/python/api/gluon/mxnet.gluon.model_zoo.rst b/docs/python_docs/python/api/gluon/mxnet.gluon.model_zoo.rst
new file mode 100644
index 000000000000..0212528d8259
--- /dev/null
+++ b/docs/python_docs/python/api/gluon/mxnet.gluon.model_zoo.rst
@@ -0,0 +1,167 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+model_zoo.vision
+=================
+
+.. automodule:: mxnet.gluon.model_zoo.vision
+
+.. autosummary::
+   :toctree: _autogen
+
+    get_model
+
+ResNet
+------
+
+.. autosummary::
+   :toctree: _autogen
+
+    resnet18_v1
+    resnet34_v1
+    resnet50_v1
+    resnet101_v1
+    resnet152_v1
+    resnet18_v2
+    resnet34_v2
+    resnet50_v2
+    resnet101_v2
+    resnet152_v2
+
+.. autosummary::
+   :toctree: _autogen
+
+    ResNetV1
+    ResNetV2
+    BasicBlockV1
+    BasicBlockV2
+    BottleneckV1
+    BottleneckV2
+    get_resnet
+
+VGG
+---
+
+.. autosummary::
+   :toctree: _autogen
+
+    vgg11
+    vgg13
+    vgg16
+    vgg19
+    vgg11_bn
+    vgg13_bn
+    vgg16_bn
+    vgg19_bn
+
+.. autosummary::
+   :toctree: _autogen
+
+    VGG
+    get_vgg
+
+Alexnet
+--------
+
+.. autosummary::
+   :toctree: _autogen
+
+    alexnet
+
+.. autosummary::
+   :toctree: _autogen
+
+    AlexNet
+
+
+DenseNet
+--------
+
+
+.. autosummary::
+   :toctree: _autogen
+
+    densenet121
+    densenet161
+    densenet169
+    densenet201
+
+
+
+.. autosummary::
+   :toctree: _autogen
+
+    DenseNet
+
+
+SqueezeNet
+------------
+
+
+.. autosummary::
+   :toctree: _autogen
+
+    squeezenet1_0
+    squeezenet1_1
+
+
+
+.. autosummary::
+   :toctree: _autogen
+
+    SqueezeNet
+
+
+Inception
+---------
+
+
+.. autosummary::
+   :toctree: _autogen
+
+    inception_v3
+
+
+
+.. autosummary::
+   :toctree: _autogen
+
+    Inception3
+
+
+MobileNet
+---------
+
+
+.. autosummary::
+   :toctree: _autogen
+
+    mobilenet1_0
+    mobilenet0_75
+    mobilenet0_5
+    mobilenet0_25
+    mobilenet_v2_1_0
+    mobilenet_v2_0_75
+    mobilenet_v2_0_5
+    mobilenet_v2_0_25
+
+
+.. autosummary::
+   :toctree: _autogen
+
+    MobileNet
+    MobileNetV2
diff --git a/docs/python_docs/python/api/gluon/mxnet.gluon.nn.Block.rst b/docs/python_docs/python/api/gluon/mxnet.gluon.nn.Block.rst
new file mode 100644
index 000000000000..fbf843dceedb
--- /dev/null
+++ b/docs/python_docs/python/api/gluon/mxnet.gluon.nn.Block.rst
@@ -0,0 +1,86 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Block
+=====
+
+.. currentmodule:: mxnet.gluon.nn
+
+.. autoclass:: Block
+   :members:
+   :inherited-members:
+
+   ..
+      .. automethod:: __init__
+
+
+   .. rubric:: Handle model parameters:
+
+   .. autosummary::
+      :toctree: _autogen
+
+      Block.initialize
+      Block.save_parameters
+      Block.load_parameters
+      Block.collect_params
+      Block.cast
+      Block.apply
+
+   .. rubric:: Run computation
+
+   .. autosummary::
+      :toctree: _autogen
+
+      Block.forward
+
+   .. rubric:: Debugging
+
+   .. autosummary::
+      :toctree: _autogen
+
+      Block.summary
+
+   .. rubric:: Advanced API for customization
+
+
+   .. autosummary::
+      :toctree: _autogen
+
+      Block.name_scope
+      Block.register_child
+      Block.register_forward_hook
+      Block.register_forward_pre_hook
+
+   .. rubric:: Attributes
+
+   .. autosummary::
+
+      Block.name
+      Block.params
+      Block.prefix
+
+
+   .. warning::
+
+      The following two APIs are deprecated since `v1.2.1
+      <https://github.com/apache/incubator-mxnet/releases/tag/1.2.1>`_.
+
+      .. autosummary::
+          :toctree: _autogen
+
+          Block.save_params
+          Block.load_params
diff --git a/docs/python_docs/python/api/gluon/mxnet.gluon.nn.HybridBlock.rst b/docs/python_docs/python/api/gluon/mxnet.gluon.nn.HybridBlock.rst
new file mode 100644
index 000000000000..4d93820f85a0
--- /dev/null
+++ b/docs/python_docs/python/api/gluon/mxnet.gluon.nn.HybridBlock.rst
@@ -0,0 +1,66 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+HybridBlock
+===========
+
+.. currentmodule:: mxnet.gluon.nn
+
+.. autoclass:: HybridBlock
+   :members:
+   :inherited-members:
+
+   .. automethod:: __init__
+
+
+   .. rubric:: Methods
+
+   .. autosummary::
+
+      ~HybridBlock.__init__
+      ~HybridBlock.apply
+      ~HybridBlock.cast
+      ~HybridBlock.collect_params
+      ~HybridBlock.export
+      ~HybridBlock.forward
+      ~HybridBlock.hybrid_forward
+      ~HybridBlock.hybridize
+      ~HybridBlock.infer_shape
+      ~HybridBlock.infer_type
+      ~HybridBlock.initialize
+      ~HybridBlock.load_parameters
+      ~HybridBlock.load_params
+      ~HybridBlock.name_scope
+      ~HybridBlock.register_child
+      ~HybridBlock.register_forward_hook
+      ~HybridBlock.register_forward_pre_hook
+      ~HybridBlock.save_parameters
+      ~HybridBlock.save_params
+      ~HybridBlock.summary
+
+
+
+
+
+   .. rubric:: Attributes
+
+   .. autosummary::
+
+      ~HybridBlock.name
+      ~HybridBlock.params
+      ~HybridBlock.prefix
+
diff --git a/docs/python_docs/python/api/gluon/mxnet.gluon.nn.SymbolBlock.rst b/docs/python_docs/python/api/gluon/mxnet.gluon.nn.SymbolBlock.rst
new file mode 100644
index 000000000000..1275adbe0a97
--- /dev/null
+++ b/docs/python_docs/python/api/gluon/mxnet.gluon.nn.SymbolBlock.rst
@@ -0,0 +1,67 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+SymbolBlock
+===========
+
+.. currentmodule:: mxnet.gluon.nn
+
+.. autoclass:: SymbolBlock
+
+   :members:
+   :inherited-members:   
+   .. automethod:: __init__
+
+
+   .. rubric:: Methods
+
+   .. autosummary::
+
+      ~SymbolBlock.__init__
+      ~SymbolBlock.apply
+      ~SymbolBlock.cast
+      ~SymbolBlock.collect_params
+      ~SymbolBlock.export
+      ~SymbolBlock.forward
+      ~SymbolBlock.hybrid_forward
+      ~SymbolBlock.hybridize
+      ~SymbolBlock.imports
+      ~SymbolBlock.infer_shape
+      ~SymbolBlock.infer_type
+      ~SymbolBlock.initialize
+      ~SymbolBlock.load_parameters
+      ~SymbolBlock.load_params
+      ~SymbolBlock.name_scope
+      ~SymbolBlock.register_child
+      ~SymbolBlock.register_forward_hook
+      ~SymbolBlock.register_forward_pre_hook
+      ~SymbolBlock.save_parameters
+      ~SymbolBlock.save_params
+      ~SymbolBlock.summary
+
+
+
+
+
+   .. rubric:: Attributes
+
+   .. autosummary::
+
+      ~SymbolBlock.name
+      ~SymbolBlock.params
+      ~SymbolBlock.prefix
+
diff --git a/docs/python_docs/python/api/gluon/mxnet.gluon.parameter.rst b/docs/python_docs/python/api/gluon/mxnet.gluon.parameter.rst
new file mode 100644
index 000000000000..e21fd92de331
--- /dev/null
+++ b/docs/python_docs/python/api/gluon/mxnet.gluon.parameter.rst
@@ -0,0 +1,68 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Parameter
+=========
+
+.. currentmodule:: mxnet.gluon
+
+.. autoclass:: Parameter
+
+
+Get and set parameters
+-----------------------
+
+.. autosummary::
+   :toctree: _autogen
+
+   Parameter.initialize
+   Parameter.data
+   Parameter.list_data
+   Parameter.list_row_sparse_data
+   Parameter.row_sparse_data
+   Parameter.set_data
+   Parameter.shape
+
+
+Get and set gradients associated with parameters
+-------------------------------------------------
+
+.. autosummary::
+   :toctree: _autogen
+
+   Parameter.grad
+   Parameter.list_grad
+   Parameter.zero_grad
+   Parameter.grad_req
+
+Handle device contexts
+------------------------
+
+.. autosummary::
+   :toctree: _autogen
+
+   Parameter.cast
+   Parameter.list_ctx
+   Parameter.reset_ctx
+
+Convert to symbol
+--------------------
+
+.. autosummary::
+   :toctree: _autogen
+
+   Parameter.var
diff --git a/docs/python_docs/python/api/gluon/mxnet.gluon.utils.rst b/docs/python_docs/python/api/gluon/mxnet.gluon.utils.rst
new file mode 100644
index 000000000000..661217aae6c7
--- /dev/null
+++ b/docs/python_docs/python/api/gluon/mxnet.gluon.utils.rst
@@ -0,0 +1,31 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+utils
+=========
+
+.. automodule:: mxnet.gluon.utils
+
+.. autosummary::
+   :toctree: _autogen
+
+   split_data
+   split_and_load
+   clip_global_norm
+   download
+   check_sha1
+
diff --git a/docs/python_docs/python/api/gluon/nn.rst b/docs/python_docs/python/api/gluon/nn.rst
new file mode 100644
index 000000000000..12d829d92720
--- /dev/null
+++ b/docs/python_docs/python/api/gluon/nn.rst
@@ -0,0 +1,156 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+   
+nn and contrib.nn
+=========================
+
+Gluon provides a large number of build-in neural network layers in the following
+two modules:
+
+..
+   Don't add toctree to these two modules, otherwise it will generate two pages in
+   the global TOC
+
+.. autosummary::
+    :nosignatures:
+
+    mxnet.gluon.nn
+    mxnet.gluon.contrib.nn
+
+
+We group all layers in these two modules according to their categories.
+
+.. currentmodule:: mxnet.gluon
+
+Blocks
+------
+
+.. autosummary::
+   :nosignatures:
+   :toctree: .
+
+   nn.Block
+   nn.HybridBlock
+   nn.SymbolBlock
+
+
+Sequential containers
+---------------------
+
+
+.. autosummary::
+    :toctree: _autogen
+    :nosignatures:
+
+    nn.Sequential
+    nn.HybridSequential
+
+Concurrent containers
+---------------------
+
+.. autosummary::
+    :toctree: _autogen
+    :nosignatures:
+
+    contrib.nn.Concurrent
+    contrib.nn.HybridConcurrent
+
+
+Basic Layers
+------------
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    nn.Dense
+    nn.Activation
+    nn.Dropout
+    nn.Flatten
+    nn.Lambda
+    nn.HybridLambda
+
+Convolutional Layers
+--------------------
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    nn.Conv1D
+    nn.Conv2D
+    nn.Conv3D
+    nn.Conv1DTranspose
+    nn.Conv2DTranspose
+    nn.Conv3DTranspose
+
+Pooling Layers
+--------------
+
+.. autosummary::
+   :nosignatures:
+   :toctree: _autogen
+
+    nn.MaxPool1D
+    nn.MaxPool2D
+    nn.MaxPool3D
+    nn.AvgPool1D
+    nn.AvgPool2D
+    nn.AvgPool3D
+    nn.GlobalMaxPool1D
+    nn.GlobalMaxPool2D
+    nn.GlobalMaxPool3D
+    nn.GlobalAvgPool1D
+    nn.GlobalAvgPool2D
+    nn.GlobalAvgPool3D
+    nn.ReflectionPad2D
+
+Normalization Layers
+--------------------
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    nn.BatchNorm
+    nn.InstanceNorm
+    nn.LayerNorm
+    contrib.nn.SyncBatchNorm
+
+Embedding Layers
+----------------
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    nn.Embedding
+    contrib.nn.SparseEmbedding
+
+
+Advanced Activation Layers
+--------------------------
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    nn.LeakyReLU
+    nn.PReLU
+    nn.ELU
+    nn.SELU
+    nn.Swish
diff --git a/docs/python_docs/python/api/gluon/rnn.rst b/docs/python_docs/python/api/gluon/rnn.rst
new file mode 100644
index 000000000000..b13df3094813
--- /dev/null
+++ b/docs/python_docs/python/api/gluon/rnn.rst
@@ -0,0 +1,68 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+rnn and contrib.rnn
+============================
+
+Build-in recurrent neural network layers are provided in the following two modules:
+
+
+.. autosummary::
+    :nosignatures:
+
+    mxnet.gluon.rnn
+    mxnet.gluon.contrib.rnn
+
+.. currentmodule:: mxnet.gluon
+
+Recurrent Cells
+----------------
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    rnn.LSTMCell
+    rnn.GRUCell
+    rnn.RecurrentCell
+    rnn.SequentialRNNCell
+    rnn.BidirectionalCell
+    rnn.DropoutCell
+    rnn.ZoneoutCell
+    rnn.ResidualCell
+    contrib.rnn.Conv1DRNNCell
+    contrib.rnn.Conv2DRNNCell
+    contrib.rnn.Conv3DRNNCell
+    contrib.rnn.Conv1DLSTMCell
+    contrib.rnn.Conv2DLSTMCell
+    contrib.rnn.Conv3DLSTMCell
+    contrib.rnn.Conv1DGRUCell
+    contrib.rnn.Conv2DGRUCell
+    contrib.rnn.Conv3DGRUCell
+    contrib.rnn.VariationalDropoutCell
+    contrib.rnn.LSTMPCell
+
+Recurrent Layers
+----------------
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    rnn.RNN
+    rnn.LSTM
+    rnn.GRU
diff --git a/docs/python_docs/python/api/index.rst b/docs/python_docs/python/api/index.rst
new file mode 100644
index 000000000000..7e08f0707550
--- /dev/null
+++ b/docs/python_docs/python/api/index.rst
@@ -0,0 +1,77 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Python API
+==========
+
+Overview
+--------
+
+This API section details functions, modules, and objects included in MXNet,
+describing what they are and what they do. The APIs are grouped into the
+following categories:
+
+
+.. container:: cards
+
+   .. card::
+      :title: NDArray module
+      :link: ndarray/index.html
+
+      Imperative APIs to manipulate multi-dimensional arrays.
+
+   .. card::
+      :title: Gluon module
+      :link: gluon/index.html
+
+      Imperative APIs to load data, construct and train neural networks.
+
+   .. card::
+      :title: Gluon-related modules
+      :link: gluon-related/index.html
+
+      Modules, including context and optimizer, that can be used with Gluon.
+
+   .. card::
+      :title: Symbol module
+      :link: symbol/index.html
+
+      Symbolic APIs for multi-dimensional arrays and neural network layers.
+
+   .. card::
+      :title: Symbol-related modules
+      :link: symbol-related/index.html
+
+      Modules to train and visualize symbolic neural network models.
+
+   .. card::
+      :title: Advanced modules
+      :link: advanced/index.html
+
+      Advanced APIs for hackers.
+
+
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+
+   ndarray/index
+   gluon/index
+   gluon-related/index
+   symbol/index
+   symbol-related/index
+   advanced/index
diff --git a/docs/python_docs/python/api/ndarray/index.rst b/docs/python_docs/python/api/ndarray/index.rst
new file mode 100644
index 000000000000..f09fd48215ff
--- /dev/null
+++ b/docs/python_docs/python/api/ndarray/index.rst
@@ -0,0 +1,124 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.ndarray
+=============
+
+The NDArray library in Apache MXNet defines the core data structure for all mathematical computations. NDArray supports fast execution on a wide range of hardware configurations and automatically parallelizes multiple operations across the available hardware.
+
+Example
+-------
+
+The following example shows how you can create an NDArray from a regular Python list using the 'array' function. 
+
+.. code-block:: python
+
+	import mxnet as mx
+	# create a 1-dimensional array with a python list
+	a = mx.nd.array([1,2,3])
+	# create a 2-dimensional array with a nested python list
+	b = mx.nd.array([[1,2,3], [2,3,4]])
+	{'a.shape':a.shape, 'b.shape':b.shape}
+
+
+.. note:: ``mxnet.ndarray`` is similar to ``numpy.ndarray`` in some aspects. But the differences are not negligible. For instance:
+
+   - ``mxnet.ndarray.NDArray.T`` does real data transpose to return new a copied
+     array, instead of returning a view of the input array.
+   - ``mxnet.ndarray.dot`` performs dot product between the last axis of the
+     first input array and the first axis of the second input, while `numpy.dot`
+     uses the second last axis of the input array.
+
+   In addition, ``mxnet.ndarray.NDArray`` supports GPU computation and various neural
+   network layers.
+
+.. note:: ``ndarray`` provides almost the same routines as ``symbol``. Most
+  routines between these two packages share the source code. But ``ndarray``
+  differs from ``symbol`` in few aspects:
+
+  - ``ndarray`` adopts imperative programming, namely sentences are executed
+    step-by-step so that the results can be obtained immediately whereas
+    ``symbol`` adopts declarative programming.
+
+  - Most binary operators in ``ndarray`` such as ``+`` and ``>`` have
+    broadcasting enabled by default.
+
+Tutorials
+---------
+
+.. container:: cards
+
+
+   .. card::
+      :title: NDArray Guide
+      :link: ../../guide/packages/ndarray/
+
+      The NDArray guide. Start here!
+
+
+
+NDArray API of MXNet
+--------------------
+
+.. container:: cards
+
+   .. card::
+      :title: NDArray
+      :link: mxnet.ndarray.NDArray.html
+
+      Imperative tensor operations using the NDArray API.
+
+   .. card::
+      :title: Routines
+      :link: routines.html
+
+      Manipulating multi-dimensional, fixed-size arrays.
+
+Sparse NDArray API of MXNet
+---------------------------
+
+.. container:: cards
+
+   .. card::
+      :title: CSRNDArray
+      :link: mxnet.ndarray.sparse.CSRNDArray.html
+
+      Representing two-dimensional, fixed-size arrays in compressed sparse row format.
+
+   .. card::
+      :title: RowSparseNDArray
+      :link: mxnet.ndarray.sparse.RowSparseNDArray.html
+
+      Representing multi-dimensional, fixed-size arrays in row sparse format.
+
+   .. card::
+      :title: Sparse routines
+      :link: sparse_routines.html
+
+      Manipulating sparse arrays.
+
+
+
+.. toctree::
+   :hidden:
+   :maxdepth: 2
+
+   mxnet.ndarray.NDArray
+   routines
+   mxnet.ndarray.sparse.CSRNDArray
+   mxnet.ndarray.sparse.RowSparseNDArray
+   sparse_routines
diff --git a/docs/python_docs/python/api/ndarray/mxnet.ndarray.NDArray.rst b/docs/python_docs/python/api/ndarray/mxnet.ndarray.NDArray.rst
new file mode 100644
index 000000000000..4c7edd4cd4d8
--- /dev/null
+++ b/docs/python_docs/python/api/ndarray/mxnet.ndarray.NDArray.rst
@@ -0,0 +1,310 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+NDArray
+=================
+
+.. currentmodule:: mxnet.ndarray
+
+.. autoclass:: NDArray
+
+
+Attributes
+----------
+
+.. autosummary::
+   :toctree: _autogen
+
+   NDArray.context
+   NDArray.dtype
+   NDArray.grad
+   NDArray.handle
+   NDArray.ndim
+   NDArray.shape
+   NDArray.size
+   NDArray.stype
+   NDArray.writable
+
+
+Array creation
+--------------
+
+.. autosummary::
+   :toctree: _autogen
+
+   NDArray.ones_like
+   NDArray.zeros_like
+
+
+Manipulation
+-------------
+
+Array conversion
+^^^^^^^^^^^^^^^^^
+
+.. autosummary::
+   :toctree: _autogen
+
+   NDArray.as_in_context
+   NDArray.asnumpy
+   NDArray.asscalar
+   NDArray.astype
+   NDArray.copy
+   NDArray.copyto
+   NDArray.tostype
+
+
+Changing shape
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autosummary::
+   :toctree: _autogen
+
+   NDArray.diag
+   NDArray.expand_dims
+   NDArray.flatten
+   NDArray.reshape
+   NDArray.reshape_like
+   NDArray.shape_array
+   NDArray.size_array
+   NDArray.split
+
+
+Expanding elements
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autosummary::
+   :toctree: _autogen
+
+   NDArray.broadcast_to
+   NDArray.broadcast_axes
+   NDArray.broadcast_like
+   NDArray.pad
+   NDArray.repeat
+   NDArray.tile
+
+
+Rearrange elements
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autosummary::
+   :toctree: _autogen
+
+   NDArray.depth_to_space
+   NDArray.flip
+   NDArray.swapaxes
+   NDArray.space_to_depth
+   NDArray.T
+   NDArray.transpose
+
+
+Sorting and searching
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autosummary::
+   :toctree: _autogen
+
+   NDArray.argmax
+   NDArray.argmax_channel
+   NDArray.argmin
+   NDArray.argsort
+   NDArray.sort
+   NDArray.topk
+
+
+Indexing
+^^^^^^^^^^
+
+.. autosummary::
+   :toctree: _autogen
+
+   NDArray.__getitem__
+   NDArray.__setitem__
+   NDArray.one_hot
+   NDArray.pick
+   NDArray.slice
+   NDArray.slice_axis
+   NDArray.slice_like
+   NDArray.take
+
+
+Lazy evaluation
+^^^^^^^^^^^^^^^^
+
+.. autosummary::
+   :toctree: _autogen
+
+   NDArray.wait_to_read
+
+
+Math
+----
+
+Arithmetic operations
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autosummary::
+   :toctree: _autogen
+
+   NDArray.__add__
+   NDArray.__div__
+   NDArray.__neg__
+   NDArray.__mod__
+   NDArray.__mul__
+   NDArray.__pow__
+   NDArray.__rdiv__
+   NDArray.__rmod__
+   NDArray.__rsub__
+   NDArray.__sub__
+
+
+Rounding
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autosummary::
+   :toctree: _autogen
+
+   NDArray.ceil
+   NDArray.fix
+   NDArray.floor
+   NDArray.rint
+   NDArray.round
+   NDArray.trunc
+
+
+Reduction
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autosummary::
+   :toctree: _autogen
+
+   NDArray.max
+   NDArray.mean
+   NDArray.min
+   NDArray.nanprod
+   NDArray.nansum
+   NDArray.norm
+   NDArray.prod
+   NDArray.sum
+
+
+In^place arithmetic operations
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autosummary::
+   :toctree: _autogen
+
+   NDArray.__iadd__
+   NDArray.__idiv__
+   NDArray.__imod__
+   NDArray.__imul__
+   NDArray.__isub__
+
+
+Comparison operators
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autosummary::
+   :toctree: _autogen
+
+   NDArray.__eq__
+   NDArray.__ge__
+   NDArray.__gt__
+   NDArray.__le__
+   NDArray.__lt__
+   NDArray.__ne__
+
+
+Trigonometric functions
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autosummary::
+   :toctree: _autogen
+
+   NDArray.arccos
+   NDArray.arcsin
+   NDArray.arctan
+   NDArray.cos
+   NDArray.degrees
+   NDArray.radians
+   NDArray.sin
+   NDArray.tan
+
+
+Hyperbolic functions
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autosummary::
+   :toctree: _autogen
+
+   NDArray.arccosh
+   NDArray.arcsinh
+   NDArray.arctanh
+   NDArray.cosh
+   NDArray.sinh
+   NDArray.tanh
+
+
+Exponents and logarithms
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autosummary::
+   :toctree: _autogen
+
+   NDArray.exp
+   NDArray.expm1
+   NDArray.log
+   NDArray.log1p
+   NDArray.log10
+   NDArray.log2
+
+
+Powers
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autosummary::
+   :toctree: _autogen
+
+   NDArray.cbrt
+   NDArray.rcbrt
+   NDArray.reciprocal
+   NDArray.rsqrt
+   NDArray.square
+   NDArray.sqrt
+
+
+Miscellaneous
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autosummary::
+   :toctree: _autogen
+
+   NDArray.clip
+   NDArray.sign
+
+
+Neural network
+------------------
+
+.. autosummary::
+   :toctree: _autogen
+
+   NDArray.log_softmax
+   NDArray.relu
+   NDArray.sigmoid
+   NDArray.softmax
+
diff --git a/docs/python_docs/python/api/ndarray/mxnet.ndarray.sparse.CSRNDArray.rst b/docs/python_docs/python/api/ndarray/mxnet.ndarray.sparse.CSRNDArray.rst
new file mode 100644
index 000000000000..ab9b9cb812c6
--- /dev/null
+++ b/docs/python_docs/python/api/ndarray/mxnet.ndarray.sparse.CSRNDArray.rst
@@ -0,0 +1,203 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+CSRNDArray
+====================
+
+.. currentmodule:: mxnet.ndarray.sparse
+
+.. autoclass:: CSRNDArray
+
+
+Array attributes
+---------------------
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    CSRNDArray.context
+    CSRNDArray.data
+    CSRNDArray.dtype
+    CSRNDArray.indices
+    CSRNDArray.indptr
+    CSRNDArray.shape
+    CSRNDArray.stype
+
+
+Array creation
+--------------
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    CSRNDArray.zeros_like
+
+
+Manipulation
+-------------
+
+Array conversion
+^^^^^^^^^^^^^^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    CSRNDArray.as_in_context
+    CSRNDArray.asnumpy
+    CSRNDArray.asscalar
+    CSRNDArray.asscipy
+    CSRNDArray.astype
+    CSRNDArray.copy
+    CSRNDArray.copyto
+    CSRNDArray.tostype
+
+
+Array inspection
+^^^^^^^^^^^^^^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    CSRNDArray.check_format
+
+
+Array reduction
+^^^^^^^^^^^^^^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    CSRNDArray.mean
+    CSRNDArray.norm
+    CSRNDArray.sum
+
+
+Indexing
+^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    CSRNDArray.__getitem__
+    CSRNDArray.__setitem__
+    CSRNDArray.slice
+
+
+Joining arrays
+^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    concat
+
+
+Lazy evaluation
+^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    CSRNDArray.wait_to_read
+
+
+Math
+----
+
+Array rounding
+^^^^^^^^^^^^^^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    CSRNDArray.ceil
+    CSRNDArray.fix
+    CSRNDArray.floor
+    CSRNDArray.round
+    CSRNDArray.rint
+    CSRNDArray.trunc
+
+
+Trigonometric functions
+^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    CSRNDArray.arcsin
+    CSRNDArray.arctan
+    CSRNDArray.degrees
+    CSRNDArray.radians
+    CSRNDArray.sin
+    CSRNDArray.tan
+
+
+Hyperbolic functions
+^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    CSRNDArray.arcsinh
+    CSRNDArray.arctanh
+    CSRNDArray.sinh
+    CSRNDArray.tanh
+
+
+Exponents and logarithms
+^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    CSRNDArray.expm1
+    CSRNDArray.log1p
+
+
+Powers
+^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    CSRNDArray.sqrt
+    CSRNDArray.square
+
+
+Miscellaneous
+^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    CSRNDArray.abs
+    CSRNDArray.clip
+    CSRNDArray.sign
diff --git a/docs/python_docs/python/api/ndarray/mxnet.ndarray.sparse.RowSparseNDArray.rst b/docs/python_docs/python/api/ndarray/mxnet.ndarray.sparse.RowSparseNDArray.rst
new file mode 100644
index 000000000000..7db86728ddea
--- /dev/null
+++ b/docs/python_docs/python/api/ndarray/mxnet.ndarray.sparse.RowSparseNDArray.rst
@@ -0,0 +1,183 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+RowSparseNDArray
+==========================
+
+.. currentmodule:: mxnet.ndarray.sparse
+
+.. autoclass:: RowSparseNDArray
+
+
+Array attributes
+-------------------------------
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    RowSparseNDArray.context
+    RowSparseNDArray.data
+    RowSparseNDArray.dtype
+    RowSparseNDArray.indices
+    RowSparseNDArray.shape
+    RowSparseNDArray.stype
+
+
+Array conversion
+-------------------------------
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    RowSparseNDArray.as_in_context
+    RowSparseNDArray.asnumpy
+    RowSparseNDArray.asscalar
+    RowSparseNDArray.astype
+    RowSparseNDArray.copy
+    RowSparseNDArray.copyto
+    RowSparseNDArray.tostype
+
+
+Array inspection
+-------------------------------
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    RowSparseNDArray.check_format
+
+
+Array creation
+-------------------------------
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    RowSparseNDArray.zeros_like
+
+
+Array reduction
+-------------------------------
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    RowSparseNDArray.norm
+
+
+Array rounding
+-------------------------------
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    RowSparseNDArray.ceil
+    RowSparseNDArray.fix
+    RowSparseNDArray.floor
+    RowSparseNDArray.rint
+    RowSparseNDArray.round
+    RowSparseNDArray.trunc
+
+
+Trigonometric functions
+-------------------------------
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    RowSparseNDArray.arcsin
+    RowSparseNDArray.arctan
+    RowSparseNDArray.degrees
+    RowSparseNDArray.radians
+    RowSparseNDArray.sin
+    RowSparseNDArray.tan
+
+
+Hyperbolic functions
+-------------------------------
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    RowSparseNDArray.arcsinh
+    RowSparseNDArray.arctanh
+    RowSparseNDArray.sinh
+    RowSparseNDArray.tanh
+
+
+Exponents and logarithms
+-------------------------------
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    RowSparseNDArray.expm1
+    RowSparseNDArray.log1p
+
+
+Powers
+-------------------------------
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    RowSparseNDArray.sqrt
+    RowSparseNDArray.square
+
+
+Indexing
+-------------------------------
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    RowSparseNDArray.__getitem__
+    RowSparseNDArray.__setitem__
+    RowSparseNDArray.retain
+
+
+Lazy evaluation
+-------------------------------
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    RowSparseNDArray.wait_to_read
+
+
+Miscellaneous
+-------------------------------
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    RowSparseNDArray.abs
+    RowSparseNDArray.clip
+    RowSparseNDArray.sign
diff --git a/docs/python_docs/python/api/ndarray/routines.rst b/docs/python_docs/python/api/ndarray/routines.rst
new file mode 100644
index 000000000000..1931e1f5383b
--- /dev/null
+++ b/docs/python_docs/python/api/ndarray/routines.rst
@@ -0,0 +1,461 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Routines
+========
+
+Create Arrays
+-------------
+
+.. currentmodule:: mxnet.ndarray
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    arange
+    array
+    diag
+    empty
+    full
+    load
+    ones
+    ones_like
+    save
+    zeros
+    zeros_like
+
+
+Manipulate
+------------
+
+Change shape and type
+^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    cast
+    flatten
+    expand_dims
+    reshape
+    reshape_like
+    shape_array
+    size_array
+
+
+Expand  elements
+^^^^^^^^^^^^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    broadcast_axes
+    broadcast_like
+    broadcast_to
+    pad
+    repeat
+    tile
+
+
+Rearrange elements
+^^^^^^^^^^^^^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    depth_to_space
+    flip
+    space_to_depth
+    swapaxes
+    transpose
+
+
+Join and split
+^^^^^^^^^^^^^^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    concat
+    split
+    stack
+
+
+Index
+^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    batch_take
+    one_hot
+    pick
+    ravel_multi_index
+    slice
+    slice_axis
+    slice_like
+    take
+    unravel_index
+    where
+
+
+Sequence
+^^^^^^^^
+
+.. currentmodule:: mxnet.ndarray
+
+.. autosummary::
+    :toctree: _autogen
+
+    SequenceLast
+    SequenceMask
+    SequenceReverse
+
+
+Math
+----
+
+Arithmetic
+^^^^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    add
+    add_n
+    batch_dot
+    divide
+    dot
+    modulo
+    multiply
+    negative
+    subtract
+
+
+Trigonometric
+^^^^^^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    arccos
+    arcsin
+    arctan
+    broadcast_hypot
+    degrees
+    cos
+    radians
+    sin
+    tan
+
+
+Hyperbolic
+^^^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    arcsinh
+    arccosh
+    arctanh
+    cosh
+    sinh
+    tanh
+
+
+Reduce
+^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    max
+    min
+    mean
+    nanprod
+    nansum
+    norm
+    prod
+    sum
+
+
+Round
+^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    ceil
+    fix
+    floor
+    round
+    rint
+    trunc
+
+
+Exponents and logarithms
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    exp
+    expm1
+    log
+    log1p
+    log10
+    log2
+
+
+Powers
+^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    cbrt
+    power
+    rcbrt
+    reciprocal
+    rsqrt
+    square
+    sqrt
+
+
+Compare
+^^^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    equal
+    greater
+    greater_equal
+    lesser
+    lesser_equal
+    not_equal
+
+
+Logical
+^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    logical_and
+    logical_not
+    logical_or
+    logical_xor
+
+
+Sort and Search
+^^^^^^^^^^^^^^^
+
+.. autosummary::
+    :toctree: _autogen
+
+    argmax
+    argmin
+    argsort
+    sort
+    topk
+
+
+Random Distribution
+^^^^^^^^^^^^^^^^^^^^
+
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    random.exponential
+    random.gamma
+    random.generalized_negative_binomial
+    random.multinomial
+    random.negative_binomial
+    random.normal
+    random.poisson
+    random.randint
+    random.randn
+    random.shuffle
+    random.uniform
+
+Linear Algebra
+^^^^^^^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    linalg.gelqf
+    linalg.gemm
+    linalg.gemm2
+    linalg.potrf
+    linalg.potri
+    linalg.sumlogdiag
+    linalg.syevd
+    linalg.syrk
+    linalg.trmm
+    linalg.trsm
+
+
+Miscellaneous
+-------------
+
+.. autosummary::
+    :toctree: _autogen
+
+    abs
+    clip
+    gamma
+    gammaln
+    maximum
+    minimum
+    sign
+
+
+Neural Network
+--------------
+
+.. autosummary::
+    :toctree: _autogen
+
+    Activation
+    BatchNorm
+    BilinearSampler
+    BlockGrad
+    Convolution
+    Correlation
+    Custom
+    Deconvolution
+    Dropout
+    Embedding
+    FullyConnected
+    GridGenerator
+    IdentityAttachKLSparseReg
+    InstanceNorm
+    L2Normalization
+    LayerNorm
+    LeakyReLU
+    LinearRegressionOutput
+    log_softmax
+    LogisticRegressionOutput
+    LRN
+    MAERegressionOutput
+    MakeLoss
+    Pooling
+    relu
+    ROIPooling
+    RNN
+    sigmoid
+    smooth_l1
+    softmax
+    softmax_cross_entropy
+    SoftmaxOutput
+    SoftmaxActivation
+    SpatialTransformer
+    SVMOutput
+    UpSampling
+
+Contributed routines
+--------------------
+
+.. automodule:: mxnet.ndarray.contrib
+
+.. note:: This package contains experimental APIs and may change in the near future.
+
+Manipulate
+^^^^^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    count_sketch
+    getnnz
+    index_copy
+
+
+FFT
+^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    fft
+    ifft
+
+
+Quantization
+^^^^^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    dequantize
+    quantize
+
+
+Neural network
+^^^^^^^^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    AdaptiveAvgPooling2D
+    BilinearResize2D
+    ctc_loss
+    DeformableConvolution
+    DeformablePSROIPooling
+    MultiBoxDetection
+    MultiBoxPrior
+    MultiBoxTarget
+    MultiProposal
+    Proposal
+    PSROIPooling
+    ROIAlign
+
+
+Control flow
+^^^^^^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    cond
+    foreach
+    while_loop
\ No newline at end of file
diff --git a/docs/python_docs/python/api/ndarray/sparse_routines.rst b/docs/python_docs/python/api/ndarray/sparse_routines.rst
new file mode 100644
index 000000000000..9f9fd5fd6287
--- /dev/null
+++ b/docs/python_docs/python/api/ndarray/sparse_routines.rst
@@ -0,0 +1,200 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Sparse routines
+=====================================
+
+.. currentmodule:: mxnet.ndarray.sparse
+
+Create Arrays
+--------------
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    array
+    csr_matrix
+    empty
+    row_sparse_array
+    zeros
+    zeros_like
+
+
+Manipulate
+------------
+
+Change shape and type
+^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    cast_storage
+
+
+Index
+^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    retain
+    slice
+    where
+
+
+Math
+----
+
+Arithmetic
+^^^^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    add_n
+    broadcast_add
+    broadcast_div
+    broadcast_mul
+    broadcast_sub
+    dot
+    elemwise_add
+    elemwise_mul
+    elemwise_sub
+    negative
+
+
+Trigonometric
+^^^^^^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    arcsin
+    arctan
+    degrees
+    radians
+    sin
+    tan
+
+
+Hyperbolic
+^^^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    arcsinh
+    arctanh
+    sinh
+    tanh
+
+
+Reduce
+^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    mean
+    norm
+    sum
+
+
+Round
+^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    ceil
+    fix
+    floor
+    rint
+    round
+    trunc
+
+
+Exponents and logarithms
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    expm1
+    log1p
+
+
+Powers
+^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    sqrt
+    square
+
+
+Miscellaneous
+^^^^^^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    abs
+    sign
+
+
+Neural network
+---------------
+
+Updater
+^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    adam_update
+    adagrad_update
+    sgd_mom_update
+    sgd_update
+
+
+More
+^^^^
+
+.. autosummary::
+    :nosignatures:
+    :toctree: _autogen
+
+    Embedding
+    LinearRegressionOutput
+    LogisticRegressionOutput
+    make_loss
+    stop_gradient
\ No newline at end of file
diff --git a/docs/python_docs/python/api/symbol-related/index.rst b/docs/python_docs/python/api/symbol-related/index.rst
new file mode 100644
index 000000000000..5cc5c14ec964
--- /dev/null
+++ b/docs/python_docs/python/api/symbol-related/index.rst
@@ -0,0 +1,53 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Symbol related modules
+======================
+
+.. container:: cards
+
+   .. card::
+      :title: mxnet.callback
+      :link: mxnet.callback.html
+
+      Functions to track various statuses during an epoch.
+
+   .. card::
+      :title: mxnet.module
+      :link: mxnet.module.html
+
+      Intermediate and high-level interface for performing computation with Symbols. 
+
+   .. card::
+      :title: mxnet.monitor
+      :link: mxnet.monitor.html
+
+      
+
+   .. card::
+      :title: mxnet.visualization
+      :link: mxnet.visualization.html
+
+      Functions for Symbol visualization.
+
+.. toctree::
+   :hidden:
+
+   mxnet.callback
+   mxnet.module
+   mxnet.monitor
+   mxnet.visualization
diff --git a/docs/python_docs/python/api/symbol-related/mxnet.callback.rst b/docs/python_docs/python/api/symbol-related/mxnet.callback.rst
new file mode 100644
index 000000000000..5b06ba719bd2
--- /dev/null
+++ b/docs/python_docs/python/api/symbol-related/mxnet.callback.rst
@@ -0,0 +1,45 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.callback
+==============
+
+.. automodule:: mxnet.callback
+
+
+
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree: _autogen
+
+      do_checkpoint
+      log_train_metric
+      module_checkpoint
+
+
+
+
+
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree: _autogen
+
+      LogValidationMetricsCallback
+      ProgressBar
+      Speedometer
diff --git a/docs/python_docs/python/api/symbol-related/mxnet.model.rst b/docs/python_docs/python/api/symbol-related/mxnet.model.rst
new file mode 100644
index 000000000000..5475fc8218ef
--- /dev/null
+++ b/docs/python_docs/python/api/symbol-related/mxnet.model.rst
@@ -0,0 +1,45 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.model
+===========
+
+.. automodule:: mxnet.model
+
+
+
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree: _autogen
+
+      load_checkpoint
+      save_checkpoint
+
+
+
+
+
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree: _autogen
+
+      BatchEndParam
+      FeedForward
+
+
diff --git a/docs/python_docs/python/api/symbol-related/mxnet.module.rst b/docs/python_docs/python/api/symbol-related/mxnet.module.rst
new file mode 100644
index 000000000000..f023d574fb89
--- /dev/null
+++ b/docs/python_docs/python/api/symbol-related/mxnet.module.rst
@@ -0,0 +1,35 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.module
+============
+
+.. automodule:: mxnet.module
+
+   .. autosummary::
+      :nosignatures:
+      :toctree: _autogen
+
+      BaseModule
+      BucketingModule
+      Module
+      PythonLossModule
+      PythonModule
+      SequentialModule
+
+
+
diff --git a/docs/python_docs/python/api/symbol-related/mxnet.monitor.rst b/docs/python_docs/python/api/symbol-related/mxnet.monitor.rst
new file mode 100644
index 000000000000..c7a9d38663c9
--- /dev/null
+++ b/docs/python_docs/python/api/symbol-related/mxnet.monitor.rst
@@ -0,0 +1,35 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.monitor
+=============
+
+.. automodule:: mxnet.monitor
+
+
+
+
+
+
+
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree: _autogen
+
+      Monitor
+
diff --git a/docs/python_docs/python/api/symbol-related/mxnet.visualization.rst b/docs/python_docs/python/api/symbol-related/mxnet.visualization.rst
new file mode 100644
index 000000000000..732fd1e443c6
--- /dev/null
+++ b/docs/python_docs/python/api/symbol-related/mxnet.visualization.rst
@@ -0,0 +1,35 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.visualization
+===================
+
+.. automodule:: mxnet.visualization
+
+
+
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree: _autogen
+
+      plot_network
+      print_summary
+
+
+
+
diff --git a/docs/python_docs/python/api/symbol/index.rst b/docs/python_docs/python/api/symbol/index.rst
new file mode 100644
index 000000000000..492c820ff5ac
--- /dev/null
+++ b/docs/python_docs/python/api/symbol/index.rst
@@ -0,0 +1,65 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.symbol
+=========================
+
+The Symbol API in Apache MXNet is an interface for symbolic programming. It features the use of computational graphs, reduced memory usage, and pre-use function optimization.
+
+
+Example
+-------
+
+The following example shows how you might build a simple expression with the Symbol API.
+
+.. code-block:: python
+
+	import mxnet as mx
+	# Two placeholers are created with mx.sym.variable
+	a = mx.sym.Variable('a')
+	b = mx.sym.Variable('b')
+	# The symbol is constructed using the '+' operator
+	c = a + b
+	(a, b, c)
+
+
+Tutorials
+---------
+.. container:: cards
+
+   .. card::
+      :title: Symbol Guide
+      :link: ../../tutorials/packages/symbol/
+
+      The Symbol guide. Start here!
+
+Symbol Package
+---------
+
+.. container:: cards
+
+   .. card::
+      :title: Symbol
+      :link: mxnet.symbol.Symbol.html
+
+      Symbolic programming using the Symbol API.
+
+.. toctree::
+   :hidden:
+
+   mxnet.symbol.Symbol
+   mxnet.symbol.linalg
\ No newline at end of file
diff --git a/docs/python_docs/python/api/symbol/mxnet.symbol.Symbol.rst b/docs/python_docs/python/api/symbol/mxnet.symbol.Symbol.rst
new file mode 100644
index 000000000000..738fde742eda
--- /dev/null
+++ b/docs/python_docs/python/api/symbol/mxnet.symbol.Symbol.rst
@@ -0,0 +1,335 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Symbol
+======
+
+Composition
+------------------
+
+Composite multiple symbols into a new one by an operator.
+
+.. currentmodule:: mxnet.symbol
+
+.. autoclass:: Symbol
+
+.. autosummary::
+   :toctree: _autogen
+
+    Symbol.__call__
+
+
+Arithmetic operations
+------------------------
+
+
+.. autosummary::
+   :toctree: _autogen
+
+    Symbol.__add__
+    Symbol.__sub__
+    Symbol.__rsub__
+    Symbol.__neg__
+    Symbol.__mul__
+    Symbol.__div__
+    Symbol.__rdiv__
+    Symbol.__mod__
+    Symbol.__rmod__
+    Symbol.__pow__
+
+
+Trigonometric functions
+------------------------
+
+
+.. autosummary::
+   :toctree: _autogen
+
+    Symbol.sin
+    Symbol.cos
+    Symbol.tan
+    Symbol.arcsin
+    Symbol.arccos
+    Symbol.arctan
+    Symbol.degrees
+    Symbol.radians
+
+
+Hyperbolic functions
+------------------------
+
+
+.. autosummary::
+   :toctree: _autogen
+
+    Symbol.sinh
+    Symbol.cosh
+    Symbol.tanh
+    Symbol.arcsinh
+    Symbol.arccosh
+    Symbol.arctanh
+
+
+Exponents and logarithms
+------------------------
+
+
+.. autosummary::
+   :toctree: _autogen
+
+    Symbol.exp
+    Symbol.expm1
+    Symbol.log
+    Symbol.log10
+    Symbol.log2
+    Symbol.log1p
+
+
+Powers
+------------------------
+
+
+.. autosummary::
+   :toctree: _autogen
+
+    Symbol.sqrt
+    Symbol.rsqrt
+    Symbol.cbrt
+    Symbol.rcbrt
+    Symbol.square
+
+
+Basic neural network functions
+----------------------------------
+
+
+.. autosummary::
+   :toctree: _autogen
+
+    Symbol.relu
+    Symbol.sigmoid
+    Symbol.softmax
+    Symbol.log_softmax
+
+
+Comparison operators
+----------------------
+
+
+.. autosummary::
+   :toctree: _autogen
+
+    Symbol.__lt__
+    Symbol.__le__
+    Symbol.__gt__
+    Symbol.__ge__
+    Symbol.__eq__
+    Symbol.__ne__
+
+
+Symbol creation
+---------------------
+
+
+.. autosummary::
+   :toctree: _autogen
+
+    Symbol.zeros_like
+    Symbol.ones_like
+    Symbol.diag
+
+
+Changing shape and type
+---------------------------
+
+
+.. autosummary::
+   :toctree: _autogen
+
+    Symbol.astype
+    Symbol.shape_array
+    Symbol.size_array
+    Symbol.reshape
+    Symbol.reshape_like
+    Symbol.flatten
+    Symbol.expand_dims
+
+
+Expanding elements
+-----------------------
+
+
+.. autosummary::
+   :toctree: _autogen
+
+    Symbol.broadcast_to
+    Symbol.broadcast_axes
+    Symbol.broadcast_like
+    Symbol.tile
+    Symbol.pad
+
+
+Rearranging elements
+----------------------
+
+
+.. autosummary::
+   :toctree: _autogen
+
+    Symbol.transpose
+    Symbol.swapaxes
+    Symbol.flip
+    Symbol.depth_to_space
+    Symbol.space_to_depth
+
+
+Reduce functions
+---------------------------
+
+
+.. autosummary::
+   :toctree: _autogen
+
+    Symbol.sum
+    Symbol.nansum
+    Symbol.prod
+    Symbol.nanprod
+    Symbol.mean
+    Symbol.max
+    Symbol.min
+    Symbol.norm
+
+
+Rounding
+---------------------
+
+
+.. autosummary::
+   :toctree: _autogen
+
+    Symbol.round
+    Symbol.rint
+    Symbol.fix
+    Symbol.floor
+    Symbol.ceil
+    Symbol.trunc
+
+
+Sorting and searching
+-----------------------------
+
+
+.. autosummary::
+   :toctree: _autogen
+
+    Symbol.sort
+    Symbol.argsort
+    Symbol.topk
+    Symbol.argmax
+    Symbol.argmin
+    Symbol.argmax_channel
+
+
+Query information
+--------------------
+
+
+.. autosummary::
+   :toctree: _autogen
+
+    Symbol.name
+    Symbol.list_arguments
+    Symbol.list_outputs
+    Symbol.list_auxiliary_states
+    Symbol.list_attr
+    Symbol.attr
+    Symbol.attr_dict
+
+
+Indexing
+-----------------------
+
+
+.. autosummary::
+   :toctree: _autogen
+
+    Symbol.slice
+    Symbol.slice_axis
+    Symbol.slice_like
+    Symbol.take
+    Symbol.one_hot
+    Symbol.pick
+
+
+Get internal and output symbol
+----------------------------------
+
+
+.. autosummary::
+   :toctree: _autogen
+
+    Symbol.__getitem__
+    Symbol.__iter__
+    Symbol.get_internals
+    Symbol.get_children
+
+
+Inference type and shape
+----------------------------------
+
+
+.. autosummary::
+   :toctree: _autogen
+
+    Symbol.infer_type
+    Symbol.infer_shape
+    Symbol.infer_shape_partial
+
+
+
+Bind
+------------------
+
+
+.. autosummary::
+   :toctree: _autogen
+
+    Symbol.bind
+    Symbol.simple_bind
+
+
+Save
+------------------
+
+
+.. autosummary::
+   :toctree: _autogen
+
+    Symbol.save
+    Symbol.tojson
+    Symbol.debug_str
+
+
+Miscellaneous
+-----------------------
+
+
+.. autosummary::
+   :toctree: _autogen
+
+    Symbol.clip
+    Symbol.sign
diff --git a/docs/python_docs/python/api/symbol/mxnet.symbol.linalg.rst b/docs/python_docs/python/api/symbol/mxnet.symbol.linalg.rst
new file mode 100644
index 000000000000..b7979dc006cb
--- /dev/null
+++ b/docs/python_docs/python/api/symbol/mxnet.symbol.linalg.rst
@@ -0,0 +1,49 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.linalg
+============
+
+.. automodule:: mxnet.symbol.linalg
+
+
+This document lists the linear algebra routines of the symbolic expression package:
+
+
+The `Linear Algebra Symbol` API, defined in the `symbol.linalg` package, provides
+symbolic expressions for linear algebra routines.
+
+In the rest of this document, we list routines provided by the `symbol.linalg` package.
+
+.. autosummary::
+   :toctree: _autogen
+
+    gemm
+    gemm2
+    potrf
+    potri
+    trmm
+    trsm
+    syrk
+    gelqf
+    syevd
+    sumlogdiag
+    extractdiag
+    makediag
+    extracttrian
+    maketrian
+    inverse
\ No newline at end of file
diff --git a/docs/python_docs/python/index.rst b/docs/python_docs/python/index.rst
new file mode 100644
index 000000000000..7c4fa7e91402
--- /dev/null
+++ b/docs/python_docs/python/index.rst
@@ -0,0 +1,52 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Python Documentation
+====================
+
+
+.. toctree::
+   :maxdepth: 2
+   :hidden:
+
+   tutorials/index
+   api/index
+
+
+Python Tutorials
+---------
+
+.. container:: cards
+
+   .. card::
+      :title: Tutorials
+      :link: tutorials/index.html
+      :is_head: true
+
+      Guides and Tutorials for using MXNet.
+
+Python API References
+---------
+
+.. container:: cards
+
+   .. card::
+      :title: API
+      :link: api/index.html
+      :is_head: true
+
+      API reference to all MXNet classes and methods.
diff --git a/docs/python_docs/python/scripts/conf.py b/docs/python_docs/python/scripts/conf.py
new file mode 100644
index 000000000000..d2733046584a
--- /dev/null
+++ b/docs/python_docs/python/scripts/conf.py
@@ -0,0 +1,243 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# -*- coding: utf-8 -*-
+import sys, os, re, subprocess
+from recommonmark.parser import CommonMarkParser
+from recommonmark.transform import AutoStructify
+
+# -- mock out modules
+MOCK_MODULES = ['scipy', 'scipy.sparse', 'sklearn']
+
+# -- General configuration -----------------------------------------------------
+
+# If your documentation needs a minimal Sphinx version, state it here.
+needs_sphinx = '1.5.6'
+
+# General information about the project.
+project = u'Apache MXNet'
+author = u'%s developers' % project
+copyright = u'2015-2018, %s' % author
+github_doc_root = 'https://github.com/apache/incubator-mxnet/tree/master/docs/'
+doc_root = 'http://mxnet.io/'
+
+# add markdown parser
+source_parsers = {
+    '.md': CommonMarkParser,
+}
+# Version information.
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom ones
+extensions = [
+    'sphinx.ext.autodoc',
+    'sphinx.ext.doctest',
+    'sphinx.ext.intersphinx',
+    'sphinx.ext.todo',
+    'sphinx.ext.mathjax',
+    'sphinx.ext.ifconfig',
+    'sphinx.ext.viewcode',
+    # 'sphinxcontrib.fulltoc',
+    'nbsphinx',
+    'IPython.sphinxext.ipython_console_highlighting',
+    # 'sphinx.ext.autodoc',
+    'sphinx.ext.autosummary',
+    'sphinx.ext.napoleon',
+    # 'sphinx.ext.mathjax',
+    # 'sphinx.ext.viewcode',
+    'breathe',
+#    'mxdoc'
+]
+
+doctest_global_setup = '''
+import mxnet as mx
+'''
+
+autodoc_member_order = 'bysource'
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['_templates']
+
+# The suffix of source filenames.
+source_suffix = ['.rst', '.ipynb', '.md', '.Rmd']
+
+# The encoding of source files.
+#source_encoding = 'utf-8-sig'
+
+# The master toctree document.
+master_doc = 'index'
+
+# The version info for the project you're documenting, acts as replacement for
+# |version| and |release|, also used in various other places throughout the
+# built documents.
+#
+# The short X.Y version.
+
+# Version and release are passed from CMake.
+#version = None
+
+# The full version, including alpha/beta/rc tags.
+#release = version
+
+# The language for content autogenerated by Sphinx. Refer to documentation
+# for a list of supported languages.
+#language = None
+
+# There are two options for replacing |today|: either, you set today to some
+# non-false value, then it is used:
+#today = ''
+# Else, today_fmt is used as the format for a strftime call.
+#today_fmt = '%B %d, %Y'
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+exclude_patterns = ['templates',
+                    # 'api',
+                    'guide/modules/others', 'guide/guide', 'blog']
+
+# The reST default role (used for this markup: `text`) to use for all documents.
+#default_role = None
+
+# If true, '()' will be appended to :func: etc. cross-reference text.
+#add_function_parentheses = True
+
+# If true, the current module name will be prepended to all description
+# unit titles (such as .. function::).
+#add_module_names = True
+
+# If true, sectionauthor and moduleauthor directives will be shown in the
+# output. They are ignored by default.
+#show_authors = False
+
+# The name of the Pygments (syntax highlighting) style to use.
+pygments_style = 'sphinx'
+
+
+# A list of ignored prefixes for module index sorting.
+#modindex_common_prefix = []
+
+suppress_warnings = [
+   'image.nonlocal_uri',
+]
+
+# -- Options for HTML output ---------------------------------------------------
+
+# Add any paths that contain custom themes here, relative to this directory.
+html_theme_path = ['../../themes/mx-theme']
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+html_theme = 'mxtheme'
+
+# Theme options are theme-specific and customize the look and feel of a theme
+# further.  For a list of options available for each theme, see the
+# documentation.
+html_theme_options = {
+    'primary_color': 'blue',
+    'accent_color': 'deep_orange',
+    'show_footer': True,
+    'relative_url': os.environ.get('SPHINX_RELATIVE_URL', '/')
+}
+
+
+# The name for this set of Sphinx documents.  If None, it defaults to
+# "<project> v<release> documentation".
+#html_title = None
+
+# A shorter title for the navigation bar.  Default is the same as html_title.
+#html_short_title = None
+
+# The name of an image file (relative to this directory) to place at the top
+# of the sidebar.
+html_logo = '../../_static/mxnet_logo.png'
+
+# The name of an image file (within the static path) to use as favicon of the
+# docs.  This file should be a Windows icon file (.ico) being 16x16 or 32x32
+# pixels large.
+html_favicon = '../../_static/mxnet-icon.png'
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ['../../_static']
+
+html_css_files = [
+    'mxnet.css',
+]
+
+html_js_files = [
+
+]
+
+# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
+# using the given strftime format.
+#html_last_updated_fmt = '%b %d, %Y'
+
+# If true, SmartyPants will be used to convert quotes and dashes to
+# typographically correct entities.
+#html_use_smartypants = True
+
+# Custom sidebar templates, maps document names to template names.
+html_sidebars = {
+  '**': 'relations.html'
+}
+
+# Additional templates that should be rendered to pages, maps page names to
+# template names.
+#html_additional_pages = {}
+
+# If false, no module index is generated.
+#html_domain_indices = True
+
+# If false, no index is generated.
+#html_use_index = True
+
+# If true, the index is split into individual pages for each letter.
+#html_split_index = False
+
+# If true, links to the reST sources are added to the pages.
+#html_show_sourcelink = True
+
+# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
+html_show_sphinx = False
+
+# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
+html_show_copyright = False
+
+# If true, an OpenSearch description file will be output, and all pages will
+# contain a <link> tag referring to it.  The value of this option must be the
+# base URL from which the finished HTML is served.
+#html_use_opensearch = ''
+
+# This is the file name suffix for HTML files (e.g. ".xhtml").
+#html_file_suffix = None
+
+# Output file base name for HTML help builder.
+htmlhelp_basename = 'formatdoc'
+
+nbsphinx_execute = 'never'
+
+# let the source file format to be xxx.ipynb instead of xxx.ipynb.txt
+html_sourcelink_suffix = ''
+
+def setup(app):
+    app.add_transform(AutoStructify)
+    app.add_config_value('recommonmark_config', {
+    }, True)
+    app.add_javascript('google_analytics.js')
+    import mxtheme
+    app.add_directive('card', mxtheme.CardDirective)
diff --git a/docs/python_docs/python/scripts/md2ipynb.py b/docs/python_docs/python/scripts/md2ipynb.py
new file mode 100644
index 000000000000..7793dea39847
--- /dev/null
+++ b/docs/python_docs/python/scripts/md2ipynb.py
@@ -0,0 +1,47 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import sys
+import os
+import time
+import notedown
+import nbformat
+
+def md2ipynb():
+    assert len(sys.argv) == 3, 'usage: input.md output.rst'
+    (src_fn, input_fn, output_fn) = sys.argv
+
+    # timeout for each notebook, in sec
+    timeout = 20 * 60
+    # if enable evaluation
+    do_eval = int(os.environ.get('EVAL', True))
+
+    reader = notedown.MarkdownReader(match='strict')
+    with open(input_fn, 'r', encoding="utf8") as f:
+        notebook = reader.read(f)
+    if do_eval:
+        tic = time.time()
+        notedown.run(notebook, timeout)
+        print('%s: Evaluated %s in %f sec'%(src_fn, input_fn, time.time()-tic))
+    # need to add language info to for syntax highlight
+    notebook['metadata'].update({'language_info':{'name':'python'}})
+    with open(output_fn, 'w') as f:
+        f.write(nbformat.writes(notebook))
+    print('%s: Write results into %s'%(src_fn, output_fn))
+
+if __name__ == '__main__':
+    md2ipynb()
diff --git a/docs/python_docs/python/scripts/process_rst.py b/docs/python_docs/python/scripts/process_rst.py
new file mode 100644
index 000000000000..5daea3abf297
--- /dev/null
+++ b/docs/python_docs/python/scripts/process_rst.py
@@ -0,0 +1,86 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import sys
+import os
+import re
+
+def has_token(token, lines):
+    for line in lines:
+        if token in line:
+            return True
+    return False
+
+def get_next_title_mark(lines):
+    available_marks = ['=', '-', '~', '^']
+    for mark in available_marks:
+        if has_token(mark*3, lines):
+            continue
+        else:
+            return mark
+    return None
+
+def add_hidden_title(inputs):
+    """
+    convert
+
+       .. autoclass:: Class
+
+    into
+
+       :hidden:`Class`
+       ~~~~~~~~~~~~~~~
+
+       .. autoclass:: Class
+    """
+    lines = inputs.split('\n')
+    if not has_token('doxygenfunction:', lines):
+        return inputs, None
+
+    outputs = """.. raw:: html
+
+   <div class="mx-api">
+
+.. role:: hidden
+    :class: hidden-section
+
+"""
+    num = 0
+    FUNC = re.compile('\.\. doxygenfunction\:\:[ ]+([\w\.]+)')
+    mark = get_next_title_mark(lines)
+    assert mark is not None
+    for line in lines:
+        m = FUNC.match(line)
+        if m is not None:
+            name = ':hidden:`' + m.groups()[0] + '`'
+            outputs += '\n' + name + '\n' + mark * len(name) + '\n\n'
+            num += 1
+        outputs += line + '\n'
+    outputs += '.. raw:: html\n\n    </div>\n'
+    return outputs, num
+
+
+if __name__ == '__main__':
+    assert len(sys.argv) == 3, 'usage: input.rst output.rst'
+    (src_fn, input_fn, output_fn) = sys.argv
+    with open(input_fn, 'r') as f:
+        inputs = f.read()
+    outputs, num = add_hidden_title(inputs)
+    if num is not None:
+        print('%s: add %d hidden sections for %s' % (src_fn, num, input_fn))
+    with open(output_fn, 'w') as f:
+        f.write(outputs)
diff --git a/docs/python_docs/python/tutorials/deploy/export/export.rst b/docs/python_docs/python/tutorials/deploy/export/export.rst
new file mode 100644
index 000000000000..a781581c1945
--- /dev/null
+++ b/docs/python_docs/python/tutorials/deploy/export/export.rst
@@ -0,0 +1,21 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Exporting Models
+================
+
+Contributions welcome!
diff --git a/docs/python_docs/python/tutorials/deploy/export/index.rst b/docs/python_docs/python/tutorials/deploy/export/index.rst
new file mode 100644
index 000000000000..2b4a86465c37
--- /dev/null
+++ b/docs/python_docs/python/tutorials/deploy/export/index.rst
@@ -0,0 +1,42 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Export
+======
+
+The following tutorials will help you learn export MXNet models.
+Models are by default exported as a couple of `params` and `json` files,
+but you also have the option to export most models to the ONNX format.
+
+.. container:: cards
+
+   .. card::
+      :title: Export ONNX Models
+
+
+      Coming Soon!
+
+   .. card::
+      :title: Export with GluonCV
+      :link: https://gluon-cv.mxnet.io/build/examples_deployment/export_network.html
+
+      How to export models trained with MXNet GluonCV.
+
+.. toctree::
+   :hidden:
+   :maxdepth: 1
+
diff --git a/docs/python_docs/python/tutorials/deploy/index.rst b/docs/python_docs/python/tutorials/deploy/index.rst
new file mode 100644
index 000000000000..9c61fc8a30b1
--- /dev/null
+++ b/docs/python_docs/python/tutorials/deploy/index.rst
@@ -0,0 +1,123 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Deployment
+==========
+
+The following tutorials will help you learn how to deploy MXNet on various
+platforms and in different language environments.
+
+Export_
+------
+The following tutorials will help you learn export MXNet models.
+
+.. container:: cards
+
+   .. card::
+      :title: Export ONNX Models
+
+      COMING SOON
+
+   .. card::
+      :title: Export with GluonCV
+      :link: https://gluon-cv.mxnet.io/build/examples_deployment/export_network.html
+
+      How to export models trained with MXNet GluonCV.
+
+Inference_
+---------
+The following tutorials will help you learn how to deploy MXNet models for inference applications.
+
+.. container:: cards
+
+   .. card::
+      :title: CPP Inference
+      :link: inference/cpp.html
+
+      How to deploy MXNet C++ Models
+
+   .. card::
+      :title: Scala Inference
+      :link: inference/scala.html
+
+      How to run Scala inference
+
+   .. card::
+      :title: GluonCV Models in a C++ Inference Application
+      :link: https://gluon-cv.mxnet.io/build/examples_deployment/cpp_inference.html
+
+      An example application that works with an exported MXNet GluonCV YOLO model.
+
+   .. card::
+      :title: Inference with Quantized Models
+      :link: https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html
+
+      How to use quantized GluonCV models for inference on Intel Xeon Processors to gain higher performance.
+
+Cloud_
+-----
+The following tutorials will show you how to use MXNet on AWS.
+
+.. container:: cards
+
+   .. card::
+      :title: MXNet on EC2
+      :link: run-on-aws/use_ec2.html
+
+      How to deploy MXNet on an Amazon EC2 instance.
+
+   .. card::
+      :title: MXNet on SageMaker
+      :link: run-on-aws/use_sagemaker.html
+
+      How to run MXNet using Amazon SageMaker.
+
+   .. card::
+      :title: MXNet on the cloud
+      :link: run-on-aws/cloud.html
+
+      How to run MXNet on the cloud
+
+   .. card::
+      :title: Training with Data from S3
+      :link: https://mxnet.incubator.apache.org/api/faq/s3_integration
+
+      How to train with data from Amazon S3 buckets.
+
+Security
+--------
+
+.. container:: cards
+
+   .. card::
+      :title: Securing MXNet
+      :link: https://mxnet.incubator.apache.org/api/faq/security
+
+      Best practices and deployment considerations.
+
+
+.. toctree::
+   :hidden:
+   :maxdepth: 0
+
+   export/index
+   inference/index
+   run-on-aws/index
+
+.. _Export: export/index.html
+.. _Inference: inference/index.html
+.. _Cloud: run-on-aws/index.html
diff --git a/docs/python_docs/python/tutorials/deploy/inference/cpp.rst b/docs/python_docs/python/tutorials/deploy/inference/cpp.rst
new file mode 100644
index 000000000000..a24f9d19c592
--- /dev/null
+++ b/docs/python_docs/python/tutorials/deploy/inference/cpp.rst
@@ -0,0 +1,21 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Deploy into C++
+===============
+
+Contributions welcome!
diff --git a/docs/python_docs/python/tutorials/deploy/inference/index.rst b/docs/python_docs/python/tutorials/deploy/inference/index.rst
new file mode 100644
index 000000000000..84facaedf1fe
--- /dev/null
+++ b/docs/python_docs/python/tutorials/deploy/inference/index.rst
@@ -0,0 +1,57 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Inference
+=========
+
+The following tutorials will help you learn how to deploy MXNet models for inference applications.
+
+.. container:: cards
+
+   .. card::
+      :title: GluonCV Models in a C++ Inference Application
+      :link: https://gluon-cv.mxnet.io/build/examples_deployment/cpp_inference.html
+
+      An example application that works with an exported MXNet GluonCV YOLO model.
+
+   .. card::
+      :title: Inference with Quantized Models
+      :link: https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html
+
+      How to use quantized GluonCV models for inference on Intel Xeon Processors to gain higher performance.
+   ..
+      PLACEHOLDER
+
+      .. card::
+         :title: Scala and Java
+         :link: scala.html
+
+         How to use MXNet models in a Scala or Java environment.
+
+      .. card::
+         :title: C++
+         :link: cpp.html
+
+         How to use MXNet models in a C++ environment.
+      PLACEHOLDER
+   ..
+
+.. toctree::
+   :hidden:
+   :maxdepth: 0
+
+   :glob:
diff --git a/docs/python_docs/python/tutorials/deploy/inference/scala.rst b/docs/python_docs/python/tutorials/deploy/inference/scala.rst
new file mode 100644
index 000000000000..81c7c71b069e
--- /dev/null
+++ b/docs/python_docs/python/tutorials/deploy/inference/scala.rst
@@ -0,0 +1,21 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Deploy into a Java or Scala Environment
+=======================================
+
+Contributions welcome!
diff --git a/docs/python_docs/python/tutorials/deploy/run-on-aws/ami.png b/docs/python_docs/python/tutorials/deploy/run-on-aws/ami.png
new file mode 100644
index 0000000000000000000000000000000000000000..a20a497238491c59856852b587aa3247340f7d9b
GIT binary patch
literal 124310
zcmZ^~2UJtRw>}D@0wSQGAT=TaN)-^08W9DNrXYfJ>AgcJDFV`)fPi%A(tC%{d+!i>
zuc0Oo5)vN%?tTAz*ZRMgwX$+Dv-ixNJ!kFxeS6Lc{-~-zd57^15fKrk;`_JiL_{Qy
ziHL|D$ZlV^l&_Rvh=}e~S;@(LRFso@{L$IL+{)IBi0FNAY#ixl4J*3t=JPQozI!3R
zMv@+<5RKVvH_eif|NQvoJsG#?+uI6`ynlnfQ?lIr<jDK|F87lVf7#c>xSI^0C)`RO
zb93XqbL{V@E=YU3*dGXpE#YA2@N>8eKhZi+`-ZFpFX`*sK;1_Nd$N(?p}A`TMECFA
zo4$qRt$x4E%E(A|FDVpO6-h#r7y%l{sUqO7uB=azpWbI8diG%DZA4It&(YTohw@u(
zwnQI2-y49;s*MsOpA<fOd*^MfXQGCAolPRG#aE#UPN8|)6>6g8Uf#GDw}?(q1}J9t
zI~j60?6$32M;J1-!@ai6@>gVsuRXrW%WuA{d1CtuqbBhjHmdx~x?KCYdMeVa>&QSV
z>TyaH_NtNPYEEdZ)5UxHiR$JZd|TA}B)EfB0<rog*X_Uo!ARi>Xh5BD^IviEP^q=8
z-|Z)New#Sy{K7F;{5YN7bY?f%)c(!DKe?4lO8NmE^IKh&##3RyNoY9mc@b9<gLt=i
z<My&RUG}@11BV;W2PryN#uA0!o}!;pK(g@8p2|B|Tl=iEN`)yfrDBG*m0j{v0SBv8
z0Ea$%&)HAwJ7aQN-iqh3oA1Q$kJ(g2#oI+_d?loxN~Djm`p_TU_UQ?vdgJr3lsK-1
z?g#PDluB;zdWK=;R%<o^|0ETVg6c<_lwOM@fh^sx+6a;t5LOGoInG;n>u$!%mdjUN
zA|tTeatGM{9SKp96H(8}mJ61Y=<yV>qkkVAg^Rh%_Kv_^=RPx^Cm(Mm_TWr!4*bOK
zf2Vbn<31y0drZ^ev+<p>_Av=r>xWW0+nfG>9_<r(zZQQ*7V(|YktX@J%AbdNx2Ie9
zMo6%&a*v7GmmfYN0kl57Df=qu#wRw-_Y`A+%4|GA#C5M1KR$jy)hSE<@lHoz$B2$4
z?dQ9(uQx`%{iVa+lAzWPuz%e&0<k1x^k0-UG<u*M@bDYu43WW2VI$BhqKF^yt<N3l
zk}2q0=9YzO@5m5~wZ*UKIs$op3|rTpeSeZ}`ONMYLx|7GTmL_+pL(CMSxe`WX@=y#
znatGrnDSTh8Tqcrt}tV0PR5R<a3w8*n#JEdvtnd#RCjb^o6(iMo&-WF{ex8w*8TCt
z?jwyb_JH8v&Mc!Z#&-m>e_IcT4`?a_&2OIG5A7T)4bb*31c*{4h3NY&ge*kbwN!ue
zE^D80Vy4Os5$+UUd*jHfCt7^3h8lDOL_QtJ)Cy^fInwdqX{4569q!&*Q#$TGray*Y
zwlIpm%(7|A@5$I4$qLBl$Q8)>e$4&2-}dxVs^xoEPI(%QV5VTR4zE?sRrXbHn&2!s
z=g0n09$8ZA3_A~YB&>o^35Bmk*}S9UqdV8_)=kz$Par4Wcl?cCJ7sogls}()R`rYL
z7uPQ}q0bC13^{id%>9)8l{1yo2AID37Qzb~_MK<-X4Pkf%WKRG`|q#cU6<O>cHZ0I
zULWeG><jPL*x=icTNhrh=>x<!vt7L*&Nce8T-5UE*~eEIj_<zx4$0V6m(H`@<=p+W
ztGOHW<jd&CsJ!3#+JzqKWx7tvin6`(&vmR~)&0kAagbBK6aFJ;wNr_#9Iqrs(3?@3
zfi|9Q@b#hlAbqsl6rblmM}H3a%=j6r3I98$*`zuC8I&)YC-X(FAmOinUeRaCKkk}H
z^o`D2suVL6A#}+GN3U7Fh<&2gGR%a&ZBl~h$!WA}$^9u)TMOy3QAx~6GSA<%9}G>*
z)cMRu&zCx7U!<fXu=Bwc<R#G*wkQi2|1y0xPG<v{I+#kG5S$1e>z%e5@hnuDFx%$b
zJD9SYW*HwJqZrdI(8@i3o%UyVWNY_u@^B*U#fz6gf-|-wrblfbJAYdJw$Zl0^aL5N
zTmLfqZ00)s$Y$V$s_>%2yvdEEnM6kwZe{zVwjB2ysd0Qx6NMlJ3*8C*7e3~f>`8S=
z8^0$LC=<p9NCr;_lLnS}$8;YwTW)0xy<6h5<s0LR(2=VAT#Bk0sIe^rTbLPlR_B-2
z+o+VCl`R@A*y5+UCOCUHzYj|5=G*;5n$Nk<Ik=y>ziIGl_~b2(yNUaNlMQ&n{;Mq3
zX@Picd8M>9eR8EDGsUEO&8i!iGLSr$93k1HZCm@w>UgxXeMn-6cdLDfhQ40Ox1WtZ
zxS*P+zp4+NmsHHuU4%f^HI#Yh<H*m&Sm{{bM7prjrbeXhNzFBWG=LktGgw%>v-oLo
zV^Iz43RVa2g1f+WkZDZy#kb2Fm+n|fA(7v<=6o&#ON+1+m;#ab<0pTlmaYCM{&`W#
zK}K~#jd+N9rVacJn168N&2P4bH>`n-0p|V$#sEqR$^tTPI(r7VXrr(%W7(5ckB;v5
zkv+&L>ddf=j!($Z@=>Ksn@ld_M&l30Ps>7$r#lxzG(y)pMY`8kGuH~%y5l_rW-Clh
z^N?e0*(CjU<BN3MP3jOQs15orJ;g>vuVTI{1}VNuJ&JwUmTw%+B}B_Z+dY%I-?XpA
zEH-36WEt(Et~hpTy0sp?PXB!9d4sxqUGcp7flVq`N%FbqqyXAz_pQ!*L|J7X>NhIj
zE?E9emnZxNFnlR0FQJaRLd%Hz*@O4x%oS&*;p8MgCZD{oRGvsK3c4$!Z_Iv7y_(J(
zpB$2`#*?X)`jw+Oq&eTFs&|*KAZ-h~PH4Q{6ws6dDLO-h+XPP4l}I+S>ZH}eA$|t|
zzLe~qa8D7YV5V_VLYzaCVf58Eje(rF8{xi<zAFGQ>@Ux+=MU(p?A;OIBL!4sBcP#1
z+hMk5!~1^jwt3k*C#&P>qWvN<Sovk%er}#!?_7P&oE@-~q`|M*#3*-fD3RtOdOmtZ
zU$%h}G!CLi5=t#PQ<<_wvq{fs&O^>W^xf!BJl{dil^xoM*EBY_WoD$SH#sY9bU=p)
za1wjcU21D)UKz~By~B~Fc6cCD!QK21kw%g0%%1Kqv6frIVQDr6llh?q%`9txn28_L
zt`msqDON2Yyosuqb~-#&+bd%$Yr1#Ss{-3jSsAdZvHS}&(QiltWq8HnSdYKEri}lJ
zuZZ3HyA{Hi-ZdF+qB75^=%VLx=y`RD<WuF_f{I+BwF}&BI&WoD%n8s4(CH=D|MIqK
zR2ezLI`?g?oIONRmvk7^&E20Gg@`QgqHScRi(TtM2K5KvCUn4B_^B9!Sd#thg7tnm
zQsO)b>9AFgtY>nVTe3fJ+P2!!pZ6{st~xwnDUqB$5IS7kr9L=h31X}jt9AY89O-h{
ziAUia0P9k}!N1SRPXBI~&99Ad?W*NN#RyHZ#$0tT)<q`JFIRWkznvWw0N=vEEYCgU
zJ-=Y}_B@dk1@uLkGMUl>@r}%$93CPU^+ygTumZ3NYI8r$9_Ak284=$25T4>ygKpZz
znE-5MDkB3UfET}yot!haV|H^Bhr@<b42|JZX?hnA&#U&6j%(r$iI)H>X38q$+Ha-?
zo_&AAtg3o@c|nlqS<n7!r0k|YN5$z$m~4eG;jYhBM};stY?k=r`0dx9S&3wOhS}f0
zbR_xy!|@pn`vQ@z9UmI@``IkQ`b+4<k1)<`4UNMnPg;w+Ej6uqdA&J#c@C5B%czMg
zajztE)sp7PiKz4|OUv#ww#)j#s+vhwqe;8Pr5K&AaV)vx`>!rUM06bgzQl^^PmhR*
zZrrr`tnI3;qAX$JV8{Q>)WO({-^0%FTAGMR%0uG%($38F+hY$qTYDD?59ufWk&w8)
z{?{z<<nez*Ty3PEXsdjDEa%{C_E?1fCI5>jGIt(7ek|o|YA&JvR{p=$um4Fuv2=BH
zln@XAfk6BqVSWc^3jslKadCkcLIOfUeAg0uE}r(T-#qy2UD*F?kpFj_w`MLT&Q^}D
zRu1-$|Bd_2*ul+J`pJ`j6aAmhf7xl~Vf8;V*}MGrvaS~>@UKTekpG3i|CgGpmHGdV
z+P|LvQv1)k{%bm^e}hSUwDK^s{rc9*&dlEB+BBJ$B0^ICndblM`5#07m!|grXo|mj
z`M)*)SI_^_{AY=TnzNPJwUhq2LPk(Z;Qwm-Z+R(!e}4L3e*3RY`A_ThuE^Yx68Jwm
zEOY1n6g85F=rxhzTiMSZ#QO`kmhF@@fk&s&2lB1nv6T;XeR$k!j7>o2OKtYD`&|b_
zBrnQnmG8w^&{F>lpg&3*-p5N@q+B2&(5=PumgMsx2lFq(i6S=U-QwpNJSB}M*+sVe
z_1hVPI^sSKD1nsVc93t*O6xCG;nN>*c0}3-4YMr$V~;MVk*2-Jin=@;<yUg)Gbw>u
zP@;e%R+);c3&N_-7$M7;e25QePDzTF;;@WbQnTj`c9P+al2}2#iS=Dlv96w{TtfK-
z1XZ&{<wManbc4^Q#fFAQNRw|LeD6VcG|z4(5WeV29Nw_Ym4zA+sE$SB-MsTD+3~G#
z@`Ua=JBF^Im)U)3kGZZQyyQc;VOd45z)Fy#(TE-fC!b8fT;|=zzA_1}T{G+__etqp
z?XjA!NKi*-#Dt~+Vib@l&ER%$-_)sklT}{<>1=Hp@iHceC54c#zA@pxK^&PX_tN7H
zJwX+=j@@R+DfBC9+|4wm0PoL^S^rYEBc;Nprp!;qs)emESdpatjWeOPS99M}7z(0b
z#?P>tEo;Wv5&;3&DEA5d4Za=SwGuEwDZ5We5-SJnqi$3{_j^J{SR@-*TzJ3Cl$}ME
z=ZEooPh}61M(N`3r$*Sk<gl$lMLmSeIZN0vX&Bx(McDX0DvhNVw`5mpXhV0?AVli#
zmo#Um*gZ1K0iHxS;)@CyzHa2E)W`5{7kVIn^%%5iF=H16%BMY^z@lJZm=lZU8H#sZ
zv~emHrf!aVhw?85H#+GJ8#n&C6RXy9=IM&JzZz)`Z(RPczi^_qOgJCo$a%59?M^xv
z657(D9w>lWq@TGxj8T81v0|0WYK7t{UJKO-D#7GQi+_?l31LWqQaEJc#w#<y<OF@t
z9_9w3FS0nMT6sbj66=}G!Z3nCu>*K9E$Ui&c3KR}&vEu(#hAS(ri@Ome^M}9#oKrE
zk7z}!`EWp~sIm>KaExa`4dZl&is;V<o^<^-<X_`)DjG@mpAg1sW&4@PrtN|PiP9du
zT~dW5Izxo+%)Ynlhd?oHaD~6yK8ni()~jSto+9~33b&XDCac*(Oa+ILixns2Lo9$g
z9Mv`s8y{c9hp0&s-UCJAdj6L7Adaibs;@G##{zCs*FueU6bvvD%%!8Z=~74X74)#N
z@=S+}#dA`)-0{HycD2c7?t&f$yT4L_rfUV2Sj`%^x>XKZu=Cxrw}g<Ozkm=AWc_wL
zyuSNT3LZsjKwQ#D>ssoT8X8}X{syhdo_S<x`l*2<MY3!qYshgp+9D~F3yfXg)0$ED
zw&*rB&HfsFUtba~I(5)WE_@QmDkWv4gy+*UsfJlot=YG12M?e|r=tZwYa?Q>?FowF
zlK8`;w*J|}ax~m|<kphs*2UbIs;~J(FL<m{4Bt=}UO4j$%4My7<M757F#S_IAj5}3
zKQ_wUgIyT41CnUfM}?iV(I(`@FOSkEexk$B6t*?koz5sAA|xOCCI7Nk!sHLOlA615
zg!r-ZAXE6pnHAKs^D0{%zP+2G>pFj4l&~P}@eDarl6FQW7DySK6{}x7GYk0+GDa}%
z-9#)2NZOWiRd=Q8WJWf{G_DTk0`WUBv8@9g_q8bnp5}_S$9n~@+_Mjcl2<=vFP&gF
z^v_N4%2I(*!tOSX?ISe)9%4r)I15D7l>4X_xP(tDNYqZ;@$E(Lnp~Dse?bW4ZDm<p
zwPnSXh`wk(c9ujr)I*@BD1~dKQInxtu?9<5m{9&8JAZVotm>yXF<!n01oLFB<GnKX
z_Xp3o@MVi%9m^jgVt%hZ#>5IzO)cjtN}CQ}ELT?!HfB?@yZ_)9!5B24yQ`>5!b}cY
z;(OCh`&pDYCj|LcB7MSz@&WRC>wK;8Mk!EM^P@&R2=GjSWrb-43+X~Mno&+kpp?+h
zT!J_6-E9`?>SMh8$y+OTmQ2lGnLGPwM<}4hcuyU`k4m2_xZnt`V4pGvMee<|n1J{i
z-DXy*=>oP<_xfp2MZg}sLdCBOwBD|^1OE_3zmJD@mQ=|V9I!J@eLAwa^vR{dL#6o$
z?Jsf6Hcg)bpnVquikLVLd6uZO`qggH#BtLA1^pe8!<LI-n!?rYGbhw3m{<RM*o~55
zf>W1%8l!;TlwdV$hWL*L#{&N3(|QCgVWP}*pOjGkciOe6wIq~MfFfbKGxbWPaeDTg
zW)J*LC+65K&Rbg9_m?3xe2dHZ!@P}@!0>UClv`2wISc?^tetBSkP2`&VA^urKYV5g
zExLNXXVPmjZDxD*aMRpMnSK9}B=snBTX;Qx77Tte91pF0ewn9m*`Enxb2>jMJ<o_g
zztw}@+iy=gR@7HQDV4?~3n;y(de3=jrn#*h-FzGNb@sSf>i`B#)(600U%Ph`FV9-M
zaak*E-N!%^Dt_?gRjl}x#lWD>rGa6pVGK3=*HVc<(-qg&l~n1WPyD2pw7~&FXQt+~
z9z8WR$#_|BMF9f;lsLNbbh|nux7aiZLgeu@l57E$j*c`J`(oU;pRnV}o4uC=F-PGd
z!d#2J1{la!$q0U%gD;6ajxCDh@Kat4rw7U^w^&GC+i}IO<fT>QQQ!1S`-b#1B1w-}
z!0N)}egqXvw5TWF4H5MThuF!bTKEggV%f1-lA0jE3t5W$!WKDZWUk(dkA>rV3eXYe
zOR(iWD#JR79Fm{@q7O`9<OWt5evvt2O_NEw^DhKe)L2-ms2u#WS>`(cO$}u?0azV1
zDcY=Ro^bbAHdauX&NGY2SWzJ!Ugp+!(D5-8%HVyq+lHZyT1NwBFFK^e2Bs)q#Z?(I
zkRmXywy^Q5Zt+BgWVUNO8s76>dY4*ChzH)M+ji)2zV<D;?B-nB8Vf9bu_55I-S%(K
zWE=|T8f#J6&_~Aa=D3n(U46J8x!6amy}RswNo&5_JryJL$=}>ojm908Nn+{-8CZ`M
zJ>lV1_ZwI94(7*jB*vg{1g2wY-9a1kU-5(t2!#tx!<*)$8uNA^v{ST0^ow0;eSe$j
zVb$~Fq9d({e0DD$850V9Q)RrqZ8!$PP*uaE@MlG(d8#!#V9gjWJtL*&lbgCtiue|h
z;h<>msHuulrLN}!e1^nwA<bnS4#ts}S{s2DQUHH*FD=`XR=O!hTshbChoaS7A^7GB
zn`#*<I*0L}2dRg|T$gnEwzC)TU23z90_5_f64>W(H-?}etz~AH_8rwb8y-H;DWuxt
zIu7+}q3!;5SHA+hSN>d^uG9T->gaM&a$7*oSO90E624D(h0;COf&9y7)j?6^L+_Fu
zaakbIHkrf6GR?Hy{L2yENu}~PH34(unp_2fw`)ud!3u;K``ftY*qxFu*$MKu$SMqt
zEp{RMrW#ebV?|Z$Br+UAZ7~v<kpL=i7P;Ln2RLxhC5Yc9e=GvoE9j!<YIrcmf}L2m
z&i$N3qM`P3(0BH5<!fe1;|jov-6TW04l0lB=>WlN^KI7kW9U70!{ips?h=r~69Hv^
zLqnAY<jt*xG~~6{I}buikVlc|o~7doq>@m3Gne-t1&!lTVgoZ(zs0nYkpTN&*}%Ab
zo?5B~sy&lC-n7CG&)%w8KSKVpb}`xw-n^Pp08ipyb`K1xjWaHxL1km(_)?iTV=j2{
zX=$!YYUuLHSh1Il_d0d)$<&z;E6P=0sWGMEB7bv=BdLH=Nz~!E&22bdzda8+Gh1E3
zQOciQ;<vzDE~sSEdo~jU%1F5!UwPDKYmtOp=)T^ih#4BdJ2fmjaz+6-Cg1f|GQ4bz
z_c+YCo4}U3T_Dj03!+x(Ja-@V8UM9~^V{x;NHv;9p~<z6`u_^xQ!W+DJ%m#d6foIB
zDi?`YbKqvvp;n3kk-`G%H+QB4UZ0W5q{rW;s(AYBhRp)Yth`va)jO7j10hCqBA)_<
z&SNQeX6@lXciBl8#Ygbq3`VjP)$POF1~~cZDDqmZ@3uGthF$-VHtUy>r0Lw-Mx5lR
zYOippv&~#}S*D@2nX*#t-kngD?jG?pCjk2)HtW7Z+2NZFPPWw&C(p#I%$k#q0J+9i
zmP9|rGwddl^lnc<X{UQ=1FaalYH%keC&DS*tlV=cFw}iOR98>50K<m%xF`KI6Hz4(
zf*%#JC<vtv*G4|LFpRLZ3d`+Wpi#T+9wb&PcX}_eNAfd3#hZA~Z!vR&T7Ff7rhSWN
z%Mx34OLC@Sn#D5?T+!{gl48<Trf@1$x$#EflZo5#6ve$DAV*m`?7h7QH^el#tEPi?
zaiTz|>y>!tP>)8WP&YJN1@|a1RQRzQeny+jqfaBX-1Dp=t*LGSX<t*<N@-tOcZRTG
zzcN}d(+m;%^bq`)#hYqW?^(pYN&m$NE6-^&qdr)_n{g(wDDX&gs0T2g&hSDyo#~>}
zjE~BC+Q0?5nMT-26NP$xDa;s^$x&V-+j#1I?aup`&Q0cf@0;Ys?pj46rQWzT1f1Wz
zq~8fUb!Ri@;FkwR;6I68G7i#6o!*~M7yoS<$rz7|stMy8$;KE4b?%O1a^CMuuUoDo
z_uL>IaTA(5Z*bdA(+y<rR5vrfd!3FnhE-z6^;oP>(ciCbmu3s5ZeDSu-gOHW`!}8^
znaVm#eq@fFhKH~FH=RA~pYxl;#3tMV*RgailkBRvts)g&IM{rJYj&`AE6}yG_Zm+s
zPD?tgDK}q+S6a8r<e+Y|3Q^5!z0yju@rsOUP;il+DYss~^~AdHVAhNH<Y-G{mbw>-
zT6;S@U<2MXq0~ZKr^$oZ)&OP^5U&{8%GfAQOejCx@)U4&3`l}q#KUHP!F)`O5kf<<
z_*MF550^oaY1eft)khBx(1Eua$E$6^_bda&=R;B_0`m;dwB4C)`paDDt=(!iq<&M+
z!mHd;|Jq%VU38DJ%GJCjIm@7LvK)@)sJ~aZ(sD|UL~F)0sO-y?icO$(rn<zq#p9@!
zE=D4}B6iYBz$hiDYnq5OLDs^z)*dy{zY&fo@H8Ff!rrOBqT0)}GYPfF3@1j8Hivc%
zwwPE70|M6zS0bVeJx2wOZ+_XU<hjyeLJFQb$p|UvPj{+mPFa%Fu@{cnkfwQcOcUP6
zz=g*(we+j%P-9AB@p{g^U(7d<NR2b~lP9M)58ZAuM|+cjSSDFbORfV=@XpIin&FF<
zAuYl+Q}sr-K>?T1;en3ZYVDweiXlBR-km!B((L4qNZ;v0v9LKx!D+fVil4lgzLrN?
zr*m@Swk(NQ;sam(>_MhQ<$9!oebG3T9j$tzBnxqXjN5W1#79d4310S7YU-e^|3J&|
zkZ!TcD?GRF)mz&7k&a>=fc2@3hYd?AH$a(uVPft)UYtrTo01nz<6x90mn3mC;S=fB
zNvd`AZa8FGJ|>c&GtiR#)_%iJcP~)pWz|%~&=9aVCU$0P?^h2sF9gHy!fLY3(^x}q
z>Th1iPObNKOmjsE6`MQ8Iv&wDu@bE8hUNnE#oGT+FyFmj0wBoMj{L6wG&K`B(4wIL
z#qL(oB#M9ohxc_6PYyQ9WHM?ZOenh+KWJ@Lv?<hcc-p*N?(3nw@CnSJ9Y&dWuPefW
z#cFlV&w?*zH^xTzdv(iiL1b2f@4BHaFdAACr5~6Q@_5vYR;X23w0G~**Is6)K(}w~
z9c|`@EVcW8`zt5>{k*~NDw7>8b6hO8I*mQfM0m;3BPumwSoJXOjJaQqs#GN_t4j2N
zmcG<d;5;r5FItGlYaYYpy!NVzlG4h_s-`YLtydPnEDV428IGVbC`w)y5Fd|YxKx)`
zd~B*Cmrkx{$gtNUk5!erBIj>pFTTbnl}c#a5AW5&Y(g!=<?2UM0)S5`N<x1s@xx7B
ztbR;N3@~)4FZs@PA7c4B+QuI4{E!K7Ag5#0w6{~Z9d{=(h}ZZP@`K^rQEYRy!fe#T
z#80ZO1uNF%YW`LV=F990Gt~OmN!T<;o~eGq=Ij>WBDSV24#8dmMUN&9UEc&IEQ|M3
zq5I_e*udS92%)2!xdFpmwSBaSpR_EwWv;!Z?^p$S6Ms{CO2hP495hzn>^5ut(L0My
zt)v2$SW)M;+~U$tAjhctmw#<=CDk>@@b&ER=4AuVan+`*8bswpabv5Nq_K&kwNxyp
zP?y9@ABC=&$cUCnqZPw3hOc?;v<yBqrVTo(BgG#h&NR!H<#pxc6l_U!#~5Z3!+JuB
zIXnYBM$aEPIDOtR9wrNg&rG8EUJ1FoUYsUtCS)d~<Y+G*E;<Q6#!Rdfpq1;<Jc!-g
z5J30Wjez2**4zlamp*(|a`cH+67{}@ds?39l!YhXZvi~4kK`~T;cn~xVs6{qti1R#
z)}3*~{GE?aGRImpFRe-*2=o%^GAT^<haCmVG?pm>Z3<4lG*-8p^*wX>5h<}e?co~=
zi4vJw0qpQJV%>gebz2IkkNn_rW-mFN%bdHaGKF&+9<3DNM_RM>j}Z^yPuWqs%NoL^
z!HaR9rlJ?`9h&aXmHwdK|0w<LUvT)+8vCvH_vrFSN$+=n)4`qfsHdKsy|`Y43_Ct?
zXuH_0%JUbk9+d=ZxOJqfW~%#S2`q?kkE(=*5smPW6LwODQMbzLAy3cW(R+@F><F8u
zwIua>w`w4Lw4^>n3`;Br0X1O=P0{;4Hd@Io$~OvLvZp}#*pW2$1s3rDp4n8Dd#k?y
zJzYGi@!+yp-7J=J(yyjD`r6*YSmXAxrKXVNag@Lgi|1Hm(T+m5PSabhZ&6woMcq1_
zX(8d{5+~bfC2IMXl?n|vv`FK<tWxCwiKuw1+}5y46Ig;+bgtL>&PaLPz6Rjn-4~QO
zK3p%IX=P`PzQp<#9>JW#NdfN*j+iJD!Rr{#pDc97E2|y!mYGkM@ii7y4{`;McA*3s
z_RF7(hFu30(=U?N4P9d5uKj$y6_%|rhBJ&1Dp{x<=S%C8H%s@C!>N??_b4zsMP_Q7
zh3p>H*sQQhAAs(fme4^NK)ic9IP8cyALmnX?}wP1EP9iX`Q+XKcs;B<Ox%V4`N?O9
zhz#oZ>!e$lO02nUOoFGmpL=#*eLc%5Ht)y}#djdNq!(CnG9h{?C|LTG)b^KK&=B^e
z`Cio~#l=RslE9pv3tA-f1oO=1IXpDAsvaRy9Yf6x9wdzoC>D{ZjdI`5WKhZ$f9k!i
zyL)(u6~1-rW@I)ut5Y|s7ra7YSnIi~k%m}`>~O)&Ex}=*_A39#$jc<kiH?nmMaB)J
zohEPD+Ph{lA?=8iB&NV+Z@uBA9J=vF-F+;c7q~v2l6k2$hT(1KG5ZZr;~ERD7DC=b
zeRqQLg;xvac9X#9*YD8_>dHU1D!nR;#<b=UE2NNY-8E`(usemMv5V*z8SAT$7HYLL
zEj*2FJht{8?M&{H)#X$(4|S(@xiiPU<3=``@LRyEizKEcOjoRFS@_nBxXyUfvs&W#
z?RX=bOZLW-@>zP(3-9Nwqvm?6YlwKghPqBZ`%xECkIqQ1;$djj#mkgfcMCty1@x3o
zf1}MrZhQPXi*Gn-xg6svjOz&#>Y<ii*&y;HP$Wi9Y4hl3V~B3~eJCZBLicIvc_X}A
z4}zXdN}6Bil-}ya4MT@2#MCa%b6ue}dqW`jX(fPJ<hhDF?j%ck{U64_CEDgAs14?!
zF8#WT&Z9Xq!CPrp7il$1t?B3HVhvwmq&ig4Rtkd=WbJs$!2}X*k7_3DHQN_^IpL^U
z&Enm0r|&kIgDzi$3F<>HHgp$8ry-S&WH$|YF1rk`&K$0$ko$Wd3*$_PE)wKS>Id!Y
zE9#I)d+*g%OO<Y`BkQa8x^93Bri+MOKpAIbv@oq3E^z>}3-(aZu_<dBm^?QWWUGF7
zF>Ii!b?F4zW<u{J?KRcf2!g&!(q3A`hJhxUZv%F^2sD7-jb?m0nc&0uJ43_J?M;4A
zozjw@v(Fp*Bj+^XoK@bctW0+5%hvY8czx*XruZ;awfPU5Sh+gj-smSA*T7|wxT$S2
z;19_`ehiXF`vFZ(ai5m|#QNvCa5njOs)u5U{q~MuuQqY<klA?j+w)u)HaF=Xid}5H
zNNT>|-znlwzE^hqpq$ncOmpUvhMOP!=ihU~AD;0b&-lSX2k0r>scY(m`Q06dJ<mcL
z_rlhi%cA><+uGyOZ3SWD2verRL;T{QoeWA2=z5AM!6Wc5j@}uf-X%3$<L;z?ZeIjX
zC_p|NzV@BpOl$`>iDqg+H?$TT*CgR<oAY%pkaoR#vR>_}{jW^d-~wj364xa&mXTN5
zBlS5Fr36VTKBv7}S0k97E|UUl9Zsmof!Iu7y|~M!GSG-`wk8*(P3v9DF0rZFhO08f
zRl#SamWipi$$QN2EtKguX;XDrdpxm;Y9>}-(Z!OKPn+_!_Qtn_pT@}S#E3eaa5{{c
zP|s^?c!gD8s4Bw-r@@1pO-r~YZ|4$F&YNcKG$AgAy(bPNw@bdLJ3z+L$J4HPu!_IG
zD>>b~Hb5>^{%g6_FOV{9qHcn;0Trzz2D<{mnlIV}F56Pq^h+fzf$MD4cBI*o`-5iF
z-hFz^<#v^9mCLK`x7z->!JhT>ceky~MGUZRt4?x$g(`_B72LTw4#bb+d{(6w*Q7<$
zuz6_+hw>h`Ri)s41Erg%#PhNrRUJu?b8zHb{XTuucBdWKu(?b$elz|q)G_EH_3$D!
zVe4HI0Gkd#G!E@G4sBYgc_TKj{VgeRz_+-{Cz^KdnO5V8)`Fqo2hC{RX1CKI1u5gx
z00&G!k^uOK-u}Qd)Z2Zo0~?cy${H*o?3UQ?8<wCxluS(bUkrh|5(Jr=+mYvxrE@#z
z5h={^)Vk!%dX7TzrCWI2_62_F3U7bj&@|V8KGwvWPOCflL}J%~m=A_KZ4F-la!hmi
zRMXbz3~y>N9wxK2DYf~++4`G?<%2OL{rt^Y`1~nhu_d(rQq@JQ;YBPQ6naQ~bwqvG
zOz`+sOrhss>zsSlsnz6h8h=eSl9uobOAC$dl8tgeaHN`%P)?ZK)g!|UCOo-lTn~p!
zIFuSnY!X4`9UXdUh|M-cfX_iN&@`#JT78x@zIHdi09TSEuA5@B#V$t1HIG^!yZAux
z92fH(AA}ej%5V+Uc)LfC*g-t%+UvbnX;W)2HVg?HP&yaRn37)@QBjbQ&z>56sU(ag
zQ*5iHM>oGAIT|o+*<z9_3b#Hyv+!Vxy_c2xhwEzU*4!N3ak58#n`4Gg{Pz4{9lFS&
z$um~VGxlJ8<oD~e?FcDM%a|$)hQ_XQ6bFKxgQR~io=PuOaCye)TJ?kxBm$`V*^fM#
zF>iFueh9o0(%?=J)k)*Pu&Rx-FdiX_@hvuXqm7$3aPxDq=586Ls)m>YW6wUe?Nw@_
zh4cy{;JTbl7H(kERQ8e99chzb`=jN(M?6xPK3d$30=Mo#C%S}80-~fb)8Liary(g`
z!8O%DZMWa~)yHz1ue|D8&T`MUUd{T6q=Z(IB+mmc+kx|7<TMnyX#zYm!Lf{e(u$>R
z*uInEKE;e^(P+2We>inv?#<0?w=E3M`9nTC=whpr->bQN<Zn)~H`5Ds+AihdzgSdg
zL>w7w*eS@H=8yV|;&&rIFkRZVk<Y!+J+>0q5tZ3{j$N(cQ)2+?nc5x~-A}2ag13!P
zT_eG~_`aYL{84gg6ZxHp0R;4N4bWVdAx-=*G&D=@1y32`CuwWMJTNAb=S5K$vwaD8
z8jpP+tsB!32Bj@>B}1H)@lWXcKqrf$^h3qu4wl|}Nv)jy3ZG5`bGcK$%%h7zvj(-c
z7v}XxKMoV=#_ge=8H@=h6ko5O$ZYb4qj$9(r@p5-BeU~zwW!?Nhu%%95!ThtoGkW_
z<*>AC1B?6D=VMHA)Mvi*@IS*0OyJAJ7$&!`p}Sh(&g(QCLT@;!zlLCwSj(5-X{wI)
zpio<PUBArj3t2p3XE@f}%3zkQ`^@;N{Lepf^=)BqxRIS-NZxaKO`Bcd6{s1hP=d`b
zsjq9@5ZlfudTtUaP;i;gq#sLvy;GJ+e64e*X=kWhv<I$~8ea-M{c=Dl10GF-?v8Jo
z{SrRwC09<NG2~kjnj-A3O2u)AuZZC1&I(%|>903D-z3lF%CUdy!M?J4hKNNkzqyp9
z_8QsTf)TbFu8J(gp6NY>9&ADFn;EWwkyhHz1xPv5hw1k)@LxNyy;nW?u~picRZ<}E
za>e^pDuRnZRN_p7IEs>t5W(}wC@++!IB@Gv1IK;N3Hy5}i!*6Ge=9@A_PF2jtEkZc
z2MVSza)T6i%XlM4Du(Cydt<(7{D7EHHKVNWx{n>x{l?GP*kisO69$(q2RD`A>(_NV
zl)S?PNr()<bu_&sTtH%H64qPAr#J-aur)urlk_H3^?vd?wnY;sG^9GWE@Fe5F?P5l
zaRAFW-eLBPDr)}x#H7$9Fi}r}xA@$<`Fsx)bE|3kTPmi5>x^=k_NT*Tm%~1NsKdFt
zLk&y+-<U=nYgkb@YjFe{{wNE8$Y1+i?Pmm3zI&$WnPM)hjn>~3Ir|L*D>`pJeR&yT
zA2vB8+^s%=uC-E2yOiX_ppSuPWsyigS>h`ew^C|qeSOtEzhKJJUme_!^!6zL2Gt0Z
zG`WUvI}^*X!uop@_>X+d@#uY>a+cf2S$_ciO$TlzgYB$mr7~ZfQ!fTA?4h_CSOeF$
z;H#n~Y!NO!aGx6UoqFaE$U``eS+qB7VflGi&M;kmQ(LEu{{rGz7VB7+aMkLk6w&jF
zr^wx<w`0!rof_$|gfL+_gH#2PCrJrX*|a~lw2=jP?j9B3YI&Tj&aY2HZxu5zq?mVU
z&Ze(WtL?4d731GlBH+T8FyT=8=4Oc(-SN2`D;w?GJI52YYRu_ATObZ7wY0Wn#nec>
zqg(u$$`VRN%wi~cM@{^RAO4tB91oPS28rmS+#eZYONwq)dUR@sKX<IooBi{LeJQsj
zms6qjTpp>|rN1dB378c8>BHr;7E8HD*q^O{@bbrPJJxz`%K;E+I^J#!VQLZr-dmnw
zYFwCu23y;7F%SF$M_fx%(@v7zfm5XSHSqOgI-9@kz(0rt;70*y>9!er@42}%wGA;<
z0QB*2Ef{tDlA^{bjH<X;;jAn0$n9V!csNW_;^-}@_8pJyb|%<o=ERnECM<&5HcNk%
zREbxMAsva47mf?b%|!%%X~q>+^{TBzLXQzf#{j+cm6fXy_Rz=zK1~a-09{iTKKnWW
zB{eMJ5AZKiYn7N+3T%X(C)P8^zs#&=QnPAr@{6K9tX{6L=SEf>2T%`@I=F71LkcGi
z^nLqlX~nMdC?pTLIyAVe0K|^sgQaxDoR&827IO@V2*@P@a?=93X(6grll$1=BF<qp
zE!so97if=IJw@Ok2QF!Y8}a&!(`sjb!!qZP6bGj-ySNg#Y(^=A90;2Z2VzQ%Rc%lk
zK^Zf8ZSbF=KxS90>xAH{l5D1H{g>P&pX}yXk#>3Pkix{v!EeAS13V3ud#lj&rE69R
zrmV;i=y7YLBK4y1y3S`X9+RA9LxVTrNZtw27;_Uz{6ka7IrXhGg@YXN!-LtJ_C#^S
z6ENI>ayYu}Fp|ReQR+tS^b_GS)QDnSgNpB)$Jf6k)R1JgRLOUo(=2R;^4NG7KK_ig
z0pyZ<?%rB)f30`5|6JfC;!l~mt>r3H_4pRcTc$?8N1g{=b41It$wq%&?xiVw!RjK+
zYpQv?PHbnuwx7tKfP`)}hu$xtxLg$=tZx3hZE-9epDawZ@Je=A?jpiest?;y6ql*4
zxE(WM4obFJFCZo8{pUx{*NW_rfr)#F{S{%5@I#j^$4<4)_sSs;Pd(R?=4%bLmx)16
zTnV@!$<wVDtHr!aN#2f5Y+X%jdk2{qPF+3R&(7QA9|}V#%9lIxYb)4(bBTtcM_Qt`
zQofumJBdw8cCL?wa@l!x0w7)AY!BMXB=_zmL-(kgM4sA*2ir=;R!bIN3YwEI?s2(#
zuV4MjmC0U^zA8B2SvyM1nSK-Ix3HfcYEXTeQe{as8+Ac!K{`WWh{=$LDb}PIhKD*7
z9?w|K?+fODG5weFImh1F-SaVi#dId`zuL5G&CeXJ1^<K``y5P~zBAap7lv<{494GK
znoAcP>iG~JS7d*1x#>VKZ+^e7@h)tp2V$@$VvyPWdP)^DsK4}it}}RJ>&S7VqIkbd
zDIj?-^CwSrN0SH58m6<6abQ09xW=oX1~G;TP4iUUM#~mh8jkS?7265QmsDA$z2L;P
z71__8Md>c(cfa@G0ZJUNG|*x?qQQN)3C5AXonPPY4)F+sLhOVdiZGlvU*RRCFS@w6
zW=fdfb~K}7lYcs`g{wX3_cDsd^Bya+)}?=nX;nLKxWY?Fy>e;iO6@V{HR!w;7`_}B
z+`DEXAjynr#)_H*c2UL}NS{}fg%GrSFkQO`TNQCBm$3hqNk+6=Ykjnnx{H-%y+3w;
zn9#`b)@oVP%T9&7j&lh{D7gjZ^?9dyRAo5VI$gioTL5vb0#>7Gc@V9kKu<vz2Ubip
ztdNCJ*8SV%^75}}2{>Wq-r+2;<MD-YcfCt{U=#L?*?IKGP-BdR!DuI4ond1ri@zOe
z$1gKRxA*hRZhKtVLuQtRO?<+zhk5fW(WSH5xp}N8zCk^G7}N8VDXlE@KFZc0JVu=J
zNPFD%Xn_>EOlog{pEQ811qX;&LYhw{n@=~dDYXSa0Je0EV361uYun4SfwNs^rk1c^
zhcF;~mGl*1K>RzNp+Jkb2x8N%H|t{Wk;!xx`qZYI*)JyWwgEvf^250|`9{axdE+@6
zE7ZejtsbxmO`0HY979;miOm|rim7sqJzX{*iJiRX=SJl+>Y{*slN{E@<<dGBfgViQ
z9471!i#l9$>1NNSY40ZJHXe6<1R*2NIoJVf;{v})a~<khTW~robbk4}S6I*#eu#q~
zcIkV+i0p;$7Qpj?2Xx?r?VlST*P6`!L*5schO2(}1R*(Ran%M3(T{dyasToO<Q{7<
zMfpFDv|C;N?hWLlx0D6%jYwWvD=?6-2818i`x+5jM7}$h1)yZ{Lt0&rzlR_GGX;0k
zH-@KHy3Fxe#p`X|J0UZG=0mj7U7r{`wr2>M2+6?%@XS41?#(AmJgFinN{-GA1iEG8
zpuwoo6|xU`LIWyD`f~mPhsO{9+ObvnIw%r;EL`;$mwj$p4+`Z1Ui2=A(L_*)DGoM_
z@1x4tz61XbHfI?ZQZ*Q)IkH)3h;8AQCruaAo7k_|sIfnxPP{`+^auD)CE`IM$p6G|
zFiHXDdL<KS%AUI5pLeF%f?ZUCy!IB*SEub??e1pVZd%-H68%TsCYM&|V4^pZI=aCF
z-LCj92ZEht>TAqmXjWQyS9<Gta7Tb-HrRN8-x7Le0dqg?QIN^#+~Si+wk2mo?lqq6
z*=}VZlnqT$k|TCh+guv5{aQxg5^YJq@qIwqKQKLf9eU1nRG@r4vY3KqY=LBn)XDR-
zljli>7=Od<y~%e&l1<~1Xu0FV%b`PCmr#KRUJE1oJoOJwH%rkZz#|fT6E0~U#fkrJ
zt9KA^!795Ml+xYxl|B{d(x#@RqAEH*ZaUVlm8r(=KxuHCyF-S2pQ~0wIVL$QV80aX
zNYd-w=iK`c$r(_!DhV=RTG2Y*ZlId-yR=K4e^7ol&@8n(DM$Yj?#Gcck9k22mt67o
zGaj8ENqIy|Z9F*suo^>ki(zxe4~X&ehO!{V_(i|EMB1q_!!UucT8B%cbdIIOV|~&(
z0moBz>WXgf)%}W8<U@Y*A})G;AIRZ3)B)z(V!!0$+RE~EviWSvq9#cjZm~LYD6%n(
z2^-!fy}Pu;KHSaL3g=L3z?|5_n_A!b;NPnnXI>nA=#gVR`3Z-v6O{zc#LNhF1&Z7b
z&`NUH*Gh#xikDuN<tzCrx2X+%vsKEehMynC>j6~PPtyDnAtQ{L8c_{0dlfP^rw50G
z1N?%!GcwIp`*5AHyUt~+TC`7biU5LcfzFuwsrR48FpG+Nyi?GsEQMDeO}!xX43QpE
z$Ymk~iKkVS*U}GmfYse5&Yn4t)2C(l=k{Y#V2!iKE$5KsO}lFlAqG39hm9V#g%ZmM
z1D*eH`P$NRO=U^<dy%k50xuR5kMR-~G)w3U*wx_`nu#B!(+r0h8=RW|m?;%465F~j
zS~kh1Lx|NHuGF*BPXLw$ZBf9NDX<JdcvMAuLigehSgyHFnLmm`O24$^IqK(y%;Blb
zg1Mtt_$-E%0HI!zLO&m0d`E=^L8d{-V@pHPsa*}BHIPVS)y5ln{#C&7#$8M5%G~I@
z9?#S;?E?G8_0=8~z4{F)?@)?|xZuOhjNaOdaeu!?pMO|yesd3guvdQxUreHcZc#|A
zzpX<@MS@KYUC^staNA+C%(}?%g%J%xPrFFKbz#g$gz%NtgbLC{1LtN^$^CJ+wMQG5
zuB7jTusMpdpL0;G+?|YD=Wru6B)ysRJIj7ddI<Cw8)>0JPZnvHB+t*P{73O;E%ONT
zFPRf|U^$?=XOfGDnCqD>BTeqA@7odJ)ve(}iU&h1+F;jf4h16wz;$|z52s=H>2tZY
ze@q&6i|;-20eqDQo+0p3_WRi}>;%Iq^-p$@KFa8q2j7#VS1h6|mt7{UK06H+M>yCK
z4l-^YBbT#W@jm*su`b%mD9=xcvY7AvY1E${Dep5|;Ooq?zxr0wz5>qH4P#+;#P)??
zv(`<j9d*9+1mR#_4lJrlIIT4|hf7p5l@ET*s=@7Gmn2MYzS-hmWV*~=%pAZ$arkrh
zbRqJxClV!a|AT7R+R>AQr2giMz2==``^GMI@+7}7l_c0v)Yj`Lfsy(buAlnTHkHe<
zrPR1mD4B^#$dAx3Qgt3|!xF}k7x&s%KdEP_Wq@Rp8wj>8Q9cIFu8uFosQgyje1<vK
zS4omp?nsL^11~dy`%a79pY;G~A<`xC9JeQ1qsEWz_Gy0SQb!k{?JKI>!xy^aIXFP>
z&#6oiv&vzE@#2uYVUHr1iO_9$*+K88o@%E@ej=9^Fo}x<sncEyaCzK%%~{Q{{hW%u
z!?4V~T>O<MUuy!jr_b;tYzbBI?J`*#;Kynbv^ZfD0>vwjEG$Jq181v7dQ|v1JFxl(
zUh#yRll}=>o9jY>k3D~-VJ15dY?`KQ(68dy^?Cc;99PlgY94QZSx0$HGE>pD_<yA|
z)&6tv2|etB9&fb#dijNkYG=My-i+Dsy}(UxJKp8h(e$2oA&*+QZtHby_a7ddh2|A@
zdwdT+yGf7QcmGYRM8!*WVPv`eEzqeYc*9`xQBb479PJ$J<VIQ<*X3#~C4PLWbvzYY
zdEhPTg>NSwXjRfX;z&CwVj>6I6*H4<kcpZ~9Y-?Gv*wqse2Vn?yyQ|OUhOTk=E!}1
z9C_^1@XT>$3^e-W3-T*U!bn27H*!CtyBnT3@5f0H?6$3d_Xtt}=)c-C_C>k^;TvoY
z?;lOaLw4fbm^z4<>LYPuiLMN4C~I98*GC1OYr^#%Q?)}a`<gN*Z!gC?E1iB;@!1jL
zKDZXd_c1n(5Aj-J=fxTxI*sk!2IXGvsCi?%B3&@hpGoqU&pA7O)|IiYgPeAJJGFb(
zbOqVy69E?7^g+hm(6vTi)fKH%Yw6N%1n5hJIcA|gN^Eq-Wj}Jr*7&(lk1H|NK^SU!
z@Z6St?p+`SyI9yrf-aCd%4x&*v4!wjZ)!j)mDB3a14Z)Rmqxaxvjv^pxEWnM>(%OY
z0nUCQt*0TuGHnHjn~B8FM56IA)J>c_T|bh>`Co?}Qxle{>(ef;6AmBnQ4)S}2D-`x
z&FD9cK_@~q@5(enrv21rjpw^)n^$V$Rtm+%TEaV-bxB9VhdY}f5^EaMXHz{>WQ`@k
zaa!C(ZgFTfLgZJ#W$tHj79!DZTP*$|FUegeL|#pozsX-7xMY4|$+AT9$?8pGW=`w-
zkE0|12&Gf09|%eEYTxsXBe`aIbLr_U%gj=?A8E$8u5CG6$F9=dY@X!ojRzT=yBXGy
zW){2yyyToWW}nX4EDj^d2tQ&yuf=Wl<r>bGwA^BD;CoCeqm_L_?zLk69o^pYXE!r=
zKt7Lyl$q;%m5;7p3?cC%HgSSZSwN>Y<;CEinuS#ZDY)FdHxysgvv2+3$b(*X!!9=|
z4X!9fVW@7{#Nm5StZXEuf5~=GbAehs!p3#EYd@yW`D0v5Sm#VU>TT-91EV=;OlB59
zx2L?6gBYsAZM)2q^uX|WR7`!FL~CDK$JY%eMZW@*Yf}A={q{g}mdn9>9idX?jSCrl
z{HDUH`#mKZS1}FsXxB~_K#)N2jiL@@#;`tPue{Vzja+5Jdr<S-0&dvzgXKYSTZ4&n
zGj5@zhC?jy!QR{XV`0etV133ki#{l2VZj+Rp8oN~t4p|e9~8y$7GxVPJC8NvX8KO4
zGEg>`GDO<gNrIKYBA2j8IJARJe=3$FH1v_$oz9=3JK))BuD{}H9ZM$rVKUPS-jkDB
z=!UF=v9eC)*+$#AF3UAHLG$oY6G#si!bp^MuQ$2LTem{Z3f0x8MnyN3##Iy&-WCwH
z6hj;M%%-K2^ta@75%CZ*2X0czH4KTrqvS2Q8Gk@U<GB2Rs=#=;677^!F5@L(2Q4b^
z2{k}w(=-Kc`-URdEh{ut;TeNI?d=J|7FZ24!Sx-Xw1!uLhK|mSa)sdTz|}=_?t_ad
z;;#CdojRhpCFyT}Nc4Ci*CirvL*T&&YwxPI^l6_D#YXhA%TuOR;%ha<%b1yPzUo!b
z@w#fg_EE=JfB>nk+LZ6BJi@zf)jhrWU`0U_USmUE2H{6@qur@a!sfb{VG0Md<F>Jq
zdSsp-Uf-6yK1>1T;aUwgeP0v43)82%9>q)dw;ImlR724%aWnT}Z%brwBR%9$;+~Z$
zth3++;qZz8u?y`F^Sjdfdz6mey3q;U`}vHkm%=%j31EMRkqLLvPOu0|H4-10J(t4!
z2<JkN_cRaj`AJ-c$LnxdpP}vf!duc8r>pc`28U?FlEe6^%rekiK>JWF_4X;B^y=Uv
zbo?DykXdz?)?EG0&P74dH;y9?@k~m~8HqUV%!54k6d6O#n!J>U$yyzLXts#3QQ|TG
zeCa3jWK_f8-rMx|&q`eQ#QN+~b?TDro1^J_=`1{$v4c_Va5wg;fk8jO<#O@xt&m1;
zdnEm^rFU$o%Pqd79aMgnnC64}&itme5ci;J*fGvz?j1aoObs_Mx&<EEa;;lVedA$m
zp1ir37?&oUK$q!&tG<4>jrE23$;CTm#Q1*F2XvJTT+5?VYumFW{AHOeX#>*4cx?QR
z=fL2DW+SfiUQJ^yc(his(gy9BPUcj0D1Ws+)UO}c`Sa2!<?@Ic@3;jLetYmzjo+hu
zadzYG>p*nR;SkkOZ7R0DRAT(3z-_yT!NtUZtNXHl9HKHHXl*rd*-k1Quq85%vx64*
zV~r|kzG^t^c5mTR0`A?^PS{=jiyV1tY3*0v)3tjgN+fhjKByBuB-={dni3)AT_Ltd
zDjg^V8s}vUHHsEn$Xj_#PbA7t%>VvY9Esm8w#OV&6us&F{sAp?_ips8C@rziiviC!
zlX&LX!os8o6_2*FNfv(BvJCAw)kG+_uH2bm5eIFpl2G(NF=}pk@XNRR&CA;_lDHc9
zh<e}LV=4$`Dv4%T8gy_vg+`fZblZ^7kI-?8qb@Ahif&zg3@*yM_<sPTKwH1f!gEo@
zCY!4G41SbV0~(J0sxy2R1V7kp_(}<`*Dq<0V=uY5`_<;fj&UrVY5tR>USYM}?b>(6
zo<6kEe@IW2scr6!#p6ziNZ~69%7V`vh-rPXs4bT1J9FktPv%}x;AiuRMqHqAfaiCw
zuXv@(p~^<$S24t?e<#3&$W3lMZ48nQo5X@Old)tTYy>tH8_vydcJrR3Bv>)zF6u??
z^$#%~`^ga7p0-JRY_}F9eo6OkuX48@w}1JBzwEJTE<IMy7w6gGQ09)Jq-wq$fMQRd
zw|QR1*y+xk|L|1`pSt+7v55<7!bVHJHEulO6p#FIo7=VnY5R{({<WrTLXx?4bB}x6
zv(J+!IAYR)6V8Q>O;3Js=<p7(&4L{AZ8X+8-x=?n?s%upQ*G^f=Y$^n)nxR#;3}V(
zve942`XGn==s0a{RC~Hn^Pk9xUFk=zn*Uvtx|k3j4(JY^?B_E%%OPIh9J#S_+uPi>
z`(2)Mu_4#HDa5z_tqd2%;@HIlm^pI+;D*ps+xLQQd)wRfxwlTlW?fg>8IL%Tt7C8M
z&lt%S&;0BH!bOMJu-44EbtfO2Yh$r4(2Hp@)func?{F^v8;`>fe>fz#nE=inA2JS>
ztcRupEnUVzo_)=QKfNv}yi$`r&Klzr>)0AN*A9{NI?%)~+3!aEnPa+q<Qaz|ugX|!
zVD=}kM7fF3_rSKM_`u<e4*R9{hj=}!<uiN^KWaBG9N4@8;6l=c3YqNn6o>ESC3`LV
z$;;hbJ}_7E2!EbwIaBQ5(Z0(o-lflxS5(iQJ=-rP<wWy}w~o!Ydw=$^`EaNdXY6$2
z$oyrFLr;!8BR0mHVezH$j4>A6Ufog|+NBa16}LuPX|&nw;!iBP*f$@>B!^jh3Rzhf
za=Y`1H7F1AnRSNGUc`Uq$69n)_e8?s-#l95d@l~1sa&MG&?QIAnJ@8bui`Ij>8kdr
zbCLDn^(lFQZ8;b5^~D#p%+)^R=D;ro+}Sri^M+2|M<*A{M_05%zMM?IJ%W7WVVhj#
z^`|q<c5B3qI^*l{++=L$uT76Rm)F1ei@&%Z^y#t2_}L9X{vpHb(x3g=pWS~FSq`wq
zvLEr6y_}EuKpt_?U`+VEGHR{KJH9XK-zj1PTKSJG7ZmnfbHN^aoC`j02^j}jF5c~R
zWXU(iFBbSsPM!Al7I>vB?YDukK`|#}S|2yN>CHAzc;XYzYkh3}Im@bhQFQbRkSMFQ
zwo(^BZBN+!zrpu`EU&TcVW4t`4@}zxICz?DlPIuEm^)>H+rABEW3zzHwPY({xijoS
z!6HeJ2+W>P9BLb{Pt|83m|OuXso&s+(<4g3<p5v<GSTj|;Ehws(6%wX@~gaZUkq$T
z!&aMwMUo=1*$B`}zyi##YDokxcsvqir!U~X@I})XwaCf19s2SQQ(35NCCfq781QCN
z>-eHyodBz_+74f8@*Sx8$KtWT+(q+6(PK+wp--DEhdurPvsmbgZ!JbiLo#z1K<5MR
zzdDXCbAN5^nHHUUz+%f9exy$yy-#9OY&Zn?@;<xF3xAo{%m<kHwy7JFLz#pw`AL!z
zi@4?Al+0)BBTJ&>Hy=KfP~ByfjIL;jSNz#jBujCqgg<d^J%~%M8mc82a<f^RTM5w^
z%!y5xOl`9K>W7Pui!Z*U@lU^g1V^8_vPqaL@~mUE@v}|b)>7v&@c68`a<#BmQsg&x
zR=uieqcIl_5%kmLfF^NTdy&f~N&F6XxMQCq<Fxro(%BgB*Y#^`ZgALG&^u`HzmnHI
z^Xp-&Mjt@1jc<Ll&53p2W2(W9Ss(fFh|G7?d4JdU3f9U<AG*L(2j1&8H|!KsVvS$K
zvIBwIA(Nl@$o$*<-TihE?LZ>d#gLpPw(vWE$~AuT{&u&&T|e~L1I&eW;V>m$?E#()
z<FOHoIkCmgy1}ut`gI&f$F*3uk66F_&0cHC#|<C{D7Lc;eM;ntdp91~CSDRt4#NE6
zf?upEOZs~*Yj*FIre>st=W8YFx_8<iw$S0iz+uOsgJ0cMcA$3{A<vq2kTlM$^~i`m
zHGep?xFF|Od#pR-ZY<!(8+x`_XY!DkWy`&q9p;zs<SBbw4ozP9L>vFn#ZUZ&$ITNs
z{xl~3q{FM3S(C}TZqT?;fhR-knGc6Gz9MI-#CAAs^4LiZeq-mW*r?`5d0P$_gNe6M
zew97qdcRHeQqHS<wOwVLHXh@Um#i87<4a?TFZo(QXU#azIDoq;>Qy)E&|JF+kymp#
zjxWsx|D>?fhfnyAU*u0WM!lY7o~@nS42nPTX!GHCcV29=TMj|TespB$Tc<hCS#Rw1
zR1h8e6nTL?#%T|BE;I)AS;zJwIJrYiRlm~FOADR&&P9NmX2#^KDc{R)Zj`V`-tt<O
zv3N4HmC*!cozWql`IvrlA&)r+*jMCtHp)NoF*!cfKDO!ek#%QG#^T1Gw}f)$pf~ev
z9cB)zZGC@TULxHE{-7s+>(3Xz=)Bg)o<8#YH~TpC_5x(9b!dzZz!+wA#rJ;O2K--3
z=)o9Kc`BVYX9>Ur6kZOnaY?Rfn<^RZ<eCicqgzapkOPK8ki(ukHsA6s-`wb?FU*mx
z;3YS9sa<S5ygfem_>9v9nd0iz;Y102@63M$*6nY1`yQ}BWD5+ouq`|S8cuMy$YBd#
zz|$u%9sc}>*2jg&nKNhl0m~P@QY33hk<I7Y#g{_%VxV>G^k>|l3#!|}me@-k8!U8f
zHYTXqI2L^9jwu#C*)B3HI&&r=C}bF$gRg{?WFOh1?!Jky*i&s8?Hl`{U4sW-YqV_S
z8+Nf#k^!^ujZu6^_`X2yeNB(D9e8cn;=_fmN2ccAyR=>(AlFABQ`_9q!*>qd?!Zfk
ze3!c%E^MuprNR!I5&IG+WV1B~)ULBlA-mD2&3R8~?ZVhWfZf(6yh4T#J8VYmlIT+;
zY4n}|dXj>+7CI{|;~wX^^7J*=4y6t^o><5iHd14A03pu?8r>N$*~T>4KiwUzu;`{M
zK0s?zFn_hU0J7oQu=z&_;@=y~LR?lEX~(|sW_{f8PVJX{TCO-|ee|3wWM=oqmdK2*
z)OD<2p|AKwUgK>4`NBB6T>PO+Zg59XTr2XF8$0~NhjOwDZu52a?Ad-Wbx?7@@XCgR
zt{-Hu7OWBRiWiML|L(NvJ7~G6H2>x}c5Y*4S~?lZV%^8ze8u-JTD`{O={Q+pRdEor
zc2n?FzoF#^G7>NRV2y~;9LmFAYjW&kO6(7QEl<}kgQAT+d}8f6=sVExn?ng(_?CV0
zsRN-Lf}T$N(XB1k9lpJ~!6)*w9OE@l7mVaoUN(du&UncQ4Ii+<U3EDDP5&u-jIHR)
zKh;m6m-od<3O&{ZdV8~1Z)&mL*(wLHA!Qt6><yd^;?<r_2LCu`(5294t&20X*^eUs
zn3}HsDfuA!!$0;rd^e1}F@>krY%}lirM@xoz40ZE&4~lG^MqGQ(YwH>-@0;P;$qXm
z-jiT))opKhzDrwe-+0QuKV|ciKlPJ+KKSm6b|Z}MtY7iddCIui491W4dGp<6&3TzE
z;zt2TOD?;e6$0;#_Q#kN<Hctk(l3X|&tBuN#Ts#L;+Mz|K7F-|S{Dg&oC_UKX?)mC
zt{ZV)Q$s_SGiSdE)Pz+z+HCWhl(p!_qjP{caS`F%bLUsM^X6XnyjPzKv?+LN(T{B7
zWSe>Y*><Mz3Ra4XG=4J|{v9#ymm9;|>Grqbuy&fzk-;9enx}g<-z}{VIo$<1+N_Vr
z=z<Mn)<<S7+wXu>VA_QEES0KYuRV>n>1)CJ-}0^i06+jqL_t*A{##3?OEMWpmO!(3
z;s8lNpcz~pfP^Z*ynidGY#<UR`a8BeobG`B;x9g`Ex6b3SEyvi*2v*l8=OaT_$3>Q
zz#?>2>#*m&J&v!193?>j-vg~BaRKQg;8Ni5(D$5YPgnG8z~`{)3Y*<FY-3ma7P~8M
z$)7z51W6+so&Zc6PwdiOD#52l!wxdo;v%R&<!H9r#Q26?NoMH78z1VY9enMBrJ&8n
zW@KL7y))+)wm`OkZq)jG7Qd~^I}l^BD>WOmQ))Y~L-k0n&u$xciVIKkBVn=`4ZpfL
z@WoWGd$>#Ey=j}W4O3FI>4D|K%eYfAc5<Km4^QrQti&BZN%RkBca|kdk9WPMK;Kv-
zXLD#%Cy#t|J%P^$F+A*r6Ay~m?D`vxaoV690Nka%^PTRzJ#4^9#$FFQd-iOPRlX7L
z(PzE#ixOXs`2jq*PB{M=VfyYq^MNGpj+C|U0z}`d2sXgX12M6~Vc7U`P$H{tdT7D;
zhoAY#`@{T)uFg3cH%0u~Q1=t&l|tr71C|neVqPttmevOw;A8h%zKzVPXVe#(DRuAL
z6WXyh4!Ox)Xnr9>zT@xo#g3fqLc~GF=IcFe7s=+v``=&l)nC&O1MUW*H80}C4W-1P
zC);i^nQwZ%a^MN~bD#6v&3Am~biot9vcrET$^ar;%sK=(5LlBoQU^^p0>rSj#ICxY
z#&~^EyDJ#oY&U-KMfUTa|GaH1hlX8sF2?rO{$_9G!8huLPaMNLSh@r4rjfnu@sE3a
z_o=+<HBmXDuD{5qlN<*(hpI=kJaX6O2TyRk(&1wb;59A<Pwc@<mbDU_(C`}`Yf>KQ
zuuFHji41V>!;^tuU%Vu4x<AZm``A;(l=a~IoOA_Fm$e{Aia+xkJesu<`FQEhMVcpI
z`py8(17z8+A~*6P8(v92__2mZ8{1N^@g9;_aoQPse8~@dDIV%gRC|uQ^<G7Fu`SNM
zs-?hr#Yx;-tJbZu%`@{j`TOdZT)lbpV;<eVV`070=ldsa%7{7Zs*krtN-f6BCTEu$
z%z2Ew*ekE3;HMi-eyK4A?)6PD^Tu`sE&0~6@9~*4Mb4y$Ol{|yx*pK*opY9T;Jb`2
z%6u2YFD;6F{LYB<d2-?VCiE%x0EPTs+OUn4OfK*=l7HkqF#eUt$aT>#2bsfKwz3+K
zEFZ<#@$t!n+7D=WEzwOGYXqEp<941(J`8Uc(EgCU2OE}2@Z?<EK9@DQht>z1eCiF&
zF277anRQG(n{-9ZLwklYrZC26brqmbNno{YA9(3S(;(sN2VY9y=?jX;1LFXnIMo3C
zn<0J`$^qG+U2OQmk;LT@x-X~+GI9ipM1tR^%EoIF-951>!ug_H?ovB!NuV~(OjhLw
zi@jtk@azpem|)K$qsv|E+yRgT98Mjk&YnHnlLLLZ;JDe%Z`L2L+XTSe^<@)!2Nb2+
z)B!ULJa&X8b*Gzy+U8^9w3v<KihsPKAG-L^_yn?pa%AC+oWRnrIPE38*zm~EKNcj(
z@t3;<XU?4I8>M+natMEqCx2#{^wm#C41cOkSZ$g$&MSVar^$%iLELuCfs3CiL*G~w
za5CMUmnfh6+~>47x@muZ*#TLS^gR!ca4l>bz5|hYjxAL-e?0xlr+0ahtN9SC+UC!O
zX%59%^jCg%u}>i%F1Ds`?sM1>gYH^8(3^k7BkeE$@-Of4=#J32bLV<&lPdw5ORs9M
z1>a-a>mZUU8zskT4|(RI&&lkoj2rGyAxHds<-kReHGW0A<6}Me*d)}6p~zyFJMHdp
zv5DO-T=?D^bqCmnf{!oR%tw4pmxA7%X0LkskQqNlPaS7`3s>Qx9qZ7)sQtSo)`z@A
zzIo01kOwlConNk&Z<}9Iw(T7#sBOKanIX}wTifZ+WWD;6$Nxz`Xz(*zyc?c2Ua=%k
z@fX|q+>Ig!VX-AA-}RO5+H<zp;Yap380C(wxRxKym3aENkNdd(V;}G?+VRmdj-%QM
z*Z(WN3Eq5*2N#j%-+FSuwTU|r*c<AhAaaZkTrL*xa=AHj$gpQP4BhqSckMK5wvjQG
z(9%;&A49X>SX030Ol+KOd&ilUKiObS$gk$Z;Zy7ypO{|rm#10Z_)Xuqd8NC)Ql}%!
zLDfx*=RdFg5@9npHg~<3PZ!9>N}rr<PRwg`+3U<d|C)1k#z>ZR)}Q>FqxmD2Y7S$s
zz7HCGPb;i{@!=^ly>gTTcg7bz{6$yfSN{da4tnK2a{J!Y;!)h;HMY<$@dtLOuJ&wY
z?N9R_dwd<cNB&B@_2ultjMtcapR(7@Z#f^?leAsbd)gr`z<ifRE|k~Ised4sKdlEe
zY#`hG<9Fv@Y`OX5`x$<?M7%uij~~}_X<a)tKbgyE?sw-Tve}uUk6$d&Y5mjZW}b5i
zo$R%S&6)2~CU=uVhZ}nGr@osd;xTcOc*>aQH7+ue4}8~1Y}vo3?=Md07B>&U<so*F
zL0&Iyo#;Eys1^BI?vpF*`)->1ULifsf}x9y^!Z-V_hBEHwWKe<dYU8mf;&HWi$^Xs
zPWxB*)%;ld(bMB)&N;n3+ue!VpKIE=(C4W2LB$-`ksmp=re}NWc1(DcaIL*0d=FL<
zR%AGAQ0x<D2dC!5>hy}k(`nj*@`WA0W?)hsJUmV`sT?k7Eto7i37B1i#oY`6AQ0VI
z698a%EGUbDJR6E904CF+N|2|-es-oE+d=~;02M)VwrviQnaxhXc*oY=a1-kF0yfy7
z+2c-J5}?KNm`6XRe|ggvjo9tVoShPdFG*SS&7*D3OE#OE)xOb2KDlx8my0;Q<O4p_
zw<s(c{&d*#7+zwbS0QUV*G<=+?oP4$6FN33XghuDMxHq`Km2AM*pU+c^wnTp-XyZ3
z7Tbqpux0C;GL|*pp|5klwP%jceZJ|7>3%j>OyT24|1O3k!RKlRDI2-OqSye$FucTx
zMl9gB=}MC3$)+LB+`&S}hYsWWHZ)H)+w$oO@~8O^oIF0K(*ca0Bza$|^&<_EoiD|~
z`$aeLn7hXAOp0-@M&Pq4{l;T|WApV7`TBm~yydNKx%s@${k$IExr-GOGB@h9;bJda
zl;FUvDZY`=y}l!th#{MSWXvwH)njDlH9r24GalYvw{*CYus!{x!yJ%DjyZD3aVJka
zS|5I<n+@)I`QkMj+#UY#5C8DKP7=%A*zzZK7to0&n%KHU&g(-ikQeIupu3h;9pZVu
z2~_FF_LhIn8}PBIt`9K2GT*+GXfA!9=q(=b7X25Jash%)%sRmPewz&&&Uco4R{_pN
zqPdb|&zw2a{cKLX`XmmGksKesl`F-&`1bKSqd7JAiIt2?pPlNI&;*u#;@&)8(Q-du
zixE!~<bHQ#!^0MBwmIy&Q!bXQ4>{A5GW-ro`L_(%9$VB(#uXc?joN{$(JFi+2KZj_
zltZ2n>t~w(;O}?e`}M<;_;qk|i1g)kYcnwud9lIT7w2O77B{~|pPSHnx7_^bvqdom
z^Xm2FbLY<WLmpo_>{%Q9=&<HXyS}f(Mi*rdeBc9nUdTF$PsDv>cLnXwIDQ9AucyjE
z-WG6&-hq=HZZ^6|HIKggVqX<M87G;w9UZBIQ#)kSeUH1}qZu*XX^t;K-_rlIe>DKw
z@Mrz%2Zkr`@K;=5V|~aUi6K0Zg?<T+CU>Oa?H%UIk2)BaAN#TIh*|MtU7~Z|aZd3z
zggD}Vb11*MP}P>>oe%g7jq_4&1U=%BkLbT}=HgQ6<7xg>*H1^5KXm6Kx7ahiYMHkK
z<Z5GfL&*8f8HmmHK2OE)W}mbM#h)MXk>|Z;>0(&>V?Xv|+V=(@*!R7nzWan9$mbKe
zSMh@k`Eer`KGur;S59K5HOLNr@DslLYz=yYhCX>je6mlm-pJu+-;Z;~y8Rt)zn5#Q
zOY7Yj#j~3oSG4>jZ@yN0%H*f3<x0PlB^TM3z0U1ysjql)tWCGVcVj#T-U((JZSxfR
z>;e2?4E&>zJFbt!WG6WA2i^7pq|)`E5iC|<DHx6xZk4tY`dX*W@U){wzrw~or$}I~
z?)=J%B<zu)Ns#;#5H>pj>K6*~9<snn@H=ey2caZ2heJW`6#<LNfy;qIaQK`#bEY2*
zC36=q7HoBTY#i&j&m^(OLJ*KP5QjAX;1--fRa}6$i=Z!<By5i{UGYD>ZF1}q<Q9gF
z?*Z-A8+Qu@=ZQnz?EmWH*jIfmnE2BIN6SBo;3v~Y%s&DhE;MAN2+m{55*>N;m>W7J
zGmAHN9Fn^vuhO&eG&zy69gm^RVb8XdJ>Q|3hrHzjDn6mt-1=oH^Jio9Sohxdx_3_s
z#%kX9Uo5z5=~1uP!Y7g0$lWbA*ES6Ac{=oq4Ht)s*G0sHWQOldi&gT(5<e=LGwqCH
zThALr9kZt~4|>pp`r7e`*hQ~@55t%wM2YuY`*NPOV*~Oi*#%H8uGu7}j6tH3tR+Hs
zM!m)`<`s4=6*kgY=K)=8H*V`za(6J%m+a|G!XIyPkcD5O_UeJc=X}L~?s&NfL}&f^
zwK}rgWwik)d|+d=iFiHI9QA`soY<+(|4)y_VJWedZRXU+25`;Q?Iyq+qbIbK_=O{r
zzsVU3*!ivxJbWr9_{1T_ynF3JUNZ0S=)|^MCQmse@|#2HKVI>Vn|r_Nz59ipwc$X-
z-{M_N@wWnIot!;;w#OX!W7`w8-)wpP>gE%N9lp20I*8`g4Z3sisbdCLjJeaazP#)%
zu!D)4K;Dh#XPdq}eWtA^@gVoJ54|{1$e{O8k9u^ECvsi9f*JRH?|0u$Zx2bSHbh2n
zV;wsK=TA9<U(^oaF6<umu!nUYfyoVQkW=;PwjTJY?-2`u!=}jOZ*xeuoZ=7%Pj_sW
ztL?4w5x5&*E()wE2QfD%ytYg~f0=U^ntrC<8s$Io6*7C=xAr(*crbRKY4c!hdd1U|
zSGrt)(r>)-kbL9y7VSRPSqC}C7JTBD3_1P<FML7wO|^GVR#{dV#~!|Q`Uu-N&!@!K
zwKhje_Zh#PBD3O+Ngm-V^X1<P5f|35H6;hiVdB<>v~}p9tGK~sO~5-}d0of_pE-73
zaz-(~UIF$*-#T%j;zEEQ?HATl<~MeYHC=&)){pX>YZtlx`GLQB;$QWPS!XlzL^gZu
zt6%k1U)l49c<@uv{_#Y)#SIS!d~o^6IE?ka?Lu8__PjmB2|xK^p^y5gkLur{;0x=*
z*xZ0q$f4hlA^5J08&~Eh@on8Xclw8ijo&{aYOiu0bzb#mf!DFbtP3|b$yxSwdB;9!
z&U~yFYfikIFBesB*e?F%Rk_!G?WdB_vCG9GTfwyX)W?Nrk41rfM9Ey>QS_7Fja}bd
z8+X>n5n}g9E{>8t=*s3sNmaDSyC-PBgPuceq7s{{=*UnzM0o5iSObp)1GPz**bLY&
z5ctw7n*t}p#f92G2Io~5-=*L<li{vSbdc>3VbUBB9kL}-wC=RoFde>4Fk2tqE&^;s
zg4tv{7<%0+i6OSa2@HwXLQ#z8KCgS9{`$(@?tZr(bYN_^ky`j3CkkW>z~b=lHTWXE
zjTOw@2)0QO?yh+q!R8BA=OuQe7PS1$@1bL}L$8A>9|(Ml5si-pcmH$u?*|8VSSZF7
z{>Wad$N57CHo_y1ZVRuEb4UnV9c>+q{9}Jh+ZvG#c8IR>*q=LX(`5tY8wo;urSRRK
z{rU9swd}41PW-yiG!{OSBqcLXk)H9)XY`m5&kic)o^QYVyS{sy#Kf1oi4GG_`s*ik
zztABL+)Z?+hiwiYbcjP23KAM0veyN<bwCyxcZi<$^ppRPr1-S1p7d8w>T%3o$<D>S
z!x;YD0X8<W>H5M4Ke%r`#^K4HSd<XO6aV3Jv2Khmo;+;;^F{WBGf;J5Z<E1(iP~nD
z`KvWsts9%4m^Bw?+hNt~lXNLH2H5Xo1NOS60~Wlsev4b)V)NiOCO#)uaTnLclr<I~
zScBdRb{Kx>Lm$$0ZDY3N&Dhxm&3`Tw#zs2KZOtcDwmqWDA8e@O-=lAxid(X6q}Rv8
zKVCm@cU(Mpq9FzpxO~_uPQ9NjPWaGa!K<_MnG1)>GiRos#dJaFE~E95IY0yUiJ$O^
zJ*SfMthT<a8~=EqJ;3V`4jS?tdNw}mKc3ZNM_$X=;KZjx1KWJ$cu%k###~g%rB}4O
zj1CWSFTD70SJ9zLTwUER09|mS^UBu!?|;8;54~tz$aqcB7fTZxV$<HjR(8ZDaCY0T
zz!f-a2izJ^_``*exD|i$5*YuZ?fz_s7Ec=8q%dy2;(NJdJJ%&Xy5RYbeVKkw6Ztm#
zf^}sKSzqa6qm#e*%8xYgg&3t<fqUxHp4wkEai`t>%VxHa$sg7?7+F5y6JPurpWr#y
z4x*m^n*;ecYg`@_NB(UJap<+7r4sqUjeT%>=rjfwA85eT`GjBHptXB+dr9{iGun(*
zuI9J+1&=uqE6z0)4kyOlMC2oK_?oZ8vvZADBHNy2%+40}d-LVyf_d^&=FUdWK4ODR
zeCF2;7_^Uj{Np;@8j<tucWz9HcZXr~8UM)x<{f_u9NWpaSBYtUzxtZ1drtqfPyMuh
z0qY#rW2kK+^oTe6%e%eXyZ3Pz%efXO&VFQ~iSLZfcU8XRi@&7H%-W0%>ddtZANzzS
zdg8YFkDcUNn{H5fl}es49{J9Gq;KEiE4B6QCe1@1`p|wB!*AX^`Ed4ham}ann9FBA
z>zVxw=&c?XFnmuwo#slMyBL=b@F>P<4#|GlH#}@Q`?j+!e<@?E^3Pt$9#ViKO<f=I
z%ZLx_2;b|^YRS5&C7PFNtQ7;dA$T3ZqBr4^RTWtI!PBqpzD}DOlTHq2Hudcv6r6s#
z5uBq<E`C?)4nkybpb11nw*!GIFjsWG_~Re}&o%|00I(6bxS)%#FTM1Veuq@@&N%3C
zrS8g=42jny;&a99&XEbtLDPYUKip|Wr`Q-=FerimJ^5^RxMXi|#wLJbgFaq%(524@
zcwGcoh!!k;Y#`s*B!}%rJx{0r!=IuZTfq3vl{Wu8?|BY~yPrixyPdBl&q2$$j8{nz
z_s;yM>Z)*aonSY|{wF(Si7)s{zeLw%6#4pmVxe1z?#Q@1EfIpjZDsQfGiDcMynA>8
zXPPYL(3vA~VNP;EBH@S=dK7acj_|;lE3pM{!HQM9-tT7zAK&KIEzWLt+de;HwLda%
zIP-2C5|ud<&pzUV90|)9#R0fDvW7AqcCmpyi5;<M44H4SXkFofV~@62G3WIA;P3Hs
zi}tv_ZAiui&lY2YGcNiiNp`WB&%~;BU-NUE@zprLu^XIC>?U8K&zjeVr;}f;Gn<5e
zAWCuZ;Vx4yn2jHe$JL&2vcraPu5D8M2S<(z3O-b#)0nicyz&`sY*T(=BY*R)Pw2<7
zbVU1;F{O_VOq^$pY;gs4LSBjajw^ADzsqd0)9#J-2dxj^+kj79A7N~DWM&MpN#ERv
zOECVESNK{9y*9j95NF^%@>+bSFV3eN{n8E>pM9QZfB2;-he7itzIq%seb%{>wRrhu
z?H?{}>xf<QrTC<aZEUojL#r>Q*vtmwvDaCn;!G|UyX3GR%`I;!2B)9D7N5rE-!*yu
z^V>gy*w&-Cr_X*O=JDiX{E<Vi_#}gTwJ~L1OYE~>oF`|;m+UkScx#?Md72FQ-fNIv
zn<B@>hzqG3T;5)<Z5RLKDeOuimn=SDqkhUr2c9wU6WbL2lo!p9G5G~Yc?{m#vS!Fu
z$mb99q_EvMvhIu_2hi-FaOR2(I`Eq-vheaZUG5_1S2DYBf9AE5ajlIBDVmC}u#ta>
z7Cj%cQO<POSBr7;qeOn>1-7{N&FkF!m~*lez89bT!WZV6TrtGg+CJt&+_Nd?l*B;%
z$lv6fL;TrGz~Xl@oLRDd%$b{EWQzO5q&Z?28RiPE+G}3vBiA~ok3Bx-lYDkZ4xj0>
z4PR`)A0PDnrNy_iU}*V+&&eYfoXys?^@krUb6Dqsy>NZLn*ZH2B+L3xz{MPS#>$Sq
zx3zrbf3n$WkJjcRxr$yf&sSv8Nk;M*U!ga4wDu}KbS5<p^Fgom<mcrtY3EsRvdpvC
zkbjJk|FyH{cEj7BKIS>EX6$ga9op2RZ@|e(8S%j_$rR(xmTWfO?FC4fMxmS~pk06%
zB&GyKXw^%~mhk8VQwj%hx&S1%52lT+!(TcbKEO;08g_<<3`x#JfSaHWn*%xvr~}&{
zK64R59-W+#FKFqz>UAi<mqnq?E`5b=u-F5J7c4$R@1t~k+a0r}s<+wT-%p6WlMQT|
z?pA<D7rBw89r<+A$CmD|rbiuoZR5{(QH;&&8#W>`e8SU>Eq}o4xVC!QK*&i6TtBj^
z{?OGrJfW|+)7p{EZ?S{FjO(BOWjtUx%(uC2aTJ~8N5+~i_?NVe>{9dC;w42gU~}dK
zT;DuoENtP|Nyh#^hK3KGUwsmPXse9q%lx3l*T>m(DR7B7ausyZtuF~H6^6$r#ag6a
z2`;wN2M^xuZFF=QD_@}V(f5fgwEA%9<G1JxPx|V>QqzrB--my$X?HeTv&(kj0On4b
zR}<05PacPRHyl5^$*}3U0P*---y?cYM)9Cgqi1I=Z3qoM<tt<IQPAQU=_{|fRm3RT
z_+ticW4HI)A2bTOTGrrbI<Apt9fWV>(dVm^7w8whKKL<y;1}}MU`ocJ220LoOXx#m
zqm0db8b6a0dNE86Jz(^QPFo=_c=Q#97A)}52KIq<d)uC3lT7@?w!RM9QwBc_kV0R^
zj6ZGTz!zS6)5ddclatu0vLYk?&6p<J9Db&C5nJ;!z@GS!>n^zUz^C?VccR@TCC4X)
zE$OQ>M-`5C?1x{?20lrf&Bh59*_o%zODArB_?u32UN^bA`*B(Wczoc<^0A(>mgoqd
zWfodGYU%zyVtrH|?atTgj3M|nUsQTL>{+`2IaX$L*1G$w#U?-SeSBJDEB?aAuJ}?P
zJ-h;kPXS|>`S7Xp+6mepaI(U$9Xf6J*wb-sPOTwr1+6uq9~hl@;uA7r6Q1N(cpu|u
zHy(u?JjoewK6FJEKK;<QZsXtOdGyhrcIwX5{?MyLFFg4^V3FU6+8=o1=)P+DoH3O?
z{&q9PnPT)~o6-&!=gupU8-G=L_Qp5PtjSe$(2XbOJpI5k&JLJqwZXH#mT-ZoBd_x`
z*;OW7?T2rK^P3-$@+*No0+*P=T<g%KUcyyaZHLEX<tiSGaX#R2a`?a%xE!WV;M!ze
zd)0J^2#y>R*k#L5txU;lKZxM3xoUTa_pcu-9X<!SK6$af%WQlCV1#wvrjKr)Iz9o0
zmnFtBwI{o-y?U1vz^mN&CG>Q}7ZnyB_}Z?vz?pn}frmb_Vl%h`j%O=#8p4EZf4A^0
zWP;&a!XaCO7ZH2Sx;VmXK2yMIKk%lhQ)O&{M@E$!T(A3FwODC)RPxh$;N<vEJedE;
zO^pW52W+dSwS(jLN?+ro6SY6#O_I?k6K&>Vih*WL6CJwfh+eb`+7Z?fGfo?xo^D$k
zKi;g>4x4G%p;TD<OJfOrXPW=SmL*tliTgdf+HCqxjq!Nj*y|W>j7au!%1v&1lm2>G
zPr$REW2YJ%t+BDSmh?jp$G7pp)Su=NEOu2Hp@FOMm@%audGs#?x5fi*9R3?GzV~%s
z`1(lxi@&<QrgO9j9Bj<_iOq^*+b9Pf?Oy!1n*&!}JLPITnLF*oZSJl|o*JDGnStX`
zl2h?teeLc}ed4$B)Um`iGRPcb1D_K4+LP{S&a*Z%PWa{6-^C-F$WZXP!1hae-}}Aa
z+rNY1&aw-1cZR(lCl2!TBlf7tsI_qg--Wk7(Gy*PtEX{Hc4)J!+J+|m$WhZ#{SjHw
zEw+-=!k6{LH{pfzUyVim&_*8ld&QJAvny@IFY(2WN|!eJL-<OUcIqWv!POdF#vOgp
z8F=UeM^}9bUvc=-hcj2!M_}sMSLYTy^MfzzFZ5`8Y1Q7`G(NQ9MembhZWDtQ7aDwN
z<5^nE@r^ou2@m;W$@sw2P954&4mjQ9ku3*Qp2TSMI^SvgScmnzy7d*A?Yxn)r61N*
z=p)-JYD)Urbw2c!YTpFehh@zy;R1_}C0S?=!SPmn^i7w*(NJo{QCPs&xmjhV-BdMQ
z`%bKiLRVqha~|2+8Jkm2G}8tU-pGnG(ueQuLVf}lAv0~o2gR|0r{3ZWw3Wao8!SM<
z*-?vsLJwKTwn#hx!<#zj+FxsjHonrY?ckT{)98Y)x|igDC0N(+OYq2F!UeV_H}ECf
zf|GQXO695T(1Io2BWt9K?SboSPrSPr>fzM7p{qL5R)@F3(hhHG@NU<F+=*vDR%6>J
zugjV34(@#GLw4JYJeu}n6rOyzOO-E-XQPQ9>Gc>sUpn_=5E*Yak@QC!!N)P8S);Ey
zM_CurALS)>4#^sMmS~Tn=^6GUxA-vrH6NVh*!e3w(Vu?h2`;eo(Q5lF@u3gx()tWf
z>d03ImUsv*@YIzyu+(s)ZJDo16WOWZM*I@2(rZT-T8AXx2k}~rw-&tG;c1|Y5Qm=7
z_eSUKkaT-}NGF2P#xPeqDr|`-^Rp&vi6=5gTx3;va%$xn+sSX>nV*qn>{neQPCxjy
zaRtA`JMr`H8g}3JRR_7XKT0?T7a6G|quLxf6&AXR(@uZH>o4I$w}y|-DtCgN)CC^8
z)Z|up)ptxga$-kxC&vUHS(RQpefYFvPx|2<>)=Cw2oHxoJR|LO?T_Oe`-kL*R_)WQ
zY?IR7&gh*(kzb@*xDiyqM>oUEK~-;6Uuo6^ho-rkMV+iVJ@QBXkp{l`!rwk=k;^$%
zM)-oq5tw!@p$WeBwX58TZ?|CdLsMm@9a-tG)m292iA^J4=tH|kH}Wm@CwK0TX^E~m
zLI^IF(T*kiqGwS+1LE27nXfg<lkheDBjrdp;`O@?Jx+VClOzV>=g8?ue+J)-bR$2O
zE~D^Onc+uQOSi2!ruNgWa_~l0;OSSn+QE<T+OKPG<XMsn=78w+67RPA0Sd)?#4bd9
zJmqkgDE!*qrS-~{U&sOXel7bZ{awUSf6y0qb)IRQ^2Kh<bb!Z(_+o@dY4G~t32v-M
z{lP~D{wkxkL#sybvxcv<W4r2#tR*=U-Q=55M*5+d`h8V*o2qTm7oH_uaG`^*{97E~
z%tAt|TjkaEl8oSkt8%KWz(YT)-BzdJ5sJ6=cWYmGK*{w1yWtIQ)V~ZY{uS}<ORDLk
z8SQASr<Uj&c~50*1mUT=tGr|2(Gx!X$Q|3EU8}QSRQ^fE#1S1MKJtQ3f8ssqM088E
zm2atiXg(s3ZM8khfm^d5ZRM@);Hvy8yUMKXs;{=IytLQk1%ASwiLsBT$_^cL^;u}D
zeWUF3*XqcC-`6wE^C){u!;l5vkMvdV60Yj6wA!IpH&rLf1mDl;Ro+rN_D6PvuEB#}
zs+m+*L)BO5lYyJ19lZAhBuuF*V{ey%$H)$EPN~Yn7n(Z2I10@gKK!XGUD|N-ymzt+
zukGlru(T)Kv}pHXljdsc1exIultUDZw3(2=EB&N*8h_d(SDLCX@G2{OeM)9q0;{~?
zsW|d#zv^CtSD9nG@`wXA#b&hd>DRUij4i=;dj9p_f{)=d=Bjt3MYk(tO})yq!|&{c
z?>5fJ$W2}OYdf;gb^a)92xFOG6IEpC)pq1{nBXq>;6~Y<D1YcIz6KuYBPV**fsM4`
zS64dywKCGxzIMu_bJq`C9ecDZzkZ6Jh&+4tY{z@8s=ssFz9W*0kMOD8sd6Cm>0@lK
z@bt&sbFhwfr5}In?AgT;J38DvH63MW-Nf)zS+PB^$f&e%wI80PI&_hvW=A(EomQi(
zI-zQ*xGHmr2ENjSH+99;c4VOg5B&rWZ0;3jR=UU&HPh;t;saN3CuL7E``_6)qs+07
zHZb)e*})Yp2b)!1^h8E<f={sH)`$4^6Q6$Onv6@@dho9n*VjZw*|DR-cY3vUaZ_p7
zm44bw_J_X>WJ}+L&VNRp^r0hP8$390pV~*WRze5g<E6<;>7FgJ0#6+q$VfZ5)PXP2
z1cz4s-|IKMDb>cn)aZjtJ<13?d}F_kapVn8;3F?Q7(6*VS83Bmr@h3#1dmL7%GeK2
z*Vph>Pw3Ufg(qm>mGDMKWKY<Ae1)M8k2<Bw0}IWbF*9*!E8UiUrm3=L(AMV47hL$q
zI{0Q#cTCkCTy!nrDhyrZgNKitk*@Z;p5m*@x8?1!JLUAJnS0_pAXRAE5th1$n3_FE
z6Ef4pRGP>E)2?##D;^DeVA`P%42?4OPlJv0WB)XMcx76WQ`_Ik-d(@9x=#jJ>(W`r
zcbW|uJ4vCf{8dBn6K((7(anQfy`dlNsBrD*i2le9JoJsSH-oRdp{cl~c7@?t^6vzP
z+$Hbu#YQwKOE5S*bqph2WOmy2r{du%9?dAL(!-~WeBg&*BQE{a;jj7wQ>Rq=3afle
zG-$z3qYG~xcVJ{xe*Kh6lXjQUrdNNISLKA>(~iLNK6CofqxL<B;Ptoi=iCx`!HK`D
znZVRzseHl3?h!ZkL!Y|hYdiR*ItPpq*W9-)WckFdAjW>+p^XhAUErawc(^5eU}!6S
zWT-=PEUXN%vxGl&WYRanj?=N*&no}OJIb&4*r2ZCuJYFSD^1#=)vxmM+L-mwb+$kC
zz4gl9@r{3}O!7O$g#PcoKFT=zdhSQ*{gKBvZH5}+Wc<e0JGy;-y$L6_)83zNy6b#Z
z2~}=r0!tnG;G=U19~j!cGY|0}Sz{f(!0~Tk)^!lA4!qI?R>*pDgpd6#{p7qw9>5)6
zU6)(#6MUnu5OjMrN?c6cGP*MEjIVpLI}`O(CJ}|Nf!P=yRA~o3aqj=!jcZ3Aii!hE
zf2>14j;k3^P$zBOBIgEsw`HD>=-R^P#u7ZRjC<r^A1mOaYwroj$bd+4^HEF$B~e7&
z9Oeysa)A4I6cc^;yU>L{!7mL2F2*mgo&ZJG3B(K-@v-p`{iuJGvBn#Cr9Y^z`M=UG
z@dq~6bNqBL9scuwEhl96=$I&iQ`hkZzLV1=pC)%ld}?ddhvB5ootR4R*HmhO>z_98
zU7Vhjcbfbvb8LrqDON|?)B2GWI&;?T-sRk&dc8EKm0-(XfYFh<pcb3`OU%uR)5@B>
zHF%X5-&NY$MmzSwa`4h7BXabW@N9iJfp_sNK4w_0YOGZFl0QfMqQFWtv}5A{ZesRh
zcEtDoyzs_xFVWJowmwGs(WbFq=?~%11%Hf<Rqj$dvVt3ZH1^TqS%ODK>d@?TEJa4B
zclC~b2U}{b(XGL&jQ9@iNjoRX;2MFAdKcqG(5UOQesf+IS48yY+WHvjVrzv*=Mrv@
zu4$$x^cAOl8h@pa|0*o)ot*Xgjty1Lj;~0XMWD;MNjrRz6OOTlN4r*z;ScYYc90ia
zLbq0L`Bw<ws}Isny~1_^yw%g>9akngd`rHH{;k|1bO~0_Q?u9+dNTc=5_tNxUc)WH
zN7&e}a!+e#+<SC=j`ko2JJ&@&!T6Wvq$-V$?fPgx&DdSL#_ZQbyx!nFYi*BpPg8QN
zG1ais+TC!8Rd&6Al7Sy%`!V9LZ+VAwUKd?>*T#HZ<W_sTob#BQ>r3xwgD)EU&27IO
z?{~}V0kzVO?L$5qX;14XS*`i}G=BK&+dt<!KgZa(Msr<s9h!#=@ySwBKBQx5Oy^6#
zB;&sV%sBpUeJsz!g~Zmfq|>2Y*Lmd1D)CS(USDH4q@&uB_W9C>?|*VF_xm*``;UJo
z!kJ0`m_6b8_UCnti|b=Q8|V7^9^)v#_N%U?{h-p;_Gx}Q4PRxgwJ$_Q9nYHF{|<0J
zBGa!W+CNU!{~^LPXL!HVT~PnJXukEI0a{QA&^=f=^j3$B?eM6_#Tk2CZ}dAMO;f!i
z(sj>zwzNe~bOjgrfk$_@A0I`nWIUno!1>Q8>jW;g4&z4`c}t5Ru*hdh?5R3xJG7A-
z+zDAhtjMhXsBLW37hT_ijB3YfyGio@XlvKee5C|8+R%~npWwQl;_J}l3&;}x!B}m)
zu_rRgNW05#{c01u64=z6c+w88&cPODoU`84aN}4)PiE|i{INb^XB$-F<}_UNR2`jX
zVUDZb9uol5v~^TuRl3@)Is%W6^kZvqYx4d-d)EP=M^XJ>?k>Ie1OiD&XrYHDAWe`W
zNS8mLC`CY;2uP74f>IQvDX4%5A__|HAiekATOc8!r4W+4%eCbHesA};_rAH^@B8j@
zcPZ>7_sz^}^XBdB?7W?_l;hhac@i1!+9tTzE~J6{a?<2y?0E=Fi$P~)z(~04t~}EF
zI+C6^AG59rtkm^!eGhpkANK_I$h6D-;q;(gP@b<B@?L-9c<rf|(Z&4$(hP~+;<7wF
zXu>@C7$<spT~pdZD9g(Y)3|qekFgvdTYXJn3=QbLJo1^IDReB$@Fdolc66YqJ;yR=
zb5BPZv}pk@#U>#=$|5h*zI>!TPl)q*PlvS0#f_5Dr+9iV2Wi%oa9=ONe9X3201NJ!
zwDMSvmrpv#3gNid0OQfI$S3r?POi;^*6;z2aLP|)#1$Z%^&vil0}mYKk>1C|SsY&v
z$_eFCmgf&?hzsGKH%x=am&r6``Iy&|@(mZ<M}U!zX;dvsZh<T?1oEh_(Yb5o<xvJ;
zJ`Hg`6v$EpWq2Oacs`~*&Zmh-?AxuULE49cvbA0aY5iOlU&K-P>+S9_kiq+-#S$#9
zB$)8PLI6V;s7!(KN)+b7N4bncSw8ROS{epH|I{Ti8s&SQFbx_rz9KxmPqQ5Gnz57z
z<)G1d9Ps3$TwI)x#&k3R&HUs8t))>9{YN-t8%uoAnI{d)V7ak)Bl9vy7m17PWJLUl
z%Vjo+UAb09u}&(s4S20Q$D7C_PUA$sSRLv}IPxqL{KOLum=EML9#~QNajj^RQf<}v
zW1V4N3(QlO7*4%dCiU>TQD4#~I2E19C!@yJW*|Q<8Ys%AEJvQ;01NTtBhK<HH?9-)
zD!|1BczH~dKGfa#V?M8YGf1zazaQK+A&rk&URVxkh-aQL^{|dn%K#m9PH3P<s3hzi
z+a!r&29`zHl#>My@sMZw5TP+;c|8s7>_Yiu^Lg?UR!~P?V^<Djkk+Ruhx~22KOj&8
z<eQw>w!GdfBcf`iBM)MhPZ<!=;u<+R@GwxmuV08mz6GAw+mMRKvjgowgMW@BIsuOU
z1VMR|v}T!3>Onz9+<d()8;gih%B=sjY}yNOt9wA_+Yrk$-k>bwr=Ce6QGzloj_W|3
z15&FKZEHn%dpTn2lF-JuEI3(-FLr-`*6Zif2|opDOc-_Xd8R?mH0eCd(y>j6_vJ!g
zAG|DFN1q2BWgtx)WWdQs;J7Kl0f;uC|Hunmm<L&+SF9805*P@Az%mjV<zt&LoU5DB
zM2Zwh0o(%GXFyLvmc!VWXCl!)!HX!C#~3ma`dA?4#M#J>%Ae51vdBw1)Q!A|tqiBP
zhrzxKUKhY9gZP42**veOr)?8G3mDVorj<p0;;0|XB^=_33yQKV=t4aE0?+H^QBDCY
zHdWC!X#!mxalKrFp*+^Xmk%=DA6+m5LM2E<W)ClxrU6rF(0C9EN^y-V!e;+w@Fxtw
z2eK1fkf5H74PAo2fL0mO@{m)u&9pp!<CqR@{VAcl7aQvBaRuoV{mS7jhbAefOuZo!
zp{Q-kl*O`(>6wMgf}2cNekPrxp&mtJrMEVs4coVsO!={}5TBL@X;FJR#X^3EYIU+)
zS)fBGN5_yh%zGuvE#LDXT`pg-vO}5el@H5Ibam}54OXoDgg%8R3zr3l2#@pW6!B$)
zAP2!7ka~x(t(1}<;?wdb7Xzp=LdahROE!tSX4yoFQ99oz6wn4TpdSM5Q;fG%U9#|5
z@B+Ow(?XY!pab$P%8c#fb@u7DsF21_7~?FtZNf*F6pg=lP`b=^Qk=5|*7S^@ML;I)
zsRd;-Ago)M2W1+pSwhQHYw24`XgvgFB9yFiioJ{JOC00YYnIJHoy*cYO$@4Bs(q#K
zwWv!hrv=>cws1fkt{_=VNC9C7W)=q>(yABgg~kflk{tMO?}$2t_G<4k%97v4o2*=2
z%hI7-VeLqlrdJxQ+#(l;w%(CClxv%E>B_ZhN70#{c)FB&z~uKwatW4{Ynvz>Bo>GJ
zBT(sUlU*#@P6yx13w1HmbW4LRR3S?qbLKr;rfu5X?(ked8J!ebuB+05QF;9Nz$p_C
zTiA41Mk#r%r7tC~9DEB~6q?0EgnHrPw>E?_O7XYQs}w^k@pyaULu>W(ykVNM7>D?@
zd{|%drok8JcOHE3(|`%QkR$=8CBib&XhInE3*jA^FQ?2jnlu>oOv8CtIc?q3Q*L|4
zp$_d?9%Pjx&-ag>-t&8Sd)B`-vP_@g{z&D|?~lYRt-Wp?O`j-MW@2%;KfFz>4aVb(
zFkNPqmKZYMFSM7^-j-#_Y{k7?mb@~F%F;7Uw=`HrnZEsGkhB1orB@5CwgK9rA4&@E
z*`#!}O*#7ecIWAt&eW@L*q0RBQHg>*AW#YwXXU9Z3MUL{54UluYcQS;v;_flDHWtV
z<1IqtWm8u#zd(*6e7>AF`lN?UmW4Qh<KpYTtf@`%fS!Dey-dm@J#apdpD^ahM|j9j
zJmIDCl!KVGNRuCFA3#GK>5=zo&?k9Z?D%?<*PH>@UI=G732*GgF%NuFmWgdHH{@lR
zghwxYo39DstPAr#_Ii<yx-jqQecJN^9_G_vX*BkhI^}*LKVk&x!*+?hmxnazeA<Be
z<6)l9<5>n_h&`Xrdz!3t8m})S#Cti#aAq?l1`XvEOH(dsktaRNAuMXn%@>4pz=Pk@
zlZSBP$iuuZ7d!|aNBRg;_)EG%XmfiA;gl7YNjcP&d?6j-VN5zNC#3Ot>OokT2Rwv@
zdE?D^R*>Il^oRPfem?fJgabyNG<+5gv_3#*1ky9_X|mF!^}I;a4vf?IL*9hO*~{}6
zO>3jKXI|VNLl)^-j)x^>IXc2TeOMRbLfNc8WgsT4m*MeDla6`BY4V5<=}DUf=X0f)
zT*_b>h$)Zy+w)4VO&YD|3)5ash|9{S@r1B2Z?at5Ns~>Uu#Y4y@JW3M)EMxJF8Ut4
zxmzu0U-lw21?NO0N$Nm5S{}U7ZJ5vzPe8~AILCRG4(?;}c^uNAT!Y2;56bZM57Sw)
zylzaB)|W?I7R=+BrX03E)7RbS30@Ov`U208$ZVy(yG))uIk-F&SzhcFE03eFGHE=N
ziAs5cdRoF+aW>Q>?>cMn5sq{VUT1Wc>FLQs81-Wed~1T|3w42<5YKYc${=khhj8$)
zPNX3Wv8O}6SV*HS7e={h^uT4ovv8i?r&&%~TrQpGEyY8;@31^S(x5@`*O7dHGo3}}
zc|9$!Yk}P82@qX#pN{Vb$SOC4`uKdPM>#UncnAymSr*`|Ulwi1i#%z~DGQ%KF#~Bl
ze;Ul=kuF9P;*dv3>JUGW01Ii0!GJ@c9Hhz5GD1A^2&9X0*}vt|c?qR>O5q_3GzhF)
zS{}F%4xSLkJmn!zxkm5ri4xZW^u+?pqI{ocxr~d+A&#_+0rzR-6FaEM?3owCfD35>
z%L?VtrqQIq!ZJb{pZ96-A&)>CS|<JpX#viHk&pHXW8l-u06dhL)@M*2X&HO|5C&Yi
zWrDU;fP923nWgxN_j&^6^I2)qA!hm1hcM3z7=qF02$N1M?MN9BRe8T&KFg*Yk7K#S
zrAa4!DLIry8I(zW!i&Y8mTAVGFAYX~77lPc-e6w_w%;@!4<lW|7xtAI?aj(mCfI`V
zd`x+?74wKmM_Abo2n!=4;xt%m@SfJo3zZD%((=W0p`l3w`7DpHG?}C!J@W-e>SZy;
z@U*%R#<+xt1YL|i4VMN>gt<g18#vO;BxE3<Oa#jGc|(->Q;zN|qm;;U>59o|p^sL#
zOGV+}rWijoy>&p8&G$Y`2#CajLBoP5h{V#dETJe!OM|p@Hxj#aN=w%Q(%s#l)Y9D{
zwJhDR)XVez{NBIsd+wN-Gv}IX&bf|5t}{M@B|cy0j~@R7gEyJTszuGCsp=_w1BdYw
z!EsNywa7c5^Ak1v`<tA)GEpVm18YW<dXjyKlIuG$xDkim18evu&Ywg}IrK&@-t(~K
ztF~q8I4db##?@p;uS^8j$!EVM<ic{JkZFxOs@dQYdjH!%JgaKLynp@t%;Fy+LW_LV
zQ%KGe+Q|JugDj(&KW=E7|8&y-+VP(<MC<DN3`g-|6{>Jh%S5@X_lEaa%b{`8>^RX+
zLKMr{GD43u_P*y5zRBbDPN0;74Ok+%tw*2K5XWz9AGZM2rV0_$+7$OH6xWq#VsrbF
zsLpTRmk|iqe7O-eS?j~mcU1)i(}(R*==27NBJSA(c(Dgu#Z$DNsqm^ZIQI{;54*^4
zxZ))X?wtu2Bz=B8t*?=x_humH<@~qwZ?YN6#{30?!#NTzebd97v@lTIJFigj`X0&q
zZ|Jkv#pa{9<OJ#F;o<^*-3@`mCMSW8^S?l@``XB<1T05*ZV%S}9No{_=G-0whtF*a
zF8d)?8Ws4o5fy2xJNB4RjNx@(0z(GT2quKlTlVh(yjrA8=(lh#_pX+0{M`+QQh9RL
z;HCiX&h_gMyg@sT6Z@0ZAtJ2P9K$rGM8Q8Bmz;UNtbY~1^=ys@ql4TBw3BA_HS#3x
z23TfJubf$y2C`~Tbl2;lJ+OXo-1~YOsHVa=@js5v^%zNmG$t_uDIq#V&3C;M0fHN0
zYt_bi{aGd7PoAe-(H+%@roU~cNV^S3WWANA0vH7FFu^Kk>zVT;yg<}LyniIxg5T~J
zXm<9f_L*%2Mv-B=&-HAfET7PxseH9#XDmtVRgvn!dH>f%=I;kv9bgo5iMWCsbr*CQ
zab%w-yCxkhC>tEj6j4W`2V#~}QS4!8(-bY<n;sbW)m;^8Cf5)Z7M~n=dNOVfftv){
z#M%Rcp)D)YMpq5xvIZ>f|Db7k%*TK2b3W|tKvQ_NDyBQ;ynTbAqC-^$0E>?eBWAf1
zZq??PBjeqt(LD&J!HHTXc`ciS7g6NUWOier6yq0Q0^Dpx&kyD~e;7=&-wsZ2?>mUf
zL%stpEqF<Qnsf$m;vHTZ!<@IYt6zA40}{)pXkLf7<REPM=Zg}P15+}F3BC+rE8WTb
zUiktpK5YN?@*IO0BN98SNVBV}v;gs8(S4OVXuy6?l4=`Up6{u%oG0X3IxGoIC9)TP
zvN}9Ib(&H_=(P$x0$=lP`X<=-(3ygJY!V>w$eKU7o0#i-a@ho%xnp0;`W?k`0txe8
z)Fd~UG=EB0-iasWZV_dmbzq=HFu}o`iV&lJl%C+cPy(Y>t+qrd(SLgh7G++(6ysE*
z=h6Wx9EjIE$9?%jY)mGxoqsoStuvn*r8HFNFf*+k*Np9tLia<mbw&;+46o^Wc`Ycg
z;e;UuE6*?SHaQiFsZCOsE9J&XrM#vijyM&;f9QIh{bn4;(Kf4Et{<tH>HRnJ#z@J>
zxfCIr+=@#SFGgl3xa4&xut!c>ASa~cC)W@QOxZ`*j(A<3FW)ToC6O#PvFbxCE^E)O
zdvsn2uTTxPy_}~<#)XN8M}pKm=OMx*rK$_E7A^sG9AWvON$1{`#4R2rwna)UcZktv
zLU&mO!aLDa|Cr4=V8I@VRw6&Sag4q&iRn$0_EOwFF2F;7>U#Hr3t=3NMtk`{*svj5
zzMDK3&T$eVP|8AQx&n(66~#=L&>uW&>FM1JNm+^=3JHt}vfhiV^uAudB(u9;Z#6$f
zlXTe|&USQk@LCLn7JeaPk00ssbfYTWDg2am4+pW+4#=E17|ES*@tnTHvjB!*v$5_w
z;1m5ZcbBI7<bXlwdp_`+fvzUUkWf>-?-%eS$Q@KpF+35t^Br1a$CdI`FA3Bmd(EII
z<UU|iOnAf?4Rk2%;FjJt`Kmw_(I2_sNCh&?HGWg2=CX^e?0f<KVB6LGQQ08JaPN!u
z8=lSHO9181bEVBV2_aFAzvQSb_v-N)ce3T~*4wY-QKt~O;4`26&M)m1TqgB-UTt#v
zz=4x1-bRM>#F+Cve0R;D?YH43L6E^43P>hE;$0#H&Ow9=C4+2yhMO=$I`7upyPUgT
zyq4LJJ;sa@Uy=!tHSgE>33qN$Xrqaejm2)sw__!`QZ{+D!!t2pNmQb`)KbE`^sQT8
zPCaGs`<a&DK<9Z!p{7@fw2u0o$!qy0lHry$t|LqL;$MIMu?f)~Ou&T#Zdw#Q;lFlX
zU-B$?Tbaa`8_#iJZZb+9T`E$snO29iu)T115`P!)1deavx<RMNz+mIK4NKOlLS#v=
z<E-4`oDx9hd7FM$@H=zcObN(GeI+}2np?(V5+k<3NXKOXDQO+w95AJ3dnZ6)Kuyfa
z#^#((U_C}k{hh#hOs=pgt}FN?$&M9xO`a-<6juTTIL;U4pYK%I?%S{q7||rm6hXf}
zs2}H%fWm3NP3G4&)h6hj+bNmKq|ala-ZgF_$KfWyRnbzKGzU>%1?Ge7djjfNi05CT
z<f&^u)45xSfwJS;a*y;96!VXBJ0XDaT-3KnLeK<Kmas~jHTGm-K+5Y+A-CV|;ZCx`
zCS%nrhZGvX=?-rt-Qfg-4VTWB-P2@fWd!%J-WR;Qi2*!9hL#mbsiJ=!LJA1m2(rZ3
zG?cCtN71)3zK`WUIt+1iOm4)PH}F+x2Fzz_5C786q#=l-icVl;4C`3|(rPKFUkgF%
z#ch4dGjO2o-SgzuAMrTP^r{U0u6E?&^yNV_6tdrjpJ};wR2%1Z;%Dn@JxZ@q#716m
z9<J2H260)yDCJ&|k8|rQ$Wg}4eGd%>KWjk##WL{;a)gK+her@AuUqY`!OK;_YoM%*
z#1{##wnR!56J^Oyq5)hJ@H|a$SZ{Z$93Wq(>v_|v@^gTPEMA@5)jQ6RP{3w%xcxY$
z0)>U&>lYP@nMBG#+suSD(%^#Z#Ptci$ZY{31>BvF4T);+<tcNKkmZ3>A5L1Nxj7yy
zz2aj4zlHC)b?NZibE0RbConKQV$d{x+r*s;kpH#oOK+0fp8&3l2CNrFLK0M4q&K`K
zBnmW$Q}=PQu!)1HBY6fBZVo1pJmBcKJabtltwR=nsG~j*2q|R1%^w&vz~6l_aBjkt
z{$8Iltjpi;9U^i2dg3T^$nO!)#3ndcUCAC}WXQxifZRMXh+S+VA~((*eye2SA>@t*
zJ|%4fCg5~}8D>p^X(e@J&!RS!h{pzbE{;efb%eOwt~H1sDZ`VlmP8dosU}BrO%4lE
zBPbQ_LV#_b8#*9-&H0(dyJ+VSnAy%m2LnmZECK`Wz6x9BCLU=S%j6k-*x;ggU&!48
z$vHN2eoLkHkvwt7M5Qe%rj7(*Liz3cZ*ms~79-HY#1*f3HS%3Bm|DL?%9k+k15n44
zALAcOsyk?605`ar85w=ac|xC;#tSjX$)fAKsUdwL7AI~0>xa$UnX<IHV&9obBd@XB
z5;Wxm-)Qh8TI$F>PeN}CW)1G)eriV9$0OhLmEXL?t0C?^b56Ebro_>$D6OIP{8L^F
z=S)T8-u<vg&WAz)+~r{7F5f4yB;8HnmN&<ggck|@!pNyel|XBl>Mf1;)cLroUxISu
z#6B<dOK2hg|E~qWN*3%GU4K%u<CeCQ)3n+SU_Vo_y%nH-UwT|P_(rf%Z)9`}U*x%g
zhUk+Ckge?GNaETAsZ=%bA9vA#PjZm!DuX|%X_}y}7Q(#N;hfXTUVl8_ifCeYtL))C
z2KLjC<3ut|g|J=9(b!1GlD%kr@ZXsyCNcbS!R{m2G^ZN?1TGEdxG%FcwbH4T7n~~M
z-c3vHg$7$5M-CV5?~f(cb=u8laUo}VQc=~s2bk~59tvftfQV0WEx}y!l!N4>-?-Qp
zFf2M`buo=nxbgk1^ZE%Wc48#yffH~t&MoO)h&^Fp#jGB@2T{R`qMGq_4ks`Mr}{)e
z;>b_js0J8HkLPLU7Pr1A3gvuWKIM@I|6yRCCs;^|&qW7iiAPRHZ1UtSVl`=o1lPV>
zn8S6xlFjyg2VyRUj8K!WygqSEx`5L4@CI@MJn{+&zRy-7uxfz^Vx<HQqBi1}*lC~@
zH__Xp?vMpiv@rqG*ZLM)c<vrA>&)-)s<*r^>VgSntisq%Diq?TQfA$@*T+$TKkX8Z
z`~pujIo1r}_CSAJ23c2@mc;mYev#3yes%xOPj`Mh>PRUO18fo{Q*jaR7rM!}nmmNG
zHtW^A5+M3a6OS~X=Mg!9#R&`ZM(!Y+dsYc2@PxAM4@{g^CZ2ZS;@a{2=|WYauk$<R
z!Qiv~7y!axrPJyUw>fZ>C!i&u{w*=WDvWSl>72|t6#cl;mY!&9OO5BxA$C>R<aJw1
zKPV<!w6M*rj~mdHN8uB=4(S4SED9tlt3+Vmn&Rk85;0+Sr}TsQujsmEcB22L{M{}m
z9XAE$19s!y{)p)|?7c$g?{uMa*C1>NQFu?j>{w!#r`DHPQF;9f69&a3W{@%0?71=1
zThqo*G4&MsEL8s(s*`9El3xWk`3^6<CdznQTzXWO5Yw|=9#u~;KIADb!;4f`GiESR
zTASq7WsdoI*%3I|9ri6{BDB>3yEmPSO8EoQSepr4_*_&sQ^nJGpj3k@y^D}0smQt(
zs;v;4@%CMtf)>)%Uxm5ctQp@fH8Y3(<rqsAOLj;NaW!Eb)^mNhA~{K}iTMi2_U_gM
z1MSJ9C$Xrp@ip0@7?8<UNopm0dh;t<fb|gNf9#_D*Em$)-0fRB`8Q-2{+IcTYTb8S
zS@@(D*RLJJTL`JIPN3jYK>#A>9DH44rXX7%#2?X4QNn1FAD{aDQqD8?7!+C&2L=sT
z);v}=9|aXJ{XHTBCva+dLIAO^tDI$@ek!7(rq*>;dPBZdR2x*!SK~`dhRD~?Tpx$)
zPR<x)A?!ZiXeU*X1^uwFlSo$<(=mH;))uCOM7mavo(S*Uhv-qVNs@2v<_&!J@Q$0}
z_%<dfjIQFCyqli)s7VklyZyK`JBEAP)MvL|yZh!;6?S}W=C(uIs{7ML{zSE_^H_(k
zmnkSPYK|h!U08xYV~7D1W+XTMX8O|=8KL0Itu;OG6Xm=pkw^&LmIXU4V<|D$^G&9f
zp8_JhxGXk-Z`{L|F-S$ZRM}GbL#tzGUXYDpA67}vk?de!KiaX!uSk1EiL}l*A6NFw
zh_x)p2|W|gu;hoP*ea!FP*fJg<W)!KON=j4?(S*Y^#A$Yz*>x^<eZ6!!0R@qPOI&n
z`ytJjO5)@jED4Z3MRv)UPv%tpPYx-hv1pTU=Y!d3Hht^p_mf)X#yMS$19csA6cx8!
z#HoC%2$0{GLtdlVJ3QM2<)P30**T(3DBeZOd#sYQ=q2P42HW?4Ol85EN#Qn{{$hVb
z{jYDm9};eYb+{|xp(smXv_1t~A@(J3IpQ2(BwGKJ7rX5s7b}+aqpS6v9hs&xLID5d
z%7!pdNZO<oGnNvNhw3H~neV0G2(Et?ayXiKMIRz*|5wWAN}z<iLxC1igYtOit**ti
zFol23`8DGW?uU}ER+~ZRKHv3lkDmGjZoZnGFL1^+Mpm)@>HimlA1)1@cTysKPx^0s
zwKNw~1YsseX}CP`>k(m4zEGh1V}Z^a^VB=ZKMr!<kGBJ*Kl0(b2PQYppXQ+K(U)#{
zl=)*Xw$|A^08&kvQ@vQXU}+GK;x<_yd$e}uIhlBr-~H%iNX$!!&V=7#z7Hq?;9zm`
zQB&LPBW0gif4^TaH(5{_&2lu;U}CXiUt&BS4a0hsWMACMSG{M_TA6G@T1Q{at&n|)
zri5<l@c{P$<Fr7)e-x>PEzJOHV!2L}<0gnuSEz+SJ_)cJ`*`i|2?GZkBYQ><HU-wa
ze&P;a7Ra>Wmbt#;pBbi2qjd|;K%n?V$r~&<X9klfyDfWqzFA6Tq9TP+l+pZplBpck
z9}CWZz7p@$>;}GsG2#z#Co!|1u;E!G1l9oHboP(j)yVq9=4iBNL;~lZr=t_y(B-Gx
zUgK9HEG8Zl6L9hhwr^dsf8!9d6EEs=bO6ZRa?IT>j~YG>reHy!3LpWG#vf&-5Zna`
zOBO^7Zw#$3Vm0D85^Ru}8~PUI^<1<22+==&tSROK%_~+DYz$f=1OHc8>&ELX)Pip+
zk_4juyZ5nsO)xLsGI)7SKy`bb(=t*D(V~mpUHP=2S!s}r|IP44tQef4lSm%BXJOSi
zNO9i!2@d6~NGS<e`sCD<v{5YGZyzNe`Hf_&=dJRea|OZ3(Pup-3A&P2J7M^?idtHm
zaqqKVipn1R!?KIG&o!4_TgV>H9e5XL2cS)Gq_ma9zEhYf)6Mm~gexm)F`RDi5(u|k
z9Swi6j@%NpWT{uwYyID^)%fG=Nd)ZEGn2<?XLhsu;6Y)qRw&<%Z1UYETe5&1oR@cU
zA(P%gH8XHrYdP)LcT2;ZH9YGt!M?l!!R{USMso9<9DyJSbpp~SoEO|(H51HqpFWd}
zXb1FGnlZD4Iq;jvhHo2!!zDQWiKKD%(9O&{<5RMaKH(%rAbdcR=Y7!@pI`I}PUdUE
zAtA6;5tZRPEKv}1L@$D=G`P{(PvJRnsPR+4DV&KY_&{QftLS%?a+Av;2Mm1{kBN;d
zQ@-3<YZvMo$o3UHI)1!LD4qEgKJ+-j|8E_6Nl-I{xpvC@dG$How-i*>8;bV0RwD@Q
zGu2SQ#r*uYWPX>QP$8?l!KC{}xjVO?gUKS=DO&<8YMe>$cA#wdJp(R-kIyoQ(Zq-h
zBwMW2{y_<LP9g+0hM#HR8P6V(a(_B~rTgBR?9+SR>94B+J}(B{|Ff?Dd-+F#1}=jI
zn&{j@C*2g3vRg9whz^S<o*NkG0$w;ijsuu%g;Snv?UwgdD~d2>$fp)nGw_&*CK$eY
z^6GgXi}-f(|Ay#gHHMx+!He;1GdoINw}NoW`d`)+V*<IAS)Haj!Nk<Ii}6<x=_I4s
z3;l}gJQEvY|8L4#U<&YCrDBgO8x!}lkJ^&0?%}QH<uP6FO;~_;xpej1kA)g<>UL{O
zp6$#PClVgE>Ks_rq;5KW_^-Vi!Jvjzj^1Tz>(yQ?_!KNrx9lpF+-}gePe93OA3fBj
zUVt#<d5sErIT%nN@hf-{i`LF}tDEH6z|R$4sZvh6Fr2!_()NKkj(EnE4TO!)YcSG@
zH-uC)R75mPhtn8hmp!Ep8t)`^W0xjyG3@11sJiC>G6Vn5;MiJl-m?}z8Saq+wlGJD
z-TGL!o!5;w{{Zgfg2a6$fIG^C)j(8<X2vUFy0lhZHzG&;Mw}`(AFX=xtsx!4Lo@nP
zqUScx*<(j0*rfm4%9&}?{}2Z+{Bw6V(dwWfg=MZ!CWBnMh4|Ck&wGl;3y7sau&eIt
zJ_%dj|2iE!kA(~y1!S!x->%(oy9^jF0l38lasd@E2K6>Dt(TBG`5tK{gT9niBI3d0
z8^*yOs72ZeJrMya*WyF=@F1U7n3Y#(zD<>EuBw`IyzQ$cz9lvHmf(}~8Xdjezr9$E
z*i~0LVdbq%L$vv#O~?F74;7s)b$OCogV!=ltq+lB(-Y>7+v`><s*czjWJpzlOi)ol
z!DQ^E<;H{s5P{?}%9Xr%dnOehkA_9NU8{YDeoRmZPV!n^uPRYc69ico)?&=7#@7iI
zCNWP~F(a6#fc+(CO63Vvb|O@?LFg*fCOCdv%|qineoZ7XW(=kghp8KkI;|-Nvm|l#
zZ%ZW1<5oRUD{-PFX>}->t2eY^vjqp05V!JxJyE3cKN7rB#XXSlT!G!dhSIZ5)H>pQ
za%4F<a$u}Ar-d7T5ZAReTOjHj-3quhB+F4;@xtMMuVSo_@c-O<6$YKQd<2oQu5M}M
zm(n{274XHQ!Wj=3E%%n~&iJd^SxgY~_irl$+u~HVS?^<bKx1U1%)fbE3u781cua<G
z6^%XajIV!N6b<TeK4xoXu-D4-DbJ|pM+SM7OQuJ_b<qQZ=os#NucmM$pE|CqzNCi+
z@vky4<B7{NSFs&=t*!KH7w)0grB>Tu`gyK(M#nuJM4^yHsgqQBS$Z9eP$t%J+nSf&
zVOU1UBra#o|0BK<F5Rbwc5-AYZopSSM*;RumU6;!PGDNyXf>n{-zCd>^d$j23No7e
zarfC0X4r<rGmcL;xCzpQZk*kEr<EfTW*>fQ0o2|A{%4(<#$?FvFlB#u&{V<7o30-r
zMP$zLc|~Q)S^Bb4V&4kwGGqe_K!2Z8-Ze0QXX#gkLh<BhLAb{57gk1?5Yo?y+J&cM
zn?}jsE}P_1PMcI+cYoi+cpz4P`i`4<0SaxeWVZMwWnf0F>>6dosf<9ikzX2SEo_XQ
z5wR}8cCU>*%Q>)%C1p%LPtBN$N8UX5zP_t*TjEUL@>%^ilF{A)wuWj=V}bPo&2T>(
zk(9uiCx@)YLHZ*Tg}v`Z?h2au-;7bSp^o~ag0Tj6=4%HAUPBFy7SU>IWA2B1e5ZSu
zldV!e?!dP3#Nlh2nO5%z)pU>f<(yNz;N!eS3QNut-|NZF8W_Gx?U`6xk|aM$_;qwN
z%j}L;oBe<Z7T)HWUgreMqjt9l+9H$+Z$&)ME1CrOC2KN^Cqb9$>aQw6YU*Tro05$c
zK-AB-gSAsG<C!7P&Odrw9>n(rTaUVY^0B_{bXEG6PV$)O-Lbxl(VNeRibnk3YQ9tB
zBhq{JS4G7r_B>JW3HFUhNmLirEso#|R+Q{mc|OjWRR=>^S>g2d7XZR@UJ_P;p|3V&
z5oN9qh3gHn`P7YSDzWRcUuiPUw7lj?$wR_(Y?a5<de!1x#@`WfD_Z7eAt31tW0cVx
zDEfsA@ay<z6Pw>uidLe73SGGE%v(#i?SB&%P}zD)kX0AeHG><>j<H^>Q-_NfA|PyZ
zWXPjsB64bMLuf7f{$Yt-QS`oGuWA9#?a)f}O8{cwKUZ*C8eQI&!Uzzv&uF>os#X^H
zQFewLQ|Sq(WnEcz#nZAqb3~0I4RrAafv~Hr+wFO88NmMF<)Wwlox!BNjdLKejJBMD
zNS=H;)8(>Dvs}tq+rBh%DL+b1(}rm7pZ3ahRS*iJtx?ISLv-g(`lP-2Fq%b0l$$v#
zYP`H=peXNhG)rR8-A~kwWNpiBt1y2vR0^1|!_yXxt7{vOE6G|8{!q7U>Unph#v_9I
zrmM>swRD#Wx!jvT(}vHvszm6RYR%nhYa=X+%d-ULu>#fl@QS)cek@z=#_k)=mi#V#
z5x7)YV{sJycrsSzIa|na#L5KYl%^ZC;&VR<Hg$r~ONG<0Xxr9Cl+^X4cwl!Y>$olC
z=>m{XQWwP7u{UQkM9;Fc>HY`DDhk+qI9|eDUid)^M*P}fH!>^t*n7Dz(S^?lZI2Si
zw0!f5ij;32n0Ad0o7TyME&^1X75h)nex=eG@$v7i+7_MfYA;F_Z}7x^MexsQMqwQ!
z-McEyae?X~1gk;?<=C|%d;^PliW{+LfwMzcA+k<9=$!VuZEqscgo#2nM|+V5UJe78
zvPwzcqbu}p`+FGvoWB0NSMGXHy;*B!ScCIcX;pE_bb0d`l3(Xk)}q6vz^D>TyT0go
zxqq@Zp*Wz>yvML>cSS_a$evrP_q%9njreJaZySYOIGy*#hsLvCVaEHlGx09dx?=7G
zFTaKR-<O+?#I)-{ebY`;^{M*vw5{2rM4dBo*L>2PmNRPB>5NXc2a_4M(;SM<cBV93
z7cB(*KM-TlQGCXRQ=+4M&sKZI?}r4I<Em9JXzLdZ-lW<z)<RAUa(d|=F0qAnYV;9F
zl}2X_V{ew4)CLfsRyMOV4?l?8iil+UruxEw|E1Ip@L}VN&`Hv|$z*|&#|tD|5S+_<
z;(Vr`6z`$WT#8-nTTgk-Psq#DgX%*}$FpPR{%7kwly{G^G($mV>0VW1c2zrMCwn}K
z1&m5|r`Yw2ry<r2V@%UKP;|el(DhXYrEXz{P5p|JqRY-(MUmqY8rao`6SNhG8Zo`#
zC3Voetswh=+F0LM5m>fRAhl7IIp|t7^!9Y}ttUt4Yd9MI*=+fLP^lVc6zDPm${xA4
ztZQYXf*6OC6u?|E+R(1wZt`VZXo`wbG8oyOLXn^~sPq0ei~D}35z)6ddzINSG+vV<
zHa$XFGiGD5BVx=N4|mG2&f3~FdjBjQv|d7dZ`drB#~kbXZ3I^O<`wkfje9%#)y_<F
z`Pt~yODA$DOD(~c%#fd@Fb*Pt$bcq0O4^E%Y9S0_Bdp1>tlP_t?L58FI#>Qh0!FUo
znybW}(@PJlLtWcwo>WBp+SG{5Gn;(Kb==4uOFhlIq*c#^+;%){6t&-}sq4e!=T@m#
zDpLG<5qX3Q%*w_EADSFps=H1yR0*7aV*B~J#w);Vn(q|QOfW4ilP*iwwY$I^SL>In
z?x_CW!D~&Esi_SOGdL@?ZA;-WTMMf<<l)Cfkoi=5DMN1tG({A{W0pIIZwCHOA~<5y
zQ`(NnM%-;8z=slZz(goSOO%Qsf$MXHIhw{|aIpg)y`ay*zil+}m|&M3B`iG+Z-CSO
zb^B}LozSlD8an(ZJRv($L<xO)n5~bxSGK(!X&=14TGkVbmhko{0RvbE!bZec+lBVY
z9k2E8-&qyHR~ByY(458N*(UmmX;Ia~tzTRh-34I9(~5c?$L`nr$@%^5Wo;jON-F$l
zj;q(5i`r7bqL&0|QG8RO!dn5cCKE$-lUl#aH|DL%eytQ`<4SFn;lZPo5E0Rv2|&g~
zy=%K_J_Y+wYab7_{!=tY%YBn~KeNZ9B*(p5VY<^pe+?ySQOA6^_a2XyY2{%Jvub>x
zc%T=k$p2VfXpKhbE^gg;@7cPutLfXq850Lnt7@H_Tk+nK_6rt<#9#kILett9ch3_(
zsg0yFW*`yiof7<0W&{bYNCYYED&M6xI#OsB4@>9KsSOA#(oNZg()>6x48>a8<0ZMn
zBj$z1m({AN?^mB|Got0zs|mk|xpDuyH7l&fC_9FTELqxizf7Lvlt<-AzHh!zDK~C|
zPVve*@NGeP=T6$q<@_)FE`oxL9!Pd63Y5*TE6}Z5#ai5j_q5mc)57EJyYxFVHUaRY
zDG$8IG;xN|p|Qwr{%6MDg!%5iE3Crk4EnmF2~8@7JcYE*C<MC3%rL|fbYa`g&3#Fg
z>n2?&t7N$oo*?V+OOoH*T#7jYX-=OY?COp5Uj-1zsF|1w$Z(x_urPfCy*bHJlS`~k
z-HJ@ziUVumOe^uZi&D3Ivq3|H)4BY^kbsU^3ec&m5REgxU&>X|d=GZq80$^NcDyMS
z7+AhJ$VWt~^EZ{7db42T5!ab~YXG&So#!6-7Q~5qsD9VbWt|%(iO2uW%f#!r812%O
zh$Y)vF_LLkzT9;AyLikp161B}`%A}R?l03rgZ`~D|B53|dM$~TQ~J~t%T;5``IeRA
z{o!z2`i~p9oX!vSJJY|3&pelq0HND=fu%M#td9){mH>`bQ17Tilw)zP$-?2Dv01-Z
z9)K|*qj(Q?`A`W4iqu-il(w}7@^4L;B<|P8_0qY2ab0l&YIzN6HlCY8adeE^u>moM
z2Q9_(>XSQE^f!CW?GWdO){`2rO@{YHAt8|KW5SeHg2?4E0(tCDP2eeSI#iG_qvzhA
z1`t$j_O`RDP44S$(%D1rmW^rUzS>}xx|LJca`{}m8!)92BS9=IlLv$RJp#f6rPcLJ
z-Y(jN?gdJVgMjq$0>WjC%gYAYrr)%Wq3+=SD7eQBodu?-fX9v#7RaTdzFax|dB+@e
zl`xOyqSWt0G_t$jajCs5p?X9xSNw0q12%Wc@l<E5sK7Bz3g0QKc1mDwyNIXS4)vaS
zX8dI+PM*#)9x&N>#3oR+y)a*A=-0i-&4OrDisaJqn5NAN<_F<!aSGW>&gp+%cpbmD
z^Z&)+G8^%|lNX?U!e*YtT<&#TMK>X=I!yuAq}{b`ddW)XoM`yDMk{>c{W*5pQ}ZpS
zZRFMuo1{DRdHpfj0v{PjTnboG^#pUB92wCmHZRmiUtCwGmws;Pe;KiPeN27LfPLn)
zHHo~Rb4SPgI{*ivY&hA%q#7Hf>Q@|uX+bt-M#b)@zfV?Iqk&aidY_IiDb6JakH6d4
zWDonPYg68#F9oP8JPH&&+3o~Bx8aJ>9{3z}85WL703feHtw${!YjX;h&r4n<C$bMy
zQWIGQ9R#}VPnlDWqO8hHsK>0VsA@l`J-R%NU+W5B`C*Y=XZM5T{K?`78WPA;`|sn`
zied~ZE)}=K3wE<Y`v^838UCez+Wq%WkpZ~++A767uc9=CGQ7CuX#*u`8=~TBr?oU@
zmTZ|sJT89Cp(g2RJT|y+gHL^_TiVK$gIM;7n-+Kbd+A~X^u5*0_*E5M+>R2gkD?97
z6=!g>J&;e|pGnddR*OBo%KKbn!G(H>(Y+^gxGZ8*En+)`M$1sP{~N&Vbu6dSZO6yE
z>+lC8617)WmXv!p_)?+Hu|G8#wg!8Uu$FRn(nN!o?EHPGF$LNI@$#=6Pa_^;Rx+G|
zIHu5fVEdb5#z`qVHyV;mcoeRnN>ZV+lcw%x7fP^o!!R^l0Gdtrx$rl8M*_hO$)EF}
zbP{?%mKGD3v|}BMDD~n0$TjWEQ+6LO{JS^k9F``j1CR85$@Yf$gQ+ldWx^f9O;xW;
z6fg}in0fePRdE9oX6Rge@pt~N_ECfCrqlDVFXExJ`t2Kgx1b%u2P&&_#+5~D0%3|b
zkdWNKOqz;M+88$2kv%mjELE9SwP&S!EK?EHIg(L}Hkz3$x1-+n+^agPfPE&%kG#{t
zypJ|P6*JRN9C^0uOn<sO_`~>G?}hPHr{I?l1YL~$rChjV8&?+7C0dR*Ljpw-iz-1#
z(Q)PsH#z+(bwtq%^+djzZ&J(Nhq~AQ!bVI0Fn6%QDh74bY=bOqU}{;rmgof6%y9Ez
zAqXP1BfIcp^FrG;E~33LIU<8@^eyssb(@6N)4HTse|iko{f;_vgb^T;W7NNGofZ^}
zv~At-5Y90_TFq)Qb1hhkTJI@0zhpVOd^p5(*X@m+trm7@4dr%0F*{!F*U!tXqrhaa
zRqxALx61s;Yg|H_H@irYrsF(qBmzlSr%$jB-ZSz-`bydGA;{6c$ZxEfkiW{#1r{~+
zpa+zmS``r-CP0W=^hp7anIutoq8S0(=p#mz=@zH-zhS2HoHZp-rwA^r;2X-W5m-=o
zt5>6#Cm%m^&VKhH_<oXKdbc(bdCr8I$p68el)+~dwB+LfKs_(O1UDZA*sB}f5eX=n
zZ5)3>>jEGwyEaEePPj$es)*W$CK&}mw0(E^Zvi5H?e^P$P6-c7IEj6jc)<5d=R_|*
zM!`jF$np(3N+UmO&`9O3rPnWyzsUYXZ4F9wNanh8YP^YDa9+JP@`qjY6JD3w{*uzP
ziKrby^wgle#O_1)vSril<KRLB^X6lC?_`$0=5LJ!A0!^`)fA6Q>4}{6ViVd>6MqbR
zK0IY{M-KPynI@DFO~a{!DKLI_%}?dPbX%_#*ckB!1C7B5hnjF|F}?J3q^YmU)$qnn
zolaSEo3!~)#T8|588cbYL;;sNJWM=HjOhY9q9gUpR_wCea~h;p>{rQ=q|vR=|IvZY
z;W(Tj-A)E|!>;;HJ2+LR%XkB19^z8t+6<)58kxXML=mwVN>J#kBE!;qMT*Y*EbB|B
z51IHVqfVZzt60jJuj-D|Q`s1O&+E_rMh}b$eHWA4%J)4}a#fS}yo2S5$u1M;e~Kh`
z*q3tZvLuUKwA6W(E7lY@e2^GeHnHAyj`YK+{=aRC@Ifce_CBBj#dP7%rc;AI4N7b1
zddLM|B9V+<i+)b&q11hp+qZgU&08`{x3J*0oTbmWuAK-?!EF@VPRAyg)^jbBdM{ts
zaPggX#Bu6`f~uKu+VIOZJ8rIF-&kKx%SlJa>#2vSJ8J6r(N%X5pY54;h|;vC6RrDs
zJQCjV{8mHHqZ296e9C{luK&P+0Ex+@|Hjq*e3E&)bGx!r6uo)2x&P_faZB#{T1kv+
zeriT%K|P?P#HgcwC8S4CTnDzq-gL4hT&LPrR`$WR@mS39>Vp_JUCZ9*t7eXFefRjO
zjs1GP#5R~mpComA)_VB|upqbdy-F9>zL<v`3ki(HmQ3*J)1}H~DQqg^M?DjwB+F4B
zOpf&D*c2sNk^&;ciT*Zhr$yzd%tlk4jp_ojEA|VmCxfhq{S3iU)Ro?)o}8v~Gt^qJ
zpzRD#u!ATE8nLo@;R%dMJ!sIsKbv7KO<MPMO&YdrF`d}OkbC$?aD(wG!O;-+2{9Iy
zsU^vg;d^MDWr%_qUFHq5dU{3B?Fo4=5+3?u1H}r8)xW*XKiL+8=Oad4YhZM(&UM(d
zpC9h;jIWLuF6h_%OSS8tiHKMgE$2LN&RLc5DJjlV84}}}b9Vb>2d(8S{Wwf_JebQz
zh&CUZ*CalygSA{6H`VG)_CGwO?p7)+EUd4$wJS7o@)^#tuG>`SQhM3B00diCJSwM$
zrHQ=qlaD>!;zKz$JRlb$sg3`}Ge0$rOUbCic|FgPz5nrT))KvD@2j`u2>lwxhkDKT
z$*l4Jf#POOj2rGzfRXp2$wQm&g&6YdKC?@CP}iu&n(QRBEK7X1y(X%|@C|eN1$8<X
zrO6$u!glD#pLoxIQ~<qiW6~Vab0-7Zv71}Mz-R)Fwna?^$ktPs78y}p*R~NGZ&bo_
z0$$X8<b2+@^y^WgZ6Y+OoUW)^z7Y{kOw`0A)!7iU>E`cT4b7jgXtjYNV&_^#AeopR
z#Iod_)u=02(Y?JSo#pgwgH7<g1$#x@H7EZ~8T@@<#c&gKU&h0Q;!QUy&Y?*LC*SIO
z?K9JZXMW2Bnp+V*BSfoQQMo}+?L}QxlYcBfG%VWRz#MyFQM)IFy3Rn<%LQQ*kVQOk
zu`J0dbnkLqVzY8vZ{`?o%lmLC-{ffgO8Cvwd_7;fUSj|S$$nuMLgi&hVmn0`L(9#D
zw&PT-K!52Eu{Vk>M|qLtQT$h%-q*1lh?GL&5RShZy+OmhhoIZA2=c?L6(h&BjB?Ix
zY{FE#^K^4XYltlu!?mIk0n&4Cm;hNr`r8hiNs6TUm_C0J0Tg&f^UV626mqILs&ow-
z^2}|QL)ss$oR7(M>Z6dvn-BMjJgA&q#@%Br=T;z|Zl%ynI1gxZNIC2DL}9oH98#YQ
z-!28ZT7wkIKX&Ma)vtJT^EZv8aH*vGlY;4i=G6qdW$jt=%oo5>3B1kMfZe@6hr_q=
z$H%_e``;3jK%TcUth+l}<pc4F4|7MizV+do^6F-46e=#Rcs-IGl7C0UG8nuehd&(s
z4|<W=y^sSR%IIa0U&+s$c}K~q`!657CNaNy*)1LVr4pby>4}`$uF(sQDEzUZz@Y<d
zJd}sBNKX=gQ@bj6m&pP1HC*PeK=QWtxPus<LvO~JY_T@1RWI08Pzd{DdXcISCjZQD
z%G!n;Bu%%v^+Ltl)vIoolNm#YG2t1;|1fi%7RIGhU|Ha2;DSUV+@RWR#cPa!T9Aq2
ztD!m#2-=|Z>h3+-&C6fB_vf^JW>nuZ!FNyALNe$;cyx{pqKD{jVvW6z8{YD*!32G{
zTM2EYsFGsrUS4qQMm4=wg3igBxzjn=Xsmz-SW5&Z?vTQDk^T}}2PC9E3jcH3OG6Z~
zhOq!Akze4^Jx#hcR~C{-h4Uhlup?z{j3U?(%J!*PdM_O3F4m-IS(^_k^COlxanWuA
z`L4J7;gl1xX6{Z5t6_Sa67G?KK8N3?bQK_*&RVb`{JIf~i6Mb$Y~V#qvu(T9xq7~=
z9h^>NaxN;q=YyD-*s-d}Q}djN%eK}fVs0B$FS_gUOLD1^M6@9!)Qk%&(J_0VmX8m&
zc@H>)GJEQ(e)BV&f=koOxueqqjk-Sh&2cCt<h0FSbn4VT26r2vth1(FaaK7U(7U<)
zdTBO9l13t<O<{Pil7IuA!pxG4sjd*8!Bd#A*wf<I#3qiwNx%34>y9K|_T%H_B@c4T
ziB!+Ulqz8m--_#xHEyPLowjI_FLB5mGX(t&S!3LMXnSCE|1h)DiK*pvM&UyIJL2Cg
zwO=PBax{=oQ1o#q0@%Z-vOFObfQ>IAz9@lqu(X%!tnYLKmq{?ss<-<;E_Hsd(XU+C
zJlEM4;MyMrUf3J1=Zin;<`Nksg0&ixh_2VK+xeOpBSjSitj-_te5s78D|s^iK97?Z
z$Ia<yrW^f-R^4`isucbMa~m!@2;@)2#nF8)TmU>6kX9;%bJ)7@V<Y8ZIgfZY(iD4i
zJ)qp(<NGUt-ouMlnDM^LG*1gxuh=N&4=YvjK`3wEkO$qJA>Q5SJ1^L<yB{@;F>*D0
znZVqL>ST|b`8I~G&vF23m)4BjJwU5~YQMuCi_$sUs|NoF+r{3`HGDHT0Oa^rSQ>M3
zfkw5}r$B6fLi+Fa-^WuYL4w)!F940PZGwbYgVo^L!wDBxT57#Ds47ZZpYX{s83m|<
z#5%eFhBF<jouTnbqHT0mC$WH#YEt78xq~6&ezQ|gJ-uMSxc<kGz7~8+jQ0j0TaI&N
z{l^iR_iWGuDTQ|)_E+$tk$`4FFjM;9-?AYu`cfOf9S4$XEu;f;Y<7HLCP(4RU%xUm
zbn`{RuYst}uqX+}RY6NOMA;au=}MgE=Ehu(bl}fV-;V1ZqAbI5n0EolJaNq9(+H8^
zKLkfHD+jLuq<7o=vt0XeUg|>Il6&n~2_rEH6%T)kZYN^D1VboksiT$mgGO<IIjs>N
z>a<D5Oar}ezi_z>TR53jxtV7Lr52Q)apus8IOj%0l5>Fz$6vipwY_KW_AT3=9vpYx
z2?9OGd*c7XEv$a0Cu`!|lkzGPNJS1lFgp(@kjj9uJIGjCT7~o5+PU(ry8k|=C#2x~
z(rUVBj~a#Y%i|K!Sp$Qyh^Sd3f7TWX<n|Ih8@cqoIpFq6o5h&R{0ju%j~OLQ1O-Q|
zO1>7GJ?!O&g|6Fq=S!_~)41$`_SRu$4`@|wvcP&GL0j@+JX{n-MU=h+PH7_YR|!5i
zm0S5j8E*~HepkDeeIA}P)=6E<0hTz<({Wz5bJ^ceQ~y(X31LgMZc26$EdZWXm&X;i
z39cD#a5GLNQ(4rohU+Ws&dMqX?`SR<xAmK9BlY4$W%6a~oTX>N70pm`J>C&yR@@Hb
z*x)+OOqw)Kt{X)8MuvtiLq42hDCu^qetS1%9s3+}R;pa;v_!gZB>^fcqFR~IQ5SMR
zLRYf_a+cQf-Y;-nbdZ`e@Fy}Zu!VNJ{HgjGC5rw6cl(F$j!w?c{ZYO=E!k$P&?l#B
z6oyHeb`rxV#boC#yy3U;(TTu059gUACF&~@8`=HiF<tNOjD(hr-A(~tPaJaoC);sO
zZD#(%+qAn?p2CV<_Q9<8_Ie~0yr1u=cW*uf+L96ju;5m5)btNpGVGZ~FGQetpu376
z*dX0w-C94>0zN#t;3ETd%ykNFc%bG3%Eel_ymH6~S%lkCzppZ<?|}@*#yhp)qU(Xc
zoQ2$ZT`gHKJ<YWRsf#jcb(elE(zt<FS~NJTL!zgr_r1Q-q%jQk(ZAXJtmU73=R-H*
zXu_FV385gP9%DDBy8I`%$2a{om-tzq(rauz?o~kWDM|S6RF*Hvy;(S9p^vG$oCee)
zrX{-8g1GkL72eoHz7rE}-G6YUMO&lJ`f9%+{;A^de$=sUd5ZyT^V-4QSPNi#ays~m
z$P9y7Ukkw_cZU598bkINIj^aDjbIm>Y<s92x)4U1o(e>etju;u1Fo8cv6eAbOC?Id
za!;@Pu1~UiQ3kpqR!dK&T0J!u+`N%7Qi$Ys@Z5#n<P6_@H^G^r*mb}yDsFu<nHe}U
zQ8OL8qJL(l_2cebH43>8wj-t?4{v4Cw5&D2q-lIA=4P*-emzI=d*hMe7;9I|J^Z=O
z4!D8dU*|gp(`kqMsI;vb2Sh%xJt{+$K=0?s9pQBB;dXxXI?y>W=aqp2)a<!3T}$9Q
zsHY*n8W?3RcdW=l1CBhR`cx7)Gm6lOvvAhrso|M-o{-=4!9EjaVJ*#>sq^%eNwuyD
z-D^1=J~`?JAR4#f#WuhBm)U;SGs${)AOa)!=f8*vHq|L)cz++JFb(5;XXL4gE{&eC
zFRb%%(WW^uU&$XyH}nsd_STII^reRj2>#_PRkXH9>}nf!r06v7IAy2P_>6IvE4L^+
zMD+#<sf^RgEc#u1Rz^ebv?(jnTBML3+J|o_T!FIm?4$#P#~-!yhHBVDsHPY!MlV8O
zKur&|@{vO@?T;@;@!@-g2!!WZeKM+{v3A~3vy!{MX?kEfOQkcDdR-{|bH#wk)sbQ7
zo(D|&at-g0**(L&i(d3Ale_85Wu_ie*`lv&4SOMPNLC5#EoK1<RP|5Zyt@kmXiQ@S
zas3o}|790;;6L}xrH7lX`LU8HOOZz%g!VDeMUXyeADJhc?mF)RxSp=Bp9-Tc&eC|X
zVn+pbZUEz{oE!ZpUI@M+7QEtwg@Hery{TYn8fYWsmKVzmCV{zqT_`yro+%XG!|&W7
zZ1ubxmkba%*9==J*u~7Ie351vI2Y}Jw)esIi<R2~X^K)(bozD*a98-^Gb=>s8n$eS
zRpB=LL{$gT;S=ILH5%U0hvw=Fw$GeVRCftFd3_alk|ssFBpAbkL${bIC`Q-0jPVYm
zA|LLi<8|vVvKgzGzk~PeGT!LCnfVGAuT8PbhnjYUq7n789o4T1nB5zovMMu`Ef)88
z9RYy8ixUd3ZFNNW6K|W`RWM+QnhTmAMIX@#=nh?9L7UQYdB6{&<w37+x3`-2-x>6+
zbMr|w0G-Gj_G!YU!M~)LKL+&RD+u=`Fc3}uD&k?F5cj^n-ags4^VW_O+M*D7&Vy3j
zbt&3Yak=8W@zo;jxIrz`4n{sM{^F5h_in|krr1Y5KK^NS-njfH!~EcyKaxLL-B$m+
zilP&YDgC0Toj+C$^JvZrkLI5YJhQ0j@Lhh`ubEJ&no`%^LC-bL_X`Zv&gill^@dOJ
zzf@#ZHlW)k(|pdJx!?Nn279qLqRH9l49RuUEBjD3Qj2V+pLGj+3%WbMS8RLn`_jqL
zj%>2AqFf^KQY7+zk^@5ZX3a3mx0+FND^QdLAX|^)fgEe@_T(rA-lsEETZztKC!8Jm
zaf}H{q#Aw@Z~M?oA9G4N<i@#_>Aycek9)KGw$*sC<CTeVdyKLgsm&7*iK=PPrWAX1
zbSMw2X38h<*HtZlMN;}FD5;SwUPH<BiwQYv6JE245CVhDdhPdZ<<E`D&8OI&GExsZ
zzBbsDS0<NVUB11o9@%S**t{`>g|j>!xrk>8CrVGZJ>3mB{|y*F{gpUV9s&}!oI~0*
zD%C9ms&SIWQwp|48C#C$S?g$?`5Wx7Z2DiW#EYe!S_w-VZmOLN^0uO)8*h=X&e$H=
z=>S$X&yU+BHXjw$lirZu-&@D>>6yK+xHVT387IN)t>!~|i0)x`mSEtT3!vKYtiN}0
zHRwZ+ldUVq=Bsvat&kS@b}0~E`PRfu6A?;G{g1u)YFG2_HIVm~4VpjyzIeI%L>$07
z84PS$83=qank|*va(U3~Sfa4heJ!}7VKzW%_V6J+?N#juqlxc<sKBVVlKMD*tx0GY
zq4p&C+#NH%6TW<jd{Ys1y*ORRT#{+QL)3)W;=_xp3&1N*qZj<Cy_j|W2Toh6yCuD!
zx1i8R@!gHJe7$KAz6I2RCv4PGwiDMKiES1FP!8$Hd~PM*?GuR$Wcqk5ZO)-?rt(|f
z1Uk#a8)k?#WB*uBlG`?o&eHy<GxYTM-t!Q#nW)dfoM!L0*Zy$txRq=+P+GriIsCe)
z^0<eZn#L@TF;o6Knr8S-T7tzy-W(gIshUCWhJ4sOv1_+-o{*FCwCuz?AkQ<Ed<8ze
z1F@`A46nY<a02Tv-i`G`9ve@4+>hGXkWr(?mll;RgxDFQa~SNK9|5D;#0Au=1=P+y
z!!28y9$3A*j|b6g<~*k1|AmS^x5N~raElG|d~#=4e;{JLj}9?Oh>t-;{xUfOcoOYz
zNIC!cV2;o7W)+4G@Detj9<&q@%$0d~U%OIJXXocbH6luL1ApE2RTHlLo!zI??!gw@
zc<Mv3?8;9=Zku7Y!YR?|1fv~m+~w6%0I_6xTSWN;d6`Q}*^HUv8TpXc;6yy_sb$KU
z&{xJKva`5a$&$D0!QruG;O^%UuTt=qHmOTYWP9A;sjUoVdA6T|Gs0i+os)iFe@~Z4
z-9*oxD<uE(HDl97BYDcc50}QuqnttT;ejR2s>D!+7dkmI@co`wnvhZ5qATJ&Xm`2P
z{%ybnMA1|;)23W;l*B%Q!t5!ab5B($BJizBU0M0H2Q>k%Y7$Ea(}A}>Ebtax6MN=&
z8Et3VmTP-|FV1l#u_3qmr>N4XcZWKw;Ef>JQz=^4XxG6R<e2^L?$+Vurk(d)Y+K{~
zph7cMuGA#)_@<2TK~=L(*->oNLo%FqY9!LQyX4Q^<uZGs&Xs3u^)&c}Nw}?f7vbQm
zM7`HDdQR~JBC0`1YFnt?hTm+V(0TadxSJ*|=@ggM1s&h7|CA-FyLo`E48!kN?=Cm=
z9~wR$wC?iH>4*jSJLPb7`>xxpj1wIrBEK!;W(bz<uHhB!4^b`08PNwftz(OH2La-3
z>KBy6GM&PNo>h?YkF9?QDeub2l;U}9eVKkZ--g{n8*Jpu7?JOFTe*gdqDy`+BGd4}
zEY^Azbug=O9)5l5?EOsPxT~k99|$V|ZoA70H=Cc5o5otYRKHcg*8Y51vx+vXdkS2q
z;(eMgMf1Ckwg9}FC|SEWqwoINZkSp=ajC7oUNkzfdM=3$HQI8wCG25SUh~4(b;$_i
z!qi*PvPB70QTHA|I(?bX;-YyBah^8}rZo<HrCw3RB%ku8{cg!_vkJix%U;(J-06W(
zQ51PguIn7L=(ZkFwOiIakAc%*Ro2+E{7W3yRAyS!h)B!(?mPnTx_;S17Vf@9=_JP)
z=7%X_s-&*#qUQ8w+4E3D3wAzbWAWnRqQlpHV@ztY@xJc@E#B_cbc@Yv&%c^ufQu#j
zXBER>YwNfdm<SNN{VGPq_s7{+0#ne`I+HPtbuoLk+wtM<ask&fE)e5e96qxV&r;%J
za8Gdo_EL9R$~N3nVcBh+$VoJl=l&}bw&}*Sww*{Uk;o1wX-01&PQpuq%>93FU+D$4
zmILws407vPKDzMFu*%F-uj4IQ3o=!6;Tg}+k(LcD%FBAUjZUGck4G3!IC0nfD+Nsm
z^6xSisqatiZZJS!Mn{ak2H*34#`^LNI1b;jsF;?u6$u8-<ZFX0LN~>|=kl%eDRueu
zn1;Ocw-mh<EK!y&uBA%7Pv?7TOre%1NlWHEjOZ;q_}CR%Nd3bkO8KZM<$7GVor$|?
zMN`XS_6g;jcichPUrrKS4pQYZO!e4D2I6<}*a2XSe?dP1$TPkAFzEp3(acT&vV)M9
z!o-W)o<nCTC*Lc3UlT(0>;v(RXe5{}6OH~ocW8gPlECe!*vm=Sdgi{=s(mK?w2P!W
z@2YLM!GU#7Bww@*c2w~r<KSe36;$5@mFmq)I_~Iw@c<oj@6jtCvDPV9AXv5+U9xyq
zQ%Rq1g(%jIl)G>Zmpw91os_H-$K$Pzc#duQgtr8zAyj-7i{o`0O=Nj1yrddymZ$RY
zVZX$ad|FoYWr+N;=Yzesurhfy4u$o4H~&+f^WD_u!-iV9NndU%2$twOr!xoUU2*qJ
z+C%=w*s)DNS&o_5QZF_sS`VM_F}$Z2Fev2zqw1~0+G@UNVI&Y-N{bYN7HEr0LvWWC
zD;1=;6e$FE4^W`EOL2FCySuwffdU1ByTeWU{_cIg@2@;L&m?Eg>^U=g@3q&OYsswM
zOI~E^I2V8B@}}Jr+-O}cz;x9~#(%si?`Ie$DqW+Z-LO-PPZcyhtNyXHamVtyRl9a_
z_)lwj>-kw#;nq~SSQ}nfe_l!f%?yR5?DX1T9o9$DRMu9Jb1C_}d!efhc|<l|wN;=(
z;z^_cPU|>>vr~Nabbp7egY|2&j`|za?vmP0m#J@>9-M2I9ctTFcZaQb;RNV|LA%pE
z#$Mll|0ym=G}*H7HC^caxOW()X~ms-wDn<Gy&mz6B*d$b+C?@1-*LjEblLAq)4tvH
z{d^v!;GV8ZJynFN2-rcJTrlcyp;!ooV9&Fxp<{+;1=66ge*HKU^z%@@9~bj&;&tm`
zf<xDBo`kF8nBtXd{*(hAm&<v1<_cpu6j@LrpU}C#r&|<P&wR1%B9>JTQ6fNhJ<{)4
zyT8nukBQgy)L328OC&Sa<eWS%9f>KaS@`ByLAnP!xm+-}N`cl(v$tFs>~bRCLF?-S
zr&bmk_ahjx#GVBjTtrEANkTO*e7Q7v)_i}l2sOOgg}1Vc-mMXSZN0ykv}$C{3bQ=J
z1QLvk-0m@`>3UQzIW9sQJe}%>BibkgPWl*zU-_J(HtM~vI&8W9iEew+&pde9`siWT
zwCznR<Do)8RoQsZAb;2{-cy?oH`npBgw1?Gx%{TwtXy`tQ!=X?w;py~uvf8rV0+h}
zx7Y?)CcXLmVAgTdR3|w3JChpUx0_GLZ91s;LG}@=YjIuK!I2ns<s)Q2J8kgL)9xqr
z?V@a};dx>^yyd{y1H0<y1t?BB!^VIvi<ir+>^o~NtD|nIa(s7F43SxL`E2Dkm;l|q
zYTS=baO9SVi-hsCC%f-?r<1A4g^hSgDL?HE7%Xs%Q+{!ae`8Sqo!XMmnsWcl;qX((
zYQw*xzi#-e_2}16br;t)8~J!{7_%s&v2F)V)8sa~wCNPTr41e*Jd94+TG?7fZ}I9?
zbl?9byfx0-zWP@3Lm(_zloKltWWaCuu#>lN@R=QbWB!jN#;T?T-n8A8x@~Ds87lB0
z9X=VoM)a+)h^=R|*cn?ydEcjH&MH{1^38h*^edR#@A3dPLPcwXqpMi8C`v1@F4dkp
z>DDcOQU4UD#YvIRobUNcow{ZJ2(WYsXxfM@BYAw)RC(q|8h|QkNX?IEJn;NeW}fru
zdomWrP|Q$fJjahas}wXlf>&c%XTHc|{JP0fKP!8*>{GP=Ik18@m$*)4?P2<=3ChJ&
zrCj#PLnQ^lHKNul!YAKHTrF~Cv)S^_G@+F#1eo!ae2=D|n(dEjXD=@+`P32|68P;J
zh6V7bxfNQ<kNNi5ys~@I5+ODo^;Z)`8|}ac6FyYS!2O!X($vq*HLK)x8xZZ-U^kOp
zXl-?L6ZQC^pWgg4%L+xl#fjVvzQ>svZsR?2keJM^)W|HMyng75U!3bqnI?ouZM@{G
z=ZJaDl`n*!WhE@;7gTyvO=%un_BZ3t_OHl#g5ha~Bm!ZvzC3=07wN4cgL{f^L~0<i
zz>0`0di(%xqI4e2=-tAPjx+US^GW=WkU96S@1%@md?~3Z5BPU^b>NIjeH{hF&93Ja
zK>C61H#9{+oMgAPGzX%kqIxaLsvrXI_{f!1uOB;Lt#Cn154-o@2lk&PB?_>9ztL+8
zr|@YPnDq6gz=S+qx$5Dwknsg^QV|bHHQmoF+OEQYh<NpQK4x_JxK*)a+ksbLc~pwk
zc3b8l@;1kWXZ*)qB+zvMa9OAL8ci|^fYnN(jn=b_UElB2cRrG(bgWiEKoeG5$O3Pw
z7+(d-7p=0Q@O_(l$*=c_zHz;k#utEk)1J{5f{PYs0i(GwjN2;fB&cj?C%&GE)BJg`
z-W$&M1@bC^xSbk#xawOyb<Yb#5&X;IyI%*09KUD`(CX@#64Q&vezr1z2Fhrn>%H}&
zoVM|I6QT=Z$H}i`&WpjG0vBvJ-~22U9{GU#AI4XF2=5^`hk>Gq=wrOL6L$9(`0^36
zW4=n9!H-Lo$_omRID2SKcWv=w_|3dCQ)9iKQ1w#{u_XB=&q5=q(ea9zbSpWBeIXaS
z8C-7jFiZE+wl(2~w_nnVOuf{9ey1R`km!vnb`|#YN*!^ZLLAo#lWk9&X0QzOWbwtn
zktt<qZX^s!TYs73fC{a1Iu!A-!RV2^nnJNeeMv0LLAG7o&bRq1AT5%a#;YrCXGV2&
znyh*K>tIRkc;fBb3sf;yY2Zp<<lw-L$-Pdm;rdyQEg>bL-W|*a_2N3!;o!SKR)tT0
zPXZ=Kg!03*#tlErA?sFYci0AKtaEg%<W2!Xt$dvXZHe7y>*<wLp;l6EUX5y{AUt^0
zH`Uv;=8CAiqQ7sWO}%cm-}7kzPx0Zj)Z2B7!fVN@D^0w9{QO3-n_mq3;~g?H&Fyh}
z7QC+~y(P-XI<|SrPxUS?-2%wlLLYW3)w}K*iPNz8X=gj+*pa$&;mx$e0#W8jjT)SK
z=A{LKqC^ILX3ZdlE@x)f0x)7MyWXkROPpEoEob&oMOIf|Xl}_e?^^*Y9)K<&Pd-D1
zK}uI_Hhgw_IK0C~i2lM_%E8PND}f|KNc~SXOZ-_+c#}=vPKgz(9(1o&?^?St(fRhr
zyKf^r-vxGrOToc1JJzI}Zn{NQJbQ)$19P&1zLpK_#9{cDE>nW9WOr1at<nWJv!FXA
z(7Sk;cD|=+L+!+0%FnZ+^Ii8HKKRM@l5$olUVlEM@OM*6I!P%D!aMVaFZ&p1c0P{h
zC7N!6*X{5u^l^CjvAIV?0sCPD&zgV>(i@fgQJMcU+|bJ9W+xAN6lUcimd2a%KHd~t
z+sOsF5pAYD-sSP*je!QUIZ0rf^6E_A@poBD>oPy%%ICCP4_hq^^(cFukl5YPZnXnx
zc&W7-Y3{dWD@Fu%$?p~UmR~gP*CY+ITNI6O93{NDUU__ACKNjI7I^LZcXL$BGbkRO
z((3`-ekyH1yrvLzEFNyr>-m%Fc2IuNI>_EQ$4an35%+z+`SO=7GBJY-j$?4=+(EBj
zzYyH`PNq{9U3zi#>(WRgMO@qid92dAq>}bxEMxE0{3W-;=93Y@!#<XV7leb2oGeks
z93-j8;v;nhHt%@J)3d~i<byX|D5fC<kkt^qM<4-T`v;Bea$a<v7wNstfo*f#3m*FZ
z&+lo*%sjYn3|5Q1y6mzWWYRzax0&eld!3!9{SoEv(MgZ2)ue5>;zDFwyzZ}}3w38T
zi0-x8V*<|Yi{ROL?7*pTS5w2F)HKKr&g~bj+YF%b+bmDsIwjzkIi7}m%@-F+4a2S*
z?utY5oiFpvW!g0zuCF2u_5QCGz;-J?fp|StT3HHuIB&1wv;ZY4xsK`%R3A#owI~u7
zWJr|}PDDxgViZ_HO2xXw(x%a<CR|i#&HY)3^zHm8js{rihYi&tivOf2*8>VZn!TP&
zV6-bg)y1tyK_$I27pMMt?2ta}fjsHgQJUsvK!XHOX;eIb+H9~;Go-vY`;NIv%pF_8
zx_5(X%tTM}s|Xt=KKqO3`0)YW1o&9(gWg-ZYI!AZ2-?<$D{7dbV{ALH-Ukx*aY{41
z*Tl3y&ZSQ}BKES>R<^`r?_J)-<{1hs7-m!w-sl<V-HZE*MsvY6OeS)=H&6-Cg+a4B
z_<rGk&UjApA1KawwX<xSlC!C%F<>{0NA%cz;@;3TG1{kns_v-lJ<*me0_msRDerki
z<ZLKNgS{IP?VdK)ZNFb}KUEYABeQzff#TZq41>^^u4dktQsZmv=McQg7itJGP$fvy
zvOec-lf$bV#vjMI=0=Z<+j0f)Zg<Y}Ye$F_I9jeUp~k(gnnORuF6K?rpdf(-esnnQ
zh4o%}FLgyXsb-%fq40}@dU77)D*Oko_43<;#$6cU;Pb_-aFK8FweQ)D9n11CMg|*p
zOU;#t<COKUpP_*}bUk-AZ7=vuk`D5L2Z}?vZ7RNYHY5C7bAQ|*wE3dOT1uk#_LZo{
zGIR4uZU3?)Ut#&O9rpq2g2&4kcN$4EZX$SfG@4aTjFl~-o;oo9OSWqdT|6qC5NUJ;
zp|mp%Ku63;Us|txJVw>`ZJf~8e;IE*7m2jZaXSo#uA#9`CM|S_qpq@idCIXITf;Ff
z_|*cp8H5|xu@hPH925(NejyYUJ__Ul;ypX5;hVs)c=2W#x!S@wH<~YoTs7ul_33F=
zoeG+o(~d#+kyzw;Pn+Cl{P7li%6+V6gSN<t?qY(uXlWY#h|`!@n$qr^xi+j?__)Ab
zCe);J?Xs<R-Q_1`rQi4^O}=qP`)obPH^l7y#vPF!ujwid4`Wbii8t%JW1dD!L3mef
zW<~(fDKI+{O!n4Hc`df7mA(XzU!Cisme5aZV`owKk*L!0j1bAXdSjoSnQ?|I%SZPP
zVuIGXohPjQo~<)0l9R2!^8Z+wv`<HP!GZVF3Y8^$dymS#D~Y&@%#Zpem*iH!8vp%h
z8G*N9C0{Z+1h1&1<>#=J_od`AR#-C;WP}X;WWRRhR||!YMg%fvwvepF3O$)6OIoBO
zR6<YIOBVV*kI|$;4{~VR171Yx8DrR%&4ZGL%SQq74Xbl{X5`5jc-duDcuHu908#tm
zgg4@18ds^n0hu<19-dJVLsuWKBxr59M&hfFx#ZM??VN6g=uF?+z-6dZv{TOnw&-fx
zqRdfS`d+~?o0uo_gU`e12xM=#{U9Bu&O0?I#^Tm{cCCf&6INoP8ZC#m+zR1ap!fA5
zc)J49#;UtK8J?*lF*^luh~l$@1$qBV3c7Xwkoanu61vXY(23Y#XSzb)Z}+_>^Bw9X
zpEP-!esNpV6CIr?tiSc8kOWS|v-7x?8ur+fR5J|-G(3HLa65>({N*Bl$-KKJ+E9DZ
z`q;8CC3IDmWnuQhVPyS;b*z9u&gFH^$7klOW>h5^hcCZuPd}eGJAa9}D>vUg0JWk*
zjGu^u@Xr<-i)#ja!^nje<BdP}i3ty(pxZ&n)}rXg49dL5#DViW%8<f~NLlK)3uwl~
zoa9?W*jMF3*p-A>ZTcS}aE@dERmNcG!K!?1V)>$(Hl%@U1lu&Dww7bUo`};Nc2BK=
zN`QOTNrQ&K1p>qHsVKH#(ap&Hh_!RQilxtg1W(SlcUMg7Z=!vI?R{}x1)0`y@lMB7
zv`2eKU!P07J8r^cI=&L2XWYRv^m9#W^MRvZ>t}PzF5pwxSJz0pjKQ>NjUCl_#dy>6
zs21UyK<YVoz{7S!#t1>KRe57}<+InVLQ78S7uA_9L$`mc+MHHglGw1lc86yz_&1PD
zgF^X=nW%lIxwp!%Kyjm<F;-u)v<#5Z#S9Cx?gU#|!nv9?PMH^K7zWskqjt%*rr<70
zp1nMD+nRt#ETCP~_*vNo4i7fDb0<u%EuF^KwhLU8nL-t(I{qrX6DBSq2|+pby(R)C
zMud7WAVtm$Mx@%r+s)L+Xxt-kn><>jMa%pfkTRMYHDftiSn3x6qGR-8YC}=0!G~9M
z+aDYF{vhW)`%AJatp-ztYh|Q_QZU9?_;->f-S~#B^F2=7^tTS}QLOheaw`Ypb`p*I
zq~eWs=APTLT);?)2_I%)Rh3vYJ18l=h+K8%uy=Tjak{$trU2kkx`y%tRe%yha7!Nk
zWufG0HuQi~@KaCDJ2PeXa&DyDc5tjNK}z27j)ASDmgcntf5~vAR!{ep_(y`2c$gpu
zn~^u}MlY`?XG&nurty!r+X8ejMkR7FLkf~OS~@*PRLYj#5DG(d`zh2%P8M(BXGxX~
z1t|g;Uyk&Nl$kL`aN~H_j9Bdr0&UPpF9Y@7hkGz&Xm<!1F&MS)SWA5xw)b|sCfuPW
zcIPB!c7;}r%aE?PBMBy2xD9$U$dl2D-s|pt6i-<V)Z?cx_Cfj$DEyz4hdT<i=8_Kv
z{iA(v+{hjNvY+J}Frq|)eq`&Kw`@guhVw#8&m?eP>e&9g;#IeK)m^?haB452t>wyW
zD5ze}drW{?i#B+A6c7#X5N)fM;+wp-J_&k0I(cock~<>fm~1fT<YTfQp)YyX_%-YN
z>CEQd*B7h%2=^FG4CSrK6zECjCVzK=y<txcKdnW7Nbu{ikv@a3uF1+|A2X-~l-DP_
zH~i~rkW!6blK!fP9{c7}32;aPD+}n*wP;0iHI{(6+xBGu?7_f;hSSon$hKcN7Ii%q
zKq|syXMc()o)JhV=I?~>C>%;rH<M`(F|6eskqwaFq+G@_t3!Gud>|s$`3_bz%&Sd5
z-rF30#_Y~q8wfT;hKvV%CDMn?Tw2`<tU^GxeRQ_l838{T(3o*YfbjwP^`uI59k~(^
zF2mliYWzJ2DS5?e8LhQHzzkwF7eOHVmu4FW#(kj;B6-{Tr(qIh){*l4`$fj5n+hap
zm@Kce0To&<sUvyvfrnc5K$bYB7L?rJiZco=1Uz36XX9DM8FG~Ua!yr1dE^!$TdzfI
z^ppYws2vw#3BFxfK;v)X{te)Mrj#4DO%T^kPsnDd^@4o7mXalm7k7+Dsl9wSH(lVF
zAM6d4|LsAma%+4=DaZ{MXm~ozS4)z4#Avs--{wt~)LOrRmDFxkze&0{2{W{}`S~=?
z8jAEf92cHmmC;eH9JHZOgQ%3!J6@?m3UFKmNcJ|!Nk;gzh8UReLjmO5M*+6hPdTUv
zw_*28)?!Eagb>Orb$aB!<2_mXfIO^sQ6%!sIYW|l&{4T0gnrIB3@L^)g7r58wdAsu
zqUr_V!?IqwSTN^E(r@4SVqIJ|6(T^X@(``5_o`YfvK0EN#73tCIW6HB9b1SA&B>7h
zjRSA{u$0peLF#&_O(92b_nS88i&OkNP^hiWxtv>$I&h)h;Uq=Y9V8c4tE0fZR_{|7
z+{^HKyz2)E5^T6l+Q)7`uDkNYGFCe3^=ABXMcR8lt7}CIfovafZuiodka0Q(u+Pzk
zwJ{WmhkKJW3{m`m;)p;%W1y3W9`4tBy09$2a4ZA(0O_#^AxWm_93@&Fs;*NjGH*Ae
zGVj^Ae=e!ATq2TBf*uzsemJY+HcZ7j1<G?1mrW-1Z|$Xa8&X>Q9U1aZ-9x?z!@708
zxvol;6R!3j_kY0@Hh^Sg?5jaHhUaZ@tAa7^SY^-3W@;af7hT;WtZ&XZjQ)h|oXk+d
z7l5>jIb&d~wLMhK$5}1AQ0PWa6pN;34vj*WKh(YKXxV9{+R$-S@K9)R1-ZGTw{tVP
z^AhFB=#xN<DXo*)F%PYpj{S#Yw5;1PYMQWpJs~*O)STwthR*FKgBAAgKW2DY3zcc%
z$~PV9OF|@H0(DJ$fdu7|R5Vh)hG-h<Xy*^Nhm^v}prH?|46uAc`*vGS)}oG<Fj;C(
zDfX5PgGpT3$BvAS01`TqhOE*tkA*6;Kc803cV@MnQbMSO7dFH>`uv2bhH*_WT@5HG
zK{;S8pJc|`Hu_4G<R3E40XMtx;j$rGGP?A5Q6jFRpAmzw(i>@wTa*k>muiVaKB{;e
zSIjgf)^NLj!Y4-#HDY;4YR0a`cGE#OTU)Ik`Nm@Wn8RvlfJX|!-ioW;xF@#o6qPSF
z1nr9cYSF40Dxbrdg#w{M!K+#0)bj`ULcN|pi+iE(O&EZF%Th*#JAwi(U7uoM9Y+&R
zM#Gz-1EMB%m8$^=u&vYEqu2p^APOWd$d8V6xlrXh-;<iiBl#KTdstU(w8?Y?L956d
z7P*KN)_g*_c)jL!2$r$jY8^^ggP4_tnCvoVG-|!ml1+d|ET~c}U({5EJucqn5Nkjh
z)YfT^ua2Y-v%Vrd(v{G4`|a9rW}S>TyBU0i<~pj=e4bT#_0Hweha+QMN6c-jxh8w+
z%`DIVcvDc=66%gA?$(L?7qPU_=b7gcKhQJLE$}MHWPyIqI*QRS$*rHRO6jG;#08RZ
zhLrR;sR?Q#0}N3_hD`OW38|^u2SXQPFunl#e4y@P6cOmuckQn-CyC?m1qRUA;_dYH
z0Q}ziJ!q&(zz14%d(R^P#Tb6W`c1lmfJ_AOM5ZOy>f0h;y3;h|d+Uu+K^0+%@ID)`
z7IPNm)IlpuEK2x~d#F?{78{0lmQRrKS|-H13Li5mFC5oK%$J!qcpks>C4-z%L(`AY
zuOwEc-xHU}f$G7wREnrjEeJhgly`@Shg>n4F1hRlp}Uq&GB3eEb;!eePo4@2W5`%n
z*UKz#YHMlZehYD2TSjA$*<O|HOFs4m`Wdd*W%U-c&jOHI2%?{rBPuO8X`Ponp65r*
zkmIaP3Cb`=R&cMHE_T1sYYX-EAXu73q3lpubplH)a}m+eP>~aCV>1YE(*ot2DttK5
z&#XyTv2wn%YK)NyS-*GPrNzNVsqz+QdT{q9fuZDKDB)Zc$dW=QmYULqQZ)6<Dp78D
zc1hNC<WHNJi>;>Je%Oq+kT`N_JU@Lfq=s}v$k2|gj=?T1k}*XPa|~a`C|GmlC*Ez%
zzhZ9JG<!J~i>jWioP#dHJ{t9E2hHQ9rlGtmNaGW0=j`l2*fA>*jka?uE2QID#-04s
zdAjh2edPJz2ot}zAVbO5Ok6U4S=zMW?G}ZJ*&*trPz`Tj$^$Ctgr-Q3`O|6*Mq?Ew
z=KdFV!Ncc)uQ7*7mK9uztN@l8m*fX7_)mGhgK=#7Cgc2rPvvS?KPNKPblr=$RFndP
ze%ux(aP()DWecT&H|eROT0CgO#nfYh2LjhVI~N&dc=z!ccayGa{ffS}f5DXmuXg50
zYTyKGaEVLgVduW>P58<~u*P_56ta%yiXiR@%R?G2kELI(Gr^ic`SlDQBS^*nZDf*l
zL|KGC;b342FZRd5U5#|NjABx#zd=6BiOqB31+WC!i`Vw6MY{IVi+fMtIqYM@FUitq
z@aB$Z7sg0aXW~={ge`)@a?Qj~X9wly8$UPX@#ff`4VWQcuohA=FEak^I<?}oiF;M;
zhV8VT8X(|4{^BcE6UNU&mZM5X5m4vYs~e&}JT|EuN^Uy26p~6&O5<55qY@qzGDLJw
z_+Qym$Hyt@#elXY+>wGo-@lSrjETuqwnqzasLc90WU-kV>vGIc+DOE6GEogflxIUM
zdjxfu#AW9FxRkXMn14x`GYwVtC3u$CQ|qYAU~s}kC$U8e@JMW^NSN%600Ld88^=$@
z6M_?YxorG72TX~3Pgp+~OfYx9S9%)nB>ZLIZHc71|DU37t#?Chf0k%Bp9RT&?_cHY
z>EsWJCS`Ws#J7ns8Tr&TahD{4%U?#e(<zh?UGbAqIxKN8k5Q47Sf9d=hu{c>rB4Eu
zOzz*;nJ5zy(QR%o<|>hE$mv4Y3!wO@#~_(|*HvK{XY8Y%KR+L9WzTMfr>_Ikp`HxU
zz&h2iQ71dm*E9Fk_ecuI-*#|l=x;Qx;hmW_T{K-ZF@Gueza@Ii7*{=@z)j$f`O4IL
z7JLW0YO9-fqA(%l*IT<odXk~M4!ebePZ|+xgpsFfx2%piZ-@Lo2HxkO#~xhL0mT#B
z!}Zn)aO|u*cfuu~38e+fQemF>9c6O>B^=(Svb=b~V1Pd)Q@<Tw_QB=@>FyrDc4>7p
zH@Cm&`?DAz1cjaGPz+<1A)Bv3Ohj+rRv7L61Co$_#+(mq!ysGef42ci7~dtd(Kw!P
z6n5T;avdisXNwrl%m}5Dk5MQ$=lUt7ghOMaVUx)h3(%+P>vaEO!5bDYgiVD_mcSUC
zk<YiDAfl-HIsV!KJ3Lx5f|aN=!2lV}h?|x1LUt;C^QN+ruOf@&^oMwulDUw|$&k|!
zVbEmAq$%<kn`}cZJY_1wlk9E{UOJ(nyneKyNv`1pp&^s*Z28|;1)qrpX`TB<(^g(;
zpSAyzHXN@##oZoeR&b&JZ{1IUqI$%9^P0l&nvpH2<kGzq9fn5)UAt=4Ug*0jrTlg<
z2C(2&S*<5^m5JiLVHWY@LCR5gP{iXhgA9*uc{i`?o~BmiY)>5d#Rh4}d{Owtt3?_j
z6!M=^d+g|M!nGkDf1xy`XkDDN9;7TZQ$SrYM3Gv5L%$_C0og9~ucAA$-6&FTHOKVb
zT63`hG8hUR3|f30?;b<>N76aaq(xnUB1BxXdM|$y6z*6I#>k+3&&ZDZ1Pg~3LhOCb
zdtyWDCH`yN-!{SIdP9DL1-_PyH}*6}@@GOKWB_c?yS^sJASQ%<ome{;D=uWX%C>L%
zL<bAAf<I`$02UNH-ZFsA`r87`*G(b#wW~uh01xvwc_rsyufz|@JxTFIsh6Df#;Ggq
z%|ef^csqT1PmSdhIdOgPNAPa~;K9miTpw=`{qm;{s{c<W=P4<2DR03Ba_CLE{H>Kq
zPi30bzLYp0pImlDyi#9obgE>4i~G)p2RUQM?F??IP3XRk@nFD8sF^#@X``dO=e?{=
zm*h<DNlmlvh%iaN`y@Qa+}pLGn?Rl^0V=>Hn)3ZhKC&b3QQ{NyLYthK+wl2DiIy6s
z9)>$b9j^eV4;joh^5TXW(soX0pBlh9#zoF5Pskxg(bvk!HSp#>MW~I4mJHS%#g6(i
z>?R5#Pe$_e(YYQavN!Kl{KXk0jS1vO8>;8pGZ58^lE>Ra*1JK6=YrWGB&z?5Af361
zjq6HM@FRgWRv&|(J+_!l&*|gdGBBA@)<*Pb9Ah)N@*)O>G3GtWTe(0Z?hXKn^oU+2
zK0%-!HyBNU6Wni<B-##$w2ShKhyXl!mq+z}=9TuuqlX*(!g%Y~Qp>QFQ_<n6qp@pY
zQIV%`<7H9lfeRhctaeyRVaA8qBHQhTth_MYqc@1yiP_bivePmXIl{!xp%Q6@6P~dp
zDW@0*CG17)nU>q+%tbE(-h5lqEU~EmsKQsQoma8%nlMnO?qE@qAt?3RXuH7?KF@Bn
zfY|vgVR`h;L3)ZWJZHFYpiXUpppkq^wQvEU=Je)e69QR?CfFmbXq*39>9_i%W+CGI
z{PbrjBy`b+^}L9EAj6TtI<zpuFu9CjWIeyIrnG_m33!8xSPt+6CL)%eI!%7pSbcLy
zc6#T6n@DH3ZuJWx>H^BhXe=6)WVd{l_)~Ac%G_;Vf+;McXi4+4MyuM$?z5ujQq$_;
zIjvu|GV(r^Gz2z42q<X4c+2F)frEd_XNJF>7x_udghB|D*Bd<Hp47I87<ygvPlBKc
z;TLLOrf#$xv_<_M-keRbd^z)zgUf!CsBvj249>Wfw3TG`eWGub<Sr@H<$qR~KiK86
z9l9I2Az>2<e^Z#^xSv;A%Py#}QW#vP8)if7lVbQO^g>R$Qn7i&CFadWra`RO>I~Bg
z5n*}8Ll*s+pZn=gttCoF9XMX%7wvufH;20bT6?3nfLsz(H<3(8xboEIe`Db(mQMYi
z_tDa!lEVnG-_ZY6n?3^T1)3c`Y-w6>R;G#Y9{RAg)DJ7>x0?%a%zOJojGnWWDA3zy
z_CYE1(@(EOnW)iQ<i!ZAp=SVYltdPW<x$U4CGV#!gPLcQ4)+$G3vtN*``6;%$Cw}*
zh4)$5=XXkUXqPb@fW!`h$|5ORq+w)WVxnGe5ighI#{YfK#em_7<zigy@&2>gziUrm
z$d801EF%<dzTW1?<`G8qdr=uP{ePYQ-%Sj~Xwf6MyyFy^$YDJ?BTIfF?N_-Ce}6{=
zKqL+9ZjodP#s2FHX8=(jiV*?+g@VoW0^(-T*f?m}%;fL0SOO4U@rbR;#oaUZe|P^O
zi~Q3cy}WOj{(F`oTY#$z{vm4s?Xo;_N=fMr{q2+ScEy?zU4(oY6Yl&}67sp%#cAWU
zLxIf}f4}@_`vy2k8dEcjG@1M^X4rYHpy@Lm8nbZ{vg4Xiu_C2+LX`igvt{ngOt>|b
zjQl|*4RQpGAIUFU1Hc)xJ`ssgs6W~auTM5(!!s@0eK4l)u1^OYQp#e2xbD&`$%OU8
z*&F)9?R6U?eq0QjbCYN=X+BCVG=R0&Wceth-o)(!N|!f~`2$#+Q^PT?p!ye4t6HJM
z|5?Xg68VId`9{lC44lhShA&Ve!W$1;8UfUcmW|Nro!lf+33{UX^BK(|N~@-F0Nrj$
zt<J2T-iv!18Uk3ioZnVy<H2ii1PS2H=6ul@vl#tH4n#bgrfP{+eZ^7|pOwz>_&Bpq
zp=;7{D&H+7yk*Cf5RN5gSlmHixY{}T9hWRld1=t_5Q|m2!G60wIb^F$O&+nXDSBA#
zF~IoWpySbx>CvVP^suM|C(Q4-dYrnxpS|*OoLnq6*Fo0EXg1c|fSa59oT7JBXJ>lV
z5zG>g>h@LD6i|=hEl-^VZv}d4WSAyfjf~|#2a~cDf)`A)-ldn1G&vN;vZ#_ycy6UQ
zsUi#NRIV+`+A$@?rG|%7mCx%9x`UnoAXcOoSVRY2kGE<_=;o`#vcv3Wud7ZP9DQ6D
zz;zosVR6lh=k3JZ?_iEnB<9!j+9C_RT<~`wL*X!ZrMTe_!o09@M4(6jk)Ktl$Twfr
zV06qV?b(F-dD#)XeEaLN)N#e*^4sz6?r9|0-zqcqDgLn5ZjQbO9UXm|+HH^PX*$Lq
z2cOp{^&3(0L1}1_tj!E4y|+tSBAZV8CxaaD5oBS{H)N+kvxlD!n``A|RExAT<(Vq>
z4-G!PP14fzsJxe(g}N>vWGDE>FOwX3;mn1tI_EY0)zjfKEns2jdDgV3&UG$mc=s3)
ze$kA8Mw=HA-meQQ)ja6zn=gzv9eh^)SXz+hvK$k>3$C|3uWndF?fbc8{NAIo#RVi2
zayh4HRaxq?Y@P<)UqCFLUC^&6=4xtd(ij$`Vci>!kIxuaoxe%D@1=fGs=7>;XWSUs
z-$^Li6rltVK@jC&;F867``v3ot3OxI8z()ZZv{`htx;0tFH|D3LniIwJOR&I@As_+
z6&m#n^V{lAafkgC!SmSw^1k_;c%3izNHC2;X3FuG%htCfXw9nzD<rs!Q1-g{sFKCe
z-i9nX)ZRxHXifJ}T4^mKQ>u*l3t!{42avT}E9yS!8J?8P%kHLuc;}mbtqWx)0U*bG
z%Z8m?I=rb_d3eJvw8vFsp#|ViX<4UVz3jLcyXS9XN(xJHy>eypylh-?81@UvJxu+o
zCQPH4#I)OB(&)<R&0fbp!0igQLgLRS#jZUj5*p~?YvGQKxKLZ@`Toy_6|>1n)fMf*
zjcM?{sgCle(f>xBro`i)p^ChkP80=y&LY)=_uZx!nPydKZhGC4E$*8aW~;JXmWxtz
zwLU7JHoT&lh1xD7%pE$>Bi_gLO6k2OCL-p07ahT8sXWNYt>1I&jZW~A=MoLZSj$#W
zT6Yq}-3Y~gdLqKf`Tj2vfU!~U52;y{P5$V@L=LUGi2XH0&8H5_n)Oh^o~*R7ECODq
z1`wR9Cq**d&!@kS!CN10rvD^a>1aDle~z^S4~Ag*0F-ei6l9oWn=j;eD=e_*W7Tvt
z_Uf<yB+1jhptVuf?YueM{&t#SUxfg!guD{1a#Bn)2lLO!pGi73hbrs|v#5!3p1ri6
zO-nRA)X_s|{ct-6E8ceWGz{q>v7Y{NLeC(sI%Iw^z=wEn2|#_s7l)o=vi{S&^wkyS
zkKC*Sle1;x+J_}v{`(mZC*dg3g`1a(G`p8S!S<bk8@nP?o9~#5Jf(y+w!&<bq(E*{
z{1YJCR1!$QvO}{=>VF#)XXW4Bz&La^xvYk@{rQHB$2!M^Zy!Un+d;hvowR%$Yk}wE
z?TQ+ij`P}BNR3{nZ6>nR4o;H4R4XYk3qNpxyM2xeqYaa*rB}CMGtW?}@fCr=%%yg3
zcHtZzm}^bvahiiC?5J;>%kcFp54(uVbC_{{8(*k%KG{$-HV$U1&9|)IjH8FAcy~Qv
znJ1nn0ekLhuDk~4%ck!$sL)XEcLHCiaH40quG~nzxbERV{n?@=_*#E*wR`nV(z&D>
z>+OU;yQAM|?UJi}wZV1Jhu&7&l07{_e)$bFCy%zDmM8Y*opsn<eG_XUy8TC=mlxh$
zS$E>|#oz%Ov+y;kkGz2~TQXi1@8S%{LH7~iD4$cnMr(y*qttb65sImiN0Fs3Z}7bg
zSgYQYt`Q2C?l5*<mXV1#{c<p=wr=m3P}7EQVd&_?-MCcCpN{kB7tj5wlDDSxs2KO4
zY1vLwJQa|q;9IiJqR(q37u<KR_}|7e<o?%jxHg2G3b(!bc2_FNGtla3i^nhF86mrY
zi(T2f?t{zzGW(4Mqwi1((iUIMBAZdM7`Q0_&-E+4jF2fvwv8{8*@`u5;a+!!{bG1{
zcro{6Xc(6fM*&FnzOMyQ&6INAB0hRd!kVsWD}0{%MWl#mT9iwd%hU9+B!TChqa|*Z
z2q{C+(SoQdt|KG~Ub(V2?p0LCN*-04j%OOMR1CM$Cdy^Ebx*Kw1u`t^%Z4LW3U<SZ
z2FG5CyQfgPT(Z{ByTO}_@C8L!)j3=HXgA|B+vO<Nt>^RuV%MLkX{p?N#l78UIo1a@
z0qpk1l`sc&66RPsm2XV|dg5L4`0}?OszPZ4L(N&r7%AHU8V;#I@pKWN_op903e|LE
z>lj|1GPL?l`pw9__?d>GGAUC9yStHKBPd+xq%VVuFLzK450k~~;WRC{X)9Pn8U<f;
zZrlVOVUe(P9>O#7GGQFoN}h1OFw_JsHE!&<-J_-4e0~Bc%QmZ}eH6SE<LMR>@}n(<
z{eJtjHt(UP0a@Bo=u04wA0kX6GzlGj%O&LXe~qR7*I4C}e8G5d44S4W)K?l2A~sxT
zfb1vp5*0?25jC{$<kZTWCHwlKrMrkvat7n#Gz_Hht|ck}v{*^zF9@!sf-tCBZ>Hge
zp#nT@a`Z^H57`~oYt}7^it{XJm23hAh7UfC_;+)Gb7>{bOYmY<H0lo3G()Wq-PT|n
z2MaiQntIJ4J1IFwYEQ37EJ(PophE`cyPjy0s$sFkGx1im_$sq4qS##I<U4D}5As7K
z=j3}a*`6Q;0p(rwI0vQY{jP4jC&n9*g@M;&o4#|nF%g;?XzS^z_<go#!*R&^iH7YQ
z26t?FHa*Ds)JJ)u4}`C)c5}+o@XK;UgBp-!K#dU|>*!V<(Yl;o;?)M5v}>ZsWptpD
z$#lW%5nVuBpLuXn!{?6(W&f!((O0V<S!BSyy3Vlart8l8@c^z%_nkq=$=e9S(Fg$w
zenSG2bIxbp?F6Kp)9|mx2S`Yz{cCa6#}H=Gev8KCnjHK_Ar^~D!na%m#0uk;2OSPY
z_k=60A<b?U<0e8M7b;uR-ih9aBAB%%;{LhFuHr~PPUro*#D3{?dCab2sOYl|AQyLV
zu%5<qpM-jun5W#w*ipti&s=Q#5;xTY^E8ZJj40SfSY|7G`?@D_>00;rNGuUszW7<J
zVYU%FB0x8pMhnwMU|@<E7m)3@<P*tfvtKyUDubn>iL)*0r{liAn1qJ=_$<+TB7D>*
z+I7_HIMO4rqil|4ifGFa*I`QxKXICd+8B}%?yDU-4cpaSP^7QJ)-_!{@6iDMWxD=t
zwE2Sd96hBi1TOk`JsRwV_JRh6)q%$`>%-gq=Dz1giX0!+4(Xoh*00Yg9voy&3oL;(
z$6^N<$`y>wPaza6>ymj4As9qhb+<=G=%N@j?ix!PH6|8wY>;O!w>1}$mCKFK=b-76
zp7-1>?!3l(#>}9yvmvb4NPukd+omu}Q4yS~i=c(`)WU3iDJeT7Kt#`ANl5UlELt%L
zY#k&fhGg%Qv|M!?w-=Zw(xO|{O%!lAV`UqJ?5sd%mvKn#+vC__bQBKU%5mn$9H}H8
zmml@b|1-xESUFt;YTumopejkASc;r-`U1AvcLjRTo^+U@D%rRcl8j@7_colKlQzJ4
z@F(pjx!O%x(fb``+?_0vAAEM9?mro8s&;!tA8gJ8WrZ>$PQDJt#TTPxGCP|wi1rCD
z5@`V<*pjSjT<Btd@qTw^a5RcoOh}YHntwhBr*9iqb~i}Qv;yOz5xsjbV`yZ6=rb6E
z98H+-#1~^!V6)6K)L_*tsjV8Fiku8v-h8E5l3ilkAs(w!CgAta@($y;k#y7#`82AT
z$D{LAwB5g38K7zG2ep<+SR3ZJ;i$K_F?lssFzpZWim*&fDygL&Ds7vD1RQd;gdw)A
zrM5#EXm%YcfCwTtRDh=T<d3S(aXaF~@5n;=(yBbe=JOf0dAthi7?pJ3NY7v}0pLwf
zRElljUB+zEWJvg`zi6D$4%rNv;sp-*-1-7fC&ASuhIxaYF>CdAkUNt7ctu}iCEo_1
zSm^lN^VaK*RI3lOLN~*A*@qS$%w#vj)f=<bOMZV6-^vQvK0RS!hZAVn%2kiH+{QRc
zi?`lwSH6w%gfZ^_%?e1RlOdP39gLiq#7_X9wrRl~nKQ&?-Z~Y<C#rQJ3t7zJsh{n#
zbD60^KK{<;zUg!ZSk&BgyX=jQ<Q8w9j^*dcJNmGGfZNwW3#<_Ae1f9eGUUXcP-Tb)
z`!LIZud-YFmK@Eir{grAo%9>SNI*U>E@WCWM8k{#0IaehKaqJBlrbxS+>N;U&R3of
z-+Dk4Vdc_Y7@F4J*r2}rTl?-yA1NA($voNsoeH#wquDq2$Hdm{@7+AM)8Kn#cVhv8
zK*!sIN~>xpvl|MHABb3{?fNMgNC1@;S7GJBNu>RCBQ+(AyBAgNogxMkwTPx8(USHG
z^O1HI;43Bc?DHKokJ=&5$N3WB3TYF%BTg8@212k=t~*T(JCqIa;aD>JxD(-EqpZ|s
ziz!uqH2+k#i^*X!)QFoSv-Ifuz>sJZW_RykaEnRLT*R@85cA!HTFmK)D15;sJ|*~2
zWtOm4%V(M}D+2sC5>}8#u2P8tww3({JqnqRbCLO;Y<>K{{Vd8!^d&cpP>EQi2A@=1
zrwmWZk-Kya?{R15E3yU@+{zKS*wGwk)y`((FB5yKi};<<r3cpEV(~2^hj$nH-J&u4
zeFeS>X?9txl{t&iHnv;;gd(spr8lIhg?Qnm(Ol46lA?)udF#5MRyComf~d=!RC7x|
zUH1mj3|1YIuX2G)ucwC{>w3}l>>9iXZ_bgr2_Fh>3)e<N?VVeqz7HT=4l|mQ<a^A_
zk55!lrs){Plt-|3Mseu}gN^U6wNf*}&^)oU0OoS_HF1U|b1u^3{W2Qplrz;Cx^c2Z
zT~6XYC`@;-w-)Mr2R+i(G6XV7mf))alX>Qv_92gpa>dnGa^4KBPjBY)Tgkl~2cobB
zt_>B=^6*_Qw?np6n`^9XKN&2gxG9C${NDv;9@d6<WHbp7-RV}4LK!XXrc99*&7xoV
zxXC>cct<Z|Y%xs4U=+x35->;isV|0+=~oBu7&bCzV|_;F3&Cu+E&27vBj|R#G+WNq
zI!fp^Lj0nuf?^|FhiAiL9Vj8|Pt(WDonLZOmRB-3?TBxzBcbGRuQOE(O5*7|w+_Y^
z8U6q#)TLJ!AH&eRTchN5b251ZJPDCw*bL;dh&1gYXOC-OL$EHMh-dn8QtG1qXqZ$M
zZ4Oc9dqm^#z(GM0XU9TK^nC1D_Gt6Mfg(q+?@Q4(G!H(n_5s8y06H|h>}6XXsbm^`
zX3Fcm(9hgbaTmw@cf`dmm{$yatArX9bf|%P>dgu`{G;KnRyhlnxBKq|=Mba9Fji5-
z`eavk^;SrT&2Py*!IOAA-K;t$N?Vl`cyI3L{Wp_^*niHyD18K~oM_m9R%JmuPg0kC
z!y49;@4Wp)wAOt~V$5}A)HG(2$?iTewDrcnh~4&yIC-Y!65HB-IuEK6y}cpkk|HL4
zI!5bzhL36Ni!u(nY1Ee>1br8B?5ni{`-w;`^Mmfh#?F;xbQiG2`a)s>J#*t^**{x3
zgML#}HC?~FGa)gc&W;rlTn)UE5%;^s;JDe5$-edvWH|64f?;^|(OqEiNGLJ8w46p-
z^f4UR<5Ckj*Of`G(rfA~obf#Y_cVy8ZU_T|>>S;uOUjMY7}a&;2b}=AD_o0*B9C89
zSB+Ny$=eJJeL20SeLARk8m^ufm1(gxie7vD5h}>~e*7NmXMx*;I+XK4NR{3U0PPti
z^98ogRc~pVetQQ72H(SRQi}ZmYdh|YkM4eEMDP>CgM}+UDq;8O_~YH$Cl8lN+GfiC
z(r`Seu<66^$=EZwp_L@CX{~Ildt7cKv!Ff9sErcgxEd_h2rVP*`lb@e8h2v-#O{MG
zg212)<FIAfiij_=el)0(LB#JUojhv4(*}$vapyl3I5RI$)1>niqaVP6aEEHKV5PUr
zmFNz_wu=VK!jo?qB8!w>PECj8=lP0A&GCO=Tg+63loMk5U;;?!KIeHcq6s-1i5o;(
zic1k`HB8YB2J(U;H&<DFLb0Mr<hpc71|zP3y6Yx}w;2J{UrNt=50K?0i;P*_r1!@e
z@lU!I${)tl!UY}}?(7kVe681tF8)H47D2S1T1!JSOng#&a9akHund&>H)DM8o6im_
zM@7EAC3CrN8?K#;j+Vww5(4_*$YbiWzxPhDy^U&L#V07c)T%&@;}%+&0F9P5OL6m{
ziGa!N)|t~OqO@%_+sl7s-1f*)cH7Y>t$(2jL!2pJR>dzE(v03zEyiH}bHzE)iMRK?
zrYW}7e+u~$ZFjI3Fg5EoiFwXwIyFmt8}`TLjF6+uL~;*RWG;vpd4_xlH^}Q%i9`XD
zc-%f;E6eym^HL#V3?Y4G;6SPmxuKa>ux^h-x0NT5gv~HF>GTV9GwE;Dva40kssQE8
z&uaiGc$B_-0hZA9kGlqCr7gym)58%t;h?Xi&Fhr97@u0GMtf|#W+*!W)^g5gaC~=6
zOiY{_uu9Uke=Q1{i2e6uF|;3l@=yq1r0>f+)!R_Y{h5D@c>TINNnthW_e;~_vP72D
zSnS9PCStUQb+lAtcp7aNNq#6@-u`gLQkFjJOzAdw8DRs5pWnUQ05G7v>h`$hzIxfW
z<hnM~J<>-%PH~TO!FuciV#OpadH5}U?@GEuBIE`J;FWzi6!2k~wfHnOiQ$!i+k-)I
za|z~=ou^W#)J?$A3)xZR`}GN0b-G$R6c8Hb&Lx6I*-Ex|loR1o<g5hXvk2YFCg;%>
z{pMA4vu`zJFCKOOFA2;^iu4yKaKFtP8xXfunm?8&Y3vQocZa0}CUVS^5YZO+L^wOJ
z`)qgur~?^(hZwy=5VLgQy*(A@B$nL>SK8p~Ln<i|&P#Sh3blvT@+5l9s72IIv=`F!
zJwK5V1!2{uOViN7*`CN_+DM^QW1}fQLsJ;(b!k?j8TLtt+620QHI13B#6EdMC}hX5
zi5s2oXn`tRA6&NLXG=R&iRg*O(r!89n)$bq?fmc5!rO4(S8;tDrEw)Teb7wp$~A{m
z-k}NKoMD@b<2Rml%6OO#64v0bOX{wwT?*e3C^Yo@HM!YWs<mxs5ZRLhFOH?tEXG<7
z=Z{=9jdaHbm2@ANX>o?CcpQlVlPs0IoSaw7G^sQVXY!Z7N1VdC)F;uEp-BDC89V*?
zI1>f$-xM=SHC>hO!bP-kUt&-i&AnDX!t<l)nN+V=T{5hF0x8I1jvMR$Bz^Sz`f~@6
zT(;5HFv#mj@ZG%-;w0g}49yrN+Nm-k=9kpR*Te}_0EAyheK^Ml$qpi#Y^)z=DX4v;
z_M{vAK$z6nT<1u>Zh2Fs#l{8W=Kj058Cr+Hpm|pc%oaLJM1;ib2(cgqx(I#J8NG4b
zBCY-z!(;`i9LU9C_(?XFWuWLHk%GYPy_qbHgl2p}wCRZ)T@(VUA_vLbF$6DJIL(Ox
z1&L*A<~P`g^nk@zADF{${ITixs3**m`?W6PcX3o<Y??bTnr5&I?o<a27;9=Mwa7Pd
zC<+|Na({4)PH^*qZ2ycBFz@`*D3n83hfE$^#)g(cQ`%oS&o_Ab11{&bN{)<%-O4SL
zk9uV={1L+FH%^3cKC-an4pG}6N9)z_X1TJBO7jOl52KyqJwH)I2lII<y7Z#+Lr5A|
zs9u-muT`oR{*(BX3SUo?SaKE^LE}{>+&Oh_Z+<*1^vqOpwgUrq&>;k!@~*}CqOYC4
z2W}7kE;=_rHX4POm!%MG(qB0sy0wRAA;|M8$25uYKMY3i4ce~#!`SSfRJE19N-4bo
zqHQ5qc`|B!gUQ#e;3-wS78@K5@s^=aUdZi*jb~}{z7Mlp5;12!10og|h<N?tiHf*f
zgcSG4vYHlyRv?b;mK-%PUMA^W0Bf4Z?*}<Qe~Y<*r`Npl+Wz<!#qo8A<Xu6+XZ|4Q
z2^IyVhh94VeOV@9xnJyuS(=;GERGjciA)SA*us0C)i{v55fr8}rvSbSbTrBYC1He|
zRSYLUOq{lZ9M9Jm&Sp1%Zn>-Ib<lXY{(9!<3f}gQmC!M^?hFO9g6ka#TNZlB`R>Q#
zw30;Kv*mS%x4bQHV#p_LRc<rbKt1o;G8I|3ibOBR!*dlCl18ytrm>ypO3K2+*GvkM
zJ7`i_Jp0`jc#N?1S*wlUCcTvde2}QiwabOnv{&|Yjqm=&7gj{-(XnTtW@dfK;%ysx
zq4F}E6YvLyr#KRSsBCyyifEt9$qtDLT9Iya0W&1ZeA1<o#^5OzHxy9)Ub%gls2xX9
z_g!OUK9`oM`)5Y4mW_4=A|cR7T>#$#ZQVOi)`#)yaT%pqV;<p!@^OBjf-(*{+uD|(
zu|gX2N!@t!)ZSBu?V};5Z3Iz2d{LtH2`pi2Sk@+de-B?!c6W54hrXxOe&&SXj#HJ}
z@T|OuU)6j!V1GP*CHMvsaz<5~5Ig-pgv}#F43pMF2x8Y=MXTiCbt{aeZ$1Zl!MPWH
z1CjBd$`T3u(kyZyy=(#TS9b8qDjjaETd^QHhwk@$pb<#&7=L!zlj)QV&Y(P2{Vfm%
zJx}GU!C6?pe6cWft`I9aUi&~Vn8A(+wiCX?y0&z@9CII#?)dfU5F)7X4{9T&MF!NY
z<XwpeXdUBsqwXng#xp;6a_^hN`qdZD3tLw<dVU|OFN91V7VNQDSnO7o=rmR@<2GkS
z7S&RWy1*Snr+TG!C<@Y!mujil<^RJD+vbSW?L7MC&Wwuwf_c3C{*QBq8hJHL=0iU#
z|HBufeZ`E@m}K3)DgNt*BLlJ<d(<lwYbkhg%^O~O4!kEq$k*hC7&hd6ww|A@h#W4?
zkAIWOxgk?j6_Z|j*J-y@&yVci5GT-K;U%$Bh*)t;3d!-1{6#gl3IB$l;WlzC->~|;
zdE>uMTW*>C>)WJ{*!|&$d-pFP{Yw9uCba(r8-0ORH3`4&!kackpAS|T2g7${|3a4p
z0B(6f!5*&Wzd`MexSli0M9pEX#!6G_%SE^G#b5<Kw}pl3X{X7IQnG6uqlGhNlY8|&
zOLLxuo1ZI*s0FadJY>Je;<3&u)S#xej1Sa3HZQ^#HvS?<vrQ|xc_l6Lo`rDc!P5Vs
zN2F>0LyyK!qQ9!aT^Er}nFk*GXGB#6nFmU9Q~x+5dHg?sqUKG>@D1{cN><6Cv=s;U
z@(t8{R&_-&ph0KDEz)dKH09i|ys>2XCBlx$MgS;$tAu=vehEF(QMo(ekaaoGKg22<
z?$WBL{1?>vq^lgK{Xg7<P2_*LN#^GN>z;DVV|0mEv7=IqwxaOxF8Fp~qhWd$;SZ#R
z4pfQ|CrANY6W=Cx9ei~7Zyx-Gvi}CCPmvKp-&3CyZ3kIB(>Uwt{U!7XP+sw0(?5u(
zUpp0fvC7N;gQK^-{u=I)>!%&5v5e6OzKcLI)({qedpm)+Ge!pKDzxGKMWiV><?M#L
zmPoi~WrXe(0mhhUl1T$5{`F>G|J~6e`0qO<$ZMwl|LX^G2HpOhK^LJrA(#10{<V3R
zpiyVF$-gt^U)JL9#~&&rran|pcjy%`$aLoR?eN>KSM+4Z|F_8#7&S3Dwf9sDZT7F0
z`h#j0?Re0vi~M7!{RKTwaeHBKjU)7Y**<?)x&^5)c|WNxnrj0+b>Kb6pZ6T~o(mKp
z17|74giAV~lYjKTlR6jGX#JpX0>5eOSB?!IcY@1`e7Ar<NGFZ=jhfE)tdv-`;N2_Y
z&qEwZN@uH)7~|~Xh3&6QlfGX%_6j))!OiR747KLJFj&@`Vrk_tS=<OV=ku=7FP|<x
z2=Df0{V+dmz|Z~<WyI(_t0waM;}tc$t1r-FH$L&m8Qa{4XI_TLEYa5V-~%CCBM+r>
z6(4zL!p*c6-9d`N^9z#vp+q5b4$(2^`!w0a!WxZjO)f=SOg-y{+TlYp18%A{PD?HX
ze@2sZr*DhWYWb3H1hYH{NLkh2-~A2`zbM0g{0*T<-In#r`ai6_cTiJN7cUAJFi0<g
z2vVgeO+f)^p@Y&Cl->y-(o}j4y;nhsNRcYiLsL4^f}sdVuhI#<BtU5I1i$ajy?5T7
zx%1}zDRU-?`>eh9TI*NVmV7!7M-8V0?BeXr@~^hNJ5Qw#`<E;Qoi-SMN@W8P8C!?L
zP;n8dsOj6Au3mty@0@)%^{04N<VP)b@b+j9cXuu!g09hP!G{07cTpyKaj>JtbB0JO
z-KY+0Ftdx7r|vn*O`shvqaXN+1EM{S4mal4hv)6fR)_#n!Z$%=BDWq_GFB$q(#=l+
zde)aS@4D}daHZR8IW+EdS;Bb^aUA^obFYn3U5Cm}y3%@`GClnnmqqmvfH-apjd$A*
zcWAjeJ`Fq&^41AxdX6YDI{d*h9cyj?(6JzVNmA5(##OiN7SM~Rw{06gQK0vodynKF
zaj2USxzBXcq2w3p>rlIHxjvLX0-)!H42xs{kTY~<TV*beI?n)_6#r@x5nM*h=s59)
zo$B>&860FsjTrY(QzF+b(R(4a@-VIl5Ulvit>9|US99mv?!|znt$vy@?!xbNl!}01
zZONodqIxkm<fLb$wN(ba3c37&ZMi&ck$YfC*FtZ&UJn3n#f4H^wj)hDzB@xpfW*B_
z8ao(ACpUF>YAHp~)I{npvF2BD>En61(VEM%Rfy>f;Egi^h#;q)?Rr<*uv>vtD16uC
zi{;6m<QaT_YmT0pyQsxZjy3BzRP~A2NnVFjh|c*qqkC<ntBzg!9*@0bIUVo*28fZG
zy?Rtlv>X70ZUhiy8vE`=neweB$fkn5&F_bctz`#AHU$R#-pO2e&1WH*b5RI6xAp9I
z2M{0>&PM|sBIxr)a&=I8lVj&du64h}MyeQxKSx5kVw}`ZUW+ouHbG^qgR<7;ul9dp
z0B{^62E0Kr-68n?oFBYiN2(J4sN(>z94wEO*#5r2Vu6!J>r(j4UmIi1oWaZH2AgCY
zx+dOHH8#Ff=_nUK((Mb8S;IvqJ#ta96kgA3fbxnq$LH6*!k6${vS!L~DIePBt<PEu
z5VruE5ZG5gbg}72KD>o*V0#dQ_g0NAv7Q^iC(v@j82h^~f7I0h97Qf3L|fX9snpUc
z7W;L4;C9G)^zQ2%Nt#%wsgPM<oU-(O!ZICX4m(<>L*a>#51mk9E`vj1=VLJAt>)8h
zPSxFuqm~vB2Y}Npk`%z%%VFmm#bHvL=2BFc`aU~Gn~R_5am%>U!^fX~DsR;SU{~Pz
zQ|^qHnS{;v2g_|%03fv-u*6HHuKwkRNIg4%Tsph{d7(_!Qz~43rjt^e%TP6xtqJQm
z_+7Y?uYZ-S96rX;jnKQjREs(XZ3}N9)-o1%Ej;7xCJ2_^=LMQ^1R<J4!lT5iu;4rF
zW#nnXxv|p;#fj!eJ-mnC>AiY}%gIhZb8JEbAB4tW!(}Yj;rSztt}8d2uw)W^AvYlF
zf8s>?d%z0ShP6KWUmc|0<W^8Uyg7b0)pBl#>(^V{(pUBX%IZ@c;H6nsrngw{=<=Dq
zIEx@3?-9fduHP2eTqt&XHqPXVB770+^NuvDT^AT~IA~gzTRRAgwyVqcSUMJz?_v(o
zOqTB)-x&5VK+yrXmH<!OLl-DxI=WOnTKGpkGR2J!M22Pds@8`Ep^IApW$_3QT{@WZ
znX2~>rp)zcbPOF{y`_|?h-h9r0=5IAm)l`oJ4S+5|9qb}Q_#04SF?%zZM2oQ&zF?B
zt|-#EMNf-GHU6e?>7bePqV^AN+_-xT2Q4uOJ+GQnYs_zx59~cmpL1>a%<;;O7o4iU
zrYi6wZ_;@{isYeu@>b=8DyLBv&;0A_Rnwg%2LL-oTcsGw`W8^%ACbJM$s>wrm=7fR
zwa<(+S*rqpCltX0fPKvuyK^>G1aA&(P?7!>Kz-q4mHACCXkw(Y2*i(J_!qnfXD-z+
z{HHZZ_sdQ=^<*mK-5kU^q%I7)xEOz4Cv2-R>W+TWB)fSWqunqX=56oJubLwz)dF|~
ze7i;=Sz$)g!Bm?pGPC1%#OqLAkNSo(IjYxvQixO*=^A45Tib}_E#o=frP-EtXbvIA
zNbGPG$M$utXlG$x)&TLCyeh*eeO&n3DVnz9b7^uh1LOBiAWAvc<To{?(0>-feD7`_
zw54|I(Q+31t--3)MT;dxZ8BU(&9XB52rZJkr1q(T7(Sa`?5B*t&bnbQCAO-yE7!Lh
zh>qqZ*Pm+M|1|FYSCL)^uo<#yi<*U8?8SA841Bwgs$)4pDBp-J%y2XC)G>S0b47UZ
ztOfDgV;bgvB$(T^mu)MzWLEj+$>m-1{BK{F2nhX0cs)p48MEYz>oNYs_c2pWEvShB
zKQ|n%E%zbeK|pIKy{S?1t+X$-q}@Wvz=3;WHC$o!?4fq-%N@Yt)aply^If#8o8y1I
z0RD6>j5V%24w=t<*TrDv8NF;StG0g8qv@xW;Xdohxn_<8ZieS7Tow`0NEOyV6?S&J
z8OVnykg|C(FlltB1;+LR)iZUuAH?F*Qndmbt07{O9ip@s0W-yJ@ojmIHO#(BepsX&
zKCvj=NGg46l5LYfz*`%{h~WZ%1+ij^cF{jc1o=cJB`lu&DK<fWlS}HmXxR-B<de-X
z+kjjiKvbW5EL3Tfkc%H@2WCX~;}Gr)KkT}dnwdNVE|vMyZSi$uyla~^$OIj&*mK_%
zN+`P<Yxb0tQ-(4LOnLE_<>HH^Ci)$w<QB!a5-W|!28+_9gl;qo6Nfpw#2#SE*&vQx
z%T@KC_mrIXKMx}`tw?Ix*TS>JS575S;A^0U?hX6mM_ho)00xmBdV#*>JBX2hav;4A
zMUVsdSght#c>I2{_>1(BXP5bBBHXSj!9gMV64k<E4?FCYs6X6ZLS#y@W5QU?tS=gF
zPDCF8cbyf-0psj$D5{OZ?=4$50T@cV{)q8nHw@C~)yZq}0$eEtnmg%t|7Ih($iVrX
z)uw=nDw;hVAaqD(dkuKcY6Ey8?7qij-EIzj(^o#%8$ZOPQ7_`|pHk0WVCpDHcn&X7
zx-Ip+K4B#sGszP^i;QzTA~#-s%9joeWRXn<-kdw46i<#(jJOL#iYT@R%kc?Qk*1Mq
zoh8>b=0i16$d8A%@?z6K@1Eji%jwL{Ga^K06i1vS94ZTC+jh=6r9Gf@9}91^GV9}X
zap{q60-h0Ixa&K1&hyOcjH>FCfPt~U@0er7^yHWB=x^Iike;AN8_6$g$Gaw#mn(@`
zXHZuh2khP3=<I&q#oi-Lp9Pu`8|j0zA!TR2L(MEVpT96Egp0#xbB=j-2+YrhVf-6|
z9|K!J@Lp7oX%_Ad;vdL~{$*!+I>~OS-CXHk>2E+(s1MSAdpxcT(IJ3Wl-?T~7)xj~
zNRJVISZ0t8@f5tuup{Km-afO>sm{+-Fe9M+3b1bQT(!#x(&UzUH-Gsk?6NTiekKO@
zf_PPg*uzr0HXjp2@DSaT4_wzSC$~)|)*n6LfpdUMmY=p<pv#f=)n374hWUrZ88QfI
zlFpLvYLN`~wZBi+lp*!;ruy+b$rvzEZ-*fYiZOba4*R*N$>TMlbU7J0u}31#aKmcO
z7sH=^LU_|>u};)<(A;gB`VJ=oPTD$mu{EayI}n9w6WluALm$p%m;_3~RrWW415VDZ
zukm_va+BgNKBSFF>+E~CRM|0~A<l?XXEn>+uZ_Nkt}(vS#qn;-;fzkTmGWi{>-CMt
zE^bTXX8?5C=moq$!$87XzoVoboj|ShF{=iPCk)ek&eR_FyVjlzcxp^^2n=gc2uz1@
z`0kkp)${aAQJ_!X4ZNrT)5}(_k_h|)vN_kE#N0`}iKkzLGaY0j=H;cPty1{9-*0)=
z(C(~R#tJl(1X>#u*;Gv0?~pqD_XraJ3f&>Xq>@h-pxc*EkM>zkX${4`ZO6X}p8D`L
z^<42=!qdC@wQY8~4*0B$s_(tlL7sNheq*>4B+^mU$|8cwH;|Gcv`}*So_u+`qFdkY
z<uK%2o2|+89Hkqjad&sTf1d~cdPd|$e1D>bcv0_LWIQ-I3DCtiBan`do|d16*J5sH
zd0E?J`3L?s7|JJ4^If3H(zBkQNhAaZZcOg;1#|=QV$+HbY<mV~dRPQAu@bH7^qPex
z(6r83+%UcLO1RvMgBi{CcA9`@Lp@{{A_k-@u{)O+J8~WTVISVTSNJsszV*zB<I*gs
z*1q<{aJ^uoEY^3&qg&$HE;os+iS>F-xwixHS!3HX%(KdA4eP%X1D<Z^p_JxoAocZo
zX-^yMQ5OBGu97EaY(L0&#(p@MMd;z|pXHPN+veVZw5iAi;_z$E1F>rAP&JTR=Ht}n
z{g34(egJtjxi(W6KWDY5bccOh6LK49aQ9SK|7-Q71-I_vLD6~RZ&^N}`!@a#S+=0A
z>UF-?|A%@#=zH*n5NlukcTt*Hd-|8GwivVVbbmZUE)LfCJsIk#tc)yK%$U;8da90B
zpKB@K&XvCd>~vDB)WR)#V+}Ppa@h{rxR!J(_60BZ1w(!#W5J<(#&TzUFUPhjqN8Uv
zl~?X;{di6X5E3Wf=8}<|FxeV`4B-4iOp(2DQY7&E$<7nbY|ksQ+D!2C+asdXcOyBU
z!j+SQiPwDWuv4>`+GlwArSIqZFDQe>9=M(XN9l7R9-xfyR+SO^-dKE}5p9*X*_03v
z5LKyVzoskbVR`EDtgVXV9oc6o8X<w?<h^zekC@Wlh;so9WrC}w2*NKaO!An`Y9hmI
zGjzj=XN3%4o|OddF_-tzcI~j1;O=i-p!=?|eh}m2vp8>9BQBIFI1#<8D6uM}Y94eN
z$RTY|%Pn&;cX>WXElJEw$d*t!)LgE*wex9iF5#J(*j>^0iL!&ZGV%+#m*f>&G<WhT
zO*&tL*A*WWV!%3iSUnP_bE$y*A8n8<o=a}SOTDenJPn_`K8-nGYI<CY%SXrtyxmVU
z;fG@-P9}8>k0-Gv07G}UbnbsE{PiJp4O5fRIVN4XDVM#4G@k|!V-DFj5Ff47M=z&z
z{f#(=W<zQ3-jXHGsTGmd4v#jS7BAZ%z2HK=MhGyCvke}(V3W-saFkYb?%xHORw{Ip
zR+Du`E*q*~kR}*7H7#5DQqc1oP!O3A9il+gaqz{nLT}+yHVM6n^TJw@AMNe?{Kz3n
z>#9>U=NnAzv2=8s(D7|UZ+VcW3W}ixl1u4&>*o?sCGe~Etkt~p4$|1QfT&Tn7&<oP
zLuq{Wviu080_s@7ZE?}~=X+Q^+4y`z0s$;neJh_m=_T6yGHUsiibMmH3M3yH-3w%P
zf8YU<K4#A2*SSU7pDW!BtQhhjh5VF8?(k*p`wE~A=?79a#Nq_(*XVg}tVo(Zs992{
zWM{Ovm(qHsYKqm```8CJZ<16V>{fd++WlkP=<?h+od(z0up1uPGD**@#X5=?z#ZY6
zs8>z^5BvOeCGI^!49;ozA^L9UZ*}TrrG7bQS@!3aHO@JkZ&0=-brWM;W2MbHqLO&F
z+L`Ex*1PP#`HxeHp`epUO&aE<IEpM(qaOggc9uv#fg-6(SFBOyZHnq_QXvQzcG@<B
zB(XZRwOS3zfan`COcLs$&*JrOVliy(U93g12y+^D2AU(p>1)O%_pd?8iP-N$c11Dz
zho?Yl-sz6-Pa2)AR7rpBiNhr2*ye7eUK=>ee8(uBR`vy@+fmY#;~k%CAMwIr=m{@b
zeUP={cZ_A*&$gX%u+N*i`pjCX>&77sFUeyk#5seO74hMUa4<oshugdW?~o4P7m|`m
zsFF5Iee}sz9fh>%r0BKSc#@ee<6Wp{`tn<4rgsX&krUivm;^N3^J-hD%JgbliYGoh
zyXd={K0JEW(!%0|Ay})sD;0OZYLs1RplfnGxdEWDr?=oyDuz4TrOyzl`Dz#zR%Ehu
zS_A|z-a|I+Hu>enG5fMQT7Ot))7~L52{4U#BEE&nn`k<w7K6K_JuO6r`7MoQ1_DmD
zEt?PHiz4K>uu+!Nb|%WcOdFt>8S8rfuzjMufPesak-_tyT>7b5mkacEn?JHS-kKNV
zwQJ=e{9o4rdG!}Q{|&5=ki#(^jmPclie^@(LHb^@J(SiN-#dqdpyTd_tjJw`!@;o4
zX9rKf_UIO+1Sx}?ciUt{T1l=+(l-mYlU!>QnD|bjb2E)cwG71Na}R&k?DMOxmz35N
zF{GU;BIV`-2KSFEfBj*99Q<na+ZIDS;DzfePC%y5tpC+x2L7%Z5*bIJIlyy<svol$
zs-F1<86LJ)aKmXzB3k)y-AMfh;Z=hntTyMHj6=b@_a0u`VLH)@yTy@}{Mh$fB5`Ga
z=&v5`EXky&V0if2J@Atcwp}I}*__Sv>`-~lsfS>Wz}HzJbxij}o_H^pqK}isPXvgZ
zguHrlS+o&lW0mZ|vY;6GXho(eto`ViFzdE{GNz{7x70q9&R_2|?kz7tz=1f@E&dPB
zWO>7EJ>hjdBxpREj0QQyC%k`7&vMp!J?s#y0b+2qU~(gAYz%q&_V2V>vu^h&5@@Wf
z?|Yeyx2qf0%PAX+^iw(D7gm-*uD^4TcdOdvx36~ZZcavz8kc)*A7mQZ%TI3A9B<k{
z3<)SsW%#TXjwFCg*&G%9CA*Wz_0)V`G9hHg;ljQ6DB57bCM#hNm+}~8iZ0f4CL6wt
z^X;`n?gY*o&vPNFpvdDcXVC<VW?mT<z=N|8(ptJ#%^GKXARt$M@(es+=@TM)vLQOW
zk=MR?m_B=00FN$Yz6EBb93R*85C4GX`bH))oh`oU7xwY--d_%jf?#sHlRi?8TI<!O
zKsx+as9?c<<U{sY_8M%Mw@Nmrl*wYG2U5l({bLi6rr%IvaiR29QKxjNy_h5Q@ozdz
zm+K+RSf8bDZ7SdU2%agD1#eD1*bR6u-Q27I-i;<qN^0LFU=?d9qDx|c0qmaP)!kgy
zX?N%3_r7;S%6c<5=88jndyGNOC^;5B^w#4BAs&JXL7%@QiY;axT5NM<fwvfiw{rNU
z>}V&#G$x57YHg?MV$1b@h#USS+A_c0QLoP30y$69)IHs5F`YQuW~i83)Xvz!3+SuN
zXcB-E331r37TRIpQ^Iv4qlO>tBd|4-O~0sw`W}{^ju%)uI8?9Nf#{MMyiPyvyrs$`
z4wi}o@OKA%ubwrR&GpkEh8-S`zm)GiuaF;BDm@xtrAIsN+7&S1-))IGQ!BIucLPXf
zm|8_$J3ZNG1h^qVR~TbG)ra7iP<ih&7NQRmdeLYxx&hsad*Ez;FH(IZko<kPJ8cI)
z_JEE@ai;NA@PuoI-iMmA<%6tCBVrl*yCRExU-hR?MC=%-#v<!@d!B=Rel~l-6u%yD
zU@52>(0!IO<nZu&77pvffEUf-mvj1LHR9VKMzjc)>cL5}BV)1eScu&%Vraa6fHCOd
zZdKA#<8fe`;dh!{gc^=x!MikDBSR6eTE!__ITsi$8Z?nb-6lT+35I2O2HF2H0RrLh
zsQ@svs?W)<YBS>n#dU{|@f0N^-ttMTBu`ftuS*}d?MKd9O5lSfq$DumV99aRCuTjL
zb30SPT9jqpIqjsv<&y8Xksylpz5Hs1ou1NE+oD;+*TkNa@=I*_2lOuH@giHh<>wPb
zUF(F$u~dYhEZ-f>LELQx^+xQ(W;Op+>EkDFwhK%3tGd6HHVjTmJD60ng!l&-O++(p
z>`g#N>DcHVbaX%d5JJmQ(VQZy53r$6<bEh-Sn0c@Z5_F>ahlY<NlTWZMsWOGMVDO5
zm~Y>dd|zr}7(8xs64|ES05*@G%&EX!d-~p~=fhMnt}kmPlEI9bNTg~Dvob-HpR>Rp
z`MhGvontjhAdK8Fa4#)%sd%#aD2RoDWr9JqsCQGj<%^0$`Cs%5jR~RMaJ@ySu~{dD
z5F7*-5BxgLBBx<Sg=tW7;fGc6A>Q8;Yagc;%^QzyWbGC8Y%kc(bv~BxB41$6s+YQ+
zNqmIDkFJas428z!XzF>8OA606Dr!)DzM(L8wj!$AvbLG<V!#G-BX!);Dcni*2x|h(
zYRSEarI@RZvf=A0ly*FFId5Vz@$AlUPr{q_Am*z{`B=F`KKfQfV73bbbXdQiZQlxO
zb$(P^X#Wr5Y3)@o%jjo0pZwh4>%v}|IRXB|A`9)j9<iSwD>GV3RmVIFFQ`9z=@lE#
zmQN7GYCD5<kK=2gdSj{*zR3|AUc56X^Js)^OeJ41=^%qP`Eq=fRl7}h={K*cLpWF5
z?@8hLExG2Mz}~A)AQ5Zh4PWbl1Q%oxPAeu?ox$<LdLzNz`;{3#CA9|_MCszo8Z|k^
z3QSS%ZMW$d>utyvw*Q+b()aGm6tC}krO~fj_fZpDBh8b8E#DJkDiwaEa*SE`ob<b0
zuzrQk=V!=h7)n>l%5WUdXKg0(1zM?NUFSS1vY3}r`h;#=pFL{b4~g8?7vcV0r55EI
z2#|wFj7?&SDX{fDuW1GX82sEA41LEfuhDE-L8e7RyW$+c^?PxBdwb6e8FJbwBpN5(
zTYQn@mGTl;P`$&%r-Jj%$DvE>-{*gle6uV6QG4h_>O0Q4o4~pM$;R6SHJ}SB7ZS`=
zABcHJlXRdgV<#!eovA&LxU6?3%$01(AUrmSl?u>>&|7Y$9rhh!(8m(<48kS^e8Bjj
z0Q@t=X~&KR<TSbVN_+TV1WZaH6U;tyLGgN2w4D1F>MDEg%ltoN&&?V}2^?8|q%xFw
zQ#;?A>Nkgwzj$h>xV-5byIiw{r;zke(jGaDc*~_Rz9#Q*UCNt62oX<^m~|wTF9?H^
zH362~#!HlM$HFb<5dwDye9sk<uic~mO?-cip0W}@!X;+D9<!GpOh9vY1YYE24p}JH
zte+a)0cxcI&gKC*sTO*7f*YWh55B;lx99?S^K@IR4@d4{f8d0OhE5GN^Iw=VH<exf
z-t=H^34d^U$Y^<UY4Wj;(SiIP5x;)Eu{*=`t%1zfx*(nN=hu~Q1@{33y+$2t#Ez<?
zsg|+p@~e=F8`9&YuthS<i}smYo|u`aR`S1!jx_<QzMIiB7EYgQHu4b<W+xfi0jt)=
z@j}>uoFv3WtPUFKH-1BBQTozp?T_hTrQ-8W$oHzWn8V5l!95+oC3>RT!08-)!vux!
zcKqAE?I?D%fPDGOV^)dAzhpQeYpm`5v%N|{)d=pa1TtsoNwk+ePDNKd+w<Xns*9NZ
z@9QE8naLy@U-kz*V9*cQpOhX7luy?SNx|TF%DkeKpMH6k7lGtOMk2>eHzzr)I|s;n
zNLqS|qE5pd;tkIUpNt4LO|mUbhS<+=`5IuBO)^qP(ZfZ-5Z5QJvZ3o=>Af}AY=T7o
z${Ta_%WDpkyH%Nf;jn*KI=7ovj?OWe`Wm<QdU$ZNu1xh3OLlpvI;SE%NOeAnuE`kI
z?VdqTp1BL2<t3Isys(El%`0kraefrS%%V8;TX(E-bhUW6R$9q5ePXQRkv$i^Shus$
zPF2=G%$FWK{3_D$lGu4(Vb*iWH@1erd2`51(Dbm#uSR<7(;aYlxLqf6P))+*r?sRk
z_BYn0-u!2JKU{WIi4z3JWHZ;F7lQCz4qwFW^02YwmAxEA0eW_$%0_tti7e})jkspu
zl69RytsfK=-c%j?GqZ_wO=lLX{;S7bp`Kx`Jw97t;~*|)Gk?3@%a+FB@3=J#@h5Jn
z-3{&F*4<RW?^01YRv9H?CxKb88xE~+wmw~Pz5{3LzG(dsE05?);(+A-8ORlg`w!cs
z3D8fdIEm%|fwKDtm0!S`s$`&#_0xJC3$@%C^%)$_6(*)fcNEsM?4@XIFBMRq>|55e
zY%%>W)0J-{!C#d=ApZ%}?-JOt$2+wd&?J_mS%aW5|MX|SoxrI~4EIlL-hd+|pI#9=
zs&VWJgAOgf1*)}umJ_wl%NZVKT9&v)q#;1}Bw#($V`^?1I<V_!T_x0?6@Y~L$&*dr
z6&DAshkVFovH%5UfT)qpLUk*d%=Y2i5xVl^2K|hyho^Df-X9W}^F>c<8LSQ8&<7m%
zgE+uMh*j#|voBW#&I&6I&u^36kw|HP%3%Au9`Rh=$NvTN&E#;D068-?PC#FN<Q@GV
zeffE$jP+BjD|Dy~creuwz+%(3sSp=o%`B{6uDAkb1+2f3(lsWSh+bOQaR1fEaZv*q
z6&H2ipT}IFt|a_*xKp0XtPV?Zj0<F`{!T;9md;q|mRd1M%Yt5Ai=pxP#q0jD0y~Ez
zrQvS-crv~PF@0(E+KYl2?LkvP-)PoFr<VsiFAwV1KW+5-yU25HTu~&xemi6T>2sq1
z@YD|OyZkz3O+RvaJOF{d4I5XI({vtvH-2u=FepCz>d4@5<TR$aem&fKCU(Xpcv^2q
z@ia5mZX++(toq{c&#GAPsI_CAcWz}?2{(UcO+nhsVOa0vc1ZMM_<MeW7gD!jsPq1n
z1MFPnuJEpbuaq8*Wkc6_|ISIH=RxSjib6v|?94@N%pa~Dp!E)rE9h}6DG>d><#Y|^
zd%0MCe~Y&KTQW|zu2;#faBxLDY)xWi#nb=se)+lSLwuo^3Eck)Oeuf>q%!b4e?E`x
zIf?Y4IbGe<J`rdh&<KB}128KGKttY+Xw92Qjs$|iOoM}xVT_Q3?j;}&KX^`;H#PT_
z>v=}U)Bm?tCj(`Zd%AJb34k8o`F01;tVfddZsS}A$~fkoh5%H4)b)KH8KbE}s;mm5
z_*ED>GvI2F{k5Z_9fysAJ~7Ade?v!bi~u;xOkk3<>%Bf435_AvzeN-PYF3})eSpEN
zkNpcyaH~*?i}=!An^gy}%hYuX;?WPBs=o?TmMI+S0genVb_Ut!cLw&Ck-9cE&YmN$
z+0;(1q9+u$e89$8rw9FFj>ncAhMEV)k;>f8Uc#|j)b0~lo-`yP%eh=tv^;@7^qS#<
zvMor|PHjghah(5}){$rm<j)}D02i_LptHWB%bABSgtU63SUK2|j&SQ%#vX5~Af~I_
zoOKqPvU8NEZu^h1MFv8pjU{kBy{&#A^lx`$hXR=S;|KdLpD16e-Ccog4nHEFZ~(rx
z<W9h6K79#kPtmIfnW+$XdCJ*Z_S95}%Rk&y(=TyblO|Ng7k>BBQT<j)5pKJ!Fyl61
z(?iWT;8);SHeC@izE4KQURmLO?EgZ#@t&BTxHQQN!7b$!<yR-U@~i<7$Z*!xb6kwL
z4aWHQ59AH#Jq6+`8<Gh393hw#>GdbYQeAd#LJ|0lfZGEx@SyeZ>jJ_5#8+adjI|UO
z+2l)H-R?{paK;?}L}Y$_`<Mz$Eo3j^a`r}Bo(z9Z{Ek?$$&dF|ekV|}EXl2Fp!YOh
zETnRuaGxoB6Ze@YM4wy3sf*_|t!O4*ZN*6R6Wzd!5E6Pi(;Yw&{{}FWSZ109;C;WE
z?fMP!5%sP`NXpwx(zBJoe)E<vU##dGfoORwZZ8O5IZ|BQ>eryay14BDmJi<?0S<~>
zG++^n#1U0e|ID)zFyHm*jJ(OYmX>kN<P`}W-`BFn{a+}+6`=$(?L6=XkR0>r#{)99
zSFjVL={Fo~Y*79B#bbbB!^SkfaE%)usLfAaM~NH~^;?bzPx@&CC-H+&f)GH7(BZEA
zDi_wv)gJjL4fs6~t<ku{m*I(h)zsQPU5RwMvBGnzMemsnlaV0i!)zhOOP~c`kL?un
zd*RT0vLYYs!LF>7C4>)HKsdMp9-6w|o0ZBqcMA>a%1<iXUcWmvp?g67RYWeaS%FHm
zj4i_1ZwK-U-CLtcJ;T(sl;RBMlkLORA^?I+U6MC2TFK58sOcIeEu#39?gX!0>&H?q
zRkWqvI37>7T46P7loW(p?HQ?mLi=Jw-wd6pbzZ$*H3A^K)Cj&7z4rkp)tR9gc7!Da
z4&EWy!d`0nqO_xetb4Bd$I2|eE;TOeL*^P|5gU`%7E?=y?l{f1+3N9PoM0Xn;IFv3
zj_TXE_SOGEKY%*;)|;kesZ3O&nlG>$|F|-^BK6JbR$ZaB-j4y_ZEYz4r7OX4WKegU
zV9K0$RFty-C|}OJ7p|P-+}6Va;EL3FJ)wV)4`4HEAjxOZ#o3GMPF~uaQ~R*XBiIN|
zY3l&*;j#lIw;z@ay`Wu=YnEx<Sps+|dNXeq$wmN)BGP%thpx-=weOkmH6iA^Rk&@~
z>x$d`8=5jK*txj5Yk_&`_u_n6!olz$utXe#=TQ$E{N1*R^T|uTBjP9Ge6o?X+ok|7
zX~gezOIPaf3k82J)cOeUE8y+WlTc&~;U6?za~xd{istaP2+i_6bH3C0Sn6=2SfJSV
zkYzviOSjZk4ca_x!!e`<E<xcv-u}%7aykJFJ#={0QPzw_p@vii`Oh~2GQ>xUa~N6|
z23T=SIgA(r;L)4K7|}8eK%Tk+Ty5nCj+&2}-T;m#0#)<(m49~oGH|7p{;q=dd_eut
z*5r5KsEFPRxCS$-Qht|V1vCMeaRScb9iLSR&U8%Us#H0sbkAFYif%M(5qqMrrg7&g
z1bR7f>!F&P(X1gx0N%uLwUW2R>Od+59n{Q>g?&RDUDj4(hE7d#OFjBRP}3mayDwnF
z>Y^X<pa2u#e(*Uk3XJ!?%G-G3j`0%6ehg?>mR<@<2?ZHv%f_+Fc_X7hL{s)v>iV@E
zBHn!QHs|aY)@K|Q*{ZG1ZG`l+Gtl*B)VJbi5Fpa!*K?T<0ePV;7W(**-^0uf^bjp8
zvv3%9ro6k?Fn3;t1+Zo#>`D}?yxS6RX7Hx|(gEMX;Ycs9=skz~iGoq|f@Of{BQgdx
zZ<&^y4cdl+Zf%Ni#lE$^Hh0ZNgvsyt?}&A}gHEs2sUp4c%Lh@tU|7txhM57OCa}V;
zqfwj`W?Tq^o7{jrm_JEh^Ji-*f9=&R;jIbWKWQ<**)HsTW}S}~UWT~kDJ2v9^<2tj
z*2h_Si~t%3?PSm$m*27gv(51({}11%`uzt;(+fC=BOD^fCnokcnC2T!xjp$hH|EBP
z;%oj&czK6k9FAO8?lxhSBDaFB>8qW3iK8TuRMsCaM5+4we`fJo`!eF#u%(l1&2~n>
z>Xu42%JS2G?gsoUeeSFvURbp!-Y##d`Fh&5a}wr5PLH=2INlhb0zX%kQN#dbl3Q@9
zBIW%ACk0cp`F1+!TqZewVv`WTt&RN+*>FEvW&CvZd0>=G^9vlR_?7wSd-qMo%j0f{
z4(!Msrt1qR)dkakPjs#ZXk?q)(k&F>#?Fo~0@G)nVU}>zJ!RF}<7YbA+<L)rc=94o
zckygam2srTLp=hncKGp!&JoTi!F#eKbA%Gtncm6}TOYH@A5>+Xmxu#N$PJyqG``_D
z|Li&Elj&8-IwlCP$v&=j9Y>3T)9%623t+TEfme6fXe_Y$cHP`N8iWKfBTtge(2GJP
zT(q)2RMWbR3pYciKLr4a?wK&_p&HL64rcxHU1dngEV|77Ro$GQx9eNw3oAiiTebo9
z3^+s9*%CL&55+*L6IOL)7FU;LOF;w&z;8i0IC&eQO8{OR<xT<f`5AzaI=K!#<$i@@
zixty1ttoPz?g7o;BGBW&1h0fwp4}3&F9M0uDDZz@5kU?Uet0Dep1>}5E?==6w_g9{
z7sFfDnBz4v*Zj@vCr##agr_m)VVtKL8rO*Snme7<RS}1w6f&GpITW%#0XAX_^@G>$
zz~Ad8fmi;E>s>%}ZJ=OUfi4i{@~7r&0HcjrOPV0HTN+b2(sG7uNwPAAKkj=EVDw+5
zk4^?Y$}DAFtY<y*R|t!fl9DD66J94^JDvz)&n~7v)!e)Wzf_P(PKy^8A9iMLNdX3B
zKSh6Dl`Ooqhr@!G({4La3ej8$s8gUOfI0=Z+j8a2-lE<DC51Rusvd}l`)8QTC#%m-
zg=18BErKKsZ$lXd2<c7qGJt8ZBazFz-#iaiZ_1s1RhIe&mCu~hPj_4~kzMJ_M$+-j
zzPIA2C5?y<jt(UakmkDs^p`4a7CLA&nQlmLT8f=+Rw4a}yr8d`fxVZ4_j9ue6X{3D
zlC0Ik=W&`CxCQ~JN#Ahvz*jgcC^SHptmTuXz7@~Lt1TC2-#K^IH<z@Da0hrFsAWNK
zPjs}6q-D6j;Wq`h<;aiuf?lS_Kk4ZjRBej=egn+4ejy%3Am?TCz-C>ZWaZGWr#!7B
zMQ(U*MLb!I7JkOaKt`w(D4<#dePIU90!u>`p&|2m_SJhzBkjP=^r0!RN#9xAhhVW)
z=`FF7+u9<KQQR4_{A_ZzKu=!+Z3k)VW3L4)^CqXvU6VzqO5nHH3?0zn|I#aA!nhah
zx!dt!;p$iG>wGidZ0>zotPkWwN?w>XL-!upmv99Rk#N3!`yZ6=?N=lb#k$BFqzn9-
zDwU5>xM6ClIt!eFo==Hi@{tIoUr=1`QjE}K9d~@#q68!G^zMhK5L?HacD~P{@Y@Zt
z>Y+MM=ACF^rHnkkv8lZoaC|ZVGx7aPV)|8}d2Im|%>DT}oM%#>+}bvA*&tcq9>K=}
zz`<iA&hIc6&h)HH(SQC_GuHd}yL=XXa@CS}m_A9sHTKJ-Ldkq6JLr`Vp%4wCsn$G>
zHMm^pK9-)9cnSK^2Sjs)5->W|S7LGP>?IKItQ)8NT?F+!kNp{rhY-l=W^lW(RbA(4
zb{&ue-4yx-!6%fw*kie@9OwQTSY`F<P_K&<3Dr-7e@~nN{%3jm)?BR#Hxs^?hjAJo
z?nx0e4?TdXkq{h=GLSk-O$Aae?&DaU(HZZUk+;Z@{v{V^C$!V6={YZm(?3?QE){|l
zVRYxjZ@&SlwosAQ<p+07<u4{vc1*$vfxX%pXP*b+pK=0ho`EFwg>AM`#PcLM>`smQ
z1eE{Q$-w2=0JRxwuL@>W`sjxS-BP67=z5*x20)8)Vp~8+%h^8xxDbc=C?L;qVcQoq
zg2}UOVPz^P7rdyRH!kx#-OD>0opR(4W**cT#{qQkRSqGVWV$B6v4__SD$AD-e>fvl
zx>43twrN>eY5Zh$+WA|Y@1~_y(Z_Sn<fifAoOmkQp8Fsj6d`*c{e?je*IUcQEmcQB
zWE$wX#17nf!rS~_nw4pIi&{|rYjX)>Oz9Q>vq!RrdC=3Gn<hU7FN}=fwroz0lOMPX
zLBtkvfTRD>8u6<OYfO4bl{ZP^Y`$D!Kgy;>i6WcC2zapF!tEwW%W=f=teKXUYd<`J
z?bp<`17tpobkWs3`e>Uk%Z@t#YFKyN`cB_DZASZ8gIJ3H${Pi}G$ab~Ngt_ee1>Pn
z@QQYV1Y|;|U_EcE>1%*K?v`riTYuQ@6i4(SYTUrFx{WAkj;O2&I2=MawQ$s#Uwln<
z$`iNF0oqmGAH(;wjP(;s?ai#61j*@6KYsKQaLmD}l3f5)FvnWSv@%Zx&s;M!|AouJ
z>f>@xbz7UKiKMc85|gl&^I}22y{M7E5RzZi;;JpjUn`7>RNV4R#<_Xr=vhEFuQPj?
zd7kf5DKo#|zpeLDB+{vF%gt(n*2wBUUkF^^)Sp@r@9}S9az5@;+!>iOU_!213QYmA
zzNPS~7N#k)|33KUvz3zBn`x0b`?t=LPw?aEuPbCP+`4dR8X4tWwD8oCBvE{Kk?ICw
z2Ui~c){<>2C|T5-HPK<2!k$!1r2R)q+m5SZL&?TV9@SMQwe?yERz;nM63V@q_&D(%
z+MMPK{%KK=#gr5C9EpdJJk~2)UgUqi<2m3h4s}PTe_T_JTvyI>piX%ulbXAh=m>2z
zObF2XZf5Rz`jKoHU8gk|_c~1au4#NutO>lI@HxRfR|5Z&gX@>ktQgn#raPwb?BPY0
z1Qb~Q2Cp^}ljni{&I3>C_(tHp>i{J0*AuEIVs-CR-@M2%$%o_8Kcc?vOwt4pCpK@u
z?gv6E8N!d{Zl^mx(62P*ydfpT@W8~q$yN2Sr_;n6-Xj-kGoJ{Va&>`?LS}qRrbTu{
ziur*Ay(Q#!6v_d$nmd>P|4*^`>Lb32G~sZZOjzSct#;DHL0U2)KD%7h+MkFB=k0re
z_sioy>Q}_camnh)C|#?>tlSF-()~I4mLJy{adlB@nQz_ab=ZPDU#qewv8}zkj^KzZ
zi3L(Ri!c%C7a#(#Tb*2N6R$)-XrN1?F1<c^Ta5aIJvy1Ey!z(d(ltsC*&wpD%I+ZP
zV90K~`d_<4!PLs>h@C)^iQo*k@k;Uay#DkCas4k1DYM4S^1=L0o(G1%3Ve{=z0fLk
zJo<V>tBK6rT#L(|Ys|ZNBUVp7`wBJdGrM7u5z*B*y~ZtwjqMduIK`Kfv?C$thsn$O
zzlmEN>rV=(lWcV<A}6s3Y>{mC*+r?>MH8a6>SlQ=Gg52>=K-m`sE)bPH?(fz#8iPB
zB`f!fo9qD;Kvy+(r-U9b9CWGr-;^jWqj8<vwIaD+DFSborJN~;9rkw&9fnZ+?UBkb
z`<b#ov0?5uV~xh0XI9oTQ&o*KJu9}V+0&xPIJ4`651r!_(q`!McxF*9`=^>^vwQvS
zNp_IUSK)!IW2&O33(bB|sj>~9!tU8S-s5cor)*FwpPi5$&b@kLWaV_7zTtEBwRq3)
zvCL47a*XeOVwO=Lfhsay`n)2@3EWT@(!(q^=U|rY)plU|+$4dwwZbK;<oy0wT#iP1
zt5Vacixj#@lYio_YjakI&_h%ETZ-0-gD@x*<#lYCMh+u%jWp{?ubTxjrj(6ib5aMb
zy0H!}4=hO8uKmg6%DeYe4~PO4J9lDI=scA8n<C$_loF!fm1c!yTCa#enUKPx825*?
z9sj>|@9PO!#Br}<D*CpAub`KA!uqHa&I-~|{12LaE=n!^V!ebVYR17q=><#hBcqh_
z-SRor_^67ir_wpnvEcHUU&*v=n^y&3GicyFR~0;2Cqx4(E~2NLvC}M8S(QLH5ff<U
znP0L)_->A|#A1bo3RA$BSDXR|oFkuwwhDdBjTQe!X?w~Hec}`FMO3+`x1+!rGkNBy
zNd)uJ{?SB_DFl;4-J$G2dGzcpt8-g9>*?8t!GsJ-m7Z7+=aFL50~CDE=mFaS>jTf3
z4O3mI(+-=I&FiM)OQkwg*}r=POzB>Gs#9tzZ&`gdW3zJsrpMBX@h!ta8s@6=_6%wM
z8#s;0#5IoG4!836%Y}X?7vKBcV>}vC#4m`7$Sxxn&K^2|JA_$}<DVS#j%VFmd}PvI
zALD$mL&q>M5^rA)8@&?vZK&b+N`t7Z*C##j;{OP=&^V>t`D?Uh^%-<x<e#c3Vjl)a
z$Bpzyz>9H?kk+REtCT4*!aw}G&NCuthLY8mU(08rqFdrO(#3;wOH&muar}yNoOZ+0
zZYW1F>KCsw;cdi+O%?lZwMjh&t{^5`<!VB@=8w~c>5a=hLOgk=R)XI8Y(FOr`e{2z
ze$=gFxHI}@r1(YGWnvq>X+Gj9RCFlK5&vWs?lyrSl~vRDp2F8Ju0xJxiPa7T=i{m3
zpO~3$5?7i}Z@{Qz0#XB0+Z3`y%1J2sUL?Bit?+->(^zf>4C9DGcA8-d=E`P%XM5c#
zo0QR3(jYH;%+_9@q;EU-(gbfh$L|WsV!lFyifr-PDj)Ua2$+yoMFrK|afK@0>!+Vm
zKzc$pKFtRiJ4w>xPhNoP#8M$jF*2YJUHZFwE21qwK!0f%d>-7#GSmc2{71f*{cCJ@
zMgGAS*rku@Q2#8Tm$e`4OgYZV8Y_!kH1S6n>}GdJ-(c+R+vWw=XaBS_qFVTvaZam}
zs5}12v#zc^y_gn1gb&~1-1HJ{8WLWtTtaCboEqa|_$(hG2RPx8a-46LPW5niQuU;4
z?TQ;ZEoqIvo&!luPB^m<{~f7Kj`8f<!c{obF1s)0<xC_3;+ojFvwYsOZERn6bZHpy
z!w=aRkN16X{7ci1SY@1$QRJkb2H6W0Zj|B`lAA<igFk`TOdLuOq&>6Xa1g`nOHP$}
z5<^0@=2MO?h3EJ-9`~=r3taYAVRiNlg6|3V#e9HdhQDn-@?+4c&9nBihy37PL$u`S
zOu|V#eiC7NY-`0ui6+UdiGT7}-^f<+3`l%&QD&7R&38aNidJ|s_8+U!x&NcpXcBtw
z7W;3^Gd{!S5=tUfV@jnCT;Xf4e}e{Y8DLxD5dTVe=Gq<VSX$m@QcWVf2*tNOdK%Cb
zW(Fk>5KA|cvr8D%$%KG-j>aM`Ak70mG&*ou3f22_>X^^c+*!n;tltFeEe(2tl}WOq
zK?xshvl%2`PaCYGRW-A;rFkEhYUl`3o?C4b(6j4i0r>4CrUiv27{a|HsaxS<j3Wmz
zq#58Q0(zuj<M!x=-Igg;Z$((C5OFe3C+Q=WPa$`rB+8ksqd2v&)!o4vlLk2}TZWi~
z3YgiTOpX0IocejiaC-IH&AW(EFpvGCu*&oMhgC29Iav3XBLcy)k%F3e+H)v`R~J-0
z`r+lV6GKY2iWwn^N86-_ekdm^6`(Kv`^dL%qem9+BKh(p<B+fM?51G(cJszIwa>A)
z(CoWCBk<<=w%487RnyV_o%+_71+iqA`U8*?kI}{PVP?7A=>475uT&ZK=eZ(Z2eWNW
z59-;ZSCId8l+FJC>L{zigS2FS5CZ;l?M6rcb<d;q6g&`<3Wy-E^?taJR;k7)!Hb);
zthqj{1ER74R`S;U32)E@qzV%s?xayTl>b=yBsWROgmKz6?P?X`2q1f3$_hy~8NAmj
z6G_DkV!W4(|6q84T)bL-p6ZQ*6$q8e@i$O1&y+P#jO~Rztvwj}%&lONCY?XgqE32^
z7#=cxKz1)TXoi46mKZX=;Aa>iP54xWnPq)+03BGRvD>axHL;t6RL*Yi=kcMnZ%ZT-
z=^wV#Pv$hXud9ynlP2<fdEMsx9s#x2F}n<{8Uq9$-12t?>_pgacTf&qj(aNKDn>ls
zbSN|Wd#A|eVV85`onZ(HFR!Zn*@J|EPLII>43M7zpaPJf*2b)nqPfBEK&%L+h2NU^
z;MWP13i{?O5(!gYcUVx055C+XC?gZfQIEG1pH?2q6>!nZTzOo+eQ$C|mcA*&cIM6c
zj4BBg*P=X&WY2F^8>aS^dSA5LHt(de6kM~=B2<_7$NNgub?<TY%$a7VU~>4wX4ezP
zRac{UzpZf%(ddamQfT#{ChbvAQIKwBVXm?_{BIQYb+_Z{J9XW)cg%~q{mS<i>-!tu
ztiwl~R_}}McUQBSHS$LZixBMI+K7075PnV}gPFD*c+;KXW@JaCz&P-r;lDPZB}!)K
z%gN%6eLkvGEl_>Ju%oN?o0qCK;rA_=2rFNYb~cHz*S`o1XH}9eOR&h-a12|Lpi)98
zW#j@5B&<&|BIR6AzJ1ESB$l`*!+eb64$98m4izPQ*;cyp)UQhoio&YdSX{S{v{1eV
zlhpZTQQKY-B53hdDjw1W;p0hT_zTe8L+tN~f-oPDjw_!WYz?`1$YI83wP7L2J)BeK
zJ$Cz!TW`;Qq^`el9MpRM53jVN@ZkPp3Y9vBGe$hFE!`-m!58g<httD|4u$jAaffkT
z{e!>Ut8Ca^f@dvGQQf@XRG&X|8C>j9m>jW8xhL`r#N`})Wj5}>g%uuM)~!;P+_*xV
zQiZ`wh7~Kl&yOl|$=<)Vu8i-J7I~tMvm5^r_RkSun=OI)UkxY?w2~QE{3g8EDPP}}
zSkWSavd^pH+RP6gga1@9qYsMKrM&;a+ZddE!X^VUZR4r6jP>?-{&GYslbo5}XfDtl
zA#aN>`{!<h!;cw6gNw|j!7e@i(S|nn|A*fEpX^-~cwilAo@S}^*qYkgv!Rzso@eID
z8sR^8o7pJtn@SjdF>I;;-?TfQzYhGM-=Q)Z1=VbcXxxt*!->)#S?65)Y`$S4@v(0s
zp7E$M|I!@D;uuEFMil?oJkGX+W%zsTiFhQ|y5%Gb^GF&rfm7`t2Fqx|3Kw?F4#c!5
z*yfV#ik*JNxXk1fp455VeXQ-)0?4tC0D{Q>wH*5)%^lyzbC|(K@*nS546!T)b@XZG
zWID#xHFm{21@gv%`%Q@I^{g)J!x+2t^4za1+AYK3+HO|>zkeM%=4I$bZOCgTW&3gV
z-*%_TEAk`!UZI<Js5cW6>Abrg0A?!ABSG0tFDjEMj*iuE7xh;cu4wKwQspQ4>AOhz
zl-Zla4IR^BMrQx&djnVnvq-A7DbvGyU2F}ww=XT1jmffM(f?TRufMX0I~}^n^lxAB
zdx2Hu2meW?IIITQ8ASK~bBy?(JPHgZf7jXZuTH<tYsb9}vH+80GgrG5SKuGGuH(Nt
z&^JOxpbdpIf0|1j@Fb?SqDA!wlbr^HWZegND4GI@{jM<6+Dtys8`V2p#ZgyY_YtkH
zDQ_jGZT)rnBPE51F1Sqk=aW&3ivoLw9{nX{eZ!2ZsH)cr!ygsQ=-7X^&wX|Eicbqc
z<f&eLdvthfAQh+96%>5CD%gqw0d4v19WAXN_|R$AY`XdV@~`P)Atk1%PlU~te>w+C
zoPErsM>;*KIJ&*RCG6~6gj&f@syUoF*wG-d|3KQ?I1smnYDXE?M=f%ZyFEsh$*WWH
z6=?9*$|(UF_Vzb_e}*L94yKipZjtwwxICPpSzqsdxZTF3z|zxl<#rD^euGzm(Q+*~
zgzsCQ=v<?LQxp0IxzyK<`$7O_Nv+mOmHA?RGjpip1cz|~7;0JM9qB4hQi8ZNpXh8?
zA)o9k8>3M%x09*bP&J)@ukAVjLqo~Qz2<&QgQdsEobFOg=r#KQ8p?41KPbjA1yW4B
zY|$w#XZZ#I494AQ;yVj8*+=;6rnL?^{t{$P=z&;NE*_5=Y2zpYBQKQiymF`+(czrB
zi}xKEpI5A{@g%<<@XRg-2m`gC`?;NM!mj7&F?u!8325RK#HBM^1^-kB9M}_ZD@D;X
z;FICe_s;DnUB`@z0X^rBY12lJcMPY4n2dexFn`w3gGUT7c`o_YKWvFoSG>625P&%_
z1hGs=E|F_xj?c8FFQj}JyaITvmoNj8RUJTkcyL4Z{3m_$^QLumr~CNC1QH1$m#QfO
z{2fPZ6rL(<KLLHLj%)WFcT#@*bi_r0Hod(Q0Mx$OX*w#yi4y^0ah)kKIXg$2h%5rv
zY(+(xo>`y)AhT=|ut5m3dY}Ync^6zXZw=`8i~9i$G4&>xna~q}&0Jl9M4sN~FR8a4
z3l18>Y2=dM9Roh``dt@*E^h2Ambs_45%Lb)u|pOEf0bAdEk}0rba;OFbald|&fsVf
z4c+caUSlc9a&gsrAO=Q<>KAMk{xfDlWWR*i^0=*yg4XJFcIq8=WDxP%avF?ju{i8+
zWH=L3jY=Evu^tTZfUU{1d;?q;(%A?~9L{qcjCFskoq-vA#p0UfYh%8;CXOf{(qQl8
zQq<oR`m0^EwjWe7Z&5enR(x@>15CJepYx4MvL7ZBoqb+vR0*GS8CEA5g<kc$=Pp-a
z7lk;uVziYF8|ycbtr5=_wf9T68P0@LB%^?qv)}sB*}*_LGy1FYNAH8xzH;fk9?dIP
zgUD?_%<yI~N72eNnbS;k#$~volKgydXVS;9U8_lpCCE{4=uXxUD~wcLrq%1jDn+*(
zaQ6rB`sO^)Hyk30+}V22_Ri>Vd9L8yaiaRaTT1}A7SaTks^-{0P)Sth<D3!Qd`t2V
zwlKV$TdS4VL2ZiuMkFu!&<~>=2M8A4^ypCbvZicL?aY5I<&;hKAWUn*)Z3{x)oznW
z9@2~le-=O0I#eC)EtlyzU!(al)*lUFfih9Tyn^P(dK}`dwove70pdnfeT?=vyIm`{
zZ#>%e*J$@Eh>)rvKrblv#mp2=*JDw|#W+*Hk@dnXlQVJLSO%Q$0B*9v3wxl|%VOxe
zkv@07nK1db0N}iVI0(|*bukSHT7G%<DmtRlg4?^Nt^b8cFuA1-(<K0f(n`Vr)Bjh%
zfDCV28p{H*EdIn?l5L++O|$ZrR{v?hF2V$x5b;pQWnB<U!50(v?lF=5GEMrp-k3Tp
z`P=+aH{`rhd_SC{I1IA+*D7KO2Vgn?^9pF_g8y9Vj116RUL9Wm?*?Zcyteyn{izJz
z7rEA)RI8U`<@t_`@7HdpDdY*(ujRbS7Ph+pAj9TAY&$q{JyHrE1fd#13br&-zEKUN
zRK5F0?F<-$Ff3H%blDEiN8Yei5}`8i)fW$z<QN0j`0`w5@p-E+9enn!VgDCz@f@(}
zM$QNiiu**T8vn4>2gczD8un|oB}kJSZBd-cHlN=q)eLJ$+|21^5t)qp(kIV?{mqV4
z(oe%Nr8|k-s>sZhex7KEU=V8?I00l$JGlg{*6iIrY`}F!9M}pU%sj^leV@t5rHGV!
z_Srql!dWET5IMZ#JzY8C4wTS2hB^e~I4A@uB!Kx0FqTH@n$e{~bOc;&`XHuEUati2
z#ag)KwYg(H@Y|iThMoKSLYz2=M#7c%8$dXPi|@8xQ_a2x3qZcoHGOpl?VtN>ZlkQu
z(Y0@6>?Mv_ffQz={LA7k$#_=Xi_E0<&F~ye8<~KKH5@zo5HbZ^hs6Tk>L}dg1lh>!
zWn6#7bu`W9mBdz~HUm9ee^ddlKY8tZ5prRP2B5<RYB4Ws$!q#o2M6E*buHj8e)#1;
z{jT)o;K)<m1FxS(w(GFe3lnJQV?Ecw(X!I!baK}w#1Z>&0qpih_oFh0=GdI(r+HYR
zYeQB>l&bh$KqJ#1E^78P>~7{%+B?P8zItqite1x5UIz+5fR%tAo$ohFcj$rCj;E&U
z@!zl%8G;^qBi1=xqj!qv01BUMO7pw4vRvh?<$y)L@kI1F4sG-rO3Zwj;WG5pYC@|q
zK@}zq2jc+Q;&K4r`)Ia^>$IdzT%yZi#wzv8Y6ubijw59V_62(OsJRZrji^L_c=7$e
zUI2cZz8<?lEJhB^imO}N8)drTPxRde9W+4@LBKvgFaXaSuyo+lXKFs^7AnL=;0R!z
z*~hkE95ytbK0?oW?``;M8xc&w6eB->fb)8R22jHA<3tFa1pLBziNHTUtN7B?jDrt#
zaHJGfvkp;Y!+f5e4jQ-va;oco%yGXB9UD|;F@A7@vv5gn+M{txe&Y)?#sz&%fZ-G)
z3;hKy0djx>pJDMFK))KRm3?}ow%^6^dh1bIx+y<re_iz`ItxSF1G2pwt!u8S$MIt}
zA8Q=$KgC0xJdpOWC35ZjarT0;q7VUZ#swy4YbHW1oZL%4_U6F;?@LAp7=60sC@`C2
z&K@%48pVtmy)0+>e`tHls3_z1TUZbYrE|z3B$N*6u2E6CyF-xf6loY3P#8c;S_MHu
z8b)e>p+QPIhK8YYfHOSL|D3hn^RD%Ndq2X>x`+AYwfD8JeZ3k?op-D9o4YyxZ+TyB
z^zY5SLr1LYN0s@DHBkH2pZ~`M$nyU%XKDxeuytS&#vAKxVTBuJvE=@LFBWr{(w!Df
z?6uTGawSUtKAc_-m)XXupqT0B_-vlBhF8*;eHwhW=l4a5EVZIFpr9hAXDVE+;ad}x
zp+0@sBeY!%vM(zjyRj}k_X+gv5hW7dn?^_~+tPmDr<P3>{^wm!+CgobP<HvyzMb^)
zVFFCX5=!5?V}^QOZ4a4Xfgu<fXCCE%A2Z5ApAneb;*p+BVUlX>v+|RKB8xa0vb&~L
z!78W67U-5*swHI>&dpyUufo&*#4q;NO&uZ{{GUvkEF3|}mwIviF<Gr;M+>|=%BN<`
z?P2u2iwDsdjS~sqHf39<vMK^VAd3_3!SB9cKl}c*rp*>jK#)4+sx@ISPS#@{E2Pl@
z85$pZne9TDT0m#16IV25ej5|ytX5-Lkg}E)C$H*AgYKPj)a0ra-N98n9hWu$>;p2=
zCyLTjLM#Vob4iVV?<yvSO(+A~5|=TvDpULH$2}uza^?2Zb(A9Pv+?ZPsl@x>UH$(R
zywhFDI6H|hY#Yfjy*166djFvAzxB5NTUQ>1KafiJQvRd-HI9+o@qlpu5wmhnR@Q(m
zTeCevL-ulQ`@ZD_j@en4Cu0yHPnghWw}AT!4V{<uJP|wutUnDu+Q8BGx~Kk6Jb2nX
zjaQh>Xdr~sdvu==7b;8Q8n5@>nvW{~tYXODd{VugiptUEZL{_0<Lh1eb%W_nW5Ke5
z9S@RTAuCqbWP6azGE=Se7kHX6BvjgZ#@8R6dz4zLd#}4GyT38nFXY#GH>?wwnV~j)
z*wQz&8kOo&H?@jwDIwLdv_Yi4;)bqI0rDqy7>YdkqC1D@A&|H1lf*yg%GUU>wcMLs
z2a=T6L8{ZwJ|w?5tb2|zU=0&``mxd`NumUYwVGT=96KgRe?e!jt!E!O0lSS*(B*Ht
zSlQmK8RiR5ejxvVbx8}%?b#3V5n{t8!ip~TgMQwc5;u<KNcMsZ!~g9>%J6^Ww0VMe
zgHf@X*%8+2Sex2q{Owb1?`e1<J@f2O8J>5s)h!qPAIiFq$*}8Y7`H;KL(%QgM=d40
zGrs?aohStc9Gu|jJbufL4HZ4vd!t0dJWY3&>vtPqr@BBWbL5J9cP~m|>F!<SlR^m<
zwZCbwlgHFg-5GB$U{pK$`~iJx(Xp^h_x|@SP--U_#?a<*rm8D1MA4e+n#cTVES*{+
z%k>csHn7WE)BZH|VbH=&uAf03W{mm?Wfk85$9R;O@#S3y8@Mr+R~nDCWOsjcKF*Yd
z)evr*<*@T+`2{PSU<@m!s6T?JSvY8rm~%OV0E%P!@~aCL_Ql;yGs%Pgq!sfE8R0Sp
zeTf*eS6qcSTq(-9SgdsPq;+}iYnC4$))BxzBrf(uID8b8n1R^75l~QOAz-g2MGzpf
zi_`R*g)MFZOH>^y_RR(>L<a_ZQ410qCtDR2w|;&;dZkL48Zn?PA!DxbR8Lh(q^tu4
zfSh50*RC3bV-MBiHT?CpEn?LM<B=7JBC*-NCs;ime=0UOtH~g$a#Icj%L(&#2yQZz
z4#?Za3VPq?1DhZZDvavpxjW-*f*COOO+I~-ah$n^M9}*jep=Y26+Ulv231WD#`;fz
z3l64M`)&D|FVS$=K5A{koH|JX5xbJ7);HqhDu9hBcaAhnvitBsp@B8pWHS8AO6%Y|
zfroo<mA<l!kUHX@s965MD&XJY)D5}`%Det#jx`+W9dr|zSDH2O)jtA_H=3h<5o&dH
zN%Y!A1#N(PLzOScHFR`!z82v7OkYT9UtI?I_lKqSmuK;+m9(s>KKmb^@PEt78+;6i
z`~7zdBP#V^+!O7z`W~C&$#y5zv-?QtPt1{c|0e(*R9=vfYm|_J^MzI~ExElm*8zjw
zGpdpnx|zFs<AHM*Kx^ry&JZp0SaJ3&uw#PuONQ3hKdWh2DiU|@104Q!9`gixPMS*z
zAM+4Z#oC!}RshaaZ||ldFt$05Obx*z2>;NM2-7BZ(L2=0VczFQyZ2fONWb%e+mW-4
zL`>d7t=A=8DAQeJPdX|Lbv^Ni)VysFZK!Ie8da4HF#2R)qTG1uWpj9_XoVc!CGCt?
zOaR)s>UV$QC$0#%`@9;a-Jjc@Vi%0@7}KC*2GhagyboR1cQF7=nHPBN3UE7%_OCQw
zrpQ^}2ZoC%-tMDrD~1X(zmL~JX<*1SwQ~ZwmwadTR+<e8x2=aLQ~+s?!tFW8%?^e_
z7M=&#6^ofQMRX7yS(xAAj?%8dT26qD7v{2oc|fiku-t#2X<U(<H2v7pNQd1&C{-&J
zGv&`&*jPBYl;$#(yGvTseOQ!t$P#XTx3FQm5^jDyx_7!0lI!{<Vuk1ksO%@Xd>ys5
zA;y(Rb!b6*Vh{=cD&D^XV-3h<@gP(zJ$fxzlA-OwHJOUgRTLVy&&4)z{)x6lG~}_1
zipol5Zbee4f1{stdu&IUBfU?NGiPC?ffKa=e!wo{z|)j$8@PgOO`K1CFU%H7d8gY^
z)cG}k+B0=Ii&f6?i9=V0^_w^~2OZy}f5O3I7k%deDt`AJp3tRvWwnFensvxUj^OK8
zJaLi-A0C!fJ>m=kdXBdlk*MVye7gK-!DqHT=EpB*;?kxf)B1d|y=&}wK*0zn?m~0S
z@mDfW<RF0;lFC4zjXT{gE~_?JM|nZqaMAlX@w1b?(GTt|OIvv7*o%I6Dn%y(b>Ev#
z?biTy$Z^(U`L)g3lbm&E;s?osH>Or43tY41QvFG-;QH|o=5#s;$7IvpxAy-(PukPC
z-_A;wM6B{;p??sVMq98f5oF=`!glB*wQ3%cz<UoU(D1V__9R?osf8QT8xne3exACO
z!Ygj@PL2&UYqJQ~eyULoEv3F1q`{ma5$#9ij0)-`B!YNlqnZrjv+b1a-FNd}M#REG
zsQi;v2mMBdW{B*zSKW`kJkE5dd<P<vn&{t6pd}6BA7BnhXS;)fjTDiEV0)bbaRFv=
zMC%Q?-=dEnEfcNS#le8%Yb9<6!grAut1_!2zw?+fQ9|giWx>VmDVK_$k1KXC3h-4B
z%SI3`XlTiMi`&G`?b%d^VcR2N;TQIvXmj)@8Uye>FiXc!I)9~oSL?68<8uL#PDVxS
ziQBw|uvy{~xFKApUeFJfPFCFd4qpU^4x1?anstOQ`PzCccv1j6pIqa)t8+#g)oa<b
zA#nC%zOcuKG;Ie_KXm_{|HsnFm9GBo58($H0(*wtZh`H2DQ&go(hoZSp8cW>4Z;Du
zpycVPksM0HQan6TixRt}^^TG%zNs^DD^oZs;)Ex@J%LgOXb9FxBM8ClW@wC@e7>o+
zzr-C*AUpVV`pxaV=#1_OrQBn08eo8cvu)-lKYLBE_H`W|0~+u=y-W23)zw3=H`~i@
z0p`6_8XgG$oX_CoOx>r=rPef%{dJLl4=g2ZCGeE*;AlU*Npgqma%s(K*l;^)Dx3Fk
z;i%F(_q|9x43weKQ#E+RJ`8Q@OSq9wQr6elCzxwC<jC=DrNO%JP9*VP_cPol`pw`s
zlcBP;z>gP4UHhMtUIb@FGaFY?$+CpSa?QW#_3@k4R-jxa&Fej_Dyx}lc1}GROk<(S
zXl%O~4#%t?gRbr!+KR$_D)ArRZKXLsUrr4MU4J3^dNHe^D1qcjJW_IyOe%=Z{hTE(
zEF>zUGYw!ls}vuNJG3f+KQj>xyT+DaT;Oo8VQVY`W7$FBp3h#_wvu$bj`<A2jSe9d
zSOQ;U(H`*;J&(p!+UZtM^H&co-g`&Ae6PO>7_H*}A^hsBTyxUO<3Am0Fei`CVr*tk
z7e$CS&HM{jUA+Q|u#pquN|G)?Nxrs=f_J3(UjBeguwac~DEI~a$+)2C;d{2WVjp!{
zo7Brm0vp6KHdtOg#BlCA@KvE1O67qn!bHaND%-7Ty0m7@Gz-hfNmEpjC_MVlzZiMU
zPuGZG_{Q{0^czOAqQ9Ev&(inDi(wnOp2yFFCDq~?_?=VpKB|8|X??PY_?0<>FYEC4
zw!CKO$79im2o@`77p^CeMg%cD1GGBpe5DTc5R~O<UjY!LhFPc#BRRgOPCTFp`y_{o
z6gF8ZjDaf8_$`0KC<miuo4|CTP}NN2FQ=@0Yn@gZLa2sTg_i|xXJ4Gje8`ir&;0PH
zVuMX#jsffp+Ji#{VHPNJz*&UUL#&_o#o<0%P&fPC#;IJS87kD^5b1d`FeH#F&iPq_
zaR8hoBGF*9Zp(Ia4=G+{k2>IFs8mmNyis4#Ve`E`7#3RJW4a+ExyPZxR+`d3UCZlT
z20VU&NpXu-y~n@4t|kOtQW%IMLq?-$CXfytogZt`L9)$ToQb}SAbF#b!{s9JQ?DlY
z1v6o@KljcS=Gt<;#f+%DlI44que8k0&V9ot!Nb2sRpRlGbDvn^iPCq@pN*Qy;XVXO
zknw9Mcox^{1P_L4%F;51nt>W^Gp6yDH}z}CDc8}NyyE)}tJ}K-DwDXbFmFm7whe!w
z@sA|Zp8ils1?%ry+*J#f(i82mMGWhvhn&}{z40%ylp5R~uTt)<7PO!j+hGC9p4d<3
z?{H1DMY~c*d8ERV1st^o7(va0le4KHnJcY{m$bF{Im?e=_pFNEcLxTE{i)q}ic)k_
zQu}!vB&=Y4>dnXT?(II~{9c_+<dng7h(-7AXTP|bVmtLoYY`74cpN*J0%QPx9DM18
zP`#DC&K%aa89I8VHUse60u*)>5`?Lyx&0DvGiU;<!zzwd{4H!il?irY1OI|6+q@80
z4}ll*G9~}Mza#m3Az$O{PDuAu@WZE5#q1Wpx0%UPugAfa3hC}!D1@D<QzFr&S7rWe
zh!|NbyiZQY@lk-2_YKD-nZ&I|$+PUpjtKz|Id)gnab)&RvDquIL)jSO{IoqUPTI#w
z1ODA;@8AI>P8v*+0CRjI)E-o<BLlyrt|*aU@^~Gnv*Lp&hR3Bl`k*+g;(k%^Gx-OA
z6IZ2;i2HhQV4t1fUS?Fuw-jjh+)LM^ORUr$<vPxtS1W6h#p>mb0ZTGRX5$LN+uq$x
zCP{IfIFp&M31g;<Y#cW2+;2Aiy@C3R$9ectabYMgjexG5Lp0l@2W{9`8HMYS*;Ths
zaXRSf9H!c6gUT&)v8D3zkhFsPb0AW$4K+p=5G2n3#Tnxzhod`$)>_~?ru0PQYq{b6
z`QmL)3s%%|4Sv|+5`IGs<vy&*#)qZ8LhJA(?n*iJ2{uC)rfmmbel4flt1E}UguR_p
zy}5@J`s_iNKEXa~xyuDsg>H}-t?RIWVVJHRQ%HOzQgO_uWzzuDiZ4FjKQ%P?b<eO%
zEGq6L{SZ`1@UiJ&mq0Rr3|h7XT<}|L6<^`|(O<-Ma#nHiT;eL#Hoi3zFTPg14>IZy
zVE0sW&2P@Br|oe~?al4A^PSl$f&!W6nMhsukayb9b=@YC@--lfy+ce^NSX&Ab5OTs
zn9D=+*+N{HaA4%?v$h#BgRK=MQ#tY3)ed=UoQ@=*4IWXAq&kmID44=yf<$%JN6SJ3
za<F3NcjI%~V1B9}0?)ew%Pp!~H7vh1lax&|&>uP~7(m5CC>A_67}G51_o7O5h2JoG
z5Te%MWSXJdbuuE`KiPf`T7T!}?$p#_LO`kpH3uWuinwK_%JDkZHD)|-DkdO8WLANT
z4%t`g{Br8{uN#8X51XgJDC0ts6*lwu^rVj|6<2Ca^!4Ww*ct(HhhXA6bbjXfsd45P
zIzJ(Gev%iSoS|d64N0(EM7_uuOsw@yY1^<nMT>r2;68W$g>YXOk;uMO=8$rBkqoV<
z)7!4fD$7{a9=A_PVq~22whbpVek&oZXR_yoaWQchE8eRcUgxc~Wnu6*=O%Ffj;se~
z$JB+4$2(~cj*f=OJH~}~E$Ry#t51h1oO}bmOTQV}rU?^jC7|o`65I7ZVP>ij8w6We
z1o9tr{id(GT5j#M3E_7U)8INe5JHBu?L?~m>r*>QzOBe~Eza@atXQq=xvV&+aQZ7w
zTSo$+6|GDgjzc3`{kGoybA(V-EWDUg{?Ib)uH@mxZ}6(vj2^p;0*lHxiU^c{WVg+(
zmj0Ja4Z-;6^N~{{3DWhgp3&ndGUs?Mhqx57_rm0<!9$KOx|2JUSki#N>{T8X<A&Le
z=Uf0>)#|+}`G{~<2<034BwC47#>a`jDA&AI+Y(Q7u9mHobB;Pmziz7acv95hQ@Sqh
z9|&V-*HFggmyO=3S%0o)H)tEkiKmQ+9o^97lV0f=z#PA0JRV35f7IG%7|}^z{Y*R?
zM7-KXGHjfV<9H+}inpbx&+(8&1udNV7YiMN<hJ;Z*128EauU|&+6Q&t()Cx2q?Dq<
z?yh~Jqpc$4Zd31gxTXILR|{=vqKk8e%0C4Q3yJslEq!S?_*~N9pW<*ro6jJ<7w+Mi
z?mb}Kx<Xa?4Ou45Q`4DnWS{&c;A$FE(8JVn7g3h4^!@ZFxkr>93h)6Mi`;$v_S?;O
z{BJS;2pE=6?pb8$&+6f;TlufrVYTC!0?kR(;kP{d{?tbg4P{IqEaaywL}VUCKSRZ)
zFzEI^Ogz625`(H7{hUd|bxFBAg(n_hkwks8?wzl>$bHA9CCZhbO2~G8G~&~d03x%-
z!3P<wfDVh~gBC11i4aX5UsB!Y$xKk_vHWG3rZH>E#xE(oM)<*Bc?s`yV})GDJl|e9
z2IeJ&J)f}GIB+hB0bZGLYK&n5am9cY_Ov_K(5AycRPi&$c<D}J>P?|-u9c&{%ALp<
zIVPmhKT5?*Xr60;?X)g_bAUR-Ctqm9Nz^$w*L_S!gJ#hS^-6bau!)&4BHH8lEW=hb
zp(ELvGBdxR)_4VONi_+E|ECgfZiz8(vkxM2_(1I>=r|Q!w<C^y5cy(2T)cv?^bOzy
zOsra$`L-tJw@X3APoUg6qE{oQFAzGF)($Eh{m&?^V`C3K$b3>PK6^YO{7^e>AOp+D
zbNJJbmjkJ^$$D7A#nGOMuMmqzB}y~PT^e@ugSyt^ZUI~N7i*)!#{JrxLS|OYXn%7i
z&bKh{u~t~H_jsF}?C1R-EkfD@?~E$WKw%f4Q|J9L8+rEyFMF}wlmJXJ!{pp&EeJ(^
zjrWmV()qKEu8?8_rrj#CTiHlJo~ON<aJX9b9Fd~1#c8%D*v~s#=j`>LSmG;-J3sb0
zaoNJo)IJ?{84Xs57mC>qb-ayq{9WuD_WINtuqD<kf4IFN#~9dZ1&!%ROREq`=QNY9
zEUyhZh7}AG5~y_mnD=uiRjq#eb*KEXui(k5Az1is0`>PvB-!HQ$K^)sn8x?u8)io1
zxr}`f=(N&DfYa=}=a05ZW?J*qrt4b%3qaDM<b~z+sXrwSy;l0OMLx4!DT0x}V}Kj8
zJlW|U0f_xty`K$JG}<KA^{r9qq3rd{F!X)>tZ(uARP%>M*&eSdc#trupyg11$KblL
z5JYKqNVW&FNc26piT*<f!URpiEgvRT&zd;7$`dc$0&=DBHKT8`Q|(FOP=NpiF)8F%
zED)Ag>$UMZF?Nj@CxVwK@k>I+_d&aGv#^I8xBq7sjDs*dkM)ciPyy2zG$^k$Z|)|N
zA^A4daGXGxy`!u1p6j_DT@uSV!2BZimhQwN-;*(Q3VD$3TG_8t6?il2rDQ3}YUxB(
zJkb`eYd&#p?>~SZ(=`wEu>kSpjN^IR>C+2jUfNBIyI{7RJrlMLQL-!?4_edt<}4_0
z{0_?kuShOx-@D^YoWP*~*8=lhsK~`zPPBIiVF3*H#^9SN9@0kwT|B!tvJ<;P*Okvc
zPijboFA`y+_qA!;u|(Q?Fwf5Ly}Rv%)dH6oW?ws#V4pJy*PN|2BE3skMuf4(Brh`c
z<rDKT5qy+99ndo;g6*^F%MI_k-%rw@vzRFKE6cAVQb*igL}#^@u>uY}y2O~-UcTw^
zAIfUM8li*v)DL_4|A9(8a@b~<D+^;~()wbR^8)>OiV6SSCv@04>6R~~F(3XQJip5D
zqn_9lm{ZpA?Qb^z5Hv67>C}TV-oE>IH;HW|jh~R%Gp4;=ZqWy_n6v6$yZholpjGeV
z{hA>tPuzT{^3_jYxd#>Tp7LV_>w)~Z(;@x+#LLr!@9#zjf%dL0+j>{J6Ur3Eh)FhC
zC-?vJ^v@C72-K2S&Yl;JQ2pxA8EE!#!l6Elj!EKiWA%*V$$!?4d}VNEs#K2Azi93M
zA^M~RaK2z2{W);1GQh>wssHd7X*kbrlo>hNyT56BRqy<tKQm0j*PR)6(6rSnnQ^=y
zWTNm!Fy`M~_uLwSw!HPiEwV4|G0fyOatRj2JoS1QE{ZQChNwaoV8+d`24Cc%?cmCy
zHY(!73?x*T*##1M8}d@5@;_sL&o=&d*U-jeUTN>xUOwnT^zWf{K~bV?#o&uVWb6On
z)c<_!hx?CJd^U6>J?AjK@qw0f2GfJ5zh+_D?SJ0#-wB;$8A)0;+ldcex0^19hY%*z
zP1&qd>8^gmyenfAAKWEU1q4O%CCVLc6|MhwTY;7Cw^hYUIwc<heE@r_y4=4h!!4qQ
z$9W#V_q$q_WeP{zSDmOzre6Mc1g@1VbCO?49@EpLdv?nd&rzLc#Vq>nq*i;Nsbk8G
z$+5jTdVj20=}oKb;CSEyI(KsQQvTdfDF-cLzVRFSaQhE$=zmbV`>?v_(WbmK?U@)3
z^EoKyr;l4?98Jj!y!MX|dasFqE;l}9s?lK#8^66DTIa`Dj~#>SYn64(xaR_`9J7NQ
zo=rtgc$^U1AO*9c1<6DGB3X{gvR+HvHsx0@aumB%xjys5<8s#4e`RvnAQY=#l1OUA
z$N7j@k2z-4XFAXW#vx4K_n-5CP!%%(5mioZlcd<{O?0(0!EXmY0F0U?RcRpq?8}1>
zstWDq1*F?NhTJb<G{k8Uoup2M@14I@*V@u!GvaXNi$$)qYR#3qxigE*HGYfGo<sVW
z+wMo2yuRFN?LkoZ0ePhcfZQf70{W^eNHakp&s9bxWOY^y;!Y~4A^0{v|EO0$c_6yn
zHO3;khqry)hz0bP5eV?~>f|cKL#RVhK3e;Evca{xK%uJ+`WS@Vjb}r-nAYu>TgCmf
z8r`om`!vuh2k9>y=aDx(%y)W3*+a)2n@i@kTZRE^`=ddsJ(si2(Fn$bXhch5feuOK
zF8-mA^r7X`y4lOap$WV5dQp>xq5zmxdIaBX10N*y!<(`;moPf)8bYkcmxK9BOs&U;
zjhoPX&&k1Jm*(4s4w<R#x)x7P-p9q)WQEAgYd+p%F%Boz(#a_G21YR;H_3ukEdwPi
zs&tt9&fgWlLVqr%sJ(E4_)?L*iKu>47?eXupKEI&StG)^ppTF`qvz%qx2&C`sy=Bg
z1*ZbdOc3gVI_;XflE^6Ff|=yJK<hb;@(i%<*l{K(eYE;iuM=4zAU_HET6!R}GG4aO
z!hzfB*_@^SDSN=#|G)(LdHraOv$Jrff3<qd35EG01eldKP%>t$=ARQM%=|kd&b+-1
z60}eIe5Zkr2(oGDL#=uwj!JyJK%2u-92#-p&@x9wB5B1qo3opKaIBuOA6w)dS?2K(
zzH%Tx-1d3-VxG~1o1bh*PkcEYCtu}I-H;8osSjP5sZMa^<OufHSRVb9*eqGQ>Vvtc
zjiNq?dioCF2A}m6{!WzHYDlSmRm-wBKv*m(XOQh1=8MVE9k3K4rz4=y6hlvM1Hx!D
z2d+w}XP<k{rC~$2VL<Re{H`P{m9c{~;B;9Hkyby<y_hIK5<i3IuZh0t#S!M%@1w0i
zWH)@`KGXf<k(^CxAyS=K{z`(fUhgb`BejlX)#3a-L6P>^v}Xe!Nlh3sH<mNjJ-1IH
zLeigkxc6lXfpD*u{r=+_(~brsN|=c0h`gA?_rrRwqv!lbk*>-7J1PL7<N?Oiij%Ur
zBz*U{uW5_f@7eCg131lUn=ZrCEEkqNv^a?i;X(dODiS*;ALrv0q(!GlF{)I|0v)16
z&h@>_!OGOFK@HM`x0T2#e|sqKshD$s?3YwQq4-VoK|+!>gh;KCnKB|mlpgg>z1rew
zE|6-NbB^wOw2^_@xt8{6X%GI-oI(uJpv?J-{s}}4H?XOC+Sqz6uNS*e?9?T0=hBBt
z>U$Jd4p;NM=?O+89+GY`F?qX^i%y1AI<I~&CRXsiGxV)GR92hX(v$tT5%IBY=%fgc
z*|oadUPz3(mAt~AH&wEPvuMuR{dMkV12(xkU3Qo$A>Ya|@+mEY6tcNhV}0+go7DnV
z^lgwNXFy)I(K<IsD@oq<;LCuG%9tJ5-zJKDE&+#{m(!@|ei+70E5HT7IvaU(_qSEG
zKW++=n!t)K{ucsZjp;(ACet=YyUd8L!3~#GXyqhqr^b>cuA@yeG}Mz7$WXtM>7Q{b
zn(9gD+P&`0y;+%$UJiIWXA#`*PZi)l@)L1qR0Te5XR%P^BZ@L@ave2nyr|BvUKm=v
z))~PxO3$I$as_%&ckNEtr408gwe8UipV!Aky~$1;TzzFA5n??;pRjnvg4fv0nh$an
z(szw($qCf~*sw^6C7^T2k20(3Y|3XP`N54l4?p99nzC@(2%ki!gy*hSK4E1^Cjt8%
zv$AdQGm$j=EHyZwwV{>tfCWk?G@ttdN5?+XWh2+x?u(Qm%@Kwtw3WmDhDG1`S+Zzf
z=;g-n$Vp!`@#bp4Jc2pgD*Y$jlz(CFBQNZ5g5S34YsGff#4LaCw|s(eX*pZ140^ow
zb_<jw+}jNUoXcg}PWSVVer}%JAr)-w+M$I$Pu)|9Pn(vHR>q_I<$A^<*!*a@=~eOS
zZAGANDsvQ}a;CCDqnb0K#sw+W6a_Vb$L3W7+)y%?x@AMXl5Iyr@UEGEr{<+57bWG7
zL{_vNS=h#As;rkTPh~8bx_7!Np3mTWCFDt2biYCswQ=bg($M#?dDHP36%0&#lY<9N
z>-1yb&4L?yFfJBsVh#u2Z^3Kj%btGT8Cg-}LI$b7C$vb6>4fpa9hLq)IXpaRh3Y>#
z<X3;p%a1Y4AtRrmlQR~p7{-^VnowP3qmgTHZ)iD6UG{Y3n%PnDjT*0mKopYrV+O?C
zn@6kddSlsa0{+3nM?d?~af472#zU>4--7oFmXEr75}}U-Z9J8@x`F7KmzQTm#Vx#0
zHJRYjWC|9Wbs|Wbl#;qBsnBMG17UiWlDdlw3#U3^;aV6_TAvu4NhH)g&@rzTy)9H`
zVevwDfo@Aq_N*j$naT#KV3O3IZCNQDwm5p!%h-c>l}?<F>b^~NE&%7&-56djRu#FF
z2|y<;9C+47H5<1l^PhLNkqe7~BCTbACZ44DJ(R?CT%hngPu_cd=>(AS>(Lz=4N6K&
zoMbE$=Bba)KWr3^>Ca4o8{`TY`~Gv`AxwNJM)<AYm8oLuR`TrgE-Ac|ip8y-gh!U@
z$X6k9R$$UkMc-!K@!K2|e4m6ru2uAKRsu|+S`hvX<YtDDW0EkgBZ_cn4Ol(8+;xub
zPqP|eY}qqxl(_<vM2|YEu5bVGR#>~10}2`Y5?|5dK-3EJ8d%N_(E?X#=efZCjgI6@
z{adU#%ux`{h5^j9=JL*LK|W9=)M(GoE!t#0HOWhIM)bbm_W9qNg3^RLh^e@)?>}i*
zMS^cKmN=5q;G~%De{1sv5sST&d;Vr-EkQ564-$IT(<+tmyg~uocXVpc&AofasxwEM
zOqJww0Cc<{70%zMa5f}=sHFCO(l6DG1?s{q5(1k7Wrv)tw9Q(kHp^NUvYb;HvJHc&
z#8jZ_MynkcVP5dXnB?OokCv<=;2N-PM{|Z(RrgNNaS@;zuO-*dch3%r;%xts7m52e
z`MY8@k4%W}y;bM8shXJvsF<2pU3@S@wTy(YenpdAL-5_oa<vjy8+|0~JR6-ABK`sl
zhGdIspOM&I)Fsoyz%w7jn1u9mYZu3={Roq!pWAjTSS2U3Ohs3>s_$Cf^)A~Wqwx56
z=`{f2X~w-@L41JcL2feo3n!~7UxtP|EiXNLzn1O^8&8s~79zgBk+H{PB3s^w;z2tB
zSi=Bn-9>_Q2+4Kg`S;}vlA?4-LEVM(vizgE7zmUG$bj%8TzDgy8Qb&F55RX^NC;zy
zMub}7ya5qZs{Lf$XQNJnvRUJ9Cg;Foev3Y_i3KzGRXIGxeaABss0qMf2Sdowy}e}Z
zv&6?S{U)sQ+-{KIm^Y!vY)}j+UK%NC$@$7Ehlc|Z1nRZXL?=m}l?$9Lcz2Cen=I*V
zy%t97SXp_`lMDIQ2)uTE(v+7No|o5E-kYZ?cX*|gj+^t<;+iD5E2JT2k0C-=E8q^}
z7k%_?sc<k-*YIDSQIdRY8Rmc!eiHugZE|bK+?y|0-*;6&3R*B>NlL_^x|OYI=&JkW
zdO)XL`t+~cjCs}m===*gal%CM!T`?byYB{sQ?$g4qi6w~T+*D?yH?90w3D=)6jaxR
zI;SmZYpq`uA(1b=%OnCM!yT{}l!Q-<dnYV=Y7ZE;+#U$Xc31#?w7@@Z1XP8@Vrz2&
zS!?xHe<JVp+G%bpl13ZqWxQ*pxm6jb5>tM2y8`&+h>O|H(+a7<X*o9DCm!uP6Dj*9
z>WqB5PgRye?&Fyld0q3G@Kg)7n>l*L6p<ju8J0&LA7V2{jk;^tWoIL+Vc%`+ZSZdq
z)9!b9`AaBZf7sYXGQoR;=>xtnU=3+E`47znUgT2>C>#tw8y)dWtb;wZ9Ve<Mjv0t+
zPY_VOeF}br+x=TCD!ak<+Z-(lV*IO?XRbPx&DYF$A=rOFAS9@$<@Aq$E%xH6&Dm*_
z`1^kFK9yjW`;u?IV+?DnRnd9xqeA3b83=LL^*ubOn>oga)XoczOj$2j_EhbyF7)F^
z77JcfD*=FhU9oq4=|t(rHg(8@<Se|N^4|SfFw}sFuI8~^hCv09k?1@Yxqs#$ATcv(
zkf=$yNlwzCD4P~UAt$&tzhDwIkTioE-Tqr}_3-C0XuDxx{EQ(zKc=e+t}*vL6(++Q
z8j}c@e@hy>L}SV*ByR7zPsjtL4ZV&^sZ#2_F5MM0zSS5J=K&|c@NKN?mx6eHUB8W#
zll>K-Y5Cq_$o?@qnx>nC`pL@W3lgGLwxcrBP+FvW;0eVwjyWh2y!f23&xXXDJcfIK
zdl8?_r5-;_qszI1MIeg|gYI4US|t$8MoFQiW>ItJ_V&oYkBt3{Tn{%u4p&g%Ft<fZ
ziI9o9EQjPSKydtf@Xp-w5f~VCM#0!KUPy7d2r)B0i`S}WEL{C3+XAE%er7U7`zimZ
z*pT|*6X16dk0ygGI#S!*!_nLR@Y`a5KNmJh;!q0pY4gkD_nhF=yZEvPZVi%?ZNSD_
z7a(I9nGVG>`WROClZcKBe3j*0?wI~X2086E52u!{e!eOm?h*rViwxi7&>g5K#Hz?C
z?$M)?#^^sT%JTET#H;r@J}(q8qcEzT{l_+wg*{kQ(#2%m)`$|z-I;4mtY>U6g;&w^
zq>mzCd6szxZccpI=5y*_@i3{$U>B-6BnP|(-Lk2!Og%|lT7;NuPePCwTPZ2ig@BHh
zqwij<mP<aHlby&;h>126IooMWOp1^kuJRe7jrC{EXjD?MGEg$42>nor)844TP5Omx
zLguo&U*zKWokJ0NC&k9DewuJNL}G2A$7U;Ibr5!r31ecC4eGm&Mj^L6s#h8W`-<q9
zl$*33o}UjnuVc!C{d-&dx<8Gvr5B>tbQpitBh_Gx&%(U5rIi`|l>l$TsDwLaqtUIB
zgg;>rqa2MWQ>H{eRGgii8+ayxsIzx=foG&wb0#gHy#E_tqC$RHT;>YIj55^@bs_-G
zg%$0R!BzM06n$^|@@;%xnZ!lBPJ)EzZiI#vxlp$AC(ss>0A)>YcUjaBfhl;%!{&^X
ztA0Zc_=qw2O;^|rpFet39CDR<&Lpa9P6sClxZO1CL4Z1#=hy@=z*AuMKdXzsU7{C;
zSzU`*qbY>lCl_yo6@SouXl%I56A)_iS9t~@v03TKr!*y}7lg$6iK+G+pKiMNB@x>V
zXXIR1?e;Z1FPT4&PwA-Y9UmGU&7qEOzsr`<7L#gQbLg}iH>@~_kxH)?NR5f8`+VtP
z)3GH0iBG8}TD_4OHM$kIm%ZIt$v<j%-$N9Acbt`f6d5JhBw-QbIPc{hn=j(B{sv))
z>%NzxB>2%7VPexoHk+<m8s_8;$5W=u>Nehpu;D#-<HuLc!rYObI3)lUs}C_uMhn0o
z23P#zkemjf0mQ+AgXlT9a-@8$6p<U+crYpZtDg<F(K9Ucb3nu{K*YAwL-6`)d%93{
zSx%HPxQ2Dk4nv;N^?zHSMu_CmcW5>?AmZ|?UlH22yZ|Sly8M#sz|V^56}N6<@<`HS
zGB5F!HWjRPcc}|+URZbigqu$@rr6$ev!|l+n^5hJw-r#8Zk{e8Kll=;w;PtgZcSL8
z)%mMYdX3_;QE$Lf+};n8U`_DuAo0#aU5kGAT}JQhBSHD&s>N>dijlwpdGzkpVW(^z
zkvV^Cu|r<0Q4A<X%g3t7N2_<ETT6oD6x|@lLP!u4mjn~pj6C%41`vvFqR1|@-9&$!
zy~XsgA&Ip!+lgNM9X<tvd;D<$a6;Zw1h=f#D<Q3_7<TiY>{5VldC<NjbV`E4AwdI7
zIgG)rR;Y~S_zfxR=(UnROFuHGs*OTX0-m|Gdb=zHg?68j0v)&*@s0@2Bm;(KH}aaU
zPsL;Y+Pow7ko6*CRi|Wphwy9b>lV%nBl4WTZ}aD*W)PkhKqW6$2%E*eskA9yY?4pR
zW-6DApDL%1i8oL7CcTQoQQ{`E#+B2vJMRNSiJQ%0!D>KZf|ZC1BO^rTr?{0*OEdi2
z{7diYsd-V;P<l10v&7H3`$_3sbeXk%9!}skf_}>xHr3(DcC|>s;xKua*QZG}`u}jv
zn09bcqP6_Qu!}`ZEI7&b2j7VNy`_0AOTXQ2`o%l0WU1(?pZmkArF$GZxU8?&*z=k%
zt%6Nygb90;+s+p`Y!v`lZkH48W@k!<)7J+-k8Q$~xOxby=M~ij#_)eB*(9%in0EN&
z`LW$5;#!izxYJzl$u%5Bl(yL8(q6_Stsi=bCdj0M_la8Z5Pp6ZX(s-ZR{T|&tUB03
z`stXd29u)s9z%4mTn|rz&}xwr^h2-+{V%m%3D1c)5Q>LAiNEN)m-6U1Y!3#l=FkG*
zA3c%MKG@VYmR&sB2mVgI>v)1g9CItvhHYG-^pWVF5mqT1^*Iu{=78(>NkpqlQyzb?
zE~Vub+?<?fgutwa5BuK&o+@O4<z&EFW~(zE&Bts&Sc4!T@J3m@Wsf!{G5J`azI^{w
zFi2*rgP!`u-EyNDq4^%^Rxa&j1`s_oY1Gn({+;!kE}TG1C|p7JtZzAvkEivvb$I?4
z$yyt5-W;~Du~i~=Plzjcmr}G@WQ2xBj2V*Mr747|I1<L!wA_vl3O70bJ3SH@HEP=A
zDdQlu0^rOjXnR-04{_n*p$fTT{d9IG{1tYwF0%~D50(HbB6Q!B>SoXg0M=g*ly#~L
zYuoQVCcwK%(&qH&^U&XHU~Y%h_YsDYOFO-iCejir+-ZR2QA<j}f^xMIun301c!KXp
zb6n8IPh5PwQ+0b+JI!D9mMgGEFTjR*j$F-$IwqL;O<g=~!baCok(bhF@zVM&)L(D*
z@Y2d#+T(n>pY)o}HCDzP7+<V0aS*?$dbyWE3jOHvpy2tPhX(XLxg1KHA$H?LHdIVd
z+S+%GA7ZNQn;7y=Z!>btwK0u0GEqrkfe-h!P{qrY;<9Q%@4ZvD!IbA<_(K<l08+ly
zMwqZ8G3at${hRN?M2yqzceut{40x4;?{q;-b1lz{Ht+a5WHneh_wMGf5D6hRG#1U7
z&XA)E@HtvG67*`-8PJx(D)*$h{Jufh^RPt}3^qi(ww;os8FsRKW!1_x@_d*Ld~WO2
z<X^re%n(AjcDB^{;S*bE>4!|Rf4^A-5=b|7PnRT3g57WfF|yg9&dN<Mxu(|8*5UED
z&YCR>w-KA_oWU@G8#Hcl73(c`#BKZtfYKr0Mk=5pYeZ^z-lR|~!XDJLtiN^hq2;ab
z^T0%J3ahPK`ptpD<IP0A1**};77kGXS#i-!NVKQNST`H;Nj}#cEo1g7Fpq(rV-dh+
zv$8FZiWRWQ|1bba(Ws%%Yj_H^>~8gTO*$}vbIrXx@h=?psHBGvbPIND_-h}^-IR%c
zXNMZTXJm70Oc~vhkm}z2a?Tm`aXsjP-mAC`Ot4-<h2>)?<xV%3KE&{hKd$iIc~aB<
zpV%2g*t0!cJXf%9hzjAGT}olxq{xslJ{u$~R8E%(=98*hd#r0as4gvq&Nj8{YCmjn
zh_paFR};ChRQTR14Sk?zy0Ty%Ioy@f{G>DX*IL}NlEk76l<|@SF2<iQ^A7}%A&xjJ
z0F27<*&@WO3AP=3ixaGA$IGnQu7OOHrRI<Je$0Z<6~foQLY@XHV0^Y1BKTk}B5ih_
zD=%jRQ-iDHMw$7gy2|(US;mLjtV~q}lb+|9eT>*|-opL4Z=<S!DQn4fKacL{S(wF1
zs#<dy&)92V5vT^Cx~3!B%mpsCc3w;DWU3)btnl@7Sy5_$n8UC_feznz3?=G0w>xS$
z@{hybQdCrlTwTJHY1z$3Xu8+2QJNSpU`j)E8*DaY*qM~Q_+stYuB!Ze<&Se&^6>jG
z^Qd1pncetrUdY>fsM76t;bE=5i~uLUa1G|+AE1!=l8|IOHa|N)K^qq(7+7bPon0{1
z#3PvjlMOA(rpx(BVxBe)I%<l6H*Xf67`D+I?Myq~&V?wY#3ya_PLmpkQupI4R*B1n
zYQ)k~mX6?8D%BG1{uPg&8i1?PgP|dcR{obH5tD&Rc2?foG<Tvy8DYzg0W(VJx{r_p
ztLqaG2P6Ik$;Ln58;#;VG-+U@=D{qYK$xVupA^@2ZKnLGvDcUPNrY7=n;$a-Z_Wag
zJSg4P1OpE_d+=~ADAWYy+*Pxqsji2E{ZH!i%gt$EBup=ieF@mQ@g*CzIx6{U9vjCg
zwER?!Dt>8CFZ!5$MtcBdPRq|a<eHB*r`&4Yb5iRsgBTjy_5D_h?m18ECft#jB1v}$
z;r>G$p!-p5>RdHA=@TQ5!QKLKoA+6&DHY;e&uVUtI)Wpc-B1j~aknAXX$Q%eXk;D>
zwa%_Sg%(2LR*c2LtWZb@r|rxT?n_p5>)&@Lw_zn@u~uSS8G*HK2fsab`<B}L7G4G*
zH$aHCYskb5r0_ic#wR?TUDpRHaTNBJC!AOCWvveY$RE#c!hh?Uss|8+fo~YyAa1HN
z{Z`Rg;XdYTH|xo>LbdM!#wNHw!!DQIXPm6EzX5_|POwU)onP&S(Q+?Jgf#?T|41R%
z)HU7V(VoMkUNREziD6ObKZcD)I^c}(*_sc>z=t<vR~LT+d_wz<Y^ht+hFjqMMXTGU
z=S`{47u=|wzgbY17L*66yGVm`*E0o$kBTl>gya?&$>|yHE_auYjs$dl6E<SHjrUND
zI*6$PudCIJ34e3&U%e={f~9sx<e>p^+OeX7M|NQ$Z`etTb{jxsDCu`sW$5zQ5y%6L
zQXMr~V6wRT%_n_zet!ngggbI?w;I7jWY02@a1gbs$WFDu(b1q<^r~%hmFM)w3tv^P
z-Y}}@_T^cU^wm({dl|wwxg_?S;e!P~Zbed48bmqmV@`R&$3N!2Fw+U7U36(mh(R51
zSY@rR*4~14QWoWIIxhswvt3ThlArL0z#T7FBUJl21joWOdWm8TiU?=<&<kGEjxt6l
zE0eUNIe}OP;i1hLSzLYJgjRZFhX-xFh{#on<&sBxed1<bqKt$+#Ti85Gb1Yy7&i9l
z>aYuCDyW;wpw@CLZLdvzCHj)<qCUFaGen@-d2Mmv<<whSCus``)P)|+uuS`S5bK|Y
z@{?rmcMBXVm(!NTR4&tU=SQ4GX<nVlK?(riFv-j_2>A!4nf%l{;%vw?JK^U<P=C$2
zkulLP9c%l`V!WiZ(nq59A^--mCI*HY7P%y}oTx1ts<6SIWJq4~=5X*cCJn__$BP^W
zN*fa=D3s(Isyfpy&shDE76MbM?cGyW_L%gnP?nlo(!qk68@JkNBgCC?4JK*Xn=}xs
zmqP~`3Y?C<h|}T;RfTQiPva;FCX3%Ppti4{u7r5Y`pmnv)a+kX1BhClCVd3wtlEWo
z^YMe#&*Or+YD2swP2k@Q_r9|>zAab{oalr?oePieriVd@Md^31B7xi*v094Jv8WH_
zG^%knVZ1U{f9J@@@95!gVn&tJM1A8<|3Tu0_Ww*<ZtuWnl}oq$7lN#5{}@<jB1o*)
zyTu;;+l~47&b5xkvg-@nVJ6wmpfWxaa--Nc?(48K0IDZkvnq6)or)5=f_{$fzb@R{
zr>}X`rf~9c5DMkoC3{FGINRvt;*zVuLfm}=1z6pgGW@<C&yeA&0^?JQvi~gxvGfEJ
zCb!#boN?Nmwbx7fLNdqL{7mR-K|A*EJU_Y^ksqy@2+|*Qi)(EcU-x|1&j0QP^hCa}
zdeD0CL7atqY+l5ce5Q9sd{A6cST;6L3&2+&H88_!`)@R;$L|Go*FqhDGQfu?QA%8W
z>1Z&yRk1zHW?&lqif2b?NvLguwJqQKRj`vZt`Xo9%jEtlK<kLjV(q8&V*|S*W%Hh&
zv81K#=4r?dP)QUSb<gI7;wS!sLc6WM^_>rzd7$QdWQ)jY{v__Zjiq5+ubz@h2m7al
zlqu(N?fED<Sp>6v1QUg7?=cgD>PrAsSYYob&o{mPrG&&WEf#ckAX%?j9P>M01ps|B
zI8|<1E@?I~vyi5zs##+`NP6!lK+Nh_4sbE>Di{z!5`W=Ow0OUX>97VXF*E`lMq$vH
zwqag~6ETrD2E)zP%~^GQy1NdDYGzl3?fw>^+284y>Jf|+Q;i9bDzhmNo)xW-Mfe1@
zG_!ZCJ6g4>Zg>TGTb?UtXBF7|<EFelzm=4Yqyp{~@I*3_bM}G5Tz1a|y$%n>B~MnS
zZQtRz+z1Gj$J}mYOopwVucuj<ikiBT<a(zDmUvm?9CHgcdl(!P*cAWTrSsqdqQIx!
zr9F66`>`VL$zOOnm>m^YMXoHaFx>g!k_1Ip>!&p^PhmiaJ`3p{yu==Xx=X+;ErL~>
z#cvy4-4~kFskWtmFV%<b1Z@thQAw$l-D1)eBt|t{|616krD8t1++oceXI<<MON9zm
z2J@Wpx-fZgO^C)Xl6kIgC)@Z%zNyjrXO0*2WGJc7W|7WwB{d_nfp=w=zvdyk_`+=A
z0g5HzRx}jPRR?js@|@u^qVhdkHi=AbK=;eb6Mhilx8RO70fV_OQQ_*1;187eY!<7T
z2ZNajQh%;(wUiB6mT$QTh~Rd7rDL=J;JgWuK`&AUFiYiNj4a%3LzSC&p>n(gnSoxt
z^(tC4sn<T?qbmn&5lEJox;8sa$uO}wGEbq7e-T!0iGDjhX@{?K7ij^!p9~MKmy;nI
zJ$QsD@UOE0BcYwOGGO<o^2(^Wx@W*}$!0R~wxxzc)HhRdi>&%|k|a^|-P1D!dhHYG
zKuXde%1_m7UJTrEhob59(cD5@B>zlYhuY-(m>hU`TAtM(=6tf~UcY_+uwV3=%uq~h
zhYS$^;`HBKgBMNWifqLc?EF%eOn<~i5(8FBEROE$h)|%2Mehy`&H?5t=dm!A3noej
z33RibyGR?udOgMf{>lW?y5JJiQQlxka;N<0zV6zk>XJI20mNU8d*5}URm=MWrFUtG
zAucK8_xOabcObfHus6_U85Qfd1%y<E3raR$XmNPo)~h(IC8xI**myCNXtk6T9Jf5B
zOvnRmoJAfS%Oo%M<R<T|T+(_}Cla6aSZSKNUmMPFipKmKxJ0T&BgB%7=zv%z#uagu
z*KJ&?tALjmZLE|eJ%;-fY*4B0YW%`rF=aeIb#Ip;*rJ4_SYd2yfZ;>;`ls~R{ZSvz
zycGu)axQj@ktLd<kV}Q&6v@;>k&c?Zw=s?~fiJL|h)GC#o*5r+yB)4{=i~H%!$}Ux
ztw7P!;-NObJk@$3A&PM_0%z3I+*YDCTD_mWD*2Hc`46&q4}a#ri&dF^9;C>WECmho
zHB;slQ#Gx2MNNNb(?J8U#bQ=MTzliZl+Jd^AJ>UgkTm&EbFam@1u)yOU9(#$qo1$!
z9b4Pm*Nb1FTIg*mjRrzTkcWe$+Dw#AoF1<}2e<I<pFi#nQW*IsVlT%KwWIt~xZ}Yz
zE4Bp7V<L|r#wIWj{q>dXlMSvFqRzX0f9MpLzW&sb)bM4qngO8JQof|nFUT|`JOq`I
zZ^<x6MHbAB%<koHyeTsDjNY(-%MUYY;gZaTmYV_%Z~OoxSv`WCNjfmmv*=*4mF%;V
zV&urh<_@jZw^p>&p~t}2!3Rx}2etfe=gFrg@HTmAGiuOXF0GISuwygQXq{!+1w*wt
zlV!a7F!0Ys1nwY(?G*Wjj#;hqL0o=K1=dmJ#DkfZ=MS8p!z9B^3TsBN426Gs6_)R0
zuV{-B$mIk}qFl2!O#u(Ka7I}c$Y9e?%%!AiF+H`EH!3@oH1Ha%yptu{OgPcQ30iG7
zg*Dd~f`>2W4Y%FlC=fzTX-0CdAeNNzuM|+0uE&P&r_SAm`3uwB%ftY?@vll`33tCK
zRMkN6WdpinHloWXz9x7EDNbSb6xdpcW!3)fJdD+H%U1%5wOLeJ)AkWq1yC2|5C7W>
zz(vF=o?Al6RkH~df8B7wpr!XqjHprRESVSG_aK5P)z#zK#;+a8>eygKot6u^XPlx$
z9nrjf1c%Q~%>r2YW@7nPW5H^din$WqkQT5SQbpXOr9XHH!t=QD9nX7eC;e5Y4*r&F
zNq9#Az76$5vm~DG2p#;LSCOj=r<Df>to4V<d@US_<kN!hib#Z8R(0GxKGoB+Rvx*#
z0?dJu!L=={w!d0M7r~HzrsK~q(TG@5Je=T541ms<rm^1IyXzY0+tC0#Gf}1^Y}&Q3
zd`?6Rw-r`;Wpqy%aN{fgcE!xxG3llmG^#VX(??k-G3`bBZ(_L6BXzud3S_-XPvtCV
z^K0ZrI%9l8?qKNV)8m};L-AJ@;zL1<pOY+%RY}5V`k$)9tQ({~t%7-6Vn&Y^hryX(
z%_i$#bv)CoZJ(H;`&Kf}>bFh;`-M2pyqBh)8P9gT0ZqMPld6ANfVuF>mE;*k-`&()
zd@c30s|{8GLuOJv-s?5{yruoy4-?IcAnIt3Q+yB0xURqmJhw-RNY6i0*rxbro-qfo
z&OuY)P{uFl-Y&k%G0-s8TaUmTzst6Bv-24jFBgf-q75^Ik@!p4j!^JXGU`BK%Wu%H
z0!#+io67HJt`WhIx=Yt90iDQaBy_w_>X{PXZm9LE2nyR}rWZS|9k*nVHJ(i_A1zva
z5OAy`35(V)q5nLilP#hxs}V5Dgt@c<;f~gW_aeb@75TdfjNc`oHPp<wKX9s2X?G+2
z#QQ@im^4WFb}M!HpOuUVA!(3=3r=^U*F|MK(Vl23Y-2$D755}rLb^I9PYxys@FK#R
znCrQC#|XjO^Lp=oG><7cP2bpx%&W*Y)Ncrt=2e5gjj6Ol{^Z^enyVh$p^;rZ4VoI}
z@0@B;#Ra&gcP*r!=SgEy9n+dKA;$vB);eLd9etW{eK(#%6P+Ghy)Q5AcApuMs;vTm
z*=T<5M7nP`PWS_4I3;w<@51Eb=64paTdMzL5YKN#ZuJcRUkcMNCl+LFc-=-!VOBw=
zFpSsn)#bTz&M`!ii_Igw=Qi1(XRJ)Aj*c&=dyB^;GGCDY%-fXlwzLO6j_PUJx#@)L
zJUqNeIICw7=pl7{JjcL2VgzM8b?ZrPwrJ!be$Zf5#PG`}c>Jk4*X&b!iQeTWE1D^J
zrn0(xkmB0e)6Bh0yP5a9qB#11@b9=U<yIeV-IvHxskd>icw@MmHzYGx;{TUOmE*=B
z5~bh)?Xj3nigvlwt7zZvU;*6OM}mhf4Hx%op{SmorlbVnbxz#T=eAiG4o0DB_qBt#
z?Ea)m_56RegwcO~DE=uQakYw^Bki{c$q(fTeHL?S^S|i&%eW}M|NkFXx<im|q+{t4
zN$D0TX=!PsmM%e%?(US7X6f!OL5T&Hlv=uf!|VO|Uisf{|LZ$DGiT1soH^(5yx;Y|
z{aa<Q6dOx-g>-23s}}shOO5+);^57XsIr#mE*DZll}~Egr~}`sGeO^}bzh~|Ke*gT
zi>b-l3I1Owx<(Pd=6ImvUB+B%u=EzUUe=w9HVZ+Nr?d&mf9v@MLbjN;qx|J@Gv4+u
zcl_W>t&foIui^m>i~s$B2|uk#EHh(Sxos_&KX5fZjUTN~DhlXDwj_ZRkQ|d<IkXg8
zZFB58@r3x}Ko`be3!XOr1u<<o5L5)J8)Vja#9jb_GprZI;A&YUEbNPBKW_=Ip!&i8
zpYW&NcM1I<eoRdJiC=%MtcTd;tIy)3c<Jrk3yIS%{{o503Ra{K+vi3)T3%5O{|lv(
zKZkag)kk>W#rP5p;zX%r5pmN$W5uq!Rzt_p()M6W*T5IlXCz2n^-<X7K{h=-y~S!9
z_<~Rjk@ld1e5Yj9Bhs8`2l8w6zYMB2=y@8x-Lq+~IK1okl&|*{0P|{MeQA$ko=($f
z@*KTJlnOZQMpHoSoBz5f50*f$ANpS_ar{@uX45^nWp{@o-#Ohs{i_klmN7+tvyzr;
z5pSqF`UuVjR(@(pUQ5pTUqTgvXPq&nLp;Ng)s6Poqqg7}&5U0eMbpOde`!=j>D+Ob
z3O;7?y#IdgL=Aigx=_of4}Os|*>{gg_aQemI4qia^_;5yZ;StO%%7f{<V#=+L#;AW
zu`pt-GTEWU{{?;j?`K^V5<tqcE<bf!DB%In=-DHrZF2p;4D$b76W#yUvUOZB_ML<O
zSnFXd)s2mQ?n(BWjsJylv7~Z`&m@QVo`4YSK8tR0&pBySRq(XolO*&A)Nto5P6?Ss
zp9N(}o}%1b6?8j_KuZWL@gDg1I_Z3HBkDvSd)zZ)@FwQo(S%eM2+wjDLiij@3|gUZ
z#0X7<vA8hUa7j%xw^rj<(gfiT^|rnObDD1MjI9=@DaTu8jKi~Am{r3%7l>}>$a_sZ
zPI8Nm#ilhpZ~;y0SeQYRu|b-JkS2WTTKDGpW<eQVe!hp3`WOGZgT?(&`pLJ}Vo*AP
zN1ke`<oROZQ^d(T0A6s7`G;F7ve@Ho89%y$<1zq-lWTd#*der^4v?p)2(Hu%%EKN2
zaTlxLs;61|n$OwzD4>b+$Z@*bk<^<!#shpB@7Nm5Iy&o1&WuPojM|$20>m+k;dllM
zfUw5)3_vp$`__JU)H(`4@r<MJnx92?vkJxEY|z+=5theB)P<~mVkvSl`>_z;o~A3V
z1LI=ef{vUJEBI$~b0a;gEa%r-NBtft(?tbIYScakdeJ`dMCnet^Qywvm65kiKf#8i
z=h$6E(3e&g2TOvj+6|+wi$oPA!8>-n=CFqIz1-OzKBQaPDQ>a0>@r@Oki!CiUQ)Zb
z>YWq^je|d{c{1IXZ7cxRUCu`cdO(I_N6WK)2xf}wt$TQ{3LsZpj#_rK>E9#;`Ei}!
z8Ul1Z*a%y7g_JK~v8lvpRrUHL>je6hbYGF6Qm_et{*Czio{21QU_Yg+JEf`SyzIe;
zq?4YqOMsgSQ+YyYGZHpn&R|s#ml2G{<;s##hBnd7t_|+}y#`pqY%gzzU)#ry(Zx6b
zh%eIVg%k7Kp4>42u&0Om#@r<A(1V#AfDBUp<<8CfM^ze(t`}TYU)6rOtYC2yI73Ca
z)<sY-7%;o9-h+DjOXPm|3BdjR`UlvIU_Z;NSc#21P4#L_BoC{frB+%aLA6>X^NtUo
zio1qDt&u(~dd??+C8yv2lF@u!d9V;jhgZC+O!{{|7SLs4;VQv~snoVB#3B&m{daR8
z_wZS613LzA4kO0^IOZoB-VoWtGc(NyppYa#mz*adJNsYO<s{hyK)VQu-R)m&%-088
zF=Ds7@b7Y>`3BzO4X}f?YcJE--QN&X9a$>inAw*tt;Q@xVJvDaPY3=F)n6zc)=|Zo
zpZCDFCIWTk3Y@kzI!lM|>vTfgiQvv7EG2XI@|_taTO|i6^<LGZ1dW0Y-r3%0*0#@B
zzpNxpe=IcR5~^{=H*;Y6@n?UP@K&t!&x)<Z44s?eT#a4qWCQ2^*P19w+8)`bRwzsB
zW6w4?m%XBt<jolIjuFJ0-(SirN05mP)KvksKvI5N?=FD)Dis>Odxku*mT1fP4nZ1$
zPOgJ^@T}uabaHX25_VH*=Lz<ml0;47X#AajiHwEssZZ<l*LhG&S~;!S4%L?T)3f@;
z_Nn`NT5tm8o418o+__196{Y8T;A?Wkl;sEIYJav)_cwHh%Xq1r2=91ll1;|nm&Eb%
z%xJuLJXd?FkM28Bv-s_ekWLWxVa_cC!E_k*c9Nw6U5D}lUyec}`@E1TiJ0oH=chHv
z@K_u_y%?#O&5uFqN>0$x4LAh>-FSnEI{TqbuH=9x)8nXJE+Nj;m3uaRrx4^f6CYZW
zYzPeuCJx5o=)^jJup9M_-@EoQQtIqoe5N=0`jz~p#S@^7X=~eJ0F@0qsrh@kRO;x7
z?|eGabRSBYHc|YIE%;YG#qWbyy|8^>*I-^4)u-zFbq)XC2iU*s*0P_0mwcp-bRDj<
zlfKoX9xH4je#=6Sh7CHVGmdzaj|%02W3}1S3>kf}c-gDv17k$@N7r8WW8UoY+onM(
z?~i)pT|w<NhKo^u+K)q>#lx@qT($}moV|LzCp>1+|6s3qAKyGuEVTpTn)x(#r6}y0
z@f>mgTJ|IkF&^q&2LJY}b{mW4Roek+eeus)UC&^;S|@1Fmsrd-xAxPs7V-gKc<OP2
zs@PTNRpzl*^%Zd=d6V&PNxw8zL9tFB?LpJpr^maLFwreq3*e?XuzkUp<#kx5zf#{G
zrZh9@*=~C6(d)XVw&VA+m^+JvLs{Cyf<70=BQh3(RDSTQc2+<9oCLkv2gWwq>9=k?
zO4WMoOg(}my|`zml92wc<_$~ISCm1(GN&V#M6}XUdVjmvSq1H5aEjX2t9=jcWK4b)
z8RGOC_<`+pq2pXs+-LKMnZU+#mOI%^W)|5nQiN5GPxYnWgBhU=ls>5){A7E2KG4Wg
zT@hN&)&3UPf<Eh^1J#+)PPrKm+4kxr`_OZFuj1J?$<6ywZzcLE#<8(`-B0mXA{U&X
zU8n1A<0IiXK~asG6I&Y$pM;#gQo|iig#FOl`q8S^xg~{*eLw+YuBBefIoa^Hd~JE8
zpV-)70)Q1Rqg-oH)uUqk3VdZh?((Lf|Dd8(wdJm=m5p%OmlZ!e>Vd?pU^DckNKIY6
zynYC`UZOLPuPelU+}mXk^qTa%VRF?e`z%GYIlnun_U%oD@%N`KVu;}<e)S!-fyw3@
z3|x33{FiNJVW$tHy=%PVl|hG+m<e4~IW^O<wMWXX`x%`wh;q`;oP>&X^+!oqQ+;8U
z=T!lR3b?CW)VU#Y@N=wPdUHf8Aa+d5a$hcke{m1bSz?prBF#=6srJ}M75&Q;x|ahR
zqMm%<g5xYy!Bdr1rnW@MGo_VC>UsUMTlx?|002hNreF~}#%LiSGC|b!EJ)zV?~n$(
z2M^Uwi78z1mZ_s=w9ZfA+iDmk7CFaqixE#bPKs(afOEtcqMGS;ef*?XU8{Pz8BQ%?
z-I^0^i!$V@{>4<A4Z%|4K4X~k8}3UJ<KAbgT8fZ*ax3RqTvP%=*Oce|Cbe-%EX!2l
zjxiv$a@!+%*|C%Do!og_-uV+cHIo}<t=sY5Q=QS^8MF5Vt1y6jL?N#8ph^ao|AVv2
zwcjH4cPkwSikzyRe%QmakWRIrKw&-ChA{1=`IoGOl0ngJ?s*~ZnQtEFBy1rbI=>5F
ze5U!080@H;PWa}Ygk{uiwRoCCU+C%ntQ<a+OAwaZd+?0!`{Ef+&na0R%nN5=hhY#A
z8@g%;U(9K;xrY?j`#s&Qt(8cZj@65QC3}g|=5<}0(q6$j{)`o~&HA7QadWE*$bGOS
z#_|K7Z+hIoQgr&eP2Q8?-Y=##sV>?5aSqPuUeJP=)XpdC&o>tPYN9kS@zuW-w|7WT
z5h$~t^R{0CC6U;i2)>XA<qBC>)h?_R34aL5ri(TfxzaP5Nv0Az@dAvTb7@pp@P-PW
z!x_Sf$#o<n?1fm~EppPmEYXB+>jXgx*pGW!ea*ZCaSST59xqT%c%WbR3w&t=OX~yP
zLS$msc!3+-PGtgYM49lW$9%S@LYrc?q{%`=YMb~oCk>`4ZO~M*j%TySH#W%3Ch8*A
zTQr^2T$@Yjw4pVI6oU_^yj(EKuny7DYS-!c!BX6V>Lx$ET33bsWtzbHq7+!A&q>ma
zrlScqef_z<-cupN8lVII8Wlr*e!}8kBZ~?OM-|ZnB_fVZ=@cIcE_o*K5Y`1}pJJ!5
z>+C^MardPc$o-b%YvUSo_zGF71cHgL?n9*Cjv}oXF?F;#IVmfzCtl!U--NOG@EYDM
z14Dn?j#I{`{a;+eg15Wt$c!U2&iJan+n_3up~<>#<-O8^CC+*K8y!#)ea^;84$ws(
zXy;a|-I4M$Qng`*hmmft-dOV+6i9?nTMSa6M%5l{eWS-?MoQd%7XMiQ<C*dvre3PE
zNVAc$lFUz!02~b>3Po3A_J=e1^RV=xe|t9f%}@QBp5!Y>amYGcdwN*Yk(9)H)~HrV
z$uZqNo1HF7c)cdRW@`c%LLIH{Gk|MX!>I<o-=s1&x`Qkx2jhx0&nSu+xM4g~Lq~`^
zulv*GPEVEdn$jMhHA6igg))ZG`&^M2%+_%q*KtRl&2oz>5vyr8Cr6+sIEamC7xN5|
zx3-h<5fTJ}0pzp7Pb0vJT9WeYT7?0xJfA~~b%%j)Xd%I_NN6tK)(tR<v=rV>H9M-K
zE+X$(nN&e?jn0hLrrZJsNHOz}Bt_MWOlkkEw0xOxr(-$U^X(Knll4Dgw2==)%lsDf
zuO*L7Jhs!U7B20xI=ivq(eaYo3O{C`B`Rb!H6_yNgxQAfOGP*}>XC)gq3BAMp6-(%
zm{}7u5Bnz+<{>0S?b`$b&tygmtJvDVMQK+jHSfP=3djd>$|Q~6ldwBCe|I(cOcXxG
zpu+>&mH*Qf{?0$fXN%r8`{pL^sgFtQ7pwU*$Cd5an#5p+IJY;C9-HT^sq?{JYXoY0
zOx;!&(`^Np8x|nvw13>tT&{ddAy%~i97$aJjGML?k2ybo?fE>Y<QVMJ(&e?WZj~$1
z2|vejbkV+Hp5*&@xVdDN<NJkjTCfat{}sc07ZJEpESDDKsbPiXIgFT1DsUP9MwXO_
zBP??$teXp?9o}8?m{Q9TwP|K)XyC0eM=qM5;Tj@0XeTOuF=s+lKu{$xKlvkJY3#*@
zhHuJtlFq=G>GdX}zaRXY`^KP&C}68(^|kmRuGo4RY}__i;~YC?=&`1C_ra&DB_~0w
zVh>|KxAtgT#%m)39K=gc%N+-TWuz()V~;M7NZDL7Bi;-&br`0vLiOH>9#qtgJX=Do
zl1?2>j^nw6_Fkaiw2CTZM4y!#Km42_q#77?*{n3tqg`$d?fN2X!fYwdmzwsEZ|2zd
z*t>zCm#MeM(k`pV{nuO`a5v-@|LcTeJC^on+#ghRR%KI2y&y@N3vb_~JTA>DpZ1s1
zGl<e95l8sLj3}3j$4K}hNQS9zj~U(E=C@BeIsG(kh3mfm*>-1{A~LO{6PI1#@Lpd<
zfd?dMLY^c#z}wEQErqdU6JKyN!f9@aJDCJMMA*u5<JrgidO!rVihQuHMg)tVCxo_5
z2<){`m#uXsVH$9XX><~#uqj<qaK<PP(_0k=J31W7N@V4};F7wZBXFB@tP%znQE1fE
zD_jwxMWhPIxs$YyxtoRv{xK0}XBOMY%TUBcc4_+7IuAUA?Gt-Gn<p#l0b()R<zz37
zeP*b6beyxxk4(5~22sqxV6f1P1+%&}i?F8}*>(O9wd;u_f5*w_W+983IgVbKK+y<u
zv(H(H$!g2)L<DWW(Yv6^X=4|(f$LY%LDq=|_k|qnu%vkTVhf>>&e{NKJr)YH=*VKe
z__A<8`q*D+Y|x@CxAZnEz6iB#ZoJb<A5so1zP$pKDpp*fyh`ez91qzk0-eeGB#UG<
zVjXY-6}=QS%~+IR)FcGG#Guv>JW)IEzb^SMZ&yi8%yisel6mCh>m*lD+$|=xZ@j1y
z?u1i3NY{$&<h3z{Z?RLFIFz&16`_@3a}?V4bwAznItdpg=@AhdI<Mzhc6LG%eYuh4
zVs6bsZo{q3va$-rf4YVFSdt8-w2id7nER;jPxqJCglmn@A-V6T-m}`eVO!3OxR<Er
z2g<=peHj?7d(g3OKE~8D`F!1f<um>snvoeBebRsv6FDm1rau$m?|LG!*xdwA4uyyK
zFPL4wsp9|j(tmx`QUWDlwoQi`p}oN50(^6uI6wIBy<!L})U5CUpqhgZv7YgpI^Uke
zA2-E+<`TOOb>jV%O$M;%kCoYv`B06KsQrU$b}KIckZb&whTd2;?DogvlMJ|j)L3H;
z-EpAHIy48LdLSTLV(@clmkSdDZPtXckW_>T42|Xf!#cX6D%F^4>M+*>J8q2ET)|H+
z=bj|$ygvM(Obz^dUdzIStmugatO)=)J2R3I-8-!77^8=C6NcDX<rLrKinQVXxZ}S7
zvkPg~Bi8{ZiR(U##wvZ*_q+#vt9_t9j7zifNeitg&LUZr-6#wp3Y~_FoJezm7c^0L
zlg|X;(I9ot55xLIbo+5ayerUz4@JNnHUlm*aiT0DeIm7D`H8IOVCWs@(yb9W8M$Ld
zHnenwjPK?!t&=0g*d6R!Y|@=D(+nfh*QN)h?5OLK_yS}`&P3l4QF<g5&gu)X$Mqce
z8;T{V;R59x=K)8lUmZm=x$N9k?kfzk?b3RMiNm%j3qtMPHwn~G7GQ#slosgjys<+R
zwHc8~P5mCAs$gz7LfFUM$e@G_pklRe=$BRSd*K8LZZnG9-nH+LS{h7y?UEX3jt@)+
zoYRGBymctUEx|`PfCyG&#E4-8rYX*{8LkqQ{QYeXgPB7U$N7{)EM5%h_wy4@{@`pY
zjn3uO-OA#zEi`&J1KJwX9r<8Uz?oNWblQ1SoN=J#4bN;|Q0}89xhuom67*v!hj-xx
z<#^?Z%MhT9P|LcVzl&reDZ|C$a2u~R@YyFg$#<00Sx$7m)=Y(Uv!}5=lHiUEwqgsC
z<(w)#Zbf~r9&@pb%&){v;)IMKta+OMme+h(Aco@mWsI*?kYO)phy)QIk8`3;UWatv
zb9pge?^p8#y!5NRpXa@9-q$z%j3Om`)P5yq%Jon4fY8eldgA-rG)#*ErsuUB6ZpdZ
z)!Ob9txKcy?R7GaSy;CYDjicg4wzPTW$o*so5k!+hPE{F!ai3Ui7h-R8iL5{D~x5r
zWGBu<{ne4V)L1oREmAfh_ZV_3I$wV0CHt3v@W8Rgk8(-SZNjDteC~kpN{_yt1DCE)
zsHZy~4?(Ke<0;C~rvQVM5|j3(ALR2Z`okRBY1oVF693y0uf#W!(PI6II|Z5v0^N%1
zP9gEC{qo4Pz4&NIr!Q&mg?xPXl#E%Q713<iO2`xi-jCp&#j6l>v-dl3I8j%C=o|LT
z1ceQ@e%vlRgDkdJxrg3&K3-I(bUys)tOTD&(<madR4Wj#89%~P4pxb!ZZ~tTJq@Wg
z-YL7T;`(3r+Gbzl`+&S}Mt^cKaK-pSe`&>?RHX=XZZD`Jec+zap3{@9ga~Je@MLk>
zzM4<WFqdR?NsPMJ*cHOSLRKW7nb>0UAKeh&!=x95rPg{(EbV->K#8_gWr>{0wbo=c
z!S=u)?;rAxz(O95WQUF473w<{ay!#gVla9U+T3jd^Hb63a*FvDO!vdc7}@nqF#A{K
z=kcEhc^$qnu(9-9)@wYjGdb>#vRQB)s76s1t}9FfBZti9sx4uL1d0g=vK6A(^?Nxo
zLjJneEx(O^@z?dkqRPzv*8w2CCI{x2RZWSaK=B(nZ;D_rY=GRMB3S37zo1Q>50>zf
zS!G1bz!BYyGsTAmM89#JZx^AIXC9ym=X*(O)E}1P-KLB_i`e2Zlca`dYyOs^T#B3*
z;W`EPJVfOaq0U9BIDHVD17zNQTj)^6#1+KvU%#THq_+isbI*vYAig=GJu}q=#S}kL
z+>|o#JZ|od*2+sp>D4kM(($x8)hE0Io#s(86W_G5COe{If-aPwDwR!l^b9Axcwe4v
zA^YM`Hg|`-eXuH-e^m7iLSDAiHRzviDo@#`E+7zxv->BvIoI@~N~>j04c2*AZ2*Ls
zPoTt=rJc-oHnBUkO1kCZp6dRhz2rGn*LypK-OOKjX&$C*{K{Vk5@MuTf6wc%4L*BN
zZkZrY(?Py`o@x7Xaj-@0J6BLH)hQIBB+}DmjYiwG*Y|IQ<|Cg=RATJeCJ8Q~zZ8fv
z&<-Q1i)Y!Avf`Xbm4p}xRj!L9aB7<5f@eQYN=Z(Y*^c~JsDKdZN0D(N!6+r#;H;{6
z<-hjH)g)>u^ryWFQo*lg#%O_TxGd*mKRvfyy`18h+72Q$QyD9dv^**E8;V4_+OL*d
z4T$MES-K2PQ&REL3CRjfu6bF15(xd9>fQek%X#SD=}sT(_VMnnPW-O!r`-dfB&a%6
zFA<&6K=LO18ECrLh4~?VwyItRqv}#J8$Me+{1PLG6!CD22>_LuXMCbcHkJQXv=u8k
zL!-o8k%+$66JIO6eNr&z(gS9q60hwVa;yGJoJ^fYk0aN@J4*L}m4iW>Zf;UMF5dh7
zPy5whx0Ag08mV)baE2Szv|!W-b_>E#*{NauS`3^-PM0Aq1E^-`<{~e}-#?mklh^!;
zDvVyCI;?9f)Jf&u5$wxbc~$`QTh9uHYYZ0`fz)rLD^_x?YK(P0Jsd9z^(ZX9)oIQT
zY-AJr=3I>>@Ykd_LJ5uxWa$_x>Pd`Nt_ZYTUVS+(WTM`U7;QPeWZTt9zUht^UHe-7
z?f?szDPO~bxoT=PCy}n2Fsi~iaKt?`XamddGhV(D`xR=MvJ)92Zo$DWexCT&OF>6`
z#JDAuGRSn>aAo0%>vKK2ujr=+iq8vOtf;Onk(*TF<o0|WO!RA3B+d=5MXgaSRpuo9
zJhePP;g4kE*Fp9(p(?j=h}oz8%#uownNfi~%E(0jj<2tc$d=KTH(X}&V@{MhzNIjB
zOutJ0m3q-C8!3*ZFWOy4N)xWwTE&e7Ng^STUA@oPQ<dwlsp-C5560n~d<fpr&U+XH
z%*AeV#oG*IrA{Fl0!C4jk^M9!y?SJnitZsjElfTvglm8q^uW4mVoZMi8OFH0j;>9a
zbsbW`pM`|nEn&UKj-E>RAw+eC@fsb&M*W{A@D6$tMX$_^heZxW@2H~R5~5@lGd^2h
zOw{SvNYbLQ7@WmkYoCmMZ3wQ+dSB6+<?4(n*tV%6BsD!>CmNBWxMbeuUbjG>8$1S4
za8}Q0=>I2#iZEB^huAw7Ky1$N?YCWUwDH?1)p}aR)NZW9=}6}O$e>tG3rVr7)*hBW
zS?>GU#<x}!lgqRfr!;o$xKFMPQSRBn@*O{Q_?WquvR6R5HS1y)3>3?TMYb4up1Rtr
zCofS=UxD~qaSjba>+c*|Q*JWGt9icLHN006Rm=?}sADQ?D+o-czIEa%eNUB8vg=ji
zb3X)prBk&eTg(@wnBH`w;`GE#hG%e*hCz2JTe56^Smpj)947Fq&5=L^RqcuHfF2mD
z-`O*6#?M|WdnG_OL#dZk!GkGj9ucPld8v0$a^wAGjY}mdTA~i6QEwc}Tk~Kon@lfM
ztK*Y^lB%<RSzN=8GZQu0rt#naD+m@_OeB$Oy&$xxQe~rP5gcYjqu%SbR)CIq5mE8M
ze;4m{N;B(Q(Vel^(wf&*O3DMXB#X3=8+?JYs_4S$0M+eqvD}=&U`UZeZaEbT6BII0
zciuKx4>`VBY2Q7nJr>*aPCe2;YXz8%(k5;+Tn#un47E+4t8oOh-(Y9`0M=se=a@!2
z?hf@+MAJT*BjMqZRiG$vp_iZWD4DJ}H*LllAK#><H9s`|VG5ZQ(pf4sL`Tqk3SrNK
z>KY*E`4kx5qgOnb0o6AlFf^V!fyhs98kb0mOlT?4$O;69GjIgnO1c(;jI9d;6ud%m
z<1FrXt#N&S=ehZh-2uS-k&^3R3dk-)<*HL6JHo1WTQ`33QU5$`*Wc#~*!Q{mY7N3+
zqPkyK%D-D(>w&R-72m2qvEY_@T93*euuNOZH-HZ5|D@V(Y#Mfy2sxiN8r^)o5!=+6
zoS;4)#@K5eOsMH~7AMo;{BvVokG_1vP9+=~?Oi7#YntHbXYDl^6O_1zy}@SVQN4TZ
z3_k4eh*|Xllo?5@n4E374qaAL-AO@vM(=n&si~4p(UvqTile9}vm1S|$gyZJr?Z&&
zKvz5ZF3l46d+h=2NlaJe<Rd|<JR;5pI_5Dak>`3RFwH#@qv1EgEgr1A#lHmd`z+Du
z4HDm5gN?TH8Jwlu&)KROXCL!q`5d+9GZ@O&TgYG%v6o^r`Khch95<~w_#DUH()cXq
zRj+bkK^63L1kW2r`f=AR-}6+HQdjn|KETuWJacZ`LG6W`KPgC1J-^ktd~3M$R3K4C
zc~SQcEyiKN8I}t=+6no!5$Ig378VSlqtxNe$b|oM8#Z>N3Q?kJOCYWn5(O~I?$!76
zxPEYXH3bn;&I8=GGqKrf^gimnqQC{L*!M+-ixddv?sD098+v(*6q>V2X&XvIc<IR$
z!j{^D=AB`oJ0;RgA!u$R@A5W6%Bt!*q0cV(!znZZTUqoi*BR~^%elas+Qbb7HNOvs
zf9)PO<bKt@6tg)ds$&nQm$akg*hHg7N{M3_i14L}j^yKs6u(mxY9rWCpa(<OE=Ii%
z6zq$e${qm8ltX|2Dsh4Kv%<7^i^+y#az5Qa3Om~}mc%b4*9s+#!K6%-19yeU-GMGQ
z+migVPaB|rtr%gKQGb`N6s?>$6)$rnjFB+0s!Yp1V^e)YAnQ4~Vf$2u#ZgTW${!r~
zO8Vtrz-hq<OLb2>A09L!oq<KUs4flH))mTvH9CeQ!7jRCE^kIpCLO_s#0h@REcLC2
z9r}isQVM_whJdX&kw5hlj+rJ;2MJwdJNjKgs6JT<Cp0B9KQmuWc6s)N<4yfz=P2sa
zo`@-jeh@ZHKyFD>c0+C%CCkKSGJ<bKY1Sy<t(3;!HzN@}Lp8NX+D23TpiWMIPt6Xl
zlwt@E?BT^_Nyy%Cu2<+z-z-o{k9LcN=f;Ce70_PvcI4eJska9|L};;A8^FG}BDdV3
zBus5yIj3jKF@rv+ymd%$&dFQVhcXw~)Fi`)8s6W7YVR~qeVCNVr^J#1J(Df+WYPxJ
z7NIiB@bQxxuWpw;K8dM@#2C7qi(g8;JWQ!Wt>^8U$s-FcDtr0WfA<iU*(lkuV~vcq
zjQ!fEsmQ3=Kl*QJ8C@#{Q}v=1I?8wc;VcRx7EvB4^ya5vS!FAL?5!>Ot2dYZN`A8W
zU#fHD|8J_(o4g~g$*7YbT}klJsDn=#TczN002h`}6bJIVZnLyx0!5*L@HB*j>8yS8
z&D%e#myN9IuGUC6OnxF!w|bL)+OO}ChHr>7jjP%=M#vc7QUztuo1hT|1)6Z*e)jzt
zmj_8Z&iraC)ML0TkhiI`?C7&eUXmGGRQIL=qWZg!wtDp-@#KC_%^y-c7~}Mn9aNH_
zka7wi=>*(Jkw?$iUdZMA+a4-QEd1Ypx*U3aA)M}ei&eJ72>UE@_&3jQ<}Vdr*jBun
z+24B|Y}WQv4SQS{{#KWDX+f2V%(vV!ybD0?4||nouTI2_y37RLptQrXjMXu;u!IQZ
zDlN!MXI3^XLs&a!@;ueEv0kmmRjf1G$oa=zd8$Z<_H4Dv50^9(f<{;wV(EgllP?lo
zNaj%8X0u+I-qYTwtF2qtgP&ouHL_mx^qV8h3_6e*;)V8RKSJ>vw`CH8c=2Xdwp5UA
zb8a>gY}MQOoMF~^n7$zTb0lgTnP@zUzpe)40&yf@<|s_Z?OScL5<84`4f^jRE8@pU
zx|=ak2JxBqsP-5ocU$jz&-dkqced`t1aG}>bc|1n{6BVRu|%IF8Z@KP=Qyd|Wb5*N
zX?4@)$%aJ*RpkU=XD1x9cUqk8X5|Q38qJyUah%Omtq0&ptN8+$?d6xfQU(?#q=j&e
z%Rk38zLmY!U1a`-6)=M1<va*ecE{2yF}IPo7TKxt1;hk{w`{!*86P_nK}JPk9!f1P
z>l43b0nNOvTb96HTBrLSiK*K`X+iW)uqLX&l4}BlnjmJGRd41GCrk<AN_unRw~sZ{
z+(xC=cGm<1j$6nt1OKXp0qnr@P1EP<x(Dl%4Atws5YqSlD!0{WYrz}rVVx0W_->S)
zY1=1!OG3ddoNFOL{0q@HW3t)zqiM3YE1Mnqmw)^_S*`hvq^99$Qr}}YB6r_Hk_!l(
zW?WsXgmUiw?(BOrWpm-c%{ii$tS5tzF@K6f<!YRfn9_yc-5(zNv{LvCb!&Z(P>T}`
zmbS)=VEOA?KNX1qP2)ThowGsHbGc3&;9oS-6gx|sOni26C6K?&%9N2WPC!xS;dp!)
zF=#)bkBo{KMrB9ys$&<(`@yufm-ww7tz~;b4CzC1sea2pMRHxLO~fw``<u#Ab${IJ
z^(p-u)$HSXB%#hlcGjCHjFFLRUZa&3e-0ZfdA?`nPQ^->3AvsLQ&PP8ddO#ijy!|K
z2GdR_WWgw|S!PbDXUW?eLgpLN6nBa5F$*P9@mm(R7}m%&q==VomnDw(aeLq$uQKlR
zZdORJ3&ckz8ZgKF*5r^M#g*s<PgWvjswVWgUut!_GW1w1Fh0B1f=OzVt%QA}Mo#A0
z?Vw(37aa+6X)IJ~=?>k%VN-l7gX>#=&Sv8yj~Ys-VG%4Y8*wtCmJJFg^@dH2q5{U(
z5cFG=HXyU`YSTe{+IOv;IQjEWQS1(lOpYX;gZIP9f<CNPzWHrX#D$s<j!9t_ijf6B
zf6KOzI3+NdHpU$L*%5hrewelrH6Sg~M9<&^PwX7Mp17_Pd8zH`qFim|1C{jemC*1w
zeMx*F1LpcS?`uI*<$aonN!evWBPN`xq^7SW(DTz42Rk6#=|n<K*Hd0+34E#95fkO;
z-i;)V!Zcpo75*~ETYm1_Kc>^TwvQqNp|l2tWn#JX)f=Gi?s*j_A^wj&)|l{BH|tXR
zG~)9l7~t3-F8~65+QbM~m+ul-e&+HH7sV<|DoI!;R%Loq2H*@Tf5$z>p9&e1zvuIl
zJACyUSjqoqPQMU4i!TfK8kzP#vO#SitGhW9SoV~m6W6xJcb}qqUYyD}1<9E)QlPQc
zafL8Id-NdYkhxVXsZrUBXW09NH)SM)R0v(p1lsm<B*SYti4PX|m7&XR>|e~-({0|C
zlL)3WrOvKWGgAF*ZHVA(56C7@+IpLpqSYe1o?s0tMX4nkh^4UpCRQbJm|?O*_mRQ_
zRgG8rqL~|N>`=1V7Zd|?vg|4JLHXH}A4{-psNqUtuCGh&lnB{tyW-JYXriLNPP{9z
zt3Q?1x&bUM_R&1F%F)?yX|~zZziu<<s^lJOL~&-xv1Hf`jzi6v^F{1=k}L#bH-yxb
z;CN~ai^k-?g~}QWED4!hguDX`-=cYd3}oA>66C(sv~XTUd`(H8V)0|MiIQqhzr3r6
z4}ja&xp@_qml2WbX$?K=`N3J?nHwHjPvCe($SZD<ht+og1I_lgQZr?q7`D}k!gTy)
zNJENh5<fkJdc9N=zjlEKCl_@@Vkue>*Yza?v2>0gO?t)=FK)ekeFu#@efPsqWCo4}
za^BJ;{aL`ZFsP9@-;MPn+slH{_|txv)dglwqBXc-4qXk)<(hYBYVxG#r#HL}aQ$gZ
zOJ?<BLiSFWvhO_yS>9R<?|FEE=dOeqmp)j_Vz6h4mf`S{xG#ZIhTQ|GAqW+uAKKk?
zmuD<Q*Y)nYo9<n%HI1-P`1(@(QQ~fqaqn%YFa3EwOwZ=@3ZZhs^lpibc^)~EDs=Rc
zXGUuK61+*i%XJRvm1D^*OzD&SLJ_b1+uxsaQgc#agd-D>R<t*)@YR+QK@VHYZg!z#
zsw|F7fyTAs)Z?BlFWyv#O@7+sC>*6wlV3{(Ts&m^9*H2kHt^M_=thPuv~b8j7Nmes
z5Ve{gJmG*jBwC7I29we@3tb&SceS5?KTfd`rQ(dHv`B`8)Pg>h&&M{=jcZpSC)OF{
z>s*Lz+!LW5j|)Y!Mfl-FD};tO@TynN6Jfj*oqljxJ19r}O>^IPnSGGDTxq1MI^6LR
z$=(OQI9UPTVS#>g)Vn~M5CcrC8WtenQ=u>reB#MC2Fr4~Fk(VuvNd-r#-cnQCw%A?
zznA6!=?Vy5;j4FcUQ{~Hr%AXC3EH^yIm_$^cqMoKiybcDr}OuUP|KJ?Y2X&c*-#Lw
z?T*P*EE%_cONanh^r@Blv*5?#pQsL(&`*eN<K3Pb<Lkeen15@^EMi^cl5Y^AgJK<_
zW|{VS`pEQ0s8jaM8U68AT)wosqO^gHdi@ZIb8^xZMH*4E)OTDAR%8tkI1@+oy;<pp
z*db~gd0g%5KR$KiOflEV#P{JKwQB<7)#<A-hU$A5V)6_Dk?hQg@&KkZn8E66G4kY$
zVp~G=EjHWDMwqeZUH6;W6Qeac>J6u!_~Dwp4aLh1RL=YUJ-6#F!Im6y2)1Vtv}kpx
z&a2R13sD@4-}Q84xbqQt)pBhMdBR{LQL*KIQ;)3YCqa+p%Ba(7S@6Cv89Y^Jh};eu
z!_;Q@s!ntlZPZgeSue!^icAld)#(tv=lTiLTHa%d@g!8s%o7S(pLjbEv!&&Kl622(
zMgp}}r<9fcw6U(&nrPJqeS1J;SBS283>!?+6ohi(e1apMZF!$=c<1A+d;rH8CC7NZ
z@rS>6Uk_Bu?o*3QV7eX>N91?QC$rfAmY&s)*5wRsQRIp9+&c&G)M<sKsxcn8B$EH~
zt`1p-ynNMW<0H7{^7UqEPrSTs`-&G5^oe9rb`#w%8eRoGWwPTF4gIc%+LK5l{7^r;
zi5Q4Fhx2|g6o-i&CPEqferFhvmA_IXf@L_r54aAuQXsNo%(rdsPJe+v$^6T0BLq8d
zK%G{UL^2H&lm(TX;zHZ%e!GWPsJgdKLKmWsA%(zl)#nN7#6rhmOvIzC5PkE6WH`f^
zNosLVA_+e-ixj!mRD0XdcGOT^POrR+f0As~{i2ilejRm`xbwW<%O%2x|G8IEv+zWB
z3oJWM#>Pn>n#*Jgq>8(#KP+l%YFDC-Cy~m%ot908om97fP>1-OEs8bF`;=s%9Vnv2
zA#R0O?G1^8=iRJ(kU)vr7F~HUp~kG{Wy)YV8!3G+>vb8CT_q-)^~txcngVEHf=~$f
zXPnpLceWMfQCpi$rR6lSr!Boq2O|Quy%EeX5ZK7^;rg*h<jT)!`~%NuWk`R<3H%Lv
zW+s}zD<AI(pWHAx@ECO)Oh>F}&22S2hr~0bZIpO*T7fF*$5nWe_wfmRRD`YFr-ilr
zaI14v$oJ#7IyjuPO&6pnoqf`EKeDWB+`xx$%mem}t+<@?X($i1Q=jd2ua@p{OCh~O
zzgPZ*xsRyl&UbIN$9OT((<9%-ykklx`&5_f_tYGx%@u#F-}vA6pK=}~?u_&zF|ORB
ztsT5d*rEq(do*vc&AO_rw&`>x$ko3Bxr#1&5oe6}eiWX41^V;-zKeg)o5=t)uv(C`
z=hpk9H$2P{>}-5sfn>)^vw2<L2@u~aLRl{(Ghe24g?3Hlo+|y`)G^z4BeTb-z+#H@
z-0&`mBZ#*C(rj(hXXw@AmE+t_98I7qQgvV0Ye5z=_O!O@`$9?kqwfnxqPWt40CS)3
z`Kd3vmGLBRB#@=`KA}W1%d)@bHhCeV&K)*$izjBrQc9RhXNmm!BVo9rF7g|)7m`8j
zYY!J0yyt)0&)ip!l<pe6J&e}=K+pI5ug31xa*p0sjf%8LR7d)NE_}FRrEw**&;W@K
zt;KL8O-%IuPXd=j^op&Pl#&XG=nh-sPi6irZ=E8wgk#PdD5chqa5lr3zQxgT{k~lD
zf4cwn%6=#E)@qgBq^;62U9%%IIFnsva5y5R#jfhs1+?=tGn6$Va!Se@r+n+aN8x+6
z`s09kr=x=Su+K_uX|<+JH7(Wn%s=(MR>eM%XRF~@=^dm>=Zjd5k4TCOQ#?M{Jjpl8
z)rC%;4JPZgj(<ff=aqOIPYg>!C)E&y_r(#{(p4w2fR0+t-y02CuZ4|O>FVXIMS82z
zOEYH?*OR<nBbeUiG90E{u+(E3376Jc#Lus|<1zO7n{xQRof*VAntT(-6#e+#ub$lA
zoOHZzz;HRw&iLlix7BCScPZP42kG!eCzLtkEaS4CG|x`|(~!+oHM`CNuJFd+o$Py!
zk#Yi<f%gZUivWrI&?T8%uzz315V<KQIgKXz$2QVUX**U6HCgT5Jk`5kYaW;4I(CF1
zv${VHtwo49LNu-Zt;==rLyD+1>0dJTRhK17SLp)$XsMGuHJ}L%{qU=Ao>gQt1(!w1
z<v`vXArdbpzJYS|ruQUB+3l^?>b;D~-$8!=J00&v3L3ltxzc?<y<zF0^wCp0a;m^_
zgK7Hny@*QlKLgu3>xbO1$0h{gyU&$9r#?cWj!G%^pXRT3rCc6MROYQvg-yrC{O7*l
zvLPf$2c%33i>=z|dmnXtse64GeMI>(A;CbYni=gQqPYy|p738aW4Qf{dgt}dNx*pg
zmu-2#r<*A!3=JWGn-OIh{^B2KB;QdP7#M(qV_h(p*vs@Y3@7kQb0kM^UT9yGyncC%
ztN-d<Wd8!&ACCiX%TKg99zEqRd5%L}{aj0dOm>XXyb(Ja!bF^BL}+hJ_Q?i1WgC6R
z3rZJ3M?36N<B-t^*GgmjQnZgqjIvz|?|(>$c^r6L_UX?5?Z+=g8QMslOVpFi6~5I&
z-(rvHHgL$sr}LBw3m(AlInaoTktiH^9T*`YV{(gE8ajAx+Da#1g)$VuHPR6CIuQI_
z;@u`UE^32&8J+rnyU<2M8lbjolX7ivcd4SAa$nh&-mR1y9oJeZHv;~rOH^jPszhgE
z7H7dh3yBrsb>Nb8E*VW-n6G_Hn1uJJ@@i00&Z-gZPvB@-5$Zy^u4jD!=I^qdOfIm4
z)V1d}^@}-nnQm3ZmH%2=844U(E*+OXTD^FR;=AP^j21@iRsU`KI*@^09Xpo6?4y!0
zFTx?{%nQvo{bltA8?a9Xx~WHl2n?z_iq2~a8~*>d!7web!7x-yX%Y{Cpi*oi7Na5U
zfBzrQ1$=G7k0(44^dBP00*Y6kkM4D#!t(|S-CCHFNC-H2xca`C;g`1L|1}p4R^+|j
z+#h^06nRgH{hg0lO#f@eJv^YvXQ0})fUvW2ld92V{(tSZjfK!2^9Du!{+bLZAnLA=
z9m*sv`Y8{bTn)wZUSQTW+m$~#yn|5-|L>gtpq&JTFokeV_;X3zzt}F$a7d5B6^|T7
zD+?X6i_y041R6{?8?RLSeY0rPg6Mq1eNYUidy$M$)N>O=bh*;2lM$YR-x!%i@xy7)
zu?2q9R6_OvSE2u{CgkDr55e(w8qNdNrfe<0{~BMH>bbYTg}$6ATNy8$BCj2P8fBYN
zt0w;6iJ{=1ED?hAt2`wE^;%Zn*{|(%+wDc;r`yGIV6~Yor4^{)__~VqQ#a#(*LIW=
zVg7~QQK%gi>)(dWpKSKgZdp)3k9leU6eBazZScGY+Z?vl{#7;#d(8V<v4H~pa`4JG
zX>C3%miVHdmoj-SOyh#(j_e_=)FDxa@g%eK{v%ALm3N78W%SokI;Q4)=Tf}c*ycO7
zXkn+F{I_*Gn?_d<olBqJ@eK=~Y#Bns7J=H290$l=vERy)C+--LWy8w<t~M)hWTdal
zJ~`attsh#S%?Tg9M32AAdAesZ^ge9ZTRx~#`EjJ1con(^%*v}CRJA&+7Hh;yEjImi
zQDWq|jHTzVqpa9n_1ug~Q?INYVNDYv{V}g?Y|s6$Ti)52;fWD@$i&bR@BhcX36|nF
z-1JHGOF$WM8iPZ@f{yA6;A~lIiMWjGEkfV=3zW8imERF`H)ezfb?m_36%dFe?A^Vu
zO-Zy;?Ow^-wbtz1Lz1iBL)+^4lu?t-CfPX(ZmIB!<3IVbN^=Bl2PWuYSE9kea(zml
z>c!_F?6$^V9{zWhI(Q`JsPZPLOmNfvU_Plkv6&R%qc5FV02;Aj+Vmo{2sh!a;!QNq
zY^dJu(zK;{6d5Qfx_3Ft&P!k6V#it{9{;qwx<lky`M!5E>F>Af)Qimv?f?SZGYrJW
z@v84G=3M(<>l8)_EHjtT-|oL#e~CM^^)RCZ8aFTUF&;PfXc&!^paZIV1_!=FI1N3a
z$TqEqjiZGRlm3rqUEbM8+V0fXU8uzki$-rBHmRTF0kf|$5Bd+V^eSV9U3>+WAQrpf
zEQ#fFgMICk+apJy9*BKyVXw-#i^+3e6ZVc>r*;2ldF@03ztnkH=D6URC^^-@gqVM7
z978A85&q;Ipfn`Kg;lk|N7@*yYVA{8<XS9!?0)qV4TD2E9}XS&AO}^W&D|@Wf6ub7
z{>4l)!(~|t{JEwZZ+g(UiF>X|Wd>Z6!*705l@!@0WO}BV(Gug_+I4uV_1&QhjDXXP
zHNVy@ndbZmE(%@auziRXRi3ChFBQ~u){kk9@#OJRRv*+Q8!W;h7|Ko@Gj$0qQd7Jp
zBy>2yzrt6OBzQlTc1p64!*X<7W0{<~x<G$KZ<HT!gkXRF=W3;aQT}kMBvIHp01Hgr
zxR!f(hn}8epsjOvSbzCY%7Ilb$nKl^S7XE@F->4hYBE}Y-geHj%#~u{$b3d+#+^jF
z)6gOG7=Hw)+My*l^Z<<5Y5(ZJFpQe{sU5(^zSp|;;2^SJS}>!nZ=VFjmc$F6<<mAm
zanbm|=(A`P@UJsjsG-;LB)t69q!mKEUDJ!BgbN;$zD1b@9J>G7(4654OeN3r9yS^F
zjl(}_Q=iq$vtP5%GTWY3vOV0Mqu<I-P?yUktCFVnL;F4F7;giCHyuFOq@>{KYPTLL
zOK#aCqCp)ep!Qm7gVa66z-9P-Kk?|SSi$ad<&qbmJxV(RlC+>=`e-4#1aqn&4Fu3s
z&o|67Awc3?J?hIVwste&F~cNw8tw{I2hkG{QRfG_A3m(vlw{2WqEe+|6CKl|?1qV*
z=NTBaFduud264|F0R;nFqMX|_l*XqKI)y;UmElS+?QG(0^>I-u2IYBF=7D3g7$vwb
zZcih|>my`09|*;UQD;}R?T^$i|14L@7_=b#yKChvu?A*NhPA);jncgf%;U2s@edqi
zYfB$_-}3!y-ge9#mvY}@oyL(tSqf)o*e_2S&|0ZWifIC{*3GuERLIXmh_6U$PYHq3
z*|%L9`WML!L5M|Qjb2N7CAE$kF=FjJhaF+gMD{5U5dx#l##M2YkVI#lCHWtaqfH3)
z)d~BcnZ!<hyC@R3_KhXVU_m$c!S92Q=kHwiDPlf&TriU1K785MIP!L+>L<BKr}%z*
z{n|c66?pJ2$+H~;|1cSNy1O~&iinb@znimZK^YT^eX{L8P_^s*sH{71`rR**hwAoY
zwDGy{YD(`abznGr`M^IJmJxj==cCGZW<T`;T>*Hvj`1Me1Un_WSqnNg04e2rE3YY_
zn11zkp{cmCX%%7|qVf4P1$uaJ83>3b&7vGQ#zqUlzbpR)YWp_ZK74UACS@zWQTdY)
zH06mdh8A0Li5k{c%DdDS3u^R=veSQOB}!YPJq1wHa!Nu`C5@BBfy{qhUQaKrT?;IE
z>8lHnU@pD`;1+4;hd>tvI5ri>{1VFcuvhu^i|5ggHcH~8XdusS%o*~-sP-2xNaz*b
zOMc9Ls<VPzrrXXAia(yZ_MkJFKdckxag_AQxBMn5XtUQ8eL27}B0~XR=Jv(Y-Lz&i
zG#M|wz26bf8N@O=PhqmW=0b{eL}J^B{K><t_7qD>@_kf&JH>4TzI8&x{oC^klGz~n
zuO-WjBEim^@g+7mu=)p@)XHwIJ>Cknb5mM6qECd#&sNoX7{iX1g!p5IW&5(CzWZ26
zb$9~9|Ew;?k&z#enPkG<u7m64#SQ`Tnzj3F!Lz^YJCpI|i8mG*Rqc^wsYcsLSFw1I
z*63Qp#hEY9L%`=@%l5DB*C7#I74%ai6H6aJglX2WB4z=z6tqwdDS4_Of`PCz>dt#$
znr_nXvQBv&s3Q7P54`1yQN^#j)B27Yu8j6(zNHjDzuSGKBJ6mzhX{k(xG?O#O1a>f
zydf(4LLdiIiq{MZAdyj*MY|bdkwy=c%`ObHlF&21NSpMrT<vwxA71r7&Khp0ZRIQ9
zZ%ji!r$<C-WL!$_x9#?hZ!+e$AmNQ9KuS_=lUiPkp8vdOey*dO&T~XuQBTv<`FML^
zwP{+xZ9Nx=D9j)m8Pz49O>EGP)XaV9i9v$SP;9?RqhtN}ULS5Mt>Yrtb`=UX09V$t
z$;&xFyG#h?3T$^Js4~sQNU)voOu7o_j-zE4i%&CEL-}0htj^QDUAdnk!(1<*6H{L&
z<p~X}>@sZi>mt#8rLuKtW_<z(^di*{lh2~qCB4>1xr&0_k&TFKma%F<#^UP}xd}VN
z8gbcd0{%mVF<y}}sk8mfU-Z3p$=yQx&2zp^2=Ae*c}jVWXlBRTece(BmfYp9IC{T#
zo?9YhG}0ULl1}Z<(WuX&6e*j&^nc?n;Hn`OP6W0DbX{Ma8%&v(;tV14TvS-SV|#ZG
z#hrvzwQQ%K1EV&PcHqIS5Ca}F<B<f)Z=O<t+)7j7^UQ}0a0N$g)VyXAT)=&*5>i1^
zG&=IR94gPtabGFT$Bq2-o9CczXd6<fZ+`6bj5OV%Mf<$RC9Q}110$R!wZ!2j<bU5l
zKBkujG~kjPKQBfr+LTO3-vj-<VCs95wHMCGLB#p^*_Xe4uPRU<g7KDl8kEdZX_~O&
zehuiNAtK!ryzSkr?KDbuUo*R^NPq}F-VL|{!NU@uPFHZzlm_UzoeE7WVjU_Gq9XJS
zt};jG*s>GlNLAl&SV}VE!|NM|7jkwJ>@7!5jA%NmfRG{e_MsINe=5fvO;ky*y!w}y
z*9JjO>R^WR8*AKd-`R}SL^_UT2LML!n|5G8rDNXfPx0>dI!|Wr^_2erazQxl&~-5E
zdHU29DC5~VwTdLkd0xug3mtH3FWNh%CqpEO!$m|pwLIq%h)e;G4Y|o$>}0vC>WiBk
zM1m@!#OdEmaFk*C|2`-eJ8?h#ST;G@qi#Y-W{VQ<as+~0=K~Y&<W^(nQ8<yVkTu|a
zISez+UHwLEl)3}c%(<N#xYjKoy(*stwDGCV({g9Qv<mLAH<zhNR|6{x-O@P1ZydeN
zQ%@~9%JK8<ZwKykCs(v>{8F7H2?W2+m8bbeskApkHTHF?{~OB#-#}zEZa8DeNbDGm
zQL)1H>fh^WP35tUWh-2H@JOh?6SxFt+AZ0syuvfHV#AZWFlZXqBq1}y2D^(PwU~+0
z2z*~F2MSltfo>y|LvI>F6OGyq%dJ1-e7hsvDMtl1zvQC{6y-j@9?|KDcW8qSoPRUx
zh#h97i#P`!5jo>Xgn6$scTCv#e&B_W4trQ4pPFsaL~0k3^2s;)(M`C@ek4VbZ-49u
zNwa;uq@I{C$^i;F4wrbP{OxVk)?z=({#QJ!Y#t+ibX`9IIWRZGTY4`0+k1{LhyW!l
zJlw068K?|a4xY&-A^&L8xi$iC=SswqEcU~QDLo5Fgm3W=JN{XKlm;n14E(qO8MY2u
z{4fpIB^nnNiFn)F0acTaLf^;P5Nt1aipPC$3?rlJSnmGSsSU6Xyg->K_qqGCHsSkm
zRX_k`JSd5{u+Gmzn+o$Vo#6I7KBesqc~fIB`u64zqw{=G*=G^<tS&DnmR$C0XdbSw
zHRBo9tTgUBzJwc~9$Hvj=p|=OA;{3gVO8Yd^V>AB>3(5ck#y8HOe~Cn5PPfVBA2?7
zZZM6Roh{uM$#7$s4gEInKQ*AFTeLWma})LHIYqm4ZFzsfyvt;kHr-Qat}0^IBmc>E
zyn)PddVQhs@AQUR+vM2mv*U+je}93ok{&b`Y&^62k&*Jkq~k^xfsS1qrA_8O3NnE~
zjHCmL|9!tf@k;e;w3}&jf+-!ysXzWx%wU+u$1o3fHr3LpLGT$MwK9i?d(x?l(3THn
zATFU&`#6!=qR}GsNXLfTRO7O7=&}$rm54v=rPY6f(veHbVa4$w;b_R_+pR2_&#DYe
zGV)!-nb;@Te$#Ie!Zk@s)9IFUc|(2kDpnda5KP?uZ7NCw4n!)&2qaOEKYVwJXQG*X
z9=ySY5%u*+*sy5JHzugiu!?y7zgmi{2Oz>RxnJko`x4YYbzHTBu55qY*~QHU)pNU_
zqsA><nubxPPZn%RjZ<&F%<1|b*!6-J<^KzP5Q6Vyz(zmx3;x;;Op$m``LZ&u-jWES
zvd+R7*FE;wBfasBZ_Kh8XZ!89Umjz6xnF<%^)hx=!4Xnv`|rPhCe(BljG2JY!xnDx
zj1w=5F=t?HvH7^+MAmJ$-j>J0c<{m*hl36}C?7hH?SS+ni%bCy+VEmvTyVh!>BTR8
zadNmG_JY3C2i9JjZN6EK!`Pz>d3)`(SJpAMpTSLA=79Q)47y39yZH=zI+$aOWH0lC
zLBX(=&wyvt#r!j_<b;=zj>eQxn%`s1yieUWqoO$|FX)@?fgX%7l}1Pw-u&VDV0;*7
z`ib>jUqT0EVNgRwpdwHas0a)qPz{hlJnCIVpc4Tk3%Or)wegj228h*lq)lF%a7aVu
zfAy<h<uZ(lfzWUgo(fMb3gZJ={n3wplsycOee7e?4m<9U)>wUwO#ZB3Tg?rF;nrKm
zSC6Az1W=nnD7ClM{kF<atqS7<kv`>=?=`F7*XO4;**1nWl|efU9Uc${2m*(|T7?%p
z4eR&6|9xUOF%HHW14~dA0TvKUeR*kf2%qtRfLRg$Nwa;3aiU$@&B!Gm@kZP{Ns2tW
zJ3qqH5?&GpfB<*o6`8g&uMRz|>NEIkQ;L8H8Y54nLTsIH;blUot=|9q=Rfaz3ZhOs
zhAZ89vh>BOI-^TC>#Vc#b~_{-!G}w1w=*s<;C<r#U|<*<`WRz@_k!mLG3OnjkF8g9
z4I*nR8?VGwS6`L(dHOzm?}lw<0>esq2A4-b+mgj217n^RTg0@<Yr`|*>1<(q$m6}T
zWs5G`ZntfIiWNidoIjqEPW{2Dc}{63>UioIPXyyT=9Ru1W4gFzoWTC#i!V;RGPZ8%
zA8(921_zIy2MzG3D68M*$8Uf8+iZL=#KK_HKk{KZ88WY|R~R9OzyI)b?s@0-jVAqC
zmOvl6n<J$W;y&J&5id(SwQfbAB2W>Sj|fx)WIm4S^42E{mhDBt@kPuz+Y^Kd0YwDE
zC@7UhOb}fLiq+9FASFB&2fp#ZOo-(<PlA+rJnthO@rb-?i%dT4X-~`K#>%_L$_4?;
z#HPSVx-{*2F-CUXW!FAgW;pPM*jsOFl52;~$yG1pB8wiU$^c<d#5ofNhJaN5?svb-
z=M%yJ*}XX*f{X@X<n19RJRMf+-~H})Cr>4c-~clskF+jaxG=Bgf95lvNn?!xB8`zj
zN8W?@T@S{Ktya7yJ3ea1yrsw)iaqz-GaC~0g<}N96fX?IOecoT8{Y7Sep&J|j!~f%
z2PTXf#v22K&Wx91k3BXUG+`L&FKpyRfH^~eS3qFMFgTrQ(C!zG{z5wT|BlUs8@`N{
z(@s0BuM@rX^~+!Wa&Gs$bI(g3``E|wxELRSj>cPPHlO9~Yz!?P1O^DhWB*s|pU3ng
zANfe$7NsoXi1&ek05@3MQpkW}B<!^FPU(y@&*;w!b$Ip6(XW2>tNGx)xheG9Z~7E?
z>O0TFPk;K;Y<Mwp7|@Khc$k6hTRbY>KjsGS46oID-}~PD#IgMv?-D~SWah>u@h9rR
zh!Jw|+YZin@_g~InP<xyw4wi{5#m~!@!;u<dDPXGGI3_4xy+w~WJaBV{GNcwFZ;Kc
z><(G|GQGTLPkPAgmx+rgJLEp<MO?~^DrMX+H;~xeG@t*Sc}-S3J?`7_fXD(v=@a2N
zP_HRDpT;9FD*ISoFP3k-g*qN4&G_T38!#5bX&j|o23GyhdcQ=ycAU$%%1VoJ1M=L=
z#CSB1#Vz}?m@f3|cz^VDZRhtgN;&1V>}r7YK&sK)LBJ&UZ>|Mzo(PB$5*S1w(j%5`
zI~LN5(3OFl@K9L&7OeO?1dPlhFbE_<=u7~Dh`2FkhP7Af7G)S3R>lQS;X3ielQO|%
zoLG7O+Sk68_wX~6)Y){CO*2{l^Pie6N6ndwwbxlYZ-1gcGOIs`E8_s!=1riRRs8Uf
zSgp3Q?rem!7Dy}dX&coiKl#ZV+tw4?vgnP3IxoN@A|P*p8M*o{FerErkYxSZr8xla
zw?-HpNUU=SR{tYdO_yf%oe_X|^6oI4CKDDlMAhm%%!~np2oBDl#P%ni8iqxX@kMto
z%DBYVHMlVToNLfm0fr1HUKR6+5vN}~B?6f|XgmheF1YZ5Tp#BBP|V3l<E=1<1oF1q
ze!J{hfO*UZh6ma3(zdZR-*}ba!${EA(1pSLr7wLc&m)K89q4a|n0L~7U5pLv)pgKZ
z9b<|3^Rk2og`vgB;`J~G=wO?laUL5R8#?FP?RVXqU-a@~ggBV)_gUztt>|A@-LAB<
zk7OI~c)=W&?!^t8XrAhJ*JnnTyqZ=Ks0dU9R#XJ40kWcw{9NjHxq{H>Wd*AIqMr+@
zA{&TP=w-FvmJ;L&@rZP~Dl7f<uYY|et4Jqfr6fDb`z-~5L2$+8SLAI>u90cv%r+z2
zkPuE}$tro-PPtkkJ<IQ5Ne9M_SUTc;^UXKqK4?Qg+-;eOutxI)qFBjS2ay_U2w)fs
zh+;f?l}CX01X=aOp)f#fky_YzG<Z&UN*q#0HWAP8h(vp0`q7<E(Km5iGfsju+uFr&
z(<VaCv&6G;Omk3OfRpwF^5Bj{yEb;}hTL3e#6K%FICyFpZvsPx0pd)A@sAaJWZszT
zPY%sE&{MvkZ3JBVF^}j^W)O3$^5cfr_#iT97&^wloN(q~G5s5P?GW;%D-SEW#Nqcl
z{(DEBS4eW?GeSLe%X~xB&2PHIoTZ2DSLUF;jL)_4wo9GN83Fdjjo$PTM>(<i5ZJ`F
zGw&niY-iF2qi8BZya&YQ11$b~`{1_8QQL|68hwd+`W<5*Mu_{sBl6TMR}lxUoANoo
z7n7aq^@iBzVx&bm_n}M3%8NJ6G>kHlpDQ<i$ZY3DT9k=6(ie-HUeIS~dXZ1<zV5yB
zkS}h_DD%qvNN<;q`^e9Ans)q;z6Um?yr?&jSLoNzUxCJBNpq*fFYs>T6!(#z>nx|^
zLHG81WuRZJ_fqsd=2ZVa%Jt^g0M6x{E3VtPg{-puT&9?x<$aWoxU5&UpX(Ik6XpAH
zp;Pp$Y$xP~Oz(H0|DG@O3z_njuLcMtYM5OFnlFl&w4sQJI+p@cfq(^>L&U6H^IBL1
z4<e65Ay4voAb1IoG24CO%dp+A{u{T+h<+n@h*%I9brHD;$_43>hwLF%K1U)%d&-8~
zxE9L~`RZts(SmI9S|EwG7$K*Ebe@3ix8FXW1yKfRM#y7T9!Aawth>&->5z9FlFt^X
z3%AR!xIEo_)6MDX#*le%<26ABLq}!$p&0wnTVAO<y%`pWvd6*Fowq|Ab;Ko!E&JMU
zYlI7i0*?WY1OtRu!`Ty_AA!fm)+y(9mK357QI1Zb7v0D4xN8ujFUrd+py`RYt8B<@
z$04JSb+1aKi{+WSUIJabBEUNC?~*evDC9(0u{2{IdHNXFQ7+_|=U(okOpy2}yDTxi
zwa>Wr*lc6PyH~!SMwK5rhMwxVj#rE)9HqIBI?6_$^Lt)G69%rEIytQukKg3Ko#onP
zmndsoR3GPdKYG~%QIuIM?tRz{O^*+wrb`2QMS9UnTSk$UFC#zF<Gvk7e#DXQen5v7
z&I5Uceo;T-EH~ai9p!R6#n=tVZMB(Y_5OtXh}&`|DjRj8t^R$q*V8ld-S)2=!R2+d
z)0PqG5l4ReKGGwO{D{l+vfW7U$330Zo2XotJB{O!+YcOzan0@a{`7Tjl?%NW%P;#D
zb;`8jI@)Q=i1dgfzkMI+5l4Q+?ew@`EG~44evuL7LSBw1F&@<b89=Vy%mxDSrChe%
zl~(4@<TYIod@IBVc08fU!QG>eJ~|(KMwk#Cu|vZiEgDaxvK`6qg7^VLfjn<q1kJT4
z>1?^>mU&AKLXC{sF2r*XGOVs!osOU_hK_(7S~(^|Ak*ubM&D=)*<Z6+(N<O<j{y~{
z;!Uk|`)#*pPYtgEV}k+0v!U;{AuVh?8(usg$hb1FDCNY#aZhNn$6v6@@BGR;-tmsK
zPGjI)*BCCg<j_e!LZ@7~_lMpB0|HL;ME(&v#vX5q@o)~oIS;tgr8GET9CFAiuicLt
zv?<t5X3Vv{@4owHBU8GqVYa2oi#D{)K#N21FtjyFU-VgElrm<Fx!|k}V>Le1eb9%z
zT%h^WgC%a47?VYRhuMv}fE+u^0xQ^?Z^qRYGlsK3H@ZjPL#NOuR=1QLxSl}4s7t1K
z5@Tf^XxE(J>2rR>*2CDMrH!Qx+SDH7!}%dwy~f7I`eU%Pa@+N^1Eau6e%!ay+I8!F
zMW7;35m*rs82@ICp7&fQaT{@baSXeU^kt5Rv_BwslrjSqdN;%B4NLEmA#IfMqm&sY
ze-_i*<2;fo!}>be^pP5yok9qs?IH-Gm2hNV@Z=@GnGj?OVYD^LZx}cTjC8`(RwOcl
z%&AMBL&-?0fUNr+2ILFzvt<jpW&{YxzVub@HvZHqk19oIZLK21VR(i>1f=K8<}tkT
z1^EXwXBLn=y4cS2yykZ{g8Vjgsp;2kndo~OlY9Z$M(+R7Y#*|3o>vE9^<*i!3byJX
zwYCz)7A^gY!}t2>Fg3gwK}Q{RRNrHw4C4Y$fzO?deqmfO_>f0|r^T59TfuH>wv*|H
zL*H;<eDK~Fn;?8kHJ@`!gMYMx81w8nw;?#kL3g@&7=ci>M+b%+?}&6|>CS6nycngn
zIyp}fUI+82^cs}JJ~z<$r~8c-7{Jk1J7WgRFhZQKVBj+17#j=#Pd1Bfa>|<TA*ZY9
zet)}F=&F*_j58zMc0GZI&Et9L2^-_dz>9<S6X}oz4EaMpb;I~@<|&?FHl)g=lgW=)
zyR!D<)y}K;6@iLCMPNloV9*27bDgw7X@3E<@7v{<`hH0J1M)^GGf<&-GpydQ^e!3F
zMkzl^nPKv0F@34Vby!~~k*?i7naL0jt|ZPPB>kq5P{x3Rx>ky9w?VQICB)coZXi00
z09%w|+Y%yo(n%-v<+#vSr2C^E{b>G?KI9qM@`NTUzv_@5TYQj4gddT=;l>-XECz_*
zqj3JhwFA6}TLgp`;c^beBX70g7uubXKy>|f2fZU0gGD{Uh7dD&kYPrYfEYR$%wVx<
zZsp#pIim<Uk1`H^Bj?hQU-k9Hfn<bSn~beEWKL(~^Z4VB&vnRA$D?au>B&tDAD$p}
zJQXW`oQ^>g7+K->NLdC_%%%2xZr^96NBBdY_Vu3u=j@01fJhrFV`ChomBIGA(@sAv
zA0p>Ta_=cto(v_#V~1VS-+^=o^dm56ml&FHYX{>N1{@>Dwj;V9cl>erU^k3-7d)kk
zVajkcr{O9vj_Alk5k_IV4Y~!M5i3((FIK0et8+DUv2~F5$dj-bu<(t;{Y#SBZlk;p
zquA5c{6MEMD9^`~Wk<a($E4j?_vLHBN`u?Z<e5wDdR=XImCFWcW9*Gmze{GBHr?{e
zwkEpPrxMd)WxMnpR(3M!qsr|nHy_7iRP4I&7`5(1<wlj;Rc<QBqf5u>mfzLpsA*kz
rj9RxW*Ot@1FUywKUFC-1RhIpKZOXIzf$e4B00000NkvXXu0mjfYd7Tq

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/deploy/run-on-aws/aws.png b/docs/python_docs/python/tutorials/deploy/run-on-aws/aws.png
new file mode 100644
index 0000000000000000000000000000000000000000..89c8d1dbda5e6a49ea7199029985b9d6d92a097d
GIT binary patch
literal 178726
zcmZ^~1CS`+t}i_H?6Gazwr$&cY}>YN+qU=Ewr$UR`+v^8@1A=1eO=YntCReatR$UU
zRlQcYysQ{36ebh^0069nxUeDs05CoP01z?+*k8*2rMMXY092EOkdVBDkPyDSgRQBB
zl?ecVczALOxU!N3>S*uFJQfXnMAB>~em20oCHZ`1bQ}OANk~whp^&j1xsiYf2o<L?
z=qS)w9$|qxItX%|T%3T24mdQc@-v{+>Kad{=kxLArk+jD%g?p<jrWN*z?g3xNC30?
zFFz-r68b+Yfw)LmVxzFYg!uS1(2p8c@qIkR#PIm6Xg|O4zyO(XY%^snzdn9`mJT_>
z30VQ;AqF6^5x8*nn8J4Sp@Y%^@*Lv2uFs|Wnel|w6~d6i>?WDXW*lCa$^JALo5~2M
zkqz7c_9bdi8!!SKo^_vUTpA09N{g)q%<SDE<ZQ15Pnp=mZ~Na4JU*%{Y=+b+4Dg*1
z|A;UNRE!&vNV&J9VhnoT2?sDQum3!_eA-IMA<bOnN3y3=Jwgi+TEF)c*FeQF@)=0n
zVn47mU6`d&Jx694wTv;@#Bfm8`f;F^igwH}NU{4`v$7F-!~|Ck6uo~ok@zcq36~@x
ziEQsVW(_?UNM4K^i~g}krTaaO9jQ17eEf2sbR4`-Z!vXN@aB;a#5?cP&Ryc<P0BVP
zw@zf)6C$h-)x;`WklM;T6Pr|nVC*5z0%}3<&|UN<`A|@0ch;OfHpx0lLH!fwo(*pi
z+Xw9y$bTpVPS6Ln3LvEqEeJR!t)0lR6?H<{uSr0co+;rfCM!>vJ#LmsSEQw(z79<9
zjcbPY(%H#$0#mSe;)nqXK;J`P=f&IFfZ*4n4q*KD!0{Cb0Dl44CT$WKtkKBv_=pC^
zan!(%PzEG(=*<XZD(>yl2iZn|@)4*H4`|pA)d!{>_bW)BMIDYc&`Tb~#g8Ws8yiSW
z0G1t4*57m%jMl$$m-G}Aco#6vPvI6ZDiBT|F#sQ1*uN}}vIz7{s3Z<$$WI&JmK+``
z&@kVP9AjU|(440Ulrlgv&*2n88Ttz-JCIgjC7<^cml-Bnz&4+b9*T4@q7KXofGz-|
zjtd(=tZ#D9m>qaJ<YCv^4nrFfFEn#k>Q=@LAf=CP4-%O_UY<FLbpmFX-@PC}!M=<*
z3BDOYC5%!;r2tdDx{P+wItk<n>IoxSL}|{c31c1dDl|s8aSr1&`qa>gqXTR!=vsJ3
z6s~Y|ZifLmigN_{Ak4tfkbXREDa}ioq6Eqj)e)4*4`bM?&#<1kieMS{A~brqM1RE~
zvL3uHdey}W&jp0be{fIPzJ%ROn?5FZa_H#bc`xRGcK_YJuuaYlQ5V`a_@#ez@9kCF
zi=yY3FW^qBZ%8ij7{ViDOprcMXkAKOwq4Y$WSa;&i8SI-c!;o>AqYcoTe`Za6}~P3
zFL4i1yf`N@xFUJD*aSgxylg^Ee27HuIFvC`1DQL9hV-^*4QXrQP5h1mIWlkr7D-Hk
z#F}V%v33b}iC04YMA{_12|Ht^F{N>a@w<ucB%<+?$(D)QnCWE7L{3UK$%kYNz8U@r
z#c09?xf+41tc|#hU=F1%1s-)Stsb==nXk@wJCJNYf4`Q0VO*)5@?OIL1d7N~v4`T?
zBF&=FBGsaTNL4{Tp%Q~gz4%>_VeEceLO?>aV$wWjF;ww`BqC;nDZkPDy!nPjsd-=%
z-2~>u^+fmt=mhM<*94vlYFS%3aRqV(a|KM9mqKx+M`cOHwX(IUcTtEclLEAgR<(=#
zRm6HFXasZ?Y8@IfEqn8B4`-E40d7f;>{ppb>w8d0Nl0x7zftapNwiu9vkco5^3+xa
z8uMsVnI^3!clDMgTC?tK^6B*>vj;|ZB5zG~%~dJQGH=0FAy2tc8B%#-<wDtmcxu_?
ztjzpY^+rBdO=&S&QLApP?oAJ`KyLob(oW@0@y}e}EdIKFr9fl9qY#Qvi9w4&_nr_@
zM0}+}h(hrkj!_lEAp6p*pM5Q)m7(PTOF4bJ2~_5K`n@z<+B3}%wUhdoo_f38Sd`VV
zO^S)CEvuC*6CCpli!G~fx45msW<hpj2dZ~#U9)&2yXjq@V*mUO<4VKQeRXm;%0|V&
zc?`?&`lv^ubL0~imQtp0W+1Dpb$N?Gi<UK)Rqxu`2ILjy<;dmJ<-yg<1{?!220@hJ
z2n%>rVCW!eC~N3Uj7v;<Oeyw{6QiS~bA_Wv&#ImM{=QM9SeA2^#<W-#39VDATk0S!
zqMEf@ie>Qy5}OWNj8^gnldI7S^2@<>Ee|z!5Uv>yuLq}x&8^jop!=H#ipP^1m7ADH
zswdy`+?~X8`3uHN^OMfW#&zf&^zFtO+t8wZLwfTcdtc8V*zat>L%&<Udp|ONQvZRz
zWqkHAK6AC%L6Uc=SE-CZ0eU7kwcA4Y!MLb6J)!!1f_#*Gxk2i|<xuBfr9rijm0;hn
z+ptyKJN$WaMeH>M60BzG3f6O@CN(FCCSp^CKOt$E^}p-8R#VuuwUxD1Hir~%iak?e
zXd87>dadl*_wHFHYis4T;#>AVCD)Rxl1mR5?&j{C@ANeKt0b3&ftU<@2iP|fyHmds
z4o23dFr=JhsU^Hp4zrXK9=X>z+|};USCX|RyoVnSlOMK=UW>6M-_kI%G_yIAQL~rR
zV&r1w_(s)sOYgbVENV0xE9CSk$I!$Xv6-HYqo9F<R0zeJv=<Y~!(e!~42kaG_V=h$
zt0Jo8DhpL}b%}aIdP_X3$JYazXBxa4e04*0M|D@~bv?h1p9Zlu*|66jtfg01>m^*3
zwoB%)cd|03hNj9heJ7j8XvV#JNj#)1b3}Dzx5wam_!dtl51}7ITR5$G9*N$?wqtof
zmq9<Ede9u8Y>_pPlTp2COwo2;$L~uUa!XoBtS{DVTEBLGUH<xx>Cdj=vXnfOYLF6j
z0loUYF0-T4_VJaI>{*k#o`kzDwMn&2MFCB1xmMb0JK9rww0Z9j6&o?~b^ZbU?91K9
zG3z^fr9VgzEhG}!gMEzC<2m#qu2d{wa;9)OS5+t%D}r<Jo_^<Oovf*%u+;k7r@4r<
zs-$tbr=>-<+s2beo2~|rtLM_~2C^232G$f-4AUZ7gsflUZXB(5=!emba#wlOV&p7@
z>D}x(w>XE!Dj;K-HJZ&alZ2+8#{1-R#<_{JIp=HXbLE`%swLt<@}yM{Uw5pN-kITJ
zU$`^xYpM89iU@~_3s*a)-K&(TsqsXzFWFMfyGHlg;ikiF>P>UQdGzAMhTevGr>S#o
z(R#!CnQdcd`g!$<d8?s!Y5rn%W4*QQMzGiNr+7oRwRYQJyPc!XIM0^e;r4pAfnZ!`
zdh6t6{<^z8*V*@8U|Q(U;DDGztR!9~U(M&%>x*c3_TgvUirq`%Eg78XDiPfe{MVf`
z^9{w3#TiqzS@TSFPHXgA)Kic8yWwN;?$Kf8XYcrr^^axYirKf*N24dVsn=ul?bPDN
zzH7xz#m+|8O&UbH+b6--;Nj?iTt)70UM5~w@9^(?D~Bhw&6Nh*ksZxma4%o>WL_2$
zeMk+aLI5axb_z(p)+GS>xHbRv$Uo3hW+z7&fu?4k7(hSaW@hIYjlg*&IDTW;00D8i
zV)5p?U;vPN@Wi5RfUE18=Rfoc3C&AYsO1<KGLoew+XN4!)98M-LP|=BQ%XuZ6>)|)
zz_Z^5F0#3-?FRtZJ^BWN5M5IOfxgYXzz3+{Q=W>z{C}6Rkapr4jsO6tB>&ui5{ksP
ze^<Dm7Ru^Q>N3)tMz+?p2FA9ACbVwWc7LS-0Jz;a{|>E9oDA^YtgUPuIo)^&|0Tis
zcl-~Tju8J}B2Jb(gz7T#_(HZ0CipD0%(V1`yioY~_}mW0rkskxqW=Z|`^H0P?&M_0
zNk`}E>PqX%L~H9{M#sRx!9hpQNXN)X^H+k#(cQ+$z>UVnk?7w_{#%c*iKCH&g`Jay
ztquM^dJPP1ot=0H3I8$lf3AP~Y2s$_KbCA9|7%)*CrI}XhmL`kp6>qzbFwh~|AGC(
z`8Vue^ZK_r?theV%3HXZSZN4bSew{5{&kI)jfIi>UuOOn=YJgi52X74AQ>2#=>HS?
zAFTg?{^JU#oP&kQ-zfbP3tk3py8lJ{FL`dde**O%f%|t>{zd(p7G5ZBy8n}6UMPm4
z;4uIIegFw!0cAJ9OK)&}WtHb|w#04En{G6q{1-}m5zdG(hzRoMh>HqK*DD+p6ln4Q
zWPzfWZ7p*GND057#~kT|^dHx2;;@?QnFe1_Z#2gAKbtSx-X}S(HxxS@VrcQ}TOidI
z$g|{m3jBqBqJYsrn4rv1=Kmt>!sYpW!?lIDK>jE7Kg7tP%%Q?YApQsOAG}Wk<XPl6
zzNp3Z;Q!Y5A6#?fx<&EZ2jtlcr`fyG=5%RN^fyNiW{lJ*x!RLgc4DL-jOf&vS6XmP
z3#0$#TiYjj9tD2tpYSr&<rLJq`i3HUTVWxi#CMB*Pb%o{cl)|#!A68KHfCLJMJHq~
zOR8d+FdxOyn~ZW8uiB2Z&WHhhGb54W5Gde}@i(l@sPmiU#BubZ+%J-^b6A-;hNc|3
z9?Rn$m1bD-TDI81{&k>f7goEvdR0k9oxzs_OP<zet^Ye#{DpnFGJrCG7U_U@Hme(&
zg=Jls!DKE#oN#?hc-Py%><aqm0({)vaqRY(chSQRhxnCOu*#?bLPkuDamVsh75*G-
z!lUDXYf%zAOfQq-LMyP#UhRy=R=gd6w2q;>ac<}-t7<04mK>X6uTz{*8SrVX28|G`
zZYfaEu&Sc6cV!y+A@oBMgu&YFdFC~=;F2-Af88hgnLz9-0_&UqR^73n`+ww#77gmO
ziMi{zysqHn;C1`Je50F26y_Y%))p@)1Y~e>0r>gZrL3k#K*WTk!EsvusY51>&5QtX
zgRHQj1Q)kP7M;zIUOOvWUP&p`Ju;;*uT0rp1f+ok7xpf~t-W3Ub4<X_A<3s%z2`TM
z>~WUT_OCNCNlBt)FfB@q!cbU#zh1uc^Yc3UNf#?FHz3>?7xo@HB-c$Q1fC9O0WT}$
zk5?$5$O#2d*{lgcRTtJ4tBvrP6dMucII3=fROm{Jga1_=v*i3*?V=$36nH0+cVk9O
z^ah{iL!x5e)EXWx7f}XkaKfFe<83e3CkQF%AeNSvn?94ql$6is>}HgNN@P2tpd>iX
zuwd#)^Umk`v?bguh%a028^di0X=U^<7~JsX*&kFQBg^ZE>EJU4^NapzZ+$#<jm-i&
z-oW=WXolmhTndMaVH~;LLGgp3eg2Yj#ZznJko^MwpJiO4I!sA7JHt%1J~6N6ct`H-
zO|{Ru|F1$4`CCW?N7dBSevaUCwKE%%Z}1SxI@UP9e_O`+wg8}h^2X(Wp`n=-TeWqz
z@5h|6QN@n+e!r16cvsl%&MG)vr_~(SD}68zlv3)?AvPHhu+tc^dN`fsCB>TN=uKk?
z!a+OHn%meY-Vs%Fmg7>4OVk-qhc+c7AbQoAv&bt2vFUjMAl2d&@}{CE&o?+69_5M{
z84-DTckLaYLt#<U;9lFDuIA&|Yt+tt>=)L!Q(#MH^Lx(y?<R6fCeN?LFYctko)_C!
z{)f74HnGm=g_l~R9t@+jAzo_@z>KOp;;hZ$QGe5S#R=-2wvO1?a@Wtt&2^itEv_DX
z!}YqS%#ZyZdoCNSpr9ilB4}#(0UY%GM?WHawU(`$;)98C@8a0_dpaqUR#_jUh2&~C
z9!42=C!(LIW{_nNta3)!U!%p4rDVzr^(e@Jg`RwSWg5ypo}kUtjxQ|rcy_N)*#;X0
zL`sPld94ZU|L%1DlYT&&IPkK9zz4Jiia_=^jyHKSzZq?^J(yT8xkQKigCNwn+}Kz9
zqJh2}<^Bw9zCoYW(}TN8!HAv=L2v*w%9-UiXa-CI%E7@!Va&pEh{^W*`(I%Mxkl&A
z!WGzwb0xZ>Hb-4S{DKi)c{EPS<^|CL>wwmtB&r7wKu&_{ib2Rk{~@3upoRMG;XVIq
z4-kauJz`L2!Gb@(9%5Lhf}4=lwH;~ViceZash!dA8#@Gm^(iJV>Hc>Q|Bs%lP5ZYa
z!%qjjViH4aZcHPN<9rlYuWX0G-6~^LTtZqn6d~@7A-C3BY}H$A;3})Cl)p6=yAEz`
zWv?~t-(9O+*k7VE$=6GGi8>f^8>ov+I0iI=&oR&-Z0qKnubNP;P=uu}-a<f<!a#$m
zBPKX=76KrFzXWAxbMzG63L?ld)M;lVCnvwt(Crteb9YhQGrHVjlA;*xU+ss9>$ws;
zUxp1mBC|8m)S~+O#2c?;{ZB6JMCADq=fyj`<GF}MlQ+7SnV~+9$nym1G)t8AB@t5z
z;J&w&%+hVxQg_(zM`3~$dm&>veewZqTZ&pyiiaZrPi3#4%O=(b-J-ai#LYgzGkpQ}
zR1I)W&&t$%LxV%gc2rcB+};M?+FTCdA5IMN`Q^15)r#+sJDkLo)|w6{fkS^8&8cA-
zAS6XZ9h55n2_4E6kNZgcX~T;*Ab7pJKp9(g&x#Po(BBI@bP<rAtz1ZgBgp&f=6`3Q
z4@D3#UWnKtdMFxq+d1OH1Gh?W9e;D-jqi7e4nb=XtWsg$jN(7v3T8cR82ErO<Noe5
z4LY+S2&Zcsf=^FR5bIg8qT*!UW59qQGYbpD_n<WEvcw4ksj><iKVDkEQ>JTy?TxOT
zNu7tZGKi?F?2wwCwW$1Wxz}6mdE)BpbRDX=^ki7Ml2;kYFrnNc`b(~OAy7g<z=F0q
z7KL#Rp8#AGBi4B$FoU_&9sNfZA$p%b5KrB`xpddv2ddKPPB_09WQ|vHyxAQd(T{yT
z8Gr=~;?XaoH3d6_^XJ!ZO8sUP+=TBTwIvnza18Q92kvQ8lSUa;>oc0CyW)*3yR^Yx
zK1%m(?ZLts8rzcQe2!~KFhr2@Gcx=i%^)6s-i$Pmgw;Rql*fDU9|%D$noAURe+vBU
z!iT&$XAkUa+OUVy+uJUB$|>9bjL>k^9rr;7d9qXFy!TEQd)F%LO6L2O_}P_Er`1gK
z5&Cl!v$*{fzR;gm_@Euod~XQrZaPt7#cz^A*!H#nz->BLD)J90A>g7)IxSg^ggXgo
zOa0#I2LDy5biFO6uKM8;_t>7F->?301r*ipLkR>7y!a9<BJyhU<>DsXVieyG7DO_<
z_b&E5g-Q79bfFVm>2aNrC^fl4X};8qZA?zWB3+W`b9@u@b-&R$Ob%L@=cZBxUWIH~
z*9QeuIIga#DWJFpV{}^Aue>e<D%AfZjwWzz!KuYBmT2H>OTUnb@wnt|M&*4hNd^p<
z@qwy**x{JNhsxFrgp0lsbI0=PzSK|FY~T&rq^!!EIP&YIRPE&2PaTzlg_T7S@;l$b
z>uu*OnNyHgj1zjbURO!Of@AvlUc=HjUQ^r(Uty|&w2d8~eh8-~XkeMe4wudbRqdR|
z^5FYN1aQr$$gZCIEY#&v==VyD+s*K3`e6y2k&v~rLnexmtr|HbbT??SrfK$EhNyop
z04OtPP@*}NGv{+|-+&07zOYmYMf%2X(=<-cYi!I@33#^@yudyj9BgGU>bodyD;QNZ
z-%Q#vr!Inif%BVZ8oR2xI{7vQg7{$58-Jnmx-M;hx48#FDYMQd<UhwBuW8~XA5TiT
zXvoO=Px~`*^W}6E=P*s?T7+pPq9NKmxJ=hMA-X&r_if6k(vLx~2Kw{FU)O!#-f<ZT
zLm9*^ueaT{@w2QDPt*Zr&3bZx>2%@U7G*U3<49nSILqPi?M}iW9!MUe1@X;bS(BX6
zz@5+gk<K6b_fKM7nJme?0lVLwo39t(50Npm3h>qe*^yLW3>N;?+aO5biW=Hcmj#li
zv`1@tPQ29%xdQ$pq|i+g;enmq$(l^GIDvI4m6#M%^6qn>RU2+FKH&y-d%taVH6KrI
zw~Grusd~x0pHk}Pu<VRqEP`CD11W?;3au5HwYM(wEE)3(@bNrdFNY(XJMD{>aUmIM
z%2tx%dKr~IYdjrY>IUaN37L;kcI!)V2M#Yr8)H(};3>#b7T<k>+}Y*}O@%;(EipmA
zDUn|{sPJ829y$L+A8v=*^!xdA(@j?z=Xx9$x5jxsb2l1eXiy2mv5|2Vk&^ANjpBt+
zDc$KZlSc~UC-_gxsL<dsn)^*iE|GEwWYdw7YJSy}mkXdanh2cu3ysRw|9->pkPuW>
z5ITeZyBvIHnYXO>L2A@H`%Hq?lcgyS5Ydn@jM!#V*T0Qc;}s3fv7TdPf&B7xE$vGv
z>mma}xHpoC<Fd&PXs>L^4+(~;F7Gl|MG{%pdoq-OHgk3-p>#HIyKuZ0J7KkR)Mo^c
zbR#@Whp44bf6up9L?N}&igq=d8cEP5+ShR^Lld}F)46(aZ85w`Ti{|dydA9FAGbi-
zH4>7G4n~Qo*7BQM(DU;aN3W8WNrhFueA&6vyMpoHt$bqlri8bh`X=KP^(Z_m*rn_u
zO>s5g-e&U+Cs$O^4y(RM*^ggA%b#6F;8l)-J)Gprc=`U#bhq$gq{>U}^u+Fw2kFZD
zMLq(16%2vLHc$t#lsug3Nc`Y5b*hl&<c3F3f3r7*a)N-i2NZ}Nz<hOED)GVgwV2`g
z?r&{5cU56|dn!g(DAr0F1-OSRg!}tA_I9aUDV^QdjcL9EhF>JwZN2GlKdcq-@9hRM
zlo$pY!G^lyp;bkj`7_a9W&u|~et;~9M1<1Q8xGwcUL}~c41L^DZt~ntK;hp9(r-Ap
zgA$4nN}!vFFIY}|Ril^LbGk~a^2BGy4s7|HTckz^NtJ3PvK)n7>?gjiyINe}EyV{+
zz<j}K0}SL%XUGe>K<Bov2}LY%Hit|YJvK(uH?328_e?bdq-&vm_tvW+U0*stJPLo$
zQs+&GYP}lS2H=YOv7*1qcTVfXc@J8hZdTcS`&KHqdp@m92<uFL@7V91iNyx#flZyY
zIqN;<=26Hk@4ex6X>}dKpA8sIQ}X*RX8beUi%@3Y^BcHTju#Q8&Itif6cM`#my8@6
zI5vK!DD+lyvV7?+2s{{;#vSL*c};yi?j>u_&{CCOJL@XnXyO>Pz4-|JAuZ^uc$@=P
zxhXl$^7|CLmJ%%F1%Cw|Qe9D4Y7;v2F*iC80aN2X#6;V{K*7(q<;OQ?@8%8CqWE-}
z1Jy+FH-tvQjx-H->EQ<I1bREzxW_IIc8mg&ZE$KN4Vaue+O-6Mu(fxsam#&N1=Y|v
zqELvsqHhJ{jF#qoX|O^tnld)<$Il}3B*zew)v(bb^H1t`!kt_bsYI*+^6*yzpw~~f
zF%dE@GG|&Y_}TJ1_gJ&AJ`+YJ%3}Q!_WoL;VFRNLM`L|3n|pe$l|wG0sS17y4{^0e
z;qABF^Y^z<L}1+l+wPbE&iB_`W1Ec~IJ?$_qtGe|&BC#>{|E32NOm-ELg%9hedLr!
zJ9qTxHdk~JGuk40)OpA8=zugdM(<`S_X%tWDRVqPgl6@O0yK;q4DLP57kf=nvMS?u
zZb|JMf!fa9CSOVIdZ!x=6{OFxZFsP<<)R#-{3r*Shm=)OI;%t1tlTnL^%o=5Sk`zG
zE|&{nZPy*@NK8iv1hlPl-JeE!eoLCVo^DUW*EXzrw=LYEP&bRZb3IYR>J=1z|K4UG
z!bm?W)uiwCJ0H0SqfxoMQ+3{TS4*K+CE#ArEC`E<De@um<(IlvB2-D1Yz|KpHYN=}
zq_3@d(>KyfqpVPJ{9i$qQ*AcrzL_5<M@RYludKvR24K`mv=e{Zm&Rp!%S+2fNS_ok
z0;_`IG9G6*JsWtlM&zxk#AIk^V_<+`daEr?0FQRwgOh)^HM#?Ig#E{$oY721%LEtE
z(wsF(c5Mp<dB*oT+Fs~EC);jE>2aZ1rrmD<7G#(*H+c#UF*SFUKuDJu+1rvQCAm*;
zniIYf88Yb%2e{?!7f@=hpu+Zi4M^0G4ozch@<VNUAK}}*`0p^ID^x}}0w&Y`(SS@J
zrWt|J@P?xGxhtEpN`aAYZRaS*Hny%ynz^B=@exgKO`6PZaX>FRNd^_vfm<&HW#y<%
zCJu9l-S%)5KF=Nq4=yp1tlPu7e6!N_@?WFB(B${y*IAa8^}FG54kh~su;GeF`fY{P
z8%=$Ulz?M$ja}L%pnN7gZ(jtWa+yRJ6A}9~Mc_z=&@<5o`Av9YSKW?{17lEmN}6Nt
zTncVQ6dBd?oB6oRuN0xk2g33sbn$(|fbgM2L_}E5A+C_}ht22v=hi@7SUu1&&kZcB
zBY$Zt<)F8t#9DKNy5SA5td6crioNFD&T{DH`#<bLTtMInBT^P2B8?F@j&qWyyGAM3
z#hRf278{xzxpH!~iMq|z417!af9l#Jh?jvHsYpgj+4Ghjeta`*-<FoHE)Hxs_~bzB
zFR`#u5+E->akn(H+;ygGs7kixuvswQZ6|}x*rEp>@#oZ1(hW<D%a^?f*d>7Pkz=!g
zz8t@6-rs}rW+vcfcS)C7CG|29gqaS1@wuhHxKjsySqAfsXZrhx-8lz=)>3R==l(80
ztiwj`J{oRI1<Sn%aS8}5$8`o9*EEhM&AHGPL6P>bbYtA=KsgXNKfQB-3>$DxwC$hc
zQMyyfrrIyBTXeu(YLM=jbhNSBeXsk088?P)T?KGkXFQe$@uJjhH-nbf!Jom-EJpe~
z=-M4=x;1<?m!T$r%4Up+XCnp^p&M=CH6Ye2yzjcD>RC}zK*9=$pCNxD^%-)GGNQMK
z<!T`d#and1rnQTG2+ajv!Q!oq#;(fJq{!3;qN1dX^NsLKmH30honDGX7_ezD*-me#
zUHy2Rs`-Hd1#$T?mLtvmwYinX;7z$A((N%a#Gl8FPcs^Re|2yb>}j?cFs$b@b+VH6
zN0c{}MbO7mrm!zr(*LITn(FD7&rRD>flg^q&$80mh7vooX<-4S{4jtx%y7*VhqfIR
zgVim-h+5i(Q`ph;@LD=m7xfTgm9#?!6hTA?v&O2}FDzkD;rX}y{gVlxa_Bz)!BKki
zU6l&nvDbK^rDOcB=W9N{_l3^g`F6aC$_sQ?!`Ra>`KotQykJ!*2tokH1ywsg2m)1h
zEtSZCM6$VHct!-$qH)tX`5ELs8sAE|=2Sg#!1`TB*X7YXH4pYc8Hs07P3bee)we5f
zbh|<62luK@qv`onh5{|@jI-wXjSd2uVjK_mPpnSNLWFtcz`~Ch{?T<tS8i_fZ)(gn
ztFqDXE3Uio`6Yg7#Qkh*a(whSvj)R@DX}(9heo#ZG);H5F0SW3?;p&mcV^htiQS><
z(#gFeVezFmNnv(}12loa2mu}=KPzl)eF@yA7U+>_l*qZSEwSESHE$5X!~pV)yUh|X
z<4a!9HrKvIE-=DBqZlKiz)%kjcOzU>V{|Um6K7MND8u4iJTjNQNtkYkHN6&u1>V{a
z2aNbHPGH+PUpT<G6kVX41?hrCpujebOpWCWuLie4mGRiu`R8+EUnVBGeWQ4_CV2^L
z1p#k=<mKaElXlh*<tgsR0V|clo>X#_5JogJA?y)1DP-Ow>GiaB+(ft$tBd9|)CJYN
z0e8CJ;IB2c{CRs)%E}oColilean|sBYL=A^)6-|BaVT{5{cC0WA|9)2EBfZX()m4n
z|GbWhzj7*iuqq$SerLZr%*m=FqGF!$$B=eEO6SDYf8fJ!VIb99ghJ?%ttvp4m6wkQ
z41}f7ZbRbmR45R}DIy@`_r~dNHw1V*qO*Z0m4G<XJ|>-5*7=UodNuwAhsO<8SG#X<
z10Qp2iJ^VOReWVxrt<F`H?~AgNZ;(A9|!b6a!;2G&u{l1WWKl6T5VJ8K4M^9N@njO
zL!(fG8<1%gF3e}ftL)v1QY6)2@AyHVEkP^U;esI0omRGiB_HFBY2`h7SlSI3LxxjV
ztT#V)7pK@hfsCmpD%tix3Gjw2w6+;aN9ttw7^F-8)lFphRo0~w)ZOfmaLSKbHZ~dh
z;rETsTgRmR$jmtlVIU)?BC%{h{k4z8{k=(@)cG<bH6t_1GWgz?JtHL44d|l)+4lR9
z{g3ah6th#ro}pmAUJ}6k*J?}PY_aQz&JT7lz=x=nMlXuzy`(*BsHb=!nW!{I#G<_1
zJT^HM?cgyBR-N^WJ^OGl(d=}<@~x<c4?CH%xCp}%R;9%&z5G`_=~lV)9Xk~`*}M`i
zkVL|qAL%+abv2Iu0ktkZw6PVL3}GO>_c1dFQ@MbQGsknaUd$1e!)`7j7}>Ssls3X|
zD#^H9mEgzzFS{%C5f)CRyL~2=tlZexUhU9`1koU}k9kWmIH=ketz4V<@}srpQbN+3
zBklp$r^jD9ly;Hx)LrB<&pZ;uM3(#)xMe+U(t3*z))jdxn^F{bjM8_i*PHcX?z|mN
zl0W_3F5<S4WVzljvAj-jUWS}`4f&7=+cgoi4m)ZCJbEQc`LGvqMF6R#y|#(N(mRNY
zD$<qqmX;f&piFq4m^0jO2x_cStHGTHmq9&k!9eOCE*2G;*i?WM-X1RfQrhT`9rVqw
z?jprTd-u)gtWNX+K%WTx=x_>P(>Zi~DO*);(^=a~Rj`OD=n=K5HvNy8w#u`$k4?s=
z&HJV!(yAZNO!7K-;}@rKO3MCNoTUf#q1PeTC`hWc8$p4gF}s^@57Jwip-3FX=9%bz
zx{0-eSFO^dk0Vu=SrdN-TDfBJ|3nJjkO*pA@ci!m!2$*06fT~p45KGCB~$3%3>ujI
zP#Ba^M@B-Y7{X%RTUvvjBeenJCXZ8I&=N&J|5ZO;#^s2Lk)z=Dec-FFU&=lbvl_1l
z!Yep7(x#B+ut6YrV!9V;#J#gd%6-2s9fi00i}kbT_|l$wO)<cqnib=8k6)@0fhZ(a
zAqOn{grmitAcUI}e4N0B-DwsM&&P+YqZmv+4d^#i?B0Qi7`?+L_XsfaJfUUypK2-l
ziv!2m!=r#I7&Pv;XKI+Mcxk`@T$UIK7x(cj7V8P)D!m?nR=($GML}wbzyom1+ucYy
zL*<{CTL<TYmmnYp(9Dq$59xLbAk=9ea5*~e=pc4E?5+4nhSn|r4V%+geO;+(nmTmQ
zn}R&2lP0X&sjQc~8TU|lq`m4S)CsiS3JG8TaAx_ssm-3_4<qNA+sNo(qy_beRPj8+
zwc%v?to-@yb@W1%!5}z8fY<5)g^Tj<vK_**Fwl;!62v9&dwco`e?eQ|<xMUG1BFaG
z>l@}%3Oh;(8R?Ct?v**M1Qis&*5fZ|o_Rxjff}In{Ea|JmJ!2s8Bh9)VBdqthhO4O
zta}itnV}drtj85Cc8*Pjpvy35$Z0x(`KWNx8?i5X9*~X>8S$s4u}qJ%q*3Wnp|;o&
zLHK&8(v7OB&P%@g&2GDgK3=cWj<2We$$T|tMmAkYKV9Xe`=C@%V^pX<(zqKz;<WZQ
z2t0QK3rgf(3+FS}p={NCbrcVG(*dQkeEmqIHQA*t)<fl*WREusxYso?Wn+9X2~RhM
zsxEw8x8Ks*ODupjF@K=Mw~=4viIP9HY}z3$(dhcNYO4hysz+qY^fMgDxbM$5(AQdN
zxOEQQlfDN&i0m(5+4dV#cLpkVOo(Wgu)ruQv_WWP8&PsE5<&aEVqrzHT7f~`NlEKl
z1P0wHxnkjlr6Q%^<JWNjWrEvYjv2}t^+LHqT0=o|snod$xXIHVX9oK0XY?h1e%TfI
z7gkWi>D6*a01AB&Elx&{%l|geQwk&5=?;L5?GC08>QpE%F!Nszg%RNvQL^pA9et@8
zzNqa9ka7Ml<I5lA)9t`Z1S3;eJ7`q!#J%Zx!qnA`hnsvrL3ce--sG_+$L4rRS(Z`f
zIMI>T(+(}R-3k}xqYTSBKoXN1jFzvL=ku^hBI&_+IuJ8DZ((V(yw5C?`V~}j3$n?n
z8?CmJ9;NItXJzq<*p`#-Ba`OxKujs7cW_LGO2obyNy^wrc1Q^5yNA_Wt60vir5kwe
z-Py}0i6%}YSr)J!UN5lgI4Wx|IKk9&BP1lHtx11!e2+F}^5qa6Hj)aRLJm3<zeWQN
zcN*E2CeHo*C>3N1URlIu8W=H#nbNk`QNP+^*~<tMw%M@PIb|0M5dxPBIqtFswf3Ha
zzs?m6l*$JBy$ls59bF`jT)cM`+rwWfi=&K6S$@Fq;NO|1P*Gc<O}kdCn1HA+6&uo5
zc#g4}y}gXRLI5Q}xBnBH!(NTA2$)l|TK6fHgv?if9JhQ{TV6B5_OODVuR|P&khv=^
zzOsiyC9k0o4q)2zJJ|2&D0evYCbLm3oNFT>vfg+P>h?vYX04n}R=$*)l2+h3om*K&
zwLdiu7}69iHnPd%`RBEfP9awZVDwQOg{`1xR@a9B6l6wPlhvv}m=fv^sGE_s&-~0!
z_uyu<r-^{w?U5J?YTEGPm_->RN96<-2GT!K%kN$vJsaunjOoP)!bl9Hgm~G<z7g7|
zJAIZrme%{WD3q!y*9|Q9@Pr||fefg;PE$djK^~%uA_SB+{0-iqH~e$|YWhXljRe8p
z<fG+ulNrtIUUtw^osKw>Ezlcc5<lp`YfeB}eXSARl%A%jz)wB@xe3iF8gz^k({F|E
z2YlpNDj1k%X6msC!grh{BNMXK-f{GaZ3hfa*Yy+WAsuWQa>n}>hV<8kcoo;(Ir7F5
zPt&98!>QjHoO{3K4+z6GG=u*7WSaYobJ%@L>1Y_MlAw~XhP)k|O&sp^mED34ZCQ;Q
z5FIr|-n^k$@mDDU9Uq&5L(lK~R=&N^m!7+p229okV0oFa{q?C%J8CL8+Gkes>hjS#
z8w+#a6WFE_%`!8!q>oWE?{InvEze@|BSSz^7}?TR5{dITEg3f@363Wi%{_~?ly)R2
zsHBqmoDyrIeA^D*w4ZV5M@y`VsvmCkeg_a26FJ)|wV8fpQXPZ0Tb@^|NQ9T}N9vND
zS+r|a#McQW$8F(98`DjBr9_@Cj86|b_kzRc(t0a3Ft9CqyqumdP2sor7-W5s5A!4I
z!a^d0aNxT#UwG~uPZ&BMd;g^@p4}Oi=?K9>XGfGx9dlv>Mv(45fWeM(dVC<9n(=zl
z<hOaQQ7`*vXENxOwE>T-FX(>E_dVS_qav~LL_B&+&X8_q%R=ZoOgS@X2Zcdw95d(c
z1azJ4o`&;}{B5fmhWAv5M-w1jpW|O?b}E>cZjRM6h2$nOL5m%&7ZD%2c4R|O44<Cf
z6o`a$dosg=*p<QY7}cjJCwD;hjfJjuFEP=QZ{la}EHK)WlbKoU1_G}^l09Mz8mmaW
zW+oQVmw}IqbsU_E3ePX0i7`1+YT%nrMnH*~s}9iyFd;dpK1M_}o{vP_&KGuWocGi0
z1W>FbM)<j03kTO+haMoWV0&ljtww+Yd*_$K?+QN3UBZA2lx_6qL(p>oSh_QzD~=?G
z6A@sLY$Ji;o&%#Bsh-)@6RRf&SHL{5SOra$X2~-(fkOE2I!w5DYwG%|uch*92b5k?
z7oIOc04%@K!KtA7Ni}&eA$J@|`J#JVC?A8OW#2{(hgzVCiefKfJ1+FT3F{rm((7%_
zL)~D!Zc&?8py*H$&sz<*Sdmjz$b+jAp!TO$bcP?F7dX;M@0)pKO-=WTUsCW>>ICk(
zbf&Ar<@s!<gBO1^na&7!Tg`6w$Q<p7nDnFDP2rEdc=6!2xB_ESh7NDfj^*U@_;kI$
z?UOUq^w&I}uScP4dqa6f9j59DHp#|FH5bV3hUIr{YyHIKb6Yc+SgTf}*>>IlTsK`B
zD**TT(N$NbujpipjOjHReUPrdMi*}F90yBpdLsZ(fj5Um_#3pslO+x8+v)jFqGdK*
z2zhB$m(Jr7v$&lzpiOW=$JA-xVH->va`9MgccM?r$Zc}8Ki>>S>JPM|7}w~eH5Q4?
zCB8ilY#uXTP-<{j_4=C+y>zJ!$@%zf+JhkfWPDNRdfU?`XG6%my)yXw?CmUm?FwHU
zv<4+UTB5}O2&$i-?$K+iBQ4LNGL%~@=qUy75sB^P??VrT*bY^{{6uDUa>?rt72a%z
z`DYuwx#DD;1(#7w4{isR?&2f|r0L5+LCZjdSw?D=Mh%edYzAN&R%4ZUp+jaeAuimI
zPeAd|!cIMoC@1HIsY9WeEn^}O%+nwjiizxAyWZQWIWT+iRFV6!^-Z(@cB)njP6x|_
zUR0FE85I<(7}7e-pcxG)B{CG4L0v*PTHqgw*p>|d-rHt7!Yd*T=v(Lu$Y7y8;<7@y
zda;0~^V|%!Ra#URlMV4hXev1~{JQ3Xfv%-58VbE%KHs}U3x`xH-DRP~*YRu>!6(2*
zOvQJmc&>WK#QJqi;6I^CS3>2Smv@Sj<7Y7Vaj-CBBgFDv?;;t9=;6E9R0i9gaSKeb
zy3``mpx7fqPG#1@2#it@FfXZCfzdB5vKbpr0`_!MoKsy<L5a=fgDBIkQm3zCKm<B7
z*+XFvP{(mEXZKr=`%###O`fiX%vQ9tsL;$1LTa8~q%|4`Q_`W%`r;QQ@KEGTmShfW
zA&^ciDb!K&iQHb_91}#8oB#-t;vz%5sJf)srnL5(S!S-tNzHiBH^TnF-)%G#hE+c<
z46-Sj1s8n+;<3;r^0tCbnKkHa+`lFn2klOZ;=`U@m1hFcWg}$qM&ti9LP*&J$}eZd
zH_wwX#!p<zxY^_KxGEb5waO4m$CrX8kV(eJ<nSQ49dVr})gj;HiO&^Cv&4zLx$~T?
zr%c4=Fw@^M;h^kk3J%VfO!v*&K+dwQ4Dti}*Y{*~b)`CD#T=%=Z+ik$Uv5HpV+LRJ
z_&`q$jv870xYQNPZ;yS$CX1~0-oRsj8pR>vgn^ZCy23_`!ZMZUf9!$RxAH;|q(tfD
zClKd9h~k1@2)}Y7Jd@o~5qhnp5dL8E<foj~Rn-a_B(fwdZ0u$9N9`0azFrm7o*|&n
z`Nu%TXGf4A8zuPj-CMB^hU8%JB*Tmt$y1mwFZGWU(p;l+!*73TBgVi(hGGxw%BC{o
zhl|qDEk7{au0@v&I6YV^^ON>eAd7I&;c<8jLewX)slp(iG-J76gZY@|73dQ?&ExOP
zq3f5_<}jOTBP4eoPbijz@c|?%(JLYfrZ)u~9B8I4o6+NDbHX`pb`UZwhT2Ve6eDNz
zwShvnvwDb>8u9Po;wK<^xcWZ!{ssu4)A1Wd`2%JyCs%CV)LQ`gIFTe*xDF5JM0`1x
zKEiKCx;QpIB_<&PyKK7wi}ef%1*QDd!O1{XL~M=FmKWhk!6!u*K4Vhr7gm6O%OXeS
z3E~=c+=v`7{?g-Tlg(8jt<Lp=jlXVCs5uzq1yqknLd?ndNysuN7(mV?yk7&3JG`CP
z4uL=P;-J<=`%BleP{HqCzuX!|SL_F)WT5*WesUtWz+?0W()Lb=2>lT~6|@y6)T3dN
zbzctFDtD1D{fOMrZclr2)V34Ic5>lHz=*_geQ3%5J_!{K?>;vi)j7Jwj~-~d<fB-y
zTJYKHnHKgZz@!KQGWo>~*|*=!arcx#EA}mTmA-u%9eCL8YtTNz<LUe6GEy2s8T7{(
zok&=j<Chi+iNdFfH&l0N3jP@9jt}G7ltrf04m>-9C*3QMp&~f5Tpg__^0VH>)u~ha
zKJ17?*bg+-tM!jgg`-Z8Ht%7GJ7p*100jskAftv1B_TVVZGz6oWQVjv0?*Y-AzG0)
zF>A~^czH3EykG{XG$VUhwCmP|*b`fSj=LfgCvNP$x8RJ!O(8c3lQWt9Ffin~ymS=5
z8jiaNR#w2z`f`@Qo%QMHR(Z>cU(Q3OIU~kHhD*8?RU^rel-A$u3NC{2Nk%q}xjFDs
zVm`X`b&!cZ%LXSl_m#e4%=G@grb73ep(2h(;Z?84IPhpLh9B$E^)X*C0pWbS27trd
z&RpG*PYF~jc^P@wNv2fnd0P^Vy4VTXP6ulXCEfq7H`=|{pY+7%V|~~!k`w92sCwVG
z_`Dd#aeG<#nB`cHW^NS0>4lSx7`2d`Z)pJfg2E7CwYCN0KtoR68!ycnF^5Xq>Tpp}
z-3RRMptyY9SIJ~}(p)yxkNAyXSXu5xt<{1}rCOtk?hN_Zg_r@+GhoZ9MfQl!iMu6E
zhM;}up1RJ59Qihb=zJ8c{(J}Sc65Pbclk{Y))X5I3hIrNuY3|?Ga!>4YJ|l@;*@Q0
z6f-kN5P@n~h?@_YP0vtH#X|vtZjTecYe$st5S$)*yFQt_U;lJc_lZ{sFH8y#C&mPN
zj{pZNzzrOOMR5x?Bs<OLcq&d!S)#vT2G-K!L5CQqBk^JiBGNkSsPwK6jDlN#NV#qE
zR()D~{P%bamaX*sYK!5TRxWV69>x2LyvMJ~RIl(G7hJ)18KCPdAD%2~4KD?^_Ie;G
zQ@-f&*q%{Ot3db3?ZI2#a^>j^gA=5v!|7I{Do?F8^>9X;@ONmjyTjFQw-Y~NeE?G?
zqP?>1LzH_Xer+hQXYtV#I41qMWg!eGWik&d{eEIt8W^gX-`37Nv{}5cLF~KQ*Y3o>
zI$`N}*rB#tsKF|*g^tJP7l7x-EK@Es<c>mP>o!Y252hp-!vZta48IGK$@j3Gaop($
zyDm@D&5*%)Xu(&g`yS^O?9&o?PoGoWzaei6&4&8WV~1R}7+8;(Nc-?%k2Fr!@lTYQ
zjeW!S6`hi9+TtOE#rmC!<Sx8s69I)lQ>NWf*?2#H-7sL-PJ6=|fj0$%9vC^X)|@V$
z{5D48yvHDdYuZ%vRzYyF`aqq`^+J^EVn!Q$AAX1#6Jv>{_L3Z&bSDML+kdUZwRujV
z);AdUS#5CSlcdWUe6_3<k#uNSj}(K$DvN?l=Xguw{>BUn1<P+~K|Ptt9UTCLuJgID
z2`YvEp7{EdG|vK?UkIZ_#Y7E+Qm;L$e&Ho7`hb+qphu16`+%BPOjPlvQ{XkG)#@g2
z@MS#NN4Yb39<G2rR3k02DAKHCi3U=G0yX~l*<f2c7;=Emy^M7Rmo?nF?`*eGy;sug
z=1*m59OXX=I#u>PI!3E{5R+PQjHeM1b|?#|FVmOOz}GCZ5W=`zBQty8KK>)dtI%Ud
z%3Q>vl21*XAW;&O?3C;FNb`KqoJK%_zbpruGWjx(yq&E2M_8~`^_n{(S&HpWiMEb(
z{FU%PiHq5{{kEGXYv41jWCf-(!M5oci=wX`Sv|J97e}}41<Y#G4!R^3%kT6QqWNx(
zp)EZ`22_lGR+1_%sH};$?d6S3zx|!Mi>DlW{=KXbe<aJ1$$A>CE$}wUgB0bqxe$@x
zzi;S<MyES|krZ=@Mv$QuBRH9_!-w}hwmtRXe7K!9z}h}4Ko(R1gGKS~4mwKoCro}I
z?eo+cKB^9ZV{L7<t_);57JfrkK!9aW0t)eTJ4R!H7(JPg3N*Z+3b-wEB2?wKZCkc>
z?24H$VC6hFC3%8M8H5(rCjHt<@`&z32x+M$-;p<Uwt<{#wSRIc5>2~IlPKf%zyRtv
zPeFZo8W0YXuEuPKW!_WM%ad#V;B5F<@zXu_o*^`ETA*(?L#Q`5{4!~B9lYk?S@UbN
z7bRBx-y?^U{(})JMX``NolXMk;`klTziX4;n3s1UZyyA{*BaG(eYzV+O%GA$JkRX!
zFs$-tkG`aq0eOE~qS)g8DBP3T?qq?6E7p*_)#X>fgtpR$WmTK4n1kjgzRkz?K0X5d
zc{K#YwI?kK8_3{Eh8S-l?Ya<;)pYP>J4|KGQ%WPtI2&(%z8x$Q(;Kd{V6qPpjdm<`
zH5#vUyxUH&KL4zx>GH3K0wO6IO<Buqnes)7l-!)Hv7~&i@&?wk!i^doao$(QK({sv
z?Hg(-;~YN8GfB}}5p!<%IS6*0%!HTnp-GwY#6U7r?ALEZM~WWh*>d*{dQ)hlWADId
zm4B;~Jqs-ftYr9@Y&WkOD?N$9P0j$ys&^2E)01Q1c<?&3MfJeGorz_Lb5_c*tlT@4
zC{EkPqoVYza}W{qqIdsI_BQko+C?!FpqV-qkcZN}{p)%=i1Ii;Ynv1e%@n}Vl_YxV
z%z1c{6snPgz`FS}UsRtr>ji&?!{DoGqY<$?L~ft#*Qdoun#GUuOz)7*5^C;ntL@Z~
zL8`2HEdA~V8=g@XKb`a?LQR7+M@I)ikq~7veg4f%QzQsmuR?8g?p<r&aztACor?nR
z{s&=~6^@OsJx2$V3;AN^xT>4sOdUJ&HMR0YC<|MQCZ*V3c5VNt#aDc;uPBL{Mz1lh
z>CXcwHi7piq9=Zc?%IxGoJt5O{UoMfRm=61U*aUx(VT>BYE6`l+S6GKqX$<a(!uWe
ze$j8^D(z;>i`Gj)9|@L|zT^p~3)pWM8QQ^E%Tpb>-*|y)dT#OcvOLNHd!9C#1xZnO
zaB9eK7T|DrCi`(~uZCx=Lu3rNr%(!AK3;t5JHC2<PX&yTIMVf7+WSybqU`oZ?|+C$
zv&y|m88RySVeO1E9My=#mA-2sBbqpO9BKz}$(f^71c7p>I}A&a#!9h4wTFN_kRVc$
z=#7zx#J4x)KBoF4021e{EHJ^fxtkx~jVlt{cwjaqIZIKe8+MxoU@ggkAk=q!Ny@#F
zxZbmQQ3-Ncm<t&=^J@}f({vU#JW>Y&h~YCAgTFgV^XrQvuRC7`1f9Yg)>)@-Rm7`G
zK?fmbEKz82!xeGQEC@$B=qqXo&rK2lo-!&ZR%@Hk=Hr|73;O#E&F3O|lAK6TC(1-O
zuQuACO>Ih;ZxEo94sbnZZ%eUxA_y)3k4tgB!ar>Nlqf(Ln-U_i5%VmZndYtsZYk4`
zP;Q*OJdmzDJvb0o#VYhTAbc~cDi@GaL*^eMRVD$J>3(q9sNbHyfa^REmoT-dyQ#fy
zY*S$lhLYXM?U5t!LmupQ&)EWNY5wjW6ukSaZk!_{fVe97H2*~Y<jhyB!4=m(t~_jF
zJ>7Q2AK4LKo%ygmI4dT5twN|FDcRRsTWpkA_wal(Jh&bl3STKtS}>$QAt5Fn)8mPC
ze|uZ)F(NzmX^&$dh$=M5iY}V$DZuPTRP-f1c{NCyH6pzeu5&W>hV*TlMJnQ!@1JXX
zDi^DBdeUd7_Yv<xNoW=C!UmkXLP$2I6{fUIik~ni>b54_UDxDbqDaTl{jdX6{30Vr
zk}R%Pl+W0`K7gwQ7^vlR;?EUW*)8F6Iq0dxEw%dt;QUKsH7^~=MS=wuPaq$8`7K?J
zdm5LovcX|3+{xOs8L!G+(6d=}!qsbsP-=YiXKhtr^rSHV6r|;fN7{7{U<l*%37Qx=
zexNtrLf^!jnyyMrP#;|qg2?fT5#E#61Jv?$VST@ZJ0>$-TJTfk{*oh<^x%Be1>5g7
zfc$hH!=MYusI2N#<QC@k=p?ly0`&$^$G^+PBDl}Y9p-IkWDX;Tz@C4O_I21TucQm&
z?a{PTo&wFb4Tni-O({P$12chD?VPJ=yHmCcQdv{>vcS-6?N@eZyTOJm_%qp7nfU|^
zt6k1S#b%mE@&u&iKp!Onn!|KqyIC7Gjg|78Pe8U-Ey0WEm&}}ohm=xi`c8)4n}CP}
z%6{R1ytyr)S*vVcJ~VmB9f>;G+BiEZr5(6lCmfZ>&_1Sz3uumX0fRvfFkt}_0#0g_
z&6cOZ;VR^-%^vsW=ez@Za0k=Ki6W-1$K21iZjP<gz`*4ucfH-PhX|L}08C+FVO$pc
zMuC2s-MbSitJP{4ha+0w_FDGYNji$;^Zt*|`{ccrJL)0$w885O)=gub9U~#pz@qw+
zN9nfxH~U?(%947A6=+TG3CF!I%l-mT@_qhscX95s)Zd)~|K8yPoy>z~zTVBB<fqvN
zLPwVwt2uNSTp6H={;Hbsp4BUoFOSOdbmrR=cCtT;8oq2qcSW<Df!7)3Tag<jkd(Vj
z`u`F4meFx7IlFLTW@cu`%#1NRro_yQF*CDcW@hG?q3t#^vtw*CGc$fZGjrc}=FIo=
zu62L(TD?h9siczXsa@Lmv61b;z1-Ft#2E64#a}mXw!Vmeui^A3^0o;VYKxh_Ht&2-
z0E>@0S$vDad5uxK9Fhyds(na}PM()Ewfg$pVRZS{9fghjt@Z9l+iOgj6ilw9FsHU9
zwg+CSVdC}Vd&AVxkJJR|7Y7b-fwUhHlHnIK!oS2Ta^r#2LK*h(oB7{Hs2k|HR5r9Z
zJUQ74P%T44lhAldYJBfMBgi8AW3t>PSNR0(xObI_KUY-g-!+&V&o%qd9;XehVn3=w
zb=NBY;Ut|3k+L5M_|udV3e8L1wUit#y31q5m>)^Q)%6aBTShl*gq7=nSwV{(r?1zY
zJo_ke+=gT}f$13OINiCb&Hu2{dXSBWNK7SpVUU^lJ5YZ-EV!xetCTh#l{`TRVyl^<
zZKZ$+F6znyexs2))M^=p=`OaxyeIL-9!<h@hqtFkU<C;aF+QhFUrCPduw-wrJ({Xo
z<`ro`NPzg9%0_F`z=r=MT&dsC5yVZ$Hf7sm7X@vutk5@tPUxgq7SS#H#BVu3mM<A#
zu0PITF_IT%ml-7;<R9C+W+*7D+B=B)lMom0H+!X)>hP*peA(j3z&B2hOJ)LjkH(!@
zDhM;<R%)?LI}(18-(WOw_DckP4HGF3znGiv9*GcV0&<ttUgP(l_ehpsGIgXpQH^Pg
z3!4p4{<%)Gcx@?KDC!KlA4x?mtE#}^^~6@r<Q0XKOuRiuuFK80%B3<Ib?2H(BsV;u
zKrqLaq+ATHRReOXC{XHL;{37@lu6omw97AyKTp#M$4(Ib8J34a`E8~pdzh(UNpCA?
z?4C9yetSwivc8|=2xs$Mf;ONmY}nj(q=(VSmHRwL^2^LMt?uVlY9r8zS=^3zV8BYO
zhSB$Xrffo>4LbM)JUonrlZiCiJgdPUT(iiAgf}aP&|h+xn>Moiq|D!ZjD}Bi&;n+b
zrJow^QjEmNQAsnAS-nOI0q`-cg@GP)*tXVfJn)vDe=<^MlG%5ZdK$aK7L0^_Er~lW
zjkUKL3|4E-qDx@ZEF(zfKVMy58^NMb<H?S_>|mjUp>7XueNu|)Y=7fN>!ILFh<sk)
zO49@1mkro$I^&5vI--?LcLLu^J(TDF^zpiu`qMa<dYu;sx-U#&>_W3h5^`R&@OIyn
zs2X>%_T-;qT_M&0z=eXm4N<n+o{1O#F^9-pOP7BdiY4svd}55=q|q{}uOQ@GGF&Kq
zPaPp!mLMx|0cNBe(Kl6C3|5(y;b&fl@n-TQV+ob#cAi6YXfQDDtq&<AHFitxnDAUQ
z-HOOW%=}cKq?fGt=NK8U#BR3Q`cJ0!C@?ZRH<;I-snt1l)W|(*Fv$Ugv&Vvt&%$F~
z&tEzzp>gYLZQs5b>k5Gl_qG2<oKlad7suYvlQ9SxFfcT>d)x4EBIF88P0_bu22ME>
z`Ab4O2f<esAnux7%&%Cd3T^}^lfuq6sumP;_k>xQ1B9E+(6I>kBQGJn<N6}ioaK;U
zOi60`*h1@+vV9@ylVv_}Vuli5F#IBfQxFe<r!k&OVEM8$6<~Qz7!-%tubwJ>+Tcbk
z(fP5ClQxAeALNjiowl^cBt{vr$U>W?`d#2iMn2zrkdz=}qh(kftJY0_4+1J@c~)32
z@*K-*P?Tx%>pnLzRLR1|#qvG#Ly>o?$Gwy((zkAvSC6g~4!R>;<8!R8-1{yPtJPt!
zFhjX^Wsxnt&SL2#yV{!^@|UgAYAo7n880t>PMejWqEWT{Z*5TV0I9S9U*S|e$26hp
zYG(5GPxEW5!!ji(8CzpKjQuUHSfqiCQ~|ufj=bDvnbblaiGqc!^L=iR;pNJ_kwcT}
zfi~o4{uw32$5NS+vQOV-ak>r5m9^=@>uSv9Xmp>gV98csoyzIEx0-DFGBhrmUBBZg
zE<1@5i~K&pX<HGCkrtNya+jSeEE~3*yj@o)f}4#7-qwm^c5?8ebVMTLkJKWHCao?P
zIc_a5IzUjsyZi_Dyat<S65ZzaD83qBp{e5ui;Fq~U*KfU&lhd#Y(CRmwrThiEIdB}
zZ1u#rW_NhA@X?pG2wf(w6iq9Eua!V6rn8Pm#ue6b;=m5v6}icco*3-!q?P{JG(nW9
z?Yy?c1NZ5EvtU{r_Na}AA!*3Brt>sc)TCPY3ul2`Y=5K&c&|%KS-&(Xuq7>5Hn%uJ
zv`GokTwtdzeva|E!lPP$q(aK%9_8B*Ii7|+Uv)({R7aWV!l990zn8<>i^vy965Ev1
z>~z{Pu;7lG`6;M1(koI;PMYz9@Q}ybomjrvJ~~T->kFrtJ7v&$6vkP`aXChz;sh;w
zQ;n<Xr&e?>0Fzzx@OQ7kgS;Y1i7AtrKf6q%jZ>>^+z6zZ?<Ss;c8H^X3*%l@X*AL3
zQXTmv!3$EEZxBR8eK-vRycju4Ve-AigNrn-Bpk_&@lZFC6}g`e!Wx|JBs3d{Yc3#U
zHwgyMf1>S*b!V1cu(4h<+peYH@R?#5k*!>gU+t=QT0kdQsMj*awftUpE)sgym!{MH
z=4N9Y&J_6MNkb`vv7y?W^ySnrG`_-7(UFq0y(B-X*z0A9twiVj{$l~B%x@??XV}>%
zlnMNqw+<kYjHE|6jb3bMNc-)YjDZOCt+QF>g^28(DW<*okAs459rz&epcT0?e?=Id
zs%PO+>_qqM)qotezz_r$<iO&t2Ua`VDDC^`<qPH{W$KY%gHuIXC;lUPFj-fBgocB;
zxXVp%5TBl(-U{zdl9US_d><RWT+GdP)8o99z5p6UT$EPV1)>M%g9vjbVp9UcfWRv7
zi%S^=gYGBrnyZXZ>4g{(Y%C~<%IJYBrNt(f)*dIlOs&lpnIMSa_j3Z&XmEH}lILQt
zKIlHyN=7yU7SUvw`S_OWi+P<d`g;VEKYySsYAOs|AK=9JZ~wMGSz(8RqgR*c9-gB{
zbM?K$bIPF)DH=4FH4+Afkf(sNwnJ27pDqPG9q39KeUtG&<%VwT4|lgUnpn{KZnW*U
zX;d|+LWowHdnpp^z`iqIfcUGZBM4jy^0+EegO<({ZL;%*9<%ymbA=8b7)(D}S};vO
z>TSfx_Xri;sC%xC&`*>a`4srGbxEyhRm*zryqs3c;e><ND5yg>g(I(yuwQ+*8{Zat
zHL0h(RDhCui7NN7mP}WYlp`?X@sUy(s7a82ruhi4bwPnAe8B~T5u$jJ$)U3NK}$@@
zo5$lN8zNsFdz=>vybF;b^<<Ry2u)U|;BL;=AU9>N>G=}~cB8h*2eyk!lF3|F>JJt<
z{h4`HM~Xd#Z7X`W7^qoX?97<k0@WJ7w!STF-TLCy?78u5*#e=}N`k+WQn80Cx>}2~
zL>Nx|GFv#K-4mng3=44iHn`??m)B}n(F2c8<r0D(frDq!V-tn`_&JM*N*s2&@9?tj
z8cullh<vEEF_d``Wgw5)Q$-&w+vCNe?S6^&fV{fD^z2lFAv2u&e0^RySc(4R=Ry_N
zGILoF#^JU6L2!zYN8Hl>-S@|f!BW_Q>4>z@RLImeji5cY7=zur?Tc@Y1=t{~Zrj}7
zOf*Z-5$m}rM*6OiXK25Q(=~DEt*5Ow)GMN-2JS=$e^fS!f^Lw;aCS#G+%6G@4(13O
zR_2S&wL0VAXLF}Be+fXdvG}nkx?#vrGa<Cy*@&F)8LUGJJR#wRMH^J@>{HDL)OP>s
z@v3;SVi~yYP)ha`Y*kLRTA6Y^fLT%LM)uk-%5?fawE&h4Z~Q1!-AbAyqVjf^D?1;w
zW=!}V#WptwiGIBYG?oGg1COch{F~sD_-MTXdid+ji1R&fgzwj~3`oB>79-IkX@tPW
z@W(K@FKN_P9FpCSI`{NB?W*jqXyd}qJESE)zgc(gJSKA!musf-`ErmoBRxGl==Ya9
zf2lI%^)ww)%nn?4c^W<Xa(S{et7J)M#|Kc|6=gMNRIfu=sOO9*1o^)2KC?Jq-Ab9{
z8D7%=>s{f_A~3Rq-R|3tQu&aEPvpUU_$-3mLHqXyHT8a@RAuoLEdE|vgRlDzXgbD1
z<Uc>JAyGBJChME(uUf$65foA^ZuyWM;88r{riCY-m&G<OUW|?&QZe3_{ECf~u=HS`
zm@w(T>Ih9|K_hax;bmqhE-*upkjv~N^H@2d!T1)}MKyh6?hj~U)gJ{|r*hnj!kz2H
z;qk!PtjOS{;yH605%VI_=AmtWbAldq`eEl(!(sTmY^~l^zCSq@i@?^PuQ<h~sNd|=
zLV?ouv7azlMctbMUq>-tJu9{Q^n|YaTyD3dHf;&CaR4=1uGx4=2hTo9jrZ#ZSUYT<
z+wH80R7_g%cP^3EFXR-p9lh`@hfYx}Sbm4%l$>1?6Ij#$n*r$SWXDMB>8zw!@)ZkQ
zn;P+y-vgvoI>hYiJf(l;$SxMYqm2vJVV!$ju$tZwg;z;;V<`T_DMkt6GZPH#URXz@
zww-ssaNaf@zMkE-7i7Z5)mZAwQ1VkYdhtde6@d$hKIk*2*X|^)7u1Z|Fjokwy9??|
zePM#gTj%Q0bGxBTV04Ph_Yffh3O!OIcR+CBr#-@sKb9I-CP*-TQaz#YqjP<?^B~Rc
zRvR^s%jV2%)a4##?7-nTba5iohO)#)#_DU>OLM9*0Mr?I&Tk}8{$@Gpz(+aTjPRgc
z>d3RcJiEF@I54RTn$qvbR%DV8;hiwc>CTQZGn@-uOI@Un8-__O>=uAnSaT72N}21K
z3lzVm_>|F8f@rCEqTck$*0xG4W@DRM&k>U>OI=PYpb<RZZaeZBCaOMvKVUfRkP>Av
z*KKb{w`kZl@3VE+k^6Vzt2IO{#)}G#fTn#Hafcma2yQlboWuBeOPp-^6S%BEx|K31
z{2|M`3I>PJvRR>b4@N+1Q7l(4%FYPAI+Q6Uz924aRtWPb`f+S?y$xbJhszjLa;*zj
zhi(mcHqB=F_u`hSHk1}q{q;{VMsC@V@CD$+qTg!pH-9)Qm(!zU#)j7vvb*dwTU6TY
ztsKI_vcav$VSodfSo6oqJUD?ir93u3Ve)ehUPip?&MVv}C)pl93cWvef^Fn8%&*-r
ziL(Odr^ECPH#Ucd;;eI62CFkMDpRgDx>&Rbhj6BJG_iqbd|Pj6u#wYt##`ugj+#~;
zu$V%PQ%$>dtR%}{tzu_xY5DOMKFd{*?o8ZDoAA^ZL6<yGB(rj!sF!?-JYK>&;|$VG
zudtt!o2^Yqdrh}mMICWtCq!h%n#rNbiP_x^d<=KD(eU;0MPHV#(2#W?$WE`%Y;hGJ
z6A?&7jGZN`De4qX@N_bXPZ!<)rs7@Q_n>~|ERf7LJkUGY7Ck0lnU?=fvF3YBiD1g>
z6XYWNg9rUfO?GOs$30WmC*N7E8oPT$_m^{*^F)=<Ea*FQgE|VO7^E97gisa(u?E;-
z`fYs~cL(b{By97-+afb<6o|-i<<m*sGh@%<reyp$r1jl{%N_#(L7~##6>E>NbpwCM
z5T{gMZMeY0DZYSwsAnSX>cIe6!aOAHn(^n>N7IIdoFYFftIwnc>H_zU>dz7+to$$&
z(RZ>@G~r-}-<bTTRGFU?n=65NXwO`}5uOsO$PK5Fm3oXJqIMKpWNw*-B}MrgM|&eq
z5e*1%9qI0O3TL3uebcpYbn#0Zeis;C3Q6YNjQ!a;qx*Dc^`>{Qw%607d{QiPg<=iu
zUSPY08})-Hizw#gb{E`~h9_WSm0CB5h7+|BNR2vu`V--70;2_iwy~~O++yN~SL@*y
zc^h4r0wtJyqk>f5HTkIF>3-bkh87wou>N*7NtcZ%Ux9!|fPLxW1#ARWFbOAPc)*w1
zSAq=i`mtuaS+<fJ_PmQ7!M5ujboC7D5K%LAab7ASgU@S`@OhMz!;qvwgKmmLi5n&|
zf@0Y6#9b$@di^iGSfLlWxer3dQcHb=F#DIq7vz(neKi*gNwY)Wlj&{Lly?G#XR9SB
zk~nD$&A#gL)Hp+`o>T@^V}p;-Y+&ybi;3ja9;D&*J{rA%V-7y0qqW@+<DKaG#(tfi
zMleJSPDrh>i2L3)_{q#f#kzd(@^pPO44PPs^H5LKT3FVK{{{D+go89wpbZ7>1lo*t
z6>CRJ)vx<fhwkz`#f&s(Eu|mCHciEgmgm3cj`bz^_iwhr5ogH_HDzJfO3hG7YC(vQ
z8=}RgN0^@NZFNg3f_MS>bdHF39yU2kM57eELmnr?5l22OMEViIVC0U)2z#XgGRjE*
zF@9Iae#i=~S`28~lMRa{2<ms36YV-2s>x3jeRU|rIWl3>c23SehLN4<J7~&E*zRq6
zu3guoL<I?Bn||WTDi3UJ^=O>xJRNWJ<(f#+*bME5zM<`yp7b3ntoYl)gQK2_FOs$s
zT}A#L;2OUX<8^=DUVN&KjnfINNmYNSxu_6;C;uiZ#r7EWKD=LuF9$^(v)eT>oD@rn
z&8~)NxS1KVF}v;I0n1}1yH97tU*`^)Y)$8%!UZ2y_okp^0tGOh%VkJH6|#lOz3!;M
zJz)q>AImBftTL-iR=HjqEKIA9T`prwwjCDVzxk*tS|3~17Zi7Vnn_@RV(=<%8aV^v
zsl_R*44>m|bj9yjp7zVAGC4)^;1Tn&)kj2>#y&gSlz?w5<k7cEXMhBS^V7c^sJvXy
z{@gj?`;=2ErAr>mo{&n5#fONXcpQt!B_;E-^mZ)2>0&nKq7NDx6<>5$wgT+Emuj*e
z`<TVOc0Trty>C&a?1RL^K~Jr$+hYscT|a(xI}g0;st<f7{|hR9P7r29^3?H1mi+om
z;vd@-v+kbeshr3|bn7c+cB=fb)A<YB3j-#KMt;Bj;UHvsniz{^C(+tU$t;+xIkA;U
z6AHS=GaC@z04hQpH7{SWuQ5^T^Sm~*C#D$cV*$4Cl9n+7nwRbberz)mtY8X#_dS4Z
z(_c{Hf!WQ#3^G;<{7slvg)EdopsLqnAiyrB6=%DLyz$QWyKtMdPj|NZ%O>4omQH0w
zSR+DkMS+Q;V!EnIV!;M|pmvN>*ZYyTw&$_X4}!F4VK|PpHD3B1`$s6ahaegiHI@AQ
zHiZ62<YANVPkWf~_Ja>vwW~z}R=*igaCV1jr{{*oM?_rqV6B$wprK*b9jFug2oNTK
z4Gp5Of#KePZDAg?BC4vKSC99a#d$AImf!(kr8rXo7B%)kOyHdnlUZL~CU6Os>!CA{
zcDr*#_C_Yx`|^75M3mLl<5-9=+d}H%(K7jZg_k)ZE{BGO)t7Pnxm#@7z(i{u*0i`Y
z^+b;el)_l$EKS5AzlBHGadL%-T+;Hp?%zI8^rFMU`kPOz`6HV#1VnT)F?4bS=_eI5
zGz5K*1KY`pv$yK9RFLiQ9CZ#w{VdKe`EG>ymDAw|%KL%Y^}BwN-b-VJ93=v;e7K37
z#jv}vnHl-@A6To^W=Kzb^{MD@nJ_J!vZQohr*!8LW$iNqGu!xQ9_S~0J@HyNZ61zJ
z_hFz70bfkN{5G8@U^~a$lAV{f(iKFWwaL{#TY!{)%U<kwL8s>(5;3i!XH?hmcbL$!
zmqvw~%#oCzx9~W`+U&5j9mLY%Huz;@N3$NKVaphZxlM61?i^WNYMSdt6AnK?e!YWQ
zktu|~Fz$wct?1bm2$oP%@cCL$8?i|rS=H25c~V4U7;?whbR%O@s3&?w6X$nC?6@W2
zS`+rV&5)=QSj3u&9?3ha{>z1d)pEAKh=(izu0Mlaso!@l`iyHTR;KTV4JFuxE$M9<
z6AgR)LqZ8ra_bCz4J=$nt)l$-+7+Al2LZb|?Y|=HE1={RBoUw0VpC*r$1)Xk*IEzp
z`PrP%I?5wN`0M58q<@}9EQh#1?bu#2KZHnblO)AX3Tng2*fQBr-O^Ht8<Lcle&5~A
z9;mvNv6~z++swuZ4W9uc;H$y5Y$6JpCda9w=T6PZh^m;St}&WrJTYd4afN$ByfFo~
zHgB&a15<CuEod`bXy@!9=_*o^lJhUL1?7!TdS9Bd`?Ds;C4`>^mE{=>^pe7mVQ}H^
z4LA5Vqf}J9B$zNZcJc%swh|^|dxNkV#UBf8sa$MVgXH23GnjexXh{s+hF01_r&_j^
z?Qpt#ER!70>Z6IfQi$9ApR@HsQ(Z1E9Lu^_G??;+W)f<H#!E?J5*=m5wI{ExCrGXj
ze0<))h&)VC4SZRGEI6h!FFSo&#wC8yv5cQFX?&t^_Y29P=43l~>n{=PnrDEHO<j|q
z$yRB+g^5BK444%3vlWJcVmYHa7lE9guGtx-jolJh_^50_7X{W5`sYmTNcXkXf`Q9s
zoKR2yBm_5tI}Yr*@=&4>8qM_^vNR25Ob;y~VE3@*HWEpzG%_=u*Gjvzm*$4=hkss$
z=yUFsF<qYITk`eDq^$*+-%!kzLqi*Ozo<`nnNw!6F%5>8mw@cSBu?^K9+eXuE9umn
zn9P@pJ0t;9`o^w=*9?1KHS!S{w$@X8crWj0i?y(=I5(BbS4FrXdd(U}_<DZAr65{q
zo`HFwC9#{Lx@B<@m*+aKF-BZzg*9qc@|b&ppV&i)8~+I|1U=@AietFp3N&WO6YI%Z
zN}k}A%pnrCW>i$>NkZ#F#&cuGfRh9QDFs^R6fI1cowc~BKyArxW~KLSZK(DBB+2){
z9vLo`OmU=N84+^C;X|ATw5z2IvvGST>hN`(gC{XgP%I#vV~vLhH#bh|y$fVRQjVnd
z;7dxEhCim~IuV-Fiy8?O?V<?3hBU3#feN>h%4U7_{H_AEn^+C1$A_@I_HIp26O`<z
zwFtsLs~85e!S_5Rq$DZa*p88&Gk8|L@J{DsKVK8z^9*YFNX47atJ<-PjvG7I=BgD?
z`C?CChGT9$W5ut*&tpvWe+1|SsB7#u=}1BpI<L<z#3RZ%kcD?aYzAJp7#>!kJk<>R
zQ7ddqN3ErWP98(_KU7i!$x9}GPkP0B1{4vdzT|3XJXl92ao4T$Pj=T@!^qW%jxPmr
zCz5COFQR|!`emgOwmaS8skf=>t_|FKvmoT{F0Lg2-4iP9zjedAbHt^sMW4Ko#yl6+
zd{lAzx~ok{senNvY#V|U;tJsBdO~qJz<?lxe?DCVs%6b|$|>~3Q)DrynS1b@EWJZK
z!|#8^4!z3~&8@cWif%VuYcjTy!f0ovoqLK&jFB1q;%Ll}O{uCUBu}cRsr*f?531Es
zP9O5-e7<b5P2ISmbl3Bdgrbs|_O4ojC#G|etd%5~nROU~Iz@SZZzrvMVyo|RyIjjF
z1Bw09L2RcpwS)Wqlq6{~d;9}7Y)?vst(*x^*_^wH*L8+8SzKCy)j*gfX1_4){9%jI
zxtFM~xWtr%BXx?+C#T){^D;t9+@ME+Besa|x2xKBb*yUik#t2=<pGN1U&m=3sFHVp
ziy}b;!sfkH^-;40qU*LEvgo<x*VMVo_n0gDOQ^RkTV*`!s{qU#d>VE7?A3WH7P1i6
z{VhF(^?>$gWP?X}4{QN;$igJS<#W0SFkef^Lg(^_3cBhZ(z7gC$QWdr=KC=AfXkMj
zqNV$9l=Bxx;wxPbj`2c{4o*V}Zm2d}vP~jtJR1|l1E1-NBru?OY>x#c(u4ug#O#N?
zqKrO_Bk=d6hF0UaE#))|taQX&yl?D^hZk%H{7D6F3e3IXaZ9o0uiI{wp{<M+e+)i@
zAFd0O4x-XZ|CZn=(2GR<t#CB$8wP0zOPQc_nW*_Zhe5&~i~_Dj3y<hL$F1@`U~h|e
zqLpFA{40_&u2E!Pvg3BUaXkuBO))d6s`({UCaKF(vfHt~X54ATEp4%w#xc*jDt{;8
zrf9sOQq6Mws+gOE+ri90jGKmK601@l^ClCxk<t;2D>EJKOQapOC!7=(tsZs#?f1o$
zbFGY>#T3u7DtbvD4MZbldX%1q7#n4IvBlWMco{9rcRWqi>ydJr3H35hJJL*9`W`al
z^o+-4mlcW+G%EfI4r7klgn1SZ8YOtEYqDsq$F}U%{?P61==|5OKLqG!)N&7b(Jv&o
zUW`}Ze21{EX#PP5K6m+ML8|+B_J`&2k=G>1>MZlQiOENfDu^}448OjM+*<)Jr1%ru
z6g4x1D^Fmie;V6&!<#fT*Dq$)w+nc!W&r_d&%aX(=E22yk}TQOTWXpkFP&tSHxB%S
z;Y1brL=+?5{h}7vvtLYd7Z^I5!4PF5QRGA8B8$@2HRvMXG3kladK-EG1g#gP?9znV
zqt9qa9d7Sl3N}g>71m{mYRM@HT$Tog7oMR_d{HcD_=XSN0wb%M@aOfunC-;C24>vH
zm0!#*{b@$OvHjoPYh8)_oG}L9-^;OO>UM2b=s2rzW)TM1XG#50z+g%9isNF1Nho5k
zMw!~Dz=TK=ebHe2r&<qj0_Eyi1_<vNLFKBp_c=?R*J~YfSvM3~bqwPH>uVDeV-r4S
zH*067NzJ*#xoLgAW@&x&zo0g?Tu%C`<k)e?#(JjWzz?QpoutwF$9&(*{z#sIcWTGj
zPVA3D0Eu7jwdBxAj%>6$o29cGF8RJb=#Xl`TQvG7KJB?mb`sqLC_S-ko#QQew&6d{
zaJ|XXl-xgGtbfr1a6~dL?VyvjHGG9P+eu(_U%BK-j^xEYf7=X6Wldx}FC5-#x!3D+
zt$t_V<k-v4<er~Ncn_X-#Sj`u14^QMFQuNd@fMGm7a1#QBt2FgN~Uz&7LLk32H(+U
zpI9s#fq8{lzSv5SnLz0TH=S(H9ef?s(DQ)HdtIwZO}3%&f6eie7bbfS?HqfYq3Y=N
z<Y<R@g(v?&{bskVL#9V7%`YZy_ad^L4s?_kzk&O7#JToYQH+(0W~k2Q`VtNcmdsi@
zC4IGOm{g!Ow7i<$Q478`H#CsQsu-w8eHu*2Wnm9KR-nTYwJ<T_)xqN|A{O|?5^)Be
zn=yH4T%bX^X`n}`6TsnX#A@@-49ip{H*j%AG&1ZnEsl8b^@(dtzFTKBfjRCBc4rXD
zku7~$-mkiMk1H&rTaXaNi=2ij>-yu93kO&Rb(_x)4^~*~dT4jsXlF*}3l~O&k0H@9
zus&r33c=|z5;;#@M(N2nX~;Rl+xOgdIcZ8VZyA<a$IDWFccSHu#G1T!4{KEO)b&^~
zDuS9151U3AjvWeR+0o>Jkv$z|59fovKgbSe);}u*^f;lP+n=0AyrXFGaxU3gM(^%z
z=|g!{Trd3;oPU%IAdYJ1igxZ@SSG%6mBV2>OYxetBe?6>xJRMHuIery#@<+6e}z#x
zz%$aE>`8Z@+{##Qf#vgv7GdH|>j&3d;Eeu!?zk4yHdz*MKB$i`M=0rcUR0ikj^vg3
zix8d=I|@(;u`D4>O@hSrhPW9vRN_^Jo;3yxXB=%YS0tyJ`WcuOI>p84TXu~urjep1
z`9PdiD2A(p*O<(>yDXn#Jw42wPB2wO&n}(!+q@=DK3XM?2xPZaZQ0V9TJ>5)EPEx{
ziEkP(9cV0Sj>ZyswmCGuQSF-EI%OPGg}|(LXEmYd(S%buK(Spr8E-fEZT;{E`>>lY
z^aaQW&wkZD$}PgCM(DMZCRH}Y^73am6=g0&q958pIz+sA5lz@yL|wr-&dNnUI!h_v
zYw6U_f&J_KE&r4hl2!#S4{iw=YNZw`zSBmu?_i#P*(O-()u$ZWyW#{5^$B!T&9Y{z
z%#eDC*#cYWnc282FY>A;^emL04x9ld{M}zDguE~jQg~{uT{SWDN^;U+qnK`mmKYZ5
zKXwa@QT#Go{`Q+KqKR@-Mf0@TDD2$IiOVBXVD3`P3{nTTVdFQV(<9hw=o^lXrl{b<
z6*FL=@Gr@~gXyGxdi_MnrM!LJPuxdhEY~$u>!iaZaMpUrq%JGh^pATy6F!6$F~4%l
z8aQ>bu6s{v3!)}VlI9K}vK~zlBV-BE{UjB0UK9eJHb{7->?co@cJDA{s=dYAkqOFS
z>??Ou?D|dPVo2C=lXmMq9l%t(6$ZcVdv&>IJhqPT;)-c8y<_4PZL1+8xM+iaNF*ME
zDwuKh>X!ZSi#q#CAnD<?4Fmyf%W}R6!+!ZpIn@X=W#WEAF4|Q5eM@E^ukyLM1q=4H
z@}o7+(I1kc8Be04dW6DZU5W)U{@#M^vVXiO1;s^iKau!^p$O!2We_1tr+mHK)i5<Q
zkYjes-jwHba}M+cH%<y<{LK*qxi|QhJY{SKEg*Pl<N-6D88w{Ro*@5y`UuLrhwYat
zzuU~F;B+`>gW99AaJ2r4Ciu<i5NmyE!rELB?4Kh3!z@0;0soxk*Wkc(Q6%r=j9zZ8
zVt~Cm`(sR@G=CWE!1VIk!enRh^IJQ?4HDB$A*{5XX3@rI)kD$u*8?brY=jG;4(*@#
za_m2kUhgAMs*0(BQxFBQRD|#ryIVh6#zqP<_59?jt-6HkO4RsLsk@c0XQZUIXnh)(
zn@H)%Ny}+f|5F-5`47jk7L#}UT<_2w!?ojkgG58WJq)wzXG1hDlMqayNtq`5?)Hm3
zM3v%cn$!9T9Yr&3@!OM%llxprqNmd%#z7Y09+G2v>4lNw;i~f9j|#7{WT1A0GMe-X
zRCVxwwI2VY^(RFFJ%MlR=}8wRnwf6D0-1bii(sJ?phlX<?jJP5)`TEE_VHU12tijS
zx|wdf0XdK7x^=q?M<pM<`ER=9wu8Nx6HC7X`<!ij0bk@s8i+nA`Y`kR1!hxM`Ofxf
zThmvu2OR-G&i0;-WERllk^T?M{T~X#W@NeOu`bIQ{IwgBhgUlyXw^UeF0W3S@J@bC
zRiyB9)jxgXf1j2_or0B;jGK4)PpW=sALe#L@*k3Z9OU-G96%KIezz3;9}0iD+Q-2G
zgjmeye-0Y|`w(xW@K00Tc0;X%|7`PLl)zyqNG)Oo2>{^2mgs+{Hr0AX<h~<B$oe@>
zf{Y1HNPp3BWxRhy@pbK}6eKUR+djwmY!8CF&MM8eUeA<sbOs>1@{KXdo-KzYfqt&5
zw$^ap74v@qkl;4qQYo-!1D2cS7^j=>#F+kZ4wXuw!Fyqif%<509)vGw<@?<0qMoA-
z)UuH|@$8k<TDDfms1XD@G_543-UY`18bH~c2Hp<4Hb4fJQ$%8GizWf<o~NCWRR=A@
zWqd(2YqjNg{vWMkw*fE9tezR(ExTeM*cr>;)rF0g3s=uqXhg>Kj3s+eyV|zT7#Ylb
z(@B}P0gCG0sr*Bdsgd4x@jhV+!>`K^KDA?&*zLJ1bGxk6770x{gvs^}&erXFM_!ii
zvjEhb8Gx1<2-u>ymwDQ+>QL5ntUeIt%+0Y-^$@qx6xwK%Vy)M9`h=}hmf&LTp6_hK
zztwBQX4MMhw8Gb#Ouh#6+1P@f+N`NTYe}sgRylVrsaXvBr_A`RjItrSncT%S)>R69
zYx9oYKyOnW`LL(k#_ijF)vM$eX@o5q2av9DyiC>(AhSs)rz-mZdbfgZ_?FyZSf|qv
z6i)~O0mhe>JZ5ZSy&bKW1$zMf)caLTpy1;Sng93q;hXNyN3`#3amB3wq>Z(_f)5=8
z7VP07)~j!Y`eSxn2yH`#t=mbm3YS};lACxSd3JH-6ospMc4pjSnaUim=MP!{p7r$h
zQfry#Vor;Kr~)Y_BAh!~E;3|KE_%x{og6;sbJjh<2#;=76CFU_&6*~sK0O)tgu8SY
z&8sPg_R(+#?X<)U%a3behPH>Hoyk{JryQsaJ9)bv21b@GtTbE;UXIRNpgH@|d{>g4
z%BXgZCxe!%6$WCM6^eoRfi=8WW*xP&WJ&-ysjc-%<uvlF^RCUK@CFqNfb(wtAmp&c
zp^3isv>Mn)_qZ0V<YU(J0#GJ4O$lwQ^!`>}R#wv7Hq^yFOk8-WA0<$iDPco|d0>jF
zgpEJYe>7H;ED7lNL9sUUeIYI*fBK>+|E=IORrS?P>KEuPLH!#MuChmM-A7fTyJ21a
zayIZuDZM@gMYM%SkIOgdz$27fi^pgb<|;4AX@#7q3lePE5%#e+GH>Q8ZXzMZ^~GDN
z)BY_}J>3fQA5Erau`5BAa2=94W`*vs<Sp;b0v3U{78&m)Pv#WGvXyYq1&oc_B;K>o
z_2PSmmKfx>6dcdv@fztJ$ej}>AyQeF^4_e2S`_=A<nV+28?4!fqxwwJi(=G0-%OPR
zwu8s=2eZxv4C#EDv+wa!UOCBr;QUgjt7WJ{$$0}Tj&?-$(MCs=Sre~|ME<s5y+f@v
zvtE1jigb*e0qC7J;O>%Lh4LBUg~^ERbe-^ROQlC0ilxUimb^g351R-*K*uVd3i$kZ
z-%7t4Le+N=#Cn<cqWQhVS$DdxO;<ZyyGbnLb>S%I<SWUZv0RTl&4$&{<rQu#gAq1E
zP)`8>T{Lpr$UDeBgCxX=efU2n6?Cz0;lVkeS9HN*j>Fg@##^7!ErsRpK0T6;hnJEN
zWWGE%_tIKV_jK!AeE}V8ueKD)!>)QSuF72t3O9V?`xc>3^4=}U)uG(!!$h&$iXFcV
zO`>~GSblQ*Xk$8W*{b^k*Dujh8V1U}2>PDzkAJV)7GUDiX`V${_kqX%{CV7;Zt}N?
z^4ExdDj~cMK4W7(SNr#ooAD#`HH$S={5_Dg{uM}C^Hc`^K9)B_IDu`UP1pUM&<6`@
zlG9*SLb|`jjSaLZ*iad<(Z3UF_$%r*;A=YnowuLN2PH;ShQAY<viV^Bf2$G8W?);u
zaj_ADE0kMsnU(IW<BdI}Qt!>WOYQE@a~bM2r_)g+oHl%4d236NG8vlmJjt*AdF)wW
zeVg|$b@{<I-0;mRz6$21Dl2K7ziFQZGB-&7J#Myu`QK@LK7`w8i1{xiq<;iv%_v#K
zzsGBHI)9`rKc_v*cHS0S!c|=@<eJxE`7}xZR}c5EVf5=|=Z3%jv?Af2mi@CU8le5b
zFYhTEDN%}*47jQ(;+$pLU7Yw!5NKL?psDI=NLD>s=|S<(k1*x%@nMx()VoqdAtFb8
zst$276AqrBImcrLo!M*^&SFKR(SBb?;9`q)M3fFwU9!W%0Aq+X^dpAOu*TpTQeCa_
z^tOT(1vVj#%-Kj0L1HWCSKjeqv|4P!;r^UNN^D>TwK}mYy_R4iaW_n0Zzh>nuku~0
zp3L*)vf8v9XkaaxR*bVy-T?~)a^aCBvY#s-yQLg3a4557bhR#`cXtHXWLX>8h8!;O
z-f<qwXgFMMCW~iF5t-ss*+wW_Qo54~iVXs4n~HQD$`Uen?*KYT2@%!vJ5@}zhzT;@
zjzCK~li`ed=VL2;Pd?en`PwE8y^;6`<R<znu`6Z}v%8j8IMXotB`Gf;s-5BXuP8|0
z)O6~Q?{LWgw4}bO;s5a(s(W+ae@pE{f&AMHc+d>ACU*vm0p!Hw1;K1ZV&2?><@mMt
zZPEzs{A}P7T0ICgY%FCBv6Guzi3?ZnY)Ts6wD407WY(7Nth_Jd%xyqabqqe}(*oa}
z0-h-9lCS3Pe!Mi!+xX5SA0-TFj%b0HE~#B?G<KdX*M3|EH*SxtUcHUTZ5@@m^)B1*
z+GuR8=v_yLb5#(2#Eg428#Fb^e*F1pI@eLO{kLrBenJXMYaz|6X+>{KQ2j0Fpmi7`
z=(RcKddpo3P43Ifqm-A7#5z)+)!^|%34+N8h7enAks&1zwkYg;UT&(d^L*fr;2M1@
zh%&sXmX<7oXuL80pwPT6`zF4Kq38ieYbN>W;V#HNwOSuY%;$*^!L}$*-wd$$`Ez^V
zXFax02l4uRGDoAcmK9LolGMH8wHV++U?aog*<@;)m@%*gz~1g@$y5(iQqt069Q>7-
zzISQ;;In1ILd*hepavdusT0+^-rh?9Ei!C8p#_EBTP4gNUoDqy%kCXp*VgVVjUo%9
z9px2g*<<W)IE-?($f@irjW`&rId13TfBgyRr>^b0d0;9jayjwv1l^-u(>P~3#-|s%
zt9yO*9jt0R=ZjMaqefkI%c^;xz?!}rWoR^70D(GEQWeU!GZX%>kN0jV`Sg5yOcNdQ
z8nW+)^aL%0q9(N5alcuuzp4MozbsgsRao~uITu+@P;E|m!N$CKHYl$^1||2~EG73J
z84@t4{Q#wA>&SHMvb3S)KK_n5=%XUQXsOo!Y1~YB{!QlZj{n7f54LX|94*aw$hbEl
zYN$di{0)K8ZJ>7?$e}3DjtsAH61a?luO(@mKgl!M#~ntr?*dnzvXqe(5p_ykntdxq
zJrry*LuD(&|Fy$P=!_HO@N<H3Xut+^KXt?Qg-Xj*OA6m*;ubCC;9Dy|H&)h)$JH4)
zHVn9?ti3B--E#n-t{W03f_U!$A1Qa{?Fn}wB4Crk`74MXeLX%x_J+qbp_0xS5ZT0I
z4~j1ooX$KIQ}cq@1w^-7gSe>81PpctEEgcH>t(%U7+I(HSL6Uyf0kI+_I3u025FuQ
zctA2ASs3N?rgr>l)lh&#?JlpY*3D_Exao03R#t?rIsu^SbUi78*doReRB{0F`0!#|
zTj)c(c8XTSILKXq!e6()g?;SmKpqiiqK_uQZzU|l7Mda22TYO(EJ)n+I*CsJDQe>!
z)ENQV=Qa^ht4wiAPljgu`Pf(?m6p%0yIYdW+bef8Pk`)p>yPgK%B#&ZY$Iy?6?i(k
zisjwvOSY|j6X-AvA3^@1H_4;^n1(^%DVx+}BkD(T7U#$)ZB<B+9Ko>A3NkdhmoWx(
zEG_{LAp5&LG7BdfEz1Pfm){$_!-%V36Cx^qTMqW{D!SZS^Hn*H0%Q&KHNNKrSZSmr
zr1|wx;r)rdlu$w#23owWPYzMEY}!f+6<MIMXJ`x1fPkld%kXlB=;bJiq%QFxr5vcO
zPl_&%oK7Y-z%WNi{bR=@I~1N#w~T~8mw-PZyA`wA4&LF!pn>Ei5Fons^vFI9eUQ=l
zD{H~d!QyL@2N!lumG+3@N97@hc}MHM`H5bqo}H?7W`Q`z32F63hm5jn%x4UN<V8?E
zDeJKK*`6X3@XyB}t2SZMB=2;>FH0?Z_JlZ#&a<D1#O{OEN!<Q!F?wP>U29Uu06ef`
zWS!ty8jRC18}%M{1Lb;5!;kbgX_*af&PxM%v^>}AeAKl8$ew(qh-E3-PTbhI`<W+q
zC%jr`mIENDxd`3wj2)}Id`M05BjZaB1esPsU!MSDYj>4K-spM;ujpDI)ID6X816ZY
zteZU)*R>frtD*@*KBCp0RZ@<EG&c+z-#OgNiQqDb6M7BlBa{JM0F?(ajLw<Rud6kg
z3BPTRm!0!Rft9@`@?TpFo`qn`TvVK9x<zPH-_0|qzjTDc&(O=LGcjJnKb8G0>+wT_
zA&JG)I0`jxyXt;-(?wmffxwFrJ}Ql;;-L>OY8H{L(E2Y&{v6@w3#%$dl45wdQ&1Zx
zINm8yKeSmLa6u82sKF?oZX*@7_g@eBbA!Ff9k7Rm$_ttYZz}M+)oqfJvmsiPmAx6c
zkr#U<rBG<Vh6xro70GmEdelaY4g(tuUidg2(ljy=6@tWK9O^AbDfCqfzTMFQRWPr+
zMMjAyoAaMY9a;tD`|BE=^Xr?rccs{q$E+OmxPN>d8NePbjQ(B5Iud!zt#SGsut<IE
zSZ5vSOWyE;%kWOV425}W{UdB7KRW#gmyol@&?pU6gOxmV$a?jl!vOL(VF58!{SI2e
zPE{6N)99VcpNT7LV^vvzj}xdIn|Qj#)Jt0L!`PQ>+fXm51u6Dv`TB=faZ0Vky!e66
zTNGs2Lh&+<i$I4~D_hA^w_cmsWx#C#NVWYTbe>~dW?5fH9vZK+S@20G^c7Jt2LWr%
zSGCUCSj$0%;V9&oTO2v?0P3-X*OUraZILARi6TiGBfw!gZe^Oqx@PPw=AgwrwsNtS
zk@`1rN7sAg-LIQSt=fn$;>TtGNO(4R<)`=Z@i*^&_)>jhO<r+>dqg1|Cgj*oFmdq}
zTNn?t>W_Bj(RjUpd@CVrwWo7IEixBKGSRvs5z-!{S=M$UUlBa2{^j$SO`gMWfR}?7
ziqpcWnCxLMch!83b%~y9J7`m$q<6#3!i@_@ea!tqXa8+tQZPEd*5SZ`UhJxww~7kL
zgOR!2o$8Q9iLJ$jtuqnT(J&0gw{$fGI38uJx0pH?*3RkctY5bhiYw876xdYITw#uG
zACO>s92%3t=1Tv2Hk=D_7b?uEz?(mdoINuo)Uh9E@?o^Y)m#~{-ZdGy7*xnJt9r%^
z{q(o-uF5NQhLop3leqJ9cTYP<?{6Ac0M@02NKnzMU#Ch+LL#M_50=08FjKBIU0qcx
zZcowhx<aV#0wngKI}G-2%0^t?3mODw{`~M`+WK^?U@(fb)XL8JY^{TD=W1QSTDhT?
zH>JwLpJ_wr*$Zl-w$;U{P$<pMhjA%UJ3Bk7kohD3?EuVRrjUKgPmU<>S9JAz(@07U
zHD8qf3(ra+LD5;ecqqm?W34Q@aDZdlKfoxIApl?gTarCx01;1KA1xKgdU!!iN{ID~
z^Ix#>|1cK*i`3^ZZBLWz{N4M^l&>ui;HQOupT!W0k#K(AgnE4J<UeLcN2X0>9c8Ea
z+l1yr^O?dg5EOM=2CQ$PiT7=|xBr4s|A(0$+9yMWZbiI*JMm8#&EJIzr4y_t>abjr
zk6i8kKd<K=qvgVePH?xJoh@YlXMg)gQ^sZ%x~99*+gr>1g)RRtM<)X?4`eCqwZ~=W
ze>?jZ{5%bjz6L%sjcV@spL~R2op8aNV3Yr~D)7GmU!w3JiuoQ|#p3v3ZDlm<H?1H4
z4sHGg^EslKn?h0mIMF~ynJ5iv|3w$UzfV=TO|{BDs4QfduejRUvZyGvNcRfRIO9Rr
zB+#_t5%WBdFR<nE4;#F5QaA-IS1mS5K7SR|NlT<mansB=FZ)Ph9dAJeuOvBDvLo@W
z_LQ0~r>hS6KD@2EIM^QvFdtuv*faJ(e^TqN*sH5cJ2T@4u;S{%7<R`L8S#-5hdr$?
zco4XH_N~P$G_5h5!=`XPeY1k3C`+;=F*q`~5;%181OYJW|H!PZU55I8h(+)5?72o{
z-zxYq`_M`|Df89>VYc3x3k{XfCDiSpDOg?$G}>BwR{3}~5bw+;NozQ8Rf&yOwQI@$
zQ7~J8-`@Jb4$b!C&EI>Pn;n`mOW!A|{wd$Y1m*PGFZ+)ap6AE=vu4b;P0XU!)!}^W
zuE6GugdVH_<mfDB?1c_Fo6vAC$GuGyC|hf-wX!j%=`Iv>OJgz&TF&@jZP{wLKy(WI
z=wzapti5Gn-BEH^X+1e-8*>-v^#L}?oDC$IFE#$v^1~Ak1MLs!^Xx<$?Nsj{>O#L=
zU%LH=U@J>NisjCKsEhi@SMS(<<rLLZMsU@msf}8D`GAgNg;6!3kd9=tRl?LKKE(2D
z74JhiZLV6DLbj)<O~*<j@lB0fo<G;;UHYp8mXWUxM+)hIeY4A63a472?BDDEN5+*X
zy6HO0^JMhf+5bP*o%P56&<U{KQM$qRAM(oodGDCFtZvr2I{qK!=ma!U+n4>dT=F+N
zgJE|>Zz(nZ`)J{Zw#Wy8)&gyO&R(SZ_X+jE(O>AnT+FA_&i^EMAPhFX0zTva!(8ca
zY+#*W!N&O^f6Lr-it^ILuq0>VCs9X8uaWwjlmF+f`o!o`yCr7*M9r-+1r~YkzMpc$
z0$sz?*5u+3HnN^mhLn2#!e{FZF8LI{<dS$N+sAWL##~GCqB6`@H}f+5heLnymzUV^
z)BIcueNjiq4-8FtN{DewYbAbl7qdz?eBrV+q(VXq*)c@pBDE+L@4mDCLcbOjERoJ#
zR7gTs!uND{8nIH*aIo`bVgRuQKAGIHm2uJW^xpv+UM7EVLr5{GJxspj&2C@8i1;ho
z8(d#@M(%s38`*742u;ke6Y@#R!lWQ$jOs@O1;I<4`Ll_xLu_C4!DzxrigNHd;4txl
z$HPHV$;I+>P97%>IC?ZU;eHM$rJYIOsfk^i_bK$lv$q1N^@W?);R`%Vu`Ms2MSPsB
z>%yNC#=aMxYQwdF-NiO{a8)k=27c4Ih-q~ov(1WIBo6DR*I>d(ba`$fB?)A9I?5R}
zu7N6*V(+8NZ)yU`jBbd?uiGk-e6KRx=M6@2GRV<@d)I}n{C1W9HIpuXg}*eYaX0b%
zp&L=CcM-Kry>@#v)!~4hfTIPOUsuJ>rsO<%+AoQCC8CqR9iF2=z7N$1iagNCcnf9K
znXDA#kGv-so#FGGTGnzgKtGnr0qL#yBJ;;MU>h{e8{*FA1<vXFK<o1uB4jYIE7bp}
zr@%4C|M+FC4#D?*w%Y!gd{Q4&g-0%QpP{xjg#Mgg<;Az={zmM!wSFtCDj2Mg8I51{
zjLOJ+PvN8h-;p_L+jRV~*}10B4*a9;lQnnKI!2}gznpMgm_I$6Ga<pU(!UMvPIzF?
z?{F|tV&l7VyZ9_-Yauv}7pOQcc0$G-4`23tTsVqnASkl1IE-aSTH3M%%_*qHo8iE7
zfQT7_ZxFkBJYcv^KqcAP^^m%)?*I!rK$RP@k^&F1C_bnDbPI!eXC8vL#14lBWE_u-
zkq!?QlR7*_%s^Slc-N4&uXB;%g2#&eY0|^oXn(4pJC%>iEi&`*5bQyAR#e3p0{i^J
z*7C9KK(Y$_MGQ1i--=4>MOJlYWV~-j0I{wE9IFL@{PmDsQqz(TE$#9$9IK>!K%%#>
z(MDPC-vgWeJ6X+oCu|n<bLKpQUa%{%FB*7^0Ms@VQs2~}_r*cNd2UUj8n#;>(T_-*
zeZ#9EQXI}^EG<r-(8m5Z+8}#V@2P&p;s&5Txv5Yis;CDQdFA=13b!Y4D^T6^A4+j=
zt>6X?t-8;|)?`_vJUF}-R((nWf}Y3rFGro%c4I%b?R)jka)<lVxZM`N-<*S>SFf*P
z*Xy~E@c0oza&9PJ-X?tqis}apR`No&i{IP99kFakCjSs?&RNK8!KzGkig`mW0WhRd
zvZ;K~5dCiR5vdAX-|Zi+t9=6DOQJ=vDkXjuxBoepNWWOD4FQ;`1vL=%^>fZz|9d}+
zgH8OBdBz1J>b4I;G*PcRK;&-)VwGe&@K&|o8QzQ!_x3d9ba~P>{elwkx`IH!WkbDI
zwe5|k)XF%D^L3Pw5`_d*r^P75OKc}9O>{4rWlPjY03XbIITN`qtjXVsw5kHF=~N9z
zj~=;=FE&&8%bUuo7}mppOr3J}$lb&Hx&ds_{5Ul1`8I`Y4^i{Nb4=jw;K<!c>p;Ac
zD7oH)t0gysHIc+W_8Ie;Bi7uxV|0)wIck0ZZ|)2gBtyA;L&*z?i>~UdY6ar7OA7h~
zwHo57_~cyUvAxDK_iY%FSzcg1FQXY3QT3)ILV+qTCAs~X4_1%y&AE$d@PioHUY@mb
zR2OyQ?hz}gmQo;T={IBMHtWiN%S(h8$TxD{))k(|dnM9+a-E*3+TOa>{d*?|3*$oc
zr%Gv3MN$k6j?Gf<_xKZQiD+7S$07xdn5}n6^!r(@bn7u1QGN5N-+qsd)*LkMANoY1
z;_z=869{<W$jg4~PN-%Q)v#!+-O|c=rBJ<9uQf%-VkjM0+>RdX%A{A)(Nh!q&4AhU
zz>@0KXRr~`;l@JF!;`kBSo^h(rXVgm9C8OCEmUdp5p3Sdw(GZ}T5jZN*?ukOYjUrk
zyWcq1TK)TSIb32QMnQ8k%tzsY2AyC$wy=EvM%+JKw_EGY=Yy(B8uEbtf%(OhB&9@l
zQj&EB#DT{>Pojo;8&MrZ+7Y_%qlD~X_r~yH+;L?&=HxBbX~VkuaY7dPccBbzyvu5o
z)6Eg#;Xia$ESn$DVL>yl2MV%Y&jT$#d!wGm-oqngeuy?5P{i5hWLQonz?a#I^`F&!
ze79Ebn>RIHu;ev5$#2J&f14-!GYabqb--pxy1tn^y-M0{L?e&bT_Pj(7TSZ4+wh!K
zptD>6Brl!<9-H;Y)3|>cyRwmRm{m&$hD03V0#jYaC^pviYn^6!t%}n|LfeL9#i3dX
z)pQ%ss>gJH6)5ERe<go1l9JPld>j#Ldpv+wzaE9R|2`?p22<qcHE5+(i-^zZ9WM80
z260xhK#S`g%BlP-obAlPpKX5@YP?iW8g`spnOcQx_SE|8Lmc50ysZC^xVMaot4Y_t
z6G$Mq2bZA1gS#ZSySqD$1=kSVT>}J{;2NX}?(WvOOXH1o^D@uOIsa#7&iD6ouT{Hx
zt=hZptM1=*6-Og$;sJI|;b4ff!nIlI6f?&=T176Jx7rQoX1?c}zU}u9E7~0$m^U(q
z1Ol*+*`JQz$ctF=7srsA#y@ZaQ8L<RQ&0bNE4?=X2~=;VEP(nmfAmQ!4D}=*(ru2;
zSZyBkl$g%u8TxlFCeyuZaA~+M_<d)th8@elryb2-N?b12G-EqsrTyFVZE7|1pBlEX
zJfk>A>G7lB3_9`%67M&)>z2M?@g|cCI41ojWI{xRZX1q%lbNGFPXLHLlR&X+Qw`(i
zqnW3!WYa7XXhBqnyGlD+sT9oGc?-A4QW#;Wz`)*y(Vbz$i^vi8BZ#U*L44w*p(PCO
zE{SDX<YrF$w>g&ZoiUlA*P#T{lP|{+Q4(7+ZHrZoAA%)`A1^wo2%K+ya<BC*GbGn^
z3Ft`wo>f1b4=~ZCcZHvqmrugi@vt5KvOgkm#P-zA#QljERsd679v=Uld}yTz8b0E$
zt}E}aDE)Qoo~GqhRD?&?QQf`ryJEJY8j#0^k2ZHO>t3On?wv09=Xb-Bc5UC|HiTv4
z{=3J>47miC9C!7#BX47MSh5VLW1{_d&Q-w-UI=qU=^BWZBy4n-=)oCxp!5Lb39t!D
z;Bun5k#WNp_NurDI8-7#cdsw##|Bp&SF_iN2z7kS-#_NzzLQ55dG2l7O!md#GrHLG
zZ3O|Mg&E~EeMFwRhYTm4oIwiBLc1EWXpIY^O{>+`>%4*Q^mIcmngA<Dr%aS**y)R*
zJ6k1gGp1n3f}!b_1c(<bXVli)b~()0QNi*Y>M?wc1oT~H0voZigx~Z<79?xKPD7)7
zg?2ox0`5)0<rTnP1K3!#&p`AD$EBE3$FPs<l&uK%rhfg5&yEL&ka^D0m`?i)<H~t0
zL}Wpq;KFP#5Ja;sG%|83_cRlC1%kB?F3PR?2Z>NU<~x>nKqfjJPpVt5xTt>cJff=!
z-)LcY>Y>W-5xkyN-s`y85dzU8mJt6CixnPw+}mBh1YZ3Bc&)de>$CodC8Sts+j{8q
z9BED!UrD_sW{~Sh13w#q<Lx=NuZnXYB*2%9RhLz*moDJ!q*zYjsYg#Rhz=BfCe?JK
zR}FbT;nn>(-C$q96T1w&o@N}pWdIW!J=XD5Wr42>qXPDrSGBE|Q~gfym(MdcoTnHj
z{we4H8t|!?Im>!g%MDBaBM#aXx{?xJ#KFo@bN$l4bbzz9UBEvj%KR;Vmcc(|9&uRH
zE!|&=&cUI&;FS05HoSu-*RewngoEyI9U@Lk;e)NE=RiyqJbH7La|?9iMZ1i>OKrAJ
z5((#23w%%oXS4x0gF9!c?z5VUTIM<DFbO9|#DALixPbSr9wx;t@*#`aN>5O-jCXgn
zr?+HJH;w%hxw$$g62<x;sb^S0g5P5?Vh6jCH7$C=eOU8JuvN!}8}idR_<2gtaHHAb
z_VcMDxU;i!T8>C~Da-j}31pxIJahOdBe4OCkcTrDT_w^~$vT1q!`AgJO`+Y*RgfIt
zZ83n9Ooo=4-tnB~>FvBP`$F3d{`ysa%)zP2vQ`I=@Y<1Uq+4OJb%)0a#zfxk_cIWZ
zmEq1Bq}L7<dMaV)V9=q}*tV$m1S1Bqc0x1&UpjHC7gW-`!Bxw9D_2$yKOsA$oELmI
z-hf+T*Y-Al_t=Q-sP(%bkBMLmFD2odUy!qJULaN$p#`z^o_0Ml|Aoa?c;_W80WBcd
zz*SNSqle{kkL6}N`*|5^sFW$Kf4d;n?!f+<&9J<^nXF@Oq@WeF9SiZl%zC~88P(0N
z80Z|&EzEiC3h?;U=eDo6?PL>POJaHA!|qrBb|IZqV^(HGkYy47)0?7J88s`<&K9T<
zs3aF~$zXTXzI?<0*e;g<`32mEcuapM;TY-w_hCTIs9bzcwV^*fRG!^fZ7w4~aj<<d
zz*HTYp@dhXmX_Xr3ZBCi$Y)mte98LbG$yZkBHQb<19Yn(I9~HVSpb)V33#=T&j<hZ
zr6LQ0y1`-djjSqf3EegykLBGSbqZ~#%-sn{Yg!5ifce1fLpq!-KMuzXTc)ZD>#t}p
zHgb4uJv^V=Z)~@>r63cxvSa#PMlqk}RzxoRz2`36Lj8*&<F)}#W0wvUp!3+mlq4MB
z1r?yny>k<R@5)GPvjaMjOO)H$a)nhLjaI5iD^k>eeI8to;3?BX>rM5DdI|KMYXtc{
zKgB^V83A5$T7cVfyD@JxJqfj!i;lF&dDtQ~-#QNF`Hdt(+5yoeNXtlCp4BXin5BB1
z$FAU}Tzz}ZH!p{dR_$xQED*ZUKr0x~@oTe(LnBBI^kY+^nE=Cj?ReV@d}guD$OAgW
z6Tc0XVx)NPfFiRV4!X7j{I9UV8#`DaC4k@H3r3x;U48?)Ul=QnsRp@aH3L_xBVv1O
zdUEaNy~#&`3k#3C9Uxlx#(AGOT8j3En-~!hk{^LJVKGsL=Zf+&7op_d4UiFm+*^ht
zQ|4toxjm7?A6z!sX#$tH;LQc$C`|`_-W35W)#5avODAx39Pl)SYmFvnLHV%b8uEN&
zsdyxj)LiXD?!a_=N^IBR^?Y9xaa?x{fHa+<JNmA5<F~Vt@Qh_St=fQ+$R7tZ-L?o~
z#CiUv_+QA&lQgBSEiIArIcOg-&lVIPTvA~T44)|>{iY8r4m~T`z=t{$ahV9BXE}4%
zn4ueb`g|z&l9q;OoC~4vSt0_T`6@9&F*2fiq+#PLxjz#@`QxA%bO#6RM!bsWN3sXM
z(<p-Z0s)z)<OMs_@it=$P-Ytvv8MfHNJ5Kk`EafExPa&x5&`%3t$bY|Yu9S2!itv|
zde2yZ*ZY9qJJhJ;^JsC;(D(ZHF;PwrTEO63=n3uWT@tbL2r0%!<8PE2n=9^Qjak)P
z)%Y)vXs3e+YR5P=1qY$>v>CJdR*bH2m|t`>t@W%3P$tvS1(1hx*CDyNm}Hreb^da5
z34*4+Y~`d7>WzTb=1ucHVb)dE6U%rycXHExzo!>2^9oPCUaraAWpig3O26gSA!Ofd
zGm*K!|BamU%vUq*i!(D`Xm#hn%{r7w@^&Py!NL8Nq@uuEe2lR_RIDt29_Fx86G;C+
zch_``3?qxGg;5F>dyuZL>pr8V5E&Qy*IkBUw?kJ)u4n5I<ON{ZzQ=X-2?~%kr(gb^
zW#Cyv#qE;u-rxJNJ!f3irm#dMm0mRNWYeyv=8M%W+w6dgl$L{&jKLzQr{!DDu7W>x
zPQwc<eS{oVaO5K27eIP@VDiY0x|Y>D@_7Iz!cO70DwL~SH^~7y1@@=bi-FB(XBq9w
zar<Aoz^=h-vA?NGgCDO9O2?_{_S8m*##WlkQxS__AnU-da{C+eOMSeLbyRHLqwidB
ze3F?l5)*h(XE3q?Vd_^)Pg*r@0wO|@YSdfQ0TjIMJI2wuUolh1eQ@k#{Yd?7ag}2l
zU&6zkz0XdYm3Rq+F7IVDXA%4?XCC=!a~~{>PjR&CZSXvo6XkhGUCSBtqIg6M!y*PS
zx(8)Qw3ssJXjN(_y27@lL>{?d?%>YzEsnTNrOtMAI`YJrh1@$j$F>{Rma|Mor(wd9
znNF1RE0MJr{PhRSw&CN^z%0ZmW*WyZHU71W95#Nujz}%j&0#Ld)XYp%$I08==-uO(
zbT_y?AdH<%`1xZiRyHkDvanGdZf?gpa%-+D70_w)P5R0~hdBopJI@tPK{D>%n`?5f
zk1W7j+gTtKV6Wea%_&&Y3oHYczq?853}Xbk@L$d(86$x0<uradPfkvyJNHTa$q)OK
zYx;}w!*=tdj(!d5Gi-&C_b4jN<Z|<R<5kiNi##y#+nC<)H!k^9Fj8a(eak_&+NVH6
z%x)a?*txhYN6Zql@IAOUrF5|Z5Td8e{myQcg0QSKLCurJmy3OVmXxCjq3r)~AwfuQ
zu9y)Yt5Yh@+T|-U$LRfypJUau!19DC;p;Rh&c+cHn=<QvXTR&h#JU(mJ<X%UUEt1F
zGRNcn?lbLp%bsKV^<@RGgRRI6PKG35*u($8JTJ_45Q}tRXpo5*CE*<w9^pL=@*sN*
z2U3!r?5}RDISu=P6{SvTsy{NkcJo67Ye|UTVS(w0p6$ASc6*eBP^`u?rIkMDM#rri
zePNfK@`M6!NWOjwMHfMyJ3c^?lY4@M)_94@U~#kh5)SebCLy9BbPi@~8I7V+b73IH
z@FPe~O5YdJWYyHCGaT59lzg+0t1&(0jwa>A+kUt@;7XOB5xgr5kh#XKstqFx;s83m
z270WGdEO1-*En4acwQPoHuiG$!$vwiNmS)P1Hm4k@AZl=*%r?*bS99tIl|PQy#J`f
zOy!{CF^|(LS`rbg5JusC1COWI0UgiIzgD#aBZnDgr)1xWNT#a!cXl-(yAUY~?<w!>
zPpV=cDcAVm=P;M{EGT8gl6mpXg83&cj2p0xpDUycNUIUdr>V_}E{9z1Z%vaYscaA5
z(lyi{n60oMZNFT2HZk)4deU&qyqn6s-8AF`md@{m5IZK<RWJbzGoYDgP&W@Tn_K<u
z8NAHrHY}6wiP?u8Z{F-!K<DP*?abAh8}I?0w0}k^Cr3Q+?(M@49(|4>oylx3Vau%*
z&sMiu92~eZ5!qX7<d}vRHY>*Fw)ifQGmoWe!vFdBs3ob_@xU`XH(NGN3lyp#;CY!E
z*_9O6oBlwm4ZKEsdMN4ap8>STok|pyR#ma#kQ@G%kyYr)a}L|Qp0P9;{?K#ah-r#t
zFvV7-2TAy|u5?V2=pL`6MQbsnw<g_d{C~7C+HM_xH^TTU{Qp_UGRDo{GX7P^Ryvf_
zABFv+9q_yU(%PZ(pC#|I`)!Kwz#3U?^Uh4A$B0jNK@8AzBGzHr(_VC6<g6j$OACc1
zQ8f;2+yZ-1rQG@e3VA@Z4c(ZytvA(t6^x`&FfSH$!!s}^J2iB<&Ks4O7dL5PwBL@L
zxi{mwbm8-F*b*S%;54^S*yY4{`B0!{b7n&M>z=r|3R4J;!1=h*p7Ecz3&wvYH;WCf
zRQWjIW%_Gg2Rm<hb)1K_?VBy&?HfxAW4KqGFC~7});QaQ^A@*@<0K0iIFR!Na(K}j
zvDdl!qz6DBF#e=pgl(>ddgD5*2hv1!hW@S=jvFx4`PLV=7G4oWbY8vw9M6I%$MX_x
z@tU#P@l)`uVUP2a2cT%E_vY@H>bXDL%BYu0N}>Nln0&9wOo`di2gCv&2+8%4$UEqz
z&D~g<MTSPKx3@lX=Vuzyug9PPoM-wkwqh|}WjPUR59bHmXQ<wjvqyNjCa*5m4b>Hs
zu(@f%6EuoH2(@L*g}iGmZH#WFn>6z+$>xXm5WXPy^<ASO$QDU&?Vo|aRQXK?X5Jez
z<5uLiAY!M&`E1ztPIm4(Ho;*wPDd?_qC(Fc?ju_5=h^6v8Q=#In+(FKj#*{B*dLq`
z1X{I70N^?1`@+dxwby7Dz8{#mGC(7BZw2VCKik;QFfiIlbD)X!=LRC?w&E=KJ|lX%
zf%H5tiuRojX4%r&@100|uCWOC++Ml3T(WtdQ<xXcM82z>yaVGRS$|YfX)uZ)qRzwr
ztM_#IW)-3QrAL)FqxtTCyJXFjJG>e%4EzAS$L5#6mR;MYx|XWH)*9cq3_i>Ezg8Uz
zRn)QfUq5eWpA3ZDMz+y({w1i)9j@m_-<T`q+Y~rlqdN2L0k4F)&(5NP>lU^zXX5NZ
zo0CJ=v=?R<I}WVdZTLSTKI0tv;kPG$hF4m*AnGUQn!D3(`$|8b#%-)0hl>l)4GiF(
z-E1=pHNjo0=o-@R7K2yHQQbi&{@xYlkK}nt#<c3o+;Q42qB_iNdY_6{$oo!8i8P!u
zXE$AP6v4oM$B1^%D0aJ(B)+3IQU~v6P@PRM%a0>2Cu+;-&O?sa{&W?{78s+*1e(;@
zw_UH;`*mMo`&@y<<S`ZMlpRc~Fu>{<fNK>H1G2anX4Dpk{9^j?j7sLep;%s&%>G(S
z-%nUk;8;G}eVWs4TiK4G#^-VWPdZi2WyivKw;u*Yfe#N(XKk&pr@fKh_Rsa2p9dmE
zBASk@U(iw#WRh6DUsJ3%C*-Esv21R6e%l%=gk)#4LzP$W?<zf?Pve~SX1h3VE@t*<
zUS684iP!irlCYQqtMBbj-D+*xltIq=VbjB3d_^B2=d1FW*>#fUlsIZlO?LzoFwWQK
z*3+q9@JTiCehn0x6tH=&c(1}{wBj?9WN_ZZ3JN=S9@}UX>rMC*OuN1kUegI(+w}!Y
zp|`)Bp6o?wZIvMnY(e<|8$~>OrXx*1Jdcz*yGgN(w&O(bjf8K^v#)kt*(`Pfg^XTD
zU?}|^OXH<LI?=+xB+N_uWe^*;CmiyyI(1FiPS1N9<;E&0WP~n8^@VUP^MVqMDM>X!
zUoBcGjdkFAHvXqaD0sV6ER-UvYTqiraz}}3X!UcF^L;pz;N6m>uiYAYv_J!fX8YCa
z`{NLkg5rD+b4nA<Po6tNFtx+W;tvI)ajJbQ;9uPfhpHu}t?f25kJYc~V6O_U?!QsD
z9)!CoXv?%+tagHXL||O_M!<WImW?~H1O49tFENIkK$-Pk3nu7kLpH2sV|t|4v5#T(
zZgXE7dT2ZAAEXI)up2^)zAb3FVJ`S~({b;HEIPwYWJrZBCnj>5@|RvcHlt?sKoc|Z
zMOW6uHcTKu;+d?EIc_xuVB{oN$6hyC32`!>^W!~1eE!4yaz@?Ogp9^1jo}#k_}Q;W
zqx6KV!$}!qy4;R4iMn+j)uw^`8#W~4o;qn<y%h^qQ@B!Vd?=+|3ze5Y%Kc26qWy4B
zDfnbV1M}R*I#`I(;<Ub$cD|Fz|CtR>k-t{oYQ`{^Ha+&p89<$IaQFVUghb6ZE;KhW
ze$EclyIL>Qv1C2)@g*3rXNE{c>~p`PXS2}VV>sQXp+g-4!TdO*p`=V|T_i%G2=i^H
z`H-}N^fTs7{7+R&EpM}4%IBFl!APSHC}&Rk)$6nNUDkBIs<<VH3rTNtKF!~I4rp*s
z->d@kn<pMe;Q7g(=1uMs<IYc9kK-TbP@I<_0nzkgSIP!z<ex-$K06~%C)u5!JVUW~
zIxgY%+Rp{KeJ`}5k9&BMukzNx+Wrt&`hBC?`5(Z(>B_A_X%WZD6@CCZDoM!Dj3glq
z*?NCyo&RAxz1b&oSp>~-9}EGziSF@kxYcKnqrNlon%q=WZl;9Q`q;@ggV_S(M{mK$
z{(1uFjh(Zw(ZV)s^kQrQ4px(~8i({a+4n=W0kuu6!b$Nw&<Wm>KyB$Ft?)%;Br98*
ztc4|2FzlwVgoBj(N9g5OjYYRJ9p1Bxf%8S2iOpp@$AJX3ezS*kkaU3tilUJ9C3~#n
z<;3+`!_f@6{FVQwdiXjAB1VPJ(mBz<V7kp|uwvgFgthk)MQi$vIW_}IlgZSHUXaXs
z9#11V>J`DOoB+<RI5CXOTsVjc-4!EEd8ub#Kf9;etm}{C&E=zIQakzXwjtN}?jn17
z1%Gnbk}mu@!d1aqwse2(6dcSpDxz*b^1j&u+vL_jmZ;A4&WH>lmY0Ao@vFumOSaeE
z&>ckRIn0XO?;Qq-J7+&^Yvn*Ht@7e~?QdA)Vhh9Hn#milXpIdQ+C_RNg4uyPrNs@H
zS4Q3>#EIzL_jHpnKryeB4{tLF38j^kJYW`+9Y|$v^#C3cpDi~M_FZG&x-Ixo15q3l
znVp&Jpoy`bjUG!C6Zq6AGqVN%R+7HHs84QNHU#S;cg0rj336#3qyl%COr@(q6Ys0V
zhUS&VlphZgpi@ao8zYxtgaIWVSPKwCXN-Nu!cdg;cdM5d8Og9ycUA!blc2!J%%kx1
z@&2eU%4ft~6)XdK&%n1@>*vJnFc<^JOI!n5i_m>qc528^GmL>|;8%J@UU&8yC}C@a
zZ<-B9uBlKetzR7QiC*E?9SEV7bfuOj?sE4Y-nQmojqU<EQr+93)ER-DdZ7SH(AMrz
zDi%}y#D_6YsHPhSyf&{L8Jer`v^$hlkB>_vm2=+iS#iG4P;(w!3{rs}`1W5?2j2>J
zxUg-^>f?_3lQK5rJ#Z^*oqM2ZIcmdRkMrMpW;;)yd@RjFDP=z#(x&3L*jX8=u|2dj
z1BiUnS{&MTL*elYH+niiyt}D{7&EVNtF`&DIX?D`-Q9!Fx{B9)#e^Dnokos8#8RVz
z_*w>jT>Y5a_j*kxBko2#+&Ub^|1MD^0tcQRWiF}cwS~xE=Ffsa%0n8alwq5r+q<wo
zk~g{I3(P`XO2`(77x5;F&1FZrsSxg!x&plwS{*_i99w(sEAo#0Q{l69c3MJYAGvQJ
zU5+n&7LU5gJfT#9z>m-SDX0OIq8Tadb>3$ENMT)!s}T#izQ|yp5I*li76H3wat8He
zW#692x-h<3D5~i5l$W+JSf>5*^%~=sh;_YgcrTB5+hO$y_~W&e)U6Z`VR2hSRySfn
zOu{-V^H~SMz5wbepHJ@iqXPy3FupxzLzxO%!3Z<%li6n}R%vmeuOb~_WACjQ%<Vi+
zk4l{+Ue8f!d43PdRM?}6Z-aMm*)~c_G+*Ng%N@Pu6Ym;=x9Pkd1{A?V*i!^~q&%UI
zgPP;slv&`nMU|ORJI3@Ularr!ARQrRZ~?g~V+Z{1RopsHe-efWI@AU!6jE{MGK(hF
zL*E3P3K;d2&%T!MewZKeyzcW5ZN5$?Zg&92%mQ3-e8k>rviK7V4EAW?V0__TJ4#w9
zAh*<)za<w5z0YOI3uBwXO=I!cmfCmOF7}J<5&Zm%EEo8I3WGFtnv8FqZuo$b5-Tp|
zU7I&~c%HM*5Vw53@XBQSmjQFoMP;Fg)5yBb>c2pS5uuT?o$Q8lX@~!IYWXh6QmMg%
zBzQ0n$HFsr+?I7wJ&sT<w*M!NnN{ySllL__hU9O1mw<z`#3>w(31K8?sZ?C&6<aFH
z_Q2`%pLNtkXbvi&@T=`kKAaJdFA`uzfYeY|=uHVJKjzw<q$S5)^i4YAPkY%orS3c`
zB~j32pLYBCSHa6+N#ETUwt%g!T7^4!E}gKS)PpiMi#_42=oJk5D6*LnVt0_wPPQ<O
zcF36*a~p&@7IT!GcB&tS2z!!Z($@-nda_CX+AV&5N=~BYM&j7m^#w;y5*<N_55-L8
z{KyWlQJ}#&T0F3I`JL{8TnIajZ0Gw{BV%pmL!i00i3!-A!|RCK6fRci$+Okv#B9|E
zEOzIqfHje7n%3$Zy$6awU`oHjhCQCVj~0Q-jtaUEDeUtKJJQtlLM^j}Bx8E8r2I*R
zguO{7Uc%pDj}Nw}!~s~ABwXyh2@Rt2mPwbR{=6|{4nErJxW0Fbex6%J5LB})*3QL*
zxwP>gG0z_0HMe?b2CwNf?zx2P!8}G7nJWS<-P|;k|GAq3z)WS5=+#iaPIMCm!INob
z_Q81eb0q6HaE1f=g7^-knp+F^;=0SEa>K~reKw1%EX@$djOBf1rItF&uHP8~sR5^#
zkPEi83YY6YB<7G&Dngo0tN=Zclz$m<|H1L~y(?UGyJGd<#n^<h@#Y3`v#JybGIFe;
zTCQY81k7h4Y}tu)i9BC@dJmTF;;CxGqKhe@L%gU-L3IE5Ss5*+-zzXT<3%4;fVxa|
z(FoWb_=^<?uzrhVxD86lHj~&F$^{7P=!(WIDhwpPmfEH>;=@6likT41i|a(nu=j)$
z^-r=rR<7Id$GypnPR@ME%D=G47I4+yp;r(*;5?(BrY^LC^iKx2ZiWPaV!In0Jps4&
z-d+Z@mo9Zv%6bl<u_<YuS*^*e&ZsDN(~B^`#{Bvd8Gvai5b~a>7vtFtpGEjy`O1y?
zb^w?~`+oWbhv#m!vM^hC@Hii|t59xnMf64|i6&dC8!fVFe^5~|{HBl_5E%eHow%D&
zV>RpLTiD2qnh=e%C!&~%*$hV5$upi~ygNur(f-0ZOU&&=;l1N7rprIKu#n$S`U))g
zOup*%Xy7){$FJ`*19pZDB|QOsG_-3E90a-B-WWYT5E5`i(^`b;C{I#ze9gF?ch*(7
z-x0H4s=;}iWz<zzy&4^*lntkwoxcNF0{c)k{W6L!f!#3m=ei3k3i?vIOj}DOC{6^2
zQn8l3@ep~U)@0QUO9mqTRH`O(bM-?|03E3q>V8w&6O0pp-O>pPdr(!ipQW`VI|3)T
zt3mQ#I{k$bvRmI`h+GrRgO@{@bnytKA=ib)^&Mf6Ykk_a=cKF9f%UCEFYv}9ihne@
zku$dohqY-^VhEWIo+m8!vG(OO51C=c39}<aIE(BiD>XCHn_p7)kdz~LvO}~P^py_u
z<jV+z7UXV@9u_3XqI#AfU~Pfy{+0_MT0*C+W3aEGIp>L&G6y=lWT<Xv=w@UBou000
z9L+88Os?j2s1+=Ta*$0_Tv<MEV+gihe}b_C?o+X6J{f2xiN4zDu}<-sSamG`Z%>kY
z$~C1v2$#OTsJ~lUfqL8SXXk=89jeUns(8vT$f-HEBAG!#JmZneP-vwFFlRO@C7JlR
z9q%d!0`%)D1BHLSS7~G8P`m=(K<zq=R-atKnPB~sm~o!w4bOx5bz&Z`OvnnN;cX=2
zQ;u}|R;!I-t%Uo;gG9AY8VL{A@_5KPEW!gK1o3?KfEa*o)?u>h{-cLl-mYm5F}<J-
zNQ^A8u>Vlo$!Jf$x0XIc4xEEXc74`%^&U)YMRq!p^x!3OKOzQl?-{xBS~$CgxsIgC
zLvgBE*!>ULdKJWCqzjcaB#1kR6~`fo50DQl)Zh%S5kIs;ObOb7-c@`sJ8a@^E*O-G
ztXa@1P289w&=L0R_Jha9ICAPxXd4cU(Y$)N<}q;!;!4YLF!vQSc&P96W<)MmASMa!
zRjK{@+hZlp%vZ5>LjY9i@x1mJ09ij?yIzYbxV19!4TcQ$o2~;tz;*nb_BkZ)yedXN
z**xAQKZITfJfA?Jz{hq0*tHM%+YEZdasVXmd16I9^T3~L>>%uKG$cQBP$5HR;5iiO
z+5{Bx2V7fPLTt-70ZtHG=x@H#&`sdzE(L|8<@yr<m{z}SO6A1Jjq{=ZzP=RjuTwi`
z5Pe#W?=RZRd}huCMoHy*V};yg@E+HJO&_Ga?HL-o3O)k`Eg|ztP7@+I1&;(oPIsdm
zPM||6t}A~8n@)7{_FGOvPd_YGmxrxxuzqN5#}z8aEn!-r2sKm5TrdX651xE7QeMa8
z!^{0U(%{I&P|~8g1JEM{iJ&(%!tHU4aIn<YcO5#D?!nKNeW9V}<F@kpD9&$fs+^8>
z+i#~)8d~e&<nYIOBC)-(HN~*Jqtinp3J&WSTOnHw^_C{)on*oo!YC8^ymWCA$7vfy
zX@*@^74sW2eYsPQEVi|c8?MoUCvNhjBIpBS{P1*SVvwCl1xnv`OnYjslm68-@-7ie
z{EcZwmi}5b$u}Gc;YA&xy1tjoR4*=aWOMZNK!E>^FY2)Zcq{x?uf<0jc9C_r5UgbU
z=VE`;Zh!PkwR~qRgnoT{N?R|Y@5*qX!gbGVY*|D5oX;RsQ!O_7DJWgghCMdZ8GD@f
zE3LQMhHvx;Eb;VV`qcAyF74v%Tak97Ez-RGvI3-yNlK%$zzI$$<z%uzsW-!1Bu`-N
z3G1+V@iJ>$i#a_k?clO`?pSlX<6bU4Cn1!m_30?yS?!bWN1+J&ex_{RZRzVgyZL<g
zZZX%ti^I=wGVO5P@Qh?6Q$r>k7Y8pU;Ww=r6epYLc`VeNIuU^4-Bi8#aL8b=8R6n1
z7urWwmV7#el)Toa`BmH;c~;8Nu@Nb-VaLHVDez`Rsgk@<RnK=xwHH`!kIy}WlSVXi
zs8Vl;Mx^FC*$g1xu^h2`-nv=Su8?&%nTe%r{muwKmr`UsIyzAE;hGz8^=I#vSiBjy
zuE_Lq=UA-+N2Fci{n}Y+AwCT{fgp*{;YCaKx2Ki5{v%}j<<=CYnZuZKypI)1eR*AC
zgTj|irdGMjO>R-b$juJjP5HJv<`#LeAeY>^O2mh03!Q81>&aUv5mf#EaiyXQlr>;2
z(OY(~rqcUcm&YanvO<@{`ZM<*ZKubyP0QQM)90D{Sjm~S`>z{RA`~?3MT((=w&Bca
zr}Nhm_SnOGP9AvlAH<BxidwKM02z9d!uCTvQt9D}jI9;re`AVyH;@V+#CG6;<WuN_
zvZCrW7(DsGnuJeMrkkH_8e+}c9n$p3UF=_-Vt9I0_a}XF;M~?;PJ3Oi(nyP_x*i?H
zt-%%3@YRvJs-*>rau!WhhH=Gkb8*Y&P!&f<Xk;Zj4vVq5dz@!EZu;Bn-^w2ZJy1CK
z;i<(NtW9{`Bo*hkZX&jqsuvyQfT2mN>o~L!Zue4qyz1~2sRReK`-#GjMPx%)vdicH
z=8?#&z_H#J7;WP>{7=$a8IJoUpio;cZ{=_Q^k2S+Tgp5}ic<+qD8t{q4gaWqzP~u6
zGbvPp{x;FSI=!b7frh)qGJost{d3ewwl~*g@v)n~D*q;g|H|VLeRGY?B!v36KJi~J
zii1yZ{I3r`%`V6P=OcdNU{16vEF<FHrXDE!npA6HB!%%WV=(AO>KxI4OXJsWS6!s~
zYD-QVq*kkx=;c83>5y4q<!PkB|M~<XN*eETmYbV|v^xJ6m?zfCxA-frq~!nN-~;|o
zLxErAfOE6d3G=<%@!DmMlTAVOWY%O7W}8sQ93Ub#qu+yGo~*htAmv|EJNW-|YOEbd
zeoA>2{{`+>Qew8kO*62T|Ci$*TRQNMe+OJQ<fk}T<fq-J{L;e?Y^Z}l)e5pyndB1x
z!-2f{^d7%?smT;@U1;bMIjCStoEjxEB%w4M<W(qCwcVTDtLV~6o1Rc%=F3|fg}#3q
zVA7foScv*DXirLovO&w=RJn+_ONj$Lc*zx)d~ZVKvLf>0nN{4gyNfPZG4C2`Osboe
z$#C^qA&?S2;jc|vZF1hm{}G;L{oV>EVrD+Y`^8^G>`ZYQj+QnSFb<5o!m*)~e!r*n
z^p8;kilKqrFK$jbGAnQ|D}VM1{kWyb4jq$ur_%{`{k51|_i$58<?(~_&1#(kqk{~~
zSCikTjDwQq`E<21Wa@zB$P=qt93j4{f(!9jJ1wPQcL7r>ptzLFREnUy(lrgnC1aB+
zergoPR9f8u;*i8Z6;8$?5?w-6)u4kA$o-~}>FZ~U1R0jr#CrY27rSk0g#I;6T5C=A
zjlQ0q)>C-Sf$YmuPHyf{b{Uu)+$p2#ME=|_4TRatt;-wT`_N5GmEn^$<uE4kuSlA2
zY1vc9%``4^x|1M6>4re>Gj>$zeh>0l5kjPnbcY>Q3d@i9>l`@IiHL<bxjFNJwNpFy
zLN4)%*xtFVr&7&-(=PwADKTS9JE#-{<9Xgv^Ze1$(kiH_!Q9>5Ev>GWssMz(6tRtb
zf7@mLdT%r7;)(PEk+gPpQ#8?FOE!}R7bcg|Gt=n%dd-e~x2G!!si~pAeleL_S`smH
z@O@a%m%GB7Nlm7)vRK;FPqYIp)S^Sw%<I-N^|umkd7HPL{G5eGddD60%(tk@<%qXt
zs*=3h6pxAw3d97YQitSMsNEdCooM&dkEivGRL9Kml<YUzHM6c{%lTxlxGeD0CkjhA
zP`0h5wE5fX;oz@G#IoJ?$t>5$k$2MG((^a@FS<97s>a{xI!rXZr#i%k48*MWc73^D
zscrfln-mdo$t$AIjjq$Pg}DTt#+XW|YuMJ)aesZ<%I_{<vNGi^-Q~ElJM~Lwh0OZt
zyMVLXBk4;n{!vKC(hoNhCl=vCmR7J1#Sni^{%U0>#v_387syd+wXUkc>QZ|2B<|)E
z_I)`X<1j2D7`Ja8)ND^a`+nq<H{W}h^$b8YEOD#zirgn)NVwsCm>bN>9(Tr*>goas
zW-U?@f^9)UE(?10Up>IK!5@XA51S=pomobj?%Qf(ovXIZcao(#2eO3+{J;{xIEP%7
zw)k5Gs#SF8z_S!o0;nd@|C_XpG_3*uhGrcjfm_FM#bs$x>LF_qIh=E_ABpBnUKu$_
z%1y!QfW7AH@q)=hl>vdE=O^!*qmb+CYYG5^+W*^M{}R0$uc+dQGVV@zOaQElj<n=d
zQn8##7)bJ#uB!9;|M9sGf{zpZl4kr>z4_?qNLg7q@HISwI30PN@63@#dMvW;%HK%w
zzXqo;fqU$usxtrjLR>Gt8Ba5CP8<rO|F<gmul)g0FbHZ2M~Qo1rbSOrzgO&<dRQJ7
zKlLBacSDDJoXeUdK7>*U9!^F`?en%Y;Q#yP_J^j%s1s=oP4mA3P)HVE%t~*f$d@Yq
zdlRcEd4ru9cw$3kTm4@iWn(y`HTdc7NBETgvM<U~eE5eM@JqV-e-0Vc@tNW$UVO*Y
zBy7Kj15B9U5q~mvw`ir+4(PwmdH}MjYFPkTwVR6+__%MUHm6eKSC;ypVa~*xfY^_W
z&8i~~v=s6yCwcIwE0?7G+&%)0<0b1bP@E|MA`3kgd9x$n_#7ckwZ!x0wu0v;80}4j
zuh&D|{q6wtEarXI!$El*R@38{i1g|AW_l;biVk;6V><@<?7#r(16TxB6sFB3w8u=Q
zr;O^^$<*ZQQDrP2%+q*Ius@IYjwfo@{l`72nNFa<)IP&{KVYE=<CC2VwZDT1rJXIw
zdT~V3!)8eD>MYQYI+XZ#)8j@HHGiRmx4zwJ^ZTv1CGPdmmEu$fxb7cASB8_b8c2ng
z<A6<pt3cD4Rs6U$tm>cWc)NJ#s_gMuNfuhCQ%(DG1;wqgXH_zvCB40OP#oj*$q4!l
z;mgCV9L(5VT-!eUhF_Q`vN4*Uo&CVaOiE}|hKNwjf<xoiGg+%*U=Srqlt*1sJhd*N
zn+BVb$p@^4w(8E#a#1o1!P}G#S#Asm|9ogAnw{r5KR~dt@i$20Q(rDCh>1x6hv7ai
z+Lc%iI@lYhNE5TS?5{J$fr2#FCyf!thsw&_T#H4RqR8{^X|9O(?;ozkf@f!OpFKjY
zM6lY#m*bAaPqv`6A40a?Jqg7e&E&e9!S^YE!TIZLv&rUH9?a0l2k;*>0T2aAXkMSR
zX`}ehsR!5-6uZ0-j~UzgR|y!nj8HfU{_3qO8}(2=>4oH$iHG?-(j_LLgEY)gZu5%d
zIgk~$yzyGuR+8I$fB$ETF^VQ&6`7Tb*bJTT&Hd8h-ZbJP$}g@q1K=PLLsX4RpSLGr
z2b=$i6OsO8oE&igzU~YRIGy6Jg$2>5(EQmhOxPnC*Y^jscDPXHC&2R9UgwFDTE!wd
z0TcV=y%^{Y@Gz<srdV+(?Y|u(uC=hDx3#m2pY!?HA4?WJO3i;f2;T%>r{;j73)5_W
zVEAR!2-jAw%C@ypdysUTTUxr>fkHeF=sJ6$TBy*(BxC#XKx?-LK4lIG?&ys74`uXg
zu{0G;lqC?+Xkxz7HGVBEozVO`<jgm{&$oAImq(%u-q!~aqi#7)4;5s2uYF}yK19lm
zyfnY;>^a!$<CD_D$T}_)#`~wSV0J$vq;#A=M9RvlXcz=jXR2FR?EkdNH5?q1uXn>a
zEq*2)!$FEIaDzB*FNh(t*Yn1n9n94Wq%3j`$P-^rEh<OlcN=^xfVr#=aq5hprAC4L
zd_H6;!bv=)SrhmaO8HT68yet6M#?F##YclWyfbk~-AN>)6hqaMaeo%pp|6z!gbvJg
z94wHK<ror@QzOXk8v?A=cl>$o+wxww$wk$+wZFH~;X7EO!r<JWPNsOx7j4H7RqMN?
zTL>WuSD^}{;3%R8zcDCZeyy07^n>MASyw;sCJXs6iZ265O?>Y&L``-yJGbzm8e+`x
zS|%R}IoIMcQFYS#2P|F$8@3fmdx^!Z9y!B-5VJSZ90Z&^WXys&ze*$yX^AVotrUr}
z6hO@pog#0UBb~{5hhNL(EdF3K&};2W8~d7gSy6^9Bb|;1bAq;l-0=}Ns88SY+@Sw0
zQ8_+fCOlLUBgXCh)ZIC$gt~r?Khx(^xz6At%JEU)F1c~>hX+-B^Zldn%0(v@etXuH
zV&Uo7N}L98bk*(vYa8@^<+<Rm)6%!6fO?N?SlCV0Zjpe8@eKEakNm_JLx|(x_}CZO
z9cA{6yJ&9g_sy=T3ALU0gOehnDr!wA#LZ`-wvI-qgp*kah$Mg~po>|G@QO=nMjc+3
z9yuCMoykJm*D3ak4R!q*Gyz$=$&P@4>rpl_`MfIZ6>djy8400Q3o9xi4n!To1b)|s
zzSyKvku$yTMT1YUn$<15C8K<tWE8|Q*GH<G%9ODCZ9mJ~m;lUlgM(vTzq-3H=$f=3
zwh<0F^||%r&^iX$GZ`;Eh~>#=h%Jh-4kNap^%v&xf`bHArR<<bLbddd!jy~9S$z_A
zU|U9DjlY;57k9N$9BBwmL!_vfKT*pJ&#TC&sQC7RS9}tFpUlsdcPysAhmg>yh%^Co
z<HlAz@|r$GjFXj2`4(o=!i6kqsDJ<5BlXjWjFbacDG8p6#gv|)+WJ>@SMk?lWo31F
z=T~JrY2KopMIno|9k2S}(YtGY!Y7#A;wcF;)R>?hf*D>YWAZ~>53zekT>LX}6KA&2
zSb~V0C0xDlQ4Q~t>AFSs=-lN;{G0t~v9^D#a2Mn?y#GqS&DH>j8#Ic2ZZ=C_V85DM
z)Xe$5Ni~p;B&~6;_{%^~lyssyeCm*3l94XQq>vnq3vaRA5e<3dIDXC*^U^wT5GVe-
zvs7@*O|XQ*!xu*PwkF5!*h|T40c%X}T?>SoXb9FpSS%6baRMqJ`(5M_eg4YK8e62<
z&i~`$w9>Dhb(vW`^WAGva?^Gbt=H~12SK0xu9o3c^zHm#;T)K!8Bf(Y%Q7&(E&0&w
z!FsjZEw_?=a9%<lg23;ZU^guEfcB^K+VQ$5DdtSaZ7=-mL7aGE2lETA`m7gO&vtY2
z4W?6-{UtlC_*S3vg0z}Y-<D0$%<`pQv)0RF`uGx7B~-<#;?wyh++u2}aI>I4uUPo)
z+Tm!tjQK$TA7UdwV9P;-tl2?}v2>J<gijJQke&HZ^GHM{r`A?nxx|0#uBKo#txKw9
zMCq~Xd@7Or%}w`eo4{HLZAjR1aLOSyjEtbRl|lE$y0A+l4v1)Sod@KsNx;3>xx2P2
zTF4*q8uTwfAO5<JHix(O%U+Vb5||NcdJ)=3L>)H_XBKrc7F)G@I;fmi1t;K<LP5_3
z@AoiCP5<p@EXzQ7{>ZE<y9=9BZ`VNzjsOpJdV#Y}%<o5@Vw~=gx*Hz9dA2rMWJyfT
zbzQGn9}+z}%|rEU#aZ@NlV%5i6rOpoVmLu4T;dLUDOv*x|5LA)D<4|_%-v<#uN=2T
z#;{vvj8}Y9ip41?tbC+v?38KQlN7GU4~>h!idhnrjz`GniFT4qbIyDh4|-&jDCN)a
z4QPf8=wy5Zhvu3F?eHreC;2#*83!UYtlqmGQf3N#?s&M9!*cXZ4xsSW+Jblms6ru1
z&b}-C&SxJJw#rxLCZf-v1CS8@5a-g80^{2dPI~&)*0+QiO9acT>rYVdaub>W%1Hct
z<v(oKVx9F%4Ux){Nhzca-&DU<W;Ig)Mn4tmw)fP$z4S;n;~#g%o-+fgOHW8lJo%nf
zfn+N9iU<}f1f~x#m|8KUi&`VA;!7327{;9q9%m?Fw=yNOri~94o+99;q~>`{kQ7Sj
zU*?veZZVctW!u-+u|JKBwIn9(_|peXZ1@kd4KZt`P8ZfOMF4pai2~RVwuBeMW*_(t
z#os8LQW1TA{CKu;fzoxWU>0uu7y!8FQjQpa7w6!EzrXjZt+(cKKVjgc`_6&X^0dUm
zruMmn2J<oHnA4!}xZ+S=i%0i}Y%;9VxEtHZ?1$y}{78_IhT_P^iwg@w4E=~v?$F*G
zPeE6d!(A7B;`lw(?7bgMg}$g>BgOJiFa-g0@0L2)nIlj&SNb`77PtIhbm}Y;nO{t5
zVQM;$nzb6S?of*1Odzi;BY|_$xVXifr}E^SoU3_#jo*%lqrAF85{X%1VM;VUH8ehL
zJ=p&pw7$Y$!0mAPjD6ONesE}rTW%JQlR$Q&kYoP3Pg$X)ug+!AL)ZRp&?r&E!lIN?
z*z32~9?FtD%yVY|y30xoD+K&OAo`w~oII%`pi2AMT~WAo-#q=5nNe$@w8(H#p_Ymo
z;KWNN_jhx$yz4Z<{RU8TZXPR%^T<xg{HmI#q~BXTHEjml58K7XH9>I!B!#1($Y^7v
zJ|o1$$EQzcwDz3aarLT(sq6@LnC+T*mUAf6R$o6-$MOvhVLEYUn58=rDz40y7fzsz
z$>Gb8zg3bS{tFJ9S_T*Mt>(9p{v{+Kp&7pYbN+TnD{#H6^#*jjvc|DCGt*YDFn4zC
z_o9IViquNR`BIQRyVifKpR8sNvc1Q7$}tePhto1#qu|2JG5Zmdlz+y-5l&8Tm(a=x
zDS0_U5Rgz-(cRU~<wTJ!!mirPK2>_RLBXIcCqTb5v`aJnsuPzVo`wXvpwZ>!x;x^=
zOhe!n)UeB@Ix5+wh=labf~{%E@owPH+aHp@%;+=n^^<W<>?p!<Kad+8vxns<A$^zH
zWbli0DLLTyiiW1DIjX+JU_N7{P-4#ZI;R1iG|Npj+)lIs7niDBC6iFDjE1*`<0^3)
zrsOln7Ah;}rt1=MPp!q*p^lo`=<8`qXj?Jz<CoxRT|@gv*C={=@e;~_uM(CeCGjz=
zDahAtsO$r-^;ejXT~!GXq12bF>&IE!pXv#zrme%;85w$oKPV{BOlj3P;%WGshzj#(
zZwju5^D7_OPZI__%YT(6mDk-k<PJ&MiDi6c8vU@x9(N$`Y*C>u6&<%*B#~itrV~QJ
z6j;<+mmR+}CrD3Uot!8yBCkf*-@jF-&{R_h=S=pFX+f0xg7YT($B(2h{SI#h@f;>3
z%qtl``Y1?5kS8pe-hT<Yq5Qsm{e<0#>P^3U&DQIfFPv(q^V8S$ddhUKpJB>mJ0Bb4
zxi2L-d76wLpd)3ZuCmDYK#7Q<svm7F=XXl<jq~+c)Tka8{{l;wP_CRZV!xe2jQYpa
zXfsm$t5LC!=vbXO_fn7P@Gcf?CY>7UbJDdslGG9RQTH^}HR(;NY?=044qBADRabB3
zF1vrpPp~~FI=+TV4Cu==efzD#@}MVGXyKAl7h%7icDPL}p`V_yGi$2(-Fbc5U6g;B
zh-35k*d!+fPu2=tGpc|({BHYLRP1+j@W2;So$_U*^(o2f-XJ5wW9|HU93jS2DaKKW
zjWnB;7#FS1QZXVY`O$h9uC$s@8;L2^j^+E;DBL7_(Wt0#Gwb^nMpS0?fr8rAnwYM(
zfF(M_Tdo#V0@jd_3ig0j3>CQtlrhaV88&taGvOSQY}#~iqnBHUN-gce>1-I+cmGc#
zL|WS8+4?oZ{ieu5V)G=VuDdiDwzi!bE^SRfU4#Kz3k!c;%hcsU6+}3~)?3RC%PXbk
z?c`crTiM3q%(y1vttHgrw~9`Cw_L8@g0%~oOVOFT%LbS33WY9$3b6a?R1C1m8U~NQ
z#A)=ZTDetO*xRmGnL>XtSt(NIa$cRonL54m`Z-}u{rjEYr?zhgo_Ka`=<fT8pY)2Y
zv>#JMP5diEUuz?g9g(<v;v~qDlct_kPhlss%Hv2i{zF-%RdI~C87>g_+>O1R)B65k
zS+&59Z(X`KSKB&L?uuiIV654!Bh<^yD~Nv*qg>m{t^V+mhErRi7GdRgW=7{XHUl-F
z=0sB+`h~D{%!N+xW~!dKk;?BT?xJ;s_@<`Ki5|irp#u0shc^pB8gFFV>J46-`RO9I
z>SQNYjl0%m8!_?-ZV?3gVT~)KMYD=3f7WNI`!rS{?-*g*qC1D?joP6UDI>Q})ziD?
zQxzs*o)f}qQBQ9IS6OM2SDExW7Eg4fkD7CFg^@~4o?CvAfl@i}o1=<MMgyrJ^`k8x
z0x&R(j5Hb8Wujw#RWKZNpZ$Q_(`U2VY;+5*X#QRxnQQg>n2;dI>0%SP$g@Q~Qy0VK
zTF30T4*B(D08njDK0;ko`bg3qEB2Qo?opJcxwWH2<_h{y0yM@wU-d~diDMcK5%Avo
zV55;|5QyA;<A&WNTGe)&KC6+&mhxvTi!kXd=L79;3vO9m#a(Q4IfW}>K2PJ^T}~Um
zv=5q(-mM?Ly{AD+K+=nRi)NZ%ron&PTz76$nm!Vw@zr0mRBHdwd8h1ywT-Vs+g1Xb
z&C2=*W~WB#j&NZ({edM7zFr#zJ+rUH!Xv$>M}o{z_joSf0WpKSZ8F*`%gn}QxcP|t
z<1*}>>g9~c+TxZO<|@Sn0l2uaf%M-$+rgvY@9s>TEWV=rY}ORha8_Qbqs}S5rnmd)
zs}>w3ACFBRVPbZF8Pkbi-3C34xXxP!JH4VJZlh4dut_^O>n;4ib!~$J5}{cWB%0GG
zdhUF`wGPZnCTVy0*Y21!8s7!n-ua=lEn8=p-bqwPEYTkpv5t$a>Yf~SN)RZ&+<%oA
zY;0u7^M*-N&CjLSN0))Z`!c>+^9%nFY>OcrqwTdxFW;!PbeiPu?2w^MJ@5N3>Eoyv
z!{Xq&WJ)-%C)<~rV}#mU8(xe`h1{_5luMtVl9@Ej!iba@_72iJTjK`7kb^UI3wbEJ
zGsp5P$Op<)@w<p3vwd-b%_3#UY$;{Y?bGdymVFpv1tfsmU>`@n%ka5gwyU4nrI`(q
z;7g*lwYGkQt{JeWF(sNUkf&uPQdSVV`#XIZF0bgV+0@mRmS(TkMklK@hgU^s`!1~m
z4rGoxo(M6P$bf*;nu<?a^x_)6y(Cpo2(>oTz@6+-(un!(n_K(FUZC_z>fL$>z2eA|
zM$dzHc|{tJD?a#Y_4I;p3$DdcJyLmxmATqPkw1P(o%w^_+T0SA#4ety>Yvc$+J<g~
zn$1sJ0C)IAJqG@=*lpZ<NUjvBHA0{AG_CZ9t4Sw~A0)h<li1y5czgU#vW3V+joa~V
zpE|8O^(3u!PnO7J0(}NF29^T<AQ@$+fN~5oYPWeA82xI^3w8O?NmF&Xm*Bj(1pln^
zWtrn>SZ2UQ6eW{NwO)WQE`aZv1H>hgK3aZo;1?96J?#Zl(KlbvNlwT?29fhgxJ(J!
zw(ISU4>m%g`?fCGocP`)QCQUvEi|&wP{I4LbF!NpSGHr34ipt=yui7$8lU2%pym$k
zQ605S6v%~7p_C~O3%@BAUS~%lk5Pdd#M5QD1uMf*7%p~uj-GUp_S+I-&U;rA9jFxY
zv1ygfT!FJZ^%>g+NYbPzZ$}@S7}*?u5VeGY%xjKI^reb{;rwAo0Ah}EA34T+tzoA?
zCyB3b2Zjvf+LAR(Hci`-9BDvv2hz1atn_WwyZD}?4GYXODy)k3eaWHbK^J+%nEfnJ
z^EZ;?L9ytJ?!_wDNj)yNPnadl;R2r*X|Yl4^{;j&P4;-?oZ7G-pCU(&56#5bd%HQ|
zukvd1*@kr>4fX7Av2q3^n?n^0QnME*BH@L9VQu5M+KVn~tGFA1ib7;E!~xXg(O-qM
zw@tt5Uoqd-eG19Z3dl8xYXlARGKJcxuCyqje?2i9BS;uO^B31_REUX2b02vHH8&dI
zaw&{SG7u^vBu@38ce6MvJjom;QCDX|`*nDltxB)<CBq-OKR#^V8b7AVR$_Z~ePz+B
zwsL}`1)7e|7MS3Diy=^=tRrw{5>LwiZIRsd=i2mVshCfiVT-eep<goELKqaI?lL~a
zc<p@`8zBFnuuJeI&rOa2VFYP$^oiXp@S!V@-kiA%FyC04P)p*#fm~ihVo>5cpOaDc
zy`hG1%Ab4{@*d6|6p2<6#s^o${Z<~gNkG7T_<>)CL8YT$nWJnmqR-_U{-*N$@YJoY
zPWIkM+;8qK4Mwtr)TEe|wsTfBkL{>2<F(d!GVnMqZ|kq9lLqb<*}vmP={Uv`M^{8_
z3Li=u3g!L?fX@nDzWlNk*KHBFcyN{wxjmr5pznjdMb~5TuAJ;$KMim59NJ@H(Ae<S
zHSa)15+?FK^MJFFAD{jh7N1X*())}*47Nu_5l$$B^h$qJhywFIehhjypcH}m{}K0A
zQIQ1OwrE46jeFzn?$WrsySuwPjThcD?v1;<yA@ElyE`=Qa5;A$yJzn^-p?EFea6Vj
z%!-(Ety~eYVy<$LK*;RnLWsMH<m4GtdDtzebZ+c)yc)cz!Fh4&DD!j377ti1O31<(
zWi#%at>RQ?nmvsfa=>Z5MY|aoi5mCE%l037*7!IVUVbQ{&*x0LbJ<I8F=@q4Fw`!o
z_3_yzckFZf@c!VJ<eK7jui?g;cI$S#R*oG9&)i)(cynwfavUDs>N^qk(2takt1syh
z3o6%TimAB1?O^Mrr>%Pv;pTNLO3@&ONt8h(E@7~4J`>`A<KM6XTt-MCtkS*M$e4Ni
zs+tqGgh4?S)aCU7&lo1<M!Yy6U8J}aGy7csBzD6$TR^N0L3<a%8@mUnu;Ve-6dNT(
zIbz6lvGpK?Hk0M&0}N%6q_Cb!ocG@QsU{hMaPfMuPuK@w2Z=d;{9Xtxk_n^4D)<7r
z>-zdq2$+qSR^l+WRd)O8II-<6^T$$Q(p~H!SvxiPW*{f{%bZF=?ANUhURqL<&s9`J
zM;^mHYrs9~Ep=l17z=}pEl5La;Pk{zh&pJMU|a0OA7PZStO#G{T&nkMjU^-0yVO*P
zHtd&@*7d;NpUlfZoig>=6BNpA2xI%-??}+y&E+zYaka3b_uOJHSrYQu?2!p!+ExOZ
zjOC9A^+fUr^?}sozhaZMt;}yUMqo!jr}c_)2iDIR-sr&bCS75Hu2P`|s{pq)k;t$%
zvSJ+RNHVJMX|%K<OqtY$nOyaQ;eFplFD*@Gs+t88%UmD`35^LwHH<$FEy$gt&A<Od
zd9RwVC9|rcr%;|WGM`C*80SB<0E}~DrE(@=QAJUmiYbey(WrzV3b2ida^!+hr$!Dx
z1tE>TP=!!U$H;pLNsCK}n;Dtw^0d!5-_@3%>*hXD!In}|CAU;org(GId23@KtlGvm
zR9cQHr84-5B;4=)sjU>D*)opH52)#Z+QOUgTdyS@hi0D%b9T}YAQ23{b>d3DD-4sH
zM$S;@Ksm>s8TZY{jRK=TedKj2Lq6md?3PkqK%rHhS=FFXv*lOggjRe^2~y!1Gq2W1
zyriw)wTyi#4u2nU*E>mH<03HeB}cx(#=|j}=LrI!&=(AFhP>YHefR5-)P5jLRKCUu
z+^!kvaH;xXP}Ox{UFtg1m|X^%3otH8;dWihKAGbXN)!^5WBvdMBTv8WeRnEPTlAeZ
zBhkNwzB5J^9NVqiCFV>&p<tlva|?T{C{mzR8QV|5&*C|3B;+NT$kaYHSJ9&%|NeG}
z_tYYboP=Y61_PY~#H1ZI(6d(tQQm8?4$^kx9q3Sp+~vvMJ<P5rBbSzi^_ZR^izZcO
zJf^rsFKQpWuHX?KOcl0Lb0jrDMj4QY?1v5h`OTwnI_meZET5Js<E*V=f9S(Eh$9|i
zWA7{p>V0j4`pRI2Tuu$pr!UG@Ewz_pcai6Fkyk4ez<$7pfzeq=<XJc}O2vc&)a_Sk
zxg=!5^wiMOks=rf8Xy4GzNnDKA&m6RrIMxUjM2LlfX%QlS3NUB73ngYqz2|9G|8pN
zvnbrUyI6x(dQ11fqTvfLi_UcUmY{dP&}nloTiMOdf~Xhd7Fbdx_rGINmAe<?)-$0`
z=3H8s=o=pXp;*9JpOa;Z4f6p7%`0)Q0r5sVDvN>ld!E{h^VU}8_%va)ibPV!mx0I3
zy;6*)lDIbVus6}MSzZf6UHacyH1I>X0sJ$Tgz)1^KG!N*izE|``xOGD6#=_+o375O
zUrkN)tj%!ra8d!8qABqPIwpLlD{MR7vn=2V<Z;~RzgQUagw#fdBsr@_L%oJrGyXo3
zHOl#l{0%1r_c?<IkqZ2{NozUKOl{l7TL}d!iy`AUBH}8Rlv7U-r}}0v$L(ufh)dJp
zYVX5Hdzb+(zSI3gclVGM2zJBXfhvPx*i4L$W7HfYoS^efCY9u&$Fs{0MoyDu8h!Fo
znq6$AdB|E+JInao*OGJ_12a|IrrKQu2roRbQksR#e%mKfWhS<30H=t5(KC0Z5yRWr
ziWi|9!NKl}jj)D?>>z3H5s|F<%Sd8hfSXAgo=5t!Kv8OVeJsK32~W~k3KgV_#locA
zS6cN%q<d*j>r%1SxJN>w<CtikN`^^PH^#O6Q?@@627=Yha-}*CB{3UT;cC`(tq-O+
z>@`VKMfmM+t_Yp1FDIRTA)hm1?EUy-2-)&ax;})tgg6$i3k{VV>Ba*R`VrBT(KMGg
zO+V};slENvQcE_EE5Aw=V;Y3wwzn<fWh5*~JrR-6%wSSp_MAVR6op;EY}`8<UWZ~Z
zF@;Iy6n=D{(G(N6&?-xsI?=mgp686^a!h!a4H{2S-+jIxzH>qTd?Xx|MiX40YjgxZ
zSp0GiCdtHfgM8n9lANQfj;5^CpHl4jCV6g7faF)aRd)^|9<bBAiUO0d-g2TMo5#Up
zmG?4N1o>q-v6uLAvz*Utx7feb$M~rx%%&RQGBwlKIoeyA9rB3y{4{QAv0qC2+hjC6
z2ubh^7iXv@fMAngjHDlY{Gel1W$7{;ynFRT07sC|8p8dJZBMZ|4tM;PsLX0I+cj4u
zH#{hYDyh8w{XX2UG2l9iHeTiM#kyElj)E)3$`B1xi5{RvCE*h))&RTnxkirp3&YB;
zDk1RZu)x7}#lGmYZ1pUaUZ|ijqISBgn~#JlgV?c+7y!LoLnny0I6J)BvlKzSpRyqN
z64)e}NNOzw%%-+zO1;;p>c01i$=w%l+=HPs+5&atwmgXZWSq&t)#fP)$iOFs>6K9<
zT=QIUHFiah5=zRvu(Vs?xzjRpNeHnit-BYqWt+45F*nyN>6K>RGxQm1?;i{>Sc}Uu
zg&%cC+42MTd*&rf2}5$*aO5pPIhV$3<IC^5^B1C?wrE1Xnw^9fpt6BzBk2R~``5Sy
z`=Q*oN=0OZPF1n}-Y0vvyXmFDEQm!%I{CBoGt;W09m~Du*(y7=UZ!u*1F97(a=uPx
z!DkM3p(VZ3lu<<dr{6tZkQ(Z*oQ$#V*vXx*P2=X#G()sYAx!XP!npG@+X#P*Lt!;l
zM(Vh`xoh>ww4O;I@fhofQBC$HN|Br-K?V5u4(Xamfxw@4P~^p}^$aS8MtZsB%??m<
z@r&(~D&;|&pk5xNUD<Dbq4CsoM9I>h6Ab4+gHG2vQ?OGLk5oI%ZPV2uCgr$1_)j`c
z9AprVzY}t2i_Pum1oq&I=O8mR@p&r-2nL7;DzdrudV8Gh{RkFJ8&?a>>pbiZpJSTb
zoXtg{3{8oBsVc|y)K-wuO@kRyrTRbql5kAWPiX4PEOC26T)dLuJ=N7I%kMEMO`3~f
z>xv;~bZtH!(z!%s8{%-_(@?Y(<auVMm-n^t<whj+Pn3iqpz+|?^53fNZsqpV=~d+%
zMI^C|V;Vxm#B%;|$)mh&DrVwLnJ$i%q@@gsOeCyty}P~1z__Bv6$<eNyDEe@jkJ$7
zs+!)!_OH-lT9|vaDY8m}1xtV*60<rkgJ;T#6`As260mu;l0URSCS+$)QziC|M(C<D
zZaVQLl&p62?mv(&#JeZtA#0?eTv8ogVyxRO8~F)K$q0R)B1stX!e(r%Hbqi7-noz_
zws0o(orVd6i+J{KI1-M7NO^seo-a6arU$}q2wW5UG!j#vK0uwb(WPx>2nwI^IWxVo
znAJI5om9@2A17bW8)1kMRQHlCaqH`p7UMwt6qD;BYO|2r?*W4&Y*8sLA1AFApQEC(
z%CAH!+l#KmtZ5dh?}i@s#Wl5RcRq<vQU<<00yn=65UCYL-k;hiZm~)htpIw02wbsE
zSo#(&;DaByblb#PRt{H{G%2yM0wPjsoZ&gv;5@VybUrChKv@sK2<l9h>b2)jO}u7e
z)C|^C`=F8-bX;@Y(Gr(6Ps1*Uv488rbic((2-NRUazm>F&BBQk6m(^9U0YiVj0ivx
z)}6v3qj-bIBN?gp{As+&Q%20-;uBU6`Vk=(r!?SVt#uTI{ISyfLG__ahB1~V;wnA(
zEt1$idD>6Y5M?}kgdx~OZvY}wwkPx}S`-Q;mKBHC1b~f-`H!Y0Y$QM@M)&A;ijJiK
zmST%V%dA#g@pE2p$(KomVE6Tu@V$9qaO-Tc?hBPgGeK37J^xx?D2xzoXvooM{}(}K
z=X(>`zfz`ZYO<ns_mdz?UJfWPa%E{PbLxlRz^>svNxN*6arH}RkgZF@)09+qNdZCY
zJ~d1WGnXa6j2Sz*$Eb|%vpgFIIa+3mYA?A0V?Q_L8#r)Q@e&5R+p?vswQ%;mX#M7!
zW-6{WU0^w!27g_BVdnRJ;^bnQ?<*3(ZGOVtO_~Qjy4*viF{Rd>ipo<8J24G>j4wW4
zDU~@{FlFA=1trtwVOOhS+McGiY-@l_F=@>_az)8|!C&M2VYh9#4;X+N_+K!@gwVh4
zl%=}MPe~+mEu&Pb7s>oiWxxRt(f2sXmo7`g980hJT51tpl;J8ys(Brud`kT9h{V{s
z;)|5ss30l*mWr)D&7@}xxGjx!?UZrptLy2chjjco#$~76nTh@;cHW286Qh=d-^pnT
zX<Q$zQeIx=2O(;L3T_eTF6<xij6I{`1oK0?q4u$mQp$bpYR<mW#&MzbJ=1UUJu*N^
zFK0)ZgS{b8F4z^Gi+<)qLoNP$A91J{-n^gUm>6VqXWpxm4s@AVj@!I`0^hO~^<7RR
z@xgSrOV&N<DNK**FR!%V?~?Hf*uY){_nYnS)P<Q;9KA-FS@<EEg6P5D+dd4;D$q%S
z)ZkEz7e73SZGj+_)&$8UBKiCqA%g3z`ld_k_9MN|OxOdlAxJt1pbiAslC%!7J1jIh
z4oXmk>dKO(s+hT1+85%A*inCDIwNUML_Q?DbT$&!Wby5|a_!rcNc9DFu^R-QFXOO4
zUgmM^B;>9WHdDxfnru~;23?+Kux4OtnYoLe11DkCxf)h^tCg_1k6MB3OQlL?6H1-{
zPXMD;0Ig_6)%Q2fgH7~|e&GRyD5Gce$jEX-?x?>j@7iGqM&ojG@9ri#upN-!C99I3
z1HDOOl|zmwpc|BeB16Oe<}MuO-4wc@_;(zwZ67Fs;*u!~%AqO1@SbouY@t<ORaEvS
z7l1NUbyPuKPPdvU@a0Z6VIJB3{6F&Zq%%)UtUd5#<A{<~5c!u`j;Z$8(DgFe-o&Uq
zCtNU$oxkx$sfCYJ8Fi7FnjU7N8}p1m3{;sgG-V*+H~Z3-TZPF8Q0rB&k1@3@eJ+V7
zgnd6Ka3PFtYcyH$2faJZ*BZG^q~oLqIyHT{$;(U#BR>fNA^24gRCR;udG>Bg>+A42
zZCk8qaJcl&g*x1olX1%Er>olRUGR1_no2jMvZJSy-LppzItmTE#y8ufq*+s9kdUGR
zpNcc$pOb@M$E1Roc%U_T>k|SCkTrCK&*uLGFU^4GN25VfhH22n$kD4C*!})gP)xB%
z^8@`AqBkjxuGuJUaUWJ&oVvF{?3g+g`LIr!eefOlk%V2^$&lleIv~9&9ORVu8DapA
z5BeilnLQf+)U(9`bbi;hVTyO4I|<nh8z1DLzZoysyCTzCfar{7nTIp&Rn0k9em=g~
zHz-(T_xfED8)D!26klO~ga64ECAA*-btaFcjE>Eo%7!wWwlht)23L%u3pYrKt*G=2
zZ|m2L2R_>%;KuVk>5?bOQSw%}Xf1*VCzl^UFK=xo5B0*OB?#sWi;s-I)V5U`9YCB%
zxXf_{Ih$ANKrmp%cYTn+0(c)aM3N|ND>a-->F+JEu?!pgGeFG4WSEPaRGU4^BKz!q
zMPlZ65|j&c9Ger7BF=z&_owN>9VX=X!H%3+NOdVkVx1=)o{XG%SWto;PXS;%ts8W`
ze4Xc^Rw^}C16dJW7yBc`=oduBK%yCeq3z+-RjP;tSNW!WmklYGEu*vjTVnfK(qKct
zgHT3}#<%Lc)DDheQY(rSqE4)t^po0DB|lGNw;oVaGa~6yI3AP_1g5aFOPYy(*N%p%
zmRHifq!Ub3>-L;`EY0fY;ZPS5aWt|}tW)*U#Qe&{Szu6-XgqGk14Ox(A1en@-}@L0
zm%602)wDUyzbF4B>G*h`W;dJ9J06ma5GsvZ8KpGKk!G7xAbPYWVMZEM%E($LZo?&a
zY$Aq2Z%M6hJ?#_frH81!#PXi!o!{RtG}}7EKF%*gl(^d?bW;v8xKfUj5*-znP*NA$
zQ*cs6E2inn<0^ZU=3wb2ZO&q;tPUJ1g2#k@8RjVGP$15-vizg-?9&Uv)b5_mZdjz*
zmvK&qU?(&>lKI?{(VIzL{C=t{A6PHIPId#1H%TLJe3lH!h+p{J3{#(5mXehh*kGH+
zF9TlnVu@d($03wb>9@$1DW6Oen8W8JP--4O#YJP#CH%uN*%D4qbLD4?xNw_yxa)IG
zaWnlm`#mWN+cM-p;Aa-sR}%NoR*l0iZamdj0egAi$09GtNG~d@Ngj>T1sPy&Cpkqd
z(yE6%gI_9%MX`^epL@+jAZ{y&)Zq|FSobRVCW^Aqv^a|wu$(eLti^Rt<&1YzEOd2O
z!61lD%^~jNfdCqCb=b}#1ix0ORSjFhNbr*)6^Y!)lqZhyF;!bX{<Qn?QOU8Qcj{a0
zwzfH2m)|M{=JKfbs|dvh#dghRSfTVg1J!q5cGQ{ZZb`<I#42ohYt`|=Fvhqyb?6pL
zy3|03f}C-Se5f+IkxYhiF$h4S-G;+6Zfail7_*YcYilpxT33*=AJ=@Eszrv#2l5|0
zCn53~wSb-&f7}6om7?I-#x=J5>NLR%bO@A6%Zc<)3G&hO#%R!}ZqRIV5l!Ol9ozEu
z(eCL=x;S1#a$^}3bGg(2F<K?<kruY-R}}hSZnfcKFC~I7&LuH>XcNMwlIR4j@5y|b
zT`>svBO}OfDQ@lHMat~Yw)9LN&RdOQH%@nA;lDwCkhZ^Nkg;yS(A&UoZ>5uN0<ka6
zvZB7@W`~!Oie>E-K`y#$riw&22f!-{lKoH}k8xOGoVWfy2F6fEsx((4;T5V=@9TBV
zOs$8A<|3WcU>nIZ<9gP|geUy1U~@7qJ(*`Fd_yy&br+;{6~y6)>wBAOi^E~~c*7U+
zGh_cjGt`YKsjszKOw>x{P~tbzpWP9jW};_K&X(5<+zy`93KN#U31vt|b`use*In<}
z2<m%!FYWD>JEg#$1){I|1#oN;^m&v_jRgfiJ(TcXaGv@C9lF02Dhybd(`!K3lkOOy
zIcCq%V1ke1m7^{&ypNn2|87Z)&#uG{LC$eI&5aDkJF`9F6)mIE8e;y4KGE!1mL#$1
z1_xWJNUpWzu~Djvu&Sj;+$)=t7WEc$G2{@y9l~f#zXdAMsn%B#eA#!lVV@IDbn8-l
z&T}G_T(e0`?eer>2I%LFk6G7PhRn3cchRWV^}r4~w`ty!dV}K?ls{RZsjvBty_hoI
zs`A)C<vsawdVrU7uwY69rvqxFvjLMaxdC;c!IR%_7upjkLm0F=3U6hi`qVq{a<)~(
z^DIi0l6Y=PvROB!ra@fr=Y82Rhg~N>K8SwRk??X~<>lZOwDAABiwK{)g<A&nm1<OF
z4C;Or_d{baU~Z3sAfYIui)i4&y|gYh5h*o>@N#S(uKiXxtxN9vyg(`}6yWo*W`F|?
zof=szvgBp<(dJrr`ASl+2D8RPH=oousB#6BffAwF2Zm^2HDKj>zGwWRY`MC$IZz*>
zY`=qRdWgD+v^Um2G!c`39IsyF=ZmAwa9X0<^lW4NjCKA4c;{g0+D5yObEJ+Q<C>UZ
zrI#3s16DJ7lWpwMM?3IqjAHQh509k^Y-4&cR^z-#&B@O`Z<I{e=5yCkyGPFAiY5Wm
zZ<Ejlg;aTdg+x5{N0+v$8zl*Mq&9a3FM51<_GdLl;yr4VnPE!BCgO72W!$DdL3<ta
zpmc5fcJZ7Aeo39nKQs~%eD#~!xP3)BK+v?IrZG0NkvXUG0*lpqljr9c8dd3;qm@KH
z71AJ9SnYNL#gY#vgKlm~DBEoLWAT!|eLEde#DvW`Z+m`0x*wI|!PV3hR9_Umi%x8!
zuXZf9%=cTL;S4I2kf)v(6eEu5?4uA%^D29+NTKl7P@l2qtoWKh6_r05W-QmF=S%F_
z*0dk4hvj>ABo(~!XHLZj6ecgUGA@==<~o66{55nVGm@HOS@B3$Hv*7~)1z2XA38hG
zgS7L#)qKn`Lt9V-b$!y9?&O%FIvQ=-TV7cmWkLrxu0*N->3({S!*v{A4)B_!O6ok$
z(+MBjX0PDPBM>T-p!y86^Ak{Ny-bpFqv8X&QCrKKNJT0(Q-n(X-0R>xSHR^CrECn@
z*j%=kC!0y;UixVdsHzW@B$Vw>lvyfSA-oH(Vl209dz1=~Ed#<8SM8<PefvBi%P4`a
z$Vth-FGL*!QYyDy%dADcBc(sNOnzpgY{3z*PPNVB(0#1;Kv_z_MgH-cDtq>;CfL&M
ztNno1bwVVrxJ|#s=9)%Q_&nR3vr5n{?c@1uW_oWV+Sev34`eZuvA9}v^`e9L)26!H
z@OaiZ(`q-$DG$`u(`9(h*FuQSNcg!tqNrFLh#$I0iA~|*P+Mi=xoe!^YvjqwsS&EC
zMP*%TD@w^AH>+4-HFu5vBT?h^HemZQFOpKIIA(%Y;zhVPvseinVKD;x(Xj2Fty_e4
z2)OMx=2(ckLS*b{^;y`x5h-XydmFgnZ9oKeOQG0E0^z`ndeyAVfHJ@lU8|PqhnSf_
z`;M08jnM;L-*GtDzyZm#;7Eve{C49{6)eOayES7j`9`XYNjV0m?O9~tAS`vBZ&IdZ
zsfqU8dT&>?U%me6drM+zE9&e~;y7tn!9s%euPnaEvkJ!Pvn_6HF<1fh1h-=|zPwa^
zuovPN<-8F1w_M~Z2WZUdIs=#ylER{tMtlxenjuIHj?E*ImLOPh>HQaa;CU24h^kjF
zQzpVLb!w20HL4hjTwy|@UGKarc;+X#W0gud9V9Z`(-T#IyzC!ZWvV+sho0M=(C|6M
z-yKbXA$YFdy8X{2a67?fV!+g2ukunGJqbNLg3L0l*-9IwG5W~2$~P@BQZ;7Y7;q~$
zm#7AkpyboJU;Smy7w7V14{%SS)z1xg;%r7339Zq;Yy}CaPWu@jgE9p%4gFm|&r_ot
ztF2%%ltgxy92xWPC^ryi(*!k<7x$ubGA)dqPeCLv1cz4u#5njV;~e{<ZUN-ZGPC_7
zuP#8SDAmrxVsRq*awf9v0mVbANnRE`Gsht$LT;Ky`Lu7sUtQ)M;}@^EZMl6v6Vn3!
zPQr8_vFnJCTQgz)QTGLC%lg?OA51~q7-+8w4@&#iaZefpNTaYGn-m?$H(at%S7_R-
zGk(MCVd>yfaK1ioimueyD@oq+1E;sC_wLWB6Jv^}9aK&Y`Jpp5%e2={_X^yl(h>D&
zH5z&RU@$IE_<b;=Jjz(Z?boNvMJq4L#-jBe(w|OeCp?dk&S^~>`qq5kU%jM=^1W`4
zP;TGq&MS*I8#Oy{@v>_CWH>F#nQXk<K_mt0v6>4?vQiQuMLP<ebBUEQKcK)uYkqwx
z)7LFgCqgS2q=5n{!BWrCC2yd6@;x;UHUu=^3>OyKBuBzcI$bkYc+;dZx*z<>>Qb<e
z)MhwbV{ByM7{}uqi-;X3ij*)Y1G8CgIr1jFlO&`*A<Edj{7NH~UF1;eY1A0`T{xeP
z7|6}cPxaLg@XZ9ZIRZhllt9mt-E;5Wpzy~WNv1v^)$n~QlX3Py>Q)|0%jO0pip~_&
zgsYw7bk<su0WS(awiJ#3!yWxVGsF#ic3H)wDXZbjQ-#3-8*_A+S9Ina8?n6|G$fay
zxb~ga_3b+%Ztzq3du~R{Z<-3myzYckS3iYWQ0NpM2iKjBSc7UBm)O?nqB-Z1vq{*d
z4|d?%dne^d>F&aWg}spT<?8~h^2h$%OqfexWwpQC$3DdMu^5WmGj*?X1W=VC4F;+f
zB0mqx%C!0ryF*&H%&K$E*Vh_n@kd4_i1oDw56Ttt?7UcU$|fS!R+@odq7Tn$!{_5o
zv*);RxcJ_Z@C}B1Cdu<t9ETaggFQJ^)!o0N8|fw(na@^LxdZ9pVdU*$gB~gKTq10C
zW*oBtkII+%Tsx03NwY?|{5x#o3l@>AH1Sfonr1-ES3cC*JkgrzG9{o!Cnh^CSEzju
z>QCwu?iqofR{C5vsU`bOC>8R#f|DdX+&pB#j$v~ZHNa6&@0<VDhR@!6Lb2kR$Lp*v
z{JALResvqY6x}|&qs!&*p~KC-0j5Z2TmPN{y!ya4##&u$iiMLCahNJ{pR2iXmg|v`
zMU65q3W@<P#O$B*6}kd5=!@;_Z(|fYEwfF2$E2{0Oe{suxV7)O5rb&a-G}PB-OHiK
zVfcPZa_HscnP9S!)}3lO##ZL4AYR9uHJ3}+TfJmmqkJw7))!otKF1cf0G6-!`GC(;
zL@GFazTnvVqgLY_BmvcVer?uEF=_M~9gYl7avwpV?-p72&BhF1^^|&=awn&QiL4ZP
z)GpsZ7s%>q7ws<$-0MG##YpHp(z$@~8e;nAxYka^SYCH*aFrQ7cwF9Pe?)&XOqH?j
z4F+i56H(R9eZ}(l=Jdl3sj*oABHR@g1-!e&`l%tYs-P>Dm)+|&i)QN^R$e_DFOy+q
zwpPXVjvPXEiqA!>gnYl6sE3P4@x~JX4kO>f1i4jK(ZG#AW&AQaT&4ud1l&5?)eAEo
zmgB?vnR9|Gdk{EDWgIU)jD4O@7Lq+|O{lvV$bQ8RD%4X}kK$JaGjHZ@JtEvjR~*J4
zhi9MmIsiLyt-g(=4kv(N5?U8v3V~IHs9QlCLAe!@M$XOneq7(C%J3^v&(cskbiEM7
zDMhzdx@@c_0z^*7(d=LDdH(ITkfGt{eMsD>Hs(2FkIWi1-Wp80<^&-kei*IweDz%%
z=^HXNU9hKm@!P@y#=geDIx&W?Lgl!LQ3auV1FJ>ZLF`MrJaFA(laz1G&*+By<TuS}
zoUsxT$)z!9M?o*;c$Ri#hmq7j?MZ?^b}>H;dMYaTuZ&2@gOfE|K8e8!qt#bEjj(4X
zCAjH|Hr;u(3ka3N>%g1TNC}B5WO+YU^TM&I7j1E{wZmMJ6p)csRJ)$I5{bHBxf=tx
z=5W_e6M(&l80HRwBt#qPv#hc1!1ebGq$5$+>6YT4(7|IUrWB{iF=P|P2*p)RTT=^e
z{L~##q%SJHY3w|m@9MJPbC9J4H)S9B_vO_7p23<<$c?*p>T&0|8ZaeuB{jEu#A>&7
zvW%lP>(cO?gEFmpSCGPtKhToOXre5VW3Uy)u6suT0=5`D)1ByB^3Wf^#!X^)UsHDC
zas#Yc{~?*7;ew_f_QHFC%~odhat@q{pEJ19a6&Q2={c4G`bL9#oXL#HGOdyRaWlwV
z?50N*LCjKTik*ybJ;|95>_F8#wU$c)XT?Ij1<mveoRf?0FXgJpTg?>ZqC|u8I`WbC
zy2km)o`O0%IUK*Q!AB8wqehQqshJxY1>P3k)x%V5!(7E|v`pbe(_p6;_@Uf|!i6Ud
zLf=|~exfn*7KOJgp;8o{ON@iHRpqo?+P9{7PcqiWN(eHtWV?RNGxy``R~MM*UE+e3
zKV*8&U8im7jQY9ye3ABCS`x${3a`o2ly%8bj{9{S5!okEiioF;Ca=`M7g4E}=5!Jo
zA4#mR(Xk1)0JlCk0iL5%^;-nIk1WFFIKj5fW6NxBX%#YSvzhEsy<-o^Ei16bD8I*)
zGfuf>pQVyoqyzJahe*GvEl4e*79(-spfYVjti>NL^UEHwhEDKKmHGTq8xov2oaUUc
z))iYaKv7*G_qOVK*$NQi4sd2SG}}M1TQAKrPuX2lVvs1-#4Ovrh%%~3Ioex_gcTa&
znoFCNH%075cW&S*J*HEc^uzL|97fk|0)b|2*?_gi30t1op_#LBO&k|$#HLPmUom@%
zOp#ws-?A)BQH2%cAW!h&X)FBa%ZTuuMoD2@q$S-~4idY+WY{OHS=IDVlqM!wpqP2B
z<6)Xalwn5+wpQUFx<l#?YjJP@Tgzu;nqdqh&enc%cQ;B{@!uk^B02MO=W+BYa{j@f
zs|&ULIaKqVElxA*V*Y^m=Pb?5L{J`FIL|urvcE*+H?tup=j|8Wp*X^>02^KeePF4Q
z%r0AUK1C($T<MiFmoyMN3?}?9Rez*3MpSZ}t{;L7)%N1loNe64u%h4vpP%$lZ??QJ
zZmxq6p;ZCGDIM5A1c{lu84%;PPRikVIAt-Cc3DyT1bn$Uu8SM`Flq4f>`68Fn5)!4
z8S1T(r{xU%b|uz!k`YIprzavxwh?zAKmq&PB;3{r!BPzCAoK5h#v$8EdkS^rb}qjY
zh_Ekn>3ZetF%=Yql*k%qKQ^h+QfKwedezYCaP&Djy2RaG@-F@LM-s8oMl;QiJ=2GR
zR?=9bZV5#<zBj`~uaXVhHcD$Q%cIA@#!0jBlOg59*_wX4IXEvgq&DqJ5_Hy@+ED6t
zT<?kLU>Op(`RaVvpUQoH`a2C)Z7!_E)b_}{0Co9_qGGGW<qd81#c*e)X5;kTa`p6q
z1fJ<kB*Lc)Nyh5y+F>BK0=r=Cz~~|guoTklPIGl;!a_UD+q<?VJmSLoqR2|T6hoya
z<_>LUGOVq+SOG}j#Qv>g+y;5Reht#`;ud@WnL<WNhB5uC`KpI*>(NaUBR8UD(NAAh
z{BQG5j`^g|JHs${`Z|m)4VmT9t~r4^mF6a`vWf5c#r+sDkx^*9YGDBT6*h%a#bgH~
z`%JM0g-aEl(L%RETS;|fTxr2MSn1|@z-3#J6mUtB5x0Tbiq|_vS8YS|a$fl{EDAUx
z^6ANcHM9MoD<%*e<+_QenHzIdc~H~Po3Mh0@U~+A_HsV{Q9-zpBhMh{>Ov#z=bBxU
zGAFdf-t0ms>FRZGq}UA41iD%Ng@Ya<;jJ+GnWFqV!FxsPCj(5FWX;CFg^>754OKYT
z9Ez;Ah9Nabp2;7DBn-s)bVS}@bWS_XN};m*27riRR_H&58iVxlfaqhULg8#S>=7y5
z1$PyjD%Ct9*aqP}!((<^Q#WTv9}RkXFWZ`(^UL4iL$6J;ehXbRs@P-8t<BC@HA1{|
zb4t$-ldbuZWQbO@+g60(Qhj$Zu#t_!L~!G`Y9);2ROMkpURQt_F9+C$L@m@?VbZc0
z#>IFr5loo*EL9etme2KMwWVt9+J+R6>W~^c-`K~^$#^(j%$9v0j9zr!-d-~^gs&Xe
zvaL-EEZX#4XHV_Z@1t&%LUO)@^dMvYm@~Yi53QP@qx@SAs8=IBFmu@ssY-pKafn3o
z9{*5aWzbov{j#7qRD?Er0B0ZbBlsvea`B`vB#O%OG`$T8?Y=Ca3YQpPhWa^#-dxq&
zFVX0CcLDP3M5>{H*EN}2KMbR5h`x52-)fZ{rh};I>VwHuT>`>&N2ZydV%V?r_@*J@
zZz`Gc7HFR$0xc+~M)^}Dd=?78M3yg_GPimA7~lC~j`Wk4<qMn)^O%N@*mS~J{LQ&g
z{86Q1VL6zd8g%$lDi`XoZ@-(nz<t)b0*7>XuWWur6q#BO!_|V0CExH$7DC^1hW4oM
z<Iy~~TXuxbAm(BmJSH+QB_f}rfpG@RBf~71QLIy`Z6HKWu-3aekLz$yGsmO`qjJzv
z`7V&ATsVANlyZTLox@uoN-U{ZTrI?=-yaFYI_&lPnV)06FeeilSsOH8#nHlTJ`p};
z8Pem}4Er()0AGoRzye6=UnVG$kVs`EEzSC1lyS|-3lA(BHZ`!cQ7<c)4m4H4FodU<
zHC}D0>*U42HlZVqz9Wv~IJc&7Sb-hz+c0mZkoYCtsF``}+a@9hYz789|I9bf;2;r!
zL$;uHmBB&2p}PC_Co;oWOxx@JNEPdm+&01m8eCKor2=);1tvdou+5P|BdP%9HL+af
zXLRF+Z3U-Nx#q+-Ga?@wt@29zoBjo33<7WM{-ElpYW=E22$g+HOyj_cF0*bto*pwz
zI)7<HEs$JDFn<F8$eg*oR|4hIeV2;ffQz&uh=EUQjl=Bcl{$;F{#^_%j(?kup`KY8
zKOL2!`owUj374kQS^oLGc&A`^>}KSw4@dY_ZeLdl?Q`)2+o~~BMXGyN$5^KIGViCD
zuNHGL9nFtwk6gCAhH{AC+RWC9tWAdb3xn53;W4)&j0}a%c9X_Z5|=8LJ*h$TXxOF7
zf59{JrT0#4hzi{LwS^4mW;%K_UqT{(?FIYcr|=Q<4OpcSM2NXP+>P?06qkmMCB+>9
zSu*%mi<e$Y=sa%%MZV+z$s!e;GtP(&23q1hvY5?8zA5UeM%$o(H}F{So`{cZ?8VHp
z+SzmhRJd)VJCdA5oL+kSQ_(}ekaca~Dvu8-b9b~oDVczYg;))pH8h3m|5`v^KhqIR
zi#iII0h$F9$*6GZ^{A?MqT1-t7Q~aqkY!qhko6W%Gb~6O9;Y%E#xaV7l&Mi;D3n+j
zsrG5X<rUMW`(s3vX+K}RX>lf&iA;O{MK&R611Q3Efm*vX20B;=%@xvSRyM2!1+%oJ
zW^|U9=}p+HKqx*<6Su-ZnsSR;`1stXlgwNX%%eAqaGtFoi8n<D;he*Q+Fw4nYzXJq
zCmb{}lrwXxu8;_}jO?>bM1Jdn<w3Rj2gP|pV<S^>j^KZaKZVB$Yy9E0nRYVlRk#0?
zb6L@w=v^|*v}4|i$_%%kXt5a{G)jtfA|okfMuD;CIzM@;u@lAiiHnVu?0l9_D2ru$
zj$*&8#F%34;}R1`qjj5;Vbsd(<-OJYmXE91C}Jy_G~@=FN|-tkACG0R(41WqRiLaL
z2^Q;Nk>_uyE}=E5sxYiKeaXJ}IjO?4%34Gs;2Y=NS%~H3Nc9$&)%`X*wDzkq!z(?M
zp%zokPb2GlXY0DY?6<-q;4S2<N-Y}3_E>Sw{6b!^V810!;dsAVw2|32>aeIIUos+K
zsx3COE#kxJqso&nJu_Bd&;*Ob0y!QQ)|7+mna%TiPLmG8n;tBX>^Ry8lSgaBJPLPA
zZ9I%_F;S=d4{O;5WNz~k<1F>v227TKTBZd3xz&fIfM?=W$$`T<e8u)%85!UHbS;}q
zfIH@PiGc|&MOp90!t|XNeWy!$F6d$H9Rb9%X%kTIf@oK~TV7k3<iB_T<3v))f&^mS
z#OPPGF;v+-j{pA3A54YYZGJ30As)$I4zbgfCHJ|J@O?6})J~!e3*way6&yozzsyAC
zk33_8Y;CFZfzfd#0tFP0?+Lsed-<`Eaw}M&D}I-Y3P8@psJx&kR4z7MdhwSM!Sh&C
z4g;?DuxeT-nW91#>Q)6VWTSGaT_vtWd)~}D`IJ6&)(us@J=rabB>BKy)i3J;{5h&9
zmPJ=R4Zlp~zsFq8BkA+a%P1B?!@uvjFASB({F+qJ#RnUJ-(AMmqzc9v`ATOeWLy$X
zY^R4(dF|(%hMx6HOp+fVw@Ow$I=`r2GEKu-Yj8!#I~wPOIouL7@Md6Tzz#rY9LbdJ
z{x;psP42q=y&+<^=74ULFeH2b7iQ*LYQ?4HVcqHxI-qQ|jsDaMvDnaEt^v*kQ$i>H
zuZ059ujZBzV)mw7_ubrfbNA+ZaNKM+#|XE)RlCDh$aN}nS`?qRyF~-%<n&~ZE3v+p
z_F`wIHAX@PT+Sa}wwMdWNyb4CB2bOmxq?@}z%FL5j-P8zb3nrJNwdSzHq@E)^ogj)
zIExCpr@P#Q<UPrXdFsm!><L<tEzvBcXXDl>a9L1yYBzy!z3_QadFjq>xTJ4KnaNt)
zHBa7?OS652bZ`{Ba$M;ethv6OjaxR|M#%|-?4vtr!`5ZPAnZPwv1}D3@|q-M_4o|q
zSo5gG2rsj`v3JFB&l|JFu?<(}g;3qW@HKFIo0ORTqVgB`h2EO8dDq7ly!B$;J<U|x
zqw(sAlhmuvG=W$VqCv6>h{}^z%g#nWUrh~%MI`n^b`qMz!N@x5WbQ}HkF5Z>u4fzV
z_jc5;GPhy~`XjIzG;P|&D9*EvU8nq&HAD;|FsDK4ea2d-N;UjQt0HU;nUk}fcJGS|
z^T{3w?-g+5Pu4jaJJYebmcXkg>k}GG^HyJEm!=O<w?&7mx)Lw-{k&7CIaF&Lm}1c`
z`-P{V(Zw0b+g>%Qgs`2{plTV;>xD!j&w1b4*dLey=aW?lX2G`iV{}rBUyDuAX`P{~
zTVtaHx!>kE_5^ZVis)+DTvf`7VYT_!GgB-sSb<Oto;Ph;ILp7DWmKHs*bu;~)Q12t
zS7Muiz1zbzMWr8B*t^HiueQpDf1J<{#Q<zcPff|k&gja1ud=-KLh_sAkGqFA&3@UA
z3tAr-@}?~c#SZ!_u>{+~APS$Eyl<<JTgoU|k55$E3i#kB&Nnk27-%X*k^T!ZW-<{r
z?K$E^)#%1q777AHtWWGCV}!x^7hFJYu?5r2dakFGn4H!p2`$AAbV{UDDD^b&ywW8#
z49cI@@sfH)Nlm}H!WMZ^T@nC>*~%(rx+9*dG>J8H_p@IzxG);iLVY^%l0+`Er=7X-
zn2Yh0eECAt=2cJuvg`0kCPF}yoYK`WFEeY1+_HyoqJx2e(25*0e-LnBwd0&ch~ck@
zOHfd=UFmtN(VL6oL=zJ8`bU#(8i1pMQQ$cKkN%gX;1<PQwZ%~*SFUSDU9TCnH+qh&
zDNR?N=gbqky~J@!1s)pI(oW&QmBgkIu9=#fBDsF8Szu<7cpwL!Od3SP_o~b4-}bPl
zV$3e(L(^yH+6C}n;~kVoNM-e^urWJ&EX>4|1V=k_#j1z~9-VC3tS^Y>kn)Sa$$>?u
zp47OXdeWsgQ%8{DSr$Hp9jzi&+}>X(Qkv@fRd(AO6*!mqhySic!JO4b!L0AKH-=qj
z@!|6PcrtxktG+XG`}Hd$$)F-g5#*LpoPY~Nbm79n`1{2fkJO&FA$nrnxnzs0!l_F4
z#B8_<&Qd{QVXvMdw*TDNWZmB)cX`}S^U`vNdjB!)&MfQ3$#rLT6DSvk(llgd(=hbo
zB`?)%6EHbMez;y-sqcNw9$InHH=d}rFeyTp$J{nBt`N(uwD@+r9}}=NZlHi#W{jbY
zyKq-|x3t@>B=M)}HOHyd3d4v&>7&vKvm+D6hTpo~0xQ&*!=|;xN)c;)kn*rZWe~Ns
zr<{J1Vp*r-xE~|)+IG+Rv?#=k{KHxA;z2h+uVTK&q^kbTu(o=ufPS%3M&xP&p=R4*
zALobsaaa!LmFrv7>Ns&KrM$C==LgY_oknlz;zYP`yA<ex6mN&+d1hrGUjcj4?QYa}
z`6zr$VOG&)S?zUo#40@_i9W$V)m4YUks<Du(bqf8F<#=;uikM%pjeL0Z<Q89HWMp@
z+K51J^~c%a5mMpi7i~FV&d#LTI%^Zl6C5*0L?>{_<j62*`2zZvp3j`CQVajXMAlu8
zKtgQ_@`vlGs1jj1bGykoz`aeAhAr_M45sA;WGZZGlG#QhrYh^e`VeHDUjkY`PRr_P
zmIx`Fktzd+G`m~vC{JVu%Eq#5JvB9{;D_A>hkg4*fAyQOi?al(jlf=)9f0<e!mY$t
zW3iCojoG3-5aWyqIPw^-_}6|Gb_1E53yOLVi*1>j1O(SKKdD>YDM<Z-RuUXId~NjP
zaeS9-qo26cRlnP~waV{W-ZK=ciSobf(P3?>bFvX}tQwOuRz#RH;OiRq8vv=DK6Q5+
zr$kY-hCi2jIId0|ab$j3Gf_s^%ekl|@ELiyU6bd+<5v2;yLet;L)@n#js!0=Ow;v_
zO!t;u7=PUey$)Ds@<>RH$<(v-{RPMShG*sy_igL?!60uWO+&Z})+2C$@sN^&&Fyro
zl_Np-%ry^O4p%VWqO67kSmeOb6|+a7lF?KoWekNDvVI717Wt7thWi=O+9}&P#Jb@A
zkwfd8L)ydb_3wR;uXN%djXAddb=N+{xY)(e44<v@h0&VixBjL?6Y`s|Slbq7PPL6g
z&_RP1uQA8?0?O#%-27L<hY!eAlI#cb%T@Y>P4g1?*X>wxhAq)vY^4;I3;dB9C{7@q
zb%?A#4%FYdAI}X+74$DwI9Rwj>6#4L8RQpZyJP9c;Td8}u>ovh%N)pXY+fGSp6feY
zV-ujyGPRT?dLgBd7(se^G~D4keA2tqSqfSVcJ|UbFP$j<U7S80zS4-CAM-EzduhGU
z(}wWUWW=ttQ#UFewXFjtwM}@2h93`=CpU3Gv*F(hG|mJ9A6<hM6q7R1-GjCa4oq?E
zl;JX&VVI6-CfMVwhdZ8?liqKht7IUkEc5=Rx+S~cL(gZVIhI|ND{a~4ry3zgb3)^D
zIVrFMm2D_m>M}pAuN1`kfyi(_ezs(4{~41-R6?n<E7N+KTOTGfj`13tcm5LJLeXie
zA@V4_-2C^@2QrYiLVpU5kuIpQ_j^mlqou_>q9?a#4TDXuu(1ye*kY6%O`sd4{YDeX
z@zIE8R`%IzYd8&F87lIU3k+AAf*2201w&%wTS?W<zJ7k*GWg&v_wCpeQb}2(H!2dG
zAea>joF=+XnYGO{Wn@WTxY#zKeyHl(5VpNEC!oA^kf#`<mGiH=2?D;%K;9jPC%bPh
zE>bMz8Xxl!Yf`CwG}7Z=>o&y|+Ao3hSwGKWwp}6r;79&NCJ|&b&l}$rUkwwVEY0JZ
z-ot)=6j@^1jMAFDJbO|<@?5p{8?)Va%|jRSVVv=5<s{T5vct3^WDlBZ2s}D!`u1iB
z4OPH`?n!7r=wD;hhWE5z$_xlAqAFcKib%;%Oi2#IwV{f4shb_>7B^E}jYl~K{Hm}Z
zFpx(45#dAq6w0MR01l#APtvGT%==8Tmk_j2)jjY`-P}G}6h?jLU4ogy>fhL%5%lO^
ziikUUT&Ks&U+%l6V%gD3QQw0WNM7Fxdphs^vQbUx<3wNU2D~M2q&di}tdHg6{LTn_
zkTuX*054KNiwI_eYqG0+Y`?)R(DV8tma|-96KsFb%`umB=U&V5a{2ybn8crL@cPzE
zGc13z9OFDZdwV&pLdpN(pfmVzY)d<12H%1E<dA5-Hrf4hSg3AQe8tFaK<L1Ey-79E
zk`wTtV32*zzk9WNxhi*Qg#QjcFlpX0@V0x2yMr=;f$QHM#OEQ5XCEfBCh+bL*<YI>
zrqi5k`@frA8KG-ZD@q2+JX0U_ZW~hRSds163{D@9FQGFXzq_njJ@U`6WLV$%-n-DE
z8JOusYg`rDm2A5gJ0TdPIZjq+?EarR^6ph?wqf-uGqE_l8uQ92&3bXT`8Jzk(LR|q
zv}x~u^|w9#L)>cU?X^8%XsmJm!SnU;MSXVpbep4}{DLrG^R&gb(LZAw^&zg?f&HzM
zz@Ali=l$w|77uIPm6yuC-uB;G(1ngE#0l#(4}&u?0dd`z1=cC4>P+rUXj39<KLrJe
zh>($CU?3pC!NCGQn}#|tL_v*dtFL3`%q;I2V(a4qk(xbXT;;kr1)Ky1%oikPAUpy+
zy0!f(Mc(emmwk{!^US9l#J}P4*&Vm#|M$Do89Sza+We1^SDy_i>Hj}QUYCKn5-#(n
z?oL{yx^aC;#*+j_^_Fur+r1LnT3)IBY8#~zPs*KBJd4GNcdcVeqaqpRB;&h(>&<HA
zt$Y63728{TfWEb!JI;sl&E1td=|{{=^vjdS$L;>|g-h{ObA|!$tNmWLQo57C$3@PS
zv)vsZfxvs$yz9Mp%Y<XZ%kbs{4}(tS=08}aBNtE|z6zhx67cF*y<0c`Z1)c;&EFE9
zIBH+4Q@sq%ME@HK5GgXUr{I)CKd2|}cf6PTv~82IFtA4d{;iP{e5(e?jog=^2}Az}
zD*pvi2GJj^QGFD7PMoKwr<sinhJ=KKnblRBv3*WT4N+nd;(y2SzwQ7F#3!yP^!Z#?
zt*MOp2@JM#C96;ChKi%3o132>-R=)RuRMy1ikg|3AxTO~Qr2uK{DI~HAr8R%@3i>G
z<IWhsy`p5K?y=S!M=4C)k^+v^q;g@ZCfin|si{0I5v7WXi_I)8C6h5zI5x)57#N7}
z{sqIZkf}OE_wV$mfGcEbgat)K;^r0>W>!|mz`E$hrjl*?|C5pb_Ua`Pa2?d%6=Urb
z_EfBw89Bf^9K%2V%JY%bF%a$7*)q(G){&)ceqF|~x9vf<(2PE%C?`oT<Ufz5&)2|D
z?wESut*-b|F?>27rIqmjyApN>f6}@k){g(QivJo-BpHY*fR7+S@<`~vf@f8HE0@`+
z@!U8J=6{>hf8F@m?9mq_`amtnyBF>h!T%RHg?|Yo3c5r7Nl*v>Uk3S)C-_#AAj=DO
zY3T6J{rI09|BFn7>?%kAJ|&X;Pw%``gJ2*E!noLc)IUeh-T0^L>%v>$B$`5ee$9eN
z$58)sfVwnLU?_<;%jpSE@c$<pJYkTRW?%u!Rcs69+nhH6Ff?8Jbd2ieAL|0mnST+#
z4&6j`Uot&G(?8oM{>Uy0c$k!_T$A55u<qjD_%D0v;$8K-V0(AJi3;ERPY=kdUsc&P
zygR(voDR}Q`ghCMA2ut$`*+JKKfb(k{D<GWbkl9p?*4}%HmARf;LDG7;+l19re9rN
zJ!EY;M_6Illk0D|7uIsXb*FvE({!ym{o`MHwDa=6dHP>@5MD?JT!^>sT0MJwbg0Vy
zf8hz*)h?n6aX$zGHene)y>U%Y<Nwtg>k2~H=p2Xi{1bGambH}!6N2_DN*EpdUobTV
zE)Y($w@p_o#Pwfr?dMMsB7k9N_W$Ah|G_n0OBe{03TT9rUrPV;>p(<QQ-}aqz7QkI
zSiWB$|B$v1k4jqp#1^`o;p+9XW}`Z{d-~*m0#1MP{}bSo9q0NGb!l>26R*w6fN+}e
zLEzbEco3`x`ULy|x*YGvgas!MUt#v&Vuoa|e;G4)-~_Zk4fbD(zvC8SKipitV>2$Q
zZ~j{(p(AJUe{u2;|N5Kok&D=@v)`rJ_MfDLB)`r2NwD)z76%T#DojL`>i}1U%=JI*
z{L>A>g6qtHElWWW+y65%$jE<T(Dm~!0C54!Ir`gvtKO79s3InBgr#^>_gfXY2`U^2
z62i+iI09Vb7{nSJDV;nbFGXTj<;QYxH|Ik+S`x)a<fvW||GVq?6)c!LgQ)?e5QQaB
z_<QgUqQ0zpz4g*(asoDeQ^awvMl)R@<fXWOfDByDievbY8Cy^B^N@DmRwV>>FzVKw
z`{~U>doG{)`THD&O#L?spFsrD9zYB@aH;>MRsLl_1PKYb^X9JHTR9+Cto&QPT3X-#
zJq*tyA%#&a^l1m(9DaXACIgwAeco(&?b(JMfuek_{fPM2Oui)bIrLzn*azx=XaNjz
z7uJP#JQCK^jGAg+M@1G8sgP?(G*Wb~;)bZQjEb`>L`K4WsuV>nUI@W%JLVxMOI_0@
z3NN8Ye(LZ-7orm<{I^fmJq@_V04!khvns{-2qpg$Y-xFA!Xe+KSF+~qrh7Hm%=JXT
zM~m;v*%wihE^__PdbxvzqBcf_4fwVx{StAb2#b2;<dB|+nG2?U0(1eUqd?iL<ikB&
z!hoE$6dpRpfMb`!bUpy3v`2j;e_U<g%YM%02f5enjRA>5OP869=1W~%jsW$?bMEBm
z-K2Ls@0Ak1&CUf5R!$HuAGXW{aFPECj5~?<i~VFg4G)rqHxgqX-^rr6y(s@j?y6bS
z+m-WaeSoGkL9E;I1PEv5zk$2;8)fM?w*Ig{*B);Zo|B`aBL7R;B=BY9f&Xc)m_Lb_
zAgQI}j3L2X+Pmu|=Q_;b&Fty&(pN)1o12<aKKmD1@#8}etk>49RO=;(5g29g7w@T8
zqEqmF$Do^pl{Pk^OQ|(+Z*3%C_qx3%EzcH@YPbB@p3e?zBFq3f=RtBT!0#{LHFUtk
zB}<y(6(^R3o0-(b$2j3AqyMW6OKS(t{^hP|q)S?W<+|=~mEDIi+>O_<>${g{PEfET
zW>jW1|Ha;4ucs%@kFqtdP3{*p1>OViiv*9i|Ha%_zqQ#d?Y0F<Y0=`EQVJ9=?odi`
zE2X%*Lvbg?-HK~)cXx_wa3{FC1Scei_ub#O_q)&e2hMNnO0MKt&&)luX5DMe%*yGo
z>d8^PcdEYG)Fs+>ZrlEtEN`}N1+&y{`f0+_X`@0ts^&D-9>1pD67#Og+lVe#tAw5W
zZHI~nraLIhDy2ia8a!Fb*5h#^zl}LU*1*y``l6Xj2lw4S<AJkEF+EE{oD=)2R<)73
zIUNS!$0=c^Q}3IWZBf8|H1%hj_B#`ox4tjLSGGAw()3}wGGcN8lG`%d)<Br4y|qVM
zY5Due?IOF35(enS{Tw_O0;jc&<!(EoS(tN|);Zs@tWL8IR++&XEbvTNm0Qe+9?p#o
zqg-bq7he=2H866LweP<0$f-ro7T-$36_$x>DULzPHcW=)2psc+td3f1(U)(i@5(B)
zs45z__rL4v;KeiO4y8m6;O<!D{vn7v>rs7PXXWPbMrxtX%gT%Gj|r05%00_{yU43D
zA$_g%AqLDLme!NGt!AXF1&&vGWCQi+1g3`)*mB&j)peHWJMIqVhR-7dWg+naXm{h9
ztMtA7AQ}a?OUg+~z*m?jnI_kl-Q6EHR3l!#O{-R;({eHys9&g+Rb^U!7)@T=ak<}n
zlG`Z0>XicSOb_;h!Q6+t_M^4z0MBL(0Afi6Y~bnR{9RyB*N*OEQFMRXRh>O-oHNb4
z)TwFvEV`+F3cjxBWM`#zF;jKevE3JycXS_vAMk-Z^{GJex7-%4?K;+O&40K_EC6!(
zD;&mv;kQEA#0uUQu}>kV6nq~9mlC?|#M1Zx0z#pbCs0aXRm!(49rC^h{E|8`c-bD=
z^3WkCl-_vz?kE8n?|dt%6~v(C)+nM~#>Noo`&YO4v!NW>^EO51s%@ZUO2X}8bds;9
z^6TNZnbM_AZ(X$@n%5OOMWaB~_j1s|x8@7FTzZ$_AeXAJyy7f53(Q$bb@Qq>1bIy<
zI=|x`HDvw8L@sM<Gcl4Mf~xzUpMkVH?T7M_oo#rFko4=0P+}&<A13+-`B$&}RA?n)
zt18XXoZJdQ!vitk_s4VI9k7#CNF$Avx_Tbjg6aolWn-?NLaA7%0u-#ZR&E^Oh?6b}
zQn^(gAslPqV|oMq{&aP&qn`uL?o`CCOkX)h9pH8~ce}ekHsS{9F=HP!H|EY-xY72W
z-Gbn8Uu$c<iq6I?G4b)9-<nvf=3BWy1MxV=rcI8$(Gc02lTFk1*d#<-JHWg>C3WW*
z*zbUK`VXHkkcQp()IUD{<N4Qt8qlNF!^8EGZ6X%%wwpjf5N!e+WZyZ5o1OT~E%5g&
z;NacQ_g!{iv|w)^o+&vgs1NB@g74d8iCJKVnG?2A!Wo+s@xgISELK`v#=7Dl!>_fr
z9X?*~qe70kc*_^kcjrG-@mcAJ_fKO+V`=j&_0)dfQBPtP+{N@)Bs?-XVP{6pPkyIk
z9(GW&Z?93J;BgSAAxOy0zx$;Hgz@p(Z~cn<LcZwtJa{!EMkHBhDA5m9!`R#E^GVZ#
z&^`s(Z-K@IR7@SJ{wP5F>`M;ZNvR7!9KL!&XOTK9`%^J1h`Q7dLTS0-=S6hC(sZ;<
zpg!-NEEM}tC5-6RKRhfg<uXc8E;Oj7fQR&4I&xnZ=jP-PQ@z0HC7$7LON-9xd@hBr
zDrLDOME9KAB7X)tE@@#WegCpAFSA-brR2iM(eVp?>SAvDTR1<_M;hF2FM=S4Wv}XJ
z`ncx$$F-lbm`=4J%TlpAY^*HZJ!KYF@dw$|#OYemtV8vU)%1Qyf|G*ZSnl#Auj?8l
zS<UI&i}KX>1zmI~K5y9=aZws`g6)r~Y@$oBTqKRrydvGrc$AsvimCJaa>K59mfci#
zev2PyVh&z$L*7Z;#7puHow{m|-(BKu`efMbA07S9@WFA6*h59F_-)u<5sw5`#`#i?
zFozHJ)H<dM(iX^rexE0c6_wk@T9&uSN0J{uW%V5Eh8`5lVqU-FQ++QHRU49J^Vs&t
z#>2*d@8tAdMUfc{X8Nuza;)7j!qG+FM<o8>PA$^XP{%cVv=>~8r!1uzW*`pw;1o(3
zvd_vpH$!bI4~h@zsh;L=95|QIqUw)ylq|Z&k|QcGPPk;dJDhj@d@oLu`|C1N=jptr
z<Gzw(wvw`TUtFJ>$?&mlZGvw1{(e7QEN-)stmA})cY4JLIvb}wR;BJQ{7*oW;izdz
zr}S)8t4lH_H{FS`v)Aa+<nN5k--b)&O^(0CD)+k`GrHYZ04_r8hnaTjmu%Pph}-@J
zK%*@@8O>En*i(pMXLSWGBVMedFMlPs7&kes3H^zfT#K%Tvv(JaFuVyJQRvuquEE}J
zYOdCUKTrY9DT<Et2Z~IE5KTKp3ay=R_yl3wpl5+)Pk>fLrIu7u1#oM5N8+ONKEiDl
z%)-Xvd7@->+ef=%0zW3D1&2ng4{=X<RNOu4Huiekx=n9_p|(v6wZEfwu7XYxrO9fc
zuMG&mv~YNYIK>U_jwt9p_b#Mg<!wGh3o#&(x*2pVG^OQmm^vc3+aD7B33yf9zsNxW
z?MWSBx4GA!S~(hmPMoH(l?=K;S3taa?T8tmULau~I0(LFEG`W)KjAq(@a_Gi+Epkh
z&fj4*b${mq>@59Sc~CG@I9e3quydocXs~r|%R&2+z#8J1&!l~);CanrMS_4uppGm&
zwCe#8y{7VwQ6+Q^2vuP#oA&yidttcw*rze?zNp33??3(Sg2sSLjEyvG+N;iXs}Bng
z;JsxKYrnrUT}P+Daq`%wD35T<#97r2u%moS?rxt4c(wg%hPmvT6dX0-9VN7OcHb-9
zx{N-?_sh#og1^Ju`<R;f)R0?X686WC!j#X7f;;tTPSBl-m3ubmrmcGcaRXQmV8395
z5WnJLP}-?!KqRk2(3-tLZU@i|(+rsCT$8=P_U?%4=|R2Sr1_k!2`WEWx8pAU+T*H<
z6#T}<tGp>6{CXgH)q5_;yx`Td`?-Ga2O25`w^Uw-Ya3{W@U40x%^k#DyXf(8qC%qa
zcYVTGR6%mq16T?Adc<M%;yL^5s`<+Oyap7a>(P)r@%!uciPst?^pFMkTW<FZYsE+h
z_M0%paAwivHTbmsq1(hvh~Ko78Jvu0d?e3bzMeCH9;JdTGcLR5<{QUgAq^=W?#r$<
z_<3`Pv4Ry-?~f#e3hvhq;F^qUB(BmUkI1{}xf6EGNvGiEy>;7vF0`b`AFB3W8u8UU
zKSv%)u&g7Fs7wYj>VsB?hC<yC539C1w_SuOgl&6biB7m1&`;Px&Pd-Mfg?>ZO=?p1
zI;w;VJg_3_VE9%y0@q72wM7`(Fnbzc>)Fx-l?5fIrnA<t8}4Z8!hX4lSjX@>Lo1S}
z455m*xp$zb$2<(grmbts-D<3$KZw)8VQT03t6g1B`IeaOF;2t`uSGX4EkBrF2Z2r%
zAgl*J&_zt4E664<G^w*gJSJOjZpbEJw1{h`*vdxcq%p58g^>u<j<FxQPC0jV!gtZU
z!UFm<v;`y6Y8`h+!fo9gBCpyH$N?NmQieOU60$dUQ#((+T=DZacDPedAugkr8K-Qx
z&|woN{Yh9i3q+6N@nZFp=r^xN1k`p1^&L85kIE$}m@JCRRAWRL3Oe-)i^=X!<sWUr
z7_#^}f56?KDwT}5pL1DoM`$viABLP?=H8{QdN##at{A;EeVdOM*|nbUfZqxd1TC!C
zrV<J?LtuYoyq7&UzcwUiBy+K^dBS@dMr<FEQyV5tlPl86D)n31AH`aTVL?&A;SYo+
z7!V+|Ll;))=I-rtH+ADRi;jMo>>&7vS(8TxRj_pn03SvnPB!yhTz}W6vf_3x?}!2)
zxDF9AK)6&lU67MQ_f5)N#~F?|y$u%vcDl;3sWe5fT9xIn146*KW%YGZ-5`blW1Xi~
z$s9S(!fRYr%=Hh456dAeUVk#z4Rjt*PubJGa;#vcBO}xPO}oOvCN^G1$xZ8=K%>z}
zmL<u-1xn~m$DLt$T7y#11ls*bDKX%L18`fTTLZWC8Y@`N=Sltq^4c9s=@;0I-{EA*
zJ0<f9b?aaC9{1cW$KAO^c#gCl4bJT3o!)Y@Z~o5rstqr<x&fXb7HWmuZ=%(c_qaPA
zj>Mt39f$@KfyJfpbc8NcDayo9^Ns`BIp#GSE<ZAFAASYmI(^*aZoFta70$-E487Zt
zST%ptIoCX8yK%-yQD3w&aqsbboI;pojEe6D>E5;MKz?+Y2)WLgg4xY-Oko!Y&YLvy
z+z2N!x#ZC&&*yz|7arE%ngOm^;=ISGWVqsj+t|Su#6uT*=UV<cx){4+t>3|odLsHY
z=skp||HUwS0FujQe*TN-FUxDc5&a*A`6gyT1B3ft=K>lBSY98QZfxQn4b1yG`d3|i
zN2v3id@u$?9bRm(p80FMkNE_+{ewDEqH{$L{hip_<9TEVU~gts-0i;QS@Ll2Nm%#=
zWBa-t1}1k&XQQGL&2MO!yS%zNuUNgjx=Jb^MIGtrE`!G$ynxou!7}#AT(n?$MM^uW
zAeXur)5p-J0#j%0YZtF`0Mqm9AOI}kk!$XO92!cioJz%lAa^hr&x;}5ADM&q1_F<l
zgl*e89!^i~&Y$+aHG5&CVrt2I?5D7W0#b7?jDpwAcG4+h-nWRwGhyiU68OiC(TC9J
zJ$4|xZ<oaF!ZvBjZy{oq7IC{94!OW&w_1QOTe;heJeix&qQHrAhyFcBdVmgeslv53
zX7|x&9~4>t4#!kTaL#z;0$9!(<<`BLJmID)3Dn3ex>JhYFSkuv@~-S^J;M^V$QDJ}
zY<ie7ZMV^JZk&cqdI(7{h3^ZVOKC=Rz_;2`{E6Flad$oypf><(rrLMmyof8Th02bH
zD*jTJQ-pUX8ST-k*K}LS?$Ls63gHf63h;hJyGc=Q)kVnU$LEt>_>nHy|M2_5DTCpT
z?kb#ws#k$Smyc8b5wUp+9NW3d7=t8Z>;kF#!4j+6!DqU+k1kI&e>|VvT=u|KR}rRZ
zBM^&r4;u^XX9G7Z?Kdt11=FwxZV25#5x<ZX^fA5zmYW<B&))XQ1P*V|9mroZ-XTq2
z3?ANL2VV0jjjP#EAmS2JS(c|j>4%e3J5N1K`y*;0CMN*LM`iA?**qFB!eh!>y!{j-
z-;~C9Qb}-!iF+nh6@Icx<RKrE#tw4rSFqEV(uZ%C-1H7vjZCT~x-CITA_VrGcG?x?
zF$(09K%PGWcwv*PthA`hG*qN?*6nviE=zoEhqx}9r%E7%#}a|A&_pT*!DBNtmig$R
z<5GJ!c44jj4T-=%XiHb3cWZf96+HW_@qnPu@zvFAP5b$&xtMoOUcz=8{*`o1rQ4i)
z$XZK|L9l}gL@Na5tGQjjq{EojI1r`-Gw>Exjnwf<7r4Q<jV=LRlwA;ZAjF%Bv!-{1
zT~A%Ml|Vuah}(SCVGMHR!y(5_C7Xl}Sn_FX1#A~M$f&FEc&csxplZ5pUD2dmZb=<6
zQ(Jf6Ek4vI=(;(zWeY?!Y@tS-;;NIxdUQP4#Q;1?TDR`FrOai|+6uipTDLfbS!P5}
zWB5J9gU9lG+=gFtGByZZp?wcKK<>#?hBOQ&)(yq@aff;y)-tlNG*_n>Gn4vba+se8
zch-NJ{49;nN9S>=Gv4H*C;t2Jm%zhaLx|@+_~Lj}pY%(2@nQSv^Six=(Sb3c$BD@w
zB9w!Sb189&^A)_el5Vr?HZf7~ebjAOPq++H`s?=tkF=)=z<rNgfyW1m&N>(0i<Sp6
zW0ALHm1|B1<qt;a>3&)+dk>(Gh{t%AVJ2>hsue8}vn!4tmOU*-Z>f;>(hIIaYMj0x
z?Tl6|Q-)j+4vayr$%^LC?X{memqU9i;%TWnXk1nn0_+EWc|Meng+T6uF2Vusxfn%>
zs@1*KiE7gNeggZvECgSZjEvt5O}c5^^i~YEnT|@m->-CBARNpAUD)3Z5w^eM){zjF
zq}{rFDzkY(B}`r6Jy<htImq;(OlRdIcgK;_g?|9n5R$g(Z7?g!K|%)KhYI&@((QcG
z#WEd9jhcaqnAD6%*_artLE!Q-A@$Zp2A>PabDIi~sEf!ekdbkEp0KJ?Is8i;O<QT{
z1M33Slg%C550kDkj=90ExXQ0JHGy4m94CpUh%WwZuma*Kzbf~%+;-68R3o~f10HYR
zIzxw$Aifw#>=!y#xyxD7*dlfD6eT#L``RB0&V+c}5GU)}A8&OFZ=8?)M!+W%w}U9`
z2HaS=jWp+iUXT5QX{2C&rNt_}XZI(rabjtFcXt;>8+(6$Kc-*cp7kQO0xnflROEAJ
z7utF5ns^=Qd*K=c+alFvJigk7bfwq7u9Z%qD}pwIbmC4u@02bA#(>K0=W(V3S-Y^z
zxa@-EC4QO$XvOd1c)EQtH)mtCY`HQqkvNi43;Y7pyz1j%&+T%3;*5-hi3OdQMNrHk
zmgBs^mtu+8oFBa@ZK7hat@m;rV5XJcY7Pi>+bXOc;{J$hY)@ujmj-^Z9#5o7zM9?j
z!UHfkk??}o&O~<W95t(y5^@blTx=fjWHPSGSEI53FKDSKZwwUuuH>WMJ31X9JhhI8
z;|CZ<c@#rJ?JpAHo2eHg{bX@$bwnmR+ddu$QHtBFxuJ+TUCYhV-<WwLJaVeTsX@N?
zSi(^C_Z>IigI61dFs!5EJn{Cxuwcjy2uKy>H6yd#Y>7GVTF#jKA=epkb#XB#q`h)c
z(;zvgZ5s`EsM;OA(O#xh#>fA@TC3oFIf#8g9K^}w&5*IOaQW`HSMRZh`*%J?i^P6i
zsy6};D$+*0Qel<UyYu1~VKT;cMmmwY%Jhb~NNlXybnFluC<BW}&GcOLo6HEP;;d@V
z1(ee+CgtAQ?Rq$6TXXmlaM%qR!8*;N9*)h4YF5;N54hch?6!Vsze+_&=$wtv4o%Dw
z&{dSi<@-r&55klh*o0|lPY+^gYqgk$4VNbcRn52sFjd1z^zbM-)1#@PN}?ZvXpJ(4
zhu>W6q0Ujn_QU_oaAA}P*pgBzTx&ld-vyA;Z%0-D<#b?|hq;{^-ZH~TH>*qlV%Id!
z^FT{6@CkD{Uw}u8#Fm%&Wg5(OHxvfLrC^Dl)gyTfhUd*Vs8ok;KiIm5KyHn=a4*3u
zj+hft0EX?b&2S7ZV`nSu6#FF4zP_Q{X|iUKcJ^s1Kj?eZ3Xizfi&?LWVU`y46Sv3I
zEfj8H(Q^KI#%=tW$!SkD@5}t|dggL=^YVY5!k+yU`$K{?L_J&XAG)_AS|-0Qh06Et
zA|}f7YsUR_!B7eCU}kc%x-PY<K?jSvU5PCg$VvM&)kyK}?s6A}#|cuSZA6U=#V<uX
zZ8Ai`%@=w~?3C$o{;b4tF}xyQN0chnBl5g?{I6BMYS?1#u`9WO@4x3J`V;()*6Fyp
zwy{s#X4ZO(B-PFxy_CL?%x&x4!q0D8a72assUD$J=wO8NzIf6?8M)l)3`O8^;9VM@
z`$^wdyzi0lYAQhARh+CvQmoG;6A?vwMqs1K@ki@KZ?b{gPmN@a$f`Zp_`uAm>D^ii
zyv^rReauNDj_R+X+U51FlD>&HMsh>$T4=T|PeW$hxY$K%U1xzw5tQ)&&KIYtY{)l^
zS`j#xH0@&ewps<s4YMQnFL6=F#M{1k4_o(s<lyoawW#l^Df6my+Z0C+DUEt&(XX3}
zg*pn~!+r@q3?5o}DaWB1?5uo~Cew7_dU-X5O;JS#-Svv-Y+IRof6sZ~BN2Eh-co-&
z(QJ&=SR>ctwy;5#w!VoUtRdn3<pxs;8}v{oSkt2;^ykV*AVaQiv-iV?Qn|pn$Ku2R
zyeM9W^2l0)MBuTCWJm!snUl@kuf<($!}TwR4gE_{uFUT0fLrGk^bt=ev7FwLhxkSY
zh4w_glSgf#s#NUJ(Mi;BkZQJF`k+P{sdrlOAI?p_6XLi?M~&k*mtKUo%0a?qYrT;*
zyH@J>V{5PX?&e)D-R-F;$he*zAKX$htiQc3&j`WX?0?^CLqt0`!>;O-a+>kwt%b_v
zIJy8A@(*<$A%_N9!@y(PlbPxUhp#o!xJ1I$w4>?GLq6O*@b6(uzubdm20llOizjhd
z<EJ>QM*=$1{aS?FmBV!Fpzj1+Rk>>|mR?$?OlQxGqL(^cHH#D@zj{vj4%z<~HuekZ
z^k3_Fu4t3ri2+hlQc3(S%mmC@!8Xgy`j>kXy?UNW_-P~;bVUNK{po?s6RF0AhpLw2
zlizYP>A|e-)1ODw*w7GZVRPu%?{JsOo&IdNRpjU^19K~5vNXUSLP+mNFTF54P*vj!
zs5KJD*$Q|Gm11JPbf-V1PTD1#XXXt^%H*9hPA3%z#4@LeMYUIK772U%JCHDim1yjc
z#gckP%Kx;>S7~^2<9gQrtPZKEjeyL=%zKh%j3OwNH+}9XIBR?8`KVCgoXIm^(0*7t
zYG2Ir$+wA7*4+{wQ<ZDOT(!AOhen|`%zmNSUWH?VnF8+t?#MB?vxXac5996mSdou)
z=q>5|hT{HNsJ#N|v-Yzuj@FdgpR~wIq5ZHNM3=}i`L+_(?k7Tr5q62z3fH1(*TD@?
z)Fzo+x+Nod5ib#HjFldidq@~F<?v!wU<Ke)`$vV(iEQir&dd57s<hyWl0=rHO*2;|
zYY83)s-^0$&17aDH7AX`*?LO-W-l5D7AW-Br*l-P$ZjIN&=bw?mO8M=bMs;mU9ni`
zIjwzq4}jB5H+<S$9sa1bmp6uR7vbMA#tI8RH&;KZPQ|+8r5z1EyYP!*-~YT_c(jS-
zBEc@l`&{?k^-1AeCF$aa%g@tUK?Bds{f1d3&U<$yJ{=@R7V5P!I&7C%nSG=HZCVZ{
zFE$ws76+3BM8q70qmu|pSiTtN1c?rxo-{8Km#D~nDSIt+X$@+<d5P6+@9Z>*ZUd<Q
zI*6iD7L|oH{pGaN(C%tdo$T%b8}nqzuL3|CCN)Z4<qF;1TTYwfq@s$xP<mC{b3yel
z$bI?zDvsm@wZ*FhHlYy@q4!i&Vg0wCYz55=!d9<IQ#e{VkCj^FEXNloBu+>ySXnZf
zzAn*o2nT3b77efA-=cV#`Vo+}bsBHNV%m5Tw<4p*`PbIJ9)7dd_8qRe6mNXdYuYJ*
z9zq%uXDV;OrZNhDHqn{RT55>mippz<+GdMcN*4I+O<7smbjdui6VVF(zMLRd8YOQE
zdmkrWjJF$Egqyn|M{G01!{9=FakG2oko!+}&Y{6hir3z1ImLBZ0WVHENTP(~N4Y2Y
z`7}v~P2o}|6<9SX94QDS-a}qjWz)&%=(Zs^{d$LO%L%s5>s)|!+FQhf%3-YqSP|N;
z?f4|URCgn|w_L-p0^)d$C94_!#Abf;OK9(oQ4Q}b&=@%&!a%b3XNaVb%U4n)#`m<T
z?RD6Dphmqu2~?`&x43i5)}q2X60}1pit}l?KY3YHII3&l`Qn-d@&+U#THDu@fbXYt
zM9ijZxX<Bey(*OJ8du;(F|&SnB;LLALNP}~>5_CDu)M_4t>hBX4I>x*#$L<$;sqNv
zDB3@|m~*XwZ{P>7YwFuDuLrD7&bV@RUDZk2)bQtQc$d4=u3oCIEBu78xOzz~uMN$N
z-e}D?5TI(gXBn2H?0m2DqVAelMirUPsRo`B8`YPinI;%Bl8zPWcR$Q$INdrgD2Au-
zy!75KOk|;c-QJn?s>ERbexPSx)B`7d-|zGN=)fS7Vq^4rNMFRkpP)eO3*9;mj0xz;
z^S$LZ3>`0+kSzR|r53Z|InV{8*=HMrMCLC?_Jpb@ul8*cEvc=dqpjG&_R<_C{{4IQ
zHNNA?-AArXzp!GG0B)xGg;W()g>h~kGjx6z&ToFLIvAJec3lu*mv=rZd^N+lA*+{<
zc-F$(7gDVx6h0~}BYBiR<T+ZY$#9aFJ^`m}K77N=5dDLDSeNle+K%boR@VDcxm4gM
zvCZ?&!Ub<+6<J+bT!%5s3)k<w{c|*UN(1fj4&&8Mnx(?x4xe3%KV5Ak@%!!k_~xbV
z@MvYG_?jCv#kQMH*<q>@)GrZX>M=GHe8l$_IHnURcSvB^nc(GWsP*ARYm@Sb>{D-x
zUjp)1AC)ts#;-hTalKcZvH7_x`XjaNh-O;oJhcX%DWS0EZ&7@327(bp<Vy-YRc0AO
z3ef7J8(-ct%}bI9zU(&`s`jDsxmeGBWBz#8Mxa3~)$Ug%@e4zHK2a1Z&XMfdeK_xU
zmP?aJh@#g7>Hc%F1?M!Atlll<{R(Yh$QF;#QRBLM>gI?qDnSRZ#(6$9x1m6>;v0pK
z@}*s3d#yuZ9PU$;SgCg)XvVrtsI}3A*t%^V9Lf$07BW~Uwxq3XnFA1-eN22qOdH{`
z64BNH_vQ46w6t>wcb_!)mVy|cH8z_rCQmrNPaG0UhT6@tD6E7BMrccKXyBw%p>7N{
zgjGJQVV3GN;kJQVux9+)_A<X~;mY|JnMOFby|6S``*nXCEQEf4<EDe`>tB;4>TcQQ
zy)aj4I78@q^H|oHA-<cZ&on(K6bhO<6=!?%j(6v#@9K@jnd{>#bc8E4+wu3xgP_Fm
zdT0<2spTiY9mV7*E5>#)=>&?krgL9jNv-RPEBR2||3Tb4WNGf_$A=CLEw2tR>^-$!
zw5LPg>>&66Pi1p@3KergUFPGf)~+$9&2j=qF_=_Q`sM}KHjbh&FLwk2+9^gyPb2&G
zxU+>#gwH>CYuCimoct#K%;c8gFEgBDaGZU5rmVteBFKs@AW2^<!jA3%xAD9_!@V`W
zbN--6nnJtC|BdBU1zYXfutF8M{AJ)(-c0ckWIR{VqgGDSr=cDG995zJcW0N~e$=8j
zVU$(hoP#jxeY=18I=&QN+j)M->Go3#*X`1@o$v^$&Av!lG9@x4T)|Y501nvpE%Ark
zpWO=N>+9MXmrmBGv5ZuH&DMPSl5L{5N3rI&l=;-}NmZr-cZ{ZTjlKNl3jOU2_x7_+
zkR4C#xeXG>`aEJ+#T}QV%(U`~34&g>6K`B3F|~Y4k<c@<3gos#8Vd*Cn)kX}#F_r4
zTrad+8`zDs>TI@_iTTcu6ev{HekmvQ)=&s9vYiUe)M80DH{jVkZ}fV9x%?6{(c~Wt
zTZQD1G708y?~!yRUU*)O=<@*pNLMd&bmv~;E#Z=TKtfXJhCdnaRPYd!E8zZd8%4qz
z9O^9cvOHTUIaL%Od2O8Zs#AS$5n4*ld#X2m%=2~uy0bYpqZuRZ<GIXp=JXU0d!;t$
z9agB~?1+>A6%}M=Xapx{cd_$<aFlw4N3!@XaRt&ObSOmd)|g^@X`9l~V$|K_U1}f<
zrP+*tdN<!8Ff7ZtL=xT76BFbO7oq_Y`YA4x5Ub38pZye5TR?$j+MIv}AO>wum~L$j
ztlUxyrQb2Z+tQwTYwlXU<sH1W-2T#fHuOT^Vn@4BX%J;anZytxx_q|cKX>*J=q2z#
zce64O9MyNQ+R^!iEl!7M7URTuWtx2!BN*{w7y<dLYe!2F$D(^^HJ>qGN37XUE@pkO
zUsGWDJ?%d}vl;DMSix4KOd^XuIa9=9lagsTG5bpq5s{{D%}|A5vfMr;tS=fIS6_)p
zOlL3bb6;lD)>Y3Ptpo;rr!sH#e0Cq@^|C(-qc<so>E~Km)Hri1{gqg*-f0Txe9^RH
zy**J#0u=nyPjVMqgYU*TWn6$_^TaB`t3pm9C~!s}l<^B3X0qW2sNalWNqk>zXUybn
z7JKcwJ9jf)j=<fM++1I!n83w{En~aH(Aac9(YZ1r{)`Qy=4fD7XvT$JZXx%OwMNQr
z$`~+5%x-aNyfhD~`Y{R|-1{V+sY5Ib)IBh8C7%*{{bl*rGspbxAW{_m_xmu`(zcb4
z4Gm(%oH~?stZ_71t9LyTu6xI5g(`A~xRWd8q_5qetNa>{Ul{~~DpY9J0~9hO>Owho
z16J~vs@6rA`D1rFx6&lOlc-~CS=8+Z+~`t7qS9RE;AiRd{3(wxXa0)$mKoID$_?tZ
zNU{6`lw-M_obikxV9sQbc^A?zMVy{mK<dlqzOhim9^=jRTEC7Y)3l=eRpB(=m^|VI
zmJ7g#3l(#%J5fAaI+}12msxLJbxePy8QHU0kAXy$T70Xx{>X<|*!x$xC`D%oF6#O1
zTKPwbHi;@zL|<%FYxqP(hhHKIptF4{ae#=!#HM{6`X?|g9hL8@nJ}tCCv@FwQE%+<
zlqy>X|3Go@Pj5~O>v?aHnZTJ8`aUFW6njm#4n^l-^G*06KLJh<=4SV~-#5+yO?C|+
z;)oa4It<WdB=foFKn|lJqGZ2gygq*7wqFqpez$N#olHw<NnOt{?Fd-@m8jU^t}Lfy
z-OISRsguB?A%3GJcW^w;{K3P&0T4{Bn;`<S!>vk9$n>`Q$$jg_w+>m=JU<JYQgj)Y
zA)4!GsZV3p68*VKRk3k<zCtwHgo7!Y?2}MO-zCI=!=EtLX>=gwah85M(I$q$v}zLL
zqJQA8>}v4y!?n=kOZPg=AY5(Ss$Hzjwtk(?r%Z#Y>kQ|>BBh>h7%|gthok;u1jVoB
z&lAU+&imW2A<uTv=94c8;u^Ni>S(U&Z)fA^QG+naeWMy!zAyw5)ST;^WjKs=CZIS}
z?c`F?gf|h2aPVqf4HU950G;g_;aX>$T>ba@<G(}-H9IR8e;g$PE3Nil-^=$1rL|+r
zh)PuBsMSzhZG6p^FhWI!CXsEF(2UYd{LW`cWa)k?mtl8tL~UfmpJ{*Gw>O!??e1)@
zuLT~Qe>W!G1qUBiT7h34@q4}qxg<xINPIVKyMnyzek1brk-l#xySmb)8=zLjf+i{f
zk%*1nZxd*6Y68okWYKlaH*FCzF1n%Q(4tGsTm<qGh9n|-PcI*Xl<bm&dEH+>g4~{`
z7jo-^ZvY0mD$3{`z<0iGu6TzE4s1uqHD_bG!r3;5wCCVSPNIbxALo($ms#IGvZE{!
zj&*K`((Sp;eXcDd%pTNZQeWLuS~|Zw;xu!b^V(a0jDH#pj_SM@PSjuCNxywKQ#aUu
z1<>$qaCQ|vNZyktD>jcf+@tb1d*psI5!|jhDVWVXX#8?Nc(Iafn43J8+A=3b1b44L
zk_fZAK(qR%(alRn^|}li0l=S`uLt^BD+L;In%NFx8GGZ^<@JnZ%`c;4eU}ea^zqZ9
zkQ{}|O?m!AsU%p;RB;EQ@BP9BmUUfkE;QP1EuXDZ$2Q3xd||V}51fA!ROLCyX0_Uc
zRbVD%>S5}F9H=b**ax0yGsT+5r0xznk<*F(m8d3kImgGS-IS4_EfiW(HE7iDnq`th
zu($jmUu*M2RFfn{Xw9cE-k}PXL`dPD3Gg(_b}2vUgDs5K(=Lyat%TZkXhbe*d#sP1
z;QZWl7@2#W?tf`%<nJZ?#twf`Z=h{?trw|EGAeC^<7m0|Edv}P8@m%l#YIQlE)qXp
zi^MbqDQI!lw)6IU-vmjNKPvDrq;?iN?oUo~Wec_c*6u9ocmTl=<ZH;gBlAorK@aU=
zFs*b(!8l!NMG8`wtQ1vsiKD7_I^g<pqtiB#o8LKK*E6lgvG5;8An(^N?bpQb1kB?}
zpEs;NW5rR5c&j8S1=$pD%IfS}%%h=xN69K?Y2+67<`*GG5F1aKx<rfHW<h~g!C>#;
zbZw0vVTHz)1(D8gO=rKKey`m5y(0p>^QN{8JS1FhA3TULu)y^b8$UOfO$vTgbNWnD
z^J5H~3i#-(=B@piOfDpJ%;_{Kk;GRBQGvH=s>G#d{Y>Uyg=uKLe_AlFi%?tJT<>)K
zhPM+)Kl8_pXedJa#=5R7=b~j-&z|~`S#0~l%m(MQIyL<G6ksEcJ-xkDOv9KNLhWZi
zm3K9aGZ3?_BSmwY(a~|@qRnY*>te$ncZb+4Y$%dXW^NlTYm4!KAIDMJ;ocmSkmy9W
zT9`vaPfz$HhA(>mMH~n{yK$Z;cR~xplp8A!%F4)O9U41#D=igX;6bI#PnhBT{E`WW
zGwz^|Eq}@j&n11wV;q}N6-&Xjg*0(z;~_}CkdKL*A%~3cKhYzN&)Zp~t_X9R=p>S>
zh8LP57p;`c`<COk;_0@*mW;AKT@e?yNBW<L*dbjifkB*2p;>vGfuCZ-VH+1!!w8$c
zy)*ejwrv+#kJ_l~*>qz_A?UY}TZ!X(4tKt?F*~`^d08K4yPav+xGT!G{UAn8!eInh
z)li4NExDOsL5&w6EgTOM7o5SFn$7m)OJf+8rNuXqD%X+swv%g0-R|}=aVz3U*iw5v
zwV}C{J(^=ZHwQM-oK)PW*Zf}+=}YD%_D4Iw``YP0^fqZIgU}}e5rcc?*Z2Ds>^axZ
z0ZVwggJafeyVBg8l}`4`=#MJfmJ~L0;Zc_BakTvK{O%KsRJ)j<%*R`bgn6gFmnqOL
zUB6v6wb(4``iod#3MpwReLJ;@|G*X=i&AiJU)_G?kPK%6h6Imrd;uNvG2NZpXi$8k
z+uM~r#S5l*DS&40_L;yr)1EnnDoZ~ESmT-yncBs8#zt6v%uvslzMmu&Nf+~aSV_oG
z3#w!#P3iHk-QyD{_SyWyw@N#zyej{OZ>BT9syT;W^r~X$Y6c)s&*x!lfgo~l>_W$Z
zgunwQc-U+S&4EBDG&n`X(cd6ir(qI5SltJYoio8&`WlcC<(p8Hd2UUw;6sHUt}5cl
zED3p0!i4rW?e^F6e3s-hdoVhikH3C>{swuE6!VhWIRp5`a?JvuXH?_IFKJWts4v2e
z_V(XcdHU%`Ipkc_T#odzrF;gj?#|wLxa{k*PsDCY)*JtLi@!F+^*Go_JzVmLo2rZO
z;oWtK$*Xf9Q0~>Ue=F_tE4;v8MDGH!u7OG7=#hx-SY|PT|2*q6D|RJI0$opFZ>-Ia
zIgQ&0RvGyLu%k-<%(aSn`Iy3Khq5~!Cun0_lSS!nroNJokn^cBq4Itx6;OulN`I)L
zUK-?U6PD7RdAGkwC;?9}I_QqmwZ3v+IG8J#d$5jIn%z|2%*L8~80I6?95#&z6Vf_u
zQ1(`j`qz-<P|J!5#L0aBvu#!7u7RK8P@AlIRW={qxT1^491v%0E1147nX%3-rucml
zZi;M%Gc8bk2^T=^g@Ujfm&0vc;9>!;Ems}OcZts4VlxQm0+!=l-P>iG7h=P@<-+T#
z953z)m?P#?g+wc5y{J$qybYyO5|irATC1cJPEGhqHZ45DYUe<>Z3u!Q#Y?MDzf_(4
zixO+fy7uMpFlwNT?B!o?*){g{&s$utq>GX=IwzlN8y<vMoNDHXjFJ(9mOm(hR`GV>
z)sQsDx+zL{M5l2&DE?wWvfrYX^nlw-)~nsUL&#eM77(W_AX@o4yRirVKMMGp)mTFW
zetw!C+?GgEB;Y?h$KUL2qZsm$t)kHHh<`^he}DYMg*;<JmSKMJ4^98=-ff6|JOlN%
zwT<@^%s&vLJ^%0TznP$IO(A_BFh4dXh{u!ti%t38zxxZU*>495P;(2jsoVaar@do&
zo=TH7tXJAP-b0K0Z+!p#`_JpIw%Cz}KYV*>n5;JNl%VtC-=_B0+XT?+O<|X&`8xZk
zh1oK-p4}4yhf?=TBTKB$EsX|5K%f<1$>m22q28>9zh$rQ#B6cjwNo;Q|8GFW3!y9%
zaC(VAr}|kUVN8S5L3AC92UpcI6$%wMOY{Y${WX1JfbyF}C+4SHW+a4OjVrb#uxg5b
z4RvRm<~4*YwJz7)JIXF>T?A%ZcpN*DWgqR@IzDQ>rW>xOi2mQ%eMDh7spXzhCs$qC
zn3Wiu+by^$ai#XsBy&GBrterJNRU#TBIMGIFXy>v_@D&Hhgp<d((Tn3?!QR^33=OX
z*-GUL$==PVR3w&pd$~WMn%7t`<8Od>lsZ(^dCoTObzgaFZYN&3{yl_06_ua8_?L}X
z#{*k7t5$lSuE-f3L>)egt$&wP=K?>c3)T6p@_*^-Ic4Unc^>4FUyls4*_v-u7vp-4
z|Hp9S|9=o^AEg?~k!}^ivWBbTYaNKWZGH`3d3|qL)bENXx1xG3eP7RWIXnEryN-!5
zHanlis-0f@KhoP*8n=FuGl#GjY-3>cx9s1Rr^BN8cz^k%Li-_ifxop0RNpnQyeqYv
z2s(VMA7VK?|JU%TTSD3PzW}jBk8ticYzq^;NgbR9%sqs4<waLp9sDIyV?pakn?P?$
zjLzyvsiFT1(;P3ju71Hfs@S;cM{cgj>=`d}swmZ*!?9(kr<Jff{u|<z>AwsY%o&$-
z_B;*0M+K&*7YPq~>VifiA11;TlXIl=>hEv!#wy|~FN>H+-u%<#;?!PLzfQO-T3`QR
z<Y}bE9|g8d(C-~$3YL+YGf2AeClc4*Uh9Ett2H^(8YnH?4*V%XTB>xMG3m)SCZ0-q
z!-Deg;}iHDSh{zD{IJsI&^wou>BnY19^RK9c>grQkAuiYuM!p(21i*hwaFm$=$kE9
zB4ZhTRXgLnOo2tJqMW@IhEt2ldv-VIX-!G8c5HU8hlG0$b1gjBas(keKeTch{B6}V
znN6K1ol3oJKQ_xJN4zm#cv*&_qKd}iQ}|!vdm?D#8m0CiQ+g{)dJKQM@|7+e?5p(^
z9z}RV2rC^9?R)%PzJB#`u~KUym(m!9L|%Tca<fF`Rt3BpAjMpaX5!~*et3@YxUNUR
z1oFzO@wVg*@fikh?raxCjd_=hx-tfRc2za^hb#N7=Z4uxWl)Y(|FAq~>SdPHlG&&J
zFSnMth5M7yoW7Ws5JIH<aJOH$vGA4~$jh<nql4&A;|(2KT?h=y?}!*bWccKLrGZYm
zh{aUWICb`6a!~daDU&4UHTF+eu47Y@K;2}qn#PjLdV6f%-0-2W&1(CUbVEuJ9rb+I
zKqKi;x-7*sx<8Gk{RBk3s4jp@Hv_I_BYw*9-sp_G9dLirN0;tEtP=B)C;_XMt$T+}
zV&}`hR<a3of<$43OxOSNsM1u=C2Xemb?jGX?P1U{;j^=|&uVJ(awM&}+?=w?NZj=F
zujA>JeLFgU89!5Bi`pN#ebR6yfv=pAUh{GHaEsn|o;UKJ9bg}YkVFtDzt^+1dbJy)
zPUKpgl@aeV%u4w4`U}T*HixxiT>qhXhOVXV-syQR2E&UhOCGNaJcA0_?u8lB_~IX8
zZa{?yAA^|K_>kIK4xZ+w?ii9|_5Hy{cWR1cOy;xm>t8jo>}j<%83lMLwCqge`?;}i
z@~+Tc`_S(18<FJ}Y(Zy!6M3K%D1bz??CH~%hbT)ejFA(e?*ylLv&ts)2BZ}_o7f9*
zyzc1p97TTi4i(~&m_^GNPrX$<S#%qb?(f}u!{b8vvdF0a?v}*WwTVB~6;fF5|6dE&
zQxkY<gI5IHHVaRDEV@#GeD6pyyWLae_A<p3aNBEwv?397tHoNhiuQZ@9ZzA@kdP1!
zKI*A2lcc`rO-OWA{bx|v>4ag|X9&(#kcINW+Jw#aS#OFU4?u#+;DGW|vAZ9-CK5+m
zVV3t5M1RC}lkPIu$}it~DQhX+dOXwmL!i7T+Tjfackm_!fz6Ckx93eUfYom6+qCsO
zvRBZhNSF!6xW|BBk|2=0Jl0|eJB+vSh07Ijh}Xmf&-o2OD{N2g67cC6ZeRGHOdl+R
z^FFVXBQh%0UON6`l6JR8eRK~Sn(V=k8u#nS;Te}`<wit%m9VE9y`{J7O5vFlS?+Qv
z>u@`xBu<R}<!;)IrB&{Clv9}!nBY9hGQ0J0F4+tYob=RLv9BQcJ?sfGusbA7HQ&om
zN!5G>eej37me3=&koUHo<LM#^n9uq1+zL+NYCC_R@C7~Bsj%@@)@WQOL&txu>3LC~
zLsI;8%C&f>%QOigsjP;5=xO{ek&7_M-q_NaQOE4N(UG3XJel+T0#MiTGC8N^jDJ{|
zOx9RejI}1KhqgfAjV%T@NyHX`1_sV@Kcz+AFpuMoFU?#=xNqy?d`9{tKh+6ddn)W5
zjr@u)s%~;b;j-eqRA6V|>8h{gwCUdEnd6awcLd3v039{1hl;1)=yK48?M<^<)j5lm
zdfijO)Uxi(`ywLlN_LY+8146Pi`;yz&f*A(N_%)i(tH8?%7Q(<ON8qyEX>pA4N&O$
z<1A8RN?$f~)I5ZrJfLFr@il&G!&hDxEUiZu-q|#vps$;3YEPL@x7#_Y6Atq^XGN<3
zQbcIi<SOFx^tJFriS!Hc<*AtPtcY5IBoo!Oi*>N8xu;8O_1T9EggXkRP3V{D&gG;#
zHQH8wa}EkOFM?5R1uDU#80T%>5pP30iqv{QRQsJ^w)0Wv3HYJX=9*z(<?=b7@EzrK
zhWt3P>0vp_(!PxBe?@G+KWBdRMD>qAvq?imM%Fu;uS&7&Y$Ji-vgvmr|0q?9$yYu5
zVCTo09Uj#iMre2fX-b)3wV3|sc)X}AQ$ljj^?c_g({|Jrk=0V|tG#Q+6csE#eJ+1O
zjd~kj_uzg2@I<ubO!}vTUd|tyL2hxj9ekG^WUjHr&?4dTKYKpf(fl|CHeR>QriLPk
z>uT-SHXIcz7-)o;8=FBC{p8&`J&Cr$VS)N9;!RG9EfyHJe8%gYcAzY&P4PXgevrF)
z+C*^(IWZ4Q&N8m<#wyO^H|lvT%xknp#|_(XcMnz_!0a2i^2hSo?k;L@XZvwW<#Ic7
zeqFJc=o(p!^=VkjicLyZ7cs?hBleBlJzkVNC~TJ&UEIxubEciEI6(Q!Xn>dN!PJ7;
zTsd!ws%(EYZ_OFFUPG<YhTy)g%zO)t?Nc|-#-<;pALwhr=<&HxM}Dxd13GE@vR@QI
zX>YLiu><Y-95uyC`Mw&&kP!}<#)@EY86kh!FUYv@-!2>QT$H~Bc@Hd(LP`vI(<Lss
zRBv6Efz9>hhk~Svvx?1F|8HuUv&Wm&ZZH^wN+Oam?ObGg)a1dhem4@~2gqmXPZj0M
z(s0AFh$SrzAAf1hy0${2cKKk-;~tQ>ls6y1`8hm|SHanN8aGS$sR5US-U_O1j^gr#
zMp}&rqQ}Mb`}vWm;Yc{vF}Bv@mkHZTrM>72ROcf|1<LP`Kz2pcZw;%duHKNJKd|&S
z{jl6}yuLELGZ&hr5Wu3Ce=29e=-Fj{1Q9}7$BGoMlNR|bPEtL9E?IzfwFpTnRbxaL
zqJ8uD*oU!SLJU|?S9ff$juw00`lUGQrm^6g&jxLQ5#{!@2xIEQ8w&U>=Or7v5C_V!
z*7!+zmaEHGFPQX82?5l_*v@X*;2<{3+Qv)Vl}f>#R$=0G7v%9T{af>Dc>f(jSfIrf
zQmR3^JH>-5Dk@C;9Zw*{4>zZC?q5bT1Z6PHQH@ND3?{P0Zr6hVwSxy#;^Ci-jB-tq
z@CtYR6o`q51@O=o25%oKR;%(1fVCF0c=C=PV@c*Ajn4LOzMgg>{j`CSSS@+yn$a%K
zyIy0y`kL<J?P@Y&nRKyFb_CzWE2k}ZCn$&{?=$mh>zr`BK@Mq>1s~Og{N)$%OnqZp
zBV*I|)YQtUMj0t7zCThxow#)#k?8rnfP&9{acf(8L2vL@vTo7~PZX%LbTB!iXlg|~
z1ZJqQhZ9S4^wk9HdPLaU<v{|0ydY4YAGt=RFX{TgNFb6r(@3m58XF7f%k;W1>(3+X
zA841^T1lx$<PM)m&@o?~@{oBDc>lWQ6tFuwqk}oqkf#^*4}@&we_wDl@YJxN|31k!
zF#l7m=34t~Zef|niRsO@uP=^w0SiS7f>Sn?7t13uO+34g3YU4?Qg0r2EXTCOJuu_F
z!ABa&31(3#CggvE+Gj~B^DBBM8){}a_6Zs>yGi)0>q@0QlF3*G1{arWd?~NPj+ukd
zNe2S{<;L@825d5&v}TS4)VXm=kfYc}qt>90GYX3jK(7{ZWveTRC1=*=iL&6`tu&sa
zfe<ecW!3Vi=%;Z_hrewOnC)-e_4YN*b8}sfc{umgMsYk|!K+KH74NUN9&hv}|4~9D
z`PyWn_{V7BaUBvCRvV$Y(XUE$aEx8_=Gtcl4P(RQ6f>U47GHBIt6xNzL4o%q&V~MI
z{=)|wq`pE_{cCM@!#G!apT5rK>8q*J@!-^vpju*mg{oX(WJkz(%6BvK^OFxw?3*dE
z|5BOAes)6s-M!N4qNUM|G6_DPXKIuq2-dy31s9y_Td>`>ynop*16}NnNw{un_Q0*i
zP1%sWbmAyodWO{@faN^{%+1JnD3ths9wSJSB_A8CMWIZ87R_4Y8S&QmKi6ZPBA9Q@
zui_ewUF{YcY5%tt05OQ|OCzljq|r_z$K!Ce%=NVGCOSGg6QjkzFjup|hUECgjptSd
zm8c>7c9c`io*u_3)mU$?>$H+GJ!KH`C!bO}S>5^U>_R+U$C~vf2ZLw<!R*gfEc^Bc
zR6sRJjz5shhdzRDeSze%ssktLJ<Sfs_3?b<RSCh`${@?L=Qjp3#dN_|T4>kAgFKq;
z_&Oe?SVK*G@BI=Em92qgA}4F%iL?vdodp80!kY~em!<iD_qk~f<w7az-G*sL+TTj{
z)6G)3@LS-EGA|`nb9Fj`@pXYAoj2Zr_gQ^+)Y5}mXno|y5_?@&UT>$P0%|a?SnbBm
zIEs(LrsJ#*SXrsF-~Q>nQs$ZPeI7p?JGoWNeHbWJm#V*(M2mwfC&&!6?!J!~Ue9Qt
zd0rQ|J_cI-yc?+8`)a~$@IU7~PsjBvK9IXH>atfm;;+Wg^r;MYN40-CIr*gg9OWZi
zynW}UVf$s#t#qjtv!G%%0!2wj#MKy>JN1cjX6nyHQx=SyGq8dg5TWeRm!teRbe5SB
z=vzL}L-R(iLBhU^^TW6nT1V#RX>=9|;1$jU^V=L+p@0HzmQH?!@NUDJCH-Da!?pGO
z;9N8A+CvBiz9(bZS$xRtyqeKsOLscSohnC5aA4HF3X6)kjHEKj#+4(#K$SP4IQr4+
z{;3vLCqpvt6o@nfF1zx}fNIerva~gwPLvjSQ#eQUU0RW<WKD;w;g;)hmz33fH6Y@>
zN7q6xF)jN|j{auC-eg?(%QwtStOVypzW~J2gXdL}nNJDJD5%*gjey4)iPY=mPdn=c
z*kXd;V?jhw1sxV4y|%*s-&m%;AQSXRNB?tY`x%Y)+xvDTgNT?WoGw91Ce8Xke|(VM
z2=8_?QrYm-@INhPYV40#0=FiB>DHY0TH27c<6{gK7MA0vo8_m=Xw&7?i|A&dMY?dG
zy^?ABo#gkep>g2IAV<3`M=Q+>x-Z4TzFxbo-}%S52Lp3z-Uze~XE{}f5S@>U-kM6F
zv;B7X)?!Y6|9MDa-hH|f{gc@S_t^eGHZT3rTX{f_pAY`|(;3?02WuUgq;xhO^aNkZ
zcR?N^TEjVFEk2T<n>SuA+pn&za!!4xfn;FI__;~qLbH|>oW6}T7(<RexIXFT&X&h1
zq|dF2IB#F1d0c4M&hzvwM6eRzOdci?Z;`bp*Ill1Bxt2Y!4ri3K6X?xsvkn=@vd>$
zvhlY)`BI{1c(l91<~YiZ#m4&nW>mdbklyXB-8`ui8eIs7OnTb+wE4q7Zp8hM$rnoP
zC5%_$boIT!>ffMtYjHU(7gMrbVajWABO*o;V4)160i9|`8An==CkiD<u*?;CDXiXQ
z!lnV-*G-BiYUO<HVv~o6W01b~H1F%G9I5lTKOykAO_dJ)K{|Yc`t6e!*Uf+CSi)6u
z?uj)Ki&>oG<lspoYOUM~A5d0ao@9FqX%|U@#0NP7#8Ijnk1J(NpyK(KEs4DtRJf6{
z!yNnq7A|eJR2su2l{#DYerCi&dj9h#zD*m~FfO7gF#hW3BRXJAGf*`n{OiwWa#mTq
zwDk4q=~Q6aD%x(JIA`?FH{OrGHdi+e0fbb1A@*6YI;p+5wZ(nGBw<Uw1FSq_U(5UM
z8VzzsuTqv3k6WbD1-CRd?#f9=n$>k}%d<h+mb39fN#v&3s$C_YW}Ei@`t+7gnqc0j
zkw&5oyVsd$SJ#n!nEZJkdh!1|YWT~cK1uh?+<|D|<60C(HABjI=YA2nLd1OIQzX~F
zd75*zm#A2x!zsrFBpRFc_$+67@skuXI{FmnNMx5U8<pzRzw~QfK}F?1plJL*ynSPI
zW!<)IC8^jI+qP}n*|BZgc2coz+h)ZTS8Ur(UcT?Vd+t5=o!i=bf8LMXc5ADxF}3De
zbI#uT7$Z4vba!z1_%4K(cA>Vgl9c8$7H5PEgrHLbtCOb_4j0djt=g9t+Iy94zMC%I
z<^6~1`wM7xdi}Q=uZAB$yl{3c6`?7<Zs6x-_Md8^KeF+dAXLmr%FpiDtu$YdM7fz@
z1G97dWoaxd)AsFBL>|l=w)L&<_pIwWKo><fX=&`1l<<I9F8g-xPXkeLi?dNCyYqNX
z>co2jf}Av-R8JM``Lmiqt_Jq9S})TG!a6)~cfno6zIl@o6TFajf9}b6wIV;c?0c7P
z4}huTzrf?gPC~!#RENjzR-|ML3$Ksa)sOd%mcqJyQk=Y(@geBmMe8|+yEP6`flzJb
zd4>>vj6cqB<>P9NK1s-g_1)x{Y_-8B?^L<V5d{J63D@Tl7;eJ;QzZ4ZcOW<ZE_sHH
zz$0cHWY?OWlNW24q092i(e7Cgxm&zCB9_xtn8nrtD#6%5f^~XzOVo?sOuO?+QWs{^
z8)iByylC{;XWm0Lf8!hPg}6{DWiKr??9^I$^VXReDJzy-wE)XiuVznw&oEl*eLs4q
zSCR*7RJE+IuGhmHCQNq&5;qG+wb_y{A<F^C^)(q2;_?g5)3b`O)fva11?yNgzE6zD
z`yM2M;iR@RoGb;FLRjoJS5ZgOT_Fsm2WE^e^XZK+uU)Y<hDNG>^ER!?{fE%yzU5Om
z<@!frp-KFazDaNp+zjBJT8~r)M^ufj*G2-^_vVgAptoidu!}QsDeweC%p^?Lfjdwt
z`M_?o&mHY7L~AsDlTj2cQP3!l16V68Z@zRDrb?@Qojh97?hasqCe!Umve2F9Cnp)W
z&v{E<c$4>bToBKuxo+8Ms)RJGur@?vvHuGG<|-r9O6A2$TxmMmpqtKB_}~U4O4Rhb
zYfddOJ$fP}V;-nVm99L!KrHs(U)zM|sJ2SV71^3!y+N~S*Z^4mIHw)XaD;ZxjBIY^
z`YY4kv1TU^$rRYyT#X>$m<ua|-TM_98_ht;U93E^JH<P6t$_?gADvA&X+?*N-VIUx
z%FR>3)!~JJf#K({rK<`RjU1VDuNuk<EPS$TG*E@vK{7Mf#Ed~k#|(-4#%GP3dV(^R
z9}7`jIP=IPPKGP!9LSU$%14+Qp9t(pM%Fqr_`w}h9Xg+g{DPjxqa^|7tr5g2_=|lG
zCecc(2Q@Mknt}Oeb`%YpTw;wHq(5)DYs6QYWf$0Y<YNknRxbvy6vN%0BAaN}FmM7x
zH+@%j`m0OybfVgt6T6akpo3cNk_jg`+ZX47x!-)BUJ&48WcZcWxL;0q+^5o#VOL|p
z#(x>b9xJV{)ueddB7a&({S$At)|XP5zYCrO6jA+?a-j5vYG3#wwxB6%Du-MCK{=@4
zg3_e?1L4noPF#5U-?08zLGFJ2-*=!f=d7aS+?xOro5KVIH#Re_e}5s-w~|z;HF5Q4
z@z0hnYz+KR#S+I+*3dm~JLCoZi?f@R4AwqtUYqf688|J8q;hILpiGgezP#?ykQXwu
zQ3zCQ7~=*5k~mYkE<jQ6Oy0(Tf0-hCh~x^JKoU}t`OkB-cB%={kr^qA;;cpn;~CC2
z)RwTOvi)5@*1{HDq6Y#Jxf?>7Q2YAf4^ybd$LvuueYK`ES#~*s`w-}D*Qhw$TZY|`
zQ`x!*l4mF1Qk!eWQUR%bsq}z>U;LwO4DIelfpCjE7RIAWqFv0_|B7y-eks6zv^Utt
zKhp%7S`niW^WB1RutnXr0*{RoQN`wB@idRHU<$EC{0`p3o3`*F$&YH!5Bt}y%p;I1
z@ZhUrbrpe?kW>EW`Q&dHo!{rX<XtGlt&&#E|3P)VK&Yva9uEtmn{ck7(-Qm(eDyaq
z8-VcZ{YFUOhlJLDg;f4gu0F({d64`J3++@d;(tfIfeZgT>J11Zz<FF=O7y?iB@Q2^
z1@hl7t}z17f;jxY+|F9{Rea;msDGZ({%-5nM|bWE-B(TH?NVg_*Ph7NLx{tRk8<MW
z|F=H;WdSGt?=5~IRK`2=3S<4ZqKYBEiuxZcu;c!{#aB^`-X2AE|JwCjBg8n||G^>*
zNU>P123hvM&gL&y0%~9bzr@O0f|Y;4eIfiAW%yy@{)Z}z-J}+u5v>1pu=vwR@hkL_
zzEbH0|I51d<s=j7C&Po!K_X>3Bl@qkL-;F7_7-#CyA(Cr<v0J=4f0>?=3jPT;<B<p
zul$uFEvP#6se20te-q0YY|huuchAS~Ln*huRBiu3cUeJ3q<7aompcvp>>){eq2C8+
z7Rd>;h{)*ve!IJl{3o;S(PediI`<c`Me%Qmt)72RdamNc-qXUZ+mb5zYF*Mnr3j9V
zjeMgh9Lz@+oQ3+|6Xrh%TKQtgJtoL?=Bu%(sYY8QSj(BJ;8o%~R+09!sgu@~4N8{#
zujaQ%#^xU={RaW6diejDt-~Id<P&poX%3m*h~adGf-E3^BdPl9DdZL+Zf!Tb0ZUj*
zrTgUnef;ZH1=fm9lY`u9uj-}yWj!gqgTe(gVlbVD&LodUsO$IeP&{L_GLT$_z#ts)
zvpeid3Xx0vgT4lBVL;t_GB!zgU4-oKc~1hY(J>X!N8p@JqESRN@pJX4s17a2lY1gM
zMka%`Z)OIuO0#9EDeT@}a)bhr=5N+Mm?#%_)?LvN)Yc2b&?c<(o!frs@AE#n{&(fB
z<mBWKo(|XDvHfv72M5tEuzQ6%0}&Zbl#-w_+LJw@+<Dl(oye%q#B2MPJ9DpxWmC7$
z_s8wCvnaA0uOGW3u}id)F$1Cyd}jOl8Ph8Gp?~eR?!V82uv1V#!ok6*b@=e8R;$D6
z>go>av<HFXVaHk|Q4Nd^1><hWGE!H}=h#zxS-gHV#by6QMLLHO1)9~CpMPIcTuM5?
z&<dM3lM@!vdL_{t!MHZ)8K#d?{hjb21RM(qP_5PX_I!IijAgr(AjjM1y6IhlLE=^U
z<v{jyH2^<Tqs#a7{A^w#1M`#7?OyWdSHpfol!iKad2w%Ky}|uJsv%mR`BF|d29<<@
ze>^+#_<TH*6?%B)+6U9a;BYE?QWVTWivBh1{1QaH_X!RSCKJ7D-dxu&jjr1V`{5In
z)PuO35v<m$JA>g!r5a6?_2f~y-C<NX-=&erY<{=wN$1KBgFKoQnid+4#*-W^RG`r+
zv8~16aM^vqC;3`!4iHdK{aby3b-tfIZ?Bg<t*6x;wH6DMMkXc)m}Jxyg<O+=7%3&*
zc}TRiXXL(w5C-_U3n0U|x=pqeSiHk@Iy*Z%^wAyxOy#P=cCU-#PEPz)D^2!dRT^eX
z4IQYwKJ#X7tsRC_nH+6A_?bF?%dT^Y5#`T$t5;*Nc>`oTU3P~g61FaOqmeNq7Py&V
zVmH{ZnM|ktVn|pPmY=qq6ibo{nas6Qygc|T$=X%K=0yraH0$)QS1fA?@bUROj?!7J
z(<>qv8QF6slPFWU-5Hy0H>-zsH-4h&dMTV@=&MW8>i!`a#kd>e(u(p*k0>@IkuaPQ
z`RdGPyx&8={~1tqhE-KgtIbxcM0j}XnN{pVm1h4&A8wN6VsDe>lHJZcg)$th$Yyet
z!g2?;!^y(HTAQPF@~3XH-00ZSo_;|jp~Ivp6$r_rs?FrMe0?^tNP(UXttJVI>9Y&~
zMEh{~Ft5r(qH|V^ziGM=ZR+^=kMGvE4WE6LKhN$1Bb)HyqoZb`!(v&zHyYa-%@)C=
z(O8yrc|=4+O=vS-{0osJq2F{zL%53AFH_=`HZm;Zr(OA_!xupHq1r~Xn=mIdT4k{h
zO>N$@IUGfq@JXdp{lbyRcIM>xf*E<K@6AeURh{&w(pjJfLgAM_+*%i(Zx5S7G5P*j
z)hGT#eb8S2=b#8WVYWf`F6R<^ZtPp9oGi{b58BJyoezx)#Z#St?YYM<ie63FF6n*1
zYQF)+|D_Iv@#UHG5Dazm^n~}2Q}X@#3fOVP{wWr3{tejVuv%N3+Z_{aI(x1hVdF)s
zBL>bNCC>f~nVSiwtEU1`Q%s-{&dv}2u)5!x-tZ!}-?DTf2m)@1V%AZ<$bR}I7Fl)t
zZIFIyYetMF&kq@G^EKp;kqzW4JOUV3&C2YSOG#gvDms*4Djkw0&fjjAc)VWdvAi#(
ztb;ziaeY@oJO+k_h#FJyO~3(d3WXH6(|JN}&-*ET19BZs#{r<wsD5M>8A}C26-`yM
zumC%S8UvF=>oOjvhEs{J6{MvJJ$VO35H)3>7#D%}C#b_;Y7;gj8y}#XxPVRHPn;az
z4-V02%p%yXojldEr#!3b6lj^sJ&B{SI_wWeXJ?UET%O;R>P!<EZ19h3j<GHZo9>5M
zR}T-1`4!wME8MI9kOE~7HoF}e<VPgY^feAzvODjfyS#ZLC@j?sto!5k!Zpqc*}*c`
z=)$WA2AF#hVaO`e`tTpxbJ}Jbk}NwSbv^e@9jfzql^&IE)P1QQzaF3FDa9?H;{4sS
zRL}FwKR5N~lPpcx1*OL{QcXOK|CI1p3K-sUiboqByjVQf8($T0#$EztKcX&oafwEu
z=SghQ%Z^+e%@nv@cGS!?o5H(2KjCM4@RoD6<pD;%d{-TX79x_arujZ|MIyi0e+Ob(
zozAey47LKF_ZN*9&3DI_UY*QVY;ZI*vH@tcD*I8_j;{>AAPDG3@OW}^Ztkt|7FsE{
z-Z}96x4>$V#b0W<N#nhS>A`SrTAcMr5C(O#<$MNiZJZ`Y{f3z5Up+XX^Y<xfnPGvA
zWAXu6AV(AQ?p2M&j(Wv7y1wcdzntLs6GEJ~_Yd^A<xK1&YAa4&2B{9~$WF-nPg;bN
z^d94<msgyNzN(4rEvF_{S;9{--W`r!k=l*r`#@hWo7#2Jl6HbQcza{wO}hhLE9)AG
zIb~+$WwrW~OT#_iWC`dLD$sp6UkxqE@zJo{O&eeMv0j~1$^6!JOQ&TIB~TH<vL#q$
z>m0pW9`C|7>z%TSoByhGZmDAC6L7bbv5}*@l7T~2OD)t<EnF2)M4M=IVz4Tcv;~gm
z<p&0fX*K|AF<+WTu*E)RZ#?aLJTKFdvU4l4(yi^u{`-&VzOol?^Uxx*>x}o;0-4S*
z@G(5^JaVkOLAg$|tf=nr6&9xg`Y`sqpG@jtH!`8*o2l*{r7q}#R+in?7{f%^3z8bz
z7s0t1T*t%Nzcl&$aO5eKo65+rmg&;!aYOGiOq>Ubq3@GT`-x`ErV!UvFU!B6pe5^b
zWwY6i@_r;TTEqA8y~YF<;RkNDOm$<4ir!+iW~ki_UQtWneVNT3pG#Tejf!w6oY`z0
zPJJoc-e8~5(OPYqiC67gL=xpTyUhGv3MV%pCZ+oB=4N(wqaDv?>SmV_+Vi1uAeDzp
z@A4$%?2dU?6I94B1?!Px{;jLQ<GX0{We}^gWq(@v-KwJ4={7ng34#&!n<c}`C1>n?
zQ%S+kif!X$UJvuzk|M2vDBa>60GZE>sn?=n+Igmy{z!E99Wtlx@$l`?^rmtlscK&}
zK$jnfjqiM7pEWE(+0D6XZ>ztk>*)TxC6%=pnDqM+WxTS479-@<vTBT{nLEkc{&<Sz
zz=6tB*h~S!?M27aQxY4!%bnSq=UHm=3k7@~K<aw2QXQ$G`w93j3C5RfC6+>|xHmz^
zXl<%pXg@0nH(G9Kav0vF%f4KdrPVFIPxjNQOILZpk>`QB8HYD;r70bMV)MK?StctZ
zz#8`c?f#ZbKEE3zUshRJ8F{6}w&q?927^(U3abaDvA_n{xqe<NrnJkmE{+sm+rB6v
z?PtK~Tyd^$4R7gYt-ncTt+b0pt4bE9^VJmB<yMAmw}7zlcOHmGGtsay%}B3EAD9#!
zxG5a1*zMcyf$lR0x2{*A)<{g84_sO&hhME@Q^FI+15Xy`mu{ZtZ0y;I5fqOP{w-9~
z#tgjR;<_rbT_<eaKMSOEq{K+6&e4xj71MUGcG6<5aA%g(+evMbcN%=Y9C^me-fp3$
z@6*QjxD8{hgnMdQ^j;f}jr_h_7*A3yDQ>Wxq-Xe_LHPd==GPv_VzU|yoaT*=XtLD5
z4`N|&vB4VVq5qL&Mhm{=i&6@38eNe==QBdDR)!KqxVQ4UUgR<prc-c}d22wMDtOta
zTKUoOu^)K-I70+MG%yfNnuS*o>TF?A-b{%PZ@$m@K#9TJGTzp~MKsQf+!Ku%>p=k2
z*eV{P+HzP}xTAQ7amML`K!>{)AfjtKqPad|=#-Ld=eUE5r<7${_OWwAp519%igsH8
zt-St|EvD#2IUz$cYq_s)2>Jn6$L`k@>>f3XDaBWys+SY_A;V=0-BEfgTWLeiYA3ef
zt8Dk@<*!&qd#SUjBTBC}y!#B#O}1C*F0ABV4rz`qE`!$~8Nc64bgwNo8PFHo2^eQ*
z9dAdc>O%QsU`$4cZxRE?oQZH~EH>Oi@XwcWBPET&#XQB>pArM1CoLIW-ESDUa>oo%
zi95dH9n{IxrmQ%=Q3NMZ0d``zE`Bl14sCAY*>=6N!p+1{`Pg#@st&Ex8%MG}21br7
zfqY(zr>5w5NMz8xfnpHJabE|yRNd6Ax?5jmA#5E4o;=8|grO~}uhU7=Ni8{DFc`i)
zS&Gek767jH8+zh^evFZ@;pRopsK*NhPdn0;vx7Ysdk73M9Tx3SK6k2kq=jC07qv<j
zZ?l(x?#P~r^77zokq3?|9ar?eHM8B&L*+-818yXydGK+LtaSth0~VF~LIN~o0VG_m
z2h^x^+5wX#F$Dsv5W>oEb8uO__CGE^u4p*VD<d~}JPC*!15m5=uIa(L&D^8(ZVx8=
z-1ko_lSD`qM;26rH;pP}ZN`QMQk##^&Sc;R9|OhmY#Xk)DcTBVDs-eeG1TDvd%OeL
z9xDYi#ugYVaYlBfpMy5f4CuMNU??#kni|`o6_JWZnbu0Zeja7bY6e?sMWcl^J$cQ_
zj7&TxC3b2$S1S{uwVP`T1|AC~gk;lr;?|frN#Z4VISa2tm^`CgVW$z7O_*7Zy@U)5
z;mRw&(@>cGLnpf!kFD7sk-m}4eG{pK{j3>Z2CwAP)Reo6iV3%nrv%=4zl*J0Vm#<N
z7plUvuDvY|Kc`$?(1=Fw^b<UE$=|c;q}A~^|5H)nE=_vo^T}R|LWvGLm}uh&9aE3s
z`iHl@>*_;ohXX8^>qQV6EDO)qH}>JD>W3QDV?8#9!}mZi*!tYjD#)?CWNVjX=zd^9
zgst5)vN<RtW)_PXOr8((5O_52&=i2L|5mTw+G-=zy3hBStDJK1_Lkd1W;Ymk{sHeC
z%@j2+ajX-W+jb$Tafe9|$6I>dLY3k*&XV?69nds`y~SWk#R~#!u>}C7^1fpOr`8Zt
zA=6Qxe)9YTO&|kOlIp%SMYICPA1+RUUOw@%ln$ADn$XHYqwlZZ34V{K;7YcI-kg<o
zPJpO%7t<$v6w}XT7xx?(haji;x*HSWm`b_ZGYWB!FM;9Fxr{(*a=w4*>3ja5P^Dp%
zo(8TOUCP6QKWe06;hk{bN*S@zVzO8i!cOnR%HGC#v0&%yUgNz!gR)>TS|7dQpRY8{
zt$1MjaHSmW&t-`?i%K>xCrxl>WTC34HsBiuZaCF&b+i(c8dziY8>b5Bo0>9DP=LtP
z{|sz{vCg5yI4^2p5K2mM=`lCIDgMPH+h@UN`xW3~jsGSAE_pZJ#wjkgY<G$_`}y84
zwN^eofWz{-D)_YUHn<<@mwV;yN|R+1JZy7OQ}(mLPK#TGx%v7~23x+@F4z%=QLc-^
zH9a-0+QP|#e0bE)cOwbg3|>%gE+;_%rprD*nI#gBTZ{E-@x52=Ss*w(rd+iwkmKdw
zs^~@Tss(SATO+-t3+tzg36V~pOY${yX5M*NV{w~Qq8(%{q(|3IB-?ZBnB&m&Jh4c-
z?{UuYS%#^60bl+_qB$e#9u;X{0XgvSphbft2$)r8?d&mWVIt%Pm>B1dMT_;dYR3l`
zm`dy(ZdmsIbmR$wYIHOGVS5A)=kWysAni02#b)umDKu$_Q~^Th9IN#m#Z^T`QAZ7!
zD8R4L4DDE7fa4i~_;U14P-)=V*t#X8(JWLwz*x8)wzf_(Ur~GIWe03!T~Ha7%xP>f
zv20975=?O7($fUo6##s$?@IEhB}*G2n44yMMg+X1n1^Oso`g8J*KmSrY9bBwSi2}k
z*lCu=YjhSK(1g&)%Kv=RXyRr~etyZQq?Pkr{u6o<-B$^o7?4?=6>_+Fb1xO_!dt`o
zT`Kb(gV5Qm%hO{(R!VAR4v(!LQ`jYd?B1-gTKXQl3#Iy|?^T26`77rsQKpCM^bGs%
z^*ttjCLk=Y=LrhM?)Zkpdf9=M_s{*sI@Seo0d7@@a|aGx#S|@7q)9)9E;V{K+e4{Z
z2}O3TT;E9=-gYDo*6%hOl&7||N&kY4?WagIJJJKj(lNgpxo?!JaKTtzb*3XoEDdvv
zrR5H}FG2lL@cifa?`F^PnNiB+x~19n`8rPbcGBS;A-AT}Ta;bLk51R3W<_JlCA0Lz
zoS|B;X&SI0?J^0YH)Y!cv^)%OGp5tKk`7QTC)x@Gg^ZJydALYFa;LaX6Oz(ntULxT
z_XOzxUol>o_uW4<xXaEYo$bj51>d&yvO|mx<+EyzWpC-K6>FB<Pwpl2=zV(-l=C?^
zht${{h`UYdTfLkvvfQw-7HYzof3i`qV14X^92do?*94Fzjt#&PTj$*5dxM|2i%;(%
zLOlG+ZBNb87X~h3Nlv3XpMR=owR-?IC?h!DI}4Y8*^u_@=@@2inLv&u$G9U?Uox25
zoj~z1xqdD%RVDfQk0hNXpfiJF;+&P~NP0>e+3&%OWIhSLzJ3rZl0?}WJ4~N*<fWXf
zTGJR;{&Jhxl*Ew|*Ev`MZMmlm4K^RxQ|IYMTX-*h>zvGe`MK)9cAzxHjf;i17P7<9
z-3fu{Tlz9i*E8=CqOv;1-5qbw)x4~nd?t-)H+IfoGa{Tba2yGU6Ig1X!+6!#Cn#jw
zEKmmgCM0AAML%YSI>21;rkK}^l5X#L#x3&H?AVEZv>r>6VwZUo_3|L)=Kj(M8<4np
zwwXPGYyz;OR}I#ZXIBCeg**&^cMOoW1(4o&O0)MpX8c369g>ngSis1p#fZY4>|E(W
zzr@V54i{6z({4FG<*S+0Y~U6Q(WbM8K+8w!OIyg;O9>Z1f!glTv#@`I$XG7z&w)5_
z$%ax)Cnh3W+kp9A7Pv<q7&%oA@3oNa&Zo^?Sz*V(4S*?$><Xe8w&yW22LM9Ekxtgd
z^B|MQ*=+jpBGC#Qis<L7m9XIX-t25_$7DSDq)*;TJWq<p+r3av;cP+2T7+8K?;9hD
zGQRjef9aU^mt%s_By&AA>xqs-W9(nm((tP75n3?ABTbH>SG!}zZufh9cgYdMN^?Wz
zhZm8odG}X>Excwb6%h(ra34IKsIk<mfq?dhp`u-118NK-FQmO*5ZSwAd&!JG^fh*v
zQ?`!?{S4RK@&qp0k|CnyN$6OXvtaNJXR$asz|}H)lPkrAhA%ST-DT)eU<QNJDQUbh
zd~7BRu~K_7LDRKhY{yKq3;HIX(}L-5tmVzClZWvqyFVbMNN1T`c{&Unjm8Wwl^e0f
zySPCE;Z0#`O^AKjjc+<}L>HgOLRzxzVWz9ngG4_kw<JLzly>s#%=QJj9nA0;cN4Z`
z+4v(Gs8)hm`wK6;%g&Nu*l<j33-PfC<bObzwS1>g^p6Uv+Z{HCm+6LYo%w~gd;<$5
zjLBnUaO^4e;B@ct8>Gp=jy@d}Anb07R|R3i3=>I~d%G%x3x1C7ieu_3Mi7HbsCm3$
zvs_PNv8EuD(L062`aA^p7;%-!?mWFdg(bxD{m&D<u3Dg{xWmz;j#(0zTb=?=)aN7d
z2QN;fOVfL!+wszrKEe7U9qvjj6=dTM>`ZMzOTqa7=5Cv;(`G2(dz0+-^E94bnxo@H
zr{_1y*Eyk;rz@3JDe#ZMuE>;~k1%`mbqZ{k<Iz8L_81#$b+DSh>>TmGPJ;HAh;HD#
zW}D#NM40}noW(Cu)7cIo-htNww}>ro;m4Xw`k-BLqua?W82k_Wlv{OIF6iq`T!Kw6
z3BpFjTPbaL1`VD}+%tp_JyqI%uCbXRbeV`LSx}yr{M_;tJfy-dLPhxS=U(<P6V%%~
z5^s%fY@Kdz^o%u1fjK&hMr|H>w)?1V<pA4xXr?=ZLdAFX`&;;}RRiYX;EwGUKD6y!
zr4c(~5|~eJCPWl3Sce7oEsa_F`DJ-l%Ju{0^|JK46O5uH4l0a{rK(=84pzg}BB}ZM
zZLkm~>^I8oq2Vn0P9-u-MJxKwCyrqkleOT1C-;5DPN&Qq_4&9R)8q^~4ONJd$aRkb
znXE6l_iR*yeTs89Pd8ozfNpqxjw~)o(6;l)qt5j{_jWTbn*-!`r^mOmc?niz0Us%J
zAeGOnWIp0wQh|I|TY>3$z50up#~Mwj1ISZdB-p+b-dxp8RdL31{X)fipU2tlq0D*1
zH`;$1Fdu7T`d7VQ@sH1JV02oNS6qzLegYX>B87EOvh^bIfaUw7vNf$Q=H5T{5DZR(
z^ZEd}T~Eth|A{bif;<nf6?+Jz!lDcZ)P<bXJi1)=>fR-}`PB74n%O;Tq|P4W-dQLH
zhA_v2VL+-qi~!V9aU;xKT}SdJ#~Da0;1}E>pKSJ!ogTx76E=N<7jeLHyXQz?G~ckM
zbV_Cf@2u(_AH{B=7Ou1o1&-$Nr8}k*>+o)Ekxi<!g5;*M3SZ-QR*d^L=J4PbPr|Od
z+VkUc&|+*}P^=fN{ooe>Apnt2ii1zlRCj>79)Iq`{zh6O=Az~}>dxr!{V~lH3;P_q
zsb_uRlXcWV6q!ncpZO<e7shq&ZiA~;pH;e&0q>sQT<YQVrkQmCa(By-MJJ{&PDTvQ
zNUF_O7>L0@;OC$;EId(n<YJX#T%V(5Q(}*FV<cZ*sA=za44*cG(4`JI0Z$7%E`{mg
zedrN-@B5k~hSTppn9m3v%&kHRIU?}QILgm_O=rNerhwi|0=XHG85{~ohSZJ|H138A
zvdpI?5T+^O-2|WA-<5*+^|SG{E~|>2ogY+;9&hx=)r-s`%;2NU`qyhkOsNm605$A{
z0P~+jzw0$XTmQ@zzBn>BTJ6gn-7avvvA!@kRSUraOD1F^hMqH|#K|VR>X^3j)M#}D
zMVd|#1yjF{5jbCK8_`c;Z-%GH4(chM#*`lKLjd?ZsKfy_vOC5T@7F}!j$7Yx!_cFE
zWKQ~1qUs$@;SCv+ibmMb>YOHo8_NWw9<H18nH6sjsCWtNP9d*AlCjo0os6WVVFJv}
zM_qA!p_IBm!FYi_(gkNqru5G9N$hgjB&L*OhuqtDs>JZ7S$M05brwPhuY{6MxqVuv
zUJo?6-+l{gMa2x8U=9x<O}aw>({+sFo$a*Yar>luCN7tW`W>`o@eOk-l=Vh~`6!r(
z-BO>-TLwhI`fhnyG2=W4iSB@U-S0|EulPb4u5LyW$P8=?&`r{1^MoxezC~o;8q`|t
z?V4#5`C(oVsC|xDysfsQVBo7n-G2+8ISF9au(zXo$dn!kB@%2J;%BXS1M&uZ`g^E!
z7=FAz7R0x3?bL`R*k54{Pq@Qhn(~FuoMTx=8+yZvULw?4-_sr4uWGRaVg}dzh0I3f
z0Ui`zkr_HZ2AM4R`lhJro}!YGtM-Ig$h)qk#Ulpj(Aahcx6KCKf_Fz=UkacwK-DqK
z8*k<~x!d<GEyN4AI`ZBXW^z2!kFEET=ANvkoTNFD!X&c53Ou_kls~Mn`MTi;R6nBl
zXh#eNele8lj;6pY!weD&&jqbpx3+&G@NzhU5}k-{mDSq0ad4U6K;0H-_q^ptMa7~-
zcK0E804>(4;=OTt5d(8)#-+L7WVFzXR7e$Xt7#G5Up<QWRrGna;dbHC%`%;aMyk8D
zjq^K|D`8B{%eVb_r{m4PU9w#4dD0&gzBAWiinVyFPSU;^DsX!*fxq(Zq$_?z4@ONY
zB@5LJ%UX-AOYDA~zRN%LuPeoshf<vwQ|kZ3i!FDZ(rxv_JDBK8Y3M1uL?<Ee3sJW3
z`c<IaDywu2B`B($(P?x^$M+t!D;Be3qcLA#no{j{cU;m-Wo&P2r*DKN46nIUT=J!B
z-+`2Vd6#<2+7E$$l@@-#$^Mm(1|?R&y0u*eUCXtAgoTSP4jn$<SJMSPN1jsPMrnbE
zTebvCGyWQ1by?v37{1YKa6HzA-fY6tKHq~B`O+ku+W}3Z?FH6Qrh~;`i`m`J)Y_pA
z`$wV6j^>yVRa;s#P<MK}sfV>dzlk+<qZr3xPSO$MPLYgtA2?f`4-c-9^K>wb;7F$e
zcv*y_)$0YHwCV-&9c@pq;q!Y?sE7IecG7Og^;(Nk9eEdUu`7;<bsqw|NfF^muZi^2
zEmdVc+TgetjHsTOwbv2{M2hU?v@Jw3%~J#&oG$j5eG7(e6gTAYs6D(qAfAkME|8<p
z0%iWoiRp`FitAgqciO%|?_)oi<!Lk|qs`$71v_8astd&x<j}=zk(3?X0nc>DUFp-j
zFW`Cm1yMBSDHg)#F-Hlj%$C3#LQ5TKfj(C|_4cK2Pnu_^)_8zEAZy*e?K7lj&(EVU
zpR}7VM-Lh~^oq!n0_Xvr?MqRGwe9BDZ4cvueY5&&y?V)+YfQ|}zBwaoC5A8Xy@z3x
zIMBal*R__k9Rt~g_Ru+3;Pmz1zFzZtSbpbHnvMc-p5`bqP_L)+!DdC}!L0jfXvb2q
zBnZo?yF1F?YP*p02Z<A#V8cj!dFDGc8{nJ<ePrTR)$fh_U_Bc^QgHLUWEHki*dLx5
z=|+1($jN3M%}Ef#)^oQv@UdROylZH%7wDEJhxXf1&XI-v5`i!3>BhTL0Ljz0ocY^6
zMC~7ZFy0%_SIE*(yy>AWod^LWT>hMoM>C-+H<TW#8ML~mTe|BvA@~9eoWszY3+;~U
znG9P)$|^W?Njy~P5;o&6Fl9ap`r<FAJL(WvtdM10Uy<?EcC)T-O>+^NY^$lRt~Z>*
z-rnBTCscXgmW-@WEI50^1-&FUj+t6{Vn369Ge|gF?QpaY-T*QW+1}f(_TO<tftj4H
z^Mh;Obnjj-l%#b!Fni|FgIt(>tEY6aYVWQvgy$;_GJj3cd7Q<$Uw==|Sqpu*yUCXg
zqx&&+KD>LU+NBZw@nqABpiDC@^{t`8|0<5&Z8{^O1VAU&aMCnGpQyT9uwmhHqJZ)`
z4JWPf&eeIc7RV9^USJYuZd@t4V42TI6U(i)@cE|$u9q^R<WuSr^1C1~BuQTIh9@}1
zEU2)8)ss2`H0l{FmYRY>EgN!O9=^yiewVnIM<~LNAG4kpuag@cDJi?bK|jJiYzZ!H
zUeDGp&ZaIV&TOA3^GUN$H+8Q0H`YMsWpVs8n{1wfokfG)${F`de3V&~2^6px9|03<
ziqMabX0S`0FcFBKF-!2pJ4FPB^y&JEt%}uft3BxkI_KRl5O~!VL65d)L+i4D@2_Cn
zDN0yzm+G*N!%-sRC&+U<z){#K4grw{+K3XeR_gv4DevL3RiXxCzfTba7+3~aA}fO_
zfqb5NRQU2Fgkjg#lTcUP(s^A!rc<+zV&FqIrL>1lL>+Qvy@lsm`wizvcNg7n>#7}8
z=Hce5-Vny$N<e=8@cgz(TDZG@n%!%SQ*i!#x=<ae{;Ym<y}qhupNobOXSNU;wXW_d
zc)DM1(3|Vsk015X(-DU~?7TjLC%>`+3;wZO0E1Aa){ULjYOXcQlKxXRyIwUBHpO+r
z1DfybPt<FUCveMmu&u4`_XwSjw>dd<POH>>AixhqP%4cwmCNyJ8{E<n=OtMh^qw_0
zy!ORIld{RCQiY_Fj8V6%9F#w3t4LMl0HMd~1#;HQ=sGIZe0L|YJ9EVu@fgEZw(Ug}
zXgitlReJYd_(4faY}E1%sF<n{0=-d>_J^HqW~!DjgvLZEoah=i_MM4m;VmINp{JTl
z1@=U<coABPH@JDobCZ&zgIw>p!!+|8%@rGqdANi$XmF`K&|I<)L?D9Rdrpi@<0d;T
zjbo`Ur|2+Ns<o(5)k;X){yOONw}7|CG73>w^MLLq|5oQ?d_JXzECGB+@`i){QI2+Z
zCM~8NFcx*XcXLvg_+@(Zh;z$DgJrs&{_!Pn9@}=Hri{3wQ6nL5(0xfkD=8EbFoH=j
zK?`bQ$8mx%Y^!jb*D8T9B)PMGrVe-m{(%j2@p0mvn_rcX2}6@Y%4G|vhFlK<8-4p~
zo>y@ZXbC-SqswlE_E=fZTQf~zo?*~c%1GJ`vIXkOAWmlZ6X5%B7-V(XeSK!y9hfo1
zB}5z|k|{ugZn!zI5f=g35zo1sc^?q6AMlN50gc*JwJ9aIMs%qp)4cXfek8~)5plIJ
zBX)=g2`}{&w^Xtg(}z>rC(#vKI$b=XuBR5Jyvo2I!t>U#B^~$u>Ds=A({=19RtOYC
z(f5t?c1;X%LNjFwbUd+rM*J_)?XW%uGGXi9JR)M381_saH*iD>jW9c4l8=@idgN%$
zj^wiV)eie(=xjFF!?Ad=I$a*y#I8s~NroDTlbD=Vr4eXZ@Eq?bLsR&)0k9h8zg3cs
zBi`6!ER=!|!{d_}gK2)!IuY<;M7mfR?{aNdUu2z6geqtV7s$F*NSRi0cY?0Q6cV+n
zf<zS_og8MU60vf~kQg$Io%4A>Y@Uq|Pez{x0MzsjH+{Fip&i#aU7q%O9hrLzS#Bj~
z3&r_bKriBLO!NDPgO+K-s*fHT-d9g{_D~??IQ6J9;5qaYG^*WcnmHn^w`)a;m4Dyq
z(mu7L_vb{(Ji2W~Dl_M8tr4Od@*?j-IT)QLTeyc2ZgJtUvz5W?Y<<Sdlr?5Su2XCM
zpqn?J$7yk**5h>|UzcxjXOlKxg`4}_2s6A!p1iRuNp-+9XOGT=X-z8WQ`r{5!H{&*
zN@eoJTmCkuSnD*hnRpx1&$RAIaUitCx^lLI7uBu(x;xwp<5hcWn>qSz*34-?Sle@^
z!N(THriKY`IFsO3T20xK`}eWN9*&Ox0yrEcx-<7FG%F+mkH8G<gk#g>F=f)nT%)>F
z=b|G_`SVMKyWoM(c#;gv8>=H}JSEt6_3H?}A7P$<p(FgJ0~RWsW-)C>a#JWhl3+;`
zpD7K)&K||_kae;1+*0=&gxnmmt@whS+w=4GB2Zwh`6=q!Xu{~1QmVpf7Elw@088#N
zz0MtuzFx`3xrlTy(0mX-30re>q}GC3q#JLi1{OQ1aO`JG`)h1zNoYq%2a5G#GYtFj
z0DY;-6zEoe90_3o0^UK(oD86+39}@W&LxM}+AKBR&vafha;}|05a&v3?!v>3w!w}U
zdGsshXd1}`5|y)93ac%TW60w)aeVmU5A!c_X-;Qkre^EmQn>fu)Bex$3ms4SN+aEF
z?Cp#?e_BDWDLB{Etqju`2P_9ve!?H?&FTwJ4`jfIR#>(ZRr|m+S7}5Obv)X^{oaz2
zZJFBwD&<!<pMn3GfH+g}8{RjP`UOf9{rgN3HaQ<V+QPE?3pfQF#Bu#BoPzKVG|wYH
z#dh4q{P0<Cgf?@w)o+QexURY8lP^c|$DWmZ&ZumRv0SkyEP%0ia8*@fJGwacJK!#g
zC1rmT67BJs*>Ji7^}NKAkWy*twY*;?Hvo+ga>6lf;;3zr@G7DL4jlS6nl9wZrAz;V
zHPG9;oKPCFgx^K7sX8gdo&2Va+Vzm(5)ypDn>&gOei1Ew-O9v8l}%CVtf5HCyh)s0
z3Jr?LT1=n);q%``gXqVS{dmXK(aE9F;~<%`nE8*=-$um~VS#LaypqbKQzG0x)2^Zt
zGOiR#AQj0|aN;?P>4(LSX^Rf;owD0Yr6StUZw&`&qps-wKzCX>hr@G~+soz+SPmbt
z6AC;<=mqt%u|d73H})so)gh8yQ)u%b+M8Sr4<w5%kh$kPTF6Dz;cbn}ptmno=#xVO
z{NjwP@>K2TsfX17R8VkX{&Buy*BTSa)Bi4l?=~$4*AuuUiOA|or*R53Fsb5?W4hCZ
z0qh1blVD4FlZ$cPnP|tRRnVTR?}oBKeKGXo(ZeTC#-^}BTcc$t!x3)V&gw^gfVVn)
zPnR5Vx@Z!>y|AjCv3t!$S_4pr$tl)6xF^yPm!2Hfpn+XHXpX*`&kD?Q8%N>?5#==N
z122acVJB=aQv0oBE<rvTjkwsfFqg%ZhHEJ{PT8ZanVWJ~<c+*>a70gg4M@3$$qyza
zvk*yd#ud>{eAjs6s;FD>61D1Ty<I6RTu$g2Nz6XoLx!!{3`!?3_W<%Z#{<=S$^u&j
zZz;$p)R`e1eUx#{xZlv-g9<1IO8-b$<bLG9D|~P80vXmk0&|<-D%Y;V)66>xLp>)9
zNrMjIxJ%5vGKbM0h@}`})c+m)cfK{2aU5x=cfOL5b&%uxwiqUwbjaTI2wuq#7nUY>
zYHwjy1!3K(TtiXAAe)b`>nAq?d4Y_$i*w~$si7l%38%sei#9kqu=i%`G@+<#?YeCI
zS#jvkH3v#BPC6l6Y_0?I23l+{z0S$N5ztNwvSc_48R%fS9!t%!;7t#l8Bu|rtOYjf
zykQWK!n7jnj@Ca|0_fmpOSdo=6iNcvnz1<p1z3cwD52AJW$w)W2v<=U6Y<yC4(3E%
z7T#gs#lV1&2y(=si+ND(%4brI<fQsclRgTagd$0FW)SlLw*bn<n(%47XP!c3UsHpF
z-LH^4l+>K1SK9GRDO^+;_yKw;ou8Csc+w1nnb`H=wOhX>k9e&hKAv1Sx!=8hCqd1)
zH~#rK38|6EyIc-f*S+#s*nrt*=c|c;Sojeam!L>(b+fLm<!1WJUz;60Cz`a=Pg>l|
zMh9g+zp~;2=Er1X^TqMN>KqmrU}UBNSx??OwBtLnBNRaaX*E7L7YcaWA1&7<axi0I
z$u3bzpn!(R0t~Ib8ZsLC%+ouq?mYd{;q@NrH1gv@!^nnv!*$208Y1OG;qq_Gevwzu
zgOx<k^pE#^C|WVWFLi`$V>(2;=rJ>y4KQX+s9OzBvsp{#!5ZkTYv^&KeRFe>FiaR8
z56fP&EXTRHxVRRGl_v>s6~on0SQ_rT6d`#%Cfvaoh^O?%t68RdS!9%A;|G0ZWM1Mq
z+^zmU4sarLAEt&UTurj(wBUb$>0Ue$?ZOyL4KsDal`q%^^K!GK-%qP*i79m(pwp8u
z;5vS6L}zGjjz>eurl~i=iH|GUaD>jd+&Io3&~qf8R__y1T_9UHFp=?S=fo`-i0hFJ
z#7}7sf(HSF+xVM^<sD-wgEtpMS|QQslp$k+lItKbIFvg>(w8MtJI@W2L7SjJQ(~vM
zKN4?Ucm}gX5$S-^!Cnl<V$xFU-^mV){XiIKM%t%LZcB;1`DBYt@KavA6%|``MV`u*
zv%7D%+H-y8GALr*ZkG=d@0e|X>#WS7vbwCy8+!LoVln$za*M2mQPZlh8Ry!b5rYq?
z3(O4lPmok0?*$3#h$ymdGZM>6=x0Gy%!Ak{c;AleN!;uoz}+}XISEV2R%!&1+lASL
zg)lQ9OX@3NFwHym$4J5Ehaoz`Dy2@8D}%m%sAQ<sjO2_fQSH|H`uaxrrMfQ%8DkbX
z^!TE_@`ka;NJvOYz@9{yIPpq4%a!3A9#I($*mGZiPC-+z*-1cn0-|%%OVDlhuV}Is
zO7hFRNGS}S#iMJ3X29hLaM#6|;RM7MQ_7u4p*h@$jnL^Mo=~$sPro=3H0f;3T0=?Q
zl`^-^ubQ!F2uAjOVDk8i8oVU6p6GkT^!R;6xV}ac(*hF<V#tp{i4E+2lP$&WA~8MQ
zVcK_WLo~<VE37)o2Ebb3$A)v$SKk5I9Hm1LdA5gj0mjNj5#&UCvrSgA5ZIqL?8og6
zH?UQh0&|thXS<<s^fPY&oW!p*WCwGOV>P!iddOZR*__Be^La<;nSsw@a@(>~7)ft!
z-w0}B7WJIWAi5ga*bsfIpD_)SJ@h6Uej=~$C~&@(yyH@b4x@B30->aHhoRljL6Olm
z-sAE=cLSSBSw{j&&_I7eImWOvfpz~JlyMyPL$BM5D{{+Fs<9exvz|vf!dW2*{5kVB
z;ab$TNqtwfh|9KA**_W=i9A9lHP|{s89|nlR1v!BfHg3rmAc<TZ<j>hTX%JCnxGKO
z<pX3yO(_~g6i2Asji^#<cWp4gTXkrW-|XLFHbYpXOe>j4SlwWI$vZeBx%egA3+oF6
zt5610A`FrnE*F5tA+y<iA8XbaZXzPFFI~qbsdfC3pxyq5>9tK2zJNn(11&#G1QqQP
zq`YY;T7l(Ps*%GOHM!DJPe~@2{R&KtmW&Dl0=7Sv=EBIiXS1QHG#xYiV_)4n7QGl%
zEFLC{Y6eXvqlCEQ&r5CC1WkL_Y_1~?L8fQ#L?pq}ivgw7yek?!S*1Eco3TaoYf;8!
zce-z4)2CMzi=c%gn4eIY%sL;LTupI+`DoCDi>!lw3nL@XyGZgHX;!4LfT*Ck_0WfV
ziWQXv4?7JX%2Nj+2%4FrGPT%m+i^wsx9%GeV6l;~ARZD<Hhm60WErOMk|n*Fhfzjw
z>QAHAGKgz`4yZxnv;S;JPh~V^IFD2)L|^+dclo^SF<5POpzvX9N5(c!6lVn-JncR(
zaHqkWBsI4wB^kaag2Ire55_Kc3wcJp8$rnlXcXu>5pkKFF4X{bPcAwxn?pzU%Ul>d
zJWD?a$?(5|F!f(;51@b$=CPEkh&e`Vi8Y*U%!l41WRY<)xYN=m{$SiW+XXyJK8m%!
zw8@$=F9`(?!wy&x?XP{MBv6ctiq^_56#LH7JFd)ScL*6K6mI16AF%+6hcm5n?Xg>L
zzAvvDLU4~QD8LB9!M-aW6nk^{!s(I84i}ud8?iuFEwCF&xHDAyxHiH(?0`X6tt^9A
zew&E4z|+t$E>GY0x?4f_#YLnJ6lC{5g969EQ6<%OjUa$fQ1G4Q|B&cB$rKHYCL1U^
zo_&VFdV25zw8F+LjD{k%29OV|ck6>sthHixa5ak+d1#TZ>d`*A5f5cT?9UVII$<}T
zaRs&0ygK$N!j*}!FSpL7FQhp!!w}UupV^gzp&2P|*gmEihLsD<fHbL-;$%N5Iy8bQ
z7u#BIO`}>zsIaGyL#9CTrEwu2a(2xb4`vSwq&MHG-avWaR%a?_Oic=1b2=O|7Gx=r
z2^9$s>??J@@SA|GfY0;SRG&xdy_W$ds}f;OmJ9nrO}vc5F#3X#G<l&x0JtE-Pu1Ej
zwB2b>1j)2oL8CdoF)1criPc1WVNRE^OkFO9JukaLZI}%y$q1p{K7WQ0qoY<TDk)x%
zQPt?ZKAfWqQqfhdG^)Z%<N+6-;eT*?vdWTuk(!UfPUrI&@2`)5d~gGr@!T>%_kOS?
zF-1K`_t9sP?pA_KMCTxrTBi+9XZex{52LC3F#~&Ig5L+qX`X3`D&e)#mK6whj?2-s
zB49FutkB}OCs?leJMAxR<}?oi@KL|{;S(Axilk+*$?~5zE18<S_Y7KZGYv$S8rxa;
zhX_$?*g7#8L0*i42K<uf2w4y}5<8S^Q`0=z{Ge0ZPbptVcrXy5bW77>xc;FILa%jW
z5b+&}G_xpTqD!ra5;{Q#J8qs}=jZq$Lf&7*S>n5b1qRl4NBtrhRg=x6rDD8t22)qn
zep@#{zluRw7>)>Pk1p0cuy0YHFQ~B&4frBjsd*g0VrK+P(5!Q%SS(()B3GCTtX{5z
z1`>zLe`QSU%L276XS(9Zhj*5pL35-v)VB(C(0!0ovZC5^uqveTarT9_ChmQYplQz|
zbe%Z8x=7(v*124(#=0of*$RP;r1v_uic0H&kh=Iv;KFfH4^mXG$!Gi>M7zUv1om`s
zdl%9+ck~J129PzLAf1e#Onlt%orWt4%NQ#Q6JR<=Y(|4bt5f2jKqP~a;L$nY-0usu
z-}HXW-v=;lp<)E1_*V5M$Z?0;7JC=moj?tjROMFO#PM23O+th4>#m=;8x0li7b-UL
zeqVQktWpEz9=VPw{hn^_dP^|rD1&&c(8N(0lFL!oaD5Rc8cU1M%TbucQC1yRyu4*I
zX^b;ISj^}k&XwSHD8a#(;lPcQDF*qz6Swnim!yl+h1C$8KKX!h*M03^6bXKLp9;TR
zG3m+>lqQ+I&Fgn$J}kqveb^yw9(zG$#maM#Ahg0=S(xQXOD@DPkhE4E%m@-}5fvQa
z4{Z$b6JcR?a7z8^H&2G*A<ylQau!6)Z;6NNq}7e0ES__$v}?gvN~-cVzjCCloK_z$
z`)=mC%`}3GSB_GoEa$)H8ALuYczYzY=yle@q0u1u;`|W|CNI+^74Py%yHmqr#p4)^
zbfnC;Z>=`V83<>M#bMy3P*ADXBVj7k%WoU6B769a+le<$Q>xNko>=v}*ERh59(G7Z
zfcNR)mr17h;@knH*L9d~5{#ABD^@u#H>%V_wmbox$cmyG{MQQ#4l<h)3>JK~SMarf
zHJ+jQac#K40woQ|6X8QtRfv^CgY5rUNa#ek`}(+k3GczHJ1#guYxht@4Zbo~MF_ZG
z^?}d)ET(F)T`lg{@!eM$y#DdK7d6^`ehy_Em1Y>A>m7_`+bxmy&T`VjS+!)DbZyq-
zB@f1CPiS{+nB@~uT&=+ce>q%efP9Z{p^6U+0XI^6vyiCh7`67vPsS|v;ehfyJ#xUp
zmpj}bXLxNh>lHG3)1(od?pCXVZ-U1jg5>G4&2|#cz21xxKoLnj6<vZg(+C$Fq^;(w
z>@*zY0r|c#j{3PQlri&mE#US_caiU4d`1XuILi@D_TvgtAn>=@9_|x#hupBFgKE{B
zU|yRbY||cl<u(p=whFloA!@z&<C+=h2+1zJ>KshXg^{D^jXiDLgWav!{<h3jM0w!1
z9tVg$!H*a}t2bGtR8tYzD0AgkP+1kfkRj+E(fbiM-T81~n>ROdZPYHSXOu#;C&{<s
z1T{|w)^OJhWzh;KmO4ZDNr)WYaduQI@{QYF$&7|wIRgxofB`o;3G{p0v0EcLKeVNf
zc6%zug-rX4%%=BV+&9GCU=x)wif4y5>MB2lyrN-sC579jSo_)HRH_{^2mN$kJM#7!
z`QDC&{8~POnDeQ+0n4sL92oRQF&Ja+>p>*9?G1*fy9Bw!Bqe>;y&L|v4so+g_CV6i
zuQ*kTvlz0Kc^eEC$jQ?0Kag}hAaOXIg{kGnO+HwW90J?fNA5(s6@5=+D77Xz^kb-&
z`z4a(SQ5yfXkW@=R_cq?OpMk=)#^OqA8@4<N2BiLvteTyxRUc08{X-y=k!^#7sbnh
zXx*pFbR4)~6Cu{i%$h#tqLEb~qxeBm-qqZla=JeV0Yp27f*HyyoyuMu?<~UIACS@h
zVNVnl2$%|2qjv$&KF|hMS7^d0kE+O5BEeh~a!buiM6$OI1z#!3;UJB8-(<=3uGieh
zDhq${RLJgq%Vn1AC#Gc~0fjcNZXt9Xkj-TG{|cu>o{*?mPEQ}>%nl>~PYGv2wBd%T
zF24-{(xK3}a--hx<<NIa*2!s#YWx(R7KcQ`S4}S^sVqUdQ{-qj9xq~7GlMy>!U70R
zDxf|q)L6LQZRTTe9DqY1mE4V&L*JSMU(&xhdA5r@3xWH|qSC3zq}X9Uruh`g4~eu(
zAER=}pr?Xp;h$@&gcBy_#`jx#tx~2#C0C(GnwRX$55Y4dxxY&lFkkd6XD^UVkl4T*
zHc19w4yh#DxJQdt%~~6*YNoSqlb!(&e}dgzw)Sw5Uqlco7P9jl*L6&!#c~0xO>A*z
zHObQ57GeOoo4jK3dl?!0*5Q5R)_N|@N${vDR#q25`(uDF-4&Lu-Sdz!dGt$Gk~tjH
z+i(Lrdt^k<{F_LU=IqsM>85(x0sVk2x6D<wRvmD{hBq4QYGZ!9lgNgpyil~S#9~-W
z(&PCOfPpBw>~X)<pYZy8wFQ9GlDfLyZOFCpa)Bib--!xduc(myNURbWCHz>u9w#i*
zUumIlHX)^b*>k89_U@gR5VI>iMx_mK`_>*dTgzK$o{~*h|G(IJ=lIH&pbK;|nItn4
zPi!X>+x8@x*tYEx+vbUF+qP}nw)4)t_j~Vq`gfmS?|piARqw7<t5#hVJXyd=a%Dtp
zzY8;V4mgGZyXx)C-?SGK&G=7u&_XlfZwEv6pN!@?PQYRlq1$&e{|*+@w_Y<!TDvV+
z1P?Gfr1Xl!UMUE69zCdZSVL@l?6Q`LAUWhGOJsAI`AFzvf>f^jj1Oen>KzCy&u>w6
zvb*2nYGRxqp%-ZR)4U|=qJkAn2*a|_5mg<Ke%4iPBG-T20c;L_J>yK5di!ZvyFV#5
zIFsPDGrucl;J*@+obyDrT2?sRck4CY&pS9VW1agdfK)qM<#7M&?|P;$FhxXO2v{!@
ze}bt++U#;@_g3T7nv-4f{*^2{_z=QF+gi(~=-^y7;^2567r|NA?yTrg0wl}FEbf`s
zF9BB8+hKxqFdCuMg2}u~bAsDeZ9pFxNAye0s!ny3Ev&f>`8-U>noFsZL`ZQuQ+x->
zG|_$`l2<IVv6uqFQ-30qw+&2QX~NGug60TYQKEk|Z9nJWJ6N~~Bw)8$bi#>h*-@q?
zJm*#9mSHlZXBuXN@wP4iPjtU?hj-$Nj4!Eo{DTq>RNpz3#qFtpd(!R3uH|vB7>>h<
zdhcQ<TW>bsW6rp{n}N3W)j)i?rw<xwro_?Q|Jq<5s^a}(*AXTHlclAHsgiE$YpO2G
zDlXAoe|1Ej^25T?Ksq<IHuAX9%iLd0dC6*my1W&_P$mXLA|BOdm=JR8M}`W2nkty{
zvmb9Qo+LMI9NC(%Co3{ETH(%5bQ4<j?*R!{ngjXVLvYb>A$z}GegiQN=qdyf%$l;r
zxZT^F2olfODt<--a>L(K7q^bzTWyT(tzMRp2lj+!Wx2Fzmxn92wtO{YkHdaC%Qe{y
z+T}GQ*-fRgS}WO267B*`p<myDszp;pJeV_jzEGSg^ey?b`S!n7`Rv=s-5&IHfL2E=
zEv>)0U&7GrJ)XLQOhzS&g~GcVqjCL_@QI7qk|F@RszUUK-y{kVjD|T8-l5ig;Ga<j
zE#BaGaaMd6)t!V#ITRB2=)D$_(Ni=v0vDaEeI%YmlGwK()flE|RqXa<)jB^|b)L!M
z=v@aCHk`O%xKBi{18vlXIGS)SsWL)%<0yAh)m?K~tZ|gxeAcLEg7Ic7p~%|9VDY({
zo0ID-wx|-Hn`R4T8>EGL`i7^Q4lw<*2|Sye2RMVdxxcn!ij<`K>PMaR5Nj#jDl<TR
zUR4yyX~80;IF?4pB7y+|&>$eMm+H*lF|%5zA1+mVPJw0ol~W^0UKFDRbd@Cpg8YUg
zjq*>r`(`F$^)6xdhC)g|L}pPvuI3u3lb!~lhH_j+2&l12h#uo-nJSx)l-=F&!Q(_6
z4&KT1swIs7-v6S#c@*fp7FLosX!zfKC+u5QQn19h_TTG%5unKX#0XOmhUc#7*E%d|
z7OmZHT)U^HN!JgpSNU=;x)}g$PABAYwR$C_VgtloOelC91tTX|i~|${ts3@>{hbO!
zWO|e7TuK2KBG(8tX7&;$><zCk7i;0i&bu}a<tVV1aO+b6J1U}4*!*lL9F=9Y(-=xA
zlbV*!z;9z()$w6YS{h_^0Se^w?1-GZQP&wkNJAmKVIB7`I~Z!^#Yp1S^fVL(1S?JQ
z)?5SX>tqwpFvWj)bA&-y<yDRd1NCg;N2s*){`NRlYp$!yuPNVRY}jTMD@ogm%BXyO
zncnOo$pG6B;IlS9D2zm&<#6bSE%oYoXPmhfTAXMjUqf3~Fww5j<H^`)g6Un26mioU
z5*8TZo#9neV^Vq~FtFJ;bw%*=%#0-1B;IMqZAVZg|I%D1P#=kO3vNJ~vViBy*;1!t
zIW{3KoJW!8Zdo_9<P)ha*&WUUUfhcK=gna2@?L`8bzWmG3O5j}jZ4lE&)Lhtj1$56
zBF%fy(+&Q3m}sh2minaNs>&bO+3`6j{lwjYU2tz5q!X%ESYQi`%R#df&uaCx)`4bw
z+|J9SP|Fk+B;`uBb(l|e7+i#U+(|UlmLcHAopd-29f`l55EtuWVE83%rQ(-;E~;z7
z$q=z)qIS39xwt!k6nEVPFK-)YuQUnCJpWE*8|1vhJ)vs<%lE3Ex*b7N^#VD@@TVJO
zxhiI&Hzu=nkY}ngsFM{h|IM<`(XfOQ_Y*AR#DLyJjW$43{A&h|P;JiIPz%Mxj}K(*
z1Do2<k}q!e6wFBox~OmDP8@oD-}5bKu&DJ+1bav#Fw^?IkE_CIm=-5fS*4GIhW1N$
z^8V^06($i%kZ=H7O2`7Nmc-HI*D+Z&ep-HGe}L{OmCY0+);*)iJjKwudp=(XC_0`^
zY@!HSVJ^vHp|yDTE^K+Me8z1~E(TS3;!s346p+kr4a92GU1z4F&pw0A!5<!*Yvtq=
zk~KrtRz6^tAgm;c$SUpL^|isFs-+7N@v`SglM;7VG6Xtd`aVN#^nBl7z1e0KoUTzq
zVL)eRYjGwT_;_ZFaA)d0w@l^3c+f+oUF51aHLzAzb%}wii8Y$*_;@iVxupVHgiyQN
z5<+t)>3uIBUY<zjMDudz1s`v9Ch^<u)^}jV-tljB8D_Y%nnPaXd0`VEpV47!29xK=
zIYadnw)IymRVtRU?$bUlr2n!GPO&|JHJHVpp70dQD#ufOqrUekw$Cp$Lle*Hv_Jjo
zS01!oVAU>DShK}{Nj^hHsbCqoc!?2IU~jzLzpuUf959of6!m0+y?}0Cfj2bTlrv{Q
z)@qJ>vGJX7cV9KQwC)}d=<<4{^-Bx62zwNWu86g(VH!F^jURn8-LmA{XX1j3&UnNU
z{$@-aNcDkCOGP?;m=*hi1tL19VFhJ_C&MHprq7q!G2vpmqSgF9{n(40jPB%nINy89
z;o@nrVoKS=>&on`oR;jM7ts*PbpqHu^0alYkJ2u92FtE%^*w>%Y)S0TOgHj`irX85
z2=U1P=kchuyI&RDQD_Z|1C7p+0#OGM_X1C+Y!&A|3t<61rT$?0Z?E>EM=_#!!UGbt
zyQRf=*|5Ujguv`KJ3L0T+BG<S(}Y0}^ZP*-M_|Onp-On&q|xbqwF6AbGFUH5<@Eoi
z=g$C%R>bEHB5KkjlNCcLWuR?9xVh>-UaZ$O?RT%*BIzXEv{Q{eL+nf?I1F)j!6H3W
z%A{htI?)gT2v@>i5S;^@`6|21r`wz?zdfCj<+LzxZ%LMl>R$rBWZQdn$9aAFk~3V)
ze|W)NA{7N$i$n@!I^8SA2oN>1DAIV>9d4w^s&rmbRpjfLv0KGTv|Zzvh#)TB;RR^B
z`wd%^34Bfh8DsClA09N{DYpYQqIMxe?XG^=Gat9i+C@;JbeuynYj95$PmZe`q4_o{
z({xptA|=Zd!J%<e-A-3H?@VZU$guw@%oK>awVy>$t)|vG2XL!Vr$%CRWb%<pNQwRt
zZ1=NKzg@l7soW?+><NRPC+vgZZxeeu=FAZ4Tg#;P+-I86FwN`emSe3W@N6}vlh!vY
zW@$w#i2kxcLrv+>tND!pS!I1B$j+~j&qv`N3m)$d$DP#(LaE#Q6qx&IObL-)780KB
z^<C@Y5#Nf`59dt(D7z`k9U$K}!d{AaSa$g1^Qj-gUdf|}_qB!pXr`&)e6@;gr+1v!
z8_UiW>#dZfJ1-Saq_)47&U1dzg7;P;!S1D}gWhGxuc-rC+6di=`K7_f=v0*p(%=jO
z#;5E!Cnx6$iS}o_gkr=Mbb8Z%s>B93s0$saOtNbe84TIEJ{cYZji-)6G0L2XS?GIQ
zBNX+eZ2ZD>P0`nd2O`oAO%L#GYEm1^ieH!JuW6}A(zq*uo}1L!`BfV#pIwq`GL`;_
z;1@#Co0k9sM(ID!<A+UK6aK;WkO8~(9Z}nXk<=!V(GdGG{-dL4%=UYEAog>&{A_Sn
zs0J<S+-Z`ylvU|dyeR%IxlFFceIU;DGR{TP+aOhV@*_cnm6+BHed0Xr38uU4IUiF6
z?id}$WgC7yKUQd_(ZQ8l%;=72T)Jg7d!@nP6P0!(X%jYPg&BE2Uanu6YOP2JYVgv$
zB<p&f8L};T3|gXB4L(coM+3Dwom+RNN|SE+i9@@3<DmCO`?Pls^bQP-+6tLhU_er_
zd|?R@JG-W-xV^Q=I>ju?2V#=}e(31I4`~Jz(aAJ1K2i(>>q<Tan9@Hqz(mQpMjcLM
zRAR?&Hy-NVwCq?{kQhpNV-wMDwr>R4-B<PW&}9OSFI;Nk2w3A8X@9sR(<NvCBUv@T
zwexLCP>+*H58|+g)c&^As7Ck@Hy)?Tv`D8~)~(7xvMhqhMfn}+ZQ^~Z+1er#g$_cB
z0HvJR9G+#&dn{q2Leg#Ie!hu3gRb;2qU8u!n#;kDl!(1CY{j4;!OhX(zoM?Bh6TfG
zQ)95>Jh5Um^<w%Nwb!<6I9Q?;+?n9?-xFM(Wn{c3{;=;X7Hzt!q5q~tr!$HAttRFE
z?ilWME$l9kY$7}4{_%X6pyHbsQ~llpP5axG+2zInX4kS~lIxWU#M89lz=%0ESIV*+
zRVvcr#T<M*%ik}22rg5VC(o-?Z-s-!WLCgp1GU@hx$OLm?Dm5abi%59d&&wCZBQI5
zf?kjFf{-e-(t}%@74aKO1cOz}nf|Fo==i#$KZzeuH#$i69Z|6qe@a`MICGZ9Xl#5!
zsAWoV1Gr62sQ_gxEv*JjN%C%5l4ncJ7>+>0Q}|gKI;Erf8(jfdmtq+`TIUWpjGi)b
z^Gr<c#tN4nOcT;G<6s?dN5s^^=-&UDEG5;Ezy4R+zB^Oe11@g8Mdx1AZ|g9npNpPN
za;0*b$+|yUM&bJpB^y*3S5<{o6LwS2onw_W(%ob+k}mJ8pGDZR7$1=12@QyY7)%4(
z@dcc9?M;hZ8FzWNggVkQTC}PBh{N(zMzE%0#)xhVkYX6BZ%!ZI%YO~9+-i~MEYqi}
zyvH0%9h7~;{DVMgwft=LouYD5)pHJUdURox+_~Lg>TeAc29I_1+t8n9JD0U=$>0cn
z3-@35UTX+^B9#7*G2bAcR&LP7N-4Tt$ZOH*UV^5Y)UBE!c5$(SF1TAL0fxDI5v&_q
zrgY<`w|YwSY{aKMqRpnW1(I0vUV97+ZWql^dZEf`Wtjuv&C>Z;Y&KbfpYqen`=U~u
z1|Gt*{AQY@=1cC8rA!$*ZnhdOcHrx0H-MKIkzyoT1`39YGmlJtGQWY}LW@d5$lxa8
z%2eazY9sRV@r=ecHx68jdTrrz+|hSr$S9OU>v$|L-5SXnllx{&>NHB=pw80QLL3n;
z{dS2_Dr<D?5jp9<E=6QTt!GT9mlZ|Gl4v*?VSg0rApn!cW|cu*=R@}Cw4k=f<;@l*
zydaP>&t#Wv2M9ILzp|$T4%3~h><^{Z>`tkJE3nGHDB>hoiv(GY)!mzx%)G8Yg7j(w
zUI)Tu!-I)gW`<h3B_&+6J)iUC`ZPqLVeSDj>1?@H`^`PPa*bUS3d?ah#whH|!<ow%
z=j2C}FG2A(%1%=V2Qj>&WSQzi^$6~^R%bFFZ}ta5ZQYB&zut_4kr(^t5~X)D0oXXu
z4eJbeGW0Nx&xc0}t=K2599e~42qp8|nfiJzWVc91&NS}0Wt@Kw$$r{ZnFSynkmF%i
zeEcNbk0@6*q3%-I28<f(Zk_1!nZ(hd+l4IXrzwbTxYu$#ulI~6WD5qBgebo}f4sjY
ze9iw`$51>{Tt$bs^K}&18y&-HdyT^<!iCzs)*Mx&rP&|iSa9@mj~^a%#xO!&6;Fcn
zI&aJMW1lqew!QIdF!K4%6EjOHNchCiZ0%NvrOD3!g>mPuFx)NMC19}qZ}i0)Ap482
zoxmWrJi3EQ61bO|N-S@!{Qb(vy#W?Bc|weKa5=nmXz6HX@t0xik_5e<3JZIIc*^HR
zLhpFb_70Jb;!O&)x>kA5D7~eny!Nh~<Hnm5u?3t)6bI|$y+~khNQX;bbH{J)c0HhF
z-xynau1@V^{D;#NtHu3a?8iVG*4lGA_>(c)vTyw5sR$_pXgB{1Px3Y1=78=gZ>$Lc
z%f(a7lZC5(TLfuS{Y_(&o#qu>R5r<Ddv1u7>e-*Ha&YzE$=7d!!=}^m&WVdOtvqN=
z!+e_Qip;flL|2a79%#i%zTo@7kUC1J(;DdTYPLrcD%rHZCG}24b9^>-g4p#%)3ujW
z%-bCf%e0<8^GP#%dgY)a#r`zYxLJ%_#Pgpo>hJ8yFTE_7S?+np39HDq*`VBE<j6kq
z3br#8%+MPH2^Bf7c|uto<WVvBqEHM)l)elHa^=fjAdi|YH4!xbp}#GZJ`hob+$9J-
z136|dZReYB5U%SVawkZe8kUjhJLOr7`_d?dZ}=jZ_I1-r_DxUYE<Z<Tb;Pl6F|H5X
zV+Vp6*)$yqNjSY4Of`bN*)x|bNky1_*cQy(SUIXww(|thK;})<_ggvRfNhXT`MDM|
zOGO$Q{IK79iu`zx5Skij8WNZ4Exb39q_U5!B7`htJNt)7U+N~bV=ad0M{r(KA{|4J
ze5?ug>9>-&A2CdyGRB0USZX;wA6PdSg{S;+`&BVCMmR!skYXO)nfNPQ%pum}Lw}>7
ztBYb>qsR45+DCe)M&dS~%$cQ(gGA+6aN}UIF5NVx>`EduDy$iq+;=#|qBW7^Na1vN
zDSKCNRQo1<m;TPtoXleE6|+6reTJH=nyFywa7Lm#fh`~j@Ot@<<!E!l1SnhZPEMB+
zV-i)TH9`Ry&LdkWg)`(KD$V;|x8mJ#-mr>1bdqS_fpIsbvO4T4d;BqT_XHB-N?>SB
z3mzhkU^^x=%;imbt^O*7$0{|D@bx3D!@5A<l_J*z67GJ=-Ydf4mwq_jAl)GLVHE4i
z&L2@n3Zp%HDv6EB;vh>Jd6;0ZJSM)6zOg*)!4RCcNuF1jMJ+dKEzf7ee_C&-HpkwH
z=-1X3!5r~{<yYSr|E`fRMuikXj~=ND#h*~2%k)D>adY&fK~0Q3prl!U-nnP<#KJPO
zbD2v-0(x_$5}1bYYJYqU=Wtr_m3bd`S$iJg)=q177`4{TD%0b8spfw*utPcE|43tJ
zJU5e?_S++osCMYRJmRpRYmsHr{oH6DQQIO<p+y@g0I4@Ei7>Vkw%9CcI+Gg>s4goL
zp4o*jhmq$JyNJh%*f?}J9EMSLRf>?js8~4XssyU(p4L?wxJ+t$@#Bz4A0{2HIUhPK
zXGocJuFKZ|V;kbA@hc{&YxkZf$IS!cC1HkGQ80fySdYY$C$ZZXFi_IGVB0Nc(UJ88
ze}9({ljQ$NoemO{L!n33jE~#a;&vln*Nw?RBr{A%_uR?DL*ukc+tY2GSS+~hC(m2*
z43%*H036zgO&?Akeg~T^kcSddz1<3GS>eDB;HT7~35N`Mnv<wIrsDK|+;FS>MMifM
z+*W7PO-N+IhPWFmMHPA~&3%u2vQS={15?odd-0D~_2ip%p@7@&c-Z(y=E$Ml;_oJ&
zt23_>NhN(M7!6n$bcYd*Mxu4QG@U}PS85i+0-Zf?9}|(7KI{?HFlOp(pkk4}A=~np
z>2gi}42kWSb)Whqr9aq+*&sH1r~>QC6?~=8JAw!N_^{gRPcQ>Tecdb=9dFJwyU$45
z9)hu;;9Xkn4vmnk5u?yF2d1ne4;7Upj&*J^4>?t=hr**oN?29&y4e_|M4v=e{3JS=
z11Uc2ZAOkLo7NeO{N50~m!1IVd3tq_)K+}`x9!Az6)ALB+ZkQnmm{Ko8Xrw^-G+0}
z)5lLn)L@@hZ&1UipjXk!EYRspb;kB6CjSk59dKkaSI2z?yb%11$}ymklHxIwBhW0<
zwp^-0)NFAeH?3A`nd$jrSWhV`vMd3NGYosK%I$T1*6#lT<+G-(TQ=`+1&YZfRbP?T
zqucW=u~@AXQpfF>{ic{=!b2+1%9G#`6`2>@K1+YUHP)#wgCX`PC#KWk^-Pas(qR&Y
zk}SXrrIk?A8m2Ir#;%;lNj*|KgY7GAIlEK?>G1YP667W0Ca92Fx)p)@4{BAap#QI<
zn?;O{kYbdk=Wx2A8B*XWBHs}diLKP?XIBk8^7yW4(Zd<n_wAV|iYt7D$=ZC6QjA+E
zWv1J^hEJ?P5c!<?AlJdhBH`t-&$}9oXn<jlNtq?d@EsKgCjumpQ3Pu$E5q6Yn~S-C
zdAWJpF5!Tvt7;PIAfjKB5W>jDrZP+)Hk6Pwhob&8gCRU;HW-QH6ayZ1{To_)(zm<E
zJHThA$D1E*e>rl1v&SXQhKB5nL?ZDw#)$)kvD3+1h!>6v<{HHIHX`b+tQy|#*HM%(
zb<|y3pFg#+pVGQ~;p;=~B7y#47DYoIA~~CsQY;xHe=DM^);giV^K-%F=2Y!ux6ynU
zcL5Va6r65$9E3)su!Vcy#kOdNQJ@%iZ#yZ=!0m+Z@W*VD-+^;SvBoT?of<Z)65v+$
z$$^`I#5R`zc`dV#o_x)wN~RNMr2_V`E38TAAdN}e;%C|hEh25xxy!P@_DS2U8IW9P
zFHDE5&*|wV!EC;4qA}l$k7<zO8PYfW(KyRMK-Ol)mDgMT@3w#N`A^%r<y38Ct+8#R
z&UWw5YcwP+gK{#$yqK7nlR$@TQ_y4*zre0HfRZ$&NSmPE7B!vCgy%@k&Ca-g0kf30
zPt1d<?{H$YPxpkQ6gY@wISA?sPY{g_TUww>OqAO8K*k=S$g5aQTRP4#XH=(Q@1&n}
zi@Fb~u|QR8(=JPzS<p1Xga`5uI+t7{-1B8SYZ=CMK{Lj@(m1-6yVW-FIJo=Yt0~(Q
z#hYoHdq5-vTY=BMkj*gw`Zh(k*ZrVBnj7OdXb#-lpHC990Q#WR_s?CETGq~gr|Ibi
z_PI+-+7|pLAy<(y+ccfiS?LeMg6jZ;e_-cd{i(T*QAl&!{gmDD4+*e6!qJz-U>%eI
zEd3c28r3uhC8Dw-u6*2z$<KVP2t}U&Flca8$T9Tap&uwHRuk6G|Kp&EbX!fugf<vC
z_mGl|gFuocYL(CdzT-4+WKHejew9$s{6Mu*oqxts{p-;>x?Me8irwDbCg=&Ni*5%1
zqV#*JRdkzrmebJ(sG6)=kk531O!MEz{Ws)FyXVU}w8}+|ewV<Bp=hyU*8lz2e{bjo
z4t~fEM$U#s4tTX@YME2lB`BMdwNU*3-s`fj%VkUm#bOt&?VS_Ll;86I{j`6E@GW<B
zxu8c&e5ooz^3Lg@%`TCfRKp(Y?+2oKAGnTx%#=LZ0o5^WQb(EI25$9MZT^4Fv2E@R
zO)Zn%<A06Rs7d^U^^o;dLT$o}Tl0Sw%&{%L&O>;+AKU5ySoikn+LCjn2gmyQc?uvm
zeGTmiV_!R?HGNj*Oz+aftU^;RVBa|FL~heCP`v=k8OOMUaWIfmv<>-=@Cn({p`mH3
zNzyHvKLC~5fvsyZ-m4k~Am>0^4U`!7s=mj37SHuGHcaV->AYszYMwd-S>V*m*s;X_
zUhP$s>tyd-)}FEZI6eOF>7SLm`FZD*KWRZ;)S0*cxj2gwfM(yc{HZ-CA-1aguN!%Z
zjdrdai$h=RjQrypKwn@ORjGp%A5A`ilGguo_5WvWKWfpRCfC5_3Oc4BZ2SfwIDUE#
z8)F+{Ts9cXI#odLHxgtQ)kU(&Jp<%#AiPhK)?FL_HH|LA-md<cY6-BeqFUm+nju%f
zX&ZmdxhYkPnP|Yh-ao51jn4MT5ndP2m;d`k0sEbTV&l8Be@PJs_AGK6^muDiy?%B}
z^V8enroWbVJj0#SM7RQz#i2VuG_ni8ngQ2ulCt)8@wVW+OOz?k(i)LS%iV&XsRhT0
zL?#L1{{OS9Z;9*iP2)ki?x{3N4f?aN2WoXV`yCv|y#)X%uf#AWy5y@;cVUeBu|2mH
z)!F|s<0p8Xt}OnER6EZV=a05S1{&c?Xw}vT78K<IxI+7s!#3Ce!SQ`{GW>vRdl&Z!
zqdY=!C8VkWnEpN6H=CEfLW#-!eFGxpq@Stwj`l%8t*JK{NO|MnlB7Jd<FqtsZ~Z9Z
zr2=Jm|GnyXRLJmk?(~m617jj>HZahTNB^KyDP)N-mi@!SINTAVa9MM)%&)CfaKyQR
z7CjeVIJmglx6HyQ>*RmmHRdU*9RK^Dqx7=2zVQ>m&anDVT9$OEj$IKR1x^WPgAjbF
zbPDw&EPWhP6HhBRyBh;=b!VCitE)R3Ub1vV?WbXm^VFpPeNc9l4LjZO>rQaQ=)D&D
zpp-Z>X%$tOddnAe=fSSQNvvnMbOA%X$(V__nDnTJcc@%j0Nu^=wdSzUULJ|g;wBb|
zRQk1SqXDm(J?*6J9dW&kyW)HUWju9WGz(a|ah*puA7YPa|AZfj%^lfPOMZ0&FQBqt
z5*%LQ^M$riAYGwGW#Yfx1c(O?`3^pVq*rCRtM*oH0|w#+ieVOYQ)himslN#4S?A1^
z^-H-TAlhN`(fY;302YgS5a&sg%nV-mcA_h@Y4!aIK}n#nu^&h~1{G~{gTL@mk9><t
z-95d8T;IGa-|>sX`8X_tn<HCkJ~VIBg3>D>^6+2}pOrNeCX{T=VodScQI-7P*Gj9)
zx7qeZ>SFUxOC#j6mhRDPBdeJ|gq;U)@;;r?E>22!I4yZ8=9jO}`)ihxkav!cFVynW
zAC8tV7vQdg)E8Iy<0y`V5tPtLaV~#t&x|mxPqiawP55taab1RmdXEp|DsP7xWlLA^
z47VzYtx$wH>N(n*=F=U*;NPB`IP>cv-^c&(_-uN<;S_6SA2FJIDS>ollhi(PTQ;dk
z^hBjRnCz)w#C+rGZeUP!HHu8kh{?~-H#Q?L#!!=!OK~El7Y+-Pp0TsPRjJ$$?d993
zBUiJ^{qw55@k)ypN0K>KOe{9v0eI(6ehhtob?E1DOD?W%t#8C_+sG)iEv#4xR<Br8
zV=dB!$6~yuJXg`H7|HF>vkGm{J1A6>M?-VN%-x`;b<e?(`lD>h{^jgGes8~LRV!Am
zoavv>$QvuY&J>R%XS-MW!&9`9lj~fCGoEFiq+s34HqS#9kSDX0#Na52ZkF^K%d3BP
zB#}irbJh+i()3b8Sxj)TwXVEcmBK9p{G4&bQK^uar@^w>NqUeEE6qo((bKP<-{aMU
z2a@^`uY`KtQvpgMW=_NMHFMKscYwRFd<n-Knsr*uHLOH=Qs<+-ei+;0-!I|sR1gCB
zS1I&im$LWV;lghji>Y31baEdITyw<s+|Tx0@vTy#v7})y&S`lF*x)$<d}zC*St>Z(
zzKG!Ol`2cA7=#CChDj2Qk4n#Yz8bV~ODAxg*JUrlPq(m!*9E<Arf!<s7ISz|g<g=P
zuM{}i+s<w+tEo3Vmcaq1N%uK{f%`}_wgUEK5#MVU$xfvuzJ;kYok=ycitlM`;UN^s
zc`|`?I2*0J-_HJ$P#78j+iTa>G@hV>inG@Ch@Jp%aR7@E>m$cQeZbzu>U4pYW793V
z)aY<eMMT*3G@1PMc3dM-mOBKOhq2c;8N$=d1TUSZ=vIWq3rzQiqqw6DuAhZY);-Rd
zl6^tDmyo^`+Y!}%TM`#5167##|3vKntegfah+{=^zsJ(%y7;}V@ebb#aK;?o{w^|C
ziTjmxCNnMEz(Vbsb0eJr0@U*szB_?sE8~NjATD7{E0k}z*cV%HDLt)Zs<%oYn?h!j
zSz*}HMd)shwGZh+i$5>j74CVTt5}>$fu;9Oraf(~R+L_U<oyX?$bg%6#g5zNh#&yo
zuFyVQ_@Fm17<YA{zPX*jx}tad3ZEs;PHXjO`iJL*b;JEulL^DMu%y?MMUPVZgo)oe
zx4!|Ey^8>%=q@rsF<*`MpP9rf!u<Xvyfu-r5rkJT$=~jjzLn5~I_s$!p}U?!IJc>p
z4%74;lx-D;__rBzRcbIBZPVQ_f(&+)J8V~!1>7oPx^BSMLaP;Oc^#{Jbo|ekvvm&*
zxt-+H)|_9ABQMrh>$F*Niv&+s&&E@=!j?@j7pBe2OAU6o8-RUHcNVK}YRsm=oNg?{
zI1Tb-ijmj5Jsc&hcLCBiY{CZ6eN8Ye#YdG91LC;c8f0=6U<Byb17<KOTO}qHb@_il
zEbA@I0A#|UUjQCso#nKGA{+abnmt-LBT*p?8%}7(ojubipqTul!Vh>+fJ%`*Md2kD
z+IWAi0nbSU02L|I<=Z&nC}-ZSXsUT2QQssul&6+Yh(2%kmKDx)K_X>Cc}|m$x`+E+
zUnx3sQGE&(NIn~pzq&%+>#_fh*Rz+K=rv3BJDCK;7D~!;-ZQY3;@{&wht8RR*1o#t
z6kAhtk3@8X!ijSr(!XbW67PA{SUI0fRbgTO1o8I?8@+!iSl^#Qp?RP_B&aHzUTECf
zrZ||{_jQKHE|G8PkE-G6wnI1(Ri|6CICy#Q(Ct{iYSz*lUxPP&eqbEUw&0p6-N@ez
z68cima1!BQs4m+)UIpR_tK7Rto0QOzz3dr>D<_-8$ulM`NaQ=_k1_tf=%*IsWJkRU
zueohPj8DlBLxiCH_VW7sSN?w3m@n2Z>GYlo>Bp}71E9@jQH+1s%{P`q<x=(w7YFQz
z3yt8Yf{0$;$&J5|c?{4_K|t$h@%_tj`(GTozTws-YC^q)$Nn*|0pfp0S664VC1X42
z+<f0OWc0u`*7|M;osC2*Dc*m1h8`(HmOaFV{V|UE)b=3%N{jj-zC`d*`$TE7v@suc
zHCo}&_O_4y&7w54`m#MD0v=YhlT<g4Igims@I08JDe(nEegS)cd-0$L$T^&I5fc*~
zoNiy3t5+IW6yJ_F_^B0wWD1Um@`r=ipO_V{HS%&Bda4-L6n>P`RVVc(3Xc(0u$7s%
zZtrBui+CV^K<^<~GO5i5&FW|=I!C2v3_&ZssHE*Ah@TyUeQ0FZuuUo`Ups6m>4M21
zuK+)+ZhmwSo+sl)(JwZzmHT@{FxBrcYvBy<BjHn1SVFDehtFJ*$vCqpC>KcuW$kEC
zwY30a-N^G;z;PHHO%zh`4w=ge!N`u$;mJYZTW!$w;*Dzf%^>3^tA;TTo2hJq+@*{j
zMi>rDi7IEf>#Httn`7t)wgK6p&TZJOw^8qOwkBhH)WLptx?!LYi$L!e+P6etWG(r!
zw;tbEJuv5Utw77w@PGlf^SW&alLmmTVZbeHNSW}GzTJmy^b}z%`vnzAi{c7RzEIzx
zc<vI0>|f3wj!%pCqy1KA-wjRH$!M&?pA7FbCkIZ)MNbGW&h3v?NXi32?X}amB=iFt
z5*+h|W-=|e9k{pp@dPBbNMC+*e}DZ}`pZX`C6_C7J*hNbuY4PYVl^c07g)YhS44#`
zXrw~gRfmS8I{R(xDi4pmY_@O2b{Zat)Gs8K=7P7BP{cp)=hEZT(*a+h4?iOnQ=PY$
zhQDQSDGNljNKSc8@i7&ZSU(u-oBbgPzyE5`L)$b8%z-36<x>4o8sxJWC8&4w!yn>l
ztIwzNy1$T@fX0Z7(0pNF&6>CGL0faG>GBVZiDnpdphKDZ1E@}GEqs<tKRW4$LNRua
zlVWezX<(L^0!cj_Ta^_LPi8Z8<C+K7R|Y5jl{;0o#O;990UjTgdGMw0;JpoynrFRy
z!Q`Kv?Vrgh@~&FYUckWBPl^v@NkTKZDBa;u2=C2%^p#vFt{et>rx^z-^*YG)R1GM+
z)fd_}B}pMIWvp9XZ6E)^AOa@7&8|Ox*@6!b`gfZjMRG%wkzm|(z@6~Kg%sjs`m+oM
z()zBcHOQO*Uzr6cYI^?e8RlOfcY8&qqGR6J-EJLRL>8QL>=wI;^mKaCMWAD3P7Vjz
zpynmP!@99lbm4^ocbBG#*oK*n_pi)?!>vL$79aS}WxV>Qz6Vh3QQ;um7y*xHG~j!p
z{iNnSpRO>TNmnjYe>!l5ekF5SBt4g6l^%EOgI=kKYnc$y%216|c|6?TGhJ3)E-5O@
zV>@pwwl>fFq?4#E4v^^-U4$PF=gs1t;o`HuWEW6(CREI49sNXsi1OD4tWGMYpcsR~
znl3L;2~RJT)#l7*y;eMt75jxeHK{5*qXF>TvT+#Y(CE;N{f+Txt(e=quC;|#hG{nc
z9{+q<S!+QA)}rjK$R^@pbY#7SiwSyUk}Fp-QzlE_c~YL%Y&Ww_dU7?ZeW5v{aN%S@
zw9b+RCwhz#s%~)*Zhq8^Tf^;o9$3G<o|Dk|TvfetKos(`trm*KJa<+0v$x#mVNPA!
zboCKu`d6Hzt3{AD(L2f7&Vxl-IYit*<HeMi`9@g)>am(&>5iP(tJS#Yz=0{Hm%C_X
zoau%8lj?arTE@bPTT2Pu;x?^0)b5M|dufNKU_krbfcnZO?C;9MALkY2Y)N@LwEkA5
zFbh%tg#OAfprbrzlWxbte81x9^_sF7ccQy{ty67k(18?Y?S2!_!aM~HD}U<CRfJjE
z=Uby>W_Su=s@ME+R~imkz1PBL&JORBZbCZ_)B^3)O6{Ba=9~Den0jKv5hu5_-$!m;
zCThiOWP0c#Lz9SugNp{z0j4-<B%(X3iG<x6-1&jpMvD0>*vicNj|K(ym4fEXVh%&<
z)&0Qg74zN9*_Edgrn2>NX%qD*jzzTU_$kTY4)n_a+8$8R_$3){Imc9PWqYpV=dtnm
z>+x(oD7(Dzz$*Cqv07PYeONKgutX$%rc%LdJlJv_0MDHMNjE~zZ@DA(bMU~Pft5q6
zpy6zlRq-AC;+2mx_f&6%Q+?Zg!UJu?>2O~q```0%BfU;^`nZ>8rOCu@=J<lc2T>GL
z;WtZZ&f<37hRl&y5S!W5Et1S^nduYgdeFpTogy{3=EhxQ$y2}x&Iy#1H&useSANxB
z*P^E842o63Sos*2sE2ZGRJ6sha#LAFB?}j5U~xXlr8vuGEies|CNWcGXIIl()3tob
zBU5;itQ?<;oy(9aXxJ%qu3OoXg(lO$-8XsUehz&&M%y&2J)oI$!sW^~oMCC%W|)nu
zf08GwdzQbu;mzz6=EMOO#Y@Rcoh?{x?|{en)v=d4o0FR+E9{TX=tZ5+X|+FcjJS)K
zP{O6j30pp0Bb75Acg`(aF3GRV)UVco2Su6;Cv)WL&lz+715y3fN5}V(M<$>ZPJO-B
zH-qtFcPoc8P%C>F_gBBG;$>Yy9wBk;Xs~(#pnR;MQQp@z53d-*!xvx_W7WM6FRSfz
zkFU@vJD)H7$TTmsC;nkNTP8*^Imr8&f4FUJHb!$Eu=-MDlZ_X%xRxjBekpj~dg(Qt
zSn;7ACOOBNsR&8dw}b&+V~{!FO^mHleh;6+BKjP;0>w)+y%Wdq$GEnBiHNgKENY|T
z1pB$i;k+#FKL5QcfDh$3?;9Bzt#Cmjp?D<N13jrz|Ee^voI^n>PoKAzCHt)5jy3MP
zGyDyd^n>H!S?_NMLLxQkuE_o`p`=wbh|ELMQG-339sS@$c!f>6f@^MzeBZ);*V43R
z88>+T2i@7I(&>y5V%jl}pic)tJ@W}gh{cB{>G*5Ws}W0x)xzN|1!QifMu|7_gsqBf
z1al-5SBkjq^9Kx>2W|LDKU#Nd?rsh~a69LS2{AQ{XAao;*_@6$=Qy9PP>H2+nz5XR
zzGiwM$9=ZpvLicRW$77e^;o$-A1f$a+!Nzsco3DyVko2;_n)BKUV`$Zx1?=vCzAko
z18JsW%<xD*(cMSKi0i?bc{Zo3FB=(8W#Y8FmaX@qaRrh#yF;r}`7V|t0#HSr;`_e?
z`IHF=VF*yjKZJpoCw@-5xO{^L?bD^2nxTJ=D&+RfiUc^|(X;`Wgv~-izvzE0dBAJ{
z-ev2;OLQbWaFJK!RcQYWPN-qfM@!=6$>=_x;fHy-e4NhJj<%kZ1cIa%bJItipIon<
z?#wr_2|iw0%FW^uDaNzQSJ@vpyJ(LQl%}R+TBk2_eSuIT0ZKxt^sw|7uj_!F25f(S
zgPkMD&_+lYAt43L1RxggRj$fWlUQbs;pOvE(=CFNby5%+UP{H{r5co9P`z*|uAj}Q
zP09kbD22h7vO=~;;Hi=zvHfceFC7^<nsfUL-39s#YzLDou|um|JtX>lmr0rSEn~;K
zF2_Y{F5ZgbVgwBaL}D<QZNmWb&#DYyAXu^rJM4D4DQ}j%zEe^eRV!Su`xe(q<Rg59
z06xfdG+3v?X(9^?h)BXx@P&`Uu*8!B$efPp?8qf~BG1->FL_h_3pdhzqLmi)ANG`|
z3QZMz2#`8*!?*J5$4CAHP<?vPxB696I>vbT2@|`MDfs?m$&=>MQHx7U_x+^l(Y~02
zgY|z#NQa(bYUUjtCqt%Qy))$B+%}OTjGdW9Y;|Lm>4f5F2vfqhL6f%38{YSuKdI}v
zEg#htDBy`PN@ov|FJ>>2W5qi~9h1f4HnR?9=i;_QbPg2Gdk@Dc+)#oE4r^sSX}HM#
zkOL8q9$qg7lfS7IF1KUgNqeH=NvHTl6_)i|OBCwKa5C^dx4lt{#!HCpFfz`?4TAba
zmIh8EZHQ<YVZxoSgd))w#$?2Xrkfw`)^4K3XqV*ELJ~uvY)RYc(&_c#&qX005aB88
z=dNUWE+<jUy;Oq$!K`py?>@=$TkqV~#4W!im~a@Ue7z@B;){LP$yuo6LOt15Wk>hh
zi^V1G5_o}=6D8?)fs^fSI=sKU9kBFbc?oWh+o_#$bk89sjzCB*L7B#Fe4QIWA2Clb
z=31=^w=IquyxaF_X7VR7<2Cj@WuC9^qH6&i?8$5{XDnR5u!3rT6jr-#OQU~ou(oaP
zVp(wCfo-@PX!*aQIn$<fd+!k(Bq50E=yWb{-14`O`YV~(>i`7TraD<WysYpXPhuwx
z`CwKm#j8AN;32;ilYp{_mN>OWeg`JYNpa&YX|19NImLHRx7`3PFBa$o!S;#dM#llg
z-j+=xotLtJquZ|9BjLDKt1}pOQ{$?;$78@-K@i{Q48Wv^44vEV`-#YqfssCsILxcd
zm-?LlaDAEY;5~njGy)ryzx>V(cZAxB&L|-0%3R1wYVf{Wa_1s{-kp`EGhek`dtD;B
z%JG4-_003Bu_r}JvzQ5<X=bBkrDe+F<zWX;Zo8u(4Ldf~810o;wgT3P;Ar7y1e+jw
zx1m$CG1BdishX%_{yOlC+}`ZW9-(}?7H!lzpN@wK(#T4XG$P>nplUO1;Ywmfhg5(O
z6#NL8<#6n-2Y=BU2(s26>!ZECzk@hhD_|LsFW#`J@K<M--54wU`r@m7eg9ZXznh8|
zGZZXh$&qF2pve?o62kp@H%ll+tIXDay_{_%43H39vS6ieH)a0^0~w)$4DbC$%?Sr>
zM1%kSwIiK_{~ZN#1?|H#h~E5Is*VVpkcnoh6MKbs3ForY6|;I!YUl1+ANx+S>4)6B
zliEpuNFIt^L(9Gl_DNIW&h8r3np!1nNpuK8;l3f!_1~{GrmB?w;>T8Xn7<2&27ZVE
zFWTu$wh-Fh&T^k@caWN_Y<Sm}3Ia#e@1pptn?w3i=lh?ZPUaE}67z;d%#*3`qYK;B
z5^I)>X{3L|r+`y`lIZbe!b`~f;qO^<NYOTk<nsJW@zvJ2bW8dI3{km5<BQp*3~uwR
z=fDQIs6rC7GZ;Db5EA1DIb#gWM95&tJnz<fWrxsD=hZx{gZ%#8!N*LYOGJ1*(aTmk
z^XE;#d37uDEdi%3OMzm7>H9x9Ul6(u%JdBUR`^LBw37Z_{g$q6OGm0zDI&#a;qX_A
zqHmuRulG!qb|)8pGw+_lituHaNg|boCNILh<%&t@=7<c%0OqE)+VA7$W8><fc<9Gc
z6bq%8bgd(Yc|5J5K}<o$8ySj~mflZb;cz#KLh^iI)M3u&_egB6VDMM?ovjaWHh>LU
zf!7Cw(dP>h<Q#nW!Sf|S+73i#`3jkEjL{*5E47We74G*)6K|}KOSqwiNOLC`Ia7@)
z1UKQPh-iII<*f61B6`PjvA<8-@MlYI-z5$v`&l!k`>Am-5r4=9@l;br`tiWq{e@hW
zrSmHyEdLj`AHjyalf`_UB&E_|z^m}M7zD-xp3fJPpPmmh=Qpi8$NSLaGey5eD8AR*
zz&()2^a-a$@s_Zh2y-+*v@THhwRkCw%7XXw81@R%ves#N+*w|P36C!7-<BwbMlxG(
zej&CT$sfQOe7gDr<MH5uJ&CL)K(A8Y5$~wY(=YfwUAC$dp7VsP@rLNkp*acI&|a2`
zec|4gOd+FD=Uq}{QazEGiop_-5YuheG7xfC<QT;B3vpob`aiV*BH05~FO|W-FE+G7
zWo_&qWe#lDO$ZmC><g2bY<Uz*vK<fI*mf=ZjkPEHJ^(Al#vIDa%W=FHHDZr1#=YWH
zPZl&&DT6jl0i;&<ZI$#l$CEaXPvlGQ^TaS`YcL(I)nkRH0Y(1*#Q>;~;$Yx4WTbI&
z{YQ`{n-?5XCAFsFPPkI?Fydq3l$atMq@)1hHk0$602TI6DhhZv=$HhP#D!6`aSUF|
zd)G7WaZT22XJWt!vUh9PYU5jWRt2g8oR25Z_j1j!J{xYO@|E(QgmPCyhF`(#O+T!u
zC4`W0NroUJodf6;3eW7nvYK%T_R3N-{E}rRGG$@?855IGWxQ_p_HvrZmKyUznm1*4
zKDNqs-7M`OkJ5-LKuO}c0o>A{pg>kXD$cGUY-JevOP<Hh8h3a|(%-3zdy2CNAlY`t
zcKdXe6RZLiUSEOXmI910mIl!lP2(5`o5%7tf=;^{$U|gA=<%U#F*=i%s%&u%E{A)F
z;yIcf_iSJ=-Ejipa7sQPVO!uDH{G!=&{2KwYn650Io@&M>C>@rF(dfdd@sF{eaPiK
zyOD6P@N>SJ=AETYxj-n2M!{)b4^C$8jN%=1&9}m<EtcqPHrNpP$$Vh)mSsG!vRF;A
zGVT3@%uaoH<ph|;ru0NuQ71Bq7Rr}g5qc$w%wI-UjnvG4eXG_%0tA;WWWUMY7xrIt
zk3CcR6v-%*%Bg$`-XIrP{L*Wt3<aAWMF0W)zsM%j_{WBkf<r>cpEyzckjSLAEJ|$p
zc#h_3@h(m={n<0Q0+ct?LM5T3@R=hD$@WYb<>Ia;IX29m-=Lou>Cq;mE=E@az@sso
z(Y-wHC^RZC0zN!m<vM8LuWfCEjXjJ3OwJ<QaeTehKT%Hy|0flM177n5C^lOE^#u+q
zP|E<nAQCT#l{YPLeULmdAtwK9rjVpxzqjY|`lczUI{jF?qZP`xeO-n<2h|F59pcmd
zgRtY}0}4A*l5l;uUs=pn<>nNI>w1orZ}D}yO3G5o!pg+%yxdH{<FItHO6MKd!07Pm
zV3R6strap;D#e$}Y7N59Hgi;yuW>7-X**H9`u!D+-t+|2%bEjmxy?d?hI6g?j+M*d
z{SVjO8HGX9ORkZFgvMYPL}8@YFZqtQA5hW=4`Wn&2L?n$)DVLoszH+*pXhono&2d%
zMOhardsalJsLn4(^nGN+M2ys<o~}sFC&a>oGsR@F5p#P0s`q2_x2E8RH*kR%qgjlY
z2tr#DHN2wu{|T>=!1WCN448|Y(Oal;CEVopPM7QA)L$iXvh95nxPZ^W#M1aWl6`*9
zBohD1s${(y3`p0=va6>su@RNnxJQiY%_x`6l|ric<|s!&$)YTsq2pi;k1fV8w?;bG
zQBO-aavbKtZhj97#~(!S7z33Obd9vx{l?509i3J(+c@@@m}EfrxXk*xI}gtr({H+@
zBdr4YoD1+vb0esxhy}7pw-NV<|Gn;U^AllnOe-RfX@gT>eZMcsBZ=o;-o1W@*s5t;
z>Et|p)H`hu=2CS7B|FvJQ7<8^aQV=7WFsl=DW@l$n3|ElzkBQnnnjqBbXlRfcm)j!
zH=0M`F0;e5$fNzl8;2x_g=Wg?647AN{*h?X98W5vETZ+$)osb6(kmB7dhQa8y~QyD
z_W~=M`HV&8bhSKeq5PFuZ4!48)PLl0nuEo<>>lU2%;~q{vMrHCKl2LQNI+vde(8tO
zP01KLh&lfcd7$<v?J%l&?(LAa{b}j{R?inS=MpM0e%Uq`8<hBe)$^&^1w$8tNG`9?
zg*0(I+0-0M&npWMvvnEw?G7t@nEBi<XQeAamy$CV?bWI2krAJI8Hw@alGFA$o7I0?
z%Oj&golU*kS#)>QSCURSPmb49Oc+tE>+^{<C&U*#-R;BM%ciB<%i3>CtiHyT1^skx
zefX>!FLZc~9v*Bo)wedQ>}kiko7VC;^sszel6Cc*v|1TUaI<6^pKhQ|&Spl<hsj>Q
zkgN~N^ioW{TfkaD(zlxFMdx{oVXXuI!Ll0HcB%im=6r6e*Ye1@p=#%___Ou>O{#a$
z#JZhXwl!awF@Yw_jQPHd(=M&y*d(;!d{H|7vs4(%k?qE`V+nHF&;wbq2*iizEaQ6%
zgZ__^@TtAFH~Q1{KQaXnC3sW@-89`(Qaz)=ar)5gOzU>ZEyaptO$Geq9^Kg=d$Z&3
z>FG&9cWqdM`zt*bf^ofcMyLF~=(*?0NbfL(hDot4?@YC3Sl#zj)hk==iCKr-7MBM~
zG*GSY9FJ#?j>nQ?-|zGL#*JyxjvHT=Dhb$ji^s$jV|&ZGfcSn}3=@!Usg&hxO9;8m
zOq;OV<kHsY{K7kjYVJomh4EH7yqUEWfd5o&Q~p^UrbT(xL8Vi0`1fF<Z0(P2wq2*6
zk-8RSgA>2l;VFmI*gA@?dWuI9E7_95!kiZ{hlw%+Z|vvI>$?OWx&5=E@$DkoAlBox
znhxvkuoWt^@K$eovqi!(0H>w(O-38;Z|#n>aF^<$G>nM*f~C71q8a>)8-7!z_R!y4
zPC1Qo9PXx;f{Lg1!qUzxM!tvA`Ck|)Q<K$3wn#2nT}*!yEOv$EqcJA=4-j8!p<({)
zUjQ-G{;wMNS?@!EOkm2Vz$s%P?f+hdQ5JhYcryYqLYhO5<I!W!(ro2S*X)Tqk}x8g
zLDl1nR`jsV-$r+4Wtv?j!#zS1pCMs{flAX?2ULcu7J>jj8i4(_tHYZMX#*}>fS1#y
zkEo6y*Zq#nU8@z{3R4CD3h9eY4I{cY0sP;tST##}>t(Yv_8M@?T8-<a+smz$3`>MY
zOWXxAMgHi*$RY155x{dg?R3E@RoQYIp1Ly4{)4Uy*TWeWRch5#QV_z0`%gvY6PEBD
z<oe=V1~fM~yxtgCiR#G4&p_FQm+tDxEX70IqxD*(anlwgC$-HTG3Ro)#ogB^yDo|5
z=)`FMP^MA!dM(Yrnor;bld6$_FEB6G8X^KKUG*kzwFySZ;4-D2(Y*=}2uQfG=0P~<
zMK^19jiqzkAF6a(RfXz*STzn=&yNXe|1xPlJ1g_^!EOJu=X~&Sl7y0l39jd{zhVVi
zOTFkDt<Z!3cU>Z%hnfJIkri655;&{RBNPQS+b?3t$Ksj2u%D9~j^4(yOxvvO2*19I
z;b#ZH&4f81KzviE(g72q3JyhqRsuCEr25UGVBwwfoh1)MKn>#iNzfj%5%+BTXw{kc
zO+fM4IdiYIb$vN``O(SvH3P5Zz5`d9_r(9i0;{4VKhfqVBZVG&H*XO;sU&XDe2k)v
z{%OJ1y0ry(zn#*-Xb;z$ukB{(>~zgzxk`f-n%?eC^<LB_Ks%imlL^B=1YBLW3zjv<
zJoQD6O!PgE?6b-3RsYFcI#*K6@yQLtjTeAO!LfPs{lLm9^f^pv$H1K<5^q?bb|Xq>
zDo&Gbub{gPC97oW&+jso;nKLLUoBM&0%8}}LqxFs@l20Hdj-ZGBY`+t`#kX<cO*ye
z@Km}|`ncYVnLLiBX)5w2=X}VA2l&j4(0aKX>~2{ie6WDz!j53)k_8-XOie)kgQ4VW
zzIebvDdN<34XJO~mhxcAQ>Ja!0#p(<PLCpd-n?u5N(fF$YHpxz4@2_reSTDJFr&Sz
zy)_vsiWv(4i$d|A(W&W^seS9)ySs`~v+EhoV*I!@x6m%+wVWk4qCkau)2oI;>ao!S
zB&{0RzWc(~Ao#bpk{(Ieb8^G*JcTE9HI1RsN+Eq;-l~g>?$<A{A<yh>3Z?OruZ`|c
z#R8}td_-A@Pty204wxe)YB64_;+&awxyx=VD+0DEvPvS5@D^dP7FJi~CqWd7eXKYz
z{|8;~99>xxeT#O`VaM*MJGPw@vtxE_+g8W6ZQCcdZQIU?otN+T#(nqQH^%#S)vj8#
zHpZ@+wbq;_lI3rcj*a<8ox)&*Z#(1hBm)4sJyOx=g8Jsvu<UXM2f>9pnbh^V->XM7
zFq#U-5tG60Csmuua`B&5nJ{RV3NE~sMyDQ;|3vwT<<ua5to0|Nrc>En$uD5_yxE+M
zt}=S89bM9~tElE--$@y)>h!2QbKatK@OnQaR{Uy6GpDxMx!T?N&iA1*sf{IIv9OUT
z8w<Pd!#xBWFa53X2Q(Z(6q_2z$=NA5xU5`Sow4pjZ9pZ1q$2anp>5zVUG9ID*(7?^
zqf)5L@d=NHtom%Z4_Qr3a(I9DH8!$8t%jffu1JXC8p$d@jErg=U(!JPLnfvX$~#YK
z6Z<3i@O2QW*oliUHgtt{yYUxm$CbrIB#*wihD|i3DPfmO<sBz>0m~RP`0Q*7G1LSo
zMl#06F#J@x|0iJEKPWY;gymLsa23N*ls-coqg$grgP3Xf;230Pf!SK*{Sr1q8q*1k
z2_~}}IW8=fVZqy#mkSFr1k@+THm?<z%IbH@|2?rs;+)Zre65-V&D7KzE}~gcj%DgK
zU|3gW_C-e>Zd8-#bO-~6HEwa;<05ddJ0i@5t<xP!__q^dwaKU+#;`oSoYwc$Se&Wx
z@uW`hMNAuyRp#49;aT>dU|;jxMkUs@Nv9$|&)7{+u}eNO3FF1uJQAc{P8QS2(s66R
zm`OE13;(*E*YqOWnxj=S5B0LcE4$gWns((hnR$H8<xaXqOrX197;697mf5ex({M}J
z^xglA{ongG4Py$)0{+wYlL-7p9P}lndbi(O$gg}m|9{*6o5FvW0l|?OCe1p)QS;*e
zpFaPqZ5rn76=fPf#^;lJ_NV!Oqy6s|Q^+c8$UA}<>O<wzfdBFI|8DzuhdJi?_4(ma
z!2193_w|u7!W`qoNP?*DDOHofl_wPc_hkPYVjGS>N5O;N(c=Zqb&d=6|8V(VJU(FH
zCQrz@%G_RM6&T^8>oZ?sU;&?vbN^>p|3!a}QZkQPal^Y|JvF#fnnxKQ_J*JLSxzuj
zbi2~~sT}%@Mdd(w?XX{(wQ%pL2b_L7qE;DbL%$i@J2@`ST2MZ5s9J)L^r^bRJE0GJ
zyK&`uDK&9&nlG=LiUQ$hQ;!`<6qmzbiTMm2QR63%7F82z;=TW8|NQ^6XBVNl>%eVH
z4bCyHgPsL#Ic2pIR|C8!v#qO5M+=BP;r~1pMtQJhPGNeN-oIAgcA-CU-Y89-x_72%
znZDg7mhWo8Js*G^?-Zv_%kDUr&H@MdRNSKOk_Elp0Qm3o^{rf|$}1*APf=5e2M&dE
z$^qf|U3<5QQIbY;DkyYOAOG2X{(tPBvU9K<^nsTqdneeJPoBSl8RYa!Vcc|!$czy7
zGMUIIjetO^o35Auol~I6+zQ-W;o|=2z7Lz{7hiZ3V4=Qs^=7}Fwmp#Bf`FdB6XkyP
zUE*C;tgwLc&$0VoFY0_{H#7VHCQkK~<{s*&ZN_7{ry|-MF6}_MpFA`-Bg<2M_FOc^
z*tvK6eDfZ>7y43oPQ!gfO+Y|h{gbo26=P?2Teh$;Z(4=-^Y1U9=DT#~QK8Rwd%E;y
zVi~r<p{fe*X)U(*j|)Gl-hr7d(DPPS)mWj92$182n^%YKolYsLw7J18&$6~M#(6lY
zx<V&0xJLeTvpsD`)RB1|5vi%P5X$D2$zoYu!2Ap{5<k;w8|QMY(JN0{?$6pqLb6Uj
zK*H#|+$dafQbJw|LOt+r^&tdSvc!cVpMMxv2@#~yW;sQBL>H+)$Ya;#;9L{b7MrSJ
zNQFx_8=ZZ@oVEgV%4rNW_OUSb^dN{LF&=Dw>?Dz;lJ<K}b)=F7hFIVpWo(JLNzvzJ
z$Fj`<6Z#%4cMgPGIJ6P@Id@&{0!-;t0N{xYq)kS5ySn;}1z7!)6h0<R+$!cPa%M;K
z^#~omu$>GeyFc;44_3aHE7xsO>YkTrwn4||S)yYs*we}uk;d2<rdAnp%O!q>S=G-}
zRdB4woST>b>+EWW;owXpqNPMpYYAMRabU#li{cEG6O<{DuO}-5wy86PGCwj%-Y_8(
zpoGqk0}P~pyNI{HgHHqHg^Dm5!A-#B1wy!u%bA4qtl7T)oZGvHU|VIpNpSu0_Bbpl
zu&Fo=9PIcgJM!XVlSZ2~zW1dHv@l=PIShu$D&nII?F>kz)zTL(GDOk<ID4kpn1rCR
zkH+n*BT5sY4}|;D1y$rY*+60{x>RVWAMF{MJo4NPjHgh46A6wIF5&$%3?#SZGLl1=
z1C*3NNP_=#FjDE2KU#HB(t0qW|BY_tRbUvB?YSB@H2BwjYX$Zuj~T=dI_=PnHb-u>
zaC9?F?Fy3g59Q8pVEtx&pGvKq)e#<-lof<L%TCo9ZmsKZa`M%QL}sy8g|;sGz$EsV
zjQe<oQKN(vpQIBN&(w9bU;W{6Yf}^V31Cx)i0p15%CQs!Tgn{?rKGUC7nPGkZeP!&
zy*D+J=Z)J#i-AXiv|pPV^6yiPiC6nf+BX?-mi*n4Yix_$KGBq0<}Vq3i$aKtWM3Yp
z-e}&fJT4BSp_CCGvNsf)FnPhIX!l69FjMjeP3hKNkI*8$HqVjrUBp#XpQTrJfVitf
zsfjr=Lrb2P0UH5#UT=+7s`>qw4Ng`I03t43g`czKIUzAq3<rlZ!JR8SPLZhu2n^V>
zZ~>uE9IW{Cum&G`-eWphFQo_lI2W^0Z^&z^T$Sg(-)iLvcG~t-2t2%qV405rg&iL!
z72ZucASqI02=mr`s9cHeX{Sh?Aqn69$gnL`(uc`EFMvto_M(dPIhFtEzDCRPGDCh+
ziNu>P4NWAaE}-RWK)_Vd5EE5%t}?_U9*JkPboD2?izToLFN`szgaf0d9oa@`iYP1`
zAnNgemotas7N=F3`j>n2;0g32n<SK{Ci2qWIUmV{zR+{W0oJK$?&Jp&6@mzk-QDXQ
zB};`4_1P=y*1a_G&}@ZBI;Dn`ZXGpwsA#vy2Q4kDrQm8T!kwuNpS#;3>S&xcm8_1T
zZ`uY%@YlPdlqR&6{2@^d;Hs1Q5N#7+(VJyiMN8E$$iCGIY$}a|frT%+u>qZ^zMR&U
zn@-PN$s3B+?(5E+(Y(<(il{Xhjo`UEkJm80Ksyo&(s$g_;z!AFlW&@!c;Wa<d&rVD
zI81V*uNPs_a+aLCGm>Tl1U#GxbNU_4Sk<cGy||MZ&D0Y_w+V&*tr;jdqg<%pu->09
zCbl}^3Rpa5CUh1K1kqASgci_epGcRmr?FjzCh3u`8_QoL4O>Z`_-<AnmsPP(#|epY
zK-YOOkLrm&xwp3YFt=WOj5t!&^AF0xtO*cyz0f$>Cz;5qStU6LxjzLqn_YZR{2?E;
z7b#R5p`Vm2G~)C56aAxWb9BfuJ>UUP3XS(I>_LPS_ib{Tqja9J-N>DGb&u#bKOin6
z@WNwhZ_|!G_X?q>{jM^^18s4JlyT0^!5Oj4qZOAaGpxpCpc-h?O>w@rd9X3n89K6|
z(bpWtZ7*A_KX;=80(sEupYBemvfVmpZgp0rWbkYEGJ4+a#=BY_+RtAwq8r$ph7Ct{
z?Jk&Q?=HZ?Gx)qGW6!qycA@0pTN$Uq7F-w?LwTS{k4%T!h~_}V=J7_|JXwX~Rd5Dx
zqDk|O-t@xE{%i%K{(Qmn(pbXO(uL=nj6B#aa=&1TG2^(!?QjyQ6h$GWH$gP;>KD&f
zB=|d(9TYotHBf}lm+O(vWxtUhAMTqSLryYE{-urRnRLP|C=q54vM@t|lkMNpLwwYQ
zqw~VdG$OetK1e5q9hKD##A6;`)YvZBESL{8%gE@j;}pWmO(mZ9li8I|3EJxNAneOZ
z4RhzQ?rXTx5xn5n0oGNCIa_x-*my+*)?*Rb%y<G=N_ZSl%~<#UXq}@o5?I<96C2FJ
zBOrK1aK~aUMwrYcT!tlv^fd*ToGllZd>dObAu3h@&)P}FAsjR3M}uEmhU&AQqaxuR
ztzA$YSzMxq0F(SE!mD_Bmb}B|)FwMGW#<#EZUQ)*?S$R+tI}jXl9EZgqWf6#MW|)B
z2S(*C7;?s^hTO(e37oDB_w}+R42)C(Y=s&1s7EFLICCWxUXM$9!){YEHKM5PJ2=@K
z6pfZq;7O@u1Y+>X_T4{rQv4Z|eSy@{U=1iPW@g&WuhE};b$vYOstvf$2CbZ#Y78xD
zvQZmi8;y@@tmBaOuw9KYQ4Qd@!&3cLM<*A!7)M%cP<w_W@RQ*{vF=&Fu6xC3Fmg*_
z8{U_XK?#3)$F)6m>_llUzz$CK4zeKlfb6=67@UrBLTIC=`g!r+LJ<xH*+>OFKCc3u
zCi@<R&3bQvx=@4B=0aT4OPR;DjY~+CQ04RV8vX)O*e75Q7AX%`m^*4s6#3pBr8ggO
z=k*>s_2$>#E3VKFm4(1?nz5jRFdj$jdKJ9BNVK0lIt^2(h}#!f1l=Y+1l=`5+Py&S
zOu5Q3=rE?a8si>3&zDW=C-<>GmY;e|Ro1E-3{0aTDW$Q_0g3dK;yvd`<hz?HUEkJQ
z_a>sdgwjPDw@(#%soiDtR+_K$XC2@f-1cD4Q@Fq`PG8k5BlN;8SNZjkLbAa0RO!mm
zi0uS*5hpU)1m;QahMI(epbqXZMXLYaTcROW6=`TbpMN_VyqlSlq$F*dmbqPTh`+lv
zfts>L0v&6o9ornu-Qd_ra6U>ug+%oiIBfhCR2!+i8%nO0__TiPkgjbJ-O?OUh)}L(
zzwG{bdUZ%-<ilmcT6l<k7e>d;=;xh!ld<n|7EdT2!N@}AL{F|pl}2iX8C}1wdQY9V
zRm>xMeJt75H@?X>#CpwzhDM$BedPq*`<y~#s4#}{!M4a%G)1|SvOJGsw?i5IuWwgc
zr3B0te<O#ECeJ>Bx?WV2Y94yQ+%6$sq4BzwCFhG3SCXZdO;fAd@kBavAm?n>MN5ob
zI1IN$Y=lN^s(9!t4$<mpbB9ZWeG!A0`up3KAb-c2!I}4OM!?Rz_qPev_64T+Vug4D
zmy{wISJl<H*m&>Uxj{(s5qW+QrAcKFyI(>kZ7>5*R1uF{x@ItsSa2KyF4gW|O>Knq
z+$TbHPL0zbV~FFU0}qO!j8LUG1>(m!a{6^*vq9<SL?%PvZf?n_HpXY&ZzHb+EjnEN
z<-(gc{P)T6zMM(%h}?X!K8l$PBh1b<lXi-^herX2pwZY`@5QeT%IL40QJ1?azEa+$
zEp`A$(&h13x#lXLcrdCxiC1XLjp<P@VF~5j`unqvqq>ZKN_}@K=&o<<9Y(;a#3+Ut
z!D}B4k$v^sU~d#4?G;2_=P9G(p?ahF_&Rj++_O|X8)`V0lD29@7BurZ#Y@tcoza<Q
zZzwd}<E(CJ09N@qVk#Mg_){{x8IOL>T7s)3o7eMHX2j;4+kv9|2i#wz4(R^#jdx}&
zR`u}?{V!@Q?kcXQC8i9T_x<Se$V1(YvcfMFY3yk4I|2fr*SEKIAcR#YysL6`u!c<w
z#^Xdr7|xtdcj%B9Q9~GcUXv8`&y6UQ=4k%g)pg>7Sj+5x$eKQ{Uat11VksE)gvD{U
zC<;?+REq0rt>>xBtv4<e6jVJ9*`47YS3u0pilWMMDggxBz3Dc)soU)BA^Ogq6c1~$
z3mGRj(LUH+8vl&BKOy0=g68U6!vhE(tmeN<oEgd-kTNlLtJ;{Wp=E^x%`wZqFZd-p
z@XCk?|K4%L%vf(nR;tJRD%(at%G#mW_M%Zc0f|dZ>;p+dA|pjZveDYho~|upf~AD4
zMVU}h6>Pa^AqMurk+RFKkzn}6ZNKf$6Dqa<J-VyaS%L2E0ujELSo_zj*DN7Uv8-h+
zzaIAg^n<u26tvflht6itf)@|&Q}pp0Qf!ck<93_TDPaK;*y;ZwG61emFT^ATi007B
z%tK0UncP>Yp|Yd@d~CIzS}}f;y+P*b&&?c{vB4mgnO_@d58^>Z#}o|y_T}7KcmGyp
zr6@Z}LAk>;VXw9RKJBx84=gRFFB%W$e~^q2;AiD@*zaY=5^-CJ5Tj90oQ+XD?>DOo
zb-!1u!oqR^@eXVfdn2ilUX~~pL&43Vx$()8Fv0U6YqpVPJR5yqD1-f?!zGy=xgzII
z+|qJ}^(<T9k1W$J83YP!ll?+=6{nGi`8m`|P6qn;HR!h`_EqUDuTep0BIuiLYro$}
z0d3bV!^+Eu;T9;;Zp{nhbh_Z*6<w@LQ36@&tO5s46}?ZcKeKg<GIrvA^bCv5SOUKs
zp=Jf!<<hl{iS0e4E9K+C<?VjP4PZC_R<=+%32hSs=y9XT8pBN<ix{_@H0aD2vH%eT
zswV-ABhGP|!CFp6LLy~_?aiSydCGGBcF4`&*!r1Z&<&jcUiG+YyQEb&#}iyPw-Q3;
z=T@YyCXD>X?8TASelXtl-Y^@~=<iH1Es|fg2d%v}*B{e|gFAqbtRgjn5&K-OJ_Ab8
zuy%cOA*R(MAnj>l1Mc=Y{yVed;X#|E$%i`A3Af7CJhR$O$V>`I7#D<u3hr+Fg&~GW
zDC7<%m-zVny&nHZsXWHBT4{(LZX$`}eO1`MoYfK?cJZj7Fs83wnsi7yhw_r3VMBtM
zzXZPH`C0pV-}aOXslJrA*O1uvkBo_UNHOdp3T;d6iet8Qez+uETph`|V9(rFe}Or_
zP_}33&xQe(I*pj*EuP*l-F?@*st~sXX#tIE+5O7?8n`4<TO|Toj^}^B*JBa%?(e+I
zPi$!{hM2V<SuS{-*^ZSAP4_l(d2=+NkSmDhD~e+fTN?w2WUO`uw{;t;@rN^RV$(5c
zS%IBHPNwMFu-d1jkGEl`Kyx{?b=Es|0G|<;_a>o#Gu+DDSdC#BBBrzC(>_ViPI2@m
zbeYRQ`V^0^fEI3jwH-wqdUq3jjkZOE=bgOg#|Xfa(~c`>%Zb>X^-jQh(u{<}q?fON
zpsW@B7aLJr^5HQKvW3fe-A3uA5#swx;EdwC$7^i8#8d<q|I}moBu}tj!;Wo6fl7xF
zaozIO?@RlW=`7lY<EupU^eiz>%9*={a&l$dhaVz1nU)Hm8|Jv<N|}Y>!mEo0ULv77
z`3^@=&ZVO4MLsk*56p(>jJm^od4NVgX0iM5K#QZ}5|ny_BZLU~ja0bffTR8rmAj|7
zOKkMTM=%d(sIy3c`f>pMr+QGd*Q>#cZLhy`1JNaxPX#e!imPi+Fw=Ox0Qc$2o;m!+
zy7CZ7b$=i3@kv{o>Km>7M(d31m`i*uRUC!!7T4}29M99oRGf|ZJSNUj8cOwaLv&;+
ziKJ!`?(3!v;MU-Wi$n=$NBYNGQ0p^jJjQC3Yx9p!C7)r{BMDcnUDA(FqYXOsyLWW|
zMaqCAoA8*IvRH8k3%JZxP+P$X-<{biW|nEskiBA@keIVgZe#l6^a<V>jjH=N<IDL1
z<3nqW_9NdXWwd8Wa<;@3-*hO$8^mg>5t_7nPGEt}&U(lt;-QPC$*)RIcl9uZD<H`u
zi<iYLEr)B`O)tBG4Y^6{b4NC%mNY(w@odoZmLmN_b6;;Mru5sni$nts*`#cA5h0)J
zU{=&U9^k=h)uQTE#ce9i*y}c1G-tj6%dYM3bWKpklsiCD43+8PCe>4}semHW*-~9>
zEZJ&*=24UQBJiZ)u-T?Gx!i!@Hov*^B~m}46e^37aafi>Rd&zDsfOE{$Lv%^XGW4Z
zO<`MW@0dTX4l2;mb<eZ4IJ(-a)A2%h?#L#GrandFTw7p?t0m^BX6aZwgE39D0<H?#
z;#4OsS=58AvWT?2Nj_{g`^I!>)XmX@o8AK_<8i2iC$l$h+_COpnb)DcAob~_`R;I8
z<NSDS;})D&t=^A}Z<IK<$$6*)YeKzgD`SK**NP87-M6JnU;_a3M>Xx0B$GtD5VlNC
z5?*$!h!WIXFZ0e(6o(^a6hNeZCN#)QaLF~(W&WXwK)d17Y?>-KYMuC}{rMm8$_7*N
zZo)a!c2bMmg1_WMGyozf3+Nj&#A!WSiKdub4l(Z^4K09u1QEa|vo&xER$fyShFI}Q
zzEopf=Fx_`8T+rH)T;!lkrd)>N+ErBjakZ9q)_;~x6+{7%4qO>-nONv*;H0TD__FE
z?8{cIA=HK_rVwk!Z8gT2&T{fV|0Fm{@bsi4LCXLbs9?e6(N<FIb#7K|atm`{HXEwz
zGM&_sJmD|U+v{7Lin05m7-XAvxf=60mOfrgwtc=-f+k5n^t5*5_uiMFBBsw~nw7m3
z_=dywd6JN2#)7wpXq~T-sdaK{QY05Ju3FZoSMIg~m(#$I&2AB_xIdz(L~$!cSqjJe
zeC*!Zvy`a5`2>m2eE3Y0iP)oT)DA>T-;c}TCh&>ZpYRH%8mH>0utdT1afG-(fYVS?
zitc>=Ub#{m+*GY0jF!#9X2VACgpMyYoMhRnGMQtM{>98xe_(ZY(SG1~y$$yAo{m(i
zfv@e33Gj0cE|%Yot-PCyusO!51)KkpaenDhO5=1Oz~{&kA827@*@=oe-};y3aqFPo
zWrcLH)|gt<^-0u-hAVE3C;K`soUz)L`ciMb$Cpja{i|5ks<@|7sKl;hx*1OT<3VNB
zWBXU-`x*4lnU2_bi_0W5<2XsPSI>NYwl)y&i&+oVYL^Kvy{|{+>XQS+$SbCL)6g=|
zO@B|yPXigDZO#;Oo%Kb}-R%a;@u{FTK!CMrft7jZ8xOiUi$Yb~f%nT@n~v#RmXIfh
z@K2+oeRVFjAflk(6aai_MS?R*C(rA~DN~4I59qzO+5ADr<Ljeh#V|{Y!r27Sqe8yS
zyP5c-++YJU561_>i_;`t+G~eiNoPv4d>tKYvHx6tlLGx_`{HfYNW$oQbwy32V{<s}
z2J^iNo3LL)N)v4woe{M5MlkQ&j?1dUHB6-jLpnicefpix-fQBZqm>@q`}HZr>{S*y
z+3e^<o7vLg!rdnGU|gcJ!Dc>8x)G!3&-*JMe6WQP-iBRS^Qi;drv$eI{lCD2mAJYS
z+B}3VFYEnUy7%J_!jFQ?*T)&S%b0ZsNtF{m>rp%jW^TCR{Kr}U+^pHpO|0VSvI`H>
z&#3#mkMP0nAo{g@f=3>qSJX%t`L++tH6hnONB4Kn6<ubJogFOM?COXxPVs<F4JWxW
zN7H&nj_bFfdU3>+gf@XqiZ5m_js|anDbnk0gN}03-eSI)q>fip2pdD6n@wMZ!3$a0
zLTrZ&-Jz*?ik#NyyK0_09QAfPZWmL`Irv9ySun8Qvt7npdMq9wcP>v`EU(WV*WKIp
zl1*uYlMvY6hZUPR9oBpy<*Nr1vynmkaRU?{zOt@BG25=4yW2Q~aRYL%#}9{olQVh$
z-_bMj1AAfysh8Ov#FU_Uy|nj>W)_fL6Qwu8F$k+@_KV(2U}!fI3|=yDC&l)?0_CYo
z^xv9!o!*pH5Gmhq-Ro$x&#fGzyq-Nr2Twa$?x37{%Ta>x2=>%)G2fP#@T_-dud0NI
zC(Lx%)Sz8)KABbcSV27W(&#mNXQ~ZeUSI8FMJD+>nfD_(PGy+_7tlEh&oofh!hJhb
z<#>oMX|`ui-jRz((Yl9Yt(>t!jgmI?rEdRyQl~AlRnrkL`t!<uIZ**<3c<P5(BK=y
zdyRd96Jwv#dZ)dJI82}2PNHKvRL(8u|B^djqbFp(%>TxoLpHG{=cWC&`+DpsaU`mJ
zo}1`-{PVbWWJ@khy;!BHj;J0>QQi(k2cJqXeUyp$gQ4R!hq3NqH+gKhqWMC!Mrku_
z9h5%ra2}9Nv1VA>d#=o2goW|b6K?kSDAZ*HC@cG(IU4~cgj2}C>c@-Ylm5fwB7I?&
z-hSc7>E%@9=ik2bN8SX(*{f_EVm}{CmY?AL`JoQQ9sJjL{M?A-s`KL9t*STu!)6H;
zh-N6c&#-q3@H{XVS%q5U5i40K9qES9UmRA4!{CX7_%XO%!eYhn(mFs5X9@JZ%q}0w
z=gzx1(Gwly=4b+(VPc$2vAHCMwZ>_Hd&@C2+^!4jjeoXVF_Qe<qFRtlr_;&6)JK7m
zlV_s7bxT>heD9zU4-fk(88N{vDn3xX@k{GKOuI<uLsO~^QDnIylfLL8JQA^6QPDg8
zS)`WcPk0f1)`82mX14bRmGLCgaXeNe;y;xmXw#V{hI)fhW>)SbIhCv)?3<S*bb4ID
z!LiNTS0zfHSwil8j|o6;v864vAtd_~WSq!|*hynL1PlQDbuSFU8Nc9oLo$Npu05LQ
z$xGNOL&!TLsZIMOY<JvCP+{rK@#Wc{oaAD>(BsGbf?N}*z~{3brUWj<bV#8p<mp{D
zt0O?N#ug*fb^wCZlS)c2S1*-G=Ay8;3bY4UOut<gXaiun=wfWlm6tVq4|fTZ@nNj|
z=sA|+yMAF$NHJnQO->fP<z+I4a$kS_fiH28B;d`|!53r&F%|RCc%L#a@h*I+E-WPI
zCTBQoC^0sk=S5xo=bi7#IWQLjR}h-oLX`E6o{Gs>3SLHR#v}iyE$5Fyq$A55&vfw<
z`L^Tdwlq6c;ur5wy)EaDx1@wQlYI+Ej>*<IF8wjVR@M-1k<6f(G>E}(!N{uJ5XkX1
zh$P7$eWr>HGk429_{hHZPLJ)A^{heJIS5+~|Ev*p2{NA{v;1p&C0pO-$WJoUQ-=Pb
zG(0EFpsjxF36B^|K8VMcr8JEjwvBmW5lAt;--&#kmzKbQvA)WurHXIyO+eUZu}{xl
z$dkE&Rc5^V(#Td%s#~A1gml*|=tzE&Z+)zphVKu}_V2Wt6r7c#Pjw{s2ISvas_2wG
z_M?Zzp&_g^JOvjYRE$iGMaqO*f+rcyymXn&PFz?SYj1d#*sXQ4d`y%!xGq3(VP;yc
zGKzT=WigvAuF2vw>1qH4#xDNsJgMq(+3T`ft#$f4U#$M&cDc;K=Wd!glwLaqw&d;f
z8`mT3t3+#m?9#75p-O5uzLKLL!=Eh&vRQ?7LcHcrIeFPyby?V1ZJkXQ%c|S~t&j(u
zEW1sFZ%2=IQPG(z!^^3w12JnyU3LQ~OiZwN6ZxlcES`bs<`uPvUYIh4101bhINEp~
z^x7{E^bwwJR0KGSVpRr*?IcStI2oMPsV}}uXYfb;bq7qM&%Zj(y=m&SHX>O4Isd5M
zt!?p7y)sxH^Yc!Osh-gyrrF=x{96-^5-}(O*t?P|q{dSd)+S{94HIDB3aj#@3b0)d
z-3^>mp2?Acf6nM5XQ)Nu)*ezFwE(*L_&vE6IgT$C%U}tAX|fzsy7XnAEu(5*rDVtZ
zU;9JbvYb;?>+IW9iEGlx8=h$cLL2v5I0G)ds(Ttc)`TqM;%=T-Ym|vYFcNEFIqkcK
zOVQ+)>uB(!Bid0AEM9Kh&!r4AWT0&z1mX`b7!D_Yz1^L8Zz{$Ocu4hkLg`LVLxYAp
zgq{rHK+;$z2iL@lmEo+3lO;=aSY+Ne_+K?*{sUd_Ay@=$NEhkQdTZw{jBks$)3r`M
zo;P!X1vf{~aw&nwnO)ae5xBNmONhEws>weL4Gr_>>$zI*uEx5v`(roS+`f09t&hLs
zUv=JLTOY3nbgAfWFO~dpSbuR<dE+sn)z?Q&Wi<JhM`F3sh5*R-_q75_j&}kW^))%G
zL+@Wl+D#57o+*K6E4jx@<%yb-Q@E#`kR@|vNWIQi{Z(f4`AuTH57+9>wSI4lU|RAM
z9SM&&HCYuFx#dXejTfP38JgEiowfx`QTjce&m6FmtM8A<+fp{2a#$Zl0W9<fNf5D2
zo7{sB-4oLL40JA@hi~L(Obn-uuZ-JiNlek8<Mf8_J(cczn~Z3#Ew@a<WLbg)$<Noy
zKBJ22)@=t+U!*JIuR~tn>4;fvH!AxWQy!fPhu^;vil|tB_*AS$$q-u$4HfmwYKH()
zGZTZi$u6e7x+}YP`D{s&rY`8I;$A|oyfQ{YQHkzcjM6DJM^98e*{R{Y2)$vNf1JW@
z2I(L7Q#~(S{X@yuF^;yy3kQrACM%n8|60%KVb2q(K?=rsRR({1M>)O<B7m?KOdo7d
z%tk>bo!F?SN^3Cli<3&SV4Q`D{(=YGY%$=YyT2XEZ(wvluMd+i2dKKbx<2LoS%ab&
zv8U@3>R{Ki`J*I!9=41LPkxH<Kd|@>$`?xAzfA0Uv{wnd&m}Nj1O$x5a@^13Poy{Y
zgd}OZo~0v*OQBlPn&Iqh0U=uL(rIb*vEqQ^MneRBt6PtH=N($|iE9DY0=}k<h7@^S
zYY_>CRm$);c7`9899nY(2x$xK2`C2(E6^QK(f#c3NCuBh(kbuv=n({~g!k|E*F6?{
zwnAIlraW(qZ;c1o&1L;EO0`=Prv+@Df)?soB?&9qZn#YviLdRzeAh}V6UP%zRxaoB
z0GZ6H!37l;fky(Z8pknM|5!6(YdkI}q%CIECSTVQl{Oz#u-Y9JAomb&bb4F!5gbc2
zbND@s638#pt&i=9buxYvn8l6x%8?K1w0sI>g?t02b&AZ<BhO;q)px!9I<f*8ALpF5
zbB5yD6QLQAi|t<NnCzyKZ}R6UYPCP!+W<Y5k>?+r;uAw^V^!-=GHBj5-bF;9GkEbQ
z(-T{0snAhVhbYL2h6_kHWALL33Z&-EvEicq00l>hIbvg&rg*w6Bn3a{(Dmosls{jv
zk58jSQcuK>SlpNiic>)Nw9mxas6Xjijlqpf{11RP^{5v6W|!CLIH|9vw&lCgt)2l&
z%eh*bJ2u^?RvyS`cG$Qs!GCxdmqV$&_nO*uK%rSs?TpmZYvMU^I=u+r<gdb@0%Ni#
z3FX3dzn9Tx1hpM{(eR-k_i`l1F=vF&)%tj#Hp4yqVy1@HzByOVu?ns#(MUuyrkK=J
zlCWe&!580C^IWeDe6m@A@`B!q>V3l#4kqjyJh%mo+00#!zWbxBUo!+b!HD*TVsiw(
z?1YCR?e<lGZvt&Bxm=tn2TlNsfm*7bEOlx{)K8?X&iv6$P6b{4{>pX_@FGWu>&q{e
z0JcD$iOAZt;fSd2MZ(YiZj&y-4&9mW2F07crKWZ=b$8YTvycM2KfcLiGY5|#R;H7u
zHC$2j$)|<g&4Fl2V7*;$)ugBm8#32-#;+>BM0Kt8Nt{8s;L8}nQ~jWlH#-u8GaEM!
z9Gb8F1Q;0TBjRTNayJqx0~(3ch&Z@DH~38-1Dd;2e-e#S-`~laQV=(&#%eqX)(RKZ
zkDrZHUruD7aRwI5h0J`xGT>#LB#qtB#H9EaT_p7l+l5>g!Icv7c_BWd<MloK;C&x>
zEk|(M9t}G;C61>b9co{)-u%RE3r&3_ikrqVWhb6`m%vTP1f8HH@39b2Y4bdFOofnH
zYj*B%D6~?<(~)MDDSSG|jE4hbpD4l%`umcYR?`MJ5+^dzor%UV;W9G2Ee^*Z?wkWq
z>q;LHb>3!pP)Y$88J0W2B@YGxqP^S835O`xfLA2&Tn~{f1o>d}nnI5NWnkQ&J!<SD
zc15^uPw%JlfUwreTBlnc`2VE2QTYEWfiJwE?Eh%=*?~;XdKq%j9bNkpS%|^CyL<yP
z<dq|DQJ2*1(^$6Mw`TcaPPX#&G~aX4=XZVXOTp`y4|6gkt8b+Tn`Np@=otjX?bBdB
zk2vfFw?lbF)L;&qG$}hEjnpU-udW>zToW6#hp_uQ_omBgXcC+26lMQ9mrv~RsyD35
zXOKI4V!#S%|2F4T$`ON2u|d9IGq3%&G8{{=>}K`5v;3cD`{_P$taRiqWWdX#<_~(i
z#0zHjCDZ`W%!X`79pu8ekZ*#U%~EZ6AwoeH1kujhk<WTdESYHkwvpjakI0Cy?zukb
z`%~afI;);IPJECYuhj@!e268HOoqKPRrju%BExQBX-cO%;bm7-WQ9TALsd^On&?ir
z2qX5}b-C-|j>mMp7a9&3ZkVxgePU%N{O+#2bd(f*L_`k|XgLpeV4loeVx9rdv#&Bs
ziCccu?%S4?6~$@suvf%pO(FevE2HM1;!?z58TLf;cbK~>i(vn8c2{)p3QV%v771Hj
zjc?FSAyeZGS|Ba6IsK-)9t>}W2i%7WQjbF))b1e)l7YPwIZgI0;^w-Fq1kqN#08zc
zl%Vd#!N1@b(vQRn1;is>wDzA3f?I5~JIWd4CRqKU@-mm3*tZnf_0kK^EHpak!r1vm
zoo@>YW{3s55+2;3H@N`+GZh0BL#iqq0Cw(&rvxUu-1>@K=x6z=%aF1acMc|!Z^Z^D
zti?lizx2fW&gh(B!m+O$0qCN;I28l)S~chxnXzhf)B74*BSKxeN>rH*f1q%^w{#X|
zVcl>k$tX-F)7L3|Gt*8>TE&ys1(%>bmq$hvzptXibNqZUirXltYPUlY4^q@qv9~x5
z_UOF7-Wn>jUjm0=E?i7ERaKaVCn{;<N*IkTWMKVIA~SvlG{CD|0B`XCXZ@#lP+(%3
z+U{BUCk;52kDG_GfCF7IGaFvdSsygW%vBSNdl@y=Mq&=MQU16$kiDokF=GnZhV8b%
z^`@IZs2tX>JkEUc!FI`Rdz5VQPhb$HgV>bllPi=Um@y|9wJH7k03asz3)vSj$#Swg
zOqHFXz>>CNkHEQQfD2!r_T`2cke7e!_@#RRQjOr$g3CUo3BPSL`m4MmczHMF>VvKQ
zp-;^goGD1m_^Vsy*rvLL=FhCkI4;(;6nnj{pnq9HSDW6Mu%$^A4cv1&e^9*u|72Q;
zi3N=q0g-URT%N)un1spTPI!uM<1KS`0`IPTA^-Q2@-MVsju*U+!a+_zg5!{W8P4Pi
zDuk;Bvpng|2rE}F9CO?Mu;%*e9@rwrtYqM@vvcI11Q7&=L@2Detc^ZEcw|s6`kQ4L
zn*;qvT7z{I;yUkgJKs+g&|{yg9>t)}M(UY!z7*CqyVxSVwx4C!>hanq7jK8s5A(Jg
zv%I&flgP{W(;Ta_zqV7MePeu<y_@-=qR{<$__wj4*Mian<CxZbK3gR@vdp}t3A7}*
z)JUJmcP+8dbhJ?y`j_6;*m!Ka5<Y!ZN(XE)^ASFGKSI9O`0Nf2{}N=nf=j+ii7wVZ
zt4gz#AeV1x`Bfu>yt>H)>FV-x(Gq`D@o1?h^UpvX_zL2FIdgQ{0ePT-&a&vGnoc;`
z#~X;tINsKgTKlL_upaS1?<PV7__=6-vOkuIZ%yOCL+x=~mP1i`WB%6^Y{6pZp}5uH
zp1jMT55IZubo$JYc#|q!jMy!KkDeD1`tN5?<k}ia|4^B`t8rg?;#xXpD6m4o05+*a
z>si)u`_^HvroI4q>FK#A+Jz4@^x*G#m}`{IF>zUIJ-2{?9Q>*lHRj0V5c7JzkNeZN
z`J4M7c$E(szyo2eZ!m#MsFB9sm%6vqY}5V(gcx2K&2EttpM{^MJ(}Ei?O@SS=SWj|
zf}CEIWzH&>!Ubf7zoJbl!OPu<(~<*Vh6pEOR6u(vOp?SAPHXqCEbEUaTTk8_u<Kjt
zzB}@=my5$d{r;te1+*_jGI_3^@ucv2U#);E($zU{n6U2(KazK%+zz@xoGq+hyo{rX
zBh{qv?MYnQWZzaSbO!5$0nMyj@_x0_?h#*$!F@p;55NWk+lC2oaV50oEKS=A-)@8l
z>U<ik4AMj1Q;_}b-|@CQ914{IKV^zSDA2BZNWL7Wh!oA^hz0)qWU>Rb$@KHx%}q}p
zjNNZJh7&}=c3?upNeW@@DOJLesW!@ptH1lF?UMog(4%6jf>-v@Wgu>)T6a%K2zfO*
z`Mfq0#Y-`^I6xZ3jg9o}+XLD8PBbL%KX>2G96{}%e!5*pIHuIUN(FvNl>|q|yy{%c
zm_tuy6+ReBJ9Rj_Lx~4vEF^<RIT(a`BN}wBV)$d*71^1YK~~K1awY*Og$CNbz5ol@
z`O^)g$0&T#WGGC`dTE2qfne!+)c9DnevLj?h8zxh>^*DvZV#QwK16MzwH(>!7xGMw
zs2XYLe^FC;P%af(;gLOs@%jz%Vrqip2PM7QFd8X|7~#+DfCsaroe4etkxJVNZ8h>)
zJq=UoFedE-QMM3~m}BvTP{qG#tbTCJ#HoM(c_CO+sp_zPrCFna)tln2wP2KE+6Z+e
zclI=h*$CtyArXZPVtux>+}cmaLtiTlon5aT9=Ps5(H^h)ojGW8DVJ+wU3N$ftQz6R
zptK=31DJmHIQ2^f^Z?B}4v$?7G~OCrdCdu?g0YFu7fX5Rb6NI}OpLaN+QjyT2Ad5Y
z)3Qtn&nKPgQ=Ty(^|zP5E4w}bEru4mAGzgIEE<#}mQR;r7AicD?zSIJ&efRhhGMdS
z4^)e>YcX}oeL+e;`a=pEIbnxtznCHI69a47nkJhN`X^Lg`9&zkTkZ4o9A&DAZo=NC
zLZ6Muc+)4TG_|Jw@K}(Te9X2CBj%Y<iGnUZCmLrre;=>Z^SL@OXZf6>(Wv_Hv$+oN
zBE_ce+gIf&jc2oCUA@<!ZHR7#3?c}*i8DZqd1pnaiKXig-gLO`XltX<auN308KZ7R
z#Sr@iMQ>DJV<A9bUGli#yYiVNlK|N7J>FU_8IF)^mBdF5saWU(ryf6H<1q_#r%NtP
zSAk^g30`*Z?_S7VyOQG?Koos|!Y~?w;y#(dgid`;EVFGNlofKfb{@~zgjKrvOjvBD
z{;98<9nGa~2$d!fuRrA8?7Zq^6V}Px7Fx=g*EPF$=6$)&7y=3BiJ^lr&3sI213n6a
zQ-FAuCd~BWrRR8d4BBQ&Ga?_wtbKn(t4hViK#NKCq9=TbRm4A(86|tdpyafVxJVxP
z4L>}w23i4HJ8r&0QboXzuk#Zl*nrs(TIm3`r6{ATzujmI(NgdH=e4@E?58=5xgB$J
z#YcD9!r4AeTw5^inZY^4YU7>ayp0ml7Cokp>;YYAOO__w4&iT=VXUjwkv=3X>m3&_
zM>OM!7gz@$t&mq6+z{g)(clHPs3+@kQ!?fIQeO`Up6mNw(&d^*^w*TDl&jsmhwa3n
z&F=kyHhuu}d_vbBqV=gUHeVC_okP9t!%@uJ>DDbd?gn074d4h~hOs1xK&(97@lnQw
zs!>5=ae98X``<56nKrm6;sa~A8V@CTUh>dpafP-92TF5IW%T7ZQnvO`x1m9(u69cJ
zD;3R9#NZ)9hrz^%OtiDxALhdwra~RzG0=VYmhfYn`IeZoQ;$55awK9s@_U!G{^5pv
zvnWiNZKsXv#J48KGsdgtgW+x7sv7RWxFyYV&3AI|eb$La&Z_0|eS+z4OHLNPiP=-#
z##vT+f>~CM%$8!#EWF<C45!Vm=tH|qJIZaO?pmJ=YYX_Uhkb-KJN!~O@8Jr>j7QCC
zDYfj-23O~PF=@PSk<q6qwn7MR&ntwg?e5i5T@8U1LV7o_G&`h;wl`vZF@tp|zm1Kk
z>@hmr`?koG{&{1X2w4>-WJUdEn>wrvuTO#3Z#>9Jl?EoxeQs%eXLws0yE5{KRYCUl
zjG|aWrStzjvRrFXiT4X<p&IQc=&{+e{FyjM6af-)e*56g+qD0c-#XhC&GgJ>xzQ?F
zm4sGnW82-eAeyjKqaAMR9dRf2>3?wnp!?HFVT}*Susj4h+`<S~^Ijl7EJdeuS%TSD
z{|ruQCs*bW*y-EiI|D&Blgr@sytTl`OrjO*+oa88*~a20Mx+p{wq-$k01xCUgIdM~
z7;J4>)13ztZ8rSJiElvz<k|W*Qe@52%T;xmC~v|?`3%%3w<bP4<u(eO&YT`vR#K?!
z;Sqm~p*h(Q`1Ld`^*K<J9yL+iL_Q8cPt8x6(k2*HRYxVw%MG;ats*|e>_q6300^S?
zh)4bXjrMiVhSPyBfi<YSi^k^dco%2Ar!k$K2WWfHLw){qu_E{U4oYvr6WVgku1$nT
zrbkvT1ZdW)ITx|{P?3nZ_U>Z96tGXQZ(N|;cX}{PwJq;B<7%Sj>CMcZRh}z(MF+SU
zBKE6@X64MNmCA1FfHqj{XT)X&;r{TV!Hp;E!tdaA5f14hSC9!b$d7qnG~sX5#%4c6
zoKNz~;J+ihM^BU13S}qf9XZR<8K_*+M40MTs!+RwCvZF9&Y9W3c9xV)GN8$Z(L1R#
zApTdoAbf1WB_x)0f$|Cow`kBa`^@1)gpSYEAav1v8h6@E=;No-qsszU=9iMseSI<C
ze7%?UWBvp!(jOWzPkBSeey&Z`kFF-h>~OIP*xIK4xTC7HEzv~A7s5l4_kO8X&2x#z
zh{!zH8HlxIHz4G)5r!x&=$f$0aX9gUx)lhroIAru9w)}10k~6TEEDHxuILZw<uy)#
zRaUp5RU^J*+e~-{^35iPyk7kbS(N^1-xnqp6mL*tFhS%A*fHRoyX$VGb&Cx_OKZFc
zH>s|U`V-9;Hk-k+&XmJ#e*Np(mzElI&|&k9HYiW*(U>M{i+w8PTC$mBVmj0zlOZKz
zvUQ?$=fvWc(c^@O<l=U)y_#$6fHn^m!#x}-*n_85Acg(f-loER;}E#lal~-z+YH)i
zTZnm`Hg&Yt#tj{E-iglU1J6G5r}R7)&>~o&y<CO1iEM|e?zBgGdph=AgSrzkbz;xp
zs6om00Yn{TzUI4>AJ(Cejf$ydp!*#;^O$h|#d%m<GAnfcuUy5v5K!F4^rG{t%K_C}
zJ|wU=4-c3}?qbWg+3{Ugb`L3uH_5kHbSw1Is#-9onp{60QSbRp=lNSu_V)ZI3V^es
zdK`W``gP;^R5FF}#i^Zb1>Wp&BU-x>cbg>|?}PV<yZzIG=`!=0&Aq|qSEJR^6+uK9
zAA(L7C)j2cCpP2$Pe@lx-LuoK>to_-VwP#7O@Qhzq}1z*K<+_6X!BuY&jnJaU$r()
zAe~iu?wupv;@dON=9?4wrT5YI%XREN;-yVz!p*hT@ATtOF!?ug3?H4pk5`*8%%3=j
z+0K9crTf6I_IV*zU%!SSXg_n`B=4omK?wppAd=B~V^4Lx65TLm34h9{fS{gWXkH93
zwO<_Zt8h3`{HrX;?^W=T8z<G>!wBh+FMsd;Lg@3>cyH;nJ@ES7j;@%9v+^xY{+v0S
zrpw9A2jg+`TnG}$Mw^Vt(jSG$cq_1;hB3Zgd2qEc65cylQHf{XW&yUc#am(Q+%VvC
ziBODR8t>!v;E}0gni`g`{zC$Q<9lEI30}OWEm%$#NgC688R8sO6I$4QG(ONlm3H3=
z?{rP>z1E4_ytNFC0Kv1PQ_Ut=-3fMC|Bxrpd}%uWLbWGi`y0y(*+d&*roe-cE=R)^
z$CbnycL9gBA4<>Jo^6F4#_P%xY{}c6dDHcfv!GB|S9j~ubezzdbkiTzf$0ae&nHy8
z&qi2Pw08v9#n~{hiI3=Nv~_C&njP>2t{ocHw>C%qJJuwh5O`+nXtt1GWr-pa$V^WV
z;C&n3vQBv#SP%)=x9pTPi1AzFALW1rd*S}|<IKpVU0I!=Cq;6r!z|-=yjpO69OJ{M
zeg`Nr<`)6^a=X@CY5~#WCo@>8ofw_NvZup+gQMOn_NUCBoVa<aHh?mXN#$zo*tN)R
z-N@~TtK488j%n}Yk$y8TOW^N{w{qp<QnY*8-*bT8a1-9M27lY%@!Ykwua$^%>s|YS
zoq8j8uYG~>(<!0hlcWP%8X-J7VRNkR%wjfglHOM3x)mPSPzauJ=kBs<VpdCel!8p}
zZ}*o&OiVru=c%XAyFuU%=H~__XM(M1xF^B=!;d}gUo+hM4$ZG!xt@CgoZ)W;5I!R{
zYkcZ5&t$;wYM?twPC^=VrmZJ^x&h;#t%N!@c*T$F5mtvDN@8nj+T{m>!??nsJ02%{
zSeMPm<+Hg}USjS1lFfLmyQ$bB7kfz62+W<|^N#x$>Zf2{EcWznm!}QgJsd5xfh);T
z!i6JGa0VcO-^44aEJyjlKUTc73*$=%-3C4LQVL|bYvQ<=FE=(*^t_=1q9LWCb9H!W
zUqPb3KO3h$^3c=sgB6KgZi8p6Y17%Ja8HH`xyP9&s0W9^lRBTLe&?J^{_TbRa2{~D
zS;L^Jeo;XQFZjXD@pjT|cB^+AqI%15xSIj_;<ZZR7Ezz4xHP2mp3x<i%WG-cZSVQY
z<PP+4fR`0{-3FSRi25{XSOv{&LK52~V;WAEwPY*c8lVMeWyI3^i0ps{<$<Sr891P!
zlQs72yw##kKNnW|@{jy1uXN{t)FJ=$7e#rf%8c9ZPPMga62*E3TLoG>r>Yts+TF6E
zYA@D8DN;YK_F|Uipj8xIqP9*NuPBw%EN`**GSFYV9r3jxW`fv+al|gIQ_b*1-rhy?
zKjZ*32QIrmZrdoyqR-J^E<0Btm!cX@3haJd6;%_&tBx-__QoycJ<=8ZW#jN_y{XZQ
zr)Ap`EVE$&|ICK56|tWGaBFbJ^l9_I=rbUkWQ<yA48WN#c~D05xpuR3*=ytJsyNc)
zK7l)#q7?OPaE<AmmGU5UO}Y1mTPhZM!nG3|B=@mwXzHS|moVK*<2woEj=UnJOgHfG
za_miZWEp(Qimsa}0#AakkEdh4gz>uF8SA&8q;Xb|8l~{Qdb@_U(!9&}{4l+=#v!+i
z1Why?nOLzugQAQ;q>hXdy?k}e{jhOjX<-)k=Iu%%<2Dv9GbqS5L9f-@NRULoS>rvr
z22C4Wk(y*2G2H^cnX{Vuse5m}Wx107OSvSQSp)bkr?_TFNC{h8gV{*kiD+qe&K8(C
z%p)SF2mYX3F>7bzqLQZ*KyJqu)9*K9qO;yKU9Kp_(`!z&@t7(c!TxTrGDF^rjF$cn
z_$0i)k2Oa8YhX5Ya3cM;u%*(jBX;_%8g%_oIv#G+aD44B<sk|y@91oP7$H5p`8YEs
zpp0U6!tog=l*4|P#M6RR(0`S6|2+KXciHer!TwV802b@8m~Z*PQ*GHOP5{)C=fL)a
z-tWJigiyxo1#n<J_s>I3Hw{mEUnOpK)L8$?#+if3Zeq7Ooe>z`&em9Wc;uMtK}8Y3
z`itpmu~@05KUNt3j))GW1yzM&N}r9M{83~@H|*b=24O$Vn-AwhSEO@lbO-D1_0KSn
zT>s%pU1_=A-(!F<UlN)#Mi}@la}Kl8&b})yjG8U6h%9aXy4I_H*)a^g%Y$+I5sJ1G
zD=sc2prDU><5o~08p#g{1(mDaH&{Yi#IcMcNfyaCR<X~K5dsKQ;uTfG6H~8r|8^8U
z2!p_Qa5K0bQnM#M;w*TJDZM==mR8Clp<Q&|@D$JCMwOLZJTEpkt;?G@u8K%{M>Q@k
zs_F`v;cBKG59PE?x2m<vgJrV!#5g$@YBoGMvKy<U1G!Wdd^myIn_0_n)67^NJ5?MK
zh#$`X(_pZJAS)3hco7s~#ltvdID#rjdQrsysq|5lr|D+s3|Nax**D~cjJW{6+A0}p
zDp0${gejqtnM7+zcL+$Ocgsg6iozGsv?oipA?P`3Ei3auUdt-F-itg4Yxbj|{rz(%
zR(^Qf-~lcZfQ+t*i|{3>4mV9fa?q0hr{4JbUhZD=w|;LlF&Q0>nP)&FTJ+O*$OXeR
zvXVpEBsA;;1ugj$f3%@!Vmdl7kp7g49_1AuNGuad(^#EF3?b_evU_-)UT7aYhXGM9
zGJbY1?xY%Bvb@da?R-eIKGHRY(M(Kis%O}RGq@ivi&*Bj^2mmy4rDqHCpW@hw^&c;
zShktA=%H?trqc!Ar@UK<h$(;Sk;gw301oB-kMyLpDgfg*7GwU&<|LTcsBFA(YuR%d
zKk<WLHu=czkpTgF2-h<aie(&w5og8QqS8k1B%6uzBB&>XUnQ!{;Kpj(g;^la24_bu
zLU6R%Z}&oFYNJ3}uDDhfBzes(@>?eRPj~6*oTM&IoHzo)&=4Rxuj$uj-by?BOkZB8
zj(N-hHoFFEz=)4@2;&}?<^N*q8v`s|mTbFh+qP}n?6SISb=lQrdzWpqyKLLGZTr<Z
z=gynCcjo`j+?g5q1y)9^5TyHqF%nIQEktiE?WUSxEmKgC1qj)ngM}6r95-Oy`6`U!
zU+Y0u5TzW{?1}h^gzo}eY-so#SY?cQs~7L5<&3D@3XjcU6G+{5_qi2k{{+q<)SmV|
zE~&?+Zpip4gjINVWq=xO9f%5IYjoe_Aaf*wg(MV~keZbRW<+)fPvrr{>KfK1T%1?)
zh?=2PSh|0)Ct*L_LU5WA?Ob77G$i;Cz0xsNoRv_uNyy|qX%tlBv2OOUPEuM}rbvC-
z*h9?OEFb`Dpxx=Gr3#Yz4Yj+LvMI)}tbbH?t~OS?HD#d7ufp}#`0WL!<v1Q*4es<2
z{T*`yn|nh&K%2qP&ocvnLb;dN0~0o{TK3Ib1!Q7jqv4PFen7f|9nR!TDxC~@U^Y`{
zB8J%O*L%7B*e0<rrhA}`e;paTyGo!sdraw#x{`a1nCyW8(WWxMS|rNzhmlRP$nt@i
zQ0^UuRc_MHu|wrgmKEWAT5K|Dhqy757O~|TKav2n!WOOFL^8Hr0374FIhotiaKi|5
zo7@Nd#)}fk)n|mDyGfa~x7C(WrBS6DR61q2*Yi2DlzI`bx}q=7Ropr2^hMo|bMxH_
zkEP9|TJJg}^YUVEE!^WE8&gZpFft88j8;NKI1WA#Lxj6RALLZQw(xqce1E%fsvYkz
z-#E`R8nN!l>B$1V2*lc=CF*V2K5;#&auioy<9#iD43f$}ct|7(E2sKCErINL-G-!^
zSBxg1UNpRN#ZK`J!WgrgRo`lgF>5fQ;JOueh7uR<Q+|B`5*c6?&n;BG<<<f6W2&0-
zv;tL0p3kn)ZE$5b*Xp>qyOgvQob~iN3ntW>LNI`EfA{ZRdp!GDl|9Pf+V-wp!*>EG
z=+copb~M@f+4_e{O!JGMsK9^RyXXl*g(AamisbNB!qB`_vs8ReEQbtSm=Ux`qpvU~
zHZHKXh5yhpu-94N*B{auYmc@-vQR&Uj{r13L|<gZ_mB0f+ELmxDMVbOFBR#(d*|IL
zHJH>e&617$`SB<#wnVLA6A(vU7y^bOWw~Q4PSdMjJFEW1s^B+j#buN1=uiuwS!FKV
ziJ*LVkY=J1$b$Z^-alv?+t)zx=LYp1s~UaP-00Q%eF+fyCv~UXcy$Gd55!SC@Ds;h
z+{E~ZTJK@ssSe9(ZkIV<SAIDuiR`Lbr%6XzbK#K|n>|V9y>RZ(8v8AwZn7vH{_~N@
zp8xq(N7=#RHoqZojb{s7*aYUVnX=mAFNia$OH4!%)(~@yKDb@^&#R96ur3V-om|?q
zz^|_6j=PA`0^HtmW1}a-5k3sPWRz?fs#7nwY=w-U-4C)2p9N<u$A!HgnUFdsZ;lqv
z>GfEjj@nO`Ubk8_e{o3M5Gy_ljg1_Akwwq9X#8eYW{vuKGWlNw7fs(l=d$);407}T
z7rVt@6@(*1gb17ExTQri|C?Nb6&NGfFAJ!d@y*Hl>mUDP@-1A%DHg)RZvgW7|2>o*
z7U=6xe>@#8{U^D_*P(C`<|uS?L)5UA{^y)u#40}^RhvLZ-zwtwY<;TtdPx7z89w=d
z(dfIG1<FZ*W;WGcIwvWxzy0S5yTpXHU|9$;f;vs}{);K(fAV+GLN)=T(bbd<Z6d30
z>X3ql=DL)Pv7*c(&3+GV;GGWz<N7YG=w8bZmo1qv6i|_D)3P*p@$6L)amURa=Mxmr
z1dt%vfo$=`Q;QCpp2YU20rkHb*)F8_RW1rx=fdEu<c1kFSyJ0}t-V7(h?uYEB&)7_
zV<@6WC%gMcP)(AMP2r~5=ROGiiyd4gUibIPga+Ef8F2#Y;_Ua+SW=@f71sSGeP~fX
z^NIBEt0N95W=WMzHuot{RouhA8Xy7S1IrrljLJuzWm71z2b*3r{~|l#gv8vhf1BuF
zJKGs@+{+v3Xy11zD0|ZE=b(fgt$2f7XBhJd_Yj&JFQZp$qgfc@@5^c2Hz26W{+s)x
zOCZdzoUvoY0(69!5F5<In;}y{^M{G|1ih~6+r-2|@A^o2L(=AO0!8hQUht;oW=&IJ
zQ7=~nSDnpI57kPod1h@#m*F(wua$6OfQo9Wm?%ZMB;Is7=E0UT@^FjUBUSfIWY|#@
z5VIv-98k)O8JpcFXCnBIy5ktldvldj%KdYL>1lv@K@2fBN2%{W^-{2tsQWhPPHRpT
zp<Ce3LIBRd&n8Ri>}5A}R6_$rWH50jngk#(E<ugy8q!V=9#Z`Z;hewr%NvOsR2c5e
z7cu3{!eq(FD07u<$69YbpPj%a-r|#<X2U3Itgp!z${AYzRDqyk7(Ax+bH-nU;n<#p
zoal)kb}8p(r&d}w7@L|B)6>J(*4ElPJDX&P-u7%bpDiJYiHS8GW_g~0j8P!zy9$m3
ziF;at(qsB7)5lM&mC%pL_fEg_)OxIg2WCoxIrv31GNd)MV0(MIiqsGM-X3roiPo(#
zn7vZ8<z}xk@H|<ET-0BL+n`T63Z%^c?u4zaxfxmj?B?Di$wO;YpCw;Ke|2*sKCabW
zvRDIU`Q(;Zdu%2hAZ<35tE<t<0-ITN9CDEBpOb-Fp<SDC?kfU&w1~#f>tvpZQ|qXD
zUH^q&d2KsBHJ(Rp;9>VZ=egEaFyH@0W%MN|2GE<E2uhA>KS#$~DjJ>`W@bmqdna_V
zVq--I2dGt+b9#Q@LY+zIWI2{1UJ|WAR<m6n5m_pZ*C?Ln(b2HI--IlwdXBd{+s{@<
zidtE^DQ<{PC;H%N0)=GaZ#B*^g&B)x^f|y-{`)BoXGdi2RCt@MY2@u6_?4SJ!r(M~
zsFmTTN7X$x)oNPhc44W%i4o`%Jov75s+rfxnvd_U<@`Y#vz!R2WW-A<V1;n;@#Vc=
zACh=a7poQg{rv%-TU!QYD#g*TtBrQqd)`M=L@l2muVFl-bJcbNC-dbP_s6p#$jHdl
zblS)#1Yde0M;S7OJZi=WVTuO<E^c3GNw7Ev@noaEavw|H`8dJdimH6dpnOL&P}Sg;
zb{n2!20k^onDZRf+sO(s_p-M^U>MQB%xq+r?hSM@!~Eqa)vCgy?n2mi@H0I-MU-OZ
zrV!nw4ezebTlK-?we2{56T-KO><2ZBA^gk};QR3X1_vc?Z|IMZkkYT+cI?%(&utC|
zU8G!5WdiofCTf$4F8{F}0wI2Szxsa<2<(X7_mG%w=zTA6W5B@w#S`$;#X+omQNqZ{
zgXF16BN;Pnn}dtdj76q<9qGV$%tngI>+<9v{10t4H3Nt8Efzo%tXrgS>)Vd*g}i*B
zq!eUtActPpV&zND;(XNJP(eZj6Ec-~|52f1hWGf>pV@a|1}Q2tGHZ+e_W1Mt^B5+M
z^~eVsODt@bRcbbEV7gyhwj?6`442e<kUq`L&2E?Nk5lcQu4Abz67gk>Xp>zaSJ&5Z
zdbzo|C7Si*Jv}{Bxgrq5y+NBnxvgEjrm3NZdDVZJM|Yx}5f!}=Ca<TyfRwvIc38I~
zu}FfS2rH~5P#AxV#JI4QT3!&mo{m*{oh1mKXg5PVUmN{lq?adZ&RNc*YJ^6hHLbIv
zvK1ARjD8Rlj4L5!WNhOd!K)(>)M(ao@_5WH?i80~=x*uKL0&cCcfRo>Wu`BR{%{~V
z6{e)3?J3P)y&8?QW4x`0GGEAJ*}z)WA7OMtm6g>+73hG*>S2eXm@7rc{pAU<5efeB
zLELZe;t*gbg3wTL;4JR_T{*)IN}KNE@!HU%Tb0w~Et2B>FN!vE&<`chJLC#qR3D}5
z(1~9qII8zX)v=Z(tsd&Vq3Zm-kgo>IR$DD-lyt(jdPF+weA^dAWw=VySV&DiB0!(X
zbs_0jJJ3yx3^C<S7;YZTqtXgTJET0!^jFta)Ff*6Y}}F)YsW}Q1r<YLl2S}J&`mS@
z;nTAs&XyHy89Mj|&<qok5=^evIy&p_ok8D;gjDK<C}j1ok`u{%0tuMxP!m=CLW3H{
z<nW8lUha;!G!a2TK@*`|?}jEO1{U>wqbz2N{LQ3mY}dIPY}daU`_3_9)8GniUt+2Y
zSbddh9fp5fg|I-8uKAeEHaX1yTzb5^4y-jtGF-wLu5A&p#lI(gTlcpL76_S6@R8Vj
zkW&=sl7SBlCCi=JL&M1Rvx%)whu~d%7L?c)70EIQwKu6Q5YTQ}Tl2CA0Ih^U7*b?9
zRUx(pZ1-iF7!)>OHa`b@-x|J>XA~xo5J0Cap+w=Zi}s4MnKZpLmpS9i!P)nY^*@qW
zKe~{j-HFslEX#-FVW>ywn)C;4$a$=VV#(Mx#D=meLB&o&SW{A*z!ow%Ku}iKVA@)L
zt~aT<?hB!29S1jdiHI@EReQ|+<xGBYk|_`yXr22i2Ys3?1IluR5mm{kAN(q^KVf=)
zesx69<sj}F{jR1}*le-X;0We|%N3pSB-*528qKczk|!cUV7*ZT)~$}vmm_ZoXAgCw
z8BU`ggSX-jCj%g%rH8|py(G{<c(=ojRJ$)D;n?N`RA>a9x4LxAH&ZPzq(;ml-A|Rs
zF~A;epgtEH)w=9c<MR4IhlLH>-@*wpa%^ZjwSzA@*-m8QC~Z5K$bfOydE+hE3551F
zks9xzyaib1X&KPzeEFj~-y74cNiz;o0nPQv#05tBuotuO@1kA<`)5;$-Lk4X_a_Sp
z=_6u<3=*+8Gho3ZVV+FAMBD`W-uGw&F$4?&9d9=>S2whgxLn8(@VMU?Qe!`$ebMy)
zdc*g>6%kA-Y-<wHXt}ZLDx}N8W*Iy3vUg9o{ganV_n6h3YqbgF<}>DKJ2XgAFQ~!5
z&`dzbLj_&FERGiyE@mjqTs8+OZl9@mceI*qrUbnI!LGEkza`{=?oy>0H2&J_rmsOP
zt=m4`<gI>Zp^ZS_;gd7F0N!psmVBcZ0cW+8g%2rW-DBbQIxqN6UO8na=*tRwQ_Cb%
z2NRg*brD)aLro26fl}EWc@?zeu!|~B(RZ+*5ARz(#=U(i0@G+90p|-@3zLU)_Y-x3
zPYEvE843>4Xnse1iNf?p@195cX%B-=yBzKUVkgwN07qJx)2P(7B=YkGb)iBl$lE#w
zC0==Z8^uq){Uy(2Tv>Hnh+NN~<~~6H8TB;3;~%=VPfFls4+O}?h~(9<cWhx(_V<^5
znmnx>fks@`M9yCP*hOtsGPiY6i7`GxGA^jQQ3-{Sr(c623Q@&zAS<JXdW-jfMW~+~
z(j9R)=rhi3<WDWympK4=`9SLR<Kg1$T>nfreW<~6mK*2r2{P&B#=sZO>qjap=NvuV
zRpo&4HRC57{i@uVU!pHJg`RsX8R^pjF){f_0V`OORhdVPl><IVJGuSdGG>Y>w$P9p
zBI5kAlaZc*T2RezyhqcG#~PNkxr;zX2t!AUpRrb%GB-2F<*y!yY4C;l!^`mp)~Cha
z-7GDs=gYoFJUP)|DO8345Z>~$ns0>&e4cW1IZk`)$pMU6V7ZT*zS%RgvovJd*9(7r
zVq0U7iD|DW@>f0$LKE~(^D48H?7cN0WCwa|{oRzag^9@tGF;2es^%9GvJopHlL(h~
ziMpE;tPiQh7FY8dH(X7t_F8o;%C&^lBRS9dZKhO*A_N}4vT(gyy=>R*7~#&g#PBZM
z(;g^BbRASv9Kt)&I0tfEJA<>lTyS+~muh@L2+|}acB1$!5>Z6-+Z|X|ce)#HHR`@+
zwa^UZu+>w@@aOAii*U_!rm*|4slB{6u_?bKYFT?TjeFW8!fHD|CBm*guO{EPpS;02
zZb~VwDR~$v!dX7_8HbNCKHTpYicf#_o&-4Amx;wh;V8In8Z7PNgltHUFMsfV93&fC
zV<|aD8&~`O%kz%@rofWi3B^Z6wq6IdZd<QyqRM_|q&X7)HmdyL=`~SQ53Rm9VouKi
z3*F<$O{gQaTBjU!+EbzWvKP%h=QY)f>a()6)bv`dV7xzjJ%M5IzQH&$K~r!xdwuuF
zkx&v9fdbaTH7((5mw+v153vVcbteX4u=dJwd^K!LO-Y^CWW;lv$&?9Ir9PW6PEpD#
z5VB*GZ$6!&Kz5#Z`B8MF!jza6Clj{{P}0BXW5s1jbqOR>782qwq!6E-StgKNDZhB~
zV-C8(8Z;Ljq;C7sfibRI8>Ku`PszZLERVM#e#t+9M{=oc(LF(0s(v@k#(YsuK$kdq
zI15Cz9j?PU4Yd7^yYC{18l880-3<}k0Sq(!toY-EI`ukcw|+fBNi_M^GM$MYu=X#e
zLO%Uv4D3&@{W7=z<_Dzqp1423{9?z`dDc~XQNGy2fxgsyD5t%hl+9KU{}jOuRZ>f3
z)_ay>ec%7Vd?8ZpE#9iory?(eU)Yad+^pY0|Bkcyz>62oakg7Kehb?vL~`6X?|IDX
zYI^gN+5B?=jI~Y|1#4kf@4}B1qxQ;Bi*5F(V(oQ=l^Z!pkjvdY5z#x42S<ExJGazV
z5x~gy%ycL4Pz6#`wsQK}@)(RRojX?d7+;!4TxT3WD+Vft+w>V83WF+U4E>814RKjJ
z*zc@e>p`#3>2m#mhvISlQICMmNpWiy^yNa%*3t2%m4ku{i$X=<R6LkDEgXu)2QB@N
zHS+0VB?=C^HEKG01xlwZCe_k}fsC@2uq}2X;7rzG>v7T~YZTrJg!BM<XyuYW$Lzsy
zu{j+%6vfK-&>e;8vATUG+g4Gq4E9r82SBRQ4{*os<O}v8cgb!ej~XTw-9*<;mP7d<
z<1^N?^JUZRlO}}8@mm9GGxi6~+b+|mefN0seNV%n2W$gAKDli@o^N~lnb&5ji+$&+
z$6Ot*d>GOIlXUN5Un$oOH)|kym3f8q*j#C3^eLk>K6=|=`5y*Gn-OO3%|7D@uDHqR
z5Y3QMG6u4?ZPPvDzV$454kTfV__HI;6_D1;ExP>p_uDk)-y4zFQIrOikM4F5bz1D<
zNe14j732>k+i?Bs8;*Ifd%+)sW3%(`lHn~w4?W;}DC&cW9!x1K(vn1|J_SLaJbyAX
zQ)nWYfQ$^it|oFYXxKYB`lYj+v(>u#wcBa(uD*V!m2nmfSJ!1um8vi?&ixVcLHTTG
z=H%7_m0@B@nBxIA2T9Cdi7d2011AscFRQhwqha<@c_1Eel0@sPtc>hdn$&S-xlp0$
zwx6UPDMc#YHs+7JIZB2*P>gGS;STxOALnb!O4sYu=|g8d9g)^i^L%m~PV%{Ct`&Uj
z^ImqVGF5KkA>%7_%y;!PR+IB?FwA@?$Y91<i@QagEThR8+A`j78L)?bF4XEm1UlNG
z3!I)sU@)|mtkju7u9@*plW*3k&Uilp)y8DY8x8+OUtjpk-mABoIXjmDUgVMDbi`9{
z;Cri>#M|W{=i7&|udy;;V?J5xGLspT>i{UHzz00Pr!OP5#|${C<Ah21_WUD%UlL3A
zlgplA+w!UA+)ZIn5p6ZI-PZad_=sEcyQ;HmP(W%bMqDLSv$b5`5HXIeg)`c@r$~#A
z37LgM^KtNsb5sLRwyL!3s(LJvCyEt?tRET{6c(d3W!j<vmYf5g_qFk2G9%3mG<Vxe
zyAHakb?47+4HDbybamcQdzntK)XxeI1|8a7y7y1bE?VfUzb~B+40B9HW7AUyhVJMg
z;3MywnxvK`f~1llgQHga^DqJt9ewq&2@FMOL>?bztKXl^9G{@Nct0=ksMz0`3|ZzY
zkv%?ze1Kqv0Ighe%Q0|{YLHIk=)1^?=b^fy@$d%I<#~ysWS9b0=^;WZ`m?$j_w(Ne
zz|_fN_vrLX?9X^}&t`ZX*xZOwR_bv$9ofe`xVF*O)j<nvud0Ks`eQlZ++exMle@A`
zxXSU!4i`uAv?+DpN*2-=#BoM!1H0Vv&0O`XT5jULa_PUE@9q2;{&*+%TP_*`CMKq(
z1`Cud_X8}qgnld+S4w<l)ptrzRA4a)KUB|LHlD_WnQk7+0$u2;k7icGg@za*j@?8_
zvta82`M0BD?R<C|0w;zeuJNp{0lIH7zfYhewf#@8ryCAw(SCFf`j_cmiH@$spVO|G
z^=MuZg`ion4t`oTZHG8jb`(o&_U#X`&Y7Qt--fvEVh>BFooy}I*O2hf^7$3?Lqes8
zYLVGU*~WV{(8U}JC<s>)>f5mJ4r{2TFeC5Q_}*qedA+@c#>jSO5D18xDDY1XHEfD9
z8-ExPef2m>ix<CUDFxLDj%>iJ5Qqyj)3c2kEPIuaZnKHHt*LboZwH(!LT{>g0fxbo
zAqHNIoWDc<wQ&Swp!)07w)*dmG<MebYbce@2S|*6fD=$rVmK|oNSUw6XOmW(e&PP+
zVB9<22mV82ge{1o3`BLg4K2ZSVs28GV~CkrKa+%JyH~(QYn$;KJQ6_CU1^OMneoa&
zJ&_qv2{Exfx(yQ7_pAwKaI^|C{rgdn_g3Tk34<|_@uYZ#&Tsg1jPbtcQ{I-t$O9Yv
zkPu&Y<t)eai=o2D?uN^RT@eToVPVqWTt5#CwgheVZr+}6k7kPz<g@v0R0S_o0O66s
zf<y+QemoOeGCSs%!9}F%@Oojw%utBx81G^DnsyE|SCc(rOCJ2V?GT-yr?4~VL+(pU
z0f!VC&jzVxHke!ve}00Wv-F-X85fX>Z;L}yK~-7rD<s6E^-~tfp@6|hmpnC5)Y-=6
zLL>7zAWhd0-zc39=B{7r4|5&}%%9Fva2-ksX?f7l#9O$KP1QW_n(N%lM|h6oEd0`4
z0gKG2hiv@<s`g#j8t@$Ae~F8{?(4|brFgqKgEOUKVfg>-lcB@G;EY>#T9_$0Qs&ye
zi;ng&mqCXfeh6`d1`6^M-ii|?)Mp{=UuE+mb#UL3y_RgZrjhJMY=F>l<;^oOA!7rF
zCcX`y({j{-P?z9T|0#TM&1jqeC9bC>cnWO-(--A<RB2ACSRxb?YvC>*KzC<3&g2eD
zzlh`u0+WHk!%q+x_Ns0h_&C7YHM+gs<g!mpuiN4W(#o;B8fL`w(+cvTT04;1A>>M`
z{IGT)*m_GLBjkGgbXpVVgHkRaG#%Y52+mHQ_1fgEN!A2_g?<x4r<2M<kCWWp-w$Rq
z5Gj;QuRB$$LSX?FvAevwn(y^|bJOQ^>vW<=l!Mr`32Beln1`i;plWdvxjAUpb?=Y2
zrR{Hu@6H}zO0I!>Mf-~luFRMs*l@Y$k<mn#b)}Q@n;!kjG9MNxLG!4MNo*=@9;Yri
zoe{TGmhFTwU~w9vaNQj8z!Y4YGC6wR50&FCLEMVJm!Y_8WnBw?rYZJ#lUCUnm^-?6
z<S{#+EN|IZC_I75!Lx~TdAcT?P8u|^$YNU?RlLaE0#6M*ldn&{@mGIeokduMXDYP$
z@*fn&Q=TR1XW4oTEZ$9#o8;uFI+7V=B$_%!w`#VMcnA7=z}&(-j<GE0aRHkNHh9<`
z$xEDu@pL51kCmxd`6A}d#i0`YFUblLT}>e}-YXDb!+h>X%F+Ew+V&HqIT5^-#B<Gf
zj=?c>@sL>YM?+L*wdUj9KG8;4;=(Al6CdGhlTS;=Aml>zgvX8jG1oRi^J#BeagZV$
z8FSH=+qS%neRjG!^>Keo;qqf8aTC#t2wJbBP_a=8#_9Z)xZ~|ad+e!(&z?P1_F`i|
zOj&v%7VrHMJ-Yi-?=ocI-ZBq270{3`&dbK0*gz~{)O<5ImU*_1*299=g4|Qj{x*n*
z&;g*+Mqh7nP8b0nG#Yf9K5(|E<6uam*7=esB{%R(MVWpSw)xC2?!&N%8vEtV(A;5A
zt9Vb^-J?NDoanQqVDD2S|HFZJs5NAJ7kgo{ctaX^f_^gQY?|aeWn}(*NX=SKPmfkq
zihbU-?TZuG%`WDyQWQ?Nt2|@95ph7w9&r_f(S*KI$*!nOlO#Xq^Ha~{H2UzuF{M&7
zb{TREo8`5>++2w!5E6#Mc2`HQ<X5Q{X~J+3zEW*&04yFUAJ0lkx8vZz0=i3vm^8Am
zg&NudP=T8{@?tY76~i8J9-ElxR{^;`sm|cEOfjV`81?#k;ny=VK1`j%&47^!ZcX&q
zJ$|z+X58+8j-gQ5%=~U39+l(6V-5vM8NolIMMPx8vGRp%E~hPRpU3WNTA~!CASVju
z?wy{V4vpUK#5}>3AY`XX;vTAiZR#1#T0mAg#E&+aE|U6*P2ct_+l;pF+A6L=o(rV(
zy0O+_*)FAxN~7w7&Bm~Sxb<pHa(WU^9)^W)=YG=hDaI|Ag*X1VOE}IyRHkA0w$lH}
zV?$X7UO|)`4^$jrbM%c?IkcigsZe}582cLbAV!D${54d%C{7MLOhzHPcYEB5$?5?*
zr-)A(&Qtb|*IhI9qQX23>?k9lhcj!Mkfn{el+GEvX*T!s_^xTYkt8Sv&rViBQqm74
z6SE@Y>n1B^K$_Rb)!d6oU_-h^8gB;1@b~X6kJ_^_XFX(^jCB9HOLWUvYe(MnVM2$H
zf|v7tw|L@Q&28`%gY{_!dXRg?gaK3tYgj`M<8pIl8jp6|k6w+p3V=j~<cRN6S77H%
z`~)fV%3$}!cY)9+5y2>Eh>Z+yal!3W$PSa4<8m!FnsUF)h;lW#yV!&YIebT$J*euc
zY$Am&27--uz<9Lj3N_=|4q62G+xVHUQoDhZ20X-y5i(H;!5f-8@_;kN<t{bsaJ+GP
zP$_ock9<c+%xRhBl018`TMkc$YG{n!Qxtb3Wv9~)cRr1dS5pc%AE7J=HNTl6t(IaU
zNnq0Zvq0*4jObE5<MxrCi|F~$*RzSfWo-^TtSK#DPlthE-IJ&pK}M9IN_M&Kj6-L1
zUyi8wWrXn&f!(#bJp`XW2kVg}<@bV=yCT}b^VHlGIC-YU#qo9Wn_X}`F+?wx!W{E4
zb{^HUgPpZltFoinaO2H_)G{HqB7Q(HdAxkq7|s&6EjH2^{!%x5C^Nb$UDlxgJ%jKv
zJ|YX2$@)u`n{jl;#LS`|X!tm7rklhQeqjab$S&rSph(`O_mv<@WRoJh_lW-cb$`mD
z!`6@<tmjvWUu>j}l~3=^_@I)_7Uo=q24I0dxv<pN%Iij%B7^@13cp$#3FEQ4hl6F&
zTyxd7tASu7=in}8dx%_jh^!F%DNmf4?*P_5n8V3(`u6p7f1fN_^SAjl@K=20S4>fP
zZ8|=_<`5z4<lYdEhQK@=!O-y}JQ1BDq4M};`nujF1TPWQ%{RbMlI8lhE_$z@t@y5<
z=l)=_l)#K6I}MZuq%)e12&X>tBqVPIvBUl5?=%y;^Le*AOI<|D?p%R8{lt&%R^Sjl
zx{i7>Ok3E_dS9k7*BRC=s$6JeIfy=q64I9w98FqfJV{qqoMr>6?|egTwY9i8kaQwu
zZj!Lu4>Wsi_XpznV{~?9>-6oE@a}vAT$4kX(P7TDNiwAFqvZ`m^@gaQge&SHW$GYl
zZrts(62{))?w5@kIUP(sS)I!=XWc1ef#~m>o(8)O@mtF@2bf7_wkd%rpr4(1zpk_o
zr+2T*AUB#JQ4Vak(Ha_}Bf#Teg%z~b%IVM&f{4zEpVhc05@kiuv$x<ecsFb)#Va-f
z!}L4Dnm^Ba3VhBDP<)Vk1#lE5fA3p5IK8wgMV@TQ{5p~NQpAdoygHyaYk{^MiV{_s
z^C4xoy$mvKYk|6Y+t0brH9Kp6M~QTZ*)CUf-@)2jAA%85+s7qGx5)a^NZu;UEA7QC
z((bSB6Y&m^ow;-DmZfVB$sqR5WK_G&1n32K(IaZ&<;UAyxG}LF?iuawQ4<ve!u0sy
zdzRI-iMPaMVD;=~L46u{yKm+>Io)HiHZLf5C(~(v``4wxX&ElhRo7e(nXE<2igRZ^
zou27_CCTYTE6e%xN9P~bdfzKsn%it$e22+ac>4D4=_*Xv=n0qArEu!gJN8_TNZ^6x
z8+hh#PZtt|<C>eM0(+p4>L*q1O5$Wq{0nz&I^UM&D-#yr?pV;2STc{+&f6ZjKZn2f
zUe2JfYgMh+UhwN-wjtXbuPFdmX`IGH#|xL+^(4~g{bhKc;PGt&=05s%q#bele?*m_
zr_54Hvl#69e7X*M**J2ejO*3<&OXo{zh&)?87SLBkg`uo)F!Q`8%`A~xRp^x)hY(2
zw{A7qU4f$m1M`(4$d3f>_?(XCF>JIKLefDH#IHW!XjHAAXp6Ep0w{d6LqUqGjD~0S
z8)8P$MK*Aw@Vz~EuZ!j_E&|B;-BPLPR8C&ESrbcUuw^)``?ukIx2i?`Ep-J3LM9u9
zpSBB^@U?-eW?(V4zIQN@k81@0B0jqoyeVq!2aHb!lLaFd`Fr|&K&Gm&Y)H+<tZ|VP
zYq47Hs6c!bAPR1CqBUPNTB(PZyDZIDYw^++s-Xd0w2SSd^MmGT%~a2$z&a5;F+maV
zgrm8{0PAJ2$s>`Jo6)a0%KeMG+jiqfvy#Y5=eIv$78<<yGA_fk9=C;P;7-%)V&9Q_
zx#DT#X6>^eBjAG{#G(i6GBP#XC5q<9W3`IGslFDxs8rtbyWUN>8J6vFa%u@d>9r<%
z9c%=d9>51IGgwfsNh4VM<+F2Z(5rs*9A`QU_2N;_O@qJj4uf8u?mLl%=Cx2DuWNZ_
zLh<h#R=Lb!l}V@j8>hnP>aVIGXgg|~4ag}dojQjkrl){C3@Q0j)!R&$U@(UHokH2S
zl*gc}+?~d19br*JaNuKWI1a6}q%7f6kSA;S751CQwOY5A{Wy)EPqf_ZkUQv_X-UT1
z2cwzB@OK~0-3M&*y!&=1p|i@gFYrwcNuN*DG;-+i7Mkb`sRq?i?I;-*yTx98bd~5(
z77g%e>RL!>OH(g47!WqgD!g<oGDz&k&Lau3_BW;qm6487?EMFJldAFujJ^|n92n}^
z<ISF0bd#-vN(yMe(EhIxe(t)3MBb!*1H|E`q%6m<L|M;t$Lh#c4H*fURP+cDwWP!n
zJfkQUP69Gh1J3an@W#eHLbe@@{Tzk)sey^0q-0ad%G#byvj<L|e+Gm`Yc<-AZ{{S^
zdzP);fXAIQ66&_)58an=EUb3qHnj&TFf)xKy_7-I_A1DcHS;Wxm}iw^<He<62J7oX
zXolq<YAWX4h_Z8a8Aq&$iEl}<Y3numu*K*mqe&4vjXjpKjB8tQ8drG}CQQRC&1YLJ
zd!sHT*Z+z-V_<<@l?3Y~fgDtzhe&SAXt9O|!WCZgS5w5ET&7*xrcWK3hFre#a{TWx
znHnI}^b6U>C45B#ZZ=G#n#eNO-VdT0slc7T8Sj=gHWX2KnaPU8HEp-DpcgMDrDRxK
zCQlBD070UY%K;6Qmwed~L3&90cB8=nx@K+wi7Rhs1ndZzPxpz-M#JT>6+KuJNz@(#
z$1{aYDhwDO<>et5O}4y(?$?=rS)g8tEqYJE8*@~A{>e{j_&f2Fsvu^k138Cgq<ZmN
zz3QCRm!zjn7=<my$`D(nYV;Wrh94J;yAEj!PzpNSm~4{IThna#-m|lZ3tOXRrqThC
zM&?t_8=SJIH0E%>@vHQsoX9eDb}(nsZ*w<U;P~3{i(Z{yo|_%9`MiC0G03yurqmoz
zzr^2$G<B-0cb}BKxeUEe7vLC?t&Wddbyz<5152uV!2aiioI`_Rylt8WTa2-ff)nj5
zqXT_RRyDqEglq8)$?h;KHna!#&z1uVXAiG^d%AYoq-PT6>VG^CD9{ULkUW3^AF>^#
zI(gIc?tm=e-5Ao_?|Zwq_8)W)c$)z-4*r<D#*iMar+(;imb3VXwpUF;BT2E)s(<4M
zf5)kU|Ck&_&_v)H!R?Ni)a$drAEI!Y$DuNW^>GcsDX#pSoGsnsL9)y7yGo3=NGw<-
zK3{wLE5`k2?B9?C6p&5wrpSnni8d%G><EN*NG&-G6@FG)Pp-;b(W$$Xf7;6wlal(s
z4TAO$iRsdP@Z<G5K!x~f<JG8Tf~7)*4+y2~{2x>QCD437!K_0+Ug$9@{F@LzW%iqp
zgz&<@kKzBX<mw8kMh(UZ|3gL*_pcTG+X6pmAT{d!7d*Mv-!|KKu(A)uSgY-gdL*t6
z^mtDv?@t4$f0_6IJ|Oxfly0fuJLoJ<eBQM*vi?s%((22cunLSm{kM_%y+_mOGkOR3
zc$;}{92|wg_=4#^JMog=01W@&bkBvKb}`BSfWy8#^v`tq|ER}Xna>}CMja6WkDk*F
zUbe>!(DXqCN!3`fzzo;JR$64$S9z#yO|u*SSbSR_rx(Dp=F7cX%*$`ir#}UyulH6z
zn5ZmYdJf8qX`<hL=38JDz228kC<s-H$>~qM(e7eMd^Artz|OwE%*R*1z0l2Szud|k
zZhp1z`3FFB(Yev?XtS?{ZM-{7{^;b>`<LE6Z`y10a^E)mQtp!s?;&_G&-CycmtwOw
z&4rzmO{no|9j1ceJ;~Dlc*Fm3+D@Pq5oYv~6xCJ#CWL?esw?mtT{rdKT3A?ci^_lA
z!@nf=EG$UZJ%S-i9slpQ;II2;FMy~BfwX~u8X-vXi9Yb8`csQhf+E{W{uYDi6qthT
zz=kuZy$jS)%Ag>1l!dRPOC0eo@H3dE-EJ3$Bg3-V=plPxfAh-rI~FPM8{g?>0~9ii
z#^I=FIF*>t<sC1KO3YnD^Wih12}`9pV`$i#rb_PLd)Pt}62udILevL!rG=S!+K*)L
z?(g-k%G}6k+o)c<Rq<I~+LS0Pb`u=UEGx}dPKgG}wbrv$<6o}!z-$hv6#mR65)7C4
z<CpZ?8Gn<NhMa4E;+;0979UMG1#9-8I~H;IZVzbwJMeHuwlFG1jB<&=-(QNN7v$1G
zJ5*+W%0Z&wY+RGCI^3-Fmn|XWVnbPApMyNCB?wtDZ}Rl)jhpLV8lo(K@pOA}>(Qgn
zp$X>WJ;>CfuFKtw*bBrYB{k`44`j%I)t8TR#Lx_liWavl@{3xFGR_--D@&@x%g|jf
zUDIls7<~7H=~ozTGg7=-tB6g_3OpJDU21jj?i~jjk|yz`4a{U|Yg;ogDzr4~cKA+(
z&kj5w1XlK_N#)Swv5Ou*%e*fen)I7aht~@ca|<R5lxd(nTh`2kkefREBo4-_1RrfE
z3^rc7R&o@Pv{BO%XHUh2JunGw*peV#(|9vTr}oNrfOgglD<KUtCZ$i+&F#5sxm$c9
zEv=wb6WIA*!50!}iItgHPdE<~V1l$m))nB8*l7=WlsUj^t;Y}X^K%WxjsQYbykE&E
zk4-0@o{e@bTDJv1y4puS=+KJO;5{RTiJcTurqdf{{w*_B4lb6hzm-7#keZJldQmuk
zYwtXSxdR`3W*L&J2}nd=GwKrEC*V)a_irZf%OaEp2OjxU%*wStPB=7hk-<62okRzV
zhw31yqcht<+CYGj6v@Bcrwar<#4HB!IsYkbW@CK>8@u}<N&!YgeFvTwU$>2+2#-I(
z{(K-jNO`?N)W&h<Te4(_%kN(L6&+R?T{IFHw7%b_+z)?Ce5h%z-W2S>Qbj@o4iXjo
z{NwMGvG&ffP^}+hHy|$-iPcW6@Yr9j%adcYC|{zLh6<Xc#F!jq>Hy<dewdkw8J4t(
z(8oSF@pRC;h_of1>4I$LidKj!qzGC*Cs~p8iubXE22T*88nBjkcWhDP@F?*<aIe^^
zXT-ZvMkU>JBRSp<iBSN0;Na|@64c8B*eS*M=y{>r%$M8k5*{`EdGa?Js0fKl0c;7S
z=8)&fu`);AO^q|`fTYpt0OkRUA3ZQ$M#K{#4%D1rGexD*T%t$@{%7sH2<cDY(k^H7
z01%bFahg(baOm;=j$?G!CSTTuym-cHFm;rt;a;`pmDs20mXON<1=9<6w7y>CX)b}H
zJlc^0x-iautJy}gXim<Ywpp#^vBw=AH*l1d8zBpi4Equ>37#2(-C%|VgcOcDYy`)+
zcr}IPp7~faOI-S>rm1|qC$D_@V7oRh9l|V6oKIdV&v&)hBP<|OG3bR{flk5JV~$$)
zdthGON>O+dqDfvX>i`ti-CFBW{st=)hF1f^Lh`o-+X@-$;7D1$gIpRmuFXn4`poDS
zYEXPA(2@=FO;hZLs<aS)-QaED`DBbuyr_kH>fZTjbf(5CqxT(xtB<Q7jh|a8vN*2-
zb9b(B_Zl+kx7r*@qj}NT_<JR01w&V~VCqGQH3=Gs-@yn9jHSQ=fvLdeH`eyR7R`*r
zmqR1CLg323`ft{JnCpzAX%Z*&)e9OfX?k4`^DvtxZyQ}NuLdY}hF4=<x{s3Fa;MWE
z47@!Lv{lSSlLnH|`9Q_Tca9;^FmM;OvC{sIPoJF|+LO!%3BX;ZjQLydQZfQtqImcd
zwPr(AY;8%7S`OXgzhSp<ln0%S!CDf!%dvu4RsvIzY1Y)$L(JIr4s>HsUdrOI!m*I8
zAua66NTUm^rC^6brD?@BqAM+pS^@5tF|<8FY-o72c0>?NMy-Nc#yxktI*PaSbL+(Y
zLmfa&EXNuQW*Ina-4BPIY%(*#IvDia&Ow|iE@NbsyPDRPHJ2sBo1@`j4z~6m2Mr$%
z@XVK+z~6Lb@dDd!*bax^Hu{o}<wGbKw2D%+2K{%BS;=S*kYFFzkmBl<A07%gO{Y*7
z;51e(E;Z9DXRzPfZc(SyVo`*wa6px^@ZnunJaarfB~Ao2c~a~t+6sD%x?{I*hl51W
z3J~2mA&)lql0a~-wA7XPfpLC}JlRcGi&(i`x5x*a&eX!C$)-mtP<Ta^I^!6uT~*mP
za?E*`f!qnG7ca`a=N1xFc*PoNn#nm%4D<$>rpooPx*Ob_I%!Z|3ur=7{e+N^=&FU%
zEAmeOIFAytsA%&80GJVr!JR?-ES<f@v;0Cs=c*OyA5g!fIVGEO_cL+46TF(WGjQJ+
zd1j_%o9TuzPDfPHE4|{?J+g`;bAOG@VAjlW&FtrjKibx<$NtVJ81?=yE`XN5{Ud*2
zVuSdnAkv(8G&mqz!sX>*K19~6ak$Vb9Zbp9Sp5jGDDWshlBm{V@If5T(pGE&l%SL$
zbJ8hpgC(w>{&6@mX+U5)i?a?~B{av%aII2kXsEEYFtT%ara6H>Mw<*^Y5`8=P=JjL
z=?9WSyqhU=f1$~b3ex6Q&Yt=}fwJbLz<`8=tk#7tlR}ak7gA#4-Z<$Bu2#>y0tG2I
zmE8&V1M9g+sb;xif61)t(BRlKcq&pIA8Ar+&^GDs!i&*(HD(*4Z;n(@RACl=5dk{S
z{hs1=Gb)Hauk3mGyOjD?Pl<P8$1^_m23m=w><OZetPzN4_+djwm<7hBO70n3C);qF
z$o%$<XAHxVGD14m$hf*|+dHK8rvq}-Jao`R{oM)4dC$p#bhlzoCiz)%RztnLNJ6YA
zDlA)jJJ1Z5@sye~2_pt_oa97o^w>;N1&YnBB>dFCQ2YfcoSfau!}2@v@){C1t)Wui
zCe657o4SG)?2{iHIJB3^%_kAi{DoLK)$9eK)25O1`>=M*bv?wvx20b4CBl$v^7Ckl
z5#5Xtr%vU?Q5PyTqEYq;*j{kjX4>I!kG02(u=J=HjzAvDmk<N=j>7fH--5A74!7>{
z6>?lO8K`icv=u_53Axatp880axSFSt)$|kZ@>|T*B$`L;b;l*62hXn{MV0yL>s^EG
z1U20ffOn2WM~ik!IxulZVHU)cEvmIK=g*ItNSBnoI%f*EPj`vjXCc5r$K7ha%??^x
zTU#6Jx#*E8Wqxct^}!rZt6%SD>l=#1m?<{9%4pm&GXv~Md-F+pOx6{0-hJswBi`P>
zl5<nRQ(23;>xjtNlZ;Dg;0$BmTF`<+n~XKMTDm3|5q!w_NULSzAsmf)h$SCRqeta~
zhDouo@O-pKI_9jgzf>a8Di}h1J}o}zd5~0SOz}G{DGRTKes24-GOKiUTI5&BW&9v1
z1mF4(tJql&_LH)|V+80ZL21iiNqH$0so_#$A||oEezLHV6i_52EDC0p($#z&Cc;>{
z5)>(VCE6_Zcu#=g{D+F`xP-VuJDF*;Esf$kp2L~rFLVJGG2B<r+@rN6&6yusFZz!h
zgJC}#QQ(|r`;XgC`$s3$*!UNB+(Un4k0WxZ1WGnJh__iADe&}^hXG>P(C)3i0~~lh
zYy2bUA!~it6=pgO^1~MAQdSM06B$I^n-xbBU1y`KL)knhVa{RlZqkyXxv^)GGqWfU
zI_KqPbWprTX@id+9)>gXVt!}V;PWc!gqd1utt_P<_ABVaPHLi`O7GlK$@HHa&$_0k
zqLKp;eZSK^1$@W4KF?qyrq&!ib-W^%vU)03>1)g8pOjjBWxC$o;Ugwf8Rem&Qm%Nl
zX)7@ybM;H#QL`Z_V%VgCy3pm235H<vk!7`6Q&hbHpo8?C>0<2NUPTP{r8K^z%wTV%
zE6g7D*JT!q0f1_b>eI!Xcf7lv;E02IcP7GPAfBM^K9qDKWEWG4H|t*)!u68y$l5Jv
zTrC`+Xh7x$+)l)au4)i-Qgu@A&v&)OI=M-!Ej#Fc*$s@;(Dt$%=f<e{d|1R8uIiWf
z?+7eJ;4p!60g_1Jt#xQU*vez;FF^kwCScg2A_c!xB*3)VOGe_Y@R_XzxA(1W`eI~v
zs_Jq+;1UV<%)-VRhmA^npkKC4%@F%w)S8d}h%1Xo+WTS@!kC2NbVl}!(dD?A<TW#!
zHKfI2&_2^;QJFcu-)A6Zl^Xpjixjuk;nZZUo7wZHFZXQcSIcNZx7iX2-L*PQC`Af&
z6(Att$1NI`<g2pzZPvfW<|%3zjL#?X*1iWZlMUSWf_Hc)h=k&yC;Rz!o?$|imZ|@)
z`i4?*O&4z$3Ps?Y%Q<2AzAdAr1<Wu?=oej;Nugna&Foznn($1OY6z<FH~u0f{7o8&
z3fU(}wgTpwG#|Lmd=M#4noh&gu})G-4y`n}Gcgf0E)7uRycY#M1nIC>w?L@RU8lsl
zHxP+k=pgV;?{X<X$X=Dg0Pbo>f|JEz_CeQ_p60gi=T?gicfQJomL2se_SSCHsL|>6
z$E|kNh<RhM(;9s{SG|7+3#ETCUk?RM<~4l6J2;F=Kn}IDl1mh|uCD`*#du9%fgpK*
zLOu*VVz7g<7kK3ktv<N|E#%<>T_EOJQgs{NxBtQLHFZ@>r#`Dj2}o)d4N?30@Z;-b
zSyT!N%=Z;!<dAX?5hAKLkLBR3ESS8H;d5P4lt~!E9IDw8p5fn)AZov;X=_tQ);fQx
zRMIA>LshmbV5Iwe$OiMxpE_TkhDRB0eyr~_W_KV>s{M@Bpxt!PycvX+v{9xU0qjKk
z|Hfo7@?UZ_=+t<dRnKc?ksDl^LtC8?9cqF|w&0w*c*{$99;B`{dvsxX9GvXi-9HDl
z#tFS2tO_kRdkB--j7A{=Z$NR`#}55r7lkA+nZ__7P9AX<n7$K2<V>nBvhgWiv0f*0
zw)#gi^3|;b(qIkWQzj#3!9!WejAK35&x<)0HTGh+f1a{Z!pZQ}@j4Ri+WfoP*z0Mm
z(DT+rtg-r6PRp0;Xs%hfx4Q>O%3~U?z~7`pk7K>+4uE#`DR1^})7<G=;i}sp%mF;P
z?RZ^JcJLWKuMvp0qWd#@?cIGR-pHZs+vFw_XZ@MHji--8re?EF_F${cz>hCH0^(fe
zeN^2aaT&hb$R_q-I%KL81A$gj<;&;nza&+6)`+z$s@p5ryR&Y_$|alma10J+ym^gN
zC+iWS3lLXt5O#?Wx_Ib$DZJiMCS^$?UYL9nJ;0lDZqjITDc>`@3-w{H5O17DRVaT9
zQ6EddY<5EuV9P>7@zE!HxZEW3$-HT%ysV~T_@Zr0tkaD(UNck_T(A`&d6o)qq8zv*
z;}SBlo;kI-9z5~w%R0yDKeV&D!M<+|JumJtS<~%IxDmeA%WCM(nTrMPQIG`65ODc6
zJbYld+fBn<^#NZf@pncLv_CPSAi2RjtR&JEjj?ijw;XB&xi4*yZ~DWzSn))rSh0L&
zq;F=c6AE5-2^n`}2F0GJxOhs+_3<pQkX;0cdego<O8P-N(QJ%%!yqF^PA#gBVWzoY
zlvi@~A|!rOON~{gTl||de;S=FmN*ob=~8ND9$QeOuX|po?4s5MPDrwJT0X`ZN-C;^
zl~Y~c2Asg?RX@%zE~+T>DvYGkUY+;mABLC9idf{n=82B$1xU=cpO*`g*aw-gKf#PQ
zv{|{24;WS&&Iw01YmFqP3$4_r^F2AH%YNue<*(8tn5&6UWD~j2rjzz=rZ?z_j#>bn
zYp(6@?j+#fgYt5R+L-N9jcOYT(qUT^rpED^v584Wq|*QSc+OO{M*l=34yaN*%m;F2
z#Vr@0@mWQ-HeWhcQVU-)aFa;jaNNzJN0(hlOdG~|u-JRz;E_AiX4bo5vt^>O-jnQp
zIhC-yk~&`XpcjY9-^7t{9dg?bjz(L&+dvL7D-sXAeEp4FG8M)x)+f+h!h$2lHi{MN
z)6rn{NP!fWLO*e#Lrz^cWB6I1(Xw0MwRoT*L;UsFbv1=FRHWn_6(-{QPTl#*)LDt4
z^?PDO3~0M)nH46RikX=ejEIR<)N5%YJ@L)M@E|Rq2n#CQ^?8eX`ZqmX4JRK1I{{37
zL#pARunl?rr9?vi{E#Z$&zJGgPO1an`<6X_v{*#7z1h6}#Kvs0{rnb<t4AgyorCSY
zXn}qedG<W(Y`cECEVqd!GM|@<{0yMs??Ablh%QhV@ZpbHSExtK@;VX2s7lE$;e=)7
za(ZvdfjTh>4tolQO&V|SA!%nCKBC~68t>)iLXKEbo?&pW3~4*@V*+(P(W>%SYY;ou
zm<Yd(BgP{-DcseBE7o(zYcBM~Z=w>z&GfNoCNND?QzT6dQ#78|wQrWUCJ$zDc}v9>
z5h8qkL$#KP^p!&P%Ay2G=)t3d*qn(*a12Dyq;WD&j4{*g|D6SP!EvRJKF^TFH2-;b
z1u+~ka{57Nuw)~fC*2$p?njqeFg7Hc|GVkVtjW}Pb|btdz2xW+tWH{H9O^eto21qS
z#&&<_pOV)-98RY@Qa!J#TAkU^h@6>(9sB1K=P5V73=5KPjqK6Jy5yNvM1!tIy(@5>
zuFvF|oj_0DZKC_t>9-D<-nWI!;v^;BHC;3{)n*2etI13yvNPIFJXM7itdr5L!S9YE
z27gKagtgd*T9|OF+vItAz3jZUedX4|-ig+noJaE3wxW*7J)>&4;AB2P-?8uw%}!^K
zC(wvtOMfIxtyc)|3u;&Sw)t^%XZ+J`x-37j05xrRG|*EJt-&*w0I?CCXfT>+wG338
zekCG*kUd|CY^6;hn&WOzVy$m~bP-B|q1jZ2pYWhc>3PM~I)a_f%HL&TiXCQTY6Xcc
z-NGakQXx+fr|C+>F{JS$3T_o)d%$Le#?T}t7%yaJra!{%dD&76bOpw+mpy1%7+%(D
zoDZaYx9>w_iT%VR(V4|SegMK&ugNbx?nlGLs?<R>64A^{RNKr3x)p^6*=Em+NvF{s
z&bz~K8LQe^hW|yS<rIdHnosCCZ|#JTjQf`)v4H?cTDtbcz0eJ9A^pMo61E26dkE*1
zHHEi+OS%Z{k(Ub+`GXYI`Ma}GTY}$1xCs*1p!ae3f^zM=VGAmHf~IIB^WUuxIq1oe
zmLd6CT!pxpf6BLGSd@+|j1x?@Gk&u%7xADi+~?EAnl`I7zgJ^D;(@eDQ!S)Ur`m@;
z<}cYOVO!~#Ls|UH@Spvmvp7tin^ek=lhS_MgO)sMDxpos!felDj5DDmm@R*<R3FK6
zek&;^&3tb4lR8Xo@=x5o*CHTOvVfG`#idCUhc_7EBxVrjdtyU#g&*IEz&sGh#wm>T
z6TX?9m=fl>ZmtJHoVZ3UuT+2BZ(qbXZcN@9gt8aUDVEAJc6y(qs~$qwinCC;V)!r0
zc6MBlvV6}={H|rqCGWT3v4{c!TU}m1eTx{$MRYmDAwaBD4Q3j!2(GgdBq{^&+xRfI
zy!ysuzwd&z;IEU4kK&Ni>+oa}v3DQh9QNEz1y&fL2n8V1$C(hL=~_))OCDv`ay4)*
z9iVeu++zb8=$t<zY1-dj>f>7xDz~+(sLvhXdrR|it_QOMAe)`w8Yl32S~9!0c7MD5
z^aQ8EHnChB#7W>ExM&>M+W&uay<>2tVY{Xs+nsc5yJOq7ZFFqgb~?5?w!LE8T(PYV
zzPx)+?U|aX@87ER`+jcJc^${ud(_uD*XYsXh(e=Y9ZI>u8_Lb*Q%=0hTtZipoSJu~
zNyv0PYn_(efQs>x8u;D3XcIFt5V*kgnj(_LerWo#<>vv;clkM-z*tr&Qa!&YZ!y}{
zreY+PwISkG11KZxj??JClNe$dnF~_K*yP5AKX>Bz1>AW46^#xjgvE^4(y|5<+eOyc
zDVURr-Xy%F-R$u1&CEux5ybnMVE7QXV72o`A^SKt@FTfFr0|xqucYDx6UIKcVI(M4
zFU{tKn!DPG<Yw+|E$+qi#wg~)kL+k+hec0cFc!{CDVdu_fp=Tht|uK97TQ>*)66Uk
z4{>)^{2r}*YZei6CVOC@_a|`m7G<+Ic=czEE<CNh!eWJ#h4<*{dVJbEiXdw>KG<H1
z8F?5TY?^e5Nhq9Tp?xquFrPL?JK{|Zh8GUzq}#D!-y9gj3_y!*Ax&f#@NV?$Cu~>W
zlAW9%!J84MP=<V;cv1Yy>^$l-IRsbAj8Uu??!QY86CG<y1qUl9y;PW~9im8LinH8|
zDt>W2%Xbpfwh@!pj!GVwii)vM5j)N$zx6}Y!YcMjwo2~c!!C}1-->+>CrFj<5<)?}
zw8?%+PQm`$iAkD#W9o?ml7N2y!t^b^$bsUiDedQuK(sI=z2K&W?~5b<;SfTRL2V%w
zF6JfeRUBeCu@@Xc;W=d@)JkPnL{m~yC>9jFYD9_!k?bp_3QILpjDnL)SKFYM&qLkV
zU<E?S3R$l9L#^_4>{vN>Jdm8Wts`Cd{MhIATs#VsKqi=LyL_bw^XAl^v&oyoi%-l}
z+8XVt>J9P6Dmrb-S_bDor)NIOE<eOqeNsW|;H{%6UEuV(kyx;~fGH=k;%Z{Ul@t`Z
z%S3*5Tu$zl&~4QnH7D5j`+?K^nJsj?-rdP995=(3oQ14?e&uh<p3+Y>`LmNoo{yQl
zlYgd!vxT6n*Q_9yI;;afE^|lnHEQ<niAhI0(Acy$vr5D(XR`8=sJGlWeLr#J2{(eJ
zfTcvj%AjHKIl&4$coE7qycAaDePI@9n6bxn08zk%L|h!y)X*&5xmkm3dS_nNohc_*
z=RjoD@7uig-^&$*`U3u_Iy?WMEQCpwcA}@@3i?MbG=HypYlt>0KS`E_<)#+OX4Y5e
z)N*k*U&iJSKklXEM5qbGi=?mgrjbD-_~%V)TLlxa$~bYo5O#f>p7C&>Ga%LE_sAhx
zT-eMak^<EVnD_|=qQ%kT_wSl3SDVe~DL&dFa(yKHr~h=j@j$s*$~K~QuypfXQIT!)
zN5c{#44mvdftl&-<pj>`ODXGn^*%7WecjSi<S1|uRg`cX*%MJ(La=|T|9bQP^;1E^
zvA<YDBLAyAvix&{la=b6A{DP&q|iCP<~!w#4PeLo4N2K>pqgw>m`2bRp~P4`xG|yc
z#vX$YbtPDbf_nSs-`cO3Kh1C(GrA(w+{8IKY%?^{Wv;uA<HBy`yHcmh3oMCZ!X$d*
zqpVU<MH8xF#p;WLt<QC#5sPQ$+*<K6GG$spzi3sxs%W%&3%MYZ8oNG3OgHxGVXGy3
zG>d9_d2Yp)OA+ogfP?Sp(D+RmK-?8Z<K<Lzgz^heN)2c-N~>|<q*y{6RYp*49g2yu
zV6-Qs*c;RH<6TMSsqiSzrC%pT68Lm_9XRm950?nb{|?%vj@It^rhb(y#L$4I@?@!+
z>YO$0{hXR~;&lQIeEhX%=HJ!nLl}m57M=No`OEtZ$>r4r)cVDHob!A2DsI9k+9t=+
zO@Mty$Hfg-sFXPzj$_d2cVTt9T?&8GbYttLQr6E4j;HhFDKu(u-(#yHm+Q?+1;UW3
ze^sR#D$o=6{dC027>E8)1l>~%GtRGa9^g+sgutHJhES{eX|{)Djd(6z-P1%*mwoE?
zp6x+)nJ@HvJ1u5ijyVG-UYO*98;x1wJ1EJsa8RsEl1S^`CyY3c9wE$UzHsz(R*0QD
z;v5|E7bu01cmE+|e&ma%P--!cRhiJ`jOuk8$-EV%%3kv9F1ZT~XAFKB-f<NDvR#a~
z`~aw&ELCM+AULRJcjL(>TpP^jB0(gLAO&JbkN4GOu|}zj@Yu)@HcNK-Lfs!Rgt|YL
z%{G2lETXa7M?@&l2z1i%Ljx3*37Q+59YtX|tRKd{y>@z-%8KKV?I5>HOz++Cvt+Xb
z)Gi5FBIY7FaO+)h9?Tl2BBd-Q3N>Z-;KPM3>B#jj=PQc6#?+h%r$%8{5u~x==3j;b
zsz-u{<Xy#$eFn$}yTyMjP1n7OUR5sVveU*~Pv&uphDD8!*F#@!rWl!g=aj$EXROp%
zi?o~3l22wUb&G533ZJg~M_B~#3Lda7J;_@qqVeKI+jm&26<Bya?$FFFG*(u~9M|X@
zKk(~re!NGpG1=>8U#z!Bm-h!9XP+-2e~;S+p4aIZgP37IyI$E%=1j9cS1Yycb7Kmc
zN(;x7@ts4|{}W@MZ+UcWu>2=h?=0~}&&BR(bV>zRY_!m`$^7Df>YYbD)K*WGiD-b#
zLNBwH!gs=De#Y^BWJS{7Kx%sm?2V_bn%6U<wwu+$?ZTQ5<zB^0C(G6y$H+pDQ+e3x
z-4{Em8q;m-<;h`c5+G1YEdJ%eGm)sGuJuBdV5(i}<s#4du0K0D?e+3D5M>dy;WiYZ
z@HViO#(i5Apvb@6!Y&ybDaMTG;E_(El~O}LbdXs=A6>|Ub2vu9(t5FK<7Ga{W@Eq>
z>GM>s!GO{;&2BoaaX$L&X~qe~rv+;@#wsK9s5PxQ@3ipe#AB7GJ*XIWOo(6{q$WYi
zMIH5`n1rm1gIbE&PD$4-*QO&qn?b)gyMVlIn;_*)kuHO<jMwL(LB&n=DJBM87bY%H
z4p+k?Va(pmZM$T)=|iF~yVdb(zmoNbKLT{QtiOvT2nUzQP<65rMv_&rA#;}sz2Jv5
z#NFTY|H9QceY|kTeC3#XNYfo=a`|)^22CMN##5+=-Bj}~ckkUVtY*_vQw}H6fe)t(
z9-ptrOon|RFE3c;{P!C_`KjJtyBj-qT#xm4k`J9_dWY5a{NSacMPIu00#lGF(lP5i
zLwB-5!!d#XHEZ!ZkduwP!PO~ce`|eNU7eF3Iiw8pu@|o}4~HAqI$?gH$idQ^H<M2d
zS3yATAMvO;FD>EbuUt+StnhNsdRG^yylYp}5n&v*o4%QeXK5t8O+LkeRs?PS74Q!p
z_EKUdgW`K)f_W<+$MD>#p5H~MrHo?sE_~xF3H2PcYXLgN=%>G#xo#$>**xxnN~;c;
zKiN-*7ImaDr2ox{i&<eev2b)&iDHc_n6;L?CtDW#pC^LfWl*LLPA}p2uvxYD)mU*l
z9dORI6g4%8#@;j*DNO|9QWVIRTxs?mgI$tJf#nd!5#($XdQQPPkROJoA{U1N0X_cZ
zldLlRq=84%qm`cdZj3hN^$O8_17o#)jnkE@-u#(;_2FfhYqyMqA;;cNBlj&&l7)>L
z^DHoo%TaANfTC)8cgj(-QqnW+y2(zXN(-fum{y@z5aSxcA$>3VE705r0y&>kIx7gv
zIc@I4jXF3VK3Eo7WEbok3Mjs#s5Me(!$$#PW*nfkmx=bcuD>(dO5-TDT6~d#Gyha?
zZTabap2`(d^#7oDnLO^#NdK9U?Bxgz$N8?7{RtXZQn{D0JR_-J8kZIMfjL+~FefSC
zK71N~h(y@Y;WCFtw~7^BL&}}s)QahI5|fKrb@|KaR{MhMr}SyF>n<K2IV(igvV4)3
zT0HujvO{wjXws1a2&zuK)1g~}M^eLLCh5Y!&=2qBxw|PB2<7nX3)}wOw3rr4lnDCY
zX~369$<^6X9?B$YI*kjKL8kNQ6{U_R7~Ke>!(h{<eCL2R4*Lmcv?LR*>WN;!&2LR^
zf12$Kai_{Xc*`M~-(#NYwj{OQEti4_^cgNLZb=XLcz7tDSN2UcdQgN48zkrSGhK_t
zT4_o$U&HOV#WsriN&MaSSlY&&w#Ov%eO@FHPtXmde6qud|CXz=6+a@Eo0=Z&t2FDe
zBfE;DUyB6L1Uc*fG5U0K;pkanYpkL10w*JFt+XtEAXpwng;xGFdfOxy97(j)?BV4z
zd3(1zs6HK;xA(f+>G@wHL;hH7zI`$00<@^rI<3P=<T5vh=?2EbG1!)yZA`+#!oNK4
zOk?nQeu6_I0m2FWBEI?Zz_Vv*E>dQv1IcgqNci6}dg)m%O<WGUln84lN`vaXU{CP(
z?P%cEPIT$_x9P7WZ6K+=iLY{1K%5`D?f3-UVjCz2z`)s(FFWdcU~j)Gp0gAzp|Dlj
zSV_Yn_A=XMwU%(SvDH(msp3=5K^h(xSlMkK!BsTDj!*XH*qZ{qCW=OCi)u%SsZ@%!
z%&^x=fc%M&#|19`gl(zbh=*qh_}H%~((Fy8wKnlcpA{b*P3smt7{8@oJ;4k|^HZCv
za|^Hm7^)?)tN)IYHd~jtbA%@JVR7a4`N+O?s<qg?whm1IXRW+jvM8o+0_4}oH6bo}
z#tP3OK#!{qeahilw#`V?T&85Gv^OdP>waG<`*3(eZvOKL{JAtfN2<eWxs1x!O=+z8
zl{lLH$&hQMMp)y9y;!jt1Iy?Gt9l+Py)-ch9EZj6hxq(Xk}JpCi>w|&)(8DCa5d=q
zbn<3`eFbT=x%_`B2<V4891drc-@7E1u+X*E70&(S^q3!&Hrs#np3Kxktk~i%P^L!4
zL5~F#d>e-{(PL<uvL^>)w~6=u$eq^<$@D+jYeS#U3aVBq6V&uzR1n_awp1I0MG@em
zoB$)T(&dfj;t6+;gPAPXxqnhxt!cduvze6*wnNZk?uG6{jf5B79w9S-ND;GBVLxDn
z`9JU-&5z9+eEh<6t@I1?GUF>x7Bz^};^yoA&kO<**gK-LL?5Wa16D}wlO3QO^2be>
z8t=A8J7(r$IhZ|%_j;o%lFkZ!KaasD#G*gw^@Bf7jV?XyqWFx|wc%XW&fYoUX2Q+9
z;NOKq6n7x2`Ue+g-V9e{&9$s>qnE0{7@OrkwA|L*(8wJhNQE-i2b-PjC-)NIO`PjX
z@fS4A!##!AP+FS$t&0eV7qTb(j=LZ3;P&0fD)L)_P^`3zq7kQCO>X?NzjjoaKsLAl
zw=RYjYn724ZYPUKz4r(dV3SG=2#4)dt7)xE_EeJ{M3_!M{<?zT@AJ%$17+>RR;LXw
zi#HEK+T(>i-C~8cT7vj4)3JZ_YNGiL4Cf?Fj!mpST_oJ+%Q8ho7K$J@0NUEq#`v^z
z{Dt-+-5+<PdoiOpvOyZT87mG*IaJbL=dci|ubx1&=MA=rg_=PWB+SeB^S+kuK;$V<
zrvlHsniy*Guf=d5-d-6mU#GT)O6)Bh&y&LQ{HG@FjO$5kPu?d{o<G~UUdkgWtn{7!
zP9+s#Ibmp@uc)8~{~t2k8qc@WvU<M{uZoatUQaYq@t8il^{j{W-?d{Ih<bC!E<Q68
zjpDIy=tf4Z-#Q?NhszC2dYu-)Zhx@j(IhqkF1ze>(w}2z!Zk)T>Z8c7N&M&6murE&
zMJ}R;H^R1ssT+z&gC4ALgB`xF((4}SBf_Ggc#(-&hNIm5T{HIHKg4c~88*_tHTpfi
zrYQ-d_2u-6{dZ4JU3TsNt}@$q@oq{VSsao|TQ4BrT|`Aje=-r+V2B(sK(o!xJL%3K
zr^$5Qc&jH?NZidBlw?GiuGY*K7+<?CO&;Dgc3|MM65wS;c>aDq_97yf%9jK*E}#Jx
z{nm}blgfA<ux_@o>SqkaHDogB<!y8*yg0~ybY>5Cbd_ssU!h>C(|bp!c3Kicb%y-j
zFG~z==0vZxS(9G(9VFOah~Gx>3wuAh;3lof-lop$emaDsr#Ca@Y!TGmEWxLF*{l6`
z*ht20u)Bc_jdhyFpfTrR(0P7tyrATzF>e-?yy3^jZ=#P$*v&vvn~PGb&U7PhB!t1b
zy@BMT)AeDkK+oR|<ikW0y&IaE-#KZ-AjvR_=AsW{bt>7Lo7<Nzc0<F4wpiGUVA-~%
z1hV`8{%hWnG00X<&heiII2BR;%GXI{zwtbk%14NT+T35QA11z@cC*mNjeeZD{Y|#o
z7i>(|S;#St(r=L1*Z+FC)bV=%;vbbQZP}o9`DUD`%qXs$@D>nJW@_hiub9lD%tcLY
zE}xRdg;`N}H6|4S{&JbbJb+nqpl=Eszg+5n$!B*Nb5i}f?RdFjyTl`;_D67h@IDTk
zi9#jxC1uCwv}YBP52GV{*u#vEVl=okoGkL|&Rp+Kt;KpiheDkQ7fR%5In(f*Jq<9r
zjv}Bh?@=LGdT4X`8n;jZggR`odv<6F_@~4)`a}yo&7BNp@KpjTXVRFD)Y8h-{%Cw1
z?H`-*h$ih3iTDwAWVboG#9GR?X|i21d~(<1{3z^Y9Q*U81hXjtaWcW8Ttu#V_m8Y3
z7zq&fqnHy$+l#!#c0=5vs6?`{b9nS8N7HvzRj;333<00^ECEWr$q16rKMB#V&yVjm
z2_KRdu1<-4owUcPu7jVUOgFD}9!m#-r`?-lTOaWC`_6(MA;FUKmh>NU<<=Co0l>=i
zGTK<LV`dUrW!d&Z;y&rxR5f;nJh<W1I?D!^AyPTruPYbcd3?=E)bWKYK;sDVo;6-I
zZH)$W&s9p;@_jD1_;QZjLOEV{D}uGb`#59)C_Pt)rw^eUq=EQp{<=pY<W!t#*}m>5
ztZoms&~G}nMyaQ10xGo@lC$M<qI4Z5Xqyb0hkfeoUR1eftvW^}SUIa<viEf>JRXd&
zakk$@%3AB9)Dwa%A4VFE`v+P8@?s@Tng$zQ#d<IL#hQIaiTekS;iOa0rkFkB8zbGK
zM;N!JX>JaS<AXqHwaS8!N<3uNgNj(B5Ci^bmE&3x5(+4*hs(E@mjdPHbr>2A;lU(v
z)Oq7Uhm#w}^)F%4;3fmojb%u+Ggd)zLR`Gf=5JkcX~8#WQAkp4%Gq)KiypZ`dKD6l
zmY2DJo+kpCCmx)5W!IpYz<Lx_LYo+kFl-lSfr3h?U~xK2;qi97rvipC`d2e_o|SvN
zQIyp=DK$5OrJp!LwvZfGv{rdb9OlMF6r$Q*!0it01OT%Bo{1!ZEU;)b3sUnvP4{Zg
z%ypp%Wt(e*D9}lD!H)vw-AiMsN{Z5w;m9d2GgBDCk-1n2%E1iM_n4MGHWZ>Sd{YrE
z+4@WZ6lk`J{Fy;O{`QqIm$J!Egz+RvlJB3ML0!*@?*z9}8@~T`=|EOiJUG@(Hm*_i
zZTdYY?UT?UA+rcY+-Vq<?He;0alCY+yIjcUmfq>@RIpn7tAR^y1tnHAHMD#HsGhKJ
zINO8-3<DO1U7%BFRFKqy$4QwU=<FzX3hh3iZrqrgoVCU{{b@pCWI~9U*#p%zJLWHH
z1gw>P?~1%=4@rl(aM5D2<|>-XVl0Z}rZZ)syuJPv2pe*7*5??qa1};(AC&9B5PN#k
zlHlAnSxilB!JE;lk{79^?iymfAn1SboVj2H7;s#VEU@`Ho{hqe#djy;c72Q&if?gK
zNkNG;X~_qkV=C+~hEl0k>R|?c>O*nCif(m9B&c8}2;PO8wG)xzf~E#3(w0}TCKcR@
z{%kLg-80j6<UrcXaFW}5UM8FT_lfwDu)6fho~pb>4RM7G)`7R$?p&6AySs*<pR^jV
zJ`gYPi6s(_l<N20F^+fMI$U`e(;o;9J=<iZ(jHRjd_3($5t8nLh|MfoI(N^C=8?dm
zno-QgKb@>DDZ0}YMXRJFncoA-=<Z(UC=B^`huo)GYax969K3HR%0kcs93NAang|)h
zv5^jrX4MRsPU<Vfo&K<=^25|}XR`dHwG^q<jNc34zEYp*q4Jdl$fv<R&2lXpt90%9
z?LCu9$7tJxJ$HOO@3!1|-3;l=xsGrg=dmv}n|z)IFw+9>d02WRA|e_23UkHmSRXCC
zbiaKGywWh5evLNT$vjx<O*0~d@w=RKf)AN?8N&^{TzNa4HD{-0Q-xhIIp80GYieb_
z7WAyqrf!uZ9PrFzQ1^ns?q#U^hNRs`cq!1+aIUN;lUbJaZB!H1!C3j7hTL<%k9ITe
zshQPH>MVCgtl+c{fuV<9c_5o-u9BZjmNx^9DsciPnC{A}j)4CsZ%|N>!#)7EJ-vt(
zJ3*`4i|s_wJVSLHLPW>El|7I2e8zsWEpWTr8+h5eBfimMJJ<YM!{=d6!g8fXPyYhl
zzb(TjvI8r|?4O(AT)PP;ju*Uo4Y?H&IqI6#a=Cgv64bIyl--neeoE^CpXu<2xegH*
ze<D<a?cUK5fRZ1*vXPugwF;YR-;=b@HOhki?jFj8-hdGF`!egQwf0i9tb-?_euNm$
z6lViyhLR5(LzZDC!X+UP;#hQCTm3CvG0mf;UwG{GXBuAM(0h6&h4P2FK((lOCU+!n
z%`0`Ur(aSG9lZMkHV^+F$%&zP`+d15BLut~%-Z{sRIPmph2~0i^&CBl>hyTfdG_(l
z0oLuN)vCVZT{Rgxj`qwGtLxYaTZqjBj<VFanl5hn9(B0<F~zr~*YCQpk%b=69-#Mv
z_w0Liyif5rff7#!cC<rx%`}T>_p4Di{WXhY8CC?gIPVO61@_*e<DCh4Vg3g_wTbgG
zVNP6?mU5btTS{Ia`_6BtAu<-%b=_(gE*ry%{lYN}zMfYRq=iG+^b9L2zwvEd{c&!E
znNhTK(V*wUIexY@_%x@mbI<Jtt>i=sr9P+7mt;>XCvs9Y97+WP3GD&3b)Qds#alUE
zrM#!)U-opRizG0Sp>OT}FgF}C<Iu?5vN_xs-ySu=*So{hi#m4AHirVG*ID2etwndu
zsVglJk>GSd3bhJUQ;3ZAtT9Wsk&)5O{e7~lyn-|1`tu=Iss`a^o0kVERP8Hxcwqxm
zxs<ohVgVZdgOvJbgR*0?x=qzaFKpf?8$K<g{@{Z?pH)0{#<E3MTVWO#L*<wi^T@%>
z2ZIDoVVTN-17f^vRhe_tFaXkMVs`tcV9LGJf+CaaMDeD(ZjzQiV=q+?|A(3Dvmoj$
zZo#|xcq)51Fh1gQQXq+k9|{;=$}wP${xG~k7hyle)B0AlIndOGgY3mCEq5x{)47Gw
z^nTPtCJ+Wv6Mf{zM_^)fASOPK<NLv7;M(<{Z~CMCkR{e`TDW1rHN0q&9x}Yx>Xv~1
zG|S`<i!a0VY=V^??^b_2+&2R&4D-jo@hBF3<&am8!S!tV%|yC-0!vRE=bU;1N`>`u
zI_3%gc4~i{ID9I1>wLLkGSfh&#GRU1>ekRg=A8MDL>%cq+(`olCkp#D8|rJ>y98&$
zbuH?qf^H-53nSZL`M58$sfqG^nXF5$LEXDTvT<sNsE`2tRwR7xkYH$J<IOh5Kc5RO
zy!VqV-|mw5;_4A<NpY{OeNZree}6naFZ9Rrm653|j*>za|4$z)zGxtyWErxTc+N&F
zDD4p`;4j?K^JVN)>~G67F2qUIqTlCdu5<6T0=H)cu2|ju(8=C>$o5wAd50hxGIAMX
zYmBo{Vou}awkRsK$39aT;@&kAgxCu6Pz)udbD+ztCqgn4j}Mb1$5XPJ`w~3bl4%Fb
zCsMbbAmkQ3leEOb4WZ_ODgQW#3}1!bamQ5?m=9!f4``KzoYWlu3q%YRbeyOQnrF0p
zAJ}z}APDnQdVQO!cc1Z7EmxPJfo%YH&ig0J&BcQ$;M42JRWn>%Xrt)MYKtY>@>t#|
z#C7fnr^22pPMm-Xwi$UC$DH@rZ|>Ur@uX8h<iq~P(vBB>4nd2;#k0_^G|nSitMPcY
zH25vNi#MA{EBHQAlwWrbwRlC8u^=h0@$UVMCn@=mXusBCcFEjjPuFl~I4X&MIBK!^
zRW!=uJMpZ~EO(2zZ^raSDUG=84^bfA$jW6V7R&cgKsR}1r2dIGKv>_9PE*>lgc2K!
zPK8rPIT?RUr>WBqf6Yuxi9nY&5edicoUKQE-z-=C#i9P>VgtV9=Ow@p*LA&|&s+E1
zxPa(eJ_&#*P6#rVbHT&UF$Csc?y;q%?w>t7QBiYYM>egb%G(6f;&<4Z)H<ft2E$~)
ze$QO|56?u!4`B+f5ZjH34dog0$ts}-D>CSB;LJYTL|lY1wZRvEWDbagT;pPbS8n~L
zK!XCdndD04sb>OW2n7fl&8NT!xkt+~@9zt#2q(!_F!>1iQxcv7TrL)sro%3_Ix+dY
zpIG^O8AHdR6K@(NZh<8~ER~xXx!I~1RBmJgDyr6B?U*N7KxUThgO&bK>`M!yyM9W6
z7BD-uqYW|`N!&Q%ae4nd2JV-$TnB2KY8IM3LRsoocU84I@ssd@LL*;`AY@?KYxSTb
z;e9}#2P+V>@`KvR>z6CFL*)_y#hXt9%o7u1K1|PE!1Ut9C<hsUaG$F^1k)~`vnG#O
z`AN0us;2w)x`1UBN{5S0Y~6T;u<jfdFzHlcoaS58L2gs@a0C;q5a|0%RBw3ip!hwa
zoJ%KmW*kv$wJ}NMbM42uDsi@hbD{ws!^*<{1J_ejg+R!mv>P=uvBMjrquJ|PIQKl&
zFc#tp6|5)9r(O7ZzenEoxtD9}=olY_@QjDzxy<VI*SgP*@9PuP?RHY5A=0STPX)9C
z{d?~_l7DmA3-)Jlx&q;9#5j!|7CLSWo!z@^PUFbqx&X>WXsTaUx*0c=UMm&m*}f6H
z?jVGdUj!fOjDuPYvl;^BI6;>2qZfm;b8f>PlWSfH(`<!W@DxHnIa{v`LV7CG4bPIW
zPddySd2wr;hG=$yF+HoFEZQ?!(IOh7>(pBJol+JEa{Nu0I?3~=yiH`>qHid}S8E}(
zbB;*`s}qzrhnx1lEVpQM9Uq9r{!;vfN&(!~kz&1UaQt~$;V+&KL6|`LNm$W3y*Cn?
zJZCm1TO|W|rdAoOo;qRlTw;5|ZO_}I->j#-pA1;~Z)W2^YKrf95<<}K<fnX2&)HYL
za;FL(0zQZbd)x2u>}0!}=UWm)CU=v@Xz<y?G64s5JVN2|e6dD~Or6c;O!4-34&BnC
zb`kf0tc@y$f5$3XGD5P^Ohe;+yH{YN8<v82Rk5>2m0%J0JuI=S%3Z#;%Uo1rIDeN{
z&p8b(@a%=Zm9;`Kh`&ClQQI=`TzLfC!e~^Q-+<0J|J_*D!|xK?7UtMWYx0qxWT{@A
zl19V=j?eyDG!eE_&s+>?oo*j%$JiZTL+z;f!h?^5t4F{B^%TgM@iyI51SFUT*1J9{
zPfEFsO!5!T7Fl5wIY#a6r8Ak0DPTFfkaDwHMnD}#9H&^)X|W*h=*A3`yIt=swM<z`
zr4L_3bLbepWpZJ8KPbp4&BK@fKWJj$zG#32zmJbkF_?0Ju^1=UZH7SCN8c|<#gIwY
zArTF?)yeBUQi+4e6B}Db(%|Vs5{-8jqIDY(<s?*GZ(MeQT!%U~DvWj-!Z6``RxoPf
zHPa9IMv278-2}ZqPJH;JI*+2Y>X~=_?+gAOkjwpF>(713{PizRGoYfyDEOI6qvNtd
z;tecE&F6NC{G9x_S#=_nBbg?ahS-x`SaGYI+cffB-0gj1(`ow&Lww%KyeB@~I$rqL
zuw$IqJ`hu2S%t}}{3jPg>z%?s2<q;u1D>X`NJuF_TTiOU-BxA@2R6#43%+k0Sj#mf
zs{DU6wt@Sq6fnNpu7ar{zu3S@=9C5gXM6i!>j(nW`)rqh%Lhw?QBQodU*yKkIh>T5
zcuJ=kB5Q`eY9SSwy}dK<$(wAJKQfEMZhyDo5T-OE0UnbvKwg}Y?scGq)OoPNYOKz!
ziLz3o<+VsiI)d%8-;h!=5Vlk#%T`)L;&|BA>`#k7AisPTFWURi%cH^x9AUE+cjd==
zQE@sxHPLO|ZFOX3;9)asF-V;lO-JAOAYw`PdjbxQ#8HA4v9sa5s<Ko*o>eH;$Ue`!
zhVJHE<Fm!d6#)qMzdv}F8hfX>UQ}qLj)&@;Q>=5?Bvw_gAjb;ff|~yf4eu!&NchA#
zX!^{jJ)M0{o!D+w@;I(a11vLGm_9=9e0%UHr{p>(Xn74p36+bzZ>t1WYLTPlCf?~j
zkfY)Yk5uq{bmkX7-kR%pG1+{L?w1xY{s*^JJ1)y#DJ*5Q^h>+_->HiAn1<g++A00V
zYsvlp9})b2!nt%45;=2w_FaJm2L5rwwLKvy%5Xu?1b}lFkKOI9PQ`Yo!)#v#nJ&5y
zQG@HsVE|zF<9T~<y!=<!dNnQ{xJ<!}Lh`I1AZwK0S)XB|!L+RHJxLDJ;w9}y;;3Eo
zte8?oeUWx%;`DHQT2$AR4sVNZj7E>b4nR@VqG6VDKaY-;OE#C|b{@iwG2Z_)>qu5S
zD~_T-`&|f7R1;6zwW*ny=vI$_<tW|omwc1~Ba3$>5|x-#YY>r^WI1lSd^sPsAsOg!
z1whS?j}*kxVv>oE9$LapXX4oBw%PKfrl%Yi_Ao|k$f+!lhP73n5Y025^$ySCIBT_Z
z=70o@iuW6&QB$YSAKbxl40$|UT@5vUC;9y4XqrbBr~ZmQ=xllWyBkTR-x+e~VSD*0
zd{%944d>lVZ0UdH%G?5w-9OoUh3*?;4Deo$#TS@mpU*X9dhX6!zk{tA#vFg|uK#AA
zzcw7l>|gKxOWB!c@b>lO@!#z8S1VBX5ZF_;y&|U|H6&vYKdhpcmp<X?(XLulDmRLj
zMtC%x9a`IqOZhLiL)qvI8#iDaQh?8HG}4fcyAIy@R8a&P6LanKNL3|>#^hz;@-U(+
zQ2JIsLLw6@pU}IF+e;WgC?+cn2I*mq?}(4U!qCJ8)<A^eg=R1dWp_*ie)9=j+C3xM
zJ-Cn}8mES#_*+7P!(Ag^GtE30VycZt?m>;QYimD2TM;vj#y)*rBY*cNR3-d#7+HtI
zXsB8-#qm~xbba&s>L^fL?Hma2uke0-!v>C>iBEhuJf2E#VO8qmJZs@c9q_7X?%_;S
z#QT430nfOg_n$3@^kPoWcS$*OtT=MV@)a!9@Fk}p{aKV>Lf>Yk{STZ_N1p*N#Y=9}
z!es*4taOmI^|k)7CB$WncPr1tmg9n7P4H=9ool+x5)htgfkpSsRotGCQ#9$Jq>nwW
zL;cyMHKL(gaD$r*8@Kww=|&R&nCDp<9BCM0j)WVm>d|S5y*P5Y@1d$m__=e(%@L6`
z&R$foMt1qgvg0pq&oBO1c*J|_TVePf!sx&G;xMFHMy>h26-l*&q)-cGonAJ@%!E{l
zEVr<9^+cE7?gbYICBSaG^vi5rm@a#<C#7B>0K&p)S-QBX{ueOxKNr0lLli?XA-6Uw
zU^{EHOcbcQVvg>ZybUVeOzG;{ECL8E&LVXSvyIKn^mxdx3l*wwcAcEwpphahwL0!K
zoMRslYgyP27oqMq9PstfjV2ihI7;v}Wjm*4rB;DsU~r>4K8*vk$#8!8U+O^{sx_~C
zxn4t-qzGf1UCXU4fR3WC={VM*3poZ;(SXrJfQt%(YBfNvjc$9_rYTX;({FXwPPxlp
zb=KC#Gb|gt$mTy&v{O=!U2l0OB|DP1|7paa&+XiG5jUe@K?<+cD}q$?yKyQu({>q!
zq9w?TGE_I6fPvbauNNPTTtoe;qb+k7T88%T;)Kxy|6Sn0WDhg%_<(p9nP^n!<nW8e
zjs2#qVZB&?_*BtWr9)l7e26kdaji(C-eHW@3{nQHisAoSJYOsjZ7hEu!z&smKJ<W!
ze&)l2(~5ON<T>GK!3XrOxPxCs+6~MAwZ>bj;9SHku=+A4^N+kXv_wH^Os%ZK<F~vf
zkhJex=)cz893bUvgEi^mV;SOWQ%I%w<<Te%PxO%Y^FpcPzJxT!b^9(Ko^DZHGW4&<
zeWRwTB?xj}T`8XY*u!DDi?zSji?j_x8IWysT>^k%af36+;`s}B{GVVP7E?j#syNgd
zxFmAOoq@3uas9(np#$qNYOmI=Qtc6g?wxKV-2FtUGUk=EL70qr11~y8>miX^+vER_
z`M{$K#=$^?`zq(`Q^M8%0D-X0&<V&^QnTEKpVo37z*4qSG}oo|@_UDAvl~}}h0t_4
zZTcvp!wpqNgB}3yUkwGfGDvxuD`u35u<scxy@azraaM9O(IdcQ`+>9>yFlk@R$8_@
zUVn-8_^hluQ59Andp?-_?1KeiGqzdtEs8Z$clkkAEI3P-m|DHY%#oQm#+sn~!yU5U
zbd82I6$AM<I<<ZZYpkT~P91@Uh)MAP08So|3j8A-j_2o{e&;mW*Iakrq>4bLT*Lm8
z)h1x@c-d%kycverOh&%kw0IZolKl1H`Y=F-diAIV!Hh3)J#H~-k|r@dJvo!cO8k#b
z7xvhX<Eh%oH|Gz(JPbn{wvo&WU$U0T8D<~CDksuZvm)Uwg?gt0G1tZ4W2uwF*Gwro
zxKd2Ck-6c19NsPl+u?Hl1{sHf@84T^WSLnu!SXd1dbOYua#oLE#(~ho|Nh$(ooPnm
z`y3tgr0`=VJiAlCb)BFoBA7uO^Bog2PsC;Td0Wa$XQr&q<n)*^e<a<H+>oxPV4}6c
ztHs+V!bf=j{@4a~Xd9SZ0h4x{X7ppve=`}E;rr*_7l}<wpv%GDkAHVgX&zc(*HV=%
z9NTif^n9PW5b=ybrU?GQ>gqKfHNN&X4v)hMouA@gJ8u2cy=d0$?eosy@|&<h#1t6x
zf1CO(aykmlrL@lgr){QoJy?s6YQ?oWLASW2M)Rk(Cuf(DO~sZ~i2Nd*Q|E?CGd-5t
zkxy+XToY4mZCR^Vn)uk}v&;y}rAgnz!p`tksA7js3|gMQB~lnqk?}sjFOL+2#g!@4
z5x-FAX<LTlmlO<N>IIYp-dVXZN}NiDO{NY9%ay7bms_7lqWq5x2kz=6TVGMHn`%R!
zF5iqPF4a9vp073WTtM)BqDR3Izu@>GD@k1IZ$d#mZG~rO90Nnr=(=x{QWtsC4)+$0
z?`Z#X^zK+7UgPTI^nS}yjB>7suaI@wCwT9zcL_z>uRUiTcpo~=Ug2>3Rv#dzC^<St
z68+_Q>46V*elyiRuR{VhZ|^V1&+p$Jt{(~$oP1R~_p}yvGv7{<FlU+Pkm28mPSdHs
z()miE8uK4VGY<JK<2?x=&8inLSgSqp;WNc&fI`=6_lbI_XATEY(n#$G;?~&q*wcYa
z{>(HSx^zJL!7sMDn8%exP&kJ_0&Z`O>^h$&K|^Sa6LpaZd_G}e%@<v`-W%v*uzz~1
zGo;FGtO)D_dWEQwtMumT%rsQ1HK7Mb3m0P(P&Ok=v`}cF5h~{KKK**iwHxSo`S94h
zfUOf^?hNXvq~7s@(O&6}kqcr&eal4<9guFKOY?`QyBZNR{op;UFMGS+5FfuF?QaE6
z?D`}Qe2(A9F8)U7+QsdC6Ji2c0JtR`mtpxG8W$&?>V0Wxaac@kx5M42D?agj-6{As
zG$-&xM_30c)t_6qZsM0wODDXjI_S^$LY=D~==91#Wi}ws`zLFM7>hOA2o~Rh2oHc>
zamP=Qa2#8uPGyV~`lHkpgi+uJ)91!!EaUesezhu;jTj|VZ62$%ovu&dH*r-~?*R}z
zLp0a*fO)#$gLw`(gQ-$ObyH;!I-K9x3No#{mySRfy2|;tr$>(RNV_oc%D7TbWTwIT
zy~p<fMFKb!?*cV5Dqvx_-O)Oc@c^+mn(gTQ!*dwq6B-it;)sf+qDJlVFNu)|q@;<$
zxa{s<!JNm+mdoWo@hsa}q_(>bv-Be#H1PynAcz-*dAPCC8E;q`RtL+V;DZsPG>V~G
zT$Gs6ilinVUb0r(fiT%J%i_T}O%DM!&erO$g!td-acr+E=*Clygy7b2IjW!xr7}sq
z{fr+OSGrhoAB{tU=Yyv}hc=b`7dFI6Rz10lX4i^ftF)Qi4vJ2XbUxDSAm{d<oRlOm
zo`#^IaHPA|en%Tmco!t-@GrzNTH@?hoqA=<QGd2mAH?lNp-zgbvGr7j5Etx<^kgyE
z^o{lAU)D>dy*Me;hWQxEP4OY}d=_O((W#7oYh!=VsF$0`+I>#_W|q?ADPrcUwJFT6
z%IFP-wq7i^h*`3W$)V5LQtH(=S**p7bm9)a%hx**r`2o<LijYR3^<aHiMyp>q-MbQ
zyC@Vo-b{S*@GASa2r&cFoYK=t=`$x!IBl=j+i13W-oazU8Ug1XG3|=4gqrB#H+r!V
zv#%he65NU0tRL_~1`Nw5C*CR~W(Q{<+w+k`|701Ta8kefvOgUJgf3o6R7f?_t193Z
z^rZmbvi@$DAMeqGzigK1Pc#bG>%f2+3*$G5+JPJavYMX6Kz*objrfV^R<F|kC<?n_
zZl0&dCGNIp|8P9gBIL1h9FKE012$7tX8*N&xx6t<{D6NQViqrLikQjBqBxIXre@(n
z^0w%SW0GRrdkDH#T*|^xaV`)AC0T0A8sm{lA_&i^>;Y`-*vybqMlsC6BvbeV6@26d
zcYeQ}-JZEA+^)iMf{%!thKaOSri2sPGH#FR(#$>|$7IaI^0q>2`1D6YF#aiJprdpa
z!_eh%qwH*X{1D>d7Y;y{awbLcf|Pxi1k1vd^0)I_v4WpR`ctOj7aG6?HZ4EBDovTX
zVyBolxiJ5Vaj<H7RioEA*Q~Es5#f-1=v~_D{~nWb=p6T-(y7^w*tXa8${Si0ZNG1y
zrq^sUyu(i<Q?;|bDjP=v<g<r6C%zYNi<0Osmyxu(KVfFYCCOIQ_zJ&n=n|V_*|P?-
zsn+QbeMdwGb9}B8m>Fg4y4Z44@$}!!htdW(Su!X`oCTfn$dPFm?@Rbdi^WI7-|YK0
zWKDfJ@yQcjrNVF+8Co&+W-1!St*gxiStGu>D;tyDl#7GsWOe<>_b{XUj9!Dznw%a2
z{0;q7My(iwmS2zabXt@8W}4Eos&Pj(R_S7g)2&w5<`wlxGY4EsP{Cs>xxT8>wTqT(
z-ry~#7+KOo4eXq3*80<{NZZyn=AwVQyl<;uKg18B=ZJhfSe(`TX8~-%)WsOCCH!>8
z*onuFd_J}l%^|Qi+^o}3EXyXe8LN`E_CbHL(GG)6?&+JjEO&0W=k;Q%6?KX@gj8U{
z!qw@XBa?JWibkWhV>Qb)r4BPgizfFIY?x}V!;g*NmSnyxygg)Ym}HJpv2{rU%fus-
z#&L9UDqFJ-U;gtvB2li>4}D5KR!5<gAs;n0jaYd*g1iI#&{!WFI_4C2DTn0Y7OZmI
z>3}Ie5prccqBt+u6Ve}7nF$`3<pZH^mCML1*&{g#HKDdHUZRq8+u$m~g3P{%nNJfZ
zo>;Pr{dpMOw~aO8dLSQWI_Q2Sgq5?LYr140F5^X$3=k_aBe-~QlYm+jBTo2^Q4u^2
zA6M!u`amXg-Vt?QWMQ4^Q4^_-cVo6c{*BeiPk?UOzxJHo$`%P!>Xotge5XM>zMM!o
z9quB!#v}u$i&s;<ll^%z=>;LZy$6)l@{R-k{f=6%GW&N~ojwzyrw8eBrw=Jf8q1V6
z#unSpEMl3w;^fD<E_n7N<9As1Y>c&W96t_qp-34U8yaTkUMquP8h1Z1ebzGtCt{r6
zF5B10ZWVfSmdopgAX;Mj)Da#q)qAksm2tmVdjPSe6Uw8kf!)DHk!hK~UjKaiU8`)3
zMt(z&jt$Gcosh{RC8yD6SsEu#t7NFRG@t^RGyPvW@)}B^GfZLo1rZCn-}~B(<k);^
zlG?jY2*dMFCBU&$T8yT&H2^O~DU%H+sS4kUIgfSJ1Te6F%M5Ab63KzUn~%1{@*lf7
zYD_!ERC=F$=&sJ9BSM76yAMm2>X)`DRK;r<7T88fEMzM-mU69j%=2X6ky%$OJm+-h
zHa3!{M34byauiyT#Kfd#=oZr?Bdi|6cQmmcI$9eUrbYa-gH6iZ+MNa3sn+HCYvDz*
zHGaWf<>~|Ym)kE0-mQ4zy)DhVrz#i*44kHgb9eKm3=-pSrZ@@ayWk5KW?Y>5>6J%*
zasV{?jNzs-(zy0X(F$txdCCL;Y>X|W?mN>(<ssIv<3Zc9&A`He=^a9Gso5wwZ3hB-
zq+!tro&>Em^u=Tf_I+GLWbGXlgp;74zXli>SlwG~ufyks)@BcebXkFg<WRo$RHn;7
zOO|ZKLZ(0T?d@$tUSDtpZ{?ogvBF6sRCdINo6E`WhRJZgS*7}m@uo+WW-ropEJ5(t
zNL-OQy|0!sQs_U-!Uyhc_hmBHmxT|5PG}l?2D2si-1pk^{JTy<h!{kWco2OaSD0}5
z?2+o5Uu2~^{gn>H9-irfKngjf^_849xDZ|LINQ!(#WF{eHd6HSZ*a~7PBBIt`@IsX
z&=uGbfy5s(#e)n~7iGnN1A+>CHCV1UKuUYzmQftaLNpsm!E*ZTgD}kET<+d*uC{sg
z*Lp}nu-nE(k@L2PrXb?<ri6t!;H`FI9tYX)$&=l>G6rcQW)(F3M$<j@471XYPbDK_
zJz;&q=MTNn*^7{u4%Q%6X#065^f5f8Sc0x4`>Q$N89c4<KjjCxq7V=AqU$;En!TR*
zu<83)d@lK=J_%h+k<dw0%T4_4ug<niZRZG+n=W|n$!H~D13AFOKfl%&>M%8G!uOGE
zZCsx20_|^=%N3aGbeZC9m|;mEkC85h5}k$G@P7*ZPSZnC>_!5k{TbR_hAWCt+r6B9
zF<LgfcM{0Q>BtXL;BLHoa1@$kMq4CxPeFAhl_eb8w9fYfO4)kVY_=UTJ69~Nxf&j)
zYZmnIhASy4C{Z$bA{Vx`&gh=M7_&OY0<5xsZ8;$MpF41MNp$hAJ``+0y5rp<*QOlC
z4a0?mx8dJ>&&Ic#{3&YXL}RhY5Od)dwe_r4<aGNG5*l{I?H<2!$@AtcDE0J-#h9U@
zv>>BjY3$@+o$eQ6<!Vg4D<la>)H3}UzB~>O<`kK%Rk0zsl;5U+9}zN;UET2gy~eh@
z3snT!R=%yDS^WtiD&bH2t7|fCo;+|*3<Ok4rMx&CzvJe`%BK_?w8Qnc;3GhKb@W}(
zynv-H>|voS-R|hM|5Q{|n7*EO9Pa?IZAn4K`5(yG56{DOM&+9Ig^|JALP$tR!)I*2
z6NG3P;|Tbo)?Ls3a9?mMsE{1Kx}!)+OJV*;!@@)@2B0L1UR@P+<lxp96&;2HN0gy`
z=0wRSb+Xh8K#4RyjVE-ZC04%{C^AdQAU+^fX%VIUz~0}iF)Pf)pbw*XgSza9{`A#P
z@k4Sa;1SX7%wnK^uKAK0ZarC)S!^JF8=n%}xx#1D&XV5GK^8!>1-mFuttEm&JRz5~
zO`L&J$fg|FZ;lCmwnt^u+=y7t@q*KS7ffc<FPzE$Ec8QN@0Wad>_o!wo(|8D&iLu9
z^8Rpou1)aJWzpMTD8@A&{0?q_P4rhwE3&mWlioO%x+ZU(;Z5B&X(k$?1vB{V5sfvD
zYEXTPANAPYI)ydUXk6qQ_Js>9!vtNoq=qN#`kiwk*f8`Jd%ABfn;BDt<kJ#mmDeo<
z5SNJVOq?SjpCZZ|3C;asfN)(shE1z*J0IXzSkp=I1H2OflV^G}$t6igyl|EI3%B2#
zQ4@*nd|l_^Y*z9u?|jXmYZV}~c=6oAiIMv^m6%xe{r0jG$NpToaHh|%5MhE}dc*so
zm4u2}i`Hacj>%cr0<<j7_$v|xd&HJ}f+o10px~GtUOR`rql;UAv$U{mXPxnH0s<i4
zKvWcrQVPA1AD6_l)pAL8dxG_n3d-d=|4_kd=fkU@EjeWgYNy>tv1?0**1rG>Uvx)b
zt7(UEgBUHQxPQYBlhu!|N8edd(#2#tdT+)9?xmZOLE;xPlkr}$Jab;nR_9o!fg#WM
zKS-5r7Igx2oPE(;9P;JK&&`J_i!abZH)6#5ClLV*3wx{Bu%xB;P8xRsm>Zttg*-0g
zkM8wG@7B6%0~w#zQ^wncnrh?aZGX|)8Gcmz-q*BmJrzp5V?7B4g}A7gSR^F;Pnho@
z|MtALcbqeaFsG}=d+7sK5M)dm^cB-DtP2C>9_*CRK>2~~HnSVNTIDbGQOz2;<xvoS
zo2BFHiQ8>|0{bFX50P6=7kx6pc1K@1t4EYVj}}qTK%uDc1Ae{VG-jbXxxre33*ko&
zr}txvq_i}O#ZwhVt;++N!zJlc-kb--^#)ys_5u@A3rhE}EGqQO4$5sLED7sL$cyJZ
z)>zTnsMLOkjkog9Q>Zrhl4D<|@p+;Q2|K@#XqY|*?XfH?n{H9>yVz(82eI<|uUUm*
zg^X5t-EIuxeG}uM=4c5(aXq-%dz8pA8<wP@>2>CxqN4ZzJm40B0%}aNSG#0yHczuy
zG@`M%%m;Fw!4^sQq$j_0&zqjHKT1}8X*2<3faqCT%AsuPOj)=K0e#)5MJVH-_$rJh
zOyM?;Ym%etE!u%X6EPo`kkwYlM9b?lszcf2z~TVWU%sWQoi<4^=bXk#oK9Dn5s}e9
zG^8lx2(~na<8zyH7onV}TLB8d3h?ViAw1r6HkhInBJP8vm(y-PX1P3MOn7yjc{@VX
z;M_-)*bxS_L~%&dF=Sv!d}{bj9X(?y{kkACU)X_5F=vlSD%N~=psaxNp<lmV>EtR3
zlc%O04$R%pWb<0I6}z31IJFSYHs2Dg)xK1yWH7*A(iJn*pq5Vx5BY)O0D%6_JmhNP
zS=$DfIMaNSq4A86bTuZ>R*L;&@@)4eNfN(}^qHSMDJ`zG@k3Llml9m{Mx}HzMW8^y
zHZWb(MYjtEmYduUPe64TIlD^~>v1&bBd#IktPq6?3(Skq@@u=hQ2F2SPzX6Zz(NKy
zDslodnuEfhdZ+)dT4hwJT-LY%!LOH_LFA#ON)0*ey8VKCCNY|s-nanmATj&%<fE79
z2HlNxAsN_<4&HFoRPO@!GIOGd$@%sbs~Bsw%6Urn4EZir@%c+I#J5MRe_UZbbI3!i
z;6E&v>cPkTAF;1m&ccy*u!CrZbj^bP%4rvAMHBpKWJ6ybF40&oG^LT~ob&Jp{4UqZ
zOm_#Z0cE>gN1<#;az)R3=fuPee+W_tp#RK$VN-0zfQrY9WOt1nUe(o`ZT@ngD;c!&
zihI>!P}=n>S<sF~=!81?awB?s@C5Y}Z``aQaGlFfGDn}*Hz<;YrK?YXRKon0n(k5y
z3#MBK^?Zo}6vx2fxE7Sj?oQ&bDq>suGgE#rM;Qg{q$`*Vz3don8Igq{<HrpjvSu78
zwLn4jFeA)yDD352`wz{fdl&jlIC1tE@!S2FnKAH$59VfPceH=-`L@yLhV*VA5pVoc
zZ=&9WB6)&4`g|c*^v9xzCybQd&YXR4sElUHi;;MFyni{$G4p74ru8zH**KG2E#2vx
ze`Rs(n}LT~zaREquQKXO74Zj#lYh->S4Ev{6h)>iQ8)GQ@sa<4ZBmtSwh0#Bjf)uK
z5ugK1bgvIgQJq*fp9Fmum$!Aff^2L0fH3muVBTuP8D1zzFuiS*cz8=<Vu?}UG@V5a
zup9xO`!MTWKkT{`TNy8}>x4-xjM1{m>`&26W(nfyB>Wt4fxQ`#LVO#U>&fbu+`x}d
z;4khA3KC;PsyH6ajvEc`<~AO4S-jAWPyM9OG96b0?=KP2aD%}SAm-lG_j67jTbn)W
zUwlSt^Bpw=0Z8Ck6URX_`^;Y7yxI+&E|ygj*~Hi!Xs7$N&?$3?WjxL0+J5uafpiKL
zvD~{dvQF6JHxii7pnw3z3aH-f4Hx`8IVVhswt#%EbD?LRS4I_y9gO|sK}Jcre>di<
z+3tjTNn<0@S<C9DuC7hG(&Z;t;Zs*_*)p3Xg!`-77=otRIsjy?PkAs!3U}?hmZXl<
zOtd(ws?wn+u{fq{t%Z2J{!XaOyaCZ-S-}QQLl7vQ6QEV(82gl<dS5@18!ct|&)S={
zMjoDEbj}jIdW&q^Ef>aSE6~`FqI0q7HrdCmCiLd$Ld}$MC%{sB#y1pcK}trxe=1s{
z-r*5Pp5anS23cclF=71wE9<JF;@GxraDr=aC%7cI1((Je2?y8U0fIwg!QHK!4jw#d
zf(CbY55Yok3D#(6^mWd;_uVJ;Sz}a<RaI-R+I!DA*Q7*tia^dJjnN(W!>7h%l}e_R
z-+qb~dY-@O3!K+27pzQH>KjmbgYPo;P#r{5n`O(C-iA8W=FMPvV;Rw>%q;zlp)r{O
zWyGgTkk?7QUI4&W!?8XAh&1MfH{=W2rI+BZn&v(WBHdL;*Xc~<KgP`Ot+DLty0B@V
zTos8F34ow(1P|_4SLsx|So~oRq$0ldnJG*2ZHV{AXgk|s2UYm;*MD)OO;{&;=ArS<
zrtwViHtJi+8Pg;xBQu}H_6<Jqf;f@nqg2Sy0zIHTK*4U^DnAyt49NIAH5KiU?ueKs
zGj^c(j`pc|cnW&gw1c)HNfzG@^fi5$NZ`VJDw?!ZR=?AJb0VWR-uvexjOX$sLwwkr
zc8%`5ha?W7{2TIoo1iA)8OI{2Lgm~}Jj2Bq+Sg~MIW6RgOT{4$Z!;AKGwP758hT8W
z*9<WlyGhiqsG+Xynf*x}5NAyC+Pop@7+tBcCw{2yZ!x!?B6OPW#~1hKtI(0tqv)?R
zjn>~wGYvcLBb1MU(dvJdBtkr-#)rDa;@97)t6@m~pvK|kn1S;yb~k`W)W8)=ayZIY
zc^$G+rx)k&Ihf^s!^iCzi>`P((S?3iR;EOeFy$G50;eQl(+Dr~%7O<=@g<k3X<QM7
zQF<YYELAVBI|%+1X3|J5`Dm1k@!1z(Ud<KJwL@NP!O+aehUEN_p=!NCH9G5>Ud8uT
zWut2+PE-{5zO~_uNOkfEk2-XBII<9<BnkK7_lfeRHkx7FJz8T`IduGysz)C|>sAx?
zxrpXdnsYN<ZbaWTb_6TQD;HW{?;=6zp_-3{yIY5QVI#RXOq>Z4(_~aM^s0BEL%gKZ
zUJqib`9~WSiFai)8d&(<DQU8O1m9``zj+9iA4k`dHDoD~Bxis%tP<sw?zOXEpkZPf
zvj;nCz>G+GJwutk+w+5n8Dcs}q*-YlR$ERSDY_^zBqOemC~vf;Cx<xbg?akNw23=i
z9;VC;HbKt3N}b_n1*fbd%N=;MQq4yNLd6#jM!2<rA&rHekHQ;0MJd#o1V7<Fr&@-|
z&JTq;CF#5Xd{6?abYNA;GhKtoE*6E5TlAHxwQ!tqy20!l+yN|&vJ67cj699tFE}yH
zO&dnA+cfSRL&(L><pl3)&W`H&u7MrW%rbcDOmJvxYtUxC%PmU<rHJpO=W}jeFZf^|
z6nw^Wc>PgCjqEhiRfiq+;T2UtGF>`D8Mqx&dQ#rTlXg6jmC$8hCS}+O5pFcS9&RHo
z%0dqNFr`n#NBgym6@5X!lNMw1Tz+p$SCo*fkfKi(i(A)|uP)<jA#U`HV7hxISq^hX
zo<&nejAzzVqxgGiqU7%~AL8(fI+UqoT*<ig(N!Ri3G<SK+VY$J0u$gYLom=TZ8=g<
zRI4ERGT`0*S$y!q1>$t_1hF|J!=49r_eW}c4JKCdkWj|T^)sP||3DeI#^&l0NU=Jy
zF916qjC^5qly2Ke{LS#nQPYpkSAlB&X{Jn^);YhXY|ZU-DW5%_QI}RG_pJIHtsTMe
z%PFrjEWXf;vK+C=_@WI{E`<?LuuFcZfrlk?kEg8w(SDyV?&p#n=M?L(OL7Ti0`I?V
zC9vSv!#{m`!^>uQTE4p`>x5Q6A+>9%f(1#f&`LA!u)T9=VKn8UsrHEouBe6cPpBs!
zd&3gkdXWauwL0!W_^Cp;<7)WgT&=59#_3@xU{y7OToLEVG8PIu!G6@LpjKM5JWj3o
zuM5R57L|&54i9OE$vDSH#9E4Y{z9-pDdr&#svY9yV*CQ@KtU-m5DejKyBMDyr}KH&
z5Oet+zZL8g+A261;--=!8PwqB?Otr_+`zG6A^+K4pPXm$!MEpbWb2~HWWXfqQ4)LG
zY}V8*Re|y@(NIi$!OwzD4;S<CpujvbXEimNQ=P8~YG^Foh?kAc&~+Zt=JA(otyMa&
zc1X{*a=u9*is6G3D<ab<_{&d*>H>YC`|*i5f@T5xeg=xxHS61wv(tSB$y2%f9Xost
z9A3u)D@AUs<|_k|14xdv+TzeYx|gW!fU7pxy7ptn`ya84c>8Mjbu_|yb4#cD*+lNx
zE$(t86#QE_6oeERWgh%(9;hb~Y=u^;6mLf3a72%xO@8kP31(m#uSeqRT0OV3^rSI?
zrg6h3mm#f-CPVgN)tla9Eu&}qFA*!eq<dEPd0J<t49FZZrJ{g$|0lI5s+)eNymGz?
zpj@@@`_Jj0_BEPYf51+*9`@OyQGrgbOzkSi03PT|tkLhja*Ks@4m(1hSzxodES^m&
z$%81GGD?ddBU3+o91nCB4<LB8nXjTMI<8omp(<oSL+hn|%*(mIjwxzjKvpP+4{HJJ
zxF~3ELgPFbqal&C@gHrK)F0y%zalxu)^cj<>&$RwKZ>K{h>#_3ZB`RPY0}-RlitKV
zFEUOuWtu)Kcg`U^hfgBh=~e}W6rNC}HjuuYfxp<~AqlwGt4Eg_y3tOb)Wy}W+K?tM
zr)b*Kl){LPar9)xJW>=&t9fy1`^=5ki1x3~-&5Y-zX7zwx0?|5OjNOsV5Nj9=I}=4
zdFjSW+S(a$<c5t*MoUVNap4gzm8Uz~hwBO-0LHvYIxNKfpFreE*SE_uC+`-6=ku3+
z9g(o$!z8Mm&gqI<;kDG9A1<4g+l$7VSI(*Dn@)%ofx8z78Ag7=RA|64;gF<n)UQ}#
zl$#vDRGqp;oC>O@H9OnXtCuLTULN+$KQ>88_Cm__`UYnmD8=8&2+y9+4N<1KzuU!x
z-CNUZDuJ@>o+t3wDzK44&72+3M@6D7%;?=5q^Xq?Yf0V3>K%x|ckOiU_2$_$dN!qN
zhhGv6;irTJ^EBH-4;>JQ=u*%;E|ey?f8#l~o1jGIul_hSHhJK27r&=NavW5B+nZtw
zuRfzY<rBV^-Av5Sud|Pq<59M(wtpH+krCI~{LMs=8pj?2@?L`I?xt$g+4Wcv0;l7r
zS(k&#5y;;3!po-Z8VWdJ$d>ULRUN&@+GwQ-{+O#mXzK>`WUB{sA-q3<{WIUPk>o*_
z*$RNxz;NZki=dw0vLz_xhNZMe+%-y<SNE}tN_S_s^0b{Kz#OAh)Th3D8<XO7?m0F&
zSh1V}DGIksBgj5od<&1G9AoBfPj!a(J><jqMYY}yE^8WpH2cj!l&M%pUd#ay4z(vr
zs1xC#S*&{8_sBh9p|f>X7<^cQ4x8s$S&9bCOC3rnVAZ6$?b^QCpDr}4^T60~P3$l5
zrI|L;%Hz;)<MIz!ww`03h5w%B7Lxo}GOiI@ra)(3WV=i8BIr_eIC*qnu*u!b+urub
zhNtmm6lMaTVD#;t&`mC3>DSt_#49Q8ZGFs1nOTbK1Hn6~VEi6OC<H7><nAuhvG<dj
zzwMf-XHSE~2OaW{m1lu0cDd#rM%cbP@zW(h9uHLOW(|#pUx(7CK3bk)W4^)13qHq5
z<|>I&rC*|uU*3)U+fW>vKRl`Yj9}Wk7HN|_wTG_uzm?qKGm6`I9-T$4Bu&em%`)(m
z3A4|Q<?XrvUOnea7GM(Q8H+gyvBHJEYV^w%0~%V;x!{Gf8L1V-XEVI!wcKV}*46%K
zInoa3*{McV>Yk)0RY$NZBSF4Y=z8vlS-nB=!1MXR+CuxhVNS0!i622DGfXCW5E<!X
zeBze`W*`3Q&K*@bJ;kyZA=Y46%9QY3X9hJT$j+)sM__3ovcHy&X^8(>p;agq$j2Yg
z6}Cstqe*v3-Y;1hmB~@gA&fG34ij+Meb(Z&%2+<U6?NE<x(E<SiQ*bcJ&n)nEw!9Y
zJEoK@;3c4^PNpAMWy}2$`WtDb(;RbR-=-0b_x4u!lhA$~6$`{_D<QVr^N_5A=0`e#
zl4e)!qJ`CIgVPwXRefhqirm@M62{SOa+jym_l+Vs7x%X(hF<T!lTj5?R9lXyENGxH
z%kU$f*tR>)hk<3NW*mqjZ+2Tqn4sI<Oh!2%cKq+U-ixDY+6R?bl?ar|`J=AB{gWbP
z9j8NtlQi4pPe8WWjg>=VY06d89k<mUY}jzch0NT+jfg?hpW>4H+2Hn%1NiZrMW|hs
z)kT3%J$DqQPq}o0SqtBzs*!3@H&0)>?(<Q=fJ&Nm#U1Rz@uk2L=w<P0Xw!iLR{JSq
z3h7dt5cYi|Y@nE!f8(%&c<*$ht~#+LN+vjvfqRmD^Rv?H3OWwO`abnxM^BDS3nV%+
zgoaYr&+n&Qp_2R*#RSwuu4MT&isN`Uskq6|!4u8cXI|vWCU^VU$8-!-YM;KjZ5gY(
z3_r22$D+>p?gZ=7_%EIMMfALJdJ`%btdBDm_k^27NBqf3UQsEn%Q_W~)A65hO@ETm
zgh<X(8u{$*$>YZ;a4t~smM<(V_kLTVNH3d|{VN$;pj(XiM+c!?WSsC+j0A4bPf++g
zGuFs>!ODam*4&`z<Dtqpw66N?pbX{ZfKct~MktDPXCHg$cD3Kgi>B6qp?g+(Thx~>
zy$siO0~!&u_qh44IZ3!LJ1DilHA{N+y(|0aFK%z2JIsG7)03@*=tcg$A4-lw4@g17
z5lM3R<|_nDfBHiy9%A+TVUzM|#MSsKPNv-F3AC4%a3Zh3QYM_%DF><X(lglG?b6(8
z_=3pT?KXw;vR3ZNCHI<!dPLrlIBK{#UBa%(e5$fwleLRh?dA4U+;y{c(J`Y2>P`(1
z?yds!^hUpxVf}_t1YK6NcTJvGSQEHXtKTlGGZ5?jqYHt)%Th>TeIs9;qTgJV=#zLm
ze5LRD-dpq{hRa1noC?)i#rG5geTX|oeIFpa${0m6ZmO-8tfMU}!VaG{e^3iInrVFo
z7%1_Pq31`CcAY6_+ugAZUQi{&ugr5MUUsywt8AeIF?5xNYs;Og^O~#guJHa6%M6$v
zVfar3wSomz$j!Fl(}H2Q0Q-7Yb+bHzw)kWgTJ3T}v?Ya2WkDX8+d^*<2lQQ)7n;vb
z!!i`CE9S4SkhoN`&gR03Mql#l%@WMJTqc;{!L$*p$p0>c?B|m?*ebav8=gY)E=_l+
zz(%NGNicxun-VhlIG&*D2a-teXRa%UM*6T_vMt^*Cg)L{d1ZoM;B={^N-?7D*SD!P
zO1!BcLvrgsE%&5SlfI25VjxveuLUWGG$`sKqq^mHAQ!B3?1)qhM+*#!pCb8;S3Kn$
zo|xO-5ISurlJ^auSz?53{C0cK(skZ&3Y%(iuCzDM@w^mmv7bFD%BwiV%q2j@enX|m
z*ZnjmIUd<whiSGqe%?@WXh74+3pB56vYMiL8Jn1>5U3__cx1Mws;^J$7;eKgeu4dA
z$(ONT*|j)r!#X8n?CQKiy5Y)=-PpJ!WMN3#_?KnmN2Q=O4adwsenStzOqSB09T|;?
zX}lp5xiOGkM?=Y`o<>6*GLP>Q8!x8Qs)<>p41v_Tq{NUEjZrWF1=$mFI`b-MN`Ti#
zqW%4DJN2piOF)deG8_IcP)d%ww@<NA0-2vs6q-?CEz`k!)=1Wqu?w<~$?ONO>pPRk
zo)Z%K3a+jUcNmzDB%C)@XLjr5p_Ixo2BeLO%nutr%!0faJgHTZ9-~?^;~0MdSxEo{
zl4)b!?4M1NU@MddW4=Mp#1g>=1O3wiW)k0fr-#Y&>UaCQ^R`*JDfDf0MH9jMX+~xv
zgQ23;uW$3z0tL%UED@u(2YCC63WBsgR|G-_?@ASkc^fgDHTv@0*T;91P0`2G)J;i@
zz>;0PKj@B~_v1Z9xbqXV|7_98{z0{yxfo$FAWjTchHLucnt4;Q43*=#W*JV7lS3~I
zCk?~>wE;teY@XglzYo!88&#)5W%@`uh5Xv%bJUBP(TPev`Qr*3pg`nieRop8P^^g$
z)T@&YDd4o<PW@iT^C|4QUVzQIe{2#yQ*JHh2BJqD&ZsJ}7FR{p=Tf>G6L1FR-_`|~
zV6m8-Zy|0xA&bcbkU-o*>^}wL(Ez*rx#8o+<nYlKXE7XY%*E+4aCmmQGf+g^n1ykZ
zx5o%446HWEUj;gXPNdUxnVyZ$yH81ANIW{*3gNyuIlLREPlM#!o=$=6%CwpO4jThi
zSAUxctprvLbENlR!}8~J@<_|Ja~AgNBgWXR94&{{+u?bh{&Ey!$QuW07M_pHR!yT5
zoO~^arKP^GPCsa=+p3K8Zic;f{Ow#{dh@GR1lPK>=+Q_3+`DK5&4ZrX1nVQlNhlG}
zevYbt99D=>+cvgWfi9tGTpv|JLz+`2Ar=IQkzi#fz1kU*sPR0aWTg4-_KRJ2SC&jt
z!lYJ+)|ZT0rm=m;ZojPz<Nm*QFGx`d&)R<)d@$X%o8Tai@Rf7mX?Ak-!swxv?&p33
zhwH-GxD5^s5kvV^&87`w-INoB9pFIT-klte0I*4$pYgWA->M-DC8X^Z7l&>f5Lpxc
z5r8D4qb#YU<wSVZo@{Sz0a^u*&A2WBwK^Jqz5+W*=%HX72Bjg0+3D0RQdf|XFgjnY
z@?V!CMQ@5BB7Tz1l!O_7jSL2bl?{=jY6+t*Ny835>(n=uwHW_419=k+1u2x0{I>vX
zZJlUJMfTfFX>_kni9WWJCN|u6@@L?iLpoFZ7L@RINhFHC2DGbsbW)3f-nW<Sv+LRZ
zfrXLr{6RJ1pZLmWoSj}gy#KU?3_!W7*s6GaYsasAwT<rWcdCR}_EpHD5y+}+uEE(Z
z?PjG4Hn|I*F)AJ99YZ`5a!rixAWXVyx6MZ%;l(BY$7b*EJOA71P6`|K=FMovo*U9C
zfjal;ucx;cL5B4RvW<OH-CD(NwqF`B3{F<47hnGBFl3i;x(j0#-W+t86_vghPL;OW
zCwK1V|H{0KRJlW<0@&*%{s7I4pB<{z$&pTBo;8_r*k*lCR`s1}bcxaEe)Q8Pb3E^V
zKF$B)RnrTjHclM%_y$K8{!YoGoNLB-$f`q!ob>%nPt42|-e;XJ1xjmC4oORLGC2!t
zrI7Rp!P57DGlID~RBMvlPyO3~_O@Hx<Y;koj#+$zGSJvB7Y+#HY+IgL&#X**=u@*q
zgh1|>@~pEmkI$a6UP5zb|D6#33ItwGY#Ax*GqqXcx(qDtda7`P7c%QdaaX&ea96iW
zH@E*yHgB|-4kR7S%f*V_y&lV9R>*uvqTmjna_=iP>75<LdOiYH$ed9k_PpidGi{2f
zE(a~N`hRq&(Jj~gDwX35+v0i3%@vg)X4*m)A!(HwqX~#AsuMJoYSWGbUEhMV-ys(u
zz3zwsx`oAsp=wx1%@`CDEvNlnd+8^Zb8}o~Y&o3B6t^P|9s^*-H33naDxIdGaG3O%
zfODNvo-u^tLf&;PmI>XhVDRNg#ve4E^bAZ;DFw!>nJc>}*_HiYD*K=GVv!Ho#c-g1
z^0M2$CTNzV;K06I{|gVyjHWmGY@xxP+x(X{Ly4);<=U~No8zpIkpHDh+i5p?F@0XR
zpNVUhn)Nq3q8+?<ku06<k@*b+SsRPzM``Vp({(Qo0SG{BYotuaJ*ooRT~1;Y8u@Kd
z*mKgs0)0+G^dplPiQLupXys(e7cLlf2_WKQ5ol9WuwVzm2HnmTcZOlK^{?<Rh8N$;
zKsC1b3$t%)uT#*<jjBl1d;&<@gO1k@@+(&DzQ|@zQQ`eh5$ivL963<rLcS3mzoT(G
zK6Jlp;HS6fFvO?-A_C{qqdw_8PR952SwC$4O#RxrY<VKno-WR6$6(XFafgFe+Ly{;
zlA&+w<-N|%m|fe7D<dEHJ(>wYuF~E(BMd5vA?)Du=GRH*hCHh)7fKQgp~S_|t^({D
z_%Y~yzvP{>&`Pzbd^yk`nZKD|RK}WXu`O~PaL?wv+<>1gifsP&qZPi(FsoCOA%oKj
zmRpv{R7E^t=4a??n$LZ4%fOw0@aId)dpht{y{u*}nqT`K{`TVidu$N~{8Xt*XWFR-
zKVh49S)o;gb{mt4t#0?q*h%k9Ld`3)Kg!yH#=#KPQY1?Eub1#2JIk%aCnt4lK}1nm
zl430$%xgcfG!Nz?(b&25aPXJk<Bj>#WhdpU%(mF1K_qTwrZLZ*m>Q@378<V|SF-C9
zR0F0OwSsBQ+Q_<HqO8tTP6~mJTB@ss0l}urXGr=+dQ|ZoUYz=1Kbp!`-FIaIh0(Ls
z8H-vyRWV!EwK~UmD<>C5t^z>wl7j(IdY&%|z$0FdN>LTPR);W|`f6l5lTYGqxrxf7
zt~xlx&;I6a`PI5zSt^9f0#nI!X4(f{cI|nISzrE0eTw<!#I$Gp&7^hjN`-^TtOW_=
zf<M&vO>YJ{k6=@#!T%f6Lqt<{G9lGSy2#E+;ofwKK-`TOGm{ABDQ5+T@f_j(uhzBO
z3KJq~`p?_yYPhacR^Lm_Xet72>?my(zC~WSi5V6Jje@Z`21Zmc@2%?^t~m5dg#^+a
zXPZrjY@_M(`Q<NuPzu3)l>-BL-Y}nR3?Mca<OS&todr4R0utt{7UIW1um?vR|72)V
zcgB0g#(B5my^KD^oaK7d@>TpFt*_WKlktEMIgPb2wn^_^_=hisUc!|1-_RNwM6f$k
zlE8_}g*qi9V^D|YVp&z0B`xM4gz3el10tnj@PAd%ziavv7R#Lb5Wv8?CO2mfUE}0o
zSJt|{Wja`~6hvp4o)Oa8@GXRUwr0C;w0z4z;T`HUuMy(LPnE~-TvSpdJ=>oJO=)Ex
zF2Cs<tH%u3e=#+}E%4$wug%NK>}+8mgG_91^p*9&jZ6(_(<(_s+;PAl3b0;dXNQWV
zf1mWlRdb*Yt)s83*CGN^m)Z)Dw{gH30rQ|USWrOKjC*Ql<`cxlTZ%l;2q}H~Sfu%g
z=j#h!rCuZSIMJ{+3mKfh#?yV5J0G?@rud4^?dv>8`uD8u-@olY5BnT-;D{E0*BT?7
zoz&InOjZ0`ar11ts0p-=!<Xja#>NrBqiR&d>LUBI)`y09>k5CKxqdFn%VnoGN7Q!}
zAG+^R*V15X7BC_aS73z6?KsXwuii-$O&vN8A8EpXyCQHNmZ~xA`OSN-Wb8-Lq0R#a
zH=uYaH<w51grkRNYOf@*NeA1L(##1;k&<caNEViK579gY=5%U)*qDu*6elaSXkIo-
jP7WQDmy&!ZY5yY%JHBENN_Fh|6Xc<)1XTPYZyEMqxKK*^

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/deploy/run-on-aws/cloud.rst b/docs/python_docs/python/tutorials/deploy/run-on-aws/cloud.rst
new file mode 100644
index 000000000000..9562cea2b95a
--- /dev/null
+++ b/docs/python_docs/python/tutorials/deploy/run-on-aws/cloud.rst
@@ -0,0 +1,106 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+MXNet on the Cloud
+==================
+
+Deep learning can require extremely powerful hardware, often for
+unpredictable durations of time. Moreover, *MXNet* can benefit from both
+multiple GPUs and multiple machines. Accordingly, cloud computing, as
+offered by AWS and others, is especially well suited to training deep
+learning models. Using AWS, we can rapidly fire up multiple machines
+with multiple GPUs each at will and maintain the resources for precisely
+the amount of time needed.
+
+Set Up an AWS GPU Cluster from Scratch
+--------------------------------------
+
+In this document, we provide a step-by-step guide that will teach you
+how to set up an AWS cluster with *MXNet*. We show how to:
+
+-  ``Use Amazon S3 to host data``\ \_
+-  ``Set up an EC2 GPU instance with all dependencies installed``\ \_
+-  ``Build and run MXNet on a single computer``\ \_
+-  ``Set up an EC2 GPU cluster for distributed training``\ \_
+
+Use Amazon S3 to Host Data
+:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`~`````````\ ~`\ ~~
+
+Amazon S3 provides distributed data storage which proves especially
+convenient for hosting large datasets. To use S3, you need
+``AWS credentials``\ \_, including an ``ACCESS_KEY_ID`` and a
+``SECRET_ACCESS_KEY``.
+
+To use *MXNet* with S3, set the environment variables
+``AWS_ACCESS_KEY_ID`` and ``AWS_SECRET_ACCESS_KEY`` by adding the
+following two lines in ``~/.bashrc`` (replacing the strings with the
+correct ones):
+
+.. code:: bash
+
+export AWS\_ACCESS\_KEY\_ID=AKIAIOSFODNN7EXAMPLE export
+AWS\_SECRET\_ACCESS\_KEY=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
+
+There are several ways to upload data to S3. One simple way is to use
+``s3cmd``\ \_. For example:
+
+.. code:: bash
+
+wget http://data.mxnet.io/mxnet/data/mnist.zip unzip mnist.zip && s3cmd
+put t\*-ubyte s3://dmlc/mnist/
+
+Use Pre-installed EC2 GPU Instance
+:sub:`:sub:`~`\ :sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`~`````````````\ ~~
+
+The ``Deep Learning AMI``\ \_ is an Amazon Linux image supported and
+maintained by Amazon Web Services for use on Amazon Elastic Compute
+Cloud (Amazon EC2). It contains ``MXNet-v0.9.3 tag``\ \_ and the
+necessary components to get going with deep learning, including Nvidia
+drivers, CUDA, cuDNN, Anaconda, Python2 and Python3. The AMI IDs are the
+following:
+
+-  us-east-1: ami-e7c96af1
+-  us-west-2: ami-dfb13ebf
+-  eu-west-1: ami-6e5d6808
+
+Now you can launch *MXNet* directly on an EC2 GPU instance. You can also
+use ``Jupyter``\ \_ notebook on EC2 machine. Here is a
+``good tutorial``\ \_ on how to connect to a Jupyter notebook running on
+an EC2 instance.
+
+Set Up an EC2 GPU Instance from Scratch
+:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`~``````\ :sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`~```````\ :sub:`:sub:`~```
+
+*MXNet* requires the following libraries:
+
+-  C++ compiler with C++11 support, such as ``gcc >= 4.8``
+-  ``CUDA`` (``CUDNN`` in optional) for GPU linear algebra
+-  ``BLAS`` (cblas, open-blas, atblas, mkl, or others)
+
+.. \_Use Amazon S3 to host data: #use-amazon-s3-to-host-data .. \_Set up
+an EC2 GPU instance with all dependencies installed:
+#set-up-an-ec2-gpu-instance .. \_Build and run MXNet on a single
+computer: #build-and-run-mxnet-on-a-gpu-instance .. \_Set up an EC2 GPU
+cluster for distributed training:
+#set-up-an-ec2-gpu-cluster-for-distributed-training .. \_AWS
+credentials:
+http://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSGettingStartedGuide/AWSCredentials.html
+.. \_s3cmd: http://s3tools.org/s3cmd .. *Deep Learning AMI:
+https://aws.amazon.com/marketplace/pp/B01M0AXXQB?qid=1475211685369&sr=0-1&ref*\ =srh\_res\_product\_title
+.. \_MXNet-v0.9.3 tag: https://github.com/dmlc/mxnet .. \_Jupyter:
+http://jupyter.org .. \_good tutorial:
+https://github.com/dmlc/mxnet-notebooks
diff --git a/docs/python_docs/python/tutorials/deploy/run-on-aws/connect.png b/docs/python_docs/python/tutorials/deploy/run-on-aws/connect.png
new file mode 100644
index 0000000000000000000000000000000000000000..36be81aa0bb2ca45f925038a0faf331bc6c074ef
GIT binary patch
literal 85927
zcmd422~<<();DhJt=cMDrIk^zqGGFv7y|?nsI>(xlZs^slafM&0D;6XBm}ft1euCd
zh5(^03L+#lgb)G*R3?!OLP8=WgefGF5Qad)tY7TC_r3T3|JM7i_5Z)?{nnSYLJsFV
z&))mD_c_nm)01)0(^c;i<4?A1*`jyh{I{34Z23s0JMP}GP4}0ccaghg%O|vmbLTEz
zICt*gMHDhTA}Valmh%}83btSIj@aMRzC2)J_36D|UzHpz+cJQ7!yMZ2@h=y@Iltp8
z+i$nI#-4bbepBzj-!8?TxVh`AL-&3@`}GIwe>1wm$NYKltFP8?9+{aTjzDnem|1JP
z(e>2jw&gbT$t_Z6zYotkoY?;LHs=7tS>@T>?98gkv@Lr-{Z#nT+KI;VV`j$2J3cMR
zOr_;+-9pGoU{%uIZoYdLsV+Ud*JR7F&&I#aNiVxK@85~6|LA$tmWxT}gA>9UuM%<(
z)gJrylW&`n2;Sig1VJC}f0}yqv{-+9&z7;?69uO}+M-?xUNVjU<d1We7E#aS^D8^N
z=9JH6_4YgFzD~U6;vzdkKNR)rikHKesr~N1M%Md%d6}pR>z)q==N&Agt-WhK@J@7k
zpbL$AbI4Qn$>#IC^CfM_h~f_}%PhCuW!dBcs3~cT(Duim@J#Teyr@I{Nf9g#`s=zW
z_0FPDhO&Uk{ECcD4$7;xZ~tDCUv$}XUy>^;?lkxAmn6#)BTx^h_01S)|D*5z#+sY@
zlD)HQe1HJ>c2RS9C;s6kD#?9%Eh^?=d4sDUS?|Ihei6~7hpmwjMDrs-7B95FM1C@G
zPL8{<{6O{{Xzu`mnpYT|<L&?U_eF=_2h466%x}B(;_mKmZhije2L(^}-}&H|vWBm4
z%|=}JXOUl6|6Jmc;OcpCUs*4_#QI@?zu)lIQ+PAD`SLmrAm8=JxE$>tu;ps<xv|dV
znD4f3A;oQZp_Zf9wr@Eo_#pP@m-~03!_jZ1t#_ebhTS@J@gu^E^&kJn`ekkArhe?X
zue960IJmF#*3?bCrh{8|Jp2C7{ZW7W`8UIvEx4~i_B(QJ8prM{-RAMz=YiXVPpw{U
zU3+%!;0G3CpBrv9e|Gq9XYJEJy!6En=XVa=b^qdc`Ui}!B^M8Vw!7=>#}_~8yxaK-
zdR_m@t_NRFy}I^z|Jp|mdxFwpzGl9{U*BQ;v*c{Z)z941KEI~-dQ0%%09O<2x8&R@
zeD-DR{?eTWPeo&=n?7;+!0vhBctEW4iCZDhCXd}b^!xQ=(Z3qqyQTj2=ieqS^&a~o
z67pon5BHvYGgtw=SoYZQ*vAT>0$`k3`N#BiK!g79o&!IJS5RLm`vdx?-WcDoXx@t7
z4!L7?F16?4DTSeTmPJ}dM%TluKB1r3JSvK0fmlDdJBR;mac^eVz@KS;xLR}D-6i*e
zZjam>$&G&6cnwGCcpYcDyXqdG3pDvn?1?~I@~8AY2_GhWEWB&-4F5cT9-4T(b&r!7
zw?{tdw$QU+uz+8YuUz=a^lbUq`5j-x?x^`#^s(#5R(C{q_C7y+iFp0|jiWC6yfaKP
z!aA`NKTKFm;Pz!aJcl~?b6(;@@MWXv&!!zB(wB;Bzqb7Xcfas{mo!=mliI5BYTPG3
zhkhMb(dk|PrRW&#*W<rh{_1u5iV@nVa+hoPZTFwuE8NRjCjLLww$--GpoRj6E)N0f
z>0u##d!@Ui;3+?pa_TE7r%&%?cAxi@)zmpDKuUjUUfBM{yKg?Iy87D~>FK3o7w!Lu
z{q9=Py+0I}A=Oceql!z49~9|_eEKiuRTn++t4+L235avQaJJXwOK8M{%Rdi%bmU{b
z?*PBqL`*lR8h8yK3>$>$g(XZKp0awWd5M3i|JBYb;w$&B+`D3YW$lNy#{)kwf8bq7
zcw$@a<a4g3`0>xxq$`fU#s8qveAxAo=g!wV@9i%Qp8xuQkKLs`z9AJU-!k3sf#<wC
ze9!$x@tVBXjqo5;mV`f%#jrC870@eI23ABt49N{@J^lTS1grxyOLEqn=Ofhe_9M&%
zvjQUDhMzIeD~x!RRO`kMdvjDdD~J{z;PD1_4g}QrRxN*B{u}p|TroE|$1gv1>P))L
z>!?>h&Og7{^-Dw%!VkXkB0W?Z`D@sfup2@{1nZP1K!Oy*J}h}ni1ql&J*MP&Wqc)=
zw^_;DX|wadezk#*mFXFa5=O~X(O|J&F^{#Cy~r+Mjh+|?_^kc9{147|qgGK?16Dau
zaKn{9m*^~d6eSrR7TVSL<j-b=2SrPfTpfwp6m;{C_R4OuApuXKe^G^t;zh`KZTvSa
z*p^E;$ok*bzh6I_cF5va%7eI(4<^UP|9tlQ;5fCS4AwXq(c@gkDjg`zab)^MHQ7fj
z^mlb|95^TB9h`jz&2B&SePNJM(|Ej(_EJ+_LN@6k4XYR}6x@^bkC*Mt_M3f^i#F3I
z<`9)&QR~Ivw&3rAM<ky}E=i^&UdcC-FDEOKyOX2w!j;C=Ya1VK#INa{1{OtyTcKH_
zlGL(P*DaufhkgT(Mf`T*w^M%}*|A%_XZZ7_V}8y>=Hkyj{HExOmT%1N8mEQ-{MI;4
zZ>L_(4&442qc+=Cz)!}MLlcReJ?C>@sPgtyWc|^3N!4H9?^c1RuncVt{XX<CB{Njm
zCAsIFIoSp5nVhJYteNa7Otc=N{`h0HYT)^!t$n)+Nziy0V_3a3W#IFIe3fLMfAd26
z1$*NBgU_Eo3C*@Vt$$p<=QVMLIpb?;$BE%wzmL9rVPNq`x%9r&;7iVzEtg#wWbx%$
z1ktjtblG;$T60zLE%f{_rJ;JMXen)1vdcH!No^lGXOHGp7c;)1`d<C&*k|YKO{v-+
zvp?Q?XYkPZ26uibDSekyP^iVio{8U03rp{ndL6IuCHfy}zt{c*LF-jm)s)NEq;Ffd
zG1Hipc#?KF8*x{_sB>&JgO)e7;cw5T{iJ7+)RqJ^$uQyBzAZrJh1`F4&6`zO@L~2(
ztv`*MC#OC>{_B^Y?cW^}Kb$=8x|G{$-a_|74$-G@d#m1rQ@)FfSP+tCNOr098`U#a
z)zQ78X1XZa`OmE_x7%S?tCSqVzSaBU`{O}pTZ|KU2?naSf5N+nCXZ|%ZC~EEd~f;t
zmml^KmZw!B${YejZ*6~G@yG9%nJBlZ&J@nuwyiPS6?-C0PdKeieLDAw+0k~_q-NKX
z?{lx_N==jE&#YaSbF<13HG@wwYuXP?n&<QH2ygI*g#t5Q=QieUvc52z=!gA7{!rK}
z!&28e^cvD8yvKfB;rE5?OEI!P5Zk=DYtntwf0Oz^{#bs``1kI?Y?z1m=mm5jdM@eR
zqRPtCN}d9Ir}3+aM|6GkXs5_}pY{I0(&k@r5v?Atv}>rBQ{&ptReS0>gBhZ|f_^-3
zOrb$I3CTB_6M~y(lbM>d$?QctBfFBAp^?a$dX>X+i3%xiRyCU-MWZpZac?4~gTy!r
zmo}$9Q0FL|Jv}E;?3tZAkZ#;$*L33|Di=N1wYjv8G?#*ll8csiEIxii5l;?SD!iVg
z*u7<*4Yg#Pk^=dfGZWJt*R=CB&flgcANVrSCCO(kP?@CKSz|z|aH@b<7q*%v9Z3YP
zHqRr~sWr*4CD}}QOlo{~S59{8=WS(Jx`wG(hnYt?HRRsSab7K2h(rDHCSOrS;AU~l
zLPFcX<$<f8FVkj97U%_YAB>uNgt>cs?Dvg;b?oLhrk<YL#zt(m9D6bIwd$<w=Offb
zb=Fxb;O(wk?>eafP1eu{7kS&hzGAlJ><g~N`7^OwZ{CSLw$EZ@OH{O#Cbj6;&~T(r
zCjU;>(MR6ib9qVn@Lf;o&#J3?E32!KgXbxGwp?GgcdYU%5r4d8cTmHhluuea&fZR?
zwQrrczr6<pHjdNzu^-2t_eXEpvj52Y?*|tyAD-W`<pW~G6~7yP9_|h>Wc0~vKO#fJ
zP9{dj>PBzb0#0<$9Y%-UxOOlxIw}V3kO(>S*9ZsQ@%z)(hYtQV<OTwA$j{^A!E;Dd
z*g@dQGbc|Sa{A=p!GmDbkKqoNzjgV?blrcDL)UNIh;^{GPDn^NnE*J6M8U0XKp>Fy
zsngb{Ph06mSfP_*Zd^;Wia}faoyb4wd>e*_p(0{$L?B}hzNdRF6p6V3IdtefqyPB)
zUCyw?h<|a4LH`4m4xsh>E7mqAPg(y5ZQWGx`?C%gBND@+{JxEd4vRtScsSVrZBBvz
zn()6}`WMT8o9h2>Q$hCsKJ(wM{9`8A`aObwL-cob{dHESFQ-qy*8ib<r%!&hJImIY
z!!+VsuPeIW58vBH_kroY$NurFI~HHcn;YA=Wy{xFE_{3TO5z7I%#VIKjP&UWcrnp8
zaiVSKPH^dWTlK_cPp$rZI#Sj(Vo><h?Bp%|zwN)J-*jS@^GWlIldZz`nN>`=(;~ij
z2m*lsQrBGEM{r!ove=qhU;m{334%EG&au8D4<fV4edeJztPg&s@2<psQk}Ozl(ZC6
z;Q`v%#zq_9X^Emmq+C`sW(mstx)av%vBgSQiw(!Xm$IiZJZf+`YTeypA#2d|8b7=V
zUpR%^4T>kClMsP|VW|AHEU>XV?2RT~QoRv8QqbS5-?!qa5;@uws=8x(lC-C_5s^P8
zTWSZxIIX_)u!h;r(`~Fx3>`rkd96w`&2GdaoRNdB1RSOBm1lO=GI=~^YNSSV8k!Jl
zG)h{38(*8L>YjDWRSw~c6T4@HdtIW_L{+1G`Oaz`j&z*$wSNk}Jg^y8g9vM(tPdo?
z8of?VI_c=a0dt>kc*gYUo9cz+d~=c&^5Yjn=yQ=nbEf=g!TL?F>`S>&i(r@xt9b#`
zyl`!;6#*6%T3hx?wmg|OmL<5#gg@#oNKwYuF@hl|E1=6iM;vj6|G3L?U1fAlFf*N`
zebI;I{#rp#Xc!qOVNf>TR9_>(5BC@cXZxq3T;rB4duVT{wwbPh6m8^9Sj_DviIX~u
z8k5z%e><6qYQ$rKyZ|2D3Z@`XUr?=KjI~;$NgIs=ozC2x7!m?Os**xuc2Hjcyz_?=
zsH7Z+4Y0;R6FODJ(rOz;?|O${@G?P}4=**g2o|V{lh$mo&^}@(ppv-RYe^)X(}M(J
z(kX#n>YN%lQgwi>l(4c@2`43ld>2!MdV&1=dcNbmLAo<2a{8ZAvSO78Nw9tAa1`#b
zx3X=o`RsmcWv}@-pHd2(2-|FerMNi@M86&YS-|XvklY>Aa4#;^HCTYFG-Wa?RI~3n
zxO(X!AQm9Od?ag4$DqX-ME<Uv<`15@NAulkiZ<!gX!GEu7}%Lc``EGvR28C5YlgHA
zq-B)3*80x@tI`7Tg41S$&!lg3&=4jI^)Xx)FIQvxKdbuyCtNsy|EFaAFi@(X7A!p8
zebluaJUOI=w~MW~rnU4h*HKnR9la?ymm(kzON6NFC4i|nD)#1mTBmp4>lCaM>UIDI
zk|hFbF@Lwds})`lzL0)g5Udx=ivCn6awWzIvio08JjM8M8ta(DxK?KD;L&(9N;p$k
zYK+qO_a$E0o?qcbj3Y;P_BBHQUhkE?QJQB{Izxxw@u$jvl<ZCNzhxl%!jE+nhFGzE
zSU>yCScWKnQ!VxL&Lv78|4w%yGG&T0$h0al;_^=9=&^!A>eDltz$*LTktKnuPyE^+
z#rN4BjGEyESa5^U7=m4gw-8wUGu;|SEA!IJM{F8GS~#U=A;6h@9_`YsJA8WZG@ZUG
z!>$aKAR=;TrFo__R6M$uN)8Y0r8qN{2Q)_u5PgXc2LbTpL>xjrKOBW8i6DXn3q5%6
z?TYFLe(HL=xOHCNU|&yFia=$+OF&Kt#6IerX9I6~PzR)n_#iN-(+@R8O2T6Ckr&<a
zg~~es!nHblv5BKw&amQ{TMizJkPacpo{Qm$isqxVd2Lv2<6`wS6v?~-E-VH-u1dqB
znr&Z}O`l*wWm?<Oz&*}5v=y6D0JvY_!-+YWBp7r*0n%0_Fe@aTsA!|y2CQuVD5yFt
zN^i6>1`E=L#q2WAuV{4O-#<qT*E@k@4@O9K`L-ylM<~M+0KK>*NC&XbEr{Xoy`9w@
zOboU{<4Ta^yXDL!e_r2vHu>O5+5P3=%eU=8ZkO$Pr-Ow>M6L8uDfB8iGqKy*%OjhZ
zw-Ob;Y;RC5I_i~$EuobjsS^HShFO<}w}U|feuX6{@(n2uUdM1?myA~{3H;}xLdr0w
zDy^56l0^)HbABTlYkQ}Mk0n%6mm-TrqGU8=V}?yT(*xz4&w+wk7*c;<07d$`T%yUr
z>i5yYzype6fKw|In3NdZ*d|b!DOd8+sM2Ts{O?aB4V5(6NQyh71yFP1@U)6<jh+mo
zGWibMO4yIm{LLfo^?J82eD~%}<$E0%|6K>Sn}HMj2Rn1v>h9d3DyWPT(Z-PY55YaG
zw|ksoi~V~6F((<x8qJVJ&)X&P@A4X6@JL=IG`61X!U0MG@`ZqX<?I==@XV=rJDD-h
z(m8G-I1|gCvFOH<LL+}kII+E4fPS0-@<o<2oZ?{es{O!)tP%k4Q~2y-a~1(ZDMhGQ
zb~3N0`wV=Kp+nha{SxfDj5M_*^L&|YW420+;GAHY#i8-2rwydHGk|WmdD3vydVc;w
zmh<yz^rpXo7c0839FRQ@uS?Y0T54QqoqV3RTXbA2Ehir~vkN86FKQwPq?hN6iyFK8
z2pdb)$Azo&o%KC_W~MNN_J~u5^acbSnmAns>>w%8IW|CG_SEFyekzx^gjyPLe4fv<
zNnYYfS>Qm#5IoAm+B`Kcn$j*DvR`bFH5X{ru1=a};#%ZUOPfhPwH-00c5rb<fVc$6
zbP1eD&DV)}>vmXKGO2Lw1<EmNoaO_I90E-C@#KV&9CBVCRUl!EDGnbc=KvY}mTi@!
zh5Dn;c*RSM6PPk%Q?R|~H36T&Jp~HlmKp|Q;p|xDS}80_uTFbx8?^^>D}uQVtyGcb
z7M2_Us}mUvJvHtmDA|4X{<p-YI0dHN6gbFRUh7;CIC$nz(7i!kp%anNd^aZI>aejj
zLd~lp2g*D2`^;cTv00>{!j)b5JPj*j{855n7oE%nb@TaW3UYk|SWdWszW#){9bP$c
z+~0KPSEPAWj|!M8#q1ReQ$1S!N#bkC(2!eJm^C5W>S;gwaX}FnU*~0tBuz>U9dCn|
z?_%PBQ+<ylv{@iBIP%4X@?-|*u4MtFTRmnNK($^lI$EVH*6(net2K%VDo1BkS4W&M
zuWKyAYabR?%t`hVOLN96%8$Zj<Ip}QSR?^qIcMk|Q};}#=jv=0P_?k+_+A03vNC3;
zpZ1TT*B_dWWJPB5{tG?e1v=Hwz}LGiP!XgIUX(pbD_!a>R3;~e4#7_B$)Nz(_my8m
zH4{NLtxFujxIhEyDIyTM)eDiCLvSxFXl?euc(2W@(GsY~upF#z;MW^CQ;kyZRQ&EE
z=+YNCI^r@YGv84uAK5n>4eraYn$u)Vk}##FF!(Rl2uQQPcD}-V^E#*nzq|6ng30~N
z9KXz?mIw=vCIA?hU2;4p0mAW`Jr>huT=BM)YRO4bObmKv*;|1!a?gx&=4XI0WP75q
zY(tteA>%nt06ob^LUuWK&?5sd7|lG5X}BS~535**eaZJ&_FZ*%m?S31jQUM_&pX<{
z@R5fFHmwo^)V!c@Y(Mg{5a!#v$TE%_bg_nMmJ`>P=zq`zHEk&kjWlQ^a)F<ASCc{~
z>-@8J;j)7(x)X|Sl;wq+_gx2CYv_@&W#!^ay9$yg_SVIrtX4x;jM{z>J*la=Tc_$8
zvnw<;88LVJJ*K(+C49hWxfQ@omz;MQ&M;DXTTpVwRU-z~Lvf1B7Cm5>jIMOv{rr{l
z=mvp|6mcV4yjl&o_w2qG_`i=%1Vng@7NeG!wsFGI)m?>!ZHyp`gcOvkjh{NhwY9sK
zCe0j&ulPjQkjZ9kjRv<z15vfPQy-a%Z@jyS`jFiuFH1UP`0Ov`H4ulk`&)KnLtgY^
z6+ve_gK~pt8~~#82NP!2V0+XkARJ-LRz(*A24>c`#VpQ@df%Sh;2U*a?X@&pFt!xl
zw@xb`51^5no3Sgy!Mjq>y_Eh<%4dqo^_t|PWpP}`#jH2kKRzrgqBsHfDGh_?c7hU>
zZn2qm)k;c@R7Idw&bfouf=il|bIECaZlhvyo})9P!tlgur$z9j6sqo;2PD@NX!4P)
zx}c)Dvt<^EbwfYIoZ~7sf<+0n%TF4|pU7CVw;ds=jpX%x#C7}08gaKHaQQ3xLMv5l
zsp8h4k0-<D`me{xac5?2IX^^q{J<_&js9aoTY|Y=-6AfoJaEu9aIka;A6q5o3NRth
z(dO{v2cpD0#t3iq2lBi6@SwjBNMZ06;mT&~aYinn?PYYUH(J<85%O1|b0g4}C(%t$
zK-A6T|AK~i7Sy8i@&%qP?gyN!zv20%7Ncitpp3dKO#;t$GzUnEfb!&RhSvnb$k=)!
zWwUVH`rPs##zXL0uF5Max`dvH(A0*3ON6B?08ukH;zV1jF7)f}Wt?w)1gzoCXV#AU
zU}p<3fj3N0o84=fk&>J?5v3C7fmjn)?hVJ2wpl8TM5#t^Pv5Y9l85hJ6Tj^|2`iav
zaEZyKy!EWJlK(@9xuCvuXXVI5_-N8Z-z(;07p;+UXd7%(5FfoDQ0N>$TPmoGyEf|r
z7RJ=Jmk4l<f`-ZnkMi}<JN_Xx&w8=l+K{yNT_c&1KbBLR=*|>cv}A%bsB>7Og3+hB
ziGucob^buz^azO-*g|<516@@4@d%Yb{A}ibBBr1*DU<HQg?3Yu*we{^1iJ}FJZy8M
zL88OQ2cxeMKP)KUW~dwiN&U1pW`EPVO=e5@acM=Zg3o=0V4bV($vI)R@N6Kk#!psZ
zZ-aedgK=djueYC(ykR@R(|bFXO(}cn2+sCVm_^&7DuGmNQsrd@#IjLK+7jin?1due
z#7=ug9t=6a{~*6m(+StekW=IiJP3N5dS3~h*WO^lYk2mnwFLyDDi>|IxLL4NeGOcb
zp`4=AiNQJRy{6Vkz1z35XD!w*!}s@jl#3yWs4VXeKWj?}FsjkHzOg&#+nkuvM@TUk
zuZW*XDVTG}ceO%?t`F2)8)@5X$>`*;%|O$x!~P+eR9`bc0^;*NOvbpkTej?FzX(b8
zf}C<ENl*OzEZY>9|Htm4)>=!a8oSiv2?tm)9*awF7@V1p=1fm`lyzpoX?)ger(~_M
zB^^fpAsZsfZ`O+FjcHPQYhnKDbVptzvae-U3JPJVdd2qm$m)JXK?Pmst;S^a@b`s0
z?S0Rp^50p^_VpW=J_r>4M6Xgp0?N93-?Oj>*4s@AXq8@ill~ro*6mD9-d$x1$KECm
zY)nmlIJbGVE(vYW-!u&xG3Ot-g3J`+0d0#@T<u*9zHX~HI>r-KU92>*@I4M1v&g@V
z^xhsAjPBF~IU{Es#Dx_=`{NAO&`X?^;tbh`Q;-ih-6&x~EYQ05f+06@0=udrRTTn6
zZQUwbuIX(d&8TWMGJUeEw48w+=H1PJm%jd{Ic{qX=x$=SYS?&Bj3|9BnK9eL%H~xS
z7&G)@b#arO(5^6$Bz-*^yt~cgKx`6wuP|Y=m7$Cc$q|gzN>9U&&(R+9nxLQnPtJ?i
z*kx@ND_ip)W_MD@j&O}EkLI|-3UZgw@BDpxjC%b$b7Qcc+7}m`2RLicQl`2TISU2Y
zd6q*Xm$bd_4Y}Q}8^+tY=!S`{_`1NeGbS*qQ4qK&&Fr`EoN>8V?A_6sOZ9GBWg#{G
z%ex1vbV*mmP{~Ht_^U!&C4!u!?s}zf8J|`J0=jS5J5H5wH+;%9V!P1Dc(%AX*tD+t
zh>L{chw6G|)402G8zmfq2t_?Ua@5YIiLLKl1=+M9@oft3rlCo<jQYGCAz*j%!iQ!f
zwQ;HoUZdBo#+bWLomiDc-WS<D@i!;w-WNqs4%o#DNh&|olb5W7><MH^F6{vV?^`ws
zc)&G%3!fe+8F3{+PyHf$upTL$s=4hIEUad;k_6s8KpHWS36UkjIZIQ+CCL>27e2=s
ziZgbry+&OMe{>E5vwT<L5L}B#5#UyuXD|AD{Z24K*#XA6G5k}7cew<DTAaqppeM#&
zE(DW=2rIc!RSufe4?p3hS_0q~fH8``id;i#+hIG@gfyIpAG1&f+f&<Q1j~>Um~?b`
zm}gdXTxpq_4Ns}zHm`e@V?$N>=Sne%>OC&Itzi`DJs>gy+<d7c(98)D`4XL7tdCq6
zdw4PlS|@~Z0G<cr&<d`fE--<wO(4YlLRWK}aQuZBX=g96Cs$D1B+erfIBA*LO=Ik>
z9f4aZrJYZCr6R+mWuOo&Pw%%TLPIU06QRN`QH`YpFL;)rQ7`Bu;DL1l$g;A!?jIL`
zQCYNxuy&vgn*IfP!pf|)-#xZgh|40*I-=n3(Pk$`R+Ud@*2R@QAhz{Gp9a+tuKKCj
zgNNzJD^zkNbj(jtG+DAeZK@U~UGZtt5ap)I8`;>WQ#Z<>CFp(S8l5bwp>Hzuk)$sf
zy_UgLl9Q70u9L4KFfdH}U~jjq*7u6|9Ts)KT`!c46?953y1B=M#+F!Cdg9hxq18G^
zQ%?@v3Mi<G$0im9PJRS#_MaTi|4SO&fqx0(1ceLIZD^%QSwsnRdtUzQFBk1mF%y5c
z$1X@*sPJI8r=Gqu$8%K%Xdi@^C3UXx^%3Q!6SV<u_*3v7PZR`FN)lavo*H~pVWv42
zX=jxA3VJbFs_HXKlrpcOpzv;e^96JoMgzys*G_lMYxNQ8r3qCvLJ_5TO1H*6?NuRn
zYZ{Eaz>Lmn_%0&i)HW|~Z(Y=TDHqIyj_@?av*~t|_gYTk(2aiNoo!8$=HNhUvKJVp
zkQONq#q55~3J;9asJNg|ERMAhinReTQaejQH5y~7XcP|-z3o&D!aeYq@vKmctMOIE
zr+uFzAoc`A|84jQ>o{fN2|4CO!WXkmd39Bf<mrqc2%zWn@uR+a$uJpX+q!<=>t-i8
zdAe69DunP~lnG678UV3L_d+o8?gJW%Dtx{7kh>qd5}h#Vs%ZyW-e1x#XYr#Ui`wgt
zR3x5-Fa^cJ?6un-6%HUbt!d?^Ht!!W8)lK|QhkBOKRQ}ZQMsD}fgE4$j{S4ShTaZx
zJ11r(PMi&T&IeTff;eTSx$G7Vbb+Xs6c{hzIM6l9_AXX<S!j>xdeg-cnfrPnmj&Lu
z7%U~Q28$AGWF05B8G~Hik-4B?ck83unYCefA0_Zrxd317oJk!8+PmU0)w!Xoq%d;M
z=1O30t5#cG&e+VrIIm_|N+3EjSPHL#6^{yrBeSs5=|Lrt+=0k>9kp9SengH_-0ROQ
zraO~~l37w!<;YSf!$%{nT{ZFz!~;<|MG`Y~Yf-TKZmfUbTCD)_n=@(sa_W9~J0dn`
zO>x=0OFj6~Ul$rW@(SSPi!*)A6sS~<)kE=g`i*=&S~_D%T<CO7K1J`tlApaM1baa2
zZEO{JRUzrFb|XAYB71KM`*#VA5A+PO!P+cpdj@$TriR~85`04Vgjfo6p1fC6=fc^`
z6Txx}c$vLttr7m^gIK+#DoluiJ28`>$GH>|q^R&qK@``(oSlRgbxgbh9+Mh6;^j1%
zk>I7!W$S>3x!CGQjm8ybNmxyDm2#e7VOw&JiBJ%eVZ@z_7S{;8sK1qT^^WS-P~mUQ
zn0qbZtoUts^<`M&NyhXvVpC8bW3I`{Z?|kT$gOU#O0c$sH?#p8PW#|=1F);r`Lrxu
zj+5T>-ra9;Y0eHqM!pn1v77@hx>8?#4~EI3Rx_7s*+1;bErXjBrkU6&4mW;Yr^$*8
zK$vP+f>^T{U6!;{GerHIS26Y0z{@>&tu#XHv>HHR27r}no_9b1Jus14G@Rsq0NiN%
z4&2<!HX<SQQo6NV!4Y3;XH0!T9LjZJzn!wv4=FrPr_6YGBn4=sW$?Gx_d_$_Go@Ck
zc|;mRF;0Pqn(o%j4$p**fnxY#z&q1=_+wDT^3Ooy(>_=Kn{cQ=&U|S(mTJF>3&m(z
zqZM`&(yTUQZGR!ffr{xJ?DHzC4J#F2YVBe>*a$?xf#?%BZ*PeYBuQTEO!(Pn!pITc
z7xEAuO~xk9fJIGb#7&nhDWpUa{E7%V`54}qwutKY7ui+YWx^4~<BBl~Z55Dh1%7Xh
zGpBvf|Hs6qrO2LdoPT57vg(PE2hEk>albA5%Ga;Vo=^q{E|!hJggl}4sb-;}F@cOQ
z7}|<=$E56K*UT~ugvo>JZ?F>8$X5KW_!L}KNX{CwN!B-{Mm$hLPD!?I7QIOpV-kK}
zo*>MTA&E?ac&o6-dp0{xy#9DGPB`+jBooorq8-yj&LVKXnd_I0_Nqo&PjRb0J>jPe
zG##aJO5BLU0#!$@y<lo`IaFJ_;0j#lC<AI1`AVpqeWbl^rkxheR&k2Vq8j$xx4S2l
z`STJ|C(G>G7x$*#8Ho%*#7waeIfze%nIZg|+dW$GT@-yKvZJr4)q@RLY)L@Q?pmn;
z1t7!(fWrKm!mk5Xd!ZIY4NUH%&!)HE1t{EVfe!7TpJXDIO{gxV{l?nR%&~s9aq1N+
zQf57TTXNl;B}=bkQ!DANqlt2LcuJ@FwGroutW)dFjSqOjQUPIbN~O_Rm$ZJdZLKVG
zvPF=D?sZ-K3sm7mb_ufxg4npkZYyrTz6V=@KUHGUX3Hf=%<z+CeI=_p6qtAEfaf#H
zVIR*BG!)@eNEiO^K&W(#<9jJ9H1@E@a3z9Tot>!7iVVdva2wy@UYV(GlZp|GwbKDL
z3!IYC)pY0QF|;}$9VG+y0MnIS){4PrFx>1pMnE!cJj`8EDI>0qwAdwV+hC%xBW3a#
zGDadEa+rB<h=4kJL?;z~MW5F<GO5!*eBHW_oUtFT1WJ+?9nBY9Uz4;nnGdH`*0e2-
zx$+&;Au)=JKbDYLXWN?9E7Cot&bR>jV!-%-BQ#f+H)w3LEg4RSa~~hAENtlsY1ppn
zcX~~jZq47hn42uM;O@Badfy*TleV5Ynx9JOzZ|tQ7}ttf-rOd<byu@Js6aGm%CK;r
zv~?of4qkKtbe?>!ViZ>q<8W{#DPcF6Vx2DpCaI`*3HYSZbh`qm;>Xm`p%QyzLFb^#
zG;miP=AChmSu5BE&0Jx1bd;92T4i5iYQk5q%gi9c!ax|LG)v{*F~wSQ!894JpVZZ~
z$e?pzVEFQFXoUk!aKC*jDASl!u95j6oC#mTuej|pD_G|ze`z{0Gx(EEE+V>&DkwLd
z6nti$#DLhLK}#ldWVCWZmdM5{@gq9f2KUju$~sM`X}*@am%5~Lns#(|8ir^HB(1AV
z;$|*bQD(e(!!K%_C!PQmTTKQ7kOjOsmSEa+mK84U?zMqG5rP-;+IP_9;Y4h=3U@%z
z#a=KL9osE<58QH{)57J)#gKalRCc~{rt~<sP5OFgc<#oM_N7fvpFTjC$pwR>a^n$M
z;SG`#=4^3=-1t7C_HI4}f@m^b`%`QVu)+;+o3e9dzwui6vnz+h#F8Y15q%VTPCZp`
z-SsS;Cgz5&&3r(u!x7-^60Fv;0Gq)>db9%B3#fVMa$Vfavwt#Tt@err4Bvyu4WI2t
z<9mUEj^5WqU?8U!dF5^<W$tuA%^Z~bM%<QH9%o|Nvz8Jqxt{j=yC#U;A|DVl7sb?Y
zo&lHJMZ-FDuEVkXyisr*6C;;lYe{j{>_qL69?(_t;M@NZbkZ`$DX(+8XT#Bn5kvVa
z>2u!%-ZTZ)kJ=-&-hIIgMYv~FIYi(Jln+_>ssz8(`7H9MoiM6GOQ(rGBevlJ+M}CP
zceJS)z`35bDR>cJNt*&ytamL`KOj(x%n;Lq&tZZbDlcFjsH8PJ;aI!?lZcw_RZ4$l
z^IGI&*OCCby_Mb$@1P(%Y1@!LjsPa2t}!b6!Ad?#SBWbFJ~>iZkj!jSzA;PWP`6u`
zHz|YA+r*7V32E?DRA*2oP#1E`=4U?W15efhiO_C{uc>p1t4B1`&}sbzkg?LRHzY@y
zU*V!PfwNO0hmQM>urgHCp0&p^Fi!ep#8_?Y>$ra3@!oVqTeDgh`y92SWDWk}8ncTF
zW^VTCd*`<PC8}cVFHuYVGnS+51+IaJc^3i1g0lmC;7v`QAw)J(4)Hfx2zqzDJ3W`U
z*3J3lFP-G*3T)Z{G_fg=HkHwb2vp6u%^s_)T2;LC+8sv_cb@h+XU4DrFD_{<z1$&c
zGMuDT)Z8}U1t1Q@h98ko$NkLXNY70Cm$U2x{Kye!xDK>ZU0D{M9hp&mB*dD$-Lkus
zdN|I)m-uLJh$s1@He+oim$1nnMAo94LbW#|bm>t%YCe?Izd4i-)mHiR6!#?%(f9ue
zQ`fLnR7DThxvr3jznI@fgsydFYgpfJ67{%i=?D*|Ru>gOlK#b8{tw=>4$a$%i@Jxv
za<-YEzU&>v-<~#94tR>o2}Ec!a#w=-jMOH|Kt1^cPg-S0X$rJSnot{8d0PGdSN8q_
zT}qa!Ym+j9kN8(WLnC{InaXDwHu4h}#4jGeBRUK75x45nnA6m7XN<-~X-Y1FT!ji>
z1eKe%`XO$lY9AJCRu9I>)MfHgX({8>+^HQ#`E|vCc+|5q2Hz4hPjB<BfY9=)%Z=6b
zmBG4sg>5#tB?~KDkSluTKoVn3dNYD^u$+CVf@!Q8^tD#{dvlwCDB&X{%WU)fah$5h
zQRnKAb8mRNwLyFB1RaCM0AZg<Io%<aEsZH&=9d6NJ)DxdWr_4+L$_cKh)iBo_0-h3
zATzPr!_JfZ?_<b-xCtQDYfr-d1S<^HHPGFf=)nK8|Lq&6#hDAF?;giUKWVYntw7BM
z_6yguNjzf#)x4Hg>)pqY`U!ILCPlKN$o=ql#~QuLO;53-4<@v<^tArZNGo_rh>Oe)
zrC%sF70OM~G#w*+WVjehC13?A3%Hf8d_r}#rlWDs(Ceq^u1N0nV8Q+J;RDOVZxF1&
zI&H9*c>%)H3YLUg5Tw&OPgj!DL?Go}Vco)bLyU~M4X8h7=*ejyn@6N)x8kw()FuRp
zE)a@YDqaFxJb6=$e+7}L2Cwhg<NXgy;7FM*g5sEok(#4Uniryaa!zUO5!sB?L^(BS
z0uWjzc)6PwtRJIgP4#vKSP1s@6*U{jEY`XPws+s<H5V@%^W9rv_8?w!%EK>g)!ONC
z)&%)cur@t&y36~nal!I}uKIK%L}t`i{{CLL6+ylw#*6=wp{@<<&(+~3<+Yr|!?bT)
zp+g8~38+GY!9Og4st3PsqnMEeMT}sH4z<j-YP+v5QCzzhq3#9P_O!4&{nRK<9HtEE
z92^A12Joqtt438mD0EqdeqAvh+w>lK%YQ(}yAanq4Fj{ae3xx|-1L(Cz3UKKXo#{`
zQYvIdPIz<QF6mNjSiwHDtp;EqJ2D+`aicXQmtzbk#Y<>`+fwJST6LqK!4;lO^$`*9
zjtaSdZs6v1NRU!Y+<UUJFw^{f4j#3{R+&n6P(z$2(<g1r#{%#I<e)q`yrjl4z*m4{
z3+4khQQ^i2<usJTPUITjPc7Enk``ITfMhQA<Uor+7k1&S5HwkiIzZ)%{)@jXt7hr%
z%*y@?W)!DUKVNx0b4^tS_y;S+(fGW?zeC8=h$Fd40t;Vf!U&C{G8)&pge&j!=0{ye
z#hmf%Q|lT)j=h-@Hqh))s2W7hl@2x6OAw7|ql!WRFE%Jr_h{=P=**?8CGDHqI6-7Z
z65Yg+i<K%|5jq!Kn-y!r4BXg3ZS%>mK*q@DU5Yl>j9g7~H4D)}Wqn!&J%JMyr0anR
z=s;@viX)m%0(JUF4a2>JCmlyyDoG674S_v^MjE15q!2YP26F|x|4PiBTny%<sAg0G
zqGH0B!YIA=L2G#Bcr)LfN$&MuG?+G;R`vT&)(*NX#@NOGouJ?^f;9%R=vJ)|H`dm;
z-Tq_s&8EnJk`|v91tyr=BH5}tkKH3ym^FFJ8)ys!Enq~Ve5-`SI%=PmIfM&k<iVI8
zrzSzGWa}~w5?f_<4uKX%F4HzsnGoI7VDZ)jg-x?<0x8~lc2WrpnY&dZ+3J+$W6e7E
zeC@+ZHsj6S0!<cNS8>v%w~gRwxoZRjIYb$e69@**VBcA_TpKc<-ML)ywkWQZE4_&c
zBBnIvk|qa>k{ALueZ%Z+7NDaH^d$GxN;aHG8OBB$t~<h%7S01D*Q!<c<^;}lEh)4D
zlXFIxSV1UCdC1sK4MFBIf;U>7)&VY6i;&?8;$GW<NC`B?V-!19)KjF<1s1(XoqH-t
zQvSlV2Lxu<!b=H&WNXVLPBeE{?Q)ztu!q%0i)ou$o`;tj&9W}x?>s|-7qbQh-W{{r
z#V9h5Ht8y|eO_-lX+&S(cy}ar7mruyvZYEPR$#w`39^D<X4c|vtUXE>!;M<FON_bG
zjZSI{rjbi~?xneMCfbFH{M>|ue#q!5iSBreyptXN6T2xp!D*l*F1*RupKC9)>sUtJ
zmy5Nmr{I`o&sm3XiJ4Lskg_LL$BJV*=xVsp4dD7kLx8Lv;=+fXF)mnuJF`~K>P;7I
zlRF5~Y8PC9Hk$-3(b&PyoM*AQ*S5q)2YK~8Qb!>2M+i(ITcZ(yp1Pt1G37nxQkz8-
zsRX$S+0a?Z5q-t{+Pn!D45MlIe<~N#aMIUxDu5wlv>wg3`>%mN&sAF7`fl9(-Gceu
zL(~t(oDL39i!QOh3)kD#*X20ZglasR=Bq2BJ4lPbn>G$@4{rCkVBhC`T{en+B45FC
za^kQW6a5|-pXY+O4`-V#&Tx||*0OdwO;&|#I})r$tetcPs%q!Sn~ru&{EGCKmTe2t
zPZ88&0nd_-8J}`rl=c2j1gaqd#GjU!F1}FLF~1~WE%+&H3mEunSeA>Rg$tS}lk3)T
zh|R_h1GhbPnCgt)fSb=e(2pJxi`szR6(Cdz?4<deJ3`j$Uvnha@TBqDr33CcHWE<A
zUW5Rb&5861fmP=L;SU?dmppdW<=8l3Uf3k9!EPiU-=3Sj^i-EgenuC*4~Ein&j=#}
zHW~Zn%UM!sbLX?u5<&z7vgud%MVz8o&dQq7#NVv*NAw+-w)e6&u&$(s=k#3mtzGyz
z@dgZnd;I7qaM526oTRunN$Kpxoir3IK6MF9!X^Up|EW50+vH5tJAw@AK{B!wPHY4x
zpaLy}TVbdu+r=iV)|Nrq$pexWq))x{bZQyaJ|q{dVdGkhBm|I|+er6mx-}-N{{?m#
z<l4Fzc$+}7n|vH*E|F-d#)&cK>hc{5>{qXQsB4E{o<SxgE_7Xb*<|{b-JV^y=b)^@
z=l$8bDn6-kupAMW!aeUvb~8OONzz$E*XU=wuZoz&Vfh6kx33UrjpDqV#U+$3AB>PC
zp1_#&iQD)#!J-gSKSavdri38%3QO8BxC0=b6Z{R;b(eMfX6-iO`uU(ZaDjaiIWG`>
zk^!+G>l|(iYq$qDDaK84ra<Y>``6u2p=mye%je;xYN{ZPT!-<7Wf!6^SHRh7K|x`2
zs6yHG|77lBp&XH$E^yF#k&Tk9iUNEhPQ;BIW9TtG6@C-a3Sci)=k;Qu8=MfTe7ER_
z+a=&ryV?*oA)mO~O<1sL!#?kdk*t<KV-6+!X?iz2+g@2=W_KBxs>m3(0p?PvtB;Rz
zS7j*8n2}}fYv7Zuc5&~s{t;HIz4?GA&-$<&Htn#him%@RQ~$q4+Y8-y8|d2wKW&Z(
z!e!cepk3}`OX_7={zy~_;N?^eB8Q8SHojy#BrnX2i0lTwcRv7sUvGe^{q@5~<Kh=>
zUsBpCs|wn1y=8N2F0a!qyrLm11R$3I=`bZ-(xt33sy{ApVIoGTcW;L;A1MI1BDl?q
z{5~idm{eU{I`d$U?nZ$lQJsT^gZyaHOfSJ$msFNNs>Eo*btCe`w3MVo;Ums<reLgU
zYF8{SpSs_%1w>7nFSSv6kYrh?RRv)ZeSkqiQFn)cam(85hJD^WM*MyGqM~HMouP!%
zk=ATo-^CI?_Nchjg2+_(Q>$q8mIy^=^<DD_sBbP9hEA%!#`YI9hnS^wdWa221<g{q
z4Ll)N8B;1YqDp2WIL8X>$RH<Sp6Dw(<$M=40Xcg9xDBTCtd$KTyBF?Jgzp(*i;nfI
z5s1*W*&_*UN|S3AIbfP*ELz|K7`rELe0(h%tenUW^V|de^9=5pG(flNG1Qkk2-I3k
zJ9r~q*I3s^!t`=soLMvBh&=-k&K%;EUOE{Pgia<~(0_;#jAz2<ByD*<p$hxk&`^{~
zK9_K6J%?ICzm%f^LTF1PZnMckyXP*ZNs-ED!rCOAGa|d5jdj7$-b^>YKA&mql8d1k
z_m!Hhb`qFUH7Sfp3H-kdw^a)28WG8Y?F--fxj;1e6E8RNYBazqk&0>PCh1BtG#5i!
zC!jH{oD|R|A&QX*;7YWvS8>PzI_aZZQJ@xC(24G~#if_g9&tcXK>O!)mBz*JsGxQq
zH{HrPcW$lg@^P3#EuG-iR>cc4^7Ge>L>7g~4<nd}3|*Ww8k~f+H_|nB$J%JpU5gUc
zPa7`fDqr<=WlodwV34Y~^?}^A{geumB-sjI>|IqQ{3tD~o$|KGrrgq+3Bk(ZM`$-;
zGVL3xmnTfNWT+gxP!5EFWm(eEoY#zM$n(dRF}3ve#muqD24AI5D<SenA_cPvwbJ#v
zweFcVa3f_^_vL4LQHjI;mY$vpcvNnnI<HbgK0<8HaI|0sLV%On3TUK}NSLe+rb7x<
zx&<ZoRAh#UlFK_z2X+lcAvgcOkaZgHz~iQ-$A66CvO2o>7G~GRhKUYX#_mb&^_)}i
z>8Ug2VByJk@hgG1*Z${p+v3t@n+eq#(_P_rZdLVu0O*EMF(<m*TH|8A&pD;gp;OIm
z9BR@9xy}DAgBmt9R^<efJDMN_3zk0?#J1$p`q~dT34eRLqoSaR@)pcQ1$YOtwSvO7
ziUSRS>gDIj`?EjyGtSw4k5u-Fd4A#MySH=iQW9ri=D0-hq*+4Z#-w@s{7I!OqkJV}
z&vMJ?=5DWr&%ELiUsff*1k^Jt0bM2;N`75j?PgcVM%TVJsau<L!gbw7bK0i@Vj9)(
zj({U3+v3uy;_JRdbSvI;@#+TeC;YJRoe-eZ7s}%g7ub|?s7NocE_V(fN%z4D<2Gny
z!W(#YYqXE^p4|q049MM9peq9(09Cs*`-}9K9qQo?(TI&%2si%6xf5n2cz4-2<84+Q
zms*8mZT{g*Ds@57yaj{b83b`_y#mVv?)!DeI&j+{3y)Lpup92XWv$w?hHF#P;K=TI
zKzoX5+3eZn%(o3FW743fzpp{i4G7gK4j*$N)Tm<`ysqu6c)Qn@_x(wm_y@Ta{mm#3
zf9E|C(R~Lax=b^<c4Mvyxm5Llcbydfvbp)qAIf4)h7#?zug}m^K18=P)8zhH?1Rdn
zyz9$Vip^(sE?<~6n6N?|SQX0kmOG~@&LZDa!pN%c{|mm_)YaCw%_b+vWw7nPv8qI}
z<_@sW&TYNwwZ}mEZngZ~>VS1OHyoY!_Jci%PdyCYz&A1{aAsR9REx9idEH^52_o!C
zv}hJXVlI2APqzu_@35Q860@e6$+<N!ZtlR=<>7nFX4KxkmEhMBWlujd)_O#Q5U&@V
z5AI&p3Py_uL!8NTtdw`{XE1ZHGf778LWX^pUp5Ap+s!_}DI`I13Hoat;RN~yhqSR4
zhnH(N!`#h+i{xi-C5_|fZXZsH{B5zQ2P~UMzSAP_+_g+qTRMYPJSWw7FL9=Kx7XT3
zujZ>M(DBxf#Bq~g_~vrzyJdCsM(SEss>4Q4>PF8W^H;nySMFBfnyUhwAflSO*_<P$
zE_=2JCtI1=Uhmt$$LC5-QMD9KuD{)4rzm&L)o;1D{4QdT#0f8Ou>rF-sa4jv#ua~V
zV#9ul(H>JrE_ARnp`EtZDd3#8X}3))w6nq?Q6#99CB(G1DW$rAdT#1@hc-$c%ns~>
zdI?YmGHM22pFoOJ@Pcen-(Y|x_ceAxSL;NLhU3579dke5hOb<lVLR3?_p`HXLd#QF
z4&RMm(`{8*tA}TnYqGq2W6@_OZ^7jLh$sIE<9C|L$IGk@aXeu0#DvpeIZ0CbtfQ5?
zycEXW@2oT$ZN2aPWB1k`4prbi$b*Sf5Tu>&#c#;Lqg*hEX?pmPgwI5EA(L5=*`(>i
z24=P*j<N?cOPWlaYg2iyF9prD&Xqv$L)0!Vwwsy5f`emOlh?a-%X7=<@Q%>|;^5#n
zB8&rE28RMtCbh~I&o&>#+u)PnC~o}hAd6P3xwzifzR~wHF?C@S0p%63FW<(-h|bqg
zIAu1v4sh4jy+_+OlbVJQ<xYqSLo?aiAzmRQV51fehGxFb+MZJeCq+)Y=T%Pn0vCAK
zFxYD1(t*}!gwLWQJ!BN4ra>2%QRCOBs@4?9E1WiY`CL4%T(@uN-g?`6N;}`Z31{4W
zH*Ws+TJiXSk#Fzu3o2K<^SV*L$1eV{L3L76&4{uHV)*ZmE4Cl*=FcR4Deyp9-HMBQ
zi+n3X?thWFc$kCUUu3+}D20!_?bkOk77T6!_`oam43yZ&9EBS%+X>dX*s05v!+WF4
z!3bv1#EMkz5V*Cpv)4vx5e=NeysNc~!!7u6A|y7jRx8H-q_w=9WCUtt+X4%ahV%sU
z@~g~11<alo3(;zUJzsjXPWXkFGocIu2u7pWMlF^7rBb6Ra*+t;kt}m<?ab0tZ%t#2
zKpb%{3p>0+RC#Tt$&ojlo(w6WY#zLvlG5!~YS<>L?cbR#Z8m?FrNXoYM>7+#nE8yF
zFLQ+T+g!{GR@@O^bbF}p^zYSm2PpOJIGFTjJubu54;Flok<Wpa9MA;wq;BuZbu)Y0
z0g7<gW^t+QQ(y15Nt&@156-8$jWCRWQvQwXr6x~|blSCf2)&7&T6+6(^JPj0DkN-V
zQ_nIE#cEzYhvNTQ2((FFt_)WbL|{b`T9aXQr`c%wHG;jLepVObkC*6km!#FMIbm})
zsN|AX;BFwJSBZJS%MFKfvxKFA=x$w(+cK%U`nXJcu_YyNbYmsXc^v(YG1@tG<fLp<
zx>=@cyYD3!w&A=1N$hAd42!YWx&NDytQ#}5@J!J`Vp8`!I|GhY^DTPnmkmK}9unib
z1hG`(pp%?Rg?A%7$sm~U3_-UCh5tDkSDV4zNH5g22V=+~zB+EK%}FQSb{}DDJ8MAz
zA>Pl28i|~h9$@&V@>1EO7&ZR|t(TU@ZB}B^l0g&2@K#?hA{NpUSPqu^^E!S%YL`?o
zyu*8Exdze<vc~ZD1x)t#4uxtjwWL5|wKn5U1M;`%_DfrZ`wXa_9@(`+JfJ;Q(m7T3
zSChqV^ef^QD2?`)#YJE)mS0gYfOe9jgWtfz0`n!HH6zcmRi}k{Qc`My1lW9Tj68h<
z!r0t0zjZfm9+9w5b4IA!UG!Ps>Q(Iv)!JiZ_fUm!aVD;9aX{WmD>7Rap63o(5Syp-
zU9+kkCk_BOl9yY7-Bpb6Q!x2quHOMQt*F^Sy~J<mcDQSq)^phoJpr7JT6byYT=xJz
zrOA#Gl^4uPFL}6UF9iX5U}QGP-@pwraj6cEn)=1DgP_}lvYBN;lnim)vva0R76wZq
zZkRWRY87RmiS%AJ?X^5fjMN5}!$$((%=O(jtSV>QRGUa^GsJtL=x0XWSePwH9~Rmv
z^5oDL*{;e$=YfX;`yvOye<l+}_cmHciETHB+v!$Wt4bcebotgjiSJ)1tJ0b+h^0&F
zr&$XTWFyvVP%pseZn9M+IR^{@k|><<jByH|U7DEfC~C_i1EAh|u_Q)@9D9Get_JOG
zffuqC4A+En=W>>d)@AlBtGd2Mn=*+U|LzicG&fOE>1Ae9Ih&rqvd10Y!#6CK5uRn0
z<%40E7s615;swu}XRI_O8tM`NtPEijRb?{mPyrm=A|#X~k}Z>Bb@?OUK*Imx?9IcH
zPTR2IX<BT?${H(84Js?Au|zY&1z~0?ou-m$ddgD6lG4%?l~hoc%F4`z%5oQKES(;=
z%-j;e6*LqwP25pIQBhGq_T{7J{odpI|NBRe!^6Yd-~GF<>%Ok@Jg<{(8&dsI;kC;L
zjdgdUyz|8|f-Zg>Qz3&!2hLW}wzrBkXEx#%HPdhN$N+3xwnF#+t(WQi|7*Ed#BVE*
z>QTlH3T0fPcNRaU0J<3ea&d%Oumw5S;$ajr|I^)eGdW~KF<PT!s0~_<!G=6_i9999
zll05#E#LJpN=T?MnoShV%}`6!)S3{`<d_?s=B>hm+)PY#vSw8iQJ;&KeBH;vJRi=0
zCLFE-ibu*>QIw9=%wSw-blx%Y%09P`Pm56_RJ^o-Tp!Trg4V4!LFc`Pwp5LI_Ajwp
zk)72wTPMA6g0Jtkhqp$ynfJ!=*PFCP>jrDj!^@-OvaZtR+H&~D{^#21y;+!=jeWt5
zg??ym|1Pjw!jXv^p-)0?dE(h8BN!6yyYjT?+rgS6s<zJRqQJ-C2(OF%&NL{8OnAm2
zMDb=veG5`VwR4iC=#~Di6<eE9l7-S1w~7?u2M<8uUn9?yBXEKV3}Uu=h=NGla`eB=
z))Q9-ZcSgn-z?9YpUt_{K(M(CONT%ubxV=&gUc;|SXToV;C%}%Hg!B0C8@@*_GSOH
zA)?RvxlizASa}7I3$QChe)0nzo3u5HTNo&~xo_;_CEFUZt7WVm8JHu=m0iikmc!?>
zqvG@O&&h5b5k2@?eRdP-wxOe))tmwvPosjm0t4x;y8X8R#76N*lDh08g!=#PBP-@<
zE7K~lsW7kKoa+yqy_iw*?O!V7e;Jd9?sX{QSS=g3vR~fn_w51t=5sC;c7H@1FzS0d
zd+fYRb)^>MY7uv}LN2;%luJ~ZM+3JeplN0L8E;s;>WoSlUoQOkHf{BcL03(5)3yOU
z<x5Ob{K!O<A>nxaa@wg5z?Gyz=1`B&q}9Ya-;U7II#EwqBqaF~$BH4;g=1o3i^nYN
zi&_dZNT<L;slT-A^E3OJ=?tFP9qoQ$E(05;W!B?5?s`s`Lnk^-cGhoqQ8`W$4Ou(m
zFG8PS=86e(y}APjw13!s%A3GyI4oYkubU&z59{SlK~_V~`!@VIiX4Obm+dnW78UI6
zs0%O<;c0kXDS6PZC$OZfn>QzNcbhg%DX-}MSUN<`NpLM(xg98!r8+K^xN@Qix-6n9
zD?`UI+<iv8KBa4k4?gM-(^e0i1}#hFlq^bB&7}X=8oI7OU{0P|r{x{i8xHrWb~M;w
zpV?iC2N;x0kYSalv3C>4(KS2ClLyF?OUL1|<90U6+93A8mx0OdIbm}J(7RZn>(^by
z*AUBikLl;bHN)ad8J^u!hQvujnAIgkwg|X%$K)WXV7@wY;KXF3yFc}kj;cISGx0t?
zzRY*IE_i=GCy~ng&YUCc9vef%87ZUAy!J4i4Mz_Pk)iV?*AMk~NulD357jG10afW#
zB?&*@iQeMoj1|`~#|<L7UNjL!N*H<XrJgZidJVUtw=)u8i{{}R`Ad<`n}SVn$;cQu
zxQNe(gzOE@4-4csQ@(OiJTyMaK|gK{Dy$$Z*Eedcj&Qj##;l;9yI69rAM&2Aqw+rl
zaSj|N=@-TN2vwLV`z$$5Nw)<`o+KR<=k$Fk{<5B>9{ay9hZZ+lo5&4oe9UxlcA|hW
zY*jBz*XzuAqow>~Mp(L%gz*2rUI2+^9NB5kk&$^s+_U~~4=Y@Nfrv-r?o)g<f)?!w
z(9h^%#}5lnjUpe(KZ3hvxbe;t*W8FY5DA9Uk|2^LjX}vXfpY8WHQBQvfWE1HMF4<y
zEqV6jCbNWv5Ld|XIapL?rp1B$KV1JHPwXI1oL<<IJe+DMp{q+y1j)=z6Fz+P<Lf&q
z+Rh0+bdk^gtHao2Q4B?R!?*8A3LHlffcV_gbV3DEJp;M9VaZ$Sk0lS6(vPv~ZqAR^
zkn^@<tna4W(u|P>*CE3;#+zM?Xja@}S}}78;#d}Um|L#|pir7>M1ADp8sj)~FK^B?
z&C{7MZbBGeI>eJ4N)I5{(v&H5j2knZ%=dZZyLD-u(4W5IZw69CgW@oYLc(JA+lVSc
z)x_iVwPBt!gcdto%LK`wB*AM0)d*4bCb7&%{6RomG9gA{23i5JfBTy%4Uq^%OrT=v
zmkP-*4WOdZ*-H%_HR6Sn2nL%G2ut6@dQc}~o&{_xc4BsW69nF5YPo0Gd<s-{3?^Ip
zoCLiMRBgXl#ZNdBje<)KlW($mIG9><`eWwG4W0J9ED1l5RB*tPBZY7#%wfyspcN`M
zxM}S(v5G8q!wKUE!jpE)*%Z1m1;kmP8#HvH^0l$~%diq^<A{nlSSRSG$b&NU7EN-F
zzZWP|7ob)D15*E!q4-j`upGtZpsN-pe*aO}9EVAYb5(uc(m&+jCw5u*I&qI}+z(+n
z;8X%Iu9JnS+5UgF+<Q1`n=nnp{pyRppSs9nk+se+sdG|=ey##qPdoC3n3ILgX>Fjg
z8f+Gc1`X+xDMK|bB<h7(XwI0KeQ}WYgAsY7uzG&5q_NmLop44I`?Y*BYGJ4zs#;2d
zX`5X7b3BKuJclP5$3%^pDKcZtr3vq+KNMrd^SU>Iy$z$)Ms7IaH~;z+>t&_NGIpKC
zX61H2!~iA>r5SD&>spDUb$f{X_Yja*y2BF4Vgl9XO{#e#o@uc3R`%GbH?{FF%fEAi
z!0~!YVTv?RlUWSAbZo&G)bvbvG$yLmI--<Iy%Up>pPEr4|89oN{(3Xt22DwG=t4Ia
zq<G;(tIs8xO2x(ufo?)Vc0u{hP`=<n8ubmv6r6DTS;q3vMJ%QZziU&bld3~dI{VqK
zf`4{XEZ|hQ+Bie?y8gVF{7p&-#7YiFMD(XFTPa?+USvdnL4D0=*w(O#l;uJTaP8*g
zT15w-60n#s+gtxRMb=?L{Aw*IF;VYQE&d+49r!%cF`?Gj7V`W!rJ0QpDD+f7JGO@R
ztE@g_A=b0r%xf7j_Al5ehOlfwTwb~!D8J4!og^fJl4z;tiji2%L51PxMS|4RJ#s1r
z@~N4P4CTXC&cRle9(hA1UweYONMvvLql`Od64*CXLD2G`>KU}<eMCMrwM<kMoXV{u
z8hNA445)}pD*Sf9t#>4(t{{JQ^N*tB1#2Pur&1lmW;3#)q91+x?TD$U*NhX5qNRX_
zB3la|X;~TmxAi{qn8kU<SN7O6p%sj4o$w~gydPL_G^HH<JO>Jq8c)^^NCx>uwX`ka
z_PBQ3Oh2AuHY#;p5I*1%7Ptrp%gWJ4vuFgPF*sD``YC5L&&?@Qhrdb-EO{M^f<sOI
zVbw0t6$$h>758IbZ}jD4&g2$gD(Y`}7Ax2)8D$(+1|XyaY26ECqwYeZD$F<_BnLR;
zPnTrqjC-#U_*q{ymMNZ*y7*nBr>RocRl-xJ^5f)|!WVGWbGX|Q@l;XN@=6$OI&70M
z(a}HRQq<qNk4#XS{9wG-#n2rrJ}5}l^b9>bL){OmZaxACv`t@;cP<QJ(cB3nt}M{F
zn1926xvS~&Fna7bbR-Nn-fhx;(_i5qT|3!>{~|63oNq_WU!$if3wKCH=!<bt@rMC7
z_lBG?8S8wY`WEo8OSepAd;GQ%^6YT5uVt^KXXt89Qj(#o5Kdb~JR<vdKC#t=vGI|W
zS^k67UbuilWc^-ZVwC`l5o>;gCtw<~Fir0Z({H3Km#v>4r052-7P`V6i`Z5kCMGQ1
zF7CoSs*A-feyE;18+OWv{`r}v2BoP<;wCh5oiF!uEvCJBt}`yXpA38Y`<CC_TAS{s
zhd58oHBbUhrCS05^1?|geI2f%wznCOkb1M>kP7@@2Kv1(wXt!s5FqU62@#i?MR4V^
zLH@z$uIkq&wb04EUZqp+ZL+z}D9_ockK3bFZK01sHa+b0wdiv7bj{Qv1qU<3o>I^a
zFxBNSytL1}Dhu}?6V9b+jH>XX3+7^UevXZ(f96$$4{n=qkWiH;^x;kpE%{H!HdoPW
z!(KlP&>t)?yF#4JBFx^O%ZtQr9ia$w4)~IM7=j@y2c}DQEj%rC>h9II8Pw90V!P=*
z@qyVyW*m-r0xgHkX>&Mc%>{O~(zdilPC~=KaT`6$!p7NQ2=4q^PW}Jl;VCnn`A0k(
z9Pq2A3Z`-1RDd2f!GAW{iI{tXm^)38u0fNtnbq~sgb;jWLrpOU5b_jRzR$C6Ws99t
z&>`;bB42pA=p=|+{e)$Af&UAD%Z}Y2h=~^~2;vOTs<T{#VI|Peq{(ox4$m0&p`iU*
z7f|JOjXCJb{d6Y|`e)SVO4^OC2UxOm00j`i5cu&0*sD9qoMHI~_(njI_Um1uuKhs~
zNZSGmpgD136=5$%$S`dy3?j|C8ju~5;7B{j#53UCL30x4t+}GlDYeDqY28yro>i?R
z4GR@?f?m3z!x81(D|MN&8<*_ZxpkH?8*c><nj{5mY;4RaF0xvBhNQS6bgAur-kSgo
zkELF~5^hBaN#R-RuQ-%XHR~My+ZKtva&c}iNHjS+yMW$1kQoINB5MsFAFJnEsM3Uo
zVQV?a{FH_;Vf7`7njHpMDRnc)eSl2`11mGtLGddBSNp~hvTZrzJfvLXR~U9t3gO|5
z4d+-VC@XaltUsJe32z;V<*zNx8w-pM5f_A5OozCqqbZ_h=gis%EGg|UoH*S=T^si>
zt#Q1x!LiW_V2Qm-9`Ya#^K1ai3U6K}i0|SgBw1IaW!3$BDxSKZz*+hbKa`+dqx_zu
zeVtU1P{KzsT7$x=j!kur74<u-Y^aLgqLW8`KwX+u-iu~IUDz<fZoud@!~DJPP27mn
zSujp(%9NHth2QJCUGCh~dk!tXH987oHTN+3{GJu&ZCP{|jGH_AnAugA-9-v)S>)+U
zr^=qDLo}V-_-Eo=f#_=s3)sE*v8w%RZ(^8jS1w``0DY*<c$fG|nu13V6(jvSy0dzR
zLgyyJ%e>2}{kJn?qPfQplJU{~A0}SMz^cg^ZZRR|$k5j&S$3N;_miQb2$O8THlrC&
z<h$wXKKMR+rruc9v&VjYmuW<$H9t3cM)#fzi|8M<!;Fm^#>6^uG|L(X`epr>l)>7E
z9^RXvPJCJ9-@{g}UeNdRH_6p~R<j*d6kOlxF^gl=pI1>*Y`zIFi0@C6VZ6nKF=Ksu
zm{65v2)x7nfp)=lH()Jlc{?f!lL5wNWN1?T%5Aww-ukMdETf`e_{WF>%Ts`QcLt`*
z#YzFb8Nn32Fya{66|!PAfTiii9q8+x(Psu7s#as}+LJDpBQ|irsmktfm%5)B*3xea
z7gH@sI~}N#F0^FLdywXRQn3*Vw{Yhdh)=IHn`eYnxk#U8W#mdqpZeF<9JQ}6e+o_b
z=yT@urfjb>Bd(qhQ9}Iq0C5{BY`PK}qL+XSlITiHYMOz|x2H|o`{@1LHS@Jhh-dZu
zZchB7U6kuZ;T5`Wn2uM@QuMP^2J49c!$XwvA*<GHLawo=JHaWLxs2I~DWeH&{f*mK
zX$ha(nuy9IB1W|{1b`Gk#+0yg@1V^1t7HPfE0gl?1oVo3;N(8U<kGjrPXz{^{w>c`
zZw%Fk!)x5Q&s2N1f+G?e2kjVQjnvr^afKvb%ZDnzu}$~01~hwI_Psq5F`658m=ZM_
zjFG>-fSjJ2^!GpGnZM{S+Hv#V)wj<6A?Sc444d}9NX4F+#FY4;(BX`Q>tLVs?iycO
zzOm{nw&)oF$IH(y!W}ZFvl4N7VMv(rKrT#O2y@4ca`B@%hT-ow61Iy^AOt7Yque<X
zuA}k!DTH3|v{cVpIk?9gY?~?_($K^jGVYIjp^F+KPp;yHuVFs(hVnl$j1v2pwyue=
zva%uoLQ!TM{v#MSoeZZ-?CH-c?HDTc`M{O)S(MMy!XEm<Fm0jB;>|9}E8Q@)d-!13
zlwq|<(`_n^Fa@CLb@2BN>)<x2^r59wgMAY0Gl-@Gz1^*7EGib4X9Ni@z70RKQSx*U
zZDf)Q*5626lV=XxIEygb0O~4H{Ap&WYUS&NvimQgW0cY@tdWbL5P?{693H96QQ(*U
zjxI;lG&+ip&Um{g?*k!gdrhVK3v~3?mnLY~^~;SrLKcin*XN~#WO=$nm-Ov5eR`Hu
zS+xhzw*2t1`3{gk8N17HvfL)-5{MFUQw!N*$)k?{vtv2Kb1-A4l0-mnH=}+lNfGfX
z5>DB>ACW&LFL@hsMjO&*3Go>x+}r<6v75u8T7|`g*7uJ92a7Yua<TDYZ%y|XBUO{r
zMp2S<t`<d$i9|LA7zZl+;L+|Eym>3tBLL5l^yZPkFqZ^x?R;T*vQdKKe0oz@Zd5?U
zh^&xYYHb|7u%#7kWI`te>C>OOcQE9!fx@nw9V0YHq6fQ5CtM^Hwn}q^u$p8kg;9G-
zMH+LhSl)d=2)|X6_CF9-YE79b>?R)lWQ}AuE`q9;ftpHS!nAeiw+pN`;5#(XE(_JR
z>>RBvKHVjxvh%PGnNbAb4k58F8<mfZ_|+s|dQ=$mAsXL^p9hl71})grBi-70>$1DT
z^$w|p6A&`|BPS=my2Dtoq+xjjt^97HksYi44z*$C3}2PWvk9i(7~yH3Zw?ze%<ZRX
zN)wG@+FVh)F@g3jpQj?~2{xm{BwLPseEKV6^F(*W^?Bp_Iw$%_JJUHh>Jv1rDGZwF
zNqU^XUWPnxq|yYCwzXb>1S;V;cq477E#E{Li=e<=EB5+sbZS)QW|ai;W4i&KE9uSA
zd576nPL00H+U&4vp1Pu}>eu*hUFgJ{K=m7VO`_kJVrB(@lMV?{<3xm)Y}8P6vqh@A
z;jCszYBMx<eS~95$QJp@*n;|P{JNF;7f4eyUP*A6T$cV0lKsy52sAQ(2XSf&-;In+
z4gr_$IG0z)sl+y~LPXf%(9lReVY%{?-cZU>mrli)It~A9!8tk|@WBxga-$EwA*D>!
z=j%a)^ALn}3Uib;aBU#~QMlyy7`-|MacqiX7eit`7gg)OHErNVLe;e25NWIs+T9E3
zeqZsv7I}MQy4ngdcTrk}+C5UYg-Z%eyWEZ~Bz-le2gR6Z&J!2}QTLFbOBO~WM+~bL
z$7;21?6E$SG>X#oqjo~SmGy$n0@v}8#56XrN^~xtk3B7j+LSejM<g&paEvL>3(+~u
z`a4WdoIVPl%soN{W|Sn+uSc&Kd9UHZ;(n)#6cfSqGxNr>2sh3!X&U-gfs?gFL=bg5
zzdjlC-OT`VeKtvzeLaw~2p;n$A*`2HZQy7yslv--#e@RSoY#vu1S9_{SFQF8zE|CR
zmiWOzsiTo&q5_s_#vA4Ks{P<4ABQes`mp``qaENo93w&z_x_d5MUeD?x4~(+Cr0Jm
z=&hMhf(7+gh{$4_sEt`Gah&BnSwGz?)}5MIIt5%Bv2)IzD&CLCruSeN1($krK0%^9
zj46XKQdr$yIR>&xaH0LYS`6PLJ^7cX17yh~ka$#uzV5I!>O1;O3T>vd^Ch>*?$s5Z
zet|~_cC-0FUE;&sCc&MAnIyu@=>iw3sDUP$I6oIxw5w7KHArDZC6Kh-c@S?iuh=Hl
zXNPkx;QPTXA%3>F`>zfd{gbZb!gmRrBckVr`5|bp#==3PP2pfxl(b)xA}_nSSmtDn
z1d1;)0qn6si>dIkiC@@NVW?eds=5fJE;3=O0`iwm+#6=6tU+KR7rXiSW=Zt95?W~e
z=e=NEB2YU%Wutg$;(6n)CO<DAh|I!BIi~9nHzYze&!FQGryoSgsbB0Hzx+c1-9zWq
z*EI)Td9{p$$&rH}+fXYPP%)T9FeY&-`_rD`j@p~_WovqrYn~a2rz+s7x&mVd4c5k=
zvEVB*=xcp2N*>Ir?Hi<dGThZckLVSr$`r+%G)TyluI(UgXB!y&1;b95upms_k9Zuy
zTwV{ny8f{Uy*@iLj2xA5jJLw?XpEcth9+u)ws!+OphxR5Y|&q7{HHJ^aeOuK=MNb6
zT@^4~eb<6Vo7BE~wc<gS8s(h?L8TfXUIy6lzI#Yv>c^y=Wzh5hVJ4+}$I4V}>YQ^b
zjbKag7)ui1WH7-o=-yX(L3;Cx4t4RVOB3vG9WG_54y3A*>rGSoA1d|*O7^jc`!PE>
z=!3g>&MTFp7So`^8PW)t#d4Y=&{*Zev_>>=u@ky$T({j~0juYJq~OQlgH4^naEa$|
zucz$IGI`#J?H2p&O1B5JawG>i(bK9Zxh0g7)W7Mfho)HlFBV~WQ_{fTX}ft4EoYtT
z?=?aba<R0&g8q&n0u!P|88B5waRsmLUvBJE#lN+Zx>#$6V}-R3wCa_n?u04h*Fk)X
z(*UrA)?!a>k>o;w6lf;aK~h{<7a^T9f<NE_Q;v_TKDe=>%ne9fFt+E@D+g%F9R5Fs
zJ%!a$>!E;B-BY0I>6Eqy!mh+6<e6nu_wXa`ck(2%P-so}aNzAtc;DstmlQaL$I9wh
zx~T;oiNunPcY{IN2`^c;xEf%apQEsjaTq7|g7LlI-mJyL+ci0K0-=D5EqF7=nH?1%
zwcmsgS#;SuvU77CNg3o^VP8!EV#X9Jb6pVUVq^7xCLs#049I3?UiJ16zoCzl()Xo1
ze4qq8gt=WL(aeAr<9A7DzjU)1<WA{1edSD=n|CLO`1#oV?HqP%-7$yIh-31G0{m5|
zTdfsp=QW(U<QspVg*q-@yB7rQG8%Q1EnGEA7`SV}y#=<AN{1hY;S)lszY?1*aLwj(
z-)M6(E<XPI3r)!xC|5O5c|o;z=P2B+y`cGzW2foo=04pFGfve`{m#W)7C~Yhe&Wc2
zU7f#ar%&|1?@x5dgx7>=%NH(j0qGxw6GLcwGRkUc;RgN9yErJEP2JIsOi-6kLWV&{
zAaT)#Xv9?Or=GQf9_TzcQybzPaDU)%@G>PvdPZ6%g^KuT&O$k1*)gxRcAKjr+Mzu@
zF9bgs5U^x$nIP~VIDm#!mxo<g3(pjzJl&{?zHEo)E|^n=(u9CSvnji=Pv0nt6&0Mc
zx9zcb<2Zg>j@en|Uz24#bs79d7<0jI;rRH^cbE?-IGanTG`1${8ZXx<6B?z)l>@!O
z;ta1EfCmIIW=sx-F3NkU6pqp|P&lv0DPk4nf#lUr3ry|U>w+zAf7ZQ|PaAj6&XKfH
z&#(0qnnNv@tl4(o*KU^a|9(p#Y&B9TZHxWPY7uZDjO?p?TcP#ZE7WQuAEN#yG@0O<
za*+4IGx>$RYHYKqHc~n6%G(<f$WWC*mI&lw^zJ-p%?gB5MlgRuBRKqy>V_&4VHnlm
z4%bnN#q`_d2gTHXD?<|EfigcO!xjc_JFz)jjC3`G4kP+Iog5E`>B>&k-LluD3kldz
znd%tTlXyTOH!5@Nq#Asb)I;c`{C4;MAC=C51w84K2n_A|JhIL>=UPz!_VSzwqI!Me
zezlE2aw|;YNj90Fz5seTbQEM0ScTpN^OroXP(EhGR1aqDpXIq*#OvqGP;lQUv|#0J
znKW0t3X|57kX)wtCEo;#$Sr7yKy+TZCU*@?2>9Q&a987Yq)<Si{Eq-lJ{>bz@yJVD
zF*BGzUw-wAxDiYGaGeHQ;FgRE!wAsVox-!EZK@Fy-K*!!U-G5Y3gOTcS;W!4n8`7f
z9{ipEQr5Kj6#YV?@CO5yqQJt3KI1w$9)#6I2@Zcma(m9{MX`jDZckV(TL*F&0aoZ_
zCz>j7OHv0BEEwPeNS)6vs~#>y5vC)I(Wr7Zoxh-<R4r?dI`ANuoT0N5|Ew|rfe4Lv
z1gknycJ_O^|0a1`tj%x!LE*`hdS*d4$;w==i7(_^oKW`uHP(l{iz>^4nNH-{=9dO%
zDoFWlkDSD|-z3c^9R@B$1R52dsdYChp=EB#=A^noWNEG8f)3FrxIVAl5xkQ!{0CID
zOgKXuk<Z*P3i54lJ#1~y_YhtF0~|3wOY52v_hgjthK=42?wC-k;SS0PX(vPvVIZ?9
zdpjt_(Tk^r#w${{--%No#3PxpOBPUS<kZ^Tz|iTypDPWB@wJF6s`|CH-Qhye3yehA
zdJXXhJ5|?F+pWy#>Paf1o)lR)L(C$h5>cRc56djPJ@ddseXOHm2QPr%ZX7iD-W1fQ
z=J;{dDtv$*S^RV=>evVerTV>u;w{ym_iuc<9r(vm3;a|BY`U!TveW`^v#gv5*&J+?
z*I7aOBZU8JlZP7d0S$RT@6vaq0zzR%1#dhEU-=Fg7?3?>eN(@6y=T7Al@|l5J|s<Z
zqq5v7uFkbE{#j2aBn0*b{Zz=r1$g*V)XA6I`9s)3&GM+xWMET;_EQCw8rUJs*RHeD
z;bG|1CG<M|wI4Yz@Tp4{b-0Fl4sS7~TIfb^G;$1pxcpkpz_c5G=h(!~Od+#;p`f!y
zj^4s)R5_c4&SK%M(t4OKQzs*QOOl=7v);pegGX<e-0g{3In7w*u%f_NBTTh+iuzX%
z=_>U2^q$Tcr_`66#5Mp(%}|aSw7Zhr84179ZVt=QFa?^`E@6bY(UD!tNu$PC>*vTN
zG&NOx7Cn3^37Zf)R7L}IP<(#vy);)$e=KFJi0+Q?`N{~Q=V#(R=b<0j)wm2Yy=%(u
z4a6c+yL<>qD31;{mZ@iQg^+rGZm~R{iy=%Z*s8E`Pllma8Es^c?D}vUt{=Sm@Dh{9
zMVLjda;tEeuCYS%mMzv-EL&An2K=kYeo0dhL{gK+nI-6Z3fxVxoW+KiOa}&k$=FW<
zB@3hloEOpcftqZYk(1+bh0qTELrZ^xpYZ2PhYhlxwbj1A8WmmEf3tj~_l<dJ0&|tj
zd(2e+kXy*!=Rw<mmEHZd?~yJix4mk_nrpwX?T^I@!qPH$W$=em7QB<{NbiFF_svwz
zb)yNSD54m3J4ADqhA5ErIf`A;o8D7@X4gK+HbN5;d%FeM(n(K?0152e(t~X#PGS7K
zjaHu8ANE;1!Zf$nW=e{0N=`^;PB?+q<g_P}Ojh%1{@@?)UTdo_O{TC_Z<Wp`x$UMt
z!33TH&pT;vNk60<H!&tu_z5Mx3iq2}b>hP@%A#}Vn4;}wBe51E`)^+3V3w11`pvrK
zj=s;Vvn)vMV9cdFtjjZy7ku*V)BHB{#$<E<Q)cOzX!o(+5YDYLH*R=_^zqg6%^`u4
zm#ch1+<={t+(X_VqeGEse(D;%60q@fcvNFu7$>Ly8B5$C2}<D&3(`#CKZh|=I2xWo
z&!T)>4*z+w?{W(MY@7$DiaOZM>tdm|8hpKoNV0S*?+L%6<sy*C$PykPqALQMme%DP
zjwGdQnMcxAb%1Xl8Oug5=et`-lchl>LPHW%_KF35UFXQ6sHDK7q4TzER@a2xe#~MA
zfxOx^&bfg%Uf7&s4i&tiz<^z-y7%2U$4~8rJ$dJ8Z#!<)+?DFML>;%Gv>lBQ=55ve
zwwAeC!-0}PM}niz-a`dqPWu^MDuXpAo?BcRmr4IsRPbj0I@PGpd_oN01w`aol_!0)
zjG7c!YM%1iXfymrc*cfm?qmt3F<R&9P74%&Wj<w=FwOm)<L8%@cSOq!)Gf(E9Pi*=
zY3_4+<lbo)SBqX3vYGM}ob8P7YMQ7smT*E>hfmFC`1fcPAt6F@GSbxyzv4?=Iawlf
zWZPK|&mNs==1zLzvzE0THr+9~{6<pwr0OYlK108`P3l}B#nL*=LN)iyd5r122`Ky@
zCisgIc6wmDJ_C&qhhbAw$-=rj-zR~NM{N*D0}wc|(2aM@vC3dIAl%T;cpxD}T;I<;
zC@%JL#zst?@!&m4XtM=O6deSF>ji*thto@-G_y71@5<*D*(=8e^mlA2y_&|{;nuZ*
z4)3PmA1XJIE9(DhmhMJtoVwgGgohsS8>GMC^$<h^oO$C4gga$})qXvp-<RrIacEGi
zT6<XjgT=CHlu(mH`E5bn3e+!63E`^#eZzw>2sA{n+w2m8zMRko$m&{IvDOGYXnc<%
z@!6bir~F1N?p1qiAzz+&Gff&l{vP35SXRFdtv#O)R*x)-92V)u{+Oxog3`n_+^>MD
z1cTp$LzDi!0UxH0om0EP&;TPv7PU#YWM){}hyR=i_<(C>3DLi*>t2$!R~&E^uB;pw
z16R_6g)ZzY8&uaEh)5eMX&jQ|%|N`A_s;5ZA%H^YL&<WllV~L>e{A8q1`rJ}UOb2)
zeq<_hoY*P|7paBWLnt!o7I768I$7LSXHfoq0a_u|b_O0_hNddm+I=4@-0(J0Bg<ns
z+<yo2#09G8D|CnP`e*da&t4opt^8LoEPLFNkM|^N^62m86f31k-0Hqz*CI!DryVa_
zco*5c+Vt-qF^=h&NT&q+>H&*spxTX2ls)p81_Y+T{=B$1#ZQ|yv}b$e?#5dgR+FZ;
z)zdOARHiD7R;OVRSFS`PzAjtHUr8kojF`x7&8GK1)#T_2=h|w&Q!-c0uj)KVd)o0}
z&!%gEcQE&sgTA+OS`0hl=2-u;*oq(zA;zlA7C*NP_y)|ptmQT}GgAP7?<`{wuZxz0
zrhYSv|NP!eeQXuCUA_W#)WV;Q#6gs<FJ1b(e#ncdkF8q-W0FJ4AC5tzg>QSUB8`Um
z3$~1ppoG#m&KC@yLHH6tanl<@#0x=X!^7`s#c#L$Y;$q%+MB=2!;m=tm(c?6<foRK
zj;y<t+PwPYlI@2cS=;NfLsP#V^S_xeviiS2ZQ9!Fl^0}or2pTuex8mO*Z%iJ-jPR7
z&Fn){E7uH_^YTTt{nh0{J;`Z0S9&P{m8}@#Q0e8Gj|RR5neH<E*g33uw|`@Yoo{~#
z%FP}2HKjX1zN@@+QsB(R5A!BZ0kAzj6nEsnBe&vCn)*sE&vHopp(?X(4_y9vU2n`}
znRiMpihKHE(Byk)rG1{e!+Te!Nk&)Z(wp7jO8nzAgwHYg8EYtxWQRqL-CV<0w#WJS
zNFJ&B{4F-m!!<4J)kj8_meLitJ>900&^x^pyIu?sAU=OBu;ZL~<oM+A`xz<EX@gxf
zQp#CP^lu&15Wp*}d$Z|vha2@<o)O7p<L7V%O+qP7j%Pe$;W$fSpf-iLEMw!Cx!|jh
z&WcImCe}u*{s<X6`-7XStLo}kZPl`|`xQg+XHNqrP8vBc5m7gY)!xqe;ysI<I=qX=
zbe#5JZcDd~>@e9*tcD7rOiG{e9W#ffP8>{#k|d3UdgL;$ifdg1FLi@6rFajuu=u*}
z<XC!uug_|{vdL(%M5a&GG{tj=c_@81T_tn3c2yUJBi5`C9;sSuW-ISiYU^PNAwoNQ
z#FG7+Tk-Y4(#=Abtz%nvu^)iV$UiT_i*8<bcaIqT#RrNWio9HPnU%thYNk}yx<biT
zP~5u!NzCDqNAib1R_qzK+S^{dJK1UD$UHj<Gve)h2B(rV@-&U`sQ{r;?U;VuhBuJ(
z3LEFd-Q96JpT#%cIs!}Z7g21w2_iF4E;EgZkVhB3B9&=r{kMPPSTu~RXJ6+^zkrC%
zvNS9`Ke!8Td~y4@%=&x)dmDi(R$qp5lWy4`G-G>i&V<YNWe!K8yrUD3S=NeX-6YzR
zHWRxYBc4@`7A>)F^cRj4KPgUM*@qf;jF7wJ`*yHM@V_bG4s0lyemgIsO9(-Z=++eX
zGTsYDKung)FW?QAGj)+C5QXk(>^xZk6dhVLJEU7Tl5R$;Ngm~xkm;@s6kiIrD*zsl
zG{UI!ZWCXd!WW~zYAEyPl<!kbz)uNhHkx%x_LUBMc-CPTbO&fpLaeL6foycaOF{Lg
zP&#8H;y&-))sasj0DxMsdv`)rS{1CrB&<{VW~Xk{q0G0!c>0Ssa;t}30{%!ZL84-y
zzXqmLZ1S|fNqfh)1Ltd1_K}%~S}I?Xym70<tzK6!?-uL`lXgBLGgb=y(_a^T2O=&5
zCls|8vtfAXkNrI3q2m6sD4hryD&p61Ir#9?XAoSeuPx54$-M){$YgZ_X13BwF&~{c
zKMtB&0q>sn(4|wFNWjIVOO@t|^JYc^`I4`#NjmZzb%LzkjRmabyV~yPFI4?UeGRf-
zUuzhz0x`64dbVm*eyhukT>Nn((o}rg2b`K8a=D^n>;~RSG}rbNwmi;3|6Mme&WV8}
zFMa>`Ma%M?Vz4y`_Lv3sz)7b*9io68n&h2ij}u3?zLu6gy0wekCSP06-$UlPH@7EW
z(J!&TiI?bg6W7Zc-~N`~#c`Hs4*<$!kS`V)w3_N0Zo@t>r#939`^EI)iZU{z>cuJT
zecQ}u((snV>fjcidI9B@=3l$6o`J{6ZAo#TVm1J;=6b~a+2@V?0(bb-^)@5)arjg_
z`fS%p3%hgkfo^}i=S>71ePCUa|9bh@9US&icPz!?uknc!(K(dw&48#%$^tBhW&Aj)
zZoBD0%JkYp{QON4oQ8c{^1;GHaxyzYKSJ`YS=vk0xJ>%nt?OP(2F&-4R-g}$OeU{g
z2B62*->py#la-N3TA1qh@jTjW9^wP?^0;Nwk>(`XxwuHl5b#J#d^<_Iw7T?9vg4LS
z*2-~fI_j5emw|=Rsv&RBsHopNN=7#)Job%);KQq3BD`;Tq&}rb4-BWf8c&i-tu5wU
zKCEpX%S$JN+Rz>yDh5?uPu=MT3JCd(-}G@PR=v*36><KaY_+9o+)I24lePyo`LE+T
z{nmW+36k|ZsBV|wpF@UQIj+Qyi-hMoGq~uC#=N)(dL!sSZ|rmIt+1a&vE9p0RdLrE
z7Ww=mw8b>T_F<P^#+e%tAB5Gvk70zE_N;PjV&cuoS360oZ1N=A!lBzZ(cNLIiaaU(
zY%D9I2C|{aTpmCVh|gz*(@Cb-t`02zgK&<4W7}a>qSdu#K@q+_4e*hMa!ui5e0lWk
z_#$aGb%JB*F!~Vx{WHJoVU1G<e$Tx0qwxL+MiK9n<x)qOx$A3PS^i;(jifc53&`xf
zEmT?a7qEXG5~6e#q)1@B*Y)~%A3i4Eh$FlmMMZ9EBJjURLnC5Wv>9{_t({OPED^bC
z5*w#l>NEr6(e)HxdN($v!ed@DDAvEB%d&i!{~<9a|8CH25$SG~oAe9RogZgoy5{s-
zH9yr<>RG0WEdBX_ievLA!Z1n`UTobe3#Lmi^Hwgy72S{O;nz<es1KjQm?O^wjg}LS
z7MMA9``CkD86<&vRx$bz4R<kFc>iFbzU4!fKPQk4Q!d}`r_<HGhPjlf6ZFy$BMA7C
zwe{dgBkI%2wyy0an!!d<eEPWn@N|qP)xSk#Mv3UEk`l{gU5U({P>UxA=<{>}&2bM~
zbc0<{esxN*UF*gkRU~0GhxLxmIhV>e2skf<L(aU0IHC@wJ@~+=RHZr*|LkKP*?6w>
z6S=6@T3V4qO2Ee_O@C@V>1b!CYV#sh#1ZEIqsrJzsJjY(U&DN2H@qaHBu4YeX)RNI
zCRgg~qcXr$Ipa8uzSZ?_>29Y47APU`UiU}t5Lebv6dPvbIraZZSmXQ^|0Sq4KmAm_
z6;J<0`q(^d)BSG|7?pS}K6=b<O7*_m6eK@3PfuUZpSSxC+D52>kBxYjIqcE~^KQ=A
zxsSp#ghiVz?j^Kn1>WA+$sY&Zot~tqq=_(PqL?N%qTTHqw-`Y}!Rz9B!}AecRp2jz
zWH@(P7R!-oE<US&u@P+1*+qJBTKWsK506$I6}Gi!#6j+YJ6(pqBn_p<pH?i)OqJ}5
z_PCNWR$Hjx-r~M09QFykah9|37rCu!ugfrEtZokfgRpm`<Mb^055)<{fxL?NuG|*|
zng`n$b<!(S*6=eM4P%qaKLRUip@Nw8cud(2qU!TxAp0l9#odgUJD};OvmYPCBwD{5
z*?U24mFQ|Ql?4#a@GQpb7Ze{yMU1d`A0~;n>_K48f<yA5w-xg2wrpw9J&}6O%e=SM
z;(J**JD>dZ=w1*qe&Oipg+H?a492-x-Rkbs*5X{@53MKvT>!2j%gy@agf?qs?`=)4
zbwW9t_wj-@lQwO&a;(5ifVm7nRq|%8Pr4Ie6$JfnMiYSq!jwFZ^=@Ho(F3N66c_l0
z6o}3+>V9BZJhHJ?vUpo*x37EfSCs<-jd4Jg>;Ho)?1Bf@{}9JHrwN~hc<n70{-#O(
zx~>(*XeTTkLx6gvhibx#!@oeUwS}#zze{exjlbG3HsExqR{u}7s$KV|{&BqM)UJ3z
z?D5NhW1YgNpXl6_vHlA-`s&fJ3ejxiPw8~=Xm-f1uDa)iPGL<__J8&owyrVo)D?O~
z5=UBgTU%ACO{FZD%q1-sl(1ui*kDPgA<Zzud#-)0i0Gn!2XpVe4K4?NM}A!2u054L
z_G;==Yj2Y3!(Swj@kq-EyjF^Esr!Pd{Si1)0KR-_7&()-wV?PYh#J58`LH!iK>88R
zm)>5B9D8QVLY2_aOS|iDSryXf8Xx;-sed{cW3T;%+~`p0iz`v?8wr~DJ-sU|?1DS<
z#Zjf)*9Q0t?W^IB2vg}R#t0Sq!)Lhij)&wmPI^Y~KOdsmwEzwMcqiL|I#3~*+rCfH
z+lKiw6;?c=-m}d{;}Xbkk`(wjK%BngRjBtAEfvSuWc)3;Usdd8pyuT+QtY6Yhne^1
zb7Fz^hil){rzetTG5@9Q;~D_=0|Dzsxl=4?k;1t*gW?k??^-UMOdfnM9`3;|j?@;e
zSo~VAm#OpXU*NBKeDdSYE>6|7(#~!fhL=fx`HAf*)xF0xx0k#fS&R>SHe9B~Z<?je
zxPYf~%tn5$rh6hva$>JdHr#HQ&c6r{zU7DVl>RorV(U3MuXoX-ePtsd(?tBMA&j_C
z@q=3<RN$+99Jnk%YrEoVp!)!+WqccHsxMt2W(u6%aTfv<<+Fp#1!&D-{o(?4Eej^P
z%&ou6mc45f1fBvG7?1gL@bnIDPk8p!(0e~`y4a|~kcFl&G=ACX#XNYIl>0!tk~^F{
zfT+;g*ODWI=tX)FpA9DP!Q=?Q@?1e!#aT4%b@#xqUF<9YDK6TUf9TuE$0q<h5Dfv!
zL+hpp2t3)m5fr7rHESze4Y}P}nJ3!5C|b5UBL_Dw^c2_9M^G!uXwR3Kd0(~bHV$7k
zh4?Fq<2^soB3Il8v>7nngWmgjLAN*^M@SB%<*;Po>HrZs^-96G`dMBl75bUGeF~DZ
zcs8Co<QK7MuDX2pD@jg}^>~B&vjP_Tgig<$da`)?qw8hyPnm=zZ&zgrc<c~S_K|F|
zZ2?Z&4!DI8Ga$P{9u57DKk~immwxc{l=727GQrUD(_)aIu^ndBiXot^vuoh3xLy)v
zX~xF{#anU)^?F_c%}H#j%5c2DTJmRp=6NVTNYli~$e-@(pDo<NELt4+LAKQ6apVHP
z$Ffa|@EX&({NMpYJ&~5}7YF=3!!m!CPrZX&&A)l3eW#skEL-)Tzbl$Ny}~?~XE%O^
z5bw2ZdO~es93$^^k+@N$ZpGO4-kN;eJERouUUqW<vVT!~qV^FhfVtEiW><ui2do^X
z#H9BG^gp&ODr0B6<Tz)>gFtrf%r~ep>^+0krL;o+Y;l2SvTgc-sGL(CqRKM2dna+y
zYd&no>E-i0##{+063lS=rywNgnMFd&W@qM6c1`C45AYRSG+=}Hx#A=iCmEm5Y3!&q
z$}3Jnx7;Wj^23MCR&T2+H~TW5I1qOjpN|m)%Cq^$f1R*wwV?dDXuAMUhe?HSXKds5
zc84-BlfPf41S}!^$?3;B&{suKk1Uz4>1MbCY;$|9s}IfmV#GVhN!QEc9%KKDLRQvV
zDrXE2)lw~<0S^cp38^W>(?r0j0g80$aO37o3%*w`iQn{1nE-2a_RAkQ*VF1vdQr#x
zvo2mGJNz18bJ+8T=E@$0`Mr}60t*3vSl*}kfGW!t*5?oRyu}D@nG^BRy!5exF*7_b
zoo!DN;miLl&udd_IpIm+jbR#3z91tdd<X=X*v|#~NN?Wc20E-O_8l~KS8_X*ZIvvJ
z*b?<v_X8YL1+PDD<ITMd!QU#%A`ZyN=_El;)-A5~STt<~vL6MMeT!t%Ta2jV*#;`m
zGEMgW)~3u?a6floO*%TjcFFd32|oZh?78l+IDS-KcP8R%#!5!s<`QzBJSvaptNK`>
z`DihG%78&c_+H|un>D$Oi?c{jFMneal<nr(#iwmLg<#B6r#Z7yP(W%!{=Z0TcHV~m
zl#`Sd=ZZt^N`EtT*~G6G30QJV{W{@qe?(Z1?3&nS{kV6mw;MNo3Q*(59OS;!D(&Is
z=)%2$m21F_D$~uVhY4w)a96DrR_7x^^E30a++rOcxd_IOUC?ZlhpZR&X~UkL8dYgj
zO;T%{Md4Rrcv{R}E=zytK>tZ{=yXhGIV%+5^h}Z6^pQ8WzzTojr(YPQOpf{KkG@#B
z)!K*;3GCO65tsY{)M2BZImlxY$bXr+kOs7GM|6hf&0*97krK0n>dP61y%e*^3dgLq
z(hlV2qtY!`tu7ty3gd)5N?8zv7I|<FWt!2&6NF4ZTB$)@VNk~zDYn4JnlX<#{3Lk{
zyJ4*;3TQnG!r|}S@hiHEpZhj;M^|o9Oy4nqRE&%@l!pNM$6}U-O*Tz!O;Eaqr*+M&
zfj*=JzdJE=?rIlC)N$2zamnrONtFa;xNCQs_eA*Os9D~P7RtO-6u~ank&n2LuBR`U
z5X4!quR($|Z1I^pNCsHZrgQZSGEW?}YjXp3+JT{ymc4A6-Dkc@RO}5pC_;&8*<~gk
zyLPu~)dvB)1U^lhUOsRfTcp?-kNhtju_KY^=bYB-!v(FeJSTSDVS$ZTB;+pP|6Ugn
zJAeygq!n>+)6QaL&t?K72(`maTmU^1q!mDL*9aiHOuumK)H&9^i1Vu>S~`#pE^ey!
zx8_-vlLzGc`d!gw#qnVoJWJ&PvXk4w3)+h0Ow5&!HOPKDUfVtr#%VSToD~)Nq51VS
zAODS;Jk|n`LvM?%f`W$nz3o8IpbgfW+6LNkEFHX*tKgLU;!K@ku0SHWcPy7;X*mgi
zQ(q}}%d7B`X<CLJ|NT=pq0v!c)cf!ShwVA*A|jSPSZI8T(pJN;4olLC7nr02cEBsH
zJx0ggA7NKoNqctVDswE-$OFo2J6FbMUt#{f&+E;%uoJxHWaJ4bJ_C&xi?>gVwitXN
zt6I;!r^4Jp>IuP~98ly8Q>*PIs^JteS&&jdf~sde&^f%FZdDXmfuZ;0bJlm)fUOc{
zamR2T*D307rd^^c@4%HU%X4U%4cE&V>NsS_pJ@HqAF%6IxHsgtLe~YQyy+;}X%ZDh
z`y%>JguStj#o3N$P<Pft-_vd=x}jM&#|FM9MXq9;sl#!UZfL#C#Nq9UnI7<TvUKV)
z0;bJ@Nkb4+ulBs!k(ou^NpTY98L7C^S7Tml!A$f@Rm5I%;3uEa9G5Ls#^A2O*e5>X
z`p=!(z{|(o;G-%p|4(V2VshtVQvHs6K6*s!8b())+HEe_6hW0v$@Z!q{DMWGGt8Dg
z=UGFcvl~NzLP+LQ(TY9kE*^g!74hR~&qlXRAQ2YmU%#|Ji+QH#E^p1%u93F$YUNDE
zxS(oG_W<0g_KQHpLwMP23#ACj?g_~HY;y|RLdn~h@ouB^&D!C2ENWEO>ga<Ww0DaA
z*{+0)ht@p#K5{^R;b3e|+>fr)J2fVvOPh?7RXoY$y3NGvMqh;6x=uH5xoD*cZ6Xix
z=l+|T9QGgbFUC5#kSX29293wz#@`3}0PDAgeVzv^f4U)}y4TNNJ&3LD9_5sXg<lhd
zUwgAc>2toi|MsGq>U34m-Bjp9lc=q7|M@^y;vy(kqis;!yj836eQ&HAE@)iThihO4
zp+TDhOc!d~6Z|8>v@XuW10gWYV4OB$-LRA9R&m<RPvNYa|6yf7Yld@cRB);4JFeG=
zBL}ax!1q`b03OXCPa3wZ?G+#M=~Jwfh)ktv-~xg5ZxHcqw<VgMaM6}ys*IYL9U6?M
z{qcR_J0D5z&VGM&E2q+4To#XLUVaGN=7sK_iSZ1`XDy%ja{IEun#e~%s?m+V+NRH5
zgROy|g*(Jgxu3s<`y<%=l`*SC1~0M<O=kJKIN%{p?PM<u4YBD1>ZQRtwEpV%0RBa@
z65S7Er+;8d3a&k8U0dtk=+7sVne9%L?@2&6uZ6y*__1-1f-y%)vPv-aerHPK{#$`t
zu`N$7MrZ{UK6$L2pTEu2QI%WHAKiQ5cd|{;NWl83hm77deP}G@gL3aZe0-`rW^G72
z8)kBQHIm8B*gXGyTj)jAH=(WMhy%jxsr5RG)%lNpX$)|Ob|a_bPEEQO(~~+YZ<I{^
z3U`tU{tf{5O{W_X<3Ih${qij0`HZM=m;5370(|?m(|G}prB|&2B{xbkjwO7$e%k-c
z<sr4i?I?~3zS?HzJ2c&}9ftLFb&B+}r4J3s%u&$lfghN$qVDaRPX7Vz$>3#IxT5uU
zqukYu)}#TBJo%NzvFPRyaVmKyasmHRGYdV?rL&F+sC!SDI^fct2qVz5UMPy96Z5Qr
zbcwJ2Qyg8W(2^}Q-af&^?h2JlF_w8`%6{)Sk~do`A35F)TjtQN<QVXYP3AzKp!%q%
z;CF6=g5wYEX6v5HLk#nj2h)2?opGH_QEyN;A`k9iBm`sUo|@-wjX3BC+r~Yn{eUhN
zP&@C9G9Bj#Dv)8SI9_rPnYp|7Ym3=gN!Ilrs~?H%V$*HIqce^qxs;*+;L+fvPIT-2
z?2DLcU-txFZJ}Y}W~{M=5XOY~k;uV)i9La#Q?#cm4iCcrQDAe#CZLWLT9%~<B1{c9
z7KvOmGfcovD^Cspbaw~1i|~=)U0)Hi#f-b%+43W>N6Vy}zpIrUN4s%+if-zZ=hP$o
zjA*kw8Gm;!i|Gcv#b}jj7kRpdzySC=8@H@DUcbet<Sz~(pTDLBGVyUQ>Y2Hs$R4Zj
z!^h;?^1#z>Z0v^lm^^;h+{sy6Pu=@}C?Akr)aWEt$Hqc&yt48)!a>l3ybbeNmS_Mg
zI|LBdj5-51=Am_m1KkLb1zCZa(!DUT=6Zq5Jrm$R><GS&!#u96`E0tI%PknWHhq!R
zx^~P}6pwRN3e7$#Bz8;^M7nR4&__E>VxF3FvzTB3X@Y6S=xyDsYqJ6;)u!2)JzR15
zh4u8h(Zln0+9&tvBaXm##n`om=gth(cfuZkrnw5bIV5L`cay!ml|xRer<jd!Oyum6
zOT;43tg^&e|NW!90UM&ejiNLMl^?sPP?jB}IbOfKVDiVMBsZ^jAmvOU$p<WxD(oEU
zVeOW_8W;HwbU~K(nWFss0EZw26Q#4KK;-;scl&nali{nPwp><rYy`S(<Y=<9*%SD%
z>O16g?JxC=Q&)3c$KC}BzB&jraV7&G>5w(;<(Pkj4N$^>NKGjwXzZ`a#eCwp30v`e
zhY6OF%?!bRnwoQiF;gnA%E=`D6{zg=My4`QS3+Ew1zD^9>EzYgCMow}fWX;qD3AW%
z>FieB*V&I_G2a=%cF)F753QvM_7-c3|4GUr&A^@Flz61R%Ak@~RKmVMqNA?dl?ur7
zRWSl7u8F0ls}2RKZodr5x4FH=puK8TY8+GFY@N6r|2yBprkfHo(9y4Dsi9Ywj@vs$
zuB0d95WlXRGZE}CmA08D1pR2e)_%Wz0BJYeuPF|y`GRzJk}$SgJA$($uRl3`2tKI&
zYqR!%8!@?<65DT6S$3Wken?ti^NiK2eVvhl)9lQXn4_3Aj2HPsWmC>${7_ibx%7*i
zit<&7*B}dM`E~vJv5%f-K-a1dl2RY2JpqmfI`fWZ%X({|$e~cDx1GBkQu^Aej#dhu
z9EfG-!|*?SN6IM2wSc3t28<wc0a+FU{C{YB^SGqb^?&%BsZ-NTX=aNH>NKXD#?r`K
zP{A{&nXGZ)%rLouT9BEdBH%9SG%ac_q?EXT%#4$axkW0tn~9*gRVuh!fRN&b2#O0n
zbiU{N`=0Oj`8}`K^LqWB=YP1a`?~J?b6@why|1q5wR<!$qjdfD5@YN1bvg2v)wKXK
z+fm-^xACOjMAhKCczhK-Tr^mj5C*~)A$a{~O8i3_9oP<%0ARz1UgLuhmaB%hyP<6|
z1MdDlTs&@UOZ^?j`|T<Sc5UV8y`a@|tSjkK-wrYEDfWd20Dgw9LtRBoT*SH$2!`?Y
ze4w;P8vDo*KZiD7Wq2;Mne4<d)MT^u#lK3qTlTUaQ|Ah#aX+p(ZTz$NGV{^&ZdCX+
z#FC(UE#<?`_bpkkNuP(9)%mryZw8Ny_3j*)xcheKZ^U{NztW{~@n6Vl7brfk@7n~?
z?9|Z8mnH3~f5;xDtc1jvF2x)Av>WFe@H|q*<WR|_==NeelKv_VAcRS`9QU87IX|z=
za+>~n03Lq1CMXa|mWk|n2P69kkn_q(C)PMQREE}TWpR{w2@^`wIB?Jxf*}#kSTpUd
zFt{AC`<D{5@X}w}n}2zRe7Bg;Jbu3lGZpk@bV_erle#xOa6fy#TOI5q(|)&OHO~tr
z#<D*v7T#ih%!kMh)Ys^-WtD?Fmr8WIVV=Sa?PSe2>x$c2S7JKBPS!K;{v#go5^_2<
z1iy(N@z2?VJP`#UzueS@S6|swsZ<!E-YZ=fGd<0hKiM3bEWta6Av{)p29>Z1vSwp<
zLcwO&oLKpcA#iAFP&z<yt9~-RA<Jf0{dd;#p%jTN9Sk}1b{k+bx}KQxhId6yRS-;H
z1;woXgJ<y)lxw35A_OdB5<gI6e_Kz*q1(udSUo$wiN;3L)xPoVeC=-6Onl-14gPBA
zdM_#MvHGa!n(7*z1Mm{V&?i+tK}a&Oi|4}3CM?zPHt@ap^Y^C@IutO($L<d`#{JzR
z(_Ipz6Y7Lpvpb)GK0|aa%scUq3i1Sp(I2mleSrODo%=dr6TgRc=r3c<%&z9b_!Fxo
zD_$>y)@3PErVDWLV9b|`u>QT1`<tkHhE}O4_n*@^Pc|}S#{v4wUvUo{pMWGQGD?nV
zE^nXJzQ7L;CXY^QGDANkUkf~}*mJezm)lN)|HJ|ae4oDZ&1u-nB&z=uNA=rd<ALPA
zH;<$-3>_P<sn;$fy?yUA1oTq%`Fy%Ed)9%=y6fciA|CaR_cl*Hv|D6#y86w$%#|^Z
z5DZ@ootPG{-(}zvz9Pxn>l;koQ1s#Stmd15kAdHNW{S5ln{6glg34+3Y{Kl1U99eV
zFpZzHqjh}3%q{ojG^ZIT%ntWHA1~Xvf}eS-sf!RS#BHoPwEs=h*Le@*5Kxf+I7%O~
z`}`PSIck>o)3L}$QJtsm8Gi}b_4np=aOcdx1Gg7rCeZsD1*+d;V>)jmKi~R-zW0=X
zcYghQ)3~H0d4uO7f9+w=)Gw>VG~<A^_Gsh#NqMHSyMA4XO{8#9lrlzGFU3Zg2Ao*s
z>c_D21WOJslwO=c^FIv-zIF6@Ld>k$=Ec-opRax?r99dP&WR$|x2IbSLf6?E{)|(K
zURW}Qe|;I2J26pnTk8K?5R>1st@D}S!p_RyM3J-YvCY%-AM-mq2}V@lz*g_X$mquE
zI(t_&lhUL*TFai)jGAqsmm!7N#orY*6+T-FF4h0+>ThmeIiY#^jU9K@H)$@WH8t4&
zh}+8R>;#?HmBW;Dt4lKlyH9q<f?S6<0qUXF^|V{D@i~xh%rtQa=k<F!8%kz_mAmpP
zsTt39u;(+pI3od*bBjTcakn!;W+#(5zPwlT<=5MX+TT00Y@KbNWmErg?P$JUDl0d&
zsl%Dv_OY{Sj{C_>w;au^e0F-<?+B1H+0vZ|JFhhpfITh{E)=(nr4T9bl2HZFCRI$@
z&ONbw7oSD^^(Fnns>bGb24d3Ae;_m<&JWqyezkcXqNJe^lNFN>LhSOzej}EblJ2!$
zorK;3j}-P2xjw$2iWXdbyz;Ba6c~0Sq&gx#AkCINAGeoWkJxZ0AT}1%$G65(zO<rG
z%06!n-5oi>X(fpd#A+rLN!k`~!rPK?%>tSHSp&SlZF}rm+wuD@F#o7edXf$|f(Gaj
z>j#d&xLPBe^JkAcF`8$l%ez~pscWPXA#r8xg%-F(v0NK<zW+oS^`|cq{!aMJC1u6D
zrX}pREwZM*T_tGS`q;WRq-!m-g?uXetLyVu&{QWsY5882di#n@?>1%bXZU+wf9eo+
z(-TN>MTj{>cLROtr<23ir{zGh;8mE(p!(T7^C@k`Dx9m09Va^%@oBBT^j%yEkHyh^
zVIi<n5w;D`pDq8S0$7WZ6*<NYF1T0U!atLiZ_nnnGr!I$>8?@%!H0lC9tqI}9B^*{
zE~HIXGk}a_qp{HI9^aKDt@Y1ABUc{)tfL|JIQU(kycNNX2a_w)^Nti)-+EKOJWpmC
zTmjTQAeYYP(9!34UuZmB+Y3-lj{67^O6{9DD0lPsvHXVXkC&@MiWs8m<$`6Aiodo-
zIq5RjJtRcRo<y}NJ{ET~Y!Jx%h>4XtQ%ia*-N(@NH-_TSv|(S+{A}}`SybTau&XZl
zNq9i2lS!-Tn%bB+IV?hk&(j2d;GYuCjKYZzf~UU1B58INfhedrrBlAP+ODk=NUe+;
zZ`%>}&nm7s*2JFq(26lQr)~BNQsxNIxTOWBzX9GuzYol$Y6oy9lxW7c;49&ag7}f6
z>qQ2-sqcH++q|#SdG>?8+qwAoB%}0`><9F*0XqJvZ5pJDF^UKm<Yc#}@4{2(VYHOX
z_4M!)ohgMg9tvovSMM|)-550~j=6Z6qy8jc!67O`HTP^4H<!l~?!8Bn6#y=8S@u_#
zZK{->G{Ko~3T(U5-T-nvdkvMofBw`J;`lWrr~zcNUtY7ZCsi~B+gaP)`dfNh;s%+-
zTj$V+;oH>YSG3pij%UKa7MBmHb3xlDrgz@4si{BlKGSLX^u!d{6a|TZoW1sF08Fcn
zQvZ0sPBm{T$#Pg8hGqGC%vYski0&2~d!TRDX1MnIL-~dFlzB1UC@W(KHr1%=cz$w4
zS}s1US-7kJL-Lkqz2U@j7)Q=5iu^)SSGsVoa3}DTDS%Mk(GBq=`!=q<;s54b0$#ZC
zoICw43ucF4mc|H&Ro^(<c*o}G_dUsjl#loTXO8w3al+ybL50!f*}pd5FF;u*^woPU
z*eR}*hzEU1Pj)Ihm&OHIhji8}=&YUaL0zKPjnQ`8kyqQ-%(m|ZZQNrR3#!fME2RNz
zlc%nyM2UW0Z;+lS=73FcFAo4+=f#I|kUDrHcnDx3cq9RDICM$Dxb1ZU$acBM^F7LN
zk8E@_mFs-x_z<vl`mmWQ^EElt+OB5_Q-U0y({}?@IEo6L|C~mu%-vrg|MiA#be$J<
zyhLk!wu^Pv7jGq*M7Lh)cZ%+b<=p$R&6e*I^K3AW_n0OKSh-Lsg!eA=jxx^al!#|x
zkCNz=iO0$Xm`1LB@xdkxgO(a?_KKo2f<kd&m^a`<WJoq<B;-K|Y;|+KQe79ACcbdX
zIe9)u5e(V_<&;+HTThhC1lZ^{5f*{>0eL*{#MLGIjS80H8W_5{X;K~GsIsbd1D{nn
z?6+6&pEBgpItM5APaJqj*_u51$R{V%SaSVO^rs9K27r}312Ree9$Q?vt=Sk!;Zrzy
zSZI{5o{K*+!P*X69sN{^uun)N?3@|E4C{9cxT*F&tr=K7oo|;hvSdW$%>AJMAJ)rP
z_hBG*;EjR-w%ZEK=_Q`*QA1=$w+^$uRvk(owe74O)D*Xu2}iw5wrHB%t0~d7%dp#5
z#oi$;>zL_@Bc(TIIy9u@{erKk2yy*`hG7+)+<6J#T(kh#Er0itv~Z=o&1nvrqxf^a
z47SaIQL9DB{f<)MhUA0^Lh)j@Bf7!*x^r?hf%8sk^hWfL0qp8!6)IV%eDEn+xIs%u
zi~`7!WE=i_Y*w<9j*YUiHgNa3HE3}%D8}eT#F#jjVXQdQ9e(M^&&NS|jD4~fQ}0th
zrg`FbX|5aW>75rd()*l3btA9ytTJu1aaN(fnf0eXa8ChySUW@7{%mzi+^)!hPOr4g
ziAPVnK8d4>-*Vj+PPU6Y&ZzkNuJsqnx50Azko9O66IgYXskk!2S;qM#S3>8W9w>re
zABh~v@NwQpjHoZ@t3S&v8kQtBr5ksaS$4@CxQ@OAl{xEq&q)=NU(mwsqCGH$_A#(k
zpgJX23|iVaUp=MdHnSUAT^!F%700{dg9kQG)4{x3Hjd9N7yE*1%mMnY07N}4?hN-$
z`Vc`iy<0Z``f#UDqYpy|FUN28XS(8m6w$f+u`S%ZixX}9(&eo&>Fj(<uez})E~Cdu
z&Z!$oYB-HO>yPt;fzBE724}NL7qIxHv5@;xsIXYD5$3WQ{+`pF?d8JS4h2?Yy7b+i
zTWfu*ofHl&6}2?+rdR!ecs=FI0UK%ZKnRF`3K{`cMZu^2x9%{PkDAkQ^3`|dn<z42
z5NhkPt~b-dX>@%5^WFAmJ&b}EtEbM_fSA+FhuW&JRFOl9dHIR>uqt<F+^AJnLu(5F
z>>YtW-7_Kwp!smLb-l8u&^{rVd8>w9W>XDpL7$}Tba{a`J>=p~N^cMcS^H?{mxKO0
zrrJKe+{&)N5!#zUdd}^1)~2D7>OT31eOx=r+37yrm&n7?3`D5&ZNcP=3+6QwD<4s9
z+t)WC?CbN-YoPwn@NcGT*QpLV(8jk*iH*kQkv6&mDG?&ZDIZ>~E9=WRSsxmvROuHt
zEHNq0MeTIm7Uc1PBx#&AkIEPqjtJ%TBdGHNmI3C6;$CKq2UK)RU?<}koFL2376V?6
zK0H=~!vR;!p=g97<~9G6sk3POT~|y5ieu;w#izSmv)sx`)Ob`&KlCbZHy}sxY|FG$
zA>-Ox%FK01x<{&(wcw{62MJKJxwUte-7R@&h(|d0?NST44mAT`dM|o}`}(u_Pj*Uv
zI<1pN+`dwn`uD#tnr#QAE=;j^2VGIcwUQUd_Ye!*b=w~9-(!}*5%0aRbb+;hG<Cav
zcf1*^(dIXP#9MFePRm0UF1NknGU-Qj%~t12jmh9}MlCG8cIiU3L386(Kp-#ASGZ~A
zA)UbLB)ss1*y`9@G-e>$@AXja^algaNT)bgJal;XumLhtcfPh%fZmXYwXr$ASdcx}
zdmWH{1Dd>0*#SG5=1Xs66}!2D-<Gu{qsMF#SVAElxvF2wFGvyDYJ~(RmG{H^bujwq
zox30)6eJnGb{^YbGO&5Td3GID2s75KF>_07vQM_B)l1swkp8zrX^I8#s-|TCz*2xj
zZYcdrI=uIIubC!3B)>A(=b(Qiy4n_jOZwr?@2*N&i?&kWLqUo{#OTi5=wr}CUsTfp
zp_yL;+haI-$!qg}Ctk%ytkzYL^3$5un9li=k}82)NS`C~7#;WQv_4G2G#*l$+N#Z&
z2h7=``nXMg{ZgBsd5uBcBzI-@)cp@^sCED?>P=5C*nZiBzSk>dgWUeh*DHE5c4Xfj
zj0^LzV~S$4C)d6~l?`rNn>GLEKL+WmqMlHXcDdmRD=rnX%yX0WDV`H^Tm}_cPZo(S
z7wkuR-k<Lc{#HjTmN+}KboRxiq6uF-{U_3B7BH26z5S+f*?>GE`*URPU2K*nY*Nc1
zX3?0?GKZB*w@WYOngw~GH&Jikq&|eX7p;IrS}Q)s(VnqE!cf8*AR(~K(7o7*QYhET
zMOKckI$0BC{&yV8vh(*CU#TXD8?9wsUXW(5GfrC;K@<4&86(Q*us&1~8iEySrC}M=
ziEQoFtGB;iL(prbu&ww1E{$~0K%q271739*w9RKJ<ZnlR1g7<#p&SDfWhHADSLV3H
z<DatrvC2OZLv_9S9`;TAveELC`=mgNe5shy)$F9^W2-apUZaB!A{g2+iLtHzQS&I7
znAzXyC70{8HP&o=ySv)Ftd85ww$;eRbl2M(NvX@rv=XuW<-5HGP(1bGX6rw&e*%0E
z|IK_ru>Qd86BJ|eE56-k*!){Q#fE3DlU-ozbtYo_Y~$YMuG+`FrlBE+aGbdw<b;(D
zj)o1ItWu`Uo7)(H&y!vRPkCoMhCRxu*Ud5bWc`t=g$S*S4vd&9v59o=eDmB6X4E_@
z-B=(Th!ey`5G+$LNO4o_z4p5V(w^$JpBk1!X4=2-?dJ)&1j8~Kk&_$9J5t&z^wkdQ
z;RjT>{%Dr)raL!bueFR7#`(T9x^ibr{eUoEnWSmyo91d426V>Vz$9b_;VM^?;$_t%
z+gIn8lQvF1yRz}C_w-InUX`&TXE!h58+zqLQH>BEZh{F)xiATbsySwcZiGd3)C^lj
zAv8<JdKV&E7II^s>$|b?n>UW}zG_cnFCFP$7H?~2cDHzWtr78O#y@q^xJ&e+{n&Ov
zU9*Rs_-)%CU@v27XnHtz=A)=dGuRfY`OxOd5nL+;^49AnMV1@fD12C>Sj=p$*Zd<{
z{H?j0M6aQ8;;Ein_FJZ^L+|3dl2yhH;==X5inCLSjwv=q%J*A$Nh)m3{zxHK*v;Q`
z({xpYe>(YHJY9F0E)0OSxxV^(X9cElUDjxk@^^{rJGu^1z59uqvG2r?l$GQT(Kk1s
zi+9lkxTalwKXqq{vSUS8JVNbTiPYwfbu}GxQ-7~m_k<in-tsVhaqp;^)g-N(=ainL
zfUN-<YRtDXrBISe`o=DOD@9e*7+14%q3<?(G))_ChjkXM%mK_@7q}!h7V%>@OY@A{
zIaMmzLa0H#AFIt%zS<eD*%=?wz3%!K>vPj6=ca3p*iTxq0_^$XRK7#wO=Hb#Td7nS
zn>^>z8f`kYZcG_?@2nZJ-)}u-35^(X$X;&?fDvYg^plpk+g0AE)Pj4I4~oFJ_+!}D
zsl46Q(q#=}dAEIL=QU|p5$CG3)ph-J>kDm%B5R*BuUofO^EOqfm`<MZNqOI#x?GW}
zH+Lf8*+-0(&77cM>HcrjPo^qej~z72xPthDIsK>j^RB$FG<HR%=|mM0w>Vo{&75O+
zW~XQ|*Ad}-prwMFU7ig~e0igCQ5=<G<X*|-Uwc>`K}{jdS9EzgfkI)U0#0QA0kb@X
zWzz45tas{qo;<vm=b#N8vo|efo;dP8<MHF!@eFE9l$RQyK#YMclf2NyDB+0PxogW?
ze$r8Qn!+aHdj#`z2<;=E2-)jalT-G%&^uo@*@HqI7;R3>2%zO6x0T;+2P9q|C|=Os
zm}~XR6M6(mGEVdG3PS81{>qP0a?TJl;S_C+-R1~kpX{3(Kj2;oR*qCORsLm53z&G3
zVYVwwo7!Lsv=BZv&a^n}Nb#07TeJO(4peYRb9f>b)Q$V5GfkVBF%7-r?y7x-h#!jk
zw=oM<DLrvB)r3fw3GnXQ<IH2lcz!w1gFDXBnUY;W=~L_te#hvOW3?L5&-C2J?k=_`
zZ(bjryid~n5vGVxSAfDt!e`%I#82NK)pb0f+czG?UtYbbu~T`si<^&1ecz87)wHbd
zTpCymnNVLm6`}Xvbb-`9IqEvT2q-S7D~1QrMp;@`qCnY9kv{a4)KhiY7`Yirc0ZYR
z3M&ahW0mf0Td_2PXly`DVWn4%KC1PCTtCle#e6`*0w)U?m-SBNj&J{NERuz%ytmoD
z$6!d_FPI`yR3H2a5HC?!O3YYLTwp7g3-UlhJXe3~!3~+oO%#&_Zbg_(>5nTb%zL1;
zOJY~Bvl;)B+UX<lS)o8Sc*T%ydY0>=xe<~blVM8-ElBnVaG0bsT%W|xXBt(rR3V3&
z!!gOYO?NB?Cfy35fn+}vySu1+;`##iOjGMO`oH$dHBXMfkjY4a!Nrj5<P59+V3!yL
zkfh|kbsa}&_~83>wLbixn5iNL^OQZfR&^vv7)9(i&)*0Gpqy&xlS3Jo@R?WF<KND_
zEV!t(gEirp9)wMP==rk6XnUxL59AWpZA-Kek%WUB+xt^Q)OoLixo$ax=$*!U%gC+g
zUN{uwekmv(II!T*e**okF6p3tT72|+KM0fA<Ahn9029B-sa>C3PH9_pu|eJh*Sw9L
zF1Jmqx-fW`kdRUDU6NCbZT(0J@A+S(1Vc7)e8XDn`bG|!PGpo~UKDGGImPIh<%$*{
z>z!<Sa7J7<NIN_)R`?yk5H_{FvA(#OFy^RBfIguqjyMxWzf*WLAWQha&gPmE$p56j
z`hP;d2i?kCBdPLTmC5b$mW8B}=iyYJMrz%E2+|vT)5PBteBNOUu`y6@GjkNkM>`tb
zi4*pvKz8u|wr@^N`&z&l6Cde{9(Z7y;7ZhPUHm_{4h^inK#>pklTv4@>6C26IVpS`
zY{UE){n^le(T6!mw(jG9wy?0o>4e>zy=uSv!EC1#zWEW36jqa`SFF@#Ypjy@?!V6v
zr!MeP7QRt@p3-3xzy7gv99T>hW(~VJXOF6WFur(4W_+pb^TL+5JLy2Wqi8edED!DF
z1>$rCd%294Nct_(n0&nc;AT0CiOND?0bYw|4|(P8X~%F1h8UMBL7@H+Z9$&%e&CmU
z(=a^h8T|SE_|0+Qth+VH4IP#7#2!3*q?B%e=ZBWU#e)S%u6Wo7Rxhq(dwC8N9Y9wZ
zB+IDzmB-MY(r$A<S^N+5?+57_4n>gXET-@ef(!naegvg4%MVji*=3JybyVJ6MiP9<
zxUi$bP1IV)VVQom*3cvrI+Kt(1I<)Q1TY`DX?-F=sc#-6sUr^>j3=TuUYVLXYTMA@
zz)OKK{KM`+h)SXg)8Z(z1Q>|k<qbK3HZx9!(CnSQ)nxSqdwA_^#jb7trBHE7TB<-E
z;wBU+en@ifr2|F-$d332=x6OX*^r>*8Lc+WS3Uf$%eP{{2sMYvd~DOm0c3Yr<d2IV
zxo4%Iy8Rg0URqQWydfDL1R2Szop!PVV=S}kQEOEM|L6IWJ5J)ZfaU>l6VpG)H{^%^
zP+R~-8(PEhJsr<+J1iJ6S`6Qd15Odq54H`-y#>_G`SN_Kl3(8AXM4T!SSgk^lvj_c
zoC74B!kED3ZRgdt@{fs~6tsUk-sF0}o}%SAttt>WBw*e22X>v>^*(6L{NG5Nto+e^
zAW=3ft}HyqfAEGq5ZdF9*AjxQ$L<KZEuYWa>-%JyTz<a*uq&o-ljAfQU%>{n#ek`r
zp$DJFRr~UL3KMuI+jg?hyB{lpX0%wa8@jM_fk7g=F!$mDa~1ZB8`S`Pi|1rAYDwgm
zO^Gf?;#A{9e#I@JKp+e4xs;XZmigvVNb=_BWj@thI+%l1iytoBi^$&WBWkFq!kOpn
zFlRx^YTiYBrKCSN5aAIf2kZ91wk3(g)rXYNlU@d!_T@)IMsw<2uV?+*ylbIE)UW<A
z_xSk(n8CBl>+WY*)bg$y5t8hXzM^4adPYBST&x#Xr-WtDbTQUdgSb%k5?}Qtly*xT
z9T%~M56<4{nqLlG$><nVzk&3XGMt2`QR&ys@vPMEpu`*6Ylp|)mK>i*%ycCrv0C)2
z35y|zz?|TBEnnWWR*Zi*F2{ekQ@l~NKjaXzdg5g;t>N(!^qmtw1+^r#QmE;Qq@p<R
zu6ILa=Otvd#J~5RJ%w4S@{W*>51NV}hC&=D2@%nm8`S+M%M=0xn$X>Q5Ay!J?oIb5
z({SE6{T%o8)NbBpm4CwU+IJ^YT%pr!SzsE{&DZttYO79RZ;O4?w=Jz}9iH3#W#!b%
zG55awCFnJ0zUD6I{*s0=j2(GZTITcWE9mtXUryODNAXXWrF+(QxM8%Om{-a!We)(V
z+;nUBLyxY6F9rt9=N@p7)7~tn#_oJSaMeUPm3puBD3H{|xWBPOd^gk@pbg5-eBQ8w
z)T<lG_H@M#>~7O{6|kE6_a=F+%hH{ep?wx<+A7w1TnyK*@#8R@&9z4^4zsBr(b4xR
z%3ACwAH+c;%G>?zE(I$+jH;3q0S9_?*#hn4bidRwG3e(hBaGsAItyTenPFyxBva2x
z;AUL`2(pqg4(8U)7NR7t3t`O!zgmiWIN_$3X|&nieoq|!)q|M^|LyN;qAE5XNb}<H
zjN}7uXdjmfN2T~-YeMoTkd*GYnL!CMA=H@^cxh%mNBBp}-mMgd^zuh;$4oG00MKk3
zJ;sX~Gf-34BV0eQwL_du&?vVP2U_a1zi!&Q^}N@;CgCk?;^{|Era)WfO4SdcDXb!%
zFNVx|FC786bVpZG6DLU|a9q&%&~{>mW0D*07n=^R)+{^NW~f;Q@X}UWfHQEwNg6tS
zV`A`C)ue@E{KL|T7(BX$dmT9yqjdq69TEpzwgyV2-Qgx(-zOi4U5d@I*Z#Zr%|}-N
zzO;2n%JGwKe^(s7<=6PQdGCH*d~`cb`|?_9tHaiN<SG9e!uMdgoR})0r>8gS)vR*V
zAm7M>-M+!?zT*ow!6}aOpSQjHyd*Gah~vhcv?DL?Tlol*1mFC}f;W0BELLX9mK1X}
z$022HhRq`eIV(SXJ$XRtSj{A@1<Hfq&B7L*4SkOp4<9cpF>9JhlZ2E{H}R^}Wp>MF
z6Frh`V=F*__kX@KxnA42H)q04BFC`|6aGo`($2pVU7Gmb`Zla4`9@)oa}0Y4hiMDl
zoc@hB?0{w)b?TX(>rQSSu!v&&5Z-k=)jx$8wa1VU3eO?=f1|OLjr@cjxYd2WJ$62F
zO2j7)t+jqjl5<<KM|8Y`JY5!548Sr?Y6fG^Y+6DbL<yhmeuS@}M|Q)|-6n2Q8vhDx
zbdKBNSdZMCF9})%S`#?A)ExLbx#;q=f0Fj||L_(nbk({3WB<r6fQ!<V=9EFYqZ3O|
zE8m=&h?jAmOf~cby|*n<neO*JMM>V0c)fU!t_h-OqkU1@ST+q+C^(*WNyWuZeb-A-
zpT3#$%J8B&#8QE;taFSJ6}98EH+>tAZ?JuJ=dN9Boh2@`xAsX_Mb~^q24hO|l^ejX
zztlVdvzTkgO>}W1p0m0#eSnPioi%Y#OE9Uz52JyDTrDQg<A%D~vA0%4?odg?c~Z8c
zE^ua&^X(b6cR?0V3obq1pyZAq<i{+xJd_;op@Y&uZ5T2n8|0`J?vCFTf}J(*HF&#v
zMgh#pSyYC%q^#RdJK;hdB3FGjvKXzrKOgT2o@r~;kJ=cO2`n;T#?&?^48vDa-}P&5
zs~o|8yXu)h2m@_~2}9ZKuk_r!hoVklt02$0z9eb7?oEpx=j-@`6)hsm8R8-gU@{>a
zi{B}2Jwjo*AQJBl#*gd!NSJ!k_u}H>hIEUW&&YBPgN8I2MB+WQU6ePl+xl)?@kD0f
z%*z_?g^?E_$SKJAl2|^A2DR%BGd1Z!V0J4(`4HG4Gw6AT*)Go}j4>Jg0ibQqRqMO)
zREhS1?McR1T0@<&gZiV6rbU0s6hFt#OdfC~E+}Pr9L6E9AX+{AjAC5)x_f$~3K6O<
zYXR3AB-_dBCW2<u%_~A3^Vob-KXfIJz}a7$C#IbP-@iJIBV*fCTAe|r3U)uDZ!rL#
zrzVVt;cxC-Kg6s9_64S7qJ{3;dQ0NV#p6dq8mJPQ+qEGtSJCRJxCzx4C9%S+3R^*p
zl?VUSB?PTOvP6Z}pY~!?B}vl-gZ|;7`P2E010v^r;ax`2-=XwH@$e8Di<n_+dGl??
zGBL-TP*KFa1IM2+1k%CV1DPP}K>D^`k5k0_yAB_q&-&s0mvlgjKoS_GVpih{ms0`=
zr?9Xs4>X9~Wy39*Z*@#y&R@+|^AYic90;cW*ZvTXVx!+d`X>r<;%F`9RVVXRcgK3M
zI1gfm>q{CS6kaTh?hTzz*z2wJdqZgIoH+!8bg9yo6q(h?vAiF@#s7VvBm>rW1DGn+
zUcN=#^$R_894aUI@d-37K3S%U=7+-f42naxFWWeBp3p?+s|NtF+@VNn3aV}dHC)k<
zaG~H)iKzmD^`7L8sGdRVle>P?<6SeIG9YAldZ<8=p7RT*2YiL&UYdcbcyGJx<<LA=
z5hsyKJAp>Fw6!ET$?D2)?+>E6OkD&RfH`$^7fJdUuZdgeP+gPXx&7U`@A_uM>|8F9
zvW6U~680Rj>;m~?+N9ZP?ZdOW1XU<=X+yv8uKb#D${v*C5W^4cr8uHdmb8rlspEO}
z(@+;j3tk>>wXk!!#Xuxf_L${X4wdPc5@YZ&y0~lN!$T>n1?D%+WJVPAlEBVwRSO86
z(?xZWG?D869~Bz>$^Vf;b=#a0n$jH4;nPE16Z-UONio^BSY*)IH1*>p=8|&a)sVT`
zdam&)qKj<o@c>=NCk`4b{4yhukgoHB1S0RJD&N`!bl^FsD}S{wOa5%{z`nx4jFOd}
z)81zTX(Eul@>L=RP#hB`tK}m3N8H=xi$K$@TXcz*TQR)SDEu?|ad0|<VSj%g`zqb;
zot|8EIcO`3n5rpYdm}MXVEYJ}xW|dWc|IM_H>gc{S?mr)TUCqo+-t5Pm$VtsY!`9T
zHU|I~9<d5SF&~Q`^-}$p_+n>mfV5lpQjgpap2?$!RYo9rCS;dFdVEKfW4|2`_svr6
z`f4pf6?IVJZ0#;#7@^;F`B&IOl^+Ux)vw1&!4p_5M61>rVYRZNu4&6*h@6L#qZQEG
z!p^2oA-Kb}UJ(ZuN1wqH4;I!2p}JwnjGh*`*mHt;(+2%6E5sB3;A)&Fsvq1o5ImhS
z<|<dow!gAww;+%?y5Yjg`Vip}nL&C+y*;j?#USB_8azjj8M3@xh>l8RMR`$1A%QqN
zYk=830s{A~8&LO&DiV`IpqN=V7&7N#Nww#69k2xywR$7Fwa?j9<cID{Y#iZ^m;~W6
zw0xF)7s;Y|BgX!Jak4T4G5MB}k58Dx=Bucp9?(QPdakoPod@&Cxg9TZq7!>AwqUZW
zqEx*tI}7If{iy|fKXQyArISeN2t8L)KgyqQR`|JVo3iEo=;?+A{yTj5IKPJ+BS{Z#
zl~;mp?%Z{t5V_vS9`pRbiiXLL+0KOS^^FX08OOrAjf1l4IZXlAPgeEBg|E)$nq}pQ
zY|D<5Tt|E-8giU16VAKJe@5Vn$b_r9I-#a$zmS8;r)yjwo|{o+;`prI+gRGT!8r3e
z!<TL+S)>@?^?gw1vB&|wsy~qX!f3Ebe6(Vzh*Lx$(zF6RPZcf-)K`&_3G4=d*(k>k
zGsv;vgAVIsB81)vpw-`}Ux7;iwL8xFQ0jyDZI0L=-Jx31Lvi6W^G81mY44jDgyMhc
z$_S;|`B8BHQor`Vx3=2S>p4S$<1;Rj>`crdYhGCd&Bbp$8Vya|d*<`H)6c)2u=&(%
zKd1};C#}s|ysp!ttng>LMdP1D1Z_nqe;a5442O6)CrogC@@mm=UC?|bwk0K}r^!7A
z|KyM^6f$hJbb1Cv?7}&4iL&z8m)#hzlzKV8Q2UPYwBx9v_*L<>>`^{ySUeO-+<bSG
z+w|17q^QZg4BJJg*36PSLp$P#q5Aq2&_Ac?DysCutkI(!J%;*^vpQ4H=l?Q*R4pbZ
zGdDk6rQw*AGiG6&3Am?q1d(IlAF3ll$%pLQA7s)JZ$t<j0%zngixw&PCp7dIZ6lah
zaUPNJvtfN<lkf;M&>B5o{bta`UTDx&2YpZ*d2etUg~<G#3~QFp-GLXBU8Tu~hZp<z
zH`We7CE~+qmz}!)Qbq%)4yBu1@4L;F)r8GD{zkTn*m8og62BLyb9xTdjB+cv&Lj4*
z!t-TIMmv3I^PKNJWTlQW?~R4Cta<}Gb)7LC>Ub~If5Z^xIW<ju=qzixX%&(ztA2AL
zjpXP#ev27~zQvITm&9wMb?~zPExVcjPuZc!1z4Id!LZXZ=f*Dl9q9g4{Tw4=>PIpt
z>4+@B<KUI)kD?A9<Uz?+i-0}ysz7)f8sBN<gZ4Hr2{U%a8N%bTQ?!p4-e1d<-&13(
zh^pfg{#ixD@obnG_2q@C`7B0RBo^9DiwUTGT-z2S_>kG@MzxHn^Q{M65N@1@X*Jt(
z8a`>@&rECcKa(O-`qbkAZ+afHl4)2(>mM>APki4e9PKTlg&A*Yee^?rTC^MMjVnDH
zWUc(HnG!T&l8u$eE&b5Wd#$9kb13euPp5io=5x604QGKSP<Q21*)9G}A_@H%+cv55
z$5?MoJX<J;y1D30YMs<8;aoF^q&|lUNgVy!kebb!#3i|>TKv`<wt?VSYEH*y2I}+b
zTXXnkU7qL*e86HRe0jFZ<PE;u$1X{Ib?VK=@l@&fjO@>*jmsfHOY7^B32#mJ`O6;G
zcaC9&`n3;$`<ii!%kQ02Gq5(wr?EEePo0fsgxdID66A%swv=QiCIUNMwd$7tH41Nt
zd{yXDG2XiIboXq-A`J;|??aFiZRn#AgjUw}|CF_Qr`7ab?pAE_O!b!2hqp1Im;iU{
z2&MNFhR{EiqF9<WZlCRa5_o-mwW6t1epWM3-!9nWf}g=ot{Y<;XQF7%RYYC0B-vtM
zV2QcEwesoB|Gp~rbhQ?!V0Ws0D-QN~1ojJog!U6A0eV0QJ{y|a&1}6&*99NKr%n#x
zJ$Er-G!5bd+kj^Q(I&CtmX0e#1Gp)`#;LR9&@=Fbrqd-AGfxL6cUW&oN&eMysh*2x
zr9woOD9<!|YSo!XEYv~_1mRn+nr$Ut-oe3o?RIL)TW4L8GjYhbrA$PMG!h&X*`@SG
z6Dy$XA{&H@*?JvvXt!v*Dght5be4;|LgGfMY0c5&srK(?!c1GP(%HV2|M?@tz{Gzo
zEnhL~jl|k<+4F=7_46);mRtF5CvWcCP_?KA{DHSN<hccZBVzB(NXkw=D|njn0LCmu
zv%G0B@JhwTPXBkPK+LjoS`;t>lb>esu+#qaxM=QM`?LWplS<+|u5?d&Wqc869|1BP
z^3DuxrJQI_Gq3R&3fGxJy6tf7C<zzNXJ70)aM(;6UL(9DJK!WEG)xdO3{<6G8w_Wx
zIcDgS5rFZg${yl4d?$C?o7TwhNgB>Kmm9h}gT}Q<ML^<XL|2ss$+2CV`ZIYWz*o&P
zHjX-<zjTiOwALCOib#tM;9p|g2pC6|88l`wAR~o7|7#@;OCQ*g)ukz-cl`zSMupsy
zlD+Aq>MK<6d;J=4y&Z}S>%%DPkrkV#p<OvS6aF#$;4=~DHa=BYFT&43pIb+qgl8eO
z{?&Y6MOWQ)Z{|#At`9P94dEd8z8$x6)5AF1Lv)z%3CB%vYJj>T_M|4nNXJi5{5@ox
z7Vdpr+`?-6{R&L=tp7*-@kM@W{!@Jlv##Tvd0%rKtYf3QsqUT!2yX~WdTLv9d`R1C
zoTBR%h?{zY{ipsqia`<ouNAmmGNpu*AURhx{?%7^j?z&TxR^=*7>RAZy#bLET$=nD
z*Ny+=?2QMxz47C4T0(sRKPf?;<hc53x=HYHx8B&G$8X&*N-NO+0@`GWliUSjg?$yy
zg0_ZXls`~nXHQH=Q+Dl;0fc{@6w|?1Y;Rga){{f~%&t#A`7Z{}U1m_PfKBc;dLy(0
za*T5=z400|SATBLC1aX_VzpQ>m=Q3`@n{+TqKD!7-ej=+1JfD@Z^3YWU($#xZQUx9
zGW2E)6#c*I(pmMJk{|AT{X*>f{N`iVmxM2EG)oqiF=&9U!B$yBU#Ra$F@ps%K`8g8
zof3fY8jor5VcU`5?1te&tk*_X#8^+-*${zK+v&(`Bx;477-tfX?tcU3p|#}}+kgbk
zqb=}ixaUqutrRyGLK0IQ`zmHHyDs*jC#*oBcRJiSsE|szq$?yP7;(qDQn2hVyymTS
z1cuxYlvu7)PR)x9@x}tWTCstuGe)<8*L*}|pp3!1eK8M~`m#6ftHet8$)n3gpmJkT
z{0s4gFvh;xr-MwpGMP7|<77`v4%L<H0Ooka_?cVBNQ#F-_qPWF%~;e`KiNiEgB?Dj
zgPjuGTU_slrX|#E`^gOYs^%;87&gnp&dl*u8G>ST@RPEw!w&Ydq?H%9ZzPXP7q;lx
z9X<=0HMF9Qr>4}m1=R>;9kekk0+(JmU&c(I5k-%8Xw^cYdNJyAun*Q9yB3s7P37$X
z8o`D8<}!CMS2|cuA2Y4RDZQtWDi#GCf1YIX&z;k5i1&nlvbN;$i)&4~I5b@IsV?9$
zpOHM!wmW7(>}uN>FVCo%?>L=3*K`#oBlnITPb(;oG|Jq7cri>S^J&DMxoCf_Wbq_;
zU*K|e@n-M8Z3T6+a1mteQ?uFw)@HrQgUnd3EdM|`rwZhDMv?I*vhTq~uZ_B@5eKc%
zUoMY1Z;j?)UzxqX(sqneFbsE9sJaARp2Vxm29_SYcmEIz+<-pZ+Bm#c?B|z|^L-8>
zIT5{H&~Wf<Enn`5!-$XE!S1}ucfizgo$8W@{Ys~Z+&2LN@J>c4<I0(FB!8?I(9nkn
z@t~$i`g^pJCq34;mH%+3`{cf$iEX_S{V{X8vj4x%V)#gBqGGrt(S=B51qKG!S}Y&q
zXR-LVKU8EhETu1=ExrK}WiL*mnEG_Ei0@@*=x!}iS*CR-s0Qe(A(av8^xlL66vz*~
z!8S>!utMj^0B(jI3?H36dKIw5iMH7aCcZRwr$T1`R=l4S5rC@ik2|C;*L-RXu4UKj
zv%yJDZNs#oWz#u?O?z9tHfM2c$cPHUWc^wWtf}1S&MXc)?{Ix~5xF@k6r0~Sa7`Md
zzXJ?@rpSUKtRfr#mT@H#EpDcO<@)yvzBHXFx{`^FXti5CrE@Uh+aMY)qK;ag5oZFj
zAZW8s<b9>luA&dewArk8+EM4D!!^8%G?drU(gAA%-xHUV$@DTi+YZ_iWjd7EcuysO
zvoqZnN5ysfvSRsW9Tw6bopG=h@IWP~%_vB9)at)4mzF}XYP*^pFGCC=n1UZ|vvZw9
z<r%;<h?Biie*&-}5zm$Qndcki*==TkQa^D`>Bfe?G%lVmm$gv2vtEZNj$pAviIUc9
zgzE`CNUg6Z%0japO+}ki=E=;l;(~ff?WM{BKD-rFH$=+TYGy3%Y0u={$85qJI+IK?
zKy5ETQoV+@sL3<m1v1dWe70d0V;>oy`Y<{S3n#oQCNq+5X8WQp^FIK&+XuEcPPQ`-
zV8fp>sug%6inAS3D1JB-&yTala4Z<0D1LR=`J5?Xe3mSZwAOVKi{cjz8ZlA+FeUf3
zm#h7Ap?Szmxg|m0dwSpr>8uwNl`gMc-v)aw6JlB3llbo~xS^hq+LBWK`-qVnHDcQm
ze`}noJ0Wi)2SLH1`pJL*^Vnl?{?0X|{25=olfAZpX@o`@YG@eF@$|yHxD~Rahs1l;
z>#uXPeTPP(3VAS$sl7Og+gQG!st&_iuEF@616w#z@$3FP<)$|Fe~fhePIh;as>F5H
z+co5-RRT?wk<=C9Vn@sCG&L#S%!tq-5>}%JEA`wD_89GO3S4iSxr!a^-)DO&hhqZ0
zM0m!1S(mfK!pUOZGV9(E1_^SgvbeW+L+kxS*wj(SU_@aZ1Idf*7~5%blMe9)=H{rH
z!Fy_6outLRnJ_@fg2k^6G!OfwWk>ITH#LbA$M92>UlX*xgna#*z^I;9ABws>gtt&D
zD8<b=FwnkT{cJDdIHJKFpH17YYRlN4w$SfzPCtY<&eo~AQNzL1)G5iVo3A`U^7#X3
zkF4>H?=mq{8>I=d7v|tyCx&jBT(zfkYCIA2VF3umjl-+a8{KwPl{Q;GPAd+xba9jh
z%xBcd7QY7IjPwQID9MOnC-lqJpX--%n>aU8MJBa_fh6ve8EQo!cfF1{&y53^6_#~S
z0oSrJV9-TAzqEO}fT%Tk;uA@qdo?ymhcQMKj`c*aJz;*yPOT3UJ$DlZ1m-wwJ(zgA
z`g1P&bJy9jlK(|DC-|7djFUVR``)_8E_RjC#vKk2;D|Sh7!vWH&>qCqP&Ug{TOBKQ
zU-$~xK8lEXH3DG-4*7PK&-OA4;c+!R^mro*<jC?WzGcj3Qae;M3xL=-?VwyJNLD3Y
zu-=hGpQ&h2^D|&cpT}LPW|G@S9OUmFwyO8t6!sOcii#fPh|J+w0{;%k5~{94AV^Dz
z=v9l@$K;@evyQX#CGeWiO5yP8>}X$kvp@`U$?i4Hh)V!ZFIYvo_kMmU=j`^Q;>6fK
zXcEMTl0tCGzdA{jaTu-lnokKvd+3`+@VEeNm1<~75qVD*NZ!NHZbh`fm~6wWpl+kE
z9jATPsfnKGV~l(o-c7IMdF9_?KV+y+QTJWbT>Jo7G2OKog;ZbF6qyQ!tGTr&+JBc?
zjJCE_Y-17^Ms7eOc;~wM>I1L)$SYlczOsFz^L7KR^?oh54640f);!uU^9*KCr$F>F
zZn|jeA!HF()i&bdn=Lw`;j&znjz7;e35H+D$D5W?Gg7Y%x<8|2on+R`F@4DDx<%*z
zN!cd;Pzb>znT4vED<lf|K*5dFTN+GKTh~V55W2=v5$fz(R?x_|TKK9&>2@xI9`5*%
zUNP}K%vB*guDEJir#Noh$dXoZ<4pURgYJoeKSTIC$;1B=q!|VxRQZfvb&-*-9dR9t
z`l33i=6!fh`JnfL9fZ7Gv>Cb}SWz~t(AW~_4OkMg6l=Fb$$FnxLb+yRco7x!A1j!M
zdr*jJeOWE^OWjM}=()cKH<j}5iynrd+wa%#?k%_2#YGGvvIpgWH<Y!Oy-B~X-%ao&
zwHEa;B6d$-T51JK&Ng`03SURVa)he*9P#3lfuhs-YBr0MBmL{raS@y11lA@DT<fNG
zCbSUofW9zVz=$1IG=73K{mNn~J^7FT6UHby9eZOz-?H&-$uj|`dKUTvy`YYiv-P&f
zT@J}9Pk9hC#dzcD6ySzq7wlL40F)l>-A-dQr*5fc9^BD7pfvbIVi5@*UOd|wLdu!b
z@-=7uPrk{{c&`XqDAbD?kso8<_%_g|#aeN)k)da@7<be`*>K^eO$;B#>`T4Xyv4@u
zRyh`I#V%6E`9nl1YT}G69RH-XIzB++zhth=xUv;vK3(nfD)lIPYSEV;IA2+WD%^b?
zyN;dFAaLi_ckUCy5HJPqK}2n3ZO>y>lQ5=Gw+E8_;Z}QTLpkpWD5<<Q<w3CK;>1*Y
z@>bmT9Cup_POR%1G5ph`>DLy%*R$Y)QOR#^+RXnnDl3jAKbr!wqKP6?cy{t$3i?qd
zQjm_go)t2fH3p**`31FX1JNxsUSFS7l|B>{njn5wt$bS2Fv_axX<=g4j?X-j6|_0=
z{_cTmbi`(1=OmA68actHfubpM5kGy*I7mnU^qdy_-c_%~b=c|aDFma1uOTMyNE~)l
zf5Ml<ZQpDlXw*t`Af&yzFwqb*)iMXHi4i-O*p23V?krn*EIxNX@O_s@OaQqpex88b
z5PBPkhV595S~Dk1k5Bm6EdkoHNO>6?{(N?>WQ;fYBg`XwLTHO!Fly~`m~|?v8DJiU
zBdwE$Y)q%9t9F!zK9~}pc>)b+b$}+NKX#`f$!DwWMz4C5m?DJ0g9)dkKHSRhf!dhj
z!db54c)=uxgSHZ``Dn{?Dlx53f!3@D%=!OGeZW|RXadh-tZ{{Dg823B20N6BOW6Rp
z;a}N>;;K4gJzNR+n*#-o(Xjm+=JErS*1nnsDv)tg*9@$K>G}g`jZ%4f0&)eD;6%-Z
z``#Q`Y1c=Ia<w2Q@G1~<BNUc^7JMjABWjJurnbR_bCmL{g1eC)>7_1mM@rt?O+(H{
zW*4-|Eug$yD&Er{iFsx+G)Scu+FI&N4gFbjnfSSY3Vg{Ujl>_h1CfVRB|MvgO@4-M
zq4a*t>Q$c18AbGcWPY=q18d0gWVuY)ZWR>IXJkYmh?4ApQuam}lD9SjK8||O8;srT
zDlLY$NseeuWniQnl~uaV4bf%d%gLDM|M#s1B30+xh4?XQ5rimq)E1wVG^CF?*x|kj
zBuGD~hYbAnYDmW^%$|HV9hpC@p34z3AhTMQFo-GCi}1oMwVqkDZ=CdcD9g@%Hly*~
z4?u6@>{A0!ZGm<OD*b_ZwTI{xmDPmFfHAqS){fDjDbRMPQ&z6q8eAV6h`KBUOn?>J
zg$BO~)2;evz_|I-u3bhmtxElnO7)s8$rE{K@D<AqmDjud<4nU^*K(-3wjHF+UD347
zEe>4uMWPVX0`+I=&`?;r#)UL&HZB?8ZGqeJCEU~^b@#0|WXDZiD9iHG2D{(AYM2yf
zXMT;KY#Z{tu2Ad6*>N82iVe6IfGU|4oX`$<eF9p%17ryc@Z3CiV`$?Mhc3>8PC;Z_
zmIEc`)?K1-@C}8YaT4v?s4d$EX2vJi`GzatSuUt*=4QySV(rEu`-Y6phQyctZeDt%
zOGnT-4Y8uPu!AmEX3%f@#13qOM&%pBiK2a&BiL4Ood|zWQVG`|OxfTY2!Lq|T^%=e
zIp|(S>#GS&W^drFQ{Y9yfJ>_7{XZOe*F9!5=hFplnO*j-UESsqql(l^5EzVq?M|^{
zRt2y!MtoF$>l8?mj&ZQw;k>9_eGlh&q!2Q55oR=IRp>KIIluU#vo-J9fRmm4f9T27
ztPSlsQBvWgSRDf<FG?N{wc&f1uR6&FGIEe{_5}?fSzTyw9&DOh5twlzGuG(5YgZ*m
z@Ntv+jFLWtm==E&IfTSHMNwx|;N~)ChE_vT(fp!|&H<=#tcRDBBZyB{zv2H1U#POA
z?5tFQs$n{fry|WT0|y61opF_+6NIZFfzYkrXNWz)gW}wa)J9b^?<s7(U?bZigde<I
z=Myg~Uwot7ytX*Tf;v6H^mu|WeI|vB?AxV0w}W1y`2!d%{z3eOJOcu!-k6etDgza!
z!RCB7N7Iq~x|xEiP+#1QLk7I}22Iwyb0a@HKyeRUkFZI|;gdPo;NI4!wy9qxZ9Ic*
zj^ElUcfN0<?Qy(W!zqASM_hQjL0Fp&L3NJ~;tYlc+a#|~qcW;CEQx4kmy`GQ(y!R;
z(c$4-Sf#cmPHPw~j{TJHh>if>3`UUmFoXCPs(~CAr^c18SMJ(goUqj%bQLHWH+9ie
z`80?(G&uhFY)Ol7^f{O6$a6;$(Q94y11{r%VQ5rkzeZB|eMnUd$~G+miqXF~4M@!6
zL_;wP7ODIgU8bK+oOG?fm)_P|HX1l!#QTFjj?9U}AXU`57W_P$<)a#V3StDxV*oRz
zs)d4~H}!l&Hrs`7;dRh1!(C^g%3sF$m0^J6P(?3-#-tCVn$2>PVy5`ZGHl8SiI{U1
zCuyQkRnIP&Dn4*;=uC{~Fz5x3&q8_6Mi5A^;*E^Vf>CaUn=`Z?1mfRJCGh{K<vTG8
zJMHQ987HwikSi!Bx}7S|Ro0X?T1japY)WReL-OF^HX+&QVOWGUui4Hz$;&?wtVB#$
zZB>R@XNS~O28;=Nta7X{eHnz!j7)4++bFltZNfdmBddOFhH5uoS??7tdDb5if$A>4
z2+J!N2+3|04>m2Gw3p2;QQH#KLu|p(iC#aEff8QaE04_XEg|Roc<u02ub>D!$*@&h
zj%cE@#Dw6~#CH;@ytP3Gq2x`5-#^(}XxaA89iA~rW@g)xSd{Ss159l@7&GjLp^f%k
zFCHA{JakAaIR&B(8YT76W2P>K^P6v9{KudEWURjO&DTHnPJxpZs|;+-X%?lH3z}!x
zQ$$nR*EjHN4Ry0>@o_RMtgqaMXu@NsL7XMg(N!_r#j~WC4E>9q!n{36753oDi#0+k
z58=>pm<P^?6_rzHB<BOQAC<eyc#C#5N!?X819Ag;6uz(%*21DnOyK-ncoQ2J&A;%k
zGlA8fiMSwQk_ERQ@v-gF|F3FbjHkVr!&Hq_gom$unnW79zieT;r=|y?Z8pFXrfNi+
zVJlXM>ONyxQx@A3k|*j4k}u5$7PDtAm=dBG(a4}fvBACM1atsJZqA~HVODZW1RTqS
zyL*zVqHQ)3!^y7MKwPNQAOlexivsLvI0h32b)x(AG*@fR>Ja!WEVA{Lz7G|<`Jqz3
z`Q>dozQ1&4W8qt!xzP*zKvh0U$R(NAAv4#k^8bso_W)|LYyO6BMMObBML?v9ph$1h
zn~DfXQ+iFPBE9zzL_kEq(2Fz`0jZ&fk`Q_?QbX?~^Z)@ufWRB?=ehsS^UnLu_kHut
zWF|Aox%S$#XV0G9-`TS#op0GV<tR-0UM2g~o=*CCH<N-vZ2@f6R9cwCZs^Ml8Yj;Q
z9#)iLsQKi+&72X+_Uw~D{?t7XSH6HJ6V}8X_M6TZ;=J!kO2TlrPgkLXNo==qxwI!7
zqjrrv!-B^g+sl{kWRn8Xe+XhH&}F(ZrO>Ml6{{p~thD#|mZ?^?__3;%DOz-{ddcNG
z_YT|i3Wnr{EDcLQ9=SMAMXJqw=H7!YmZPs-6q3@2q#dUvO988qd;^j}UgMF@GY0T-
z4{m7rD2ZjUm`<~!rk&cxdJ(1+$IsrG6FDYY)!xTlPk`K8VF{b}W)TY;<(j0@|9{dh
zIR8+0zp-E-7a2?q5mz}jMAS(28BaI5AM$FOxi_xoRBCNeQh<BJA>idoMrqqI-|&1=
zp|&Y_#)w~^^jq)F!!h~fXsFcD5*kJV%&bTOo3z%%b#9b+ePHvLb^4jTT%@{er^?J!
z72WdPrV_u~7x&m4zwAN-oZV9^Yl4<H^s{Q0KqD%4nG+q(?aX|>`}fp|U*X~;^0u^F
zUp9-F6Wr_aj8KvCg{yj|5fBZWcZ-(P22f5!`!D+so(IQvDA>Hb%>4P@KxZs$VSZ_%
zhn2|^1-q#9K0oxT3AULFGE0R77Q@zp1v;w_M=Bb`F{B(dkzgGwc2rfP{=DO<SI{Yc
zXLRmt9c_7E!@OgF1}W$8LOf`$5WUQ{DR2Vpb$1RDDsm*$HwK;9@^23uPPi=kB2jKy
z|7Sa5Rk2IIKlQwLwT3B)uhapUVn5W;3bsJX6&pVnw?6+}&5k#69l9|2;<%Y_>*Y8=
zN@cSKJ8w;udk39H^I6;b?o^MI>7$4>WkoY>q*Omcv+S_rPWf@u17z~@)QP?H+*jPa
zMn{LVno?J!NTzG>S0L#~NwcCm>AaI;2fUEgwGY|^jl{+LMfao1^^74PDlkc`bnc#t
z)$m+lodhaq3Yo0939B`fUfx)7lqDS*+0jv~8~&&Q%$y<RJq7xVL$~C=AVB+{`;Den
z_TpyHS|NN23Rf`nt#$tPgRM?wN$NAV8Zg$Ube$z9u6tgkL>82Rp!-zHQc3!OK;2hl
z-L&hO_&Ecf*Uk(vrYK_XI>yrd&~R#WqbR}kvn_aV;3NsrS)QT|C?}nqBh}WKAIy5&
zI@0+h+eI3Zbx;28?566#J@4_V%+xAo--2v2`_>$&)($C#l$YH!JYwqvDEPO(<#*mc
z?h-)7(Kh?vTj5^G!7Q5IC<?3z?iF!zhkB55+?LfercA|EJi3!4s!ujn(n%S`m6c#y
ztozn#f+6WB=pzVZ2DUlkBy})^6OO9!h?KR$SJXZ9#k=ktF$c#W+YvoXhOUR&|L;`r
z%=wdUYa|Z7|KNKzTS??w2PV4y&4kdkUJ@7N4HKHBRUE^H+ytJZUH7Tmeo6gYko73Z
zL^^-D@xhPdW|7}v=gC(kovX|fKoi&Qa&5+B-iAn9M~Vd(fCU)6k5Ddx%U{%j)kBj|
zGF~Vf4VFJmNEz(>?r4}7YOxn(hF(x_AQ(z7sqTN)?@coD+#oqz?o~#IiDHlPstKc3
zq8~NQjAOdbl9PdmaPw6b^)H-+sgD#+oh6c40nm?dKawwl7#P~tu8l2DQuMTlw&vW<
z&JkMnNbObV80{x*_mI*fqjNP8V235sWlpG3K5}^BrXQro(P3eLq(dq0oj;Jd&05RY
zdu|<Nw?O~t0y4LOhMmTH{4M`>n*}K~CXOY?=<xdPZruMjCJlUWnsPB|XOYOd+hh?W
zDW!~rLA*3OwBD=S@EO-lc}_|JyIon);Bqga8ofIr0R@nf^rT;wZ>-#OB%Rh=5&gnY
zl;zRUc6PCqISt9g=y%LddX}+r$3VQdVrw}B^uOk57F@YJ6h+C!75ns>{-@Be<G=+<
zm#2sjs(^NCNwfQxc$e<qzy6p~k37DErhL;nqm2!MDeK3B0>R6jy;%Hq+N|?nR@&jq
zImZ$^c}K)%;8EW|pX~frWHQ`T>Pt7{z{b}|P<F0j4P-jWnjyROR2dl579(}I;L<tv
zn3f%Az_%m*yi>6-o53>%dRJtLWebbQ;LNBU3_ySkif_@NSqNZ(rQ^c>b%H`R;J_}p
z7$t*l^jz^>4Rb6{0o58=nhZRQU=?8-z1<KVRkGT&@Irk~fTeSV%8qat;*1(*Z<v+A
zh(rx85`REV+&Q)6Yp31Cds;hF#y$te{61GX9NsrM3Djb7BbbP=eTOJ?C_qFvKH)nd
zh^F<YGj{E((%-Zs#-&I%d<#G+LPL||R2m0|qt2_-esSkd1np0hX1EC)#nUqKY4p~u
zlyP>1npnZy%BxxKX1wce2wzSGE%GB3%C=2TtYM}Yxwm`LaNvPHpG#W-g|BSsOaJ5J
z3KMteEt&H-#Hv(BQ2VD_7>i)+D$8P9(D>$RrB#fZFs2Ldc&pRUS!)naIQXG>YK<5d
z!Dn(J`MAv-H@^_57zLfoCk%!g?d`j*muhmpO2BL3+;s{XEGM5Lu+=ScC={)WArQ++
zQ;zq_mM+^ZNIH~<(M9FZL-mcEu?l^s$4)KPO=T7pjifra3ZUA#PEWvcqm=s)r^%i}
z^{$){`)s*Ig$Y1q=FwKj=H>l+AY%c{hc_x&_u7x+df_gMy%i3p?e}V3CLsY<7{7ra
z_fMHX970IkqOFiFZ$f~$5Jv``?Beh#k_qVHjvy5bZLiLiNyf1n0;Gj8JQz}S)4;99
zjjCU)$~h|YR2f;jkxBT*m4c}8z(86o-KjF0{A5(tE=vUSDc?>}P6Ten@r~qXHo1xm
zvXzj=tQ=ozU4dG^<AW=$l-SMulT=7`BZV~7JzIJvfckW((tv8-E(Ke?ykS*hC2+*5
z3;{b;sH{}5d#@}P185ohN<7N*_xU}_Kf+Bhl-xcvh$K%Sixmb1!m3l>E#H`EEpd|q
zglabmF{nf-<h(mj&BUAtV>z=>JQN?varK|tiB=`pX9JEihS*y8OT?%4qOtM)rGV{P
z8LU9|D(V6MPCHwVbP6%7a-k*1a7BjpOTeh#!6Qud$`9{SkG6p*F&Rh(&an}ndo?<>
z@pDq^FfE((?$;O=D_CJpKFH&^BFnINtetM4uz$xNvuM3Ym<vkXSoj=)C8kE00TpY_
zgO=Ia=0<|hZkys;k}fr=b_!VX4#0|BN9S<LgyXJlbq3W-R)2>(%POTOeqS2J*@)f#
z0(I{)i&)BWAZ>g*hYz?P-#GgIR*_b!73?5Xg$DkXGh(7-V~r=9rLh=8k4oM!8t^sq
zUv0|B&sg5?RxvXZ!0Jbt4nLx0-;{@iMlRzP0tIlf%rKQcz9SRR_8)QB(ZB$evy&4E
za~qiIh22Cuc7lo9%b~R)f?LzCd>WQ4d>m9x+^s2t3e5msxpjJ@k-{G~K(DpMvaLA~
zWZ5S09n*ZOL-w-R!}9a&FvI^{CQJ9~tvdJ05B3nJ!eh;clW=yN)ZpgIDcep0&IB~*
zA9K3YmJ1(z!4D{@NoJHh`YT~<zrG~Ye<u~f{OqbBnP6guWDNWsg#yCIBu7R79ShvN
zB`pr1wtgG%;8(4Alrj9}%!GQXCkfY)+n`sOm)@^V6+hA+2y9In+h>sv8&PRASQ!&W
zhiww49?8>+ms*?Snm7h;R1=yqOqnCIs?DgQ&Pt`0%2W1D)(m@35926uN^7*)4SI(f
zdIL23p_Uu^t>N|}!OTtyqsLjD*kyk$&{4u*)|cz}@Wz%5tQ4rMOE*j2L3*$k!U9dw
z2ABy5!*cr8-kg=PeF>ySup|ImPlX4aJ0Yj#`TVF7g|<0eqrSsEhx(>`#=wA?>If-*
zsn3q`VY2xhMV5UU+J{YyNY}C2FW&uR5prVhgysXy=InCvt%JZTzU{Xl@=2)ux=bZ`
zIS(^2@7=E6Bg^Ux)Hn!QKoI92xTKQ2in`AaNEbd{Iq?&x0R@!jcbW%&YW<K?JFZmg
z=>Wgi(RZLn3)(w1h}kqk$+>ljg&yrAI#+`v0%XQ9DqusFy$M}^f$VAN(N@k*aa{@s
z+=(O%f9HPu*RR0VB^EcE>Qp4P1pC5cuX$+^Mp8~jz|o!j8Y_Uip`yn|s5U})V}gnX
zgy}*}Y4v}SURpvO<+8I(c9HsaJtYmUodR5zc{Fc06DlY6iAv?X7B0m<&saS8^8fFf
z9lt~O2YfS3{vL&eYo2WGC{ozcQRKeHC#x2-QI!f*@C>E<mvFb_ciXY1djW0U#g1%J
zD{0RT8}YU9WB!2Kkjl7udKI}NeLye9hvsm%%e{ZJnNw&wV$1_zibLd~QDssTEXD$8
z*U)|nDau)2tv+RdKwg%7dqGL6;mjB$<77csC|c&|1KA%HtkD&-`civb9kfoNBC+pq
z*R#d;5q4x1Rr{qIPmjMpcw?z0jvZ%8ftwN0{?7QusF&cjWItB+=Iwz|uUP8cE%Rmn
z?6ua;fVuLov<xNqPYPykR{XX6cazPcGpV?zO7Hmv>MNqNM}5{muuZR<ZtihyRw0uI
zXTl>ZGyPAcvRZvNE8oM&K2oecKrKIYRl-I<O7@y-T5n12TWKIWZG(`4NJm#pYk}p8
zjkQ9@K+Kt-3nN>gzXtWVr+H(o$+<h$3>!{*#!9fGu)wk!aEtTpxO>oo^`dpNEo%|M
zVkiKz)*5sC)4c>py}60hRY2tF=PQ`VMeH|}5szhh4}Hv;Wvts#pWPY9<0aYa9Ts+3
zM*Z9z7!<~(zl?#(-XT;D7GHvfYsaA<4-e;gvH>H7y6gg3N(6HwROeR*&|yDX8U)*D
z<zJX%21*NH0%jhyhT>a0uWl$U`bhD9g5Omft>XShv#BS{Uw4-ZYPs96UuaoZL(+Tx
zQ>)BPqkam~^7Mk;rHwnrk?3FqyvW>N7cY@H9NNc}P|?}Ro*uc&MPSG6F1b?NpzTpE
z?F^awI7iEFx*X~p!RfNno(hKao`B682iIBNUT%GvF>xw|25&v1W>G*p$VrJ)v2U_;
zIw%-b&2gT%aVprGZ5p<ooM02Rq4r}15Qf3;XM+4d$)8&I&x2~w4SehauOkls9=~vu
z@Ugl7SlMyvT@-}^g93xu_&<X@^GW+2>SRn9d-1y&&drvu=<+LbOWq!;wz=dqvO#xx
z)IKufISfb`Z{PfCkUb%}JZoqHRe;H+1#-{K2ESxGXP@Nn6YCYzz}00|p?-S5LD?Th
z28qoDk-sI@Wuid|M_;Q16&n7KdJ8|L+#199z|dKwGVOnU2UlqpX>X<P?eLWuW>07K
zA?S8o6gfvSy#KsVc{^{6`>5X!Pd`yKxb4y}-yg)iEpBgq_AZ^oh;PH*c=K7hYh&H)
z*OlDM1y6ZnDS!Sye>H_st~8&8wb&Z}Z*7}YFM-X)4^_!^LiFy_r+&FyA1Y+{pB7uL
z<TW&C=qz~GFaVE#h#C9eU#9V+sg~?mPW9`Hv0}A9Bo;z06J$=GMjiZ3?hjRB=~K2o
zEZ$BEDn$S9xBU5He-`4Wx)OmE!E-t#<o>$@^yDW1PkBQg&By;~`qnH&E;W0P`*ElG
zzj*wch=t^xT_s<d;HI)$5YLt<zm9)X;3W5BjR|;<lX(?QZpdo173cn+zVy{x_7_&1
z9@N%K`7dEL(HXucW2XtcvzcvmR`UIx+`oI@Wc|8n&JF-Y!mlnnb{+>1@8)$S|3}cf
zsdu_U37uR{HJbm;mJshkgdF&q&_zzVWKSpiJg;v`O7GOv24}>948PS-gp~8Y`7yr~
zynV;o((*ft{D~K#ud=geZ<(8I<`$B0k1Cri0N~oZ*}(PnKYUi^k;FJSB^HnS?{O|u
zr(AQ32+dq{n>eYEiA93eTPx}VTpdLhP5EZT2RVie6f#M}`bT5{a=-1_2hMCQ>@r@z
zGW>U(q`^xzr(fk#nWG<Tf`#u^Dc&co->BVNJnXuERN-G@PNpaI(irUHQS;AFfBwme
zz09)K<xwUWxZX<JGsdUrL>pELs55KC4@|EjE{Irk*?EBfn%nvH{<P`8`7O()|IKl_
zN0;BDE)@J7w3lSqTQFibg_<A9me(C1PEc)I!b_3z-&|=h(!W#26al^X%9sAm|H!)u
z{N_F_8s96HhKl=-ObSRk?RFFD0h*+}z*^P$9KMPo66epo{g;8~?k%hp{QvI6ZF0cH
ziFS%B2*>!!a|_@lbpi5}Qbe)LZQcZfg`vo^li!PSfo>Ms84~|`;!hvnYKNH1rXq3O
zF%@U)ownO^9>%V7IbL5KE!_+F*#ZjX4*M!s*X*>kD!q?h{kIALd77TVY20hc&cQed
zamjQTw9aplLg2_Sn83JtIv;2UH1`E){H+Fo${~XnZG5YY!34u;G6Ew&YJNufVsacc
zdsy!YfX>Slqn5UvS1^lky{@Ex`rTK0S^LZaK(WsbM9Lku9t%c#t6ppsRQf-ifB1Uo
zABjEXb^AvDyZKBhVn2W{9c^)rJjZQ&xZ37|V0F6ZCBdf{vXVCd(BEt{lr%X)gAr$2
zl8weR?9w`o2OEyIVDNYOF!sJd2n|LO`vaB1GJt75b5V4x3;t(D{f0Kt$)_UyS3^Bc
z$E$sQaP~9a>4dNgMbNSfo`g!6oV0|v(wJJL(~i1zqBb@hi~#^a80of7U+kQ7gIv&-
zm$W-N+}TIN<R5~6Y}hF<<XBuUuoL7d985eJ^VlP@C-N!6^%`+@^K%!^SLpNpn7J~F
zJIB?oL?7uOblv_EVX)e_f71;NpuqJj&W*o}*f&W4yCNuTx^DfCDhK5>(aEObl?a}g
zfh6nBFVzoDB*V^LWRv+crrsPPem$v%3$qRY>%9sDx?2ffJtF^O=$fACzlXMZ-Jb^&
z$5?t?Z%Fo>ViD74?kMI}_Qq4m)&5{&ekiXt!1wQdh~+k=AWsrj^TB9LPb3|8F9r}$
z$ImaA93I|&^k>*u1~yL@;Ev$Dtol&-z8RwE<JJnF;OgSyc^>>nK3nHnlW{Cm5*T<R
zy)TFul8$~8toEHSk(n-}9-8a%?p)R1VR60UiSMuObqXy{3e!&biJk@RV>curnvLeL
z4l$%9ivlUoUZpzen<E>+4t&yHu>iONJ(u^xk0^nly>^d-wINoZ00nkp;u{qsb|6!B
zb*Vkdxx2d(gL8KN;m4H!KMP4yi0*qO|H&gDX}XL`b_Fn}`_8$5H{I$$7;U7v%iLXJ
z&2k9cV<D-(rwx<tLR+`)Y6^H2)vMc&9{~m&@@+StSxIHM&U=DMw2#@0B@B7XOet@m
zCB8z&Vmt!31M0jmM310VkGg7~N`C0P3+*N<o%!;|ZPWvqWZ>fn3hg9ob?%-t&cPJM
zY;c`igID1VNrv=R0vf`K`7`a(3@nrsy_o0h73{Fpo-}CMr10h;G)1Snm^|Kv2LFNv
zErh^poo4OZ4Q9>O>nZ@e0#gjg_LW0}E3d(@R8d`!ATP|>YHv#V1qXkUr{cM-N#1{3
z^o6IqO(-oe3{HcV97pvA!w+8?1A%<OXAwx`A%!LFPgVb+zvo<E?B#l@b4f4;-(!W7
z-*yBNO^i((Xyhbl(cVuLk-y<s5U$fA-L-HUTZAhmFa_+>ERaSXAkl+B?-EBUOk9$*
z$DxUieC)}P9dqY1)762Fd|TUP7h`AI{qc0D<h{6${j>X?Dm`Mj&Yo_i!6Z)=053PK
zA}|XeW&GvI#PEV@TDGawdk6Hx{L7UEO&hxaV}P?lC$nt5DL9wV2Xq0G`Bo$L>`I>?
z$Mg^Ur_U}52{ZClzt0}^4U5?pNS6(Y>nxPB_j$tLbGqke1RP|ol<Yt;cr#(&7b}?J
z>sp*t9fL-t1~5e6wp~S@IlKI2^p!m^MT<W%`{Hb9-xL^_NBrw`x&U|?SnKvZXpjJR
zY*bm$_^+ANX!xG_LG5eS0b1`)6YJ?vLU3?Sv3M}4&e{1I?YE!*7JTX@^2Wn-q$xuR
zL$4#yo(7$c9LsAZwCCm?2{=!M641L~;oqhda%VM{W@;tKQert-Zjz7pWLAj!mdqF-
zUMtiRi5`K+Z=^>-TYnxFn1Cbtr-*^8F1T?CKIUFq@1SaFDig<@3g<Z;S0dQc+Rpx*
zy62?hx#!d1Qn<(dA$o@(lxU5!OD8+Aqxtl~Kdqo?i_gt*HK6Wz{UQ-!P)fS@!Bn8N
zu3A)b9t0dZ%ZGK&7&QP5u++V0Zsq=tg*mGSL!PT=dqT<9Dq8*L?egERU$Fo6*TsdF
zLYDJC1;Mv7DJ;_42^~QHjx?<d1mmP^*p+KsmLCx+_94McxruMS{eI3!Zt~KYoHFdW
zajtE0h2dSx@LOFplYx_J8B%jKHiQcW?rc!}L+Hephl&(?u$`TT(^>BIM$BB`pl<22
zCtH1z%v%!gChEL1@2p}}@jJ5*<>lqMuqAa0gyqu)o7FEm<-T~oCzLc^QSs6d9QL6U
z^tV2&AGZ7!<nh?-8yGGM(h<Jr<Kf}KB_^gjJe8-O0w7Z=-FtsU^`DOkWyt59>;l1~
zULu$Wmy%Y3+DuZWYNT_uFF0FXJT;-bJ)ZEVwbh5Fb-N3r_yk>>g9-QeuT!t$y%ejH
zToU3BK^1<tZ*z%o3^~c8S7lxc_$of)?Rnn8e+hZ~Q{&aoCZv+jsbp8_Fm*+aU!`BJ
z(bUiCxH~~_<*&2tB?aboncjcc_<-jy=@YDvPa#|TRW-Ygp4kKc6Ia(a<yBf+k3(gn
z6GOIDUR_AEKIE)U`N{QDEuLEs8)<C8FLk}`X2`w=@|XzVIZRP~WBh%Ke~OnGm!j^|
zEy2SlG_H*_JiBgtLKt_w1j<r}>*%b|Ya9Qldi9P|e(ET}l4<#;T?2UdQEtr7N7eCR
zVK!>JTpc&VKMEA)mxmLaH<LdPn(~rS(A@C4lQGb&EaaR*Z~QRt<=SJtwY^JmbHDa4
zjm_rl!~H{EOf3*7hvuYdz73fjTv*QPtaeuhhW+N{;fXsWS;PVL!H}Hi|H%E@>aE9o
zd<io%h`gosbyiy-z{GcZhU4awTcV;e>aQFMN&b*-p_SdUXN}Uc<<|MTy)gk9+1cFE
zfk&!VKYXGO9yVI%N1MzG1){W?vA=-iGZN0zubg`UdPcv<mIM2Oda69fML3nFs_adR
zPU|&f?TtC)p7^+JrWHC*<T}DS8dt&H>_lEU>^tp|&SC3oxObe{gM3t&B4H>Ic8aZc
zMkYceA6~kB|I+PG8VlSPz6G}yON1fw9f^s0^rBH*93pBa`PIoa&eQ9?6^+W9$Sd?4
z+rHox7XRcc_mbZmQvRH%)b$BK^>(z_>O2n2RVwl+Y~{M^-uM%)`uQweN5`P}0jR*Q
zdj8V0#$t`r`*}h-c|kXm@CRm*F6s#0rIeqlFax6Pb4Y=xs`81Jn!H{a3X{RXd+7kV
zH|dHhMS$(xZgTQI2K=H6sSss-fumQ|+S8iUsZvwKm~z2D>35Pazl5Py2<YqmS2!+Y
z)oaQF1Y@@4*j_tU{U(okeDsQo$J2;gqQ8hYnSQ&!_i~w5(n&aE(sVk*!%rGhk~d^`
zG1*{#Fl%C}1o0OymT+9Widjwbb!uumwKiAqj1lZ}HIbfwvj6+LQfN#JO^YWE-_r`*
zl3@M#J`xU~dAG5qbmh=@ZRgsv^0usN88>2#Eaic5PfMk$a?Wg?M>J98OY>BCtiGy5
z`L2b17MqFv>pJOErFWTBcHpxa=zKEogdKEJ5}od5k@*I+H_7d>O`p=!LB08W_<V6(
zb|t>IG`?!QL}@^>O<qZ~xc!n0740nE(0}c`;kgwgTS^V=ivzCgGO2`T_|h^6We6b=
ziz)ltC}4fQZO5BGCG6{gG<QY$SK#vRn}}VZc>Ox=Qs7=M^Gc6nX-f2fxzj=Hb_PTt
zmbsLmOrK|He#%^TR_K`PnNrHJci}U*y}y9)?QUwO2+M$Yl{s%RNFPRft_TGaO>O6&
zbAshg<q7@Xh6Z)qJBPoE^H;j8zeFXP_hnT+pssnD0WC|#uP@vNL^FaE2e091XNW1O
zC7e`Rm${x^-cwyr!CZ`9+v8M$3_Uw~LVwL<9c%m{BY1$P0z7NNc~9=B5663xi|Gv;
zcIZe}k<Gu11e(3iH@n>?L~!8AUNz;BeTC}QnwN<CDY`yfQ%R|RJswzNNC~Rbd7TXX
zU|xPCKKl46we9nZ0_9R^ie5A1ojWN#JMiaf>TCQIUibJ~Wxk{=Zg;Ao3=n$Hb;<{i
zjo-aw`TFe5c!{}yZU_03T3Kw6s^G~aeg8K-{^xQxAMc}k=32JnbAOyA$5+QM58eUl
zWF@|8yi;V6Z5S^ktz1_xkl3PAU5sDE@t+to^6&_xRPFl=^X0-QaiO+)DQ`r-jl5nT
zZp^N79xET(-RceGp{%wUx-9Ln@j`4(-nIMdg6!75t@=tS@=R^c9~Z3O1IEv@#j$4w
z$DIT6_39YFR>*6($88sgGEIZ6Yev0>JbKEQW_j24fWQGCVO|K?J^3%rGm8%57o*bh
z&UZ#QjH?}zr1ON?+ge!vO!9Z-SB~_#f;aY5qEnfv7oO?TBpJ>fKQ+nCCC}N~ln-SZ
z^ROe<i+i-Z(Ip?p47DVo&P;@3Z?as0emd^B03@pmN^zTrdY&U)gC0j{%0A$tk>MH1
zi>8)lT(=l7q_S=MCBl)ibAs#la1}S0y4hnFETJ86xI?EA5fT@ycsjV?t|{Be<{K|Z
zJS*xbykylLgRFn`J9#WTN~mG@#tVy@XL48ghOdPCSO3+lE;55^(gD8-Z$Asg&)uRO
zvt|9Wmfd%`mVsNQ3T=@yn|zuPA?#0hi0P$0ISuS{=k6hcD5zN~xR}WK@WwCj=oTG|
zcI%Y83}ulnF53L%5~geNFjDCIi|_`k>O`Sfo$5q<GCBWhi7@RYHsVe8EB?TC#o11O
zC|k+~GJg09kDyvo$o9)_H8Sl4j&)A`U3CL@1%;Eh)RV(^(9H3q3NY76g)HI^rPcc}
z@TLTwhgpb?`aC_#ApeRYH`tcIK+5x4=o@u^Ad><ToO!5|=TZdHdHqAo8h$-~!|@@)
z<S=oFDaTd=e_ga5Z?iuB9Tu2=b$UJpuVeZtKif|a9JEdE(X(#XTg-joJjVeHK73er
z(BkQw_-PtUr09tNg1A{_!s@+gAu}s2a)7^X<tf0MpWM7Exmegi{fK7NoA{X;iSDR8
z-C)x9E$YzFI_P3Fq!Aw43gYQAb(o3&+Y*6}&t*O4(zx&4jC--PajgVU{hTIK>cQPr
zc5OwW5s2JNiC@+Cz&}gKiHDNsZ@|O`Me=3wTw$P&8Gq+M>H|f7bEh5JH>Z(yz{EvP
z6)G8?o3kYr7b~7Zy^;ADxA-w=O=g>=uAW09m)iBWbqhbT(+WDY<<Eb~`ORlG%OO)?
zdNKv4Y+<{BEfwbw*c=by^eCa9S+YwY_&5f1vgbs{n3*IE6uMDgL)c27UF}PNh@f^U
zH1Z3?gME$!>rRhMSAwB%i&E-)axWB4eIniCBIpL?abak}_bS)<W;5Rli!#d;6Kq5B
z4J3j<x+DG&DLyn8v?*S<tCCNVaJC)Cz4FWYi=@Fv>{|6E^xS{fL@H+?{O)vl(1Wt_
z@%_}V*EgTknpCua?Cm4Z<eie0qxF;8|16mywVZB8kb#tyL_MZl23%QkgmCsZg;gQs
z!DZYu`1pWx^$k5u{HPV%Il6?8`TaHa_YhgB;@{wh--go@{g1k8;`J+Ruqirg-En~R
z4O#_p<D-+x+~3*UfZ&4q0bTcj`r?9{i$jtkBu(GY|AYh6AG!@3=vTe1Q_iih^=&+G
z5;xLf_4*jSBll)FUUYOGluk6kqWwH!>=*dXtmMkUhHugg3AUd(F7NUHe+fER@E{d}
zBe(Wk?x=OsLsiTT*JW<iIq)DGgjM#{KkID@pqeE?dF`L}$sgvPWW_!1&XS1ND|P&y
z=>M~tThm-;{p1#FM0ZFKO~NH6rJv3w6w98C-^)l=tU!RQF?c{~?=fK?C+%_33h9Li
z)f!0@1NkUo3Ni|)IbjzKE27I1l$Bqg@U4pv*a_jCdg2a6A(7}TY6<R&<!Jt}ppO4W
zA-t-1e^^OJaw%Xz_;qlQ@eNhnr#!|Fof10j-iWudcQ1uBUS|Jc|J5~E`nOr9{4GeS
z6YsH`(x2sot4#aK!rW@4F>RV0AYfabvuFB-Z&56$r*FN&qn);6y9dMjz!WB+$<x<B
zMtif_E?7aE(F&UJ=#zbrm>rAL)ms5a8v;NOjDI1;fNy_3<(xAGct)q2YwChjG4C?7
zD`J$wpU!u+(#|*^0sRhHrI*&y-p<pbEcJKS6tk)J$|=}?FlGgQ?E_yp(_O^i6_a=(
zSl+&^es(_?yN&AiH)@BlDh_Qe)5<7gvV%UWQOwufJUBg~B5fUVDo+vyky(k3VE4$n
zjpXaL(ucK7&m`rsU^&A_T3>I+_s$3@h`YPpUIiU)L!NKY-Ijhy<CJ~P6h~{|aPr}X
zCEF&KpMEURsQj2OoN(7(kAdXKe`~)fwK<cW+72w?OV^`Eb2KSkGir1y(2zg7=D(7C
zQD|f#)(j_GmY)FQVRv5_ze}L&!f(fC=JJhU4rIEIT=jj2m4^+?Ws<o!zG~S@>-+$(
z#&EnBx%%tQQZLITw&!qpiDaCV1ToR0+b~6<!6WLYl9g}T?^O$?G?6z~^C0nBB}<<2
z-yXR>or}Fv$PudxSC?2VN#1UEwDp~Q$)h<@f>Z`nCl03con?0DuvMDw5)My$z2BF>
zpC{tGvs$d|p`q_`WMks9HgpvP#FZI+2I9nxi^fLu3km$RSQ#yvH3=QjDe)OhT_ZME
z@MCe`w&=-w0D2VT5G~;~am`xT2hR#i^^7x9-tOv2T&IADEy>+jHWO&-pWcxy5Qd~r
z(KLtPp&>4%ojxlhP^is|nS%Q=itlsO=Cx<-jULDc1$AcM@#%iJQYIED!@U#+9EX%2
zN!6ZS7RdUm%{Z){|M%s^ppBnz(47)rjYzlZx^|IcG932LQR+~tnf&{;Eoa=Gj9pzr
z)-l2Wqu2K%>xFe;JMs=0J0C&&`yS-1<h5*($utThB~c7~?>S4DXFTfNXFOU`*usJ&
zo0JDu%9Z*GWOw{uTTs6j-o)a2*<DYPCSjS3o(pI0`FExUG%PJcSfm_cEh(e?ep0*A
z!8EGP>6%Wiw2@AydX$`s2)j=_%juW5B)ZMdUsztbk^d`ie!sU@D4Fq`NOGZl&-Y`S
zsU=;Cx2C}iHR$Zd-6iEo{<FdsIsR3~zHGa<igG6l^5r@L$$tliCRZ-;^+E26%Pd#}
z7=tzSq6kG`a2)@IWdTdj)^!i$z<JL|_*zActC)sxPH}$y`_Mx<vg=P6Lbl@jD`c>a
zlQ}=kt@x0BI$lGaT&%nJJ8G83A?3-Ph#~xYH^#Eqh*-vB3eFpx8~WD{(E;t^*&RZ>
zhuw1@tkjNRDQqQIdv>+xY&`~<hVz5ou~#=9w#r_LzN4d~+kd0wa3h~J6d()`q2hJy
zNHzX6_{NWYY;I&!8TV{{#yj}#d1DAu(KO6ioY1sto83svK}UvjJA=FIwl|XUSd5PA
zqK=$peR|4Zw38p_M*uHaUrU%aYBHmG=Q3Et=&BL*Gf#sYDc6*8Xx@n>A}`)Q%cI>E
zE#{#GKm;z!Qo_1uCvi5Wd%Qfara0!p%#r}={m-Fk{tcs=nvq=V!SyS({(`UcehQ5a
z78{zQxQ<7Dx~5+et?uNgUvLDfX{pD?NN?I4^-NaC+CguqvhwCMXvT+L)lQgWsuqJ6
zQX@!_O1*#ZL36`?j+mA(w&mu;9??QzZ4Z3bZAkTDp4LSME(<6@_FfZ5z$DUygPSfT
zy*<BMp6xg9AdnR|qWLTbYiKrJ>+_TSbKoZl*DL3{@XPCsNS9B9C0JcCAm;S*?@su`
z7J<J*6SK>ukDZnGr+yn%d(>&jyZwx$6v1;dUM()4tunIlnkj2UkJ{U1Ir4W7#rd||
zej2VE%cKGC&boCELgSgift6lc6W#L8M??ONm@m`OuUWviqH+NK+tkfo<Av<3PS<9)
zr>beSAC#)I@x^n-Zvqu`>{{sI+f@obL!o$S_WtDXjT|`>m3I2W*}%7j@81`|{NALx
zE!fyF-ZtYqSGA>#ET$+sSJ+!Cc*~({<|%OM9xYLSu`}I2dO-yauphN=Oy&$D`q82H
zp69aWeiNpZl(@SUA`D?{R^ffC{GO5QPd8(m&bi&t(uy=DeM+gJ?Q&s+BJZ0_3g~j5
zUK0AD>H^>4<^1yWigHsQf`W~he`^OHEMWA(kE;yvE{1;s7gAp`$(uQyuWnTc^cV}*
zEI&yS;ximFy-;crGtPV70JPgAM29;!@v@aVZvB876i1jc9~TJfl6|;+sB8YZ*v0Kt
z!o%(q7lsgdmtCg%`%a|O4<{bgr9AhW=1+fHd(4{$ONarT!P>-AdYpg#@mXb&_-NTn
zkyH}Gta~Byps}CX|NF(~$<*MJS!#*Rg`>F6>?ASom(k2l?02pTuN48lRce(qXfR_*
z5m~}1+eyU18dKRuyFV%^=CstxT<ZC2(D@il$xM0b?Du@o&Z?wMOIzqx!2$zej(|yD
zUBz6`%SB}F(Q$WY;?K=X5&JsJ&l7e$pYOJ{KYQE!$hg9>loH4Hlk1@=qyIqi)(59<
zfq{hQ&{B4jow%wOna%p8j?dOkUo;Ay)f<{mRH<)paZW)J%QHKB+IA>j!F)1vxkP~A
zi8`Y>vy*zK?kPbg+aMd86f)b6wPl;9u=k6GN>8Uru5jSR^toSx+|N>Z=<zDG2QC{c
zid6~LXDm84YxO;wrIowq{(CwI16@aWlR~3k3vH(c`d#}5R-Fv{@IyWzPgO*AtH|@G
zS)FYBjR$uh9n+)S==ab?!VnAPAWGa-ii>{*<c~w@&n)Vj91;c&?J<H98+qQi8}kvV
z=X)u*(8g)^pm38GRt0IV@=?`wGzigU<UKtA^8O%tw!kVYfB!=R*|qFJg6e?O`nv@A
zVk}$m5xM=W%t9;O_=(+y_)q0u1(lW$q=U^i$-T}XX-vB~?2Cems7sMzq{7Yc=x++*
zn%7@kEse#Fm%KucWw_a?gq_wv9~oCRhA~=A3w`G5Q;Ln?W0q^JN_8FA>~4u80SHeo
z+hqi08K2QJ;BKN9trgx7yMehaa*eRXm$U{&HzoF1UBmqwyeURZ?<*M8-Ma21Xv6{B
z4lrj7ntc!?bNIXZ=8FnM+J>pZ{9S`7Q>Ffpz<YAp$9BqoM!hM9ET+wDKj7Mf1ie(D
zF=ehOuf47xEgn2Ps`1}hJ9|mREfV84E?Wab8;p4O?}@p5;0H)4rV5<Y-^_Kqjdq@&
z-ad`Mu=YJ^p<zgtBNnrhLyf*G`aS5;s$DG&)J9#)tHE;AY999)8g4;!q8IG(S<MME
zk!qJ^!t%0pvu#MX80I!0<NB2qRZHy}R+4v;GiEMlb}xxf6|r9(Ve_`kA#-`?z@m>f
zRQK0*T%Y!N&oXsrVZP>_cz~VIwhR8{t~d13xYoH5j@%;ao~cu>PsGQ)nSIoAFx1)g
zjZH~^O>4YLv+|dxH#T?Y6jU2x=p7S@t*)=Nn00MAI}S877|+oOm9%QuesYEL9hp*?
zB!=uHXji9=H39haCvdmuJI9;HeGv-_m;Sbw?=HJwssxujoQLZaS4zjCHL0RlG>95%
zYH`7c<N7^A7(iuC=#4rHT#vI!=BkU;&Di;Fv3&CO-2`n}^>epP*5M*wpmD?7B(tLt
z+=nQ*N$=h1G^kdW&uq`a!=*IFbGw&Bjsd`rs0r}idS_n7W3ruyl>BK|0wS&PvuEj`
z4-4v!nWKLj(Pz)0`2eMQsWhO*Ws)m<{r;sLdnIR?Q)%hIm9^GK<RzaU)Ge12^kInh
zy<AAz4I?=BIN6ErVhhOPmV~)me}1V`)wZPwZLhycUuS@0<=G#U=H*{4pbFkBPL4iD
z#Rmc<)!=;YuCe-LO7IB94Vk6`3UD9^e#&46e%ld=kMZ8rL;+b}>>aiwiN0PIiG8Yc
zd*XGOhQj<F@K6z<7T~*88e|3$yjiC!fTq$!w!V^Hi+Gya$#7x8<7)OzFC+9<xOFz=
z-py46{lzP|0*{_$<F}_5+lRfoU+7jkt%Z_A9Ksu*&GZ${0NAYmpZk}PhJe_?(Qxwd
zXN6J1uBW4M`{1)Q#+5RA6;+oVfd4|4Y=5-wjO5I<HcDcR#@fZa+Xy=e7~R?5mh_A{
zwu9D{ip>Vr6W^Y)S&cN^ytes_npxzPL@AIPW#+E78+2LBeF(4eRJP9XS@SD)>|^=B
zBMC8Y!7<;fsQ7tAK7tWeuJ%oX(dlTtIEA%-ZL<fiT%o0iN%y*0`gArnkHI&&t>Q^s
zn@2a_V2bnHc08t6?0HbisG#VBpr6s*clnkZwDjipH)H{*^oi1B{JiUhN#lI&YldQg
z;N0an4%Uyo6IshP`(>fI!La7|uQLEfVb`&>CWio_>dKWpL3vD3INH-N{rO_U<x10=
z!tbtzio)?KRGN?aXxZeXj&FZziALJxN-*$G?s3pMIdizPl!0hL-qS*Ep0jO|JbWtV
zd;{bFQ6-#-A5j?h&<Y2rd>b>F%g3x9X8Jc?RCi@&z+Q<8_st>P9p%u0u;VK7yVk?a
z8kBkq!!ftK%9M%y$^dm2Xc)TmzBdV3kj~H`tof7NY^b}#%H2n%kgqT2?Cy=Vkz8?~
z{wnk1crUUY+}H>DH$hhQ?&VlPiKyjG*R!0n6W`z#mo_&N^m!Gi=M*+IhfCvTPhbZ7
z*Qptd1UbzunrZ~UV7mF6p^RlCS0TV!{@W}MNk{oA{^+Pc(qF~)y1J&_pW<=~ui;XS
zDK3U!h2Q+<w80L5Xp%jgC+xVI1O+pi@_LRp_*@ys2qkG(0OHLX4~JzDMU=Sf6{rSN
z#vlRF!TVWCS2#P0Ccc!+Ml7n-)x(-a_%ShmJcxh+O5Cmp&}b{^3`B&XidxQI)VZ~+
zik5c%oT4xLYE&8<^X}&20t4kG+t767=?JLHqeA-OSp{#uxy9tQ(#`f=xHI@M#SI^e
z`&QCR8Hd3!L54ScvFlkd@28-H0t^W8nuOv<Dbbl3X6&4*A8VN0kf{Y9?Xh`m9q>GJ
zpF`yLr%z9CE%5Q(hPpy+EUB;4uNv-{|7b)rsu{c2-O#wpmzfZ_=BiU+ld_#`Ti44q
zogz@6#v}SI2q;}U-7Ly${qtb`w_&y^(rLhyPjSnHci!dvC3pgEB0&O|N_wWcCrYht
z&F0HREqA5Xr@dpVw6^(q)I!pxVw6iHu({*fatc)1L=2K+5LD$+S2=VGgLEB8Zp~w7
z5~!D+|JedJjW72Vf=b;0NgDzxg`%Xa@0|3gzx)it#0uX3P4Qp?CspB}-1~$1V|woq
zd14FbJ;TMnFsRY_r2dEv#&fuscJZ6y>P&lB!nV(+#lYKw1}&kRA6C0DfMhytX{F*|
z**0qI@96`G{F&rrT++jwj_4#hEgkkmlBfQij*sZlvX5zr5)|V+a#O*fNFZu#$5s5p
zAHT|83(8y+-@L&4J$uGIlQ|VQM_}xhryVFP&sn08S^&oZt~yC?F?e?@RhJ37QIIc~
zVAu!c*!Le;j#xy6+dwiUq@`*v@#W72T|Pgb^&8tp?zedEN2v>4dl}+7M5m=7@5j<B
z>$hG_d|;HjZdztiS`ch#ooQhI$C%bvM6M>kom6{&Whw4GJ=#(vTW7K7(Io}+m=#+K
z2<0Nbl1Yn99Cy|mea$gSi6bW`i0X!m_4Ty6x@j_i_PV}!JD6SG+)wUONfYYP!kVln
zWm$$1%&?@5FvbWIZ3mI!$H3ADatz;4rK;h~>F*txQ82h+J3%MLu($>E>`lR)l~XQ7
z6d$Jy>$@<*o;<TbDunBOXvTJH1M$S|M-3KxRA3%=*5e2k2!?MD?Aln(+J|ABaiJmC
zUK}(MuZI2fruH-wnL*?S&W&G6t7SNn&_$6)m_}TjZK~D1L|WgRJ6$Qdhx*`wwa1|)
zB(57-Dv<2E;fg@g!^tSyXXwjnI9L*=EW2azl5F+%;|BH0)oIpwRiAZ1HP?}6C;9jk
zck@nlSdDqE+;=^R)zTEGWH3h*OejuV7N+{P=A>XyF!9TECef=cAVqp~M05gpRxefC
zz*3=nFN`n+Pg#=f8Ms}rjoT`1{y2W#K8Dmm9t2Y}X+#~|c~T(k3cT_rRwS5OW2BSO
zZS_+&J*s4lEq3lZl45BgVBsq>9VS*%P+yQL%I`r`FzZcpf5`cFVOKRb^}kW21s|=F
zb~%3K-)S*-%9?uD%9le~J=@CHAGE_V*F*DIc%g{X$Xn2D`Jufp5)TD+?50fSTYJu<
zu*u41M0p*io$+@2;xRZvVf$0g>IlScTB%ampGMxP-uM?KvJPcVsQgxa(x#?sGoCP*
z#_0c|Lmjc{k^iVyR#+Z0n)ZlwSn1~R!iein>--xmFR90$U%v%5s-N)svh`&0osi}y
zS?&CzEVog;t)4%K+rm_S1=Lc@@RTC-(t>lVQe~Eo?_Z2lt*P@<=XY!Fph*?YjQ;?a
z3@^VuvONE~8O+CcarUb6e)0J+dAL6abFqU$b<tI_75eP;FnmlHbAEc+QNQZ+-Dy-s
zj#u@;Zv`>4$rXZ!zMfEwAL9sZNUmHJ3-u*N3Hm5RVe|Vv0jyCUBeSGLu8z?+h2k+{
zd)EL(4`Dg@k#*pcVm{{0+oJH_F6?Y#x+5d9A60Hm<9(H#VL%{?WEj^jT7~n?8;&^e
zUgmc7OWy;v@$eEdQG(6RWhUtWCsnXi8NSNfZJz2!650BXSvu5RtCaWNwv`Br(9iqx
zy)EWcHsEn9d-J)MUv>hbSte|M+~~Xe*p@Aue;&~XLd#a|+&7>0PMTv4D2fPxI`x?g
zL{&0$&ro~XjtzKM<bQijG4bl>lTJRS$V^*3)Em+77gwa;t=KI0<mp>csJ(i}Vkl7^
zB;-wj_z`3<V?aFG6k2_edFVnGjVg<M@^*Uk&R4Tk82JZb2o%U!NzoWF<SLBWBKM@e
zeh{$xRa4iUjoGvR?MJ$6^!5xFG8R0agy=wvQ1gayvphcL(2(Fi`x@Vr>D*-V4J?9J
z+k<<B1f>-pq>uo_M;Cz@Yky4t1rvM1C!3LQZa3MUAy8i{Qs&Cu?!rlsiru}X{c^Fq
zyY5r{d~)VLt<R3+36O~PLjlL-<`eYy&Mck9bAtp)nFVddi^3+@X|3fYD$h@A*`Vrg
zJx||H8$V<HA<!Xn(co%>jt<=xa1`jFpp%j%K_y9?`C3oLakX}uLdvQq9jDopQza6Y
z7?xnOAl)wh4-pr8(HVz1U4u%_`%YincQq!!Z{U<Trj3rhFtX7Lr;os?jU`XpDWSeM
zp$J=9x&7vo7n7#LET$iAAN<k5uh&Pp`hWq1F^@y2OYwug<x{rJA^7=*`W>`mN8jDU
z`(=WzX`rfYdU~{>8j8l*OCZdn8;{oK>thDucpEuMdqkW)*0;J}UF_T5qi6vEN<aH6
zKe#?;AX?wcB^}roE?Rhc9FQpSM9uky?dja>Oet1D2kVYk=KD0*`Khy(!Rg@!EOY5!
z?Auv2G^*^|tQ~VBl(=7^@)5oi#nXvHbx{J(-KbFaBUqnI9gNIYT;X)%esq1J9SB30
zBw)pmvEj0q?)VH_ogF%+IEm+^3O_;=_{<-n?((@+W)zFJU&PZTwC8{^#On_$MC?jH
zjh0{dr|Msf?@T-hq{MM?3~|mqK$`Rg=7w^cfAnR0kZ_fiYovokfGqIFb^dc+ajGeA
za+4JNa2*iDo6jcM3U7EB!03Y|4hLt6?^te2-;E^_N>3dh_Hb$@T*rl84Hw*!*wAuT
zB@P<Znv%1bQlrC%RF8h6_?A-TKkdH8x*Onc<v*a-O06|$hDsq1Bsn#+j%dUE)j$00
z=-m%d%^aD!hJzCE%5`T;*>~R$$yB?jOj6EOA8OYHjD7w_b&s!aIvh9)-x&T-MsvK9
zgSW+I$sdb|NA<TnXG0e0a<6~TW*+Y@@txsQMD9<SAf7JF?Np7cvdkUN_j+`bW3EJ9
zXM6%tJpEARVt!E&dDQ+_+RJ+GnF)<_x$)6_i5(B8#iO16xJqqliP~Dh0pu~#_s<sg
z)MNL`7(2^G^-T{#?|~@OtoUv`d%;i@GY`=Duq!5;D-y=9b0e$h%Tr912hST9|J)in
zfVmTZleFFW?y~rG9BOV?wr;J0?*S#wP5DxG-O!81kAi$SN8>9JXlUY~*nIr<L%yGx
z=WKzu5jrb;dhB~c{Ae;Oxs!*-&L34>%_f=<Sy{r3t7G9YhAOB<-$qqpFJ`@~zB8S7
zUCrD^1E%KDQYL1rn)13oUtu)o`lJis2ytoYNtn!m*z8}xy?oZ-uqKaj`s&Si-($Ox
zqrld|>C<|W^4(?om+}>`$KZ~#Tv)g2-mN~Z?@yQLYKyz{$i>(#hU4hEQ}68kdDnF7
zFhcHzx&o97C_@mlc^ARq;fGYRwZY#SmPe$J_LIkJ^Lu!@lVkiHZHA7>_t!VHZEeTz
zO`010$lmi%(^A!8Km5@G*B+*m?fI3ZWV64(!`nBQcxm>Yo#lRgW6G2<W~_g>!xxne
z_6?Og`5n}J^_(aGqhipKnDX9rI+WGq?VDzNcE^5&Rh{fdKzm>UXEnvE=teN9uo_K9
z*nOaTcN=v^i5#7qowZ<hMfyu}p97&Cq|2;~r$!XvKEuu*nB{5I!<z-3^_+aVHBOUU
z-D=(XwM{6(C8dv%n~H){iF(%-$2VrY53R6~U!2X&CMRgzy$xg@%Njti<A{iZ?P;LS
z7@q$!f3vQRZqX<Ocd__iM<q}-m>4jVIR!uGuOuji(c>x^uIv0zVR*Ln+><)EX~9tP
zoTG5{DHxnpzGMn_3&{XYrFE2O371#mj&o?g792~_cX#HRvZpj8C@0OjDTcmd<{uRU
zt*r67FE55|LoAuK8E^@K$-VL_1g{OpZ4(}C?+=yd=^C4asYTK9fqMpzP?|&D*Pp=4
zd7tg1PVbIuJn}r}Am!7%`m@M>b}U6bJK7|LCC3BEJwwB^YSThG)Q`DTM%8auFHcjV
zde6+Wdy;>!w{3PCP%sdd_=MgVGB}tpbF6XNe*l&&TC&nw&YkII<BSf(--%!k)gw<*
zgm_0;4m%r4OeScw*dJLpuyax16?IPXA}CjGx#%zRz_84kdNo0QHVkT`A63DlmK66;
zF{QuEQ{b(N=`|PZ;5ma|JYoLVqQ~1G_S|w#cB-`;QdVqeVMv&kx*wYaGfuVv3q<(_
zaYuPFbRWSrhP*9viv&kyN~hhJ<{gVQwiMOstFfio=H9?cJ4D~?SsnibTs7I%n6F{q
zlS@SUSx@inHp8vBVNl!;7NnQ+i1H(9HXhxN8p+*CZ&kAg$nIqrn9r;4)m6N79~x(B
zb=`nM5zn&-m|_e6>~V4{2+DhI<K9uau;STKdtNcRxAu8hM0sG)<^887r|mmvT&reI
zC@1}2Dwd53VQC-#B&{taJk?~~dRP51Hn1yf@onMTCn!;$5TlhnNPc#T?%*|}FAeyr
z=YGdQW_yHxCoeoNMJx?}8}z+)V)RK|?u>w*?+L||GbDas??ISoZEgyh+}mtm^S5;1
zEe@XfBL0#HZQSYDHBGZ_cuh|;b2d-fLtdtxd-Pet&k}0AIfsj`986HjIyT(JPj|Fp
zkJq_bcz|GqPNUQ2dG@OuGYb`g_~p9G7sq4dMicD8E8jbkXqUd9y9OVJ3ud7Z^)@0w
z)GT}P;m+`DII`j1)yV`UWrD4&{C>~=72{O;H481Qp`aaPR~=GA|LT2>RbJzrjPFaJ
zcK(x87gXM3IA5R8{Ayg(+$)FA)B9|su$U&~YZxI5anfaaB0J8jPk}jXm=ip&w;Oa3
zW2TKNXmFgDGWJZlaunH=70+*+jeJpdZR_I)+xX319VJF2m)~V%!_@Y#+ZNxhZ~A4M
zv?+bUZ-UcECe++I%wHa;WAgTe|23R40kA$)4`G?YlT>5*KGUdwl}*(oUcuD7%~S6|
z{NOqqZYI4Hd;RVBTe<7FV;X;SH>nyr5a2R37zA}^zVpAD`^u;&yQpotV-Nx97L{&+
zp+Q7ILg{X#Ye?yo29Xd1l$P$HJEWy+NC9DJL>j((jOTgYU*G%h`&_eJYgl*ev-dvx
zTxVbV?AfRDAd?IifUfjx`q~opJ)FxmxqPOb(fqVzqxIcn<Mh+1MO*nt%89;@dSZ9P
z6!Y-X*+j=1DNohmRnr4x@Ycljd5wY6biId%7fk%jvi;2rA>OX#rH{3B<VDCwb6cr+
zjwDjKteWt)xOIeW4DiXfj}5F_3AsczsvMG^?u#LlmDTEbgf*NH3iczYU1eFmd$<P0
z#`#}@&jV1U<+#HnQ(_OgsX#NV94{a2JgJ+0K|eiVy-0&;z=1lgYIqvr(!DQ5R+YtN
z*h$<z0H~d5G!P5Q;K?+QZ1vzk#I$4>)EluWASS%QLhS-2@;269)#+(G4aNdr9(&S9
z%Gu397thRkBdctvk7rqE`+>ZNseM4ZzufJ4y0+rfB5@G99Ie2^?;Yq<Yzm~DSR8>A
zU%aSL1oIXcps0A|O_{z2k9iOLZ8CkE3nES)vmNKt+)EM0WA$yMD-nq%d6nsXjxS`f
z@n)t(0LsZ59v%?#u@eQ}H4|*#i?gF5H)!G*L~T_t{HZtEZ=Tj6A#AU8AYJ$<W$jzx
zra@ssKBf3;+J!@Kj+I=MQd;dwGGrT)`fX#6e{;S7H}fekSp*L5q=R+En{l9e@sTIr
z5A3?kyM!ID1KsXsichlmh@f4>&QUq%>o~Vo2v=@hO}@Q4%8q|0W=r!S^T7y53{~C7
z*<LT-Qm)$|oMW;VDX}N_&CgBaB$N>47KICL(>_Ppw6}aRDZNq$Deq$?zOrFZieW_T
zoRB?@7uQYNWJ{b)TS~B#waI+}XL^;ZQCS`#ciV}d;%6S>q17jy6-Kuo##MF+f;Wq`
z-X*(qZPzTj3va!ne+JR&c`D?6C1m|kL|cc&^64`ZO2K)rU0r&DH`zX73u0I7bxS}2
z07Sp9`Byp9zJ6V*%i;Y&(+~L-(w8X>A7igCyj=#_OmLUe%38Sg#eP1fkoNvCKf|b^
zqrni8>se_$^61?6l5G2vMN9Eaz0S1bTv2AH##puw8Tpi{zpM4Qfq0|AY<#1xhOHrI
zqw{IFA#A!J?9rh&aSq=IOZRNZm{>(K-X8zU9zrn{0%}hv)u>BBL))|DiJVZ~A2k5w
zee1I&-eYH1EonQAM3@;?XW_?ziaEK2KH^Kf5q9Ni9Af&u4fR!*JB;7gV~|W>VG`|X
z0CQpCJO5jhc$8HUH3oe;G?!87qSeUaD^UOmhW|l=Dk%1tjT{|dXYGS)u#nruH>1#4
z@#8>=8CZjPmA2k)SZ+Y7@k$`nTbU`e?W&B-@U6Y5SN~Wy?s^n)EY8L&pO!VWD~$|~
z9}`)Gt_$#5k$v7s&Jz7*H4(y1N>v)Ck`UqIb|9xRt613W`OUqS!)#l1Ao+9>e`r#?
z6)RuKk$ur=l{OYk*Lpitvq4dezUO!5WLh6vZRq>+joy#9RfGIj6{npy?5(#-;V1q+
zueedT7#@Gr%E!)d94q9x?rZb!99ITg=b<s+?42L1jr4sC8{sPTSSBV*!WL*c*cNf_
z`pUceakimFw7n)T#A~Wdm+@^c0ro~f%p|(_b?V)_1>}%mf3f~(KPdlq#wDLORk*08
z)X0L+UFu!t?hp8?)0?Bsns%paN-1HuX|2>Rgp_Vp_V0YF&;7W&Tp18|+fUw-wf-7{
za@^j0HC9ATTA?PMvVF19xJxseVK{r~daR`INOPKqhIk|+b1q-+qM9(AXr2#7mZqML
zHOZ{_l1&BI>C-4>U!x&r{2S4$gwLlT;-?|0G}_6;=Pf@CpttnR1CVEqS(#04v2q)B
zWu9%wxa%|%<D6R;G&vVxQ_A{!c7;069Yx%83`>Q%xC$z~>f|^)80FdNRo!%P?15wy
zMVf?NuxI87OWlHRT|=mdLPSKw(5H<veYt|&Hi+poaAuAcIW}?YQ;dOj)hVW@`#8Mv
zzI!1J*>?v<i|VZ%{Ojhnk&x>z5`JA+2QwYf(e}|Qgz*L;;IcBsXOlWD(5E4u*F?_`
z@(NOtIo7-s6UTc$uy-~_YgBM*A4lG#8S3`iXL9KH4MhjNu7Iv#vt)7z2~)0Dgwb-r
z1!pe1)3{>2L>>33kCMf&hc&)7X+}}-K3~G!K*MC(>9)e#h1i=Wx5YG-97%70wbRd=
z+4}UD(p>7SZfhhgdzqe2*HSaF3(5BXs7lLk6%}#$`OK+M)2AP9Qf~fcm3_juaE^hQ
zELow9K-8>b6r0DmPm;%Xw0$i5MS;4PH0bk)_f|DyOp>92Z^b!S?!6F=*gVC0(@LG7
z!rr1ky@p4<`~I*-a;aY&GAd@6Ar1{tPqH5q7t`QUk@)GfjDDF{>}4=v(&#KU&M^r9
zYxZTZXO+HUn$9YjN<XofE;Dpk9qoOM^@!#-EM{7c$U7mDq4hp<I0~bbE-}}O1BHoS
zt%_`o(u;b`HCrB-1%EgmZhgAv>8KH|4i5fC9P^%{+AKD1P^+bJ>PM;i>zuR4n>cU*
z0)uV8^T<eavGdQk;>|zEoG)R7_m&P<P?0BdtELxXyzTb4@+!`a;EPKike7R$PD}WM
zy-OQ>!pU7WISGdy9Suhp$hZUv>zcFQ&Fh>q>ic9!pZeC!#tItWydi$gQ>n1b@28Lf
z{Jy~Sj=}l@kvYnM=U@xlk>|HyvO7alM~%MEQq#d`kuS8*+YE?itEU<LF2JhG{oNSh
z_+^>v?}kfcr;7XuY_11b5(`N(qlsorDzvq}i?<f5an)&j|18&cy~Nb_HAv=(JNA!w
z*hJAwtk#_mOsbvjasJ**MJ*@X5&;)b$!Og5_<iE_Ns@P`Rnzmii8c*=xa*(TGVPE0
zXvJ~a;&7L)!#;nfeJGI_wtf|{Q=TRlG3Wv;&X<aG30$#)+eMtMPp4D0KT25?E}!1z
zt>t=Pztp}fFa{iu)_Y(fff9<uN8P&fV3A?Kd2^r=c5Hq=+?}vL&rCg^^Yjth=7}Kc
zstC$W1L+Ym{2WKsKrUC|+3O4CkaHX|0exLaGU_E26R{U9lc|?t!rtzM70sE?{OJ;H
zoibTTYv@QE$&Sj+Hp)c^A|<m$JAL0_-Vkr!W=_oH%+uRpx~y4u&+4@LRPw^g=ehUD
zK@0mfZOdpbyWANmhNyVU^6J4jI0h93omr@o)gilh$e?ibib1=^=AG@}>;vyem#EsJ
zH5Y60vMT-Raota$=H_D81_vx}dXt&fnZG1tj2T=&wmGD|2@@)Udi&xG7;`N1CC9|r
zvn98n`yn+$_cA|@Pv(C95JK~X{WILRruO{FW%29}fKoc$UlKRMH+0$@&(M0PP)m}+
z2;ZD&$Z6NCu1KFQIvcL#BtKh?`SA|kBN=+uA#o888~;|L5VrD?OC^Qy(=x#?w$uY<
zkB<*3OD+svexJ)>BzAt_BeYq)_L-@EB`<A#Q&XQ6Q5{lmv5&p92?E++O}>+4^`JZq
zkh^`jd2@Cf*c$Q^T<iGZ==5@*;3C1jO!P5az9zi2?d&aNgwx=$q5aM7NsnNow=bgj
zUC9rs)T8tmXn<RJ`;)S)AAkDlQ@;_C0Z3&yJ64qb9_^l0jQDC?R*3I`6u*d{-_9$c
z%O>1F?gPr3IX^?bDuO^3R<|DI^qld?TaKA0ID1l}_h#DsQLjv%;FdKVq+hL$^?S=j
zT8@NqHo-X#yysFkDNdzUPOEmK!5whxV@Qe6_9fL^lXX?iHIuC=>|Hc#c=nz$n_{9L
zN7UlR%IgmH$@rzUM?Ye}Q-Sb1*5*xrh&tS`sali~E(|2D*=D*vFA}33W84L@9Q~GQ
zD6dMm9F9xvZAvEWsI-n+-SRYrL%duolY{0RIbai?Z*s?*)iDSHu^+i{jXDh%$sbKK
zX5$_gjF9ZJ6i&X(G4S$;0pCZyGoy(TAABAhEK`5;egGY};C74CJ?*9wqNz{HFlg#F
zCrSu@<a6<JX_|pc6#Jz@R$HB>n`d2re>(Hg&{TaTzuyNYCX($aC2IDmOq}l`8vEEf
zeQ6<vs7c3SBqz`42jNd$<(*QQE++J|@;^|GzA8|p2J1g@rDfHh`;jE@P`J4`AP|1E
zT9JRl_<dRy=iZO^C#RQE7ETN_7u|?_XKIl>1HEnIwTj@r9F3pGF!+>3z0z0NveLkD
zxG=C~g9<{Qnz8t`t76aT(-0cuvZeIi-s1SKU&HknFlFEy+&W8c!*aBJqV9|LfwQ2w
z4;iPL8xBwFQcfR;5TA68w7%tDyZ^F~MY&51*-*OzXRp4J*CDNVzWKSvo*w<c%dDmM
zRq8ks&lZbt`O{7^SZx*i2V6O#eSZ+Uy5TnUpDOOtn_|QyuK0xZWf`F6>h@-g-LHR}
z!&C@SKn(?IwFqc<CZO-41@6_ZhUGe+efw<FtgiXvsf`-+FikOKf4VHwQ>d0R==O}~
z#omP#CEr<Fa+UoKzwNHP>){Z0OY@MYDAB`UJ;B0nJ=xv^xnZujJx|0a1#G!`t5XO~
zR(?&SxHhpn{*bxalC<c^x1T;qjSIzoa{$lT)!NP#j6)#r4b>`Lx8{`8bSHOo2|q;d
z=!--%$Mn9XgH4h=S5rGl>d;P|A5su5{;pGJ&;Ef&SdLY#&$DY}p=GE^<{Tofx*v2o
zoBTPh58u__+kUA*HX<TYF(w598o{j;0M+W4ul=ku(NEc5%+oW_sN)lsA9E%4yRlDT
zTMY0uy6&LOOZsvc#9A`Riwi!w8bb?^@G<ji9i$4D-9ho}+}=MUOE7nM(_;I*e!4T*
zQ>&;oE9r2_R)MS3#8-HV^9{+Xl{=e{NUDe!u#cfPQ`<6<G-T22Z&yfA(V?5lONw{2
zMi{}_xQhf^!M-kSAMn^t#K+nL+r9QWxVJOz*Brf93ae~!d_Mx6krZ|<6JOeiubXa5
z_{z9hczb;aL)~(`?xwx-ZK;2`65F>#uHN+(HMF)!<(o~3?t6{3kDhN5NXqLZ?_dCF
z>L*P8=wmO`)XcvXd`<Xt9imY(R=t#U>sp~%Y?_ecgMIaUbA6qhjMA|pR18}twC4(D
z-}Q(Wevff^e3DwBABVZfDzbQ3{}b<L(t*kkk!H$=1ZxgrKo2TnV&azt)3i+2M1hmw
zVw)A^#e5E~rd(v>#iG?FO??Abn-UIAFXhCqqCEqn9Hu$mmtxyt4K+7ES@s2aa+#Sc
zP34l3rdj3R$d4(Ig+*<SkS#(q;1_DtBIYUWsQ$flA(wkA4d=yQq&!;rsT`U{(5}`~
zqH{By3@aKO29t+o;bvbqJS&~1b!K%_$}$bF6%&h!I*)?eitk-3CX>SJgPBg~m!4lh
zXdkR$(lA{&;3w9;vil)IuOu#AIG0S2X3;RhTw;i!b;-)>lIF>|534i0<|#D&{Df?y
zu0;2dm_y-@?Ma1%x07XYINy<1^vT|PzpiJ4<u!ZZe_u`G<Y8>ly*=76;}Tm%H;_j0
zLoIwXw)&}_!qJhp!P3U_dTb@23bf0=JYWW&DoMx@i}fO)G9Nx9{8hUL<Pw1rC?=Fc
zromGyO3((qhn%zgi)6RmxJO4DLM>UB6q?|=^QbKy4#E*UJhbJ7v`4{9H|8cJ#$x$h
zjQOop+ccZsk$vkSI&I=2rI|mS+t`($T(>(3Xr3=&BhZ!Han=x5n`^OV!}<j=SpM~<
z{{x2*_cQ{p-d2d}!VO8}s_)0uOMF`9FF#s*>(|01ghqE;;6*Z?KI-4ug66B!<z9Ib
z@l0-Bk>4r_>|w@`e^1sWAk5LP<dyXO>1HLg+guNvAg}_X)R1ftPe#xff>MBA=WSop
zeryX~9jtFbGZJdeHmfRzRq2u1-Wt?y*ntc^13a#~)pQI#U2Z$eYX%2%W5{?yn^cCB
z?@a8<Ru;f1m2F<V&5|<cMRqo7{lb$o(1%f%SmprJ(Gi~Z>8)xIm>OJDCXn=RSq}mF
z?=N~^LaLrmq?eIBTk=JfT0Ni>!#3MA|FT_dUg<MJ`h`=?crhO?`JIBO=SaFJO4jgj
z_WAQ?UoEqvf9x#C!KR~<hIbo#S>n7VK7S24|DZc9uj(7)y0@TB`LTp()-KFwJmXIN
zQEpA8nYTo`AbwaKZWHs%@U2?zAPDdD$<yq5<glNzE-z!Jj!vsE<(FbELe+^JOR};g
z+;-=fU)hc4Mm|q90uC(w`2-|AY5SR69u0{Ea!#NUfJ4uZtjf4jb0(qPsj*w)4Le`g
z+}5R$oPPh7*g%m$yKi#C1!mTo&JPBSED|5;N1nHSp1E#)_t8Y~YN*w_R8yxYAX6lK
z8Rxh$0_@zcApZod?zD5Nd9(E;G;GOZdjGODiD|oj^pc*MZG(Hp=$vjpO<gN<itIi&
z!`H48<gN9iZ*RoL@xvfDoQt@LU)XIRksI#^&%*#7PZdig#o=!8U^NkNK<m6|3?|&j
zrck?KA2+$3OmS1ddt9gRTs7wIJJM9`>^T2BC#{LWFHa;taKPq&Cf-nA4vdycBMyvF
zk2feZPCUPaskIPsb;~4`j(82Wcs{w_jLr0jt4QfBo@24N7X5DF(oY}urO3hWu89&Y
z@jEkmxNMu&FxeJEnZY$%^116zT(NOa;cTh=MC+#$Bd*RSx+EKuJO-97xw(0T>#rMp
zlWa2R4a!iujXnW07uUIp+57Uz2c6_cqFz6^R_oWz&@Xx?N`}%DD$OSesD;>Hz^Se^
zdf-y;;(c7k@=6z0yLr?<*b;1wJ$p{^fj*g<;+|&(XGZv&>%l=qeQ=HpkJpd40=txM
z#sU3fj<NrqMt;wUJTKlMkP+-zw#3NNoMNUoFcH=Q@7c)4r#Zh)?<@UCr*jLxo9U!L
zET0nAm`Yqj<sBhi@>I`V5VR5N_wjT0)g|23SX<wb9}Eq`0Bu-qp;SKTlwik2A<cok
z-&bvX*Ty|;Op1(`l=lNCeS9N{b<&3bgEo$!H@<j1UO*>{i#8f><gLUwQ5M&dYq4j3
zC_|Q5z9@5L5#0-o0`YBpZ-Ks_O}mx(j<G}=K94r&nLHD6)J<4PRz#k~x`~=g<RkMG
z_o{3yXq<d-zNfEo?ZbzCp8pj)Ys6!%tTZ8?#xJMG+o#zv7mvzSBKq(iy%@F@?GG^s
zBq%Ni<+Zi5H$+pKUQ8!auz@R<QrBI8oSeMJdJ$)@R)wE>-eV@Tw`SUrroF{>?$`~N
z)_Zn#*45NB;7?ToBFo5tJ=3DrgnbNpZ_|kNsxi^oREXe^V(Q!J>kki-qmD6QbxqY^
z$-UeAm1(3op1W*iu2cM<={h<?2H%TjWOX-@z#&5oaVnI?G`Q0p>PjAt=uWJItp=*0
z2hguEC0u?3D-BQWWM?^jS>=6hF)l{Gj@56%oJUO-nLB;%SP7%t-AIe!-=9!H`_ZfB
z%B@MV?X2LOJF5FYwx?mrMsN0P0JU`dj?C>i<gMDB7g0-F0uSwSe2Uyil4{QEim&(8
zrQfBWzr5pbc+FxV%H(%(UB;>`?9Ce^ta0;t`r5fI&UjdRua*yG3RAQe>wr2l3ogk=
zMtd|=A^Khh_)X$tq|p*xLH(>e=bWw<s@3<fI^A@DqPS19+V)J0m36>O*5-UW{D|W3
zav{+~Tz<_0KyVWphI8G`i@?bAmMq+NVh48W#v)Bh6)D@qEN4*;9^r7Ut`n~sraIrr
zQ`r>vk`>tH)RPonq);^^r2FxRhTwJf)(fsP?&wZko{H=nFfVCW&C@1H<+znCC%qh0
zbh4%lw%AP2y_XX28#t2SNkJX2nfk$UDACIP9k17THxOqSKM}XBra0PF0jYT6LI`|n
ztWjBfYc5)>GyBCF$(``$S@OZ23%>4C7GFkYUp+$^I2W*+y9amI<vV5ms?)8@>d{p(
z-MJP<+jv->nVHGi=#JUKHJ9fe{~UHP!}RA|-|xf%qtWHFnI6Eydb~HxGV!+1Y{2pQ
z4Ypq&a$=G`;%E7E<>XV#IJDIO-HOIc1`r#nhWF)ae;z;%ou%})b@Xu{C-ad_jvY}4
zp_Zku5T9*?aoy@drg<$*)p2FI8m-k*U0>;QD7Z28MG7wmNEnOtqjrxojRvDV7!;(p
zU5xlM-zich0Ft^OjCD9a$4GXuw&xT~isX;Ak8|XqT}?lCegJ>`L^yNAviKW)KX4}d
znGwZ<eoDNn-v_%8&fbCD1+YQQWrll?8MyRGl`cPXG<el<v{0}c5lzi*dO|+Grt8^E
zBmOo`%Nzmk^)V#%vwLKG6GLv!(`;x&w=y(|?R=T64v!*72gdQP_925d`nKC<iCMjA
zs4~GW^><6ZIvAs7;nMkmKRNpC%N5!ThIW&UV|j~q9h~Ep91lelC4>RTc{izn+}W>s
zjS4KFx+wNpTglL({(Sc)KGsIGI(M3bnsbEf_l2OyW*xeJO;K%y0wj+cHI{3QGYymI
z2!|utUbcEPD0|i5VvReRx~xzl(;ia0>ema79LhUOxqkIhBWzvpQ%u#?A~~%L^xzZZ
zuLaLMiCl|-)j-TqBhQ1p&({~T`=dN!yz~S`<XdJ*t*?D&(OR#TQrfa)En51>g?X2E
zR?LqEf=~j*c8u(=!K#b8WaZ&?>&>SRGO5()7aKWjhN)Fk-M$Q?ozKpzlh1>p2v9-=
z%xuaqR+ryh0q6p*C9}SSc<Rby{Rr{&?$%YCS!NLJI{P*;HP)byiE~8yxDG*j{QhL2
zttzF+WfKgj0DZtN0t1DHcOM<~g~3I63`;s*sEcQOPCAjpe?a)gGz8Km2R)4A5i)-&
zQQgCR<N+MdvK=^1sUGJe)QV^HxTX=>>SmhNy`%3+*oAjxXJt%Nf3x{A6YnV@9+QOG
z?Q7ocbfbldba1sESDaChzLvlOr}&sp6Sw4|Av~VLPG*>TC0T5j_4sO+<qePJ3khiE
zVws+OCQW(H>E}gp@c~BdbDDZNvi@#v;5dlH8Zod4oJDzJ{sA0Tk5Dd^QXD?x9Etrq
zIti3_XsfCza=Up2=;tuGBzd)hLwED;&Yv6hhka#Yc#t)e!>jmIG4gqKI>j(%Y?=<K
z>-!|d;zgk1=<mK7Myl%2N}-hynY9b%6bW|<>6HCEsSCg7C(qz!r_a?-^^4r~sA1il
zu~eG8MUIA-TZQWu8UX|MI&Mg|2`nWo3Tk{O)ei6gX{AAcw4l4Gn17-hA0Z+d%ZZfV
z{a4sgCbU)c6wD^KhwIMsKX_{Hatca$FlCho`u(^>^R!fQMZeva_pM3ijC|lx24g%D
z(dUD#Ek2{C_;iVze;$?V`TULCb8jK}2%nGc?jh&klkXl!3SaIISD+58Z*-z)e2*Pg
zPP8P`WKV|gZOk%b>)bpe<0<#|JJ8CEnr=t48FJXiWBoa;!QAI#{gsA>ZhOfycw|rj
zt2MMzyky<O!fDb~JX7VqW$c6G>YTP^bc}}_euDC(`vu>KGH7EF`RN#>kn>Q}FX@QM
z27HYC02{To>iVCu{0j^3Gl51rn!2%wsO&8<+ynioKvBof2G&JR+6k7v*WhcR)zn>N
z_exdNZ&zA;Y_R{rYL|h+w++tKtar5~0S)p_b`PG`l$(ULiM2FN2<h9A^UwhL?Ysl%
zw;Y^G`A5Gy%7A`PS$r1L{)1mqBosg!Y4;vF$RWI6=?M@W-hSMO{v&PHS8M<Ux^^}F
zIwODx#z@$Fv<NdP>y|Si%%q|Te1kuWI{X_?&Jk~YJs-*p6t4V*y%;PK#x3pfJkr04
zv3U^p<MBJl{F!gj&w-a_Gj8Djc`2fb>>S}6ujc}pff59|fCk{1hMFc(%75+{N(Pj=
zF)zSI{&!&n#CnX3bBrI8NRBARm3WJQLuLh@jDKf>D~hDC?^;iEv@h@CZ%h2K$L$@*
z5YL^p%%%P0%1WNY^Vh2<Uglpv$y!`?5i9R3bsX*e_C|WN=R)gu<$?1ME1h9!cGlLP
zmuZKYSy&X*)DoEP-+v}073cxZSQfv8(I$`+{xg)HtbkaTB;pRgx<&kcl#)VQ1g}P7
zKG#z9m8T#=!WlAzX+mhme^(O6NgOkP03~TwXi@xCB0>*6bO6*ZPwcI*VK-D$V>sRz
zBDURcx!mh{fO5?AOWg%50ms84BU*j2<h)OGYOI-Gy?V7l`_ouxUuUS(WKm~$e*ea6
zG3m=c@=*i;s(j+#&-TZD2<6w`DPP3{3N6!YSS$1pB7;(ai~96DlKwc3Nj&gf%a%VW
z^<PigmItgT+RpvUBz&j{8%r{lWBnd6)2IYuEd@Qb_-8ScK&g-b?qI9FGw>8))_B0v
zVh2Mk|Hwf{ium5GVVe3M!7_k{OcPVR1x^mdN$}DEPDRd#68Sf7<I4cgLk%z!`14ML
z;JCmYy;7_dQUCj4WN~y{6>C>*dy?J%?ZQ@gC@ddb6LRwM`ct?~bJUA85|WZEZj5O+
z-@bjz{P3at9b)3{wZo@?2h7gx-}F2n{$mWlum!Y$`A^n*Uj9d>w`j$WgG!e6$!rJu
z`@1%e-vAwq5)$sD3OZ!Ws-?t!xo`0i81rfhpyS8(dK%Hcbez?V@aWa|=_Ekr^Y^^6
z`T`8D?%R|l47l9n1HvQ@l1Z6=O!5fOUzu6E=)e9G?hP1at>uljB;ZPq?*lL=hB8_G
z&lsH$&w}2!4EaahIy`{r?DZ$w?!e_*zzi0f(v4gH6SV@K0ncL9{_j?3H3N_U>R_S~
z2XwcHL0E6C$9(d)qv=o~o>lrq+xV|I0ZEF0wG`}qral0dPa!TpAvHVy^Y*L+;91W|
z7yc7Npa==}(o#qOM-Mkej0EbW&Hw9h0i<%kvm$MrHB$ciV-_=FDNNAKga87ELx9Uo
zo6)s>|Gd3`32>bR^wWQ4!Be_DganuOEztk}P<=oW-5?7PRrVK~a}|;~64KMz3S59%
zf*n|+CJ!%fZdO*-EEokF8+)t)Vsm(Tt)``QuST!nMUh5{qW){K%tGbrzCJ=^&+O6y
z@yB$cj}sFUWfbO?H=UFh8Vd@Zs_c0X7O>u^lZ_@BA>YRv3X;&#d4Jxt%o{$U;6eaQ
zT^D${?H`v_5d^dc?}@DV$1|SN<zaU<a*3Pw-`yI|SFW)`3-H}rs87huG_-4KFbH0b
z@B_8l5QAxAsoWQw#`2UXiLZg)vGGc20<wx;KfcN-czHF{V;EnT)GhLu_<DQ;8Z-3|
zrt+eDW|u7aoa)OfDZOKidEAjcUt^Q}`sfSG$`^`tD9}ie$Fv9U?J3Yb(0slBU9@xN
zM&edGCao<-i1my6)oX;}S9CU0#f3_)J+GKhvgKlvj5|Y97sSt&QF;BRpVnE&**+W;
z#Cl>O^>-Nw1iZ}RD;^u#f0hya01x1F!=cwsMemeV$&T91c2SwxKoa|2q1XP}O)srq
zQ$@qj@UXzDyB(VY@Jh$6sgkt%{ZM>Lv&AM)^?oE=+i)k)HBlTVTGztD!Xqb4ynA$*
zG#*((gf>9d=Uzby*t1fo&a{`{{KvR*QI1U%vCrjC{6%+Q&8f7W{M-Xos_H+^3L;4G
z1^Q7IDr#$MAI(~3PCmqE6r@6~8(Q?cy$;VT3T>Qk*_1K%T_WfC2kmhHb67Ar$w=s5
zzs|zO>dNNIOiVNjwz0LFY4)yTGi=eLOUNGnAPh-p55iQ4r4hAF%#!t#hSmnCU%X+-
zumUpKgRSQqX+-REL4sv7<Us${D01E&s`d4C?BI4sNm}y^VTkq9u@GFc{_V*k>EV^O
z58UNIjpX^w<$kmZ-XW1OureK;OdF<M_uK>_BPU{hWCkj(B2g9{AASpsio!bvH*9jz
z1qG0Y<{^;5R_bxgKk*S6@V0ZN-%#V%?hls%6x(rS97cT~*61!+5`H0B9kW4$=DTT}
zYu`N<>b$#(b39r99@}qXuFiq8m&!$P^kmSctXZR*?6T_(vH5aa0KI&+c8jSa($wWL
zlDp`9tsO5Uq|6=djGvI~Mw-XIEhq`-kG0gI-*UDZ87{NZg0=UiSo|VAohnt-+pP-<
zo`oVb%VD_j&ms;&gxl}79wC1N4iK<oB!E0dsWOC*^tBtOZf)_Ln+vSjA1K4lW)H_v
z0Dio4F7=AedVit5Dxw^pjCblC0opA9IXtgVrj@{K+d3~EuP44>VrK5{y1hCcK`&LZ
z#X6OC7?G>&@BLJEpdHJTWe_jpo3?1L>X!I5;^f|g?=uzI^-<ngE&S|r5tXKGsJx_c
z7*!-~#JC3?o5E5M(qp5j%4{}+vgtwR@F|-6RZq@>gxeCRb*l}XoGK*P$_n>ZWSwLe
zt%6ttqA#hQSSxpR9!GByuA@+P6&3286e{?P&3N&B6KJAJ8L9*tS{Brmk2LuB9(o4Y
zJopwQ`LOP~tx`ISU_(gya&Dw@-on+Kn1rA6AKUSv08u|0>7Di;RD)QxU6_%O2RQJo
z(Y3MSLFIH;lAsV>d|S(*_mg@>L~LYNh0@D1{*E_4=N&L*H;i)1sV8nHUMTZR@o>oM
z91ulH#@yp(_&yCAGU<*WsWkg?$ECjp$9-+)8XcEZre?9ui9qy+%GPXEzEH3?h1S*l
z@<nMFx<J3ZCFW}&?f~ggC;iO5QgngfpNz5Ku^dRZ3G35O*T%E>NpAEyX)GDZH8#^Y
z+e9=f0*Nr3^F7F*j6at(?1#*q4Y?SqhmPx}KAVkOs=md@Mr7Y1mP}cS2Ostu=mI6Y
zfhdK^KDtthX>Yc$JY0NswiY{tBmsf6`P2d}XtfgS1W?(a$>tG0$^0>$LAorj7Z=cd
zR`PuppiWPW2$aK2i=+L8KSc>l<a0pe*Tmpw#mxYaqt`p2qJm7Q<~@EuwvCFsfB|l_
zVhrE7XVeA(dt-U*afK6aX-iQRE0?=&sinP})ge><X^^5GI8uD=+lhz9_5<&u?^uq!
z#lheZ($?}G_PQht^kA?t82T4UGmXw5TwB0+<Db7spbUj0bv<C!>jn}oUPkTeTDpvF
z`LCfKWcZ?J^6<AbtDMjo$3FV7r)37sJH}llCzE>-@8TJx+QQ-t_5HK3-1)J}32qKL
z34^{~p6)xy$fl==`?b7kJY4HdgRh)GWR2YqkQg{i>7gbSluLTW8_H*jXj-(U75Z4K
zHQ1$Gv2H<6?a7);I7CT5%cjO-lo}cxMuEhZS0Xx~_VbvRSPxh%?q>~^#6|96v!sKZ
zjOgMX9gy)N(S2(i3HOG<+*2EhVUOKtPr`JW(WXskZ0KhMYMmJExUG=fWwAi#aGm6a
zJY<nZEN-n}P6<3!svvd&BMH;-Jf)wb;d>g;I$UB#+QHfs0lW9F){MSki7da7b+!|k
z8KH^cRx$2|q}^RtP*x@syZreYq6@^HuPeV{2!P81LIGIS^6?Fi_+Oj@q#}CI=uEmJ
zxd?9;G#;QGJx$t&*hM%x5p^(JO|BV~e+mn?>U3Z7u_WD*p}^|V7z_*9zc_(MFE=`^
zJz3-F%zIM}fgRGZFDz>*nAj-Ax_R$6pDNBvg5)S07{p>$dU|x4fZ7?2wytLViW!o4
zyLzM4DB(^PQ4Cxz{{W;&d+1fgNUR85AlxXpTcI6W;{a%1F%8l)_T8DO;4$lqYeT<|
zlyz9~#WG><_i97NM3dE77-{(CG~ZK#$x}JaHG@X9NaE{6z30qjgVgTq;&ZB7v;(BY
z8J)9Ve-LpOI6UlKCkK-o_nPZ`i1n-{ld;C?#j0&8J&Qgz3X&w!@?8@m=eJI5gMk8B
zH>UGBPULw_d!p#b>XcMzyoYL$GZ@2<1)^v~=U#_8+G5727;1fOth&Eb6>Lc^)mp+i
zR?quvVUuFo7HME%L65fxJ!*HEXzm`WyA>JUx8>9d`_HyOZYv4x-~5e|B!C8AsBDP-
zXBkWdm3KU<t+3}N{CW?CJ6JH>-l6I6Nx1SWIyow6Q@NCTj)lKJ7LCT;Zqi;25BG8C
zr%I9+MAOtvj<U9bc92WAuNqxWvPN*fMF_RYS_8xl_6Zuue&m9#s5$B;Yc@f@fHFs-
z<OFMot>BP}6YNMf^nJB3g%*S2M?b79DDc=+R!>kl-Mys6DGG_6bm~w;rG}x@CYN>(
zr#qq}fU2uH6aEa{Trw48tG#d$HX<ego<32svk;?;PGe!)ZN0s@%zD>C4GLP0t;`y-
zEkE6#t0}6W>GeE4uRJWT+1+nwb=tQ%b{?1c48&}x?;$MYt*Rin=!|McMN7+S0k%9V
zDUmi8fF%;X;hO;>tdd>s>BDz)3Od07Em$u4lse&;LN^9ei510-JJ87atUw&8HXby3
zna7V~$#M4v@o|o`I{xmnm_}bBVg|{pMSv&zyO(~Cj2v#LK^FxvUL?uZk00yaX>f8H
zX}r`ZfRM35&}{`dX4*}tlWI$~c9WR(?zxZ4&AWHYv7$(4iEX5qDtgyX^}fhc<Ter6
zb0CX^$o3yp$~A7_RF%8+&Y`TdlQfm2Z08jXKcX_PE45_c4(;0>CUD%hV_$X}u4Uhj
z1ec(n0F7xGKw<i-Gdebm)Z0N`D-DpKAn-CJ^{0yUa44vqyA5f;mC~pJUr-v$@tqos
zAx9v3peLi~l?^<s(v%LniiytAv=+U19p>KvoaT?hI29^V*57{H<1-BV>7tK4D}ZDp
z@_3a%Z?=X`h0{Gr4%*11>wq^3g=1<7KG}1L_O3@&SpK$QH$DU2onj{&R!ylhL*yd=
z?ACAqV3QWevoHAveFBJQC>RNiEnH)!=PlR1&R9bMoMEuBR3MJ_Dp9w{E{z2$398;K
zS>D7+DmAhmpR9(k$Cs4vMU3y6<n1w(X*J!^nB8N5Y5<&{E4pHuV5v3{IizE?%yI1w
zjV)Drm=*T)qtZTVcIwr}mNC0jJ=|BiZe93<d$w4bP`CNM2L1q**!DYUc-DBdVhdV@
zy5h%HlyE<4j^}jCpN8$vcdoy>0pK$f9fR6&zX2^-({60Vz$kpgBnOG0oG0u6-yK|D
zP-kzruvshyDO9FNB;p8AA?2z@m3BN1u5GXHjwHjKBX1{&fmX5EHa32GJf@v2d*675
z)*XZ#$(X$h+oY%><{Iz9VEZ0{j932op#z))=M~V?FYw=lGaovDAe1CiPk-wW@qd7Y
z6cU3v80WZ+QvPMo87B8QiC6`p;UUP8sMtzRQ*~0Qv8)43S{@R@iROn@>NvpbbK$x+
zLTMLjnl9(KkN%OWWz%vvoxFP0ojr_biG(W~`*`J596`IeNr>lkD|oD4V<X^YK<l0l
zyXkZ@)|F4^?z3tnZfZRhKlqd053)${)aI4;E};G0x+Q@<dC-WCMdzuw1ruGcu6ZLW
zMSMT<fL9_l>2VD<2WKMYUfgm^FFq)e`v7%?HtAvo`B0VPY^(Q}k{#LsUIiBW6Ztiq
z3k|b64r8vk00Dnp%8BuYN6z(Hno4{A*d7uo?&A6O%`jwvG|QaLfjlQnw+^A%$8PW>
z_A&SH=xM!LElt#&<qT|I78T8_PO;&&)OKF9o3qkh_lWD^-lTxP6ZC&3)PDP9z!Mgv
ziqQx@P`e|ZDqc(SN;Y{1MX_GR2Ywrk>|{$rdw(y9rjQ3ADsM;Oi>wJFPScOboZ7p9
zy8l<ZVx|0v-QXkFkngeLH#27xVQm<h^8gc6TgSg^so#)wO&4PtuYl!bB?u*SLVvE=
z7R5BkxMme-2DN$<fW$huw)lXh_#GqqX$wB{VD=E~QhG4tI{^_Tq!*<I$kOpn9Xz1L
z;B&+?ChNV|)i*Li_>e>9HQUb!WU59F`=@B41-n)2N0lhf9tM-tjY*3FH2wHa<UweT
z`KB<q%$;=j3t)+Nl6AmiYvYg8$8B#A=)l7ec^tU;RcwCmc=lxn&;-Ea`GF|eL#Q3;
zt_$cP>_%A5*w-(9)8}snVg*PDIC$g@#{X5JKiYeC<nY&r41=hZ@X!vDYinyH*1HTD
zB+(@z1gCmR=QD5+`HFC-PLnj9TelNsSwji+JqgNoeB5BW>Ua8A6H0KMj7~D4V9VgA
zCgrSgg27yVT|{zd`l)<1tdtW@S7(QkE7$W5t^7%yph!DdMG6ZWl*p(eCY1=Zkj<WF
z&7g55d1-EDhNXwAod>-ukLEr;>!bM%qCF4ix7+3Q_W&iRicjE<1mkP`C@oY3;Je+}
za;htgyj}2?K8(sF!$(AAy_RSJruad4Qm?lvLYST}hn4b@yf%s}3%&J+o10Wl$VWRp
zcVj(ZLB;vMEvVsw#w~X9wLQ_eT~fjLs-)bIL)~eXpXnqgdg3$XiU<H`ml(&+R=t9Q
zi9EI4-Q7*UXKsB*QJTkvKyT@A_*F$@dc&p>Ow?|U?=*4tb|cLJ4Y#}DBYlV`G&c3I
zlgX$xWn|f>*Ff}la2n!qBby01&`a(%+?5Dx9DT6x-H6@*tN);e5D0S1k+T@(3q2?A
zST!kbH*1SQUrTaD=f0A(5@C*z$~fk9^TVI_=EYXS$c$+QcWjZqQ5V9TXh_E$byo>w
zCi6KM<zkB6uXisnmjg#4PJ|rDXWQcs;F-P`g*yxN#FJb>&-YMDR_sF+nXXZ9_}Oo)
zGda^R*kDb&f&D)lU=1TU|GQWp%mc)Fsqb9^8v3(ZIiP;}v$=A&U7b__9gE`4(m1vu
z_td;u_m9+=2tvY2rYf(MZDU9RxxkhQ(C7%ETmD@84X5QC>W<AE?L(b;k@t^)_H-Fu
zpDVCxonS{ZE;9nvTub#XmZ{vPq4nr{*i)b`7|8CRv&QVnYn%1Gyl#_xEu*^-nQ%T+
zq$=XJ0gS}Xh>n#Y6afMZ9Xf{j<qhPbeZZiz3Ko=m*15t(HfiM*A)YpGVlF=pyR}bT
zOrXQ>XoW&bT1Bjk;d=ykgK-xT+YdJ~)F(cpPwpu?p%x!jJV!!lDY6xTj?`S^HEu10
z1jY+u7;=?=!+=vRWn}-`kpKV_qjwQ70^NKu=s!~o8yV{*z%inhz~5u^f{>?+wHbj$
z=*e=;o;<@j$X!-cb5L5W94qWG;UV>^c(4!ay~a;GXqH+$rno(hrYMFR<tR_)=7Jqr
z4RZ>Isd4oBvkU}eTqaj>QtW+8!>N?wqVBRJ97K1k(hsju+-tuB!=L1SQs0g(HleBk
zm!{s*r=J=cshR8bWcz_*xH<KCk>vF*gzZ^GZACjIDA)x)T_(5+o+3rkoIqU#OW9%S
z>KSm@yp?CaF_J);+QcfjtTZ1O<h8nPGqpk8Mp1CYlTpe5YkU3NdBr*TnFG`We-Bh5
ziOfR?c9dWv(m9r0&GzHLgcHl)Ib>c#x|`1CyB0Dt%LHS90xw_BLgSANfY!(4cpaxm
zM{b@B`qd)=LsdeyTR;zlYLtP$hzwsEK)jufW*q-yQJ6r)XmrJre4PRYY#ZzzaPN5<
z&AN~{JXXDOM_Ey-PT44kIx#yTRt?CDK7)bo16&wzs12!31Hz?-4vtdoOxVhyqb=;s
zj18z~WtbTZMag3&Z9EO+=U(Ut#uSWU)iH@zgX#yEyV^-THVt2Oq<*R2#*5?50aSRX
zf2!BGgx=%02yMK0({+DLsz}}brPFHFh!}4W_-pC>acQhaa!s?r_>^7I`_(yk$}BQB
zrN;3=p}~uC$n5k*7@<jfrg*<`5D>6y(?#6v-)UoqN7i;(4ik9H{#cm@_8tMPUX*$o
zd&lNwrZ)Q9(9O^D?wN<%tGgpM|C8aONCMI{q3d53{=unFv$ToBEsbfGjo0zoDL$Fz
zAi-_JJsfpSLVo<sXMO6t6<DYU#{IC2sY;8hr31H31EDZ3Y`5Lk;m04<PBYl))9yf%
zAtQGlauoX**F;Goqp?Cmdm+GPAgi|H738!6SlfdeQIILM^uu&}*KRyLJ)f|Jsq;uN
z5iCcBXfwXK1g*7RY_pnTJMe@b^j_%n<~piAUeLK%ixrP~aIhu>v@^i(YCFL`is5?<
z`uMa5hcb{qa6njX!D_S3cA`MF-L2-33kxmMX|T&bqzPXmvGf;GT@+j>d2v>SlLusF
z1-~{Mlo?5yiNu*7m+L|&+AVuOAe)pKW1wy~zdm>N?zi1_iMAxRVKr+1_7pjj_#3r7
zbu4tg&OtEK6mLw{tA}yDu60B;Woe1M?egs}<`1A05hR2oTPpE?5~kiLpiH`NCX^1O
z#~iQ2LqX?2b}_>d0-O`IU*qVG)*Kh>YMLt1rL+K%(T@gFOEV*D2?z0aIbxN8Vpfv!
zYLV^Gdq5ZM_r!YWdj!lSsF&wyq-uN&4hH!kLJPv#U=QEJrT`WU5nCi)s7JuSeem=i
zuXbj`1WPo#g0%f1p6mC~b|^?nBzW4}a4)15ef;`7eOKY!?tknGL6<<R>Gh?o#Q1lL
zsMw%@4s-&!3~lJ5eo;`|2m%R@pEUB>ABfHmu#kqRbO!%WE_4AN3;+$0q4O335R+D~
zfQQyqlUn`(4XX&6%)?%P>NjQcYira2l+h8QcKl*K!|4$`L_vkt%fCDXf_$SuI^_9R
zon6EL;ys*rUyKVR%M=)aY(j3$WBBhbE}#*_AY9;;_Wuq7gkWs%foOiQo_K(HXr-gO
zKmDZ<@qrtrk;0Szle*6U1d7d{GSdg9gboGpGWL>gZNpyz1$ZDxp$y$*$bWbjE<ol^
z{{w*30Z68BK;C=SY^3l0L~LsW2Ztfir1<Zi<`AJoaLs)<kYyhoLlhCdVjsTvEl?LP
zVsHx$#eXSjAXONQkQpO53^62-G-7}%rWF7|^XrK!sK5>UNUP@mrc=UM0D;yD`L)S_
zy+9IT^a?gkQ^~&sqRT+AIGa=Euz%+}T@)ZQ7abVEj|SXBSTB?!Quc3I2n8Tu88V3T
zkAovJlwgEF$|YYK0O6|nfS2j&L{_!@7U&Q`T7m00;7|S?urVNW81f1|!iHTSKwj<%
zEw)F0EINVUIPv{G{zH_mzCn12wbB%jJI=~QOsFxP^poELg<B#9R}azt#R~u4^k4x_
zAcluUh_E4DD8hOldrVaS!c+|41{<WVUq^O->kOofP~r=lO2lfw9u2%KX==5Y^Oryp
zV1O2*9Vr=TKv1ZECiOi)W@8DD|5sC>?G`nF^3sAwMfe30@K0X$xlE~)QQ-dp&8o)@

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/deploy/run-on-aws/disk.png b/docs/python_docs/python/tutorials/deploy/run-on-aws/disk.png
new file mode 100644
index 0000000000000000000000000000000000000000..a1abd3a4fd93f5d61e9d34d86c6326af12aa7ee4
GIT binary patch
literal 165192
zcmd41byS;8v@eVl_u|E&EiD9h3vIDdyif>U9E!UWC|b0%#jQY*;_d_|6fN!qcL|z6
zAY9&a&$;KU^{w~({XJ_Q$;|AT>^<|Fy=Tw<qN%P#LimIb0|SFZ`K|nW3=A9^3=GVD
zeB6f|*WSmK7#M_gc5-r>%5rk7nr_b4b`Dk;7;nEMCFAL6+tENf5EBeM<k3I=X0T>q
zOxVAeD2@AyLGUah?5DY$rOOM8*9wogC3SG1SOY)V^FVZusH!x+zE&{CBNElQ!c>`?
zmudCC+Fn{Rb%G%7j{{EvcZ@Iwz*Ubv3xi05J%S4Phi5~XDH)WYB-re%tQABT`VNVm
zGEbk9vx?H*e`3MK6lQYHP_w^7-QQ1dN_=4##n8m>CSatHVp<c5f_)?kOT+l-_O<PJ
zU$sk^MGmADMHS^TB+NPHh7jf)uCcUMlTYRBKEdq#t`9I1#Ms<x-_<{~l#f(Zn)^Jq
zc1n@GG8;Z(<x0L10`I=K_%OK?QKi)_yZ7{7LFjeKAM<A_z6~k#J^rWipM_@@?$5n$
zomDg_$BuGi#M8Jhh~(HEZy*H~gkJ?^&6MGc=i)*KHmTfKF_{((142&m5`fBJ37R6s
zmN5Ypaj>JP7`tD5cxm^qo7=i)A@A?x3x7~5tgN|b_MV%)JNRR%IP6Np_vsH~%=;ef
zZN%A+?eKMeyOF)TYrk({z=@z{=|Ydd=VIL&3mo>42zjm8I#Kn!lt$sPgdY_YmVR$V
zaO~L5+kaiZJI^a~*{e?*7!rlP1l)i1%_6r;=p^z9eYGiv$JR+WhoSP3=ndw8s?m3e
z<=7prE(12cv<zi$C3Q{qtUopxe7_9!K?~UYfy_26h}Kr29pb#T9d~*h41NfkO8~I3
zh9bBDgkgDoE^!x%L4JVQq-qrtu3sy0aY2hKu~qYmq8LkUGf)uA`fZ?BCqWY%H7fKY
zIi`6RVJEK9SJJSLA|Nu+&;ZRxUcoXy85yyZUXzGps)tzr#pMktU46cbgT0FRHCPM&
zI5w2*<Kxe)jPfDHU%7ta?8y~=rS1(jVs(B&P8e#Q>+^zsUC!KArVfYe^ShsJyZAap
zcUW1Wysu|-fxFDY#Br~kbNToQpZ7#p;W}XOeWtIHV#HAD99pv!#~zJ1Uv+e$HzHt(
z%ve={tNCCgck-<fP`ygj6#gMPNZj|zH}A8SYw^<`<n<IEqPP@3<UP?WFXo+c{PF0L
z@RB}GL3`Yzj(&mQC^BBYcAS1UZr9vHq6K$3>{uSANR|&BhY3){O7>H2M46eJ^Z!Ym
zP7P3fSxD$k<BlT~Oy3vKX<jW{#x}!z5E<83_;J=OrW&s*ZqCbr#p{t&NY9#%YoWM}
z5r2I6Q18~p)mr=p@A{2vev^jJV-q6ha6}h$?HXwm@Df4_#)Kt+BXXV&P+Snihke9~
ztV*uRa*6%>u1SIJnd;-MFA?%K=8puzo6<mv4y-0@z^4$7#J7@9@!r4qqBO{sl&Jo#
zA~8Za=MSMJWeuk<y}s&-V#V{u?<a{ctrt|-(G1VxzbVfv{w^>olr9YTmOGd_#BU`o
zm|@9fnQnPwWis^G^3rPAN^ih=D0whDx&0aHT|BD|>&{E)x5XEFY~JclZ=K#`b2-0c
z0p#$S0!%r<#^`1&^{!W4h9UW1i(GWpYP#{M6{ZXP-p^0*Oj%5EPiZNX<>j(#3n)~-
zJq<HYSa16F`CHuk9}`as2n){NJ$@2x{mNou!gkTF$TqZ&Z}7?B@!*$1oI#SoyFr!@
zG{sH7pO#RS2$v8S2WS<P`jr-z9P2ph2L6iB71AR5U|8;@c@(`+iW5zgNmE5j#VcOF
z3Xv>x%405ssNWU)HQt0p6h>4=yt2sYw~EtC7giG+p&D6Erxk|Q6&vsxNS7}g(AJy$
zP1-%a5cZ>Q{|>AuuQ)2=nE~c4=ga)ID5m`Vy>znp{B26{&|kHQ<?_W`sfwZk+Fy+(
zl_n<;;Om^+v1wQ-tl)mU^Dpbd$I{oeUG8$!a)o9MX6<Wo9MOrTS`k_W<4hK10zKl>
z$Y9q>0ta*ZZjs;o%~pgN3u$MnWT{s?y#No;fGMciWgymJ+bP*f*ICp-U5HG0OvG6f
z-2T>CYbh@)riCW3v8w)Ut+@4R=liY+nBZ*9^jUe*7wW~5?)`X?z6Ge?ch8th1_td6
z`HWCeZ^z#auNw><r5rlu=NAc%o*eccULI~7Ar{F5sOZ^Z&HF|0VnZW)o=1vCj>UV$
zr^OdB-g^kTzw<0{_k+y2xUR2T#3*HYX6lbBWj^EeNbyPO;eD*<sF!SCa6l<$EEaE=
zv?%0lfuKTc99PN!q{EnPWPpB>eok-)#2ephKWe{AA8sEdzZ8GjtMOCitKSHEME#}l
z&f;<8DG_{ePpo(9V@+E9uq)XAo&=qRxfu)(J`3gyc^=Z;Im0SGAZx4lx98c73Q{FK
z^fkYbj~+aqyyt7|S5vv_T((^5T#X(;&rGCexOR_T#B4Y?3LZ7be9AiU;yvR$#WRL_
zK#AzSMV+39a@|vFt>K8&jOtI-ZF9-uMn*bDAC`LGpA`5f$Me=2CwDlwG_ReB3{_TY
z8YVWZ-@cnqDoZNb5I7w_^*lAz?<#vYBabCy2JRML{NA2&_idwpeuQ4dLmi+Tki40x
z^X)=<Ucy)JjBYl`a4@j%d^72M<rlJm@!fUmlT3pw$t0SrnbdfV1PxiJ-fGdAl%8FM
zL2Ze~N3H?dw}Om9SC+9v*kK>o-_{vTefu3n3|uznI3-(O1EiEimuZyd>*knnbVPI%
z`j`J%_*_3$6IcT_i8O(l%vPKD-)&#^Ff55N&f`0(&dpUTduy*0jx)kC(?@zoerJG(
z>IZoK1a>_0Q?buhG*Mp}AcM$G?F?-aokuoEI?7yd+$gOi$l%Q2pa>zfZiLQM`cz3Y
zfjri<Fyx=JqMDq-hG&ik^G=O-tE7jd==iRz3Mu<{n<_Obie5NJpBB_$#!aZZ>?Hq+
zl!YJ63o1+8E8H)M0QU1mjm}U=<<`<!2w_5W|J^={=n5>|De)Kl_vm8}Tb!IiB!qFB
z3F6<2c&lCT+G<bhaJ(#EBSC@b;3n<V-7(4FgVuE8r=a>@&&vvHXCMs?>g`VcGDdtA
zEK-m|pA&*gN*)qx5~U}0aSEJW%BO#5J9_U0eYjS2pmzOx5kfaMS2+dQJPx1JXGG(~
z+%ul>RPzMx+>UwHN!DlIP2bM$iyk#ZpTFB_G-WjzXyx}5K&{KS{=Azm*i_+Q;+A4I
zif;}m5~{1+dDod_uNPQha_n}};xlq>u;>Xr*m2@_5{A{a&QCcm240CR!bY#=E`^tC
zI;JNM{w^*wHl4ibu)i-@G-<3{F<WVtsIvU&%<l$2{#(QL$9Qyk2QhKn-kjqJK6^bX
zH{8>$<d*P`rBt@!b{UCK{F1eKTQ%!~_zveJQ=H@AyC=Up^%OpNxn;K}RBls0R-WA$
zcO82P0o|Bi6s&G-mfm*!xp%y`&!4qHd!TClrL6-l;@~4wi|gJcCnd1Owv$x+G@o0x
zyPi#Gw?;|MC!i3}JMato%)#waZ)vv1xgTcGffoQ4PXdZO`$(WKl#fB^D*lonxN#ao
z^Xq)bLd-Ccip|azeW<m~Ej`x#7aN;>`daLtg-pQ%j2NH4<|rlFuHs@4tdT!eY{Hyd
zFxbE6*ZNjJT}Jbpo?h);QQ->P`SVe_U}rgP?e8Pn+OU$Z<|o*H(Pm!iIik%Q7>tmQ
zW<B_ABd<fj^&Qw7G<YNa@3CJ!n6U&dZ}r_VFle6rb7LyMe+qxFaI@`nKpr49RY?nH
zM_w~aXLBoFA4ivm+87wpK9Ucgj#eIKtUitoPVSOEGVK3RL-OJKpKLyM*8iyDVK2iD
zQqyFWb9S?072y@;<!1*Hva+&DyIERGzL!`0Px6OfGVHb<9xjr6eBR#Pyxu~*&Tck*
z0umAueEfoZf`U8`HF(^8ojlBZc%0lh{%<4y)sDQCyM>#bi-(=F6YD?inwdL$ddRS|
z|I^X`yZ-P0wDPh0@1C68|C6l;2KoLe;S=EH=lg$cKafiQlPjrd=VRpnlDBiTa&mv@
z11KQGFDU&V1pl9+|L*dCkm~<8sjvk9|D^mMCI3k&&G!!n|AV9dE3W^@eUKNBP@3=m
z$sR};?N{CRFdU3_@)|l1Z>)dD=Ha@0xL^F|`w|0F18W-Flp6!%6^63>YaJiV!z`RO
zroQ<|yy#ZB@l|HlrUK3Pwz)snk2MW`QW>;?ToCIl$K*!XVXM4sAcEs#QpXNo(Y8p$
zaF&eMnA))#`n&1zv3^=wpqH1|cE?GZQ2)I-J|R0R*1x8|k3s~V0z_Y{L`DbWNBv8p
zdh`@f!uoHe5Bb7sue!}A2RuIiYZFnyVG2o@6y*PwLJA4qf*hWtssC#YOp32n#e)AE
zE5eurF=`x>1pmq`E7oJ054iHL9{pQNf6i)FZD%R@`d@3vzv|8#{1-9(BcNB453&-C
zQ|0_$r1ci0L;t_9X`3D#)>oLg_vqiH^}s0C!>00IhgFU8Yt@{vVAj9aU}rVcvif&H
z$p5r^kX43aA?3e`>LLGOQV{<yZ2tdeQpmEQ_FGwc#AI)akRpGO7Ll$&0@joKo)6RY
zRUnq%%)ZbB#e+2S$wK%bcLD*34fqxG2W31%Kb^%zvzPJj|1qmyjdA{U4DU;kMRGB^
z9-NzvC|KinWPw|F+3x<#!Xh_L=xBJs52^=E3;R$Yu(7!jIg+wZ?PXv$%0H7T+jw@5
z=paYCZheIxO65d8^HZa^sRVXNVt&{>lZ|M*wwv2JV>A193MHhB7t$5?j_K=}d$xX*
zkrP0P4j8vED>IFyfZVNhm=#g);Ft0uhXQ(?4yH9A)T2$PE&k&<i-@s~YXQcpn}FPt
zOl?}FuV~o{y2t(W_QQXjv+a6dV4)N#_Y0%I^-GN&rm+s%74+fa=b_9=RUK)2Ayv_!
z#gMwQvcxa$$6yg7nw+th&*?ZbDTjuePSG!QD4T5NBG<lV<PgE`k+2d$)4(4=p46B~
z7Ox@qKA|bwCrjMZa&Z$+1fcDR>&CSOHs?s%6%;!>JIDH)Yft5Nx+GxUevsD}O6Gq*
z<YMWtv2=6jACkaBW9v99d-v2VCCEKomRCB|k=btO#9UwIHhHJ#Swb6g(CyfLG~!Io
zzk-T4a5Jt`%DY9mpUB3s0rWx2?w3^q5hc*|Z<WjW<Vl1;{=N#zYX1xLsC$a4{y#>o
zbKq&G|MeF!V0l`2fptdB(FqVJ?BsSOr_dVOu>ObPilA0a#B3yBYC(C?8mf|wZ`yw#
z0UmnxB*^<tOf(Gbw@0})-zZCVch3(lkj+w2Ia*~*8n;+fG7V&@2wE?P-?rvN-uE}S
zH&WAhckKU$gAtI&&zYUe_z!==lA<-sR~j-RAe5rAh(vt0_r(;?8NJI~7DoenmL}DW
z82i>#n1hsj_Conu0nQ>dlNR2NUZXv0{R2zgeTx8k-84QvI@swg%B)?8xk|1lpd<6?
zQu{%)h?Msdw1AXFXNgwobSe%Nc1e@<8@ab(WRbhqGoQid6dC8GVq(xCdz}sU2+-f`
zP9C}-R4Q-RR9k62A8e?<DWia)XJskXA?JO0LWo^iRJeX&>Q6x0xL50R5(eBVNFGz*
z)Z2rC?tMioi4Rw1AdGu&yQKr%*y*v>U7lR#pGmswhowYz>rEAzGxq#qNEu2bcGzUJ
zD`Is(f)ZX2bqZ)e7N<FBfI0YAt{Ney7V~@)#vF%;Cq+xN$APCk@BHxX<q)P4w*pQ|
zj0ztaT3~~Y&iNl6AL4RDmZ6skyWSLgi+D##JJXTlPMS}DggdFOE;@IR$lk)`===|>
zdwe$HX5-vT$~<kbBKyg%pi5V8MgUGNQ-C$(B~68|YOU0m|LQaxVT*5J7E|CKZZTs#
z5G2}Ohjy+#sUYjP<pul&e?+dy-j^>nYDzWjEegKNbb+glg>N`j1}U}_RQY_+0XDX;
zj$=vAXeXSUXEX#+yjp#;ThrOopg^U4Gb-@`xsAFHJJ<;7B_{&SjJp(Ks$rk#L6_*W
zzvQXvZH`PZCMD~r6ETZdRTbA97)S<f<<=$>d6S`7%Y66ml8l#fPza=FXI=&1<Lj1N
zEpOeUEB2TKO75@sF*G#!7D$cT%&J%ShNS=xn;gG9xYKRkR=Krk0$+`V)G51ALzeGM
zR1Z;1!pS7xuyAvjf`z?`MX$5zwj)@NkLPau^~Zr~X&D3Mds8YJPCA<=WN6>K|2S55
zW$&Z?%IhErSE_w*LjaV}q24yUeXQi12D&>o!0}adoikx(Tw7IKeOieN=z?y&cV1+N
zAJHgDXEHr(9#k24&<*-tGyG#7l(bvhd66ciHQ!cy7FNM!<UwnS>T}J;?VB7^OQ>bW
zNW1&S7qG=jekG(hWDJ42yESXh$Hh^kg)hmr-*Y)S=6haxiZFCIBTl9NYq-bndTeVE
z6`U-x9=m_x%icXo+H7A(WMw&i1qCJ4Z7lG1+@8U=Gn{qTvs}6fdPEl;DEk>}>&Mx=
zVS(O0@Gd+et&<FA2!VZhy>6;aw(q|6CAj*lsmHj&+8qMMcXqasARBSiu*j<__~)tT
z*jDgPwm<KZ$E2FsT$NdM4h>6c4py6Wmgfw75r!58)M*~9$MzL%?aE;=Vaep_+Ik17
zju-HujOx%e{93{;erugGi!3wuj#tZ<*Nw7azE`aUKKHW+hQ&+{-u4&zg)Ex_yt(B|
zJYfn?%4vZ}1IBOHYE74Eh!W;vFEIn{#2B-g9fiq{vP)^^A%DA5ZfG}XeY*)=-%bU~
z@@YQ<G)Orsyvbk95jysjIP|>yTf!ZR`1)ezJ^AFvq@ND}7H7N(7C&qQ_>}TU_3MQJ
zN5}O+(k~6}naImA2(;CNoyN`j*dv3*Yy9gV>~O4Zu4mrhXzVA*$$6W&r>b?IuEOVD
zT`8u?#Zu624&6qzyizf}TpR1{h(r6?pmx+0LWvunB;22iaVXH;NGKT$QLM@hWujIX
zfvD}sSKe7{N(=zt(%@a31|&Xt89}p7zmwf?nrF7GU^A>R2^XzptfIU^-36>UcVtfn
z=_Zpw&a_Tl<IFU}LT5p&w_AF(dRGa>)RHvr(0H8dInS-;9+QqceOA^47Vvc-$faLd
z>6#4aadjAaHsC{heWA2-H5A2#SZ2$59f})1#M@f-jXJE)B0)y<cAfU9)&zYqsnN6I
zf5L=5&sjqoD-*Lh!2%H^Lc@R486q&Tal=Be@pRAHr*#d(pQV&JrzOxe-J?;Fh!y`z
z@st3@0C<;K$?w9F-Xz1dHf;?V5n}qY+Hk#N@rKL&Y3@S^>X<qri&|)CV*Dywqch>N
z9B0$V(r0<6ePd%^jMv1+t@Nans5WCQ-^OTpMWvQMI=B}_9e+{;4R8I2&d{=8$oHau
zHt7C2+kMMxC~Pa=l{wcCKW1e~RAJ?KbRReiWKwTGC}2-3ux4@+p@o^w!*8IbkqTtZ
z?HG~M>+W}Y1mELi)eAzDSF*pD?pHtcxy-NZR;o^nGwt+UuiEpYCYb?5rO3+XC}^t-
ztM7{Rg`I|Hk0?&}>yG+!-u(5yk6D`$(pD0%yTJ|Ey*@}2x$#Q1Y^yFk8{Z`(Tz(w6
zOkr{jFYT@|>zLl=4iI$#&PXB?--Uko93)j1dD!YU1I<2Rym4<ESl?hYX4pQ})hT24
z>DnxN)h-gZv(qfvvNCT&GQ>t%K2S-Jbw&J@Y*5guMYpqZV5s^wkhU-Ti-knC=ODp8
zFsIjMPxw4GKGtT0E^)^Iad==GcV`|z&-xgSLq)V|psR@-@i~}uj^6m4Zyi6A*Svvf
zJx+sHcJa+HJyw=Ukkxo{zR{PP1qz>T^)}M3y)Mjruqk5R0J@9zFTVE+K0Zh=SVM_V
zZA^m{$y1A4cj6uPr`PJ&Hw^rhxE+D%iC(Rd(T`;jg=sbO$M$8OcQRHmxyk@WU75XX
z_SU9galfojF*F_b8bf0=q=xQY$$W8cdps0grdG#QP%c>_PrF`Z5PGODfi`2PjD|k1
zw>8O4Mc;-Bhb8t2!H?Wo;7yBdt?CobvLTYoZ8`=*&zt8l5uP`~oMjEAOydKWF0s?p
zQv&TasbwW<(`jukCr_wpc*x>rYUA33Y?Ur$KDnHZx0|5Cy|{sK1gi4lA2NyYxj*~m
zCZ4TsR2|eIxU4EoBuhf2yUs2fSNymcJ^sG_knH_k1L!M$@mJ2U;puQe1V$8jY6K(@
z6%i-4BEG!z_hb}52LB64<1sl6nZ)K-{;eO{GpdO77(P<(Wly~ki`&tacK^tuMi?aO
zLEBIZOhSt;wY~>8&d9nQt(YB%mwc3eB=jwJ%w3&|vauaLo<{=N1$>V*^xikTR3F-k
zL@eRC_7Vp&m40`%`kbr7W0r^}<skpdUuL=`3z*qA+&t?NU3nST5Nhb_te?GcAklCR
zwST*4TW6~6rM3Xf3{KJxD#-J0B&YEWMlqS*nSVT^d7|wxxF9kdf&16!^g!wjHW`Q`
z$W1g(u8^L_5jxr2fcyOOA}pUj;)J0VQ1wP{yNER8fR9$jBr4hqpt25SH@mm(`g+hN
z=Fd#B)?P{fWHdU6Yi+j&={m5&Hjedqt-?<H=s4xgJwx`9i_Fj{&?-~f;tRhO6?Qs-
zYWmrMMI_AdR-Tc9dp8ZymTMikDBmey{-#!4#_T76yo#>RO_*ebaC6pOgA%s`{@Qf}
z!d@pX(P+{m9S7FARuia-cTA&BI(h3KG$LpVt&NaXpd3N6yG`K6gmL8a2}@1@FVcL^
zTs{C8?22$>@*Vkn0McjsP&C{;aO=8|#Kxfp6zM)UKbb9JA-{5zea#mqtX{=yUi_JC
zN`ha6h-VS{l*Hdeq^_9LkwGO}X}`W_cHekPtF_;c>^{1ARUs<G)h#-U4_qTCJjWty
z7y#j0^xBKI*dN`@IEy=xEOVQbB&*)~5qA@ow4K(^Y04PMmqC=**G9Hie-jv2hB|<s
z@IP;z+rC|M0uGk_EfzvCd3z@|lF;TzFt%)C^|?^32;lYkq0;G~`IUk1lSuYGfD5ca
z1(-C(u5osfGN+ATZ=<HmZ)@Iv<XwfmuK6Q^8`0)M)K<mQ2fcysVEHEib!T~pLQ>=X
zsMjIVzn~nTlgBx~xtaZI<@9qnqStMlT0m4Y&&w^RSHJ<R?U&9&k?oa1Eya{vc)iqN
zXzoTsNr3Q%r2@+GRL+?%OcG3d;Gvrxb!;apeb6O-J&V#$;kWV}P*fRaq=~&Ye}sLy
zy1h+X&0Hgz<fx+b^)GHdPruiKV^eQu&4!*5S-fKrd3JO@3mtuzWx%5qob8NOCdViH
zG(#Mhg(TruW4=O{fK!nP;Q1*n?Kawn#$kIQ%`&nk9e2N-C{I=|<F;)60iXGI4|`0L
z_RD$iyKVEV(Gz;DV0DF0)32E{lbkxmSam&3!FQh10>lKs^(c;o=K%JS2h0Wa{oN=D
zTPG{yNC4jKnv1))RIZ#*yaWWD_R*Rp$XpfIG+E*!_Dd_7pJzDKz!&T*sq;8yKXn;e
z*0euBb$!BZ>RBhsFWdr`b{*{~b<YJ5JhwCss)oRNrQ#98&Khl;(lm?yg|B~Hr^%ww
zdfl>>zAbVvCexKtL^jZYMfUxTcZxA_AojjHPzMzpQ`WN)IW;mC*kp?bf_1ZKJ1=*1
z1W3*0%nthxjrt3!7uTvW7G_`J&_)4&BW{-4%!ba_Z|PlyqwA@~E6da-<{Kbeb{+<2
zK(SDPx-jT^6aZ#@K+|pRC+C9);;1O&FWeD6Ta0-gGc2s1`D07Za~ix}klU{+COjCh
zvR{x!3M;VCu&B}fsU^#=PP5_szIG>3>A5WZ#R<ax*@q}X;R~LrbIy<?N)n$kh9@N8
z*26z8W*78*J@!dLwZ;Oi>}xkn$wWi);}dxH7d1yeWdWMQO@p(iHGfEFWCD@(-(&&m
zsaol)+;3QRb^!o`<`)M~{kiGKDlry%bG)SOpYABs8vH4ukuk}9DV3mpmG6%r(ut+<
zTd&=wpO-o3^Js0Td6oyLg*|@K9F(0SBxWoO2>9#B^n@e1pWuLxQB>b8*?a~>IpJX1
z!pON2NxS~YI100TJ3}(TtW@~hCmcSXi6}!!La|DPwL-uuQL-f0CjvDbFcX@|+8m$H
z`iT>nJgj;z@+s=^+izmGp3UZGT<7lEe3XldW{Lej!z<bpRq{Fcu95t!r;UWJgfWkv
z+n<NfQ}6{LFU`Np7zTlb4pvyyB+Hs~rK6&L$YxVwv6$1w<gv_PScTMt#?apSJibL$
z&S{=SN}j^TQzq~Dz*M5w9g#Ikr<o8Bc!^n`1s*M=Fz|XiEPox!@~FbEYxKN3&++_-
zWxd*_+dEd3%9`PG@_n#nH+$A^17OV)$4R7D&R8gT^uV}sx^|>)Y?pkLI?$+)r9wHz
z{}x1Ly=Qxqh--BXcU-X^3a!7cy4rPzJ2C+!uZ5evV<UbBaTJrDdF)r_xDCFXH)}tl
zp`lo=3I-5K9ahP8wp8jOck>gvBUysz4vGY#*@m=L1r(aD7VL>kBfU)KHEjWu9XI=>
z>r1BSvlO$uFV8<2Z2F_GcY0i*Og75l56{M^9+Z`(t>-=ZuCy0ABDx3wv>w)k+ivzv
zD@K@hppIMDYR7=w6Y{}XK{tCDk3XKKeNo`b)vGl5@a8ndG>D#D<$CNZg5Y^7>&M^k
zB<tS{x~+fVtnl=i21fL3%sx)^%yjK15E>P?(4N(%o_}!>O4eiuP3W9LpejV2eZ-%<
zXrqcV>W`7^T$)rf8XoE_hI-+ud>40=&p}|uEmAEN{la{mNM?Ka8SL$PIHJ)(X_FJw
zv|`tX)?JWA-=+FDilJ86IjjTq`6kf@3`euE74Cdf7HP*jko1_$qnlA0(z}h%Yoo<t
zX7E=q>$=A@ffgkz{J*Gv-CdEWE=Kf`CC5yvN}C8g{Td+>g7+t)vml6){LhV2P6+Xb
z=JjzgBFu1xH*mQM1Bo#*Q}Z_=7I||G=C|e}n~cz9U>W(%TPXKP3h+#EZtdGzPlY(1
zN}(y#-Nff)Xk7(wV~=3lz6PnaLx!e?n})yGovb0nEZXUP1J0>pZ+<R0)_!&>7NgI(
zcLCx^Id9g@pu_F5K-pe4pAmT1s&t#b&fQ=MbCdjP_wns@0N#=Tf5Cf*{VX-17Kcnl
zA`H#ka8s5TPdkE)3mzbLa98lg4mI`ZsCv8UH&97(v@HpDj<mwTEN!BkHT^|*gPJ?Y
z31i|7$`jNfKFCISv+y2YOxcVG)ueQQb9*p=sNVi@#c`}-=s={+U4`wuN|nuDaCqKi
zi+kB*m7pTBW&ev<zVoO}ObRX&G<O{5QQOCaOA1p`3aetVD4XiJaU1+IqtokqVbaN`
z9~19(Ahmd92NAt|@!%GC^*Rt)GxHAeWf4XC3?headIfS%r0*uv`XRAC1J>J@?oCS}
z)2|;0&H24D_@UA^JV>LCOX=&YzidDBX=tQW(8vQg30Dd7gL+%*8O{x4Sr0H+qp-0o
znR+=h3%duo{v2^#$_TXB_~uV=Pl-#y4VdI1y{|6ht{k6Wm`(<9wUI|o<WL5Vt{^6q
z`f7Wms^=3vy`=S5ZwBM{mwUaN`NA?`+e_g?<GbzCA@nObqD8;<J#+(tlkyk>+?Mv&
zt%lp|Qn8@ua1~bbJDt~ud!l06dc2zh-fE2&6Wu7NoK#z+bB_o)UTyFtKjLQc)}tT;
zyLi6w--d$J?P#-30HH_Cw(=lhFX=^Vo>UXcJu}Z{-GJlkS5k_%jT;3=cK@|ZDCY!T
z?$28<+Kjc`>6_KR3;fa<6%IVnS#U2M7Bfb&HiQgYk!^*T`In-fzA!~>E}D&jWVJat
z@)jOpylnN`3Q3UX2W|ZDkFoh|Z*?*Fd;DbgaeLv5L2{Zy-K(~!-;($SaX8O`W?0{i
zY#Xb59@c`$2aNNj=u|td`0kKMowT`o$#&1#6=ltIEmS|iJY4*;n@D!-aXMj4g7n#2
zam124_V-$3)tq@u_yhhW0&Y*?6wl*`!$%2X9@ZfrwgLSmd~1*&<ZO_;@A>*xOVd>(
z3oq@5WP?w?tG1F2X?Dn=Bw|bCPLS(uu8?n8y^RCxF<~QOu(o#?7rq;*;M)zBwY{Po
z(#9n>m1iv5a%X=ybBI%n+>~E!4u$L6WYwo?a$bsE_fl$v)s9M*Vh<_M8rRXicS3Vj
zuRrCHExYZbH6`B3bXR91F0$jT?tN?)!_!cV%u_`qkaiqpX&)HaN#)QgofWV|+GlAu
zB-~6dL`-d1JMN&GOth<f+9>cA%d%wt@fi=9cVtsU74jgxQ&{##e7XA@Fw%QHT;B??
z?C}fdoj7&f&|I}97Jy|JP}%kz{EzeJ5y`VuQ9T|%{+Ki1Wp%E29@g3yFgeb6{xYgG
z6;fkrRPL2&!86^a>+@1`ut`Nx&&BF?=aCexbx#VcIoLmi!!tylL3eHi(-fF-79$K%
z{U1Jkcq1)50CvBHuQ^*~!4${*H|rC)Yq>IPUp=9L*9Y%AGm+hLE~s`n<YRBK@Nzvn
zKiR1Kv(9bkcOd4RyB5wegq_7g1Q{QKvvak;6x?0j>>qS%5cdEkb>LoQmH2^~`K5h%
zrz1SgP;TfMLLLzL2qrMVWa?}vl`S#>=ko@zl9L_v^#<LGj2$=9{-KCHjuk4oarcop
zt?vtiGJy9t8_#0BA|DnKL0oh=X_0#)sEv-aOR_6wU<TBsk@4liI%$(Z6|H~nL_pep
z-Rv^#$fpzWc0mGy9d~{Zv#x~2g2Zwd<66g`nJnMTko>dHH&Y37ohQvBNp1=F%WBj%
z^37~z@A+@xPi|X;_l+^~LyVGV{gEvt<Exb%j+)n%6Td&Sg3T6b?OAB|OlwBohveP@
zeaxu2RrcS&Tn?h`FZL3+4zfEsp6`>(*3F&Rdn}hBe#Z<x=Pjj8=_qRU7CHLvsov4y
zg7?PgYUT2zW65`gq7f|nUT@*4d|S*t{QJ2NjPz`4aW;3aHU0LGh7(Upim>64RHbn@
zdp9QsjXAaEl(R19W<}$?Xk3TMJ{_glq)lz2-&M$qxrzsuCQhj0f=ylA+k3HbWAc+7
zkGhb1P5b0^g-zMJTnk_GTjc7ZQ~3`((<}0qISIAD&Jzbd-@kZ64-Fp|tG)&8XnB53
z9OTpPhz6aSDO*#P)@g$*n?&BEjEKj<>$lVGX=WmlnQ7dkJXUTJGA)=Q--gf4yoGnK
zGkGsjq=vU7zRKRe1_kUDq;w}LG3!$^?W}QpBe1Z}_T4VKWb#Eos7u+Adw4=`H)0Yk
zst*J1E@#~%KQwd^@q!#5w^e~82Fm9y*|<czpSH7*NyA^U{cU+6RTeKY0}9`@wM(m-
z8wl)t|LuS+ew;_MPHnT+9?C@LIafYjPX@UIIMgnctwrrJ7uR;pR@&VUDQ+c3IQyUb
z-wCl0H_D0ybms@=OJ!#1rGFnuTIIRDZ5?Xdp`dmRUB~X;cu;unDQL@`*Ajj=9>Wr@
zAdL_Z_X!WIaj0FY9jDOv06A{s$9<RXH!CW6f;hq(3n6Rm!_NTOiC%fZZM;t<B?M1-
zd1AAa7rY^G>{_qy@dL_>J8`(B`j3zS`bscDQA(=RuWJf&VA6x2IqL2muIlr-L0_HV
zZ5O8SpqSzpLc{jib7}D>OWijZI~C#+{RutiCqY`LcR##5coHD2Tav_HnLOI+V~kbr
zlE8`<y{EqwzG}pziC4d>N|U<#swasz{7$|o%I&k~3nsZR?a%+94YHxbD4u@*ddlR^
z6rpRv`okX7T{rzV-X^XtP~r~(7rNljok`=EDs?I0Ai5lUU`YI3rLqUrlArH&*g|wU
z_m9FBJTcuxrwNdvuZ4%ifln7wWa9Ig=cUxE>W~0!9moJ=#FDpqC3Qj#|Kr}y{{EtQ
zXylH2T(~v9C`#OQ(sq8Mq^R4<^)ulco3l8Dglkt3t@5a;yVozIdDIsaT0p6juN%|$
zCnM6ctY;Nug1qZivc#gN;&8`gx7P1=LG67)N&{asfE9IJcGtPuC*f3pR<p&VSzEh=
zcPjoGE@QqOpjE_C>je*B&B0JeCC^}E3e_EW8k${C<bx1p2x$M*?fwLhiRzrljsxcy
zyw?+*x}_vP*SzA_)xi=tUu*NjJ>br4i=kBBzVg@B!h~`;A9>CK8^D0cI*aCHtma|&
z32sNDeSr_2p`89NAJXk|PILQ<PEN>->;_Cv=NPJu5gofh?1V3mvGH}w7!*uXHQF!r
zKQ!06jb6tkKLBk)l;M=z7HllGd6ZgoPX(#K?M-}M5*kta`x4P{f9Jf$4L=`~<s-G&
z<?ivOFSK7bl?v}uVhI$^^xaNN0_ZRKAJyw6O)s?^R509J9_UHLAFpToAM1M(OVQU1
zi3FgI8pFxA$|=-9!y~UonvriTZ3SF~s1T@=NDb*9(PoSpxQ`}`y_f6XiY?o8&@T*q
zbt*%GIKR#iaAs;og(GV&mOoBdy}f+c&>G|akQf@RhBCKSsap=zkVQpD<%00jtKJ6C
zfc*kdhqW5whgCf#08)yJD!PD;II->rbNwKnuZ%rK-#QKcpjWA8k0jhbasb@rR-2gz
zG2|8l`CX@-8JK2%JEutB#^)VHiy$1wW#*S$fBe9SU=q4b?UaI+5jldnheDnGc;jC%
zI_(WXoy*2~kU{Y*BD;MZ-Rv?gRD!ZCUc4$o&uhGAW526uQmD`Cib}>Q)2C%7uEOs7
zB5=`2M1DQxA=C1qe~ffSpR})NK2HuwCBa<1UyMS$W1Z>7#<ZUZCM@V9+1S9P6u{7q
zB|(=a2E7Kbr~9zMfF6qIO-taQlqnx~B8$#Ckopxce--U}Q$Igyws#Jd8|Htey^tZ1
zkjBt{nO4vhI>RaX)7X1Fjc^FN_T7aksd#ebUvVH4WdR%ROdMuM+8Og&tuCB~oDuq$
zaYMe$h#T#PAAoZP=?}=Q9~>5{XR8d_E%9V_+xZp&%N8A;mzBijS_Aif;l70n_vVCX
z#oAkL!wrT5^qnsCWBTgZ0q?!sbb)Bdd)*hP=b}YfYB{hvj=)|6qN@jtW|LHmWz*ij
z8JlJj&Mad-=L^VKo3Ay`V%v~LNK1MA46M2cln#=$Tc(brJ^yfdDo({DD}ql9^mq;<
z-s@*i?Bu^zOG)oj$}&a>CA$YVwpCL?_Imlr$7G>>0sUIE3soTJKSwz4SyHCmla)H^
z>}wQf(_95(wDVIx<u1?Kp2nA`$kRvkYD3AJ61Eq9CD}K`V<U>n*m7cKSK2-3%9b=)
zl0B^_Y%-+%xto`4Nlg21QRB{yHb0~b?O4g#Q>X1KU9#0!t;ZCk!ul?lCXfHoYzI}W
z#Qy)FPPp^|a|La8cc)w7X7<<{VO))}h#;Tstg&dn<h2Q!0LYzx?fBX10j+(g{*sl7
z6!fZ;RPgci&_BlqADb0lMZIxJw@$a~=50NEPm!g1D_6I#`f5}1YR)2*rJ`-(9e4QU
zjA3OeTY6d^kE+yB-C8g<X)zKrU^~sG`wM5bZEMUkxnPP`eB$%=hwW{QC(5FGJxR}m
z-l_FSbkT^o)@nUT-`Xto?j8&BEXQyE_aIs^Oo7z;?TMBW(+7#l8N%7xNp&e|G^<3s
zNA+M2;rCbRAUpQI6yyt6%igf)=@*MZ_b3?c_nxZJzuONE<Ysop8pnh3M&7X3lr-~-
z{2_^EljkF1rfi*V8XA&&zt-@2JN9I(eJ)nJLb*|KTR&fic3Y(w>&~mEy@H27Gv`3i
z+N%@wKVF+O<wG?i@CO)sB%qA$s#|{Ua>YEOT7k;<KeW^)`w-}+@qHc=<eANx@aMQD
z<pjHn#>T7d%oQPZgEWj7(RKeEu-+-lXe*1J`~~bTvZV%rbvnQM+8>W~;u_(i-`WrU
zb#Ve-cX;{$zV;I}ysC4CK<8`e+5JU*aBoEt!h`$n$xu!*69MuHJ9F~t(DoVNroT|{
zPRbnR8*~3oWvdI2)l7OlW^)UO0Cio_#0(pF%vNyU^l%@btxw7C*(8reiGr<14fzNo
zg~P~Vg?>#`_mb)HeiV$`tJ4tkHfkxSFcWapEObS}bTUP&i53Hc$^_5f6nIvYf5)-0
z-`yTn+bH{h>3+CNp1NsXF3vb4S#BhRKH2@LkeNk#<TFF}B*R}BIb48ftpeTt5yT&g
zn<X_CS$qNKlOF8p5H<P*1={!H^Io<6noW^>Rv7f66q+h?)F~ctd}lkINfc$GcW;#z
zZ53OPV=qoVtDXYXx4KJ;sTI1ungM~tD_eJ6O2dpK8%E9?Iu3Xk;%3)RZ{3Xo0<AxX
z@eL6(*dWEbph=XN05$79Cc4PIIsct9e^UZZD18kB$%u(X{^`}`1`=6wnCPu&+GDCi
z^2~87{V=ev`8RoTOoEN@-J1lFIm}tIM%EV$*(1J?$>80c918!LfOJUgH6kB&JT^4V
zvO-&&&{OnXs*<VOToS8KRt0!hbLr>!B*)nw^4?6qdju_QN><fAenszlb^2?sitIjO
z2MFhiOvcS|9)rU4q~>AXDr5VqED21e4R!8=S4E3TP|4i~4Mad|;$wz`^WfDYn*oD1
z%#xJgGcHM?h9G4gJc=LWWJMWHEx{VV2>gt2CKUOMb*fnedx6ewH{W{0qLbktoE=|q
z#NjLA9xOg4ze615_#u+93NfnmbA0?@(8qL^zBR*hF#Y?0zpwGA(YWpp=5NNeAfuq=
zTQ(m9RhM8qBY=_nh)DQ}0xHgGGY5T96y2BSXw$sxwHOWzywMS5!va1)JiOq!tKcou
z>G#fG;x;*fUP-TP0Tdh*zC6E@3-%o5dr4FPDlfII_oA!-vqM23{t(jdYquy^&kg}=
z5Ty_g4^Jc|yq_|QnkEqZn*t(g&^0?*FO21WYVIk<o>hNufBC%)Q}I|vi?0$0M&Chw
zM<9&>*%{O{%9Zm$Bs(uQ36N*|ajb{C<L^MdSePC9_>R)5d-ANfeSAlyk17)n@?l(z
z6^qzuz!zTGB87)+%;PjM_%Sp-J~{DnI423}yVvP~h$^yD$!lWlP5UqUxBLL>Yl%Wa
zm{srF0D&Nu$R>agO6LP*G031nwFB~TUKtp$+J1XPc3%$S)!#|CaEmh;MPwDQp(+@+
zm+zB<O~nDl$MNPouK$3eE}~{^Y`5+>64QMd-uiHo%4IsR(3QtD_JmsZ22jw0H;Ia?
zvfcVMGX(A6e@Rwu0Ej=diSxIL1vUQT$k`c`8q;p~i=F?*5{>R8FQuDs0KQa)qLQjF
zCU5>e5n|N%h~NDr@$$2f>XT(bRU>4Qh^a-^pGI1>Pa^Q)cW&#p4IR!@HfTOV=c(~1
z=}C1#r?(}=<}@p(K{xM5B43;{N!)(=bbooP&6`O8K3}tt1EIa<Df?($WdC46Z<;<e
z=T@0b8BxwS5L@gG+CO!ck#HgNIgvpe@3^{E6A*U%*qimJ7#rJm*s4o9xPpk>NtFV4
z<wtN(WW>Z9Hg&z{u0ANX@tl{49|7BdrZ3!q4cak%OCm_Y?E|8Y<(!nlL<7L~ElZ-<
z0<NefESo4yp|sY~(2u1ozFL&}B&6;b;aTmBY{*yJsGQyDIzL@%()}#U=k0h{ISb4`
zK7GIw@=RGONx2mHA|&Y=Yf{4iKQ-VZ{JNqqK47O}2o}1;FFcy>sFo2M+!`xW5%kVe
z^EJmkCpFor4HRzEASB1H8}J;94gJr-w}O1T-(?AiV%vfj<~OM(Mh&4l!k@656TuuO
z!TcLxTI<+Htw+q@P=#5#_FqSe)uSwrgu{ec-sg_=R(V`mInxhZOC(lmnT=1_B-@qY
zpUOrl6kf;F-8ERg71qst<lJS0m3hss8a}1dxe*{Q4tT^fEPH<&{=xE7^?*!I$yect
z;GZ#3fBxD8I4*oeUWi-xu7==6JRF|1&Zz|~$x=R@#~-?&1F>@z0bfoEPryL=>{tov
zA%4W(W>r~E-%g_;R(^MTQrlG`w5nuw$VNo2xT9(ULgoZaL`5G2p*E(VD0ALWBKUQJ
zBb8Fn&T+`JT7K_#@=R*BaqSKTyP7q;LA*_m-})l%!@l3%I3b;nxfv1Y9CX~X7E!eF
z+YfdfAi7X_x?|g(-dTm{ARb~AD1MP|(psIb$ZeL0H!10kF>swMzz=9o6L|9!O~uIo
z&TL_FgJD>_5T=O{m2z9KyV%o*esu&Du3A(los;A8`5`a!C^{TH07FP`B2e+#d4w__
zeg@w%_hD7{VIN$Jt0rsR;|gU{SPSKwOY^V|5>T8I9wp!10I<7(+pwo=NO2?mPye8@
z0rnRQ@=QAMNMp-F+t1!06R!%GNhuaDJ7f5L8y$C80l0*`X*jbM=4BN0;19{1DH?mv
z(VeG%4X-&%u0pp2aOOl)u*Arm>TjgSQXE{JA^0wge0R{ly4jg-W0-?@+V1um`&5EP
z=L9a-)<H$9c*(2FuRu3;Jza*j0^ErH@%t1qbd0mj+1*Df*^x@FV~&cqnVR1Vogck7
zWp%lgX~AUWA17e<dSVhU*HG!df3$8eYhu~&upuY|4f037q?PJ_6g2qq@@_OmR^MZ=
z-FB!NSaDtLa}R|lyr3ad;hURlB|K<&I2g$Z(lFYguWIS;08iLg|M}u`vijUSaaa9U
z(&`<XYfzfAM$BS|lH=8l_B#HfT99xlj?_wWn;wc$Fkqd{U%egaG?vaZHjyG=YKlBU
zw;A1q9ALO#k&fvJ&8)8=a%E_zq-J<)ZwtHGg>+1)Z364!wPei34IefBHxgsZWydNI
z=P%ksCV?Bvn;WNlRm%Y(T@EgpmM<=G>@RD_8lB0mQJ=0yjZ;Xrc2aA|7)2$>?AU;$
z@wF)r24o3S3MiRPG|gx3G%DVZT&nGZF_dGlkyU&v;AwIW1)=F@;sgbc3v=q7&9eFT
zM5^&-yOka46>OYO1Sf@sm?#J1UwWZ0b{;Ie@~EFkg3JfQu=Kfom&RI__sYE4m3;&w
zhr@!VsVvpP4mXoqCM6984#kyfZ!aNvuIob}RQP4imRS9<r4%C63?3C7_lT0Fo$k}@
zNAu#og9L$OmH_zc57|$Y-90X9cNb%RcgR_6qsX(W>#iiRPoiqf{-EVJ92Jdr0n!_m
zNr}4kbvPNTeJhwz9y3>L6<y<z;w-rntDHW6+BqjSCShn9^Ap(>?eez9f!cb?!828<
z)IZz&pW(9$?>e5h)7+ucf^)K@JguNwGP<B&A4%hqeJr4F1}(9qsp3FCkc8M6O68MM
z4)w!jWx6<owjJU`Fm2}`T?es{SdU3k-v#_pk<D9&FA}fa2QqRZf}kWh)C-2%4Q|ZT
z*&^F+N$hb>nXQ!MAF9p-xUnXJEKic6m5rmZgKjLcxPxZ)$-AIjfcSPU19>!8kn0#u
ze0}uiyXWM|B7T85!>1eaIoiEzJQ_ozmy3ZtnLdgs7^R;1;iFmA)f#;P2AQ4sIbm^+
zAe#JBar_55l(=Pj2jQbY``#S?<bW15WFcnkk!^gB;EOEkCy8x{*Nq>_tSVt<&9>oE
zR=aj%>8|{Wx1tZe#%%7BAK`V({zv(hT$flvWz3XPbwFfFL!dyxaqR^K4)gtvGuLI6
z;|@+j{qzsletx1~V_~n6K^$|M{kmr(zbKv)e%IYU2}K^o@NY3!`wo8T|2?cLxj03s
z$WqDuA)wQ(ZKp@%8(7v|20BHEmJA3T%-P^{tv=eke>i|3c9&3E1M<d8Jp{#ee;T(b
zCxZ+xKH_r@IP**~$=B#8sxYK~b}t%Q(?P?BYallbboyx5FBI~j)cf#wOk=ivPQCdF
zT~;?Al@y-c>Ix4%Y<#<<dnL|<f5Z4r=OV`z^6g8gTMYH~PY{&yFx~Ca(zR0q96*15
z84h#uBq~Dg^rW1qjRw6~y1i&sC`-Q5P^L0%Y<GSQ&R)h#{gs?*B-Arr&3=mbIV0j~
zx-ea+{GaOZFx@?q*KfDJaKWa?VX7#)v61}k1~}r63%eW<lE4u8Go-KAVbgyX%}q-q
zUO(sRblhT_zwlIlsddWy(e_ji2w=d8Xt%wzsD0zDAY(r}ubraWUA^ek`XNF}%#A6F
zg2uh}h1j?4c&TGL8Y7o3QXrjs_j)iU_GiSTt$8Z#Gqv4M^2Fy*WE=aS!2B?0PQd&5
z;VOqmZNk64Pt(b*j}o{fTTAiKxaU31T;0M(Gtq)RhVaiCx#NV5=$QuA*GNIXW&PfI
zLAke@Lph}P`Ba^UhT>U@vDbXbS>&rBFxF==A$z`ufXKd+@vVgW-4XJQ*I?7!5|hgl
z+EgQ2&I*ud{ANDTNjC+4JTp9^M{ffo6I1I(7@}!NLa$VaL))fv!QR*O$*5sz-zKY~
zt<s9b2C@@PhTog8d#h`eMie3n3~RhuIXf~s8;)E-8k9adQ5mneMA@oZ`6&tArN8@S
z@aHj7?yzH-R;l~(=(71~m7mA5O)iu$CQ4PbmKmqm!8XFUnCIo+gUlc>9U~LkuyfD~
zO1%?BnW4%IiIWS&yGMm~mz;dxjm=zTfbU^<k+VMO0fIQ@2}PMG?hCRlW@F{B9s&%c
z{|SjFodsTdC1$4q{bQ1JM=g?JN%5_v1CM=w=3Q@ePttxL;Kvru<B0S8><PL}A*k5t
zeIbhW%eL!Ia4qb52xx!C=)OqQcdA2bWvV0IX~;GsmAo<X8*mWyD!{d4Y~*8_t~F$%
zTSB+FM*W37KkBHNv=d&dbkt#zTA*tB*oHZulAPsB3BQ+v!z~@d^Wk?9wVs2wZ>rtR
zt&zIcCF+B>yN9)kKbmd+aBiKHCMD@qt;Gy}VE`EYsKhfxiy!*@*_i(l>bFp9%@|A3
z5f}201?ykIe2&HBy0+nlG958dHRy5=yl}kdji9R<ll4vpdX5~I4l`AG7<nZB!6tbg
z;rQ66;ksn#tHu6Lu!)c`DJCNj`#B0>?6?ANG##UcAde&Y$ATKEKEKPqE`*!`jj!Wu
zS02LtHv-YTqd=LuRPhLpHiaq4A~M?f`FK#!)IxiYxhw9WHxTvQAY6vQDXxM8A8imr
zLUMb08yLzZGfkv+SS$HA2Kxd2YmT)$&O0y5M(lKq;j8wT_UuIHXRrrv=-%aK0a(wC
z>4z@>6B>QF@pA{3Q0pP9MS*xR*?276#&*DPK$#$>OGjg4@k_m&7CB)b^j#v^zqwDa
zy*v4IV|!RzRrALzW<m!wX~)*}pyrExz5skfsUDd2#Acf5OGv5d-R?mZG`Og{r!vUM
zGBhx@<`wza^!L8kvUjIe!?NB%t<yZ2-%4c%a%gkJNpq&^Y*tX~i&ZD}V>$~=hv#GA
zC;YDMNV+Wcps1#eYeaO~#2zDgKt{=mNPVfATnpMSkXf2#EY2MtE#10Ov_U*F(l8bU
zjffF4*!;Q+32|#YYnu$Gj%PAjBXr)6aD_70B7>XGtu%B8`L3DaI|Xtui6W=p(nx6+
z$EmbFr9Gq;*tV@OYrXbtFw(TTv^b-x?kVzY;n=U??N7XWyFPP@81Y-W_0TbEO<YXi
z<I`L{?$z3p+(_mU&$nJVBq#0b(cIk+Q9&jW{SAw_y%0f`7!%MYQpeVQ2Fh+%;Ci(`
zU7Bjl&@~+C(IiOx9}z=X)O-Lp=33#!O&-<aP=#C^hBtAVHfs-lRT5&pM$K&t*9^x7
zf*3wX6O%4rlD_D!%AIuJOPvaDv*lYH4eM|H6OT*Zzma5I4(pqOnoH^v?{p_Adw?E)
z{3IPslHbJ=*3-PM^i9L!KI&C#>=(?POs_ong<(!)htVy_=luIx&$lBvUWGh;<8DK?
z_p*ydm{w6~s(rwzIaC&fVqgNK){W3Hby+vNmF|O&THP5;C|*<8v8$tXBIxlAG|~Q&
z*T`1-@gjLsNG*Zxm!*z-?l05&rzcAO_v7k6ZPN;5w1jU~*vH&^QQcvDkIq#69Y4=$
z+|910bYHrLCE~Q|%9{TOjJx_j?7e4Hl*_g?tOx><MRF2QP{}!i2@pvl2u)6sQF3lT
zkSIwcNN$xRIp@#_l0%bo&auffnYVhMbMAZY*?W(1$M^pF#<>5qc|z5yRaL9jTys8F
z?DQP_n9#G3aod@neDCf|FzB~>48HT1wt9ZjxXOhHxp(KAjKB2!EaTZlJL;w=_92Fy
zwsBWanWkCi-m=&@zP}(|ASpHh-o-*1P8kN-=r{OPc6L;Mhg;UQukumnQpFnZA3hx)
z>z(iD#2c2W|6FPc-G^d-pB|wIN0AZb775NzXVi}f$IjyM!Bs<Ez-Ki4rrq;XQl01N
z$Ud5>ZlC^qKtHh}WEeX-mTo<|-{zKLRp%<YpG#7K%XRdn(5$`xaAa}c-(V+kAyE6;
zMA6z8ihWRf#P*wIVk9a>ptEF12t)++Ue={eop>`YEgYAQ?RAx&Utt`1=X^G;Op?aZ
z@9E4Z^sL$)+KI&yHOu3n1H?M#y%RSsRXd(0*ib8a{y^xSF(rycIM7e-k2H53R$Z7(
zh+h?>Fjw61_`Oz4%Z{Axfhgc%Nq%jM*2${e8R4g>Bs%Zb!URoIbFN{ZNqOG_mr?Z%
zV{K|4Nz#Wo!@^3avl&%sp+_iWxzT;yBSX1Dh&YT_o%|;pF=Z6Ib2i<?p?z_NSjX|s
z=Q6IxKiRqzzfyWp0FM-nF1>KO{FsK&s7B7*xTHU>9TVBI93W<}i}x3DD?G~Z?vW6J
zVT(m<lo9pil_J>bLnfyCyq+>lXjrvl(?8eV&q+33*{*?e>SVDqh!wFMEcbu!k}TV{
zc@&kAMcAQo>PAz4XV)R_X8FQ)Z-5*#1|2c0CeJQ++3<vQN%yUd5`Ox#utRD|>bk4n
zQkT0ChE+?kIpc!J%7p+iWM!)5)7zMArh2BUcI&A&d1b@5a&n7bMt0>@OjGAquRoG6
zh9tZJK*Tg<ehsp^L-IFg?)oywO+EenG7vM7(scM^4C&O%N9#o)xa!0lBwAD{;(*oA
z5IWcrX>83z+rQJubQX%+%IrK()BjxXsG$CIy}7G5R|*s!%=wWj{T$a9WGl6s700m4
zusFkgSE=8X@J>B2$$0*4&z@ty94o<okC=^JM4>&cS(4qO#*(7N>be5?`OS$6n^D^)
zf)K;GwB|z8h`x8SuJoweN^*$n7HYNlt#9X$Qgy}h(q80B(P-ee_pHY#A8+Mo>3OMF
z{iup6jE->BHe+~^+${I`cRD?;V-#zvpnVv*8B)UVtRprh(EG}+wqlT7Y{sTyZh6t|
zFlsv|DE*QWjy$R8FW9xSkWe<L5Vn{cL&3)l%_{DYx2K;B?mk08k#!GwF`kItYh%zZ
z4;LWl>FpkKbf<0V4iC|Pcup)C{tH2Q+0wCRO6MuM&i}ZM@~N@w*UrG_8@_Y;&K!#L
zzVK$v1k@+}#JaLiTQ*1HkM4>ph}RyKHO~Kda(ZT7-l0%At--WHE3$K9JGs>)+I6p+
zJC<}<JB^apayL!Br{a6Jj&fZAmvg;{_L5K|2Dvf|drAua$RTRVMS-1jW^89g@~mfN
z4itiNLJZ!NwLaP(eVE$a5C6Tc>!{@{B(XAQJJ9R$X`ohGt<KfjI|ngt5>Zc54nN*)
z)ME9F4XWAnDv6l$6VusyUb)>&bfQV-T22DDZ_@T?ELrAiS*J;{I?w0+6-_a2j{&#&
zmUcM$VYTLtzGjtsWUz*7{&}ymA7Q|#)X_L=O!e}Ycd@tFWEqN%NzOZ^BV2we?~t|0
ze#%35ml`ek7P>BHguKCQG~HK}Kt-5`dCom6(AYCfda+$A?B_cCkhN5?`L>E(D@5k}
ztCH9?dFOzS#&W~fJBMl=BdyXYMFwZWpOR`@Yqhb(dR+D_UPzMVeJngU@;N2XQ9f@^
z|DKc2qnOEh#mTlWu4*OgLITl0aY-+5v41AMQDhLb?=>Pp*HgbeL}(J@uu+CB>>ZJl
za*HA+`Y=jplhnP{YzQNk5Ffko(MiW@iXZM1=#EJjQQ6_d#aZ;JahU2h+q!!0&3;3*
zD$!`?JA_E(FfS%p5QEo_)hC)y5XX?mfrhCi^y{y-4-#F*3&&oU+{u4*?$M6)DL|bI
z6e&rH4P+D@2etZ1bX<QHUPNhKmU6!yR1rZ2nQt;FkpAQ%E+yHEGo*eZL{Pip3^5Gf
zX>~17z)$;FzL=|8szx7lRTp<zP9YiVSun5V-^ZDy>N)geEjjB-jE*Jj%{bVEwQwGb
zs5~Oe@4rJ27$LSjA}eZw%;fWC#J_k6*1GyO%`m=_sYAXuZT4(F+kAVRNAYV^sSp~Y
zX=X&wD!H#Be4Yyn+#bG7t}5gFzAio3TO{21{q}TS6QS59@#~zYN6h1l4PR^+6bgUu
z@g&za3m-3V)gAOVVyC@(cJ1v06e#zhMS_&uBc5omN|5a(YXxZ0vIhqm6X%k=P=xs1
zFOh?}&If}k@hkoT1=X%wNS8&^Uq@%Mhfb1d)(o=3edUKfE=u`CTN9pPtG6#xp~%!?
z<$+-|8}1Vu0n1urHSE=JfZ+qw&Au<8DiW6$nn`!AdWBj<BT*qlwJU_@P3LxLzXqCR
zUtL|IT8Fey;{DFt+YP2O+8dmrlTI4>Hmzc2D>2KT)H7xR2e!ScY+HAsS2N_P$Tnjt
zhNGoZ0c(w)${&tsZ#q<F7XhxUbmwdkq<=L7<79JU&J@A_fg;7CIN-dkEJ3P%p0_l*
z12)%Nml0Rk-;hsc#<pTeu;WGhi604pKQMLROkC>74B4@0#K`zPpe$HFljlTY9^BC8
zI$Zy0zAtfC)0mP8(U7aE^r|T1rxQu)S;=w%tY<^5NRJkksrkjqu5&Au@bs=0L}kW}
zA(@|ZNA6jinAa<F0WTPfoe}Qwkh0{HteT%qmRNe9@E6WlVo7`v#(DdqH4$9Zbwsof
ze2w#GchnKVQI%=GLd1@S$l;9zm(epaGYrfd)B_u9J#Z4m9lViEkE$W7AGwnIJ!ZSi
zZ9AvIuBM?&WJR1PA0EX(k5esLjCUrU&toR|8IBS+sE#fv4EG=!J6$TMJq4BR1mY2^
zZJLnqPZgC93v>RZ1>je*uX$Q7D^}3jI*~li54JyXF6~VA-i)-V*!mU9e$;F0g*`@s
z43_2bsyz0h%Jh$|9x>d?2YA{Am+Ewb!fR^$`%UUuuzte^k-3i73$D+fvyZWNiL4|U
zj_A6S!QRM+E9b4nIZ=klv0U4Yicw3BJDJ;Ww(bU$xcAFH!xM>{+M3i<6^HCHC-y*2
zmpm#xtuak3&5{hS4(xfA#T;d6rF4R0w|v*0VAH9^`;Q%E%KK@B^D&kJU`&D~jPK%|
zn~~UXbWn)GtEijMd&M(PJ8t9kfCF^5v)R|Ari+$62S*|(rJG23g)8Symbk3rNMGBT
z+gsbz2$T1d#QWwoLbuIB>ggudbQXf3Qi5mGys5c*eJ?&ZC}tLJ@XvT>`KR%io55S=
z2YK&MCzL(A&z}AKpLFzw7k6!~X&Zfq#DB4nyeygXZ&nXTP1{|oz;PtIUXs!>&np!C
zg~2(c<OMPt`cRK#)dCX$Qe_bFwX~>P8+<kSju=D}J-6i>9Vs327DQN+r^d8}>j7KL
z=Rot6yYD%vzt_d7|3(44-pHr&U$76M@vzzN=E3U7_LmtjuTyT<o!NN^OmAuNOAOfD
zAL%%cnas_B8bQCvNkg1eaI;qfR~iw{!$s_ws9bGs{or%cGf*-33ZF<BO98V|nq2c9
zcPQ0N82RhI-rRfd;2aEYi;<0A_`_Tpaaq~t>rm6Mzy+fxl?chRJL);kl14mH1Gb(8
zwR<n)JdeikI%RD7Uer@|gr}!t_1B&6bvCWQG_<pe7-VSPm8$5fjLi4H#+1RlE|t|o
z(#LS`GSPE~Q{Vow<Dy7wTNIH0Yr^x4$p0bN;PVIr$|He1qWJNqFV60G-d2Om>veqg
z#|0wXkcT7}$b8HmkJ%MNEil&i`fa8yJ~1{$J2{9naPuTWGHUcf_U>aay-K|HEJIP}
z?F+sp3mSrBtMkng#HYDxd`21JE$heE?oCHXT?*gr;=L1R*o+gq5TWlqNOs8@PmCQR
z6(Gt?o#7+r5Cr@1S@dvFRl`(8DdOFM(1{m-s&CB^;mKh>j*G@(Nfo&~UI<}a#jlYW
zeU4MQ(<jT|?}?hKU_!cEX^6GNPCm=W?tGO`0Lqcd7E15<L@M4rl<P_3N@N#sh_gPo
zyHz2NrZnFVp=fQFi~pcz_#4P52+zl}N&lh5;L{iV;VRn_=jPk<=`qQGpJ)a#&*n4o
z5E<!C<cn(`VNUb+Y(H8lV909Fc`F|0tNlac!94u2s2d)}1Jcz$D$XGu;})4%ELTOd
zfv+Y6Ii$a_-Zp=0U&#@Rd~+<j9XulRXi>9q{--U46Tv?O9wdCP@x@*p{r0%^o!Glx
zp{;n@h%aV28CNfkdvk16r^eecW6Grm6fM709wa28l*6=*z64Y`f1y+&Np?qE2zP$<
ztm7QuK=k(ey<lE@9i59N#yhfMK!h_S1v`1;PQBR0lqRIVWfHprU4RhH!d=8=-`5!p
zmWC!Fr>mir0+DuW<*JsHBxT$Xi4)|*?LP}oq+(rXF%toSGsbX;iO<|x%~p9L^nUF1
zerFP5pK`Pqg>%}cSAQX@Q610#2)k4P_*AdEw8Fe!oWBGG{BZm_4-_GQoYoVbljWdW
z8%_MV5P~6-DE;?}6&*l7gxxGv;?IryuV3y1#fsPueWrh}tKnl09KUnSJmL@SfWMUa
zc#I3gH|`shYW_uU{;8GmEeQyC*b9y`|E)d;)2pXIFa?+L-9IPTU#X%zU<FzvNnuC-
zT+;rkG5>!v%j1E@c~D-aMxS^T)L^b;(D5+Sn(exT&AaBTHefrMyVr*TnTgB4+`dlo
zlc^VZy5gP5rAY?&0w&;BHVECKx4cZFHv0)vyAkeuD2%b?WfQi#@~qJ)g3Z3e)`g{a
zfXxrtrS3+0on%!LQ;p#$_bh$#Q^U{a-=+>D{F+s=99Oku$ieV2ht&3O;bM+oOn)gH
zB}G>6J;1n+>F)0T%3gDv9X)7@q^~^xsh`rzg0YqFeUH6-t>r;NA6lBF@APP6euLN?
z<pHP*bhG0e7*p|&l?#Zg`Lvf5F`s`kBLO#;AivtPVWi&naUPMnO)i_>|J{m86Zl^^
z``*h6R`ks|6L=vKa~TgJ{>wlA{109EZx#$8x`}knoPY02y`(rVAe*=@a&CA2%8$^i
zkk5M=;BP`Tj2+>B>)8jG!1p~Gbzjr}?JWD22sC&`yWa;D|GjHV_y7~7*nW+c{o84_
zY6=YDTtf3>^KX6ovGQHu{j8JsmsI|Co>fzUp`6+bcp&y?!~OG@|2573n&v+QjQ-ba
z{vRilV|?K;fR_D*rd^q95BlZjKp=_-^E}f0WgN=Wf`ew}fpROV#@vz~>ObP(VTkb|
zrg}#z5L=?S{Vm1ya7d#`+op~T@NklMX09$PUc8&MYb5}Mitpk~Vl_Wqp_%Ly%THB8
z{%X(TfB9{zah^Fs{5EIR;umg^Qj^&yj@9=%Dco(L2^=$n)y+9L&bBe{fC6VO9q)_7
zIUxE$h|?UUD5weXJRE9@GY%Ko;~je10q!wDC&qGm&M(ilo6h$U49*kpDk8lkG<2Qb
z%w7SB8I47fCtE?x;j6_^#>?%hl@t)adp+nsLn}>4o^6tn>$%Q7lG!?cl5pcD{)5Y=
z`bh2}HC1#`Cs%IyxTPWB1X?WKq5I3#vKI>z@PCcs(IONS!@+sWq&{|l>x%<<@X%2o
z17rb}g?Acmuu+{ib9s?))*sI|-YzRH!p_B~=6^uzJZea1-*K4aGVSIcqZ63SY<^>Y
zOG#|&PKx*C$;(gh^Chk(aMmMj)7S6=LMRy@d)?a@1O4vDLVj$u+j(-=ul@+p3a25w
zI71PkQGian)#rb;Ui%E=mP$iv0iUUnr<HcVopUlCa+fhhfv=nYy_MHt0L^=xuX^ql
z%|GzubAX*C&40d4(GV*)O0QhYNW5vIJ51^??UzUAJj#eqBgh_N0!9HB1blAJGeM&-
zE(;UTMRoT@{F)sQ5*3~7<X0El-hqJ7QS^nLO_!0~3%H6Qh$S}$WpQBb1cIY?t?Lel
zy;Pm)GGyu9-p-14SCeN1MIDKG{1E(0`MS5bj#?0q#=&j^DV4;qNx>eFG%;My$|Q_Z
z;FWW|x<G}5UwB{cGfYew1~)T3d%Qv5P>Vh)#hayQiojWK>BMxQK9KpvquE+?0ad8y
z$m6R?%bdKIS?F?De{+fAbMNr%MkX)PiZRo~#w$yz^W9bk_`Ey`A^ISuOJWZYTil%d
zYD>-N<|CbxElalE1gd_ezp(Y{9H!`f_IZYPsbPZI#d>`7cpRm#81HrgyVJ3legAmX
zh{xpz%Jrqh&v%-fUk=@lyjJv09mK^UN&o}|o;??>s<cLIr+kO}YWwQ4fS6EBCv@V2
zR1+vktmI&R@1#aX4vvbOjo4N%zwr8zE!<+u{ahxTkxwP=YAd`D%2#|yurfkZFUcF1
zF_Ja&7aIrnBa+qVLRWD5#0+50ypGob^d&M9OW?bV6@Xh8Xj6ZZVJY?WIumAz%vUVj
z5R<p#@VZ!kePvQ|Dqh4QgTnM{wps0+*^fk=EI+fHbTl?;sF)xcME<6f5)4eQtxQZk
zE=S;(L!WJi7`)GUlAOma&<NJnhYRql)2LNJov3vM0rSVK^t|-Hh+;xNVQP_+!!M2|
zo+im*{o<&YEG8yO@Oi_cHMH-*YFI?ok;l-v^9H#Abfl8Q(lT*Fh$IeP@k|uKDKVe_
zeq<_8!OdG>^)|>rmxFzoUC++N78JGl0m5%X;fQYIP5od{YUVLigs%<XEbhGZ74MCh
z*C`Kg-16S{cYkH8Y+`dwNLMg%5M~heA(VM}Hl2yF^#CZzBmsVLIw@P)N~i+k0~f{A
zj&k>=0FT?Kq{_ETvER#O>73Mt-Wj_hMK^L)%icXdZ(Ay1Ri17<Ts(KUJVAI90y{Vl
zyeJZEKIOS6RIxffZ8I1@@c&D-z||+1<B!-mD`$P@0DYR148zK<{lw}w#@de%&Rgh(
z`e<+v<CYIz$bFXSSLQ@MV5MN$tljU4X^Vpden+BN7be=ux5?wh%}crkbEv~73iRQH
zt@Iv#I#jfpQ68cAB<!f7Z@MK<SMmcf(4d4rAdY0dj!n>P$DKxJIOZk<D3m#g@S9p}
zQceAPNCpJ|!t;MBe#>i|-}XY)>;PIyo%5&r9%~0SV5Yw*Y0mH^W1=8`3APp)9>zbg
z7f}0N{xXyn4l+sp?JSA8#qTzYS_yGdl2o~BkLN?K=LoPwKluR4<qJNkvLu-Q;GTi*
zlab1^1D>Uirqu=(^p^xk6ZnXe>}&{i^AGred7F2vIGN@E@?u#17duTvcts)59`XGy
z=@jq79DRQvj1s}$_i+wM^S;tBhU;m-H>OFWb~DC0<#LIN161!(Q-IuSts=)VVQO6_
zahTJv=9@qUpizcp({b0=LiBqtpr2EagX)fKhlGpB>20*&(1R%a>!!m8XiDdfyyX%z
ztFM4n(Tl2TFpoku+yEvKvZpYXE{*^^Rl}2|uYau3>EaRQhLQHFHjOb_N$3|8f?S<C
zU2zMwl^=S|9F3Lym=?&@imo29C*&Kt+J{}?@35pHES4I&K1~f^GeEh;!M-A$>~r-Z
zWiBft;ZYY@4-B)?V{gfG)j6)UMk<v4K}i+&_`MGVvjV|}SF;Yq+yQz^WKLwLP<~*d
zshv!zi-#1tcJGdhJrDY1Zy;vCg03k(usGAv1Xds_cWFGky&5nQ73b{pvQfOKh8sk8
z4#!tu-g=9{ysgjm*zzllc4yXmx9iV1@u_&zUMmZ#>XY(}*aEdC|Fdm4JT7m#zr;P-
z>ZIGoOEt@-<U{G74*}myFy4`o7n#-oW}s*n#?%Ys>L`%bIGjUQhXYhj&+b;TrC$ml
zQB<zd+5nfCD;v?Xx>w!76BLTxvC<c`#^%&hpnL7MqlST4qRodGbi;~5Q~cJoyDb7(
zQUR7m63qgT$F3$BR_tl{U@f1}aR{btTohDL;9<G}BqE!LxG?eMM_5{4VEsg+%3V`U
zGKdOjY4vZ7dr{?gyC9G6r9%zq<iDNMxz75O*FeUdG~QY7V9%4sI)zQ!JeOgAO5#S`
zPNK`s|A?JQ!O$WIGWDXtH)`*^U!Kb!t;Dk)onH=Fq}I@LTgv9Ax!C=M&2WdbYA2WR
zC36eoJU}G1r0-Oj6-HKWc;0<d+bG@C;B$i}IA8dt&xVcnnRXmlK&?`m0!ag=&)gRP
zvjhidW?8FXGyOJdP7e43@C>ALej!9L&)jt838f=^Dfp`O;;_J*Ag7?-y*Trn6>%rZ
zZTFo2wjh{7ABc2Q@E}^|Y4ytxOu3$g<h~aTykytihBeLJE!zG?h|_!5C)U3i6uy_Q
zKFC}v<#m&%mPHEvP<vFm7%pEiM7{nZ3BtW(8mG7*=S{i|$j>??PX;b<Fo7f%Hm&b<
zBgCvZ*_d_1>)XF9o)e5IU<vxsJio>sCd$mdjGPO)O^)WfG$LSTFeT#X8k<8Ftlixc
zr(8YsKAg=viyF}~GZ&17fXQaFR4C)ujNUmxnHE-z1GVmVaby2#@E48bekDVm72`;I
zr#XL$0MLfoM^nz{ZBv~x^BkmW!W7@XdPMbDPraz6{pi2+k}Xfo-IWtDdBaam=k}}Z
z<);Z|>w>0jE!ibQudE<dK)de+@?D9-Pojqd_<<F#a_Y;C^}J4&o*rAvu$MV(>{fuJ
z<DX9G=An-}8;!;maqop7L`8o=xXamkRi*(i9pl+)0<NPD0)%md<RQNUu|i;2mGQ13
zu}VAOuj)n4`ztG$7;t!uQ$Q!Z`|5XHPEMn$;5QlDmBxpqJB@f0W60n}Ce+!tkA&I9
zKO1>By7&<6$W%Ck!nH3w!lCJF**W>^L^)N{R4PU6^X`pI&uXdGr+Ruh4I{<Vqc}7r
zNn)vt5QSIH0qPv&7q*r#h5&J9q>CW8S|YzaqlOKwLl;l;s~I4ywy5;5JiXB<AOCBL
zj&Yzj(jl-8ykAqkeX=ObI!B|0gr^QMA~UZLu{HzuhM2~i^mm_f{3)N&ivj#ay5DGZ
z0wlpdE0Xt_mJO-np)vP4?aqJ%Q=<2_3uvw{8f`ZkWo}AeTg~(dzH9wJ;EYl$a7oE>
zEpVn5@ID?&)a^*M_V)UdYdx6t-U5dST=Yg?x%fps+fPue$a?gx(%nJ_>)>y`HvG9H
zyLK(FlmguO9)>YsShbcYU}3@w=r(X{vrMRbsT&jgQy#D{vzR_C3GdJpjHR<uIR7$-
z0F~}fhCW+Oz@{B?Yr*V{`2c@qlWg94X2Q|uzaKCBkOHhq`9G41em~tAP?q9gAESIN
zFOo+#{t!H?XV-!cpY}aurL4&}vN+UM+<LU|C2ys~j?bA7kvd+`+Oc5S++cZK><1ZI
zNc|TZmW0Ob8B%3&6@1O3CL{-EtYGb!jj=?Y&`;SchIIm-<!9Fp$*(g62CZ7$<y<Fv
zF3@os3Qh|lJEt23fncruL3s<7(j=gAQ&@-q`T@upa1@)EgAkZAV5Es$>PNK5x|M!}
z(M3`$T+5_Z{bUW)4;+IqoFN0)k|_})Fb&Q@V??r(1%PlVrXY23uy!BdDF2zcc^vge
z^ty73@ml9M_v^nRg0C08TqnC|of9f5VFVSfyI<z2NtJ{RyMJ~D*zvjHH(l8$oBw9Y
zC3R@ZC?Yi4BVl;$)39w!SM<Tq%|tyJhYAd{RbaS~Tc_q&`fkOkn8_!iMsx*}C2t_m
zG84&B6+KN;5j=Cr8JS1O7le2or#&;5d!|I`cb1izqNUIlNe{c^Ea}Q|LCm;s<Qc_r
zkk%|djbD9jDgjWvw+ZPM)U1uS)orI+b=?)j!1}%X43pvBI+mX^yJPzqI1K75KnKSn
zNOf#P@4%{qHk75rkI&~4NhyeI;1}gHI=QO)*1$o>_T?go7%LTCe01TMMa2x!==m+w
ze_=U%I3Kt2G@;5YG}!&8&5H{5MHa#NCVZoB${+XT%s`OZ={0GH!6jL_h=CwKT$;V@
zLTqA^bkI*k{~OT?Q&kd4A9T4-jy(RUh}9*7^x(W2XZh2Fd8PPGwUJ4sOmvZSH$sl5
zkl%1vHSvC_s7IN1I))N61{s|i=6##y=a0%nn<Oa|4t~9*mAsq46zgjDL*eeMcz(Q`
zg*`?_#MkzR#9{+Vc4jQ3ySteiHnqDljF984W2uExsQDYnJ;DH9UOAi+xYpxMgK`K0
zaa3j;Pa3cuL-u^pan-`ID!x-Yel&b(1nf>aC~6Ug&XURJPfdQvE9ItICj+RO4yntD
zK#N=$RKDXZ)rQ;)QxCK@mR1nad>bjPRV>&UtjgV8^>T;35VGv1c!M<Qe2&Qbg^<g!
zuApCnW>OL4=(~E|FFPD(1&n7W-B`IP@!#4;sC1ag-Lr(_QeX?JM@e^!d9TY;Q|}tS
z5H)%Kprga}Jq|zV+^g7HfA<6iFJpmE(wSzB&K=O2z!}nlKWUVQDq)Ipuw&ryVCK+b
zep0_NPu4_46)^g<VenI798G`a@7wZsnl7l@J(S|o_LB+o*hq0Y*gtgi_YibS-+es$
zJE2gQk+q)D{jOKqcfLj=>XD%1<eHTrwc`jjQWvV1?QdBm?|$HY8~r`mWX=bh{sDW{
z`6cfLzjvrU7tLNgsjfXLtg=lk%9x+JL+6I0f8mQ1dJtF{9c`&?TJXc6L66@)8%+cm
z6>8BiGodm_7tdj&ol*}5@x}m%(PkL59FlRJ;vlVoI=ZjaZEW?J-xJ&W>MJ!Ioa}6b
z4QDA-HsKFR&T4-Ren*XKqF!Eg7D>vKSag>~s>;1IN3xgS{z6pdKT|?7WEk&A$O&%x
zw!P8NCCi6jp5{tD70VyK9U1=J%JFpFUq<YgBz-_P5Bo3%-Joe8d1fQEU3!ycCnkpJ
zRP%av-{VYCA<V+55*uUxV2A}HtNUpE!D(l1!sc;gU01NFi6oM}z{FQK@9ZmSeoz6G
zVQW9)gsVH4Q>3p{lQD|F=<}p`YM5x(=ER#uCX2=mke}iDt;OE2MSzdn^6s(3cL^77
zdj(HPdnT?WIu(MDDw$`Ro}aZaztU-JxE;97O1`F0DcJQ{PK}Vf1!5e?nysrh=SG%6
zoJY9>;^vy+$=J^}c`b|Nrt-;)wwP{H|EbBkRsyVTwiF7iKOInfhH*;YKn^q;lxayR
z@x$vuDgGu8Jm<R;d&druhSjlN31Gp@zbhYO;a(hhT5uDZJ62SFF9qykg_f0%qV90J
z1!!t7V`7LF2Dx;fetkP1aDzy^_X}67NmPp(-QaA5W1OLc)wh&;;Mkd3Np#3g^yF(S
zSNT#vT}d*CGNtY;H7bw#a7SsC5K2`a=?AQX1?cDv_R{mOlW81z5ThkOird>S4g2&b
zL9rK|9(1?a=tmk@J=uWT+ri4QQwO`7Nt>Hyo33$qmXwqbezm4b=X~c0w}SMFPTjAQ
z?w*(LEd)#aq?&Rstan5VNpfC5#d&u>`>U__CB3mldMzTamjv<MtB?PjBdcmy5r6FX
z2ZH$Dc7Vq?x-9JOm5z@=3_tbZ?qM5q#GR|U<N1igjV#Ctswm}_RFzVp6zhmZt0mTx
z&vTPuH-9UcCa_52Sp*7yRcG@h+Fkk0)1R|K)3c{;6~Eu!f?pwWWkIeCPM_EW)M^lG
z&1p?R-n8h2!ste!L<%iSP17Z$YHF-$+RBY69dywCX5LAwMOf1F=4gP_W1U<4H1@eN
z(S>o#WU)BP2nm#qfva08c^D*<v#<ff!m)W<-LIK;Mm>HpSAIYggfn;j_<U}W#+34L
zdkC1&lF;;V2(h{4D|IV+4XyGm?Zaw@Wjcj_h%v#v1wAL%M4be;=}n)az7Kh<E%a9Z
z=XMy{gDDeK7<VJIP#7dAjUG^U7Jnw>IciN$zSLnIWq_5G?w*O2D7D5Y<#byoZSYt0
zpRg#4?ApX?n&N_LKAGmH#WA(arD$bO|DOgcLQIgOJBE=Por;6cL=0nfDu-`D?k?xP
z>x3#V`$@B}5_1vXOT-QPWU$uzrr)sg3WR2T3ii!KB5dtQrrI4l4EY6gL1Urd;jsPX
z)00;p1T5e4{*;N=g3?BqOoF4v`@~NsIZ<AVEF^NU3Z1%0(ur)|!3HAs$0UNg?}8y`
z9c%^sw(1X9g5}Jtm0{jY>;DYos6P!5`r527Gu90kdl=X`I%^)Wy9nwfbN%OLm5S#R
z2^^#Woa)ti1}X5;`3>?MmSQwa{e4aSo0HNN6ZCT6sw#Y}#DgW6?DB~{PNF`Tmy4@A
zO_@Ws;5bat_#+u1z|U(fR95@4U}~ObtP?nUUEG_Rsgno$-La%PT4bEmWV){nZ~D{>
z1$)|`Y;!jLfCoYx(BVt7pNdyaUS?CsJ28dC_}cp}M53z04o*)`eeSv_<S^XR13DLt
z{dTsFvMMaUsS}G$knX$kt{_HIPwsyyVzCIOz>_zP$>)8KCX`rm2L2NtGl*vF1sc!h
z{XTEM_<$38$&E?oaABr{wSmm0BhbCUuLglQVnvKwA677Z)dPPvnu0Vl0S*><|ADnf
zYG)GJJ-*G$B`)uW0eWAXKiVE1FGRQy--{=swPMaa$01eZS5dr*Guf?hI0Jz{`AvMv
z-kt|+c1wQINw#6l^XQs2k%B%l<jJtMjqVZ=zgc(8t&rc|$f^}Vf#858i?+{<&G#N0
zC7K{bFD@gi!ihgAH>mNm?W3><O#P(uIfQnVbD+Q)?#d3W{A^x!$(1gY!nOvg6=wNW
z%eO#v+G{d|jzrXO&AopZ!&c!`B|R%7+hGTsAheDPavkEF|D39iCN`IY{4W07{+u`-
z2b9BD2+w}F@zOIfCT+@)FZKmGgnn_s6|3o)PB#hW1nZam_|{8*3cX5XA%sEsy(f^v
zA_Dp%`u=iCcOk}M0*!!Z6DL-;EXWe_E0Z$W3lHZugkoHxa4U?{p~;y>q9Sr({iN0U
zUU(1fN81ieF&#4vOcn~!^`EKP0m6KfcV?pXRv)p|9PSMUtv;pA+L3nKdA7HazJPLx
zX=WN}+_8q<4+oHj^p~!mUEI*b&9;@)_`}IX0TX;`6U#LRsAzP(9-Z;Za;yjse5#Sj
z?Q%uxO7obsYsu#wM2@Mx2Z)h3+qY1e;SVVADcac-VxqH5Y?;gXLanuAhCD(wjvXIE
z7fmBl9C}1T498<a%L^MX9@m<w+x_*9bB}%A<vm!pa$o*|3S68X=8O+_L=2kI+C^(_
z^0Too|E@=iM}SWN-Y+QYX-^-@Zi^0KqkF2!##QB6CG5~zR!!jZjV`LQG(nPy{AmG^
zs-nOfMy9(4Lex8&$6!Zw*(nQ}wFf8+7A7yA$Q;h~6##44Xg#)4!<2_z-+OI3XLZ7^
z?VMg1r=?#mnx&Y4N_w0^+9X_{k|9a(+k-`E_d6L$*uI0L-oT15N+)*6TT;0^h4f-n
z2$+D3LBJh_)ZI6Ju9afX?ocnRoi$;P;$&sdaC<7;BGCdWoXx-fMIN`|1yk>r!Z^~q
zvD)ve3IR0Ya?0DUbdMy~DR;owA=v=-eNv$&Pbq(Hp*s&fo1|GVgB@d+)jqc^eIG8@
z$*dpV?M7}`UnVFb7+|EIM`yB+!%T)i8hPs1sa+>W1wOj?>HZq6D%5a(zfiNYUq>e_
zi1V!}I!%wiYc06={6bxtA0~)mrYjCJ==?knB-*AK#t+<)B5XO>_Df8ygJw*5#-k(I
z@*k7f0tP>cxJZ~B!Vs(*(W)H_=;DU5km5p6nysNZqE0vKl{S)S98a@{!0%>FvVE$G
zaSEhC-bCYlh0ZDY*w1ljI+Vn~K;pDHxA#&f@6WR+)D?pRH>66AC+@l57=$&>z*kAP
z87OX$_ki?pws3z>#g=s%sdZ~{7cO4j@)=q}4(5I7Mk3+6gn`HY3ZJ#~z#ike(Jgf-
zUl;dBy5U~lIwhq>hI##~(~*E41l{wL3UDH9*qO@5ImArNe=#uMMIa|Lnrf{G6!M)R
zs-2UzU4!f4dohH`_er4aOa?-vU|mURocbWe+RD$WUdFV(9FYFSxyG;A@yKpo$zQWv
zfW{ufS_z+ddU79TyuSaLv`;uw(@Ljb<}>toF3|J9aJvpgh|U3;Xy;Fxt3iJIV>ZsC
zX1w)-xj0f|=<c&+`J0BDa3&Yet=w<FlQ>{_ZrwE%;aL12d;$P3w=}SGg{a$vhL2eG
z09rO`*bcPZZ}ZTE$+rgo2`<EE1-=x7<8vhnB|q&o#~y_(jI2R3pRtVTn_9%)dJjU9
z2-qRWc2)17mMi$aH-UX_Q)oyB;dF^6+Sxun;Im+K!9PndQERDwlf&N$<4B|;?-FtB
zQ02ixf{dmYWA5p3?hPA@u%9M9d-)~gWyfSwAVDpvgW*NK+h;N7i^ur@GHMfhoG88=
zXG~p8x!{`eang@Wqz|j;Enw0UL<=z_l&!wp&T1YaG>ojYS2v4JbQG>ipIGHxiT&R7
zvJ|WxtU6yROal|)0O0heTrLRNP&P9}4AF!QQ&oA4b`dWnRVL-Aio6FDpR5E((8g0W
z)`Bp<B;j_qD&7<ao>$HhR_V$0T*9$?Bbqs5S{9bDKZPsnm|C~VeZFXwQ$AoJgc$mh
zZJMceCLMo>nLz_#3c{adVx8|eB+nisO=5C(YjG@}#|ap@J=d#DK`Y&$v&a_1#c-R-
zxd5Y}?=x~<yF7wm^*ifkww9o~HG_j9i4pPZp2U2db#2tz%l-$RU7GLxCA=ISDIdI6
z0Y0c8ik4Vb$<a{k*{g@FlD6y!bLRQ?92-N<07QCvdpM?Rdg_Jb#bJ?N?Dj#*sGEpk
z5y?gmt-fC#$Oj1Tun*&<b-sF=%i!6jXgf${$m7W=mqXRLtfQ-XWIY1_UOH2;l~EZ#
zBiCp}ZdLUb2fkY7g94zbNN=$wS~9fH6KZe%UDHZQOj-WVu|EvAl$|{m7wdW05M$I2
zS1C;?)_8pnkoRr^LBo)t8OMySh+*VhzT>HtKZw=E0g_GkCXgE!J9#9SCE=%~$LUAI
zS?|*UShioP&Wac-F+?Zm0XoFv`R@C!lY{Dgn1Bg2b(z6Sgw@ElT0@(zHzx-sR={t`
z-QT2dNV%W@0d4LoKw~w?(u{HfD&BjWK#l9oN~zJ58rd&pDf{94wXz8T-smxG%q4(|
z7k5<mZC4!XT)xwfU;R=Nus|E24;b6O1YpnhIvoO+^f^oAA@`8Q5hUUskZXbqOxIK*
zq5Gj}*Ee3VJe0dJ(y@<ejAx?oPO390LbaUv<>iL{)vbkaF;1xxAAX>V*I)~#uk$FZ
z#Jvl6pyakl&bW+J!X$|Ln@i$Nc{ygvuWGjI*)XB(qMoyw#&O4>pnX$FV|eZt@hZRx
zs*1Y{qNgH?J<v#x#5vLhzOUj4;gT<*vg#p>wEXy=o*DG`$u|?j$P$NGj}|hY@VO+Q
zaS5?tCC$mF(1#XLiYA=WGpD!@dy066vgMV&9tFIfN3qX?Tp=09uZHE)NyF}UgKYZL
zFW*~?#<LAR(wU>J&7x7v-27vKNsk-YGlI-9^9dyJe<|gqtd<f{a(`R>b|$RJwXvGn
z>a>K`%wM}lzg5eE>6gR>KOE7AQ&x{@vUCwDj=jty8DRWo@|0n1frjw=gEGN9Cj`fg
z$q&4duxqXWCK>x4bHC&|b3->s-^RQv-vg~6Bjp_0D;WbBXEOm)H^ZuN%N&1!5gooR
z05zpIwVZtOF%KCkK;1Xr9ZU$tRjS%Ufe=H4y`+n`*`bXGjcEiD;fr{GNRzwpb)Cpm
zh7r1X0bSE)K+?DglqHyt<*;{3#x1Ug$@n)<em1<FhJV+RkIkLB5(V*F=^$)<Jrk|T
z7&~;8^zljqDw}}NT}OO>7I-sYbsrk-U(DOHi&KP#I0IFI-04Z9q7Ur*6d}l*RWt%b
zd*;c9d?#J!{MbK3ork!NFCl59;fD;*gsl8Q?oG5yXa?$YKiS!o!*i=_L=>MuVd1A)
z*z4h<!`-`&c-gq<<QUK?1zRAXe5x4Z^jr$_7A7I)U3MQd<ATulHmgD2?=*p|SJAgH
z(<1IR+imV&XYd61d+*1tPc|Qx&@UwMdPhmt=j&~Ug4iT9Qe%`mx3q*jq{<LE*;)Mk
zpxgdO?aE#R<rk%8l^kd!@+lsNT97B?aJXhA7NPY6&-W0>sP`K#TZ&Pr&`5nl2W-3N
zQ{Gn>Z<?NNRXTzZimY@{cd|&B<YOmvyNt6Aw0bt!9Qci)BOxWQjP_C>%h=xMED$Hi
zNiXjM7OhhR5sop9O|2a^yx1Avgt9)!b-Q+7sR3hx#iiKTx;+pRYPa0sp=D#Ml-kHN
z@|AR$W2$O7fvrmdHR&%c<U0WhC-kB+lUoHC6;zeoHX3Bsy(PI-jx2*mVhKBYqV4>o
zQ`p$`rA`*3jDFBcGA%tl<+<w;TK?z#ZjiP2p<(3en$9rwxVjYH33DNn7C74Mi+PW0
znLb%o&R%-PVYa)9$g2pCvprqQG`o-jc?nEXzqG;mJU!8JI2FvuSxJMvCI=+GvL|yi
z3g+nr_}Jm(CA?RGRqe0squK^P)lAm|8?G?rzAtJfr}Nuz-yI&Gd2bk58n;A&H<Q$C
zm{&VE9(b+2Ybwjh-d*W^`PM-kFC&pplv$xlBzxG`86fS~i&I<mIY+2tFG`Gokst@~
z2`N*)0^Cx7!s%bJKBO$-6mI>~WymL<0gXr9l}Mq(6N#KYDtZpd2+e#}c)@cxe0I1&
z;S5p^gyb2AJp*2UUcST8sXeXeV^IM5Ux;#88aNe$n9?lpy_S}j8z?`$!zXd}&Z|yO
zmaLOQw58kmchS5E&>4LC{+MnJpeyVhtAps1nZ+kk-+NKs2U`;(`r|LO(SAqsX`C>m
zxw!q!xPV9bFV4jKac7?dEU0&ug5TKiU8g^ueO`LRiLs6wlIC+<27ua|0ds<+#08YG
z=n4gm<YBT#CS2}=v&AUoKNNu9xaXZ46x07^sT{&HS^5prkZ)_Uk0g3nDQzyut%*nO
z57~+F*Qrxsld5tp%%27yJaVXcS@Da|N{RjQ_}eqd{#UU?zonmh24o6RyXqBQHZx?H
z1;4rf)HsjmdNkd%07OTS=BEeMd*$TpKYtXk*A3$xDS7)XIt4#b_OhMiQ3ZoIx{|eG
z>+%8F1hAOtx&je6F$k$Zzsdzb6+$$f;_)+vU&-fV<Go<9(KHIaZ>gup;d9N-_j@sg
zuA@r#!RqD$gDpTPv&9${ySqx=%pS4Nn}u7o6Kp9&waknG1_`9Bst=bXTj{0OVy;H;
zSH|H(mVLP&*=Kw!8w5TllMaZj*~F-}2den3ve2yi27C(ieIJ7(b`-x7*5sRbCYqiC
zmDh(X@>wWaCTB+11!iLTrvX8yy>A+Sr28B)=zybZLW1?Ct2R>)4>PyAP4~3E$K_2T
zdYwV4wa-g3Q2Y6g>me&>HcGKt_3u0kzS~XJdTm2Ac?!I~hmS}*2WgH7WcO~$?gs{q
zhRl8qNNWBebyJ0geiAWB{I)|#(zeP5Ut`%bGIrNA{}7xJI8$so-18dF{6wQStjlu;
zKWUu(N8us(?>sG2kpl{zf6WKdir*1LCcGhg{Hy;hs9)ebzddh5k^@KAug!NBvhDR*
zCmnmgeJ~3^`&alw&;1??r!tEJ+NwVqr2PKC0pm%}Jhma$lfiywdlt=AvOpZ8@58xm
z<FA$-dtWd%qz26T!a#U%QA(`tN>5oIVm?ZSqUj6WQ>Yd^pu8SzhJXfz-=U0u?nWL!
zK%>4h7e7nIguwZY@3c&n1S%MsKnh7koOx<)?D2yPFJ}1hVaa;5hbX8LB6rIduyVZl
zOj}50<%`t0<SDPL=10~;Th3Pua~our(JGbukr}b~zefZ*a!8ajKg#%_*W+HA;<`3a
z=;R$Xc?09pvI=`KvAVPms-2|uso|;w_5OQx`8&<42OJpp`S64WyNGY$kG~oxe*Ve%
zDb9HCxFz#F>+2-xaxY6CKcBRFf<tmX^*Km=Q0XQXS?Qe2tUB8xttaMLayaFehy|*B
zp%okYZMP}ZMQ9bJgV&6c&w@C@*hvo|&~kPn0-bR|QjBsH>KnitynHS&&ajT~Hvjlx
zb{wy^LZ4kv`>HCZ`ZS~&F-0*7v;N0b8x(PucVR9U1)LC@nXdnS-@iSmHiTjn4uMh>
z2*2IbXD5s({*HcuB&*(C@24)8G1pFN<Q(=~aF%Dn;@~9*>nA=8$37Eq-^SB5q9?rY
zvwv^Rzdd!6;<!@J;L2!M!c*#qi+pCF2|0?sH01(oZ(F5{A!#W}%3wVaev(MQake(I
z67T)?+~%?yezN{GMt+~=U;X=UPefZ_Hr37NBBU90kcV!xGEt2&3Yfz6h@uTHg(5ol
zk=kjI*b7u#BJw<`E;y?e+M(aWo>^38SeZG&g-JbMMO|IaASyTh)*4U@u0rpk<CJ>2
zGm4PyHA9z8^reVPjFV@Udm+u|zqmc?m$d7bjxrNhh_Lq<3Zta5PPZ?o;AcA^aXc_^
zHo4E*S>b~CzeDsNUD$#C;|0@RXyZt1hEM&`D_5WM*Sp&ALv4mVR%2yWxLOx1^g>$y
zK)OOIM%?@9$vU*v1=`xQeM|>8mbQWZryu;+OC<aM7)|AwxIe5^DCUg5G-4TRFR=2w
zNb0C{dR@}{JmP9MaOOnJdqvyCdjD^2A&$TU9#Nm^0`n@0yIK&rl0A_8N577B+<ahH
zCou2hNQRThK=>6ft1iP@PW^fgef?Pt{A|vue%T7S_ip!Z9}X4&!w&RtimN^S%g#X9
zX%@`AY_1c2-pSy#U~?W;usrooA9q;7+QVys9S6f)k29|hV3Ozi-j@gluhUHL(=cy^
z(-8P^2t)mm75r$92EI$fP(MdgKgS4%Oq%_@nYPeoIxmHLpB??}Q2pC{OIgAKUJP3r
zL-qfy!~fd^g#rK7XZISPfqVY_1o^k?5qlWG-4J`59&3MVuzwiikAYSOP39t(+S;C}
zzxbbAZ}!KWF>^brbNpM2{M(Gn1YM*EfgPc2X1x9<*O$~bTsF^_h@a_I|9OS_FVFnf
z3uXsElS7)7&(11r;Qy2BHUC&gG*dje&{O44&-;%bb}ZlxQ($PTU}sfL%I9FZUwItg
zhcAAmwd{FeRw=dbxp|&>s+r+I<8W2vO*DPvwLM=V+H^57y;XGqTQ`eGzU#UObYE~O
z8lXZ-4Vt;?dq@r;c&8@f&QOQWVyL;fXSMs5t+%Ak>cxj#Vr%ASnkCID^|#K|c9aiG
z6i|Z8m+#;d+f1EQ$eE+}h^;F%sZK{5V<hY>OzJDjJIF<E3o>W6eW5m^iKM#XJOZyC
zLq;V3>oTdM?O*GlNqoXh7nUnEzhMU<&6zs(dkPTAq}?|&5w>q!7LA1xQuRN(Gn}SU
zdn)l3xE5Hp)%uToWuq$B>C^AK=g{w?!kR2H6DA2IoJr)9kX(<X5C63CGFhB7mVF_o
z-bHVS?qoD9K<KUWq2xl2<Z05y{>8HX%td(0RKaxFZabghE|vbF?8#AS{>rfF@+OQ=
zZ{BGO<=$O8rYOp7NsG)p=^m5?)!0H?Hl7ICQ|5e!I94KvhDZNCRYTS>Z;g%DeDAB&
zcEgdyc<%GT9O~_tHZTwStmVn3nv~t)e&rP0km=)W*01hw0#hvY3%h9R&ARfp_=FA@
zS@u7nvRP`>QMF&@YV`iqo4T`Hm^kThO`hG%Syz>fE+VhHOVd-ov4RMZ{iVj+Yx;a6
zbEas;OBrrl$#=1+@2S7tt+mCP$>xF!zFJ8Cc2*H9HDROPMKJ|8(Ow-TJyGK;HX%Wu
zErQxnb3o1^))(Uo7b*1*>$bCzZLRP5Ww()@s}Yt5BJH*Fb=i#j<a+w~Z%6H=y``hN
z<gjIu9had(r#m80o1TN?*V}#biJ);O!<DRGEqf%+v&)a<;1C5@Y&T~q-L#V~5vW6d
zg{eOlD!#ZYb9*y|HH?p??j+S~32Xc1@GTojKCQ}pGjXac*_o(R+RFU5`wZss4z(*J
zCz~sG*=juyVNqq-eJi#M$Uc#jDdnneGgIu-VN4kJ!Qex@@Np3dSs_C<spFXH4)DC?
ze|sJj{wsXhTzGSpdYaOG#a9zAzy*v-o-}M-H}8p=Vxi>j678C3&B`$=yK4^#>L<io
z*X>u$m(s>WL@0Uw29>f*_Dqqdga?t1MsXBP_=1FiYo=SXK)B1BsvD>|`o&jUu)>{L
zHF1fLu%UMB^6a;b`pJC~Wk>ZM_Tv-#(L>Y-WPy9?f(Oj2Bd*Zg^GCZ~?8!m$z<!!t
zOPY}PU_tGyZoAf8x0wvy$YCfe^U(HaeM%j6J{$Fp#d|`6GGI7a7lN631J8_HxqZQ!
z{K-@oUi3{9*hESKNc!o+&<MeVq*NEt4xfz#1>-+#=9ZUe%9~(C>-8oT(eCB3mUW~I
zakYJvlJX0T@eS=aymJLTt0`I@GZG?OTPF{4%78_wy3Kla5k^*x$`D)F!%paF&zf7m
zuyKE))VhY=PX61xon^jM{|)z+s7Ut%2@&wNmxA6?;GPuO#S+-Z+#RHZkP>;B8v@x^
zBM1{|b+J)A#>GSOnZzs4h@`;ILz?v(Cn=1mTpgjL7uSxOQWfu;^?!HK+sF(}WfwN{
z73uU?RTW{AqRPs%Qxe|OY7J+L>0%rA;8*3OJbb{FK<h1N3_cH;vXkQ(=HUzrEg|(J
ztl9n8<TLa86eGOe;j<P6fx~`RR-Vrd7k^_Zz9B9Cmfl}HH8QXP>J>ljH4qm^xn<OR
zh1vi5DI4FncubHo-}H<=ezxL1xIg7$_C<V+AR_70FrY}Q5uU|o4gcT?APgUikpS(f
zjOo!dlZZE~_BeS+5$YaIr5HZbKzWf?#CmS2zmPH&^(%kbcgA7U1%{7Ub2xY77CfZ*
zmli++H6oM($b5(lD^ee?BHZ^E3!r_=Nn5qS>+;)%k{yoEH^v#>A+g@uxbM6HDVq#&
z*MlBGQ(B^$CPf=zH|ox$lD$A<Wh*F=<b<aBaq6?>$mNHV(Rea-tU@WY(|-82!aIo&
z{f0gl!q*$F#-)7?srrU3yM<y&e0&bM(#H67v?XjFS_?_GY{?}(?z{*`)ZQO9<x8N2
z`|zB~87m(T)wf90uzu9-#)7YmZc9?746GdQe-KEa;-WrlbjVHQ-|2%LF1PES{OA^$
za3dZ1I^CqeviFdNlT4!eAuDZV-SHfs3!(eDZlalzv{jcd^ikkNmzq@{<umNo&5o2|
zVb;l)E4d{pU3^n@=b-5l9ic3(HU^<y{iCE_Wj6k;O@>7At_@(C_}sLy-01s|YJwaW
zcX*^_++uSc!d2jHsV&lV;=(@O=r2WDdmM6Det_kI_|>H6?CUc8CfV~yc*OO<w&hpb
z{w95WPRT4ab0+w_JLk@?v*fOLmz#BU!}bvdi{G(~-{;wxjmu`Ztzd3D_*a-!2l8cz
z{f@}UDVYmRK6HKU0F`n#(;3PW-^7D=@V=LzM-C;q^ov>fNAg+JkxD{lekGk8h%9dr
ztc=xF9kg@9*EJ-O#0(da<a8cCx_2e$BlinjN99>uS#^fS%px4ZWQojZL&Xuuxf)Tt
zd>(u1iWDL24VT6V@e((WCJ$#DP6?FwhDQAm-6)kl!?rbN2_Ih+R!Tg+ai9Hcyzbm4
z*0WY?0-Z;~lr9~U5(#Q&$2vP~oWfe}rL#Q-QUbK1&{b9f+95Fw5%a9Y*I@%LoP5xA
zzj&Ctc$P>#U!A9h6C2T4V?lAKc0{0uz)JE#kCx%0g4VVl(Xb1*nWSepXoVYSg(q)U
z!Kt~!?7+enb@+`|X!^Wdw;`)5FvVZ@LUJ2}p?0P{$<3^H!iochBSWvny*y0qlHvI_
z!9{6DQp43F!{hTWFaG2I)(gndN2iDk&w=E=gayuH#NJ%iZe%N69X37zZOp~stZT31
zA<}jrG;lJ1C(E{_2i*&(<j#@)-?YN(u+D;uemXLKv&+^XWlB3SMc3@21L(N);7_kS
zk}_e%^VZn*;d35dkn7&9=@VPXet%HQE%lzqqxWhPak)9C-&J&5M7KOOReFV<ZZz5=
z_w23G^stGtl|-Oa2QFgH;cd%}*6FUrCIJ~js(kZZQHmb-;zp{9VF{PifKEe}h+Nyr
z?^SXOxd)QdPfT4`c*Vph_d7<YdPLUeUW;trC52{BvL$(x8mMLzAu}s5<c|hFR>_D>
z`GD16*KWr(gB2z+L@DyN@<;Oo>MoFLv9;g%=+bN@PU-SPIV`}ff2QrL=#g*w__KPa
z#RVTL+Ub~D17zO^#rT%$Z826N>!GHa|1Xa-p03JkLU^jz_t=J?$&H8u4Q3M;Z$jQw
zZHvaSK=J}l+pRghsNu04*~`>A(pKbKrNCs@K853GtmT(2F$$%+?RZFfI*4Vs3)O~X
zb^C-UZ(f;Dt;oi}*};^%jJ%{nHV<@KY<G9pt$*d4gXi0DsldbOkP#?1|3OwCxAG~?
z$?Fx5g^z)tLuK22^v4eeAgFq8hcTjU`Yr+JxxsKIDmdU{D_>m+ODHX`PHLbsLr<W!
z#_>aJ_+ehi4Ak`hKBrZ$P|c%DtitKI3W2t~ozz-_8AVMNkS~2=>d2-jeAYW(MwBlx
zY1J=EbJ^xy>M}o?+_hFn%9Vf%D{jbmIUHjR`a|&l+Mf{=p;?66N;#(kA*yQL!?CuW
zi%;*!M>)oGZZZ`skvGgqs_r_ZI8$<T>gs``S5ceXW5TaDQR2FHh}`X|g_M<yUB^U*
zTz@#+EV&qtcVS6TTUhN*mFs@uP|7E2KlRZ`Ix69r^QpPW>`~&g3760e5vZE@0JK!d
zy>?iHwQk$GZg;iqg%q8ntizG!jUIYvO5d4YpHC)h=5CjWD??^$fdTpj4~_S<&8Fkc
zUx%d%MX(59D_Sa@i;%t#JDH}E5_y}xtWbFokv($8=RqMZVmmw7B9V!RKe{PvrEAOJ
za4^@NrYzB6Wpg0u%8Jh=mPxH|#yw$^>}K{uP9S_*JYkTQUsf!Yc|-!D!Jj4aHZvmj
z#(TI(sb}l|;qBeyl1$gV@inVe?xvDGZ`&LdVb6AtuE|DAG(`mVY*(?SQdXm7YHE!Z
zrkMv+R79q%Sd#~)am-MJ%1JU$6`DuzG--H%@O%VG<=_EO5D*mJr&)Xb_Tl}#`?vS!
z{ruiP^z(s-=en=!y6@*ce6R0yJ+Y-dm^ZK?46&jwz8pVn;*UE}3-h%j61*o4TG~75
zpb7<%gNqg-ls!wI;w^9aeitXkB5LxSUE1p86>o|d(dY}$VXz83=s{NcWPue2mr^+=
z=kf9(ZZ@jhk}I-c)_#w*CVo3i__IAG5fTt*U0P8tyuSjeaRsz`HG&!Vs7VZ-=0<xC
zNh;pcq-@T5lFK%O*ra)vdw+M}nrXLH&j7zFOia^-%d-Ek+OwHt)o$=B@}Bz$38x8r
zp1v<X+P+F({^_gA|ATQLrU?EP?3mNQ5E7$O=M}tbet2t9pJLth)5!F|wEl<N-g9~1
zO^6c`FKW%0$9u~UecN=k+nLcGT!(WcJ@;yoEZN4Nr{0DnT*ckOdW9+P3h*z$?bMN#
z%tjYqS%Vm^b>(p8W(Feig;*OTkyrT|*UkH<vLmSnZ5{m}%eTG9VUdUZybRC#M(owq
zT)F8$ISm|tqi4W<^}^WK4udq;dauPtS1OP5SM&FVZxxJaPSSY3D{D(`y>iw)250r>
zUj-s|vI#j2LE|3-GcIENDP-+^UF@`&p!d6$3omL5@Cs9%I5^8nmX3nOyie`qw5nKG
zz4CIRe(?pfMUy|mF5|=m3D3IF_j}Ife$1FA4nB=qS_r>JAF&%8b*hEy*QExC`rt%c
zR-x@+&QXo1rwnpV%lGMT<cq2Z{Qf@KUIB3mgIxnJNPG_77(_Bg=ixZQOX<~&tvj>l
z${V`c2}_N4!3~3>y|nUUmt8xOvqZm#h2l7z**Bl=1Pj?E<)gY!JBDWxk<G{S>Z&fh
zv9L?s;0*37jnU@2*%YGEHXfZmfsc>Oz8U@h<%}8Cf&YRDBZ-J1d3o|r@#UN#6}^}$
zILOeyqH^P9T~>NpQnYfWWNS5Wyn&phy2gB|jk$%jIe-uz4RXK*4k-|~=HA*yy@Lg<
z<D><{;p9<FS&tbQHPs({>EK9wCVzG?aF4z6tKHZ&q;$A`W4*CAOV2O=RV;08T*wN;
zJ4EKZyxWun_(o|)9NNh{K?y{b#N9CdeQaBA{1F)SCzKV}<j`X9$jlFTt%h^R!=l_x
z5qoNCd^o7K0yIGpL|fN14#o;ng?Q-U3(Nh9390Z)!O`AbUADw&tCCW7>qx<<ZT77j
zYd___j^E$w0ld;Degs<1i@v^bSD@G_(m`tUj3NH(2aGsJQZp(Wg1=OI<s`8(k74>#
z*Eb<K@f8SP*90|K^-nWizR_ziF-bc18O#s4`Su`RA9+pHMO00#UXNE)jSDF&J+fm~
z<6&1DaINwdkj;^_rKaPp;G@r9md(%SA=h#%>b?~{h+0E-hY+rTTDj3u`WRw{!ne~o
z^p(UNVCy9n)5NC)Gt$nrRx?R`U5yqj!o<j_pes~l?|H_2&dk>Y_i>`&sx168wy_X(
z5sIS1UyOTHAV*FJNXqrezQE<5793j+vhmK$E#;K=*g3P1Sfj&k=`V3lXXDuH+NZ2A
z473i!oC05nrwJK~Co)v<i_v27v^=HrS?`Tox$e3KP+{x6%<!B>{oh2*Jw?img*5zm
z)pe*ZZZZ)Z_1tSvM4`s1in$t=BL-g0!L(#N6T!Cz3FOTit552(t9M*?&b5DVY2&}R
zbbIjOuJ|uu)36BbEal?|tsCJE5!RoMO$H>X-!OJf6@*>Ol^OW4Z6|RxBx#$V-8beg
z3(EnpG+?eF+I7t%bK71!;{zcBbfRgb3@>iIR*1qGrDu%9mt&+X)qQ3E+<7xmm1mBS
zrk$74U72~N=5$7jRy`_3MhPD!CJ5vg!;9cJOE#K2tVNkyGJ-i~hDS%j8Yi)6BGe2i
znz2_`y?!mLX2^AdsfGAW2i;&<U6O&g(l)qKGYB`YJoUopkt5wf%+f1lBUn7uGH#Dv
zM&OGt&ysM^<l2a#av_XsV~rjo`srh*#uyA?3!pOJEMWG9l!|c1_(y3}C5ucd$qZqK
zO=z^&!anG;?Z+M0qL@YjwWE*9+~8Um6N&%4$A<ASYRsviMGGI9v1&T5DITF!?_W?7
z`#zbN9-Fm8E`&?eL7YB3^);cfNj8qV5g?`SF#VfwpNfHo9*ft7JKB1cM?HHRDZN|!
z5p$V#h~azX)A$I%_;3@T6n<wGwPl;P^OfN{6&uyZC!z&Gs#vCpsD~<w%3wQIUH9u-
z^l2R;d|D1vp9!Vmm@;@eW3Fn8B@&|a<(A|46g=uFQ>EWLgi#s52&xj6jOA32lq^T=
zn;*+GU+mB$wrW%>oXWY7^a$aZsV-4(TmN-GM2bi!`+Ga+d+yYT46Xy3HqYWTzqZXL
z>D9cU%o%1IA-RKaKUsjI>fq@)&k#m-O9#PnAJqbf_6P(*q-setK@8A0zgoF>wclEI
zTy*a0!qk1uYqS;5@VELQ##m<Ij^HJJb1TH~tKcyPYyMZk9e_ow?8*#fivT&z60u0o
zZ)HW~my;!=34G4v#&4i2Tazk+pluVU*kaT)2~iWGrWQ>Q2WrCY{CYwE*9(0j*<_}`
zfwZb8mI*d4GS^ykBF%Zh)18Z%4rFV6<^+SeQj31wc1EQ>#u-cPAigjkL5hnuU&f+!
z0`*I=ilK@oKVYbMSq^LXHBuRZ{CkzGf5w1s79e!nk43szL0^W8SRdWNRAr5SXm-$2
zXOK;s>Xb~n%(!2*W}Bzae&m$UOjnfT8_wGs!aE1tLyq}jjNkV(J%{%9m|abEu|E>d
z5d=3HaGSCEPfBLLXTD$~9(MGRe?CycCUcoO%O8BQ!nsi)p2Ennxw1U-7n=-(<F%fp
zRVG`YVzSxH`wCeubB2ewEX)n!WyGZwn%M2J_(D_(jQ>hCgx4W=gbX%9#Fm{Q3hJEJ
ziUsW9Gc6&<P~!VJ@71--l#Lgbey(fjV#L@S+aY0snEpZf8L}g=pw5$;sy}5o?ffDM
z9|J92wiJ^>Jb7);+i>2~?5qaUpHC})!9HwsHLo&;5PhVTtit0sV@qO!l)XySR62&d
zs6gTf?LU(1rHpdSv@0|IDJ`Y`+icN)CeThbr#e+erz_By$(O^O%(;QqCi9yoJ?$CG
zA=AAunbxV{#`OB%T`yiC)_7t}(+gX_pP4-@4s{Js!yMZQ_A9EcpT1`hwb!Y7x}pbb
zT0hGoB?Xl$k1B4>6>)}-bEn0vW97;>AuZg#Q}~8zB!l{DsCJ{gark%WkuD<Zr9!rk
z!NIXr5~M4-IbL;iV&~M1A+;o?u};JEtxq9Qi>0wRq9621S@;qn{^{PF%3OV5v|tl2
zHGxT7SLVwd%v82cceLKnON^YuKh5KcMPd5cgmZ!hy;>+;yn#Wj7$Y&L2E){D(dMZ`
zjN;eH=}-=urh1~w@c_i{Sqq-|)vBKy218wkY#iaDYc(BKLC#r9p=J?2+?`gQ!djK5
z7?rM;fbBz3=l^2K>;J_PGvo|S-hvV6G6Xc#b^{ceHf`lSrm&XtU!BihK)P5UM~voG
zT^m&=r(aV^Du#G-h1uhqz`xja_P*?M^)c@wt~to8F=8tZ@rkva7$d~>z;xB?KF!(J
zEnj#)WS=i}^-XCXxj+V0p3%-$;E3(AGtiuAD%E!_HyoH@T@w~)cq6|yd`=NL+mZ-u
z8Xh1<KPAPX=1=ueK-A+lNSh;#psg<6>BK~-;ta9EX(Y3V-2L_+tq$lz&!twywl>{m
zko2=-5PeKKCk5)c`1!&Ygl<e>hwZA|v^neh>g{W8BaY5~Tjr53ks#-v@xEHv;j<`B
zwl%Ia(P_R7S$4&_2<*97d$={&b1vLpfI{suoNese>BldjV&9$~=McgaJl#$^CBoIQ
zCBu)^1S%lFnGjLri@KOBcX-Hlc?s=I%DW$$p3A=xrY0Uhnhg8rF@Qp@f@0I7&)h??
zEeBC4+xX7HF=SUb=FpV<8<({y&dfr4^^UN-IV4VeXU;IxBS90}zHo{Rl<jsY5``J1
zo#nQxvE+DOQn_c$lLc-LD@phMt6i8A*h<%}E;XykTa#MJ5#Cp{^U#-Le$2gVs`;w{
zT%t0LO7v)pYysJC{Ft2X;wQqP?OYwV{_iXQaQWxY9gun*@ebP-Wbbo0%OipZx{Ky;
zXe`)TLE#x$fO)SI;Qd+LIpNbM3Xy%5ZVn$poGqH#>?ghZp(LL*yB7*KN4}^~O=xI0
z-cMm3=mAV5k0RKMIgyA)Zqe3%^+e<vN$6*GPW<M;q+okc&>ki|p)Fjv6%>#fPf7EZ
zS54*o-7C(){f@+2Z5WIfp^;jGT>QD0xeLQcej%4jX3F4{_77(yQLje@(^Jwl<*-xC
z<!~so^2%K*dq=oJGAz@!ci(tlV$^0kzbf}<_P-Rh?~D%<8f}%QjuX{C8*}F6leyMl
z$F!%2?uGJ=W)Rq}x9i8RK&sij`vhGB^m{CQc&?lMO1*Y%4LMT28oIe^E#Oq%Lwiwn
zs3T=jEd!jXpi0=}twLniTt!7`+jQRmB;}h0!A&QD&xKU2dTogAFIvzVY!ahsLTsse
zezm}SN-Prj-$)&~Nhm0!Ym*fCtjNpW>!(RZKK*EXx9o+xm(_uc7fP3MFA03ngZ0V@
ziHdsxzPjvW{PRc<FYh?TPUMq08ruqbus?gSimrRaI_W*P>-v%llU{WapX%-L?*2!7
zzjMTQL7am}?BFA^uNGQ|9VP2*7JKmi{g165&>)Zl7vuPGAq$J&6<Ho`7+sq;LyZ4;
zCUQZ^+y4MD1q4yzto3BNXnT48r#PC6)qbQ`29w9$=!;jzL7~K9Jm9Fz-Yy^+26Q!1
z3V!il?S*pq<?-8s%TaOG_UcP>Sl{?={M##mS1?)GqnF@=qZsWcEY^><3D&Uo<c(*-
zROk^q!^0r>+E>_u8)+bXHCXq9_gHM9@{fCbJvwQuI8fGe!mZDpa7Mq_iy^H}emT0-
z2XK#Xy1G0@dMa+79tvHHql1$+ph1!3-A(3G%-<1-pxKpx#B_Dt*&m7^Zlpj#0gZ)S
z`p21O+^d?Ws^$GYIGRlJ-DB1smFBMO&OG~GdMpsJ-rcd@Rlp)gU*4hY4~y@d9vGyo
zRlD@ePObVGN9la)laX14ssreA+Te}aJNOf}`4Lh@h_9L#UkIGgiQV`K8(TWK-O!!f
z8Rh`7{_?_M6YnIu|0e6bKa_sP^AckO?@ko@5c>1@_SpvunbTq7T|>`*6qHvie0Qwr
z5Lhr32}mY+`-G%+O<rN-Jl(}?m&$pk8(sVe5y@S=t~__JRZXL0=>E<6%moF2oBlDH
zANlG$Tv&=IE5`_wi`vfw%_=MOIpT0pVnTe)X+a$^RWyRaYsWL9GL-g1zwCCgf<*aP
zUx@GNdA$4d74%f>dAK?_Tb^5Vtw8lp<Y3-sPGgA9sS{p-*eg){^6f5Ya;$H38Yb1y
zoEW0f28$^S5>MiJ{95hJKJ0GHK1M&cy0o~Y=3HQh0Ps>)dOZrWFoG-7{doHGzOetq
zkc)u=syrTy&WOX6UppW9s@Y!#yhQK3Ovr9yOB|S}nFi*?!|?I$mM8mq-85QPN5i5Q
z1{?VtDQQ``w=d8-GF#$vsrCSfa_C6$PE1AUQfJWmV^CnF7kAgr+0QBY!K&ZjY{z_%
z#iYTigL}xoJXYk7ISM5U+=!u(AQCi7_c>)`^5@38s2rIOm3Zr{P;YGrjxS6~|5Nyi
zW9<22PWEfL&!xfI-F^7iw#eda@d3U~#L;vbD@xJnQV-AC_-1XdAR^=<01Ut1*Y{C;
zLWWPnrK(jIhoQq(jA`!ts?)B7!^Xe(rVw4?<vN?G#Tqe>Ir)!*0}YC#mq~LKP5Lv@
z5b_K97gk6wU;SX!ns@r*%?jTUyWXiQp!SwaBi~=$IGKm$cwtBl;8!rjJ_a~0NiTmU
zoy7u(Z53Ggkrg9uKZtpGBQ&uiIc3t{8rF!EuUcu`vQpmpfC`UzX22>~r3ekhWhBRA
zUh+&T{8{>kBK%MBN@oby!2XJmuI3i8=Zfy1Iu1llV!kP8d<`eQyOs7P&#f5YaGFY3
z7EaF?#~)JmJ%B9%vyY4cZ`|oE^h*lH`S$p<t={yuz3wYKBZ>v)dFeCi1ON1^xUM4q
z518#3-Sj=^u5)n@a=tUDKq7&iL9MJK+>xjZL2bf;E$+V3QEoV%1`m3GR|t#BX^5gv
z_V4_>ufx|ckC^eB3ihiifcN=kY3n5I?l8E5^dxr$;+QRk6=ZIArBs5oqw-l8>$Q0D
zzE$D!8QiD15~NysE5N0c=kTRjI}Y9yro82^9?3{~_F=m2J}c~D1rpzN)o!bcP#3K2
zaef|DLX$JHwN5%L`$JJrp|;@uLAO3X@L0Byh!?V*>_|`6xoY`(?y+z&N5iweJK+}@
z=c6A#?yazo4qQqC`KZ4@e+qa$?XLcZym}1~Mk-=leC1d1Ss(bqVP8!Ul_DsgBF^=|
ztt`)<+#xUszHQ0)AnS*&#9K?&F}LcKOZ$l|$d|Mb&)MDXj8R|+*pT8~I*yrm-%%<`
zF-4^Z1FTJJdzE$xYSenT0|YsBX>gpN%6^qA@uHU4^Er(}0j<5jq&d6GI=dg#i%U9D
z*8f=vZ5cUN@$%n|tER0x$>uLFO-i;ugjO5|jwJga$6B>r@QWYi+lj_9mBQ4M@qPqm
z{^}l8e_@4hX6N*T4?Ygs@qJqF!ZkiVV?{DuGS!#(TSz?>pG!zT>X|_kHdSUydC)|t
zSn_8!f5%2}3QETdIOQ_Nk(BbUp#{h}3iVKXynzh^q|E@*m<rgVrbE}C3j^R?wSv_J
z?~kaWzLqXt1@=U4N#@gI&;u-X1?;X{afE`mE82RZ4^S)ZSGD$w?HGVs_a4~S5{UpB
zwzlB?lAqx>lxdxaylC<;83H;NHj{6t`PO&$xWkO&o&vwEg|!6)IJve=YG8}o!utw)
ziG?@1J9#uzJxLp!y*P<YJg~FqjZx)i7;k&uAjKmWuI=2migyf^%By0Zb-@>J9_wlI
z6(CFbu>~?^(fzskh^gdYfI?!j|IZXSzVtg;gY1v0oz*?j&J$_V!tARaH6UdRF?&9u
zhgr_sH7@F<1)0>Dx59DR?`sbH#eiR;6PgE6{5w|uZKVKE=MNIhW=dEigx9@vQ*0$X
zLr1|2%5TgMxEFP#)plV~h;O<kv4`w}J=6{Sb`uXIP!29W^=7~+CS+jZa#!W42bzj9
z^`+Ho$f?-3KErIMu#L`#ytMLkPbO{uN-j|efo4poak!1|8k+!w7M0DIAGc%r#P@`Q
zCb)yB^$QMeY${&T^{(!!wrc!c4~!6LP#E~iaA1}5z1F2{p=xG}Y>xENv|Vcob8pAr
ziZ=n?VZ}SJ)vXpFPG4Fy)@wYRRQ~-$hVg50sVfW~RM4SPobkJa%*Bp{8S4zUWCpib
zs%KJF%y;m8`S*_7>;4p8)WTlHVABFu=JVUId+d$DI%u-c(UIs90Wb&Pm_!>0DXWlk
zY4mFl0sA@bIZv`ydB!j4e<M*RC*E~t@NdQYO()G4@w2t0Qe66jW9j#I_0i+%lbiXm
zL&Y>sP=O`Z3Zm2QN1cmTw7uWQSZI5M9PWJj<44jXM-NZOPWmSVNcpdz$T4_rE=-BC
zi<JjWk$*YS<SUZo7o(cff)ZEnnPVq77t>8zQ<C@dMy+ezI%Ay$NtQoZm(=wRGp~3F
z&g7&NY(jigjT44qQ$vmyoA7*Hi!9Hzs==wN!cL2AqSRpV?zQ`S+=}-f#oatBJlO+@
zi}xTr*corKc1!EWku$O;=IJ^srZV|wN*n7H;W;m}AIE*gBngKJpSxrfs&482xU1yU
zo`>wtXR$k+u(doEd)1|AnLgfS2hbjXh}<gr{gZG<RZ{OM=xl41clrr(!%C!E8_7s&
zb^v<BW+Fpi2%`F=3Jpmi(Mo|w8SW}}qhJ%&ETXkEb~%&OL=ZeEV7#HsG2=YU5WMzO
zb<h1jv|Di*)aXS+4CH8oWewK+W>d_g$Ji?!E%We$y0?Bg2@JTGOn#ud0xNiFoA0ZT
z)Qz`U&c<54O%8j1x&?zEJ+kTb?h*BN2N2bs9mBT>X@$hpf-BO(THb!~Y(WM!Y7#Sa
zSdWb;&j+LMvKKvLMi^PPc^!>-^4MD}Vht{4QSg-{*BbHQRQc4j4f(-GSO7=)9`v9m
zA}pE<$8}H{q|wzR%V;_8?FU8#;$kgYLx8|CX>(;*6)~Xge58pEmmEYdowO1a=hw;e
ztp+4+wa3JCya*#e#icism-#V^Y8ry#SU1kxLe%3-Q=c5*Z0RCtJoxaF$AQyMZbm7s
zicfzbrD;g;kqS;2tG%}pzO_xijY}u*ipr)PZLV16DA&)lxaPM_&s=D}24YA<!qj#A
zWNO{Z!P%EyG)Y<8C#v;*re~o`=BnzV`#m@%IQ>s$swrQ+{MJ&2_+z#+;|tZ_fJ&m7
zCX8;~H6tW`wgN!!^)~{p8l*RVA#Qi0;?B2_mzV!fb$yXkp2w|DQwjsNT=Nk#|8;Gf
z!^l!qJ7W!p@zhFZR_r*OA)HP|e^s7K?(VKt^O}Zlp#f0zcQ3RNV??}atfQL9t)^qw
z%+L+)NZsB6{kM0}n<j0Yrn!&PqF<iUyd3>-(a2TBi$8pUe!z5Bu2;=j{6&OmugjSO
zHhe@>EkBt&2A%7-3gnb#)|=y$=jMdw?{`&X32xImraP+XB0{2)V-)FUHLP?CAx%su
z9uaqZXkO2iG0=@B@>^42oAVKIk4=tBKLJ;&c%fUJRUt6dSkw^bIbs@}d?{E(JF3y^
zRKC<5y;M|<6ZC!aZ*Cm05s78ht-A7|4;RsMmiOKaEC8zux|v0SzW!*(a@SHPi&VGI
zZ(`rYttrlv%sP{b!|Vk<*tI^{@<gq1mslIWWm3_2)dq|_RYICt3n%HYkNot4o?=1v
z3C%2X0e{8PVIo+Ry0bYu^_ETMFPPR1cJc|$*V&UK9#*;=1tm*SP&5qvLkP-bb0D)#
zX>0gf_oO9U%CO&->O#Bmapltxj=VB|uRl4Mxs2$iH-j2+)1<q}-pe#INi8ZDQN)b;
zoMW@1Vm?h(t&d=|Mg#0P*DdU31n;%fe{G##4Nxp1H#w)v2`qhPzo1$}Ps5_GoS7^h
zWEtyX!ZnX|8L}}(+u3@f=qbN##FTx?0xu<+*am!d{!Uw~D?At#FPN{wfLn|AY>T3f
zymzvE0R%o@xK8~qZ4@Z0J`n#2bPwm%ei`w(>eOp5XdD$_sJ1w4K37F7w<9<Lvp6cJ
zfG}g46FbwuheMIMALiflr8b4C8g4CmE2#H&`P`M_v^Ru$X_dZ>APCaV78rlZZ`#Zt
zWHG4{G$k7?USoXV0J_p}+$Q6jZ@cjZ^iP5Z$(L8&CRw+#m%;HN&0dQ7<>|idU^MD*
zX0?3;AE~-7n1X1OA^N@C;oX83S)Df!LtUeksmB~wC6G&1F;AI>dAR%?eT|sP**YF`
z+sSG!G-53rcv<kd36zv&TtCYTACsqKFkMkZzE(O{uRNIjbRPpRh)BF!5;YBNemQnS
zHm^a>cRds=$B92ywF9H;7<!CKJtca6vZTj!aeWG3>8;91W6rxqsOdyy7OZ`HIH!4U
z)La)t`O$+@=Z)WkM6&B4E5u%G0!s7Ags~b3KGkLOt7+VU`+aoPWqM8Tc{cf_d~mUW
zvRXHO|C6C&@017ly7}>+qqf4EN}0=jn24oZ%)hqA*a?bI+>&V{SW!pn8b(mjqJX=E
zFybhpuhr&8fbrj{I3g|rmPsUzIM{|BkfFvi-SrPsb<K(B+1t#yv^Pb?tJQ??E6E+~
zdrV&iwRt>h>|_ak2_HG6jVSb!W`h=Dyk3_&P|+8xnaRVmzuSD{>p>6NSdxSD&j0uO
zqde_F?u4&LcK||tW(+R<(4#JMA9&cnbJQV9Y4L-;#b>!!u<G3QFiY`2*+C+VU@diI
z`zEJi)9_90E|p-nC`;zs=M#zQIs00(e)H+;37WiphS1IIKSl-Z516ol(*4h0O?sh&
z4kZQ40@^y?U15Eg`#4!Q(4oPyr&a{yz%M2JlEA^mW2{?ehgvAuC9UkY*hZ25f+T~7
z?oUUdUX1E|56&$I8iL>><z4SmBi3g>H*#}d>K1xnLnE>qgZdcCN~%>x`|4F`;9xDt
z`s9fj8&ok~fa_eKbE7;Lp;s#6E&cJ?8^%BhYnBUQ$XV?7EvNlNyR65}6?TQCt6J2e
z{b?#?x*g|Hen&8SJ1x+o27k37NmrYtioDjzTeFAkUkh3Zwe}lpbGiP;O7DJ1_;iD@
zcvfiG$=!OCc7Xho9V!c|z4WhE%xdh^J}i~(fi29Ge7a-9p4rX~GJMgb%5^a+YBH1?
zD$7ns#}wCC-C;aGwO5tdXo{sELp<<%Q=9g24Bd_oS)@zB4H1$cIM?aZu`F@)otYBz
z?-71*1$?%a(_4|?j$ff8t{Cx;pZ>@@a|LtN)YUaj_Ut)Z1WZ^LqZR9aD$}2-WUFaq
zFZf^IP2=Yu;&acb=3Ec>v_$meJ|@K>2X3;m^tWp>#O_?MD7kb;Dtwh^KWH`U)lNvg
zYLJrvwn-oT<C9)82&x!1_YwcUs{`;>qHER@w1WF3_r|@N31@QBF=e^t<gD@m`m$p!
z$nta45o5}cKTf6&js(?$9{B4<#lLg!@Y&_Q{ZkjE>GiRzDbF(IX@IT)cR)?cLJ74{
z6gnQdz}*}a9XNR>`l`1N!3xtf9Z%=Rd)!+30n41Gdr9!k*m&qvU%lboo+USJm3n9@
z(T#zU0s-<p1D_F>6K|D7(Qn%3^s8|%-H<t+K$Mo&gM}Cr5ZxarVkEIDX>=YqWfeG6
z9Ho-Zci-U&NvYMGUPYU;(*^4-ZPn1@<boT%s~=;qja<TQCnk4Of>(Dp2~0H!Xc#=$
zPdx}65-PhX;hS<b2BSK%Qj;vsx~ke!(S3~VKyS;f2w(FG6noiqrMzWf6ZoZS_$;fD
zy1EdO9h;kCYYaBncuUqabO?4LiJk;j{?LLPzoQb|fz*3x5B3F`Dh94yZ|$krU`Gjm
zw1mEi|Ai>8dgjLxY<YD^CuZZ!&^J2}?<d^U-hO{n_P4EsR9R`f_>@CLApt?*W=hpD
zj{@#6m7hScsX@>Jkne}ndn*;N^`O<=UdFY)+n@LNaH`2KA4W(ghaxTtGbzU!08JUj
zviT)j-25G;M{`AE!LcI$H@gI2$o_=G+0Eyu*5p(z{}ta}UyIY-IC>ZiYwK!3__U5i
zE@&f)J~=|kHjYYb_A+}&VWzjJoHvx?zb8D#-ib;+C_)Jo>!}C&_V!7vX*1Cg2J=Pv
zdYC^td9?xEO7E>i_#H-@Y`)xLo@+nZPGQ{Z#~Z<~B(3k)?zsV{_32CS_RW7CF%naF
z2?TAQ5vLiHxPZ1(LBuCLDh4Om=L0&_da|g;T9b4sw(~#NELMDH3Z(}IOYOiQzbX-_
zKgAIIbnKeL!1iL)5$SKUk4C*cJzOE_9;`lf<8bJu!PV%yeG99Xz{(;TV?S;h1O?e|
zSAl&}az~23y5XZOvKE=gF-0R8Dxc}K?Y@QyagacZ%MxGNNb-*J)arB)pClY27v&WO
z)p<U!tJVLmZ<i=cncIPs^8R_@$kCL^zWr3nk<>2yq>Qn@A@*XT6kxo}TVe0?P@J0y
z&3#m>LcfK@qP(W{3ew_2GFS618Pp6zw#b9lBsWIYTHQ4M=WBJUHL&WdzQC(8A?r9u
zI(P}JyISeegIJ?btqz!jt-&o_3f|v)gN<KXC6ZSvc-Ptq;I(vf^z@3V4QGULR34el
z4qo<I0DcT^W|x1(w66sb?FaJ5iML=CpY>!kgFPTDK~`54|2;nKZlgjdWTgXO>O*0D
z>gv3kJc!qsS1BugP!s+x&z+%&5N1*>g>L{tlgU9v6a47ARhZ$&-{1RnWHUG`{frOX
zApBsUlWhU4wU0_^`vFJKPFX)LLcG0B2QP?-pLc9>_vN7cE#C?wO-63D=_cWG@8Tmz
zuj-EVEF@n&8mQGh*@u=$Yrj^_N%`sX(a_Klw^z@E(jV@df8R%&D~hJKS8vtoBf6K8
z^i_9p9h~teU+=~;i{O;~#^7cxeBAFJ>c!Sy66A2>rI<%fRwB07qL-Lf<OX^5&dPrU
zVHbY@W*p>Sxk_PozI}0=#OJgWQIMnI>OA+^J6mt__JH(Hx?%N)5#R8B8(m0v-$f@K
z<Zl0u4vzevvCWzB!}t;{d=1o#YT)~$6}KLz9bouJ8Y{r<a<xQZYAgiKglDkI3gdb;
za+YGho5RsP`giKp!UtZ7a@2LP-*6n}KA~DAJg2Ho39K6kmT!-d@BRH%_XUrFS~p5z
zUhQ@lq6cDfcFai~|G~eYk_u2d_;0Tl;1+ZzN36cI$ywTk=_~hO2|mlq+7<O)Tcz`0
zy>`C<q$y-ZWk|Jf`AE<Gwdr=57DJ(b&3<n$p!YZ;_K7kY%A-ZIi`3V!)xFfjVF?Cz
z06rRPe2ADXx@ZlFOCkzeo;>ZY&7PMVF49`|g$6fZL_8|>9<yA8Bd-i4yG(rV(o?+C
zYjl$ivnkxI!Sc&gQjs;<H~#J8lOuf>jbL2N>t1#;u|kpi;Ny;sn-vRzv=P}4Cpb6V
zla0YdTF{K`5z3sQ-Q5qE4zvo%+w=qx-dN$!|2Z_d0M_PQuL2(_r6<W@AHpCSIu~S~
zuNDE226EM<Z(7Qur_`L7paE3-NvjFS3P8-E>~8?LUr?L+jwhsoY@I3E4M}*Ch{AV#
zkJJVDZp&*vqu2*P@d`Zs{*=9-cdeij6n1#K->R?>aV3!He`s?L_ZLOb+LgeBs2AY$
z1r1j{sK7tbcSOJ3!i=q4l#YrnyKz)+IbkDqx*zWQDmsOs{?=ZlC;(*jabkNh3T7Mv
zpbf!>Vi4Go&`(EEjw~O@{(gitJwTu4UV^Xo%19Ex0gtdC%<Fg!V+87$;yiROo<8N3
zNs?U>F5dQMeh6vNWm=NjDr!^rf~C5QhL0b|N`H#}vK1hgtA0T>>n?~(2jMXd$H5n}
zpnknit^njed=a{p|H8$O`!xkx?zQ-*3PrRs7MlXkc-FOcLU!-L$4wuckG~>yi#aQV
zWV!&`{G9YG=1#45U-?B~Z!!M-N7y=7^+{qKx~xSj!sNvJ_CP$oadGtd(3c`l_uti%
zf167On;rAMEJN)ds7Mpc+R;dG!6HH?U*>m}`T41n4syF!3$A+MpjHR)anV2)hN36A
z849zpoJ+{&ze;?w-ZFI!{V6<Yb|0g4_%SOCC_)&Jm8yH`O;2v98^6`JONrV(oY-2%
znPXR1<C}B!-95?B1=kv{>M6oM<8MEH9CjL7*t7Gn%1|G-fPX-8b<0{=)gxDBk?P=-
zw-*#oFnQi%gk%uzOOoo6vlu?Ruilk=DPv^eCU9)Axti!%4LAA3J`8e*uKbUZ3I#)f
zrmnX)7b4Z8#n#BNEDx9K!xd~QN7A{T{zi2VS`XJ_LWhp^VY%#A`wq10dnE8u3EiJ6
zp1M>guf;2G&tRz(cv|=Qjth$r9nQPi6r1f%%yQBE#P0ti1eKLOf0N)Aru4lw1dr~X
zQp`HlXlkCyxU?21cnp3CGaWlf?7O$1fh-jrthE%L@T-e0$fTHJJ+Kskb>A5-{=D7#
z`wABjs2wkS6+K*v@4d|)X~A5Og`O6WV0g68b0EOqAbhAVwCR%}`&}s4m3Jur-pODe
zQJo=TeZfm+{(UuEYTyyhr$4@_&++lY!034SI>KSlhLZ+xDou*@qpZM(TU9cYG{q`O
zFEr%nv^S(*;EM12WouVEcd0Hm%%=S}b%+qoV6dAwJd~?{AG)@`P=Mst@xP*?Z6JlF
z*&V_Wqix>p6dK#E+&yN$UnBHy=c@BoEJx`++wuyUaw0OdDq|-$=DW}m6w7PT?^S)K
zv@WK}-51xBOLNhjIuvG_kvWdHHT5A|Vq3g{AkqA&!n(J$uY@G+tO7twHm!YhynDAm
z5;z|$+T)n<9KL-?LS(_hCNc+OYT8A)y;IT=EN&@p+{D`r!zzo9H0YWHh#Ep7m6#$-
zNxT|I%%2${DD0w^?*YLl(<Gty!~~*}c0Hvf{P3r3^u1Ss)0FPfXR`aGAs8WI>k0mN
z%OxqV{l`1wW?d#_Q@NIm1$uwzOajO!tu^<ha(&CRM;ZtHm7CkE2uXXYY9HJ|l~0A1
z3v)739#Z4tiG|)53iSc4W%8wx@0mx5vjB^64Tyq{gf-8@w+Fuq+NzOv1`SE9_K6YY
z!d8B?bM(?C#FKxVnd9pk)gZ#UJ<Lq0k9pkKK@wf&5Q(!G*v_{vVFIb>jV#d*@1wi~
z5!g|nB#%nd{khipORFi)57S~^S{;D@|78K+sACTC<G;M92Ir)AR%KsJov)IrbML6i
z`LPkQV?{4FvOxB@GAz2h*gZ_;Vms*967uTZv>2s&A>tsFyb}W)TGiS|tU$uiaU@rS
zi}1w3;6;br?BG);Kvs;bUWvY(Wx8LW1hPN1SN`T73^jG_q^ZS65o@u6pA!pI6Bh;J
z>166_PKc}m*d(9aWbEBXzD*I#VEmb_8?%2fdMKRt4}3zAMg*%Kvz*aL+$P&foVJvP
zf;3b+uF<r$pCKAs7`1C=NCL5*k3l$F=Px4<9y0$8$U;YpTO4qku-^c`FGsmgj}_|9
z7{?8$q0Q?FdVTB+F%30+>{p2Gg&LD#kZr>U%=+YQnxbli3*f4-oTo3TC@(+&*$K-%
zW4BmeqmBqC_hQi3I_HD7rj`Y938|><Z;x5x%ILZ<#3oNVIfEdZWn{AiYFQv}tFdMt
zI&<@MmEno$UcKQSrQTw!CmRwv)|!ynTP0+8vbS3gdCT3hB!TW!khSXFQR>~^%G2D%
z(-7_!Pz6F#Vjhk%o+_&(=(g`R-z7F&;rKz;9{L=8ZX0mH;N<6rcG6b4Vtt@1KtAO>
z*s}jS$FbsxHg&~cynA4(>VIP@VN+97Lm%V9lvi^wQQ1~m%MI`U+?J*Kq6gqv@t1Mt
zlZ<Sd$J?k-N!x34J~e0fyDruSdyF|YEkEz#8;JX^ez_m9cCu|+$oN`pFRuQ7?*M_|
zW72Z}K|XFc0H6a}56z4&;?JXl)Da;f1wS?fF%46MFPJ<_wt%sb`g;<^DzOIFUNk;C
zDlWS7V42igi5%|+vZBk{V=h)z4?4L+mhbES+|~!J6)ao%lvd1sUv^ZLHN)5*rH{=C
zGozxB&}%~hJIK)3DqaOE5YZVn!q%w&<nrZgDCwRpX!CmCy^ddR#{DC3vs2;r*CQ7M
z83J86hKxNF_FQl0)D|C~l`f(+ZB=9f`+5+Cl2sYCt~XR{W|p9F`q>^r^K_8_=hJH9
z&_Xu#@@#Q60jcTKBhP27C}~s=s!zH)?}X*x?<LMaP5+{6WRW(}vevPq4zVmpd2v=d
z6lGk61Z3_)8<tD{{E4}myjc%14>Gr4^?qBs7eE#@2%W7c$x&2WkV6sA>VNScDP=^{
zUN~i6(AdQmZ`nQmjbHFtLgNRGXy#V6`=Q#WOr6&_Of>4m_cc0^@PolB*va&XaqVh5
zEK!5e0C7R6HE8op$9vT}W(Bsh-3eU^oV|U2)9vsU5a1$ov{DDFjl|TLp!y8qH#2y5
zk(nqRtBV0AhY-%#6>P4q{#Ed3%)207c^u%MNRvxK{CQ1Kd&aZsRoFI!+<?N6zx-8h
z=ERIHC?^_8lzoD8+_}$%UybvSSJkIP`5Cv};a{C?wB*GKiL>r%Vf#N>UXVR)XKbhb
zQoO+B0|;$_FAG-9B=SII#6jI_{e_)x*AwvT7CF@{@1a@@R9NKmC{tsUNsY6tGD({R
z;5El&O(KN5(#BmWE{Qr~ejc@19eKHJ>xFxgF7FM9SjI`{=-k@ZP6epgVq^Qq<g0p>
z={$h<6UF+;EufssYg<FMab>=I$1xAP<1E#e`eiK3hFkRP$7tfN)Y0*J^*C~3`?$Vs
zne&_|PV7WJQxV0KK^g3<AYEJ?t_U$qnL%&ReLIt1@BE3m>Ax!Za7Fb0T<I9vsmrQg
z7U^Jm219(@q7jm{cU8c$x_WAd$K2*oR3@$ppv{n1nsQcpejO~*wT|dxmL0Xb`AvsG
z{TRSW1?fv26;IH_-~2@((%UW4&bRjkr*?i|Do8+0gtG$O+<(%d1)TUc5U{FELwkl}
zu0_tB>gX2e$iFuyC<$Z4bNW%i8PsMKT-C1!;3Dq80O7n8Y<ZR3{p4EWi;9t~s}n$V
zk&Qn~e-Bmu(ZLNjeG}aXbB>B%RpmS_PdfUXUH7eSc7~lB*Ktfg{&Qj-PCGx!%hP=^
z))K=~GF1~6B|oQR5WbU6m2ap0ddI=~zub{kPaX!^qVM2ellexR6WX?SmZG^<hijM*
zR*&{(p@vJtH>=xi81y8#S==M9^6q*+U=t8NwU@61QpDRc-pcPm+Hq<<5C~qhyrpix
zT@x3)ts50<%W_6~phkbP{4=6OVA(>0fIo|j0yRE{W*LAYFX%9x`drlJIgr@I5fe>q
zhb%J7HWP&23_@=%L#c)CmdM`ro8p@Sc`5KJm|J(Zo<Tlvn-TOWyP|q89id}5(C5>!
z(2!%qVPO3aI=NEMFE56>sP=hZ7Iv~aQo;nQrE~=`Px=LM_3J=>Lq*#787?82^~J9I
ztkn9XB0hsi+<2yQz$XS6rCBFIfxA0gqKAEPV_1aX)q9?TSuj}LUM5*8akXC=VIQQ=
zVM3EK)_)M%ATxj%Mp7^yyM}PZ7mCyVZ6A62<9`AX0Qxs<t&=(Z9~D)uO?}lUen^ZT
z#?IbuR4vbj((sHGnT`kSjCYydU#i==Ih2OJR{t%pw4qlLgf}YZ4-#PX?9ZT~K$8b2
zE(ml{yg#EUg0DZs6cyVHq2riNdp!|P!>6DF<MUa}2Hm@$b#c(bk~&?quOv&r_?l>g
zm_BhgUJG#l>C#T>;jP%LJ=pT*-mKS_Qzy6KKowU9!~4@ReZKP9YZi`JHb7rsI>yfn
zAGX<TF8#S^bv$so`oeFWYm*J5U=dTW7nSVY!?WGEzqy~`n_RGY-BO%yz#HgVZ}poJ
zzRGUyYywuGJ6-~(=&|C`OK?mW0NqiQO_nS#fv<k{(w#1r5$RFymmO7$nel>yeWas%
z!zDuL3&IlT`^cQG5tKkq-7viQ*8JVywFBil3Q1MMoffoS2Kn$L+Sbnl^?1lzJyFKf
zWW{G=GF$SxE!6xLy5$7wQ19zde{Ak{-|nVBx4WS2JH)@+B`x`$H^_rd|7el8Sysq9
z^$sF2TKn0M<DKQmzt#fyoaoEj!devQIrEE$8RR$h1{+Y+R&msp`bw1UiZ|Tktop@k
z94|Z5{!;rd{>p3Y$~s91o*99O%y0g|hj6-_e^~WAeFJ-8gP&v=rE?L6U*`6^M7_m~
z!!nkEyV*M-9`AwN<zaEBZIo{ZE)~;=M9$1y1RnC$hCP=1<B+Vw=BC;ZO$}aPw%Nm6
z*1v7vP?H)V2@{J+^ACeuuHOOjzC^`(=d7*WSd(8x2SB03LADBgbO2}hTt0FAc#OS^
zs~4+-`d=P0+sOC>W~W-DHh8-#=4V+zJHiLI(UVr*5b`o6AZ$Th1t;kpEbj-Lh(&aA
zixG$+-hpnH>~~9TxL>{XzI?@Q=dc4K5fZs@iFZ^<42#ZQ4nKXhVRjcZ%u_0PR~d#$
z%-Lw-m-1r@_b^RrL(j+zhS~pFZX6voMq33HQb|K6ilQw}8^)oBRWZkyV_!Ic^zea(
zLciziw_ic6_eUJ|1Bdo@IU_^IzpIUlpRz&fJM#_|WcoA!Szu+%?OJvvj8@SlL;0Bj
z2vEpMdHcwo)e%OlCvn;9%33Gty>XZ^$vik-+LK2W6ioQoRT=VYjpIMsA&KfIC!!(N
z3D48CQWx?{jpB?nR^0-kqWw4|Z57z#S+63foM)alOio<%gzL}%RF+RWbqJQ$AImST
z0+<EXo^{fZo~LNva?;OUghf~%@>$H$iUWN8?gJN|Z?@;AsmHr}o|>nlO!0~8DKfLi
z|89IUSnM<zW!OiSNSHfpvhI{|AZN;*X!uaHEQBn-hmKPJT1V=w`_2a1Vj^2qGwz2p
zRD^|MQ9~i-QKm7dKBZd*y<>jwZhl|p0>q(>dYJjcR0=WoM{T)+$STzi=a_TH#X<3v
zEw3nzSG~ybGbWOaCSMipFUV|``-}t0tbyk7(1Rd90Q~AzaCSw!01FfvByVfE-n<-+
zG<{2s?^#OflhObO#vOpmdu~2HeTU*S;4}HN%Yglha|vIzJ&{2&ginrvD96WqvoruT
zX*|^#?`fE&^g$USlrh-aE_gv(#g8E;Wv|A&m4Z#s`xA-1XFkuP`Cb7^>5p0sT(+_g
z(E!wKXgMEPU=SO$1xT>RL*X8o>DHbjV_Bju-;8*PWYTL(nEqxaLt33VejoGs1kjr!
z5fX{o-s<$-^+9la=lRCQn3DqJf44JbQB=Rx%c{dez8lXn{3<}+S9*FRUO9R(P)Q6#
zu#*1vcAeY7*}jsmAwZ3N%r?eXHd53>?^gm8bUkEcxNN0{;X*p9>%=+RGBSs!F;w?P
zs%mMrx5df}zKS`01tYtO*mb1XFFMHCcVqqa1Mteb%Gc)D;}b|?v#ln0z4c^Y*$*FK
zPfCle3tZv7z5vF+YT2Qg>I_*dfRV`~&C>(K(}M<i5RveL4y2fnot6I1Vaw4QWr-9Q
zaH#wl5Pu7#$p_Q8uu4dMeClk%^d}5tky|!2C7;cHIVk7!wQ8ll)v~Xl`ABs(V-U`8
zN$?acA?~cOhmZ3k<5DK65BduS<#LuCGIOvh$hq~4gLN>!3V6SAZnb+K(;hfrMyz#}
zl)|n-X%k>TXUb_+50LnqIt1@4RnmfZe`GEo`4uWtqwG+M`VRQC0dYu^cS0jtB2KM9
z+&3*|vBjkRu%MF{R2TA59g`+xhfMF6z2Kd*um+`E1toU5sWUbxP1$+^nJ_)G?rvq_
zi?<L>Q6G1gpCeWb$;?IM4J>L=RqBTzzsGKW|Ce*yCce9TVL_zG+(o_%s|6lb{?OoM
z%vIfsT_F1jc}R9MolpD&l$5mn?fS${Ym}bmhx-`^g$HdqZh<o-9&NZt#VtKeIZRvs
z-N093O+oh7n~S(j@adJ@msX<0-y{Oyri=g;wb0-|0?cf#J(?C<#Xo6VxUm0dDq{po
z6Xh|^_#}CJa8)lZ;po+jqo}Bil(y+ycQ4#9%A+1Gt$GhC5%m!~3Muw{lHec^8CBqo
z1rOI*y01nIo<-4oT1=?-x{g4d@G6bTy^=1V`vNKfoG}FK;tQ}A36z(J8caJcc-esZ
zlsLcTN5lJJr(@d(5Z#-h*Bo%}|1!p|rfNs>8Gh2yFAmQAw0FXeA)ME*LO`Etf?HVe
zsq44?D)?V)ZTXPt>Hg$*K?nb+{xaK;R8XM;{qa2c(U@C`3luUTPswuEA0q$={w)Z~
z26Rz8RjPZubVFk5JllgM9SxL)QF|Yn)G_Nc^`E+kAiqrh-oAWgjSk|`UXtHiNh97b
z>bKTks?#-$(~;Sc(L$-gjrz9i)5zwS#Rlrs7$m8E=ol*nYaBJu%F}j5<$RUvHWsFL
z|FZsLaN5!VfuiY!ZNV}9BFm_G4-*lA1e1HH;;lW9?8~ZPM4vsrcPc)AbGDe4AFCL$
zV_{_3>33m;$GxnS$ywWRM@ZJ0N%PdgzWP{?E|aFE$sGwE1GJ@!F1y=DD|u&@V3NnT
z^pK2=Z}2t#_@_EX#@k947U7I$@d_qIzW!rC`F7IUSBNp*!H7Wd<K%W=w5T74C$IWr
zyaGG=ANuvmBX-<++i-*=i({S+?@qp&J!FT0t|o35bgZi<EXF9+X1#Q~qPJtS$vjR;
zG~7+hFdXQRc>8THN9|NcC10D=)LS+?AVBrT&!aXz2a#72cWNmex(V~N1GfSw2RPU<
za*XT}W1cG@8$cF`xA~J*r$l8DI^UUW<-~+#DS4*dc;=K<q5X~J+~h3EW^8^72zwq-
z{wZLX=OR!WAa4tCXea<4Q{%I+=+Gm8NQ0v@Bt=4dLB`q&=O8BMaF#LHFKN~ZYz6Se
zE&(LvdxiGdfH2>QEKgw1@8Svv3WfoFYrmlmqexG!A=iHhzy_)wN_hi?i|-abFZ11!
zOy{OQFLG(6bNv@GSj&ic|EBkol)^ZAPVuqKd|{h0A|J?xOj2G#+vCS84)nH7!Y&)u
zfZ7R3++8G}l#6R0VP?Si!vr851Ck_iL|Fx78ASUaA0o8PRiNI__Bq<1#vT%zM;C9e
z>;gu?eaeKdXGomtd&>2!lq?EPHqQ!#ADpmTw_AYd0xx7Rxh`OMT@pvHj5-o%34}!D
z@8%qZfo=A$rCyG2x5B<kna?^YTi)qyQyr1YCrco7lhwer1MTaWrLrglh@`G+nz2uk
z?Jo1|6<TS^*YIKy3>2w*gbgf2M+=g?A9H`Od&|Ks%xP5C{bJe=BOU+LanqeUX*&o*
z+Mv8Bf+^-_S%&Zm_o2-rit2Gw)J^}9(82r0!ueU|v++&`%*u48JQqtM3co|RKg7TL
z6azgHDz6%^6x<9SD|Yrl3~TTYIu@K*nj4SQ*BE|XKC1|#i?+(JuD$*xQhzCOjQ?#R
zgQVzc0Wt*udE{X3R-@*f6*H@?_9H^%i?5@>q@!XWl)Hx1%wGW+7Z(jM-G0Ks<$bi9
zv&7>$qkRiVVciRd&q%UTZ!6KHXT?(aMae~8l9vFt4_z)LzP93j+9GN8&(Jd*2oKT}
zAsrWf83|=5s$v#~(BE!jEOIExpt9_A>~b7;H$HLCGH^QddXd>v9wYr(s=BaUr!v-s
zL|RhRV)G1{xUi!N7(}NyZugP5`=T;qx2b2b9X|~+|FF|W)~mQXCL4<cy-fk3b<ya%
zo9D>`iCbTS#O5_IKqX#CHZrDu!NHB@tphVNTl_rTU>+(=+=bHff2ZnqKcu&q`{QdZ
znlDwo>-v}CgG0eQwcgFwqm^*iYo6cs{sgpEHGloXk6VW`Fh;QGOFLxG+~1HcNBzB?
zQ2^as&%idguAcm(xJ5d2)BE^PY>0SY#$mg<q^j)K>XB<)9C1GXzC$76HbK>rj0>IM
z$oM{`l4pxQOSK%Ru+P+!d$#CG9WJ#_l-O<px`nR-@ppK@U;+X(<rI`*w8{2Bv*s4u
zsvue0_2V5zT!InYd@kbJEw&TMT8}*s45xpNnsj)J)M&B!KJgo!J!g4mhQ8;|IvFpM
zZ!ov>PPmn2E%{$qfRGP^O1M~JsXqUKZ*tc>K=c2oE=@gORh<~)Q>eN1awM~$iBiKF
z9YejEJdxnXhU<%Te}uaCF_~vF@QY9FnGK6Cb`9Mxv2+i*AKKcT<yUSc^^2dyPG2HT
zkF?_&M!(;;huJS#n-8-BG3*D8mjH@1OY{Dih{12ir7IVsZEXr<=g`-3Yk@=o^L7`o
za>&0aw+cK2A8+ns-g~frLicb5^5t0jF<`4l_-<by_5={|mcIK_S(9zR^L5<?LZWJx
zH{f>IUQnGgyeC|di_b`0kqmXQ0M$hCVp<bPXacX3s7|#o7MC*^^hQwjXfGfI;LG@*
zJnAX!3aU8a4}7}!8ZNCkRp<q*<)j@|i$w<vvL1c5KtF5#)8DC(%NkYSwi$RYI|bPL
zl9vJ=ME#Gt8~~{K1G8zHuiEBQm}UjjJcxe3{h=^xj$x={%Ui5YG`&tF=2{{;RtU&#
zu2^pp86vlH$!ZY9yb)y{sIMDv_v+A|?$B!M%N}kc7jo*mMNg8If7kO@d)N#M;}`cs
z>kZa^BCxqM>47OW%%SGr_7_Hff_jK)3A?+Jwb-a*oH6mUWPxEy%!!4}F`yS*NlDhK
z1Y|qh5WG+{PFzE1Ub|;8ow%yxpVOqHO(mJn4Vec$yA)Ni|C6%!3~Mrt)`iD$MrOvs
zjHsZLj4~3MAfR-TaTq}e5CLgYLKP_iDWRq~qJz{3p-PEL2|dy~8KfpOsi7l;B7pz_
zLJ0)U%j~^>eCK@oI{W<0d%by|^{l6@weI^_E7cU!`NQAsnnCUrPxF9Z{mLEaM*!tG
zC2bqS=P?N;Rrcn3^Y1<CHz`)o0fsd6lwf*h<P{<X{LB{LFfvVWo3UD-)a&BqJ=G+0
z^@Kfh_<Ls?i^Em;i0<ft5P1l;bg${?z+%UMw65s(&u5cn(-^>yS_6Dq99ZyF$#)0u
z)4~~wvr|e8?LGr!-_;AaoOsr?8Gk{KFU=*Z?uHF&)DOGev1woD&mQe;^FkmIqRS-b
zB29q{Ni_D-3;rdsTS3y^9?;`g!Q8eRS68d}<%UBowA9f6dNF4kT%^IQP4n0`ExEg4
zud88h^Z)-j12Jsxo-Q@lg$;1#{#cnb1Km<vCOhBn@R1vBOgu+-HV7;|Bepu-*x!*x
zkPYcut%)#d`r-|rAuka|<FNYeG^JVld=Z@7-m|s-7o^Rq9cvXgwv`I&RMrRATxCd4
z|KhX0#A=_iyp2cCm}bC5(FJd8&e^7c63sy%xNF!K`j_c3Zm0J<W<9Ftm@@qM_)L%6
zn@+Wx5igU{eY>SUyqSHJ@Z0Qz&U9zVn?J?9^nH9b^|7wYgRJZNC=vZXTOL<PyWf2m
zTv@=uK@Zf3OFRfYbSiXxMkzxjGqiZAw&b9$38>V4Fg?ht-<`hd(HhmbJpfZ72lsgN
z1}%Ny>eqh3jt^#5IdB|yB`fes78=v6&I2Lf54+~W+oN0iOGGHg*!Fw9qur+6Mp8%m
zzWN=M#4l<_2><^3q3BXab_l(GT5K%u1t{|1wdzV%)8*tl7_(sRsbo%T$wD$ZJ)Oek
zeZmq3W~gKq&S%s_M4ndcU7j(RjD2^P@)*AQ7z>5yC}-GQ9g4@i7qi`TOw^`t=lHa`
z^Huu$B<RTm{6<z~+DldWIa@Cfj1Nua&kwhjBt{XKuKAEp*U43mIpVwrCGb36uDsiL
zY|zKAvL5J&SGh9@-YqNQQaiSC5yoWCv=qnEoG&oQN_)!hXOAn$%0SbIXA=dx3@7J7
zYT~_HV{)9Y@q(cubVSx<w?o22x+ZdIuNZvA@9#=KPp2|M8%i|V>o>aBd98DxtFFBW
zW<2~r4I6V_NS}`9+A;W6t5n%03{i>Vrec_GY0`Ga)HPxGlqHEP>==3dYFiy2olR;#
z4HrHuuHCZ_v)y!qc92U?X)kk)XFLv{PhA1qxi_?A4_Bi)tg7e48DS)UUKY8v1wVgT
z#R7T`w2b>7HJNrdrFZv%@)CIvRTj67oEN8vt2Hh8!?<y*2TfJC#`|nX-wIbucyAj!
z#m;{TB3~zjcw<(qRg!IdsoMo{?%o*@5HZ6w$n=1rY&>I-c(hHehGc6x_C)>(<K&~y
z-<2=*V;P=9)A13hgX@B>SQDPe%gl6&(XQQ&$0q5Qt;uV`w-ajFrS1HXs<w#aImSmA
z2WhJwS)IMUIx<?+?BAqYriRNXA;h4)#X<X`RJIn$dI0aKyIF}G8xhmYnZW5Z>1<Z2
zz0d&k?EOFJw5ak_WLxWt_x?PsNVRy<%*~_Q=${#%!B>(mTHxNCksXs;<`?I~h+MXv
z_}lm}Y#e_W;OnHP(fvTV?4HqbKv5hyth3SH?yaSbu+vb<-5ppJEX1j(Edpv3@_F)t
zJH^mC=H?W>SUI*lC*nRE<_}d{_M-;qQAY4JU12527Rl=W?VD;PjIWZEuufg1#%nmT
znlm-4k$Ti;UU#RoMtOaF2X|17n>ak693NCL*H4f{Wzen<sQTckA9vo^ZQ5mTGnt~<
zCeX+H15%>6E9rl&asH<KSOe3Htl!^?W6`m!#mwD?C9(Z`5?4}Joqc`MOb%Rytv~Wr
z&PZgIW@oUd`-LlsS%e;B)LmnKcZ$T{LQZ;#<oDvj^9h74i<zh_>`Fh6?Q9JapE*um
z;9Cy6IdQX*S9Jw^q`G|1Pa}_~jJ%uV53L$LgGgvmzaToyvFHgcX&QpULNI|fz>)~Y
z#bgvQ^p_7WD8*YG7-Ncgk0&LKXoX~8G5c)!)pjo{_EpwY>_3<X^r}><$}EpVBXPx*
zh$w4;YHk7NE+qCRrR1)3xh<<GSX$P+sH;UI(`>@Yxm|1l5>H}@P)4w~yyTMVn1v&4
zwqpKr$!fNXbyt#=!G}it>9;U}RrCEZXZ;8PDM)7zIZxeE9p5?1+-0&VBkD6AyPZYa
z6@H5tKOQ6U#unLlTth(wcE=UBvzQOZFc&cZx^orrszm0>{==@bb{j9Wb{_4G(po@A
z8_P~ILos89vL_Y)()xIlecvm~qTeiX>6D{I^Xj_cpUV-iYEe6Wb*<#KoF42RVT*1=
z7PY{U$8=6QJ+S-OW;}gEL+e@9_S9Isu8Lf@;kd(&xHZH3<y9(uvGk-@hLb?2=zFTt
z-8OQ5b=|JF<d8@5o4P1oWv=|l4*Ri<px;Wweb6hh@6F_T-kIrS%tgDZT+PRfOOS!Y
zN4BUTI`^wD6TJc-Z__jwvq_huRF-mNhSz?Da1?Ys8d_uh%`(-@Gmdn8<x?^&MGW70
z$Pdd`(DF=;+INS}Tk{GrsqJHADMZVPMM2L!G}9^eTA$Bi<o$(V^V&Ncpgmv3-m>Ho
zcBTBF^ugT)k073YrP^yarn~=|e5tr?qHC-fi4td{<^OVdo|f&mb5(r5!Z5g_&tX_k
z<3M`ij5U%z#NS21Hhk1t-4c=Ev$~hE0Mt|)on*%brvKSa`nIp2{a|tl5(>$k6HKux
z?F<#Th5dbD5W*Ui=m)H=b?;1ev}@jUjzulaESJAyukCBJ75IwU6UB(&92r};GC`Rs
zRLGy*HX{BBYuEgxM0xscpjI%%s28y_-@BKSG|_RCl&J$cuOhhg>%?P<NjdcYS$Z7z
zDmxV~<VG?*^S7_BC=y>pvORXs;1zh@8YVxg58E5+5GiI~)EF=eUbI93V`s*2p5DlZ
zH_a7=y`NVz67`>Vnh}Uvw!vO%{qCLZ3%BHkfVHnfw_w;-y+QBIulc6xM8b_Dvx$er
zPjn$Ba{QG=RCv)N=*WBS>S4@=s4}KxK`Ee}xtGwh54Y)%Auyxr7J&FESz;vOKKS#n
zb|Gfic%3W7is$nded|JnJI-=UgI7NHf;VNC!3z@`;vlKy@W&(bVDkdd%0o=Rk<Q+Q
zGzfk3s)iGlE?nMA9*w&Yoa{Y*lYd(MPFFm!_0tMzbL4#neBNpRPOF~%p9<wN)Z{L1
z_m(?9tX&-!mJ_|1nm{^QE^i4`T&?KW%@J?(U4C4DQ{+gtvavn>MVPDEuL~dh{Ls%9
zeDI-@^BTuf$49#}lY8wdnT|4Geu?Y*HLxonhZ4eb16jycYTx9B%c55f<HX(bE(vOr
z@yguJ1|!oS8BjKW3Jk}!*9G$yWSZt=l=-n6VY|_ZIDl_4rup?A|9IOhe$>d_F%45d
zmKHo+IfTqc$1=eR|D|3zHh77gA;#ne=C2oE=fxK`A9i7@-eb2<4ZALRzg+*g?01V;
zs$||l^6bUUsoSMKLJZx?+A$Mm4-A3HVs$YhvMvdI*_s?UQM8HRK#GA#i!LX^=;OC$
zH(c{scatvJY>uP+FNDXMFs{Su=S@jkinMs5=+N@<vP$sJ+%<IO&c_zBi2{TvX#YX2
z=1`dgwA6%W8O|)<Qu!1(x?P*pH+r>@VXC|Y^%2Fd=9N%ZlcQlJBk!$RNd}&@dDCRg
z5Ddc)ArLj}fO5Y$&<ZBog$hgQyj2bMzl2O6zvm{pJ89*sKaII5r?6>evGOHBZ}7@=
zkQrXi#ZPlocF#alop}khN%**L!4mi&mk*SI0kQK>Yv=RcX`%!^?z@WQzj?g-BKS_}
zQv<(#{yUhPl$Tc>I6t!Ha%N)b<VVq*Z05VWNj0sV7PiG<rV@tMK@2mkx`ZCKefbw9
z_SZbqAu9aSBBB!hx-Oe*s)K3O=(Q|O5pw!9YgC2yJ_3&YQRx#?V7v`R5hWw?g;w;2
z?|I1jM8uKn9`lh5-d1h27l!ytr7@bHG`r6A8<X+~#Fsr3cjC_|Jq{_F`x-7Pq|f48
z$WMFT17$pTHB`5Gvu?yF{Ssv(VZ6`GD-^r|IdU=RE`|-dfMIE^Gz($orHPwnXNr|O
zWtju`I(=SSgOP0S7!!n`lDy4-g>3xAKZ)K50~xSeaqX_?_QhP4#^pr;C|f`UwD%96
zvUkW}nld_~HtA%Onz1<;r;A}576c%(0?LARL9=Vwh;d@zba0x74da@ym~HRo%rS2!
z4S);f)0>!wWyy`6TQrL%#o@uMLG;&G5iNm@E4FFC2}PV@5Y65gSYo?7(A{g;?9&H-
zy!8$0a2U>ljfwCpM>%QFeEs;J-H)~yX2!o@^Nj*0&$3uK-Uw#o2Mq47iF%c}Mn1w8
z|2Y7vI@>XFjGM#&s%t|+OCc<>q1}wAym1NjFtXM>m?>euC6C-bZ4m6FDD&6@Y~*f`
zhf$bQg5RD<ijF8oxp7yV-3XTZ?%ElS_1Uy{!)H?O+j5O9@-647QE6Lod-IjWkhTW`
zV0>&D5t)l{<5uwJFzYObA*FyU_JAxTqf+F5&9o}m%vVLbO}AupJ4~&iF1ypihU0b`
zZp@dH;*m)5`d0b4Laks$o+t3wZall3MAqP?8&FJ2O?g9<jA@g)-|KZ<{nJb8dIOy<
zetQH0NLR|<XofIJ5F?y)R#hTU`ij=kekjWWQNQ$J8-VsrRaEN7dUB&6e!qB&aY58$
z4&oTKwL&>ssb8Pp2SBf5q?cdXghB0CNw*%3tKt=YIm@u;I#R~mwz5B&o6X#Q)%zhj
zgqEK*$d8IdEywz=AFE|R-rWs|2`9=j6U`2cVv81?Y^5bw85_$IS1Q9Eci%*%lF<WR
z4J-0ePWZIja?@n=f(*Ri8vpy!!2rEjh~Gt`ZxAfT1N7`P75hWZ^MjM}JUt!Q)ea+0
zx1pW-DY=8>viUYiSb>~hp(1nCIiGc9kUH2qDg~QXT~yjU+bdkYCK}b=55jJzs&ARi
zEVn*r`(qH7NDrDzG_4k**F8FJAE~0XX$xd!Y*xU8_qy-dy&X-sDX(K?f=abcH13dy
z+V=qG0wwE8O_LgYk`5yWM{2B+;hx<4h^<n&%UR^O#vsWy^hL6f)}wlwY2ODVSDTC>
zS&)b&vAs46@3~>sL!+I8uAAduJA{_CMMsA$g1P#k3nr{qf59sI=$wqbp_@7osdb(C
z#>fMybR1~&O}IgBmhO8sD7Fs>s$@n`?v{G_L|Gev5VH)$iE-S_E@oy?onG5})nQfe
z(&oT-B-vmET6{ql@}bL6DK&oLk6`XFQ61PC5<frzk6{{Aj$1W7_*vh_x)c6s4=fhf
zaL2yBt`!x6e4o@^Ov?i&)v29yGOJu@A|$8XYE#B2l<~wCQo1aN>FKC@6y2n=Gxz?|
zmR^H8=OIfPt`$|a%-^_^VV8leQ&;y(3}^CBn>dNXiByj)kW)54%H9;Quq?w$gjEVS
zi8hwCUNcP<S{>5k^#7}WrEqCJ`CJBg!R0tQO%eIN-3TfbUj3VnhJT`<e2>U{j^jHi
zm|b*PHdC=$IxE)jir<Kl0T`~~1oV?3yMLKf>{BjB9Nms;TGVc|E|o0VO04VF7UKeb
z0c8+99u7Tp7#x4QS>lFG=MkX$gk?PV?w;`u(}Kd?&hk{hLrQ3dZ<-$Oc=bd|bJhgc
z#^c^P+qaDVVqfzhcqmKkCi;Vc!G?#zd41u8p&8(uh08A(iOby~0>+mS;@3aDAmdSY
z1BOk>_>uVov^tcVAknVMd?!VKzqLfaFVFUN`P`aqbD%FHff*7Su5Q|n+ba=ea!gm2
z!3r7jABVAz=<<&!8*{T#SNSpoyHSq6*mB28r}v5KycN38T)z@fw&5jM^6`E0vsW^n
zmU?mWF)AP@X6LAxx2;W122t(YQOfJ3u8i8b`B2JksESHW^ZDMd6W54AQibf83?Y|?
zESX{<CO1zZ!6S=Y)D<SW%*n#7jOU6rFuDU}15{lGn5S#isNev-q*@v&MsdP%N~pag
zWMxj8b=d6?O-M<gf(>QapRL@{3lYX=hMNW=-<RO8Y1b-qX?2>TaduE{bqR8u3Tykl
zke8RKd==Kf?Y6Q_beR$vAT<qH(dAY)+ds&an3Osed8DcR3Jd)>SAbyxfr`l>Ze=Vq
zQf+m}r~{6Oh)XsHb?WBWjppLr3F?@)0;8Vc+`y)-j5Lzwf>b?J(~M}F5hw%Me$PL}
zdtaMoIq&0K@=JAw&~MYlfbZR-wdRAD+CGU>ds$vh#s3+oW>!D$b)fSWQqbL`cMiRS
zU{;VT90zn`j4>-PP((G-Ez2$y`VZiBb6HPtwT5MOE@LxaRvDAZQiV;eH+8N)A|{%j
zLjmJPtL(QJS!j;k=Jr!i$U!4J=`)X6x||BC<=n7qFS9^)W$_J!lha!lV3n+L1?7_%
zdD+Q!qWhH9c-UnNmZ?hF@_hRs8Fgsf?LZ1p$8z<f8!EWJH$6)7eQ<D%FQb+kr4qm;
zmlbu9KrY;mUgw-F8i9`lU>9$<>lx7I2@**`UuEw$ZMI5=xg*@#9@2GV-rERcTx`Ww
zACN&jYq~P)WSsz$37!^hh8tx$K258OCN!vN?Udp(GPgemSkoE=*Nd@0Io_s@J`$Qn
z^33EExf;@+RN_3uS|@k2J`;S-DKl;P9;NHO#{F|z?Bo9DaLAvi>cb6yYPp#1*E?1|
zn3a8R%7V`uy9Ly<1$cxWfe+Wz)!T%;OO*bJmU9fgBY_+Q`Q#P$)ojoRH|RQIZ&RZC
z*_X*6Ot#qci%dr}h6F(^!=oz(d-lnDMff16dxL?rr6A-*(}|+qA3@+NOXiD?1gxSF
z*uuOcrIh|ze(#0+=(Eqy?!Qy!R`K`b(3+#Jvd#D}K&5nbX8(?kXlqaiZ50<&;1#?-
zU-MR%dyPGsA7-*`#WSRJ9mRZf9B}1;b)-ks{d;k3u3EtzsrdLDgj1?8YQowlL(_#j
zAcv|tow3=iz~SbrDwx?Z%qU+9yERR`CbvmqZ<RMim0c41LO5fUXEj}3PW7*e)>k$<
zW>-=F<Aa!1Jeq`#RUx*HLV@*;rXO_+2(ReHd7LHY(28NrG;OI=0ZsxK-}`}|c{$5S
zEu|Z}z<gKvC_MyY6&`?mm`f}o;pf>4t#iidOB?Y06W-=|kY6yT5TT{f+*xqw%^UH~
z5Yuh%RrX)EoG<*W`BM*yLE%kMP9bd9{7D+fHT06kx)*pebQy-I+wYk<>>24;!b~q2
z3d9vX%Eqy@w@m-`*`aJopC<7KMRJ(s<x{1qQ_#CcOjFl$96gOGNx@aX<0EyP;wjKA
zzDn)*n9O^L2S0VJQ+OvP+oqoIp|3aK+jOqVr(6h6NE<U?q_RYrD~HT^XfmthQR35R
z6R0b3Z<UXw1a}?*2K@>ImS~;)A~D=V5zLf=i)Gvg4aoq`@a<F_4X{k}RXN<U3#{>X
z22d+Fh`Mq%Ey2UXfHTA)BOT`};YI2!6R=puW-VGh9ip@2w<&jJFF|1NgQk=^a{hfp
zYS$^*o9rRZ*w_b>y>|YnUJw((6><1?fGJIFu9()S;wDo_M;gBn_zM3PH|la!cShsE
z)Ose&$$Tla2*)I0-5-}qrg*#u@%lkim>2Xa{|UjV5rL?Hmlu9)c%q$RpEGsf)x;L2
z`1mE)PrAIw^;TbV;13;LTt8Y{->I1`6`rONEWa6&T6DLynp?@ni>`Ln323Or;KXgg
zx=HK>6v?g@HStE(x&7zl0A`|_wOgg=a<cC~CEPMQWXxYp%!*%5SKbmew_1|HL<g-N
z-h_a!w2@}!hx?4#pF^_lxYCzvt{#?x4oas42N{HT7QJLAIXDkd5EW)Ft0-}4J&<yc
zQR)~cIBOa$5B(9&Q*vDxL2A!!UpI5LEs*CVUUPbBil@kS#=JKwYAPy(hX!FJciX#l
z-w(=_Qav<u+q9*Ylj}l)^ovPF%mVnz=+9s_l>`scA6|bDc!c-Q5V_j6w@KI-f^ulZ
zcy61}+<@1m7Rv$mLYo4t6yJv9EWMR)X1@&*)udij)@#8PVlNhfp7}Ku^wvEJead}Q
zVt_V5CK&fA#z$RU)=*Q7+>b^-mATAkmMibP3=gIaT!2I!nIxf&WToM0|MvT#e$N)@
zm<>{~(bzP;<ge-Ssd9J;-MeJ4Zqq7($;NQprc}ps_=EJBb?S<COU(q;`F1C$<}K~x
zjmV6+7_~R9n}p_k94!xj`)jwnly78!i3_1@-@?iKb;zL$b(GoqqHE6%oRLl`;|mNr
zD0Nq_<)vXfW}L`k*q6VM>>|@0Z3{e2l#kuJe=`10-N~w=Hr-^%{41Zg-`gPCjWc(s
zg$V7K<fbLGSq4f<VODCH5YpycKOo^VYNfMz6XTl9t_U|TLb-(x3)Xp-ZzlY;jP9ZK
z8xP3s-<)#R+=3$>(oCA>26Ghi>zIReJ{O6a+?+ZE^`bhaw(Jla%%5ZLbk(6XKiRxK
zp#`rWW97pv<evby8Oa{L-E)gNe-pF%!06!G!aLhx58Nd>q8qPu#KzBToNDA<S!%S*
zND)YwKNuoGIMIOy6QruaBaP3kAB;R(t5B=Vmtu;-_rwi_)C6-iy;5IYwt;RbL6vOt
zZonH<`ThnxMT@PEVY)I`N=q9vpr|i(1IwJ#nKrs9p5bW0(^ec~r`LbwlHJ20QCOW|
zi7R%RhmuAyW5cD@&YnDyx6d%%H%}dm_H{JQ*h8+2>#YcB_tsyctaddSbZ$=@2Rxmz
zttqUDuKn`jta4yYi|Ed4YLw%I2Zp~Ue^f62=ameobHj6D*wt6)6S4dr8~$Ny9TbJl
zTdFeUH>{r*AfVfZgt#xqp|rAC+5Zv&&Fwk-QZHQI`qg7TYfxPH^48leQn4j7f*gXH
zD~=L2o{+li+PFnB^32JwgW6M(FBR5}D3?g>-;fO`w6f{_`^q%GmvkLW)%D<QTU(8%
z?Q7$|R&k9ln1RibkJN43M<jSsE1vyh)%kF{12t(o>5;liYMN~|rVC}b2$<Sox~-#b
zjWjXJrJ=T|n3CcOBtOXsSw!r=4O~?IbfXX5YKEwk^ZFAR{wST(OH?mS3<%*p&zLw<
zRk~IB{}rQruF;4V(f0PZigQhTWuih)cE>htxUi5DJ;DA`kJS&3DK6OxFB%jt_h%Q_
zE@_9l{WtoEr{_AxSu0i@3CNdl&HneEq`EQ*bZl&7a%834{R?n~wMVt9h&huBP}k(q
zUrK08j#s@)yqibQVDx%iC+Y%3+XUNEHin^!k(PU{rldRzixrJLHjbfx|MB`^zJRqG
ziOz57E9y`tx;aeYFtggfA<1IQG<bT0Pku~O_@e7mKrHb>^IrcY4Lhe<msNB0K~%H9
z3$x#Wne1TwcyMB@hGxqB7A`!!@pt|1%8O&bx%BYs3!U~0xm0H)3t_pzCwQ?DtmjXT
zmtBDX%{9;&mp@+UO7B;W{*gsIoCkYbV)cB)*`==qn>Jp3lAt+#-mUG!NKBuZBlIp<
zJXMcGyo2%vp%}l)LR;i6Piy(RW7J(MBiR=V$)0C@Hr(tUIvbry<qLXtWm*@Ugjki@
z<DHZab^9Yi{q)+2!NYrfPLccf;oiXY0E@bTMCAtO!9DwJYbnsV_wqpwYTqA^@z3=y
z%Wvu0bzPghD8a}*j?+}oZ4PZt7l;b~rDNy!6`_`6mRDeX&(u10S1N&bfyV=a89n7h
z-s+Tsi=5~23G@wNJb#y2x=F|J=xHY?M&_Rl!hdym?O$}gg=$!|G?cCB?kOc6e76^;
zrEVAayB(xS$4>KGm1^nlG|sO3I}7(ie48`V(%{@|_{9N}1JF&)wkm_f#hHIadh$cZ
z)mqRKh0KaAEt*wYT+`Mtb$f6*p~qp-e;kw`*Y*&xY+}Ry@<l3nNwo`|>numY*@|UE
zZ|nG)f|hlCSvxQ`Y`KbI(>-+4TP(w1%BO!#a4W2}%5b1pP+gw|N9u5UEx?0tlBsfd
z>Yy9$VcjKdOb*PaEi8;Qgw1@(_C8<Y2v``dCFiPPe!`L(p8xSe@M}IkxLPDzNi7z4
zRi!m*=|!v9&6x!{wbS7@6-pcy00=3UC<$H~y=?}zn~VL_As&KMWjJ2RCz`WUs4R|I
zDkLnE2Bp$kx|b`UlD3iWn+9@f=9-&L-JyV2TYaYbCADTY#D{3Ks(k2!z-h4~;FIOw
z#_bm`X)dV&#a-<uSXpav?t%w@IsA1A{_dk~iiHz)mH|Ct1oA%fCm*!l&};bez5M9m
zIGJ~C)vI=5YE9X&PV(I^(2dj3kHG-=QTX%&x_BL+Ai%(4-7<)MT2GrG0^R;HS_l1I
z*zW#N;PCLUvs$SoYA<B31q5k?_r|=44*c;1h59SwC*gm?u_S6Yf;WtqQj((4lqB|2
zz7KeztKqn1oF=~?FQ4k^KJ(n^!|RSuWS<z1r&ZyK6WzMcg7kuK&37OC9W+1L{77M_
z9GP+CnT*;GR2-lEe#-$a`vinyNFpSOmU|cQ>4J3Jf(7>Gs+WWA((ldm^tw-Ef!#WK
zqcU)xhNzhzobip5YKPs_(l>L-W}hOmdPmV;PJ?QeEPl588&~XM>j4jv=&>@k*|olB
zAh(Gf>Q|kvvgN*tt*T79*;vz(9b4X#X{p&Q!bVME{3h8K$|*^{hyl<OIo;X9E($^P
z{_61xB$?}`hkL|=uAZG}>D{V1Sg2u$KEd5uXv&_ENIn75ti#7TFfX$Ra=;^KUH63!
zzhoR(BmF7J$}S1`?{VC)@I>`u&B0O)134q^6qPfXyzSKI8ZNN~?Ol;yY-837`e6%q
zF6;q?gh69U$^WQ`1!-Lep{V>-?p^4MxQaMma|77`L{}rttfVY>fRI^H?@LbmV$Zok
zkm^n<yMfo!G5>n&vU+0RFdGX}kL%4NsE)DC!g1g$O6D1s+pm)Ma6KM&<*ow?EFH~q
zYRR3|+j~8U8ICEIE_$mb4j(lE*E7x5t!kbjet-S@t+6|$oulZZEg~hDw}POM(!7Cy
zjprJGn61;M0sO{*VQ+EUUT9E6=p-6%B=ExET-}SG33YwfL#I~kR2II0_wxx%a2U60
z;cpI33{Rs%xj@;a<8t?{s_I6T*k|l`Wk<7>rBAvjr}aZe<z3s4=L6=w=be57?WYm~
zo7BhB@ksw=jeR6K-JXB?w5#6?(XbXt=rTr(dJ(#mNBf^%+ZUqhVi)H#$VSRbPQtca
zBdjneJVr|m$D-)gBS)z_AP2Gw@Km0e9WbQzOnH|#mM}4v5ar~UmR{A&qzuI2;~K`v
z`6sPiLwMW<TDw~X%1>c59DV2m(E1hS{Gdhprg(7{GM?-<L2q?5biR~Fu&`zp{ats=
zTc^0jhy8fYe2YD;OgnLNkaBfjKWNFF1SP%Y8a6(;`M&v4QS7d*wXey=H1*&bR_hH`
z9wGdk7$V3DEJ|OjeObSFmt5<RGYW)zI}ART5Es!Kr7?V3U+>i`_1Lc{zb&^Q#XQ=-
zRu>a9s!wm#i{7C%*h9XyI&#uD>g}&t0M?x<P*Lt+Q+qKfa7W>NQ`JYuIC+Rd(-=`m
z{)%(7#g3XpF*RT8O%rfq<=NJrsr>7O%cuPWqE0qx$n19G-;0qQVHF-yBYEVd)rz=Y
zheA1|h^v8YZo|0ja3Qi7ze=Ce*)eNE;6{ywT&;Fi<S`;w5Ptcb9My?AHYvtmN{3lp
z`ir$2*Sf_6`pKMUf|++UiiPWo!aChGohK61X2hZG3FhgW+r>qW`B7nH<mzgxh3rmJ
znBC||O{G`5K7FoWD(~M0+(9h*%n4ZTAy?TnA8M_6R1r5K^e;N|9w!NAxRRiU>osxG
z2<?+5m*1*J^g~>z7CaLzBz<!)d#LnfQM-1PVd|FFrjvnNn-qM8CY@BLx;Nh5YlLod
zoJs7}4PS8{t{Tx0cJ}ZQKaidwLsum$$vZ3LMU)WdEv#6+ZS)Erip+*7m{3ksK8WZ5
zEpY^EhY$ABXA1UFdy&gb&F>EzmbAP>Y-gGzu4s@tS0~l9)B|2bE|gByDMp!D74?4G
zr8Ku5+!wl2^W=|L<a-nz%bO_irwO%nRvX0qe7<lqh<;-|hau{1wYf5g`nDr>N%Wa!
zJJo5_$}$55ES|uNwWf5cHuy?ET~GI1+?J&>9^bTV=skW&BOmJjJ<7rKnaU4-r+xe=
zS|P>KnPY{~9LB2dPWq8{CwcSmqxnqiuY3^kUZ(zpkIu#9ADPN4&+H|+Oi$_DtuO=Y
zeASf!>FTEQoEO1fj-~B|NByQxL&J-m&J_tnZDjNS02|IzY~wlK>VDgE<bwM&H{=_~
z6Q07sVj)o2JeNvIrW`H|j((yni0#&NECTh$JyZ;iv}S*VKh*38m_kiyY17-e0)3&^
zuEJ}#t+Xii(GpkObbHQ*uXe5aty-`0qhvMQ^n}H>QaU_=*LMwrH{>VoL(qnq7>Tp2
z4^XMvTSW|~5`P|o?Qv*sz<rk9Q*3EN6X(f0C{>|dO<hKE#sex^Bn$nW@#qT7$lgC<
zA~{=G_#b~hPJn4whKnx8nMSL9$|(nSc*@;?_6O5JCa94dGcSyAq<kc=)Y;leewO}K
z7m^4|Mn@3jW=)C8;t;kdG2p95W4W<Jl?iX7;>;`lx#16niPpMs2Vr@KO1<&1N2w^c
zaf>FP;fiq+KJBY9V|O7RiCbCx&#V$)rpbp7@ac|g;*-}9mk0NOO~^%_wSf|T^m5|Q
z$DEO+!)VV$7{_cGc*)m>0p2PoHBD})g$-LJo7ZxXl1-939r4JR#+QgJ(EcMi+s&Is
zbuZzV@#wwQK=k-~GN)H3byS~o+IC!LoLO$Ned$ovq3{-`H<wlf(}2>{qs#Ow^?v$W
zk(vD8$y7dH-EMR+-=hckxD~Wwa7LPC5q98$n3fBx=6lMW(i8=a26hVBQQ;a~K+)Br
z3&`@q?^T1Jp|^g}d*uEmA?;0B<onoIJ#7j8@;ATA{IYM~5(YZ_q9q_=kfLDKN%ywA
zmsof?wdRYlt8(V)MCuRIhT-yOA(oA8P8p+6Ackjl*AT1uFOqBa)$0#Q(817C;Ec&e
z3_B3kX<7VrR3UTcOx)3aJ?mF6WYyt)F?v1>Z#lH9`XLKbWYPl7u@+^{n@{O{(YU(#
z_+oNHjZsxLEN#9L_BJFaa-ylQa4=l3Tdgz?o^QFbY+=>*(1IQ#4%j0<to{^W@MRc4
z%L&1_>jKZLd_P(5D*?=co}0-wj;ObPE{guNeUJC-;<}OZ!moAEy)fvgy@!LQ!cmIn
zlJ*}{tv3j)B4zY;7N~ga&pwUy2DkY5NZ#M_j+TdQVW4l^>&4iwbytn%SF$_=1xn_}
zE>~+a)vcHd0?BBSg@j=PL)JpJAlY1F{!$h7hcIk5Ec97rQGY$<buq+5p8rXLDR4b=
z*xaD!IwRt*NK?R+3K@^xW0tp|uo`3WNY_E?w$eb3W-u#0S1dE0XR&gE*^#*d#LYWh
zDL{z!F>5=+cdxa@wON9bZ`~4Dm0^ImI)iR2G|f~t%awEE{z5)y!|gk3tzd$A4A*eE
zoP}dN(PCA?>U7A6Qa65LNbtPh{s*GWu({`dCxf5w(ReV*gdS)%!T|}*TZw?A!URb`
zrag}P%y41fFYPHzdb_^OrpKXw(5gvh1B3v`>S$e8SGOI9=71p!m*Ju|ZI*x%FeDKO
zd&8<uH6JlISUqZ#q#nF=+1rcOmR}9|Fqi#B8sDu9Cd*2NL_*#rsGSR8Z(Le&qhyU8
z;9@dIFO`OBt%?>pw)G7c5{uGswC&WgZKAKA+5b&x$<WovL}o|GxJ;oCQ(P3}G>e5&
zHdh-px2%^DP}Gf!!5P+H+&YK{wM6Yo>vX}15XU$?#PX}8uqi=c&=~ods$|^E_`ENx
z65~1$J!UM?S+*zSU#$<for~C-0D_r=CWZ@RwWtACqi_t%nft*=O#Rf$k`hb*i?67L
zKkb`EtKE)sLTp`6K2%f|lCb<|sM&8V-z;)hvvNF6&+O1N$S#wbj1F{KbW6`BIP8-s
ztx__dwT4^QjnfP4$q*Fnos~3%lM17i!bMTrcDFT3J5_Zdf~O}+o`%WotG9nvD(%#-
zm(z8gqWwNIMUA8!TD~bX<*6%`j!lz8h6{HLzNnR`<~4l1FF(Ffi8`we7zPt7tswlA
zlEV+V)_Thj;|>Lu{Rg6aDna1;tGwm{?X3lEITa`;an~JQFLr|9!HRkapi6ZDrShLk
zR^ipnubE12i>Uz-%RzII<dFH2w-B07l26a~`c&-ulBUg}`hi)~BxUj7Hs`6sidQD9
z^!YZU=Rin%wE|vX>zZZsSb?S#-s&u}F#g+p-Tb{)fAn18bP(4ZT9zO5ENMH=cRL?Q
z0qd9$RUL~p)YAxF-O#pu+vI-E$A0^I?#Lis-MPKLWX=S^FiX1|;&hGFb$==yr!Yzs
z!L7!KzQshgV*L;D0w*GkDU5|}+`_QYLH1*mGBf*f)+9O~EWwbZOGv5fw}B=032Z{6
z57+X;5X*r`Kl(lyQ=H-NoUH7)=sBcUsQ_A7glQ@Gu`7npJZ48M@3mTB7USyT4BNuW
zokLB58jeGScG14<k$$q28yGK&&4UeE#9Y4+4QU+;;Tnf`tB#x5Fg+UF%XPdB82+_+
zX<PE`DW3_9H;rZ5<=%0l#4%S|+v3hN+n0OXDTi!*>Y~LfB$!A0ZslaA)CP^apDz9n
z<v_L`Om*hYHCuf)mc#qNCe@9<iahs&*()AL?p}76jW4%F3#E`=l#4K19o3a36*UIW
z_s^CopkFWacw_xO<t9d=zq?_fd}>4CsYEA}K|xpUoB^OVG3cYZ-Ku30WX+&MNw0aS
zqW;J08lRQtC`PYU9q7fD-5pjeqI=)Vp9&RIMamvU2P>wU@P#SEhE$0gKN4@>D>wY1
z8~&JQriOxXFIsTK)gaR5l;L)tmv&9<Mj6=FZq|n5ds`qo*Q}yoyKzb6_XzNn9$g}k
z8s%7>by{t<w`NW>%HW5^R>yY4oK;!3$G6Oyj@Z(k<dNW2HO4d$c9@u)mrzNy!Ao2r
z15Akwh2~xjVQR9EPChdIWil^o3iSfQfob9c5Im*@)aji6gdt+5@=<LCO}DAl3`1Lz
z_!z@i3`rOZbZlEth1n*8A($tzSG9p{DpEj65N1$mQ6kTgO4fBf=R<q<#e@uBaLek8
zgcqe$47$dUG?58zWbb@mw?sTTqL3RmX%j;THWT*eCJGW!)1@@|_eIYEF2T`y{!G60
zMsqO!6F*-%X)laU2|-Ht1daPEu9_IxYHEJ3^?h)gQDm3@=3Kr-+IF!#>jYs1bTy<w
z9L*cdtlJ!FrpF)k8$;EO(${i~iQ^WZTm4Zk4-Y1g%K;`A+T&Abq>P|^btX4adgUY2
z6VCmGh;1ztz=_cH+V6ne7M&qXX+zLvVlKb`5Jy`y^8>n|m6EUm<8&$`TircYE56UN
zX;6I1CBSiR;&!RnoX!~AQl|U4`K0YI&v}^5Fn)ewPZ8=l&%$qKUATJRAi3p&;}S*H
zJr(CTA9MY_oIL9z^PTKZY>~4$4=OoNaBG@r5GC&g?u%RlNWSGkWXO_$FlyY?B2#_u
z@c>la$sXq`qCRDv)l`gma(o>wtW|5f8pt-215h;@$#(poLQ}7d75#P<dc2K=2{UL(
zJ^oXW>#FI}^uS){HlL9iI?<FI!puMmBi!Wq8#UW+1=DM<bG!G8yMbbfG9tF!3G~&e
z4`4w-WTT_P3|0G&fT{-6Q;A8c%wonmdTF}}I-j%_b?4j-${9ljla8cpX;A$9x~!)+
zqxVkiTy#6#kM&x8nIH;PDSet*QE9|gJN1&RZuOz}eA}5~Auy=48s~A&ACmc)4_|xB
zc!D<nTbn3MW91>N8rf4i_i@>otKW!?qmmH&?)7KVSfBC1!@_dy!H)CwHp9zNAq#OR
zLXeT{lTgL>oMb!o=l6%%|CI{>(<;fqUapHxZk1&>Jbq3M+;q%&)^y{W<HLpDo{EaE
z26BEu?&hfLtlX12=xWj*bEqg>!5w^;!G2>!KVy}gtbCcoFL{5`2)RU2?ojBBPGr}o
ze~zkb9Cx;bVC+>a==h4LEQG@6?Nr~rYvlw0nQy8fxF@G1<TP+$AU7xL==_qxzI}t^
z%OtquP0acOF3ZZVOKALFBFyv*9&}W*$Kd0~4l(eg^9`pqZ@giAk4rDKvhBv3H<W}W
zel8m6+sbTupAZd@%6W*4J8W7usd&dXKbidK?EIyJhhhbCF0^Lr0&mu~)MW%R&LVm=
z`?8Z3Z}{v)mTKQv(_8D2g|@ean?+r&8mp%4$`YILwtlkE)Ak2yXu&L6^l3YUV>Vac
zfM#OH%fVNa-fDkD2LP4a<H(jJ>Ym!xcr3Qm87D({G85};OY{J@ncK9R{%seWn1g4M
z2A6eZIcmh^thwz|Ioa!?)ad{>Zv*yKZH+u%H`>UTRv~;{gTGNOrrkoq3=GWc`|UlA
zOIDdQMmV~+d_PP~wYM}KrbhD3Z2WY&<TlTzgrixAki2=|bMa+#T@Y%7@Sz-@J;#A-
zInkmDE4G}vTW@I8#zGoFBiJmmudwq>GoE_~>1!=TH!6_N`%j&ZD>kj5TtwHSQ4*IL
zOPm}045=W-%-%Ak{fg3G00`E^8Oj>8vqQJ%Rf(@I_#FL~tgbT8(>%K*xhR8RPoamO
z`usxH2Eq^@L}3?gmvUuQe%g~|CZO<l3t_aZ-lEJ8{xVmBJ>zxHE5Scb)z6F+Sr)A$
zS_KJ`MZGj3v?L>ujK0at|EQpYcx9f9c?fp)b&4z`$X$4|X8hFCE2u?yZB1y=OiDw(
za8UK?!Z)XrS%m>ZJ+S)7_3h5qoF*<jy-p3ic$%m%-{vf8H8qQw$0daA`0eKb#(CPi
z-iZ57dJ*hSU5M}2(Jlqo(d=@}`}$DyY@Tg?S99Q_(Y`g{c8HP8m58?RhrW~8e~EQb
zl)Wew@8B-$0NahV`(j}Mf7q|lBfUhR3sUg0^2d4|=?^65oAEzHFNIY_1(ut9g&s-}
z6&D2TH^}AirzBnFYan3qCyl#cO*Y0ZOwg%AZUk0^HzBgma{LbsPo}_WZv{CH(>m2M
zFK8m6td$xyc%jxO{zmnAD#<|2|B+(mk-)^VKr*)??4)-M%8seB^LVPZ+|lUnmJXkq
z${&Ygh`lAt=A-WW?*v~M+%6;f0m)`Mr2G^z*_^utQJ~rFv=EHj+2Rld8vC@o;Y7k!
zdCvHC0^NJ=%b*3bpkYK~qPs1qlX-p&0LVK#QpPR>ZzbPQnW<QD=6=>`Xp+86)?C>b
zwGj1nzM?SlzWo)V436eRmB`H1skXxm7Cd^FU8MjiNyh|s`Jl{BIM3$5LhGr*7%_sl
z;*D&zpfF`FlSD%pzunK=r^5P82l`^2*N>w~$A9{BTXP_+F9Ia53*;C}KQh)`i#1Y3
z*?JFR#4_(fdx`0;wIBxGuNXRMOw{P}Em;sr6Nomb2+Ca0>_#j;+A4$hoPEpt6@hs@
z<BhOKAe96eHy7vCLr8QMslUpY|8aNhLND$h>G2LZr9xK%vOufQla^Cxzt>*yXZu}7
zng^|+<j;<y&$bpL>^0O)r%ax`SHr}Gtv5H;H!HOwU{}#qaM4G&4f}uX8SYz6ZpZg-
zso>EIL_gy%QX)j18LQC{$NsbZqIAZw*URzJ%1p7RKO=6H;I+VKj*RgkL)&>0=xSf4
zj!IpB#ZPCo+HCBA(D1);IMLbsWB-zM=M{6=arPA5&o(1R$#;n%vmPZsw%Gnv!a7KE
zDB{}TDj(yU{RH@a!ZJez$0gz1^#|#pV%zWdSR*d0{BDWXzzH4c)#^cN8i<2n+{Kkq
zG--*I&4)itxRyi*?8O;!_+y3I%QJUS(eLZ0SYFlpN#6TOwi;V6H;=TZ^HX23dYtd#
z1?lYg^?MJ}e(@Hr`!Ue-b=zMVpH97$H?*$f^<q9Oz5+>H!8jB{m_Ioh{m>UOQm*U`
zti~P9Im7xl3~<yw>6~5dBTTUjXzVvgFe|}KeLx%<>toKa4v&{<^HDZ4KPu1ohk#Io
zdv3x4n1d3*9KIZGf;kShSwQA&lZ^h)4s6(Lvz@*xd<)%c<~Kq~d{^t{5dS7h4R?PB
z-QM=AV6=^h`qc;!bCeB}I=D-@Q$FL`{nmo{MJje?$c;~w+Lm_`w$N{b^|zRIyA|9y
z9yo&<;rwL-r4?nZ=yUZj;1eU}s2_ab9@~dC!1g{7&Vb|$2k6;xDl?CU^!j6hSVP*n
z0#S}KhSsQP*ZHLWy%BSfNA+=rjxxlfcyi5Spf8tI3P^^V;)hkf82v(Cd;`-%i<eLM
zAj}@mC;Mju*~woUW|T!z>ogO}0wy16IK`k}!_yU|)Po{}vtze%md5xoU$qB=xAJ<+
zuU0SKxjA|W69&j!e4C!uBeV7KSQBV`n3@`%Hv`2l1VN)$^h>mZnTlkYdiH!xd}OH{
za4=j>Q+K&cK<#%qO_o6e1fqH|sg#Nq`m1LZOAjq%fd>I^BW~W;W!q;N1rICacfOp?
zdnORSF4`*JrMzEIk(1W>W$|zF?X9zKzDJ<*UW~1&FNg7KWC}}9Pjos*zJhx#Ws4Tl
z_I!~^EpIJ<6FeZNRrq{c!TjpfRDl67zgvUjhbJjm7U!bmrxQ!vW90>e#j8mT{&dL{
zHQRpz;03g3S1mV=o2Y(jxgj(WG}g!3XfJ8X!yiiVIh6o&(&^%1FF-(B>rq#kHZVHg
zlt145J2`74o>8*Z;qgsSv>J>zSBD>6`t2LCz!%3{nP?~^vo+GZYqE9jgpiC33U^Af
zafwccI5_fvylnUY2ZLCpBp@S5$H!C7^^;<@^{UT(6`l}RY=oJjL`7t_pSr@sXIAbP
zzQ#JGr?yD0tEpAlPIGOv2XUL@0g5Qydof_qw!Dy)0e3ppbc8Ks%^*AGF~qwjcM8}=
zD^9&h)O$F+MUhU>fmRj7x2^TprM|<Qlu2J8f%UJGw|&+Y@F@lld{1?Pnp6ctHEC6;
z-ZW`wIA9fDnka6fDd{jTlJTu_>~Z<oAy-eA&)eeihUu1)`<WuRogG_NVOCzfZFZIr
zz&w1+QV*u7LtH!#&AI`L;wm(YF+Dj-^Z8D$s}qz0&>J9&o=Q3k^l@Q%KRqj&NA%5%
zpz(irQcKh<^*&!D3;%o`F<~zU^S9R6NT|ygz&BJZKEymE9{lAr&qo<fE=@2ccA07V
zU(!VYcX3N*{|^O(*iubBqA}ay!M0SC6x1=0o;YvRAnP~LHQ8oO??^9J{@lel)bsL?
zxPm-8L7MZE>)MH4{3a-V_jj2qESuB!G_06_VKDjpwnH)@$juZi`FS`I-kEaFA@MTa
z9}A#hDH1;)0~eEj9QOK`xKIs*qDdhc+0=-(lG6%~-!3Uv*Y^B<IR+gtF>HILGsYOq
zGjRoVJ8WAiq=LaWC0_5{{O3g``NGyyRgK$-i;F9xRyJ-)Wi9ha;`VJci9El%Qa>@P
zLCNrxdos5olvcjt@7ZOu-*JkefDi6o1g4t&?mqzCnu5KS_64`Ti}l_bNN)1SLqHUB
zV{lrccChQG7O|a=T6}X}#AhNt_H(~@?bOSz$^-d?5K8;Am8ZOM2NR}^2iMa8kmAn0
zCh+I$7L)A*YP-Ohzc`m=O<0P@mmA~KSu{&3$b}-L8zNLXV6nOP1au4}<y`6kh_ka(
zfgkGzK6j+1wl1l*v<c((-hJfv6ZwMQx&ckOtrr3BH9h>p(7`c|9u&k-`mu}h*1{O%
zfijfyxP72k$L&$qe1(Mv$N2dk=4<%|acZ&921$)fMf1&YQ84cpQvLh&xxlh*vwh^o
zguO?4c`f!og*pB&VZvuasU71%uo{Nu$qB3bPi<MkdO9n;<mB5f%ZE9~gsl`UpS?TP
zU9+K-L$b_?a>rAe;>k_&c%Lqa8ThB#CxNpzJ*6#6=)}ERwdbb|Wv_j?EfrHBlR7{l
z2uiJ}-{6S$8SMB0d|tPl@3<%a3<Y$&*9{Elccy$vWgvsavHcX0zQcL|JXAbi*Ixw5
ztsrJBh}ZgS96D52$Ce@w2ag;0w;$8%M#RdIm=X2|C5cmG)peirXSRAZyz%a9@4o#Q
ziup&3FXs=tTItP?<7S^Kh8ZF=z(9=aU@x7;`yKS6w#MQ+Y+}ErpPLWQ!|jFRQp~U3
zU<lgxS{r=p>-Px)dvAN_=BlAadd{Bw^qshjQcBT$*7ZaEm{>%9aOG*44+2m%=zd_H
z)-&HjwV^eeg>Ih1<-H&7g~xRO>+{q3JI^pe6UZ+iGHjq7b3J04eP1$oX^f>ZK=pZg
z0MW>6ySED%Nf3%7``v`Ab6<eQ3it{}b4wEZ7JHKV=dpvOMginZ%ye_F;aNOl^L=8@
z3(&M_hpx2b-j}Gx{k@wis@-2V&+-Gfm#tje?|p`&c-|=9Pz8p5bHgHbq4fhYrA3Hr
z1bItB_&-_V0oRW9m8N@07!yyw?`@dfQZ60Mt~Nomr$sjdq^fVO`lRPoWq6hwPCmmO
z@8XUZ3oIH7K4|$bb58xZ7>6|{KVB7VKQ9o)G2@GlOWznQv9_`S^XQ9hI}5yQdQJOX
ze`tT@&U26Y9aVSfs(NG5{I9C*W7uoYgm?-|PDa&+O@7!#|B2Rb3gwrpcINoq$wZ`E
z`zunBRxEc-SmK*h%WU+BD`sT-=$gbSOv9fzyW3?q;&e0A1P{C<CB)TcOgc{&soFJe
z0J7~8)*>w$=KM=I8Bv4{Md2VPDd<mcF;$4;j@hiJeyXQ76G&9DLpyr9<HEU3DjdzJ
zYs!Z0#T~ud8D6LE<!hDMV!T4Tm#d;S`!PpvR|;EJ8MUg+*SgC9ZDKH3d5h(1M)_Ng
z<3n0^ZfE*~JFbVi0_EXMwR)CYi*mRxHiv%Njd5*3EmZA?k<KUdA30cqKYy87Q1GI9
zxH&5Lt#7CyTm}~xzp1h-?L6^+zP-ex3XeL=+$S4&nusM9tl#;w&*?L|QI*k^)v+XO
zgxUM6LzZoPmhc-|HY{+w*)?A#`!hY~Lj(UB7&`~v(R7dI_ub3&=H@QT^rJ1?KRJyW
z{7@>LW%R>h=-O)3%znm9>NF@{OX$o?8OUo>-W#ym{@63*41j!l+U<Q3%wz+FtPjyG
zD(IH&ju=pzNUpvqTul=)AQ8Mhbj#i&f3Kjr=?WiKDmO)r=oL;UBHJLyp5g&xk?9s{
zfwose@=t_mdecs&ib^fjP#GOJR?|K(Et2}PbLf)B*|y*J)m8CFh-G~XeYRLr?5%``
z!MKi<81lW{UK6q8Ovqi(L|OxIUsdqjmLsw0gx8^U(aP!PD8~7n=%tb(H^Bf~3W~i7
z_je;S9Cf?SlL(*=RV8^zeCY|0__k>y1K&-U0Q|uPxA;_romwFB?N&u%NtPjs0$CG`
z`JbgLXJXD5v)lB~d#{ZYW^B5LAD$3^76Eh-o*SXKW8I|MD+EepW{p%TfqnH1HtpM*
z1A{FzYf6fmuST6cV<-GcErFL#{j>M}VQ2e(?qc)z;;LfaM?jL}v}GSxi67L!?B-mw
zicIY<;Z4q-FI*Mlsru%BC^mPhQT;N5oO&y@Gb+Z{NU`Vd69+`yjuFunx~!=x`;iI-
z%q)~;qB4g+*fYibws=<*APmnmOc3YwG%OprN!1N5Eq8y0URY9#FYTy*eR5N4&2DL^
z>HA}#EKuQK&soSa%+9o>=EKJuM4OZiJL%}a_F1*1bfvZ~R=nWXPGH~cfOmX{eoGiq
z9V-7S@3Jvm!lVxs;vdt))#6}|@q98D#8!=Ci6`-+$p&_e_oN@Pw~Eqy-s|^0W%%ZQ
z+&_Ao&bYKx(a>Hkx`RP3UHbK?G;$S4N2U^h*FIGBU9p}C!S_AqACoicx$jgukkF-#
z)TVtYv>hhrTYxG{_<JJ``{g6_#AH#h#h(4YEEvzy;kBdsg);?xT*)VGMG<Mr9{X=S
zMwC=~L(^STpVn&jmWwRaL2e_K^QOkG+~z(m8zk<GOA)iU5E|nRdxhP(LXkfxllPd=
z_wrj($fSMwUsRn}R1@tN?o||2nu=1SL_tBM2`DurDxV<IRHR9jD$=AANC6QMX(C85
zKvYUZkVx;ngbosVhfqTggc1^RhW}aT;#>eXvsRdy%)I;E`+0s3^3!%w#D6X^vOn&*
zk#6@Oz1PbZ^-!>`7amPBFCz|OOKT{9Dxtg~#9{>iQf+l-f;9!0%@J5A!=m>TNv0ZO
zu6BN~Xuox=;D*KIy+s#Cva`pq9g1&Tsl|1{SqL`qPI9sI4f>aHmDjU0gZj<JRwiJm
zyiA=)lDuFu>AVu&T=BE5yO7BCH13h5{pin}-Egah;6I^gAc@fE@l%t3_}{ERxBNw_
z$5$c?AEHk8qNm%tvInJ&^(Jx}WYV5GffF1Go^wuRy+I8&6y7<rQDoy84yW8~HiDLv
z6M_r9m3;b02Cz)N91>6s=jIp2bSlzqdmbdtzJcHm6l9%+MrZvl4^`DYRT+<VpiL<a
z7o@^EKewGMQ50hrjq>+?HU&&K0`yb;eZGErlm>5prYK{4H<{<IHZ>UgnT5x)h&6EZ
zQQ&Ky-DJnnCGAhRezPG<K~HVc+<;aw|1y`KIWaQDlJeVF7h1rJiUx3D7E=_9H4mN2
zywui$3DZPOA(w4rw5aF-N2O5$a$3Ad-4d!JFn8Nuv=&JgLwW9fFhFMvlkhdmvwTdW
zGtW7DZm1H&+AfK1C(-t4Jq+4;vvD40H<eBEw(=;yY>%0`Gkl69+5kdVbywFddfy}3
zuqF4uIUYS}qw=#js}Ov&fPd<#{5r2_8_dFhKuXRs=V6o5NLYJDwzd%B50)M5p^?-S
z{dV*rCDE5W09kX%f$WHAfWCgxpYl?e2$g~m^jntThRG$vBOLxMXEw?mR1JEM9V=+j
z*``+=O}!CTwApioJ28GXQ}fb(BNL3y&}~7U@<`K;<4Lj8^YfiD^gfxj1MAeJo9&O}
zpt-U~;uOTBv*k@f^Bs5Z&C8`TFZlWg4EbFPt?C`rVD!fZVmQ4go#|lI);|U0$DW4#
zbwF1!$p^BN$FHY{>-f&I2vHId337<*OzPFhAZhoeOJbHNL0uiPpzj|^dYwV58S(`t
z2zwMx5EW1AH)9d0ouWH=z8$HhzK>9`;nz0p`-ZN#=Ubc#oHeTz(XS%Yf4@ywsC{b0
zGwr74;iJi_HiB?VP1|mU%X{Or7mQ`I+Hh?l`ZJ-hkB=Y}-k_AW&11dOUxPMZcP}Ag
zRl`B}p~sVw@(9&nha(TTFb+Mj>frc2>4JT1|JdYiiM)5}v{^013=5i90sKXiz&YF7
zl$Ur{d94M&ERDY<j&%oQj+FdPPg~Qar%ZTQqLSkdl4Qu4CC=y<heZRWN-JjilD-n$
z9^dMJ@z>ei)=>7nVumn8jb6ik)Q5&^A@MdY>B#@&-S1fFY%jwz5CB5unH4#GQefv<
zHbfQf2>8<|*_<ocnu#UWKtD*s|BfLRL`)7N??_+(r`5dpim$(|k3x&!t9VD%=Lz{U
z1dM`Br{DW*|5XhIs=&W*wmb$?sWC<Rmbr2yZDc<>1af=8F>J%dAnOl=@=A@e)W@y!
z2Wt@q&GXp($y5D$VC6z5Px~9N8?8;%tCTZ7lU?o;{hF>09^2E6Y||eVbC<O5?|Np@
z7a0fUB~8fq=?C=1WrarpK4wNduy8A9P9pW#cpUWo8@P6LJSS-jikO;&$r*~V&q90v
z`KKNm^2GG1&mNzoxDs>0oqo3+RHu{gH+(Qw`%8*6<fZxh0A3V?7ocKeL=OBN^{S7M
zf}WDgzRy9Edjb|^A@l5)`~GUgf%|RHf3yoZFU5K0_uk$hS<rU!TVnP}R<O)z7o381
zg?aSKzB+BFdfkVhxdwEnto$b;p>KqVP7&HN)t7yJl=u42vqu~3=TGEbp0TLYvze6?
zldc%pcV?0+pFb=c(M)=6CpX@}(w}9u+>vD99z7U0=vw-|*{Ji2>wu#<;+EM}MXE~s
zR^wI>r=r8umnkmHep8n+)+pwAr}`+uJDNCKy@A+JGjVb9Dly=Df$GZ?OWLu8zA+ZB
zsr<6r7GbC{!AtsbmD7|Q9y^3Rx`ctxa^(oLYhJS=e@V=E?VLs@3|QSmE__ot=;tyi
z*ZPHpyb1Ai{gPD;iM*~0hJVF!+GIPXVAvN;k#$7y%5AspxWQf1Qj1-yKQV9HCA$2n
zr{WL^z@-Rpeq-jkgB_=3J4-OPbGD~A7VOVxZ`%CZSOm8mL^x}4KTcvrt~ig#9bM|d
z%hH2xLin7)k4&K%<#UUAeieid(8DuT%DyGLEERLv7%AfM6sUrjIxe{&sjFNyHVV1G
zV#sL-IVY3TVH+5~sBb%z63M9>>H2)CSW31{X2eAen0M#tjQ{7{*RPwQt;9tLyNhUx
z0X$p29{<oRv|9-(uM^%x<i$CqM=OOFrzzQwe$P2rRxdAV%mVFP-g^4PrvG{(t}IuK
z8JezgBxzF-K#|gz8vG%Sb`DVp2VUg;RV0p$(-qXFL3o#XZh%W92KWGV@o;UZ>S!t>
z{DCk>d9!)HajKFNP|{wpvNh9BDIoO(kY-}Fx8!G|Vv}|<YYKr6h_L!e?b&ZHGGxiL
z05(heqY4|JC-e`FsbTDj4uClL7Ul%@Xu!8Z_g{KWCc#f`tsmg~aT&`ly+O8a{xDZ*
z05}g*_iTjvx+j%dF56BFYrm-c#>!JZwd*>iP^_s#{eZO-QvFgbH&)lhA;AedeYM?A
zm<ur;)TvgmQ2A5IL9V>@Hq6Ku`1AXb^isvcO8QMS{hgM^s=xPDl5MWV(Z0Mp&7|qK
zfg8wx2QwV+Bw}RVF5wO)K5jNu<RI^u!FIy0aQ?aJv2aoY)*emVS2sT*ogSxepcq}o
zhymlZEJmjz#LKJgX5#8#=_?Yu5Z9+;7d)Q?H{w)qcrzSNQOK`5){LfD{!q6XY?HK%
zs&l?CIWn~qLKzoo&m4~Sr;CarvNu*Gq*rDWe;xhMR73hlU6fcW{2#=%e{Uf=8Z!TZ
zaxuogiEkqmfHZ*05Bh+$WP%RN#OPzbq&7cbAN5szAsvNS;{Tmz^C#jAR)^oaH5YI)
z6*azbjBLt~p`_wI6(UU}xKmB;D!$%*WQ;3tadc%ed9vuGS-5SbItD~Yh?65V)m$$D
ztOolOM`e7P?xN{fJpFs5LPm<p%`rpR&WOzHkK!A7EvL=w`(vm||3&_55*{))<?s};
z^^~oh51Nv8YcZQmemr;Y<>i{vI*ErRQbi0+)Q%>01?n<!M1FdwJ(KjW%?q()QVYgO
zm<p17>=&HVlI7*bSoE_gw+TWpYFKIixviLf*arZw<E%OC<onx4wV-cYNyL&L@qEaA
z)LY4Z_77IZZi!$_PL1;Sp$3suZAtnn4cD<tOqS45Q9~>H*1{xr>O-7)ZUAZ7AmuWG
z4AinLp!g=ERNwII0-%%SwN4^+o{KF181@sR`Yb%yceholB?B_cC4rkNdKQ+;z11$k
zjG#WLDZGf|c*BM;)5Jov<zCPJGT1qZvmDDH<*>oH;U6Dx=s$xPO6PP$Y}`tElas3Q
znn!HRz_m#N=ufwhu1@A8g<iQg?!Ub+86&*O^sf+bY)*K{lt4xu$NGnMhaG2E&9-`r
zE@G%fkK6<;QO^OV@iT0{NqeNk@l-D13@o*?BfMFfdv{^<*-pAY={uEsZ2q~J@!!IX
z)EbpkxjSIJc9FI!w-bS)`xSmS-iX+P^KBx(elk~*ZEKWx=szAh^nSlAX8%d@M?3Jt
zwh+IWO^EO<m`Sg_h4Kr*_~#z&V)omdaGN3=lJFw3;M0KPY?HNZRTDtWLd{VJrR|-t
z@bw1nNaRjV%#(UR>mr(Tkdzk6e=AzC#y!sOB;a;rl#wjXujc{EgoR`81@`NiDq_4J
z%i@#&>Y=noDEQB>dyt{5%VZlJxevTyOO;n5nyf=v6sea*e@`BC`{_wYi9MisT>@Ri
zo%07oX4N@29Ys^&@2wuKfwtd2?=?;?>1(={QxYDhk8y$)3~+o?Hq)<67lKZ_5sWTJ
zo|fIV4P1OLeXu}!z8&_`C4#aFvfCBpiYR}XjXv=hH2SdXnJG^?aNp#h^dy7lf0q^4
zdQC5d=%kRF+8hNup5NI#9($<#d6|p%6(Z*5q?hgf+Spw%_X<-Te-)r;PE}yBe?H%$
zs5$TD<MWgi>HN&S^GUrkwujUhKz+iun_|_Iuq`<hlHw>{BJO2uG<Ho$g@ld$b5yKr
zB_4wP*i!?IURiQ`Cxa_*b?t&h8vdx7qS?y?j_@5aL-t>hegIASb%Q&UVWtnntGq~f
z=01rp?fkE1v6v;C+Zm;ePxGo=E`HsJ)xwlol5)w>cjB(jlU27vN4-ct$SHrAl`$Fe
zq(yb;^kw|l@==bJr}llZdgTbhz&}BJ+07xz5j~%K`kUbd%PPvJe5<BqKY+$_y3dr^
z&>=_<%XVAuH&Aeqa$MWzE6sPrS}I=DK3)B)J%HulyD4jdIK_VCBymL})dmcE9ah}6
zwD_r{Pg%0!Q@WdM7GQOb2o;rv`3zqhDzW)3v(>KYyKInKWO-+?-CWiQu*SW_DHys}
zKVRz;OUkoTRjr{uW>W#`3cZAyY4KU#H`|QnCyxxUNfF?p#!==SuSaIf0^AnO&q{d>
zOo8F}L#{-?b0p;0H2qIjnkF^rvUT>vL6SU=c<$}l_wHNFpU`%nHY9EBlO<aJE-s8B
z-m_k|)aVLq?L(CarlAqicl?b-xt`56{>QXC4kken*0zFAke8)9VekanXW8rRH%x3?
z#r)?kBb9GLV%BZ6Idp<9)>>NI_N3p|aWkoEYX<QI>0|}l8VVtvPVxOty{syIo{!}z
z$$V-5>>VYqdnl?9pM(cFtE-Jen#0uM@jqs8)nyAxMCZF`_+|_gHDNCxS-kw6E5f{&
zyf3mf0*Pc2;uH@1R_iM4K6c(}EWvlF@I|i5{2-yWU`I8~c<7+cn3~U_3vd3IlT_4E
zHmY4ONZG=h_+*}rW7TQN$H}Fwn20Kho&qMc%-2}CkUHclpdQlRTEG49eUm*(0V3zN
zbZf_WZ%GvNymGKOuNcn^PrdwTp`*jJTpI%R?J_AY>l@pDPU#n)`vNZDK#%cu{(3DT
zI1_Iy;1ePH)!yRh7oO8L#Lph6zBqFn_hW^(89@9@F*}adI&0f2PTX(ui1OmZE3{lw
zOcKDUVU~<vy06!l+r-vb=gM-g^6U&h->Fy1|JZ@w^sG`b%72v-XgO28X<Ruv;Je+#
z39Y-cTz|li5|BtPI|duXL%z!>F`jSstVnO#Mu+37Tn*fGNW*?(O>`apl3;{mCB>Gb
zpDRqtq{QB49J#|2r??dBp;lM^t?yuvC7^_fNd2GgNN-VfwOsl_7^go%x6XhO57@Vv
za28}2=p=i9LW<*dSnW4OWo%@{&9Tr<q}K8E<(+Hqi`jj?_QU60i}N$@gg1_CJK0<q
z&z<z}N0>_^yI>8tD9^E+%CS|P2U+LP88u7(Cl9&cJo!8?7*_pQ$eEWx`-q3MG}-9!
z7CcNczR=$w8^-BwcO66)PI#17awBFk<q~VO?ZJqzg;Vqbd#bZR$)A!+>Jun6)W%PM
zn6S^io%cfRu8(-)5T>l?V!B&mnw&~%=r2AJu3=fg@v_A-{89o6*qNFb%Pa)ie3y4<
z{*yiAKYT5HT9MK|p!*#-&lvJ!kO;r*a}TYYZh)vqKKy5h&aBA)Zj+fR?IBW4-eGdj
z9@97w&<%V$pjmC5C*f5b>|ohnm>~_jW1;3cBdBRz2r(?7853WnAq{W>kwDxq<z-)(
z3SwC^@`ZWd??P$aTpH4Qz#-dB@|TGdR@)p4C3$FIhT85(w*ovwtiHsX&T|4N^PtUo
zc4>_|kJvR5f6dQ!?M*53_`mHz10t~eFVryb-1Rsa>c@v{(sj}``?F31Pc2V4iB%8$
zal41OK7LL^6ujMV7SoT~Rz!KdG|fnj=MUbXPi{<DocSTp9tw7yIrZkn{`@`!@CNRb
zoleS==~1H~6(K?oK>v)3Hzj0HlS<iQer|B&f+Nx)iRobbWa^6fqKbiwixP+IIr$;?
zrs-;xV~<v~szwxRGpB1TCdq*DIWsz3&GQDg;%}YXawDWJjPKVz&fNRghg^k12RV0L
zoJD!I>P_YxzTu4|ld%QS?>cNM4HRZ8dBq8Si}D^K3~|tmCb?*Bzw^)bkJVo>&^Zl8
zKmPlMZ)v5gTj1E3UiJ{@_iJ|OZ7Q=UyrK^=EHX9Hy5C`Y29nfD`vRE5oFcvCjX|Lf
z#b_)4o+De&cLNi!NTPFEQCkDcBTvJ!oc}g$ts-<^0^0rXuTwF3fF&jB%oPdU36Dt3
zI=ya+EHzb7emrtVpI^M}_G@Kp9RK~K90%ZHGuDgAi;2dRR?SN(!gIe15lQV@Em*Dy
z9hFO}5gXf`w(1C4T6o?ulY?M_<9eIhr?!~lrJx@;O6XP^<U(uK@OA8T9jCWBE`={D
zb$!Y@COJ7S4M)T44}L~Se~ZRl2}<+gUi9rYi?3KqBvEr+HP(?W3ct9>>y7u^5dGgE
z(-j|(eziBmAVKv*m8MSS5kpKnIcNA;xdN-iE*<7=+BVSF{c3ebLkeXwQeD5^+;su>
zI3Vxs<v|)^=2BzgUunK#0Q+~g8ogeDX8KDt=Fb=j?wLI=AwUh^kHV`?21iUZByK39
zo{?g%wnnaO-8{^Jd|J!@1bV*`7}lfaL#OT=fNprB8Wn4l7R%kAYxdl`#uX8MM*pO(
zv?<Qh+wstU!_)H1kJ8ANTxtg1<0!Y>F>hhE#K6&lu29s{=(}<yf|3|ay1SySC9{k@
zEi)E0vUyd+$M>;K{4%aF=1wXmK5Bo*80K<o?nm|iG&%a{-$HHm**Qg2-huq|{u(nf
z*0Odmvo(%K@#ht^56p=YJpO#_-`b{i4lZ{EN0n>&V#(+Q_@f!p2zQYoj*2-{hCYm%
z16DeJ;FPX&aGIA!$oj46`IdD14@AQUFze_i(z1teKH}uLn1Ga6Xb#e|Sy=gPetlPm
zF>>B`eX`2YvMmJrR=!&%_p<c!s_&T7A*ei^S)0?C+~3n5ur;4i){s9h1ITe0@Sm3=
zX*?PhND+|Et-qJrTt{@DM7k`CiM}!o{)o`)%Ufd-6)M;2Ah#&icx)F!4-3Vwl6C6f
zz(cy-kD4H5cmK8Y_f3i)ILhe$%_;pxTxS(1IoTBva>m0ghX?)<kn)*1(H_HJbDrzj
ztkdSQ!X<qG+4M^33oB;{y>H0l;Vou=P9iTS0{24F^V4AvIURCzA#AM`vSG(T4AW9K
zy75In^={n<Ee}z=b3S5tEAN)*cT<bzrb~w+F+Lu-ZMLBhn$l@X`IU&JD5jnpXYk)8
zeJ~4K`NzRmqoIE>o5lVhipH};BoCY~PS!y2OUN^0r_r9R;b(8$w2%5h`2B2zc8w23
zN6^-Kn7IeJTZr|jSDIlZ<o24~j?^#lLsr*Jj)!cql>Tl+Mc+I8tyeJrd+4`W`$e_5
zup;LdA3F{{)wHi20;40FZ{~s{;)_WgO%{2-Ux?jgzDYMI?V3QgG|f_g(%1<zuT)1&
z=caxQ=Z<ElT2JL|blW{oFqXIz+gc-S1obM)OKM8kYP_-Y&G{-acdq!{Wf$=xsMqJj
zcUox`p)p@mft}b?Ft~lFN<tS-zA`k>Y<!zrIWt<Gg7*+0?QU0fkM_VKX$P_yc^q?^
zFD8pyYZO1NJZT#hqi9OU7}D-rp<F~DLx$#71y6&pOa(Zvz@W^2f7Lz$qgYvLWF{WN
zPD}-Lmg`NYPC=p`B`n<19nq6mU|=nZ0f8V&fLPLzByg)-N**^yK$?}808Kb@$9<{v
z=_|$nEg<JeX~j#qbD2XG|138aKDN)YRT#CVU@~^cQ65VWx~-@3C6k@@I!yoYP2meg
zmgP%Fw<!Pjc<ldwe30&*wlitF4hIka7Ex3=tpytYTzD#slRqVJ<g^}Xe$Q)LodYr$
z3^F=bK+fkjx_yUlU}C?#;Rjms7eKD!F{-(MKFYN+X!!t{)`*D!i{0O8ZolBBaSQOp
zAj3T$?4>M%9sRC!22O6LITiS|NmI=K^SoH;YqPb0g_JcClP=>QP~$KJ+5G+EyeqEw
zuQEE4eTl=JSA=qiv$2M<xuW)HPp^(SY0%1aY-4#t)QzzqwA!dPxP@g)^RUreb-~7P
z;uR3^$U;$egkn1cG-w_vi=?K{iYn&4g=#C1M`-{4WY?OhK0yg^^mL(WLOyCBscLtc
z9Z)=_-A%dMsadqr1#-2XB?9*KWEagbryEmTZy(KHLzd+K87A^VbJGsi0Y9l)-0R5e
zV&9k203a)caK38w_jV5y_41X1%7R!4S?>w?<@wc>-7c>u12Yo%Z#64P+xM=HPaCBw
zuw))I3e}JjM-pj;o}a#!yX-y7wiWiUs4Q@=&<eB?`3|x)Y~bWFHQ9nkn2p=)g|+lc
zZ3&@h`Jb<l!ER7REi}Z*J@s!fXGVA;Xj!R;oFqkDby4b(`xkE??`N~HuY9OHk<#;9
zHJbRe6yw!BJ-T{rQ8v$ycM7()!aFaZ%#(<0g$Pu4e*``7zxS;4x9Z`SbBOgbN&AcV
zIWT^}#3@lZql(y5veK?HS(y^sRqUacc+{j7V>|9ZBQCfu{?L&W@*_KHgH^X&+7{E5
zV?x{_$_0<oi>)|Ev7kS6g`ssKuVe1fY$6@5KVYtx`+k}(rK9ITuw8b$)Ei7pl`-e_
zIB9^QINHyx0RjuBVjD!5NF2<3^+H?&@dj8Zp;_jyZ`_&bva{(3tDQ$ycdIKyH49gC
z8&WtS!*2mvr|tQ+Gr}<+=j~1@CLf3B-P45(YF&sNmRU#cRqFHDf&0j`MJS5=mEk}B
zUMs?laz=^zSs?lST<jB}w)Te{;Ms=Lx5i=>2zsIqrS_*29ZM89Op5de+_k-IV%iLi
zV<iuimzspff{->~*>l=6ADq|DOg~48Q3+~q1@M@F@zw#}33fhP^Z=5gexWKt5`VaC
z<dQ|a9QSXiYT+LC9Bthsmh0+0>`rl0;k#tSbyhsO;_7?bFIGSFRy8jHPjlGOY|=Np
zot@r&*$)+l2M}gDU`lp<=@T6lN)*Q}TQ{HI=p%-KP;*>F<&c2ta;u81p8Tv@m4yUR
zZZ+Iy2;1WO<Kb<^Xr{Hv2<wNoflqC`6THQsZZ%4XFqGLNtpizyI=WNF?|;)^EYfsW
zSMg#4?2Gd$oEph?c1KM@FnMS5+y@<&>LDgc_)LI}w_1?JUSrOpZ8xCN8LO>^ZADn6
zT+bS?C<-}BmRRD$c!h3DLTh>_<F;4L^(iXUVcC_f60^;IGhE`J?TG}yq95}hvXORw
z<W^sw^52M#eLK&Ip^d*F56OkmuXH7CA*K@8OBT>-1bjGjXiLE8;I+U;I<d|p70T&6
zwl>sbs+`{%UFv<p>dod$kcfb1`>RFSPo@27^d(oOc&1A{vq#srdwlzhj+E|u_Jw#7
zok5TH;yS*tmrq+&yob8bJ=QnF{<U<o$er(B0}7|{B9ZN$n)KR<ZWrgHvQ2MQ?PkBr
znD87*_kzpkSh1U4(Dtg;M4+Ss2XqoGEQ9_y@yBevB37<dpic`C<`KpT)X*1mmhgGq
z({vrsX1B0Wf(b8r59ojI>FB+g(&fIRQT~1V(b4{^*(h{N%Jf`Wv#m!{uA+!J*h6Gi
z<Ra~7yKOt<RLgtr+9!qX-dqu|^#5pXh98qE0$9O3;d#;-dNU3m_GBE|@G^ikwHf(@
zAD?{JM;lDPU*nQfxb?vi>e{YgtM7pU9q_jihCDVkV}XSRMT!c9oO*&LDLG(vWepCC
z8?)oG1mnhNA7k++b{`1s{oMTMfWZGmCfv%Y+icDV$&46Ui<y;?c>;Ydu+7p%RBGW=
zF7S*$mnW%rf2&(MdT-J$WTqmNs0RJpe}xnoNBt!~^e8fsw#5$8c`F}m=)UfiYbdy-
zHrZ%KI*@nY-@Yd@Yx)R4i!SOKmY9`}{z`+kCHmbkYbCTrxxLHNoA%35_Y!bdMZQ6|
z<?MC=L&6??+`ZNmMD6V%CW^W)<Ut+fKea@HWgd%Ci^)PBOI>ddEM>EicT{~vIaR4f
z_@>9$=FZK-nGs~#M3%0X$(U-`2yR^|T73e!Uh`m#WJ@m!1))}WdSe}}B3)olKIC^-
z3T{X247(f9PHOPyE${Ce3dsya-I7Qg87gCjygsN^nw+rg3)v0c#ml-y`S6zGD9=2O
zmrozey6_o%yq=@_4-s?S+(AMZ?X3J4=ft8$Upbd}Z9)uKrTsbdqt#EZ{J~+Ho=@CJ
z`r?^I^9x?@d}rIV#Pm{V9v;ojd@=9e1~=P<#6_el2;R^f>pfVqo6Wppuriw=Lhs{<
z5w!1TS*~pyC@aoG^j&n&i*~CWLtoL)z50sn?Zf5KPmDZ7@}2_im(<uk?<)$TH-#eq
zsZb*?hA80JWJh7nxk^&y(Nr#!3yTmD1vW{XcoylW<c4t2?XnYX{6$s!q?QYKkkRE$
z33)}~gYKK;hBO|M*OrwSEBWTo$Oo0=2YEV1Zv}D`FO6>9(-mSW-z~;fzapPNOYT^u
zo_@ucUXaYM@=HQvB`0+T_@!3Ku@Kh<>P4Nz2zoB3M{$o}V0aFg7kRL7QR;lo0~ws=
zk8srSIIHJVa~`|1{r<||)>85P@4dCDwYPJ8Y{e6ACq~baeu(INbrejC;eDndp;q^l
z#QP0^n7k(jygT;;JURFNysr<&)5_;3=FT+f)916$e5WDZovkvy82@J^q|&+4eSA7y
z9~d6_?H#(^Gbx2$eYYz~$sTsi2K*lMmVZ6wG@=U1Bl;KKwL~#4EJfzKDV(ihOqz6w
z(+btuw)H~n%5%Kzmn+Mr;7N&e89uL?bY9pxix1c{Dizmr!b&!oDd34BY{~Ys4^(xr
z-<Apx?A5R<je;ww#_!(h^;|(FW6SQvEBJBk7rR#&zR>&d_l+QUQJqyHM0vwY?Wq`d
zwa_oyz2gE!vC6E^W=gg#Hcnwx;us2&1-cz|E&*hRT3<x!ZHJFWUzK>Ubjt^%!|$oY
z-(5J@YFDk#Y!_YhV=(qvytZEkmJu_Dy@*8s`>at1wF9COhYuHAh}f37ZWWymj)@xW
z9}y;wRz#Q`I=ygwm%1%l&dc9Uhin`ys6XnQ$rYdAoGxb{+&|$}61`VWtMv6PAf?;9
zp#vfap};q^Z!(mN4mYfbwgAT6joG;_w?kcO<f)xazjkr9Kcz2Lje1isiG;FC7Z@GU
zZK$uk2lNqH=MTdpWTe**Zhko@%5f{@v)99me7s^;@_*<j>CDE2XQo)&{fuP=-3s7*
zIXv*Ce5|J(z&fpE<mr-rv!yDK-dG&oeB<V&TE$DeMDTCztTzcaW**3z<q~mwzYH%F
zEWb5tlj6#u*1GFp&pd{PmfRbw;@(bjj!U8s8=+rXars>aS!HWmiQ{Am(jILwF7Z7R
z!&ngPS7!7%SyJZcjWmz?u0FFV>R=aOX{uPHOOI-mx7Lnyx%sx}zT?rWstkRHvKffD
zAyc!1KOc9z2S*?G!BaeekTzMZ;tjVOo>wpmb5CVjS_iaxi)C<*KeqrXe3F~Z=0x$I
zyqce!72xV~*>Q#AMO(kP4_~-pq>c0(yaNu!4<A4fV+pqnI3D4fgz2qYb14RjKb)bH
z4f)Jt^cve(TOMMZrUSffw;U@pu$^j>UKLA_F~k(iIXQBBUtZ%<oh{Y}kivrAZOm%T
z@adc=AMvETNI1}C3@fAfKix}Kd9~zm)4i291pdBl+jFR;2tCHqmW|$=X59n~22r~@
zHgDJGOehTitts-e@-r<Yg^6q8Xo<Jk)NM#Uh4b`;@8wT=43R0Dzgkw}Lsriz&(~rC
zXuK>`0CMjKc+a*~|5k>6U1*gqpZj9L7a8;5cx-ji=P-C+7e0M60$CCM$S7A<W2Kxq
zFlKK#M~`jcy~V&h<Y!F4`t-Mya|7neX7n=sx6yfrKah#HJpBI~-P2VmSuHJ>HQv6S
zfFPQXb_I>oYi9Znb-f<;D0pqu*8sfya-r#BiF^2ps3jhwKLusNKpF;Bn4*WwatSZ(
z3RmUtDV!Uc{|57;#@8E+b8lw%1bj{JD46Tmez?bEN=+C7$;v@VHq(XP&};sYc!w@n
z?sm1^SaVmE1~_iD3Oy+1e#uejDfh`uX=fu9F9CbVcZnS?`~?h0L&EbzT%qpBh{B}u
zho0hkUtiZ^P$6P-bg6W(8H6trY*t`d{w1}P=xyyH0lw+VVQ=GsU+ZRgr@`4HqaS*b
zO%<2B@pk?r0P7nv#|8KT^mWL98sER;mK8;dz(RhhZ%X=t*~aT$<uMVs?|<V9mz>yZ
z(NAT!W^Q!Vvn0o9q^v5}-MM})jn(ItB4XI%M}x?hT$`qDfgdBgS{LO==~c~Hkb~d)
z7I=~Lep%VyXhj7tRd9edk6(s*&1F@3a4Gv{{2MF#r4cvtJLT0QM=n#YrHa1$Ht@Hb
z_Qk))#%g{a=xHUOfaRv{tCoS$_?pdEWB$2a!Z&cS{%=m(`+yr~@y1y`ANsGHyA(~e
z?%@qS*41<u?Ong!vCd$c(#SUJ0zh0cC!|8kJZ3_9(mdqXby;oIQN007#Acr8a{pJM
zQwnDSjU;^)FHP>x^axC^L|v0}yG8#-%mKtjUB^uEYnGC^Z)dh#X!6Q!wlHon=4ICN
zNz(dmS^jKsd)D(&mkw`OZ4oR%`^PNc;>Dr4$WUY+h7t#K_65&?S~J8$7Hw5>MQ4(F
zWB?tPk!)ZU3*l>so}`=}UumQkvTSa+Iikr_@a32_eDr?4TDw6)MJ#A@Hw;1u1IG)C
zB1DE5DKfItQp=plyTA}vWwlilAp|^ACO(+#oLd?jJw$w@WL0gvnA9x+{Qk2!z>T~u
z-9)@NB$|%c1m4-}?{RT=&w4eeV|RTlx<1s@po=F>vMf=%)t6!9SrypqwD_Fy`~vvo
zET$}jdUiNXHfr4DN(3CBmr(D@5OaDe@?8=E<;bBQAB#EKVP=2Mwl|W=sLTVL56An#
z@e6<`FmoXM(w@aez|h^1`1APx)G$heN}@)ByE-cJg9Tkaa)E9(vN4ne1F_{4_>B)9
zw_CuRa`{P+kHp<}SNrJR_#t@j7?%2A@K4cPYJ<*eGSk=jg{{#G-SU)kAJzkclw8vB
ziH4aqM;Yx=#s9U14n&QJjoN`SI@dDT+ByHwf_#BKJe08wMO8rV>X*i@U7yOUZWW*X
zO^^14=Ms!mw^GMbnw~;|J)`0WGH>lJ*ntrX1@Actq?fJdzhxtagqUx8<9XhK4IAk&
zZczpd{_UKR=n1K&nMq8b3fy*&k*#Vbr2Siptp{_B_wmBj(<g&ZybCfvb?(BuAZSp-
z^o7TdPs9k<$6kEQcI)B0hZin0tH0yz!j;kccf82EV>u*v=gxAu#Ij`|4p;3vkVnto
z%~Q#p&)RtFrIM(KJlZF64)_i0%lLCPowW<V-sxpv7QR~GvZMQ&oK-f1s~C%~OHD=X
znU-@7LOl*-5b<eu*IKm$lM_&$)*g=2p3~Pc(MiVL4b8psu0k$1r&;@2bB0VTS6*xK
zFPL@1$pczrT5;04n7+ZQ`WAQO5^`vEB2FZ&?Ku`M8D^PNY#I5QVGW>E*3^sjA}Ru+
z7f)1pd+T)a*8Q{hYylx<e~9${hYy3W+J$^caG&_Y;=R&WmAAodb?Bu3EnuO!ul{}_
zi<CVzc(2TV#YrD+ea+P;74#VV`FbiG*4J7pwN361?yjq+SF{~=Rb#8)))IK=|MnKz
zSVWkncD|I+O7-<e`i(Vnb!HR75|J6XK965wrL-$I2b?VvY_E>99%@c>Q@>in?Z{m#
zH9<#TS{!|)YpEoO0#)nS#`U8fC<8ukC{6uD>%Ksx`CO~769%;{nwhW<zY_j$bE!=X
zV_tLatDITzBgkJyJqyS@o28<<Lt72z6B}N5pV5*LW${u^pL~A%s(+VCQeuR<5B5w<
z@FVV*bQJ+H?lr&SdY{#TBQK9PF@!6-g<XDMBBVdB<vJyZbo-uQhrLrO(xhId!cO%`
zX26b0gQ#4W!IUOp+skp*)H|XRky57><gZ1eOlx<=RwVSV9^|_Bp|N=;rWASimm_UZ
zy9~30yA-|V1RP@PMRb9pTK6+OmT|Ukz0q6wA?Np<F+?X;da?BM@|a7Sa5}qBZ@o2V
zh2UJULtV(-`^oz(IymQ1+;vU#2FN`*4c|OGUt1(I5%5xaCnH<LNXDb1v7_X3fvH}f
zysPPGD0Wge#wy;voN({M)O1ewlCDacNq?SC@_K&oZ_9Cl)uCXokn<+~$Z{;6(#Q!N
zKUl_MeAiWcuMyV%IllMY@%l0&e>0kecRfg%3g$Y^9cZ34o@g21<R|0PNuG|&VmR8m
zo_}Zp_2JhKZU!$$AYATP!pUu79Tn>Q8#RaPsa7aD<;ir?wQJBaCY_!#Xj!_2D}|n}
zv!I;oMQsw^zA$Oj+w#6#6mArJq8Q$BNu8xC+IioD&(W0e%)N!W8+hHER+win5wCnj
zUqS5ZE*eQ7^4jb5C?mjAQwx)6D$#$I4@9*wCCxlFuHBrJXL+B8{<~_TGUZZK`$2E&
z{$?8NN|5>JO3i7$cgrdLy06vz6Owl`{!Nx!b=JCjS!xA}?SJANd;r?2BydULn3i*8
z05*)?=XLsK##*{1<C(*t$aV(73h|~YtI)pG6?567XxdhqHV5{7AC9>@FDWlu6|XDg
z{XhlQ_ptpj6PE2jeJ(y<QkMG8H})R8zQuf9msLl4M_orl<e96eR<a5o=cNdBZWo>L
zW$UCArGHp~9p&OyPzVn9+&M1%gxs)Aezvannf>@=lQfeW*xNpxkl>P=h*_!giHvx4
zQ1AN9z{Erd`wOxty+~b!UgTmwxSyS#YcHC%^Y7-_PK1Nz$HVAu-KmY5G-@`(`gIV+
zOfC^dumQ4c$Yd#=@@$<YcG45~!`|YY)f)tA`!5L)_#mMO?dC|Bs~ZXf&UDsv(ObBu
zyiQA%n+cAVMQzqSiopH44Lwtp1gDcbWc+FvM}f`UoiZBR`QA81dM6%)f@i{+A_x!P
z4XK^Ic4G9qW%XHtG4-}y{WA^(=%go2Gx5=_`a8(j@{mZ{n;n1^4wD*QJavc1&kUOx
z@FUa;ZW}ukNx@W1gK+tCT4=TxPw|$wRN5Ya>eBXxi|p?wX0878-qIIBYgI*e`UJOV
zTne_@<i5tZ6Z}Xt%^Y>7?%fjEQ2WFQjxSpOJ$R9@(kMC%X{FwFMzlJPR(V=-;$nCD
zwGOksayf^=v0tAJ6-cUAct#mmS|-Ha?Q_zf$e8kGs2|YJ1Jkkwo|fD1)L=GD)WeP1
zTAFEX)4p!6Sg>Q8H7DXxt*DdrMOC_hjb@F)c7QH+o(w}%MNy}PH|p*9kqKW9`iqZ7
zO&Xn8lZ&v?zF$-Ao(~o@le_;R3v~1<_ndpE!V0{P(`tS(bylr+xA8wXM0^9|*-jap
zE`IOg?9zfbm`^O=PN|na+nIjOdZHGEn%S1YM>{pPegCodMwgj!z*J#j#&Rlx{)Zr-
z$}``A8vUYB%#L{O`vu^l1rPS{-MxmQy`yM#KbZEa9-g)v&#H1tDc0N1j9J<(-mbW#
zXK#H`?Uag(T_!}~Qo&?zSDYn5n{V!Pi+_tLrz@C)nCnxwsI%flXvOAwMpZMD8ZkSA
zb47{N-tK>#x+fuvS^IC>l{Bowdt~lx=(P^54xReesl`S8*1ApIWd_lDAcrmo$dUqU
z9KGmY131k?|7k*%>wU`wf+E$fCY4&&DdE-F<ZLfkrzP)N{U@Cg7j3!y{am|l@S~3V
z3Ix&<7Dv8OopRv8vX>IuvO{U}9PFGlomr~;d2ui`xZ|XBR0HAFd`7UD5npP`?R2&>
zS3ih---4aGg-6HdA&j11&HS_(#L4=1rM-k|+xHv+r~5D!@goyn@(ERIkAlzj$%5y(
z``PkN>>kK}e0<eRG9lLEU!-n`G2`fPqfho4KBO%MeCL{QPSw$JqP<k8B~81=UQ|PG
zBCBKUC$=;G1G9DdtE<00YoVUwEAB?o>_7pd-*#e8ej9=@)k!ube}6kAzMZwM$O`A7
z>X6H(2LGu5G46SlM-n-CDmL?S`T@F@<xQ?kmIOKXtCA*Z;N=A|Q@bxQQQab5gtb)+
z>ZrVA>4!PDk>ua2X9X_BYhU}`a>K#ZQ}3_zE2+Q3)E)KJq&~bqf>(dv$n@RGVsVqY
zd-nBvm<R&=6kRa*lX=o#)i!5CbN9J3OQ#{P@eP;9=Mr&K$ip?~dN5De;7NXi&-I}T
zceOgX6qXEte46n}znE2y&EV8GL5@hNze>gOuY-n06xdzAgz0_98-(b5<C8yo-lG<_
zP#o^kkRAL8{?pPWa#MxFmYv_|=vmEo#&XDA`x5`wR?Qf1l9<ezcpTY4#ffC*kT_&-
zKY6zzA$51K&lHIrsGro`gT@a$K&Ay+ZhIaDv+;AdfbaJ1y%kmt8KIf5&^1_u^A|Y{
zbsn%VT{VvppN<6@6PhVHv&MI8kEkoo^IMW+ol^!UoK=bkDI%CXJb1tVFiQH)$UqkO
zZ!dV+tYGmql==}>b<|yL#dHWeWw7)Q<9$EMcNX{1%yCq^RxY(9tL`V3vdpg>jv=z2
zlwLg<UtcyD{I-Q7B&_=6tr!&(hE@w<eJ~wwLL!MzF1$)K2uzt3`@@c0dJnBX5zwB*
z%u4DAnY4enU4D)IR*#qNTMzc(_wbGCJzp|GDMIR0Xv&-Z^+EWF<jJ7v$p?V-YF(Bd
zwx@)124=9l)8Fpus_+US*ml)UJRGZz@<@OroVdGYiP@5UP{AwYbZw&+23x2PyFcbW
zHvCzGDnReOY_c;_$1<Jd5ea&3m!1*wNnn2_44o9`DVU|4?q$<n6y;N~J%Osj3d)0O
z0{5gxChpmT)7^ipzDN4TCGN3R>_IlTyvHhCj}%5#y)FPh+IfoK8O|nre}}*;g8{84
z`>;EgO<r)M>}Kj0X>nh@e^58K-B3nLL_Q?J`}!6DFIj*22buG=LiSo+PY#JvpYIt7
z8!t)49Te{*tVwH+WVjoOcjySN`OCLrQ>Cg)(xsM1|1q1qPTOfBJuona*6Wm=la_sY
z@%6)naIv&ugh&L7IL`;2nx%37X}<SbgzW2@Z$AYy*C`aVZ&h5yDL!0GY~qdgH;L6p
zaRaY6s<fUZ*6c%7@9w<ozkxHQYgxKX0~v&X<A1<=>{{)aVXbN8_#j=hb?LwVFl8)3
z6kQY=t7k(gI=O!Mjht+x*x^Et*Q(Ujugoe&2axN+hMx+JI9dPu;5nP&con%IABB;8
z>u<!bl)XpF!AXnNo9u61q*>;sH%eRvUS_K#XQR{KB;&{`)AM=jG0-FbHU8+7X!!hB
zWA35N*f@~OhZ{;TH20-eS!L={dQ3v|=?CI9xMr>Qr>z8&A-z2h-wE1KrKyUd)^z$K
z?kg$ds}<!ZyyZiwxHI?sea;`!*Z!m3XzkEkT=r5{ZdAO+<=;|suz=2lCQP0*Uq2bl
zo7OgD8R$B)`d~0HT?qDLEFZkIOBv^(s{2bmbW$vr&6L^#o=U~MGQZUXA`|ZSQz6PC
zq_j4{NszyT#eFN@z(InO6MCtaDFd6TgXLJZg^AZDbj!UIg=M%`Fn-XW44T)uPSf3@
zTNrLedx+_4`Pymk;eO5QalY7^hPvx}uh08i6M3&aZ4$frOC+NExb((yM15z+8IFqj
zc6Ky*yMNXVSV=NwPWem~FMOnrAdKp&!K_^|focCu_2E*mR!B}`P(4rv4i&qL^%*Jg
zF*@aYBp=ke*V1=KlVXE&Eyu?Om(W??<>rs}W{c}fCj(YE(7?A@AzUTsGx~Y;lD^R1
zi6w;`o$mRuXr!D*;pdIG`puEMb7PGzI)&#p$J}eB<_~(T-|bc9dxpD0*Er?e3Vg=C
zwdlIeiajvlxbqtASQYZFRb&Mol<!<o+RgH}8LPD^ADo{Ca!c=-LAOsK_9mo%eH5Ty
z6sr^1GbklcS*usrqI~NeM2VBGF@?E@WypP(%8t87q5>V-z<2)}VvfHmZIKsW!CA+%
zm__wzRnoY&cGTQ9zs^}ykG?HaODh=JI{e<9eWl%5{Euw@iM07_bEITAB<Qe5<0^D%
z_<rSEqBP93WUuwmhW`ny-I1+!4R#Y@)ACT%im;I@=DqTyVnxC|B6_kM-;>7#$qn}T
zT<e1<S#3}~03iOkSJKX)xv@m}4RrHNcHKqa8h%f<B2YG~{$IDas=N6r29{~No%#h*
z>A6Zi@tIykv5Dlz7rbZx`@!YK!cM7Yd&5RzbMfwF8<x~dzN?F?dB%~-w092HPCR*&
zsvi_f-qh~ZbfP8$FM+R#QzkV^2U=#@A{a2`$I1PFoZyq*Fzug5nQDaDOB42cTYm2(
z&f$h!C*Ad=FTt~<gOZAlVwo#yQc>4@C=5t)*bG+M>11u37=m3QCn|e#>4~w>sPgE@
zWu5d@^`JIpR%Q5lC_5q?d;M=HTm3JNn)1qa4k<FbPSIJC&;4i0`pkc%rsK^Tm&K0q
z%WR$#?oU0c9hrXr$)Y<C9ieJhRcr86L|>b-msU|F-lI8tyyeJb8|&e*f_u1Xnp$ud
zpVp>m1=y^8MB|GeV%TqwULd~be=J#6=Dwux6qUCzg<>GWwo;tiOVRto`w=oV{`2Xx
zq~{y~k>WJjbD~N6I|O|hu~l)g4mnqPD|*nOPfiL2O7qSL4wm-9bg3UCgut^n2d2cZ
zjOuHZ;U3YY{3g9;!4k~pGJ>&O*W0ENyBFaB%piN~e+Di4|M4$zxfWUI+4tcDJsG=g
zU^b|a7VCa0c-h&+)b%?mdaJ{8D?(0Lt$n%V9NW8_oT=-janQ`<o=cx+A*Uu31i6-2
z^z&nMTR75~NJF+c#7_^@+m#-*IKC^V*)c&&rn9kWFHgOm^paH>?g*40al*v5E#C3@
z+Z--aqdZhMT+i&tOfESU`v(KVh4cS(Y2V}L5Qk<?yh!rbGOL7GYUW)>ov;-Zqqy6q
z%tHtXTiR0vLinu0$z;f2_c!>Vf3!#L;R*Okop6yB2laZL-=hQHpRT7;h0MO&TYFU0
z^ffJ=pqGcS^fw-%faS_>(w`WmJ0pz?ol(WvrTL-PCKgfTi{JL9KdW3<ZM*6E1QST3
z(f6hjJ-So~lf(CC*u`hhvgd9juehA_qB*HOiu@Q=d{fBVBRJ2k+1<2idOoKYG4r&G
zH4WY+%5JKcyR&6x&snehT0od_M{fBr+qcBUWh;wZ<%8#V%GN6>v|P78V6>Awys2_h
z)$cS9|E-SDYh=mO$&s$?Pc1;?AfzAPA=ycWfM5F4w#%fi+9TKJw>(=kQq+x=Tt?91
z7b~{>Tv!l!ht)U;zJDM9^fR4}*Z=YwtLo@YwFldr;tcb(NCW;Vhr4S=dcfwSu;)G6
zLgzcrIRmSX;&tPm6u9h<>3fc+*zrL)(SL4-fu>=@KROrF0^P)(STzfW9<5xMPAAI%
zgzLWOfMC+>=QA;DktZv7ZZmUB#Tb|yd^SYd)ZP>8@*Uh$hYSN-l;Wb@X-Acn#w;#=
z!|@OVzhUdOjy$mH)Q-N+)Ah;fz(pWf+^QvEC)Cq3{nH6K+j9?+ozidn$p6`x4QG)$
zNY;Z94)@yyBgWhoIrIHKMf(n@X)+qC=gTv7&sII8b*pY9O}uI`wX_G%jbt#cPfz1o
z98g6YeB%MWzTLSu&TbaAwqIj`-iSMSQ7>qd{+o^MPZEFIBqkQ7^@5pgzsj<JQ)Bem
zkL|wZHmK%dzPX1?bOzVyIWHa;|GT}+2U~gSgWSW{r!}WUc4i@CSBF9jTq6VVCu~`8
zcD6ZCp0_U6|2!)#u$b0fjn_E@N0y@N8i`wO-xv~1-MhU9yDUf+)IXL@S68kgeUIx<
zSE2h;EEptpS&k!f3QkB|q<ls?UNs{O){xN~UL9q4Q2{Z{Eux2@<5um;*g+mK9{NaA
z^-o|l@-;R0Df9jgJG@gVD{X2^K_;yok)+T%c89JJZU5)h!gu(WrjiQLp`9B2&Ek6A
zt}1fLsA?GY(&m-k+Bv~5-4zZ2+Is?PgXHAI#dGfPIVn~J`VW2gcXzmV(a%Sj+$r_S
zyEM)dCm4)X^pW!aBh{@|!*X3Lo*hly+6L>(RSAb!*9HB&@QlXv_`dy9;nr*4l%^#^
z&YnGgi+bi!?Dg2kr%>0<^TyvgEBEo&g$M7x%bgWY@4S|-Lq447fov>}FAn}G-diUu
zrUY75eND-GX{fJe=)4A*s5IVKujys@JybAjWdR~MrFyrYK4H`{_81DsfUt#N`F}Y2
zC~kr_35a7C$N+_!jg%w5bDWWfmO(6QqX7gXW3q}t-<X1T4Z*wq7bv)J-ngt`!G@=D
z_(SpX9IMrS!$KSoys>aBd;!?&u&xCn;P_rRPSX3o+0F}!wFe7Vhsrb(OMVEI0}ebf
zg=3btf<Uda<@PtisMVY>58t438^=G`4_1rUB>U)s#*B?)$Vt`?0Df{$-g!0Ivq%L>
zjdg$b!k1WE<;m6rP8gPJZCbhea0D30GhB`U3XkXZu{V-BC0N|WI0QU}xGq3fsE9cQ
zn_;yogLQkQ6>h8uIh%HfsV!@vk2Ow*WtCI(b+qc#K{p{u2!JymS#N((ta=5_J2`di
z3uJdD1Hqy?dkett)rDiG0~TKb5GpWJ#}xmmn2BXQzftq8PsZcm71N&4Jtz3Y5Mp9M
z3`G`GTuk*#%R}gbdJyYgiZZB!jy=Rc8DMR%0>Vhv3y){kYM~X2VsF65QEWoaU5r_t
z+Oi)#$A?!a#K7l7{ukM|i{}OS=3!YiQ^zU3vwoZGQ=9At2yd|+bw&^B2spcgPDj1q
z@uS_+sd0e&t=3$+R)<$lGe~0XkpL@B*B;YJ)Vhz|isR-xKY3CYHW*y~gL^<UeCz*s
zj`7D4VFB&sz}2cOAD@?2EPlv1R{JXCCelFkB~7uZuX)~?=UAXUHyK0Cwy`U{2XZwn
zkUTub5qda+=BeFpK9rhR(oh@)ufF0`ooBR6AG6H6{ug}lKiUz5zN`;u)V6D3jKe8Z
z#Rl!zWp<f}Y#^TAD_%!!KHv=KJ+gg?kW>w=Vl#2<c0P`=bopP9+;K$9+U3)GGgh=_
ztCS-s+xT|c-uULQx57t8BCS@iL|}+6cDMkk&+lA3IXkr_I3=q#DW>4MzyQ$drRYVq
z4cHp}(;_H(<oMnJJ<WFFaJmrX7ouXld_x!D7#wZ)LpI;nQyIXmpZ1;#1Oho~K<$j+
z0Mu%lu_d6@*Y8Qa<ek(`plBTdKBQWm9<>RW6deh0cQ$tX!nMt9NSKt6anu{=<{aZX
z@v@~pZ5#+T7sn?sR*sd>`~g}s`x^m}I`h<_KIM}uT?ciF2!f(Lf0zU69<*mcrP4aJ
zXSjr_Boy{*OQM!6flX0eEO%Xlb#3!HzeSIYJ9w2=PPlr_OIVdZ*CD#Dl-y`;D;J9i
z!6DY1wdeY;<4yuz@j#b6pFhs2SC!uXGv21FB|5Gnkpg>Wfv{!?%Y4{^JZAZA<H~u<
zME|@O82vNdYL>^DIxGK+ZtpelhU}RyHHEAiIf5kFSp9e(F5|%qvgl(ImeL&yJ?5Is
z$%H%FTP{U5D@yrZyXI-KKIFG7GZ_)JK-#+j`q-b&pT;&~XCLQL#7zr9?G|Dtj&puf
z6nJ2<?GZ2qo@2nQE`OY<CR#`-+rfZ*oDlCVcV9?s_qFBQysvH$XD_Z&R{aH`c_@6a
zZL+jj`5e*2qBnYPoEDW`Y7ytUiGS9J)F|%mr$@329F=YK@{4VN^)`PdccsKX+ZeOH
z&9Y}k+8%BsUP1$0s#nIj#P!f7!S%Oc%XT24!7n^4$4NlgE{(~jd}<4AZEYZa2ikIF
zf2Q=k`E7^JS0AfHq=iZWtP!Vvb;yhdI;uE8K;vLPnFX*fAAxtH7a(|B$-o8KG(}ne
z=;@^Vb01@l@&Ev|UJT`+<*UCCruBMaTHTz+4*-X0-b|aBhn;8JJ98Jd?;D%aaW&0#
z%PZw;M}B~`pQ{T`9x;G+91^2iealNP1-r%EWH0T-XYpr&@uiDXNG3_lUxBKOe{A|P
zBy(S2=()7>J`lI)jd{nYJVtV)D2Qk>LOg%(#l_SqwO_^iFh=Z6D-OJBXY)IG7jvpi
z3%siacj{qxdveak%vU|b6NgJ|+%14x7G_H9SbQZEqqfn5AGdJMT~=q~`42pMF-FOL
z+o9+c*bh?Ybv=e32C3u!?EHUNd-JF!&-PvPYi-q5g(_9104fSr6htHgQ);cCBBY2I
zlmJQ;WG0CL0wJ_TMI<C5%9KEAQ4oP(WDFpPgam|yGKG0cLLxIEObLVg<@?S)=XZW<
z@3r<i>-_)b$uqt8`#kq`-Pd(r)<KP#Mt24t3lbz%hK5l@H(rl9S}PqkIWK1vQVhHe
zTiYPU=;PMj;KB+IR3zH%W|nMVUfWSI3=xV37kXX4k@%|8=JVqs^r^u*0%);D%|LgR
z9^3DcwnNQ4cUZx!w$+`R3nx8MrqiMlWR<fOh4>hSA`d8=gH%*?7v;v#RNI{dWB)Ge
zhlF+HZNvlON3^=N>N;HHW5N5%BFKU2h#Kcx)U&48{$J+*tsAs>|2N31-s^38ZqEM-
z0lsd|h(T!%;TORCMVUNt{-UZNYl-D*Z>;HaTGLxEk=BWWEthmTjLRNqBOcjge5`MV
zsH-g<sgcS@e^Eo`%yR@*iqVf6=5t+US?f6LN9%)k?`z(-Ii*xXXT{@?JtV1=0qdWX
zTx68%3^t{T4}O_<q+>%1#?DV#W$)q9vWbU9l_u;1s;(TR*2p!#qSCZT``*bmOj*7Q
zHBk#cv#pMxd4KKt44emZklm?aCX(<{Q2`n=3GCj%=LDd0@#~IOYmU*h8qPHe2uZ84
zQa7KJaJkej8)8OIkrDGHRkl-KOfXE)6wJTV<|n#%{j8w#cir`V`9lTRW}R@l8K>6P
z4RWoloXmo@S<W|KH0V3mzg%gb65mUOJ#S;(7`#A@bXXO@kuB*_G$O7`a&16S5zK5Y
z2H~Bm^<lH>gr;-3dv1%fd#xU7UT;W)iTIG*5llBs-3O1752e#J0XL*hw+1e|O1ve;
zvMghD#S{K}uTKFxwYk;-ym7FsZLfYT1R-~{$?#xH+&i8d%6DWSq)6+)SDqNW&Btr8
zl!85eo(OYP_v)6eS=H|^s$btv;%>Qt<&~Z{4MlZpa+Zi6bdCfTbRvVfpLT}YSh^pP
zRXN$Q?Tm$h-mEUH$F5Lz9ejxp+=Po*c2pm@ASjk<yY@iEE|=@7d^!PzWtF7SlWjT_
z6;aCu`81iXBo}6sy;Z`cpi^^ms2@LUY`kqV*)W(?HP;5vF_15<?bncMiliLslv1HL
z7s<QVo^6Yv%<5JTO&!PF1=rUvZ2$Fp-i#(`Ofz@a*B!+%cXbm@(>ZsE7b;$NL%wRO
z%O?Qx6m|Gd4ln&x`9Rp*+<w^X>Zl<V?llT6SL>=ViEpCrNmG*dj@*)5vN0&_g^mp^
ztRG9WY|00JIT6%_Iluj5RwmV`!Ih<PFyyn@8s#6-oc4~kM7!D7sQ2eideP+{sj&iB
zA0C}-Z8X<(3L$OPOOEg-bbQjEx#b^{k>Vb_^vF!ydjWfzsU=p~N|k@MXelp^YvycE
zTRw*Pa-k(z6=Xd;D0?!U%nD2_dW4~8YCR>0T>U7$nI3Y{>56{oC?Z-djTMYF-ML~R
z3<_~(YTPIU-}qx&KJVvQa$c~Vu7^#sJThngmp`e-hX5V#!Ctp29iWA5%Ycx^#FoJT
zS{!@^hT@htUo0%YAz+)EgX1GZZf(H6lO*j4l770l!z`++W_674q2OTCIN@Q+UWy^|
z&g(HL?6K^8b_r4bjah6TvcC^f9+;h;96YPDXUCQ3>nXd8qTOj0pIW^3b$0)gC+J_e
zXIze3t>Pv)4crhOqDGVx;yvU!&|!!97V{42wJdyx^B{c$y{on#+v`e^cbj|_NnWUV
zAx%%51+>4daM%sGEV3&?nisr>=x^+6YA?VH#|C&0^%BVU(!`*4T;R^U^k(Q6qd@Js
zx1v`3;4Kp_X42FQhBK9pZ+z=b(I_6NarQ`b@856zc+5d+_lmn;4iLP>xw5^{Pebq7
zjUs=4l=HAm@*(~zSw6WVuuy7#EkGZZg|Nwl9)=gYs4EDmGXKrj4SCxmDN@Tq8d$k%
zZFwc+-Isltdf@~-)2Hj=YlJ77a=ij4Yd4;2Dby6jAfmC^mHAT>PzC2rBh-0lJOPmf
z8}3Ok;1Q0lM9i+Ocdu33`o{KKrlNzF`axlWFF?%Ql3TJ_KdvJfo6<caIPXL^(R3YF
z2`ii#trxuetwT>#y}iph-vG`{x>h4L9(E6a_PXCbd<o32Z>IkJJA2A=&MF4^I5N5b
z;ZyTu?AQ4%uet}$x;`~D!AjKp@2GKKz$|{z)0B-dg5$>CiSpnZ6Exs1z_-Nf;9iYy
z)oS@G)OmyJo^Z?6Qf-r<X}M>-jXCDk;p)vAck#Ew^!X#)UGIb8-=8#A7HZHE!@`0F
zf7e@jY|zX`PIsiQOY$SdW!p(H(meI(MRjk?bqhA<4rt^;d42uaFMcg@=^vSM&Ny`P
zF!m&BDr8pRvBzk})bD<?P0*1*BxBXNXE5PJNPzQwqY~jqe~LRdN7MIaz#L^fbw}(t
z_}UT}iY@vHA@ws@KFDmDTAjz!7n-*{H2x3_IWWW1nY@8Sbtgbl?SeRVtO5ivFVIx{
zEHlM?`i7(Tj9*BQnhxDk+hjta{1XC>Y?XaFr>`Kq5GC&C*1%*JLCT;LRqUqihP)Kn
zaqyw{QGaUME-61{e?D(*Nm_FipTm-qp3n|+E44Pro#7|cl8)qJ@fg|9h0D((*&O%0
z0PHi<YlIK_G6jgY=@+}~x6+oMt}gSe>AUf2YCl=g0XFN|#|XSb<TwD8^N8$L#M;)q
z1+w)84a}G*_^Nm3*e1hgk0q;AgBBit(E#kj=<PPH?X*n8T;extEd!hWwI93}_qAFx
z{uW*7!|66BHM)-*4rBhLDN>BL>rPaz95xQTDqw@K8s2Pj4nSI6YzC~`oi2{&N}tV_
z=6?vR229C<Ezcyy&h(IiRaUwE2IARoS)pf)^bfDS-HHB{E?#u{y)qxPH?vjJq-9C=
z&)aTNp)=xQ+lI}ao%%(_u)UkJ*SwDgwleP=U8n6e+Q93z7Hh5J$^^D*SvBh2W!RI}
za69T(V3B-SH!I2{E%R=F-rop{)n(ONcAdiaD@|iPDdE*hgutRi+B~aTyVhu$G`20S
zG8cc+gohan?W-&bC2jtrzVVNg5>X@W3S5C&eJgr6*T3a=2PM!u|LtkBb@cjX@@fk1
zVC<a4-R}Dtk@PII>{X}wZyPR}H0ihBnFU4GXV89M$}$bWJa60*<=QAXkWZ){7z$A>
zbU9>wX>1)n6adG!CNhj|#=h&^by!npxA)`qEt`}5r2h2HSXl(gPt_F0j0;Tpjw`<t
zE3}NKrfh5pZa({DuWsOw+ZOedHrQL!gbps>kRKpKVw%iH(Y=6tTy>siiY#lj{u?mZ
zb=pFP9wfkwH>47GYur<d4IdNgW><52@amEBlA>fCRF;NaV0SwE<`XMnytIA=2>F7<
zO**Z1w(@t25Q@09XM5c5?P=w#@9v3mefCugA3Ew2tWO$LeXrzN2yI!nYf8p1?qN=~
zdj+(jlv7%<Qk}Uoy^>-r(NA2te}M*4Ousk(O|!;}kKz@;9$X!94*I11hAx5(u9`X&
z<DckYDDWm{_TI22ymdHiQ1xkWa3-1F%BLrfSaFmJa|SO4@?AR_c2;tGJDJ!uN_doH
zpPjNQ9zsJ5_2Rw?L<rrc989`Cb5$ifa9uv2oJO>tEh|9uD3TRDoWgb#PkR>9$w?kP
z=}TmqM;Sq7@ukurcJ-3(@LM<Hw#wxTHfv{w{ok^;?>}eL2(=5w0~)}iO0;XT!OU-~
z*jUoD9C1Tl<h0hn+A+T$i-}>)FPul7It||FHB8oxISWN#iySfu(i$9TBEptzA-tC8
zGKI|Z9?-e@WZHFiKeSvc_+F7qjHQ=yAEUPntQ343ePrQ<vf#_PurT~?_}ww8eY&AB
z+G|c8#mrWZZ2UX$nXQ|?@=BbG<R*&1d7b28NZ5x)jofWj7`Nrv6fe-LS`;T3Uymw<
zh<#m<jn7AARs3FpwZT2XP^IFGudO(PQBF_=*lpsd=b~jl<BPETs-^Ig^}vA}9~s8w
zW9Z8Mn?IaVb$}mAYXt_fC)r4>U@nrT_*7GeW2_9OSUryEts^ZC4rc8D+(hZg3=pRu
zC|Yf--3<@piNE`JV?NG=7<%Fg<Um5)_~0P#nxIX2%Vv1hFT3VqYql=!!v!B>UV?69
zc)$C-<b(Ildfq1LKO!GJ;F0gXE@83Ui`^yPB~VpQU1RY-7>wW=G*akGInXc#1(vw@
z*#Vw8cI~5j&TkC`r&s^P)~eqwdYdAs7yawlvQ<THLbFY+Vf);%G3$%H?)N=vdfzBz
zcU+&EoKRX`V^-cC3c;q(f<j`ZJm$U}58&gb6EDnu2y#A5Z1tbuX|(3SWd-2WOf3;{
zU*HLGub%HSMhtm#tjG{Nb*Hue^RvP%(zK*C9X643fH!^F0Fd2TiDjr}DHT)6G1i(s
z{&ASiZom*8c;kd1%p(M<%%WoXc5N9acO<=5^7_5^53_zS^<ccfI9Nn+6-U^<P2(&*
z-$v}Gn6w>Jr!Q%sN!$QE^SeGxkI>&|S6&2u51Gm=@ao*rD{)V8oocv6U3if!aoi0y
zi>wr7X-2KH*-gy}#uhdX3n<Qa-y0vIuVT8tL2lqgerA*J-xyEb=!~SW<Po(lb9z+w
zzzD6t1PrQ74)a`dDpcg(xV$t_AfMjBhzvfH-oEP{6B2m)vi`kGjlQjG-(;eT)5TiB
zC#PLdaQ9%G_EegqLHf~Tgxb8nHPa@?CQOJRE42&ooWF3UFm5VjV?hTI5^S0NEEy=>
zlog}$JA<j`nYa3HpJJZ=v`xmk{C_=N1QY#0s|@f95n3YO=Tc|Q&yomTeLHF;kcV)E
zHCe9H!aia^e?*ARNvUYgK{9Nl(#nR<aq&IKooxlgOTWu*c+FRjHBBMSbAZ$OH@)t^
z65l@#&U%w@@;lh0sppX~Zi)FM?oWN{BzgbcDKsNN#+pvhe`ck*-ZyoOIl^v{K`mJ>
zF^Ve@g9{00)SYKL1gZpcIJ%6YRce{C%^)`D2sXA&acOoc<kZu)m<%VZzyy{yN`IG)
zG^zlze_H+{G;Mgim?-?nNEcU*yfbdFdoFrtpgDfXCbbLkXM)p)1z<Z(3;nVj%0L0e
zC)N-za(o<29t|QcF`nYp?lu0MIPN3;<HZGT()Gdf;U{V-)k)_g&ebpM9j(fb*m8+i
zYy(VT*CM=_r&=S@-oKFpTv=o_t7+w%%`ODM5#9BCy>PiRd%1K&^V0Il+j!d`MbgM>
zlIxxWI;IcU@>%V=ng%!rmknI$w#fck*=I1!Di;(3m4-rwQCJ#tT>URWkXm`tO6;Be
zgY@Yt)&;l?i*;y1I=56DyD`D@2I>}^>T0{V@8`NO%s*IN*jMVh@WY`Cw+q+eHBBN!
zoyHU^pSRD+Q;u0ERr5ttE-1A2*-{X67_czfvt0aJS!@)-nuj;#{=>GDCe8`}peLt?
zqV&1Ki)4B5)RDTHZa=-iALhK$GrZ-?&#VIP7K~$1_;F*?3U`>jdziqXknMT-gt;8+
zH;f8NL&8AR$vZKD^(J;RkME!wqg3O!Y$ki$<DdJz473eXU7?Ne9q8%K)KjV~U9%|Q
z6K7zTt-flZHmrGuz7yL~ZvZy`=HwiGJFO;bk9~3?E4<sM@vku_y4HrzVnLt)73<UN
zWX*nCl!5YDG*8`@q*Z|SSryoB(oq(|Bd?g(A%dmuhucn|ar4?evdRW3wm#g)7A?FP
zl*Q2+DR*8-i3WoF-+r>$8u@sYM9X8ZH;dDm7px!k%tV;YnQUW$R>@Pi!Lfvd7Sy!!
zJ57;ihz~i`gV*2e7Q-z(TT@^tSO<EKZArX#I#>N<8itsGG;j9A@jDspUbhbl-qZ!i
zJ_Q@BjLd$ZIrBV0Db*Vk)YlaHA@=9gHGI0Rd}^pzN=bwHkaY{tQ!kQnZD`{;Mzjfb
zp`7Un5#WUnzxsc5{_8xNM1u8Rsp(j=CYcZSjAdFEPX~<QNZLJV{gDs@jE|M=M-WdT
zOY~a&+kdXPc2!kpY$s3Xj50SE{_FJ!D{^39I9#!{#XGYp1md@&aezyMKY#K85y)N-
zO9yIa2Ebbh=?4he%4~b^dsY*?G%@#~w#N%)au@yaQ9;(-5OgwVvsa?{*@c>PZlSV4
z9&--`ZVRcvZaH?V=r9B0m@cKk12DJy{s}wlv7PHdnev!}KarN{)PH@MEZz)w2v#S}
z$3Y<PC!CBK3L>B)P@~++1I5shwhM;~1ThBpY7g<6+V&>~o%zFB7dU!*d&<Vrq3`%n
z>4rPls^bVD;<D<!f%51y0iO4e(EHqg@s#c#fr$wSB(DNGG7*L!vL?iQ?%}$_!$oTy
zHrU!B6mF;^yTuXJZyB$3Ws0&cjp(<UREslXn*n!QnQNIsS82h_nxYf9?Mrq?C|ckv
z=BuZ<kw==-(iihi`Zhx}jUP(ZwW;eIdmp_e5XHUdy1W?;_o>2zh2sf~f(%0Q15ZT&
z+{cO{d1J$oO!{yWj#iZS>m(H0aXt6Ix|);il)}~HzGs|c`(^sE&e_J5v%@>I0;6rN
zS1$(USOEXU-b}a{+wYS~#9@3MDJ}>Ep*5Ytvpt%NeqzFvWe=*cqCjrtC%VM+mTEhA
z7zQgOZ?xWOqiyz02_sdV2P!1AM2&NF2W&V=oK;S#Y8ch+E?kwEJ6x_1aI0+O_-yH4
z7Kl20U^WT!`IU|yoPh1t;ryWJG939vtp+Xs%zRiAAkR@AQ9E%ZCXkol7)@PTG&#_=
zb?|1e?3c7y4<~x;{mAkQRpNhvH~d>l{{4rO8#qaYZ(dQ(?5R~D)|d*zR&A0?R>aUR
zQT4mL-Vy-VB-BKAg-*<=8*r~tWX;*|X{ZOb$L(&l<dG*M!0*zy#>v?Lrtv)kd_Z|s
zzS!KYk)l8M^=uyfjC=a4XQ<mO{ckME{cajlq?$(RFSIS81ktmEdPOhKF8oH*mXu^3
zu|hqO6LDw(GZuoDZwbXgMmB~<_AJ%@V{MSNEYAC8u!h*|=!N~nJf-_I==Qt(2|D}7
zjR$7Lv4>tis+tI+5t}|;hIKH~k2)aaZZ7~0$yPHJ!}^vUp9hl&kUvZg41E^mu!7DU
z64Xr{il#N(3E^Xc?9f4FY(u)dYY0x{cBX2MpwyQwI<TLNH8&UiagB%5ju-|_>#fL_
zzrQ}Y2ABiM;)j}#*)24T(Z^fL+ACgKZ<f`5%GzjMdUszz;@*+qY`W$ur@c?AnZ~$T
zYp!1qI(hd?{~GU<x!Y_?eoTwH-_1~}S|Ja;2izxJh!W$7t0siBX-aHSG1d?6h_j4P
zGv4mW)}w{C1>hW@Vz-`korszPJBIaJ%lvqg!BGBLfc}NYlarC+=R3&noKCR(TOQ~u
zO&*%ZJa$k_?B*o>?f+ZIho1x%F#B4yZB(Mv&0AB`6{nTBBL%~Cvv=RfC`PEESId%K
z={Gu%unuKUBbsMddsN3GmOJ@NgNmN~70Vuzo=c3tz(Q*ypMP>zn?jwj?j|vg;k-H_
zlHKeojk=zgvda?(@`yJt?;qSNi0rnk<LVVR{!y_5H`B+8(sjoDW7!tvsfU(yM~;7$
z6Qto7)Q^4Pu=)5FooA`&5Y&{`9~1Q0SYl?9Q{$kAHN#aMM!8KMJdxZuud>UqaI>|?
zuhx9MpL>NWGK8~pZZuclj#v+jd2YRSJ+pv~gPMQ-G}B9rzDA=#y}lfZkJ)S-zPy1s
z0ncw4*7&%I*f_SBHG40QA3>5uIBQNh{iO)4e3zWm?7cbeJse@7PBN2hsVqpZ0}eB_
z@RjoLDjy7kWsY^ho$U<i$c+P6`LIHo<lBy*-ee$;4g)EV5#q>7%t&c|x^5J)9*l%u
zVem6ldWq_>s||T(W4U?L@4c>l@>TP`e;r71v&qEGg}!68!Zs>l-X%ylRihAiY=}=*
zhn#qq-{dgXd-sz;=-8T>b@S4+sJr4M=_dob`?2eLD~wwSjXvXi%)(Vt;88DYg_Ows
zX93qJ`gSbRZ_dF#B9QI$zD(&@`6_Z*vO(5d4qVt5mlO#XX4A)w6ua?u=+B?#H17D6
zv#O)Ik-UL<1I_Rr{T`{=SgHtbTi$3k-Vh)8WngLZ)_X~JgjQ6Kk{adSP`%x-&b@3i
zcnPcwbPmdA(0d>8yrZ$Hv{s8QCHaNPm(QE84ESts(!KBX1@Z43M)GBx9_sfC_NSH#
zFAnz|cbqEkTIpXu3c`5r%!=OZ_gH#m>0t%t?HwuO{im~@iNSJ5<@_N#@GA6*qFm2-
zuAGfq^_0OyUjNU(mn%ixxTSxO=b!&Fp1P@x!UALrp1wgpxy-a>^1VNvY@CV;PW1j1
zAIjIQp1&O&bbFTMSV-#cF*yMHa%eb)C)z@h_*+nNC-d@c`2ECmK&yT8_8WeZ?sGGe
zJQ2aJ43Q-@ZhSA(g(-Eco6?)%03J60)MtkFA=+~Bf1*pw>l~5|4vkf7P8BD-Q%vT%
z47uN5y9ns<-fPYqoUCH)Ol?b63?&fbMQ4vho7XXK=_NYydGD3Xi_R~L!^;Ys(*9aF
zpG%@!v;60(SWV3~-futI_v`>nh9-_TZ%I<=+O@<Q-R9poxitQey0RDXSF`o*%^P11
z(lsSwPnib~A0s5I$Dy6F8aR<~MZ!@w)yK}E9yY0b9e8Ib*)#gJ*kzdh<rGx9+Lqee
zXVoU2oH{iLx-UKms~+~NN@~pcEItN>&55ZHi1OC9O=#Y&>4;dY^ZmE^O)y>Auf#v^
z8|)Ru?^#tuH+`1BeJQA*s9%wtf~sAqXo0P)cNcvcQ%<s&;<&3;yD4#9wzKpSd^WGU
z=T;YNC+{qlvQUfpaRc<NAM@-&URJueG*BH+eBz}RCq9U$TKn;BsFUl7ioe^re}}#e
z*G=5ATY&(YT`}M-g~cl6me*O|QH9C3)$h&CHm(2JpIG7+vGd(A{byCsA@1Q<!vJjV
zOiN7tmUkzM4W(bT13b_&>@?JvAtyu%hwE=$Cq;4s?!eqQfYAHvCWc&+GgmJ_zmx9p
z-4dTnI+(ZWnc_+}-oC2*_9%E6n&In8g)?;|C3CoUQ{2N5mzk-za}wds{}hv!3J|&_
zpgM~~g*&kSsIpH*@5Z6!Z>1JisewvmeuU~adIdOG6U`==r*EVre|bGwG-gZAu3_MH
z69c2%<6vaFtwk#4J<#U+Nk+<wJtHHUZOOG`(9hz#MRilCaQ;X+!*WS@v%Cn%XUoq9
zv^-}IB@|V9;)SqdNM60~3r6ZF?1|zB$dB5J9S`9PduMs{-xNH;!B~3n+xjNLQ?x+W
zR&IB<4M!LvexR=7P=P+x^Mdj0=*=4?s(7llUP^Lok6QTH(9xAss)u4AfJYux#}d_^
zcH-aeYBObfnmN`sypGTUImYPw5zjDyr-%RN)96L$y{j_sR>Gpo-L_CE_(0#vO|zgQ
zbYm8T=az^MJ+bncatDo>`RYKtS2e3QX7AC&j~5MhJtD3o%sES}e|#Q-#w_Y3mQma9
z{1x<W{(Ks!rah}!75|DI@MP}#uZBTU*zv<r&f!Dux6diIEZ<96%`DOJi?!AJX3Srb
z`unIPJU6^->{p@ww&?l$%7eOz*b_@$7)P;WTMNcomjXdX&&Onf=EP5{u9eMRsS#CZ
z8?Q7u+nG<232^Hst`9G5uz!EW8L)a*Z>a_jw4Cwg{ndz{YFqk`0FRM?$7TW=5`xyC
zmz-zvDD_vb$K}Id0#d~IpDU-J?*L2mq6~3qN!_Hk>yFm5&OtrGH<hn2%k<vT`nQ-S
z-4c52Jl81n^@5Qp@-VhG-nZ5Fg>ie(>8HG6CoHoGb|}+amt3>AMY*rdU-DB~`DTvL
z5uYZiUQ8P_Tmu%OYQJE~BMk$6{i@1Q?pbwVHaTnEr%QXF`=JK#j+Fr}TW!TWbDn?0
zXZQEDpDs$y;(DZRgB`24{fuSt#nPj*uV*aWqVs335YzA%|I3f_splLBn@gp6w!sR3
zuM<^=hkri<+q<@NN#i}>3Zh%F<@N<J*qo1FQFM-K)UVI(MEMcX=DMANxYsjh|K$o<
z#6qsBS$ZE-0%%>=n12;1?^Xp6rN%X~=kL2Rm&KZC*E!F&(7R5G=?ETKm&Y2Q7uKEN
zpCz@T^)0ggWjuE_$hnaHI7uUz36e;3NY1G;FjO6gbn>xZv*yZ)BWuHutd}J(CJl@y
zf70(+yhUA4(7pAhj*^1b9ng@*1)T`TzcULy^D&NWhnsST_xJXN)v&KT{j#4zupM3w
zs-vj8wj|k4ZVMOgGZbLbQfj!e`LN6sOW8W7hM{*G%@XF9O&hNX{X+Dn7~;v<;*1L6
ztm@51mmIVL?uBKW8bNgAcIF=^BZ}-{%_g!ddvJv>^p#0D4(XG{VhQw8w3Da#-J?Cu
z1HHHI1{xt{79p+rfe3DWU_p<HQ&5!EuXQ8;{V2RCp@^7Y6pt9_@9Oq*)|+ZuFRfSb
za0awLv*+HzcE*xQwQ2nG;GHn<U!g9(=)E>FTV=a{Wc?aZx}Pj{{>B1=!)09b*#A^F
z=^pR5!{#DT1gtrbsHUC=>y>5KIormzl;sCDMmh(~g2D&V2k&ywL-I#^WtA-#y~Mak
zC_ldp0PvGeb$j!gG0&DWt9<^s#LOyZHT)yI%oO+yrrQd$$#Ff`ikA|Q9@b^%rB|Zc
z&4hAOD|YG3A1YO$3s2Rq-O03)5A=r}0+gPx-K<UL(72fe84;V4+|D{;WKS2H8WWP*
z#>O>U+>=`{dH<dv%B~TY>{xkR{nkx##Zi&DkpKu`+;jdlmcFaoAoLbmJ`50-?hz*+
zj@h+;7e{rBJAd4C<+B>G@$jC*Lw{s`jY&xi&K@&0Xa(yg0;H_yyg9dL7M6*hA`YMS
z52)uC|I*`h7Q1>Y)!>WVfMMtR$?Pm#tlgIGd^u(A5P)AxjQ~3sPQ$~aBbf9B+YyCr
z=l-W%DjooS@c}kb&2Gew2XQ$Pz-}VACg4=|nUeOEEvL=?%aUp}B|FqP-NCqN$B_F=
z{fJAo9_@4Cx8v?dWw#C(Z`?9jg0?lbUNnu#uSoD{Tl;y2r{N1Za2+r)eODX4d`l0V
zvz}@3u{ln9eAlKxBA0%ZoD|61EsuRXOpnNN5NM$Gi`|fecj@@?f6++1vI@zWh?k8z
zX|A#3<#uu)D&f#BC?82-)YWQXQ%z<`uwJF4$;;YXXsK@d=BE)IGDr$F(qu8-w@*}X
z3+(jzqTew=2Rr8~ms817N~yaTJ6Sv^H1<MEj{vXc0H?^I=#@<X=o>V}DmtsA-Xy2&
zlxYP_)LC5Il5cMQnR1R%Y2^$DuN#uxEuoFIt!^5K0gZtYIl*-8WhmrF!Y~@03~?f;
ziTEeM8icqOnW2v|_c)4Vlc~_mhWEXcfOeWPiAQFUi65G`=lo*#bShK)Q=}+&xzHP!
zSuwx~coIinv@Y+VbPgtid6jgPa#BwM1h?igKU@32VLgT#W6G)KvHXom$Qgm1WNT7#
z*tb!XH}wZqWyZ1u+Na<ck)^LQcEa(!(_mEBjZZn;v!|r5OL}0sHK%Xv5@TtGb{ncH
zx{gNNEOarUK)i@<fPZ}C7Uw$Mg0hq+2k#EHTfT4{A+lFm`rUVAw${&SC4FdD=H47U
zVmG*!ivi`-a<q!k&GU)vKmw&ZA)};(2^#I3TRj+a#BZFnee;v8<~HipS=+hhfnX63
zIlE(Q4@Tdli%*3L@~V=~ue@44b9@cu7WuvnNE(hGM`j2`#ha7G&PuDFMLRYr=beAG
zJ;r{bpKf|h7j$bW&bx$lOrPI&D<Um)A?D%wr86;qoQpu75b9T(8^lQ!Q*=PEYeDic
zCG<-l&2}GhA2Nnhc_W$c=1lLCh+nQ@fh6O-Kd0rV)cJae8k1XS8x8q>(yfyh2y>C7
zm26QwXXkSL)bLGWNj99cHxobF;T~|^!4sEdxXFQzz_MVYsoaH@??}%e$yt^1`+Z60
zYXfioG-8E)U40`*nSutg8|r3%MxN!!rRVn<T(o0~`BHNv2V1k(>jVEHGY;qK=OtQ<
zs3hNtm|~!?ulWwfOx1W10UwmTNwGlB193d!X*m9Pyi$0{_T#lW7&gC?8Vv7EXlLr6
z9%G38Zizi`9Wx-Bj{Wl4+iU4Z09ePUtWn&zIM1qdP=NVoW-f^k=%v?^S0`?DuTZn}
z<5y0FKJ%jreS>OQ)9sPt8Z;|~1hJ>wr?qDe%IfD%S4g`sQao&bo7Tl33d_QwMvIqG
z930}4(42R`FWb1>PpqK7@35AgAs&t^{?}N*x%>QI1&vS_0pz!>fP91?$ddgecu%Z7
z%+-BKhhPLvMus1GFO-)wOCw=MA4a$DK7Avib6=VrHNg%aKj*Xj`aqmM<_I_#SC##N
zkbQ4fIn|{(ir-@kT!%0{x*M@$n_^IwnGPmPdIma|2F!SZQ*{fi$$5~hGR%H&p53qy
zdAg0>HF`W(YUNy5ZVp}Yk3h%V#R#{kHjPquGA!(;RgfQQ09D%SoY+z`S%7;|BGSqu
zs|rZC!W?IK_;R||%o{#Z*RRkMAMaFyDN|Cc_iWcLs)HpsdJ8<@RSmjrTH(S27Q9=O
z^yx$B=Pg#N)gYX87L1bytpB&0$J**9xd&yZsj=(gJe+RS7%`9ZqY99n9g@}p5@BaF
zRm3f!3qnr<Vmx=g`rsBpHE-!p*>A<}_s}fUV|!k<oxkcAXBeb%08Y^XM+{Qdw>XA;
zgN=5BSJ76m+wsp$_PW|5PZ;I)2fYak>#fYmRzv|VFOZE>mm4F{q&=zw6_t%YDVf@<
zk~?&Bv8wU3)cQc*vD*y^8osq_f;|UkuikY1@cTGLq3Wq4^&gYmR{@vdfx*RGNS1TI
z2Eu?&j(!D-xGap*2ot7iY#hJdNqDJFRI!rsyy>iVE%;$)J4<tdM&8I)l&&^`wdMgC
zuE;A^C*&ODWoh0FJ+l6OOfCgtpp7<65zi!^kzHNGXA6=enFGfVbvR$t`Ps$1Vqqba
zyX?~%m0_Z0yWn?Ojra&A{G}(Uku!Z~i7<Nn%j<_E`So)HH}W;8!F(5r7PQsLPQl^(
zFBIk_IeRJR4WEa$MZYodPRoNU`A9!_mKni>Y94*Yn=UUSg-M%kg>3bBgow^z(M1wh
z8WNK=0$Tgmwz<1OPHa~sld_`*8eCd~GnWH=OkBcGs`d*rCNX*uQm1c>L*5iq>c>*d
z#1jbAQnXJr*siV51bJWq#oK-`^!90Q@Q(z;Bpir&RYGXOhn;J;5nPW{Xn@+T(@QFi
zZZ=|O7n7;P+x<@@Hjnl=uGY5%5C0mrxyL;EyvYPF&76zPOe{_@&90R^chyHP|NWmH
zOCqx++`nxW9-s5Dw%mNy%1h1bmo%j){d-Wb!esYpi3P-5nn3H6Tu10;)hvuT93RPE
zxeBi}MZ_-9iUlMq?}ckXYj<m*;=qm3*5ro4MYJxWPRX@L!y23Oz{Mh~I@t18+lSkG
zGqHJ0l%9GdrvENM<|AqpVx;?A7ju&Pkt(a%R@zSI!K>bSaSg`a+)hU2B;=tWdwg0X
z+P6(jd+7<@#Mex*=%M60#aKlR*7e?i$dU)kOqCfPN)WPEKt(XgQx)Z`u4U-L(a>>o
zUn=&IMXgX>0D`qT!OP`9wR+H*R98B2GRNkNs3zWIU!T}zi+Ot)9&kCv^aqcAcj6XD
zcZ+Q6uX-6f%_9Zjw&=SfJ)W$l=gFN9e2u@)vN7yOXzSw9nEUt6CRP7Mb~wsEWY_O5
zdHD|egy(yxy6~szw{}~c%yEgvERed~a@7M94Gtsc-NugBt^sCooerUoV4AXeHFH|#
zmJM6L<Qbzc0F7pCvEle)fR~`>SFv{I4x<g}yxuSL-%I2?-m_%3M%a9i8um2bElu*y
zpL3MCl_Om#zL3)v_4Q^um2_``aOb7*C^Nx!9AvFa$JGng12l?#n1kG@8%ZL|5e;b-
z>sV;FbZR8m5BE<7a)VNG0ALbJCX>p{D#KyJgAT8oA0AfMs{OeIUyit^nxPRJBe{zi
zQ~?N=q@g?hIW3mcJ@$^WG|tLRCr6aOH_~SU*ZImfMaA&7w@h<9zn(!5Em{j~<oWeL
z&Rg|#7a1l>%2`TqEQD5C3@c@meka%+W_|&z0H2H;Zv#2oPGiS4a~>2Od`f#R=+yo>
zD7*Dr#?#(O$PRM3R*Yw@IqUOd+~vaM40F+?nXl@4B_HOXmZS$YI#Yl`)d<TR*4?}d
z{3>y5iZj@nUpf7tHxm?ABY#xMg6_%GRqpQFC4N=Q-|N9%Oterli>2p;y*i9M@$S3I
z#fLw&C6e5BDMa67Lf|mu1gVRnqkQ<2>}-$`)|9h7C-g78;w5sUFGBDjlADTcJ)h_{
zb*`m=8{gYDe0NSryk{)cGQfNSGWbL8)0Oh(KpSOI@MTZjyk9nSgJOXo288)^Flr@V
zqbZT%yD_4DCTg?fh)uRi+mX50l5wLj(_Q+x$AQRf<NV>)YA$fsz09@Nz1(Q+Y>KPT
zJ5sLVf0c96-a9Sy@`)-DNTVRemt1)z`hyu5HgD~m<b9}^Yh|!H84+~Z8NcwS(}d5e
zl{o}=+%)=7xsd}MZ{kpN$Zli#J|dfp9&l7y6sz?7vRAuCIWOTc&EIlCa&EhdbMzW$
zP<w72w`7runn<%KbGKx*wii=ZQH+Fb1?gxy3k)nWIQ?-19VPt}aWQC#w#|O+bz5Eb
z#<X9k7f^>VT_FmMo3}q9JHM3k4?w!ngKTNlt;lQoRHHlKkt$LE9rc%#N7uHX0;h+b
zrr$G$pV)r&*<gQD$Pvf^+>3paho?&JN0!M!K+$@E`bK6?jc-Ho*vr8$n=2!O0DUbR
zwfy0)0rxEmPbC}BPy+|QlZzO9t=hnNKG@m0#0&~5kYg5F>~|Z#roVmK=0H|fszLAD
zwYnhwP=s<`wSkBNLrl;nyN_eN*2Qxe8FF~|sNKMLEa<me3V~frmCJ>b=A_#@Pefck
zh01@lY<O<ewTi7>5h45AV5sq_$qc9EkYA&=fj=T02PkO%0e>YdB*66h7Ve-7bl|_7
zq!`4Ker;VFZ~=u<%*@_SI&gA*od<hEm(%QejO6_Rr!uG`7@tyCW2W#Dsw|L7Y3yc^
zmQZx9jwSKN3GeizN(n2MDC*|oY~EQH+MOX>K$>jy3SYwjG>U@Z1#GzKIv6gd*{xY3
z=;GFPdn9W7tGFW}_xsLeM|niwJMo6HR3H;^o-i4vsVM)3L;AYOC8FiOTPm83k-kTZ
z%dIf|3rCSlc%^C|tyYqgYCiR52?-qG!!C^T{C4YpWx+G41*}IwVb`puZpy5s2}g6A
z=6Y5CcZW&|Hg%ZcdMc-A<CD6;uW5|mu_f%<nFob_8QbG4nCV3oex$EI)^yH=#fmt+
z2;x7~SbbwCFk%SIjU(dOA9w!pBbf%=Rlgd6G?#<~e8ts6dWyF$xwX*SMJNNjC!EJ^
zzo5Sh?b9ym@Kdu3dMR2&)K^{{+^I1i(>eU+z+V7I834{M^dK}HnWbYtP?MUjW1`!~
z2P7#bB~OtaS<qnR(*ji$F($#ud%>k89p9}g0DD<NUoMC(9wz;sA>QxJe&ekMEy%*Z
zeg3L{v@ivxvKWah3Lcr5d#jSz_n@|J$9AW8L?0>B<W0V+nQj2#rH>uGdjcEB*H1$*
zhL=TUGewg+5vVdkcUy0E6{*S{`zi}11ST7cW^r2^8CTc=UKCiU=x?hBJ@WP6chO=m
z_%wcYh$!nYJ3x=gv}knRT|Vr71aq;<$CILQxu#aFm|6~0S{a8{-uS_0SH{QYv6Sq^
zm;Q=f@JK>+QsWNW&u2S%zo!6(-+|74o@^}JBH~8`Jo2b4hoN!XG<N*c`7^ZNQDZRG
zQ`|!911cP%2g~rwc~-D}RP>VOzVQ^d_SWo--8UiPfOW3yR6>iswLs$NJPk|UMK*3_
zvnroC)(Dv};b-Y@tRbj^+%n<NPcUyBaWM#mG)=JpL8O+Yw{7f7+$Cp100~P&mchD5
zk{aLC-vihFxb_20hmM&hkFAinT#*hd92-u!9uJL-(W_NeS!FOV8>+&1@HM3aT8_&^
z$qW%p2Ps7~v7@-DfXf&Zy(#xyia@ghPGu#THQI4<xBG#t(Xcd5GXRT@?uY47?}<9>
zPOO8}S*RVX<zy?C%48Um7f8|CxXU3)*Ar+@Gj$bv#w!cTI2lTUk4bD^V3t}OVwN+d
zJN^=`h&-192`4y<lY+8)gzLxj+T!VJH5)UbjN!Y|00(_PfKM?M8bVUg=Dv(x5H>FF
z=~{QO0(-H4VNhLgS7yIFX6vK5f=<|I0fu;i=c=%XFq>NRz&TUtvwI7+xA(1HnLc;I
zs<;v{-IVIegrS&8%U80Ao_cHBQ-Fa#^5ALJS{^1jH;9RnQhjl&USp@aS28jQ&Te!I
z=1<OXeY$U2>3TnodE~T501fA2O%qZ^BJ=7^uvYpLoL4dKt1x{Zwwlk3A>L;RStEAt
z0hz1XA8oWMH6TYVAyVB>7-V3Ag<>345cu9D+j3uV=(Ci~)=q{}yQf&4=ot-5y@$nr
z(G;FCVk~JH^NwYcVDjbyp)u)Y5$tiEH}Ku@6FXukKCW>Vlq?qAQ6zy3`_Dh7Q`#K>
z$PC8cO3AvCBJS^+3Beb5+|gYN7hLpP&H~2vq59z(OM_zA=l(MN{M%$JKqbAV2*bUk
zZ$m`AYIJ)Tl#BnE=AtrzG$#&ie142P5ssPwK6dEPEvV1YrYtwI)L0U<9eaf$Z7bN#
z{`jPzzw+xO4PUT#!l57?mCIplrB$-!@}vW^_DEXN`5;{2oJQYA{xLs^_g^0SXDd5p
z58L{a&Oi1gL6}MSNb{x5B5yCAa_*D4_}g?ODs1p=egkWGDKe!^zASF5{qQcWYiMv9
zZ<-1+q!DlXClwkaiV9+1mQd1zVNJT@qa(`?uJa%wgQeX-l}iK1zn~<fpIgz6Qx0(U
z(ZKJ!<X2)xlV4d@9UN>6pgQ8bb?1boilZYaBGBSMZ`fg6;Hh|)aaoyzIC-Qd8H~t+
z#!Ql5Ie{=tQ8%Z<mt1Je`qo1+rFHpIlEt;pCETP0@gAq))<vpfk80fMiU7&U$dj_4
zC=O(|^r0k=N!fM1D9M}icKe3Dvz4Ph<sJ$BO#%8uV-b(&@YzqVM7Lr3N2lB=$=j(X
zijsLZvOOn*TE+++{SfYG@kTex_H`hg^{y10(0R=`^nW!FtCST6z2Pp&FjlJHHCt)z
zz0lSu^J!)!?J>SWb;Y*cn!Xr3eAi}y2I-LIYvduaSfvSu0=Jq>_kOba@#lFIlTctF
zshhlgc}32{rAeVVvW1F-qu}7EeDSNz4~C9`67#gsG^-us2Scw$NGgP;DyX1t6R0~S
zDHikKF?CLLI$?`e!(#hiY{9Eo+YX~^<G&d11Og-KaMAfx?!dwyGD$JXdC<zMsOjN_
z$M`Cf16!H1G+M2|NHfDjC_z*&@EebVdGwnlhnf;BkvgZP0?v0<v9-I}cnl!3=M6qE
zgpv%!3e)+QgefUe&ZT`pJ~d9EhSN<99PE*;Xl7kyb8FZajR_7F_HI{CMf)(T#PgAg
zzaJNvs)-|UW!uf(*bEC_sY@;o*UfxuTVa~uSlyCeYXzL7sc*AJ*0jx9t0KpTeQQn{
zMBbj+X!Q~bC&IE2!(DwIs!nHSsYaZwW<hWJ>OqjF^HR6`wcY04a0x?vLqk^{zA324
z<z{>Ug%|G_o5FUaE%#gY8~M>R26A4j8*dy2G$8>ce6S?x^nC?o)_Pyw&B3tQU)0zn
z-eKAAMEEJ>!384Nm~JNjPV3A^AlaP!*%|x3liAC5Z=`T{jD5xecSeULc^n@C6lP?J
zEz5x}z8sE~3Y<O^7j=$V8PMI{Rp{X8D1TjKTO;ShL|oWC7yt?5V#sd|zkqr&dmmLO
zB5Exy1>Z>DM%(W6PD`mR?P0c`#g5wD9e!OLto&do(~q||iR!4E>3`Zc4&2cgg^^X3
zA1Uj37D2L!NU9hB_zsayrN2LT-%cMWIYlrw!^n1z*Jl0zAMlqR<9x>)^A%5hr5^cV
z^Y7$_+`iz@=mGwg?&QSrg1v1WT+y+)KSi>kh+;2b5y3mJn{8_0;x)t<wIZy*27PyL
zHzf1V2~((bRoV%N)?3AmVBY52zy=^JxCEW(pL5~2qnjU!z8#OOHu{1r<qfa(fsw?Z
zYI`AID+${Cv+irZ0oF*F!NT5bM(vF|J(gBYRiMU%qL^tb1K<{fc`CE0i+TV7!_+fU
zJRt{Q2vL%I-HrPSJ^j2i@DW**Dj*TSvQ+`rfBW?i!(*nQ(|yzZ>dGd7TiIZD+M%st
ziUn7xwYfpx?1xUGV^K2iV!-_tyj5Jek~U!)!ETX;L{txKuD&Lp>vJDmI#|e`3k&+|
z5dG@A@4&{=ocic8eO-&7T)oA513SrI%4PaSaKoK!o4e7!l=Mj)Xaonw9(!q<0nWjF
z7kCdW!&i@x$B39zu38|W9c^G+r<ZXwruSa}Dxf#C?;-SESG95L5La|1iCMf@#Ah_{
z8w<x-l2w)Yasza@%RW{3kDYx#?6pJX<lKB#u{0}R{u;85+8cLIH!-KCOyN$0$I}w~
z;<d^)JuQh%?6tnC=b)n4r5+5g_WmIXNXVLq`p~nKT$?;3KU$s`QJdMI)l7)6J7uZK
z@F@%s=UbC&IsR>p_UW^n@I6#wmKV^VA#h@d27sUmzKg!``+z676xc1pg@XnUQd5Z8
z5cVWP@s%K+36#VlXfz+;U0I%2%C_8wh}tru!ygKp2_VNjQP9HrWq~ES*Y%EvW_k&F
z&)r)URHV+m#^ekGSW<Ou>M2fggB93QeYMK21M<$gmJ==)AaPoTusOkwT&c*F>=tZ;
zk8e+d&aivI>2Wvt7Xe}Hd0Ztsy^QmEY3|L~xrKCt`<=C2fiNqg%%dfutI{PDm{m`2
zM=eoP8Bn2nQPKSUou80E$=$+GEFod=e`*03o-0YS_*iePS;7Qntt<W=Yv-*4_4f7=
z(;Vjy&mqmxzo}iCdFr9)!Df92(scrD3n*bXrNd`$t`0uJD(e?y4lnzH7S|q-6OJ0x
z*CmmKXX|RP*ETub>98ztapIWbK6}9ZNVakr`i9|-?YXp|f4_l9F`;omGPZbzTg4kl
zx}&-ivR*VJzC8NQw?Aizsl}UYZHOmRFg~5Lm5-)YNW%B09O>Ywt~mP5@?`qJS=j3e
z#}Gi%5N{NA-D0La6G=wnrDNypc~F*r1d6lN66idRKqX~#`YWomYF%m+Qw6GngOU?`
zba0l-Mx3h1It?Cgo|~FSsgOtLCQ6Kr!KZKZ{Aauan6U1HjN>l=)<yIXf2;g_Vh<vl
zA-xajQd<z8#4Zq<3H$1HZ<P;4zlq(_?f2ehm^;<uKH0!IitN8`HuD%$$ONi+&~*1{
zT${Hs@<@-xOv<CqB8xv-GW{9DL)m#gpaKhZgLliD=n&rd%XKv(lUPweE8Usa(>R|b
z%JTN^-Z5t6=dT|)MV>V13LRpv!^q}kHhAK1b%gvBU0v;>P@fcZv-5Bq_BV9eU}jzF
z)PLp<|KH~>t%v@JQK<uX*7zxu$evIcZq_p>FX_ByOzB^^2f@DIrN~PIH;-)>Y;ykU
zgzpg<VKM@plaRGK>XRGRL8=ZWO1mL7x(JSIbvlU<XbjjrYD?mmP$C!0pT-;w<r$VI
zx-oj+)K4hx!vLE8p&YN)te@j(M=t{uG-t*QxQ>BOtk0X+uN$32r;$vZVxpyf*V<G@
zZK(;8`*z%Gstts5ZAnpZ%GE_Uw4%^Z|9#PKCDlIdUhl49tr?4M(Odl+Z_g7_fg}ba
zeUkArCZ)3Vc>&kNJ88$#Jn3*Qb^_Tl<8Hg40TKEuaM`^#pR-v6;PqgD0`JX)nmSB_
zxaqxb5~d2m`FOy^<gdLwL8S*o*0nmgLa$Hxm2ngY!UUpv%F$b74|YcbK<X((P~+`E
zP$_DnV0oXB*uN!59bj+5-^LG)Zin=uw&-|FebAEe4x|iMHbxs+m-1V@mf!Im;HvhC
zPruU!B}TL@E%nascpvK-T9`yGHhTnDT?rfr<;76}-jET1CAn-iA+lM{ErIVjw|Ccl
zEo2cmRt#9S@whG;7pZ*e&Fl(my>&Xf9Hi<}wXgo5Y`psAo5wWH9VQB>?+CDYy+Xb7
zf4(*a<sj{z9)HYTj-@XD+&U=DoE5N<Z;=Ybk$;cg8qli|*@rKGZ?9eS0>d&y2`%y_
z|Gk6`|C=Su8!|rq(34$NX?v$NbD|S0^f$*E8KGDAzoqaEh050JIVIr>;cTC3_oYAh
zCI`|~sD}6Vn<M*|a}h-bN$QwA9AG?FUwna$2z@&E=k>q(4QtrikRB~`FkW@s%XA%=
zc$()1)620i+sGPPI&6ww{p=P}=3WL%uzbiQ09B8RFHAzy%!bSE)Ew!9^uoME&#mQ#
zG@F;ge&B#_=^b+#+i{5kqib)q+Xj+lR8AH|vgeV7=Ihu$9cio%r8Q&qP5tp@5fr`p
zF3)8TZl8%km9!fZ-yYAb@vL37GFUfkuB|6W29l&j&Dvvpeq|Z?j(1kEX#C^cjdZ`D
zw54Y)Rk;4BzQk3scACe@Yr)PlaIrw!n`g1`M{$i_I43a{=vq3^HCX)Pw!zZ+LuJD3
z6-g0nvbDZEJZTEve6QnOZuVnKiudqj%OK|-YT*gGMk=Wk3*()G7LMA-iX6&`QW-Q@
z(QZicVY8aF)b7(iLB9RO-x(Uhb+Y8Tdt})$wTOUmoO!U(7S$*ld;n*<;PxKi2EBjF
zZsLlz_F+B`ut`^SQtRAzk*`F5(n?3Q(DRgcar->P*Ym{M%F+B9y#s?yh!d}XFzsx2
zWy(n)^}Sy~CDTI4ta7WE{brh^o(>yyS*RGBZyhVs@Ge`LA4p99FQfi<I|1rs>uEZ}
z-M9}T|Nm#un|pIrCT8;EH-88KNQ7dk4!W?&*}Jjj!3aCUFQ_XX6`%m+SOfX?a(%mK
z^K5f5wxID1KYzo|X6l}eq4&%mnD{J(#?+BDBRig>jqEph1`Q0|+99(;zJ)DLCziM7
zk#Ffsvc0BVtOcXj^%mx+PrnE{aii7ONTm4UQr7-c-w!07&*aOc0u%kZ80c9`)?-b_
z!pFUPeShp>5C!KL^qZZ6$c0Dd6u}@+TQ5vC#+4Nr8^KB&Pcfkc4zpd)n^%%lTtEx_
zd2l!dNc`Az^W3vbEp}9ML5U@1Ywg9%$aSBDZXg|H;vK*;Y(2(=v#0+LnjHZlk}^j4
z=2OOMb!c;G4@|rG|64=<y??5IE{&h^E$WgSao_^(xjK^+51XpxD+((zejMzOWh>GM
zMP+T4%0!|oiN(0RXga1AO{r@bjZo|V{Nuut!VHWe$#3_-`Xa9TkZvN;jo0@U0AX#c
z)YtCWu|r^Kdxwelq(Gg0O5oLsh9mLeGcNF`E`$%j*_v@OUA*GGjAo((Gec2zRU&yJ
z-Y&przgepL{{PI3a`ZwSeYFGZcOUYf`NrNU3K(E)O8z;}P${zCNFSJ>6pQ<~J>4&Q
zUfmG>W=ySxsiB3J_8_shFUR`4D<|EPy49giJ+?I$hQ99-xki*pF3@dOI}W7S%{V7k
z*f(k!C=+xYPn6@@!Av7FG$n}^8mj6Bl5qi~Mr!2PUh9u!k!q$ryTi=5wv=DrGnSNN
zQK16vC)W%5g`EsgzmFDj@U;QXdPt798Cb2dTC955K;ksG$r8*kFs^;;95#VQEqYXj
zu#`X63}i-4B{gn&Ah|O4SbS~@whun5YvJulNDW)KhFWNC{Lme_{0pjC*<E$B)s?!?
z0T+FRzD!h+U${<VenbTI_u<Owk9u<*KodP+L=t@FC|&Mct)|CVCEqfH74J1=H@>#;
zcJ|3g(efD>ts0cf1_ZlqE4;Gi6Lj(vr8}o)v1fDqtgZ&b4t<?bX-g3`KM!9<1&1}K
z$@e;_%J{BgMD$4t{dgH%%%tMGI4OSLrL9YS)oMX`PP;D}gfG8~@2xJ1aigQl*p_A+
zp0;u>CuixEAd>%Wh5b8OKni2&boQWOofeSM^EVqKNAwfIi6S7uDXH1#ciF%SSB3FO
zYH6$NPe`k7=&R5nhfAB&0$-TnH4%((se8P8QPwIa*_)?2?O<?Yv$WB7sK&=!EP2(G
z2Tp}?J1P$JjAI-B1`0`X;oom8d4E~zjIa?ZPYIUp&8pf3dprOdg_aeyVqM#yFQEtw
zyKW|KhAbo=n&pbRnc-#4S^tB#HxFy_%=d@SOy|f<t5BuN7N8b|BAWqO!V>B*g<3SI
z2tg8v5|Kh!Od_i!OoveflAy9nAaxKT27*BXgtgWr>_L_g44Wn-fkeOnL5M8xLp$f3
zIoEmLbN#OCcU|wlko$Xom-~61`@4O<kcps}FzLp@RiU=}O?KIF<Vsl?WCKTa0Uskw
zyzV!PSawl}-VOwoAy)rI^|j79RxXS0IN~=C9HbVMHB1?uPg_4ENTG*iQ3BHW&+b#<
z64HWC6wDP?i`_JWd#G{AZXbg)hXtOSIJ>o~CCS350Hpey!Pcj_t*Am!@_C<rTGK=*
ze80HF#r@lGHjCmxz+hLd$BW<3xh*<7bAJj8j7`BU@%@OByBgLYT<TM6A*x7T4n`?S
zqN&<qpc76{xUURQx99&*cW@k7={~!LE~_7JJ_6WJ4tg&G+z4Ytg6)cp%L9Mv!#|yn
z;70hqtuK?PrL(fnMfC#e{W!Zcb6H}EuE)ydNn;Yju3Fi*U`TF}PZ%LyI&{h%edm}h
z9rHE#Cn;cUz6E+gv~*KG0oM+kp}By0NOg=fXh;?u>$<ezAsAT1-BrP(1U*efA9NJ7
z4c$?p;pSY!K50yDO)Jjb*Wz^ofBb!JmLFnf&c#xfeC<H@{M;)VXyc1*Ae&%C$A|Zw
zq3BLx?PZ@A`<Fv3CPSH}`0vuQ7Gg;0xXYPXHt$jxa96nh&J~T>;JvK0OTd}vXZbeV
zLEps92k54MuVs7KnU&+m6DZO#?iYBE^eiWb#A1KXRU?J=2f7Pg`yS4^9&=J26BW)&
zGE}eYnm^zG*@r}p%0KQbu^SiamqB4ohpdq!u*x}8Y|i4%ai<xqu}|5{WZM;~^_bUk
z`)crRN<Y*5_7buEwt3#$oNq-x6IDo1ravNGC<yA3+W*E|ot*<z5pfn|+-po3els01
z=W#@KG&r&*oF~GrU$2#(y0e5E@y@ZzYGNaE<C&(_4Dib3{{PbdwXQw&o0-L>=1-Qm
zoqFI5s?7Jvz`Y3^sh^Hk&r_ch@gTZob;;yg4{B;n8&Xm_<R<d-03zX8T{+HynP9lb
z>WQY8cbk(p?hOcwCu$ieR1A8>qiED!3*>Wn)v%p|$r466o0Lf9zY2qC{nT@>nu42?
z@Fh%sk5!(tAslg5SU3tc#tyQan(m;|2O~*6VL)yp5cs}EtXcW-0ZtYMSoLeAmGwZT
zC);MlPIlKXd4htsw&J)803?8KOmaVWyWHClEnU#}j$y8r1;iWB1wAx^Pz=Dzzy$a8
zNYpS>f^zN!yxRgb=&DD)srrgP1%s2MV-`De{_UzC18TO7^lg~Rg9(HBQ9C0cQ&a#}
zYZj>}LcbGdss#>oWFirtZd&g=R@z0*YZv>=5r{B!;(q3;Fdh8EV}t=%4zx;u=w@mw
zlq5_q{rknCk`QcA?n@NQ$f*LV-e}D4S}oJ-%+2>+=`Y8|CCU?|7WIOot95lumis=@
zW=yBysgb%|L%c^Am=c(`k?){p>Oia3qt4d0JoRe_QeT_?XsnKKg^^F!8d_9Vkgfb8
z@&gd%5PXtkw4%S)5c1tRFJ;bkA8|5G;&GtfYvaOvE9-}VD+g~)n3N9`qq<sG`>l(;
zN-_oQzam`$MT#AxA50s5<nd>YJEZn56hCsJi-ArI;(|X)oW6voO{Q;2&1o*LHV!K>
zsE^{ZJ+CCN=G{R*)4cRR+?zoki|N+8rT>;$mV>3HacKqFA12-eHgsvFXbJDS85B>>
zZLTIJDvAT`Ap@tAQ!86SeiM8B!RBRe%>42*;PT7+>3)0|7x>QpMk75t+3USE+Z!Fh
z3-XfGrAh`+#<?C(icov^977{btS979S-My`Q2Jb-wvL4Hdrmhv@GZK)?<dFgXQNXV
zk4hK1eecpIay$DfQFdMEMk=oN0=OJ?b|r>UcVUyvu%Te<N<$>p&cWSRJZK>PMIfsz
z1p<~200tgTQM@7g%V}={V}P8y<f)l@+!HGwBKxs5{a1$|e4vKFql=0Yb}Ba9!!f{@
zaCY1zSM!&#UuupI{Y^Yy^{1TE$q%;p>Z3ZBPXSGF-VMBe@SYT>m)<(>fr;$sgJUQ8
z`m@-#KDcT9sP>na%+cyp^M&*)_iNdT%bi}|!5Xd;*)JlOOS2A`^=cl1u=&(h#fp1-
zYB_AHN8kyLi4ZH-B9~4t&Xr4<Eujh46&t#_dcD#@H>qj0)-X2!x>Y{rE<ffM@xTHM
z_@Mpe{i^r9@I`LkyMcq1mes&r7l?VoW*|#B3{8<Fd->VM=Xu~H2`RZm=>+fc#GUss
zH+)R_(7xI{ITxGBR$dfb1XVa2suDLiTNiA%qoc~>-o1SDMB{6co88|k+$WmO8_;p^
zMG1SO_s{O3p+uwe$WRx}M%$Lmp^LM@bFi-!?wu&0O^!yq|B!rc#~yb-H+Ev8eXYuY
z(6{|W)fV@|?dL%0?H3li;ug1?-M95Fr){ZozV-gp>-d5A1{k}paOd%Yw%0AG*rQnV
z)+UOg@7(~rZx?^nYU<l3=v2QQHWoWi`OLiG=I+8tVYl06t!VS-uOilcRq@TeulHdz
zRn#gsPt3`2)qDJAWr%iE;66E#_2Po+MfTiwT?8m<JEs3WarG5Od6R=rHHBh;SvwXU
zc<z(zXvSzUM~9uCOlgzEbX28CJZz!ImJPC<X0ATXW2J1&0oFSRzEwFLpU)7Vj0#|1
zTn|B0@|_419HNnGiyvs-$Co^L!OYezI}lyZ%SJJ55=`r53pap!5ibc$_<_qFML~-A
zg;9UXu$1`Y`!{-)w6It{P}qDksaUkxW4dXx9Eu!77{h1W!~1BtR&cR1J$J#!La{Up
zq<BA4RKdd*yQi_v3DHQENPTe!>B$*kLc-3P8$*)4Jv2m__L)nx6Vov#Z_9c2ODTH_
zgVkAhdv<PMFv{Myo_lavHG1W3^<>C9<xS`lzn#z|JNwm%FXx_~W}5xQ9$(rYJJA!7
znXuQWur&|`ef~Db^Ax5x=mF5Ac-XbLg?n7wFg>;Q=wM#<PSFqi%c<9Y>OQoR!;43=
z%Fv3+;rm^%2s@_Sjtq$HARFT4+bljD<*hxsU*GgGAnX&qfRigs0b+7EKlS}C>BM+*
z(Jj09No~}p-mWx`;r1cF_WFQK)A-i3^1MS98FpPq;H!YLnkmIAioL7l;6jA<)!lC2
z3|WXVyyEy)>0O6GJE=vR%7Yb#L}dZhF@8kO7zQg5Df@1TH@K$qv4q{QE8#%n*zb9q
z_ebl$@p_VVHe>E)*lzi9j|l|+rWX0*XIjGBzs>AkPNlp3t=fLRyGGTuYWiZNyt4hT
zb>B3zQku@b-2(}1<aF;dhdbb^zMB10Z&2>m#Zw;^V7fqshxL1S=StzAmG@@v0Yx*C
z({o*3WuI7c1_i)XF7}zWF8}R)mpedb{xclq2IFwcms1&X%Q4ODrrb@y6V(08Iqde_
z=Emt=+M;$jJu0biXi14c0UponvmOkO;IB8}!nyoOMW)<x=)$DmYXx(vO>V9co4R<f
ze(U?8EZ)m~7Kyryo}8MSKJJ=I5kvc+$+8*e-6taZ&F00!p139KDY*K|dJ_i4c=r&s
zDFC2xKD37G;k|s3!sU>1LY*d3V0??xoLl$S^J`00G(&VFzK!)qR~#tMd+Y9}=9$+k
zmp?Jz__?t(rCf!K(lU9uVecYLIzK+k&y2Udxcn=Csy={<DhJ>yhjx-Z8t(UYKl0PG
znvHKXL13n{%uQ28D9t2yue3Ky5o9U?uz6aw-HRT5=ti3l%+2nOqPFq=kKx^kB`UDw
zDY0Am4pZlVAD_Uths7RHoCaSGKy(~XG(q0nm!_CaUN`f*J)J~oT0p~>!d9CXG_gM~
z;btd~yDErCOs9q)(GfB1D}-bpzh!TQj(&N$d;CRS%l(3uMtz6i*#|}RnAJ5W*ksE?
z!-l3#q;hV-?S$^ulj;!mlybnIl*DfX5z2vypk`qdDBiSEQ{y!L+TTs8q@d@@`g=pf
zH-xOMje{a<Y_wH)ZRc^j0HJ&#irR#A249OKaK?H^v6ny_7tXO_IKt9m!$nduMC)D-
zn)V#HoU2b0Ppw5q*~Uo{sgQ~Fvi}Q?FxW7A=&I0S%6<p3bRo7b<L`)32hJb6<G7>1
zNZ50xC*|)ekKI+^R~Ym+ePtXfsp>$YDT2b#$;Pt3+GeS{{T{rYZ`*@Q4SO*$-MvlW
z{!8(7O+A*3iH*NmX%e>wLm1CY9mIr2paQczUBH{p%3q-suYa{yhuuL-={yZ}%%SN`
z!UX{AH$|~k`4IO+c0MXRqS0wa_6Cn;5x;PUj5EI-H<a~!=CT-1O`pJB4;N<}FcQx6
z2Lc#3M)Wx3x&4<BDEq{fo1nuw#7lv-q4A0G<Gvo!FiI!P%Fi{=-jBwi3~R@XRPWd=
z_6D>~<-vG^UC@WGo5sC=saq&U{$1)KS=cIVuGTH70;!&eR{FeJmye^w_|!L_L!2BI
z_wayLjZC8PPmk2ouwuTD`}r-Q_Xw_rCsb22E(PkcLH}xfHs)yFZyWkgwz#AH_bwhI
zcb-$~GR?6&+5R5t2#x6vXqH9ss}kc3vE(U?)RacC0OOLNkXMNZ2mI}_+|d)jy<~21
z;tMxyAi<gbvb1s^x?+R#Fk(QzjEMe0df9<xkrDAspI*nDI0)S?EnEL%KJncbD~A=T
zkTA@=pGhFy0V`a3U!Gt;6e3^nk^1?rKKz?vw&#(+FiC5Zz#m|AJ{)~JZ~+0g6?2YR
zO2t6AP|qO|C@b;RUM?Kwfwn-@hEo3re@yxYtp5?SWDRYxa(w`(#fF#)K+cA%+#%r?
z=!%Io6CabIv~1H&{Y;?7D3M2bzW{_D{eYj-@724Zx@$1yq^qJ73R_r;AWR!>ArpG(
zc6!D~QM3}^l1mRuZJnUj=e{T<#y)1g966ked-C?XGOShhaByaT$LeSv`Mqgw?7I5?
z56rLUDtpt9Jv`ve{HhlJ`<K_}+GaG&!M#vp^z--M!&^t>|4|=3eWNh!)5M9!Yk}%*
z0P5yap3N7V_I@son}2jM?|}6})svXT<5=L52uxk(8v1(y=mxgfK`bezG~Xl_aQ!F$
zr<Iub|37KrTD<8Y?;oZ7l85KTzw_dq{q3Y2+B+$DJOhMBuKe}Pj6Gc&1FP$w0I{L0
z`FE#1q~hL30b#d{uKEF(G=Md8&y=$SOX+fxKzJAWChsPT`*^R*ud?np^h3~*fho1u
z8u{B##rHzZjTel6&lm$jil?I}MEu|d6L`J49|e(p)1PmgQJilm>8+0gvP<QLLjZP8
zd4F$-=f(s73MFI1U60e;YtfxHaXC}n^k8%SDy{rZm;9PHyEBBnDQQK1{I@`9uZ4~~
zeyt2c5x1m~Q9FIsI-wO~8*NnX=K;hxJkN27n<D@5`4aHF%)KZ^u3SR#M5#R;=gc7E
zFPrWdqpbMCE6)n&##{{o%+&J(!w$`m7^%{kxM@QN{g8T5x{N#I<GINmJlx-+FD=y(
zwBjK~`8p_EZy@0?N^$gqOX^&Ro(JaSOt9lD^xgB(_fdi;;k+@Hr(6`ep^%q$^!><8
z1kqvb&N1y;C%@Qne-r?*QjQk0VJla1%So#VYbO0vu3en%-<;uf0cIcZ?Oy~`T%ZtJ
zHr-B@<!=4i$y@%nyus5<<Xl$J#?s3z!ME?zLE8U9o9?%K8^pvF4(_G)qJ2ipI42$~
z<8H1#0Hp(%;)mBWetdY&S5eY|q2DpEh4NS+Z6I71jlpsMAscqF973YxIbgapw2k$H
zJ#t0G=zxUhSPgyc*M&dn7l-WN>+P$d;(n5yA$RqkA*n3JLm@?<pbF>uCLU6`{h9Ru
zZv2T(fzMK(bFm+6y*y;IX}C1OeEoHFi&NAkMualNY!zd-g<l;Amwh90rvzHf7(0h=
zmja+sRSv9(R|4>2s8#AS9*n=~swGFlvYs>9hbrx|j5F#ig1!D}kpz_d37H|<kk$Vu
zC;QcNA{V%=6h?|Nb5jXq05(&4@9_XlP<naR)a2ZKwAZ`Z7b%F|^pDSmY6DXT1NB|a
z0beB7N^kRf`F#E)h>g|sWc)<__PhMgJ$Th8U}uXl6mTLg#IH78X%Fr83Ga699#voN
zQgV$Ek|SBwt`J8D!s{*~P9z>bZ882rpyiKPHLd@f%W>((Ab`Q8joIDcRn^sx8>%l+
z6W{f=5K~CK*8C6(V+aXed8&_MjRz*HA!aMJHack(y0m|)QJ;~KjQ~BInd@m^i`>=r
zhG7so<PaSXm5D!XLj1`@0ZHl_a2BTw6r}=~;RzD%<DrLg!@&H?FxE;<TMNfj531@r
z%H+>o$W^V#zK_+)TX@;`KbhWa=nT1miGvK^O?>utDP=9Y&g)*;1MHSOyC#uaVigX;
zE{?5KHNn%PftD<1ZcZ<B#BxR$AmHW>dSd4L!A%6C`Y9uWOtb7wqvdoyxyGZba~Mgk
z49nyii@M!ZS8agrXP1+Q^o_44?&j35ERlV=u*?=PMV|A^5OMwas9(YBQ`c7WYz$ss
zX@3{#(AK+*JSP8e{6QuGz>QD&<=Mer-=kmMIncic(V*{bQn2&sDWKqCL71~{YPU2?
z!|6)ip*%~B8J(1@^wpwRN#Q5v57>r4wKmB3Xz;&ROoe<AHT|%%OESmq#5O|sV-a@|
zezvTFJ<G-aYPm?gXsh7eL(OiD_~7KuciyTxw(Kt!>sfJOn4Uc<y<1*G3m7DrP4T>J
zKx=8#WFIezs0V`Pl|vPC*!}i|Xp{U(=~>UF_UJOJad4<-6C_AK5E#!jFIM$LQBo2W
zqNq65{E62dEN)3XP+NR=!TW29Cl|^d#+d}>9*y^o$ga8x7#!!pgj7%*Zhhy$(iXSy
z9+f|DU5(tmoKR<HWz6!9RfYNp#w4%4lP{;jP|}>5#JS$q4Q^FkfGz8bTf$->M6D~@
z9AGosV{nZ&!O%nl*JkO9s4xfs4_EN&A2lC7E6Df;%w^s8G}KmqGd0n&8FRGMswjlF
z;FDYS@~>t2efJ6X{E$R;dIaz8vSfp6vb@sZ%PVX4?I#C6K%in&LJ+vudb~u8lv1))
z`i!Dttk|gAr7NQ04&*R~5{jg-hkNe6oEj?)OnvYfqIL(Q@&^$}C*R1qo)Bt2gzIEk
z(GmjQ@Ktls7GV|ayZw0`bv<Dpd$F>{@QAe^&k5G70^||{INr3Z3>~)y%Hq^G(N~Tc
zG=*$K#*5wLj=%i1On-ImM%cj73b2~X%X#6NV`XNtuu}UilK3v}+a+-T#fGjqRH+w>
ztM*PEa9%P5s#(zeh&U{+gqDKm=A7|{;^)3%W>||-Vp8Xx#gCss$;YXr-rN9clH?gL
zClC%oqkq`54F6eSPJUL)Mh>|7rJVnj8=g_*lfiRjYV<Lk4-F#%Vx&0mk$ykw>p#q)
z1zT?weSao$HV*+V=1tjGCU@ia9db2*?o8d>0){$xhSl^~9!G9n`3&1K@nr5(Kj`nv
z2@{_|Q-tg&w4}|g^I)<(lAKeVOm4CgWm4ys+-l+YpBoP5ZkXm~7rJWx0L7xsNo<)p
zwxrA*e?rfV^Na_eVRdp}yF!m|t|f-XLt?_$PxzX#BHs6Bm8(I3cEiU{X6SWH*G;i$
zAh@sUxUc!d+}Vtk63i6XkdWP7)GU_Fo%B}A?;g%4rptT2Mebf?s>fD(u1?qHcMjA6
zpDhEL+amXB?qNF9jn98|mk?7)zG`#WN)M|(j9&U9rd@i^-_E65JP*+Ynh;^KGxo78
zExr~=@}RcvVj5kIXZ!5@fWiP?k7e+F;^s4_3Uut_{(fWm@}uUq!nXB%Y2ObW-1$$+
zs0#sRc(TX7N(c4CN-zT1eHA7wMR#yNU*6IVz!7CB|3w-H>(tL2orP`ezV^^hK{WjX
zn($45(YN~wzs@`JmkWQuR@{E`d4%MPsMSBbus+y*_mhY}{^)l4lrfrgQ1O=sZ|*)n
zJ@(ys>^B#F@B7X9&;LR_{QJ3d!c6ka?l^+c_Tk2zTgy*pmuH>^Y3iQ}*&o-I!a7<_
z{Ignqnf}V^a-3I33$<1s;c=XxakBJG^jC*sM`lyrACDdU_fF;AKx=H!K2`cSe}pct
zkxrS#)omF$5V*l%67pw26Tp<q;zFe@g|%|gi%J7O7<zpuYwG3tnigF=F1Xy@sjbPP
z$Dcb83gwIT!QoQ68TIc(YwrnekWt$vKvD<lSC`tnqW^Mg!J<idh_<Pp1>K(JS0qU8
z=aceV3kGuqp78;a(1uR!am(L0qqXoue#ybxzhS@-D*zqp>Sf?VF);i9f2*;1C8~Yx
zAR;l_)!q|J!A~~Q^BUaveg2voME-X(4W-`F+|lbqbWlgiJ8?{VFo`>_T8{+9dO0Rb
zgbRV)^ZE`j`*3DBRPkB0Levmx8+l{=k;@U*=?;+BHcO<L9Bdg!oz3&`-aeE?$(T8E
z&gowDmk!7mdBWSZr~W>9?ONnN7zd)i9y$9Ml5K8d#Mw52A%i_7{^<L6{?QCa`-h5&
ztZas(p`<#z>xhnykW&l9X3>$Ji4gizf6oRoPuu=|?0LE8IB0ivG#Bh3rRJ?enDxZ$
zbEYdGw&byQ=bq?J<{M8(i#!3VDE6#kg>RgjGF$v#t<oGTAKH*Kmd=I*IheII8P^}S
z0Bc(bMmmVRf%23@?HE|g&CSm(W)u%?{%v8C9c$!oG$nRl!)eyIcVd{ww^$<eu2#EQ
zWtSN5BMyw`oymCiD=RzV#WTplL0X!$+0Bg*x<1B!R_b?)9~ecRix{ck++&bgJ1a`5
z+hX5)h$B&N^!bN1{tNrM^4t`HCQvV6`eB@h8uI%g_*c(Cy)H+}2hRK?Fo>%EQYkB~
zu6juce>yr|J%i;rENDb}lmI23s{Z*0hL%)mhrZrfRIAtIOcZ>Q9>BNMNb1$c`5?iB
zpe>@@rw;WQ3cr9ko2tA|zD+fZge2W%6|7#gswW1`HZ<Sd5K{TymrVi<gcyw^=G~ZL
z%TdoTE@&CVo1;3y1LAqAtHU075Xkl|^)2j3vUL`#?kYLj7Goi3!6(HT1(xsBwM6^3
zrzJv)8*7p$PY}=4cM<t8LVO_qlgc`!6Qzk%x*6I4ofzMM=)LzcDjUn~;ujm@-OwJ(
zIf=S#qFwr6RXtc%<L7b&7g1mn0CMTsJeR5B_r2#BX8Xq~H34A(NwA4qlTG9VIgDfQ
zNk>Z`BQJ^YMqq@X&|f6(26?B)+G>M;Am~r#4v#c%g}SE?`wM1SE*c7J5;+%X-#&Kx
z&QZn}(SH<wacw~Ptw}ETcTxEJcaEL&<)8X8_27QsU-oH?Q&54|s)EWr>7eQHAIou7
zNIidEn_FAvp`>jEnT9i;#-UmAe$T}4I`{12dd)#N+TC13vH4Zr_wL=GD9>~@fKE6(
z-dC6!IIc5o?5+4+rxiLsO)c6ugw~CURfMqwkH+h7*K^jKt35S6{6E7;I9c|~eUPr`
z8`B|1Py-8W_PLIo8db00Pqz{#o796-)*m8%GJvOQhM9U(5Olx#RLfGAvKVTu%Cskp
zy)0g{yX7Bci}uj#-uOmo3dGF}=;rMLtL{=AR9p}Z+sMX6&J<`)H}i*5|GL~e@i=_T
zxszM+(r7XjUW<RC!Mw%T6Uf#9UO4_p(dJk$hWa$&HNt)CmA~NSp8DAJ!UTd%Bl%nX
z<I8d1>E&+}=-0p7*Lqc{Na0gg1CS0gpz0{q0)}G7RH`Q2@M$MI9s=!3^w^5yow)(z
z)Vqex)za{H72)2S#>j}Y2K9r?+iA3*h(gR(MTTUpE?sx@xn>Q6hVQz&2&iUf>#qwP
zx~<EivX>=+Xl6Fc&QfhHnj*1$Iv$?mub_V2^G{eu>dUoQukAChRDb{9_bm!9ryBO%
z=xMDEpZ<fJHlPfh5|CxLZ6jcTwhsMx+cAD9%06D4=7=2~?|;synCNV;N`y9BCP$><
zFvKg5oD;`pH)^4C<_CNqX6{Ij+iq1}?pf7;VQ7weNOA#1n>BntoKbuJFInXo+ESDF
zoiX;@ul$4ndZ^<(swYBVM(>Otu3ipheYXY4ShcFQ$~~uhL@E4=)#pqc^2Ta|p;-&B
z)3j_BBg51r3Z_>Lf37T+77RY;CaY_}dIu}{H>Yx^M;Q6~K1RBiMPrPzfAiao#_wNV
zcvh9cgA`?=zIt|^zqmQTJBJ9c1Vl+q5}F`pa-U!8b^v>E4x8IHR##|68(zEI1)%+Q
z&sHKnb0;yD3h^olAAE|U1iB0zn&r{?o_`@3<D=UxqX5}$^g^C|Fj`CJ4sD_w^DMH;
zgKg!1aJ-_tk7z1_b;aOq4Vv#POY*TFG(#bM`~t?Zn%z>d3)PPHco{qwE8Uu_q8M6t
zfgwi5F|<SYM=}5RC3pcpY2Y<LTab3C49#hAPB);D+Uhg-7hS56guHs5X6wT+&3B1Y
zA`I^4*m)s@A}l}Tf%<k4%-PYe&tJ_Tn9mybtHp7j)7o8WMbJcR{XB5vrS3m9zNgt7
zdCm%1jl5e_S2ao(gZg=OZ-rTl3R*&<yh)DC{tkaX<K6)ul;oE_-vnB9&3CaoasGlc
zopW7Bn!kuL_;?gQ<a|W2(im%3J_(Pb$Ry+M!_pm>I(LlxUzgxCUY$ootFwL?#vDaN
zFv3*Pv=$qL>NY~3;TwdyFI}e;6ieVAp`Fe}QugGR(9{H|9YJ}~(hU7W4MqQlyxo|Y
z{lW(JP3`}w*y@LdS3-$)<?5${Eaa4NXa3Nz`Q{9n=bZ2H#q~vBCeJh`JnQcBMQSt8
zJc1HsDvg=o*Di<`!*cs46m|(7ia52RgHRl}!)Ax&wtWYHjsm|#J&p_WD9(LG#^Zn9
z7nkbZkhH49y!4YW#P(jGsbKN5(w5tJ^B>#3A1=&c27i&+wHxdBOZ2IKWX+W(o0q1@
zQTkDct%oJ_+oc%WieN$T6x$h*i;=lGOgG_Yj%0*SvIA!RgZOH4SAf@@W!{sYr=a(p
zkh%V})uQI|)uxTrna;2kl=WmwlE=6t#!fHI=H`LUQ}QndxQJ6xn72cr$sECuLuc?b
zVsdPx{Cb^8j>ttgiG28d0A{Bm)=1hu*PlonFln9oup9>kK+9qTP3I`1zl1Ya+b*s@
z_ZiQig?!vtUmvV0@m~+0id&oet5Q90iyInTy+`J8;!2+n4ldWm7OS>OTrYjFxmgz%
zv+#4gKYciaS~f|Io8-{;?cmDISMLbYb}xT4s;yInPCab@2tar@yEgv|O(O0Hr@2&d
z^cjBgRFubGmI!Dha}_;ve!A-wyQ!w?lUt#UF0-Li(F1t<57fm|@`!;tliWRYJB`<C
zBVFfI$u~>a64M{J{e8eID*)M&V7GLLt{qd4=eG#1irlhRJT6hgn!UGi_STs-(ltiX
zvx`eved46Ie3xL;<_P(e;(a!TcbPGH9Hyy}vN<;K|6J4>M#{F6?fg_5XAmEY7GiC#
z$E!e4Y@Hh>v5i{;O>B2b<}Dg4&J(k80-G_#Si$kg2qBh?T1>Pho|%2FLUB@3*&?vp
z*SpXH{kAWL+_Z+3+U!yLoqL}6>Z$&>)Mp;`&cu$C8rLNLpl5cFU`mME?}@FM4W9Xx
zl{m$h#64CfkLyA&5(WRlvdh3*eNoQy;AVfSGO!zihfguFy0YXaZi#J4MhfIb%V1N{
zhA_NaQoQEXwo{l}48LWXnYps;17Z2ANtJovr-1Jw(d%`J>hp(CP23dC<XvPsI%;ZQ
zbY*Z#=&jE7-)>s5$36`(P4LiBxA7qbvyUV#ULW66{rkArs0AO3apLAeazd?pJg*-j
z*yE-$Ud4}q=68RIpUUpxHCdv-S9nJeU(X%UY-vXAt?o2<COZ2?Jhj^%mxO{tTZ7xj
zs@lgh{v^uDhW(>D;)FpU$8C-OltzXgwy|IA392`ZC-v#)w>(8TE%AKyWLw<pNbKP<
zLs>-^q_JNeSVR}JtZ<+BGs7yH3X0!le;fZcuo$OV+;n&w3LZW)cIm7PAeuE3qIq5e
z#dPzFRPfcG>J1aMZdToeANE*sC63!BVkB1lmX*e;_G<yQxX);<1&oBD-<axqm|d7#
z<GJ`hd2)-~KqBAWo4n!KI@6sVG0t_oGc{p1+Zf4Q6FRWOiWHl0Jrr7HQ%}UFu>_OY
zl?tH3y*3Z&f&O|mXnztG2!381ngxkv8#euc&6dOn($N4!pZ_}dtLFDL{v7T4R`HFx
zlKEpXC#rGa!d!-y!-C>iq@&)5xtt3+S%+@ba@KY$0E`h?3P2z^dQHU&TAnb(X$l=)
zi{3c-_6dvC55j2NDr%C9@WEW-qj?S{$^#&a3-wSAvCwMY<u=Eu)T1B%*maB$<EhCG
zdqAFEIfsxtURNGs5GeFzkS>B;SKqE3THrM`!4ZRW;sMuYBrSL#=XW-`sqvHu8%y+w
z{!x0>%Zh@MF&j)VqdvX2a~}<Sk=aQqey2TVFKe_3i~@MCL4wk*^$gj>&Eu(#E>E=P
z7>8|?_h!}j=6xt`m~>P@MuL@Z&PU`q7w^ojiUQg|36Yxu^0%K{d*x40Ca{K`YSxAw
z;`-h{Ru(rE@OoL+FRR9pjlHAw5{zy7p`i)AN8*^;MhkyFhCG;{%fe5&-PpcJPY@2i
z1z0~*hEI%L8W81cYLM;iVk2~ZD1`ljY!MJvXyotgr_CIWC%l3fT&vO6)FY{&Hb<l*
z$_9W?W^5RLM&mjOCZZXv5rfMkbZ3_?2v>lW{;csY?+kLyGME%w#tDQhjuR??A0@ax
zFfjZuHz^Lds-u5)f-4!eu!>IkpQAd1?a{`SICPGaujE;c0S&@4uB438Z~soO)MpnG
zPhI;bwX(@-*b>rI1gRXFvQ`Ch3{%-od`mViCey@^85bX$juyEA1$K%2B6KDxm%2l$
zQdXKV&sDg{8jt$OpP~s5=o!lUquy9+IIV&P<^Q30HMZBNO=}cDN|HRT4GZ5MgWJO>
zVMg|)Lz|3%3gTfD`fYIv-^e4*IavFZzqXMtcg>oB#DiLhHKWZ+`TGdO<l{k(yGyd%
z|1!vvfPge&UUqNvTy*G6;<!Q5B3rHjvVj-iYUd-+>?kU0G#Fjw(C|0_6HCP3Etm3t
zR291u81cFeP3&`GDg%v=>a3^@kV#}hfnnKLU(JDd5T}X5Qj^1^Cz(xSfJ<_~(H^ID
zDteli*5Z;XE*QPDzm!4IoT=EU@q_JhtB`~=qSjEKx_au4A^K8VntEc?YEk@3BWYaY
z7nz_a;ec!j_SdyR>11JFK1&f%RlkZ^AU!$|uN#L4wLhkzTRMZfTe1vtJ7jsLga@oc
zv-zVAk?NI3YjplVuN&dw5nxObW|C9~jCy+2Y=CM54n)VV2SPMX{x<$L^LOWze~5|T
zTN;#kE^7ODGB@>vDV@4eHKl!t=`0d>YK7o5>$v$)wm!oSD;|@z97eRcG>3U15y}(H
z2;~&163W45-^U`*toOziYHQW=TwQa#krf9ln|NwFv+0=R!Ltl#>%?IpFwdlJYb&C4
z^UnK+^;J#)bR;SKdlPre1N@<Xp}qPf3*#Tg3p!g~h8?qVwFf)KV{DI9fVq7vxRL2&
znj<oHrP0DFJFwdjw*!Ge)Y*wJ9QJUf*-Y6dP2r%}dw6%Vu)LvkA-{zmWOZaFr!K54
zb*y>oRS<PYD@*78?E(#HzKBQ10qkxEjz<}oLgkJy;53)L4DRA8Q5XPmBj`Hy81wLF
zNSme|t@|BDJ_f%(%aMr&>wmcuNbBrdVANu?rFvy)*g{M(+<fg2*0jv0YwitOWn)l`
zzNR+vmT|3)ye^SoJk@oE^G{KxDmaVel)=XMgBr#j7Q3W~%LA3%7(9|-1PA0a)bz4W
zb9=V<wG-Uz7bxm~iWrryD3liH(?S%-4B%kp+YZPT74OA92wgg?*%*Mwkub=_h_`hU
zVs@z?p<geIw@{A~_}K#VG{aEW@qP8+5${Ms((qffz4VxCbqgd`<6pbO4!$yQA(DSa
z63Y6S9ighPN4L0hT)J<3Ep<QQ^gD^SW`RNN>}##2Z30g>JQYyF!sIUp84;R;^&N$-
zaffNr0&*rSLpCQl;p>HLcjq4>Ce|LPaN$dw%+tO@6k16WlOj`Wnv&w=7iL#{;8?8+
zuDA{Ym^$jqk0bMb!H~H0+98)#XEj;nx%Ok3O{(~QKDP$F|8gD(ID1@oD~=o3!}*BQ
zUHv4-iL=)qaGyBsYuCn%*1N`UR_8TLx#8CC;c>NL%xQ2t|7Prd0!UhEzpe4n5wco)
zCpPj*<UJdQW2WY=jC47>!1ykATvCLxkjqSS^bhA^35RIvi~FQ*_$U1#K}Ygt#63Ls
zq#1r;Zoa6b#&ut7QHJImsbQ)GM;Lq?(uQn9r;ryW47F~2zc8&TOZO={F`uP22M^v?
z+l;mJzIdF0iKUVNjJx+w`&kW0Z~l3kCYMF+`r4aA`C!&*;fIi09jJt<73X3Xm0K|!
zA$Uo>u~^gZId5v>W8QsbEGXSy&$rJ|Ikyor<R+F~0nT~Vl(CA&B3B&<PlN;)sk#kA
z^$|2~RC~b-HvvfL7+lCZrW2@m!jzDr!Ze@I^9&Oyx029TT!ClfsoPqOezVKI6jmL_
zI1eOQJ5z>RAt^u%d$e-$<=WbYkzc%Rs^(k=-bb`j-<f@($OO);hk!7}nB@KS;9q&#
zc;cV2{pxgLZM>t)jzu*PoVso73MpAjAd~Z1<VU}9b;qg{!5m~kMp5M<Dg1M7#0XW3
ztjsWygPof{igzEk7cCivqupKZKq=}A#XH(ZnaYsqYq48?XYtpzK6Q@Aom`kTHnBnj
zY5R61uGUrrIBAwm(CncU>r5;aG8S2TBKM4LwctV+&uFMssRJ_yD?uOL@qdnoMz&nS
z&Iek>$L$B+=Xhr`2zMdhMK)(N1PKn~kGX=Cjv3-Wz3~*>woK@%5gZKb@6uZOUXDj{
zI^{S1@6OBwNZ1wGd_-UcYy#uM_M|o>{CZm4fYSnbOWE;YRzLZ7fzuJIZrtb5oFJ-{
zdp*rk?iQ(5+Q0s5NjJ3~{2;2Gir@ny?;>OQ=kL(HG<FEF#_4)I-R)#_J@m~_&L!x%
z1G}yX>_*&i;2gQ5_<e<#OpoOg9(R14??}-q4ed1lWf3|uRV;jQ>zbfXKkuBQrL=%C
zO?76nLOc22+1o?bNfyw=zKOM<|J=aRlW<XQoCRJRQiHC4I<qm<F1)s79EcPL0{lD&
zkK74R+RT&`!U2vS<qy1Ylf?{ivhzQrlkX#Ga}(wIY1aRpUiBs-?WTlu)V>>Uc*D*y
zfo;^q|Dt$(%*a6zOKN;u+1W>V`wD5h_?`ss7SB!C1R*e_yvd54E2HD9=SVx*K`s>~
z51&gKe6@G6`{D3ZG1tnseD!C`8gy-*OX3Y|#9V~w5SCS_Sf^)52J&EM3wf9<pSq2C
z-qE4^Epu<m^HZO~Jkkh-`nmhr5)gv2D~d;=1q=Ae!ABVsa$&2qc6)aaagbjR=2dwm
z^7|nsK4lwonD=t-P)U$WbrQZwvL~IV)abJ|ykmec+Am?hjQf>Oom+R2beC-+D#52I
z5Cl>gIL(k%Sk|mWU=Nig5r(rbFhQ}}rfVnp2Zve?`?o>)45>1&<AI^34%{?wf1**7
zuAkq@AJeON92UmA3WtL!ZzE0wlx=XI;LRqEC6YA5)w=dD60#PZstM9tzA*kME7^>n
zYzsv*oT6-q<oul=K??u(`S@2>fXYda_6OWjBAOaiQ3F#j?3Iyl!q2BMC%b!0b`oY{
zqJf;QSd-XEDzp*pOP&cz+MgA(zq@-}<^742-7?Wf4oa0gJHx+D#j(+uF-SCf&z{JY
zj$qqEo|)kAhmTX9-9vA(n(9UNKm<51tl<j9rk?e*K1su=17;Qy4C%Bajld2laL31n
z^YKJUzR7HfHuDUCo}o&CIk-rTy5x8yi(gEi-r#@XV;fhPdtuguQJf2y3N){;a9R9E
zw^eUTjZg4$`>V)se&N~$6oJhciWAatgjaNw<V3X~(3>#O9}>&|TqKc@YP=`LvYmWQ
zfjjzjPpJs#mmc3(2_6`W1YL`>%V0t1Iii4>=Zq|WBUBiUFcySbR3auT>yhUC1$tNq
z-oM>&8&p=TrqVX%_^#DES1ww?X5A}Ba|cuS^#J5tty&iC+6m@;?vIc~j17_5K!QPN
zP7s2N(k6kSlUZ09e*oJzVdP)h)+Y>@rRK5L&XFK#t&LXIhn*I+Y&qSI(?Tk9SImq=
zhA;AyY~iTXA*Pu<#Gt!SRoSVHfzxP(&_Y!vbo&q-oLHe<K!@`qobI$7F^D?5{h}I1
z1uK8koA+v!J!`?Cvx>13p;()E01&;VxnV73_%ribl?<SzXlx-cRXpl|Jsha0p+V)5
zwl3(q7%Y}QW*)2L8dXvhYZ1VhJPfq^FeW`!Oinlp=8gn&3^l;j#wZ|6<A+KlRn4qt
zm-b&pa=?I}lEl9#XW}%Yt_rj*K_5-2uwTrM=ACUL)@^2IDyU-~$&37Za3ue83!Tp(
zMdp}<jf_3fBEu7VH5cH3)sov35~a_;np0L9`dB!LNEz%;5d@^;q$YtGCjktSg#T`}
zM&PJK+^wp^fma$UF`NGgpyH>$%?o&d4a}<jjqHo1>5x-bU_TXy?1x$p@j@~``Gmbv
z-PJJFj!)N}d|m~WJ=8IoYuBI5ck+$6NNX^JxXc|}p@*GSGQ746^?vL%v@WacZhm3>
ziTRVD3@}dGFSG(&&In-i9)0TNk3UlBZYf%uetl8C;+Yph2><{+J1y{Fc|p*}EH&0+
zKm0iIA6Njn_t8GEzU`2XjXrjB%S6$+=|k7+2)clCrUH!=;q-L&t*jo>3CM^{m87kJ
znxgy(ea<>AJ*ierR#7DGV`wB87u$2jAkn)YqDV)qxP)xi`2dD3YDvvkjXq^Gv8P%N
z*N2BMwvn8UI9|f!8@{8+YJ6aaECNg%)4o=Xzl8m~5kU1kYZW2Bc;qJ-1u8Ss-G8l)
z`(hMzxuT`QGi&knMH+7K#{;;T?8ROq3vf-qWfI5)cab=w<iVL8JYG|*zdJ!@mkz9F
z{B!-B;!E9%oxT;WW)2cNRHQAy=*He#g~bL4471M=`Xc5g?&S+`yS=G%pn}nghIO;q
zeVF~}KLu~|Y_Y4BB6<)!4#UCPgm<)ylZ~QmuRR&J1W%au2WC-^`Bu1GTRT5qmsS2(
z@-DaE<X%1mjd?rPE0X-|GahS<{D86vRvwys@K3#~S^uK<-*i0`_Ak1&T(nlrN++*4
z-5~;=3*Al@1DMeZWY{x=0(ov1%CZAz7_5m;+k+K<C<4L|+Q(ipv+L#r?b3;6`4d<;
zno->RPAO0Nbn6j>giYf#F84K)+xB(s8-19dK^W^-Mx?a{cNQ@ZJ7A*&I9ad#Xq|0+
zb&#j|q6wwUuC0|rFv$2p7flVfUu;_mlQgA|E5`-T4?7S>D{y+x^Pq&}LInPjRXt1C
zk4Q?yfBbys6^QTFffOgpCi!8RsLbfWX06OkR+OOp`OkH&mi96d4YwCNN7?uat7tB%
z4x&eE&H<)nbh@ab{dO3m-wltm2?zRN{$I$~b^UJp%^rQN8}z_T9}DO~g4U%hl&u%s
zDj}>bv@xb0R=TH>>%wOm7-f^L@3Z75`3tg<PMcYmCF3pQvzX4kv`KS%pw5vPp)=Mx
zYO6qhG#{}U3Vv$8-Pn7Jqgz#<s+{oj>R3OLuvsf)OV~H-Qh-52eO?1l?b>uu{NRQ1
zc7Cu!!fPRW=g3;758hBZFWoqwKG}kKk2wk1JwmLrmk#s-ivso*Js{TeBldY0Oiexs
zlQsj?+W52M%bxfI57Cd_>Hn^~y}9?ttl0-i+|_~UieX`m*P7y0j%cJjh&|-=;c}c*
zSfaQ`QyoQ@^P^b(TS;mNnd89oT=PVzLtCp-P5X!y96j~{1Bl>}YVy~0=LG*>>*~p=
z^P5e;ei%iw!;&X{+2h08+4`R!d5ePEiv&htE&PAztFWz;v9ChKN4i{=4lOmVFtgkC
zJwLTO`fO;B5f$h#8yWs_W9dPHIW11nYDc7Ts+OZ=Vfx7jEEQ?ND^u<A*eK_&f#-|q
zO}r>3VQ#dq&2q_j06N*kSe|N6bJ47X;g+Rs{+$&=K#Ky8Uffk>1-%KOmNm*V##`gO
z^WxN5;);K+denq3b2GRaJ}J10*mBX7ysQYGamI?S$mPV)CQhm*Rw-*6h%=uJThXK^
zm3G@hJGSh+)Q{BL!ly7)^P-}`u9KGP(f=EN*W2nK`_tmXy~@%y?VVnCCl{yN0Wv(Z
z+j&FgjsM}*Y0Xc<Uz&3MYR|IH4KQ`Qv>H?k^-tN><qh6?lEQ!Oyu>8Rr?aBsuRZy|
zDFx<-l^;%!Ee*EaUL17#ZN(TPe_{^5VLkD?U8m*W`M@3}k=q!vN98om{YNv3qRo*2
z!cf<3A$^5gZEK&{b3UOnB7f*D{ff@kP`l24$Zhf77Fi`3a^E{?w6e_+zv*x1F!Iup
zg6WDPUEuvZUJty?$2~hr49?7op*PP@*N0BbG#zt^RM+_2iRsJ{M*XRN_EAf!-r*s)
zHvY75s6A`0j`LHdQt|qib@_vd0v=u0=;`K(VA}C^VwObib;(ou&NvzFy}I<$$<=Y}
z<J3StRB8Ve%4vxE_}zNr%?aS6LiFl9&?H~rd1AI7#{F;=@OyS+uHKp~_&=n!E~_i)
z*lc{(v6X%9a=P-~qnY*GbM`x5l6m`)R*%Sk_cEG1+UvQ(W;MUayge~<+<OtXb@4j&
z?+5xq1#d^2t>C+H-A2vY>T{`o$~5hZ8%w4*@Oa4w_#lk@swbz@87>ZlZ5_Ra?icZ=
z_URol{+Qg>>dzj~vMr|018MxPE{gK?GyeD**a5t^1D`m~T)JrYoVfVMxW$Xe_VFvE
zaK#0Js)e8Mnq1y~J5Z;1D}1;bn#vYp_1cbP?SfpS3AuHh6ijCoi&)yFeH;f9gk;?-
z&q|LQh<ktK5q2vYo;(@4t}MZ(;co3~!wg!WOrlLZL!LBGmyWjJVy&%$O{UPLt)R+D
zz(#T1rHp?HBTgzSk{rSx*ay0_$3cj>V!9#V$NmgEs(VYLC6lNfW2L2tFT&39*nA+S
zz-h@Y%g2zaur@`|74BoEmDSGY4XJY&bbO}HXRKM<&FErjv7_`@?7ff2RzPMS4+ZUM
zuIqnj&Jt^vu1s5*@=Rux`gOYS|L(0_)>gj$bjYA%{@da&GVE(y0yiG9`5L`H&(0Jy
z_UjkB%lckaX5zy8@{jKL*Rp;|?-h(T<SxD@IWWh4Iy|NGs;N;+Vm3X1?@u7#cZ;*z
z3N;0v7zgna;?NOkiT?XpF`(7HL3p?Ft@zD(ey!Bqfne3m&k)Q0bfz39doz3*;vnn&
zeE0Zx$W`d;;G1@B6BUCSk{GYCekV$&z$;EtoT9zfJ|;5E9v%OsUU<-_Hr+m+{q~bc
zQ)SCITVDEmE<o$5d#R24BH#)t+6#uQ-s%ZcbHGq(0lWW}i!@gy8?L~vB`!tlcX3Cp
z02p2Vchn}}Jxwt@jM0@gH-W(&t;g?#IKu`popXTGpNrog;Aq~;eF86rX2<o;uZeW)
z|4?nv6=9TH2tTX54@9G5mezOyyKBzjGP_uEF;x5%g=s%E1jK1sswp=6hH#(nZsVhX
zU-kifoSFS^Y=k{G)xu|Ti?v01?yJ8A|Fit`vTysyqsf?#wQ`WjZgjELvBbW@Og|l2
zdx4eDR&faW_U1LjF#8<<pDoR0XwEwTc_sG`joB#4LcaPU3VjTQ_^d0~kCfw_rAADa
zl!Q+o@~JhZ%bigx+^_q25Fj9-7Zx$b@TYN`uHDpPvf#fT%|)?DygnOCP0vM)y>qfw
z0t`4bu3uR@_u8$)FapCIPl@jAfRtJ+)&w%AI4{e)N=Q|t15a1aeiXY&)^3}i!W{C}
zsf<qkxw4Jp6;LAwjkB+$k^LT%$bAxmH|%1QvloPQheaX$qFiTY%Xo`?R1^Y)1nFM?
zAzM>8>pL_!^K542?!y;(y4UXIm+q5!#|gc!0zuwSqd+J7a5phETDLnsa(G37-)^>w
zJZ$1aGXJ~uh=_i>t_fs851{B5L-Z@d%Rz)bNl}XSaja*G08S7-N;nA44<(kuA)C}L
zHJM=!wT>b+2JLdAef+jablZAxc>upG-w8(MO>Fhuc%n#-_1ZB%JMxgTF#1a{f^vTp
zm?7N1dCa%N9ilDZ*{RpgyiR%VKKm4N)hW2|N`B*uM+3)UfX8cS(|^l-4fD11ugUwf
zN>F|IEA!si4shBwx3MXx3`slEB*u}bPza2g`X>8AJ#6M>_Ocq15qn-xjMbj3ut49f
zVZDm-l+9gvzZbK$^&U4<-PfFXy|qxeznBXtvTEY%jOGlFWS94&VJ)1wpyH5Kqc-R9
zYE;BUzM$nc;LAkw>R6%J(Y%e07XBfJaflJZzEiS>k{HqX5wXZfx3N`^CbL~V%ig%}
z!U{FfR~dHQJq!6#Ze%BL!ma#h4K2r+dZ0cIn3<*)pD^<}#?+c4@^(f1yKdT(!9^c#
z&Wq0Tf46L?TZ&4(Xtr&|boEWx5AJyQLjdi_c#0V3=}O}Yk^GAZAP)Yio7xMi=o(Cy
zOXzjZUE`oJ8EDLChy~Q}%qcg4qPh?Qx!9BC3~dP}6~ST|W&m6QXKua_vwyWFfy_4Y
z23rFe$zR|=+>AZQi{)AEdtCP)+_e8M+_3K`0bk-jEyv)D=#&vx#;TSdc<ynNmP&fm
z!8V;JTEnlNazRp4fx>0&6_MYharusaI-sZWZ^}a~59jJ$?jy%6geKjv^J+b|`Z&BC
z%w;589b|LKXKY#gp%x_<a))lO7#qoSjgKH#dd2bYXWkEWi}mUtu0W_RtQBfTq56VV
zCs3+pAE(s#hsQyDi{uhZ-S&`3^tE?I%t#&4NHZ=6n9Ouq7`2}z50%Jq2n{^Jp5xa2
zer$o3%A1#_>z5f4hOMn!Vg2;O9*W{l{7y6jT3XpB|DL%!<#LUhlf2*6xA2yshM$bY
z<b|8!B*G6jIdy`19NO}TX#o~UtStrFg$a5~-i%q8z{4<97imxSQTC0Aw6yIxyjxs^
z3^IE{S_w-hXNDogW2)kI;E?W*wB_SV8h^YUiVbZ+uN{z<q0Bj~>Fe7zP%IESq2*?J
zS>u07CQ&C<NZG{k#9p?L^Elkd`wY-AkcNFf#(tlj7(ah;>FStb>Q$+gM)|CH&8vKs
zCgq>q@BX#a3}at+#$SO{J6pN|1zkZRWBQ;QF5){oP}5$~69`r|F;(4I6>bYUJMFS2
zxnDI_RckskQt6KiuYpJHS1S=Acsne*wT^%3|4dtT$ap#yhZw02N3O*5gB=T4hC^3J
z_8p1gMZcvCyOI)!ggHJxUiao<S_=?99OAvGD1f~3@J+Jk`%}Vj9uF|1-K_RP9Uv~I
zZIsaSa6FGYCY60&q_nLE)C{HYN6f;l)~Vo=bu93xJl&D$j-|W>Zeot+XpQ({lo?o?
z<J<}AxI{=}jj61bH_;-?;P_f<UIWs$osa!E2ZV%`jA^i^wc;L!XXTP;{o$k%0bx~j
z%E#dy6aOrBASX6d)|~tr{n^okg~p)x_A!Q&PSY{X{767Y3SZ1ymjKQ+x{3+Bf^mbY
zsb?4li;N+yU9b|lg1y2b-Y3Wc&i2QLr$hwOHVa3Y^)8Z<&k#xkI<eGyo8`~P?GNzg
zXJhT>A8{Z0P{LBPGjAVCuH`^=njSlIUU)>Q@mNP<pOAjoRoxe_CoRAu(H<zYTS9lQ
z=G@tU;*xM+TnkDX89v9YKIfSvYg94nZZld3ZUa%VAd!hjUL&JFL`elE-Y_=CscQ`E
zMVh19M%W@0Or!=ux}BS_FTRN%QqPa<{z}g9A8xMd`9q@(nfDJdbmKkW^+o%^${N&b
z3uJY2e9w7Pb4)!g7u|vtE?~kn5o~&RIkF5qFx-F+L_x4))-N%hU>qykN2k;w6084a
z-dj3=-5#|{-1wgFYNo9SGf&a@n=b+*Fb0t;MKFZy%A+xJjD2~&`^Lj538~)@Vl90P
zl_@h1bdiAa>P9*VFivlaQZA<_03I^;^R_~7uw5JeZ&ogvQq*M+XidLiX#hc};qwF9
zT1i#9Of$&#SY@UmCaTuV=un1d%r1(s*56_`)JWey4v^Y>`u~vk-ce1p-P@oIu^}qb
z5flU-rHN7n5*s290xHs*f`HTjkrGOxqN0W(A~hjVQ2}WJLI_n_=mbQ1Ado;J2!tfG
zKuG4`JKxOvt(moc^Z%^#2g|IObIyJ5v-h?4zRtc-C~V!U+1X=P=Yc_p-{*VwV}^fu
z!ME2fUXRw8x-n?yOtVDHzb%nz>Q5}GGb#xE_VyycMeXh@rLX#=Q5|P&3^iH6#Lvk)
zq44cM^MBqUE%zdHWuD;6(U<cZs85K`PIMyUtCTA!@nfTh{pRwq5%dgBOQRw)!I5O=
zogdWy1wEE_K6HBr;x%=_(IWl*ieJ*enzbCYZWXr59|h$^9S>;E1gx<<A0>@^a+Rhh
zTkf7Tl-^-R3V&slR`RQ1Kp_Vk@#u*ALfnADRYzU1TG5E$g!rtFN6#J0&nN9~?=J#h
z)ZMFJ(&6T)=T8Vi+xflKJkd#Cz8As#_8CI6tku&i`6?8x=>7r1c}T=tXTc&Ops(H0
z!6$r^0nHv1Ny2Te;<Uw0mSUt^QcGv~5ECt{`mS75Ll@&@<b;@xOS1DQQ3L#%o`tGT
zdwRmmHlZ?n_@^#A$3+HhH}InFZAmXK>N}nEwFgM%^;<M8@CoJZE0#s(0f#gT{f{P(
zeLU>4^7}NqZJ?>oR8rwTSK8dTs8>RJB7GsmEMmSkd_b2y<OJ2B+&z{!;ok{+i0uc4
zwpl}ZWOCw+T04Tbl6{k<uihL_ZGL512XIMB!||&!3d#dgs|3m2e4trZk`FKuZo6A!
zCx+21eW%Vr)KwLi9>>U;jBH9thQ`(%EU&}uMAL;(AiJ}4@lVJSp*Ejw`XB}Xi`G|J
z=;BRcdfrt`zdNHPzb>PmfG#_l^EP~W8jH*+2K4}Kf6&%fBD%ZM4VWE&BBqdSityN)
zo}1cGFo3Kitcc`;f=1)0dL_fB-7lsRXB(C&_sz&%*odG&H*4vQe+gH}tUe4h{~~*N
znRdKYZ|35l(hwN0gV${S<!_2kpWY^?Ak%z_n!$Gp??cd<OT?fK$?6Uo@Mfva%4~f1
z8EjLzU#<Rk%1lkUEk^&WEReKntZV?SGJtd`DUN>&l&i9n&tG>-X{iVteffMm$gO*~
zuIF6p@^B>cD}+{zr};JKotCW~OJ(<ha-fGr^C!>%5i~L(WoAQpNJ;ladX?HMk>sd$
z%<wx$`0w`x_8TYvC|g$Ze*do@-M&3-jfz?+q-3B)QS$v7tr8o<t?5^$Kmrdvy|-HL
z!Nap{U8IEDLx}eY$*8Ld=Jpef`65>Mpymc>9i>0P;%oUWVFqV4Dh{<O)x<m&+Kypg
z_yUD+e9SQLj*X|C7r&4lM(hTgWYqZD6=F&uZErYT$r6t!-;2k4q)ZU*gL;%1wVI2X
zDbkKIhU}M?1{d=NuNc+P$Jm+yqrQh)&4v{td!VL8CFrZ2kodyqz?HoVxrdf$4cs5q
z^3V(v+o#lOX%7-NyE#KJC8LYifhF@%`_&X#XCz1cQcDP|dFEV7=f=&F9P38k=3q<f
zCQB3ITJC0)eBZmfg3{|z=VK{7eDw}ZJ^)y@3|%0OxP6aeCI{EwP77+W0Yv8=n|y2V
zdcJ;YH-Y^7GY1Kk9)z!En7QJ008R&f_<J6(9CkrV|1YN|HF_>;2gl!^Pi06s9!$RY
z;2&IKkef`C;*w~#8b{_jN(St~aZ&~DT{-&y<7%E4v+^MPlsYv0`f3{ItC9abVbcs`
zCD6z9n)vmZgzRIrSaxh(cP5BB2EFQoNV`ybS{&Vd@85|%q~;&1hx1qfy}>W1_$FuD
zrXtNzz_KGn1&N&3|L@CAOz)|k$ybA|n2n*GsKgfg|NN4Nv!pUX(`i?Ih<lRb2VeQ!
zfEJ$&s??Ba4Pck1586=gmWhEOJN?AZ`Y&5-?E`8$O0M?DJ~~aDv3qTdj%y7U@*urq
zW+Bt;fV-b){yzPyFuuPL=)tsYd?}pCw*WXKrA~fGAf8XXVf(cFo?T^Xd-k|{A9iC}
zr%D$G&mSonx;NC-5N<Uo$_Gg2LEW5zTd%BBZT%8Nl7rp8ssgMXdza{7Qd4g1RkM}u
z^p4GS&p7SP9pQ9z@LXJmA-!^M_1)OGG#G8V3w0g@Wgg2{B`ad<Sa^vZ{~}pQK}m<0
zAq{-p+?RVmI>mAeI6UO0Qk1a2sEB3brm5ygm>aQzsKQ1aSW+aZLnSTkkyV3+#~c?3
zKs9sId?lnN5kfeLW8WrIfMgiA^!wXY-(b}ic?*Jj&5QE^9qT<UW(C1BW*o=$)FN>c
zzw-RptL}U}4^lpOwzc|Kr-O*HN9&T!mPo#8YmUh)@AkYuEtl$_nHik_HJZ>KxQtyF
z8?r@#+RNMf2b2F^*{fpU$^!nkD|3*k^DUuF&!VaZqkv1>X#9ZR%GtO$(W>kIW{i^y
zVFG!rWB8-t58C>vZby}=nl4u*bv>f!6h#|*P@1l$ntq5c_2t+g09}IJE5e1dWe=Z-
zs2{H?NylBm=}gx=68lQC)hG@nK!HktDNbj?eyU5sHIx5g0Scr0jrBCyu@w2`m7n#z
z?lZcrYiqRA^nn!nlcO}FAdr@%(arB&#n(V9t`ez7m=SDi4aiJpS^1J#RXMZgfR`-i
zCs)`X+iBo67Ai}4tHHqqs*T@4msMZ~WWu`p*?(q<fF@~DG0uN(rjYdixB_}g7;52n
z_wCvmQE1O#qsHtY!9WEKn>J&EzelSx0G2IG!mMtZ+*tm|EU0G`ZUZH0TM~<Nz<)Nf
zJnJTOzNX=5=KwNr+9O|TmS~S5>oZ4g6qFX$Q?Xzer$`>;8!px2x4)EHq4K3<c(fOp
zTSl!94H%;g5o&K#vL^`a9z+rd8BH(sSHrb14C>iS^->kgf<IOM=C~H@%BqY!83v(#
zV9Q;cIOSvL7I)y*6aTete`a=vZKy!SxLiexYr=p5!{6``?`mtc47gvDj&ugT(&x_<
zkNh)MRxMRgQacmd9{^0G8F1YiX!4Ltx-RfPAL^Y4;s_KEPD#9J;(@sT;Xp$KDgsn7
zj7KJ`x1apa2lCdFKD(NI*v9mx+#mnb;bF7=<u1@S&GG-A{j%iU$pOUXi|%}WXc?z-
zsOCO<bjV)^q8Ed<wR$am!hhZzQaVDKi-&xN^`ra~cpoIS&Yt_PBXGv%o|afKi5TiP
z^1rXLqG<T4TrKY{Qvw|z5D@+AUJct{2jxh5q$OdA`>C+;-`|E=13yB{XDKlL`_tSX
zlD3Ztls_)~7u_m++V%dg#?zN$^M9BJL}h!IQx+h+7eeQo`C-u)Y|bUnD0km5$^ZS|
zMXk3t^&_aKL^FHWe;->13}^7gV>Of3TH>4O_ebh?>EfB5(8RIvaOhh03;AaLTGduO
zp`#^Fidz5Q_MnotE4w6IF*?)T@qagN&$COFUNxbFi#KD7eq=$Q+1=5MN54D_<pYp)
zbX1}>GPa2MQun`?txi-TzJ*=LZv*}P^c!p--=!SJ>2$5*{1r7c4+u`~_78xfx*h9g
z90RsxVaV?<{vF#732&0tDF(!1baiWI&2@FF`r>tpf%z{w{53w0+zmW`uI9Y$Suwi6
zV1H*F-8v>SaYT9#-r{l{nA-$Kb6>)cjwCneA4hToG{;~(@6To!Vl6Cr4OP>Qx{1cu
zPb+GS)U1{lX9bfvJz*21sl#EvKg(=vuLQK{2Nezn_G(!{ud1Kxf~$wmP{s*reO?Z{
zB)_}&{;y+lS>>D*GRKvgbpP*X@9m$EH*3jb+&_q|VqL_NAfoqsJ!7+uL1TmYY+YQ{
zK%vDvnZHG@=L|x&2EDn~t}E77Rt)l1H6qN-_Wfl}daI!)8^hpWzFO#Y|4=rXHAQ)W
z*cr-I$CtK9#4S>HY*bq;I@j9}&PhXXX!gX2L0bI8?|2<omZEDZjFpTfp$*KtHFy+_
z2@;^nYsi#q+%}h%+#T^PX|?kn;_l~d*HtGdY7=e|3pzMs@b4mB3<e)wjUqpUcB2xk
zk+$n5U>(T+eUD$i{&=YBVlB5eJiNU)$U9HS>+$5_s3~#y6y=d!^tvmr6OoY#dt6ph
z`8#UQ`zPVpO%i?2zgEm-Fef?3IzhQ3;SzM|vT#K2gU==F0<KE}DDp-jIn(B5sFV6_
z@M=k-zmAT^&iIDy3(c_kL6A2u_6>-ncAL}1b+EIfqGu0^jUqy44)W($tk{DD`LPvs
zNJw&bRsepahH0*Y+9WYMR4(SzTsMBES*R@WX%L?0%35>EQbwY`gs6mQdhw31p+NDT
z1wj8nD6E16{6&RH3~t{|XVJE?k3>IkZ`^)((jo;O{xq7uFKl4H(E9os0kT;EspcT-
z*OA@*nAPvy06ilvxjU*^LAHdGp2$h}Ci$pR*7jqU_M>nx6mG<DbA&h%#nwfzUpRjG
z5EeZYmStlce8*%$W8<U7Xz#CQb{Xrhu<Nf-VUf5nR6yPr?{$Cjs=u;%t8WqBldVQ(
zsrCE!4TK#CT$YP(72f<NJVu)L)E(n8_*?b5WabI-2k+O!bjN(bcp#8C#T^*7i%N{F
z9iS~R&e`;25pq~0Tfn<@(YL0Qq>&Sc&0832skRotE4dUXhK-)REt_+@^C%S9+{upo
z9f=dL*08A7zI!z+&}fOVZIHlqcjj5%QtOxPK!vsx;ITCL!9IIRzwX{xh45Fq$(xY~
z#@lwol|0LF?k0lIP<GZ4^mS)qxygu9*m{#3{1pQV?0)z5oD$PC71{Q+61mM1X+3k}
zfIRpg9Q==26pTjM);P-3Y=_EbNDte%GjAtG{GvS(j+#{D0b0_C8qRmi&F^OfUkGpY
z3TFk->-K2v-QF9z%}zkJI+B5t(m1C7*>Z}5V7=%j@_RnVw?vW_ucL6$Z0ZZjR`DAG
zrI_1M?1$lV>6X?H6ptk~OiEVcYG%}Okk&bOCys`0-~Rdh9rQPbf?Uz!@D`e}MnvED
z(hT#-Bz-^}Ww&}+p=AFFz;GUJdu&H*$*TYON2`KM{;HsjzQl=|?Z;eEQh#S81sHr=
zD9imJ%>B_V7~LyqzUrsy*?%re|H<Y&kvBgpC}-Zl*qUHu1%TTLZbjE`y1F#7Zp*FQ
zwsL<%kSE@rRy<c;()O=$KuW+-6>*rX!ZHEEC7^N)XU_B^?>h{CkOr<pZ|uN#DWvVc
z*?;_0U`>8RUUv58`^4Wq4^=&_nDDh~GOZ}A_K(oP^IHFj0PzR7fb0*CHUwW7QtEuD
zu5&=^E@0gExA*rvx0mqF1i&roi~~#8>52UHL@?g1b1=|sAMQ9An9Vs(;;rHsMXSKj
zb?}A>{~?>m8v$2e)-2th9&h}Zzio;8i3jc8K73H>zyx5Hfu+mb`60XM=bDhUP)OP-
ziwD6`q}FH>4Y+;cmxal}`D*gkqO{EgK);zc_sh?4XY3^rR`ditx@t_e_f!I4YV&qa
zkfU~gEy~hy*$TX3We_mJsdiQa2<-^ku4Bs+z!Fk}#Od{a;w1sQ4;2mn8$?=Mxh>uk
z4BjFG1jL|uP=n^w?I&j2J~?gBJ?=lztLM|M`mM2gqgog~C60cIb^W1@Cd<Z4D&UTe
z7?{BZL42<JBm=JjHuSDjL%lKOZC{h=!2CF6;sdR{HA8j<-5tx9d$@5hwl*<{y9k`i
z4AbyZ&HGG%TdW}v)xJ3biyDNhDr8nUNFl-NF<?C-tDu6rPGz=-x&ViEsdyJK!zTmj
zq$;<KDFQj$NJ4{54CspbYlg);RQA-*qUyZa1ztw^)rwkUb-X|Ia3~N2eH$OR15pjZ
zT@Fs2L&w*S7G!6ve|FvYtgP`cuTFnaTP!UAq6EfWsdo02Mrz%K?badAN&P*`#qGe`
zpQm!W_yyr(zNo$h;)h_=k6BUZHCe!%D$kx>+E#?Bit#FNs5uK-EJ?Br+_sGayuj8k
ze?5z==z4YiX0?#5zxT%0PmQggF9zaFNfq2}*>|fZHEE>I{4pfSSG5ipwTYPksbRiQ
zV-f4I5MX%g=1qu3E%gLLuXa2`ym85p;No7QqY|%7<o-r0<F2qL7iIhGriF%9bQWac
zzqc*1q8NxU0j_1`vbI;<nREn?t_zHXHPdk~r;OIRO>XZa?d;@NV;8r!@ryCr|5PV#
zW9k_WKHh%YRXGtv!vP88FxKGo;MfIkE#5Xp$k9Dto#Dz!an(2K^uZF`Hds0jCoV>p
zp~kz8i}1$)qv*clesofYXvcj4S%0y}me4Q-{q;0CE&!%7tUcx`6mJZPfiK0#)!+vT
z141HNWRm_qIr#?ycXCC*Oc5OBf`%5i9m{Q>RYXuje;0P@$^ch)Qy|nwl@(RQXM%aH
z?PfbtD`<zqDw}9oWKNdu(q-UL^cAQ)v0?(BHmQJjs^#~>w}ueWY){ClCo0x(`c%Jk
zsD^`3=y-)Qj;iY0$b22!)!!8v8x|hhjjHFM7#9~}I5TWfFwGt0$$n{K_)&5bkkBR3
z=<r3l5-Oips-IhQk^cI!t#~Pas}sS|u#|lV0H&>h{@2NKn}Pr^LB)cWKO|S-+>R<}
z*u%QqWv#|@G&LT=0BvgRb~Bv0?(Rb~+u=CR`W}`bpO0}}ZxpxCu%qDfAS@-w&n)Fu
zmv2RP>SoO$RY2T>p@dYj9kpC0etzv}>1S3RsB;HV6hM@_F5g=eu7p1kCPx<pr`h%O
zjJd<?yZ*XK`rXmJu0=brBY)#6@Ep7PP0STe_(Z&5mku#c9Ep-eY~&zDpR<jK#y_{`
zTJ}2&a%s|GvX44LiuUsA;L9e6M^y$&{JL$_Xo+w&bn_B-8<+y~>HH|RB$g@J56SX(
z`vw64!73|3xn`a5u}{5Tp<;`dKgJzGYz_6VDQjGQ7QT08TLJ4Y3Oz6m<9@ZR^<i%Z
z^nOOR<<>9Dtm*QGbn-!$rKds~@Mr1bWCpHorO@{IaM-2`05<~GYa8o!*S0v;w9qnr
zi|kjjzC#wRrNdvG^jT^GfT*<8@_d*f3d`tFWa0E$8HwDsM6?lBK`iW7bc=0N%kc_a
z<%1l8ya<0dackI`_`4(C4aOFaY7oEa3d#dRGQgwct#NWzsD(v115gpqT6=e$chF|%
zD(r9_Np_=%u~F1tn1!A$`Ve*BxcKOIr5^A5t0NW8^hc@MoNh9=d$zL4$$16$r$%@t
z4R^YKeRCUl3aRW%4u>v#MGt!U!x?LE$en7&e7_ynmOtm}Ul}jtlXWdnHEOs?R;RXG
z@{sFK0G5!?Yd3Ky0E*f8I2-EkcLegO$Tm&?Hhy)~*V5(zYxfe6+?yqwAn^Z&YL_R@
z(nyZ;lmK#qeGBnB7XHxTXtzMtzfKQmA5D#hq~co~@{szxIshAcyK#@`pJvAVHnoiS
z+1~Q~^DsKO?^z`?9#XUC#A^0|rM1m7Sk{?-2ySd*wF1T9_Ap3GVKvvH))3nWbonh#
zu^^NZT=zqu?Xr0GuBZg9-FA|CCaUj;pI7#F8GA^*`}-6YO7m6io`QH(Fg$HJXRd64
z@zS}DPuh_XLo&aXoome9JEK1vYp1rz*bgVdw-Fp|TNX)WM)BfeE=B#*1pE=8n-%6@
z7q~e_<{bkIZU)F548bftQ@%-@*7|~`_SCP7^WCbJKB;r+@D~P+-|AO*xh5Q};~Mpy
zLuYf<>XtXtse(Y2)sU`lwVPGHh2w|D7)-R4Te&CE4oL4Hrw$KKJgA5d`6sGKcB2;n
zKi%7&Q10yVOX%~vlU|5bubkbIDE4;BfZ8PUdHJ}m-MWW$Vr>ktI|H%)V<8b<*2yl=
zfFYP%M<OhUbxq_j#Vl(;F6tCC?h~`^>yd>hd-yiqqgjMi^?RBxl<t2C>^);`Vv-X#
z9o-uvs};SI-4th?<)mrRxCRBUL3J4`E(~qw=LPY`-x%u&j7m;!;zn*as%{n)uq{Ju
zNqqV}<433wr`-}`WAcD>>Mw3|6$tfK4ZhhfetstCbHIM^>$#FoZW8_1D4e374^<sr
zA-gTJOWG0#h}<q>&4cIi*7rnZz@e~ft$Ysf^zMCr-(#<M(RC<u?`Qmzr({1Zv8e*p
zjcrt99YTd*ukQu_gHu$$8ys-qca+KxuXPBoyIvpf-ZIk5osvViY8W~=A71!r$7Ra#
zncj%ZT{2s(0yF*yGuK}PN=3aw=oT+KRI6?sb>Dhcrtcg@uZB2hT!CzrEqOngX?N{h
zS{hZW^#}sB{0%J^IQ4NN>l<U$h$=}O{&vajmBT^h)$ZDNY{P}ZIuhX9`Qhvl?AAzG
zzXLn+=Z^Vg{xX)=?>$`IpAhZCAjlgKhoZ-a%mtMWJN?`qWG;lO{noWX=8Yi?)NBV<
zO|i?S%E?w?7%q$$Iq{wKLcf4>vIknEur^}*cq<d!LkIIY(nYMFOCvO~M1Ni&G2+iE
z5EST<V%qmJ43+N`Tu)WM<DRDL3kQ5<l@)4+<kDzn^S-9>*O)BG7WH^!$7Fby_@b{1
zJ98vzpe;o;6WKi7;V=6~MW=OpdTNd$Cr_HS?dJhll189da+@vv>Q;U0S07MdPWrcH
z_v1@<8>y7ok93`F*$$-{HkTr&J^;yJd`rP}ONuZs_P=$X__uY7cMm04&Mj)y$H)f6
z=tZcmA%SYio3^}b32ruF&wY|{1{73CI&EJ|wT0~GF!9DcCgDLqdd>Px`B1;+vNdOG
zX`c6Y``cyoY<b(sMxkwS)*gw*%V(Dk)-N6G=X`4jM;Xwuu(7ka@2t%igh8F<?dQWv
zHK4?Dqf}$Fw6KS-?&cmeMn1Y3#lKw*ct%F1RPbPufsr?l>D+#R;MAhGrYFBe5ARvo
z&ggQU?}di@0ofN>XF-8tSI%pY<2LkDUS0%?oe1oC)0!S#k+}D_GJ>UyRrR6DJfmrg
zD{~enBcHg-G=JQ?xTm_)d69CL_`O2elz8KzwwUR7weyI#KiiSJ9fJ(D%km6@;kwd^
z40?=~7=ODWVAa&}BZd9>f%{%*;sOQvqTP89;0cjSXPij-)BgE|`6taTkZHnUX9hnk
zYzB-KvGwCK1JP4viC)(OJ)S3L{abt8sU>S5{kj|x7d2Rh8Mw&_1u!=%7R;SOg+=6q
zT`U2!Gl(QZAVt9tY8O6;>4@o{f^}G7&W~fBieUAH>bEI#R)-Rrc)j!tZ|oWjtE(+o
zIdDl{80RXHoc}rMKG3ra;N&*fa2o6X^s(>l{fh<*4)~4PP3yIs6-X5E6KWN*y;2<l
zBVBHlp1^yYC9bUR{GlKl*K)RP4Z&Z-UWp@oOsN^>_Qo66U40mAN}_bt%l7qBLUlEy
zSkcV_x|qv4xvo#HYKy^a*wr<i)sIB_zP&$s#((VIPsmb}_2YUC;z0PI9zASiX+&9s
z{>?C^>=-1RC(NG$-wg?iIx2<S<R%QSZECopZzNS-SndZc^sDEIt6a735@6QOW!+4B
zY;!0*Kk+(cy&dxGGt~=p;Q4XxVOafIWW8A_GglGrdTB-GK6$~NKN;%(@}QVsWYNn9
z+KL*7KknQ=b0T+>hS}vZ$&ZLPUOY|Rtinn7EI@+PW&0*Q<tEooAlQXe!LUcNpGxd3
z)g8;}tsvs>$G=am6}>Ad>pwoZ^M|VAO#J6Z#xUj&snJ2KjFuSQ;XsL7QnHK9@={Dx
zZ|)A)xS9`Pizj<_#=j8iR<)T6_T4G;s3@~wtH}eKJ>q^f&dZjG`?*04X_0i@2W{r5
zl^<3$8zbxvz`N1Wl~wIe&@ltYPJC3-KyW&9+;_SjkU1D0dN9O$h7Q3hp@qr@xe@V|
zVymhlQ5ZvsEV-N9Nl!<+DJ!64)27dy=@j(@S%F+ZLx!}zoA(5EY18){EiGvyKNWvB
z+zRL{q?y|u7+(`+&Zk`s`14YGJ7<X&r~n+gDn-m1v(m99>7L7bcqt8R_+u3_GMxg6
z3La>AsCp*}X&LLM%8Gez{m;C?5fv)FJY&l%VZEp7FG3&_wX27{^ug>^qkv*hMK!X&
z8f9~st=@zB3MNCMlvlYIl-S&C@J6-}{mu~;0e#*1qjYnw{|rZpu7y4rWN}Ml@F<qy
zYJ*~Kb?Wb~Pjk@7Gq~%XWFR5C^jL6N=l)l5tf0!f4~TWZOxnjK&FG)@t{A=y8gw&{
z{;GkM!|u#?YPXDE=LVN!V2<dmCu!Gi`MxA#iGkg)!}Jie8I!}tzfF$5*IjqjW!2Gk
z7YiSJuzJ(ew@B^87?#-Pw83@IjHq(u&A4J*4h)y|$4!3xB~|rLe2X)}WPWoiEzyJO
zgpT+{4v$L3-$Fj$A-8&8uHs8g+C4q7V3LPi6IRdc9r9a5w<2xrTefI1xAqtG8Q@?l
zLF7c(o!uMSDWya2!Xy@xt7(-lsV?qG_?~ganhJ13c4_6lLqXh00iP5SpIA`R=Ko1+
z{^a2{OHjn3jjYUw_rnQp4iI)MiXPBE>F<BfNmB{?S%(DU*Mmse6A5EY!doe}Pii{)
zBD@bO>l^00$iI=IH#_;SnzB~G=wpX?HMgawaZZ!~)x%x$YjUhpavyyn0N>4DwzPDg
zQ0gi>?C{QYS7ALK?@!(HTIHA_@uRVpAzt2Du1<LMh&Cq!%gN~8p1GbZClqqn5cwv?
z$^RUs)p~?`{tFNN?rP!2(6BqHXh-X?pK1~Q{jUVA?|%WJ{S$vFBw8V;XipG#x;nvn
z|BA`(Rc=p4^&LzlH$W=h_-i<G78u^*x>@dOVE=>&YJtRPAKbqVa%+jt-;!g!iM<KQ
z?w)%xrPikUAn>{0D2zG0T~_jTwIM=fqIG06UQEG8(Y{N&YmVi+|M;Hy9<x@CF>8BH
zH!+&K-QSTI*A6?8n81P{jl~LhO#d!nAkEXovh}eS@aHT8F6`--5AJ<mJ5;+E;s(km
z$nS@WevLPVhX)S{v)|(hFpV*XSEEeN`=Bze)6lP2s2e1$rqyUsv(_o0TEzh?*L@rG
zX97C+aPN7O9u>!DVBB+DX5~aC=WSZer<sde=UQ<C=xLeEYRGVy%54o<wW*TQQAc;)
z;bHQ_qDxvE57$pTP4C<uXg_ib-B}h9{Yw3cdy@X*hJk$jP$-AjsbpDHkso44DoTWO
z4j`O%OhY_g1#8Q<*7+u?&);=(rAoB;1Sh)JO{&pr7gjx}ah0_xnWA*YyXfE{|C*On
zQb53E#N=){z38-awsnAzQ4Q_4er8IkdM&v%>I-j`iqmG8dOJ%ngY>-y?YaWIFgHq*
zoGyP9>8>3yYfcl=IB;XM1$#x!IVF9Jd4d0?m~>$J$#-5~(A#&mqeFK;5bg~~2zt47
zA64EtUwj0I4QB0U;(kRITml`;4mlVG8JmNuez_LU*Q<p7Od)>LyJj9d5&vEJD_&z!
zq&Mk@*`Dz9^LPJ%&$J@)&%SY0UwL-l`teSw?b^i=Q9B?OwU!W{cz8?!8I&y=P*P3U
z$?5un$2G8x8VZZhhoB$-&3-Mn#<L3;w+TCS0OxiLD47je=F<7?(mG(i0YXpvmQE|u
zP<O3zJYEN~No$3itK&P$vPDm6$V}e?0+$u*`7x49r_W29>>}$uQs<mbJ|C7VGrj8P
z#!Ma6cN35hhdYa=#kc5#{n5LFJKc_TU(1XzYc71YU5tY7at{)qL%HU?OB<QRoJ=#R
zk2)izl93eBk=*@Zp93-bm}}Rq_SX?Fh~@nuy*3Vx?$?d(r?tZ8TaQ>~#$+HCXEe1G
zYnr;hoDLfN6+57hW?Y$elm~@jS_nS9pSC41`rXn$OZ-ZYUY`IecCR)^iCA}8jmki>
z!Vd8j?*#QNrOB{wW3ad3*C$u)vC-{l@8k2EghimQz{p4#zu>Jba&V{Kn#)JzPdmSE
zXgA|!bUmNNnS~M!KiRU{OhCbzo{*V)zS6&fyxwUfqN`)>)}W)`)_W)5<<_6bg$FAq
zhlZ=myVuh)vI=CLt&V^)xtI&n|2Cfq<hBNZZFNe?WhecyB>X=0JI?<kQQv7;)6`w-
zOomOOoNg4`e*03Ap4d36U7A;|VlDjBZaKBmCB;5Ca^W_+dG{mr&Ijj7$yv6~DoYy<
zHy6TW-g|3J4}9@EJU>UAyHJ!Hr1(Y!4*TH&C)xavD<JY_{l|J;@uK(0td4`7KM|9H
z@X6^tI~-+ea(B7@G;B*9^Q#niHU>yOut;J0l~A`yQLnmr{SV>vFgSg7aKD9~-VHMl
z@-;yYA;&cj+~p#7>mX)WOQGQy4)ic2V2q`sO<@Z(lMiazpLmlWg+Jf;M4EKU=Pmfb
z$YHWx*ORx@eaf;$w|>;ogvNajqno9)Tz7(8iycZVVTyqhzIJ~LMh*gl01mj<>7xoj
zqDSj!oBB0}*2hlc!;!ux%i9K6l3qYja1`?sLj-U#=gym}AfyKNOsmZj>p~A5zml0E
ziebA`XLc9J#yG?qOQjCUZoRm_BA4jhk+x?EcR8j-R7UGlxOyfXULf<p`gE8xQ*Wm_
zVZ-rdG?uN474f9{W!~VnazycX=yUc>+v%O{_`ZiTPnp8JNnuPl)Z$<G%Nj4BDi<aS
zKEE}7-9L{x%H>D`HD_h%u&g&LQ({KA+fJC)JJ}nY{@i;4?}R`J(alSPUce0F54<SP
za>6?EDkh}@t<3zr&-N^fv!rBg^Q<rZl&;2|q;<dbxRX64_%}_j&0jhVd&hlq4eD8W
zDqeH1lquJ(?6O$$0?rgi1Vt3Nx!HTS>o8F<TNk9+uYB9l`PC5>9>o{M1o|Yf*+`O|
z6WhQI%K)#QsCUfkqssBuXTxmsT}EpDXvaCg=U5e!&XCVKspzeVG0Ijo5s6$`o2nRV
z^|^)0gLdCyEg3aBorFW#n=1uAzD7NnP(PsZ4r=H?$R;%~n;0JlPbB!R`l3o@OdPlN
zR%j!t<vx|P#kQp>3mU%?F~Ja9dM*L)H}B|b=yN<#DNNQdI)^`RCYKo{<5!!d$$zuD
zqHH2|8ik?Bapwwl2!UM-UDl9JEQ^rJs;&b=r{Qmcf(6n_OV-Q4n`O$b%V;#K^$+>^
zdW|z#Gy?6n-s>D#IPem0i7R@zQUP=n^xq66@3hzmzq#Wbai{(@bJoeB$VI_?ZTs^5
z9!AQ>)cQF5Uh+Q8yC#6;FO8_r$7Gl2g(88DR@p9JDWu^6>UMSUi_mgqyXL(`$`x7P
zNZX42!^jIjZM>EDD3{>o|0!yw>t8*oEAzVCO7EjOSks&C1Ovk#W&FX`e}cqC>9>!N
zy>!+~^PVqY)@tbAgL}V-T$H+o4r96>COe6yt2Eg?+3r`;-nN4JzEfH*Oh0*O$IKQ7
z;v_90Ui@^Wj}qPF>f60OJ@I;$h>d>BiAhX6zC7N@?ey(2ZJ~Q01skh7yMr(L?amjs
z|4^xe2ypK~m`9%%<Zz<e7|@S>mpPmZJxW<Pr|j6djysR)`J44%qc3f83&6XALV;Ru
z8Buft1<b7pB~1P!Hk7KnQ=FsEu>5%cN8!s`{x6k^6g3LZO@|rA{|m>d-sTm#Hu|_m
zKsRS5>`3&@VjZ!qp7PN4kyc-kcIs8&9bl^LT4guTVI?{T<1PtTfAYF5ktp@vGPxOU
z&hCW;9crgdRE(KiNEuuFFu&8oVumZzW+jarqlHSxA&$Zz*hxr-_(Y&;0W!{~F#L(v
z9hZ9nE619HZQr*(tCdFXb#7uj$}upHKVB4f11NMQTVND|y8E`sGS3|4+;pxig~0yQ
z@tQkcw2|7+4PUmk6mY_~%DIDxPj;WSEr{+pC8&1zZlTD8-_mV;;fLs3bRT2P<;J<1
zf2UauoZ%?3>!9})A#Xw!*T1L!@`d<2%5q_Qk9L0|>Cmn<CCB~?G+@H8Me}ZCoKlyz
zaB%IwOWy#~ld11C3+pQyaQQokr0J(eH)S@r-v4ci7Vk0xIxliF8BRb|X!-rD!JZ6v
z{wRS$dfnoJ?z8*UA&xyfVArK|h%T>XKG$OXwc&t9fNqvTrYdRXGTN!I?S#0YaDg#N
zXeRNLm`1Nm#}~1ZQtx!lwidiV^Z027Q<kAwcoteNAOGQRn!b3?6g=(J2Q&<Qsa=(`
zSa#>Y%U2&}XAazyb{Q_4tsoZmX84-&9t*EOP9Dn~q7nVOIK_FEw0i+E#SvkcAlc5W
z8%I>G;8$cQ7p7Oixwh_pTGs1A2blUSgD24YsGi^Y34@La-i_-dlT5;|T%8ep{>(ym
zie6}N1C}gyMEtRyu&Wu+%BsJx&|iRAND_Y+vD*LB)il1vzn-tR$ZS-wNxf1_1`kJ=
zpY}6rz9P;D?_{hh)?>y8{04diB-Kgl+}o>T0HCLbZ`h^cMUh?EJQ@|(0m3^P<yF<v
zjTRR~WNh^?CHsWGb~iZWkJ@!XFRQBuohrCPqpgS0;*Ae~!RUR<_7I85Pbt*x{&w_U
zSx|$xy5Mv)cdJw~zNHEk7gXn91X&AlU2YYn;ypz-)ol6PY%vt#rJ>jJ05D{kdh$*4
z>q{0gl~sE<9w}_9uzs4dNE6*FP!To5;RMbt7mi&|@Eg298@Vt&bKp4c^iV$a-f&N}
z?-iMsDyOvtZJ&ET40@9-t6q-7v|<+cX@}AN#UHl&paASTuX1k4?$ZdhEN-25KO|12
zfu(2J|GCJa$9;ihJi2WaHHNFWejnA<%<yUV@&Or67pNyT>bAgn`aV_9T)GX~eY-i1
z5j%4vO)j4cnzFHSoj5-f)v1)GqE>c_z&S?TzwD$)w-9+P*FNFXc|bnEq&q#p@{frc
z{RVfdB-*q&d+OSWT2WnTC^{?xwM44$xcPtwC~CQtuMxQWW@ci#L>*AzKj~~&D{NO%
zeVf4A|C$A0J~k)=^-yWOn0fk*w%bI}1Ah)%KfLq1oy}HIPO=86A#v|1oD8(iJo_^|
z;9l@VjeaJZGzi&M@rrzqlpxft4N|^D2d|3Gan0(N&HB$;N$34Aac90T?qpO<FcO;m
zHn&V%pVhrcjF_9b=Q*!ZU1T?1=0Sd{H_`WSG}i83;{higJbX{O5X7ytI5_Be8ZPkA
zh}WizM7*(>PT<k6A%3SJ@Wa272ow-L64n-S`(Xff@<NAo8*DSO&{{`~eSumwU;AB=
zJQUd7x2pkgCo(tfF&zU}ly1*v5Tj^Ha?M%!`K8GnX@<&&>x^;<_t9n-?M+)8zbrH_
zQW&M9eRh7<n{$IqkItC6Q$>$zh9YZ+OfL^Zoc-J2X~H>1JD<O?eZ(!Dggr4-&I$P`
zS)_Y}-FenEIC!Ca2;-DOw0`S!X2u5n3GctVRkSebh2@4k`=n<;+T>Xk#9HqupYsA$
zi(rf3%Fi!E5Ewy<`;lWYen$DAR;XZ4vk>erm1NQfXoZ%fE5GHg&wLi+H0`byf1|fX
z{g^`D`AaRaoa1cm>p&fTFY}+y00>C!*?Xn@Kvq3_oyUD_R9QE#!E2OC<hF|WEA?Nz
zZjAiX)C1Wq?fVLw%%mrru?Zfro32Sr`}SSx^2l`X1jyY_-Lmrwf`E4PHa%oh%Pig3
zlVtatAj}Z+a92m!4*GT{dICLF<4mtYvJaa0G<p+<*qrUV6jnVpuAcF8lncbr+KmSZ
zc$%sRHOU$CC)hCCekW4_?QhR24q-y4*^_!B@~etsy-SJWYfHe3zSae5^%!+9S=KWi
zvo*WTD0pF_#Tg5aC!f-^{rP&^kvvqTUCG0fbvSX9&q|JP5XV3*QuEb_EpJ=!ceXJh
zao>_Yl3O@m77bHbR#y>QYp<nA;34CyD_CFEW2n6v`SYg|LNL%7eaCV7Z>zwh3}Pt`
zJVZ=c7xY#=U*dGsc=B`rmTj-}d*Lm#oIaZU;-&d+S-2xMCMl?Nhu>-C)0^v?=*m93
zuCu}!;Nu9xAo<R;ELYxLW!p#RCOqp`gmvXGW^v{3AZLmBM>vW}f^Rk+lRo=HWertg
z$3DaMRk|J-eY@$`e)ZS0$|z<~rxnP~h;2O*VH2Iet}NM$t-HG2UIx6y&$GS0sx&A9
zCMiJoi4wozbb~6UrtT6H+#&33Gr81@sEc9Q{h5o1PG&c~B<vtd!`>Q5A?-gSRph>w
zv~gbM9~^Oz-DC;#8o+k7Q=E^-r9Enlv>?XL(p`>uWhHtzDOJ>Cop*x;1%BPJ$ohh>
z-YtGp_~^8;@S$5en9^C#+ClC^8nlBTPrvPSZz6S&=Gt%fofdL9BPQjkWq6}Vl?tjd
zvUOktD3IzfKOXIg)MwDs2|>li<?8y%nKN2n?OO&Ef`d1;HB_^Fudj0Z!0QddE-S}G
zZT>m$8XUlWn~EpJRyhABKmgS-8YPLlfLn$=D30L?M!)NSZ<sh)*+G$BtuF7b9b7GW
zOM0>9<en5=Q)DqcTxA(RgZFvom9#;884n&f);K1mu}aQLf2~h6!cyJ#C5q!|54GJs
zZ=_$D2X=OpQ?UPpSDH*#vPu1)*H?Kb_4bdnBDBP|p6w+bn3g-x**((wRxhxG5h(C{
zch65AFq(92tLp5VWM5wRE9X~Zr%qZt1$tSVSx#s6{_!8=O+lXz47@A+^807S0153B
zB)tZ-1R>_iHwrEl#b39G(`zT;l>CIvjqBc>#`HJ55)stG^mTwOzD7$Ft{f**WMxVU
z8*pxi#<v>>xj$0wJaKW?B>UjID<T^H;DPdgJ4%|4A-_{@;D*y<1#$d%0!7Cf44(fW
zh4kC0F}S|X3G#MdKbi@lJC8PQH^lm1UkTLd&=aTFXpk|TsBWMmTWvwN)W!%Y^c1d_
zbR4L5vC7}5$b5`e4pJ8^ab>?1ld?|H8)DWRmd0V9Kw1jwR$An>URA~*@$6hF8<A3n
zWT{KQfZwjY4q0T99tsY3DI_S-2MYn*Kpd6nN+!~@Y*amkD$h0jd&wq^)hHqL(sVvX
z)nXF2htBSj`*^gmj`WcP!9Sm9MV5d2UFwamI68v-JfZZSs*S$BB_o-<x6L?PyIx^+
zi`Q8_=0GIiltxZ|y1ZNtl&r>Scu*ibe`^}(f&BW%wW?oz>mBUN@BT!WLoIb3y!NnA
z<a#y5xMseSBMrkYP@g7=?~F>yLkQ{%CKRe4y27|p5gm+6xShe4Z~GN7yBc0MHXo_!
z1s8?}C5V5#xN7s;?;2~fdX1Mn54m|6m{FEYkZ-TdA=7n++r0$a)XzJ6Vk3X=nYjl$
zygy&=RNZi;`F%MAmY$CIR6B#|abHrY-^Ke7c<I4_tg^3ZNWF*aBiDar&fB37asGZY
z2f#&oK?kohCXEjTSWTXsD=T^_FvC9o@E?|Yk6;RJb>_3bf)X^Q&03h$_1rN}ZgaR5
zqa^{0yg=-0uWxboc`JCG1%1l<AqYCcJVu#z2U6QJ?GpcCJ{J`IjRNHSm=inwRcZKN
zq;>>_wF%J@!{u#o?hB9Db&1D+Gv<U&;MJy`o_R@>YFyU&`jAk0;4M_Ir;sM4fnij>
z_Qet?+l3ZPP3~#wh{A<Q8%N#@Z14`XAb`EiZm<L&|ASXBx9(|wj~1lcfxx{H*5pB_
zc9e>ketqPR-Lkz;d**V>^G)di7kJx!!fstSRo9{<m}j=qa0-{?sn+`4kwg*ehV?!6
z2$A12NDp^VSjFPKRDZsq!Cn{aJ>GdBfVN|tm5Qsm41IH5LA+I8JgsiNv(C@q@&o@=
zkTfW90<z0piMSP9v^ZOKy6o#b6hw!n-ct^p<GukX9uG*z)Yk}Z@Zts5R$8|i<W#`G
znV>>4ObyHJ))%jyK_)~e5K+;uqN4*Yn*(u^jX_<dkm|V6H+WGWe0uN{v=@P-6Go`d
z4GsvySPgJkT<3)BBV?H2VAsLORpoC%r=F3QZxy$9GZXigl0Wbk!DYbdau@%(sVAKP
z?R2!)Ug?bm9Z!1|-p~v*?69?4`%?vD&|^VgnEZjePq<g}AeJ2$r}GEsRh&YVJx)j4
zVzh#`V!C8HYQDG8-wo<MpDB~FA2%t>+O0k<{VBf8b4>42;6RMX1is#+D1x)si0`>t
zXzI+H@Ts_itNHOp@CvSsWw=_<V9PI)V%xLOry)kulGke%igm&q*;8!F*L4+FPKP&{
z@+?)P4KH`MP>-HR9Z^NUFFTu}tA)|dz{;I646nyIJccnPgh47rtCtHvA11<Ts3&rm
zMf?&3JNva)+ys!v{dW0Q`41MS?YNmy5<Q=z5joQN#r;q@jj7CiGn!h_Z5}m82zo)c
z4>Fu)JWB!j@+{`OdI}rE>eyL1X^w>%n`Qu$)O$r&`yS$hr{5+CUZJ$8o(>e{w-Sgi
zMApQDRNX@MY=R`<N1$lRZM~Y43YGy(#5NOf>t0cbll~KsCCkLU#AePd0Wj!k#wnD|
zylrh-<pXU!WtQTe-(`}?02L#OEz~}nh=!nOHL(M}n_6OJL4?r2!~GX4zDYHUM=j|Z
zC%+m1o5f$~5n56ZW-@(^UYxbn_H<9Ew?ui`7dxa~EW#e8=T~cqy`uX3BrSHONi6I0
zKW{uH`akIMB7%D5ds6VCM>=p<=uq$lM0`@n7fn;c%oLDo(4tL5&rI~rgDcA(V4V*A
ziaL5^otL?Oyna!HSE2^bdsnd)&CKUkq5lN2rTMzTpnHQ%szK&<!LYNoq>{1bx+=fE
znX57J366go7gRhOqc*Dyrzn461UxFDm~Fo8B73OI!T^@6X6>)CnKbjf8tX{xf&!xG
z8uyds#-~|3J2qQFO?KyA4`>*cq}^&PL$A{XD1<d>q75W%`*w2cugq5Q;3!HZL9;X@
zNQo>V)RR_?ws+SI(~>^wcXuMQJ$=A3F=`qhFV!0Fv|I5wRhm>*4AzInvO5emF59{a
zu!Ns$Y)<^!Tt)6H2js@&;HC}3uEn*hqsalF>-@kZw*K*`SKof6)2<x@NSHf%7Fy9a
zSW5$bE`dAR=%&9>g3u7(3N=*X`ihMZD@v|z`D8%T)QI@z;AyO6`{mansS!WB##-0!
zyn9#`9JwyzyiAbqK4bGZSn-=NsiP%jez0Q~2!V-i*ja0g?qz!16rswCfZu^OvcVCf
zL?TrJjr$gOo^s*7gSNZer&lg=`*)=3=0ZPOmidTN*y!rBQAr?%KlnmG_LSh!?%IEU
z#V6aqoeDqrL#=e-)6kVFW3BIcj9iV_9Y_uELw(u`2nyfBGAtHjUJ<nTsmIK3*33#V
zuF+gp%^R|F%G_PPG>yRzrQz-o843voztn&tJ^diPvk%}d&Wcn@grfGF;dF|!{EK9o
zVxy7u8Qb0Ds>}OUHEp7ptcxXFXV2^cojZCJOD`y=GangRwBx2m+eUH#<%{CYs$v7!
zbuBtnh8@foY$|!va`XtBMoRQ|@j(Z{ud|b>a^k1;um#$b3frDBLmLmM3M#<9*jxSr
z|2Er?7aUOI>)to;>65SamR{PFotlR`BoDMct#kfMk)azMsBh~wiBM)9C&HJrEeZHU
z!>!+;mg?_s6vWMVJU6YqShnxQ)&Y54+{RFtV6P0Nl4z{n7UAgv7YQut6hNJ(K<m2c
zW>E>nQHY+S9M<#e^9Der6i=;dcB<^vGT{m>$+Rn0k))BEaQ*<<mGp!_J<g)IgNmLJ
z$)Yf5xrfX0t>G|-dLepLe`;7;66&Bq$k843yX~{)I2IFMYZ<FszY}@C+x@Gj>6Puf
zBwmq8pwS)U+hj<r)7?>BoWA<moC!1B6@#_;*1VW8Y@2}D@^qA{vD=?IOyvb%RZn(N
z(KE`yRd374(RQ@{4VTDYZ>B!I+ZAZttYLa7Du|voTgTJarIdY)OF0x3#4-eEeU<`x
z_-z7!_SO%8bhXzuv69jzYW8X?bL@>bH|S@hngpKwlr1cdcY$4Ll~Z<I<^GuQ*|5TH
z-2Ejo({VT?5Hr@8i2oF!eMWd!p_j%NNRiTIOK3`RaH@Iz#z65zU?6?u`Y~lR<~zeG
z+3?&kl!%IjgWefx>@KiU__v5-ot55mG?ZC?LR6A8GHQSSn*eRcU;|dCy^%_8u4Un?
zP<>@Z8HLDr;UIc@+3hTGOpludsbUIwx=lB%T*G#zr#n8P>?7Qf!qAe|h5Zcg18g01
z!Nzhk`+kv9!SN3bHP^<bS4{?flJ{c`66+=_6=s60oD;obZr&`Kw;7^%)e?a`A$>Dv
z%-VLAUE7c-W%D2isbn5`#`gGo>3CxmXbI<oG}2FvuE}|8@~B{xc11+!VnV+b6mY;M
zDiSZ{GPnVg@+k31y}kWEk}DrJ2!~)Uw>>`%w_UI86u7BA%EHw{0NSc{hP1dKzO8j3
z9}S*4XjJ;4{Ffdz{2dO#9`6YgCu?g-d*5WH&Y$iv@jg4w=dEEEU+58m;?EyGE=+y0
z!rFs_z4!iP+tk@By-7%^183idpK83ZJL%5%^QMZo4z@^3-Z(RHr=#G|v(p;?JXkdp
zn~SkLu<`ib@A^Zp_NfZsCHBb*qnm%6oT*1p(P5lIMx6eTlkY+;am2Tt3R{(RPXK6g
zaZD7nxOT9bAkb5zY&FSaO{5B!MR~W13v1p~QSA^Pn_Ft)@2>kb6bJ60ZqcQryZ@QV
z{K3BJ3gdSb<l140`@3wWwbh4aWKRVy$tG02H0avlHfZ>Sezmg@>fDjx-~R9ExnRis
zPmhP&*}cls^tTteYTpbqG_K4ltGKMo$E__#H{~Yx!Vo<dhLnFBHQpI_Ptvqt^Ak7D
z_1aiEDYOMA4kJ<(^XH+(y=!Lcl~Qp=tKs#UrfrNA=4EoY^XE9pfSS59OMdi)<}Z?R
z{!50lD_E~oFC;yEtu?UUcOwc{sOeu2#O0D!B(HA~t0Xx;mDlAuy-r+l;n<z0!<i9<
zBH8W8vU|$M#5jUq%(d{aa@{l^ryy=>=Ea(QzG6arRr19(Av<1dZk6WXneUpAQ2=bF
zK)RcMmQzQRe{>yfzO=A=6sQgDc|XloDu?vS70q)h&`tN3ujFxMhCJbIO5l19<DkLl
zlt3kMc&Swj+I_^vpj_hUJ4p13C)`0P`Z;Pi?e>K(9h>_}Lux0&vzbf&(wkkSw}H-p
z{bJsavKLDQN7gz{ChF3;{VfcSG-sXVofzHEH5ncp8sT_2AwBE;R@m&A+iTTtZiBwu
zr|%cA!MxPOj6M6Rqu$TN$dfU1$UWigy<h(zUyVk;2m3ct2*LHOwO&DAwA^1vU$73p
z5WX^CD_ko%9WP(JbKimTa{*)PZN+B*Y8kuZ&}Emh^-}q|qdgE{j&5)ibLvRaW3N3)
zF_8I{>#322pHB||w4G4SL(}*0WllFpM^C(D(r%ur)3lnn(sJ;O>Q6c-Qk#W%9Vz0q
z=3wWPYXGW;i(j$(6k9`J!LJl&XGe5*odZ2&8wa2{CPov;b7MLFijSk%GNmvhMA{x+
z5ll$w+U@)Y&s?45Cx_}*%W2>HEe;N@oOW$vWxN<Nx{_uIn?yov^x{M$6!=~l`ucg_
zB)p_pF#orb$YsgP@fuRgReC-$OxfkBrbzGo_SqA*{W@;v`PD11yIc1i#p>SE*#Wj~
zMBxe;xwmM;u!(v4k<GW0dlKgio@y3jVLE=cjyLz+JJy(6U%U=4=)`ucgL4-$-<foM
z+A)D!7AiZbdVbK9&%d3v_Bd+fh%)32cJ~#9g=IXSB)52mc+oZYTXg%Uom20uwX-p&
zxa^B5X>3aa5J+Tp1@C*v-=Qz6Y+<g+EtMH@)Jt{lQf>w4!T1h?<^3*0_NQ7ur5Wx&
zxA7XA4+G1Lp>Eh+lcaTPL(rR`++o6*ujP`~QI%8yS;t)(!x7=It3;&#nBZaALGL;C
zw~v#%tUe@Go_1_4yL?H`FI<Gcv(Mf|JOK$!zntx59TE<Q?VNV>0cjm8{dPmvEI52o
zqh{%fdh9OH5zhMF#7B-VKVJ0@b>x~1dHF5xH>kt6Q9lZIUAbRna3Nyli(NG8#r_aZ
zZzJ;f&Qmub-_nia_L-q-Qh#67=bz3~2(sF`RUm$8y+Pk1E3Ny*OMT9pA+6_JzaR3L
zw<o;TriZL1a@m<OSH&9bK1TLl7Bo&4dQ01(9_@~3JrPzg6x(uKjPpVpaU*{}l5}uC
z{82h2iKs`7`%rJ%EWen;a|z!~4wV>*h;d0fIyMCxNDGzTfLlfoB`l8<^&8&WcQYZ{
ziE4-sy53_a<HuA2qc>)Air1d#!40Z|XDlu1vl$Ml7S5gl^)qS^$%tC2_U~c*fM`Vg
zYeZ8}`>|sEn(J)1>CUOEkjb`AYU=ovX7(x7gpGSmr(E~xL}bxmgXHUDVnQ2y$IUlY
zM`Xkr=|gpFDrFUwFZZx+d1dVN<bdI~iqEwbW1nEZR#B<QEx8gMmzN;b@5JZF#AbC}
zzfNH@>?2%Q;dUj>m_JdnKN2!tcl{Ij2R$iDPF&LM5*LgzwG}B{KcBF6Jg76}pQsj(
zX1wq*{KG`zVNTh8tYUb5gSd+R67QA9XV#s&oMueP_g7&r^9o3mkl0m8<tJKY)Ntqf
zU-oZ)J-R+d-EulEmLbrw^9-QJC1DGn!p;<Q{Ze?eQg|o2R@IZJ*@RMD+Ec3o)Yjsr
zj4-KI=aDCZl6qr>Z=S}KGW2%s-sNPTJH?}7*HX)WcsXN?>&{W^y9)A%k^cu@ZvhqM
z*R_o+qJV-hG}0g?LkyjRv`UF|_editjUe4fhoH3d&<(=Sg9u0~NK3<z!w~<Q_j%vv
z|9;=&?^|mYi#5z*?z7K1JFdO=wJXmq`U$ZZhdwQDYbJwgf|D?s1~Z))r<C))WZ#kp
zoby`;EPNZzvN;`<&LQ%0x+3#_r@v%>jh<l1rqfa=N$UoC<&1z5FRn=*UCn#$rv1vy
z5w}lc>Bp3u`OCPwM)Owq9eNRVFYML^UT?Iv^U5}pKNMYwXBvDBH?AL#jXQPs6<Xpa
z4eT(1z9u=tO68GWDw%v$wL4YoplCv^kufES7v&3;cBMR?&F(l_zvKkF@9ShEa}T=W
zI~-t~B&Zj85yA4JCdFxCDPh4Bc2@(3Z({k!VYBBC1i3N4&hV<-LRDVmppuI4+oWkH
zT(7>Tk@ZXIVo`gziqSa9o!`3(ta66(n|P8SanumQ>O2qqE)IpiHk>cV#yG2|3Nvp~
zU{NSR6+&#%I)j9Bya|5y#8Ro=Z(UqAFvH>}J3mr%7fun0Y`aP8-J^ckcg5IjJxMY`
ztOO(CSUMp<&*kdo2p8#lPan(UNjPQ9v4x%B?ypcg?;aQPI{p&b&De7emOmfD5y@7=
z0;4!~>xBB3n-o5+IdR>e7Bqu=19b0xVdQ&vm*02Fpl*ksL5wBj<{juN#T>8U-%bh(
z#i)DQT}!?TPQ6IRC*Ex`S?f)<KIQ30**(JI(QFl(;f71pH0PjH0o7TdtK~~;pNb{w
zJ@$E5o~2B-s}t>ScyXc#9{0!KUQD>(oIOHhxBhDNWNq2|m6`L0`eh7!qEsifW3>L%
zm7JW7vGvUT=#j9A4|r0c)8|$&Tf6VlaU|hrt14u<X$SoUtxh+>D0Sh0G7dc@K!Q#F
zynBWq*0@qU=#bqbSkE(>cSSYd=bYz3Nr|_I9^TG6vEgqI$cU?7@5ag5OB3zJh+k1S
ztDT3l7&$1KfdbA=umX0aRwbmCYkT<Lzr-~VIg~glkK;1HRgc32^X$s!q{F5UFSgo*
zp`MjQd(IS56?`!fuftE74G;Y@JK(KsmZ|i~J&COYvTpJ}XxZ$MT}vX%;q}WG*HOpM
zT-6$-4w{xPH$(!M&rFv{FHOhrDnW;2o_izLZf$WZ4@iRB+AlLbd|Tfx*OA49cORU!
zY#ydtwNHPe=!+R3yyhXokEr9CYrLFe59x8nG4mney-lL~0{blN$?XXLhGEAc4`S{m
zFlYO*f^XSf*n|zMy-H)J6h9SFm2IF?`*gSB5F&Rt-#vY%Zw!AP%(<`9aMD?Ntl<#}
zB1xD2`f8TO)8fvE-^mOc1wjR~&OA1x<{joMb#g`(Wk?%4lz^r}Nv<U$r^27Pm{E@D
zo{4i0+3KCV;2w1ILaW}N4BB5F@%{Oy(cZq9&x;Rq!^)AHiw0{Mzw#Xp8KY;OZQD$+
z*p9I}#m@Z96z;ts@>CE|B9nfn_oGg4TfKE^fz5$`C`<TI!USntHf$SVfH?BnRq9(?
zoHD;3wd^(pNU~NaYsVZnirUf5(Tjt`nuI(Rm6q~i!Rxv!)e5%(w;6YKIarUB93b^{
zP%$OP)z!@kY54<5dxr<+zzQeVXNvqYT35@&{Us^9AH|(QMf@1{<CdKC6P~)2Ifax1
z29KEgG=IAJv_ot*2Nk39ZVA3Hl~&%aG4pZ=pr{*oMUPOyS}3VGiflVQd-;l_bE(>;
zJwodEhsLTcdn`XSNELmHTbjdZQ&JP|^3|2m%o(~XTtgiD(V$+5z6DFE425?ph=HDh
zx#f2=Ak=<erFN(IZ05qfad(mD;ipMd3`vYv2e-pn0v#5r`nrp4N32>GjuL00G6n{T
zMRrB$E2dj0ocZn<-&u<8=Z3=Pee=1%-*9@G<5sIkGxYW)I$B|C>&>b?O{`TS*kCaC
z!*zYmS%VK&+u$i$wtep|jFzz)svf$01~11;>kMTvHFNGpz1?Cp#Ttr#O8PkWou=&9
zhDLB3%d?+P!K3~{$f^=NW?Z&y!F)gb<&9sh5zE-BCzP6=K-0IO>WY;Dof6x$aFUe-
zwj3W+xmwOUquG|T&$%$CH_f3DriW``et`{Rt2W_TBEppO9>x=r2iNP_B26s<xPwlZ
zt<p)EG&8Fxdwy}>W_Jvy*|Te1s4W#90Si9Ow^D0rtyC|Q+PPfQdzzFI9_`e~NZy^)
z=38q*aA}t*t&}da;8SR&y*)6+DkFD0j^v)2=qjn8MhvEw_Vpd66qg_g{wG9auGVuk
z?Dq$MEbc!(ftzJMK)giQr7RJpIS*1`SJ9H0c11r_jbVKmW;N%RdJu<_D*4&9#4eMV
z%7bHeFrIVerZ+o8!I!ZpE()0v2t+dz0axxMA*+K9?4(BX&;<$UizWy>eRd&b*}~e6
z{r2W2X55H<7_(-a2^={yEOqNhtXB@t{N$1h2@m3^yu=R37a`gAD0&1pU0(2Hw)ed|
z0}=lGFfE|+>yK0+CKhu4<+jp5pn!7+qnwx!&E)e>dijx929{l6AO~NX*9~9d#BPOe
zaW9hW>O-mb?8?;qp#&5iN9mYPM8HrrE;{#)<&EBO#I`|NDhK0Z=1;`UDL$oyetYTL
zbLJ0UORs4ErrYbdzG^P(^_z_q(qiLC^z$~2KW$(7gsob#bKlPsj{h;e{^HU)&)Ju$
z-sP;@0>IiUSl8+RE!Mh3<5Eu(EZ-h)e%;LmJ&2fa;0t|s)`d-;aBHvp45f}?u#M}H
z-ww*mcz3IYb_OT=k`_(;{VTSzsSR;eRinUOrjG*_YgI5F17Fq8_rhQEu=CmD-6Hh6
z0vk_mI-lMIF{9BxxaAaw<ClZAQ^clViSWl-7#<ff^bI`9q^qj0<A-$4f%MKqhFgTY
z+n;CO;U9Bjf8dezr+D~k+251+#TTjAs%b$BI4iiCG{Yz6rIIP0#3`J7Y3x{iBxEzL
zc|l#d;GvHOp9Vk9xqlZP8~#bNWr{s?LtqjpMa=c%0_eKH^x99sFq?oKy(9wXocg?&
z&81YIuV+o`S?tuO8)aOs*W0h94AKS^xfJr(k|rzsB+&*_8__d37@ulGZ+K8hPY81x
zASc+?)wtB9hUlNg@3MqOB55b~SQf;;uNlL5iCvfY_y=f({IN{3#Ld!99dT&gUe@%{
z`Pg63dJw8~VwV7?NE<FPj;-8@O&)to2`2i}tUW|NC7}A6J~sku=mk;D<4FdFp7$2b
zC)PiR4L5BoJD+cr_#Jchs$<)dsO1r0k%swMfAKi#d7cBtB*zy~y_qt4YXp#xwAZJ>
zZrjnA!OH_gD)6GhGcpK7Ux4#R#P0egaqS#8EjZi%l!Yld*3ZDK;-r1qI|;s`tB1Hb
z^^CBd+&l1hgCty29^MYJ8pzOlK}CNHL}fR0ZecWbbDVyB<*CsT(Gm(}7wVSaeh$69
zI2_`0o;k$l&kSPmKw7q5oo{Jrn#1T&X7%KrLPW37nd=B2k;7B@Oaz!-+{2L&*@H`6
zTGdEtVe&^P<$V>dELYe`Sx<<6?)CyUEb|?V)0^tZe&KbTqZbiypd@x{6m5V51p<Ye
zkFek7pgSLB{mpv^<+tZ!atLGv9j%jY_&YN4V#RFP2?LqwX!7pJghuOg7k-&rs<u=F
z6+1*D<IPeQ8*eYVbzGiM<2E79FA!`{^%GP=Wuq_?mX0x$ES0a}n9xMfEFqm_mcnIq
zFxiT}z0ude@1@@^Rgl4yE9Bc{EWe0_LEf0ra=v4ZPs-do!WwKZZ41*qncm?vOfb|z
zk=nP~iCJmc)l{KK^-ZC&jJQuCpOVgPNXB{`q11ic7PcH;)YIP;lH%3FkdeDeogEje
zJgH2*SEF(MwPw#xWWqA48oxVm3tul7UOqi6s9c!bZaw_w%7bsG2oz^$3dGI&kGsIH
znJ0UdlA~Z;W7kDemm5*2pWIs7yA610Pe0zeeqGtP6#44UOr*QQH^IOHxz4VoSIq{y
zrGOn!CNiD;@bJL~aXh8HA)Nt<kBD{1xYhEuB+Tlg|M|uQq0e&dM>91IVNB$dI(A76
z$l&CzoF|oV6(KX`Y~vIDLmE@jGMGExgP3juk)QWMamAhdwhV?|J$X28nqopq-U35S
zz;gM-ciby|j5dUFS^52DVXG6t13UF|j@JFl^Lw7*1Mztv3Pl=cIZ;T<aM_4tl)Fy3
zz82+z+qlYm`)t49);kYHOyj56MvHc=`8S=wbl<wS$8Oj~WKXkRF9GBa&yL%B?ief7
zCH93DQxWRgl1RC>*;KNCp{t`U0+?+PU;X3ScB25Uz%6ab?-w1{H00#;ZQX^#*>jF#
zPcI^r40SD&vV1FKb0l1ycl=JBNO%MNu>~|hTWzQFEu#CKcQ9JD$$+pFlmN*o_}Kk~
z0H4rfdG{`#r6(NZjJBe4C9Tt4iemWkW-)|9=yjHJDECk_&qG4d?W*p3UZS8Ld~9x2
zpEblh)GVXf=qeBQ2P;_NHcsF}h}M$n$?z+ayX|+^zehK2Yg^@+V=KS_=HVacr)htf
zzsVKERfVLd2*+iN_PUM=X1aaAd?Z{n!xJ@s;;_o#c)Ab}AezP)l`+~rBAF;z<Cvb%
z|G@;Pdb3zhxk6pUQJ>e+I*7!Fb-;zBg|A{Z&{1BqCbg4lB6I;42dgj1@uI+-ueDi&
z1dGb)y;c(HN?3U6yX)_=WqX>J?)Z{D)3p@(HRvGZAY?F`xW4vas1@CG-KXL@9&p{*
zba^rt1_RSo4ZY6@8*zBhg8s>T-S`o9$D5SGtL?X-BM_B?F$*3J0T8FEoqoU)JSX}c
zz=D%{z_L6nykK~unZHa<mWtSMWmEY?ar;438}AJ8sZQ**xLZzDZr;4c>0GDLuO%$n
z?p1$Gu`y3ez30l%19Zclz)H*O^+(i@{m?obvs(2>(&6pSuekXyMMzmre6pI6NZSYM
z*dMAA9r7BdV0mIliIp&a=98-6<D6@Bj@D1{lqyQhlsnd&+VPFCa3yCC+bNf+MteP`
zWJKHZy|-CbB~W*+p0yY$EJG-*sBDzQPBXo;^4V|n%BMsdA?v+y51O~I^giO(-R(<c
zmDXv=kM2sXu6i3Do|1MvHnj^BQA)O&&(fSHR~UZgSSqa29^7N&+vq5TrGNkOlPK*0
z1$~pv;U^VgoBBEPMA&NNOghJ4^&fzdr4V=HvUqwBkW>?isR3xdLG{qsw2A-dOvTiK
z8F1GuT9|j^fMZzAKok-MRKh}5e@BCXUYK~0qCgEK`w2mrGfa#zxT;|;wf(4|OO)P{
zc(HFhFTE~AM8w8OHOcY8AqOc$_NC7nV>}$0Ol4VnyPGCWt6RByTvglw@FH2hyfHr^
zs1aROmfLLjSccq~)psc;3Ed#%S8;0DsULHj+HN>gmV?;OV#@ufH{Kz}#?p_X#t}f@
z=t$~M?orD3&{O<S^&cUx7w+i4nhNE{)R)iXXgV6ekj7yLKOAE)K@YgCMW`}6!{`y;
zXYtD}gd?l^qw$9&EZ1x$(pyCgPai<RiHZVjtve1*b^rxVQ8}kkBd*z~83+%l@^F&R
zTK|`Yqv#R9P!L<i{sA|-Vcy#=Jpy6JI@0hS5AgJ#zPrw$f%T;pwCpyQ-j$akXXSkl
z*wb&nRgU9<qXk&qhMz7<dNN<KU2qZnIcR006hf=4ec#n&ofHXJ=0~61CV85cKe<sE
zKn=1a%XPf$L!0@FHDSWd<L8K(fax5!vF8Lnn?0Rj2pu4I@xG0EMB)&6@3vb;^Jswb
zDJZf^@d({T(f+c8=@tB^z(|?wwCrzYIT%acDuq%&4!{vZaU0Zu-cJF?!`J6=fHI%Y
z^@qlx>W`aOJ}3=lr`l$x7pVDdYPjnyl~%SlgJ-m1q~t|y3vfTgr0X})qAdHe`fxA0
z^z@>{$Pw|qR#?$Tk4RS84M6S?tsp)uu3T!FIe=Dwt`^iI?dcD++)#><Wndyb`^%)U
z{KmgR557MhB)SjUw|Kh0ntwp;*!bDfPKXm9#F;Mm{P$xU?8&p9+YYWwf?W+kSG{g+
zFAwOA9nG9SPfuR#o>!@4^hx>djIEaoFXYeoU}i|j?3O*UZ{&XS>O?UDhZOzOF75|#
zgqfM%RS~6<jSHUa0qmaH<7Ij^h2b!usr~exq(a&m8n}y0BE8<>jA8WtXHRd@XyW2w
z6Q#VZP9+Geuk+P_ZQt$tpq*00Fz>g<pQEmx4iAZs;N?^K71*8hhm%zP^L>hNdE_nM
zE|Tn*C%cP?#m1E;hZKEVe`^KvIhRGS06agn?YMs4du#1{tC9!9F<6Psy0g(&)@Ttc
z3act2^1z|@ebddS*L%xpm^6tHFKW)QW#_vq-@s*TjmT<F{l=Xd(QBYs!<nH%y<#S%
zy1d74SI=-QDlRV$+U(l&c!Yzxv1YrS^T;Oi+)Ie*rltCDu&lFXue}9*)+YrO@eQhO
z4C5FP#d!K3mzoq5V_*57iOakxAjExt@IAj#>vn(?@=VKgp(z!x_*RAYMu=-D(|;9w
z4{Ne7iji{u6_>HKcj0`P*VJ~47Vraya!zL*cLKR;gR;*4&<feq{{-CfF7<}jHz2^x
zE091Muuh?$c7*^ZQrR^27G{LjXB#d*y@}Oj1`f9bvwVrrG;-G>mnTF`E5*%N((I0s
z5misWe;B9)SS1u_nVmF7e{$IDHgbiAjL0VauD(Hg##V2^H=`PwWn<r&unG|N2o$q@
zoPw^hOc@WgDOHQF>i_aXFKJstYGJQ^X9{L9IlJXLvV=dL5vM<2j8Xdtbr^b^74fB2
zAGts|qLigIeJa{#V!RZ~J20@FYV2)O-t@WkrbSLZIEhG7v+)*%3Fcw$(w*Vc-Cz0W
zuYQC{M_2+i_D0olOWLmdByXpe0;`Ld7~+ZPMe~$CrS8O>blxoXzh3u%d;ajHh#3|T
z(MGAg>z4c`wpI3Z-WQG&uCz_k2Uftd1kYI!epL@LJ(3lBpt8dFDm&l`Z6(S&By4j>
zI{0bEj1ge64AUpH`MN7@Htz|uP=(~qR&U#nDD$NC%ve^Nu`-5PRmrtFg?BO4oJ~8a
zx7Nf}`mS;g`vAxK>$dD}f~vUx#WKAKjDqRggv?YMOo5tzC2gP=Uq-I*rUBkcVa4~C
z6AZf=KV!VW)s!aOqE_BplhS>}Gxlnq@Vct$0yU)A^ytZJ1qP++magJ@SECaNOQ$}4
z6HjARk;ULiy${jv(_?-{iHNH^RJmnKf=}n1q^t-4VxlFAq3z|_g{_C;+uv!#PkXTV
z1?I9F7+)W{1y=p3^2FA?YTfF7G@@xJvEn%J-x5<cO~BLDHMIQg`0eH$TWlh(G_67^
z2PDx?f6NYr{F|o|pB-R!jD}24Z@e}6pC!kFYbqMT=jo4Cq(Cu5+e9$aP~95Jz?nj%
zwS~gs=0xd`f2_UoJlh}RaVKm!A^z9sBlEk#-{+h<+O|2Kndm+lhHX06&zm1xLFNL5
zM?@{MSG=TYTDK}%1_vg$bNXvSd1u&)xgGc)q;)u?j5>u^t%VT^*!fQ<%|xDFTRYkX
z6O2YsIVR3TWDeULBJ~Xq;$f_Ww&{y=>z~vArG?(%DL)170rq#pp8r|q|D2G2ofF_^
zia6kI3|->wpZ)vv{QGbJ{fj9U7Jxs#TYCbWroX?ce%=UgSM6P&(?5K|6pI81RYYzJ
z=s^B;MgQ|>;HoO{ur2{u68`^lkN@LCY46^J^x%DJiu@l%`#%f)Z@mkE4R3*mO~sJ>
zJ^+x1f4}>G{L+aHTo2V-!&?9MLjNrs!0Dhg&}~JpWu}Vr$1VJ`@FZ`9cLn0T^^Z?X
znbSGlwREP^82S%Sb7L*aK!9xQ-u_trXW_^*00srb(1Q1m-;^c@to-#(thdD9HwoCh
zDqLXY+*p>v|D_rJ`mq`XU=zxIsmJyG<EqI6flZKqx6S&`@|(Ot9P7k45B?9q|L+ay
zWV#7mapQjl|D#-xj{xb`cqmHJ{o|T-v4J&H+^)|2Z^i!~2L^~uZrsJm-Qqrre>|E|
zH@Af8sRzklC35p7uicI3G)sHz`Oj+x0c$qI?sx3^N5R2j0Kw_C<k111>;IO;|N0$_
z73f7mu2X6J>&8iFTF>^$!0fg-ftq)3q%FjXmFd*(RkNC6AjGL%W?jGFkBRwWw;T1h
z|JR8{l%}sM_p%jGSYTG}&SAI`iEart5!KrnD~bovO2_w_7yh!~@9#S`yOy{bevXyt
z*Ods=^pG0}%sDg|9Rfv*wO#-tmmx}M#xij&5Qok>950_1c&rqmz_Zh`4rc{~@+$qs
zZ=B(eZ|7%z`s}q?#f$GYTGRk5;Muv|a{8BVGsV)vB6$#{uKCk_%4i7rI9L`hLS0T%
zERqOfw53;`B_U%k>xKGUZC~VKj@B;YL%#}`Ls=;{%?`QSy1V*RN5Jh#H41B&tm(&-
zNNoMoe{pC2iK?|vHlemlHAx=O(do!lDJ!Z^R#cAXS$*B&SzdcSQn#6yGLkh?7i3=@
zr{BKWFH~PpUEgRYVH?#vQ?x0c$t_-2>D|ow4cZ@N5wSW5q%jz|0q3b)(*ev)j(=tK
zmvh9gXv$=kusS0A%S|Kj0BGZ_p0O=0kK8BfiA}3ekCKra)?Te^KKv-`U|@7?kv<kl
zrKqeXUcqhB1UBibZyBtnovOp6?o@4T2~Wms*la19EazkD=U`JQQmXXibM;O+R*Gd$
zGw5MF^PbBwEIt7IVoc8UH1;n#+f)Hp`7zkoby(19hMAvC?<(3lpWZoP`tjG#tYGy*
z-wBPf3n3}NmJBI$f}aj}j&jI&`Fq`xHD_iTn|ShuSno7RwaQT*-)hhKa(+?y!ld?a
zC5p6Rf`y(ssQ0<p8i!F$gu5osdP0t@PR9>A|K{!Lky-aL`tj*ryRqP(rxm`(mD-ge
znV140Qce?RBz<!1YKdCbAt0LZSH1xHmYg27-PE|(j_G}(`d1wdcE+-J5S3BhpB?G5
zQ>SwXw3{}D-^YD$pP0&AS&vj%Q=xsc*9Bz}F7!f!E`-de)HwB{z4=G$N|)p3R`51`
zvALlDoy$LLa}CGLYs=!8mrdr$oZ3)0DjaW<(<V7VyLeT-@Pab2@`^*n@Ii?Vr$)BD
z3(Zh6tM;6;GQ2e<Wlxv<FiAW5<5;9c_86i`s+vvP>v<Wb0F|cL%w+<klFM}qU6$%U
zLDlKdJ@pok#3vG}p7~xLPo+vy=y*>uh12{+OPIoNqq)HIZ@LMFGX(7hBPuh>!yBK4
zFLfh{P)~u+_c9Un$d7F|^bZ`|T<w#et|M^Ynd!eN6VqiD-ukLytM9-Sd)tjtKp@VT
z)jtbaZnNuvGkwc1)Zv>Pu_vN&b~5%RLXlb~Q<2G1Phw9XDw0O)x>g9dli`$1MWx-0
zi%R&Zv`{36j#yt3N3rr0M4Jl2Sl?GSI5!3d_~WvPhNjjo5JlT$rH02YCgxtX>eMR}
zn?EjwQU_PwpNp2as<*qg=?B>EthT@55-+eK!7yY}4#n!$!wtQX64e-Z3YFHHO{!n_
zG8M@O00hI}N($qLv>c)U`u!iCgi_{>olMp-)>F7>?2~k?!q31T){i!qmD3oC^MZWb
za;Ci-omJ>q%R#Y`da*8&@g=&o-VNLx`soT${ffw6K47LoJ5<G_!?ti3dOotDn_5ko
z+mTjTyKTj`5NTD^n8HI}{WW#lT-nP}(VcY2cnTrPW1={h(KcvzZ0l7W2l2o@mP$$Z
z=C5GXCpP-FjADE=TR?hM)Yzc;<ZZdKmtad)Sh;@%P{;bpxAQiR9DmVYE+3!^c>}#b
z<RIl)xx^65JRuN|Q2o;d+sjw&OU@X{=WWk7SNs>>zogcv{TNrD+o8Vkd^R&*bvMkH
z{o87dx8yg^^z!~g8Gv;5YVU3YU@ZLjB)I>>`US2&Gqp8S4{Nx^6ZP;QL*VvjX|!me
zp;x{|KiY7e$WMcO%M!yK2%e1ccWIwZ-TPCMB3$8j^7+IfS}n%FW=XeU*!XgI6-9D5
zH~Z2*_CWe&`xuMsZmO><?RYb*c06GZBuC&F?+IJQ-I<9&^Iy|B^#V{ADzB!U>B7OJ
zBD+lv%baR;{|f3#i$?(^<H@l*PgIm!P(^mhiXAIGb`zfgj2`4_|Ce2^d2VWMb^wp_
zvj>q95$86hLF5B-xjt@1H>c2>2O;pso-j+Q#MAeT>L8<RX5tmYFPHS|BL_5IW}Bld
z^gSF7*bS0NI1yK046PjCn;H$;uQi)4CO1pU$Qx$*d<)2?E><LFo_t$%m>O+QVT~iA
zYCxB)D(2Lo^o?~s>dzEI*Tm5Je#K};H7VR^<NI9tf7PHKxXLVGCU#q=o{V=)2R1Z@
zO{(qR+6%epr%Y;#9PUPNHfQhYE^B7i7PVH?pHCP2*AGusYm~Y44CV-ZyJ}?pK{-@E
z#CodeZtl;;laiPcvt&qDiJ6weWI2{Dq~vUkOx4XsLE4wwUh3H=x?Jk&Mq6eH{l#^e
zVdRBZH3D%aKI{n-_?aOCE5j2+ZTvv&7V!T7KIu)ljyk(l5NE$)D{Bl-&@2nr^%Lee
zB0qMc(uT?Axe@lvpV`|J+2@6JgBHd#vfFmZI0v~*Zj&UC?amXEOZL#OLTjb|+i~Zg
zyn)S|&@n<Kvt@2G8<!fFksQw2ySAUx%sJHzIP^5b+s}bPRa|TG0V}1y?B`%bVhBO%
z)ghDm&l2}$dL#<I3=!t$hRCZs|8>*lO^rk3zcaAb;?emH+IiYRlPUaq-N2Qr>nQye
zxUqrPH>X3Tc9~QL=1gGnp8}T})Yb~3X46xWYB7jsbXhsWuI6sH^8@w5V$0u(zOGvR
za?JHh^Ng|IOK=O#)S#VL$y5Wrc+hz|hAlue_9|IB%b{ua$<WD6id|Q6yIB!^a1Rhx
zRs1+y<huJl;NQW~|L=nV(0|%nGxgkO`OF}hTx=@`4+kMZAGEqb6E_;7=3(UQ$Y$K<
zmKtYR=pCt*UD<-lb5Jf-jpksB=0DMWofP1E3jZ2Q<X1Voaotj0F{>jnXfa%oB0M-E
zlQK>(Gv{Z=IVD2c2kn(8aLw9jIVW<kc$5RDv-lnt((`Q4=0EQDZIU!vER6?I2ftI;
zx!H|mLw*zW#8{Kalg$(xPK~}jDX`l_)cW=Z9I10gdAt}>g`ao3s}6;VoXSp(R-Wfc
zpf%br+>`sN?JMojiCpNxL;|b9C=Wq{i2>(f9B6W7n)eHmhLDQp66Q?6A@Y4qolK%p
z_zBu$SCB_oU|}>fF`FbQRFb%3sS&km06$N+7);htN-R2g&WUJ8v`$m3l9a~is@a%x
zg`7n9JRAP&0tm1US;6&qgo&mfbVs!sh6+o|hSp`0W5(%co>Uxjq^{acd|91BSxT84
z_|}aAv)}{T&!q;{9HmS&IsnO44t`N8#tRrNEH(5%jG8R?AJ;|gAOw!l@G8nLB7ZX=
zHS(rU%nlT#&ecIuZhUSE8YI*p>T~P~S}V>LN{u2X@lh$Li_z+;??lv2eH{lqV53GW
zeG^iL+-d!a2>~k!M@1wTiL5)vI1MIqM*uX|%sG)?a@}gRvJKa^o*zyCRiNtj210d^
zUVlXGMUMzZQ_rBS-glz3XSp8a_hUB7YvwICiMa0?9}2-Un^5^lEa}j~Ox=>^FHTP9
zHVE*|f4s8BBWL(<Ml?RC0ZuaH><>e^m7^#XfyrUOHJn4(6=>nm6F;+std;<kTbdP=
znQ5`^F6|W&uHnkLM#v&n?x?9GMW18<aPK41W6x=Z$aSo|URW7WaChgz*e4FT8<naV
z$vEn`Po`Y0yiGL%C#sqIesS>naIMQ$sAb|&lImdIK30+^G~tgN@hYLd?5iEL_^DM<
z57%kQw}4Jz4}wTJG%k)S1KN@Rpf>1h$KM<qR$OH;7+{>fvVUK!OG1wMutCgsem?!u
zBkw$W=<qmn^R)N-ZduLrbYIaGhjI98tOa7S2re);wiQO;FIta43D`(=rO)Y~^XVhV
z5ZI{XqR%DDQ={C4TEeqE0;~5&*NeSNNS5_!S2=p2g<3fhGTV2iM(c;~iObXiQ@UY_
zbc}1IM(CHVlhv5FndK<5<&at#`$P$&(pFe$H59)2x&!XGor%J%46xRN4qY;z<i+SJ
zPE9LjR<y5(RXZGhM(OgbtzPI$RmW@&3H6O;^U1(o@r_q*<4CJZk&}~vO)~yh$oAiA
zq!XVgP1p?XkkiASCYK<?6ihie*uT7<8^vwA3&e4OX`!FIrp>|xfmrGngIbs33G;Pb
znpmlJxh;3w(byV5(*N?HZdhneAdJr28YE}yo>b)Q+~$3}x%NSc+s1#h$iL+1ZBHaw
z4-RxgtyU`|?N&n+PkUpun%{!&;czB%tcdw{w$;@8t{v^CZF_<@Fc=Phm(ZMj&CRR=
z?Ze+VQl7<<bp_s$b0{t2DA$jkwBt>VI(oGs3jlXuKq|O*NYekLCg+!Z-Guh2{ma;e
zt;&w}Ce$G3gSQo5T=&1?2m>JdA(877b=7|Gjbpc2dY<bqaS)+q4-m~&27dA7$XnA8
zY67p7N}?m2!5aDyj|CFD&$n)I7(bVj)`ZY*anKGuC;1bFvdG>fDwP02iBe{to>6Un
za<q6=6+7QQ8DlQ6Y7J>KVXuKxTb<YCmPtqUhPoo{L@U66ZOkJDvxGCJa9rLbw1zVm
zE?`FFkUgGj6c07xYx^Sg!kNNhp>UgxX>9!1RNwW*$h82#JI|Sc+3N^+nS)R=MtxWl
z9r611GMn2aXCPpidSQASs0`uLt8FA)R{toy?+vid&Km-Oh-~2vz!~*aJgI3nNGt$g
zLH=RrKA|Ps!{fX7AQOPXz1f|z9$egzSxSSrwDtgsT#5{Uc6wp7V3`ivllx5Cu(oRY
zPMXT!?hqcaF0UCVF!WvH)8G0V6<fM-M}1OP2WvN-BL|=Wi1=PN{nfr{z5Y?9Tl5zL
zyK5l%=W%#L6n<waoMA8E8bb}^ej+C|8aCMCTU=r9UVe77t5H>+du^wK`G})sB>#xL
z2XFwT+c#HEc%M%Hbm{=Oc3u>#wz3Y3Xcp=UAqJx$Xy=P3Q78F$Nj<zJ^BosM9Rhm*
z$-PDL8bESwNTpL2b$bf88aFP18hJx0UxIW&6qW{@Mfhn_ue2S0+_S&hS-2udB%YPq
z-ln%eGtv$<X}@e4)wU8%A?!&yEzj91mYnFL@EVPk2&wl?CK*z229HO}!%*J6=8u<a
z|AJy5?*)Dd?g98`uD2vwS?*I2^9FMPrWo7-#E%&Zz}ImzeLz`&Aq48oVHqR#-Gcl~
zYGNDt(x9auH$2<0D6V>TuNlHAY&;G`p08=8cZrg+Y45soAFc4?_;N0r3^s{Hyx}iJ
z*<!_|&TA)C0dsTnlW#sfl=fK7UDYuNSn{-ZBzz!N=t=?y7uFCOS;&g4rCB`MZNdur
zA-bM5Zr^r1xfQbkun3rBj;l!-6|xvNxmdZL%)AzUyWn^F(j+W;(rCqBJ`<P{U^G0T
zV<Opqo(%B0jtTQI&27srQ)()o3|3BbJ0W%Sp%acs9phoA+C0Ph(Hwi*<77pe9dmsl
z2Ov+Ka3%#1C`UG_g^&yO{b%&VT6Kg6DB+t43Ea79DKaF?yviiGjX^vJTDk1{H$a6r
zft>V2`HV-hRl-BFgZK0orZ*tc!=^##lDi4L?87OeDxTj5ee@<|<PdZlFk@k@^9~_R
zNyc0rk=X$x&I=j;%hw12S6!|dBJ|<7x|wkh|4Gr{W>!qb&8(Cy!J${KC+X3PT9w{f
zGj^yjsS7hCowyMRxyey31p#_gHyn?6>imMBcsIV=ox;dU5ECOv`0&D-qc)@`=2*4K
z<S&cj6%G6nA|DJv#IuJZYMTos%w;l(@?7k7A7Sb${bDQydGlhZZ1wADwq8j)#IL<0
zOChQ`O6ykj03=_HJQD}T%Y3JcRoB(b`J(pwQ|k43<@M%Hp3oNLB;ttOEaAN&bU%<#
zfY7U7_Fi-!8BpT=tYbsUBG*Lfa$M?{Jx~d=>b>)4@~Tj6nuM8r92#hM7giEUAhe%v
zc=243dZtU;KbOlUrT(V)a~Mc@(FtlS-As>Iz3R_a-evTmTeJEOOyf|z&z44P{lH*X
z{FI(vf<Mj^!JJ{_F;g(zADiZ~>4q0I-KjX_`=zfD*V#Tg$L^S#dXtEqxi}hXHuPCS
zc){28Ag*edzZpRB9&T3^UBrs-vU|}O*8(1`5TV~6*|1seZ{0k;HDPQt?U-MCDa6@D
zzdYs~_VJ7NN)!wrYJOT?U}m0K&S6h3aAcar-<(ZJEDgLUVlqKH$da^YJSAv+dSwgn
z?^JPbvUQTR0@_xPwcjMF;o1fO0m~WsX1L7fZV}6F{El_#Y11kZ(W;eM!iAvSB<a^E
zjc`{}G(^ct^qHJA^fCB}RPQGJb~6n8YJI<(&?#2(WcqdkkY04))zE;Z!~4Onf_$0Z
z0r?<$&LZ0(?%wbyB+6%nnO?6N)H>}%y!aw;JYG~+K9@2J{+aOpXlOkZNe!V>>w!YL
z03py^nh7v^Nl|BIl|zX+e`l_KC%!kJIBmHg7tDM2WXqxzQfAmxExJ`!lW_w}uIibJ
z9--HB77YE-tE2YuYjn<MBfx+K9s)dT$|{5yPsmjaFkt6Zs-zT9Hl#*)u?=*}4Byxl
z!icEZYsj~(l{d7iso$)^t8xDT$n5U-_E_6@tNTFJl8fsQSA)P!xliUF4IvutZhl0X
z3*{-*cfSPsU-Y{w<;v*kMAqVm@L>6eG)$fxZeM@@^}v&07m|~+U_I9imyxAloKXty
zufO5{AN*jsY7tfy^eo&nzq5>6P7=>#kWGQXg2MsoEIPX)u!PT#T}OI;GUvoIlu&f=
zS%{Ci0!_sN=JV)1<Ci+*@0nmX12MxoASW3_1y}5_Ccz*=KlC!7QbI2ukfo{gRa=~h
zKK8xFEI6r393U9KQaCFw(fi5&kgbyY@yZkM3XD{nC*8RU3FMTxGLs341onXBZZmk~
z?ua6O?J4p#v?iJY6f5SpJ45HQtsbng+mhWQWCns}G0@$G!7jgop#i+F6|5}ds?LCf
zus`n{UrLYjkFleu`j)Q5oQsuX#M<-hz<MET6W=QJ0CVFqB`Dx%F_ZlbV==AAMMhPH
z_s55Xg>$6z2nZRjcSZ|iv&u={O&N3%6Tq4STD%IknT&atqh{XXQla90M@%E^yPai7
z$=ACT=}fknt)h>ufZp`))${rRRP+`_0pNvLoNuOae+KcDeVP6^oAcEypJSf~o3^+(
zPSW!q&mY9T#n-Yp`K+4N4h$O@{4yYW&y@m9uaKYT=lgw%@*B`wQ;jXk10pB1Pha%f
zC`c6w0EqQKr9uGPb%!oO$NozW;ZY#o&?hg2#)X?=yimC|#@ERYV0W`!207lZhqX_^
zz%G^SQEUn|W#?^seoj;kyMoT-QSJfz2}4heGT5Uqz1!+oxtn-wS-dCwovsSkW=0Pl
zS#T$|4K^ug)>frGN6-ERD};r0w&F(pe#)C(*%{3roK5L}tP@Zt>ftEL+eKc}VQwmV
zc*Ax2fyHcjkh9XzsRN454y$H-49QGLhuxsrZd$?OFffw~yn{K&0Dt++bTaS!{rBA3
zi-a)G^pGB4&cOmW=sTMf`gwXv+Chz5g9;GV{CU9L_z4YE5JEIlsosP6VTKe&Zt9{A
zqK|kpA67)ioh;jTGsj6(ub5rmj0PLN8O+sJ!ZN(bsz~du`J5Rw8j2M}e;Fb3ItGV{
zG-&#!?Dg~N_aE!oy!@#Da_Ip22l)!6R+nFV)-VmAKbhVE&_p8y$$+;rZzXyFq<&w@
zeZ8R-kX`Pk%mLf2(@g}+XY-tAz2;{EN@Dd->vyuN-7vraB+b%zWqJ@6mDC7L4S}oa
zBx?|4xol=%xVuIr?T9MAGh=SI8HU7$FsRT;NvK_TatLVt72d7CiGp4{mZp?_8DIRA
zCgk22q>><K(;{HrYh5r0pzOyfT!GW1b}LM~5+<<G{S4ekJpTrsWBL7HakZqhLVu(W
z&?ut)vIw(1i4!x;r`V%r9OLKKa~2R(@v!d24Y>(ZMcePYJd}^v{N8s<p8gzf-F2_j
zNL4~civ-K3Of$XgDX8xNM%Wl5hIAK!f*#~|<Wt{{J-<Cp9B+p9UMe5Je|KGs7`7?;
zD5QHI`I(XUffpUoIwiG_#XOLGdA}AbvJ^gFvrczX6_yP50{~l^A8dh6Fw5%KkNx5T
zsiJEGDf{?#L_C5!ZglVa*YCY?r?!U&YDznmr4UkxVc^Ek8K#8D+uNRMo)&5MUL}UZ
zz5Q=m|BrX~BFQ)(oxDvVz=6}e{pl<qjC)~paWmu!q;mLb+d2F8Y$W5AL+?d}KtRpK
zK62l}3?7v-^fy=8h8T#mKwq&L1BpU#Bdi_ZRbR3+d_ldrZ~EthaBab^@}Ay+ChN#<
zM7S93boaUF`6AjVXWpywPgvgx480?LwMc*U8EmB`MI=o`&hgqqH<J*-3H$Dj{)Er^
z$iPxIgAY-DQ-~tFEKif`Z~{UWJ?4*1ZCzoJf2xra0jeN61?G@A2|9U?XWo;qJvg80
zXa|!iV(0T9^W}h{*RkpF@ar3faM_)%U_CF~f?4qdiE~xD)B9gjF<th}U~fMDB&#vp
zrUV>6#(2fwj{+!6e4U#XSX_Vcq%kzw_GS<M>AP`W`B)L0Cr_hqlZVl`JlkL%xyvLV
zMf2>ifXC0qG}80F-G$Xbe76_T8g4X8WCd;%%?pygS1m<9>4O(Cs&F-Ce>Vl+t&vYR
zdUSm+EqQ3#YnMpsKsG0rfL{SL=+w9v%J^^@dIxDd;YnGuOK7v2SX67@s11c&uHY0R
z1z67hU*SH0j(%czvri&_FCn4-(-st>lm^gNU%T~_ekJ<6|4n=k92V{i_zn(NfXn6G
zzxL_<LtA6a>=aN}b_B8Gj^qXxtdag^CuPE~0CJ4DLN1}WK~?=}g~uQ@zLaa4aaD41
zP`YkxD-aHoGV*+zf4@<aeO$&Q)@33Ml$#u&UOCT1M}jJVnLGX!xS?L6d_Kl}7Noaz
z0@FPXux+qKQ*0E!5`R4aW*Inz!9XSc(Z^*Ba*Wxu;tkv4_kyL)`=SRZwZCz_hZV8}
zv6|Z$%8ayG+6c&)lM1HW47}Q@8G-uzzRyS{g*&AuvCRp##jo=z!Dw*4r|#nHVhe2V
zl!A632Q4d~_e#GTzhQ79m|<w=vvY=K2hJ|I+K60?dCF>Q=~_x;!zurs?WmD9oe$}e
zuzNlQN<}?3mJ1A8?K{w1(yio-@eF)fJE3hrW)@vgp`K>uwFi_LX&8Q|3p`2p>s{oz
zPfQM?o%1aQ*yB{6!%jZ-vR}iwy#zl$mmybrCECf#?KOcfjn)7!-Zvuzz4_326@v`T
zH-gbBNRz1B0ogY$8QOLbz9<;`vpN==yUq0>P>A;l{hkooi2D&Y;C{mMwLpTPCrVzR
z`xd5!EV<73`u9|zN}oj=S?<Z12g}ucIhkhT+a(+Dn>u`7MM-EqdfACH^1`8)=~TUa
zF(HTcSI+Du3{S`h{N%gp{M*DfTK6X+9)ePv>$M}da$#!)?Z+`@i&Rm5HZ%WKX!Czv
z@Dx@c+>?jtGQMGuc^1x~p!vOS4011dkh%V*70#-KxWrs9N5C9;GYyc*jZs7%0BxgY
z3#5X<3XHsr_n9=A9IfcVeOi>OvPz%faX?-IdQpulZ(<YHZr||BV;BHp3{IxH+qs@K
zmPEYRrti1uFbdQQ!lCmtJ*+K~EN`Zva*oZpiFtbnFtjd@_=cS86j}VN6s#rALi{1i
zNmlLA2FM?&`gB-K81`x>n9e)Gp-+qWeG(pndNOJq6BW}YfO9E}GySGxCMrQpPvS?7
ztVx;?rMw~nin7iT+9y<kO~W37wJIZGx9JD?Y>s0@M$F}XSD>~ufRtXZJ?-f8x|e<a
zqv%zE&i6E{vMXl-r3$mHg6~W#f6E2_%f1s+^!0p@$5TC&1bPkn-1)130O;2cG!j{8
zv8HcW81<&x-v^l+$@oAyoDnw)>6<mFxKQxjp^M3vXogY^YRMWKD1b#*GoQ9d`5L@*
z$LzyQXdae1*%Eg&5mua01C8iB+nH(KD+f9G6PDKP>Y;nN`u2CEBkm~Xc9IXxjn$6e
zBbMW&+K2O3Dh(U(^}ASuW~t}*LqCgEnVLvNc#wX8c`*4re^RBhl>Rzfuh%FO&Mr*_
zN=O_aD^5NA+IrAS9d^=$&^|3<eO#&xwG#@D!%e&|NR2mkcz|F0@wr7d)v#`Dl?aFY
z6#riU!<fv@pa)UmBH{RT4Dc|QVB(W!mh|#>QF_KvjS5Xz=y0+CV5(3}wj>i_O~1&q
z;BGZ@k&XPsP&rm?o)h&S1HIEPbS*_4Qwi-i0#f7H!LWyq#GNy%*r`v1R`P#?Ge&Xl
z;;9`;?X~r}SjtU!(i1H&LH+kH0KQm6wb{kd7ElkSK_UymnC=1%B337KLvD6~>5(@?
zYJ*o{Np=#|1zaf7KHn4ze!I$YKl}7#@3X})!W6}290=bxzFz<q5w+iUMmqg||A&U!
zgjK`_Cr(FIn3;;MWt?g-pDJhEl5i`C+U)beNB2p+;hTwEjMw2|$VUj)1*c!)6O}VB
zrMOdfiOH$9;I6rER(J>aKVAK~Lg>ElRzMFfXAp(RbCsSha^?Q@0Hr_!BY@W8hp-C9
zH$)A1_{40%FLGt;*9De+d2!4Mu~Ihs!^~4E_?WpvZ>l||vCSvRix~Uf(W}m_u2`fT
zG0{|$irov&y{X6r=X{*R!7%nRkRQp~EP(|ehtUwCDtY!<ARZmc4n26h{Kue+grCc-
zE06>7W{ig)AB5j=bzGTh)eD}{GF4#4Vq7j#1JhsqD1r)3Yp8o5ke^oeY2>tarDRw~
zo<7_MxQuh4y7xW;nEf7)gR2UL!tjl>E8t)a*s~Yr=-(@^pX>crCX-UvO%#~IGFif0
z1oR&$yvH5zwC&1;fsMU43bwdL*RB{MnM23cODmdRIO0H-7z6n0Fy(xb6oS;dl_@eB
z#x<j^-M0n8chOYS07y2t$Za%oONMv(tCY0gBmDf=bh%9KO==fF+7A2UU(<FByvbF?
zlH?$Z$8wh76=vR*_(KpLPt(c=M*fHQ7L%5Q7LXFMypgxnLs-hicWO2C2Eo$Ivu*_-
zz#kEP?iohwkv6E91)rW|wb&vi*NSsyTmgp1@RV?KrHIIPfI_RH_$OD2xD=FS&&C$n
z-<WD!tSR}O=vb!EVK|sxK@K6`1bRZxk?-V8OMZ3uUVkBx)xk_dIFr)@?Iz2pG~Wv!
z_tLh&4u1hopZpukxSqziTx{`2=3g3B>FEW2GJnQsv@ASHq`t~V(S5E4HDu5qlPsoo
zhxM+;=iELH)7|N91-!JwZ1}W%ztvx0n}1<Zd_IwSCgBWTCpx5z&q$V#Yy~VL)uibf
z;dw-%BE3j)+H*$wo|&IK7cRQ}Q2<(fELX0fDq4U{k`2$3IM=$Yr!;%f#gscE0H6qw
zw;gPe`N<{#nzSE8fo~6&&6oaT$-x~~xWcd);hUmb)A1N5{D=wz%s~9PmR#v;wb=j4
z^f-aaU$7ndQUcY_JjBR?1Y52#gq0&Y{of&lCV-o1(|T|PWVOuw#vFDu?$rPYdTw}T
zNgNMl48I4=h3NyIqiRJjPM7&lMbKB()A!IefbxarNs&}u&@SOlr`SA2as*+38hU;p
z)WcH8$1!a~3@_{qe?KG;tDVWDK&wL6Z>&yE-u$YtqT}lP)yhAqaNq_4C|5)jF++7H
z1EUO(!50WKTCw_$WRmSPBiF-+Urib~{qnu(HFav*y|LG!ETdot`A##~Tn~i_&95YZ
znt38dH7I(8>19I1rvYLc*cD@>@)oo3Ba9jq7_}`R2M(r7PDT9WNzvwa1%XbQw<<I|
zB0$+hK6tnp;32CM#vy-zy`t+wP5{JTuHy1^q2rAVcJ$?Yo*|pV5Bezt%)g!YKrxQ(
z=(Xb$Fp%;xo}dA+WUK4JKm0(f0<|dEY5n<82*lr{Nc?qQo*@p-{EXk?!#eYWh3V0N
z;3io@$=aWHQV`4^NJLuMxJB3f8Mul6knm%Hm_=(@anjm>p_S}EMJb;J&b-_1ekKKw
zE!eAtc<%LE_yX-WOYx?34*Tkv6O@>Nnjl{2H~!8s&+OZmekB2QBJ<(}WEN@}&bqo~
zqptt1zyDw982~}{A|vmU4|*4Pi;)pLrMME#-{K<Lbr%Z+HUIR`ou%SsXs63oc|!_S
z_%t&mO5*|2aQwq>^u9Fpqte-NK^e-L-{wzo4eD&QkdN}y3nHqL@8l8<1+AuD-a|9~
zj=N;yPz<+72V)+V7@<(ci9hU?hswlLut}`rtk$P{pF*KF27a$-cA9Ta8QUo%^p1l?
z5uL?)yWKXfTlf@GndO->t9JP5d*5u)r3>Y+fz|rIuf9JVA?bRJ-~4v4^itB2I{S8Z
z2&K7{Sq$&bQ>t+HypgnB*K5n}%XgsDc={3D=*R!0hw7@r*+bGk%^0iy1~Ekt7;*y1
zhipC5tP7euS0bh%L|dknc7wVnQM#n>gZZ|JZ1!_Pc(yqdgOLtO-!;X<1B{?VX}ZKT
zvZQPWiSI=wu$j)|s@OAiQze4o?7r%BAw|&3!iOF|EL!iElu~|<*2{wM&g<fQ#{UYw
zhZlg7EsNj#Y_C~iXWpAJ*B46_ka0}0(}Q2}vqs-&Qv(MAf+xRL@%!+GGAGqjsyNIk
zc)*u%CDs2xS)Sg!w<hVX^zQB4ymQqYuL}1VlbLwdeM2Q0qs(#AQQe^0P4lo$N(_^a
zc`r%M?Fzh-k-D*!Eq!MsbNN%+v;A&QFgP;$uV5#TS;vdQ<wm;6q$MywxF2DMqf0XV
zdhD>tu^CagM;`%?^;Z$$*;C3_@q|VseL@!cLZUqN3tENeM$Q#+Twb{1N7T;Gg>VDH
zOZ<Av#f%(Exh{_D;;xEiUk0qmqTywf9v82Slqtnex$fIV(|)exLYx~3@zNekyzGeU
zn2zlQlf%J9nDbZ~O+^~FkA4kCL(slaI_U(LMuY!%AqW6DlB)w9y$a5F&Wt$8>%K;6
z2C?)Je0j5#brWnnb1QZ+@(0B(GcnZ5squYZPDm|C#tk`Z(VrxysDLE*<hh*Jx4jZs
zJx7g`yusXGG6gNu?sa<98eElYdBfc6am|j0D$rB*NgaKNP(Gf+DTz8LFoCKk8?O5j
zo%*-j4C#&Q@bJD_FP)TgZ?4=uJR(S_p-NT1TpnTa7vniWbe(tJHcM==|F+W+#a!Hm
zu9^c!tcNI9=1(YkQ+8Ov>x{Wy+uyw1o@^b85SxkHB#J{(Ijkyrp))R~9qSYQYSs1Y
zA1Q99j?DyAaMB}6n5ofPdRzgs=eErC<*nQcn<Vh_0*Q)MS%_6|y&Z}y7id01d2&&k
zPsFUQ*z_{hkV#rVh+Y2y7(?9n1TZs5p7fjev-lyQAMV>4%4%ad>c@)7<7@1h>Z?r)
z(<J9EaaR^%m<e}!YWqB(Byd&9`5V1^x%YA}N~!F7YT|apD%;wyj`yR5lF}QEZ@xOB
z7_BC=@+|vA2k=?xlXklQg3Ad(Z;s*iXs>;bh=ux65F*q$OxbTjOW%1|M@MfMy7r}F
zNmoH1E&9#l(7oJaCC)C$lU=G8uBsz8$rv+Yt!CWHs`6*f1M;wAg|jb>y^@pGkj$rI
zf?|SA`(=8CE6tXi!Rl*_`jp?+fGYfAQy+rP$2;O#OGV=O*P)An$_wjit`6-`E@|K8
zSATALpOR?bblzq;;rUB~fH$x}4XE34j*~p0+y(}BL}BAS07U`&O$zB%WqMIYG7gtG
z9~3eidTSt-qDYOxn+b3<@aPe_jC7yd98l0&<(Vy2_KF<Pxzvrj@Kh0-RF|B2Ln~Wf
z0K92MU-_J5ML4y`AHlczN30JX?Pl<aPTFCJdu>SA$_PCgwq2Ze@h8kz1{t+cSJ2(D
zsv%+iW%i|Uz1Bx-9Xp;idHS=RCmJoI+*2b~Mt-l1&{n-nOH4jseO*$rzh%mJ#jy*C
z$@T8V6o*fDmdS^(!`PjmOX~iL$#I+AkB|Gx`717qp{HC6L^~dJdYq-J$FvJFv&S1#
zzBAE}6mvX!4<PhcwmXYDT}(=!I{+YvjDG~MJuZK<Jz$la;$}c(ul`hMd=8PT$#SDz
z2DbIKS<okxe!4x`kb--%8qLrO|N54`dDMlRk3Fd^+E!5r^?6H|tq2}*0yjL4NUjt~
z;V|L^D!b->i_JbMPeV~ARi^QcLHYw~6Fv^+`qgWsqw=nGO;$%6P^3ds#f`(pXvF;^
z>8VY%+99tJ;U_6^)~R#qddN7NWD>;y2(3dkPzqv#5-8NIaI4jz%gqgzANeP78DO)L
zlh=`o(a$p_Fh=tD#Sa)`g<cd_49-;yq{b{+D*<`SP*kp;vIcbX5p{_DTyh*&AhgG=
zEYB^IVAYntx5-$?Z)PyHqUXgFTq3onUaJtIUa5vbsRNrgkUFcb2h<Kf*&`_(8GV~t
zX;^62Q~NxF$j{}Y!%`zq)T<~NQh%YoTIUUyoM8J_->hv|QxHubbo0Q``Ttxsr<(~+
z>q&HmJU66jnD?Di8;etSZyszJETo;2Ip5u!%imGeuPo&mYO<HzPika08|5cYJtX-9
z|Ib<-B;SpegMk4~2T#waVEi7V{bqaiDCwF{=EeqM=j8zJ4m6MJN>ib^TxF(M;=JGL
zOucwUJ>~Mpwyj@*qmEA!Nv?`=CD-M^o?%L59?#fj18VtL#H*U60?l9|y?PYmww;Hv
zq_#+w_ZhUCa&YK3F=#@q>Rb-NFW6&`&5e>v;|$}rl%VF||F69>3u@{L<G503OYIEC
z1%;r9qErZK9F-XBQWfIDmI;dz0vL(GK%hj4Wl7v>MNp_zNLVW>1jvM##6+N0X(0%?
zbqQ^@3uqN@5ec<K#Ksux33O~{h_8L>eY$hzdpP&pGv7V;`~UvuxTv79&M3h1;jxh%
zr4)$<kc_Bf>hk;hi0AE7Ml)<S7NA(zOax*BAWw3?INAv2{X@UuDRVsw^<lGo5-#EL
zmNqpQn;#i3K?-3IR~qbhQ}iv<Y@lDy2bm8JD!aqgguZ^dQMooe))!(+i4_Mp3Ud_K
zJ+CV_X)PtgN2oN=6;<UIhWky5B=cFITUYRl3!*Dkv!OtV>0r>hDs5_!MuqsdE7i?$
zIj7HK$>&xqxM3Yi^&hu004+c8-8ik}q|%n3_Q>`J%Ye_?-#JU)`o7EsH3#L*aB$2Z
zUVhT*tLxOM34mo8+<DEM2;IHWYz(i#X+?;VRc(PgD{)$4X1M!cXjvrL5K88Pvy7AA
zC6DQ8U1L`@(9n}+Eqmae!x&?5ad3TH3Bec=Wla=-gKE`4T)=Q`s#v7Wkg;2Gaqo1;
z59}sL!$Lr0=WPD`=V1S$lI=ho%R;%xz1+M$L=mrq;rkd6uY>3-2r^^OJ_Fp156Mz5
z&Zdw~wE%7}Fg<}M-uIo!vpc4SO?9^-nQ9N(G)+V)4o0|ytYm_thIhB&l@DotwlFrC
zzy@Sw(XyqRYG`wxM}!3BFzYd}u8a8s&JMIhqgA9c9C8@iNARXzu<C5x5MH}P^lIlU
zf(Ld!B;|;{*Kaa9sYs<s`!S~0GfHwwQ+4^mn_xEw)5&dhkL^j4@eaEx0hzsMv6l4&
zmjtjZ)MCbu?S<aRRKPtQnz=bFq2}Tv99a!rSg&rnXXi^->~#$65q3K|p>15S^;76C
z8pR#4j8bH_V>@Q`qEA0*TZJr(ETJO(eAbo`G?U7y-Zvj1de>NDZn0T-OMTHdI>7@y
z?TFc}jJUTD|EH0>0#=UHa?|A~YulmQbWFhkv$xB$n2oA9(RJSy7M&ar&c$<~gKk;J
z+Y2E}tPU0->E>3j#@c*jHF+55EC+sX?8`5FX2^XP4~pV%-~Htj2yNSaA%5k-v@)i`
z)&18+>aEzoHIgkz4hl;tZxC*67L`kPr-q}Yym^3RI)5t+@&^2E8=hj8q>PBJs$@5#
zGg3|^X$olT{Lu0$Rzo*@I$g>cAqmAM_dddCQ;=CgNoG|f3RW39=_4phNCpOaL2Y{4
z8LQLJ_@$tH`N=`mj$0eG4V|PMW-Ue4oc$QjRH#yAcM<t;U8w1^p9(@w?Dx<DX-D=k
z9e6gA^=p3sTg|++vc0_lvMCU#j(g_j8LJg9v}B$k@A4&_CyE+#$aHQ{4$Lv|nsg^7
zhw;2OLM#dqaZGS_z$R?(Z-U}DVH70PMBHy2t>^WnSv$abRji^e7~qGN-eVW+de|dQ
zGyXBB`)g+^qI+G3fqg3=hEAXFMi}K;YqNVA#U6&<BX6v?fi~CO11giW&;VFgi(8)F
zJvrU-Te>_!`J`jz9v2Qk<<`9HJ^Y}{Ri4EF?zIck^rSQ%g+092Z~9Kd9XtGeeCtfW
zRffMr&D%59af9LF8laRoVLTjiPJ^jVlTno)P0xk;6<L=jmn|TE%XOzbh+lOo#SXYW
z5f9}90~hTuv?1PJ5*RN{@+(2efGa$1!T*Oa{?`vFX1&d!*z5Q5sGSFxcwO<CAVfe<
zbe`PJzgw;tZEiF6wVwCvRG=nmq{jw;DYLw?YJ=Tg*cw};HgBAvm*s4)v^+6~YP>;6
r=2xSW3>$B|_Wz*RnJ@q0puF*L>|ckj%qUqn1zbA<Lbf$*i9h}qB{F~b

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/deploy/run-on-aws/ec2.png b/docs/python_docs/python/tutorials/deploy/run-on-aws/ec2.png
new file mode 100644
index 0000000000000000000000000000000000000000..6a52ba8e6d9d878b45c233a331cfe0c8b20cab11
GIT binary patch
literal 283254
zcmZ^J19WBI(r1zmI_j`v+qTnD$F|KI+qP}nwrzH7+qiM2|L^<en>XvtoVCtc=j>g-
z(muCt?OhcnCnE+Ag9QTu0s=4oTUY@E<l6}d2pIQw$iE{#-gR>zATW*QLPB!lLP7*`
z_BJNwR>mM8zr&J}p_CNO(MEcJ^VqbA;fb>u1X&>SmK5_9QL!M<q``r?20})*6o!H#
z;M8175F=otxkUMD7~rV2vay09+EB1;N>8AYE33R69#2Ob8@krrz^^N>Yp-K1kWrsn
zaDNuHAHI&>#SD|n0eC3bVk7Y1hzJO(VIS13;(B>WNDv9w(7!D3AwgMiZ8D@SKi<B+
z77sYXh}b~nzV}1pAaUdFGKX&K!3L&*<l4t}UY$wxvEU1-$%mqb+D@>L&)5T5$S3QK
zOr(WV$@{NCdlS@Y^qD{oPP<OjFN}mkq{LSIXLfIqvbUClri|?nxBPDUA0Ct!HiB#A
z`}t2vzC@S>%f}2zCEc1+Fb6zth5cEU*1qnY-)$siQD!dlA~@2hA7F)u0I%Ie)iAM4
z{QBZIIQJaP=ccLDPZ61h&7;iL(VR3jzMN<!qU|$`k{mt&Hg+QS=%9-J!q@jkQa^<c
z;o?LTk*!^a%)xtoiSscd(MdZrdW$if2!(-fN5H$pqo6$o^QqJP7x(x;zIpF9o?=HY
zGWLGiHDb%|U}5>lMmCxJlopnmn8a!%Blq8|5axvUU4<_a_xY8!r%h?26Ko@tG+(i9
zS%~H_y|AtU0tZ4+guO5;Ad-5pzd%Q&v=TTsBaf;2GzjU_GQ?fPWaNml#!NHl3pLf%
z*1j=#;hEwCJ35$;;qrHn9WWt47`h2<J^5Pdk$jugK#X4QIX?nG5YItdrHmtj)EhV-
z9?&5<59<YxO2MQLyqLgDetS9hLbnqBd<)P+1U2Y`>4nsa{Sm0gs)oQ8;3)_0?8}>r
zg99ce2+sj3<7YApN#|FwLv{l3Z3i^gSN;Y$G5|pj*`EMM*snB}su1E-s5tiLpsyBz
z4Fw`hfI*%s1?HZRff;Wj1eL!;uKme(CD;$JtN=Q}<vhL<JQlbpL7O~!1{kt|@LEVK
z5PE;iT5cQ=vEGSYBaUy=!S_1=TTCrz{E&<t$s1``kmO$aU1(H+I60O?wsE*20k?dA
zdAm}QM8qZ}<xnaS<$Nr;s#3Z?fJE>|m`BVg5yd&jM$9$n%aCZ{hB?fWs1pN6&UVPn
zz$@WxQG|k#xot+&NUmYj{ZM@a1BS8G#Z*rz%3>G?GzSP~U(6xTUV}Q8O2Q?)^N^^a
zV!dVkh&rg+s1;``d}nZOzkyvPyJ8MgEr#fziNVADr`_m%y1iGsf>v2qWF1(WAYk9f
z?#r{5CuR2!AJFX>pWqylQKSdx=s-QNklN(hEZfLgiB=H|QYqxauwY?R18~Nm)-*Ly
zD*_!tK9X+YxZhkPPzn@bV&jBKaWe7Malzs_V=zW2_2h1t>QY;x)nqLR*KynO6sX_A
zu}P!j#aBhkinNM(iaq1=##1L4j5(MxjHry#jb4p)CXkIDjW>-|M@=S@$Fq~WNZ%x)
z2}}u&DM#YhDO3qvWUPN%|H`Jap~R=jq0^<&CHK+(Yy*?&6X?_QD~K(zRoboZ|Nc{C
zvB+Iv^$+bI!#~u2<V7m;^N17~Me2Uv1{%cdwZ{9$M=2!EV->*^-Af>2g_{T%&d-~z
zo0pgcG}4b_jbDw2jYEvXkAIBgE2EXRmXVaBma~+@m3qn-Rk&9amtQFXRJ;m<RhZ>r
zl{KrJ<u1e5Dj>pPGtp|%QRz6EcDlJLt@H7UyJbE~-CJG*gNuV}f&~n7hK-|C(^;h1
zr%<Oh)6rQ*8cQ|kG<d2uHPD-MW|K~?9$4HlyAt@StEw+cXqWi%Hw$>n3`<eU5-Ju-
z?|-M1PRvTrZ&t16aaWfVp%=F3)aYDy^9knU%`9$LY!`ja_0AHk=~W0e^f?Iq6e`wl
z*6-RCA`XwMkPntGn!`1$WE|jFeD<}gfwnTR>}M@wXfuY%SWCN;LP&k09i(wo8`V{7
zvmK4JI<ihSR<U8Tl3_+*nPIhI^Xd9+BfpWK71567)l%E^yMe>xwpXEVew%5ze(|m<
zDeUKZdH-27>(JVWdxBHMBQ~~ThHypzn+u?<S+H3Xz-`sDy1EX1iFGl2@p!R+30y~D
zM8zbGG#F-uiVO%DAPZp&nTd9ePKz$V`Eq1(kZ>w@aPMBRwcFb>j1bFo%2b~g%Os_9
zOmR&aphH##s3uz$ouja8vqx(ttuwnA0#SkcS2eseJb`$oynOCl?$$R}z+Y}J?myih
zU8!Bg+*3UGpXP4GpUQxkz@|s-<MpeMTiBcRQ})3>di7~dlXgBHU+|w<pa;G;zIVRl
zeq?_Ay-Nffqx@#7vje2BlFyRq0fG$7uBtZ$hy$^avARNad4zdC^JE8T29`pcf)oc-
zgO`JRLT^G>@NNm_DHL#4kw~$dXv*2n3>#G)#T!XX<R^nuGwLkrI#-f8w6v78ls5(y
zu8Ta9qv;y7lY6Xe+jj3*Cu(ZsG~=50-X&I(Dw9g~8E@xqoo;p2`zj@tgu$5gefl}p
z6S`79;`fJFr!XZQWoX1blMgbL;vaZcIo(w6FqV@v$GwK`50dV;3ZIK`BwkXnGBvWe
zlF+i2Qln*KWcf!_cS`QKRn4n48p>t$s7BF$GvP2l8AZZ=3sffh-Kg~^zAO}uZ_|MI
z7GZCfCZ#gGQnsQ%B}a$2C%C8BqiSr;ziFo4tKLT^L}x^2xlYI9<LGe!dxITk^*can
zWu;EsMRBWm4re<veQI#3EW>A_X_R)%tB2HG(lT3AM`mjjp_~8D@x%e_eMmDGfcJs;
zRctGU7h(zG4W=929>xY$9W@Efi`E2v`+4lHq&}y(nG|roYTfd&^W);jXLMgyHMgb2
zfn>d;s58W+#hUcCcI(?mc9KVR%331cn&bxc7BwX-jpb@di_J)P&EdwKA52X6@W<I3
z?2`{qFXyb!?4{lSVU&<aNH@+AZnwuE@V8=-pz*2v#av~9Y>Wu*`D@y(10YF5S$?s_
z!n>)Etg^Uask^yZrpwxcSBt(HpS%0Q^%}Ycg%;ifUJT1TN`$;m{B{hzXYh;3m1;+6
z#C-TPnEBQ8DW@o#*2+J9i7krVA%m2*j@Ik=ea5Mgt10_q@qPJ>?Xo%iUgEe#mq2H<
zgTaaMZBMu(_hYf>K#~}jnj23my3MnMxv}9`qBqG>)vH?P%Kp0Db?QZ9-D%|f*qXtb
zWxKIs^$%d(>xq4Rd-`eRk!7>KXL0^~c73g-_4-$j<yX<VPD{;}{#F}jtx>KGgZ<6b
zY(3$a_Vng4aQ>>REyu~{PH<Xia-d(#K1Kq+g1`EG^BE`_mUZx6yKD<gxFJUnT_L9b
zLj1UOV!5U~G(TmoGHsfv%5I5ziG1u<do_3{+BrO^c<&ke0(@B(ESr8hzBPF8n0P)!
z-Aw&i-*YLyF5h17yiWa|=K4<fF>o-_FI%2t!N<(!;uZFJXJ!AWy0KhuGrX<Q1Lf(%
zk;KPJst2vkTmS-N$3Y40+p-8E7rW}W7BLAcX?lE!8DL`ijtTY^W@>td+3+p57}s|c
z2gE-%M=Z{42NDE&7m-A?6?A1y<Lrw;KE7$O60HmqQ(B^=c#H6!Y#PJYMo3XHVM<YP
zyFAw5`rGWMzOzgYTiZSePPd-^!1vB6!2qA8o^ShTP*WZX-vs`qvCy`^)g3@U&`AHe
zLB$nFZvJMtkmgEij%w0UT!uCPI(;J>17kW@fbCyu5D*?$uD_1}V@G`gSAdnZ1D7i=
z(Z49T{yzUROix7cFA_&fULrMVIRYUYdt(AtIu<$xB0d-b0s<a;BNHwKVbTAF|9#^n
zGIMma<)Wu|adDw@VWzXOH>GFf<m9AhV4`PYqWw!j>)>YXsP9T^?LhorO#U|?VPgkF
zdvjYya~o@dfB5Pf*f=@z5)u6)=zp&N($m=0{QpR@cKC0z{u)UC&kQ{y9RvOU3+8BU
z^8bPTGxJ}tf7$h4;&}dH#wBO&YHX!0Yz{ECcKE9rA0s0Z1JA$Y{KwS)(e&S-YX1i1
zWc$CM|2Fk+(0_E{lC?KC{_CcHoWaM)L;oLR|4q+B|Bt8s&2#^?EdLt)yDoe%JoNvw
z#(XeCjn*|FAOaxb!h%Y!pcmRudOwHOcq#w<&F{W<kpymeKp`O_p(-g|tkmu{oWDJ;
za;x=1=KK2kuLu+t_&!8GY<bY<LB@Y&UYiV%lA%ib?!X;fb#*i_H<*@qdV%Fvut@ky
z{s0Z}73wA2LA3qO0geOqZ?9TP;9&F?m(}+FKLQSLI6u9Rfd4o8og2vv!v~~k-|#C7
zAxBh}mzV)H0nDLL+&o%P76n6_@ZTi;k6?m*LV~0BPu~R0n%xwIh_F1vSw&YuP*6~3
zHp1Qh*2N0G#nLr8r7TLGX70oP)c7AhDWG7OIsw_dmL^6&e)Qj>klXGFcXq`~z2l;y
zhOQ8g*fR0;rJmGYs3T1}gt%U*Z9uzDc3OX*N``0`J4V3eoVM&{r!MB!OrCt77`&5R
z>*7jbbs#euLya5|i`jo3OBfxrZ)kw4EnXQAi_!-s?eO9LyVL&1lwLwx>+eQtklGS=
zQhk1E9EOyO%Nbs>*ob3>WS?6`J%;6MOh+Flw;Yo>T*E2kKBS3ssL>ou8+(Jy5Rf}F
z=KF4!;6BG<rxwPpwAoT@n08FN5vDazd#bBlq*ScJYELw3wHDa5mPm+d$fH^CD;|k%
zDjL}*RPb(62RE0IZ~w8J|E*c?D$-~zoO`mApjx{bHlD|w3`?adHUtc+z~`uEXhg(7
zHLpjl(HN7X`=g-|yo7*~5y$Jp8(N$F(ctWE!JNx8K7}GdOIi{Awi_b8^B0D^{1Btf
zCs^ma7sxnj7z6F=JS+Fja{_ywc)_b$k3NdK=LasD9D(h^WtFX12v9yx3-bVaCW_Ys
zsK0pDl*;KrxAkSg;N}biJ^Iew0Bmp(NE!Igud%feF*U^&xgqXl-r^7Gd^#3KbUCQt
z=~6n}Snfon*KQ>L?dh@2{;mv=%+KLqRNiPJdC{A_R1=%kQc377I!1rYpVn;d0tS^P
z79ZkHEdLsP6A5_{*<s0As3Ta}&Sr!g5e>_qtI1*~DF$kuh7T|APom+B!N!_Ch=odp
zKSV_G4u6bu|Ncy;Z;GyK@?<x?#CG05h<v3P(p$$}xafCiX#MW5e1pq!6R?~=9KfOf
zG{N?}5eWETrVBh>DyWA0ZpQON7;S0z18jK;A*N%F$=b14x7CY8<eL|fN{xB|fZLha
zG+It;Gas`Wu!*cptvU!BvmN7c)ef&%GBX%;SuN7%|AM4Y%qKIujgzsQ@BA1&oO!-z
zGLwzl(qiok`ll9s3Oks(x*Jq}D11#bhsUWM7vJOA+i?0=Vg1wT%WtLGf&DJ{_?MKF
zcw=ilGzkd^w<C|&wzmEM9S1mqe9c-X`}#)XIadt6aAeWYPn2YUQ~$E>?{9oG7VdS|
z&@Qx00s0H*O@DFzfH5`Z07}Ri+hZ$ZweEt!%l`s$|EU#9l|+?`bx%2^Tl@X8+ESYN
z`S-?hJ7c>S<KT1(z2;i<gi57on!=o!S_>DX=Oc8pNm;OFx$>`Up50+b1Ux3>Dw|eZ
zk#N-J_nfC!tS<gH5cwo5y{YDpUYVCZC_)!0mUXlM;s+0x>=VeO|Ni*VfBGS6XhY0j
zo)82CwjcbB7g{{vsQeU(f*vgwe7@S$CIl?)WD=rB!+t_yCzvOt#SuUgt*O6(skbOl
zAlPV@H7F++(>+gXRlZdPK$G4C9;zJym?-V~L8Jd%t=6`N@gS9UfS`l8*^L<-a((?9
z-W^n_Qtk)G4fIS%-mjN>4qFDazfpn}vP<}Ouah6u?mT7TpsT&6Qn`ZB?tp>C(u+14
zhtI#ZR*uK<8h54EoO&2OV=&iI6G^8YD<Af#?{lSq!;WdobLH~LXH3jCmgsomI{P{}
z?vlO`!f(lO@qB(;Ou&$;=s-^z`lk#t99>$NmyS3cBi22gw#N70k|{yl)@CVD5X>eH
z@hc(7N!Uc8*V9wzDN7ypRc-G`v#BUiu>q4WnPUZW)k0#9SC4#rr*qi+@E1{7@R|nv
zX%7kHZTB)3u=9rqitRkRXD>b+!V3_qjg&0y62xK_MevC!%9NP#g6O+RQGv?_kaiPk
z<WO6ucRXIb5k!{BG$hHg28ZRW6dHpwrbG<n2CCEjd&Uz{f+;3hI9!-lD&G+hM*0Gv
z7_1IBc-{rXn68ceI^}z>0w_cPt@9ghK6m#R!|*xLf1~)E=cmP=T-{gMZ{58xOxF%a
zQ^zl57=Nx|g~LEd1Wy3hCJ<W1`04VfmGl%0h?-A0z1dZD4=CS7mHf26?>0M(L_3^w
zGtr;6F_}g1F8VML;*IRj&!8VLc?CVGrM>+4_6_XiqsB6j1q-<uCkcA*5U!t-C*!*m
zV3DFh3b5R2KVWT}BXU5W`pwNDI8E;i!zJXoW#93KT(Jk*qb4I*1lx6ftrMM&M+LRL
zT<uyHP{2bLnc`KeXugZG8Z4x@2M;ug6=3WU?=Qu8%)V{M|M@m)SgKVAb|(_Je^O_N
zal3#(p<dKHRriHP8q$36lWonMBGxgo(v#$dH-)5K%->*>UhAvfJx7v~E=Fi4hcLHE
z#W61aRloB)Y05mf-`_xJsi0YJO>W&)%%}6Q?LdL4-QQaq7Ud-bSD(V-XNP7uGv>eQ
zaFpk^Kt`d^8f(6GzEo-^y#B|WnZfQ)%u3z95GtJ#ViQ%efU6oCYRQ)y4p0@J+nY(Q
zv|hx!=gJSAp>`cf)Y(Q)e!oJs3hram*}+59xd#dN^B%ds0dn2Ik+5lW>t3g{01d(e
zA!Dr2YSWimgH`Od_{Hwvgz=rtgK<eJCz9xAc?N+9*5d<{wkDSgZcFR>7%&Z8{wHjS
zCNDb7GORYju?kIALw4tjj!M<3Ktjc*tDp}w3-q)moN<I$YA<Qm7BO{4VFDU1po0RK
z{?nm^&?iWvwMtsZXs_+ilFeYP73n*T2A;85Ii<n*cKE`OcUXD~(|y)&OTHLQOE!QP
ziq6>=BNlt5sef^j<<jng^>%@0)ZSAzowJAb#}7ZBZ(w~!(+K$7(NP&c_F~2q<-;Oh
zc7;M}tvB6;g@t!QY`Xg+(-8K;i?a}w2@#;ZneLBAOHGvC8%1fdyP~hs)Jx&Uj~jRn
z)~H24q<Cb|rfurM*04P=GM?}S`L=TCO-rlRG(5D%UB8#ohYlkyCdDZ<)1*aGZVN+b
z<NV|t>K}-tkBzuad$kpHAC8z)k>640Fg4+T)P`%p=+3RJbiI8ckK){}b3ljm27Xhq
zuV^5Uxo9h$vhV;{!Xlf_XC{Q4qU+#pf4yRJZ7w6d+==e&j;s}^)fohToGn^jw8pR5
ziWu1}*F!g3ZHFO3Mrn={IDUJJ5@<V<#}N>a`eblyJ%vmx+r(*U?t)<*%~ajjHR|p(
zTde1F@9{|NJ6S7G$O`6ge0UymZ9y-TF`P~T3<20#hChIw7XDF{>J2;Ktf3*TH=k{`
zj5;1YP@7Oc2ou*FX`2lo1HJ_i7*|cMyrb($%cW=jVH7cvT&q4v`*EI`Lc$qs>NY<W
zgE5CPgn_QN864$$c1XwJ{7_1JHBMWWJ)X!K@b@I{Ftv?ZwiNG+D^bi#+DRR=Qg^;@
zf)w-HnCaRmg!jQ{HpSph7))krAX814+{zR!cn|J7v%60oqg<d=lt1%pEi@XTpn$?)
zbQ%c-iZfQgmdC@0>~lFBsk+89Y-2nS>QKJxjI><wTEdt*2dL>Kdfe4s7<IE2+v%B9
zQV8@Y=F8=wQbgmXcD@k$#36h(nFX=%9$#1_ojl_o=W8*!EO>se0L9?4ARA8_xABWn
z>RDzxk9fx&xy>tdE?MGF;7{4?MMx?!a?sebsqsu(Kd*mnd+qSKtC0liRIj1&r86F%
zJLC4wIZfQmk@MQF(_SL=aS%yBA>a+>NU9L<Q1O(-JntWLbN@|u&0~CzatJ;FTJ@L<
z#h<=RMTTZ`^APx*Z+PVWneP)pG3~rzVwk=zXNldVsx7SJ%ay**t${t2W9HlK%qgXq
z%RX$<e?yT=&dV86by3X6v8aY2ZeQ%SDS=1w<cj?R36Z}n5tN}x=0V_5#;w<3DXs};
zy8(8s4Woetn^1;5<4Ut7zDd+q5Uj9W^&IJB{A|yZsdZ3?J0$@@Hd%qX)0n%{T%|Ik
z?t)6aCv<;mTq9fbdH~bncx*BE+w<eAi^RvH)Uwj)>hRv`RS)WxhXeqzi_$8^dNtz~
z!x1T85)F{Y`PxLGSd2<;o2z>LIb2Ds>UJ=J?vG1tk{+lczwuc6o5N2_pD^`t#I{nc
z4#vr1S)h%nNWMc<8K<PsbOV0@uEhL7(Q2tN>@uucMA&@J;I(C>My$Egxb7MxP1?Fg
zrDTkC#e)Ei{sYK*#Nj&{9>vgU!>&}a>d{1%#3_$P2I@Nh-6AHnxt}fpp)24K?q>cf
zea-c<=-BFwYu8g@Mk-hgysi>~=<Yx;qB+K1a5_Ry!fQy+s<;qfbHRven)qHDYsK*^
zs*qt^*klS4^}aL`zT;`VUo8-slavvcER}E}CCuCE_TUa-WT-%CEIu@v&d&<9I5qg*
z7OcPClx-p;$*5FD4iWJuk_j15Og#kyBZ+=&n^1H^x9)?${>B5TU7{3s1i4`B?3M8;
zYLOdA93ANvm$@28&61JkuFG}>3;w<`hZ0I9zX7gDZbloQ7FlX@mVkIjG9t?pq$fYz
z34F5y8&7!o<H7R_FglOx5f^oIOG?yus3(AYPj|5wy6?^;A$H;{#~{XbFcz1!lKl_`
zyO*Op=Mi>2f9c_73S#gaEu8yIQUfhI%ZR-l1`g7Fs`=uNvX3)(o0)23c>RJSDA>0$
z-tCll40`}qcQ*tVQHHjU4>&ZfyL;4>a#Lx(C-A~szKj-`wn~#r^%A&NsYdo3EF>A$
z?T%x7u`ie*w_&yx;WVrjaiuD|MmMpFI4MPD@$rEQ5R(-fLjDt4>)phYIv4J8$sedK
z7G0orCC41MJlN#-Jc!5bm%mFmiYeiFfZX3r#xIS6b+WpO_ep+)Ooj-GcQ}^k(rXpC
zNggQITf`d@P0XeHV_JDI&P&@VA~%7-lDMX#`Zo;Q{UOYdT{G>~;NeedVnqfB38)_h
z@<kY(pC9O@LjblUEBGBo@|I!Go#_m_V*q=B(Z0&l6>D?XdGe~)Kf5pUaNi~4A!zs8
z!|BrT+%6#h9m%WDBW3xLjlM&6r8eI6JHc@UIM3%vfa~6>%yaDo0m^qv-fp=t#d>Jz
z!~Q}}!TXM4wK<4p6B3wA)*7m_&Lwnp$lnFDQ%z#%E144>3J$O+A4urvA)DmLH~4(O
zY3+_1<(Mni{RqC+auG$xuLoGqKTZ5pe++8%r}rhw6*=g#yI^qf%_5>(fJ~CJ0#rPX
zPBYodj7BRIov-!?iDep-OGumh3c~_B-_2JMs}3))G5t&#AnmVAI&YT@&AgmoVT)-s
zjJ~?PFqo}*vOQl>600%7z_S;gL&<T=Zb{lj3^is_7xw~4yYZb>ve;ik9HWPG@5mnY
zy*bAji4Xlx7}dwzDE+|Whtp4RHjkmXI&Z1db;FAy;~zXHny*J_rz}i(AU6fb@+bjS
zWHW{%F@{H-BBM%y=@+H`sf?Dia<yDj(-mqe+o&Fsp>A2Jye{30C(>Ap?9#7WT^@`F
z9oxQONmDM`WS2}m$e>j$o)6cNn5w~i3L|yLYCI}mFHke}4n|bJC~cgGD@&sBqfePl
zu>-6X%>~d(7qqG=QPS##ELC=nT)t)|-w_<>pVb>o^!o#$DaO@60Benm+ujhX9`C!(
z&!yY+!z}n(9Y3e4&3$>=9Cl_7&s$SIXf)WA*l5q{ojgX}5bFf?ntk}It(Or&-?{cu
z+yc2l+9HAp@Y8ri^mW=E5f}AwY(a0IFYaf`KB5Mf0NiykeV_dl0_qG23SGXHtyjSs
z&A!uGomj9XGrw5Q6^@q;I&5DZQ7g2*v9nZ13x&iO{Gmsk7d{eb>vyx-)n;*^tRF%Z
znn*v4>eJ|O<35tj_9Hr!Tpx_rYR_8hP@x~44AO>ed#%7AmK~^Q{=GujFC5X83USz$
z8}<{gD0h!zWR6d!8iicupc+DKwA55Ii_3}PW;~?x0m;haa`6PXsG4a7tAF)pbyT|@
zuG&UzYIyr5$29H@TC^=bjtoSd#gbI}*Ik}+RlUhoqxrh^WTrsF&HUUbX8a<a&D`YB
z_o%R69k0rlFCoa+flU)~?HZgO{1YxXAWyYIwHu|Q42CN$j3E1cQz2tckom5C43#R`
z0gV=;;~^_exT-n}E2&pWmh3Mi9WN_a!%5Nv%uc%!(VK1$b~H150$^dT^ao23MHWlH
z-l#Dsn4S<t5i<M<y1(YfCjT_`(&R9M=6o_`GE$9WtGum>eG#a105AHIRCUHSal=!8
z{yLVyWpwQ|vw}q!nwE|nL;MA1G4kV&r3$Q5zk|W*8N3>k4N4I{sM%sOB!t*%ZRDjk
zhZ7S_N=+bxMuydf%lEqVXxnJ$Tw@prbS=k0Et}QO7#wa0M3Dj8*g(yTACN;N2~h>y
zbB)S|>sNO3?}uu<4w&OAKxq1@Kqdxejpi~@$>hqFc1wif$gEHtd3g!}QEd)a|KNs4
z?nmkE{!?P2#$5NnN&|U{Xg@TA?U#n6<fr8Z`QC<v>(lDx9C3qb)p@S+-+8~9bxMcN
z+Y(0TTef8r)~@52se=967_eQA1BBLEHj+*dTRo9tixR<3$A8)`B-0LOE?fOU_CT<Y
zM!C;G3`{MLx;ga4zn%R6=E`@iJu`edA@7n}(X}Ctn&F}7_qgfOm}e9f;{RG{#_cZu
z2ID|?biF@;l7Sq_Q&f)4TOSYhj&hztl&?e@7-GUo<~3J2^mj8{RF297T5&YBphyHL
z{dO)*@GTD&+XM0K>B5A-pxVxF)yldF_MKts<)UdAk#|r{yu;;kur^QVH_(c`&oE&)
z>h{18!M=;a_wCvvjE+YhGo11^`+nQq^VRQ%>roiGnVTlH@`R=nx;joc=Z~G#`E*iI
z)#BN9vUY`sL)|ycwR|d(@%}WQy^k23zHh*duz?VSIJDOQm@pKxl~O`HBZe!SM!L@H
ztr78URf4P;+VXF(wKdlV_~xlZ%1BdAF1v7)qT&;p<9_{zRIzyDRx@>GWFs12rzG@q
z^JF~5EO!e@hSVb`I>V@jklTyB29+hPQQJihEwRmex*(@?i$$nv$c#y_m@3^Uls@Gw
z`seCw5_yGfl8)`qoH5QN8w8v2;4C5LZraqv3_5PRX^|tj8IEOo6wOwf)_x+W7}X1#
zEjuaD_lyzX7qskb24X&`SQ|u;`T5P;TeqU`EZ$^c^H|T9F=ed*Sg1B0O^iKJ#}OO8
zSaZg6K08L8@BL(=4)kL(_Q;l}_>WFGb)`yOh-uyM7~EIXsG*U1gFw3@ZdSJ|WE{f2
z_XCHofkhS@)p=fyw!rQ(2)lPCRy*M1497uHu@;t!cy%NFJNI>`A;#}<rzuv|j7CYf
zS^#Ku&zJm$*SfUPPDJeJOk_Y^9cuApUjV&NXu*TzoBnJE#R%;+8r{*}sCWrl5Pm#U
z&r?r;N^~Vj_r)#l+O{)%wtHnl8@^N~%i;#6L4JhcNAMH|w-euIW;-H%*BT~3qe)<^
z(=CdV+jhryaq%?CdK5oC&0AA!uH*W+RJ#IlD$&8#AZ(zxgss3d$L1y~|ITzOP?pbQ
z@m<B?Xwuwxu)Zr|rTMGt9@>DDaBNUF@-qz?e*p5W{!r_rZ30BqEyL(;UP@!Eu>(8#
z7W=U`j#R!~&Kf@<I~v7TIrBmYF1@i6=Fk1$*VaI_6D;k>ehhzDijMo-4FdGov^YJO
zgR0GlDQ|nO{s$Kcoc-OJc<1w^v**X9-ovwVBV**XlN$_ey0+g&n}cSXy_=e-YFrEd
zh2NuQv*ASaz{%5Ch1sv39j!Kdb?q8CIX3fgM@R1u<|P#0KK!0Yn8R_s%U5{UyP=jb
z0{VQDU1=7;hOgH#Vz$>Qp!0olpj6Tlqbp?qm8rBdDx)RW`{Re}{?s&1!c=UCAi<;O
z8xX&j1tyTIYaTbq!DQ$*5ai+O2o|M$Oo=xdEA~;=dT=D1g2x1HT;!0stdi^k0^4P8
zc>RhQ?EYX3<!?(&fu*V_{^x<ArAL&8)9s~ijk&l&ufLpVB7JlUUN^3EjalH7)eIM(
z&;nVMZR9gs$0;b!r>n=bB7pLJbi&PF!}F<%R5E2ZK-R<XtToOR7*K7txF<1)#}zqK
zs(kvjXnOKK=+OE${o_Km?}S?q5p%jS(!-ATX%<2sKM3My*<y;xQLkS>qTW|@N%kJy
zPo%Wl`3WFn>7nw`F=bxF*3eqh`CQfvr+=VPnP;0-p6n9o=ba)(_^r+(c@RBxBsY>8
zA}RIHCJ3p0C&n<bL7Srv*yCT=05mvs4Q9Y+$_urA{~+bkH5Pm6h>H_=!AR7IQ<bKM
zt}?&(?dq4_dGscQ4h8b|18y+Q&|Mxz6}gO-2^xftTi8{g&<+t%EU_4dv%Ekli6xtj
z_sxbqTFthxgZ^B9H%QNCUEqAj>QOzr3ksh11DDpq9j(<$Ygh+Jk{BC|+%d!No60ty
z{DCN4yRLD;Vpk}>*GhI#_^d$k>~2_EO-GS!8%V>QlQIgy-76sf(_r<c$IUE?wtTY`
zAMwT4y!UiTeul81E5}JTtL3u(;aZgAz}T4E>PTyZpjxLB!rJy)B3-(RVc+{p>&q3?
zT-O9Pg>GK{B5ROcqVvUuANE>v-{k83?D;w)1v-kkL-P_t3LOAE94!wqbg7kWQX;SH
zjQ9*{qD*~=j4BtSF@23lcwWQt&3=9|mmBbeOfmFHR?+c5L^#xGz3qa?inYw9??SUV
zjOTtJ5BKsyZt2to8O4yRtE;teM#W+=2j1oow7c)3JM5@Ij!v&LSP2&bj~@hV|1s>c
z4HPrK)5xcd<hxjc<#xFqI2suFd(dB#D<iGZd%L#f^=6k?R|V<y#SHoI@l&-05GdBs
zW8F`oZt*GDemmCUA+PQb$+EgIn!`W8v06S0FYmc=jY7WP#_o|PrL?7FjvF<g$Fx83
zxgjC4%e9p86QVS5&E5CcAZ=s*kI`t~{5c8{%ee>CqjTJv)E-RW=F*S2xECh806JSL
zwCLX4Pa!+Ww`P*gYPRj`OW3k0Vo<HXgpTh^GaipaSuI<_kaSSxO4YugUf!Zp!AXop
zv&$=YclV=XL%+-y%UPW#f0ZFd(-<KpNM8u^nNRkMcrw~a#t}D?gw4PYTP4w}qP;4v
zbsLr-4Y?8Dt=LGP+4$UGj)*@dk{eGX-SQ8*5)ojdmjgV#Ev82?He6)In1ogJKZd7L
z84V%Vv|PTS(Pz$hirC&~A<W|LP~I!1#I$-klM2iglJI{$u{SoUficd>|4pVW*QzIt
zv<$|i4ou0Xei2F~h(6vN9^>Z?B}G}ib-KcUipIRaCt)wORNdVPR~D#vW9xWVF>~?1
zgJ>hwCz$`u7|0QkbS9;)goJ+CI=r1TI<DBV^xVN@U<`I1=PSwZtw4<GRqgGq)SG*u
z?EThgvjkfI7~QwBBpgg6*%zcWI=93V2*M(rIvB3~eVAl2CdD8BqWSlfOLlIuCsCS7
zC^$xBCT9dWMS#Z#_}E(iHwaV^Od5a;nC4tg+ZRm2aVHTi95<Rc@P%#}8!~NKMxYG{
z!laZdRaj$$xh)IRgdO!>;pf2z(WjRV!AYzr)C!QmFt+YJ;daPT`0#Cb053_b>J8ZI
zxSbDb+XP0XbjXIIUb_F*y@tO-GnRz>ke`@Lq_=_7mXxs7I_k}8+4e^siimHeG&YfG
zUXgp%2t}#Ul2xh!cQ_M*LB#C4EvYAI+MIFjUU-D&nZ0#3GR6K8H};dTkg@!IwZU`%
zw`D6*{({@!WHD(GiUl8)YLKs&>*?x?pGLDGj1EJ6iU&3$B~Rn9<6?`zpwVDR!f@J>
zwHxx>8L#(ZHN(971uI_?wdm#u!<kmy=A%eyi~`ljt;s^Er3$mfX4?<S+B`}IxD|A>
z(Po9SqDG;CA@2xHsYbwm7KI)DZnQB`-*ch5J&9!T92j!^>ohg~DHGW}sFwa5IA<8y
zhB#UG>r#>o?=iDqcZL`61onG9o?2BVUEoSzt~ekv3CTRd=;~%8)ydS9c>rDBmP|jP
zuzlT}Yb*B`s=M<`j($%X*gSg&uy1$e+h`&!xGvBGR)iy%<lyvH*(Wli14qPc`dU3=
zMX2yTWKu>F9cIKgE@Ae(0pYf{Kz8@uTD#V96=s-R{o52hkt3B|#+3f{BOBRg-`iFH
z`L$;#M8y&@)LTR<M9SY{wI7SHuW+-1n%VDoQ7s1vg*_6h9?I6AeYE)-^J!?yg&7M+
z5owdzT7DGO+|n5=EYxLyLL1?njg>2n`Lrr2MT&DKi0>+u1@c1OMILI5EV;uzcYiRI
zgyBSO{dk&rui1tf`tvqDtAA?>@sjbBHF?jFL5hgiMW(a&AQO*#_-%_#iTPpabGY$r
zHM*~G#F$)O+jsMZQd^$}4xxD0vAQgdE0-#j5j6$m4(UO`QK<g7tOgk0>&7-FOVw{V
zoX(5tjVG3&x~Iq_`Ay2Su)cWoJiQ8;$+a}k(e_AZhD&ROa65WznjQVTfX%`ykSq24
zblm4Rl5RB|qS{#KPHL-<#$sJdy>FLZ$LLk`!4JOOC*Midv=&WxWv#WF_<@bzWT`^#
z<NRm%#UMJ3k(jT@zKHB-Cdc%=)_HhbI=xh+p%_EJc;*qHY54X85N%PS(G6}?{r;0<
zkirMNz4MlT-ae?#w(pm4tgdS)md6w=aN8jTkf)vV9J;mI=0F}U8sAK$-RAru`hdZ)
z=8s#_xUOgteQaFevQv>gqxng!^!$L&gIbgyo+tmI@nnTEdeZaxno=s)U=)8Yt^uUV
zAop+BBQV!07+Yxfhn3HMx6h|a`n3+FD-c8fX7HHq^u36`p|MRUII36Lh0jd0*lV?g
zzq`2kbbl)VQJXWEZzPc^C`DwZ=2gGHkTMyFZe5d1+zvTafIzHRXK}Lm#U1(KMI;*i
z3IC0<PB4#ZrRxh(La97#3CJ6T(C!l)mcYqSim6zFh>}~i^!JYhWo>f{dcT-n@HRB_
zsKAl1wiUw(r@*9xfz?@8uA<A4Qo4`Q7;T@JtP!&*c!u`_T=WONXy#je0$|X~#fbq@
z4R-6?rZdblE-sY~(B4H4UtCeslLe<oL);s&`n{hFM!frK8Q~k0*N9;ICd-C_Xrg0c
zTUa0=@0*X!a^W0mK}wB)jHXnBVh2S}we@DlzP<#I&1%C8kU0GQrQ-(sXeLTBb^W(k
zLs1ki++vOIAaGqoX$9IaOo596kh&*Ke%o{jkk1!J9gcd7y39AqNci2RQG_I^=wYMZ
z6kK#eSWI52_Jc;V`45H$ZSUoJljyoAtF3GXS?g7L#VAc;$<q@?GQDna<<NclAbn8&
z{A1$iH%79vnVPN}lJ3uUUbv4}!^UD%rNGhG$Md-cRebBHdl1%@sArjdM)P?iW*?uA
zDR%R|u7pTkaO;k;ENIEXvPE!q&*$&LB4X=y1HteQQPck1*FKTP&&@<iZS<Wt{O`aX
z`dK35EXIAJ=yWXy<{$EsbY*22YB6q!nB9}NPCD1u9GfzOp%T4y2_^?0tER(|m=Vo`
z%KiZWqtFh=(_er)#g$(R5{ymmK>E&CHVZ{AkI2>-495OfftYMw^Sz$!_t&d}oVz9C
zg{ew8Z@GAtV*<8vF%H1mFG@=R`HL0&2>LBAy!&JLAs;*s)G7__08N`VXctE->EN`F
zG{9mY_0dbmb@u>}|J@8s1o~T}ucvs5Uxv-8ZyK{HrXe-$Gwel9dgjo{*{U&HaUqCz
zw<$0S1&t3>i3g458O3G8jlKJ9Gb0)7%^WLzj0Pi}wc64jIf)o7*JupGa-iMGa!R(#
zZjnh%1a!cBuzu0`H6)vmTzba&Vk^SQvMq?TN)JmecHw19GnQ41j%V6%;MED!v4MiE
zQa9w1+l3gRh2wr}{j{iPn?6bm+BRa&v$`({a5F@Hz0Zye3lOi3#<}l6`Wi3j*+A7O
z?TKi;n*G$U(&WM=B=Oh!r{X{V15}e3B~4x(PF%0zpaysOJw8jtGE`R|%x)+_KK^Oq
zszvwoO+S#aB6PpAyzTMrx<~AEzUcN@QFi@Wxk4rVEql63t1Z6&mBMm1u0YHxJ32y9
zV8`l@PWD4vq}?akh_Rc(=Wo<s&)SLp;triOuN0VMD580s$5ah)<Jy=*5$FSzs}48z
z$8V##s6$Zrp1xS9pFc2}0wwRntyW9)iq6eBDT3l+@SaAtYp8&a$U?bqA=(&fRfrI3
za105*=G8_Oc<@gL56LLX<+^ETdDAhcNZS=O59x?$@kCQv9Ut{oTMnoB>={+oB#Pj`
zU{ag5y&(gWWA(pn3MLiOaEv<S>!Z<YgmzqCg?2ioB^or)o!06P1n21wh?Z)$;3c_7
zt_$_O0DwIH!;btq9VSt8n~N380^8jmm64l;{9CT?GE~Ol`x!RfBAf1yh$tvR5xq^V
zuT&ZgPzOzQR710{^xdI%hZBk@Uu|<j$m52~$%VOtO9DLzQB9dhd3@f%;~M(zrXkpB
zS4m2v$%H=j_!DKRV+uS>+U5`YgXKy?U?;DSvc)HGPnL$kFMl^`8tWL4!toE_ewj3|
zhlQsj8qV2Xqe_ib7A#g{y~OnQKxfn{IegH>cM1??%=BvKw`VBF^Vect8&@gXd&^lg
z0(lnQ6MlQ9S0C|vxuSk$fF-MRtbr0yn>Ej4j+p1NeeyBRr~qW-A5__UV)K;=)zYXn
z()%?PDW;U%N00Wj^k*zPJ3dxW3b}Rs{on%k$6eRs%NF?_!EX|du!K;oxoKsaiIi$k
z78Ss&P1zO@-CmlPlIGTOi1ZdSKP?s?G|-#`B;fgSwdk-IsH%UO5B-+=8}7qfdkyc#
zirs#x8@-YI9scW#16pK61sg5CivD4zxN{5!T#}b`z!_Q4!ebCD`tG8tjy`5uss5BD
z**!L!HCce}ILqBgTkd$E5nP&MT9VqH*ZkUa7I(}?gy7)l$+p7`ubJE9;&7(V+hxo1
z&b{|2GMcsE(e$wIZx1yJ)$i?I4^a^0o)>c=6qnV3AzWJ)fLO}yLGgsy!+@()Rzu>C
z;DS*Woj}_5=M5~y`Co5RRi0SzZ5R}nD{TqLGlVRX*XF4dwFcJd(J`3Z$&*?6A#YbH
zL46ubn-T`|>)VbfUh(OA3&W3z^`DU=Ij&$eouAF-2|=6B&?s{arU(<dMHb2%ZX*D)
zld_RZgdtx|M2>FFnF~p8RnT{U*wWb7FiV8|<=j22<K-RRraIT|(hv5AN$|-mX7;g5
zA|6?AT6(H<X#FAy<*Myag80S2*VN$_(Tdmj#*b0uu|O*rl9B@vKJ>msPG<CyKy6f*
z<d)pnwPty#TCsz}h?_^il)OX$6vA+VEMM43c$3|x69BH}C$-9sIn0klcQ0i)?QM^B
zEy(edFvbLt&59ln4GoHy>`iomIlRo7YBtFNX0w|Z3|9NV59NYIp)9T@Kxe2cYV_I2
zR0caUbA1#xJA=g+h3BFR2-<u|mz3WI8Nk%8j4a9pf?{o4zyX2(okiz;djVB=Bl1X<
zg@rw3{O1JbE^lamrK;{_xW6%VYR}uytY`&&xcIg>bEm=2^Zlif&yY4l$o(fxYnBvD
zKtu_!&~t?27~#VSeCw-{KA(!6B~P=QpcVF?<{mfV+}>oEEp_1ddF#<jA|}<K&m~Ek
z(S6;t&1QR&C2PR5eM;sg+&Hz5>X1j9+*n2fI-Rz;@|8&?cH5X0v7$a@Erm;Q80#wp
zs9R+BLi$06Y@aMM>GXQK{LJhXDw$Rl?bh0`__!)QKYRraD&Ae@Mr8jl%!p%xmWqt&
z&{YXPjRG(XpO9a3h3kVm9f|UY&#xJRt>?{e@^O|ep0Nq!=3WI9uAfIEV@EQtdk(Vm
zNi}@t%X*U;>@jp{yf@T_Cy8Y`gvzv@cT8V%*MpyF;*E$h&w=kSSVIB|>SeWxT!OFi
zESXChfGQ4)Kfk%@+g0t*vDhq(!e!Js#)%YuRofR8NVPGAu(S@N|GFoZbl$e+G{`TY
zEaV!!W%r(UM6I3$$v~41Znmh@YylZxtS|<noeSuKlp=sv^+SVf+)J#TTsvQP8pVpc
z5NdSy(^@@ke}-tRSRO#&^_5Uc{jJ7s@qp2JxAdmgvd=M|Op{v8(w<KFN)oTqMXT97
z=}LChpTwBoI)wJkaxSLPq6-a8{*@su_##f3BFwnZ#MxH*>Zhfu(MXw9U<#fFO70}V
z1hbyN(T)KPp<e*u^dy6mbfC66a;17iQ|&T&u+EFq)D<~%{%Aou1A<}%`e1a^1x3T~
z0a=q4S_IHe@3L?>0)Fymx^E|{&u_fsel4p*(?JAA1`o`+b^}MG&d2^36EL=~8GcG=
z%g{Bav;5U0=~JJr@cCmVw%Id91+?FyMA>kzKVChSE9D2!7@R!C+l{)bL1_{PZAS|`
zw`Z8;zs4;YjX$|0;!M_gS5LX!CEv~wC8|<V314c6eZOr&X4fc&C`4R$RS0P*A*sPc
zr6&y>fAbzV&OtCL<~<n+lY110r9aJxVZ2Y{oFEaMd?m)oO!G&cEtNNnkWnvIg7Y#q
zm<^p#Yw;%D_`JDy?m6SiXjJecZ8V+?gs_ZlQnhh(-_cPj?WHvu<&ZYaZI|W04q99{
z#Qa0L_F9J!0lUr|m}fithR94A0nX^Otm8oogH9)9Zos;ZMjjpS#W-P?$Rm<OAxQg6
z+_JQBXg;6%^|?XYoglP>Ov@>0=30GTCM~bRN2y8&Me!7>x{xad=lFy|=_gX2dTZ9Y
zph3Ku&ZOmQ35lo3!JI(6*Jy`zN@XMgoty=5@?}EGT{Ck{8-jY_ZBw=a!-#SsD3^ur
z^%?!g@K&WOzV~a|Hj3hEz45b@!E{=V_gRNCK0CZ<81gRP7cZ0J_s~W?TV%E0p{PHV
zGmkO!qCRd)a=Z;@qy>~=Fi1S*@g4!Bv@&-u*-Qb|cp@0B^%5fIIcHaBSt$;pRggn<
z=e>-EXjQrjvj?R5BozcjBvzdfJXAerDA9)&Y(vObru2LAGx1@8dpm>$w(mEf8?O6N
zXfb)IQm5uRDlsb5@T)EO9#5n+It^)kQKc`pwLt3auZrffg$<4L>sk-|bo=_A4cmdo
zzrBma`u8Xh44dvgBLqH+Y90iv;^>4sdAb3la$|!2P9Kv$(a;N#Sl{`0ye41D7cFlI
zA!KsA9;%^jf4)yg^k1k(zMcJTQ+)phUT=%H)@FeA`8HajrC6W4%+61&d%ETW9Y~tv
z*f#LNrq8rFlfL&_k+BoC>^`5V6gMe5750@&dF)tWvk0B*@5h3hFh^eKR&QM39DFI%
zuV0O@wkgahvU!Wz^LF%}pvGm>K#9%4c^<AUdmE5K6R|Bq-{SH>mlm{;ETkgqtbso2
zFSVri$O)9e;2qTJBnj2#F~kWPt|4ze^sPeZ3>zw?rPCh&j!LUFnw}!OPhwfQ<W6Zb
zo<bj<70JEuSRgyeC1dC7?zM_dY+6H*r9PGzd-~EWt%jPqocMh{a$-%XZ@X|8UE24V
zb%Rh6)S@lux|sF=)DtE;6HC@_oH}@HL^kTni|2Is(qxm0OEmK7Hc$^m!FWn$nQy3_
zvvqwB*3CAPhZ2kBdwX}zQi4J+1cV|+(e0q_*`<Zp<Jnv)voUlK!~^c}^P~3eE}Zzg
zuA7F+Cyz1Vh?nmC889vF-2>nA7xp+!<Y{b5a@2^|{u$ii`+}q$>M-hP0?K-XX)nc9
z{vtXh2R>s>W&#Rv*g=BPpN3f5C1<HO!?Dy;CT>TGcnT-Spba2G$F+1~J6W)VkIp<N
zec8e#zc4>ccD$|LVH`o@apq<%M%rhm-RSTq=17y#rT5NA5ol^%21-}z!vS-(svaay
zxZuK)`YHyXUB8Qq<ehj_Ky@Jg=Q+d4IbJHG7Z#h<S}?Gq6F?nXXkbvvY&vcgUltAA
zni0f_u}_p)tWjM?d^R4$U@_kz&IP0%P6ltjwR&l9A+Cd6qg@%(Y(KylOJWC*GyrqN
z-lN}*C0B$AT!#`VN5j86bk%wOiJmaJieaQ~wRuEn&A0oO-flBo&T1Jsx8U({H&Jr}
z{D5ovf}hJ5aj-asH8>4lbbSg^jpHSO^8Rq*n2IsY1+Dtl`Fj(FZY48r<n5g!`;&&H
z?Dm|RvP-5(1Pz1qB;(^elGKR9Bk!IVfl1*U$#eEnk!G_qC4ligpYeFfMB3V_lx&qR
zAjEJrlmDWz@HU3;4zY$bHRt6OJ_F7M3GN#de176B;h#J=1!hr3dck3v#q`JL8Nl*F
zBqjgw#}hW0Y<lEclV#rD+PslP!6d{l!R^Af#NaN5kEJ>{JTnrJiJ)WY*a9}3W1~yP
z)m6%<O>en`S3y`Va-OaOcxpzK%p$i}3g?TpspB1;%ZrjA*oJpQNk;Pvg?A34)U+1r
z(ET+hYz|$^^Gw&*c%N_tzSq<vW#hTCv*bTK#|1riToHwMQ<pOjS9d94{U^5xe;W=i
z=>#dQXLPRf7Cyd)_8stqvP;J?_zQcWvpqGg6|0mMDE11#PeRobLR%MzQrL<70UTjz
zC}Mje=}i$k3LcJ|z>5c7BW-(p`SAqum`ATEK`L#Zae8wn)b)k^eFTp0;l%v3U^IIa
zLEr!lHj&%CAV#M(7Kh{6X|$<ycJ{tRDUzB(%GE%5e4=<dY*WKo<s6nh_wM%xL!ZL}
zJ`+2_srT!iujmxMiztPMThkww%aq`*`y#)?Pr7Z7ka>XHZ>K^k%WH+ru*^{(cKhU5
z4qK%!PAmOhfw&IdNILNqclDyZX5{kIBpI3umu4?_NvR`M)|PDT>2+KOF%tfijOIEO
zS|u!79uc67`0e;D8;;+$Tn`~&CMTW)({zR%`O0y--Byk@NThQ<2SGFm=%x-Q3pkVU
zMc*fie$Oq$)dnRi0Mm(n`5mB|NjS(5xMUEvr6#iUKtWg(D<O57V3eupZc^p2t9wJ2
z9{NPKlBx-<6=5CU$a*{uT;f^}CS>8~&*x_W;>zuZ=U5fP13|atFa;Y7N%3D_9ZwZR
z1^hilk()o`HMm)`8@mADn>5E&TT(4+d1&0nBey>JR;X3azbl@d8vca8h#fcmv?`+-
zx*01GvO@@1TB_8Ea=b8gy$f?$U6t8eWZxDNn!n3nw;6`ge%qNFxvsAxPM3mV3Pkt!
zJS;*S$IXUv*>VeMD&eS#;dV9)fmSqOR+-jsaP9iY4w-D!_VZ(9V@X!2Qa4pep2}bo
z-lpdht)gDD%{ZhOMp}y*QVe!j;jw(gWhD(WI1k;r8(vwRLkc#IK<&-7YlvfZ;m|gA
zF&tYGqEM~uh0vcq-0uk-?@ZD0`i`%=%CT=Y%c_gJhohe8KUocsHP|{5=)8j}X{`lU
zDs0w(8%AOpIpzInu8>qOj<tlZF;t+#_<A)zE+o~lmdcr$u8K!26u@v10wSznJ6P35
zpF#4s5o0S8<8y(7=BhC;pboFdsL%CssKidOLxnALT%MY80UN$lBm#Y5JHdP1$f_vH
z>!m1#!SwT&C?6nvd}CV1uhtunbB<+v9&N3E#9VYzz#B3xoxz<&rA7k~nNVnDppjgo
z*5`|fB9|xAFeozB^7Yz_q<H%*q$zb?MWFeu=gseb@%7Hpl?ChCZ=8;8cWm3;9ox3;
zj%{@8j@hwo+gP!k4p+=??S0QZ=j{9KeMkMZ#+YNwSvBAJ)>~E2^IPCO$azYT@aSLr
z>%udal?q4lY4I>J_6)=@5?h}{nk&GnI}2EY$3hFYTecJM7z1otTlS~3J5EYaBZPwe
zM5nYHp>}|h%1r*Am%eWLzH%j2tQ*MoCr{9Ko5aQ+4=OmeB~l6r2yE9X7XgGk&M4Pb
zmXM0osvNoO7^$`<u8!7uL$WfziShBHNc@Qg>^LuL8SgSVtb!i5uFShTr+(}8?N~qG
zU}PA&-6L~9>k<U9TYLx1yeemE4WxhlHLcZfTp+$Ajon&F930}aS}Ht4wXz&O&ttP*
zU%ly{=kF(jflHIFwf$&dPBG(aF3;5C<d95_#vwosQSP|<?a6D9LcrrHd-<4dUZFym
zk&5EJa^eXR&C_`y#2H~<CPoZc@+;=K>!JPp<RcM@lGadh(M_rd_Vb!uzS0*(3&_oS
z{#){401rcazQ;F)k)}k@95o5#(NHw;MJ+a#X44js8(QU-?XSe9Le~*O@&Lm-W{DwU
zg{>elkZX8(yCY-xMpAjHS#Jydh-N2`Q8U^3iR)WJ%{Hni*I^L7!Z@h>TG3*=wTO++
zI8V1uC9q5`dVqvzOaWAaUXDI;k>nv`ui6uX->c@gXJ_=2)RY(aID`EoE7$WzLpfRx
z<_j)#${x*ES~MiTpXZVA?dd4oh|mC*^`%9MMm-*i3{F9>2QzUQ(r$O}dAw{YN@wn(
zLBI94=$Au3;4FQv;X<sS`KjTuXWF*`Jpzy^3=Sgd^!m85j%&om7c_kV{*IYfZH?#C
z{^IqIvQ+*$@S7JFj<%iR;jNw-q@4#IDTDSu`174Nk4`WBjQgRu$k{plqDsl#nTtiL
zrEVkBsEFV|yIC}uOVm75s+b7aOeMePj5gf7Vdpuf%BGOHOK5>!8AUDz7S?!8ZZ##g
z%+SzI2Ili@88mr{t6PM{t+cCUH|!PNg=<U`;lDUawPY3taSZOwP%9K}`q{H@7TIUC
zIx(g`zlXm~-WTD5d`mw>|62O2`m;d#NT}?eVX!^X7j|;B?iYvl#t9>l0k!*<580b!
z-&OBqwd`r%Ct}Qa#v_i6r^O7YVvE=}KjLaUFYPHHu5YMwcT>^~Oi9<g9kEU^Xf;e3
zJp-H^51jv0VR70ojuhv8@7G_XF@SaGS;EoaC=bU*jG!r=u^;x2G7iww`n)IPfarN+
zOLkq(KAG!^rnND(D|bqc=6I!N?iL&R&MqGBU=o;ecuXt`t#P&G`*&3}O`%MbgXhEa
z<_~L1q>0R$w}Wgp+!xx-aZKirobAuR+AJ-lW}4vb#Ae`81pDTzLe2BpPWKI_B@KMn
zqCDPLtDp(d!1I{kUVQ5kl0Po!b`v9ofJzY`X*<p(Rhq<Legr4|UnX1oZ|r0i_`=yR
z;L+mt%MFG)VB!oY{Ri3Lnxa+Rm25EjXmK{HOfx^Ye^vCH44tkH6&*(~4OgKH)&Bv^
z$I^|rH9AXwoyYp+Hl4NV@%KJXsZ5gCTGGTaM<q~uyka&HoT%{&ZNV$g%V+GX8%ull
z%~zaq+vcYXsKId~yNzk9wU~@WV*vM&6r`FC7mZ{Z6{Q?l)HRKqv7@HC&*D2q=5W{K
zMKyfEg<DmXpqB+(-^bB<V9e1r2GR-84>)zfsQj}m9e0{jsKnxM9P4DQDqJjA^+56h
z9OMB)*_#U|sSRYx&D3hJ*ngd4S1N&eKcg(;jWeBZOaFRuKY<Bl4kx~<9Lh}R!lWD0
z1pQnHqY=~E2=K=I8w`&1I2R>)BszbYTHybT4ljoJ_wQxL71aW+m`s5DFljk1w9jj~
zf-t<$H>Cz`GmPO47r+IDu07{pOgaDE{?MmJ9gR$4i@w4*A=?tq7I#aBeJMMookl-{
zNAjEILC0!Y7)|^;b2)<bgFo+FOA?*gkOwVA%@v!*+6_vIuC`0WK3j#<`aZio*l!oZ
z1Z&Q{<p8!(reMs6%lD@HKzf3uzI%YdOy1AK6>b@Vg)%nGyX&qvq3?ClZn!dgt(&Ez
ze-CXq2gu_DY*~>_yKB6f_(OaKhe9g+%ZS$M<&r9)q|VY-8;I&YDB14>Sf8(j_K`3t
z>hQ6M#8jrKq-Lmu%h1Th3k<GusBA62|5AZ!`is?S65?}wXjAR1Rx_STD$_&RnCK|t
z9hVspJwuxGDZ1?&m*LOU6wyC7H)rYZt0Vqsmt7Q^w!IdQwy)Uv&4`@zLUiWl`@RkY
zPo#9a-!*#y-4E^f%xJsi%WWUDVzKd*ksSm75xENo!`d+-%e7lFd@Y9lyj=_ymqpUI
zr?CmDjrjF>aBcCnf4I_$kRWb{K3s<BI$!<VT|3XY);afSrVvQH&pvIZ76=QT)jwk_
zk0A1RDOE};SS)tg81(Bg;{<O5(5`B_)(8$OT&iSEc@|1SOVhqm;d;`orDlH*M=oym
z4}IU7!sR2!lXNA*gTsue|4k*E6+sz|!&)M#KMfEr1T(?WTPVj0DtpHlw1xr%GVuI)
zrr_X)e)C~r+tc3eOz!pk9Y<Hj28^rM3rCGdVYiqX9&0UZ6xj&cm@94WiP^^|!pOiF
z2z!gYJnV4!Jw0+Wt>a*Pn@rn$s54hEB)8*lwm@GQg`R@XV*Uj(6Ww6R212jK*}anj
z?F6_K_eZfJQS1FBy>an(+4)(`kc)+cA+h#z0t^q%F#A3F%vDt56IMB<c&-q6ozZ~T
zhPQ$Q2C5V*i}RqP55FU---{E2)t|6Naot}=J#RtJ2-wPCqI_YOcw!S+B}3K-ou7Uh
zbPnYNa%`t@P3at-_-wEc+5GO!h7m|oz#82)cs0{!33^%D^+DgwM?(vNS5u1ysEt-X
zqL;gms5%ErQOzP4Dz@sm`q3{ga_S;2OE$|&X#Fq8pHlAQmfD>!A?g<1#&c?iQOB(M
z;-Ymk5AxIMTW$I{af*pdD+VYZ&^IF}W~4CL3N*tVh|pvTF$n#4Q{WyQVgf?eF6i0T
z(1wdqB0eo&!CaahrN^@Z-WD5=$uWo~ahPzZplyM;KS|pBevWdqt&#{Ak)>UAc+~qY
z9?TT!T~2x;1}L(DlPtza^s($51&V6Q?SlV_)oMx0&_WxTH%p^1qWe*`=YYVySH664
zJ^m>ee3D4{i+9dA&#nR+q}L9ti&C=<X!4FL*pzeMIsEiqlg%E|q#cdKP6rqqW24PA
zvE0N7<3`wZW#k{{Q)CatgU6ZlmO<d&ubU<x{DjTVH|o3s3yaMerTG|gikB^d@TsCQ
zVf0UP-dOM?ecapXu;Av~@(qUuLnt@}R_pQKK$2TNl3df5FZRHr2(3gO8CT2|NhMK~
z_+P9wZCG@`{v?bY!4VAK?G?J@_u9W7rjW`cp7^u(Agj<NmAh0=bt<qOI`dm|0(<(x
zTL^g=f%E!v9_|;9agkg*hAgfyF+Hx8{*i!A%T;}Vp%@<8=L{u1uE&9fHpM~7Yp*`S
z2Jmh{!9{t?5To*e1t-7BX<uBN;wo6G!b-oJ6MF<%c&KXLUd+^w%^vZqbNZ1^l+fC9
z@uOnBwMpkx*8ykUMEI&~7q9rwIqluj-0(dAPrn%8Y$BD))vPgHtdh0O3KUaP88iZR
zdQA(}%;w!d=6E(3^2p`L5#zd5q|?d<5}jHhSP>x3G`(f_VcnjcO0PeU$Tj*zc!yw_
z6<m#=@yAFk5hH$$&%T))nMO>L&GMcde0DS8)Z_<|Il2)FH<>Aw;{VG6fMb$Y>w#$5
z@}kc%tnDa~`>nmLxEiIGXZg8gb*UDU8DmBdz#YI(WW<Q9fkb3!j249sMoS=vDm1U^
zYv$+Qi~JO@Rtg-ltx!<N=0mFKdcdb+FB#^37bg?2_}OT+xfVhW%bR0bqM!IJapUeV
za|(>^7ye>HH~Fw1>L!wOBHYZ_V?Js0w*M#Oi=QBVJ|+C#hdhdoEHt!ZGMH#gcR_2<
zQZYXlA)Gizl@UkIjC*Dxg`SJfvo*9G>*9u2x0%OE$%qm*5Bzn@Z~PA~qRSC{czXC$
z+Q_d7RV@2)DJO4t2p*@;)N`Fva_M!ZjF6#W=}YonjYBD?s820qBHGz^GvFA+(tnt^
zlc*YX7cyyaXtf&COEgy-v7J6<JF5>&)~q}wxmm0l67qnj{2h`j1|>iA?{;5iO92*N
zXmnTo91342^xQYCF6dPD;rWFgCl`J)XceQG_$QBaT;2DAj(Tm5--H_p;(emAI)-us
z=YiH+B*1m34eu+#pE5&yQ>WM~X*2E633qcd1gV?LBymEs)vTZ`>j46X&Ok~`eF4W$
zUstpP?5ZNUA~A!8pG0RIUCTcoP=Qn>LcVA~f91#b+^>rH>SmX0Zj&L;$I7WMp<8wR
zq)(4kE^UvwM@pOFqm~+f)UcB@F8dvdBIRW~$5~hO7+vHtcr0lBp8dd@(ux(sV%m)T
ztX_B7I5VByiMDy9+tXN!i>%)BUXM4Hn+1+0v!j(`%-qzXEB@%NMtu}g0lV>6#IpD(
zDjIaB=%>){o{xRZ#9eH$5l{w-o+nuUjyTY~(?`yyM5VL!6R1etmpp}jL!E|`K28-L
z`u4FbHHFgG$m4Nd)q>g!yStd~l!#9+_u?}IYxqgHC_rpSKj@+n1!cR16F|8R$?EnU
zY`7Yw7qcCF7qyf&vv0zSF6c4znmN66(6NGn^<Dp~j2+<aw;1~@nxFwM*aNUtV!RQu
zlU4^}eBSmw(-_VYn<%b46de@}LROm~fa;w_wCf(~3`e0CQW$o3{TBybN!|{`Fpts#
zvm~1ej`SF%oKB0OvvGEE&QpBDw)X5`uhayM`KxNTiX4N+xwOg8v>CiySGE#uldWT&
z#^OzEmqw$x#iH{>RGUCRf1~r(;KFn`8_S}zxV=DkjuPp+iX^T0Dzi0uCu-&&#w?*w
zjipHW;y1H1QtHQU?A_WQ<{lksS?%dhRU8={f=SPn_57xH6h0c$f}#0e4JJZ#k`lL8
z#f3LGPeT>?YYkzF^WoOzz3&K>I2GeD3bw|gF1#I$2i_K{&%@JQL`!<fqEnT~>-<+w
zJ)dIf)EUe_BAtJ0a=ohN@(7YEW(DQCA$0k{CL3bfFIBl?^b6HmLc)HgLYHo*wg}Mu
zz^V;pN!ES~T^Pys%kGIvuCeHwHBCJcW{nQORKJDQW$|Uo%>|cbP=X&FEgmKO-jJR0
zlggn?d!@~p9e5olWp<Wfrp{F2Bpl3Ouf;Z1{p_#$XLB-0Vcg&AhT%alGom=v>+URg
z9XCUFeNymquj1tO&|)%kB{k1d^^a0e^qkb+UxG126Bcj9xLwDU6zEC9#|CWXI~bX3
zO~1tnsc&a(UCBs^qrwQrpYRX>7b5%RtVgM}M(g$s-HLwyRC6_o1}w0u+pPrCTc;Fk
z+sc~^7DHEPRN(KsZ%ovA5K^L7T^{&3Zx1Lq@ypnw44-iV?AO{67D}tXSzVqJQuBSa
z%S^GR9p+I%s5WeX<(06p$IMO$R$4Ga^N7&Nqm3M%#c#`XsknsO^(kojW)rf}*oaK~
z<)rE&lbl4(kuvYvqJSbGzNxn}REE`Sp77v+{Yntk#->6y8`)7_c&t~fe?~a+2;O8J
zyK-kOL-O}va#cji1b5=!wGlmf{Mmc9L&>Fz<IjXjj?cu+$LL>R?r8Z&Lqq;z3b`vm
zuaKj6PsRFhq9^zaN6&W$zi^=Au&Q?7Q{lQf`p$a$BKIXsB!bAV@a~K7E$|Tv?y<78
zf;zMaQS`R`1FJoWaMfv6rmaRwP&#~Lzz57(qr8gdl<-~PcP`+bRl6O0vV^!e<vIP5
z0RAsYZ|?@j!GP!Gue$7n!PVi5P)v5sxF?6+f$I|#J7m^%Hdy%wfonjM5s2EfPJpy^
zL(8Q_EtmBY&K#mI86(BXl>)oOPn1=Q@-)*&@(KA{sH+Cx9_LsLPQJN7rtD8y;+Iq$
zd68XRI2t_ggVJs9+7O7v<i~8H2%OTJtxAq!ziX)FOE{5>ZqYxZKB}Yo%TfqdG<f<*
zY^>+fXPVM=i_2DX%Jn{k@oaH`{?bs%$>gFi{1t7FgU{m7YcR@VFHY%p!3an_ooEll
z6>z4Ub_uEI;-JQp?%>Xu-xP~5a$kf*znS|z%Hem1F1GxEQR(mt!Iwlbw(BVpwKYFy
z&_*o4rIdp(uB?x*Q(g_n-JrTjsQife$hR0n`<BZuALhkP9OlJjgf(o_r+eo%HK4Zw
z&n!3SMQto}PrPqgFA@JFbn-YL&@b;N)m~kXi7SbFra%>c2FaDs!1Vq=_0s_z(4nR;
z_o4L^yJWB^;VSQKZ*Aa~lK(*wG%nL#{kA$POMMT%H1zPojBZ`H)%6m9{pFed^gU-v
zRp<TNj6YA^mAQg0gKC)LhJp<O5#^UehtBZdYt)@hmZz(Cy4K!Y+R2u`MU5;ySNv-1
ztR2iB!~~Pp$}MtkUwwS$&edSx+s7D`w$A2}Heq6aq!@c5S?pHV(GcEWE+W@3?S3s#
zF#&F)<I4G}hJcaD_G`S`0Rq3r;Ppq;M*5DsFZuHQ!;TmVvJTt5q8NRmSr<yuhbVdf
zj5jYe8AcWt)xM`;lB9r~dUz{mChn{uhfl7Eu7dT6aqNy~_<}O=(8A#JA@#}$@Un>?
zm-<-OXr{u5lfKEGW9H>P>p96*1AbiVNnWtqAWEmuyJnUM9Jb42<(+FQG{H!TkaAZ}
z>e|#N2+e7qPdSxdUi-QrdO81Ks@hJAD!991%_3xuG!Z?wy>&|P%6&LoYMtYD+X8D3
zwza4|{&rAvdrS^F#9`F$f&9lZxk6MU_D815aa(2S%61YU?XBffuHx?Tn(uLqog(;B
zFo$DnK4J!EL2m21tsKr$;`7Qm!Srd6{1!T~M1tW|f4+akB?OV*cg19^SA}=f@}PHE
zGxtCRLx(2gHs~+z0q2;h;!O@Gc<2cr=XVUBo>%207)&5P9!>z#9WO}-+n1Ut<X&^u
zdtDVFTlBc~@}WXtt)Y*^p%$;Ug<kBMi;!J?(e997CZ}=wAm(dW)>TV(W87dMLlAFh
z%E2V$n1tbLIZxNd;wA95L^TaSr4zB5noNa#SeL16RiiO{=y+$8emCeBcd+Znca*vS
z_MrFJL3VSVvE7um{TQp9V_Aj#dVS7(#@(G}ELQYz>3i6Kq#n5a-i=V|n^T37mAZNr
zeyO73^DcM;qUwdKou$)Vwe9V1|Fpxxiy(rH%X15+`=d<;151neWHyQU_lOtBzj$z;
zmfXp-?eQ+t?7s>p^_TKa3a-u{5A)KA2;wv-!2Fw4N*Bd$UW9F!+fapq+a5Be7;9j6
zY0X;=EZs7L(PV3yhDw>P8k=?EwI&3-pu}yFnN+_9oQkiF>MfzgC!?3lBmRBAv~u|f
z{SfO3F?!k&s|ovwRdm@)V<n<H+d~}oF&lKhToRK}@`y>1-7o*1#yj)b;7`8=rjP8*
zcC#QR_FKb`t`5=wf6>++RfuZ(>;N{?MTGfIy~%c}!HvaKiO};7hha=cZ`ox+fwP#m
zCE6X%0-c^;lgn%UB+id3Qlh<QdV%S+T&TGWaJ{Kr;1PT34ohWbxdZ1LDR2hUU;5oE
z0-`F9QD?O981`=EyGsRrDS;4tP3bJf^@&-Y`SZrfY*A&K#}&iYvd`i+QSO3={WFVO
zst8j7ILZTxf;^lXGZas=zQ;j&bb4fPeLcwum8@kgI${xROLIbZltZp(yvmo5v|EEo
z?N`i{uT58#b}D@q;5(}8$r&94EdJeut8GZ5h&~IC2ssF5vLWN<O@`}JwCe`Sx9(1U
zGtX&zOi37c8Baa3`hngt6NKPXaUXrE7?P4>^EB}zAWBfMh7<*KDv0Pnc><urSebxD
z)w0cI%=Y}n1KbpM$ToZDae=!m|GtC%BfyuB+k_<~JR#pW5xRl_jG6Q3?Hg%YKAtNI
zsa2U7p>|+Qy>bmF_(>D#EXOGTQ*KA-&~$N3@|>A_YPQKz@=rw*DFNCwHZ+_~Q(PN$
z<B~k7&Z&EMw!cd=j3J%xc)0zJTqkQ3+B*HhUp=b7-j-czu{{P^*b%=Li_)lFh$n!1
z7yktv`bS1$VAP~+^EWx_pOTX#<qllHdu}YBO4$c(ll)VpIA!1*o^&rg#3yH!F}kVy
zM{Jq!XwJw=<2U`-_Z{WAS+5HxiEt6?FA>5RzxL5nhSM;<tikFbh-k6xYW-o{mVxP*
z*aLIjRkzJ+^ZFfhM@^kH4K_w2Z{Q50B~F}~e>~dH)c5Iq`nx;$=xp8cp_?6hr7>6Z
z<fJ1<M6T`YfQ5Nmic1}^Hr8a4q=H%2crfmoyCIJd_=hN#m)mk99K=G4AN~>%pQ3&?
zC>2ThU;V;E0Jri-Arb5d<24aXv8nneF-?*miisXZuQpzyS#Y-Pf~7uQhDm}xQudBM
zjdIpu1K$q!k?9c{|5zyl3zsQjj>}05RbhTokMJkh9IaT-o$WKa?Qbr-C8`Fu%~XL%
zGMrt8;cVD{lu`c{=;o;ZMfWFS`{#m7Dg2A=%jEVD2M%Qe5skmApGCYJ|2V(OWK+8R
z`&jL_$xh#6*ebx(Om=t!)#iTUKOgqr(8NBFA<{U5FHs`WE!Ke>PIDi%tylly-jSrF
zhjssdq&ZV4!eGAygMf;yf5*-Ghj;!PDUZ#O|F4t|@BM#&lbvu8M+mp(8~sJw?NGx@
zsf(u{!trn2CEWUb(Z>!q71O?M{|dJHuPz1pBS3WYqkws~JvJJwHNAsjUpe^{kWfe&
zED+S}z+O3IsAjzG{WJXjud)3khN7$bjbOXtu?DG8t`CobUGsB|_i-Rx#A_d`)_RE!
z2e5I^_3!X`*UUdi0VoD!0PbLU@7ux@={?IK{L*X}VV^IidOQSLIF)=zvA!*vLB8Bu
z-fL|=ujD*@d@om9=a(-3z88owBHwbp3{u<l;LaY%TGlgMmXbj1qM?l@iTk*b770HB
z@~Qn4B(Hs?_`EXSZoL4Nc4L=78|JHnp26ErV^>+e<6Pcn_nwzdZ?&AOo|G)juB$A+
zKe`puHL*o!N!?lHG^dz7D~}78`*DK@6&I?4%fz3Z`j85nJFlslApoY&m*hT@@Sa5@
zfbWpz7DxHOIN!Zx_wj?;P6b2R)LX{g=Hrkza(2rDm0<i;OHIJw%%J!E#Kcj`F_GY;
zuP2iak$y+w*7rRZN}<3j$DEm6@6<sZHT}JJyfWl%Z!Q+)-d+^c?WF~wuH!|Q^~Z(S
zNDqn}V6_goex2X50{zO>$f0hyF!t8dEU+Xj#otdjBC)$9B;`{OuM7F}xWSW_sJ5NO
zjiQSU8U0=1)!qJTt>Vb%?0UH5Qts_CF4Fh$mLk<=>FIdMlaur&3lLMZ{c()Z)4`Xh
zc<Xz^avRHcUMs!emlDznqL}8S`T~{`A_9bm&&J1`<U8%#-hosz$KYp&kFk7L{<(fs
zJ={&5V?ezlH_I-k(_AvgHNssKi#_+Nc6Wg)E@uBdR$TB>y|71q(q4D6({$HeRllpu
zY$=B0Mwdt}NY@&Txb3;=Tso#h2Yo+1(DS7n`}ep{*Hbocx<_A8U8yR+zKy?6a8`_W
zT&WHhy>zNXN?diit9GR(9s}uanW#VHtubb(Q+78iTt2o1QPC$PUrw8bgc~-`JK~jz
zKD3g$z1=8Nj@62O7AdE03rbx)+5@Y#bkG&Nj?v?!+;oVyrD~Wqjm%y)lZf3k1@*sc
z0)(puG;)NJx_S^*M>1Hi^&_tElKj1{W*%E3uQ=)&UbZu=iD<imO;dEI-Bt>zAl%9)
zp2$ZCw`LATOnFW%q&2mgCyHVha<35gA4v?|jLxpTJ#8y~q0~%0e8s5oKJF~sw%Y<M
ztb5ZH0&*Eh6mngNN~31wy4NuffF#GIDHSARKrd<rlf7__W_Q_Bp^l@pK!c>CxWNUf
znZIW|&^}*Nkt-HnwUrH|@(n1DWC7jhXO36oWhf=PKKF62YRcf36WijwIwhTj@Gk^5
z$?LH14n5%Rh5sqNfdug_a9(Rhr_%o)Am0CzH}K#C1J3RPN7z)8nA?W&!>ks|!Pfe`
z)vv5ni|B{{Qx-!G1d8=$wB|A!Gxhw(8-s(7<ds7Hsh0eAiQ^xf&Man#SW#hvL0!N9
zbVU&)1WuT!f&#O9jqO!BGAX4U(#aVQfBR`<f<{Nh!1uksBM5;hfG7l|G|;ctzY^g~
ztE{)qJe2|z(|!UPmD8LC(wsTxUB{U-O9_oQuF{HG_Zw|{KGo04<=47bf&ib-qS)k1
zL4eC=k#BXZi`dQvP%q-q>9Wd+mk8oGCIA85eOj?C=0BurR7qhdrWhE(_q8kOpZ_+W
zfmFI?fdMA7%+Y`o;{Ja>oNRocVVF5_m~Gnrzb}wP3k(T?I=PY^t7e~Az0EE|be|%N
zSN{S<F*rgE;>A{tyBz)(5%XX0r-3p!;H>*^IPvf(6ykzSHffjN+fwzIzCcmGz4?E!
z1j5JDMh}WVQBSt>HGF7E3qX;QT7gkqouqCn6cnG20sk;C8(0H;v8av~4EKdUla(_*
z05gxTt7|UJ=f^q*+6O(D>&f#j7wau3eITe}Cg=I7vTT=fjl#2BiC1-RuLz-_*W!!(
z!{1dEx(A2c=(xC%<tm*7VShg;Xz2GUXMw*nt_?4?B0Hz=53<jjoc|gDfeqx2w+B+T
z>&-~fILyN!NmQNh>rX-<AKVN!GZ0JBQ?W=AEty^eSxHH0G?5Ykw09gvDU-Uh1=`m1
z-t{LB7uKV}UoP$m53qDCZnz2euenRUx;?ww`u)hX(2mr%BDgt{Ub+zr<g@t&Og9nM
zC+8ig3|EpqHM-mRK*<)!{p?bY*dDREf6WGHY?hu|B`90BXjE$`XfGY6yeh<f*gdR0
zDNM-5z21M^mvVVsEY9WfwroeHct2ah1%(Vgo^Ph9UqnYo^Xq9k5<`32@uq$32m;y;
z1mmb$EB_^$KGeQ=+yU}IST>NBM>dNG4R|y)kR#~rbfU=cV_oVdkx_>$Bs7$aj}Ko%
z&n<=Z1SED6Dp#vWW%BU6aBj?r;|7X+@OyZ?6^vgy-j4%gg;If-+o#!pxx$XIghYwE
zo?F0`{_}?$UE113)8+&a&@uBV%ffaj^x2`@W&)_z<8i+G5(JTCg&&GQB4E(%!-d-k
z3(~M6JX~(tpios=)fo@j8hQkHJ|{~gG9l#=@;Yl?jET-(5%K?A2>|vk@JjW6{ra^s
z_TaJT7S}?s%Nt~hz5Gt)h5!dAb%v?`Jb!e#(S`x?7m0%y2T4%K7K}XEY?972R{3jb
z%nm}P<5{%X*3!R?yB;t77b(u9s$BK2o(3Z^O6dgbOta0Bv)j+Jx;@`dv)EqEZeIpK
z@4D6#Xnv03mv&Kc`^U%X<i7M^(?a;4MeF~<|Mq&4k!s(JCXlBK`wRKK-)opmgIEcX
z?QZ9JuBY?E1Q9KoP1Y$^%Q&EOq{hs~GvlN4<tlIe`)nEWxNe;uKfz}E)u`E8#bjEQ
zVFn2)<U|eLG-ak-0pjdW_Z&%q0EVxFE?5kjvz4R*KJODiqfEBikNe*p!KJ1T%%nFR
zx5WHECmHAmb<VmX{WLz&3^bc<wf2=%tiQt#W#6l^pv&iQE@ZHIpcKLQK5n|7Y;|>T
zvT<=O+b=-E4gOti*gda#r9}F2_<l1&!s_$nIDvzK5o}Z427@Aahbo;yXLhVQ+_LTW
zkJF3I$rv-Q`Fb;%MjOC%I?+<uLEyvf1hdKEc#1hOBkItin7DYRYUKbUkz%=P8O4#r
z<4f>~@1uU|r<<?Fd^Bp(N}&Ww=hI#^E~j;})VizuBW>>4MynHfCZ`REyxIz(*Y3(n
zwQ0iio*`(IW=IC+LqzRKpjztB-??2rQ8CjY0br=3(PDT4xbO6W3*3&9FV}Uy-O8Mg
z`nSz=e4_Jt*)29mKYh&~x!E7Nct7hpn$9Lb7ot#@{FqTGP?Un!;-6-*oKar|4YlJ0
zV@nq6@0W>S6rqS_yLD}n+no0p?zZTE`Dh3sXusB)PvI9X4UMKS8*Etuqp;||#k8k0
zSk0X;mIW_WXqcSL=9{lo1KGTBh*DF>3eQz7TI@Hkcf(1vZ;Y>tx)dZ)nQyLG+#hQ}
zF9y$iDl^pU{*2Pt@u!W&m3|wYYMJs7i_cypb<8$8%8}k<taCOIw|y3EBmsw|VQ1E(
zeyMy$6cPb9E;Dsbf5%A2B~cep`Nu^4Uia%INDS|9Z-zv`g{UC(OhK`uL?Tn+>ZR~R
z5c4Ztl=&1Kln5<kRW5bgeg)1$o-=cBOj7(9e+9kg{T%O0*0N>)&sPJVyEzFO)jz0y
zPrI<Lmm7HVmGt))>o_265!=|0V(F9}RzBhPyCl8GPsL5+aTH<!%X>)zT62$`-oTU9
z25RjVD?2JC;|GB-ZGfOT;I-Ph6K6MjLCIgboyzK0;s{~+_mlQm{lsC{&yROd;b9PV
zt4TB{e+&vwW#VS;CV}>u&&-shC!x3VBq{^tA5A*<=E(=U^+p3QNr_g5k}IU<dXS>;
zgCsx>-)fc3H4WR_ZO{tu80QnD$S?+BrEvLwR?1;#vQYTE93&}J*8D?@j>~R7X&|lc
zzvM0RktSdw{VX6VciV}MNXg7x+`W4!<=5lqZRP<;27p3prbqVEWy`&0N*`}a3D9Np
z;n{V*HP?|kKR2--`@dr0jUN?NFt5>KhFCV64~yMm`Ugnh3v&7Z@}2gFX>?m_wGN`V
zQn4y4Z6T$4qIWS|s)hA1kTO%pY|=tt3O<V*F|XK7h7l{MnqHI(9Dev0Co50>j*z&0
zJI(}5q`mpQ%lpaoBrVwM6N3uV+*ow^4bS6jRH~2TixDlM5S^dhJ?d53MtdQVRHiq~
z-|y)nacs7`1;yitvrRqE^<I77u0U}Z4C-4=+DOl4{X_Tr!@3;WMHjP{JH6*y#A7Gl
z%eGC%Q^K03-eV8;-%Uda!3p7$IAi$Y-Q*+9H4(L<qVRZZy`$3xi<#k$t4>JPjd>4H
zAg{p$y$MkS;_^Bdu`)7hH?j=3`@O$-0H?T>3dND$-heZ$rzO8xJa0`G3T#k4-Tn|m
zV<%CFBX*9f=ul*%nNaj3fkVP*X5)K!86~Dio?`4Irb<O$v8v23pS9nX=^|N5wF-o3
z-bacWNCbvJNJ-+Cf??!9iQ6pz>3Y)}U)cO<wCanN^1ZuoDyC_Fbx>HN(~bTe_GUU%
zdR=rfJE{S)WoYo<Bf@*+|FjM`*Se4Cm-4*ZV9Qq`P_5>Rr>cla4r!t=@IuZYghT0a
zxT5)c@3XZd39yjyWD+Q)>(Ze-UD%5ku<8s658q$bacJt^ZnRSI2HYf<z$j<Z5h_c6
zXnsjbzV*~R{d4duZ4qML)6DDM^U<^CJ#K75QJ=ZT{qFX6u_V0cb*InAz1Mb^jXvJX
zh28@1=aoM3=l*K->#^JR|3aRRK7H}9=}G1V%@M5l`k4!$DHi`%&H0BE)wO|Q0=1W3
zW0U3UouZ{D$JE{FLY?F8Hv!-zySW$x(EH|Ffkjud_l3;IIzO}ils9r5*E}a^x#<&W
z@HTNH=y#TB{n=?j$NtQD)xEg3ldT~Xr=Jq=gO-D15wy%oF`-WM^#Bb1Mj{r#b&uDo
zR_Q?I+gg}|BC79wIJ=3BVkNDByRq=<k>29Gv6mh%JZPHexb&CbJ}hUHF+BuYjJi$9
z{G)Fv`%UZoRKi!A6~L{g1-v`|-YoCXbDGFNTxZU~*NecfR?GTqdhYIjEvYWHJXddf
z?&f7$cf$w}C9QmlDN|s&zJ<cWgatenp_NI+spjf8;@Z8BxgLJ5@}r1UL>0!>V3vXY
zy$afs#yDE=Aq>~s7@afoN1<lVpeFh+J@QmbjyVOB$lPIcPJB6)Bu#WIXnH#AzkfJH
zp`cGUfm05b@7pa`j#u6|9&ty=o!--{Z2bH>)05MGiFMKB`A#WXbaR}?X=Bep0tXcL
zO}Dg#(p=ySzi^g+-?NZp9M?+@n~en`X!46k`I>B{OISNi;;|E0OhKLFQP5E}Ye_Tt
zSLGN=5{7)XQw(@LIuIAq^{_Va9Q^vBBtfJ$K7&LIji}9s9`$Kenu|^BeOJe4We@7T
zh*s-<Eoy%=Sn^6Zi60N5pOqsnn`uPNB<%ty8J|{!IF%}G)cX`C)w78HZip!O7))vJ
zHNo_NN$2xTTC#8&7Iz}t*#%c5K@zz<WnQ;y(Ob!Tgei8x)Ql13h1H}nX0u=o8~TFX
zvT_cAOnX!0{rUdbHB*bbXnm-juk&bsX+IEzgbIxCy=nDv3Qe;m>Ei@3GKJ%3&Jg@h
zgOdmLFrq2?MfLLF`0#RzJg>SQHwKJ;+cn>WGOBZX?(X7&ZN^w3TN~X-TJW%3f`rkq
zJ21V)^|ZN-3~Y71eNq(xC#12SL7`0gf9{~M`*##NXAd*x74}5_yY*@WL3-$ZEw$~q
zXzq5y=KWD0Ts?|wJ0%uNO1v7zGi~=x$c91DbF^~w(soJE@Dmb3$1~wrLGXUD7L>};
zPE^`PzvG_ep%gCq6j~9jJN)WQO$#3lwxD$zz7F5eRZ&q9Mb;fVWIu~bqYFp__ef}g
znw7(UI4e9jLy)P<3PX&X42_A9g%djXW-25<V37~C74%#9K=AB`_JMi;Js$zQBdA8j
z+4&wxjG=h55k{pu!P;p&D<))`zpu0XqJFYcS4ds@9_Gu!5JSj1kL5W2ok?sulzTRy
zg<Zhw{T?8rQ&2|kg?USHOPhmrQaw8F`*P>Ri>=L?+7IhN5pb0pD4P5>I{l_(aT2I;
zVv`*G7VRYO3rUz9dXv(6eP{=3zWO%<+u`~!E%f~Bn|5DDTeXFG<g_ibr@6QFdz`n4
zhk5r)*vCYr|L4ad1^eNOOkD~c3N;f90Y!!yM?=`Xp^AJxaGHMxsPB20Qd~4{kxk8R
zwzWS6e|1wL`MMPHILW7OJ~>&rZ@Qe9GyM>5!4<%+qMD_C!e--U9@RQx;BzySK^u{w
z_0Fy`$ll=S<#lHGD>b7-);xP=;rG%4P%#y$V3IFA&GY#%&7#E6tYyPl#$5erQvHTa
zC9ggWrsv>vYKRh4xF|k6=r~w3d+ZoBk8GX_z5Z=@U)6nY$Y?M(JK~MkI-a(5>UByF
z%FR#{Zcyu+#3rp5em_;nXp^)sFTUq!o)IPx^6~iD2aRLO<<Y4p?k}+B6z%X)i}>D4
zur`R<7}IS8uV>D*yV@HF$<E(ccn3Q(DK_ef@O)5rf%Hoq;|3PrQbEU2+j67dD5<Wc
zP>w2={1UhU>Ckx-R$AxFf#-{t_sJ%IlJ+e8lJvsOvsel|JuxUj<rkVWLEzou23k#>
zQ?P5WC``n4NwVq4-ujtTq!2TCSyA79o=6DO-3(8gjoK>T+!!;7P-c$Fc<Ff1g$|jm
z3sp{1-72?nS)~qpWlbZ4kL7smMJn?h!Mylpi9hyy+}FtXsCLQVEy(@%S0S?33qJ-v
zMmzdz6l(OPBx|Xx0+0LcxdATMtR%*)B+txT>QVR-j{OS2=ZSr6^pOUwg~CgmY5(FY
zI_>|^X7Ro%;H!sIfM|+6#&|+9YLsZ_+T@bXX4-}uA2|n`EUw`S0c3h8>W^knW2$0A
zH-&^jq{$b15WJzL=951)*<*g0v+uY_T{Zqrg*S(omV@m(8xxE$>wKK;@49g!KWhO)
zoyM9#<k{RXCZKPY{H2bd2@m89PT0aG7l30UnYq?56#qSmX^pb&(DPmoQV1<w`QJnv
znEr$b*j^V6z;ik{MDd+M6!wdgwFz5O&caJzSE+no#a5GM<9Vr~-L2lv#Q8zn<@j|!
zNQKl>&+ePv64TCC65ra+tI8oJI@w&I0mc>0XH7dJwa|g>#)3X;$X;uR-iH1dk(MkY
z-;w%Q9bS6uN?XI5;ic>@_@_@ub*&z*yMZWZdj85N!}nq78F-NYKz*h5O^NH0QrB#0
z#Oy5)me>&?hjwE3tfJ<j9gkfwj+WX!A0z{bW^8t3tK9?FMCMH)M&Ml@3!aLKIA^<j
zuFUBauB^5G;p^D7>XGzo094&yR>*Sk_%I7DyILCFI|NmU)b)q(f4Q%XJ@Xl8AK!(y
z#pxBp-*S4P;82>u*TLi44Z-NNpPd?CxP>|+K=|&`^j;v8<<z#pj6MBzLaPq)ByQ&*
z&YNMMPa_aZwQa2yUy{TGv<&Z!V$%6>J<jv-wC{V5>T_o>Jji-<FDl?WgKggbi+k%N
zyioUky~0OViTFM4oiDkMj;W%m@@doW^+F#ed^J#^7uS?Csgw13V?-T&b5B2}_dK}q
zPE1|Vq3eFx5P>!)&G&&YJt7TJ-SsR+2hVNa5v5ZFjNzCtr(g5f7XD1scjcwuqn`k3
zLRf_OIx)vdgl`z3)2Ndz7M%k5f11xd4`~}x1~CyS%vgU#O<j9OcVPqR2yjI3^4epc
z6GjA<IAL_B<eGQ<!hL0{ri=UJBBwR0FO&f$FPzjQpPux}h_M@#yab7P<GU75)JL7o
zH*N$*RS3Cc^(fcLw%?y;{C|2*CM%~U^hRezFbusLeB9!+92w6YUfWl0*>?O$MR(P%
zO7cb`uNfBc134`=+F)lDyfjxF{9koSVeya=_&BZ_f%3b1(acV57!_>JjK{G~nml((
zIGs7*yFl1?Of?~fT@UnqBRv(B{kjuTFlQ1UnD{o{;24+Oc;1AXRTw!TWYaKETHAfw
zd80nbXF7@0+*{3w31{QO>=YFWW3r4fGgzb>H|FQOCna$kPTZ<75KeP)8o}u$7vT%+
zZ!^w-olQ7zZ}q+|_?-1<_{(m?0^X++984do#EK>keG+mIm@!BmvY<#)X}cCK<w}gH
z5rBcB^urL5r>~SPLL>OBd>X27GUzxON;L=Be%XBFm*ETqgsT#9<E)dD|1m18?k2d7
zC)|6?+$?3uaLghElv%lt?yb^GU~>s-T{w|QB=)AOJ@_Rm&5zbZRD}_7r~Y@C-fUuS
zv<H1lXR@rZAKB1p1_8dO*O&scVS7KiYoIWXBE`$-UMrW>qoWkpSx*#bsxt+8!@s?}
zxy-=oh}}bzA|rvP%kq%j)(#*er0F_GcJ!gfzRnb<>7OyN9BJ)V3*Olos<_bhcj=1w
zi&+(uuq-=n_7i;pqqY}?n4fpoQZz=~TOL3NWmSUf#Q}u6#!$Z$YCXvqm>AA~w>p-A
zpxMW#ztd{Ysf`b)p9(@}{%UE$ikQX>?$d`A(RP?1L5xgb*Q#`?h(=|O-3Lac(OkE~
zm0Bv-I?w4u`rplP=m@Nyj}4p+z4~xeVbmpYuFUuQtzOnorM6-=rQU?|_|Us1x&u;E
z@8-P%o<fX2vdC!Se*G(fb>abkhXYq=UR#aHUB*TO(V&L(0uUn$rtu7L4Q8Q>7&6_f
z=*y)i@C>dIwPK(lV=71EP8jB8lGQc#o}!J4#@cp<Ys*8?EX7NWbeQfO^|4*|&5?KJ
z<vby7L}qSMT%Z-_6A+r*iPBzzk@-f=bYoHy;da^Ev6zl=eP}hnYIezPxy%+cz~=j5
zlRaJKn~5q2O|22@>U<(AC-?d@EOJhLmb<;i&sWM!KnRIsX3RXDdPY&tiF-j)w?1R~
zlb_hGL}qA%s<N-bYVltJd1;#e5*Z!_3G;yoJyzc3;WkKsi)8FHx~nsy{#`Al&%2@F
zhe<&*P>N6-HlnGJVZjUS-5`U2M@EXm%9dZ~^hB^A>=&%CoE7JLP|TxfNQ6eZSi?r<
zL`}*kGp9RL@rS`ik~7KYX?VmA5QeBqzlnkP<(9C7H{XCiAmk21yzJ-F^O*Fd_-+E1
zIISwSZ=7J9q{8a&zPU)=4hN^AyMch^4@yrXs}dh7M0JSWKwx;aF$b^jP-wOX#ArB>
zfmAO`eRoMeG`a>=*KJ7+!PF#iC;pqdZH6S6I=j}~z&At|TCmx^5Wq+!Ko*~Y>*ieo
zY$&r%jPS|QVZCIxGjAqez(mwyE>eso;9YhYlO#9>rIRx~85iRqKh7rT9`XJyZ-;#g
zm!6L~h$Y!RKq$r;S^i9v@YEI|aD*LY_7j}Sei(8;pmDbUU<|=@r&qMO+|&59S4`id
zQ!zbnmqcT@(=dO%dPO6-N2<Ck=4>_7qe=Hx+Qs^QP=dC?7N{C_!*D})#)Gt>K2`x!
zyGW8f{wG)1$rb|$@0lK4qJ)^x#F|KEF$f&~HUZ1}uC`TyXi9I&Z`mJ>hKIFJ`XbKG
z4nqdaA1x@&Lv6TfvzW>0+ZgRC(){@iZlnU65|x}HiVvPt9FbH$dUAx!bySgNFgX}K
zx>(n$t)D=^H=&e;CV!D!Mmp?%5b5nt^&;dd8lP+g!j#-r5;FR^2phy{RA;PhI&2f(
z3*NA9Hs=q%JAM!G43ENo3)+*xLRb+5FYv=ZK9+7TQC5=lc>+)1xxI4whbs7bY8D={
zxfoY)?kJHCB)EPL4Y|(+LDNWzTQAj3nP1DWMa!Jc80d_M8o(M71HLL-;zl=$jtW+n
z(!h)mdds$cw4V=%;piQ}QxgTGeAT6M^zOY601t&mI_LVv4Nncv6jNd{@TI@FC|Z=J
z#V0o_<tv{sJX{o96k?PRJR%a}td?R?J`VKHu6r*p$ZOx9>J9eY&*lz<$Pjr(sBfE)
zd84JR7<2KgEPoW4G);Fr{5O~sXK^h#IXR2v1J~h~?KgspRpU#V=EU^yTCZUZ-H`ZB
zz!uZg2uMpHKSXOTm~XJiDx=+3zJ)v;;bmS+T+D`uaYST-j~<BUnkar#iDWvDUX?~*
zqB~5mX3M)`<1ryVUf{Z`?9_~2Wy_C@@EN>;L#-UHT%5DltjTa}nwRPpxchUuD=Pco
z;m$&vAG03|nP&-<Lc`SCQ;Io#Yz=QX5$l0X=iq#<zczVw>Mx8FI9vXS?T&dI=^~8p
z4X>{K;)hr%4{?i~KW&XNj8uIN_N?{C#Ak;p<W-3{FJTDs!y}SKLGfzczGEtB&+a>s
zrsRS&t%sUEvBB!`-V+l6Kr;{&p}ZOYh*0bNVcqU9wPsJc0o*i?lq<oM+=>G=Gbpve
zBDQivWmYMS8)+bfGp94Z7uYR(JzUi9y$WjAITh)T-o0PHx!DAn0HemEhog5i>AtU*
zr(rU#D5f}jvoi5!wmoeaq^odQC!(V-y<ga!Yn!kpI%8a&v=4+mnF{huBt`p1$Z;a>
zar$@A!lW!yKX&18_9WP-I<b4qtit^Q_ii<Y&go28yhy_~gv=xAf$do+J7W4`#7AHb
z&EdoMkS%M0-h#m$x@{q3%nk8Dgvh-!(s?gC`QRX$rWPBMvtS<Edwx!CJ3OC&&M0`H
zv<eIszKK07O}1;bK%U3`bf48}g`4)&BkJJm4=0Tie<S~vFUFiDE}i!vJPw{Ii728b
zrk*G>{PwJ9>HeT=cBZ@+Uo#OHs(tt2QDSdwe^5uKykWTK+iD=XkfJ7LfcgW`HFfW1
zmTRjzSnCsftukcg$F&5tF>8=7!f}pw^~l^!UtdvQWbk#Q;7KJ6oTSVuA{E0E1NN*G
z*ew{1Q}Pr9RPQ%1FqFKwE)p6G&`SGz_%6GfbJ}AB)?N5mELXtxlD7ZvWUL`pv2DN)
zf;0(l7kFN5C+t+k*^I{1eVU7r%wrggco@!$N=Cdp$^Z;etr&0=bxU>iivWZ;NLY3A
zo1nL#Cw(K}5x`C3tDF!~BZq^!_bs32E^jnm=ABp#HpSWR9aXOTBeMZNH+6?GdV!++
ztv%?&+EI!3Kinp<DJqPX8}FqZ1G~Zko$`JuN!5{%|I_H1U~6&7g-wdSi%L`vtDu^I
z*NZdgaA4uYXVbtwLw$$1C9X#$uG3BUt;kdtQn)B&#spwvB0Y~UA>-={L=cYp`Q56l
z^q@`JO3Y;v<1lidwT1#9szOYcBEg5}LzluBOcBj^B0M_3h__$y6yYPeZj5VI_w98R
z=&A~gL5}vB`X<?=7%72(K@4oD4$^1DQsQIHqF<(GSa*GM?)|U^8!iwqoEtWVX)uDi
zq^4m78;7Z``wE(}X=I`z$4i3?eTIp&WsRVJNJBm=O+K4~SAf{rEpVHFi_2$CLqm}U
z<3SN)6zEhwOad4bCal>$VvS@fS{)}`c(!|3VS25Dp(dDOGyu%ScWNYW=|9v_xY-%b
zwBP9!7?AT07U1$pWca0d{WZ~fzr+q)HnNFtf}Mp24WAJlHsFgBKQVk@D>H5P;Lnze
zv7h76xFRyZs8z<7=hE1H2RJAF`0hzJjB{^9Tkh;nCV0qFR(}R-IVV;vQrUcl2&16*
zW^@zvA>DJ31O4>{$2SoN-R!=>_s~845o(-ii~de<V^^!Kn0ZI;+jD`@El$#AaTJ!$
z3+e8i-Anl}i4oWnt;TLh-@b=YY-5O(T7r{^@5y;cQrb&B0M2I;R=aADD5ufW@E)i0
zI=l^1pz<ql<Qw6BiqjCpMiW4npnL9-Am1fzIw!ml4juM?nz}CXb+v@N2y<_3Xk`Ey
zA_5n&@2JkG%;;ibQ)U!FVge5fPUb9;qnLzxqHIuQR06oWwdIenK&Fp)Kpx(Hc(T5!
zIAW;BX2EyTbt6qv2QC=?F^6P{=M!?Du#6!%SIo0m4Yu_!b0i^YjiZVT#CbJR4ngvk
zs(QT$j?m%rLjtOsd$nFyHW%VpN%;#K?w`KZ`?Y|a2Mm(@(1&5v$Wpqu;Uv{9aC7cQ
zF|hf_kJh|#BP5KH_Ik};eXl24eI=NB=73J(V9P?=U6E>>^aDLdxBGyhWCJ5?e<bk1
z*_EtsqXvk^=uxKM$0^`5>BPj&`JI#yNl~os1-n~qHwSdF!5Y8hIacqSTG_Kf>{gYP
zDa9K!#pQ%M3n&S}q$d9)BSkihuVZdQuLbK33IfyrQj?55G^9}+IgmQy4WkQpvcuJu
zcajhGsvcro^dd=Xhs7P8SW4E|Ki0d{MJ1k1A25%QCt2wDaTDi%XNM;HCkT(-Jgi}w
zZ=!0C;d19|+z6Dtifk{~e)I*hZJLOv1X@WAOQcHxK{!zX)JW3kzJkDI#U#TQ5flvO
zabD><Ob0m{TY#a>7arpsMZv(Ax?#%KoID9PN+M!N53lR_{`;IE3a~U^Dn3Sav@m}D
zn|Iq?aA-mY3;{cjK&v{$joCpzpXs)A_LiNoeHXC}1CjLqDaT2j3`6$=Oc@Xj{x*p(
zi;DNFV7EbP8TGtBBD`L;^o($Z#*k_enBf;I9lw$04ztlsN^JFk*ju)>59Ad_;77Jh
zmf~0OZ(?4}(7-1AJ=O!VE3$Jkl)YaJtH^j5#aN-Kf*042tMft{7wb^G;WAcz(cw(D
zI55(k$zwAl(NMVf;B$RkKTH{8QWGBq`o6xAHbAg4dUE`2@ZWvL*}$dmqMea=*eT>z
zf}LhC7KK>L<a9V%4^oSDtjHa#r*eXC;aAXpt;}lDSjy%)$}`7Sff}v4j&RJz2_?fb
zWQ9T%KBXS58lAlE)Jo@?EQqFuLSn|`sh1YE9+lQm3yN}9;H$n7baL{c)SX48x9nG9
z5_B?V@=R6-@SqsEq7I$XY4;W04Z=APVjo8iwBdLV{kkG{$d!#=Ks&w(%e|I<+PlVO
ze!M@#m>O^B)Xz|aZddue$UT~3A*ClK<Ld(e6WcXU0Iy)DE(d-y^5@G1K|9TBx4q6u
zba{_aMU(9r!JMARlIVFf$4rzhqM>`oiOfzY6oqz2isZDL!`^mL{2kMrsRTO>!5IFY
z*YxD{Xlj<3-6y74HtRW_d`W$2m0xV|ZdnV38mbGL0>^vMlhvraxy)mtd0qquBk7e1
zOJZ5on6_B<NN|Rrhh;kJ6_BSiTS^mS*>l{s2u<fx!9i9(z#!}_Y8=I$!O~ZJks_4*
z@1~j08=xCx93s%W=V!nVg{1Yx^Yo%+>!;Q#9w}KpTx%dJWHeJ4W;R7!Wx4QyTMQY^
z8rV3u)2K0x_o$SA`>TRc%$VV*?WkLt-p>p`C}YQ{wyg`)(>RKEEhUOkyeRLni#Z=9
z5gxoD>ker1-Dx%Uef{qR6(Y}|5EA2+l&S3Xu9iT5GigC($NypLt)trNqIGXviln$h
zp`}34;;t{H#id1xrW7k0C=fI_6mM}2PATreArvWY#U;U=;0|Bjd%iQyx#ym-$AILY
zy?3_EwdR_?=b4}UcexI8(b@aDsZ5_is|n_xu*K&`SPojBF)<}Eb}PAej2HvL0~+o>
zMN<Hhm@TPqN9T5<6#?<2l^+`<EjZynu}3j^C99U`B{EJ+w^^pRDpF8-xbrV!u>rzM
z9(#@>at&#&^5V=Sbxc;S4e}ZqB=44_0=e%(-bh=#^I7)VYN|KQE<g^FRocwmdFguu
z{SGV~#ngk<a3vIfwyqv_(6(xD<`7~R5wa4hRT5Bzh-wQ-Sqau+ZvB%{FIF3vl;-pH
zVyAd5#a}rZD+42w^tJ$3NW(g^T#h>vf0rP2V%P4i4Mk4)-0$ymTOz7RQP#vm$7eR8
z@4O{lW<yLPu~qI=u3x8?8dy8+yHCU~x8J7D4LA~Vh>3o;YB*_>|EQPD6kgtL-9W$X
z<sb(u7Bb+46lMP^Ehn}vDo8=ihU{H>(0;=6Tg9exBzq<V2-N-P27R@(5R#nc)4nFg
zE?3*Vt_lKW<qp35L(b%%;QwD^204DaBqL`8(9C>YRZm#xGvxwvdRkY0?EZMF`uBZH
zv+rq4ln1VnMvZL|hRdw-cEbce(yTf(Kdze@zc-X0sphRn<8pU@E7~J1^t=ztPD6s{
z&%i66PCYHFO`|ILj{|w?@^;_f4L5hlf0QDY7IGdMlExPN>>^+DlWXCDnDp;a?1Hl=
z<0>}c5bQ_!N~r;1qoWtk#@ZXN5^h1}R^h?gVx3I6*|=@xUiNY?y=Kh-9>*}KE}iwy
zfuYi=+BvZ`HRQTNag`$$v-NU8MO3^t?7P)F8z}jx1E>qsi4Rw&<Nq_gi8O)RFF&EX
zmzSUWk@Cl;;yQb}T|e2>|9ZpUd7027yFQTpZM=Aux=X+Z4ijw`V<46NY!$FQuRunG
z?Z<~^w1ZQ|pd0eNripUD4feD_=ZTL@qVqd5x{qad$Obq|xIxvAQWx8CnNl!14`I-3
z2Jd0{B<w4%mO7vUKZx@T|EkpInG^fn&IW$l1QQG_EpB?#V+9(0#a{X?vl{~nBHMD2
zJc)Q|`ff1tS*dRw5%f*>-%KSfPJA^{1C-9iO9p51`FiI@4CclCu3+|TKT((#a++!q
z-w_c6C@6F&>^V?5JgIZb1q``LR_)iy1boolg;N8{_J3i6DgJ8#@dYV*39+ic3NtDE
zE@<Xiz+xD2A<UOMm3;ez*sBU>kez-{qy`moOaWi?!Ud?6A%Dzt1~1}Nt<V_}^D7Sk
z`6cYCZ#D<1%qrIVn&b8hc!Lpo%%7EcMk(Z=v}pU(Lu0q^EAblN4_+j&?I~h|zyd#5
zFdq9X)v1~9s|VxvD`}j{PWn^&-3|A?L#T?m!Qb&qgj{prOkT$-K!?$PoQl-fKQ>@f
z;FTwK35m!1AthQ1N!K}K97ATXedbGOz#$AoQZ-!S@uA<9a#<dp5j_uQV_35apn1i2
z;jmG}<AQ<6mzGxgZVK}1`!X^|P$yHDV8d=q-C5jX*!J}uqhT|np*XHzGIu0rRCUk@
zE1hJj!_IV(hbsjW{kr$&N3LjbJXdyYS<^i=l}wkvDQ1zC=)d}Q1EzESPg{~^!o$UL
z=x<i*&XG9gO2-B7;>G`Kt6FuFKMAgwGQ^85`spV@S72gTSfgD0Ip4RLb_QO6tCiU(
z+e%UxbGixY9X<AA%8Y<Wyl(t7{|R_~bz}QePd{_)aIZT-Z8vUXB8AAF)4ASDJiCdp
zf-+gWYRK(bcHM65QL~m>E!$YX#Z*AF9knTl9uAJ3%oXk%^)iWko^|5J&J+*9{4RS-
zPSt!tjEm&S5Y1!pRn6#QN#Rd_3jn7{NQVUHrwa}imIF9IUi6*c|NC44QQC+32d7z7
z1fLV1PNjx?lqAViGnSlmV?!cN4FvMvHQ?h#7F6OnE7^5nKI_{6MHWelt6!`OAaygp
z?d2KmsDf~4e}lb72BSj=Pwc!#!oQ1ZIcGx8^0@f8<5a{Gr`P6yUmlzA2ae^?CayGF
zB};QhKhf38&XaCT${|;6Mw*ShWK~*h?cgeM%=5x;90Vr@X!l>qzAhX(USoZD$;m2s
zX-}2@nYlk}-9^gTwe+=3VTGt;{Z5FODaC)A29x6_<KPec;XF?=){GrkLo|`EpU$Ls
z08#qrYXQ}wncM*l(-g>Pxn-(co+Cfdn+_K8(!9A*MJCz4N|Dvvb9H~OJdqMCoh_bq
zm~h?7xTQOL(iK?Nu-5mhS^q3KPW8pB|NC(N8BL!szJ21oOE}1j|B6T5$nOi)<wk?+
zH`MRI|97io#7~ZnlG+?Dnn(*Vo++|bL{5t&>7-$6{K+>dN#fmjTJ4hK$5v<(^FD4s
zI9XLy?7tGf|M)aWi@)yFD*l;W!UY7o{5#z8newbicf?K%IbF{+uM2b6uzJ5KuXn;}
z@TKsZ(6VvXU`=N}OQLm_E#|*F#YOSSD?ItH>A-({G9;6TU+<)<{*ckGsrE*}`Nq3|
zoE-m)gm$4TZ>hw}vn{E3Ur4obR4)@bx{w(PNs*ELCog&IYl?V0fE7>uKfM6{+iQW2
zsyrZ!>-Zy!)1`x+&{4TZ=^->dsGu~q%BFd3U`r}oAzGtY`|5*}(WZQoq*uuk-H|Oc
zQMQamj08OP4Ie!upER$kexpIY5l>CD8Ofwe7jr%Y(_nrW`0;-o27MqL@qnHTEgRN8
z5xk*n87T__xb~!>I74tnh~?a-d_LDUiF<RA$FcUchUrMnQYJTndz1i2uMvH^-VXN}
zMdB8k+YB=>^I}I6g@rn4#s0AAkM>QgD?j@wYX0XPw93Y|M|%_qYFV<m%&q;qZ{4@a
z`cx?%*J`E9+@G6>>`BcDUj81CNIpfInwIPEY$m-OgX<JtQD*<ov)%{gy#5;X0-w+4
zpjleGZuUttWj4C~bW2SzQQzJ5Y1`qV7F1T|--<qPE6cM&l}uNqLYQ-)heFhS_x+08
zBATm{^yN=!*9<zVQgq3o&&qC>@AzSSTUyz^>s~oELm&KF<6Fz?Z8h=#d`|!Ke|i}9
zJ5mSw_KIyoh6AZ>XY@+m74-2MDgavjEnqc}*oibu;LsvXc&TwB_xgukEv|01*QVK%
zL`gTsO5xvJ*)nVxL|8N?E(){)76EdQw7-W;I^M`joE@9cHF?L%yf5z1_eMAHXgVRR
zND0WUdGjmN=9mAip7mb?6AuX~PXn&$KaCD^f_DgfIfCLWk96Z29q)p-+!}>j;=H#(
z)h-QaAo8>iw64OX@&~S4kx(r$Yk#&q?oUf&b)3eV6p9PILgQN!bgyN{lPU(~@IMhX
z&&yUM&1ZE>zEn?rx;dONv(3@aw^4ZeKkw#$d=K6D_dZG<YeP#0xZnLtt#FU^G5eH3
zZAo%RWiCywjv$W=BjRhskC|O)%5!Tp(*t(BnGv2JD>)$NcRsGd5{fpUmvd;Cvqt{m
zfD*Gn?m*kq<r&8N5{r8S+?B+fW&mlFJQ$5JbW3byu06ReZ(RN|6G!4XShW@_6^7pZ
zZLVcsEW6r0T#&oa+(Y8&!jMkysXXLGt6s6@GED>v8tLTTxo%G%&B*@WLl8Rh(GU0d
z6(*%Oao?gDnC-~X2(sr`5b1pBQ(grVL|+Vp!LOMm&!q!uP#IEe>8qajYQ%Fmy41?7
zDY21=LKp7B%<oodvT33uRWIjkpiCYM?u{-tJH=9Fly0)X`i*4cW)UVUzi=iG7b31?
zUz3$l+1oOge%A01=-oO8GFIx4GbzhS|1;_Di?exeRWftO`oAXc%+Z%qZHs-XNY(Ry
z2D1P7t`?3RW3Avkp63PLukM5E8#`{`H}w4VLR+_Yql@R83YqC`Ba(YCs=G_Ed)Zb;
z(3&hAji&7N=f}mrXrk<3$Eo%BP2-QpyDd2Y+D_-93Y}2N?xXol|2Vk+EHFG2jlROE
zT*{7G_G{F{5dqk66j^t7H$ZNG;9;iyqQ0t!+8Bqlx_gN!K5r9%eu5G5Wj`{iTg^Iu
z@UdsLce3fClY;+kD;y4dqc=7;8RZ)NY1scO6XFf-jES3uC))hyWUw1;Pqn*)PB5}i
z=amqdv)#Ykt>@FmBjJ#{HI_iR1DcnpCC?T2J*i6>?=!?|)yV*NI4P6lmA^Yye{dnw
zc7vu^W4~q0;~LB3b`-YML7m<#y5=k++k1q$W7D)HzdvQx+uf#luRUOiKFQK{Jr1Dv
z|BUJXe`F70v=zo>3N7}#y})clO20K)K?g7sF1gE0w{A4z(GBWj+8St0d|tMMK;(#%
zy8GN6_jSKva9mG5I$UZAw;r~aIpG4(co=mz?PlX*E$;mLbO?Oi)^5zcJXjFR^xiE&
z8g$$wvB;++p80a~LPn2iFhAvcDN%p@@~a)5{gv7|48US0`BfV<$f82t^S?a?gF^W9
z8VV{U<Cq|?u<g2Y60t>edAVb2*=JF3B-at+b}~>ybnZ7GXpOn>zG1<M2z`_NceI<X
z_KXr|&@A|myioX3tCw3%7(n)_llK^nlYP3HZfxIxz<O)tT{hEfnu88upUOwvil7_c
zursImBY3x;lwPPlD(M)=m5Z9Iq6f17WgAQVKNn97`7c3(WLUWh<K8ty`^MWdTMuO-
z^-=#K8RcvEzNY~$zmY0e3NEllY#E=6vxHfRru!~@mNS`MShM8;8X|W<H(*JJ4#bl4
zb$UObV?v_IPIc?!20sK;*j7~3O9fioj+5A=H9)uTJ%Y3-XN@*WOznCord15mq2rl1
z(}aD?yDWq%fak&%RIHo+|BNR8arR?VjJC@w79b*pu4^<rx<o#bG@1kYcw4+CM}nr#
zvRm5FluobN(67=mdbAx21*z0oRn@ZhUQ<xa#*c(qo!Pnk2Q+iuvWM<Pu)?g}Ah8&e
zunbauLxx|Zp0>IH5->rFJQdL-*Zmo|Y-{EEqOltt{SNeRY@mMA#~{wx>n-Tf&JUlK
zT9>ptw2X|}<71rzl877Kvh}QC#K&fA%dnv9WSGVLiTs};$F0;$WjofQfsdjepksOD
zXPbero7K~ul*=u4)0tkrQJN5k<KmC4pysRg*9+Aps7<?qO!g1@?fXT(`faP+8ykDS
zwtSoKVEU5dnQ;c-=4n@XX=6&Wq(Ni8vWB|Q7Hk7{-wk+@Y`gxil!^7VdUgR)gO5Ms
zx@n3;5kFr-WU~fhEB_*lXy@sLcu&7xSh>49PPvw@J6k5Fx$v4Zaz?W*^Y_U0i_N(x
z?6}PRG5`wGl|(5d&&>5&H(jfN5-A?3A}w!Sk%ww5mi43O-}PdxkD<OB+dly-6A{LL
z2=ryrTYX=SxSEoY|K{O2<}}+^z+Z(av&P@=V}uW|8(v5RD2~u!Ihe~MnMEB^eYD0{
zRx1YbNL9VGPTMVVZGQ32)^RdxilfP9xl|XUiUX|sbRC#@?MjWwT&1`pQQi4TZ=A`B
z$>CmY{9R>3`^H2Pk#Ya3!S=ewAE@fdn|fkX^lwvL(Dtnt{$BAKv@tlmHX_%zvlkUL
z$vb>osIWG{>1KB#p0p%@?^-#AD=19aAs@EEgTbq(>XChRvzjO>SET>dzhpUPbCoY-
zqvDg(JBd*c%|@m$MzXGvT2dwBh^`?kgMAS@J5+olhRQ88=|ta0kEc69EQ!26FKPU}
z@2|Y!sgz%DKT3R?w<O!m^$!#4RO4@3H)lZTL9lw<1ehSc!xn!(81SAO71~Fari5;G
z5S$xspCFCWT;H#}w(N!3kSq%x9>kkhmBx3FGMyKU=YE$|L!dqSE_;|q8Un(w7zUmi
z7GwDIC$Zji#d-dpVb9oO+RW#m#<A!Oe2ON@|H{d{%SI1ZLksDvi7(B+){hQ6q6cbN
zA33ym$azvbT~(YRk^?%S9mlE^2E*4qT5eNAdYNqu>OT0wvKb;TzcyWHFDtiGAM1#Q
zM`&hC&#f>?W4Fc`&Z1YEk9*{@B`7w~w2(`j08i$@d?S&uV^dNwYuqj-6pfG{L=E|B
zQjV4@V<L@bTcgq|b6s~4<qa}CrVnk5DbF)~H(hU{cYvqEqLneR^KYXr>%Z4~B64Kj
zcEe^e2UE>0V?<g_-<pz!4^;N(`xJ{)M_&&RuMt)9z%fw@VwT=$j+E;UPqbGr;eF^a
z0TZJ?kt4iBcNy|{Kau%NBGkWcT1FaIY{8*cm${W7&8M@^0JMwvRU^}nU71rT>yO%5
zh?BkP?`Tkx90)7Y$yXWAIjruLQiFDU@VonRd|vOBRVV$suJ4lQMKsnxLB9Hf62D_F
zX7?>xA`o=+SJ3PEP~n2)9d9TfIREPxvCTIlbvjo;67*aJ1KvFPb|uk0pP+($4!)je
zG(c3w8obrW4X%1&x2qv)DCFzcEu!SM*Qn0xQ9}QzHA(PqN^h|scV#<U5~O`8zFql1
zVyTcVsfhCF5|~a@fG*{^Y!-D`9F*E)p{~~J5B2U~4!aY4-3Yk>kQI{+2}o*93ngpU
z57!4KFlQ;MNj>x3-1h>GEv$7X%SeUROEz%Dy#5>fX{~uk2oA>Mup>4FKd$k^JH>iK
zV`dI$Jsv*N^dU=lV(XIRuh*_>fXO+G%;Sc9e7$KM{N0DXKYAcx<L=Kc-!j@=S$<m6
z0{ZK%0ATy*{#4mC^yW=9kyeQtzD;LQ<PCXA)tu6X(bA#Jdh$69=EPq^b>3v~;63{)
zbGhi&?zgt&x$Lg4%hqwVO7s}nH7vGbvfHxz=n8s)eJ|!;LAlu<B?p3XY!B`a>Izpt
zuCILQqz!_6%_bC}Td(~$KuN1SOXxv-^U13PJ?WJ@McJhA_(Gr6rWqjv7af{Vi65%~
z5ZaKfl*szY7DW`~c46hrw!6`TRXgxnkkK>L!{&CFRB9gq{gEjE49bt#qgi7+>3F<%
zhM`$<8K(Py%D3@m&&1TT_d>Sj8CyXTAaBZ{gN6k@^>pDs;}aeKgx@60{IQHWptuYd
zghjpEGs)>drz-yX1gh~aLL7?5>d=AF6b89crcjmEXECUx3Z0Be6#|-{JTg(FNl9@W
zOyU^eo+d7IW@8L{xY_HjpE28mpikfk8$bHwn-<2hnd=|+Wlt8bbuxoa|I>_>5VVvo
zEd24lsyLVV-)v<T{d9MuiEt|ahf`>{(^7Njzw<w4=%bFz$=QyPZ;6mcjMy)lt532h
z#y*g_xmlqBbHxpJP56sK!GGe4`C|cxr&i#Xw>pg181+$dln}<0C83Ij*;WtCgu2+7
z^b*Y`9qWyIG{oNBLu0<bun=MGgp@Z#chD-F{EK}MF8C`q23Oz)-v){Cnz<Lp3iO5)
zH41qUa;WOULsqmLx?0_!Uk_5Ap%Zo4Bo0P!_(ALGh=LoT_$+<DzU;`#IGhZbS=@a^
z!Suz)Wn)49>d#gyPizWW^bs1x*UTBsBA5MB(w)o?49)tACK)<0+BkRr%}8e4#wLS(
zyRPddHHpx2E<y(uO6CDf{70M(+o-&~0)vl#xqG>NUj<9_O_r*I)p9VAXn7wofBV&^
ztJ{^win98dpGQCRA*wkiB|assp9)fpGNta@4Q0n`t%pxMELpMp`?d!E^d)et#9p7g
z(+m0}qdKYqN*J(Zte_76EtgHlEGoNG;&PV%yxX$7D1<1Rp26elILD=XWrditQ$5L?
zdZQMG5GUUF09lsqu_<g+=|ff^F67e{SM(y`f7cuZrh8{%jaOSCKUhNWE&+F(CYNIi
zz>8LRON#dSj;nY-Hgf`uhe?aI^%!ZB>-^XSoF9U3RT1hf_7%a7AP;Ils9ZapzyD(k
za_h2<;^LuutZIW$EBy3`mV7g{>}|W>2rZ*5t!cb_Kan%1Qft6Vi|p5D6F%mExOVXg
zDDoiOk|Ugi-(QvyGE;?`uNsxkWZC5To7<V9m<ZPHH0ops52Se75kbNHH_BurJZTk%
z#W%XxIZ9rgBPOquW!v_97l*G`$#Tt@SXXzN{Wm5_Ec^RKte&wg+A>4!tHl*`mm<1~
z`=xp$<na=+<PIff40|iCIRC1&Oc$<Tc5cLi8W;LD5Sk>Ya{MIfn5^MPxk;}!sd@XI
zxF6RDM?DswQQ06;mr?xb8;JzPt+(sE?d*#^cvC`NO>xk2Fj3hC{su9<a6j*60lxl0
z)h{1@m&(>=@|WIC_>?r^eV6D)Nu&lQpimb1ciyY3lALluRF|0<)#&w%?^6qHDsjw(
zg3^vtl&c8?6cwtYuRPIG2X|$10I@vs!9wmeGaQ^OP%3WvA^BuCt^5V24kPRuP4Rkr
z89Ua0_y)-{y$;bDHKBT$IjsN7l;HyQlQ&6pWVG$1*U!+7%HSJyPoH>K_cc3uGUx$A
z1QDs~YbGf({;LIq1!>-PJ7bix5y>-pNA`R_)f++Q)#lM_Wfas~5nFmt&L}h~)XtwQ
zsH|oJaCw#K6VQAd=OS~fajfFbOH<kUVt9>;^f-3wD%)m5hYpkulRhu>ZB21LBMMOg
zubrVAv{e~PQ5Dw>aL-d}!4BU=1wG`m-1jFWB=c%He8>7E*`HqpBVHmZ=6LncpP?nZ
zttxzvy^+Oh#IwqX7N9H}80N4>A9!=_+~1tLaZ4qC9nhA@aA^LPqWIXkfQ9f2up#w}
z?D;oEON3uPA#ukR9{JD#bIQ)SQd*+yYyGV_iJnjFSLfxUEIOu5cqA?ExAw$%q%+Z4
z7<BHBeU(QlZ)!BQddIT8?wgy+UqE)KdLJwdV*VTKl9Sk>&Z79;>LKDZL1om$>*%Dt
zD{|yh5UK~(M8j$37wED?fiE5-@D6QlK6K8L*I(K>`Q|uJV`F`-t3=|prV=c>Nvp~i
zgt0+vk4pU}1XpNmW#W?rFS+yr8VJzM7{m&Nh#DVmvs_l2oWo4T_Q)G{+ltPc6dxlO
zOo08;2=X!R9To3{S8+G=JKDubD#sz*WhSkwoPP$Z_TJX|%RM63QKv_gs+J0-Uz57Y
zQgj#vbN%e~#0=s-jKE=Rj)T<t^RI^CofHkk3(j@PS<gk}hH82O@bUd=V_Ph2#a(Wl
zfRovqG1K){wt?>!<u}~;&0GNSn|UdH;l>6fyv`GeMy$S)9O|t$9b+X+1Hks-@L@-P
zoQz%VP7|rkGh)r8Tmf_odA1l7*xUXVoXHJg?DO-}R`c^K(KT7^6db`Mq3@60tSWpX
zf=v+^fxZ9qndc%%g@(G_5ubK-K4Q!Ro%=Lf#C=#t1DzlRW-F#kg~gX-@Tbw><L8+>
zwLt_Rn8C%t!VDFr2Kr?RuZau@7mPN3!8b>cVd&oCd30DOgX+Y7P!6rI_jDf=qL!bq
z=$b?4<9@DOWqi}FG^K>@&tov=b>3F3vrUoE3UpF|a2R=1(GrzoWda_>srs3lwnY+J
znzMh0F|@37xm*&4f7rB@JtE{{xr0e_u3+0UoQTbt%j@yHU<jSO!k2!K(Hh+$?8(cG
z>q!kmX01_EdRk3m#4&ZDFE!_b#7#bs`E$v6u(C7xvXev+F~0fs!9PycC*-fb`7o~)
zr<38~7w@T%C6AS1yHNjm7H(b)QXCJst-+!I)c?kgreDGwvk?N0w9pdwT|pOeWq94g
zjtuvnigro3Amk06e);kxvzTDiGP+S|C@8pwK*NFSS$4Zp76zr-$tN$BF8PSU!#WDb
zT-o(+QF5G5(d!;DGfDhb(igLqhYeb{x-2)8L}VxoS)FOIi@P_rPJF)THfmKZOsC&8
zZc3^1U1mbx%*Qqqj9NN|Sl{s76E2$Q(Dug#VFPyfc_=OF^D0odjEEplG!eusi>@FU
zH|K^vsQCYl5yjPJMzD_(%x(l!drTYIghKB3%-J^>n_WCNg>t88N;yUiwL!rt@&4Pn
z;mzz>vRYDOMR&#0(zdpGH-f%7ary7Z%vo+nEs^;EIoC_@IH}`Wuiuv_w5)eFSGwI>
z@%Eij_dMYdC*acQMnXEj)xgZ3T|Mn%6`$I1=e(HXZe5iRbg7$&zZG#xbl`?ofrbCe
z6?@^y9s72LrpUfDQvw_eU;UnxA9!_dc>bL9VLU87_-(NV20jLJujEzQ-YjX*CFZrA
zC8c~XyW>A*0g__|%--9qEPKA^R&g>H1TtT|uQK}<!_PCSwN?s_0$FnIx0MWy>>xKx
zzF{NMj@h|zfa0q&ofR(8M$93dXkb9oS;oqpwK!U@-3FkbUOQ^l-$E@N`OhSoB$@`G
zWv`zy1yf#gz<uDJi*_n3a;V52(_Z!?nq7@Yyq{ve3~1@;X_O;&%V9(5CI6=KRX=Uf
z%5f}a=1V<dTASZYerMyFQhwhmS5RgFo~tret0XuiL!mrxb7v<Tcqq}L8wkltjkmMI
z5tD78N5BafP|`8s|632R35^k*?_Wri?e!sA7JNgiS4VODdtAlnMUFF^<%Ss2`uryg
zQSM$vOGTr^*YOnGRk>4^t3PflewU#K4NH9cHG6!^9NRQt!q@HFG*d(t{$8$~2}pFW
z>tk=h_Mh1I;SU4SB#x+X*{<6dP>+e0w0mW>7ly^M#d0yfE}&P)5zgY}H6#E0KG{()
zZ$YRSx)F|Dku>gU9WLNyHv3?oWhzmO#hpR!We7co597yp_Bdq^b6h@bwN)iFnN_Zo
z=g*KSpF3;$J#7#vtT(vSMCRJaK5_h5PatHS)^gbb4gX*)0`P0tCij>xD{X>SQZ9?_
zoiJ>^YWFT~0HPM4VT6-gq2)eh^cQovFU#7$a6=wHU)=a0%RxZI9yH{bx<hBH0a$r3
zO(J5d53)a!gEEm#n-?pk%6OVx-PY#Az3uBrj$$(%%-7Qy9#Du3;AN#JfWh$YZLPQ5
z1-u_M0^0#D#g@Y{rOpZ3qQ2@KbUDWnVVl(p77dg5IJfJkiVuEfEp-Fp6??aaM`@XC
zTIaeCT92eqH(-P-LR_F`NZ7%fP@rXTh{hLn1u^Eicu;5F!<AZow|zH9s08)2W&Jcz
zE+05d>Ie_U4hyXm0U+l*Il^KO!bAD&8vp<~u~#8Z<#3rte<=qWC$x|Bya+Lin25A2
z_&wNjF?y`YZ?G!k*fm_E<%*W^<l#yH8D#`~&V6~<w+t|LT9haoZ}3VLcGuh<tFVF{
zrRe~v@{f%d$|;)5#$}}~%<%GG4wJUUKv(9QUVb%gGflNS5JPN?BSSA`0d6rDG0hE)
z4TfT0t1n8d)y25qi`@TPVGtI@2ajXHlpik|JKTw0@SByrGBmDYWc{aRV5I{Bp`k7a
zU|Yu^fEDyaa>*l_P;eqOMHVFeKIa)HFM&P*7h;P&PX5UvdQB9W={7<rhh}w5!CBH)
zuCSZk(j^g^TxNE9@9I>^+24dt0o;nr1Cbb*Yv=_@x1NA11}9k8j`CXt`Ou5fispd?
zJ<pIo=ne@@y1_|!M3iu9)#1zbOBqi+j@PPdTK*>qs3`M0cqsh|8v&C`CuYTB{F|?=
zLu?Vx<ylkNrdWNEyKhY)*MsKGpQ2Eaiq8Yd*vY!67g0}$URMkgysyhb>8XUg2Y(X`
z3zG6&Ht{s?&5PXQ#G3tJV7`&eK}j6o``Xt0)fMQuvdce#|8OZiz3X_qmD7sN>%qcs
z#MEY1Y?+UDnNPeQN$ioq+Q6Y$M2bbW+e+S{y4(D6Lr-+-K;1=t;5W9t1B2~5ssRFo
zE7j@24A}!3)LnF9Aj*V)FHP<YUK^GZ;`X2eI*Fwja&Y9kB~^HUrzL$1<8Dd(^NB2q
zo700cc`Dcj#~2(7|F>8GE9HgF_R2$ZhmlN{+aVO1F1X)rEC~Bd=|dfZ%^0q*Fzv4L
zBIe&8JduTK2V607T1NYKvp8anK{2SGVq8VI5|H9-`5ZR&-3q&h9HytI>vrU3VG>>~
zmO8Y1xc(+nf&FD#Mvp%<hreg;jzlUxk1cbubAB1SsMXUtzUdaa5gdqI1Q-5Oy^Tk5
z=6E6S{0>_S;4AsBA3()6IN)BO_o(TT8S;YZ{t>nod+HIy!MS^;W@`z2B0J$sXM@^8
z=Y*b~{)vn?4EMnv*t1v|e7z#DZOcS}t=Sp=ZLR~C4o~X1oX(dtIqiWRbXP5}PLaDF
z8ndsVw1ttN;0a<#PJ(P_9>YDw$0=s8XwK7M7IwmUTAw<N>;~VoYn$`XSkY`zVXN<6
zY4@Xb?Dr88Nhb&rdG92JCGGy%T3N`lFUu&pBbn`07r-CkCa}gFu!2fZ=BM_s#cu$P
zjqtaN&)yQa(fSr?{971-#-*YSmB#z}Fixrcf+X|n;N0@}-+ivSlexJLc^JFIZgMc@
z>99Fh`&b~Ymym6xG@HZEJn+jjeb07yFg*I?oy5>Rk1BSw-+bDMb`8|;&}eHM7|yhL
zo<rP_SvB@#l-9hJ-;l>LQ^?F{arWz^hF`l4W?)myB`%RHkM$AS+vK4_x24xz+3R6P
z3$lAe3l@DGynYy2U+QCf7Abm$UO{&^Gw)qdzDE<_vZw{wBW*m#0W4xSR^jTW1BSS?
z#zS_|YV+SNowSTG3bO*jnSJ}WCb%nGC;B9BZ*{KT0mh(SEECJ}>Mi&M(2XE$Q4>bV
ziZ4;pB98F(#&)uY*S26<;J^X#f?nJFuk!9vnoGik)N|jHItgSy_X;6PELWWboy|}h
zjX!uigIYfxSJMytd3Is3wd1rApj63s&>b;q6SfrR`*KkZp~P=9J6nSEv1z5hF}rY0
zb^2&;pqB~gILtzI+nW<46fXqMy&1jN@}u>T{Gv^J^M|;5oG%mAzdd$ZR}9B*kYlh%
zfgYKeJv!{(UfodfhpMdHvq@i%CklL>$VB!2o<klszMNy6;PJhiJJWXCoJ6)3_l3nw
zo3SsVR*Qk%llWntZ>Ve`SD|FUaQ|R<sbx`kOmjJT>`fm&61A{QitgrRgzkw2o2n_a
z`->OtWwE$5sX?2`q;IEZc=nk+-;4E?iSOthvSn*-6<TQOmXy>T_jm^szXvG6If~xP
z-E<`iOi<(C`AKq1Uw3^r|L1BZ!Vc&s^zQM?HVrV7Ypz=^i+);a&#v_G=UAzIi@@ih
zx~jF;y@MR#UUU4dr$)1v>0<C@gAUpeh)bQ(*;aV*O8V1_y-VNOolP+wty=-`I$CnB
z%pb<QMe^jxS5oM!dA`OK)P98|Wl?G>h56y`s>-D^RLKEwuL?Z>@G;bXni}8yUNQAk
z6wy#2NvyqEJQ{CJLk195q_y1%QiuvtJP6f}e74RVR5$#i9?yNBCLf>nEHQ24kDXW{
zyHM_HAt0*|QlysVPEdzq9EhcB&5s(w5v*GpD#OxTsYFj8hssRS2*0v>Ys8-mzYMT0
za<G2M`FTpq0{+E1$k#fk7GEc9;Pr-tQ0^<Cz^*j}5^YbFl{U6MYJK3ch`>c~ouziJ
zkKU!mLmk_Or_%_*%oiJXAI;L`zo)@XRf+udW(6sF1t~rW)+SoLlulmN=syL|CkSf1
zJ@r7hmY8WXS;#k5wI49FhS#hGnr#abDRV6^z7o>I5YkJR=BW_FeCcBKk|1e9Y0qQ9
zDPt}#JbYYkh)OiL1J#EE8(K_J+tHvMP)7`^?;Hypzb8aw5js4)^-+KlUPNB_tK9Ro
z><a>FLy4S;n15yvCNeuwGmMtDtK}G_s{!k9VZ%YIJ6(N3=a@rzY3|#zuPw_`sf2~P
z_=K!veD)3+rL&CcrJ3ZS$xhN&_xxggD5lhoi`0Fj4{&~tG^dYcr#(v_uM8M{>ASg;
zOA`=hlkH_~t*TtoLCka$R{UD6r5M>~VlQOOd2F+=`#nv-G?Ic}!}dZTNdqcY___r>
z{K_nb8270?Qg$ls;y{p?(xNHnLy6C|K1JsDE#yVZO`7ArjIe2#5fehn*5ZZ@QIa`B
zYB#Nl*1h~}x#!=nY1IXGzsb_(btxQ8`;?Yeteyq>b+Xf4^uqgImd=>=w#d-xWulKU
zArNhlBlMu2d*UrX^TdH{^p?fZBFZSuNY?N0MYg~V$tlTww(O`lyGbXwxvJmT_{!z(
zi%^mck*n&V+w0aM^IzuU__z)7YxhgkR6@otN`$ECw(6v$@e%o8!Z;1qOP2|qu$$+B
z4cjUASAB99UV%0z-{OV<1!6tfoGig-2B#lSJPCjewmWu11G}e;JB|u*ocO&@foJ^B
zdPi@=#dizD$Ne7tE^UeetcMsd?q?|9YiTiWE@e11|GC~I%ws|Ri&_*nDi1hZp^*Ln
zEhq%(<-_l=dtj#u4PW?VVjV>?hk47Qy^71}Xx?;5wyV-apYi)&Veg2ovAi$ZjUPe{
zP$rTV>w*b)q}ODmDVluY82v1#TG@o{o%YC;ArNrMFzV5nEqB^2m01;6KD-?lEq2bb
z45*`Xqr3l0%`44&<b`?4d_C-XbghmYb^tX^(QrnAuW;p~cy*Ho0-VN2eBXTB#&{F=
zYN%<GWOMY6fW<ezBc7QaCt41!UNYj)C0gtFB#moYvxArO%13wOHDs18F;1a3z%l8>
zGi&*F$aE8wFt^&4uVR+tyXhzozGJ=TF<`86+7kFY8?qlisJ<+@-o@j48_1J+W_z*c
zV?JnB>tZo6HD`2n{CsXw>CLr}Ttcz&EC^tOZN?SC<gt<C&?fYd;dggUtcAV1@-Wxi
z0|Zzn6b+AL`%SL@XnRbSIe`<zZH2okt#nucWW?BRGX+Wg1zJwB4TRbV`!qAZhbFKL
zYc?N;IyC(8?lZs5EWB0`MV-Gc6kBwDxUlw^`a5dv?Q^$($^RMO*z`$lQ=4;H=Fy|K
zg~teQaP1$VVM_ni=W}UdfRRngJz3Cv=Key8Y|^>(23avhGh*;A&%y3?vKDn8UQ>GL
zC*t#Y41cZz>MhrDDR*RTs^uk2gfEjCbM^I$C}_8ghw(;RbbSdlS2uTB<Facxr>Df$
zbWWEc7k6zSYSqg)W|IMZ{Or-!LF9%x%HM9)dfg>QGU~uwn@|CfH6BJm1WP*Ac3pOs
zD(U%I24ph?xqS$Hr5@Q^kj~DZ8!nem?+%ZYYF)0B=Bi$YN<8KtPu8IFA=ZyR5Csp`
z@$$G?skORCYp519feErPIR~huP1WER+|ANSsBtzwHxcz#EZ;wj;J$l7<Cy`Z;DIu_
zq>Quee2jrBcFJ%s&xgY}>uZl!=Q``f<^6q{n%Q^mPDeaFbt#@KHsi(^a#TO*cc;7T
zkoCI(e>bG-k1G_$Lwy&+8~-LkT1rffRra!M+fAE_?^^)jKTaFpqnZgZowx*$L%iIE
zi7pEaCj(C#ZJW89%5hgo9cm(s5x*H!Y~@~VH~qz?(c$4VZ11wi`tq}+1XTLc32F$g
z%EsP*#B_O1X+c(aU)7J0uowvTp$cGWw~*)&Oy)PgMyfH|1euACHBj0@IPNjki1a`5
zP#7kn%sM4iKJsK4`XFeg>1{EgU?TrA?f_B`HaUzK*hISGroKjYiI5i#D*4V3HAfWb
z9tYNsvI9ut*z%VNI)rvQQrJJ^>uo?G^yzM}kSju^g48C9QAlnJXT6S3!_?c9Aibf*
zBua0P7_|j&cTk}>mx(chOKphRKw+GPMo-?iaJ*?=@IP|c{C>EGUK{%UJ#8NYlvSsn
zU3C7yS7T1L@WSgqr}9@C-AFM%^dmc>2sp+7h{z(Cqzj;(_&ft=S*9M#g=cA+13iAC
z73PaYC|vtj_tei~<#yQ(K2TKb)#Y>4vGn5jeY?m%QW86eRi+(et$-K3NHMAXhTj-a
z&guZ!nW&GNRDl))s;vUN<)xA(H;EQE=D6dMVuRaJI9+~T!OINb1q+=XS2gC{e^$D&
z`rE<1(!vv3U)1=|jE+N3s&bUX1mMy_s8?Ni1fBM+8n%x~#ubcJQ){8Y8<flzB)wQs
zG`Q4%p2tZ}^kE&qVdd3c0Ur4=J&<Fp#abJxO(EN2t=?0tuL4>>_yA9WkAhya#2vRk
zGjJ=L#n=Gu;3GubL->WFmdRndLe&4Ha17nsnmiqnxyBvy&9BGiTS+|qZd7utS<{1i
zv}gEst$C6KL1Yn`?rZ~pHJo~2G&xyT;(UG8$^FFswZ1)1sm#pY&VstF5YM3DE*&?R
z$t8f{K(iz7AeE?U5o`53cr$Pua74Mudh-;+4|Jbb;O=BMX*XHaBBpG9x`}D)Inc{C
zl!?dOOJ)&O%N$#u(L8}~$9=MTfa3?Q*q`$p{UHMN9f~t-t5&p>V3GBO#bJ;4-(qXu
zBWUWzi%;ZfvoVKiJ5#GI?^K}{PI-qYs?KiAWdYe$35^Bv%u3NJu(K|+8@*%gO8cz@
z^DaK``j)9PQM#-(?O^L=V8g{Ba_RRK7D{?tpYEJ4?yVdeRBhO;e{*#@*Ak|)RviA_
znjK<uw(hdpp%PXLc~k(Oq%*-iHWityL5MGoRQK+{WK<b#82jxRQ%i`uzP;s|b$eTJ
zdbnTX9<x7aPX)NYCB#$d{@ba%dq#}wjN7`Tvb^JsIQU4UJN{A*u$Rst<+O^wzxZ6h
zEim7{SQL5aJRS1<;Xw!X+hmJlIF&|o+5VtvZ__@70H~f;bD)bzOgn{hTiS#yTa!Q#
z-!7K~SE~I=1?+nlT~<QUkPC+693zA)OC>(GO+UOUjOnt5*zdNNW=K|Eb2sBs<z6_h
zdzPn%u8=UZylFR)?sQyELBh<K;98oyRNiHOWut5T?toT4aYo>lB*&3!ck|1C!23-|
z;8e&CGSa3mlxfD$sK3P9j_=tRv4$w&e5RxI6`flLv8SJ~h)GjQQ^vwKRD-s~?R3Um
za&Wc>=aLGr)#y-z9gu6Y=YkURZds5KM|oczT4yO%)=Qyi=Nkp7nl(<VdhkL$iGi8Y
z&J=#OR@rzyPD>)bk13Vmm&MX_nsZI|vz?j+9ru0afUJ3HX~I;S-Qa?pnZ7K_Ms>HG
z9O3C7{ZYgUX>w*iP0mj$G(IeuDq$B(@`S#Jf0(T8#eOP`@ls}cN<p5S*y~R|C)=~9
zFBK+*=Cqn5ci3YUL!SmCtdr$DJyisfpT)98!rA0)RX7FqO1Z%WO0N~T;ya(;q*dn7
zZQwp_8+(q73&p@uGBG8i*J;=0xw$+#y55{?f4R|vTa~5}iADGYp~zZ5syay3{&w~v
zLQsx;BqB(b5+8GPibmQ*hjUI>-iW|q3R@Z7f@cHFBW?1*obru)s|6VbGZ9pU8iIJv
z&o*DbAh{&{{u;qm0VnXlI7}lz7b^%n34(cEr1s2)nukbXNd5%KFOf;Eg>tPfG;r;E
zVtFpk<b=zGN|4_Mz9i+zlbN-N6mXyxG?09{4Dz%t#pWyLygft0)awH)5Ivy=omd0|
zIPU-0DX`}A{??m74md5dSNt>1g85f#O`$a(nscYb|J6l22P2cbj;e^;odZB5{;^6o
zvx49c!L~!U#UPGweB6UVn@)hvu3@4gW}x-Mkn2vA^qJhr`YlCWf|jEAR}9Wc9w!B%
zYuZxA{?H#id8C%<)uHe=9UCDoCUtbByL+o@YI+5j+c+tg6x=D`-OkzxI0q&0oc%^1
zie)G_U_uAauItY#<`dewQ+$%K?mY5HwgoKOM^5%3HJF`{SoPDFzOm9bteLNwPot^}
zBsf$*ZYAFkiu-D*z67rFUhZ0QjxJL4U9CQozi7rry*b-?)m}x=E0=wpp9RaCMP`q}
z3LfsNBj9i<j+?cMVGr6}Ow9od!+2nBaF6098<S#x>gU(w54k9GQ;p?rw@tn)LUZ)`
zYRUmY9g7>E)sIRjFLjM0&ZXa`ND`OX&KzMiOLj@rF0q-M(x+tJ&RNDh-vSO^qEH^V
zApS^=@DT1U|JWNA1s!|@dJXo153H1R#{AwZ+CJ&%^pw7ICO1u1!8p*kZks7ru(fJ6
zZeo3}q%Kp1&6_K(!NQ+i#ws8Km!oO?RP!Zi>zHn_7WIb1>fb(nCn_!?ea@>ei16aI
zTGm=QKPPr#iNCp;&p~no%xTB}8WGTYgI^Ti@zC`frLpjQsn*TsajzClu%g8kq0cM*
zW$!jh$bhG24V|AWUH24m1%SC}!&VjVTGlP_h3D@kOB{6`UcFSRCU(rY&ix{rtZ!%J
zLy)Nvh1hd=a>D4AqSB<CyIyM*U_Ja{o>tSCK)wt5X<*NPua5gS&24kBh<8~AeeAUZ
z4S+>GlL{~W_Euf((MnXtp$)l<>`#wvr(srxX$&gTtz*uE-e&IV6TG_jt~wa+ILbVL
zUtG*JHE~YjF|OhfVX*Xtl{Mi$_4$*f1g8q_4fhi@=Vhz`hNJRhq{0}&HF<{3KkLn{
z^Zx*PUM!2<CGZB^-vxYNm|kE8-`0P?rMzg;4=<mwJBT96j$l!nX>fOwb6;tl&N9cz
z2+WMa=IBo#HDhkr!pk*iBt^bkh=Q!Iu`KMaO95B1q(U<w&5qknx2u2U62&0A+9?3X
zeH&YT!KP{*shKu*T{@Bz{ev9)U^;46h3ANicSXA6WDmZQ?jAUJWs;MQA38ql>7%V_
z*c1PTJQu&h(a~6aVt2Nh+}S@!%Sww>r#7ysDJChT3&iXRuxW#RQ`wHRQ?U3a7fUX(
zi`v-1j-%Jyl>FK37Dr*&-Rl+Y2JP4p$<wkQRY5hu^^}1x@CN?*DgPP+T*ODPL(oWY
z&IurpH9Xdtt|n(Q+5K!u$h=M?2gC0S*AD*?>SD_m8EYQ&6ieXt)mk%+E44pi990i2
zTNR%r7ZXT^BwLn;Vh~Y*3iKelCMFe$tUOknMtNlRG+3NpJ2PNQKb?v8RHhD^wxC2D
zccX-b9$jo&e$}#A1ec4?<7-oBm6_UWpvi(?zlhj<z&qR6Q>VQZ+3Bbg!Dp*B(1Ww8
zV%}bcV*6sPR!!$=Tk`hZ=2}H1FSFR0^B~9v);vBuTs<*8<G2w@u3fo5^lYC=7CsZB
z)H~NHJ2hmoW1&{VA%KCyEF42vrU<hq9;>ExidzLW@821a{w{@?p6+<3e<H3DSA@iZ
zSckgRNQbd2T2G!d?kYfeEJITE)I$0;Cd_}z-F~dDB9y)BZ?Bg@3LRY<66-`<b*o0z
zbTjKy|2Eu~2d=&<v>c+PARsn047fzmeSFV%P+C_IwGc0XEGf=*)gn95bEbZFbG}D2
zB6TF)SR}*`;_p1^r!9;zr@zyk^-9TF@{SE6jnih7hzbxA%)Go<o@Of*Nx-fvF%2;|
z+ihC>aGxd~qg<%xU%?5+NdB^EfYH;xSh<wGhdCbCi|eRi9<ji}?OUwLWD0r%?5)L7
zmN~5D`B-HerYyc$x%)?2@pe4<_g*XM4Yp<U{dz%9RLCX0aBTpnm(C{fXMuUA4-agy
z_H8GNU&!$YRwpo=S^tau@e|o7v1e=9-Zk;zRvGPQwa|MWVz6rfRDsnjK3O6{nf78v
ziF=LjQ0)w|Z8c<z8Sg20guNY}!owz?(JQl->YLb|#~4MrkBKh_9P7E=FqA50UFpq(
zmIioO`A6*i6o>!pxS{vUe2AVQIu+}@ixDF}m0Al_e#dk7@kQ?sfVg?Q8nLYsFFoS8
zTbljjitpZV)y%Swg!yW_cE1q<^03^`1NhGPURL3GNRc3Q#<y+9I#!=rz>b(WvEMJf
zt8^nB+^k{m7SrKh9_S{Lok&OE-{c0*R#>Xz=-R<80;qTZ*KpP&g|f&>+;#qF5Z;pz
z@c&_y+);JEUlcN$30(flq7E=u&!W<|j|=?5ek(oK>XPp_ec<U+Mj>frm@2>#ohC30
z@HTFWSSti{5M-3ky{Z!(6b?^oGTWFXVv1i8wQh3)irLSa?Zy`UJ_O>=WO><^<FDJZ
z_%mA%XRh5=l}{sO762W}NA*K%zvQ>ZzFQlK;e0Jswdh_{AR>@PBiupL_HVv+i+#QN
z?b2hK-;FT#ILAJFd&wdtQ1N)Wrd0aLYiw~~R}WwNu-v~i^;5fEhV?WbZe@0|^#Pu2
zRrhw#0haB|G}vZz^Q}3)t=dUSFMB90C4VUX?SVyW9+SD>Rzj6oqb4%?$QcOnAo%I`
z#c6Lcn<RK83-QF8ODQyiP3qNeQGJ)}n_f5(Gg=$<8bp-hviA$s+xn!jBIcVhEX3F#
zRBW@J%q7JqH~DQe%0M;TA^_2?U<NbIyGwlNRk;Mg4x^4+%l_n-nys@-Qlphf|A>8c
zy`njc;G9kjKPSe_3J&s4e;Qm9fe6C*77(LMv4NYb!tX`cgy3*ES?4$TrefPo{wGj`
zLIYC~A>T>K^d7S?F1Sehy$+A|188u=f)B}&b@J@lsxEOB=b&fmo=A8vYe+mEjnOLE
zTcnIIHbE#t2@@a2&#9*q{85~UVk}}TQvT~LQ)7$0(0MO;+$#MfyIBwgkeD5}i!h2H
zNG~&47=wrql{C7{+W(0)RG%~@-y}p!-u(jqHW2a(z{*G@NUf0g2He%<&&k8Mif$Ot
zyP+p`lE3c?qt&;Yk2vA0KPrSQ@%#Z(VW5$mihcxJDMflUjP2)hlv)Ii`H-Fwg6Z4+
zPf`?~+Ee*9vfm)n;&N`xJ9%+sG@UJ4z5tf8+))wVl9%6IYMyQIk_HXVA8Hlj`i~j(
zt3djGeESQqc&*6ykn(ur%AepLb|M6)WVs5!Z9@BQRuFs&t@V4Tc!5~f?9hQk*#&Z}
zVk5sOE#=R465Ur9V&GC>GRW3)2IaX<9yy=*4}ZcUGBjkb{=5d)=s?Rm22GLip%n9U
zEmKi~mh_wqq3n2+(uqblGIzP=E}T^Q>hr0tRc8}65h}oJp+<{qrEvui@HOs@p{?Tg
za05;n&d6vG97$%$tkyw|%;7Bpn5}&XfD;_0;qO$!8YVB(#5%)!GR3KXA;|1#XME0<
zy5-&xIOl#iAm;`hw~CD3aL7MeEUsf_Cu7I(Q>P(_<kL1Vm}}zuY4ca|hv3}1ovG0a
zN`4_1TVMS)38{Iu-G&kvJ$ixP!qZm=rMt1n4oAW7#~=toH?F%?gyMWqbGIduIwqQb
zo`n5-j}+#W_9rhb&P-*}Wq|K%jCrFM7V1@Pf&R<IS}e{fu(*Iq<w0s$2xxTYerz+6
z9aA}{f@j{vg;PGu5aZJzi5r8Lt=c9ox^6|~>76r1T0g-P6^WJvM}ctU3>^Xhoa-ot
z(Wlf;ZTWAc?^7JHhCb9(m|tgbw>|2Jp2i%^U3$td8e|1*tAOgLd?&FK5`QlQy8gzd
zv?KkwW;z55Ze-LZ%!(GG=H6bL3iMZkSUO<7iu1XVb$dy~$%yr5RSIV4E3;F~flGSu
zRg2&@Pmy`GeN+*TsBU)>M3>E<ZE_kxzbBDYJ)2o)w-Tr~+l|T84XA`jNDG}&-0sh-
zdyckW9s`jR3o0S)#vTomy#}N!A4}a~yWf!>0dsYzcCILzrStT(VWQ;^&vrj?#F$@J
zRwJjwS4h^>y;QiCACqUg&3r1znmx_fhib2dhO$}s+E43I6-+l5)lH!*SDhKGt<oa<
z?OrKWW-}kUJueKO?}H9XkzP_8S=fIMZ;oz}6Qy#*TL;Dz^XZCG9u51u^}AH8!72*|
zy333-Md=xr$9~f!HcR3>caxe)EOJ&JV5`}Xs=sNYAsw=pAj4A21(y+7`|$1xI(ocT
z(do5EC;YW6j};XlgK@ymgTW?J>-j2^>toL9N78!e>)JEry*fuz&xA`_sWK<{)Esgy
z*Ai{>)=DRto^fZm#(Va|uOB}F&h7C_<}$YLBq1d<EaB#YAmwod<uU_V_LS`eT%BY&
zmyvym)|wn%S~>Fy{wP@;JL4KIj!ne*<0DuU&S7kda)9jz0&{Qq59Hg)D6#+vhA!2P
z#HFcGJ|bgz>HlzPtajG)W0D}G9SP(KL<q3d@#-lQWv#kLIjKD&^nj3~Pkc6Z?50@s
zUs+9nT;dnAYwFLI$CG3<h2LnafIA(@*kjr@3VPt3uVmqd`^=<#s9FQ6LZ}>$Ddnab
zU)u{t;0oa~cv52#>$(54r&Ci?Fm_6@As=-z<e6y*6%G*&!-QhxNe;muw#i&ee6;HD
zNIBrmEX&C*x_mvVMTF1ftGzu{od9)+2@2MZ%PI0}BPc~wX5BoAq!_@76u_dV{z(1&
zsl!ugortQ6O$FiA-KdG^iLiKL|BxzL?bU4tjA`;#Oqc36v%gg%gIII*4hrrF^w;q_
z0}(L`d=>Kq$pn)GhXlD82ON7(VO3=awy2;0WilH5kk0SVRZ_`Y-C=`ewo_0KR^BLF
zf)LKlfQwj5ql7$cGo#;}M9MsPt9Dx8fz>CG63HTEsR&E+bsBT^SG+Ge)waX<(LsmN
zFna1CvX~{OAU~uciM#fFdlGMs^Mp+ai=sgC`~Q!vcMOlLjly(0wrzE+j%{}Aik*&a
zcRF^*wr$%T8y%<8u_|_Q>YHoknmKdM&-zun_FM19TF<)gr&yqL-pCN-0alvBu@4xZ
zG6;r8pWPR;s^LyhvXtEgj>6v)m`qS1GQGoy@we(%*<q8#3Joq9wObY((yMo?@c%*U
zD`&kgv{%vPRU}{}P5m@7cwjUl7o#v8h~WN;I>~0SXb4JwC=x>dr@Ej+jDbdvM~-rU
zpc3(Hhtn;FK}EKo?x;G%^YRgIXZs~ufQdcm>hQDcZ_VJH(ZmnrZ-C#a>SfCO&tGNY
z8^afV{u0em$**_7#jYA$Q{RDQ-N!au`~YQI;u6*yFN{4&RJ=vTDvuJ$;ck3W(M@mC
zRxwu+OEBM5!LtHld5P*fUM}bDWxO=eGJUum&#?{f!~z1=Cs1Ep4-hR9KVoeCb64+h
zPUZMwR7bz;6I&p{L_LgUMfwd+3pLN}$d0{DrkmT%iKH{`qWq+k4J?Q+bb8z>a!lv7
zE@bqz!&@*sGIf6|Y_zdM4P$wd5n${d>lE@c;@&5@!u#S#maMy;<sIgK>$Ug|ZU_li
zJ_G86ib-~^tO1gPXEE%K`URJ^*i~5v(o6Ca|KX#){_iUHE!&Vc#!?Pje>V(jvN^7a
zPJ6z>&+FxC9TNv=+IJ{jZH0X0{SHwQ9psWYre>EF$SX|T({!kGc^!pA14w2jzsB82
zAacN|MBgtXx?1q)81{*K?WsIdAsen2uCk`lJzoo#{}QUh?Z>1asspzBsa)R|?T{kj
zrJU=juE-I>ohqAaFA{hWSC}jKZN0bo$BZ3NGSnJ-{`Dcm!w`onLO&1^sUk*I^zK#S
zMaO|x^t15dsd{C@(t&KI>nyhZEHv1Du--!&BbT35Kk#Mr5OGT&KPIQ+^TlL!2NmJW
z;H5gy<9w<H(3H}siMZk(oo7y)MAor!x;Hbhf8KnL@p01X@6(3HL)@lmd^gR0AZWM5
z(#(zo8ch>)%$e9`eV}iFt8I1_T4Bt_aDBht2zl`ACiHkonKwa#+)K*Xdx_>jfxG%~
zo404>=)KvpeW~cfO<-SAzfo!%+B7ZIHQT`&f)~)_`czx;zE$BdI(wz?Y_x$)xNjM*
z{%}e-^DH({7_d%}_4557n}&?Pjore%#A#~AHY`mz*FhVm#Wg<7vi5P51&*Z?VAGcg
zIg|Uq%%jWk_Vn9*(6i2e$ct|(`5%*ht9qK#<-VEtXJ0SL<<p7BUQF-S5XQsBb5-7_
zphEX}E6`Z6?M;gWt`%?7Ehr~Du*-W;kbMDms~S{f&mTG&*Q^|>8`bg9tKAY>5}$8S
zbqIs^L5EDb%0emXS@5&<5cui4;TW*y&Kf$+>Ni6Zkz#-ErPcoQC)b4bbxMz`KG8i_
zmqjy)s^mMdl{l*LxTNg9)Cdw15=0aUYgc<!p(JSVP-+4RyC5=XPwuz7HA><LbqYH@
zS(ORXKUPFTXvt*k5aCmJhxtf7I$Yw@sc7LinGQ<Q0T>IY?ccZs^3P^I=o9F>krHzh
zWWS)rbE{#!De%u&eld8X*V0KOH918aI#E^M#{87-c>;%WFqCzg;W(k@#Ii7?iM`s(
zm?40&!3!bOaKv<JOq{<_DF_c>Ea1gd@4SAz2Ek6i?xH-&27{>4g1Ybj7Yl&Aw*w)u
zW&<0V;zq@BLCq2&+k5k0xZxMSs51NGra4(&Hv}uH>o1d0rv=<a0flLPGq>f+qF8}I
zjAo@A?&VN{7q~Bd{fdNEkYw&@AQ?fodTofDI}0zwS!-575_}2xJncjJVBX`>I`Jf2
zP7T)DVRhU=R`2qp^*o=-z$jF&F`#7EL#RC#kgpwMtss_jGl}dAZQv4;;O-GYZvNVk
z@2TR}mu0f;h#ywbW7)JZ0d!b`r@>K>c)VZFv1Zx_F=e-`SP!9(DVaG3Y>%<X*S>}2
zZycd+H;-6N6x#uT4N8Wm-1aY=S7k9HUfV&ir%)=6r({rmB_cnQdKG$>O4)XHIh;Y6
zAME~Nu|!4julVJRci=dZ&YP;ya*}siYcSdr7h7M%e*9gGbw}FK5zfR<vf`ld(_4n%
z7cafGrp}edHp%eOpX)gR1Z@T#!lDFN!dd9NSWt#49(*bW13_x#e3&IEtq`(MqO8rv
zeA0Z5Tb1APtUj<<l!@OXgpXJ8DKqbuV7i;-v-7z3;(b^Y$b!=5v}>6rOy-e2eR0`1
zJqkh_RR~X5lgG&3WjMuXRcb%`IbP7-Rj;*+#)tHwh3?FfUAr;UkGL#3q)XGK$lBCi
z;$H*=sYGtE+#xtE*;uR?w>_DP>vv^*$o5Crcx_9k#|};4gimXHM3LTg-c?<Z-20=9
zz|d#E0)2C=Lj}QBZOKrXxf-@FLjR!fE+j>;zHO(IX(+dFnUQQZFK+TZC|S4VQ?e4D
zbiO&|FZJ1>@v@xcWm30jB5ip52=A%swGqQLA#A9{7tUj6RA`%Z+VkjHvAM8|UMKe4
za4@Oe5O@P4pO#UH1ne9)xH&u;dx$baS*lu?JKRMab~d-qwjT%EkvvhDr`qo_bkbH&
z=V;v6+uZUc!!H^>>=qPu&bC%IdsaCCz9Y(9Jl^_K8_nDV%a9o^9ZEy~+oP&k+>BlB
z>34ZLLc%<rFCJk<XM{AK+A!Mn;wA@Omwq&{!OQ#`Vs7Bzc5{IJje%Zkjx-jyx1jvw
zHncM;Tt?I83mW@*;JfbjkJGo0h;>a_WDm)lNxgWZ#*P%eW>EIkjnGeTqgU+m1cg>=
zuTglXdWT~o`Sb6;j1L2Erfu7Gal|}Hs2_w|Ot7M?7i#cXw$1CWQy_>?_86L~Ed4Xf
zhSk8h@!s}vy}jKGD|^JA3-AjzQC))9@^)PLpmGu2F{h6dUF)|bWAI>m7;pb%0v-!1
zJ#>ha>KqY6QKpxodH0$K>ASOb*#^HmbgGS$M$+h5914oz3R}6K!G8P8B_6j>`@{h~
zo)&%0F$J26Lp5NqtoxY}S8h|Tp?}Y291_YQOuMEbAcc`CzNp|xj#CqHcE2jQR;~R8
z3+><8oQm*4-9zTsBX~eMGqf|UE6Q|SjBW5G!Z=bTE-`Ud-CjqY!GP~&B9x0pXczA0
zLQ2~#_2Pi(pML(0duS;>cC9V(>YRUvptH6gt3KkQ=AQm!=*V@nOCKHYB>1psd@5tB
zxN|@(6D?{4h^t(qHQx;OdAAtjq$CbuyD{oFZ$FB5Smk!`lu`yXqu90FTU8s}5vcL%
zu)3k#@=2^G4``9;Wa4G813@4C(Wjkfb$Yv7gk{lJlPR=5JAqg4^?TApoq%!S*GsHY
zg<RmuUCWC8q|#;I{6R{X6NXW346jkAku#r)(Z*tpYQm|!nUBe_%i;cBB8fma8CkOq
ziFCuyQ1+*_T~(8vv#>Dbx}8Pr+h0Is@jcefRhIfuSs<sgSI4WI8-xC;i#HLKorzVA
z1}VpPgNm--jcnF+Ayv9BL?a_T+vAPIa172&p|q$>RKyXloVaALx@MvQ{gv5*0imu2
z<TIr>ZQ?09cMNHUZez}a%KF5lWy}74%xPNJgB@Rbq#)2TpUKJ=d-g%cgOf$ddB60?
z^Wq6j9Hz7#oSuhZ)H#>N^}dQ*Fro}Nck*s8Vp}u(cx-m|33ZLVy&+B;;1Er}89Uhr
zJK!lS4fqkFZ|P7>CX85^;sE@6C(E|}I|8xjb~~0HXEXl|iI)YZsn4V+duim>My<ML
zvy=vVm1q(QVaaH}DQNSN8+0@ee$VymNEr<2D{#>)1<~czGIR+G$`uKA9$IU53bKT{
z*07>>-;W&aOUlFmEHJ?HPS*Rcf=UODbahTnIACCr&f@`azx&zcj`#=<HI!bFRQZi~
zCrw^iaBr}}a+x|$L<w)~#PIp%<Yq4l(0;?1ZQJ5J0fl-nBscAi-yU9+KPlJyMQO>9
zQ9l2PSAjPrtP1EXG^Uw%0kESOI-N>|&_96yEvJbn?GqFVdS&e9&fp>yHHOHaB&Ss!
z&-a`TaP;q0iC&@jzCw7O2U`+BrTVw(-=D3{rnnwp^~yCf3g_jr1M8o1+rhwpI7I(n
zK1(Uo2&*c(`8(*U#7Hr%SNE6LoplDF2Qhlqe^t{vE7h&<9_(ncn;j_0((iHgE^fn|
z+Rmtj6`+6SYxXAkh<Ps+>~!7w1X?cieNLkQC;C8Q(j7*{1ZG@nLtHO%pOCW6CLpIT
zy<!0|2-|exy1*}e-q>62NmK)#VbWH)YB*G!@q;@Gh6D*2E0xAo&ebIrUW?p$Bg#O8
zQ7wtG_l8&-AL*r9r)j#UCp)?;K23U$`W>&$_yK>kwlwyaAcggBs42$wBKEdFTPjIz
z!5Qkm8j1U3v3dt(yehr*SW6o-)QrJCj($KG)Ko{i1rN;dEsv4zUjEQ%_Kq`inx41W
zV*D9juFO;l(~shgrUt%j(DGOif$Px*PsatP`&;W|Ke6j0caKo>%ta1M5+@8Z9I-YN
zQYVeBQi#xs((dr-NJuCbY)Ot6>izEx%!#<4ZjcKI06)!4m+SB8*pI!Pt3dHpNaS+t
zUeQ4tC_5H~_Zr<FT!#FkLcVv+%4k!WWQ29eb+|N7;Vp8nU$K<?bT9I3vUpWc;4<a-
z%IvmFrX;>ts)5i{)_}dXe+kEo%=#Gn$5sSBpkocfK&7@oQ9jgW<^b_(ePR>3D+DcK
zRHoElwUNo{tj~g|a6ds6gs3jSWK&`5K?*j01t~<&DPcvY!IVBN@ITO(w?2K?&nQ5Z
zp2cDQGE{F>YDg`R1zfBASc=AF-0Wl_Onq_Xf^=AKC2rUAT>68vs1tBs<l<F{z<AG!
zNyL{jQ3(qe;f(FCifYGc_&Q-#*l0B*xP#YbAZ63DN<Gk2q9I*$>qfs^wEdu${6{1H
zv_p%zu2K<D`_JZ9D~lyXwI!yQuWd$Ok2$YOfp(KYe!9p>D6L0J71nDKSV`&lmz^2i
zPjkKt-$H}IHwdIL3dvM~U;ouhvjH?3``b!?99~MZCMosmHCA43uoarOUK_FVc)wY}
zA2wvM6hb&{=t%zy|8mz=?^&5KkY?)0>S+oMQ6P!*Qe)WlhK6?E<7{R6QO3ohCD92L
zWAESei~+x|;ozCVpkhh3I>g>eO8qD-_;fT}2P?O`+fT6LLV5nht4cCT14W%MOdBGJ
zsgrDK+2tIC_NzNCbS_u$oKVWJM6yfjV<j(t^~38)sZ~^ohl$dSvfyM;PBRt4-8}(9
z7yD?;_Skr9+IPB81G3uI-6~?%9|QReB~2`j!fl@Q?K@4qEheL^=zaC12ym{o658xC
zkR+~5Q&o+gOWHN0BXLl+nGg)QWF_~%TFExwS8^wpJIj98PW=K$I6?*~mEkY)Q#c`b
z0mZ3jFYIh9^JBH|gG)c~{oZ5<g$Fra@FrsHwgMTIdXFf}??>>J_d}B-@McRzn0AOZ
z<*Am=`s1ao@8waC&jh<$VelD8Q6@8>7n2wyF*dQ(tmc(bybSZ)Xzu=9z4HH$N;4cJ
z1*REk&#5LK^yV6M4Bz<QjMIjT1;d6Kc>Eo~@z&qT^FGYz|DH>CK)2IAT2$;xzWSf$
zK+Ej`<G;zZKC^L8zUSOyHW@Ok){THJC-5NE%MstH_B>t+68(<Lc+k2l)>)zveYZp9
zY2J;0=Sw!d#s4AP`QJ(^(R*SmY$Tr!U@^F+vlIfJZSF`Ex=eC50ju;bYiBAhwYOCJ
z>pBhz0W$-^dT**f#Kv4uQ8)LVwGNFgKcl+_R`05v0ngh#zMmq#s}L$9*WDs1EQqpj
zQs`0*kA|j^k&&ggn2TnoA_jqMJsI24Yz9f?CqkuoSiV;VO!=}{8p)`qfxSI#@ng3I
z$`JCKn%i3!7c-Xuo&r2g99>mW%LnC1w`S<*(3K-qg9A3T$9(!kzfmAzA%cI3^^+T;
ze}l(|M*i=ks3N(F8DgiI#nEhDUGim?V~5U#e?~?|h1TDHsW|!<2CS50HEj5UgM&*G
z)7_<2uRJ?`!GB$UF!FeB|0jfO@Vqh{qnzpx>yHy{L0;#b(o?%5uDyWC!1}+x>Ax>l
ziG|mQj_#^@v0^;z`~0ZdoX2C)FRwn<KY7pb+!oW)N?j+*c?FeT$f|$66JGoe$U+j&
zZ^J5o87<@mGS+vS|IdH?pF49dUkn}<b(A_yA6RLH<(WyPvb9JW1M|)qSfFtG|M{U~
z2tlW9`O$j(!L6oh1D9YSm+o@T#B(aMUjGP?w=es4<@Nq7+;?m{8tmw$OM3*HF?Ycn
zD{+M)d?~Xx@D%t#&PGdUcsL`#YhiKaVLv2^{!l|aAGUgHxfirq5mHuB^?#k#SPD_I
z-`o!Tct9`ZJOQ$tTTo_WmB5Mq0hco^Zjen={w++tnxU3%rEW36-EpIpIhDmA3e0L_
zILJK87Wn`Tp%v-1IZPmafM+s_T#sgK=6N03q)T%2s=R%CfQ^$Z>d^!CoAnrA^3llS
zPGDUvc$BktV*Vgk8(;C{(;fQm=VD+_PO1O@wc=xV5X_eRXIwL@{5h@{*^7Vca$>aL
zP>JhV&o2~)^z#JamlEN$nJ0}-@Pc0R%SuZrSy)g%K0Yc-z(*tH<HIi}CpTC89SeF1
zt^W<?b~)fsA3JWvS$Eyl)phywf>EnlMeA-niB_VczpR9Uni?j%Nr}<V&#&9XdDS9U
z&?z%h0|%lPjyQc!`0=h$R!&Zh@zvMY7l5x*{0P&wzVAEg8Fhu8ks5GRsf(7A7ZY>k
zs-b~>hmGt%OZ@*?RZX#<FA63m1&O}&SNw23;4Xme_Tq*%qg2jBo0W62GKqzT%zH#9
zj)e~FsfwYY;pygE_9nbW7EBH+q7i)U%!~-^`!AJ9V4fN8S#{IW0a)$|+H{@Wix>G&
zaRBl0@p&Dj0Or6Qh>~m$j1;O5K3K&KwwY`q=!h={K9#OjT=%sIQds|E8~vZVYWdd}
z#bQcBwV7h!VS~D3lk>APTOK*pzW=ZZ^;Ywg|IOn(ffrblpOD%550Gs209d%*<8$A5
zW~hI5b~`IF2BzFa{rhLGY2e^1WV1vEmgd&#Rysy35koHX!x=E15LY;B<a=K{jrp#_
z1pN6;v1>FGhT;q+K~aAFDoKC3YT%h;!?k$Y{j!<hF)e0Bmv;^i5^^G7i1f%<nU-aE
zr}>|o|9>BKB@Be>3>tB>{>%x=%M{1#Ps1_fCI4~&4mNiAML87})iNre@8hLvJ@I9f
z$Z=Q`U{F$uxcJ)ad9|G`;8{B%aghBu*2hK)RzLTycV;3ApR_pckTRy$setdUGF4rN
zN0O8(_PB0a@WOuJ^Vmrx02lWJ8p#(|LVB6z(L)-TZ|MMs3MXJB7=dwj#IrfpnP-bg
zK@;1d6To;Jl7%XV%rRZOq}l)T()Ckpw=d}D!_zrfU#iyKp#_*sWxjAZ0weKKfMD^T
zBO~7Z)D*JY#kywAWC=#TixQeS!!fuFsS)k$DsRIwb#Xk9ot_@yezsz=nY0gD1{h^@
z9252@fwV6WU*?+=<j_)_X0ohSs&zuEb@}Shtyz#EQQ<)(e564K#C44}g+jOr!*XcZ
zDR~01C7Qb3gZ(ftiYJmN#`X3#a!_!p3^o4uo4mOen#!>#IR9M@6Fxg8uQ+XLpGdC~
zR<*n1gWFPlE2Bi#A=6Lj1|h7G`3y9p=6Beqo9<g`!wyfnSFG=ZTsgs4Y8zAtLMF$q
z3vTcP+v5a-QZV%pa3uPo%fy+P)zOiRi!A3c`MyWXeyIw8rZLRdm%VcLxb2GuW*n+H
zHxHln^Brs4br`0D*~^gOnXR_@y)$HA#weQ;Yq!?C&3!yYkTY9_`4?(iT>~1vrn55<
zqdw{}<NpN->+^L3{tBp(xR;|6%2a+{(+v%d4@QaS+*=}e$x!{nI9tCNE86dfGt-Nc
z_a)v&p_Y})(jbY0ewg3%q?53$dT1n`;C1@B=tWHFR-h@g@?~72(tLs!I#G^FiKDu5
z7;SU-)XC!5_n#~7PGj#L|A%Z-YWbSL4ZnY_cdmPLZmX0Lq6?~~RX7l-Iz(d5Vtu3E
z7bl6gxYn!k_PF!Z?nRf({ncwa|5mg3@a5EMfmB=!aQvGHhbm5n07qVMf{|y#Iaz~|
zhQ~r<c;5*y230kbay2USz*QsZ+E!WD_aUv*o597(ik9bh6M4ga*zLxG3l@sRm>JQr
zFla@OD&`3rOmoFYMyevYxHepXL5w;j>Pnc(KR(#d@YWA!bZWb^w#sY2yJVyDvf#Q+
zA2Zq}eSFjK_;k7kpHWhl3$~pme@{gM8R>f6Z!Q!^#2x=`u$NWgWC#U4yIEK(Y%ICN
z7CWS|Zzdilk1G_{79PiopN~#diQM%S#O6=#*tp8RbikB=s`o)oPkTv|AW<XeC-2i@
zF5hMxM@GWu5ewPB8SykGAE4_>bs-A@&ID+8oxi@(3B6tD*9jk>+e*qaH@YGZ`|#*f
z>maF;us>Smh4e*`#Nj=>)Af~@fv#9p6BFc)i;ph1@+#V$WbQpeDzmFV^|(hy-yh}~
z8%?(jb@j-kaem6|fAAnIOcmg3k7Db;klBq-VF~LZ#Y&uMeFcPi@ZN^L4^Hw}o=ZT{
zb4Y%08d0Ko(4xnyJ>%NV{0YwUBpD7_N^$%W5_njU@7326`?bvo_#TqSoy@7Rwlxs#
zK9}C5o+q(DEG2+Ce&!I8WzjA_W5KT;Ca<k8{Q;<V`P*I;z2sjO+Md7I<3FBAeJcC-
z0DgSXqu(i(mFo4rJ+!HP!BQ41NIrji^K03k2j9A*f}r3kNgC2uW@qE!M(UO#pZQAA
z%L|je<yw|C9yrDN4NQPePP_}de4jaTLAAfJt5fY}#8xeF3k~1-*UEabC0_`!fuVg5
zDSJl=%+5+_VL%#-5>JPs(Y?t&9+Rmuy&!TEf@h5<YHDh2jPpV>|8F0Ohwb@*>Zl*2
zu<rw^csD~<KTPJVZWaasm*X8~qyX!507499^$KQ?*Z4Z}U6&n8q9v->KFFt%q}Z9a
zvv{EY1=MlsQsd(aRwX-rwrKjzoVw|Qp4ja^yvQ>pS3J9zmQf1^iYV=?$WAyeucS71
zsU{?5xm6dqM|Fc9&$Zbn*z#iWlLT6a*if3j@h&<KugN+8<LpE(walosveG@YLg!6@
zub&0IPpzv*to9iMTYh{`!_(i^?OXRUlSQr->I$VhC9QNMIT8BUU5&+my!H4Q94N)z
zJ2vVK-(yeh1$_9?9_U4+f4!~d7JD~z#^ct?V{pqQT|e7?p2gj3yw&=IJ6jPmtJ^Da
z9o$Po$d1_cfLri961Y2-#&4os$4lINEqdgl-}2q8k+u&za&~%*iiF5`(>+I(fz$9c
zfW+{V0|k`8jf>Q_%WD})Y(ZPv^&(|_9<dT(vuimfp9av$(y*A02S02%X}})`5Mkp-
z=s9#qO;h{_rWBKZ0^=KNRt_n}bbI(#vmgKCn!cvmHdm^Hxq`J@s~k=2^SU1=A9eAY
zz3EXRw~MicV`Q44ia<h{e!62W)CT%pFRFY31I-EdQq^e4ZF+1sd#Yf+>+O*S5UxBQ
z6pV;bL#|(sXvm6`FFLN(wDZCVqivx%Mo<lIZ+9;>Vdzsq``ypR_J7gtr`K1BPV+<Z
z-yRjo?yPWBD{c<UpH6S?!o`f5zz`#as(t;E`Fmu{vv&it{k<bIoze2{s(h<tr0(MH
zzq%*E<$g>3++P-_m>IisqDTaxS~+rQ2bBSxC@-W$#kUFULPS;+W}90(_TsN9WOsJ9
z_S6-p3@sY>u9ZopWknaOs7vm|lk-P2_KWvbOtCY7y<Q0dXjQS(H98vZ0dglujQZ^}
zT{@<}@xHXoBmepuL3<>TR@-!6`dF2Qa`sm!>&lVQ^tV4E-gBlw4cF6-jy91I#v;y)
z2bB}Jb#(LsKdl<RHL=3M2j02YH=cd$Zwk3}3+oD&6Mqv@@lo*_a_zH|{)r521+yh_
z%6>S?D;eGP8&P~ysVqAr%Jq;Z)0V!Mg0shtDu~JGFHoWfbn9-9etblc3o+;m{iiRj
zbU*UD-bf~k+#9ojYIsJ;qQI)o>OcYF;S%@>fEO0pilhv3re>i%XT7mxGWq^3im_J3
z;o|G~M_Oj5K}VhE>C(6beNf@QJkM?KbPiusSXBISE1UHx(EH<#+4)+7jk{MGw8Fpd
zK|fXJl%vLs!IegMm2XZs_%+ZN03PKeUUug~3A_=a@Yd?N(|^^1`XAphOJl5qXQsu^
z#Q~v`Gk9NV#0}!L_N?euu*c9<cHp+~W~FUL4Mnl<1{2v&`#?BVSZ4#G6s@wG3Ozy=
zvq}Z(Um-aiuYEiX5!hX$lQkK2t<Ed~lqc4Aj|rT<!#%?4o0IQ;f^#a+78f6X8=av9
z-8ISI1ZyB7#GSn#lkZ5?qz`i`&r=~nVDG!pY23~0k&@K@Y!9jxUoc>=0wz<EAbC`8
zZ+5MQ6QCw5nZ-NWig!`~G1Z%<2jWCq{eTuRq>TkP=U7&i+Qw|eetL*%kuuWc@gCFx
zV59D;I*9L!k?vtJ?r@EUAW3?BFok=`mRpHM7k(5Bk*Pvkz!qv}4L352=XVSK1(Rp%
zF2rQh!UpnDLZnjq)Yh^n0ak12$uBW=#>ZZH?q&uP#Y%2x_PbXlOYS*g78&3ryN3UR
z#N>DwcH#T&ISpB%w+^M2pF>Qu%5_rqAMV9iaNt#cxvOsZhsMdPZ9;;dOC#Aay&tP!
zIpxlxJu|xDDC#T|gDmZWKZAr9?yHT^!|RumM+M8Sx@Ym+_PNznKe0<jL;80%mVTt|
z!IkoFO&f5}zWb4k8_{e#KnS0ozk@N(cbM`73wyU>jWp!}a&cP1dLi$kFx?GwhyEK<
z13QzzqVB&fnqS!;Qg){;_`GDFTth(2iR5+LRDxOEylTMXkGc(WS{o(s^L|i~^iRLw
zpy(e)J|`vVcY9yK|0pOhv#dgtk(YP4SC+V~dgb}90j#^Xd2QJn3NP|~yu=sus#jb1
zZvK38Ajg--?_TL&=blq1PH_kfp)$t4!}@?%fQiEinn;g|9V21<D{+f3E4Zw``4_&?
z2T=)GP=&sjef2ZcyS>z%oQ1^rBD)`Z#ZrvA=1r#gfYRC8a*dU$XG+RKKtEahDazOq
zCzK~ZJKRX?d{`On0e3({VFr_3vi$7fO<z!?9z!~+|LwB5MLVSvO(+Y_YzdI{Q!P;-
zf)<WvcnhyBtPeiW=(7o7he=^fN{A3RG0UUtkfwGy^iIX*BdLd>-Ba(@W5>g8!nZJb
zFIBv>KmR>i6#%^i3$fjPR#o!_$8*mmiPgSzq68=6ma#9lfgsJFg7dbRq>wJ21ib<l
zf|BTIcb-uoUtd9Une|iEzM1f41<f`gIU{~XG1zGzkR_Gax(=H9E!}=QsAV%_>vAY8
z_%SZ$*KL({K*K13TEq{#UqZxBVg2gh=5I%VNhzpG_tmKD#q!O|Qvmr4H@GCL@%j&K
z6=<^5uL?zNENg`Mex7lILT>$exi9N_c@SBDff*4Hm}IUf@Eh)1rzbBd;a?^CxoWpY
z<jj~?HymMo2F6xQq`!{$^WZt2GoV)Ds9uZh<tg$ew*^<~6<uNicD`>lt6^U!N@pv)
z`Xk;S&R=K3`l0UDBft*^$0)%CgdtP5(@n{Pi~-N8_1A2Mzi;@^ai|av3ODSv85EgC
z?<6d#ViJxzJcX9x=!UPf5-YX!Piq(zdInpj`7*NANsSy+Mro1r3uT8Ta(SkC2?3sf
zRdm5`jXlV7&&FL-ucVLTh<>8ID&g-p<R5i<-*UTav0BgnNTrP(j*DjC2~wY(r6~>l
zqU|hn_k({MdkYP$bqwX;=T>WZ_Wg?*^KQqS+UU5jeg#0v(_cVNyLYTi_K6+at1?9L
z4WY-yPKo+;Sp$n2S<tpr<*!fv&6ifr;;VC>pFr-LIiKMhkHjfcaQa|0t6X)wWvxT>
zAqrG%Vu080X@lT?42*mQ`HWb`#ys~Go&OMs1KtPvONwyYd0e)*4t?()I3t2_(LL4~
zS6~Xsz+z(=|H+(1FPO0?tE4n4fhH6J2BR8-MJ1WVe7VK&_%QEZ{lAsDx11-=?5WPh
z{NdAdr$b)ZG>@XkEEY~dfz?J(+TOYG(aR6u^T$eD{kQk%+sWXs*rnUG3>j1u*SZ}+
zgggJ2_Oj6EjXvr!PEB%x*eT4w^ddLIuoxCbBjjx^Gdoi^!Y%`ELWK!(XIO-t&G}Z?
z&tH)eSP1#e-T{%++ZU@dav|Wyk@SSi+eIJWlh%!YfHx#2A>3YF=my)v@>e(PlBdDm
zYf6o%{GMKwj>{y6ggO6{XzaU7{GTGDZldp<hF(`kvF$IhT*sS&(2=IjpB4e^EWXC{
zm%Wt<$ZDJe!VQf(?;}3d22FtE87qqcpz&zJHdX5V<rRUK=c1t>W9=s!#yx018(WZ9
z7(ob2VqTd^Z8o3@myC5M)Y`LIEZvKvT47=|wyh5R3|u&N6_fY#P)F*1e&m*X@dZ{C
zU|3PvPi+HYlxCSk{ru71Tx9e@q7EEJ!k9L{1@tV5eDO4xLu`Kd<fVlZahM%Tmd`BI
z)Q5WOVnQi0(uuE~{^e8we=K03Vh)-_`u%klSVg2uDNKVNy=o3D#am%O3p;W=|8>BQ
zK6iH6(f(5XO`*NB#RVncCc5SLVGZ(Y-(ND6Q=g*OXnCja9D&{L7K~nz7Vl}^8%`Sr
zljeq%tSrZi!|;cG*9yG!=yPwi@!H+1is$j^DuMR_YJ9AT!nYSd<Pd4ENW2IxC##BE
zmP*5!7wT~_JK3%1H#HqLo$9?oLXCVFK|?Q6Z<w07e<jHDz=Eay0`KE2ZM41}>R=0C
z4~L^B?4_TzkPp0Lmzyz%;NvrcU%d-jZ=dF&XY?fh^B~F~9;CU$Tg_41Fzsqv{}P|T
zlE$OuY`jb|2sJfUF?8^j7ZaZ8x7bfHG^XMDTrsbcKz4FEs3fD0CK$kGFEj*}kh4Sl
z=t}=Dco4rf*AieQVP{@+@650f6*d?S4*9E-*wid;Bs=MO<QD?uoEHG5;oA=ZVWLG{
zukTQHijyNHtkwN(zNyAL?Ey9_Db05rDw0$G<6&0;9!y4r&kOwE(`|F8X)Rpwk9M67
zeA<Ki`c*quZ00?7RbjH@_!CR2JFgmX*86<(HCwOx9ZamA2+eiloHnb>AJ^CiZ>FA6
z3T_x;F35wMVUs3)4w{@bG17Te8GpBIwrYZHRV=>m>{fs?d()N<XBW)eE`C&md59=J
z^>#Pfa{4FJkskX&N@obXpk&y)#`iziFy0B;j@zWGutIt=3~Ig2OA9=U6m)`8^!LpO
zfi_%T7*>`$OSnCD&xGTMf8sw=!ceSVol5^%@SzuYy-eHB_enGtt|%~ZnOT;aMORBs
z<!b>R+F+5ExU#SgL*pH;D648#Et7Rk0v4`5$z^bLFqQ2EKhLy~iLqmxi};Ij73tp9
zpm$duXA|8Mi??53DxnFP|DEDo9Bprs3h9PbH6EJ^Up{hH><GKrqE!FY328Dt10oYR
zf$%)<Y7q(|&__IEJRnS^zQtpiyuX%YL?Ap2EC<)1U1pj$c!WRiF%FbUBpFM!c`Kkh
zJPG0O0TXBYmmgKVzVzv@VoG#IlCw~V)e{JDpfI*wjFh+tDdV=tXC_69?3~Amk<^5i
z%Cc(eU(?!8jr0I}`48e1k2e_M9d{Oy0w#J4f6x!ZP~8MBURTipj~?S-8s+C~wZF~$
zhJb@@<&qObfgD-R5gRID0k8iqG-ZC5-B0AHYvpjkjJTTzo+{9DvYX$FH0=Rwfc9Q;
zj($XBCw)#0?n_VSlCEeh3@Ee=e0f204Cp{JwJ@&LW}3@nby`QxgxlTH6DX7#`l1=y
zek{8A?9bl*18&iNlBM}cyi8h-{)*C%g8dJiPNTXc)t=NHduE5Q%(QKvf*16V5~{<5
zxhLyJ_fuq`Jw##mx4qBKazTZ~YE|-5d)EHU3tuTQOHG}<8<p#*3xRU%o8)uT?$>?Y
zCA?Yc09Ic3Zx&*(TGMO62=UEY*Jq2NN7BAU@gPhksnbOC!oyuw{j^Z1(_j_DI6e+=
z#VZa4g4vPV<|{0;v#X`kKTZ9m*xSjRea(;ynZ@1Q<Lp^5>znU5eDX|oEIUsdI_6h^
z=`~MD9whv4zT62hB#S=gHPnBpI&x{85j6{sacjbQz?p`7a|#M&i-D5Fb~j&A+!E|U
zl}$1(W|%Zg<H-t!YfT_$qRm;GbT6d}>|d;k2FMJg460iq0@OLYo&+@Ah`~SoN1{*x
zwtf6-8IC#$$zLYiT1BHjsDZs?Bs432T9VKjj;Q#uqi=~&f2J7+vr{_Ky=)8BW*(!B
zb!3uneo)HhW#E%rn+vDWc!Zc`X#dM!CwIti&EM#MKGw&6UU(>Iza8f)Z#z>KT%<+p
z$>I^+_)&nrKC~Ve=~Cm>$AGthIUuvLCo#E3e(qn$%5H9WGN>s~Z(#{h$~>9q4H@cz
z1{)gN-cQ4CJVcvG{&bO;k~y*xmO|f1BT9r(tQS(X?i?hl^u1kKE2r0JVmzgzPNeK#
zfx%DO-e=Dw%n82TfsChNhk?u-e2R$y>YiWDv#w{LCI!9rX5X*tW3Z-A8O-PmT7j^Z
z)S^<EWWoIilA>sGjfi)c9rr~k=^}wV!d?v|(%Gt)EI!2yQOt7q7@!N38U`lJF+a?j
zm84x=41G7nI<)y7Z#oYVns*)nwA-{Y(<Hk69aaqayIeJ}zstf&U=K;TPR95!&#Tpu
z)8{Lv=vm0OmU{y0MS<}5WuQw;LVpbAtgt?%MQ@)A59ujhMY8LSbYmC!-gGqCWz9ml
z;N=@#C)_u8DQ=JN8N|8=rk6cK@1Wz!Ytw>wO^ufp<PoAg2Z<rJ#cSg}y6ky&Xhn0;
z-P}F<Wq%oJajA4NaGiEH9eob5^-_IiN1I_I#x>59X(}VkBiNI+j*7%J0Ek}B4Q?PL
zPWD?pe<8k<a>=M8q;lU<GAF5Q;QRDed+{c*mEckrSb3Y0(Oj61GQ`oPWv?5h1YjSh
zeJYL|1P`KHHZhSY2IeEv_GY&SXv+^zP@1+nFgxVIp>ud6NC&nfSbD~b71DKLYZDOb
z{2EMLulj)%^@aJGe=s!{vT*ve=rW?Y+(5?NABj3vS9PreU9(an$s(zCTDR`hq^Qd3
z`}^_BOulvEcbL`tauqUPDowfYyc>q9(A$TN^-1hgT%_ifJyd6aJ&8J5AglaXFcL>l
zn?<iz1l)F4Cs;E$@=9lC>w-uXL|2y&UFY9J7#~GViqh?rHFbMVuE*=Y-rr>UUR*#Z
zvW!Zh@%2I@n)|zf$2doP)sc>gDt#|n0Cyb(YKR>U^AJ@TKEdt600gA4<&1AnZw(!C
z>}h0lR~Zw{8d93u-(P_%QNF@Q>(N=v>reZ2MC-=+Ufjo?F&8}G5=9}#?yD^6pfx$!
zTQh1~PbaD6gX7PVt2>GC(nh*cXwMy;2RNH?A*><v%Vu60M>eMfUcwf4qbXeF97_?S
zNWf>p2kBmLm)??09e$u66(Sd&pIQ5UA-B~=F~mn=$^w}-`N=j$FZyX@Tb)A~kp+an
zR=B3=yZQ`WataZ!N3yC5^&f;?+V+{j7}=$4?vU67Y-9BBY*81LMz?7@fPX`Ad$?!L
zy}ca3gxj+pE`nDETy*d7I$Sh2A#DNuvtVntloV+BUDDNVlL@Z8eS>e8{`uaaFI<r>
z&A5SMhSuX!T<}`j<&9SI!!(Wua=A1g@IXng#9Fh!I(F?qK3Uc5KwHROIz>h4#4gBM
z!bH+gN*f4S!$@Pl5gZ%+0306!E)5#S;nK9}Q9uF%oNX=hA*TY}NBI4<#^?keuer@9
z8FtWN4D_l~UOT|}4uDj?hKyde9votcoE(etv^;Ya|Be~MYtIc+E5~Gx`kQpa_rqlV
zF!6@&Ko*A`o>nAbg@7?)kHqk$c<AE~B@@5>R)gaOc5(eXBA}tOqvGuNn6F6fkm8kt
zWLs;3714(1g;ez@rrpJx{WYo}e*i~7Ko8POlmI>6)IRRiW9?cfRP*}T?xxDPvR?(A
z({D<J<g*%J=LAfHpA`MkAHE)#kCf+n*VU`xgZIq*G-dm{l;<}zqs8wJ>Mgm<fKsEZ
zM!gW%>mR?LK{_}3DbxnlC$kYZ4bqne@AC|AJ7tZ$nx^d-Q}?&u#<1dkj$9qXP)4%^
zv|v8Q?u4KXOZgUecWr9^a)A%-+QCoJ4QMZdhJt${_D(OP!oajgTRi-_zg)2anmyZ^
z*YRiprS$^ng1w5$%BtK&j#2x*<3^l+&1HUHm)Q>-#9J_j3%y?C-d$;1F*ELhKhP<D
zX}FW}ohwsIB^*wM7(sCpNXtJpF?+xY+$qN$?y5G*Zam${w4)uu3O}v%!c|v(LN24N
zpqFc1(seL1NHn+btHrtL$1!O)fc<nakBw&iR2uZZvX^?~#=svQ;2$rmvZgXY1vhj1
zzyJK%C3y9<yt3jK3dDr7CgMwQo7c6eLMA5yng1OIjZ${`jQ2ZONoEW-J)A-nE;fqh
z3K$9i2De^i3dK4{Mn)=8u~kTh5+<A!Y-f~7pewU561FR9>mB81aS#;rJESNCQ}Yii
zeFgGG>8EHaUR+rk14)u`(ij$EArJVQ@c**ssfC|JgDX~5(4>uj@S$orntL^{Jr2U)
zBe;j!bp5{j$!PkF<$YlT{4F)noejO+vR9)xYOk`7ATE}4KLrq307=(HPksEAbrhe+
zu&Z&U)Mp1ie~i)?`zCO;8{+{<<XgDX@Yfh>*2xclgcnx7$aeV^lWYoCEDO1U)|PPO
zOm?%^aM{zk`;e`i?w=@~J1mVqFe`|dSCK1#>v5&qRvUr}^hYsz&2rTzS-0j1c#}7E
z&~9T#6xP~Fu#m_XyOK5P>fS4pNam_-<{g+*FhoiaUJAHDzQ;Qx@F~n0GY)^hD8HqK
zO;h^0cJX~!OafJM$kL;C4l<~(rer1|0)b_GdIPj$Fm*4@k;CxgZO)Dr$l_MHrxCvx
zX25UyN?#yTPdnW9l{R@S7<;{IsiaY?3zA*6q_16MO4W5x?CY9N_t_&fH%60f5Ox|D
zo$`lT7`yE!IPokck&K$LV6rFphGM7(KNF*uy1jfVn^Qly946QWjAWBXDBwI}(0l8R
zO1qM8LlL7mS&LaTQpfj4-B3rKj694Rn7)<$TC%)v-D=-ENm<M7U3KZeGyI{c{;14{
z7`SlxG&OdM9!X^Yi@Kc%qiZ)eG3FcT$TR>`Hw_RB(ejz;AtSf7{rwlay^EojVF3JF
z{zrSeKLLE#LwcD=VPpgMqXoh8fJz01J{TAnhRZM_hkh_K_-B4Td6!yks6NdPUC+7}
z@yqmWZ>3Pz%upmeAeb9J5Y5_K-pcBvbXAF3iE%haP7YuG_<fS644)F1Qd>>f#2x+1
z&;>oAQIue4p4-wfh;GL=`dmDl3R(Xz(f1MU+-|scfwBCSDOG7Df*EfU7KdYk&;vbJ
zHt(KfR-|6%Pkq=++qfklVk+%HKyQt#R#4jy!^7wl*sN%V;)1z$NiFKD^<g;)9xW#3
z=B@$SE8OV1<{3<u8eN9Ag#)QIS|lm9P5%HS1zlk@d(!OK`NC?)zXrdqR<9&#yhK`D
zx>F_xr!i<tR9LBCQ@>1iY_IJt!Qy7CechNyAy;8qV>QAEXJf6CvC-<Kn-cpoML*0K
zSh8OYG0@1B&=NuNC`-zEd2Xv#wtsN8Q6#UP;hNWtI%Jd^9?c&{#m9l>IGxIOJXk~a
z<}Tn9*&^p52B)RrN9w1XHr_9*@VmuJudY?K{bW6?{cv1UyWSZOlW-ZJ*EFs3J6go>
zm~ujbL}x^p^l^GdG@6a$`gksE;{E+2&?ACU1qtsiCc(SS-FYRYfcI+L;+GySrNnzZ
zHy<9^XLh8egEb8j=%fOYkPF;r&h~;XWliw(l1B(gPh*&HCcH8K789+M`X@PC#ThB^
z-@1|wk>rU~_(5nCo;~y6r=oU*sE)?3MbC1OnzQl_Ad2yYd(a{n^P4?@jquM48giu(
z>t+O_VR~$ky?BEXu3c6;xr~-XUGQEWwTY7C7aGHP=ps_XSo4g$8GFJebCq+8J%}FI
zFx@jtcoL(%s{KIuvBUE~&_YXNPazXSq*P;6SNjsjrjwR>cefL5r=TESJeqy+`r|Gu
zxbCn*Mg_;EX+5!FnWOqFEzRaJf^?PGzlLl*#vi#Tm?SuBaxUio+5T-S?TBk2LM9FP
z2F$?Uq>(HICIe3hP8uuC4csEyRuG*3r4>8=`k!eYVj@U)(ljSKok>?!9m5`<mi4<2
zFa$!pFW`MC*B1&775ekCeIFH>fE~J_p+OGA@+C15)8g;L-JOOrHcQLGG!<CC`Sc<z
zzi92%S6!`uTgm_pp#Rr0GwJryS>7&duUzkbrsW4V(X~IU+g{wIho!o&NTd~<B!wZ+
z!a@iy@;8oEhKGl1^Vph!%~TVwL91~=vyNckpUqr2l!#wk%>JvWn1oUl_HnOb^w%8(
zpPx=f6Y8#)aa?13fkqTBtsogR)ywLXrN5S&!4d7L*lFYx+eAXT(UYa|WfOnSyN{TE
znY#@IgYc^4Y%!Kh&Cd!9YtHuar%JYe(^sWLOTFJ$nHyb{3OD-r`43Op?7O90;qM36
z0x2)dnkuobop#vWB9O#gb^OYkn~|9by~j^M7!RDurA0PrbOqdExao|TRsNFuy2tbv
zH%UI;sVVus3PR2Hgu1;XiQ0jXl#lW`gBUD&siAPsrW#jP!>`Ws?P{E<I7>#IrCbvp
z<GK5oVea<k2ku_+Yf6_Tn{>6U$v73Qocwfx<<U&0W88FY0ps#PJk<^a>)(mmfqnuR
zx9e>4%G(9vUn9R=GSk;y5rw|WG>b)O7ZG3<y(gg4x$chWkrz0VcOvyXm<BOUvX0`8
zV^Oj^*BLCr4(amrD-|r3d?(M0xM$#QjkEDI$V!Q8Ha$%)blrbxoYSw@z34F7aB7ab
z9Yp<$=TjpX?XlPEb&}d@gT&jr5!8tHQeKM@agW!w5#x9M8zPReINQ_lG=GI*H&s4(
zLxx<qnR~${>NjPSY)JdcHb*yot#DI@Pa5*h*BhE_Wcd0#_VY<b$u2G72xL&=35h#8
z<whmU_)h{nRa<hsFn24AA2nf@b7PE4c_ccr7kH#ghA+xV#*R8ggIEdoJ?IpHr^i=8
zxQ+Q{>VGkc8vYPol<YVYjg2;r%4DSq|4BNb&@&gUO<3ByKU+8cU41?iu-2zT&T@<w
z-x?`Ah!=%p1p3|U)x*^Na2&DX(Tk2}h^H8AOC`HzuSd{~gbE6E6vgNqXF=R!cfzG4
z{RZ$rmB|J*6vsdoU(%1Z7z~28xi03w2s+okuiTu8GE4LcTNJ;3lWz-#RHga}`10Io
zvOH$STN5)YR69x`HQYQp=#jl5Mg4AWLbm`u1WJXYce`uq4OiY&Ux~pGPlx`mjfe%=
z5)yf<?lvVB2rc~aWCm{cvdUqU{wk4haA1Lig1QD<D$PK<19S`wgU!~9ATaYc;+K91
zIN`SkwtPr~Lu6`dY9O%ih8!GdYxc1;Hz()kC!~^zliX-skt-OX9(lS?Gr|#K0AXNY
zwEs(#pDU5a)BFc!{6_!$`NfIx7XlVd1UgalR+r}_=~P7oI1?i0Q>9gtEag9w%_lct
zwN_uiK*h#({X0I>A)Jqt47&WcYk$aW{FSPj8fL5G4yvQ0sy(rxCc`AL@m|+Lapbv0
zk1|s}rTx=ubV>ms6kOEPrKF5hQJl=PuR*L-wo9>H;~B=8q5uc8()zu+<*r$=ad|MK
z&y2O3?pV>GP@J<2zhzEuMuU_`guu^9IBhMO!|_e^kch0${-?=(=dN>(0Pt(On#hdM
z!KPbh9tUw`7~`Al5P@>^dO=^DJvnpd{A&cxJR5K69<Ez+z4c2qS#W@sT5~3#?&>zz
zEWj`e+_uZXa5`VCeTlC6@NM=Bdg6an1o#6U_1*=upQ~?Ml#Wi1*I2eKb55j;Eoo&W
zrZgdFX8vbHi-BBao=InKW!`A6@O#O)dUg8iNT(zt&rv>y%KVR~-C(q2sdun1LTdus
zV23a8y=hihU26_(-a?Bd;?cT1-h^{HU8=BJttH`h*o^owooRBKT#-L1c+{z0@Bt2<
z&bQbCn6&FfNir_M!b^>V9M>5>Ir^P?6DV%S?U<Ds3z6BE`)ja%YPU4YK`j_s<Jd6p
z-JO8WDzdtoDI_e6l)>#ZK3=#oRpU0kv?hL8!7yR5`M!Z>vK(REVWAqeWFP@{_PK6d
zLQxpbKu3?j4oX65GD6AiU(t-ReNADMrA}<iu<T3aSNizd;udzxS;wm2-XA`Svz4aa
zLhgiP?2Ph78zr~LwyiPP1hX#MWfwZ$_^wxk=Uxq8jy&Q<Np*@(;wo&Hw#A|N|GYVs
zrimD^;tcRsnIK9~6j|Nmn(1_xCQf$8cuwudgwB+mO?6jl5ZTMTaVlC8#;tv8E0tQ;
zdS1zr*VW~tWj5qE<`jIy4$3+ZSTOGHJ)C%Ux4X4-_je;ouO0nW6|TA!n!&y)vt&@c
zsd!Xr?s=<~sMOa~E6Xvn^_c3$KtHIo-hjrJyYU)5mw%AC7(4OG+Juz%TUe8HEOFxO
za1*w)<%Q(6RnF!rE9KsRG39=esS-Mx{x93y!S4WzptkfhYm(;$3h#_^7xt8dHsIFu
znFBwdM3cIX(We%uKv1jVO!8<nFyVftP%V}(2hL#rQo=K5=xvr>Je9TZ-)OaeIw{Ko
z>v^%~Y<esvaZ9pi^F+wDI^ETA95HvGV+pwj8!aZ$2?#2H<;IU!J4QL`2xS)U;N7X7
z>dbIt6!^}?0v18J_krWE6F9k1fn`cL=)}a;$=<p^?|2h%gfGA4?d7ZvkgCmM=m}Qx
zRlI8a(HX5b89FCpN*TYF33R%fzczHwv0Z=BHgF?3v08sQ_Nwcgu}y5vau>|b-=m3?
z*bY6-_m=%T2F0jHpjq>|cT+`493tR9?Z^sMmvbv%ROO~6om=U|WUYS)rV_M`m$ZLx
zjjFmj3o83jy0^V!UR$Xh%;Knnb|tuj%RCaRj#SWUvxcUHrAf_LL*{68kaiU3t?`_=
z)e$$dswFHmXEHy>`8}A^;9&mQYboeN+w7jn&YO#Qj8m{pgtcnnrL~92AaL8M)I?by
zi}v96DztlaDgBVY-PXv&mRqfy2ahB53Bws#Z4|$L_N7R;{E%<l!HH}1j<#Go;cKGX
z!8_of@%F1FVQnqJ(dN~;#dYcFPRf_TSIng?xtF$-!W*N*mNc0k6c{h-xo4n}jU@0?
z8c$u<i9U{6N_XvXM-+!eyZDIvW*`jXY7IvNY!Cy5iajEKsIG3~$*5tMmc3KUVt2P7
z_4z{O^zR(|8gB3c^f82HdEadUV~Mya1qJm>Z08F@`4=FSJ?~v-g#h$hCnt^Mk<c|>
zX{o7|d}CnQQfp(xqJOSyf@9CUR+qfsB1aq)Z1#=<z?$I^&%g}>oJ)5Cn@WnnhG8&7
z_=~u>xQ0L4^YQNpgfeDGKeFbM-Da3Qe8K?1-X0ls=7|#Q-yQaUQ5xZ%K|u&>w71>#
zwqY<Y;}5Q>m`3XFJKtG^O_y;3l*mb$Q4As&bzgFY`Eg2$F2>B5H<bFb!7p}H(A6Jm
z4FJAJilZ~L(6O8k|LSIggQ{Aeu0ng@|4agl)*$H%eN%xr)Hm)-5rFMXR<|x&n1boX
z$Hc@m@5=9#b3W8JV>ovcDWkk#P%+k$ON>|u2nY!Ox+kQ2K~UbjC}miJXRUv<EK|BC
zIv9n+SnqM6%cR>35B8hFO2^;_aoMiW0X!MZ6dVT;#D5a1<A}S9h!kZ5_Dr(Pb^g{=
z6fbUKlWVzsoa`RI)a*~q|Gz`C;OLYf#@rc>W`~0Abj2Sl>MTS72|K%LewPMb-D-mJ
z7;u;A86OPlR6DjNC^N0WOIyVC)LrdB-vm3am$GjnK=AI0Qkg-~kqyK%u4UkNRpGN;
zxdKXiBO}{r`WYi-B_%D!>3C8hLVazeu2IIp!=qtJm63*oOnm(cgiKo8e~WEy3@3iM
z8cP+)xA8|amqlf);-v@wS5lo8G05ds700%JdD+#)<vi80X3oi%0hVFU?f+rxETiJ+
z8Z8~1pur(H1h*i;odChz-QC>-1P|`+65MGThv4pRjWq7A)9*L$+?jRn{He9-*Qu(b
zb<W<;)+6Pz&(gk$cA4cdc!f;+gFCT8F_0{{rR3-7``-hUv4TMW0JmE%@{j;znz8P8
zuO5@%dxPkzC9n6#lPg{zGfmWTg9QUzu8uLQCN#h-axZc-t54AD%Cre?Z6cd*y4A&^
zDTDu5I3(8%EDc$fJs~>&l;{eWU-*2iR?g`hIu#))On0<UIfi9GpaSzhj1FY~bc19a
z=93Z}O1~nRCm3}85A^*gjhZet9Mu!@$6S-GVZ7HaO>WwYlCcC;mHV=N;%m6fnq{E$
zT;vX?O&k09fvQzYGcbiCL>3OR<FH$~`Gm}iSLXBf2%axLb*%w+o>b!9OO&n(tnajz
z^W&wnZ_h~zclpfEV~W>As=pqUN9CLRUz5lG(RsgkV3b`69)txCEGI~br!CmR1ADJ0
zoi?q{yyW1t9Xtj)s=N#sbYpG+KZ$UBfvq+fG52mAy8VYft`#)I%tRjCpRIA|l%~)m
zWoG&JDVZAI1KG+WG44gcdB~-dL{_A(%n;5ode*&2jWzKD$~V-`hPANlRoHrc-nX>e
z2C;#oqCMf}-b09xSU>NSxY=OwZv<q-QbkpVXSm036e`X<S4;McrR(F5mHxy{a;`=t
z-iCL&-*!w=C#{^$Mh`V0eskG9B3?&5LZ+w&TRt9iR!*T^w{Twuo7l=W<y?gqU05Jz
z_FEFZx_KgReMgPtxm}4tD4FVwm1vi`u=$xD5(@6-Xnp`hQ$2ee%-hL2uCzz8gHOG$
z)KPnXjZIP_#@8YgBPN(RqOF)www>&I^mj|q%5Ka${!<exNdT<e0Ws+hyVTuhX3qW}
z7QkXYue>b5S)pYuo2FWjFy#3&&y(rIy16vfuHb>;?pO?XJnOM+W?BB4t((S)Gh|zw
z(BI1Q+R8)T@_6b0u6WCZ_=5gMAflkCYBlxifsKj;7a!HKHcVCbcyA=Z(;hc&aG$f{
ztb2z8g>gtj{5#dGeqWOXUc0GTX14#gPV>KSai9nFL>7}BRSRx-*28%QvjRsN)Ysfd
z1`8*4@I4yA^CCNzqa24eBv74v^RKpM+2<KG06GzJ;i3v|36r>g6z3?=^N~OIUM!qw
z*mDf3ral}0ThP|8?vE-{*+)iQbLyu@>a{0uX^HRr(BCf4vW{?F?WqP>;gM3Lr;>K+
z>=;VsEuwNV^e7t3v96IuTA8aeHnoivJ4Ej==)H@eW8+>mGiq`gOe;6sNo3??NrJm0
zM1J~pf39sB0lS@2dam&-CbcKF&m2i|o_%pDAI+J|uJBGn`g2jJl+C|EVB%u;Zv)4{
z;6B85y~Zee_&GK|9N7KGRzzci7W<1`eQ^q1?D(v3#U!o(({Xv=H}cAJ^;MM$_aOCg
z4)}TXNy?^|A8-r8e6gygw@vGcY=~LDh?Z~rv?>RJ+to+b;Ov=p(9COjDk(O_21!%Z
zb)vRY6-h}!ndO#z=b){%>3<&JEfF!x73nDe(`>d(g#=2H{9M)gH?DGv`AL7hTl$iU
zV(3#RiDl}iP;*q6B(+yZVDV7yi1W>8kX>4CBl25Llah2p>#q-+yy3{VV#nr2mPGqC
zAXjfUCu|{L&S>-Q*6i}C)8rKF_iq<{bBCF=29a&%hO>ov?hi!>MQ?EZ^*+HC#Ga9P
zr$lRb8%(w(;|e_`W4>$rMLaL<N3`g0BMm?~<V6dks}noG@kvRL7g?jYeCqRhyPalY
zJ%UGWE=296;N$Q2t{EFbWfqxI(=xbCa#@$a^@svZ!vqaHKc%DX?^Uz<F+LGyk-cKM
z`~^Q7Q`BzcHoik<=zh^|HM+)}dMCY~PpQ2HHoNe}Q4Q?)`+zVEaa##ki3JzR;D38v
zpvovc(j(BT0)6C7C)h1P8Z|Nz7*YR<umwJ~YIl;K6XV?b3~6+w#L!{VrKQBMB|W9|
z(5moya7eQAB-TGon--@d@^Fd%kZ}^Q!{BuR;T^_DyDzvF$+jl~SUuc;2}@|Psq+_2
znJyQ*I#Lh*a%gWWjR;-#Yr&-!MMeiZqZmHtT@T*-xN5+{{E*1nlJP>p#ra*6dfjuc
z$;kD1y`QPn_9FgDe<A@#i`cSyY4!$C!h3bIO_ubZGs=jxVg%$5j%@TK2C9AM+O8GG
zES{-qb5Q8Bvqm=(+Z8W)W2Frt%l-UV2GcEL$mY*;cZjL_u-nFIx>wr89K7lNu8wKt
zYB`F8Vx0eOR23}~pW%9{fgavMVLLN#Ve)UgIk%}pHKYiZ#KclToru*9y)P+3G(e!S
zv#YPfUGI2=$QHK5PrtxOx2H4F-=0or6Tkr0jZd8(w~IPcj|=G!)}j3su0n3}s_Qkl
z_CunhtPK}S27DK)HHf~PY*2a>FOG{9$6s%Xy@rz5G;yEf&txWE{>BTo<p3RygUdHZ
zTF;iB{Jl0)_%N-enM;di|1`68F`>?~F>cl)E66Sf850yW1VzW3c0%G1Y-(boL-(^k
z2M>2S{EaKM2!!$H<xaJjS7;F}Ws`)=hQgHH{{8+1G5o1Q>w~VJJz`GGGE(>s(NiVL
z6`2``ne&+w33Orm(U(ZVTw_jmTPyNLS}x_IqbpoXZL;d}a;p{Igr@8z&8F&jz8Re|
z<K+{{@On)SKZJ{k4Eyt6EPu}HL|719C5&JdSxV@{6UfaCvnj3Y0S$^OZpNyw_D!<h
zU@<UeJb(&y_$bNiNg}@=R#5JzMe{j3ZSE%Hzb&!7JOk06nxd6S$tSK|A%lBv1hXV*
zlS;dL@;wH!fN{Ipdc;{u-TF3p3X8UXC8yTu%+NJ)?U~jsOR0@a{+Ukk1x&a1!;>e}
zYNVNs<bLCR(G`rc;N;v$#c`3<QK4IlFSCsvDY`5A%`Y{Xji!U6;1;~qU6mf~niXRi
z#mQ2%Puo;(4YUnJ#-%j`$GUh76DcChOUxxSdO2}*cth;clPtl#s@2Q>;u;fbajw+|
zN2~vc_vb&=>IW#L6@wgJYjFdZSA$!zybE=Vxt;HDwwpg9vl_2C7x@uK43Qb6`YPPY
z9TF0;_?&~kkj-Holb%HE-*6{59+DY4t}+1}$Bcbf*$G<E<Xk~+^ywW6+8QUBxbM^L
zahK(rE0~&l+D^Q&ywY9F=cQvsK)^`FY&@3ctHDUzHIOq0#6%*10+ijD-CH^?j2IR0
zEBAuCK1)Sua8C}0TgSw^6-X%!-oMQ#iQ)REIdqE(S3T}bS#U6)u%iHDj~+&1zU0pU
z^VGCRG%hpHEeU>GRRUF$1Y9>p78vUfl2mwf`G_9HTF3V0J_Yts4;&OItQPh=*#IAr
zPCwIspIsQtpp}?ikW`V_L!A2}Az!@mkfCDRO6EEgT^R0Y`b9GS^dMn@Or1!PY*co5
zr0<5x=i-t$LYl{-4dS90{DqteN&u@Hu1@eb+7p?w{_UI7XFW>@&U+inOYT%pxSuEF
z-2z9mUu<s<A1u==>q^<sd5?0M8@&OuhejC0S3_^7TEsX+kDixs`a433)7scjM^&7+
z>1H;xvMH2A1KgFq+%~K?F`&DBx3*pCUc?kGJ}EYy*9MRirawzhag3qsVfDq4Rqs5d
zo1M}94Vov=Y%s}1%xaqYGKaUW6H=(8DNwo5js+<bCc6Erjd*A|^7`v>gbfb@`+kk|
zV6R-8>TBCygnW-S%XxLCq>?>s!|Ecf#FVfy?;P$18A_t0bD8+G%25O-Tl59@xcwhN
zSDae8qy<BY$j|9NsU39kYTFoUG@AmK6fMv2@xtwcm=IJNe|0#!aN<^PMi965ha+7`
zJU_r6%uo;!v?15(ewD^@_~olKRXV=%JH@Fd9Pf8MEa5^xmEjK6*=xk~?No!@m!Ox9
z(}zQEfLmkJbXDVE#!%9>pfe4Yy4s(pHm~eitr?1${^Hj^R(~EY_HhrU*>tz@=-a``
zO2l8w<~irj84%_vGFWhTP_|j|`k!Pr>yoZtK}UL$@F>q7_7JS3?I7o&3Ce2yol%wS
zq}3un5n=Pb;Q-KfE-|nTB2QF%HzqCFk|t@el;c(Za^BMkdJRwVU!^05Jq3wj2gu;~
zx0}mwVt(f8e%jSbquG!qf6IxKw;(XRAS3@srHp=6zNh0s+2SY!>hmo=cUehj5<m67
zQaWE*>SQNvNGUb?a}j^>w88fCb099;j|wlULq_eyK&ng+BM&2j6-1!}3eRsyw}aJw
z{hH0>ST<A+V)57kX>}hX>QGhJ-H`(_X!dGWI#W+RN5sD!ko$gVPY^*@JEVN&78)Iy
zY0I2Ydn?~{6Q5MeqU*z<uiklM&}_BO7HF2`9ag_AVxU{CF~y`(7+ycv0wQj4=>?r7
z9`*Lec1}Vy%<FjM|0p?Z!s*-|wQ@_?0HH9m0mEk+X&IEzY^d4DgVn2V{j!s7-AuHZ
zt>>$w9~=Xy66<7f#{z+40^-u<R<x&5t7>GynFvWQ!#+tpzC|$*TNnLPWb+#2P+C?*
zo8_gxvJ4g>q(xi;mQ4Ssx0!oUWO2`xmxL7V)p8@NP}~60BMZmR<-O%}NY{s58m&0&
z{P<U1&WaWrZ>O{QB{NH?t@!3cvNzSHWC!L2Ja&kzF4}#4r}U8Qiz<rV?05N{F$o7J
zFg@(CiN4QTG8vNm3JasH!nie^r!)+s%~G9OM^ZK@wv{%fuf&xQ)xkd!@T0sqT9f5(
zX+gxGD3W`zVzT#jMx}WACR_@y?`bTG$&2=*C-&YoH8vp^^X&}x{+Q07#AItq@sA4e
z%Xt*j!SY`P>Xy`oVyMK$0)?M6{+|g;%DJ5~ru^KiEGypt)m~&d4_<9)o(m>-eR@+e
zuz!gu(qcW`1oxTOqmZA%3WTvYpEozcCG?<cU#2@BG?K3PHdcQ%NB9E*LIHru)rgzY
zXS(CNY~|btQis7UWXd=d)lh_7&9LV|jvh)4H&vI>MZ3-56c(lr3sMBZeXLZah+KYk
z1Rr~HED5wU1Zh8uuLisE4|S|iEU|MtkV*ySlJaKf22(F~v?9*FkMt~qe??pce7Nsw
zgz95K8GO*;jrw#FW`I|;mE|N8Nb8_W%gz6$knzw|J>cNrE^cWlfa2v(1zpIGai)a~
z7O02b6qtu1XESiWxOS;C-qS}p!1%pBGhoJ2Ifz3^ak=pB<}#Wn+A`%|Iqac{XFM|X
zgXqzd%}6a72*_+naFw$1na|S*+c)|ly&f#b7P}@RfwyDwd8_1Fn~sP9=Yt(-3Q(UU
zybK%?;o=Ue=t}%rCi5HRt9L~^b~zsZnhq1uV0cqO8Jd?V!HO$tcloZb?lcu_u5B_e
ze+$}X#{vP~O{7@<*q;B>ERN@X@c4nJ1Y!o9&(8L_%Fo{0v}arNZ*jHc-|#GA^%y@p
z=oxy@Ri9u)(%_jxvsCsom|!}A#jE;N5IkRx)$kcqVnlz{xTQi3Nt#8Xyl@&mCJIbd
zTZLbd{Y+Zi=h@bn$zjMu*s3EfVyqe0yq9%ow8So3*!Hv@VdH<6XER2C&DR02kmNF+
zy+`}jUge)5mV<*uYAUh)8DYFO+#Gwb7%nfDQo>_U{+4m!f!ewnrV2G`F&jyJEhvGv
zXandbKuZ3pCW|2GGe@Ch{!y`dK;~K8Z%n2Cxrx@mkWQ3F_<HI5(kSm4RJmRV`v=o$
z`m!n-J+yy&21;(51;#srM(ZkBJjppQMRE^2(O)Yzb7P(Cbsc7PgIf#*{f(Op#%@oa
zQQf<qbx)f){YF69bWJ3F^FZX`xZQN^&EfN%fRJKI-^t)^7gv583Vw4my82<`d{$#;
zoEtaEGZW5X%`G0mq_&Tk4{GQ2j&^&OJJ(x#2Qs{@=I`H-1kd@9e!69NFEh`V6U#L>
zDMHv4_%Hj_<9}<>)eSWgP@E;6KeVz{)jfRBCs>AU(VLPc_*B`fV=6zm=`01H)|qT6
zFX1hK?6-fLMuQ9&as<hgim3SUQmt6@GNtKRd>r&LJ3oTKE4)Ye23q<pI~7nBl*wxJ
zvZ<X1BowyghR0!o{-Ay0-9StXkSccyFfL_xCqG0}F)lmOxNePc9e~9p*nz8(YaNCG
zlNmAJftzBG5RDc)(T|-z>G?i%_h3F?F8fGNyxFn#5gd6jf<D#TL~w8mq|N4Jz&QS<
zne?nkC}u!``$#CR=yy`Wy~bX^bR_k%le(9GDaJniT$N1g%MLBXv=-BvYi{yZ^O`Ik
z(R-o*(`>Zlhg`>Ghi>0hh6*1w-}WfLezDqd$|}rg<eL9}uy~vHu!A=CuDNE^uPn!1
zf+{uec+Vu#rEbLC=3~-+v1e^%@^QawnlM}Z0{r6+eI^E@rdo6`bb-RFvf)-7Q`I(&
z9@*<VvG2v8`AaiDOfkrN0-XdV`Hq0leR!}rCR@Do?NnRVpb;S;`aGudlu)`n_~CU7
z8zr%|QE!J6lry>_!#6us)g<Kab%Mh2iKyPpaCc=9Z>xZXlj3;!ZR!4z2cOT1t~tuh
z?=JQ=*_hSEi(Y+fIl61`{_jtBrE>30l$GDx?=7$%=&MyLu5&d#N*0Raf<RK0&}YwV
zSxvQK=Cge_=~9*k7ja>f+g=+`!Q1o=N?s0PKM%!+NVUgRpA}HApi{E(NVSxpg3T>4
zm}^jD^_ul+8mD)c6SVU8>0p@+n>Z7eoSakBGwwp^W8)|CN;Ha)H}smUK){8!r@sby
zXcq_G`S?mc_RTlrTkri#)Q5xY7{JRItmj7qf8`QHs^95<_>C+NHCzkY9nAsg8$Rt^
z#-?O$?V9QO@8;hmy4uk?ifq06e!nuq#ctxqhE<>Bl{DBUiG=UByha(|mC(awBZJ7R
zV9^aoqTVAE$Dl%1CnrSjAtT*ghHP=(I|moRWXxvdeXey#yhW2@l=()02pVsD?FSG4
zT9i$}DTMyS$(~>5i-tFq9H#z!uto*GED7J_I-i-44PuXfMm=*Xoxa-*aiS1>BaI3*
zGqKdTq)*T!j8eH@e{$kV{AY7r`~Nd83@VE-<Dw@^K80+nD+$W<C*){A5=_*KwnH@*
zjrW>$+dakbGL^06#Anm#7`R8Wxn4U_&o`G)xKM0D*j9nxtm8IQL`HGP3bAd&Vk~2^
zHu}hPkuf-$hvH|A?EM1-{5xB+m8S>>2gS^CZb?_@kYfF6KQfpJ6ogNt1j%bf*AMPA
zuwC3u;cG1|;t?#1;|KVgY@<5SVmu~pzyypGB)14Qz}I2C0n8nt)jzVFxV`i1E^^_=
z4si_pU?stpS|J6OY-Knx^cPMdFXpVTsl$Z9{}?;Oj)@J@PkgY39J*jBDEzymSA~)A
zZmhi$t>jBCu3&uoaYJ;@H?!5+xga^%IOwa7s!9CEwjUsaE=qTV79jxeZXJtm*68sE
z#T_`7Cgf-hbrYz%=dOVMFpYVpYr3q?i^twg*DvX$Yrily8+|$>{uJ>ss^P1zqM&*8
z@IXH}%;d);cX4;3#H2cgr^^o%`*+i*Tcdbx`j;y=klmO;CY1q}f^L94@+gxm$mB1M
z%fx!NX|g#%2u@3R4#n=oOR*3zvW@0ADN!Sc=<%nbFgPqI-(Sy(4r>%xqZvXwv>y?*
z7(ClzGWUo&{e=^D8MHEtu8gNN$9G#6%VXm~QKAIn`{i28po<oF{THVgu@?(7e<5A9
zc2*3B7O_D9{%OG<XmKjIzc%D%3K0kQ^*=KkrAqfaoFg<r#lmLWGC23O8s=u(hw*jz
zOYtK-!M;R31I9N)vTF?bNE%rxjMa7enirBJ=1j{mG0M4?Jw_u3IiNfBNZ<_f{^h<e
zEFG<|pgRhQ+n3G@GIM4*u{2F+lR{{xK*Xh0a|tp)jv2um<vx8dpHPgQE6r<N5vv&>
zc4hdNn%WV_Ucc1!iH)TTHqL97v}!e^Rl!{xA$=erBRlMwnz>Khp|UrW8dT1tFYZlJ
z#NNGoZOZ2=u=(=cfKfj+L=DvYwo$(RaIk*co%4Dy&4uWoI}e|2rsj#%`Gcjeqi`qL
zd17puqVi4}vG<vwp}*<&LxI>4sO9|WV{D}2h*u{7FiV_7eW`5>)eu&x%<pIrgxGMK
z<R}|88x7_h-03O>)!-E9j&^@I^4e-qTgE4Y_ICkhN7?wSNLtVVBhij`3=p``q-xuy
zIB_a?3Vh>fUgG-dXH!h`R(W~=H?keie?`)%v3}P`a1LG!Y8XaoUh{p!JEvx&Bga!*
z1YzXVKf_g-yi4n4NX(1by5`B=M)WiX1YyuJ46qY@BQ>Iaz3}u0Y(Y*!Im#0jaRfA+
zN>DV)05Q)shI>FaOq*|UT|VFUw>(lt%Fvihpr^^Y5mNbUu_?W|Bx)8gq!-q{z}R!R
zVk2!@ViEp|>Fp}(X4cQF-p$ud_io^&LyqXp1&~b5+!8Eh<&^S6?R@`e&vMv`vnz2)
zM31ZSurOYk%`mjs9GP!?zhN9t*juc#D4K>se}>`Ts$9}AMd=WHLW_k3RC$oDol4AD
z%~4`c|D3qQ^I<mCY)h8a_CT;x(2T3L717UZ%0R9MgMfx{N^;?W*1=-Oi0Sfpg=h@Z
z<nIuBAbNld^yg<#e`ec?89FH|g&xz$GliDxeR?{d<21o#Sxq}q5mGa+<(hn|NfX_{
z5e!wCT1hw2;oJ6y8tIL`>2LQa&}U@b_X=IXXGKEkGmaBq+O2y=o4aGHb)euhwUf%}
zOotI*ZjXmJZQW|KkxG4KLAe<!f-M=M`Y{i4$bFO8`L0U#>6(SmTTGWR_L}3LqdpBc
za?Ts<iuT{M`a8}{P&7tfi>3Ka;XgQSA22676LZt}=Zsyp`}O+Tj?s1-Jb$4XO$I8N
zn<-wihwRKo|IUxosUDWJ2R*H)d2YJmltzLFtyBal#C)Ch?J<h3?+W9`x{Rm19!0E4
z34h{@HdOgC`zQOPBc7uR#Qzkm4<}h)zZs0iy@3ePV`$#i$a%f(u!jwyoJZn5naD{&
z4$UPfc1kR^g{=sx1^J)?Y&U4^OGkeivfM=9_x0D`&cbz=6vMOC=w*~_YKe<o&92|i
zKkIDeOtx%A)F=mPlCCY&D@p(fM;n8GsJT!$XZRzlJ&#WYz(BERH#Ur3^k>^m6*k@4
zb44R{`%!9duXdSJNbP&5h-kdFC*b9Xl6RN_><5<DjpsI*@RBGr_6^>fzThm%ZG^jQ
z8Uz+OS(JI@&3Omg;=0x2R?CUJsmf@6^{~t{?9_NTBiyX+J-KbG)n~LE*6L;0vpjb_
z`B|VjxYArzv@)^HNk90i4QtqUr`sgAf1|T%-2GfJ<uT@)m(Rr<Qxk>k{oTYeb|67}
ziJ4*RoIKcVk0PhOS*rav!cgQ1!o|*n@fyZ?&HjkaCijQ~XnW$ZO!=VG_fIBlf9`yI
z)1T-CC)BT2vJARHZj>8Y)EggrCG9aMgQA`}20mz6{0eNpbHypg`2f+E=6x~@YsN=;
zhG~Ip$#SK806OKn?vy~;4J4zc0w3uQ)@JV3!cq-|&&gnijbj%#nxJz#$T$4gKNn6~
z5Qd}^b-(YMZHKtEYI-YZv@=B+3BDe>>vC~GTeVU>yvAmy-8d5J(%>HadAIHFlJdDY
zSAi+QRoClG0+gnjnVid5&=ZjBV&4?~=D*2oCyAySKS(F|u?E0HK07tiSxN~`M!?Ah
zG-zPkznU|g0Ri2DQx`2L4>ayou@4d=w2at1iYtfw(XkRAM+9zz{Y>j7jN9pN-!HDr
zrFm#@t%X%bN_20=+=wsJimGHtn73W`DfiZNM4W7eaM_#ndq-$3`?Jxtb2U;HST}I&
zm^?{Pi6t}kU%H}y-cG!<tRC1OxBLCoX1p?puv=v-uKc*EB1zsWP`{4WyXm&6LYk({
z7AuuBqGw#}?=C_;VXKPg&jzA<!DTX?F3@1cz@zfQZ>LVY#oWu@b0W`oeH>DZP!)+8
zNubK|6Vhw0bV!S`CRU9aI<y_@Lnk5-d<Os!!J_NT)v6|v-v)@qGC|oAP)j+j=&z)@
zmNjkW555ZcWiKV+-QE^$hFObM`M*_NxP?pLe)uve<!KqHvHYo>=&NVqtzT)FOBZ$U
zA~Uk8J<a1T<cA?8@Z9p!%QVv|1smJZSH&2r2T_ac$a&Mt!=pkB%Yy2<mhYYbmgW+$
zarSRdmQUwFZGzIhdb1v6k9GR>WKmsuQZ!UE+s14j25a!CIn%Jm-=_>{aC-*Li+7?G
z_P0hY_>4WfsAVk$7}bD}tcY0|h#a3ZY~%0ddcWI3m0rcYe;*bVN4&0`B=(r+z2dz3
z^`%ZQ+S`Lp{}Y3k$EEVNAq`&Q?q-uk%cs#f|Fx?bZ>PgGi;wljJj%0Lw%^sdQm+Mi
zNe={pj(XI<bzVX+I?6!PD`5o`mp6A)iWg`_P0b8PKflAT@=ZY+!3?eKf}pO1{RZzm
zV=a}ZOaG}3h4Tf-kE{_f@3Ske^>A>eZ!CqEL*XlWjj>-hD#N*fL;X_rcfw-C>#Np>
zE&}gQ(;#hJ8#3Z6ain*#wn0OIQ9Qw`o)?sME^FqEfD%-DZZ0L|_;87zt+HPcv-T0W
zeYNTM4mP$A56Pn%A4ld|UWXF?@PIp;p;gXoLKqOJXuJId#1=BW{NWZN`}y97{mb~C
z6ns7HE1jVX7}f7eg=Bkr@4YMFjMKNzrr_96sdR3q;XzFJEPP{ShQ|rFz5-K1wQI9x
z!DHwSEb33$3U}fM9kZ8vX5)@VU7pMK3z&a3HJs#CujEO!Ofk=G6*s;UIhy*B=4OU?
zgmS76q;w>0n#R0RYt>lvE!fUeD?eIoqrKc3!Jj==A|TO5o$(oRxkDlwNQe+WfyoGV
zD>dwWHPj$fMf#EC!EorF@$vC~$cew6@|GkuA0Ht^(SAg!#Ow-`)hADAyuQAUPfRp{
zL?{ou)zuAVK#+zZA2!p=;ca{yvPS;gby}em{H?+aY^n=qNR`Qp8>v;q=ldl&sA!nH
zg=^Z&<lD|<KIyfpI??Y#lKa|(@yLF^Rjxm6oz$fDU9WPIR@HuA%(U~qm5t_-hzA$h
zM4!GQ1|drPDALlPy_QiD`xui7C6T1><(n1-)YVuyMu3cXxT&g4er6-HLXxtUr8so5
zOK6L~cI^J-gk)r;=F9Vy?3$k(C4Yd5hElW1Cl+;fEmZMV_f&L=AA)r94kD_Hi^#zg
zuW^|Mk%4R(e)fy_YTyQ5uD=pao6?g@Q9qPCTX9y_zVXdJ1lqsrJ8i#<--X8R_p(g%
zl;_|u>@AyaJNIiz^>X%TtT+pI20;a(G8wb0oBat{r|Z6s?R_L=@Gv%P0CJO|-`D*a
zy}X}MZDM;DXUn~N%xv2ZL*RvGxQb&ub3)l|aNWL-hx*XjwK*==Q<#`auBtl;|AWsV
z@$rFy^cZ>Q;9l8j+su&He^3t64o49G#@9TCCye)lzF2m)i0}JS7Gl3~%CwAft~@b4
zFIO)6e6`!)oTr&{B@aVs8VcC$63~AFm@4(SD}u=j5K~9CHiRmQW53<>O0waE1e@Ar
z!ZO^eGsl>M<;G}nZ~N<I;DYy~_<pr8>Czv6=RobH)<S&><pNX6+!>yKIlI+BvyV|2
zxa(1A;p!{BZm82~PHaGb;!*zvTO6}S7aaUctl;$mRKE@~H_!}3U`Y;yHO9lYlp40h
z^4Onp%ObbGy>IVA6l8l?m1OkSbv-{Hoalzb3j@{6Q-D9#DEt2Xib-MLYZd*|1%V$M
z<*vqfwZ*#$r8#kVE>#2qsQd^pRLh_O53O)j{DoVORX1xjP>?f$S&8J5S@972gaL}Z
ztrr_-xlXuzdJ@Ppo)AtKJN?~<Oy~moP!waL8fZBMVV@-SMt3FU^RL<+R#mF=WemBd
z_-Dghf|Z?`Qibuni*TcS+V+b7Uilt-Zv_gu=k@npPn)k%H2Rp5lVl`@d~|5UVksK1
z)(0AAB`ok9Q0T2W%X@3<l7>8veIvn!9{yb_ml+!H_KI-cd0kyOWLqUMM1)%?7|u`)
zfL_%1VPfujai$9A1qxe!maUoeGBpH3Bse>r&u4uQ0Q1)Qc})yCIl1L>eX;bUD{n4u
zDi^?==@IiEa*PCQ<GirN;(1}G;9)GM{2<Rt;nOcr&TyugQQimFxYi#w{UGPjK`HzV
zn(tK~tef@_NijEY&)TH-1@=Pb)sRe5#!Q}k2RMCi)IL{#Cb06T;ct`SkWj$1Ipgb1
z3v2mq=x3nh>S0vI=<L2jHI0~rv4-q3xOFOZec@fP2T<QnQh9iGd*l55?1k`&PN#19
zNeJv^W4<0n8W>D?F|>>0d3DrGvG!yfg2KU1kDSWy=ZEzxqBs)<aNPnnD%Nxp{TrF`
z1<Cy#T|L72G|*J(WOXQu>6Wn{*vLVLpj6NFqmuU0WFoRJu3~?fQ;fkeUNeDb(z%4T
z+heT*I&t&7vRTaA%#rf9zrTR?P>`c8N6bRKlu*qe%JRTjWFUq+oUWfQk^ZN7Yy|i3
zw8MA|u#Xfe;f93tE^{0FlKb02&G6%eFHW6|jlJkccma03i=;){QRuvs5*P0}%?6*j
z><IVyK@k!dx5*&^V)ed^X1roBnTC3_cDkurKdFw{r|YE<7YBP2W2?m*D<cC^d}#%~
z|JWQuKUsk0IH054!6bUL40%dN4(_H4{5f9(`kT2AB##GJ_i8t7dL5VKFOloyzit*K
zv(0GW+5JLi<Mt3^m?-GsSyfp36gIfAR=LK+a!815KrqHfo8rox<bGWg4e+$&{wxVk
zriTmo)uNJ%YrLzMQp=#1n8W5XTKfZgOT9PD9gGXwt6BJ=`$su}j)S4XtrkH-UMrRi
zqkc7N&!4MGHFp#`xgUYGObi?PdtA=LBN}TYHUtT@l9yu}uc=4|{ep{eM(Ek6fo-uT
zEt6B2Udo${Y*pJlVZg}m`I4l{&+bKM-vvkpOuHNpbFAI%!>{=pabViD`F!>JQ<=-E
zkOVie2VTLg5y<}KsI}zm`Cek3$FAp|^Wg6yIB<s)Oe*jTA1$OWlB)|oa26iQUATfW
zZgJ`FXBqW4<mw`Q+S!$6C;IheLE0iW2%yy9l^w24JcTZ$6OSSIeNmq%nl}B8f>AVn
z)(;?D0+xIAgBgf~CSqw5)g{>pYp!pOlcte+6evaM&nsCS%A;6J`h-B>6DNa%JsEec
zoJrUTB5w9kU++g&c)Bawm<BvyG2iJ>=roV`-q?$T7m|O6j;0Rjo!lUtR^`6}iI-qF
ze4n3K8Ts1BVJ%0rsr>^hr>C2jB<WFjh4RW^TZB|WVL+ykx17JUK~~uePLG^X$jr=a
zsl%oPT4&ITJZVtJp#x`ndf}yrx6zUZHakY?NtcuWkB~5`&SbE6bW~<N_p1?fC>n7&
zwrjRLDGutj85s(-DkXHxxyHAIDpuv)3R7Dm0>6WL&ZU$#STPbkMh3!BQK9tygBhw%
zle)|5cMtn(CzJ|&bS3+lYC$ggn$*z6R{_`&Dr@YkbA3MOEd$|im(tyZZ0WR75ii%O
zcsIGV(79_oScs`?E!ZF+a^b~p@~LVjg1dEitiENK&8A)$%{%sXifFbikF(mnp=^Br
zlIxGeWQF4h^Wk&M!#CDyEcmVlq|FT~*X0x@xO#w25@W%f=ziDY0Fwn*&m#+qWZV0A
zijiduB5AjuU*F}+6;alGSFz@M@hvOCS`*Znrw!%V11B8c0Qz4o<8J}rWZ$)`4fvPy
zHoFnIDDI92WkOv>($Aqv%J6t9txhlATNPEHk|qOOrSd32VwgIs8c*mYLGz&YqGkh4
zHfISfxO)wTklRtaIZN>!oIW?Pj0yqy^A|^~v!o|TEV<_VhIbR{1eOg?`W?bz3xsva
zC|=XHj}(k8gV#cffke8oxWcT7ZjBQp1p(nb+B)ex*H$3TUV0u`5oCFBeSGj`@H-1k
z64mvVhm}BUTp7ChXZ9^ev=m7gxk#j3CDSR{L;*UGli6t03Kjk##f?5IN^(c~wGzEr
z*OB3&T-)*5yD);h*He>tIW=dh^<9czcWKeoF6y0+<+Djxi!u@bGp*rn)dH+3paZPF
z==&{TW~~){4IKWk`I{L3Wr$)~{fF*_N6OYae29@{DjEOnoOz=)ZV;@NYqazYBoqAW
zt{j18kvq#Hh9*K6|2)BX>s02iO@q^*hJl*Di!GD2M@jV8{e>Nulc%T2ZqX!ML#aa0
z#>nMW^%lH%$;JmXQM`WFqLR8VJ=(Hq<gS1Yvy3sBF51uN(Y6)_?G193Nm?mE`hDhG
z9b79QiMq5dIu~PycSlN+2WGXakGVL4Yh4CkAx<ORX?JjD0p>pxgn-Y1S8tNV%Zb6R
zn2hH_$0XpaS)_GsT%X;X-IIp%jkS*-<8SA1E3na1!f)HP`aZ~%e-aT>c{S^C|NZ&}
zZq(qMDfptkEOYHT1Bk*?&%y&E5El6hfwkl6A>L%9+E~j(S;df^!BW4>Jt2-3VId#W
zGG`&%=51~zoU}iW&1;^SpMzGy>yR~mgc21sDZ~mU8OOjgn8`8+;vC}AUlII-{?<VZ
zzOfH7T%~CCCfnZWY5^76!FkFZ*@uW7ms`Cp#x}unfc(cqg8z}gzFb{Trm?w|Yv%H!
zV?435vHhhQkz6Z*j?c=fWZX7;9<BABw|2`(ODj98(QZO|zB?7`3#~73PNZh*Rh85O
z@9mM((jwB)(Rpr%;7#R=A)pX*s&Y2Rs_sEZndAZj#2XtM5cf<SIy{j}pX>luP<ORn
z+uZeTTd&0=80h!xeydAtpw^V9u2k1f>2xHp%_l~cL4xaBn4La4%^AvkW*Y3uXZ}c}
zTVOHt2iNs|jpnzIIOC9y>1fWAagRMCZpjCy>$&^bxoV=iyInBeD0`jYSNe{hx{C%+
zjduD)>`eA<NkA9UyNHDG*Wg2v{$z1txSdnudFjNCB?9i66@{Zl9|ghQ;Me(PAtJLq
z-zmg8*W+3jh5V`UK0zlIbc`sM_DVxpAa5M?y@v(LJtAsqyRH?7M$++Jy*E3PtZL)^
z8Ivk=Q4i?q)b1I-$b2v28-qbr;{=cT-w0RNY`XB$p>{~p*9T_~9(i?9umE$6D3|AJ
zyouh84?v?*r_D5;=S=KO0Xb{Uk?3N%we-XL<c8Y^btT(Vbi_k)SY<LeM1i;|9|UA1
zdT4qIPQ_Y}e&!$?pGT$dIUPcKfkCKA8$RdX0>6C8FQs|Nb-0@t-*;B|2^&1o-O<Lk
zH@19kJ~@%@vM-x5mbIAjI@%<|5OO8xRHcA@ukyweCbLT0Ggx5LBt-9Og~Lx#{pjk4
zjDa46=;SsVVc#@tMU&JYtKNKcWxPv9u75z1w5jseNcAI%hN8Fp&s~PW`UZs44Q7CT
zB9OsDo;`z+oWGuE+Hu<@KuWL>NyYOB0HWoJ57aF&)RU{F?81(`^||$j2$rkov0L*p
zY0+I(hRj`PIC0-JB#l_;j6B%7D9Wb;6fDh#y;b1be&eR6F<yl}=eA(LWjmM8?#R^c
zbm-GlJaG<#^8}SmWtifj?vfbUs92fw>=sq^Jj<!r*q1PbuWP?s3z1*-B+xB<(2&FZ
z@x|v1ffQ4fOd;`$B^YYBRGE}s#`Y+DtYOp&enck4|0={nD1P!rmxB5ug-=5Ncn-#q
z4N?h4P?aBx1z*kn8rg-zcj3`EEX*6*t*Gm%Kk^^W@TX*}#wR8x2XBf$0?u!lJdaYm
zJ?$X?h#lDrzW_ts8f}1Nv*Zm<`Zu3PL0#u#<;sWr7VR13!?VqcO_5tCwOk3$>wtPV
z5X*DzaqF3NWrKQif!+NK8~+?De$D#y$9Ek7Lhy!um>P?lcqQ$HOFzR!!6kzeWM@GZ
z(1P6g;)blB)j>*VM(R59wX4m)F92S`M0he3^k-+ADE6XdmMuC*@V=u37E5FFuGV;c
z^Hvpa$|NF!Zy#2OgU~>45mBtaEfk(m02iDVwn#L|w4C;9f)c!Qc3gNdm1iz(Z!U9^
zOLy&VwA*mZdOPlpPqun){epcNu&t49MFg7wqvjDIJ%P%#_LI<|U5R+pa|hLS9f$+*
zHI!*JP*W@9R5G5Z4wd~>!6@y0hY0pT5dYp_fq2x#j0}qrME>jv$zqtxxJhQvFuu!e
zVBPNc_;A{?>3BWPwX>k(poX-rn~rLp3HeojKMpYT=o%EFQV@EmzE@H0wCMm<Bk$8z
zX}gD%1Ke?{e*0c8?`|bQujI%}KFlYH5D1-N9)4YBH6}rb^6oBMWzCgl%)Ic3L_$?0
z)tfN+iQ?;q1YU8_F@FB!qz4|%NvnDGkZefr(@V6|OkIUKWD0a8S(S_wos1QT*NC#P
zaoB}tE3^M(R6`DCQ`*Y#43M2SRUAdfva%9X3-t}*)}6U+Qj>Hi$5?dpKatv`iOn+n
zxSJxwEY(ta|Gj9d0&CSV1(?$=`&dUKx0quw@t^MEJMh3YPP<=M?*I@;zH?YFFdC+#
zzk2~t%Z+M0{b5LSlYM&J@v1#tHrMlDiyyUpFiN<r(57Kd!J=~DTQ#jdi@NnBp~i}O
zS;D#wF@cRJ7>^Zj3L@)eM%Pp+SGn|1dy|q>;8ZsMwMzZQL3*}Z$=q;n11b7lAGT8D
z1F4Ll<fKBx;59oIQw=~iojkQ_fI23or?xW%{Bh@c%%_aWyS*KH^2bHfozaG7=N0Wn
z>Q<E9mkn5&KKw#auOY-Tyq1(=tJ%A}H>~R^KnhoLgwJDb$^6CBJ;D^LJ^ItvGMxv`
zMlW|(?S<>Ul^cGoqXWv-UTyiaEZQ|!-S+!mLZ9i*EU|v;Y^((7nA3m`0B6=~y=+E$
zeq}peBY>b+d0!J263<G(qon~_Wi_uCTEV?JYcM|I@V8`_68ZY#GZqK;?&9;B!S`2I
z!qn!Ey)4iDideZJ!;>76@A9{*0{L6qZ+z%xC3$VZlgyP?fkkeabQ~Pm=bg{YxGY*h
z^2B_`4;R}9D@_aov7|MEzO12PVJ7=y>Cz$=eA*Iyy}e1GiQ5Ca)#m<i3`*fBhU9Aq
z>z*VS0UcLHy#x>?5DUTA=Zg)ZvDxVH;^}+5@LkUtWbc%>zv;hO`6c1WPmmTLU_Ec@
zO*cu?Zl4>qA<Qsd7fG!=(Zveh7^)#_``L`K*nwVc+~*?->CokQGioU(W75^W%#y?i
zx-FJ2#P^gI3RoRGug3^Q?u#ckFuz*`HFG*!Q>E}cEWTKOuIz4psWq6-%_^gwU|Xm^
z!Vnv63zEFki#y@R-Yu`rw##y}jsC{sEqr3QxE$K3nY{<>04=&JWDs31=Sa<(ne6}J
zjPX@BBBai-0n+cdcDfFh&hEP&X9!BiYKr$B*?Q91tr{Qk<YN5k>k81De{88{j}*~p
zY7nphmd`hCd8tlj9F=Z8n&`cv99g6;@-DZPnni3#t;L}7IcFXJCE(FVx6bxOh5~oQ
zrv=Bo@-*KcpC}wzE%)D5PS%L>OA#|MWS#DQ7Y|Ma0@2-v-6KPJg_^SM4UP(T1f?qt
zR=m#;v*mO=Zr1kF1=sRHg6FwG$<J*nlbBKXNluWdHjbTQ(mTg%6T%C5jH(l9M^2x+
z{(?>l!$RG$D(S^<9=RuQ#2IkeShu!HaN~9ls7Cw*1Eb=So~Phhm3o!5Bmc?m3rT1$
zzKO)Gj1pF)>I1sqs-D;BE_koP*?YGr$^&)wr~Nb_kEsfkBH&jpo`ZFC7_-9{zHh4V
zG{ot&=&m!RsXwMR?Oe+9fwE+goy_ezxA=p`i#qFSx+v=dw`Ip{hPw5#yeHV`r7&R)
zjy0Z814Eq`E42BSj&|=3KxPfNb+j*42T$U{#TmE|SEH<wlF|{k<Cd_KQ<M3xOb)9c
zi1Dk;*Wb*-f&p&_;!56wmSf<<@$fHOWB);Xox$Z0({=`wna+2fFb$~t^LzGnCLp<V
z_007sQxIvY;DWb872YrfDgx7Co9Hed-ZbWIqDTk)GlokI8!FZJ*#)e;`)fdt*hkN&
z^++3s7d}<I7PKPaR)SCzlcP1b5nCYV?Mujmq&uoctY(`GVDN}zajpFUoNLwt?un0_
z60MZ;S*@<(=-4yt{sBsoWG794ie$r+rn(LbVSn28<9w`W*e-6glPN3B!H~>WDbGnT
z>YH}A4w!Ilwo&mB0;0uM+j&)~^bjz~sM})^<V`#^BKKNr;<_>K)qk6oo1d0il^;^-
z(=xZ&R!5WaBF|ZyHYN_+lSwpbN?17r77GFoSM6437F-q|Y*&DVVCKo2Z8V&FuXpUq
zwasky)I{6(`lK<v8Si-i9M97YzR*b`RYjc{rO0X`O4Cvj|C2wG$jdr<m{zUV+(n>?
zdc(`^ZOO$Zeq@_68xMos_*b?vheBYA1?HjU22WtO86R#(FPrG>?A7m6(P9UGB&uQX
zTyBY0*@=PswkK24xE_|Hf7;D7@m^LIks0cu8Oql|Q*@HlOokrJ6REeaQi;c$Whcj+
z->l~p)o%|DEPoU8;Wk;%D?{xy+b$bB*iB5*z)ome<~D`01lWSH3_~^kb((P-VAR5Q
zvVBc0=EG}@QH0{+Nty{>zP{~5+5E?*6Y^ob?~u;qU9oh#!9hd9tNN(EbB7Y4l+n<q
zB74g1Ks<x<`jM{8#ES@b$|*^Pn8`&mb=be^S~YMK&n&@)^o}0RZ`DOXpk=c((c!A*
zg1hOhi?vi1$CZ8w<<p?)_=|Q1?&KrP!Cz7dh9%xu$wl6f>c>>t2)oB=s5XEk?_zx{
z0XzI`slp({l{W=&GHH6#Js-6%EeubqiHsKR$sHlMvU;t`GBT;qB8cOzc#|{v_r)F-
z$-13T2kDE6x87`ivT|8}dO#rSHe+rqS{3r$U#C(_y(K2~`PSUhveQ~J?&4-wvBT(*
zbZB!|acoU;^X-|I`OupmNUiavJ9@eZcMsmm<X>TU8J!#*>(3tHsamzNva;sm&~8nC
zfSEyE9~>Nfl<Wt(+wdHJ)y#SwEK#mWcHH;z*3zleVyR8!cHRv?a=1TR508rK%i?jF
zYOqk4g6o9j;FLRz2LrzvfYG5X9)FGG%A`}jy)M6VjwS)f&Hu}EC(`Y`h)?^kC3I^u
zMmU}BK$mht*zR{V<f0nWn&Uf}BcR#o+o~-FA(^LHF7G#M@>=!hNM>t7sO@P#d<Jd^
zq{&N-6+*I}%ikf(rH8KfXQB4@x95ZT3cP<ny7T8nQi-a9>zi`C=os8G$h<U8Bg*;I
z0{*gW|FLlWH*?rtgmC(4aA&}~f3fH6k=&jx!w-IN=Pm*|iE8!1+Gdvhfon*OPD?eA
zi~elP2EsFlT$qrxZ%Ii`hJ%kP6wQVF0O~FZ2naac94Hk(sAxH?H2zV};j~*Rq5w7s
zN%&X@dePaKRq+R)h!Gxf?QTjO-ib^q`{XUw7#PSsTsTauAatByyS@ozPd?{mcG3M?
z>Hif)1tIUfh-_8jxNaOB()Lult?MP0s9*HE)7fAm`KIU{H`ps3x*p+*_wDBDz~XOr
zH6rl}FccIPCMU?pB3I?5QVmLrD1=LzemBiGBQRSI%QyS4%J_eZ3KvcX0905D*v4Yp
zibk~tn)Xz^Z(5mWM>uNIPuw`{h&|;E8gi=7SGca-2#(WlCdImMxfVT!Uf5nw_a=d5
zovyk6p}GB!2%QPsKsKeR;qOvNb$Uhx1$GK&D=Wu<ls5h2w^NSsM<G(E=gp*7`>2tY
zMV|HRt2{3tf7;W3t0Ks4w-8|<o66Mt^4Ih>zT8iadbmos?eAEO2XObo=Ls`~z3w;B
z+bQ^G3Cv*SB#Lru>j43t`7b*8x-LfldEF5bsL{Gu%u3g*;fcT$JMm#bpY<zVn)jq`
z@yopPNuZbp^Tec&dk6Br{<$~1e;CV~cTw-iXQ`3KTGYj|DH7L=qw=G1xCrUh_ucI?
zXqr(fB~k<W_Ih}ezDh9TAf0sO|9nwJZ`dTpgrk%L_jatL`ig7m60=l0szv7K&(hM!
z$KoBcu3ppMewALI<cyMti~VmOa#p}U<-I(QqYP4!DdOU~R?XEjn29SP*?lC|=r(@#
zBN`nOcb6CR?GV@kn+ZJKIeVO3H}A~O#Ao|pPD7$j$`~)Hizvd{v<VdWa-xeUwZhA`
zgOB`kCVcq>^tjmh(0x?c6}gu1AiwY>W;OpTa{u|N%VdVBHiP14W|3>xmz6;ua?8<{
z)qvbo&$DcfqNeJuWkUP{^tmct#b!G_g<9NMIl)g90Tt?Wnj~eTvkg~qj<hgprqn@L
zNa|0>Y9X7#t((H}!zt~|Z#myO`e>ZH1?kQwZv}G49qd>BF|xr224F%<h`iIhX6);a
zpacd5#)!|{0>^miB}KjCyoKS@9TZl7Y2Z@Bc+KPon8_ZIMrShH+oE=xn(h-Vwq8+E
zwxO3g^jr&_4&Hfq7Z`mfd*h{p5m1_24h;q_me~Fs6*?bm=JMgib{(b4DQbk1R>(M6
z3&3`|9~<1i_b#wnCu4XZK}cbKJ?f!om|qK5H6-3hWS;H%IQq@c<G$w!2^kZMzS_;z
zNAi$vuRQ(npAlmM8TNr+g){3@|4Z;;=H<otrEpB`nc7E(o9JGqS7XKTQdgbYH*l&Q
zKD!+$h~`GZ)bb~lq%K@I3=UJK=byct!XYo(=*np~&q@mCJ8-`btM3ZQm)P@=DQkUs
zl1AUn98Z)g18wV%BRiNsHIP*>%hU<Z!z;V00%k?)bJ&y+aT1C%HnQeF;&XT*V{V*O
zQR}G*Y&3Ax?=lkNr4$0~$We5UYH<>)P3pALV$}Nd!E??Q258zVlrlRKpJzsfoKQ=6
zKEp~23kxLr{?tu~fg1qpM?VwQD+mR{nS7;7y}PqnS5GwEa`0(Qi0AaSJ<rg!0;H_g
zL=`&Jls-?lFOAZ#v?@(8tgskAD8U5IbPMNibG}m5_zV1S(%HUYzZLU)VtUWP)z%~#
zAL_4t*9ql0j@fEZVhzWv7;+fBGno^!*}haaLpfj7#Qzdg3O&#SB6_*8#Rvrpt%W47
zsw4+h4_KoaW%+)=Vwsfrd%7Nkp~vu(l41Ez{#T}u>;_~Wr8aKOu51$B)&kSgt+?$8
zyq}Oti@>}=drSVEigd?&hX?gMy+n&fbl(G&Esv|m-aYG?Rw%OND<T|0Fj)BVrjW_{
z^f-?Hu4&>LQ4#WZoN%$lpc9wqff<WtW#t1}U(2R|t<#iA$E3GE;;yeWwsF)Z6KCWp
zR27#jN#S$X62r807*=&FhOaA+Da@`<*P!;33}Z&1zvRw{vUm#J=|zhl8Xwo-aISZ`
zoNxC7+$YPGVS%Ec^p5D@GC}&N@*#QIML=}(G9Peia$1cqg6eA!?vuY})Xj~X2r4qn
zeCiw>T4@k38j)Rl>&T9%pOipJP$#nYqE~s)e_G)hMP4q}D+*BEIc0Ug?G8BMJlHUF
z2h>fywA5(U8}+KXK6yR9yW?MU(JSSC0!CL?)G$0<uF)PyZ>3^kLvoi=B)K5h48cf=
zJe05W%9rk^$$qVJ7L$K@yy7Nv<XSu?Wzj-uSyF9({LMO~OM6o1Lpk_db>tCQ6^*U5
zCi&8ctl?T6oH5^?4MW5yA~0vQoJT!H@u90my0`o6c3Z1B;b3Xur_)X~hoK2|H9k)M
z+UBw#C7*%r2D^AGf;HS@ZySABS~KY=^+Chm+azD_Kk>EmKAlthWEspEu74&IplBu!
zd()kNpXIb7qTWcjZI+jdlmnJ#nb?Omv3j8N=@s7nv7fX+IRV1D!aPo&l=?K_*WrSt
z+;f<jD~CY^lfyxA!ZpzM0r^F&wcHGS=MXG?wr+-CmSU2paV<Rtg+<7WvD%8ad~b8&
zOtV`R_>*r0=o^RklOVsZ2M0-KbrWrxFc`4Hosk|k=pQp!i%dvfUs9vfD#`qS=3HJw
zffp&QJ)x|u7~}W{y<Z0~M8FcSB@2w9in>a!98Mjx2Kmc-iC=lMCAr2R#|}%fCli$u
z@4~RKq*K+pm$;3FeqJJ(l)UDhpAu+R&*w!wUg)+s-Z!3&;V8~yh$-shkFQIb)&=ge
z$mEEA&!HF3t@3JnqAd09O?JL31FZN&R#wybK9Bq_w$8Gl%_z#&rMNp3Z*kY+?(QBO
zN^y6$;#S-V#WfUncPL&66nAKFcWyd!XFkl#KS<u3cjxTA*IG|946UNE8*&Jux5-8Q
zx9GFTKlQs~BcGr*a@s9P%BnWzan;8XQr(HtVzM9wVY3|iPO=OyyayB<!hpQv085m!
z=twRs2EBQFpdz-$AUBXfYTc%jjW)i=<I_fG%cT-1C}nRp<EZGmJRh}MVOlww8#O;I
zwDMLcV2VmpJ*LL-1Z0?D1CW=OUuyTLO3^VkCa;9ok%WdmZK@B!b{aQ)MPIBnsZB3u
zZT_z;0KAh{0vGG$LYhjgio!p;`}^j5<7rEsK3tVLH8BJ%0BHEslzgJlcM(V~pUZm=
z4vxd$6)Ba9#-1mC!tcE=+>-~}q1eHwxHzf#Qsp{_6&@v^2pN@3oY>9Zg$07u>Vd?4
z;K}I9pC&uYP)@>lVr~g&bk5e!&Q5g^gj~=QO|w)<9D1w7CL=pxri+hBpNbDP&VR2C
zHE3Om=|T0?^Rm$WHjIq_xk5@zrLI<>jrCsvOL570#!J2S73O*$8x-igzoj984)7U6
zd%^AMa_dAL9#%kVQ<O``+(e;WV`gp6^%>(+2sG;1;6r|9J!Ybb9~$}qtTZ3MVptR$
zv*M5I1a(yw$|+^_psK2_L@QU!)pm*sMEaQ>Yb(2muZ)JesCRQ??LhXT6Lotxig5C~
zN?;iUWi$i_EgOzvV}BmrrgN5KjNk%65iQq*`tyih%4<ae2_Sv^GjzL%;?$iFuam3_
z9=&BaBJK4sj;-ZMZuO>9*)OeE6Us~eYWGRvJDa62AN&GPKNH^hV#JiOu7>q(*u#Gb
zRz%1_TGIvDQsNSNs8<M?9M9>t(ap;qLH!14!a7!AHa05+>WR3na=&EWZm|{kr<3GG
z7}E)xK;aiA!&I-EE|H;*(HP(y@OYaA+A*LSM7AAc4lnv^C48jRaRel3C<%8RcRf6x
z>Q_UxJB~x$phLU4<-}{@punm;x7~x$>1#%P+FsuVpa8Nhl(H;^t8X~T{=xo11-5wn
ztx<?BVm?5dRhXQ51k=$KlR-Z@DuRqD48!Q^I+>s)bg}3ATyMa>o$XmSCA!3__r(tt
zE>_~1kPg=Vx;(Q-&=PidQZe~`1sl0q%>Xo<Jwngj(HPaR`SWg9n)m&-Z)TSO_M5(k
z)S*tl^lZ2HNol$@wvi-yDG?&{x8>Q%H@VeU#3<hQp<KCj^ur~pLer>=B6-)#Z*S$s
z+s&r(CKNmC^<g32*#Q`#P#VN`mSMw`%>LYuka^ytoY~BU>bHIzp7(^Y6*@)|?5>y~
zyYT&PKs(+g%NyPLc~5H0+k<(!mhzQ$qEQk0#J7Y9_0sZlaO_-<E7IhRmPN=H4jxAZ
zZXexmnhjEnCb*UTUjHss&DNTT#*y-CFzgT!5m~Qy@^72(q=vr!%k=t}I{Gii<@nfs
z)&FYp{`KjM0}6=Mehb4C=@q_P4a+e0MP{gAs66%B2#AF8GyxvGR#T+cr)wmbBKIF9
zBqe8yWHFU;`LK&*;x+2GCfIrgxu%7R9_ikoG}PXB-qmuv>w!p&tsjx({T^VWGV3G1
zhKY_WF01YBShnHA*&-<O7ro?H3Sslkw-7Sm+v{CV>&>D;o&7RrNx{lRx%I(;lz2;+
z=HfeOl$74$HNJ57hrLg{0jY70`ledN@M{->)~rhz9a^+PNDdwE+`-e!`)Tk%nu`MV
zae<0ea(!I`dTWQ^I9yyztHP$;4za4y%dS!u!8L;z%dgui)%c2WB?Y?u;&rZ_^kL1j
z!nV;RtB7wYee;`sI-C1uO&9CRb)&ebBUPe~BSVSILlm_z0e_@i?MD#hXAi)j-}Z3P
zpE^IvrjNN5w+oOpg1AberH>jmFbZ7kKYm?bQJ5@PXH~c>sjYo4(Z%tSpa+NGy|V^v
zHXroMnQ8K&CzjP2>GVLHd3vR34N&8wUBio)G!LLSANu-yeu+cs+k~0CyN;M>5KmW|
zAZDUI+5nf*Qsy)g3?Z->QW9-+_;B1AG$f}V*aq9le3X(7Ey7X6)#Id94arU=&%%aJ
zGn1{?GBOo~&H!@mKPbm+uAwn51az#i^5M*f(u)ngPsXWNpAxGFM}KVHdrQG04`xMz
zgH|I@rB7cTeCe(oiCA5^;0f2-;06#Pw&Nk$V_!lL15Kdis<Va6A?KSMlpD5aNQ(;8
z3*UmZv$(H8m%_*&%#k=K+l!njtKt4}n1D~S6TlRk@twYkb1R$5wV|GVlwZH?-hWX=
zEOntf?{9f2BK4*fgd=uV3hx+r7A5JSKAPbm*Z+ds{0S6@<(NLEOvAUkcuhTYf3~>O
zl;~+Ox5H`b^dU4=@~HJ;VzPDnN?Fby>P)GXA052G<u$*SZmQd@z>t&U52+GaisF51
zAxb$t3$5JNVK@6LBg7eqY8?0z+qWe#b!@>M?cN=?JJ5zhWB4wj77m^4d>Ds$B`1WT
zDEkxh5~e8IQY;C%A^HX~`G?=a#F1Q==Qov-_r5P?h8i0bT&Gdu$*c45JG$FH)|;HA
z!2*{c!RWiM_9J@(>81-xL5-!P8TxoPyajJ!t1$XHKBb9sR1d0smDgT|_R2TRis-_(
zzc1qzm~gK=mXoMtfBTqzM5l#U5XRSAwBk>j4-O8N*o*n@Z}X>fy~T;b*k=!4=yH^*
zl225W;@R8Rm-5pmafVeB{|opCbdu6>5+3`gDo{I$Y$CZd0FbRQRY7e6-5pmN&C@Eo
z9OQWwLeW6ym)9EO50Mms<#-l2P$DZ4sC65~&~1_%2?a$%=MotBh@4^MnN_G7+cn%T
zdHMqiOpYN0j4acw%=k{bKi|8odhf)C<Go^#36yu&cHIJ?q*T?dzDLD$W_6*NcF+nF
zCH^%em;Ue(vr>qC2ATsX^r)uUA+dC=&e?7ml0^O?cP?i_$yz-CKP?EQ%bfH0v$tap
zYw5BLej1`O9V|w*ZMY6DvL}w{ul4AsS{nw|$h11T2R_=VRoLHnU>YJgf{f3*lY`ng
zCzWQy<5GsSpi5)UcZNNht1G;g=Dm{GqvKDgVXqxzP`do)q@ZA>sY@{l)ndvmRKV2)
z^eQZ)Khz!qIC_DvYXI`hj|nD*&R+{->EBoB$@1@QVl;KK?{s5W>lrMzDf)8Kz_!G2
zcKaKWG}_@+Uw*^u&S_FxWp~qN?&Q#H-iny8+gkGzltgWJnB|_mpAzNpx`Ba<wxga?
zEzChnXtJ}8Md<Su^-XE$9!jD#X*SuDSsE@q8bFXiE<>ooc~_60f%ES@wOWdF)Co^F
zQy*7xtyfK+Dx3KHzB(lDBteCLp%dZ>!si<q6^I;c7urQsM_|M3t7`E|Ek8;xj>h&O
zN)y0z8vjOt?=B-P3ncbFI87+ua#ALq33zF9!jUqzq>>#xH|RN$DLWPS_R#YCow_HW
zRCi-na2AXDJ>9Z~spX#j7e329((Pba58Y-T5T`l^{~_*6);GT7SicDA@#4x(MCy{{
zM1V-9Z;;^U_)5@aHfrsiYND(}wvw%!#vSYDqe~Ut`!vpIfvpR^xX$VdMnXi_)t<8t
z`42SY{B1tx1x!IQA~JV1@dvVvgg)r*_QWx|n4}|AIKW->Xii{OR+wz}ay-W!_h8Wg
z9}Tm7Cn%7R_d3?ZFzm&gUz8bWd4*}QW)*-%N3Nn|WZwUsiO)pT15ra@vFRNDteRus
z%Teec_Br9*8%@qoEEB(OC%$b_B(sI7EzQ}tbPry7WA791CcP#0A|cH!V41%!XFw`B
z*eyi+sLjPdlW9WBHR$<5R_JckO#ykYq19%dw)1{F_MhhBr~iYi{YJMDLmw(3tJb1D
zpwuQB0~J-Xkr$ey3B@7GGOXEe)EMwuW?>*BtH)IOc9+mT6?3XuX1XJyeFz?i#AJf!
zgJ$zF3{q$2GEZkvGp-2Ksg^=FU6dRg>iR1yEl#zU<@e2wYs3{56_=}4=Fk~wxlspC
z1R7D{W0}WmVJxeLdxdoa`BQvUl$y_O<bgnvDjOS{|K%8+Lf`%%?ApdzJA`#_c<OOC
zPw2?6C@nua1x;y}frduf=biu4Ny{dkNvAxM@7&H}%v9Od*~YhE?o!5!gxLB<J2PF%
z5DlG=!hus|#dQ3CxY7Ftcz^fd*4mH6DM!xm%u$>%t4g0_bIMikZ@I&>U9L;@Uai}W
zW)JXoEDpn(ob_u;CKd(O*xoy6Q_jp`vXF!`W~d*J_L3!j{%0wpf5l7R;zSB)wSpsm
zwk1rMcpPH9l=#VXD5Z_gKK717=LL##HJv<0b5ZT0UzH7=c@U%;eey;jt0P`Ozp=ni
z>nwjhc%|uY#nBiF<)Jf9y>I<{Z#Jb8eVALe$gn^5$J5tl$|xhw4-^Bx{p@lCl^5>y
z!x>VZ)=vI~+fMpXvq@L<tNvAR&Of^d8hxuKf~1!3ECkwZ8pWx}vO7}de|JG|p0}MG
zc<a%j2>}R~i~?Mav1036%{2MJu5233N&a1`aO96Jm>NTF2Zhb*at;7ghRFFk2VKTy
z>Qak8m$3EBh}#kX{Z2p5i-UdKO!n$-jj1a#0xnPq`9d4d={?IjzZruU-7(&3NcIe#
z^mCRXrL+17eci{{8~Je}X2;29Euq^5#|vt`$PsCdgcRg)Dz<AK|LP^@cY>CMZo`a3
z!MGXklmF&Iy4pnihr{y3dGBtv;lEScJrUZAeshT(TwCjj3Flgte?vX7kVutKB2p~L
zv-{=p8PiY)pKZ~#c>hNabB+1-Ap2Q|Zk2O!01jG^C|hScHM#X>IKF|#PhUF}*FCaL
zx=eDRPb<4Md@+);)qIqE`pA-gVBK+k8|kPLvdNcOr(+bf9(wYZKfehw9>j$B8o8h2
zN$I3$^XQQNfwFOFmDC6z&=mzEh>!(PoVo;PvSk3D(dB!;R~5bbyzUEODfzhJ3Sm)@
z54>CR>TBACMtqN_JvV`kC4GI{UAZ>e-00SW-U7<zl=}f$CH+c!H(ht@136CJ(R&l=
zMJ8{7-p6$#sf7WDYS|jEU0s&0^GU7E9pYgKl|h$d(eR=@p>-dO_95WfT3Vvk(_8{O
z5LMZVV9lmGum{R7$LCgg=X`Pxy_r@o7P<)w7|k4Us!Hej&q?Qx3$Z+3e<-M52N?;T
zcQ0W+l;ZS2$NJnH;u%j+xg{kdqr#22&y;)u6oR}Ripr{XgE}S{V1a*ZjdzW58}`8y
z4(3Vh#|N=SoWB5RRC4h1d|Pt|Up=t!QGhJ_pq_Q46i*?a6Uo=(i{Et&dS6TprzQhy
z=)98+0tFG45xZv_O21@xOla<d@Ap5F%#+@JOgv}&am4kkFHNE;=X1dVIKRqcP`*nI
z3C@6V7@pBbVKuo;>JY{X;=^>;l1>wS`RYm?K_um#g;Efy;~_isKe3l5NS(#0zBWax
zTV(k7=wlWe4_bpXEQ-H+=Oix*PFZCF?~&JXIwA{<TYLWK@@<8aXYx4F1#bAl>AnH0
z%~EfE5Z+XWAX6C>CAE%0LfVT^-To~W7}?(gK-T(&8Q0e7TthFU%Xl~fBQJHMUQwU-
zXg|5CQ8PbK_0d>9anW|Hq9S9pW!z+B+bN}+ezg$MkH<_WPc|jyv{`N?1}xqVXeAa^
zu9@I{tJhgwF<5+(que+StH@@N%TCkG^cjM1Amb3?2RlwOPb6t$wze8kCn(NLalFNj
z)VySWlrwzZc>T&f{0k3Wa1^(Nw<e;r4{E5s!-Axrs<$*JaAlnK1v|AcSw7)ZY;cWZ
z>W+Ooy1U4NXOA&ln7na)@4<2>{>s@}tYDb?JQ$xRzXn9IKZO{FO<+)$l)5Ds;Lx)1
zziT7I_u7p@I>F!;;xKoHt5i@E=!V7GW;nw`rMo1>-%cDn@H79jns@I3F*6kIOWw)2
zu8ampwj|5zQjOsE?pO6wiCLz^98m;4!$7ln#e!}YbX%bUiPZNBd+DI9#DqXChC$`*
z&wXH%fSOe8Mg11XJd`OJn(nX4$fry;(75J;ro6_bd|5!MjnTgE$!GOmo2G;8bEeu(
z4gHysM2bY{*{|qz&fhKs)f^IwF%G!9fgR5qzjJZ4lB78UuH`kEPPC?cUVC9NM|blO
zjRS79n*sy+Lg7tkc-m(-+~<`Uok_#`Z`ukH>zf<MAzZ>w#|@14q|;D-yL`1?orE&l
zs^^LW;|J0!A7JBet}f_n^mnwDi0do8y|+nj<EY#;JL{>6`!8sonvi6{cZsPBwJi?y
z@E#JzD$)<L@LVDn^sEGTy{~zYioU`RnHML=WVTWzh9ZicB#`}zpFGDn-<Iu?os-kg
zCEK>UZED1?ze<@PAL0UOSyK~=KNT&&7q;rAZMysZ9{cfeBH$a5OgNpYZ{Ei>#)Xdn
zT-V=eD=xGFw$qw$WbW5ucRF<<YYgI<b3+ovhzU8jWa&|7mvh(|3IMND(hrea<_-dk
zdMwNINq;vCW21XcdYM`pHIsJo^8Oi-#5X&|3Oqg39|utaHq~g&NsuONZ&jqy>BsSS
z0$<JBzs`jYw!6M5o9)S^^4`+gMxtPccO=ixuIWUU2_~1YWnu4P;6r1?h$wz34$Tn?
zj@1LRk+2}H61D=vff)tfn&PXKejnqX`IAp0A;U*EnpzsiP*a1T3A=g%V;enu8nvi`
zP~u^z3Q{TCax4d~r^T?~$!wQL@pjnk4AAhoBRkIh_EXHyGCkl<_z28&?NExY^yX`o
zL9Z_5G?(v_ps2&}UCu6<1`;}n+AYqQ&Z2u(uqm7u?WG)(pwY_P*}+L`x0TW0Vls`a
zhSxZ3o@HT6rBvoL;jz#zuaa*1)Azq3+Zt@y9n}?g-~DOF-M0s<uUm|WVsm!AucZhG
zCfXlE&Oov+M+O%`^}7nn=H&$Y6Cc$sur@d7;$gI8*t)JY@Uh}ic=5sij41P}qS{KD
zZO2rgeJ#%lQq9@Yv@G>m{fwlUNJB0uhJ)e}%(ck}GiP1|^&&w*d|Q?GYAz>#nyLV`
z<Z3Kpv{Q^scKrC8i6W0k2F><$4D|rY=sL;O@iv0h{Bpf{ZlK-5+w+y`_1^_W{1il5
z#-+INDJki!6x7B>$g2&JmAU&J(CWF`a1n~|QQwQ$sJB#v{u6+~4USOML!DWe((p2l
z(Hap=zK)-ajn!u#8TbF-ovXruD#{eQ6KfMU)!B8LlFHS}hN50jhRV!_Kc2UIDu7?x
z815x<SE~9vFG6J6^jDAsD0gj0%h-=x$Zqm9fPr>I&I~&J!%`u3Um4TPhH)Du9^6VI
zGAB%0%cj^zqS;dDRrYc4cf(g3NEH4~q0WNK#BZY|m(B7LG{ph#dKGAzm!FG4S@!tf
zAJvDP)|Th|rRTwwdbz0;&7#|;u{x(jsJHZXu{z}yy>5B!!t&bMBl%^v_qqfnPsOgd
zWDAr?cT+L4g=m|V$Y+MN-f9SbWM|olX$F|v(@oJ)<b1exUKAQDFbm1tM*)JeMf$Ko
zdph!js<e=m^%Q>If90L?{Z@TA@!zNI6j9GuFV$`||0;T-tFhdG<g@U0v%*FNMrLh$
zuU+rMg++_?G@8R~i}K>nCN>9r5ew&%Ej$Y@SB*wXS?;SG$a=_3gH8Ucsa@XXZsjO}
z%{*It?Ja0p9X4wGR@n`{JsO>=C@g?Y*`|t51_MJDJL>~6B2P=qDbb0)<fSO|lXtOX
z=jwk-Y20nnXpSq;0~DB6E`8nl)&0-QR^2Q_;iAA={+G!5OQ*4m98IaNDk205?8?Po
z`oLygx-GPq>31r3{=3iNpgJ?ge!;Y>FUTN%JC9_&%_;>f#=eLdZygOCm$`ZQF{iPE
z4yyL_L5%40LAnM{{y<FBX&~WA--N0|G8xyaL{-EktA1L@klkHuXDzcLLM3CHs97c4
ztG^rm2l)Jz3VGRfRF7vP$`NpnFRhM)s<we|#Moj!p;sjx%*p#Nf-R&Zj^Wu*H;-7j
zRDp61kAhjLs}lTmg5arhyR9xZzYJ58UEww;?fE%{GkS>HJk;)&zKpIjV4A2kp7q*d
zI8Gg!ef$M<Boe6fC4>IJcajI}tl3u5lqbHA$KTWyCZIho0t<S0(sxZA7qL+%Dt-U!
zW)r|+g{p$(Oes#Mg2kQ~;D#>XV=*@(;PJQIZk_2YHU-AhmwES_q-m4b-kgZd95p_3
zLVAFTF}uR^`PRUKZ@}f)HI#MA1on}MEj#Fi&H&M5%D}A^3i!tpzM5L>rD%c(Au->A
ziI2<G6{WW;y^Tk?aMzy5y_t!Nj90!j`6d1cbO=&1riQII%JM!x<*}_bMiDXU@vg*a
zltr-3Y{Yl_BK+j1I?OPX!Ie$g+U4!Q^~>KDnOK;gg6*cJ=-Z2?=#ZjXX=Ur3tKEL8
zU$R9&Iv1Z2gtpK7;iaop*crP$n&kEPhT-1q$pycYMG;|9X(e&r)C`b-bol<1?{K);
z-ZFZ&4Vv3-LBMHandE*JOq+RqEg>b$iMaD5Q!#sAgtLSc?>}p>DB{{18If;&^K34(
z!bJMY`SID!0)?lBFCqhxC$tO=P8E@4G7`E4MSCBF`QG>EP|c^F&eBg2!u7;QZCA5Z
z`s`mFl!m5zO1B$aSQcJahD25{$i6Fv{^UiyQ#DNZyRQ8^nfSIZda8@=^1RQ%;o?-8
zW#%#D!TSE%KQ6;zo?bWqMLyL$I#uLyZg^a>TyfxIT%ZH$DFHtrA=$UR`h=&65}Yp$
z086wtPt<fpwZ=$9-hpDvE3BvA?>m2+Ae7Hd?{G;QlZ_4o3z6pk9<wq`_zcUhzRf%S
z!hRT&S8M;S$cN$ItkWzcn3M<v+82HCcyT7OcN*_KIb#+-Blxt`^v1WI7o<keuiJk}
z<mW7onsnyxb^N{8gL9T>40+Ys>H5i3IDaKTYV8gemF70e-fYA}mSSwsspIRmbdxY5
zQEFMUyO1Wr+6BKYZ-T(5z<YNo+|}zr0cyFK8JH7WGsS21&a1URAudpKH4$bq_W|8S
zFPR6&`K)%Ug>}7sxIS5IbMbc7S<3m^z0P@tml7k(dA((6z|VCi74L)#nH9oxQ+uHE
z{djUEe2!*a-~+aP)e7Lk@Yu;=XE~P`?v)kp`a@jby0sY;j$NBp?>E8HLZ5!mdwc*X
zOI2V5_Vx8K0>yVegm*!SLwwW*$womhH;nYTo`P>TU=!^@4-i)w&PjHua!#q?N?Tw~
z-&Q6cznGT~LVA_XBntajz_3l)SM!eorBS0uLsUgAD@8vWl$;`US{#im8Op-*QZSVS
zJ&)CVwEl=fK`Pw(=RI#P(?^w4EHjzjJzCp$$IC^x>IcFptYPzA7sDjBM#5=c?QJPM
z;1aGnber#Nfrs2;&mR`@ao-yPgP`5|55)}D%I;xfBdvm3#%<4Yd-&~<pO<X1Vzr4s
zsxT>}P)I@Qvk<k<LjDDOD<i$w&xct&?LTLH4M(Mocm=|!C~Q&QO;Os0xFLL*^1)4D
z8|#QPU6O7li%sg9@uJS}<v~XI%{@+0yRnEvMm@)BrLRbr3#a~x_+uDQ04C^15!@aT
z{mMF={5hYneH_y5$O4%pwmuC7WXL>~axs=f@Y{Bn0ie=h8tJ_e-izebOp~+&T#R&?
zxYS4U9N$<bOIjY3AhN#*n+rcC2e&nl(VPh>7qNi3_J+(tE%C@rLc-xmow<g;(QBEp
zmj-^ULc%@MtJ!WaCu9kucNM`eYA(^FiyUZMX_sQCI{jPrXig+?n3hK7-_V4rCjLy>
z{fVHI7;m8|vDnyRUPd&!b)c9<BW$X*AhIoHGq*{GcKOcTA5F(9Gkv*d>e56thT?s@
z;4G^s^zc>Donn}z9kqY3?-&$DMH)zzF;0w4NgL)>Mtwi|^L`MvETYvIam+Q3I6-w~
zxGxHO&)0MWi$->lHn$fWIfB1aG1H!V83!ufXi;X|yP>Qqmpc-~C@pGhb?b4nLv2K<
zVmxcHcX!aht?NJge*2@G<<c#0hTof-MAg_3k=Fz(I$@!zFpR$6xUhWXq4fA`Z<F(z
zzdPm~J`MD`Cg@##qzSLvjM1k!u=C|oGu2xDCLGv0ypBW7ifb?gFDiAqL&nfkT%lLE
zyEaoKkDipXj)a*ccRV>TSbrA(s$5m{)fl6hJXH?T4_WG-^**nE9336kfb6Wu4PFD9
zR13pKtq|-Re&S|4tqLuB48Z1n!vN=xEY?Ol*5i)W6%pxS2N~*5Qgk(kRL3W(+s)e4
zow1I4+Doz>D++{oT#6v!PnTVI4Hp8vc`jNMrblJ2OflW<31}BxndHban2f*zcw1=<
zBV1T)FcJ{myZ+1^^BI$LPh@?0>9<8@?y-+**jNR1Vbh=S$FQkn=ygw_umKa=yg@3t
zxtKI2_J@J7@wB`cE~B=#ApV+uLoSsGc5tp+01voe-uG9{@CU8<arz@S!_h&iCv%ea
z?6>>*L?jSV_4{UyaRJzR7mBOD3pF1wb|Y7c)YYwiawB*^R$ACvYx%%ATmT`esRq?c
zX^1Ge0*X3W;itp5<}NE#(RH7*$hVvfRMUU7fq8>EB(y|~_Ex;CYYTf-^M!~S4u0v}
z4Ms;mxjM%1gCxArO>9xof{qiFY`oOf*U5!1@Koz=Ht*;J_>OupsRp=a$j>6}cA?{Q
zn?2WB4&4H+pKKS*uFnAcIF5m-9$Xm4T|AYz4`Vmnxn#TT#3HS?qELR3yUW(VSTFG^
zYEu93#)fMf1Jm{WIqe`_d1TJ1^nt;gM5h0IK&}o7MLTl3dOd;#0f(Q-a(zV74rO~8
z7Xt%T*<#wbh7YMb$L!C1wzN*No0`?RpsO^CvdMJM^-)aRmPm{Gt8oZvol0FdtFFiH
zL>ldt@;`kNBhUL=E1+s%x?b23PYuw@?_qg8+9;jAjV^EIDI(f3l@c)3HcphjA7x*-
zS~owIEF`1mrN1nh9G)!QrKXca==tbRII}a_?H*-d`3JgpNzg1cJ*4^Yw*~Pm&A?Q^
zQTxX3Y+x$NPQ->oEVo+}9+z&BOl%DDZn^CFEBs!#4QhV^Mf%D@U9E(x;i-1U%1V}7
zn3%Mrm?)jQVwq(dv|LYh^^Gy5nG+Ws28uYX3k`{|jG&R0v8*-nsAh3W>u0s;<i>EN
zd0TcSTS<yq*jGR8e8?fNC)c0i-YBkETfvN#_8rF*rPuYD`-10*kh%kK1{_wzHIVtt
z!yF#kPQ8p(V7ZZSUrO!e-)wJh|Ni!VA~RE!2AJ-Y@ODHwjfw82b$Bq9SCtyD&UI&G
z<$l{2hFDc6x}xc{IS5CGWZha6D+JDS3k|*-9W3H`40ssII1JJcS}?XKVATG}R#c$-
zlVW#cDnVmLSW5hkL0^1&d^{cTSPQ<3nViodyew4Z$IvpbF4OD7rGX91o(el~OxW7j
z-mYxS&ZzWCwXK=40vmTkt1MOU8$%UGQi2`Xe#f*XjXlkYcc85>*b@YHNBqwJRl|Ai
zCw-RFzb@8aq8Epz3`6eZ@<;L0?B$P9UrOtk6btEbakGSH?Pi9{(=T~lLCO<pgnndT
z&VZVg_5msQgW_IhiLac-QA4HL;&%rMDFH`9`5jdwcZ_Ri9s*4X+?mbuW^VxUjzzW@
zjH{Cqcj13{LnowjCu31et(mqVBG^$5;EpEapb}_0q8Sa+>?z=hcKmSq=O-vH0@B3V
zFy9DVInr#i3J{#Wy%jjmb98d5x@uL)QxL+P$oh<aIh{ye)zQp$1i-EmGa-LOh~1Ij
z@OSa}Ot#nrTv=(|hu9g(-tA=9PWZUj6xDi=tt}dLcL=RbspK4FCv>eEk2I~OHQNcg
z1rQ_Qb@s$MeDRwmtkfmw3FkhpA5G4GX;-s4=nEen_%yLg#OR$c^({bo+Q`62XGV96
zLepqDur>K_1E$f)I`BzId&PKixmhldv=c9NqCh`U3ss$y@So)I;al*H^@R5~r7H;9
zZoVM@4hF1Ityn_qXqV=0T&H?*%)W99c%<+U58Tqh2^ONGU2N64P-B-D)Ay_F)HWzg
z`i>eABdZMz4?ol0<2%hdP>N|TGFHDk=nUd<zP8dm^x%&@kv|}u!RJ4n>*?x-c4S)a
zaE%E+-kQhQGW_NgCOP*tXFY1lYHyO20vyULdc5`_0@6*hd)W~$0N{$vU$@1U`4*<B
zYKyWFw~p;o0I2q4ysb-kVoX#to6cy8lQv+|mAf)Sgvs@C1`WuP;wkWG7-9-5cF@2E
z=3EO>e`xP)ZDxiV<mYA|ZlRJezwSJYX`>V-2P0#xZdTrrdTFtqIHL;Aja#b&fd3!x
zv(Z|ds7lV%###Y02g8xEN=xm0jfut^Xpd%=tztDTzm4ag3=$LWH`Xy}{D49-6=+ZT
zVjoajo#ZLIVU&LgCIb#FfKOrs(tj@+1xe4=A7n^@jlsDj|2k37BeG9Pl|Y6|y;8g)
z&2$s=S8NoBb}WKG?BWx<J#|47?nYj+MrG9bii~t8$=)-2r}r~H*%Sdr>WSorohBOQ
z!S96|G_;J!_JJM^)ZR!&M228ku6G}BjXon`S%tQGW;&u9y8YtD`!zi}s^*ANc1`1(
zT$WJ=Q0%)Iv9Ep8)6Wn^JBx&*oj_PJRbQ77t(=z-ulPu$jrpktnh1QqMzG@j@_NCJ
z4%yFpX<)mY5^DCU2|{_JEmFe%4xA0B+|Bg$GVzsZd-mfP`(Mx?5ca08Yo0=pQ)+2K
z>4=5=jUr>qysMY7##8e6G<@Re{cFdiQ0u#%a;cJU3Jg>m9M?hW3IYMIZ<#ldf0(Z8
zCs;Mr-w};D9nSpN4D>CQ+-&m^hDH205((djBAhnTLRBoYQQV=1;(z>|Us+tT;SXh(
z${C1VpkR~>D0Q=v5}8wi0yfGBy7!IYU5CZDLl7|CgmE;lU8DIBo|A--HDVo@iw7mq
zccX}i4Q0Ex<-HrYNHIAj;<H<1*23>riFvQGE}vs58G#1VLS<SkuEM9bbx@~SrULy;
zl1U%%!|TSppi<$#{Xx<D#;KR5rGhE{OG92>j$|#VZiBZR<BJ(lJ4dzI_wzxZ?GM?5
ztQaasnm6Nw*4L)`nw3K+`4i#YY_!5zC_JhbwOH3_u#Sy`Hjv+~RG@552|fV2G|S}i
z$kqV0k-aOzo0AZ++h_i4z4Q*cWMH)A#kY}x`%)}%DM6E$f+(QQumRPOwce3aVRP>7
z9Wc-URN2}8e!T0qz>_$wk~OY44}xweY~RG1KjkTYL4~=T3cUYenV)`T68Io}BQIj(
zL5H~t`+Wuev*T*3T$~)ZSmUE}^SFn;-i#4B0u`3xMMoCn|B#|vCIk~jrt=r$XBQyi
zE7b(M+M#xZBeX4|)M#`|i?gt5C$j?|6C<z7?`dgi8SQ+bLNx99@w6`n=<41_{VI2^
z5r3_OX&L9WvMUDX-%nxIET&m9VVt@-==MWNrd8u4pRI$nt8^;R{~iIR1IJ)l)?x|S
zbdp-|1I+U7`n!peatJP=yfEXF>5*=Jo8Fh_5BorWb^e25m^6RzApMTkp$*4@NSIQm
z=;>sc(Y?`1CS+c!;9iuQj^pXucdisV`=Dlpz)zuzm&h5+ZZ`jy6<{0>1`{wU?YFlL
zTQbk*L8T5&9*G0aQP+?*u!4b_KT1OtTRh1t=z-6WV#XrQNr$S|GO{$I_L(cUK6t`%
z5Z@=UaDXfmwJ`0icuBy{aJ&M86BO*|s$DiW=~h~O%-m?RhS1@8qTzSoQY$oPZV-q;
z%2)8Xh$8w_sG$nsy*surVq7^+iRsMm{iu@rd9yocH|<VZQRL|SF<FsS{bj{Pjc6Zo
zjZI(T>hLm@uVAc-mj`eq=JzB&J}49K*W!j&tN-sX>HY8bwJa<c(Wtq?S_2#G8^tN6
zVS_*6VF`jE99lGDR3It|m6FcZxnMD9HchBzC@WN&Xw1gKGB;H0_Pwz^UaFI1SZ&Oe
zm6a{Bn$B0y{~83Ct*59k)vP!GxFT3tkT|NS!sm^9II4iE-R_`@4PkbdZK*O;As@@_
zmp`!mb@3{<ZkLjT0Rf4IRMXu1&=IMo<5T0R8;<GA<IQOLODoA|+u(d*Uq*Q2mRa}7
zi_h53ZwH<zNrlXZmI*-&5B%ZcDgWtX_#fX(1r{a|3VGXsZr8+<TZ@?N>OPlhFDn{$
zI<;PW-9vZ<%g?Gc-a1l&@U0BeuTlxD89M_WulwkL{)MU5pslauN1Ezi#rOgbbyU2(
z0VQn&xpo5;zwAl?6!N(!!f9{N9YJ}wwWXB_)FY%&N2j`ME?owF;*YCsZnLt?S8<yv
z0`5juGkncEZOw}*v}va@@x&F~HhUDGt-o(>hCipI6T)1W3Ez%b#~5{ZYIky3uR_--
zAwHLh;+HW4L&)Fx8HizcgX~TjGpgX%7C${PRI(XxyV?F<=W+@qWLyi-)fKSG2DAD6
zg@p%fakaxl0ibVkwT*q-Kb~>yV=hjO2`#Cab%!a_&Q+hcdHIUx#ki9~tNb49#^vJ2
z4d{io_~ofqjAsJNM;dkOPyuTtSdp?%Q+z)u`t(rg_Oa1>e!N@Fpx-i2Xl-^(RzoAX
zvi4#3@eXQZt+2k#B{dacIaytd|9rnieB0%BXMVXm#)u>IR5&3Bdoo)exI2nna-Z@u
zKttT)OZODfd&_v?x2cmL$icD9R6|0Hb0foi2OnWTD#JiTCbY#pPZSGI(DHA2&LOL$
z4`LyG#TKn-L;g<_i5d3!`R&axJ}gMh@mOU~r<i@Z;wQvobBc4NW~nB|-3UC+%5vJM
z1o>U6Ome)^qADF`Wy%M-!>A@el5s6d%Rv!XJ0^OL4*^@BEJiQd);OypTZy8L%b>QX
zAR{B|wK$TQo13FB92U=a`FSZp@8R0QSwR{#k!1!#Ea!wK{&lWt2U0LC%CWJrl3H4+
zG4H3Jz6OERSGj|B^KjvEe-+&1gHG)g*<fhByer;cX`dpc^51f9Y3+ZWAcMan(?bE>
z_;=K*E3uNq8agZci)0y`e&By}A^W5`M^6!{sqbdF;8Wx6>BcsNvOfrYPs?YJ%1gX3
zl8U8Ms?Zpf^O7Aei{q@mIBq06VPyRnp$5IANmX@@SA5o;`^YDt?qm7;lM6HT4q1Kr
zLs~p33sovQ(SJU<;9$fw9mcF%!uY_4pTsTn4f{%{#BKR?EG)kog}Br!Zm_F_aBXJO
za$_!`jlcpo#8o4h^+YSbLe6cH7o0Xv3BnH`A0t`eI!UTou&ya}okD&$_Z7JMC(fX6
zmQf|Pc|Xhpy45RMOsLgwkXfLseR;ea$X#|jhr;xj0CC+@1s>3&q4V++<Ehk7$mU!?
zl+A>e`T6(yDQqoiR93^+ZKz~R4TXP&3yb_LDcZj-UfwF|oh*S>5?o;48#7WX4~?kR
zSPIiv&rbX1*(F+^OK|0d_V?E38p3OJ3@^RG#L6PM)KaI(4j++;tve2Cs8=2dMg*e3
z;U2}wLfnW}U|-k|FwC3#S^4<Gla)btSYfsAitVV{7N1G=PL~+4r#&(jhR`=VOQi6i
zxzxzCg#ue4?l1f$lB_1zuFY4$;=hZH)i<#c*l79fhLVUsWmuaZ(^39*2ly?dTMwOt
zsVMT|rnv#y9_BXrQvySrUwH6M0_AcZ+)>M1Rlr_=xsw|omG<fh9wG4QL1p1ith?T@
zKqZ^c51k5oWrCkEtl;OvK7MdF*Cr{n2U`Gp=mYNxAPnx*iQr?P?6$=EQNpx5oO#g-
z#FKHrKab%3_18^6&@1g=w%;!<1yNz#`;NoGjqD}aSY9b;8O>9~xHm|(+&&`(VkM47
z$sG);>d%Q1Hd@0_dTM_WYx3AIt6mQH5Y{eTB~FXY;`mW^bZ?rhhc$7e-bmK@pd=pK
zaB||Z)y#mwu%3aa2P71n&F#-|xgBv1V!WVIC@}c*wD@u+CzNnTa2%3|7I$?y_?7kL
zGM{mxVZFFnw7n%p%tKUltl{26*5oQREhP~%XgMYw-L^p1zQ%l|waDV~pZ}?cA_To-
z{-@>Lr!TQW1xmKdQl47Km+G$KJz>13{L~(E-?J<hY!<G63rMd=OPbm%@`W;>-fr)t
zA=aJfVfcb?2NoO#*jxRCzv-~8nj|%jR|Io@zsq)IM#T+UOJEX|q68U#Po7?Gd*H;V
zL{r5S4)=LfvVKIapkud?7ZNc^jtXbUvc^-x56cD*7N{gyQ{a}ae*$#p2eOrhLo1Rg
zwDPu6ZYHH=l^|@AE>#ZBw(sP^x?!AFEd3?SFIr?kta+{%Gx+I2vz8u{p#nH}OLX&_
zdk~mb0k0cpT**C3r?oMG1DqwqR8fBBPtgf8220Z{TD0l>^$=b&Hp$u92orNr_(S9}
zcpxrHVI{SB<{(9*laE6>JvEY9%e%SKNLuU#VnS(GGEYMyaO%Y2aa0}3yz?}(_FCpt
zM;NfzmCp}Zw!<u#o>vTUcCz>FOex7tDW%3HGdHU%upoFajm8kDs9~$*gfxUu$C`LB
z-jq;0WH7?c(7iQ~X%jybDl0qHeSLwaNL0`x1!CBb_VzO2vL8)h#PEtl9w%PLdVwBy
z32SMtq`tDA1_W}g2b5+>ikI;wTuT^x2)50*<N43!9y4S=bN=fE_zsW`y^d4c1Uc>>
znkc=5unGRg@21JBH)(TM2mOT2mO`ksdWDF3nJ(EuLbA27jaNtz(<z`Dr@EI}73&Y!
z<*9nQM8!^WcMawSD1Q?5Dbo=*hXU23s<*=UbLO27m`E;e>ZSbO)D9#R1S~U?u_^6S
z{ko!1{3ue{2pkl77?P~2c61Q_tRT5agFnSyh6hI~0ceXYH?w4Ue=P8ZD2R(D=f{4k
zWf@e?8*mBOpv6&)a@9(%VKYX_qTH(+HeH+2YV^0vf_oer?9!FqN+8Ex%E^wVvpN~M
zSM}N{8Zj{S-WZPQ&g1Q1wlslX;(JV+I;l5a>tG6!7%a3Hvr-0LGv|^u?zXc}=?FeO
z$u1mZ$p6Qcc~eNNmx_NGC4E0%R}s79iHBuX39#&G(sSLGjbZX$fJ5~F(m6+dGg18c
z#)D$$rUoCD`~m3TPElfGLM)<`cPU;_z0vo0$4cQik->-<Y}ZUSIQ1fXu`nm4WaNef
z_QYuL0JOuzCbq<>L0kwCszm6@zt*fL#^&ossMJ*LbUX=AR+iS5{6_J>NWVVoCHQNY
z?_sVlZ`aAg>RoBm@uf;}x{}k3Hod?JfoHHl1U-pG=bTZ3PBQ`c)((Hy*$xl(ROsdI
zA})u?%S&$vLfrcMn!j|pGlYZ_E0_|%sd8mcfhUk%F@}(D$6s(Y`On5l9|TvPs}4FA
z^nM>k=QO;g8?m!YVn8*zfr`pQU3l;>g8FRbd^PG;k1hSitQIs9rYo}<4FMFk|JJYL
zC(4sr4!ns}_I}Hs3da)=yn9@p#$Dz=I&csxK;#i#CgM<E&~Ab}zJCFn3~CqYZU^Pv
zZ8rxh*X-|3)2=l3mp?4J4!nxYE_jgn`QhLbq#plvkdvJCh;o32CMbXDH`@tSRPSiV
z?->DuaT_%1Ih0l}v*?*T>QAS|4LH}vEGF&dwv-zE?ZWTx-oo*5+l!P_)D&kkJO|hu
zLd!%A;<3-{r3_z)t-<uISWbZSdXu4+ctn<zEH=_|KcT(#61grm(&~<N!6Qxvfa`A|
zNYSau-B5db+yO1oe|SG*;(}!%JT=VnC*w3F*^**&0yQRBpoOX1_UglY<h<=aqfC-(
z*#dyFGE+6fr-q@SMm8dt8%HnAnrf5&;xby>)oX3_hHA6K8|ybKv-j%;&@3Kx96gRD
zRHDM)1k^?iA4k@*>V2&yh?Qiw;w&#(kE1PH8+F-fHx^*FwgHta)TCmiWYfdj9*93h
zw)jG!DZBi%cq}9cJJ!A!1ht~UqgbMYJV-7WnS61EUEKQV9lJj?4O@xTlDYDJJ59b?
zV$%m0bV{39`4l*58_pKNoIV7yVU*^oE%48*P=cPT1|OT~SWrK~<Xe-H2j5QG_JJE>
z>S@X31u;}di;KKrkE2B6U@MNSDM%7ZNH9zn@{3xp>Ptt*grmb<F>`JycCc`i2VQ1>
z_^{N?85r<nADFxe`~%z}BJoKrV7Ty)gu2HOgW<Vhs|%`yw{3SKKz&OYxj#PW_i!mA
z_h4syZu}ed?r4b_4Hsk=+|j)rJ`#~D=AL=1MfFf7GNL1UwCuy@{24I}f>@9w$v;gP
zo}n1dYE2RTNO(4I`sHFXso2II&+Bn02ZQp+8zVdV*U@5ObN$cxP-Pl^gFm&YG~!jB
z<Pz<p8Kv8-s%?0z6Jdw=jplBJza({J1jmZ1_afINnRsX^L1svl63=zMPvSIm79`>3
zuYcjFNymnJK_22+1joAC?0gB@2AGt^wlwxj0dR^^>yHy~LxE#XhAjw%cDiosycti-
zfEJ2R+|N>DpfLl6)K|l-ch!6ez>U+W=E7V>$vyc&<nTX~S7#lC46j#`IQzBy1$fu@
zVIy_=?1vxg-Ooab?tPST9FNK3qfCRG+xjXUDkxL9uvv`NPO>8kZOA6VIAzlrm;pJ#
z1!5*Q`7=F=8UO*E2{<<r{cJ3P-PkmP<RrELaP?`dB}vN96H{8^h{r<B2DQ&fgwx0)
zXY#+&9p(B<VJQ#x?&`mIIb{9X;-vTL@%gdB^KZro9pF027I5A@>_4t+iX`|WSkJQo
zd-4nhpwz#J;QUW5mKF2$uYNRL^`%GSZu%y6(%A^<V31QZE-vfvFFIdS_YO7EDaNQ;
zQ*;=`QzM26iE0IH%wPB=H}!-%+xa2b_xJfK;i+!hjk*Y-HN(G#_Sir}+g&5hBZ+=9
zwoCR(292`js9Hl@qNh@;c1$jMsUGnVGxfujc%y_`f<eI=;Yae(JOM(M?n<=g-%Zy=
zELzL_IBycGcPs|j?0%L)=BIhFW<aNb2w2YVZ$50kYt2X$Q%lu<=7zfFB`3}&VqpJ1
zCPKN=R}X0)?j9Jgy92R}fw+kQ3`eby7t6!DT@?Q=9(0OY74f%OCidqwOFk&s&57tB
z>mM67mkO@2+L}>eUv<Mn+?|E#6t!c6Gzbn@D-!qT)8Z+sPyH(~cO_--w8mzeN?wI=
zrqcMyuOwuCJI?77F6L>T6kr*m#7ot7<ep6?ryt^+$}at0u4SB8-9^@^x2qf}oA#sc
zt;*m!J0exkS|p8Mg2g#k75441f89NWE@%9@$aFv(fsrgruOcdLo#B_AG)gL};wjLe
zMLF~3&Hgm+R@0k<dP*XomwI$}t3;B}B9^{Y$z*almer}YuZW^XDrgMYBtRsXtWp4(
zkzV=`glFeP*5Auvr*7nU>T!|z_j%veUQsASaa}JGS=3Se^ELQu)@bKuW%yF3Dy=ZJ
zx(^NQpnxJRRyQF1Tj^S5k)pkWwhA6U%=kMOC6`(5@0ar>A3iO*m&W@g7M#sjv7bUb
ztZ&XGggAT<Ii+~s>g_4aowCA6L^on?qK{g)Vo##l9570MH8hXx`97?()eaM0-B$X6
zdnQz};*NOTkv+#>I087FC<H{>;Lw4&iIDD%@OW1fa8D01`WZ0xp?)>SfBtXKLXM)#
z@{)FD{RI{+NN$)0lWk=5E`2L%C$H#;sSqj?`*%0g5`(LzckIuk`B|MMEj&=3i2vPb
zAX!{2(L_bOpV%}vIbfJ*v0(3cVc37rwi*`}A^L|*ayJF}N##$F{=Hl*vOm+#lTXL_
z`SqHi8a<>%5{uxF1_obsd{g7Ss6q<qr`JI%yS6X5f^>J{0d<R{xZ(GCZ=iv+T9EkM
zh?%#C9GdT11Cqflxr|kvEr$bMh8F4g&^anexgB8Vgf%h7pjLs8UOZbvbvGuq1K;^_
z>4I4)zYc#XFP;{MG`brmK;+LiPY#&O3q9R$aK1Z|L?1*2To}4j`}WHxa(*y|(MtPK
z^^V{Kg}8fU_<8$yFb2L=qx!}y*d6VM-J1h&O1oAGbloe;Qr{>J&iZ?|ChF=<h*@pP
zScTIt1U)4G<0<hm!Ikq~MWbeve12wsVMt%8P{cjoDV4M~T2Z7$&#CfPWsCN5V@LGE
zAeGEB74??igeF9+NKe6ONr3c5Vii&?QXj|*qt@M|as4~3Xd;BV5Ujh=TSnjtEtHS|
z3z!mAS@cL)x~{bCzfE`p$=3Y}upNo8s@^v^kozJ{4zmnTY&iA%d3g@n*Ln7wPkAa4
zlSKX?{G6QUDD?BI7|I`XV60cE<G<Y6Ld~DNp`9#yy4dM#+zdnquuZ>YL3Y|1@{zyo
znHBj$!bR`Y(;;N?BOyBVZV9846N2EO^*02n{qS(_gE^$QPNgsTt8a>wP87g43QXg?
zAI!JKJ*IigE}isPT>#nhxlgmqZRsgFkwk?`i808bJ&E|Da(=R8+h4*rC9BKv{IniW
zLvVl#BZ|%#SG_s0$rqEbE{EVc3{O<27h^zp&Qs*pLyiHH;=Ms6Cw1wl0$REylEILx
ziAnXwYg4D%z%)O#_VahA!ACt#1K0cR5GB&M6S4$91a95?9_JHv!?#7w>w8nOgii8M
zhx%N*QzwSZ8%J5g86K3}oQJ?E474r}n$y#q7-N=!g|jL;e=XVnI_K<gxpUvyM1|GB
zp^6ktAEqP7!h$7wA9Fll5$^gK!sm`kEydGOk?rU;)ci&ATz-xX9y}<~v8C4gQ(|HU
z)uU7SMQ#PtkLw0C5<x7#6EnrQ<kPn{Rvu%u4A&AAZ>MzRP%%@B6{1=VOpo9~Su*$6
zUKiWz`QO%0C_{$3?*oPc%R)so3wwDTwh}-5DDlyzOJKtnD^I7B8Q<+4rMLr#vW?U|
zH4OR&;;4@Gw(3IUns{FBl3dD3h=bMCr`*2PN^YOZ#R7Cn#>eBeUefD(wPv-h;eosU
zzkF#42ph52seSlqxd|MEq9V2y)XF?RvO;tyItarHp|Ne8L=WYr>?Q~yZV~Sy8FHC-
zlc9@xHyed+Yg2M#a=ie061$eNkA+{2>h8w}fpFg;{5~vS6ZxXs&0sRSt=WdB<gn0N
zDgpZ%r1R<3&!{yd;U6E`^7UvTVDJgww+jfS#ddYP*rWXQsXlJ42}SH;y}Q+G_KTRc
z_Vp=F4J|{W<rgYWAtWbb9YRc{wRqWD`H?dwX(9r<Ywj+RejtZ1brb&Myvf-|$EC$7
z_BK}&pUXNrn(hXc37)GX*6FNK%!roM+rG+5;_fj47j0U8@z?dy@3w)IBb{XR$NfCm
z-u{;=E<vi|-UsdDVMi`?yNQrbj`x&I53U98K^ue$A~BrDbW4@H-abkOotQ11?J(t3
z4ty(2*q2U+q;aZ)Ih80T<4<lEz*%K>sqGMU7Tk<Lq&G~=81Zkdmsc%2a1&JEVW|4j
ziYf4Kxb6sY(ikNOm1amWy{Uv1)iurP8=>P7SZWB%%!f)pNYqnK)6n-$<2ZRwp?kX|
zJhz8ED4?FX<WlxajqQiks!yJ5Nqh1MG}tnpgrUoZ|AyQ6STJRw#xOoy%tUZgNYI3x
z(uJszS>fpuu+`wg2Dp62bkK+5QHmn=Ky{XnRedw^cb^5grO!9|Sa2r<-urKC)BSZH
z$d-kVfmDPMrNIPW=nmqzs-)NvxAD8s1>-c?wa%!TMAtKMn29jL;=1<9<?N#iVMfsO
zv`T*&m|H7M9HSlOf|<Jd_3C_(X6ukT?2MGCypC+d4c!j$qmChq&4u~jCB6?m%<&_c
z(|f1kxc`*}0RG5Lh1r0^2~*d87h45F_)&@T;7ZMnRRbwmwBn3?X-O8p>%bh^%xCj5
zea7Uxciaqn%7I5YyHV4n6tD8V)E2nb^0@l9e@caUZ)<}JK9y@f?U!ja7Lk|bba_K$
znlb5g_f$s0xx2H?ymB_UezX?$6=lF4mFD?u`!`Y-<PbG0dYn!ZK!X`T?^cfL#?&++
z0M-O{aJjljb$SMxBn2k{$G+^zLfBvG9C5&s)Jgu^#wIaBS?pS&A-GYT716kK;XTa7
zq&Va`h-2SMEaOY%ii3~YbiS6_t9wez3QL#rRaOW*kEbCczqN~4Ysl_qQB?@Is=Q=I
z<CZGHr+gQS8y!703S;*e3vA11U!tk!3wS}k?x;!A){WAm{7qRHY8B(EYS)%l{d`Ip
z6ch+`Hf~2t#Th?(QZcH#EXr4U*y9w|IxEr2&5mN4)nnvE=gtB@X$X;&@1Efsw)Xg<
zKPab3O#MHq-Z8q;Xj$7%2OX=!j%{?%F*~+x+gh=0+qP}nwrx9k*WTax#yIEK^LLFk
zYSx@pb=P%^bDUy3XgZ|!pfM~Wk=XI2yu9DY5DF7L6Bocd6&UW=-vY4cQNW|cD|~CW
zNk?G&AQ4&1qkK2d#s(?}`zZ-HPtyJW+Yu_EfJ*pCDx=82;sMGhQ9u(7VXG8=B6y?F
zn`~B&`xGxcu%$he`eD<Vf!h2|X-xLvkG~4*Ex};b)z6>A<Sq!rx>wva75iD@;u6kR
z&*4i&fE(;IHuz8^9^%+iq)p^MHyiJ)SKp#lMtB;l>1btE=|B>0X&__1ladrI9ci&=
zKkT!lT{W#><oPnVkD>QCwGVht`x#QIftxJ|v6l~o@{A1;x2fmm1I{ICTB!E!wEx&1
z?c87enEa#q(<$9Z>$phE4B#a9YwOLHrwdSt7NoVZBLDOl(t;%PUp`a<257zV<*+q0
zv$H|<8<C3PXcUD@qhM&)d!6vh({msj$ruNkBb^falm4HlaW@C)lfO|geZOi0_HUdO
zVQWx36u=|@R1%jl0S-35)%<G((!=|Hm%Wve@l(FGHhM?Y9c{f{v5>%edbc@CG(49I
zr0o<?tf5lE8JyCduTY26g#I#mD5)4kK;IQ!9eNIxrxU8Nn`EZlUPP10$Y_W$U_Z=?
zD`oK=`B*Vm+CAj|+Z=LGBBM4Y7-1^a-7cdA=W4+{w3QKhSQk+&Jq~iKUY2`y;`Nto
zi!s#B!QcGF&<SfOwD=z`^o7`9cPs$)Hh*4YzNzH>u+dFs47{@1<%f;Ay%Nps8?HGn
ztqq;}&hLRpN`XIe=pO7U-M)YJ2v<tQw3Yk;ihT^yL2PkFLepSm+T&O0n4VIwaOVm-
zqgRofbGF}pf*mZb^k4688k&psKd8|sjM+?6|0jk-p;Y5)iR=sk3{Z-Hz`uSayZ#+1
z(e=ng=4|ra0`9}S49}~ZRIO`~i=V;!oA1{~H<c6cO&}(k_TVT5{Zt$rv?8`MI&Q-x
zz_0qNLv^*cffpGn7YnI{8bSZH5ovRLHB^ZaP<GNsL;cGlRod_mVUfpdSz>uPTmu`$
zd{SP(K)xzBJ$yI!9QyN^+d}w2WqfS({NN{({7?6+sKyd=q%)j>vB}Yrs<M765tpyk
zy1<S2?5enuKAw|#DjXNYu>Lc<&Y5-{_>WdXG&RN=l5Cv+DQCdIai-V&q$n{Tmwu9j
z6HN)KxD=tvIi9XfJkGrviUjvgPx9aGI-*f_2>I(Co_+4;0d9kCg}H-C8`*5A)$6^}
zQ-e;!3nNK3quXNW5vfMzva>1j8`*CGe=w{D?vL)Io&NCgB)gQAJ4dQg%58#?ZV5SB
zd3V%PDG3=BU+iF6m!%<kcEBKd)d^d<pp)7&DMVa#5aas$%K?X6*NQoX6AEkMH7@B_
z>Z&#8)U?;kl~c8if}-ww^=h0qr+0=C?SI5?MSqta(yhxk&0T&4;W_zKroKovRihxE
z!6A?<DZZJ_;(aviL4mUI=f>YC2TridQ_vn_&({io&M};VIACKwSi6&*iRtp{q?(pe
zGM{WsC$&vQ3AVaeY$-Mtwtr<biqz1brnD_RDvLS-3Xbx9!4WdZd-t;5JpTImf7s?$
zRCZsVp|ibc(l4B&Q>;f6jr?*BzEDP;Nao+!An2(D&1PDjZaw|Hz7E_=w-+K;!Ktv&
zQh%83t3A29qnYj2bA8e))*=wPYz1<tZSjZ+h-xfb$y*u8)SBRuvas+05{TUX*m1Gx
z)<P0t*>ELUUCMcwBCk2$-Yg}L$B#zmd9$7t<9GwuH}id)q6xZu?nc{tD&6Y-CfTdT
zYW@L`>bt?uW#P}qcmtgG2>PPW9Ok;hLg`?F%LQ+TISUNphl)R@*-I9{B$|Mxd-68r
z;8x->v~HL`Uj4IK^gbfMLT)Moz!+_tP0>_OmE4o1Ux{4pCeS6*+J3kj`b@m9G(IC4
zUyt%7fScVpQ`|rC*Hcd)t^KE`$O%61vphs=PMOPUfav|r<vekMonqYlOEb}jA7z=i
zOu|VpKndmC-Y68w3kp$E8;r`&!>c>172d|H2IKm<vb<sk2dT~7tC7o$1pV2>x;*-L
zaQ-;aW=Z$>i+F|J@t0tbq*e`fKF?~HZ*m}!_?rc-Mvz_H_13pWSrf|sL9cx6k_&b-
z9Iq&eUDZC`g3z8u1($F0SGzWZv_WQdKyh`~M)bIWMW4zV<QUfJXt;uspuHOyZPMpv
zuos!d7U>NZWH3jmZ~CSBX0Vh<k<gX^mL=MCwpL$r)=B}XcSQxe_uf6Osy!PdH;FD*
zd}Kn~5~iaKaum6>jZg9RmTzr#)c{LqQZ@w&;Vsd8cSb9kN=KtIs;Na@ub6+`!jL6>
z>W-iR{=<tcZPF-<8ReZjZLHI&Kd&k=Q)AZ&L43329BCi^^b{w76N2CK*Kim>^Zk&e
zu}R0e*oS^K@SrRD&?WJ519LwB=5;2Tm9R<%p1D#5W!Ug{AFPZGdUdH(Nb>fX&xi54
zkn{CwdT5F)G#mLf<lQmRT_wUqGk;D7(hk$u@z`Vk6-D^hXsFBZu~hUnpt_1pbr~If
za93|v6y`AdrGCW~7^bh!mXET)$-)~MRGk=b9E9S#S3zVlko+MwAND09Iy^9GZ}78n
zruaFd%qlGIK4YIw-LOQGiElb0c5?RH*9h~qmudn~2Z`6+1Tj~bO`W9%v4Al?H>>%V
z`et7xljO}#iu8tpd~Gm*kZKLFmvPOKv^Op#>=*tS>t<?r2G_V>6-1YL!J69hJRi+H
zB7C{O`}S&>yY~ipQn5#a%|-D0`%<4q_VE5-juSvnP-6#>wI&>Lxw!La+uO-doen3d
z<4%k4>{$o){OM9H=!GuV$_<9`P#U_M%&d=g>{$Z)|GmwqLES88<qn20q8oc2<5_lb
zQ+EVnQdC`x%N`fwt<4HSYZ#!`CvI`=W&;{iyq>uTRL@9^4IZ>+TlkkMFp??6*s}C`
znA~<}jNWFepLb@bUBHlkQGSM0jH0{RogX&dCP>P<jT&m<-p-}IOS(T~)coAuX2*#6
zySI|*JKnbR;r&zv_NOcl8nN>y6d?)Uzh7?Goz#AyVI{gk5U^BXL^&(e6y2&dy8U4*
zH9yb=<!V6Y3M62WpgIL$pdYhswkD14TO^_mLVVh7o3AgYrl!2EH^<X6SJm9CKzMD~
zc2n=B=HXA0T$E=^PS=e_k^D5HFy;N}E@hQW-CQE_hmS7{5D=#h30y9Mvh5RsTuMd1
z*JDoLL4q%*jrFZK<Bs_$dUu<EKVSEwA1i3KUWa_HexGHwteQS2jceD((Qcf=%|joQ
zd!Yem5YSY{q?5s)kJ(dxvTK2Lr<^8qS|zhQI}-XK8F^^(R^^z-Epqa?`?wNMm}y+G
zc`RA87n$TgQ2H;j8#xgmulwaWc{<jNp)k&0-@<JW4+tSTPt1dG`FoJ#WvO@Us1t4e
zFu3QsaF@&8aS&3i@`d6ka}MS9+D=P@_;U;a_T!^ky`Dq}onG|u`qjS#)S!VU5v!3x
zAbV>mh1~c~Oz;hPda2OYrlQEV10fUXx0e=`mX{uz*7j=eDp>kWgATgyd?X0j2lDuj
zZUxV%T$zsRfZGBbFYlMr7x!=W&AUwNFF;no&D3wmRc?xmVU3t<>J?+w%xt5xO|S^@
z`zp-AmGqy`)4C|sln3;ZTWH4Dh4NWifQAppgQ-WK2WfFJPVrH$s2lmIzw>wViN^*=
z8RXliXTGm*U~4+bRB_ee`_}m#e_fU#4>#_an>`Jqjjb8h+Fj^vO65bs(5c6kmOvYv
zFV)2*B@c3VgkGX~+Fx&ZNmRw>=jSQu=%Rq!vVTC}*_{EMTHOKVwoR9C;Xd0`)xt@)
ze4+To9A?8S&8htR&7ybB*oLR2N%@_e*J$a8+|*$-s7u1_3wx`IXY6VxePWzE;yq74
z;T}~PB76h)P;^Y?g?$Jg?_s}qSU4r>o79DM)Q%_sbJ!mB5GD(Qfy?e@ryswf2GV3k
z0+)JjXY0{ZoAvS=>Ds9)@)>1_W3ukT`3et%WSMcMY9B<@Jsv>;sy$nIA48$#U!R+Q
zW0{hxyxx73f1QXu=KWedZ(^LQrCe;`;HBbg4F+esJf1G=szArnUBCxn<WXZK;8iIt
z9vQYq5E7lhHEP5X-?!hm7KTH)^)2$H74PlBr$$wy%Si<Wch86(pckeu!Llcs@_Ds$
ztPI*N1~h<!)^JoRRTOTbWitDK*EHJ+>I!|Pl+i6`&_<AOz^^@&7@0z-S)j&!>EXy2
znMUsj2}-1)`Apn7hCo&FM3y=@#>|_>-%C~yWU3xxyd|C+HzC(pVzu>7VGG#PiQT-t
z^?-+^rCSfX+(_?}2#gxL7th`-{g`X<REcYGO73fl&4>fIk9Tr;Q8e#<Xwn@)M+DQ-
zo^P_m8%J3S;ja2cVHh;~(ZRip<E*16xpLcRG{;vi8M%7DP~l`S1u@7Rc_G;Dbclbj
zLQKnzsFZTKGt_XlY102O;D5fq_af$%c`nA;^*n%;GxFLBVidmO*_n$bm9*;+GEqI<
z``F+WMgC|8n_a~}>fBy-kZr96)0oZLj3DMs@1;&^HtQenjDoPf2>p15+(H95Cm&lN
zPQw5gQ$A1G{L|iBU99kVkl0je?iiR+Pdk$a!Zvlcly5qnI+(!>h7bj|I;3Yh8^z~)
zvKj%+DRcj#lZnkB84(jpVR!MCw>yDL-L6qhqUuFfjW{2T2n9E6PGnFS&Zeg$lOlL(
z1^7p&2OHYIb0ShrJ0C2R?R|OPjc<}tj=Sp;lDO9VuxxwAin}$Xqd!|0?M?^A(bu&(
z5)WVSE~#e+JFtb#q8@igVi{b0jYI^twfJWBa9(1`Wt=2W@Wlq_7}8a9w_=$DyfP&4
zw>V_k9eYUhL>MdAJbcy`D5p_C0|AsKL|R;wSx>xc(qBuumM5s6X4?pm$Lo=i^SsLp
zo$We_YU@4%Gt5y73yNO1yyAu8M&O8cf{p>p`+W9j4~nKTg;E3_OtXL(;KBVjcq}IS
z=gz6v!KTX_lsp_6?)L`Q#xeP`+Zk!Gf3XIP3w|sfsoLoWU*+Ofur#|D`vBzVd(16P
z{~YS%dW>1005W}*EpjDZWnmU1;rzc?-ag-FW4g78b8Ll@^FI!VTX1eTr5=N9EAojH
z!=T_lHuit1yPnkQg_)fX1{xU;m;eK*5PJQMU{?<l=y2fCfY2cqWjA=+a>EeMrP@lT
zLYX`XcDDY6j3oKb@IR-g%Paqf%igiuj!E%!wm1$N4n9+<Ul8MmH1SJnV00?z=jWLN
z2)%i21<l^RG#Nck@V&FqOA7R%>=b)E#=5Dp;Vnb_-4FP@p?!lQRpq@{K5{#r2=NBW
zsj%YbS167z-8!NI)uDP=<QZ3KAD&ky++c2cF?iQbt~mUXOb)#Eks*_sfc<!Oc8f2k
z?1#7tl6`;h-?p7GJ^66d`$lC(f-cMUoicp;<<V@@EBrbsv<D&N@)?QWb&0Rnu-?qM
zo%yCU%iQ~sqbXEV+32KpLwWyPCHy^N&~kdu#-5Pb;#_?bb<8rR?4rppL~}U}#Aj$X
z%Oz&U{(VF>n3=#lx>>MKZaRl}0eoEgZzcci@d(S$9<rWL&U4h`4U6Y@lP3`7;_wc{
zkF_GuJa(>CEE~|v>^jzQAy^#lAemvypx8LWZAE{Rppc=&#<|up#YG<KJXVVct(r$c
zkFVnB6kJPCpWJo3%yGIj-B=cm48VZn7u6nm@(v5kfvZKDQ!4+KtdPxh#1jpnUC`ES
z0Mh;Y*MGXWdm&%qEGxa}rw{M1%Xgrpahy^mUeWt<*ITB(-Dt|t`b#3s;K)>=IYR6K
z7o5mF{)1hBfrj2yLjnJ5*QSg$Vw2Q5od0OCRFT`zz+9%&?UnT4do#yF;LmC1p3r{o
zxu}-~Wt;9~o%QswEz;Am?MF5_+AK4pdQ3{J>z~_#uemx(aM?n5<vBgAp0<M#m7VnE
zC5yQA`{8#6e{hopJBNLz3I=kec;0Yzc&>U!GKc(;h?+1bjv2mb_Tn|-<3H+dB_8yy
z9fG($zBPj*1=rGwqkeQxprN<4tI;CwiDey>D!4R0=nE<F>9w-gy+@S&ee2Oj5K{#w
z`|D?$YmN>{u*#9k57tVa>j&3d`+wHgm?;=%OtZF~V`ZPB*w%(mxdv!mk|#54(<V16
z9zF2<>s@K}p;h0OjV%G*kD>-mTqaH02AW-Q^Tom4{2;3tCfql&hbhr5MEKMr9Ky`E
zW04$Hwg&N`KOZP!4pIqrKyOyYm!22JMA%YrKCoayoDc?bM7ZPbf+eM)55^^l1D?i-
zY}b%&uedhiagJlnZ8IRWrh==mmkHkD#N4{O{G4l>2~iz4hC>``qdJvpBp{NC0_rqc
z$N(ulb>VCgH#Ma|(wL|zr2kb=C5~V$clb`LLSp5eUKTC%GkI}+u|yl-098Nw-v<jq
zU0wV24q=!cF?dt7<Y6HYPJ^Co<rrQ!EN-RE2#D#_U#dlO#pj}M-9tjriGk(Se`w7#
z;2NkOCX$EWx~k!l2|245h2hq@d#BOrITs7K=Qs?L8+Su9To;OIJls#}P>>nscR0Bt
zgPyWxe|fCq7xA9B2^u)~`3rz!Y$Mg7U~TZRL#XC&G(^Wmu|1?Mlxyj;>av4WxtTVg
z9TQmkQ|7EUeyHE4saC~|%OEl*^Ka|bA9r%X7GvZq6G$D%FbYZN^k+FJY|p#?#ZWNU
zB1S1t_mc}<MBIVL5_BcuL5G*UbTOA&R@A+}X!Ob{Nq0*FE!gJwRsVKr=Bc)Np>7$*
zk}0V;BlwI-z{fTSZlw_*mi>4PhT!mUs;BozdeaU@e#|;hq*jLq!zi5<OJ`D5O#&ft
zX*2m72jqbtg&qfe7P+w&okzlnIOeTK#+^x{0$DboB=<5=_ctzMlg>|UP*6K`JpI3_
zo?ByXG)70Ys4qX52d4W!u^*(abycJA^QN1t`4~FOW`rj5jV|-Pwm$SO%A;w_xbr7y
z?1xe{em{Q<Fx4jTz&9|en-JBLqyGr+>Gtt?jktYYN5zV^{4%XW**wT?_yVLh@MKd?
zfH0>qMl}qSF5BB!H|-kGE-RO?yyj^F4SFg1MH{IT^W-w~O9n5EN(DSkBqWOLsp?<R
zzv$Gh%$CfuyH--2O!ujwBg;XJ(%xJu#Z_v5`6io?JPZ2Fr7}WiDz9u1-Ov;I6#Ber
zFbl=J@Mz}3tPSWa&neMY<*Yplm>xDUhbq<X^(J*#Sy6aC4!fOw-Z8=)vLU_iTu-8(
z+TfXqcCrOQBn@=jEi$%w{{#JNsT!7avD5Uy^lD+NU_R=L>8({4S@ohzdg)QiR1N0R
zq3N}|X-?<u$&UN_@+-|ZCxVxJ&fjT#E*_D-*hR-im{hga@_WN_ZGeH8V-Y4d74~^P
z=L+rg6dNlX_Du$sWj-)}b>-Kfwm^uL;|&xopn~-8p%tZP*L*MF$`y~$e~~Ma+r0*Y
zB&kuCsMac9+|eld?g8>T$wBED3kPf<wP4-^ZEG-hfavlVI_Mp6+QW$xUf`N3&+Pr>
z)mE$`gykvWptg_qzF^5gpGPsdk||C5FMOs0;{7B_f@=1jl@+a;IO5w+4mOlX&1;A%
zA@RV|eg3pO{rqljm>KJ4h^lP3-KznUF}=&H06K9`3^BqE1QIN>fjiS2eO#ve7(a(n
zhBa4WZOC=F!XTT?f_U?<8Fyo~V#e1lfXJQAS!8^K$dSKsj@iB!&s?qQ&!m>E&)0G{
zjE&NUEE$@x1JbMrSw(SBq&TDuKfP)Wfl^5i6JdunCd}j0<$9^*YF&f-vh`Y%XFAUC
zz`zKHHkU0T?w)8)M-zrc@OT2H0MKftZt7)+Uwho`01AyxGo{IDT{t&4SFyoVPR*vn
zXdHbFh;mG%kn;m-2nuoE)><8kRWEDFTm}At-cw4}GSnJYi3GuZ4Mk!@y8-}gLP}wK
zt4PxVS%*Nn3Zm6ogV=$8##vc3Zy<0VBCir?Lkf?K{F~4-`-5D*>{8_q(#VTD4xBan
zzVCK7Veo5Q3{y}O+8^)=TXx*B^I+!Fe#vPoYvdYWo~Hrr<>&i${#%JI{9@`?*AsnE
zPqu3#LcB#cEol<_Qnd2)!W#tQWuaFQP_>>a9=?2ujNw9YalTr%{(Ro}a8WpG2~W=-
zzUMNll%F))9L-u3uDZKEev22>UGH=CZA&SIxfbMB4>{q#Ni|2z%7S8KJ23z>03E(f
zSSx~&%5Dz5!tYG#!kUkuv;5s}HBVwJP>z8y_odQyV~?RsCxJThkd6Ugl3*Z3H?{QM
z@9;FYLG)_6+fb$Go4gC>ZjuZBp~c<iKj4}&+%DeG{lIFj(~i?vjxUB0r7C)jP$IUh
zr5KJ-h@XqWt0g(Pr7Gzz;(XUqysvUj-+TU3n@!sD%R`zAk%qQHD;J!0rLUGn)t7$c
z>S8Ca+ex<+`>X4|pUTbTl}G)3%@qeFAC*?UJw0ucH=JWhDkn;cTEQ^(qamEzItkH&
ze{oge{+J#6WKZO)NUzge433E6SVmJZ_NUe=AI*u4j5_~wHyV9B=(3J#JPZb<;OEQR
zgl8*vV~KW@*O3_9T8#7~r*e*+zj}RFO15x>j;M=Ad)AbVwi+20K{nX{bsl{1$(n?I
zsm}9SgWJ5e`NIK@_}<^=+vw(8ilLXs>g3rN{9mU{66bZreO_({xrxk|;Y|yzsRkYL
z+O1;@<v`0x8W;}L^QB6S`Op6AVvFkbB;ja0sl_s-e7pT2G5mIy>mt4WAi4aYNYjN9
zIeAw70jPhk4%6Hsz*Uh#;C7MRU>KSiP%@pD!TWL3uh?j=<aRrVG5GjdR$i4ina(<{
z1YAQxg8$7J@b-8vGR<=jw^*%`{Sy&S5{fN|S6X)B{rOrZlhY;XNw#Mj@40DtjTdN~
zNTpJR!wn>wIvysf$OCcPlsr7zhMz!FJE#YNVE95pf#4C6foR^Z6I~<C5KUhb>}*%4
zlldOv-wC4W_R%1^5OF;Uyl<eX?pI0vLOn#fz_@<-$CurlmMt~LK1A%z`Evbp4{pT}
znJpcu2Unv>^@p3&ES~wt5Rv)o(l2u+YsyDY|7KZ94&hCW;SpOeorXl1B})EA*XHy}
z9NE|?JFR%pw=A^d#S3FE1x+!mMnn2f#^}h%QL}|=4caxfyvz&jo5PApN0D#Swhm7m
z>af?<$2PX>9@%UP!pNoiJry6~YzM*Zc)fen(djU>DU3JPA~MS#!Qn6D_fK;H=jP$(
z97&$d7P7uaU$%IS6=+lFSC?GpvLR`A44^D{;=md?%{C*uP^=-YnNM)+t@zAsl!c^!
z4;4QS1|s4(<RPms>G|E*x^NalFm&9FsQpPK06qUJb8P;diAC&Y>~eJYWH!89c5|g}
zp#4IAe=RmZi{!}xX&NvTfRLS|;kHy%*RcUP4r_g)9KQ^YnpkT<y=m&)I~*abB=do+
z>xaw#_FN)#xB{CCC*!8M(LrRj8Y^8lq)jprgJZE&stu(zU&vuYn-RSS80Ale89dpw
zw(CRT^kFU=wPOdUg!M~m#Dihde>cLqg9*;{f|d5x=Lpbw&IWOE(CmzgQ9kSj<A>#W
z4x*{dEUM^8Eo(Z!(`t7FXnTJ;sdu{lT`ZCFN2k*iolN_ie?P?;jR)BIL8V-gqB{5=
zU&Pu9@7KfRhdx;PnXyFbK;SA(EGh;Dxi}ZiwQLo+bcSd=xl~b7rTX06;drT7JlR6C
zjSg@N1BLr>K_M?<I_<ASp53<nIIZK?>#>6WPk6ys;cgwzM>H*u+d*(x9x~K}`wde!
zU~CGm4uk&~dft6OECsq2>A$klKG<trEdTcP*$VDOJ#xH(EAlWwp?9Upwz3H}LFrLe
z4)+1Z<IcYw%@^z-h8OQj7x<Mdb?`4I@Qije<vm)<QO`O72&{$TAK19jVUJEs*C#W8
z3DCEL;zmVpK3H*+=Er<ne=j|{OECkZ7^lu6%iXGtng(!r_opOdMUOt9eVi95^;UR^
zn?I6Uz6`wfyk>-R{oHC@84Bth6m^_xF|K;ta+M0jzjoKpRvPtTca@;8lf_k8kBDV)
zg+=|keFWY0{`2BcSH-&uyJ2)YOcB_vqhF`#?CBxIU-I?K=yP%i8=@N>&ZD4;#jW+&
zQAoSt>wtLIi9jTwL;$8MX%R%S)ovyh)m=bP+h=>)=p1?l8f0*?u~q*2*TE<#Vm=uu
zto-{J=_#9xHIl}RfNL?HR1btdU-voy5~@ry!X@373yM!6f%Yg?ZV;E|1xK7Iq?QRO
zxTF9j1y>gWw_R1#8*YPv=CAG#Cc^<_HxMrJ5*0&+Euj!{5!<QHYZJT>#2m4=9%xp|
zF^m1=s%?qPVcJ|=k58tO1P&;6yu2Dd0rp^`$ORw)to3&e2nPKX2#fcYhn-HfQhAc<
z*sdEwA%?5Cco@aB={k`9_-}_zwN9@v*r<bo`Zf@TK{Ue_0isRo-PwK%XuQ>SQ8!L9
zgR`->?S9Rj#_3X}k_6;k&H>G=0`O@LWcPAs1XwH!oo0nFLP5RPt(xV40c-YjvC0@a
zQ==!)RyUovknfI&=a2H6#nk&D<DBviTV;(MamhvdDb}v}r^K7j>rQaiI`4iBBlzXo
zTfAoV@aX^*{oBlKZuLAn`h&r{Y(xDtt@5ft0V+DR5YaoAYz1iH{h~pR6<A@t=fTzL
zZ;C13xy;k${$vl|q{E+J>moT&;L^z$%fujN@@lL!lj~hUeTy6Q!7i<FdJq5Bq1^1Q
zq}}|SdXbbVu$Affd3i?K;1c+ySA^{mA&JW;)@kqF^xRITmL5<cE!W4!J_ojco%rS6
zo9#l9QWiPtiEm7Yu@(QbOcnYyXuplTN<@RE!8YPvsW^(!WU=5?;FGA*s3o8lF!)K3
z;4a-4=hSZFr7^sInN%R!q^ywI*I*C8R*UvRjfb7U*AQLW17L{Ceq_pxl%9{vy&ylp
zi}`C;9qgE0Ppt^qY^wXa82F6v&=Dk~Q4QSHz7V?)$yY}$s+ve1*MW94UK2JcAG<Uq
z8m1EXld=E&sVR#dkI~VD7f%Y;5mWG|YMr%UqzR}m!}T$u0KW#4W<<B9siQLZbI&BI
zSjZIg5`4hCtLv3zah||{Hjiq!T5KEPYY|MJdur2I@N=<$HSwF71X8x^w*+TB`w6H<
zjUK((0q`Z3JI(u@vGu*<DHpffX8kjzAM3$@>)jXeSHjcl{c-plJnK`kj#ug&0KQ!x
zw2+&HMcH(gb<M1YUC>{|uMij7sph8Hd{KO3@%egN26#6Uw~Pvu2BUofvHYkIxE9;H
z9KdB|hc+M#-)NFk^SD-7YnVdP|2B}iG+X!Peo)|*m{&{eolWZlY&Se4x>b^#^4P8Z
zFwUmv$kkU#`ytnVjQe72)Z6;~COWcxNdHiH;JbBjjF;sqjjt9aVQLcp*tZEjVG7ug
zDMF{#fdbb~N_S_R(FQhgi4xo2d2X~nWQ{x6f@;F+v-tV_g|JGAss*<C(y;vs_$f8&
zP6&FnIOFo&_nEv;mCZlzJj6FL#f`2rr151mXwTcv?G<meFZEZr*VJM+tF&FQ{@O8p
zHky-1r#2HCV%$K_@2`esMC%hv$bmO`YbJ|BcQ|3ru|yGk_1c6kBt7E=Q`}9BGoH2E
zkZPmgV<`n}WuwbLBH$uteOqowByTsxGzUu*885PD4W22k^%(6|>?lLgir->8E0hl(
z&(KAsMUk{Mn#|DrN)PGPDO+L7&dxz67Uycxme}R_(yotx>tNKWSior9t9=y>bS>JU
zpl%=yN6m3h51Qrdwkg{02-GO7*+cD1Iwc>bKN1Ugv@|k#T*8b_Mzi=t%7r0AM!?G#
z>%M0+KS~}0NM-Z_F1aoQEljXlg)0t-*SO;-jwa?M8MFQwr<(Nqi*8MsIO=2LnZ_wd
zfqumzyxC;Q+9+8f3|M20Dzc60@>ZugLBdNi?VMo~@f*YrWAba&%XF#M?u0_OZfPrD
ze*qdX5W_I95$)joS}HL3d}H1A5U{he`z2|8e?%Jv3kHRWSh&ce<hx`~aVv^7VEy)R
zs@Td%e|GHSpi0kcJb`J0n*adM_m@X}Gs*T$!t=P{SG)fLvKr-plz>$!Zw3)@ONy|z
zCraFvYKLDxGJkNALz+Mu{ZE|$tfBvNogq1b=hRKEq)OxNt*DQSB`V?=a5BBQ(N}g`
z;5Qy_qTyZjbJ9M1`&V5l$R!L>D!9xAh+xb5r!YRKS7V;B+W(H!sqxQ~K+8k?UJ=?{
z559FfLMoGE2zdMI%v9gz41aSe=c@;FU;V`)^k9U{=wG`4tyl@VcRMkz;M-nhh#RWf
z`@8b4c57J9?N}mELt<kOmlgwSQAhq<ErXktB6Dq>CHA~PB|!gO6Zp0zAKTVkAxYZ$
zmFWAo^nCNaL}VKL7B1zFD`%&Nj8s{lMP+Obb}1d%d2=#;+~Z~m=Lq_z4#09ZwA(K(
zzsKPlGFo0kFsHk|6ns)ZBN$Shjq-L!k7H`dxPU)-A&-jKSYgE{`{YU*`C&|6``v4Y
zg4<icpteiFBTcZ`McobcYJgdMPt12nssChr7)$QD7-L?`!MO%4m0y<>*ZNDub^^Ku
zxthM(Zk+z%F<%OkD#-yCJ2=xkS4;$3``89mjJ+$)bvU$|-R~nf>z8DJ<qlBFGbOEn
zwQ;gsJgN*-%>^iYocvw5={;Q_;~)XRa8%4~jU0Q(<$R=tQAgGCawI%V^Snm@9it`p
zhodn9vBg<w7HyS8`h<F&t!kPCQiI*!ftY0pp!rSQo6&gEY<2fnpT&4~!hIi3<K_kI
zGFBMtTE7<Fk25l<G!l!^S{6JMPz-HAuIgnkip65>6K>V5C=^z(t`iUe`fC&D3-D{E
z%H?`XpMUu6C08JL49jum3_idheF_eIB3S1SNndB;=@-mTh4KFXzSRFyf&MqLb}|I{
zwz0!&#~(Y|vFs0S`WU4tpG_UD%!B76rZwbK_oO%XKhxx*ljLv!G^xo>**V2;3q?U$
zo3PgBO3}+c$;AVgVigj2#n@|#aXlu5s%pY+=UFU{7Rgo(@yjYYb2&JRZQK#&!vW%2
z>`fzIp57Mfb#BGrvh7`-%tArIGf%jXf~i)$8nu&dbuBK@cBUDZu6+bwi_#B>*Snc<
z&IxW@sQi^!iX@@ZQ<{)Z6#|!RdGBXZ??#=EJJySACu**ClR>}EJHqS#4V6MuXLoz8
zCXxTbd4BfI+&?KB+Wp0i+u)OLSh}a!Y5$vQ3Z|u0<CK$o<0AV^YaALd{9uREVKvN@
zX*$chZU!6eYJd61T$?qH>PGuYb)DPY(IITtbNz3kfy6gHak?u*Yl)DN`l~gCm0on<
zuL!-<8hORzz-~ZYZz_l>l1dW#XEnS{XOiUN{=ZVBYL)N%)g#3O;yVj-)k>UC_O`%%
z`RTx;#?B5UW~UOhrFL4NAhSN0IZs@*YDY|}ETND$+nUPv`VT+Twf~d-<*pnuGb{(q
zf@(RutRAVzgHubHg<oBV<Eeb<FqUmgnakyxQpJgE3%O(p0SwbgZQxp_%a+&*;wO+e
zQH00sChGV&8JbSY)`Q6V@gMJ{{BQ1OUbovo45Lmr`#1x_<gXX;{yz9}E>x$M3LV>C
zhz9*Z7&8|j#$+A{q#T^=6pbbNwTf#oqelwaGHpu&oU^`(E@f|fy<aXA%iy=2SG5aP
zHE+2|_-*Q0am0Or9Fnl=v~b$tTV!Etf2~p30P*FAMO;uFc8CyhAG4{v7kn=-c?_;Q
zs_%^Bo{gM^bp*PJR^G0&!7#BHo~t~lmlqD<N>7-SfrH|)BRq(x<$R|ekC>+Ge5aQ9
zt?tcGsK<P#QxE665?y@b_Wr(?#IoR^#pB`Uv60LWD|c%h))2XKE}|5fY;@B<w#J%6
z5;-npeIEBK&l#+^{VBKjUmFNsY-3nFCJL^L2i1OD%&?7B#PQ@5`;e7sLVpbyl||oD
zgwJ05jE-h?Jt!Z8`O9NG_f<oxzK?ZyxL>8RNiolsu7u9mP(2yD2#t>q&32OJSdT>|
z?|*h!2*u?i=IXrJUE@@daEh^pVNBZX<S#CNFfuNzuiUr(*ZxMW`H?`W-Gposki+0)
zeW-gkIRVHc9YtkY_)ULfJur?Ca$_eSP|Q8mgCT)>Gn&A)GI$3L$|EW{=KG~u^oDb)
zJ(Y=FFW(v$5M6ZC`0{oKNr~?QQiem$JRGPx4D*V<^#mukfP8Ab)#0iL#3aJNIy`L-
zsM}DS)Ts8)MLBJRG)3v!)|3IkB^s#uP=1UQm$Gkqi$RCDW4s$vvnoKdu0m@drh1`J
z0%O$ymsxc@3Op4F6(P*a(y}6<7Y1>h{fR$1BqRrD0R{l;)VtdOdWR=9o)A<i6h{dH
zM09Ljg6j}n`GY-I2~ma$4U!QqRAiBSFd(iVE53*C{2DEm<@NRRXe|ENC;==B3XA0u
zTc{KZ#nEm}dmjIF`00{KvgCSeT_H(sxlWFM6fyw`Tp3NjqhXF`_w;%vB%J|ld+I4h
z#S5$60hrJA?`a^c>Hm|({?mS7b3uyf(fv5ytOV@g_Q;9i`r}XGZuZHY-4Gcy$?*Ub
zKxnmpKXmf1!<hf;u~HjdD7ePSqOyh}Lx@~TeK3Vw<@5K-_}Y;x!$EFFxOSgnKij-a
z{N;4SmRP+cG39Y{RPw>N=ql~_5f(aDd6x<(wbb@VfgJmz=LdCOEu+ovzim?3K}Nk2
zqTfvyNVcIBT-BKq1O^{8<+raR?43GX^7)R{+#a<W>IJ9P$>__ga$t}Ol0QacGy!5|
zpV;${(}79;$=K8RfvhrJNBOwx<?T$z)ldl%OXp*vi9J!l)o{!X&DM!|TJd)Q%3-Bz
z%!;voyu?;s4u&B_rz!}Hn>q*eu^l<(iJDfkWSy~H9+x7&h>yU45|sHEsj~0b-Z{^+
z5;_I1jw3?%1me;>W5;~o!rwfc2Vb%}2*~fCNB*n5Gww+Q_XWtq)i{4UKp@;h&l56*
zeDU*Ym3)TSGBIl%_!^5W$Kdb;m=!if=BP&Wf4_Hs@A=k$iksXm?f>Un>@mqT)8sps
z+VRN7YLzJB+<HmO8NhfpjaI=ZY#2QS3O?adv8aJyE87K@hpAGEBfhw>H@WX4{$x{>
zV~PVv9q=)lVTAShb2ofQs-VN%`6!tlx;2kZ&_iXulsjQr?Zh{kY5@|MFPcx|-4&7Z
zaP?&uRlpUK-Z2S#gbQid&s%ZvJg-dUcU$P<chtLcdX)QLDak$;W(U||>~t%gb?swY
z+qldj7baKTCH;(@@%RX@ud7b6Em90Kg6R)d?9JfWis$J-!}BnbEtC2f6A8E({hz4s
zibQTjdx3le*e->#;3D{gZ6!J1pOat{_IHXwswqE&^i?qt*||=~2RMC+bFJ6vvZHX>
z#UTLWF)+x5C0OloKP2L>NXUHAUt$Soj(*Ip@__n@g?w449vXN2w-{Jff@!eIacn~D
zNIB$!_|IRh6L(NlN$=G)W{KY=d#mMKrF-b^*z^D=)Z;pAzcp{JOeOVV+SiVuy>qv>
zw7~Ou$S*h3ivzZl?3f|2$`aqc#l^aw8>v>zl_2autKxQ#Gko<m>A;HU;%&@L7vBYV
zKOf(0=AOm||M(2}Q@U48R~(o6EGX@I;KX^%9xu~W^J2S(g~et?EQiJG0dt&>!6`CZ
ziLtz=i(erKnIr4<CBX&%#pwGSejp;uD3U4IfS^3oYau&s&wLQphKh~+?{*zQe)HXA
zmcFCv_#+6dGmPxbwhC5ur+k0^G#HXNe$;;!e@kuFRO29T$1k~<n0BA28~w@$z(m~H
z-J|}_HGqO&@5-5A8}+u@gE9l*nJQ+uCk*Mz8Edy)RiLLsj&x~2BXM8P7|MQ&PJ*Z-
zANupTN(Aa8?_mwnJ|PwTjmGFnQ<ZgLY&rpc`1dl!`8k;SN0*0|z{0BG|9)TZNDA?7
z>oB&i-H*q;dOg~V<1qu>gz-Wyw~2C~_*|i!{fHc!NQLw^p36;d6uxH+o)ru4v4rMy
zzAp+0iB-m&F`jicWl?Fp#nYo&L58MbUPV-vb4uz4DKev10n+s<7X`yS;yI5UTEhvD
zoNS1!mYlh^c62bbyzrCfL;&&Be8)U|ig_xgsmw=l>Cio!IX0t>p?Rp^V*$djy}{hN
zH9Vq;PB+RN_b(({L0qW-Z27+SpD2J}U~s`|>j=v@l^#%>^o+;!vOAsN;dh4&c{GG(
z;m8KQIhOr9R4X0s%2hD2DIl~NnD=^81bFemJ~`Twu`@kh?v}0?CaDSU;})}PFPPr-
z6c%scR6N3@to(D<6so7Eyw$xl(nb7VX|@I`gA^*{-_>>Di4LAX7RAZYxNXgFdB<Nw
z7Vq6)48<;=rs#`Vco^Y4B*X1w-Z0$}BsjdU1E9`pHTZ6;>l+FF%bFM-3rQe)V<n(@
zenbQpTP4rG192Zrfo0YSLot3NOP}X2s(7NDgR57qyL+)PnkqkVUv{AsT4dnBIoh)`
zl;u+VXe1SH`u8-L446rQt#><OQD_{7dflHuR3|)cDss%ex{i0<HcLI^-dHJQLe#J>
zs+XMAbdMC?Q^mAq5@>oQa3LZN)TR6i8jM{v|9+eggYzv#yYA|dF?z$EB$D+fytqz2
z>Hu_%t)e|>9u3!Y1l2xbHmVY9Vn6$qOMgfrw|_%gG{k58#ryqiWl9uj40P7Vz92Li
z47Q^a-K_H|&#ZIz{pPbXTaSeZI(i+SB9+wRKue?3tXzrHUt7k9ea3P)%?E6_hqtJ^
zM$jp!t6jX2@3chYs592XzRcoBmoj6FMY(l>5<%`BZ?guBdirz{Y(tp1Ml>+?yif^#
zwP=PDwu^u0BC0J%i6+KZn@IQH*bKJg6X>jv+0j&w#qPMgWh``>qI-7Xk;)_7$!(Xk
z#n*!|!YmPKlvn>9kU}9*(|bxI0Q(L3z^sDy5<*)jM4$_P2LxuCj6$o9(Lg0OAF0J0
zb&TB?S-p?o_2uXqnzO9Bxc(CQWBA8)XN8Nmv>yjO10jnl>}zv<;Jnw&fa9@+RzAVu
z0zUMTyp6S7blMBFkYiA8rcqCs-Ms-{-v|j>98bFk7LA5)viy!GeSLX+D~+@kpM0Ku
zjD8kb8LB(2HR|fq8P`h2eveNuaYBY(OF5G6TviFyh$0kUMn{6xv)gy`I5j9+d|3%L
z_r1D8VkP*O@+QgEbLX|n+)g%2{m?}Iz7@r=CaM&xj;2P!VWEZd>?b~L{(Ae+EMzz7
zoXc&;CDDAx3T>MDT~|u9XP2MXs$)pVV0)+bZ?}uKAVtCo@MscztBqK}X^*yuE$y~w
zqv>HzZ`1;n>Ixx@<}w-sTvjKv%B>7R+VbSWuK1-}78Dxdd)F=rRQS_*pRTM#zRqnO
zemhc_8SIVcPT&MM>^)5(v*iyt5jk~VN@1WM_t&$OAiTuPqgENL4aI~et)uLeJ5YlP
zCWD`=50Q!Ad$5XqdhV)mT2!@o@@b<o8(=irV07`iu>;P{uE)^XX~`>sZ=^hUKM$Kp
zH7CVwKI&DrQva}oVRFk;wCngz>J)&N^i^o#A{Sp9ZaUgI39&AR@`X;?Ng@N_V)KO6
zVY^Cp?cC%JdULL%ba%kJ)y)1<Sh|PrSz!`;9veKFEkBc|-irUL5|)w^Gnot~#3I;Q
z(c}q^pzEW0c=l@iUbEbf2<mF5^Lolrvj=j$*^}~`Tj%-mV@SkKTlX+uL&Fexgcj5_
zFXvcUbbX7+mI?}7Ll1=4W2y|??iEqtmqul+HdjLBkf`}B?I6r==KfAG&FN7Ax?!*G
z6rEV2DPk~m)dG^H2pO#g{}OqAs{W*9$1D)O?y%*C*7TD!e%RA4_q`JLa#N1J$Ni$p
zVxDT6V)(y(M$seB-kMU)ytNT{9kfYq4UxIrmXF;J@re(&Bi*L+^|+KA9pK#eqxx8X
zd3?99)Pv8I;PY3<o91`q$^}mY(|Hm`P9@?^-_nejkH*;CYFw<|oYC7O3^JcFpdTcs
zO|e|9xYb{#5!&t(l~9L!SFVm5w>VeK@i_IHTCQbU3+WOKsUOIBtMes(PUmlL(Fx*W
zDdu>VikvQ*t)DCox|{vWE>k2egY$eo7RGwoGdEXf$~YSgnuEnqDq^T_mJ1dqJ;`-D
zEMe9dh*hO`F{XYYpnUw}`84h27J_za+r(#p4yJ5b0IE7aT}pHv{Ce^zb%3e!E+0KJ
z4Er=QJpQckUZVf7=#%D1a0sQv>dZsC7V{5(OEJ7mC^>f<-KsFxV0mV!)9hi+ind@|
zW|>^!aV(3Q?G?r#PoQf0!au<oqrnY8f|Hv}ZdJI_MtYK-elE|RWN~*P#jJZ^e=FEh
zZ-Sdv3!r}Vc4p^hC5TdIDWO<CzLQa&MMRt^n!I)as%zztFSawFOuGNimGJ?ITm$pw
zeUq1cE-<wS*N$l0L%+i1>FA&yCU`|meu7C|V@%aM#vEAxo2|z+7-fc8?f18(8<)@>
z)&sF6fAIOEOt{O;a=i0QPP#rtSbl5uh6f+2H+(~_p#|gP1-=xqwHbdPZf4e9t^N0)
zjKcs7V)ElUc~!peXWr>(x<*ancC5XzsNC7Z4cA&LYoPpG^h}8h%E>xLTD=mdFx1*Y
zXwIJ~RPXtf#D!{X4YyrJ?;FfcCkrbqkZ=Zaim3b|n__Zz#RAB8mZZA2TBFlL2u`#8
zB@*`Rw`Jj`uj2WjWljYe8E}F|1JZ|{+3nN8>t*@#H#D4PCvAhT^PV6_JjgDAGEJU<
ztknAOq)OnxL^U+JtATEl)tmfpxm=QUM@`X;t~BJ!xgmt+EafAn5(0G)JKS}_$g%^=
zto)HehwTqanL~?3wkEhoOI+frOBy(C0KbsiPMR>z?`{Imk9#cjhhv_!FMEr#je@v`
z#UR?%kO}W;>hjX<SEUmEMthtddS0|A$%2szXoWE+XzHB<4?63!LzvlwUfL2KcWQ#a
z)wqOJUoN*!tz`;I94Qm=Y=L*Pue1gwW{rPx)qX1k@f?IkI1XUfjQ-yi5`ig{Dg?Kw
zth(?qBWRgnrl)}+ZET{p1372}VPvyreu6<%AZrdk1HST{_PaX<X`Mb@sMD7*DePQ&
z^in?cboCi3KXqW-C1!xxN@1fJy++Qm&6ddfQ{mj+fd9j7MkN<}Pc&Z9>ZnlzVPsWH
za~;L;u<C7axyyPRa)W^f2Gz91Ecs_sREZ|PdN)%bSzSBVQhhR=c8K%!ZWHyKsnMPn
z%&|j@8>@bJ6IrxfS|0bidY^vexoVZ^k0&o)el8n+*k1^;fiQum)oANx=+r})>I_f6
zM2|hj`Z^o3mo>*_$Ix<zEL)cqk<hR8JLoc&Er|<@a0xHoAQ69%bfX(7uSA0qOXG!5
z=&4Y3wp$`DLQ@I`uR3`iUALcxT2P1k(LLkplyzm6huBAvf=Mi0=l`@ClZAEO6q);b
zh|v^bT<8IBwVo>Yn47~?Ige6edtiwTozZ{;beyN{D`An*B=A*19{R3pN;JD5*DyW4
zA}O1!dK;j7p3KUS>gQC-k$xOgXe^m~IFOn|8rc3;4b1=ikDBm*ok2D<@CrXKDpR$!
zde9%xR7XnQ_GERV{nd4eOiQk7;;0Nuii>%bnfUu4`gKVhvKc)g&C(xFQDlmloBi{u
z$bo*)$**&&E>~h@jzn$jb7PV2r?)aHGYN-~lx5=3ZMou5f1_!H>y<gsEy0ca?=bNC
z=$<qF#Da7n$H0%V%X3_#AV}Hn^<_4M(ykV&u}d3XwrQ36Io|qEG~r;xg<PvTM1@?Y
zJwS;UlK_6$`o)EVINpG)#836^&qd8Tbd$|_6;TfzzPEG;toAt$zoHF1Y9<(3yZy4s
zr&r?udCKA`AYR6J3M`HRZE)P@Pld9*I1~hHU(C^Pjbu-PCt<?FDEK&P^ni^rf|a9$
z27xq6AqR`f%K4ca2=HV>Oo?wB#R$gWPX$ewK*3a9M^F=V&b1%9>O!1LRk2h>)&$L6
zBt^>Yn8Xe|DE6=DY1U8vJhX8xu=$qE2`xvQ`R)=&h5v54ihVHL4npl6C!adn4(T!Y
zzezBI5O_{L;aZWDBX$UV8k<_B$fE1#Iyb@iP0f_;SMxg4^V5h+`+#Dm`PC?_DeFDa
zRl=gj8qIz*Y<#I5Xir%*A@ETDI%tU3rRQXM7#r-SyH;mK1}?#ho_qG3^_1z=u&Gka
zmzua1BONTlB6ifdj}7#yYkKeIu&Qe$iQGZ}98ShXWIDFOWQqK*h$;%|Vn|h2&Gti(
z#lycl<x7`IjZb|GpM`zyWB2!_(YB5cL=}n3#)v-3hhcNkh>aMeXjSh}dC4Qlr-*fM
zMyd=vp!IFx!R@da25Ei8URRVfljAT!O%yWp9^HXg7qu#W4g9CW&gN}}YeAKerl)6y
zXX`z+OXH7J3Mu}#?9t|`SL3LoG6u^9v(%Q2h==DtjBpO$;}INGpYyr&<YQoZ`=-BP
z9SWR6=-fTWT3l%)tNSTOt#2Hf3-8V#qEM%;c6wv7@2%R|TXeCKZy)&|KI=LTc6_^}
zObOiKF81%Uk8Fy5OjpK$__DfG2kx%_pOOzaQUM3&qUQ1M8Lb1>G=xwoQJw5)qX)U-
zR?@DY*q4rez?D?7JSw5n%v9`C5bCjzwkYm)-jv!{3^R<P6x%99f>5JGt&Ks(@rMy#
zSe}#~*+E4zrW6DieHaaYj0@@f^Yj3P%CTH+j7zh77L-7A-NRZCrGMXxVYBb^%ky5!
zExTJ$%!&Tv+>bpl96kIOXNpEHmks;O4qs&2%9+t)b#oEWe%t6I^25H~Vc2ejmal`K
zY4MbK0d;rhO<2jggE$<lZsuyl5npMI*-4eF{G?&1J5VQqs!tyeF9C&)pD#u$Z<}(?
zdiu(hN$aD8P2~3py>*e4uq~~`{0Ir74Bq_wK~o#m5=uOTSYKB}>fN6?tfu%UEEl3h
zyZgx_98Pw*$F#YO*Z)5Yfbn<qEUTfldidrGkf61|4()Ly83b~E%Uemh2ldK;Gl1X-
zFAzSHW;L2H<PTBRa;5W8E2fVK5R7*^pFg@P+{UoEy;~dRKiNv}@-0!e3#(EM8gfAD
z4s-byUk9**c7W&;{X3aMPzuR|9dELnoAFZn-zlsH3LN5x1wSH8MAB$$kPlUh$$3l8
z9|6~)uQe1mmEtZ$TK)4AnltrooX=K!L{Anw&dzVRjrdg#cq^&Dmb_rned^~<r+5+-
z0@hUhYjD>4IsF{BaTS6(DwWLK%zFNU?$xSQY@am%aw)Ao-va=3buo`NkQ8nqtzca#
z!F`=7(=P8-T)wWDC$%1Lc2JydIs&EypmkzvcfVhI=5IWyilsJn{_xTU!<D|(h&y?O
z#y-tW-zx!)CbXlrVQr$4@>?#YVN9)|lQNuTgnL~63@&|u9;W^FGoE#{Tb^9-TG4r!
zSxhXo7ZfAE*>q_)T^pYyt=U9SEo#PjqT1MTf?IoTa@_ig)Ouj&my6aFa=STrMD2bh
zfWK5o22OLKwNnUsAeZy;svo~jXUP^GSA`UvR%{*=m%|ie$>o}E-`+%ut#h3jJh=74
z=BL0m$cq(ee(Vr9pGYel7tK2MJFTCzAxoTAD|I+?7co~SZ}Tg7Z%G*hFBhugEaH~x
z)I+sBm!c_l>}Xy!!%3;vM0!P-jKBCA{CdP==9=yk_xy48Hb;czwZ#&)f9uWSf<1Xa
z;F!%jn=*F$bymra2h!P`MThfBzBv=N`Q=KZ@U_)z{ad6|%F1MtcLi`n)w4wQ-(lx}
zv}FIiy?k+qWOtyY*)84WnL2#l#JjW)JZB-~i#%WUOCP@guNKGAU)vNI=d=Q@EF)N!
z9zbX8fu$}`#TYHO`}?$GAl7vbh>ShlxPV>3Idob8N<r_~Jzi>dlv@5&8zB%mw`9+F
zi;9Mf7>wB-;d3Tk0;ir^9oo=qz3y4l>lu=}I3$;`=@xTnIJwx?r+J!2xmrKY6aF7z
zZy6O=mvxQe?(Ptr-~@LG5Zr>hCBZ$oySuxS;O<bkdvJGm_wS_Nx9{zK#+83{3Pz1O
zQfsd@_mn@5VSD9&mK=Jo&?eFx?q5S4%5a&Lw^-_bt7MV8TG{7+%*9gL;x1oE620V8
zF0sgDA+XaW{;0nLRlXK4AwVF0p=P@KOLt~E!Epm^Oq%=45Ybob1=DKwDs|mTde7~n
zU6=4mj07M{yRDa!ciduww5@@6b)`HB;KImx#9kQx`K2WObKqwKx4)lW-GZU{n6!zd
zNr(<pg7}5N95-(Off{Nu?IA8lR6*0#qgh`OrB)qPs*yBy9`3oB@IFUW9H%6An8)d$
z-=8FkmY!GQL#T6Hy`brm5A+=zMxuSHoo1hv!suNxs^&c$&ORlqb`x6)Y_JJ(|JM4&
zt~<*cmlsY{oIOvHqyC_h&<+dJ5M2=anyy%Ufblg;_x$)Pd_3II?80)6RXg0rVN8-O
zjEP6rTM7+y=250Msk#``3KFf9tfpqp+f9q#B#=(r({5iKy|hLan0KmgO9&;R^_1ed
zI|%Ey?{Xt%a64%fgFjLo{u1OkJfC&PTQ>RKV=*mxk>nttH4xG^#qVot7+>QUk>AX`
z`TYHQP3)^s9(TK~JEXZLjWRWAodWp4Ka0o?p07ul&j#^T9kt`8SE^7saaP~x`>}?|
z2j;IOXxNNeU$JigXRCtzyi@lxLyVhQZd-A2v5Jlkk?Jqyz__@$9TIxY8cKR`4de8{
z{$H{+!{W6@h!mBo67K*5nOs3lZoHTG+Do*gj{T<tQ@8bcRNA4&qQY(Cq3>S7=9W}9
zZwXhbC#jLXoh%GUoEYAB2_!jD7}Ke&DF_wfKT9N)CGSyUIR#U(q@j&GVMAF<T1<Ii
zQ_IL4Slq+#-EQ?5x$QCE6Lx$y*Kyld`JyKW%K<8|N6i%OLJi6K37k@&4c9nTRlY0Y
z@t_{`WKUVf>Ri06mnOa|gI!c?W6h)rS|5{n^dXBB>27A$vtGMf`R|5);}1IA@9^g&
zM(u;fb+3G4zMmoWtkl#&yp9LLz+Uz3BZgJSj}~!B;<%1M^Vg|-$wk$Bye?()IXY%q
zm+QT;_%}*rj!s$8AwOt9rMZNKe`qOJ@DgL8T`L>yZ#Z_xg>y=1GxuB3NEUBBmO|1b
zLh&;PPYm}*|HtXF+xL1M?AZAJQ(;HtOFHZ(NBs7n;@)i}vi{eCd*rf=-yXc%o2^VB
zLa6~%H(d5LrH}Nv(8S(#nIE&c2X^Upl>SU<cpd7XJdIwNzPz!*)bYC_p+sI8P^a!V
zJOwl8v9`MmY9QewUfGF6+edM1^QsX;j*so^l{@CxxupLYueLQ1?9L9aLINW#h`7tK
zf0}2jwIQ--rmIy+VpS_tGS!-xO1|n*hGNhM10uvey`{S5%1uRRqB9gG?<XPRFxCHa
zIx2=U`Qgp8lf&#uuU3X8vj><-54tlHPh|<X9{_O@`QVT+lK{PaHxMn1?I1cERGN7m
z7viw%%O)@|lG71lj(_wwbd!L?Jb|`K+GbC2veoHW0<h`N&n|wKUH5($czwB^aGck5
zR$6UwGTZD9B)h-!H}=%LrGwQI5n%3((KeG!1wjt!7VYn85Dg<~{n{23M)ZhLtI&}G
zV$zEtVO`6aXv%xSt9nN0;)mZal7;%f@D>vQekGSj_+@)9X~pz2t^dbkieNz&oxr&s
z`DL?p?%+6Ne8nKB3fg(L1Pi_oF>Yno{pY=(U~nU^Y-J8El$hklEdx4}%=PuwbbLD!
zlIQ5#_pH~XgZW!2UrhPOuP2CV>9VgJuV?I4z7;e0Ps`=+^SdxBl}l66MXkQqf6mg>
zISx;D^#t|c`zI#~{O@}3KU^3CbT|lz=9-+Iyg*zY4G*E$8<m!;VfsTLR}R;L!=!aD
z&7wp;lh+Z5k)JMaTszY3DXr|N3NkKd#n3~F1);+4Z<O8+M3HW9e1@i_q6!2u=p+E5
zbJJvVr?7sJEIt972^>%XqN+IYO|=^>3q_n!^Zv3q2skYdcEj&J!am(m!44Y?sWneY
z?Dkeu*CJWntn8c@-KqxLbpl_69UyR1-5c$PugRF8$|FKFMY%C5$iTYx{<)qR&R=75
z-agaz+Wn2~_Qf;NbkA=+vz5|izc{{8h|a-XADX;2y+6SAU^cRCn8##PI!m}By3=mS
z{vQh?2yHOY?<{5O%N&Hwazmu~T-l-O_>_Ro@L!VTN4>VDFSS4v0>GAJQZ3Vn1tR*G
z;X72@-JFgVYiYzk+ZGteYpA~fJOOD>EiD3oV;<S!bS|NknD?VU0{>>!d0nYcI`PNN
z!IUGwG?yVYcg2BusRzQjD@Ex=dYTwM@`mv`pUI9SF=8I`CER(xJqCL{Y{FJwsFx^;
z?D+71v1OI?uKhky1ZYIfmmA6lo4$<^909ptjJhpf8CKPAI{Y9d?F`uD5pft|ZiizK
zk4xq&^_g*AfT+&UbfMwn`2p*R&p`D%7EFQ$y>@8Uescu)o$%{_83_HHdxfljZo4b~
z(o54meUBQfwOJQLKt(ME4BhpY;66P&X`zEm!sS;o=AsxJ!i7mFd~WwR|Itfk@WAaH
zoywLoDuQx93qBp>vja>mtu`0d)3tW(gwwaz=NVOXT`CG7bb$UZzs5+eQm4754xh)K
zaupyz<bCx&0D{Yk&Q@D7vGzEvme6$en9rSftk&8z8CF9|kV~8P7DeDK*c0ew$zHBT
zm@!UDHL8vB;CgmF&pX;3f#{9NzszVLfT7NATf*=WNd64pC7pZt%e3xz+#@t!ZQ*FY
zTe8>+BOxVauDBbB=CMBfadW@!Jre+r*~^<96C(`Vw?rwhi}iX7T#g@;J}gq;i+>Y=
zl`dKx56Lm{1l(?7hG1nC$u{t*;m8PC`mewZe5If#{hfg#E&lDsB2h92ZDptb&rKGb
zwL((5_{O@J4{7Af%ftOsqk#5*_ueKFWX?E_d(h%A(mMcKoH4fHFUk9U&Asx*&uJY<
zizozsa-oTMvCXl<kkFX$XM0sz>vjXgd1ULJ_p4joQ9r*hYSp2U0O<~KPCJS^)g3Q~
z#o0-2H>mdx^?BPwZMg>TZ_npj)ToHu3D@?!!&7oxtCXi3Vecok6Vs5W{ABx86+K7D
zrJVitueb9v1Ek)v^Lp;(e*l)$oD1zd9&>^R?aH^0N_kLyeSNOFZ?J&=0k|4*F`zM0
z^!^XH)C^VQF95wWfy@n$o*H(eBkGdeh=-crN9C7t;g8`2x^74rI3r`@K1{GAm&s*E
zHi()V^7!quA@;?@gPr)~@f*ZDoF&L`&|)N*(D1+1n*WKf_@fQh`&EP?VM%EWxZj^K
zS}%v}{3ZE?E<YGY3rHvkK2jPI^ECAO?GnHbv+FG64%#eKi7*URm2sI(e?BsN4Wc3*
z=35zv%?ZgTO8Q1y(}BToVrXaxrKfn13lgi=t1$_!lO*G5N}*_ZfhF|-9WTXex}wA$
zT<4he4YMi(><ubd)TUg0`ynbelnDG9F_=#xOedR>9UCnunm}wh^F9c6*{N&wHHgqU
z2l#*(-k&G5@%UAv#^qk+TJ-_MJHc#~FTS|ca5*o=MtMs45>VlM{lNwE!t&#Rl`C2|
zlY$FjlGdiYxE0~V|5kkb2gYQpg$|S?6e{fD4_MOxolT2jRYM=ef<^Fjxe*u9HCxaV
zF6mMfI`g$$HTTb5%K!&Mca?qmEuH|wyg8!;@zUlteJ+VKr_Qcn<XphunFeO`?Wx8!
z$}T>Cu7|&>y?u>F!v`)zvawOdaYZn7uStqr0iE$x_}61fhzGGn349j6!Zwe`tANw}
zEVm;%r3co_i#ctZ&_Gh(O&DYXN=yXFX3{z_zf%6PgAn29`h%*`O6^AZB?HEmt%Ic9
zGJ%Og`|>7eix<GvQ6|-oDty=Y-&4rHR~-N*XobrtGu$&Mv;8oj<uF5~v-snJi?+Oq
ziGqc&LD!!H58=WW64vD0vB^=)wU5`jOf?vq2!REnPlOIy03$((_U4GG^0mo<w*O0&
zc|NI=jbsc*Qy#@65yR9`H;UFtHcBBcCnrvE9jjq}(|XS>F+BLTBdOB`sO6^$e!z5u
zUH4K{{W0D%W8(_B)ny>a9*_CJ2a+1WKp<Ed_=9cfK!ao6L#=&<@e3g2F+l<-Jg5x@
zJ!}xNoWdf(Nc(YdXEs-Thz0v#6(=3aELAM4J9b1RvK>3srvJ+`{l7-9zb8$G2IYrA
zi4t}|XwxPFbQqbmX||-nBLKm5rtkDM%l#~NaN8U@xRtMLCBz=3l(lS7gA2v^-s|-#
zRE96Xv|N(%0Z0=Zasfde4|%^_N-FKB^2~PqCNFIMC7jHptK|pkB9C_3h;asDSulK1
z_*tcV1aku@Ty&EwbWN_nDh^}7E1pugh5fq!3YKCa!B1Ftz)QKGAiY0Z24lPq(PW2M
z@FlpHHA=GZ(q6)01p7D~pt@{AMnlR98YqK@z5My5Vj8iaLc(+gkk-lvQFlp52Ys-R
z0>9#Ae$e0J7>WHNQuTA6FE&-G!gP2_*plKOtE>Y&cw3_wE{LXB$`xu!?=Y64d7^g9
z;bWcVB@eOFN9~_&iA-E6`KT8>cKnb%Cr@K$o)wG-XjsFDSQZ*l=vc@I)GJ-pbA)q-
z8bPrJv;99!HH^K0dN@R2Q8v~;XHiX3$^p^X{&ZBj)gQ^Lv1%v+L|au~2y1J!Z`Pn7
zvgsUs@Tb;jMUNSqe{$5={63aOg+5!IrITQu#Xvi-oFoDtgiUz%@C6bU1xto=C)g-D
z$8sT=sHS_2`0Ogi0Z@~DO-MZ2JCT3i3TKuk?vJ)Jl4<&$<tj1POcBP1Wxo*s=Ae;{
z8b~5?xS>a?!XrV=Gs#s25<G`P2o|;j2=qMdz<Pv1MS6Bw1=6?c!%i)G|M5NE->0cy
zA17k;$+gIxp|Q-${U4X@odn`H*Y4bf|6Or+Pk|6NzI<Nb@UENW8&_psrCswbj~SA+
zAJ{}<bkzIT3)Ct^W=+8}2C>##oa`lEbpvY{XXmC^F)3tZWb8uS=2u{4WOqDJtd`D4
zZou@X=VloHG7Q)~j)0r0C0dok+7R_Pt+vY(?Mg<G<U%|mhS*nnQossnY~<bm)(Nh;
zWX7W4zKb!!#U9ngyngjvG8=z~8lVf;;D_Asit3oexiYYa;LmL<mEm{k5+^;L5Q&C|
z{gitKq^Vs&&fM`z41#sUh?&YsNug_QKvcFRtnIjSk{T<+K7cbv5N1JN7=Q$Ps?8yo
z%*+mn99iTcP`C8DQK9EqQc^Z}bKXm&mA>ZWI82lvaW?)pCJz8wem))9-RU^}oCi`D
zYqQQPDoPJ%$p|uj=(YsBzE^iNut{h=+qfjnaB-2RRn-9+{?5<lm8L94-5LlA`);>K
zrpw7f_<p)k&NqKm4<dMaKrAR2anE=rpXo0)q*g!)gZV0b3=u11<_mqJ3H91PeKNw-
zDtn_*jvAm&#@=jiH;-Z~Cc@&8b717jMIIq`_}MQn6lxc*;#kor*TvXCaeN9z>XB?<
z3}#o?Myr-2j4<J=YF=UWKB5&O?#B~V^D*AZe4#q7-9REYB_$NzM5*~We}$vmuhk%W
zg_}TDKZ(R2s^f`5SaKI!*yeST0!J6yf9_~ikjNliewF?3cRGJiN^KK<&z5^+!}^Ko
zZ(vOR^47zWzOZEckB3S{OnCj$FoW>Zw+M#SK>NtTiZ3M-OsVYp9+!c=HGUta^p)Lc
zYc#0Cck5a5$one8lkXJUVlOHX{mK0-jb#pc%1X`r*bu~TCWPMp<au+<+lf^6T5GPh
zI^!;S!WY)DG#Db`5h67g$AKUpKqb$;m<ogSVo_x41lK2DAD~4E0bK~Oh^+?;+;zUw
zU~Y&YFqY|sKU`uC1mc{sKW=R{3Wk#z4o_qr2SO+~>uUvZF3}uBUq(h^=`rc7tc>wG
zl=0i2qsqFb-frYt#il!Pa~=zavr#fme4g+$YevhI(O(qG&zNeN3#Eq&OV8S;<uXxR
zap)c3{Kr<#g91g)dC>(@pfZvvI*wUyiRjh6JnF92vY$jWIws<r4F}rCrjCOt7~T{q
z{g$t0EvF63v{A|L=6(Y#Ngv-YdQcrB67}T@MWv-!E*6?J7~_aM#@-Z5OjF?=KOe5#
z0_k@9r7OBH{N1jp_COA+CxKNWuKn#?(rWh~$;=VCW{^H+)UA$#>{pM6&EPHMC=GK!
zxR;2RQIG7BD&?APx>306R(9(a#XoN`nfPn3!<wbuk?Y5#Fhn7p+?z+oK5U(yYs>Y@
zpVsA;3BaYp#|Dh@Bs{AA;>q}8k<++BWa*(Gmf&4TLg7|QXs*n~EZu?J+3>(dHfzLQ
z*-6pXeh3XKi$=;E@ju$Hzw_a^6Vz8+KhX|g)OcEdf1}Z+RY+r(N^o)+6lI;cmm{Ju
z<Li?=-i$1pb1oMn1fW*!1ifX7q&XmrjZUs!--S!PQcsuPdvmw6PPJ4u(O_{2b+|rq
zI>iaFN=Ua)wX!`aLJerV3d9!Rpi)(M-P;I-a+6zX%t~(_4*B_!&z|8|AQK)o_Ab>u
zO=ZK38@uhj*`4tmw64g#^zpVZ^sSPU8WuRW>i7hL?}XZiPt;ZR>=4(%F%&R@1ir;`
z|6G54RYZi>Wvb5zD-IXL5!Cd{zW4te1rUUo%!~vSl<wb6eFFm{R|d4(8h7D2vp@rv
z*X?0RT^JllG7?(dPdpN3NYp2<sn`*F<B4NYBch%!fvhxKO+vgZ*#k}Lw7`fs;<8+4
zMqz!KqT$m=8YaL1nmhy{9v|W|>&XInV@8D)fK(3(GCC^iE)15-w)fHxyYt&v+RePa
zH~QQpDW6xwM@jXh>YljD8bb!#M*X@Uu?CPUWZ@O0m0I=YMb&sPb~f2ws0(1luViP~
zbMZg|v?PR|v1H|JsXC$>s8B2zrKS##6-37Tlh*&GD$7_DTAFumM{KP}62;+BS*yhb
z-fFEzGf95FTw6V&wdyKgVe-Qd?kb5%hhDB{AZgPKm{c8aXVt^Gz;?QDR$PDQJVPHd
z{PrQ_o4Cyo$H)%M+r!ZF`~q#Uvw>@w)fG2(RsG-Pm-`xBqs8%;CH5;YcZ7F`o+iiU
z<cOdLp(`dz9uO=X!Y^}DbcN$q|2pe@_d1%d%$^j*S96;*aQ`+b=BpO1f0E`jBgZAR
zLQi{KBDU0IM`0*QKd4-8&atS8AkFi$Ta<Xtsd0n&chxgC);)ms3`Z#|PUdFxkViMz
z#!HG;@s$M!`z@C!QMds@TJbJ%Vx4{1HJ+F>E(<1U+J*%QMK0RXQnhl7LT6w&VbcV2
zquo*9e}szkv?`0hUohAL7r8VtHacuQ@1&sZ+*_Dps!FJq1$#BteXBC;F%kV6G8h|*
zDN80y*XaZ*znKnahW{3V>l2{cn_~cE9;Q`UaZ}5pu@K2XRuOYy{Jhd4>`eN3wjhP8
z>xd3$o*YDjc@07Zg`Q77;S2pl6g!&A50;g_Ia}5Kd3tqf%v5kUf(xO>b00XIsv0Df
zU{I7H0Om*MyZaB}l5N8WALlQ4t~ryU=#qZogzy&KhCF?o6IH?#BEB#J3^R%ixhEoa
z{d3bo?=Q8V8H4i>N&$~qwLtg98q>V5VoE`If<W_U25E|6QEUj_-{K?(*ANLc;`Rpd
zcnAO$*x?*cQIV0@efV=;lfUj+7_QP-T)}o<0Bw96V@a_>Hq-Ip6b#>2k*R>1ieFag
z4c(YEAT|{IY8-DMC{i@|w3x%tiYvi^_7MaPer8Z83Dz1A316ei@x$R8{|HN+k<Nt#
zs9~Wh!a_gg3qF7hMY_9ZY#<-M0`TaMr8+b6``u(+rnwt!n|9UD{Vk5P`_3zN!8%-%
zjM^=-OWR9^u(iMdgvdzEWgq}6zo12A?1xPpCU}-OIQZpo;Nt;A@+)sL8r&VF^f(FF
zK1SY8hJRFo;xHcq*PNF~JXXITR8l6U-3>@CGmYw5Mq<+=UKD4Gg4Xj6?XsnXE_gn9
z!|<wqb$6ovTnDjVy8wled!IR?8*P%Jun213f=9M$vtu!1Q*>!M&(9P;KE9tjqSI1f
zg@J+3z@$pmRNa$@D#K^e<C2pibTcVtL@ZIoLwSS9<*c!Q{z`=N^F%h#$Es>HpR_66
z7y}aCQ3KH%5NL;K^Yw#sU0ScUN(nH`ec5v<Aj_#7wQf=DXEkICNIXbM!Aq?I^}_o-
zd@%dtzTqE=SWshVy4xfgh>%y}ebEK$xaPVKk5hGGY`M^NT$FATrvaIw;TtZ5VLQO)
z2VhH*fA3C<IMLjISnT;yJ^gN|Uj>)%7n~jDzclzoz7u{J&1K2E0~qLhFysx7=x5ok
zn64dP<Lj00#XPk9#Qdv9JL05kg0DKLHl8g1;ynJSuF-&w65dDvUsG>(yIT-ExxMfg
z)>~2cYC$RE2Dx*weDf-?B=z#SRLa2Bcs!V6P^Pj!Tj?yovp#K#MkV4V3nRc%qHU>a
z$wRuat?YkWv$Y5oCxVx*`8aH=8cfeSCkiNQaP7;K1~}(6I!}rQcn?RIJ9QF-5+y@)
z^c7>2P{oN5>Ywk6_7#f&AcZz|^+R~8P+8>w!5Q`mG??JyqB3$JLB)~nh@`S9DJBwz
zX6ExORHID<@|=mKRnY75Cc^t{vOavMJ5uouPq?#NbTQ+Dj}Qyu{tyJF@hYb_jqoJv
zTq=b~?3@L7@+~wX&LqdYj#zaW2$I5sMX;_<8Jw~gO4x6fWY`rL#d<SIObMFgpS$&L
zU29-gktqY<)s)xLV8d;w1+(1y?_%MxvuQ-SXt%k5#BQ5SvlZA?BG{-g2b=e1es16b
zlkfh$z>w|MYcM?ARRC8ot#8-}n;XJc1;#~x_h`}h|Ccn#=$9Adj-$3f>YYJdF=qQc
z*c4wHAB%0LY9Y@O<O819E2<QfNHEM-HvMHI|6V9TQsYN%ia%_EY#Cv&ldgYP>J1zd
zY&iGIP%T1!;mc-d8nd^AH!sQ9tRFF8(SiwuN3$aw1$8U{QF*#Y=8|IRW5JK56)*7&
z@j7KJZ{lPX%{WzsLS+4<_-7L$3giR`Jpbev_Vc>ciTkyI(`+2%scW*sGAH)!l(t6u
zq8N*GKg6J2AWKzGy17u~pn!whl{+<qRlt(((zmm7c3k!PS>Jp|WrXMw+i*PdxBhZ?
zENT9AS?s@iTJUgTYN(r6betIge%2@(s0!<uMmEJnA2l?S*r<<*iDTcM$Di&n@b$}k
z5LHr_aiH$^$ivY|5IgkO=Flk~D@ZaL+}y?VQTMGI@sm$f@cxiJNy@OFg6Ct&9jsjH
zlESuyhw$kLguCr_T0h@=N_f1-eCQeq^|c2Byv$v4qke@dg6VpLZ78w%ql3EOQJaQI
z(M=R~oAb#+EIeY2k(5to0H>>w$UTh@V<X(dXV3tu@PVSWeB5Jo&ht*{NVlxe8qo!B
zvjEH%$qzNtZ@OdyXwwBMxwRi>cz3>h%+{<hMG?PSKWo@?Rr5@7T)aIj;+U`^O|6U#
zMYiRbnD|!an1F{GxAtl8VP#vDptx@c_+M#23zoxty3Rkx=3G(pI9!`I+ammQA%1+O
zV8+h%m^SdOz0;m6@~&_<E&X)NxOPpyIlcdGlPss{hxJx5o>>KZTB}*T4xmY)%N&~6
z**sogKrF~SrGNzQlXt-EMEbb@#f;r~2C%?d(wpFLZdBo|T_G(`vZ$~EdfNEp=^wxc
z@a<kDi<Xs1C)X04d;YdPdiuMVYR-KWb5#wjSKJh>Gkk@yi_d!6{@PN#9$9lnV3CFH
zfyDG@Axjq9p0<td7v0u02{AEM*x!Za)s`=~5T|QH)=gs}_r%D4W9NP<exNwXWb&w5
zs&JU<&OuU`TjSIT0_;lUL*VT5l0_x<adSj6<CP{=U2v-)mR(tXCoqOnr8R36P2n8Z
zax7@6wbhi3>?sH(I5!6BKBk7X8$q7L`UJX;VSjJhr|2pL(>QXPa{G1TVTOND2EF>Z
zpClU}?%^4^b@=twXQ!RBk;3lP$5S8&;?OVKDsEEnI#|C`;n!#Z>JHv(s^gNknyFT>
zPPClhx74c<rg_c|d&cY8;5oA_RUzqZ`gykHV24gR65w4v!s_W~NMml@?ZFYjQ40f-
z<As&x;XV3^FtuZd!yer7R-wYFl^qL`^xL=LRraOiBn#^XuIMp0?_Fzx<>2%EfY5BC
zf@Y(V=5)0Y4%E8L>C_ill8&gc8sViyLAEt9EWR<h7uk$ad+$1}ispuShcP*TlhHi=
z<E&`behkv8*pa$(l>cgbHDZ0`=9MwVSwNrtw`JEYQJBMD3HR}YFAu|r?Y0C;<Ke=B
z#-badcG_4bpE}m_U$h#V0KGqVL-eu_)@&~#H4jWuT-<%N2pylu>@Of?IaVNf<8o4o
zEH2a<@G}Pe>H}ANma!E#){oDc^Mf}_q-75w-6&ixnlfY3TnWk%D$D8oc9uy!R;2L9
zS8}^VrUY=PHM{J&%=-x6ZCNJdG}&k~C~D=Sx3{)TG9Lj*?Zfl1KL+L}5TpztFibj_
zMGT=+s!rCoW4U#V*5qhT-b%3ZO&iyW0aw|9IKT3eTgYVnlic@hjR~04vD7lztKpmP
zMR6)ZLoc!jKl-Ou;m_bb8^(0&<GnH(Q{@XjfS_8hTo^sh)c(1_OIC??QL^Khrzmo?
zPy&&;-W1o?xe2(S@_Cclo3(bnziH1YiRA{GYCFO2`Z~}$SS>Zgl3S_=oz9{S5rX%l
zQ4vcs-;D?Dx&ba50c7TigSk!gLQuqFe3QoIpYnQoY9h-=p8(IH{cLBiqUsWlw0wsP
zQGGb>VA|8FEehV|7CSL{%E2kX5k_++>a9wUXqY>}{)Fo7kBWj6mLpV+7=(UVI@fa?
z!UXP79jZn_eqNqQ9=J4b-lTCRR|CD6KKB5pL&}4X$B$z0RoSe!C^`JLfqp(AliQ@$
z6xz9u^y93dKBs%Oo@AtXXWL7la4<ZhiF<$v;_iR{xP5x`(S_+u(~0&I4bsM9Moo(W
zUUGg2zB)Iq|12RHy{tpmTZWnWxQi*lR~9CJnW2fHdd1zBk@Y#H>~qY6`0!Gt*$|Ai
zU^D8})6oAlKKfr69MtItwzk-CCR6NAGcZ$5ct4}teiW9s=SGb5g>x1kg@m70nt;HJ
z{U_10TXC6wWrwBu-Cnv4U{<k0hOnB#YD^3GRNO7}3ofSt`$AMoT?3JX`>K|?w0<PR
zj14?Jl9&h8(f7b+=5O)5-fc=o0pnF(h%}(HjSFobs3s=J8!FYyvskf15y!VaVQ#zd
zlLd3ZzwQFYfgc+nUyMPNT|8zT5G#(pu_S2<94O6w5Ow4wl56eDUu-v-k-S{&F`M5t
zE&?(h4bKM1(jLrBGP8l4_ifqm*NZgK3i4_n$`?CG`84YqQe^^?1^PNRQv?A32dNp$
z(B!h;`O>nRq}^sQp2$qCz5;J7+O2WL`r)iyho($K_&aYYSdc;Fpr?k4EhkL`Bo`JF
zTtAJ{y^aNzEw~b?GFw0+=HR1Az?~E4b|T?owG-sGZXecfM^#)VfM~P{Jhky-1>}ms
z)<Qkh6!WEj4@Y;I9t&%4G}No4YFHiwbY^OgaDOoyL)f~U+?tN)WzVwm?nRzs1fjdv
zvu{;3`#-yCd~jMZ7x(pej|jI8mT-xikbJ~+@%@8L<#q5}LPKPpHHBQ4Z>EQR&(sV(
zKOkjVC2WGNs||Z#7S)N)`xJYPHKzif+1EU)gSmA&>J>E7MWSgha)S9<q)>S_w4xg{
zCXgGQPgpBXFRhVN?NDBkZ(_g^1hHJ=agw3z4WgZP4p66dtFxOVXa^Q@;rt~Yb7i0X
z-vXSL@F4H7)detc>^FR+eA#kMDRW3{r8YQ@=+!F{oZr(pETpv$vNimp?dbFeg9`Sm
z&Wv>?W$H<ZlZ_0fAti63kA1viZLNGD?@ZP2-FKtiTrdn67MM_Ld|)&TR+>8S2%n(V
zrwe5elYSKQSH1JO<mdvPsit9?k$;8Uyih4<5V%mW@YPdoFw~Rb5WeiWvX~F4LuF#k
zoo>#}R~Pewd_1EaAj0iO08!p4>OAjwZu=yRz-^5K<?V9beorY)F}&*OUz}u0bzZUB
z`s20bHlQ8asTE3vgopyVdnDInWkWd=U(3dQvT0Xot>Y-i@Tp$UBA92?KR0K;$|8Hr
zmiW@R2bB~xOBnM4DyY(<IYBysNrcPsm$X#yLzZw1k`C<3RmE}aFWKW&9;it+qGxHX
z%4R9!q{C`H*!HAUtFi5qqeL9XG{+V0d1EdZ>0|5S-41)M@~mt`Pg+5|c#>AuN#3#5
z+r4HZe>N^b&H8!XF}bWW<K=;&g%;Lnz!;nUdg<8iCr|%jv;^yg_;x7+y`bi-nb6-A
z>;3tU(e4U|HPMaEIi3D}OeLGu{&;O(y=R7pcgy9>HJMSMb7NfJ)eN(@O@8*CRbH~S
z^BoyiE8Gp<`D(MP`rN(!wI_dT$L;E;Vn^RPF0)Z%=WNZ{X0x&DvL#d1?^m{@&*uB3
zqpM7lK+U{zF0ZRBJA*Hr67>JJ)Ncv?jhe*!#ZJj2XTd;i2T=RRdOmI}`Mkb-%~b`D
zDOF5McUqjyM~)LxESP>!Zc#auSvHt6KAbjXyWF@#@X_uDOp{Drp<8C~$?`3D{&*!8
zYevH()B2`!0?|q6qXohu>Z2a@N?z2Y0&~1$yW&#xJoENrk-r&^h4)J%7vI+T$B$sq
zs&sz0x%3~Y^9%=VR@YHrsn@6EBUX1uZBVQolPMis;`V>p`$f{~5Mz-##Xgfew-3w)
zQOJ{Rrv1Ld0ptR6D`b3!ESO{7WvC7(d)0)L<uf`*kiEl}#w5#vY-ox)?2?hL86t;7
zW|@1+ZYVma_|e~qcpFaaBUVWx{gbXUL;LUSFuviGj^4wMv#oyWZx%cB_?}k{7}cg|
zma(nUDAGk;7Ar3rech<qwzd>?{5bGCW1E-+{`{h2ZCfs@<Hu!(Rfj`sw45j8OKrY7
zk1TKEh-Ia{Zzr=BalB?QTZRScFl2MoSc3YoQn3{gK8U7g^4Iz8$Lxj~hnvIjN@m^!
z-#N>0V~<gjD4AoAL_lGCzGryVFA$53TFdkUffq)*QcvKV+y$G3nlq>$*m`AxSDX}q
z#RYF-0!2a~zHBo228e(P!Ej`pE`<dW)ZilBXv=MRU`U}*g%{i?%dUk5{1D54-N~a(
zK`>{edJj?Hlp%9`>DjT2ZlFsUb?`u_9`($!yg4NM3yLJe_Cs-09(r&$kp-Sz;^40a
zapb<MtQ$w*Tv$zNWSidt!Xb4OfgvDZ1nOXu+V4wrNibhTgD*Y8gOPoXPP6@%grC>_
z>H0wON+<$+BTfV+jy|#kc-r>Bf~vbiUIcbZE(x0mN;|c!kncUDj?hM)UqHi|g`eAC
z4mbQ}=S5#~pYzoV@|`Zef!W?V9sR-^??@2oHP4y)q~+3*`G1c*zWm}~=Z$Ma5juAf
z&L_h0%vx>1+xbK5)tcwg{kqm!&lb`4laG0~s{7I7^9ywf&cs3K(fJ(~eo^^DV?y+c
zb;$Pmjvd2wk(IpqrqY$3CQcRX8{71$FP|jN!>_Q0Z9j=^CZ;d_P_Od&1e>!V=KFc4
z6Id|Qf{^gZ!#03z>5q&#WZiOYlJ_Jv_rzFQO=HGZ-~m}&HYBr^`ucgBhBobpOijXb
zA95~%?Jubbf%^<7loG(3@xnKbkd*X15#~6x8V53r8Ov~Tpuc}Q1f7CXGQ=Yxzp^hJ
zS0xyQm{J%#@9Oq=UV;3B=;53unLe0tMu{X;PwQEW%#O>=lw{v5sRtm8h*<M_wn|0w
z84y`+usW>KOCB=$mrqAXZwS0EuhY?Ffz*1)%R(M_*kyjSz{f5;AOrb+-7C3;ncC|E
zu<wUL{BfCN1t3`b>Cklt?E2~>pb_p4_9aK~Sidpw;r(1^31xwEW!3b9iw0=0MY%me
z!%MKAV?TF8_4$B9!o}jTqUgkPt-H*)%dO076YNgm>QiSA%{yI6;Te2**bSoTy<z-)
zJ`L7~5W~Ryq^NY2$GjYzm+5I;JxoGTSfxRSvAB;@dcvxV5S~aL`(JPKfIh;?)g<>Q
z1Af-XzBzOUiZ=$CdiCasw0#=!ktH4>cqQ)dB9Fv^9^8gXLJ9Vt;a(2%qe~q2$MYE8
z0IE!#!KUcb?z}#wGU;7wPXkm{ia!@QnP_T%md}sqQ`jmJA_RVh-4d}*fPa+lyzZ&+
zq*-O)i;I2&9#Y~1aAFE4XuRZsrnJ{6x~22&Ne0W@QW8x4=Vz1v9hGAF#QJ>=$#-DL
zGHs?_Sw<%nj(y+gK{aIqkB3ds!Q=bMP`(Qf$pGzx@d|*<Biz-{V#@Y$pWOoNqrUin
z&};O6F79GHUI6f-k*Oz!=iB1`4M01R3FtEul8a~BL<T$2s{8T5iZv^(-W<UO%|M|S
zmc+%Us`IZaxxz>?b8&TLWFe}ipbIM*8zq*_i7B_~71q;Eyn;y_n0+47>sSVl%7cg<
zspCBv{2I*Kdy!w%n`fcH2$UDkUTWc{z=*WI*ocTGU@>O2E3V&wD$e)3Vw5N#A#GT#
zOj+}vHg*5`wu6r<N}36Ca3R29Ab=2YunV@}O5oD4+suk`piQyG90<C#9r{Z#p=||j
zL!uk)99%l;{MF<2Uik7OubP1fY~vX~atg5zqg_=THdL}+?`Ur_?74l;v&Nl@R}iEd
zX2{$9AhH<H%8ikrMO*Cobb}6!IIvKP!n8pe`F`+zq6=%F+BZ!CPg#KGW+MfcdmpgI
z2Qi)Q7(=cK3X_gs`+5Q`4i2s)ETV)K1CWYx8z-gU0`9!mGg^m+r~;3!+utkV93~c5
z7Z(?K0XMKVe!xaJz4%d}lG1=!;|_q=6Fz+77Xx^aw@YB=`H`H76HR!Hl(tY3pKdR=
z`{_DH6@qR4DC!4DxvRl?nIzCLTwGtPAcCbu)>^u~@Iid?)Pk6N$=us3fUiGdh|j{M
zJ0UY32dx8-)2y2^i?E8TUc4|pSX!CN^SA}!7X_H^)oSD5v|lq^V9{h3dg9XwR-z(s
zfwWj3(eyMlQo6Tn^h$+gpxm5HSn4K|fe7MaU(DrMl!3SB*x!e7y@D9cI-$B)wn`vz
za9n7ef4%U1$Aiwg>R@CN+!RvdQtx`_z;ZE1;!RxxTwUoYeh`%?@yS%~wc%C(qwDaO
zI5rB&s6VyyCw2TZa?~mJ7qPzzk{$s{ni``Gz}+hV4DN?P%B6Besmbjs>Ej@euC6YG
z<lPy|tfF=2^R7iL3@#3(^R@QY2*`ZdYj^W98ilN~sFlG#aduP=((#UL#aJKz09!Bh
zkL-NK%bqaZ-!czE*HX5x-97wKW|dQdn!jr$N#dFKn0=dQg#;kX5*+9V8aY};12V?8
zE|2(T>e@{5z)H4VEU$|Yu!$3uL6I2;m@UZ}@8?9AG8N@N!FE_s7u?{Ghr-|D5AkU~
zUxGAmSdII8%`vqot6qTl=T~7@jx+4gxGtaS#-z8fcVRn(3yyb|qTAu9ju1-NIFhS}
z)hl<E@fH8BDj7`G>Aa9q5Zzfe?TBhn?$0d++;WWKSgFmt#N5yS9|scyIf;w@Y2UX3
zDO0*#g;EWS)C<BPP!RYzU^0=A>N_Ret!PI_mV=RiNL=RnE+qbJxxGXr{!}AgGm06h
zq8gcGCi5Wq+Mj7&GyGXg4&6mbDc|<m=%XX3Ayb{q1AfgGs&H=^j<>_^A^f3V8w`kJ
zrFKw&$s%w_8gZEC*B)!V9L!N!RtQRwU;Tk1|AIIA=$bX~ZIRk`vahe{vh{5{Q7g(|
zI_-Pt7S&ga@#_nINcj&i6WQ^0&I)@>TomP{M=74O!`RBr(%2mO%mD9`*M)-kfF;V8
z8%^?!!UOXlBVv8fQYC)^<l>btk%hz^2-A46qv>MacWNbC3flnx3Q=b<FMrNbpq912
zYX-CaTa{kr&x~jP*UjpUC9?|+unaczwV9y9uK~xi|M#={zYpyOE1eG`v#8~<B&78H
z(aK0FD!hOk`L!(VsaWr26Yc%-ihHYHI0UMUZ*hN4{H9#e=rkH3OxpV3juNKajBS>~
zw~a^hG>=ag-OgD9MVzVoB|~4L@K{C9F}}O8`LkCS(ec;M16$DXF)?Ukt@!)1d_NCp
z`NQQ-s|8BbRs>SbXTOi8<c@MpbbgA7WkyBj8Eg6Vff`aq?z#Y#CN#S<8v!~c>ifn}
zuY;lz80dOgE{#3ZaM)^^qX%?36zYTIjgby3+D-9ERs=G2+o&(>SEFm%`Al!ND1sK6
z;>`F<9mIxBP>*C($+v9zOWK_#x82_eXwp8K-^siJtWXOCHdzEFj%Io@)xa%L(+2Mk
zro=K&ZVzW9+t0601nrDAOhjRw|NOp|6%2%X$>g%pyS%=}<URP*4ep-$KGNxA|E^z=
zQ)<nG&_uhD=MxU`KQBPuAE28gw>cWDttW;~`KnVL`>AA}h_Kw}%6Y$azA322KRaBB
zzdJU+e|^>2<tIew-8;(4L2{$fZ?yJwgk>j=MIz^z<G<F)5eA>dh&L?QW_80XUgVUk
zH?x;3O54wNl_S8nH?Q9B4u6cv6}ypTCmx?dUjjA4gcT<jyJ2Uuo5h)L5A~{CpA!9m
zn7H2lGdWYr2We+9(@?J1su0OOQHV)uesstl^}PeQH>IP`ZW%q}k+ti8G*o8BJ}uZm
z_sFWKC_LPdD8##9a*JHZa%GO8!;D?JzGW1hnxseq&<5Zf)q!F(9kKslW-46HyQvs1
z$1f$8cBFFrm+AHEYjX_(Zb-TSTG8c}{D4babO)d#m<C@vfRDe~awcI-0bG}~FfLvM
zmVNgwuw)fOME9~G(5I7-1TzoEvIMjzwwFhq_1~?YuHW4RbZ;I0v&;gI?wd9(=+#M?
zWsl2dJzhrL*3&^t;Uqj)q(_e^?IUefR(w1zIaOw5unVOOxwGxmqfm44Cv~a!#>tt$
z{Z4;~iCp958&Gr<R&8y5JU)Axb$<J<bDC`Yulz%9j=Gor+8useIqG<nsllpgaD2#3
zmMo-?o!AT-a1oY1{ZnO`S`NIN+cxHoLq4_q=DT!oWRMC)(;MPN*%7RT>O~%yl5(t@
z=r-n4sq@x(oyN~7i|yYV(+1-0sF1WEVhm03kzjB*HVC<_67s~ue&Y*_6clBR&*9}+
zOAZCy5`NVl=lvugcm8Oy!Ns=4;Loj+BQnOpaZ*NeNf|HeHmym*v1w8giiqzv=lhT@
z6-;0^J+DECh7*mX7}I@&6;M?H<2@a{c0RN)WAAHalrb5*=Kk|MmN1s<4}{PC>Wm?f
z=UxC?j`{<=>u2FEL^R(dJeeJb?+p#(%j;{RL~pti&3KVK^=Ul+A`V0L-h1!9p?}xr
zt66%X?VGG}wTl{j=MV*~28#|bsr-B8`*$@7r3JMo5#FhC<(&-&XfB(q1YOtU*@Qst
z%fQxNxi832$KJN~6buIw?v~f2?k4Vu_hQOEt-9NNIt<v~e9Iu>cGE0kTA<i=Pj)AN
zNl9hV?GpEuECgYGI$)S&p+Hq}@i3Ev<wPnXRui?0*6Q$0M<cP@G`Q-!bqq4~ZhYr-
z-F~`bv7?qVQuzKbeoo!_Xq~m!>UzZ|rgIa*96j{hG*R4@BV@g3x2pp4N{M$<7Cn@s
z0PCMM(U&tIu07B7&-1aP1rdK#CAq(_*@|8^d05Ghqt0*DX}QmMS*6h!-cw+v2Vghz
zcuuJx6Y~9u&qI^nz^9AN3|*N>vQpi(QBheAauuRQyfa#wubOBtzRIo)fvQ!mv!@uA
zrDUjseeGmNoO^EYB_`U^>tLv}siRo7Ml7zV)C3J#@E(dhu4PLY=)XtIa9^Jxg`dMr
zG;Qz{(-7@n$$1`3Z}EJA#U}CcAJ!@(X1ZoeX-U5t<J!N<7^x}dGJD(+3i_dQai)_*
z{gC|1dh2ff!?&$yEgI=lmiR{OW)s_gm8*Z%t=>Ga#^0&RJ@6b^nsGgsH@AG#rdOQ!
ziaiPvFZf|~_f-Dw$~@5zqh{^4YQ*|EA_LT1I2i}H^Bs(7SQVu2JP7>QFqsF-_`Lf^
zc>Plzjh%$l7lvCY)E#Yi=P*gG{kY!;7V)DsOznthjM6NFvFR&CAQbpW#t$<`8jI_D
zD0<F_GOm|q((CT7Fl5e&YzF#x#zF52{E<~#LP~ctbn&Gi|I<fD147#O&-m{qn(v=I
zbxc!m<X#T*RJYjK=ydAX62AOD9=yHQZJmWa(OFMu&ks^40Sfqcy60Zz*5ZtiHj35G
zb$Y>H@lx`rT#HUOK^_Y{o$RH4+o_ZI-JsXh^A02`T?Jfdl3)mhNZ>Yhzgt}KTaX-e
z2BEz(ecsO2%l7i`(^n&BmqJT~qQC@yshR@59Za%!9)mi5&{C+7Zk=?71a)AzS`fzn
z+rd-<7~Po&q5Qib@zD;Zf-{$^>mB&eUgp=hBIo4g5Wb(=6&{cFAI#Kv6ytny_py6X
z3oY{^y0LYz9#EA)PIT+uAr}=!*%=U*W|P~sT5cy8%{XtE$D76!2g3*dvnItrR1g=I
zlK#Evpm%13n8gmni4(mu^5)vYJgG{y%|=$@t=bOQZ!++E#OIgX!QtYpwd<cIG-4}o
zfl`Z)(1ds69W-8{oCOZOHDk^fwIkGy!PE7Qd*<$H{Z<VGeJY)sb=yO{hE%d5Un*?g
zSPoYo85#`GSh{`Q`b-lYlnydrG0XcTFc<w@J-XPRRE{irjoXC*LttCBEIT#l;rh*U
zWM9Uk@1wv5Z2$20!kP|ED|~&y{qr;$-?!b2H=)O~h|=rUPu8J*e8tY~1BzrHmQuL|
zNxAUwqSN0s_uL7Z1d#D2YNFny7Y-DLHW#dB$FG%N^W}~LGi9HD9XC?ZWl3$2ReU7s
zD__NgsXSxtNZ2Y5Kg`<0w-Hqj2#1)6f?$GqU7VVOeiZr)K02Tv#E+OVk#fAhnmrUJ
zK-{|uHiCQ~K~3})Iy>qMNm535{7{#={No#JqN%<SPw2dW;V?g*g%t)|@<lv<pZo~2
z9{b9J$M+n)g4KBa=lhc*^wkc9s#Ka)Da1LB%?pK_)$b}nd=*eN{WMEho^+{vmH3gZ
z4JU+_4yZZ;y>-^yJN?TPQWnmZw)ojDRnsv=3f?*?)1)w{@tl{%cTP8uDxE2;#F9J>
z%~W=Sp@kukUPhWEJa6l&FlKU3X|8|ti0=ff!nyYg1CF~I+^-(FH`z{_1jo;VwP`Se
zUTn9I7Uz1pFFNy)GG|yC=1*Z9-iE#reZiHA7IgV|Rl&{_qb9%BM6qaFPSDmZl^D%A
z0>g5c(KxDu_T3_7x0`=kcIW9J4^&&|6E5Q2aN{KMuAc+?lwOdHZD2uBYu+cFDs-NF
z5J=kvJv1dRYg|~%IR5bS^vDSwHTp{dNBL7P$c3djw01Z|=&cLS9LATB-Ma}%)EX>V
zbH*zMb3j3Q8VYCm;EF^P4<i#tb^pA;K#wUvH-@z1Sufkf<UtqG&hF(?6K`kw`JP$y
zzKyk!LMA`eOy{#axgzOY_&WZHncQ2jYn<Q^sEU~1R`Hu$_J>Am@x!7ylw{kpwPyNc
zX5FcWU|$ObW7~X#fKJTc!2;R0&mt|0GkE+q>m7`>-$xTX-S6o=u2-|>9-Z0sPzcQV
zTXrHtzBJqKYO!0%bu1iWl7v^!5WulcJZux6{;CU;%3i4a;c`jIetGgqo69EN(590S
z((OLMjzVJ4w;i_roGnt|B9l-hYksB4x;Db~T)?&7nc4L<QSj<CS$`<DXJE^uOjeV%
zia|@a-JbaMPGWT5#j9^8I#Pjuh&UTFTPB4C=BLi&s74D-GP8bSI+wNitIcXlAnScQ
z(SoyZAXZypFX)4S&1zd<2bT=WiFZsCwQfeCZ%0HlF~*rXt6AnX)jpN2*T200_~nhR
zF6Vm^W6{oXOxTf)dK%U=$^)*_oA!k}`w-t_kS(}qDb8f0mz5fKG4?W-8_)TD8t5p_
zu6Rg=XaKEOa_UTx@tP3&chYG3%6u4<7h`3-4C9X8w?xIZP=^YAk7E;$Lu2WP-1k{Y
z4GZI|fwx2#*Xo&p+ayKz-o&@d4gsC@NL0>}qVCgWXYry};xP9=*3uyg$~>-|(t=hQ
z+Jfi0YSbJE3ESVTrKR*srY1fQk77$gckEO#`AJ2iRvov7L%c&E81Onl168V#79{l?
z>Q5Z-dXEX&0O|7Nqv<SKSY)_=XLZ$A5XznIq`rPi#rMvM$jf!{2Z9AMRUpVchTog(
zQ6`nkUCl2&PlJU>_ERTo3U|K9W;q=7I7#I$-!8}HXgsAEhN%f~sp(#q8j~o3QH*A+
zy1w&b*6}NLgSPG9++C@Ov!|MPj>ZGi(|=me_4HKA%*&tdo}_+yvYy7o&4(w?gzHH#
z-AHAVZI)>5cJ6kDd~x_bA>AlXOt~e2<e!Dc06D{tY<awD?#a2nI8$8U(27gNb|t~k
zmO4(nf5D{RLTPhzO!a(!2Cnk$V1I?wxHG**RzPaz;mGi!Rwy;QF0zPlt$fYAux3Ns
z4;}66Q@t6LyYu<i7yR$T-_7?uB4oPndW7y+<xmM|{9{aq@`QH$tMjqc0q^thrYgC-
z8Au5o-$UfJ0Glg%5|p;QXAT24L28Zj0_^KFe%8yNyE$CQ<w-n{<po0%4C&fztHU!2
z9jP)ByBsx#xd8J(G;!j$divq&`~hQl&Xi#8wXSi;{y%iezhtXE>!2npn-0r#b&H#J
zwPhE6wia6Ka0*6nK_c;x*oMH0eB|%!6#5qMQvVR`vJZs_GZ0&C5F$s|zwH0II($r<
zPE7Ej!lk?6z`QsfI&p(1h{ERcaC+rUoRu~gqO}%;170BOJc;a@l3_e$moOy~<onqw
zAKP2Iv&lJA>G=n8-f_VoxhIV#-dj`ys}GpRHc0%iMGWM;8n%pS_-(S9c3t72;V|gO
zKx%vhs>IT@M&~vO;*=|~)u7#lf!>I8VAGZLp{Wec$hypXSp6OgDs&crv&0DM@(mxV
z&^TEx1yYPoc#l6E!oGaC_CDT|bN%H>$V1@vd6^SuQ&zONcnh)3crWwwp2av5Xnvz-
z9RbpI%qCK-v+lsKJU$}*YIdLPe19>FetEcH-iu&D$GEVXA?@M0wGva$0$El?E<tpZ
zs8TJpdX9$hjXL`EFk^kgsy_+aX5y0Fo3M=(wWva*g!H9bDjWAos=BSC#?CvyV~H-i
zP2<y-wu^SF^0q6bV2>I@o2CS&1ZB5BBlN!&B?U_x1@smxbeItMEf5Tb9smA-bg?!d
zZa(`iX4~$yM`yy&cw>?f7Soi4(Y?oJMX?D82vDgcS+*#3zkTK++R#KRqe@0(vG@{6
zz(pigm9iSr6e#yXe0g=HrlMJ-;1@!l%oOg<<_zx5;2az6+Qd`M3A0iHCdkF`fI&hM
z+DX2)mbH*WCeT(_#c+XUkNNF8myN_nkF-4ram_*d>(6-Tn_Q}78{6B3EN3g#!>nc$
z<m8lrm8j#fuF^y1vsH&~wg|@BQy$6gNrdPD!PjO(@$}nv!Ft9SOB_XKO7^g6+6v6f
zDPXOejbG+07k38_S)^jSaUD0mWf2O*mT2Eoy|@Jw$)PwH#y}!X$Q73TYP@6^X^D^8
zoVO8PKBrz2Br(8Pag-5%ad<ZM#{v1p{`ueo2TS~gKN~9x;km*eMH*{tXpA9VqiQj)
zz^lf@+8)sbCYb@bjX5M*0l^B=qBa!ytlT}Pvya_sy$Qz-4XbJjErez{{2gzh!^D<s
zy{i!~O+uJL4vW8?Uqpq>H-`OJ`p(@%#S&6{_Ij$qGyX8yDKM6rg?4ou`j*-VTO%_B
z?_QEVqvDTS?K9;Ho=M*qEGS+6;MhbLGz8b}?YmhWgsUSZ7=pAqMfr5j=PZm`5E>R1
zr$Q5z@7-eNxqx$`U^#JNf&OqB)k3MF1Gj!OkE8c8QrCOUJ53$P-h-GG0_XplMri1K
zb3DlXRNua~@kmL&1#{(gs6io@81m!Oo#$OPP*paaF%l-~4jO5D1yL4)kjkFR)17yZ
zX0<1&y=aK}+9@khJk&h|RttWFqQO7zSuD0Y1Vil%=iW>|<>=KpYSh{%-W*Iz6}@sx
z9&!8#XrcS^>seF`|E3@8aJ9Mo<!WA9Ds@=>aI}L2J&Fe$uJ}tAYp7RP3+8u(WO7y#
z9-0;K=^|8&JTvDcS4iq2tP)rE=L0+w(yAk)TU{~O*PxFzmFaH;Q8nkemZV9MY}{V;
zry^Ww`|G``(seMGH&==tJbR-L)JWrI)+N2;jD%A`nSr&?16sD1d3eEs-D`yCBoz+n
zG)sx6TZe~Kh)I?6JpQNFtQTdNPhbNqPqDbe##+xSQoNd0d%vs(U7{2)zmSrcPCfdZ
z%IRDRt@9+MW|5w}GUD^%J`-+_@*|)v!Kj&wjilHV@e8J^9r|$o9{|@tD8Gup+)i3~
zV{T=i;MifXkA-CD5MCGy92;i-(s>(gc+;s3nV0QLy6D`7v5~?%nWRpWR_8^=#{|qD
zs3)oM%e;9qeB1{(8;tn~nK`zJC&xYejkh=MsYcq{XlpYT6<@SP>NIKO$e9Z|j@UMC
z=zJ`n|CE>j#JR+pD@a?b{#<9SLLkL;EqcDj9Lu|~y?q?(6n?uAN5C-n5Gv!@P84pr
zL6~E$lbma?R-mu&ND0R`=}f^V*Cq-EqoOQaIEL6yZ$;e#t=$oyOxu$<6F<`*IOaV(
zJYcPM75y>U18c1rQ!IyLkn5W1KZIlTA0Ab))~9QjXFWWUqb-u^T4vglbz^=8_DR;6
zf!|Es*&ic0wg8kk{hNdK4a~*4v-`^TJxj^KQ&*)*(X8sN{T99_1HD>T&m)=Bc}w2x
zsl-3CkM28|FW#X2Dc)FDgo^_I2nD!axq^!V|0)V3IB`A%rcaumMOa&jn8F?#W&&$K
z4D_e>i+EGGz`)H%<c%BH6eNWva<9z&X_oYSWdJtXDRcv|AEvNB{xDVg40r>Z&ZIJD
z_Dor|d>J+ycO-54^wPS0C&`i1Up|8to|??#M~}$RcP~nZF5M+-_Dpgc%-(a@8F1t9
z)2tavGislf{J9XDuB1}FQdMc#q`7Vgxw%~O=TiA~&QI9PU6b?~GfA5coh5(4{JOcK
z+2dSj=r^ocrSOV_!EEtj8*Io@Y5b-Qo8{-3KgjuWXC!0htWvIGWvh<X&P+E{T4%g-
z(|G#SS(vF#SG?JFY0{>X7OmS!p`wL!lh1F&o12~M*tpG~`?IWEwnV?B%aBnTHERLw
zVu*yDzpONB)~Yo&J{~glo3A8afkLuv^G4aRZHwg2lTR9;jt39ylZC&|(~W8AauuXW
zix-uq)22+(CpT_eH*e4lQ<ds9rFOj+;8E5ao1%LXaqR*&ThR7mQ*`$1MVa&COxdz&
z12(8pQno^6z-lF_z%ZE&wW;5yA5N4P+jW%uh4P_J5O%<H_Y-_8589;9kU<vwGDlwS
z(;rp_hh^b{U!_X*8uIh3nXm>pC27;8m+S#SLKypM)J9uXmg!Tzkrr**fwA+`&Dy1l
zm*w-1-j`M=BX5wu96E9W#|ple&?ASG-s#e3lxD5kNbzC?k^a71zjRvL#QY(B4X!_Y
z|E=Wo3y@tqw#f--dD*`jHESuQ%asMQc^k(FzD3&<18%2f@~0olh>0J`j;&jyK%pYo
zB;J9Bcdqmv@S6Dg`U0k?Iri6|eJtI3zar@Xi*3CgeTVeq2Kj}?&Co|G%Jk_!$xkz<
z>n8K{K0T#l*O#Rj@ZGXyr?%@kl*e`@E|sg*&<&~`e=?#pAt!uVC6M_qQ@)}!0Q?lb
z-r6RAp<n;<^N(OKu1hBL&6aK2OHq_@;>20ird6s^P5%6Cz8pSyKyn5INWT09rB3|@
z+OJuc-+o;n$3jD;bGMh}8kqQ>(9bulTdnOMQmU*pZP{8fXG$ZB7cG@Dz&B6c{Hnbs
z&IJk<mTWn4V2r!T4KOUEPySM+bYpF=2bs(p5w~>?=+(EM)ItB_CY%B@HfF|*Y2;<h
z4fB5bQLbIPD(TYs%Ee39F)rp{PFSk&*zenQ?xM70zqxejf-Hq~!l45P(9imz9nwVi
zQ8FEOA#hj(bLZcGoeL&CLb7GgDQ!BwB)I}}$+RioD&yR;bzALgWMWn<TaJ3K!F==@
z82USy4`#~h6@Q~|-$oxVBCXoCmmJwL%9=GB<?!Kyk~d#|)ix#r5!0YQH)z}xV>2&s
zu!J&f_sf?rgW<U%nREEb^l!h$e6UNJVmxKanq93&wr$xgo7S&YhL3GptyV1^N2EhY
zsWRw)h2?vU`BbS>%cTn!lomb^*43%s5Ect{(07um;O1A(AAc`VTq{<ou60YBHm%gE
zTUW;h#|Ih1o!j@QP^D|nKA=Zd?E}}Zh0EkGK9Qoug8{ReY}&L<=3)*wbNVD`?I{&1
zSC!|{Us9*?l9emh%KEjdrE8Bq%IvvguG_qEt1N{jLyx}w(LX&P(6Huz3Mv2kW09PM
zaEkaZ`fULOEw@ol2<F1Jm=n|JINrT;ui`|lHkq-8O<U+#pdjQ9Y=;*w{1bh29@;Qm
zGH1!AZ5Wg{Nb{aNaa#N99teae0IO540hrVaV6t<PchsQs0GQa7E7r(2UwkHY>(!Hq
zqen=U8a1U!^QNfhdCW^QK%1>tv+hYC=2XtNIsI~If3nZj6u8oot76saSetUnm!C`m
zeX2=`kmBfX5C&oVeDTSL+CF}M{_^n$6QoG7;yPDv$DAFII}hgTW~zbbd`*D_tw<U-
zZz(BL`=HOwk&9vHbzNJ(dZpHZ@eP|c$DEoPM`gn0w_oPT-%A!@fxWNuXmik#>%gQ5
zV{mL@kDNSyOx^)4yY%P{dS%6!nyc-@qZJf9a{hk-eU)Q^b1vsz_QNHA{;v2PI<Oye
zwj0*kOV(POEfW|@GRkhU8tu=vei`##>NKgf4Z_Y}kS{*A>RhsP3G~r5^6T86<l=?%
zSdYA=M(yXNI;?BFAv{^TdOeP}td;gJb=5x4<6#@tZIHFAR!Xma17zLm)v&?|l_1P9
zBL}@DonGz@L0-MY8Co;h{D+?GBNW2;W%7|+S-^l}E~K}13Lp5SVC?yl+3=flLyX`j
zUUZ+)zKvrczQ8&>Y#<nWa)oO9X3BXKm=9Yv(;4{ODO1c79e|Xng;mvupmlWS2Y+Ze
zHkc2lNc_vg;1N?F!n4)s5z00&5~BH|)(3JoS8!3lMS+AUpiFN96Kg_w6OJ=eS;DCv
zC05b2&6i|YpfFOY{HXVaV$+Y63xPuO%a<?7=n?PfWV`r}-=%x!4q&KTNY`F{u}O`R
zGQ|sn$v7s3ix!6_&qsVyrUFA<NEuEo$FV|i(g{6uP)(|LZrds^W20ELW*vF?rH;Cp
z^u*@-FEH&d)UGb2%9hu7H*g14ixz;H=_zci?#skc!)5mL@1$*~F3J#2{&*rbJY#hO
zyKnD4sZ=^xSETNJ24FK*N=6NSOExT70__913<TmsJjifU3r}s}+*#9<fo<Qp8_tlQ
zmyh2chqy>hV<uB-9+v$1ht|DG%hss-D_|HSrApb7VDisH<9ka6zCHj<%Qb9P@5rq0
zCrgw1btI>Mfb{J5nwr>GC|OK4Vbjy|l~+|LK@H`0nA37ouoa9p>3SVZPp^Kj3pekb
z+P)|=r+%l)8YlD1mu@Oe!@)p}es8cWoIg+6b?OR+q@xV&+a2eFKhq5wnH~l-DTE$A
zvSZ68`SbS$`c6jg=+RJY>IN$FOM$>UZ@!KV?j10W;qumN{pGdZ-L-C&tJRdipggEg
zlw{A5Q}X67ptKt?Y%sKJ;kxj%X=&-e{=to91hnq0>es~PZnd=T&>8<;0)0-%_>n_Z
zW6$=yeZ!%aQ7o<YAboE}M#_+P2FfdKTS^`<RiqoWY&9#DlkHo!DV;WLSSzph?gqMi
zD-D{pQf=!upM3;|^c1wYsbwts$G$zgA^5S{dfn<(vV76+%6LqHNhoPfrs*YYD6?eC
zCiQDo1!Hmon|wc5L1a^miWD!Q>GvN9m9ixYqd&vk5lm@`kkZod`I@q3)e2?)**9oe
zz_a{kAn57Vrnzd~diQ@_s@1Hm{rX2R>@*%&ws?{D>(b>asxX9fC{w(U96x?WHR7L7
z7%8uG>nMj09gydtvG#^V!@K=oMt``b@ToQY?1ORQrCP%z^8VP7GVPl$R5;eQV;A}6
zvq|#F2jk@yhV4Z#h+R9hl@ag0DS6OlxpD`|i|yM(h*Jq;$kN9B0YSi;b?YGnD4<$-
zYqJ0?CB`88#LC4Br7Q#tp3t`6z735%q(aG{!GvN}iWDs<?+hIV#xc9x0OL*p$b?}7
zRj}2*b5}5UzoNZ|sm7T?hGb}rbm=p|M7{{d6f8n)^A28>scY7tE*P;|(yiw!ItJP_
ztSuD$`1+=lZXMfUT!w+keGb~XD4C4*CWA;};b)WHmvKYil4dWqg;1cY{0+R{d-pBv
zhu0zC>))=m4Cv7Xg1(H(@N7cAT>Qsx(aioi97$oH$Oa3Tf?)0=(XVs(`AcB#+}hVu
zr%o%qyLZvf#CD}1vQfR-=rbpkIs1Cjc(uA<U)T=jseP;FItTQ9ZJ-_rXxaD$2#F4<
z26QSI|L&dJOTC6obne^;hM(_e&sqX2Ll^J~+W+1kKNd{US{-MkQ|OVyGHBqN=&Sy+
zXU{>YfFlbWw_SVml}c5s>Ad>&=bs2i7lj1VCx3=|T4Rk<!ufM&Wh}-Qg#gwJVGTh3
zW*qDJ`!92qv2NV_MR{YuE0|+`704FUS4H_E`6W3lT6*>!Ai)qyG^$%m1+|R-9ZYOf
zv|(ik_bBY6z_(3<nsWZ^d03y^kil=hD!<SB3H`7m=Bo~1MrX-KAG~jA&~5#J8f^+T
z3Kl7X{&QV|^5v7P5RzQJd|d_s#&q;~3Zhy=fU|l18tK-qB^dX!=nIx0l{MmA!XrEV
zA!xgTzDyeY@Xco!7h%zJRutwm&Nb9RGyc0z#!J%|p4WM<Hw4kAPM(m;5WN2V*COeT
z`c#E*viHkfWF3TiC`#w^S9-lHyLW7t&fR;eu;P_o-Q`mVskwA6U-Fl9?bsF!eJkk(
zCixD8hZRZ|mt&ZFiwBp~dXtt#O9ZP>_z#Sk*3BD35LrQmiA6C7wSTd>3hRj9Uw<sn
ze!ODY5(tOts=$qFE$6)GwN{(cYS}cfAQ(M-nC#xQQw40K34f0nIa~!6>(_3Snw855
z({=CFPf7r%R!thJ00c>O9h^UBmReG9j%WVUCr`?Z@4nLUOImip95dw20a6rsId?j`
znqu+v|5){U8gMz+QHXsD3^==<VU+mfJHIpDe195cJ_-!ZB`iC>e`ZheFfiG6_Q2QZ
zZ>X#(!=C18{f=Fan6k7?6k_H(;=^xG5)8~IgOM;EvFpS3vDL{|pTuxTh~{Q0H1#nw
zN(?5ZU{`Qa;D3t(?EFtSFT^5<HQmSh^zbi^k!c2K?2P>SWD{5nJO?al&!Y>&4}!;o
zW}b!e=ZT&$nS>i*|Ew8w!^#atmCBVRU%`TsJ0Lr>Qw5}CnR1dLV<stDq=0M%Gnf&-
zTefJTnmU@A+G$M9V%@s+<jq0DBz+nmY*vD0<<cc`4jaTvF#Y~y;ux9y{d9Q|%qcet
zWYlPOyn4klDS!?Bhwo32t$Pni!MwS2Bbs6Md?^hrBsXmvp;fB}bN06e50g|Wy`(I(
z1ZmTyle#skV1p5xe#Ge}&MAff_qPVWCx!C_2t^X-&Ypp0`Ue$IJog+-78#J1j14tM
z70Q=aZJA$=%+OHgL*2_O&A3qx^3SFWh-K>PCSQL0JsAJi0$x!roIfw22lvS<uMd=5
z*)uA$yI}5Yxq9V_!lyR(<(|Ez|C{fKH%tvfz|3<4*A{f7W}MnT9thx*X31ZC`ibld
zJtYBtS+wcquib_-#zmz*Hvd6+1HmlcSDaHr+tV4$=*JUAL#XkRWP)~wTK$HN8c9%|
zKxmWKtLZ%HLD&q<TDO<B*Z}*0Avy)ifa%|UsoMW?!9`@>(bM9eCB4>D6XQ=3?A|E;
zAoE2nW|KA@q<O10lGiV*+`{+sFme8T(uC**nF}5Jl|As?%`K^9z|}V6zx@&I*;Lz~
zn@?^Af5Cap+z=M9JyWJmBV#`JL~;TCRR}Anp=Zby=r8R%c7o%n)e!KMky~Kq{#@{j
z^mzkj-M(IW{NT@JtE4(MrVKpJF!bHGRU22OTq$UTj;Y40{Y#y6EQ}jD5^d5;`lHQw
zHBa&2V%UV<m2bcJ6wG=_%}?zwWIb~A+7;OiXJKy_EvEgnB-(;na?<AF<p}xY{jpB{
z8bT*n2>gw6xYU%n!9>4C-4~=6gg0qYcmUQBd82<{XxjElrShd@cW9{my?ljy@%>CW
zcyParA2Uj}BTc>_f2~WVpXW;mEF@^nLM<pmpV!`!YUul9s@Z?J=#$a=^Y?|i`Jeau
zB53pi^*90BpBrpGZ-BYkdni;gWXh!9$e15Ia0pf*U#nFO>%{&}jUd~SjLw?PJ0t`G
z2eN}%vrm`8z^h4%*3zO`Bl!bX86`@UmR4v}oGt(^R_oJh;{3S_GIq#7nKgI5G-y~)
z+nhq7_AT~8i?>p(5LlP}phFJyyMthy=FIt728|pG?WQGYV%a57Hc$tnFj<OVZs^*x
zFDw^)<r=>AdutH1d-cT~6Ur?dWB3GPflOkSOlf5c#zKu6HRO4i?sJ~|aLjNB4Qs1V
z==7y<%st0s<hTjatydp`MK^>JIpo*bKj@sqc`jGpf~ujmXq!X^y#A*2>HnJeKnora
z7%209o+ancpOdub(ksLJ%fiJ{8v;lMGTIwfE`tCj#x$64Xxg7OTV|Q?;m5M*_xV!m
z`FiNT$(0%I{Yrny4vp~pV@FA=7hCJxl{%%Tlr9^h%v$4mwbd$zLZ$3}xuWM@Qk-Vf
zAr&hsofHAvA9vu8Ev>h_JAAYRVje?xk<sJdM_=0~P|BhW=S#b;-Q@MR2TO8ykrJ4n
z$T-fNIz`uj@x$MhALlKQhG5cJ7T1M_TQ<n14eKxtvdFBNGi1}weNw0())vHT-B&D9
z;gmr`>keTMEmzoflRq3QFEpAe`LWLY`o~}L!?dZgVf$_=QaHb63CI;F#V`j@tJ}0?
zGY1%!&dqZYsZymf#uc}&&q+W~f#~^-30YQ&qD9c=ZYpT;Op!{y9QzRj2yFz_Q(@(N
z;QaQ`5#k4H3^L4rU@o}=VKwn5Eo;}Vt?O9U%xSgGi=p|iQL(JFYSRH4^vhDBSW#u3
zbAg_u2Q4j9rAj453)j)LDhmV;Em|~H`Vz<4zx*ckAtYdLNRuY5<PFTJYZS*DHQZUV
z`^k_|V<ltyRO-Bp)>!G&XNa}da-E}qfI_+cue|{Q!(tV*aa=EgkaqNh_klxd8S>@p
z^6Hxdr7z|PF9_j+OO?>|rZd*7+V$$|T$v$L77|M9Myk?^z%MWleqrv!+Qcwm;GpO^
z=Gii}R=aAUPvH;S|G_}P6~4zA_h4%MLbMnh&nLywFoCx*N3Py=MwV|mEw^Dg_8bJH
z!TB?&Nxm;8B9qQ49K}1zx1&yrfEyURo{$jp*a!pP89XtklLng(U6gZ|Z%CbB9-pI-
z%c_1{@H8X-Xi<)e#zjS@Gx$ZKyro-D$=V%farOhacza0I;H*+Ae@0dQFl{8pJda^`
zJC4Ee8-u}tzwPzny9oy02$VzoSucCAaJ7()3kLCIP+A<kAQ!IPlv>5D<DQNxYP_k>
zsS6RZaQz86a^i}vRcX?CNzIUKQqa$`GBWsRCZ|6J#?qY-D1vYm3ucN=!Lk_7I$XvP
zsQDXDNuA=EB|YYLlh1)_oV0kV3_1&pE5to$9!zQSKWzF3Aw2lyQD8nw+6POr8iL>B
z!v9HOJC{dzah2no?*HR^Tm$w=>G0UJcFc5fPk3>EeH>h)@xz%%!zNi1ktyRo&Pao4
zv<D<O0WC4})~r)k=FXfZ1#oV)SKn7uYfjBv`t)ff0@^g5kG+jeFpj=r184O;mP6>w
zPjkJJWy?cL;v*4YBDta9S#E0CsQHV4_uVa<HmcT%j(&I^b?ds7(4^#7v*DtJ3drmq
zrpiok9E7Lm>j0bP)Q6#+r*?|xajDs(IWHN!;zbIBDX<nMwmI{0Gm$o3Drwoe9ZZuq
zs%du^Hqw7#LmX1Bg1z<_uoPT~YN_V^I9>AO%p$$|yaH3};!>tudC3g3--Cya!IYn8
zu8|o3Q=?9eGe{s%Dl4rEmnf;G#Z(~Q1*6AJ7jfY~YWu3e6xIvOc?8aJdcvF_Ux9*(
zM;MrUGQSM02TjlOgRz=6WimA8G>1%zCpaGj3K4?x1u9Iwlj)*K?ArAk<mizj*qoJ+
zc|XpO7SOtbhmhMi9~l{b87a9q16hPhFbm~*QyeW+GhWt-XM?R0a5fz6WYTH|WFvLS
zoTp0RA=IL7+q_ASB2eJ5WZ|z;xOhn=4by91(2Uj}^Ui*9?dE+oVJ9l#K!E0!6aZ1P
zM-4yC^XsCGb7EtD0|?R7m1h66_+WkNHfSsz!02`A+Cv$mpMIJxZx0)(n}Np7TgaYW
zJLSWVKb5d^=T!rjKPVSFgrKT`VVzBn!jTNt_b4=$?1K}>j8uyOFK=&HfE<!vf1D+w
zKlm7Jl^pcNh8UT1<nWVG<8YLqX+vzXUr@nKFqqO(Fg@oP?6aqiYd<@J{Dhs{(@PHR
z-=jx3%2lW=Cqj>_*4-EMyM_)!i-iuII;$ynSup54%E0%GnKQ%uf4k%f$O-TJ-z%fc
z4Lrvk!)ch;(~_Zb<w{Z<79=<hi+aI221F*KeGKJLQ~2EvKf<K7hHmB=$5!Etz`(vn
zCgBFojWdpYh#@1i&^{?rC<Fg`zrJdIPc0`I8Xo=lb<S)Zj~vSkC&Ap_ydR={h5b{<
z0UB1vEr4?DUx8`)&B$;tSu3D{-lXkFA>P5An`JYYJDS+P_SV~KB44FyMcKJyo7AjX
z1IH@@WaaYZLN;MO9M|#afQ}{5f`QuRELpNj8VsA#7^Cb@x6nz0K@*OJ!v_ySbD38D
zTDU+O<CqE0M2B5OU2qnc^rK*j;S$V$sadD&ioyjR$w>pINaqs}hkb@rW7#~T&bf_O
z(=jc-r2`XBMmFr+IZ2LmWSlVdpp1KJ_FSW0L+xW`P_Px4p0p`nsfrBf+Y5dJ1|eq_
znKN^$ygOnXenX=Tfkox&H6<lBa1j_MG@+-7HpAu17xl?{u-+zc%yPbXAfS!eCu=ll
zg(C~;bzHFt*$!+=`jX(>$a#pC8$5bM3lCbVgq=GB&Fl`zoGqsw{h<{BVKIK)`i*jM
z|30+>sRwiQELpP&cnz&z_MADSX}fL`dGiM76oV0I#y-n&M|}CD;FM+8um1w*mq#FX
z)40r8vq-1jePM-i6!mhfHJorAAqseq00htZ!K@V;%{G7_iu7Up{=-Khu*|2w(KNs*
ziG)<Bph6EG2_Y_IP-%(9^x;^i%2%!mbM~~Dr!2GbfLytSqOtSm&#JY`l+Qm_;X=<o
zeRZAWk(Vr4Gplt6;j!<qF04O=Fm%NE^_P>?nqfEk`97>E6!tP)08Abs$^hm(5(si_
z<q<RIwU%kwe|SuSYjtgmneLzwsq)bW6IGDLez9-oc0Jxh<~#xwq(6b+l4Y1(+l%dB
zttl4$2!J?qjwb>5EL{qIEJ~Nv`R^_ojmOxYT_Dw;b$E<2Ocuw}ygm-5+806h2proP
zF?Fkqnt4Ln*3KZgGkMDySkE;axmD_xJ|>fUm6D99l52aIa@bxBY$L+q{1JLCTsltK
zE?=}MDEV=wp5gsDyCpT+yhnoq`o3b<IT<kjm|XoNi}=Ey4)ZhE;jzIT;5A<392Mo5
z;!l&Fc?p8=WX=ap-XtF^I4!;FWCFA4AxjTjk$0wq%CvWiNb9PBOfK_Q9F`47uFAwN
z!3s+;AY4<Z!i;&haAc`5-zLr31>Y*kq-O&jg|xn#r$IjYUE$%6EkED39e`EHpC=@|
zP-aV5KtxcC$+v0$c?lV@0n!aO8CWX4-U{^nrt>o3%R@5joubmbQUH=$^taQDunEJ2
zsVk1jfr~fg?*f_B_YKD~+soAV8WxZC!?w%DPrV%sx<ZJKw_wGz31gocgKjEokhMP(
ziqmusf|-gWnm!hAyjX2&J?(LfvUPCt+bK!(Yhv-7%(SfN95Fu<!`Tiy&a?)$dB%W_
z^Oqf7oC)Ik_>i#U%KFgX{)sXi$k@h4EcD`}Z>$uFzc|C-`=f+tGQ`SezFJSi;Ktv9
zW2cW@z6F?Q(G2j^$<s3R+ppCeH6WKCHu%<t+~gttNt1c#c(Ce%L4&d3i;XTfIfbDC
z`RR|P*dXpx^K6<bc7+M*-)lEPt5_n|L}Xyv#{(G&o^9v&Fnu$Id4^LfFu6JX{UsnE
z5Sus4q<h?_IF}p{pc_JJ`168srN&2@GMKvlw)k(DrTVG1fVesbN+nIOn#VfxeE!@;
z>EE*pHXC=O0nQF`L(KEDix$r3+#MZ=C+Soe=hA1*U#y$X?KoHd3$$b1I<?bH4>z_~
za4y^mq#@ZBlnXdKI>2-Oq$d|N%*7gbW}3mmiN6es#NP;xhzI)=@2B=u20jUxDQ?uL
z(O9skrV26~U=;+lr`*W)?f<GWJgK2w-MW6MjGpwV#w&hA5P{~}>7l_U6UKbp8?x@)
zteQYv?!qd9Od-)z6dm-X2L8<XE7IzPT9PeifHH7o_6rp$DpMzaVL&KpZRE9+S~c;e
zUziNQKygE9x-sRsdF(;0z_LkW{KrpZQh1>kwM%DCosu@~a30yuUpJv2j2kUKPy1GS
zygE>s;fgQ|Zd0=&U}Hf?9(%cj$I4~MlvxS`Cao=46wOoHcj=~mm^d&GHNxIL$@P47
z?Yi}qVcE1E%q2F*WZLORAUOn0{^-*@@(>AA{$%LK!6i#!!=4rW<2m4%T^&h*>{?d}
zXzDj=D&yW8r04fn{k>G`HEN<-T(-A6+MilEo{QIU0A`9>MxHfafiuoI{d1v@SPsgF
zBkRcaJa+7;q(m7!M^9eHVg?=Ut95~SHMD%yo_}6%cc@&gn$kKKw81_5_2b?|4#O;;
z3_6)r62%+eg7W5t`8}N~MXC8YHO0B}<b~kCnqQQjC|4UxsjV+czlduW^(aL~(3;03
zsGa;|>KwJo@xU;uRHeGS-M@#t)V-&yU%d+E?Ck_{EH%GPnbHzuS@S`Zc!Dl-fBRFd
zTzKX^1jjTea9}@WJ8>+$_3mIjQj-b7j-?A`sTPpf*!zI#pB!ICIEJd~IOcp|rFNtu
zaCAbB2@l{-x+<@NCm9(-f3S;6Z#*<`yje#@2pFI6Pv?3RL_R22U~Xv!p%Twr^Z3Vw
zvnN$}MXL?=nOK4OZGr90bIexR92r&{n;SQ5NwwbW2fk=`JCmhC9~7fw3xYvfN%0sH
z`!7qU;EzWaNS|{!2VV;oAlV>T;#i^3kQN&3?_}D|9M5u<TzHaRT5o>T;HU)4M4ZN>
zB#Sf{qp|8tVgWPIigh(1=9l@x@@P$T2mRmb2TV@bx1-o1F@ZE-&~`>53R&rRkZ9te
zLa>;!$OJgX4e}TiQo+iD^)CR6te+Mx!928I>%Ra3EZzvPWYs#HWiJ)o4_Foh*W^Ag
zca@Q2CqU?1K`K|RAt#T8T66=xY)c;ZK^I0~z4;)a902fY?Gr4TaQ>#nS^glbn_&D|
z-wNf+>6oPXe=^J)42E9jsozi;(t>TMZ4a{GCyK%x3qr6&u4B}&GmVl>@gS5Kd<e@5
z=DR5+t~B<v{LM5bAK%Sy^W-;sFyHxP_$SIhF{U^z^GW|$b(s3+0U0&@xGWr7Qfh}}
zj|SSQ78t~StK@^syJS>n9OXk`n=sJH9rroObc|R?alEYBcuDTJ(Iook==lh_NtgAG
z(4lI8v@Dk$45`(Z$tW^Vh<FDulT+1;VD)c{@#Xss2z|XE@U`!)_?>}cgm~>dd_g|=
z;e`DCQ5mTOXM7B=L69@-+fCAD!Y-)*MmR@$Z@F~krmQ)7ExN_DMC9jayahzufrFc)
zo`hlQ%r<7dJz&Ci@@%-I@wNIi;|&f>ABi%P!%3vc7ZV2|l<vzQj0B975Yp!L#hl4Z
zj$rNAaD?sB4G9^uQCbv8Derd+mdvRv3zF9$gqZZ>c4^UXv*iCdl@#=cCf#Jl(`r#W
z&LU%57s9y4{Om|*Xl`ig<pJqvb~sw5X&!=!2g!BZ5m$zodOSr2okTi=b6+c0u7srV
zyiOcBa^#S5<;n>+kM=?h5{eD}FfUE(R>MI>mMmE$cW&#P7~>7`O<Fz)i$UWI)y&fo
z&p4)AwQ3d4p=A?)fBy$?APj;+QjOcNVFM(NUQ(z~p;&37akrJhybN|+cJ0~)M&y>1
zC{e<Sbi@;vqeqY8eAyl;U%tF*+oECGVXzB0m0^c(fO3K5*(YwCo#_)-dXtZ^6O6av
z8=c4YBcZ$|&Z>e_C2TOB=1;cUvkK01ebjQIQFHuo^jHTN>mwJ*m|*{HzM605X}<Gm
zzHtKyQ(Tx-j~F^woxv2y?I#iV#&dd{%(=0ow^=&zqBbZqr1eltK<fxk&qazU&>x_A
z<3_D!wTe=;LTPo<)1gIU*@aCc8FbQ`ng$4C<p#8imKHq*AKffFu(IlEnO0MiON~~B
z%-O)SWS2(J@-ZI!RQY)}9P(TU1A~)GHgDaop2zdSY>@G%FJ6NtajN*|%7t@_MJ?@c
z>vrlWiOUeR5}USd$0jmq^jgY3$Vu;L=wX>RcdqOWJ*no{gm>idK^-gZ<P0=eiw*DN
z<$d849@MB@UaFw(FShL<wJQY6A)Fhg1G1DE(>n6dL$0-0I6f$Sx!GrHupG9j*+4T5
ztq9hwUMcl?{utZ`(;Yf^fKdwjJT!0&tVd?deOtHhRLvmG&6)Ptxl8JRFK_<*+Rofi
zvTt$@NRGzfndtAn{#>=tt-JKcT$e*>%Z;073d<y#Sb4y*f%G9vdmsZuija8<faxYR
zpC0Htckz$srWxo-eczs4&``nxV(#zof}2hkS!&UkAJdTHlp<a1MNBh0{zTwOh9n3Z
zT+$&DYS-;sH|g15o~NfrWCsC>t_CuStOwa9GI?(g9tt7LZxVX=kTSXSTE6GNaTz&m
zi2Su;tyDwXF>KheM=l~0aU&?!oAsl&WjfvR4-8VrVb9lxlP|=hs<LwZ78Mi~D-nV>
zGF;doKwGtM_d%4oKst5nAw^)p(7ah=W#&JFX*-=S<;ho2&p|f=ECwjf;B0X&OkuC6
z&j#WjLwAlF(KFk0lKAtiAM~tjkz&?*kvVHN*|}#Q%u-8B^f-W{wi(|~(J?@+r!pJp
zXj(6n#lFBzJIByZ%h$j%!5a*&gGnMn2$}{PqSR@8;RLWf%-~ySA1e#<e{>`5SC?_5
zU=A!WGyp!GTU`cR;#MqwZf)<2FwvV1XMWjp`l%;tT8q%jI4v9^z|?3x**5_*C78mD
z*#gypU43Zu8E9eh^UNQ?@O$ff0j#qbF)u8D6~zf?OFO^ZOS=FCHD|Bhft5^QDN!Iu
zzlB}DrPsp+<;{n&;?M|@4j=@X(`0a<RkL<o89RK4Tty#l-ThUyR)CK)tv`^{K0s|Z
z>A^8-4}`<{jZdV2kRoPn<@iQ+(=9a{<5^!avJ9M$f-vuTv(M~50*&bx^7-T`8mE5%
zn4rmodtt-a9CO*vaAHU2l|5b^Bx%#8#u&H{flh#09ndLRDqomEA`dMq7-+&w-yrAD
zoKVI-HLNmdZ9?k<GF4>coCEuxJrD<aBIka~%R4!4`N*nf{k7}X7Rc}vM~($LG~~H+
z3XS{&b4!ET)zzAY{d@X%({!!yhbj8Pg$vaQUE#veQe$I(<mf5+eeRFarT42!GplTt
z>c9;&O9pEO&chsgH0A#FmtS;D(@Y*SxEzOb#kteJk`a?8qs=VmUsk(V(DcR&j8C>#
zhK!lz0<0m(hup`%1F$sNA9_Oj2veuUVi7AZ)(7UZf24Ce_6cH0VbqGHi*cl+qhx{w
z6)`_?{FsE|CJzd0=fYf_p3&>pt|pZ+e&|&5TEpjc?g%MeO5sr048^rvxN<|j{BWFF
zwX|u~Oy`;XpwE#VTl5$Wwdhvga?HzyOhANtJNsHoL|6~raF7P$xcBSt%FpnFK><al
z?!DC_>HfXs;s<U2<_+tmYW13!XVFK2KP|}^c)Wmq074JocmsqK8QgvJxEifCD8$h|
zZX*_d@|)Ew_;e0Ln87@)x48e>)5P`L{>=`-d^b;fobzvcnz(-3zuEJe@8+qMwh}mg
z@?m1_ITQPBX9QoK7b>H=1x7RHdW;JFF*r{KncBC2Y&dvXuHU>PDUv6Zu&a^s!{URo
z`tVg{GV2!0D9x+omYoOB%fz|+B|{Dm`DFGkDOG@iqua9m#C17_IdJgT8>Cm$LUQV2
zgzPzdQCil_BTF_Omt$wIOGbFHpRwwsGU*j_r<69e@`x{Ph+xfj9lb2GmK>7JXCtL}
zMo%eKFryqjc|}^+3<85}Y4uIlxJ)ymlx~tWy-#%Y$tbt09i(&o9dw%6%MQtm4QKTj
z;J`08Nc-BkB~Omj^4r>D@*B*&&tv^hkBvr6)T=>-T=21SSEeo8E2Ro&k-ZQA%-VcT
zie&JVJ?A1N`GuP@{JYK4scv4$f;wzD7$&op9+CZ*qa<gFWYV@;fCT5wpkprr=2UZ+
zACkXzhDmtTJ!xDrvz)&S%Qn#2>dQnqW*@Y8(@8jYxg*2c6jtV(>nqdIEWdL-T+J7H
z4f&xO0E7PZZ+j(Y1~^>|$pMO4VzY1XEf*{yDwoJ1+i<La&P|^$=dXQb>!C3DaapMB
z3A-u5S-fyOEFB~UxbcA-a)fJ<w`Jb)!}2F^g>FQu<w+y0Y6PK8thGhgDNXqVf6Su1
zckf<3CmIwKBvq?cg~{s;>DjZVv~1Z@KKS4RHKSu%6IeI{3Buo8Fvg7=hqF5^ltE{l
zPMXML(wg*U(J}B$d|dBi#*C51jT?)<zrU8nye2*2QQA$3Df9mH(@!uhOQ{UH6=&gV
z@L-d0ad3{)5BRfY%^DRleD&2=x*0I|F#Z6f{&nHebssd8^t8)*n|c_0?B7gYdmP~~
zPbkg{PntAI-hTUS$$-s_Ny~VX-h4B-GmhU(nHrBKu(l0W9Mjs<8(7R6A8z(?Oc~mB
z&1mMwM-4IEB*C1v?v!A<$BAUU?DaQvdSqCRy2L%XGm<f;!p^@Rt~7Kd-7r<HQU#8k
zK0+J!)ic-cy!E<#@bPDm*kPlBGrr{1zri_Mdg&#zL?>?fatC0OkOWsYU6Bs0Tj*wj
z8?QrH#Fs8xCVg>EmrY3%Kzd_NpCKd6{3>E&npQWIrkQEVO($~pKrIrT^`uIXTspus
zxlQxNa_y^cp{;>aHJG`+(zClP22;UJcc;!>q*KerGH}=^)%1P?r$~!`o~jlB6`%p9
zqoYwn-_>)N=V4wwZunp=ii{npMm$V^;8i*d^qd`rGsWEC_V3b3O2V{}=9|TX$q<2&
zKn8lar#Wx!Divhvte;?F9iS%A+js5NGxzKVN6%c;w45t8!lO6OE3;6ROD*SBoL`J-
z$@>sGz_}b%Wynjx$OjX~VFR8HnyC7EPjzdY-yJb#ykx>zS)Pw(ph+W5=U;5oR=Tup
zDFfabq?*#t;VkPo%>0|RY#m)crf1(Iv(7uL=>Tu=+i%LkCCjmS#9W4WYPJp>IgVeJ
z*5qW^^=JYqu9(L-7tqr+%{mJeEsFER<z&LB_prGgqRzX<+i08u^CAPi!54y1hnv2>
zJ-P{xCLBC)M9VvN7Ia`EkTABuZ>2(lW$<ggm9Ze>wS3uM^5Mh@&^)cz4KL06xdt$+
zW=Ed0Fze?xg2a9m4xMUAweqE<NWuIv2xn?hebCnvXF`hzoiTBvP3K@Aj2WS&Q@BKv
zd2Xm@eEk*9*Or0+tFB%z7F;N=woOf#pwe@Ei$?Wj{0ASQ90+_!AOzw;29yr=c*KBS
zzDZYlQ4R>o2WAS6#o#Q8j?f~mTVCk5;ur^=PAx^fIA-=8Ii_=`!b6+g#s-@j-ZB*`
z>)GY^Mvl?5ygWLRJ5L_z(YuedYf@hZjT*1x;JdHCkZIq312fnTs>SA6S?7ReRO^pH
zul13yrvD6&=Xqg5Y}OZ6#%d5U<29eJFR$T@D(UzM%x-C!K!<f?l2<KTB&m>;$5nW{
zLcam8N#p8eWWtxzlsWlv##H%a(uXSe!cfp-D)fX+Z5uU%yqb*~1YVDoJ_8uf{%=ad
zIyG@d{|5*%^5KfN74kaH->(1z$URDG2=3}NY9@Vpbd}c!zJs>%SB4=78hW0ieW5}T
zne^QpopabvY0Aqhwm9Zb961<62GEs43OXL6#m!iF2>%^p!E%IWRo@FjAX){{w3^!D
zBipw`_csD!n}_b(rQi5+=EU*nzDLJwY62Wx;(fV%@d6yCMWF4{t2G7tz_-(8f(c52
zGyPxLQsSpYe*Fei(H}%82%3g;pU#*hYjL9hzw_+=3yqozeL|4&uivN%{23gRwm9QI
zboeL;v$E?vv2)J>nfmS5>f?Y%2;O<)HK|v>KH##gV+U$|IltKhl?a@d*>9@j_)Q3y
zYg*Sl4=0OfF}F6yQ4-Ei>C>lCKMxJ-)X+Ah*Zego@3nq?Wcj+yYU$IuZ9D1Rr5(<=
zk5%F2G~l&s-$6ZMV=vSE=A5$+^U@r6ZNC639FEDR&6-OuT<^!ZpXT>pe*B?i$r+@6
z3P>v}7$Ra_1`IN$Y+rg$AM$E1%%z!iZ1Tnt2F7#Vxrlxdt>p&@T1o8Nyc#KDXm$DG
zMf6#U9xTTA&p-c^(ZdEw^B3F6b<pnB9-V|f0ysbO<_@l#JWApZ;W3X~EMKuw`eVMv
zGOGK&-*7a8f`{T*dxFbW#5(k=I<Dl=k|_|ThTXakI9ss~ObJJOSFJwB>H#Ir>zv1a
zpC8v+i$olF=ukAP{{sF$*e_olI2d)LaKQsUM!LZKy@Fa^@zxIBT0jjy$2xCnV7u>t
z5OBuS@APQb`ZcSC`-GM+zNqvfJl<^a=z;WfRKnij;{;^VGSdD#&cwElCarneq8|8Z
zPh<c6KtiVvOlu3Q5`yDly*$caN73glghSeXOKRhsHUq~h1!X3XS#DFydb@_B39Tls
zlO<r5=d{fyN1;V-_4anT35{{_Jn1D0n%JvQyW*gdc;LFn2(&%*)ZQrT7KKk>MmHb2
zBrnYgl_u5mKxh??xp=3fX_!_%uIrDRLT*d<(YqxH*1GOsLigj`b)mObOSwQVd8=+N
z*|7hjyzt&;cy)A>MwJ63jRQ$T#eht(l=Ot<#<+DdzCM`ap!AY1Wpcg3?t^Y6aopve
z`~eM+Cj|{CD+2x54QK6_ZaFFSTCI^mLxQA!>GZN>+gWM+&NkUJ*%!=v8tK3MjD#-+
z!;CKeM)9<g1i}Lz++n%l5R$q>0J!_;B`NXBYUxrdqjaf~LuRi$At8NM$&Sw}NuhwW
zGH&)x8T!Q`nb0Rl0<-$c=!K!OcF$FLp;S7+AtWo9z5V(<VbZQ*S_#Nv?QaZ!#8ere
zO`rR2Wz9g1{_wSM7+WZfGU)6PoNizG<Ai)%DU&kjb2pulgD`Qg2L_#Z7Vp0b?VVbB
z*7QevT$YBP?o<;7UZu(~Wa>6~|Iah>eRo)-0a%BR_Q;`g5i+_<NwqBCn&dVFK~q1*
zAH#@o0{rNskCgG8K7G2>s#Qy>SFbM3nl+OiJ$gtUY&?n=FRl{+31Dq5X<`(^EE5K`
z-31C1kbGEZ4MS)M$;p<#%{$YZ{JJRw{wCfYM~!|!K!6l3TsS(teNHg&2xIf+&FZbX
zbZJWyW8iU<NU1&Foy+38p~FF(f4c;&C7shSy$Q^B^5jXGGG&VN>C*?Bb#y*Q-5F=c
zS340J#*}T3BRqa*8Ex9MkvHFbQ#Jm?!N4QkOgg4zoPlNDqtm04+2fhdxx9qpA0|$b
zd4xYSvm@=e>0_N(S9;LqyFIWjv?8E&2LI6nli#Q*L$&b#V^67C3}G>_jx66drLW?n
zA0P4$VXWJUYr!CC%$eC&qM+F_aAQEQ9P^J~?Z0DE#rKQwIJkLFaG|0l)O>Y5&Ta0-
zSu~oCHhU4yaom$5-bad-s;K7BbhbtM^n9hCq|J~?O2Z)<%^EpQ=-g@>&Ws*8bWq-S
zXNa22^31olw=d4nozv@SxbaAx+Pe0O<x@+4@xn#jaOZ)hm*b1%qj%UGIsI^LPLTNf
z=Z1#7q%2vqP}Z$lp_=nC6DPsxS6OUKlVCGbS|y8gc*8TQ0XWk+;mhx#IfVv0bsDK#
zzX3F^TU5J2CsNd06ew6o&!T6}!p)gQTi!dI3h5cgE8_@@$P9RUkP0n0mWjX88F*6T
z9t4x(z}&gv&3&QJ3;n*myHWSSx&bD$LUXTDWy<L$)*bzjrqSu4S!XzZE)4j)t5%iF
z{%uHtFI)&ylfx`ov#Ce+#?4x&bDp1Y#-Ez09=MW<*Bp6!df;5?RXFp3GpKSEFfXLQ
zhPAAEN~T9@UWbzp^%w$XjdY0RtOxXEU8$k>Ns&_V=T%H}N=MrB+PV~IbDE;-ReRuY
zY3k3r%PC)AK3*qBK@B&&rOTFAEo|uF!|IX#)X5XjB6WtwJC8CnfjA>d28uK#bIy(Q
zt8c!oR#VjM(WE;$tP9Fjtc*DcWmwY?#x1?nU&XaiGvL%~uriYLQXhUjTt?9B6Xuyw
zQMYjx6_ykb4Eg!{>%zxAeeu#obtaf2XAaf$roe`l7EWX`>23NhEICs6`bh4)1(a5F
zlE*&R0avLJZ+h7-S*C(AcqyPEBHr{}Kxb9-m`wVvMtyjN-~cd$yfTjW$g^zDJ*0QK
zbg6Micw?m#uK?p2e46|6TCt1}uw;kHe=z#fU$`QPmJ&QuJ9^@W5Mq@9;~RlZ`*TvV
zbQx$$Q=^Y!E=HfDcX*DcyLX~+PP!Ewlw}6vMT-b`Fm5H$9&YNLpEm<g2=LiA-%EBl
zb3AtZBsSsRYC=!^Fg0o5%>}1Vo{?imk6PzvAv|Lptht8si8VI(jdw5yLl9FKRw=~!
zFIXgOgx738|3G<j@NfyjcaGU?Su#k0BEjODHUqr3&qtcIs_p&s>nTW^Q<u>6m^-Ll
zJc>S3svP`J_;^RRJIf~XRJd>vT+{WN(uUX2P5k(CjFsZb#FHWQ^z;E;;D8X`#VJhT
zxq8kwMT><<>hxLEGdr(dq=UTAP&OTNojHr^qkMdIOme;_9eA~vkB<i|7p}{h<Du}*
z|El(NYN=>RLF)wiDIgBK1;PzNJo<vjk}ZqkOvWt|6SSIlL+E`8T6kVHl^slR*o83d
z`~H|8K|(M^chNU5DZ}QMGn*7CUJ7HRsNzcJe=k7jK*2ek@f9sbr)z8g{*V7a7vkiL
zetY7?3CRLSYj-$IYybd207*naRGiDWzOlb$&XP&$)NP=1Fr8!a$Waemi$^Dc?%0@9
z2wEJ>{n}N_)gc+@#+PtCpAXK4(^(m{x29k4nlyUMr?Wa*La@)^e;g4xqi3)S6~R0P
zLCW*>8tOc`V%ZWM3$MT--ivKJNK(vafq{9Dx0uz2Xoss;F3b22KgApx0>PJ?&R4WT
z;+g-=IR3#a;VR-^X4EThf&5x`)>#ARD+nz819CxVx>dEvrOTGW8c+$>V%-)V_u%ap
z{sDpV&U>Rl^X#$aJiVa<=jIoH0WOEREWj^^WXzfa`}rhlb-;1l0K(@YXp8(f(nRa0
zC>*US9$X3nY$`H%6T&6&O_vD{)+#B@c|_^#*|R$L<PHpwWlNVzvEn6gl%j}QIPj{+
zx(%D+3dEk03eHdcbLGK)Hm|N@#P!hreQ>Zh$l>23DcUEk`pBTg7U@s`0#vTWoSS(5
zyea0G(pXE@LRh8!5NijIjAX_cc527nVG6*xf&MLyh91$iifgUT9iXGtf9}IsYAQHb
zgmXT$E3a&1?_xir4}dC|^XZj-%f|I8KzQw~clByHYU@*At>ApMX614?5?!tRsZ-bP
zm{+|qSC&!1Y;w%8T#t+iu6ACl1rzw|Xk&KtM1yl0=lA#km~`eDUpjtwPUHL@UtTL6
z*R6{%(cXdc%QZ{oh%fO&eP^3dLp^olS$U^PZt;a6h2NIJ$=b9(_RChBf!?_5tZdj9
zCVAkLY|ZvlGW^#Qa^|Dz5(4J83>>c&$?GLAezaT0v@a+X;8bq>{39~6Z?IH`L$Z+E
z>0}?Y<wY}l$<TJi#T$Mrc7j2?d?ixaRq~UA$1lm;bJu0lyJc{5P8MkF)5D78rYzfk
zMP96!QzkFmFMokC+%c?*6b?uOhjp37Cux){K6+hVuM;3XI0MeWeGvD<X;XSiy&`EO
zA}n0qUUFJK{_dcR-FQ-Nhh2puRCmb^^Y)NHC~z=VkAPwQp<h{bkh*%u8L3z)jl9*m
znB>k1MQC6;nXvJMG%K23{IdAUiZ#cuKHitD@0ORQ75t<UwD`LZoR_?~I&XNpV%T#e
zlh-D1kurIG<%`~BBq&=dsarOO91puBhfiIToPg1E<QAFnR$=K<FTdo$`TQoOv&kat
z!MVq5g|zNqj=5}-{&ZsZ*^*Exk<D8w70IG?yb9}p&wtr1tGAw%4SUY%{{1Q}6auoQ
zl66~g#c4(_DG6;o1K}*&a6(GwOd&<lP8;`!i8tog+Hfv6Vb%^gi{F0^tRO`|)5?W2
zNuX~MnTvD#eVYZ!`Kvdj?Wi5HWl{yH6`Wm)f)@2c(o4rr_DI8!bdouZm(m*aFw?n~
z`AC15l@+D7yjQPYn9KuZ;J|^gU{IS(jdAnl%{A@u<HzNXKmL%T*i7&`2{OY~p!uW5
zsdw+*QWBf^tk~@R{rBIRuPU_hrAk?*Lu5XwrCtsvGIT7IAIt!?(!5fFAq-cx5cdB4
z`xQ1lKY#YwXR6t6-MY2RpFdxjIhvU>5PwQbQ^4$}T)DC`Y}hdTP9gnpCXay(H%+)s
zpFRx<Z6<jh=l5tKz(9s@DJJuc(3nKth}1Heo|^Jeqag{#qG(7<8nHgC=b=M~#NXc^
z_w!CySft0sjT^NNHEY&XK?dO;gXElLGoG7L3OE=~4g8Nk{wOrZuUxsZRDf2H#|T10
zL$&UwPMy+)k3tc8BV~O^uLwwQY5urq(IQ=#La@M6kictg7<TN~AxoAlQ5ur@AnamT
zm`xzvSqE-#^5-ugb?VenK?50{)reaKK?wT+=|tfSg)F9j7#I)pBtuS9&g7oSl~E^6
zxsf5BhMt7O%?33Aq$L^4>(By{&g@f<Q?4^^Ds;gl&pyZU*(aTa1kQQv?*@nWr)`$P
z`7=qy;#nmej<RAFJ$ve>^P5`6NH`YaJ;v@>_*15Wq!Ym~c9e14f(a0XFdiN_3xjw3
z)P)`71ik42{Vvj5$0w+brDlqoUoKcCa3kP6YcM~**}v1+gt@r6V7;|`z`uD5CP!c-
z2_M5=+l%ngd~jfe^MNS9%i9xqsG+vz7}kwxmDxmJpm{fOcP^83=f;$IqhM~t%`h1s
zmVF(bJh@46hgQ_!$@JX#F+FKR?Hv1`2jGJYnvQX-Gx6g;dzt1N<4xc<=83$4E81S|
zi)*H8OH()*yL=78Coo^0kPI`up$pUVow)GsV!UF!!Vwfcdiz)mx1ob6!~SGh48+SY
zX0h-hK8`Z1lvX`VK7J!DNh6lSxtJ!Fy#5L@u;{uo{Vm-0#d18rEVwC6BattuYMP0+
zoepeAIyE|S__)*x$s>Pm+z%&UIbcQ~p&B03{w$xc?R1U(4WkidvJbId4EAz}cNE(W
zl_0~0t_}L#1_OT&?dYMf9oaM=VcGi=&6Vj)i8L@9VXGADFVnIwaK7Oj!~R4(NGt8v
zh`$AgQq)$`X8=v(9SS)un@pXq<~E>aZ159J-q`Be{$?DY?{J)YdwVGiQx?m$*WG~R
zchZn?v<$I&k%^6Yn*L6jkw&zhAkBPJ`e=UAfb?NMGGj6lcGR2`ynMWLj-WSk(t-1Z
z2^eOv>d11;n7;|L&09BbsZfX}%N)lH1{dosre1~)tWN|sOf<9hM%frTpsNMfP6wtZ
zT(;3*Xagx2>(RH5whsprX<^5eG-7_%lmASx_rdsno3zYNm{+fc>y>4t%9fL$z?>R=
z9>;U4g%pxelfHm6yxQm&#+rfUVN77&yB;0|OPLnRur+Sp1pSu;WdDW<wqvg29)s%~
z{T7hEWB|HAMZe%kQ<h7ba(?hYU*tR6Ev|l{^#EiP>j+lc+39EcH}4SVSoZREcZ>z#
z?UqC>u(&SJ`hbEx&I|0{+D@3S*th9$j@E_L;*zGs#ZFsOI?E+aCU9+{poVEVw^%|X
zI&HJY7wN&kxrE~?47X3TZ`MEt4jl=BV|k?u%eoEIf6nd6p;e~<(v+|D2W*22!P)Ch
z*d`6>IBvv9^mA|N178f+aioI`JdXk}5Z4FR3aqov>m-U}w^JMl)6A))ttaZh<2`hW
z$+^OmO<G#HtbT<T4O$OQAPzb(ofqhT26U)XK3FDxJ_W*o3fjj=E7O1IT#xfOl^tfS
zH0up<GW!6|Wqjw@AquhS6N8h%=l{c#H1@!Ja)3<rf;}?6ce%%|wlQ&E0j9gsyEr2D
zMO7RT^Hq31tvV{RHk^@cI3K;|+znZ@{<0kSvYM>kbX+?AbVSD2$_7V;x503`$%#w1
zWYSkBWzDQgF!S~jzkYwqwqa$J`6I9%pRAV*UP)v~N9%~zl*I?+lNG0A^{^`P-TXcB
z1<cJ?4XL8XCfLYd|GHa#-3}A%w<<}kkt^hd{ApwWOyloh!<RIvo2&<OT6*YuIrCX{
z$z+&CM<28k;}p(tUx0PMcF?N*oFibuqGaQ!vZ_`8bk0thv*Us+M!6gh>`ezwUX_h|
z;g~WkLKYvqDhsz>k!8b5NohD%^X$7wCO@A;Iz12Gfome>x;`JRRg>@GIGazVHelpx
zc_CF&DdC5kJm7Sf=HN57hDl1y7v1XzN%OCE$-z+-BoH*d1B(M{tG`&VS0=1GE87ND
z6fX#W*}fbnQP`KZ8ofdaK(H~alSQ{Ml=1GYU6O<oF3SJx@3S(XMiv>}sf08cyGp9%
zP9=RBSx$bLq~zdb(yL?!=~%yjyz~85xsJ7RWapC7VCYhL0fL7<O$$fU^u&2w_1OhN
zP%!2D&xA|EN!w+3ZQRxae1H-BP~4ZnA0LsQ#}}8zl>>Dyv`v+EvOnB!GkKDMBx6mJ
z>yJPFSa$B*shUqNFkQNIQL}ZXr52gY+gzLvEdyyC8AdYE^XARd0_nu&rI%jPcru=3
z)|l?d5i~pk)A2jg@tk+NcJ1Vyciz#B6B)Gr{rl^;B1MX*1iO28+#&q!x4Hpg{+Tmp
z%DQ#Xs5t^RKr?3Gnh-GA3~V@lW1z`Cnen64N`Rr?z8xNR!So)6wt(TAZ@y6`m6A*f
z5NJX0^2;wPL+rd6(+rM3TyRO30Rsl8aDt3)<;s<{j;s^U@srW*)~%Z?TeeJ@Z_<~7
z2nrY|n79CwJK{-ta#Kjb1ew&uix(>{)X-BP!t@MVwm=Gu{Nu-um*C)FWvuJNB#zbv
z3}p66Gcx%VD^`@#XHKg?VC~wq8vn~LzbGCw|1SpZVcWJaM}r`S3&8sI>!os~O5zJ4
z2iuB53kpssU|^e*rjHz~H9662?ACFHhs+;mUdKl)b+MvNUyt?iVP0Z`amo=7{!XbL
z_LYk=O>{HB11*_zc4fnylUCd)aPsA*j?KdK+yF3e@#Z_@J)zlk!^TYG@tt)i9Kz$j
zxSG^#h%9K<Uv3Kdo%y-(B0QS!`K0iUhHoZI!Y6*oQ8t-#=Ht7eJM%JdqfQ(ZH>7dK
zMau%tyatXNB5ptki`SER!aKK}IVU`(XWb2LJSiB8hHsV0{0uCMzwKq3Z&nKHFYC(h
zq&dweY5tGufv%p|+`4O7ER$v1^Yfi}6Bo7t=|Fnh>0mF%{3cFZEOb!=3<f(~4ii%c
zlZWy4HsyCM2a6JEMn)hCG&A{FC#F}%9Rd!1XFRp#{ASwPz_&lSxjuz+V4T+&ys-f`
z{maY`gh!n0!TwF#6*%x4X=e|_n{7tCIc#r&*`aAVX~?>hv9b4E=C=1IY9{$^+Qbf<
zzZpl|l7rc2``Oz>`!(pG{o2=2FTC69MI4-ip%?38<{E8(GY(J|nRCXQ_K(%3P5>Ea
zjZH4~@lQG|gZ1Qi;8;kXA&oNU%&TJxafD^Zh4Tf+9dWjpbF1)J{l}?}ro9N0<Io4R
z(LSZ)*h*}0VI3(1G{Kb3yjnJ_gOm<<XPli5q%)bOmCIMj#L*-5I7#)|&tr23Xoxek
zu;Xg-^Jxz(!+eYHw@J@-C(U@4efiSGQYg2d{QUDT@;e-;4d~TP(&H*V9_3*g;v5s6
zlRuDV?C_y-VE1-du2j&vX)kc3W4#&JpE;lNoqd>aST6emzmqn^lkt4U6|^3);$qW}
zd6=K|W*fT$rk02H;L$&}3*R~Cra&8V)68+tpzQ=)SVztSoV$q|X>I7J32lGOJi<JN
zHdY=A%^;X})XDUDW@Q^wvwZ5rIr(_vI5jD!V>O05(1LM%kruix0JoU(ExOoo;csVJ
zDUBS7Xoh|n`%W13=y>OvL3sSf`f}_LH*1}w65+vhk~Cx8Ok0s=tQXUAZsH!0b321o
zHh;zR5te1>qiCUAP!#2JE{MWW%y}|;=pgaOF`L3rqL8^JZv4lVVS90`u}rg8nz_y3
z#P5dIru~UUZ2H9D#JFb@tiJSYU>m59QO47%x})gybUqe&Pc`Ypii(4#<^5!K|1$Fa
zOTqGb-9Vh{PO1*XC|IVaF%JmJyr8YVgL%48U>f=U!x9n%vu!$gV?6tWb9nFz%0i?1
zL2#7}^P~y9?oBn~fI(+#1ZfXkv*Q4-NfxXQl{q*&&5#w$dy$|F(yk^f)`nKVxus;X
zWcyi-qXRg?bqp=}8k+jNJ(tOEyDmr?oF(sD%MVT{xSl#DDAXwx>Z`$lD*L>tC*ee5
z{!0ogH+Kllypi@E=D}`NbI7o^g~bPR1kK|)4(u>^9UX?0EpQIRoC10j$m}ie?Yt~k
zc%=hES~#Tpylb$0`f^E`)Gb766-+6qpsZnbGV~n749w4BAlr&XCayO)#jfR<EAWs~
z^KeY6dv^k3W$>C5Rso*yHR1-PO<K@$>bMfn##<VC!iOCF$%2LGbJD7nb*JwSKm4GY
za6;_|6WZ!Esw)FZCX<Xgwdqr*PKD_p&cq^6!%Ne0YL_Xc@7lGieE8vqm^_k8S(v|4
z<4X-T(>H0-M76wCp;c!fGn*Ob`8sy&h)F3*rcIls8f-e5W1v|(HN~dEs0C&D_CTj}
z<{Rt6MS|hWFTa!_Lx#xU!Grak8e?wG$OLo4L1vt1)`=4XnRqg+#3P}=GB`n#Q6@t^
zXwV=nlX%kFfEsYpftr14(5VF{b4}|6YH(<AKn*wFsfDL09fb!JM)177sS9aC?K=a%
z@#p~SM0zn$&_HcJ>Hg`bpUN9=ydlGf57)X;XhLatXlSSkB^owtsO><FI|FefEh$W)
zkRTV%Vzq45Qt@Kj@O-@4jBAEx^ymNByAHrAil;w{w1gfY5JC#QccdysK(HeyO#ub0
zU{}PhpeQI<u>n#<q=^Ml5v2DLdWQr;2&p8H5c++;-`v~0%S$0Z;1mB@$h*6}-PxJh
z*}a|J*;zCA@&un*yl1-XJ{$STZu;z}vm0*q(`TKMc=7|xzLkN`26yENisO6xoueaj
zrDgnu-ZM?+fv_{C$>5>z9rzho<~;`z3{Kq5Gy62>drg?ZkAdZ;#pXk1c|Fsc@-hIO
z?I_}!2TRQN+}|DM=Wu?6XWM(SyG}9!W^i%hYT`0VZ{n2e?9tN%^3pTToxl?=pQ+?y
zY^lNd-rQ%Grv$(}ltoU&at|a;PVIrG?RUh$v`fqC*QUXwH&>IxE{C)lxq)JlD(C6>
zZFt4LnE04{_Fej4Q(i`OV%}vY*Xf~U9b@2SR94nG&D+fDJ2iz%PKa|xeT8Q_H!e1R
zJ>Q#W&#R%u@P@^jIFmN@*@cs5yiwelE;X%5<htT>s;1ZUn=yaM3bZ$#0gtly!WA;h
zYu;b%mGDTH7xsV-AN84TyIAtid{{7@P^J5h5u@RN%~vPfoMm&6MxO_X3Gp(1+Ar9{
zUOHVGlitmDcb%Gd%2OcCXA^g^G??@z|AoqP%1U<fnl#R<DJ!G6ENN2Np|i*`>bIm)
zm8z+VDPd)VJGkR4<^uN|v<rMg+=Pi-PDB8vYgF&V+1wjk3Dm^7@7;Y7<2bQ(&NYlT
z{*Ku1IP)=`2^qy3O6b=jY~Nm;>>d8mrx@GO!KOWV!2FudC7T(+Gq4T*d^T|={nfo1
z%C2OoSEG!K@i)8;@+&6yw<{(6f5a&{Fp(}(+@**jTOwA;zx!*iYI`)nE=gq`6DV9s
z_JY#Nz?JH`E5gJJ3Y|J>L%kKqpH_X#k35D_N+3XKP#nw)<YS<&NF_Z#619Pe_BX4K
z$e?>_;=FOHoFYrM#Xy<V+teSn4`yGoD_%za5g`%q*6s(T(gZrK6mMAj7=;^Y={$~Q
zorG8PN2ctNulrY#NAAQnNQ9UqD8;9@Nq&^c$pQv`lRX0!Y=+`co^LOnPip!XmO*z_
zPffNArt?WjCnXSO=yLRo>^X20j@t@@g|GkxxZgvfbb`VOnx(MBPEE_p(D7k1XX$=*
znrHAJ@Fg1}<gewi(xysrRm3a6(jgKH4GiRIJS=S1h9}FQ<~F1GxjDmcLf-48PML!8
z$Bv`&5GFqgx+-EDtPCU$K9j%@ZUdmF<{oV;NdPP&7?QEgZR)(8>dQhS@RPQNP3%Qn
zjR10WwO_w}x>`=9jr)3NZcgR${rBHjv+;H7)~RAkWt%fJR8;%+?W@F6vE_<66-FxO
z3^cvtsx=i~Dx3`TgdQFqu8OcR&khL*!C*L#uIvT}2WvY1)Wcq+u3fvPD$P`U%?i0m
zq;a%~as?{iGzZ_faigvXQ|UM6;fV=}x~ffulu9or5EuxPtJZhjb(i92c*1u&^O@Rg
z7-Ls16=d#5*VQLT4ks!&p};-ZoHTg)DV#@(NdZ~~aAle%<KzW{nFJv23|#$w@4feg
zUgXL9y?ge`=+UEP#E234o-o@$ai((420;91iq1Nqx8V&NLSz&iHj#GHLY&@x_g#G^
zzZtk~fD<Kpyo_t$L(_b&xRVx|+0!Xtty;CTP6^+Xr2wwtPfnEUgu#3MT}&_?K-PJj
zshsy&=gR7>Q}*n1PPpz5d1f%u=Qk;xa7^6AUo(eKSXtxe>cf%F?#wS#x@TGTGwH}U
zxYK8zCgYrGV~Holh3)Ac*90D$s#9Sgty$6G#w9Im_fyvS-RUmwE^WE#%Sij4eHr0r
zTpvu!)g~(DefmAD${+`(?DyRveHrd|=I+92(&OdCDdYTU^JJXHV8CnI^v*X~^TD0+
z!eyAW8S^ze`LmP8E+xBV*mOJLI`0j}nO(DP(`j=#;oA3E@tk@2o{slA-EpV>QMByO
zq7#<06lc8i-kBz&`}Ap9HkG5I*dA~%y!+pM@4edYF%;1zVYUJ|p|M}+g)vXBK7G*+
z+!juDnzUtPMSI%1OPkMGUi$mY)21)aoi=TG>7l00>&}-xrDu#gZDwT<HhJk=##gs4
z5CZGi`HfCne&%Il)$g)zYKH|8_8h#=3?D*IU!I!|&(b~Nq|KXoJ~tdY4b7$bO&RpU
z3rYs%$7yNcPVb3d`ZChu;LPbv>%4cS$>{!4(_U)6jBs$qIpI3*ooO<<XWE<#tb5j5
zK`3pncdsSWUtcb*hc1y%deo2rm|;i7Cd)^^?Up&)k6|T^mR#qgMcr~Z+<u!p`SCKG
z|7~v-9W;KEtd7Dt+8wLeQ$5&nGHY?Dw1d;RCfNVXBg<xQh0m7dVbY<Yzd9M)gRK+{
zXP_h=hw^6!n-|<xwI3$p5FzQ(yu6I~It<68t(CiP4wjWTL;dCHp;Dl1e#PCD5f?k}
zL+vWc$oYq**GDVmm3ym-AABq&;FOJtfA7P-;AH7E5cnf-rT@=8$K;ol2ZZ~<n*|h<
z#oJ@0Vf8pD>}O@ftQ{f;Q{bd75vP{GA_nQt0x!=nXIs3~S$$A$!T$My?Stgjfor5F
zw*9oiw$VKY6XfoJYh~1H)#TPDfij@IuXGw2BEP=?s{ttNKP}oP)8|FOQrkDp7~YHy
znqYtbh-a$GgKun>L$N31)}{fvO1>KV)*qR^U#%Jvv2B!ms#&g(Jo(LTxuuS;l)xm)
zSHA%N)yZPrZ)1EK--2V<YRB%sXWI(Wtp753uUv?9YXir_k;mlD3EQC5!UE~cIUL*O
zgCmB$rNjHnWo+NtQnXM$`TXad^39wm3Hu>H0g@A!rJ$P*^7ygi^2@KksJS|oz-ra1
z>0j&C_C98Ma_`=)yLK*IO<^!f53f`Td9}@k+%rpzqcX|?ol!>Vu#24w<GG?tvvrzO
z(_EV-qy{#x?96BmPdFyt3=TC8wWJ-DQzii0q-U8loo6{zayi*hp+W`4mvMye473uk
zTaP;y^ONoe#~ooCUJwVv1G8^v#K;lSt$R1!pU!>sG#wu`YLrfN7#h?J1u&^Zb48!<
zsMI&Uv9Ub-@WZ+{o<)+L;@|^=>6lp#@h2ZS>u)AIn1<;MY~sm1{$@p-`IvUvv}x*X
zp9(oAPiQ93I(X)pXLN#v4*zH&Kv<?s!oC_IC(26$!^p`+AfIuqdjfE#a^8EUyPRhy
zT<878!pu(I?4-%6-0bAfPMUvD863bfq>(2(Mt?8;Cp&z|PMWMR$WH#O(~xd<zZ^(1
zTpAy;E+Z=n?04D8pPe*Wm6V<Q*-2x{$S7?_aVEXFW+zQnX*0|3jCbA}SeacfHEs6u
z@f&Mace#=ZkweHVzcb!>pBerpKI62MXU{fn%R2Gkur7Tt>j5^dPPoqd^!d`pIMX`s
z)22%Qbg5}CHD7uR(#AO9I`2JF=@^NMvwN_XzAs6e$Nl7l>%4cTFn4KJ&9hEx^4M2r
zTIYS*ROz3xlh#gS#*&%KPkQL-amy@?iFe}Tyf^PNyIyMAOU;)V9wy!i*LiQ=XLe;;
zw~S?$>SE&6lpG(yT7ku7*YKO=)1S9X$FH_v&l^-ZD9JB3D<!XXts$TOu~+*pdPLud
zecaDY-Xbl&SP#=`kq3jkWybSO)yY{oA8#3ccYq9;7fy?ARnWWL94H&Zw@R0Z+a(MR
z+I+B@eTQ!W?FD^{7m!*wI?WC?FN)!)$6JGJA4NA-_mwr@x02^3ZkG2yS|`1$7LiYT
zRgkCt2v1$vW`D|lmE-y90mWqFu;wxn3U-GtH)EL`bYKFYW4VH|^26p*A5QBST0)_|
zZCjk&^UYRSJ-D%a+7}khpRSiBUv7{?NdIL!e|5U{%3op949@NzsaQylm}6d!8Cc#u
zEdyo4wC!^Dr0ue=d{OCuBhID_KP|mx?-f}Tq2-QwyQVzUsT#A%&_4C0nAaA$eF7F1
zF=_EYaAA3;cd$ezL&@e+zAKn<38S9Dp7LNEpZ3hpdu7NHnCHXvonh|4Iudm>MPlKr
zh2c(kXJ3J1@9v$rRT5Aq?=|z4M;{N6Lhx44P^M@;tx^v8xTk*Z$BpIDX}e_D`dFz5
zza#IrE+<RM+9%!6y#AkOZ<Kd`+#)xP*{Ha7^vN%qzHNo0+-yHul$*2~rWSMYKgsZP
z%zepJmU&E?Gtd-T18C;C=-dNN@9xISIro**oO}52;ZU%-*Nh_P4opX(FkX{sZcT;U
zgx`PvT@}dS;9y<B<w`PFvl+PJJ9g|?>CnD|K67QBiZuf(h@OPG&zk3+o4E6sETE##
z^0+ch=W-@+FE??aB2GAT_{MYw#|`T@s2RLTpVifv(wj8IhcF2{3f{80H@$P`&boTe
zmG&7kX6U|hS}B;kIw1f6bLY<06>)AIpw$5b6=XViF(D(qQrV^@fIsSptKi(<Z|a47
z<YWscC1}~eE6wCh;MN1K`g0FD%cS)H|L7BdGGG++?0-ZJ<m_Kr7a0Y{=V-YoCbP0K
zDnCtJ#weyqlKb%A<v@<g;Wf;rTs~aGm3NJxuGVTC)m`OaWZPKF*(kWby_un7@@4iu
zH$E)~Y`$LE`n`7QB!|a1X<427l*8jT$`!|u+(Z8PlTGDqtT-n?@uQ+lWtyQeysPun
z4-RYV!mIbZw^~aQ_C^zxqS%j4$tMgtI(xZ$U3uw#o=j!8hh|W+{~3z2z>$V!mB*@f
z$HwJV*X-K5qTJi6qR~5%LZo~9s?v=GA#B5W-NzD7$l`agwN+z8W?<!+1$()aPgk_U
zR{U_J+2j{n%EzFC$HGxb^(g|M46f!$$aO3F$ZxN=QaA?Bl+o?g+?}TUUNF(;k!&x)
z#Gl^Z$2`>>@a>XJ`ZiGV7uPA98qc>qVHSSxO*k$N<rV}Co*U0VO9fv{Zj9*PSYGJ?
z$EH|~FM*Ri$Re8EoBiXa3|`4k?#=Ida|OAhS&%xSW1MfX0#X2`_X-zqNDs}@C&D9r
zG8}X==y~P1a$6w7Q+M0?k&6WBgcbeW4ax)0)5;&x`vTHxl_3j{s~h)BbNLXCP|pHC
z;%e8wyKS5L&;l;?hx?%E9F8mHRMP3xjZVTAEn1|`&gkJiFfdRZ#BqN)&Bm!bQ(318
zIvvbW*`p%pIeA4Tl8(@PeSLMGbl<*xb<aBYK@-j$cie&F1QK*lH}U5_Y%0z)$>zE0
z4ClEC2!TpGCjw|{PyD|4;tN$IO{dJ&Vsp$F9nkS8vtNJxRrg#|Vdjc3P2XuDK&APi
zhx)35N?gW`8z&!s^0BTivw}SXSIAA^d*bJrj(frxsHpSocABY&hK8p8ae{-&F!5wq
zxNxE3M&9k-vs<=p+ot=@IVnMp^ha@23|GEMANlAERM?H_e(yegbRR#JZf;E=&2PW`
zwmQl4_xD%lf1IeGNjqt!p8+1FM?C3eogU_wELo!O$a{K?H$DbDX>{Unx%Z~t(!()L
zB>kJK9`i3k<*L)3TVO5+aygL8fvdy;v%+|lic43FyZqcvKOJ*&BXXDnxiXf+487vu
zI9{a^$v~yNB90F;$~ni$RL&TT;z%cJRLqMxlykx`>V;8anTK&)aW!LNzH`HK-!m-(
z-<$mVqm!lGeamFb%+0cPXS94jZ@1k4;szOWXCRbNEcgL7aWaATq=Qi<K}QAOLQ)!v
zF_m*O-sd~s6DLy+pQ-ec9xAK$GxpqLZ#mNuUtXO-VIZD#8QKjTmPzHD&-kx-dC$Ny
zh#RtMFv_|8<o-<y^LpJ69tcI8cp6y5-_S!GshIOm`2tXeK3+8~5)%s>&+C;pq?dvD
zwT_s^g==x(T8fh!c*i(>uc<H7#N8&&c6vsWY94X*)?06>l1g)HIw`Xi*@v_^?%AeC
zaIVgBMVLw|J&X73*;CD}x%ZmK;y452%%1cYu=j@doCpXC3KFi|)AYNRYh{<pGZo7|
zP&lc4(vvyw&8jdxmvg`R(@#IGMR3LUrI%hxHSeZ5yjhi|b2d6*<9jN`oOGa*H7cs4
zn;z>)Bk87sOeb%~;hPhGb5Gt-X{C8Ok6NMv?r9=Ve5g3HJUXKrI&`SanKMTxD2N-K
z?2&%1+H<9vlN%EzOc2tweED+qm{0R_I(hs0>#uckgcBHC38%8|48+~g$`x-a+vIO>
zg<#pa-9E~VE9wl~2hWKCPFir1gZtLWcdn=tSMGHuAGlAR=Jw<RP3^CAFan;w95xTM
zfPX`<`-^`ACb>A|av+xjxg5ylKrRPzIgrbNTn_v{aKI>_9J`uPD)kAcQbx2?x8jW|
zaTv?;KhiO{F8a#c^U9>=SrcaS=XR_d|2^vl`S7n$8MG`)d|{3~bwqvX+%i}p7(AWe
zNQVw>v!KJ!U}J6L?;f+;G%I<4WN34i!Fv;LXtU`vlN5?UYMQj^?RS8nJTdt_;oEui
zSCwNot(nw`0h<NRHZ=T_N1EQwr2N3GsUtUS251Ilf??(@;a*Z82vZo30pm(I70u$s
zi^G9RQHAMD!&P0bR8v`H8WXr5nsAH>H}f!#imXwxnU)G7^A{^tRL$ZUXW*Ycbs8(`
zM->KPo7H9mlkkWm%cj!Hm2Kk6_ouPSOkpx<372WP51q=ap@r$0pJkF(!r(q_#&M;Z
z%B<lBVK6Wc%jP?#qe4uYjFO%at)$WH{pKV9^P0diNt=OT=wi8~oA9_APP|wK(>wDs
zJ=5}?!Ik^_i9hK#-rx;iS#DxtqP{0DjB;=AVP4Y2G7O)bc}=<tUCB?D&xr`qc4_CJ
zyBooU%Dd2e)*GD?a;t`^s|)4fdqexxJO@2HcwKB7(!pS4<YM#ZrqAWTzkvgkWB#%4
zy)uFA#0*8QObG_6wn@;E*6CpCTYPKm@A1E(%^JQFyDP_gLwouw&mCvI{2O?Y9=pqn
zams<=bM|?heR(yo$-~U}sJM{RNOLv9)u>mc`vSy}+MJ--l%+hrK$*F(uPg^x7ddqf
z`jy2rXQ9)*!@0BP)XR8Y*Y;TQA}8cKXEFWXftDkC&{=`3NCvJH^UoPfC7U#!3Z8lI
zR3Mq3a6O%uF^-Bg{}@bOg9DF3qdB|@PGy^ESq7C>6Q~68k0BovLlX?HPI^onKZ2%Q
zv)|q1bK+~_cx4`A3BYI1bWU1LIou0x9N#gIDU(+x-hB3?i}1K|Zi1oFJUe-9;2Ncz
zX&A>~_{ytMycuuun5#3d^F8mcb^xk58c6#khs(%%$z|qHM%L{OaI`_4#$$)XC6(Zm
zsY?QI4QINVv|r7#u8k^Te+d&h{N;okXGF8ByF`DP(c7GhW8Eq(XqP-nEPdcNlTJr%
zhgz4_cclk@Q8C1oCV%<z+N&*?6Ta#pFVDusrz|sX*H;~(94e5!wfkmz1qPoX+k8HY
zooq%Hvnt9?lbyl0U59y5hip6CZs{rFTnprThO5J8l!tmH4QE07m5_0cepGfbLgqcG
z<CVeO^Lhrm4XN1J)a&bu{vjQCQFqjdc>cOQrF79?ax>&42QH^Va}tlM088Z>LP(SP
zJg$A$_821b3Aw9)R&EjCK#nNr<fQ2&?TU5tmhtADxjNH0-!tB%F%a1K1A;#CoindV
zW3Ha>_1QB&yIGY;lgo{}JH{R7S)ReqTs_|thI!|N$9pGz&U=&2z%u#Gvw3ggc~t`$
z(7}7>Gt-)M&N7^QWE$r?XL`P4FmdKSD_oc+v-f;=wSy<EmsCDw#MMbFugt?G0lOA6
z*@{41>{0ddU)HbOWfQ>xB*Yb#AD0d43IlLdeomAGnmGW2F$NT;^nF}T6yb!y!SdBA
z%kE5F4aJE>j^1B-eX#9vpq+F)S>j@2q+Iy`Jm>5xI%S%&O{LhZUgs$JE?|b1<HwRx
zX~~1LZ=K&WTKt6W3zy<>l*-BFD~V55G!oZ5;7Q7fQ?hmQPD~(UuVSUjh)auaRxonn
zU3gf8+>rMMDUC^c;*{E{D{#-}oG8%~M$VU^yoEv@A9qxa!*Z=W{QIONr$|h6l$0&!
zuk<=)A`M350osG<kJwLS7L2^5oJf)Vkr6ol)n5t}ERas7*uD-&!22-*&nds+GKU6t
z=-^>YL=}({B}=%RG-bCBx^R6k1xS(<9GSH9P?FL`=!|DDC2=1vopEs=ERU^nj+yL~
z7ydPM8IB{K=rpVn97pBHkzALn^k2B-+!VPSusNXej5ej+Bcl*;DYBf)&z$E#&W==A
zx2S06Uq&cr_C6z_Fv_`s<IHRB%{yLAoPm`tKcvr@#-ww?aNb`it>^o!;5pwp?@f6I
zmglo)Jl~TpCk*qPCXORVSKxbRaHccyrko4CyOj5)jH_|=r1g@@Ov^5NLgbPFUA;{F
z*mHo+KbioVXI>ec&vu$j;x#{&4w0pc{*jhd%1bh~deG3s3B%kwDKv4;EAM&Y(7H8i
z<<H;$z$(k-FQpm$o#hxjoN?}Z`>p#K(>NYH@`P#NG2Z+!zvbeud2e8vXI@Pln;=h%
znD)a|2@4HVMUL+nGAmDCUf{<wFJZDQZe96#=1;P1>sDQ9HDwumoL5tXO%M5D%a6&E
zQ5mUuQqzJ~7e7pTcN!y$Y2ljpSzVpDn0u36%VC|k>d*5%)0sG4sgEBDKPX+=wUEfe
z5m<e*aW?SWX*0*yoyOF^%`d`^kBgTdzn>yUaNH!7QnPI;J$y67chx;;U`PWym9H7M
zO}5FcTQ<pKeY(T*Zlb8Z3pgy3;SB1(R^=dB{LcccT%#Psvwlq7n7Y=)t{=7o^PX4p
z=S*`k_cqU|oS&4jUw$qv8r73eMh=%#aP~^YhqyA0vs}V=^1<MfQMt}^hBjw9zGvWd
z%B1nAn~7LqW{f?-Z(viABn~E+c;~Y-&RMQWlhM_r)4W<ndighSo%Lbz@oLArabr1#
zU(A~aa<FC7Ch2ikd-SJg;h;5KYF7!C1EKq&e5d856Yh1WfD=i{GJEDM*|l@0whb*0
z@KbR@`Sv}x{^6+N17UL4P4#fPOk`?$la_dK<#+OTV`bWuN!rhId|+^Omd&*3gIyNk
z8JMPQlUDH~Pqe*bdm&_K_*<{bSD$^X-zdDoJA<?PYVz~y#>Y*k^S$$)u*k!x_+)7^
zbcHl|e}&wLza}GBN|O=xJ)fHnTPX*lle7)mvc$59r@NgKZbos8XF6KLB*826tCKfM
zv*#DeMtIa_8#gd`HTik(eD;hdOa>P3d}rU=?>MQzXV1S3%VRnMa|Kg2<I><_669X5
zX%2Awpnumi75^>d(G^^Uz&4J`zaqiif8>^NAqHPfUR|mBz7UOg&yPn&lL*<!nwrED
z#>K+Bk|Ljqq~nJ8=F10jbPkRXPxjl@4HHP{!+g(Gz8h%fixq6%V@#wbi6-98M^B?X
zNq~d?>!eP-`ZDqN1yE}8YI^da08D6Y*n~^?HorLtx&gEQH(&&1VBz!#ygx66P~PsH
zJ7n47h0*~I1UU#fb?V~liF7a&D1cF+D{w+aO%1v1fZ#N%wUfAczyT3gkBAr3F~{|;
z{8X-Nv9;q*yYMTBvTVpUG;)_{kZy*&;5*I3PQg5p35g@)SQmtM17<e*J=jy0tl1!i
z@&w6Im}N3JX&_8fPuY<*XI_><(?}|d1q$So;jh0WZ;t##n$)W$iO}0P*r!P-y@Ybe
zPkKrJ860uKvhtHYpu~D--I14+4bL*LJq^|mHtt3a3_RvVRvc|&o1K!rd?*v(H5eQ@
zFuV|bPFZ!8rZC7K2I6JMySscq6;ofze^?PHy-0|vfLy}I$f*>2j~!qzKT*@Nkd}0l
zCsa1<cYvpSz!Uzbl_|UaPo6vnZ?yFGdS3eXc|bPp+K>9h(YA<VU6}f0`{Ge9gl8-$
zj7+f&q=gNnNQ<k@8hK@%<iqhgE=yv`SAgf*a-fXVD2&c_Nzb`+dF9tX=SyI41*NB;
z7ror#ovR+0iqGygYnuxLJ*C@*LL6-T?YoQnB~SAM(YV-Td3Ep$GJnZRY236a%#qJ(
z*<LvR7bqauNEuoUA4n^CMm#;YBslBV6NbUk&`Rhx+yD)e3Gv5z%7f#5SQnIA%B0~1
z%l3j?QGSh?HroZN(Kb7V+%gU6a;CxSG{K3VU55RdWsxq{KjE<c*`9b$e8~geo9zHh
z&+hC@YTwHK-EL3Bxd6b@Ckk3H@I5W1OP7&Zzx^R4d`hAWyG+madkV0~4(1#A&TI1C
z=rPi;RyFM-$g8yN3Nna3g=H~#6)g#6J)a(VOnIZcxcdWQO9=QQpV4{fcfe&V!FOkX
z6VDh=-}cE1)Pt5{e%$rK7=G6n&Y$P^3vj^LKK&onZ<5jqByRw3(tLLSFb(hE_=<JO
zI${3@S4E&LBYZ#wDui`a2Ioz0db<VS!nY9Wf7q0blK(nOUhUlwJU$Ou$b&RE8W?32
zEJ)~tZOe;u1>6htF%7>DHYJ)=c}seBABdNqb|#1v&nthfJtU2BWR`x%fuEYbBMtjw
zPI8k^gu$&0oIEiy2GQ33g+STw(Ta*``Dbuqnu7RtJ8{5kO~ZD^W2x9S_-!_D`I2(O
zK-px!MqKoYXcVQgOQk)1-D<YnpIi>)a^Mm<aCH=P&mQ3tk<BpMm4s~y%q_!k;=<Rf
z!K({kkrN*ZPZ<1|Ure}~Y#?qN$nW2GKv%j8z<0sUZCmkewWMODDpI9dbqtuUli27u
ztRNnSsc9vwzHG!t{*1J2b+aTU9LI|5W~=}nhJ(QpQmsZ!@hewG2YSWu%oVTeF*@J?
z;XDReAt763ACA{62uFT3YSoc|Ab%ay=7n+|7ndO0w``VZm|K=8;Um>+)`Hn%Y1z1K
zw?rHY$6zbJtX{EFf-6>p*=cFOzPJxN>xHXFdv=G)t{vN<be$4^oFq`aW^D|T>=Pik
zqO^6(c3n;40KH=6D&Rw93_Pxb!^dRVgVQ7Y%a@mZd-uRp@_+;dSCksHYfChoDsI}i
z0Rz<nQngwQ391l`LFIYG?SN@#K{#womc6@o;q-<=QUm4r2b9x6R``JkISS{7%4dvj
zSQj+GOu)$rW#L?K3r?44(X6TL!U+e3kT<A8MIAux-+Mr|Z`*=_<Qc`0eCHrNGbNTH
z$U5MlX$SbZch4?Wdi?y$OHJT^J(dYKg{+r~)oRIs{rhG0#%)ruVkPm$`I<4&@yN4P
zqNAe3r(`Ktz1V~KT|4*bpq)M@R;^erm8({hN|h_h_N}`hQ+rhbC|R<!<|$sHs1A@h
z2oKq?P4<I_<V~$Qb(LN!2xZD*fC)Sgg&&b^SnZ|t1r^4cwd;WHJXzMcQE=kp5?%Nn
z5iguL5fB)pcu|?z47$A4eEU3j8-js)K9yOXKtXxp;56z;Eb!k9B{5d2R<D5pGMtu{
zE|+G6%(5sGM<Sz<J`z4V$^qZapd(&Q;cM5e2QykY`~)p$pkZv;v_<(*$h$D)1#%x)
zRt`tS%lfrz<i<t~wM`Kw+ZC_mBPV@gV&Y(Sz7KU=846T!tZIXXP1_+Sc_1@YG3fyr
z+qPX{)BL_d#mZ8-N>!ayNk~XiIj(?u+`VfjR-rM;fcBUV$`S{~0D*}v$ouAPfOAIM
zqd&~}=@W#?%chXcQVdQ*Q&LXIw#}Qx8_q^+)`IMoFQ=0&oJcy%dIGPG9ZQtzHEKyg
zI02<|(yHh;E;iUay&jHQPsoOKYouX=dfFEp2n&M@1j$*<Qf%3@6*Prvy;Wk{1Yaok
z$w|O*|32-XLLqmD!o#Ii+jd$9AXRzFy5+VTm1XoXg$fl$`>mkn^9EO@rJUfRfqe$!
z*gqwpuiJs+NFxs)k|ITmDUAVv<=_nVq-@-<9kN>%ZMH0wOp(2qaG~>4%1^<9`EW8!
z3TWD<eTQ!u-*j?J+S&Fa4@cX5DSX}RL7g2xehjh_jJjbTl~3zy`_`ROJ|GbNWvpyP
zU)-i$2fQmNtJiOkgWy#Ww8dI=>H(ip+P6|p!_lXZZdy)o;*<_u3l}M>{RL0IVH;sv
zWFQ`F*DRk3ynp#XZKElPr)B+`RT5OGs)T~qYc~06-$-9Qo6xSpLiYpSIaT=CAC>ee
z4Bnyb_>@K;5)B*<NCfJooU6|%Qk3t>Z!nxZC!e7#$Hyl^CO1h8`Ws*Hv`*dnpvhZl
z(X@&sl|8#3VH;=vN_<(T<P~L<@b>Kqg#`qTXM!BBTE0~2q$xk~Vm+}u{qAhtg8mJT
zanT1=L!FiN^G)U5fzU(f54LDK2(C~`+raghjmxO71sPgy0ZfQh^eduD=qZ#_xZn+v
zA95O8)?2YZcqCc2?TZ!vvc+W0mIyh8zQ+&eCszve(PNmXl=E-(7RV`%WvU#=Z>cSt
z1VH7MlRdw$4wr{&7L%5B%Syiq+oW&DB&krg5GFvblL(l;M@Ao)vc5%R-L^<cz}e3=
z1Byw_N~I-aSBz{ua8!?!YEZSbl*92*RKJs9n!jaRw1gfxjtLDM9aXxJ)TvxrP3Jf5
ziiJF$gnV#Pmhy`4I{qpJ_~0z+8*mQvY1z6vTEb$I#ivj{sadh41eLYxcz?t(Nk}>+
zK1ID`{f;AY9OYCiUktEI*^^$NmAv~;1<;fmayjt-#Q_fYazic${!<*FVtTp36NYn8
zaZ#`@AC+tsM}=t7g1@C{{aVuZzPn^41~Pxo{atRXTLUJ~(=o8XfOGe5xvxWG`C{}(
z(*EY=P;@q65F8<cpB*T-wr(lAckGZ4-g#Zx-&jir_B`rw#j?e6|81@1<f)T7;+^pA
zSJJ#e9oY;;aNJj)OO1-<C1hQQ&deVQKP&_L_mu(n-!A*1zz=`(73p*TJy53iz_k50
zne)?hRW^n^^EecuBN!Z{C5;0H&p&4hzT@ov;suMOZq*7hd-_yWs9HB8O&>yeIgJ6?
zS^4IR&!k1ex){tYlexdnmWDN}$S*(5P^B>uCqT4n+EDsG*i9yamQ5ShOWT%B<n31m
z%j11}LW$WV<G%bt8Ug-(C{<i78uk7#xutd`dFGLaa0KBt`Rv2tQm2AHc(h3{W`k0@
zOkR0@5LOaSsbWAJmn{58^U-SH{kL9`!bOYA#BavP_^-zRkLzXGl4WX&{Ks#<=r#h@
z8|#UIvPN3d2hb0mEm^n#D_>P*-XFiq>Xpl-P4mX`9cYPw8TaVnL-mA<IkRWVNAJCj
z0pU405OzogfZjn5_e5Rnlp#+)D&6jEj|q<5IPX*Bms!*0UDVY(ue~hy-*bmfR4iGz
zNE+3yCV%|)vus?yPHt_}Qa*;6a!LwLe*j`Xem_Z?H>?BYbhXU+d6qofql?Pk&h1+T
zi?&d9HcB-puu~?C!`6mXa&wagGI88Ds*F+@%ZNV4QL%`Qj+2)mlXs%b9f0@!_;00g
zo$635R%`u#_sy5m9rzFG-y4eV2AMSWE0qVznHN@w_U{dqem%R$vjZN)B+PCZ{PYtt
za@bo^(zlE~@&^9)Al&BX9@J4wm|;Kh@B=b)>SUedxCJut^UP@~GpC_ce*O8!(z0=V
zooxAZ<a<&r$Peu)6w1SH88Wb+l=3YrqlUd9f6e(-{`vbac^oF_REjaimo+O_2;prG
z*&tY4hoYAxUE4PWkJf7yPlkDL)B3e#`Qk;gVBVkFW~YL`)Oli|6m{#^LOvTcLgjYR
zKl4?oIRj;!E1}TQgl)2A<9e<WKP+hgd1smI6Fz+RZROQVPd%X%S|iZD`=Ea~91*E)
zcH8Fd(y3(w8T0W-RsII{dqD1K+f?g~<vRoGC#yfE<$KCMC*$6F>1ip0eaj=>8lp0C
z>f|Z;X3S^G$F-|h%J0AYq`3a}^G}*5HYP@%e7Ki<`N?Q$hCXHr_;T*tS>*#2IeX$I
zMqUIh{kn9}{%S1Xk=`wvLUeVQ<(pumZ)qE#$?}oN7<u`bf$001=>#U*arIz-$nXZp
zT^{LmdrSFk_AFJLxI#J}a(2^=^|cOIZ=7UdTjRtVX>^ulXlFT8OquV&dvBLt(YDyf
zv~Jc2CfTFWN1Vg4sVVZ_o3F~4Pe;i;ZJNltue~6#u`&A0zHA33?cN;xEX>zCK)&{?
zOmEq=S(;ZWC!c@xfm$Lx+PfRJ4}1#cm#2&5k;8|u@`^UXtsFdZ_wPUdl={^xfu5Nt
ze}l@-l!@b^{D(p&-qMN9Z$JG=X8kxtdCWRsAGC1(Uzk8$F73dhuRs3;E641QusRI|
z{-r^WqHaFYNy-kbTgY3lyoAY;c-N}-Ih)VuPa+OQ%Af&#<z=L2f6%ebP4dp`FXI##
zn1f@Y!N?HhlUs7$d+Sx{(%~lbPY(#&;^&Zc*2|`ljgk+v%==@Gd@%G)@C--G-gP@B
zKf~oa?MuJZ{)>~L&py^)Zf|?D?87(i!(nftf9n8w$0RcPgVifnNsS8S<)>*s$l6sa
zwXID1VKVTr`&uI}o>waWr!gT-%a=2_a-TW>xDw91^Vc1cW~0~1;BjlE%aAql&GJLi
zdhiNa3*|Kr@TR}5gHWzpJ-JNwK)Ge#jhtExd7$zfpCTg{MoRnoWu<jpKXeY~WX(40
z=ZEkzJr(fQLsm%DcbCe9Wrt+u+DK_VY?VAddWp39GDJ3oB}lJvTcyFs6%u#sl$-*d
z?@iet?O$0X3%AG0lob)u?x|%mYvCRN88UWJn6&?PtF)cEL!O<zTUx)pN?Lum7LyC-
zB{J@$+%<BU+&+ArYzR$|?lX5u?Pvdy#Tz44rj~C#A~y|QCN+jGm1%1Z%iIl-()O{%
z^5?2Bn>I(=QzHKhKOTMHe}SZ28m=J@prgnQxg7Y{a3C%&&cdpNg%7s{<-*#zbEkzf
z;jJZ0mRO4yFHZeix^$^EfBt-H-@bi{JM-W;yd0;4IE00TSy-*ODA*WKYWh*%ue0W_
zKV+T4Q+&#KD*-n-`*+?tdh)!Lbk4HA`~F9M{jER$Sz;xgwyc=rr>w8XPt>>_p@%H2
zsOtU0{RdbZckZ*I5>u?V-yLo>YEaMGv}>Pr3?zm|##xU%_Jno!J@;5KNvABB%i8yV
zwQ$86&A(>Tb{9S8tPei=*y_^tJ}VmU-~Hf2t9jEV*0y~It>b4cYyaU`t4ZTVDC2#_
z@r!T9DeQyM35uU5ACkec6W~)a{&*!#CXJzSPxukb-^bf}|HIMNk;D_$aisZs$x6j*
z#rjRw{H6B&Z-4!Ryyvahq|?^eiBt6b=3V=(up{wSa5-P=wYT1}4j)ajj;5TmMtwF$
z-!ECcLCf8-|B!_<BCMb0{HZuR_T(U|W2a8aGtw1yB+h#5$$?6DZ1QPq)~|D|2YUCm
zB9A63U8G~uk29?Y`}9>F?ml?L3i2yu&7HqkdAu_$0yvbircD1y;gi-m^ZrT2k#v(@
zLp!g0W}U{JIBPxi?DN*+0|)8-N#y-&;WDdj>(<t;1BaES_U+qS-%pzbc{_{pPg~Ev
z@RD`g?YCQ$0oE~jc;9{ZTm2t-3^2}EUyZY6<l`?PdvX8(KmbWZK~yikvRF5y@74|-
ztj|$jqy@!Vzy7trs#-D7IvA5+t=qCwafL~(;ux2F#`@yxaT-S%p)9m(*Uln5;!Yfw
ztX{A0m#y8X^?UT>xis`KT|DX{1!-sgHdoW`IuL2G)scqV@3_MnJ86pI^ZHvut!fp6
ztmW%ADgX8!iniL_a*Mtv?}ohghII>`cZMBSIouZ!Z4G$zajRC<%GQ?Mp;|u6;vadx
zVA*OdZ_KyjwJhR7o=}!a%iINv^?k_pJu3HX7q7ngw)MoIr!+lG+x0!=Ckgyox@Lnu
zZ$NtD^Tyl5^m)XnkCi^QAGTB0!?JZ7^`5-6aXM>Fnemgxt=+Q2+I=w63MyAx+aTL7
z>;Dh5@7r#>P3!xUF<)Ca$kd|j18pn%*h%I6&vWNlNsv{_Ne9&B#vOZ=R^l4yTf+MN
z?}aK;eIM?pa&RE(sJ126Pk?VR>&>C>rnb?eXh-A;X(7Lyf2OTcPMC&iDXWyT4cm5G
z<x3W}2$Ok#`QvY;i)Aw{dHc<GlQeEK>SbR<jMbufGwYVNZMAOMp2!oXHFZTk*}RLg
z0Md$xJ8pGF|MTsH$u7CF%U4{0$D2dnvxW};K+7L7`cumbydqx2FFYpEdSUPot9i4g
zR%F5nYy9LNl&@^l#HI7S_gXL|xBfu=BROPZg2ld*_0RmCZPeh%`XP?rPWax6McW`Q
z?2{QcZ}D=a`{4nPSY?acV9fz9DBqKQoMF|eQO(-EKU~|}5#TfE>1Qp<8T+|j5BAo+
z^FUO*$~5_P=UsPcyN)`RV)40Bpdb3)1He66%c5*h9*FDj^A~B{s*tT#7+~Cb>#f%G
z*}o|)_KC!8>a1T>K7p?F!_1$p`gLn7{^S?mvma-l$v$wkONL1!+xKg4zGDr2e}uk!
z<DFqvKhVeeW8bkOG{S1xqPaEkhw0jW$v5rKfET|(Y?~cBc7%-X)BFdbk6Hsk`@LPd
z>NjlC)ahFOh*6(ty|BMwdGF)fLs@)p#79<xdUdUBdk<<o?>`)){lS~>ysLHGuzp>Y
z58}=KWx>)_sl1@vX4Dt4&WsG{w-g!sj9P9z^6?5Q5qO_KzVH7*yT5g&_2|dTt?d!V
zt%C_?th?S_Y(4t<Dk~mLA#UUT+GELMe^>{Op0<u7pOKk(@PO~9E&`0+zghcYQY@fn
zy*z%MRd2|AE8^H$<?Eack(S&&+Zr)_ixmf)SjO$c&>ubWyR|+vL1FwAa@dkPezMl#
zdENd5OIpmZ{tk)M{KtXkQ)5<JeMT;|;*tL_gyF!^Q&u$SnZ5duC3pQ~{kSwt+y3D1
z&~M%Ui?wo3yp;%K!jR#i50`0Khk(yCq>~Oatx5CuTJfmZC@`Tv_|a?RGAkDJCIY^p
z&+zYBypjgi&Bp!l*7Kh%!(DF3<-q?B2QY`38*(|2%YiG+fh#u=YeVL07o9NmeBeQ8
z+Ttdif%o#xC%4{yr;-*1b4KI6y6=F;q!w0)&ti3I30C9=zA!|pRKcEA%%c01DWUuO
ze);)l;fj{MclVU8+Hv)`6ZVgDHGRd(wJ@RFu4ad`X3kJk;|*9%ef*ghr6N`<xL3K9
zua8WgHAlKX&|CMd^JqctWv6*E@kN)H%68r_U9?oDem_-ynfbF!o%{n#5_jk-JKymS
zmUvhV4wXZ3$#Q4sE->RQsOG{A8a0+Jd%}dKiz}8cmL~>2CCzWT8T4X*Bg_EWckCn$
z>eg2CNt7y~5mD0Vj=QBqF>l>}?OUd-U@)j=qhJB>_LYXW+@h=J#PbLoi`{#FPpJx?
zao=`X-xAWZ_d`OuX+oU`%V}I4bW=mx&cl@Tq?+-1!+bO0aEzLt!io?pynBH|j1($d
zSVGo^U<GfZmPKZ4Sie?RK6QyEl{;MNAx!Qc&7<bLUw;0XuEwuLUYhui|M4f7otD)t
z1bJcBpAWn)QXmh^VH0G|j49Ip@j((CM6>4e65#JEj|_Z9#(wjat_Bj<HmzGrmu@|z
zbg80Jv{-TZdeV>5q2p~ZqYstkE7!?N@HL=fRje*vuUiA4NXbpD+9<Bv=X}ep9TnRf
zuyhm|aY!(Erk=Cu&}_}>4Op=|EmbQ9Y5kB66;KC#yrq1?+Fx4U+(w|7D#0O}cgRAl
zNN-xX5Oq~R`H%>jy7YPoCcAYYm)I)`GvHeF8fY0@rCT^}t~~tclM)nIR_m*rUn#k-
z=L521*B)ggSeY7lPm^AT*0;15FUSk5+F|NlQx5IlsVjUTFxAFni<&!A?rDiqrmUZg
z`Rr3Th>OFKpT#vl<r)iiYR=!dVFR@mNlHqA-0hIJhrI{$=(UmrPtDvmfJrUgY7nw^
zjoja@JGPp%Q4{QZSgLK??pA@qq9*AC92OBRj}CfP>cJX@`?vFePf0Mn{^H}0<mo3K
zl}Ufhmqu7|K7sx040II7_JXQ{>>if8yLN*qeMQv0VCzabX@1LXaNu@Aai1`Lob>AR
zu=tiPhV2}JHc(d{9`usR1!YbHz;d;dE6qQkUH&v<w#=G7OJ+@*C9`q=Biiheg-cZC
zX=2N=xx!4d<>=VsYBEm?9$E}M_QXK6%~lG}+tok$dS9pJ)^uRD9NS+Wf93^g1nUg%
zLS8Bp#DTIwYk`(Gw}xyS0WD`_J!E4s`mbHvH)C50OuKFJZJf<7aN%}<6UnF4g6GqT
zQ&6{+wH=f$Sw!yZ@c{a=Rcex6uR$Z3J9n<~B_<|Hx3WC)%yYW6AQ^j>H*Q!bH#Td5
z_<ZOqGv1m*mhmj%pt3&F4*gC+nAor$nlx`IJ$m-i?LX&G5izhJdSTcnsO#29lUG;W
zTeNMjTV<B5Sfi%qzL2rGvuCJP&-TqBD#K+;7gw3CQaMO^_UR{Kdv~Hupa6s;2M&Wz
zdDMD=&$r!qH~N_T%9f@mpFR?}Rf0#X(qh01^2$}-!bJ)TCR3$h;~Qlc(kxxJO51yI
z<*Ede%GTTOx<~s=_U*N>TL10`Bhj80Npi|L)Hlq(;Z;2jp5<3AS)^Nz0M$S$zi2K`
zTH@nkq<XD7x)M&m4eXaqV4X)DIV@dK4^^w#{cD+0#iVQZp7PVInfeW4e^Iw)HMzg%
zgHq0~B>Fa5j+{n&$%{6565p`D<;mv;V>=JOJLtblmy|Cj{2=_!aSP6FSUMFi;RB1Y
z&FG8Pt9-J2ZfCNC8y{DM<C5_^t>^PVtWaKgv~z8#<X1>a6}>@vv<#51mqbZ4ws>$K
z`&UcD<-OMBq(qUt_}<zwLwdMhJ}JeLnX3*-=PE@cHZetZgdY>XlHRg$`*GQIAWmS6
zpx<Oc-cHSf1QzE~2=e4xynsB{q>NN5UsSFKEZ>ra(FqoS*ZH$j*}tgln9)i6d<)B#
z-7)gliUab)o&+fYJlUST!MifW@<~+8G3osAdKuQUlHA!MP>v;?kq_2H$><)Hq+TU#
z!#M+U{o?tg?`_pYB2UYKsN-sNB)<8jb;AI)$SYj%I;mZ$ghZnLPhksCYJcJ2-Zg)l
zarIyGWaScYZEyge(W?;9iP)|C3Y10ICS0k;lv{@3@P)62S68YoE{uUaIKAAjg@??h
zcde2&A|BbZPMkw;X$D%dR4MgXd=`qNb^dw`{=B3i2Ho7RNQE3LNWwkK7;nnSloTj6
zCvm=gG2JUi1^o>2@R@<;hhU~E>CDgPgQ0uXG@N@>Ind-Fa@hMLkpH|KOE{{>J3(i%
z&2Ud&C*{lIcq2aJhxqZ2dvvL2GT4d)_FbcF?g<Zpg35jHRD5)=bgi`eU^xj<qiQAH
zKgt37Sqz2=t4gInb!>AiArZ$81p((gpqbvH^C<q6u&4Ye_7<}|1`h5pULtvl508JM
zqIUgx?2!gtXJD?bFOim>u(>CcIeCB9x*n4QShqwti?pPPdnA#;WJy&lED%mJ^Sp2r
zM7UH1PXTsGJpcOB3{{l)o_qbDdvP%K>z4&QLLxY)5@0JnGy$hV#bfUl&7TKT@TU~!
z$kCtbqyd%KbD)EDjqxES2|x$hLSgPn22hqo{JEc#LF*N18aHn#g$oxE>=Hn+@H2pV
z7>_LsRD|j`ZVI#9lNf;K*Yq^e_VW)=zEa`00FQD8OAM6*!sIT^d2{EevdA*MybH-4
zUAn{fKqcKbYIx<`I>K^E9~JOPW4~5KyL#<<$WvL$1y@rUA<pzW;9I3EcufA2=RE)U
z4DwQmJ0pj~cB>o$HIxOJK6ky8#J>AV!R3`U!f*!GQSItgb>fIJ1^q?qHNRJ3t#9HV
zwr{VR)smmAM;_Pq>KkuCj;Jgk69VH;gU8&<e_yXYvJPh4tS2mDN$2}}$eL#dsYh(a
zcf<q{osz}J$0<*UyK%rr*`%KW9-(FAm3w~Cr16aE1mR1TEt6_cdV2SL7~4mFQ73Fg
zig<%JlpOxqfjCiCnU8Ws<?-CPGb*p<GzH2wc|}<+fFq+ymG;#wC<X?v%uloR<xsxp
zTY@}cphCz=pc|XFkS0iDx?QGc8mf-01NJwZu%di(-}@;jYUj~5sXW(i+E!^LZe<Ex
zk4Xf7mEE&wzjnJ~`L=BEsJzMFjgx==nj>`@G?sv%U@2Cjl(t>s;|%-+0Sz5{unyv5
zqIHkC>5EQ7E;xxmMd&E{o4O4e3alnzmc1Q{a-1}3+FWX5ANWj|fpda01og&=AHp>C
z;+9`6iKS_OP^o5pO!lBYpy*J(IKjZaGASuZ`OJJlz@0d9|2uiK3lpGYzx+(w8taXG
zc%c8|Q1l>IXn)*qPOg)$=y9;u+6Qt^&e~-cgu=wWh-K8MTtV9;l|=H9ePq=t!J0BU
zIz{neo3Z;qOZRq9_;w5?QVUAmhK*HbIT=UToWQGByQWSk(%hS6oI95X-<s00diheD
z9?+)~Y-k^RzjE1PnAWdDKLo`MP|BbkaB?Gkpe&qJnS%%_ZS0@OI|gnCVEekMT}N%7
z?4Q`~NwH4a;F}a4x(DsdPi2_>Go8u#l`Ab^hi(@r1cj7+EqO}bvCf}IUrQeVKsAH9
z)S|Qg0qnE*W)%x4AZ6hw?gSb>owGHrfo(K-w#d%VIJ~!D?tV-@?Oj)$>amv~FDN@S
z_1_nERA%i+WQ@!nvINBA)*Hm-7g@03ur#k#THi@wv=i9+qZ&~!Xh-M>yozxDJf2;6
zka$jf@ZHRR_Q>liqr|^he(C1#CH^Rj`DmV>5BCRRPD<_fS4yvjKJv(&H6eR!`}jed
zEML-gdPj$Gr_Ws{-rVQrl}C=B0OQ%HeG6(iXsRwpfp7>2l-=!s6y}Cp4&-tmCpd6*
z6!a_EX0Ie{!{OXA3@0vpy&Al_02c0##RU>(_1cpT&*!Z2;@w3izW}uCy{6Nf@B;@h
zAUlr_I=yxaR(Oxg`i+}(Mb#c~VPNB0ap9^{0Vq~<{)2D3DjUcKr&iHgAQdu>xOKGx
zS__}+Hf$opM}3NqbRPXE(>WTCuA%~w0)?Ne?f{_!LM)bJ@At15Fdmm~y?W_@iYqsC
zXoQOnFto1l-DiCF2l^mgZ+j2}7#x6cRgY=RfS9W^+js3%M_zPb#DV9r#1ry4_KUV@
z*B%2wKiyk@1{(znU~mC_PX~aj7ym82`t}DbVrLJM09MBwgwH{x)*9mYY|=3;&ryzz
zj#9j60gxAI=-i2c$`n_m=)8-3#iR1_SmH4oh6`REWa6qe)p&0VK2Myq<hhq$Q@(Kk
z%E2R@-f{AT;~>@_uNY6J1`fbZVuZ>%zPU|XtdQLyL*5*Ueb^y#NBcHX8;+xzwQQ-d
z=!_3~W~$Suc&zkeY^D|r#6Jqolo*H;icwzknfUM9dr&&xeyjY5x})=|;#h@Sy*5M!
zVvi^%0I0~XUA92?Qv3K65q?B=9f_26*mq93prfi}#633X8FegoiY}88xF5d+9A`1m
zVHbm~l%js|&#ni-UeT$t49EM8|8cgiRC7gp7xuDpAZzEc>&V&2sI+qMT>$b?sb&M+
zlglE`q3*bU_%K%WcZG(dZdg_Z0lc;AU8O+pPoBWOQLGHodo`7{SHNqgVLOY9iB*M_
z6DzT)?WKV7*aAZuG`dmu9D8~HsSe-RJ_3S+Wx-#6sAIyeJ$fnn%uj`#9?rRv%61qB
zg>BnrI2OdJI<Qh?DgP)s^**3XG04xW>VS{_2pZkkOnN-n!$#ggAo1lR^J;$kN81x+
zgn|2(YhfSvb+CjunQ~SYt0M53V8AF0JamLLPKQpnYaXJaa9vlnU&<$CR)dxYnRmUX
zjCgr@>Hg)@Pz3VBx`S;q9_?oSf(7!-;8(Q|FaQWYm0-QKVNV+Wt?v7uH1jtoWn~o!
z?yGk4hgmfc2iy=R;^te<UstBDzmC=lwp`FB1r?`4g$rSIGLLlb)(ieSmPl0OVOY)d
z&<TJ8Vf)lKK)oBAqP_ZSzm>jyYDrow<zo#jNI1b#u3RZ7M)b;m9uC&RbmE0F#s0?B
zO+lP3&d{<|TY2`SA*%4wDu?}Q((&Uu!Lkk3AMvrUXhJ&nL2L(nU!Y(iX4Q~_6<PL~
zwAk5>z3c3Eeb7hdMIRX-cU(q&`I+3+xvTau+}qE-%L{04-nC15-q%^MEl8STqNO;N
zYXAOciM%!Bd9@B=zqWD9Hl5(C5a<Vf(u%{Lu<<S92lzNC0Q4B;msVF@y7!Vh@3{wl
z5l$(uxqqJ%hnzU2LdZalv2y5?kPZP!lj&dSSdW44I6**XpX8T{FXGuQSSD%k3kcS>
zXt#HqbO0vJ{p}ApKTXj7=Pb-}o_=u%+F@n%pO~;jptC*V&cM24Ko^`oD3T^R&4Wf3
zK@|HG{!t<KEm1&T@8~Z-t%;EOQAyIac2TM5532{)cZ``x0~O{)Arbf(+#su-X{-*}
z*cXyN6aL&K@BR}Z1MY-#LT<vxBo8N;Si1hX@+$(aI5tLp3m1ok%j=W&%i>WjvG2Z=
z6vr&Zuqm6c0-gj|T(v(h?@ZnxZA;~s4<2bEMZKWtBX2&MQWUyDc1IkS+W|j6v~KpB
z39wL*<7cIKkplSUvRAk9ch-YOrv3n!VwoC|d*kAO-8YgyjsQloPIE)%9I*Lj%U$NF
z{+n;?p6S0yTP{_(9LO;a7zHuM0Ol|tc4AZ%Xa6({&X;CmaFzlk@)*v$ou(zxTQUcQ
zB}!pHhc5yZzrsZd$!&LamPa0XP}b5683UPJ@GL&+{h`w5p@(6jTt-(wIq2gDxo-W2
z^23zxVb%z9R=}d-{xh5)^}3@K6uo?MSC{+c)u$ek&0BV2uyam!?AR;ayWFiSI{f}q
z5v56Z_`!pSJE;RMCmT4x;J}#*F|Qoxn!v#sC;5XaRFEK;IZpe2qC`a>h3V>PI5wK6
z$2oD8wrPu&GHJqh7@+-v0rCk<HYCantbiRj60hEvO+6Bx3B1n((`;wpAe!+YQyqYf
z8TA32!L0=iXR-1TA>YC|)dRiz=qeJGte<DkR+E1oUlp=pvy6uWC$2<t^^AN^K7Jk^
zh9l)jVv6mty<Q!x>dXX=iK>`z#kv(7Npa;a1%ukj-%XG;t5)M3$FN+*vK2J0G7foe
zkPLtO4GfqQb!EI96b}YX2Yk6Q76S!59Q@(xX|H}y$eVE36|xDQzEQ_px9^dmue_iu
z_C7v7b{(@I2C#*e6%1S<D}c$AP?(#~m^wv@g3c6pTBf)9Fqp*7o-qx|X`<{8J1kV3
z7`VF4l|Pzr(~(>(6j=`7cVjyQ9hrqg8RS58#*fqBOzj_4oHQfkmsLW!iO1we)f%;Q
z#g4QrT(CqYPnxK!*<3}UJZfUiX#cRjxVl}mXhG?8_q}k^)=M^Q+^Y4pb9boX2Y(34
zBS*H>TRo1;^`t{6Aae=|4p(rwWn}b+BhW@xs#41B43j5}g;TwkP`CD(-popcDfVcR
zPAbuZJL%yuWAz(1QRR<tUV83nDN?MM1Ox=B#Yf{N&E&UVf0a3Kn3f2S?eXzR+Gdw7
zTdwUb!_gaLp=zDRs=U;IVvW7?(yw<<C>?P6hY1QQN=CkUB_1a5mR|2!PbEwF2=@BQ
zh@o%D4tV@d0Pb^t{YAd{>PxIvdV^Ocmwn|bKij3VjheQ~a!mTv;*j49tABjKmV6^7
zIcQSPwwZJ+1<nAEq8(3C;FYRW)w(HAxI~5|GZEV&Cje=M!AT-Ig`7X{FZuTCue6O(
z!E(|{d^mAK8S?hd52f(|8Te>_ZO60*Vmqeuzb@F`q7!2%gLO3P`|+{~+ZtGoPf2(L
zuTfi`e|jKHz?;Cjr;sEbJuXuwPEzM%rhO_A_#w+EpHwfWPn|5$F$t=q(sAId88c9)
z4bZ>w$Vv#Zi)ZY5I2rTKo3EkoSf@CjfR)NOUwkT`j~)qYkP`C3;8)=2@J(#f*sqg0
z>({f7`AjNQt*(4y`6bKRCvI?Mn1OAU{a@1YQ!*bDLvO$Ss!p)6K1e_16??$7&tdxF
zm^@=YzH5KDO#fjr`hP#WFF=Ks%zvHyFkw9Uf+O0d)~s45@4WGv+|=e4-F88lqm#_y
z#S2T@TRX|upN@u8y)Y<#d9lqRQs~rf>7s?H{Ta(6uh?h#;Dm@VqlU{WY#-r775f`H
z=)=By-6liJ4sG+y<O-Y+A`L~*-rIG&Lk17%Et|INK>l;EfY<|Pa<_r*V|qeB&Biyw
z5#|i^mkFxq(}`cJrj4M~9|T-MKm0SC>djLbB6v;~rUu6?({cjM1j<@oos_Z@QQkP|
zbZe6UnY1ZRo}IN%`n3)eZ&*vIw|HbEy*xTCCgGF}SrQ@dwDgm@m3`DCy*xa|2bJ-b
zj?Dt$kuXIz!J9hemyO3!tZPERVBxx=V3r?u9Fw7b`K2hff6*Fg#r7C^>z_j~ZO245
zCUCz0eVcs0Dn_2Zs~RQ^&dc7&WO#c|R!{CDTlvc?<MzlRtlq=bh3q(#B=7yWMf|Ga
z<Pn(6pGJBg5@-T$$pc4>dGp`7+Etq)^eC_JWB19fD<E_7B?pVi5nOW;h>lAxf<QaH
zJA=(PyT8auysiaUX-B2k0>-&G=5pXFaDbhJ8RW7!=lw+$@9t+VD%(XT##fSqq!ulj
zL2>#KW|`mX^PJy)6F57Q0GRw82*v*Gm?Vx8^kdF4x?ry#k5{`Hp3UJfN`9L>PHwur
zvpk0tEpM3Ma?r(9YaRtwt4=K$KY6O$b6W@Ldf)v}j+12L$^|m{x5eU9qOf$nuRHc@
z?vNX?KXJgLkLkeq>E~XMd!V>8a1j1@|A%BMl>7zQ=h(D)6To-#nS&T-V8?7e|K$H!
ztR$2vTUx&T@mJ|{S1UEk<Rk(o4Zg>o*a{W7kKR%od-u2x`^vlMkwmMon)TaX3nUP$
zKL;`RV<4{>SO#f~io|LLpYca5wqnoonKRGpdk&`SU`6z;AunJRw1TW#u~_`d2g``h
zzSQ%Kd92mL{U4F`81!}P-52J;5fTKmY%1TBBd#bv@Z=!5@9sO~K`8Z~eLF!u{`x!V
z-l@6FnLR_(PMJJO;4llv3suL!F;e;T*;n5xj}$K;O4_^&7sRUhFnRE<HrVUAP-?&g
zcj9;B<r(mqN7`YrOFG`&S@Y9eclz)1rSBt;NhIiNigVE)>epYs8#h)b7X~~rNWCC)
zrHOS(;;A=K!G2=U)6(vyrt$y=$$L?!jheQQy<6AmzUQ~!e_#GwxKw(!ze&EsL_}QV
zLAkTX!@_;(G-ECdxq11`cjU!^kHQmsQ)%3ymEs5|PeO&1D|;i~9wPU2?}HWkw#sqV
zFY7-)29md6fKSiQVf**0IV!h5^c(P~JoG>htb$cjeiC1YcxXM-R2JmRz4!HiN%}r%
z2G8i6Xxa>As|w7)<BlA{DjrXcP8+yF7lI?ySXT^uPm6}=6P8Y1G-%XF;NVbhYu8%3
z_h5U8kOWL3eDU26Y66;s_QCzd9oya{gI{?~hP*WtTL_B5?7W4{m_A*P<>H4nD5wI#
z>we_IFh@Um%F^Scew_7-PGrF`qgs3X2@l-Uf1ZP*?t-vd9Hx0V(PI#&!m@ege8?w`
zbHiKuW0;3i5{AD1vV8Q}*Vt+jugVK$I~IGXx&Pk;(#Vxq$~>&u<>?oY2NQDj;PJaI
zWRJ&hk!L4UFoA|lHb?B-%<E#20hfAZSq4Go(MMb(kJFSL+gfnn@W!SBM}P7_w=S5#
z>52)7qq2VC0)YcFZJYa{7{WoJ))A7Y2`rmlukXCKtK8kOot`4WNf|F3$wmJF4?oyb
z>cKR-YPCv`L3?$YGDT1NT-_vo<P(ooY}4XK>C^WiS-$Wu39eET7D1n3VuuO{>Im&S
zxN>z>WQ$@Vh3A!Xy8%OO92LfTOioUgM<4DZpM3rm+GkUJ=j0U4tZ5RyW6NfFs9R?o
z+g4ttd_P$}M|<WzcWw_l91cq-)Fbhtm3oVtTFFPBd@gNUG|@H}x_38Luk*^NG2deK
zvLN<TKZ^a-yQL|73ET(Vw_^WvaHYzS^F3N8oG=ZGj8}!aQpEs)V?(shR<Z(p1Wn_o
zPx}!v^_v6+1wh8m$jxm#NdG<$s84_=pM634q5t9p+Jk-i$ZnY0H*Su$jecVKl*uqn
zZ!QI~Po1mUy}RBi)oa()iI!L2cw5@Gzf~uiPJkz8&Vmro)3sYqZGUyD2BTjbAT!5)
zD-R4Dgm&3Ib@iM~Gct1SEVt1#m)Bo>2L3gw%DPocb#mY%(C!6G3tA%BE9!1(WFNu_
zTuv)?L3yFz!;N)n=yzulCT!qHQ|{^7O^OsQAj3ZVM4o)8r~Eqo2dRrm7WS8)e))~m
zZ`ep><#SBFa*sT%lCDDssw?~kIderhaL@>AZ+8UE$@eAF{=yEdOKxeX5>P@Os^cTy
zZNWB)YGu;U%#<)PlJz^IL{=V?wu7*p025CqVPOF&Rcniienq8hlj1UEVW`~GDi|-Z
zH4LN^J~<EzE0+>HDg)2>uL_<-pVkS67Bzj5Xt&gRYl&1V>m@rAPRrfFh2(Q6<?(qp
z$`9*cIfa$<yT@-*xN7nfby^k;Ybt#@RhP}-$EDNjtEFip9BCJZZ$i<$vh>MDx)RTQ
z=5a}9Q~Ly#oq|&dVqg(N-2Rgxhv=dbeiZ&k_>HqIjci#ijw4X>xR}Sj^}onR_QMRM
zI6Lf2O%I0fo<|GjMO)`2)Bjqy4j(>SQZAG7%)l6CUk-Q!o3Nbsmjg4W@?HoZzR!pU
z7s{7Y_+4vo;$bpPvo)SY%~|q2d-h<}EgS=PnBqD_l{4yGF$)e3mI@Ur-~$dPCXkOp
zaD0=KfvcZ~4jqDXnsOL0Wxs<k^*(yadZ|;rjNIG+XYInQnVoY^2bPr&AKi)LCcv@U
zCK>$N+v;?QN(PlJp6knSEb+LSW-~4y%q01-;9j_sI6CXVfiNg($-25$9!|$NVC4}}
z^RUYK9gYV3<m>TR9VvoArEL<+J>m>>Iz^=!PVTWX1QWXBDZ0-#5l0bmZ!1@!yu9*5
zxwMsHo;OV=R$N`?DjjjisC_8#OnxvfhmsVnXWnuZg?rTl0t0oR>iQPwDm~5b37bkh
z&*rB1&`KaO;*gXqU0O{+SwviH90twFYHm)aMN|lB_DknqJWqN+?;dcpb-y|<<2mU0
zVUijc7>qrjML{>sFLAyyR<k1@CuW~5%}kFU!}+z~4_7j{dP96@<xsXP%tB#S866d?
zrkzwQ>2$0-XfXIsojg(Hd%)w5;F^|45PeJn*!0ti!5c~nSIxY=3xJ<gAYeHV9i^*I
zJlDCPS3a#5n#OY*0^3sgz#!1(rS-|PgDL04fwIK2yN}_#=tGANVla<YX(%c*(dHS&
zY?H*3XPYxEm6gJ9cE^3M58iv5d_8%()Tv)b2c<`G7BbIG=AQLZrF`K$Em(O)p8qxX
zcXb-}@*8jHiZkP!f%!Obz)A~*(!>cBmS4FVR(?>2{M?s@Ge6cT16KpdTlK*J{v^YK
zgfh+j(X<38iIva<z@yyJq?mHYa6RfK>3EV_cCcMg=_7v=u|**P^~Lj+^TV+cSN$my
zlwUgG<KE{|rAw&XvrWgx#jC=}EhA;hmeF>8H2$cTNx1p*=Y{pbNlcU+hS_mR@RaA2
zyZQrk2@&CkbRRSaa-|@fe&zfWzZ1wqb8DU@&2zFDxP5|WwR2xD`%2;)hw_Op=_B8~
zVX|GKL~-yxTvytCO8RJ>u%06$BA~PtR>h4n$@WN-=hIO5i5rgsB)?yL^;KN8$cy@+
zT#zT67$Ci+z<)1qdcUv(Eog{81NWq}ZBlM&e$2^2ruQz4z5)C(eS?9?dZTQxUOC(g
zhVo4R2>YR+RjXbzm9HirWt)M007MsQ&uITd`6cft*LFL$%VPUG4E|72^DSf7V*=W4
z0wxpK*HOkObF>bjHOV7SK8`-c&P(1>p`SnRANlN~59G`5rl4M8(3U87WwhO}uQ-F5
z0Lm*B=VB$`{{wWgkDxrz2`1~0)(YhVg2V?7)6QVRf&B%~aOe9INy*x;u-zplCSn38
zKqq7<n`{%@N<f)75*ejiJJ??Q{QZ&6HqYlfehYZC8@D->^Dn1m(#HTTgt!fb^};PT
zbR@@qfs;BV(9g1dXyHPup+NK@1#qrAWrSt3K6pMnzbUlfVt>PW=6UmM!|b2fj#xKZ
zpP-pm5Imx>&w$6JQIn>+1)?CfUa@~F;RD`4F1Q7YlTpOO)GP7eb{f)43j}_5xP^eu
z4>_Ssx$tt8$BCVon0P(Eof9^tQ5OM0fy!g@Twy^tN~7%<_$oAxAJ+NdXlz@8T>F(O
ztSjj;2`O;;cuLBbg|jxL3A%4yeCLq1-{=(*R6M`D)~BK1xB;-n{oUtnRQw6_i+0|;
zkO_82sVVqAHVOSc&$;)4BfJzTgEDz8JJS&lI*Ke)vXJ7D4Cin>E4{Q&At_ckuY_Yo
zJPMXL+*j`JTSN-Mso~bWv2gHLP`t1YfL03BmJCXSSrU1DisqB!Fo6$^I10K>tJyng
zD_JzZ)&tGm6L5yQf0?2v-=6e}1|2+x(yw%pRCo@yU6Z$z&HS(!*&TWmix1o7!>3wY
zG98o~@;M20^xXIj^6fs@)*wzR5|@4fUec>gkhH2>PV3U7Ck|->fTYR?UZi`Ew=73<
zA+J-8opCl;#t@pj4AUmvW!mt}vuAk*z8juNX|8tu6QFzPwkW9`SWNtUXqDw||0bn<
zb>WZqD|bXoVQf#V9b|8Z8~D{4Oi1OGcY4>?zSmCg#+}dU@kvjgo$uQE%`pW%GqTOh
zzhPiz>9P}VDmOc6vy;XMT~6SZo${_!n*RzN<WEj0=s77T9CUGlpd1Eg9K2^Y7#v1_
z4+R~LW3GmRK4I*5J;V0P@#E~y9>}r-<p7d_iWlb?FxGK(@a};UELT$ltc-Ofl@6JH
znDiaapI#+n#!trNhqn$Q^Wo!VJ{BB6Q8{NDBF!9VI@v^po`WE5W5|Rtv95A*jF1|i
zT5@)7>{UDtx;W5eAaz`YVlaHiwqZApNN~o*11b*0!QchNbk8DTK~G5y%+HmSQ>V^h
z0NE9LQ+r6S9`~ygFn$Q3!|RH^p__wJDgX=|@Gvh|N{AP6GVcwpjPs;{4!)Q!J}v>~
z(}UIXdTp$F6L)7HrFrv#ep3l3GnF3PJL`aTO&R8ZKTW-G!o)RLhQ6u1Vp;|#zIKca
zpFE;B?hTu^OOtvv)iR^S&8>moX+1Of*O^o0*9FUAl3NVCv6TuGn;OVp@{W@P@4xe=
zbh`6yOnS6X`84#Qc7cmsM@-9Lltl8E_t2d*oOR*L2i7TNopQw+ClC0Z`Iw*YJrzHb
z)@~Oz{m!;xCXt|<r3R<mGm^X|t*m>~zD%9lbzm#4#FMx<6&2FXctfw!fjXzGZ~}pC
z)8NfSY{N>6wqsX)p*>)nuj#0;&=P`Wu&q)SO&cYyhF6sB=LbHf%Jl0)heH{FZvmVW
z-RYKAx~km<*Hc)%Q!`s;yojK<qyAWyQ#O^>Y_?4!6Q0GIwoUP1U&TOvm~x!?oU%>1
zHtpZem9{PzN4%+IF?iM^<(K7f;_1~Fo|Y${eG#kbHB;r+DCmE~TIZGLo|O43L!=m1
zzlkDo<-C&ZFwU;;jO4&6V_Eevlo9@!ev4Pipy2`Wr!zj&N2pAppXQZ;^382W5Do2L
z*ak=&1CwCBMipN^5Ko28t1AuD8JRTlt8prm>D!;d%hZk5g-b#2ad#&fFz^}a()rE|
z`&X9Dw!!+agWY!$Ps%lMG;P?VaF(b2rQ>_SG^|U;EADL9fZ>L3prpN0mNgxCqcFik
z<-ZFrPOcqGIwf28#mXITua$)(;5e~HX{?Sz?o8t*2&7}*&;EjVuns66&cHU$ENS{J
zBxGD#JU~0(Fpl-XZ)!fkW*<qI?58M4<RNj_;3|w4M)AtL+(S?A@@z9Eupi+W@JvM*
zinEJeLr2=WF*&c@E9uIQwzC^f{3g$Vf<CClB@;tfl(8iAwRa6$ELBV7mwufpp`D(W
z#FR7g^U6c=)yzow^x3L1urn;+P?E{xS*VFO*Ua8~#$W0)P*-`&s656onD*>hhRJgb
z&IOC!F-Mkt(o|a1_Jb_4Eo7dLaN54PP;RYOTn6{3r(mc4wG-dL>!n+}Dp~S|GD=ze
zF9kN+90{Do=4uvKSQuDATm>~sjj6CpDeMGhJ@a6+%atFN!$8=V6D|iIXSu|Ou*~X)
z^Bq?qxWdHMG{YD3{v=j^&*3Y=cb5}9>F|8M9N5=x-hW>!wY#$`!BHDBf4RED)lCjg
zI9SvU7V&0)O@)kR;_OsS$I2@emXoTivEAA@=7%P>VBViH@ym~8<d+jtm2<v5jX^t;
zDVIH9ae&~de3-I0I6ZaJF2IR{U1D~B?M|2jGnI9d4ZXhp13px3PNSTRaO5EBB;Gm8
zH?W-h9I2R?=h9e7<DOUQ_&g@a<TZ5hN}6@BNWR#N!N>;XU<Bv+o^Tn*)pxs&Y@8X#
zXYT8Le#q;(;zcEt@y?)mPn*21K^D@ObfylNhjCmDG;su_WjfO9<OL#)oG_1}hb1c?
zO&qS3t5y}9;Vy5y_>}g^VOzJzO?P&dFDFh_<(zUx8Yycy`d4u<{SsH8_^<!t1F=FF
zU=NBN<nbK?&6G_rbdkUIy<M{=p6Td>ixR1Ez|l=_D#2PmeDBQ1duLkn-n11{SH$1Q
zO)4$OYXWh@(ZGOBo~NyI1CxrrfnjjqRpph!W&c+gh8B0*bmN^`H^^&XJ9%x|AK$5h
z?Mi2GHN53^f~TH;Nk)%&Pg+#-mC^xKu?-+x#!j4~`^7nEcOhf4X8TIcWJE7{$3B4X
zja&ge1bd5vCx7_H8JOR+ZN?iu8rd+<CeIm%T<~2gPDtP`UlGtOyt7T@2|Il~^27j+
zX$HUXj+%>8sdJV|m~`+~yFUFLpueC3MlA_}>G-GdocM4(n|G-)h6qpGnATnIsR;nf
zZfl-0OqlkhBp@j;l#i)VfYD&&n~!Yw8Np^6lflT8`F^lmC;psVWTl!oCqABOZ4BJM
zW&QniD+@u;yT5_KD}&vyvOH*=8fcvXo*MokS!#KBujHC{cqSaeu<^iqCClBOGHN4s
zIca}Sv5g|wTP6S#{Bgr#x!1{S)3(U>Qx3?`K~<!7#Zo#cgObt!GWZZ2Ni=ji6RG!q
z*L$`@?KfRuhzD<(kNI^XN55%yoXM;8;Q~rUpFt_G$*?0_zl1}&s6d^CA3efnGE)wd
zsf^%iCL0VLOlRVl?mrb=yfVDFRNBbjWB{oVPz2i^unoZ#ZfzVOHOp_6K_Bmun`)QA
z5qBlEUgP06A~Z4)M`xZBe;mCRSf+^bnBy1n`Z&zK_Z?1zUx71Hws=8(pAR#%Ctx8I
z3(Jl&Sglsae<<f9EF5U*K#Pl0xIYH#h~n_wup=y9Xnj$!OkpVp-$dcC!U#Q*Bt^XQ
zO679Ju*DJ1A6@PIC?=lv9!iu`m{_RfS6IrFu=SRBSXFTALt*&%*b{zKj-Na$!M=s0
zd}-Tf!2#eX!TBXNK1HoBiXa{3<_PdPi1IioRH`T}5<wp=Ng`o6QWO(kXRvjGIMX>{
z$L2wXU7Gg{@`QmmhXJz~a1Dl)M-iSjfItfmuG$wZR6uqfjK}E;=OnOnAqfCJ*J8Nr
z6?9X9KjYlwapT8f&kvlpQ29iUK-1&)?b}PMR;^TlHEDVE49-r<yyukzq)C$|sj1*o
zPdz15r%sj4n>Wkw;lmX~YFh0c*`3&(l-}USyf@G0%JTN_-><7VHEPt*iZ|~(^JIk^
z&--1xc(J_p+G{#kHF=rmAWZoN3>YAvfAP6gu2fk+)QsmJ^G%yJO+NbQBQ>Kn`8ZfJ
z9ePIP8XV1)Y2sjR92OQP)vH%m^ELy^q%l_~JQMeCyZ&2N&`mvXaCKQL=vkV$Uv9T=
zYL9~#m?dMc9<MZgbT_d}D+6G3a*3;>#bGMz8e^IQGr3+`S(yOKB>L)>!M2nDbA3*v
z(fhocubH5i?dXi@V0mUW%b3n)8)UkK8JCeR*+oRKU2uTFdD!TvI5q2LDKw=oS`^1h
zaqvjt$s~|Iu3~bP#<qIM4vyJLlSzTuj{e^(gLT2JF-aKI(5pY&I#<!l`eENJ*g>Ty
z<GTFc<CPJk)b@#`4JxHh{utRlfh{K7<6jh8WZY_wfp8_R*KU1~XEcMSgHJl7<g=|7
zUI{)C=9PkP!;OqfmSiYNflwOq!$N?{a~9p<We|C#=y0O+zgY%lFm(m}vQ6CWJ|Y>4
z;w|qimiuazkmtMOI8sc6upC;pMM4qvdvbw%)4Pfc=u}nK?248qqt|1$>bz9N`S!au
zACuv|E6UTIYr+$M9@!O<D7SsOR`wrCksB)(k~JZT^3py2@?O72vSVM2)P7`{9Q>i3
zl!s$KhBZ3@@0C?@czk==ap0)j^8R|cr*1L%ITFrbF(F1r4)3=pBQGzHlKRE-NywIC
zGN8S$j2_TfmGq5!Vx<0vwJ@J^na><PDf3=!DD4~i%ftnHrSH^T(k`%&%*XM47#xV~
zO_r4-n~Q}(Lh~Ujq=tV!*`9D#np7$xD_*-<mT!j7qv4>hbUq0vc!L~Vc2qulv6^&h
z9xRnctiq`QfWt<EV|pV#Y$adJ+A2rEi!lS6Vn6z6d2i}w896H)Tj`3*=CEWb?_W^<
ze4?S$2rMo?%-bcszS|{t)F>izVlg|)p~$`yvUu2ya&ujK3!k&kRtEixf6SERWuwUb
zef#(Am&YD^OkQ~Y1$p}Er`0UDQ>RYy`fIPJDi2h6$RN)@yJNN$YV(fDIRjzu+__Wl
z>2=r-M=3c2)AJ}0lP@D!<{hu*{U3k)A)kEmiRL3bI<sMLrgf%gKJ(7t#>ogO=-eY^
zf`QLTih1+qVOH2a+Jo=SJDPcp9XnPhGfX^TUkV+Z2xtUHM%)L=a3LP+H%e1pfgkqi
z|Alawbfjiay~&6vnJY3GrOrGKD?=!!81}Um!QOG+yThfG!Eq8z+Dnuyt^;)=w{Ex@
z-CbH)89~ZC?&_AoHdzvTjj5cc<!j~`X75eLbg(=sM#j-q_F!d}D&sOTOP!r~P9Ab_
z!4;{}zCQ4bUkMX~l~h(Pq?{8z>EmP%tuXA69UQZh#s%(@FaLXGur63<1-)=IUF9I5
zNBDC7Wpt&?t(>RnOfI6%F1aH9)n%CWN%`fyGZ@+SF64#%(^Sr{R!ED-l_~YgltG@A
zD($1nIr|lRM15r_u%TWVIt<<czTQ$ZsF>t~qQcc^4-v^h!mbn@P84&?PzBG)lFPW~
zn96XJa>0pe25<Or2&w>|HCPotioYf!*GbPnFF81}wXA)swfr==t_&HrQ~q3iP>1eM
zjt!A~|Hs~S07hA4{~wTE2}uYf0g}*5=)D&!h^QcnUF;sV^E`XOcJ}UhdV2Qz+t5={
zL`9L_d+)v1KpG_M|NZ9M+5L92n=HvDQf6Rx=9_u*=FM-v-F$D}n>S*~Uin;a{PAdC
z{PJiAIf-L6#?O*?BNFQbac)l;m$-3qDvkUL;~o_WDA*!768rwq59>zvM3w{`yzht2
zm~nf1%zUyxzPdXb|NeeEw(rkV7W#J2%*Io_B(}*51CaCL8F=}!ES&YieC)|7M6Foa
zAF?_h0~*E&zy8?ycpr@Dm5A5ATP6F>6EOez9!fd?{MzRD;jXS~@v8SDvv7a^+Bo>!
z0IYgu06u=C6>j@{JtQ6^wm#GY15=~$V3*q1EXsMZ#7*G$C{|){F#Y$J)p-8njri@!
zPM9v(ta!E;q6CZK|C$Fn{KQMV1*|Vb-zEvzFZ0_ux;F-Os)_d}ZiF~2s^4s1f6-hx
zonVEaxwundZP%_HdiCn%nquF+ebKXLPju<h1)aNe#_-|8l|`J2>$Yv%ByiGx2}+Zw
z0&ww%S}VW|e8P6tHEP5lQ&I8&Xa)Zwzph=oy2c>QjvYHB2*_?lS63`Kq}RL{JdEvh
zzmJ!oRVflwgu!8`AX5$oBw=5+(M6bvWAE9sM_IP()vKr6)2Z}wjts!GRf0^BAFbka
zyJr9*%Hf^7>+DJnd3ET}LGffiUUS~tw{KSpb{%o6)n^8i5Bsu>)^fVjvz?f5VSo!5
zJ$m#|ZJY;XQjub19{F<4Y%K}sLR>h8#>HFKpk0@QA!w`vW#o-}p#AK0b{_V!ou;(1
z3kFxAH0p}CaTvOwF_lMN)zRyX59!=M87zKY>`$6Yx^*&D6%HJys>UkMhoEKCZlf9-
zU0)VY^;79`S1pU$v{xnq{LJ`0cmH<qKDQo*tZ}R82dpwD8D!q-K36w!Nye;Jznjuz
zeIE>dB~kLMv0T_SUY>0y+f^a|R>*(LCEa}XC)?*&LSE}ne~IdHSNOOkI7Ep2aPauO
zrLylG5>@}9N4lY&xVAH0*gFe1-mnwn=I=nm2DLC@Ne(95-yTiU;#K<@9qVDzcYTm4
zZsLdKh*o9ARxP$SWn83zP_)#<Fx+^4GbD&xc)Yy2*NedwZIjTYS#8xJyK%Buw<Wr~
zSoc?L*elT{3vs3#9hfgc4Y{b$Q(W=!-WtpiOTSt%NR5ya0EC~on==h;Ul%X@x&bj_
zac@c)#nUJwISPp}a=fPm4O#M1Pw~nW7kufwckf~B&Jjy@ObwwA5!du^#7V!}qWGuR
zvI2`1%HOA2Q8kb+&$rJ`-+`xYXo!C8)74l_(`({CBU_`#ozunTKSvfI1mJq%xc0o}
zsHxoELvcmVbo}+#O|spj7@_n6AmBbe2gUFNd$a!~u8?R{{Af+)`KnCCj4mOmbLY;u
z@4ovmcI;Sr2q;pDBi+;M*RQYK!qa8{(FY%XpxXEcZQi_j7|?$JuDId~+<NP+xaF2x
zFl*K<*PipQzWNHc-FBPeO@1GK_@PohAAa~@&{}`hRaYt3b@E!da;2PcIusXQd@&w)
z-~mwCe*5jWm8F?7JpaNAShIGGMDMKave5tQU;k2;|3@Bq1euwcc>ek4l`>D|cJ11=
zBG=^@Fkk@gx#u1^T4#i^kTcS=UHMkR+1~VidG15kd(pW_K!sfQn?8NIQnIhT_F9Y@
zHA*7cCS&~g@rnx<5<d9g10~nJ_ueZ<$uz}fmt7{2V7Ft;m@(+zzrR}OpsW5javn1m
zB(A^ydUWsJ9rxdNKZr5sZ5`|4_8_o{cv2QMcHYAKtM{fW_1gBsTp@loKBbNqght-;
z)<a<sm<fwu^9zntRnNuX;2U$|!rw){f_dNh9?jHN76P81W6&N6DoflGp0D}6IKGd_
zjaL=^u{=Hz1E%KoA1W~?$@71}^Ezz_gZV*{m-7~9MvFRAktmL9w&f!!E?lhWqO7Jx
zsNj;Sje8^{wabjh*1h@ID^_he>koCEd#Y(&CZk@hQ;{xvt$9{Gqws3p?CUhtv#7k^
zkP;@x7h1~n5m9>c#k9;Q={ix7MKy=oo^ent`iQQMq3_H`okymi_Jfl!@SO!f(`bg+
zkvI&;kdYB7hE8{X)<uYNPYp0%_JdO%9!ba)B03QFu|M?D0z}?35tqC&3-3>t6CfgM
z$bNK-hEw1^b9>_V>ZmC9o8*|k#`Ud*2~A&@3mFmB<P;*t1Cw6jI!|R^KerbP-$>~%
z|4+IUGz&Usu6}6cVU3Bd+60Joty{Oov}x0nW%$!iKgHKye~neER>?6P4KQ=&O!V&E
z8_k<FNAniVae<r#d->&;D;IfMiogB#TU>qh)%eHLPb<FUM{73I)TvW(%{ABHyYIe3
zzkdBx1aK<$+z(ELmP$0;;XnE0Q?Zc8VZww77&K@Q&OiTr+;!JoO6lkRVjks1<()3?
zTo9N$cP?7BYK3LXmMO1*#*G^*xwr^G1)DDIv|vw~G)XNA+;r1TN*?bweS`e?<By8J
zc7vy-e)jCyYW@s3L(4do>Bk>`95>#0qq6XSA?N3wbIv(RHY)QYM~(#d`41d8P%QXc
zanVH=NidR6l)Rk(lTSX088c>}Wy_XoZ#&C#=FCxYa^CDm?}O~@Y}e=FE$_*8&3nOj
z@>lPh+Gnj7i*o<T_2T0>7Jp4jCGaFK*Hu-Fp#`govCJ46?Xq)&RoNJ63WJqKqU_vY
zRW?SN!eFJ5I5Bebc?AAB7_78H9XMv*l<wy{Dv#Q&oeHOZ^qam+j)Gj8ix%~2B8Lj?
z-XkbrE=iw}zHMItYKjFqB2pA#*^iw|MP9YboACPjkF{th*6Iiz%^>42j*U)-@`aaW
zFSV*f`PB-%5<^IpqVMdWo~uR05*-`SJZo3_{&+`c70ivww`g_PCb1Tp*RP3jGdH59
zjAdU0;Jg^jjuKe&wHB?&e#Mpp=y2;4ymUhr)?Ly{g2TmP_R{U>`qW$*&ytxk@iYN@
z;+;rC0QbS~*)MzNdF>?O)&Q;+gwnm=mhAe071r3+c#;%@s-XLuGl8PJHeK2o<n8Z&
z|GTmVbI){}Hf_|t^+}T_$%kqM<l;VdD!@#%6jKq;mGdoiqT){F_qX4F!<~2DiNS*h
zt0ua9fAGPFa(?e-)&9&g&nVaR<m4n}HKzrc?(bBJ6A}`XyE}uD(T$#Mv_?Po+;eEs
zq=}+$*svj9dF2)S@WT(tYM7;DpE+x$3Vz1{sX)`hPG#GEGiW)PXJ%%qMTi9p7PuA`
zsN~bFp7UUtLGPI8F+i7pCR);o)m%}syNmle6ZhfMN}iFCp_Fwh?wm8nV1O{rgV(HI
zzkWTIE?ugETlMYRS9ufAlFsB$R@HbCz3Byzk|GEH4-121l`ECn&GSd7T>QN{@rRc`
zaPH*3DB0$w@>M#16y0O_;#XRoT{dFve?de%K*-1`T=_>VDUEeM$;h4nmLv>HO)w?*
zCy*J#A2KED4OS&F@mFK8@`p^xdV^I-O#Iatto$KUvff}-5)*$l6~L+jT`g(cPf_u5
z{9Tb)y!oP)nlTq9FW!#9FD}He3sTS`D^W!*hnzey=C{?jeRx}hiSoNhj_rHm(+zm<
zt`<ruMqCa2--NYjA?Jigi2}W3{XPu6W)8mnv<uqPJzW&|RT6PMRid`j0{;0Q8<Z8C
z@~CGU=YDQ=EpH5FsFh=jM&2XDz$)_9Ppv81w&)&fcB57Q#%xydbbV+Vmc7tRg6V~E
zh9)%t06+jqL_t&_)a!YPiwe;ak)6qUrV=Z?bHQQj?p)a;Ujt|L$P}0SSY=KBWzJSr
z=g@rQVUV5xG{cfhxbQk6iwZF!_oLYfxb@4mIJ;9ivg(RUzeJ6H_lK20dbo-NzhRSX
z5wOjVGH`D3Tx~cBQ<W&_wD?ltrb~Ctnl)8$w71@ROYH%Vkp0qyvY)$Ivt}ylIW6J(
z=rJnIRHhjKjn-_PsMOLLy=L_qWx-}Wt?IlPva&MOi*;01=@L%`o8xjpfa7Szjy$wg
zn{C{${?kuCffjF$%@rG3ySsJkhTXe%E5*Eavb9e`%SP{j$&)8@I9Jlj1-}eblsP^l
ztlJaEpsPOR=22`HUU;EeG@xvJaUVJN?ynKI`b#do6qBb+7B7N-sO<ve^MbhN4;wa2
zvE<eQ%F3K+$dDm;?X}mGb$#Q;jVh8pBjD3Z;8aTf6e)l8f6Ck|`~!b{Qgc;NDX>1e
zmDc8iT>Uuu<3)P?JqiZicG<*X+V33nnEcVDZ1PnJN+Msqx*aT@K@rR_F$fq03<3rL
zgMdMx>JZS+Z9#gzpoMymDBkycydL*V-YHgTVSGp|zq!Y7bH8Lfaz$H&$}>%xxNCp)
zw>B8|$UJ<yWIwv4$Kb7Rw%}Z$8QeVsHRb5DaW`h;+(+hO+@^^b*eD(^e!B%%jY`9r
zvKO7=4(pkSeh<&a9fP;wvvoPxxl1h0@}Dv&7x)bY%EC=BNk41f5j08?S)56^m#-;6
zjwtt8S+V%<Uz+3M2N$Bxx!Y85xwrne6}MlQie~i_FmvS|%-&t-@f^mZ-zM&~+|5H7
zlujbipZVB4+%R}2?!BOyQl?uyI2nKGk%Bi@%Q0-UfN#vlJL6X2o{O{5v~CRk{_nN;
z`<%Vl_E2}x4~wyd`_v_k9M}|}tT=#%xBQ0N2BzWlWulO;&&SV?bU>_F*Z0YBaM)()
zLC)!b9s~>bh>W`8B+7%XpzDWqo$uPUODUEA{O3PaFttsaH>rKmJbH~vF%@72Iiuy6
zZtrw?*DmZ-#F=*P-02G7#%n%IlRx)<(*mv&>8X%%uXs~AtC#NVT3OA=$WRLatk<$p
zsnugp4qBe6^m5Po;oQR#v>{X}&)h?<qSVW=Z2BB_&4aRW5y76kEr1TEOTJbvbYCtG
zPzLSh&TA^>x}9^QQq9GHjvYEGcYiK+Q0ag6*=O<FZ@;PS0+gM~xt4?TpsPNu@Ehd#
zwfXbsD=z~sR&Y=Lsh;%xaYD?q3V#*k_gDS!db<Z{9Ezxf%J+tAiCmr3e}i<pJ*SHj
z`T65T+AvuW=C&bs#AiE)z{^yK>47B+xk@I+x}%IDsz)N7TIrSgd^sO9L{>%BwFlMl
zT?k7xRt*U*k}oUIRMJbE^YL*v)CK{AfI+|@U=T0}oKy(#!GOu@8N{kQj(6unOr*G;
z|NDv-$i480^;{$T9rEm-oE(d$;@Zw<AAQF8&|aBX^q)8h(6&z$Y6mWSvJSdr*F~JT
zxAWQd98tuV{VPU}RNEuxm=|N*gYD3xO`1A_jgq~2Q%79cW4q`uM{s`oI%p)W=$qs`
z@&t)+-a4}u{&=Jv;+%WNY3TmslBS4>vI1nYe|lmRCcMy5g49_-+D4qwL|N-8Pk~te
zhd<K^U0bCpX!>TSU~26sM25O8)MvF%#k8!N>bWXLWL|Z5XVJanC`H-l&NJ87-ro%i
zR_#`9{KGodLGwn*m^^<gqNSZ&MqStf1G1CgkVTAGS&$fcc0-p7etp?zKjVR($}Rq&
z%&$-51hf{ffyU|9zVtqAQ!(kjSdZtzer@Vtx}2;K;k4>|KUZ?jCu;K4EGr--6<sQd
z+VadBiOM98F=HJSa=N)w(OkTEvD(wk=;sW8MrD)xqZx^u(Z_%P{dZh)$t6lrU9w~e
zUV7;zFgO~MQmEzTrV`qzQzu+^-F0d|Hr=<W*uVMan`+#@{`Ie5U+#IQ)tqVJ!i7qy
z*Gf6ZW}<v_eW%5G;>3w6&>UrBq9vM^b-JIoZ{Hr@fB(I5C8sjXU~FH0`K5B#*UC5h
zXa$>Pm7I5kreQl@Nr_hU_sq;pwSSyrpMCb(stfmzzxUpIYVSUS-EmJm74wXY47GQi
z+XZgD<yNJblNaa0c`(ts&M5OwJn@9;N0)udX<y7xgs13R3H&QKPyKn7i%ms;-~$+x
z_f@zk%j0Hk!T%)E=aZMUK`n`>S;SRZ8J`{<49xu2_bZSnKZ2JHXoPC2%`;#i=Sf~v
zynmd%4O3R^#=S$cRWwezWhoq-@^CeF?UqCGqc5izNN^J>>e5~HcZn<&-qY9L0VSBM
zX)<!@&iw_r<Ku-GJ)|kJ<d4-;pD#rB#)&w$OS(D+O35Wm$)790V<xV_O<%3S0|V;e
zZ^N?XN1a|Lu2Geo?kj7Rm~!Z`$xkPixL7f6`X*fZ^-7#mHyR)QrHk6jWv#1OjCCK)
zgTlDESlkq)QxHwB$1%A<z#w1{FbEg~3<4(s0^Wbz%PF#E6Dk^b#};+{a^Z6YjVFAD
zkoauK&P-G<dk4~}XCcX(i}ji>dvZQ{ptc-Mc7D$+dlTwAgUUsQMWS|l%afM$bVu*p
z(k&P35@W;Bzf*nHpVxd`YSSo5z3fc6ppYSv<N4Bw^i3$2O}56eH=Zw@vQp#0mpu*Z
z+t78HBJ!tnY~Z@iZftoi3u`{asprjeyJfiAoz-kagSzqRr5h<vqk0MQ(lsYn9yA4=
ziZzvC?uF)!MC&*ePAa_&5Jx4EiF?NB?#(^t!!I7L0=zLe8zYx9pd0sHUn(x$4AjQx
z>hHY$j@r9UWtfS|^5P|nRhbrY20x>fnTk30dH?(0|Hi^a3z3zXrP|nj_uY46`SRtM
zGG&UAk7H1dw6rv(cvHTQKKclkj<{6iRQ4HkjS=Pl^Pm5yDDvF1&i(0UoN<Oy{JA*5
zKHPtP=9y=@9{wqq{vv6GS-a6Ip`CBUYyN`yDndL1w{anZd)gUoo{I$xbobzc4=VS5
z((;J9%*;%+m_e6#29)E%1-%Hu!o!rMoLF<~0HfJ6dOj8Uu3fr<`{uc@FnaW8HM*A1
zF3-tWrl*3h<n6)Wi{^Ohh!<ZcYNBLRz<2M<#mAGk<Dp@#P(8*TFvCJ*&)z&7I$)id
z&OaQSQ;7WGkcC`X;IUS4`3Hc!b_uUn7w*R07gC89EAuhy{Aw!wRI2l2<+j7q)A0Fa
zO);c*Ch`g_d>EXBtHHdIpqsAmg1+b2mt%{5uiB+33u_LHUAYhUNEA-48@#k&7wX6J
z5Ds_ZeyEhaWmH^Iwlx}@AVGq=gy8P(?!n#Nf;$8V?!gk=-L23fxCM823U@E$tK9Cs
zw{L&%y)oXeb4Kkyb<UQv)?9PWB@;Qz>)Ic%dNX0+^*X}v*!v^@At^4^jc@hBtUi9U
z?g1gp>}$0`{ti7p&BwhZATwzi5cm!)M?*_9Urllg7azN<DjreFzVUg)Rm96yGP5K3
z)?gr4zyp6acYo}^4*vfNM}j_*LcLt%U6aZDu}Z*{&1RovPrLn{mo5KLY@;(~MX>**
z6T+KfCS?6!3u=fs>s|F(OjO8DTr_Svv?2m&Pxrw=k4@C?w-4S>#p|j}K7R+)s=PGW
z`;3y+%w6ZG)Iv0Z3_Qh3nHi}$?z$wY*ur}46=SsRD(z24b+QG6Gw!?Y^@X{#2Qhu)
z{&S7?cDU~FHU+ooR<7(AUY!e>IHvG&W^OKYbLy$TN+Z}7@nJGmyI)SOC)9<jcCRws
z;pHISa5m#+^9MM}$6p~p<t3o&WvmHL1mF|b%RX_qtOS=5CH(kBiX=_SP7wEFi;*BN
ztqw85pxIgrnMi*I5_?ELYht#hx9)b?r>^I#QCmlWjLhmox?xn4wQ?2dBIh?=T#}zR
zxG{SWxU<}Bwls!)N~|WuR=1ZHcfyxCyDvWKo^wIJVBD%iPk?ebDVvs^QjL*9#ZqdZ
zq(}Lgh8{FN^dP{c#QbA%PX$G3nme`u@bG%nc?jM4*^rve(q*))@D4nS#ZCT*v3t&z
zgp8~z_a@ZQ^hf;Yp_hq6-THobJv*dTI*~^6;^z?huKXmbPx$vpeidcaAL8a{Q`sTX
zbw>GN^KtPaZW5^PNyOmP*CkpRF&(s;5z8Xd$zA-(0El1<QkCzp%(5}DDoBhI+Qp`$
zGyKi+9A9+1%`6zCsP;B0CAQiRe?diHczz}soYELT#Z6-oWVZV}Pg)+rTmOrW6$=;D
zI%-y8paz|FSL-A<ru|$c&*fSUGeCWy>=W+jy?y{4I)))}I~qx1XoMLZ0qyb6pQJnt
z3WYBxhNx3bk+2Pa_&@vYS$}w+Trd4Zlp^H*ks`U0zi&B*`J+-?d@`?kT7huo2B#14
z?x=T;-iKY<1>b7uk7(mB5<pJ|tUvAlcI+bkW5ND*5kSh%N}aG@j~uBB5V>*$WNTHC
zRCA4q)9Je_72nlq_j?cvuFIOz4vr)H27<MjbTJPu3#Wg~8JMIQ+}DBD1x|Mo$JY&Y
zGxl}nz0%Jl{!~QfAN#2@c6phLPCirlws+c>ESSQMg!{i-<@->)$?sT@>ZO%6sy*H+
z`4DCK0*>uQm29>)745p3)0P&%y4N!fe_qD=wTz71)6;~7?1eEfadlhng(=v?9IQ4m
zyabTqk;yXCl%6y3(8%cN<vR~_x?7#|`Zu24H+F`-mB0EiksS)Y2Hh;MbvzI2H#({!
zbgq=h_Q`sZ)r)w+%JpLRL|oWDoIlL9ydHCE->L@|e*PYPHN*_{HY(g&W%Oj47`6C_
z>#QgIdE^-Pqvm$8)57Hf-qK|r-fH3JeTRwOE@}7P^J^}%8#FtXMhndB3^O=Rn~%6r
z_fDsua+Fccg$3ab=OoEfSf5HHwvyU?P4jPBPu?^qGf^Y*106gVSNZc%35H0DV%x7S
zZc30Z5@8>{(SQ;{gK)#mO?Go}76WVFS#|PYlN!tsC=z@wxc}I-!@Tyzd8}12C7NIO
z4zVsfKC1(&_pJ%>l<Hup!vW6gy8d&EtqQx}%L;-1%NT)O3RxE(z+I-tfo%}JiSlyk
z{J73*jKs}4JqAO0qSs%Gdq6zRoF0f_GRNY4sf6NZ@qs|re2SA-@<H#-=RjNdLl@Ip
ztPrE!&dA{YcF{_P$pik_#YxHa#nb8<QjM-EIUajGDj!!()ho`JUK0W!un`^kgUs)b
zs%K92X%S%QMoAaNV^^N3F3+HKyRh;9J}OJ-p#40M)2u=?2=8`w&eOI+S9~+e)i|&D
zx-k%=;IhziI?5iel<XUXGnZ}LmomcS63@&)vPBB<z4d1ke2Np78ah}XCF#@ou(6Cd
z($gS=M!`8rPB!(<L|Gjc*ZU=d``(uxy7jfA6<`}5Fz4t~x(n+eAHU#eo8hRZ7|0P%
z9$Rf<En~>2lY8w_KP|)Rn=3Dgj^EUBcwO370{!EJ)BfkW*Qh)^&RxB?hD;;x8MWw(
z<<6?f8Z-YtKN@Iie0|?flIgZ;slGRAr$Ml@l{7v&&6=AcgL&m8JdZO7@~yFspJ1|!
zlz(%gJv+|~c5SyZUW*fNIT`EsvbFvkc4zm!DdXO3GHWK`^UHZxIq{j771sc>aKw8w
zH_21osh=#aFlW#Aa+xcwK<&NWTFWJqx@_EGf7{{m?;c9Y?kF{ra>0=T0xU4f3hPn#
zR6lG6i<>T2P8eIBg1o(+t1a;uq&@8y1JH4SY-qmMQ5OTJYZig6FwD=N2E7>8!?m1j
zqsEuY*n|U=8MtpgA97v}^vANwVLgMwIK%>VWOSN5xYu7S2-gkj(P~yd`+p)EX?V+u
zU%)->cJg!+>o}Qunm`+!I$zS!zP`ez`zCu{V!3%HMEkIrxxd(Y&oUlhmwmEl^qsoq
zxDe>uXB5UU{NK|LAM_C)>Sa(DL|tDg)vSgS^kaY9MJWXd1zZ0-jk#ZHX4w^7tNS>A
znoe!wmQ@TeYWS0;R6bVrZpGV{VD2n=sXcDtspydva6RS1dY{4(IJTlxuyUi;4lH&#
zqw}5UZ>8C)Fc|IO`ph4jx+L^krgB$DQdry%7inDLa*YC{5h}4H9Wc~<g)mIFwq1xR
zrB#VAEbd?4AG01s!ME5NyK?;aZJn0Agcz@%kT*VA2yRcW(T*eqcA)Y_uXrw4Mta>=
zDk!YDAzorypD{#!LRv~&uXAR+vDF=2p0DqU7_=H`P2rr>qVR^K=mB`MRY61+h^vbw
zLdAm1FrB({Qc`jp1ad4qn9<1bMXgfN^6jSq0OcRIH}KXJE_XI56oW2UJNEcpCbk^w
z&o-#lM2T^FOty?uDrT%I(4#Ns<9FMNwZs{oyAfB+zL#h?W+xg;EqgtwnEf20rvPeY
zc%VEk_aoHlO#jiT2wL4w&r;H86`yxRG(Fr}fF5GpCb2@}yQc}g%Qbh0DWDD;K~aC%
zCAtm_d#e2)DP|DX`@ELRo_S0bDtLp2<IRSOhRcCMVr>AYG?u$v-J}q;8rS~-Fq^Ov
zzjw%wV3Q{z+;qv%v5=o4za@8tgFlKk?<8)i{~Emf)7sG(aKqabW#~O<(&ZA!batB`
zg<@oB)YW@|7Tb^r?)3hy?byxw+*0!H++8UYV)yL`JOQM>%$^dC4#TN=(zu+>N%dNP
z>XQ^}&Z+QU#|rLV#Ov-<3GQPhPK;gm?n%RtG$NTJ^7QXO@{C{m-t6Av>|f)<1F(Zx
zn)u*`K6+czz`b)>lP3Eoyx?bj`@4w5my@XI4%L<~m?dOBLP#xbWMwK>;@)$Y*Fdoj
zN@;h9rQ7bv`X!C(lg4)y=fMrC>T79yjB0NbT`q+$$R9)V4~`3#hLXUu*T|d`rr1*k
zr!^BXSb&FR+dl@rA1eC4TMaVwcIGf^uu)%VWAMJYzCiN%gV3ZCjjp@BvPH(TQ`KPF
z`$uv8eKev!NrKwWwonD>u0gR^8Rife;h*5v@Anbku74fECjW_WBOq7dMB*dWq(2S=
z2rp!?%5}L{U{;JgG05fQ+0HS&KqWj;UG6(~;N1Ln^yW|LJket4LcNRE<d1CUp^Jw;
zhA~M*vW=>A+#L~e4q$GbB-g-1S4_6F+6Lb3Z=+3=p4gK29i6F{uDPT9?)IR2)yi5S
z0!3#$Ud4iI)yEHrZZDBC0baP-xZLM@8|gs~2uPO)p^8%i1vnpMYXR%=W3%~3Ch}vn
z!G<&@<K7Z7YZy}6Rv9wVRyf#AYQbZnkyM#`sri-RGL!t<-QGUboWf<Flq+;-`faPR
zz2QefXzl`e0T{N_611U0`kIi=KKWvnaty%CTBOsNf{VqXrK1+{YR>Pn3GJr%?TKDU
zU#lqPDo@>JJah1sZTt{-7&AAHMV=7Ec0AwNAQlFaaaf`t2kmr^;8xY}IoZSHP5Jvx
zJ(W{-cCH)FcRcUy6)`#q2f7^i@EiH+0KlW8@_Oq5ue+1C9>BYL$NRLjwC0YN=i$8L
zQ@>M-wKHaBW(RNZW0Ls7%H#5A+nSM~;qukpHFbD3;JPWzy}hfWqjB#Pr2N6a31r87
z>=DRoqsWGsiHA6}V~<@JB@muUMdE#|rPH|Qu<$%4Fe0U5rom-Vq-sr7O#$O|eF0>O
z_A~}WWsMjzMYo@OU{|qKwn@CAVN@`w@7}0HGL7S6FQ`l@Noq+X!zS}H(|@Q@S_vZt
z$CD{lg4)rDx<P8(^6$7u%g1Q46K5^60&8f93;d;QLbE6$KFfJN$k2{!x50?#JR8uq
zhVT_`J4WKYow+lzdR&`SqKCfRyN6#MaXPT(d&29=qc^|AWHKSyYkNgt@2N3N>#D}3
zuv#bIrC`PWQH2{w5@AXk7RdhD2&Z?CL!qRw!0}VruvT$cQ~Iv$8iDB#7g~<MBMb%J
z;pLpwm^TvZrJQV(>gk82j`b1^w((;X+^XHoX=dum?&h`dDGGXwYpy+VMp^<;V^v7d
z=@FTN!U5@PBU0lcXb;<XZp7V1V%MZ;(}eNv;a4s*l>a>3v@kkB&Y!H5pc}OB12HD#
zUj?)u^KUgku?}P$ZSl;yM(%}xg_Y6twEt^LyuEEkS0-|Z+SbPx4ChZ@udb$30Xf3F
zZk<^*os}yu|G(FSFo|xUfI_Dz84sd*ESw-uJpmJZ&^Pp2=^3<A@plq~cA{wCiV}z>
zMjQ``9<8WyKtIQLB78S~mBopxS*1q1v^`F$K0AC>E1Gpo5OL-}++eAFzuxY|Hty@l
z#^jI_%<AfCx;4-%WA~fpVVjAGY4^G>bfqk(q8lx`-yiAt`!_lJ7@}~l=bb$-Q)#BB
zCdJ;qG4CM)L5!NLEOMrU{uWTXdrO`HCY##cPf6a_)2Ddct)Np(6~`6w4i*#SuPune
zW2@iSPp5+zLwP7Por-mQkytYwK|-&Zbm{JoBbbWJ*BS7f{edKzF+vjx1HCW=#!JN^
zUZ*d&UWeH@rD>RvIr~_quj`Z~*D4`A8>Pv(thTGVqr}<bI2WrejN6K2vC@4tK;X%*
zz}s!9mglY3pRTt;jg`znd!H)@gHgRPi-3%}=JR41@D)>6>l+tWwNex-tk0)w*e!>d
zZKagHt5(Vxk*|X7+Y7QYQw>^LyOGn5jPMUXa}8RM(n34~A94F*wnz!jaY9nKDPC((
zHDBv49a*)#DYxI2jraaFu)j>El7cwvrSL`LgY^I#VshBK35ObT1h;D$c6h)}ogFVP
zFI5~*L9q5G!U#y2ZuWR6ky6eE1sVBcCzDqcT8b*k77ih9#V)O)$2r#skJ+V1;ith|
z(KVhX1YI2~gYVr*Nq+HB!7GKLh)2=enPg1MO$Fi!3_<HZ`ZPL2wf)pg#iv?4IM`+|
z6PToP45lej>!jn2<F$jr-Q}gbESoMy!$Mk`oZu<?1UnSR(9OB!zKG7y*qCgo-Xu_t
z<0%U2S1xu^$>{AQm|AF@Gbe6JyOoE`=2I?qu;cQ&e144hK-+TGv5$$xgk&`zEw0{;
zVZV|Tp6-(RRFKvvF*D1uv|$*dsUvHhuq$#rFkDJ&RZ_wqmm7Lwqg+FL9Mj^sC4nr2
zOI-h@yuhGg`AA5%3_CK~eD$G6%zoJ&Z*K3p4IEXBH{G6h;-A(qd>uVWMXO-myx-lA
z0Ji$gc6{`iNSqK~m{mysZWlLCGE1c~P4Ivdk40%&Z<ypxsXooW;DpzR3K?aMedlsH
z_H>Gj3Lc~W3rne@fw>fM?{q9e-MulnR90H#F#-U~DfEb<SCzwl5#3?gQ)En+Q*eK`
z4V$hAy#qYj<z<QTw&(@GtL(tI40NcyijVb-?j9k;cw2y{|2vQg)DDe6SX+mI({8lo
z9zXiBL5Sz-fL@9T8|?DICq>2<o|h3_1(X{;!h3*WkrQG_Bd23&X&LD{8S)-0zGkJ~
zBAO2&3mGBkvx7yzqZ%(lr}tiVh|;f?c}2k8(bd(hRZ91y1g?fd{?7KyWkrH-%R5S{
zC-07jsof}y$JDf9v7w6r2SIe&#6em8PdMgR`+j1QTSXNDRU-=ZVHDuTDi?B?VGNQ(
zadDwR(|2vAv}$D^KH|E>jlL|^6YHPCl_X}4urh9aPi33_IaUh#gH0OU=|jypNLH0O
zRQ&XX=_>W}-a1n+9noTd0{nIA(!jVzA%Xy?=dx8=Kw^K``_S7(e|pHF%sgKo<->%i
zPBGbYh*GNrt`=T{!6`2;zAd6~09W#GC&C(M2kk%OjBgM?;43uQZ>y-UOOWWdGc!a;
zb6hkHM4p5K9S9G7rEU8|bQ4zm+p^}n$f%^6J|DV>Zn@{~{R;izr7=&Zd3b3Rmn~KE
z5310eyvx#C96c829)fj+Iro1ko||2S+lEwa0vXcSfoc1jY0_<UdmSoq#A-;LA7w1^
za9<Ucq5W@4Ve-@TcHR>YdN#?|B@ncJnkL22rNTWd%x0wfow=(XQ`X0xm}+)hPsg)S
zC_ANt{4O=;`+n8o(hPytGH<HEXMT3QU*fpVz%PSBlGD=0>J~qo^>vuh7d+pKQRCK=
zkgX$e;V()5{7ORpMU!oDUxnfM0aJ-7Tdo~|zJ;EWT**ow*_u>^Px;jkRTIAAHwI$n
z^s3fBZmu|hH9YQrUj-Yql;~TM@H_SnG}uyFxP(%kN4eJ{|0zwMEH$Z({HJQh8U=MG
zf9J*p3_@KOaHD+hD%!+vCuD%xIZz;zs(ZqaXbB2+FUF~PCTo>Hu}d%oZ(^zXt$FEC
zr+FbM37oFK)^YlBvO`O^wNu=v%h{QclOYE1zOFM$T2``Rw&PRmWvzrGyT>b>@;@N|
z{p--qGlRO*A6QtHxhrUC5o0=lNTd;x(oOdj7P^Gv{+~AjvF8<XnQd!}W`RnZp!^kW
zCuZM3%hXO8;Y%+-TaNUJKxf&BSyB#m`>>*gljnl&gmB?q+VkzTwA0ZG<=?1Ncyy#~
zi_<9!oMZQ>$KjNfmXq|Boemg(QA_QTyq@=d<b6EHM_J!h4eBFpj(U|*PYy>8LmDrF
zW_dW3OFXb$s}xn_y{asZjuW09VLMT8G(a8HxwzN3uf7Kgjho0NAY>RtLKTX|s*Z@@
zxD}y4nA#rwv%34YK)}O_URns^ItR%Kxh|0cZd@z4vxu=g^$sWS(*gT7pziwp0#~OQ
zNGx{my6{N4qG)z*GqNX|Ym4RfmSf><es@yfeZ1Ph$)g4N&mp~YNzwaNt`qZ{`Znu)
zXdkqcnYuR(L`hx)5(MBFU&`|Z^wqv%%aPX5=`kiU09?<V>G_<T0wCXzzXiU3Mdq?0
zB2)qZn|}BEZs?e-2Dqi$?WsE1dU;hQ%=>odFB@dM1XX+HKO-@Xf694u&AS`a+K?Q7
z(mqYzYoD8Hm2|O1VMOrrpih1YJdB*en`F{3ebG+RZgv(9zy^Z*)#}q>F>KdqFkV(l
z$XU7c`w#N~=6SGqhOIWFT<2rrF8Vy_b2U_h^I>h~1@C-L=2l!-%=R4Ju^j7$6#9YM
zu!@QiD%Y|R@3mZ3a7k`VgX}2AgLM(M2>0$ClkL_VL3<&UvvlWm-ebGbB_tCy0!2f%
z&ZuX;b2JCtDNA435tF07?UnBw9~;^Nz7Wz&JG%$0$P>5b$G#3CzGgnhYfS01fHPQE
zwUag2v{OP8+SiQt2J$O^beA)HUp<AV=f<%}LD94z-*x2HZmpxKP{-Czp$ch#k>=I)
z*o}3adQ0bf*zoT}Cp0NDfy@7t^R^_{F~_Vydtg@LG%)=lt&hZKm6tMhtM_5^2n;fr
z%r);Di4;^nD@!0v_-MUs-dCXILHu1NdHqdEJ9acjFPO{0jI~7v`ok`9e?bkil<q#?
zv$H2IN9D(8m{#eF-{9+u`5J!KS~JDxmQM)8_4yZbr&)>Qr@E{Q?Ai^tWYs?QLgR;N
z5njBB;V#%C1l<7F`vTP+yk{RRE8aCJrA=qs9=dsS(!o#_!3=&*v*rdxyZt-o%GS1(
zrGdg6oz9vi%ei5fpxRkTi@GU}bSs;y1Fovs>vZIgPw6}AQfx>*7P+=wnkZ1m?b-n(
zAMVqD&k6#*N6ZaY4)3(aTOYD91DojhHTv;<K4`F};)@p;csSP55mG9e)K1GJXAxG8
z49KYARdQfjfZ!|YS*Nk!>0>39ym@P<(d_w{eG|UWK0M1vvPj2yKickdp|tW6Z&dTg
z^xNI%`i}CIGpBSKzRz>vs3=OM8p3Ej95-(Kw8_xO12J(b4#0c$mX|IZWd)*iR9Fqy
z_3V{q+KhT^UrFO^_w#3LrXuUgUKkxx+RQ$Jr%qmJTXxpG@xUsi&wRe@>rX!P#U1M~
z29l{3gm-zj1v3|C$`u!VoW#kiKKggmPK9ARjQITGSkCZ!RpikyuBqoD0C34pAUoGi
z964jMfbJt>0$D4DOFHVC4MS;W5;Hh_l$Ix56L6j0?Hw=U`@NBUX+=HsRPo*_rPEIW
zhj|yBn2K_xu><8`w9vb}AG($4Yn(I*aJfeUM^XNf^K2LU#2o9!n9}w^gD5C!{c<k&
zt*I_>E8Z^n?@Ru2vByBG2VuTRSN(r7-)Q)`wNqjfi&?a7K8=iz2BicT&v-9|B+H4y
zu-tYR9p9OrY!m$nY{vc3We~=i#NCGF#x{hzmoTsM*{6Dj?TnrlQ+2(dM@*zC1F4#S
z_>gn@zSn3W-?K}%WvMLldQoSpV5-p&8rWV$6_5jffq-)?0aHx+e5Fi2Jh({p?6zBZ
z?9(hBW1_2pg?Li}%nP5x2=o;8P0T;jW<tx<g%DA%RvR#$2>)sDXQ1*P$v?$ox1ek$
z!Wj+7lF@gOqU2DD)rz`L4%NFHn;lzB)%$+)QzdhO3iV~VgnsWSLgBUKknr-_FR`u`
zkI_!r)H;WO!zqDGdI@J|=aPPkjuL83Pv`7SeS0H`^0JLfQ7prf)(f1`f3VDjdfean
zR)D{9@me}Q68qL*q-ip~-y4C|;louGiu}YO(b00nKBm`6Xr!bc-?ZH{S*yvzM4Aw)
z`75&AVLr;>!Drg1lHUuftp2NuC76$=LLR26|J&r^looODjU{6lY~(K!Gjtwu^xBu7
zu<|6;g?~wDRrSDm57`P+6hT9c7>j{P>$Ly34bj?NZXd%D+AT+^>S6ey^2Rk|e3Yb6
z9m$_=9I)LSU^&XuIIQp+zshc>Xv5Rw`b>XLi+;a|t)6?N(c?@oCWQBKu|XKu_a0=D
zrjJWPi%HdreqJf?U7l}2G+BE*#f1r}sn-UZWR((`d2OD!Nn(kJvzC>lt1whMnsHxR
zGSR615uNX|BpJi^W-D0|bfmnC=>h+28ea(<*!{}I$j9gpxw30`a~98)u?L(kQOl!3
z`FeDQMzv^)B)_5rdEvBy0nAUUoF6grpN*E-iTN$^a^EJ)eo}zXe!bp~VWPD9e)wT0
zWo!*n_p5Ug)?F2RPtQ%N0zPnhQeA!HCRT6s<EOJ)U4J^{t4!;B)Sqo}f5vGH^!^YQ
zKoRBV0^58#F_@5Mxd~}rZEW0*<>r|D%`RIn3M`yi<MjszuA`S&%~=G8gy}djEL0DT
z5?npJ*m8}|H4K{B10F7BX~k6d%!{L5!tyxvaXhoxB^@1KG(_YR&_7r*9ALvvx(Q#R
z(*T-QCe)M7;%0~k&?4V#<4L*R%!~W~{oDCXbW>SML>O?`0lj_jGOBmCyS)_2f7$C$
zl>%G=7_>bY)cjUgm192L7*a#{U@oAFr5BqGMZ{FxN`RQMunisK-Tu+1Spmv}Sk&j#
zx?ffp=)$Ky?QUA8Sn$dJ{aHw$c=FC$>_#lD(;Yf;-3r`lrzUx4-@$WVATGJoT`s%I
z>VUZ9gCW+%@zeZ3?YhnoJwp%gn@*S~i9;kIJV50c!OiwtdcnZXeXAZiDI-c8aW7(B
zDF8?Q!F!D@Va@n`Pamc6=vi;ePG^&slbc<PKuOuridGz)<Lv?IgY`3=z*l$a8-@4M
zjjr_X-ao{|H6luk1o*Y(+*jg9VZ-(Ryfs%gpO5SXb=CA&@*>net-V?&<J5Q+Q%P>k
zT^`rTLhp3m_R>Ay_HcA~iBr#ccmNdiFLovT$Z>A(#_V9gQD0BLDct<2rmyD`>7C{I
zK0Rmj169eL_z$35b@Fkz?L5+W#HrOV0f1j%ug@vP$J{=6sRkS5nLBSA=;pIAbcuX^
zW&-N|jq^I-a(}_lYHXY*QLif)vefF6oLem;c`<+}0Ti*8%TOeHfyv{28MugzI$xfi
zytKbQIrlE2^<OxeuXmkz&DLrIoa?UXdYmn7T+7|x8C2+vjthc)4G-6WcJ38cXVLQw
z<Nq8GbTw+sc0b!d^X=?B8Lr#aAZtvQ0_~bIr|ll;d_=4&o@&MK{U{Dj8IS?N)ARFX
zTf`=gqiSY9EY@&H@d>NeVYbpxB#l>9Aazb83vQv~9_|sMZGLqmcR`B!rknThMZcCA
ztB9*m$~{eEc|l~k=hXINr(X}TL*ftg$ozaWIaL8V?<Dx91o`!;IcJvp(rA6$QYO_P
zAsWrQz6Y7Q9}UF>I5ivLgd2L_@aaEnmEgALO4E08uZLfKmh&!Xn=<b6pgTadSjBF?
zu2Wl`LoUj6Eq#Yx<Uf~6)N%C)y&EYom6)F#UFQGi*}&^&J5|PxIUCkfp+^zMwgAwQ
zfp(S|hjVLZdRI>@qRr_{sjNZ4F6aM4A~UeoiVm0vM6e(gHkgf;==P+3%r(#9uP9|v
zUQ&xd3*-DU!fRt$wH8l5E+-n+vT!X<l?w83_!e}v{CVnhV<R*E_z0@TzYab8dthZt
zr_-WQL{9%pp!EjB&2dhIVPH;N#e{<Bn`P6ac<_a@NnX7gB*+sI+Q?N@fJE+-cq@32
zC>X#AS5@kLEMOhu5Ce(tSJHzuQJ~L77&l)Jp65s>h)xAY%tSrAH|Z9o0d#ZaITg#)
zc@<3^p_{Q75q?3F$y`Z)p;y<Z*eycrji#c!ou&Q1OG53bdalX>l>H)D<L0!{)Yf#e
z+Rz0`IXi9F1f*v9-raXhcS07E=EN#)l~vY12YmK{)`G{YU8HwDzDr4F!xbXdoU!A>
z{f11#a}A%-brlO-*~6&A?&$w(fq1wHj1o{nz6S!Abw)NZZUOnGRLdDSs-Qa?BI#dI
zWQ=W(^=aVIL%qaI4BGCMJL8HLdFt;PgSCY$eoYT#nvSdCsi;XRWFysG68%Zf`m)w~
z(YO4xOg$P!$n{i&OJx|^jUJ>dztcZ3KDrH7lOsb3csLA=M%SK=y$&fvc8Aw<gVV!t
zw^He+FMdflIjQguu*nVQQjw$YOU`{gT~bW=!u|y<(vZ=vU}tip&VGnw_G3kpeX*4}
z7gl(fTpb_M_ZhGjm!$3&j`UFsSNZIdXq|1ri{l?_Ezig9zw7%SE<=^-t;w!2;Pgp9
zmLMQv8@A?u!yVq`5r!{Tlijy~b4ZfQ`lP}UA8lsJh|7Rk!04`JHY6EqmOtgH8lv9M
zwopJ${`I?-+RpRo35xr!LF(r9%S$JV*(KLA(4M>c)tU05hpi*P%JqE<b@#<XxB8cI
z4!AYPpqtb+ql_+}yZdHipOAbork^_{^5&ghbn3fX+4s!n!1b$N$4M|y=e}a{Rp)U2
z22+{$ab0yIyQj}%Uo9<u5JE$^x&K4&f)5gY5B2i9_}V2ZFGo^qc>QpK`mFnV34cLX
zi{0_lscGlS(b)#8$r%^Nu!2|zO|kA;0f}1qSqZi}Y5g1)@`YlS8p}~9XG`G*iJN_9
zVeqi(<=991JL)9vJoz2#S=>F>?cl1G{S=Fb$5<wwhJv3Ens!ch;gMuBrD>>DerU%0
zn*#3-RN{g<kP5l3W$5sTg!|2~k}A`dQtCs%GCT|43rgLO^TgJi1$KEjJzt+sEZeQa
z53<k;2F_CA7N|xH+3@a3rVJC%Q+XxT>)~8YJqa|L;7`sL8%btx1(Y(r77F&mTG42>
z42jC;hiMe)MT6Vb&KVa&7Sby8Qszk<I#_2=PK*Qf((Pv2Q4mK5@bnu65<sahG#bTK
z>#QfN3W23D@FNPkUv>+U*U^r5i^Hl^OtK`B)Y^hi$r)i~$268(ASn~v{lb2Y;l`gk
zSzm}3<Wrc(#(t_x&GMrxZ<1%AfzwqI0@TYdU-^@o^^7yG>d~<o?};VH&K>;uNrHb-
zrvEW5>e$dvL5iR~q6KUs$7LyzO3*0*H9xOT%<+6wkJ-!7c8##ZfQYlJGG@gShVz`I
z>G;mDA61dxKJHF<8DW9~uBYmc#~8Vaul8<u@NP1LwLsL%huzbIn?4jj)_Pvu(Z%p<
zq|5b|xrZz8+RvMsGh5b8V2xIu&M4mZuyE6!=c=P2uC}TfcOazrvyNVi+PZr0C|jHp
zTfv^IZ`uND%O4|ZAa*elipuh^#rv>dXJ(z462Y6FY!vWf|13}(uYDD7a<Qj&`MM%K
zrISBM%BTOWhHGy-Pf2xXT&`#A#ct=s3bYm8#hakY{lVz1?n90r?fe~}ja>I`nLDNQ
z%rj0Wym~ANg9$uu^kDkFwt2F?8?zo-cW`YnYffo(Z^IyVigc`3F(hN_8*Dk@#XhwD
z0v*)Y%=TC31$F(a1F^oy?G25)U+l}Tp<3fT)NhXQZd=0kyY?N=VtEXoXVvB(mIz%Y
zmGh^=pHT8a#~<m942Gr(sP1XJ8jvs=x?a&cR&Lx?6Pwb-mbbS}_xLs|NU_bP=S#^m
z9xXy8sqah(!CutI14*5zp`qo<US2KI8)h!9IQ&ulW8Gu%0*moxUia}45i0IdviDNU
z`e3o#O5Y7FQZ=jp0V@6r+Iu3;tttn*#2b$+hj@9az$`ToTY8&CIp&89X01>W$1AP`
zHR*lGwQ>1mN?A2@Nb78J^XdK<g@ld$DhEN$Yuo$lehMY&Yh6>~4WCUK$CIDm&+^Tu
zU;OU;*FGJru%Wy`+~!)-K`nU=EkI=~NkG~pQF7<SVLeTPTO}Ulx*p<k;jxT)i-j-p
z<s$xJeQrYGIJ##=TYXbj_fon<tNx#+2$MX{ufDz7k&OWfE8WgX+6DL+uRUmK1xkK2
z9z&{jIi1X!J~{%%g}0vTClPjCI^lgrTqaKAs;dcE-;NQuQrIV`-mNvACZM;&hO8R}
z+g74Kog1Bfe|142oY}+FcN`cHKX{t(HVvu@D`_v;{4rWl#=Z=v>sE~$S&As1R+zso
z7H0+CIb>?NO)7fWEYcZDWP}GaDfhup>e2^@`&ewPA$*eKH#qHQV+zmPIbF~*Yk@sS
z##RnhUOx_|2kOQW%rqBhvvlMda;&#jql&>@kIYh5jAaY1#C2k(I;qR?uwbt~-5yc0
z(yGq2BHF_}UA|q7GRqwb?ZmLNK-!8z^o1~1Ij&swYz>u!)5B^5YN!5BH=;Aen%{-u
z*iCw1Pu4pLLlDrU6k?j*L`Q2)LnZh!`7~%>3uEKrq~b{g6H-!&3V(9i;rvzlm=`f5
zKTeWAUbJ~g4#pAJd0m=Pp@ChTbCt6A4psonwsYkgWt+=nZ2j*r76DGAViprfxT-A?
z4L-NF_Uj$^`pH|Ml8?e>OpE3a=@Ix_4);0kI)Ar9M^3Z-QZR&uN~N6JK})~q{W#NV
z%?J{|bIb|rg?kn{%xMte#pnBvvwuYD+l1p*J(~C2)60YQ_<X=tGYK}q4<yq%0cn?W
zK$BM6Cw^Vv8jj6bO={=Lba;G+8|l50`dKt2txb^S7E)`=iPh-^DZHSJ0KvQVE^R^K
z5n{jSG{Il3U|p_2JSR&1^Vtv2N5|eNeocD(HYbtu?o}a&g4^L#2LuJZ>^*IQSA1Yr
zZG-GgR}#9QqwNBQ1=r56-5&7v{B-b(GCBznyWS^1XxPK|bPih|C6Qq6_7;=F*j$Zx
z2k%pJpiW$n845UHRuk8S46BEdl04P(XTpcXB4L{viUcAl6PQmq%`bR#tZ5(179&E`
zQKsbTnFC;RQcq(ExhW{vdc>XYiYLa#G?3mOm+_n~H6V<pF-luX6;n{vJU{y}7y*gE
z_vcz1=?K`@DfC(t%*@KMbM+ST3uBUdPk&{JMyjQRoVJBn_VfM_p=-<42z_F9b{q`C
z2bxj-6lZ@m5?%^v6q%LzD!t2IG~ryW8bi6n0u+eWkq{zA4BzSvr?*hKpSt?FN@gtm
z?H<8sPZ9e-cb!~O_&?@J7z2tfa;`#eq)3=mEEB1rGfS?mOG2`%EecpoTWag``AA<E
zum!a1yiZqeGnDaTdgtYG)j?sDW(TlJ`VwWw11cX#xWiL*Gs~>oy=j?euM<Dr*e7w!
zS{!e+;cNf#htGCD`PUwcP79*z8MRelC)w|MA?R3(ti^ETk6ZaakNf!S1m+_ywallY
z5p4xjw{E>u>qUO2DcMuwr2G=g&(F^c0M}n_VQHet&{Hvv`#EbAHcHr);G}x{`3XQs
zrD2`E_Y}0ld{*(PL%rc>^pZw1Kd2dZ%gV}f8Pqp3IPHonQwp;LyxFWBAj<d>JFn?4
zv_|#jkzaZam$UWx@gb%!o>K?VRmaIi5Sg<azLg+jqwwnkWlO0_gQcRPtxL!^WmvD8
z<#VkZi6ZqY&*tYNf7%$uq<@ceph840&=U~2rYcfGIq?)8pS4=cGjC%K1;YXyvFDVs
zMH!LVsitlrEM7&>9bc+H6@OKY$NV8HPcw<+f{qU(wzF?Wdwg-jvh0rIjKu_(<d-8t
z@3t^XoMw=ZvNr>lF8qQQL#ZfZ(b~G>E#j-MJKehapZ;HB&LsrZhF?6@v0Q=Bvh%hf
zlCl-P5IHmkAz`%p@qB9=vtBa_Dj}D;9BWEIad0qnQ8Ks9G_1o)bN|!raY_aMinUG&
zl|KEtPvSRZ1Y85R#QLbD>U6)PE7$83i)>_Qw<JrWX;<fO3vPKO4C*H~I$c{~gvs00
zPkK2YqCexq%8~u{T?q(GBcmzmA&-_0N2=;)<VOow!7|z($6eCi5KCRP2y_QNc`s-T
z;H}pmvOXe+q;=YyEZo%>TRGMM(oP|qjR|lMAz<Y9kpyWk*l=or02$oGbgVy<>~3V^
zbFP2CaEsk7cYQIgoXpy&KMz>F7MgWfZxt5kIP9ny!@~grFW9vof%n0YDcl23%Q31D
zhmTm#05;Qp-iVS$xz?*y4-OgICcw6REc)vG^gwQuxO`nvvQJzE_%O5TuAaKdT|=|I
z6K(L0lgUQT&K02N=8<jD<UMm_H&Qh?>wdRtd(EDGWJe3S6H3wfkO!Sf?YJ>0QKtE0
zMXEqDW4Ky-G5rOn49_*k>*eW|)$>f>qO;iaPiG$}%QO2+A*b>@tBOGgJZiE<r{h}N
z$XXV-&t@wQ;;{ry<XwdLI$S_xz%<A_Rhz5gN~3(E836kM?hU_!WS4amHw#h@>jW3O
zuFYWNWFohMlA@fK7GZ;PlY4%Q8Dn%Z=;t8XYwA&M^;k&n?3Xe<mRwgs2`QQHN=vWX
zPtH3vL5zp=Sq-vj?v6Dx2JRW-g4X4xD2x&E%RgM~mfX(B3mUo{XIhyFxUD>8XM91x
zzf_w26DIHbnT5bRyt~U&wz_r3&h|o^4p(K{xurXs)DtIFue+=LL;q7*;#Jm@j@y=b
zM8_rhf;zRs*L8237fuHo4r(yo8E;!4s(V(~Dk;h9fxBSc^j^ISdsuhW{X4a#`>5^_
zu@Pg9?SRCLaQNx3k(f%yk8hTaqB}9LaJaWA<+VSLCI)ulp(xOQB)KAP1b~H4QUrH6
zGcjh%jKf1iw}X+HzCg{LY#qUb#>#n0Nx^?s%8bb3_oR#Fg7H#?T9{fKju!phB6G2W
zCQP9|U|!3tJJs%aUJV`MY{bWSrGSDZRV2*D_T^N=%82fuabSjSy(=&<jb5lrrE#+%
z7U!XOES=*1D-*<5ph$nG>*<I-29Fi`_GnHG8GV1WgiMHP-P+&z4gW(=a!Y>s+Z8Ew
zdjnsw*~#ju*LJ~U%Vh_SOw3I8*^o*NtDXX}da+FvsEEmTH(kl)l}dH^u(_Aco$-8H
zn(-SiSl4YHd>OlSW+U5Qm$_5U>c)S3ay`ZNp3@?s&}TRUcD3;bQw6+1KQai<@P*++
z$qb&-N#+C1s-Ll>zJ5;GQ%7V`XhF*L@&+gm{6nhr^x|rH00h}aZJ;?0ItLOB{;*g3
z8!P;diC)3@9sH2Svy47)z~p^g%F5#v@JHOUfh(j*_g5bN13g{9FIE)IGvCnrxF>R#
z{bpa8wJ3P{5RHTY`ASl%N1NSW%hH!~eVjk3Kwmew8#8XDLr)?5zZ@?JovzjmYJ*&k
zgK_G)hrOIMWlKyo^YE2Krk=iqK}{62?xb6W%b=Zmm(Tt{_*l=#z@Ts{UBb3rtVn1<
zTk=IT?S|KNzjSEHEvY0J=sv31PtVG#D(O@r03m1`_17)N%eB|M?3xY6X>|DBpH+FO
zeAYb{hB#^!RjP?{AI^%Am5#KEm~{bQB;_{JFkEi>Mi)GP)4%D3Asc1HHLK3ey&MIc
zd`K?HWB*qn?;km8DGKZXJn)Os$|2JrWIlbZW|P_bJI!1*23o>OM1SK#lVhkRwpbsi
zSlBevQ_Pv?a2s^ntfjh-vZY{I)HTHB-bVwhr^Fa;Ht68e!4tnqMVuMV-dhQi{2DN8
zax>k%m5jzQfoNe5Ci7qhUN}COY?%x}G?FeyW;0%6ue*|I9!wlx^$poe`m^N(Va8Z%
ziK@=+ck<ua911y2mxX_8Goo+XwwcN8JU<n|*y$VqPGRJET}*Se+^RrD#k<f)CWSb}
zh<KB58$gO1q2>P%#SH<KO6a0%PSad}m?Czwh)YS4$v|JfEmd8EzrS2_YW}@c4B-Kv
z;B~by9{=D4QK0jzUHXxT=^0S?Q};=h*C6F=lr}=>i4W}Ph#08Q!^6XJ&MDj$q9tS<
zB}-Cv+}G1TG$rslVk5bmG;I%jNN@?mllf@nwo2N4+~3DfAfDPSRKsC=(SdaHsL|RA
z<4J`LPc_aLYI0nh-(JDzDehUw@CuD>u15SH!S;)_P-X)$s*S5wO31!xOa`-tu5Dwk
ziQlfxP03mzXJ_kOu3A8nk`QcZ1XxbAE_cN-GU}oMNZ8l|^S<s(lE%m-<3EE?VO*#q
z7AfurSK*Ku{~OyCz<C0AGCrSD7Y-W)v1ZG&7{#$w26SblLaqJ8BZ`|p$={~v8-Voq
zq8oC{DWMpDRUa>tNct?mpx@fL@;H5(&9U1bV863;s9x!9z+c2g(L<RG=C9gy3jv7*
zzl`<?6>1C)wLYDzAuP<4SFdfI?$DX|cVdvx+4J2?s!j2Y>4C8KKown(Vzdzlc2xzL
zlMmMcWYZz;ps*y60i-S(rX!=-;NU{*hklgd=z7IpUV~oHC#}lyABQ>7*58PP85vQ@
z$MH}D+LCpvJ^_Y}=;~}Z{`bOu_=ujHcAmphi9bzdEKVk&GgQ=5#6P5{quBVq$V&DX
z)b>eKO;wF_;t0pbs`OPfM`e`xfj-tDc_TZ5DwIPzI<a*mj<wUrdTKf|1-|8uPQ^4%
zC!-hljN;pjW9IVl7fj7uQ3#F*r~P<Z4k@xTo4^s!B>@219jg9C#zW{RBwR~a{MXS=
z;(b`C|Ilmy8zuzh1S{6;xVCKo>S(^3nEvuBJA402@C)s+y2r0YN9Km{4bGLHewVW&
z4URJcl_Y1*Cp(8+zJ|JiYnCP4dr62;*xPgA9hEh?!KbJ5j%V1UJQ;NVJEHw75GN}|
z{LWJEqZ2vrOm|d7)eddO+@P$p0DOR-mYz9S#U(YsMT{-xd>jt?CLGe(^|bRVMLM??
zi4Hu-UY*DJ-|uq*JreC-UI70c?kO3;igm1wDI3xJ`J;>n`|MTiI|^D(-I_CYD7q*Y
z7?(#wZX4P;?RL&QLHT#JBSbR%+a^N9W+GwWn-{@4I+Ox$VPl5WJvVo}RL`zPut+YB
zov`m@h>0j*BI&*>yvNar!{YyXG*Z|Yl05`GwNu<6bk~`O{sE561H^}=5E2yRHvc32
zzAMkdjI8nG|LesQ1esl-c(a_-MD-Ikw2i&%x0*{Jm_sMUFPBwxME^>TzD@TlSaj1|
z{C7NDmeqT89=X-wu+YSX|ES`87ZD+hzKX-z2~x({zJ@5`nW@dnq}6SY9{YLP4LO7e
z=kPfT#Ii~(mn3mF6idXMy!o$#Cjbg<C&eaJcTvTc8fiM`N^Ln+1H6cP?$?0jSd4{O
zHI1~GV8gkHjLYD#Z^D3U<G0)gLTc-etTF$_2>HvyP$GwItScqZbmaYR($wp`#vA*!
zv2@XWTO&4s01>Sz_^TEmV<C%1Wq6;x3fwwhR#)NSyL7Z!Opl+tf?8=MT=Y{urKrRg
z0Ov8=oB^PPLxw8IML?GAEg8YM0C`jgP6ksYbU6q2Z(RJF*}&h$%aej$n`}S_^v<ua
zvg#|4ALR?6_M&;CnRl8HjoY<p%2(-9BC?gS5}G+%ObdTF_&RA2(7z8;nuCcPPV<C;
zk@tnP*b67xJzxQ--}l}?TO0LcIU~C7I}L=h7d>_*hn*cez3ohMj+pAS8c)DkPT8Qu
zfVo4V9>_#(zvPXmceh_A8R9d{DpQSrsAV{#(u#-&*XeS}xk`@#6`1M(MlxjyyXRJ7
z@ggMoH)Z-?;z|h<G^CXm*n=0a-|;2+jNIu@_ed4okX(FU+g><}kG21*uMOe%@AJRE
zX<3?iU8if;vm%%QS3>KYZu?4=ad%p;3AeTle?<*vi-*c|da`|9Tf$L1nv-~1OZw{W
zK_s-Q`VNa2#sh0RvitDCW)DhXj2`}u6lz<%?)BAi2tD*|jZ|xM;9e6NU-frTfyfrC
zLSXL4wq_aCdIa#&)9g!Q2z!H^>g28B!ErA`Rjk^TBdCd4sQVXi%Y}_n#Uj-~=Y`{b
zbJ(v0Z|Q%nX>~zpH}V=>;$S13Q&IgzOsvRy1<$))ILVe=C-G0z1I9xct{Bwc>2pp)
z+hkdP@;D)ncbTQqj8C;}pA-+pA$K1rFPPk1z<WJiLq(v|KL$*-ii<PcdyPk}tYB!O
za(?%8Pd_c{@Nyzg-I$ZT^n0Co^m>SGdf1Jee<%w4Sh?JS=HT=QLqC5i>SzDS(a)9l
z)!Uykrh|+S!3F8-SOxpV;v(k*PzOmYlp18x+N~uRR<aY3lAoJa{3m&S0PT1C8u-cW
zIpI&H|9?H^qv|xWG?0q5=|AQV9!eRulsjC$@h+#|l(^1MI8KJgldi*c@2T<(f3f2j
zU2|M0;+NB%#7AdQD}vQ3D3UG*Qo~1ocBZ2h#Qtf}Np)lk=*lAktLQnmrJIxi6Q(%7
zdEM>T4(%Fk37+!$D%MzE`uHqG!)H^U`98@PLgaW`>qJ##Ym$QPp0bzord}4ixqwCk
zpxWx?$BX%@ELDR>mri)&WLG$|lunJP;DDRb7AYAd)LQ|*&BZ!1W4%}cPISy|U+ZQy
zd{d)!W9c{|)teJD2%B(1>^B3}i_TDn^Z%O3%uK`xra1TTs0;OT^;K`@W6Y$V9p(rL
z1aXC4cFYf|b+t2W>*a<FB){s3rQ>kSoJ3ir++;LIAZDy*+$A1L70uGy#tQc6J3hL>
z{Ye^dn2JC%b=0MWae5jc3X&1o^A4r0@xFf+%Tq(q!RsF);|*2)dFuCpgwwFMNd;9O
z_B-V;zJjIHziK;}2)G@fzh1@%ZZ28nz>O-X|M{g^r5nnB)&j%a^~`p4eXT(ZJ_NRV
zoc4sE2}Mxn_+dh#R5{+jqq35Veq19OU7(FmRy%=0H<^kKB2VBSy6Da=yZ=CDZ7F^a
z%2XlSWJ!Y(F(*;Oqm!Y>mh9ksw3PC`WQu?KsRyLlRUNlJpZ$}9+|E+vIJ1q_l}RyI
zeCwgR?rxeRX2@UBxE0BCS!w%&B>tS_Fr1P3zPHh;ZSyAsR%2IS8<L7NiLT+;#BNIT
zx6^{EMUkfvJ%~*LiT{nN0K{;}_?PTPvbVRl7sO)i^1kbh*lxa3)#0QBJf6jmJ0big
zP^^?i=gSeL-eogWY_e2u!KAyca+lWGMZxA&A_z^TiA5g~(K<+nn0x5>?@zYLAxS2s
zKT?KxysMcJ<QAFwVae=u!)Sr*zVt*HH`H%$KBVPw^!CAS&|E?~EFyci6^@5b05*%V
z+-xrFC7tT?Fwp&kF8Oubxu-lDxNEB!<*QJtzIarKx!dFeCjX>=Lutvd6xY~C<7TIU
z%bk@(g^bWogN58Ks}cXrN%(pvIRr_;U1z{VDB#)40uHnk0Ld970gh|xggVoToe(%=
zu`5uxSUD#SVwaUxuc&ngF}*2(*oT#BRHRl2v9e;Qs$#0DU}C$r{XSps)Z2Ui{(S@_
zO?M#DtAQ^=y0-lHhy7PTQ%(d`zLo0!#2s0<&fdHHD$kl6$H0f`p2qh+5l^_Ba~$Y|
z^V-gbfXf*-7%l85`SOQmm8TTHrQ35p)sJYL*;I?waD>ZPwWsDJJ!&P}FbcaJ`Kt=_
zxPKD`!jNE?n6m|L^Y3Q?Ue~oNV@qL@jl0o1=nBPZUM?<N;~a^=b1iNhN`&HE(CG}z
zRXzuH-`QFYU?;WPU3G=1N$R0o08UbL;fvE+8+L(o{6H`qvWm8L1d~DA=6;5gRHx4^
z)_cao#Kc5AE_=KIKTdc^Qy4hxuf?~=3p@84frAjwv+&4|)a87@sUPFGGsTJ-7Z4*e
zv(*;ozlluL6Maoba1acFPc<T~?X^c61zDH3sN%92WvK!x`~Qwxg48gf95`YUUUrk5
zhNb)zTGC6ZL7_}Jb7dr{uhj6-TIV>?x2!Rp^n?a_CByi8Tee+~eJk~<+*8ZQ)j7`6
z+4NAGU(9MlK&hj66Z%u|{2n9{y^TD)$bu-sT?_cAyP2+8<Mvv(Nk@aSlibJTybAjJ
z)T;~d`0hKi!QG?W9z8{C&B$H)ock9D$5AL{5m?Cl?%s?Q`&L&Yl4PYwPciuHllhX!
z9JaGVMd}loyfa0)u5^vrInD$4WT*L((UUbs!koPjR1n4=d?#6=lyxzuWz=dphE1Xi
z`UQM_vSToS5W;-p<KhP1UN+t!>_t&IXAp%*k<L_=B#YgKR6>gIy+o13x(|bvI6CrQ
zPU_HdKOJ7jZ~-(e&3%vq$?l+5?GJ9@mx7XO<Jf|S#eEEQAn+#~OKOkm*|m%GCYmvd
zQ9iL&7KE<cf}#meABd>FyN=lCl<NjGNBMiPjENHLf{k*`b;EuX@ViBAtfsj7#R@G+
z_O-42L0}{y49R(-`7kPKLIjw7tG$sg8tk*QM4aT)HgIp_gwN+5DD*>Wc>R{0Zw`=D
zkf+__&etFhqw$GRZ3ez+Hrir7G>YCo&?+$QBQAavJHPMMOrwwLw}sCdsL<H!A=OSe
z@cv*(Z9@Z{hi<}6u*5w>?goNE&)Ivav8m{1@wq3PzCd8=Qb_wcNaFX`tI^cVtj=MD
zE1pn$<BywDiN)(?cSxyDLvelcL<YBVYo8XKzAo*8w*^YE_wlniX#vHR7CMZ7m%0S(
z=-bd(-YQa2M(CHT+xyUHr6)=6M^n8?HQw4nqP-tf-|A-dJG%w5Wf54yEVX`JPnsq3
z3&(l?WF3hW*(4qVVfa<j1>5{12W}Su!^>F>)W)Cyzakbgsbv05j4dUfp0+_1<1t&v
z*c<L8NSZ(^kg7bu4frgWQ=cuzX7g~8?~0A0#B*r3W;7H!ZZP5D8~EYuO#GPpkP&FV
z!<kgP#nWaW%&+6%s9O>4T|);;XD<pu7$&srkHV%~e)<dh9oKY~^PORrtUSHkZ^T1>
zDM!osxPxKWnEAHH9N6uDzlP8@Fq6MtZNLvH&gk6Ey-V<PuEWmV{2|ZO-R6Eg2?0>d
zEiC>*3@s=1qXKuO#{r*(Z!r>bSaXtg<5OQDty5Lv<O0p$Mxs{u8>ta-f3_g@_kW&e
zO%=1XEsvTK33%eira3pBITU`^y+2w-r*96~_F%*T<9Nw{6wdgOgt?1<OD#8NB}B5=
z4F_i3%sqa13->-9DHFV8?&N#Yt?zEFg*P(sVan9ccmaH8bOrzTYLKg#z#04>#=bHr
zu5Q~FC&3fkLkMmO!8Le-H3auy!QG|N00|m`1$PMU&{$)^8h3XZcX!_A+`8wT@4P>+
z_K#*$1zpv>_F8j}G3FR7l+Mbcr~U8aP*X9uMa`M%yk9-;*Db{clv-#_K^%;0+)^;^
z0J=i$3;%(PL8B{Mdq)YFj0^>g=IQAvpzpQME!7B+Q-rFK?8o6Zi_Knc+-X!2m)zF{
zVSb`TuBLZ?wy4`qGCl<*U&7Oby^etYy_%oyb28o4<^}P6e1Kf7hK*!Opikl2dUO?<
zQ!@iV;J!4sLqVZlW9_fTETQuN<YqnT2(nzFjZ)~%PG}`tjcA}E@b}-pKYK$*7bj~)
z_K#ZSsiJ1UKqCA+d>+V47zG%XD%V##fgr+^eD(-ax=z@(^>CtuRRuaf_3EP1CJey%
zl-beA>9}jx?vL$yFXMqgP*6~Tk4)~6grl?o;cws(34{p*4g<!cUOQsCW65`CVt(<C
z2A`G^ptCR?nKbw(Z6Y+j_2Yna-v|C1*<#eiy6Pli0)piP1E034A(0||p=-Y-n!{tr
zhgN}sA?TNkT_)<?eH+)redopMB#x5Xx+RCk8z158^%&;KK-;mVJ#SfZ(AaK@?CIW2
zl^O+xo{YY{_QnVIISXfl1p_Z;C4Ms@EUN3Psg{R{P6KRuJ5Sxz-3I}NR9pP*oEDdo
z-H&GZ6W%VO3f;ROEabAQPI>>UN%dDD_Qzn_A|ul7sk5HRDqb(nMj8xJ(1sRB0&Z+v
z&(C*t+R&#5$Lr<H<SE|~0_=@+SUj65CH1Psi%n#91FUBnb+&rnLd8ME?hW;2y7iw?
zv(}VtRK3Y~tpD63)s12KTSDW$`i81V4Ii78a6~X18o=5~FhQdC)t?8`J$IONeecg<
z0wuR;UI+Ee_N$@~$GAdg!xB4-wzJg*4Ugx#k^Fz9pkzbU)prT9DhTJ0BvB{5{M%g~
zLF&n4LAfcfNvk13JMEM5(-EOPmxd_eseRp)u}@RSvkBo1ierM15V_*p3!erKCp}P9
z5O8WTm67Rn+hE)QqA8c;u$I&JRi{Q;mlb2X0$v6SLyBgHhk0}$t2qg+>V~M^|5{Lr
zV3c_f4F0>wr#y#c4>)%ZLnrmK(Pp$^Djmhau+#*xHOSNj;NR!M#GpDo6NR`m-SyXe
zubOj|J8tq6pbAtd2fBEu`no#(=;Y%4ycTq7tqWlN*X7ppb3aBcVkv3|cxo-YSQKmX
z9EmA?h>(#Cd!+K}mV9*TUEd#wdwVxE7%(y5*E#FDuLMn(8wOThJlvfQWdtZ%y^S+v
zuD)ZpKMs9IcBLn)sOXB_>~^R>tP->&<3nt0WTcwBG2{t_)O?FKJ;^y4_!2pLpKKhR
zMs&aPawaCs9P6&9;*(W(EEUJPimtajwDFjo%9Sj^UIg<Xh|o4<<+qSe^pb%vaXi0_
zFma{wofN+rwa?>BROQH8@b9+{RPxqqB5nieP8gWjP6L5WeCX_H_K|nZG_HR?4F!_m
z75(|cs5bPQ3@E`Z`(tAXrVd|r11R6Lpxc2e$uABI{@}b34{j~{6<{#`?dE1%=f_5@
zb+cdp26FxdF0@*F^AloyVEROrBNx6~q=+9kNw3Zwqxb?vQq1Z5%XPe<P8wL8L;$Y9
zU(|UTDiWL@PRpJOX+kqJZO<rQR(%*q0sl23<03e_mnM~TaQ*J+rQ77<t4u(ND|Cdw
z{h1q`^F`-SAaVDXUz%8vZi^n>G`0sjNo%2QKa%oMWSr-LeLi$Zh8mnRYijjl&EWAS
z0hkY06Yhopxe|oUuYdBgb)Q*`QM%1Lgh<o|L(G8+-Md@It{IL;rxxAEL)gu~nX-l#
z23^~$8gl)JC*2J+CR^=sj*md87RgcyY3~fI2ArEqZ~A*Aqw+}vmYf%2nN(Cof6&r=
zQ_p6tEpRYd8KCKA6ySXSo-l%OBXYO2age5smN2vHW8_!o9(BYJ3i#G9!@u8O;AmZ`
zq(`$d^HBlr&D|cGshdB4li&<GEVugO3@WTaEzg-l+?o0QHg<Ogo|4>8n|A}58+ut`
zg3xw}RTIXC%HNq=?>RXsc<1c{jR70BQ1ziGVtjge`+4uv0e0w1s}6@D;Y+@a{zR1k
zbs}4eN`@t=xq2tFZl@vP&0lSU8ZJC`|J5Dm3n39!X6(9PiG9)vxzj3#i>YV@eFSOS
zjI7nzj=3y0xexqxrEh?o!~(N@Mya;OGj9Svyu~|$<fmNfcwCLHGGkD&Lza=aEJh_O
zjvmsEk}mm$WhIZDuqAfc>Vn@QWHYv10iP@ZwK85DJmI!z+ps+b&$i{>mGIj^`r}=`
zK&3rU5968YfO$7Ken;6ZG%^(lUADdVL1sJ1!Yi7_tIm4z#?-Q#tF^&Y4v3q80FB7b
zN6CmzfcC@ELTHsD*8BGOaM=P|9msyUT{-mUY+b<);hxe>;crm}v{BK`X)xOVR1y*W
za;fEPRL-&ww%QpM*Z2q&^KZZQ1OCLr2$f&_qiJQB%MwSjWr|7=QqUdh0VAe~7ZICj
zUXkt5-ev<yxy8F@(itu(ldj6G?{+w{KX3#A@2}e{KYP5^y*AQKMOZ*#sXMv>hZp$y
z+<8M%H-0drDN%`JSr>7_Gi82We9hqf$@+}3e$jmmTdL2PPD)I}xe}78nDL5&!hlGu
z6~RAT<^#uTtyZOzBgyd1bYJ~meWG~%;Uu~L>I7ma5j_w9TM+y9SN2(&!`!b3Yck5T
zs=8=&L@0pABVU3*q;oReH1?cg$aGt;Mpny%?jk^fK&g8)S59xzTKQ)j{2W-~N96}&
z?ecvNn}d23SzJZO>%S$W<UlB1Nus{cLf@V3{Mc@!IrF&uiNr})2UEA`F>Tfs7aXl;
z_TS}di+li|D;U{8ttkCN=1xtG4*vG_EZbH?B<FXe{hul%6xEEol2NEy-b%)!%mZ&^
zs~a8wpI+`n*tyUf&L&Li9}1b~>EI30pn~{MYZm~G;iY+N!JnV2M?_7Lb;bgX)gW+z
zXTEx|rDr_KBEE{s`0(CrQ1!g>yp;x4n{76fhNDGdKT`P$6EE~4$*NTO+_5-^R!~q!
z>zXbtI4s_K`-K=Q6GKc_uapVKf3J5gqzTXkDjMzMc9`E-eHIGB!;T!Dx)|!EI59DC
zf$;i#h^}^)Y2u`|wq2t6fT^!%#P_25P}xD@nQE9X;4HVJ$<t+8eoEL0+ILCkwxTz0
zHYEd!ap=`5=2_g<@x?ls%vE1mie(lStbM>Le+q+~1f2R~((#~R@<#=&`NY?Bz!{lI
z4s!AgZ|0u9k5rnwiaJnaG+?PjhgXdOlfY6jynRrTj9yt?jXjJ?^YU>o6+J_(bHq=^
z-B~P`@#C>DQYZ$G(q5J_0e6K=1U9#2KecgGUNT7p_rPDtY+o*TR9CQac2~f{pA5;@
z8Lt?hF^+nj-<=LLJ1s#w>JJw#7g3oSj}G|F++4r9)~2MU&Isc(%u?CCF2BLg*LjX%
z5bB5CPVWg{OmF+qd9#%r#k`C<c=?_D(4SScf>`8vH)0%0+VRV2z+oP9XnyOpfA7bI
z{Q+i0igJ|oGE5{_sX>5)dLe`Y><R$^8mRWm!rM79{TNX}sS*bmE{Gqnl#%{<kvE}O
zPLc3jt#%`oPQ7GwwAabdh|hITxf^H+M!^wXQ{ux>Uwicyhn<B;>4QIwJ%og7!qJ9(
zR837S$Qt4jMZsUWOr?MnN^#vbB<6?`JB^IP*GeEUdNqo~zWDdF`*Pah9w(4x=WoIF
z<S#x}9CzJL2Z?U@Vl0U8xlrYIKgWTU2uPUIpQP<gDaqPTT`N8mkzcvg{9R1Rq*9kL
z1VXR2^V4k);JgdYF}=CK>TAzJF>w4aNYzB4d+j9U2FMh6cu!L(7ONe%7kzKXDQ#~k
z__o`&Pxyt9IGq<E3|H^bn_sxj+3+_08bo?l&3~s*vPm?A8}=MUSVpWXirOyZ^19t`
zvm-YRd2hDX#;W|7LH5-@t}kmpKd27#MtUy3l}pUrQdXMyp-RJI5M5zXL^Ye?*V$;d
z1my&x1(e;6`VT`JS3`^j7L8-oF5((Zn<^_u30y<gxLgSYTx59ridixkV;Qdix<CH@
zVs1)NGQ{G7Yss;Xt^L6nX-P|F#?+vRich0lUdKpUOgUDIpjCTN#ESK5ki5FSyATI7
z%!?`<!g@Njo+#^J<-|NorKg;yyQi$7;av5hios_z=M!2Lux*A?;h5bX=vmPFH{I2T
z^Xc8=`d$UL{fcx+ZoXJv$9w_(AWr7-465`6SIS2-=he3`6d}RGdHhy>vq1`-H_P7s
ziB(^T6cZRy1-`ZjZz6qb`bi`a`{5r~YcPlZpA5Szm!_5Y^6#i%1%m0gnw!;)Qv=P4
zYa9XF0dHwMT^f;=?q7YGb`Z<Pb3)HjEJ2&%-Sok`aS_2kUUOEA8OyIZFgWaiD3+L_
z(gv7_-qn+PpF0heX)-9uVLEwiXk~~gS22v`NHhBIdQSLc{&a{C$Iz*$>=c8l?kbOP
z49@_0N&rg=2xeaYiw`0YHeA&Gd##$3wqXZ1UNJoLm62Pbyd{F6fL7YkLiRIzvbU;)
z5qz$*u^PG!oPFJU3Kyo-iO{=*Q~jERKO{E|KYqZC%~oV~sR-&oI1YCQ0vY;UsNjpH
zy>UXV4V4BM;C#}u|9pOS<54UJt1M5{=L8o|C(w|2Qf-fld`ajaQHJ1z5A%Ug=+v@w
zzv#h)R_1&lzVi+l#h5q3P;01TSb*a4U%UB!Wi(1^jWqsuDP#T5J-;OBEfl6uWhmc7
ziR_rK-0q+hm!!No&|p1gyz#Chr@I3JZQ`Daq6=Q*R2hDT<&Bb%^a=Y$H>eW90%bZ2
zCT7)Ue=T@~F%r@xC~U9Hb9H?8mk_hCmPoFUTOYT+suL|%Z#{H^>hdCt13Im{)GKt|
z(Hq=x{h>BC$PfMqPcY!=Oc~o!j*UjeeC5u&#c*rsJvYxealGcTrh|G`Wgp)`XiIhE
z+=}`*?=)1yr8vT%Bsy-&C`W{8j&3b#-`6}=F8`0`=S%Z11gBp{zBSEj!XQON4t<l2
zX8!4vBz+3bykd@*nt~WNJJR(hz1<GCHQv9A-tpVEsOQHX4*v7D6boWSUMwyR#FT{L
zo=&<@ueQ?7rQd67JiMh0x@|fgd~d}a?>xWYpknqGmHIZ}nw406K?NkJ2DmR%_bCdr
z5y~I<%lzoj4o4+&vsrqSzgcIB8Bj<Y`kA@xr5`JKp^4v`nV&LeXaT6{crnYy+GoSs
zVQC%&J)LqyI#l;xA$ksN-S`N6Na%@%K~pMA1K1*}^4PDg9LN-sic2%Rv=Q``F7!5H
zV;r%)+%>V~xDPHN5%^cSr2dtFA@AaaL)AzBCc?}(Y!+TPgI}^UPNcCYM5q0eQ`((u
zN{I8kA0o0zl&~xD{K5J7b-+ZLl>hBP<51->6CaTv-<N}oK%8HPzK6FLq{3?oH1-x|
zn1~?<6`HNDC9}mzZ;VpUXd?uqR1v19O(uSy#3$9MLd5A%cyl+{2-!dALD;zM3hYt;
zwkZ^N$OU045!=cQ=rHRdI@qgNE`DA?8V{Q5T4#Gzutq=nN8*FnN$~5UjE^fNiX5!0
zI@gIp?;H!~Z`<G0Z(jena9awCSx>i<k0y0WAlL|C$4NhB)q{Sfa)%=)3OKzOBTg>!
z!)wA>n*wdJ#og*ZeAr;qwx!35LeFm_XDE6^*z_(<BQq8OJujvtzMR-xgR@kvU07Ye
z)i8FP_bE|iT1x%rtsbd;eq6dyKVc9HIxZ7S+1QEIyR>%qFQj+VA%nbq72h8Y+*+zc
zp`?pN8XPKI4#M5P20m?w-mFAn$mRj5eJwM`V#Y_yZ5Pe9if)_ai{8l;+9^bXZv7SJ
zmj?@T1U8EL-8JMvI-+e~<4sN|-Ix6<0eElDt*e3_Xv7Qh2r5G;I;qp9vm@mA(EmET
zywiZ55R&p(|Bgeq61x2ZjIQC_zU!vIC-SZF(vakL9t=!~?tqWBtt|Y)I%G(7uGIA&
z>C8%ZW^t!4ccbWxKa8kKPw~sb9(r<gR<L;J;)NQ9J?n`W8++=DVn{igf%Zi{lYs+$
zp~4{D292unB0#WZP*nZ6sO+HtJa-3J-qs!_YN)HvirJ5IFtk-|ru%iK1O)?!$q>xG
zmoDLBx(K*<QS*=CF+9a-kO|T}l|R5g<jN)?`p2JRmu9r*m#pi27kyDzY~6TMZoBEk
z$EQUR#&uL`<IecBPP9_E=<SH0#gLD*a?CRh<e|ltavI;I#2!~94q@afM12)=HF0r8
zFST-bBFz?4SVl{7!<&sV&bBLp*!PMAN;t&~rD+TryyGZ*VOA$c>6brCZ`D7sYvGR4
zDI+GHM(10MDVTJnbe_bi=*Nu=;lvMkx++bllx2yLc`FuNUZZ$Cf7m-(9thO&yt}@8
zGpu@<uYZ0W9Tl66VkwINFR_Tp`#e(IPC%d=)gjSKktBxNiA`+!pp^B!<F&O}1Gnop
zL)Ed?S7_kkN!y6Q+}I0D(DJLt46#5IIj#^t2aMabC{cEZWcD9D&+XiHpA#UViimZI
zaf8t>1q-@gJ4Z&<9fBlaznm5TC*lW`bM3{Y%MBYzRwe*Y*~M}4O4L32MY<d^hwP49
z%c-8lTxkhY@X<J+z;inOx`GhY3>^44#k8XCd@Fau61Z^(!VP_pinU@a#_glXTb_g2
zSVF2Xf4=#vdU5oi*xMk?>sU?vQ!+nvq~klo)ciZv*B?THB7v^Do4^j%iiC7<cNB(7
z-B)$$*TkQK%j<3;bIpYA>>mQU@L$vND?YORkl8Kh#|8VO^9^UxR<w8FI*^x$^Q>{R
zw%N~pe)^pJWYjpB%GI5+^6M_*d1%v&=;4VNlp)1FKvhlcxf!m#r67?L(M$HmJ<jaS
z^5saH|EPSvfPa0k6{-Flw}q~s#JV)!Sar90=kK^DBD@}p%nbjHND04KQOTM4mFqa|
zXB$+Hdzv0sD9T%A{@TnZx6#^QLSLI_ir>M<_M2t1xP$s1-Yl;-wN?xAg8~-h+fX$f
z3-wH$+mrHT?Z}sh9ts>o$5OKxDKsvyjcqcu-}%L$UyFDi3z7`}>U!ixD@KWvqR%sa
zuAzljwm%roM=G}~eeB2!t!#4M=G>mCl6~|ff|5qO)KKh1Lw#|>=)T=CyF<J1lYzD4
z;Mah#SHns&Ui(C_=r;+mZn{)F_BRL;U-0M=kcA24bb5yQU?E|AXFo8Hwe#*k0%;&W
zu>?;?4QqRDlNYo(Ds$kTjuX>9)80`!>8O)o^=CBlWE#JNPa+6~)~Vf1{3%+s((zX5
z)B6Pe!r$sX-}dYc;BLLbr^EaARj&W3b?G{6{H4!uRC)sbZB2rl;w+sgD3dX{r^eSu
zWie66z{l%Y#Bu5Un(2^Fs%d+O)B5p9IxNm$o5e0Njb8Bl;LTiN5I9JCF`e|CR+^_%
zL%;4#qV-fouD8dM>~;~xUH%U9V~<?S`ZTg<VT2|A^=`}|jD5~E_Ql~~?3BoF4&k}F
zpD{SazkGxRldv`mS?&UdnmNkC(`nhpksjgxdT?5o+{2}!z>T@e$@+0mHNaT1%E}D)
z#*;Op=ca+hziF{vAh3wsUL&M{wkz(BK&z~xG$}HJFYAZpjehFT77pgCFZvjxc+{Ba
zjMsOWuYBXZ)k$<IaZ<dal7(kWzc-2x=-aG5qiK>__x;WQ(Oo9hWrXOLau=jX%y|Ur
z1F1(PO{^a?QUq}a&3afazr7V*%6EJCBte9-x>J-AGh~l$MhGcxi`&4~e0Tx+gFT6`
z3i^+VwAGWvUybLokMyPEX{>_DMu6$b**A|$_*XWQpoX^e+bCSqe1oW1<3}#V**a_H
zKJJ5WyALdfii4>Yr!r~{A>O;#=WlJ8Ww5tiKa#wcxdES|UfXwxC$_lPr-0k$IL#hD
zYh_^~5#Kpv?OkS1nXc}cI?hw+^sycGN2mI{`}~zdm%V5_%V)&Ku4ksMg$3Emjz-Y7
zDv{!%apa^XY%v~JMBkiq5Codk@vwUqo?wI901|guEU3D&99btbvPIIN)L9fv-<o~H
zzf_oFTM#S$U~?i*!zcq4S}x*HG$*$UHH!%ktdNDXwVTSd?t*Jn%jzax%0`)g)SC)g
za1dDXu`!>MU30oo7&jHd(y~_^^FUZ!xg%RW@)~v2!=l*~w>iR5DJ>eqxJIk%4so^&
z!#qXa48x>KkK_HD>M{f|TnnNXpjzg1AY}}cdtQ!4=$nP|f3Hd2=Woq+CiBG8l#ASN
z;}5r}oVl~Mn%2r_&0t4>cj%>0OG35fqG}z5`ghyb81%9Q1Lg@eus`#{Xm)P3-JJLa
zn-<gN!gcAqW2fuMfbnhOvx~X<Fp9gX1h}x;@~w5Y!6vC6uOa^ZSiMvT#`Vm~0vs0O
z$}>&LtoGt@<CN3t!07rYCeHB^nYu5mZlCt{V&8gu&UH>6=X#6VV&Qe&_i|{jyTp1O
zD~3S6?)&F#@{&%cgEjOV`F%Olhr4o3Xpc$!Db}|+jZi+!yJAz1@q{549H{oC&ZZ^{
zy2PUN{j6q6?rLv9UuaPn_5Kc-$ddE|9<;-eu+M;1q+bUH1|N;1*AmRWp#RT3a^WX}
z%4~cg*OH(f;lW6i&Eplbi^kzhW-e9>*p9SXP9w3=K9Tiz%V&0>70kSpj(Spw@wny!
zva7s;+xlVx(s9wt#7VWnbvMhY&(e2iuPpK)-M%^KgC(#8;s8&K1s`x7Oy+y_l4|oC
z7U#vhkD@68Ad@McQQt;3%xz>hB~3l7)?a{Rn#Nz6WvletiH(s+;J3ySov3Nvej0_l
zlHQ|1!NchlCGR3qk!k8afAw5>?^ItAD=y<MEGN-mjT2A)X{$a%5Csza$R)0Oh3{La
zL<V0u>{2<QT|@7RckAq>ON)^sE76itVuhxPZ3;G=ZX@jF_kUcPQ*=L*`&T~*I<(1N
zcOvTaNinQLxneeSn+MfAhzfSb3k8@&iN8JoL3x%-g<p6zE?qJC<$k2GRDUKs`^ukf
z<f5nuj;Y<DKlBgWk#w=6?TCkd=zd3{jx3wvB{=A=SM}1oI^baMxF8!KjdY-Axg0YV
zBY(4hN0RdniugOPN6RER?uHhz2nl&nct99_4jNp^dy}O#XFtXEsj91Eq_cPed20Jr
zvbdsm_F^>jxa`I88p&p}s3c9^wcC4TsW$22H|sH=y>FX(t}~qkr&Ib~3QuWyD#K$!
zd{N6deJB(|G4u+fhqQ-=OBow)EH~57elk=|Tcw=JdScbcA!wNfm9tll?vjUue0-;U
z-bvV=kUWG}w^W8rW)(9z%LvL@;nu$7h85b9^8fw66_^L*x2eyk@fgIb7q?HZo9-P}
z|Ekj+ZoyBlPE9K{PnB8_<R1FYgHlFs>SH$hbG3Xs#>zr=<(o);OT}I8wHk|+dJpI>
zrZ;^=%rv&F?@9NHD(z<}a$&RgHSv8}YMf#VGj$6Msag}nyD#=^e2$vt-Aq8FkU;H@
z(<?ToNNdC>;hh5+o)5MaQsmJNVWy+qm?qs+$>jZ8pQ@x5bzT$hy#CiBt7O`u@a}X=
zx%T0_<oW}osLos_T%(KvR237Yperk5MQwOCTd?#FHevL)%a!+(GwBi}INw*_>-h8#
zhKRZx%-8!d{I$EB#cmgCb4b0Lh=D`aqN;}EkGG4SfFr0zGf>LT{phEVhM#p5tvL!A
zl3n%$3<+Lm`5n5@MJx-!)6_Xyy1RFGUOqFW$|DNHHQaHsbIIASp3j{3{(4-;W6rlw
zBgQgnZ!raqs4Ucx#(7~1|MllqyRcX`s4q+NspWYc(KWy(8|PGDzv~G$Io7b$H8#Ps
zh-LO4U73^y6KvA++DUpP=EADADo$HV8>W71v-eRR=^&?X=rmc!oox+kD~h<w@vqj|
z^;NO4m;j-9>knr1-<yOqT$?ppydoW;nx|G@#}dXn!QAANqmuC@nsmrFe>9xxh#WT#
zrF%FOck|O*_1L=~w<)718%WBv^T_7^hSV_n10OHrI%8kTn|(^8T%x|~DBn9{6-VWq
zCt7gE@Ro8<r!6*Sh_V@g(ksUl1!pX}=ARlF*2BZ*%QP_VbW0$XP`b1yJWj)8Z@MBc
z7Wnm|H0}*bDG(iA$ui_!MpE=NooCgAI0S&oh@EMo0qLt)kBrkPd;OaU%h+WIM95cP
zPepF`jD=3Y7R7szS&5dyLfMQ!R`-oD^&*iz8=TB0j;5mkt)Xiu=j07QA&-;}w^>H9
z8vW5h(yXmQT9b<q<G}nw<-jv-8dHdmc|Jr9Za;dfPwqRhNE4ZZAR&oS`ZMpixQ470
zVQ|?act}-0OPJsYFFJrFhd-jpJ5`@XxM$a0qjAGQ&>&di_?kOJyifUC@QCA;5l1+7
zVsL!@t3!dEj+MIxZN2qxdJ8;f9yJ1bLl*8j8x7jzm?qntCstsS8g&aLnYQ22r5vDB
zt3a4Ch>WJB9js1`wT^8-Q#=)s(0%Pn(vo*N^A?c}JFSkyI>%5vv{D@FT4Y`0+h<6W
zO`Rvzi=Nz9>95!+Zsmi?+9+~J_RvPqPvMs!$mfG#H0V2#3<*q&xk7C?4A%@bwx_aB
zovl!Bnj4khbtwsivspaLn_5#;-C|Zv;k@gss;sPA*d%&fB)pEPV@H1o8Uw=(%yYXw
zoPV1-%#bTnx5O^X6K^_Gr<B>YE<|rrs6R%yeptvhSt4J=vzSozc@Jsyc7n%}Nnas3
z9d9^Ae{iv~c;i9#GIR@|7BDCGfH<i>e2@^$Q<#cY%?!DSNe|vr{52+qfcgcH_16cm
zZl-T@POl4$Bl)GDqTYc9^;e3^q}ECD(p{Ix_F137!{^eb6WMxTNKU?96cF1*ECP^_
zJ|m)6mvS*Nhdyh9o@?Sf06m`okuUmb9vh_mfI1?AD6|~+t;u?tDC<1q70MBw(jZ5}
z*6Vrz!GKTd*wr*&0ElGi-8RVg7qDa%9ssszjxenU7c|^_+(m9P641QACyIWkrlDur
z^ptlLd|700UkGk|Lce()ei9Ph0V=K8&C2-`+wi~XWG^!YOoJ74<p$*0*f=Ayo)s7Y
zF#_PT5gATE3D`b^^&oy)v{_flh~1aKK@2PEAS}%ZviB(qWN+IJo{}EvX~h)T>~=8I
zvl_?U3*sDD5=>ve^lA}MObZLbjtlN<Ej3Iv8xNTZGLVZ?%Te}h)Sj!%!f6(%e`$G?
z;jmJLXmXcxD<c2`oAa@n+(FTN<o@)UVQa!(gd-lUX8Lg$l#*SatYvl(b+xcgf6RVG
z-zc`uY?fe$T`#RArPV0{@qFxQu}ds7&RN?l2u5ol$yBSNcbw|<dT&4b{(dBI$!;>Q
z-rIiPf1}~RBDqjpbfqD1Pt~>1a`&PH9&d*go4%7j8ox{9U^D(}F;7Y{qG)GcG5+BO
zZy{>&FO;liuZv^SUSp|Hf?UgUtZh^r6t<bXeW;Mc#G78%IN!PAK-N{%*f=lv2|Mgs
z_-KMvK=8OBo=%AewK+>GNa$)g&~dM#J83msTmZ<*Nd)p{wFW%&tIeY+9D8W;iaq(2
z%`a!II8^><73@sbB<s451O?~_$+|v1+z&`mbP}Ob!zrZ5ci%rr$ntu;XgQWAq+zSA
zfH`eY;H-f2)~lznLajfK_T7{xVd;<a1YW)#LDwB#M^Bt|tS3%Jhoj>1TcP4@#$%Ak
zb&kjLjM$i`pr3dh+xpJp%h(@6PMiJQm%g!wKVs-*)lB1>Zgz@J0Ky*=c2nATVCi*L
zH)kuTgW%U?8AJV=H&{v7uBfKk7OaQOI1-&(5hy3%`$LGIV=?$@@ETa0`y<C>|L)TM
zr(Y|Nlxy8|fPbk<q&`qbAr<giL4XKhopta{Y48lOO!&=+=eJixHA}_pNUXGbw{k0!
z#5%Qqu4(dh*xGfx(9&BcH<(qA-*y`VL7b|s7n|eGiY)Ekl9xOf<{I$&!16MxX1`?(
zh}keMK+Z?)?JZhXLs{1S(W%Ojr@juQrCKRnpCAVkE|4X5fw|6URT`8BpO?=2;i8g~
z?U`M+KG@>q>(qKD$|M@K7+qtKa$CkStut;zy4D&Z#lgFFzAPHvw{xS}GPv2hsd!X(
zR3<)4Wd<z)<PDAWOF$gDBd{YRXZbAqqm$uk&f*H6DRqS9`i?2^vQhkY>x|<kT^9BO
zzJYNMMK@{z=RdmkZKxZwky2?_5dt}JYp>fGdHlBcR4<gA-(}#)!tJ991104n|4|Em
zf2k=$=ZD%<T5mxlPqHcSV^@w~Vhw_8*x@z$?C*A&^WnI(w5aFL`Ln3YoJY3oalNbe
z^^TX3jt>bpJzq<bCx*j%UUY2I{_89p)(oxq8G?v<lb3AEX(ga;x7_|=O_t@I&nt5K
zgRg_a>}M1RAt#aTv0ItX)@6PebTT|UX(N8dx3>%^PHKdg_VCSKAWjR82SlF8KO=W@
z2PL^7UHni%V}&y=L%P=jB1b{3sJ<bTuI0izOV<D}I6Ij27f=<0t43QMZgw|jmjGZ<
z%z;6a=pLBh>II_DZC(U|w>DFpBI6E$!#SJ6b=nY?lF6|IGDl_SwcQ-GC}z4-%#XM7
z%*-VkUi}nH%P7%cLolQ%)On3j_91X?t$m(qbILhsQ=vPg>DSas=n9i`3&|p@P%BW&
z9FMyi$Uc1j!h=HQ68Ane%|{wd%OTTxCpir@osihVqz@`m2@6o@&Q0Id+00#cBWVfd
zYKIg9>L$EM85)X98u{--hxbWwL)~nnuOs%{o>e-KI?_o38;_Pm&*QiK_Bo7CK8I6p
zK?13p5t(|qrb_9X4Y8*wSQ_2G<H~*)OH3avRsgvc`z>~A==G=V&Fhn<4RIPF!RkBg
zL$P(5V8KXtJ+GIH$pQM|ra1b$WBE}`8<3#29V+JSJ^XG$s$2mUpap|vS^+ET=l*t0
z%<M#Npzt9i)Cs8(oxmaXipTQZ5+J>RGl*XQU;qO8uFkZQd2SN@Um~Y}X#_U^f%Ey6
z?Z{PJN`gn_2H!MJqnRh!06c<s@h$2DWG%8vnDr<B<K1b=%4tDO%N?9!qdiKdHHF`4
z9Vp&wMMdCU`d+0eucP*0vsy?&Re!UlsI6^={{*twy?61ONJ`lfajhG6viXB5{ASfV
zx2)A&*XBse!jR(9LTJUw{qS07u#74l8oa#%ex|`H@~vPW8he-9YW<9BhuKU(_&yw~
za#eh0oL3lAgoS;yCmswoi9=0}loOq*8<gukeCva4@y$WYMW0m|ljkq_B5}vcu@AOG
z-l4?r;@h|E{ky>3*eLKdO@c{?H_qK#f-Sah@#uF&!ZA-hn+!Ut&%n=OT&TN(JcJj^
z47G1WzV$ZXuC+H&0pA%Ln7Z9$A;iH@KkCxzcRUo=uo5tIdt26h%PlXr6!KQ<(opyJ
z)Y?5+Uhe<*+hL!lRT{OfA2m!~3-86&6yJ0OGdI;}mRgrU5V4e2D1w742cGW&jB<tO
zh0!V7Sz#fw%3#G8;~&y(fF`yO+(F^Uh4m1M%DU~-z%RQ8D18)^TL1`qZ@B&|A%B9q
zJ*!GCX^~LKkRFg<&1pUTR`6%U{gGyEZ~6pj*oXntsJ>b)Opu}7>4?&6*#X`iFbKV3
zaW)VdQpuCA0AJXi+s$O<vPqw9RYb%^NTIr~wAKU4uQ^5A?hXWx<&7aD<S=4Zb^@Jp
zM*8%KcTi0yCiSTE=xCPB^>^?l_pxH!*=OYcwY}rf9o2vG6W;F6OyrnLcl?!047SCR
zZvV`KDS&YdG?y$Pxcu`M(T&Oc?1vDCs;K1F6+6uknQ#)rNn_nEFsPm-H!}c!3B+9{
z@`#vq8QMh&U;N%@+KjQFkPmT`D@V)2#_8ih?_U5?9$jAxIbV&FrCYC6yNJZ09h8gR
zG>>ul;&%3Kj$cf+a1@3;5TD`Or`FBN0%w<iV7V<IaYzt6Y#O_WR#BgyUcuJ5pmd#Y
z6KUo#Q#^-y5n(HBT(1$h6Oq`PiLQR9m2V3vTUv5WX8W3RU4_(@9)RNP-HW9kmG}eu
z2dj8K%^#yQM*si5hE$O1`JGdiebQW%$oCagpR<P9{KTJC{-r!F5vv&pZmE0UUa+H?
zrYwD(Lv~5A9E>T1UV;cBL$u<Wd<v^iG)?3yp4Qw|-f|#CM@wp5=8Q50c2<q6>wZGt
zBP8#>cs31{!M0t0N2N3_1^12;N?td6g%rM#E3*}ZU4erg);F!bfCN2Uq=}CmCbO)w
z)f$5<wC8}1Hd6x->B~fich>S6I4>mW$6uU~i25|K>6UZ9w$>QmAI<H{F&kpO-?ccA
zd?$0!%B}vX+cJ`nG73$ImX8C$$LOD+#Q)xDs{CuiA`gTs{cb(P&@O;!yld!O4U5ZU
zzcSj^I@WTqXvl5NIkjdSI#(Xny8E*GH%Vz&FRe7=w4Tf0%W<`P#D5)d|MQ<2^HE?M
ztC63zSaS$>ST#d>KPw8ef9uVXYFOKVk~DRjtsL>?_d0!_8tbsCa1Psam%KmZlF*ED
zN~6s0zIAGv1R=rWsjpxWRX%i~!~e-D|L@P_fha(|Fjd%^(TF}rVHr3eE7v{Y?mDka
zlMPc9FzL)S93A35fduSVkG9_EB8z<NRuEtn{A8i&<vM@uh{6bSBOle&lZR)nagq76
zHGfgYQhNuncgWRTY*>|`m=`R03uUKp$x9GZ-$lVTGm>Bev4%{UFHA8OHJGmBktY|E
zsVx2rlW@V){}*TYeeUq-HDwLE>|f;K-~G#{*TLi9e`6S3>n;>7dbAM@Z&XvEnLULK
zlE>__8P+cu=a>#^9INwU#l8#JwoJl4BI8rm8IXU^o3xIcE2}}`eLDhhzW(Nx1L|n=
z+o<72XQ2Su%Mr}LBJ#JbS@f2h1=PF-dsQT0QHo7A_;)sg&;nAu3zgQIV51HBUg`L0
z9L%Ps4lr8EtWPvnwDh?ylN^ytXB_k9=atH>xF=GyYq$rd12p?rVO+9tAGYnSvPEP!
z>BXg=$Flm@WM?GwE24zIWYj~txURyouX8nf!_CNCox%G|te@cj*+rg;MWukJ6`;>j
z!oA(K3c1&2p~aSWp7*hNm84w-5ZRL$^R1HI`%`FH1>AE<e^cqPO1KGOs*#_GFkzN)
zpuTK*_{Z!4@6td3p;3mH1fqF!Cn-MX;_Nd>??D?rax{&8Zki;B^QMli1K8F#u#Zi1
zL&~KN$(16rAfa>wnTD9oNgGWnX-8Df54u<`G|*=e;-37rlXwy3^C@nhkC<7|hqU-Y
zhmS5)e|o%tu*Md3PS74&I%`O%zsXW1BL(OUeUxS?v-JTK4nlSorvi^%zbZ~s>t3BD
zSTtRhxGp6pV=MOteV}yUocTKuPICfe8+&g`t|s};BjPSZ)Mthzsr4F0q0-NYH#nrT
zbFK&?ew`GHz2Y$D)^Rs46$`K;;f4=m40=y_YW1lnSV35K$!>tpxvaNq6LQ}`|7H(z
zPi8@o(3%vh>`-V5`oz-<{#sJ;eQKjMmY-y@N`%uPmie22d7KoH%kT#aE3Q-l^H}Cc
z?G>^QfWL^cFF$0*hV&@9bjlJ!k#M1F?WnIKMy3=wJ8YUqm>DgyL>mioPyg|rQtO*E
zSuu2odp%7oTiO#Gy}4o)%N;0<JVG?2{!JQNq(={>lyCJ_?%$?uSBfux9FmzfUs{;N
z8P`-!W)KN+1xWklUd(Zh{Hc*Q%~G}!o1`PF;nbEfDx}D*9T6y>lvf=2lL*9#3Yd@2
z>LU@2!Y{ppL=q3bSU|)*jccX^U@yuWy6i;9E}de$J;ZC<uevz>=arID71xfp@5CMn
zJIvsWpJnu?BH)I`11+uuXAx=*74uf=NGs2Xm6*FTzQjCAudd_Ugb{P_z@Mdw$cY%|
zyhjZ+LmLH#`{MRbZA_WmqWyXBqyp`tQI~#gCSO=FE7{2<uoH^3O#$>~7t5|y=!oa4
z9)P~q(CtjmMH(b?S-iHHt!AozuQZgzEvH_rX{f4QXKPcr+{PfbsQy!>>gxas#jZ|<
zH_$}aRgO&&vFn;&R#%{65`6^r_Y&)LvwGb@_De1fz|)E2++zZ!R%w3x+G4JDe{*({
zDZQ@=Kn8Owm(2Or>ntajwE(-K;lZ$)wJsRHNo?ilB#hrR<VrN^Hjc&FTdPu&XTf<a
zi}OzCY8KCY^ujznTqE60lr3L~2^XH_&0_!?>fWLbZ#(O@pHJp@$~&T>9Ui=*<qN-5
zUi=hJ^I-jfygh30Udb_`^iF=GUyn8Gkagd>vU8&(*Hj8W4%w-ri<nD;I-(mHZL@c$
z?iq}*=xm|UIj-7DjYc7PoV9foUSu*8I%E>YGZ4BSIa2fK74ufjCx*O|xtAX;anJ^f
zajzb9BU$+5uocto8qxt({>A6jpqfr1o$0VJV!|O{v@AySZUwh;EmCmy%@{*MP4n-{
zD}*#+Acy(Zz+ws7e6}@`eKg8~2GVM;#zXhEts3A#r7)})2K5+|Y+f2tg*3zCb>x+W
zfWp>lcrbvD9h3MtGV0d()lmAw8ACmwNq{G`+~w|-ci?ctqJ!|gx37LmS5!MUovwOR
zjatdkw&V-4+vD-N?5NfQPFpr7;f^14Xwo{rA1pNX4f$NWFS?aX^y>IuF95)?q(}I2
zo|bpfJzGABt9RJ@ILsh6jJ@F_j_}!VK>H1#GwPMV6@EkQbCp>m_XQ1`%)~wQYY*}z
zjh#ME)T!Cf+Od#MlIz3~U3%WiHAU1WNEA6lT>25H?A_L_3~rN}j@mKv-llqNBs$L7
z)b#@0JCla4Lr@AJs;C5JMa(8!*3b=-q$|w6JWrciajf9>^*7AAzL>MqF$iE-?r56#
zeFAg<ieh`J%plXYHMVR2Iwf?C?DBxG{-9xZow(&H$a!yiga}cBANkOOGG8=?sOwtz
z*sv$y()nbVNXt7LoaVmz!?6_+eQREYA^lna_tZ<ICC`E=&;|0Al5F?^=oi-iWJmsL
zx!<=N1-iWK=I8jGcR#V`1s}mOvo@28%q%RJrokb221&u9f@L_OfHl%JAlRT;0`$%{
zd#)lBrJ7Kq?Li53k)gG>LV_vX*G^<Sl%R%92hA_vZ<jl8*S(f*)9!$5`UK!9+%Wlb
zoW_8ekRJrOTP#x?+IDYR!yNmxHZw*sS%$MawBm9uG>hrDXSP~|%O0YaDjkUo!o6Oh
zavyc)eFRi4F;Bxfw$SUZ0NHIuQ^J$7&6OV$8KQ1u;gXw>L@b12cZkoRSk2QB(AW~Q
z74@X41BeW%(uN&om3Gplhm(Ys9zX>M%y5`TukZob8XPbp`YCqph7%18SH_1>`%G_0
zg_{CyeepmbIKvV>W45Uthi?;GIN4)??&UIH{pC`XRFZ|i^KT)CsEd>4wcC#^X_Qtr
z#4Z!~0f=8G?{ZHr(49;^l;Molx;S+v#^^OH@QDzc3oINfq&I6WrV1FK+0H3z*Zff@
zUD$aX2@e`Q&6|R!V!FmqGSBS_GV&f(k{s{psKBec>0w?8`*f+ZHRaVw6PgR4)F}6o
z_zyMJMQf{iv8|po{zk~Ip36)!hGYTwMH{9Gdh`lOg`ByM4>waSFkn7leQxQYR)N*S
z!EJYPU|M43XgRR{se}-rQ1w7ebA&PFWpvmP2|<KJ^yt^G*Qg&gp(jB9`+3E=!%6_x
zf@;%U!u>!PT3A?!u<H@p=9%W~Sz#6;vr-Q{;p;#z`qL~OJW1ee?&0~aVcR_<L|<UQ
zgOLd2`Fm-l<7)QgYg=|~miyz<<9)g{KtSc)y(104@vja3_4iG3@A4B!3)m#SD;jt|
zBt&a)Gn%jty*-ElbSaCmj`k^liu`1v%t!6v{?Hc_<0d;wgjt5dLGt6`LF!bwJQex}
zlEcJQj%9I8&?R5GP|ljiV7pw?B_XW;4G#a8f$L4tM+*&;b6`Ri1}Kw|-6NKrJ9?aT
zuXL^ZB;PHz?waQN9#(Wyo$M5q&1Q-H?mpW2eFRJbbnoodOzFRm_dX#%S>_va_-ovj
zwP*`jLF*ghi$Rq!QuihyUL>!)i#pw(dOL9X^Wp&F5cHhSWtD~{J>SE}m)+HcFM*sd
z6%C{PCyfNge*2ml0a1_F!uiw;lA5WhX&wavmIWkV1xI4QU?&dRGo6Oh3EMgLVdFL+
zi!|Gexn=dHB5p^NJF1>nIp;9F0RU#(Fkve44ctTF)S|UhtKUR<UOpkGW+Axg9bO<@
zhad-{vN*Lvp;@0$cyFj1bgbG6&H_S~x&Q{hY9boLKQV6Az@5k}L;!hRQ-@Ot+rVka
z(Y%+pjmMaWd)|Q1Q7iHZfGf_XAluR3P#J4VNd<Ylt@wRDVCSPkvP6>4*cheN<m3*j
zuonWPy)zd1sl}jF?~~qkK`X$dFb06M2Z`=t<#IJuYWh|!^unE_T>pBd!wwRfS!J-M
zb@f|ZLE8`ymP8S!XkDMO`1_DD*cJIjg1(Mj-ifibPIIx##29m&mkQ;sh+5E1Mp?^i
z6)F*~vW(4H&x#wiV7EB!u4JACNs$yD5Khz85~tbpN?wLwQ=fox!^vTN<(5^SaG||k
zt#eR6C9gqzK|V1}pMpq4*-!q(kE+AQwA0IP%B2Oo6>*tAsbhUR^22;XB!nySe95Y;
zSqN6DIVyU8red*>SL3|UuxnXrh=ZlCp6|a2?_qWVoQuLIf$0isBGqEo)@s`c?Idoy
zua{%_AYP5-0p6wgWd{rC%oX@^ocYX(o$MoLB}^gp8GwBto)1bD3B@gk<gGxZGg8*!
ziFZvYnKBgc5YhRYXYyIX%X+PHDZ~Qt`myXpEcSA4bU~bz8SmT{b!Qs-CgZzg!GMex
zBgUzFQzJGL3ciKaLC@K)_W?-rNv>pFGojAokf{q<c^v{wz9(Ck6Gh{QGG0QYjH83y
zaNmoh(!3W*%;ZR*AB9SIZO_+N(b1T#@1GCEiNgHC()t1#q<DrM>y>e?5Q$iLVBGF2
zbS#k3cVAi&wig=Rh`wu4D-Fs-kkJ9%w-X{-(W*C`P)n&yGo{Ucg3|5>Fyp$k^$2Vf
zKP7PZ|GHm_ND7MaB-_*`_I9ncbx30Ny){J(_GAF>mekeOK0}tDE#Hyo)=MwYm>nTX
zw$jB^zjDHVwk{=PKa9-bm^pZamAUZJTxVXRu+jw(?U5L))45Y39ELX0t^2kH%{OCv
zB<J~D(SCs=Mpn|eZ5ig$`6YM;;Job#@vhD<$7z#G&!IABSjL(oxQ?dgt6gx_3CumP
z@I^Thk1?CY1`onb5U$hn6q1rANW7_fLMB2_-1df*Yv(QH<N|O3-j~9s?GCllyVEfS
zeY@v{i0IWaa*^Q9uXpVhPPyqypfjt{X5Ibj!TBT}z{E8M6a0hI-G{`1F4^k_?e4YN
z)d`fW-K)`0kr93N)qYs5<8ddk4q5p)F@%U!_Z2nhQIp4}v7im1?>9QtI!>7gPaCFb
zmfi@%2&!MM0`2=*JF}8>3<ViiV1%G3LTvE8OH?=JJ~SdSOixw1`UNO|;B9G}s0|ir
zKupCqzV!(C9uR-iVH>T#yE%f4$kj$_t9$5XOw_xxPq2@^Nk<PTGnHfU>O+GvI64Q!
zLIzju#2EVlH?LYQ3D;5e-OZZB72Pd^-~qlch2)*#iol5b>NaQUyvSAsGDo^$Vsl?1
zLAE0?%mxv9vy;-eAgrzTIb(IrQJ>lEJBYzQE2up>e800a<sTk}V70<+Dr+C2V~hq2
z%<<EFA3WNO!jW!(9ajqqAd%$MIW*)C=1yn)?m5NdY~Vmosji;cZqSLc`e=cZdk*Cb
zbMo=%k-^>S`LSTe_xormi@u~f-u)}7-}<-l!_AJwXYRpfC;`-qA>S6<mYTnqKl`<R
z5iLVi5o9P-+kQ!8Oa<NrD!>>zxrE<YgZFBdF8NLU$;-!2Q+Im+t2?RQE+9LmrIB94
zpY%Dj=<@QwLk4YsUh%<uMx;_|cfPT>P)_hV`N78Nre(dBfxaig?0m{K-`KyQ4fNm+
z?bj{uXB`r*9py*0J<=Z5I*tDrL<VMNPJ(%<Y-s9EsN3X%8{;d*PX~UN=wmeN^n`1L
z<$&jiMjK2cC%FI_3K`DBpw8BOvBUvb<`&^l<~>nQ^^Ba!SUlk<xKANBReLF+Oq1^Y
zCi{y5Ynm}pJ#!&GXh6TkCZ&Vj5C<$EJY$4ts#e_235@7=+53K@D9*Ruy43b?q0XAc
zCYH((=9Zwlc$FOE$8$rHg)&L{d@Cbm>kZvVuWA-Yj0rHt)~f;%j{EaDdMm8-DF48y
z7oY%An}FK6OVH!2)Aq6fX=W?qwi6p&Xe;dfNP>Q#{V(4Mvzz>>WZMR&YoKzly?ixB
z?35+@rb8=><4BUh_y@D{4@KLEctsOS-H9M!QZ93)tOMiT%n(Y;>v1~Pk>^fX9Osg8
zMUru2>piql^3INlTZPV*;Pid`npHXhrnl}cLEI@fNQ%yI0p+4km2<X@T1n7nF~9&A
z-}X|AujAE=5C#Fnx0>z@e)#$Zp4)U@8|aDnGWU4nrb}rPM#)<_G$U#!CU;lI@p(iK
zl5ygVfS+SgjB%7O4?p0+{7V9zI%x~$nKc<C$$%S=Q$l^Wj2;~3uBQNk`aDeGyJRXj
zx0}ATxPAqyL15U~N!og|a8jr?@#R|C$}|f<a2;?w0O1H7eTWHTkALGf?-A15ZkiKc
zVX_hs6c1t`2}W<)X|TGimT3q1-l%v<>o2RgHA%9K{ZK?JiJZt?8eK79(EZ^_PhfHk
zjB7FoBr2*_lvmO)tpCBp3ZCoRsGO3*l6xB4ViUd(l>bS`dGSGCjeYNh#~-zso45vl
z2Dc?}U8h&0dZ!4#@fPVM{U`f|YQ(DsphLh;qdpmq^gYX_)=JUlFZS;7^BjF1f3EOy
z8=z}>6jB<-CcisrFG~R@s%HT^Y;zW>ZH^V+K)dx<WF&~9Lll@Tl*`5*wao}jF-Jc`
z?LtR_qy#HoVMShmhJR!WKXDKj{ZY0NAIJ4XM!@wyJPkDkz(G(%>>CRITCr3EV3_zh
zxEsiR{rJ*lic9n6wT%(rg!n_zhYcu}))_=Aco4AMoAtKeKz}A&6k_W9XgJm3NeD9l
z?1C)7`V5Wnio*VKdQkk-_1WgjfjPStVP&iM8pKV1&#6vUFI8SumZ*j_ZI3^4sjUp*
zf+*{u)~z5NbyH^k-Qud3YF}9+I0M3<NZ@7jD1ni>yTK~qt*9wfxZqYsAl5^)3^h}$
zfY3gY=ahkOb0n<Z74awg;F}%(FZbBm;qMD2=^DP9?vsmr*FbHb4YZ~GNRpt+&*R;I
z-DQ3(ar*^Jy${kELGtdkauBnj_mP8`uoG(pGzTN@oMC9U>2r6>otmOFJF@%bMR2im
z<By1Q{loA!lp7u!`MNEVrK3im^r0UQL*Map-DrYfwdLa5kV`O8i#1zVH#Gx7X{@C_
zjuz|VK^9z}Aun~pX33Ifv<{M}J#*?p2wg+m-<vy!Zc4L5;?Y51^!M8(xBt7ScmD2O
zE~Vk;h08k4M?yQI*;w&HO&ug5U~f-=j4m!ISWQd;nvx%$mzv~`GX|c$xox+?Q)nq}
zUDQXkf`@_nb!V7Ys&wN!#C%;g>mThYE;2;VY`&NajtgIkM85qx$}oB_Q3ukzz)M&f
zqjK@*QkHYbO!f4AgTE$x-Vf<+Oy!aBT^5GjcFp0rY^{ZODw7&iYs{hxL4jtv3mF`+
zspqyZVIpSmC0u-C92NZCxs29i%_hc;uBjc<UCoe3(|5@$(VNv&9x0)Y*Z9>{=3Z>j
zyTA`VCqT;LRQPWH`BrxW9JYLyDw((v<UC}@yE_u4of$FbMn53C{uBL|Z$uAJsIVmB
z@0jOt(loty_{#ZpRKD@iWH4*JI$2+S9TE~_1HE^C-HjtRcn$CC4X>~F7|MMV<eb@s
z<^O8-;S-9+OqVy=%KnjZIb<@*fcfju%g5-o46dAO^Oi&7!xe`e>0pa+$zgqVFr8xs
z^l^}3KhtEIg<5lV6khZzT{Ppch?ZKmLH9xRwV6<owd-+Auh-ZHLyM1d%E+PbG5!yV
zV<pF3(JoD@qCTO+L(qiB$_g$$Z5P<uBDSyZei`%TL3Dq_WHyDacNESNgK2y3_PSd%
zr5Yl?ZC)RI=kCsFqkeJQ_S0rvwD@%w9$oq1#|$A1PupP@nN0W0jEwhMb{~%re>d?i
zIXh+DmSF}~HU!UL9`rJ#Re!>RVa4#fNYt90e7jDxFOaKbN@q0L;k3I>RBfY$?b`XA
zCW{Mj=)_W*!1_{&UvZ2B#b}~<M-D_KU-7vtUbV8iqToJPa;!qK$bkM94KCvVE5emz
z6c>^7cwyWhIS;W<MvmX+HTjmMhWZlx#M@E)Tg4HZ{!B~~*lN`Ne7?e?M*mR;F|%0y
z<v7L{3^8r7%jX?WVj2%!QdKu_a>~1RG#Tc8F+XVVP3hyzQeA30E3N{=m45jdo*-gf
zG6MO~j4T-F_+0eZ@^6(eextm`|6<pCuAa?p{TqsPPU<ahzw&?BddsM&qOf6`t|3KA
zVrWoeB%~QiKpI3!=@9Ae?k;KR5((*cXsH2dK|s2D7`ngX^S;lwo_N>%V4Yu_wa?!7
zzIWW$6{SEz%6(<=SxzKIV45fykb97F=H>rUQa8b!u6>{VWRdC1w+inbW&(>~1*T-a
zlxcszTQXK*lOMRp-F+Ci&*&0pGMFdbaoDAD`|H~*=YKA<H72uel7kZ2l11U&%(h#V
znc9S&%tyULU~B3O>&pfRQG?c`IH8v(Z8^31TP;1SXd%SE6L9>Qg|Jy3Bof#3IbK_O
zL|gC$aOfRJtK+E7od)?}Zzm~^_k^{ggZ>;?p)I8-@15LGSE;#DM-8bjz9dMWWr(3-
z|8|EO<D19<tB`F_uO<!$uW0mtBJG;2vWh^+gA3akjOH<;Q4fEfdVB4FIqj3S&e6o@
zVehAusa}VN{y1tICJ*)PQKA8bDhK0^hYEm%PPazu9}14XHmS>rcpA7jVD2SY-n>e7
zV!e$eip3n4?=P;`mCBFLaoCWW2R~lP?|+zPIK=8>w|yj33wUc|Vlt5^pyW@U^Ko=~
zsCwx~@Tjy#mdb)I_{52zgy`<f>PFaaf(*^GNG;TfGi^^8YX<8Z1^Io`xRCkYA=-Rs
z&3&O`7w8agX}4MI@%Ztfx|=tgufJ%To{4<#jl9%FM<`P9w&;7^AU5n$9NfDj6Q32v
z=!lxY_zMkX^MogjM`=vDMkE+F-O7{b{`(JmJ2&8FK{d5ng9_RvI-74{rF4w@_E^{W
zmflW@S{;2an#gW!&R~MK8&yr9?6bq%8~T?=_j@{>b#3y(IvmY$Wz(q;a@fU<4Hk71
z6S#=V!%AlSBd4iU+=^Y=2}Pz5w~k0}n~PVSvFZ9f?0N=EH;~EvoM>sl`n>TBfephb
z-3E=pM0XOF`gb`7gFVP%63$HK3*HYW&n|n4>oqmpw0~d1NFx*j;SPq2J)TNDnu$p!
z`%CG035wq;+dCzR&?BnoA#Nerp4)Xe1>4Vc+aRtrH9dcRW=MmDWGMU#Wb+R_=X~!r
z+k-}ch?n;<JNK37EQE>;H#gz7h5)I5ue$d9d4t`yI6+>>`^x|W4ZmGG+Uj`Umzn%_
zxjY7yVjQk}-n(|yWluyoy(~u9hVDu@k}2f(*G@HvSe9E)3H<mfOjhV<%?R5hTGwlN
zhF~llpPe+{NkLC1HgqoAb2xRIUU+S2`E}+Z6E@0{z$`8lT!LTfei<c8Kt=i|ExGm6
z^zX8CJ_jui{~k8$I=;Ky=vXDiU;ep!N_xGuuCTz7tIAqo>bLAd`b7AiWZkk~OfdeF
zpY;d&>9YKQ^Ca^o<jLB?`}m+s7PWTaS9(=?^6k}fDGWp`9#DZ9L`&rX!$Sr}Va$(s
zT^?KACoeCnoXK&Iv$G>q?`y-a&kP6nT_mOyoL65~cSjHkzz)VHakE?d%H1aQ8)br9
zx}6g7xJRrT$r`^zsw)2ap2YiugPz+#1hqz{%h8W1?*;yrV!8|8aI0y({0F1?LQ*uQ
zBBtQh*K0E~B`)cSt`j~xO`qn1!#aYMYE>`re9L63SEM6`^JfX}R?^^0pL&38R){r^
z;H+v6{-DvhI@QI8{o=7nVb1X0n(4{6T_(6U=!U_n7_YhVwxHi2lA^|P-(R>(egTtX
z(_mlj_Fy#EEmZw@6EO@C6m;;>_8yG9>E<HV(}c8`mG_t*RUMJXWM9e5v?1t4l1R1Q
z;)kOHf(a<<rA7|>!m6+c3BQNTnj6y0zVh!nR#`Hw3*S$-q!r{E-K_kTR7VO?u49-M
z$JZg_)xy6+lU+09J>Do_?C(zM1uxKAZAIcjxZU7d-`_S_NT$--BwHKsH_KeqR;-z0
zXP2RwbNADufFsc2Xn5Yb27MLK#<Mb1)$d^|t0;ZJu{Bv19XX+0(tVM#Up%>&AyAb(
zA^m{pcWV7$kRjK;j}~_r^)4ngFhTtD)`hT7TI<=~S`;mRBuB#w{rOIBUB5C@)@=8<
ztjb~Rzcg|Y9K1m{+BUFOtI7C3ALiQSJzS(pMtcIOIX+0@41N|V_3H)uJ$csfN-O{S
zJ$=BAXS)!${yR6MjomZ<MJ>Hld>giwSaJ|I$E4e5+jV#pTh1pltcdcmCl>Z!&lI%W
z4~Tg-<noPxgxwrVtwGk)f*LERqar47YjlLKA)9Wq@K8%z`+e)~hcjXGHB?>mezI%?
zc02kRu}GDz#cDRD)(B_Jx{gxAU&nib9z$)SK^zamy}XAnRiKp}wUiJ^>Oczpy_{y+
z^Dc$$<-lNY!ll2`4{50FLcvnL67_mzU_^wtty#<qg~Bw|{Yx79elI+XrrebyB_ZQ+
zRQC+>s?OM?Gjw=kTw9$B<=6M@763&>po3{DSyg0cKK}(wo6fvXNGXe@cp+YPYrT25
zHtyU~>*_Uy%WDc7uFR&e*wvWYK*>dErBnwOt&a(>c+Ij|R?;f9{Y5>(Tp)i8Pl4_E
z_sco_R-fZu!O;J3ci$>ds2y<PZ^-9ub3R?BlUuCVTxy~UTCn&fexAHvwD9Lg3{>%h
zTeGdl^4ISt{u}K>qEk~&Qy+&hgLp87nlL8FyJSBENl4c|1W|juuewlu+i+%Sz}$R-
zB24FtV3;rMusx0fHhiT;&F_A;=SdLi76Dh_YJN<V+)XdnZ4MWIn6=k7yb%h5657G#
zy8QAa?+iSaSw@&B^E|f<d>0Oz^%hZNeAZrS?Ct$nzPIlVdpMg?gPipO*toy_MsT{e
zKgr;>24REB+LI)x?7c?m!aI92KWd?B^rM(CM9xob$$kRDWQ+KEC^l^yrz5<TU#Ho1
zajN}&{`F>&<^JxG;G<33-F<LJ-RwoH9SA6YVGOw6ig$+7nr(ir-hl?#RWG`NZ>=IO
zlD49&4Vr&#1TTsX#R~7`R`E9-e?4^5VDxLA(-+XxxluXWmvRvEPduCd8nVM<$7?y^
ztXFwpJFD6(^5QT;UO7EkZI^L?2;Nxcp0fb=(K%uvmH5K3DCqFKUS!|ucKFF|>6L(1
zv&Zl6zw9I=-f2^?&Bozb)Q0-f)U@pEvwBgL_?&Pw+j1?qE$A<`-qX+38MvS~hKN_x
z_KBuS+LKetlKeY8e}^V8aoJtU{w@gv`I|D?CDJpqwv#aIa~CKvojMsK1=>jbUQFTv
zDNnM_k`NsA*#lS%>{|6~gCO3l%`4{4(#D-s5Ia@{>+_e{%5@pV!3kaFd45h9Yty`@
zLS3@zqUDD_nj-GhW_~uw+!Cgx_o1+UvF@%RjUOtsw|1}NvucG2&nx@h1&V!2?oW*(
zIm<lY$z(xM^2!k6vBG2fw6Utkg;h9Xoz+s&IDu$2EhOLSu{vT`VK>Auh@TPHZdF^>
z4SkS|*^?esev^nB{R$8Dyax?8x?$L)4^AfFWRX|!{#Pa8?=PRuBPL~0C^nG_1sqS6
z6K&PLcQ{&^bzPf=SWJT<U<>oL^0Zf3x?XKYnFqXHGPURVg%fwJ$yFMJx{0fjbSapg
zucPFIH!%<eG7<C4<GaFyRf9PSS;xon&vjzzQ^`tV`$$<$HZ~I!s3VH5gRz7nFNtUJ
z2Cm7dWMaf)HVOz%E4A4)&!!o*)D3s|G8xq1Y)f5|$s$E`e_X~4{m3;xQFB(-sLSRR
z66*3xJAHld>HrpUvRu;IkIF}x<2}@8!e>Kl1LF*%m)?`^-;5eZl2fQY?T_a#_fYf7
zL<JgwNi(viwEr|K(G%Qo%g^Im6tyQmA{~slNtg3JosXDALHo^lR)pS!(BYY7&HTPC
z=^k#uNJtTXH$S+JM}>!ST_!<v#^R~eY&B^GS$Vhr<GJic(P3fs)A{I`qHT-6($_j_
zWSZl8v1SaB9U~ndg13vl-!h`!^<wLQscA=fH%}R`H;B|Q;3E_Q`7$5WZ3>_NF8E9E
z@^RhyHC=48;G$<T7yjoFA%U}zg0CBe4UZQy%kV~t9{>y4PrW(?{hCDo>-gSznBh#n
z&{?A251O+#%S_+S|12@E8~a)(oWyo~pRopx15wy@8^CZPXnH9Fj{geSmy^3-`qW<9
z@W6zPKp&xsLKLvV{A-zl^BFO0Q5z`ImZr2r4|Dkz&%=Jr`O<mJQ^4d7V;^!*`w@o0
zcI~$c2QpAE53gHY|HH;vJWxbz?Ou-YVWhYGgl0@tVm!cC+OR<%4C|s?ZJ})5sW0Yx
zpjUc&k?h70jAEpBA95FuE(AxZZc7~?XW2<+-@^!CD+om{VUf?#TcYu=EKuA<uG6AJ
z_huJF6(x`%@{SX%+1wVAt6K&`YxqGoML83>IEM0P9B6K@jaS3MtoG7*C&0GVtxKio
zyRczh3`84|$qwH%6P^!cwi>y$XQ?MOX6`i}a@&T6#@PCJKn%hVf4*s`rz0HgYF(kY
z1JmO5S#{q1>MeL8Ffo{JcQyEYepy>F53)hd?o;s8#+b<S^rK3N51WDc?*iP-_p~e<
z!&-KYD(^b6wXFd^a{>4Myxy5UcAxyG-{q~G6D?B?UVvq|N%!K@;%mc4^Rm0KDq8%<
z&QeI43-;FN@9x0Yi<d`jnfFNn6TFCBub0t8n11JJ`I27<CCJ&SrNAn?GgEu%oU`bf
z&C7~Mt=^WxyFs|l*C8Tm8g$i<&K|;>iA%zs=?X{BAIdQbgXyxmvDi?3c0Up^5xl5S
zd2=Sal%FX4WfRMe6{9Nh7L=<%qDTZ`1u3RU#iu`uMq%qRTQ%m5sSEClSEy(!Co>%1
zkTJ1H&54RS5>JK06*YM?QDkCxYu_jEO3}xo=5ml~#V8FW^5@l11wJ4OEqT|yGP=I~
z$kG#BCD}}v`jEN9`@EDU{DYGh3M2sI3XLLJ3NC3}r))eKITCo%CGM5^#-u<>;iEL3
z5zXhz9j1i&ZZTb-l^lgn44t2uk1f65TTup@Fcu#be?)z_Z4YSf9n@9tDj$12ZWMmG
z?9r*BKK;GM*e%c~g+_-4W~-ZpQ+4Gd25*f(bD+~$OKox*T#-3=XTWLO05(75uYncl
zGib6pD*vrPn)u;SSkchc{p}^XGey<(kP#)usi?_iFwSOROMi~I<0)f`_g&)FH#A4$
zwlfSq3USRd;qk_BDcfo&*M6?+>P186Jx50jCX?+v>UFO^hk%|nD6o!<cTK9bm)n%)
zxDp-vGREO@q$%FeD2BGB%^M9m=nCwVKYkqw@_f+8(i0QL*Kw3ik9yU8I+2#t3c)B|
zz)3cr7f3cBFI^#F0o%+UHZO96Req@?w%_@Og>!uWOPZZW+`mBLH|E7et9w;gmGt~x
zbYwu>OUA#IE%yO0;fxK6`exbmM4_4V=}lU~`br@A;aYh`6)$E=+vIo5ewWB)#xrc;
zC;C<6u3k6dRv5WSJe&FAjwQcN6gZz^?d3E{KB6`?NiLgqTmLI|U@v@a;D6n02iuym
zjU_k}?>9+dy?V`K64XO+l#R9dHNYgu{~u3gSa$Kxca<LWLsY6d6v@0(=fF~c8IZz#
zL(n~JO++QJ(Kk6pL7Nz4|Ldi<ZWZpJP>K<|%YaG%i_oK(!z|l2RWMqF4vO8=7>>O4
zB3l>-;;uV6(usO=dY2Ly1QI)BpR6yN`pmvd%!Aj1+Qt@2kH2xuPZ;c>u6U`R<wvZI
zN^>kdhmq>#GkM-hPi%^pR~=kr%FQ!_$YYb~*f|0W<-)(98R7kGFXh>KOTl@jt}{&%
zfxlFghaQUMtNz%4J|JcIurh?cDc^blRbA+@9{UrDHe&Lv_}1rhpB&T*eoJAI)zzZ-
zXl|0WA|{<BDIylg#0hrv4kyIqYpDcpi|6!v2I-{Puh+=h!T0L0+^-O;!P_W@7#+F|
z3^jr4F$)D^Tuy$sLqrsZ&FVzh;&1G(zFF02su8x9#JiL>LJUtbY0kE5oj)cBIIDmS
zR^DOyF0ExdABr3^rr3QCnYqd2TNGp9;t%4V567-%5Pfb?l(oetA!uEW553g79)5Md
zp(FD{kw{$q=tzEGFoa3?O+%Ecy(UB6sDbsThSE8mh-o{6CCbaP?6bZl>yoj!WE$mk
zvdRY?);eQ_c*}M6v6;^$!X2v$27fjO{a#q?K8RaPo?Y{rAZ@QFzaJ0UXi^M48Z@Lg
zsDng*DNC6RD$##f(-6}@+popwk*X-@!(qFAn6$;IN~B<=`<1n=4gb*a$?E8X?y+am
z=YHm-RCmKIWqzE|0k{^&ulXT3FSkdFM<Kt{7;_*67`=o;qU!HjVKin61w>o2bDQM=
z;sBwzzBXe_BJ+%rq3HXKPVRyRF=Lfr57%XM8u(Zkof@M+<;&BJ7^2YW^w3|45ZIJ2
z__)@MhJ}<z?FX3oQSHRYOm=Lqj2h$Jt*6easTPH=@*h!>NoKA*2HI_SjTsM;Nvja*
z6P4aMpIeT+=9pwyD`W`4rkf_by{0%8p*oe-$`88iCOn}xAr#1ifUbv+eO=xt3B@K}
ztm@5T5!z4Ig3O+fVQv3jAC#BORsMLro(t0*)h#v-4wnrTLNbY_pp)CMOZdK|+9nld
z*GAwksaL6vRqOuLu&y=V?^X%Bh++)c=c_{m!lHWD<P9$Fq}4wBWS|w%rNl*~lj&}B
zzaL&B#KS=rFP<z~rKrx62i09G^gz`Z;w5&-d)255?M&M-!5}U6UG@m}Lwmn-XsEu%
zOZARV!)Mfvr`yr_=^<(_ng(YXki|KkIl{`GCCfe4mmlW2xsTW}e<qKXM&dwO9o4=V
zm1(Zj(A19yGKN~jDjdf^+CL%D(}u+0F%X(sSxF)bzP?CTIC`haN{T03>c`J^s$)uW
z;vkFes0&G-LNEhpd)P^xXjf=bUEgEDO;Ff&&_p|Lr4XzEw(<=4NR6dP3dcT-uZ@-T
zjv#C${pD3vS-J6+)^#$%7e=>|(=_DyMhw*=8k7D?c8+0%R;u%>ZL!&RW(aUKzuX$J
z>&cFOh)&f#0Ps$x`>vnVI~Q_J<;p*LG<Yd$<TcpMGbq}$aZ!=p^y>DDq6s>txS79}
zq~rWDF<a)awkyc#Ij*e{DBcB*VT_&VLo-A5i|Ods1##^Sk#!<S6=1W8WV|K%x{G?Y
zK@=N(jM&j-bST!xSe{aIrR`TUgn#oj`ggXm15S+1Ku{eUzWHgZfWh1@(TN2NMZxo6
zvNWio#AN%PKog@{zciE$S8ZC@E!y<R4@E2<6vKTUmQHdQd->u%Ck!3!Z0bJbA-75p
z!4LrL8|a}EqLL^ZSp02}(}&h?*l&!S#LC9rp?A1Cmwc9MG#Czz$0qj6zWGWM+uV^q
zhW2X4mAvK?=`_)G2HSYRuIMzu)oK#vlPgo(6@xVk0;^MdqHEAp%cXy+KbwZY-W|6A
zrxf`^E+bLyC-&{w)|J^)S6lxsX%O7&`7tx1a=-glx#UHkgmB#teb;>-<j7z`1Ljn&
z0LkxPo87DHZYW<MmIO!7bKWH_d)+oU_ZE?wr@dd`I4izT+lC|?J)HUpw>XH3Pqdd9
zb2)siTB}NALeJ$u;t6x_gl>BE_Wl`ab8I^P=zR6i9yYbK&9M{^|Am3}9gjoVb^Okh
zp}&}IG^|?jly$y8okMN*?`?ecT{Es6lOSZg)q*aMVK(nC^&9j0CK1csImIgKi(Fq`
zk0`2?PmpA&9y<k8Kq36>@4L2d6?ZihOX(_OUea6K`mBC!4pne(wd)}Ov#^_~q|<xI
zFK}UVKH_SY{+cr&MUozsmEPo=iWlzc<mJX`I(6D-B4~y=so|QZMt$_iPc$21?Pry4
zF!Ri_#B>GJ*{xsB5YY>Bo(a=nxOo;X^CirY!%aG}XV9FeFJlM?VxcdS8Otjyqi4q0
z7mAlIp@YAYHG~bZPBFXc5vNV}AyTfZ`D1n{=_O_2Vga&1*+jchW*r<ZzAcrwFH4P6
z;7ER+cfg~7p!l(F`F5X_^vbd)HZ?Gc@pijr@8uj~rEb%rIs#+vbr?SaP<!HX4|zQ9
z+Aqd<L_d#KO}|s_FQXt@!3HOjZA85tok3&Ood#`govJ8Ttwwb*6UX4Y^%ckXYy@rw
z;zHbFDw9N=fb|?jvX>{KYa?w)n-Ug_kXaGEgq{@o4rYk_(4yc=B;&5({5u}+=od!l
zb2}t2%ntUI#O4s%RrXI3ztuY@E=q(Ak<figP;Sb2=cJ0(5GBv8T!X#Ke1^UJ7^K(q
z&F4a(t+~IfG~1d~aqSZ@=|VIKf^PyxGU%tUez*^}8VAR;_aR0wawYvatq`k*#M4}3
zx=EQC5kUe{*O@|vgKPFazqNFzaQJKcO-f3ZaQ#c#r;JvxL8A8SXEHl{K?CR0y=$^D
zE>3WCyPQ6(d2lptLq0*kT{jV_si&KgIIlmq16P>DQ@+0^!c&1x!IGjyG#jOlg<`=8
zwJ0EVJRCUnI{IPA(6Q9s-Y_EuuX$~_!ZoQHlwnNlKK1~nd6evXX1T|P51MWp@<ijB
zZ&)`CV816_vO4AL;K8*#K%l63j@5ugXfr}PuV=EHx5oK??TrZqyHO?(l9MfBX8Mj}
zQJ9gOhfm6gdX!-3jeL1EbtCS`o(iox89}3|E23M?ynwe1>V|(tJLa?{K@dx7^!=g%
zvE7Gv#OQzXFjq885iXU2dNRM1OHu`I;^Xy`1NFy@2(?GkY?_Ay$09W`kXwuJZ4U|@
zx7%q_!0K_uY$X2yccZ&K!`t#C?q<Cld!CG3(N817nNSPFa+t=G8E7l!*o1ZrZvM6w
z7GjFo8Oze@<WLZ@|5LnHmU&(P3GDJ)($J)UWfQ}g%#SuDtELH}S<DFY{A9von|0^K
zYgT&yY@$N-aJIw~2{|Xn*`8CMD}XIlqdb^FQHY2h_-(lBRgwN+5{mu9N|H$FF^@gH
zL%;PN28Rf!^UPzxoAjI9pzlFOEoKu91s<<oT5URUN3t$Ey|JvNez)_no2ppdg~(PW
zj1x9K`W&*(=9m325)8GAbzQ2Jc<dV+GUZ;IW?APq!?E~y@Ks_`&WWkC()T^-+}j8k
z$a6pgi>QH`yrfr1?et=-MUwg}eykVgvxB-*Zs)0Af_ls00*8TITDC;%QxQ6kXVx|!
zVkyS+)!srkWWnaK17`8=j$4_QB17lMJ-fAl_<1ST5nWDfBZLF(vdGZGf;~g8+}P&;
z2?-_p>n3lf!*RVAu9Vlyd^ZY@#~m+dt0}pcsas0)Ps{z+TxOi9+zyv)&C;Xl-KfnF
zqf+e0V|uI{3mfEa@A<2Z$&|c}kcEHUx7_P4<8xlyQ?cDXYzPZWTP<Cs+2`r!FI0W3
zPSNZX()zy9zR{xtO*35fR`qv%N91LHN8|Mn*NQ|E_{?**l(qdCrdf1HeqVK-rCNYf
zVPA5eQNd?4C<t<eZRD@lelwMvHs>ZC(@e{@bGzD%O-;R4xko0SdNg`3Scsl)S$*A>
z&==G1@axik5yOI9(d65RSMo?{;!3e3xHg$ST2Fw>VCpYY7ridTfLh`rq+e@nV2%!l
zQ4mGbLK17rkZ8T)_2uR8zXf`STP(xHapVS7@uN!UH|+R`sf@1TpmdUCWjI=AN_rF+
z^2*R&7oy(MyHfL$CrFlcWi>1o4aEUNd$!h|iGx*1*)vlgeRv~L0Xx|X-n~JnKi-SB
z%3pI;_^k^Br>MB)_PDa>NoY(_Ac13%Gi+#<DH8aq6SA@uqmONT#(gf<4LZjfD_VMH
z!SVg53ZG!|n*5G%;n;Z7q!0}PtH~6*x10_D>X5-T6ykP2y&fdl{FJjZVoyvjJv$jm
zS$UMrHCXeHu^;KtVWc<5Gs~ZN%cO>+xRqa)@gddiS(AJdaun#bbsbTTlR4nS#h7=+
zGRbWgYnl@c((VU8%nIx!z95rg^Ik5oz8w7W(rNN_Ce*I&ciey%RacOH64>rDiuh3y
zm$p%JMnNbKNr_=#Dd)5x2%U!iY$nTrYIy?_J3tvK>UO|<k9~mkvG-VF6y1LNK+jS-
z%2dH0kxa3aFHfV0YBw+}nI)N2g0EXqru39%k4`LpdDp2gc%=A?yRzsyootjqtSv6*
z3-y2yS0|KKzNF&pSXAXMDr)r+={tY5+fYkHZI=}7XYfNtm$$J7$g9q?XM@t0Jz2T#
zIa@R}|7ubf-=TY6`(^FLFTTSF_o%lAyiU-aEY94C{%!?o3(lfN^^Y6Fuf;ZSMRLLK
z5rY}hMkUH;*oce8+G^K>=?P9m0}(u0zxMrGx_L~y2mxgg7$XI+F@uwm!vkN>mzOBv
zett@aewj$O=#z2x&8$CAkrsq=Mm3KI7vgo#<<baHA6W^zkZDa~!{c|B`)Y!KjhBv6
zdE7|~H@otV(px^IOp&_elnmbpp+lAe8RN~zSPVh0=c4P(XJvdh#Lm(M=G`FzvbTYP
z@IAB8(=8o`4-Gz_n20v^-4Y^5*70^5ED##x8_M?#HvQxXl5e0Z2C&tc*|E<wpIz9)
z>--$`u?>dY8(DG;a?M2ATsgXIOV5IUZnk4Gv@^<pq*scN`{*|2#Dq%}UO7XIm2M(6
z1QGXS4p!akEnB0)j_2rhd6z<NXk%TBmgGXdW1@#4JeeKx)K0!~?*jT`Y0VUQJp)k%
zErZ9mY6K$OqQt%_-MrHd1g~`W`DEM_bl!@FcvPM$`X7UTl4J`m$tSa^O`M1~2bx56
z6V-hUK=|KD-g5AMePkq^T$#ERGebWNmKA$yvb4&+I7me~&wT&&?E=*)E8->?g}JrV
zZ>oukue+}Y?O46vctdt&af~w1gTxABN4*;Mq5a;Q%BzzBvPFte$tR>zdJZ~2US~9W
zdQbFhiPcLUF<NM#{;}T?85|;!yXym$e~Grgn*grtzZ^HZJ$Ci-S)<A1RU^7zsBAr$
zt*$?l_xio!*b>-z68{W6t#bVMvtV-2ecPK7x+OW!{dTGsSz2`wbad&|C9;N8;wtZ7
zO|@#}jh54}A;0!0X9=ja?F!D^IxOoMQlHkw-WtdIvH3-ma}~$<2i)aV`?CMAUC_9g
ztF71TFuERlci%5!SzycP^P>IG$JXTRjKh}K`C#A%`UN8H(~$1pUcMcJmH0R@wY<6c
zsGQ6L-fzOCZM$(>cz3bfnB}_v=ErF3soI1?Str!H?XYh<@W+y(8Bs~XdEUh6FzMQ2
z#&J<_2hZ$-i0*|m`_3jOIfTyeg)-%{iu}@RB!z)UN5|L-1LCdC8%HR>AM1KsSgjNT
zS0pk?A|suwB;Mk32XFPF^%f_QxxvGv1JAdwNaNE%oo*(m{6A&Q2dl*^Z0j~YkLpNL
z{!Kw64V-P^Vg)%qnH2jiSJBjv_-%Ej9C?E`)dC??69JQou`0EU^3KUCc<B49Y&&`D
zPgxL{*bE;#T2bcWHm-7|rC)LSmaI5u_f=WJ`d08xsQ5mc9)3+^Ybk@Y&#%-^PCD&c
zTC&jQ`D1Z&jGm@@|LRF1+rAR}RZV9!+XzKi{W)`a6T>g9{Q(ebM6$uyIwfvXTU-Yf
z&3w=lpZV<eo>lUY`s(?HUuUyAod;nHfD#yR{p*bD)`B^0&YLJ9eQP<brn{LZRA!;;
zWyh};`^6+&ad&S<H`wyZni|9+4fcXL`ZXiXWLm40$PIt@twi@lAPZjXixO~RIvDU<
z_pVwso@`kV?f&#xJPJna5mtWsO^8WXjq?Fd!;u=eXtIk+|C4e{tvAd{6+J^5rm6sH
zXb=j!6$>IZVo-xhSedk)Vi4+6<irm|AXXU7H$6PD=m$u)&Rwe-Pf}!=LdOaZy;{Qg
z<0#U}6w@CBDceufXhK`2u5)Jjj~U{m{S!&E<w%6L1L3zyjqI&;nDF9X1rw;(r20{^
z_LyFq$GxuTs`o;KV_7Ud14;b^wv=y(PDNEYs?jD{ueKSKFK-cwt<qmMKvqJ>U4WlC
zPiRFC$E1%g237k*B}Ge8y&$0Fa<-3WSW0BQ3k|X?B}48x=5D!t?w4&FlAI7x85ncX
zM)Ba$E8e}TKgCi_aQSQUWdJDMN65d--S<@8zT8d}@bvV*I|(TNv7_;^{dScIIU^Wd
zwIRKf-7);!_oC(Ow@G$~QM;kQ#29^vl_#C!33h_N+Oq&FDj~xvy(=+^T`Q{4vpYJW
z;Hhos&wU}!2t>k)(?yqBhe}W{UFxx`o|s)D%v{HFJIm*x@~<{eMYE3D7|S;DnaMrw
zuE=*ZUnn4xpu7BeBL8MsEYyt>edO`otP`OF4(XB)-P)v3{o15&=*ty&D%th4uVBO`
zE>Qh*+WmWd(l@-}j?VLlRsiI|{zyD?8tz3yH2acHH@92%%W%47<#zxozR0z0k5`Gk
zS&2nAaBMfb>v+zO{JKel?D2aBRrU~LNElUn=0MIBK`_<g(Uwa{TiPwYi(&qmEL*_J
zN}q1qOXe>fcI*^G040Dx8riPVzh)BZOcSJJqUoRNg{5f(z<eJLbd0;@+tP<ka^%%c
zEeF(K17TM*OY4(5$iR>lfY2T~roZe0GyKX=m`lsbVdocJ!7b{?WXqP4WS9Z!nu9vq
zm3QeS|E@T;9C)TepAg}iEg?#CMf1$}+Pd~YxZ@MW?-O-LZxk{FrmyZ6P(y-GaD+Pp
zJ=^niw!qzCC)wH9%G`)#;0ki<309T$F6l7_{(Hm-1pxP&yzE0hjZp(WGQaN)RZC@y
ztAAFDUMZq#ow#D#!`J_Ncx4Ainl@*Lh~-fgrGD+zONp1EHFUjaZ;xjcKy7VISpe9^
ziTkJoPT#Z192f(sa%!nr>yIeRUFcDFfkh(U^F2ZMCxBSb1J7yFl@pBk-Z9PmE17@w
z0yT@QS_6;7>pvlrOf2f%bd`mun;JS+jxl(bz8|~mkE7d}F)>x#MzuI}<{q?>b(dlH
z%pD0OflSr;YtlmxbQgdAJgCRX2VsIqT`xY?1$-9gFlah5&Et{ITqh;4nl-zq4Gc*Y
zL`qXoI<5+UAO|+4dH7Zb3kqr?b0HY|lq)&+Z5<NWb4e7N49!ndLfrK$@z&@F5)i>O
zbIF)s!rnUaXq-|l^aQkE-VE6gR|LJ~r+S@{%T-}0oKOY+3m0}M+93EPMX=IlyL))s
zO$K_gU%NJH$Z+9QyB&vOZ{8Ec8@4GF7eTb#-0%PVZ_+B?MwGK~b-1ngO#$t^Dct+#
z>CNa1yNHmXD^N&t*-P%FYAW0-N)0`|=XWH9cPjV*?%TLM8RZoFhChJkHSYiURW3aF
zCOPz->t#k!WnowAWJb8<%61nq;jAQyi|iANxMX#W(Cch;HnqaRuw*gK9<2o?NNIxe
zH|$co_pnJhtL<V1$HJ8Y^TL-mY?4=;gxCZH>V;2xnDBV84f54HZ(fwn`~Q0CLm0Ex
zMRdhL6wN6l%tO9O)q#^j6?PqT3r^Jj{(&rdc$J!cOoot}(Qcu!krX0qjP9WiiAawN
z@em?OmJzHGVUQu`apXf^q97Bx3)SS;xYP4p$!8%H31Yk2aMdF;VIbO+3ECo+TRGKK
z$zhG58Q)lWautfbm)`z^6JP8uA>xqV@u}TIV(K<60LvLl(}XIlJ3b{ROcX5@)l`L#
z4Sm&b=gPlgs+RIrCyl6<%#vx){dD4vGDbt$6IG&GO+W2P%96-{I{(xzOoHg=2}SRG
zvZOAN#)FO8QPSHK$yS!GJBIN8BsiSQppp3rr6v#6Aot;<n#M<SqBU<*Up@U$`LjSe
zT?=O5F$Vz8J;3#<e3>#*GA7&1Z&H4pZOo@St_Yn!=}S@L$o~WAKeJ!s*+Iup{3`2x
z_b^$A`;wCdfS#dNbA^l|g0@eUTX2Y))B$GkG@?E_*+=^OzxZq)1++Xi!$9Qo+wTW`
z;5up~9ag(|s6rZ}*9WQ*@i%49$%x_HdF<W+Yq#!iU);46#);|s>#q`h0c>V%&zFle
z<)92b3ia%J+24lI(DTq&&CLw+*-v~Irq^r7fBa<^&D%0w4I1mD*W9MK%WlFYfF|_>
z<Kc<ICHSwW2ZfD92d-qJ{8c4tgM!uWCDS#<uQ_p|uK;M$rx;95w8T75=mhHWG}*4N
zB4{%kg<I9K1Rv4Ah63Visy5ifO87|%<GiTJy{k+Rt7a?wo~p7zXJJ$kIiqM=8PUkN
z^@jPs06pmyyO_r@`g~Jg7}#rZ=|@dTJgs|+=E$DxtBu-6VnMi>5ExAU;xnc%@oeA%
zf7-f~b(rjG-xt-DGvnr&U<#mf=NuQCrecwc=;r!88S=}TvBRJ7+J<jS06Vk?rxNTl
zDrX3Wzbn{8bhanMIy)Yyny9%!RsiQDm7s#x!UdLT@s@^t&KHXHSGQ_4OM;yAN*OON
zOOjy=&Zcqo4$Gndzvb*Llyh%92n$5i(pbbU1MZtoJyj3tHYKDr;+OpB`2_!MYazX+
zL<+6%G~bw^CHo;6*)M$Z%qMSvd+7JJwV~qQ#)H{D`G~t1l^s=cVhMGQvvs&t%xFve
zm8s8w#r_ewtM5Rz6UOk@kpuTM{^u`|AqX#}T;*Mv=HYM?Sp;D*`woZ36F%OrMd~QE
zDS{3YFa&{IiX@@Cw8{GGaZ}VyVp;NiE^W(>_Mv7X4c2SJ&3_e;9?Ndcu7@+fJ3{8%
z_N%_;hm57NRXxbVII7qy*>807uESZ!QZhLZ>^FB7m@8LX93X;}Fj<Tg?JH)XI9J1q
z)}q9l2fcNTm;_lWI(G1K;?MJi&J#*xsM3#s4^La`0=o+_xwkEIy}my0Bm_@ZO=9A7
zuz3MI#AmdhP7K~Z%CTAGa}O#ZI4%d<Eyv1(*VJfyH4Qh=I|q|QOcF#u3}R-o5v<Fc
z=V7Q#lq=Vg{^p@bLI>S*al*TOO?K_seN8IH|E7ZYPiV(}6UK|0ku~jj(RvPsZ<8uo
zL5Bt0+w7kd3=1m0vu_wQcqTnhVz(D7BEF%s_&e9T68((a`NFDb#YC&WkyaKUO>92F
zB$uV2ya=mjyRFyC8v^M6DF*xmBH_bn6wZo#V|3X>DHGu>dh7NH7SFQTzaV1Jjv@-f
z8nRh8KceQ#C7p7BJ0w?}opQw-ZlVhl&YxY^sO4g!Lj^ZM_$gx+T^+|jq0Db`6!pDj
z?|rUYWwNU6gMIljR+KzK1PyXcD99x@sN0#4s7Nl3_vyhso1;4ls+amBpF~dC0+aT8
zLt`OZdppc>iK56j#yqXD62>!_o->3D+i|l;6a0ll;J=Ng|D#edK2*pz4La)GLTMf<
zFBa6XP;U&(zICZcbMD5Z*y}(j7VhD*XCEl_v&^I8VJhPKG|Pk~xT^KOh%+5s+O1vh
zSCpOlYiMzyY38&U%|eMugh^s?eLicHPGRIJRsMnl6kRu^Y@{(#dxMN1TSPx$&()1L
z@;ldB$U3@RGDu>i?IZ0=m!zuQs$ZV}ei9FPm+MF(|J<OKw^?3PIV8XXHG&Y{fxT@7
z2voK{2mB<F*UjW;%pRa|i6tJB@@|=5e+I^Kbea^u-MRMXN2N=I_>6Z4aro5+Qj5fp
z7(zxth-o4a=C7<*g4u(iZSEiu>;WfLR7;%1JUblVob0m#+#(BLM7$yE6Cs0*a4&@O
zZhJoaBJsaKy8p;By3H)(#bs^kMJ<P=inBl|K9|pa?xx?xf)3VED{!XBLd)^3e9A2i
zZL`}ToAwVGo?7pX;tJJfdZ1g2;$oigpM#C$Gqq}(wQ$pTvYe2`K!(6P@zr+k%NM%a
zQG$K)ivyaLxcL9c0-$Ac!YCaJa%7)g>Ba+3T7reheZ(-GGo2~?yD8FeS@K9XG^Ps_
z+CJp8dfe#VX-w7{i_%T1XVgO1N5_KELBuT)W~rBVE3`bOq0jqP`mGbzK;V}4dXyf8
zv%zp@lriw?ujZwc0Xm;bgmUI!NF$gi>ZMRMT}00>M%)+_3^n;~O9^rNd;dr}3v|J%
zNA~sN+T&be_zWX7A`d16#fJZ8ZdbiCmT1E9^b>xk>p?g0d!06-R3pL@R=gh5?bAVQ
z2{06Lm-D7(+a{+FC&K@a?w4=!<eTx>Rgia`@C!P;(9s8%j5BB{JvU!i*Q9kpkQ=q2
zE7DkRlenp5I1TRZttjR-mwq>IQ=MtLJ)`%O5b4PN<wvYc@R*yEw@@@+43@75J_~ue
z$5T4arN3q;tbyRobmA8f-)icZS7$dDhc<XO0Kwl<s5ZLDv+%XcGgvFqX3j<Q#O1br
zjw!wlW<`DPs*lV(q>?XyZY0Vmo5*Q^0npKeTeE-F!H;czFK7go5oe&fHavlbQ1syC
za7alOvfeVgcimBHz>S9G8gSh-IClqp(?=gLeEe6sPtK=XlMfV0@xEmxOg6JYapF`o
zMz!SWLU)tZXXi+<y<&i157!=NtNs0lj&&EgZCbFmv5=A~{)>UAaqI%mcwC{d#bEnF
zM^BhY_oBVs`j)-)_;hT3*<iVe*D`LCstR?BG5#rBvCC^)O$f}cI}(rM*yS7<f8g%C
zLrVLc^H*V{3nTp1d?T0h#22R`)lS!Ha>=1C;Q#3j`VWiHHS;*|8j$vAT#*2kKLfu4
za#OK<#x#1iD&CHit+fb~_TY+-Der0gRfo0|Dns<zex_(33tpSzZjDW~B4ZgNO{3iA
z$y9do2MexDRJQpYIrnH%!ZBgMR%=I@fqHqW5kd;PF9#)olQ<9r;;#{}@Wv}`42h7~
zIT?DXs&gskFN?cLwE2W3llNrg66P^)#kNlLC1r6c$jO2-MqH7H`wRfOsODJnikTxA
z>MTmR;snS)!GkB`3stE;@H?1*P!P(-Nhnk|Wk-)=OI|2{wBGsM6<_8iVhyn}H;gkh
zjAx-uqT{_uDg=DSAT2Ar7ykKZj4G(L^ly^mU2(*N5~V*`B}#icEMMo`jxKn493L$$
zHtWe`1o>SqWdgpnyJJ7drNs6Kz7WaUXag#a_-#w_4um;_Yn1YjBzVc8ko;oF#-X?G
z#4Wh}q3gUvcGw)^im3Yb?PYdNND%fL!Gg1c?$IvL7@A)(LCuBx#IPiW2I|ggM_Y5c
z?@gCqzTwRr4@)=pP3v((1osUtR6hb&{7(pWp2R#eBz6*KbKht5zQpe)M%*j;GTG;B
z@z{SBaS8RJ`jgK$twd!P3>IqPly_$|cHBes5~?-TXqv%jyk<AOXzs_bz4Y1aJgF`d
zoDWXhOoYckx<0Mxn|+LMSqw%rlAuV**Yu3UB#}6;fyZm&PrMd;rD5j_c)6$fbdY@*
zEa2`$e(OH8_^#S;H0QOXdQRo<zBKijD8b@IT!ryNEQzqliFhK%7B2r{DI8YjK?>)J
z(a0KAH{K0VhEoB9rnVcC4l{zeu<@iG=UI#EXFf^z+o7%J5)p|m39W(+za&x73C_@|
z#iZnRnb2uC^9SfrC~s%{xpj_6I6vhDfd?29R0m=riDn3l2~ayE{y|b?-^J32&lj>}
z5K7GfDlyA@GrtgQI?paf*~k|rQj%Cnk^L(Ps7!@a@nXm~Q>R9YA-Q3sRuN^`ud`_Y
zsm#YUuI9g{SDwrmQ+&F!fjfkc_7dmQnoWuHA;f>2$EC36@+t8K&F;|{C3c9~=9Y8a
zVF6w>k-Yz-t^PeiF}{rEzFH6~@l;Cg6z*H+<XZ2`!~99F@`}aq&+s+K&2l#=QOb~E
z+mLW*C40k5&wJ2CZ(|447VSmlazNGSn4>4}`f;=LG%>_hq`NrVU7_#d6%p^j1`U;3
z)mU|}NbdH4dfEk0t8i0Z0p1dwA$~inAUsrs4x``Z*5uT=6^Dp@38n#04<(l;S8z)t
zsHQ9IBWxtv^z8<&PbmfgPQu3+R9+Mppt|o*^bPSNkn2q#0cf8OwK4r<@GC4ze4*7)
z(%5mCuSUKnvbz)Q5hP}eni;vMnf+OWUTke^8D{adjVOcwrkzUIImK%#a}>3k?S1Is
zb!3yVH`c;kQU+`Ht0ba-6G~D5<7>q9)Yvfh59NR0Vy44EhF%o(p*)Hohy>r}UnTn<
zv345+T&85T@B~T!q3kg&g8&XC{7Ac$E{%2BpXG9%ig+%jT5n6MC~D*GBENI#ZXP?O
z#Nq%~@2@9IhW;Lwzv3KRG(5v?c1P~K=HfCk@PE%e=H}LR2fnNMJ9baGr?!3V_HE#_
z$2&VqM`*Qpw{XqP%`FBkEV-uLHFMvQ3N8-uJ96uF9#a}_gPKnvcWq`#wlu-`Jup{l
zz4J*Vw`RJTA?@!Wt#l(J>hW!n`wSf&J_D`1pIuMT!M^GL<etKS`<N@B-G85I*WyKJ
z^-d=MR?hOsKF899C3I&M^oGM@Gx<gq{ATnlGr8UQTFz%a#ElfimN5d3<yJXz=7Lk=
z%hOR&HEsaPOr2ud)+6zFt@hzV;4AE!b)XO_{a-R@YeIl_GeGXeBp;trQQa~B)c~LJ
zWfxp;CGk0%=Z>E5cy3ZpR~+s%(vK(8S;Cb1nj}_Nwi3ydXvDkiqBXk=e}ZmG&Eh!i
zNm5%*lKZ(QTQ_yOY+u%vNy5|yq<!r}!@`8tO7dyc)YK^D-HgPQ6dSdoT7F&sl-S{B
zQdw=%JCoKM`1d?s%SQbtn~AB8T@f%IrLWvx0)LVg-`T%X{U|YWh~Imx53Hsz&_@3H
z%t#wMNzMof0r+XV_7LD&H2LfsMC(fE1)njN#z!eBw#7v284-lK?;D8%K<GIRB_ZQ{
zCofz9C;h1QM+Em6nndUMOMz2AGKcfo8(r}bP=`CJosiU}zh21|$^Q*a|7G;0gh@VV
zRl}{mDm!bMO;s%<yuzEUA9uU0&9%0!+-kemEpikG88n0U%f9woS7>^)t!n)y=uC46
zO+x0k?_r^%CB0WOuykP=%*AmVkQsf3!HM)Z{Ur#>Jpo`cf3~t+09YP|f?UMDNtMXy
zvEFrOVWG6D$S!lxxXd>NnoNkQX;)a2EsuCyY+z!tuQ>LPaiXmpK9oQ)i_kt&XSFxt
zOTHH{OG~ULuRABOo#CC0AARD$wQ<i@pFfY)O$GXTUphgB^G#N>y<ux5b@IPIf5JzD
zhS3--4VT)yHtW7{mD;CuRKotqk-wPp0zTPXdrT`yP#rP5!LuKnTCd(!#moOzXYokd
zzc4$Bb|ZFO^skip{kITkidK#pseesDsO1t<rMjpUtLxW!R1xCYHac!S*83w4JNx&g
zUL--G%FcBdg(3Stsxv+q3VRdDu$XuZEJzLhp)ZB+&!z~d+{rx8f9w1rjW@*c%NXoc
z>M-4mUTX?JhMmUS6=Bcy%McD(PKdy+ImerUvVIB=JYxnZdAUoLpPN4x<uk5V{ikFL
zg8`yU+9VZw#O&L1gC7K|_0!_K_P0`F>~`KblH&-UB4>Var2n=RcB?<L*z6OiQy&`r
z@Sz5NWH1tk1AOLPYG1u_)6Gc9b;gy~Jd4(D*H*3lE$$8;JJ%Ot^V_}vRbTGwiGKYc
z^(h=z!V+@*4+I0-IE#{iZ6$6bKyL=g!+d~Ka$AQpz##E!E2}73c18Cui9@T;)DT-O
zG4lu1x<avy+(d6z>3#=*uLDZAEND>?nETnpYaS#jb^|QBo-BUWP#y2v$EmigzJ0jA
z&5YaW69sGjms#`wc1?9Uw77LC$XLIwC?ld%R#M(8m2P$ztLG>19-lyF14;zleKCho
zVsBoa{?yxuOa0BTW1G`o=v3JQ?3NE?34t1%9{!Qst6I0732&xoZhThau1%de@s~Lg
zT(IQ>CCihmiHW`pjnZ1iEn%yz6E)AO@xBbMHx*lGNqRs;NKjgJ0*ZP&6ca#>C9qBF
zJciN*d`<02OR_cj|Htc+qp3`XP{{pFE8*0R$4QCl`qDylQyi6CthNl*+OpX`<S?rJ
z0=wCDhAjcGyQYimkx~no-PY%IuZSH|)&#Z%@r}Xa{p&_>QUTOXdeTp{uZG4?zdk|a
zIcI(B|7+ax<DgB1EYZJ|W3hRHMxa8kM_<JnZ17>(rec5dk4n$}5`dX0e1Dt(ZV$AH
z1a1`%ygD5{arALs+_ga_u}K|VGkH-Pj`9qXlg5MpOE~zKxcqwzVI;=&4(XvveT9Q&
zG2l84p=oDb$QhYo*0j$|-mT$6iY+HB5zboab#lXlEnYdc#Lh}pZ-;Fgs_N+ycFtC2
z{I7I~BnV47UkXPAg19TuiR<tG0`Gxtq=NQ;o;E%W3Op-OFi}X*;@;1kDpl=6V_`|1
zxr<4}nEhz?UcfI0JN$*)+c<3<x*;#TZ=O{DGm5d324OMmz`88kf6BB~aDWa+IVfhL
zwrwQP#3%rm@8>8|wlvq7yvC0zS#z+ReqE`*3-|<j=Uc;dd*>AVG2JL0w-MU~4S7DT
zVDUUN6)zJ#Pr8{)7szGNwoB!62<I?BSd-3a%+Z7>>*=;0?ZuniSDrS0Cjgr()>zm5
zdTCYnn^2E7!RvptqYViff%Le<!<E^Bj2BQuCY$gz*}lomREZU?8>%R*qo7+ktda*c
zAm`MjCnF=XA%WF$Y|^8g98H$QobCNq`l_613d)d5(W!d>oFlC|(f|I2DoCE2d3@DN
z_%OoFOLYp@xnpmBxwL-W?REYtiI8T1Ad{4Xr+!iYe@jGdDA6nqLyn5W+P?r>bDY4r
za??>5-7jFSRTEh9H2a|UrAJG#>RUwYEZZZ%&q@YXFVos7TVH;c^@hUs&s`#)iAeiZ
z%_dYaDX~^mvg@2hw!IS*c!q|_J<?7e>Id8qZ`u~*_~NoyWhd&hL^&eGnp20I<aKMW
zIu@{TvtxtH|L^43$PnY%&k!iUO_p&45Q23}-urXHh9hAh3OE3WvGWn|IGq2D^c$`5
z5oVYK6j`|=s#Ur<^U0*Is(M`@+dDUt^0V^(xpnI4-<NVMpE!JaBL)EnNjLCb#z;}-
z#sQ&43D5=FCWc#v9CrEU_aEETuDUk3)aDbiAs!^h%2~Cr(f0DWqUHYgtC0k~C;3Di
zywvPq^e=XAo(zD~0YpRTn@~3840<5`eA9QIv-mj3mgmd(&);4%`^v59k0}Ub;?BXK
zZ_6VqK6w8z%kHBraSdSPQQsQPv2hc!bolt@5rCqZeRy2}LK8IoiF3N)(gEZ%A8_~;
z)3`pc0%h}Qv0#a%EiMND2UQ1<0KfQO1o;C~=_bHQ<2CTK1vJ5$``8$rc&H8UPs|z_
zQG~TsW4FKfNWRAWnY|s75?@m@f^`>|7-4vdfYoINcI$lXuaE4qmZXQ;RG?0O<Ng0v
zCO|*!dKMw6he5lB=VfXy{PUlDF%2L@|9InT<a79H4zTvd9drP?8utzWSTbzrbIh`Q
zw<WmwkAJ8iaDUEvyrll-4?2+;JrL7BXE}7V;`{+PQV>YTRpA|gwMwQi^puJhztEin
zFenzchs_kOq=ub<d*c~Ngm2N~S_~u&1c8m){xckG-kpx6=iT2~BG*8W`7TttfS%*p
zRlxAbbFGlfs`uj&z}IX5zCo@gu-Y@Kpe_?y!o*M#693#L&Mf8966=nt=6G6xbk>E#
zx9WzJ7VK?ni(Ftj8BzUQF^Msew?`;po4MevRvHUt2xtsQ@!tYDtM6Emm|CH|%Fubq
z!W*O6VzWbQv)_@HvW9eenh|Dt|2@8Uy^jW(O8h6rTjk)hwaotBdjN|xha>uW4_o3Y
z=-$9}LUyYgy0v4r5r_^Pj~ung_}{IAvplA8bq3;T|9PDYy#Irg3D|6qLTm=kCoFaA
zs-n+?|7l3L@Si<dsxlCz;x&&Xp!`S3+tOkKkNNy#wiyKg>`L8*(p>NIyR%Y>##6v<
z(tmaY4AHGj31D+Yv3<ueYQ81V>f1mBki3Y$n||L1WGgdx=fs~R7tgi&T%LZu@jmOp
za+9Wguua?R>6ye3cJM}CD)CkPD0jfFm`YDwrnK|?5jQcDo=;$)C!?*!u;U7g-&G3K
zq9rLa6f@Nl7ufv_)7?x}+TRv&ScEwPYl#fG$~xk~Je^U#%;wZTa)!llpQF>}x0VV^
z&+pqOOCQ=k*Ti0qBMhc39IZc>b9m622v51|e6RGC_{ym?y7292$8L7IW7h57Kj)}o
zd-ihYrq_Q)XLj$KPQ0H5*AohNEF?aa|4u;!CpGUKK%~?GMjOTTLH*c96kG1gYIgvo
zW_$F~<@bprpL~v^d!DAUh5Z9^b6DoQ4oIp@=@@oBo12$Se3Ex3iXy_G#{m3KZghw_
zG}qU4bqI*Y>%RzL2+zN`3IFsI<};Og&iz!D8FtM}-W7muSM=j)s*a(a;;2Sj+o0Ds
zA3LiMXlmvjk?g%q+mP>E?>mPC?{Cb0hV!r68ceFFiwK$<c;=(+gytK;OxlfR8&60$
z<O?s%3@edvi^h!qNJr(ojv-!7qa4j&mlHT|kz1)DQnpMGwCl}h>;4<f{mcw@Ff(cG
zdK??weqrS9hFksrN7q|NMfJz;zA7yZ3epWq3(_SZoq}|CgET`oBGTO@Al*HHfYLB@
z3?<z)42<;Ie9yY)=e=j$zgWYXKW5GB{n_t$p4T%Zd6$&Psv?JVF>vl1lY<6=B!Z7j
zJFsrPIuOBqvW2hDhrZvWhp8JH*a)~?8MUz@8hdntop|^)N~-N~1*0W{)5#S73YzSA
zm7je-0p?*%9i-n=kp-A&Afv(WB~#K>4soMWU5^Sy26&xj!GGhPk+y+xF4D~?8PP?*
z=W^Y@;%otKeureQp;y#_p&LgJ3lM)xlfM<x0qc=Wx<77#=p08%uWKov70r2RVIyw*
zahVeFM6oE1WJ{i;WUWTakx2AUvF~TOffpU&+64de!2X#L7I=<PJXMFngWr7^{2Ok_
z5d!%}A*7kYX2ckRhRsXDQONh1baskXjRdDx^OxbVOjDQT%|4Lj)89nTi&Gfte0#Iu
zz{WYWW@>K%o{%ORWX!au{lMXKx33x^)&UgH|KWNkz+v_^-Kb$~4dpO;s6N=M_8X9=
z2DM#ms=R2V`J1#T003pv-#I5>kqQr7uV2k9<nndtlP%+rPgivHX>KgHxtG6O9q&Xv
z%kz=Zp2mjo<eZ~X`Kl@lY`w56w<y>2*fgQJk}zT&Y67kqviQp})fM?I5c;XE#F*R?
zGEC$wFdTzyd@b?-<hrAyH*HpR4P1vTk5TXV?nI4doI&`7vC}x)6%S{z0q<f3X-;}0
zaEU36)qc!f^lxNDy_a0A-oMZgbxO82-_l44x9Z*?wdC6cnKex0p~_f%{qMUa?*97U
zr0Ku9<4;uJlaqwcgy{Uf<^a#B?r7Yh{4@4YdU*SVrrt1+k78v|$lgvG4^$PjtAG3W
zDn#{%6AyL<UYNlHPP@@Gg`HM}TaP{GNb(t<rC;=ni6Hc^W}7=1fCH^C3!vRff1lE4
zo*orwx&sbm-o-z@X8keT_)sju&0KgNnbZ9GiegB(8OUE(v?6n7>7ZWqug9X3RS4?z
z`K_>EojF+a_T(hsq5T^Pci%5zv2tZ4P(?a`_ghS#YGZ7zr~?W(%Ii_tQ5~yn2Ce&b
zc_d6V#r={M+W|pC8{vg-KfLCz0Jsv<p;L$3-Xtvvlm0zl*HyGTdcmU?eHevUpBhM>
zXTJ=ZDCQKu2G#VubGjEMONP478H7o#zv9oM@KLR9SH1@zCn}cyqEkd)H%u6(bFK~m
zLLwiJjbHl7s7c6H*17$Sk$|^t*}=6!mkiI$51;hrkBAJrPtowy+M#+?8M~_^-BPMu
zrF+#sJN}CP`e5+2nY&94<n7fIBu{C~fKeC$J{<u4B_6tI9d^yWKd)1jao7QBC-&Ug
zqdB7y-ouHH_F6t)9X2EQO=nKElS6F&Yf`;a|99hhPi++x$IbD0(&~(+sPicpX&Tzo
zVJ07f8&S#+S2PB<L?T1gPamtaYTqrE$$URFJ*FsMrhcysoe!CzCE4aDc_?5;<*e7-
zmLoMkEIdkdYmCKf+3SG0>pKns^>1gN6CX*kq!?nr=w<6_Czr2;X=Pc<$Zw{YiO^((
z)a0;k%R~G3G@8X{`q_O`L<KNu8Klp6$F^m}uSK%~5xuFvpI-J@tqkpL)ip2c6k|N`
zVwY^iRc@(B?WU4D{(IqDPPogyeGvOeGxImBM<lr1|L(wukNk~fE>-=0y8*jZ&$T!`
z-(ajqK2^T&^^(O9j<XDYjw9A~I441`KAZ|@zSKT`G&y=gZF1sO?0jDT?8fLGM%)T?
zaGPJ~VmL{*lOgHc{qPzQQd}n76M%nYPRHxjuwWS-ipWCaRQ#s)ep{q9!f!93sA-uL
zIV*X4q8AC5x_QYZN(5Xn?>}!6vGDwC-W%AL4mmV0TSkrLLpoo1tV6YWYvBuq*w{aY
z8jGUwfDnh5Ct9zy=FYJ?G&IaHG)6acdKvFol=kf8dxgbRsLB%+cB~9a&L`XMNLx8U
zom3sHen(g2cFdStw@X`hX=8|k)3g-q;kq${m00$~&P(4Ip5!nW|6`ax>nLKU+qIl>
z>LPAns9d+6zJyXket)O+i^Hb)oA|3b6Ak5n^T%f1J@v7tCtmz92l<5G1m@pKW?{xr
z9x5gKQF`{S=_8#mLl`j7V8|}J?^#tc^1rgk1iH)28kgB)(+T_H;~s<0?=MeQS6{3J
zSw^tNGBUbu4#X2(#t55}iH5pP5Y8QXT_on-{vw)V^u7uwbL7N9=5w8l5suNQa?V<x
z5!?Hos905ucwtWu&Iv+BCH<pf^0#U3_Y?$2KXif+1zSb2q{e8Es|#l}mqJo(l4vm2
z-_af3A*AT2<;~e`@!HdeTqQR{$-mZLzIqN}1|@<`fgHQ$Fk)AfL-5>Q_>`cRQoqZ~
z9xFe^$0pq{X@AU2GvNo%v*H10?5_16J>qB-=Aq<;B-uf#u96@;^vs<g0|4?2Xc5yV
z$WEwf4!^wy&SzS9aLYr}5mc8b<QYIPFB!curVnXarc-G_TWomi5?bjsn?4uhk2ZAC
zw?P{IWOa1%#5iQB{$2XEz>f@D$oY=dSzR)0T}XX1d19()aB}j3plm-{z>e%0OD0e9
zS9hVo6}@Vf1HeW(%&mLl+Ss&4ctiMq9%+p)p9T<TiTE_dYx3j9$kKXtAv#XdJ<0Aj
zZGHwm-99xJ;n=UK;mJa#{N=54bJEqNw9GJ_EwJ{PRAt17sW$VaJN4=*SwV&YPK4zY
z<Nnrc4XA~%>HRp(3jL0jNBni5HMgh#V~kTH7(KRM@5ImTjBgd#=L1N^#l8%GHOR&F
zS#PXpk~1kIg!)-lygwJy%Fzd6eH^C;fRyYgczUgp#wkAE!IE|TjnyG9!k;?ioY-Jw
z3c;J+^>EyNLRy7IiHD&I+;UsfAu6<z>+>ZlRP|AGw<)_Qs63yU3A=v=qI((h1)R>*
zR2Tm^eJXuwv5zOJvKnVW{e*1(nk?q4eh9uB-;){%<3Y3+q=iUp1K1Y+lC5uY29Vy}
z_e__nGbKi73$+$!jY<4q+iO26W0cctkb&`1U*Cdgb3^@}K#bDBhH1nQGK{CPo<u5g
ze7UqcFK$vf*>qR!;1&lX*21sYPIs*OmKa=CfDGcwlM%wb(!wk@H*aZY>W@OD*DN>k
zCF<U-BRvIaxn&zOmC9vUc<`HiBZ9GnVnz1W3SN%aRrjA>P7teH-jx+o5T7^IS9OeB
zEX0qH0{H7Wsdw3_vu1NVzAZ8PA11ffcy@00*k8c<KEy|err@ux=!ZMDD4%(jmgzEo
zSJyPuWi<S!qpc~X{h0*|xIfQ@MTp&YXT`@!61bMq?27skE58H!+be*0ZGoe-BkNUk
z(ssb!3QX00H8+RHZ3Mc>lqL=<@hwlgo!>uy8j45YFcPD6{C>uLQz?AcYM{im{Ls^o
zkVgU|l71+?v{zBGPZNqj7^0$3`u{b_WQirelPo!BmwjV+4|IrvA4z?C?!GYpWSo5D
zYOmg3HjjFYJ$@`b|9RZuaa^XI$4sfMx?%SEAlzcv#2Sp96VJ|bigtGY-GdpC2XH^_
zQ|d9+wEj<aS=}R<R-<)5U$;Pn9{?oKh90#7!I%<c)+YJqoCdC=JiJAVBBv%KJI3#9
zr(a(Iv4<S7V)tHc^Qdy2H_Inb*YQ8d6sLEnVSN1HxA{`+8yB1_76vN1Ilc*VTT3!5
z0~=k;*Io({FZQB8cj#P9SjcVlxIkk1r6qo1XgZ7vTs_~sy+_rt$iA-sq#>6QFPF1e
zWB*&e^$&;BP~{0n(wgKAFm+OLAAZkomht(eo5mil%Zjo#(+jL&5u>h%K|UJJ#e)dg
zi<0VkU=RWPp83b8<Occ?T$}OwXglm12~{CnYEAYQ?UA2gdv#S)?qOF|+`yOzl5pOn
zzhc3kpasPdP;yuOu9Q2*1==QGIL_cR@A)k>_;ZEA!63MJEf1CX>%~#Q>6&0K*o*T2
zoCRNgW_%0m!tGI~ZlDK4=(AG1s%e%+K$a12nvxwC>WW5}rNhyU!2R^w)rp@&*VBZz
z)6Bl@in}?RN^lwl+__%?EHqs=!OO!1b!GlF<g0F6e`7<VVj?FXRe{TToVpz8sy}~E
zS;;Oh92XUz`sRZ);fnIS@O5pZsM;90W6!=$8krEr43jPitE|2mx1O<N+8&=f&CPBw
znA=PIm56$5<X6VFbZ@Wg+U)F~=m@5dsUN=oEJ`(GevAr0srz=!>j2dzIX`V>gi|fO
zLO13_;)>;?bP`jq=#E|PuQ~GUy2-|V(bD_M=QLrpcq*?px3Y(1`cEewga6}J%otrA
zMRvtVtgev{f71(5I!ul1<*d~8McTL~Y&QaxX;(U~9Q-*Wn}=Y<tpn~}knb%b>ayvw
zcO7@x^ZsKo#ol!l6Xk;pdZ=(o6=1FoB7~^VeDYcO@^)&ouX%gGWbrcj4XHqbwT?*;
zcTRuBz6R+86}LY`k(b&gg;-y&qlbsZFQdbIG&X<HamNH{X4(_~>seFc;gZVpIensG
zjl6j(5so1`y_%BKUi~)_&s0hUU!%LXq*1&%{lt%Zqr*DM)nEA`b?&2h^XmQ!*xk~B
zc9Z{$o04ammMDXOqt`(PU{HYGb+cI)GaYyw&~w?<F5Ru-ZkEV6?1{-u<EYKYSy^p?
zbcFvIE7dU4{z1+jw?Sv2)Q)8gHzzobiFjp-{;?5(c{C9C_311HxB<^`MbZeg^>Xn!
zDc3h*vT7mM1)Ym!48adccH3czagt*6iCnWt=N}gFBqYjp9LVeTF548Z(T!-|kx4+5
z_33*SY6n-7YZH;5TGVtWrrzws;+zplbgsay3k^gHO|9`le|w3Sp#1QK620VEO_?wT
zfr{64tUsLYYAW~M-0oYg5V0Q<KE;!(u=n+h89ptMS&X+7)t6%TXSj5`ac*<%0M}11
z=gkD}q1ESS20IUM-@Wm-3!>x{SZ;W0Ki@yI%L$4gbQO~r*V*~TGyf{??1LL0(p#+Q
znq8_=6HR9X(fV83aaHu!rQK-yx#l5nFG{u5vlZ3rxDtgqK!`gMS_9qTgqKR@-D;Wz
z(_#)094vab3r2&jDIigqA^9Qh{ZH|@ZsRGbAwP06kMk<%#elw>MmW4pWcA`Nj|@uT
zyb7fGQvK-raL+-5VRfwICODe;Dy+_J)quT{&wdK7)^$oei*Zpj9K~xRQrRnFlpipB
z(Ff74k?=U4F}&xtOkve6h|=AU?I0>rOU8;*9{e@@&kS|e4LK{R&gW&0g-1#-HJ4b%
zX8wMx*kwyRH?C{-D^-&NhmOmAJEELXoKIe9XCHiCUq6aa1gWFxah)PQOMV9hbhj&h
zxdG-E)|hVG5t&kT7*l<y=%OaOBVqlCQ61CeZ274k)D5geX!*rT;Q2U%0p1)(ruKYM
zbXFuHV{udb&--h?8L4`XTJjT{PQ8T$5Me);mABW7+-EeT$Ev3c+U7-ZT#jA--~^#@
zxV<g$gErU@Igb~)5>Jh$xL5c|!+bWH)FU=|opGsmc1y5?Ub)e*Qr4Xyx|#RT{HWhj
z|NMihw-o;qYjVu+49g|7v|D619|oLkuhD|82^@-~_DAfEW6g@&Dn=&3jEAgwiz=*h
zZw6SBD_)MSH&;{J;#dZ*ladhZWVPwjWERkBxst%1w|%uh5%4#=Xy(QLyzFgZt1k9)
zC8^oQ9aAI9TjPgXO#L6|;v3Crt~({yDaDr4UMEm~m1zX#bHW+TqQCjc^6+%UaB7`n
zlIGQEtn`A(p2!)wJ<oKBi(uKwwFY^??;xB~ljE0)O_A~NqHFSoX_O6J)~UpT8oteb
zf?v<mbwKP?vHy(ZD{Ro;FHyV4Np7nUmMNzRg(jfXr*n_?IN{CSP_-B-3trLFfu|3?
zuh<)XInv6#8`{kETF1yFJTkNaH_)SB1DafI`fxle0`Dwxm*|pIu`kS<E`4wkuKT+)
z7l4jWYGd^$@boi6Ux=p2QBd4a2ZeGSBJ4nmgf7J-oOQJW%=Svy*0<^TDq{Jl2u7Gs
z=5b1Y3n>SmS~VWH?$i8QVPYCP93A_5`Qhn%H=TiKlzs!;?`h-kgX*r80e;MlAZ$e7
zM&MJVZ}c~gCWeq$N1;0USmiu&J>gtl*|_71Uq6x30QPZD&$&)wVCC+v7|mVihEFc7
zI7EVTyAmILlNL$>wrGqy-xNwGOO>yOE6UL>y;5POkKt0cqVkSL-wCs`qv!Xm(y`Yc
zuHcVz?ih$WLY|Pi?GJI`2x-sAzRbU<pe4wFgHMG%2`3ofu~j+mv}wEI!0ca!Ai-8*
z7S`oMcoSLg@yqQAUq)61=NSee3I3&<gYUgnl=7*2t}#AzA)6_s1H%M)zn;mQ+}Nfg
zW~@H(dp-1W|G>*FxX_Rr&rTII7k759Y9k!`&u9ZP>dA>=96ynZSrC`-OeiPeSWF2y
z7wSSET}(f59jZ7(qbdw%jb166OM{42Q6l$IfeiBH?7FR}AFD%|PLqjd^7sn`oci-_
z#mNXGZr7F@1&-xM221X8I=rD&=fBRF>H3|4a{W^^FAp#glcm?8jzQiF&8r_09_(0N
zyD53Oi)NmYyej8)ikRl~K^NucJRwu@xHecRM6FO;>&1A7go$OyNfC%LpVq6y-|ke3
z+JuFSw*pym(X2(Z&7(P6;tS+PoLHFXfyO!v+aUgT3njTjxNc+j6rM{a@pu5^zFo<P
zdNOeLDXNg~X}&Z1^}g>@Ht-X&&*cXUd`<B?t%~O4<)|&hdmL89_#_Y_Bjkq!^Vz|8
zor2z*Peq2B;t5)&MD|J#x0o4KQqD@H-&-~%?1EfrI9TgkAsj@X)kxJGqFTxH6m{ah
z5K&wk+QOv#Pzs_j#B&CT4!L-|Dfv$-UtRr#o91Mneq^oqqz}^Eb@^DVU0z1P&?bL`
z<>0X_%;%rW+!%hDv`IADaDDx;$Owgu%?RS~6^Zy$jXcUSgKY=($jA`pF!xxg#7#N(
zZ@^Z5W6@idb-!Jisl|T4zV-O0DgL&22EoU~F$UTiYNgU~Ki^I^Gx|Rh(P`u8L^fF#
zN;4wdMPO<*>S}jVYHJjg!ZvcQgwB`0KB8p#(mCNI;(q9h*0%;O;^pm9JCYOTI3J14
z$0&Kay@xAEz_(Kg%R7;j%Ab#TWK5wF$G<tfPoB;JTefFsOuv>zS}fo5P;I}T@}}Rm
zq4HdE{@smo{dol!!U8>hWsJM?Cy>eKv3cV`pq!6~Y32TSW}g=gO*jbuR$q1lG|JZT
z&*R)s(S7lLdhV^0VohN{1XkR(TN4jgcHiEK?p__!lq@~GJ|DlV=thQ%R)wI)xoSpO
z-8iA53gc_w7x$%y7_L$sj!*87O&kTx3?^lsT$_p6%1qXl8Pr)ug0-y%u84nZquR1#
zyU&B9g%%E(@L6B(U}lYcJmYYwq7zf;P8B=XPj|BPx9r&LFa98^k<00OGj&a_U_PNU
zV0NO&QJ*{E<|DqWD6*?ES9;(+9=@n{G*JtubGQPzSl*olyeQi(Ev__O4S^;2$DbT|
zJ;0%Ady)jgaXKhEAo1jiDeq5%YU*Z&_Jxl^qAq*w<f&D#`}t7v(w82o$B~sO*}?<8
z<yt~=D)A23Wvd}xgU`uO;1c-BY2%El-X6>F%$`z;o1RqzL1C-G%}E^t7*r8I#ZiUa
zkIm=2e06HDgR%23k;Q?4jt~cvx0+I`fPnw2q2Zj$yzqIc&XbsNN4NMaYp2$&l<kjJ
zPq{8BBBo*e?QDi_V;GIa2KF!w$Z~iEo%@|g`qfdz2bIt#fN~J!66#Z(@Oy1MsAJlM
zU1Y_!=hW}Mqgnh1-(qY~EaW$$E+a<yXC!i9R^oiY*iKSCIvFO+nQFdESaHP9>hoi0
zR$a$LZ{50w+gJR-w^sRl+p%VJe$RS=O;q~PQazB?9&Y<p8-y+rLO>&cMo9ZacIBjx
zW|zUTFY3#C9AsN6euX6S0r4-STvx<(?1Boc<Ct!t0g+6N37cxtl=<U=nZIL_&088A
zi*m{?qAcXG6uy|2-4*DwUFirm&`m0K|5UCQqAtl_sd20|68AA_onzTX`PG!B^&_+M
zvTd#Q1FC^zAr+EPMMa#`MV-0C)v2&#N_SL^A5|VF%*h>7MQu=0EwzMY=$``gRO<bc
z$w9Cy5fL4|h_BXKmM7AeJRctnSA^718q3jQw74$KDxSnXzj%07-8^Y%ES@WF5CS&Z
zJikN~Xr@<9^|0H|i+(!TpF1yXgNl3FS<geln*-zp{XSX%`;(g4yF)rt-<D9s@`R=p
zI$_EPcvGJ#dywQ=FO(HkR*dC64Z#Ewc2a?L=_L3eiQ6gLH-T`rqL%2(+rgdI5T+cs
zf?KlNcJl)e3f1f)F%STo%AKEeVrA?vJH>OSMb*~HMOmlbjq@U)Ii;7_)``yvJCODI
z8!6GE)U4AEQ0zy|VknQ#X0(jhfH;QS3zI-;w74Y?Ku1J%`}Z+2>Hu#fM`Y3d=>wV5
zZJ|Q|T6hB?Mq43KtR42VR*8*+$;AqzFx~x<@}Ix68m{mQhFc%RO^K_t8C+8;lcd^o
z+U`EvwLYXVxg>wrZ#DTC&Na6KGhDq8a^7sS)hVJd@td=8r{27#Vo88ZFbopUhad0C
zquc0qk(wW=TGd|KHC;vtu6VdqfN_rMxQ>hkIT|XHgO{WOx?jD1&wh)!CA@l0Ak?`0
ztQBYR^`29py2Y|YE*G@iz69RsWgr%;p!jbIlZ}3QBJwNgYCBGVV8vA>DJ)>qsVXWo
zex5cuY*sc-ci@;VR@k&WK=PfOVXCXF*j1dQCV`dnI|8oxi8?8T%#s)xlAbnTNuu!Z
z^}b1p^n$v<>lmZm(vFMZqHc<rZf#H1OSb%A0T_(h(+DE|NP3*%)yoL|B7ElGbH?=Q
z?QhrUJJu}eU9qOO^MTnPaNZj)r&yp&l0(CYGdDv?@8V1~ovn;Ru8!c*Zj+HrgOB-T
zkLh?df5?$A;1B&`_qAK~`{0tVU!P|_KyvoH+PhZ??~H|-BPhKGPA{KgT)zEp*W_^X
z^C{+&1mB+)Kn8J*Y$T9OvBMh|OmX}6mGkeHsf$o#*2{isrHv7BgyC<q4}XBW&%H~X
z2Oh>Ol~5(-cKh?x2Tex_ozx8sBVYI}|H`M)k+IS}`Q%F8(rPbfPc%5(+(u-W6MIlc
zZC@=%%e%s!V`67gN`VKdyhXa_9CK3e>2h&-`Nt3_5BH>q`<Z;uYpqL^Y!tnQUaS<>
zIe&NC1x3MTYi5ND-;7xdbv3?`H`5%WTu}o(%mzpXD>)@}S@O4w%nBzZZK>tyj&EI(
z{Wi<?c?9yYc9I2_az7X)#D>#{x!0Y8EiG(<OZVq!zkrLzwTYwq|Lr=SBYPpt{NDxS
zYY7~Zf5@0th-5a|4i&HW%48Bg7%_>MCn!^b39^UmWS)9{sX943;+hXcWRE)$eKo^{
z0osFciH0+K!kAHEmwVX(AA`A*IX1nfy3Q;hCGL0fNW2wwkkm@r>ZtJNggNmCU?Fe3
zcCM_i!x~%Ty=|}&Wa-Q+`}S4sx?ujv3h||Gz;^lrQO9LfDz68iDL=pUkrmh^_t9Ry
zcM7=41Z`t1Mzjz{KdB9WQqLi{Nu@}$7iV4_<xh)z>QY}VaIJln6MBdBJTC-gdBtl3
zo6PGyuKO2bV{t)Lrns;r$tJ&5pX&k9BS572ZZVGLS+IY7&XYT1e<7Gs!&(JGiJRX4
z12QSu)9v8X&&`g7m`u~B<}j4?iLEXm3JU5=WPkDU71@wur+H`-XEg&wSshHJD_&#n
zzUq4^qg4*Ol8R{aUnI{d5vSp1qg#EClPMj>Q7gO3vWkjUOC>FdPNil|m%Fzi=GkMF
zTgh*8hgUrPs56@#7^9LPjKAKNmbW*_Vcr!|;?;IFxquiTa=Rez8X6-peqMC}iOKxm
zalPANE>(INIgGkS&6zbG@9N{MB|+8khVZGCfmW8yl)LMJHKB_X$PQbw>v0ZswTcm|
zh%T>2^#gM~oy|+z96Y$Q?SLH<PD=Ew1ILMq!Ry|WENUfp+*2akG>=92WQ>caPC)><
z95kz8dW?5&YjLnZY{1M@uhIu@nO&rCLj)U&IN!WiA(HpxiEFc!uZ{J(=`i|(inF_3
zt&#7PG}SeJOvJp@^I3j>%EMw3--boSR<)~6_{DcL@k##%b+Sx8BlaUJNaBNjU1o#L
zOpiSVgPnKPGl#Qx|ENZIUOZj-D9IHGv{p0eCvetEI<{OHD@qbWX|Khy%8nYpxdRyv
z1tXtMs>*DF)UT=2zE~IGJJZjrdtK+!l6NdX`*f})jubLUPm$hu?bfTgAH}H*MLEIq
ztn$gu?-7q<A4a=4%mpQ-x@c`p(-i+VB3R^r70*uEhbePF(IVw2i4AeQBoBSvdpN7y
zl`(YfhOD%8H4&OtmDD7UmHOkby~abE=DCLdm%334@3!}HFmr6W2(RjLbJf(R@e9m+
zN(XLg5>Tm*^9^@yM_He+l&%oU`BKgK4`@i^*p0>66AGFGFa}-ajeW*d%FIum)i|Ty
z3%#!w8?4!~XM9AQx7E_;i_@z<KtJ$)5+brqHvbY>=AB>aS&{<nvYhOY#AH}wvB;={
zT3AY|lb{_GQ&>O_njIWNvl(afL7qY0gSFM;5VnR6#57WuUF@t&-~G$7`qBnJkA$V`
zG4j&g6}9aH>)-9KY8q5!WJq{F=`{+4_kLnKDSp+UzGb#kN`9x2E%^!JE8<k?@;nM$
zuZpH_yuv&ucQd^@l*mPkD$T~6R30tTcp}n3k*7#7o5){QQ?yAgd@IwYiii8}lf(l3
z{ja}>m50%tHeQ!)leK*NAH5>uG@k~%MPTKL`B(xgXTCRYho?Nb47Gm9=r{~6N<<(c
z3KvPBG3=0?C5J+adZ`1D#MlMMP|3Seadq)8$1>xbBU4+2Q&>}(U8-P?qUYL5t3f`_
zNyO+&ttDA@;{Dpr^BfXm3y`=`M^5umKPtnn8#MY7l~qL~OW?Rwt8{T<HRw*e8G7{{
zv+Zg8i{M-pahx~JtFZ!Q(>lKZWy{!emrsDQ<+7-}KSX`GH>dZyK=vgCyj*^0Xk>sz
zn2(jHZ;lF<A<H1+Wx$=R6ON{fVNS+SHym}|p00;2V8q}6??>u+E+Fc8t~}XZm|(!L
zdIHg=ZSD|LX=9cP{eyF`q(;QD=jSx5mOa&}7Vc)v?(?Gr6hm+vyThKi)}g02e^n@7
z0a>m{#M)ia?F(p<t)dW-L2a1rc-vSR@2WXq^76?1=Kcr{(vlFgh`-%JS`JY^(hGOU
zNMY5ZQGnkD8?-6J#;|QIAa;gC2#JTJ<>AD30d5Wo$!Z%H4;I51@^ghl)#Iy?9Z8Cw
zpG<E?8W>=s_YM||{zseVSHoLvo|eOE4uy@g*`;1tO&?`Q<|`TE54pVeX|K*hulf>L
z1P@e*AAY<Jaje32n?9(G(t0&0>W+lHFlmjv?+a^n+<EJt(<q|oJYT79mtMT-J=C)W
ztEXs}FX0{C`i*hEd9ok0s@QueHrX8o0@5%X96Wp7-Ha4}BLptS#kg$Z?F%lf_AdA-
zsQLXr`z%zb==}~T66SKu5ia?NOm^_3&(t9i=LP~^wQ)J>BosH{VwCo9IePh(#Dl=s
ztBAV1Nxdv-x3W?~WXO!{O8n;k-gFN9WgC80lmUL+bq?DWXSL~E*TGE5bbgrc02T%d
z#F)z(ru!O}4Zu<dR0aDM>02)MF~jk%r`y0REin!E`-c_mVwEMxC4UxvRCSslomKYz
zZSZeyPBXGD{BA%zS=fvzoujQDxEVFsiUn9mD#&BC<9Y<mt!C)D#4*}Dp)zbO+Yri7
z+b6%i)FmJmV=dm47rTgix>IYampd*vc2ON@3gtEca;r{V_e9&*%Vi~U()?a8&A6a;
zUv6IP*PMUznC@ZT_A+eyMMNKxzVMK2<iXXV`xG=s6;xwzxBfzga&b3y+(({Sg=9&5
zOw2qGZd77W>#*&W%WqkHc-?>If>`sj$gT7lEU70R{>T@T@}O+M8gSM3WWj8Tl52mX
z)n)ixeG5j1n!Yu)!<y_b0!^c|{|nWdGGFcTvo_rFiQQ!~T5f7?V7%0yQe&89vYWqi
zgocw;fJSGESN+Y9fJl#x?@kAKKb3I<s8<6#+OTX?to`$I;gy_Rr5{)7wB7IT=euVk
zREjy0M3<quuSEnqTZerG?we|P(@v)c$%ZVE1c^U}f2O+He?d|bg0VxMHj+$h{_w+K
zE^(Rn<c-ymfHb>kUamc~s~$8m5rMIq$*<*cHT6mW)bG9-Dl$726j&sPp6B}Yoo*1E
zf?=Uu^gc4{e)q*flZ?VuFFuk*xwt_^KoZAzP_&Ox25rA6r`)xNJv)R_MAl=er5yx)
z^N_QWnwk4oyYpe>-2vj6!@E5J6>*nvaw}fd<%+l++(;_?x=##}uJy3z>oXMlABzMv
zX14oz%fgD~yZ^^9z|1Gf{l7Qt1F?G<F&Zg;pz|S+p(z;%e}H4|6@spd$L^c?S$4&#
zV^iO&?eKSfe!~l>+5&Q`t`HFiXa3cw6=%14GMF~6I3LV_^B!nIlG~5L3esJC(Cvdd
z(>1=%GwcQH!f1j7N@4UB9l^w7V<n4$h^@vMgbU-Q7~t`6nUrH%D0o7-9_J`Doy%?(
zAdX7j=5h2@`)%0e_k$N<>vGF@KQ?)I<|NrK7&iQqe5fQSvLX;IlX<LkiC}_yc+WmD
z+_*}e-whN!$y-72veIY!qn3p489tfpDm$A+{MefR_3v7)JR{Eque+SOVaDaH#Y}(8
z^XiF(Hi3NkGRer-=S6ar@WmBH5*G!w5Piw9)m(Th{CL(7+Gz8}$FViW_pbF#-T6-(
zmw}i;33@`YOge=W_Ehl$_e6sTx77A<j+C%{3&uuGl>qe_#QiUb#rsuu>FtP<B`tQ4
zUoq>a$wkFQV;rvp=Ga`8DMyRTNchQwX7N~B$!a;{WWAja$x?d<d9wwvPHET6OcnvA
z{Xc_RjM|^NYaCDqo-SU*^A?s2FK&n?(;QUOjq>RG?SE^9^@ZxVtcfF8oL@loTvfXG
zm76cD*%lpVRl?y1uZgjbG~w%>4y%)6{Q+F+sHV+Om%lL`TX3e5A)bg&1LN2UCy2Dz
zVuhW{$|5ZU4s3LJZt8U~O5Jdta`?q_iW)E1rB8bkMI9B3<%wBMt4~M@&kvkP`R0W8
zFiA*igofuGU7A}lJ15&obTH)hNiLryGW(iug>9V)|5GjjrF>pKexUkkjNziGH}-<#
zI;Gu{=r|*E5Q1`f3(y+Yiy-=<$*jmGT;XFAFMvF~hpe>Eim9!>)&+ALuTt7`YL?Ea
z=j;vsC@f1h#Gk`dW9OJ4g-`jr0HMSd;6_-VP5|Dln>+k_gBbVeu|+Qfx-QKA94tP`
zA0FWS5yq;!C`Ho5q{k2f<xNgCdU$W>cOcn%m+!e5C?pm)kvqbkvCytWh>6S8>%LK^
z^lXyQ3C4`g(XXfCeLSjSeu^JEAfgh}Xh)>F_<qOZq!CnyySZuOYH`Anlh~aKran<j
zv&`Q!;K9=G5=<FCzX<@p2~qLZm+s6x)Edk>J#tq@&@=2xD>mZ7*|L|yBXh^%Ma8_;
z*ju-F$lTsnm4^>kPtlwWnK9FxKstq#D1Ek&K=3PPmpQYO;S|1aNx#E_f^UqjKj47j
zN+7#UCfE=e)qofk6`RkBpt7OHWG8uZdvOfuHSRIJs8egecl*!NX0X1r(v&%Qp#WGP
zh57J>(Bg$%A=7Z5)xFkOi+`xEplc_zNiiJfm|n;}DGo8$@5e}dZ};u?s1?`zzQ96|
zs;l&{IV@!l>|V^mf8TleWX@xC0$6VGkc$1|XwWXl`{7H4zKXqmMT|{MDepyfK9>DZ
zIVPI4CEm~ZWn{8Q!QnyVGOGPgFy>dIile4Q<^S@#I}j^&3X60q1vozI_`Q(Hhu@rk
zpS>}~@VSgJlk`lf@?2N&OPp!G)Ty^xuwAvsfB%eiNS1Y>OHD+dLdc!93OxFJNzbvh
zsKIt-2r3ocv_Sg4i}7;y_^1Xh+NPZ$)`0EfS1r;U<}o2<uxWtS54bSGpTwq$wqM>d
z83hp-vnu4$v2Y~KPZh50@m}}O6)RxI^KG*ym#k*KS%sEM#6Yd+1TJdWFej~L!82=S
z{SwZsF4A6p35RlXR_|K91E6XK$(7BDf3r%r+S=N^YctDapuN!5JFl2;dMn~hhRu!k
zm&01$eG-ef%q&@2Pj2+mpP|FPkU-y`xSJ=8b+6clg!)K)^>cDLP_MlUCV^_HOl`A%
zBoi85j9Rqby&~8vcl1AOIcklpn=5XU_bKtHdtfKq&1Hx2dggz--*pAK&SG6by$?<$
zO>G5tXGsL(4UYp{+prF2R6G`oyDFeogibv%Uh9}*V)7;5pJ@L-EP%t*VIl5@<Li#i
z#B_PE#xtQs76qr$JEjg!X*AInnVv*@J$_;!H8LYwqt#N0%T=z0YxCW{j;-!<pUjE?
ztmDEisj`%pXNkE<o3e&35eXj5J|{dIZ~wX58}Wk%&}MVmp=pRZ*5Knf?u;j;;m90@
zU~S#zS+=@&wGk9IT>{SjfP!}OH?dIzTlu^Zj%gQQ0a%oV*_(xV0;^oqi{0SwRL-0D
zP5B~%{x^%Oy7z#5#v#(junboOt}1ZcvhgmfG2}=K@hCNc26`c;MNlAsQgcxSSH~^y
zVM2zg(eD5i*nZqRbJ=9b5V?AEprvz~1TS=r&Mpohlpk`AF8c3-4(HIiOK`icMx91F
z%d6UkHZyq~7&Dz}TOXC4O~jN(JloOlLgwmlnRp(e9K|il^}GtkE?=31PzySjPv^yE
z$Mc!R>W&4_>wR(P;{weLRLHYw(@3H<B@U}BNDJ;sIM(m5B{1bs(XnJUC;554vUb-;
zBmTUey1mzS+@|G4X7Fh&aq*`+=JL7%Q>Q{bQCpT?&>`=HotV><^p5qx<faoKE4jEQ
z8zMpy92z#Wd?|yYlU&CpIK5qb?Z+@dRfXZuujjNRyqAzn|2vsv6z(IhFKU8Nf(w1H
zt-`bw=CrBRzl)8pJM+%|USOxNU~J5>SDOD<$YDjZN_Rmb2K->wIQ7foSc1ZO9Oca4
z=PYKj+J}tp4+mIO-10#F(C36xR(C<z8>$F(D8MXepj|PluJltc3?bZQ*|ne3+}T!n
z_|yltW$+~L2xGUN(=XnA=EE|b2?@QLApOYcypB~_t*~F(ISY-zEiR`JWbWw!!P{Fx
zB`iEI`dUa#ib?SPUkA!Cp7D(_x$nJWV~RV6hZPTvDv|h($wnBJ3gRgmIdufN%NGaE
zM=1`J@c?hrz~keb7LE9Nz-4{z;9f~CP1p5I^|xt4B1qri>fN#D7NF}&aq>IRgPr!`
zE`l!rlky_^-<s_3N3pc&hcwP}V5w;MxEmqiw*1x@rFHYTC>vdUln~Ov4vVmLteymF
zAZpIK(Wu^43y{r7{sEMq_N{;@{82!Z_-M=^+x^XpO6vb8F#2(i&9`KHv$72MMz$Np
zu6GNSsPK_Qyy0;`{_r3#*ord<d%HINl1>gGWYlFA#4^+2(!rw1Yk@h9;$0V@jTB@$
zQvnXmVxoDQyp<(B^{s3VF(6!yveHz={tR&{Kt|TODNq;pnW4M#Ip#N!zqe>Eho#lS
zBA$mp5Yv(+!>@BU23NI+Cbmr%gBK1N>_p9HZTlvSrH*c(AI%Ge?x!`bD~cWc=ftdu
zI<CZWQV<V8G;mZ_mx_u`p2;FpfJI=!G|^<0J7zj3gU(Be>US)4z0wm3b)1feGhG@(
zs-r$CnVgzK0{agbL}7LYYZM}Q-jl-EBmuiE*cJf>-;bAZEeDEIg_$C-u2mfctcdpt
z&W9FDON8{@+%`HXHnAELHP+;+qpuV_1P-NIxA#Xa#4Z)58YDWdO}Y|0neK|oOJl8A
zlTe@@!#I`ZCzZ4Dy?u~)zjqaPY0E5l@d2IhKB7qzDMaud9r=?Z{`A8dJC8QOA$$pj
zZR5*dV#Q{kM!#%p2Dzq7TKi2}lpj#_LD-D+zJE-hN-VGVW()iJa$W8pQ;Qw-v(Hrg
zVL~i(?+FX#fL9jy@ES3|^`8d@*cGrPR|tuC->@(cATRJ}Xm`8$>N(wd+JiaLJ|<wE
zBCrqUq*J-e_EeTd+oB)x<^1#L;CZxK!)Pua04><oqnoG~M`&cgx!SPfTPWgSPKCV#
zuGY;CUQtG8@BC{D#7)P|MC3heaRk)HvmI9c_)+r|S(a}P;F2_Uhtxv^^*mP1JV8Jx
z)<|ZIU+C^*!Q%q>Iu%mc4eZwfOLD!TUph5rW9`*;iq5pjo}H#KFEJRrk<1epUbX2U
zF=ug*_cBm3IFK+_3ZoRcN>8>H#cXNRFjdmk!k`K;$v9o`O>Z|YZ8%<%?yNmwbU96n
zPGV*J-cX8@;y%$<Z3n*27ea8y!b#97s5G@yyfJO^SN3N2-!e6B{@B#Z{+W>*>T!^=
z3TwaVAGokOdYuhzj2n)D{sO?%_T`s1H>-)g5f{zZ4tjiZVSnJCw4C@1yrISJ3Ybow
zrjD!{H`0srLbhV@bd7z9mF7fumG1e11R+nHVskB#{RVy>*ZobAgbTIlLl2%j-Mc`D
zCTq0G8cqfGXTMzHqnuYlBXAq~#_rg4zU^mm=_4b@l<j#O*~vOCNRR%`(EHG_2y;0e
z+Gxjs@NHAY`Fp~DH5-(<_e9F)>Sj-G!T&6Y8~5%@S9J2~$*g*(&#q<N)t?-$3$2?y
zO&7VIsE7RsSBEYy;oWWrcG(v=-c!-vBU**g8E_S~UqR^phjH<@fcAapXJ9$eMyq;-
zsyCN`0%FQ1_nbFDySW+j>@+*Cllk__1lbP=_<j^e=wgEpM`kXOL5v?LpCO$UjR6)C
z_FTWqbbL=hs{^Fk4&UFLIBA?^Y@jrsx{am_c~;>g=wkSLBt~>Z{8O_%<L@r#Qmrq9
zz4`qR$IF$XRMR`S29JBCwcdzK8FDU&tQKJS`}RjA>)phD+=<UBzM4wfjaJvSM(ZJL
z*c{mYTU2*kCIIjnK5s}Ada83ls1!*5=uODddoQxk?{h2OjRZFJBd(+G)qnn?c0R3p
z8K-}AxD+<LdF06m)wkXbWn!PFmq(!msBL?&y>BQkxBg_z`O_UE$qk)XV^|DTqsfZR
z(<l=u!&@o=jFNL?mF20`B@h$tD|r0^tb22z?&@b1m-pl|&_jI~Y<5>pKG+>{GU)1d
z(O}7CvrS9F<54$uH(NR@h<lsTysdy(6{A~Sa-!Rt$aa5~G0?E*x_R8+H$>sYo<M;H
zNyMO1P8J}Q5OvK7clWer+z`9cNA+}KP82g@%{^!`KWY>kZs>~K@J$ADdwgZxpV91N
z!63RW<;B`x)?CSqi-QWt=g4&M$uYaBTt=!}4Xd1F(r0I|#`}4^%IhK?6nv4pXM3d5
z0n4vy5>=1<Y<G2j=QQM~cQNsXr*boK4|7x8fc5xq2bw(Mn8IPWEwxX{)ck|DyF+0Y
zF6KLAzglp$2W0Mb2*i?&eO^R?v0_TG_<;Ppa)vu#T_ks)=L&O^r59DMAID+lJ{dN_
z#J!o})4zLX&9>@eUdO7}pnADzljo+{-V#Ar;>*q_%&B>QU#qM)SGl<T7EMg5r8QIO
zBIHLfbIwIl#n-+U|0pnZ0|RAVRiPL}8d!fo9-`e9c8jypl<nFrh+;MO4Z-G(i)pha
z4DcE~W0%xVFUKaaoM`40_of-&rA@j8tR$(A20k3YU23{EqZ{blx1Z_}O}_EGb89)y
zviET`lAC~MBK6VNx#EW~z)ZHnX@yWY%~cu1AF8i*y%T-%2e1QN{sMa)(#1o-dMZX&
zLt6IiLP&UzM#;xVGm&P%svm-6!qZn@l=2|($u~xoGBAbIUK}zp_pq=|vRy-7T|7a(
zvGer~BvUS4<5a2s6^P?vHzuR;LsQ2e2K||#?N1?Qfn3|7)g|Sj2i#tRt}4Tq%X)(>
z^~vM`RaYz;-KG~f<;|du9EYvA%)UK@Q<nX7XY04WpDa>Jg9R`@v>gzciwF+-iTUS5
zk4i1n+^=dnz7mpg^xE{}HPZ@jjlnW(&ep#;e%89*x?@q_>F_-o%4-UvWoV!<54oq<
z^uhIGb-Byj|LVwIi3*RqH^>|YL>C{<R|xi&blWaZ*pH^iifUXP+L{R-I;tnQDD>lw
zkG><kmo2EOtoGe=mD`j6o`-Ni`r+B|T%(x$ANTHNKq)kcpv+-(S?%ZgZe_RVdV2&(
z`ow3)c5u08ZMb`~B&M?&<7}JiJ)60&q7$X*GQ$lS9$%@n_;#!$>QsFBvE#mtBkOpl
zc)rmd;5Hoy4b^LON*o=}N{oT*6S7<rp#_w1M`-HT9EweNL`l?=*vF=mMa}Iu%7sh^
z5*{7xvSC#PU3C_E0yh(TE=?&;Qzebk4b_T6a}%wwY~5jU?76uozK))2$NL={_cm%>
zZQJEI?bZISje%3aST1k>;s4ZlP^)gfu#@pL<siifszYyE9O&%_f4+<m+R1X2-`4rF
z!1$JlOn|A~!~i_OTJXN*8W3yqncxWRnPpE620L^n2+0s-(Ajrb4AYZeRyMB?^gv0_
zTYycsMf0*pVzviV?$K_%0F0ov#)M8jiBP?1-nB+TUSXF3Z#oi&U|`|Y3@iddF-3bI
zZh>fa><<#gv(==*>I~64xH_2||He0Ndmn-ANkZ|O1<T9@UpL%DzO}y}J&~zdpGK6n
z2cuTxHzi43H&Vs9ZoenPWx?Yra{dk4k*Cu!Qbcu{R*?MlhDPJrPuDi>L1S(acD)5@
z!;c*9xY0YuP)W4rPYJLqd7RYU_cMR|4Yphvis_Q3wRUe0Q-pU4ZUW2yNX$$)CyFvc
z47bi3l$|cdJYOYjI^VTcxrhSktTwZamESbiA-6|Ccj^6|@|L!*Kwaz(MbYf@sB2jz
zg4WGOsUDNk2u!Q0TYU#Q5`G0$y{bFIt^hupd}J@3d|y8ba_hG>5v&tW1>NwRJ3CXF
zwYwr0_*J^>LXy5zYcGhes3%~RR%)ws^+F-7%&~Ode#-&i();|7U!4a99$jTCf0gBn
z(LBSMndVv5^v5S0%kM1_zKs)y$KR3g^z3q={4y88kUyQ-(_=AT&s+><IwGa_+?u+>
zugXG7kwd(hJUXhj*jgy37c{$bt1l9rUw&yxH}~Lb>TUe0D<r3L`oYBf-<AG<z=fw$
zd91-WZvwsC4rY}oy?1H!YK{OEe*#c%L$>|6Wimx3Xih>?>irgeMas3b6hhW9Du!^b
zENS?A`UI>@_yD?%_)xpYk*(n#OI3@+PJ2_5B#y{0i9hMFV-iky_C50v*kp{r)sqQ7
zTAbVdrQQ|r@LX^04rm>CeFo{)DEixR&cf#jt3+?Vtht;?5ibVg7y-s>hqKnoCi$jh
zQ$1dy7_%A^z%$jv>E8p>xuVF_35}`Tii;|Z#|uQS#0e(c-vY<Hslch-@==555yM|r
zPbjte=UTD|R2{0?^d=?#oxGgU=*=&R+cTYX`LfN*P{Ee`=5j^=ikbXL;zKkJv-*dP
zilS?spWpHWEm$(GDJ793vGImLf?{NK3&TV@>M$o{zI4IBxmP_=T*Ds)2JUGc(1_&8
z2GLw=6T_)z>dI8Bp1BHm3cTz-ZiQN!YMbuZXH((+J~q(ajrP#-9W#e>%Fv<J#M5xw
z);*!9W%qMWcK^;?J6#@ZprC2%7Hmj#G%Fb8Rui)McT7!@1YJk#B9B<aP?}6%{rG5P
zrW^0DpMA46wXbh*ol8LbzAQP?VUy^*LzwcPs_2pY)d@#Wk{XwUr;&{TxGzf3ugeci
zo8*wojaV^uAOpTU(y^vHyoo`#C1DNlP1^rz_q(~=?kL8I8l^!Z$rT3GfdR2~&#mir
z0HHL_f|ZlI#4MH9pQo7!B_y%G-MID_UOl7zU_NQ$Y+JT!FzhSnl4cFhf1r^GkugPd
zzD{$AxIZv7VelFt{FRHcOT9`@R8T02gpKtIuc>R4cX0rKAlXt%5*5=0KRFS&Ao=;F
zGza~*dO^YK01mElW3D(>9N{M?hL2todvFb^xCk4?3QOw7XE7GP7uoe5%Vn5vs6YDt
zlrE~TnT3qmX6*KI1zElVW3KSU)R3EzyNFKyrpVxUVQcUx`EWhy^lzTZ!5l<StHnH8
z6Vorz)(>4W=y8Qb8NZ(dD?w&4r_^`34X?LY;#Az|WhyP3j0>f=Gf5l8YkTyZB#BU#
z#$!F~enH0%e(Brp36S!n`<jwH+kI;0ZKQ~zq0ccV9=G*djbps<jby=RQ?EdSgl730
znY4C>)Gh5vRW)+4|L;%Zi@qz=)ft(RK53qGgnatC5y<%!F0v37J4j2}B6S%fvc|LA
zF5!Oihh~4*_$?EzShJm95Dwb2Iw;0@9n>v2n;rAOSy>>!-G-x*&X5V6b0$a}4^=B`
z!`!5|1H*YIo$l@UhB<q=;W7A`4eh7QAGn%piYgH8tCH1$GP?k+g1W)<5{0QmNaIfK
z=iSVE9<|Q9UoS}7l*vE=F~=boge<$tIV@#Nesb5p-1oEyj?*~#bLuyzFIv8zDLmKU
zU7NtzFf(!`$T|FgyCY`^^L34}cfoRS5lXz4Yt1xODn0%snX@YyV{V=~1T{9r+zGo)
zXd=A&keaK7WB>9)V_sZ|ZA)`<q^NT?eC9my{^hlu`)j%VuuCQwN^9_kf;To&3i36~
zjS*1h%5+Qdr~0XK?A;~f^7-JxF*ECxwn+F#j$69a!zz}c1CMW!#UE6~0GYs7i8K0t
zCSkzb+qm)r(2MO#(UzMRM|v0P0rCnBSsD^_NI8p4>Olzk6;EhZ^77gD#4J|v5m0<M
zz+}s2O^k@CVJ$yuP`u6%tm-|unQ(x+RGBd&J$<DRG(WANka0FGBezd<-NB}jY+=rf
z8{P+7hSE&smq_oy2h8dwg{QYAW?g>$l~p?<feT)KXR;7Rq^L?zU^0|o`^{p5gDF`g
zP$3SJmgAw@@ueAx!V@}hjdo*kzN6M755sAz<Vvw^3cmFi&ifOINy2Gu^|EFHmCg>i
z^PC--#Ns%Y$_;k`xdGQnCd45c#l#eH)cO<gg5o@u)>DtCeTNOJ7{v6kOTV57c?!QP
zM;GJrR@!Y}NU~;r{pR-gugIkSC(h4iqHcCcBc|M8-iE3z4U(FM+LZtHRF9{}EGjZ-
zXr2%rAk&tbb%ie{r%p*cht$KsOGe`~3;tuzPpz`uQ^LyC=guXoLA9bz;A^ot-~&3P
znh92WIm{568YO~LTiJeJ8{)-YNan%fOO5V$e#|oSlFaQyN{tib6tCh9Fn(7)k2=KK
z?xXT!GD1O*V2sb+RIhrLA)ey*V*f99nmKLm>$)~{BDb`Dp$bWV+N1^(Z_MjxP{#ZG
z3N@94d>6{e7@cq2^xF)AZ}aDAL^O0Zu8Uf2b;X@tE7W7O&k`yz7CgrUy)g5%@M);b
zs4%2eVMuiCD3%<?%9JS8lr<;tW88;jSYW&U(bNv;aox9Ne6`#CF8`ZrjPGtMF2Vah
z3jrOa&l<CK3XCJ<|E=%;H(`+znKUV{Fg{iFo<lAybv6M^$}UlGuoEhB0#HnT>*hE)
z%{34=#M?;LTE-9_NQ`659rY)DEnq9X6*Opb6q_w|Q8oeol{K?ZKa1BkaVDmW5n1Jn
zdYE1op%k*PV%IG*smn)UUAfvM#X4U6M(5MWOH>K~(d%Vc)i2@CfREs6e9NZrF->dQ
z1_hWWDO7R6c73?s8yoGi`Z>O6ET7-;2*U;$eiXK8{=0Q?8Eh}~n<ycLc!q(A!4i~C
zl_(zuq2{hIFWN0xI>I!^Y&=r^LeIHZAiMV6%RqJO;`!C*Ge%OmTIF_2*kps0%UY92
zk$ZU$gyXrEOu^Ai(c)3G13y6{)&J;nEtaz%<+;Ckzls&=s7`16r^?mA{=Y5czn8we
z*Fk^BFAixa4l*Rj&Pu{F1-=PIq@fXje8#G7VCnw&GM|eZfW8fXO4aG0apMb(QQUx8
zr3jnpvYpm%tF0T%n<VDA{EB0T#p3P^93M0$@11My&Mt*omLos#$Kzd|SJ=U96<2h{
z2RJ0NP3~%h%N@)st1uJrbBNiODU{z1N_{=DMFr^-58sA9H~a(g-kh#C2&Efb+8VTp
z&aJAlj?k#8mH+5D59pTFrjqR}wH+72u$sF*(>Is0^R2cza4*|Pq&8%>tHnV50yUW4
z<<!k2XeL4l^vwQa^_~5rI`7;3Pj&wBN+>r>$^rC9;z*q_{Qsk*ZQ#M{6e}vS7?qM4
z&g>mN`~-~=*E$K8W4&&i5a|mzuVk~>zNj8l-`M`@rllnqI+FDp1yzUt#Z7uAJJF_a
zX;tUWjo4THo7QrV^a`%jwOtn-kcmO+dExL)V??c)jQhos^bo)aDBw=Qs1~SZ3;SPm
zy>~dAVYu!)>Wof^&IC~sokSgD^hmT6EqX){y^lV6qKg)ZPJ)mSos2d_kD4F|GkQ0A
z=X};WYwxx8KIeM>cU|9j>;2r%?`~9<@1{xa^UY2#+Pfo~R-){AgY;_kiCIv~TbSEe
zTBTcy$YDU4l>3%cr}cl+@BlAQD-(H$D1*|KG8bE%FdM<Lsh9rmAtKrEI@_pvTVtb)
z<=cV(wl)_mW@j}ygPrnWX9J%0m7d0!*~|`KaMSA#_)q0YM~%NVbNsWRRr|@yn{Hc9
z$4TfihP0^VZg*|>Ci84K-;+PVjR69<#vYH0)m<^gN8sJ;ZH;j{bB`^uEUOHFk0soq
z7ayJ4nB9pD%35gU7)vH-lKYUhmNQzx!}kSh>}$^WKbmk#AlM3==(*2q`GpzXRQqn@
zK8e}=KSzg2-)nx1;SI^_m|&DpAU`~arq20FSMlASH($WNx8i29`DF8X+s~NEPOtWB
zJ3C9&P__yuO(h0}jf9-|)64FgUX0y0#~EkLFMP_4c-M*L-@D9|I8m@PuOrU--o^M@
za1y_RT3!yH?C#4R{%=#8g#mG<UE~GL?9&T0e7jto*1<RLL7hd8xAK(Xk+0o0tBV5_
zbxQuG?95SgC5bDKlEpt=<KGsF#JlhIVxwk)SJuW|^9&w}T4bK`)&AK%rFvl!NcHyM
zvIv5w#53%`I{v#fF`3W`aB3C-z*VYR3?uIhHkAJDX2DdA13#NnbrL|vQ1?695?(K>
z(z&VYuMLqc`3SR#?#~ogkpj?9X5*bw?lrH?b*2Cv>qGxXX<hFbU)$A*@77t9B|VXU
z9+Pqc*YoTHTRgU{F?F-|UC`HfsrAxir-XKnpQZ`cinasnEC7o6Ai}9zVRli*Z>HLQ
zbRk(6p7gbL^6uYwX@=-!I$%B#vY<1-O>R#N<c)nbC!T!Bl#a}b8;-R8$6OJdG=;rf
z=2<o#)HBHD`Jc^InvmnOaiH$G_`%^@l2v<TPpYBmopf`*5>vpi>ptVV+yi)cZ8Ky&
zK5x_<neXOkTv{)ws9z*d?z?jEed{@rQeOwPUdJ8~Pp{X*B!HfbsEx+;Z|)Zd<9*9H
z!Om*mcv3t9^PEL#76AYlO@fs#a!bN0JI8<OT3G>DlSbD#Y2T}hqibH)JK&Q0{M8DG
z_-zQ)8Ztk79goI;*hHDt^?hFK*6l-@Po#}|{Ow8R@B3qDCY%#+{{1G`x|6@c%SdXD
zyHAUBgp+WD3L=9CCN51jl>o3WZAA&3^uD<s@=}IpRJDG3AWzi9R(<|{cw^ya_gF^S
z-R(y4NmmNCc5!rVHSWC)w9o%rrB^4g*Z!|13%FNdlfORPPhS7_Lq`XG^77v=8~fp8
z9pk<<;l+q$3$b#kS}wNFWj=aG@6P1SKT0_f>w3Ju6`Z01$_YBPKLKclA{y*&s*i6w
zcG)xy9YmhY@JR%5ruqM?fWb7<!kx>ZY1aHU$dBKXl}IXl0qEA^wcR=Zs3O*$8haOQ
zdR@G-5IbU%O{bcY`gw}GAhS82?n|T^{J!~yC*j`B84)ID+m@h<G8zT{!+R`)%iK;h
zCehdQAii-K;w=An2LtweDJM7p3HrIQe+|H6W+>ad0uIx(5|OG#z!3FWFC&AhSpJ;w
znS;K%f5V!7sn{Kem+JuZPXBnRYW;$wV7Y4UE_eH3FNRdkAy#Vocc<O@qhZo4t)en?
zwbt6{qY(Wsg9lBqMvEFEq6WT;mCpyRDVL5sC(B=&x2^tv5lR)WdfxGle9;%=$xIv&
z!_o0kvcA(k1z<CR01}1~T@19YhgQ4HnggHWPvsBzo2W(r?pXQrrfVr>sXvS7*GFkl
zU&9C?5z~4-PXBZ9YxVh>NtEro#KIzGi3&KzMKQWx^a_@D1F0W(`LmJvzDRrj-i13O
zKL4848OVMd?E|cZ-ZykSLeGMMUhS$ksIo+-ndBW}Yv|_D>ZnGA+dr@I`}+Io-Wu`j
zcd(e&(5Qb;6X0QgfChc&>hF@|Yw}d(HES)51}2~)#lc-=JDB#(>_=~!iL*U%deBZC
zfa{yO1tz+6O&Q%^pVf_Ylu+dZfOMiQ=asZ@9XrKQ5b%NW?Q7Ri7g+06k_Kmzb%KJy
ztr1;?i<DSa+1{=3>#kIDr|xPGf{LG2331or<booNqLBPXMOazgjNjPN*V>tf>gact
zY72y?8Ec4B0J~L|5Ds*ZfvH$;U!#Dkou&-%ht`TE?)jl+-##atUKS*Fu>)5InAh6f
zd0=vkpZYEUSBJgdC8e>ztT0<&PI5u~gTL0du436-{9;%z^72`q+JEqXQ*7>;rzaJc
zt<Bh}a9pFbYQ!f`pHwIP=_!MOn?G19e0f$-a=J00elSt^sQG{OAbr{<8UIi4=|Y#M
zcGum$X0-1jO!Rvk8nTe?5PO!?&I-w*7mWuNmY})HlLXl%X5ZCM>9L@hptDUW@X0lP
z&iP+s51R{`%R~^zrr=jyvwu9Fz$P2f|0HNwjj88EQK3a9Sm7S{-PHoUZf__Fg(J1x
zOQw9kN&BJ<!_#Zdn{m~vp8D_eu@7be#N9}ZiQ%Ykp<hJN%Cz^56JXb<iI|^XjTF1u
z=~R4PFT4Gd){wtkv}M1tM;RgSxpM0|JSNHZ&7IZHADt!K6$4K_*`BpVLN8V{B!Vvk
zSfrgZVMo&*>UJzbmWYrLf^I+!Qg}^7v454`a&A+J@@T6Kqk&JrwpWV&0o*-{G*J@~
zdVZaIgs~Tv2LSk(Eh%XIS6%-MKyWY)IvvgF&5PoEU6@S}0ete~!$}X(syMu7_c;LY
zL%n`9qw5;}d;Xvqm|zi?w2?GcE!cC4g=^HGSy*1O=RVYD!_6J&?q`(>Z<@lkhtcpg
zuIjgOO~`QWD;LdiaSvf3FJt3(9oM(j!g$Xrhjdb(-S+@!dR{SmUrzMDbjH3x+=rC0
z?wG1;27H!4yfyw`He{m$I@FN&_RPPf;H!i3;QN(tnK*WP0@kmYu@88%-MgKE`XTX%
zU!QOt^Xe-RscZ*WEl{{_4!8m<iiMs64MVe-Vj4iJmjxuvev?g|+x)+fAGoT*$Do1x
z!|JTVVN7fZ^0(=y2ISU-tPog$0V^()aN7CUeZ?w@)wolVDkMV8|M&oYw!s7ZR1F(d
z+i)q1%%hhsXPZebY+M&OnqWIlyb85!;e%E#93}O1@YRKsZFcA3gf-1Iv5!ull;qa@
ze^3wSm|Dbi<%loB>2+u;h}ce5gI{UoSaG`Lldbdt;ILa$j#Y8LOl$|Fv)v*40J=)M
z0dRh3M-KTy&O0>eZ63uvXf3^3x43$q9e8NWGl!hiRXBLYQe<@)_NPjF5}3Gg9rKH%
zf;WyB<}QP;j)E%z^yKqkqm2aFJ)!+5g+!N)p8&n(t|buj0Z``|nsUnD7i9-1&lsyF
zE*!<}Js48r`dmXv(5*!z;rZvY_45-kKi|T#k(U1x)Y?1^K{Vns=Zt@j#(+5z<gz^=
z?BOYj51?;d?I2>R3B+#vl<W$L&5v1GFK?QeQoqnpfCzRlR<7LsdzTbSYj@PIJh1bu
z3SpMOsUY-{-37)u3-HYQVSka$EUKuKdEZx-D7|CwpV3EbW#qctu~{VlFy35KwB`@)
zg(I)aGlJ#lkxA~`0jS6cl~c`$4iE{&lF}2P;^Nv)%J$DZUX>l0uh1<s<K+5tz@fZp
zY_br(a9KYeFBxz+<)X+lveMbEZFHeRqj38=A?L3aXUw5et!8*HeY~Q&VPdc@4<kNa
z@{Q60fyX@8iZj!0rgbU*7T0d|U=tPn5t~qJ<k<sN;)Sdl0)SFosP}t-N2#1pv)||`
zZMRyLBocY7GOQOkeCvo(-`JPsg}5NMo)qDZu^=0!_<xiC<6=SEg^n(f{AmqitHeE&
zOw?g`rA!5-)6Cy0-J1Ou)K7sr_QN;UMoZx%2`OIdWB=YLEYoT%DNBlS&`7^z{@Zjo
z*;k92XHO7YNDHT;WSw?xr&>Cx)w;m@Y@2!i{UkHXvFi)?evG<LfMd>ooxkIzi^N;m
zaqSQH#dT%&7Bs){SEs|Et*G1d3az<>ds7+wy6<IX+|l#R#q5pvW(y=PVoc-Z>Cv{!
zOkfvX*7_RSP1ll!R7()Vv{26!-gFia;7I0V9aF=tDSEtXI(Fc`|0W{me1Tu;ABupE
zR5Eh&UjG;n%`P~5z^z0x9JB1eK&ZVdwol2@EOSZXQcP{*KSVLx5&8P|8bAV!K?IC_
ziQr@WFSxL(C6zjeaLe4XJ2M%!JGxzJz^LlvPSp@5RB6Zhw&4$(bA8^TM_d%s2-^*f
zU4>6mcEkDaE|qGHk(;F!7himjo2XUmoTj`Vax$^t`0o8<@@r>={h?hD8-^cL?4>-_
z`L2j*<l5~2zCBe}&aop=P-<WGc}x`D6JC^j>(|7()Ov|;WgwkG#Vw&r;7Clq?nWD=
z-g%{aa*fCtlQQ?dA-GKQ&$;;5aMx&j;MH-L;zLr^d!sK;TAepA1>2kAuC3Evq)L@H
z0$u-VS(xYDcue1)Q&g_XR}aVExe?H`-JQg0n^Yg_wnIx-Oi!3fr0Ml#YzVjM<p_)Z
zZ|I=^Op2fkc}SS%_?VQ;gPT-=@@<W;kDzS9AB{AUS^Uymh)woA&X?Uhf@$=X+5&#X
z{NXd6q%6p{3QCSGXHDDW9zQeI;XVVV6w<b-`v7%y22=UhI;&v=x*jwejs;q8uWC<L
zX#H<&?t)ABw1Tj$A)krPAW^nE_egn+E}EWp>9_+~>pdEBOxd&^OHs1`FYo8croD+_
znUQ@Vl;*N}b+oKWxOM`wb_K9$D)Ii^(pGB#BnT)>a-FU`Z+uy@c`=aN3cWhkFu_y*
zc=NFJGp*zryiBn2YE?J5QqODS)%LN%(e$XN@q8QD2~p9&v>^&zBkzbVno!|jTq2TR
znyW%^)+W{u2ARu-KPltX<tDW7BtEt&mHsjK?}TrPl3rG_4#^M;`OQY)KelEjZx9Q}
zWdE-j6B`_Y@(*?3cA-Uz@&|C-VESCaE<tTP`?~b2<2ShjqlGxUvx{IE#cKPH=1S}x
zb@=U-Zcg!Yh>2&MSO8=A58%e-lvE2?2u%Jtd~4`re8{trb%Nh0rKa=Iz3)_i!M_sf
zTxL4J?bP28)t>{v#TcK*k01N;UTkpau!NC^=SfOu>Cb6Suu|J$hv93*1uuQF>Hv+d
z-^n%zzPC^2(<P`ITAdsKmz4@!mY)@OkvFLmuRs{d){5C)DP-9W)H=l9*QnKOAJt{M
z=0gB+>X~f>P!O5;t4Fnflr^<9K)<^kvy#CTLDNNYfxIM3w^bm_r;(Zv%g7nbrHHw3
z+YTHmcx#{)HyZwW-d8o(jn0xdT8(97&tb#>Pl6j>Oc3Q#G`Owoh0L$1SGr*#p#1ij
z;8q#m=l}LJ`OhDdlNhP{(>kUu&F8o0oI<iYx^*MsSAf@y!;WKeF-=2wy`R8+a|0M=
zOMf|wMfZMqa-&r~T+TO2O)>9~*3%Ie_|ZMMNS)uWBBEh#1p|*?7|fI%h4oao&w1Yd
zj5?Lm{uXmGg&wqq)+#wGbY#}P_eo%4NswrSO|u35&V!%mME>QAhldda-b&K2z^5e_
zt8h|G4~yzfXu*#qr87Nh1Hz6Ak^?Hdr%J%)$RkZk(%aMA_XBIO6vRxI_0pDFW{y4s
zs5mRmqYr1RjjQt-tlA)}F6Roa-|!orVFE#AcOVR8er#r-g2+6cD{)*@2nnll=uBdu
zcqrUHzii6K?Cgeg;a0KW|CF+CaVIi`BtBUBI`97H&sSl^QkY<Z_v^mFyMw(RAKA{{
z+dMKb*ADzTu(Y=SvPZi3kyxqK?gO3T%GmDY<pP0j<=*3#+oE<|FUfh7zsXng{KhTf
zh7UfUC6CdKiD!|p!2p6y4YagsRz3Ik-uh-e8fuo4es+IR-%%eGpxU=y1VG_a=iNjA
zm2O?`(SoUQDi4L+vsZjx5BKgq^2ogDSfYQUA@|?&MGtc1thVV?ca)OO;$xxz`3=}d
zA*dFW(=)He(d&1!yA`M0`DEY4dkw|P-!wL*qpK3ei{?vCXX202B+>`(9!V^qU5tJ6
zva2&a1Rp5r!<B!nJ)JOcI*fQeQW?at=JBH&Fr#biz<#y^GHev7Z<v)ia%=HG_BB&X
zVL!C8+POt1ovECN!=5c#qK)Gamac7)yzSJe<<&L0QLd@mXyyz%MaQR`u-zdt?W`Id
zM=0N26WDKwHsv;$w+&oN5uoJ!{qB5Ut!>HEyJ0-<A~R}yaV`Gf|E%V)Lw+ApvjA#U
zypEh`;HLW@j_VYXHJinV)7OIKF^<}emA$(QEZaR;Vq-5Ald6IBC;Y-Di*i4uM;CcF
z!H3dEftwGBLn)Rx_SeS;=T^fu-aW;jj6<`Hf~1f~ojt*qC(BN=vT3c$nG?agnzTwU
z_A}^PVHRt2!MYz;l;ax+OkI9#*8Y?wFXQh1yjQ*GL1V-YSRH-F)rg0bV2XizL$;@_
zgoN<mFei{v2TJODSnYeBwrtOSi}ygC&*|Y{cEkjVeUH${4#WXGuB>nDvLjvpsw6J%
zmFSxEbGcToU#QW<Kx0|&_wGN9Zz^+?0U%)Q5r066KqbHQ5vkXEleQO>j_wIx9ZST5
z&XJ=ek=IJnMaWGVy;&z8wiU@eok*baJKq_XOh(VgaF#&nA!hWOfc-ts&TgNmiDK)!
z6W0C<yWz?RA&XjcJ&LHd;z9dRo=S4V-BQnQ(UF<fQ}sLZFUQTi@581^*DbRXBw#C(
zH#ZHl)pvU=<%9pb@8Q34kLW`FyhS`U$Sqx^+5LHAH}UBc#>ZSVm3?1=u4aKesptCa
zG{Gnh(GlW8IN~QG8>`se7u)}ce_4?DYi~TZ-r!}u-#K&BUz*kb?Tds^GuoR3YHGAe
z+)ZegAdju-x^34j|D&4(C*Ig*kAbwmfB!1{kheLy#V&UX&Bbu~3K{;8ngr3hSOE%x
z6rG>r!vOaGWRD-@13NLwHCl{<iW(9O=`Hj^h%K?07PbJL@WP_X!k800u6^^iQh663
zr%~}&>q$j1<E{MS(H3Qd_XzJImqg#YAiIxN3KBkGd&nXon|$?)=a@Sm(U3j?isKD}
zC@3O0+g3I&X^{EaLFo-53cX*?>3d8U!iPEy5nV{$i$=Qc2`d;1xFXvCrz9`Hx{w<7
zb|o&N$6V1i|B)h;M)*JG^zxb63jo|f88=8sdH^wDBU?@>re+!ZHgQlAHZQH!vK-Y)
zb1Sp`2f$Kz@{l#<y{nE)K(Wa0XScs8Jx)zF2pkdYVzTA|bN_z~2^l?%^Z_WGi&&HR
zPP#_DMt8@D{u&7cYT>tB*@98WcTOV6Uq~)US-NY*nnZ^v^e!q<_Oxoa@>7t<w><Jl
z5Jp^C)Zmp)Z@XN7$)wRgx>hEU=XN^*oFIxF;Ghz`em@yUQ-xfv{`*lk4cP1szsd=1
z4-s<Ul8zxWG%!5syqKScyZT8{XB?|Z1`QmaEA$-w;BYoLxU^`mH^QZNv9Q^x_HU=q
zsDL1?JDoFuZUQemjwJzPM_kOrf1dXeI~6iSs#q~nWYzy(e0S@OkIi=AVXZC)?Y6;{
zp}DWxn3<2+yzG8a#GLvUG$NN&faoWQ#-+UL2+qvspUt`2F~2bKI>AoU4on;{Sy5g6
zyUJMMv5YG>cQ4^7$Z+}VxI#`|H`{iU8s8v)`@~nXx~*3HA6`r&kMN6V_Ba36FmjX8
zgZivToIe8{trIpaOIwiWe6v>?EOlX<JqSUD55ogb-&-C6o>t=<n0A*Vx;K5YglwT}
z=#xDm=-@eMM06}+L{u+;+UWSA-aq^SD39(Z!D^9ij+JsiQG8ba-FVAiM+5|A%~*B1
z5X-lym|JnEHJj(k7ew0O))D+rL!rw6nhLk%&O;7(3hlz9z3};+ygh&~nS9L$9W!^>
zJTC%ass#jHd84QqC?SU(fId8P7>BUKu{<wkGeq{?L?E`BUT>r2(I!1~$H4yxAvl_P
zXZ3VvW3@rlBojYPyAC~8&VVdtOLZv022BY@2<!Ww@=dW<V5(|BY9xg4)%H`9NQ-mb
zpMHM|WwxiXr5D1Y{v3V~DX2YuaoHX6e-8_3@IzCv!5fH*OOYp5vV!ri0hj~5Ron&E
z$Q4)=-c_KgB0o3dhL86{q0XZlP%wWevY<0=qkZSnOy1U3;I~!A&&v2xSg73AABV3s
zY1q31wha&sywiAdRxF$1fY3B#93k*#?-#u6H{=H-0g^lvl1m1iNN%#4rE;>6H}D(5
zeTm~n(afswDC<A?IQl547n91l*vi*3)iK?E2RzxWVvJ|#lVJ2N+x=y7PukE&=-dcH
zz(DDEv-Q_M;@9_QD!2W|&3x|TnAdCP_f8*Mq!5FAEA6Vgr=ctF@A<xK=u=@*JUl$w
z_&X|ud-_?+m@sO7zcf8=gfN7??kp)M_TDxK56YG@blhR09Q)b*mnCwOHJwF|>Cg%P
zhm;>plGdu^N_vqdbF6a7yGy#G|Bvx*iyl({_)By-1-j;|hyK0GksU6Ykt1WL2B9^!
z5&b)g)j5o;M|b;}=4iGB`<@z`a<Q-C@v>;$d7Z8k$yIcm*4xhqK+Jf2Z#IWT9=apS
z?POn0z^H+MBQ>?^-rPv_m{&&2z01bW(sMEYG0lsqz`OHJ-$d70D_w0P9)F7vB*T3e
zZ?9V7FS;MQC)VO;3}bq@Y)}4E{+sgn<dSH3o!;??2#wrB*RMf9%#qV<#gBX*EY7_7
z1SRa4BN?v$C&E%<%x!w=>}dNxLLvgEn>6P;q90m8zC-dqefs1pC}h(pru{((QK4>^
z1)Ply7V{r|DLD!d)4nJnQidt>dxoc3*-4+?&-CMZ_WXmX%ssj-WvBLcsulcH{w0r6
z4X6>;!LnyNbj=(&7I&_$`xaPfpJ^m;I-QOG9c`X+{$UK)eRLt`wRZb#qbk98c?W_J
z#{+)Pj~_4N<+tp}iOP1^?|}oU>*G|4Oumu%pbLqZvI&3F<x+FuH{8OLnM_RyT>VXs
z35U7egDWv;>xw_12yiduB-C}%{cov{TV5+gvH6yqwzfj1#(kxYP3$?oG|DidYQi%D
zzpyvj-ynj!$I$z96yJ#XSk&-2T&O+3o-^EQ$KSjGf3T1Lr0G3u^EyXUcjo8N>hb8g
z0H2EiYgc;wo~vkAjwaKLVVY>x*i_e=nH#fq!JXp`8%gKSC(G6E{%$KMSY^-5LrfU4
ziz**yuWopTdtLG3H8PWku1DWHqSzc%f>+Q5ubule?)|3K>h2eEANEyG&g{-E4>>38
z(U*&R6)$@6)vFGT!XrDzgEe<VDJ;aTcs-T}s@jK}4c6^>YFyWa9KUU8f0Rl(kebk&
ztaZ}R`z$W$fK$qY*g+iF7nVXrarNrfi^9Ufi{A;KjZGMRlO@R}TBeQsTSZki>8~&U
zi=7D%#M;hcCeGjoMI~v}ZJcP4l({D}joJ=Xtp!>LV=+D5K%dr!2_L2Qlffa#YXH}~
z_(e_%@lZ)GJSm^(n72+*mjw0K-+O?>UFR3dLu@)JyAW3m1e8}mF-~>P|Fj-s;4ZE_
zcEIA3?0<bEz&DYgoAvm)Ot7pC$6y42#u-_Oh#c|{=`Zx+r6g2{4h1^%L@Y?1j{te8
zVt6b!$Q@!1f8r7_{GNKRwg8TATQ@zUHtXn>p_R)LDeB5`aMzyi{^D<gR8YJ~6^0_y
za#i!GR!oor6*p0v1kImrbu6O`vaolixb+YOSn9S>zk0>w$aAE_kV_tyRy)@lf%=ts
zq#x2^_uT5aYs6rXt_*?`Q=k8!{ZXVOW3H^A)4NjkBE{sK=3i%;in~726j66#?^a@l
z3Rh{|&H@?XbL0{JaNT4Po`9v9RDC?CoHfP&lfpxr3Bh*9Qte}4LcT0ppro8Ep{3&F
zOe0Z%`I|l{I`}&+m=e!Vv#ERK`l(>voqd6eWAkK(AKJ(GrQ+9XT{e~{<#&Bo&tf0%
z3&YZxrzWE3tC0!<gYD=IPxe6pILq>gMNz9D1xOmvB$JrZxHFS$X%j$#*4Y;y*sv6_
zVka)<H#bH;5>F#Q7h#Jy+7`DVZaUVCo8db%5;Jo<Hi>MdnWQsxy$kHL44<}Ju9`V&
z$^IdEovL@#M`|98-!7e_2&t^<J-D|jgApu9KNh1oLi<xJi6$21_)a#zC)9=bz*b=}
z!*r36y>C^*v4?xYFR||UUFB0)d}&vdmD<1M$%5YMpP&1bpE=IF82u?xU2vEF$-FkB
ziA4MBWNCUUbxR?husbB71`Osmi?hK|^PguWp%UJU%V1s8_R{%9x%Fk^CnFUdOXeC~
z0jdDaCX=lzV-R9X)oZ#b^FE9@J@1zdY3<qRp}~@J5F(BJ&5^hw{`6Fey{O4_iFt)=
zK6B>FH+;f*ndlcHbB)2|phNtA>fT^(Rpa8PooCApLG(8}jsVeEcCNSpNN%A?RiT#G
zqE?)PumlXXjeIs^%V=>88UZ~tVpYx~QQsl5JT~y_p0X`aEVazAb<-%QV#YlrI<1nX
zHS8i1v7r6_W=`^zCo|)Z*1K7T;-%s;0Z9>F9DQ<8X}(17(n#1S>SoLK*xGdq@I(Sc
zmITUwg=WxPrUG-VQp6s_E-QR9dmc2@;Vz&Mwnv_JS%=jfRSB7DP|*+~FA$9=(LLH_
zQL@EhS&(bh1GOYj(8!H0{$Zo={1ilmmDS0KZBNe{w#HV${vQnqar$1XeV7Z{GiANH
zkRMaw)JjJPbIr6uYHz1+xT(GP;@0VY{=0>+kW;Wje-{B&b;B5v*jX<_L&d>2{GhpL
z{Bqz8uH|UKZ{0`2n0C);|1)3Y1L_MV-9!qJ`qpqaT@`2}ki+&>^bAGhl#1I!V~_k>
zZY+5YjpI3Y<dCc_k@gC)r9|ikphM_l7$3P>UnJC-<5@rd{g^?Q!^(i&DgvXBW3gwm
z(!H`szkJeQyG|9QL@03CoPm$ycy$5<3oTB)+XronbKVPTjs#9>-Vw!A1%FfIeUiu}
zk4B7)Ydov$Lm-|?*g3t`DWpMh->UW-!|TD^)iR+jpDqrc_dc|P__+hD;|)8v?)*XD
zdHZ?^XZTJBeP_8CJ8sMAW*I?>Rl1pXMw21Z1DE9qyeX#+Ip#P-VMW4v>@v9^8K9c5
zx2Z0(N9r_xa?TMFggIu~{j>h_00^F{I|9MdEK2)RWjY;?+r4>F?Xp1i|2f;0Ea+zA
zAbj2N1YbZS+^ol9RiBz?*4qb}uLoDo3FUM~tOjPjpkTFzSdjIoj5tZUCGmB%H_Z=5
z^TfK*@iOjxQ+X3sA=Id#{bqKL$xC=UG?iae%8W7X#UA5Fk+eJxp%^l?$Ul58Nov2N
zn~l`JvkSogjo9toEw(Cqak*R0k(4PO45W;t(Oa|58~F(VcY^10cP}gS4|2n3-T_^I
zcSQA)b553kM`qm#y&uSq73lcNs`^=#K3J4}1;p!hSLb~GYx>iVGQpe@a!w6D#_rar
z4B-?M!D>8)eivw%g-UMb3~(b{0Q>|!31{YAcyet7^U@Kv`KZ}HnYiR0=Zby1V0Eq|
zBJzb(y9aYlvyW-nIDiXe4DRnwPWb&%841(Y-zgNd;x^Y8mZr52zwsrGOlWLfnVPsy
z!Nd|DMc7SLfyI-hx$5v(xLh1;4BexD=EW5z7W(N;r;k!tkMERIUClmLNhM6Zizz*v
z0D?`y6~)l2YlH;dQ*0hml0|)#@$XtqH2P#mDfHY(iPn{%*Q>3AaG&M_Cvm#{=w}uW
zI>%xP1iL4<qZ{qC$d8S8<?<9tc5T11{PsdEvo!Rql4=1O{8G&W$e{c-x=Q%esM=?B
z@+aex(b4x;1t=8Q=xHlaPB9Ngk&(1azR;S2fYGH_-A##edQ~&#Q(841#Y9^wPw}|S
zd&?f%OIK>TMMEJjC&WpB|JZb~x=9F$t+r)E5IHRb5{@l4Y8dQdtV&2Map5E{daHHE
zc5*i^5c02TT-zxIUSPEdgn+!3mL|G5ZoX$}cxUV<aX?nKAFYkMlkwoq5UK8472nXg
zsOtTv$8k^I!gUq%5>V3RfBTzqGR;t^4qL(mxl@5Nh8dvb{Ax_)RD83?_NX;xH|COX
zXl$Rqt4qFJUI*ru()UC8FCc`+*Y#ap%wDm4Eev~gdUM-qO3LYLP$4h$fQM?EL}oW&
zsIpjcUyr|+OEEA-Y=-_+Y##*;LI_@@+2WsyVl>A;Ys>1s4dX0J$1ZUYindP7he|B!
zbro*1K!5fEf8xgCuvE25Oxo+sab3zA8coOKM7_$W#`}&bj#&i!C(0<!l|5IyxOc0|
zu0$<e`X5ds8QHE-8D!`SRAbwtU1j-W-}Dlud1g2K8-Jm+AxhC=hp70;_)}3|pX78%
zwuG_dKqwokZeho}fKoK(^eLzPon%)#hflBqp~=+m>Eg)&=XM&M93xLSt7YNKH^E-{
zC@rFUiJTt-tXkhwP(Gt6X#sGDOKK7?yJ-dcR{whcaB?zL&FDAnGU_>NHN_eDb!snB
z^}51`)7T%Z1$M4$HrZRuFs%)qJ!tvFfivsoN%@J^K-T11TIw_aolS*6$e8-kn2C#U
z0&MrqwCB9GJ1&Ch`AK|Nm+fTY5#!A8=ah9=fhkf~k(Yf&4{VoPKGc%ib4Iu`^hkau
z`?2Ag@>TAofUH1YQ`ZU)Hh*iz#drL@e=g2nzf$-~&`>81Mxi}K>bwHRwgc_RiC4Q6
z$W@;HmK5FRm|{iGC!8=;5Pp=}+(m`?N|pM02=TMyzpu{tZ`bUs1KtM7-uH?b>jY!e
z)Y&uToLl3{IL^T|8<^D&=@byF?0C_0+%xUlFW%K{^*Y=pp2R2}4xmw9O{?U-;v^Bf
zdk$N-wI4%gQ0glk0~$<ix%JLsDZohR$?}=DH`nh^=zgY?Y?1K@KV4|L^ekndKjC1F
z-+_Ei?^I$osViik>-J+`t-$sFVgW3=zhdrUU-C#uU*CzBj7vBPyAM^r_44)-%a{T}
z&go5z%#v^|o^W+{#NOdXPHItjdzTiocUwWGp@A=4cq!G3`+V)@Ty$TpZars0sZ7i>
zVmiKkGw+$7We>RETgJC#u<j2Ec6=pU`z*PaQrsCUskP2M?=o-Mge451P_1>N_NbAO
zCnm1nX?<G1jx!mVlX`NxVhL&!t*k1J%>4~pDX734o{7Tdc6PmmFh+zB#7)80s(p@~
zD?+FD(St6^5Nho#dq+n1%%DN;;S<!9WDMOh{r+Rl7-7`tiZLW9M~zZhKAM10ZXwTn
zBLXaHZ`8V@#cCmh8;cfO@B)i^sh6rn*@#Oc@mO-@+Xvz0%?}RIL5o;;ud80A18GA*
zeBI=OHDB9r-U9)o$3}Sp<uMj}cLZAqXf8Z}BoeVtltk<oAK@XcYV1zGZgpE~LREG`
zc@r*OfwV%C4}iaEfYg9N(N1#JL8LGnIrp~qjSXF#GLWWDBb}d{<8IV_34~syB<sla
z%n+S|$+LrjgrHg@uyl<3<armgs2gxA@BuEkc;n+$kL!g(1s&5Q4kSGWOkI=2CYWPF
zg0PA`N1(IR`&D}ne~zQU!_!*_;f#zYzvG%Q4b%&^l?Y25eI&XdfST?(p50P8q*k!J
z@ri+n*L`|9{C=(o9%P;v^)_Bjo^X)%W(Sg#hEXq^JjDz`5UaURFo32&2UmAJUQ-H3
zh1?vyNju~}?bc-+w6(r^`yq=~1+Zm*i<1l(ahs^HI;1oMd>RR4E@nTx?py5)247bF
zVVeP=yMmyHqAL*^ro$uo_C@BV_VS%z>Qu*?C#AfT^LhsX`NFF-3mRN)sg-RZ(nQf}
zooY~;RUU01Zsw}#LUA5UzYKUw%BYyxyvU!(CGVsks`v+-t`>b1d+T%Kf-u{1HKIKB
zih6$;I5tL8NQ_EYY?<X(<N+1z(=S1Ro2!PgkF=_*d93+maEhC4GV^Fc>0agIbkbc~
zYDN2}c7*&$BiD1FJ|{7~u;&x%kg%n&@Kt(N!u;10CHfh$oC@T7N&ome`g)lXoTDz+
zJY`m$a4qWnj%bwf0$?wxtM&NAAHxFsV1!0ctm5df6y%JEOBU@@8gwr7iN%_V1%iuG
zc;9R->Il@!TI)A_HOq1tAIl8KEUAV&pWfZ09<xgs-S<BwIA3ncjZJ``KX7i^G+;9c
z8X?i(=WvOaIfr;e6>duwiCf@RPC6zqXFZvnY3xixB@{4K+}0EsmJWRwre7j>46%0p
zG&=+}qDRnQyj*vxO$%z-<d>dyRe^I@XtzD33Ibx^49>c1r8;vFxQkFQKFlrhvVEGO
z1<P?y?LRd=qih*$v{tA066FH((%qog;r=G0;f#Wex(8jyJ*g*5kG#)Pr=|i1nA17e
z&od4v=lLBVXisr+t6PLddqia(%%d#x;>7s@MU+c&BR2-5ro(9DCPu2_l^d{~yo9<y
zU|a*B3IB@TQrE=<p4-6G)vdm&vHb3v<NYfE4obn6X>)Y<vG*sO$L<N(-Yot4H0i0y
zJr}EaxCFTa`7u$VPaB3r;-IS}a&%F3$$ZSQj0i8b)H~h&p)!3yw2VH{Um!sY&;69E
z?t6Ba^BibNV$2A%*v3yBxMXf;wW$;2LNP|vo!ejVI}Ew|nC4sc%>Y{k>T(R);LYWo
z9r8+MqI*yW$x8>N(;i7PhR2pR2rD6C0?|y^Gtv-Dh?;{w2&g9)l1L+XA3l1tIm3d;
zlt!zqKrwG@Lk9ChEg;#_y5*k_B<xV;3VkG6uFQ4^9HLs(ZS7XhjY5pyMV|RLEG%XZ
zbcqsU2*#Zw+=8}|v@@o;x?R4kjLObGDMnqP&s`Ma3AnUopUS&JstG6%t$lc8_UOS*
z{imR>L==rjWJOKc)Hm)E;BO%-m~C%$eqFt$Rb2L?v|tp5j4igAZjr($-%jnLYk$u#
zUJBdr=QF%zuhGXwf0!MaWsLW^8Ez2|MXBU!+p{t7Ll`J)heGn-`b34W2Lzw=SvcnU
zZR~UjQKlnLJt06SYa&>UkjU#eJ~>-FqOC84U<0s*)dj^_#-fy<UHQS)<S#B#rR!ne
zE4IhWbMAnKKsl}$GO5UE?<psDECKifZ8yq)t)yu3rSe4v2rpo@ivV>zkLD-a>a7=R
zdrGP(ltKgF&lNt!aV=I<XuR!I4a;pqJZ!r3UYad@+^+taJue=@OO3vw+&41DL6ynq
zL1DuCxppW<QvYLpmhf#_n04igcdEr<&ZJ%pp!z%>(7ZEyXdT#zvc*h=`rCEYSO^oV
zC|!*I@zL*buC}eRavRE<!!M)kaff(M{;;}HxO7KI)M2C!-+h!gC}igv;JT=ErUPey
zE^rB7L^elaADWzR`LtA#<<zS5MBhj|M2zaPM}LTaodn+>Qi-*#U&0y0WB7=-{jJ{r
zTQ7avN(wFJ*=D}T_(}TkVfa%9LqxYnr`#law%DD~Ljif?-vNrN{_FneANZRQL=SJ^
zN5`%`6^;a|u#2;4kAeFRMB7XcZ!#)i7p6l12jUmyzbQQ(+8;57yJtoN=b8$jCEZR-
zyVVMJ@VW`s#?&%I61PsK4vv9!f*4;3ctn1wq){Uxgh$g(emra=bZw85o;V9tj<Yr8
z{A+#fYh$BX4TGG5wn=831(;ZlR;Q*_paI0pE=V6c7X~$S9k!JX<yp<!izvebF38e2
zqD(T7NGA|?Gcn`JRTf5J1?4)wPNe1KTFr$<a9)t}eimfS+x$}cO#=Svm#w<xIlU|`
zFe)fzupU&_Y@OsOwPi^xgYV}v45#~2ZI_6btj7Gygx@pikxMepdG*2iU>(c}Lo3H6
zA7-oKG4k)+nA(!PZj&;EYcDZ}dyc8smr5#%A4v>I8Ww<T23{((n-cztU4-P6{l%|^
z2KBx|^EOeGtuBi1vD#RD`b;@yhb5$)$YY8S-#<m`WcY8^r2qO3X)bI>h20ute7vN;
zw8P^`A!TpuA{Ir^U*h=pe7xnG9uLNZPX%Lw*6^A1<@Y@;Df;EAW|kp;C+Yxt{%YXW
zug>Yno#e*K0TnLl0qb~$P6nbf@c@pF)A$;`V;THM--MF{St>XWvWnFDtE?*);XogI
zd&@X_oPD$Ve1JX9R<e^~$=K!5nm$zZy!lMz<S)R!rQW+ZqW_9uBtS`ya)v72y5G@|
zwj03_1H%vq-J14iCXAkyzngHhj9;@?VTB24E!%_%o+owg0ZN*GP?upye5NDg+`)eQ
z=!iogd$~p1vOP@q4S$XZf0-})$!-fuSEqcLqMUPiTx?+b?fmi}a4~FmosTGa4U?j4
zelTx?jGf;<^*fem_(0O+&Fe5&!V!8)I+P)lDolt-T$DfNF@~1B!d1dZT%cXUd<D3t
zu?f&q<uBZO`$ANbLO;CL>aNx>qtFXF30hTUr&oy3e-f?S6<qArzAxrb+5f!%{USBD
zn>6^UPpOKXXE~=HT2j1+fe;8~nu$@2ioZ!t^ZX6wN+*#gx`Davg<$r327XTZq!$Kg
zLa}f@Vp0#jn<G}*HbW}32wwv8?IOWCi@&6<y|45>Ipb5>GBpWs?77Xh=LeKAY~^Xv
zKrQZV8cW=LLq3l1a{ujyaZ~if-7rg{dOc+`Ix)IJI_EnvnvhIbSf6nEH{bijEWyQ5
zMD}eF+$1)X^J4U5P82k`(ptLkY=zibRaCeSu$ARyh-Zqr({@P)sGeX{JBZ`FCeA36
ziH-#Xi80-Msm-ydMPEIGmrZ*^pmL+zi8QBJdNh$hj2~BZ`;X2q0lA8M&aD(PWPE13
ze$Ya8f1Rd}y(AQ1AnX=sCpMJbtR5|M7ND=UEu}*Qff?EM#Tjo4_~D&H%qJ5L)lQU|
zHziijG{;<4gSD_|wrZ-2IC#+bKOdG&e6q?cvd*5O7CMyPzC&yd`UW~|R%7-e3u&Cc
zeDD!LvCU2)Kbh*o@(30Xrp|aD&C|g{OSDh4)z&VreZd0GP52(6MovM@7zR>S@Z3?^
zS*n(}suIvVqOy3CSC8^R6JcLjMdXLos=9S5JbZY{flx#l?k)2!H?`_cGCiO0LxB^D
z*ox=Dn^g#=K%rZ!8X&Z7$ooH6LQ4O&^ke^X>61GVw>Af!O@4@xjr`7F^}&l=S;&Kd
z+_%=A0OA>LbsGVCxuw+h>pjgex?8~mg3^0i#OA=#smZW|bSH=%M3P8qfXXhI6<LxW
zr=0oT>lX%jfaj$nZ=_(#%4v<n%WfG-DptITh~G!61uapI>GkVcH#4gwlGQtss1Nr8
zyXBx9!E@RVCHy;Of%xnW5U9cc@fY8A1~tlhKdKim36?mr8bW*rHpT0Z!gX-C$MH#b
zdjfON7z^^XxT5tfX7M?*d6L!lU3Q{Nda(uq#A3H9ypbET2`p6SZrP*GkY#~b@ps?(
zZb3abP`Lw>VYQ(mYax#*3+UFVmf$0~o>srw<)Uq+8<DaHKudal%*2L;bPbYT<CUDT
z4Pp_k;wP6KXr;mIoMOdHksvlRval-?kN=?a$kc!tr=yGHoo;Kt_*))7xVrD!!q{uO
zS_9|YHpJV6be-Lw4pU|Z7CB+SE;>j%eIAGVGm3n#cy^ys+Psi4mhtnx4T105a{6wO
z7n;9WRx3$kcqI)uZ_Giq1qV|(`ff$(MGu7@(tKqlyghiy-AEYwlW)~{TG9^BLlq`_
z)X`>3tm)H!9a%0M=)KzW14K<KOZsfugN~J%5?O9mr5QAtP^meXu*+W0Pl;V4jHJ-X
z9++#7!e;yFi5%EeDxGzs?mzn)M41hV1Fg1F%rbWXzcQQ+me*nv(?LSXx+eW#y0v>A
zy$fgGO%Xee!?!Agf?*w5<bzCQ8CuUM_mSgbC1Nb%NIB#`IuYg#x}gdT_qp|B+eCFM
z0^Pg__h=H=_Sy25(@9dl6fi73EzN*7n!E<QC=)niD3BvIDcujYFs&f#^0E^l#fZX&
z(u@xZO($udvkUau-_9YjD*X6)GX&KY5H^-mOCEAb5ywmQ&b3R8b?r@JPlsj$q^kOk
z$fVXjpd0x>hT6F!d)OU>d_iRt$ZGUu=X&MXhqsH~m!Tb-Y;g+P94C5WTo2}kflDG;
zmKw1WSdYIv*pG6jHZNW%(})=quA+$Y0bfL4J}`UCr>8qmf}*Luv)?M|ddty+*GY!@
zOaWK@L-8qTV4FQ|&25ObR@f<<<oDp$ae=$&9N8Q>?=`z<%}MI%6aqv{krz7rv4}mv
za+sn6;_@FiCud@p+YLr8_glcQ^qm7)!w6-`t<YhCrFR@SYAGbfzc0Pu2=(Dz=L*+l
zSiLp^FRB^;Iwj^I#qe5N%NRVhNO@moO!`HcK3AxDP1Uc{zH_Q(%)F1-4bQ_MIWAr<
zmcTX5vr#E!<3!p7QkL4I^$>h$F4R?MD=%apcv;cWpcpP*BrdvDJN0dMq)*hCa-9#x
zEgp~<39Zf(Qb(!{$FvSKCdgP=OAHVdWd(g;f#k*^@k%u29{oQc7oc8BWQ;h$OkV%b
zzLbU!vMCKgnDFwrWw`AV_>6Vg_InRK!it0V8vXz-s0|_H1TvojzBuo0&kl?gMBc{U
zQIZWm^S(b;E?{Z&u^!Cvkd)HX(N>Lx2pPC8d1822@B)YFb?;J4B5Jt@?xm_rO;1S@
zsW#BhZg@8V5<NbTD7mfiib3?=c0$WUJ&h!7LBvb!VH+ETh^TCsNEr2VT~gs*-Z%?m
zJxLkdyJ*H6kN85tIHURzWr%9hc;AujH=(hUXri1U_twy##35pRsQ5%|Yim1}Opo6J
z;g7bsQ>wa0h|8az&xjq$%r8dd{=5(QD*D{G7rzrJNO*5dnt=_nSC}Imoc)LC6r%=T
z+||mU+`td{k~@dHkUn3Fd`IYTn}~;=g;0J`i~x5T)A{l3+mP%YLRU_SZi$tA8OseJ
zeXb8Zbib;%_qdxz8Fx{&p0GkUJicz$cZ%<2EwR|}3#v~S@7W(bEmnXj9>-oaz*Wpj
z=qF7<LAg}HK)K8S>?)6h2UC}0!DS+{YNd+)nY@roRKh;!=$su=<ai8;(^~LfIKB6f
z=5TfRl50rEuFO{v<T^Cl?nA=Vs#l^chtzoPLW4nXR~jkz#kpF1*@Wo9V<?(Khrjwu
z)BELRy=4<tyrAuqAr!Ltj<m80S>zODIi(DU6t&8TG`+%8hYbGPo7fwi>#EsaP&!s!
zr>3cRb}Ub6K4aVo;`8n#(7{DW5qsG!bFiK*8VSZr=)qCRNAX8JCmMt)^k~6YlGSDe
zr%Q{h$Fh;hNfp<)A}l!py7?f>mmgWjoHHSzo@2w+&J1<}8JaRT#We-4Y;Tn2hzCaE
zUeP_0V|;w@=*D8<5zn{m*~Cs@ru;{(m<1eQYNKU4fVb^*UsC84$GlIjmtd55RjH$D
z*M7k&C`xVw`_bA)bE_#KDpP^b^1y~8)F%5r)gI&oG!_%v4jF?K783ZaANRArnUcdH
zjm2G_P<y!P)_j&6cNL+P$v86F!-hir8c!zKJ{Zl5FK?hnMP50*iX(9z;P%)S>P6A?
z+oX_e{=TWivP}S;HLlBZL<|!o7L|PSy#o#Lsrh3l`BT=dY+T^ueW(ZR)J8FXbVx|!
zXXPYHc&kE3eN@`kzeW#-JyF9kzy8t%E9U}JR1IYBu2blgTra~&?wcqm$cZ8`sv)IP
z9Nwy%!t*=oKHp}Hzq}LUh|0{3Z6V;%J41H&5&b+jIE{k(mPbk-p_lR#?K4GDeoPVt
zF}j%2lk!>kUO$E3lCkY%(8GJq8|65gSG4msOP>}!7&i7)L5RL+vG_BoErgwATLRyA
zpYj>^jwB6+GD$)zWx7^W)tU2;kFA~It@%(Uo^XQ{BFad1c<0>v-Jy>Bg90Ja0KQBi
z%Um^DkdfsX>ZjxpRyhwPfdw8PJ=>{jEN|Fu9;g|$<qur)kZK-IVA%LbUE38IFK}G3
zLlOA4R2T26^vny>LKT))#4pvbpC1)E@Nsh3fpC91A|Y0T>fd&$f93l?y{%M3U}jMq
zG)xriCY~f_luY`Qlt`@gwvqw?a-E(qG>2vv-r)X8C!B{?LcGm?k{p*52Ky_mt}v81
zuY@11j@}HB_m;6wX2Maz>|9DjF}(C*fv>};q{XBqj*L(`yX}MfnIN-pqH1V8M}^S*
zjr0K(7zNb;yUbcKkMIo1lR2zBJz5*v0H@*%hnZq3{D=m4wn`MAJ5jI9bL3l$@Z%Kx
z>yfX5CC=Wp9G_FHR;feRi>i5?qHnRz5%CAcM36;8C+Tvb%3!ZfIW2haJgs`T$0-@P
z6az=qa=(;cF-0H~j@{6C#zDuwOPelxm=qp46ioXfD<NQ;J%sTba+)$2q~@Amtc3iW
zh0WRX75rBTg05Ka+jkO}3j8hTB3ynKZ2H0`KdI<y7`jS<Cfes(Q?hvJ)-L~M1ax^O
zuPvMMRt;pCggGP#1`Fw3wNl{aEm(4xqp%zpsjXlEY(+g*ooui^|C!Z!UIM;(cS}!7
zB)6Jre{@WU)R*ZyXI>`R8UMvQ@$hrH|Mcsn*;3cv7+DTU921NEp0e}4A;*_gSGdz?
zNp|V(5|MNYDhzGZp&9e;y?FKk>1xLMPK7E`RG;LY2gM$dwUC35q8{=lRo9oen_1#F
z*JWB8!@ed%QUt1495Pzo&&w>66oB`L?-4+XsUhRB!|N^;&5Qm(k-$-YJ|v9h-UnkB
zTAHk~b=R{_jx5>C8es>pg-<JeuZ=kgcG2`Jj|atame?}T^whBg*kyZP0WZ9sX$`+6
zrsIyuvGJmKnoN9b@;ZSF%~(EFDtzhg8nw9{u0I3E6ZtU|<t2BV!J|1ZlfgzX2kAn!
zqjeSD6S*ZF$&9pVEV;M#x#TAA$-<r;RGIGRF0_jF6EIeJx|+Y~ZIu_BCpc=ZSg;xF
z(vQsjQa>%z-q)Qw@#WKV*L}58>SOr`%ePokC#-c_f;WQ`9}yqL(B;J`TM#K3<E+pM
zjwn=!h$obg5JX6_RRfTHtgu!}{mN<8Sq7^wDLjccX_bR=OFDWZ#R!ErG)bXhyn{vw
z(o>$21tgQX97>^WSs~E26oU8=Vi=_qD!nbWmM6On(pVzzMNHex98dy|aa*w*Qbi&l
zp`2@!!MUls8Mm0RcGmaukX%EIl(9nRJ?k-n#0=Ruf`d>_QsZ55%j5*P!~}w*N}1SL
z_uRKVLlO2uA1MpRwn9e0nRrk@HC31tIv*~Q8<y@~Oml!@f+U*zoDyOK_G%*r&sRJ|
z^mbMyT`hS(1r2GlPKHU{67@u}U4|x~%Fp6>tB2yTsst)9`zD1v5>Cuhy8}&9)9~bY
zxsjO}XwPUxVcvl?5}Z>8?-l$Ou0+4D^r-IJzgVa_BcZfgN|YcgJ?lwsLM;(;Nn<vA
z5Rcj&PHg8H;d1oLiGqgj9MFvn>G*F~)j9sIW6zmi?8OiUKv0)DdOj<md@Dufg-UOt
zj__1W2+Z*`Ys}3VwNrr@${7u0XPYzr*Q^;(lsh0WLJg;D_8FlJ!P|t&(t|=}4{Ndr
z7+)G>*Akrg=5+TFMqNg`#!qonV|0&+TUJ{3gBhsziB4$(v-}WD6ym+87E^o4HSo}K
z1?s8PL;7A+PT_%fUe2g(D~ao(0*cR0<qmOHVH8se&0t>4VV>5STEmd`--Ttfyg;`$
zRbM5U0A)=C^mF#P9sQ)h1)N13m~9APv`Q?Y1}x>0-a1viIJXV5gsGBIRK&a9igk;t
z0h2%Pd`v0Ruif9oe1qB0m|mRv%qaF&+!Hnx>Un-^;d$<P;d#ZgzQ>o9hpH=Tkj~@Q
z=^j`c4KIjL+FA(Q^@MX>xooM-x^+F8VChcthH_xV6Mvb5TxX+|oq!i28kT&qn-;CZ
z8XUrcHRe4G#Pff0Lot>>WkD)xuA$qMI%vAD1nfaF1<nvvR;!~x8=YmS<YqMxs1Z4k
zg?rT2w}F==Qik#1QfUvNPPW5wN$Dp(DfIn3{f4HG-|(mwoJaZekK?nw2-1RrhS~!n
zF)Wkf@jG73&F&o^qN=O;$*ISf%iQu(ML)9pP2^R+-qw$kO5q*ab|TKfvQV*exmz~i
zP|e{sjYy$U5|>Yk95O=Y;0lQ?FL7JNyZK@2S${Mvx+OSN%VO8KwMTJ-L0$o?%e@$i
zPY1A{<{SwYqbKAMhN<85;F$J$JiNnL0H&4fl{1hYbO`%H#cv^}>y^{Af&b7+*ZF4&
z9w&XVQ9p`XT~=O&Wjf@+tUi>Tc{cBR7;s|lJ~9MsjRC8!U3m&M$|+HXkl#_txI#gO
zW!#rHLZf6&8+(I_lGU8fG;L5!)m$Z}`*TdRgqcjrY7mkQ>Xr9Q>ScGL#DNT1g#lW7
z{-NUf<%L8|bh<brA?Nlut%(8i&KmCV=O}1e$)l#YPo@V9Q<dMXyCVPY)e91@g{5!e
zR^3MivqjAR(_Ed#3l4B>v|iyTKI!{^3Of&=Cf@zsgNUFsl_o{10#X&}RY5>HNJ}U|
zq=sIM5C~NPr6bZIs34KvdjJKgp-BlMz4sP632>M5`=2}iIrq%m>|}Q)lgaER?|$=^
z=lMK_zrndv3AT<WLIyNwIcUyM!jKDrN3XxOY@fd}F;Bcsixli<T*8N;jrqX;J}K|B
zWo}4rTizq?X1kHbv7}gOVm-idZ)`P!WE9eBhtbao5kyc(Q`G>ns%o>^8|k)d5f-`F
zHBlo`PzpbtVCv{^=!@COxsewxyS|`I`E}$e0aQHxyW8-A%dX>WjnAYcB=<(JD1AM~
zaM#JI$D$t9pcQ&XAUuJel75jZc`|qKLZn)hegI=gR85$%)_HeSpV-rF4OsxwGbHII
zGT4%kQH%NV0c_CdYer9!^Olnq^<%3<VVaW6!{fcUf|eXD(sfb#xy@ARXbC+xz?0>_
zZM&0NZW#O`H-fAnFom7<*LoM;SmzkUM#Ic>*@9+>gryz!{@AC=6xLnwcSv5TC(H-Q
zSH2@+ZmGX|m%QhImLk@SzToY&gQ3iqQL$HbJ!~;=sK_mqw>F=s3>V)RAd-rCSQPG6
z^*ZgooO|5PO*ywT23=(YGIwIiZ-{95wvU{xv^;XOBxm=vIk8CpoTtmu>ic<xUyRtX
zUC)EpY=Fl^6ZD+XgL+DrFgA2jF~k^V2HvRYt9pxC)G!BCN9bcMGm{=7rCPrpye2@9
z^OUV;Q}YB3wvI)sZjE&TWXEn;zfNRFe6nB584}Q|%Ybb7<uZBBHsH0s8OfN6`NP^B
z>7I3XOQ~32As|aCW@As%^kG(lz%%?k-UX!jDPGszn}S|#Rt6=F#@F7YnO$gW2_hwq
zR=7DhE7dBhG?8;87NBbyupwZdPRU>|NCJ9UTKqU5K0Iswe#tZn!^=u>1Akg}`Vy|Y
zEY9rdf73iD^nT47-N~QPZEw9;N~LBVs5F)t$4%|NushD6-$GLk!S8yuwQK%U;XDCT
zS!-!$xj4<bFw)prbDph4p3MAr+0>l$)l#aD34hA?=2f046#kmIiilbYdVx-!DBqgr
zGm(pzx<~IM1~CN<lwC8p#%AB+RLHcO-_jkWMrZsQmF`(eRI!|P60tifKk|bqWLdwh
zAhWjx9SqCdMQ&>JaDw89`+R0;zxv#K!h3L!9YW$G^@6>+boNWTd6;vMcxELL`fBM0
zNhi^8!U<`{FiStN+^x=jHn(Ht0Q93g8Z+@xb8BrVpzQ`vjeS(Ts~W=$Wpl5jf{~Wa
z8PEvsgJx3u9SoL?+;pj_+p~!x<;i+qF`=S&JKr*b)`{1ivy?90{M_W~;>~uu$?)oa
z>605pn(h+;MVb?Eb2P<=nx{=(3-fDhcBBuot2g5q2S#A!qBHli1p_GWd<lY>G4qXj
zl$I{s-}?KlK#WiQu-Q=61U83`T3g6-{rgQ{6`5<!-luq>;g)oxKj~V@E2g%iAGW^)
zuD)cTX<Fe!&U71uB&(2HTdxFw;Klc?;fz1t4vNvJ;P}_&UkL=6f}Qo}U01=YdUCca
zZMb{K(S1a@1l+QtMWm22TyOsHgDu_kJ<P3=2woX(+xUadPB21G(E4P~h<?~6#-mOS
z?D38mJg;MX%9>B8ud45dX3To|Z2O$0(I-=5eC?e3MG;a{Yvw;`-k~*;ndUGN^OSVe
znq`d0qL-XGyY`DTLtd7A+RP?KC;ZbZ>3tTh%W)p!&-5Rr&mTJdd{_11Ih_Q(%oCRZ
z+a-&q-QO$T1hIGdC3h8fUs(?cyYIg67gg?Az*6&!X+%Zu+3Dri5oH;q2A|N}waeRa
zhVJku=_5+Os_1-<V+LK9*-tWdT$LfC9*m}fbgD1dS{sPiuur60Nd*eNa-6X}4X0S-
zaIYU+rG%H+(rIcjw6!J0m-sMz<ZpS!Wb}~lJtW45UeD-koNHa*|JTzPkm`>pe%It{
zCHY*O5BcJUJLKA*9FcRezP&kP^{e4_dHJqrW`EoYd-o&Oh;V$N{UE~)nt1|JU{P8D
zsZ8#n9}C>B^RM9+cX?~keVmX%Khu^W*c-&J_`a+Mv^#`zt$+Z&&JV5pF;|$#p}YGr
zBGKvYy2_BPlTazSXq1=vY{fE3c<YN>1C6CV(X1e5)}3T4=qGy2|12rHN_}>^B}f|;
z%+rwe(gIKK=o)QM;JC!dT~7#64J@V~Op*TA+|`W9k6=M!LHTQ8SNM#|<s{;Gk3#cP
z`xtXIlhXqZD??j8@KTpTRonywVqZheSC<RLICYl7#iRgrZ*G4}vhZVp=5P;(yJoHK
z+x;zx)D#}F)R;Sj>#Lfm>wB_W98TxeI;5|M?p*bgaEbq{^6djAxrxqI$*M?r<x5O;
zhvbSPLoRbVIjob|0>nW2RMzLTe#<Sa1mv6X9=NK@YXPaUcE$!X&CiQ+J+O4f&IzzH
zx&7N-l%z>L9QySQJU^vBp*KyULVisrJsiJRUXRTSVsoVY9%mv-_c8g6N@iFbGHqa{
z#lJpg23Kke?oS(r*7dcvl;8Q1BG$aaHT?ISQP{f9HS)RnzpB+`pX}e;j~kuDmdhf#
zHx^{*2c*lk6faJ6&vO)YC>I*KGeQ_|l(fG$Ckek&SyR`+lAV7uN(pThu;Z!0j=F+t
ze0j*El_g=pEbeN9BEbK?Dz;+sU1*X!+aEg2RKy(u28|}(PH+1MkF_)r*vGT0zNn#i
z9*XD7aqUlQJM7!wP4s7JRe`oDC0>P=|M!IcePY1zK&n)(TdV32^CmcsdtRD!8_>@s
z0ujM~<99FZP5vpF*(Z5&&=ZesMVhyX0oxuR)$0Y6&sjp|d86hJF1ZzWLrT<Nq}P`8
z*XhoR1*2IikJGbA5wrs@!5`Pf`zGq*lHyDJ<M-Jm^yR*nZ%h5nU>3O<*q#qXPbl91
zOTnnnuUTlL0-IWGI6RFWXIger6oE;&<{Y{JFEsow=lnyYCD;@^VDa+Vu|EK{Sp>KV
z2^isqp288m0`_#t^|}C7NQ~mSPeBkxDIzPezu6{J45RK#INr)aiBuovsNLbY037oI
zfT8elq;yuqY{T0<#(SuJ`pD6v<h{mvmzr_o{p_Q!>;;TxL5=Y_>{T^x6k7ntiKoz7
zpNv&;x;Hxpv_srv-|4+4*St5t<rR1X9Q0G&*FA39BTPz(fSiI}$AGN19-B{4?3T#D
zsNtrWh(|8}<x+qjKwDZ*fOwKZ<#_SEuR{A4zU;#~wkRE(bT-6K9&w3<0z8$ih2!Ka
zz-mW{bMx6D&p^oX2VP_;`;|Aq(u580N(D`;HC6LslG$bU^G73Na9f1x@wHLlO?-%s
ztvnP`^a}yt{?I8r?C+RvCg3#jwP(GO(}H}YY_%7}Cw488OKE+1{xfFq#Y8H0SS_IR
z066nUAEQrQZ9<wxmqQsLae(W=GK`YxRm-DkPtuzoPaev{?fWvISHx`N4*^Nj$4KG&
z&K0GZhS@L{t@pe%`&7(g&;QoAxozPt1C3cPQ6>}&FU|J=uc^f45}MYSJ3!Re0HEWG
z08_SFrV2XL?o-poV0LG_>3GfyNa^^JMAPJ2I~!IG_#0Vh0ux*87Gr4J@!w9!*i`?i
zQ2%@Xotli_LOI`gq3`qJD!Do~(=PnR(9LY8K?Iw>Y7=f*%p%4ME{45d`Cv2jJq>??
z5e*GD7}RXjRh&WM;d;i_PUf(_k2UQ9#+5pBY*@~FxyHg<kg;6t^lpi9XX_Te7SR^L
zkX-;bX0x2>E%7B)>AJP0Z-H^q!mxoZ{X5KE?+N{LfK<S&eQ6Wis2YWo13yUj160PV
zkmJk15~VXG`uF{c7l_oy-mb`icXI&x+T`ZOt(XV+Ki0A0kte?EMfOT~fUD-RD*5Ln
zNrg?e*bn+<AT7f4aLgW~#_s>A<7hUVy?grN+<SGn%s?TE<r8UZ7*zs5z|k|UadqkW
zb)%2w$s-Re*#3I)7O3avTXzJ&*SMpdz@uG`-mp|$O0iyu4J2fnBX-+xqs`|d4p{{-
z835>QGlhnS#VQVOwor)B(Gh=&9nft8V&Eom2S9x}{)N4Q%k-I3Z+AP67$k`e#fUUd
z?7(Fg<1d(9BYeOK-9q5hme%2qP>RrZa`OrZ;ZM9_YX1^Y{v)IOXaA0M2WGQmM9cMu
z^Lt{)@;g_HK&(AgnzCI>{gfHV)rGlz`J+*-RNswYOqa5f*^7((=iT|?;U2Ij3QND|
z#0G|Z2qN&%h#d5sM&6&L4~4V)*uNoLowo`BqJcDR->sV{olJf}?VJe&Zm^XDNiQk3
zcZgYunK^F@h#K3yGoVf~bEz7+RAR?}Q4a406reA~mG6)$wm8}wUHO!hWQy8%<mSM8
zuTN+Mc5;R<>?r~st0mRg#X6G`2%wLcmMT6Xx*{XyRyAVWuJ_LU6HyM?%iBQ|idgqG
z(who!oJ}17Lelz?D@@*1JM31!nrqB+PzO+uwY%H_t6i!sOK_tB>e=z~XX&fHg-f-#
zlKQoLdNj@V^yKY`AV356*isUA4#;$Hz|)LrBe)rG185V6h=%y%eC`q)e-7)Z=xt{k
z-BNc-d#BJTqV)fk6C+mlryf%wZMcnw>CWNQz2V2n+9}spO!928Xq9q<Y2m>JFM)Ry
zsry5$@TJU>#UT;Y4Tj-2o9UQC7VpIAeP^fC+j-AyHoS)xhprFhJX737+Z|yC8?7}X
zkM5Tx%iEuGog7^>B|+|{X<mrSte9dW)XATp9oV|+?5npKpMi`5B2ibQneBSb_-LAU
z6Zf}=Ag8av>I_a-3~zxKMFwl_!veXf#4ci3yfLHJUvcZZHN2gwjpN;_1yz#gSd;dc
zOey7a{O-Mzvt0RwX#Iq5^BU2%*&`;^RcMTHsH35ge5V>SiWZp+ZFsTet=N~usF1<u
zcd*SPlrEkhCv1Vl^aIQ>hNvRK!|qr6Q;5CZqO&1*{=o*l$`e(N4$&wUS;a$p?x0tR
z?TxIOq9H;_R`2!!YMCy?Z&Mr&C_j7Z6|CDSz3jt@cyWNSPVx(*0B!=%<J<Ax#z@bE
zms<IIf5yFQp#NLK7JI;!{AQ))Z>)F#peHSL=;$DE-2$?yKdla-{EQpsm}BoZ0q!%;
zpQMbPKJt_$!MT~Dwh<e3%MiE;;_Bkhw`A$=TR!@N&gfl$0sLz_O5qa`lye}fk&S$c
zh>c{&A7HR0m{|Icm~Tmw%)IB0d3c{#<J#ePb`%k7tbCzEAfhsV-Zzov%k@{=%P@1T
zXN>=n?_5fF{@F@HJYP<IZNnY$c69zc3KVvWK79(g@F_>{UT<b!NIl}svv>EDz~ZfQ
zpQ8*QNpH8<xvA2{J}nicb>7|9NrJ`R;fZaOkm%1O3-^D}NaL?A?C)Dh34d9+FLm6E
z`@=FX)WkOUipYymIO)P~#>?m?BAG(_{(xS>EklUX%tA*6hKl@az_x=xJ1_KlLCUm2
z0pi+i(#X+*d1d&+1+4C3!mc71F-Nb*<_1_&sKoYI*!@LhCr>|UrC0oLza@^?x0%c*
zASd=W?ie6G?-M}~+h)>)2;FC3kq*VfIyqc-?L96N_7Y{!UX{hQ%>#~M=z*VK-$-KV
zv%H{;6gom{<(TL~A&m`Ax8j5G^EKo|@&zf4Cn;_YNSm<<dqs(Lwmb)rcg`%Yiv(97
zL=0P(DPj|~e);R-!qWt-xtBKWOK%`2+-q~GRLr~w@O_rt^^AdP)T#}%X+-I5V_%iD
z1E#w;MZF^Z>@Z`_*m(`P#AiD$C4N}5N{3sjEM6auvKZ3HCN{}j9}P@Q{06;L54Q{2
zr`}lI0%UjK@iE$dVi&;q_b@m`jC>SGM~egZL2q0=#TJ?1Hb_g1!|a0I(6Kg6XHr_7
z#Bglzv4zdOMf+_x);u6r1rU6#;WU!AU6)w2@WZ0YK@GTqPt)RQ+Is&~Gyk{5%0+Ek
zbta0Cr2;X(FV8D|IBj+nqGFH!Sta}5`|D&MNgYY(r_$E8ahna@9|A$T4BxA&5VNK{
zRN}5{{d+d81=ER}vXb%X7ZbYmd5f%45TT9a34HJde21KA&(RM$@%`B~i`_DFe|I-&
zn^@TX%({x_i9dTca@3z|x@hdFF?3w;CO@syzKjctI)-O#4pI)$!8Uj?z9MCSexjAY
zjLy)Hwthj}p6WMy*7%IsddYZ)KtA=u`QV<zU}*p(r3$^ttWl6lwYM9sv8Ji+NE~~U
zGNwp!Ly%GNAQN!`#1Pi|vFLl+(zW<E+u?SPDr^QTr3dq12H@`m|I=)CAh5MTBRwg4
z;UM8OZ&&Qg7s!?)Y3{j?#gbD1Jtjw8olDkNUJmKZr;sK#P|s&E>|4#xk|ycM(Q8!3
zH5tFmj}sK^-n9~I0-%eV2+?Y@y*gJibbp0ej{zOL?JI`O@trZX1;tN+;X?3Z+23~s
z9*4I`Q?fr&YL8_RVL6-}m_VzoJl>ijUn9@ThzV%9&if|-CivUo4r_aqe)RQ5&0x>8
z?{S;`Yz)ZTx}fZ^=vt7>yOUOG#h$~6?5aCo$$rebZuM~AZFwBB=i0E%bA~^tc{_PH
z;idZ(7Zj<mwGWh_l+dSs&&h=MKx?y95fnknGXSvkZbd%#6E6+91K14H4o`bS(02V$
zQ0*{Dt@2D&kZl&p;%9bSa>Y+npn);WMFH)ASK?ulb)wLI2NT^d)YK+Nu3ZiJy2|et
zkM|#${Kp^8j~i{lIMOvOdWgl&GY*mPg>!LOWYX`7#kqB|6!)#-9U*4PxYjpY+?HC%
zqGf%dkK4QjqdUi>xNsNa<Xrw99R7^qo98)+B~Y`6Z<k#0y|QTAGf+MmVF3~L)O*##
zd~l%ac|l=qGyJJ;&@glzCQ}plQ?^hzlWuh0Eh-^Zu|L_rW<7hKw=T=a3*Cbh*S+R4
zuExf47}}Epq#Q?UF7!z@CnoXdxL%|miaHdG<b0SS*+$5yZ~4J95YqlluU(C~8V=XF
zWRJG3aN4r;f0|RI44Mc>9k^fL^9JkS9|+SwkfQEqed<=HqfUdU0%ipxAGV`2EwD1_
zepX`G&~zqO@|F=l3ay~uG~mneSvU2O2^+h(GMhq9Mcs#H!nyZgm5ag2rf=^;Qefac
zMS_@#MkSJu#DPMNo_fNADpfY{%LJaVjB969>>`x=4rUuEk9lK|gEhKjsZy|NxqKvr
z*;#di+`(Tdrh``2x~-J}+>}Gnc<HXF-Cx95y|=*X6d3AkKJAlU@+3H2q#a>}T<DkE
zi|TsQ8BK_jj-UQBr)TZ{g_PnLKrK3KZPb=r_9{eQ9g~mi#;{1cZfBrl@KRY1WG~r+
z-I%=fW+O?ro)aMT?qQ9$;P8eKzzRI-J&nBC{nUe1FdT*lMnSaie01dP-TcO@U9{h;
z(h?(YmOAUEJmj>rdUpUMMLa|L;69)q_8>i|Ve|NvV*<$+_f6P@Pj<96xm}6;!PDBM
zjt85yH6&iI*@=TNSA<WfSwD9y_e=fS0>&7Unglk#4dHP1fZDsDaJ<|>Gr*n*O;`k|
z50G)TpwG?E>8}p7=54UP=zwmKulWe-NtiO|geaaYBxnPlhQDOp@^%=o)6oQtwn9y<
zia!M8D#2O~3vTMSl6iUe^P$34URD1m(t$pWWRaqSf&XSXng80pv?#P&>uj~!vQg*!
z(W4&!lLIlZ_Zh<=D!pW^t+0t4J1JWkhu57n$8P*qH6A*y*=FY`O1FmT>;y_!)jzMe
zHdq;wh(>P+GF0far-)m13xe?rql;Z-k8O8?T+2uPZZ1~(b#x6ZE7Ul<<(K;QHzdSG
z?(Iy3CoFUw@r>;?Sn4GF><HkHq)g`scQN8;dE@p#S?A<A#8)xCV~Y8tabNZnmI@Sg
z)(UOi)xqiy8|BkxxL@n@IZdXYM_o-VW9nQgqs{}W3D5?J<2wsJGo++BkCx8y?c(L}
z!ePVeKc!XZ{Be??-2_AMBkQJ(a%djq+Mw1H%U*V9EZ+F!kym`-SV5-z_R+>D?vytn
z%8bI0+I%1<UJP)j^N7$%&rbB){yJX$$`ZcVBRid(?KYt6STd@AI9*wQmI}yqem#k|
zyPNgx^<Dgq4|sjBX7Q6`l}@86rj({BQTu|B+oNxA?lFG!QN4np>obdjzNDiYlR(o<
zR=*;CLatmomg;jc`Lg-R{oFqxV+Aop*IU?yu^H`s5QoAxB3|VDX|MVgQFs%lx#u6I
zD?Hd${C4y7JFPkC6IP1DNRcK!V3dX4!~-dkjeg!=HxnFz<j96Pl9xqlKh$bnhil{O
zsw+zFe!&KdZM-Ebm9e&3O5fN<pT#&=ui7#6v3Uyi4TQu|=2-M*LN~a##|lvX`I~_K
z%OrzJkS6>`G@JZi!I7O1|D6p@vLBjEJ7KylRCi^DTL1!sk8I*yZ+O5Cu^jm--(#tL
zDyzFkzSN(U$B3qPB#0QXfjSs9IB{b}9E}?v$rEVH7Z3BsXu-Kr99bgxAqU;PaU-Xv
zA@#<pcbcL+L%8Pu%Zxz`Nmt9Q*=jRPnCDg`?cxM#<VCS35T|rFq_o=T;RIigA{K=m
zUIC-Dt)cl0wm$$3nNm^Lt=wR0$C0Jwok9F`_qGV($+^c${-EvR1cT*m#)<&rdy4s0
zCn>#uyXwf??roahV{7MbO5;qh7cS-co2Sy?S8#6v<BaMK<{5ta<(uiTZ8+Am|N0s2
z5l-8;)6cU1(=E&Md*eszoXLvfs&6Lp8z+1TxsID9D6%P7MZN#wq*j%KNYW!kkxxV3
z8(fTR^=07hQGOQz?^p!5=>>6PIzJDQPcZ9S$)B!HmGR@MT1k;&h~Yo%wws6AHqpXG
zHse*}U2TpkimoDOukKtq<&XX*=IuW9nOb}J0DZN!nzhEPJemoeEWZ=n*|)jS@{{Gc
zeB#KCyU!j@+a{qZp%uL@K8$l^fnKSHlQ<aBtd1$2!weO(ZPUFVNoU?`e4_*?EzG-w
zSJq*&zVZa5Q;WykAWJhaW_SrwmGahgbMtD(nm+Mv=e##(9PQry(}g@26lU@A4QG6R
z*6xXjPSIOF-l%=@e9*v2v6Sl`IVxy*QLidm;&11i9D9rS07{p=!g*YIiw56*BE18a
z4B&hsutf?w%%gby?urbwRi@nD|Hl%^)nu<ht1+_UrFxPqDK8GHeXj_)@BvN-5}oGJ
z=*D9NOQtXW!+!Cf86w&4?DGtFn(djyq19t));^)5n78HVvdz#%K<(SzxbbWB!7#Dw
z8cB3ty2dJoujD%-k*f$KaU8|dYrt5$r(!>Vj4dcg|E3={R4>*YVJlTr(4-16>gCqG
z1z*hgerhUM5}}=J^r{RVsm+Kfm9A>v3n)VfOHAUX*ueYrEXFKszeFPzpX{zCsru37
z@H}9JpWwk13k$?|7k4a9|1^2DxLdpB`X!AQ+>B~CpqzB;J72vGYp`R|VkQ#l+kQPQ
zjtbec*1l-nuk&o3&t51TZ||~IE8<t%u8V1$YEl*N*Hz-+BvnnE<JdfPv{i803BzG|
zX>Bu1cNL8^4-ZPTK2{v9Sk-;@XxbF2F=x0IO$bVL1S{0{Yw%>?)t4p>fB)z@@TydZ
zjY4w9Qm<OKoN&pP6$P92UQ}OPOY+XIUkH@+F55TD_78Ayd>`@^rkq;hhgRMh9JOd{
zj6!&ZaLfcsxz<yTy|mTDs*&O!#YQER<n|<)C}i^6y9_=}%Nv(}>DNu`c~E)K_Cj~D
zJRN!GK5AnB8^UuvX%J#D5llg|CcrxEJwKn-S!1vSKu)#e6^b-&H+_70%Eg2>%DcYI
zCvlEXtZ#IhyxGUN0w$s|`aJDRBHlM8Z%oTeIv6vty!a3)HjvfY@02hYELO!Z@Kp6F
z$DSXfDV)0XEOmcF9Z)d}cY4O^Wif1AKhpRXYBm=U9LI2VHrX;@%zR3^mYUJ?M`}|0
zGAw6ESzwBoniE1pQG!f%dM*n^%ee=Qk~$&fDZdfBp4E1daBg?9&m9qRxSQvfr7x~6
zKzu0*))iqv;||7h8)wD0ML!yT_evx$r+p_?dt~2ix14Y1aBXwsmmUlUG$Vtu|9>;$
z%etuW(KiBJHY5vUc-xYYgoR6YXSbO%$YmwcmJSm<HzX8&$oE|Pg;ZeRi{L6Vd~Uhv
zsOK1G4;F0EoP{`-m0c-}H0=Z*t+xzKOa!JV=+?6xew+y0NE3a2gbWm0A_H%Scn>2i
zxZEHO(5X2ykKU|;T1YMCgF}}QyL+Y@=-g+*5PU?q=t|2>>*QpCeR`ViO{<f+OzS}8
z>8VWr<Yx#`AE(pltrheH4TL&?<h^pCYOQOOyH*ai0qQDJ4}cXa=p}2l+TC)(TULL1
zQ!`XQw{2lPVcUvzf*G7cJzCXtJ_RBkDNivE_I@nX@kzLbEBB>C%goxJEVmbcq(0^?
z-zS<SK+mEkZ!JfEWOQBObt-tNYiqaCwWDTn5i^0`6+6KsW!hq@Op?-N#1dL93D_Nw
zg(1joO3q#iTTT23g(0c8o%}(a{W#!$KRCRtj`e4%jq~z3fDZna$>zCr?N##G?P4Au
z$Ai((J6Az*`2N<rb+k$J+fezK>Vy4-<*E4=`woi{@o0(~_d>!2VOg>FHucs+ZDw|l
zgZ2A@nnU_v1yo|eGPi$9xL>T7j2v4{;Ka24JtDEMs4}9ndFWlTS+*r;fRV{m{wogd
zyJxw=#K<MJ?D}V{9xDGa@mDy&>d=|i{E^u;%ItuKVj(`cxzdolKLB?e$<6a!<Wvc4
zKXNGQe~18Yf1UO0k|^jz3)EmJmTfc^$VGAxoKeGrQoQ%LAL2+9ck*vk4wfR(-cw1Q
zgAgYS1Iz2gYy1x-yW6}63<lILI%g+7xEJH95f81x#D?UanD%kd)q^D^ok3r3e8@eO
z$FU0*CBysV*}L^68`CrFvNWBp|LD-pwMqL_^8&*o>hreRbH>`TJBa7toBgOnQk8aO
zJ8t=4y!+_|8@KhyT(j!Vq)&c%=e@xA*b$+CWdVgx;lk5;)vM{9hIFSRM~@e#)OfqY
z?GKVQhZp_B&%X`UCHOw?A1!gtvz^db*46njV@Np9ANH}`H#E=?8x-mf+I(w5s#eS+
z>erWXBRt*g>jb-8iFA7OX4jpUkF;8KqpA=e(dI6<mh#&gVL#u+CcaXabWEw0@ksmz
ztg<ws!fyc`?i?}^&YdOh8xvB<E5AwK3Sg3BD(w9`q*kKOPKa*YWG=ax<3Xo!<V21t
zPNad3RSle5EE5U+k6!QJ%V3ws{3W7w)hhn=Z)G>WisY@sB;{FMf=yGvx#h--W-}z%
z&3o&41t*K)Mf@F^7kOJ0StzosgHnS5%d?kXS^e5jo`+VAJ%8~77;D3{3~q~&+ZTVs
zr@MNjDQJU1yDoK8{DUtP!3@lKJxw`_ltXUCM-C>CLnV5y@#5iU3=b?s;YdoqBd66u
zzv?W+o>Se)uA5D=kZdQNd}E{Ts4?#ESXO7VBn$U?VN*E)zgUL*m3``RXsuE8>}(>X
z{!PEx%KE&`h=OI7{rVSoXWeFZ{EL&;>)RUrF}=10R1KZv^1sgpo#;D8Skxa2E)OxL
zh)>6@vbT!a>q9;ty8DC<Oq<8M4m#bho5x<qjVm5~j4>rE+y$M~fU+~qVHlq=Xur<j
z{i9SFoUT4%V-uyY4#`K7x$FnY(|^yptMGKyvT&U8&#TX*g@bw!_>q}BT1OEtzoo|X
z8jngVMn++WW=*i3HrQ9R?LWsrp@)y(tO_-puijlP6h4#PUl(T2_ahK1t2wc0f4-V5
z1i2)CC%xXr8Ug)EsdDJqZRLlspW}oO3gObCapyp*&(}1Fh^|JcKYMCuNAY+$ESg;r
zQe1bMR?j#P9b!4gey3{UM)hBNfIOIFCKFvRL&!cTURL0Sy|2#we%?Imr_DsS_n$ZG
zf>fN~%&rfc-e0AuQ$$(@{tqLZGuAD;SsvCzIocl%+y0{wGvdFHIWC^iEGKQ;htD$2
zjMrFC>>3yf<PQAfXw)igp$M&B=Mm`I6p5)g8y7MPvdKC=;>R^t_{!1_&_0qhsZJ{M
zhH5h9p^CEb$9HJM;#CyWDnQg*BT71dy)C3q)3a<l8jS9YRPq*&m~Kf02r(h>jJRf+
zrua>X)dzuiFE{B!MQdA)F_1KDvf6WLw?IR-3PK`3Ooga6O{IOV<v96QBB{N;hV75n
z9>lYwjbiZ_)q&|Q3FOV{H|49bJZ2d?@s{r$@gHC=%25#(S{(~)P8b}ihqt|5eeVU*
zr6=9IR(x;VC609|?DaF3(X~m)sq>WS;bu)l{ba!#qB?bWEhId5uuR+FMExk*4O<*v
zh(!kz))g(kqb+zo;fg+94DL23%Ep2Ws1ZI<g}B3e;B1u-6Ac$@o{O56n71O)iPnea
zni7^M+YpmIj)YA@!wdM#V5sNHyk&P+iT0=7*O;H*8jj+uxC1NuYvXhE-LYwQ1~Lh6
zJW{{9O*e^pE<C?nTG-Td@PT<rFk4a<pvZ4`#by9^b<!iUT%Alu{J!+n64B3IL$H%s
z{fFMd!cwnL<Free&7!PrzsfR$bPqgkHwOkbr63+@|Hs76lLaCrvAF4ksFhG6<XiX2
z2qWJJ)8Av5$tELf=r?1JA6WF81RJ6LOxi9IL=xvwM>qCY2Duy21K)L9rN4!B6#L*D
zql%nD_DX`W_Q8VJRg2K9gNIT(K5|FHZ4QHo6JMz5+{dqmtiAN=nM?UKPn3b#aroAb
z#E!42Wrh7Lw*@nr@Tx^!(?74-p9a~%YJr@_WEGK(3+ZiN(eYOUKh)M89gVCacE!zb
zl<dV2e4Y{t2~OY0j>WO3p)jQ|qMG?59D#Z6>3M3l@Gki(Xf!)$%RFLqF@)qx5Zjvx
zF^|`W3o4KoPjpd5y<T(!POXh8lK+x2Tv~Dd**^1Ab8v7wqC%yy_0Lnt!(de4^^J#u
z=o0lYFzodHx&bY7S3+qM*LFd^pYfy?M!z?V-%w|^w@19lR<$YTkto+1N2dqAids+j
zLyM{K?-W5Muo|bpb0*jzwKQb-wOBp{^V$_PBYA6;2i%srZZ(J>BJQ*pK4f0JNxVs$
z*XQF;Du)H8(Qai?f7nQ+O2tl%g1<&m2$yd&gcm-^2dNUb+guf)g4oif(?6R&IFEEs
z=Ao(+7E`d=ynok9cn{u05I2dN@8wT^D1yQ1OGw@*rX+_dGsVe;?=He`v#JN2^LU5#
zor6HarDz@XfF0xSy71~7-p~wst^-cB02!$-vh-=~i!hq?s4ebU|Cm04*B?9jM~S0C
v>ega#<wXZ_%=7rfK&@_V_hZhP1$^KG_H60oY^*5}@K9ILc~-3SGUWdNc^m>Y

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/deploy/run-on-aws/index.rst b/docs/python_docs/python/tutorials/deploy/run-on-aws/index.rst
new file mode 100644
index 000000000000..d59c1a1fca16
--- /dev/null
+++ b/docs/python_docs/python/tutorials/deploy/run-on-aws/index.rst
@@ -0,0 +1,55 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Run on AWS
+==========
+
+The following tutorials will help you learn how to deploy MXNet on various AWS platforms.
+
+.. container:: cards
+
+   .. card::
+      :title: MXNet on EC2
+      :link: use_ec2.html
+
+      How to deploy MXNet on an Amazon EC2 instance.
+
+   .. card::
+      :title: MXNet on SageMaker
+      :link: use_sagemaker.html
+
+      How to run MXNet using Amazon SageMaker.
+
+    .. card::
+      :title: MXNet on the Cloud
+      :link: cloud.html
+
+      How to run MXNet in the cloud.
+
+   .. card::
+      :title: Training with Data from S3
+      :link: https://mxnet.incubator.apache.org/versions/master/faq/s3_integration.html
+
+      How to train with data from Amazon S3 buckets.
+
+.. toctree::
+   :hidden:
+   :maxdepth: 1
+
+   use_ec2
+   use_sagemaker
+   cloud
diff --git a/docs/python_docs/python/tutorials/deploy/run-on-aws/keypair.png b/docs/python_docs/python/tutorials/deploy/run-on-aws/keypair.png
new file mode 100644
index 0000000000000000000000000000000000000000..7e5b0f61e444074cb29ed30679044846313f9d5d
GIT binary patch
literal 193217
zcmZ^}2Q-}D(>P895iN;?=t2->5kxN$y+`!Ak`TS^>K4(Xi{2$f-B_{uS_BbxS6d~b
z*VV1w+u!H=egD7rJ#X1__MH3NJ2Q9g%slhVotfy@I;xa+8SWAg5KyYCDZL>eAYvmR
zAY8vgax-(0%bP+#aJSAuQSr69q9WUCklP0bXIlaSwdk~TQUiSl+U~aFAx6NxnAE`>
zwmgC%r`4A6J7h_(U#Z>U6;mQnb>}bp^pTSJww^oxM+#o{m<WZJgy*;E4Mx0vu<`Pq
zf8^ZU%%7GG0K4J^#b?ihk6Vvh!2$$J@+P+wr1(i+lE|As#&0Mj#>bW}MiSh=cW;dN
zjK5xOj)j5Y&b^%2;JQR2g6ssJ-jcej%j;{$z1*kw83~>}oL5TtlozsN`pXSY+~G{{
z+E2~O$F|-&JCXhSGo`yqjegnsADW!9Y3xjeYq*4`Xy&O2=1~0ULc|1n2UZ76-ggTX
zOE{d{S9VVB=x%RxEW;)4Y`^q<tD>?j+Q9CddZH`E6O7eLb%dMnyvg6P{k>x)^O-HL
z?(7=Ld_5^V)CCS$W!G80d)e_>EvMDZA&c<MGB-(7@AE{FnxM$0cWq^oA7W(|S28>I
z?q<ICFiSmWstMm8TLy8wUpC347Z_bBB_-83Ov!ws^T1EF*F$(9il>r0hhDN<61h4j
zNn5OZyLTJM(?{MlKa?$^w14=NJn-8k$WLqS%-Qu@L9OZ^2&H<ViGxe-Q$aU}d{$0N
zj-I0=$Gby{D*@`qY0Juz_lKNnK4-Wj=$l@B-Iw}0#1cZcLlV*xMfED=(GSA(pS0nG
zNqM!r0SNj5t#-#Jf)P2|KB_veALOCzas<Dbo0?1z2?es)u^yiXh^$Z)&aZ$?%?Yd_
zigUjpuF6CNl^z5=dn@2GQUbO=gzgb&T5|9Q@amc%1qf{$!v2~#yXV~Ic5l+z=0_TL
zMcyOQCu|RXh2TC?HnI`jY1jBc>wG)n%i~RgfR~aIcM?7_xIf4x(f;ztf@JI`V36pn
zU6GBDWA4#oBG&e&w-qEl-O_tvs75{%rS*jG6JgWKnb&L&sk#)%Uf=x{^=r_~p2mP8
z?ImvTZ5izuu@tpsr0dI;!9e>v3=uO5K<kHEk&oU|{w1)wEn@8>L68uh(az&en@djj
zb8=3&@va=9ct^&(xw|}n2(W$e*+=%T_Rm~W>0?6nlp?+?=%JoHag?pNV;EEMYP87g
zbzYhDGqUv;>mm%XC53DDBDFLV)XWhdifRTou;y6YDuX8nf+&zwHk?H<xcjxx`eXe#
zj>zcfu5Z>x@9sV?&UEaR>^0Pq|8RT%er(s!k4Te%@2p}}IWd-D(=pSDE<fwv22^+c
z^<bhZjS=aRTzuuuZy^S|*Ff!a%ZF?%im^SgBW1_Tmk&uT$1>2pvZ%S+y-T+nc(QV$
z9?GOpFsF9siTfQW*(8}N86bQz{C>w%y?lE$PcD@Q`q7Nhw!i!r3>P>S0v<$vQv|U^
zeD?h&^M-!y;hL1gr-Q8TFU6h&BxfXdEx9bcUlQ93+zYrH@$RKZ(Jy^C&*ZbZRK8U1
zR9#^MdN6$nh3bbetq83mt%6=g)6nm&-<vl<;}+v@#zo)_wm{7NC5j~(oC#<H$Gg;z
zp+v`H^l<>3;*!Wx1DZ9X?aB2k!cyxmbCo~!p1qbRbXR_x8B@6aMz-8}ooiih-EjRA
zyAk&F=km-7lkdK7s?9yL)D=)FJZ27QZz6_>ImsxMMZP?DSgYNt9j+yuptGg4^}#*G
z0nmr&Kr{_6xxth{vO$aigTa|$YuS)ti{Y?=PlZ^yoRK0lt1P0t(m?u)x8c^|tuA66
z^1tLUw7FJ0FPV+R^{9=3ML|j}nt>LI`kls#U#fK%V}3hnXP4xBs91LGi_I=FGXT&5
z^8dJ2YMKeIX?XhhOSQz!D6kG2jU5fsI<fx2|H&VDJ`z2I8gm%*`>r`+yUMkJ|Kl>o
zJUl!^K4cCxE<Ju(@MU0dWqo^ed!#@}Nc7Y5zs`d;I~}jPk{mLfOzcj2KD}FVOtm$z
z^&ETb)GMSTGUGP&{#MT4Y<F#5E!Uim67Le3;meX1^5^8tw0m?$047n6oTeOH=4cjW
z)^IOT-+o_C?=1h2`NKB*mBN1IS%5QO2#{bVQ)}?!prN<Hxf)_;`>w0L;s?S>yZWel
z#(LWM^3U%PF4XeJK3Ve$m!z!^lirhV-bdcAn*E#g0%Xr`pKF{eM6z?FR;PJP6E4op
z|7ia@I$u+i_r8A7p<6z$H+LvELAu4nxlzJl7u(g@FV)Y#(%Juj4xt%}c|sQrt>?qk
zp%2S*V2s_B6I)Hq)d3afWXIwxv@EX@!7McS3Hci`lgQUrtyaob(=&Hx^k#4~x)4vu
z8^}83H^e1y?4<tm?ZvGN?=wo_7n#l<0N~!)nc%!&RRT#i_AfGX4qw#22>sx^L$yae
z@#x^0iF_vO)Wch^GM_ZRVu@mi{19=)5J^c+3B40Q>q_4$h7<{9sAgaA{nf3O*t7MS
zx+t#jm);f@j@2x3D&l^Je5dj5X?5(ov96gI{n*8>7u|~sMT^kI?hIeS@fsVO@~xqc
zVj>JhMx~ke`=*J#100=E56rq!BITp{Cv}PZowP?C74PD?g=zR`y8q^Hwrm<RiTAtq
z+b4tHs1NPitSluj(ed>2G`~@4f=#`_JLPj%<sOTT3LaXoE19WHRM(atWFACPKvZ7+
z_G`T*A3yuKJgbSf#@L$o*+VrrQ_Yc0JQ-2=D7#v%)<|yUCki>scO1La3tyQsa$|CJ
z`HGD5O*z|Q+A5stQ0o9_!OGdv6_TVSvZW-j@@OL7De6yCl{AvYte~+qFbp3VO3C5Z
z>i2>%nsHd{D&6ffF!}nees4+ot@u!6=sYVVxQs8A=OHbXtM>$CNA(~P$=cjt;x^uZ
z3%Fmp`k`9c!(n%<a<fuA7=BT{Sz7LbnnW~Ay2$?^Y7T3AZ(X|4pZ(x8c`A9{QlXi_
zXV`~s>*|MHS3YC0STX7GgX5TE4fHKc_VL=*Wc9X_WCOCTqp0xfn--8J?pIL%RV$Gz
z={mI|6TjRE?%wubOJ{2oBb1^-Bhfl>iOJ7f^vr%`Ag;g(I$9A6ZDU?!O&JLv^BkEN
z`@>=^-`YY2qZy0OH}NlA`8I}H_OChXq^ylx(4R{^8NmP_1kp<{yCW_s7PT%-FKg0P
z%2r|+zWyGKf3H2or4F_LZ~I;EZvk`wD?u-=4^5!nPF=*s<dcFA1ZgdD5vc(VNbSL+
zGY}d#fAnaJy6Trz)8zd>*uWQa>xWKqV=zyIj}-zBX*rBsjNcch7te7WpLX1YZ%G~J
zY`Lu<wh)YNle4aPk5z{?%c+3sfx7KI<|^qiyzutSIyHWq`4dB<c%$cQP$GD{>+;~-
zjde*T6Ows+XTNN<dTMcqdtJ98Nc^fr;T?CA(9(;ML(zq`&bLQ9P<f?b2s4kbil5P$
z#fIM&Ih3xlNUlg$Fayct$Lagx6tUyB7Yv2GKUm%@a1Hj3?@EYAK5EVLZ#ZmOKY!2a
zELWQtl^}nbx$6NcTuoUo%^rvw$OFD>l_{_|eRN#6nX}uFzD+pGs%@*KO=j}yPw%sj
zub6anNam)W6FlqLe7U8t9Kl($zZa)aBXUI%a{a4D<S=fW@bxgsO9K`Hg`NQpHBon>
zkKyjm9&k((IJ*E2gEOCvPdFOIj)cc?73=G7fA*uXqxjj-US5tWDKB>$RjZ~Zus@fO
zF4fJMA|s%(to>1a7ul&07F^dxw2(~NEh)p`aTCXqxvQCi2?%I8|M?TDzj?YtKyZuL
z!NA1RL|aShy_<``TN}4`wgSE`?l;f`1Twx-H$xX&&$n#8F3zrCDPLLke?dsyjQ^Py
zWM}&qh^Lb*yNUK|Hbpm(E!ztLQ2`-#xw~v^Y%(C54^nTGRQ?0~=1G>_-qX`vN>I?p
z$49_NM8FMXC-_`aQc_SzSWs9Pa03AV`?-3)^#!<sIsQ%L|EHs53w{rBaQAd@b7lL7
z?%Q{6UY@e-?Eh@^|9=1G)7IDFKX-Bk|HrXz4k-A~ir{krA;JF*&C}t-|AF?;%D>V6
zb*_JJC-V<6sn-s^w$7$X4lcH?;2YNDB%ce*{A-*4vGSjc{ufM>|HOPQDk=0|QUA-*
ze?k3+6)9bigYAu!{*i^;a~Z+^G4~(vGJ^jI>c0r?-&*<C?2TIF?#c-MKNZW}ZSF$)
z6A-*4P*+kg@Fm>LBSqR8dR<cw+Y#u++$}`jf33I4mHqce>~DeR(L74=<y;KfZY4RH
z?<z`69=yo@Td4Q^Ij^3&Gq&ubk{%)7-O!=8Bv<Fn{ij>!(i?d2Y(Qt++4YiZqtABB
znN`4+e}7=jY%K7qVgq4o=5M_gQcRl8V)EYxtyC<3cf1Cx4XO`*4iRZm^UMDDZ-a2s
zbkZbSw*R^CubHtvA`DT_Sl_6o^8bc>10(a1|NZ~9xhu=xgTo=uX)jy@9-^@j&~|y|
zh|*Ag$;(M8A4`p35ng_NreM>v5ls?=eJtPgoi50Fq0;LUDbZ~x;zQZzEd{$qYe6`3
zg2@A34l?<_U&Q!Wv8i|6-|UF{25fFhuWESNXol%}wyWC;{hSn-Rp3Z|M!okeB^-MC
z+I`Z=Z!T>!n@$p*6}qa}>RXn&<SA=Il5#215z0rAsq{clsJMN~^t)OCPFT%3nVUYD
z@$gNwf4RX40a2A0Dd2D~q(8+P>_=D@UxXC0vmDaj4^i<?VlICoGWW!N(3k+Ox97i{
zbty1#s%lGiUqmu4OaKQC6dUx*A^dEUco0?gRXaow_{Q9!82q!GK{$>ssQ71E%#!g}
zA_}P`C}juKVUC0Iw4WWa)L$m-rz2qf<-BcK-bcOBddK5{RP9vPmh{C%X)8^WS{YyD
zvlN4<NYNo`>mQskisODZ*&u9#ebSTD(ce$oxF6q9rY=gx67i1eB}3xtUK%ZG#P1~$
z3j#X=SPb4jWUF&}Ip+UJ<Z*aV@|+cm+AScNlF44LbZN~BQ()8@k>J6M&Yc>H=Za=7
zJ8+w4f)YNuJCd@vC(#KN=%uF(9Gm<?@yB17Z`-ln7Od_0Quy?oe*g)islR{Cri$pK
zMe#PKPZMd{Ji)9&1&Q_LdfueTBokC*Oih-7FbUWDSA0}|b#;WUx1bAh@*mxI{{DEY
z*kFrEcU}A)e_p|?+4O%H7Aec$mH;20z87PjV>jkLhGT>4QnNA1iMh=TS$g3fP1BbK
z*Pbt&0@v;5gaQA<uK#7e;zLB297WnWVqU6DLBsgJ=F?IvkKON$kLfBNjT44<lIqI#
zU;XNCw!0o0@J{p};b@MMznGs+V3tN~zyQ>3>-+Gkl*Pj-mls~Kt4u4lDChR@bN`iq
z$@ID&^Z4JpR_ShLLi4Fr5hN3bLzQV(-kY&ZjB5Jt-@E<Iv|aLdYrihIO4)Kkl}^ZZ
zHGc}a@2W9#y}ff-?|1)_eaio`R@}DISp48z<C?uG)_udJb0)RUY8r6eB&&~;#V*PS
zjLpw#;biNurzPmIhCG4eGxUV<MSx?`zIIwwRo}m!X|4)`+xF6jI5QxX0n<*vN!b<g
z;JWjz6}c+u^|CblI<#u0G%6_&L=er*ird*m)yUtEO4QepEN-5?#E-d5BfNB{YL-m@
zo8$NoR}#1slA?Rnjt96&76<gcXPLajvtFqCEcLnFud>;Kaz8oW+6pGKY&-p1=VW}G
zx_u49_qhdueHNm*Bb$HjnwI=;PAQ$`<afYF#YifKs-QzCA|?Nz7T+a!vj6UJm5qh;
zQovY$%k}MVxq>&Ep2|C{7P(Q%<}cs95Y$Ck1ZxpAB%py_)8YTH8g7<BxyyEHKYO3!
zMOSQ|558&oc{huBP<NoMpSIKb^o}fcWPkH8nDm>>;-@z6!(u_;y&LZ9UCd<;=XR>K
z{&%uFeK^9SHDs}87!ma*%jIgfCtVPZZ+Ffb*wRotphJ972i=<V3HQmUvJ3<hkBj$a
zx7_avQ2Z_4OE?=IQ+gpue9h(DAwKc#I%R)SST6hRF=cdwEl~9Q9ht>Hf<Fv+3I2P;
zJh|9cyf-m)7V-V_`vT^dLT3{jzHZJg>hY1;dyZb}xaioShq~$9{<sbfO<|d1_<q@@
zU)qaW1?1qJm4i?|AAf9}HQ-Fc*y0T)Vi1#DZXt2)K=p$jvI%U@n_UyJIP8g%V8K$?
z-z~9#RHWF^7wTC#+<xX4SuU5xxY5CWkxckCBTvs0ic5Ziu<&Vo6?9vK@nZl;DvdAt
zFxr#EEgSr61yrqwFgy83`CH0Lh9i)peKE$j^n|G`9rR}dRO)UDTg^4L#U_|z$4cB6
z`eb_5E0;jOai#@7xpS9Rrfg%4Ac1m4?pB*`0#%*^K@1tQ7*~utqPO6MjajMB1ntyY
z0aZu>omSJTxhNp>BsCW31zOpMFWxq0xp1H$bzX?|psw0qfsa|n%`I7ZXY2Xhu`DI6
zLfA<3o+@*j402~>eNZyzg(*sacI#~V=SfviAggwg<eu;Wp>=?NJM#*TzmyXwEXo}V
z^y{MTu!yw3tv`L}-=sVis=mVRrw=hL7#me-e+q1x%a_)%#SQBHvD2IONL5c>Y(mR+
zy%dR)-BknjccrW$VW8MQZ=0szn73BTUi@z+?%I4FgSH+A6D=%%Lv`p?!o^uG3K5GM
zs|Fra1H!^E;d1LEom~Wlpr#VvfM=6jF~+veH{55c!nV+18Fudp3oW;<$jRd;jl*wT
z+s%b`E9o-Uz=4%wpWJve0-aEt?=_CiY-)WiVVE-ajpVz0;k<lqdn8F@W7V;K%1zl*
zk`OGGp+INFb}b8?_XneY0BAf(Rb0eb3^c|aBZmFy8FT>_<Rppb1GcyOrLd<h{4DJt
z7QVQySHE*&B7&0hb2anlprayr!vUtAOH{{u598;uyz3ArG6OPmW<o1^lEZ$}7y*5E
zTry6SDu-9PErSl_A(fUUeseQGuL--s?9AWsXKJC$evNfR3og~LW%p`rdkJc)kkf(!
z=N^7sO^uxecl(Vy7`V;{!tPl~LadI?9$Fwu_Ig)@^GcOhPI%<7wI|FaaL}$FULq|`
z7fa}qJ&M*pNfNo8HQZ>GR=PuFY}(|js2d~QtvzoJ`emo6_@yO%dy+^^7_7%}xOkwI
zGC%Sa)nw3z%@{Qddm}SQ!kk|>$uS=Rh1>&3kRW8?ihex|ypA~spH!Oc`KGO#SDCke
zXt*V!Z!6ZHJnL?2fAxejgFDrx*1gd5hF^!Lnm>#z8+%4o6=B48T7#xnqf|%8!!|rK
z4}23KSi6bAyjCT0iPc!gO%$?_hRMJES;K?cLJzr#vcF&?y5~XF3XI4MMG+7U{r$K?
zRHpzvq$<GpN-*d##Ul$bHQ4?gRhE+xo!y@PUPh@{mxTc-W_)eCH83GX=PPH5x1YQn
za$WzTBPCX4t3t}z3hY0h=kvhLJF;agex-G;CNphwwSiDz9G-$$<#in_l9D{oDfRDh
zShKlE=5p@=(I*2Qe}BSwKZa;?qAF2wg|5<?L4Sd$U^v&=30VIGbK}LOtR&hE3JM=F
z`>wg{-br8MG;Z2S#(`S+{jzjL>t^|Bg{f;-X{NmFa@Uu>eNs0EcQHzI>6G8WZ)Ft{
z>VV9~Y45};cYT_m_dN&E-%7M0Gud-4+OJ0gGNTzE71J;;&``Wu^XBTdV?gyBm<FZu
zWrb=cfR>`UY`B3Uz%(5}62a91>&?lhrsD!?Mm6nzUo!a7-_(N^`Y$LdC%EYsD48hl
zrVo`psap;G^Df)J-orXkOIS%&K)TQ(^~|5sw2&2B;-@XTR3lmwOfo559OzB3_P$)4
ziPX2dR;!@Hu5n#5L#y2>?-NV%qN86VOw1_vB<ZbZ@`~Sbd80M6h%WYnF9n)i%^lZc
zWZKmEY;0r@=wXz^go|`s2VMB1P362OpP}hl;W#-Qaps!A*oXUa?MVJiko(iBfTr&L
zVCg<H^!|h2Nj&rlwp%6mS}QsWza1*zP0AH|Uwe(5Gm0puLK9|>1ad;tjQvE?IG7)~
z^<E`r)$hRD!SAb-R}Mj(&xq@s?g~zf?hH!CXZTHRk%!s%;}58eB`3?JPI{pT5bCuY
zF|}ztN@y4glR5eGSbqU^S|J?iXSPM#wr`l(#c-0#JN<Fz%)nHbNicJd7DgN<*J)@h
zkOi3F$@H~Q5vby(!fhy@Sp=0}8Xac`Gb#dR7RVk4qJI~Cqt#BNUo`W4RL`cu8y^lU
zq!HeI=98IGx-+;mQs`w;QrdcFwu3BvKa+&(VyiZ_^qq$1+m$)DD8KQ^1^2YHSRF6i
zPeY@82Sc|Zs{M@pitmtLv!CW^^EMcECm~G?Q#ePh1ZyrQz|NrffvN>bBVj-_a)63$
zT!o=BvPVImF_qSWke_K!ahXBKeIY{C78m*%qcTqFT$j2P5>!i;pocKgIO!q*rLTEC
zEuR)J6xz#u6+{@&3_7RV<rr|e2XG%N^^UEN2#Z=eE0+d%ablYi!((%oX=hPf6J_un
z$A-=Im5AMT(uwqhq=?Y3!LLeMNv8E)TpP)<QRuwzJc#(52Or;OV_YzAC0<rX%<ZSn
zk0>v~Z_@?#2<1eO3NWC^OdPPe!K7w&#0U#2s|E*!@l#9j*2ATv@_5297&cfy6PcXh
z@mBs7iDW{Q>IJ=k#f~}f)<94S)m$<uGYYpXCP`(sGo)&%?s-29#o7^_ZUF+_c*69S
z<lzcw*9v~W4(JSeT5hM(JDS?BD3bvD35-hpK9{poY7<o^dc~As8*PPPPtXkRQR&K@
zBu#{5&nJM}a7PL5@w-M_$a)E`t|n*01Rc(C++Mq6Wk#N^41Dy|OlX&vE`D#vRsqe0
zZ5sFze?)l+^dprUzg@LhXmz0iVrVDUH!oJ*vE~PM*vZX<VfAd?eqF2ww3saURSiwi
z**o%_d<PVA$vVDBCgI4;%DrT&44_}1PPZ}riSgyT=TaTeSNJ`W9KH$npM~<F#ccd<
zbGXNq1x`bwbpoLXo7)2f2045Jr>aPF7b8Sv1Mw)aFv57RhG{fSIuAqN-nSA%hfA&z
z-^UUvO%GC6S7-lB6FmDh%+4Z}_>ijj?X813$!^LyE(@JERt}6Q#qQ|58v_}PL!(qs
znP^pHHrpRQB7aHY@i42gC(=e?A;w9t2ycyn)Yg*TulHl!0e*lpExPLWau4J{u_Eo}
z!XnPF{Ijp<?2pZ^*krpVj$A4KkiPyZ;jPl|a%o!ghQ|5xR>vOC8bP~!K8+cCR$DwD
zxn{VI<Bre#F+<y@YQL?GBec8v*}e=l{R}sM80NIV3YouBjo81t^27N}mv$`~gkNYm
zAmj6-9!^rPo)2}Gx&E;Yee8Nd4llR<GPHPWFD<FbGL(<5W#hZVVLWKTZl`KBE!M8G
zX%Z)lwD8KB;SW|SNCHfmO~gLlMdZO%9eK55h;Dp0Xjcmxw^Q=keO$habth!NfevfT
z!YJL(H~mEHs6fRelaECqoe8$rQve-g@P|F~V?$0=3_PbMa4WK>MS&*s`Z38xG8;X$
z(9zF}qE;{|hFxP1&xyw7@EDm%@mFTb{%K)*pb>7*K5SMCbj`=A)TN#ILy78no=<p9
zj6FP8ujK?j>-J5%4b{}zCGl~N<0}3$WoIbJg>W#jjMC!N+!R-EkAHhCLHZTsf{636
z+=YGX#5a{&uRA^WDwK@AR2LFa0ItcGa|EqJ1U)-Qb<?ZAu<S)fA}CN#xFZBo7QGYD
zUF@jOl|}wooE-fw?J2;LkRMpsca;Op$y;C^fBmr{EF(`r`meDUrC3N<w}Yzi_kv&K
ziIZZX)M6U>n7geij3xG8xtm}t!q?Nugsj(mbEVD>DAv02J73+HxyWB_hqn44zPBxO
z^mxciR5tWs7PtrMQ848#I#$WEr=6Ei0t=sTlL<&es}#g8?#~r+lvWHcyb`hpn_dh-
z5%z}00cFt5rd1HEo{vs$0{UF^SD1S~W+qoUJ|j<HuCc5@fY;R3DWU5JrQ?3{(ye|R
zn|9#WBaL*(=(odG2FR0ET3Mm4C`^}C^2G31q!js(kavWS)p4|^G=(X>0OL>Fi7%`Z
zul(wk;6*YAIT$Yn6iu8|?MR-g=TgG{9#j9>OJPf$^a9(PS6svrW@olOWVonauYKbE
zxtCvl!f0uy?cmdIoNv>D$n3p<xtxPypqi;J0VZR=ObaI3#f|heC_qEC82R*Tf0~1c
z$xW517S+`yMp4B2tamduLRVz=qh?+`ytE6d^rUR7RHf_F%Xz9V_9W^$t>nzM^6aT<
z)5c9a&eJfFKVRhiu;~4a$aztGaiz9wp<wHQ*emGmDUZ-?s0<Ywqd0zSYr@OLXWVei
zlM(awU3b0q^zV24r1#y8fi=y|3+vO<C2wY`>w@h&CX_BKLYpsQrl<e(F(S6i#k`t*
zvdR@H5}9I2x!<@p=JM=uS@k?AC~AMLnNWp$uKqgFy*Svh#hM%QN32xA8fKTd*h0aq
zE(CdHsz4*RuCC(Q&rxerHdE+fQZnF$+6@9RG`QSmJ^S@G8FywJ7QeF`5Eal=JmBIM
zeJ`;=SvaCd0NI&K)p*9gs&ApwUO^$^s-ar!(&LJ%0zJG&bK3XtS(Zb;AJs3#Sk~6*
zF#h?Xc!1_qmE5UI?b>eI*G8d4`&nfaIVz~__47GO7|hf-XTfL|R@m?wFQg;!k(>WV
z_1|VYfQh89-sTYPAt~*!fvr!%U-g1s3N+L$eK0why<kIv#;~EvIlcX5??0kE=^qt@
zSVj4m3|QI$ayIXRuio_USonat{pcC0_>744@o*%P!KyY(U`$yx-(!@kOSN-%XD5q+
z5pXIgptN|_VzEnp)~a6cP8C#0?<e=z-W<dW<o%vjuT1KUw$nc-1BQe5l}hCv3XBmC
z_$?bVBaFu1Cf8Y6rU@QS8X!Meothm+-&7p%xX~S*4IFx9@%Qqh9r88sxxOq$aOn2L
z#wiySQ$f=l9G^PeHfZUK!%KKhEhlYFhb71tO50xprq@;ii_ed1TBj^ct-KK5ncm>R
zv0;dS>1D3z`aZ_y#K5Bk{Gna$?3Tchr725ruID79PeXH8J6L6bLeB4Hk2+y#eCL;9
zyu|K@zq#y0jT9kccWGpsZcJGR?M;Xgz04Kc_!(1LEv6oCLmN)o?3+~e6j&ygLC1K0
zh#Z?vp6B*o^O1ycxRSlh?kntrgJu7I*X_EA`*sm%bXTdAr23JRh0w0AfK*gD(-!jt
z!(J?7i>{JtKCTQe>G}5o{5$_S2zZBzRULjdanZ08>ahEpDftYxCpy8EYlD)*xrR7{
z3ncSF#aXLREkTxc+o*LkXTu+tO(n+2dP&QlH&NU&!iLAk%3Av@C^Opjj}?g)YrbKD
z%@_G|5$mwTFDda4_@`st`xon*1)?;?{K#3Pz;V{^tL00-y!bN7{$t~w?0ddVO*s#<
z-4t@zlt+i1NqzCUYn*00osCUvlDucd0NGpeINUXVGf&aUy+->|PeSg9i^ze1&51V4
z0x;_Lchv(|Cjhm_bLGW7o&bsuQ-x=#E?M0!8QO!N3gE-9kHZ5m1pTo$!KPKZ?n4ay
z%FTtXzVn%ME=XsH)Z$17t2NJQeDYo29p#0zUJ4z9JZ8mfmKvKL)LA`V{yoC*`wl4e
zhvd4V^If@8r#Y01r+*rXb8M|fs}%Tf18xk5w<MeIFVT$+^J-ch1*c^!vy5Yt9}yMC
zH_i-d(^-0Z9Ty%4mq@RF1Wc#kJ!<end{t{!J68kVjK}A%j(<usVO#JA`^}TA^})v<
z7pb@V&-XZ|!82Y~J_m0}JxTF@hh$eS3pRolPC5~b=&y^5IHA3$>-{dgIOZ!(Omzz=
z1@(L^Qe9lGVs!G-Y9qqf7WZi%9?km5T+}H<fhKQkkRu59>;OI{!o;NRe)aix=BB@U
z1Zf|`rPz=9njxy=)&r?({n{OEGn%`JxBQhfS4vZHv2^e_jL>|VX6-uKIE=h%>hu|O
zwmsll!cxLZY%HSee(Otv0m)yXeQEdOCY^V~R8Xh4_aXON7v3}&G?Fu*&!Aebc#KlX
zbp#|sG^Lb`9o{(K5s(_7n^#&9)vIqsNvu1?8>6^=Br6fa75w=;1#vq>`Vo($c>-iU
zG*tu$T=8TcTgKA1(jtlZ!eJ%pIkk@_>2ga9trGM4w;fC7^hI@mm@TfzPfpFd7nDLL
zbL#IK(d7N(8FOg6OmBxuPZvDB=L<>tDk(|UuHreheBOcDBhKx~ICD+e$t1r&@1ll#
zcb!;R?iKd=E$atXdKPliC3Xr!0HXRA`bbk%Si{LpLV%|AXg~D%5+qgqCL3uqtA;0l
z(eKptubHaAjK8Wkbc!UdYcsx6@WGp!LPD1ie}L^Zz+d8Nv4S~lzWtU7?rA>nU_SY>
z0JpP?hz#!ngrm=KZNThz=>w9<clsAZJ`>X|7oEUOyb%>QsoV75z-1?6FFMHK9==3;
z0(*&9Ox&W9b9Xxas%tgK+~V@y$2Y{%kP0B)GJk|W*~&pALY5csMh%S~WHHt1b$Uc5
zDUS`^XF{w<st~@fX!ih(5n*y0w~X``Xe^}4q=P*n*hOD^voqfKwKAbh29%fWL^_p{
z4MMVdV^F?(lJ6%OhpfKT^i{r>H&i?#r9W=K3tRnVc+52{!?Mcv7WMEwj*1N<_C3)b
zujCPtqNB$gS?ZC#)uvKLHHKT{Ry7T1USj8cv5z_X#&{p6;I~vS7BFJ*&L7L3X2`3~
zru_0DCyg@*l&a<XxaOo}?%qV@r>Xitq|k$&w~9;gL=V<4a71o(_#wE|B(un^wn&-d
z6n|ZaJYqemJ23?H)a2_;zcch8up+Zmpni!PF~X|Iop53vV<{euf0Y?N@Q>Z6`$a9&
zzinGrx~xuE`|Wd8-Us{GFoPrQkd2W>y1d1YezNFdj|C5lot!I4i{wWKSGGv1eBOqo
zSlZlV2&YMW;zJsNDnsue8{_OkjjN>oTukJuu`ee0kyuY?X~z;djxFkG%Q(w?TC8h@
zz)AML4aV{8Gox7C^5JPchMPeo%T7-kPT4e5`&J>WM&R=YUzjRm<a1UDMvmn9Qv(XA
zK8vhOt`C|j7fFuD-uuS{<K;3bhCv&Xw3VI6&AkeK(hBE2;%u~HYj@oJde4CanDAP5
zt}MWH!g8iSs)UKjw|G5!FXj;mGwA0ZS07Y^^G$BXvw@@eT4MhKf697daoh#1+F}7O
zg-o|3G^_h)`*AuMpCXSqfX}KvSdO{yRe`2$x|i`k_s%~%kxwzIgp~zkzko;iPh+eN
zjaisb;v5!&pbN^&L5D)GoZE~dX%k;aWmtD>SNXp0d`h>lwON605UucxMdXB2hZ%?|
z$}97k7V0(MlKVT4wDO*@pv?ny6D^7iy||}^C9OORS+YH=`%~vo=2zd{{N<~UM%&yA
zK_slGL-Wbx`Xwhql3_8$0{dsmxnQyTIL+Nf?C~kGFEUjCbXaMvupASw+jZZCa9PUp
zCQq6s-DhB0TWKL9Ne6J3b1Y)w^~dtKwTtkNbxCKGUXfq$26@pBDJEQ2XzIi#C@kLr
zenRSGC0@+gqBwl=KZ>_170IM%wbNnaeU~_J(H=@U?s_ge?J1LZ5Hc=K*=v3&cofQ0
z5=<rDLn08s%Syb++|JDlIAwR4XIjS5CN3Hp$*BT26SxN*2G|gKa<zI_Gy)h{tNqDS
zdvX~}5<kxL11#3H&kt@3*ju(Y^VaQ&@NwOwdv95i;h+lBZU5NtCYBXJF>p8mRFNK)
zUof13b98>Glpac_7U_JQK{a45=Q6&{-bLMu(J2z%`a`HLlmUE86QR6Ex4^ZurM*)^
ziJNmjN^3?yG4qQbw{l`PFA=c<&3CIjjkhM4<#mEjEZt^gk)A^t82zL+KGM$s+*-E+
zu6s4=(HZN>QJO2}Hzh9Qz-xMZ1voTa#`H4D^lxljR(ULgRFRp5qxZqP^yhyz39Grq
z+G~uYEv^jcO*M3CXqf@o<qFvjV9PBtC{Va<f&lV@enw|k8&~ME=G30FxYOd2ILR}~
zw2WZBIuvMTwwf99u7$u$rD~=t_<-8@hk0}8(j|!*Or==8wE$gE@~WAaJ52Qgkm(+f
z+)T*}WR={hU(y<vCC;EsIEL>m(dTdIG?(I(g-4B(nfGCpg0a3`DGne@PbZZ5Rn&m3
zza+*YoM!7`-$M%s6sLc&S3M!7EY8X5pS&L+n{;J+$?g6p%T@<V)>`WKr&+Dv-yRpM
zym!RqiWX~6+t$v5qF$8_`Cnq;W43DEk$#p4SvQnd=Jo~?UKq*GFwox2+!394_NnAK
zi&AsL9;GT1JlIFcZ^#3!DhH%`Z0uPeckMLIhuJXpk#|;YD7r9&+7)<P`K;3eSG~7d
z>nB^izzs!%t^m@wB3H=E<EvQMrkpoEVj3TMK1H129lSF$;0**s(Ou<MN%u0g&~}!t
zhS*Ix9=flZr84y8YIW)@T_UxJki<lIb<uo%l@jiGk>2^#mR1Ir-vN0b&3tQ9U6^M2
zs$l^-Zx%BM)wvi0IXLNb*fyR&$mWq+F(;27)^|l&Y4=0;;3FQg7;u3Ejw!EBdXaWl
z(zQ+)#3{W3-GvbgW)&LBuFMhNh_ZJckk1oTT8u1XHE*2bfoS})Ptb4B_S6^Zj{WVD
z+iCZ*yeRmCUYIpOWdzBPmbk4hIsOL<p0z6abrRxav%y=x3H+vdNhcaHekuaQsO(~T
zXa~&P3mtF*!<a0wMVXEWeYq~^kuKI&Zj=ryod6I1HH*Hv+%@>#rU7^f23Wdyv83H(
z2@lh;(rLm1W5$aZm+V3yIg*plny6;-53jT{;Ot9Qvfx#E?;JgZ7t*K7Vf^OILNruE
zC-NqLyi{m-lTC+O5XMm_?eL}uY7F6;&<o(Kp9XY!1B46Xlcyz&X=9c7X8=`iw~JM}
z<~FIVDc`#F#;}I&#Qt{K4S^`1Yshj2g1R8s)W^`z(h2tyFr8;ig{Nw2Kf-7E$RYy{
zvw(OV*_8!66i0?eHOPIFKHCXHOxwO4cF-V!>WxHzGgrflW;&!T{<*P)MOCHFxnW<W
z&sVPfbeKx_W%Z&|*t!7Zp{ANDPSOVMKm6z(J8`#Q5loL<S`zls%^EvZBBexmSb1B-
zie;X@it~!wAOU32Q^7`6V^`<1G<Hxr_D`eW8&nKj?Zh6Wx;g^7jT=NM6gRbs`cVCx
zpLV&vsICHV=`I}O)ri(dn7OZok6}g>bv+umhDHxMMADXhV|DMe{D?+E24^=?fActq
z_}0^F^*q%%VyjzH*U~K9q>W#3jq)uDB+}E*=&IijpttKf(R6V}rO3_Os1EDoP^+Bf
zHm1_e!y%<!<_8+#o9i32H03V+8_S=7KX{~evW#Rl`dYLXT-s$~%n_3^ElR8AzH3gn
zo4QnL!^xD`YAT#EOJK39)Zzdz4Q)5`Mcw$UWJ8w?(qWeF$R%qUe>JM2nIUS(Ti(k7
z6VS|IkB{8i{Wf<7DBRe6m6*ou`L}-T&)jrGx0KfdQHUCNkIPyCImd@Vc|TO1Z{WF~
z6&&NLJ0~!S31D}sl*u`1`HhaJ-puzeV4f~E-r^0ubZql|6q{Sh(w))f?$tXlzA7Ln
zZ!l|dHJ!MH8-?!87|r2-fYf}PCEYE84q?8O8y~mK5gQ9168m$!7~=I*-#r$WxAJ*E
z>JGZ%aK4R^(ODG!W9B*@(wolG9zGyep5WTj#_-iZCEC|E<K}jb<`mq2MeYg^wty{3
z@}r+?_MY9%99~bSl4i=s-BE32sKj_mF7yeF7Ax+7o$pY~+M>X|X?50_4Xa(^Y0Yx>
zaMz4-xV_oI&yq8i+|toiGv8f1#*gGGcw8@0LamydFY1fd10ZmtEw3bE0HU)EG9W2%
zuz+PuU-j0g<vZUsb%3a~V!TZOjXRZo>Jn?Yi-MMVW=uBV#t^@edQp6YzJM%|Q{RXH
z?v+~UBwmy^rMuB3uI*>FyU8sst2H6dhst3VoV(BH+={jRf2mT%50fv8W-k(V9uIM5
zuHfm~i%G4<XW+UZ+msVy4Cn0HQ_NdWVlm(B1usT3c&^~UeuAPgltql9THQ2=agAZ8
zLxJ10P@gK{DH2^#rJ@=j5|8=9Um%f}@ij|hFd8UI1(xKMZn>^g*-dG%53wsC)PEpw
zAd((AuQZJ#4(bq0Ru*7sM+(r-4Kk7i7Q4}%BDtAb0XiQWA*0YL`_7+SxrT~W6MT(J
zO_d2?X(-Emic@wgwI#5f)2>8N1%H5feWR8jN34Hj1eMR<)S(Ts#9+fD<1(>~Sq~<Q
zeBVLM3!?Dm#i%9AC36h*NJH9K^|CuIx~Cq%xcHi#33)kXMIvxb8Lv--Qb!xt%yyQb
z^h4{(3LePZZW;v4j9TA(!)%hJr!a*M?ctbH_6g-ieml6_Z8MhFH&&SfA_=#@Df;Qo
zGvme76L!!cH-$(lH<SnBFqIAkl16UUHwu*Q?mcYTugE>UZQROzLYvrGf*z7glLnTU
zhGS|+#$HkP6<&dw1O_l!C7ikkL)<LwhQ@NlZu3}Gv`o{?!XvHIPo;4IZn5s@uA1~I
zznjRCvwJs`XF#Hk8S*<?^^_g*+pooojbVOl?V87&b2yMavkDB|g|_53Yei{ZIST#R
z&YkN6)yT}Jac~U#PGbOO)&fp3Do5<pXSCCfAR7$36gE6`-syhJs&F5>uVz#yGLC5!
zTU~~4WMgXmwf%#iZ^46|%M4^Loac&W<!DtX=qx#@*rH_2>$Ww_H@M?}tKtbpy@tOk
zVBGJS?)+`z8TjN|Q@nxYo-m8Vo|;a|+qngOCU%STx`uVed*KiIzg#HuQ?bbM!$TKe
zvReedVV)RVyeoVXgIyGb(G1S4K6h#UQ@G9i{^0$K$joP4XWu!-EMpcs5}L#krHzm}
zj6shfPy7<-68C6zgmz=SW&PZ`Gdpp9ep;hrX1-B=rWZd?5+CB)g9{&N8C1l|Js#T`
zT>zWV-amT-d^M|)tiS(NR!6#vyw6dh`=&C_3AME?VI2Q(^r|#*xQu^Xha>YK)|)p2
zkUW0@pw|)1%2I`d#;UsS^7tUF^D$!Wo*XL>-W8Mbo%W0#_e>j-+*P+<kA&LIn-)2B
zJ@viXRsB3NPe4?8)>b53zI~)-qhAX&+U42K;r^AYIq!sh|IB->&>_d3yOJ;5n%N1Q
z<2qCR5ND(Ldros+iE)plz&5YYaAw1*v~tMp?&t2Vf}8y1=cBInqrrluCYOW(cNB$e
zF*{@=MGKn%sv&GVY00&fjK`Q!;ys2Iz(Ins^}a@3J?5fpOnBVS5b^Bv-fG&$-@*ZE
z*YS|CuLuxUpzt&0(yswC{ncl`hSI#)y5Cl)o|mVF_xB10_{UsHV?3B}aT!xu84g{f
zvimLT{VPA7<-{^^vskOJ?^v*SqGsXJn8PzO+yD;~0Zbh8D6w;F<neR-TNd4ybMauk
zV2Ny+7oB}HT6swepFwN!6Kv>!!9w)5QDsq4UOWgh^F2(;lb))r6G@@tAnT6RSu=ks
z5-Lj!8ieK;F;fN%alq|2$rVOf*7cTa*TqUC?gjRf>wkKPM3b*hwElwLgseIZpS+cf
zdzo4ip6B+filU~JB}fVvTc&Cq9+OK32^ZwtYk`@E6Fyh2x(7N9eX-@COasqr<5aAF
zMx7mLv9OMaY|E?aq7~<6Ng=|P#S_igmglO*&%L!v-jEny5b{i>UGw%w+2c|?SB09D
zN~bRM-YP3VH-((b4!?0s8lNK{P6cCwKbS8&jY4$}7!@Kos{ZP40I~_i_N0Hj_?wJd
zMwG|iFtq6j@FgeFrE&F^p25Hda5mW%bil}gQ=M?mf!auhM`1A5TPZRE7z~F3)Idfj
zx0bVtj8WN4uzgZ63t%<hGfl-xs-S;1xVw;(oHcJ%z^nP8s_JhI5c^nT62TY9GE3_=
zSbHG(9UbCb)~JG&`o8z2`Dv>%-N$*pPBY<EK29v(fcq#)MEh~uGpkB!^W7)oU=a3&
zZ%G6V@Ojyp+vf|7s*b^+j4j*GpNSIjc}>08fI24Kmq!)9B7d|h+-)Wa2@!UIF|K{j
zRN7E#_E`MwI$WYXz1)|>QL0r+CQl>8_cigG`5=9XAepX<;whlF@Td0#Klj*}BDve3
z>X&=`HM%_j?M7GM<g~-@Mg!j8jev&vGuydro~Mr8w;g7eNrr4CvRkpFjjJ-o<EI~O
z8?EW73KrPjz5T>G2QZt{=ULU~Z@5VXn+y4lUj3*XQSNrDd$!N2zEk$scyQ@N7#WjX
zB-P|==$(t!_m2R+5D!+|*t2L`y4||d2!y+-)K>8K7!T8bk3+Q^{ZsT6c+n)#xH9-X
z0(Osh^6BNr0eFj!DIy!xON+I=`Vcthw0oxn*(I3U4gC2B%Y{lRF!LAwzDIFq6lK^i
z10C%ug}gXZK5_15&yu61MfFN#WF6X?t?S5lP=l?0NA)76vpsAO^dAr-$()jC5uc;j
zRZ#wE_`r_EfTxvl(=v)W)Gn(@PMK83y>md&QI`C5;JaKYpg<>s)YJFubn*L%YV;Vm
zPq;}A#KeLWW?;Ec;wfzjpuVUWgD?mFf*79K&oaCzDVXSvaeWH^N`#<wq236TORlEo
zJtXUqOR`iRVj}@VtJBK#-2DZ`)ijk3U$08ICk1ag0XGENF81&hP*@C6&p#c5Tayhw
z=i6<;|FkUrdJ@+6KD`pL_g&ZB=Jj$>XM#I={t%R&wy|+I9MpAH_$9`}(h7FliC>~B
zqiZIVOclRn5-+w0p2q3S3+JA}ESp0gnM**DZ(f$S3f92FklDpw^IEq6vJW`SMvXD%
z2YAh0#T9q-x86h-DXk3xxxy!&w9~8MBUw}T1;2~IvJA}CWzcz~{_W;SDhwoHAC#W4
zF}RR1-Rx6C;mIg|K=%lMpzZZ4&s_t_4r|XVtG3c+9}cP?9e&<G@&>fsDY1}>u0E-Z
z=rf#~!xg2TBJsEqomlzOH0Qm;jy&;<f4)zW`+$mA-BWh6?k0IA9aDI!9rDlfx$k*n
z+}dkNy<Ur$)S+x+qZE$7BIFZIP^xs`=6x8S^g%}y(9G%Ma6l91z%H+yR29?$9P4&M
zobkQ<agS#qHGL2NbxO&}O^0tY=fGRu6^jRhiVuSHB<~CxV{~ll=A!G2O6{k)?o0P#
z<_xQva(R}Mb=S}rv8zvE>(zb+LfB`dtt?qO<lFaXA2jIh1^N?2t575)r`X$wU?)Ny
zCAEL_uDGM@3Qs!dS_fOUT--LUE@!V>&WAEuALbDVGX9XBnx~o$V>$SOH*Y&MZw8px
z|56E$R#pd%(B~@>vG{Y98n^DHO_$0YmgCWURhuj)KS{l*gLcA*U}73Zn)TMnJS<|d
zyq#5`*nG-|tf()6*-z)4cI|SW0&AD7XyrCCnC3+imws|HWPAmsb2RRyTV$;G?%pF>
z$-AipF3`0#UgU@tS9x0w%N~~NZY9X<^{le4T96zj4R0rxB@Drw&|LWu_G(hSNx^5s
z1$e82LOQR>dY4$`yRza4Y<wXQPW5+sCXTf9VU-ZoqJm6+6?`?%_+aV!B=P!hUf|LA
z^r$hAdDrjyd^WhBv@$b=0+rF4-ZuoWfHlV@bq-6$AHK1!2=g&)ZI=g3!l_0@0GEwk
z`+x9lR~v1Jv9|fc>4Yt51V@5I;rYz=<;<$$afHn?pL4i{z1a_1$md)hdiYmPT_Mn+
z0=bbb-7#jR5PBteSr`_I0ZE}X95vGbN+@PpU7IMmC?$9_#$-K4+hSpdZ1T=M&7Ncd
zU#~KlH+5~Yrr}wdu?Iy>^~uX7NScCL_cQJfm|!qm@y#_SeLrcWP(NmlU6Kn<%h`O3
zA>s@DL^f_w{!iQffPCBe#tp0Xq)rpZ!Pmz=-7EplU}k;k*Tuq3rGsHWbnuCZ+=Bro
zmoT7cdu#z|_Ee-j@JT*g8DxavH1>EFSmi$oMI;7aELN-f{v=`<+}*`+Wj~e)qXft%
z19F{9ZX&X^7XBby@}lr<Fhia459L!wZ~-P+U`-W<)>%0lVF9Hv>q>T(r&yHS81{6i
z@x<3)10PebE469{^oiq+MuL43saDfm(bPMov-VhBTTpj^sVJ-)Gp8OZS#|z<i_T?W
zJQ%}0*RipSdl=A$bfadY7<ls0#vew`aymFiODVK1x*MGi!TbSCo3ctn&vC#joCIWQ
zKmRx`eoUFpa)jCaC*iaLDf394|9bf}xZr#We;l-Zj3|}C@l>x_(`@I3IcMZ-%&qcM
zvHsJC%h5Ky<=QYPH@*QE))G@YW;YWXU&#+}Jths_A;ps9?_G@OUK;ouU{$+mSeYv<
z(l_+ScPrtYzZRQr(tiR73*boN!mh=IBwhU62oFbr;(X4`LCH?v`OI-EK3W&DFg34r
zfqwsY{EH%O?7`&h)sckr%p8gMC&k{xi=ISJ^M>@c$HqgCj4iO2Sk_BDpRwkd7Z?4v
zW*iGVlQ34e1pOvk&8|nyf093869YHjPj6TqBk;N4{xw%{&g5-#&8kiVBRt}!A6w+6
zBs#{39g8f7o!wr2)tc|3ElRYOS>Kp&b&57_9l9JZxH9$WmY%6@9<6j84P5aFvXf!9
zOvKZ=kc*9SVR_vq_^eT~FyX`=Gxw-UGl46ILzLN4^CoTJZ;|cRIby+2iZZ$tZQJLq
zRn6Oh+l*kboU!sH5R&53K(_Pzk+(+YV}Y3_$UXLxPnb<QE5AWYap$3Uhj8%|vlwlw
zGkEKw{5Io4G|`-s)qb`K<Lr%yyb~cK931_b(zTIlZ5yvmo9ufY$v2j!hlnp%(rUqf
zZQQhLc!o{U>WD&dSs7Av;(~!|n$3biyRp;#dD7AXZmDJm9XOfcyMm3Nc3EI}n2s;5
zf8&Irph6dwm5e*!<;zGY_v0ImZY6eWpW^Bc7<Vw+d-R^k+qk%I*J(G;OT$>Ow(5Dl
zr^5Hx#dMwDeL*<Z^FJqw^{iA+&85t*9`)m-d*_qI(O`4co)+VAZqlTMwrqP<R|&OT
zBN_@TfL21(Y@Wrbh621vvUw+zyjlKgQ64XMeLCPn?boBYaI~P~gV3L{*fChj+lJUV
zv$0;Ts`|(ugXPDj7ib$+pRHK`{2)#p2ygcSZv-y&B~mao%)^yymw01!5A6$>UexB9
zzoNW@4PXiTHmD&Gd!xSHgquoGi$nL5ji}(0sBW>jkq{Qiy|1A01*sT<1$q0<--IZF
zdj*}UNTd7l8?UpF#=;x&?<?iN^z9<|2r~o;$W;4D&DRncXLcg><S%wQp<6n9r-12X
za@nfA%TD<VMW2l3Db9DwyP+%#__Csem_EL!S1T#7bQY|#`I{fng5i~WIQV=K{sQT(
zmJ9Y>7+h*(avP^TY|3F{9@4ZF!lFoUKZ_gIKd}+r|0}2I8A3myl=akd<~o$lCVrZY
zrS%tw)H)Nt`Vumk$Nbbqt6UU!Tk$lszT^k1bwqePO#gBFFJ0MW$diubSlZXMbxUXO
zNq-p9kjx~FCJl?<=6M<yUZx9E{E;i}5JR}TzQVNItMXO2?IMo;7ajPpdFBxIcT;xx
zc_NufZSkGgtoj3>r$EsP@%=(*VjSV*-tfnYqZh=7FUJ+N10_}E8k-hK^Bi-|-as6I
zRR#4KeiZW1BJ$w5yU5bDzz1T3Rf-q3OhF{Sx!;s8lD|`1%;D~g3v)Y7cUu#4$#o$+
z^X>pjkjqBLe@?!*cMnDPVB!`{{Lj$@!k5>FN4*oA47i1^<-J9ftRZo~!Ospwa<zsM
z4a!0&BPu4T2ViceJNvR%Wai{l7Z2qj_(VxFC2E1(&PNY_NcuXGN|~Wf9mfwM7C%gn
z#l8gc^wn{<e^z~vx70AFpvr-{p8Nl3y7qXc_y3=J!raQGjS-D<r{-=U<X$<2+|8}i
z+@;Bw``pPT<T95;Ct=8?WG*xJ+`7yq_sf*nSPa9@_mAIS`|q>I<8yhxpSRcZZ8S51
zJy?8C;~MP$rEops-pOwVrv3Ib-H^RMJ8ZdPUxgPxACHx5%j8|Uv8s@IhJw!WtbxWL
zuPLQ^h@eeDbJey||H;{Ap{{r{19n2+s_y}$Xyioxn93@Hp3ggA&Fk=*XXR7k#Y(a}
z?u+O8*5;2u!=)dWBqdB-ZDe;icezblqBG*ggXGlFzh-t9EM6mO^_;afJ(a@k_Y+NY
z%8yoLU{kuky-U97ZW1ux_D?yZ)^*l31AhnOkA5ooHMf0pWr^VqsV}4Tus-+ULW<wZ
zE?s6x{Sn;yIXhyd+~f;!XZzhnKF3uXl#Pc<8CACB(8ou+U3Zk2$s>FN)metMl=}OA
z^)WA#g=<6GN>eH?T>>^8`$Zqx;33uOY>b!89U03+`aZT5Rc_#)&W|#;>IN{RKlPh7
z+@qWWP#gipz2J_=d7z+>eyi!1N79EeYnSj6hU#1tclEk&ltX@`0RDKf(>TWKUrMa7
znAWPz3Y9g@$W*!HOe}6wb6$_mU==@Z;1oHI7Hi{KD9ug#dhOlkueo0p8CJi3(}GVg
zl#m^Tlu?Ow^*7zi`!Vb%BMx5M;pUM_bu{m?sfISh#K!dfbc1V#>k)>9D@QS#p?Q0y
zn1B9qbEvuY&wqGQuh|Rnov~Xf6A(ppd-uyVtO8L9)<*^GZ$B%d(jzpP4242%-k`V7
zb~?hE@S{x`z09`CRPeyvc?sd4sIPhU7-9he(JLJE?oVuSC)9VV%5AXW9xg@)nNyve
zP@|Nm*|o0V)2(iZ9-oWidFt^l&E~MQPw8I_P0K|ocyn8U_IIpV*^eH>J3lToNSI6b
z30&e_mBfGq_Ec)eOfJj|SE<g2&2+XoZ{}@xDVR|nJ6<>bc7_c<)m3>sMj2SgaA31o
zW7&~}w~TxJU54g`vk4UIvUwv%;pcjbLszm@n?CtGxn|C;CA<6LXO4X^^kpooCvtJp
z?af_GanLIy&Lprrw11n$(1Q*XA9#pPGHJ%hfB!GYwQF5EfMSGYd!4!)RGKM1@LHiM
z@3~cKF3+mEgEY##o41K0C?wO7BS`w)^o$^ff(s=K#e9SVAK|tzkz|alF%X$6td+(&
z*d%xq4m><b>rkGl)Mll6j=bvOx&1%Jt*P+xPSsmaNy$Cczmodq;ny>|j10HvZ-d6l
z`a>Q(inSbY$|!Lr+WCBYkm>CFdNeTS$Dq2AOxOaa^%6D5#ww}hY9!4BePge><qU93
z)s}Yco4K6q<bC=dB2ARZZfB;qppRO9TKb`u{qlNqe1up13iH&gdwKj`i!0N7<>9v2
z2Br3sxBX4J<_CFW-rLPC=*44VG@2Cf?B0D~jO%4JR-{IFwMpg|6zmuBX)@^b^L~TZ
zqQfhJUGc(1Vz%lT{yEa4hAd2&EAcFa&n}DqOp+-Psk=_qx`5{`Yn<v7l#q`CK{Ucj
z0*M7bEAyNM1cwF(%^0C85cM`w3tEH8FVDP%6^oC;E~vmTwzc7{n#RLyc|qalVNNL{
zHP%`EF&G(9@8YKqbv`fNyaG|T8fJaxw_~*1HX5P0Il06#e>Klh0I$FBkPF=En}bUm
z$Wq0-l)mrp5?bI8N4mCWb%%F?hBHUjht)>8FE7PI9Yd!)#p<9DTaDO1(2KyZ3q3rS
zHE9zwM~NlBo_Yh3X`8f+lDUYzU@XrJ{g@R$x%i+V4ObuX(-wdDtyQuY)|6y<@;*62
z{y+J(^_(9mimeMVvK7FY$6R=p7ka3(S*pu`YI=%DsbxRWn_kev9vK7vxq`^LL)-Y@
z6kO)hVU<2sCErr)9qngZ@_0FtcwHaALx0))!ull+=cFa&DC{<BUHe&!V%=##VNKwj
z!e_$adWYi~q2R+Yj2XA{<-mb#9hw_giugu#ZpBC7&w#yf!H}?>kB2PzgP{Jfcy4^=
zvN|I}0mMqqp0tMPhA_j6W>5B7c(oX8sXs1KIRBU^Lj9Di**AcPQOy%Vv>=VP$5ghH
zO0aHM2Ix@W(IK)jRU5?v<BTA%M-c3m9zMpq-6x1MM&c|T)zWd8Shk=<`@^UaQo;n(
zxhqB-xL7$~A5HBPF%U-4_jR|!tZ8f!d;KPQsva?U;8mL-kMXrLq$=n+Ty!tQk*^ty
zqI9Wj`G*ZT5Z@fVn!wPsm(w*oYO)i<*UFxqE!;m%^mblst*PU~_Ukda@*$E~>CZWp
z+0;8TNlKelPN_yHqs>Vu0up-T<>LBZH37INY*}XRsSKn(?c^%Ehzt43%P`+Gpw6#;
zX*WSFJ(rRja1J;#>|B-7<Nf#k5x)U2z9;{x9Y?(%zDb)oV>0Mxg(+JOLa@#hSY0b4
zd8-0T_nDR0xgqeJ2IugVep%1<eAH7|Gr+^i?)&-8<-hhQc3$fSTQe6p*6%VGOIpka
zh{u@mn#)qo>t=p+Fk^wEXP-77*eCYm=SKRoU(LSr57&GCHKeYJdg|$ZBJ|(%wBQ>W
zo3u?;S*tQn@uiC&`G7<DxpJc2V4<1F&-tzTde+Kxr#^4J-2$?q&;UGL7qVHS;@~=#
zm1E*bH|TpaVZ|^64b_b`ggzFp{Gwxqq9iaSom9Gzw|Q12Zac=BsDlxnHRBk{vbFm;
z^mXIMT#<JjJf(Ebn1?grN<*U2_j~U9jh<pzC})h*wPQ}O@<hUt+V^V=0~dX3)b+jm
z77?x;n2t_yPvnZS(%hz(dJ3shmt<T%nqP6VMa_Etj3xXRnY$g+in?ce|5LSgk(S0C
z^M}W<H@|ru^@nUtC8a*z%zs|mky6^x$96}HXa4urpMR!;rK_F(wR|adW0kA5g-0`5
zE-icb+(l&}Ed>hy$nDR{@rm?bV&8vYpf^1mz?{F={s?|X6mwz~-J)B*{drh$-1~Do
zazD{_2}APGIf#CFbK|Rx^`Db7j&^nF*BKvrhSt}kzkj6Ml=gAvT~MVQ+lLCrCl{Ai
zCiVU1<#hANL(Jcv=H>kTou%~5sUUVp;#Jb6*jwXt(T~sA2ZyRAaxP#BeAPwxR-a8h
zqNo0Seo3VyY2`n;p(QmR_BHhOXJ~*AgO4Nlx!ljFfxm-yR~G1YHG@mW1@X~>G8_Pi
z5%fw3eOY|Jbt_KLs=5YYX6IvnBfse6SKY~f;k|Os{DN1rm9harwhteEbUCba`PK1f
zrbA(+Re@abg+q+>>nUUOGz5KEg?{AMH*XW-B+R8M_<PmXaZZ;U&HJl_n(9r6!}tkg
zYZWRuOD@xfDqJ9Dy5j4U-E7!F_Rk9OztyB0yHd|rsQNN{r(4@6e)0=S=!2miXAiZm
z>u%uYhR)+%*l92WkYJi0?~-IG`-j+3rTgdc&qdE@d_BS^k}8bGh5z_8|KJzq8Gi2x
zUI?~hGI~aXdSDjcEf@^(7)$Pt<2TK3yMq~|IOoU;{qU*GA}1kLXf;K(tO5&WZ**Ne
zRqvptQD?-UrC7z3Z}^T?g}{Zf(a1e-baVLp(foYGTlsbvg#|Wtya+yVlNaX>g<eNY
zPl1$4Q!6`K-?mRrwlR(YX7UE)qrWbvf06MSY3sZv3%sF>mx;Xq7?=M;w*T(}sMw}v
zpiw_J8^*6fI^pU-Ss|P{{$S*%8unnA?|24(Ji`xUK$Fr~Q$&IK(8uWgXK3L%rU`ZW
z>aQjBlUAk{e>(F4DG`f5`h@><)XAa87uXG9IRfaej8Q)9tC?NOs4QJ$OTEzKf4tuk
znh{2WFMUu&?aJYI<Z$^h-a(B2?X6^U$Ze=_Q<|j!Py-RZlZ~C_&!!z2o-Nhn@aZpd
z(IDD}%^?pu;;023-q$^x->HOnt#O`wQ@mHHP&ASy>XZ~R`2&4+dxQ?Bd{on?bS7bB
z?^M1aa+ffpHYH6VVz}!?(|w{sbFfC_UL#garISH;vl`y99$t<gKTt>Ss;hW2&UChj
zCZO}-VmB9l;`nUuw|hn|!w`u@w%P>_lscK|C^32p%#3oT%vFO_dC!NJdRBBNP))6$
z-$(g5`ZbAwNyaE;ajMKNFA(G&PtZV;4Z-)<zE1W0y8$tgp;8&^Fg#pGzy)ML%ID8^
zWIuarVEp(%CgWKKLy2LqpT6@6!Z^~#?`j8<!n#~Omwuv`r$_<l@XPefs)2sb*mRf2
zEo&7nvZ~@I6pV@dfcOcSV>ww*#_L9liV44Zzvot|Qc?^0_y;;||5w4MX1|%shxx&g
zj6Sr^VZPy++2D6hREEHY;h-0cYW6S52t9Ux7%JO`&N*19+RxLB5$VDsYH>*fP?t^H
z%<S6tpZ*M@|Lqzgdm|;x0D$XZAkig7;Cl3aYoDIt(osv~?-m1b9FN$Ird(Y42aKjT
zFkebE_|7SUdMQ}3Fu<APWCMG$kw2+<TQx&JgSY<ohBf**fje>)vs|NOfs-kw6-#EH
zcn;{E6<ICq-!K73SlHv!$<8}jdAEzy9->Mz{l6LmcAJ3O_(zQ0v-_^R7O@o9LxCMn
z!i<LbT87OT&D`jQIexJ8+WDoT=G{-%4`1?JqIQFb&j+`!C!*JG9kPWN%;axp_HJLl
z+3Z<}vL0UGn5L@1cGcXJ@Uhb4bH4`#FL|h%C@VeY>Hgt={Yk%n9@5)S!&~Fl9?GPz
zm0r7jSi5CE`uyRrH9a5ZzSF+0%bP_`!P1kki5&~PX`R-Ng4^_HUi_u9Q9Gra`Wc&A
zt{*We-vXV5>UQ|8h7F)Q{JO~Hz@ovy@QDtm<?m^^=hso?d=_B|@2om5TN1;yA2FC3
zExp%9pOS6O-#429p0L1^TP619_V!wOThle)ocHpT*lOeUtf?1Lh6N^i;|KHk-+O%2
z1?-+hq|;e`xRS4YELDKI^u5|W$qW5?;vIVA<g7@I(9VlLOTO0ie_Z2odPY9}v=y2m
z+mfX7O)2kT29ma1G9vM_5L`6yX0Kn$4+I23^s*o<gF&|v?u}~vib-+4A^8r~TULem
zx8S^8oV~m~pJrVBSUsg-!_ytk@5v9BIuFhk&E~*!E!Hj9<XCd0%s)f*J-cm1QGR=T
zfISU+W}w+#-!GBkz&kzQJ|gEd3y?}-+rni;AXvo;`N##_1z!Q$w0h(cr8cO?^j3q9
z1eTMrl5GuW`}lsnIky*#Ht72er__tOFB@pvRZ}<{?ECABdBB?ba_E6|vb(^#$&~yo
zqIGJsIAp?9h0o5ewd=X5eN$%Ok54}p{GusCUFupIAq|?k6-`v_!FPW20;7ax^orDR
zHWT!OoD?bb@6hQ)U8F~F-?A_Cjr?uKCETY=PVxj0E9GX&#pW^LSu53z#{5Zt#N5<B
z6Z0Ss<f|yGef7YGKY7D@x_`+^bY`l3B7?xGw{{mZS|FRq3l*e+pF$@(L*7}Oy326A
zKlwHFxaza^W(ACtgS)o`E#SG?;)ZxNHH-`Md~r22bZb`h#MAIgnp*P$P-sBr(oua1
z$wSa|GvHq5n*tU!L4l64`Tkw{4s~a%p|w%}s`FOFcK9Xqd|UW$hxyAVr9TNuh5FEl
zY1u#$T;)v~Jt}`WvsYv3W#))h#23kYL#CEx!sX*Q-b6M5mCm?iC5jS<;8Hp=6lgRg
zvLeyt?@Lj~Pr2KCk;^5(v?#hTM|P`CR}P>bbdMB+PGkerkudX=n=JzbR^Kiwzf8?x
zeizbkf<U<D?Z6=`?#7L!eNK7S)X`;sG>X>7cx^|FJiJhtBnFiP{hKJ}9=Eq3g~Iqx
z-Cg1;sjeRzt0TxF5lhM=BBk>pL+y%dAID6(orT_GPY<w49Qc;J(D|!G0hRtYuk`S%
z3eSj;WD?$?O`^3X)gNUsTOaN1I0-YE+w33PU*Lefrmq0Hb~l4udw2=L5uFKK;Yp^@
z5<0l>v38ye=sWdhL=+ZF*R@VP>BbIQp5dLx<A6gW%)cIL&-lG**YZSC`Dp_FAZ3w8
za#@|IaH<KTYqp0exvc_rJ4eQS6?S54phzWl#ivO{WQZ$DB^qsdoRnFTm)}a@q7cY&
z!z<1QV-Ep8hmT2S*xesliIQtJj93>@77)Z<I1x<OI1@`s>v&~<caO7Kp_JEEz4`kH
zAUF?@%7fD%mJ-%jj<Q?ILU?*!CCcJPjDQhl*v9Cx5ds*t*{A67R^eY>s^L6ec8=0V
zvYg8HI64&vljo@9$is^al)Z}8+~w1A0|iN})Zc-r5c5++%!lV`mxRrmfHU?ziBAr5
zW2YjLb(@6kDGfpk{+phDkU3ml+z7pxFgxTp*bCad{^w-$3@DKe-Ot(AqzzX(636|P
z_~(QB!~U^n5qU<>)XdWa6pp8i5Jcg?%d{Z37_olBK0*75F2;Z;_`Fr#L$PeQ3kX)3
zgGcs|uXro+n@7L;Ffy}2oAd5m<r-8F??sc<fBvV9_sGnS%eJDPmqAL_%slHN0AmJi
zn=PNNmfM^;v)ySylL{I-eu9*!q|%iJ>wvn^=>ZY!daekyTG8Qit!mgC?^!|hRPdaZ
z8*h(WG2LCh{(Jl0hq^Qk=WFo7wir8i#GRL$dE%J$pf^touh;t56CUUebDaJy*$>St
zK8@b$hi^|gH7=I=;s0Qs{`^k#o;fG=gF_2rGar;)t4&F){j~T3S%n(>-Dd#^S6l3V
zZv+Wi<qO)bnWpRG$knwEL)$lv#Z%6w%+{SDP@*((r6<PJJ$sOOz!D(X8!@!DE9toe
z+k*D>zH=X=^rvugh(k;w$38b&I9pUBhy|UU)KAQi`;eQ{|HV03WLn%EYm~?$CDxDq
zY?7D7UN8K720CF$10_b-9)3YZK^b&X9077+N+)o^W7xfiXW^}<R3BmZ{Y4<iFCssx
zP0~}}OVZ77^!9c1Sl5=)LM<0Qz<ktFfJ+Ss7aCD-U*u6)Y`E85M^WM5b!+S-CRy@k
ztDd;w+!s4~#I{7Zc({c0JW4MK9@<IC=`Bh0G5wkH4DNZ8U@6%WDU+ioNiG;Wkn=>O
zw>i1Tc3$aXj|R&6y&pH9*?7XPdfZhWB_{v8=^nHqn=PS3QU<W#E!b6VI@99&^Lz1W
z<?eD+-ydL7Jl0T@OBTbrS$X<m)K{GA5(m#ArqGaK5Wec~&JwYL5J#Hr#>wbNLcug`
z^`U-u7E=e(X5+PRBm2|?wQ)qk88OM^;Yk7gk*ca2!zga_tT{d`F0#GSg4l5R)71j1
z4SN*LJJl{2JXUN2@TC`9%5yN&f&<)ltE#vu!6IJ4MtGd*nh+K`h6e2=!lB;uLLQ#b
z;A%KkCY=m!vECDmO}=@UYYBjL!^_v!XbP(X!iSor<ycFmLO$qK6iKK4Ac<<b)~epI
zkw#s~!V%arr~}4n9px@#tIPSSDx(|@6t2QcC3^04#E1`{)pZu+C20Iy92o19>taBu
z=|Q;%AH_V?Fn<tHcgO_sCAQ+3<T_CxK^t%S`0B=bR(xA5I6!bR)tt!P;Jo|mM~KpM
zYKnVsvUTD!cTDr!<J3z+#`G}tf;6R**JvEmBftZCL4Nr0Zx^sNl0=z!D0BxpPve5W
z;MG`4J{!B4;*;7wb1&eeMBiEW>}5gd8jUM35x?Npu4-kaKn=kx`_GF#CzS=>@#2pB
zyHppd6n-$YBr(FCFY}6U!MDN*$EjAeh$gBPhU*PmB0j!|S48!%3g&@Q_5H53tz>3c
z{&O*US^Y4L2S)8$OfZCU*sUpT_Njvp$R_TM7Bztt=>WPFikw7^i^6riDJl$g=hB`(
z{En8xw8`{3enAbi_mkkYoj(wciFLURrd>jto+!ye)sxweAI>V5#=3>blvo*mF!ZtW
zVn7ebq+L0(;GLE1a#5_n?zy^z2|R)0H^7r9w$nOGPr=hu@?FRdYwX{p*X|+Sq=Drb
zj)-wZ=pT?(qCSdH1_*ZuX5Bg9Qzs4CN&0Eh8}yV<s8UY^_Ef(lI;?@*lWsG1a*E7_
zzgyA*Gzpa#goSCaohcf>IY9@0E@DSm=W^Ycco13gr&LD;xNbPS9G>#vI!Zm8eCzpC
zQq_WuPT&oZf4t@#u{mx~?TkE6IM??qIjz+tTSerT_!i%|TQ`NR*>ViH*#B2=Fjvm>
z-RujC-VZh2<%|zShSxob7s9RR8u`jZQTQg8onfL03?)CY`0=cs;7@)w{8+&;ZftAk
zzkRWi`{LuX740!kHPz24-207j>g#C3rtD=AIDuwzE3BiU!_uzQJY>Ivm58hU4_Whs
zATo(jzkp+MQ*|p_RDSKC59!S7pl!orP~mMiJq$d1hKWfIeZhn5shnwFykl1Q%F~#+
zu7@CoyWWaTWFUk%prP`vvj!?txG}1gF7PpQY?gR$&6Jf(gp13QOezYNb;}-JVoB|r
zM1)Rhj2j~}M_<2B;(zqZqB$oV{e^M)ZyC%7%%KSJgo;@Z{^ep}c?7Y~TIkG7v9`}A
zbv7UyG*@z^{U)fK9Uc>qF2Uy*Rpk|2zq<_35n37eE^Lek&g9W(?n1r5aRVW;;D%dV
z%SqD-g{yG^wCts)WP@+Pad!tlzHJPU$mn<t)*uz{7wmT95ur)KfI<W!Gff--oDNWy
z91{k0@5Ge+72u^a!vdwOQ7YqOdQb}sD4Fa#SEooM$fZyRR+C7+L+s4>#)3s_MoIXW
zHr%aGwLa_h<a4&4jA9uMTkW2Nt7l|4j5g_ivgU7G17`_spft9*GI{q{yNto)|D#70
zx<QQHqtbYQVvK<`W*us+3@m5E5W1TD0PqhvGp!+8N3ZAe<-Gt|_-4T5fQZwzLWQN5
zfobz3`LdMOTlkW=<1@X*(_(@2;PrPFmIy0gr5Ym2;G<2AJIh{hr+1^pa3k(J@%3KB
zVudAwfPB^2>RAzwC4u;11g_=yKntBlx7GP;?u~a+UFDKgBiH}(pL|sd3_1<EtKYFn
zFv=;b#3nvCWNUK_sc&_Y0ygyNo&*LaMb{z!HO4+cWxpF<qz+g=4+3q5luDh5f@e#y
zccH;FrNCjvpc}wt!esW26b7oYtqN_6Fh<k#akhJt9K&*?QTn)JWrrpuilxVP7~Ygd
zYV+0nT5>$lSg}1-q$=bWnNrxd8b!PkDFfM1^T_@WZ&m=iXz;1kX+<@fWd_%mGoN`W
zyfhnJFO1*m@u9Zt+`Zf3BdpTh>fu*jM`ekK(k1n|c-_|HU<GJ(YMfiN)!pzyG%Ai+
zDUQ#b8PP_d`n`I9im<CN+FiU*b46u;(VPf{Th`qw!_eW;CPIaxy~4itc0!qA6aK~<
z3gkI9ML$h2gzWz3@+%-8!mgpCyl-fpI*tXIi8K;apla%qUVvG`q?u^n@Tp_{Sf1mB
zfOCd=c~6_{_pX2UYz$%mc>b)74R)odMs#X*KDv!kfyidt!8K50ryyusA|X%u5Tkl>
zyLlnn!evJ?B$bKAAl9844?jo^6eHYT-vp!tZ2<n)(^R#?;{@eUf4*<?#>T1b_gPN3
zV-VnQPYV?jHs>`UX`9?YXRlv2{5rPRA^$}W^K$-T)>-gkG*)XsxFb({RLQbjs?JyO
z&e>deuW&mHFLFFZsLe4w;JlmfYWa&mLq_mf`{~PL@h6JAmx61D0uEy8)JH?d9Y5Ia
z$XVWOzc?r|CH#1@gYT8+gtFyM_gmamY1aR`9xzo(Xt|~g_IuxV<u4EZz(01yltWFk
zF}iK!{m~Cs5;?fkbq9XM@mjW&x&`8ky?lNmJ>{;g^HWcYT5{COf$XIFoEn@*8l8N1
zq@h;rdGF&QW@&*^gq7j0Sgq`vn9D*)At}f+=)-<ERZ_O?_<?~U5-VHgdH*}JQsKgj
zd77|^9rwaIF-L|^UI4_7OzwJ}CMI8Fwqyd?tGNif_fUJPbZ&?2K#WU0b!wqW#X@^V
zCDW1$)7WD(X6zNT#_H>F`Hi>q-xaDe`oeoDu5v&JXkZhDP3*bYEf2NB7fC1v5t9D{
zO3R6^QsNF3a)CC`OHR7<gj|saLzXK|=6aB-t>-IBtE7~S%%qH3S)Ac(Wn8^a5Sg&%
z{N=;XABqQ$=yCb3k|`shRN-R_^gaA!@%BY)p-hFJD<}IWmGi-e@wm=74ke(-<}?l-
zzL9+*5r?I!N3QbShEZO39VO&1QWCIJ9&W$Qk~pA}vmOny!HMZSX9l3Sbz){O(qlMk
z|234g#)6H+&ZA1@_oTsr5d=t1)ju@eX1?uIQ2~`9NMYgCrB^(>P#kCQ=ZOLL(u;cX
zk<#NbAXyT!`0g1PIR0jv8|y1+i!f)`gt`Q6T*ZmX_)VSrw{aMy-^~H~zG1SInWjxO
zlFuZA5nfbDz((*iBtrk6j(nv`L7?a-d7u71-mxCcXnp)Vr$h>`9vq&bs7FuX0p&^3
zt8T&lmg_hh9uutmR(&!M`hbWGy%iPAIOI_H6Irn2g22oInZ?=GeR9O^Zg@R!!491C
z`4hT38Bb-~y@^p@^8i3WcNSuWfc5;5D`VRv<E-K89Ho<T@N!Au7afj2+Ex)JWLJxR
zy%mYr)_zrKE)6i^^-o9X+0Del-j7k1^wg?5dCsAa>0lVVQSp7fm>%?SB3k}1XnpVt
zS$MPO+3*qKzqYHD??HF-x9bbqq7224*>yY0{l{amk7Y0{?&prpCf<Gwnlf@x>qxRZ
z%rh56Qn-BGK)>pr-RcrV>!{=crepq4x+_Rsr~U4Tz^L)(eOHNQqQA!iO?xC0pl-Z4
zD+BZa9<sTxalJ!w@3GQHiX2AqkX3ZplFn|gbBSQwD`UM$8SndYXt!U+8!5Xo=2f{_
zHJ4DJOS%Q4ykX8$Aw$Z{uMM5ss-M=Ie*9-BBt7`#zdpu4Zd=Ms=35MBp`;#T&yDK1
z>%&1+9KW7^xLJ*9)^fhjTBwZo#@<S{J^Al1B894L`9a_Jrq<%wKu#xT?X26<RpWFG
zMj|r42uFC-IospdH*2kfvubo1@(pauYAKmLc(6Cd0%;AwpipkO&*H@?KKoQA!eyJ9
z#DxEBDpXco%Lh8X-VBkllT<vn7~wiu`b)b1f|4L0TO$Bc0R)A_+J)rPH8Lp~fFN_)
zply4@(Mr{!=}XhpSPntO!uQ46M{H4?H)UdCEmt&Gqq?HI{u-vVb=K<xZJ{%>s_O))
znfDIOM33_dU1uytn)p7y1d*7*1At!FmpPtN8jE5{2d~0V1CtQH<W|i{(=f}__$Z+N
zRUu9GbEZ4FnD#4qZ0x6I7@u$9oqyl9l7Dw#&jg?gTsI4q00}gGD})_caC!s_i+ic}
zhL_Y|yz|m#Nhqa9f`e~?j>}z<5|Z$n^3mon6!L}MN8F#fLE!~7sCY|$#S2chpr0EG
z942(;W;`$H_}n?h#jQFO!~4M4m|jEURi4%lKWpgIw+7YU5=7_p3bP#vWCV>b217&X
zId<z0l5%r%eYAI|N?cIA#U8h>ydC$ue6?EY5v`=mg>dH&3x99VMXWz8DICivo;nqI
zg@4og`!+#Xr)k`m>xg}^DP>P8+h}e>wsC{m+CfBnY!b4$vd|SDBLgr1RX6Di0PMHW
zT?6AoUg`1Ybop&7cmMkt;$h*{7-%4-^Sr*8DnApw>;jE#0P9NT8JLo#=fcv`R3poM
z8-nuzu|gtXk1VqKVcglPcSz41d#k#0Xv=;ybQziqYW4&pX*+o}q+1iAerdQNL0>pZ
zHBy7n$;(stFOd%;WDR=rbSwy|C}q+tV2cpKVKq~JgsoUpsf0M&#@5w0_!Z%SC_p~6
zm+?I+O~$%fcVmOCiSkf)Eo2~6FBe{M_xc1-OC7=8wBiPitnjC5;~xwqu|Oj_iTSpr
zKKp+L9s9JpKStpX>K~Vlv>(P<5P?9tb;OR%-)k(#<#f+459B?Io%^f_yoUw6nmRi@
z!6n4EzMyIx$0O%UI)7nS3h0sF2w_3bk@?|ogp1UHZ2qNKyM-)f5scaQhX8&MpXs;L
zdxa5idB;Xb(blGsL@+4CZh0)_Grg#p@+sG5NYH;R+?sUtBq}cw+aO8t^l@@HX=VA?
zI_nBDPy!Fxb~@G$c7Ni@F<e(}@whxNsPq1L|Fy&#rJhBeSL1s6atC%h7q|2Oa~f)1
z|JVvrj9B=Ze!tW7UY(8#|0v=m_h_$7Nn3kjv1$I_<2y>B+^K#J=L%%Z1a#Y~_>ZIL
zgGMe%DgUflk`I(?6K#kJPp&Sz{E4z$%@n>q`%850F~`#vN%PccDK9Ob1AmvQvv}||
z-$cl5cr`Nl<4u*zo2-=}hOUc-ZrNLAZ-XVT#Amq$A^Eq$UXN(+Timj^i1>)q`9kP_
z&n74?cuDXB)T2M9WZsc-u~YrLJW+?I?bok&F&Rm(IIG_HSPQ2>t89@|d=x!}Lr++A
zg1kU;1G&EXR?lryz6MvmD9SBfF?C~Ui1y0P`6=BCVziTJXYn;kGzz+TCQp1|6S^6>
z-pMYGr?^Eg6#pCJV%q$!BzTjLCp6OoGZX)Z)Y;8cVpL%JpjCnBQXZecOQR>FGnmmf
zYn=p_D9w}5<L>50#Os51hO;L-|HPgQHTOeS0$Z1_U8s}9C9RoWXgQI$7z<>v#4d6<
ze~uOp%Ubk(_x#t_i<WgR-6?&+x!=5g5mX}5te_%<8+8br=<=;wUyNCFTEa^lZB<%z
z_zSiLQN!=`QedifiD3%y^8o%K)K_&-5QQH-VqEx$QKj*~rJ>-KA_uRZcyy0BNTooa
zPhD@5sPfi*x&KQU6O){Vfam5yK(upL`P~kUg;*0_mE<j3MvvT5-yjVJg;AJzH`6~q
zIOPy+t@LnS5OQjO_-1Bhy>lsQei^ZF>CpnJKCiVmbW@7cq=1X}Fs+SE<Bx=G`DrhQ
zMeD1Sfc|K6ndMh>2}wge_r?o6V1RLq;rii=UJhWs%bos}7Nyzif_deA+sO~slmgoQ
z>ts?K>6GPPXG>2yb)c-|M|)|mc#wc*$4mUTNJf9xi#S-=rSm8B7Lf$1P;d}c@~~jY
zN6NI}+L_o`M-F@&<s_3+^@wl*qlEAfUno!1d~j`ZQ&=L}z2oVWM9i1ziEhI_B>Yq7
z$DTL2^Be=%g=<f~^g2%uIyT>*?wza*9_$r<a>;s4ltsEECSaU6-T7$m%d^jFs1e8L
z`J3WpzEi8s%Q7^aP>_$SUnfk5Y4xUZq|JbSUgY>;FN5~knlY!LfkY{r@PV6RLno_9
z1e^1Uks8Q=4OX5%kS`)FsrQJx|J(=mFZSd~|4I%E2}+7lvNu0Fal?cWaBHnR?gx&<
z#ixd%A4}w^5F+TWYik;D*K`*)OawHaUd_#A@y`{5%)R1WdUQj<ZuQB<+jWl8XEPY7
zC*n@iYENEhc(swEkzvN;ik{Ps!nRz_Zn1SwoGg0XTkMgmy<=um@)v1#2G+R;h4;B-
zpRZavvE6CMKH3Ae1FFp>*2GIfc5d@Wp5b1GK%ZjWOT+E<Z<k=Qqfa|yzM)n=N2{EF
z-=2|tL^~${ttuTim|TyLyVfEA5co3G<*5MyLk%qM6v<9?HZp0I$=}c^_Y8`|@&d0w
z41MzNYRU)54Xj{5el}tMb|<`KD>ls{B=Bik1jw&sVIms%wV<`HO2@nYfCaZ6cm~b?
z1Mzb0zoIhLun5nBxN&8??rNlZFjqQRd`C<iyt5W|VSH0f!pr+r)9Ro-DZ26ii<735
zk26Na%?seUuGO58+am`3%q+Z-i_o;ta$DaVR2~Q7<=WF|E}$&;amqFY*5M`D`%p*+
zDnGV_cI7rZj5cB~tS{u<Ysg%X$#OPYcpw&u>X+k^^E8YGS<^-BX)@2RRbYA()6{?<
zz*WXgpfmmxI;^V1kTbw92FD}5%Ft$`Uq+wXz3rrYVBWSc7<~p(fi~_O^SgF0I~Ja?
z5;zK3{OD7iL2~lCgHBc?xYzhY#z1tDOtd_K@h~QTsZNx7;fpT*xMcCkQeLd2%9og7
zW1jiRXZ=ilBTcBp&jf(_o3S0~n8*d)phHe@!-iBz*^p_+U2U?qhbn*^n>&FeNPQIL
z1&QFev_mws|G=zMY&%JbRG?(!>vSF3vJz1cn5MUwKc`6_&PJSrpa=E3ikv+WZ$?Qp
z+?27BCeQ>W<Ghp$T`-?!vrd2D^AkYLlH(jDxo%xKD{&4BtRSxO&)Bz$D9t*b(ei~a
zF@3Ej_^g%;G;78|B9-+xe%17<?^25_0UVSR&KTzHI_P1SFOshwzQ(Xh&|8-1X3sbw
z3L}c9SOuh{H>7RB3mhmTn2oXG$0YC?%h~5AKQcfmwt=n=AB?GGAhQ5VIccisdzSbS
zBvI`w+IA$^4Oo-%m`FfiXD&BP+WBt;i+FxWxf%6?R?jih_SCz*QGCocFE6x6t%hr%
z;8|igz)eTgRfn&K(F;R(sbyFor|znWcdFQqIY+KUQY1O7_dYNxa8cVE+C<*p;JP<U
z&!GB;PM2`T!$BJEkp{fMn)tNxx)-LWflSphz9h5oM9rn-o2hMyH`AahdH3GvjxF($
zF9A!2T87u@GVV<@Ae0YFXD*x~1WNQgL~*CiJMw3Ppa(rtXO>METW&KSR*O>3@tN!6
zC-q7Tu&xsfdl%w{wvqxhaD3!PrE`{xpLe=w8M^lOd5)WGvd1V$Z$r?D*#sD!Mrrzn
z5T%NId7AE%Z~-~+U@GjN1Rb(P^NOZriXx>~*z*IA(71WsN>kAI!#1lMB<2x|hhX*p
zIBF@h7vo?(B=eA5B||S&rOPc=r57X9)E;&Y98i)d_+z4McBGVBN2bez3%OQN)am``
zo7$hb+L=oy&KJeRVkWDKmv~;!9=g6*IJ0_#iUvJ;60(f)l3vYoQue=x;qwME*%CN^
zA1EOs5#0sV1?Q&%#O^^JLc;v~3Oro?fm(a#J~OnRC3W&CBks0|p;2M(3oZ3qHzD>R
zjb-qx+e$!H=&`&qi3rK#fvJx(?6Q-x<Kryrpby#yyDioKz#IEAw5NvasUP=0Z(qV(
zU&$7^FaIwBL88@@;9&iDmlgb+?J)!!<+bBEKF+LPz$1`BuxDI#(KF5)R8(`VER2s$
zf33w|+8_jn9?uTf*3=LtxuD<Wy_%vUm!Vl;8csiQG<;q#<#NYrT`(@QeyUS1vgRF+
zi0_(^kSI_+9epR!nGwx>@?4}&ow5=SE?P#TZs7y%DtSx21yijL_x?so5lO?54LZ0h
zp-pxMb<lV32ibU;BY3joQHH1EV8WHaF6J+Pq|h%?s{#@7t!2v#w`y+v&QAV)t1d(9
z*O<VNMF2q;Xtu&qp+uKc$DVet!^`14(I{GgLy{knEz+~6FW+5qJ?f-6C?9TU=E7%#
zP^55bO(A38v;3bH8nG)CMU&QN3BlBpl@F#XllW8?q8dhOsc7Uz#On^v9^<2^MVrG!
z+lKs=7gI+`$f914KUpt+tWkGVnh!ZN_Hcpg4bhqjM$1=MZ^8hXbe2!`g)&d*X@(cA
z#Y9Ib&s_$E8{Sn4KR-+ii~_ACf13TPNL>*)rWfeJRp2M?u?fB)ZYM0QTn%N1aD(?T
z2pT%I4CBPumGD8lCjnC2bFPqh79NuMlwA^Vzdd$T<NKS-;7RAhb0Wu`jNURVa_6K<
z@aw|`#O*NEKK2)p6&Yb-Ihxspk(-_V1o>dL-HWh~+i2DZ_f&_oO0K^}d-tk%j<)Cf
z&eMj+K~_F3sP~SHXpxSn4-03*FDQCEbip>HL4WP3vbzX=V$K$T(?<miT+w9oyr%G<
zO1md$rUaS|;~sza6wvb+R<YVf<?RTEH1DGaOdAY3DV3o)YhQ2UC9$!j0U0caL%_9$
ztHyz#vU{bjCeY3;bw~I$1q?*4od*aG*Xh`MpLhk2ma#emSP+cIO3G{J4+XU%Gv26N
zk6rPex~>sIk-~h)Dr!BkNHY3<E=}(HVr~Ob`C<O2tKiScqrpR6Vn_BSae2-gQh7Xs
zY=UXKiNxS4uDmLZpy@|m9G)SRw^p^%`9CUjGA)*Rl~>A#+WL(t%%E=^+ALnJkh<}h
zyrM_q`BCA64aNTaP7BG^7IlZ^D6873^rw_8FV*r#zoUVan)uDDZRg8T?;Pf@D-Mb5
zg7Vv{msMYq*7&+@9EiMd%)!w5XhFxuL3Ink7kD&Db*}X_ds6PR=hfj~5_Qqr$gH^5
z=vLchgW}f-T-yP;9Ni7OCDhKA%8<+IDpfB{91xF?zES(P-ZtW-aU|t~fZ#!9V#ZiO
z`+@L%P2ZgkHLmf#p`{~Q6-)|ngF>-V%NUueZ{TYvZ49V?>+TN6dAKctZQ)s-ZY%ZO
z#u?d)8Q=4{3;H=_p+EYUltMRMp!lYEe3Q|+AoD)`m>+ECEe7QW`{OZ|8YwXlb0HMO
z2H_q%$fI8P@aH4r9!%Yny3c-1nw^m+W$NPSY!MNJ4k@*<Uxn|h@V{~?MXr?UQ3{KC
z1KpOhXdmb)bO8|FRLrAT_|SRR&CLK>m!L96dB!+T?~sYZM(_T*RL5LDu#}LlXDDMG
zNLpEX;?a0ba)YnyN}BVb&}je*k%<*Ojz<jpsKbilvC*S%irDlRj+<Um1pWBPb?|Es
zW;be&66)-vtX1G)Ts)sfA#>JIEV67H$Ma~a?_F&Bv-dm@*cC6F%y_6rwBbwiSz0~b
zkT-M^uvy`fbcqqg+ZHBGOjIDUsE@Q~^)BYOE^u;YLkb1_+8)DzR}hhRk{)c_*27$e
zrp;=UXIlzF)!JhA_iXl7o^HmS($uVYflr?dzD|{gmZu6s-6&%9r%bp-c9YFXTY9do
zoU@Iyigrh8AeoHbe3SY!_1Y5j&^!NKxr-S$oTbUk59?YIE90zLL^ZpLF+G+F>t9rk
z(L?pjvO4uEp_0LrhS9$RedO#vE2-9NT#A~cb4zMaG##WH_@tv@;&a*X69RvsJfNki
z012PVve>BjNl`1&a2(qnMSWNMBse(fA+QTsb{~5Ao_b~r3m+EfwJvmXmHW0g8xE^l
zQ?=Dfj3?Cz0J!S3hP<Cvy{ZZJ(b~U6@s7n&&fVMbX-cR0S9XwBv%;3jQ<Rbo`E`b?
zbdEjLQsuLz4GQ)Uip$x}J1Yrc`GKQdsVtg$1{@e;dcEeMXQtGAY__B8<rxB(DKdy&
zFhu4(%T%$V6N=|wQuD_0PYbj)avG>!pO^Sd&!Sa!FtQZ!*9Xt`KBk?St#PO41C^55
zdLBgsd8sTThBUclLG)zkCKc-Xq_!f(&V9?PXvu^K+6!Xpy2{1@0bF1tq!-O214Rk>
zw!`DjhrmyX{3RWMly9UyTgrr@z*F~BykB3XaERH?hhHl@DxN-?QTW#&nr3T*bN$?8
z9f2*7fc+kyU|0pX`ZdXV@51m10rG_F$+^H(FqQXXeFk(K%iqXmJmHvOku_tcd8D40
zP?s$CH<Dvg%8@;@RIUN_GRLX=Q?0Yv0g>pAml+6+r09D#l?(be>p#uIh8b)vzy|J#
zGyV}G$v*lq-gpu3P93|k@u2X(&FiH~8pXfa6rE*1*J#n`E)7l(*D!tAyLTbZqT8u5
zig#Nt!ntvMQYCkWM>m;F30HrMsq{M<pq`-agz`=2A(&TIB_1~!1r@zi#g<fk{8YCc
zyB!gHj?-{RLYxOXb?ZG3=<<yRXEo2UmR8p<fd=prBklw5HPrhoC>GVT?SbzsO;p>I
z7jgMGCa<N~g!mVxj4<?0`&JwNbe7c0$9)E#<5PGpSgRmE+^XYp;|*&2DWx)S$?)To
zLH8c!FT8Vf6>aqwh=)_N;DJ9+?IrS~3N#8*-)771>gnwTSM;za4}gx^><xr<bXMJ7
zl#Mjy^=(t?!xmlR#pIFFh<`FwkkNHDHMeIDFza@V)-n%$2$fvSW^^jV&cz7?3dumi
z0QaR8B;*55Gd}O0Yp<!^I`us0nh>hwG@E&F@7i_J6@Au9d%^MQ>U(#~&rX<vo70s3
z6%Y)+nq*fm1-buC`la1@BmK_g-0PqB<KvHD*x>DFb8Wk89s7|E=N18sx#1#B-!Da)
zHd_(?rsN(QZVCzXq+9|9R-eufAYlpaoza*caGfkAg6VMRw-D$eSrDiN9YW|Fz@yiq
zjL`KQg`v<vd3X6&@{m-y?BaM9T~9S3aiM^YuY>M{rMGe7vkBD8%t&ck1SR$vB|eX;
z4@M;!meGkyH-B+(+YQszneKpcJmfaJtS<yn4r+Z{@60l*TA}cXX>1+cl5sP)iRb4%
zmg8Ik&-JpCGCN-6d#Cz0-DFfZisogw%Pf#$(m)+Ozc10XPQ|(P(+xTBvI!mFCUm2h
z)Giv}MF%5?FQMWLrMNz$(gM9F_0O(CPAd)z>IaoOX|+x0g1ftj?LtHdhJ}wIVgnUI
zEYS~qd|69QW7f4(d6;&MJL1nDrWR9!1$OyG>B@Q|V67*p{C9{3Q@UN#Tb6J=GWdjm
zSL*b_Y7!*lMig`y?mZoXvGNFo8UA0R&A=yV{2iH9jvU``$5~g+8N?#2t=?jspl>R4
zmyQ{sW`L*4UO=5!CrOQue>i@pFB+oy(jt{JG<2v<zu$Rn_d=BbM>Mz4dySn(T{gD>
z6oR!!{jIJ2qGQHFU0Yw>lNX7c?E-z)^Je~$ndd|a%Rm3P#`3B*gU&CEyt(w5VzPGa
zw3Z#m7rwJCE6LkIN;?=z5T9d=_vPo~e~bDvGF7^GMPyFmgRY`jkAi)q7F+tSn0nVW
zJ%y5oeNRM84$=b>yFiFLAl5sCu#%wQTM*}LiqV>yiufnNeTo>ALBn?V=+M>QAytQw
za4A|3dp~h4RptqBHz!ci>e^cF;lc$H$KK*n!hx~qWuz)JmFm{K79e}2OV|-Gb_n3g
zGx%!nkK<Z?Ui&`RS{>a(1d-29j@99GU+(ov{mWHQ6G4mDOoFW*UeWIEj0S*Zyn?UY
zaaY4|1CDCGeT<XxTg+fMssdrCD3(i{GlE>>Si)Qs7TyO=8YhTnt?>pWnbxF86EPS6
zrg-{ugK-KkS(1g#efi%s>;OKG#NHna6nn=Ze5it2yspG~ZH)7MkH038uYwMPQ2!C1
zao7C7(Z=bOh~svuu}Kd!@*S)I<$g0x$E()ajTUS>o@`LMn0_o{MxJ^sywi%S=aukV
z>KhKcN<EWv&;BZ2&@I9<0AA>0Yt%7ag{R~q?$}yz6CJ&OKHdDA>Tj#9<ND;&g9w~&
zy&sudLI72u96NcByAIG7&92n_AoP%#2XEXh{E~BJHX-00VeL{0t2&g4RbD*87yoll
z+=El%!gE&r7(u4ITLfmNRHKAKJ*Ym&_`CtRoB4KT`Iqi9Z84HH*y9RVw>3=4HE{Cw
zIWM&&L$`M%84q0b6)Z~z^mL5xb=l(3%Hg+5AxxNFfbK%?m0J>P#!8B}8gLMnmL5b0
zDS#hg&uvGq=T8y+IHu%h5Y3<Xb`n-{8CQSbm_umGCNd=1|8D2v-Oi<YglJdWCrQ`M
zJ0zbCOY?VXkwFP)`vN0y#91nz7OfhwG{9fzx7o+eqx-_zMN>LZq!gAT>b>~^Np*Kw
zDK(LS-bCnBSEw&DyM$n5+BFF(>9Z0IZSm=^S!8b@Iz`ntO-s*VM))<P55GY2Rou8Y
zWcCd8JOyr$hrF1aLCSU_<6QHe70+UiZzv)eDqdQSJKOgIVwn|zYA6A1dF2R;w=33{
z{Y&Ped9l4-f(T6UlxzJ9x0(%YF7@Z;)>Da9Qp23nPX&8O_4aYRNgWkZd#AO&dtuDI
z3Q(XEprwEK>1~+6au;|{?{8n#LypfY5?}{_6@pl=UU&%+k8PM5w<{M6S~Ln+m*`&<
z6FEr*2ih&=3a?Zs?J_$|D$xv|9cEycceCQHNGV~XBE8Zx%tJ@fP6v^M$5q(1U2J40
zSNNiXj$2RNxY$&xy6QM@VFg&L;>n7FJta9wOLap%a2b&TCS7<Wq_%Qk<J`!ZgpAf<
zFp2rdK>n5Q1v>DZTB(=d)nVgnMLcWGGrkVGG)dmS)LY(ZOrgdc0cGCYWUfL_zg_8U
zJ9di*59h@c>5%Rp1tyeUV*>kMp5lBHT3V!vUh;#8vuA*$R-7N!rn{-rmNR2vrC4Ir
z$0eC2X{s?8dPt6S4C#7C^4TrUZ~L}hB)oi1F*v!3vw^=I*?nS@Z8Gc5pgwUI3giHH
zSp^m?#^QG{77YH9RJNocfFHH2E0V@J)9-)8IR7(=2SMu6R}3!vn~ppCNj?~5cqsmR
z5;dyNrOjtT4JND421pfF?C<|g=3+YFXs>5NJ8DsTBxwa<UAz|q!p5uWo=t?7JRwbg
zu?YqcK_HQE#R(B@M67=guZAA6y%bE_g#rt~r}P^cY&E$}i{1olMpR@!`<Oe6d)4;`
zj9jXl--hiRxiDZun-(}*Eoao~y23Bh9ls4y?clHb=gVI{D?rB64av5vuRyS+mt?69
z`fAu~!Hyk}?cY0sZWAVdpduoqzSkMv-M*qm;yLh7KX@|E%OeRGqqhLXNMwT(`|;df
z+^{4gy~l*`>?9#Y$teWatFvpn#3G!b!=`W`qH5aDr~8yz{M|U*jL3q9i>uDV=v}=3
z*g8IIbYh21uj>Ur^Q?Lhd`_h&$h^J>8hQ>lIhQ8X`p|)GFfTQFQ6)#{Y78Vc_FnYw
z7uUO3@<E)=V*;|Ab_kD4I-=aJtos`MtSaNnAdLcgW(CV1M8M<qh`A4lMnh)xorw+S
z8~q#{#ZwYp90xD$@4LcdZbZcA9U6*0+F*|t@NM8_px}wl-%S=Mo$O`}f`#Ul?R6yf
z&$+h^iz9FP{rvhm{3}Z&O?ZlQ(Vom(6sJeAJ&lrzJ?X-Po)ByfyUaOJ)ZG){$$Ln~
z35z#EsCilb*b^_y;yCak4L*;1dl(H8pm*kq=sI)pqv@G;gs@-4?sQ$`s~+J`mFn9x
zFruZ(VamN9x$!oG766$t=`(qcZ-?ATju1$qNt>wqym*f*!}ha%@ArWJU=;EoxJ`B1
z7Af&OB-X&}FFZF~0v@6(bX@!4!)41GY=sXM0Z4q2>j-;_O4n#UUh3c#$B1KgEd>KU
zWRA6J7%zlPM`dU7qJyTmP(IP4GF>bkbSZdkAVvw@pHN4W<fxS{_*WQpKiy(Q(zeit
zA}Y7R1mMJ`?!4ypUAVK=6uA=2`kc;e!jUzkUYze8OqxD4n-IAp1lJeE-ebmGJW2r1
z1#t!Xw)-z#I+Ac6T;)A;LBRUn2j`KL0x0`sm-Qzo<i{>{Tbw0aUppd;S28=!VvsC3
zMM`F^JzP3>bWZ*79i_BasD>%f!oSDg;V8+(CB5cO3M0W^?P31|5Q{AwV<m3#-U$xi
zc2<crWGUj(4T93z=PvMItV$5OA^&~du2yRzcQeI_yfufEgakyvD-z}|+tBq5uh_y0
zGUzgY)6vrmmD~IG{=Vp_-X`U=$cu#2?p`SgqI@NBIS*Evy!Cz1wI&s;*ZR(E{*$W9
zrW|k?y%w<dW4Q4?zU%7E)~l%xSPT_|m-jTHQbS+8D^)`1{Um>xNu44ETL{3%r=Qm-
zFmamzr!<tW%jn4Q(Kxp*sNyW6lYnr|Xb+q9@8Tfl(e4jIAH(@FFG{R(_ZzV%snVN_
zBAO0d^{@DhL-Kd2ku52aPk<l#jI6ZG_xb}1jU1k*P8W)sIJHYbSU^)8#UCh-yD$P_
zW(&K*E<6LPd~~UZx%AB!iWQdWv~w;g63ENi8&P`g(_YCM=NYm+eKJk4w?Bm>c!R)s
zGl~YODMU!RnvSuSI`jo^wWU_hVXdV|tMmr9b;2X~TH0Q8<ScXTr!OB*%y_K%kS)bV
zuPilyLC%7$+gVpnB6y}-nQ{>u$)Mat1<lS({_I1b+ql(bOvnGR_11Aw1#R57fQpEK
zfD#g`EU+lCAWE~SNJ*@SlyrC3E-c*=E8U=gAl=>F-JMG#yX4Z(`n>P`zE9l$p3gaR
z=FGX~nwe{^`F%@j_WAhk<y}U-;UgEBS(}Uk$sqKSRcW`PsILCR8R8p=NcY=ZkuPly
zbKd?W5vU#|ipQ={MS2%bzblVM4K3$+CYqpMcuPe#yy<F=qo4H$(R_VqVLpcNiX|Zr
z&W>FrN!{u}Wrv#rB7;9o?#a~q6KJg(z#vjjs=g(R0!Q`Z*oNG!eEfio-Q@0jg!|8Y
zx#W5859bV}6G9E%2|Ha#`lM}q$G+tp$Ts~Yg{<IUjbb_o2(QTc6`Zz%T=!zX*4ODg
zp$j8_vmD8`d6X3aweSTlMeZpI3jl>XrhO4B?1ZZJ9h5XOB8qu@OZsxal-ZaNsQ3b6
zly8IVe#o0=bZpBlG{aoq&Oh{IMfyKl#e)jLRy!#PVLaLJNP;5BwY@-f`~AtUH)sOK
z@n>|K4ePQl+qrL{kdJkpVyBAkgjo>bu&@}sWKlG0SaJ~_9%0rgRl&faM~BK5tt#g4
z^0y@QCQ~rRt5Dp41g=#LPeo@|);>P|rL&dI?4BcQ(u?kO!U#DNUD=CZY_+nERjVVV
zQonesT!eQ1v7p1qI&&M7mNoiu)dfu-F!Mg4C>qx3u^Fyi8(&6RCT%UyhNf2}I4TK~
zEMRoelH4mjgWXhQz3}vbxs)9G7hT^3Pn2EnM0{VgyV3JWeG)e1yV}WL=9mbgRg?tj
zw%<(_+l(f%#*Qq~>n9KP%E2%pukR@_&ixFt#W(PS|K3-~qzpq#0I|}j2+r{hk$nOW
zLAk6bj#(C9*TAlX2gT%upuA<QT#LSz;}%o3ZtIzlufB}HI%oIFZ;<6`hGzNW2LYa+
z!>tCnFCRmeB~QE443fG(6|(_tvTr#;F9F3(o(XX-qw0U)s87MFSFnZUWnZ#Wmq??s
zZuVX;1*_f;SvV>3*gVAkE~R_<JtzqFt>w%VOTkl=wdKWpB_nvN|Mt3#w0^N$?9^Yx
z&@?$6(POOeAiVI^87iH9o9Wrw*1&S-8x^v!5)#Pxh?H5o#t+t^l~qa!Vyc&v9LJti
z2{j@xR&90K`FgVSgVE48O8xhs)xG``#|0%;i>&nahJQqN-X|?9l8yXe&tX9HqRDkI
zpHs&^Q;QmzwuE6<C4^<Hdg->dlx(+OJZs63N-zf<$PP=ouAmTiL8=-GAOV3AdDIs-
zdb$+}T6#@vIJv{6rJJaHk=h}%Z<t4>clZ<3n;#oKUaEn(O~RV@J}QvxT7E-7BoC3Q
zz&}LXbj;+liU_Ht%5RphPIG*JZ1r6SYBiEQ%T8<RPnMF}kJU00`Dt-6XWMPZI$@xp
z1OMg;)XE{NDezlj$RPhcEA=DQj@xhgAgIWGFoamxK^Y3CYo?#fq3--Swph%07!E7p
zNT~4)QJxC9t5tKJE9#|2Hm+wclv`W-Yj6`iJ}B9IgWPvV&<B!R8wE@CcVxGV!$xHU
zZAXYe8&NO`k=|9{GZZ<vH|z}yJto8|J-FULy;_h1hwc)TJCUZdV_0N+&SL;?SIyAP
zWseh?Fi^PDJ*VYCN6R!M1B25!jciyovxo}{b(A{Zm83uhQT8o=HhHn31-<b%@E{LV
zjfCAsb)?)&CLrPua6L#F5VgewBEJOR(lG8MkN~-iB5jqa<(w+Y;*S{cwe#+D$}=DQ
zJbD(R9ryiB$)3+F85Z3ra^T|7)<f+(rB!sU%(x<x1(OC<MCEa=excH=HdocciN1uO
zP-fI&_84W+jWI(a{?iO(8th~xmm$^Ns7HxQn`4WQfSw*Dg;TyG1ldr6SAvntxnrqg
z-vdNe71R7=>%L@W%uA_`5;8_Kw)1AQW01va0ZSamt?b*8W6;*$@-KJD`^u|+G0m9g
zi}urDlt{Va$N|^WQr9xe7a@tB7RH#>j%{0woxrUaO2YF-dLAE8N_!kc)T@j6Mvy*v
zI?F!ucXFR%-SR52yC3RmoC+>E+g|maZ3sXYhA?0_XJHgIpm1Va*|>;COYmE8G4@_=
z=?kM|%p$FRQ8u6tYuD~-d~!~e(vAj-^|CZZNMf7gWfokC_i6L8D)u}jYNq=g6Jy7v
z(3wcx@q0;~CK7Y111|3-wX!e5-3blnF0IUTKHTOFQmbvnKjIHLJnBgE-vsa~^f0na
z0~`RV{(oI0>0%u5J6ThG{k2bY9Y{j~YHIG*PM%s`9Wcm|sq*wV{9FE->=EX9JNwtI
z@Wrwn>86#IGz#~-9xhN8F?I5!d`l1Z^ge`{4Rk>@m}4G(*l6C!dY;h?VpZct50LYC
z!v`&lj!Jl{(ZM|aG!AZ==UX+ej%zYdMc*L>a;Yw=mvXyj<)@=G#eeOIurJ&OE_>->
ze|z5HD;!{{MR^w_f}YRvs)gf8_a41=!vECs*+=3V`Z5wfYK$Bux+s$n@Tv5Z2$SeO
zIS$+6eq2)+h3){Z9>@60a?z5j76(!HEq9Iw0^pK}n2AD7w7SDKISLF0_aw(adj0rD
zQLY`e=;%#*SzB9sxS&=zFu@g&Kvd+Cw9>);scPCK+7IO6Za-cqvXf(<N~{XEB&0He
zCoOc85CzfU9cOcnI&W3qE4|gnk8AGIk!yZ`lH|hPCWQbpq9CyFDb+N|gIXH9J=sB7
z5x=}snK#Q=C4WBc;6T%!>=9p}5UN=k-r&ub=6;&|k#gIB_SqMiDT<~C8MigiW($fT
zO_lXHQ;nrxkfrD&Ur6A|HmAB^<B_gjY4ltUH9yv$lFIa6nhiw$wwvr)dQ`({<w2oq
zf7}d`uzZ2<{?$^y!3jK(G4i*7FGovyvKp05V8RYp#sczVy_pf#5ms@RJnOZfOy%Qx
z7TUE8=j>g~gqcW(+ZQguaNqB&?5sU^VjT|>qLx2;l*nnw!8P)gSY@;`LyS7*8+l3i
zD`jI3yLoZsR-BYeY!xG_>kxr`ziBWTpKrjsJ>Qk&TmdJ|u)K26YUd3s+PioNc$c1T
zo#_+MnlSknMw9jSb^ga)%QC(|!AN&*h?Omdo^hm`v%r?3;NfH{*z4cVWIC!3bRn-2
zpZ@BXt;v?+$}VmdMuwNn=>&T!Z5kKVY20#;GA;frEYDP5JDrkabs(F=)qb0z&%OSm
zlpDvUQ^iYav5A;X+X;izwb)sG029Qv;yv{o6ClRS<;utNJKvB*9^y;gtY4m{P_9<Z
z>=FFFNS92tokCS}aP_$ec}D!%@-gPAS&8Tx3q2}5CALm}*(g{%-g5e#@ny6eD!q5N
z8ORX}8u?QI24l#rbD-p6XGF8pPu6X*5<(boOBcGd<a_z=Lz;#csNy%)de^}QacgnM
z(R)nRlv@nH$GJQdjRE<3`e}CVLi!&V{-Xupe;fKTE^P`_O7uG7fDQtDj5O_{0IWF8
zW`&vrcG^1Vq>EN*1bS+0qwywt!)(E4-9aM2u(DU}RY3r|yF+jRL~3QX*(VVPHyMZ^
z_DZjbqQLfuZz6`ch`%c<uSU;J(e$yHJz$KR%Dj)3S#yi_>!s(H?^q(T#I3m;y{|!s
z1~WX&WhljHy`W$PEbugEyrswSOVZ)TH@~>eu0THq^343np3e`zUPiQJoxg02KnOh+
zrtbrC&^65mYXFfgFG?#PoY~tl?u1V&><uze-G`#P*<G3yk{qlcAL-sT4ANa{X7aL<
zFSZ<%$VSp#MCn@n%5)>TFJyZo&2YZo|D9W^7m`(BvX%2tD^1~fN2O^-`|~HCE`&XI
z+YqdyZv+cfmx&;^efJ|EJAkk9)jm$9Ob5<#esP9|9xLwrI&3EniD4aX5)LQI?uy#0
z!Z{>Y^<v9W#(!mn!I_lhC32!^yQE27fxBCl^Jrd0)<izO;O<K)>NXnld|-H|DuSsI
zt)Dt4SR^&C7G7zu{=Nw3MOYRi$g1IKxGe@(_O5+A4}u_5?ZNZ#7jpRVp(4R)hxI8!
zR?H{wt8<pTZCWFiMv)(S;Z&cE@5>l7v$fPW<SQ^KFHtl<WKpv`V0Y;(C41TGYu!_6
z<yP3nakFx0S713Ples*G_A5fXy;NivkZKVjyKq_l6?x{%svB0v|LCxh@bD%*Lb5h8
znP=6fk$0pb)KDxJ4(K#Ax-%jPf0YNw1>LAfJMMqmwkOcsKQE^QqtJ5IUuU1p0YmMH
z0D>}0Dxi^=!yt<-k|AzV30iMOnWo`A3Bw;*82ijUf@j&pLPl)t1+^atA6r)x*6zG&
zDp`}&yE2f=7*}B1y_?cv>Mp}DXgB{rFaom%;`C2i3!QKPTb{p!tz#*s2(z`yTf+n$
zM>$(xo{cyyKXSFml@VI6KXMJOyz5(d*F}xy_0D3^PaUqY-LYA%pYF#eJi|vwu7}`o
zcqE;jyciTuvcX{PzQO7y2bR36uV9O<$Z!nOVsQ2CBSyM%Xn7SR;&Q@mT%OJMq9|R{
zew-RL`eHD6nlCGS=CuvdspsWLkLoy;oiZ!3Hu{*d;SlnyVkl8Ikbdcs8o_3htzGK9
zE0JBs<AT@_zaK|`IDcQ!c$_zufl!pUk!vUgY;WccUd=<YxDMzTkYDc5YcTE{sJW@`
z)m!Xj98&n!%R6m<XfRM3o3XLhqq2G&A0l<<gxU>uOliouWpL}{*#G$}=eDvBk>?k#
z%XI;RQ;H8;CJIT84+L1|SVh*hMPPYl%MLP`8xIkn-_QAXHr(I8izc^qh%MBaWOTYO
zb~v;RngD7II#6g??q(4DrJEauuB@uQYLi8sUnFgflT84j+uvFhHC_hB-LP&yHTn+q
zF<N82m8}(c_Z2m77Y03uN|C!?n$ai|_u4IR&_{`hnxW?6YpaHOF-pkmXL94m&LRI)
z_rLIo48VENPD$PVMqosty_&=NkA+HWl13p0XN^va*W)l*G3GSp);(V^@)1n3HrZvd
zseZx9TyCR7zpkqPfB?c=Wk<zvA<*FwG->DSAu*eJs-W{ad3uBIwa)Vn`$8np5aFt~
z$bHe>r8oZil|+iy#tFHxQZIbtVq;&hVTYwYInK66*tOy`h<ij*EmJ4;5CNA7OfZb;
zbiDteN@JHlYFM^5S~o&!5t4IT9$}x@4UUyvW0-qMELAgG5udZzGZ22xgfB|8pgBY_
z_~hEn#?u!a?tX}|++|39r}2s@>P<<L_wc0|v)!W&7Mo5Q+K;U2T!CTT_Q#YS=QJSN
z1O_(@MGNf3EU;2xRl-yHFafnfZj>Vu0p&xkP1h8bh)%Pt%7kw!buvv!oPJJ)-BTwr
z_TP#N&LZR=kvAK<jGUrOOZ_kkEFbC>hoP4}XFvzpYI#~G@lbR2Z5lI^m(bOf3%V!~
z(odiZnJ)|MV~|6;zFlu^=SR=M8}?lnKQJ8IQ(YRR_NnAzHk2fA85K*N1k?z0kb_V)
zz#69v{b#b$lm5F-YY(Nbf=Q=PG%f#VO~xNB_&b`lr?X#50u{=8{nMuIrNu%sEu6aT
z>OHwDooA1!jU*D)!Ub~X4`ohLKSV37s~6MeGim9I;_<IMd8la3;oCd*rsb+fN|sE7
zbtSarp^&#mtIOOG`Im6j^Z`5k8tSp>4H6X>55=rtz}i%9H1;zRX5|(bcEMK92UuI!
zc|OrO+fgS$W1&NI9I4eSd-w@PO6<-#%k;RlD$d22y=%8joU9(1#m2w(;_VoW_;a^^
zl>IKXS-#Et09#?cFCA!2sR<43QQ<TfMJhzsx)44<zIU#HYhkvYK-TY-<54|0m~ZXn
zt8JQ-AfuI+Lv#8m_W2<JBCX1Un0rXas9qe#FKsG>FQL8KihUcX-BCGwJFOr%3IEja
zIuiRT&?I^++oFBw<g>;)uu<@lfV*8Hw^%#xYL~sajalbx`Vw)1CWKzkD`CysI5f@n
zwUDw=_E)mC7+4#C=a;<G7DfF;M!PKcN?xid$@1a&<HH9?ES`}$&w&ZEZR%@%(;u3L
zU-Bt)(a91t#1+&>ld0u`2fD(T5c`*Br+BmP04Twt;Vjo)-VcFQkFo}oSNOWh*ql(Y
zU8!EoE&DyTi2`L>Mc(?i9sC&L0NG--&vl2xe4Tk#yUFN>2d0ujy-^C@?=@e2_sk3t
zp-g|6CHy86BGM8A`of=YF3)vNzH=q7pa4#$2%GIT&{>5MNgbnAXXYNRF(pE@#tZBH
z;&f?m@A9vzDb#<4WXM?E(~#7x((|^F35O~43iFW!v3M91o4J3kLv`D!|MGljh~5uX
z9R{9OW#qxCs%xwjrdFlHr(+FK$F2K)n_)%olhgSR=NXMTkN+OmD0j|T71ok+HkpE?
z`qs-c2bFRkWQiO^Ev;)vcdxXjsajt|?`e<!#*N~}52bO>shEoosVTUch&WtR$hU_W
znap>}FSn=+h749<dk7_pL|*c~Vy@wozCPrkY9exwOtyzK1M`bGE>Hs?%x`cH>wY}m
zm+$3Kx+R<d{)(982r;7>szkNa_6#!WYAiI0!R}>TBn)N6j(?CLA!+HL5$|pfBR~%w
zc0IWbeck@k&}Ej%I#G=2`@S7lPJVrd%f+Kuq@tAe2Azy$CvcQG`NNAi<#%T9Xgw_{
zyYMq*UJF}s7Ln#di*3dGdyRsThY*#-rH<2Ku`n|;OZ!<!0#SHTfI#F}QRv0A0kMaP
z@NL1q=I7BI&S;@Jua4x~e99d0&LBvK>tK@WV(JtF=<#S_Q@|q35J(08^C-O6TPvGp
zPI(X>@@~I`G!(qWPSmyk{3ptXAbrpCmlpa_^cF5v8Wv2AKlen>-95NE`WF|5D)mrC
zfJ-&VUnKkK_pu&UE3vzj3jN}$!0M{xzNa>*#~T9rtjW`mo~-aGQh(D9_MvSl0I6-$
zZv#X8rtUT)+MiaZti2>d_sgKFBYbL{g+1e*KE&W5+%jk%-ay7BVsDE(6GK0DXm8Cs
zB9t~su^1R@l8LNtUK^mXiuz({gLN#4S}4&)?s0X%ID3D-Jq6nI<!Bk-p>o<P=YQJs
zla;oYpqAQ*38TXG4TgI8fl0?Q&H9%{XNl-0kXDfX%M{S!2bcVuG;z;o#vUfiSo;j6
z^<hpaMv}kMNHs!m*Hh4e&KeQE{7DEK%Vu8fVS72}af?~j!0oWtYwlai;5j*wXIF|E
z^)U=9p|f;X0cQ_{YQC-lzbnpN!M=My1WWaA?Z0hDwjI@?yJd}5WMggb4sCmF3g8Ub
z0dcOhz92hyA>KZ4elj9ieHlCB^@g~JjUa8k=?leIY(WqYtvML;I3Fr$PVIpAu;%#X
z8$oGz@b;J#`!fv3=rcP&!<LHVA{_t2aMMZv9byF~iz24x;t&FTKcC5v62}3TZz5j&
zu5TkRv`^iX#0WttTYpMm&dmgV-An8)E8-Ac&R(~t5PvTPk7hXc;mO6Fn2yoy6gYj+
z(lKk;>Fj-Jk0V83KB4KUE?-Ng5#*<iQGc!#P`Y@d<FOp)n98kLsGk{(ioEfW;YZ8V
zSXK}>fzPI9e0|D>Pga;=yKFhBL`aV}uaF3e<9Sl|`qz^Sn2JtFEq!6FJ!W9n$D*?R
zRCANuT9s_%<CR)KxCT$8?NKSe&@e>g$=g^zq;Fe%x<&R54avc44^`_BNUTiNpSs@P
zmECtZ{ef6DEs}8I+y~$xDH{2g2fSGK>!<tOfXfpcZLnSQnoy-RN<mQ}^ME}LCNV#~
zsCr-L5LnQgo@AU6F!YCT0TME|SL0wl@7!-ZYUXUM)&J3k0bn-YK|3)Er`Y&x9TIsR
z`N-gj)e8TdL{7#z4F;Ho8@Yg5*>PDT610L<GFF0xI}xn0W~y2>rk|`>l7rIX91vj+
zq(j^GmjaDp?h0{hGtZIo%stVMFrT@q4<XgK2yqcQ<Cf^YHAjeZ<2enI_F@KP5K1j{
z`eKYqh)<jElZ}mU`&|h8=sNV><3S}PE(fntXd~Vr7l;%otzBOYVW4^%^)vI!D>Ye-
zPCIOu&!EsH{cU>f*3oC?YS=64RQqKjei}J!FC{5<WG~8;DV3%|@QPw6Rw~d24~kvR
z;i!ab*`ms!ZRCG5=Zy%@SB2RR%wXuDDAVZ8{oJ8!hIyIN_9z5}4yMPBLS#A@sT!<O
zwFXZ*w%^xiBIiJfqQ7`j%CjB!JYs9;kd_6c_fJ#xHT38ILPD~)*QN-Ewv~raD1LYM
ztB=%kFI{#Plk9F4N;MELr!D-nX~~2qdfG#sODCpwDbEo>f$wtjmKv_?X%hxlvqes&
zisNOi{bG7qAd5|MY0~V^y4a`R?+;6pVKNJyQ4ucMRE>wzT&cK}hN`EIN|*V9_A%*=
zt70;QBN0-w57H(lTiz<O;+DAM>cyH0;gtJHaX{o(>uUn{ben8v_K&oR2MU7Is^ujN
zYWYekqNq;b&STVf?P_fsBS~#?1^AcV^Elg#`o>c;gudH)cIhz`-V)m#j!5T<fC5sF
z2G0YgpEFMDxK@XHQ5tpHr)js6AE@EtdKD2a>q?tVDr6NojN4xa=MHPpLV`Cmd%$Ns
zS*g7Rkk)PVcFxBO#wUdQuqbX71&vLjZlgFY6)pSi-xJ13*59nEM*>n*AEyg^VA~7}
zhlZ}^W`xuF>ry|WJ4YF<Y@76UDzrs3KMnS(w6xuf+Kw^!q~ddiu^{h6lQ3RB>-({O
z5-V@Rr*ow(d2gn#t<KP`>b)@tqq3Myy&qx1AwKr&0FywfkoNO|jr;R#hnaomzdv2*
zg)1HV1jW9APP1jT^JA{^{NfEw);_lm=mp0HFw%9=-F#dEvhVDnQU#|XlH!dG7?<5U
z*uXnrSP}gX;+I|2Y4|&ipEoQbRJpR-@@?oJnS~Or{b-?+KI4s}K6m6v`qn;lO%rN%
zyh?liVB?r_LpgoBMBIGnIDV*ZUXi-y!+PE+MgQEeRe<LcdBnw`X%|GjS=L~<i??fk
zO(T8C&Ov+lV+s%{tx+nU-N4|vIYB9#?1Cg)cm24in2mZF5_ia>ZXe%V@3&h#Tm`AS
z$ETfq58UgJ{yf`gR9GRB8ifl8acI`kV<_hzHrR%h*vCKlq5yE`nqn!Uo@V(x$p5z7
zpGW%_(!E@8pxiZD5HL@wRje@xeA`q0jqF>JaN0b7ZGQt_XH|!cEe2W6Ub6>%M}uh0
zwjGh6hBuyXB&qHe)};d8nf66J?>%zfQRrGuR6JH~l`Z~Ya$+If^PH$=MfPd(;%T#z
zP_!+9hH>m|47we-LU_|`pIEQmDpNoEAu~b*)fy(?6LHo8T4|S2O-FLah#qX$*<Kv~
zTCiDkyJ8x_-F|oI<Y}^<EQ(R%BV-hMLuob<%PQoR`s4&p0y5{jNE}{^G9s5?P^uYH
zFfLP%#Iix(upB<RA9j(pr9p4AOS{J0_Ar<W-(Uud63CU6{4jJ(iFh>htR1*2_w2xL
z3D|ZGMoh^fdMK^#x{8y=Yj#b-MVuEC6#Cf7ghozLqo4N|!-yKGFdyk0W=&dx-;^Ye
zZJY!p#dX=rIzQs>B6Gsft%-}{fmg*d#9EH(yZNQecUiPq)X3l_k2L?@y}pF4yK`-W
z)pU~O_6wjni|h`a0m;z(Jh?)9Lk*uCtv3wGv{s;RnD200r$Z|b?K5mED^wE=Q*GoJ
ztOG?U`keFs5JPKhMQn^>XWPq)<Pr7mk_!?U_3au~?l`=_31Q)KdWe+|5wtqWmMLtD
zp6cQiM3Bpady;Ak*G0GkmY9eN>p#}L)^m8UdXl$W)YMqJS1Nm5k;7Rv-KtkLZ{IiV
z6KH$E=iK}%S+dzz%#$OzL<!E&_?zgd89fNmUa3aP)}aruE>r@fE(=0h$5o!p;{yi~
zu#dHB;UY}+&_MbwNw#03ledife>@`y4`qB_BDtm55Fa()f~KW8Z74)`WPO*TdIx`A
z2Vp5!CJP|{j4iE=x51yTONQHi25BFiU3%E)yv#$S1GiKX++h+qd_c`rvuk?YYXzOc
zMG&Q_``~Wd!qntqu@19pNR)I`oIl7dk4xl0YG~R!3JqHBerhkw`i)2l5*%myAw0b1
zhNU69r|*QAN%Gp#d-b<wbqQmixhf>bN+nqxw(d-BbJ%zjjv43#Ru!xvIXznVeTJ{L
z<xF>;bUAL7hrfziEiL|3|A`yz;9&qu;AA#{oYO#oRC4e^C|YC)v!OvPHqzm$Q{%_3
zmMX;v*U*kD!XI*%tB%m&5Dx3uG0Wj^{QmX)uB6?f+~|u+-0ZfkF-G0ef*hBUHfd56
zPM`O2!9ZjE>54mwUdIU)QFu2)wADbpoKjCOx!GZfCw}hzqm78Zvrn(}2<C4QJnWWx
zn=dS$<u>_rh`s7Q6XUAiS~VYYv-)CHyb{3Ri<0~P>K-r*UDy02&>MAMQ48_OaruFE
zE3%j#>VyGNi_Lzd>uY@)n%pEKgJ>o%)Wx}UGJ3i9nbe@I7mJb9yAj%@_xg5EFL_~A
z*~x(y?}1h&q;e-;l1%-m;Zj1gF?Ur(H>{JELt6)o>gX?&n*DX!ktjA<BSQf(JdQZc
zckrZPCIyZ$MtEnbsR830F2Exr^dR-@<)mnkf5~Z*Oj(p^hiW4mY#mP_nzl;<yG~B{
zEN)@+Az5`KI>%*}R%%Rrf6<tG;OWY!f;w!KIR9=O{RolRByKkTP*o6m?nh~)$n`t9
z2u(pIG=G{<S#p}{!FutWI=-=uS<5fmHT4o(C1yohxK0c`_UUpBPpiTuOK7jY)WoEd
zGJ3MWcWm0J$uQZeRe1#jCTu<pN2S=HSXU0IYu1>g7l3o{LB}R`mEj}`0w=YWYd?Qu
zB1ePzlWp4}Tqz;yr`ju_VNNNB02b=D_Mkdyx0vKfkA#%K?UmgGf+dBeW9oGE4SQp@
z>ju`mmf32)B(&9h60U|IH@c?i<(D`2LycA*Y}CIXsrM8Ad-Q}MG;Qsf_UA6es6stP
zP{TLF6@mAsFSO`G|Gs~>R|I=-zj3Wn3)}(>TmKOygyn7in5|i_?2;vln2_WW0`mtP
zgetCs7#OHJr3F@5>e&MA+m+~wgP>Z)@x(f>|7;rRe)l&AA&F8`XE`bh_DTWo9>a3n
z&(;FyY;r5zhm9xFB|@%3)=S0|xm{PLu6~c_lz$fgYGN-gA@w92hH`2#V6@X$7jQ40
z&K3|5Y~1PXh^xOFvj57L+GxGp08y;Rw`FG^0*2$Zs|LYEz8TR~Iix!Glx^>2<MtnL
zxsWYAyXhflwebDljX4WPlM08wg3@-ku-ISMo7bc3JKXfjkqWH97^#7t`{fcfc)z8k
z+=A1#^ffqS0}hj+?VOlijO+<dZ9n1Lk&Jw`LAbeQyDD5Es_m^aIXKq8NO06ld^442
zcbSFtdTk^SAPoxtjb2vTiJFbsG+=)=o59t(YXQaXUp;PIXG7vnq>X23mY7mq;PyUi
z6iVdnFYLR1+q{r)ondKxcT%%tU8$dhCa4Q<0JZY!gdnsw`~;%bJ}LOQ^a>z^LBi<g
zz}Aeb2de4@!`n?ErvBwOlqWmO-+W<2+2Axk>XSP*6n_zO%}Ak;Mb#=FFk9yc*|vl_
z<N)Q<Fu`?`>kHl4BDb1<;{xdrcN`^3p_F?fSj25S`r=gFFaj!-N}n(BHOYSS>DA{e
zX4w-8PfU=&nEu;0INt?nm8~y>CoHSbv{})~A{>MO_%rn>tSlZ3+O1^$xSg&yVNV^B
zOI(GGefqR>>%uhXD^MROtQT#CCpN7s)B3cmD(;<LBZ+B$nTK%lolO<Ra%xN_G_$zR
z+L<N_J!Q4yg>U~9%9$s6eMNDrj3-Wb-}edBM==(f8|k4Jc}8NoE?zUXSy>WS88b7N
z$^)(U)u!zoH|LTSaS#X{r%LddGLc%5(2ka2(QZAkQWYyJAuo#LBDJ{n*ItncKki*w
zOzn9r-yVkgQl|^&su2{Kbb1i|2<78qJCJ2g2qE?yA+Bmy6_}ZgZZ3P$-bBV<IYwAi
zMXa@_1aS95u2dPsgCiA<ach@N#E~l?WtqGQSa)JFD{Ko4eX}Y~TJKlH*QMM<QWb5l
z4sZAv)plWev7COe8#gNp4>YNfRhaCXGezUlh%<t)<q=H%c>ZLYl#K^bNB)_2*;}GS
zIx3%oG4aEjUW@FM$;_Z=Ajjd$fivxSQoX9kW1ANE0{mN(>=Gef+#8v{nU#=P?Zinb
z%|Gkut<j$I?)vkV2`_jL>el7@<w0AkVEdERO~~kll8vVuZ44a~V5%n--RZ|5&Aa_j
zzYtu^*Q27CHTwxpXoIg~>d#IatYcC1wF+NgORy4PSvy<K*)<*B6aHbY^f>dOhaEEJ
z<n5?H(lBIlviGIY^2Ji-`&Z;#gqHsRW%+;YkZxab+ya@3mHTvG>1^tI?2ES}e_kb8
z<g07B4OC1R#p!5sHf(1ZbJv-QTNO9!dbi;F-n&oyh>C`TTTS=h1!3=d(@jS<ceG7(
z`qT^>!bX2tDgWVf8JJMLTv@I^0{>w=4@%iP$vXixdiYZa-FY?ibgkVvh5=GJZ69KO
zxnG%QU5}BxwRdYjqRVN|F-y1?yS9G928`u-wd!c_i_)$A*Q3-lgt)KaM@A2U{zsqF
zNi2mVV3C=DhIiOw?#aUQKu(cZSO>&-LgMP<RNNV6w^@;;OlF;sYroJUgbpR8O`dwx
z-36-5ONKpN7YJ`TDT_cS6^X2{$IXkR&8D7fwA!46Uwuhq8t(N0&E|`$__M2N1AMET
zzu!DH`>Miq(TJd8xHMJ0uX$)Pq59e%?Ap>ZL8S**ckE3WtXFW}_uWK%MPhvh5~aSy
z#0>QVkLHrOHu;S+mX{2y1bOo|uO70Iklyxg`q41gQZ!M>Vaqmli0h?84>%B@6#=lp
zRIuB<OZiovyqm()N2f^%OQAudqBBKW?88#mDs&vm&U0#_X;Q2FGWE9YedmeI$#Jk~
z!x7f2Abw&wY}6qxzzP}}&Vsx|7T3#(Ld$x+NokG!3<Hx@oa|X<q^t(BE8NG`vAw`X
z-6;E&knElGUxmyme?V8m{4M%*alS*J(_%XvAC$bQ%t_unLIE%x?5#pZyoAt_No}`%
z^P_>PKZ8xjxo>WTVC*@sU2xotuc|r@@)e!W942rOiki(a?I!*1AjOPd7I^P-lJ=h#
z`de^0qNdbMQifkYA^cyB<v-R_&HGIk7xOMxZR!NCt7a8CY^L23iZRb0G484+&5FGf
zkWEDS7~&li*twRsUs}|5#QWcR@%}#s_n$KUUkltf!Am_x_f@ZF4*CYf<af>|U(-G3
zpLKbIJv_fSy->#VaV30yvii977rw+)d=j0oF3jyG9qIEH8gaY-V$ciY_{4?PJGndg
zM>n6XDz}X%aOs4DyJq)5%s1CR$_AT?YvJ1i43u0AM(Wv+um4+D_)pESYsJkRHX@uh
z(2dhs2Qo27w11R_O@q^(jASqmlve<eU()$(Vo>-%6DPhMK5?He^ak_c#^{7a!zz_=
zB^vgq_&+86f4%JAg}=#EYQKwgt?Cv|Z-gdFjjCq7e_hTEj`g>^H%PLo#E26YS>pth
zE)9|_>oI#xM56M;!;t^pX2#yi7kAgGE(0IL#BRDh!_C=4x`m<8E3@wat1m~N|F_rv
z=M$H$t-Whdf{q`P3fSD&-$wuUA8VnZ+oDzHCVudr$Ns02_|NxL^Q!9#c+DqB8Vno#
zm&Q+N#?L#H-D<;ISKw~;efWQPn*Z4ba#U(DIv)U}&t0-27#(T<<Awh70vZlmP;oQ*
zF0OzF|GfY%Dw<TUc`ZrTt<C@9&FZS}1*jJPT_q54wD~U;L~wG~`@Gc&A;JEay4@${
zsJf?uVpnr;T1d9}bo=w?>@ebsTaHzYoA6`O|KDT&FLe$4_Vf7eM-$)1hNfA?dcTa*
z8XSPJA>Dq?s*2^~&f^iuM4LKF62O0b*_GY5=OUTIE$>YT21$g#!-BoUR2*or2~Y~>
z25u)e*Sw%&@=)w?m@hyCH(0A^xhyz&aI3rRSLFE<Q@hgQJs>=rwJ0jEZaA4VPjQ;e
znao`16@T`BHz>E-GI9z(&%dpmvuZw)@sp<e$;4ug)09?KVp7tP!yPhCHn3K0c`Ytp
zdiA(<MlbB`BuxrKzIy^@W$#_0@L@NQ!R~CqC9iVPwS)j%cRad-y}fMOIqU=Tdpk@v
zeycdj0B9{7+?=`GE36dnBLXFV*0Npkzsd9?=r?d#sF>C7E71JGTjML{_(eQy6!`N`
zs%`d)M51slLUro=bW11t{rTXV8^tP!4khXSR^f$tOhZKtJ=prY_SMCDp=I@Y?+x4?
zzCI8bXnW)Vs!nn+9KOqszx}9S+2>@!L2%qeUT{c#`|m55OYDs-*<LP2Tam7aBXzZ`
z^f<9XCsLzTDsH;5zBX7Yz3NQd{1QAN+nU|n2~%ikDtnW0_Wjt+=vcfmsog?(w;Oxp
z8SP+FJiwfE#Z%5%Oh^K{MoUe!0ZzG<stOy=2X1pFHC#l+bd%i{;ZFmj<oRbZXHQCy
z1(Qu(qKH1bzcrgGw<acpq|fsTDt~@+NvX)wVl0z&O1rfDyBQh+t}VW3Aqy`OXvVG>
zr!|}vPL?S3%79Wd7Z67S3b6U9CT%U3Yo`fDw>?GC^B!n!<-C5#jzOA0e(f+ReX`HZ
zWz%h4?P@Q3UPIU^j=ZSvG~~8eCbTiVmdjpL-%7?-CV<MNzat1BrA;Y?Zsfz<k6rF+
z3N4LPrb3r~A91`nYDw|e38!@T<6B>#jU*gXNi!4bk!)2;k>=KE3Oaq<G}p%{bD*ND
zt-~m4j2fA;u`*AFIww-v*v|+azF(>*&HqS<fXIk!h&!mZD&HEN3Fq33`XY>e$8;zm
zRARNu(7`uc=RAQ@+G+ka?ZFNd)t6ogfJXMn%&G6_YM`lV`4YRh$&B@v#@Fj|t1bRX
zmcOch>?F$tlxtEg%Vz`JZ<8iEsCD+pm}0paYJ|?CmGb;8IP+6a%d?g1{+7x*Z4`Mb
zUGJ!xsGMqr@09j&6*Ri8E8a+sQ>LQ*c8H+UU1=VO4F{b+g*6+`Ja_EI6}!ITLdLNv
z?5Udj7;KlSg9cbzle5CNYov>QH!y@4T-{8gr7e0;GgJb4rlC^NVBTR6JGWOpV4=F1
z=Be$}B0=qTX?)hmulVzu=k-b0M&87M=k4W=p7XcSWw2_I>JMpeG!6k%FetDVYF^ZE
znp>F(ptL6TIL-hl$7$LJ)9w*@c<m^?aQO?VKW_zXQ^AQ2E_REG8m$A{r9@WACvb-Y
zh1@9>GW+`E#p281eSaWf(6$UQY>2jC;9QaC+U^7a0Nrekb0{gfNAmnQ>BP=mcb*ze
zsuW8l8oR?%$H$sZj8Febk|V_i+%Cq_v_%gikb7G!2>X$b(;TStVWP9EqoJb4%Y}oB
z`c(?AbEzbXBVquy==K8K6t+y^b;i|%3A4ejd)CYEolI(gw>f4T;V*0c5wA{s*E~O4
zm9I#8)HG)(RmeE^XJf*uqk8!lVHx~K%ujJBR8;g1eu((71uMhD8?Poib)Q?gUH&Sb
zX=`5+KC~<~BXCUzN3ANl;@kYEnw}TNH`}ehY&C&jH}RtICy<)EoobcoRP3mqmtDUn
zD)pGT>$VMWqgy4pl<#rgr&CLsG;KAK>h@P%^!OLq{)CM4s~H9we%Wb5-0b6tIS4!=
z;fKrNQOdD_ps*2B9H@`T%hUEGzp=K@`*E{T=Qf^~bBtrOWG%9WQL~@0%|Bn9oD*BE
zy3Lwp*8oIt2W<-f;Xvm2x~wxUg&yc7G5eZx5vHM#B?Db96b$v5)DFSKAFliaif4?I
ze)FLZnHy`k1Q#=F4IM7e*8sQE2BQXyM>IN0P~q$H9X(-4{;XX#L!OQ2=G+8)GgbAv
zC+$kcj^te@vuJVA#>qElv)bALK|>1m+r6Qie(;CivNAdqC<BRPHP@prbwOHPn|8s@
zD$~8iCR};#QL5|eKzN75!J3rN$=g=_ns$e0VO#!z$P%&_pc``jbA!<47U}~Wu1Xn+
z<DMmdbzF_w9Gh(cugtgBm+-VxZP|4^RsGt<;F8=mdpW-eQ6R8Nvx$NEh-G%-R}{ES
zlcvr6eNXo1<{0XNDKf;(IXWlWtWsFH#!l;&ZU!gkD)JVb=L%+R+elKdB`U_Zt@LYq
z?=<J!&$^V)ib1RhcLO|tgTsJ}kY#3WUQtbL?U`}XEPkzBNArj7PA`9lo=6tHYE>2D
zFe17u1EgS<(CM^6?wTjscjuW~c8I*MCvu(inV=v=QrSQ3>x4HUqZ)&O&?yUA#*%IE
z`mf6+PLn#><Bt}%{eArESwC3~IYIYDs<{r{)sM8z1}V))`6i-${O`+ysRi7lI1cZ0
z-Ppha=zQ43h43s)rG%uzZRuMetRrhumk_I^9pR;Og+3cYaSUw_uC_QX?gSwO?L8Pj
zUiJ3c;Klb+X0aiE0#6F(@7oqSmx*A_dStg=X0$zH?%7Ib$(`(Rep^2f1RdiMr)_U`
z@()MjI#subYWa%jZUL?k*tpHj<!<7@W2ftwPoA0Q$(thC`eprL_hf3*#nVXWORksi
zl-?4b7U6(eL$G(`WPFQ}>@-f87<!vBo}FEd^^xz6^KE2e^+M}E#6_Ot-S$8|zFgyA
z$9ZQFuGZqZk=sIdJ%5Da&|w<ZfIVge*pF=G^=%0Q^<zuZXo2|0;=_UME502kLBQ}o
z|9DTm20v|kX6T!6Oj|SoPD7L4RO|+WRHYZ0esE~&inEF1Hd1UPRQ=^Cv06C`<l^>b
z={B$NWWYwS_DbT-9nj1}ky)Aap8d@*D`8YnQKkA}HvoatBCNNpneDkW{NrhEB0>YQ
zWQS;*iv5TflQwQwO5mGOb$uREy2TU@6Zc#V2C0A%E5v#Kc(!43yNR%ozqQl#!Q8#*
zskC+J$$zA6^V{pQqHwVrn3|*GPcESi1D)2TrSotXSIz3Z(mwiuOWil^P?jDl=makJ
zv-N`dlH$C14M$j#Kgl<N^mUmuU7sGo9S_N%g`|iP?+FvgTs!3zRnj$1667howBq=k
z<jY@_+k}-<x}@l50^8{!T=q$JZJ&I|WU#OLPUQ{8xac>|@^ntj#kh%Z%7J86gr^ox
zpXr=h=s}@2mXy$y*sXgZHRvQrg3c_K=oE)Gu5`sYrPS4!y^Ve2S2OR?e|%vN0);$F
z7*Dp?%d&3wOvH|4sr9!uA!ju^2}6jiuJ@hT%ca{!Bywv7tnL#*Oobw571_z`7Uyo{
zLjS`zZM)sW;vY%x>Sj8y?gtuvtT9(jGJN3S(m!Jpp?1kPv##wCH2fl}tl4TmaY&Vq
z%BG=0CwX)bKo|j<mSA!$=M_a($qpKX3)WsFGEi*lasYYT1q8FZof*gW_NTO53W&5#
zL(t}njoVpC-x?E%H(B*}J2X^7s0t3SF5`sP%?Z`P>j8(j(V*!1{vr5g75wU0*{u)&
z+AOG??KLy=4NQ)7&0K5Fq9bha&l7qx%?&k2THOumTwG|Tg|~us+T}T`)<dW`?a4=_
z4%?NS?0d5EA<>4zTo==e(R2(9UHGr*O6};s)rtHVkblG9I-ST&H{6-FQ{EB<kb2&|
z!#a#AU;PcJF3}l}VbZ*gd9>Y6a)u*Jvo39kQ=fl9ij1?)PE*-UkUn~5=wPrA2!srq
z(W_Pwdtc6-6;xLC<b&Q>Le5`2z+{8Ta7^!cNOw(@XxO?6l|YhK<b`a4t{3OMD9zbu
zQ>pt75;E9aDIu!ov%mQT^3>%%H{(y~%^>huA;XmMXcOxpX^~ab#`h4i(Nc}L8Q@|T
z=i#yB<?!@pRbjdk91q%U5wOxhFJdU(7sbpcxz)09(0JBRl%cO<Z9jwFp(1N)A0%@f
z_C;XaM=8?`#hOd9#3F>QkH_+S$sp4NXdHY|uj%>{$6{J8xfj6kT{e~j17fx$J<>qT
z+|@jJISX(b&Q5Vzq>avOvi%4Ikc&<H!pmyRQ{YNYmUNKXUsjM^#bY#z+%ibwpO)eh
z7uboL-4^3FIiwtk3b}s^Jeg^wn<I0K)b>J~Ak^(Cp>05KVjYU2=eAKpyIrl+MWhRC
zhOG6P;k@N!T<B`pimj|q8cflIlu|aRE^~V0&ZXCe$5~U_)7&@U-#T?j=}PZ_B-u#I
zSE)P1R#Cd;QSm@}0W}Am)6%$e`qk}apvUn8&(a{$Z$)p4R@fJ|=!E`oC6Jtrfb4$w
zir9T;ZcvUZ>ULiKtm3&&-TaaObUU66LB;)O&aj-H+SXX>;KMu#6*9Ux^mssh!#wh)
z;xOBE9Hhj&Ek{RpgX2DXO1DR3o7#&~ls+Bz?1`}Yio9Y<nN0}!=K~wSp>3WxHW$`=
z3kRLf?y|Ykv;Z8B2A9Ivk^8Fv%-s-?OfmbL{iT}%*W1e_GMUnbt4AUm6|j$u(H&9F
z+mv=K5mszV3z6`07uuxaDNqkYGk~pq!Q5Fp&&q}Qvbj>^v91jsiJ;rlJ)${dWgB&`
z;=iuDDsh^w2}^&>#|B9-<Wh*=uHzYu$_rzBO?Nq|w%u^x@~0o-7E1Fk&WAi>>A$tw
z9B4*`Ne$OGV$!J_y7?c1J!ksz8qQ|sgp>21Mz6i)ebfO6rZdv+Y4)ypYDC!`oZ6A5
zTEVx3aPTC#Lx=88P~}kj0UQ6=3K2=n9PGT;us%Iav4~z4emmcYen=NG!*!~Va?yMr
z^^YKVZA_GE`D&%Yu!q94yeEvH==FP9PomSics>+DZL+Up(i{c_Z|<({l?|PC=w_!l
z=Q>va$eLeD<3MXQ1Nf&u0!4638J;?oUCwVqp5!EgA=KbgDeL)<c8^;@KRWK3jGbw@
z(T7`eJkgkT30!U(;G4s)o*$*%&R>!+R6UueF{sn9shemtDgWIx+G@oEEhr{-zG??S
z6!#l1*DQHmbfy^Gj=HJciUsFAF87th8YuS(8~gDBZfx@gB)wHxe*OxR2UYQh?&}3I
zsEAHBN_W6|Rf_QrrkZXlj&vRFa0TMyv`o`%V}M8!4uQ}$+`4I9KqO#;i0OLcaX=WF
zvoneg-?Am@1?!Gd+QnCZ6l{gEeza{PISGQe-;{OGJzga96Le~fP!EcLwx&S^Fn&8A
zldJZ$%;FW)34pJ71jH}{StbH>lI!@Ss*X=Pu94Lb1~rw;=u&!VQGXMwT(TDSbPD#r
zQ}+R>!~K`JlOnCz_;RlddGx{LqEB<V^^UqFe(`>y#haT4B-fj)7P|$Ng{LJhp9JH7
z#7j*-`o;&wk~9X0>FRW*2x9(m22Z=O(1xXFpe^N%95QGkR*?k8;~C#~9CeTC8!0`O
zehn$c?K}_phutgCz8H;Dp##rPudy`tva2$0(5!XENBro*ie1hOIct(NFODLl0hXVd
z21PgL%{kpvV|_`TvzxjY-SUy5d@b6|7(hd5S2g#I@LsX6*!|77B9{G85XY7?wx+|F
zqI?))5V0}bu7-X^4*3L-(Ol;4tRM7RLf3*k2>xbfdOQ!o`ZJ(es2H#YWLL*H(%5Oq
z2MCW>MZme&-$*=ChaffSeEq$a-7*F~Jir1*{VI&6bDk9<!@oVGX>^5ALOc@tecSDG
zzv1K+7!glUKkIE{qlfvfN?N&Kt%2kfAy=O=J>$FW{CnlYd}_q?*daa&%i?ZC(o$8t
zySD%K<rTgrf_M?#nq5NZfelH!O&7vsQeJy~V7O*1>odr`mxr8udJ>!rmzMk0#Jq>8
zTe==}(hGmQ3G2ORIPe@=S+-m87ID;{A1nhW_+-O+EU31iavM*H2gG;s@AdN|9?bLu
z^`GprL4w_Bl{y16F~IwJWEZjsqXuTkFCg8NfOd2O_0@`O+RbscmDcpa`D;Qn@3BGH
zNM3k8lDYHFHI0roaqC$ESw0~s)mHx`?Dis{C_(3HmHIjdm9bCgA{~kJ=;zTc7=H%U
z3DVO1gGtO^9RYh_h^xp?4J|RMmLKRPpc_B1s~Y(==ng9Q#R{3{mK~9BIxqC=NEpm<
z-cIM}ygduxsVi5^W7Us0No+U<on1kaEgKxuc^X(Z?I;)7GYlh3TB%FK1QG*lv`Yr-
zzu@EUU;IHMzSGZp@jWb_u%C1C<BT_~S=)=p5GvEpLi&2MoVDz4Tc9ixSBSb)&_VMs
zPNV<vE-tbJO**5iMp(+G1p)i%F8oE~pKkR&3(?en3dz6%bqG3Po;OEnE-7YlgIvqj
zhb0$2BP#=BKx1JR;n}KFO<m_EDW}PqP3*TGBLcoovL;7t-2Q%?81L;;H)5YjW2~}5
z6vY%m*q6J0LeKo*k%nc-Z_3xjkNQz@IvaR<OvGvegi&cSXG@hM*apEjP4P}UIoV$i
zZnl0+3{=8LIwc=2Y72(HPjCC@`VJ^#=;W*o!bNtZ$?)gHiHb7fXupS1(-Gt2W_Q=-
zo_=8&WzY49SqbP=SV`$f><!5^Zw|q?qqym!Kv8S)gN;*NU>mDnvqH1D6f0xYq1f$_
zSjs)h6HXg=NRx5L2Z_(i%0K==I*)X}%dyhq#W+S^N75Fp*@pCmJ=p)0$bA>Udg``#
z$vDLzI{mmfn2B5$N2T7p06KQk>OAMZ>1(<Q0S9mdp^bs9Rs=06J@U&yfgf9BbcPhf
z<2a73&;h{Xqw5WE>H%KN>!NN<12HQ$fX;HOwh71UIvF5(pL{R4xv$l3LRs~qlgfQ0
z>G^4}xY!DgqolvQ^-L8t9pI0g3Z`MF`s!%p+q2r@9SJ%5M)-P~`^QgeGW>A@0QQ|H
zD&SrBht6rt?7be;>@g+eeH0I@M^<|^1Xx7Xa6VvjYPYWPpyrs!b0vqqQ8FD`uX2ap
z{mHmlS1KU@QQQ>S?)?p!Nc6W#&DP{S%kr9qWpYasE)tbo1ju)G&$kgE8ZCl#u+JY1
zhxpz6_8a28Ii>_OgCBI>wcC?KZz=<=Ep%*<jV*%5!{Y8GdQoy>Kcw{WT8mmt%6^62
zvzm_Th?MY`XGQMy1cNP)i^;F`$1c(dbfOXDsq@YwI+p%L?l`n*ernm017byADVPI2
zEyn{m%4{s_j+(}nHXrR!(Xmko&=+EqJN-XCYNp=eT^pc(&J!pjbk*{2>XrunzSa8P
zi>B{>6t*S6XB}b)kqT!Jm@fWdT{962sca|TV556y=}-0|B#g$ROGJ+m)63}m^ZF$e
z{^{gj=wi`x^%>;y)OYExyWUk07{^x)yejTvO$mEzKOEO?jHh8s-ZI}JIqgltD0A~S
z291~O@sqs@+n$dL+VpoFc@dEQSv*?|5ZmAK3lqSS%T7!G^40IK(-9st5IBl=)^;<n
zKF7=HvcaV%s?vJ$d4)brddIh%n#jUr7HYE`SI_4IUUBFL^F@E}p#QPeMYo8)-(_I+
zg6j&4Q=}PMJeX;WFt};mq}?cLSEry8y^5>x8sd$cZsu;p64((vTgAUZ^K?oQil;I!
z^Ll)q({rl=#eKukTRqq8LgmSc+%|*lmdP~q#5yCV(u3^&js9(udD_>n4u-m{7Ab~G
z@OJwurZjiylu{>u(LrRGI*(?-7&#1)fJ11B!A*(wsLfTx0Ow7wfPw64uw%?E_vzdW
z>fUL`X}hQ8kdpX$r0uVX`-$;g?*Vm3<?-VDH=RQA{at)pxo~7bO|)<L4Z!_)q%nMD
zX*1a^d@cpv)}p;8wS+J+qP1V{5_nnSY^&a08$<%5@FL@xZ2zRsib-r^b-Sa@^Sg=1
z&h`?=`d+QXQ_z{vAr-ZZP1x;!Z!1`@tF$5F#%0-BU98RIJMd<y#YnV`wA-LzW7PXD
zSUajM4kLO>bQ}j$=zmWd2N`bmWp=Osg8i9N!jh9O#Aig(dJw!jeUkyW{xc+hMR6^N
zSaxe?cQj7fK$jgfUb>2{^wmp(Ryj+~I=~(!UBWn-oyF#mcv#Cl>JO_Bq*w_^j|7xu
z2WwNwQ?ENE9DW{`Oi<2cSWIj2+N0y5Phd%l(n4paspIorwjtxL#UTEi#wBF;ikQPV
zW!>TqVKXIEq-CQ$S<BMWGA>9%nOX}GT+p+WGgwtp7w>fLpaUuO*la@l^cXL6n*^L!
zK_}A0R{vb2++N+d4uM1qJo*`-`7U#pFHZ~@Z~ZPf4`8a-+u$3i>jdz%$@O0{#xG*<
zC6g)PgG!f%%SC7bpkYq?WcPotb>87@_TS^L+K&;f7A3Y)wP);5MeVkpmRhlQY-+@+
zU3=6nElQQzd&G{tYsTKQ5`q}vo9A;~zyE&!<c}m*a_749zVGus=e%C$?soNR3WC+Z
zm5i~zVZEK+*vCO`K40nO-4tG0nPhYN!JmVN_rA6*X;cpX@h;f%%1$+H&<>wHs+TWe
zXQX(2rbNp8uhHugXvj~f#~Y-Tmz-E2lxJswF6yN%Q}+U08vceH-~#O|3B>!C*R0Vs
zOg9U39Sd}Z#RTP&W~iF*^7@f44an7*^8nJ>sr&DtArnL1*-2IlaVk*S9^VC${Q413
zs#r|I9X27}C8VzYTk@emCLNojk(NV?$uH<6cWz|h&A1;v`&1{b0VVip$&=_Lwk=F?
zC?P7)(H!7JdnDKYaV6klLGo-FOwoMCw1QQgB_N_mts_oekh~`$Gsj_oESmP6r}}^k
zxd`gGL@DJ7(9G0-_sAgrwLqN^oz86IVDxiwnQe}{rJgGK;FbKsoMe5xV~pwv;U<!M
z{@PNBEO~(%-*G`N)CZU!k(94v_M4On3H^Ok_d833GlcIBp+k4*fGnG?kD94povj}z
z5xCc4Y((Qgfvsak?FZz+U7!ThzN@A6A!SFxhDegKdMXWQgKQmid>;^q3O$q1hv*?c
z{fi_Sxtvq&@kQnvBK3-{%4$a4jgX(VBx}P#mx%(eByOVw&r^^(E}b%$O10U{_ga@E
z!F2&2zt@K<LSvMnO7my6RBxc2?e!N50mD$GiX+6t&V7$jNm!jg>S#ASdtDU$(aCNl
z;1yoSKSH)M5XsE#VDxB7?I*GYHM(K6awYjrH)|FP^dR!xEw!Tf-0hUI$|h@dDQ6Ry
zEM8O<tGm(e&E^V3r269GKelqN-C=COUhuhltkqQSpiF|S9yil;2E}#9Wms%SN?5$N
z&qqVgI-~vtNuN4sj>8D+USUR$905J>M?t*IuX=4;C@J|xQ``A+slttBq`#PVt#as;
z-SW6uBsg--B~}(4J7><sUtH5WgOmW=TVU@t$FiKxQ*#ffC3YLL>m(Z=PoSh@0V9O|
z?xIDs8|LZU3PI>6SN0VpKQ5t6`jPXhonG96>?uuJU9%ab<MTyB=6}^B?_SOI<sSa2
zPeRME_UI$%?|255O{)MFvCv>g<g1zbs?z9#9<uGP&`)ySG<rwd3mXDeRN$VO`Vwyw
zC&InzBeQj{`F`p+(}uX>1EsR+p^z*Q_O%LcgI`d+O^>MCdE$gAnvc5xw|Q!jI_RPU
zGVBs7rh^%f)RvIsTm$bj!L3PO=XqelFCBF!U^>#(0d~JT-_J$)C~0-q_kSNXcu;KR
zcv*d8(#k;SIi2$Z;BAMt@z39r%Um%GO*GGL*-ZCtEJM3(v)qpPnP+1c1k-XNVH&s^
z3hG&HQ3KAkfve#KwXVpJudvC1(e$$!$^2ymDLUEP;xTzpZ*G}B*TCC&$SNl`=3$_D
zw)t`7IfJ*NzQ@So3ldAkmFSXG^>xj~Xh)!Hod_uE$z(KKM74~M>!#1SNPbk$ma1xq
zs*hbKB`Tmt6w@J;VU+qoGkU1MI%af%TEpHXoO-@Ypocoy^F(7!*)j}J8KG`FL%6AK
z`^uh^rD`l+cka=h)skLyciGg08YY6&;{|!1wV=qwD=h6n%=&YG&^CWSiU7*qU*_*=
z+O06_QE<`~g5g2l(fE2j7JpC3*<8=wFYs*S+hKG;kRZWlPsU1CE|P*O^583{2m0{y
zxHDS-c=F;ha^83KX@!q=+X@A~(Gk>s#Cl*d)wC9UW~#f~a9ey4I5$SM+Kw|hE@f>P
z(>pL>X-LRp-X2EX{<{%QdUy;X-+nax-n)Ca#B*;l`_R+!*lRw8VCl1aOkaV#&NW_T
z*47?aSHDzSRuhkV3%IK*HrKoH*4(BHyf3MS&VJ<l@9^3UA~HoJ8hKBr>zCSAqtf<v
zL^grW@S?&lcZwdUfjTFwiF{KsjLFcMY2`Hc0vkuyV7!RQ(9>sfnv>JnNzGkx=b*h@
zw3)NmPgtu0H*hZ&wQfsPppml9>QK=perI%}N)_8t0`Tl*?<ZxIcJ=x_H(c&XDi2b-
ze>dqTXn_=Df8#H-_!n)MdU&?DBj4b+03Z2Hb<-5yt?ICY2!_zFRY`*0E$W;+eeJBR
z?q}sp(0ScUMFskZNiurOlH`sYDzH6NwTS(;F*H@-d*t}*z`3#9?`9(l;wZg7q&9b~
z4qD;N&y#%cmeu!uUHj6Y8^zessYQ$SS|p){<o5YdsrSM82mgi6DIf()pkrj5di}l2
z-_NbBo|UYeJa9H|OezSf-$OBRv9x$(zt<Q)eHo#)EEoMPPj0@8?#{8{_aYhUGvS8s
z1{J6Xt`up)?<!-o>Cw`CKsMa1av*eqWO7b!Y*VJswOMO;6Xg6H^)%V%$VNB7$2|<M
zL28&=wWDNi!YKlBUEP(33Sh341b&G=+Tl#qU$rHpeLM9i+k!??2%QI$EMwxqy&JsL
zx{rO{MH|^CBk5<i3|uh(&jkRjc$=sRw)(hD1TvL9sx8<!?c`(Tf@Sh>U>@hKtHvND
zavRNz&}<2EUmJ0XEJs+0b48HT=R8-wG5Wrys=lD(w)wWD^B&Ue(e(BkYy3R0pmoy4
z47ZITZI08lS(jIfpZ1F$7&`Aae&4F&-A*(y^`Ue#F;pH`gBOm<ZAp|MNMxIUxo_uG
z$5!rmMw2rxP*%$e5lh!Grm-O=pBtSqkyW*!HK(EROv;{p0HFq+#U9Fa@@R%Wy$`Tl
zCO0e%?cpcxxDcm9qBX*KE@$Ux2&z{5rPp`dc?<LXMQ%oVl#k)}Sqbrksz~TtSo}#U
z{Yn?BL1>gp&qzig#W_2RC9U(Xq_HF64M)<*SIL7|(%WuYO;2w0Ba$HN95mlUbeL4v
z6zL^9T(|2kNx;u}8tSnFSCKv6;XRK8W7hT$Jx0A*H%drA!@Hw?L$t(KeZZ$xGhHbR
z!Gf_o@KHXuxkuS-k-mdRzgv_W)2x7%Ppar|do-_9x)c^kXvdPG<0_kS9p-En@+xUS
zjl?#%&V~%gu<T7rp>)_mK3@P=r(V@on)OJE&-Od5ehxa07=6L$fGU^W;hau@1UyM$
zK=Ru<>(yX-lzAbOvs0i*JdfI39JYs+d7Dxlo0@5acYXcgJ(U>{%e{@=PYjap$+Mf#
z&)r8|Jrv2k>}3gd<523>lign2EB<P_gmUtKYZ+jpWzi*wsY~@adkB|v<AghT+(B<f
zC~p$OoV`(=AO2*D4Vzfd--hzvM7!;9vqB8b8vbyd^Wu{e1dL8<T5R=``Ztlq)a{o?
zF29&rn>XyrzWUuLd#AMhJImN}rJUa$OQlQTd;j!mwtX3w(eN(@Lnr$^;USLWd&)Va
z$Kd>%9C2vcS*-7~rp@7b`3pN!UTe9izTwA%*pA|($8crp%M9>Y{tC*<_--%ncED07
zhSuNsuvp@q%#z88wPeuUiHcB-R9np0mw@x-zV;2@gWp4oGjk_v^*Sly=U4&V7nxJF
zr@AnHV@GIqu{Nbat4|d;?rE;u`;5?x=%^%#Ot(j$fSqAI?xd9LH*cIJMOqyr^=d*C
zgX@qxM_+fJ`8`N=)7ba^tm8oA<SRjBYazxDV_(HM(PYv%VGUpgQmz^~DFcIu6gEl$
zTwgyd<OvcT#S}^_?)$PoB6wA)tH?0pX+loN<CJ|A55vhXnuPew3@0bMSvFY{VR5C{
z;67E3W2QzTPC7<`+*Op?xAlYp&EU7PGM2QMqCEbe9pfQ{!4Ku|$!hBAI3Ui}&ALX5
z<5l}8OZb(pq!F{pULU>!<b6y}s!Z%GN$*5LE_kA{MdU-L$nwR#W7DkvdtzT1hS?;w
zawTqt2TDR*T;cS5PpHIltezTZTKo0mt?x8Dt87m|WcTC19-mDLa&a!ZSNI};dbYb$
zl-A_gtjTdTx_Fm%e5*NFH>j!s)A><daHW~gm)Z#rkp_91hXyl$NVIUJU$K)#(B|2M
zw*RM3jgku6V^d?Q3eopDL`;HkPOd&%!OCDCr)u9mEcQ-zIZ(aggIUNLSly;2I?c)5
z^QDVt$4vj5<Po$cN=o>2pUiT2?i)Q*UO&L_G66dtPr15MH^RFDT0496^oDsQ-zAm_
zuYn%~vPn6O>m$N@T5Z`kghzhdui#7enGQ9$$Ks)J`gxYs=SwJYra{jSVs%S^g4<{C
zHcdJM>|%@g7#4Xz#!~-=mA7VMrl==#p0w)8(i7!}+46|D<VGj|449w$5xG$)@WX+y
z41Z%el*~UO3HnPu67k*FOv_l3yT1e{m&&Y{Os&NDG1Ry2WY$XSbu<XoNZ=l;CAmx@
zLpGC3^TbeAdveI#25tVwLVQuTb+U_q2y9yudbmjXk^|1Di(M}OimUJPeE@c$i1({j
z9BkV<Sfj3;T<6Ev_%L~%cwgD2qgAZH!Sf2+2MJQ&Vj?lA0zWrqxl9MPDtn}=?IRmd
z(*QLmaInbeu<wkdq|4`;IiC$}$#buGiCCY0?)1h%zep{Aht(XZO*2}-JgIx`&vv=G
z%EQ}!in&Kir+5?Y>HvvmbF;8Yk6UPYa_vvU2wfpvru%rf2^Q^5?N&B0ta(K&c&aQX
zUi2%*cKBn!Vfi$HkX#1<F$^+|*?esI5Efz%@`+}x(6Iy`#*9elIFPD-7(gPQd#`>Q
z1X}40|9F<JH2g*|?<LRIF9#@|4>Et&haBe<{J#~<Jdg~D6@YiH#fosTxL!KwvL4B5
zkFXGbnEcQ@n%vErmjckV^?V6NWOU#25a-^;XP=<E8-##|6_*dL*Fti4U*&KRbf;B^
z5FUjNnLVsViQWFoEeX5323Lk2lk4a*Rprf6XXeE|p?ASnVd#Wx{Xp2t^hd+`lCD9W
zLED}zWD2DchLX)A2-Rzi`=RpS8nnY<`~ZWjoo792mqa9#r}*iPr)V>iGszptVtH1l
ziTI+FO8tQN(<HJRidka==G(3AfGV>ZP1{VfR{D{TvIZHu6_{}9f=VA03lD7)e#(oX
zlNK2V|Eg2r{H`#&s|KCxpq3z=@N%5>REKJ=G9_L@H|VB#-N~Vy{MU)=6(vh2i7#00
zGIE+4u-r-iKyrUkgA5G#n_RG%S;wkn%q`uV)DsHtN(m3Y5R~pn>fjOKZ9*4O8SK@Q
z7+Day-kC}IwzheUtoL^zxMSJS!K6->4T3`MO{?p&i$yhLmVBR>DL{C4uyE^`4j4Nc
z4+Ji)5mS?@NQN!uqk8`FLx;zI(=>b9nDhs7p9NMaqMm}_gEF@aMx1}n5@G{7kQ?-y
zZH$amxxUmC3|uVpXbA%zc11+hjy4})dk7Q4P30K;a|bJ*O(Ixhh?uBhV=mHhxe6(Z
z-1WPmJS@v*tR1hg;?cy3#K|MT^VJ>b_f7<n@meTF4!lZawVobXJ$~{sQYPDL=~4CN
zPGYdGtELL;T&sm69kUPf$3r)Tnc*K**?NC!6<!71P+UJJ&}S0<GH>irIyxnc2=8T(
zJtYcN{tcst4X&m?WO@$64^^rP38#nAvA`)hsd(`Drzk=)NTRLY0EsGvR1Dj3+ldLo
zVVu1B+^5@Wf*mu}uF;!jdd%)Hmd_segmrLn-%WEMPT?3arN7MvscfIW6?rxv4X;8c
zLHft`2;qeA!<pO*W3JA-?*L!BULmhHHVXM*T{i}0>g50Gs6PFT8?<s&(pV^|{&`k-
z-%IqDzxx!@GPHn9(mB!~Vub(p<O4bMx3VxnCVmG=YQOvJayMGHJg|YC<gntn8&8mX
z6mm5X|1S<8H+<-pz~L@cyQayt9g88RUn-2@oQu4PtKyNYGPfG7R6rHRWa}e7T_PCV
zGdSbT9VKtp0pKqZ@J;N~YP5)za6JkU<oP;TaBzbBHuecRK2B7j?I;v`vg4<148~E;
zro;(~{BtcbdholUALqy<Ye$pqE83|?V<8QmQFQt;@-)HNn_XYJe#hF=in{u*`&yrI
zeg!5@Idl814ylWsM1@4eo1#JN`TY66nQ^jA2D!tcMc8Shm6I8aqs2OJ%Z_MC0ju|B
z2~~5IH?JUVP-BjnQzGwWkMm8UH{$(4F+MA;_vFb$`5eLRp^A^#r?ZFG?Kozaig<;6
zyOmHyG0<!eXm4~;MSsur?A^#yBEMBJVEz{?3c(YUE+(($;;f=|T_b^@OpWWZv5zKZ
zK7e;0K&`8XVx3`4#(v`zAu!2c&2n)luFy9Q3`eR~rST{MPwMV0jFU*Iswi59c#Jaw
z`bny&slYj1b2OIG==JD3+Lk2s!$fTlh4qgg`+W)jxwZfWwG3=Sw1967`t+*3QI?<2
z3xH#EBcn@|oXNCI9Ktx2h*V<z+=dlXFG}Su^QJi1fRYe8?g0fa0VApH#ExoiHrpqG
z6jRUfw}>V;i~AfQ*}b80oFD}vVY+)cDP~>viP{xyQ<<?Naoiz~q)5}@iX$8drHEoC
zEjdqi8uZiNr}euY-J_~REhke56ua5UejJP5WCY757q<qb6}8jwx(Xe$v+3nqU<`Y-
z0?-EZO^(~9d*G#&G6J6i%M5m6Y@D|i>)lk1u#%y@R+J1P(wB56Qom14kibLR`Z<g?
zrGpby*-<HC-pT;ztLsfN8{&BRg49&6YnzD789(6f8wm6ah^j2$CKaEI>p&cd1jwH1
z+sXi$EIpE`BPW+a&I#JI#VYbiBVS6c0EIiZIuInWB1-LV*IhMSH^}KC|1p(c8+a0p
ze!5!#KDVg3uPe!qnqBXx<e>Gp<h%((+w#A1;_u|aihA#ZK>kh{Xxke)*0tBeHjFH=
z%N%9Nm|YH}-6pxhZg3#~3>L3Vj0s2?Jvw=B<gyh>LHF4CF<SG=Zbc7`&s1=mLvjs7
zy!i{UbTJ&f7kZv&Rg!s4CxhEFw7@I+lq)hgbsFjqY}4yO@+Vel1z6Kw)b9YNxcAYs
zuU`Q(=6h)Qq^v|$TXTEXLT2|~%_SFe%$?}6Ot=0b1j5!y6vp(Z<o7xvoEW7PQJW9&
z4dmOzk=)x19+_e76Fd(i10+GliZn5+eWj6HXEA1Ds-NI%n!AIGtI<^6FVkT0mtg`p
z$pz|D4Io)Ns#mBH2#b;)P<s3UXoKwnWdm+9w23^vAn{`sP=upvsUy(ZQUsY?Zbk>;
zG7#uv(JNQXx;nsGNGomGVUTosMOcwi`9X<0o6v%*{!7j)%`!HT_sPnww((z3oxP@W
z3^@fy<iv2Tb}75R@aw<JMO6d12VY;!0TecY!BNmyW`)6KWsX2->jdJjt(^TQo(k6q
zeM#BzWCp$cMc8RW@3s+El|E=~^^-~TN}0&8u4F9N-tOaTv({*0{6yA1DMgQ9?0PzU
z7Ya<xD8-5ej;w_;yp+Q&9cK+C$7umzSrUmN1wuVtRezelIF-SJ>7g%8pmQ<U2<MxP
zFuCa>mtm0ewe+`(Gb$51J<3Gmt^PMJa;dLcZwFxJ)YawatK@)<t~YA~GGWx4N%Yon
zLiAp+4_RK}ScHtC#h7C3)*js{04Av1q}^B4V|6uDYS!%2(}%5=L(9hJ4H`ds07CmC
zlBN4)hV?wLr)tgHS*2fd8$0#*bMJ|QtK^w3mBDU5`<xcOv(J<S?e?;#XiC8HZs+31
zW~H`eZ`CPnM=ZZ`oWBCoF9(uEXk8q8Q%w`Zc|}r4Z?3mj_+0Y+B7_RPfGJYbtMv#W
zLJT_booew*QjN4LPc+x%;X{C5l`9;0yba-(?ZI_6#<xajh_EFzTJe!j(|;5#)R7cE
zawIFZ?2?dKRPj(G6KyS+^2fHW;<bWd+WRPN$MeS<NpCDaS&f+`gh`4{kXiYR?+Qu$
zq`~()U<YrmH+SH$mO%0)U?1%JxsbErokNTIMKkfkyY<9{BSf0{xlm-ty+!WAjT2(0
z?8QAzeaR5q&^(<dj$+C?ML#=brk>%NKApu5l2zR+6F^SGE!pXi{pj{7zfsTE3M;)N
zeu`&xZP~k!N3|+*?O3a9w%HL$FP&ncnE6eN&`vUvpwEpTdzntOM*#`roIg^5#w4eo
z0tV_vUaWW0b3-@QZ=A&Txah`lL-wdG3XggzpOz&W{}y7M3ZyDWbR}_3{WpE&M-iY6
zMbpM%)ecMDQt(x}^jaUiPiI}6$5mGab09EX2z9!pLlJQst`%^ZD0KB=C{G~ume_Ex
z-q(G({?!l3u+qFisQH<Y_zS`yM6xKP`d`1L75Hd-<F97BFG!mnd#tFeWYviFx;;gF
zN=T_&J>jPfqt_R#?25U!*lJ;gXy!wj#MRn@Y<X>0hK;axg7!Py@eV|l9&i$R6w_EQ
zTqv|dP=nODBOqRzH}CeU3EqQnJ<eIo)%kO7C9tQXgzLTQsNylW-GWfeIJ277zd02W
zR0V&AU3M{IHDF2ek~`&Kh#$9lyz9RWBZ1YC&{hX{rBjSfR>CUs{;E{{&ntSmBxnU0
zc5Z>=7@|=-6mf;G(4mA_{$Z6f`e|H<7@!kw071g6w8SG))iDobWE1_D=++h_38`zs
z7tFirw5z%=Cl%C14<pLAW3VXmzy*#2?;oS-?of6QnAH$<J%NVDxTy-p=RTad6-P0-
ziY9(*HcY>sx=t<Z-*o_yDazUYjWZzhx@^1p<zuI-|4=vZ=Y1Ts#!%=(YtLSW?C=|(
zxrhO;Z8e&oNbZG${hBLtrXZIFP1d_ADt{{85hnlSR5;~zDFg%&j~IAL4+8VELD)&0
zEg+vvy8}_dLMftPh8^Q_tA%Z@%jX_vAI>cau+7X|4%Z*W9FU4$o<F((a`<m(-=$i?
z+NP7lCoqScTAL%WGO>Uwf}BgcJDs@h=<9X!?7%zr`;1Y)VH5h$&0!wd4w>QHaannY
zM!b}_&xOG^1A1hXw#Wc=k3G7M@OQx+i749I>F^;>obRT6<Tj(<Q`yuR@90g4^Y+j%
zqitOZ1)}+$5JJOE5#r6WECM<Fb|^LZRp<$ypql)laO!s0)eoQ%`7HIqEuPXI20d+I
za0AXzZKuxYZz4NKcB-NAj<Qjgtz9d<v|mrT=23X<V<>fOTry6K==Q0P&s-<h<y!U%
zW;@G3d2+#w)T5!TE$KD#d&lK~X@3$wyaSr;vf@D#S$oTurUKa@m3tN20NrmBDr@&n
ztT=l8npRJJSirNEN7r;G6h|gQ7}SPdQv*CZAa$P8eZ!NRQkbW|kbrcCp1)ZQc8rYr
zQzjq6Y!e`)EE_Pv5<RC8OEB83ZmdiydsxKkR7u#_P_tu^M<{6d%PU@rD7L)<wiQ)N
z_fR>tQUpxdlkg(((Cq6vxk8MPq6;azi>*)}{p1(wTGo#5%qtvU4%rsL=f(lo!N2t?
zU`N?7@EAPMI+wP`QQ{w)xi4v2zF@RRX)UsV^?lp8PIHGRQ}rZKU93|juFyK0q`hrp
zCSj^Hd7(*t6*E$jqgS`On#p-RLIk30-dlel*`U1Wdh#ed+jzIhcP;I6ifn^zQi6bL
zVAnQX<Rj$2+cOM-D>vH>S_#+K-#nlPasuP&p5ImIPMWIb-G>CqpxK_5_DV>Xy+)O-
z`ozBWMe(kJRIY3!y(wgkBm}+6{Z&8r&*<K5?9|v#!Im4*2HpnlRo9drC|QNN+riKX
z@c<p`Y#tNi-faxzW)iXHG@H%D<IF`bWO~@RJ8&e^AU*C*(PQaDK^-_X$S2?YWLuq7
z(!g(O(qSMV-3Q#%Ygsm)f@DOzb+>GvW<5(cQqNu8#4hf$88r;Av->%7?TK0H+@Xn`
z!<wo#mYki5I=2A+?gzbdK+I(5uXGm7+xCzb_0Db#oDV@zxi_ol4ioujUxgYHSNFeq
zId`KeoT7v*0M0{=ZAHiUxm0Pp))e{I0^++N%o8a+RNNIAu7t#~3U)X64)Z?{=h4J)
zEmQ_YY=QutLL~Yzf|PZ;j`Hx69D*L^<itdELBHJ&UmVhXee&|HyL%HRo5|;JWQrC>
z*yMLYa{Dyh^eyg6zuo)V)iOL6ONiUS4|adW<4he*Fh{zKY1r7AU3vZ0eV;dwVoV!W
zo_@o)97~Gp(yx*}Wm(Seq-%8ub1`SiwL^g$!2PN`NZ@4-r|If-mfpJ3-M=<DfmbPc
z@6%1aOS+>T&k;b_+)C9~I*Z4K<zb;!JM|Bx0xaYhm^_OrE=(q4zgkpnvz|r=ibh~-
zEOst~q}pXp(`}nq?sZ2@X1ndklEW^-kzQy|;-gTR`o6qG-MnbM#r2Ku-MEp}zBt}!
zsXjg4b>fCvE(;l-Vfx)YdbbSe1QRQ<typ6BCf=tu=%<06Ti#!gFpG58wK1qjMf1sX
zM&16VkUpE;U}r8wyi{z0NQGyN3+bC#fP55jEC`+ek7aj@7fF<xVZDlU#ykn)qSk{{
z7e>3Je($3jmS2D9k=CSGW&w+4pQAN-wSJ5blc?7ycDvf&RCmgQP(INn9oBzWyZhN#
z?t;$$5{_5r_cLLMAH43ADF0ylX!ekCMq((~v1?jlCs;aC!nQAm(55%|1HH$+k0!*l
zKZ(Fg68XXT`S}^$_wjy^>3};~-00%<a&{(2in79HS$@a!XL)(~uk!N09u;Jjp4Gfx
zb0i|G11-ax#QRFE48m5Z$6U*%;BM^cSfxmG^wEO}>IBgsMzx^e*jKY4Kg0~=Qp0kd
z>hE)@JEiwu?zE8tXQ44zWl$SGw3szfx)s5L<4}|StO4o26j_li!r64Xc^MnztqHSu
z&vhX$xq0M@#FnMGx;IxcB2{6XA920@qFOFBn)x}BsDT{sUOtm5&p4+7TgGq5#wiz>
zjLVEzhlJK--vACrAD#E8KI}p?Ct;u^#i~S;3cYl{LA9P~+Qr$CYohHrB?G;vee2x4
z^ASOHz#75g!gwCWNZLEgi9xnDS?sD1H>X~$s<xgjExqQ`7)aoeDDo0;dqCK9KS=(l
zqQE8aAp1+Cs-N>tCh$2$tqQl?6v%A!2mOM?&IAbeht-=2Z#g$P?w&uIEnlj+VY6dL
z#w&>CRyg&22tsW@QsZp1`<FDjGXn`ev768FZ&RLL@7yY!McTjlv$^vt2Ta&K_tvn_
zsz`gV)BZ&vQGC^$!(VpmtJ*KkdEv5qO*)P)86enuTSG%vs?&%uUV6Fm)b_~Ueax0S
zxb&tB^JmDbX)84WW<_A(pF8zcMKNItSLreU`RgTJ%S%{PC-M8%eQI)+{ddXnZWL#1
z4!N3*)3L`%GPrbExGjifhFRgl+IFc}r<@lA)46gl?{3C<1c9k$Yj5oBOJw<6W_*5v
z%2IZWLtEMh31bGRY`Y$i9TWVvr%3Nboi!FOjk(*|Owa(kB_qdpXCaZ+)%WaMK2GPs
z%dX8YH^($|1Izc2?t^2`Yj`)Zy{`|%Tc;+kWaygUuRFhRB1$}7YOX>Jm$r(|pg#$V
z1~M@vQpHm3gCs>W_D91GoM&mxBo)2EY<U|}@eS5a$zZB63*V_h9%z+c&54b4?_g*y
zpfkyFVZ^IShIa~LyD{Mmafh)k`$C2+LM-!CG?6A{;T64A9q$IItA-tFgh0QCYvDge
zD`C0J7Y}1$&evT;ogszA%jcfjE7kX!ZmAX;P<7vyW(<P)<?I5uw>Wyc#Dk_X{_M0I
zKdu_nG_en3X-sFi;T6(tCTp(A9Sy(wrSU-T+oCv`>BK#@U&+<>kY@MC_I7q0G?Rqt
zdLXImE8cX+T^;$ozk0Us;hsU??Dlhc?4NxFTe28_(#@=$n4LXOOoRX4EX~98y7J8`
zFX%Frv(+{N;0oF@8Sfx|-`dmZU{)`<Chv3E4;I0$(rmqyA(JQk6}leBx*xX<AK7H&
z?fjVt{JYiJ@^8==J)6Z*pPWM9G=)NN?I67hrmJ|jro!s{?}UZ$Z>lYSnQPI%xHH(h
z_l9g20hxu<b?8y6BZa+^Y{&fw{chW>KS#b38(C?fQl|yrOj0hK_@iroiln2QXyD*?
zllfIws@%d62OjIG+|sBrlVu6V8Li!vW#xrby2XIN$w_$YA9X_?7RD>bK_c6ePoUY&
z7V3kje+laiE`zU8V*w`|6ahy2TuT9Vf2XcbR0x-#clRf+L<3|NzklDaAI^oR9#wod
zr(6CYwU%g0+UalMopnTVDYN|{2H+J@+btpheU;vvJ=uVG`i@)f?`0DFeaNM#`T^24
z&u@@!G(xggT^ao9gRJ4^QvNV?6qy29Zu2NIwXoQE@YSqpE?;|{4)tWND8W`LIg38c
zax?2BKL@z1ASg5Yx^-N{iPUuK#Z;}#Af<VfE#WPdXHl%<Nq~9u5?2Tx#FVpfjamFy
z&JcDFXHiiyEDjtDqwi!kE&iIS4&Q;h{EgQ4miPo<-OL<nn{8+lQb1W-G8`-+_Kb3?
zO%8*dq?ytpL#m#IPJgD==+b)KW;KjHanD<d+dMN!LHtCH*7UY#yg5E7Uv^nl1d}km
zy{rFd-I5i*a6bC}hb3Kojo9BHqkkGK4z_z|E-<tJt+@{G7R!4Br9IOLViKfRRfor8
zv0vRWFTZtEaaa4jkDPl?A$FR{hZ_-Ts$mS`Sh4C!S8FVHgy^hhygwOrkeg@3)lF?@
z6c)Vp9&P(`jD0jWgKhmiQ6qui3|OorbPug3toDjGgVM{g8ClX4tqrd$o4%iU-w%)G
zPV5}=DCa#s{Bc2~_~fQO>O=UJ$2Zo{t=Wa<r&Qlnq69XbIE#B@{c{^y4qCBAdCDxq
zlaWX90NW($C$^URLT@I;9B1|EnJLubp%Uf!tvQDsAKpb;PVv7E=(b!f8%)3i;rkct
zi@OuzbwXo1Yg@%cslJ*Secy>ayq>|l)~9FV6Hb0Gy7iCUVBX_!U*fqo?R*BI7YJ2{
zCihBhZ!3!E&rYNxzhWb4kz>;t&VY@Dq5bpmyf2BBDDy8Mil(N)gzy`K5}p&f<!04e
zcaswD`bT5+s$*&AwtD+6C<n<OGbe-Pf{JN<#VVT6U#`v$mnuw<kwqJ+N||!o5wZzM
z&fwB8r-!8P&%7jC-uu=hf=X`;`V+pu{&DdzQC}==tj{4d@*uU^YS(uhx^DSw{)Mm<
zXt34sob<hP<zUD=>N0n;|DM9zgDacw&FheQ*BSl)-(XB##?rZ9JX+@`4Qc%CbWF8Z
zvQytzCjd8YBX)xPZXOJQ>Rh~!w8GEHI+{Q{P0M>|T;os{My11~Y^*-(otN%ukHhgZ
zrfWdRftqzy(wE<dK$BEE$?B%ci>E~{wg%i|HG>ykpq)U+jg?1P^-m4QaSh+pvTv`@
zqc~yQ@Gx>~9A1ZQuR{qI>l(eaal{`De3`&rQ(}dmIA5;GIr<@VV3q11BDWBbmjZ7y
z9xKymkifPBP5Fz5XTCulSB=%@sUhP{$vzFL?PFR}`pM~h(T;Qg+q$Y8%m-_mIthV*
zr><JvEl=$|Ek&B?hBR9OGJl|cj20OewA=H{YIj!E4VH5WbO$<9kMOCFxi5_Qj5&OK
zf93A#qQ11Xy25B(PdC!Z6zD>=Fy<uHP{C=C4_*S*V&mddbQOFt>EOE`la@d_=EC0d
zY<ylwfvot)$j%RFJMEh6Ovv#EwLDf%V>g4av=IkKX>3vIULU92&-3EcuMk;5o#k4;
zDF-KqrmZx*f>LzS!qbmayiAf`Y#2fpB>7AH6~nm)HfOzNG9}M(a4QSreI`p0NN=Ud
z4$)`+V92JH9RvHPEhgWv?8d@3h69JRj)=^LhLQx>bO+)NAhRnjfv4FMD3>=(ppH5Q
z{hRp7|5(leK*A4J^;y<LPw=lkL>9&S7c6yzV~t5@>&_i+jI~nR4;Gu%X<a|1vHz&z
znVkFYu8x-hYJ<wMyQe2kI$iqUd`1|b1SlLarvP;zy^T$;{bUZl-uZA*(vqp)Ze(Rj
zqP*<%&!9bSG4+bqvGv4ALmlaa1jpE(?rFmuS_d!*K{p%8oJVSiING*Rz^V4`p39Zw
z6~=qz)H~4&p&<BTBasGoK}y%JoKVot%8_wG3CP}Uu=11tnV%iy_|MhvtXhcyq#zyJ
zgNGU(*tN(69htpCMw+pU)*X2@HK6ZHc|8tmJveJ!p@;X?O`-n$W!ul^Vj~<}LGZXg
zN9fD&v0^Z8FZ}PEWdifk{`&?-93Dn2-GSe1!X0_+m762SrkBlDoD5CQBKCYoy*{<9
zA)i4Fg2s!QSMF{v)YMo5JhjE!*2BjrTy;vo_F(8pLV}+%S$VTJOwU=#6r>Q(|L-l)
zaaZ-mB2}KU9FsfZ<J8HIy=%PgLHKH4zg<S;#Uvr`DW1l%#l*S5EJG-t>-q4AJO50-
z!qxR<V}4bvoWl0<Jo2i`c^lZEiY|liaOBHW5~e4~>9NaL1nB*X71h5ck>fjC%SjKZ
zA1-Zkty%9EX$4V@D*SG*xY3!plDkm;2?KVv^Ij&gpP>Y{+`oQyVVM<&m83iF1M;rV
zuSCBnbmTQR72V*O=%tCJzupb*3WeGp;t^iIoG~`JtT~Uoj{+}RR(7dBwszwm819z#
zbUregTr|%z<95}+zZ}}Aa#xvS!#}08{;CTp@1~Y53sl>~b9-yB(Z)kO$ay<)R{pVV
zq5*#PTSFJDfR5IUWw}`;oy|dI&9>Uq-rZgc{R9Ck)VFW6cgYmM9RIZ?y8~Z3c=80f
zf1M?q=4rgHy>#0{sxSSTiZjYv9x{M{B&RFXt^-Q`gJn~k&N}<>pzegWvwO;f(eg=J
zKFBNCg&l?M0|$Cm_%A!YWa&tPj)n=ra%G&~xuXG4yW{`P`|c3r_JoVgZCIo)H8uye
zLHkb#vBO!z#+O)f0Q$$+ffe_oe{mmfvW3KXr(CVI26~1}&#RwLAk)~F*G`FMVcGk+
ze*(1<VyWrHDXu04g-A$&t{pwj&*|ZfBoR=+)G4ruJYK=Xkkc1)gx6Ymu3RsF^o-Ut
z&bJFX_Fs|p`|FWsq_Q{FjzhLjTfFJ2G#Q&>8>sNd9jIO(HLMi&SKe}24r&h$?jJ`h
z<^Fe)pSGueEj&AWtHx;Jhv&5nL)J`LL?cSlWtzC<_4krQD31OEgbV&_cKssYa$nEN
zH@Sgo(MkKHK8DkPB;}i{j@An`pj*%GC;C=1@;!XLMf`s-UiWT#31Nxer+F(S97{4-
zOTTDzPLN-rB+D-)?2_pdS^Af{a8eo*vohn5=BdC%KB?kK(Qp2SPa4xxa=lXluPl2o
zzKrqs82;c2&+pn`@DxrXx6)X6g-PU@&5z>vlc{|!DBaP(*kd@$TNm@&h)!*|3@*SG
zV~-0sx%PuNQdA)q&%Fg6o*Z1AZNatvf>9xWR55G>sP%jDDiOtA_Rhvl<G&$)Ij#Nu
zl8X%TmfsmAE~o2eDvaQ004DoG#`yIBJZe21eb=UYd>qO-tDvhAPu}+U-)~6B^};MS
zI5|z`yU=VQz>VrOeKKGxl+Vldhrp{9HidTrQpTN$<y+f;gU97n?sUs6A0d)hp6~_j
z!=$Lipxd1w9j9^e>#2=>H_Bt_idp!1o!WnYWVPa4KZmoFC8@1HdaC~+?4?r1$}stW
zfguU#=4BUsv#Ycg{(N7kI?k307~`28Rv7Bl!pHX0rHOYHe$&ZMQsBgQzM~$w6PKeO
z;a8nv?X@}QpJ*uv2Tm7PiurSZsr2pG-gjG=rYSXBq+54uvS0bT;y(FteEQ4%XrUop
zZuZ9{ai!GnPxSYVF+B-8C8US2oA$elyXVJ}4Ncn8Spz9TtDWTS!`6a&8{9{D-9~9|
z4s)TRPvXu8&!;OA)>2J(c%0|$geUmDMg>56GjYQHH?y#pvoi*O{8v(H=|cFo(}`ZO
zP)&FIMrTvMir`yMZN7p)2#+;B)}AY2t!={Lo2G7eduVlmUk{9TejTcR)uDUqvM~Kg
zxaHjd<TD907;e0;qnS7UM|4DbKPfcE<j`VEEs3}TRP}Qj{B3z$^-;NLv|&?3`RY1-
zi_fK;3Pdwo6{0)TID@Sb%Dzsu*1sR#BL6JqW^*g+D);)@{t~7J+eK3!Q0>TE+RN%6
z6$<DpW#!#$@=RMz9<1@-RGIKAie}V*uW$|L(~5njgy&(gkrU-De+|3XvkDHw@t2F}
zkn(2jGyzH}Y0B@g_!RY2g7@utjfIYyen5WFN1bBo^Cxt~=%Jm!camE-gJt40gbd-{
zTMXa2XMRBTe^A<4-D7x4tLbV_%8cp__IP%4RF8C~pbQUuCe%iHmQv2VRa=oj-`GL?
znPlrs``Yd>=qNix!@qaPcXP0hylGcO$na)5Z=8Hq$gq~oc=d*!;i&%lMf3KAmd}2s
zc~Q@T8k0iU&VpnF_OEFm_dU7kT(r9oWAX6H9d1SU>Jz}V6MBLgwvXOO^~PN6fbV?I
zMuZxR;x;#az+nH<Q{%CL;=1<il^dI<^xf!*_9>m~1G{vSk0ogFTRV24VE!lcirkaV
zPd!<a=KoNc?5d`CR>!hnQHRI3e~%eQDIV#56U`lT<g6}{%U%60jyc4a;=*a$wboB2
zS>!Gn`{cQ$>ZnL_fZRvxabPpKKN5l7f&;Z8vCAd8=@UN^?ASCXr<3AYu!T%JyDKG7
zEgdJz@^}#=Mcb)>)(B;Js47@l-|BH3)3ne&Xcw-qdT<<gaXm8|9`KsgHKQ()9`&7~
ztvon3#mh54a+EOODP*5G-FAb6in1?UKy171hM{!zPtm~pm2Twf^RQE?y4lmGzD<L(
zk^f!E3;wrSPgM1{Fp#@g0cJ{pCt@2>bbg<~yMVGb%)&&RvuDACTOV_J;d8Yj=Z`JO
zIBMCO%8B330j29p1MM=tT=&jWi{*YSR8JKI1XWo(kzMv0c`sNHzl{GeWjmJM{O8B#
z_W@yq2C!$e+2o`F_pcPwE%Kzb5zqZ-fC!RRf6QA(CIVI_8oytwtKHN!fvxyEJ5YW{
zdaT6cXQ}bl^As7xxqEtxvA}EomoJ;w#LgG4C)45L%?s1z@1=@H)U))`a>-5zJf9T*
zU<>G>xGIJCP_kaXB0-IzS#hlUgfe|sn3i8^tgAT)K&uVYE|ttgDOGJmJXqp3uMMr&
zlA}~@+Yrw};8i%iXwf-$+iAaOA$t~no6j`zg!63eO?*JN5$=!V{0so=-vav0x)8zR
zyPu+e<RB!OWbB}JomrepsBH3uI`4|@rbEn=9B90!q5s_P$R~`IH@)<>e+XzR$8k)B
z|CC)GR-e-2k_6kTS#rvLO$g6pB-Mur{hNLRdw{~xK?)q;MHc|0fF|(-ATCfZ<t<a2
z-eoULg7!cy^CPMtI697RW$tbTk_Dc=p*?+w)Cx+i<Es$b4F4JqfSv?bZ_)&fzsfta
zek|#_sue`U*M<9U0sK&;JkoaYo67Pe`!dia*saWRlK6Sp<Y-@?Ngi)?<m-%n3&Zo3
z6*8&4T&H+*0L*hzNcTB`m~;>xgX52X^UzH5ny#TyMY}Nfhgt|3oIGLbNa|DR(UBB#
z_e@_{73BMVdwwj?dt!ArqnQKxC_;1z9sd#7hPME*%tG^axBtk}-5amG%f4Wh$44<`
zCzF#iLAcqV$3gCoUa)MOjYleYV(J^(epB|~*|)pczj%Z$Vdl4yeoB+$RlNQfY+J0<
zDM25D`TMcmU%Pg4M~C&}KhBL#*J5<BfAJBCy^4bzP`+DvHhU4n{g|qB`20<h{p<|u
zk?8$n-!XrOhtqVVo8|!jxn1l=LLP%;Ya^aPRJfYO9|QR^9*d0$KLG^YD91^gDr}1+
zl(^3}je4m90{WE?3{qc^2CjB6R;0r*KfExj=MC++Pn=Y{c$rV&Yipn_&4_Z#0Bir<
zd^DJE8FD$O&w}UjFBp&9GUW$(*&=oQ7p61>8F)7K`^oyTtFxi3H{T{msc#gSLxs!p
z8&+c=*!5lf4~I^)jVWCJ+wvdC`CN$AhZyzJq>ou~5@7dWl2mS$pjT6sR^BnIETyK-
zakl<w4adC`_#cY>o@rCiY&Jfn>y2>kRb<3}j;8`m$kil8{DUHDQ<?Mo{Yk_F|E=+4
zp=ucZ$ZTJ_gpjY}Z=~;}3qQh7zw#w2>OKjWfKpFC#sify)QWki5j4DI`KVmtgR7u)
zO?{L0A3L6)1%FQ@K`$sCjfc~ACsj6xF<;tfp1>~!oE0YPP0~7&)L1^Wf@Wr$XX;ch
z@WoXgclu>jow!pzQ}pL7%wJfs?2Ocu%FQH8<YosU0EpWF-&!*JbC8bVsov6r%@=UH
zO)zc>{0{y~5*J(ZTkvvAoape}ok1%i2Ni&~!Fld}kDMv5a9cATccgv(<l+3ENXsDM
z<?vp$>f!)ueqZd73*z{vJ>e0yJ1`j2#6rUAW=JxSqIMb2mdL4ABPGB4zh&?|^u4=v
z>-=kXyh*@CD)$!+X~=DoyeHvCc=1KkSb!?)^<1CBdirQY&}sHc8S{fN%pHE!q#!5o
z?9t4GCI00J3RBdlWQvo<@_E@CBE*5w%I$l!^2_VAhSsfJC4k@S`En_x!Dz+;U%Z`*
zvFZt8SdgfaQ<;8%Mi^d!S2hf;NxWz43uAuF2_EyCsc{fV0Ov5Cn0H3x=3XR8*T3;U
zO!~?*s&i;+W&EV&g;Q~Lko^fQPqad|9<a0OTNF;msA(PaWIbo%Y3ra{MQRE4ND!BH
zp1|ihmd;LmX=~kjNIGqE<*(Fb+mOlMw_}J%SOLIgj>a9<(6apdz7RfozW$sb51ve0
z@;<T3eZgwNeY_ptzg?Og=Zp6Xy|cIbes?vdW0$~e&PTDmvy%YZ<g{j;dn9?w4hZbu
zB>^=m&5K!L@SDU6tE5}0dK;GJLu(<U`g>pLkeRb*DN~d@&84hR#DS8GpID$17mpzr
z6;}>Em>*;O{h_7^8xa8r*i{by6vsyD$*UL`8!#9Kxl*>p0v0M?Tl3YXa2h~y?-FIe
z3Tu7Vq2<0Kj%lq^va%n(d3oLEy~77hfz?KKw_LXRc*mC}>0#nVz*Uxv()#WUfs5?7
z!{n;`Y|mx%vlFeARSTheffJ=X@_Xu>9>UC%F2&@jby4PHyM>&=1XpVr+(?3c23d;X
z<BOI%oQBRE!Rgng|9KqZM0~5*+;09zr5NF=G(UqrHXWD@j%KEhNA#6eh7ig+H_Ce~
ztEiLbDDCvfj$L+;DpaP<{KDY%N$%Q|VG*Hyyr0GRIcj%u{dHo+ti|10QN%)JkHwT=
zeX`4MsHxT-lCNpGc}mNLjHB_pSijA~meYNrObd^)mzf=}2MV8d^^#=;YpDVskc5i9
zv7i-F3l6DbAaF~?5C}hW@jpO1P|P_%jK0SZ+M<(W29XaeoV59|swFB33~JC7{ATfY
zV1zfV)$vV72;GAo@?j=+z5wdN=xnsOv@l&Pjmmad2226J%a|^#zgYIy&AAMb7i1CT
z0+LZv?%??F#i1t<M7Q)i%YO1_IdR@PIRhP4i8aTb=Q1Q+t|I?%GvCOs9+C13n<5T)
zz?b%M6)@@@yuNDTb8wVR#yEoz#cS#(k!hBLm9Cq)x5LHs3YUoJwT&DfNfCE5)KLD&
zeKISU7C#-_PV&1ehd?fKY2#X)vw^1vODt922$5s;y$?N&4Q4akmJTO8HQf9upQ~to
ze`pWH?fw^k?tZgrJWIiM0k_}z4^RU)BP^#06_{i@IzH<hq?~Iha=%NwZ3KPjQBRHc
zZnmZb<FC*X#e3WZ^liH;Pw_BKF9EZ}_e8MYhFw&Zxa4N6SOHMM{oH%eyiWxF-t>v6
z+UQ>DEqIMw_oo|2&?Z8bH?wk=YQBMHW?4<)CTSffQSKI()zMa?OwvEi14a)do5Rd>
zF`9X(=Q1Pg8-G|}D5Zi>BQUD>Q&<f}f2{hOtSYWZZb2Ec)EzP-vnTYHb~n=Ghw<l9
zwDPOU+<F@?623HOa--ZWQnZ_G(BDTYf^Y}DF3o1&RdokQFFD6BJJ(?BZ6SFKp^p?W
zhJeM@OyJ)-x6L<dX$n(%F%})$|C}(;igv{3XLfVFwHuBVY1v?D$8U+K+*L*g#AM<m
z)b1s|n=sm20Ducqn6HRZ6Xe{9_(|r(n-LGz*=kl040)`W4$?1_6A&4S^eS^BG)2Wc
zt;`RwPv1m+MjhFzR1C}}q*v(yLvYOPz1BB?ppnO$M#8<LalXM~(8UOvOuYZGwuZ-v
zb_sO1X6u>65|x-;v%7jByEBB{CCt{Mj`)`6^D=M_AfBx0t-%&`G^OJby7~(FH?E2J
zX~1ML(;|wbSD*Sje4zV3&DO}{XT$zHhdduTD-r?^3nWynEE!-EB#NPK<J6bq`V?9o
zq$U)upSEfymUS(V#Mb=F{48^@)^XCSUhb*~9CH8%yq^IMGnk&HYn}!9J5XC_jt}cF
z>QV4j_ry=^2#IHoL(cDkX}K6jKVB&n-7~W8jN?9ybh8C7U*}$=#dfO2OV2s|bTdDg
ziwl+(K8>jDkFVNVNz@Y!W{G)G-7WcOIF85j%Ao$3S62|9XX#)EeJpPK4jczH{*nJS
zFKu~xDFlOKRdtAe5Ru<&UbrJ)1qW_yMP}z!;<YHL07b|E(Q*oa!a|ZXW0pvM-sAHA
z^G{<wiGh3<vr`}#I>D2IR{VZ_TKRq4n!ew$reaLs<dYLKAnHrGV@e*0-09=F&wx7L
zQ3mNxc8iz#wvxLMR=w&g_Prvxon)3bTB5**Iynbf5{?>CWOST8!i4{*+C2&E7|*U$
zA?9uSBH1fEPh%X>l^^_OttW=zL7~(MkvL+;YA{jHO2;DDL+;J6phhBWaAemV!5}-q
z-_f)o{=nvQ&#H&3*5noY{o(#Mf>>S<XHUpKVUn`4V?Ntr8oVl1&+e;;d{~gUn(ON;
zy?g2Kr(x*ZN^IS2hL7|;`HcQZ4VCZc$wCdZR&tJ1?Q0vIuOgN5|1c<xeuEd7NJ|+D
z_@FwttaI+cg>ASw5OD=vuRQWS3e`3<mHQ0VmP(3q3`coM307Cca?!Djx?{i(s4yNm
z_{k!&)57i+TMVWPpev5e;^bHKN5mF{7J-iMwzGC$_H`UN2enKf0Y3iHBtA~D+8u}o
zW3eBwdzl44b$v8!f_p<eDJG;Yefr2)Zc0M=CTW4FC!9KE;Ynz*zX%%BB(SKZ=muq$
zRB!>GcPHxN??vPF%{iv)mG<T<U(Ed1@ANrtv`_jorj@S~;Zr&k=-s}oqHINftB@M{
zjfBAXdkec6=(_RZDMWl=$(T~5S)XQ-xLM~6VPpzngNxC#wKoVL_}*pk*r$k7l0(X9
z)2uKQC9^ks^4NSOt>wB(iUR4&*PYU1zUA)NnL3sHU64YDt;o}d5i7w!Hw#Rt$E(|U
zA@}0={wy={$W+eFT{5GIP`7~xL>m?`VhF!5zo1kxW{K!|6hSVMdtyk!RDP>uqd<Lf
zPVmO^Sjy2eed9BC4A)5;!)mA-ezM<kXn!TVzd$TM4iW0~86}&goGTT2D4|9XKhD(j
zPledBQ`KrjN7&V7n0)O$c6z$qLmhAaxk6MzxodMPD}(GyxKt95ocifCS>!5RhkpHR
z4uS4{F)I$+?U5%{nVZU<IVmq@q?_CAaAGW|4L7@Ko)ePYU}COF2-B05!JNA&1w(uR
zR4BMLS(`f3KX?}S9B-e!`k`x;Y&je<#quGJx$N_ivaA*1k!SE%(KLjb{0-^%VwUCv
z=COK9fx}uC=X~0IgVYIsNvZnyk$l?T;`&C*Zt&^+SZe$W-UTiA2s;oe^Dr}!m2voS
z6|q&chtoulpjr_T`Mg!4Vw}IbGU<pBofn<@w*O6!nn#uNbF5HMc-4p!>2+6bCSbhJ
zlnLUU+y;Mt%6reh@Xk)OqKoNC`f;Yb*0+@9%Dt=NI}}<3`z+XrIRNuUqJgY~A&7!r
z{GPW?-pnx4#HThg$aPhEVWY;*h{aBY!dnKZ^R#B0rrNQJ8kN475sD@{lCM-hq<NB$
zys7ob-N>=los*8q+huK@WC7-WY4~Zz85>P}&m<usG<~6%)UMv~lgmL?4grOjxY2H6
zuc9MwgO@c^E$Iu7zb5E+M!^D4@9669-L*E3rB~0<RVGD(uBiK)q|=t9YdT-({N_KS
zu&wz&w%#(X$@c;KmIi52a)5$@NH?Q<A|?8hE~TYwbeBqrG>k3<X&Buyn$a=3M|ZPv
zkN@*|-rS%2?cVI#cI~{*IKIa>v0KV;rkwABY(X+Ytb`3eUikTYC<`~>k%B@!A%l`u
zrYqM?2Y<0jD-pj1R_=dnN<UOqR-A?Vv<UgM(<<d{(?_?&oYht~bklXkfOrgdiN~jR
z?0Lk&qRPuV)Aim3g3DUo#H43}e{m&xBo+OYu%@AT#8Ur8Te^oFwNRh4flpa5&uh2)
zqbug5yiDsC#;JsK=2Qa4D+@w+^o1)bBs&~KDcg}ZY`j_uk$B4N=W2Frwy?>3>tApE
zH1dBJ_7P<h`{Tb>M$T-$cL8uo2ge^zld*RX|8$d(Y?ngw>mspX^Yv-p#zin5PYov)
z7V4s-Ke1$(!4&`7KWw+v-IfC><EwWept&1D$DIMybYwd({NVWCI%_aT^oIr-VUa%^
zdCY&vlT|2E2!UR?pCCv8aK=5^h&{gi2Dwgx71PoR!K{&Yy((kfhk9)dOuAw-$u8gG
zBXU$z?ehN+pXuC^h<H_xgSL<#*t&a_V3E}W%YqjV1+l7db+wx(^kR_!!I~WYMn}L%
zSA&q;<Y;`ZyYKK4HhC{7uaE5{i=XP6^d;&3O?%E072$^65I5hALEV3r1Q8?^nh>?f
zaZJGV-@j*>owm~>%vY|}wl@Nyz0Ll4k>l#%K8|ae4rs**$>nx5%_Ax2&11z(Ru<|+
zi1XSiVN{=#B&5dCuql<O^ybT`ZIup9cC^2%2#{jW&WVm(;7>C;o?NnC&qwnIDW7tG
zXj_9zprdC9L()EVqOvH#<DW#tA_C@jirjK=Ryb_DzJkj5<(6lUUOUKl&bHh=K1}B<
zl<Jn501X;t`!KkoTWv|2@XBnHdW|P7wrGz(v%&{Zmk#X?#w&X*Y&@<WQw!~T?7L%-
z|5%bxnPW*|eGc+D$a)b`t~AU2K3X_}(72{hb3JyrU32r&e)wr-Iu0(9Z4D9qM<hDW
z8~m-e@KFlh%NBE!Jc<5ro=d8f<ai{vKDTTF9;}Vv6l3knpAWisHr-cSEvPI!^t7wY
z3RK(4d$v@i8#By+BayZFt3hOn`a3mqPO_mA^rC)`=@|(h+#oPz052!1Qh*v6gZ;gq
z;bUYm)0pPP;}MXE=JW5Yf0>&OTMeQaA{NNxf*CsUmd=J(pZ#XOi2{0wG9AUKHoDAw
zj+mx(N!Sj&9$7^IhG|xw$K!dM{_P6T5%cz&)Xs{<@5$Z9mFiG+Kcex+>L-T7x1=1-
zmjyPhYu<mMnHDmBOjvzw{#qJTJC0N_>8lby{$6E!Rp!xJV%OS=9M9qI{jG7Ahf9)M
z?RCI7cBYx09RSYSGNk&i=G&?jolnQpHGr2;(#hbgTf>&<t@%nY4OV?gIgUZvo&ARR
z)I_PmkhW1EiQVkfwp_%dsRqfmuXvbD4-htI+{apvF9VOte*NTC!Z1I@cD=9QhXf<V
zpsJ-C?K&Q-4*thKXt4Sd`4DmyUuY~g|4V<Qjzm7vD)TyFOV01AeYDBUA<^}+%Ii!E
zlq}eRRb<Fw05Oewq!qRp6v0`aDBKshi7ZhWDVF@{zdl>5*(FLQH@0huIk||KW}HhA
z!e;9`Du_k)qw?FAmDBG%`ojeWs-hW;)BjT<fQuZu6@I;$8$V#Wm(9u&FC)R9XE^C`
zc_Kay4P)FQE2EX6VnR#k<Fb70_}pnq$ZZ$y_386b&LX};w9USkP@RIH{=P|;h^!V>
z5|P@|!Sw;UzEtg+LGKl2CQsCN`XX|LI~$3(*-WeIop!nMP8|5r^7Wsh4E@MApl4!R
z)EY5-tF_&a{Bk2;lJ>6e#T?H1;~tOdirePCr4uMq|Jlo9MfI9>N%f!g#oiAYY=Sd`
z1upaGI+^%ret7<Gjh*zjj4S-3PX65A)D!_k8qh|tCeogbMWC<Mi9XhG*yD^i+Oq&a
z*J#Q>4(}sx>E8y0;xBAnHyuY#I4=VE*t|C;PL&8etRwX=+F3|ZSQj0^OE165xAa8h
zV*meL0P`EXqI&g3`)`_Z`mZWi_^8cok?%R$uMOU}eYNLW`URf^|CHs`$y8v^T*&9<
z<FXceh2p_SyP#vmU3|+jnAC90`ZNwE_Vl=T7I8<s5jJb(;p%B6-Ywx5?l&&$X<Tts
zO{{i2-m{O1NwnOFpLD!DA)YETlJM@4Skj8@Mg{^EM?#`Tu=cOJh_&1#XsLU{yRgTv
z49rJfxoQITzh?VcrvscIqib*D!9b1i=cqi}GyYQ&;8Gp;7eH3WX+ie%Z9?~2j@mNa
z`%IjEmD=U?8BEI`;6zv*w$70CLd|nZK*RCy^#!Qt<CNqlfy!NAKk$MqG~*QVWF%85
z)|rBj;w*3O41xV>pX~Ne07M&{!n6}1aJ%O^Q00{xFCW2a*e|YZe(dOx@OL?+l~yK_
z6-!i{AS*BdFYOrakP=U6KEEo1Rfv3#7&r~y#TT}Im;7rj1M9|qTGs$MB4gHhQE=#_
z<S;?z099(AHss*5oen(^fA*+{;wXoCtGEBWETH9`=527iZ#^#2Ep!DStxC+}rQp3m
z@Ilm#&?zTUgIK6LCY=*EYsT3n-MeG0KVtWV@3z<cmLun72H{^Z6^4j$?dI=-k3N_4
zl>(`x_}bULSxaez2Ium*k%;4{^Zgwp5>P7qOHzuI$`-@j_d5(Qp~H^H{`PaDpJSD4
z_L#yMiub1T^#^Lu+u7G&U!>mm*6CW)WRn8C{$1h0Xy3#K(iD=5EeE=gh(VEp+v4IK
zBS9nU(vM-*eA&Y<i`!Zm8Q<3QPO_*b;JklN_HExFu3pSc8{OBVK$)vp`o;`xmTS1L
zXU=0LF9(D<`yK@cnOjdozyIvO{iAuz+tg#h9#=?9=B6}DhZ83o!|J<3Pi#X%l{a{F
zDXGF>W7SAGlf%Yn76gwfTEL^i2Cht`)~{?<gg~s6BHRp?hLQLqa4cK}?+?m8NSBRv
zS{lE2SR9)s8~d>0x?*OVS}2RDHRbi#uyC2QbduHIWq};58!WMb_Ix&VI(+GNJuC!a
zeyJpRxmg<Q&nL(1A{Vi*)L1O(H!t6$T)y11yIKoeV_?_zlGWt;+bac%9EMdsFNq6z
zGcTPsr(JgiS~`x#L9fL6*26s2cF8sp;y6b?QvrdU15v;QnN6P_H8!T^^P*NW`<OYW
zUaoMlm_y9o&TcQ^tz2wxE>iOPxaZIE(Qfkb%jr~IN?+^9gYTIc6R;2x`nnqSB}#Jl
zIi%0u$^n+h;SRV;UoxBZsrPh?@%i}G3a9TC9x~EQ>+0ZJ?;7*vRodRRI68hin8Qpj
z{?w$0?D_nWvhobCMDGoO&KYf%lIymA-&=)D@wISj|4t$Yr;y#`Q$N{iGdBU3y|+D>
zb)a&&--4|z$r0C2{tF1S&@Jy|#$Kc^mjbs<NTg3N!y|O~3d`>#ug>i|Nrgo}!lE$Z
zp+K_Va$HW}t5s^N1-~j<#sj(a?m(<<FP!yN?!G6vfVSuQ>l7oR;R;5c4&Dx)NKcKQ
zN|Jog^$f!}dyAHm=WiS<S(Z}HDA2z_1OUbft5eNItq;<EqB?^v&B4he|5{Zeqh2n3
zVz{9-g5I)<y>%FZTi3cy&$UeGB%?Eq@~GbQd0KaQj%+L-B;I*a(7j`Rw)oi?y-@uM
z)~&@G*$l97sUd?$6)?&-$rhwX%b@PaK$+3P6@r=m6|ayLeBYV=%ef|p$_D`Jsl_~Z
zuh<ApdP%aBfLF*@1cpnv>Rc4te5o$~s;5J)E!k)!_q{q2)`8$|L4z-pmY}Iz)j4Xi
zv0g$#$B_AIe)hKiwaUP$HNm5UwqW_a^^ls>#!MfBpVCO5rPlTY4Chi|WGTxO*exSV
z+>jH$`7}G4Dh;lX?43$K_RD|vcpN<vGv~*@MDKCuOhRJhNqqGwc&z142c~yt)}$M#
zs1o%msJ4pDE#a3|6h#q$Ak^uRM!xG9+=h}ozNxpsnji1`_#Q3sT1SN|lZ)L8`7~SH
zOu=rsT*4TMR6^RXZhT@apQQMd5l)aF^66KJR*w$Bmrau|Uo!KVQ$HCF+a%!(JNm`H
zB^OE>TIg-fto5AislwNs{6j4YEmBZ|B81M4jJ<1P;|&OJqy-8j%54Dn4A-mdHAFim
z@LxsjdqFKqHQCW;|8}yF5q)EcK_FFTydYv!*;54N%l&WkE`(whY}Royuq0QD(XH1(
z$ATB|)E>Ck1gPni0^hdfD#|tzvx!)89gGkaL94w3Xph?8tz7ZHzJ<Pit|!1_?J|$e
z4TIyF8upSr_W);*-K@)fVm4A}RgrK(8&N5LGSA`y-WHp%<axdj=phUdgB%EEn&S*X
zzbozCqvZp5o6KRf>CLo8Ri^UiWC@r}hw7O`2MFCq4G`Ozd<`btAJC)4?03ndPJZLy
zQXpEETgmn+P68!arBn&rSeFvt+fWdQ_je&Y$AVW6uy#_nXtvbs>M=bRHUADNG2?IX
zN8R+15uo+{^CSp1tRz2RzREtXR1))+68xhlxsBrs8yJ{@NH=a<hkncQ2<s&W=7yM?
z{2M<ei^)`acFhmFF>hYxyPn<1S3L{#Tb_T+l%E9&Vgd+0aawj;O_xUIJyOy1S$|KT
z*6bs%?RwTO?{nC4!87+bfZEN5(~M4h`Pd4wo{I6Riz(-x!e;nsP=cR{A)(>R5U@Jk
zFh%<spfa(CsUm{9$oXZWf@=Apn|LEHGp?v!!7fbUQHK~UIWfLB^6jriY#9j4TG#j7
zqVoB}#9F73X|ZI0E_C^9e5+W}=X-ffRc{FS%uvoaH2|C)B}~`%rSS1kKz77}o842#
zW2v+BEI^_>Z_BDK>fo|1UD0&NVsRiYc`fZvIwuqEgcYdwXGVN)nXgNUedKdu2VcC7
zkL3G#XKmXV9>|#lMh`>USDHXqRtY;m%Urd?!c{u$<mYlp_O~K-tYVoLXGNc6abk~B
z2Az|R{SYif_DD9?cEb`zaTe=}LT+n02<TLT>8A;p@~0I=lVTAtKe_qeE8EO3Ju^>G
z6>kObi8i-Ei0}|QCC3`|fIh~#EiaVd^mKhkQS8aAD{<PgoJ`YD;oKiA7{_(PyPiY;
z<}Pxt75+42Vg{cpQ&Wk(i@GK*(3=pw{AG>1tlT$|Gr82V`ZzXo^U56Yn92Ua&1Q*G
zFwzvNavMXTpOVJOtvxFOBI(n!dub9A9dtO~l+}d&&X)%IQ__c>>>t=h5Jflkawboz
zv0b_9)G6hbTLV*y!0DDE{WCHz7Oj6RF$tT8Nw`g3>7AJs?dL9;HE9J8Qlw2N2CFeW
z<tGj~n^eps4C^J_3Nu3;i&P4}f3{(V4_lYFo)qQiYM5?p=@+AzqD^0ZwtrMg5bHN@
zHZb#Tf^VMfu}-khBG~-37RkAiFY7vlYMA<!nKdm?USlP1WGA<Koee?ow^jY6&ywe1
zF)w!{_ob|X_3)P;@u`CGV?cfYJ&2mt_^$Gi+4HJ3h03!oe!i<I;l&>#sh$zdv~}V!
zY^kz9lc-?p=ru}S&lTc+uMe?fh^b)-`rn5g`Ga(uo`PZth%Y00#(@!lN7pPLx8XVl
zX$zF`X#(G!a7CF?5+1d(>nHk3D)T-U%vE7i(#U%K*!0`lBfUo}5zZ6rTFmk7?qz-%
zZ%QD7rMWSHz?E5tFj^fd2+_D6nCvmTViSqY4a{?mv-da`GnSG3?t8N2`w;|r>mL0_
zW&D`KOod4epT&;)QTvzv&X4I=JXPj{<l>%yS1gf`axvXcHvOP-D^Er*b**!5iWSyz
z>Xh6=i2X1v!3iEbKu+ljC{FztA{8esE-#9F*-CGAV%og2?3<bpA(YGN@~G|mV7=bA
zlz(D-S0S2iBdc_+zTfdODrr~NM-6H~0l*My^7Wt6&U{gEf-~zR4j{{!G@M0$+5M`K
zeN0nmT!9P#PaKIit2m`3yRVN8eaB>l>&Pvjo^PHNEl=#Fk`-O2l75%1VgTSi$8^n#
z`cm7dGw%0pS<^<;aw8utSEcMQ2KT4jzc)Ez1=qp{q<lK~seH3(Y|^8~Xte(G10Z%M
zJSIVbB5RzF_nBx3E)@B?Cs|msnKJ1D+B+EW0X2~{H*ed36zBtIxKGru$KvTT`@KA+
zSb-}LDDku4HUx~<zVq7Dps=#*N`7lwjhf+xsMqxQngW!!<zkFmz(;=NMXVEOF~Q0;
zI^J4fL?)mgNzfF=Lsrh}mE17MMAcBVwBE1Pex$)D1QI<Wl17<xa)tM1n6X)}?6vML
z9KPw{i3HlL=M#uM0zQRM4)#w^t`hj;B@~zt-Bng2U)(3xufJ06VfOIFBL@16w;OT?
z)|K_qF+Y+%Z7^k&2oW`JuO(=__6QJ#Zk_a2Wslx$4pyml--;yd2{UvXrwr^V%B-%$
zzE1A~|59o`&!m!YTx=VoD*sQ8GiDdHY0?PY?a}Lwv<bYFG8uV(^g`@&X275svmj!7
zPhxUZ>P~#~wukHAbR$h_b?-l)pOsGKSzaQCm}SLUJi*CeSYH>h9}59YKFg&!Hd?TU
z=-UygH=%z$uF}y@6q_R~5%rXUa%A`VCwN6bJ2k5>@#0up9H>~AO`e{{YIqdK+{$dC
zk{!JN5tiGzjZ@X3`5$bX_-wh3AKOkB{1F+}Z6?EHx7Q<cd^}n`B~!8qI=rqx9d*i;
z<J!yun@0mq2@uVL0hS?Qh^nfI!y~8%bA9d=(PKQ9K}-0qp_yA2xb>61Od#i6SlG<#
z$8xM<xKm)ZS1!za#}sFT5I3#8CG^x~>7lNc=|-O7iINE|mKK55s<ftm#u4ot;s;c)
z-zmjuq+QoyB}iGGNuPtn`>umiKkEi)wNytFLJr!oR`9uLp`|6Zim4{0Z*Ev+%-gWt
zfpo`UAy4TT;nrPqZFeR<eRk8N_;xeW{+zMU5q5P6<mY_a-!?zN8(Qm%Y*ARLJEzCb
zDS5<vtgI0!syJ7%MRdvU?3mev0#YfC^?`1$RfkbQBQK=fRvFD^xAIJ~A_VlpHf0Kr
zOAgaTlDl|EIq*k9W2uFLdr5!;*gmhV`UQq>^2%g{6s<;(#xj<2@TJUT1c6Ej`$dqQ
z#cmo^%0&Vw(oyi#o^9+{i#eZRR}H-5N*4XBSz#f9(`;vPwL43CT&G?z^W>b>-~zDG
zTIM^FEQ<CF6NLqQi=EFi5C_pl-#lUJohI;2>%HC{`fRPW)|+f=ZfIaFkE35Si?t2=
zDB)B{4tta}@~hIkM64IW9Yfo^a}IW&-zCkmF<@S%mxnW>BJF6`qGClXM`iwP0ma6W
z6FL~Y#>R_2Uyof{uKB)Dc;5WYD3NC<m`1}_!Pd#Dd6}iIe<VIfJ+O!NyjjJ-I$`lN
z!p=IRbRIR|G(G+8^7@F5cz>u0$Met7k@oB&ld^O?^CE~W0fWzs6I?vozj=$s4)z52
zVbO|3oV}r$>miabxCKiJon1u;;fne%A2xt(mv}rfPqkfQWiG+kxN5i`j}n^W%NOzU
zu+9Vo)nmeI@=4uSx^Zf{V%gmm?=%+QLFw=38M<Z+&J&b0sdJ@_>*X{xPiiB;&7=V<
z<4LW!OSD~AoFm+ZT$cNUxSA)ooXsa)@vW#FNHIvH#{Mq+F4~?Pz1+#^b+q#7D&pYC
zN_$}>QM$gW#}0ad!ZM>a%B{Bht@Oj_hu=km5;2LdcNHzGrnR8-rnKaU^kQQ~B!)g`
zrmwN$>SF`!)gNajk;jTG|H?wQS8~hW9Q9Mv-gLwUXay1Zb<ZV+?|;R50#mnmvSjNY
zG_ob?WnJ-t{%12`_3I28JS8E;D5aTNobbnjwJ5s%!VI){tv@FpXG?k01d0Q*<%FDB
ziz!8xnz4W>6_vf`QGQ3~4{=-3EpgIVA-?rLHF{oBnP_81K5lzRwO7{okqWD>@6PP?
z_^ojCj~6lg=;&SYfsRC>9t%a{OT#)Vd%QWncg7|TGyeVS;)6?R@M?sbcUjKoD=t5M
z{eI1+H{{_0(`(y@A!hn%%>`o{A$t4V>;bxU6g<cL(PcI^Y<4{cxCQ`&$^o_~v<CHx
zf~uChlWngkx$@d;{JHZG6D$KA!li?M{Wa|KRUzT37k|&ncGwm!u5NC-f2wgN3&s5Y
zR>Sx<s5Jlbs<7bvdi=m@lCE%TmaNtYTP_**zhF^|lV;$wjaax%m({+SssWeZ+qnw#
zQA1DnpUrZB3-U!Cf%I}v0UZ3MaFPyM4cW5s%nvHC8?R~H8G4s28fs&4dr`C@v9nL8
z4JH+x%Zx6%$)z#sLKp65X1+jKg>Rj)693$-%NUihFTm?vT{#iNsS~GXrD%B+nu=P@
zgKvq6Xr&Q;iYRl)Ag;^c{<X~I_uPJCR@kN^t48t?bG^4U%ZW*6`Lp)U_)VHwXRaTt
zedFCE=odlGBBS7S8AEV0_uMI7>j=G&A4B!YxbhB3xs-;O<qF-@fU9*%coFaTma3-I
z<+!!lb!KMkaD}mn1`~2X2kq$uOAYb;?w1}fzo5Ppe!HyM@>Crob1yMd0yh8{=^dgu
zlN+UmTe&aYVF8lmPj}wgQmLDWUi!^pZi*tv)_jrS*so0SK27hkK4xi7-cmUE!LrN#
zY0=7iw(SN@MtC-yxhP5|Ub^IN;?6SQSEj9riT;?8sFh*yVsku&cgVOan|Yi{kK+B&
zt;__CgRXD(tGM!9;+B?C!cK7F(%OF2j)<4vz4^-p)CaKyB3w-qnalB`vyHBvghOYx
zAz}AlSv3Xy`MzZx@!n$*LTtPiA4MN{3tgG*hKp;Il&{x;GrPS$YC^?0;29Qpp0h{W
z8^SR?t%<IXvbe2j)re(3k54KwqU$+SoWp9+76}+zCXm7e()-V9&fGsp+jwb6#9Mif
zZF`J;EH9GNf4=eS5K;6>%I443SDp4a7L$wR8kh?;a|c!P8i!p7&w!?}rYb_+IP=yv
z)<=6m@BFs0Bee=++N(hux5_{VVnU3byQB$p-JH+QioiQ08XT|x93s|`<G0>p#1r<p
zI#5?8xbLvuO_kApAeZ>By<r!8|MwU>wS(JPC-_p4mSCfJ-^M2LN$oR1f|~3(Xn}n@
z05AQ_0%%Thq+VL}U#fIl8+nzDP2Ye!&>~@B0#hEqEDnV|BQ;He@W`3L#xJVOc%WhG
zFcsc;1whx<n+a*l0(P0{gaP60xf}P7+eQx{(4084_zj@c>8@W3y9$T8S3?IpdlDxe
zd2C(TJb{6BUpaHv4YkfseGTAmj@&q};okBmc1rx4(9;45w@jdeF3G^_>r&QdX1`Y=
zok$$wvwNCUp26EYRYz#0ChvV2lFWV+XZMUQ@xHF1Bgddtf@vnjkF$CxS%@dN<dcd8
z`wnkKcKtti3Oaf>r+@$QszfPC+e23SS4lOb@iT)F5GU^R@P~zI@jD%d;4k`!m!~AH
zbR%Cr9gw)1%9;GCpK%X#{5o3De|C-X`>C#~YFswJk+)ZvWZ-Z;QYl=fI$ELV&%!%Q
zd%2YvZ~T(nnIvnxTyMJ%F1+EQegpMi_|Kb$vrTsqIvf!rO&qJ@^kwmn0jAV))?!y@
zhYOb1eexloVLI_&?YzjXi$Mvh`-mrLdK?P59r{<>ciP9LAHIJH7d)tDoo-tbAVy7`
zF&$1|&&jSn5mMGvx~rxN+re8KTSFe!)%ajOyQ)p<hoxMztC+!rFXQnytwW7j#;(_Y
zq!u@(#k<})U=WNFR$WdhmV`8H(D%G`&V^yRFkZjU`NMmDz&uZ|r5*d4U6fAG^`KaA
zfJE9BV&H(9ro`p6jv++QY+x?9xx&ZPAbZ0R7%s&;ywlt(7UlItTCIP_-C((W2iyF+
zR(AVb<P%3tKfNzA!(+20YraWLXzrb6M}&UZCo%{fu%cyf+@SyVleFDn$EQq;#b%>0
zD*1eXyTxWh8|_moQi_@!`+stQ7?w~37F0_2OW!Ea&Mc(m-SGd77XFVmL{9;97qIjW
z2!%8@?rxPoB@J|U_YZB^bk(Wb;f6E<BOA0D=!gVuvb6kDB>!e@sTY`itIH(toBJyu
z6@!-UEi8D&JH3&h1^zg|{i$^$BI|`kce%B0R1Ak#<d~4=DK_NcK6fitH7SlR9!n(u
zq29oQfIU_0ccirYdfwg7n^cDzw|z-{Y1Nw1S!ddw{M^*FTC^4(nQ-XEx9=bS)-a#Z
zGdO^HxB#OwPtWHaMd!~8ILJrtQcw?k1naj7olXh)Qr$6#3iqXf$JZXCZ8nlJMC{rE
zGTmkHfcoBu!l$!8C)O5ziZ>XDlqnr?G4$k5b_}u96TI5x-?wN!<r+u*cW*(TyOB?7
zJ+}wN_e>f;s~WK5IcQz{d;98Jd#sYWORNWxVWegG(@Z(m2WIoun;_r$&C)mi<<Bj;
z4O)nr(C+$$6T<O9!HbZ_bngSFj|1d=hr1EO33SrzezVhZONCPg#cEkgO9ZnuHR~IV
z5P^P&uMGwLb$7i})TaV|bjHbYUCN&}mx|NheFUB6FIGgaxIV^+yBfYMH=eyo+cNwZ
zKvf<I<;R=Oze{MgcxC^lB{I>3%aQWS&aOgJ9ZONn3d?zsg+0GX;fstU=pVmKE&m3;
zVVzP9Rtq^q6!?QB2bwF9SyHfEwMM3TF(E25=|vXxKu9tC5^~e!=0$8uwN2jo0&NjH
z9Soc6sz9N=rce|FLK(*Dea0yhYep#_H3wj|gqap=DP!oAZ+dj&<KxC<9JOE8o`#l%
z_!kXDBhoH2Zi<Ny2t29R?YFx&R}XfF8cl+sM`o6lOoxN=1HHN&k^|OX3x3$?5K~|u
zN@V$EnEOlG7SQ5pKJ72Z!yHX7l`%`52)C#l&LN)af4G0Z0+fp&F8#;{+${F%>}4)}
z>wpaEvWYo@+20c-URu7aE-|27ukX|;*v5B@N$GudE8sM;af^#IH2;OJGu46}WB3{F
zw7Xms##wP3ZT*AfhQ9Mv^l~<<O^BrbwyFue(WXE$oe>5@n*SXwSF7%=#^7(Y)yo>o
z)o|$}p^_EGh57-3zp3LcDvE7G50!&Jj9wGSto!RM(1Z3cdGGdvU+%q=<H_X$Z<r_N
zdISL`8`_u08Y}+A4ax`?`9And$M3UAx!=C+bnN$nyUky@(s~y@8q@VK;liDN4}tnL
zh4uT==cv0y{v`&#ONk}!mU;|rA|6KyUM!cm$JiYijaDA(yk1btFhu!JK4ecR8G-`L
zG1=dn3nV57d5py1Rx7<aQ-yn*7lKvF<k8b`j6^Gr`JhGacEj8Rr?ucpG@0kT6@6Zw
z%yo^o#~n6Ul5Pg7jV6BxjQ?QYj4ka;UcN|wjmc>zgO)HZQqaS7?Rw-~Dc_%I-cOh>
zvv0J~7A5`+c@YpRw)$;!7V`$9lV#xA(O8W8ca*;eAlSHgURHPHF%g~!9k*HwI+VyL
z9qO<e<Gq@B$G|DPI@c}n9G~tz#$mdvm-k?(IfuBJaHn#EY#Pet1*H>GCmh}9x2_+8
z&d5U1mTe>^=f0nxv8?_-tCUhC5o!L=GHGi*=P4%mUO}ShbY`(p6m!e;0sT8KhF%I2
zMb8{@A5&qEcJLZ7i1Ek31UknPn%B!m0PzoOUR}2R;Ud=<m`%pZOXC{d(3Z6hU%98P
zyHoKN<*6P^&}6jmzC)oZr1f!fBpyQHSs>AdKJ|Vrjh6LES9UX%2s13k$s%WluI6R6
zYny8Y1UH!)PXr@aSNM;{QM~|qce=csC|mQ;`SP+KH+Z1j?6Bo>F|W7sZo+y0@Z+>_
z&4tBST`T1F;T!Irdw_Zq3+A^^n|F4bG_N0!#=Jw{Es1l;Sq+C4;_t<}4#TO-23@|!
z(K#H2sy_>t-kg25U?Y0=n*qX+zGZTEM>Bbe@ujs^txn+eR5*0i1l2A09r@p_F*CPp
za68iJ@r6tr+VuIIoC4B56j2NY<g4vWOrL}CCbTSn$;fCM#YH=eFY;>KTq8F{o38)A
zfa?RHfjr0LK#`=5o?S@ca<3uld(<WGmdeI2A``0lY=mtyS-#)&-78+5vlfi3iLnL4
z$#50PHddj|A9D3l>o3Stk9gPkVGTi9f5uM>C~9M+hg#YmNp{t&H_)35Hy`^<`j^u$
z+)ULm(Ty`r++#3Q{Wkr3j4mVZCRbz0(RISuXaCb_0-m(k-BH)h>^!c#Y5=c2$J^8p
zGI8rOm}qMyHE@S;aK9QZeXih{si4`filgFD&fDd?9dz_&!~6Phpgm4t?14j!I`Hg~
z(6El5?#h>p@o-<bgzq@5&W>U<GnMaq32Yp7TLtDo{gvYnCa0a`z731N6NlT6r7do5
zmy?-f<xMbCX#Z8Jtl!5n%YiswbKYUPs;@TdTJ=QOU0#&-OfC&D$jjj8s57?DS_VjC
ze9e=ml|4)nH^~9l$v6B~L>$bEXZED0g^9_98i<eV^&d=9U%ceCh?h~jgb4Jdf715u
z&(|=SYC+3vaA?w@){A8R{7u-sU05nYL){kA7&zx9u|vPeVEB=ATE-CT_EO1e*4(ID
z!cM2`g4^8yPK69w(hXFjTP{K7$0^|-FZ6?{;|PD=a4<kJaV{~^8Fr1tn%2Kt6Le#l
zTQaC)&Af;4>Y3kLKa3`IcG3c?b;3DJ)NSyq0;<){IzCf;yUC?b(>T)ya3Y%}+PBo<
zm>A_eK+umVektIH7sp&{KzW9WL8&kDKelZ@1W(i6hf(FY44aVw-B?z)_6D<aoB6FX
zrJI_Q@sn|KuRuVVL0wr{BHukFq+ja(8eaakiVF^VT_2BMF8)U}wvgw<0bHH_30FT@
z05b(&x)k%)|EP?>ruE1JJ90Ud`EGFPfcNbNPm3K{&qhlhLey03TCcop#$6FyX=(BN
zxEP{T|B&5%;uEi!H~lXH69lnQ;uJDz5i^OCA{`6N;2hn<uqsooxqov?%MFBTPswbi
z<J#vV@a1SJ-<~`!*0AN=SRO#tNV<<%IyDv!zigYGke2aezdn^bGpsnKcxmN!kpUC$
zHg3fXS0#ACN}UfxwEj19ok22{+;5Y{Yr^z*1*nk&dOm0nu@O=}?-X(==>;)7eu4pJ
z$bVl%<GghXi1K)WA>+G<-Mx()E(yHF<F)A)K-Az|SnlQnr~+cX?gf2~D{EPvv9DWD
zo^CmC2W4FkTJ;}eHY-r8G;LyytIx{C5Uy}3_We0^@o9&xH^v-G<02)zj5Z7lh@fkX
z=~}c>#uIPm)ua0qM}~nPXRU*w0Sw_^e=&;_>#(1uI*w_Sy^+SiA@mPUF+)C@!sw%l
zeo@~RrZ8hKjsT1sJc%(8KmS(MXJ=j7z1Z|p?%?i;DAL}kR<MomF`0!6xyg|#!CDQe
z1?-Os^XX*nwu+|rhm62_43;P4{iTVf1`G2j-$LMx<0EfZ8yx3c$tS_0_&MD5l3Dk6
zTjh+R=e?bu%3KmV=_ET2ftOO6%Ok_}Sq!Jzi~)T*Orc5MNa+te_Fnj1XCr+7ismc!
z{?-U_>i!aWa6qy(9Q|g{H(xitff5?T7Ym{&|B@7bzpTze*_GbdOGWD5KcGD7|1gSY
zL;F4i(UEE!6kl+KoItYEil$qn%uEg1X1`&)_l2fp6|w$Je*poew%L3uCSJg$ulC5N
zuJ?Ep#B3B#tN%K$cLL+6yr1F;Gt<IoC^mL=$^YdN<i4_|P!GPxz<zG_wixWMF2lcr
z*w`tTVu#Z7jLB;v?inRbs!KjARcE;ddJ_3BD?`lXpKAL5KqT9#zZ?p`%Ls)sGn_h!
z&N+)uB6{r48jxQtV(wSSOCV;}xi<Yf5{rfN58U_aQXhMkVVnF8Avsg__Jrj~tn>~t
zca{<h^+Bd>`OVn+Te8K>Ci{*1t_>cYJ4}CMfXQ4x3F2YSV^ofmM(>u}2T-b#Wt8k&
zqPiRdcJd~VA4}r)F#60RMcYtA$g1R)JtRh8>U(_ufEg=Aj|5ViJtV^?aB~C3AMq+C
zbb2&@NM;Qpi*GwtWbofD@wXP0j34$AB8#f8Jc!lquu6{fHQM=*T#QGV+OHVJQ-7wX
znBiLCu+X?V%Z%~L|BsIzBRFSFeEtz4v7KxydGNyfd9+&LQXGxjvP7nV*p(`y#kNMw
z(;XS{V;M7SM~JkGrb&?Vn&*mU4COMTRe$~L<T$Uvjrm2*M@@zYJdo4xpD2n&bZ0l&
znF0Hz_4wQ|3VpUE^DOq9h&y)}QOJ6;MoWxBq4}41d#1HS!R?iJE+t$&xy%YT(bMaJ
zYSxjy7-QnS$99mlDf!7c)uD@BxWf*P9>4TWDXGQL4ia{IDB=g|dd1`Xeax!u%tv>Q
zNRVwAqg0(hx+J&FTkogU7UW%Z7G`It(GieQ0y{(n%F4|<DHVq89j(NKkd?b7(rL)&
zH={}-(F6>f8GEbhCk#HxxS%{SG-TIiB$gM2QPN=}GZ}?!>~iq-?J?@~QLo`~YKH%y
zwan-Y^mN$hkvyL!^L3XF_?^#{%5<+FdPV)lvKio-BLUMj!7^SG%w+PPS;<>~xA+`K
zLg-|~HLS|VJ>8(!VH%(8nEM}{My!bgY%X*I2HCOP#-y48-JFNgg!|}Gf1|^~zVow$
zVA-RO$pauG->s~#KUZ&g#u|fD$~0iyiA|i6Zgb7WSpYxDzTD^`tIO8Y{lh38rw-!(
zjIYc{j?TZ8Gx3XWC$lHs728S{)&2=E*D&d0&NQ)%9f&G(AF_=G&-vA@vP$I>AjcfZ
zMD>f?0`AO)UePohDIp|J$P>eRU{4&&Fl0%xy+~{3Hjn;MX6tSb=HH}y{VamX8;LB|
zY1lzN)@hdSdxD6n74N5Khkn(UiIVURywG&$A&V8i`Ry`sp5@yKA}EQqZ*3lSFZ;L_
zJPrGyu|va@gu4DJF;zQvAY5Lk4i<u51GXf4kUtg3{&=mHm-@K}R@udH%95j|psgbu
z7q&CWH7h?%b}F{*_7lr|3q~z2Y2g;;f_|SaV(m$8pfhh9i#1`<GB8U|kuBr0=yA+V
z(U-fA8TI{VUtbivH5BAh$&6a_NaRie|6ISYdmhl=`~&n{=rrgG;Fd@pD2-x^ydq1!
z*~@9IG~1)`sRHjKI=e-7s-0t^J9+QGPX*Aiox!i1B%t;Ha;f!^(NAUGiS(S;>pwK^
zF)-Gf^k4!{O7Fy3ZPUTNQU&#Da427ZjX75e!`RlTs_92?>tHiQ>_8!!&Li41-Hi!j
zWK5Ee%ufyE;9N1)-6x?q+LGJ?p4(O7{#p7s_>FR+u4Ui8Hp3UQtZGfjqDmJB95&5w
z@Z>#AtXX^hvrD|6YeN~RFNf}8S|2>=$SI|I2sP|ueqHj@LN76<Z>0eY(D%#5jZpIH
zK|c4tf|14|&s-gk`YuN07(6EpNB0tg=&5oOlVN&1;{Al}2a#x`@efRM;!*>Nq#__7
zjsi4J>mu2>zwcl`hJz>6@pTO|2hlt~|D);mRlPo;%8}LHuZSaC@ZVDn^BC+|a9fYm
zffiM&c$7eoBSv>$f(MXJn|aN8)gHwBx<GQZm-XGs!`wNR3G>uUIv$7nES_d630j?^
zqS@I#geS1>A?G>>m<>IBn=#~X8Mm<{L6oT3hG7djCr-{q*b>n=o^kLWG5H4h{mDvR
zgw)>WJeuZZJa<EGVns6t$>Y6;h6iL2xvwc*0<DO<TyDwzxyik7r$jh^dpoT{OzE)@
zbxaC!ts0>PjHfnx%x-2ef8d?mG$kf^Q5nnKrB=3K(A>+8vrfxo2K%|YMbW!Mm38%R
zjG*Uar0$eQBDt~!CS1<nh?$-l?a%O7)c|{+uSxz(CJL)|!4$Q$Tz$)W7)T@A%CP1R
z0q$G-ebdH1ho9Eeb<{LuC2WY9;w$(k$WZ4|%3d*4Kbc($P(6~H{2M6}{#6*XnOE|v
zeT0EsE<~sK<e6}s3;oSS&G#(oU21-|ibwM>;($JcYu4%fuXD$MtvO*14&F2i$(3vZ
zKsOTr_55;qJPYln#F%v)Wt9*^Hz0fCkQ3w(pZ*<DNe5j&jt%hb%gkd~53MHXy{$Fy
z0!OQj5X7l_&X9{J>j|KC;{%M$)HHfql!BEm8r^r5551?G9y5DwRm5?FzuW!t0Y8zt
zQ4#6wZCW}#$lb=Y(wJ;F_329+1UZ7|W^m58$rt-IO-ee3?y^;>u^*np#%sH;pS1!I
zd)OnQC{Ij_k?{?Dk%7Gea`6$Ld{z~HV^MSqu4^(njx;NEyc!A$S)zQaK{TRsqp2*7
z`7IJ%F(YO~VwXG^3mwl&7SjzvBqsZu+zwhvSHqI_=P*V@Tj>x32H81m@mNfi%2%xR
zgKje4QsB-Bqg;P*&6}Zs6q=26s$O!dyXpvEC^9$Cjc@<#Tt2Ju0qE|}%){Im=O!R*
zjUyKX6Q*Aw`hU$n8tC)BR|K2#E_g)^F*sL+sHOipa6zH_cWV)MHE|`TI4C|xeXh9u
zmP>@s+2A?+@3lOzDdzzIA7JY2wydT@j?i3}`wx{2T!ezaGY@4uI_YN#IMy$e{D+h_
zqt;?2TB*+;Dxhq+wlV5SW}!c3WKYlX3EbXgMJp`DW(Me8Wu8UGE0cR66@FYgo8ytL
zNUT>!MBUC%r|)Hp@8Qo*X#}>^uaG+Enz3_V%+6qnZ?N*pMT~Qn0J5S08zC#)4~v3J
zLH?&cGqNItG7)~?)x^*}t?3c7Rs!FstCaC?fE4~BGVViEuwRUVZA+`vb+6}s8)-C|
z4HGcmaZElQd^IM}R;{ubhAYu~<%+mdZtdmCSmq4ueSgp=Zu0od<@o|OBu3vZrDwa*
zEB?@$3Fpj^BCw6->Kg8EY!-`tkxj|Wt(JLtx0R^&CPW%K5GQuiwe(`mZk}tFZdIB!
zOY|rXF0)TAtU_*)$smM+$S%N>2T)jtw<Gg|ZI{Da=Q@{O4(>}yx7U~*lGBcc%Yd_w
zuD8-xTCR?M1L+L=uCD5jb{lJ@(*&OFP<N!*#vB?_rFc7pNKCWA8bs*e!b*Q?*v?${
zqcE@UG_aW`7WzBYsJNCXxo@T&4GGJY+-(Aeg^Ntfh|q?SEKmLa+K1beDFKQHF(PVk
z8BoR5e)Tt6L;*M*>QP4byK{l0%P_ruiwV=Xq<J+Obzn%<+)(&5ERXJcgUIZJU*zA(
zx3z*$SNp1xnoUJ*{dC%0pwL?Rt>wSXH0+@q8li&HlOe3MF5;0~j>IVvA1P6b5`W>M
zl1u}|JZn2LjIrViJZsbG6Y8Pc#&t-gL>xK0Z`d>>tY891*v5~|XWa{JstcNT%eWtv
z07qRl{ZHcAB^`h0h@~qCPg8TaF9A+FC!=QPZ_anO5cwt>=^^~nfaOL<sv$#Hu@YZb
z<r3eUX#jdv0Y*fcO?TR0iQG#2T()JYSkmfjgI@z{1_EC(XJTavDSX(7!uqBr+V{uG
zPa)EoOejQpnyv6x^=67TN@P|CdTbpD1)j^gdcTuCBl_7Q;ykUp?CMIriRk=#@NnYk
zi?Sik>zsvTyh|UMFvnW%#zaUrTkjHir>n*7gUc%4F4cgh=2VMa&6&^E7TDGsq#SFu
z3~%h`k-Fv;>VyRZSMzMWL~u-vv#6%09P@@~kyEjn@S4;#i>a$Uc8Wypd7?km%)}K&
zG4k=|vR|jQDsvlCnn}6*$)FxC%<f&L4owVi)Aa15`Z2W$(a~Sx6~_jpt8v0Xn4b?3
zDt3vfiL)pJ@+B@&^HKTNd~X~}>-u10-pojqwhRtY-VYY()4tvuq%o>|b|Rp?t?IhG
zdDO_=ys7`wlt*Zs$n9%7Pt9wjq3ap@`S$m_%^5j^eVM%#^7I*KZ$>3iO*go&*a|F*
zdRPKd8Ne)6x&nONGzrYe6$GzY)A!P(K|M1F>WP$bvdj>}-Op%N$?W(cO3eJN92n?Y
zhUxTb*_?5^C<oHJaxV)`1Imk|d7vfxu%}DCuC7yCX|6KUZ0H8+R4#uG2(;FU$5i!8
z(=SoYF@Vn;ptzXIpohn54}qO~6-0n&XAt3h<Y*w7r1AEnvP>_#lyy*P1N1jWer*4w
zGxlg{CS(SM3;}+NE{MdKo1a$5Z2sJ|OH#|`6)EBtk2H3Do;kp}6$Jf_`D;0ha@qH$
zCn&J%*+sVFiInWWf2!flq-R69|4xw2b3FLdE1DG<L|H({F3nQOkfmmAI&?0#ey4g|
zWdI=OQBWJEoM_Y03OuDnBLfUfpZ1N;%}8G4<ArR_sQcM8Jy-g8V`bN8X1AD3cKZR7
zh|BbC&NINeVUHdnI0`pDv30PH%&fv$ujd|Nud;(oMl)yoAH~u6{c`lh?*HZP{b(%7
z$NMcTs}`;28sK1`7glEO&t_szQ)GwhM-R=oPRYRWGl?oWfk{)`U?v%iW7Le|OV`6P
zqgw7m1!vg5Bk$e9)hapc2!K=6bGaG@hW~k)EvND<*WOawl}NhJytv>G<Xs^}*WvX8
zt_kc%KeRHn5y27i<_G?lElb!Yaok@c$8(cO>cY0?;TixqyHj=>_wUVK>9+=+__<l!
zvfA*g1@Mfm<f^MmlR~xQr31R)`YEoa6r_XM=JpwH!0bFL)BQxlrQiB8(BH3=YW2`+
zfzOwz@bbFBBjbK<+rtF?mZ58i;K_*NC}a_Hx+=BRpxg#TmgX^*Ro`#2E{3Mr`+nW9
zerBsWGW9}irH6gOIU2K0v~Y*W3Uz>U?CmMZDH|cY!;cr<X}j%Wy5MZ>Z=oH(SqXB>
zaskE(HT?v+qtNU`J)iB`yv={wPe&%}VuV)y`qocY(nCq#^apDglxR-^%Skl_2g90(
zoQ#wRfAPM<N;{bnMqOl+qLP!V3%BEc8$o$XkCdLKb$-h49k9v0w|)|)qZ8VxXB}6v
z*{=QaMZJ9Tw_-DE>k5OHoiA^YgD!&DVRMu;ptiKi-=&ih3&$GW<fxIt*MV>A@|ajm
zS@3doWQFqRraQTd?TML4VMDsss(Nd$KEy3>jIxN0`t|z0R1~6iJet0!dRw|iXOEo)
z_{d5Hl|7a-Cm6Qdjr=exJc`*>>gCV%-}@CSI`oZtVVz8<r<uish4XvNx5b*ivh@$Q
zH~DQaIUy)r;ST_PY_07SHT)dZrnnm}q{M@i6LiUQ%{2Q<&Z1%8@iDIKEE<Avrz)5(
zV-3a2_;P=K)|PblLz+(1>NL6mT=SV=pEA}{aZ=O#yMtIimZC?L9t`@ViQH51h!{b8
z|F<vdcQGTYEGU{oI!KW;kLFBQamsV9zlZ!J#?h0@8@ep^zAGn-rM>lPCFyp)DhBU|
z{@moe#kA3z9bSY(GHVsD!0WGXesD7?GVgpwr^bYc<V=0~kJ@Pbxr8;HV2_GEMIQ3g
zVjS}tE6bMdh<mx~!l<gr#n{-h9uUBuGR(~mw*s{I=edlmTe-SZqDGsN0;5I4e{<@O
z;usIFe-kTy32Ig}I({X#t?k;`uPszmmW#S=acQGh5Rey%ra_Mxfe=a9;DJ#d-%c4n
z3z`|$H69lmf@d-UtzaXi@6m1Oy1rKMIv@`DVr{yJ^sp<SUeIBM)Q4`dBI`LA(b6bo
zepir_h3OGx;MI}%klmnsZk*yHhKy-sIq_)PUD%nej|hmFQDRT9V4kHEut_e?z2JVw
zgY7`T-AEWfZl1KrplNX)Z}e&jTpe)=GMSe0?^JJFIGnm6>?CNSk4pr^jfZDY$N3A$
zISf`i?bSMzSE+(m03jS1Gdb@yz^NB@5_WVTiMKaJa9H?W1#ewj*Z%HY_=He69f2>_
zdgem)u2m20m>xxu2$GQck_W&E7CgMoFq?Kwu#2}fmBz6noXh1t`6^1H4(1%ZT#2Z%
zOIq_Dx-P$jAj$RcWBjq;M|8|?z{hrVa+q;0&ko{6?7%LGd?``H*xdIl!#Wwd%hUAK
zp&4mEX;T!_#!bZ-$hdtQTt3vnTxu(=xkPiZXc#ShC(t(Qjwg|6-_uAYgE?rBp$Vw^
z_{~8e-1z1{v*}F%*NoP*@jbGi<o1;jp3~GP79M*p70*&NuO6Gn{FS=ViZwopp<((d
zrE~g|QLHuCf7W><d`-tL4>(hX*OV6nG)UKw{i)+;Y5S%6{3G)W`?L*(iBMHYj~jvm
zyQ++F4SfVPi!Ow||LbqgRkR|RHwK=u*&20}x$Pf41m9R(M@on=IlNi!uhGZU229uL
zApV0yCe=NfttHw9mR0I7B!mqka-Zouj}PX@O_kNhZLNUOCUr3Hi}52v9SPaq_tSQ~
zc#a$UJ{a21qcp5T`Y!T_QWqBL+BVR9`m7)#1Pgj@>oYUu&8BNl@<L1@;_fU`Kt1I1
zeF!UxoXm5??8(NNpi9h;4$h&M9$B}GMg3}s)MVNkM0nKc79IR}F!)_M#5zVKVpxmI
z;DBWHK0`qI$!7!IG_WAu+@@uUS}6C<$Fr2bbi~T-o#ErjG>LQ?zF?sUEs;OY*>ov>
zb8&b=&aHGcncAq<ktLJgjRobNn}*T^&wh{Ra#;W*BO+i^K7?JBU!u6=6S~?Gbf&BQ
zZM$&4o(RwGyGHu$v{0d{f4>h`SiU0IGL~|I^}6iQ=__+a_8vjTEx1_B5PNcseXQ5c
zX)FUr%OG>LC7f-YOue`X^77y;SIZB4FPvX<X@KqHMcy(1UPUVS8PJ1YuayJ*Z;H6h
z9_JD?D95gaT9I_}iZhP*Td%sMCjdkR<mL`&{{D1aUE_Ph?29O3U>jQ|Ts%@uO%M%l
z!IA#^(Y?uw^SoDHmbri(Q#;}JE+dFt)h*a8#4=Jg=8d!sC?HCT9+`@l<_x_O9cJ^-
z=9(3u!|&9AuapYNdP(Y4TdGSLjOadZJvBP~yXJH<kqqZ2%RfAc_$d_WD=rc$#v?_#
zE)pYvlEr!3=IzzdRKx#oAK1Kqnh??DJj~^};S<39)4R)-7pMOh9kBq&_pBVUryS2)
zK#R>_S*c`s%qN=!d`o@dEPjt^V@S$#tmF|J%k6fL3MDNF0qonzvdx%)(gr~>uPYlM
zVjwqQmfZ)KH}&EA?(HA@W{>T4X=02+=tZDHlPpE(+E<dvY0XCHqs=XbbM9|>w@TwN
z68|w`5qL-FK;D|T(*r|P8LN6bFqK-F+GMrT`lb~n)U53+bIuxMb={n37*S#@e$C^5
zftX5D@&du&xErh)DM|J0yeC^SXK~R_h0Mrj-UZ>K@a;WH%VM0m|7b7HL)!5qcq{s}
zHV4JUcV{>P+n8C;E_{E>hr}9zXB#}ta`47zJfm6~tn^Ke{xeM+KSZ$|E>-HF5u20Q
ziN|JV9d|j0l>^oZG%ZseE}YSO{h~{!#JGgln(GOW^zrNWG00;hTs7e3$&3<M>L+g*
zA3>^XU$jy#o>U9J<1%NvYhclHXv)xpCEV&dvmn09hT<BYsQESLX|oNxtIOWLkfD_u
z!Z7~QsJtHDOTTiqX<NFE{nBsNW{Wu5<U*x0$#`yY)OrirxUkV_n=b3Hr|Zap?%tuo
z55T20pX}4xfANl~rv@&lVz2?zRbEz1f27bwTU**K;M-C;^sh{97Ot!+8=8EgHzeWO
zS3&WvUv1P<4w1aHq{+$4Y`=Z~S73}tS{yjX>pe<5m+v~hLtYdnSEoR=M&L--uo>3L
z`-93#NXU7Hi{dCrfjy&IJqP%h#64jsacxNV_tsv4>r_9#2#Yo~89?9H$G|5dw1c8g
zIQ2?jV)Q*RWBh)aWiLm?>+gi&r%Qp9R_0HLh)a#%;*fCb_kQ=siWH{GL|>ZP(RC-J
zuTRRxvNz?7vU`k02`aReDT#@-Je>J{`=LTvewX@M9StF}h$hWk5sbjwPvsgTZx*3u
z8@I^@cq<4(WKCp`6sOFd-ZsBM=ABcu1$q$69<7##F)JJZcq2w%`W?6)8e1%j!eFlj
z-V-KWe-4^fK*m6dOh$JThG=1AFQv}a9blpJe0Nvy?|jDA!Ye@u$-^SO@teFtPHg8x
zJRh$@tx);TBy-HBYI(PqjGMvdd$wP==eNT><RBrNFXuQ^jJ!$6Qewk>*!y|7L8xd)
z?=_??D0WHZePJ>s9IJkerpns4;Ck(EAC>3x<lGcUZ!Zs>;DO#usOGi;0$CoX;Hylv
zpyfDqWm#2Nz3p*CniYTWhPO(BE25PtVRzuHD&_2KNKi_IXZBBEWc<HTQA@B1P=i`T
z!6)ahwLmK(Rj_V^Ti|8AS<iTr6xCL7+z;a`u}bRK!P0<FLOB-ny&=i=mYjH^Gy24n
zVo2=&!`gdCH5skh!yvr{5u`{ciqbm>p(BWh2#84U1O%i;kluR@AiXycrAsfN1_;ut
z^b#V~0HJsI#XEE7-kCLPee=i1`GaI7S;>3KbDsC?{p>A;91{hOAL*ta$*p~zQsnR^
zf9|=JLy+WTDco>#T)VG&+?l|$;0DBycI+$}bwG9yihjM1PrqApVA^7D-IRXv%XU49
zx7xRWu-+NjdM`dRg_G6$n5@^Oo|?H}$?eIgbN_Aj_|k@i;XcMljj~Y0*ViS`nDMav
zlx5I`ws2kYz@b73Z(g|5eO!ROWg6(><tPgt!C!j>_RXExtCjil!K8DZceT~7K5vJe
z@*vDtV)qQS?K^XAQfC^AQWEg`!7kVSfP{7U&IS3*<HK(3vEJoRK)P#v5K||S+aRdQ
zuD6pb@~MNLZt92?3_V`NEV0HoD&fEMX}?;wJ;(>K^n4o4t?!y<z4K^xY%h@C(p}Fh
zNO%2(G`P^qPqlP0fvXe$SnI_8utl*Y2Rv=SNl+=AcPfD3n(wfls4?dE`98+)5Q~{!
zr`{bsvMMr1xNUmXe)ccUrS);h)5(~7f}s*tc+H7ZR6oRBZGY!UH~ULM`7ed;RW$*1
z2ShPK=7nE?i`RzFa}({~_C2<0N&d(MXZ4JnfD{YAiX|L=owW>#1HReX{1VfKk|BHO
z@o@yiE^LfH<X>jo;`V6LxB2o&j@mP%{d;Fn0lR%7^+tzKBKGVd&+A3QW(dJsliJrk
zD^XMxn_8LgMs0=7q+6v+DPzQC^OC7*cph8eH9*WZF;Ne#j4;nVMI+G#c<O}RXFu^G
zX@o#fXnrSvtk7y7o`5j4WhI<T;BFl2{Vod8!`W)zAL#xDAR5{i)|4DG{xn%<M<9KA
zSD%&ccT^e9i~B5N!R{tFu9hLlAD>1dR|E|L3gAJ1Mo+p#y)eoT@V2a6eLhNeMK?Tp
z`jn&r72-h<miYWheO#a7(X-`U*A1zuT^eP5(>_vtqDbPe8p$)VePl*wSAEJdACC@n
zVLsJQvW2u#kZr$<`1Pko`6(j|7;cdYdBXEVH)#Qnz^?Kv-gzxZ-%T<<N!1gWUhH0f
z!hL}d@kwe4Vn1C_AG`I^Oj^LtbhrHpj|kEXV!VPB%kV<A_c2X9I<5<ji?~BbjSxzv
zd<jt4bPl_x49i<u8IR9O0gL3YFQ%?U<s{XcbZuv-JeK8x7Fu+hN5@&FSv%%jfMy3{
zmz#5ZH>1x2`a5z7&J{;K_tdhzk9-jY+~k>kB2e%d+KxNSH!JqDBzIpm(^deyJ8POV
z;(l|O3=e2<jwe9zbl{E>b1)O=I#X7n`C+aK{xR9@o5Txa9F41a1R8Os`s!c$>;hm_
z+o*HkKeYhxexU68n(O=O7@zlo&Y*tVM3S5L=a6g8V@e1SkY<)Tv^j$7x*>Y?ELRef
z_^g(!zheY~B3Gm3JbWIyj?t3oq&fIQA61$ygz&}93}E-l<h+zFAxIr%UMk9Nix(x+
zg^4YXGgVg)47bGadjyjG0wPkd&aSA`<;&*XSOUY(VA=7}-<#RSR#N3QOxx+!U?%6R
z<86ohbu#mq?`EudS^?4%GT;y4A^ggKqeQ$1GNu#jyk?p&e~tJD9;PnRdfu*H(Us?M
z1rRx}{HW)9yd(VM$(aQdCCwPkyv)Y3-}jTQVKZ6@t0?@0$T>c+PkpXmTkeVgI4~$8
zA-K*e*1kt(urnF|G%xlDp*n50nDggDswJYHacz@r#&e@)x^B*~MN@uxTGa1*uID*c
z+fr|ToPMjJ=@%E_p{(F-HH3DeHxgzbsK=WZYl2fuFvENjYESZ_3r|=lU#gmfrNy>^
z>!JuIfzKa!$16fiHd7v_c@RaLI=(3?=oap`JK9WngGJXz$<)u=_lIqL?wLlxW$h4I
zRz^D(h^67K>3mr{$INoaKfx2%YF8I$rvoh|Ei)Hg?#<=)%IgngLAnq29=&^C1wylH
z5qex-99zXpIN%SQYbe-1T5E)SR!hNucb!HS^#0yTjNo(jJ5(OjWJDq3)by<5-Dy#I
zg7*DsNu9Hq!ckvmBtH>)Ec>4F5hc~gpGgFSWD2do;Lq~fFOn;Dd=up#Z6=S(g76<<
zl{~&|Z1bE$od84+#UwV+e{72#Kw|O_IqThx0*r|hv-+Zlt9pWL2*WeSDEuek9Ecgt
z1kNC1Vuv(6wXQ5cRE+y03U-p(xBOt#w}=SBVEDP%cMsVwzK29PhDY>rnnIc~4p(hT
zs=wr1e3kT2oawJYyEhi{4wV^cM}da`w51?tn^o70Cp+EQrcv=tM259c5mY67zjZ7}
zyhP=vLeKQ6?@BOByi8=oq~d8HM{k^>-N%Pu%h)k1%h%4d)G>_KvmhjqNujPQjc>S^
z@({Flx_$q6jM3ya%pfnLHiquzqXIfBja5PtXPCKwavKuHSKm6(MTKQ&<QOfRS!|G*
zqUn)To)0fsp_;MojP7J0X<V!p>{$e%=h{n1hs5398G4-g!$+}L`J8bRXMXFz!p|i6
zq{IFznc1NrVRl(*bmwDguw%^T6XWWD`Zugn7b=9$>0-IAt2;LIo<oC0M(hbLKq~B2
z0VhB2WfgSWJeGQNjV2M)lx%?BS|(<XQjUqQ{K<~HMeb`|8WGG9>EmMxRj%>xeo?LQ
z?uN!wTNm<KGUYyWDQI#{mBvdfx~Wn+8n<AIWtVDJ3By<IgZ2#ETJK5Y>~KfPwr$1c
zj4F8NyDOwFm6E=qDiUY>7D3Q?z##SZKG&Tf*Jny4N8GIqqpAuDn|I&7RQQ~FIpnl^
zy^NNUIEA^J?otSt(;W=wEYAMpFP(2gST^CwJ}t0-#5!Fi1I0xzhoJ>WHpg4jS_;OD
z1|JdeTw|ECBSa$n<WI&5>6bRmvRL<Xnu*7)y;8IWddaHD_^a{;q<uq?gPMHXd3;9#
zURfw#3}3WjqsRD*UMU~*X)*~SIZ38qoQptn`@0?Cz#dqBuC!A>hY9pjCdQZvX~s)6
zx(HI~<LG6+fZizPuGCgh2N`LKpE1}SF$fR5k_c?hOIV2}`sL_Kd=V^tq3GMAw_?N7
ztc!XByBkB>8BbtI)YQSMr~wwWxXVVp`EZ>B%Am+`AvSM4+MR~{#^<i%d}2%X?3k+9
zfw$jm6a2CuypiYGttOq7t%yT7;bL^YY_hU|?J1FoFrm;d`odl@MDnNRw8Ey1hsG}@
zwtF-c_de_=KnCcowV9TL+dn)bT#Qi<IHk`Xv`s3eOmOmuh33iWcjx(n^k(G^I|huD
zfNc=DC|Q)<+Z9EB3+WmWymp3-P4(W&J1y@Ae;%>%+=jsX9y0lI%_6f|x+eCYU9g-h
zzd-T{tIki-UL-+OZ+Rl(Iu0ps@F?T9yt~q^bJkqQKi&?vSTXg{TMWMC_^2X9DP|bq
z87{$W+*Lpf);I{Zuf~aF?g@J&7`K`yw6sZi2f%sVnaF0aVovp$NlZeoAd>@Dz<V8*
z7?%jYiijx?=W40rqg{y^qXV^V($U_7klT`Z#Vv~3OWL;%R5mWyj$y@T##s28%bVv)
z_}%;XK@zVb2PfM5?p+7UJeR4v5LJ-cDClhvrj)XtF#O&%y0e#R^mxAIZdGBrD|;B_
zivgWX`}gi7fMWoZNKqN`i4fqF_0wiFPf0DU{$o)u{=nltR{1_n5dC6EW6k{y5@z<-
zNuXX4>N5UGb9*hKJz}bU5?tCCj2Q?g`tGAzk4t*1(ZFh@8I{XrnOm}OtO+wENM|#>
zEPeED|JhsCsH{Ded0j=Lz!GK|23BGr(3>c74?@aPYhkQXYr~>jDFbb3^FAfz8z{Zd
zsDh!;M@;}e@~i2V;-+<;#Ss!mzF4PTPoFhUI^d9G<cr-QpV+CGB3Tp}=zvS$rod=T
zFhPAj{rO!#at^{0AX)t%r<QB6VhT7pTVLC2WbS9nJ7!Nzj)WNs&}n7qwVk-+uae{?
zcdS1a<W8E5r#;sEO%K8Z7vhvz>}xoC1(7i*dYU&rWSo3vxFYAH1W2Ge(8PdDUn{(Y
zu?=zMv&3_-v;2N{jpL^yF0{SLLBlH@tbW1ZNz3OI#cp|52|$@3wFK$gWLXLa7*Dg;
z9%oH+mvYorPbg>B9fbJ&kSx(Q+J5ogCh<<ShBwLUYEI)MQRtnI_<nb;Rpr1Df_|HJ
zG;*cQ^vXftn5rnEcH-ABKm(f(*a_JEvmVYB?Gx<%e)mnrWaeg*>AWK#<VB29kR?%b
zx6^p-+{oi+X0RXexN`A)S;8x6GwC#tdmYbi;nq_s`aKL24GTW1Gv){;pM%bQM5B$Z
zE$-l-fLj<iZB)!dqn#ST`V2t7hkt?3&^6i{V74H#E6ABo=O<{QACd<I6G%e7t~|lb
z;8J7^n4%xQ79;0$rStvb7I8WW$8tNP*n4rn@jpmqxC=P|REY3zm3{hO02tOpQ2w^5
zWrWulSB%3>L=wkvI_#?x{lug)^D%Y2@&!3VZ+bW8nQ7sC8qp}lwo44qGWkU0>&ks~
z)@x$fZ4PLD@DfhS&6hn(qr&+W5|7S@o{Ii3+3n*h_=!}l{uNa&r{Z!6;KHDqU~#Z%
z5-xXVn0KehY0NuDIp$d}*#+L>i#Fi<O)H;HIm_r!{J8-I#dPiD9!c*&|33O^V)ARX
zb~qUjk0BD1+>>@ZZMYnVqd<q%FDwLBhh!B9OtEY;=J4tWeH9WN<C=^#QA&Nbcl2vZ
z)-PV`_cN@>%1St5Ty;}pu4U_eW?0IaL5U9n`2|}^>Gfb?(`!4ibD)6Oi!NPR{d;&*
z%6TwENveL<E3>Nmvxq;Va%~>tC^ZH@TRmX6Wv_XjNdzdg9h!PX<r~TTyw@fb^9Ep@
z+XlyB@Ah0!6m%Jz*cmXEb!=EDKz9g=(Tnr}911B{C@%2MgAtRlP(ry+2vnvQ@ezq|
zH|WdaU^;JK0JTXKPN3Eu_3@^O=ccUGlcD_idx2KF92DtxLf2pGF(IZMU3TlrmJA#O
zHxGFhp02CkIUaWhHO7ElMXyt>`|Pqb<(e}*?;>7o)lxR+(Md&0g#O@Z3GvwuZn)YM
z@ljYGRqg1xx|1SrxN;wy`eWE5G<@?ukp4_f1~fWY>6hBAM=@A@f0EK)@>l~oHO6NA
zo*sc|7&Fgtk*_9?BC^WjU`N<?+ZFM|StmHtBp2H&D=0g=;<H!qQ09m#XnKbz2Gc0{
z98p364a;sV8le6Seag1Rtks?55xd~+*m+ivOsJfnm$6uHQB{qnP#Y-DgKCMlY$>|G
zw%9dGM{$xkgLF&K)20$Imq^40qwh8Pu%^l`EdLZzV>P^8XV!Ti;Tfl>X+o|HBNh|T
zI2a5G#tZ!s2;tY0sX4CIO<jrZcgJV1^fAeB{*+WfFUv?I_i%T)Kr*@5nwDj67Us1a
zDq^U<#q^ymEIZpHB@FFNwQErIYGhdJ72PnpW!!Up&1z!KJ8qu}afTvh3KNtnnmy1z
zLrZ;5dYa$yEMjed8m&Rweild_vOh>QT!t&Enttb6o3hNN<als33K?}^(FpCu+6K)*
z&Eq;_P{EztOgbSvsv6#VLKVFBS<?v~^*$#uy-3fOuXSw5IStkKym~!oM%H`w&}TpT
z5YSxOuy_0#V##!<HwIVs0&-zP(ONlgo-3L(eK1(cD}C=KUEtP-YaSS&l#4$51L~cN
zm$9=R%9aq+U?PDF3SR#qPcTa~jZWY8A#|5e@2n5Xm&6~?`ovAKO9BNQlZ3ekC6)CP
zn^eINuG}mh$y{LWTWwiv^1w${S>Geb%?5<D=ID-*d!T_T7RSLjFa_G&aqt6tIn|8(
zv1x96JlR#DGUsu^JFdTi2ELZ^j-+92OLw#-IwJ{NOL_Moef<ocUB+Y_ThNhia=jok
zRP~pQHL@iL<}=A?w|_{Qu8>}RPQB-(Av0NtqAM}feeDOgN_JV-$I1Bn!q8{cJ(W|S
zWE4X~GU&GYfW&BF8Kzad`K{QvAUXLRRL^<gky}cKTHm-*M*rtx_qgZ&78wzT0x9Lh
zt|^jHbLu83?5)8fnU$hB1yWFi)xf!kfMN75@9>#SFAM4|FdfyVcij4j4Ti{X_30#4
z-;?e!T>G9qE4eECxe%lVNv2-nWjWZ!WoLX&v=v1rZd(v2T4XqAez?MwSe_|r2A+)@
zc#oX-rjyNCv7`&1vV_h~kxnAyg660u<RxeIuc_kp>^=KEaLOY|c`E6n4IJFhUgd1Z
z>}$W0GVb_*UaOM})0Xxlm9yOzSzQ^dFMTh?Y4$Mg1+pwu<ETc<H-*Wm`J~y-w;?6l
zT;X*t2lnz79wYWv)>zgK&&Ev*lUTGj#^~ut*4(Blc=&T2R9`#Aq&Au*1DJbIZY1sF
z&I=%u-iik;Y9N+LHU?bTd9kJBO34si4J4iMt7$C<q;ccRfiCN1?Kb#GeMud>=L22}
zqQ0S0Y}9^j&;>x8owoCmp?>w9nCaHOR?u5@yZM@?PkODh@u3612EHhoRkJh05fJ2%
zsuuGI%xAnh-(h}^yILA((iZf!sHmsmqOM2Xq#kOp_u$*CFn}e2A(ex<U5f97qmshY
zxvrl~qx_0qmyaGfD#By^#d`mpxyxK~XzcQ#v(;g!l%E3iZO=nP{>vrW6tUVxuc59#
z**9yO^z905h#c=TS`maXXm9Wbr6qd`?v$b@l9g}1!P9nDxT70%u%x230=jeL?55|=
zP+i;eNpB*i!a_Dk>gz-PO0Sv`-q+p^BVCB&?>8{fDaH-13TtxeVyclgGMy27KVFyF
z1MWg=?{lH7RcY(*6Pxw|zxTxrUw9mD_J1W2>!m90fpkt5Jd8HPc)dRaFU(5!TTVzG
zR5XLQ5yotk?3WPZpr2TZDVcJr<#G62E|XIiBMSMAT-gltwIF_~rmNT|IX5|_{v+=h
zZ`L@<x*lrZG6zrKqud4YQRepEZT`?1N_*rTun-y<M~V>b-KRe-`&74vSBp48?9&&o
zDxG^aTAbOpIls@<zpzU#B@$O|ni)fJ(kT;?K+cC@8_5s(0JHV+sj0N7TlXL#<9DP>
z&aNmpN|DM{XGe~=8vB5kaEYY5?10Ar*EBq(7fPBY;XF3?%yhq&tEj2lP!o|#bfmFb
z#@2QzaMf%9u`E*|UUi?r4J@sgs!mrq_DISg5lyTkA`73#FvVB*_dDsm%D$AyQ;AIZ
z5^1};H}ayPLsykMOqJFh>x`%e&9C7ZOIb7Y(g2=`f=2K6hV<I%=B-U^;!SbMVs7*|
zLOtH@iL9Hif{7Y+h1QXVHE!|`b^~1V^hqrF*X7^(xj;6C9*%DiGNd!wWGOBtm#6?Y
ztAQ_3wF^)EfTh>eVaD|=EqBaSQY5FzhJG8)!xys)1(II@U;ejdLrC=baE!cyJu6q3
z=ri5{m%Dt4EQIxcw7gOXtG8=;!qF3=F9{21e117oUDTkD@)${YPF+8j=?E`yt4=|a
z*=lZ}GSuDV<t=Z6ugPZnWLS6LznZ?p=pdWk7ND=}DyNXEW}?Z;-+bLkusZ}yL)JiU
z*-FNrnKMKO94UZ7e<K<rtp5zJkEreK?0?OvA5``EUc5*m_HREc&kll<`9+@tM@|ax
zQn_-UuCvDz^4Ev#E9`-W-%Q+Si~HV#yXQaoX>PLD9nHsotC(+dSV_~v$@m-dq{#Kb
zZe1ZHH)IWFzm+}Ch6wLCU!f2=XX_wO2&+?n5?7OF$9`$k#8-7P-^-yZ9d9F90sca!
zg(&w;Ei$GxNUJkb=!j_VyzI~%vv091-Z*kv47Ta~eHOoLYS&<Eb{oIgTCBaaZCcfd
z%16^g*WngDuI-c=y%K$}T~w3MxLwY8_I}DVq=B<?Nlf&fZtJIweSxpVIxvyrZYv8I
z#7j5IJ*BtNsQe6FFVZX3(wL#$RwLTG_Myi*b)9|>z53zR`T8}WPudfoOi}%|<eI9<
zxgV8I;p1GUBkMVJ7pY~CB><d)NoVjL?)l$(H=?TtfV_rZ4a6#@?*%4~`E6vohbdA@
zO!~izWzm^;Y}x4|pfkGbEEa%V{{1r%E{84k!C;FL@<~tYDt-B`uthEu6N4UvVWw>|
z+usViWKI`E$oN1k*pfF5IJV?PXNc_%Q^>Tbzl_gigi|;boAL!VNr;^P;);>Ey*e~s
zP)5NVi%{>$+A`MZ3+3*&ZMYiaTaIxXE#x{p^C&&hpUj;SJ>a<?$#4GV!mctF!&1Ze
z^h1*MmaYbY04RAvL?GAC0~IUY6+y*Ta=hMY4cV5VEbc!X9EzKFaJV0HMwdDgTMEl*
zk~bFoVdC|-pPS7l7!}^OI`9Vx^p~d2yjU%1a9qjM_NQ7ZfO*Z2zBD@im5pdm%Q4s=
zo}^BV-0$5o=33H!fcjXsS~EZVk3QBpA0DSqExn&>oB@W_M3<7R4e{C<q!k12GFS-1
zJh>?V+$K(bMit*UI!e9X5m}uVyXU;ZJ1I&vA!XwUYw4Hzycy>oB;cic1eMw)#`dy<
z9ys=Cmm;MM4YTL(1ZS)rO_(A;qMM140iJ5~QDw$D*S8Co2)H6hDUFe*Z{B_5{?f5&
zjaGSDvh`_}_ij_5X$AYYq3%wXHUA27aBmE9N^v3V7ehG`d#g^uqLH=f`GHN~gE#~U
z6n8Yz^F5w|lL@rRe@tfiO=bdm*Ne;42()v06VXP`VpV<CHxib%T%hRjKCo_8L+1Eh
znyBq(Yb#`^>G3v~Pmr#pZx1C-&stwJQu{^irc79Vj5GJrtj|>9Ws*jPHUAsB*JY_o
zkIo8U{4+;k@$dd~azCm6|6y`x4D>h1*iGQ9T0h-*5KtS;X&GU@@=^4CAOS4~tVoke
zrR;NcwjWriK_*b^yg61wq|$VLI3nl2IZ>?f)D-kh`B|^=>Fy#E(O$RE%kSX)P}+}g
z+#a%L1P=cw#)|QM_n;s3m*1RjxPs<u%-ZMiO>TLzmzCW1GCGlC?HQkg*3-Zxu8vim
zgi9xXYh2B-C!U`ctg|35K-WhTaAY`(iBjvyoL%^|avQU{?D_Ai0sfYdoK~-}C#go&
zURP0Wce^LBw@h<DYBponvZ^amr2p-u=`sDiqmxa|^*`9E=S^qyu;n~$I~g6KW0M6c
z+UXy3wkvS|mln~Q_V6Kh)he53x7@`hTX>;>g~tK05L4YnqA}ye8&HV#`C2OF>9W6n
z-OkFp`C08p6~Yjy-Un(T!os9{wV7aN2;)oTl$ohj51<d#kWgy}>t&y=4@|xGk$>&O
zl+JN-7suw*^r%5U`*x2x_V(2)c}2(3H=`nS-^kd1{YSK-v-eVbS<*11gi-Q5zBm7O
zrBL|PYzuT5Ch0mcK8P$`YCb%po6zD}O7~h77Fq(e^7Kflf{iM4eB9<4P}#;tL)^>%
zxsp^}V%*&zPpf^N>(V$_Qsg56eZ|PZpH-;Me(aoOV8&|Qi%$xEYd<b`3RM$doTSi*
zslg7V+=4Tl!u;ZB%e?2lE;;?SU3p!~MjVsWuQ-8(IAjE#3#OAO+}M3_DvpzaQEYHo
z=zxSi5^HA50)NF*PJ<nrN@wf`8*v-gGwi~$-RC5-Tn|)`Rd4_qmWuBH+JhZlL6vTA
zt`}{wC-~V+h)G*5c-~EL*Su3ZQyaD_E1a#(_xa4nQod5c&GLq&ZA9^33)avcT0`DJ
z&h*6bax~i6hcuW8r3bzDvi+z?plK}nVPvODOCT;YDg}oL*yMFMB)*sy8t&A3tn<|L
zg=iIhPwo9QP{baPF^CeR5Y69g6Yxu0N53bI8Zt;0oR<Ay8(jRm5d|fyy~RdbUwUE7
zmsme4|361Y{@2HCF2Q&~`9qLuZ2fE>q=bJvJcSIMHaj5D4#cwTZ$<__LpyKbEH{Ga
zhGjo<Y(+D%hV=gPv-#zl^_JK4$UkK@(7n6m^>ZH_ZLmda{=}IM_32V(T_Xvgv!fv=
zzlpf4R!kQezud|qHp;d-jDGbqJKYYURww@J4%U_TEV%2huT0f;N$JH8QQ-3|g5TlB
zT)XSAi|tY*x@H7GDSMW8I(tu*mkKI<@Uob~Z)K3xU23OM3v?5Q2tS@Sl?xy`@07^f
z;i-gGr{?D%;o;#m!;%A@p$l!;(b|>wK`}04nE2<^mTyrkx6bEZvX3~FF>P0C0#X#U
zr`x5KZcA($eH17{#Jgd#E47FJmI<uX^x*QgzXv_6;cpnR@<~QUP=!)m_48DoC7uP~
z;w^OihTg$N#b*cntz&FEh<n(yBQ_s4R;cVP8}=Jpvxn#YN?l?t34^un;{R)3M~QU`
z%Vpd`KQ_bXHY2xqxjl2Q^m_T`q+zk+=I0MN5=Mtw&BlX%9(a!TE(1Us(}BVIK+aMq
zk+)!VH>bw};O=1ia2)vFXH@~nM#O!`x$Z@Z7_ThKJB0aju(cG!X7i{Y=h#hoeouR)
zkGEz}*l&SUkvfaMQSM?a4(0&D2VMrfkD32lPM|I6+zoo)BS)@((<!x57roMvEI0bh
zYz>Hr<Jdv^5kc|P$q~&fB!rT~lPjIU@SDrMy<?}g^L6v$Y`2eW^v!uziE3gbSg+I)
zDI6@HC9ZX(qtq>!Woe~x6hVJPsv&!B*@zu@JOJVFrq`eHjb3p807|+u^FGr7*1qL^
z5#YuZz(^0|z5^Y_Rz?3gs5<^-*fQ}Yn<%-{43Yn4KG%C!!u>5o+WCRAG*tG&M(&X^
zgIm@L<p{Q_eQ91j6M2l?`RH}@%n9~t-~c&Z-nz-RyRT6lE1VB=K~7k&I(yJYmP;1C
zB|cHe$wS>f@#|*nHhHdxLo5l(AX=7Ks>7BxHsT#%XWsw>bA_dQuh!<c&DjpKZCmO_
z=lPwa9&!BJR4`jwm!Cgg|5{szlwwuOuf}^Rv?>uDN7cJ6RXRlfjnT0&zfwUC8^m#o
z@8D74B?sVoOsI*6BA9&#naGx!Y=-)!aKU%kk_vgyX#qTT0Uu#6T1d-|z-8v-j@Uvb
z#{~=G)#qreCIB??@&QPQKD6vx4YklLxxJepNLXo+ehFO?Q_J?YY{jJ^m=$G6h{T?M
z?H+xM3tl9OakP2=Dbx5axuy;HX?s!8lZ8*y#%<hy3cmW8M{k(HG~gUIwWLt%GQW+`
z-Sw;A1Re@f9v^yuUVc7<alQ><m&|C<ADKADAWI+-+!>+LLDJ!TvnJrbB}KntMi)tT
z0wPD-tdQRe$u;Jj>+q!oa(D!HzHX)8<;d|UIHhYLjTUHYOvTcXtl(HRqYdX;Mg&0l
zG2keF{NiW_1tl0}Y$1SD;o019d#?PHb7ORNg-8;kby&ehZ<V2&Y(Oo`yBl|v@s&Tb
zedy~2$H}a>n44a2>$XZ``uJMFAEX5Pgq}%h_f=&548$V}`}<NL6Skwg1+an$%Mb)P
zQHcB^I_9A=Xc?`C<b+e-adlpZW|Fb#Ng&WUmU?rM2#Wu&CaB?(m+tq{@Q)q8Wns)C
zHz~o3Mtk2eCA#w{ffu||^wGT45qs~<L`>WlKlwc_srwYhupu+l$HKtu(+7p8{ZQ#h
zdtw%kq9OmgH7t8>Fu5ySJ-NWk0^ZSqf2TwQ;Wz?ubk>FBg)Z_E@7uTegqlbGC_PXN
z2T%Zic6M~*dS2}#t~jXx_n$dTQKP8Ct?Uf55%O!_?tl6*(N!X+9N+QtgS2wDbC}+<
zjc@nDq@capy2fh=CqZGZf$0hxJcoSdm=Bx1td39F+SMYisN8U9^6)mo3oSlo*fh+M
zI0og=_oJa#zp?&bl8}=XveS206;V4~-2h?_(AOiZbc0Nm^D4=1o;+LFt?%jb1_;#O
z%}+iYZqp&uncm@H9h$4blBf$1WxpUzYJ~{>olR9*ig}e*7;lAP7LRCL4bJ3##4l%8
z*M=Y_DoYv^^TkzT4wLc(fbMA2Z>ndP`1Cc%Bvkr`VsOn1ss2w%Q+Vn*cYIayZfoIi
zq31!E{-2iOgnq0%=&luc<m<Mx)oA3ns=y~}NeR~B=6_ppD>a>gQND=6dk!8g^OCIZ
zXVRy}>+g*dL%f)Qf&jyZ;$a+>k+{J$;Arq;^xG=xkJ!+9Nh&vGfw|-leHxlg6=O&)
zl!Lw3@b@7KC{)ESw+W_%(bt0>(T62z%I<YwG;rZm-~?^gJD&9G^sZ&EYE%`?=mOY;
z=+}VxV@?4poIkv~K8WTHT3H7hQvr$VG&N$}hId><!?oRZTQG6nxM;wNhx2lf=v?2n
zSTE=W=+<%poc3|B1cnkGXJUn_^aQ$%h3VfZ5UM$!vbPC&A9RMdM>9<gx}$N%jWRix
z{_Y+o5ly=O=$+@Y`EVA{LY#Rg<y>v=ww;G8W-k4aI@49~bZz*JR_of;Hz=ubhb<8f
z{jtox0j_@f7LIe*kEyj6m_E+obW57>ILsL;E*!h+8(z`XQq4@wekd)!B4&}21qt-2
zTqy~!eA5lA=i<y+BN%3}?MLJ@+PLd|)wWDxVrk2GA(_wSpqssw{$Fb(9wbQTl4l>3
zmTh^G_>>JXnYq(D>+oKrTToB%tM^LWyAK3UpBiQqUw&|$t=jrk<CD;2;N7G5XmQYu
zXS(0>;eaFI&nW|$p!~4>{t_ud350<lrb#((`Mp`FZWv9N3>~~lJdipde^5^6JBct_
zuu*V$M!TtlUmDzK(cvN*Dle=2K~JJLL9=622Zy89{5Okz>g@MK|1zCmMm#~WAT9(1
z2<>q$4!)!?@TF6iDSn-^dQ>BvLor#Z?V;0g{+70daW=0|Wt2CVE<8d$BSuT7Cz%Bm
zUl^}Oy;r@pF9q?vCcAN=i6)!Emw1E;loC_rZ3-zq3@_9W4l^_`xay;AF%Vz&so9HI
zF;ftS()UV9QFbWu_!6~)K8UsdB)OU%rVy;nF7*0H%td`_ogCrzXf?BAg-M-D{hpLM
za3229vN-CJeKK58Ay55xr2h(1oea_62pswG)G`uf;CY>YvFeu~G6*HaUHUx2-;b?W
zWpp<RBWGA`aSMMgOnSQ#XUS+?_Dy{%J0}wFU+s{PXwb+e<H@pb7fL)lGH&rMyNtVk
z+R0eUd;QaI&O_whF0uw3P>Mto&quF)R(}a1$8N*oa$CiH4!#;+Jq_>XO^-^nuo~3M
z5MECf+)r<z*zFa(86GHuIS-{n&8AY=UKl;!;3PNw^vG<*JRS^#jNpJ{e1_K#>mIL2
ztap=&xDz&(c7z_-RW$VqTv}9B2Y2ps@}n(8501aJW5t8oS{?=KmnY%&so&YygLq*L
z+|+@zi%vLzLVhvynJ}HJ^E;xAU~e+W-Q6cd0$X(Lka!Sxa7~Y=e;avE(QoYC%Zxkz
zOhV@tvW;k`r_&3&rWZ%o(Q>`Kr;jW}$>cP8tS|`I$*2k1813X(<fQu4)2rEoT>{t|
z=sxrw;11pI+17KcjuTRP<&X9(4q&su*ymk<fX&t};QE=QkHo3dhL>q4ON=8>2<mcN
z&xF@z5UMDD3#V?2@0AK`P%x27LTEX>1%^K{B&5HF@2^-$A+neJ+j$GoF}C5hUVmKO
zr4y)ax+D~#kQZB~ZZ&5a^ibRu7o|X--$aN|ZzbRkoMiPB>|wn@?y2}|#;S8x@dYF-
zUUOHmlgtks#%8#n{P5dt)V8|kzee@%qdL%N&8G$W`KmP9udUavX_D((g3G<8Br<eB
zo0R11lMW&$>_Eb}2I=J<x&D#jbp5j^_u3yTv-;C+LNRx(ffP@tCc1O|OVU@hPYBCx
zyN0m0dx80<U8<qBY{jWeNd`~FIhnweC}^^iY&*8{*>5^-$a#<ej}(>Ua)gd?&cDPK
z*d0$nfRwvTMrJLH^K%>)LbcAE&Ss57LDU^jYz`ns#OO3<t_LW3ML>9X3x4xJ0J6L>
z13}<S{>&MZ%m&rIJ}O3pvRqa9gyr7%Vt~=R0gF1#CAPjK!uYc|80NcFylur(Ye<av
zd(+lu?mMLH_a!`D0sT^vr91k`=9g;xa>GK}!IC3Hia$ge_4z_D%+?h6oJb>R^LKh^
zs6Y_>+j<+3DIuzH277b1L$@*8vwHIrFmJfL;8r?;n9c!x{L=hRPU<24MHE;K6b>>Z
z1u=l)kFZ<235{&}L8QMV-OXkWr4Q;QwL5r)`UR-B@giQVYCxNYQ#n8u$l?}V-NKG8
zi&vL&CO_w%*<Qcv<Z%4M1y0u$&(rlj(~)-g#_s&1tm#$|^TjbznfZi?&26O78A=^2
zjg-IJJrClSq{mwz37Yn~p;tcoLak9^Vt{Cp2QYB>B$H>*6Zud<1Ms?)sT-}66jtT`
zt94k1;N8EhU?|^lTROK+eO13Ld0wR4H91BZ-p1o(4een!@BJNVeg&Tn`voaf$bFa@
z*2Z^Y$k6IFe|c#>UgI9R1<s{7bny6AG_RX&e`?uZa|>uqnUy>;whb0sR=|T(Oq<fx
zFZ$7m=UJwS72@h=(f<Gu(fgKhyEM_^nTnd({P_G@R-MHCF|n|-5J;#V@}Tw))$}vw
z_Orzyo#k0%N7ZBJ?&xNriy7?>!TajlT^$@Os7%Q<S9zW);DBa_O6RxtR?ODVJd#GV
zY^C$Dn)=H>Ds9zHnjxIdeHI|>!YMWSsyknM09Y*ZbFn09>&9J52zSB1p6wu&bqFTv
zz==lDpz^cPdJ+6k*)cl$oiF<#gl8dmI2xc=>#5U2j4AMh@=Py?fwRpSDa{pYpOBe&
zs6cuo;z$iRL&BCT-!6}Lt>n*2HmZ<r&?5gq${}xP1qC2>rOpeluL0+S1Ax&eQ=J!J
z4-Wv~0?#v<?fG&L*{pnhKJ}IK6RmC4vr0C-J`tF_iHf3|ZDKD$turO5$s4E-Qp@yv
z>{8XgWhJZ-vz*lc`vJM^i(;%cNP-_Jfnj+Hg{V~Q-pQ$|06)YXJm(GWI`bYOX-!>}
z9&BF%nD_q~35{cEe{^KvlP$P>Ar-pYx<g$fg7%2q@p#Q}tdMqv=36hbzwae5cCUBY
z-E;F-jT5%;)jMSOkdOW%oyse*yuk6t4I%RG{L}aZ5vdTjFJ<(pq1w$k*+U~K(aZC2
zCiuOPU<8&KWEq;3><x9gA1VzDiaT6wJUHg<r+!_S>-lNDNYkm@mE1qZL+8(|N~cFJ
zhz^#cKWA5Djhw-?$HJus$J3dzWUT}wJ4i=?cAQWB|9+gGrVL{BHd6<&x=l>+t2N9`
zdo@EOrl4AC4FXSRuc5!VSmwt{eyj)Z?hPZzRjuzgzssP#;BE5Rtm_NR_s+d>bNb!-
zCc~n1o{Zv$6stfu!$fiD=kf)0pY5-A#kl>UZtBNmr3-=rjI|GPGMzYVq7Hs)`7t|A
z-AB<%dqSGcl833YuHY?}8^PdbMDtc=emsijLb4iVSKmW3(H@AryTfzEc_(XAx=G6C
zAUWs;a(Eo>EvB|sT^o=onnlT9;ZhvRA{3KN*<X@n!_`#`J7bt&GlI0O{__5HM%o^m
zTq3|vUEJdl8eF_WPzs~ZX<qfymgxTm>nEYy5(2E1mgNME4Cv-e%V-%{jM8ZQH=JWr
zkO+OK<``&*OYVMSt~5*F(n2?0?#(hkySdZX->cknAqZ|l1Q7U&cQd8!x@nk?Uz#6a
ztjX~_qeFgD#*i+y30Vu|E86_J>#|xXSChCrTdlOVD!pri>EVaAJ1;yLsfx*19c%Ov
z6Y$$3Hllj1$$Z>>4lxq7oT%Q@2Xd$SmH-h2yVc&jK<d?dzU8cE!@nDJX|wbTr7K^H
z@m(@cq@)&$R`L*5bSJY|TVX#W#JgW`RRH77Im-FBq%R&c%1rd$Pfje%QhT8-Oq*Nn
z7_2K&(+_xMyPYIBv>W8Q=X8qOJiDv%=gTN~EE(>@=rijse=!f}y91`H9@udZ;2Y^q
zv=AZ^#5jvJei@WgP|k{@M_BVgQTb#uk*2wIOBy?B|JHYD)WC1{GNLeUKQ2D%!JmS~
zLD_MRi<fg&hYf=yLdsokGBd2mM;DXsCX}c>*{41Sy#x8m?Kk;`xuNhFE|g;2IUS%i
z6?o5!(t!KO1Xct~{M?yiawx&d5ppT~^Sp0EQ|!LUeq6lHR?ULS-hbb5jT7-4nBsR&
z6ob$<Gar2R0)rJBZi3;p4LLPrTO+C2BrO55JJ$_e9ECac8VCG9FMh$%EU1#gNRJL_
z`5q-@VotkUt@_z$R3=rno80y;B*tbp=rOn%To$Z5bSA8b_@MFTzklPQ7!l}lGDh%_
zI7Na<gniZ#aWpmPX+t41*tFSjpmS2kbD7XpT=`>}*mKNH?jr2h4>qQX(|8Ak%AS-T
ziwO+*%cOs>-XY6?T$q<(+Qm}D8-ubZF)cR*qYgEEna^Ch{yl^L=N<3pbAaf;d4Q$6
z{8gVurELCbe7539D6N2akDJh0Y~1cO9kJH3&#WKSVw>CXe#j`evuNkA#V|}(wbDLy
zbce3cp*Xlu_s|KWbSb?@YCae4lpcIwp0*FzsLS~Du`F^y{TnsWz5mf<Q9zro_m?NK
z@mo-1;5LG;JB+)(Pbtx6zUgcAJmc1pYYI+olIT^57N*4=z)Nu#oinw&^W0og4C^7T
zELHOKRTu2}$JCekHL|T;3g8`g#pZD#-i1b}b2Tyla5U=PC`@#1Xv@0Lc8j>}#BB8n
z#J}{rh)ZJx+JR#zV5E#xQWnV>mH+mnI5tlxn(fl$tJ&`FT${bV8l4uqWWK+sO{kNj
zM&*B=LmzzqKL87@`!A3`OB-q$|Drga$5V;4sPbYX+ImfoY7fsn-d!x9=3(M{{H4d^
z4_9ia15FO^5__92)GWORJpAIPA76|8)nIKvzwHU`o6Cm^8lM_%9gs6yRbay<zZQ=1
z);*ld>1Bvm!pX&(5&O50rbmq+0Vo;RB;#QHsAjCz72nib5aI`<b2=DLDz;WO3n-_n
zD8v*LGK`$q*a)<<iCRG{mqQD6nh%a?vT|PSZI5_u{k_mX+diATzQ^@V#BH3FCKvxW
zVGnfDxmNPP!i5fsY>+8Kcjj9QuMa&^S{g>|y^O`vj7~H;FfXZSDSlm^99>fxTcIVd
zek*gvP>$%-)8SR<z*N-6l&*$BOhe8`{TXbptU9b-4ob1U`|NJ{$`<X`7;awv&fvA)
zlUU<>evI9@5Wgr6Eb{$7o6ow$=r3F@OCqT9!vO1)XOcKGvL*Nl|1BW>$1E0%gXHa4
zdoYfI#8lq?cZ~Tzo~4mV2ojx8VKMB%P<!>q|J%291ok^Bh=||+`ySk4E)wPcO2Gb=
zipf(;1gI_ZwzMGuFGX65*bhMY>i-t4{`t)jpwa($PmOd@1RYn#|MH8mt8T?dbfChi
z5pSGV<9p<l+W)dj?h!ruk9X}Y4f(YP`^OTmY%R4SW!2bTs)GLKlKT66dqo~B9iq>J
z_8XC*3_D%E9c2pteBJN))I-#(@_iDUgBg!!uFru=8fYJUsriX#nzfi+%faj4Bkn)1
z!A6*<YQxU^j*cf<eDcc^7R6JkJzvonrc2GuU#)1AhgDeoDOR>k%29%G3T9KRXe;A&
zL@dyH^r3P8tCWJBon2LVvPFU2y$s?1?T5-T0n%9B{5baPrwpJqy0@gVrz3Gpvmr$e
zjW%0I<!nbdY@NgRm>zhY?lKt^HxOnK-@W%$J^Q`CYQAoz3DmMThM`Thi>lOi;w!<D
z_c8ju*AbfPYlAwajCYd%JKxoA6=-pBQSm=x+`s=9q=R($1lmp%XST{MM()nm6g%;+
zyjvl1-P1fFcqKBZvAH<lOLTyt@L1Kji=iVvW5}^SCq&!I<oc|pSM_uAgaroElEg`0
zd2G&CR_Y$~msS#GMLbE><1MRO-_F1Fx$gQB#}SG}Q-xwY4|n8?ur&`PrRDV>_1>o!
zb+N@sGn-T~#hYLOyO!N%fh_O!S@T0O@#9y`-7Ibak{{#C{}I4GnEih*Yz-UM$<Grn
zM$>u{d<C=w-48Ts-GA$sF4pzacG9g)b%{3=PtULaMM=HRC;tyX+LA{h=J(XR{sN@Q
zU>}RFs6_Ao(&8tZ=aklm!Q5%xu^5|lWt-Qy1?)I!pi7Jti;e>jrW#T*uj%D_e1oQR
z47qq)(Br#4ud^keN}n#Mnon*DnKw&tstUAPra86Ynjd`mX}<I67rF1n5rD-HBqn`0
zcz6?1Yn93X{G|teIs&KKUre6g^AwF?l<O?@0T0e2n;CaWD`CRpSy1h+d(i?9Dq6R4
zFkR6Cvk4`;$e$eH;kUC1e0QG>Httiyt#pl8mc0ICkG43mxafH`hfg4+v#Vyte{H>+
zS5n|B<X1JJGRmSugt7^!dAeN8+89U%Rm!Ac^TB9wPuu>98aBa>Uu4so9%ExM9HKO1
znn}}|Ue%!Ly)G>t<8v=kb&VqTW9!zVzuo)kldNk>o7cP%zG;%KfNyNKw$oyVc<Id}
z7#6pz&;Pq$NX7XJ$<(c8f9K$*M#WD2i4`Z+Flo*1VZx&NBc+u&we(IhYt>b;U5Pyd
z-qsKVmSAzXUCP1NYpBu0YhQ?Yhgp4WUk6vm!jR!1`|E5A&J)H+a0^P!<97l_7e&JY
zHLa=r8$FjHZTRMxQ<z;vJ6ntx%N>LT7N16D*f{~%UO-PMphbiN%#N^QSpkj>JZTwh
zv3k5ia#Fidk68Z!UogC)#gT<p&+JBUvEH;dHq$J15)sCwB}>BMBlzoZ+Q9D9saser
zFXL{@t@*N}zWb(uc?Q$G*THvdID4ie4Z9k%lX8v0cec=fQEmS9s3`z%VQtAd_)`}l
zBYB;1oFlTo)JaUa!tiXf5Q5CP%q4E|_sgupVsMS{A4lWCaE|E(`ipl^3oITuP|u)A
zdg-^Lx~uBE`r$_pEyZoyHoMD99bzn&kakHY^SwLxSyj2P$Il*k0PZ!OY3(Zc^wl9{
zmFhNXK}Fwfwy5iq_!1MlMiYOTrO6npRDIppi$6^w3i`vo`;}9>A@BBw31ZYA|D|00
z&xYS{%Tir>U#9t#M1#^_c2GPow<dHk{v7?P_pK`5)`@Jr9HP=7#c)oHOZFpTm2JLp
zbGg0JH*Nd=DNm!Stmi?Jpx^3mOrV3!O2FNIaYW>~?RUrgN~WT&NX%pJ)gGJM58rk^
z^1?6*=11RzkVU#LLgx3pIC<MPyUbf0#K{H(mhUCw8PiGnt(B|z|8Ma#3e-`7kZF*6
z@XOTQcR7t-+|i;3nMUh1Z|np$=xI|pXVd8vXL&!<O9((1I<@~f?XgZ1=-ln&pLcpd
zYhu~?L82<jwu(sT?zXC)*T?74nP{ikH5WITFmio<dwZ$JsNPu0X%qiaf4@{w*;Z8s
z{z?~?$N&2nhvQ8Y*M(ul$_4FfhG<Xrzi+_daTKS<RO&BVPdghiKYWuujOGRID%92w
z6Jr(mayCxIl!FCKkUZ#Gl-QEl)tw4R48vhcc1gzact0b}$<y*Eysio&^$6hS2#pTx
zx&4MQ<=LK(GhvMUlh%Tfo%*?=WDPVkaXZgJ0@Hjy{C<9R%^1|NkLDjFEWV$rT1#1e
zVi_sW6+24y;W*T)8a9+3p^3?>j=Mh5n7E3fNGOzCY1Td^<?uq$Etttyx@|c@6IkDj
zYkh&%zq|wEY|88+EH{eoZx~fI{hI=bV}?rFiJde}S{-a2S9tvyMw!0WDEF}5jvVOr
zKpj=k?D59_4V$wL*8kufl=@iP#$fZH5&j{erP-dTzJqm2XY;F_hGmjC*$d0f<;n(A
ztfWjn0sM0`-#x5~<vs=r*QK)wOGqD2VlZ=mz*p81&F*{VEF}iq(oe6}Fjt2H`sv?I
z92=QXk{dy{Au1<Dynf*L(ZgU$4v!}XZ;pmc>w>c>xi{w<oMt`_#KMoo7pVA~&?Qyf
z7I<(*r&h*waVMMODP0IA5RVSPqLFd0;%bBfdBRu;XxXVQ9rd{wiOQYgf2iaPF#*zz
z{~r?Qov~39KV_T#)xI_|<#N}xKU^T0<fW<H<8ec5RGgkVAJxCuc5wMiLTf#G`j=>i
zw_J7+jiFtenh*QSiQ1LE={cH=a{rps_&P>heec!&e{!A{-FiK&>2z!Q|4Bp}2mYmY
zQ1Fg$eXT)@z@+x-Qzck@uT&cwmLX0vz75%3j;1pYjr>tN>tsed(_($5Zd*?lQde%=
z2jZ>ZCx#Bdisq9OK}(4i9t-H8;hx`Jz>GEBG6ODQZsCSG(VqV7JreWqorW^MSE;XI
zx%rCIM;xkB19MLjuhBY}9xY~Fag-J9H*@9*O0?BvoQLOBe2!gvw{?p<QtiIzQ+Wfq
z<mk<Ps%m>vnl&r)%wrQ-k?`)HJJ}538zB|)#nf-lwPt~KOTQoJ>L@R4l?ZvXJfM^s
zFMB@$Jmi5Y8lK2}O}#X7YZO+$m|PuNPk10!yshb*8_FU5<mE_@1OMlCdPm1t@ls#@
z;+P=~PmkfqePNF6G{ZHXv24(&4Z!Sn?<iCt46Dlh%kQw#$zyNv6n?}|?wAWmtMs2a
z9B7%7iTmleAppa4-9#EgtsqvxEZhsug`eQ0JoPgio*WNsi#mpXK8of=L&oM@Fe^_M
zJM*f0(3x$V{byu4Yj@UaKW87V-fJG0SCH@&dCeQmFWKkREzml-5``)#Ss$c+@PVrY
z7Fij=jk&wcryJtJUg6Ojfr>4!j7Wj5YhM*WgBr-DIolL;3on|D3pQ}%6VPG>7ip$b
zvP#{taZ{&RQO!|@xVG49&oF(bQTV$-)8n~|NKS0Qk6)nsLrMo6X-!*OK42ql>EQQu
zBO8eaqQ~E>l&`m4MdO2{o9tYfC?31hzFQsOW#D=UG2=(#(EU8m-|q}gtzu!zx|>qJ
zd<)hM$wh%SU<2@aLr0phik5mQpwrwnCY;%q-QK!|xdTJZ95das(|~{6!_)g5ZP*c(
zbCK>E@ac-1d6V^#M@Hx7A~lcyT*rLnBlS9OR-cU0a($*$8xxq#3f<b8CQSp6fWO;#
zUa0?72Y8TkoOr0L#iaSqnjznnfr``1`4}~5rP+HYn#H=giOnO4XO$k6ZSm;)Q)<l5
ztQZ$}y)gI3NOHu^;};p8OhWCSMp5cZ8%K_8C-TO=syroOUI*y(o~2SaT<~Q|(6!nb
zX`kyPbTSk%SQ#&`)Qe_1DV?FDFX|Ima=sr3tm+z>CK<)nr<xKBMh>da0$q(2bl1nU
zlL#q^Jz)aesR!$tfJP0$q3b(s7TOt0A0%N@eqX`zGmsWFIMd1fh2k8~&N#DJ&p)kI
z>)Sjb+B(MHhkAp3NFkg&hB9rn8_e*eKCS&m`7HLdGcLUxPBZ@Eb}TC;-zwb--u$Ox
z{ugINAR~=(li~=LPsO5+r2}0e6A948$kY?B??EsjaBm1!A6vk@FaDu<C&552E}L<%
zy0ef9i{DxPqW}3!eeT6j7hN)*vSz#me5?h_5`nL!nlkUU-`-%bE&(A37Juml$OaSg
zWZsEV9GkfMTA}$W_pl@zYd!1iPg!wF$24neW0TB2SsGdOYFQv5tOW>Hy>ETIXLYph
z<=d*sL_{#mM+fiRHyz6p7=dmcACRb0i+LRM$5+V9-hOkM+|>LBxrnE{_A?9ayJoY>
zru7Fc^vCb8EXo7QweDl$Fl+Bmy;V%HWY)~4)t?Rk3sQS9)Irmc@IXjt>@>uC4zBw=
z6~lV_mjF|4Q?`C3i8>PTy{!F9zrbpTBC*o#mDk@!=hkF}n4|eGZU;q1T=jv{9_tnF
zy&X!EbBnYcw0u3!Kj3|I>1D8<IYp#?^n53&(erfJmLuxzmh}At8@pf>c6(XJcFj^;
zaW9CIOk<Sd`r?F#apFE3_0`T4+k*gatowp*KH;@O9_1+6UnU!h52e6SIyqRp{nKvX
z^|$mNCo6{svx|OMB+Yj!N-CgAFphTWb5FhyQ4aQkME(*5NU(83VFtUsU)gbeQIvx{
z7fUsnvs|t}=+r7To|Sy(uAFn6vO_NIT60FYEZ8^Z(nt1jz?wV3cup;CQ7mY~$^SC*
zb{lgyb2{pb_OQ6h_!r_R(|Hj~1>sNJ3`;XvYsWHskXbInB+J*s?#7OPnIa!9-&q@}
z&(<o1{8J0S){9i*B-`}u&1PYo&u))-PY!6OcEU*ccyGDgKsn|zil>6OvhCu7-lr+;
zR*FIK4?JgJy{Y1jWL=Z36F8Q|Wx|IC>)%M$&T+Q$`F6YIbRh?r6^7)j2K}t0{#MOD
zFmP%_{NoMJvWUyg)gRN_kD2)Mg`5$xW(V6g0=XZ9A_sWNX-&p!o>?7Wx^1~RJeCd*
zz>Of&wN=s1;b@Jy^A$YGupcNyD3)e5T&X7rP-(dJ)deRfhleQbsOm<~h8`V<RhWzY
zdEj<k;2tjnFzv@Aaor9aeE3!LjkkLNW5VoIaRib72ieaZy)GM@Qk^Sm`xdKe>c877
zpr(*_#?gy;mvJD_g8qMOon=s4U7&Vx*Wzx)ic4`V1&S9a?(Xgo+#$HTLyHu5hY~36
zEmAB<umS-}@SFF}cmI7q&txXao;kAjKI>Uei#xohM91UD{X4Rnu`7v=t9w2m3=V)U
zvi_+^ky>ZP$Lal#gpohbVdQK1uhQf3En0FH?d})JNv5JUL)n1#oy5yB)zF8f5;18V
z{L7KCr76A4a`Tq^6LcIGmTz!Sapou2+OL(CTkM*3q9do<Or_`FRw+KCChk|)aLYUn
zzVws3&y>b31`CMYP)R!uT#0FVzh+lR2Tr_V`wl4Ho62O@E}V1gzpt(!l`4+V3s^bl
zr5MN{dp~;2f_qOV<eaN<(pTaEI$n_~<@w%oQd>Kn*4R^edb6a_$z#!{)<OlE2m2_J
z9GkQuaZvwgWxy^Oqd2OY6Lm`<e1y}oB0Uo1q&Vb9<n1Uh>_Ui^q@@upp1;qg$2cmG
z1Bv(Q8g6mMfkSbAxx5}9ME|6Y`G3io6#YC~LZmJ-O!hW2I?ZRprBlbI188UaJY;ja
z(CcnpQqEAV<S;T0C%&6si!K1Hn{Y0Tq)lL4k$ISIBxh_mmkCHM%I^%1*1Af#_FWxG
zaJ^JWQHghK+6B018Ft|965<A2OlljLXR^Rx-}($MC+D&?#b(j5nRo`DS|W!ONlO%x
zC=V|3C&Kk8_=)xUJqDnAf;;;5vcJ9oXbbH+A_on(tSR~^y_gh$806Jr_w-LA_wd^q
z>l56cjI%9%67*N%or%j1#Zo<>Dvb_(SMe}e?1b&n`-pfB#BdMCcg_QWC|3klio0ow
zv>uATw$*NivZ~~o)>spDgtwgsZ=7WX#}1UpTqMAKc`lqwBh+_Ujes#JlLyYR+VeRc
z0WVdXt6FD@`1K5FT~w-H7GHE7e5uc$!qGX2A-OnREes&();~5~oX@TMdYP_3<fryY
zo73d&RvY`eL#>mds=xWcLK(gbBN@;r#zH(^o>vEP!HqWJ7cUa4EFE1OI2e$>=B<)L
zHlzQg<yDOG@EhH!`B-=nzOD=R<G2kXvROhVD@Xo1OLpoA8;i_qr91p|l|t4W#K5Ok
zC6fXYpMXdDp>NxYKi^jf>VDugB>oTM^>f9Of|;8&Yl<nmKi~ekYfMf<bE%ioB15E%
z$}k~e5!MY<O1!2PK~6zbjPvu}<8R9nY8|Qcg<9*h)I49}ga>{hlYnfD+t&dS47&CJ
zFyuKUCIUNt?VwNEw8!(M|58~Ce}MljqCJR#kbxiiloj&V<*2L5@0^QjH3Shg;<iz>
zXW3tqu8#%U3yg=cV?yX@?>Jm2#C_|pi}}k_;Q9qxiloR(xN*j@3UiLYhyhi)H*x|;
z9AyN(A(5*?zd#5}_<%zA1~a^4*vp>vGrsI)Gfo<d81+{ut9m5q0|Mo8j)znLIRgL^
zAw?(VZSSL_=~5u(;+ShYE>))4i#bxo>WNFc|Dg?ZYi)xqa-5LsySD=v^5u)Bk|>Y<
zC+EdE>JhMli3_At`!_UC7m0=oW#vyO{NewF^mp!D-*`YD$Jd5{2Q2b~asF>D^O3u(
zl;Jb0M1yu7_R#7S%>R_Qb-iC}BUwva-o}+IGxZE<%m;Pg%>kj;Ti286q)XpDJt(0g
zLG?W0L{eFr`HN{(Nkq`pq~Q)o6b?~~%jcqZCBO70mx$JP$iz?f3anGbP!tPG#o~@u
zG<qge@wq{71zSZ(H;DPTNvHppf`*3^P>5;SIPPbbA`!-X80;J)7Xu=UtUkLe!E6z+
zXet5jFL395?H)<7##tx2;rX7k#NY54G*Y3xQfT`jy7`I%#4bdSVsB33T2+S?p-$ok
z=hZS;QA(EApgVHgL#jo-cUX4faQxu7vum(VCP^AXsU91bEWvvqi+o;r9@T{hL&BZU
z&EcV*Fz|H_*L(8r6Zz468Q>xifQlCZ{`ORf0<M8qz$%3lh;gb39%JG_nY%eCGf2`8
z`xOn&96J&NWWA7OX2q#GZ=(Vx5oIsexGhm{;Qds(zu~0wXmx?B;lX$mCeb>MZgR0P
zoF^PujE~r}AEFI9+vTPH6?oRHsD=;}WtA+-a@M;&U;z=68~tQ7^EfX3vb)iaaiweI
zLh>J_+*_{jiDv+2sppGM?Jm>4C4!WM(Jq$khjhVau~l}k6?B0M|8hNY$-p*^^~dwh
z5-Kxqexe6B8GVsVitHOrqKmQLdP2&9G-kjTb9hu7G#ed76ZfAY{4XpFpD8kn^8`hB
zV}8d7l#DEkA5#a3(oK$kf|nZnqhT$~fX8u`?n{S*E`smidr$))Ki1#3;f+HQaME?a
z1P-olJbExC=TnEkc9RR3?5SnTQa;k%djyHr{!4oXO`}n{66hLx$Up83m#@4;eHUrs
zN1M3}8*<!-XM%QIw%{`*uhXaXUjw#VPx;(L)JNibaZKyvxMwJ-gYe{oi27Ad7F311
z|LuQK2X3;_SbY3QG(4=0i>6@M(~-1U(OWaX)!5LLm@Y{gVj*6kR&x9>ScSX$?b*QN
z->s139Jo2Hq?PWGnk)p~au8!N8WRnNVn>dvIfDa^t8yNG;PYTmGm+mvOt`}=Vjn+7
zcpk!*|D%=1nMB@sz(X`W5^1bkm)7Mo*!vby^DHbn54882fa@8Pzj;j<)HyONBBpVi
zjm?LYyJ63G7%J<E+&@@%h;yb9=c0gCZRZ^u@KxKN6;>hlrqnQF06bd2opEX?UMg>e
zzGg?wtG+|@WXVbJwsAtB7fX24J#y3?1#N9MgX_UO5?9FmsJ^PIDq)mxozD8|KTf^E
zP483>n9cJ$L&WB1h2*l*_F@hk8Ba^l+n9BIqiCm380)$<c$MVV)f$dxL4BF>uc2k~
zodtZ3=$BU-;*VIe_9xQqKT<ixCJ8l3R%h}=gyT1a5QlWVAI?j6;ULI~DeCDrMB5p;
zcH8{Eb9#)qA_B$!7sTT3y%}yDEx}a`Ihv?NmX$nF@pVm=+^1UG$3Pb&q#a6u*AEGw
z!9x%O0!f^-fcas@Mkr^B<}Jmu3LFOs?z|%z_ZKyc#e)}b1i`U!kAWfN<Tyrgh^4zd
zz}S01-j$l^6rLL^?j)(el#Iud8T^JzP%a|&0_EVw=NbI6HEjMX|KE@kNX+WwzoZ2h
z3lj?DAdUoEk=$dy0z6MPu|zfq8*3`1sf+q`7S%KnWg7Ks$3}vX-0X(y0WlH_08yhx
zu-=8E@x3_#OTJ2QI(`2Veu_)=y*!+Dyy=dsVbX>KQdBR8mnA>RaQJ)ZLs*d{tOhcy
zP*N5&CXfJs;7PQ}ahsrZzVZaV5oLip(ln!oTR6CW4Ng5<2S@Sx@zPS{oT0dT=Y*`m
zy}Bb|FUW<V(`lDcMIXJz#KQ}b=;uS{hX7IPu&uiRvj3X)^l)`1C9ZxDZJ}AyzLX|n
z0KFaviud~He+?~41B1M|u*pyCc)r|^PD(2X@OJ9I0tE*c>3|OVb_ZE{z6R9%)^SU5
z=R@E5Ze>_o!cAqR(ohd2V3AL9(hnr}2fyFL*jmBK;ynXQ_<6E^!+O?qV6tFPVdYEN
zwmp2m8UR|{3Oc7jTTdA#{2gFb-`b@V#xmLISHBQ`j^CH#dfP^A1U<GZ#?vzniJsyY
z1ngp)T?(0BY8MxAcym1z0d8=6WjX8T{mxq8D+eK0bly4b;uqUOdX-Cb{e3HVMgZE(
z{cvNwHL7d5NpRE+Ujm2^HXRK$rv`I692NO~UsN!7*n@{@d`ysD(^O7sBx(>+MXigk
zem+gk(q2<SAK+AQKoQUhgyS-m!L$G~@V7n6pKa@gXKFpII|RIadf+~i_}BlMAU0)o
z3g3qh6S^st!#*3cArl|<oHkIoQ|1bO{-?s(9Hqu`-JME!u{{^^ypV~Dhnpd0^O+x=
z*54ckUk9A{lz!s%q_8i-#i@tK;Koxd_%yZfkE+*Z7MkAxwTT-~6p#53@&MN4_U`ji
zIQqow31OaXMI`P(3K+Ct`w4V93>y`ZZMG!X#reTO-WLB8m6rE7_>cK#*X5+<(kCt<
z9;!(;)6BOC6vzk%F_9CMpF~+$kun$)5cJol-p=J&p-JxUC35@r@U^9gKr!?d*%C%R
z=kdaAFPp0L<OpoR{=jC9B30;e{AMX<c-bHyw~q8}7_uU959XyBA}!rD*s4$gSOS0b
z#LwA&qqVDI_Mo*xPKVf6s0PA}9N%A<n_%x6lBHv6oFEMTLD*{cv_}?_qI_GcjuVPQ
zz@m@TGQ59NLHF`C@FS+&iK6sF+7i&>5#FCQ9S{p_%d?LXJc_3-tN)K0>`4c)TUkcg
zk$>H>c>e<Lz^c+{1NOu7YEzFuD+<-G=}Pk|4n;nz2Fb`wIL2-0#S#ug#E1d%v}<`~
z>clU9SC3Q&RDDrEl*xkX47|X(NEbyyCfQrwV=xM%hKSh^0Xly8&uG43Zv5vMXDuEd
zj9BD#x(7@#UI4GIh;qDzQsDrCv=}O-`#XJvVGE`qMby0=1TrvofShZp4v-Xvg?pJM
zl@Su&0S*Z)OwnVJeX%Iz3pqBsGVtL`=H)*|#<67yNp63F$CTUydG&YbVyLV#F-uUx
zy=EoHH!8)YRp&+0`J_cm$HW!5z$HplfxGV?&?F{WK%(jtF#zB9CK9_!kC;aVWiv1Z
zx*b4REFW?a#J-TiS9_m+_o><ZBh6)+`<Qg&C0`Ol(%A>r=aD@`@Ck&D`aDqTkix^8
z(dQ3fX=c_8XHm~AV1<VCfdbHmkE{}Y6?np46QgO{wulB(2rXq=@R+~_$kgz^z?*p7
zdZ+iun;>%L>ayJ3qwe#v==YY$F8_@I5-dw2QZ@dxPk!MBqK{!$Ac7~nu9G<#awf>c
z$ezOm<jlNq@W7d(*xjYHg``LlA78o?BTawE%JIDJpi8@3sq<H@*n`kx7!hO9t<oPB
zj3y}VA)($v5)$ye3~JJWQlkYG$PQfaGI+v^h|riS?5|h==U7jU=%C5!VL9{D<v2YB
z?&CCmU+&{2czsiu)<>ulICpPGIZon{y@dEx$z875`s@nr)uU1_W5Ob1wmxKsTXD<B
zmYwdtpT+YW`Mnx}p*UKWCrYKikH}OI&1aoGMrv<E8|^mTJhXI#azk>BjwbQkc|9xM
zOARk7Yv#V2U=r6P%T<4xlrp>W_!R0%P4mnt>SyiqqPfk~ds^S0Jf{P6Bb}YU`;CpV
zj$kq&-9vzmK{<bbLTyJHb1a7g@JDu`_OnrC64>AUA)x;LwKhc2GBh{QqdrvwmU2Dv
zJ4X7q>vA`wRK7@v^bKa`mS^2HZC=E`m0$XQ#=9SgjnG;1MRWPakkG}D*Q);PD6Y<D
zT;?KZ932z9=SdLi_!&C5U#`XUTI9EYS~b=9q<368iqBOPx)s18UX@P4{^Gn!xBCk3
z2^{A%Yl-i%=8&Tmjvc9M5C3~nEun0CmZkbL+nJwxtx}75N|%-zgw|cNhN8zWZ!q%;
z?`AV@LDb`CHdGUc-nDVwyA%`<RIvqoxo#y~aKvg<#^)8{rCvsBuGz3Q;&LQXZO0RM
zC#h~kGOfasclEgib38+}TQ|V9?lT@f-Iq@Gh;;VlboR^iqJmq1HpT}%{%BKvRD%do
zs`-_N(gkAq<2LCM-DqaZJ8Na3ysYUij=V+AweG8Q3S=E}ZDMAsu3|KtVC#h8YnRl=
z-iG`yySt4Ph|B_7Dwj*dZR2$aq|GEuJc^-j>;+zlouO=bP_Ddi)0X&t>!a9yuKhao
z2cvLbEmi=dE6C($W=4z3;Z4@GjPDu)-}eijQ=$TI<qoJ$G%FJKx}$;`rkI+8SPF`D
zoes0MH?#K0td~H9N=G30qWYssN4`G)0qdoZ&tlGDpGYHO6K1V-LYFlMfZ6Eaiob*M
zON)*~jz@ui5$O)bVO-WU?4QW9+=d$FW6-9Mz>D#r8lSico_VOVr8fY`@sr;J6;z`3
zmbqYAk{yH@>XOq!d`*`p2yU=O%UhBahYWUUlo5sGH|z3)*TEe~)$eFlbz>xpJQxjH
zSAM_4fftVuT^Q|C111cpr~SXJ%JqPfiaQJq`9~*iaqc@PPc9D&uTKzZe&q66mDloE
z(jr8?lyReOlzB=lGFKL88?<DJjaz_<j^sL#o|UMk^KReT6VIj6K-p?8=jHIT&iEVe
zn4jI${o1#~`H)wpzVgiEa<0|(CFpr@>}F<Yu;*FrKIl3?-kj)c=ML{&+)&5@mX}D?
zCAM|h`f+S_n@8@CO3e5pd##f*(`&ugCvAS}s`vMKBR+J(C!5|I=bhc$XeAU%D=iS(
zn@G{~Vzj}Ji4>Aj>J&U!@abA^n%<j+T5NJqV?$5Qbm>3FW&A$#ud#r`H|9EMMR8}E
z!U02ZEap4t{?kqWUKER`!?=buQ1?#@Tjv&>ZZTik5}n_q3V<ZMN=BaVvaXg`jJkdO
zXM%_3@dQv*Y>9P`XD4|BNlf2B=x!(im98m{DLW_LgEy()mavkStkJK%XKJzrh5p0|
zK)0A)qTMiPBt<maR5+1*fq{$kN;dYr)MMAN&CcVpPeTa4(lWG~<fe;OzXZbP`XZ)Y
zhM6Y(Og+}vG*X9S8@YNA^8L)ALZ?J}?wmd*VOzGGJy9c16g+>MS^J-bP9+)aa^ynJ
z`ah{<QVNj4RytO3AD!_Jt=xaQh7DqM@E)Hx$rEU#139E0+oEwwG*HOy-anhqHoYcJ
zm6bSq%pEIel(vGyxMWHQtj+i|9I5TOJN`(<?xu)NN#*c`61D`RN_ipl|Jk?V;T4AO
zY)dz;)8x}R*Ze-^kDYxK?znUQwR%6Ao5RV6S7q09mpc4=%ZiiH3qlj&f53A+L3g!5
zs-wIu`tWs8db)W0yIW-mz41ZSN3eaxhb4(BF%0Xfge;<tfBLuE@WiDHiC@V0(24LC
zx6zMk0)WpLaahoHeV506_zIeCg=!bTXISiqo|(R+#rs?rDS`4@4i$P^k_4SE$u!gI
zrQAq$Jy`4PL*P|Md=9aB<n#KTytBuDjCGFd?WGbNT+Ym%d6_Hvy59N$P2q<w7SUlv
zq}-Pdr`ewM+|4u7@3_Oo^yRH|BES@v*(3(K{m$jkhR?dL&h(G9G?$n7A*~5RCqfQF
zxioZjSt#1MFM{&>9G@y%`6UiA*jI`u{YTz$gl92d+Hd^#hGvfhQ+Gi>AY5ISSO1tL
z385cFn^EAjO-JZt*B<%Yd<BO#uleNW>7Z>e%IxJNhc@=tRl*y}_z=QIcpp-=%UN5K
zmweF;vLb6KZl0$9UB6#&PBK%Yki~-|PcJ{piW0S$!F#JLAPK8(_q6#9iL<WEnxidA
z#)_bBpMh)?3zGlSap=aYoVbCk-6=))B0O=39R5#+103Ufl_DbvB(#n*VZFsYzKK-(
z&OKP4pU1H0K9A9`Jf6VYi<n6yJ!W>P9n&=YKn2t0t*<0Z756Q|U~*xND<Zf-s&m@0
zJNo1jmV(lrM6y{fV=`}(@f9p*HbfUJi6*fe`PV`*Y^t>$_{LJ|(&{^q5OtDIH``z}
z8Eq}JUPu5ggapT2%{rEdim7w((+3YHFj_?j-tuvz)&FjW&P>vB_ydD|6T_^S(~4x1
zRp<49;@(W*629qQjjTxe<{Md$@1)%a8wD%evXZ4Nm@_=aMaoT3cCW}@zOZ0VHuF2j
zM4$5dVl1<^&EasGtqdMl7u0%t3Puby<NB+b<$E0{hRti<+#5Q5KM;3<NF^UM87fQj
z9qMyc_m>--NBH{Ur8*sAfZmS_>0c!yAM6oy;&#*jZ~%kW7Bo-AL8%~~XSvtbz$97)
z)A?VE1rQwFhy@br11rB68jc4b0<F0Zq0xazL<CMNBR*XjI`vrsuiwHrTv*2yKA?c#
z$~fmJg}8a9U#7TIx4$aD5o*|AMuCk5(E&-*xyrf<7FX5<llf=q1jiOs$eoqOk&Reb
zUU<qHK*?LD`S$MSeWn7@{TB^x5Anzm{e9CZoMLG`zh2Pu&aUlds`3{vADMoI#<pCh
z@UHyLXr}tTxVU%mR7PzTBW0t9{up+ZOk3dwAv)AW5^T-q*>WdNwlcUuRLsm(Y?j2>
ziR!fF4cgX9@&6FIptu~bZf9J&J%$Yu5P!RDGdpm%Qq+Mh_-7r_Gzne~P1D*Z<<02$
zw2(_r6@)-B#=Wqal5ADTw?)o+NkL(*mjd=HoNkgAc}2&&BFjr~A%>><Ai2m1EwEnl
z@$j0GkDm#$&tIJ4n2ft1FWO5ZZk2NE(23_v^Q5aJIoR8BzOhFZqP0RPNERc1g_zw$
zgK&DtpG7oc6G9F(<AwUV4@@2D*E<}$!{fxmv7*CdLmrMpuMmfKWQX4SP0*7#Mv+!=
zFp1JOVQ|^-nUcG!epYQ_qrhP%rXFTR(Y7tZT@GJ}Z{o1uNsskpES)Yx2FJ3F^Zhpb
zctunx!MiFd7Nup5v@e1_iJ{LzLyU+;$V6ex#*v9-r^R>pRU1)k2YC|B2BolJ2exx?
z#-Bh(jbk2RAJ%9P&D7_JFXDiXhXY#1+ql>ftffwn6A6H-OOD9p?@4@H(yiTum``o!
z3e<wKh`kEL8YHPzh72mySxD9urLWy!t)rtcRL-B?cO!O(|3dkhWUq+n2}BX&<rfJY
z+n;(HON8Ek5b!IeTb@@U&wIZxm8Xy|gaTWnqGSCegFfR;a2#M>SY!p|YR&9vZaSnW
zPglIUnNiHY1$FK%Vla8r1VJ*vZAGc5ZS+-LsjoQ!pRGOJIB)Lf(gG{8E7$`0=c4|4
zvuxAqrvMj<fImT*I=SY&${5Sc$YCK-)xQ)n$2mcH4J>*A`RBszB-HY<_e(WucVP1T
zn~#Z?UjpdLkYiq><OIF?KJv>@KFNfHRm?zG)<;AZ7*YE6{o~!ksQi=Tc(+^*7I2M{
zEi42*jB(KMOooXhZfMYui8x`rEkk8On4NlWo@LefnlKz$56BB3$`P<#p7$^2JLc}P
zJf6riDC={BSGELQTkTRmc!y+m)QP;y{X~2%IL=+e_WZpV^7vwkhv+*@=yQaV7tPPy
zHzWAiO=Sf<V8bJ$z8fwVHmR&P^a?n<PfTCc-i^nKv;^Sk-!IkaU7R4r`6mDt(Jyg}
z-qI)%3L*tuW=ojwAS>!Sq3-Z`I?#bgXF0>AkkIjfH$q-y5<k`~7jTf1I}(K2Q7Lo*
z*?}DV_1?FyoZyafG6y$<B~<Cf8qX{3d*!Ls8U4?tQlO~U>}=*m*2}md#y8lGBJR{`
zW5X+&oVVS`hfjz+<G=S1O#Nl4cF4vN4R$OgEbVr}nub1x#SSI<dcTbknU#LPD@qxV
za~%_S_;h#tg(n!TVQ5}&5}oIDBI9qlguhw+bQ#d$&<~VBsxZWn|3&eilGN=n`-46H
z&5nK;Rng?rhv$xK@c3R6-wu`HuX#-ohlx_}_g(rQM}F!ONcOt`2$oG@oCd`8#XIri
zSjT*W0c56o^J<WlRtf3H#OE|lP2F>ATGr3B#pU=`&#<e?l;_`!cyAB`rLh_Ednng8
z^D4T_?4?bt-(X32&lXtDTj4OaVd!cUaANTE!6G=EFc~v&IEY4_b>zYkK{BPBSjDnq
z%zmh7l6CD``a{#Map?G6%e2M$XMDD?I22k937FA6I?^Ix<6WaoI}$9)xmijS)%D3O
zdr0Sn86bo&=8A7$_}@elqhin*u*)@bm^-%w;!8|Yfa7{9W%N<Uzsks}SU9F@1^wIb
zla|h5FPv93(ZV7B)TS34eJ!)`gKCm}mZ)kjfzVFffO*><oU}=9`=)l);rwW^5-Ay9
zIaWqIm1Y(ddCmZ0s2uz7dAi<`fv!9)%jkny2F)z1S4XB`Lq}8K*h#{5<)WD*@<EHI
zOnz*_EHZvwZ@u}b*FxOxc%{mAZ7`pyyZIA{p@8maftGp-E>!7xXLd<j6Du{IDi<>l
zx_mc^gq>ZaVXqj&`wp{Dh2>b~0hBxub>6`x&PKO}#Wz{pW8J0AnuxY{)C&R&D-7;>
z9{6G6@c39oVWBbU$l5K73N#eGg+lBiCi@%)AHxM|nj=S*QlB1FZqQ8`3etDw(YW`X
zR^13N@iTZi=7S%yi;i%nOiYI>5JFS$x_IK4rXY0w@tmHqh~Q`hiMpUG$5PmISCsTw
z`c;Z&{ZNb5Py{M;be7~UmH2BSiJRIFM~?eTxvpFF{(An_d8QCcclT;jv4}^;?r>`&
zxsy^2cfbyrcdJ!GVOv`p45q&V0p4<>3G~uYs?^)!J+AQ5HCB50Aq7(8kRON?aq_0l
zA|YIC8Z?7lTt1D)Szbk7RiYy3-mb}77S<+_e?qYz7Xd)ySx5Em?j5O&`P0L~-JTMW
zZ_4x@*DQ&8d@3n?P=4ZrcMl8PpgIO8Squj<3v|byb_|?Hy7$-((<m-03ibwU7sB`^
zZJeMBOVi;%u7x50^m=)nS)lGi3QCgE`tV4syZtyt@Um%`9!l}NnSST+m=*EMh}T1m
z?}BL|-ys5s5p>zm`RAmU<%DSEC`<Q6Cc>YxCbHxKeoLXdql1k<cf##zLq3$^E)$s0
zZ{5lA6uz6e$f@E;!OY|fZ+^)eA77?*qX%<PPSmJvzFNkT9L0D{uwFC!O<;pRi-|Ng
z>}VqRaf10I)RA91HXT*AO|&R(u+JlpHfwewk$@yWX{~VkunY!jlk36Xyt5KZ_E$!j
zL}uKUC5NaLN6441Q5IObf2e2Hi-kSQb_&!mgx)kF{iA)TVrr@9_d0J~bP)(@yyCh#
zkP~AkO^5aZV>+@v;6Me5s$D{!R#iv9fy8rITptn;t&XsRQ462oh<bNfQqI6@$m3%+
zUjo8tYN8WHk#07{5DqWS8#HnbntIM-HUuL4I@fyJ(&=zTLJRv2vX(Z`r53#hr3cVT
z*O0fml!U49C+$(B4qhfV!(R<FoLK}8UN%YlOv@;=WbU2aQKoe)DHcXwgxsk2`VP`W
z6v_A*RFeeyx(1&`#$R{+OS<6g+`?ui3KSoBG6?I^aO@lCWto%#aHkp|{YZZ}p0x<#
zu=BBbQ9)Oh52*_ndo_GQb1$*B7(-^zjVc`O4P+~(a&0T4YZ%$J{IGH$nrtMCF0zYw
ze3nnJgPVgCK1^?6$-q?y3u8`-PnH_?K%xT)m>umdp0#v1{ybq4|GYm;rt9H=oQxNY
z^4H%6V<3YRy`AJ>vKiYvRSBjdZI06doqk6Y{6jmY$Ne}Shiz7%E=-3^11JFtD@zPd
z(2VUPfFLK!(+826dY5$)ky8hpjK@u*f#?xOi9^CJ+>TymmQpqCs|R;QXC-+j(saqw
zEbJ|N3s(RvGUh+DVR3yO@QbJ!g`1<U#H{|N)jBb2(PA`2CyQ$z!7eY$_G-vM{#Xa;
zRzN6~Sy$#b2<e9JTbjZn9{}<io|rkPg$#6u{(zy-j*}EGlr+ntN(9SMYT&#pL{84I
zU@PyOmxs{#_F;@@6bjmd*$5xvANB?Ln$u$!Qiy;!NTce9tB3ZB_{-QP5Q=?lS~+x|
zdB6F@iXr?Tns^n!A{^ZS1BNZ<r5XBB>Bz~97I|4TPB6_;+Li1M2Azj);X?PkQlYOl
zUD?>fLn$)8=W`Ta(iZDH-hL|h(>ywUrP(boiS7X}GO2@K{TSRjD={_Boh%7YKoepU
z%_D^*`@z{3{0Q%;QxOiiqAPzd`bT65pBn*WOq0k+4Ab@TT=Uz&&919~Fr>L__aW^<
zeQ25yX%0Er1iBZc_W}zw7jX1!Gg=mh^MO|XkmnF_@{)>aIs;gv|NW!1#))3<f(_8<
z=AI};dKdZ=R%2?N6Oh%cP5cGl{Y-(6?9*T9<n5`Rl!78{+Yr-E-Zk&+E>jP}vqG`%
zx=u>*pPsp=b0Vt)&If|c8xE0NHwXM=b^zn2YzX!P)x(0p1hwY}`lOe`ABeqZC?J~S
z%$j5o-j}c%byD1Up9yp@?Uu25@Yn(eY4~r%vA^cjj5YIPaGM#%En(Q;Lv3B1&b)7X
z$;Xp2&VP6<*v}FRK9`x%<D+<z(>?@(^p9uUE&lP-A2<ZHg)bm9g>PU=pXoBUCqs2Y
zJI>R;kqaejYfzW=<KkH{UtDJ6N8_~0+w8xI#aVZt!d8F;kXnQ>VW2-x{4S*)k8;3Q
zoZH=9t)@E`j)2_d%)aD_wtJJxc}=~~km8Os_Ffx%(IAuKei$FCO5}Lu?z$;oyeG@y
zsXaB|D1nw7GJBg4@S{3<@?A%yvWKS!XCVqb9LPge`R=3NEp~-DAsNH>>{>93o$-w$
z7)wI^HbY`-)+%={j4eCC%(TK)I{`xU&wr>3_#+Ajs|zJobl0g@fh?h&h&vioZN;uc
zPVH?pG*^P!fMSrm&7)(E+3X=B)6JU9)s!njZn?9Uhsrr`<md)gABEz|6m+kEVrK=w
zUHhR<O+9&=g}ZSBhj8#K8Xoto4?M-h^KSj4$;{CR%{@A;cd4|C=dFq<Lf_p>5VKJ{
z)SEk5GnOK?%B6E-b!kY+fO|VH-KWrY`jJ^+v3ufAsLX46gQIvM#aBGJ0`L&?BMkcX
z*`-&8SzP=iXZqU`u~LCpxAl0m2dXxR*{kcx^XmiCcf~8hRcYgdia*u!g$niu5v8Ik
z$#`N)tv}1H(LVglH2tajPBIBQbzW}3EWhAEDSn_u!#B`ig{jA1IoY}8((`hPxGwT{
z<-UWl))#`1f_cfGzP-x-P^nY2Q`COD9MrT=YZUZJ;7jFZoaS7gca?z^V@SN?SE$z=
zU-@;NwLA&dOMQp*{MCZ))}1cW8@95#yytQmlf>@*b`TGwI!x-)DYCfT_UlY8vtQog
zc0>Nubl#C&bd>+S&95+SSqY8ZO=k`0pft$hf|tgqlc3-GN=$9feDkE<YlonBQgq>s
ziZHliZUUFmMHN=7`z2CTnjt6=lOPW%O4;D23a-QDEOHK8-T;c1TtW!{^>D0B_wqp~
z_hW)9cZN#n4}bzKEENqlqsf&=2=?ICQ!nNpvxysE-S*{pO6T|RCJ6Ukl3Ikt))VfD
zLCp5!0NkPl9)CT!{cC9;Tu_(AJLq0bu`Mi6s--bZy$VS2rP#V;`PNUW1F>O054?<4
z!d2+sBWnKv4A7*SDgAmv$o-Zn7G<5uaQZhiMK^3aStWb=>p$#0-yf4a>Z97?$QQSl
zvTwW-XzXtNlJvaeD8!3n&hj&ka~$s>QAmBF@E6F+wqF9%DMYG>)Fp<eE)jt`dPXbY
zt4+(g9cEZ9!VH8Q=B8dgdcBJUV!x?K^!LOpG{<WhWU}g@+V|LHHS<alb!L8y`eyc2
zqd_5T8-}!4eetct!@2Hn@py@Uh`FkkbYQk<Vi-5;3^j8o+4l3;i+v%)3S)>L&Dmmn
zDo>m1b7SI@I0s$mP6Zy&WyB;~q4+#m_m=M_SN8nAcHHbNMO6J|7-MUf=v?T?XpQ+$
z`Z5?c9Q(3>X0&tV=PgS2i5|b3K6iANE|(s+7p@gm$Z<}awa9&hC~hG7igw7e(>(&!
z1YG<3{6}m~8}nU0@Ky|1k4M&tK&*^@zmudsW|L}@DLVA-qGk9#ARO7<_5_vdOLFWj
zHVuE?jYP`)`51e~_y`e%Hs__jm+~whSRs%0n~t5n_)Rf%UG_#wP|g^f9B5WIZ3b~b
zg$_T@g=XF7Rb_;9@DyTh23TRct)$eZS|o~@{*t`QlwKv;2%;>Q=svd<p*7<5@dphe
zov)0$k_%jtJfv}b$mY%}*eG$#;V!$@{AO$N;UZ?~@&)$IVzNz3KagHA^GrXoUnAGq
zG@$XX#RBU2SJ?~N!<E)&`#SOL*Rq3@f=zhD?~Ihxq|^`ctEMmo+3m+8s;Vw|=lTyR
zQM(c=Ca%+FLq#D@{n*~4No72a8E3scLZVDXF<NnPYBAQ?6WU_lEiPJNjtQ|2S%bb1
zT~;*)*!Otd2n8VdE{EF=T1m3>j(n>Y1~&~Zf(G?Rd=931RfO@$l0}zGosCi*1jzCN
zO~ac-Qr&ue6N|BAF8y^*iDim$16&&$Y)S`d3CmN>@GQoYCfOshKQWwcWS@KQF6sQK
z-UntjDm!hYk>Ken?67zJC+0DNo!G%J&4$O;AP2FW7x(4pV)k~8Tm2(ikvY&T!Uxyo
z(|^4f;BnDL2mHGB5ljgY2Of!<k{v{syO)peKVpBnk^33tbEy?sS4VkS5Xd&?is!K|
zYu0q4UR3|_oBBgZVqJZ8vljQmm_UEzD9#5nzI~BVj>dsEhg7^~@Y0E^2E*az^yx_w
z=ntC@_IB@aA7=F3*DT&AhLpJlAZZg1$#1vt*I5bFV=W1%b4@Ohxd8)4ZL&0!nMRkf
zL+s1v^oKL2<85{E)39O4I4<(DVLHL(Da{|K()9d+#=L){u|>v?72Suetn;nD7&_Pq
zr*yPV3R>=CY_mtI`zvga$JToi`J>}ErDN|Wc7YP6vad2isyTZdFIC#;jdsFV;CKEB
zQvR4ij9HHQC)ojV^!-G9AcL<UB;(~@0lLtxo?cCRi-N~)g+`d-7NqWH2CIR%)ITP^
zh*&&G@?%WTE=jPQ=sqWiYCOCQj<f`d*v8j?D+!lhg@{Ab%ucjQliZAYf%Zr205763
zQ2zRGMzfNcj4%K>1?fD>^pd|C>^a1QJi!ZyDnWs;;EFBipq#*lV0@!6?D|E?rI#k`
znj)hIeQ++TM3~eKT_6rE@2Y&F`{fsDBo+9j>oq5R^nkKf2^9Ul5`f%~=(>(35q=fB
z*_@uhxo9`h=KFSdxQo9=lygA;epB?8k2_5c7SX4jjUc_l@D%Sc?>8X|4!@fDYrn~|
zt{%iLN9}K&EJNq;7mD(TtTvBT58~lRaNeR$-uz4_q18XMqz6IN?LTG((a5&Q^zOt?
z`rF>bdaq;SS(A2yX~fz#!ZuSu{?R-?Fdd277IH4}Xf<{3(B2PxsagjD7yTd^T{Aa4
zRv4QQ>2*Y&6+MW{4heZ@X1K1qI=M6h5D;t~Fg|s20cWOn0M$Id&&fDl2sI|;SiTYk
z#*gYj=!brh7Yp9fiN$$RGWCtpfrx^$$actr6-&kt4P*vV8GJ=ccY_NA0`HEA3{~$w
z9qzV@5=(KH=D^b<Vt>?8B~Hh;cBla1EXQ;Y1o>$``{6R;N)_rq)l@Y&y(*W49*`&q
zxaQ+u!lZ9R5e+N>3koMSgWhL_FEQJfoW)cyLY1>O(9}@-U%B3u*E@7Sw@cKYwf%i=
zK5bvw{GFqEZQXcs-F`YSQ?`>95pou-H2WPiDnA-Lmx7x!2=&mn8ZO>lyK1ml3hy70
zqQb>WoVGLL&0nYd8}hfpeNBaE^d$RrGitSIK=N+e|1-IL@`SgS)-;c|Y<>nFhAP1l
zlsr8rCufzG7R}O>H7qez$FVr~n$@Ut>lT=3z2wijzp{eXVVT;glt^e~91v*RQg^S6
zp8WZXUr-kmng~-iX@^?%K<%}p=A8T6-H{Y&v4H1>vq@e!ktsOVQy8ouwM2GH9QtH~
zF<IU~PykK*_`#lt&GnX2N&up&&<xSL&=!%szJGgse+~Vii`R=SJ~jbGGbwN>`Efsn
zf*|A@|C-(VLH^>F`#!(QV(0F6Th0nM#nC?p6iZttkOIz!k%^|TF0eA0t*`4uIG(-^
z)S2H*kETVJ9MD{VUQ$kHMn3BJ6EmS9M>G!ey%qwX>^hPrUxavgc({zr7J`BfoDRsU
zw9TkO$RphaPcY?S!)j6t-)jV5i6=kz8c{b)`vclQ@S#Qv#7wC;@s#k(_k8>6x}taU
z<v9>b=&@L>F$>?=)zsz*OGB}^eH}OCLx0QY!nl6&HDp>P5gzif+YY~F%BCn{q#h!&
zT#uI17(U6<QwS&u^eyKR)6qc3{`SXHMrgy?%WFL!9Cp)pGKRU^Tfj|iNP0m>8|q`+
z1^7{Uc|GHT>ZJL2Ro+|@B|r<1-n=?fv`9%J!_`R2-I*QtD8RwtQn|S+O2_7W&EBPp
z?&lngJ^pwtE0smhjCI|j{-IFO_%)2A_4ciX?0sJb-j;}(yGJmN)`vnC5y@d@I5yiS
zVsZd(BNyV<keIy=d^ba1MBfv|6LTlH!Q~$Pxr}SJ#`($gODFi<j#DANNn1v!ya*Uj
z;>R)hw(}2t`;K76)|oR8!}lD2pmxAeT5@M79PfL?=k)kqvJx<Ny83VP8|yk9<PyN4
zVlIzX_@J17Lcn=}($0{ak#it=|4y#3^p2BLBwCMmt*r|PwGT2SsE4+6DK##tqOZLM
z@V>*ZKlx(polg*W-}e%4fm#yF3U@UCC$VcLtxF=jy0wwd?qAPL(9ORHFVed6<ZczH
z!FhO$<17>{?xKAy?t@P}eoCL1l5SLB8L3;RReIcIZ<ptTE@sTFkU=D{qk_)X>=M6U
zy|!viX+w2)l?(PaN^X7%l*bl6Y9(Y<m|s#fzi`h4rkO+MLwFrh53Bf2NCN_ZPQ?Z(
z2Bz>%%)A@hSH{6lCdJK_2mJ?yyBxQUy)!+*S26}}0r-4>+xvvK1wvE}P+eR*#o_!(
zU^LOe&Qy|c3sb`<gJ9YZGzF@S!S%O22P=VstN(UsLoren`Bnjy1r97``wlV&xGepR
zg8PqHJ1)?>$)UZx?G&~;ABb0+VB2UWvIAvX(&Ul}J~XZRzRRzMoyV{KoNo$kuj=Mq
z-ByKasn~luLAE;ncXWOON)-XAG{;?vpJ}`P$-6DG<NwJ2?(i-n0KXW+HLx<^F{M#+
zwNg`}(xmu^%tkPm-zguo18$_5kei2YCNRz=O_m92NpE8mm0Xpcf8JuNLwq9GbP>2n
zGmDBn6_>ZsdNEylzCna++I&{jk4PE7z~1KNlfBE%ic8D!tyPdT{8H6_=K0>qNyua|
z!UX+y*94EqM_i||g^3QOo2|wEWpS>Y?7tJ$X)|xW%15G@o||`uQ#O0s_P!0Ybw$Iq
z>Z}iZZ|Og3k!xszCG;23zU4B@u6lQ!W*RVaomCwWce~5K1Tc97gE{&u1^07^Rl5NT
z;8=`Tg3WtdgBd-_q^i;*WQY9k&4U0n{BOXT?g4)e>CWj-!l&-rOd5l~n~YOBA$uPQ
zIa8d9d!_ovp>P}GNCPsWk5lnmf2q_%b>WK{Co^b4-?Wo(!(iak)t$WAn@|C>Mm5pt
z9>Hzkj9B74rQ8~s8lj=BzF~g4wZ|{3MesVnRB$ebGyjM~==lA%Nux$S!dxwM;kU`w
z44rrQ!;FdPeZGg1VAW5*oHRGqIqQhkhB=PS^4~lK=@}XQbM%u=Y@n$LZAt;5<6H3s
zw}IFk13G&(+Ad)}kYt4Tj0vdh-IMoHZvbqPhttHE#mq1|N09+AHqg2$F%x85aL$HF
z=4Wud#gWL`{MOVp@OsN+wI2PKYu=w5+X0>jnJ1(LCrQcz0Md%XV&1Rtb?3u8o11l$
zlIntHC2)54r((V?Rk3SIzzn~wWVp4@wT!6Q&0oP*Zee_Zxy(^jzZ|~fcMtDXrNA5v
z&NE5QaOa|(GycL>Jz&xXY_ZkhoZlH-P;YH6&y<oQ_}SwjWcG-ugY0a&%w~}Ia7q<(
zPW52_H9A}I_f8Wq*$(9f8||e`gyB=<ECA9k9y1)VR4T|u*WcMJKg387f^u27yY?f5
z;7_dfVk>0`JEvk|82p3J&4g^&VB|P?&_`%G6E`f{w22{5IIdz97tEgH#@kIa+^W8y
zN<^iT_wQQamfOebn^gviTgY2ma4tc?%Kc7Z!XNAopK)BI9^<5DlXO|cD)i956gglt
z&W+EBLuOH7lnjpknK$(>@|Nq4Tu7vdi8A*ehML4C9Z@U!$>}Ud8Thy(D981xjeYwg
zuyx0)<DbS^)7`f=+k*~1*0aPDwh&5IxfbsGD68)U(`DKJ?djiZ(V-ws#3c>K3PS*5
zKA~33hk+>RNJ^Jnh$fkl9-dII>$9iO49oua*M=4phFXhv);zUY+OK7MEUhN66kiYH
zZ}#bqGeg~3efD&cB@Iw-7W;6-8kDG+#}u`G?V|*G8&b~w`&;SyGro=Ih`yg!Ix^;0
z;%=v;VpD@BPGcB~(+Fz0!1c7#Ii;CHzF!;Ied-O)V7mP@;M%bH3Vu0JqRSz#e?E?=
z(i5FzjKRTpIfJ=RKn`~Y{NA#az`mUg|9DwHZ*-=AkO_E!W!>F;-k^??`fU1DBsxRy
zt54)W{R#dLVfe8N25aNz(|^$=>?wn%i_2EoeE0dsWu~!-DUE?DQyZK|7wdY#WtDjV
zIrgJ!TrxUgv(23BPJ`L)_Qr<~N&@#WTC-|Y+)~^FHr?L|zD;{Xxo#-Ldn>*w-#ni5
z-^`?|dT@02xsaSJ<<D)}fy?EeQF`nU7M67$AsbbI?-Dg8(*>)K?cWp@`YiSvH^csp
z)WaI2t7g?G;%27>8na>>1!u(9J-Tc#W6YEUZ3hAxe#4ZPB$6V_5M91{4=>hH+Ysiw
zl~cMOp4f*EUXS)=@x7T<)sp^F%B{$k0OSSqN%2(=wgy01hbTdCY2W7q?El7#7HqIW
z7v}~)-XKsnEn7Xume!^X{Z;jCoYM;Weo3w$kBrXjHv9iGP}hMj9xk`m!a*mq+vs##
zC~j3@fsir47~Zv+sS^hrQTJjRx~+AQ+X4@N*Z=)WI2t5v4jAeRYer(KVlIh@mSlV)
zbv6)917yFsAHQM5z_ts%O`65qeFI`qm~1{dq<`eqA6V=`%=US_G`@roJtataFnEt>
zvT$LmqM!qKF{;k%Dsbd}*Ip+hy7s9*b>(p+SuzC={ZJIM-M_yCDE5}@$^^Wl+aKa&
zoD|!>CZ8EMqkBgy)PbRo!ihAFjHLLmL4;B9$<J06G4pLX%-+sRY%44|Oc13$llbR2
z$m`z16FQ}(UlX$!yr;N&e<dOIrd$f;E+zQ5^7T~Z+lMG!8XA;Hx7LXtk^K7Im3H&e
z3KTA8m+fd8#}nwA5m~(cTQ*AHa^s#XzD^(qL<g{Z2!ET)w=*_Q&bkXhj%~COk+E%1
zJkYBc(JNkzqb}Y(J*dh1e}4~t!6nKVIB-Y1i9%&+V$gIY%#C(Tmt@L+Z2_2Y?_p(<
zs}YoR$H&KQ7NcnI*x1;-2ve$?wGWeav4qYh%V{Xq$|-47f;kgtraGP6xEnP7&=C{+
z=T?T|oFr`y6j6w#znT3P-vTneJJ?@>HUzP!=#dhR7{u~&MyB9?11j07>pyG;pv7+s
zXKJ)&UsN6x{JYfLK0u6SLf0K<TOV*%VX-;73OJ;mUaiwi9gt4Zg^CO($hp4A8g{!h
z&Rb1qqj4Yf2AOelZDlgRz~Y<x(MN#`Ae^In--Gzlr*Jzb%$)6U7n%F>pcV(zDT6RN
zOe)U*?pR^+r5_l>w*R&~Wb@dDu|>erc3(px2cKkYp7^n?V!}{|fYLV7!4KX&@D3aM
z*APX`G0i&V8o@Zk&T1#)x7(u|Mb(O(f4BMz;GSyi^&X$P)UepWFwSq0nA1Id$HhIW
zq{@0g&`x;9%;qg=_R>ERWz^_#I?3g#x-G*`R$GQLeKIWIkzF&kRw(zrzQmj!jr?(9
z&H~`lFDFNEZX<LszdbMik5HCxb9r>>xn6hS?_IQm;~6C<`)74K>dWIr!NYS@!H3%b
zyztKN<xr8l=o0w=j%xYGeC?{Cy>^G?<b#d8kRhYk37)Q?j6RnyTN4&<+<N4`-Qcm<
z=9@YNm^Clb>#C}W&Coq8mfXw-VHX;W{a}m4X)PR`Vi1;Qkj5GK6XXQ}<=APLB-Si4
zyKx;7-BMdkXUb0HIeZ<(`P=t_0&4@TL3)6k9DM+|D_UgyCx2Q=qQ$Oekx+9Xq5QD9
z();cCdC5T~UjS|2<z!Px5Z{K-mUCUf<!`=1w{zv0Wn|aPWr@R9LQ#5bb7h-hPBsw|
z9UZJ#;Z`HGIaAE!k?PrRJrF-@$#xSU8qDM~V8Q?Ab=NYWglMvq);z8$FX=jYZn`}A
z?n(W0js62Ha9zwX=tywAPaH0fNtcT|i-kW8%?X<@GVxI)CM808r`{2J4T~)%6qeL8
z9ccAHVT3oRq&UcVSN{;=$z~XG6>A&~lK!mbybR?7`r4+aROcoq%i)A^3MseoCwS!t
z@xe%raq2MP&yA%|{E$T7zE!<UJxxk{xum|nw)!vT0GXd*LD4;d_M{J*r|~<d1&_;C
zpRj_+9zzRDo4wGBP+g!pW}3<x9vW|N!v||p1oiGQ2GF?l=%Ksh_EFqw>5z6!wS%Jf
zT|Alie(FQm!c4GW;oNxIXP9OFm6ne3w}MY1Sbx_;a3KgrdW`7ToSE&HSjN<%!JTv3
zY6}zE2$L*Z`|<zi=_7!XCXZ#jV-{fbyQT~%3k#mlZp}Xv3+3IcHO0Rn=vkIhA^J=S
zIbP7h=F*nHfS?B%QKy0IU*7#@V*-a4FX_sIg92eaf{kOY#pDJ9cZ9?liKlzCIlG#i
z8XSB2R~(sDzf(QGnzV3Wk11Td|JPwa1k~R+s@60WQ>Z$^#+%#{BNiTK!?8`HjrI>U
zK}=bxjA9G)bylf0lAQgP)cWpcW?IK=##txVtU!zFqiG<3g^CZi;&?{7V{r{R{hJ}6
z&5u)|bhb9@xLw+BA$N)Yo5|7%@cq2+NC}4)yOSi<X)}PcT>|lGi_Q9g_%c2|bp!2x
zCqL$dUq53|M>qa!qM{GgLI3&&;UILWS}ijzPZskE^Gl<tWUY2hvR-{<<=e@LiBc}3
zj?adMlq-#9CHX=Q%7keEATQNBUS$Het-ibtw{7)>g@xs>noK+?eSH$^%~t3uUo|7&
z5E1oHuFn>US2<_B(~0t{Q7^)92>N%%uzZXMF?t%Rv866m2546pN^lu;7U+?`)4U^n
zKXz_;o{h6U+gj>VH>OnwTJay1ww6#e(z<K*d^6e>>%cHqKkgoQ3evf^N%d%pnHn;w
z4g4V3?))VwMI#wHnLGTK=Cs2`D^Qnq#j2Pcw^r+e>f7E*8q4A!_OyUiOWJwG(q9*n
zsfxXK)bK?Pk%RlM81J|&Mo2}kKr1_IXIQOHQcZio61$HX?R#tHZ>Yawb95PHC{zxz
zVpfdJXgR*P6nr5%kd_uMp<Ld*n<AGzm8&xHyQq0DF@<$dAMpIv@w0&J{@NsT_)mrA
zpSL4ZJhHt7e<Jz$@juI{JLMQhQvOme{pgB665<<R*!f=DxV5&K@mHJan-%4RDo)oW
zQr(HK2AoyZ=QYbW{<X8`kd=Yr%a0|_8dK>5@hK@~Bvf!FjS5dKPxw-+7yEo;_;-CY
z6&850r@>yA{r_A5N<JguctcgP4IhU!P!{m3k8%$EmxUGYH^;v7PylU`96i2dc*Jsd
zhF`4@{pD8j&|FS`?4!H`OZ{#9^1Y7sS}iV#^Gw6^TnYS7`0$_NHTgVW^gF+5yLeA>
zBATE4wwL~5AAkD1VawY<(7XvyvY`+<`>kTI3CfROSgtfB`7WSPNjnmZCqXcO6d=K2
zmSdwoo4eoA^v7uVP4}wnwxVdEUSl99^RGr_4`+S0P<X_QKlagzXiC<noDC1%sHqKI
zge%ys<7QhS8&Tl=d3_}-r`)n|gbR;HS<N<&7mpqrZ~XUwQ;7sm-lLZTMK7vFy_`d`
z-E{VMm5ML4WU~U=CZGyGKp8`E*vrPxT-VKJjfYO#Ec@@Q@wNOsStbsIHPA^NZYun%
zX3e&kags~NJqjXTUUTWQohuaNuOSU6>{x|&?sVriq6JX>Y}UxT)$=31mOwAG25<<-
zEA9wwjsa<MhhbF3q>dRhk2;#KefzFC9i6(hi*tZXYwe(kbgFQ~Uz|T^63b>G#i6hE
z4UKl*R`*w26W9pjh~wr^@@;pK0!4PBC*H!cyf}(V93n96&aSnSa0QqR;ISjP^j|$1
zE^y`fzsK+sQ`iNJACF`NK{FZ=Z4@rYa0`UL9Dzh&bC;$_zvRMKeaG;Eq^f`C8;O>P
zV5{j|zBbPTWw$NZ75Mn!w6O*T^PilZt%g?+8U57|KNsY)&E_=tuWyB@$a_)AvQN-u
z9qv9aP(Vhz%==O74%c01&lO94lZ`|ZwETe~25%{XzzrOg@NT3y)4*fq-3Cu(!nwxR
zI_ZY8Dk}bOL9Mmk<1~rKpvMg@IbHLY`$_5tYX!0Q&RH@>N4AUaj|59=zW@Cnw%#%-
zj&AGP#@*c;LU4C?3+@dhxVyVca3>IgTX46~xI=JvcX#)<@AI7V$Tz;fJ-Vw$?W$dS
z*IaAPOYEwm1jYMZ*}A_vVb!+;tMczK&2qW~^do2&`zx>zp2n^i57pRetf!sR+EdSa
z?>Rd^cg&)&;6VQ7aB~29HaT8t9pnkf;?Jd4Z2JRGOllh=$d)0^6;=FTf5sw1FEN1r
z(G*tY<HBW^>4_ElVawLavo{mobXQ!xw02bX0GvLuE1UMPc~HNzR&B5VQVaRX6Sr7N
z`a~I5nqKFhak{?hs;ZSBp>y2-iHwNNEhie78Zmj6@VKHMtvLAd=zGjh^fYRmKTzRF
zYB*Ru3iW(vux3Te&|{#mA^Gw8!2W!7*5T?LKM<Gnw8?4RGkh8)6E@w%!7~k8%~;r=
zrBu`lk3Ox*GFDVg&`DqU<i|z4xK}8RQyE|hF{YC(cOU{vXmMA}tp^y0TA-9DUcA=>
zV*NZI?S_OD3|8mk^&jnVg@k=*?n(*eIXq0U#rVE8=LkP}@DOp)N||Q0ITEA#C9Mob
zky1InfKZtV7^VWLH7$Ek{b=C^;25Dex_T}@j~ntm3vounHB^azv==s=hI?Lzz`9<%
z5SO^WNG>=2G_xm?$@rKvh$;PJp`8DaeW-ws?m$Pf)pmOs9(|bBf0X>R%gQFIQ6Sk^
z{azDwp3@g)GDoZ9h1s=WIy1u4P}G^T4vYaUwc!`=JV)%wl@d#w;GY)0``Jtm@;u~+
zacv*9;hU=$mnGizD83yQY|KwF)w@ssXg{8=Y+}r{Ik%EJ0MO=cu{e4S#9G%+)tLg;
zT-~9)U0prOdIliUk9C@IA3Pb~-kSGji(R1HI^WTW1X>7DPt@Y!+Uo+}U7Xq19=F*)
zku|O45X$8!>r3z`X)De$Cq`^YN%|lk))VtjdWEFdils@=#gG1WIUbx&ZCzSJi`C1L
z(n7CR)LdHh_ujWJ>mGJ~&iCVtI|1GFjIX+aK@MtuaK&|M!jFjV%{%YqBew$0XEbP4
zQWqPu(Wp<_ELqbZ2dyc$m%l4<bbg=){Ov1;Y+#JeZmz;z^HlOF8-4SmX{D8}3%Z_<
zZExQb`BfKZ|7ylT>bWn;-KB#DxAz=*WAz-yXgy%3J$M&K01Vd$wGeZa5wb|r)+!rv
zDo5CwXjO(;yi3kd-{wK;5lu^TYuo$Uk@*RPtUd8lPt`T*%ogcD`T4jgTd$K3eWT&Q
zo{NC2WoZqR{%DBIKGaK9I-5ye_N@==$ZigWtAWfP2qEEA6AH6GQwdp5uZ;x*ec;1b
zdg@|*4ns84IM0(!TOPt=)xu37JdO;S>jD3IApi>}i5OiTg7C__tKXl%lI}=AmjWpi
z3OoT|<R?=B*sGihtk#!sJDD%ksW+Du)~yCROVE#}3y~GmIU|K1H=&=eM_7))!9>Lu
zogey^VBb4Dn{Z9|4mg-4KPTWl4IK7H!T+@T!)mR?4F`K?6ir5w_ncBKLZoKcDFN4j
zfKI(gA+JzA73l(;a-<7X6)kqQChU96`>+HOwF`ORzuos6x(8Xxq6gF@2rqSI2<H%g
zDIdrZ?G#x^)z8Y5yLfAp^Sm{oI!rZtIegzzwA^^awz~EcI(*=IIqXg=<@^W|ZrGf{
zKHL!ym@dw-FF0%a>KtfM+$!`!!zJPyfhWurh$qCGTqHQ3;s2{M{M-Z(Q1R8*)g8%?
z->#`bRFrx2IY3w><y@p3ldBYyhNF#Zo5TmV{xUgTd8}M|ricb^COtfSTAioy_sY7y
zgpanE!j8^Hp7Lm+ya2G^E;Hw97ZFT*S3KJ6Q79WK$21QN4u|-7OpIsz9U0&tMW~!5
z`fU)sP(Zm%rZ+otFr&Br=(&DnrbPZ^iteK+0(cYi(&wOUWY+I8Jtkx-t(Q#8{ryuj
zQ$3zTL3PKn?99Mj-kLJn0XO>RZQQ_cVFM+a2iYQr>|@27C6L$0Po-OGVoObJ#MmvJ
z7a|ggbfQQ`${XQwH!e9+k(fWig1@SSD#@}wuIPsk2#sjNBnBr6PP}Jx6_wHQZfU}%
z7<!4Ptbx=gmlX{>ILGZ|u?crTpo&HHveetVP{{jwC%0d~0)&p+v*EOL%R{Bx?MbhY
zWv>a%WZeP0It{n)KpS^z`fR0K0ZXZJ&ginV&T1-wjDV-~d)kKkB>VyAk2x&(!5*aA
zVDXH)qf%iv+LXN&K6_dr7dPbRL1=la=cKdg@pbg^BJAVAN$COaE6QHWV|Bm7csi>F
zXTNmLJnkJ;Sidof#GPQXwA<@hrrVuC=)-fD<w+RsS-APg!m)`Q(U%<FAmP?(hIU?m
zY(<?miQ$r~po~O`Z<8lY9=U0P_C;-z$|!SR+`|imyj*uIY>xcKzkNK4K6{<xwK%OJ
zdAlhHEWaJ9EI%${w3uk?5qUibejRo0zm8IB??ZTYJ`;XE8%9g!^5<>$k%v!z+)?`a
zha+QNP&i_~R*e5XsmS#r{+s1De&Pg@z<45TA5ST(%eR1OsjRZ;qT;a07N=gz=^de?
zRM}Qb%IY?Rc3(XI&qKc^3gBB7ib!oQ&$jugS0|(FNt-hLGnzs#?GpM}Oe%*15SFnI
z%f}-Vomr|(j-L8XiW+ve-WvjQGCpEYq&~80GqPWNBa=g7^EV!KlS}+@h*QTGZYcpQ
zc@Fqzc^iNWiFz^;@f*N}G6OehE`vc9%>>QywV={yk5?7mO%w*xP<~L>u_{)4aRwas
z@2k%9Tt(Z_OVK3$t^I|c<pfi$6l2CFeu%@KJ=HgVriW0stt?lTeUrq4CN5YD8~@R2
z|9J`3z<?1HG0rYgAkw5j!5|0QTw*xJbN5D4)mQ!q_Kpm|y}p$LE9F#G<EBq&tt1O%
z;_-T6&egzq!rW`%I3wG@SMqbUA2iBEU@y81kSzfnzw)H2H7=dU;hf9#H;wPFP{)ZL
zdO0~cq)NTkpXr=dpYA}hxXCQ18s>c**$#ShX<@D=IX*zJ_sI<2&sU7W{iIY3_Ox!c
zK+n(nHgkZ}p1n1;);WI0NgBOUzCg)REV4qw)qZFglHnc@V=Jb&$V45a$Lrlt$#BlB
za*gM(*emzfKb^M)+szvIBgvD>SmgljcAU%ZD3-3Zf6mPCAT0lJRDYmjo8(X3*7e9K
zSdPUncKO?T+D`M=A8y}iCc=I8mzIrnpUkwZ5Rt5a^9_>a)U&HjJLsSX?ezY~7fl_n
zmz5#A!{i_?FK&Qb3_E?_R(V=RsbUkotK0|9b|{QHOJp9hvMn9EVs@t@P?M~qfg=Z<
zRJM}rDdhd`hWq)v!+y4oqVw|)XrFN}T%NYhFi%J`+}7q(!_+FJp*gP<Q}Q)8ppBK!
ziu5T~s?2QIT8|34&noz<m%;KxT#>~#<F@jRe4UW3Lz!qK(>IHEhZvRH*qN3VWNYqX
z@KynC*<PKm4eqm*7H<4qVHr!pml7Dao?KhyN5o!pMre8*-!0m!PsSKv6WgvH$c1h?
z6<sf+6ic-R!(0>CzQ3&c74sBNERin`*nxbtk^J5|IhSkJR1*#=SE(5A{rGE6e!=yp
zTo}=+j=;3+pQ339(1jgeZp>LU6wcECi&@O=aUUv<9$X8x98P5wt^1Mp>uBid#C3<d
zu-NhM`8P}fMLb@a*{iK|)xR|I$^`F=%q~k6iD!<;o3=*%hd2*Xu}hSR$>K>i<6s#3
zvLe{5*AkpWy3Rdg%hXWGs)J|Ze7!G{fVWN9-&EOeUTvT8T6Xi{T%YLd`m{rBo37~L
z3i~EAsRU{gqy@Aw{m&~*iF!<Uvr}%)?M`au2oybHEEUI=^vdZ?mg8~l^@nflf8zxS
zDwD3ZP;YFc3>d6s5#5wQ-@f!N>Z&sJ=oCUYC5AyOlc~fU+*vdgv}i7ACZdipj9V%T
zDVK?LzBX&(1=U<12rnV)1xuh|{BDog@;hiMxIbkFhVBv1w_~<DdLeszThpJd=dhe7
zkF!Up!!3A*Ie~ima{kN+6s)Lxpxr$+$LBJ?GETHq)2t0)V~&$y6{Bguo4{Y8yZlk9
zXO5gLvqClE1-ba?=w;T<vSB46EL80gKC$mJU+8BE=XB^;KGcltpNZJ!F~D>FCVC~A
z6gIIXHc^Z?`;XkDK?0-O8DuuaYT;ea$P19l=C4PjG&-Iwy?wozP`9?GFDxvcN*+=_
z1n(E{tsGyObqfr&$qoCV2E-1=vkF(&+kNObIDWLao$QZ=s{zY_Ohkg7H3YQY->b)2
zkthNT?=b@qnw!UE6^P1xla8K5H-d>?DAo8stGdL0LhW=c!<6-S_u$gIT(XwI>}jA~
z6;8^hy%80@coS661FH+mUQeUYUzM0fSAC!!#i4_~5WYNmE)B4q+FUdc`{3oyHkkEi
z)}c*j$1omG*P+E4r&d&dtcu}Z+TPi^W9FMX&GI)FIVLY@^0v5iwHne=#U<q?dqQ~*
z!(rOb;WY$}%$MP@G^>c_WxCmEgeu=Vng7l*jtksO@AtoEP<xuqsa3b^lrgdS%W>)0
zyL?7Blg{S-GK$mk8cOB5-o@`K-ZYTCEQF~nd{F;MFxST9c>(pvb?!T^Z4GN!W5MCx
zR;*QD7Y>DBm;Wt<2r(O!0i`IK?lx71Mbnqxiv$DM>cc4c4Sd5_V}oSG*1chxpIW1w
z0<ZD=r8g3O&RL2)#HUjH3ZjE2B=!nFT7Ccy2_4f0s*QxPubL)-;&V0?yLRwiKZS)N
z1YheOaIgQEZscNE8e-!#8Bzl3+f1UnG|gzX1y9e;6i<uvER~WX-87#j;g3X>5v@Df
zB4iJi^Q+IBM<q_RSt&kmKcL>8jyAg{@a$KcY0=Cc9GT0wPb9Yxa8_=Y3Nf@3*%L2i
z18W7QE(0d;@SA=R)(2!~ijywZ{MhWCyV3qDjA{G*uQ{|llxMw<eiN{9mpi2PTXLGR
zhkFT?z5@XdE49t#dUD4Yw$)9xuFhD9fX>bHxVE$y-}0|m3Xq@G-$74T*uRjBn*)o%
z52wDP=c+6diiHxkIMos>J6^D(GY0(B$*}j6x^u~~c0XYSMe#@c)|;$wzfQa~IOgN8
zp5NPeDT~<&*iVsd;}%Q%UP1;7hmS3i7gR_jr=^^nI$)8JNw1<1AXv2>gm9E1UTld$
zuyn1IR~iM%p#_NYR(T@lsNDS};nsNASO1VHmhH6fet-&adi5B0XCrXCKr{m+r!*wK
z5ly8{uoS(p81*`1D}S~f&0DwKCF|k1z?3axt8UaUZE-A=?L?2Y^|1o4r2iCF@F2xR
z-4>mZ!|5MbQ3m2j<*}(1a=~Q2AN;O|qH|@MKZGdw_$mTj0@;Eno!1~=glpu2eF;84
zJ*j-k3IwRyb_gf<iC*8zXlija_|xrZf-;c{HT64;X|c}ACD|P2M|m}j@JW{g63&IH
z`P>{dzm2cOZwo&D8mxVhg<d~x|B*sETP~JNQl*ANqWtNEah;rF<+PYZ4yM>zl}+u}
z1;zSfoa53awd?*P>@7qqucBT{G}(z(yz)q75~9w$UxX0obM85RCI;OIno|0Ypo!k?
zahP5T8Jsc8YUZYll#(y%ls7n1XI_lGvWi%T-O>DY#_lS~-nO{|!}9DC!IXw~2guwB
z5qe*R4bsIH>dWU4^@!!NeOVoBHNvA87e`uWVl5g|=5j($uW#cY<|i}Ss!ixys|;s^
zMK3+lv2@kSE*n6Pr#_B2wS>;2$Ai6NVOkQ7Wq456lf)VB7V4yIP)-N3Fp%(codP++
zt;LiM>+uC!JY%`SOj=WJ7Jp3eL2{>8nxY<eC5i`VN_lU#D5BhdBTeF0u7@FY^T<fo
z6FQ8VKtXM4_t$Nl>8>UgON%Jb2y$hFtoPHN@o?L6yn7Izt~SWH-HEO|SNiNthtL%Y
zvz=0oN?pF)SbyW1mq%BRW>b%jVcHwgR9OKbOu#vJd1!MpD0>@5*cuzuF=rJqF3T3{
z2}Hl&O!~J3>Fv|AjTv#0xVe1(bltc;zBHYh;?)TaHNdB^OhP+ElF8@W5WUi4A{^(G
zG{=Mo_gn*K#dnMqrbPcdM4Esu_~&$4Io9DlR%stMws^u;jPytO)fUW?@9636C&AI{
z1il|@8{~xd$(DLvwwKa3Le{;<GM`115vFN;QF|4&q(0L;U=;K9l!MrVy2!=23v_x0
zpDu4%QUzl6_|ZxL8-$kfL=Hs`gT+yhB11dqvs^ZG)#gQeQ}qYjh3TsvytPQ@x+6{J
z#ZDp@xdNU6GU)GdPC(^HpFpn%+g^s^6ucP?_kwT3e#;J`ZaQgSgEU2ySAJsw08uQ<
zLAejYU+j|pz0)K)K;Z4v#)W{X&~3ZgCpPxoL|C1#{1&Ym^hVBGi3BMPy$)Mvox*_}
z>r>%v2>5cb-oE#nfh6N)jA!GobvO}WbSh&}yzpxT5s&?-@M|rn<s>=Z5t3lu>ds!+
zq5u{?arkEkl?9eJv3qj@e3ju#m$O&X8)4mb2e1s5Q0p8$QxoP|A@jB}mpAt9esZF-
z8e59vCvr2c5-oiH@z#<pEuSLWP771&yLOGHb)4}IWYl<5TWto>z~zwic&X6Z*SS2j
zvF)QJ2jH~i`ez?&WtnDprex-L|6Z9OV3H81XY_``tv)yc1nUq{NCj^hbnph7fXl+>
zV<+r@WU){wPF1dwc#^D^;=i7*sQC1|2+|j8nmpUB|E^V}GHE7CWvtwQrJ#EMJa5V5
zWqPdLdit{XU}!*hS4=F7TR$!I^|Wcub%CM21bFAkU;9m><*kSLmZ-IAbPM${t<A~+
z$FOM79}~tf-alcc@sY?b<vt=;Y2q(U00cA$Fm-L4-WjD8$s6d7*qY_X&9deKmHZjs
zo!-8?Tn^@LXRMPo{rtrTNl(OxipezKrOfxUb<Xou1C>xX{|!>Fq5VtOeFZ`t#r-)t
zLpuYU#jTZsfeZ0~UrRFFRS=}!ruSe_ISZBwu2O$`ogsP-%m9sYUZ_GtnpP3Df^1<P
zsr3wX8?+gdgIwnyfvmYEx>gxY+y^$$QlFQSDU=fbcp&<%a(5cv`3T<^o;M4)x2}wj
zZ}qJN+z}#v5(dWs?si`XknEWeC<wl9A&BknvgM0aT|yF}@d}-MPo{TWxh0K}`Xl)5
zi$NbjrLl)-g?C@C=Pra-8{|UO6^k2NsR<Epa<;YQlDWIdPD*ES)v=t#os~G9?=$Y|
zrXlA}G6SOA>1yrN(2n(#{#Q5;X|ygg;)~uVk@Fk(JM0pogWg7`xj05PHC8m2#m8TN
zOnJDrUB4p`oSq(YW;|??AG+{*L2fL>@2mH1n`uhEZfRUw62P4*4iWFr1?jEh@mHzW
z1%!FU?D$dWW$)uM4l-}OJE7PRH~}UW<t*Gw<aly6(wK$np<{)MDfOJaeOi9<j1_SB
zn=MEuLWl5&#ZobcPzGEGl|p9Stg}BaRIeQV*-{LZrsjEkku)0gfB*{KkvVShb<utI
z{J^AkbDW_5k=*nj1<4ST6_fUXfqa>O01g@+<#&_k9VG>%OcPm=$GVpcN?es*>-1!v
zL^~4dec(S?(9V(<G&#PX;1IaWOhTSYR0|Hl;jMY#j6EscCdU|NrN2G;iT-cTs=B%)
z;I!i$={OSE4ZjCc229mkFn(t8`R?TDm)g2^ml%+QcR&tBX_$X+R(&apX5b)(T$hLY
zQlarZX{Thd>0>^Lxzh&nVvM)TUK-VMJ)N^<rxy2idhwn;<O$wkm9)FQmdMZ)W1=dW
zIGIKjr~eFg+k7@{P4S#0OI%B^<NJntX79OR<5BU_x5G3K?wJe|fuTs5F64m;skP(D
z&T~EKf-0NQ!LGV!Ja}3cAMLH(J=o^K#PW<qvwjUGG;#Um^a&OfhTL&MVxR5}qDdX}
z@nVbWwTTzw{G;Uyo|@o62i36cPs>p?*Z!I7t%y@vC#8@R!vXANxBx!gwDV_MDkcm3
z1evM|agmSawDa}=F27rEkK>_hEi<iO`2HE<JPMgI2CG7oK@J|-An!R-lpHVLtO~Ns
z#r7bQ_Loa#;*x+Hn6mM``zzJ9$=`qvTf*4GnTYY@RL1)A($lcugdt|WR*-V$Jzvi-
z*{@qDzNkOy(aWxEO&1NdE=SEa_cx%(+>_34EX9gJ%XAj{5c~t-Jci{f3vO+lyGFvC
zxg*E{YWrZh=k1^|uS!c{ONe^ru+C`6x6K|+kK2eYYpuR$Ct%|AtCPt3Y$JdB<Cghz
zXDh#oqI=K{&q$0g<$1ln^7!us=|9E=pMuYgn}ols`*8hiXH;>`d2{B|69mx#gcGC|
zd|SfcFN~h8-qEaA^5bHP+uMwTAwt~|SCa)P5+8KBL<yZf1ihUUxEnV$)8_;W-Ul;<
zT#r!i9v$g!IVd5;Ku3q<PCLulEbJk%ZBjm3TS8H~9$y*KS<Q*EUT#sI7YN6e3`Yw_
zHgsfX*%HI<ZBI}8H~m>RL?}2OM+bEwaqt<7Ld2_rmgGx%eY`z~V&8l10BpI4cDuNp
zCmq^@Kw{#(wwwYsKw9)pW>96A+#+R9xkgrYmLed|vU|`_W(M2fS`v=f)&f-lkDbIx
z@ID3g*0;6a_U}S2I<-Tta)4andWcOFiB}Dl=c?ZIu`^qZcVnr9$Q4=a!N!KT!;K5U
z1d{Ommnr7Wm{bMlJ|9z7^BSzDo_?moeJA<Qh0Q8dOL}5v?2c9GocJ^PiRi!4?5mUg
zzP}$V9PEl&sPB_C*(>I|$sq=Ksh^*_-43hbc_ufMtj`EsbmL=Gnf;yP;W*FF?j%XE
zN@?zsoG@aZo`5k|q@pV~eZ1lwvtzFo<C(JHywPGlmwlCZG9l0YFfDhLQq+H|$lzxH
zMg;G{DyB*73uQ+iI_7#-t@#*{oc-$ec!4u|5v9i~hG_D7+id=o2CH5M*l(8I!LXbT
z>w?_W<EcBIPV9In34Pxs<*a^wvf>5e%N!YtAl%0o!oolnn`eQwpHCmBOZ8U$QMh#!
zq{MTz_%L@y)rcsGPd5iMtIaN<fcpr;I~d|kBM7mdsBP>}3pHTmlowAA16nZL&GNtn
zr)5`^VL&$FpwDNF<-*xq=Ckdi;?UCDr+mEzH<}k`gPC+@fzsGf@f(K1QnxqBBA3^z
zt&2_$rVuSO>l*<Il8r}3|G}O=r|qt43(wbak?U=2yoF+)Z&sx^fERhL>zB`ujc1m#
zoBR1+#212XJ@NbQ<=`K+XH5M);EBnV;OsUMWn&*_%k?V~Hs6rl&~PUcm{BFKf<}FG
zR9F32qm@kdU@usBD`|4F`>WnW>P9AmQGJeTXVg5^**Z-^ar0(8sW+@T)oBJ4u!!kW
zUCZS$TOXzdT|B0%8K$i;09G$Yr0u^97~9|1koaGAmtFg<#xd(y*`IHOlSS}~)mjH+
zugOBcLkatp%llc6_<U};SNKgQ?=jPmV#VpA2$$9410Z|)CbOPEDj(UVm8+8?*Whjf
z*NRgM2q=DTps@qY?Zx){dEJ?zcV-k)Par&Od4wF6{k2z$uFO=XxwoWc)-S%dmV8Y|
zd$vDE`#Qw&ZlbJc1n_qMu_N3U=6fVJkpN#scq;reEg+Dmo+qDv6?m$}n`iaO&LB&g
zuF<%Z#eq?{R0}n|o<8<{(0M{9Rh9?E<_o&Pa;S1{6qX4@aZ0z)$&6I%(GbJ(h>wBf
zhUq&(_VKRwuz0rSfe>mFy1l}M(vpG*qkMtz-S*)fam$r}Nm(rgUayaHzSVr0YkCKJ
z7WXBAPZDa%xZ?I<0o9Q!5FaR5?@}u406HI!x<*?MPa<)O2)mBLT8&ZB0_dler?^IR
zd`plEV2z&>K%2~>R6G16OaggsvRDW)r7_^g=rf*Y%OA=n2{66n<xlVkR61T)Q3?r3
z`W7GmAyjO^<R8aznA+cJdiq*vqv)Ng#c=FGmi!nz+CEz;g?jOhf4P>?7$4?VMe_>+
zg1}MriO;wz`WxkEb0cyOQvVA%8-Ki_D}s!nvyETgf!XE-O(yM@&*%Ufa2sIDr})2P
zw}+jwZC?YKL>^G=6dV-RB}JH56f14|ruzb1^QwF=p~cnp^Gbnd7>ec<ojV8ahBwx(
zOGBt$D>7z8eG^Fc#{HV(1+C*&N&$>3QWxV(VoRsbcYo*XtUil`JF>{{yeT!OI3JyM
z#7Zq~)bNBT?e0XK%zxR026siY;?>@A++??Wik&Yun|YkFJ7N?Dbx&Q9wozO#uJ_`O
ztb{R(lv2g>=+AA1nasi2i3@2*u68i|cy6kqm~eF66=PGFa?td$-Nm__8dy6S=0F>D
z1ogzNw!0z`-*P<?_S~q@wFD|{X{uFh2C*G0Dj)b!l7OvepEgoG|0LRn{7;wDWtNc3
zyuxzt#Z0e}+@i4x-o{SKy6*+)D|IMSS3Cg{uWl0D!<n+ywRK3xCW~zfjNx~HU|^^w
zg5M6`DwHAX;V?{;MA2!ct-qH}8@~2Kd|{PkSFX6IFcg1}mQa${cR&%!rORLpt*&~$
z&;X(&!rjB_ZrX$#SzJZ`JDe?w4C_t2iZ5M9wBK}R#-BK)<Bb>t@`KZju5Sb3LYEB0
z;I8UZv$d!>tKmS(z^xY5{v9Nkehs}K60c}u!=GGx2gci9L+WbDJj$mxEw1~xUe5!J
z7794vxKcf&$?Zg{O*Na}@V$*x{93F`i;d5qV+mK;%XJBiwCzLz<)h{=7hZ45U;B}6
z20c)FvQ1TKHQOb0+WohCVL9zw@cdjX0#r?%aOz2@MmWvav}L<0xMPS>;A7B7?N6>+
zi$?bwhod<w(2V*a1(60dri9r?wwIIVMme5(LJ`HBSogR`zB}7php%?c+X|q;f4L<o
zMDF-{u;_8%(rGc(_YEFFdI>HYP0z^3NsZ`OwfSwRDL%)J1WP3%Ex=4~4}ztFByWSY
zs`iSsdo&ehr#H4SGDwU%wc~nqZ_Vtq{Xb;_6qwtl-DZoN9ehOx3>LD($)b0Xs-IzN
zu=2RMj)(+E0N8O(0z<|wa(3uL2<r9ZxZJ(jo*ADX=!d~$JYL)U$(sZ`wB6EBBXN{c
zduZv9<*9i@0NRsFNw0eWi^6^l&n}e}Dj1o|JWzMy(QxX5O7F&>sf^eAJ%a4+fSc4t
zP8)AHMNnP+1d)bxpFONfEP7C&ld6}d&a{tNg{vcGPwfDahOk(0<mz7XxIxAHoh{kq
zVJcAlpmfFZ71nNXGB%gTUsvK4_=V_E*+jifL^?CBMUyPysuDCWYJQ-&H4c&_TNa7u
znV!&Z%Od;8)Z!pJr5Fewh9=_3^;7wMV7B>pUxK~2av)p>?#^URQENScW+#NFEft5H
zWtg&yH*Wt;10fxV_2<2VCYfb81F0=CMMGm4{Za-(gzXp=R)5)^0Dx9_gIHk80N_K}
z+vxkU=e+Cw%m(K}FDQ0%M^;-@ni%fGOz0@czau3#QkGdYDyvf^h_P;8p{Ub6g%YKl
z$ayoM|LtEgQ?zKmOxNQUD|7Gic@kGFM!Abd5(aW;q;A0XEP$)b5VXxh`;Cb{ZRt;S
zw%c{R@zDjr-(E*~BsU@)iuU5Kj>!<TCA8Xg&R8fu)iI%C_rs(LPn(t`g+v)vS_&lM
z9}jQV{6}`eNU2(!<C`WL79E0JCsjQu_$E3U(u1OtoEQwuUnk*_ht&94e6#C@<*o0v
zw9{T9j7V}fGG>)>ajWzE$`AW^QcacurT-}W7BL_Z)l0`G`*s>4mH0VD2AlNVdyvi4
z6%JC-L@SRi$1Xp$%7M}$3|`X-Cadm?Lyr&pxv>`I=sBgR{cJct=jJ-$pi=rQOJ7u@
z+H;)ddyi9;bLgvIDm|a37p=jn&DWvAkOBx1^ai!AHkM_WVxdYRt-^uEopxrgsA9U*
zpXwbTo*PZ}Qe>WNiz)or>Gay(oV}lQkBmah^yNbv{~6RkDN&5eSK+PYr$$5(W+RX8
z9=61*fIMX2BSjsFd^7}5+}V=_i926fN=7xt2};e%iwt{DP`O~K!_wa(qGc#0G{(`X
z#~Ln<AWRO0iYEaJQH%cZ8GpP#HB@5|U3n7`daT~i`IU3^&+Na7Z`v?%#4xVTXUzwT
zur@z-g53i3YUpbhR6r7;Um+0@lxnDT$@w`Z+UBGD3a!?s;(D3nSzl3zP>ewdREM=i
ztNt!5A<93NX)o}r3@uf7!C0W9?hGaf5Cg6apg4^Ig&dVm6b{B>dae21cbgP_{Xx)C
zh-xgoJVOtY8syY8<5vy)T4>WTAXi*l1UPYuxPI=(K0`@ds6|s$xei_47rhO-4#pZF
zN1|Z~v71aa(+nW*0DpqFDnam1yF9A5$J>7U|M&$4r`@c~!1U%;;c54D-}q!LofMYJ
z7<0aW1sVGKp@%PCq4k2h*Wc@~*iPXhkbFn`g_HSn*x~+Va4_3tP`rtAC$ZNF-m;hk
zttsScoamzk_2k}(?=XyI95o|^!TrvghLyj-ESS_Qy_^#Kv_pxI=4PJ%d8e1sxOT%?
z*5jWO3Vr)OCsa3f5YSI2sXxfiDK>;}o$-$o09lS8gf_Dc<&T)bPtCWWN4R0+ua4+O
zyk4PSc@Ed^G9KQm<kjPK26k<V0P+ikDN|tg!}FIFt7{=OeKXCD2KMy5S3N-KDD?*A
zp62-QPR$Ez^yuq%3(!-Tj>1UQC}zSz8I*{w(o38^bO@JVgGqe0=~t8fSm@?f%~jya
zPo4jo!Cg*~5I6|)du&&9@;?e|a8=k7$gp(i_^Da{Sj$APlBu=dV`}XSk#FkomBDlb
z*)%r)_g4NtVskXubP|XfC#O24k!J8mkB>e5ArL2<_PCLLwr5h?>~>X)eWo1_g&56P
zfTZYeMIlGS7j5Im0YF@QvENN6_3;`HqaYLZzdKa_`=EhRo3!LzGA96SzHqeJa&euP
z2lc`bFX_S>M$WEuM|cn6je1CRx=&$f_`8?ENS!;;vFTr;XrJRycrsKj7`lfeH&b7v
zu5CQq@svw+8UM3{{FgrduRn8a!0d_|400%*2`aNn#-B+r&TxG=t@=k5->m)AgNzpL
zNJbk>^eNMzl>I=<+?$(|w-c*6pJX*v>FJw)!C`Wq2Hzi|Jv#&GL_|9u0ke=N(~foi
z<n@%I!AolUFBaWM$-@KxKcmGK*R>T4RmT6!9%c3G3m@^mitC#>j%Ir{BV$($Z)V0=
zG-{#-AOBA`KW@&N&8`e8C*6xJDE~Kce>8`|2sfbc;;$j)6(x9%c~xI||3qgs#pQ{6
zx!tX{)tVm12v|N6T-oqnEYVoN^`B|~UO4~PS*wI0kO+TK<sw$qJ^7Pd`}%Eo3G^K`
z{A+TnAE{Y&<}lnBYGM*hoV8r&{>D=(22ve){{p#MSyxs+O44DagA;3<jdPf&s+nZ8
z+HPlF2fznuuAj?+%K5&?laLwjdSS);Fb46+^1q)f|L$b`r^=7O1C`>5OPNYP*rzTT
z>n;g@Q%Y_^V>afMp;wkqmp8J6Gt)q0#`2QjJkPQ_j6TynTAAGHjwQu)DvGElQ&U@;
z<2(<B-Dux^IdwzSjFZnV@gAeC`-;5|ZTz2I-v2sHL(I!ho1Sdlo8;?iiLGw1IWZ}c
zk$Osn5%gSUX3)}d=g)*VdMr}hX@i^q=_{s|Xa=B|xxx6(TvD)LSznmKXWcqa<u9&T
zru2VT#~8@D|IeZSbCZfe`0zglKe&_d*MvW~fN@NT@|c>$BV`D>Wf-5z=Jtfh+fsHg
z9KIAw;B+&PEdGk~BOmX;Sg$~1VX*xo^7o~uRttu$x<ngVlALi3UVn1C?RTN;o*mu)
zoeM$NAzfGbFPi%gfxE?a1tC5^|3=9amUwbYPam=pWAH8^73-0X4{gWw0?}>(t#(7D
z+MsTLQfW^P4MMEG)*oi=DESgLxZEdm>m^@@BD(!$_>wLNz@L2kX^)%h`&6-ul!@JF
z$<>!!hJl%pu8B~9@bJdS1b|eMZRMhXh+|`+F^#&vk+~0HRf#2bpJJ`;eigZ+re$oi
zXh(3bxHffFvHXF^2eG@)#RPazG~*Mi+SfL~v=hEbs+E2b8U6P#0q@KQzdf~6>|}3i
zztl`BSX@`INohA9GN6BHL<^a=av=RYZtdy(MdeTx5Atm@IzG{S_@sT_H#RiwW~-2G
z`gYoa%#-|=3Dm>6Yo)x&B>7?cfRgo<vPZ|ux?WF~!}<<Up5XSumvg`Dkbw#H@fDV3
ze?^bMs8j(s(&D3=O=jwcuhJZEYppZgUpv1+@6j3b<w_v9zw*uYpIoWtgg03JzN47a
zE0!&NU1hW69gEFrTKfw`dDUZ3zI2ACoY;kCzgcf{ufJR;ZiBH<_9<i}0+hVzQ)eUp
zz)xCzkTY-4OA-ywb%RwMx6PQa5fS`NMmfP4EeQV6Pv;I{6n%4_LR-GBYWAG#-<?xp
zjSKj2d*qdBk~n7uypeV6Safyf`0lmV1NQ2zv=&3H$?ZRRvUyJAo_suR$PyepCuEd`
zQ5oV8E*>YvN=?-Hmu3ua+x6=(O&$Vii75K`53g@-tnQBI8mwlhhsVa4uF1o?&Lh5D
zg~p;i+&6}2{5WSQcAc}zfKFkK92n)PPALXE*=Dq@cEg9~tT_~q8ZtYzk2vpT@M+g7
zcrf&2y%He1U-kz4Z0=7J|CYEhpr`0uBvxBFpS^dh!&PaI*qjavn>$FPEd1ti5{KqO
zKMKm`^=LN1%Or=H;f5EZ|4iLZ3_r$z*lBpIrr$mJ6ISsbhg<c2%J&>%4}&u;{f_H6
z>Gb*Px?`c^d$&qgnNJzlqA`a42kE{k;|c;_7rJN9d|!C6=-OFEMe~m2JU*YPkVRn9
zGy1OzH!yK?{{%Namr6zB;dHZ{s58dH^qSN$5`X3EoG2>EUQjnJh-=%%lu+d&Qqf%~
zr?XgENN#e2%=w<u&8$s_vr!h`rk${r*bbN`v*0zX!L=b%`?OuI4gH&do$B+Up@8Ei
z#2dJG8i$lrvGQ9}5xr)aC+;AY_M+|oXI46Yhq&!*_j#C(!ey}R4MXKL{fiM5xz*$Y
z&u63+mrn1)Tc-DpE_W!HS?5wS7-GQtmsv~uDWY^C7>(@jF6t56EAx%DGd<pQ`E7eL
zPyS6d`YGhrzyPP!#092Rztw3Ktb}B{8m~x^n7mEkcXo2ugzh!#<pwK_0%_Far5~x;
zi)xbTFpN?JZ~#X0EJ9i!eudO-E0d&a5{;sHZ(5GePsp|e8usqf-OX~cdjIx&t?>-|
zORkeS-_p@Xm5%h^uI4dq#%H^nOee98_OU_e9~>b=T3*)a%KRbWvImtje;;)8&720S
zoHtQbHT5qC0~9$<nAPIZg_oHN8A>afm>^9x$j-G-L@U@wKu|0)p5c<|>b`B(Yt(=}
zsvUTcrKr|*WvWOANa*ZgxbUG$TgUR0`9RUbDA09J@AT`5W6G6GwHwY(!Jg1-^7w9n
z6_L-LM+)6fhb#3jQGM^1>Z4qM&{58?Z`rbuiB^L3ATBGj*XJX^<R{v87cEbRqL=pj
zOQ24QlF5(GqUN1XoR-N4PtL_B4@p`|T5IdB0F$L}#o;2Xy@lV|2hbNcJ6{B*)LSL>
zpGGZU`34?P?bmoInN(Cd<V@BXSjIR^5(yEb63vOOX<7Ev8SkgihX=H5=a1g~8y_Jb
zySf4Xw1s}5C&zQ;>fnoQH4sC{3AVry@w=AnQdWz@zS~uiWNbEMp~AHpPTrWwreG&6
zUY5|ur`j?f_)_C|hYxhOI=iA2@9Z+f``beG4aB&0vqK_YdxQIDVIIoQ*RHIed!0#3
zO8f~WrSW=DL-q_IJ<1M{v1`$;MGYE)j%&Tf@nIcriM~?>=KB?xvQj4kfqp7vFU=Kf
zs;73g|0(?knZQE2)$)5L;Myv8aZUT&9ZQLPymHuWbYvYbRI~6y`)@F47z)i5GxrgA
zD)Kkztemk)wh?UZGAxp%UyM02BXJpPg|*+scY&*-rK$zeIJme}J|elfPsoRYAS>KU
zo3n@0rJXZ{M@(vJYVTNl6l(%5?eTKQkVOEmSpphK5SJqg&gG@XemIoDXJZs+zRm0(
zKPasZzvZColNX8Y=2f+|wK>mCEN*;fvnQGq<D5(S@3l2|l_GG%|8?DN6{HFj+%!gC
z6L;g#l^C=Hv$7W;=saRk)C|GsWM>gLDJKc&0olYPDf?HOajCzjv5^ocp&$@1VT}0!
zC>1pK*cCPO@wV|X)fEI?YQNTU?Eyx7&CHgno2m4QcALBZw%e%HgA>Nl3_xO?ZVxCj
z%(ijR4@~p@F#sPe{ys|iYcn1&&GlGtwU56%o3tCPKkYjS$wGkoPq(wcIVeO;UA>*S
zZ`IckeFr+~?s{*;>fwA%Ut$+rCH@VjB=toLSz@jVexAD7Vo)BTBi)q{aWL$h3V6TC
zX>d7EN_@aDN8qJJFI@16jJ<DpO273!i))M-Qnwmqv)Q*HBp|zJyFqIQLs+H2(nO(_
z{=Y4$TavkdAAVd35sdTGv$I4S9JWVqfOR0F{L80<Y<wZqfrxkN;DTwxPlT?xd^pyQ
zTMksOih<^(AHUUKQ_wrx22o^OSj$xO0pd!c!Z^m632tLt3zNUT4NQih3)BAN@w|>E
zAiN~)lXlPY%HehTv}Y>7aY=C5@>=XhF#wqECT4q9x}kut>PFwY3Wp!cLT@p=Rt;|z
z98MSWl9A@Ui&Rj^J)*4^JNfRLUhNfuD>4S{hm*r)Hyl`(;&jj1G=q{WMg|nwA5D{3
zT3T{O=iil*=DW^AneHOa7-DWY`~>dU1ZVxWjgBfT|FO%chziUZF8h^HN6Rv4xsnhT
z$mt<UwCfGNl>LvG>#x^jFqfL^2@|`Bx()5QSGcc*AwV-#!bY+o4o`VvLWg>ERlHw@
zbIM+ktBjO~T{HjXf>XgmICOyW%vdl})sot^GB(%O>iZK`uJ~9PdVceA(K!?S;gz`V
zeZn-g!x~i5+50v1MSq4tVO0As8G59=0}ZRAN?X*MlrerQkEn$)5#oL$O{sPtXG$_z
zny*2cg*U_10QQpawi&R(G*me-3C@gQct6MGVEWbTrc^$adENcwJ&|KVP~E)ONOrJ)
zaA+6_sLdJ@ulW;fyHgEg3qcD-AKvNkd!c1uh{nRg+P!7kHIl`EL`MUQo64yT)^lY;
zY80NwA%#!0@I0)uA)LGA;)sRV3^24TK!1PzS^z|$OlC8Wx;a!AvNMG!^=zKt%=9BH
zw*qqZ7m2Jg5_!mXQQ_r4=W}kuBFHCNXhv6AoL_<XtHu0o$C4jlx|gK+?v6Y(&Cj!q
zPJ>4%<yll=+Hh1ekc{7E^c-T}UkM_pUC8}S=(b7Weq^r___|;+c<fxk>+W7sjb<4#
zQx)6q0Afnk<SuNx{^6M65<yjX&)f>QT`@Qqk1V-xc0xkJ9<3LR!SCCUAt-k(FPC6$
zDb70lQYZH7W%!<jA<`(g)tCP1zW2XhA@*<~-|Tun?=n=kiG#tx^h|m!$sJGo#A#m!
z94ed3H1t;pGgm8Y#8Ts<%>2mCE&A^Ww@j9iK+?1t%6-hVKdBJ~F|+I48pY1a8^W>X
zVUx+I5=}YAhImJj)XadBbv%{i$4j!v04=svYh%z3u=1;v$zU86=+5<gRVSHCbra-~
z+n^Wlb~oGM=c7uSr~SuvDyc{z?Nj+@$D!a)yxzB#UI@9#;#)6Z+}z<m2;<S;63t|6
z#xPmn(l!nNz!G(l&Si55X6PME<gg$)IXl-~9!?d6-*$tm)JKgA`UTClIAV06@&w0+
zBDtLe1S)D<_BT7|B5M9(!iB$oM`W^Q=hkdPN{R~9@Yk9gp#9?lvz)Kc$%m1E3{$|@
z_sp9Vyk9h#t<d36_3wP!{}vS)n<wn|Jn6QeC(~%Nz-;^-LD`;$%>VLrCtl>cMFzLs
zA>iXN+%WXM6O3}Z1ry%BaR-SRy@Q)Dk49KJe}jivF6KMFfp6k@%L((<#OqOU&UY<o
z^RQdl)N#GT9FuoCj%P6OHG{yDrTsR^mbzC6SUfonzCuYVc*VN8{<Dw;=x`&7JZqob
zphYTBas$}DL4}|iXdm*>9(K)<PD;ZNpmZTgFO^q!$O*Wg=7Q<h7;&!pAV&(tU#vmk
zwTH%4A`B%jK=%WEcx<Gw^`aw#8MFz-zn84ur!6W}LM-?X;?#N-R6A*ug76Bqw5dB?
zglutIO@F2fxf!AO)?~l-Tg2$pc+~K;n?S6YW1Lsw8DnoV7*=5tUgZ6el1abq7kKn#
z9yS6*P{{<fYos$Kxk&+_rtbf_?;s%w@E+fKOgF&;K@o7N)*Y$!I0jZDIs*cxc7`Q<
z;<N{bF_Y`9sP;H3CHMrNj)h>$5lmzNj19bk55^3ajeXfrDt@0^S%uggR*YE$lvTmG
z+h<(H4Y|~w?0L~q!h3WPhZqyv(zTFDt;bqbr{XLZJIBFJ!C3J9XnEj!Fh5gFOoAT@
z=fM4JSv4Gu^fT%(b@B(e?H!mrYE#_~OtvP#XEn|3lfdjl5n^;9z;Pr6Lxaovr1rtN
z(?w$$JR?HMYP4mna_fE%ddDkGEMplW@%#-JqRJv~4Yn2Dn?dlVMWhUzo_`&yxMEOg
zufFsGVm|kQIdtidVt;Ix)ON#OEoI!5{(w@tbGuO>xlPA1`F^~=KXi^D8mX*KF%wWE
zSiH{c6rLwi%BcOIR{W=J7oGvJOWNY-qWOTV`y9;KOle3p@Yg|snuYAgALe@$YYr2;
zYwCPc#Id~m&W`4N3N5x)oa5ip@bn#9Dd7c-L`*d#hvG?wMz4^j01R{Ae<v3uBOp;Z
z&O7bp?HY#k-0(Wcx&?DOqZ6;dXrD-8(wJXgxooE30sf*BLJye)*<}fo?(ny$Kt=oZ
zJ7D)I_>RZhf%c1K;r|^^%%j8&>Q+QyKwYHN05=amU-qI~P3DT#4MP{e_y7$FD9{bE
zTV~_DRpDa{37EQY-@>k-DKWw%SN_-)PEg!{os<lI2nEP+pRQHVV~jv?6oym?XM<NW
zT@?l!{_90LJH5!E?f=#Xg08<qd??6B$wcbkfo+<RWChrvtl}LX?><vYn^HlD%~53Z
z;49y>B`|EGpLN{_aYg%V-kY~f@Z7mBzNh*kYMeZ!(7w;hUivZwatpqOH)4gn9iGJo
zm_vUSrpPWkH-5nNpL@O{%6%0hlnit(87_&0hifX5@apx)qy?#@W9D9pclR^$t>zl~
z8vc0$vjK6Xu%hUJ(p8{sB>sa;mw(FZb=HAY5(GR5thzOb@8v9=`N_LDs?<IY=S;))
zS^RDY(y))8sey^8tKfajZ^1ylR`-<aUrg`@w!h$6P!sT_HNa0Z+R4qxz;J9xJ7`q%
zS+*6l5QuZxP2><52G&$vXT)!FZNG<1;2<07Eyjw#^UWZJ`EM9RFVwHiH3n9Fa!Q;{
znru&T)AxnWcdDb@&|uUyqtUKH1V}jY7>Ww<ZqXik%-t}y;Ixme-td_kqh8YwRA34@
zeDFZL;B^E6UD98G%DNFoZf6ZqND2{wG(v0!CtezPIQ?$K)C2-iujBJ`Mzz@-OgS@n
zLg0@YE@lS2Kfyq&*7m6e{PJoBhgf_@hoicb^x?iNawibcUJJA#K!KgDgtY65v;!l(
zYi_xw!4FLOa?(TfieI?~SD^ymwZ38)dIrPUNe0&b7Gy)iG$fBD;*wkT6!ayOFZiEk
z_b)J)0;pN2&v3V_Piy9|S~R!0Gol-63#Ukk!BZu@r;tB5H~Hm`r*b|S1+of@!6r1H
zLeU+Rh)QWo<|jFZNmN({{UhkA4-|AGXTeetKvt=ncc}qz_-UhAnH|F#+?kWbP#jU*
zlLxcmR(B8#U}!JDz$?>c`n}wyxPmS>(dC4aWkAqD@VXT>6EMtjCD;rMDvlyAryOji
zh9zBgCE(~1Tg%Z@9X~J!T^Ycexd^ids6TXM5$At*<Rp#xRFU1q+7mS~npQ7K_SPjF
zhItT&@%1#X>lkIY+cVJ+MUEC6h-O=Kf3_T*kv6)|5tl$LjSKW0=RaX&qus;`b<gtL
zj@-^b>?dG{l-YzW<V}l3A%XY@)IQaZZ@?%G6@!@^VvOe1MI6OFY-k3<UXmF2TniPr
zgnkKq{s<)wA(R{dBJchJ&9n^D#7=<RVhr(hFWd$Oq_gHc!Zb}rpw0@_1qf($KTGNe
zF$xiuZmj{pLlShU^gQj<TTI|Vn+_#W)u{!gaQ4ebJV9%V#;H=u3=P8#z?cTbXw^(1
zQDR7=0xP<R?HvwTKr>s9Vum5)i;g~+D)2oR@wHIdax{ehdnbYc`8sc8Ja5qD&~Qxn
zZb`4iubg?kz9CUDv4c(%9wuOzKCi=osKtmCW-#f<(^l~$HWea|4y7BE``gHo@@-P=
z;0us8{?}7jU2zmsvhJj)mm~_Ta*Flkw9VKc5)>2}(&dAj8g#OqrV^VCDi7u8adDjy
zBt8oP<01zXp$Y(nxT0edz5WvH{sw^<)8;#g#7vgj;m-|i7OISig&_Zv1&~5`UtUGT
z#6X$=3yE%PES}#0V5bxXqNa!WG~iS4Q-3|~mxGJJt;p;;Ll}UET87dhhar%8<bWQ|
z(+7R-E<~}{@$;J74hQ&Sgg~8tbM$~_rjMSl^u@6taI|b%qLG8<Qy~j5_u4CUH79mz
z2b<3M{E(4ER@3}*?ptq(haq{mrYG$Qd|4eg&0$k%ZxL4qYt*1jAQ=FQ@*4H#iVgsq
z_6Q36`?W(Z<Jr6c9AKm&j1`~<qT~6vyq`<uWilD?DvI2T@MNB?VJ!0BDzeHrMwbJz
zKYkwINMF~Jr;Ez#Ip$~^Hh4om^U>;kJ+<B(j)i%aYhJ6Z^J;RV=k?$*9fn0MGC3|v
z=aWt-wP?5)pW>Dft~OWTH3~jeg{vrAn0SPaN9H^X5pDdUXKY|l>@|Ovo4d}M)8>7M
zCMIp^-w**zC{IzNx6KgrK0D<4djyr)TIayA^IuotJxH)vVF>R7mkNUBr)<zw%)rGP
z$X*0UIqEp1FxC74%xNdo0+ZLrt82q;QW^wH_<PbW47H_3Ch;zSWe%D(2l}lgys@om
z_;q~3An>vJ<3%%B|FR!N=-$C_hpU6Jh6n0QI)j1*_{osd&<yU;5FLKmla%3ju@9{P
znMS~<2%1J9h-SIqTCVUFl28hf2WZ8K&i0Hw5Zi%>>()cilRDQ0iAI*ou@d0}0}3#j
zKv~T_?>Ml3uGq6MM(Eju*kJGsg@VI^0uB`^Wym2$jvpCYBH!C~A&3FG`cbb<Z0V}h
z5=2;)CIhrf%e1Pq{|4jY`qmm#A(Wdtf}IK}glFAyD9a(6KLK~3neUVS|MMcB`3Zs)
zqO*1JCQ?@Jm23x{AIG1j)<9!pO;ag@plva1T^;D{msjt3X$<?D(-e=VCa_P2V+My&
zrM(-RH5fuxrHP;d#g-AfWDROEy#M+jWH|4Amfl$B0Rc_a@#GlUJq&$6o?FqamMls;
zs{+iE6!!}26n+%->X$wTqngD~2toMWHQjV7i=!weuo;tE69{J5_46vePQf_(i|~8c
z_UaLN-%Y|uiKi*Bfffm9T8tn*qX{T71vhnZ0ZqjIeGl5o%_ng90lEBc2xFO~eEt<=
z|GF(rT|yW<NtM`UND9>D+jXZwR0e@=Qf^G|W}pr3YB&ZoyzNgJFWPO^U=<ct@z&ZY
zT{B59m{XgC6y)^)s2$|6nBi#mE#(+=j2F-6gm6D9*5#VvU_W+NPg9WrmLD+6{qT&d
zkZ|<R(0<PcIRV2%Ca`T5Nav0dA|Hb7cYj>&d4Yp9R<Em=2Wt9NE+}li8b-NBR4{&F
zxL7lflirxn@F1IR?Or(&rL$nM7(W`;s37Q`d8Tjeei0!^hF>5>3}F<;HlfitT77j}
z;SHV=o);6Rw>?g8rC@(b2B=R-zUh~DjiLI?F#2~(Agv*H7A*QiK^x}EZ1Rw0<2dew
zRTX+mT<V78GQw{Zno$`qYKba<;_0NQw2gubXTpeX1*@w|u}oM^%pfu%i~<e!Afdgc
z@}=@uP3jx%dBc-D$WH=&hk}v$NeswTGOzlP&2V4fE)xs^5axGIpXtr7^Ab${v}l$C
z*;i2cGYLX9v29zVg%&|5WS~K(dWpqwz>6$7F*heAph{lF|DyFmc*(-)dWa?(GPnsO
z!4@{nXlDxP6bt|V=z7bjxVmj=7z+?IB*8s|;BLVkg1dWg2(FF0yAwQE2=4Cg?k<gM
z1Hs|loO{mw?sLZYelnoxy_d{2S5?iLm^BM}zL6_m`LOzXyBHh%OwZdNPPzZ+mA93A
z6R<|@c!T6&5N22ds00g`xTA3qDTmZUQ+Ytlo>!CNpBN&jO1L)iBjw9xdEY*0PQW+(
zAnnM8UCZvbczX|BEXeq=1GyMlzO1r{&?%>M!3DZx_5{__k9ik8OIKJGC(4X$Wzlh(
zuc9{$E&^9ia2OU{--q`r<9oXbDE9hpsDREtC%&}me*<F^)6;3)xz5QETQ11ox~(eZ
z;#fx98?<bFGs5|Z<F2mK6~}zoBZ;>Y@c7jnxlpq+q=%1m^&GAHLmyQHAhEB@ylG2#
z&_`>tUr%YzhQ?}dhq~G>m%QYdebnWCj-k{Er}N6$4qBIX#nrce*90#c9)K3OuJ@?>
zJ+X(I6PN2@i#+P$Y^sWx4@xATW8Iyi<Z+V8cHX!BX`}7wKvK0YX4lz<Y7N;q-A@&M
zdJ=j+n`cYv-4Mx&OGT=BqRKaP|4xsMM7~eVet+`-LLBx~!ZzHuIi{({2MviX>9NA%
zR~dsmr1Fjc=}QCxWzT{#it+m^`HmcU7@HMg_6hwYenYt+V4osR%BbukB>19igfq*V
z<a{$s#W;Hki3&@S8JGWhLDF?nDEI_F<t@*5Ee^#DFa@eL$TVz}$heA%iajUDPC{se
z2xb&z2IncA;n?-i5CbQ8=9v-|B^2Bl=(P@nQ<EX|6Zv|E4Y*ur3Qmc%ujn4OdGs*n
zy>^XUSZGF<Qb;!^*xK^&Lwq|vKL-32LYf%8CY?9X@fyN%bW0|(2_Pis_eeG^<+`O!
z4XWntJ`}DS$SCyk8_~|$1WUiSVVp>_qgAkG^2Lu~91^;RK1O`Rsb*ZNAxcmoP#{+A
z_Q8!%2F}0p4s_3`ri<kG3}J31moS136i-BK+5~^-5cXWF7}SL=T^(=@{ft(bkk4L=
zcI|fg`?;+fHBxWwS<B~wq~)`p?G^pBUOE2J>s<TH=V2Y2^j|M9&RV)`$RJnsL{0Es
z-WTp6gWZbJ<A%r0%F7okK<m(@QGBxwALw8FiGgT(1vDf%E}pnah<&#L*MbuUP`~2O
zP2s0+->FLXv%kgM!4Bzqn=b=9B$P8Z)<?_okzWRu@T1HzLPs|LfcLMR%k249ylh_u
zed?@ig*PAWPTgYAMc*Tp!sl(GGnB(J;2_J-u#t?i^2MN`!~_M)--MG8dg!a0Yv~gR
z!qY`7P08~Q`Ol$z6Bgk5=-4Z@?1KOmgnwE)+5!Ld;2C|V6A=piF$YC9bt=2%YJWZ8
z7Eba{+)nRXr$0Xyc%pd{KNw%sxviqZZo|^cGqgUw&)`4wU>ew1g2Vn&LYs<C>-vl5
zylWF~Bh-B$x-A&P*D}u^>j@17W5f}Mq1pNDeKK{+k4i89`!(hzv0jXuTWG&Y9|q+T
z^&H}N^60mp^yPqtr!l5~N!wgIKcsXobS*%k`e`J8PDqX>$-IP2CA5pkhOg_vJY1+2
z-KA+-w_84U$yVP6qVajvGf^Sl6wgz%$!2i5#I5t<;N7QLM~Pl?T`e{A*gfjFJ>rn$
z<Kea;3qsH4DtId46!A2ilV>BP#gYxBq3KfQ;#@%jH*sUW`A-FtIF`T>eSgC|o-0Dl
z0M2oIK1334n=5L`?h&$nOC+U>sWqS?;`K#nTA5GDI@tFz)G_~}z}<GHsfKpz6SFO}
zN>yxOgWKKto2gnJGa0v1?7N?ZH5<MkM$m|yhdTCsi}6)?tZ(~KzcU(;!-~J(ZY5cA
zuC`WEP3+EqchfUAFxzxQYlz&mg>6t3Ro)%lLkPF#<DgJJuXAH#uha}~s^P^;yDl#I
zCJYrB8VV|{`Hxj1U~?)e1l9gRCLWpaR_M|B&qa=v2;Y@J2IZ_HK0>q)n}A>ckW}hx
zxTD+f3!H^f!OA)Qqqs-ztcSBix{nb?8~=T<M}l5a(LDUKo3+O-?zPVdMPz45o`;eh
zTNtKp5(&%g_IOXfR-w46ix;Uw+jqQ;&kGeyexv8R$FdljE0nDixrlv4D~V#pkRkuA
z7EYZKa&4N{VC;QggtnLpY=j5?jVd@WK{2<wogiOZw!jhnnSl~dMsb3(9j*%$dISp&
z``1`lnvaaMJ!aPm2z9r_e6H#dm<?8)sIrkrVARap%et&#%7UL!>cW+l=zS*miOETv
z7EC>~Xld(*wVp!nL^R^p2&5=&cJGSc8Nd6e*JwA3k3vkPhDA)TA^GQ5NQqzq+ksR#
zafL%N8#sZB5m5*S!Q{>s@ZDAR@-<TZ8TVb!qgFbbCtvW`0R<1P|C5)=vWE_GU9kWD
zB}w0Jfz4og0#7pm!nt7qoZBvxL`%2qD$#%|e<=(+gAroB%yvi!u@}&>3996(Udem?
zyzi=J)qIP(^npLzKtb^R#I-SI3A7@TQI1j931Xqbrl_N>ly97A_eC&NTYANNKYrA2
zG{sQ!w2!14QW!y^+1b|G##Bn#_g0zzt0Ea9gSlN*&J_j+8^CNEG80Gn&*OK1m$5UZ
zmKXS8d~2~^z>mAPj9yPuN%bfWBwdHSyla(Ef$#h$g0??Ed))o@i)J#2ink^E<@81?
zno6y~xbpFTlY3hpQmA?wg7?54_eM0nOM6i>M9yd{xw;4J>Wn#V9@Jcf4W8r=88@07
z4}=B>2kn*VWw8~F%VVZ=|1D;Eb@=_4r=bg6_Dv@&{D%pygOYoFkWCu@-6kJ)2|^}6
z;K|x;?Q>iF`%}K*yFZn_AACnX<WehgTWS68bG-fsb-1uk*+Jr>x=#Y(GzQTsxNj_d
z65gv5I$DGz#6{AX{D*FPb;|_IaiZSI6IKv}YI7+Jk3DB^_d~sPMx}a}k-FDN&x|<w
zIOXJYD;cPjq<@<zVz`?&zhnXU=oZ>BvSy<u(yO~A=F)y2@Yy)?MGua!COUN&_k)CH
z4G~Enm#;n#!T&-}*o)Q!$)VSd`tHMm1H+X5M<X`;=UKo5_<1SF7J^i3t#_LWxoxJG
z;WfdHFHiNN)k#(r1Ds)x$gB!qMQ@2JcT+=VNe+6!QmXMb4X#14=sh&Wwm@upPT=CG
z^4%ZdY3n--c7FpWE{eq@x$>xUGX!faM78%_>2WTh*428SipcA0Cf19`kd|V`tM~2H
zf4px?(YZJ!qo>2KVyDbYt{*Y&B$yKth$BW`Uj@9$pmWKl$`#4>q#iAt$nk1>Ye*P8
zHS`}9oJ!ljo_-?v-x|1D)Xg1Ci`BMs7Vw3O&QG@y<}Z3IPwGwd|CaDA$;O|>La2)&
z|53ylv)>U>l`~W7C2bM%!+%TRh7a<WBTqk?Y>E4$+*E-_lquP%C9HnyUUDUwDC&tl
zN8LW>P||;j{g&_uqIvg^SyvW;>$3Jq$gg!QarB{Hb7fi?N~AFdN<hCyZ0L~CY1YIj
zy_bwr0T~$?O#l!zSmzUf`cN0CPuv+Wag8iB7+(Bpl}Te)kd=*U`BBc3;T_rLcdY)#
zd@3)rMStK&X6yeL-~O*Aj516><KQ|mT-9O6CGhf8eC$YlCp!ps%$JDUnKr{|PU976
z1_=XAqFN6CYM}s^#Ux}uTU4OLTi%C_5OldWUWi7>HKAqG3iezsnCO}vR01So?86Np
z>Wr!Es3N7pDS*(DD^@Fq2USAi_9Zxmo53u@TfaB^iQJ{f+SVHEaw`Nh66}&M`;`vB
zGTTI?tfQmd@j3zRQ<UEMf+ZfTa$q5%h@MPdcSV4eF}v96{T9cONW&jV-u?gG_Wozn
z4b0V5`H&vdkQkHhRCav`qf^CVOnV-pDW7Pt+upyLmMeTEPTy~R*zeEs%44&fo3N^1
z7bEt(jUB1kE=}FlnsPFRL1HMoUUNi`W9{AvnEv25S6+#aok!*~4<N`A<9rV#IG+Gl
zO2_!2Rt7%m==PR{6m_a%h(!se87J7^u~jThu>3|7{>J1dWWu8wEgtPovbZwUMz_DO
zZwkPbDS|?X(vFMuYGm>MtzZ7%fPo5QY~e$oR80D=PyX<!3m0zn`RTzNz>d1M07SbG
z;K2u-ZirTm*<d+rNBxS?6SP}69RRN?IG198wzF7k0adyL3upwrkAxES+zyJYaavYQ
z&9_-;U;<uLI>Aw8s}ir$`9yQ6)tv(Xz#p*oXJn8(Y_op;r1o|NicgL5)C|P=4p(X|
zlUu%F(7-X$|C#?SOGQNm$WU|wu&xjne4?Pbk)W-lq@>~>dU`}aG(1zAs_@B$D=XTs
zuAL19zI%WT;M?2O8W0E+hqib$S4so2kSDPMkn5>3K(>HP=-Ls|VFAC=@iD%ngm$UI
zz<&TX_l-J-)sphcviwc9pufl%JStv@wpQJbSZ5)=l8-sw6=9f|nBeLYGqqHB1kLts
zWhTz|xM3IP9BWVc%`|FUk*jz8hn~M7uTjdSr0(S#80p_(irVyFVQL!Ny-l*S+cZ|-
z1Sw%JUc;vc*zfX@?IquDaUmZES*D&Ykhpl$Tl~scMY@0pl>j7FA)EtrUXK~7NGpD{
ziW&`4s9qKqYVMWHCLrxg3&9v60?aD?z_)&AVcZ{&0HSmXsQKyzBLzWybfN6Gl{;K(
zZDzIX9l~@xshw8@XdttN3WJ$karCC$pO!rW(9mrk-(`z*s{@s?dfpj7*=)pM5zz8T
zg@ZymA*an}VGxvFJfu5-e3t^y@AsKc_tzgZydE#aHES&rC&tq`mDXnX?03Fd%$H?Q
z&MRVgn`Jocjpc_kw5b~raye37pUfu%Dji|q#@=##$r$;GuGkJJQ8hL-4JTmyUDp2W
zrhzN;416^m4=MI7V56Fu>ALlHlM^7Wm<Ub*&Sa_HCac09k_9A7Vo2x0KFhc;D5by_
zS6N~;8Nu0P^SHI`#JI^;lRj;^lx2cKKqsPIZ~}<R9Y0?<4e<yZQl}EVc3s9EuMC>9
z2ESL9>~NuyY}K|KRfq!f1(O)Z9lDSBe*2p+6VbE!y_5@bwj*BiW%TTWJQ!7knDR{B
z>Lqpv^WP$6m9c+?=K_Ym9*KUz<DYz&Fe%3=s-Z%GD4mVxt<ihVvO^VTn)k4c>FD+q
z^hU=#Bmjual%^NR7!EPS<F}wCBKX>FhVw)kQ8A3?---dj1B{>LS!jCsgpFlyk_?a<
zZ+=j19!X-1RQth?v<X-C>qY}Zr~TJQ+qUz~6%aP20e?8EYKXd>&zAd(=gl-=07px&
zHJn8M0sK5I$B%hKH*#vI0wWmd2mGN1O2TBWIPweN7dLUYmnE01<BU7{lT*s<<udC9
zkvJc8)_P-<xILpJmx$@}1{o{lSCs2kPbe!0VkG(P+xeV7!>1D06|aYrD}V)^2zdL!
ztUrnn4i;Vv{UZp66OlT3<s3nk514zpuor;D7iMUGtQrV^`l5c}E9m@p(G<WT>Y$?2
zteZkl+h4rypC8WN_6g3H>6il%qKX=>EgXyP{a<vRb0~5Ois9>*Y8N6X`;Yx=<;X*v
zWS=+SguY>s{DO)NV1!4hW7_bEME$aGW`K{-7VPlX0qrZl|JyHkj5yNoI_pHnIK{~m
z0_~Gd{}$_=%xRyk9Ive%$foC6A2_=;6&Dx3Wc+DzH|grM4>^@TPe*N*d?jli_xN9!
zr0E-X-gh!~+lgX9zMF>Z@pSC{v@B!7WjtLF?YfJlhXA&H;o4qzV0&OS4lSBySv3=j
zef0hWgW($@AVTu#9YzS^k8nh|0s(i-cIT54wHfu_u}f>Pc}?y%qvDZhI+4lLs{|VL
zs(5qdljpi__&A^N^xB;pC+8fGLX)Vki<43@V5096hmBUX7(xkL<=qFLqRvIB#7Ott
z4vPwr#6hpLo%^XjO&F6k@YF0XB^uRmhW<M6ANO;X(QA*G`U%L6<<DCX`#o(5a=R!S
zz)4!inJQkHtNNw^I`M$<mn@O@mK`puYYgvv<-@|NRc~en=CdmcW-~DX=l6c!T<M+h
zQv~$J?Z*egTh6$E&CW5@usc90bUALiZ|N-IZx?`!3Vlk&&yB$I(sfZ9?RAw4=_Y=j
zh<Lxnlwy1AQBk}hp5;1_iLlLNE6a88`EHk^o$8eb2VW16fx5#8<9BYAE~fl>&O2cL
z%0IcUKj?0cJA4J~RkJo9i{v~d`7T1`aBNWc88-&fh(Bl*pWq-Q<>b;HXP+}ePxYG$
z2|51a|JOi^!2Qp-Kaj{bA5J3A+V1}m>$5#*0U8im?b+QCVyLLiOS_BBwc3?maqXZK
zop66kZdajF5zaGsve39Vk5rs3s=mrRHyE^1r=THf(zLjbP8eH`j=Q$u`%LqNpHhYR
z+414qDw?)~94~Dn1(EVtil;?Ds)P3hCfFnQyPdjBDFL6Gy9QrPFzvFTcxt}5yU7s?
zqJ!yJ?BY6?S|X$qvJK5^+*4BsPU=RdJ^y&Ok-b)Rdjg(1m_jy6!6TX=cs%x0bXe5n
zysz!wsdy3}T*~s&p}+SaYPaU)!X5qH{6epT-AZa&o=KzQ=8`yxE}gUzuaqx_dBCzL
zfm_`=koyuBd9zL*uL}}!qh2R{X%Vt#;QKM;vX-)OqG@qXV4E`Ictmp?SK-hteAIB$
zvd8OuOl9TzxeJ?zhhehS-9@9yD3o`)vkVc5cR}B$a=EknuA!`HctXaUbE$WdDT0ZB
zo47e@MI{roi_2}%7h6S(pqGxXcnbtm18<&fzno4MavZ{BUALl>djWrp9yBEt#jJo`
zLU1?9W(aow3L{^`%_>G=`27FwkJo8iriLlQsCT-vbU$`Unkm<yIx^*>veawRNe(^A
ze=t)?Qn;VVQpzv+dfCGK8$(h_T_LEhDSU<@i=r5-m7dq_Z=x*sew2iu-4Dy0Wq-ZX
zP=wDn%lyhcKmAQIV2Ah)_iV(`@h9|lDk)A*z3bGvt$Hmm%5t{7AUct(a!17m*Jl9%
zVhZTfF`W_h$7jlBs%03B``<6vo*v0I?|NqDHy>l4>$nqk(ZVTb((jTFtcL~p@BLd^
zZ>{bI<-9)e#8+BeQ{N3ndw&mW;Qyg(6m2f~(IqPBmC!SB)pnf>G*R#H*C9Z87bRIq
zBZz~ZVhC4Z*h!2YS>4@LhC1%0?R2f;EP_H7qLW6u*B8Uj0)y%i>#su@5dzo=4@7}H
zH<9&XxX)UYf>7~If(8<(l&aK!Rt9W#Rntz2{(IT~C*1JC{b@dB6?2QgWmpAV6<q}D
z;+<HsVj_pJ4l7F@tKSmz4mth_`NRHG#7=~!-a2sR3g>ex+E)d)_6Nhb;`Nvwak+jW
z{gWlOsUimo&dq=i#~}-VNAihE7v-HZL9s}yLta>}E)GuC^Ob3y=E1_r#v%%V&03cM
ziM|<u{QzlQvyVcD!xtWhPV2Yg^#!sS7JW!!Z~My`Sr<-h{kw4v&<$F^;T{X1&W(<W
z4j&*-kHF>Ssh%p7BW8(M=*8u7G=prko>|zogmaj04#2wYgwp?Ge~V;O!EA0hO)ty^
z&+M}QiOn8TPbTFa3OW%xiY;!Qh0^$(aersw$aUm5DOe%iNEo+FspG;cZ}EVtnn{k5
zJU5J|R7VP<bCuX!@@$Axq11T_+l&#uuKQ!$`JBD1ru*GSDh5F_uKRIi^Wz2re?_h;
zPr^70A&<{ky@Kn0`X0Nh?hd;5t$+R|sS5|ZD^@^o-zHGp-oLzDA3?btoDqiMwiC9k
zLNMRuvzdw$B^^s_v)R?Kk=SwmS?_s;atl>`V66Lq0N0<`JQh2czv&-40mFCH--jqh
z?MN_@MfayxF8Z${SduucX2ov#p_M8#Ssb0lGChV_-r&RPPp~}^cwk(ee$njuw!v@?
z@l}t@(ZA;pfOrm!u>RspweLYprZD9H#V3ycAVlKt1lPFthHD2=KgZWwcBJx)*7Gi8
zFd!(a2|nhW>s>C5;yV1jvxvUG*tn63uDdv__1m>teeM*Y=2-Q%du!*i3z=bHkMD-l
z{mlj7E@IA2+Y*<Yflm}+8XTl{nrS~o1=$K`Sh^8#?b`-Y5svK;LhK<(>zU_xRPY^B
z$<-J#NE{nD5C0RUf%Q63hxl<!qp=9XEKT$-piiW8JwRC|#o{=%Yq^T?M;&D{>6x#)
z<m_#C&Buh)+b)$zngo?KqPgtigCmfZy6{-S;#eRVZ@~7St7gJStC_^-$fjlGT=8YD
z1Ew#TrGkT|;w0s_yYKVcHScy4*5P^k0u<L8rA)UCcw_>oJ+YxcHp)LyllZdAA-~nS
z6^t)4g5W))#EDw<?9urRAp!h6C|-c-X9>SXzKd7p+Xrg!L^^0LRf4t3ZYFg8t|?cV
zC)wAxW4Z8<<B&zvqU0+v3gTJ<SPZQ{`!CRW!^1v!G4&wPkw>db92HcIwXVA<97K@O
z71BY9U<3I+|K>TsL7Dc%Rn%rb+oh29JuWY;F4;da2M0trYP$_9B^<}*T)V<tuioW9
zqq9;f*q*gzggHWF%E5YUHR)5T2WS%AO|$pA#VYq$C1rQFJJ|O^KR^GFx?oLgws_1@
z^qfm(LH3zi&Nc?)hP->zWjFf<Ho{%V;(D!=x3K7~*_63_i!dhO^qINx@Bw9vCC@H%
zrh=78;P-g?2~og0qVLB&c;V-=i}_2^O#b#8RvadxzT{aQfhgWe<W454D6_`q4L@X~
znX+6+J#Hz1ze!V@Mue&4BdOz>Yv84aHd+~Y=>1gTwB^+PKQbc`IG98n8~5MeYE^3g
z?Ed*%>-CJX$oQgCbjStl)_nz9r-BN${h=p_sN%ABNZD<%eUS~P=FJ?<y`*j?p#=Y)
zZ^#L0LFn`tKVAIt$8#YKok~%GF5$hK#0l4?-6>XV^ekGuy)!klp+T_Y=IouD`hBJ?
zIx^_t6iK}F)(gRA_`7&ud7@#8z|lcEhnh2g8zixOfO9Y1u)hwmo6?s(ub+4qyDt_#
z8BJ<^uV~BW-y!zjD;Ns#qh+mCu*69dX!^R_SIT(xsz2YLSNeeKP@PQxekn>^2Hn$8
z-rB#!^GSjGz4B^pa7os~pC7JAc14FPYTG}oTGjDXKG#dn+c!=l*|=u)WwQ-gJD3mk
zHVdK4G{z-vV<dIn1scdaQn7H3;u8_{Z;h(wIIMY*kL{?sMQPd{4-v9jkGQ7jbw3K}
z5@k6#akZOF{-|T@9nb8W;u^NMA~pOee!q4}hk1J81^m*@RlL1UdCmR`yv4<ZR+?Cz
zZ%i+)M_<+p$?wa?DqQb@v(iiwNV4}B?tMc`ed)~j*j1GXQB$p$QYKxWQXcKE!OSX2
z*Bs`SRvXGn)RXEGYNL}#F0i8EGjCg|PjX2ad(yVcX)}P<x{z~6MXseGzRmRvsrYjQ
zb-;E1^?=>pCR=6smtYeLJ{5j40X6<E4|#D`lUQ=*{2|()^OW%pMwP+oa1;Ko^<TDy
z+~ke$Px1zD!GnW!NyQv}6+DDRf<aZv2eK+{!>p20>QdSR<wi|ES~*D>db_)y4&P4Q
z1egYLdb(Os>EvG-5Lun7Jv!<YeqOGNVHzd#JWEYRx^Zpi%FyF&$<@=Mw&A?Dw<9DB
zo*SB<VouIwz@GnpC+U)fRc#^T$4)0jBEZ=O-h#nO1aaed87xZP(a@-y|B10N|Fdhj
z6eg!CTM8yDpaG8$m6IEgt|#S5P1j*&vFe>_0i(0$)!xM+h5p&Y(&2=ScINNb4QbB{
z71Jj#alNObjQ-MdZA3k;v&ub@qVi=Oyeq>%k2kFaYWzh9Yg&gcIH>n-NX24VxCdWa
z(Vp|>s;Fx{`1ABht*sI_z!zfuiNU!bF<-UTx;f7F&z{I5GE={*la#bw=H^FF;~2-?
zb*C#hu4^08rWX&r1Us!2O)x*x(9-D*kP*(8-H;#l><ntWsj>XaXcJzQyrb~TBP~aM
z&TE-BtCY>(<<DB3Di60?W8LOGJ*RVR&DfCsf(fWbxqv;}M)vRS77dmGet-0y^lP%W
z{-qnINW`WHXBkaWy<mt#Vr!|aTa2wN>ay>1GtB~bdhL90822^tPa`3+IpJHAlI*Kp
z+Q_ypwRSJrC3xQDGqfD;2um`!*2)X{tEZcF8yll7?vFr?kacb1>Ke|q$8+Zw4U{!&
z?t`|6Q<*Lfs%*}Pw9s-hpC3WupS%MdF~O;fR^HL|<s<3Tv`QvRLi#_w0T-tfwZSWJ
zz&@fKW>Ps>$P%e>$gR_W#o|%`Qh)N*LTzngE$`5Wi)B`2;l~XT{2jxAXx`@(=`{9H
zzHIGN+_^COvwcHzZfnY!-7A%{(CTfaP7|rkuPPX*Jpo5rksYIt+QNJ*#mHa5uxo?s
z?C-u1KoNVK3tC2aIHjqzy`MM8z!Nv0m+E#qpmEF%|G#J5I}9_C4i^r@w@*MxO!fTp
zI^e|&86b;J!FW$JY_TJsQ;zC5jpSfKsL_dLrn3bpf)IbAm76v*3UD;fd2A!2*~)oL
z+sI-ujne28C-$sq?F=msvQdOxfzzOt&>W?)RSNjaX8Xi0;rM^<p0ANA60zd<aWnoV
z42zd;RQIsst4R6b_5y&g>XvfVPa11ZvkJ^ac<+CT>H{k2s_iIlO|l(I)uMW4wqY{|
zj0QWL-9iJxMKdJP$mf5gDrvCL5h#3SEw-CNo6Uwe1e-@McM(uFS39}GuW~Y!n9?y!
zXOkrd{{rK3p{2ujXvoT(Bgd`*dN*i|376Bqr;{AHO0@5B{yQn~Lag{2-f1@}ioM8G
zTw?f_mM0ehFg&Y!f?FKo6o>s>wBfvDrdVg~I5Ri5WG&&}W=uOD^O7klQ`#mfjo&;?
zBD?Q$QPssm|IgD@1dbgQ8U-CC>eUs$?K0zRAW==13c!Wy2{GooADhfMoz7=Y^4<sM
zTRXt!K|BNGBce8J+y|Lx85tMay6N=VR@$J}@*S`brfS3Q)XFC2@rw<cqs3(KP%z6*
zX-kysQu=!>|9M1o6p;F$1<}`;O@3N_a=mF@rvuE|>Q{2IXc3*NWQL80rB;7O)|DPx
zJ);bm01bw*d;_$WB~Bw<fs!fvr|bD)zK3HnIi8Dvr;Fo(L5eI|g6$?i^=0hOhbX%s
z&IiE&%{Q-+zk<ChK<;P1WL`KZ?<s{@&ZS%B7^>~M8EC&^*FTu)eoDpTQbp$ylsEK?
z;vcy*K8cTR@A^soIz52?q!Iu*Z$Yx>!}2=&Dj=EuyYwYmJBP477~{Q*K%CgJ^Z9ol
zTRvOB6%{gD?xo_Y!k~JsVVX%keVrQ!*rvvmdw^;DoIJbUQViU*zj)qFXFVJB4FDy7
z0ex+5*A_>JDzhqc8|`<aHbZgbQV|S~|Gh&N9N|1RZyWbA<6j{{zS~apC4~||n*U8l
zCqvu)q_*nEA~SbR)j^{bGyzQF*K;i7vL?!gT2+cjZj!g@R)sUm2za%MUnP4_;rset
z(HJpA@ha<<3$eSqac}xyLgy5)tYQ20oR%@5k8;+w+!=_ypS%-WeixqZBKN>lDzdX5
z`p-SP@ELBR+O>Js2O4%KPKsVaxAUm^Q@Uzr`)GpzIejf`SMZRf<?Vp5Y~{A_E}esw
zwa<^n*=gsv%WG)@RZ?^YU}U}KQp{IyFq)6B2V9oSDJ+jCF21~RmR>j^8QrSKR>~5M
zQftMimvQp>1h*ygkLB}M&<A0{Wx*ggZe()vcKORyg#bBP%UgE+3cv>}mD{E5pu9WS
z#wPrfZV^=iC8k6qUtsK)DE0niTBzK?c<98kUohc27k>owI>{UZL!;YSCO8&8r2U(o
zc`Tx@H;iU~it$`T(p_4_{_6&b2eYr`;d&0#XVq+9rt80TVssW@l)a@#6dO=ILy~Oi
znwlvJ17xIJz^I-k13e??VehYF(Y^O?zrcqqzl+Lcft>e1nR{HmZv-cPPcWdk3eeW*
zz4;owmnqFv1d;Zjpk|P+iM;RZt)zYfjRQgOYGDd@2*M%X2-ya`(_KAc&A;tA|3|FB
z@J%A~f%a>2x<4pH9{1Xq=w5T(Ms){G06OUFB$ib_rJd`C#~?q3;@m>#(-{rb5<RN&
z7ItCT=Z#54wffwH#*JXZ$_%I5`>smbW4{SIZDc+y$uG#*^i#p<$$z$%v$1s1pQ^T@
zl!6ojE`@gDIm{l9?;P>~A>TcF0V<#e&zCaA<#CN6B0v1XVmhMGohzy9dZ5C2<>TMQ
z!`;w+GLmomQV3L1h~D}d10ZI21xbXMY~a<|`ubYTHK4s;)T(f$d;<EVGu~Gah2iw1
z{nz`clKOb{;)?+gs^$0z#ANCQxH%O>1BJ>`uK?KZwdG-5q?*$G@B6n0RjNn1FvH1o
z$$)WOQS6*BY8_jXS)2Ar;8>My=cfby?5vWBajBNl==aAB+h|yPHm5M2X-CY($p8T`
z(Bb<u`mLx}5p<n6Zw%47k@Q{_J6J0XZ@J&qs=6V{HQMU&r(>eIZNT$nbURr>wSOQ~
z%Ga77Gby>+pOi5f*B}vwM`wgJT5Acc>ORkbobxJy(^<wV9G4;EN9FChH9Gz5Ye1_K
zi@9IhUCFF~$DO&OKRjw2@zcJzeIKsrz|LU&C<G{VXpMACb4MJ44*Mq*bP@s8U+gM;
z;Qci3JqQ5Hv@MuNKs0HMz-8%+z~Gl$AN$_2eQe1+JCNo5g6;Wq{&h8B-@4HL-JgO2
zRi|f=2@49*gxYE2nZlxR4;u4do#sI+7wK5uzEf&Bp6g${>Cds~=rKT%ipH1qC!b@*
z`c`Wh<-wX%2k^P@-&xb#n>bC{^ukOpL9x>oe?PB=Hs^?w0(1MPNSA|$XmJUhRkYBm
z667lr)a!#)CAv&Z`<Z6+X%@TX;fMnk@UGSixf3_)w1fUxJm9xEv>d)i0n@(y&DsRo
zRo7pP2jC+>2l>g49Om+Vy#Uo$Bc!X0Y^(1_Q+~jEs~^!yU-!X&jB{_{p>Y1U@nWr(
zTB$!5=gZ?I=DgS6KWjCjO{wA7)&EE<DpW{)tOHMj$^Zt`bP+j#?=p2fMX<{}+iGx;
ziCUwuFnxFMw?eb}WG4cF^BRbP^B^D>9L0SWn;XrgtoNtJzm3N{lxXx%;$Eh==oc9t
z`Rh4C<GGSW`{5mT@KZk#{TUwjPbCe=ugs{XgE}g=rNXQk`{O@0>$(<`BlE#F1xH7^
z!G~yljCUg%#IZ!YZf3?i(fW2ghzPKC+Rnt>k}QPQ@DeZ3PuJn=wanJhF6O=0l?Vz!
zHtn9j^tHOI4#Wk&&xhoy0&ObTb35@F#7kTmGfMfol?_&{n@@glhY_OT@!Rxv$ukwU
zd=;Osx6vzh-O7coqGKyipZh;sh#dija_vZ&e>-=){SnMy{PJ{D|Hg3bHbHGDJReB6
zl?kei*R2*R6!;2=dx$vgO{b-F&O~fZRvN2$gA~f$=<liPy5h)(jqZkKnKyk}KQ>nu
zl#gZds`W)INjR??MgXCGK%Gs*WW+;CcP?J7_8RrtenRkJ*z<;$_GaZFq(q}x7asMP
z#y!$K*;u;3+9TyU2gOx3FPV=*=&Lv7ytd^vK6cOS%#IOk7z8WO8$9JaZK~tSPS#1D
zo4yf*A(Y#DmKY4ZDPBY?T@5DEWwq|=m_|VrjoFhxq>+q|)k6vUucJu>PMNsd6B3`r
z`X=qo_Qb6Kg>bu8qkTRvc2#Jj)4<NGW*<kvXhpBRteArCw1$f2<FgOvy@mf0#-E<0
z^}owDruVcdrP{Tc#N#+-3wJI;t_g+}e1^|c!#3Yepy~u2Z?;klzZ?|zs}67VO8r>g
zY4Tnnryy_%IIbSgOiCq9DF1NDZf1h$zjch2&Fi5vu}!14$Bpz?aPudIc|W0Z0s@h1
z#~57I@MfQ{K~74*7!56}Ds4p5l=msY@HtVtVOnqSZ3)j?jyYZIz%irE=UjtO=@13l
zIwkV?xVx{SIXv`*2*L9);`=Td;ayz;zU$^Aj1D=-kYEE@?9ZcDz*X+UFC<30r7z~Z
zzw+lz7=MI&qz4-s-)#JT;YF#;DEol{1Ip=7EPknKVP~s)b1kYW3N9wG`bNAp6QGuA
zpEsQ^(akeucjBc74WEM|TBRDpo+r2ct}hT|bT@4-jJK_1Hf(Uadu5uKpFWZ}l+-&v
zofPhXS2o~#$=Pe|mlYMFr(Q?fZjd${ttY7P(~ZU~H|e)xQvv74Xe!)`6Fg<1Q`(W3
z?rt)#K+VC9XweOzA;Vsxl}eReSz#q?$ET~6#-^_F@t_}(Wt!_|*8{Ce#>Xj@_5+>U
zC)`dFoRiqdP1asx6ePq!zOgrl!-+F>8@Q%s$RDx#X*_`A_fstnp;7@KNv;ciQNNe-
z{2nI!AydhT@tl^!D2ZKhF#J&z%2b%xvt@_@mTYUjc3Sf4Fb`n7uz3dDsH)3TFAHc~
zWVdh1v^g5l@jR+OC<`2U#6gQ3k+aAlB8yr~h{ZS)q#ossr>6{W=!ukHd@(fB&Y#XR
z({8JHlt^Jle+mRuniC<$4)sryr(i@+SGf_?pWZ;%g+7YGJtUBxjAqA_U;6Sm8o2JG
zo4@z|gMy{|spMPcI~y5g<}4`x&vG|;5IdrzXg`x1pLrCozA~T4%WP{QP!5$vcu|W^
z9+n=>CM@dM5relHgo6t9Bj&l>ej>YCuUt;aF5Q*jb@EWw6(|K3I-|{224Y*pyLP7K
zUK)VT+91&4A@J8Gw&Y(QsH?r5tZP{{kuV1B2#$#tf+g2YnFf)DCDlHSJO~ebQ8JM`
zn`aaZ;_r$66m%nz33fG4-z;khSMlu|;d<|-%h&$NIB9)oO%oQb#dd9X`sPmkf!s!W
z@FH>_CQIatREHcs=O|gxkx%}n(5^08q(jLg5-5p3mhBsUh4mFYSPJGchIKcsO%CxA
z<j_pMwP)|2#qG@Z%3c>07guv6b4B*s5YBsmKs$aqQL=1z9JQJn*TG-5%U5e5hqu~V
zjC>tok&&dr@I&o|eBbNdM7wP49>N}v^S%5bsCda#TrQNeIVkq7U*YQ=v%Ns+CwQvb
zELlW0UT?jrDE+|PPvxCfoJ^}@E*?ND?>+G2`IoT?RTADxdfm1zKE4_!ZtDkP+YM;Q
zxiI=LP=ZTECKkac)u`R%!d!N?RiAGo>64FFr_A5*LFXU2X@~#_ZsO%ucKPaH`Xo2t
z;k3D&oKcv*25Pu^QRw@n>H&$TF+FjA!Eq$LH=NbvGN?7{Je_FxXLHPhSd)&+s;I8J
zRny9O^q~EQYo=vcoH((&82r^4nW5Mjb?vN0B|*tDO-rLir(a-|#~qS;(smDeix%cl
z21M*l5&|xz9&P5u!%5lM%ZbiA%9o4IZi-{$@=fFZ9&J8}wRH@yP9r5_MWQ;SLV=mX
z;sG?yop;NAX-tMe=W5!5cS78rFwo2&5Bt?%SAP3`86}h1zA*R-?mEBxN@JSRA4e{O
z`VN&P72`{`lg5xveAVy5Z&5-4!FER;D|#dn^b=e%uGh@29&rM^L?-iokEiH%BqM~d
za4+38J;vYz^ROw)jKXQRdsbc^JMdJY`6HjSsX^f1EfD_pMqwjQm*c6N9;|cd$POVS
z=nLU4GdF)Z%ii9mIUgy*p2pzp-96jRS=GB_%u@pck9`2La?@z&Cx7(6N9?A{aLE($
z{=_#L&FDh3r#8+)v)28n`BryqzC3fToal7kev?!tX}$aL(0G%mp7X*1_c4r?kG_FN
zSGD~M#N4M)N-u-m(}@dR?iAT7@SJhP_-!{Y-w~2dQ`_6_#+6KZTAB-@Q?;%Y5P&Ii
zAzvbcgwnv;Xj|!e_dgTq^KNb#(Ubif7|{zy;}*?o8T_ib<(5SXvZK<I3pX`I^fpp=
zF{j*rI;!8V4bQI3PHSXRTP`^Bj2VkOSB&&<d*8VK+|i_Lzud5>!D#0}8v#R~ZcGbj
zdGlRO2%i`W1*!CDn}^ao8MF;_>zap+3ibDOWAqcyJe+cjEh#k7T?EYuqv27kUWKi#
zYqY~MAH=ktm?hTnARnzZC$~*H#kJjCt31tZeBWlV9-q7qT3yYH_Cn7~LPv(&&jW7f
z6t{O<?C8tqd)&mjns6I}mv2Ax;gfN&yX|xPQOI!3Fn^BZ_%1h3T4trt`G&~A6i}%A
z_GGLOPN+<HH=UJuzkEg;wrV_MT{WC1A!=~(<sTPmk!%p0P<{*O@cl=toU|nUR;tc@
z$oAEV3eWeBSQc+Y;wS9Ytms&|bk~qO&_=pUC|NX5i9W&(v3fAV=7%rGSqmS2xc&D0
zep5S!5_GH6NPp8b??E6sOcorg9_6JZu>afq@+&Ffu4TV7rxl#}yid-zUeW(TPUM+j
z${5tr^^wVpI3m?9?!|;PJt7$9AEXvh1D3^tej>GT@7auh8q1XJi+9#Akd4Po#@xNq
zI$}fDQaa|+1cAiLLvsa<X52qC;k+sr{tr`AAw#Oq^&OX&sZ_Y&44Q@zYi(~mxmHFH
z4;lZTfB5>j1Yr`(rzQeVsjXkoQ}1g&S^Zb!5Ye|3%jTtvhOwT6lE9lV5#SyyCUdQ;
zg*d~F<f8l+e6<_}J#~CJH~Rh~zfGoSCgxw++Da2+^Q3Hbh~z+8?%QqQ_PqqmT|2MR
z0QjY(DHX{x`<i}C+@V1Q`Z7h(6wlPnSfO%5|HJIT<KZUZ(~=@+crrQsX#Z=CYFkoL
zBY|gQL(<TEQj6wKj;oVI?^o1UQ!}V;XLhQk`WzWd1-6mlGx!-}gR`dSt#vUtRA1ZD
z?U?_laaa49c4s-x(R`_HD}Hy_(-LR`Bzad2v6N6Bs3$*|s<G<rzpG>8-^15V^ZLXc
zvht_Js&a!Eg7}1{teVnqb!w?KtoXKl*R1Py;%vbv5R|S*ljB>L@0d8%OmJdnB^w0b
zt+7<E$Tg2-pnHp6##lMg+GIIhV*TqgM@0!+f-TKcUth+u$ir%|I3Fauu6viftwmp!
zZ)_%|OE=1m+%BJ~_&m4sq2TRZ&?en=J%HbPydu>9KlpuF0zXZAQb3@GRvf*|vZ}|F
z$MS{O@*=dk_&c(r|C4P0i^b`f3lMO(4iJGgvb3P8r2VgH!|-Jn$mZox9f5kgKHOXc
zsVl5Vqt?2+(kj~l535i+YtaBLQ@^|Z*UIn{$o>Ss_PL3UXBLMTfVKQuAVWukg>xK%
zcJ9{mKf<&}3s>#QZQ6cld|cK9RWpVd{MXlt0*u-B4Z2T@GierUXpB}%w5cqnnfuAJ
z*9@lKq=sE}Uh{7F4AJ-t#*b9CqML;919Qps-5*Jj-v4tMPtYUQ6#lglui$bypslT~
zy&rUpUHbX+3pXhnuEB}o)L)||+OX00zsWHW)z1yJeY6JoWfVUa-sHW_!1uJW*bf8+
zJb&IE>G)h1TKFEaYvy+w6cqF&$Qq+f296RAj)$OLddi2MWexUqR&^VKoc#EePL+RF
z)&G=}Xha4Mmz4So>E7hgLS>_-^aFP}HR_e^r$a}t>+>itI<oR@J>>xAtNbOarf23m
zEaNJWO-%6L73ySP@kNO71jSv!*84`q&9|PMa;dCoD=z*XQc1Pa&w}>&u$rIcUYEF^
zG@)AZYI#h+Gl@iai&bZ?gdWnc>d^WpDwYu8Hrz>~9q@M3q7Ii-s|!eSFfMAG4K94x
z+$%pNuWOozt|_tQ8$Ja2DbosA$+i1S5fMA_2VEPA&m#eJh$we;B#XPBtj6;#+)-_W
zJ^k@W*j~?Y(0OOSQ8aJg!1<=I@phkE=WPnRRnYTj!o~JY5+ItD^z>|{o?ZNI9BfVh
zduV66L?77m?L#ty<{TXrZo;x_+UfDVmX1OUlPP}^tp#qV7aR2w9gmdr2^q4mLWOCa
zLMAx+R7j*!e`zXr{Q9%R-lRwc<EJdWYel{@Ith0%ORe+)yN4t6(#NOI&hXr}JzfPR
z=kn6Bi{#9%Ei-t%aDcjf)<bfxybr{eih|5+9fxvYUvS2|t++{ardD`a9PDysi3?)6
zpcB^nQ<{I5ceXAO(vnfDS%AVb1R2Vf8zSymye;&%(9o6WLXqpBAxz`k%?qV&VW84W
zeH5O{b0N~jHt_iHuMh(xW{@dZ0n$l&%u9;0dT#s0%sL*_S-B8+lmFODrRjwYp^IHw
zHV)nYi!M9-`-Xfk){j*p<DOSCq`3o-251A5AVUL+W#2Ttmsb*#@F4F>%rzfs3TZOp
zGiXwL{v=!4@b<3m6R4T|?G93l)lZ#YZ<u{vEn0R*bVvZ_hd4Z3)VZ&RvxuO>qCF$<
zpV_HWEe<Uv8VwC<&9_g@GJ)2_CUo&DD2SLwV`1=(ce9a@@&kNuXZ<1A;K`kGJG!Ac
zaW_kn+nanL43FlHCz)2`6VOvK33R!;m@TAJ6X?KKtX4XrKhF*_Vx-62fxPN00WAo1
zwaF;6Cc0^i%@`-0*g5abiXo3#G|`&&eNs=gP@~;;Uk{}#;8_GNYd(#u2Q(Q!G~+4E
zH&SaM(gen@(8<pF%X58+ZflbS-vrP7el7$kJKfo)?kFKQj;dK=@OQJJBht8j+=-VP
z`XI*>TM@9EVY@a~AeTiCI7F4xQPwI)(Jm6!)9PwmnRCTUiFa_Uxe8-<5se&^9k7nB
zy0;A{Gb96wNJK-rvF<b}J!&t*r#=|~r<lsO2Q+C%>X5qYyWJgg`nH7h0QV&zx(v;#
z>Cw5Y*aeSdaF6%l>~#3v0NA++yw9|j$Q%9%7HeKW*=B!KTL0y)Rd}=+CR9u8J9ygp
z4$w*u>%H8oY1CV1Fqu)w(hakvEnThvQsdW-E;73ljF^`wmwC<NM4&voHM0VH$<f~Y
zUHb9dxZ-4J-5Tyb(!lz`2H+m3c~m`JAj49ZSr*5mDFnM-N2w+SS@~p!JpJLeW?nk6
z<&9-`P~=puxXYq<JFoG5#9|%3pJ0{ipkgYoOYCt`^Cv*OD+!-W2h-vFKCUesx-+U@
z$7XI=>Jr~*2>%)~u677EdD_N%mhr09wVrQxPuT5!gK9rt<EBx^PBE);XQ5M}@7n?Z
zmMX$$MRD(^5mPownc%$S<mtU;hYH{kjR&^&9-j^$8%-uZWbP3Dc=>E#q->klpQBD}
z*MniP&u4m*nQVihwWNJ9`EJkj?Nu=J?W=-BxUYK(Vs~bcZ?=)<O!OB}mka^A8{t%b
z@3vTAu+^gY?#)tpALyEc>FKaBGEH&haIDADIU%Jso;3XXhpl_Yv4L5Ds9$gG>C`1n
zP%`GSb3DL)Hc@Q|SVS(?Y|I_}l;OkRc$1at|9AnI>wJmZZ~-;+2VNbxhfB={lOuWI
zv94WyyByjnWER+?m;P`++=tNpJ*Dr-cp4rQ?0Bn?=XDXT$BO6kGx~A|@1D0*FSI-7
z{27wmo)AprHgMYc>MxV@3QDO4%kHJ%#y$tMte)9vs1<G=KF5r8cey@({TP?Keo18T
zt3NS!w4bBS>Qu*h&*cU7X4uB-5}(FBlP?S1(d*2dR=~T9CnL&k6DY8^?rA5n8GzN?
z1M>`BXxEs<O8;b~Qs~$hr>`$$@d{)b0okKIO;q{V_kz{bJ?+mtgt%@#WP!05bZO5N
zj+yFgWzUR}y3_gXG&Zq*^8<pU46JP*NEQGiLN21cSC)1FFxSKo#Q{A$LhtEN%}S6W
zJo)mv;arm75tj@dxKPcKx-EF>UW^*<9ds8zWMCUtR{=ey3g4Dz@=0LND18fFzlFcZ
z>kZ^NUv7;uJVh^`uxWq#3-Nxr@fLMJ+e~bbhu@Rp8de%6(p@En$OId}c>)9(8yT}q
z{}aF&?NIRFh4S1l<YLL(hts#nKLbzo`DQG>m>YfgryAfpMehB$O$_FLxs&Vap1zwM
zKDZfU$UwLl*&Hdv>1|Nc^Hgbjc{)Q5Vn*ZL{E&4k@(UZ#GU5tu_Wx_dWnyQ%eOKt$
ztQ!mzwhH;5ig+$uz3c|2gRYjVvYuCU=@+3%VSR}CJW@(@>UGt=AxS3lrLR?sbG(uO
zDn<@|HCew$!7W7>iBhN67h7HMsm;uV+(f9%hALiCt&;Ve-R)y+Dv&v`*hPGif?~q+
zkf||^*!X1D(i``v#!Xi=ieUu?R+y-Fwx~}%iD5Tn2wte5c{5#&$Mkl+&B*PaqS0Ha
z1G&cIasAyb>F2l{*6Fx4W89mY;NmGmL%}rz9&^U1n*~GkX8UoUxTI)zbQWFBkL~Dp
zAind?J6`|Co3<NX?x1g9A!ilcXaIYGZT&&_{EAh(n}>&3O8#4SPe+&()jXj14EK{;
z>MYlLd7dg(V+h|`yW9v94q~Le`hdP3<MNY;wwrYq{ScNYr<&Q)L*4pDIC+;C@}+F{
zqUnb!y(c-SaJZAVxJ&P0X+F-aj}grn?P-UYtGzJt@c`(+qq@@R&vi6ko>fccns=Ab
z1^qn6k)bxw{UoatMa(+jdfO8!Ypqt!vDZ6$SpZ55*ZUK$;tkM`cy=`UO4Kr%e8j{H
z=0K;a)$kn%ell{VdD}TnuLDq{P%v-|(1d-|FC&JAi-@eR>-};Q?>@)y*nr~*Ag*@i
zwpbn_hV~b54bFu%g3(s#aW8X_hm~2&;F<G=_a27><4+`Y0>1YLuB!Ca08Hob5F-H2
z8*rEcJZP--YIACUxvs9)I1Q!Pma*wA8Sz~RE-9QMU;o&2u-c7qcw9<CM!s#=$LN?o
z7=Pkusi`+OZ;bl-57|N}tkm^HFSw#Gh@UZ{ftzW)!HqxhW5;<w0l}BN3*ZL5N0<~_
z5z}|+NMyicaHVq?RfoehUJiJY)J@cuRCJ5pKf}VS<7VHqJW3MgfhG1N+IRBc#b}~j
z$K~SF9K2TY?l?=6QN5yh|E72x8xmL7;FRj)Pn}ec^EpG{VfIcA-b(FZ4E?ZM%H+wo
zbkZF|K=~Kr3c(x>9ZDWXHX^+!v_EMuRwu_}z!Wb$o-Ry|@SOh!db&2R0_5NWM>f*Q
zOt~(xr(9pbYHUNM@;rpeg(vEji4tg-iIu8Z4=K5Q{X?^yASvv32ri1@eEPoi)$iNt
z>U8;-6IfN4jSUh<bjhkDU1Uuxmz?m6^NI_Q<Jf$AxEHztk*#2i-?VDYTUR|*oEQVq
zK#Gf^*j_rUy#2Z90*C2)fa@MAccy8D{wV=Sv3@HxL_WxLG@aau>@WgByi@&zbHfKV
zubsM?9=1i#b@L53dq=>S5uf9}JbuKWdq1H|LQ@mh@OOYauWJg<VG=;#BcMquaXYS=
zmSv}G(3Vy$5#o8i-DU<_n;fi{w;#8^JhOMWt)aN>FuDKzf$R-%>BjLKR#*tp^=od!
zj6%-<PNmY}W2if@fy8a>3Q&($iC7v`WW~;z=q)%YU!yt{3W00<lq!5|FrS}Y$KiX}
zJzyFvk;tzF7BdjS-Wq$lUo20R+wkLZ+UMa)mAtp#F$OG7>W~@%0v9>B+)(iO^oGVD
zCztpWnb!2HebOv3cojJPM}PYh)0`jwRzRoJTDGdsFp$)cfF;7QrDqWr{5lu7#o>Cc
zUyL4w9GiLX&(W7W-OS@Pe){g9F%H3X;u&WpHh7kH$DlX4Nl14oQ!cxtONn6vjW^{4
zDAWmMmP*}>h6yC*lsJn=e1dt_UjaB3Twb@D39${)hae7`AGfIuv8Y_hSrCC?Cgi2t
zi+*AoaTQ=t4To?~BDl!Cjq*|B`QZbBIKflGUfm4UR^9>$Gl-_`QTTh{w&U{`_De$i
z-)*td<oH#NTLsxA26J9wBPV`vf!+lF{nddX__bp1vntUN^!2w)o_6s@1$Mi0{S{mB
zJ1TB)UWER>)BaTtV8nWfB}b?vt}mb`U}C#@O9IAYvq%6#^G{9Obo#QkEeLtamv6`}
zQb?!hGYjH#F))O&f+oM<evs(Lc{5i-MbL~q0At-V9ki&B12nG03YbJ!@Uz{K@h+5+
z-#gFdW%9}%VIsQ65qQZtn=)OXgQeIJWbb3V=XVn11e^#>x3ivecnJKF`Wcoe%s15v
z4L>;fpae_<r3gh@<w}eNa$WxROwW_vl=ace0ZQ<FgneAM3aUfhn4GpVutDrmtR>D$
zSofAiRjwH%=-hhiw|yKx9&<+h-x#9x2T&q733C_H)`&Ca)5(9?6<flzvXe_}6}v1f
zGh4Q4yC^v3_wqg>rl+wx4;TlEEv!m@wVAKiz#fhh*6c{U6iV~SDxLxUs?~L4JWPi7
zD4^53y&rtsK%!Zi<%b)Np)bAa-<`Srw}vx`?^QrR{xorkTpoq~b-J$ze@Dz@C-22}
zTwQ=jw_IPiJ-81vK}5yS3_H?TgR$T86vI$6LkRx9m#A|ExFsqqJMvq8h~S-Iv3a+i
zZ69g0wjj3`si`9Jr|15oRnHYl@4z(_*0$Nv2>un}<Hqp`N_vjLiZF+M1Zjj_Xq#M`
zBd-tp8}B}>9Ru2IIeUVm^KSHd8pCZ8ovQ+#8svMX7S6!!1$h$)6bEq1MC9tjI-lof
z`7u1j*sU+WGeoSJd1Guq5ybxOr7+sJ)KHFZxn|nHH&!rBJgvEpTosJA!8#S&91$aG
z!!U_<8APj9sd#wkEDdk7l?zM5_Eq`DYJx23IuHrTZgOwLc=JB}c)C^B%T=3$(^)x@
zr?pOy%RHDA)g0N-dob)8O@M+AuzD+rXH0!OT7RkPjGh%+xko*+g890i?f?I{de3k;
zzp!sNLWme85iN!x61@$g_aul;qW2(Ll+k-H(FW0@2NAs{YIH`6E_yc@y_;e1-v4Jm
z`*`=UzxZY_);;T5*SgO0SH?7+Fhg<&%)TTmtLA-1^I)bR_L{O@o>#?1mYp5s8%gg~
z6FAO2Ny%yCJN++eb7t?oW4Qw+6f8q7A5a;Zh9CeO$6NqZ;_8YI2g)nS{_x}{NgntR
za7dp=>nUC1{=fOq`U)s7dL?q)8@MZ<NeFFp*&MjTj@-=z#?;6!fttD@z>JPNCu$_%
zwl=c@QAs@+iGbojVgGsBe%z}G_5%bjgLx73wuuh~Sml(Zf#XfS%2^*Gim0X?LBL`S
z)L`z7+ux%<w_jfgwK~{DS{>b8)VFbMWqD+?HBSx}7iHUqKNe4WLG9aNlYKzX2j%a7
z&Ov?(Ow(`ylF0<%#4_zXrKL05t>&LBYGHa(0AlSwvb+aa4QUX8pnv3UZW;a4#B5Hb
zI&ar=?yyB*;Aqs(PH*$m<Y*=AfwFtqCwjL@jZf;nK+&A^=W=eIIf#tKF=ui_Ug}Dt
z6asn!f5tchxG?eT2DcDWx7EM;sZ6}b=2!>FPDb(58mvnT^2Axo(En8l-^LXsfs6cV
z8X0AH5>?1{({2U-t@0Nz7v^7mo8|ct;AdT8ehjZ!t!UhQ_MRba#hN84k%9-*UnS1^
z2&|E7oQ7Tk%B1wNqg)eHr$DIU+6#%{)5a0M;UgG*Uu3?TFG|Uz<^m5N+#}0o`XKol
zm_xYvgg36eT{QWU<Yl~|{A@eUgD>yq*82&z8bV&#llG8GdGd)|KPCj_@dRK7nK}Hf
z{v!ow@Z+S88;)^&Qq%7ZsCs9YpNIysID3hci)~^1Nx^MLVLZAy8;$pQW&6$FPEg4T
z8wZ^Pjl*T5#{)3PRD;$HKO}|Dw~vZ?Hl-^t;1K-Hd3`;^(iknnC=T!$7CC;G3BY<d
z{joCY&g=eT$Hh{34)-QoQo+B@qgQ2zw6VSCI{f7*WRAC&ln9y>bQ^o0fal26cYTH_
ziX6e0S9HrN6;2kl0aR3W@=6^*Pexce9mY6Jsg6DtEwn%QJ@4?3lqPW%QB7tc`)Q8Y
z4Og{JWe(!mmFfdNd3?~Dwe>DTYQJtmdG=~7&fg;Zl_G=UD|4Q+$?A0#gTlqY$NTu;
za?Uu*)Uexolznme0Utm6Zoh``y-lsrsElDM^2dJYg10I)OT)ThYS(Yr+~pVYBIv$>
zdrqAyO0_F$Fa)hhvEHi~P|P!T?}cjq5JO;16~&s=5y5pd|GiX7)fE%Lyk;4U3pg=f
zKMo@GZQn`Am#TBAvI4iL$MFPht;mGf8y|Z=@IlO;o|R5>S>~(p_kPFnJMg!DY!4O-
z2d%SsjaO9sxzNwxXO$9Xod*sCHW`rP_lKkTZ}Q`d=)}`rL-lE*MgDbX237|SOVgm1
zFR3<T#h91PICUGYhpZ$Mw`oixKG!ceN1vzPUEba1#3GNdz<gBpWOQoIabjynLo5W5
zIVc>&7=`OG_bP40Dzjz(_q;MeUQA3v3^Hbs`UmwxuuuNT1Qp;mJOAgv%ifN^xXodL
zi8^l<G7jkC{WP@klLPMmX8A2kX9E7LH7>AA6~&6=-0WGElVWeK+6)X3o^X<?N37^y
z64H%(102o}Om~s9mG5Tef~Ag;S-_pgv5E7c6eei_CxR~d`e(0^>uh0To3Wqe0qydQ
z@j<PLIM3yl61QNJQYF^zV4Q=8=c*ZfGUy!8yn3}@W0jTAys;Mj%YM^Yf7N}zPwJfN
z=6O9V?(y4z&_bgSNfEfguL?#FqzkRHNmh$s{q9})Fr9P%w}rebq2uvajUn8|iDI%h
zC%cx7MW4zB%k`TjJvzfqoqdcO*z2!zrZ-w)TXPGqMjZRfjZ+cGscGZaF8wwULQ8Yd
z=n7GcZVSm7<!~IktB97r%c$+vZ%?f&^0HqpdTAsAjW4C{K4~<vZYAd2DyR}jt#8$d
z_HBNkspr*V<@{5@zTw&_*v2j@vRGW@Ne6N+>E1jugsJLqxB%6@o+`n&On2k;yn&#?
zG5&l)3Cqc3*68VXf^3ICWJXpd$xjh2-&k#e!Aufm5jv2IZNtVS*}&A)6!L>iwWkqX
ze+QMnX~^~A-R<E*BujSM1mV<y7GYnO*JgSnG#|0y8b5$Cbv!G<>WWK=vg)O&3Q$t6
zsb);|OBO|`s8T3KPe+Ou;eJ$2CI__5d5TeEzB}*dQip|^iWFlOLsC@(Md+ONmrf@M
z=Z{FQ!+~%l3Y|1V<K<CLu<rZIBqL&?^><k)hRpY2GY4*NgKKX0YkmA(orjCZ(k5_3
ziWFOVm~i2|bfGRK0<(YU$NGwoQ<BDZac`GC#Um^=3un<H7ony16M<Qjm>gA8O497?
zgK%1hDqgKn=aSsCoQp%x?E(uk+fe<Cyq2c>pMdh!Q;XB3#FM$MJLr#UyT~hUmpY29
zK<5(Amo>W^mS<<gnOptIQag$)OPQ*Dl$E`KV<CCZl-`<^WWE#@ra!tzB(a{8pJ^&5
zrrp(t_B3GJ`>!M5BD$*I3dcQZ%(vHc*9zr)xRf1-C{A#MO)@o<J~6!%g>8N4A1WUI
z+8xqGuylu$7~wtEKX=7rp9x%%9vISJm9c-PBvm`<D86rDR;QE6E+q1*!ud=Bj9Nh<
zUn<8k5xqBGxZ*!9QBN1|o?zCB9LjPXe<z|9N~kyo@*j{D`?PTEJXWtIGEf}suuj_I
zMj=>K`b|XZsfuwg)W~vpj3wSAc_5R$rAnYkQA?F-x@`aF{j9$2<0OORT(DozMGv|_
zG41%f<fQswtdyzmLAmTKDsi&>S#`heiajJ7XwcP)yG|`y;1NjFq^_)(OYTGD0nVuZ
zW#eNexf$Ibz26Hj1qWt|<@{7*$?`7+*K`KTOPjU*A5stYksDIKrw;J@Tu^2!t()~)
zNzYm+<}&86p^CE(X5^;Y>u3>J|7=*!ZXE&ioF*ua7Ha2GfxxC6CE^dwMG(K6T@$qd
zHHOo#&*DX1iN-PyC7uLxIfuL?5$)wZ(as+|X|Gf)?K#CI2`!e%oLJQC+RJp2v~$5b
zmaO=B->!j7PHe2F(&7M@#V@@2r=Kj9D1uI;FHZ0P4vXNu0<V*wQqGJ!z=m`}F`i}7
zx^ij(T~Peu1P8OGZ7Z{Eh!EO_>dbzQ@O%)m&WO1ZRuud9K#cbK-IhIEa1~zthFE?u
z(I0<l<L^weu$GQ^z>cv8?bpVxQ;XrwJSE=LQs}2kTh|R&SU+~i;7p^J80^Agp3jZ<
z!i5)A?7`bHrp=Z(;?`G4q)OQ<E>8o@1#SYi@sDTN?O=P=94|#r-iYnB!YK)13^?`0
zOs}|cYTNBprAyBBIF)jae5Km%l|9T+iN$WZ>=%V?+3UZCI=amR+l%fAJxV$|&=fki
zQpFJ(+vF3`CALdRTWztrCI&LPa+A9mswPL35M?7h9*GhczPFWBdm1l&_Gk3Cvzl}r
z^bI*e#HPQL@Tl-|Segby;aXfk5*59LTkI5-J7q&rYhBlzEjb5q>$Bb+3hg#kU44_-
zQpIdc_VvDFv(bXxU5)8)##!%f43q?2s~>(1^up3N`Y)gy?PUU0-}@AqO6kygGUmuH
zwY_EGHW9xm6n=~E?(8UUPL)p2?8K6g@nJ6df6Mkp3Glgqqm#9@O&YBVBj||TdE%4l
z?$PC{Fl!(7C7xcCSyb)E4SlAo9XJ~w<e_2SlU(UDVc|3&Hb4Ku2P~Vr-vSsx`yw+g
zVqH#?)=v0Sy^rN2Cm$^r>JvT2nOMq{cV5`Pkh<wt3)7MVM!~Dikf|+xT3^lY3&WkO
zGq&A62T@?9D<379jXYu|5z$k@^Y~pMawQ{{S61e<6Kch=v)7_dRrXNRWG`kV*X%rj
z!erpMdHH4-J4sMlR3=)xI6NGzs+tn+?KG>0%H;Qf_jg3G86YQV_Q^%(Sj42tw7-T+
zxzGHZmkGhw^U)5Q&^G*Yvl&O)Vmgm0b05prqccy&su;nLY1DB4(PlNr)6&PEIl9YC
zt804M1xo|Vd`-?$qdz0#7YTP_^7YG7*2LAc7mg-cKddSZE7k7a{|E312~hW6x)hdE
zG`<TO#-2`~Sa*etqHIk5l64}Opc4bT?<Du>D7W%*XKG?jSyy$3Y%6Wu%8d*GC*}x0
zqt&j^D-LDhsDJ<{X*ri>T$z(*oY39rCxh7^oPE)mbz}4rKGYRw9!{HmgaTy_E_nA5
z2gH`0WAb_C3mrkUaPLk={{0%0JOAGt-D&OYoT?P~>+j|*g{E|z_5N5`g}6UWnR)^s
zwO~lzpDNnOy(4VH0p1v8JIJ^$09|#m=T>Ff;oBAL(@Uz=@Y#|YG-LTuE7kv<52FN{
z@8CIyrJ2?Jr6mv4#F1uU&v0+fQ4qPsp9`O=PnIc0xY}_Y59K8fj%o)zZrr8^6AmTE
zdxhwqMBM2kW&E;FXI;t`(NMlCesikxE|TZ)EI#nLe4F_e4%KFl#D0Q%)SfU^52`_v
zeolg;tBzJ{Gisny(M?;=dU}XpIK#Cpb0P%>o~PmS{33xTrHxPoyU8scl@Y;xX)<IZ
zlx<sH)P%$8aCtSublbz+rG3t~2xpEzVte)*y5Qk7H6HupQ|<$L304Pr{s18nB1j@L
z*b-#katzH7NxI$D9Xt4t_bNSBcpWj%ZO-5|+B?gZPbz-0&6E-h15#+M<M!58smW=&
zXvo`B+y~Ig0bOG(s0dRMhrHKvp1~sfUvJ-vpv{#{>jSGH@_u9RAG>M8Egh<)``NdH
z6cQoO$ltHMGnz|IZZh3>Jh!XTg)It5Ylymh-&}9JcyNk?%v0?ny}3Wz<t<OoE9(x%
zuHp!Sg}R}Ke9K=+{3mgnX;#8sbHCdBT>lVbLq#jJv20<3hqV?eC=ViqpV*HIDw+Jq
zl!WM7U~P2pY&Gghq_7OQSp(mX$7gNr>@1|)mP+n{eucM7!`jh)XA14vbCJ!uJ>D#K
zI)q}+PIGJ1t-Y7Epw04eI!0ox`d6-h`=K6=@l4kn8$+*s`dub;`nIxvrgqiuGBw#e
zxtRK-v>8H37q)Nu+n7e3`sb&SbbE4e6gRmNKwh{>jvpZykdOZDbTU9bHGGJh-N+g0
zbBzA3-ne!zOAUXr+T(+c=SrN*%gk~MsX_RY(E#ay#0Vy%CMMGXyn%`er`Z=Bx#RDX
z=|n|C9^#Tjf#n<e;`m_jBzubi`~V$8F!=$^B+Zw!Bd3a_arkO1bPXN}ih#9uzm^1P
zCz)+!$qSJDPz-q<HF(-D_1Y9i|FgxpGdaHMs0g!CD%Y=iZ>LO406w)4^Ba9CBEAGW
zFHJ?Db%2Ya4uUNdVg3-7apZ#pyz`ifnGWA4No^<2H~puGXzv)sPDDRWSdM6cL#{ld
zd2{wR<9dg3`av*TQ+`qs(~wV=>eqm_)kDD88@$#7<;HhmRtJG&<ay<dZ><q^YFK3j
zW%d{`;SJJxo&hj9`J<O^Dta3DHYTIa;xQ-B4{f}`xc=1nNyINB*0nQn{w3>++?-A9
zbMp2=$unr*F=wQtG(|`a?|%6mqB(#U#ze2DaZf!sbND$Ca2->L$7Gjv@g5PxVbc*T
zDJRLAmUC^WuBx@9Q6hfGOPWN$Yzo)}xOh?d-8H;{&%>bo&__=dPrjSL{(+euViE{i
z`@S4^*6Ogbj%x6*;(`54gWX>~()+`CRA9W=5$D>XMM$8wbJxO?V<jG0bf@k($z%Uy
zJ<L7W#n48ojs{&shZPy5gA<INO<x2uNeP|YD3Gld>`t;dI>9QNKYnli@tapRU=vO9
zhzNdS-eU9vpNp@ZAS~uMjPC`KvyQx%_XN*wT!X}ObW_rQ2N7%akJMMUuER-&QSLDk
zD>^`SL-snH2|79^sqQO<T_vUEz*1+_`8<`U^lmK^o?wRA?0JrYr)TI-%^M!<kfM3r
zbH8XVCq0;L;RGfWs{}XC!WhRLyikwKi?`lA>wVJc)JIA`DdB$-gx@oY&_B66OHl~G
zE@j{d2$ODQ!|rXt&aA-Z;+7q-Axd#r-KFS$$S5O`j@oN+;gGzzxAdFX#><NxXU}vj
z{WFiF4$}gzu~zC*;oz8sVVC~3myxUME$sx@hL48ho3MqhME|~w7qz|g+Ydc58wo_l
zuQInW!gS&&vHERTPpRut?%_l|D*sG=n~TR1FGrJP&pIx~+*F0ml#I8&_NTP4xj?j;
zDe)&btuGB#C*R{uO9CG2NO|KmU4@S_gj<%+O*Sdd@B=FPtKI1fJgod)W4s$jBmWqQ
z{uT7H7sdV-0STUvg41-~7T;q=Cv#{IyjN>uFh|4)yiJJess4&2&i(sxR+D->u`6oq
zTL0i&CVnUU-1+R_4>B%Vuy1BxR4MgaW~P)^`rcf9+(uG(f!=VJ4Lb6DrHAsOw~%FK
zvMD|;KF@Get*A_dWtC}s{_VDIfw^Y4wOMkxj*5S<nrG?OF~LD<*9RH1sc|uG+!V2q
z44=a?Rt3pp@&iQ)x@;-!PCYIQr*4H3V8)f8KMkzcvz`tDhuq^!<RKT5!%t)zAaUkD
za<&}*UIs$5j_0k@BZ({rS@4o5pX%`->QED3UpN@%x$@`cWhRh{J?q=GELf1#O`tX&
z9>M3y`h1h^xlPls`gVC0%u@=b{y<IG*INt3rHfQBlA=Q^e$s_L4qC?G$-KL%{TMF$
zNRpbo?B^Jzwfup)F;rYPuTxkYQ;<kZzzouU<mDb_^oZGcpf|Zqy+a0n=cvBPd<~!I
zSb4k2EobtjTi=@?CgK2AyuNOSdx}twA-LF#`d#KLW_rtKVc~*m5(zBgE0NE`hxXKS
zKaAt6Qzk%=u@u%%A#;TIPs!PD9==Qy?0aN8*BE0g&uXEZDG7ZN*Tws>XK7xw4`GuT
zfe>+w+fr=CG2aNyq35U)?A_>>Iyp|(Lwo99LC%FWbnKvHtYXa{sEw)3>!d&|?ig*9
zN2czF?`me?U=gwRN3#AqF|B=YW_)(O$0Ww4L#j(j#qXnd8Mx5nt)910KGzeH!67U*
z8V4xzGn^x7)hmVpej-ZWhg9<V;B%{Jx(+An$5*(-mgm)o`ItD;mF;1~gSJceF25|e
z%9)lsvHCze84`k!B^`0$b$HyH7I8dK%c0%ciKlLq2;s4jH;pnw@QcK7nG*bK6~;5+
z$+_;&u!_=6_@tAZ3=klz@)@r?EU4n#<@Jxc$che}_B3;cs%e1$(3reYjbY~f3I-R}
z5GWf~SJdd|N6B`l0?w89HJlwU6mH6=-ab0%=^#Rm@Ah{2f&`uueHH+>502aEd#uQd
zxk{q=9&eo=*iZ{O)d}%%jJ!~fSo(odtJCWx?qy8Pa4le59u<L*p2FHE7=7j#`B7<?
zo(G}+J>LoZzAn<Zt1VVH7|<oncdg<$3gJE6qLBcj`TT1irXu??q!49~r+!S1#;4Mk
z*JPQ4UY>!jc#6naLx7Rk@XUu11P%O4rtRa}G;OOr_o<>^#rzpM9i5;Tr)$ZqA5^9m
z@A4_a^{Ju(Ho~z5S@Cn+-rxBjSR1Ajwv=d1KlAy|rARoo)qk<SDdN4grKroO{ihr@
zmRnq~mF@G+|K_^QmgU1s?>&dN#NDWYcke6mQk5=;5n8NX__MZJPvP4dOpqqKWMwah
zNbF_zlJm^AgEMCGturF3qQ?AkxW<?R%2Z5YkkMvTdQK5|U`SYKtSho2^Zxg=#%9%?
z7w?m>pm!-aJA6iFt3N}<KsZU9%sZ|G;hV(-Im-0^nCOk)Rxogeya9Ipw|N#<48x&=
zkHpB6D@&@7a9LU5M~nmQ)d;X1$G!vDlfEEGh@5NwTsarSUdE>>H-KOl3?~kuCg*qT
zJE`GqhidRjLmR6UW4?zNj^-8^x*@d$^}1506u=>DQS_KVx>9p--nbB*?a$IvTs}EY
z)CJ-8Im=!TQM?Ykc>T16!4#0EpO{ZmO}u8c-`;pesp${%Z>-;b+j+)8j<FQkxWJiO
zXXB3|+JDdkE)0zvVbM=wb8;^H-2LUT;H;BtEaBEsWCSfo6^@d&bJx{M$0v9PW@Aqm
zx>}1Ie$3%MmR8r7b7jHzTq;SJzWdYLX}Xi#vN3#N%1~H$=+1$L#1Cn4O!c=gXfU5L
zjk;>bv-POR@ZZume5=snQMI%U>wtn)YerJ{@qcNcVhG6PDVYmf8k-P${2wfdNZdM6
zxGKA%nR9PD$OU3#svzbmEst5~;5Pz{-tuD0_LB}jwu2!^a4ym7Gq(Z4USTJXJvMT3
zF*yPJN6ivvUhtT)oJK1axHE`eywCFq;`L8f<H@)|Jb&r|a6<dNyv2Bj@yCf}#?~Xd
z|2E_B842tYDSY6;PG!XM8O#f?*EsEcrr7Ul?;Y!?C<%F;ZKyUP%tCMpC^cus%%(dK
z($Sn)b@w@<b<lvsezf4BA2qIUhxqnL-&?TP%61&<QQ6Q}h?smGDOk6byc>6A68Dja
z+eir)ioEer*8|~?oK9~ddmdIX(cMI~CTV}|$Mu@=DqNZgTAf;GvBsgFB*dqa6F<ju
z7&XNUp)+TLyLZg=6VY2sb+4{uxrbJ3?!izTmhCGKMk3{v>w0ivNO_1{h|K@c`ZEUf
zg6QvEICs)|$r6}cnWymx_{V9yolRx`CwwKuBe<dvH!-8puyrc`*oJ`G;jZBrBDfSt
zb3W_<Au;qrX1ed&e-k#g4wi{kj#(Ve5ynosP|Ib7#6>_is|Mn*brNvAt1$u3P`KAa
z`RgMiyMZ8FQqKvYg(@ebK;ePct!Gfm_uGyRKY3fnQ`0?az-R`?ajK**C&BNxb8&Fz
zxWc8De|}sr1zQ>B-38O3R{Bkf8>5B6(e#{f=g%?zAk{i8ddZ?T{_M3rT8t5MuQI$Z
zs95Z(c>TbvKnA~8*jTfD?a6xhM0G}AFb+}B3gSD-Oe3yP4A-U-*xBbA_RSNB${8y_
zeHo{2W0N#^R<wrB$JuLf$;v#*TM{)Ro|KzGJgd?vGa0nhrA{17a2;)U^fE%(j>&}0
zJD)z-Icm8z#Jj)1fNC&N!j57vGxJ>c510?ghRF4Nz^rp=t$#QQLLan$gLg=GGYsJo
z(&2JA#+yF*xRxml`%Y(Ws&4rzslHMgar`pLZk)U2oZTwJkW_e$A-y2#i-(~$i|@Ul
zzBrBCg1oxw?*q2b(jU}#)HI;rGV6w8O&uL8x^!L>BWajf`xJqRJzt0rM!G9X6kczV
z`+))1G1;s$7tBg<^vNo$2B5=&F5Fpu7ieU{Kwm*+CX@Vk>r>li&rP)yATbmhW@~bc
zACIgaB$^9>|Cq+VMZKLxA9%FcwO|P_?U1mIbD<MO$^m9_@tQE)7k>E4X}(Zf-5ax4
z6U$FJBQn$(8m!1ZRarS%-8WB8_WmeMTrL03{5}Z_X=>~)7IT`URz0qC%X(BtwG+J#
zxN7*x#Rl$NwZ5f4oTW3<l=gZ{LqJ;9nLuxT9~m$D-GRCV%Ao#9Ll6wvW+RQdH)5De
zrYY!T%o`9?C_iS!@*pJe#9ZO~2N>rLX#h5)e*kYtk(>O%Iee@mV$A=1&?Fu9SE84c
zJXLbW&OX?^vmoZ{;G1CJiYM|bp8F&l2b`LR70DW`EdF0{TmL3cfFZh3HfMNwBW^a&
zX?!?c!0RF;i~p&|QjMVRjFZSE(bl&F6@Tg&c;qw_di;V_=v>;1jbK1&dmES9qizhB
zM7(_t*R5AWW}qh{#G2*zw<5wWXquJnPl%+o{GGhUCb7LZuB<@;L|rb)Y>v?!-~4_Q
z_jxGnb<S%{?p?KJjI5G@Jfp?iET6R%0{k;b($ikXIpWfdmMju^N$RML5b#Evv+^~1
z*rV0_24Wf1WNMhZG+W&Hg-OlxIj$eIlpn`_J5?cdNJtM}Fa8`G$3RSU-wL4di;?Pm
zv%M%B%oEOz>|o3h>xBAyH8bo#cuCb0Ssi>;AQ1vVn<`AmS+|urPsH&u>zcWhb&zbC
zi~4VPo&L4yr&ch?A7&$M4l-4o4~8@_3Hb%LY%dERxrm<p_~Rue5WEtBWetflT3Gju
zF$0<3wr8D;z2BH?GqQUmezP<vOV7Fm^i5+ZVz^l6CyC7;ug6|K*j^1f<=shfzH<2t
zK7K*G{?Hx{jjm6{h|AJi4t*k7D|Z*}rOz%hwaS7~6{cQv&!}B@KzsRn{ZP>+Ul-*L
zKQ;gClh43=@4nJ07?cB>JPsmoVR*!aL{#^6&-7yhVX=7w8J#V0&ooveE<(&Nlh<96
zFyU>>re>+A?~Kk&AtPbb_0)`<KhXCHk>>JQVRG)m9qGS=2l*Zn%IE!~s+uu;vvl^l
z*GQ;6uOs1*&SS|eN_3sSxv1_j<wLy{Q$x!Gt1{qD{3>_EMLZ5lng4G-Gfsu`)2J%7
zfgH!CaFrQ-Py}swDEa1odZT~#u>JLZa}IX1z70A{LrcRWi$Kfzw2uYMzO@j~BeAwJ
z?aFB5l};Opr@D!(35?FcGi~j}=>GNW@f<|=3a=sgzT0Dp*elm}3jZ2WsMSOm+T>!P
z(!9c6`HyS{Fz@6M7Qw>U+z{k@QRtHt2Q<>Ng&am4fGOPYzmE7`RyurB_94g3SR-wh
zeOG96<3eZ$**|;k+_g*0mz(I|VM|=D+bly_Ihri;2Q^(<>A#h)O<PklD$MRw{6oyf
z;@2L3nH+q!P)>B}L-F%=+Yt*|Q<|YatJldg;UsDVYb`geqB7$hG*30=)TPy%)W7Mz
z#C3ei120gg;^0BIx;Agre0MGrze#kS2z>lwvbr-Up<Q~P<D8YHOd2}+l}cRif%$jY
zgpXCGD=MT-WoZmNj|5xd)2Gbo<<2QYL0sCrgr_cZ%BCP)P=&;Y<2kk1$m8p{`Z&?&
z!dvrfkPy;DDe{Np?6?dZ+`^u`B7qME*fF`EU538kBnMGghWS`n@b(0nTTWJo$6d1(
z4R$lUTT~|n^-4m$_2O|j<H<qPGbl0TAJk7x`|&oSB;=lkfQ6w7Us>P+K)d0?q+N<;
z$0O3SBX_-GT6`1jYc>{Kp&ACrqZc)>4Z_A7R&XyZM|d3t{;oQH)dR<zI`siVnPc&r
zsI|dTo{lU+uxJvllx&YGs3wSL|IxFP7nv07;MEOzAI5BV8H{i8BTzAs6I1qyx<{Q6
zxb%pcqK_zC89PaSY<lk#Yy2czO&;3QM*#n79ci`}k-bR15t>3sYMJP}O~e||7h$%G
z8u$Gvf6E(RO!d@`_q!S+6Wkl$&P3y_$oI_AAA{+`5oe=bjWp&qgDbK$Fjj@6uql-}
zn)L_gA!E_s!|qGBSv|q0^*c@O2YqsLbL$<Xu;!H(<jswRa_6e&x~^HDZoD;`fV7YX
z(+<2uxVq1JQLVpg?oD8^Q3nQC_Q__U&*P&nWEx?na(4)_H}4qWFrxf4XDD+ZjzHa=
z`O{MzTkN_u)tQVtC3Z+yks0x{KlLgv+bJpJ%(UL@2A`&WTKN4p-gdpwpcGN_vLJ$e
z>X6v#!Fk~tO@9zk5`Cpu+ql)XPxo~KyOX&P>4Hu386l+?^#rU==j5Z|xk&OwnDUB}
zl*es~bO1KHby!$jV;<j}(LSRZgd2YRG%V*g2;R_mx;Ze(_TE0IsIf*pf*M2pHKx9=
zTX)a?8O`>*@D<4I&9hz*=)p-SzPL@|nbbhAc-^Zx9GmuNOblPHxMnJ+!f1-H+<>l3
zbB5C>-tjtYo43wpj>=0`DdVNd99|mlZ?6o~p#rHf`9HB=Q(zHcFLz?y)tY5UEG(fQ
z3~zP~*gk2I_X=d6A!<SLJ(i!6-<{tW`p*;Sajp;=3j=p*K?0KgW5)44$EVO9F9!Z-
z?xVcy<JYfmRl9s||9$q`zpcis|DGiD?RaFkJ-<zZE>`O$8j#JvXX=&NqDY|wB~X~t
z83kcd1beaa5QK)%2ElMl_1R1JAJj$Ak9~=Pii@Kc5)k0jss`y8OR+JDDL*3uy;}1<
z(EZ`HY$W_d4rtIoJkgnQexni81Z-B`d4GW<5`XX(k-uM13)HS@wI@wM%+_wY`yQi<
z$5QT1>sJnn1x8}BsN|ee{%DN^9L<&0vh18?1`p+V){$X{_=t0rUs=fkvF=B1wCA{L
z-;`4I7b$!5;j!0lJ5rwUs>)ri(ldH-KR^RHe?YK{fsrEHOlhp-?l)EPaa})S=O6*>
zkE%I?FJ-r#TZKDAdpHw1W|?Yww_y~t`EI6<2q#eEm+|So-o#MW7i!#ksILm=yj8WG
zg9V+BzKdzgP=C-pE>uZz*2ALk^^>`r^?dcar6VWAqYmDqe<yh;m~;yzJBo5Er(WpD
z8o<Ma4U4ZTU$lPEN2d&CpcM^8=nK~r!y+)he&(>LqMTC(Lxc@QW4CftGg>`FE4YwI
z8=<hG)1%T;*;(O}zIRPuZ>S9J9Rsd2qhg=Vi3YLq9?KPQZ|X_2Ogu$SO7W9#&NiLG
z?jYz#jbM%JPT>(2x|IK3sC!j^&ViRim@hm*$D!=iW^s=5Nr6J5K@35Xp8IR&62?)@
z)Zs+Ijyp}H`z>dthH8>&(uYlYcYQr&^MiM%=dQ_yN`+h$#)KoB-+piC5LW)qWt*}Z
zJZ2`zilZyk7HbM&;XyjSI+g8=Km2iI%OnX$ZSqQ=C`roXW$Xsxpi+?Ma&>H)MV@<t
zRf=ul6<aOm1=4p8#5FNY$hn6lC1;t1#x|sp@9lqA(u;uSD)oDdSE0_*OW*Ju*%vq@
zV%re35P`L)<wY*^4uyP-UsI&8Hbp!K|M=Uyo~<by+Mue2N4%U4xswP3)+Tqow6u^W
z(6r6<x;)=dk$|nge%S<`o5&#UW7<})blq9Y!f-l#I9rwXu<bL_(;Cg5&~5Cg?`eal
zt_Drtn=Go*YH^HnIO;SB!`H=>z%?QhknwR-AiLiDprzAu3k8!T1BO+)=B*Yw;-t_b
zj%zVF^f)idPu*Lydk?cxCtjk6+|)ehUe-PRMMiA0g}Qa->!^L_%dWPDacLR&1wn@$
zXA35=Z9MSX&djcIeRgpA;P15kt)z0+B{}`cu{Yr<@ion{**8rj$i>;a(-2;@5Sj=X
zUYi=ktwc+d4<@#<sA$Y}N|>b>bI=yvLrB0Icvbo)#-H*A31;;U(w-dgZZB%IQke_|
z#AfsII6l`2Y~CWwIwkHBm(UrB_T<4`&2vd7bn!<Mviym7>E9C}N-`l6=c2!c<)PcW
z<fgUkfsH)kCmpOk>U1FPB{){K3k`#y#VR$uMBv@)xrmoGgGmoW-RJeTJIGdbmGx89
z-M3+Xk%YuRk5!f)4VAlLg`Yk`y^A{)<{>gWh@h7Wh%s593Zocj!fjRnkFCz)$B=@}
z{;P+#1UMPi3H*geCf;EsvmnQLL#px(*RQifEk&#|NieZ2`_<`QU8hdj{)akS{SpCs
z!zQ^h#%4{f->Nc?R_V7~QJ!m^&ccqJ#ciGONo`zxn<x6Fs}Z~-Aq)pu!}A#l;XNEk
zo}<s_FE{3GnZH$)qYEth>X$y4(k==@rRTmJYX1%Q{-nH$p6nL&3sFvG_bT`JRW{1<
zmypL<t}HR!*at<~|I=TK&%yc^{n)^1Zd@o`9;^LoIJ&!7s>LIq?{%R9zP%FF3&)KK
zVbqgNUvbF|9rh)gE<25_sV@SMqQBoy8_EQ@b4bCif^VPG2ngSAuLl1vo@MSmL_As9
zMcoWYC!;0SW9&pkvcwD6HfJCDKKGDG`6#-T)b>P&TXJaReYnlIO&1{xxS&&D7*WX>
zH+p!yI<-a{t^cCA=)AOu<Lf)zjP3~gRx(mz`wVY2^A%L>TI8wciP|zbWm6gYhnY-J
zS9g$97PB9zjVPyI4!2eNQp;!rx60x1(rqV|QNih>!z{@SBXEw9Rd!C%sld_TjthOx
zk4qpuwI`I;v0)5eN(PW1z7m|u85DB9&v-gzjTf5pACh=bw($;O&8%FQ!|W7xHBePY
z^w{_*r%60;mn`9hxz<r&YZ8%{h`mBiFJnw?-OB-4Fdr;4Y+W5};G*|hZyXz`6hXuD
zEK+o2xiT~iiC9@HWc7~$l-c4EbUK+6jZ!T<RB~X%7tdkSuk@>(8mjQRlbEpOBFtm@
z@G$>4rHIGb+z}J;EdFf~zmt?E3cbE^7Z7@d`xl+siNcTS_`8^_`OPF5?e%|b9=yx=
z!S<CUpXg~16`xu}@>iauS6B1&LDqqFPu}->(-9TaK4V;unC|<DBchjX8K}!*F{+OH
zhB#T$=u{%KF7#K#Yngd{qkrwIA$(SHn9@G`PhZBJbzYc@k=di{^F=Tj(T)TP^$%0H
z>;wLe)w}N5!cANlyuGtgn3YI~a-W1ap|!l4(%?eMcTbU<fm#x5*zkI1)!$VXlU~!|
zrPF%0ipJYr%MD*Co~(8Zx{VE!qO|LEVt7BGfmaPHTvVIkel-jBs!QBX*r$w=_9;;W
ztTlB-Rr>}3T&bCa{o(XCsCkt*U6jdhJ5YVKqT_OZKPQiQzVa-eqevcTpv;$wa^&;#
znasq_l#mZe0?(_EOk~&Y#yH$%_IhEh6H&#n9T^&ri$)LMi1iJeX7&0H9*$Mx(LKS@
zdJIIrY~DnbnFXi~8fygbx~Z64XQ{RnU;c=9WKZ;X;kWxj=+zQCd*xk|{^3Fc>CIuw
z>gr0<2mdxp6MN)P$Mp9PlZ3zbp39?{?$5utU+qY+Zos?wXCl_jWqNRPnACSUQhXWZ
z9gA_yfB5rwbs4@|ZnZill-;%3S}oNenI5n?le5Sc29`e)opQJJ!7ca@Zr~#v{#KYO
z>BMngxclV)ugPwFcmx{)2*dA{LdLe085Nr{pWOCBo$$U2wU<v%CuNv@H>0NT9&4z$
zP)-XlXMGdsi9Yf^P3K>>6_D=Sr+6ZuFNguXW<DUbdFVoKxJd6j=NbF|CFcJhfL>Ja
z{;#H8SfM}Ezh~h50yaiWd$Rn%u-f*Q?{>HOc^J9NMr4t(x_N%IUqjQ6ANpvtU0drF
zZ0+W9{IGs6{+LT9JEGzr72cocaT3RwlOH)v70~i&fuU7LOrgK7B#1bvGHK~-o*g_6
z^t<P&_e`l8dAmXXN9|i{`)_5hcGiU}g~TQ;Z8$UVTm$I&DN)yZ8&-KGy7S1>D+ima
zi(JQ8<$~D-%bzbs6s_lQyW*|vjzAMSYljb!Wp9R4Mn97*=s#C0_`iRc!E^OWMA?$Z
z+Pm>W7Z#MDb)f%|vsw&pE$P+8ZmA<J@N>K`<C)?{No7ljgR+15&N_Rc$BHaYvfd-R
zSRj><^t~cT+7K=GPzD&OU;VLgT%7ArWQW&}_Ven^sBf%Kq1@!p<556qa^r1D>a)yu
z=we*so9_2El}C%)sA&ik(`t)6QhYP)EwT=z#q7BMeL~St>rdfsJeFSl8}C!?u{T<;
zAGQ2@Td;oIn6TF-om~TO!iK%y>OJtK_Q^L*=MS0(nl@cWDhZ4m?}wMaiqDl8A~$TJ
z8&Z;rzhZwUIqPVqD3Jdz4Q?QLe@JPbl<s+{wxhaHTSlw1_ZQSv66g;Ew=kpC-=}Z1
zL(|;-jVhpJFR5EZo|HQB8JMlK<0u;C1a>Tl*v~18{dmx(KMa#7p&ZO_qT};4m6F)~
z^>5gkP6TD@hcwJ8Gp^S^Xt67?Y_XkscC(^kwfI%f62E`UwgUud{X_GNh>|m@`Jh#&
zJVT4m;z+u2J*U18q2_>PcY7{#_WAL{GK2czo#Z&I8=ibfQMtSJ`acTkwsDOG$Us!b
z5acjsB#P8#ejtw1l)ypvS`WV1Lrg2AFwJdL`#po$F1n^6b3c+8xe+I7_YW0rV31Tg
z>pK@{3go!_6Y4ZtS25c~a!<j9!aS7!ujb(&L;JzokR5^Zt6%o6Y@0t@2HSWu8t_}Y
z%R4S$c2izxW_1;}^<uV>)W0OzH0hG&DU<sGhHaLUS|vc}h$UvkFRouBk-6_3ORJ8m
zBubd3X_283^Dkf*mo-`|vLt!9M48LDMVUmxA^;QbP@3tx1{-fPaZL9`e0Zh%Z+lu{
zx_|MmN$2VU^%5e6!~+cV<`!#Xht;R1*<LN~zIZLLJky)o%eK`2kjr-=KzZ?Y^H04B
zi=xpx^4r4i(I?Kg{zeT>dA`5|Ic0^gXcw(x3nTD0^Y%=kzPQ|VkxFs?2W#x%YKC4r
z(B0NC#oiUJ*BoU1t32xc3q;xgel~FWE4!4%2rt=apVcs`4eXtW6kBwJ7*Ctf+6tnJ
z3P3CYdFD6w&2vgxNthNJ>4w<h;1<fZyG}eV`19Ywh#8v&M^G>%^)%foJK=O#Obr3=
zq1>hR5-qANrB^bn{ul;$4>)Q*{rwhbu(a#t{f!xq21n0EG0gIX+T3H9S?hn(?;F6#
zx66}Fm|jbkqv-I2Y1w~y(10PcX2^XHm8o{Mb39yGzCM9n^ZQ@P!e^$3PbIkHr9Slp
zr{4-8TDm%UNs~{pfbT{L*<X~MY<nX~FUBb9!t5&UJG^lFZx2(GHAj6^?7vpLg&f7b
z{90`;KR>NSfYPfZB)<@{{0rA$8?%+r|8+g1!RLL(YVg-$cOw_~GSRYWzS^|o`QTZ3
zW3<JxA7XZ_T;K1{Ka#G9^tVY=E=p)FS#KpWPjX96BjaZjQ7aQ_QuZ7(dNi1la>T5k
zP0EilB&~xv6L+D22wh7Se#rXOZl#X7N4aIcgh?(^sl5>V2#6~(&PeJop|uhx>V4vW
zE3Q^SuN~$1ZD~SUjiC7hWI?vNI}C_+BKUjgf7x8XnQfwv&oqn%X!oLx8S}U)|N0k@
zQoxXYB%T$!T&e#{@fKYqH2@@-ag}^M%l?Oa!Fa1la^-W_D8y#<CGj+{mzX@J1W3!-
z?L^G?j$5wvqOu|eHMtMV1W>$NSv>@Ea?{&Ix5fcC=h%U`Ck@JaE$`yW%rYZjg(M#H
z_CmXk^an`pWPDK%MwKs-*9NqQ*!5Tc>yP<ug!jy<RSuM9;<@S5(b?mC{z|Cdq_V-7
zyKU{z8Ivs3$3OCiJ%X;K(o_k33c(~@U2we8)~?jEavb~UP-0Z?@CO8*S%ycGMe>aT
zky&E!Uy3>Im5x7kUX~2dGc&%2e^~LpJSY^Smrx0^QU8{GwNuXa9#RGJIp1LuGV(|U
zyP+RreCq?3OZ-^AWEzYr4)&Fj&A9M6s^l==wOl<DK~7jHwK2Z7=*MDCwsLOehQ$wS
z`K+7sYHWL)dsOQ4o7`UGLdB|D{qt7`uj9OWXdJTGCqq4U7J6dD=6jzf%9d}PmL?#t
z_WnZdE~!rXM{>C45BW5oeIWeaYJ2toXX;8jh@eO{A=W!!#%C$taH?jqSEJ1=pFP0t
zGZm4@PbqY%;0tZzM272#=`vX87rC$QdCnn_XKl~8cy0M~IE@I4_N&GR$C3fR8dYHk
zr6@W#Nrc#JQy?yZN&Xq}<wj)8TZHK&%qF?DL@}1t103<OKCtEbV&Cp^G?Xs{d4e3Y
zzmB)c7(WJx2S!H_0|>maJ5`#Bd|Nj-DEI<SN4pVPWFBeA<W8S3>isMR2Sn9+q1<Tw
zO8<Rt1SB=}3>P1yFiw6T&dbR=?tQUmBR|sVO2~BHZVo7Ct)B}Gyb_PEr?uCi!@~O1
zB)E*41d1V@e-4h;TQb*sV-q9j{eONTbr#%sW)UgL(K}W~CRV95Quzw`JhsT7(tMuJ
z&p%eYMG_2|!=Vm6^Fq-EM9({#qftdU4ybLP0))mVry`R6xP1GAFZnlP#w|m}CjM^v
z74T&s$jmbKWUr*9<)#2x_;;MrhToWe3x~_?fz(<S&*}k7_B9}>)YGM=8hCp1zao{Y
zea85l)p4X8Ro}bH?8W_99c#8xXu#S2ynHyQ|Io<^B6>yxO2rbDzOmg0rLt2<G9oKk
zb<ZzMBUK|(9uJr+bZsb8le3aTNDKCg#26OO4Rx%tc!AHvcxsNkdvsGKzb=D1O+*ON
zrbN?CT>ic~DPj{+ScVexsdz}S7iGDB1OG%!S73Kl_<SQ4k;qh&l>aFDq-=nr=>jq)
ziqh7gnBVgoKwwoW;2Rcy@2<zzwp($n5+uzibw2qXiC&N6(R|T&2*50fmHy|Ynr+J+
zCw&tW7DG)H&Oqwa;LB9Q=PWtCHM!f1wWj54(&QteBT+}dw+#Y`CdgZXz5mw*z~ZMh
zXP_-V>IYD^{FYTuUWQzqU%4xbFS<<|S@C57z||)OPW?IsAn^!-BAIE`)}HGIyv3|i
z*RvxVb|%-~XmEh^sz5{cSxa|;$O}HRPP{IA^wBDx<B~YwK=x+>9H{lHK-S)6+LKFQ
z!!nTs827uoizU4FijqD^#nPo}QLQUw&664#-It$bq^0SmVLdQ8DKjV6NsZnySnsUU
zWZP9N(HfsW`xU}o?(t)ajt4Si6!Cr$WU}{Xr&XK8-Z?HG>RkTYJ#|=WzA?V^$vP+?
zITKTwDlR!~-a=|Ex(xpTqJf>R0Hm#OM*MoOw&_>I#b^qU$@T8PUuHdiawz7tLdLmw
zM@ngBt3GSuFr^oFEBd3zVaqAsdNW{Bc2gq8g}^afj$Y9%tHh3@(mlPOsd=|_8y+Bj
zQ~u2mQd>HPd-MSeY2`3_Odi4o@cJXjJOap9mK<+QT(SY5#a-hf%a8M6E*~~m_;{}X
zAdBU{H*iuT3(!2YY#rXJss^^GOTqr^l$37FFW(BbuFyFH3{=Tbed~ES`=QU>sRRaK
z%}avVvQ~utpj1u<bXxhggjws!W%yiv_~{~BF65{GM;wtQ@0$A`sJ?^!c(12htNK4g
z>)UB_0ALzt!$*K!Vp=v|Rrb%=3c8RSeRJ6bXIU8M^A8KZZV2u=B*?NISZTrb@c1(V
z%cN*(sST6SmY~yiHFW~qfxWMs^=P{`YCP_x<%eHn9Q%2Tw6Z)a=<qk@pXhHqHn}@D
zaRw@dwdQlBRlzDCORW!Bh2MYsBRNdQRsyH@075)>uH9?M&i@v(wfl;3IFme6D6<Qo
z1_t|BNTh&&#j7gc5uYWd|Ck>`V3+Zf?dx|2Ho={pzkq;m>H@%y4ep3HX3$1Rw@(aj
zKr^YoHHCC)|5FEV?~DLXQAN|w4D){(Aav{K?|Q}~Glq`CS6d{Nga2`d$^FvTHG|w;
z`J`K8Zx+8Js!x_l@Ff5@@`sER9+L`B7!m!}5x_zhV(ab}5B=-$3&RVaV7neW;em1F
z+aEt9YgentCSDaRwN#25qg|@z$ZIn>BOR)L*o0Ei!sqTan1z<p`Ji%ovYNSX%r@h$
zHvbxIU{vNUo@BdPjtw-qjSwBOo*-gKHa1lZEUt$o8e9Ykhd=UXrWgy?pVu61W1P>%
zh%8V~777t@8>?vzDjS77e#Q8YhfI!wseUHPZ>_zCT*d#V1gU5_QS?IP{P~a-?sL#6
zZ?+P53n;iN;7_xtML_`s@~0Cyn7T5<#wS)*w}uC;*A7Txo~6m+_fydM@NzcS1(TDn
zsXt@lfJa`>z}j;xecLq?2wH3UC|v}H6J=?MzEbXQm!*wHrvU|^XySjlg%|p@pJIIv
zTyowcVmva_sqc;D{oq%LZ~-joO;^$^XMo1J5+5ec(b3$g?26uw8K2EhRN~y8%n0dr
zcNkmE3aGFiP2UpVD0zsRd<3t3)J=d|`2Ln4L6s}(-Q4YWsy_-L4<`mqH_B><H3`yG
z#X{(fb9iSBKi%uE=Iujo%S)B0a>8;yGw-};&B1Q4AnPX75`i#7*-E+)Jp2mf0{)r2
z{K4*42)dnM!|%{1NAx?fZjkt68RXnAyX0xtM30^*ivQB<cGw?m(QgrN4J4biXd?Pd
zFW=`}CCw?sG}y<oO~0}lHQUz-*tI1NGy%*er4pcf)VcuxAuw@EE95Yl8`!b-pN*cF
z-9cLVFGdw1s<UQ`yY)cFjXH3Y(%5`BlE)9FbW)T0N7+pry%OS0XA(0dRzVGYD0SRT
z{tL3jljXK1pQ$RgFMx5u6YJkKU#6-MaJuDPF(vCfWYlrqZV%WsSRS{NE4mYE!^ED<
z+CISLnd`TcrE%TE{IPR%l(M!4?Hso3s@iX{Kd5|FKMAdRc`=EAIb$<xyOXYHED|HO
zzHVdr@0rIVq%Q!u%m`kOxKGbY^l5yJmkP%5K?oglw$EA{E!H7^xUmaX10k<`4BfzD
z;_lF-f3!iZfNnu|)osHA?(RfOv&Fl=+NSXzw^Vue1Exxy%|0siKaYYF9$Wzr?PKaM
z`|zG4od4@~kE0Kd`~1--nme>X4$tvr@}j3hX7N?bglfv+%?f^TD@HK$R5uv%*P(IW
z!(;I4TNjwn9_ybF1lK(^$v>(k{x=4Ct-eq&ToozefRe>3sA&}KCXuHOTn2cCv}|Wi
z9B)_9I4{PwrSJolMGhT#O%_$qFvH<w?T}cED~YF0mIptENfi-1%sp{`!L8*MF-=70
zx^!-F)GIdKqVQP^pq<YuM6%Y_;S#D@>;L|}o15kqJ=c(=TW`CWZ%eN9J1;HCQ5}UL
z=!$0S2z*DOMeFU&8-~!;9bED(?Io_l{9&mbCX<SHg>>N<-QS<nm&%cfq=R5Fy4RwT
z3;Gj+#VY=H*ULn>FYxjI`|Ld<IsNa2QBj0FF3N_DX1Sf2!_N*4C#pZL8M%puQEsS{
zgb*{&sV$&h@S)ACIxWokw)%VJ8(po_J&V$2ZU7WUBO-=xTVTB3^7)Sz)f{X$%PV!W
z_WYBhLbH$YRzVRDB*a0E9S_<}UH3evvpp`-Srg|sKKBHl05yly#f*8AcXwk~^Qdw(
z6;H~qk+P|kNWahEOd^o*OlqlQCi#NsYZ}Py7U>|c%cph(mqJ0|4VrTD3kPeZ!RQbY
zA%t{93JK5Q_yp9lM&vb}qwo6LXVfkaLUHJH%G0)U`N>vV29tm0OsA)jy*5@FegKb*
z3=$1_;!o8ei)h5Gk_PBnp7?a~D}Z!GwDA{}!BmKm_V6I{%`4VdkM8Mhus@~?_`w=~
z)ilgw9Kh@^)YoMiR0=UHUkUOWh7ObZM+irV;IBDM2l_?QYzZgY)0*enOvkyT-8Ax9
zchJ}v`mI2aPh2V#dD~apsIVR(%eccl10aNQW<!nHT@|?_eup7fIv_%w>%bsPLzk=P
z9hy~0f4}d&Uv|rTvlVb#bW7VV9V&#^z}Id1O_(1hMjv8Z5lp6|1`lLfc_@5eUikhD
zLmt79T}f?V`$*$0$suCZMQPQs;8&PPfIRK7kC>Hqgyf3t>ey+4H}{e@dNbNM2vQ)w
zA}xg{Zziq1(vFE_p^t!+c?*Y+>4hX0l?9f`G_GHIl)i>xVM;3xEBk~YR@sf3q`6YZ
z8xu8V>y)!io5e$POQT;RjRoUTE!9I3b7p+<sivnWvjsZ`4)Tq4dY_7<qk~t6FGjK@
z46^V=eOrj60&cPDhP}ttUDK0;ZzA9xwp`V!h%k6dme`7s-od-AENvfl@+@QmK7*jU
zX!Zhk?-bbKxDm6Naeg3i*ylV?ob^%kgDV3{AR*2}ukmk;?r3p-q96Cc#>DJ;)Dy&8
zf|yA2JnjML-pr@{g2$x)P%(Uu9YveWKAPbBZ-!P6OUHObG~UDpk?TCS#yw;p7yH<A
z;d9umbErJgVfpc1#~WHy8BEzVGm<4I>J3Xyq#zXbAS&`=%uF1U#M-ceW7m}+a;U&R
zR^0|fcDlBJlm$P}<k~xS?NC&f#%8s2N3H)7>45S=h0XY~T1tn-&L;csz0mniEH2;v
zrYNQV%0-Ee9GBo0_BN^`Vpi4=wJ@IvR9GRtzF&>gJj=N>gm9m_l&QdDqo>1EMMFrK
zPaYFhm_pk(2W0r}L1**mGpH7xSw>RRR)EN8`uKQF?{(&;ordo#yZ<TUL0F!CoeqkW
z34TeW9La=hgFo0p3BsrO=|TcKk0>Tkdu%b+CUxZzx=f580{YnaCtgM(<o)ow@icVS
z=3gQ>VpBrL*|Xe+O2??-&u4$EBcYe>CNVj)!~R+<E<P7ls5_W*9G&&+UUvomyoAX8
z|5<uqy@yBji4pyFcBRK+3U|!Uqz3mrX&3`uC$_~kl07F4h}fxCvRzuxJK1a6dMpKw
z<7cqm;F!Qj#`$Mnw<p9Yt9Mx4)i9m*tGi>52hZi<_E;RNHB$OrU%G2lQPy*otOnVg
z71MQQMv}K%CQ<#C8*gma(_YO|MDoP!r*4HY32qwB&O8^to?^^<j}S|6^=;@0R%N+7
znvhXto=ENY%6roPe|US#sJNDHYZ!M+2<{HS-5UrRJP<6nLvW{YcMI;W!QDMbqk#l>
zcWd0=&N<Jy=iWQMG2Ze0d4KiZyP>OgRjpZb&8k|<<T4%n@H1@}ZsU53M02%rS67kN
zY2+<sNM*X}2k8iHqy1y+L1?cklqu+Z@rm{}=eYgcY2*TQ*OW}ea^^RTo8oC5*b1I$
zd>D@3%(hGgZD^~LoJ4cE9?4lTjp?v$HX13iG^YTEW`ajQBIYQZ7eR8`?zgEi{LS>b
zkPN_wGV!tI@_iC%@@CyTzcU!$pw>rv9~fMoHt}a2+Zpp}A<)>Ep#Zg-a~b#%KcXgp
z8egP1h0)NCJr&ap#X5_Z9;lrj(>2?l+w)v>M$QQ{0QAZnrks9C6Kegl$2<#TmE+(V
z<{#O04560^0xhPIOLccE?Kn%&uY^jP1*&$&=sUp=^r8OxgRWQ$gFxxM>^1nR2YXY0
zf4D54Dkujcd=0nnTY_>IaX98+r^W>B{5xS|tPwSq_88*O4I|gu=($*znDEPR9)c`>
zSi#Ln*TbF6y>YA?npe!Kd+4SKDQF|UZ2Jy9*T0D;-_x6Qx#6QjD+gnawr`f*G(6x0
zhusW@1=}kx{#eVWgDz<kL|eu0DJF`3&yX}Di608ce4gw#jzd>miV}X1(QVa|y{|0D
zQVQ7^MJd0w)!mla<}}G~#zuYdvl{t9wP1{qMJGVeAt*Z(<kkUnOGH$@mk!<j!{)hV
z41^ERNP}>$2yO~%<)s5+FtgCA*nXDP{UG1a;}hMDyY!gy=cSvuW5P7m)=qUA#or6E
zG5!;}u8RDeO`zG#!8C}G^bzw|LmS`1q5QLp+IFgwCkp1*OJbyx9o-dsO6-XZ>z)aT
z6S;OKKIBI%aZ;VVEJsgSEFdr?<;L+5`NE*57l%X+7e8czV`sYSPks%Lxg13Qlf_HL
z&Pu5?pVeMHKd7-x(-D(=64%fimntyZ)Sqn#_=z0!oIvXmpV|8%y0RIHee*i25gdXT
zkTW2Vu8}<rqjLK$_Y(Bu_LKd9|7AKzeY{0>=r-eNvsiz>iL%O%-C?=orAb(p2%F3-
zk~?%R^^%o1C+QmGurDh7C&=O<^3cF-sXfi$;J6R%oJlHRch;=6G1z7Gdhb2H1|ogV
zo<k~{QzabPYzjZgi^--Jd5q4RZP#*$C#<2t2Yd;43fps9eSNt#->n|!UJWuh?>z~H
z>;~<g{ITXf53!3h(D8aEo%-?Qi8|V6nF1Z3SH3}hr#%^6o2&`QEJ^#~+2fmVOzZdZ
z=y!9z#%g+n3Gq@O&We9O8MJ_*!^pK@>JvK0su#a+=zf7>+BC}$i$3-3a^!7rTpAoL
z3Rj-<X{t<yh#il>)TfAjg6x)@3wB<D!TzRx_6x*xx5oo=htY&*gOXTo6vZ%xW)1sx
z#4JPGybX0!ft`+{bk4is7%PrBhTqa9q28@_IgIax_C(X1^;!_0Oy{wcLIbpI?@aQ^
z^zld<CR{4{AV`~s!KjqQqt^WG;y`$rQrtW<2J^PVsXWwXFR!4#zd?w%w^H^;M9YOe
zJ9cx$r*8OO+8qAty}D?w#UT-a2c6)JL%I01<2@ksp$U1P8hN(DMlyr#>&AP8Nhk<g
zt%l8VN01cajaT9&X>YWe`z{372wRHV-*-4Z=JZ~4?ra|y@3-Cxgm0P5!JuR32u<Q|
zMsYYIsYZn59obc9YMR2Ot?wV~XT6|`1>{2r5fgtbAA+7!)lYlkL@~tV7FF{qiO^)H
z5NHH_+2NiPr4l1w+mfyKP|dJY3wtg`qWxB*DR_&j6OJJCr7<%iaT#O&4l~GzWQ2bL
zod4v4<wV%^s)4B{t4@ec(neMY!9D+`6gzdfgVu3J&5DpOgxM#Mu#i(twr9XXx6%~}
z@i=}mD+tw0o>2zAi3tq9TT0~zpqc<XTexG_aGEa81*$n89lVk}oamM)7eTF3+IK5k
zUokLO>d8!N$AEmkFJAmMJC+`ULE2mlIoelu?|2a6JKpr%4~XuR^Vtnv03H8>FMU{H
z-V@(mig!Om*4=3jWT|j#2G2oJ<56v4a$K^kepf3oTzj;BVk;6X;@=@Ijuv_+E1l!^
zaKB{}BMU~%z$nrc6(DjJ-LJSj;94e{sFZW|Xff)&q;U30tBCE@_(T97M4R*APLH1=
zgPo-%AT00v9f*{?6m-W$3gI5Vf#xZ0(f99&0l{I4&Q%h0As+6ulxd|YT&oMaBhbG-
z(q$L&-xhM>Z>-+Kw>;W~$1qRo*k?LN9O(Qa*vuF?czTPsC^1AQFX(mx$;VRQL=(QX
zrA}icZ63|=BRch*3E>Pp&9GHqe_#Ucsj+d|QVsi)J%-+_BYQbt>IK4Vi9C2y!F*<;
zK+SY9s+_M91o}_Yc-->_qM-GVIfK#J{TSZBn7QuL>o&>I%OLHqsbdEwin_~={TEL9
zfa<wO`$2zmi+MT?qn_T9ybA#*Im~JXSagBo4q9SF)AMC?SR_5h`nBy^MQvvMhfvI?
zz(wiS%#`Jaeka+)-rQvWw}Ee0J4>7tb<yO*%0O%j$*Q51)>_IomnlLJzm}bF=K@Oz
zGgcqCv**$n)=L#AIKfDI2nn$%zhnp;K~k+c%Ns9`OW{#%*40LUC1%C0-RkyinAr{%
za9_bc=a{;IBpu+QHs2+G%yF7%73k*EI9>!ZAvNDQm`2Q<LgKT|ktaiwHD6xjAqfpn
zv<{x=q7CAAfZI5Ftoz@mA2hR1(Y`G8S&dwpWJc-?Y<x!~(|TH%^j60#andH6WLt@_
zG3!9?0m_pixHN?Gsq(<m0&Ewz&C!1{{zIk_V9tGIlkPl6)&_QWY58xL)l@FV&%78P
z@w^h#@47;yj+uAWL!2h~<X8rI$ls5-q}V5Z#{jymI&8ZV+8SC~TO^(cHXSulEv5I9
z*+Ja8EJmX3lC6H4QWWB^O|V<bBNB*V7#%kLG8i$6@6JA}U}UK~<yLeh_+!`?s~Rg}
zpLtC~n`F7Ef#fnY_kM-qC!Sl1&f*>xIt?BT3Ln$57S@l|a`tRCQ7-;SJjNR-rZT;;
z_e6hWcd+hgAFW+-&82o7!S_8UM%LI5BVsS|c2==Din~z3BjFGLc3{ivZ5k_?ZK7VZ
zz(+q=64~rEZf3bGzFD;8NV`l|!9D0O+upgM8QIOVFzVxQI<)x#2_ZU>n${cE;(!w&
zWX8D%W0QL+J!T0zlr)JjvU?X!Ff{Z7YBp0D!Gf4>CzPXNtjqPpy6Y|DqA20xlomH(
zwUUM#o}SR}eCL?7tz6Zy66p)&Mv%JkdEVCY3d2e2+0zR)Z$|a@#?xkIF;m9vk0M0Y
z$?Xw>Jw*1JGvq95AEhvbs@w>U`_A0TOiWk&%v6q^t;Skr!{7Z5j--oEySUW0-!_4y
z2r!@vWdL@yuuYy&doq(Yc_Sv`h#<S|qCq)vCL%7^dD{{X&xo<$-h1|Z;>|v0)rQy`
z4mPSR?M?hAhb|S7y>Oao;Pb-TOQor@qBcY9!v~+6Ew2*Y#37)ydA8=>GC2zfP(11M
z&%A{R{kXCfy;w3=Ig`=6a;|i|vrt6r0D{GOnV(9-cY{XiulNAd%GKp(hQ}~BFf1w8
zL~}$-r{6x6Mh6Tj*Vfk~5}<Sb=^mPNhPjR+U)Oz6vI7ECaHetM-6((wnIV73Jh>TJ
zEvBwLhTjxynt;74)htw4t7Gk1=UXLU$`Oozkmtn}YBS55gD!=6>D6(+*x$vpw^h&I
zU*gjV319Ua55bW;T=D&+@3}oUtX35<fn9n>;c89xXo!x@{r2AG_zdo$Nm?%`nOVCw
z_{6)duP-Su@L}MRWx=iGLCo<jNk{?RJnHw|1cIheQ|u9*VjZU`cMf2!d*MaN6bA|_
zQ=*Ex|NHv+ks=WVTSDg17MS-%iE4grftv9T{T^Llu@4c}NISR-E@Si3UybKk^??n6
zNINm=>a7p4zXxmdyTBGU7=P}OAC~O;adO0=>U=6QgV#I}$X$)g;ofsl=41D!9qywB
z-`y3=aljx46DAMh{k)^H5t(nGtq2uaA516=y|(8vT&v02QC6=boeoJDZ?E$*hiW!r
z+nFX9Q7!%<tr7|vo?-pS`Du2-qspgsktuAfNF*_(Fhg-PnwKnRDtZm<^%VS5!HkbZ
z*eV!kYC{{^dJ$@)j&To4nXKyIIFHRn5UI`Hc&HY#DY7lt;)ez}ChtYu@o8mq4_})!
zDrYYk@MvclBQNy<3Tv~ijRjTdslF0?9i`(u7ALLpQ&UqvnEf+EXE|*>N74e$eCS1(
zw7Y9;_#P9kOjb;GM-sLuFqbM;j%&JRZM`g=3xd7nV|JlQwNCEeTRvGfc!9x<DJtp!
z63dgF=^R?#=wvf1mM;vZJMkhq+A6-9lNzLoyY^Sgr}iD85damUexLi10uohvw}!fr
z8Q(&lkY{j`w3X9e_dj!XlSY5UBa)lqzqa6{Jvk`OTDs5UCMPs}KuDVviN+l%HtuB>
zY_g#%4Bjd1h%?*K5o%qWzWZfiSvPOjaK*JEHJTKwZddivy6AY&)THy&x)}AEOZ=K|
zagL1zzTV5+NG@&OaH#RpZndf3Qm41QH>6amd$o7DAwtG{EGqh7sy-;aem8+h3bO~K
zwv8R(PFB{3GzgJW9kUiMVDwLu+)aaZyd4{ZIi~k58<ay*Nd73=_HY>Da3eS_aLP$S
zP#a(4OD-azNKgedpBRVY7w<?~wRu$FtS8t^41e~D$n91rNrb8m1-p=EJJr-<@Q@Pa
z?EMGdV|w0%ims2PSfyx!VB@GxDHVF4e_f=9VQj$#J=Cx->_);@6VGq3#kQcB7z+>&
zI?c=Tk%j%Gv&8h|i5o!3c9j2k=xIQUne;l6^t!$CH0BUH$;PkWp=S^+22zSLCEUo)
z+@3GyFcLbZx{VZlZ~K4z<-flEqu4-xNh(*z$D1Yw#sIbN=Q+mrKxZuPFcQJi(0yDQ
z_)Qo^R)N1=?Em&JEmGw6+vClwjh#tqny_V6g8&PeU9wT@`X5H<i+99T(1{;o^+zh1
zm=Y$sykcFr1vCoSYjp4bT@?7w+Q1*yeW<JJ4L+~;G%+?O!$^u4vnWg6+HI84R&7Lh
z^Z1CcQ)@0@IzIb69b0Oo=UG{i8r$qJC-rHhYZV%3y`rbpZqx0jR?MS5VmVq~RMj>e
zh?S%NPy7C@NyYTQN&2CpAYsa8zkj{0vH`D@w3%?g#L&Xw=zG?vR^9({HC&&HrHRcr
zU1HH#Zd6S_{9ag8rPB3OqU#8|_<$81_cDs;I=pwb%%G-*!{MY7t@G^MtmAph)9R*+
zr>-($=m=uB_y1p|`ca9(4fc3fzx2<-f^W9IYzGf1eItZJ8yd5SBO*IX)PuvHv^n{9
zH0wM%#IheJS)X2;&rUxY`!(M8bsU%9r5AwC=5u9|qT~8t!hf*;@ry`XfnF6bSNpUR
znDJSc3xVpqZ1eAK{;MDVe%T^11GI@xpL2feQR#bnRa>8(rkElDzGPT!PpkbZs;&BR
zx=^cc#;XQ8t<r0~Ez5dX_O0?dZ$N6+;Wm^kY;8s9{g{9thv@NGR8XnD>S7!D)M1{C
zz+NQQSM+cH|I@A;exld<RP*iz<D@_o=z*cL<ve4L{tg8G+ZIs37RmGiFL`t2!8Gi?
z7a?w}PluOYK?9ZY!Xi)<mqK(yy&T&6v-*`vHK?+Yqo`3z|J;SoZ`=6ak@2^w5&KV*
zE3ed7D^2LE(w0C9BN<V$*-vJ4C^nuNLr7cJq=|O#o6(-gWI^XnA~a!}n6L59k(Z2f
zSQqVH?k-W93}i#vsISJj+Rzz(UfQ-2S)e!1!4*<0=elWo_xA+)pMw5;r1l_v_Bjtx
zaGXnMcR@->w=$hg6!`W&Q$#G6y}VP8=j<efZuaDp2Rdo(<5QEj<2@_yXmQqjw&ZFy
zbgQpI$+@NGa@O_sr7oC?_-o2Z>D`++WFhWl=l@ZVKNSwNJJ?ii#!hpV>DBv;h2PY>
zS-vl!%BLRKoheYMUfetb+Q!X9<Ts4>f>!Dh0O~f&BRub-KW@*+WtdJhAe2>>#mR|w
zANH<*dv?92zeK)Fx{eB}yADr83OJh<S|_&~{Erg<yc#_!%#Mz9sm}%SNu9NNp!23T
z`7>-_ouh35F?Xlf7sBtRP}lXzO`s=BP2>d^QP>DhJRZ>ULA1(f3MM7jmqxU!GM73h
zyXq_d*Q@*JI<F0rR#U1~*!;Z@BLU&4E`R$(|Ms3ga+qi%zyl9*j<OlcaqeATmQTle
z0oh}jrdfKZp9yII1|TG*`er|J?ac=PE!;7SMEHu_*Y$P3-|JcAki0~e+5cHG`R^ZW
zaNk?I%kh-jBHoH4G_8vQv4qj4j9IXpe%*6bK$n@4W6MQ&L<LS-X!aAu58_St)qNbF
zF>9!ukh77a0EcWL9~dswfM(3NMo~8?>}tJ&!5Jhw!<MkB=PQwfo0BT@sfphzGWxtu
zq?(uSn3``cvhl3!2z?1{I=0wU9wSJcAlH?ekFpHqnk(C^DKA{DmJLQj%CcGmhZDj2
z7D`=UnX4f053Poe?(hHM6+;9gB$XA8*JNeUhTUE}Q;WoZ?m4bXfIG4??{wZsVe@z7
zSU>a@2RW1KH78ESay+E&1`{UyRef}7jf|;50oF$5;9;T|y24P?ep<*ACf1<PjsY;v
zh@ER971>0{#(wa8;-9KV{XeL>%1!La`x2n3x1udpWa8W8Z=m<TQ@Oh0aFfULb!?=F
zjyirfy`!|_%Z?*WM$dpesqerd1>=rt(04C<ipk_DT=*u%_&d8W%4B7G+lsesXVAR0
zG4{4Y)AJW(a^E5e{kEuaociZ_U{T{w)6)93UX_3p3UCBNR7T#byRkfd@PzP)e7a-o
z5%i@YRp5V!h2VtpsfB4$`Kyc#+T#vm?CkDe_jSHrA?=ywtuF9|i9DfGDw-@sF>n5@
zbMF7W&R%>(Aq99wswz@-wjAI_q7}!j`sKwTPrZuHKdk5?reEfS(RJ+4wb2P<QH7C9
zLbuMtukGTg2oiAyQ=D=^JfA$_`w^-s>>K2rkdv)4p?|oM`TrL;l3f(xh!V^kF*O2d
zhz?q@X-pq&_WgG|_%m<z<3m4O?SPNuNd2Ogj+E&G7`*zKCDEnHRIOD8*(n8O$?f>O
zLJEGJesFj83SPAGN{&U!eCsM(Td-bRlIj!}<@Eq=q%$ST+4=b3Uir^BkMV7A#Sz@)
zdf{5MI3eD8LG<pHEfPU5;QCT_75gnS@Emn8SrT<o+6Kb<q8C$zV5Ghp|D3!S4Yc`E
zza?G5h@SUpO12B$>ho-3y;Gi*41?E(;iFD5hV<l2iM0*G7XG)1x5GpjIUD;8?O3EF
z`gu2mIXhG^B|%v#$Mo*U47<eCR5@K<Cm$CE&4KX%y`zD}jKLf!qUtTOO5K4N{%DbC
zo-cw!Ew5s0)hY){{q)i5@1KpS_sE|DYE7pCZj2O&I?iG_+$YZ>Ph9cOFC|e5pibFD
z4V=aKobS{!9g9C_IzQ}>kr|#<oUI_4ZXc<Gp?;LO0ZKbp{@Jhqp?5SeXaI!jr_DUB
zUKc@_0g7|IWiMd~Xe4yz6n%X#RH0WScWX6X3JyVDB_wf;dbJ)i<UC?)^TE%W56bde
zsFsEc@}m&k3D3skXxec9;GxN5?bt{#4*uXitj(mK_hcr`@3`2LXqTPbHwdu<;W`7O
zP4gza_&p-GuCBj{Hm!$et+C>kQh@?jCWM*(L7MOo{pnSE>-!N9p#OKQ$1VZ?SA0@B
zL6|LM|7)w9Z`{qxl#rm`2?U&GrLo!*N#Wb`{ndK$Jn0?~cInmi%OXnt$O_L85@UUO
zaq-I?ZWdRbqSJrDwtqi{fBK`3=X^8>(b=7f&3jPZQt;ly(!%1WfBKrBdI8H{uO9Sm
zI!{LDem#fozfUlZ6l_Rzw&ZxdOT_*T5u(Do*8PPazmiM_nRg0y=<pzZ8Vb^aX28P@
z9v=4?5vL%@9QwDhWANb+5FLZaO2}AoO0vCw7_7sRbR(gydbbagk&SUE?^$~-8^{M>
zekBW>35u`wGs)u_OsdsOCfwR+75KLj`0y}+y;-{0s<mM2vW_dVio;ea`(Z5no+n~?
zIC32@AMx{2d^aNXklSONu8hX<|6)$^I&gyq{FLySj`!Czn4%cW@(f70Uo{E!Wl%?p
zdH(MpT!L=tS~!K$NXI~>#mj%c7;W2np=EorCCoaz`p1~uhYSmE{x+;HwKqvB>a}ek
zeU@iSM+nNr<R#Wc%w(Z<|IGH<`L>^C;oVn@XY2{_Qd{wXt?uHjt@~SfHvfQJ*I%HP
zC$i?F0tvv@1z)c7ZMxKf`FG9V5Zv<r63#fM9)gTn2X7z_v?Dv%@gl_0X=+G(=|A}E
z>Xo<LQHNqQvg9cdq)Ef$n{)!n^kEKfBB9YE_`m&-P!xa1O0mKyI(1N}V3M3C1o>1#
zwy6K@V0$=PG#IaA%ln&I1$_MFpKmC7`nRYK`FsSD9H1l-$e(Cwr^aS@d}^HJ{yS9C
z<N8BOkR=`Z(L84x(eS^1i-6jTvo%gHRi%&U32pVd-}I{yW|H3g9*H{XNRhgt{>fig
z_hGmu{_K<x0SaPW|I&Ik54e`=pk8Sa2^SZ#xCm$g<*6@WK;ci3JV%I2n6C99`|2Fo
zCfPZp{I$iCS8;12X4p$1+fvW@O{LtMycjBuqha(@<|@?(#Q7nMp&o?#Rhc@pa~?rn
zv&oFSM+vPLv`ekwVPbv<oWk4998PlL&(bkuWVbr1+RW(OYVVUTEw^K*(JMI_hsa~S
zftN>@R%7P+^Q6gCA7=u+JMT?t8C=!+ZX}kV^uV0+8dR{}h&?VfeKqC(c&&dm3qC7S
zs+_p_#gw_*$gQmwm594n%J1tNX**>)SnL%dMl12n{^cC%N&DxwMiEsp>Ms_Lp$K%Q
zG)EM6RgS}x9I9_8OI@~e@-SoxssNd^S9>S7ut_+MUeX0RjyIniUr={tZE#?6-yl*H
zkzUychS?i<u_5Vazj&FNzKKNPnzJ{VFtHJ9ZM(!2QTb8^K(hBWu0L*j_)^Aq8=w;I
z!vAk`uOlVmLEo`04>(+UPmaRt_Yjqp@PdGxv~s3uIK;ygue=8uQ{8nYT(jccjO|9t
z>&U4Uc0UWF482=84&)PyOuY<bzK8NBG}c?&Fwv>fM{+gdw2l~0H1SERE7x5N7Mna!
z?@+rC6<mCZSrGd|&$>-PQTT1{p>FKmVI9X(zYlG~Npc?#W4VHIjmIPaxPV9c?bZDN
z<9?4fi`-=<3GslN{{UzqDPUe!ila7D_c&NySpT)$+06}lZ+c02r4Ec<RHw0nW{_4_
zR`!YZSuFwW2CueFMTJo(^=~KsZ$B!S%D^0;Za2@2_jDTw&fifjENYZ=y+#{(>9XeE
zwKi+UJ&U4ytj|_*Wm)yI85pJy;mzqX@#Ht5bxADHd*5EWPQkf&w0&*P3zGJ^&SW~a
zh;P<w!KaOtfPIXzAv``yn8kaI=E`Z4ec#?*3!@SORalqDPpB3ap5(McfzUwL+wUEp
zgdVLT*-E$@LelZyp|R&qJeQ+u&HLoUN^+5~u(e1l`;0iw$9lg+FrmXLn+L`zdKf$T
z*8>Flz8MN7@5QG`jSfum2#SA;&9<JL_D7i%XG=pRn$|MoAnc{|Ehhbj)6dk^(iz9p
zVVh@rGvkrZUST$p*4ImzWa7LlBZRTy0_9XvD?6dZuJCe{ythrmxF<VQ=rLAcG#N-e
z73Q3Hjm^UW$Ys^%4zojmjr)PGCZbU9$~k<PT)#3hw)L4AR>%m4E{leJCbchf2MZB=
zltgkU62?j1=S2RV*Mx^A`+r!p4;CBUHPnR1^2;`h7Jua@*E6EHE+108n6+RivM{gU
z;y7+qo=|26>IK`=1Q!WmIk$L`WOLh07rF0-x|UObys%6q+){BfxVM+*(tY`peuC<$
zpwAX&Otk@MBB*!29^xFY!V|x?`vffp)Ge3+<xw7K+C#Y#OoYZ1J_mEhg9x`U0wd_&
z^RTtdT5l2`T2v&h0ah5bn@_@F<J|ci)*Lbhx`dwZ$BvN1(JWeKxMRf(7tRg;m=Hni
z2KV>6bt<jq8zJhfchIqT{}HGDGV@erBs8GROsBWXlxFz4v2qJ@<m88Gt{)uEXGzpe
zdmk5Rw!?v(`C~gqf!vfOWaZQ@)(cn+VVuZW8}dY2nqoupucg|vC3UF<V=NZK`;_Lz
z(6<T38)n5LgWhK4L)-KKHs)EGPj{SpGzA>^SC~UMV9Rbz#m_N}#gOe@W5^`i(MxF=
z)Gqp4Z3pT+>M#Hjs3QkYDVO}}P2BG=BttD1=P=IHanJa7VQ(Kuzb9T?pyzF%LFd$d
znPA}`k?cY=;TuqN`!99^Eo~4$5$DiAiI~(yN%qvQKtdSs(?7qT;4#2NV^3&+mn(;{
zzwZSLJ93&sBoUL$e~s+mlaxg)e2^FI5V7FSrx0twttt=ps4*1X4S5pyo4wiGpL|6_
zTuqE=Uhi@xwjFdWtvLmNn}tsf3os?hzB1+uERwW${yL1RQQu>(8GWsFJ|n=QFkP}T
z=27r`%CM|2%1zBnBc1RsWgq!0H4lv*SnI!tXf0(1C79bD95RUWF%3V5C)-i;2wH|;
z*(L2?Tw`;<${oV@)MlC{m=j^H&lY}HNxN9bACmbqidXi2zhGsP<1wg?gAx=fBCu5~
zxNUq3{hS-L%mCTXk@t-4p*3Cy%Cy1yY-b(Fzwtg$chhwz+%!E4SCqcmC6|<1SMq1T
z_wfj%C^gfB6fc=PjLNM*fBBG!_ytjf!u>J@@m}rU9yN+qbAY>C`J_n9-S_H5!~P~M
z65P)X4)yuoFxj$L(YC;!*I_IkP>vG%K4cyLf)!e8#v5E*zjILbq01nKEdqm+?A8sF
zqdLtmV;T6zFRy-?3<wQOB{SQUkg%Y!-u3PaqCRpI)kz95Ae!naCUaF~9%+58$`wZ*
zO4B>o)-<C4x8c#)heh-F@kMS#b}PYm!F0)=>(rg<1PbD%-KES#C!=T>0<Zm)Ko{hk
z-pbN<4s7}?T3ZbY4onF)Gf{8R#f^r3v?VoCyyQ}V%%SO)WSYBcs{iMNmxE;DDSQ<;
zc0avLTvT}B;5i5^!avD0K|8!R`BsEQ2$aWs+=gU=inFyjHU}+QU-vJjh03v25V4L1
z(_VoNaTmo2v?P8fIL|WokXKLj`!ErqOEPN<k9{0X88!zwYp#Ifh8CG7#kQ6SwzmyF
ze@MfoPh+(MS_p)G?MEKf5)vE38bndAIz1ur(2xdni2EDz<B7oEUSA*RLjqU+%WzWp
zkj`&~%r~P4GlRsJ_)J*sf^e<m`l9>h@5ye@%gd93C|&*Lb)p?fa5i6I*Uy6MGpERI
zBa6`ZmmGZIITCG7%m9a6{^MGphd3+*jM?k~>!f{~<;H#0z)NN!oo-CTlS|hPa(kmp
zXWNu1cFMD0F~pyICfNKTUrQxG>1mt<s7H6kgC*^U>csnpjl#x*lK}aOU$_O@L{4(E
z(!eQ;RzqXwt>#P~8cXYXANDkbufFlT3sq4LbS4j2uvy~bHpd*N*n?9<kKn5Vf*`aM
z%9#B!2c{*OVR!;lwcFd<A^E`(#mwYyaZJJNk<(E0=n<3zj*{OIYd*ydPM^0h$o-%w
zM?+^_3Hpo6Di~Gq6hyn|ebq+!#!IXugE<<a0C}RNhlB}?++j>yJEoUbBuWK)Ps$k@
zLws{mEjIQG&LvdPWi%wC5`=Ys0RXW(JLlO;Ad5s117&|$w!(|!0VGkH6r(FzZN;#@
zfcua*eT)$N9kpS~#we&Z=-SIvhFfKyI(iu(8z_(T^Hnn%D*VMpN|&FK2OW?d#Jf5t
z9*Yz0l2nK0i}vAa#b!_`Z#SuyxOpk>7%kc{a1F}S=<_>-AO1MNIIhUiPor@tIB!PT
z35YQRi$X1rUWR-471XA~c)m;);xD@6z`p7Fe0JX@wy>hWLSHd}e{_+^_RbiGJ~zH5
zSj|ek%dd)y3taPxi}GR5wSz%NpZCqVKh{B*4e}0thG8kk0yI41lCpCZ!U_A57>_F8
zk@}v2wVLVe$Dy2seDp=6-#}<E%}HXcyTy_XyFZX7d${Mm;I6-7*jf^mdl2=)Mdb^z
zI27BX;^8=SjW%Y@M;BdPlFN9S_hsePDF{zxk~9fIvSJ~N_D0ipAHHbvq(PQgcxHiR
zHVhK0_CgfGOhi18CuTqjp>5{mYGt4L0^)mn9yDpzAH{#<9h3i`@(#kViZo<ZX82O@
zX7nW1ZRB(U_ApLu_A_Wia@_H0*{sr>xKUGSKd7*5ViYfyMjUepH}RvnQNltECh2Qs
ztjoy6DS+SnW8_%6PD&obWer&|`AAt{9XFPkVkp-Y6=*;$Ncti>)EAJapy$)~t}Nj8
zseQ<G1o5>-A;zjA`57zsW}poSoAb=Y7QQ5p=^x7_?P2W5HHgNVI!~e45bn*vq0FO^
zV#B6%ux%))0U#ASzgXZb8POW09j)r@1TW~@#UToKZag``Q??mn&dPtN$TRjD>Z83M
ztv8cF&;KSOR4cdo`e;*^pQmy&Mg25&(X=ksTp$*(8M=I2sJvLj*Mo~dCM4r~nD}L0
zfd1&_1_XekYRK?P41MLUB^{}kYBC2&2ik;Ei6m^~s(E3djzoh@rpP1tmp)^nP++>G
z>SiJmkQ~3GzM*3^weaQ9G!p07`6>q{ju1Gm3MHEqVPVoF|0Js_>w~0QKulf@WMC$#
zWXi&UNBX$2mCAuffvAt$38iLg3Nl<Py~3fi!_Q{&ay&Piy2RH;igg{Q5Gj$_su!aB
zh0i^WP%QLHjw5s<BReq}XtE8FE^Z1xL%#e-R()-Bq5zRDqUSDRXRC~cG9d~M<LtyA
z@s9Tn^T8)tRBD37a!a;g=UsC5R3_~U(N%`DD@c;pI@w0oy@2q{%eXv-OCx-_NxVv+
zME51f$-MN}Ai&l9w+~r3?D+Q}x?*P^HZhi0AQc&~A$*nat(8|IgQc|!fkg#chf4wu
zx0=LKK~F!x^X7UK(RSs1tWc+t&+Wt^9~81ry_(!2B6FBEMap4y*enU<QfcVPH#C9V
zLs3H-LkH==%*F;?WY2btu;n_*uY`yjnbOc<uF$^NXe9-mbb107K|Nm^TmoX#Gd4Ef
z*2DJXp~g97M&Ff+O)x7Z&cO+s@nVvnA+C|?kapC;^(qG+7n#StGL$3x!!*Ah54e=W
z+o8gs3H%K3vz*!d=y;gVGw>Ah*bHKAI!ghy^ecF_F!+*fr`GHC1Ms(M>xp)bO{eg0
zPx>+NqH56*+{8szXG+^{L^Xtu*VUO9KRDGW=U=l(5OtPf!y>SI<FARgnCS~Oi?4c3
zSRbsev64MraDw}1JWbvsR4jG&bR#~8AsdmDeGoY;{meh*Vh+`h2=APqCDe$II3$nt
z4sn00(L`i(UIjkmlX;CKLKC;B58nRrmsu1Kk?pHo42BlK6!*S=@1w)i(Qs_F7+#p@
zMlKF!oXc_|x<f={{zt<nIohV$U+(hwqF2I9yPlu#L}f|+-c@--@uPP3FnHa>mVe0(
zWQ_z2_6y4BVmxAOG8E@w0sGHQHWC7PBeRE^KDaknVchdeLS#Yyw82sbd?FN5&xlq;
z&~OQ-zNbplg;sW9?3_pKO)&)22aoQgtu+4D$#AU9|46MwPY}scik!+krNN<S$`NIB
zO(rGM*xkZACG)i)x(<o7ZyGcMZn~reQM>D|b-ATje-H)FL4qmppENpdAfN0*-=cV@
z8s#XO)&x(W?$9{o`D)_a<|PbL-ZQQ<aS#ZbO*JXwdh@)Lt(Hb6#}2sWOVg^A1(s(;
zJcPjycsri~8s@jm%i@|s!ZCQ5`R)CxED~?X{G=y*40g$|oe(fEPj_dpP|~JUBptw9
z0nqMkj$?&i5BzIpvLKQ~G6`;f#-QB74s-=d>0ZoEBu~THw2kt<{KE3-j7lZryXjwy
zks0L>IrrkWhvf>^+rc<VNNmz~30mtn$|S++qYsMIruJG+{~_Xz21Tm70p5tg#?AY5
zDTyl*Tw>$pp@*rKB{#7u3=;n2K-~W1A&gwAK_Hs*jB!P*kyljqp?0|`t%=AOO+u9(
ze=AAec$1!GZ%6vFGIOPiS{;G^IL|zrok!(zJ+?ow$;3cvOMJtZ=IHBD6@y0P8?y~4
zo#YQM#>?tWp?seYh>dD;`LLA6qUi|kY(&VT6_Pl*n@}!YlzC1<ndUV)%cOQG7Tt3b
zU<nhq(kVN&*|SNa5GZ^px-et0<)Z9xog4ge%zZdG5#f~!f>@|aVaXH{k(3Lxvk$D%
zx0o=ygJ^h8hLJ^O(J~|dOUA^&gWKnu$}6e9dKPRxsww`h0m9SclvB){tAH$SHH>6*
z*DIPBS9`s7tvCW;ux^0vx+y|Yxu(LI%^u4|+3?a=+XdPty;mL!b&r0_?QN=r6jmPE
zSx*NC!r<jqpETHPRP~0B(nKpe^egRVvCFU4Db~UD{MZ;<5&Y4P(|x!!9AOKQg1g<m
zUd=*Xra7x(`W%&t6Pf}a7dM!ueIB}qp}a3us7LZ((~<(d+k*083HiQ#k;)Lreh*a+
zl`9fZ6cC#|6~nHSRAw`uAJc#*)(@+67T>(m@lm}O`sGzmSdNx=X1HMTBuUP<^V2RQ
zmj47}Z2SNx;ZCoM=XP7D+lcAj_%zhfw`<RxAMlkNPG1Vx_U}(%wRf_*S19Ei<X%7V
zf%N&h#nqYZQq+e9{g$35t>+nEy&t8tI*W+32lC%Ip#IKKOruQcZj($WvNHJ<LwbKW
zJ_&l&U1A>1%>Z=6SYbH-R?r}hNz<a!2M#Rn%OhL!`}jnTWzQD&j-Qb((%QYQ;8tW_
zfoM_@?S#w4%@k}BztT6ccf%XM>`K;~&cyR1jm%{FdR}}2Pv5t1lXNfBYh()Qs*^0S
zBxoPKkvEVmZPz&Ua4hNAN*)J)w`>Gg%}pjFz|qH6tWh!Uez?7&MUVHnJ$G%rN>->t
z;}Z(-+pgbJOmK?H2$)I#!tbXR)kt(Zf@ilv&1-<cF4|)MrBlas0*&88z5X|O?tGh7
zx6b8|0yr(yb<`Qon@*uB>Gt(rUW=`r;Ng2Ak!H0|l`2a&uwc2lL@AlP#Skk$oZ!_O
zkZWBShE=Ojs6-^oZ+!z*NL<<`{||dNprjC0LSDU;f<tq&S*#w#veR27h9@JxV0n>;
zL)GYTEEei)l{V`!EGF~h%-lq+TEIVfvq_3S(o1rd4iemHZJtcIZb^7HUt6J`j#*lh
zU*`Ow{cD&-Z-qwxX%sk(M0c7lVx7dh+Q1#(@6E1*Aoe1{noVmb;6!-pehcc>YuD?#
zGSFza4KJtp>2%?c1-_45M=DU1R!aaXBuiIHdaJzCaLb4N!4EgZzZVw|jj5m<Dva`>
zWoQ2l-*<gJ-;j>S9aPS)G6JW~r;jR_(hpR8ypFveNmL@Cs&Jr_p04=I23KCy?6db2
zEaM=0hm!RY9bP7%ZhLmUQF{1KF9zcMEMaf(WcM=D-0L+ZaHfp2?!Z1bR=fxNYf^;i
zdHFTybEJ)y9iNqJh+S_3LfV2J>slMwa3w$@3`a%y3d<Pv)JKA)M7}ZskA!UDyice}
z=W^cmemetiv>`c<9GfT=i>*QSu~0w@Ap_T$%Tl!8MvIQwI$<z`uob18A|;cT&Nu}}
zQHSkgUME@ayqsc$z59lPYIMc@+x4H}J$uh0EA(?Z9m;yXgB45y4qu@8?Vb+{RAPa*
zGm$AE>rA}5278Z!Oen#_YM=_h=hpRpvpwcpQ4Od3=qG)ya><FuMPo-OOh+SoVhWTh
ziqwY4i2`@M-BiiE`9$!rXEBoSb|D`IVB56?`d?q)J`9VkJKXbv*FFQ;D_fG2@He5r
zrch*W`vcvWRs394=6iFNH0qU>`j;d@HzT46Kd*bqlj&Q#(d}Bfy-_Wa#_9HYjaj=D
zL^?d%!G^E=&~6E9-KC#MJC7~yrx#A<t5j*$mTUB9d&~6Ni=<cY%54bky`BSHfv0D9
zpglc4E!e2;7Q=Gu56V{wA~q?k!pk&gfIKaA@j>G=E&3GVCF(V=200&xwVU}ad?BK!
z9eBg^aUrM@Z4Hs173BnITwL>EFp(C)umMz<!Ug`Cwb557PuC7<&@rcxN~b^9jgP8G
zTd!W9KCIrC>4PRZk*?xmB(XS<dqr;n**!f3XxX)Xnb23!BNl<GD|1VslEK3>C#U@6
zYfm6=$0hG04#1gZBrPZ-#_C&2Cpij$$7YR@?0JyHajDaaL4DT%XY&x%+^|`lI*{?L
zQsoj$&g;v{2fbpg6LfIb0CQbaxe{h2BZ4gGW&JxU<_qV^(i)@2>oN5G_{yY7sygY)
z`)bYkOk{x1{SbHE`n1*dsWdPB<VRGxQR$ak=+=9s$^FKoft%8DAF!Vn`0;jh)DP#E
zllIN<=<7iV_)Nc5;pSkL=Uc)aFY2Um>M7~|%MJQZbbi9yyfs<$p5Rd^C>oNF5@Mg%
z&)>sMR_pMq?pQC)(7f&)u8oenRZl%wM_uUdjVE`D_%<Ps>1u{Q2srEe%$%7XP1u$V
zHM%eH*KfNFk&C92dLxqhc$71F4`J>UuT{@l36S}Z$eD8wbKZ5aVbCZFMdrT_%!z!5
zzm#c=-KO%ijgjb+c}!HTul9?&`tC0B4+^cbN%WjHhiFIg$*&&l^uc8rK#q9FEY7WR
zNduxznCM69Gun4B`6R|FS%mTw_u#uytIUL=p4!col1w>?Ox{k=dZB2yxZmU=`X05A
zAUvM0Im%O=4lAXoThDonem`hy{QjmK){A4BEc;;g*Q`?wjTz(3z3PCo_15NlQ0I~7
z_U|HZj&Mf0-x_srCGju?uiSp??(fK&Hdv3QGzmICo%hB>?d-y4j#js+PDnxVl)*DX
zjmE}f<2A`qonCROZN5=)xvQ_|@W2gGWO}K0Vs~XeUR=I7mulUvx7w~y6WrO06s-BG
z5{gqM#_y|@p@%B4we~WA4L)02V<`bNDglBGib<B@NWEQ4#)JW1gbnPScdnI&@wz)d
z70cTub$eXAZ7d1q1B0X+{EQ{inu5Y_QWbnXyy`nn>dSYck}4hhr_(rEi%8k=Fy@A!
zaUQcAo~&D6#K-H9q31DeJ(O)d>`Qc>dwlrK`IGzN@JF5;#{u`f;=(HTJ8K{U`xhdj
z!|j0*GUgK2w0Qox{)^DIGY{+fj_ZgL)2VP~$GuI0>D5I)V-{F&ylFxqhEv>uubq$V
z%Z_i;2pC7#ktT$lU}?O`uzW_VM7o?QYXz~evs3bmc19r`V3L!lckyin(sgf#0I>$Y
z*@Q{!OaC6|;baB;OPef3n{1hBZBdyD)gJv8b_h*?NwjhTd$*Lfwm>0!Gn9`H>t9$}
zNBZ5nk<(`Zll|+#W{Si^Ky^7_Aa@I)f=B1I$Ce*qk9E~$Lmr4`E&{l1CvhCVM51i#
z<tzF!v016$p(lR@{V5yMyau^0twr~%fI9r_hCCwnB!ZLR%*FiDS^#95&!>rZ*5V$F
z0a(SI^P_9XN<PRiDX}8+46n|J>bj}+>t5^_AQ68XUC%c?=TG5>MHBg|u5<$D&cvbW
z0?Ly$arl);Q@^MI;>b7>(#yp^9L}GH68QF?@_yM%KOeVpR93S0j|lFj(_?%H`B4C!
zl&;zBf^_YC4ZDp)qN1fA=!ECIMn#}@5lQb+W8`>tgQC?@C*EY3@iP+eOwK}ierl7b
zVOWlbJ*j&&!xNs#q3e1r)r)1bZmbbbrQyZ($w4QI$QdAEn#ph`y|yJ#2*32*!yXIK
zN<$@7d^P8ud7^2oGTuosEwr9E+VP3~*N*#7zFB^!reR)QVdk6nNQYU$5Ucjwk(>wI
z)crD}iVIgKD;=s)QH3FId&u<!hH`DzJo`v_%1}F@a9;F0KhG~Tufvn1n!`Hw?vcI~
zJFndjIzV#WW0)Lc(45h3R33d=cPaOO#zDjw_I+I^y)RE;O(0PHVgl-5c#zI6ie$Te
z-D7BlW%MRVb@RX_J3-O%Q!`=pl+X_@<#GTR<BuBERQn~utK%L1v+gZV40mU*QLM#B
z{{jOC(>;*j({-s_lG)y%hC3ONxohK2`RldQszfJLdc|kGP<A1@gdht@GU2xFcbzhk
z%~Z|rf-*sYHtgwhB_vckPZOENjzB=*i|bKQA$#xNej0nWKR~etZTZ2z4p!T*%X7)h
zdu}joNHN(plL!do(G|=|fk!$QBKXVw>U}_rjk`3+1dh?hUi4N4IrI&mAHjV;?mUBY
zGnQtL%yWI$x14H!88a8rtPP4_Ho0b<lYaa=>M|E3jESsIjCNh;___-Vbp>)Sd*-*J
z^*y)ZzKtpZw7hI%$|cL$=|zmF8MkuW+3#j%-(~0EPxs^X8<rs(SAr@sOVilpymcFi
zdFwyE+%~z?8A+BeKY}hz2Dd$0Z!n2|GtjYW=v;j?nh*Fa33O0-gRij*?;#*nK!{x#
z^A=WhkpoKmHwzahlpyp{O?Izp$?WUH$Ajfs>Ev;{lzOE!)?c)|Ac7&+VL?2*L>6rt
z|1jwqoU<THZt%k;uHp3ulaml#a3>Mzc3|bUJ_|rR-2p%ibSQVvCv8JSWOOm5=J|MG
z(u#{X6g$uwTMkh!cA0FxiFiNh9Ej##m6*FiBTh^a62BiRD5~~^(w@M&YvKhF6CaEC
zwO$GB8Z)ae`#2AMS?KhR9m`%M-^*-M-W^Y+9^F+)?&fo8dpZoYDk42&KWJz*M_|)H
zOY4{NGj$JU+UnH7$RP0J077xBR(CD!{WD+^37`c!o&8z`E{c;*z-dG6Ca3}7x4O3-
zI*yJRT*lWMLr5~STcX_hE$4eB^0PRhJXe-|R3B3|h2L;Gq~2caxsA|PS^MR5<q7<1
zAsJW0VKydqzSIUr5-QT-N#eS!mnpAzW0ce5Eq5wR0Gbqh!sr}yT^ZE{es&#19XKAN
zov(Kcbsn|tSPMm$Lz5mdyV)e%E;nlX*!O-0B4@uV5<`a}c<3)sXam1NL~u@V4(>zn
z3n^7VCNNLw9kG-v87Ux3526D|GEzIA18YP)&Z~xJSA-oG#s#-geY>#S?h5)R8y;^+
z9t(MWexKxPRe}eanG<*F(|>Et)|PTcG8@)jnw7s3DmN;JCB7jeTcfoT^aZ%}fVB1a
zv~MUV+pv@p1dtJ{(+<lKIAVXF49SL$hzX9$Kd05R3bp&8{(#Hc-byVKpA`5>3ZLmM
zMnTdFIv7M5Tk9}L?>M4Cts&>4*KgnW1YA*58X*%{<bpE9vxVN;aH5rmz28HPjo<TA
z6H&K5pT@KvUt)Z9JhHvN8IY^hWp>mk%V?TUrFzWc_3tN%-LllgWBIAp&YR6G=h~l*
z$CAxd!W3nHw*pD;S9a~bNC?wIa57$JZ-}@%OA36pL^U0l`n7$q=L&*t=N;+pwoF^R
ztrw|C((pNez6a=y4M7E!X*63&^4ZtxnT0cTNd>mtp#7o@&DaN0!l0=TJb6HEUiSXG
zp(nq6qh|x&{pw^=e};m!-62w__P}r1`D7P~1kj0sv6ZBG@>6`D&(D)ZUC!spIX2Um
z83Rv0b_NU3o@9>1b8UoEpiisT7rqj4BHHpzNjfpS9Ip9Pl)S!NUxR%@XZgMU&?q|V
z^SJAMWR79?ShWK=$x<?0IpJKVSDSOg>~MK)8@QZX*Kt7Db0<PNTV3!f{0pbmj<Zli
z3dRAqrrJ7H!0n~2b1f(!vW1M~IgcDJ8jFBG-rwNHA6qxH{a9Y8ddgq=7|hBDp(6uJ
zHzU&pi`(<?s;1|M8fM>swqob%;F9$#MJuZ~+{{nzw>ivXn`PD2YUZ7QS+z+TnEm3%
z)t^|E@$z4*5~?@h{i_8{(I*s{5?g;|>=fWJj}L2g`m}dEKbG%IU}oprpf}DC(mRZ5
z3O5n6&uC~^Vi8I@T{N-j)O?YM_&xCBL=6p0Sb-S``vVO}Q_O-(+a6>4yH!qNq+$k(
z>SFq~oj$PQMe?mgw!3SF7m1Rxps8Uw&&8XTy{w=9oVdbuolZGiiZ=0h+3tyGKDYEo
z`9Rdc;gXQFQ-LTkx_2dV740}eYhA_}9T$l+;0U#$;bVqq$9tj>d(C>ol8#4#pXc-&
zIH>2P`tuJrDXf#zmrIB|!*o8OPCV{G=lT36;0Vs$QSuiio7DvxkuI`W!ojV4PcCvJ
zdju+^cm(n<VTMt=1(h7>QGHJ0UFVQc<xE9p2qb9uo*o{B)zx#baVomhDO>-5jJz}4
z{`;weyZVHZVPkic*%hUe-bdl}-~Gs0;gyOIpD8hENM0qImajL*cib8{Bi?Sp8GsHD
zz6okouxCLy=gNa^@zjsJW&J&jsn4r$Cegh0sZM^JZ|@>OBqKubB9H?w*2%Q(u7jY?
z%N15HV4isi>qo<usXJq=ZF0iat>5{Z9{u}BAUGxVG~V2e_@|rCm{oZ)=NqrFG?h>%
z%4hJUQ^|0)>IJ1>rjnuTk9Uj7w}Fbr`GL0Gn_1o@%pL(Wjx<#2UA38vT(Ls+;-_7{
z7D?n?&{aP4lfRdr$Mo*6`?a^ltM#Z=OPa(MbA>ux%K_Ji^g>2%jH{83@AQUmmDap9
z0S}8TX`G(lzj`@en=Q;NL=W{fjS5@cj<iA6PKSh*j&K`8vzH4PKXugm4y`C{Df(0{
zha*BEsVnG#qoudYCv8$V6*D7;Za0Mq@%t03nudL!O}Npj6A*LesT(^z&-Id^fj;J#
z`j|0(1|`dp#+1u2%^uj<VZkNI7lyEH=|K>u-OVV<>Ples^@lC+>m%U)1d=ZdJ_%C@
z*{37MTTs30^zIj7T4bg1?S?MpZPgOpS^FjraEK9i6s^MR_w45*?E2ZNCEA=j5Fy<6
zXz;!E+-SphQhpEH_USX-PF0`Bbt&L2Bc9w+p&7CEK}rc$#g(Cp1xAyou)nl~+h!Jd
zv+)>nDitOGdWFy87rJ)YBmG#gNTt})m=thlMrk-DdyT?8M@T6>qjm<JuL9vTr|pGd
zfqkBS&BL|(O8-I|_$Y@5svt;kX`iiHSpkoV0H|K?JcX$9YQ<K)2FSLls`cnmA)=8-
zX7dIr0k8EUgMR0MWvhcz(%4Y1PZxJqFJ6D|9TG+r+ajNS3tPZzD$|_oCo-O+h{Mls
zcU3IS&TD;NeHI$6QYKCg55Jb(?YdPnBzciA6=W0_)BcI|wUhZvDLIn0Rf39i2~YE_
zgwB7??)=&IlZfvBlWdQ`yb5VvHMsOQF=D&;h$Q0acS4q6$7tS#WI6Q~c$D2zC$h8E
zDMw1z;RPvgSpC;1G`)Ko)k@%i=NdCk%1fYtlrx+D>y-A+w}|&;B&CU2_R|D@_7)AC
zguu^qMHovTvzG#4@01L825)?V*ReWU=j-}mXNX*7q@}a0&6X6I2<*w*r|`;2Ib*Zm
z6Wy0;Rj6=n5KG`Cxop}&rSnqMq{0%YI><bJTz1)O4G(q~8UAg?1hb*AVpU@ll!AmZ
zK{J*y^<6t}R=6A08*}&roV!&EP0T~g*A(NLHt;*kqkRJ^BOM{#U1NA-8@dM(e|CFQ
z_|O6KUkkH1j4`zzvXA;QKskQ4O%&wmLS>&^0WmnNnXqpwD95s9DYI%!=N3=g`}wmq
z5j;~2cGi(cbh}|=jjEFPk!EMNtYeQu$^pI+W5KJsJ)XA@S(>DQ#8sKx&?N$Jae}T}
zW0I9ZN=t6ybSdNbOyfTU`33w2zRt^L7@)dvBF(qq@{!9V0BtzjXah{<AC3{C>g~Fj
zM3qORcSmB75D#}VfyuESXh~JeFC@xkh3hF@&^el6`8K<mYwTXX%&&c+aKbXIE{Qrx
zoT9xv->$Vq2Vl+lr{tt76H8q6Y6Za4X!$iazn+40!RMh$Nyh%n8b^1UM>-5^PZd9&
zh#`Sb>lp#J{MO=2kl?|U@l{n-V++~e<_&s>Vop+*#@*YrjHzmFnKh|;ryu|8;7uqu
zjPu0UM%5?taP)7JW8Yhq230FbWJ;)Ti-r|SGE1Ws5f@Ugm|)WrY?k+p{n3C|aa?yG
zX?ACn#^_x*M@|mV=<As1Td6n;_MM}g<O}GucXC9f=>i_>&Xc{#IkT#J@4r1K(y`QZ
zU<$bpr*IK-iZ@XX_c-9Bap5Rw!PI`we=C@aC9#h2Vr~6@dOP!IsJs7x&sgUngC@%`
z3<g=V?~HxM7Gn}Zwjo*5G=yZ|$}*NQB+8y$BTD5F*<~jQJt$g4A%w(lTAr_-=X-wV
z`JVIro!|U3XFg}{=iWQ_p1aI_zg{0tuTDh3#fV$KeHEQsNM3S!c(&rk!dse|d5DL1
z?j5e(ODj~(Ao<ueaNlhvfJczZ4c;efa-SwQw1lqD=?q5Tv^*vL&;#!T=Gv?B3<Q5&
zQce!%1`rAdVLtj^T9<Eha*-%EWhP#Q%SLR%V_V!uOp@3w_7`cYdCk}5HSMx=ow?rT
z-Hz2*C&Ltudy!|Q7tc<G<lDS{$#$7~Y*q}xO|pD><62UD{tO4rt+01`H$%)yp`d0!
z`2Zio&6t`q*mzf^N{XIcX_p6sK)Cige#y6Muy>@N#m$sc_w!387E~Y<d|^^i05=pr
zVKKQvQl6aRBU&umPZmTDDbL<H%P%s={&=YMy(Wi!n;n_p99sg{U^t)bzgO;VcT$R$
zT{zj$^~@(r8J=iUI>Fqd*8=5J#9U?}s^@p}^72L!I<Az8R(Z{N`Z!e0PH)WrIsZ|c
zT8EQ{`s~W2a$+p&c(ARinV^M%_QWaIl~glMgP3Jb9}N7G?Ta9>ICHr?b?U=(MYpSG
zjPNX)`SBI<VBz;Z36LlCR|WztmWD<=wZuAJz0psmd@&mZYqe;$_c#{g7_A5lwC)Uy
zX-Em7bFn_HWJyPM<l$`F7I@ZjqhxJJLA*~WV%NO;R)w|dR<T@}i+K{aKOyR9ouYbu
zt;}bA4aVZ-E$*rah`FKI$LPY#w!svg)T?)?kK()AjjZ6(oRR~O#iJu7_&)RRo{YQ&
z9qt9LT>R|XfZzcTDft$U*6QG21DBbbQ6Oiw-h6Q$X`eV)X>}0Lg_O^Gd<l)jvhez8
zP5pN1EIRRJ1U(y_p#-f-0^pvX%W1>tyPO8ihZYmy8>``dTGKPcPfV1r#lBsmcS!UO
zX7d9k<2rom0?M{HPLIgU;dN<@A#|c}+q&GomfK%ZQPC}mbpXMt(+hZzNcc0b?Av+K
zSh+`*yugBS0hd0Hx%E;sCjuYj7($A44v-yoi!|yCFFp?qPd%hRo?}|uI|QRYMi?jp
zueg`Y?7Vl0ENGr&B&biXpF)TLPa7KGt%!?;r#H^VmszwIE;}5Ls7wl!wLFrT3a1#{
zoP<Y4GjIxc8?;TQ0}E(;%mhY^7DeH?rmuO}u+4Lz`;%Z)O5#40cs22OKFAfKaHm2y
z;_kbx35(-I?UsB-DN?}OHbSLCXuI0u3Zt+%g;I8iaGl{)e?8A-Oc+6sko$YPU>Vws
zn?0v!Me>8G%c@QzOCm5=xnuqcIk`8`HM*s?w$|dF|EgFVaxMu3RAto>o%Hd)bPzb0
z9|~$Wm&r42X4ba>TJiD(i6R0W5a<lEVF~>sUj84oGvo}-z1kC%Z!4@0sIneIbIiZ4
zuWW4O>$C1PDzY@{<d?h6qU`1dekWMlJV-0GxO+#?40QM`DNMq`%t|Y5TrBlvW8wL9
zH6~#i-Pv1*j)RQk84Bf?EDC4!-P3HYolG?;`I=L=zDV&?MDH%~p6_T{%HK_Gi%9BE
zbH=_rGHQi#qh%4XZ|A?~-dIk1WN5?Unvt9SG_j+PzGX|*QF%^V#8c$;APCF34`Nfr
z)1>xQflxOqv%k|+Jyi7TpJ6b_e(RG=lkX8lej%U!8`?*~488~H^VMj|CoDc~uqG;P
zqTZb=><$c2Z#4)fUEF?`J!)0M6k{%Ny^ufuW%$~vEdIz5190G5O>ZC9x;}gBlGzgZ
zdiQnxx;Tb;1ksKybGxUwy4aic5-K_ED~!>8rJN4e)`)o~%5bU-1GZoP&Bf08qLy`w
zGmGJ5GL5@@(1{BT_QINV_8R&}Y(n+Eid7z7LrET;FP2(-%Hr70yHAm!B={I;kK?{B
zWf(Ul7{y&D_axFR80e;18)2m@(jJfPC7jsq&ZoV)Q>o|s3QZ#LmtCIZOJ2Hp)Ufaq
z8*9;P7#mCtXtZ7p-SaA4jjg}!gT2rI^{x7Zox&zG{V(jx+MEw}ietiYV`$HVp6+LU
zgeS>^cyIW};Gd`Z8el|2?a#2ShT6Y{ZBe=Yst*oZv77UfL<5Tfq&^dazig9UG*f-+
z!{l9?AWQ)yvm7;?uv0;L5+y<?_SO<s6B-Ig&ON36Xc3Dp9JZl7+?%q1$x$Dd5W$BO
zg;<putl@zXkW_b0gru`Q3{b{gvxY#1r5oD>#R<`QTicah+w9yW8v=pah~wlmb4CaH
z+cb}L+#IGkH{2e9vdXl*$4iw9_|M0s6JjLLU1t0#dD4BJvL*hx=RXQc4KZ|z9Rcz}
z7A?>9CNK77Pxuuv%ZWHCBHd4zZ{8IU<6*ge{<I70;B?v2)}gB#z4Io0W8fK@&hlqQ
zZ%~STh@$|tP?<_%-@y(W6|J)bvYxwNenBH=-XjR!Azs~3*Kiasj(8*<DAQAEc){t^
z6&_s5#NoS!ys3o@@4Iuw!H!n8V;SalqMpyEo$DpU@yV~4awb5*CJMop5KSZpM?m71
z74o^I664LwndtlRNFn>a*JUrZeCDA~Db?c|`uE~&k1AQlnLK69$d(QVsmOP1mM29x
zy@z^@3kXT?Ched@6mnd2N)_nbQWKO6FU#X}GSB6DRC*1r_t(c1VS~4HbQHz<8&>Z#
zYOXaFsD<ge3*V4>h0Ginn|;txv<YyVb_!D&I;dsu6#$>K-<68Td5bD}O6ca$DN)Z3
z)8!Pea<X*LMS3_JN8DlPBV|w0cH<<!`f17?o{@$mVx#l}??EPP?z`#$Z_GJaa*^+*
zOJ5bE`+^5wteM&e$cs7~bZ#Yx&>bJrd6^VYru5-T-=q{eBPaI3;xMc4*qH@%PtGY1
zoAFn#lkN~p3k>3P7=MF8tFn>gBDZ<7+8`g)Gh~IvBClt+KP$ykyuc~8%hwL2z09-m
z<{E!SJKFf(>}uBq`^p6si;tP%r<EW??UX-u9D8pyh#Wc=tRyMGpGlyXGL2#sIOQMI
zaZue91G>Ub+(I5T+2Z>j-*a|r@2x<aRBJ3b=aS!loj{SEL7(99xLc%)J!yrPRv0y$
z(hy03>2*8a_o`S&E3xYzG<t?0@4H>C4|__L`MdMsr-wP;7PY}*`6q!&wpA}I?}=Vz
zf^Zjxx!|nE6`)tn*c<oAKXZE<B*xOS-tjcLGt1M$i?}$3dt)(?B{$xxS|GRM6s#|0
zJAQ2GNlnhgV>^G5RxY`A2Rry;tKc=hN3HhHZ3v1leOF9{JSoX`&^K@xyHmDpPJ}qk
zi`Vk;AAsGeHvxV(4x)!i@W^hiP6-fG*6*9D*VOLoBKImA@Ldc|;!!eH&RA9@iJe=B
zR0wcmdUY5h4Og$$|KsbTJARBL+OA?5LAo;-<Q=meByx4BjT!d22x9|z)N&>!P#%@o
z_ArT#RYx8Z^vl0cDQ<L214=V{dp2)m(wWWGOq6{-keHgz4rL%PVl)oFdHMOKcHBAY
z$;Iy=({G|zz?qanv}Zr(^h|onUZdn<_`N1VFmDtaGSla5ffs=%l-{j=D>(emgSHlS
z)g-orC^qm}%$fS9?`K>THQ~M7h?Y#^Fg@G>3!O4ZCaxbcVjR%kSbG2L>2+)j|AUC`
z1vX(Ppe6TTGzgI(pkG&Eo8cR|%`6xXI$xDeHl903HlOeGRoLcmk682PBTEk|sw-0W
zdJCG1pjU3}gPP6wBM!|iiX0TipH3b$6bx(F&rW+yk9K}3FcjYyzpBT*cUv<@Kp6Hr
zqktgKpIW?c>?q<;B=)`)n%f6C-NqA%50xZv2&1HJZPlvIuebh6W~I_buJE4&*Qro1
z>h!R$0E*{RAL#s=F_kuOekp|caC`cm^YQ8XbL3z7idhhB97Mt?jIBguI(Wl|CzPd}
zfY3c2)%b&6@qKsW4`LJhY}d}sVvNWspacQrQH>9I{+12(r!i^fv`h_Q=8*EZHEf$6
zTHJ81RpPC@YR19cU@25|FyP@KSPJbYx#OyIs$8t*E+Bu|1_$Xc1$S8o=ayN=?x>Ck
zwBL0cb|h;7#GhqexPNdkCcDSJ^(tCELHnD~lud9*MON$_@`2@mW^f(h;xl94fRbj2
zTXAdr91nvadVsS^AWA_1|JpNDP+IExe*hByAHvUF)NLVzDUxP-Z{>^SRf?|T3Ez@>
zMXPr+Wlo*YCg6)c4eH{DrsihU4-UTIT4n!PTwc~BS#O@D3o_DH=aNCaLgsp`|A*@~
z(}IddZ`x>_qQ=PC*vX7<Bzf+CMUn@>(aw5;7##w8n6`<cvfuBET_&J?lT0yjb!K^L
z)HVPsvF?y^&}=_C0XHIFuD>h`DHI_uXjUo^#xO5?Lj$RJ&<{XtWvUBOX`e?Lm^2F_
z9CHd^q?Ce3Gi6kgx8Hsf)~E$vOUFrC$8A{jDyWgslP3GnMWxf*_|Y3gzva#>az<89
zC;Vw#O~KRAHH;JX#;}w{hJwMUnuZnMXnJz~5tE`%aQzAL2HQ?*4mKNU0#z(!6CV8@
z`aDVnc>iHp^B*I<9Em5sg(_a({UOn@UgG|46u7^_yxe1R3~SM0e2aNsUYvB@6Xp?{
zHp#vD#D;$vqkq3B^JL8)qCsvct5(zQ`uERqd(n*axDre3<}K%iPemrUurw@_l4!C_
zajd(7$Uk$d|KIWe`Sn_{Hf^s>VJU4|Y#j4zp@Y504+$fJFS55+Z0vSR+ltr>rxeUV
z&b_bN=c?APEYOc{u0{ebZ!qnATph23Sqw@~V0xk4+^IP^JjuYFFJDS3Dol2EcC1j(
zY1qJp57ZA^#QwWc!Z}o^B|ozKK?RR4ZD1zZIguL|+1ZJDx44*2CUYjUaA%XG0p+M6
z3hoW~<izM`5&P4vn5pWY0>FRCE>e8x8iO!SPBK)LUb`3lOxVDQwIWkeYfH4Gz@<%s
z$?4O-GYC?t;vnbPCz9y?9P$0tG-8iR&0fm-ChCcF5pUGzpU%>M-4Ygw7i{53JkG1q
z?Y_T4<WVHKa>U%d+t6TaVUb5g3=v(A+f^Q2>VMmI9VRCGa_(^AUx4f%3>cLsS!wj`
zk;r@+(oDAs#C^l3v(v0xJ#?!2f6Bvtai4I{fJm-iPU2%kPvByNDWrfCRwmaFSts94
zIj((LEeGfi8Vt0?C)5ZZ4MZWk>o)rMzjTkeA;_msT%n(}DdUS>dw#u*38Ph~%k^Bc
z{{t+6Lp-mD<&FQ|huAe}*iK8*VVL!kbC#asB`x^w!VweRbd506629}EC46>c;0{}G
z>;Oei4QAiGX8Sz7CVtPn{39o;=b<j-n0l!Aw=UmAqRjg5qT=zvtGs!>+aYk>k3jJ;
zjov|(BcW^qu+Wl8v8<0?e2+JmD8#;0pY}R$2?}7Bsi6pie3(LC#{WD&aVhi6MR5C7
z(g0OjS%N{f)#^|Tr|fIMu?3j2%@GqxtM>)-lFJFElFOKv>D$Ppa`t#gnDOjm%7Wz4
zFR+}D?I_liqp(YIub8QV2`)r!Xj96}+uPDIVMTYOojxMO06mP6_x?HqQirw{Tftdi
zx2@oE$oF<RXk;K-M-#AW{ENTm=TwA?i(nMMF*Hd2y{o~1tU96K@5AM<samqX<&dqr
zeD&XQXrX1hNqaEE{Y)i$21K1@&tM)39gMC&%e4Y%fAiDu^UGTjl!hvrfR5F4A1qma
m=0&Z7+OLKAkqQ^rvd<c@GI~Zv!EqiyeHj~?8`S7INBs{?p7f#s

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/deploy/run-on-aws/launching.png b/docs/python_docs/python/tutorials/deploy/run-on-aws/launching.png
new file mode 100644
index 0000000000000000000000000000000000000000..661cf9e360fae8b7b5bfabd8c170b908eb239495
GIT binary patch
literal 62956
zcmdqIWmKHovNnoza3{E1AOs5}xI4iu!QI{6-Ccvb1%d^4x8Q`v-5YoRnzi>idnN0f
z`{Vw(-?)qc-MlqxO3j)zOP)7WPDTtF0UrSZ0s>h=Tv!1D0-6;3eFzQ){8v+q5*-2p
z;fJY^keq~&5UHG<wUMcXAq0eYXkrqqlA<YgfBW?$0X<q+!gv~K2E?TKZtENze2ko+
zI2<jjFpQWDU2%vHG7&)DhRz3xmOLy_fFJT6fTuL=SV>Atd+$R90%b1oxZ7JDF|w`P
zdtbL*x7jm5Z1Sl<32@TE^26|{;~pIdL`Q}fZ3ID}qoK{dx}&QTUnR!JheJyX_x=$L
z4e>R~d8F{i)8q5A*;zUzIspXD>owu1kPP1w)gJ5WS6vnma<1Z9&W3e5U!%#t(Fh|5
zH@JRPG-@>eifOFMT=Rx`5pxX{Vs(%%iRl%@*@e~xp%X%`P$7jy=g!Fuob2&I*LF2K
z+%dn4uBhnthkA00gd17Tx84&{31-zQZxu2R4f{^CxMN8(e%w8`5Irw2Pxjio?~==G
zBRqD+il?<%o2Ei4Y*WJokGzj&sqqSG)N3#1Fbe11*h%R=LrBrLRZF-htO+=u-L|99
z-&RS%W0=_~f`$Eb8K0scgW)PRV#_=h{I;Ar4UeOrqh)uM1G_*3Fmk;0_9tTR+T>Rj
z;qyyMM9=(3J6EZ_I}5A)tXi>O9>@~8DyEj{l#JG<nIu%26a!Z=W(bo)JMI$KiQ6I^
z=#%C(vB{QEimFdJ=bSl{#J)HuFunu9D1yFNm5@mt*a46+8MU<TO?YEcon~Z=foamt
zVlr|V8H2`YjQQ%SD)Z1xp2Wr^*Z1x$J4m@}JNByT5IP=0t34i8BG3@!wh#knJN9?5
z5Tw5#Z32g|5$%oaclQ{P?1l_|$>m;s9k@3DjKti5d@yZ<Xs=+&NHKbRw|tNrNTK06
zKUHE|00OgdK@jfz9PDsWKKM2m=`hmSSQ;?19rWYScb!6{kQA#}xX>h>lmG$t5GZ*v
zWpTvGU@5Y9A&`yy%W|ZzQF;a7<q&#;d&bpFFqM!J`M1V(i?Q!saiVGlS@E}ydz!%E
z2QCX}>%5i<!qP<sLTCY4be!2Cq5_gT-`ZfOBjR){tTHzs@Itb6C9kR5@X`5dcW%)5
zkms1tSSH|w`JM>}X0OW+(vX?)l)@>8l?u+}smWy&bJ4)>f81xm4=>E!GhwO4oJS=J
zG|H<PKbTOT*xJQ+plE{jgyjh!7V_?wW7@}6jGzb#4eia>`K*WVz97YHgkwZmiq8me
zjvn4SSsA3_{*8ncB`r+Te<^G!+Onfg*S)G6XiJDv6vooau_0(fr@>l*R*&io<qSU?
zOwj4s6@Q}U@~#Dymw2pyXG8L|{}kuc^JeEp!jDiOYgHVM%m%IuegR$#o<3k90KJP+
zKGQ_p;f*MUVkkkVVUOFo@;b%3J4R@}kR54YtV=$(0^Z*1Jx<e*i_~xYtYq$S$#K1#
zmYe#UtY@BQ?g)W;{I+>Liq&ryXnrKTOQ24WWmdwo$16k<GxC=Tl**IJ8X-{i`_}fY
z8Dux7F{d!cQeAJTJ&eAIw8_1tVt24byE!_HJQO*sxJADuw8^qrKSYw;PWCJaS)`M_
zTHYZ~BgdX=BchuUmb<UOQ)03IW?z0^c|U~w^MqV%NlK~8Hy4E}bz3P3fkDx?YNm+_
zfs?PO;E_dGvfrET)gIPP)k4nW7$O@wZ&7a14_yv<4q?(FDlIC-DTOKFE8Qu#6;CR+
zDo-gnm$H`deikZAEe<RxSK`WcQa-$d>U|}H2t*9SPS-l&C;H4LkNQPB&r7&f(o;i7
zvHOcqc9rZ#Sf9D{*TOWT(rv4s;a~IAl<0BjGk;l?OR6#MeR6Pi<7|ys79g4WJbN{T
zZBFv*=vU_S`{~fh!CBLB*Kd;3hP!VLj(%Cr5=~7_B2KE8eJQ%;&&nPf-`PK&IiAj9
zV)_vB9%wOcaMC5$8)KScu3~&M5TdtfmSCu4=rD_GKEfo!vTVJm50wV|Y9mc6WtG-d
z=v2r(^;p=7_#Tl6`wZtZJ>dt6w8pfpl$liI)Tt5ZpXWc*Mpo!1)nB)p?BtG$tk7G~
zPtr%Jan~wUUeu4&TU2=%8|wAel~y*HOIKZ0E$b{<JpSsNelxi3^OHxt)H3GKXu)a0
z+Ud$ku-UCy-ks+jaR2FEAc&kIp(@dK336j~t+F#`X00YKL%(jrw4X0yBz-bHimO${
zqJiD?bfUL=lyj7Br+X9wr%BRpm<%VhtnS_LkD<$wv<ia$^7+HY<|_Bnd-!WMVr*i;
zXnSJJ%&5!*?u8aPtu`$Yt)*pzW%=c;Wmyjg4+W2Xk3J7e&)J*0Tipk!2d6t^=8q{B
zM)dY0E6d&)-eM3Oq~zJ$tESl!*-VvGa42V}^H>)&Dtsv<i?5*sQ^=YHiG%Tji~^tV
zgOCxC%i!Fxt?=4dTUh+?tH{?~diuqq2M%LV^CEJ4<PRsRCnWRC^QiS&^giiPR)y=$
z_AZAhhHvzK?B7_=+bG-UPj+FPt1&PrIh^b&fF4FlE?0BXZ=657*uwcdP@z-K9`7R&
zBEg<{l8Dt+suxMkjQI|;AD9Vh1$`l88?_oWiL+Obm^?Sw*^Jx7c{}>HSwXb1Vo~A9
zJd^r+`Zene<E75Nu$uUMRc*;d%0&>8hp1qmYa0|_<VtKwY9npU7adxf*W%TLHCG0a
z@X!G><l?nb)9K|QNW7YQ6sM@`IfTjSVd=8(^1fuMQniP*mzw_=+@~+g+PT|&YJq7D
zYAy6EznYIU5B}BoovVddEvuo;)Bh;Q51GQX&GjQeD8Ur#Q<8P8cHFbB;z(f<RHR>v
z-x`UBck#P~x395LteoaOPQ)&vTS%JgRjlXgx7^W-c8#h;Y)wyR%R%LA-qjB!prR7X
z!G)&!1xvn4=w|<ReVw9%(XSY{af@+lngY%E&Qs1fhfkHpy_p0BtOc;w7}sIfpN61@
zzh3VhE>s<xbJVxAcje{gD74y1ZuNMLKD9wx!S17)5z_JAY@r>Gw|2J$6O<v9eu~zK
z-XwH&`fz8mGZv9$UN%!2Ue->uK@vY5FzYZqKl_XL3tw9+N(JU@WTuK+?oR&f;I>=M
zT{m)V(7NJk!p-!sHij1-JCRM(BhrS{qv~T#;!g2S7=BLQOr*Z_;u{Hj4f|u)=kr5)
z8TuWskI$DXWlrY3uL=+s7%>>JHPV|B+)Z1g$FJ_}hPKwOuntkb_h>aPp#Pfi{J6S*
zY0f)a;n3u))pX?1dKt74dCrE%mS#1#WCp4}<h)KhwBBhtY$C8;Sg|^?-8J3QTy(D*
z`*D0m^qp(=i1~PVANA;%C<MQOt-(RgF53RM_wnN1nq-qZ#Ute!?!0)nYH?$ddSABG
zi|wgZK##hSY4hXs<%jjXZr!VsGCpB%52Cj&qOPCsG!9%35zBDO^LX=k7?WEFU8!6?
z-Zq_BpLv&g=wEDuvaGzFB737ETd>+P-0Ck|_wV&dEO=|9gQNIvQ%-H|a(CnRi@uIU
zjAdx+wQ*-@++tn-0HvMQCmlnskVqR!NyDoM{u-h25hRq6fmvO84?!~k;y)DF4y39%
zKZ_8kVR=IGeeS7Yxr~^Dl$(O#S0aWG7#O1v|6l{{6JSGwL9qm3VM%}KokBA=Z}vHS
zIw0arfuiDZtShE5Qb&DfNy%VgNs0B0coiyy$vrz)k!;!`JOqknZDkcgOSgc(_m6hy
z^*Gpm4sLu~upbL=Bd%%>0f9~R@(U@UKzRZI0rkpMNyR}$T8dNO+LA%nz*^6c!Nt-B
z+!_Lc+l3SS)zZ*Gm(<15!pff0g@^pF7M$SkFOM0?N&jl%V9rCXA}vQMWNl|i`jO!S
z0~0wf0x2mex1E6zr-HEPzuCe6@sOK1IM{G9GCDguGdQy_SlbyhzUScJU}R!uWM-xZ
zx1hIowQ|sPp|`T9_*W<Y)sL{Dy}q5Pjf1JR73oXAx_Z`*4m{-KF9ZGe&%fqr=wkYx
zk*w_hy)5to8DA)j?-`gF|0gyFQ=|Wd?S=9$w!hZ(ui>~~I^&cxbuqM16*jdrw6X_J
zjhBUmiTkf%{z3WAME}OA@*mFkEKKZwXZ;)LZ>%p<;gq#AH3UoPMHam8xf%cQ?BDIV
z8D9kTH^KdDSN?hm-WFa2ZpQ!KVqOHSd729d2!03&VF4u<NRTG1k8<w}Z!-vLn4$GL
z%12zjB#g9J=a}kiZXYgYo~dZ$o3BP*krLnJb3<EkKG2dP<dVYCOu8RC?$Ir*c(*kk
zw`8=suQVGb=cfBEHL}>xd)TkE*&ijOVQ&b3eGMtXk1hxJ`v*G`Ts{d5QK{e`&tF>k
z`@oR8QP4nz`H*MB{pX`YSBM`vrW@u5t-^0zfmwVbXwY;PLM(_RBqb>xOc0`cR{IC!
z)$Vfo8FCnTWpEjZ1(=^*=^T;#qcap}f9qHY#rK1gj3cJo78AEwqoIjEAS7E_gt~fs
zPRSt=a>Hh~IKp$N>~GWXA(wLn2*Q%uU^s(-0<fsW`DQbv@_C|>#iEh;iS?Vfu{s-H
zTHPGYCiCG7ioQI4V&9GB*IjAzthL<`RcrU+v0QFR4GRl{Yf59YmMK@SFT|!-lRKRM
z@e>Z{(LWGl!iM#mxKJpd@Kl1j-}w&3H4|E+_t$1;`#haSdyhk-a`S68Ipox@c%`{r
z><SbV6a@FgGO*C~D97<8#G@s^NU`#61yH_q8Nhz<nK~cilWo8KX}#NYP?6V9;++nj
zwqa7OM2DPADgAI8gP2kqX%hk>qUqV@;Kg>D_2lkEUWrTwXTT|!&8kmhg=)<k%cZ8o
zik7oacTd;tvPJ23Veo1JB*V3pQs46P14t1s@$m58xV-&dg;C&=<%4KZhCu{rTdL!z
z)P~>}6&E&iK}~C|)W#GOID?)<Tn87usS7&Ky^P<m62lvOTF{>Y&Y~EetaQC|wHZB0
z@Zx-!?>48~V8=HUt9pwdi-+*^MSc@8dh<z5LGfnLwmvT+KMNHW_Q6SC=@-UfY3Y_o
zO)h!zEbvQy>DRAJxGocJGs=;MQLkee8wZlAHx(FNQ{33f6R2=f(a$dINOa&T`NoN%
z)@by$^~bXXjIN+Z_vDj3?sP!NpD+VPk$q56jUt#1d=^=DE4SvVLFQ?FO-ZYeHH6@u
z&Se?njyD?1l$K6oEl5u%+@C4K$*>>gm~ON;#{A%P@GBCVK|{PK)2$R<iS(02SmHVg
zg<6df@{-F&q{HRRm#GHZ4Jo1vB@qmcG*-*pH0!q1ipFhx%xS^8+Q46!5Y)1%^0(Z9
zM6VJP_hglpi#>E#5y~j&cr4I~2fKkgA_vs{OHtKdG6tOLB|Kv^T&PgHFcbS^gSFk2
zT35OLEil~0+yNYL``v%bc&<#wpm`ymSDc|G4t6c{(?(JE_oZJ1LBF;ooiu08;`ZRx
zQJYbcEp;zmL>G8yP3s00-S{U}yOLyXsn)d5Lvi6FaIU-3yEZbXiW(YHsi~>(#vlET
z^uNB6y+<_^%sx5KdR_z&s4zGml-EzLcKS=B`JD=pHtO{vxNStTncQ9MIXvC&q@~7O
z9WG1_QfFDr_woW*vLPS0lC>FTl;aj^=-?8zM^c51GaNrB4tys2-2#Y_K;=NA*}Gv}
zL(3Me^?ZKw%C(DLcEgf~HS0_B_?tZY*#*P3ALrSe7Q~HLn_Gj>BZPBqhvsXEi;6)X
z)Hn=$oMtODFZRp6PmbpZnJzUsUhL*YPwo|ES;=!I1Ka)aVyUw{;<R+#=n4o}jF2vm
zmKNxKR2$dxHye*-_Aql*&03~Se~~j%PS~h=qX6D?v7cW*vanc?;;0HT8rDh>RV=;5
z%2?Xoj<=(o6U=M;&3f^F00<s?^=0~$irWCzc5feDI3if1i|B2OSn)D6NqSHi3FL56
zX6>qVt?9}w{2K=PL>(3w?$u1V@Au^!b-Q6|>Y;1sx`R=RAfaJ!X#o*>Y<2)#RaI4o
z8RakXl3Ex+$fw$#mspl_)xG2TB}5oTO-zV!X=&zAnA4%HdT9ec4rQF^xkY6@?qCO?
z0Bh`bM#DHq)<0A4qNT1%Ck4PXW@OJ=Njh0B7@r^f_<ibI=x{vHaY}}++Kg}U@O#jE
zjCKnolPBY-Bu$Q&Ta83tMbWbwI&)MYUb43Zt1?rfkP(W~QA6>tigfGt#pNKReFtLG
zp{v(fq{c2Z*y`_(=lI+5=6K(pZEaIBa@+P3hqSbCe`WI~rcY__7zoU~yF8eZpw2M3
z`jkz_x^T6DUjqdcs&YBis|m%g1ujM+-z_yev)S?ET<83x@?%fs@!LQqtbBmG8WIJ;
zAmhtDKRrr6FWyDQylw|D0%1JNlo+`Rkg6$DA^iO*Kw_Q5F$>o1OCKH|kG<J$A8yYh
zGt6fzK6(mzSCSZX`n{_4e)a+z$Hd#Js<~G8`cCNKA55QjsV`O&=tn9EofmD5j>AuM
z=+4j2r8@wet3gvu*#sMYt?$4#oMj`_{S~zvB*C~V)j;nJd5rI9g=RDUV=gK;-1zor
zhPfSp$8OAVe>yaZ@5!Z9z22(v^lCEC@pnZ>N7w16lhYHM948v!LTkQ1YS#C>zluaA
zWM}G&N-r@MV@OCBLB#2`YFG=x<%)Z+wd~K~*MrKHUX?_z-sg4VPtx<NLS0>kmxO|#
zdG!Gi>CpS>oMh@vT@LAN&=FK{l<e0qTYf#0({Lh-?&np1)IQ)|^r==TE-VlHV`X5*
zAud<Xd##~_)j*3_RF0`a>F`r{g+kPpoEAbpuIeg8mFJ=?FN8^9KcZ;vGw}#$Um&B`
zX*lqkYK<P-l7nJ|Y%*>C*l1<!FttiAKwaHgFx!#ruqB$@Y2LJ`*X4@B>9iN8ADAO6
zCqV)tjBHVQ*H=T*M~ni4{AgLfWMr}4N>Rl!%1+ToFt1No+#|z%Dy%q0osGvi!DTq~
zIu8{%w-IW-d_laSL+&)I>D<fC&g609xZ-)OJPx_Qal>*s#&eyXvqegOb)fw`Kdt*f
zc^Z-;Sq16PV4Zz#%y+K};@8TI>0=)c<+%sd^m(BKAVmTP1<a5flQo@GBm6r7(OUm;
zJ6@a+C3>hFWB!wiWfrQEI@37^7Q`yMtxrv-{cK;UH&f_43U_q7gHnq?Do-q44=(2k
z?vwcv7X#hVXj~<Q$kivcO}cqOqTg2T#cV)ySFiblA2&&cqnOTpob_$c1ZDXiKiD#f
z7-xCbam*psptHAO7m0Z8aIL(TT{Xmuzu1YF<t1}E3eB)R56B>Ls2PJ|^}O9m?&%D5
zRg3n-B@7skArlIi*<=qysDW?4UpgKcR50leRZek*e4f?zwp^a@m0kftM9sxy!?WPK
zghi(&S}xR0JYKKJ@~ta=<2=Xo8ewR}<T@6`S%xQv*AGmEp=@Ch?n7x1V8y72^*J0#
zW%>EW8HIR+4#VbP_Is9TKhjVDJmv`d^W}H%kH;iys~xqv5iu4-DezSX6DUONy@>D|
zZwEd3{7_g(QpkLf?(2Gj+zCA++>u$N3CHEYcOH@qCbbOZ$nxq_^V8kI&#K--mu-<d
z`nwNbesk@L312M!T!5AUeJ1t7a%MO`AqRW&aoc@`?bQi6H;B(?Bic}5#)Xfcmn+Do
z>G>?VJ!MPQrf01~Z!b#X8!U4SCEKfL?eS15u7N`UFCB?mtw}6A)QL>|yNZS`_C7e$
zN{7Aj`eo+hU4EUjPFRwZEtPef?o4&7#$LcHAkOK+%F{0ZDzF%N2QLbA6V033?{dy|
zcQr1An%YLn<U@ZGd~K?u{VECZ5q$T3@PvUGkCxuFZh3;?;n<g`v4HPHfkNX7slKO}
zH3{|@xCOgyQac&W#F2y?lXRg?KC+Ea6)Jkub(ZS-sD2qBwYkv9EtRMM%6l%Cps<S8
zE47*rpeU{*CU<_X%QEc=s$>m4KQTI8A;IkP^>D_o5P&OvcpRC<IR00X-xykU1)FxR
zuhF1h1KusvTeCUoJutRjDAcGDOLm;(3KQOfRl{qR?q?i7z|Ej#X(N=CD~AXYW;YPK
z|6x7j<9M_e1zJ}e6Ch8eBKSJH<>74D2$Q$(xf_*_GBdThcmIz|0gDuk6T+6yRt6YD
zej`49^6J;N_rqzw5lN?yFZ4(=)n+JbdhfTds@t#Mu(#XDR0Wa9c#*%Frnp$I1^W~L
zi2JK!mj1EzN6fjoxk7l>4%<F1#!A`MnlWCM(Nlrt=omcjGt~RF+M|&r3BJ3D=w0`f
z`^=D!9E6MSII>lTSqPn%U5iTmS>8dyVW;Jc*@U6rJX{p<Nw-K7-*rA`l(h@lZ;!sO
z5pbc6=3%z!n>fM=;U|NN26F{JJ?!wMsiF-8EHSp+Kz+3SwK#IoL7W~HxQfEU3{><7
z5Q)PX#8f6jKlZkv091j=y3`|-0yuUfbs8>ffrYA@5DPg%L0_FcPO2IFKSBu&1anKc
z9K-o>jkM@&)0+8dP0%x=oj~*xaXF=I1R09LU&BI4YMqI-I1I10n$6JGpuU2{gZc!B
zkT`ai&;G+tAbklgBnrBWk<kcO;W^UXTMi&)H`YSBRRa_)xqjI^jl;lBRLGX{89*Bx
z&9(N^Rr&cNdAgi2!poTUM-ly0v<J%dps-{3ku4wS5E=uWCq!^k-PVFNY&Bd_LsGYB
z=xC+rNCdgOR>wE?xQ;oPI)hILbRQ5|b^W=u-h?1ur_jU4krKFc*#=nx#rYifm1-o2
zC7VBg*RnK%9Y^Kf>qnXj+j=*G^vaKH1jx^3ki8NE{n!C>sRs)RWSG_;oZ`7&)MqW!
zXW@#AD6wR5VR|yrH<|(vq9N(^k<;@;R-19KV3^c80_}1&gc$qEG@G3Q@Tf)tbYOaj
z?E%6rEIfWZ7ZP{hJH=l2B#Qk#KC}juGC-UrWfce3gaP_dhBZL|G0Z6XbUE&vzL9Q3
z#ulArd0$mOT3HK(q*tAIy$W=c?78bL;k@kGb^c*)pb}t{W0|c2_<C6E;e}s=jdV2|
zm0^W~1D#f8*mgp)2&fcJS6FgYDvhHOv+p>iy#+kHTF61X=VXcpcvQ8(`@$x9t_6CV
zy9B{N4oFaE86<h?hGLU2=@?lWw7;5$XCTLPoYV$}7C5M|8d3wJvix0PO%LkIKj`-%
zofkT`=mQ{;gK2^^PMFy36I(oqBCSBkLNVH>?muHV>of3-0fZ!{lk5Y%-*B!<biTvM
zv<sg2lD^U}l=!<4L{`Vi<ZdB)PAQ<(X-3(hAlSQXws(G}8E1}nbM(pAvj4)ap@2g}
zOILF2!aR&jW1W;+CXFgO*2PGoSc4so<a9pLW0m#SCNzzulHszuSDPn;mhrm8pD8F6
zWMKAHO`+uMq-aT*=nOJ{xY(QO2Q_yf!9`8?7J9cdpt*BZu8}ZO3p0`5Del$X!?>pC
z3XF$zzyu@fNVVUrBVwwc;C~nx$xZ2?)ks!Im8<&MC27Ry^O3bpe{tFSm}kFF4B>f`
zn(sX%tY1tJGQn3*Rv!1O55rHD?IK4rVF<ONzb%!N7HX}<nvwpLdPGb5h|_j=HHG(b
zTG42Vkx)>=IGTGQT`s^AD+RvutxEN&x{2TRxM8~Wy=g<Qh60EVw#z~!C18sqMWVy4
z24t}#{B|!{J?gNd`TJS`#ZBl#!b&-vRuFwN2O7<~D$GFtGAcX<60sC)#xC2Hn$BAc
znVT-DaZFimUBBuo{r;~!lquzBl9SE=LY20FpSy25bVPSWW1Q)&4f^|>0qJm}Bik2f
z*V42Een(>+Aml5|(}|zkO3b`ZK#+Uq-WQi(#H-HL+V_M~K0G`?hiFojyW}AlOU2*r
zJGdzRE$A4Zrk+?K9p4RETMUf!NfM2FXnYR6d{?N*qG^}2h@Zb~lx#7QEU;yY;cqqx
zWmvt!6hRpspfL6=_*-5aoBgWfZ0uv@j_|V>&}!FDl-pb(KV<fw4MB<;$`U7m(%x&4
zJM60AE7pR~*WS;@S6}&~cl&<;0R%PxhW4=@@HzBAGZ+ki;7_9ql>uO{_Ge;y@kOLc
z2<Z4j!03na1Nxu!lD6b|@sRon`oHK<VP;foSzMEMsbpta;qP%7aoX%-U+s|I^oAO`
zyk9HwP-2eK>a!h!E3|68{Oq<@kUVn6yjPUWTmHw=gFAE)`FrR9#97`?jjodH>HO&s
zTbEV{b0Clt<zeK}x>A@0SHBkqoqb%M&LL}~3>G_FBv=18NSSCT#>^yrKL1A)#PRU%
zuv84M*lZ32+_X+x=0piXVddTADTUh=I)WMk7e4r;nzhX$aQsPaf$?h6xfAW4WHr6p
z2-wGXgQ+k^_+folUy;p$9N1y14OCzISVK9ymwQ1ZS+dC5&7`VFCA!|$nbKf9Di>|&
zE#9y)z8sZ&L#C^ZfFjc2^0Yzn%v870!$gPN^Moz^yGySe%s*Epi*7hSN?8&A7~;ZB
z-;Z7f-c24@8<;8s*wGRm3SUDt6>R-kfuMo@kIQ_%0i4`}{iqeOmG!<WpbQs3OWJp*
zlIhw25}Qw_4kX86z}mSvO2(#Mlwv=ja8)=9VKJrv%B$2avYW(=cuUsL$1CkC+-q6v
zy15g6Isz7$NCikxpq177oHQK-7Cer*QOV~X;R|gqIqF9r#dd>143;SNA#8$kbdQ0Q
zAv-z%1g^WqnNxJhZp^AeB{o+iZzi*Et9VL=Eo(K`F-+CH@0JqqB+7kcTWodMI+HC8
zWSh<u2ZZwiP{Ik@Jg~HLECb=CVtR-b$F+YKMWP_79R$=4N>nPhX^{Ur6til=q^><o
zW87iSn96VPGVi=P!Y^-u(LVY%Rr%r$KG+|!`;BzJ&Go-A9;op?VSsN@LOHIBDOz4F
z4-25ud&xtks`|7fYk3UrqAlpcR!p$9+@KuKbxhO(y?rPfQU$S|psd!jH-4U_K$`W-
z-jKN6khwiSUPn$b;tt+~l*2_;IztN}YWV>(*i1Ga2`gu0aJf#%bE3tw1#EorUbiej
zKZr9I4#ZZ@uHWutu?-*bN2^jecUi#rVNQ@;`Jv2;!+o+vVos7KgGUAl$lVXU`%amZ
z-pRTLZQ;G0YP@?ntIg&f-tmrAFlbkpWKtD7DQ8eZha8&wkIX1-F#jf%iVd(52TA}U
z>9%7Hi+W<1l06AH=1aZm@`=ae#-@e@!unUAe3i;syf@Rzh?))_(&h$iC#;Ne*OUp}
zsjq&7V)$yn`rti9G`C*VWiKx@H<sAR>B{rd?6<vsOiM~z227_a)I+VNcoTa~mrK*0
z5OkyNk46Z65qSD{k=L#RLDua3F9B34WG6H6h);*D=qFiwLQ{AZjeYKLEYNj6t^K5J
z*o>z0lHqbtl%bW2SU+g4<|hcCMINF9Df6)p38<HfYlJKp4FPow?JE<32kHSrsJCDd
zAHO#3_eC}#L#z?NMQy1>cLat+)mQ3s;M}Z@Z!kf&AQQWLW#e3?7{;`c?V|V<`E^0n
z1OsZY;QUUO(dk^fA0g)s7wU^No1Lphu`f=#QPc9g(>_}F-Ft-nG&KX(_->@#Umh4A
z;amt*9)NQUjXP)#r0gPH)$exs1%3SR%3jhtYG-t_w3)vCyp%vXJE_2@-*d?!5&ZcO
z02Dm0)z{<@z)D-_nQv<?a^sfpgun<hyjr6}{BtkV5n@ubP8c2b+?@<};<d=#m_RB;
z6=HuU_6e10=prl>ZdQ~HA}l!Z%H;K9@8@P$_m9g5KJ_;G(XR~j_@L6~_#W0!x8HIQ
zZXkq`fHn%81>z2S{D>cegdV`n(<-*6GIZE3x@~*=%-O4f<48MLGOaCddC&(c)kKeD
zIBM7FHp-V%<~ph=04UGy$KFQX&`@L}eH}6&bjanDw}Pv_hLSd`@<D`EjkbAxMue4g
zR!<MNM%^4xJR@&NRXY1DBhZb4nS%1fqFFi7Yo*dye*%hKI?0x6qx9VlX1*J1b%aoO
ze{VkGWI=)xMH|qeV876^|M{^tXhrc1R5OO3F5m#xYW&6XGnAC;6diaHKV)E(&+ASU
z6!s3!kGfp|iZ4*G5p&?hxZ`s=6t8t65uk8D@cG4olSW^iTUNcg8_2|Co&DfYM6>^p
zfAMRmoMfHO_lc=6>|Vg`iE?wcb0tEV`g>T}Vf7zYhDH!*xH^jL>CWs2aJXMt&COT~
zx=#bXkvyFQZ|||nSo_fdaDiiF_dMh30P5q@X2KH(LK$j{u1Ny+UO27FX;{Xu-5B^M
zon${Shqgnx#vt-Psn7ya>#TBuGo4pFlSlcUJ@QCcSV%lhd~H46v-q@ixQg<f#BuG$
zE2$}%C7I~LkSqInQ35b_^<x0R1hsGxC(DQ<DyxqKy@4V|5(GwNm5T_}3c7<BOnoC5
zM!BmF-DF~M9f;NA^()J_>YQr<m`F(~_`yo=hB<<8Yp`6B;qD?I@o+b6H~La_@}&=0
z-_UfFXk0J5fA6&03QIJqX}%!g?C{qy9f%<_lK2vQMHGQD!c681BLH9$(ec3%%th`*
zG3rA5AkhDkJTcO?8Efb#s(#-L#SPYeQbC`9Y%KVz>!X>42r{U5l)E$ezt=Jx;qt5e
zv4*<+C?dDydZ}7Se4Ya(;NU7P5aDG$%4l?9kb<Usx}%VnZJZ1bdD5lW<CxG<-e~0p
zo4b}GbD{P4pp3prSFs34iVv^k16+0C^%vT%b>Dfhx2I%4qS`t8W%5XYgXC3s_ROON
z2R&H7_q$@+Xh@wX>=!nmwN-NXat$HA8&Xue*33$OF;GBF`iM+k$3}8Fri%4S+mZz{
zPD>6!%4%r)Dnn=Z>Lzyh)0_ZwpHvc)FZLCA2P8IJrjP$XlC2nja4HT6Qh*dPVkAQD
zL92`R^<2F_Zwm6M#|o)L78;t~;wgsYDje4dG3KsdI~qWj5Wu$jt|bPVyHX7HVHfi*
z+^Ykdjwt?qY#TD`&2RN8-ysTcs9W;w6T74MV7f-_tAf+`*is@l+BCF9mshBDi=_^F
z-9u13UJn+p-4!qUWt|Ndbp5j;NI!TnS7wP^t%<iUaTt&i?cev8e;NM!Wk`q!Cyc<~
zBM=T1!|qsOK$#coHgQ1RZB_3oA5AhncS-)`jMSkHWi6gN)gp}=57_`P!Pum*RmaQ7
zS*tG_u@aG#Ot|3n-D!f_!3w_whtrvB-+vsA&%_eXN^5-Xu#5UK`+B`<??UZ&ubLCe
z^kJIo^XOVv_lnKCvRrdMrF5|-)L?Nw!W}z($Kn$%1v^x9E3r~8F%4_OlZXL#m7|Hu
zy1;_|4*2NJL_#S(4cgk8WrSd5U>$Il%u;f<G$5jb_R-<y2khrf0hiZ9GCIe)R+FVd
zbEq_t<k~KjvSuEYFn@7rJ$f`$mgiQ9i<g_Z!gnUNoZ#Po3X>WlhJj{O``(m4)WtTi
zBX|}N(6EefDHt=t@ZIf;reI;2mT>SO#ZR9A`Rv-iOYq-$Iyk5t*-i}6pTC#R{wg@a
zg+q{_ZtB@4YyMmb(Tm{)6u=Aaz)&GXs|O`ms$Tu5{USoFWF^;B%&MIDP;8fbCUSV3
zvt1_ToqHy_h3GG$LZrK=8-5~A+{eqP#>a8wl+d~_T-PkU@4pEyRiR}Te|RZX<?X*(
znV{hcla_bsTE!M=1Gg8=!YR)pbmkh;awK0^dUGjYx0c8aC#!_dN91)_x6D+_s$G3h
znv^Uh3v>fIHdr9$^@sl|W3cd6oCx<A3<TF(BwIIi_m^rFk~9oN2d1i`6cp_MQExn=
zYU#z)ywBumS1@Kqi#@h}kStL3j=MiR+MJqQm!2mPS6s;rA{+NsNt6TKqnk+>v<|f#
zmnXYth-n+wzRB$?!R_L5?82eMHNThqd|ZgASn(;f36>J+?9=ac&X?kAq9FgZj}ii}
z;pg81m6w_;6PyVI1`K_H@;@k_$g&~D3sx14emh1lRZX~;+OF=ng4#bQV9SsH|6-sR
z^dK&5`(?biq$DF?z6F9tB6c^Uml)F2+=m7RF@i5&`+ByebVJTvi6l7iY~2+5BgkRz
zwj$Pr)0!Eq(tx|Ed6=hO9ZIoVb@CRpvsQC3Za@l$n7t&(TPLXUler6W3gygIeP2Oi
zg9W!+H5-gYe*FXbese&=WA(!3d<KD8GI1v+=T?)HnX7v*L(VHrz4UaMq`5RBewm9|
zOT?kL&1IWt-xHtF@#emtWuxPEV!}p(K{M79@9Z!=Pnuy>OZT`~k~Sc1itU|!4&vtE
z?gA`nY^qN$6R?iI_%S@)e&kFcVV#rMlw`0}NLrlELmY|Xq-^#s^z(Mz!^$8ZKIujh
zi>Um%m}EMY=C+qML!AR#qp`rTFA}9e0V`c<aNe+&kIbRK;b|KY&@T=Bz+^?<twUPa
z<67{OF&M@Ed#^2>aqyJMe8G2S*22LqG{bI!{(8Y?^KXl8i%xzkGDl8tsZD@hx6Mh#
z8;Cja+{$UIF}v^Xc6<&id*2IU#YMitk;1!lTFIW(G^V{tu7ck%@wA?|jM@)iMrxAC
z{bG=(07~$h|4_)s<bOMS>heIujBxWsFlrW8-Epfj%$ry^xHeR3@E!0CvV>hdeTP~l
zM0oj_hCQFZ2QMkb_W7gkCYOKsu${g+?fM3TXJT8<&pHCbaZ1a6AZI;-_`BUI!%zkx
z+~+N;Wah8&dmpY`A_~w=mly|kBT%6A7DHT;#w{Z;_bFWmB~RecclxY|N$$_rByf)X
zD?fQ#s;!t0e~#7PAy?eTI7=C;lbw&HHc`-6aLA>Ig|4}2)rcDXXq4NZ;ZGgQ4RV-j
z9^Urq@f;jn`kXV;v(17tM#R9h&R4JMO#nJYCG_zz1((B=)YK5nRjLL0XqbjTsB()Y
zG_a|I0+g2xb@9^F7e^YqOP5-P<AQ}5aTC6xCIbGHSL{?f_0tkZBF6O+&Gg*|^L3Ig
zi;hmMp2(g~pZZ|&_tK=T7Y-ch9dpGt!?~L^=i{~pUF58XldA^=@Fa%f+-D>R>?G<U
z6@nMTu`dM~X9q0R7Cz_vI0#^biw2qwpRW5$Npw*daybuIE!Z4dIVZR7E;%YVEtn<?
zP*evlzr(@b6rW^k=1tXLB#tF}plSXd2*VLqD|s}{*?!2`C>pg{H(Wyhys%xhXNk9J
zGnJUUUYj?HN)r37mV7pe+A&TtEg_bo@epfCrtDnZMJ~>K(2}!x$&=o}inp0=R;%%Z
z;<_6B(u#MQOrq5@60E+@G>GM!;Q1C}c}*`27RI`%WA85-=GgD#3K@KAPM{Lwsh|q=
z7?eM56U`t&HHlelv1)Z{?v70tb2(i6xj`ih;<s9$Z<%DUE6^HpIx76J6>7C9&YD%r
zL2uZ8=+>Cj5;4?ReQ;==V?R4*c#nUfa5Xe>RJWuVT+KCCJ#IA2k9X*FW|qv!Ml?je
zVpVfsaT`M+VL~a^I{s#gx;Ad1+J@dEx1U~iU*MhP;7!112l&?V?lNfuM4};a1(Y~0
z!cpr9-Pyq(wGvTo_iDQ!H`8Pd6Hv8=<WVOHI|QV)ynHM?KiXry6ckFxH}lXKg|p8B
z`e-~DfdvZ5A@fZ5yqa}eq`-53@!dy@k4}A;uvMy?L4*E7mo*<FWEkhOos?F89KEj<
zNsLbzqiXkiD#i-_#K6=68=dtHrWJKbQ#>p%ZCn|KByM&E?)!az*(mnj4DmGU8nV<_
zUHDR${#i_x_kvd=wyT2E&XE%^>RAtU*Ao9;%|ODtAe|0JYcPI7S;SL;s6qP1+H9yj
z(e`U;WnQTvpm)BU?JcpbV7<l;Z^#ozA5Q|PS<)d?8mujJhB-p271CZbd?caw`Jfup
zi-Vo+2hYm>U)B#88w0?#5{IFlhBWu+P*A(TTh|tR#YJze&Ek0XcdpGjc6X~&kp|2d
zm%LYVbvJ9~Q(+z}W5Lm0vGH^`8F7di2~^2$gYh$x%^A)2^eu6S<!Lheid()b0VsyI
zx3}Bd;#6`Ae}@mfz=Xh8h<FhHDg?N!TmF05=r1ZM4OEUyXBQou7bxMIjEMzUD+oVa
z{Ya4Cd5UG-6vw(5(Wp_>&Y=%o%C!6T6pAdGQ!u5;co^4wnYUaKZ$2o^^@f{%Nvk?v
z^UUDGBtv8Dv9B*qyQcAZI8w7_#Ubkn(8Fu?$f+4bwepMV(2Ceubr(eVfi>gHT)E^f
zRkBkv9Vdvh(I~QzLq00HJ|S>+v_RU<RA}z0>VW6F=5;dtSW^6mXzbg@6LB!a0_V1_
zMOd?CWi^(wv2L#bg(GhK)6w+j2l_)@d-b@V?`BP|n?yBfn*(Kcg@ZOHk46ujntyuI
z8|EYvUWyM66+72+5TZmc8BKjPpKS#dP^)I_#yCw-;C^Zyr<&imj!Xy~9V5<RbEx25
zu&m=Psdr2{xY-$4VrX%)niA*PAK}}RBow3IvP%1Dyt`2=2)2l`6_{Rwfp1h*_j@aZ
zRV`{SnhkwF?^+Uo%!M|Zv@M8pusEI5YMbrZGBoSH2g>eKit#8^Gw&~1qV!K(-d0-L
zU5kN|7niJ(51-x~y*hZRy>uC@If$G1)T;h{IvklC(MkN%bZ>tz(By~v;A_{(?Ig15
z0?nZ=>Wjh*M~T+7WrpMp+a;mNH+G<>f;eARN<KIBwjAd@S!HTVCo66DLD6PPQ!&*$
z<NCQnh3_n+egWJ6a13n*(BVEp+YOY<zxoI5q+ApXC$qjhG28h=vwZUqA(8o`Do*9h
z|D;%d1bZnSWx-##ihc{Dvf&6d;qseN&^uV~t-+=7;Oq|qyNe+rYqrBN)nIg0(pBkz
z{}A#goSDMXI1*(UrMBWno@tN&g7;Ly{a*s_Y0UJ)g8X18{|}eW1a24On@Pjl<lm4X
zFST)D{?%O#KSS9=;H&@_ZU8lnG#Y2Uro77{bH2rJ_4k5S+Oups|4ES_C8e8UiI|Cu
zX5M9fjEO%><`<imYLBYZcvU%QwV7SDE_EFP3W8<UVi&=+aYrw^#X}nRnNS($v}xGE
zKyV<$EpPe&jxfY#5Zm(PonEKqo&<E#hk2~Ua!33>dFGa_x+a!w2D8fajH$%Vtyx;N
z_Y)0<LG)lB+yBHJ40U=Hi<KY>_OCp^e)c{Us^W2BV)Z6Di(rLhCAm)_-9Hou4H;6?
z4-1)X>-W^ri%Sgm;u8Na@&i-;f5nsEuUB=JUZHo}XD$9#oCIo8se>90AYcF+3ym-N
znNr<vx5^=(oX_A+#FU>afLoRLB~}>(v9DhycCwHk>vl4Iv((*tZ_`ELN(DQGnv8Jx
zZMKX-SCk6$X`nnHB7n#X=OwUu+$=KVLolSSlr^kxK9swHZu$$>w!|7tcfY2PlX2-b
zLpA-mt0G|W(||Z|&3Www)|N!_cUaq)6ICA#owdr{zfggJKgeFSu9CX?eW<7xY(=B2
z87^B^PLA0t#hK7O`+)uVm0nk|(mw+f_Rr|!R4V=Z1Lp02M47%6KH#WE48lRV<WHke
z2382WZ&58*>c2(c{OIgZFBAa(m&8BxtD`Cm+}Z!P!|-!9uJ|7@kU-`Q&Asl;B;^(`
z^&4+|AJ(;zL}sX-<Vk<191ELBkibwXtxUyNnqKT7f3J?^QDPx8v;FI^&@p-GVsJ?Q
zHg52|6!pchIUq!DYB>dzoWEc_!8k;Rray3qJf&JEM3L2bz}JD)NLGKrJasQzC+=Qg
zo*-rF>Lp7B5=yYw;(sDsa5}X%YjuDuWPVQ7Tw&%qc+qh1c#*pl-AB)2p}Hwa9I)~d
zMTdOL{y?s6w-)~g^0OAV2+m_D_m6!7XA$^K08yiOG2=@%?*Z^7ev^P4`;zaIyNkc9
zwVNG37ZU!JBB)dPzdA+W2tx^F@;@wY0&QG+cfv-I;U7_V$cyHp^DAQilM)1`{4YHG
zq5$HqPSqRd={G4!+xnVv_#vn9Tc5Fn>(Z!I=aLMysq>;0NM0mH!|GG?-)0~6qqgB4
zW+EG{HZEP)KAa?~dVts&)78KCIl|!iu*wu<9?X1W4NjD>8@qgZNtCR8OtK~XmueIt
zy?QRAISUlC^QJF(`>@%tATK&O7VHo0)%>2;Jk&{f<}6oLK6cm~^iDxpS{fIr#iVt=
zw2xUVY#>>~K=PkJRyf|tNsz23p!|Q3SwAIIjuAiQ4bV%6T3Y>MJnJD)ylQe%AA|j{
z_8)g(fEcL_1bJuwI{oloI;vGZwyN}Q^|laAnVK~1mbdBFwqQ3wF5w?1-G}_-P>ggO
zgAShe*U=)s&`OKX(b46*rp=!7Vy{a#d?%0lWAwwEKL(RCgu5B_N%4Gh%0n_X>XyM^
z;Y#f3-I16u`izo97}0iGg1}lyDMA)vkrTTpsZz?yct_*t#W@}oV97RU5<f%G2hr|#
zR+K9_gWUlT!cYn#$<TIZcF#R?9mG_PbDvNuWC+n<{5mxKstV^Uw$v<5Mb4e5T={uH
z()%_2Uzak)9Q!=jkru*xs6Y8coX)~+$V>!ASGx?BaFi-kB-1sIw#ir~p0k0y?FIx{
zT#l}d#OAGrVJ&X?gVIlRe`UnPS)*7ZJrCtzp25yO><*G_s#Nf_B(bm^?mX$4-{j5S
zr8#ODEVzJO<!qMwC}XG%UWTQWoAX?_*uLL*lW9I(qk2;F@)E-qQ{zB_hRc#+iEQjW
z4C(qMcGByKfJp6eXd$a2q$lS}_N>x^^S5pC)_%nvZ#m;l5XjX{C}o+{aZ2Xtsy}Jo
z?Nu%unyZ^uyQRIEag=*fawwTs*sELc8#>85H?Mb8n|;3>K42yhsvSl;_)bBo;>~{v
z2Po-<)@JZQ>#Suz?(|DtTdBh>*jhCY?*?lcYX=|Zfkl(VhvfN}XmC>nOv?T(nyVUm
zEXz_>zDB2J^{8fxsn1tUr3Ux^D=S*6u8IEuA{ebb`<>@oW@#L?o8qA4%$o;r)a#sV
zsdnv%rp6Zef@=TEdy)Ojcvu3?GisRpC6c1FqJ37#TwE@RRM~b~I(G3$^N&pBZfHr!
zaa!I4Xdx%Jx2z88_rJeoAwt@^s0dx`<gV+K`u+3?b;-s{rC&Vp?QgG5&_J!=10Gfa
z7+6Rb4R<D^#M?fyn~444E<=i}GLf#5_%y5|e>r7JDW?4=eZkMi8{*7zujESTj~h}B
z5XBC5wh2$k<e>lH^VNrie1_<mhqeQC<nsJ8|0v`O?ld=z7v<Fvt>PcuvcEw;LFJE<
zP^q2z$9c4&K?Bnq;D6<GXd3?hng|UP*dRPXhA?z;zOhF7hY0|4ibuC=R!Y^m_jM~f
zg;fkOALXsnhB#J12$RC|e=gy@AFN;S2NL$;s*BM+2^H`qj?!2P+;1d3O7CSY92smC
zv-4{y(b48jm65#YREtXi10Byz05vU2uDabz+Ijc76p>bTx8<z#C@mSKy(s|MD?m)3
z8EBTK7ajSfS=wB?C|7#MXt;R)fW|Gbzb}W`=|v-j+P`D~SFPgjNL`h$=|g1Lq`P38
zg~F;&@=4)mUMpq39DlhyVHCSYlp%sdh6rND`Ju&N0SoL5E1u9hO2Neo!8aZXqDK|+
zcFcIJ-&Y)A8jaaKhGc5e24sJ(S)%G`szKyJP$nLwWT7<kg#468s&>p@=TidXgCZue
zYx%6^RuY~X4`n__g+XtM^yV^iw%)B#jg^gtf7$Pv^p|zXT_<g#x(0g&Y<ImO4QS1~
zg*ZF{cCm_hcmfysxe~ybuw5EcOm$0p3sB3vrP@8`%kfbcRn``pcn7YuCRPp0Ml~<(
z;!v!I9fJ2f!jim*nYj!6)`K)~btWZW#wG4Ua0LqOSoG+3o?=~cv9oNC#2m!D;qWWD
z6`|aza%%ak{BtWeGECmL6wb2}9H~fYv&Zw^@JRt&wh|S&Sss@EQ*z-&!^qcU(tDMq
z;3g6OaeK-E9$r+bty7Vh>5;&qZ3SrB{D&bwi4-E;ATX|q?tH(D=kINLgQK1Q#!=;G
zk`@-?tcS(;Ka=MtqNENGcD184DV6^I(Pn!y#cpbj;QK|SN{==&Q9<;FS;FA6+!qyv
z6n;Ud1mVCxK+Iq#r-Mx7r3>-$P#QuO!kNjbw=6vIPB=$-R13)yzgibew=x}T%XAnW
zi>Jcor%ffK2|g6CUaBi_J`0hOvrwVWe11!Cr-NZ#-3gK|O~^LP&3hlhIe#b_SYG!Q
z$_p+<*6iWj?St3T1L)c;dXoA!-CLdMq)X)B-{%(v84Tx*nXWj^dC#1vG-9Ll{d2|@
z&elYcB=sb)0Mq0l$ho+-IAO9B&pOj(-CvLDed$6rm*$ieAY)b^;AlRd8YUTjOJ}Q9
z;Rr}JTnp#RfR2hV!wth71NIc>*HV^9!+o8cnw0K%wLCegfGJ+kwQEzai;cjlXK`ZB
zj$s4sbv4=Co%6+!&>e2|TrJXA*|vYJ=FU=F<!jg&J+GJmZ_Q1*t`T0zCd~uEg(WWL
zq5of-wqgW$fR4$dU#}E})=v0XHj^Le&NjtY7?C?6sh$5J6TU>V2D)HYsz}d@4Taac
z(MaLrd}4cAX8JkPqx<c<ixX4teETNUB9*t^YiE4zs<4uliJD=SfxUsXp2sr!<{9`L
zN{nSFSjn%yzzL$!bxQD8{&;82W|(g*=l{)((8rLOF`wubKxlvNG~wIDJya4aeOa^r
zmGoFn{d0{PGD}cz&og%=*Yut8Q<X2o(J@pePw&nT7`_{m1kGoHCEHs9hf10i4=ejp
zW5uPfKjN<j$5~(ksYGh^NuGbMw@XCU5XfCd><4ezXHGL&{yVpJBT5#-FtrO`w&eA#
z4D?zv5q^6}yMd~CI2W>04B3fu_IOTww%wU4QtOKR<G_*N9H9qc2-d{%B-GHx1L-p%
z4SoJn0qooOJ%UY#oRLYorpxZE@psx$g$^hxt4c~8zE}dz%#V6v1$Kbf+=`Wq&k&Cu
zpjL+o0=7w>jU#72L~h+JCy9nWW)^n7Xs^51ksgO`O^zcxeWv^*7HVtoUz1HGe(I(w
z`i|@L0`Le)Te*ypkiBhjJ=u@-hs1}A6D+Y_yDF_}siZc4#kSCrYCgY8A*DwkF1g#u
zxhnpSz*(V16dERC=-@WLrtFHlkMSt3abiK?A2%rQeDOnj>LhurP+l>!fIL$~`s%uy
zbqZD1nQOY~plhJUbZK^yN(^?4w8(HWH&W6BC|NXxIhDDQs+;L6eZ>~bXYO(m+w4v4
z`Rv_ZShn1l=wC&&ZBJ~lEo^~KkWWY%-GwP!K@yp)<!;e3A3rE1l2%He+Crh6Rr%3&
z%`#Nh-fysvMc!1iVSx->Rf(mZ_uFUeCt%Evvz{UeI~W3LDqjJ9bpB<~T9v7&i+RVD
zKkuwH1uwcifUw=zGMYhw0)B67(P1XeYPrvkzOLI~-MSZR*nzJlyVr^DUN$iXWd}ps
z&)ucHKZ}}2O0r&hi#k@5V_;w>8`_<V_RVysFHJ<^$u|kdQuQ*5C6-H>JA=6$qhoOq
zlpW+4t-Dr}ylKdLaxb-&1}CAWoSnp4<7Wl@nmcKH9%rO*f5Z;o*qJmWr`NpdtdW_i
z&j2h!6^WH&FZ3U!3ya*R!CtF=ymuTcRv!7$#r2nXsh^Rpum)K9Olso>e3sNvK-vCf
zXD0W)8HK2%_6EuCfpvw|vnQ&L)rZg|Np4stNR#v=&!MAUR+RJxVzlwL)qBEaRq7xy
z2INx>UIZskj!Xo?tylbdu6JjLM{(n@_$&)nZ76X=efbeh>7K;6K9l0I@1`KR)-;l4
z*w08MEnt!3l4XLM)~(eulfN09EqWHy%#~DaEqs7+ZtiM=LG^l34tB>IDLtp}F2`P8
zHkIFzFvCYJNiGIk4NKPNnGxUB*{6MkS*_WV9_(64I&iey7-lOWd&|NRX&f$DY@iK)
zxH=~};jeM0Hw=JLX=^e%N1AUqJrG8N`C3iPLD&_&C}Y?(Y-XvJrdHAFGSq;&ln7Rj
z7O;A##)q*gt24WbmGbJEuXinAWNicDBaL$>n{4WZDw;>hWouu}Xh*SWiBqi<^1Yr-
ztZFU=!JGO2b+NmbeUg#$%r5&j+U<ziczH8Ne__SxJcsBHkX|Q8%H1-3Ox9WZ-d-Ty
zhBOq;@lar9I<uQ662F_5Xsy_tzF}f4p`}1^A`%8hrRAc${`SGU(QL*<))X&tZ<&!e
z&*~?qVmRkdc3hg$O3>Sqaas!>O=BmLPb&*H6s-+6Z=1YFH>TSUC8o=IPZPA`tUnl@
zF_%f-*NyvY^I8t!IJE_haz8m7DTdl&jH4J6!aIs=zkSoB#_Dq8C)W6_FNWHS<8n(q
zaGo8u1KsT=YmQH`b4h3E!P;OaXZp6ZZn|~Lw#O<mmQf+nzMBo*=+mSt(pt3+5l_<a
zoET;JxxcN`RX=+tu8CxW_v9D2V36n@htZg9P;BUYHRUL(-{;^laW+{u-`0Wu!_!;%
zMfE=K!y*XMD$*q)C9!m~fC>l*OG$S(OV<L@N=mMDhtge33rlx*EwIwCG%OGA*YERv
z{(y7tbMBgJX0Ev=UXS)PzkMx`@bg$?QFnbtF`!1EHNC8(FPT_3pVtRT-5UjRU`N~%
zKj@obXY#^mqN;`n`U?d}<^7XPRKmper2<}jn)7q%9q6QnbUe9P&+k(1#IC^@p)jR?
z0`S}PXt+1rZgj?0Vm=Do_*i|7CrHXSW3l;`_ftg^%N$*lIGM^*d`7+)>fJ4%96f?A
zjHbojD3YiNRA9d|7D&$#{AF;>BI8b{5}8BZPsxswIn+5q&v{VF?m@NErurqG-95t;
z?~`&_B{ofnu5hI{o43&k+OHkr&-}W*w!9{%kwJXk@$1QE2&BBzs@YM~!RKlSK4evd
z0r>baBkm<R+ht;KT<}Mp2fsSFe68>N_Y|f{(2SEGO^A%^|HC$cPQs7yw@-COT9^?0
z#D(}USBR_)WHY^&szc0?Kd=E93f}))r8vUG64-_fz<j?2sMYSbY(A>R@y4{lUKY6)
z{F5}EEO>C2+NR5w#<lBx@e;{KgDpI*t$Y#OrZtha$D)cxBT%aVqn!3kU#N~%O!W5E
z<wZKNwfiA7F>^A=asSd_V5Hb0eIu1pfK4cX96?IQ@7GtZ8c8)(Y?c0Wo|vIAY2){M
z8YZ~b`&Fy@9_N8Z(G@z(pI?ZFs?u%c(8>es!k-E4rYkBh|Dr-^bI=E%LX3l3sx;??
zU(OL5)${ijIGhO}mc@^^XP0U9&2|6O4x;So7<ay#7l-i$-%EJI+#DCt`Ze=(F$M-J
z^a%!>k$4-my<+kpGlQX?`rfOBV|Xi>u+Rq9!Ip$n$Mb4FKHg&gG9QfuTM8ZA9X3an
zpjRA9^IsdG7qm+_qa(rw?!0!?Vzks437c!C#0qqy9S)1_(F%$Rtc;A4#u|LBmbpex
z7M%~T3=wSyoFc}`cEL4aJa!s2Z6cynmzdm`oIw?~cTQHFjzpPfV={l@bb>EurZ#o0
zINLhOyAs}YK6!FrDncQ7{Dgde{CDRj!zLDuDJ1rn|H;~B+md>1tOxy^gWF7Rp#F#d
z(*iJGZY1ZuHtdUD#Hu?Vrr73Ae)a>%%!9+lv<H65M)DUtd=V`6JDYoz<A;5pFWxtn
zWNL-gF&sW-<g|^^3C2Qijs2Cr#%Sq?u#hoFdcr|A35L2nm2&3VZSq6sx@`N;7cek|
z#a3a>%b#hVPpahR1+I-h)8ICm)7>?AH*uKNm4Jz9*DR7gQ$n()%XHvT4NI%Va_z@D
z{5~8cW0tSjKe~4jRYkL(H1%1f3mq)lau@5gG7Aj-aoU$YwloX7Hxb4*#d&DOYMW!!
z3cvK48Xj8rE&CoUWJe+~D7zMZi+{OQe=k-XB00(`WEy3*6!wiYK>gpTpDXeQImLXB
zEYY6D{5O%_%^<0;ZmxyufR>z!k*sjpc|z~RJ}kca=tknWzq0nnaEYHiS+w<IbBP_D
zD*pigcSuB^uEL&#v{RgzJFbWI@Y})P{V~YHG#Mw&Og<2+3=+y}und16jCV!*=1TC>
zap!9KYxV^xQ2Mo#Xv-~SFHeQKM7Sdw6?^xt2BWpjoPbd5kpItDi9syPQ-oI|HhByw
zO@}h!;^YHiW0Jqf37L=6bP2!tx>R%HtjPA?jEdROynqpEi!aSbTSjVHnRS5)-vL)Y
z@FfdIh5TfU_H}I2lNru^RUWomZT_Bg7wagSiE5{Vo~tE08b||1eIlFrk)NRFz)@k_
z4niUe!*OXJyOG;km&NzZxuY^uMF`wl+Z`$&+l9!fJZ~Y<)<xsgW;fB+8i+V2`;sys
z%T?0dcC37<G;VX(a%@h9WVhQQYphSXXtIy4dzj>)q1`_gQD#wu_@G;3A<$ZX<(FFR
z$tKX>mxDfU9`9L}o%o%BL!ryp-Id30bNxEC*7Uv7yhxTH>6RM2v~a4(kzBOwUW&?P
ziLKYM&!E~}q-?SM2fU3dIuq`q<u7fJ>XCZ}5*{2YU9QUn8VdhtTWI@PxNrDL2Lf{I
zca)dd^C*miypXQYgj2KN#*7NDH1L|;+c$fxP1oA)+4{JSQ{1?Xe)t28lL`LkQE=80
zlig`w<UDoV)-~4|mtK44dgF%RzY+HYmW`}_fX~`01yh~pPqq*gk}RV5M;MN!UwrM8
zK5b+ov6&e7jT(=supu5@AuO2<4y&i@@X9a=dP?)<XTS0ykzuc(f9I#vdK*Ed<(gmT
zy~Ut|n5mXdXE(t~567j>N0FpV_uh92Ge--b%*Y|1Ts=70!lh%IX^{{hX}Mfv`8HoI
zdjO!d*EcUBG7@<kT%LQq*Ph=%9$iqSw^9U^75`XNyQi3T;ZzXoec^l>20-%l@})uf
zGv_ObGkW-pirgv9pAsx-Ii?xoAJ*N6;_RMRtmFjmzc3oRwNI^wV7Io|J{<;DCNE6c
ze-5Tb<tzGT$iZurn4EDa8xf@BzjQ`NXGi!i|893JyQv*q@K-tmsK)&$XyIltZ%ocd
z&QT-##6uI=iZ|(U8Pq4M&(V<1@r+gzV6+~0=iJG?zm{60&#)J+QQ$R%W3KU+VFq8S
z?yQaU>OsIWhPe+v8Sg*;#r=WL^0{N@#U&BV1@4_rdX+bqUz=}EXN{`|zG$X{_+I1W
z?||@xFJL^e!iu~26&>N^5eYPhXr0-^C&}^p41~DY{nsuDu))KT%HmC#&B=@fYKwey
z(n4Z%r`7R%H@f0Mj@~g#UQC2SOr!{t&`zMk_y-OquNbxtyJsoF`Qex4p|+IKzJYAC
z@EHA206liyp<;InaZYIzRg^aDW4eWN7P-Ct;{qvX|5&sfzv;@ry@sNIvot?Dzl{<3
zvmWn9%?f9a=27GL$1Y6SSpZibEU9to_N;n0TAMblm$q$c3ypEk2OJfP1jK%n3!l8T
zH7xou|2Ia`Qn(X*e;AEI-<_a(l0{HGZ5C6!HFO4Jrra6h?$7<|JnD5Oy!{?Zep8;&
z{?@Ub`}4m7vFv{@p3{Cylz3Ju%GmWeXcL}rR_i@W_MUT##X;xp6Mq0>!L+@Ur06=?
z&C#FB-`UoZVy{K+wxZfquUXoS2_-ywNqXmpV#PMWcxe_hMZ>VaHt2HF+MP8+SK9;*
zN<1_5uj3m(-nF>_49+Bce0w5MMS!gAHV0qz1kq@u5npD}1Wcc5W!;pBBkU}`nm`xk
zCvv~BDLI)-n4lzCGVL_z&E{8$5}@jqG}DUB56JW*7C-<T#obq$ov0<m&g4Z)W)WCc
zU0!wsFMD5ueykeZQVypJB`Y8C&$-zI_|vr>Ppk9S!b5ApvkE>ZB`i@D@v7ljZZ)p&
zKuHH^v;N+tVf@`P<fwX4vqO_xp(?lcG67Y~KPBC|07gI9zC%q&fnhL-^<cROYGg(S
zbvOV;PDie0qYsq%6}?x|#4-=BwxAli9T;ky{%r;&oSaL%DP*M&QHJ_g{1Un-Ky&4e
z2<jwt6xl7}q@r09qrqOo)7pAt@qU0aR}sfh5sDIrY98szNut-dbgNDNjHjqpFdkuw
zN=>@%jFb16Atg(U3QnMHvb5B7n=OA5g{lELJ5~>A9;62a#L4ZB&6*9W#0#A<7cF{L
zlMkA0vZI*~PYDko5ze+eddC;DN0SCw=w6z8{b-O;iFyOh@ahHSu+A-RwC)vawCJ9V
zXvr;-xv}fZQZd`;WVaDes>+n;GgYl6EfO^zG0vaqd~JJnxx4bRL3NqR-I}EW{;+w~
zK8Z7ExKlI;`SKn>vW0(>n9(mkXomWi8oL`ENw@B~SbOR=bo`nc)bSV=`z-16psZW1
z%Dvlx;e)HR$OA1r**vkDX#;I?p#km=Q{z}A#GKwqm=};gsx{2_4!v2!6o0R@?Up_+
z5^qlc;7f|?TG6h@rK>4IXs7I1!O?Xow8+ig&-U>oKR{uv+EM%KMk_qwC%$Gq;kCAx
zP<}p73?-c#pv9KCO4)*e&3Qet*0!79$GcmbDxlwWD#t|ZOvRWUhx6g)PRGagg3+E@
z>`p`^F>^Y|vXmM$sTJ@!meV^kQ*4hxv}{w#BiC+%x&%I1Na*_8cam!fKr};%k5!-L
z48B3_!-pF$oIGFEmCi0<#d%Et-4CFsJMz5U+ah$L6`$7+@&Lj8#9h^mr)49r1cv(V
z9PcnuJh6<aCDV3;ick4$X2N2T>~uTiP)Q{xBi>Y>2GP#%`aOZ2gX#|-;SKxB!8YH1
ze|&=(bry-Yy9!xN+=7mgwgUJaTCTvM4VIFiMSRckiTgd9y<QQG)@x_+k~EO|Y;?h>
zR&>rVPW|LTx04`B+eILY*rL&Y-^u3o!rRi@)bgIIR>BFtwaSL9wdv|vv*REL2`zf~
zWjoUmg@dCD!?I(>0acVipb;Jp1w*|DgxHhX=&2+#YxcnhL}iu=(WsOuv-1=zd{`TD
znLijc(n<qs<KL02H6Nu~=*-Fzu(MLJOqhXWne!2mcoklo=~nr2E_;qRi5&m7q5D>f
zFg}4poVd27p1}*Pj_+#&F$?5oK}9Udtse?#hVsw~MEczR9esO8F+5TM|H~mj%>KKl
z8ru?x?c|2Z`NfQJ6p1EI{r0Z#`7$ofs-fw+!!}dLlD{k@i^_LFvwmKjlJ`Toy5j29
z6=2f)y&+Y%ixaEk_?V}z`dfk?xrYy1JPQ!8goTx0J}lm#HttfZ_Rs7_<po?btf*Am
zCm|t4Nr>o*uC>MD{lQA>FWIx|5=-~AiTv{iGZq-pvgn#<jRs4DF}Vac0#~=zhIiU-
zEE?_n?_ht(jqj~hS(>6JC?K)Wi2`(~^hb_3-Bsvog<*KslQUiP0_9Yttbtx#Q!mtq
z&;KS}m8E+u=T(uNh2T(5L5t@2RJ0i~amH%AX?bLWGBIYV&=G%AWz(iuEQgrVoiCcv
zdf}?D^HrQ%y~)Nu&)q^`!^Bq>gAXDCuGY702dIY8%<NG?h*Sl@EB2;t?_<;5)I*Lp
zfNG`%dsT4<B|Ito<JtZ%386hg9}_g|&28z!ScmfcF8XDAff2zK!?MJQ6Tj!JxsUZ+
z-c)$7Vmd__qxWj+Am2M02!W#~(fw&6^>3Mb%U)^yDhGjk=Yj582>eTGjWdS3^;GhC
z!OIwi;lAIpW-~BpE&2e=<*_5%f&KxWBDDC$b%6C!Y6G&Y$4m!~Ynie*CBTO)?{8oC
ztrEPiojrH+&cgTB<Vw3Z#zyS4L81)}@91xkI{}41hDBB7GI@O5FT_w|sN3IUI3EJU
z&V}66(hPf%=}nG;%lAEu{677WQw7d)&E9M7{GOX1sA@I{ZCAJ6-E7*l^EPhsRl3|#
z-3;!;x=8Uv_X1Kr%@V_Ug)Oe^2dss&NDXs)qz}@>*+bUMK8K&}?gTq#letr3GVAxa
zp!pFmb(_sZ{i-h5NxpUTi?J0k`qkO-NL)=DiCi5>&$_q#9SW**m`bHfM}^=5!)hhN
z9Zht~u6r_nNnfo75hG1J_5*~@iEI~(2{RVjB#(^VuQ`)M4uMYr@P0?=a=PZhZ;+Aw
z&G$n}fBKANhhNwBTpLf0J+mY~?KK1p`?TfP6E6N*+Y7#?pHw%J?2?vKZ+2Y+U72)o
zXXX^kr(M|R$Q9m)1DB712Djs!{Ob`7-pp0B1}ir2B-&gtIJcnjL;y<DWH(yYkvydq
zA=l%HjyWE?Gl*!}*&9CgIw%xVc||yZ_n*atx51+Y{>@SIfXAmF|0>}26}d8ga)4js
zE(uYoociT<@a|z}gBqOq;L9enzshOs?>uX&0J5Sp3Ny_jQ2=8}KOgZ`)EfXv_%$bb
zscHnZ9|NfxhhQ^2=aWhy{4>d)nbbF8``O9=(!qAMkuw1My0a5Tj0~)xFR;>T{Yak4
z9q%I~mT5&;KY1j1Ep{;1<G1DekQ^v!&Y_nkWp)~YbUTWI@6LOJ#&CyQCJ?P$+!$0r
zOG&r0s*$>!$RjPuk<-PL8)7eyjS3@)Poo^vGUORx!E^hWWmFl}sBI$If#5~%)7wB^
zYW{2yF(`OU3bm93Q1d(l9AeO9I>S)`6Tj-5#=cT><xYwca1U}H-6kGwIy_2&l!cEw
z1p5#1`5X)rjKCFHCh4uv(Nt6WUwm!B#|{@JNemVQ|9{Eqs`h_s433_Umq~hiz{Is;
zb#d``!TlW;GfJi1wZLh&)ioSz@GS<-udT_S%E7x$aiinJy?(J5WPf-2UqZ||0X00G
zIFfNg5{+qSr!Gd+NHvr-ynSoExF^E%biBbgio{d<c2kX6u`0%H=?C4eFZ=REKVUa5
z6mVjC-MTHmMeFA;BO%<^L3JUMmOQ~?w_4z~B6i&Ov&BYxM}2<TW5(48eD{2_@@{LB
z+GeSF-0i$K(tCd5i3hN;;%<cmOY|_L!lR}t-DvK6Q4cdiEa`DutOI}L^>d%ybjj2B
za^})!X>ljPMRBAgnS~oYvDQ$ml!ChuRJt;B5){57Md)=zWC=;N5GtfcH3b0;E-`5P
zHvLb%jBQ2)iHJ_Hn#3*}3?znL2_tn41^Low(+NB#JWjPeoaRz&r-uah7ETh|o=J`$
zj%AnWh{Z&g>WOr8hOOKA7=F25K=IVjfUEUp9F?uk)JiPU3i%whnU3Bq5i?ywSf#=v
zcZIqgV~z${bGPS}c5m8NbE@oCa;I?w(+Wi9&Q7+wcr2MWbCt=*K*}wd|6G5D79l&3
zh0>DQvGqgQ4tH2SXNP4PLM>cb#uwPJI-(NdkFn39J+P`G`K9$ppd!&dc)t$yFP?b!
zbcK9u21dJ$biy<Yh$XxquBM{);dr^=2PVGf4G?&Y=DeR<vWpS3TqOAccqv7<#l%~3
z;mGY$e&kAeZnuo%32T}S?EF4m<Aoia1%&I2YyLH(W?%VuegzI!WjxAD(7oW_6gRt{
zMJ-HN6@Q%sqKaO6s=#j!Uj6e2Udg(~qRsvpA=QC8<p7*`u(*s?5Vt=(yNzEG>fbep
zTR{s>N*UjzqFO*|G}~N~HScZeKvoZUr^GH06Ccm6X(jL8-I25CgJ!c_qFsxzCWWlh
zkLWI3buZA^3w?w0y<C}64VgZe858vY2{dVn#MfE6#)yB%rON}$cGg2Nfa~HzW>}Ub
z3Fl^kueOgi6l&(U+z{M0CmNh8u&=~PU3*;F!qFJ%$uQ2eFS*cpSNx#daAxy-v@4sa
zhO&%e^UNc?pYQeN&<@%8^9uxD{8cHXjm0MUrf(z$@2^~3ul1TVP0lQ|*d2(!a=CvS
z*!^Yq5$xs2?-h<mIgr=@F4&z@n|+vv7jGyfRdzo&5MNWCJNm<N<SHNS$eE4Op{~!y
zV7T=~?=|xi=?)^j7fuqw`&@>$vjB#N36jwwPfbvEXj+kqYEBIZU(^&P8L4o&$(uQc
ze#Lb*y4jtX%@<2OkQHKDL%b8T!f11PLy211(E9!D@(ly$N_7;oy%*F<cyIhbe9!HD
zEamRCK#7}D`=GT%`zo^ij4sc1I-`BknJM-EzzhC=;Du%!ct#G>(d?oIiA~w`C{G9)
z`b;MR*8!^x%gQ{;Ov&0AGd6D|+_wXwT?6aZm5)`I?ezoJ65z-TQb3jKbf^^wuTku-
zV6ezUV8q6{Z7bILVPhcD`vFh6N7VP+0>vOHQh;ins<@UiBoE*ci=g>-0!1yADGr^6
z^DRW@106uzpk@Srwpu2VVy+4ht?kAWZE!-yIamD`>&^I0Z;ZS=*M02~aa0XdYv10u
zm(i_ajWLA?OG{hSRr7XoPQF6Xiz+%XQ#HD}+BSt;?hs)ic<YttZjgyY4vid5+-FVF
zW}%}UNA8KvxBOV$UH_f9<RzmnlJbg+8^6=;+rkoyf8s^rMSsLeivSNlRQl815;{uW
zligg~2&pmBt$yew3@6{0Y|N@ZeU&ik&8DF1-uS};x3z1cSbdQ&J4|4YX}`4BV;lVK
z=SYoX8j)~Xz}ei4jh-bn{Os)twOWIZ!^hZp4q-JVJE1}?F0VfOUxzdK_@lz51r{xq
zDlMXZS6lVi;%Ai;`|2GMzxr$3-5Xb1sR#;1ni6d0FTRR1j`wryrFJWp`%4~TG~a|o
zUiEFuPSqNch&I4=VERY=B+n`Bo#~<O!8zb`eGa*s?n<NajH2Z)!*g^?VsrcW1jlav
z1KT>xZ-Ro^*%Gn@;a=D~GybbQop5QoGpf6Mx{>$Ee=`<}8*tUg9Q7=Ld|>?=FGhaL
zxIfbaxz2h;lb4r<GG*S$1ipX)O0rqA3r(=`cP4dkZVo1{Z_;DH!?Aq!aK%{gjnhI)
zlE<Mz$K_{Qs__F9mc-3>O1k}w8`W7w<O)dS*T$#}!)3Knx8~HZgQY4vt|OkgPi5Mc
zT<zlAPK&0!8-_P7d*V0ddm6g%!AT#11IrZ#^)6q)2-52rU~q%v#4Ckj>y{<6Y3tj8
z?n;Ssk36|5=1Op&8TavoIu>Tj#X)YrUE$e&mTOkh?Ucp~qnx)nD@)M3Ymp|IfPY<s
zgyT;8;jHp-46>BF4-?py_IUWmLeZC<$B(@HU?w-e%a@kz6m%oTQIU;=X{ZWnF9AY{
zLk$wtTh^{O>ur){*YcK<FNnOkiy8~eh`%)B=987io_wTbSv#&zv^dU82@_c!9-=!G
zI1@4V-?bjPqz_Ob;XPi-jE9*v=iY`+B|vkfSt_GWtVAO(^)r^;DI{u+ozY%vFEIX3
zWMh^2F+u-g)(FijggEa01~3s9DeW0#6rFJ#CgPMl*=E@#@%C0bxl+4f2f%GqTW}F)
z@jp!raMA#r!cud)&*5hP61u_%&VZDEC;0f6$CENNceqyVnsuTyFn&ZuUp32mc)Mqe
z>?Z6q;nrtA-^lm$D{8$^{_c}A$$ja=>Q!`i^icH+;hAfgtfZ#C0%q!k<qlxb`O`HU
z^D9eX+cWiL$8)i93J+a8BGHkIJdAdOVBHxNbOdcBg|ge9$<PHM>+VA=v8VdvLp(K~
zszlG+l4gHd%?|JozU>w^-|y(c3Hl2D1Kt=p!dd?Es4f0TNBlJm>TVY#(l26$U8iTM
zcsk7|g?WgCVp0N;kr^c7HbO4HR_ClTCB)pHb3pC&l<7IqiGo{>*)qOb<IpCO-`r3k
zrr8olOS%0NDvF>2*2mIw!N4ymu18D0-2MS~SX@hb+Ws1xk)VTh&!#uhl3qWLb&z9~
zobFrnhIg`&_*Xxl9NKYJ#I~O<vo}qfF~9G1irL{=bTuYEKN3vzpMFsGF<%Mhcbre-
zcSNRH{#c?^0^~aqil#7G`z@jbvv6y|Rt3t(LUn$IJ%%2hrtVm^DW|y#@nDzO$R5Hq
zU<p>r{~pzlh_J9Qvn^w&%5|cfPfPKEB)d3j(imB4Ju#@3G+>^PkvBtN(_{qtRFUPH
zvDW(m@_4bTcOa9hoOjcGiEET?AQ5`%z;_rJdC@gPV<r{PF)e|kjkCT&2X>Zm3Y0=)
zn0DQRo6G6J*h6llF2>EVP<4Td$;5JNfhfAoOO2t;0{i>L^v{a*72Gw?{d6y~eIT~8
zA)!u_GZ}iUy%>F`ob2-=%>NeApNPP`B9)xaAz7GK=?k^7#A05ahuSOf>M+gISL6L+
zEb8kQ29^Pex`nO;jjmdMUq)MzN=64YzS&#S`t@~Rx4l)Qhq&=M_LvjegNM4jgAksz
zo|Am8cWmPbVZ{lza4~vnWUuMe6#zQEmX9Ll6CANIw*=)-qPiF_aoh?NeihiHO^W-|
z2<-O?pX^}N<T@Mn<9iD}lLk>|G7W5h!^~pYb@fpnxRhmF`O-azn3GL@t;O#Bp1oG-
z|54=DtmQf+pLAYEp9`%vi@){jhLW^#>j~Q0WTqaivmnoH-E&4~+Cl9P#Z>pm(p50`
z_>eFsccrYXdyU2p`bTWaTAsi7m6YW1$oGxh4F1NRH-Cor?huL>-`$4PBG8Xi4NgPD
zq|Q!N`2}g$q2Ds5>5PypGY9_NVQ8}*c<poA)*ny(*Lu3?1ay&NRWZ%`G{!Y`awTb^
zG9rhKfB66(P?!&E8m9eobAwrh^LE=4r4IPWvtgt0)1{PJg0z0}eATR;Ze-5OOBG)r
z1M6s`J}FbzwSOAuqS;{rFckRg`RkiAwdyqAy9gfvq%z;^NUo~u30H80gey(O>A6}w
zLcvR@3&US`aOW+x)u;o;ZlD0sN8{DGruMo1;cYVwUx5vh=CxlsxaIzSD~i=gLR4n^
z+<f3cq+~7wY~8Q3P)1zuM8K@-TW<xO5`FpiamJcS<cd1lG`Zx7kQj!g@NOf(@Y;v}
z-hY608&`yTckzy+xd84kmFG-;V}7pT<33)4;C_Op`I<5s!w8Ry(!m%0b>KxWJGjs~
zAX2f*a`9vek!^~Ty#C8saH(Erqz;pSgQIA68HcSqu&4F#Jce8@+qIeOCNgA{r*d!n
z4|Tz?Lf$lN_L<zKGJ>R)mwGCXjF2XrymLYn7>LVKth8{$<a9ckOE=tHja+&mEK;LI
z54cWryxR@e7^oq#f&DGFdi;ejuGw+<qKpn-56#JYK6IB~(n(A#sa40Y)K_Ktg-w1n
zdX}zJ=}8bjejc*n<7n9(G%qn0*)v448Cmf4ZFf>yK_Z=>01Ee-$g?X|xR*<{+%!m3
z)u^qPjlo(uDP5|^L1F+=P!ONv@IOYT9Ls-H%R$G>*}#(PpX4V9y*_25OEUQQ8ne}8
zB4?J0W;7Y|^K0|_me;qevH>D{F;`L86~o&p#8~uBfB*)bGYrOYVj8$lr5C+{+tTxu
zsF9<bH5Ty3Uz*dHE7lGz4jQ2AdvZw(4o^eLg}MGNl2rs`gFrdItA%C@pEia%na>`<
z!~`}kIca&mA42&$fg&UX^Y3Z>37g)J>>ksO7L4~vLrRRwA5X=e6E#vAWj>ovZy4t2
z5zSg1<;~yKXfC+&S?Jx{cm8`MHVDk0-(#NG863?gx?kvR+31rASd%jAzOSY&*}b47
z24_|8&IbESYn_~QX}=G-?3gOojC48kNS()CL(~r#cwEvS2=^Bm`fwVW_Ly}j8hs&d
z>cpL?<S$AD?rdsmZIy2jt_5G!=A`y|+IF$j-#ctACk|7uobWH~Cv|w8wzjed5U+;I
zOeJP1Z%R-Le`vBQIdpsakgml&!NH?f?eMI1ByAq<c=rn}eyjR#bnu}0q}TnJ>Ch+{
z+NtQOxm~_a2q$*&!(MhC$bkGk4ZS-g@E9e;p0D*EWv&@gO!q6wS|VxN+46rXY(4r%
zC(;yhx};O4oRb!2II6hHt@uV+;ILoq%)9#)7tKSIgfMBa*pt7y8^-Hu@lf^{LF*25
zY{g|6*v*B|I(k_4$!6~n8ykXBHT@=(9^&XzzdJ(Rx;QS^RI{rO?Dg1OQF7A_W|)cx
zk#zHPPgCz4pY$3{Iz#f$Cmbes2OMGH;jxWM9_@?uU0h=($R*~LSPvnmf4xJ~?tYGi
z)Hg;S-LGW)!05eW8%P#MT34gUMRNa>-Vqm8oEM+nbd|ETYFT1l{wsJUVtoZ?5OBBv
z+!?IRepDmE3Hrq|pSH7)`x5pA+-ugJ9-IrfnyTo%b0p%h<Liu5EG@ztRakr~812BS
zcu}X$z8Tf7c;y>xmT`7v{1T>z4cm62jy;rJ!3?_h{tLHbIRK};ZT(*f*%dZ}$_n2(
z?~*W{1!1|VPPa*O&}1NBR}{8_{<i`h+p01X`ME~kdlJ^?qhnw_vG6dyy!kR@$x(T_
z$!UWT)ouaXkGO>F_Y^8CUp*}y1~e`7P^;e72`4CTJhF7uSG%Xevu$ZgH{0h$dPv){
ztJWz%6;<@>b<;=V(~E#@iC4?R%n-=3)rsPgr9--0MY(Un>{gLF4owl|j!avHah9`i
zRLwxuGsDL4y5lnsQMkLN8)deR{~HGLr*?0X*x%VY2MXD2J`3D)6`JVT^zR?pI6*=A
z{69utPh|x~eK6cHjBOTtbSxBr5(2JVVsT);45oK)1rnf(CV8ARe-2>YBE4|03w!W;
zgUL%Z+pZ$W`ZGs?NxQL&y9|Dh1M`{(4B>C9hYfw~R>m;nblb;9HZ()VHP<t(cm8n#
ze8pC2biGyZq0gK|%Js*JxB>h~y7$`cQHBpIX;V2eIEL@1BDZ&xa|-k{$H2ft3hI+}
z@bp?joM1KME&K=ZI67C{XRWm5YAgG<+bT?Mx6{A9XwWBP^b#W2b7jSf!zVnxJ1Ml|
z`pu;Srqzv<_O?@PNQpI!Aw{SyV*4Sh6S%Sqbzo7kJl}vrc#t~pOr4;fxR9RxH>!q~
zQKBb)i6e?Nz%89(c4GYx4tvMA+BCaCBrIY|h)dTcek91vxr70Qzv3BO5PMb19P~CX
z@VCR)<eAf(bZqU3Aj~k7Zdq{%bW~|a>CEA_*29ps?^2O7Gh;EnA!EgGn+AU=BKBQx
z<aK2f0U-6xFx++bS|)Edsp)89BA9)b!(WKd@L(=qnX$RJrvOEil{sxK?<dF>ja?`D
z!}bjS=JXoPOVngR&9!W~>4Y5=WdPH6gm(%+T)yhvV9>{p6*?*g^Q>sq#u^D;kN;!a
z{;szJbWJWuX^DPzIIuW;aU4l<P4Bj`avu>(9CO0k%I{AcmCw7}xIu4zK}gN=dZ*39
zgVxmvq%RrGwd@J3D_`!~uj%Z@*}Yx8b4Km5z$%K5h>>@Wj8{hr^B=Lt5nE~z>|oN(
zIt1TG#*jl80sI1H#hUQ|Z0#hDDPMA_s#*PB1uqpirfBhcD_6&{fafyUI9MXCsbEuM
zrLr(P8Jl!yD@huN_x!IijZ_uzo_)%$M~L{bWB|*^<&?GyO1J7Qmp?oA=Ev~%swA4(
z$Nw;4U&SpigC=Ba>R+FnOH5uGaIRZ!VI~(z4Nsda9|$5geA-R43$zbX7L#hoa3K-c
zmcSW_%dr5t#(pkZQkZ8e9RVfai%3<Hq(`mXox(1nZ%$6*DN$ovsu0u3(K+e;4s)K=
z8gGpI=-6FOglC$YgU5T5tS7H%-juFCn6kICI`j~%g!n{w&oA@(e|R|F3OJjTUFo38
zqTxZE7^UFiyqVjj4o^5r`RmYazb&eY(dpsxeI09qK89wwJ=ll>+VTzigbs>-o8HZ*
zQ8z@FDA2TL-7_*R`$qY&Gp&5RgVC!~{j|<kP~|rGU}%ooT|A%KOYj*Wr5k^B?VAb7
z^<K?NL4TU?li!)LINp+k$@Rk0#b%KX22FO3vK^7P#L~xK;TejqLSJ&><gF%;42F0k
z^!JpPFu-O?O2uiQR9dn0qTy7tD!Z*t37f&vA%PF1*WSl?Az_<ho<#3*t7w-J@^6;t
z>}dxmx$R>{+T~8&6``>3x)MyUVJyMbuuzjwS1n6ZVu*rn?NKD0r(nKuxDyNYV^e2D
zlk1o9kAvlo3y!7Q5GwaA3Hm5%ZVHJ#zZEn7ap6D0@`~{~RAIg&Px|W8&2&J36|wOG
zBwMZ9*$!$qv1pjh$YFk6u1f-iih{Cw9_SC)NOaLy=??^Ie+bLz0_@OrsWDs(kklzh
zPBLE-CzEd&hg!mr=)w``WP95U3w`??!SuF}H}L^)AJzi(g+pOcn^&Br<MmL~!@NPp
z6UHohLGv??4z@B-;STwW;kDK`8nCpR`mXLUiqRM5XDjI*uk~7?hDP@<+gl3QoemHG
za?)^r<zJXXYKJS%#iUT~9EFm!I2(5csBf1WNpbF-(mkh})L;3IrlXI${TFd50xoeR
z>fz4nylM>_-S}le;^kqV?JdH(!WtuWfu_t8zOo+h@gn>5YLCx04zrurjNsXEty!PC
z7*V!ujol=SR(&@GV3<RV?FS`}v$i)367lHA^6^82C+f6|1~6kwaRh|TP3|22a%L;a
zz57LBaT-Y<a?D1g#@mFdC{-hf9@Vaj%a%UqFY`U2K<zNu^6jqMfF>(&9zq_%>hdJS
z`9%EDpL}eP0l$5sXT%l1YYi)OC1FUvkNHj0DOU%VrB(Nv8F~Ly@F`Qnah4>Zl>O(n
zs@X?`x98Ma;`K>12EYw5nb~5w7k1%;8qEn>PVe2dg%MdA_$dzy)`E9T+a6i}eZv=t
zaHIXt;O&}gd{-+5`V@IFq*3>SlAqq?wtDq|pfy!j-Uc~feZ8ph*&QlXf%sjrK=#pG
z@p4GU56R3M*XE0EYMZX+OY4bU5jTsP^(aN$)~0Jl4@krDEz!TpEbNCM(DrY5j2wfz
z!%E7HfLKCd1uaN2(`)CU#*_5hS6W5iZ>)jue*RoLJUmoUV3Ub};)UY5dxqYWd?5Y(
z5#PaC7q2JB3yl90>(z%PgSER=p`FaF?f8&4Ntft-r9+8oF)==|7O@(!7Iq<e`h1q2
z2Z}ZY;yYWlCVV2S#Y`b8pG@F`_B*Pq$OGq`EoklL>;Yswrpt)%1r=R~9C?y>108iN
z8|bkGoYx0o^3fU8Z-!WR2-%JWj0xc6tBiiWz)>f$CWz@@+SNq+gH_Ke-Ep}pv#Eds
zL{JfGFkat{*qYCv|FMt$lxfTJ5{Hzi*V+w7gzy2~9{^*`BRcPjk*4?^EAghY%D02w
zUR-6yXk|qr4b9J+b2h9XCL{#@n^5a|@xzNb3&b;zv4mrib5&n?QZ0Tg=WV*#UCj)B
zSHEBO7H0$0w^B@)x;-6pi462yBHblR52(h_d!UnOkzq#rd<py7is2}VK&YIu%~Ln2
z&I+_f@Sb^GBzCG4N=i;BD6fIrPT1*xzVyF0{@$cWF!D`@_Bt&cH2AYcRQw$wPyCma
z*JD!sT9AKQf1+tSs1)7q7@DjO*j>(YGJW1&&CK1qh%!qJn4E}vAEQ?)+%)sG5)R`d
zXn#j7X&^8L!|_B20~HPpdCTD4yv_)ZYvis_H3P3y`Po7Cz%gDrQ#_65m(-a9F0)GN
z#3r)NFZeG!FkUbyPzp1K=-rbM3=zs;=(4gW;v{3vx_;RgrrLqgB$O2eB;ZuGqSm?@
z3nKKGu1RnduzQYX>ewnS{i(%-1qK&8BYHm>v^Q%njz4D^2siB=GqClZ9TT^%Jc+{l
zS1miEgWQsQTJuQ&ACbr00qu{3cq5c`(hvTGxqfZFbxYu6U`JF9PnvJ`mvJkz_jQJl
zR1=4(iZqHy8zUyW@xfAgefO<LiGsp%h5FBkDW2;g>JhPnYk!u(mZ}jP_&qrZ<!#wz
ze?GCuU?{4e7se@Na!V3^>V*`LZ|yvTX;Jo&WEP!RHrSl*hrz4P&x)#P_rgVQ>*KvB
zWY|YJdvXd>cyq5EM8ALZ!jid4A6KjV=2Y%(DqDZ5cD4QGM{j?$bSlkog7f#iXZB=2
z@i0Z`MffB(Ys80}L+df<M^su@_ME4JajQ>Ic=NAX)Uj_blGTyxF5;%tBX4Y39mxme
zsXT55vf_BxdD<sb1_ITH5?!(rF&|oK8f!7SGP}!%XJdAZqyVJO<@3kVEEh9BcMAH;
zu}{}m8Z@LIu@htFh@n)P|J9Cr8BWC{*jm%lBb^_$_rBuN`$My-70u1?;^ZaZjgqRd
zm0@QA%nfA=h}%hUQ^L&RDSjC=0+YKRsdn4?a1C6u((!d{;SfG6ySVj7q$$Q0&h}>r
zH{_C%O;2=~9(QVH04{yMtSs&`-)_VV=JK8K`}>W?oNl-uZT#J@IpwYYfk-YP;8NN5
z6HikRJF?sev3u_ZB?3DIpCa`8DeZ(S^GKX~vxx5+Gr-xoX?!MnYUa&(;F7sNhGhli
za_)ZJ0$2&n$|?^i`WUVxP8Dt@sxXSJ%Et&6<@COXL_*&007iG{x`|^$1NAc~%T;{$
z&i&^F%CBv9#zL9II55^Dhx-piOquaUA9mOYKn=a8=<F0>$$Xf2&AFevyeOBn{1>zD
zMRqd#1Qv9!tw8L^wNyOG3QaY5UBp3lH_ft=&B8fIk&jhTQMoK`TD9FQpSH$Md1azx
z`$bdUy|%qTB%gL;CQh|B$>Aqgr<>RzV(oYA!885UHTcEjAEeKrd;ek*eZ`g|9Fp|d
z4K$L;2_Lk#_fr9SU7mySu2J}<>F?z?BOEL=iWszKg9K=~|5(3K;VSL01D>6gzO5+_
z_y}AORrFw@foAD!Qy|L;fNh@l1SdhNCs&0H?*tH7_0Ngo0+s#pZF-tr=vuN)RAf&>
zq%DZtLw7(NR860@57E+s-A{DDH)4KA+q$4bn}^D!mAJ(@O+L@g>TWqkB(!O!fY~zm
zmAKd(TQWqRi+Y>EZw07U2CHZ*$~Bsv1OaJ84g8lTHIuQ+;#Tu2B?PYYu^G1vN{j#r
z&dFaMdRxCdSDE=;=|ZSrKtXyr)vDTtr_YhMXI5g~$ZYT}P#wEQN~y}RP(2C#D&0jh
zOn@@~TJ)5M-svo235?l37+4`--0^BhLAVZG&=%Vl4#rs|aiy?mR$+uX|K^;$frSQB
z+V$J%NrsKLD8?{jBct^2+nG<NvjJOW4q5&EsOKr<D5eySkG8RKRR>jm7vqxVY)cKo
z30>LY-+jKyHl-X;RSRJH&(}|*j>G%Dl4(r=5O2M}t}Qvutyko}ZP0J|BrY^KFRF>A
zPpvV79YdlVeG?q04NEfu7ZVbo^!g~s>&$0amBNvl#O|)_1e^m@m{$E%8rBuP;Ur7U
zl;7>HBck(j<f6rO_?ad`R7nxYt%D>_DK_unGg-}8Jw_wbda&nT;I)KftXGnAlCK!>
zy`S^vEk5d%xY~Wl$02*M$HTzKb^jhA#fGCG@_61KM2QkRzo$$mkw%Ccsb8=4xy4q<
z4#3v=*22bBK#ZgGN$>fQtEVb*pT|#}Bj;MJi2B14(kvXuo<<#;BnQ*I@|bcj7L;;y
z^lC+g-C~bE%f{%Ix<N+=!glI&-v#B}k)Kc>W#qiJ3L?s+qr;qiHZd4l0M!1n7eV=U
zBv~8vl8&6-6@T?J%^381Fb1+somMZ|r>iV<^!ICSK=b>!263lg<aJbpvV2e`=t*c=
zxyDdzpqsVMI?(AQj8NMU_-zzI!B14JIQ(U@2W2B-^XCp6X$asrW^(u^XwEKn8m70Q
zHeQtcwVdWu79G+ZOqi!;_72z1Xu#}s`eLk)=`3H^<cWz-v5~=kAp&vu+bROQ9j-t+
zxrl=g0_JtewrPta>@msjWQ~_`PQY?NenBsfK5y2p=E@l^VD!s!+V2XHUqw(s>)j*+
zO@)DY4H0E!5D}LlnHhcl(UklMZURWv0XryuhgHCepp>^B4&~N1-IDh42%{S)7VoGZ
z7Jm{i)?W4HBbe3Tsd)~tUbm3u_^<bb#kls9LMB&j8v3-<ZwF;nKk2jHBmDl-D~Qap
z%nzpf>F%XgQiivO_6s1_KT8fK+68^j>kZupuY0~CDt))wxLi+ueBSL*#-IEv#$u}v
zKM8(DuEAW~rx(aA@}$#<YLR>zz@FO;uu9Vg6^D=n6c(ks@_o0qzw5x{6`Abi3~Qpu
zXz>g+Y2b9kQypw+cH~patkPEluP9*s%ccAN-BtNd=UA?EMtS(T&K+A(Z57wV!Ov=>
zHaYDcx$|3zAx6S*Rozq2BeAHToXbDNgi`-PoCUCtiTLha<5(sBe6o#8IFD=W^SW=z
z{ydj~K9O}nr`Qa8+YZ^TRl?gT(ki?y5l%kZ;7^p+3Hl7HK$my#Rl?BRpQtpD%&P<e
z<W_MS)2A*WcCmX8H}3NQtHfsG`wDUwHoqR1wY!h%h^|Drh-mej)AnFYsKd9RaEysv
zBlB-RhxhI0*_=%rup#Yc@tbZKrzu-td0CuM%<(zzuYa`VXMI(4hBTiQnd6;H>Llhz
z(tBJbf3S&-EwCP|SUr_z@zcP@Q}5*mZfrZO;ulBB^?bv_KQp80GH!!dMi(#a=q=TS
z*7!BUtiBD$nsFydZkV<)5)o*4yaud&*o)8y=PQ}L1$s-RQ~nC?s+RD~hvuz)DT;VC
zG4>|gMNuY#e%pvYvyeK*@-OTK6vY$3^|o-qY1`?Pv;l9Dr){DaT7UI7u=Zs#>GL#H
z8X}DzYEj5*GPoFFG*{8Znma_fnu(J<^b-fh#qs*@50r~(Z+?>atUF66Q?0LMvZpjq
zx1tV<HT>>LCaIU72&8@j(VvMa-Uc8dW^dheGC4l={zAzw2V^ZN)q==jhMrGukeHk{
zwL%gM6|p&V>Ibq}GqoEL>sY_;SRv?*Q6Mp}0bF_2Zw1B7LOzb|ITSZx^AH6-2o7bm
z*|6`|p?NXyH2(Tie;m_C;P~W1*%jNI{(AH@ecxTTD<V1BvtKbdYs{d!6jtW-u<DP`
zC&lGv!zR0v`G6>`d{@lMi|1=JSCczoG;-FCcZgV1p@Pz|*4ULKF8`2$c;rkysWR&g
zxVAupjd5{Jrtcb_dDs((A3$l}$WhvDw3pRR?JP9q`i4X#?!;)kVv;9$=r+>$OgS`i
zYifR#PJ8n#>X_`g@sA$z+a0Q`UR>aXbe6ha6<fy^B_=6rSeG#}@<W@3O?BNmS^AyT
zOhOPY?OE07rmH_q3)Y@Q4r}37aI_X)3hFGnhyK>{OYqJY_)9(Z;}o0PZ$g?rdj$ux
zFG0Y1aL(c@$%{@O&lA3@&hF=yeR)dbOq$oy&L0R2#xfq}VDI0xnY~_Nk9s?hegxRY
zc+9bdu4m#@sHEhn>Anc)1#qa0Te5zkDweX-i3f5rkpzW;<ge`ut0_gyd4ryH!&=m?
zvANTnEju?WW<KJGsEc%oCD(l&xv=4ichXDIt+wysj9?+7jX6ou9A3&FuOfO`{w&Q|
z5pNaCCb=p(M@hL<ZT4ne#nD^1OSt-~_f8x<qDoTgE<#f;Z>aN-xioH?c;$8b&wdcw
z-r%nLM@40CYw6OM{Wm(Go#Sg$gt;{5eZh&U=v)2wI1iCDW9ytam|FpHBM%k)1`AJ|
zt&eCOAHG8JPC8?vX_HzvKD@h5AKe#Q4j?_1d87UcqWB6<2T?t#v!{LCm%Fjh?F!Re
z5qqv+GP*xWniPA!gpuwy0kdOwT)|wFarPD~WQ)SNIHuWzqB8u%&r++c4GivP(ESDA
z>R7$0bHthfO*_X`PiM`>>&p`F*CaW@S3Rxl1m?~teJ+kJz#nn0q}Z}X{A_E)(bb5L
zBYjh))y!=?%yp<+!R|g8D~uG{9y+eaL7-%ADjgh^Mg<F4#y{<~1kJyy{cw?P7=8vA
zM2Z44Ax9yYjFP+PYPa&pIv;HsBN}^Om_mi(e+KnG0KK+4ijjVY_3ps8P;u$(yj`SH
zouf2wV&rr+%8SjhfQI!y^NY>`e{<lUy*}2@O8ID%@N<{#RpN{HJT}hFKik^0IDf}3
zFWZ{j+B7S=w9T}o-2`+Ms`37Bgu79fj{a22E2G@8@v!$%f<BnqjANm#JA8w4H!Na6
zUtNVDSJm3rmrKA{)6JxBRy_p|Eh|ZhbaD{%d$j2Gndh<*8~yjyq|*)CQ;sM=Xeax=
zj)_f~6_h#)S<0_9fWz*zcc})ZS>+3OYqBb<BaCDZ_tXS3UDvG%Z|lU@&hipP|Ku3b
zbj1uK5-us`tY@|i_}g*jM}a1!4%NO>3$Zu4fU)nl`sv^9ZETQTauH)Q8f>cMLTFEG
zC+5yN83rtt4e??&eJwEC(9k^`O6!xRcSErB#@SaZ1*ff3xAALgZ-_$HQ@c5SN~4Dn
zTk6{Sa!teG5Qp+oaoV$f-xzzmNh8`#y*X`UsV`a-SaTQ|WRi8EHmgiNO!hkLpdkBh
za0MEIbsJW~ic)-9R9rjzuStT4sq<Rlv~D!75f$(i-GlIEI#N)GW!!%tyP-MZ`iEAy
zmh#dXh%D)^;TFXU)|#v7n$R8P5&}-@*pw#{Yp%>36|4!j&E9dovzOX;dK~^bacefn
zRe)O|9wfgp=8(MeZ2c4Ih{4ChZ?rywuEP;c7$4==?vk82{O5N}Mq9d87U6a6TwIRM
zN2kV#tZIh)uMG5YKeI<m&?_YByo}>|20v-tc49CIwh7pyX#Z}H?`PlHL`4z$v@Ix{
zjEhO$N5r{;38skf{e8*QSs+F81h&nMZ~imDRpk0L*=0pC13L%~InCTjP+J?|X*-`#
zuv$KwJxOsldQEU!3vEVc>0-LDnJTVA|8w#F+lC$Ok(m6Vc#VDkB*cIvCAXgIdJ#(h
zA;V*kOLncn6-!e9=b7N;7%cgPqDj*KJ@tRs@qfPJ<BNhYEt<$7footp*Z$#1(>9N&
z#LF8aXVq8x97dZPFPPKgJKhuZPPg5PJ@(21e3p~nG~gW8N<%B0+=+Sl)zum(-<0I+
zJctojgn5Q0jTc-YuQIR2fHmR10#`cV3hXcmt_S)GPC7%b1Fg}sl5lX7Fge+Su9-8N
zSInIy1a1|&?#(pH(FpBADi}W`*^~>k%~Guh<QcfnwBxjt3?pnOjZ&>FiIRtOPXI|y
zsz>;~hgU;90QOP#>sA!ii|~1_vzhhvq^&#hLN;6vrj!)`Zn~XJuS{4r?OM=NsnQ|L
z`Tbn|X$uorB^<8HqM<E*8&$@y34#0GHqM?d@MI~j>u9VnaXP{8B7!bKk^nY||2|2)
z_R6IP|1;jlEh@<xaIJYQ>#<gMOP6tc*0y%6P8#i*=T<{Xz_|A7`@+g4d|=J#dpucr
zZ;coKBwan38k64*7i?&Y!}R||Zg{JLxq3HG-)L_}LN_ZM3UYAL%^YW11_QNTJ3Pz8
z<Q5tLaDaMiuF9@=))C(`a8>On-iprf+};fd<6XU_DudyLI}$TJ+yFKe{mG!PW*P{m
zF&RDH;{*g+b>1rmkj}HiN8*V$v=gJW&tu|?PU4o6w!D~Z_PvX|zOCf0|8k-+dri9<
zVmNi%l_>^v9V#)-sD-y(*%h`t&fjC{AO+E=f>T8%CSo9^rEM#+$=mUVg+uk2w^AU7
zUST?&PqLAGIq(7U+)}P1AuWZ4n3~-NNJc0g6=j1P9A1;e?*}A;dKMJP=EduVR~Z=o
z8;rfz+O|6RQyg}3T6OQM_QEK$(SdgA>T=7+yGdIZP0Ne?PC57$RP&&?S8i6W<>55F
z*<J!PpiH)WzoD?AdtUgik_Kre*C$O&HKJ+dTj0LbwW&M3%HGqx5v(pk)h0YwgGDQJ
z-7(WnbNxh?n~>O&iIdD|AW2{~1Ar@q|HIUu)Bog&`nG)IlFR&wl=?=e6=IEFS)4NV
z<ls6TN74DyYN+tL7FKNwPHiH7UGOjBvr6I+xSVDi`MQj@$I-zgI;~uuS}_eF9|90Q
z&Nxz1%C;`$q<c-fxq2C9o?t#xvhhzw=Tq*|PXNqPBu)JG);gCaDY)Z-#2Xr-fOuT`
zQ^8(3E{mfH?B}iwEcTs;6FwR@zDYF3GM_=9(eSQ;3Kq^hRZ9nHV;Yb2Qk2NN$j0_z
zVG^t4__R?~=XyDRsh7OG{GTt$03*Znj0{CAaS4f!)b>I`LhClu`TaffGU@36PD`eN
z?C+oFb>@90_|NVKsbyZvd3e;Pj}FUFHj)R9CrL0LDqEP;o@{#MUL*zHEvg}c=;w8G
zHX?Y9H_=W6e|gyX6!rBP5|jhVzKBkYR$HJSyAyBx`xkashJgeB^>F7WD;qXIzxWF>
zeU=3ue2F;|M^Gy=!ZorWs}qrDCJw(Ps8zHW8&M@H^&l<FB&vU5N!jN_W}Y%@udYC&
z(mUdPekmSr;OAshFR;_o3TxTWHPwF+ho|l$%XJgYyK(qMrqDX759hrumqq$JTDP5z
zH=oJ#o#7sRIut5zIXq-+bc7S02_X#T$PI`D$>A26BPx|w@^S%z_C2)cK?02UmzsJb
zUXHuBF2~}*I2uo1_HH7%n|4$Ur9zz3_l?)doYS8S_67G?{hg=9G)vhv;&X{>Cowiz
zdq<7wpdpAm7<$hEZWey><mqj{{YLnnILu|Fq*|NK=HUMWT0o`0@zMnUvaftzl~cZ3
zyh3eIx=1B?Sx(hM-q{zl$pdmBn+5h?7D!1}&IQTDzq1qM8!}#4$cAiW=H!Yy<UBL;
zJkBOUdjVM?ul}xZ9+@SlpBFde?TkA7ZwtgI7j-5*GZ(xmE3WvzoRF54TLJ^*-$(c5
z(D<*8Yc{~n5)pY$roA*xl_Grxh2Nushg?A8nw*lXBg+X13DUc7Z>d+mzARa^L>_+h
zA!*X2DGu{uv&@FI_<|zbOU0qG!A^JUrY$me_LuV0!iBPN>n7>hqo?l2Z>6_~#cLWy
zR0L*!_JvfeSW)i0d$J@aCChv7yer+hcaxh&3{$xFg<%iF{`uux6NYO!SJ}FAtNgOy
zXL<hR7o>W%>dF%fSHeRy?RbiVO;~Jbi(@0>xRiB~U3+)QcVB%gFTVPc)T&Xd0B<xu
zG|v39$H6{tN7^mJFEb-kqN1b3%hOA|D|u_DYM)MF1BasE+Q(ITKunQR0XRx=aRdXP
zBd+#&SopY<bCDB)C*-Zy-jruwd`?<6Zz);1Sp^J{OTlr3TipGnV3{SE4}mi6>f|b?
zLqfEkJ^a|i(z#O?Ew_@CIUyCt^Go6bUOR(F$B!MCkKX%8rao}5bnMv4R(Fe*IY&D3
zDY~+&;_zf%9C*md$&op;=IHgk_fOR}NtN<T$O$nj4!(skrLGvMw4xOYi}^U0bC$To
zIC*3G8*<(C*U8`;2J3dd_T|KQtkCv#fW|TGX(#{yKmbWZK~(Y5#Y-h5I7A+K;!&yS
zQBmR(<0Ua6QL6Y=5e~SN!z|UNKex%U#mnS{S6>wWD*gr96om^+t-P%IOJgA{B2wFK
zA0MBh>n|~~M=FnM^TL8p8u#qmBMavLD33q=gw(E88%7dKs`$Vt99_(SQvBkNN2Ng}
z7kOZKlOnTZejI6~ak2J5xa>ZWAQ7p#(#Xd}Iydu`4$b^vSYg%ya6efdC}Bz2^6nii
zp%`JXgi5Fbj~%hIFt#U|c{-!tI2)fXI}Sw1{xd0R5H$C9mu_vUO0(MDFfL3Zg#Amv
zsW{njC`yh+XG#?hCuv!$f^=_NUHrUUQ8qA^5GNurL$>aVkOOCuB`y1cw60!WI=1we
zR&^^WEOR7VWaGhevg=r!1Se!mJ#S}eS<hQ~w5=u;Tw&xuq1t>nQo_$Bse)?ZPJB5S
zm<zkIO;cZKUb~Xy;Mv}wIN5$MN)AV5h`)!kw5jI_{HuvyMHk@3wmP317A(6*`7iCR
zN)oTcaS*Wp4!E~?wPoKqIdIzKd$X$U(z&IdGzYJoksvTELG~Puh5~LHu0&6HfOk|n
zYgF@;F3tT=S1@~mcccmI(2J7Lcd0mlbj<?|XBv!0BU0qSg@H1#rn@{frkN_wASthY
zu<k6}94a4djgS`x)s{WS<D`yXd6_b(zT#vfQ~OUoxYo3g&n%-;=aQw{?8CBfL|qxs
zwgwF8T)Q?WpFP(if0muSoa95#zp{^G;dm}CO?uBdBs2O~lVRQKXgwoGih@PnfxW03
zB47UzQmC1qpChlBqBQ(v9MH}SBQUjU*OHp}uivnNc3^8(t&u|q4@sT6b)|OQIxsxy
ziGS6qQ1sJf_7}4xH7zX<Cw7Ds2>#m#-fIVehegG);v1-e9DP3*eok%}Fi1z-KA7>L
zsR%;|ii5>2J|{Ov_66*dlP6E=><3+OHm~0VrSG(rMIH>ovN~_#XW?z-Vcjz!DPkOp
z4hxtSmwCs=#L6u2Br!2jN7JnMjKhBnc4{zZl`IJ6ujY?jI3mj2EV%r23<?YR#mB|T
zmvg_=St5?!Ip)V%B+|zbHT%Hy3d2@At$PwlrRQ9DxZHWmB-yfgi^k)72L4*~YdIo;
z;_dK04<6Dee{K0G5ZhNCEa0H9>|oo+v*5GSwTEjThu3_^GDw3wF8FbQq^7278CdW5
zp25nCa%Ke!7b}kAbL-y1h4J{G2g?TAipO7zPl!~IZ@`MPAH*>^IfW%nwli9x^jWwu
zucGo}`6@naQEhqizOVp^kW9=heEZEekeg&%?#1VCP`R@O6CV?{g<D~H5hvxBQdf|+
zqUg{pY<y+feS7vvNKl9lfG`iHW60yJ-kD#Di;ZT+Gu;IW@_?MS(o$BK#vXTtVQUAh
z=f&cRtO<v0&c`!m%Ao@XZPc1LTJQ;jm|OS6m`?(a2g<Zprs*sP$H<w!N&UtWdispk
zL&l4YjFQhk`&?2}QV}EH&5N)UE@0=u1-Y#AY=0(<8ZW=C{7vsInhDQ}%d|ww{vF>l
z6o|une9}>`KK<lViHVLe>y?eynxMEZ<Ux2}1jNb0?NNEkuw!zTlp3B;mcRV<qzrv>
zpG@11yr5}6^-Gxac{V^+ZVyv>Xn>puNs{$}NstYuwDAH!S?-R(qK7T3wyw5#d`{!z
zbWEB|`gEU6c<G27J(nht@tHF9y%W;rodDS%9ItU`G;DxT(fO4<^6+=3B?B`$tBxhg
z*ryN4^WPklxRflN(TGUMkVj|lms_4WB>O^BB`!Hz?)m7Xw4c65wj7DVh(6~+%E;nP
zr=|as0rKo05#kI3=ebQ$a?=z0<ntxRbyS?n=gNa|a@)J7Wc;_MWx`h>GGR`z+_E54
zCd?0!+wMIqn-9i7BAjIH-gDCFW#IMwX=qdzWcL1e8U6GDdFHEw5}lldJS`bPd4p!k
zqIN|#J~;AAd5Cw6Q-*Q0eJL2ro~MLo*0+n}@VWBcCI@Cb&JvZ8bKuqevku5DuO5+g
zLCKPwmLvDg3X%@s)i&}9pA+FIoA-ld+&7^AEBwz5kz2kyBNG;d%5AS7m+7m{U;u}+
zPmlszJIH3xzMKnUW#S5<%;y2qIY#;m?^*YAG4oZcN_nZ2ku9&S42P1R3WEz+126nR
z&M|9<5&iEshRMOu6scU%RX*DtBMbM(AuY-U?}@a1SuvsB+i>T#mYp3fEH|saupX0k
z$7g&;Jcw&XR*oe69E|~zOuc7(tNe)ud2Qv*cMOH)Q}DS)%gZ;94yCIg9cD~BS{Vny
zl1uh!Ij|(cH^xdrd`4bbl7|+aW(vD+r42CfC@MOwbOywZHGlghJ0mLUK2?0>>F1x(
zLgPs7so+zxdBY~@H=v(9{P-gfdMLSQEZv;lWbOL3il>VU+(^(2o-b>z(cmaF5AUMu
z6X8+-IC8|sB*(~s&~TK<h0Z;&Qu8foi)c0lI$@1;bRL*6ae`bALxj`G9L=W7N**l4
zAuI;IV_=>YJuAX+DbL!Y_mzEp@?02*4}+VNo8rKk8{)?q1q(l}8R3UG@&sgn)Q-=^
z3GN5@FrFpY935nO(me3Gff5`RB9(nAYaDlH(>024Sdnus<mhO;lel2oI9u^hd;p*L
z5C(Zdn2gVl=7)6lfupCyp=a-2$S+uYe0?!>pN(nU{QMZ-qK)ntId=J3@QDutXK#pO
zRt`I>a_Z7`c>8{(%dIR%_5=sCLCG?C@60<bM;eM;Z(;F`e1H>*(m~uUIbeRI!zypm
zOd6cakmuRTOU9uQWzQFur9EF*E*eM61WYR=5C;a!{fc?HIJqb<bB$cZ!?~=<U((O_
z%$qWo4Z1A3Mbo9vsq|&zgIl;#xaZMk$*&_$glFNMi`c9$mON#i&je;B?ZlC|GT#g3
zE@-|cpD>*anY8LoS&Rsir<TkWR(IS`S1s8g-Q-y=KKZmTH#4vt%G!A3jFDqfZj?h^
z1D=ksNegi&53Kx&Bg>Zcg7~{=d0$Z3w({UFabi3k>_?X`XD8GXJ2;fNqHtt-((j3S
zOxGh<H&;!YmnZ5f<;3DOe@$MRa?Tp=<^bP{%XZDmm$Wcgv$s|`k(YFR$+gvU^2L&6
z;$;P1Q>H3asw5%dr^O8}Z5Pmv7KCHQVgB~~<@%ooF?-C}9nRJ=xVX6LFWm>p^(@ql
z8*aQog3pGCudR+VPr}jqgo6`#PkH8jc5b$or#oCS1NH`}LeKK$AM2jxVQVkROUi*;
zA-uHU=d~XIV7Us?jn!U;6lf7&WEGVJS7#{s)4G7CtW)e0SiTO4@Q}tpz{xmy>PINt
zH~Px-$!(;16%Uy_I9B@2I3VK}1xrY?DpJD-?znJC@pA|MuD}iMTA6TNqPvbea<N>s
zm<Rbz_bQfW7I@BVUD0MKzaaA%c9MCkgJjdz1o`@v<}#vBJ)L2g*!!#ufB&$|Sbj>r
zc(8+HL$RN>B3Pt+Sy}g7Tj_*R_q2>0`MPDGJpUOK<K8u8aJyQva>rR&ye3vYeW8hr
zyS|~$Ufk9<LWX>LL|*yjl&oy*D~TzYa_jsc8Q$AlUI&d0s#lZ;qf+Ia`G@6&`DZYK
zTT^E?9=)NyOdU{1@u6}}SDlztDCwW?7yro~a&xcR5|frL5C0q_;$2R*zSLgYHK`&m
z!x;K{*>QR4t5CVFwXaO<S08+!s}I($pe*6kr;4f*+6vZE@{57>hWw#x7+OZuY1V&}
zr`d>uTP?T3c<Kh8=AsPDH8EztZ@IOR?}SahGnXtFm2An^?TwHXYhvV`hZ@QqLmEnX
zH)px6U!)9pZJ+$I`K)xpj8~sFeiAspm(HXyUmB^HRFcnsJ1H}>&&zAW>Wc?tfwNDp
z>Q0LH$dADp`C)|x;+yxD+*$N-28HyI2ln)7;=k~Va!Ec_F7G0vdsdg<HXV_}koz`u
zDrv5iO=rmT@iXzV?nIhAIk2j9Z{j0IUhb@Q7R9J_h`e+|S+Vp;BWa<tsx?EwciL9l
z-1-QdGaBSdE@U9b#uqB;E@+>%PS|8Y5uq?3l!Kf|4YVYH&p<xX)rp@hrosct5_7Za
z9`y+J=;%?=_OMT6I+Ne*3HY2mpnD=qhiwB}UD83hp;l*=8}no!J#58t5r+o4JN}s;
zrAwCxR7P8%Y0+mx)F{8CS7pGak18O35g+qMT|6)C2yMmAgHU>L%{c%8(SkJVE5Ji(
zg#V$X%u$GRB%05mR8;f9ObA9(cfu8D|Gxc-PMtb+q(i3;;_c<FN-GtQl++a2zjwd7
z8)Ri?iN@*JL419DwexrE)Q_AvDm%CDklNL2$+4qHk$)L!+@uNaYfEfmtQ^>XKtj)i
zN=9ae)TmKinzw8r_3NUMRgpsFX2-(HMVY}r*GbUA)&`7~3E|+8gL3l3Nr}P;qqn!0
z&iJ%!)lxj%JR~9t)91*8vq8<8HA7nXYJ$$Kd$wxY_8r<wc@MZq;1hTvP=ZbcNyknd
zwUZAC2~oufnv-nZz7-XsoK&w-L-y|3E8&<~@bvPO=FOW+>$a`)@FD#N4j%xm$0Rm3
zR;pF2CT-ibm56ia)R1b^wv9=T<_?lT3l)Ek@`qq%r(#7<Rn)fc+OD+Mu3cLKcJD!c
z=b#*UOVj4fq*d!ynwrYdnb0#j+8O~j4Jx74s#ljbz^9sjb=iO90Lp5gwCd4LM`+i0
ztWkHn26Y=qLP~-h*k|&dGYxd_s9CFqv;nT=UCT>EG+Y>vwn?+5l7i9wJ-hZOK2@vw
zOXsd##NW@~RFWVQ=^5#=bN@~W2JZ-qvucf-G?pe!o8<9@bShtt9F_R^cvZq$wP`Jl
z8#h*i*~X3nKTM;6<&6*5{+vB`7W5ucezN89fkE1&X=7;wIF#*EK_}(Z$y3s)OJ}KE
zsj`+M721%~Au1!Cx^w~@XW6u4qf|xN(CFT^V;5#HlGR<NZTq%TuU>u7YStCX-roHI
z5(sy&__%n@uYQC2Lis8S1<V?yqMRNMJS@9+?uLJF7I@$<UF`To!zndARkrWlE+<Z$
zfE=Hf+I4D4d(<62A3s(4DLa(q<2Kpx^zxFLHEXK;P_ea)K6@sSM^x!^0dN1&1G0Db
zUSYkg4mqG4`}qMjj35^%@pQ}N3~2b-a4k#nu2G|g(yC=^bs5@$dRntqO|2(v)QJz<
zhF$x>AFSVMhq~hH<tx$g(JC8f&YaQmt>2)5upW7OdR~O1)?*YS`N)5|jPXA*GE#gh
zS5|p$fjZB!BM)g*#3#jL=5wzEW5$FyG-y~~+O==5ZiK3sL*}w`vK24FN<-aYJ)`W_
ztzDPhp`1N?7Wo|0wkA3{TI($9M=jK+j-5Mdn`X~X^3lTKZ0K33RJoE=@u?!G!%k~m
zZqcHJ;(P#YBiD0~zg@a@kxJf`v`vYNjnkP84wxK;jPKgHOX}9FBRjTjmq^sjb{*Pj
z-8db5TIq0gbwzvPB!MRbwY;N%qbp>QWzw=$E2&evjsymukbS6wUAuGzFM{QdjcXyB
z^|Z~U41}Bs(Y7t(T!d7pSOIMj+6L4q(m`I+C<{Ivj50rn`eM3qnQfb?q%sb>bk@hp
z_Ci&}HAF(?{cLn93<Z%Yl`$|>M4+;C95W6Qmn(M;t}hL%RD^<&BOMz0$;_d(Wy-6^
zBsw-#YWh^rnUC{OHVz?vU~qy2#brsgig06UP)Ry8tpY{W9O<P|b1orW-FQw#rm;Ja
z25_%x4Mn}Nzo$f}Wyzl>5~WvXFS)5#9jORqmI}@FU2DrzgX84g-y`M4acNSulDlk=
z&zAe!dP~oiRWUPg9&U5)(!E_Z5uY$ghgO^k8Jf2vQlz1~4DVM@D!V~pLf_G^V-2~#
zeYkwKFH!x}Lu0Vca%Z{>o6ta7R`-@P4E!|p_m=BBRF!Xji-Am=3VYQ`G(^!}0B5T1
zRK6CkKP&;~GUdSQU8I^<MF|Lwl#qpS^5$EOq<d4eui#e=Pj?y8yN<lH@*IrUWTnR$
z%6SxA!vc;*OK?;g+~N$W3Vt=J?WO!ev!qHQzjmF7kwb7bql+7P+5~ah!x-Q?1>#1f
zE(|U&yAManNw|Eup<tWWhAUL7s;2uG*2mBo;0(v!qfv4qGF_F;`c>TFHdaOISFHd_
zu#N^J{NhnghQi?TFV6wN^U|S(zqD=XB`Gj2GV$-@?jm0JryQ~D*ypa<7bY{8oRbAl
zHkTftIaAxJa-b_k_8p6n)3ICwT1KjQI!lX&KGM7%>KpK;(#qL`?Z@G&76kXc<Q%Dv
zIE{gO`=)-n#=%mSiQiutNDFD|)EKo0iu1-}@iL@mws=xzfGdVq<Y-6&ruwpE(9?cO
zdq{XP`g%9<^~U-}q~!p@!P98>NOL0EZ%-F#+t^3iH^P7<%3$k}bCQS|u%7LzO9gif
z93jKQh=1aAf;6q~BTZ4R#3MC5M}FTIDwW|z*be-pn`52|i1*s3f^q!SJ{86X%a*Qj
zr=!y$a|UG51$^|9*2vox_40H?lI#nNQ+K-Rz7^1)m;(f1Xb1KkkCBcos%n1g_ME}&
zjvMqMH`%d20x}2r^>UW>O?;(Aeeec(YS{z%fK!yq1PM#X62A&g(x_%dl}pw$4s@_^
z4V3YLP`D!kR$RsfsR6go9<BYQZdLX(=Abx8GKGnNd5NT^R%qUr=$niy4cDFn)-<My
znvKaScoaU{KXYZ-`S|<Ftj}gi*A87|50t3o@KwIyy20|@*WameKn03xM&5ttJsHra
zzx=U!jcnPtMTQQ(QC^?+nncG$YlmtHHsP`(+=8_qQzlK3wjJBaqJ@iOQ2#*^2X~|R
zlz8}Bzb@DH8z_r^{sk@?du8NJBW2u(v9fdL4m8#nouf1r68M~r5`UL1StjjTww19r
zkC9_Xj>y~7-;^GmyUW}!=16*0x+-JDap4aOO(Uv|Gp6aHWniBH`cF3m9qEVly?p61
z8Ghq1g-r!$K=1w%6%~#9vhwwuucd#NesbTGsq*B*Pe7qRE>my4OFFmjq{=yG5QxvO
zD}I&st=h>w<L;7EfhXm@Tkn<o?wTrndi0SszpqB+fDLa)7cbcmAAnKE{30UGDed2X
z^PTkR-dm<lxmR9%`gu7C1^D)fw@c@Com9EuY{pR-bobwVpNt$f5=!(=&6~8o{QS!j
z6CWd+H*S(UZ@*J2L(%*0%WpCJbiEoyX<2Z=nDL30<4->>l%t1_$b_-urB}CJ>gqwI
zgi6g#H{2x8J@KscZrxKhtXq$=+$=W_zggb@&wG-Xl%TT_Tu(In^Vy2)=O2G2fyV-6
z%*fHwqeWNQgc&y)S5$N?zP$a~TPUZa$ZvwQY|=vZ?A~pT`r7ftvB=3Uj(Y|L1<BKo
zJ|)A3+$1~x+%DU;{wbqJjFMZ&+^R+iNAPJp6Yqrh1m!E!QW@F}h49Afhe%Rl5;ii(
zkV&I&mq#9WL`L5<O6Gs}y&7@jM~{=oAAC&Lbx?VuLc9#V{hh$`JICLqhRqu<zb@T6
zc9owO{ERNvY})JAr~~<0Al;gFlHY&(UDrE}0-v~EDIqbzRI0HifNM4S_3A6@R<A|f
z*^Ig~RGxq8IW?*{YquHCD9e+_PF8w8{m3k%ufP0SjXc&Td)XnMc;B{j2l-*%d}-IB
zt*rZFt!!GqQAP|ME;By(2<4Q7(Iz(uJ$pu;dEy!60p(&Bj1AVAc97LWM-HhGNWQ-K
z^b0!3z;dGt5RFgL`@{D?2uI_?qr>Ip7haaZXdAYp41Qbj8|w6R$ZLigaWwYqaU}2A
z{%{?{;X{XI{K&B~{e@|A^7sk4b;7OEu}ueA_3J84!!{;767ZjUNo7@KdihGU5!cB_
zpd~&bUS*C3*8K10DPHrxnWt`=qaX*jql}ImJEAi=r{K0ZWY7?K>d_~qd#7%)7xj7R
zqNR9tgM2w_uC{58{A4{Oy}i2il;um7<F2d(!I-@X<@D^6&nTRo+jq+ED_5xu&Vk%>
z;DoM@KYTY|x_9m-(P%F|`RHSL{i$ivqHRlg^OZNW%+pfSbReOB?|u>!6Qei=1O&*K
zn??(9KeYd#tX#Gd<u*cIc;*EOKNqg@Jz>mvHA=Q_-YSE@hr@>sLCT$EC+hpy5o1*T
zX=uIu>KoDpZTIq}%axau-~9*n%bgP^$*AF@)L>k+@E2*-w57^w`?l@0UQ&j_;U;#e
zp!Em}0hOViUmTU^<_BW*(oAIy2r-UjkW=ZR!Xy>TK}q${x}xhB@Vq8wLd30%ghTn@
z=q=X-?1J*!<&C{^`;RcHPAALMZ-b@h)4OHCAEyA*9PK?7ku0~*+$*D=I3V-(#>(m=
z2{PuDBhuvUJ#y?^k}9O(P*iF{>7jznMdw*?=b*~#4}}iXCXxsRjhi(L@^_QZjwI=N
zi?SG@PKH4n03}=!&WkS;bt*A8HujQt`$M_HI*bg!;wUc_N+p|GKIpAqt%7{@aU1E?
z$`8-ai-)tTL}4al=doB$KWJX;U&%MZ$b>;p<74H{FnQwbAo*@mBXjKs)Kpg(Op_n3
z4ke4QptwQ_<F6`Y><ZE65wl1bAJ4)iYsBLR<g*<yvh8G&O!@qj^nQAetcPovDc2$k
z)&@)0r+3NSUkA%kxRBkqG)!H^zFZNg1_6zaL!pT>=-oXscKT7-9GECitdEcz?g@~O
z79K(W2@eVI>QrQkO#Ec8i~!$f?TnUnM-yZm_}=xs0674Ikuy@B6~JFCZr-pjQc^%8
zhSlWAsaOnxCBry!!Jw9tj)HT>Bm@5>=U7CdT>su7nLN-}Zs>+}kC+AIAm1_Ibp5*l
zGVYC|vg&YxEI9!8wbzeH`?vN=z==30#Ta!*A^ZUPdOWdP?)^4c4#TzS9=LM#eKbJ6
z#EeQdM(QE1nh&!2O9RW7eL*$I^^`uo^3|qTiH^gj6!>t)Aqj@@_u!lK^pvJ`V5GuW
z>-XUinZ7(&Wt(y6lGyI)oiceI<v#`TA0l0!*d@!ho>3Y4eNUtezjd#i#_Sq*l;aS>
z!i}LacG5ms_GcLGI3rd@LLyUT1mf*H9<OD``bhj71M3uZkx;lSJ%U-dQ7;^lc>%Gq
z;dr7<`XErczZjryiqykSgeJ+@JNHQ_@}~SzuVmVNFd#<Xfk8~TJyO1h{tztP-`yuI
zr|*`#mWRo{kYu^%latc%wE)D0`y=}=;J)n7Q0WZ0obvfeIf%7Y_y2lUu6uN^e6$EI
zix>=GxrvXnEZlNV+P<_)?%Nn8Yaw%YzI9y2&DbYrW8r26!^pm$DL@}y6dRzB=zF+{
z7#(K~F#;n`f+oXA%S|^j&S#eXUqc?G=Vlm~PBo65I&O4q-@$n2-FJ+%9Ps%<7Wi$x
zTeW7D;pgRR3>`Yu*tUDSk&=~aoH-Y2JpbbJY$pu34FJLg<FhY5)BB|>ml^S?2}WE>
zyuoY6nfd9*dF7UVJ|pisJ|)4}uyv!x`SGV8jqvDmMr=}?@zKXK^x0S6er-g?MH&gI
ziN>ZsHyPc!b~F0)>0_LY3O5qdl8i}{CK=DZ@SKsFou+9A4j5p3H1i{smw<zN6~-NR
zPBIQ0J!m9lBpav0&luC+n6A$kFJGc~Jcsl{h72*_TA_0H``Xn;+cs?tENnJbuUn(f
znBR>z-e|n}&YMOKIJRQdN`-O!RG<O(3FGBgUpA_E`WmlJe^vRBnw4g3-T9}{xnn2e
zg_mE{IGF9xG<V;7x4~=1!6>Hjz(Wt}^H09`RAIABEIH>jdHegi)f(r>$)kEd?RAp|
z=6Cws8Kr;gjz5j6mHdsj|MRwyh4P#G&6k>H-KO;h;lw1x8sE<UPM`g>=qDpNGsV~l
zp0;n-&Y1n>Y%NpL_8ZEZWj1T>EF&^LO5+_pdCchEt-JB;^Uop11p~7uM(0kQjUGLE
z7%PAO)ksK7G@=t?P|h<o?Uo%|m2W>U`9<&F{op+#Eb^?8l9{S$J9g}-&m&?Zjf2My
z>GgEbbS5HH^V@fLzcFg`D9GH6#@Xm_a5zV4AYUk-<jIbJoto~?UE4ITX|GMwdyE9D
zyd<S38|$}h&}ZNOI8Wm&UAa{6PlTLQ7=*?0TeNhMQQfDS0k=9M0`hk2trLy9wdxvQ
zefyOWlNe(prY9Mc7xIO?V_;q2{lkwwtmR1BPMrzTdp>6w9}fyNYWdeRumIdx@%zd=
zK7EY(z&IA4=6p3r?|%UvFuw7hU@Tv?LZ7kjF#o&nx!dU9zrV_IMsB9cvPCoV!eU{w
z?t({Fz232Bhst30?mZyOs})XUT$I)k=D+vQKBeXDci+~$Rzj{>Zz&sFwr|mAKcHSR
zeEH2+de5?<obZ41HZ#tuKYrITdim8?G|u|X>kaZh8oc|?CUZY5{Got+6G!r%@t%D8
zNdvAL#_Mmrp>mjvx_St5ap#oD3Xk=daV>h+ZCa=GS>gZiy~bfa46L)H?ep1RXjzhX
z<Z~y;>(u+E8fn?-2J1@0`VF<-Q+`M<<?K1gw|`}SBPcZZB3wytc+5Ei%DM6B7oTZf
z*f7Ir+OUZ+<i;Udm)ORfjXG;gdwrVW>Evk~I(}H=z5Btt#=wCCjdL*(%9CGg^@4P;
zZ6Pl#`3edPR$kt8(@>>j&w&6fx47hZ<EO<x>od|roCEN#3G#pM!}qlAu-zpcT{?F$
z#!nb;91c8!b|zKj`h}NX(6r<wc`|PNIHN_g7RFlO!1^1T9A_-}d4bWmVPmwjU5%Zn
zUko8HJ!s^%s#oAYYxX;NZ4~QdI^;16pM=bGqyK9gje9@bVkGBefQ~G1E&o5qcAb=y
zX*~TU>hFXVMsRdIWH;MLMSTALc_PA)G0TnbHUt`k@X#lJ8ZvaT@!03vjFT}5MnYDG
zu`4LjxbBtphTOQs*m*L-NJo1&^)uAR8x|Y$)*dw?Q&WxDjC5n+)>DRb`Pmr%-e$cY
z_4)>*>SL>o;Mhb^mV;I#+epaHFz%VT)sT@(jm0}d@I2Sp3Hb8BD#PdT)yBI&1{jZi
zw#|?cOO2Pl*@b=)v|a$s=g~*<A9!cto;70izB7i5TV_oD_)jA#D;-ZrI~;{@wfA(C
z@!8^i#yuaQ&fL7z_;}@E?PryLwmQrBp$SPw#Ya~g17BTlM1u}KqkLq7M$*X-^CYk0
z!HZ|U-et(Z#m3TI!NC23@!i^EhSZs7%=zuG@`3!?cr@IQdw(_Vo4X6}a*REs54^h`
zdF%;}0uM5cpqNBs^81?&sr#d`;c%Fdf-<`PHMF0TR~qXMg(;s-$0r$Y{;<c8p1&AN
zb_4-~9OKd1I}91L*qF2Gh;c3@#fU?G%kWNm{9^QdW0R2xKEy*V9+`u9<Chtaez6@q
z-D5aE{JZk_*x4ARh5AN1_@He(=zRX0-Qd$Q#q~V$A<e}7L6$RcTnTz(P{wh9y>ch=
z8?eZj4VuY=jfc-_d5(R3y>T!!TIo9$5pPU@97x>-#+IXJwM+<;`Pu(dwl5{`_VG({
zP1;Dq_v-_J=VIgQwa0Ns?q#bi%aA3;w0V1QpJRk4r5OEQT5H@ldnZad+c*>!W60>`
z#)P*w8b{8>8LT_|&O{p{Aj^DyIxf-Jcm(tgUut}@`k2aZXkxN4{OwJsM@x*`Q5RVs
zDDR*AcEpg|Rv5cc=9C}eMf@%N4un81`YtvWZa9wkhVkAn`&E|auRm@?rKPF7ZGvpb
z=%vQX-|j|?3&z^LA%<MH#Mpk!<Y_9}SJJU!XRzY81NAdDBh4864tPIisqyN(0LXZ<
zmdTRsC^yLAn?LRatn)@dNEGl}Y4mw^jj{Vwgb|Or8HBp}@GO*hoga*K2hJF$aWD7$
zX2>nejoH7Wt^=>=^mJp!a_AA@;}2U-nPqFEjcpU_XEONq0%WRm1_Zt1e@9?{1=|@W
zXLM4mjJ;)?jw;eexOTN#>T1LH+@d!TE;~Oh_({;o$)LelQ0`tHqnaLi&1W1bri%(k
zPOV`Fj(k_D;0@QR8U&+m4b`hugR56MW<!3FM;?1bMvfkdseC_mgX-D6mpuN=6S8U3
zCUs*dhtU_pun&-8(^Udv0Nb{1(^pgOnIf&5wNiJQI@N2-ZFk%*9XoW?*%!Ky^zPe7
ze*5h=iN$OO!x4=5`}_ODovJ$AkW6<ox*`1f>#uNMY6F`eBP`f`hJmBE4BSG~zg9IF
zGk&bpt5H|C>FfaanO^<+$Qig!akSiWU7L9Ot<t<vGa>$s>ot-Iw@y$PjN=$g=*S0j
zX|Vlp``N|~8p|!WO_Vy-YODKEn|5tv;$64u?oip-S%@QD40JK!sH(T8w~QP$Qecp%
zyG%vb3d;MWIPS*dC$&(f3^i)jkRN|uAUBU5CH|Gbd$?S2belfmZt&5!!mi!BWZ1~z
zGT^!a;(@ZR;_WMa`}fn=TzrjAQ%7lA!-Z$;gt1c7zlQRq1$L}@f5ry_qsMgN!Dy|Q
zQ$@rXCpD|qRJ>cYXeIaDcaN-G`735kj$=H9zTAZeYXqln|Gu*9*JaWR>t(z>ycA~-
z4~(WF(5;F=M+@<r$4`A~-k*z$$a|iMnUHS1y2*%}M`HTeM?Bp<;kMODW2a|i=!h0w
zy%_GCa;KIf`O~0weYt1qJ@O$oQ>aj(f|h$&)L9ulZnUl&%Hz`jlTVZ*D2?*{*Wb(2
z&pvJP66xsn*SB9^d1cxwvTn^<-Ke2FXo-)F$0&M~Bqb*aWqC4Wd+o+`>Pp4+I-L2l
z54_*CXE*B62pQ0Spt|q*qds-%-c_S<7c}y<YqzcfgGc&bH&7~9sHA1?=4P%lqAL&s
zT?rXDTg1T8SGoYv$D8#cA|gVjzdB7|gi5boy|t{p;3js{@L_Vtq&sB&+Vzs0iXHYa
zLRo<SK$&ewFVw^Fw~W`e#To8KEioE@*FAS>UUZv_0^P5?^oo4^*(cJYS5Msrm+qfK
zh7OT2W5?=DBNTkuuzrJ#7&!v@-Xy-2eG$(^x^(Fx_dk5UeDV1g5*!?)+u?HE+r--^
z>dXaQ?P^r1F5P=}m#FwCoz3M4Wnm#DF~zRkF$9AUGHT3d>Dj#}wmWu}8h$mT|9}CK
zoSU4t)j8c+k`fYieMJ?xsrh2}D+anF*6^ziR~M{3v8~;(1pVFz?vo8$HcE%q?WH2*
z&jT`19$VuwkcX_l)lpYNPlX8iT)S#5@$&H2*|D9wcFGj+u6g6;h+RhN)T|@pz*`2k
zY2@d^1q)^BLsO+6W?eV~<yFy3>+`sY<K@($lc<NLi&8bX`CU3difk1$<2=`>T);?W
z8A-)xZZO<r&S0@O|3h#coq{zf=u_Oeg(l)!>8w0B($5(XQ<S;zA8ov!i`+Z1i8S=Z
znm>qRr}|ao@evInIEL&#8Y2-{kov`z7`bnVzl`YDKzuPP;pJ9NZtPk^zIwHp3~lYB
z+xYfu=_8TblVuhbfFH)JNDxL#xrSo)pK&@H!_jAkm?X^3AjE}d%2T^AJ9#ik*Vlx>
z{fqCkp26j+4E{L_z#Vpe*?L;8|KI>{FDnlX#X1KUtij;SMh@q34D_LJ>sl8hvyY~r
zK`JAWahd9-MLwZIXnriK4FM67vOiU(URN8lE3USM*XE2q1W!kp-OvYzVK=Wg=O2=H
zz7CZKM^~3Vt*~<%=1-c|@|5{=+siPlaq)-i7}rhc3<UUH0W&|$X~RK`xP#3P4{IPD
z>iJ?a2D3GApSd4Ae)G-7QV9#8k6@-@_3k8@adTbi)v}uIR8_rFc^PwkBk}84K{oA+
zmZNYLnsFEl^auFJxPkS>zk;iHVaBRIT+!xDYatWbR91Jq<n(Nb!wd+gCO$flC{OK-
zmz+5Cp}41;S~mM_@Bx}5)@WkaD7YlO3AeImhgH{E0j}HNd%C%e?BXv!ecet5cB&!1
zZZ2>I!w#NUW5J>E@=Bi}0mm`Jlz3hq8P!Bu)$+j_0z;a@HRqX8jpdEk8b~F`2j^K#
z8MCqeFZ^LkLK$e^)JLSIi)=d_Bk2$t&Pr^DdlL(#8)nJW4G+A?<m{G;L>bw505flB
zKkgn5x8-VHNMT6p+CFmE&<4uCE!YvQSzT|DYA&)HJFQWRNPsKivXFG?+1yLM3&aKq
zxaaddc;C2&o4Uqv9;)yb4pOL$xggz|RFdyzwUePe>WCj)=PE#^O<rTY5XzZBMK>MP
z<;k!<CP`3?_?#CwW&ty1QrSf&-Oxa4dAo}z%BlxsR%*IQCgek1(SqQXc`{R;8`DTS
zHh}H{8LbaD%m;32B-5rh5`Vak(RGfqX=6H6mQj7{N|o}?QUz{jH*~75GLW2PE>15%
zo8mw`uHYcT0)R{yrQd%s2PouL2W$yey?XU@N}L_#1-OJ*fr@TCTv{l!C*V5qJY0gP
zATw}Ze|3l1zFpmWxV@wmDC3-(r1H*?gNB+R0UdQDTzs0hXdxBKS3qTik_0I(hXs!G
zt!JQ5J{SFR;jT@>^*O(Y2Mq%%-k90II)X+JZgVkpR+(%ybOsC>pnl&xH>45Xd%#ov
z=n>N=oGw7)Z=0ZNVm5BrC>YgLgQsolwz`%;GtvrbW=TF};argR9os<>tfC7_X&4a)
zZZ*k3{HZv$YTgQd^)`Hfe|Fg%Y1pu#v~Sy9{r*je`QJs!J`JZ9ty_pMHWR?em?j`U
zsA$s%micw>(Ot%j86$MlXa|>y{@3+apZeZ?dg+=Mc8lD`vm6v>{&I0U@ow3yrLIxf
zxnqaAuh1oK%Z4oyurELc4jM>mYz>r1Gx(L$sHD$*Su`kS!$k$j+yUy){zEc+)Nu8w
z$7qf!<QK}IVe8&adc&Z^Y#My%k4w9r9ptl_pQ4=w?)c|9Fe&ieXQ0~(oehfOh1Irn
zff7yM{zX48Qa3L8)~{Z+N?~xSxwv2%l8nRhq^nJz{(V(Z%{rf@{_^B61Ld6?A+S9A
z^zJQwFjm>|lXvMxrZj5OSeiBgU!a&{v{}=TPfX{?C$1OZ+N#jt(@-X>%d0QEqTAAf
zk*choI&o5s<LPg`riSX5Gv=zx5oRNW>ruF_f#pmER1f2{lS|Fm_QiEoq>*w%-dc5<
zzx;IX(Nn_0Lg8ApRAoH^y!&(GR$FRv-@san25Lyq-Dk|WvFg6KX4Pt$2Hd!Cdfz^*
zCu-SBcJJ7Y^*&FkOpy*(SGbR1ce-j&mbq(_X9Z8xdy|*UfHK2+*uGPHth=eI@=Rm=
zeAx>+2*5b8Frc}Ht4X6K>RuFd>J-`-Y#IUnQ876XuwPC>IlukR+jY$c%kSN{|6^-Y
zxTx1z+XKSldIxSE!N4_Jq>X%G`@%9}u;f^89KP&jh0?;nHiXZ)rLYq^X)4$Z_Hc-+
zm6iq<1LJc812dRuC`WXo>OZi*t~s0Y^<3TAFZg7TY+SuTs#f;TGk{oc-7r8vpJ&|&
z0n~(}4m7OSP?r3%80EQ4>l}T^tuj9s9-(fR%#&>_4N20^;Oyig-FkGD*QQOkNYm#f
z`m(E_Xo65@%E320T6#@~QV!(^#N$wp^D-Pt(WiH{p=g+S*rj7WYH+}?u&OLwje687
zFV(O%BMWXgv`5O8Mf(STe(`p~+L~;MfV&obtXeeihW^j`$wm8Uhwyd#;3fpCydTx4
z4*bSbun0U{-r0x&Cq{xXFs_}KEIg7TwXpT+$>?Mm`u;(w>gy~&z1$wkT_tsk`EhNC
zJU1%{gEE2g$|T?qm~;s`j;%{)FFz?W7M_!SeJja_ceIceNJC$Eu8*Kn$umECV0+=H
zce(>sj?7(oO5XS)M5_9@$$d9AM%fWoS&2=}k~tg0rFG|u(zS)@YGtv9<;QlFu4-#`
zhsm(-14T|{$UBcTk||K?Jm4SC_Oo4MKdevkkl(OK{s3H(BIB_W-%0FXiEWOZV1!Wd
zj)}`anYc<zD6TlsO6|b(+#G3*?Sb3WgVBJtYr)nK5zlh+$DSzJbub3JHx*X;{YM9*
z&GK@RvoIcv1h@<hsRH*$Y^DI3S<Fu0>$m|8Rl!b2nax}rDDxIa$R|%UkrBN)U{+4{
z2FA+ZIY;IC4-ZM;+aA&Y+w780nJ_li!U&U;^Kw(y>M)LQE(UZCw#yub>e0TY1cT>u
zSDljMC*vgu>vdM0!de?ov;X13`!LWfP0Q=X23j+KR65p=uq<2E^#u>GFdIT@Uq6BN
zUl_O^5W8XdddR(<ePq_|IC&InBx?9okhO=QbT=svnZY*rz>7GMhZIe=O)+rS;)V!w
zmITVI3xQgGE@)qpxk5iGNA{nHm$7~8%hO$}$bWXm$_p?yB4X2}fk#<+ba*4#`uQP=
z0t`0~H~HpRip(5VTgpRWXFF3U{S-JAIzu|Z@T^ufO@7{TMh;?YVAivrPXHfOLgH9_
z$d5r<;$V!7Z|Vu1&IPiBH5#CS12d?k+FoKl66;Me(U`hPt*RA4CrS>lF>{1<9TnyI
z+uN#K21a1LOeQAb>SHhh#&srK_9%ObGMeu^^zD(;m*xYEUtC_uC*$(cmBzku4j2Y1
zfvpFyI)d^TP0@0M@3&*1(#TP7S9e!w*tj8-caFw#vEz9x5N(H<izlI|`KeG@X_*H<
z`JHv%8?7AHb3U~yiuKF<h!+LBu)vO<_;a>KJAR7@Mz%fYLSGs`T!4Pt{gc%Xx_!s?
zGI!2gx%Yvoz^km>J^3zq?!{-Z4&tcv?$=utBFdwMhm|)gGiM2`-EH(eA~IXXCoUX)
zXFPMJ!)#=jp7&G$3ZsQ-HRN5`ZUK+G1`)?RTmXQIHjOfFDA1&SWBF{(=kma#56Y24
zM=%1tPTqg_eFXql4jGNL800Yx78WddM&IzAdv;0>_{DQJ;`Vzc>0;>{Z@N)tyg$RH
z(3v>!n<KLH_hevM<_Ew<*+)T<<t}Bqp#g9suB4NSI6oHM{H;-|rjEptALLU-ES&8>
zu%EQVqI%+1R3J>g<vNQyZoNa`>LP<~7$luKcfyG7F!<vSGYbN7ESa|GweGo|Auj+w
z&P;H@aGpY{w3+lVE^$mrNf9qBGA3<S;1y}f$jJ~7caOZX(X$@VeR8KL@+prV@Px>x
zCa3ETNmOL1Ofry99kG7n);n(1x<keDP}pIW#e>*5VKtQh*QdX(%I1Ql3#Co#Ht1As
zyJ{(f{4bvIQ&fNi$<K=x3D>yv>e)+%+%!an4jZcf6K@=^yk%#=*`$%9Z`SoA+(C~#
z;H=;?FFd0PGnMMBj4bh}=pl`;@LvrCTX|1^`!zL^yuH2Ap;!vHDRab&<xmbYEn1c~
zIk8IIy9^iD0|VCA^h_wvm2?vW8vTq<dGFk{Gs?nSH%p-L!P%$!4KPBe!K`~+=*^v`
z$m2-N)-dp3Kn9gdEf>ALj4u;Q9@@upb%bNdmjYy2+XCZRp@2N#A!Uw(3Lm}ykv#v*
zb21jj9%p7aa4>YlO>*n_iO9(e3dasEpEA$Xq!b;T@WW;YYzz2|hTymJzQINwE!C)J
zTzlE&Jxjz1##({k0KdSyyc=MYPagQd!6gIAdU^TimO45L&)4UnJXgSa0FIt|VGRUF
zNSToDh$DHC1|?i#axn{MDiwUEC#B<;!J=KO9Tg5d-~nUM)?i!%rX3=LnSs8DB=B&=
zn%?E*gL~V{<bE({xs4~<<aXF?Yx7ZT9lYZxW{_Ou<T=bx#N^1EcQ%q<&8tHx&JmyT
zt}<n4BUyy?2B*SPVSHm@I@T@xzAH?I&OEAg%z37%4DDH4s$dp?YZ<tc)iIpy5g3{*
z{o43rA-gjar843NMeuHn=)So#Mh*nVOUCtBGz}W)KhKf>tp}3jtvl*rLkH}5N2MPG
zk`5XuiP*mOlf}nm`X^_k=itin=6fxrBNmO5Y8q}dFqZ8IlhN;B;G;r0nbOlw26gn8
zaebWR{)It0GeuaroU%ctde|IzqddizE{cY{cwm<*eFj6YBGzbd5ioZL<3nU2@3+8$
zZ#+|W!%$#ZS%GE9kb(Aq@MDs*W!b3|>D%9&CGo{VbMA~Zs9jC@YDBtBe)AYSC{X6r
zU|@j8m_&?#Z;X^-y(&xn8W>n%!6LAnVPncyD+A@BH;;?JNPE14uiVkPirm@ff{gv(
zgeiAWWaF`QGS}E>zoV&58c845*-#d^w&L#!S|3oB?ilPF*2!OHeG(>NSTlh~vSMwV
z%zU(w&N`Xqn~4><$g(mk2Rsw7o~XPUnb<`NEb+ngzyOqO>sl3c9n<w~V1UdDmC*1c
z*%6#9?R{OPPct9skJ-aLSZ|>R(VoecF4$#@yMbjO^E@33+2jRWn<ZFA7HtfXiEkVh
zspls5clE=%r%G~b-*Pg1_6gNzm_~6l6IiI?*Oc2ln7Bwn-S@|NI^33-!InhnV@Xif
z6Vq94&?szuD&tU|xEmG+s7b%d7hrip=A6(MV`#YmEVAsPVE63O21p)eCK!j?i(r>N
zxb_@ih2cMg#ed6vh5LnDjSSyYS>!@yjzqR@(?+J=I~9!vnhnHr#-i@yCywdf^4zJ1
z!fJKC7To+C%qLWIcqm?2SePnQ9KGb|E=QEl;u(YPOa-e~Eoci-eM5Oh#lpuE`EB30
zO&3i2c~_Bi$-rQjizH?y>d1ccmd({@;39Ln?JR%-yMEnz92nPGcRT{JQomL`EU?~y
zQQ;%%uECv_v~t<<vVMp;Y2(K}5H%W0R4Tl%E7p<YM<gsX6c&C>Y{|+6kj`=nqqzqU
zA4H>=--uf<@_3YYk^hD~DJ&2UM_UdbKdisKyZ2TXn>!}oAx}N`v^;e0gSrrT@Q}gC
z6SE&UDVwtxu4P=Y05C(=uKoj~f7RsI4XbpgD&ol5i<uwHRGHwSLMwpeLIY)jZZsXb
zbi|1GF3G$%Q#@QfFhYh?!LdMo)gP;Hh~zAJ=Y#*?aLXpTVZlVWlhGXoKB>B0a7;ps
zx?fSYiVJj~iA{_VF3P6c-g7TKuiK>(Z}``1QfoU}Yl~6JFJ*-HEVrPbV8L3Aylk}7
z2MvV<&H$>b4MxxZgHiVPQ{U5F;%HpBV?j68RmlB!-6ylYo{iCI(`8K?X**z6agu+W
zCG@LNMH)70D0fZ08}$eV4)S(FnQ+%GD%9*Ov7SSB=Hf!>zWw{keGlEQM&-@JZq^0g
z-2Lq0x@BfcMJvC~=3mim4C||8!Mq=2(w&p!wYOeZ*>^=fp*vG5YK<kA<X5xiO~nsx
zdEA5{6$___!M&=Xj{d_-1_KNyf=<YD-JX|8Fa#+x6c`#fTpUehfJVC22Iw!|@s~U}
ziA@=}2|!g}?6?Ox<R~nSyhI}fyVrRGFJHkLE}?7VZ4+<T89Pfk<3<TinA!0{yFp`s
zGQrsp&+-^m#Wud&zBWAKtnR4CwuR^4Fpw|$>rij?p~F``*X&=|N>VF=RjT>#FMeWs
zXL~{Zvt7%d1%V>!;;g#}()gJ1$qbn^<xZ71mI-Bx!IEc!A^+$mM@Zb>k~@B3?UoFM
z3tFeP9ko7jV1Wzb_wU~?RjO3cHk)wHV1aqxo_)29E!p82B~}e5cdWb^zt~_$tnNYt
zg}6?&iZXRrQ-@@5vq26Vg@slkVKEs}uZou{tCZ4n9Gy=+FV&%d(*&V1vKfoexj?)Y
z42TSjsB?SIvvBERdS@6jH7n!vc;p`j<ALf3l^oIo6y(+K58w=|Y<c^h_OfArI285s
za$}d;;FnpVY(+UjFP={7;t`I~WBLto+h1@ON;$A{;$nCw?9>DvLuP2`>^T`LL#H2<
zF<rgn`P*7xYgM?{Aa{=B(pX@*F57liUiv&l_J7w)+Q9Hg&&`IM!fgv9x7NrnnZv%5
zC#4@hohTB2UV37-gz}$lKq0g7fHNO+R-BOOU!9RppKGLB|N3CSPiMYRP&^fy=Sj@`
z{j@Zy?=CBz?1+)$3TW4zBpQQ*WfzB#997ypU?fRgj=Dcp^8`<kw>$dT9oT(nAP#w3
zcnfx-gu;s>>SbuBs&Y%O28aW<Uf>Bd;K~yvJ}@M+(N>9%lN<<2lyQA&oR~tu`HByI
zJ_`4{EO~r%6L{H_g;4{A5q#0v2NVpq3igEi3~^#f;0F|!f$JCC-9j^^d!MS}3p{hs
z-ZHqL4GhMD=7)YeD-%ci%e!~CmCBy3P^MiZC<deADaRC+8|ZE13wc-@1D8+i#97G$
zLtW?5He*M_lQGg|<^kDoS2O9=2F4|4RzTC=5ls3FX@~<arC&u^v^!dwSI5j0_}89B
z0q~&gfCc<h<D?w;%Uz>rgWWc;p0uu62}Ock3(+^k!foj==;16139k)fO*(nVAA6(Z
z*Aq!HzP+z_!;tLQ$Xgcg$GI7B9qiSmqSQp4r1wJsvh7&_E0402fI+w?eh-6Zho}7f
zcn94af_*C1Tfh=Lp|U`}u}l>h0J8ij1C;#}IA@3T$O;q<)*bS}F>sq?7r?3K3l~$&
z>Ln&aC#ecP0aJIhT}RF)NXvHu<c~>>r9mC&1E{oY_X>pktO`8V8@qQ_+jeoJ(kK5I
z2UrQI)LWg6ebMq=H7Esn6&3ednQV;>l}-9$4;pg4y!gcPvTge|b7ULToDL0<2kv=5
zUVP?7b;%%%Ji)OO3KC8woq=av+<+1H>90(ewQJT&W)?ac1T3_L(mGq3H*KzNF3gvT
z2B*Op09wzrVD;sWHC=mj!{|$_?$AR;lq02yX^FCY*>ZJj;dYzc8k8;w?J+tU4HpmY
z21QpTZX4{4J^#nvK3=|_{k6J(G{cs<W<!Y%$%d=N6Rzi=Ym3>XDLVR94hk;cF<x8D
z1Tp-)@E5^iYgGcU*j0DP(HBTvRA9r;VEa*+q}?%4S=3n{M5HTHo91l<3suz^r18Y<
zK{--P#fSXnEQJR~`cvT2hK0Yn-7mMwZQ81-Dsogh=_d6DjFJ^AR+#QP+IU&`6vWxW
zk6V~}x>eMX=_N~+VE3hUy6YH6xI#el+*xyV6qeijwrt%}=(D~8<$?v!%B$q`WchL4
zkGeY;4N;b7p4`}ows=oEGBJ~N`edjoSI#sl5GQqgWmFtXw|0;K2_6z;@DMBz++Bjh
zpusH=oDh6)4NQRG?hs^1Ajm*~!C`O+GPuhy!QK7ioO{pv-tWHa_Mg3at*WkFyPjRo
z?&|8=SaU*&*mzVA3vRUH4?a?yppw=I+U+KgWs`ENZW5c%V=?lOCRvWE8C84_5a`U>
zwA8P14_j4hQFxDQxEJ@<M`(M6g*hY0OhA70F+{6NnP;<H6qOFi8O5L%7VVs}fx|!a
zf7D)+{Nz%{P9+37#`NCFS9qLBU^|oCBf{1|^e(i@HkusOUdQpM<TW1Fb-Yw{kKjP`
zQOCyw7(drSf-N0aC6%rPm=!-S)ipaRro#HC;ZYk2VZWeTYs%$P0H+{9v!)HTt>2-}
zJ~oYyR7=_=%sjuLUod*#aysn0_$2pW_G!_Rinvch`aAOtCfn98@m{XVG5T=Ne<9zR
zsDN~nt+ZkXl&E3|41^dtl1PBEU3(f{<Y-CwPCD)g)hcW8aAicd{q%*Akbzv0Pu9G{
zOMX(7`KU;^I=3afQFFY8V$q0IB@3d`+EeC9C0i#3RX#<vUF-?WSc)8l1$Q%Ulg-hU
z-WH%Kr}AL|OjF|h{>)RD*z~MtSy?XA#ir)gm1n{`To=mIja+zd4sQM0y9pyHZMuc(
z5tJuxVD=$&eDI;r`LQsRP#%9yYcihNxzRYdVWd%=mVe0hQD?`*foq6pQXsW~!b^Y`
z*7DX@{sEQa{Oj;w4{eQCmQOo-A3b6zQF<$_<6PX;^b3M=eC9V^Wi-vr0(i($BVQ={
z_%O8bsfm`DPy<cko28!wAxEL7WVY(?XJe>FetIz}DMNvy_$`S&M75lpx4-|Zn{9@?
z+5>i`cuALp%j<!TU(N1d=_(SZR}2u$-XlqMI3u&3*67`B1DlS7DG6ij`6&slx*)%d
z(9_0?VP}t?kkbl@%hr9I<cY>-MQFf-;+OoOca>g~phO!d2S$7cA0<aatf8&m@Bt@|
zS*@IMwA4xwji(Qrf{n>u#($sYA>DNnTfBwR)YGb@hLblJLlwAJt`gt0$LWpi?MKzh
z(#IWhakXc*Rlf3GfoOQI#LV(IIWy$FlmGUJ7cJc2WzwiS(V%okeT}hQ_}Juv-7=ap
zyEQ)9Puy?^nk11V{Gu8oH*%FU)FlnGY~s09yY}k&*cETWnGJMrXQEJE?hq~j=X`_T
z?evzr#G$xpSLivbfALOI#Z*rGYyb^Ut;^%f<ygg;ntQ6}wmI0JJ#41YFD7z_J4)IK
zES<9*utJxob`PN_saJRw0C%7(yO^o<E%8%H#d5Yh;a*RyIR#76_qKHiQe{}g8}CP2
z2Yt(`WS%_76`089@^p3)Qq&pPAolOQ>9T``--hi?$^3dmg2NnSlN7At&EC3(-{4%W
z*#c&*GumdKJv&)?HVr;*!adrdC^Ji|W|OB04UufoW^_!9D!@C4*~RjHnB*f?JkCj5
z8gi_L`V(CRxZfc^<lWuh^svI5Iz5Gbu)yvwP4UNrZRT_n1f54vcO&ju%rbfnt_aHN
zccL<gwV(K!JeUsoH<c|t^C4*?xL6<S%-OYoQ0@V;^aM71HcJ&}@+J0c{T(8hIlmV^
zs0CNxb~xJgkBM%rB`J4Kd6AUgRG)2nGb#(9$IcM09HeB~C85_ZZbHWj8P5S&+&<f0
zJa+(E;+j%%eRYkJ%QkDGaD_5H7Kw;Dddx8Qu=n=q)C)kAKi1&%UXB?5FN{X;Yq_p@
zBTOP)yZTzH$|RAPEvH4O-0dPic8$QK=xfmQPnL=?q#G$&GAY)nZ+M<t{MFgVxrw^_
z2<BX(zzes)vvy_?dzkke>Gx$#w2)lMRr7Y6MZB45i9{2U<h(hig<=lP`K#l0FfD@r
z38(n|w{D{vNA9v^tRFfDgXpiv@UrmpRTN`Q#YfOkKeng9ypl;hGy$pt<x6x0-7%Ao
z(<BG)(HKy%{T`v%>6_-jvDjP=fN`P7WlrDpQ5*wXxfZUIwD0+TGINfP*6o-Z_f_{B
zO<rz?JZ}rW=-|7}dF90H82{ytvynIr6!9=`+n(@I+xRsqc1c&IRr(f{n4YUx=d@6$
z87kklv=-{`6QwXILXorHMueT~<<x9MmuI}{T-&?3%xj;vz~%l$HSqF&w(%KPW8`ap
zsK;5{P|N6o5~vrkIWiOaibydJgZ}+<bWBS;6?5_=%$Im^K`fu;NTnZByzO+6#+I~^
z{|zo+IF61)=OKBaFs~8X`5rTbIME65B>lePddPR8%YT0nbPRR<(Hh`Ux|O93x%<&j
zch(?nEjS!kI%yVKMA(eVrM>+j7K#+tGlBM-SWh?m5`L#P*^WUr`@^Y^me*-Jp}7>U
zb8qVUy4Fozy-8zS$w>oC9#`^NMhtswm&4hoK7eR3k(135Kfqf(zkl;(S*>7w*pr1}
zX4=DIm)!{cfdNN5>X?v^eNv9b`y%<J^PW8lYY9;tE(*~cQWyyeWfDhS3Tq^}O;fwq
zD=eNZnyFD=Q8C3YqWSlvh0brRpfpyW@e$p{@|Sg={Imtce<?Z7#?Sj-FYMht-3zm|
zvvW~Et1xOEYR(dvua{;rAMSQ*_2XEwy&FO<#cQXs_jf%v$Vy%gccmE{a23J54A)Ly
zi{#hqCCs@%<A};air7<DxzI3wQSvvzu&xc*NrUMw2^s_6!f$E)7&*PCHji%i{mp<G
zbyAagj;_K0QMefTx#*I=F@SEncuue$C1`Y4DR+mJ+3#(6_J-1_Jtt^o&y>WI6xI|W
zB<Jxw8_WCz;Sd;^B8z&y%v>I1Qi|99MJSbK(QoXxQ*GS2x8HT<QUa~t^%J`+F3r9A
zQvyL2=bbFe1<i>0*5mfmDN#Gxd$6zOan%{6uZ53g>*QlH6Bc#9^CzJ3ldJ8i#)hW7
z43BBO@h_FQBV1i_Q7|hDVUMixvYsp#1Gkmy1-$tzn42^EY2CaO8nBaOlG?Pv>+ZE<
zL*UNRLhl{_c7?}4hg0((CaPX8-9_JaWf1F%XdbgoN6H%$CN}3~%+w&QNy}JqCbNqp
zvap_)$3Os}i{5Pb0BBiYB3c%MBkAZzEGTerAz|JVY|SiL&$Vky^i~afeQ;nR2HnQf
z%PYLQK!u;v*d-VbE`P!)b|uFpzfX|zq6Jhx3|4vS{g!Lr;Zkf1KbHItJ-Qeo0}OK3
zO+N+g7CE5)cu=nK#;ywOK7JFDIIuQfozmAGxWGbY;!~A*c)5{HD~EmPFXWCCV&@l;
z(XB8E6@3Nt$ejNqC2VbCO)=8Y7Q^*hr8kp)@B28RM~@zwlz7bVE^*Hn)^2X}F$TFa
z(-{JuLB_sUim=P}-$zA~J8`^Q*M0E%8KZ#F*SUs*#Mj(c=6xehmkfK(Z3m5I_pkRp
zPuINTu<s}exDQi94mWY=Rdl{*L1M1}W1Wujd3*YA<^tJ|ofEH?as&q3z3-<g#|f&@
z$Z|xmJ{=E1Pc8tkLj}%(bU`sTHq!gP1uK`O*1Y`Bj7;lqN@S6k*R#$&?Yrya{mJK&
zoMx9OLKH?1e!D-($D`y=c?^@&M!FpKarf{A972g`P0-ghPX7agOB2eBpc@wGX((`1
z)cNav_k=d;^Pl%n`%w)TYK*g2Qld^xm!B|Bq0;U;b;(~1sLv%el%==bLQz&N3ffEH
z6o<WI5<KasOCYn5Uoya^3b;esOzTH8@=@OTmWtinXhXvW-i>Hj*;G(hQxIl}8|vl=
z8IoEe&wr+QT+Y@qqxy^Oy_d=6KFodM21JK1D73W5{js3<{bWqOcdJ^ySKMs+<@w!}
z2h3?p>T<r?2z4$w&m?GU$gF1hV-#oaCE*)dX2`;!wy@7{LKH$fJb!O59=qi*0Y@G&
z>_EYQQB}6TUrBM5n{ypeW8A2un|-DL8TO}?<}Fj!%?=61a5Oayu!x?&AJjE)U0@PE
zE+i`=@HN|$ve7iPPxa-bW%gij24RSV`f{b!Tn1L1??dXhAA`(G=xeBc(Te0j*t5s=
z_O8Zpk6!f;a{uyQ8?w<L<r;gb;2wY6dgV(2_NST0yl<&COMcj8K$rV?k1dvl<s51I
zM=!_E#0mBd?Hs7XkSS!Q*mpf#d3T)9Ev%manq%O!*rYkDVgJ$Y`gY#rGjtIjh5b&J
zMgiP-|A>kWojTJ+p*#F|>%#(bAY1Omi~!8@jP7#O8t-Yq@e7#O$7rfp%k;U!#dSR5
z{c$35xHzNiyttT<m_19%(%lPwLX{P3AXS!1^<<~<?B?f{XZ)9HnKzHKXYB_iOazJy
zC9u5JKlvM`0EJ}s-p=@wWX=gYR|X(S(_<QXpgbUDNdw>WiqX+aToIv#wZ;3+yC3C5
zS3I2g-BZtEB^5H52+e!Mc;4pNCBG3x{lco9J^;2Ya8I>1>Pp-kuA^GanmM*@@Kd5C
z1Jwk??=s3vw3){$=bd?D=2C2yq`=}=;ak3mq3B1dnT}?pm%KEojWBG(iafVP6c)J{
znW`#<dDz;sM6iIr!6N_NbJg<|1edBpcywF{k#B$GBL#)a(b9?v5ZODI8ci2#^3-Ez
zB%gZw$``5yF?uV7ZO=MZ-#=yPh-!7tI>$)Jm@_Mm!hzS;K*XD4RI!nu`Anxyj{@~O
zfn4FOo>+0oj^9E`V1ZAoe|j%ON8-)lp<SW)M&&B^%i!`<Z{{I4#wb+ifL~09kDI9@
z_$3n2mL8cQqFQCC4BOmbs`G3B40suiI#cnuR~q~EbwT~QT&HZU=KWZm*OJH@qjR}v
zn2F!CsU4EjhphY_Ea>?fe>qKL7#q$ms$pE-scC7^5w$vvvoZTKYCQJi7Xhnv>kqxd
zSc<$N0!af{&nbPFImMJ7BBAc6u0`KFWpB=u$b(Lf91H%BSMFf#FT}`F%IK#;mJOmm
z!x@UB)e`3=6`~AaX#`t67eXE0Yb6X-)_xJHwpl?~qa0K3*UAWE5XngOG-h(0fr@0C
zJ3e&>&(ztfP;T#|4|N8))Vb|#JGFC?FOQ8#^WtZW?6>Vr3?1wp=8J4Yt4^3+lT6<{
z@!>#QiO|HTlFs2(<+UD;E1*x*=OvsTZ9M!#X(a%FHfln=OZjZ1rz=svN9N~$C)StO
zA3a^Ww5Xd3W2m8L+QEz=500W5ZKDb{2ns*6tjgME*^C}Np`HWPC+0}FZ-cA;_<!4;
zS9=Tj3_<5tvrFLa)$T6<PFeI#BHWzU9|IP(AwUma$y0p%wo0_<MiIKG)o`D-Unfny
zF8$-y?nLqpw-||<)&_U5mJ~Z=S5RQ=E%8phM^E~F)~bzVf=5WUao6{cHk1PA_@HcY
znpbU<wFZls85*Fx>s%MeP?oow@xltq*;nL!C%N52N=ui$MeuImLhwvU`CcK^LBcC~
z`#DtNf*fW*nDivY%(GWWw*A5&07eCFIC`nc@%;zw)N)G4spz)~BaS}ThLv(7<7|jT
zbX#R{Eosr7+F%}aFjUBJ$ka1t&Ia!PoZa_^QoucJFf%ZOPXO47`W)zz4X++HVRERr
z3u;WN4vi_PaC{V(3aM?r79X%WeV1A=RMQxvajSXiI)i#hH~X``EHV`j>iCP@KK>Bd
z0}G{{wl#H~+;lzln4%k!pCaCxYphU}6<$YuCfXCoJ?<EEZ~@Q~YAz3wirRZ95(*x=
z{ZVjvFP@3B@XK_}jGHmXPmow}Qapv;v)Epf&?}wgSmQXATE*19*I)7W+H&SCC9p7E
zbZXQk;zerk?=)c&u*_Fbs&@mfV*Ym8755%Oo<la%%e_D}@7vRX3;oGqv31qD)bjF{
zp=QF%7IJPX2&P&Fop!O{z@Vj#u<?e%4$yRZ6-!M&SEvMjz8IOzW_-CK^>q7?6_UFu
z@@I|<-VHk3Vw-&Hf#wmpA1pnAQFNQWuhhC8fZ7Va+i6Gii)-7Ij?MVpzkI@DFG#Rf
zvdcy?>MS!ioxMxQ_N|@s@I%fq=^AT&*2j*G*Wcmo&zm*6W2qNq%MBOlqGE)fMROl~
z^V{jhxmz^|{ji%b%0oom`J1z@@8-Su8luPpdNf`&9T<eWpmF9<Id6NoW-om&z1T?u
zHrb$R+D?;poq{1<QnBRIG^el&G<`{d9RS|v*f!>qdAy5kklz3nP^)hUW!T~q0LM~B
z_~>~$iO+zh_<3*b)LfCIyxp*fOUZndW0NXi!pyV_kuxg3loUpOn;VnpZ&;asz4m^g
z$jTO!9(%vZ;Jfmz6AeM8Vs5QY^(qnI)9aiBTUvSk(=PPKT2oiShg}M!)hoa_l@M~!
zNYtICavk4bTu3UTEo#Skli8WwH}KA_lrQ>cg50m>4}XH35Io<o_t>PHHg*Q3Z%C?w
zo6sm>J(g~F8}ZpzS|Ijq8D`XHVsX?EE&x?naMkDel}V6x2AeVmyO(`1gzr6k-^;x9
zv_jNM>;APtG;r`Jaoa_cb{mHvr|tLdo5UYhcv!_|au_Mp<!-T*?4WF3>28bg&t4M*
zyKIpB0$yu6M9@sGAUBEI><OCdxi3@0?+ZWdNxV%q8&wLHQ(3P0koZ%9!Lu5w3B7Vt
z30+c#iqatA<BX5JyK{4e-zj(vH=0h@y4*%Wt-`YdbG-Or<(wRdE>pmo66LCBX;tfd
z15~pZuZxDVH=DcGuzRJm)GiIp767Jr(^TzQvnP+NQ=a6gNG&?vnvbw`ba*%&vv{l+
zQg}Qz?0yi#woi@I?`$4I0^#$7hXn{7db-jI`vap-6fASS7&57`3b<qW9#;z?HJoPJ
zvm#ZnZB#cHPqRG-a9LyvoWCO(ZN_`$qGA&QcFNExCzrM)7CvaSnjGa=hA)3>Z%;Fk
zrNmt-_UZHx6LDhxO&+}eCe>CU?aICy&-Ih`K7C_Vg)LEv+?kv~$D^K-AuZW5%@p9z
z%{O%g?7t~Pab+}9GPazn7=W6Yno9~;dX`~mCre>s`ppN5Fu(WF<Ahu6)y`=$fjbO8
zwh<hO@@FpCXa~(w2j4*+WbR)pRm;#od6iUrmgW|($0B7FJ*6gDb|}5?g?~ZOQBc|x
zjw7zmTPs42`Dh;01AP}9E6Nz2%U1*ojMdEeDH-`A>SJjGmZ?+ZTpgt7pOd(NbS0PV
z!e{xQ{&n?MY{7H^^DTmcut9O&7ji-P*aFJ+WFh!pJ=uS0z?;5Il7Zxs+a|f2oS)JX
zm3Kx9Od@i_8M4x-WMv<Lx;kQ`Rm#IZ_TUI0--p5llZLVQkk+*NHi{dxeH|+JS0yxG
zV+&T?t4H3wk9~qmg3_*V%C1*nP{y#+XN3_yUe`iCCgo=m3S9x}w?)N^*?Sq#p|lAF
zl60`I;!PEt7)-o?Y8<<9Miat@0dfm|@cM7X6lWASXM#?z&N>tSs34~!ym@GT_2Kq9
z69%yxG+}a{#uVIY`WYildNF9`l-S`H4fGPlinp+{>_cr32Y8u>JJp7Urf_9L7=L(e
z#U|FKu3iiyd@!&pXkcY}6FIB1Me@fWEj39gpdTc{r9N_Co>LN|N_)u?TX*e>Aw+4{
zIfRlcAJj|%pbiJ{2XLeRkc@@ZQDJe+KBQ2;7SPAq7`_=N4Ad*5D9(=&d-4?(vAuiS
z&~~ApQV;G;rsiAL0V{r56D5!EIOou>%YqAb%5DQUhA$dr0y$`xu{>$;mI)~6oM1;4
zRx|I1h(SpvDHwp(ArZoxk^t|odDQ|Jzkm*RVbfI{^<uIFmGKqRmqPWfUGC8MJ>~4s
zM;G#?+jFxnKGX+$qvEr9KQ|Ayv$p!1ibop|SMqF=UqEtKL5z1_z!vM<%Kl!iBsTFi
zi?$8vobzs-1_WFCt=JtWrH-edeUTPU9cc(9c73pSxg%E#!O3DH_lb>!-K7PEyT_w_
zv94%T%#wg!im?y(w<k0y(k&$E=Y~KE&ZVEmA>UEyP`eZHcPwmTwxKGY8tT!A-ZU0k
z&6nYR4L!|JY1a2l$Idv<O5Qy?U@jaJKaXep<cYjk6y(&;23-?Utyod7;<&Mfmcc>T
z13Ynif*Ro)f41wf=f&`!RIq_1ry6-_OUM|V6lAlvyL{0}%G1{e3sYEQ&=ZTPJErS5
z(L`RtDaf|o3h#CBiXGN4TC|abDZ;yJQZNL%e{jjb4`;K>cceKtq~9p^Fnaho$aixx
z3UeZ)B`Bqp2gGSU+Ge@+z3^E7!l?j;3w^iwLVcSi@Y^O@?xTHM^9xPv*WY{@Qvd8N
z7yMDhjSbihzR=`0@!po-SsCWPNE*ehA@FT*Tk6Kq&eFj)^*NOfO%Y-#b~e|c9q^`a
z?~35#4K&Ug2@dy2AftS?ue<eFP&DtxQ0`F9Tix&v;r&ye5kVevH$QinHY`v51)?p>
zoMN@AM=UaR-*a`SE-mGaMem86RrqE>QeDQ-!)b~Qem8dnXhr?L*d5OaiqY)rkGh`q
ziHqI++|h}$%DIA4U7QS->bgIx4=g6L)eldbEWwjIjqp}B9f~BnlQE84pD5LM*9|Yy
zyJ!%F?Nsu{8vDkC<mn3rbsKk5QuvrJ5mkD6M<^OM%fh55T)aEn4{~dE#KQL<Z;_A6
z_xLv+y`>JqPo-K_1)dYoed0`_PWd!Rwg=t`cWx+19Mo_5=I+``!mWAF70qrmIwgU}
zx?=rua7PsLLTed2I*{*bb<`%{(Iv#`*UShqx4~{KHk?-M&aF_*l~?a8GvC)Jm+EH?
z=$XE3BW6~I>Dl<6Pu)oeH@|3sanG|98w`k@rF9n7Bm?r6+RR>IaOdSgHD!<=>LDA<
zs)X>(9tJ<z1yAl>TfgAWzV`(|Oi6w97sOkmFBqsw#8ORs(%FscHOiaLUW2B3aWl>0
zN~cWUG_e&>Fl5~HhKKW{V{7`gq3>B7?&2X*AV`H2a*;C8KShX(Lnu2J%^lkwo=pK*
zh}R6+|2eVZ65kZC1(Y=yr|)O}6LB29+M6ti&{=T=d8S0768oPHM4Oo^7HqomxJC1y
zLTHf?j#*+YSdEJd@f%3yY~krGYp4i|?#)xr4uGxMzJ@~bi<Rg8Bdp+Xt4KxI?GrDR
z*L(5h)B`-*qyv$;^30vrnzyT4y81aXJAQQn?yMO+29VhUh!fprPBx`v|2AR;#!u~S
zu`kW7&H2V=OWhPO`#N`+UA<EcK7Mu_R&Bq7bGiRCH6TyzdyC^07&ZHZnfeaSqoGXT
zrj5G3?7W!weNwPsztJNSHb4+xEJF=x9r|D=Tttigj3rdw>KZ!D`oTv@h22hueG}3|
z@F|J8=sh|9WrUS5?MIsQA5DAty@YQYf#^$jU%}CeI^1=ASyquNe4p0V4%1DWM=mTR
zYC0sp4G@dWZcuh>S5L=9Y?&ny6j?B2hexvp5Y0nL#5A!1vtGAC;?pZ@s(8IY@90f-
z=AF+3h7Rz7N=fj*oSP!Mo#<cN<#^sumQeSw8U?3^lm%j2YD)1bfM+uSS(-j08lkEH
zr`11G5gZmh`@|-y7y}I|41TuXnxal({JsQ!COc;wxezMhP=+Hx6vP8e3Mc6tfZ51K
zL?y5`Aw2MPZ(l4zsoC#dkzkBu6S+pmx?3WsO1x=u6<1CKvZhzdAAGxqaX7$NwN;CD
z&B82FoEQ*5t#vhNY$|7^xc%@s)-nmS+;@)SzcEZ_4Whis$~QYXNijn}%l4divlTfg
zk46MutHdFJ1tGC;fR*rT70sDK<KHBUhPi?;p<6!guP5WtVC4gYQ>!FHj+qFQqtXe%
z7k7)SpX5o}G;wQVefU=`l5jty$VLlToR$j5mOf^zSYJP@^=h+c6lU;JnH<p>U&21c
zGTf{)uHIGTQLWX8hylkz(%7#OYtx*{D(#i~K3JhPIMszae(oR5s7taR$BJqipr8|}
z?}rDTvWo&<G>iGQ3%`bd*UetHG?|OBWY6J6PGu*O6e`;JNl2`7%Qh*){XBTYO#E8v
zxlkx@>it*K+a$e(K?mb60F{Co`^NflG*M0lCW48~wW1B3EJTUX?uglfcUegT7(ch!
z;+q9kw8Ed8<lzd@Ed_LcYvY@Tvo?o_kF`baLae=KItk<#{LZRy;Tr_eM^~FTb~1HG
zC4sD_MH5GbPN<@v0h->g-HVgMSHi|iKLuATA5StT|Blmg^}CdaMY<*D6*w{vSZdKs
zn!Fw7R)d}AZ;fa^>lMGWt5lUG>DC<l=|hHlep?_C)Ga7hkU+-oB!ckDqig#WvD|fT
zZ%ZzSs-F#}1=~p#UJ>i+;e7e12+zy9F@87DsI;|VxjGC!q_=qqRpf+--n^?tlu)NK
z;YX>cNT|}9m+oxf6T|rZznV=H|4v%3>IpsaSv?9Ua@Ug}gCrj78o2PeY%*qVZExof
zQ?IQdRD6mVRB5$05IwX+U0vX~Tc;Y;=PHn5%@@bVKR!qV(XSPK6_QNeodd*Pv6|7F
z;d;Xf%EHGyh1SU@%upjS#|43D%s*sJX=r2?<G`-Ak(Ubj@ma1|+ElbJxZP~&iAd9$
zM;BjQTJZB1Eq|eI^<`zYa;NU)Qw~^LrxDd-TA>P{0S^?>Q?*lviKEXy|G|(&oyNA-
zn?MWv)PgUTC>H~Tto&3o#TMbctFK4i=uElp;xOKLel9iUj*(E}B$HDG@XC>Lb#+m%
zlT~(!R3B;T0~U4o2IHQtDLJB_C)%V9xo#`=(n34|8A2_}m&u88N)JC-u-y|?SS3)Q
zlvN-nU5*f72(Zhm`l3hfMQVze$sN5!t<Yi|B0@1iwiq)*!zoMjxhDIb#hnWep=T>b
za!S1y928#goi51R`x4xbqrMk}NsKkrZ~I;gFBl`}QL5Mp+@B63hCB%5%x51IM&`6B
ztDjF8<U?9SZ;nHb9v<|!05hWZQOD8E2w*&WWHBU5iss4l?esp3KjEZ$sE0nYsJQ;$
zrK!+yUV)^hkq5=%O+B)7-o@YOH2`Sa&Pm-pGWck+ZA4s{cz=l!K6D>CX&0DuEZkZu
zQr3$8zuWq^&;SH)#98Dr?Y#f&lzT?!4=ivs%;0mseugC(a1TpxDNfEzDNksyicfww
zT;^xsK2Iy=o0wD9WN_KvUNA4zUj*UkFYpw;>KbM0djj8#Io6(epLPYQO4i%u6T6Q5
zV?IK!G7H_xPUeh?vGay*6pGnHqD#q560*3m_8y=_8~!~IVB|dkgjZMC_K)UM&GKF8
z8k%MZX^$RY_UL@}Ro4NExzxA>_*T4=4(Zo3k{$n9dsS)@6R23~^y{59X=a^{2VZ`p
z<p%wVa^h#@(H#dVKk?gj<?c*fC&LEitrR(3UGeH8t5KK0gB>*==#e#w4LjBq$&#AW
zz!E!)+8|TNe8r4MxX=PzaWnQSy3whfEwVlLm~PLLP2sbi5M?tXr5-!_FskG|4y4rM
zZg?g9-fN@ScxgD0gVcsQ5cRH+eBtikAyRX-9osOq^R`=6m4o|de8)M@DarC$wME{X
znU;oNCRfykml%CK)oY}>kJDYSFCAp}JbG5ft`*($&uIPkpgo9Sa?Aw5d~DU^jZ@5*
zzwQ1F5(=V?laup>-zW<G^PI44uGgb1iU}lSH0u!7*Ds3slsp+fyy|+t!bogBBj9%N
zGHxF2i77`}qBUMHoEYtTktzmeZ^wg4@Cj2y@|2hOE|dE&oGcT`w56C+%cNWV*fYWm
zX*p|&9xSB%2bT|6IS`PegJD=`(|j8Hc6g>z`{K0DEXOGF-$*mRXZWircc|v-BO*5%
z0UZ4q2}da6nZ>^~$L2=w^OZtJeM}O1)S2}U0NVnj%!kKkpx3w8^xtYUb?lK;d!9s8
z6^7%TVLg}MgPIX+JxzK%I>gm%(T}!+(%~4vP*FNYQ(`do%ao`80DYP6t@&`0K}R6(
z-_NDm!g<Bn4yEbI&*>>{{kPZr?P|^=bPuYo@D<viL&z**|54<>eDLqPB>9KVt{y5R
z(oa4<q^nZ+r<H$s6$?i28o`%VOkDrip}kq{?axWURq=rTVXwcvT8iMWQZivAuYWY0
z$4Tx^zb?Oi?@uJx?f4%LRE&^{)UN!-8ux=^;X`|`<r>?4wWjsgoR{xRN<XacWv}`$
z+_67luwH+An~-zsj4Gn;Q4i#&r@h^T$X`zUtUU^=4Zp7KKeh@Bcn$WuD)Z!S<swL~
z@Uv%={D`=?JJCj7Uk?L-bLUg*BOLM-cght`OOsr`_y<AP!+5c7V`hiTZdnn?<HIL#
zmA(VJDDj@dvDgznm#ZJ_zm(P`dtjSn8F32mO?8s)-LdOoVh;ssKI4acL)2G{u;f&C
zJ{O<wfh+^u%gixTkO`Y#$(%KCXAf}g(f8H{yHeVZ0JYewF-|;fNyL?Vi)8M^1>YSn
z{9!RYs%y8W;6z!a#X=sb)kto#QSorJ?vWkRJrY+>GFq@tO(d7e&)Vxt{6271uh}2A
zmVbDAv(8d;!D(lw<GB_Y5EXWLbZe8FIt(bT4F@}&>KVahMe+0mKjx-5J+#RdP1|k5
zFF}1P1KJ=9ANyIXKYyYpQKV;xI{lbCxqfP=EPj1c$i`f*QO>D?D#=AK!IU6;_G}fO
z9Q3)3?D(_xEQXb80DE)!?BRA9$PL8``AwbhSpd-JcKi%}<LqBd-9N!~m6{+c0?3tg
zCrexzIL*y&n^)hcaQc<3yHI1NliT}FF;snh8+Fary1za~n)v<Bo~^avJ!tlriD2w3
zIf{N(_Ss;N<Kopfs{0KKSh@GlCbxcz@8u%jFiM%HX#VYD|3aS^E_(I|%siE0)RuW|
zo%2?i)Zah(>lVMv5P3k>$qoX8x<lvXGb9)Kf51$riCA#~jUI3IiMRK4=~3sg`ap`F
z>GQ!nszsa^*`Gv-`eM>^W-IKeHZIN`EZ#s>ar7x7z;B+U79Mzg|Kor|MT7Gy`M&S~
zj`3JU&JeRq?5!dzfj#)*#WCR~!Pmwz0-Sf_z}{fZDGUDpqM!dUp~+GNKhGQyWs3Ec
zo_;a|87RL2X5P<hCa;$BiP_naN3|*xdzFR7@j-FNi*o%8?%eFkk7UOB)l~FMcOGvk
z_YJ>hW;3IIe0~2tX{e|}Moo5hgc?ESW`km5;v8!Ii->Hp9*8K526e&s7ZDX7E{RJ-
z-&1K8-@h~cHRhJ0p8i}x%j=SxRER)+#=yP5LtBdek~PW?r70?$f_Sdv+Tq+_R~GPA
z=s>WugeYP6JK_joAOHN5@ggKSI|b;WA&PHC^)br~VDJFGmf8M?_r(dbUDq2~Lyyv*
zJiOCZ!$BXu!1e#8-GBG{FA{dfDQv~=e8l^|ZGt9i&PiNJ&})iryYnB@P#jJ0kkdk*
z`ml!yxIrl~yoS$iL#7FO@R?_Pci<?Ozv-E$A<S+Qx~0Q<X@L5G8pv4m|6M1z+1H<$
zTtUU&A6;PVv-M5PKDFRM>wn0W`d?(5n9Rf)Q+QBzn6Pi2lr0Bg7eg(l$h91mm;n7;
zYW_CHLv-ayUVLZgh<YcAudbU>D3s7~>ot&ndKoFMXJ}gG)qe^xy;iAnU5zM+K4uea
zF4}n4GOPT+%Kx8T1TYfN_$>eCnSVj}FNSl(nPxh1tfxr*U-Pp$&IhSBYbl-}Yxe&%
z3O?F)&<FVM`v0&p9R~U&THm9Ls(*!3|5TO|z%0W^z!yaF{BQdQ<KgIIzrwKX{vRy$
z-?e6lDhU(?=D!a72Q~B1nN!E%<xxcar`od1%S}%fTHlWb0yDe+LN#I}%*SfwiB-v5
zDoWD9i*cvMbqrxO@m=evy1%bKmA^T8nXSm}dnMOioPay{q-);bGO{n~m{Tyub&~>e
zR6-3=5Dxh7vEUoxQoS9U?l1KO<dpF#xx5OqiB9#yF4$ogv07opu46w+R@il2jVe$}
zAPFC%UWY*8T&L=mY!$P1UQooVuK4GBJw^mShxK+~h@;3Zk}Kw}frD2W8rCqU<O)x~
zozSdMD<2erDDnH1to{(4w9cX~z%^UCMpo99yDvNbj~+jS4OaihaNH9XoC|K4(I3V>
PdbpJ2fN#rX%tQVk8Doc0

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/deploy/run-on-aws/limits.png b/docs/python_docs/python/tutorials/deploy/run-on-aws/limits.png
new file mode 100644
index 0000000000000000000000000000000000000000..a7f6a948fe4c8abfbafe073671f6646b0f06972f
GIT binary patch
literal 79889
zcmZ^J1yo$i(k^bn5`ue3fZ*;LEChFV5AF<32#}z`T@u{g-3A5^?lugrgZrDDbMO1_
z|L%FS*6g+S?&{jzRn=A9U)6VIMHws%5)3#vI4s$ZlB#fUh`X@+S~L{cl>~uD0S*qM
z-bO+~Syn=VO4-HH(#FmL4({Xk#3WR8H5-C~&g)51X58?EiFB$=xJldH_60QbAIcv-
zqA~DDqR2Qg6@3fDB1ckjVhY4$coQBXE(U*(M65pTUPZ;ga3A>Y@Gxsd*vHl3n3Z?!
zKH$3Jy2F(PZc9`fL0o_dRSZQ`kLdV7JSHll5F85k5*K$4`HrdK<2nTi2^wyCL_mEE
zB3xRu=SV^Q)8q5=XK2RTm!xp?uQw#4zhwrU>hw9*Aotk8DFZ$ldRjCXr^UP}qnE^x
zYyzaISvK3I;aTZ$)Y5V+<89!;t%I17*pT6%7ls#P9vHtQ3TW)QcTR86luizMwrlv%
zPQ<)Sq@}mH8sFF@+$agW5BM#g@VQ3&y=vBx1@P2RD3&U-{_eSr{CSCEvftHb_l@E<
z#$!+H$Mg<In^bt!Z90^&5%w6)TK~{y)6OD(%LpOxPD(EnBgM>FH{qVF_UrlFwhN8f
zwss0J%gjz8D(a`p_!L#eR{)t2XO8i(_vLiy#QX#NZM*CI1o=`(BPWpeqc8e5Cet`2
z&oAG;@XLF20m$#&**WB8*U8NKV#)r}wz1E6%j#&8MfuKvX7K9AXN*aS9Us~2#BC}5
zmy@=&vB~z)YC2Cp&jo%?QUnp6q67_wVSfn1uYynNBKQjbBeRadr-gW2zWXyZYe>3+
zr;MWVt4xqpI%}Suj`kuVn;(T0<@LP}=MLttjU87VJvd`uiS<5T2Ps6ja%Z?f=#J|h
zDjd};yi>>!!3$SQ*WEo<OqU^xpf}3MX@mFXNFzV)4g>L=Bp9wxsi|J|1wjI_ny3)b
zx<6GB*dc}F5FNt#i1G8GMF)~Ny~;pQ$ide~nd@SnK)magpn|7a$0tIh?0$<R&i4&L
zg<9j|i^(u~YQ}Hy&0?#{RIjo7#nF{9`oj7qbgl8!F%!ig6DCCjcgO-b2B8jO?Gt|1
zXe1%4;zq`=<wNmJu;$?mkvNS#`QW0zCU?JgBFK0_*tN9I(S#ua&)bu{q30yZ6lBy5
zrVo7c)0*BsfjB$}DjAZqsRE*>{w!RArV(E9VJ25sIkQNR9(|vCpOYk_;MbltXC2-m
z4ta=WZtcXuZ@u4;T@p8%7DPW(;ja`D0Rzfx`$TGyG@;+W_vaaFnqsi$r+gmaAJLE(
zwM0698PPvk6{_u1M#+Pn9&Ql4621~+-_>B^Q{6l7Oom+;&e_io{@}!<&r^Zhh~tUi
zi9Q!b+U?g9f2!-n*oGrQF+Q*ZmOC3bBRuoF*}0MZLMEQQ{t=DZ39S@;30(%A`Rmfx
zmpyM)vaCP4(MrEk`%e1ZqR)F%W0Pjn=hgQ-2^Xr6Sg$-GRpPzZdjdA!E>g?Hc&L5i
zlH>Zf?6=Iec%XhzAB+%FG3VSqwVL-!^z{jh33Lfc9O}fb#08i#mcjBN^11TaBcwWC
z$~wwg4_y}Y7gQHGYZ@(#hF@-BZV5rOT@D}&TVummLs7$O5N3$P7Ux#u5M^>F_45b#
zLgSqE@-7v6WxiidQYI<kzxGvyi|zJl_f_^a_P@Q+{H+{YoKm7)=A~M#=PWNP4w8Pa
zYm=xNGKu^S9ZQNchuvnc?x=364t|l)0?Wb^@)p88bUEZVgvangeOWzDJzSkc{Z6B!
zXi}qHV@ln#gr`_UQ=&ArD5SVtT`<Q(<LDBhA6fCm{EKjc48v0~a!p<p94(_<f5~<^
zKYa<cUM-27Y9(+u&{iR>Al<TL+hH^!Emv2anUFba)}dTZmv!%xo2R!xd*rG(<&@^!
z)f9m(<?Qin)-?O{_es#4%><xKZrWm(_TYHdevW)<YVyUTUa3~$wODq}_{7fs$;`=g
zHX9q)H}-kE3G>q)<^CTwDYn{HH-q0yw>~FWs9U(r5!sHgDRQnlE}J2w&!;&lFvvTk
z_Y`;(2u(c}w7+0~K~4ZA)MO^(qDgN~hosD;Vx>-vAda4orjM*KP3pbwwBGqOCbh<F
z$2`d#tt(WgUUktp(r8!hYh_{D-%wK3VyjSnRlRDwV)r-;oTdeB2aXEsmDv9{vRv|5
za`d?J_|WRzs^TMjk97a(UOe;-O+s~|^9nq8eWR-T=gdZJZl+lS*k(X9b0lLjBU-Rs
z+pdYv=InQW@0h?C(@yW$E5a7JFT>P?-%A@9hwFzfi_<Ge2g(<Znp>-VO779Gc_|1e
zKE$|E;AKT;9SAM8DI0beN*S)KVyvpHLROW0-F#Jj_kDrB_I`6W4Ywu_2oD~2SRC9b
zc9zVpBWtSxnE^6z{8Vppgw}0xWOLZ6-l1VbaTf6}=(R;tD3@O&d`O{g{Xh{$5^5Rp
zL=uYi0;?3whrofjgQt!23rY2xO|QOzk1>Nsu{gPrzxq^;e%Jh#%eBp=Gi@{dWcs!`
z!gQ{GHC!zM+|NA#-pmD;f(MelSQl!|&5MsFd-4&7F_X)6J<OUHp%)NB&A|%ea=!RL
z*>AFZS*MBkJtd}5bR2k$cmwlUhwX=2WV~YzW7cu5s<M;k<~v()TZHe&-nXhsH&-mH
z9@}QoRc2iC%&=Y>?@Q`_T&%7uzDT(U#q^c_00eX(h(@i&7N<5d)M^<s(7*mzLsolb
z9)*thb>_{-I{E30@^6?T2BtJ;IGaDok~6|Hlo)fhvUJ{chIf|O)`Rw$OS5<Gww~Hh
z+C$q5{K~Huqin-wn=1v|D0H)%I{bo<L%(3r06GBNq~A%Wc%G6RV~ygTP1HsTk`SW4
zw0+s2^bIItOnCpA0NcT1(f3s5BBqV9wNcw~p%LQqvT)b3TFTkxY_9yUoHwB6q4=<{
z*dDah(zs+VT7}pe+-YW9crcds>NajUZo@#lmBe$(lkn)N%Bnw$G@mCQ_4?Iy`1PkD
zgyFR7y`!b-6I=eqw$7g1Uq4mbUF0Br{$o!ahz_XxIG@RwL~bCsCll?x9bu%Um?fWL
zjAORQ03KX-);r^o*|w!KB@v~a<Y3D9>92Ec(~EPn6k4Jk?bsD~b5U8^-oJM8=0Mxt
zwRgQ(b)lPT>j^j0!$z<CiTTK#S{^aM@{c-?wTU}LJK-chfiqEN3d^*zuKKPgfamig
zW<};5f9~f??NSfhe&qZYORTS03G_2s5`1jh6eh0jT!tVUSNKObm3@ZIOD|`C`*E-D
zU)qYyRk*cy8nzt!wqJ&Vqt1DWdD9&hRz4rr90^>fA35%{9JP=-F0DBnJMY@;87%u$
zkJq0-$twltjyX<N_i>I-$iI;^@iw_ByTrJj^gmwQJ5p{5rTC^?qn#J+Rxg7m>GqXM
z{CS_+#ZBp&*|xZ+FS$1NdQGlQOGPCEe97N?Ndq+R^bY_>FG>l^b47B6S(Dqy0PnoG
zZ(B|sp#i17W*6Is*$x37QT@?TZTKCT-i?>-`}by)b|Q5#VbP+uDQC_uzjovI3)99U
z$1{ygJA|_JZ}G3|57W;YlTP5*C>1Q^70|Um%#P3pejrm+L|I>9hoc`n6gv{%4tZC5
z4viGA<$S^ndhV;`yo_9cSDr!<Q>TCv9~`Io$mN6>_|=L270n8qojvnqKnnfB;%Ci>
z>93Kr`D$t>u>d?P%&x}n;$l!iak1mf$7&on>w7-ILZ$R&bU18-x~gi7wqEhzfcj3v
z%{bHnejyTP*gh8B>7$M-92~*BKYw^x)wic`a0tjY>e_DF3i1MGj`l1j=8mQoEME3b
zFlsnBAuj>gt-Xbt36+<<or9}@m++gvC<I{le_pe`q56x&%~tr0wt_O1grkcE6*mhP
z3)>qJ3@R!rAs2H?0aZ!qe|3jF3BR#+b8`}4W%cy*Wbx!=adfd_W#{MTXJzAH<=|k3
zQ82p#9NbL2m>pbc{$}!TK9Ux$W-c~PZZ?h%RDbxIm^!+<3BP&s=R^PZ_xCt0ylnpG
zOAfC8nigz=tbbZq*;&|F|EZgsjphHR+n<)dyZtq<zdtARhnaw~jhBU;j--vfg@Y?>
zXd;~Kd_sSH=D%A0XQ2PlQ~Q5<vh(u(ude^m^dDXS3`Ic6#l`|Ar9ZL|VHaZkuXq2V
z7h?S*sQ(b$-&*<WEle#U7(%T7SH&V26cx(v;o!vJWF^Jbz2FZsQFAo<e-EH~U92J}
zl%N%%(SIRVI7}n*VXc-l<Mrt5bFlj^ji<n+f~OG4e6>odDJiC&mh*a#s{Q=PaV+!v
ziek*~q~poUk#KcIxRtZE_IX9k(sL|J;K1MqE&>94P@tF?6%{Tn8X6MPe>@hcBJFG1
z9Q}m;bMqhS*FnZPVL|`Z^WTg<2a2&xl#0_K;r|EQe>eWk74w1(<^R{HjD?Ga;?2GL
zHRr=WjqeYmNvw<T<vC#m4-Qtq;kBBIg@}Q>g4Wk38;&U`wEr!kW5t6n<j$>wN%Uyy
zW)J%7%%ULwyqGSkvIaVNaen^bX0fYoc$rETF)VkTClnnI(A28%Wn@*zh#C3i_NjEr
zTo*GcTM-Q6Wi{fzoz!V>-hK2HwfEH^)=Ch8FgNBwb9gBth7ot52^M!ZExK8hT9TO^
z4^VkW`Ie;om3*7RGj08(J3qsCt7;?5J$;L2XcX%j5r2MSA}~DxLV3T(_4wc-nwiI3
z!X&Gg8U4HUjT>w6NO;bl%VBaJ3)vFo<=q~aaNL6p7cHHZPQ}BfIiXgToP(}EV36U1
zbmnPlJ=e5^;r~xQ41_@!_!mPn)YAn2uX_Kj2i+K$l58Jw(cu1<_W#`+DCRCkMYKEf
zlkf}nKU<3O!OM@l6A@lE?IYjvL5kb&Vje$5Fr4p|*tXPMu*wNHd$~fAQEZKpvd7+)
zwpqJ?8XtW3*%wQ`P5ny)T7PMP@ssD)P6>^czm6M7#!^Kff5x7JSuyv$y<rE(c|NBw
z^yhdOa7#m4f8_!+vmLjO-hQT?A?Km7bKjy_&tg&D*<xledqo^zvuGnkkX}{;q#fVn
z0yGb~Kng+h3^@NO^UVK|`ARh7hwHeZGPFC`Wzb)C-*mv=4kqw$2Dw~TSwrWV2u4;}
z1BkfZR}qu=CMX==;Qa1Or}TH9G6mdA>;+3&{bvZ87IRWsyDIfMS?cp6_x6P<)Dp2%
zXGq{Hq#p7^THL309;U4SrK0(HT<yj&gZt%N)0NK;ZB5lCBo_)YF!}B^eO>-*MJUtY
zqQwhx{~|4wqUq5tAy~mp__r!ZqQ%6q9F43E#d5TfG%F364`$2b?=BAr<<h0AEJsO-
zwJNio@)MJ}W5PBqhLdE$z^Hd+%8B@+X}ylXNhoC4;^F3O5k{G|$EV-oR{UeFREyPS
zrKP^YD>U&UZR%R%_bXCI(TCGu`D_t`N*RT8p2T|FxuLjsa???bYnqRr{K(70c|rsb
z|MD5mX!G@skTiFW<E7@wHXX#h-_07%c0YxvlxdMlmMNoxVw@%I0zB-h*FNMHjyw&@
zy`W{)Y97z68*}g9sjgGfGTf_cumE`|lnmb0?lW|YyH6J*?H9(GKCl?JX;X5sH_Vl}
z2e52+tHpqZ$)+6rJA^Y-4OM~!tj02mFE!Q4#gBmzV&dXKzV}Cs&{CtvPdsbBu6w_8
z!|d8GXQ97kC^hEuegh*kC)&!;*ko@uBUtx|%*xoe$3&lfQw1F>*a#&47A-eAd@Wm2
zxh~_92%9Fwy_5H{5Mu=Ahc{HpifHP^5rRNNSxxuwU)RGQ@#1Ry(&9E7SBX#D_mdvU
zE!5AA3}mO?rzQ)r6dyyRj0`U5Pp+7B>pp5rIsX0?q8NSy1}@qG$nJxDHDk0_9OkQj
z8mT+j(83oeW-=JGxS^M-K|()|;+liwXW626#<MH&_{;J<59d`#_ok*JHN!O3@e!W1
z)3|Mu){Dg{%%a!{|8|B&#b{-&b1eWlO}Sui_dJXENr3*Wy4{D?A<7_z^y3E7$1lh)
z%%NK#(vdc^n<|E85N-O$Ld8te-tS=()n)@jJs~gfqi7Y<cIEw-8tgCUw_M<`pzIlo
zjgFMWyz^RFnf4b%I(T%7BQDgIkQ#$lkI80Nt5IIv@}dM$qMcIB@=2bzOO|g&cS(62
zGTaVk72N4#S`_?k%r1KBV?}nS3dd3KsPYr_G<@smwBQ^y!*6=Uwqv*$B_m%vg>R0(
z<K03<8p4Ao4UW{JO}VKRqv*C~677QzntaoxUE>q00GC<@P3z+UP-FiUmS)i5c|vX1
z+Qi*e-|wmhzbtY=^qb6+<?uQ+=B{D?0l-N?4fHr#0wsXdwpashBQgNnsdqp4KTp#)
z-|d$|_6oCSSxkTwz23<N9<ij3Z7Los?ppc1RRLEER*;y0hg>GzwvS#;^M2PWkhb%&
zc;<%rH~w9x_xOaHz1V{C2hYc?CtN_>clrQGqRZxYI?MAGE2ACUlZc7BXEr;WLFqnr
zX^K+pU=*yl0m=aVgT8s1H3Sar%z6L&BS?AUYMe=LXtB68mp}RG+XL##w_MFP^?s?e
zX~#6ukw0q3Mf60TAI?8>I*a+p;aRJ_qTABeV;=&nxvaJU1d4kkaqxKL%O(gDZSo4E
z>L=^?Pp9RPhz26{p3uk&qmI%R?j}lxB)fq4bD}Q<R)@NKN47%v{tD^pGDr_TcJ9-x
zyXvmEMI4ALRCS)SYz2*rpBZ(UT`V4)z6X6hVB>v8$D<t#gh$-!Lc~c>61kPsa*FM4
zO%Ax5$~JFY@rY+P0~Y;!29i1s{tTmFXk7I!eK@odRZAOYX_YUKPkn<Ue4)HwAiEpH
z?y-~UIn;4~T#7>6;`?|x=Q7OPIEcl&Ol9PMo4;?3QtyM*VA)q409_1@#9w4*FNMx}
z@DSA+KV0rhq4=<n#3Q%oE1*<opA`C~y)~$jUrM(5HPemsX*h{_hEHMA(Qd%zQHGSQ
zgV@K5Z6u3R4`aW1s&AvqOS{S`sH1XB5+_AFK*MW^H{(UgDT4$uDIr3njGz)P%V=-|
zDGdUTNgMk<h1=gYl`l@UY$g}1yZ#;vLBk;@eX`9c;&&PW+qdWQ>}PBKP$6J!h<)RV
zCQJK;DquYbbt{C>P`Nh~|H>?yUs`K*!J@(QWc9P#5lnqO`+Th=g~&WFdhKxxnAV3@
z8Xb-J*8>r^D4%b_9-gi{wUPmtoS1hDwN|CEvn3jg?){F_MX}XGk;I&C?=mnA07rG-
zr&*mS5RTWv@fqC^={$6SxOq`c&~q7M>M~g{?PvO$FA;_C@%X#51q$hy3g#I!wu^N(
zcTYCw+oSk&k-HxiC3|VDcZ0RyvQV2Ia`6aBhqPy`EQm-Jrz?#_Z6<PpTI#?CVrip+
zDQseMX-=`fC?_M`7A^UzK08SB`QT1GrPf+v@J$gKt1Of*HDAnP<B0xu#P5=h5c_H5
zj?dd|B&ApaeY{>9rhGa@b?4?KbdRLHr@^_tril?!#WIRC)SO@+TX={tmbQ0pwI>y3
zEc)!*?9Dd0kbkuiMj4HDEJNw1j#a0oB&;#U2gcl6s4d+8_EWCQj~wy%hXl6D^-YCA
z>rgO`5D{_X7}K(4y8TxB^{QHCjOdfs?d^C#Md;<}MsH&F9jZO=M+%=qc49uK9DS#5
z<jKx}0C%vJrzJw@+mo+G&i(iTHqM}T$welQ5k?OmoTPY349WR6zT(x9CmZ=UPaeY7
zJNqbOW!oDWa`yzG^AXOH^9>oY3WpWgLddgg*((@|@TVgS_yVI7tTrjZx9byaj&ucf
zEjRy;?z7C8k|%c@BeN<SOXZZIQ*!(Di$k+f*eZ&vop;j#tpxQy_tbyK8)BrAQH;C7
zZ2uC{78A0CeI}dLUQ0D@O@MPyqcIm*JkaZ%vE&NO6MdF6F$TveM6$~pCpHxsyL@-v
z;6#i%K~Gl!hF`-19P{<EYYfROk)31PQz)71A^Ydunh$<`FJ+4K9$+4?;!gCe5AQ>o
z=LY+7s!$j5^V;)MV^z&K!=;W@=DUd_J~tVI;{3<T&Zm}TpOf|)n4{Ze;1?1+Zpy7V
z_1K*d*SY{GJ<n(v&0H^@;Wk|@+RoHxU-#*}w?YWgg6FfG;`czZ;s75tt|c8cEGcRY
zu-~z^?7q2OT6-#phm-d@ReVz5UCCQTTmRNQcE2O<KfNWE&ADyIWL`f*3?>>(;H+R6
z9j)b{g|8tY!TyJ1z>sv#5SCjcwSHq|^ESzflrTD6Xg=|$rdYi7>bAGSw}*!g@X5yk
zj|WCB8zF=x=|Vtfn=kDz#xR5g-+@(QUOKp6?k-65o0frH6uX2;{G3*-^8H}`NIjUi
z7d!dlpt4mF%R5rY<L8bb@DCS@y_$T1>eI6U%3+vcSDy6I;IP6i-|B!U`Cal_GR!h+
z(>mu2eZ+sc%zup0@T3*KS_=p$C*}1sM5W51Io@w7Ut&MennG<O5$i$>RJfi1InhOx
z3Ilyu9ufg&w`ZF9a=9*2X6rHM;T5G^M>s<Ny}rb5-zY;+1VT|+_HaaQa$5YBXq~z6
z5JT{dc|OyevhYxkYMW@J1hPlMf9e{&Z|r<5Z@ZY3gD$&{7yo=w>3BnL@={dVR+_hB
zr;RRpW4Pq9=y$I9HV$V<)SlM~=_7mJNy`BX|4xSM7HmH>y8>p9))C%$yrv9zP~mMq
z07qIzx|;vuL=ise#w$t=^bzj1c1m1x?0lT=a-88vtD5&dD6h44Y82hg39iH5s(lr$
zGHU5vHeDMReOwpR^%XuS5rB|gcn@RlCLyv~Pg`ctsHt@s)=UmD@;Ty8ij^obMzYJ$
z)9;=^KKqB+<I})q-%{UMuSb5}xnX<TS5{><A59^wd$sJU#LSWV3h}j;({is4`5zZu
z24q4JEuAgns~uX-mBHcm9$mkJ+}m(QejlZ$jZ%q#kBAUz?FXv{oOYv@Qny_j`n)*t
z1D9aqz-u{S+H@>L-`|}0Ukkc356ooE*wenVd((}{=AK%i_Rs)Vmuj3IPuywgEn0R5
z4j+9B-#TTqqj`AymYnf<U6we=+u5%%3NkUgpQFzXYSJEXpMguZUBqt;3r%99XL&EQ
zb&gumY^^%4T00TL4jC@qSJ?D5P=y@~{$bL^xJ5iD-YX*|p1O=YOLm;Mmz4p`b4CH}
zB;8H6_o4Wqf{a^DnP&Pm3&g<N2-c1w=^D@lfQgm(-eFDi>goQZ^!^$arwWc&+rVQL
z$4z5P7`5fl^C$k+*7HY~)tT)TcacjV62gh!pWV(~Gv97pX!L%vPAZRq^Ls1Pu1J7p
z*FyPI-ISu5r-vJvsDIlrmm^oxcrfZo);TjE(v|1S63jo8%YM#CUlK``cPSe$%d3qW
zr}ZdH0MABmMB80F8sDJq&)~jT{Dv3mY?J;2h$AiUyN({j8=^3u(0~m#)7SOWXqU~{
zGimwP#^isUHt8vmN}__sfVM<I+ndoZ1D$(I1WH@O$UT}IT7cnvYg{{1HWLBoqrBcD
z82oFj18keZ4ju*rd|8_ve$m~1;cSd)ho8oLz(lGYKfBo(PY;R4dFOry3u%ri3GBTk
z7H}&f^S{{$7dZJ@t9QQziqH4CKEB)I;ZdVu(ykIa5V)E*n^E4$bP$5OaU-_h;<TH#
zt>&=sALOMWHDDicY&(<QTh6bw>s-0o-wasuyME8ja$X0?i&vTip&23@;|bHE-Z%&b
zVXaSuby@Xp!?y9-La_ZDbXrZxCcP+3;L^ZbOBCMBX}-#&r}a?YyQ)`E`hJ+Tld19f
z%;S;_E<H;4?SPo<QhaC-@y<oQf?a~T1B*8)Y4Z>xO}48GDDGXz4t8&#iJ7x(!YP0$
zGcq<gDOpJvxHA>E6E^ZSWmdeSzF&zH3qU(%3Z49tNC(!h+tORHsU8m-(eK{jCYhq1
ztA^l_+Uwf6Y05xTe7~GY%lu{qAc5=ohL|5N&!;B0bgI0eQSrWH_udQ672f4pZ!>=v
z_D|?mwH(ebnz}GIxR?%e8g^pma;9xLtK3f9)x_1&4x2JIrufR?>Cwxj|JXR_b5`{b
zaqp2iwef3CqEwOUOxhVpxH+me%l*#xB7k{qvnP&TN~C-4-O&4$N)Tr%ZU27bJoIsX
zXwo!ZMn_j5J~O29_sewo+;cO=a|_ZKB`q@P4zdH9c<?#xUfh64%*@HukDXyo64UIl
zx$dEFQOR|bOlvptNx$dWt3x)ee{zb*;oZCECz3{;5w&M42{5M!iH5QySKJN;0+W*y
zq=>sO>iAL%C65Qp7pf`sbW~}%XnC~`Vc3xVg}=xJQfYEsqF!3cj{-AwRc%Wc6i}m3
zL3!`yFJMG51@elDF)JOI?2|qj*uu+cG=`9+%XcpCmMsrOj(bIpxmEKquR&omv9+)n
z(6nrU4e58fp>%wbBY+|xk#zTJfpfWkss|P=D}Yyl@JkrkRK3O!JQJV8>M)YHGE7R(
zW#_vw-s7DzKC_R|u}gB#<?5i1k&Zkc<w&fEN_=Y~6TL-G(RpDOA3Jovh9trdbl4{X
z-5=Lhwml3D-=Q~&2czuWZ`*AGqj$=3auJVD%eKwRdgWGpff2HBqU%>ruzgp_S+Gdn
zxbU?;|7C*OjMQ^5j)*daW<#szNky5~<1vGca%Yffk04|^_=Rwt?^GtfzeukK6hv40
zJ8D>%anH9Q3WVL^skp9Y*U{ZPr6{;(>1I4Uc09Nt?7P@?e<f0mF^?ATfS#sWoSixm
zNT_3M>Nmvl=tZB<9Ig6Z^GUvLbaC*lwt|h;Ee-x#l>c{c^eIM+{CeHK!EY}=;mZ`o
zjn4NhVcUjj#&0|x5mL>mq3*VG#U_K;I|SM<fgz+?j>|O*ud#zklbsQOh@~?&C`j(6
zeYM{9m+AVLjOMQ*lT&8QMXaP3cTA*6Xh<c}uA6oI1_mLnPKYR&69)4-QC2f)8ZwwX
znr*-1!NpSrpCY!I-K;#~hiSdm;mPgIxZ7aoUWRyuQ*H5lihK2pvu+84vejR7DP30Z
z@;47@-mj)h8QZ<DCb@tDq(jv`I2F^wJJa$k?3ZI{+;R|T3A|Soh`jVp9<z^AQgcE2
zwVvR@9%Y+74iatDX$Dn8wejd?iU?|0ki&<9u1j=3#pk&3eGV@dvVR0T>CqAPP`rm!
zePq89$R2=r5Q{&0S60z`5248h51GZp=P#JQ01vP6F%xL^NUBEHgEoEpZtX|=t!@c+
zXyyt*VU=8bKzA=N6)VkJVNA%|SGN;hOtZjA(p|*NAj2FK0X$YOF}B~mJEsgI<ih1q
z{#n^qi@I5|y;!Mv-0Ll7?s}T0s>3&?6aziCLwuW^1(|K;k-oz;_ADxG5sqfc7TyBZ
zse6_Wsov_L*R?^1kx$j7N%6Ms`Fq8`+FE{6r|3s9b22Q#F=nrR+!UHh7eFg8a!a_h
zb^lzj*C!q~N3NG%V~WFR<dme}*`-9c&)MA3QB9UT8!ygJZC}=BPEiS}<tqTa-Si!Z
z&yS&_*_H)yKAB_2K;AdJ2PgaP*BOJCqob5!%8x%lzm3GFOYLB9-qCzwSaPyU5duca
zF`}f3@c2g=(B0nLSbQ5_pnUI6!s_c^<xPYm5|tgj^kV{_pWQ&kLR#is9sf&HViu#R
zS%=nRBDGLLk}n3ezW!4)TlBWCVn&=*d_!B;cy-^4`s*<t?U7C8Po#;^^3LMvE+$Vi
zyO#apm^(Jt1tiovb-#H*ibQ%`P#IIM#ahGdb*Y=QXL)(c!EAp7`b=rIw14UT9hs1J
zov!FfMKvata3_8pw_5rHq(T004oax?D53I)zNi+OLggoT5Ry~dK1j0ZEH)`4v08iy
zj{ofw3}z}QTJd;NGk+wHPhIAt5&UV-F%mi3zv!6iYul(~3kVl|<HxjYqMv18;FeHK
z7XjRNJEF}tJ_=t-*qHhtdc+~0)IV`!133$L^{^N%Z`41|94d#cQ|+siv`tJDDqA2_
z!1@WNbQn}6QZ?Uaxwjuvym`U4@ZOA3p|Od%jwXgD1v4TstbqhOZ|X}SZ@+VU-b!bk
zv+=EscmJiHfntYJc1WQSug#aJU`$0FC`S#IoToD{wQ_>zsd>8U87Nkjkn7>4ulF#g
z@gvw5ay!!<543WL2Lj?&cf+Tqwv3B<&3yZ4@kr$K_+2zaQKbwV3q7;PQIG+^WR0xi
z-XS+-t2@Mn0=;DvnJWLx0QOv*H^Mpj9jd6=GfF$Jv<<nk9ys<YT`&?6)b0|LO@RD8
zFG{!QF<`dc0CkB5pMkgT_OyAGP&=0=3DR{Jafm4x<$x2qS!Xte1(QuoGAr!mT3PSH
zg6zBHfc7pHQhUW^Kr413;%3rF?%ZlVwZ`98*qo;1u}V9Iemz{ovWtDyd-ww)*ysy3
zE$=?oT)KsbSW1%)%kG*&`lQXOjS6gH=B$|%C+}``?z}imvl4{BO2AX?tjid3J+k2t
zf?s*Q&+|&8X0)twSu)9qI5Aq5Z@8|YeiU*8k0*Q977VjM=f#^-bG=NFN=R{r=@w%-
z9qao6(W@zjr733ogUB}`M#~Pi+3>r2RY7)@Ssz>mjD-lS_G^7RSNT}uKkl295!|-{
zro6q0I?r?;3G|xD2^Y2_Re@&JP^zxdx#V1Tz0`hU9a)OeD;t-*a^ZsD{${u8{fGG-
zQ0mD{_6+vG`gyvD&e55d-)-|`M;jAA@JGT9`9PatR<+F`WT1}t%U1RHPV((u3NW5@
z2k@O~L`YY3ZL0uMOdm5YNg6|L)lYo)HY&i*CW5z!PNnd-4rY7ySc%aKJpKZ;0<*^k
zhUNf!P!$j=Y>>eEe$Bja@FS@VEe+$<K3f*n{<U@FoOO9UyV5T#&_fZrVh(7e&&EyK
z%!0rcALxjY*N>j2?I~kh!^CrS!zWeQH1bWD-@Mqjbjmb$RL!sj=4lzI3u0}sMm5~y
zL_q0G<L~HFjZ`}}cjF;s_uagq%xgPmQ~u5QkMo6>sF}ab6Yssf7{uc-2TRl%+15Tz
zTF1AM_EwLYCTY%nFn@#2jhsBW`SRu;&aO4o$`3M)!?dDKs7NKu2;Dgq4F-6xU;G7T
z{>K?$`V-QZAVbDhVJ_X5^vCUqaBBaQ%PErlL-pM_tfYhxp{XhP-EJ0~#Y<eTzmfR=
z-6Jpzme3Jq`%vRSt*l9ST?3tCsgtn%2g=Pq={0Ax5GYB6n2uK0@Q{&N5Ej;djSvax
zQwWTs9I~Eq2|8MWJ91mu12=r{MCgC#1#=#7i=g9WiAHK)@&d&`xW>qBOwu0O;;h80
zGqH;&y>(~GNoOjHS80lgI@c}k2Q}0A$I!C1#RQG-g7o78aW#zR^NU&(N9;PgMyi0*
z?aebtl6u40cAP<~_PzU0!T_6W=F7bgEmu@sUPTVzx~m+cdS-*2I}zqn*=3juwr=$&
zS>V>JjHnoOKUx~J$$1?ghWSC(DZ-7OSc(1f^!}!C&fNZS<w_JQD1;8HxCVO9(JK)W
zBmFLa<#FIdLe!o_-J?@o!_Njq5wIT@YOi5pcbQ~mcf|O@^7NHF!CsvZ)D<~<u26Q0
zk$D_uD71}lg+v0R@_!GzF;)tE3I_mYNVgJj5Fo-7Lo%gQ`5d_PLsU^?wiSelKV@%c
zW5>Bci(~eZl@S)|)BuV^{V)y&-1&DzyC%5Px=2myZ&^RQgYRY0J~huOm+Q%r(jySA
zK?(_O!>#HtYOqUvxB<tgRvNZr-RRbTeuqWEHBZ&~hLZ?YE}3Pf^XdqOl=>1B7Mj4V
z)Y(kcDNZ6IBmcqdZI+v=NxoT-r%!V_t_tbYS}L?ng2IL88*HX%9daviyzQ9a+jmA%
zX<+%Dna;_&-W_;4pjdAxR=!HEG)Az|G5A9VB>}T;p1?#Hfv@4v-uU)ty6s^yu^&gM
z<?(X!q7k!R15+}4Y*}vJ09QKdkw&Q|2$p=vvtMcuaFAo5i|Qx?3oBR-mTDtCoKqc<
z70oXOU+f2V!3+QrMyWNZOPF3A&OBD)01ic-tk7{|9p6dQB3}d_7|*?JJ-)Tb(<;}Y
z6J^q8u}|ktu#CbmU)@ZsS=^|S9o~J4%^TnGYUr(TSP_`407$&!B@f@sY<3FaDl*bq
z<SJj@Dfs2Km@<rUvPtU7_~jTrs6?zQ{X6q$S=kExA$$`|8lPWlNU43+%M5*-uQr2R
zOerN}lM4*OVA%`mY!1xaOpis`XpuW>Vj(Z5wm5V}5NRUl*T`g_|0%%bw;0aKx0f{L
z*?w2CfAI5ybO#2a+A=4?cX+FHE~ODJqS{Y~3$+l@=i8V+z@&fm!`)nG(jUYV1)FSl
zOB(p&EN>>hKZBpm8*@zXq)koaW*w1RNUKbnx>Tz&=@0%{WzxH~9*mR3+_=nvuN8j)
zmdOHLxwEI$3B~7TYmOYJH5oEWD5`Lj`CIQ2F1jq)H-fg)9V^U07gYjLtD+5cGZtrm
zpiS1c(=X9dkz>MF^Ai47wb>A^hsPG_7z$=BBft7(7}i(=OAz(h2ZmN(Oi18>VCX}+
z#t97Y6lf6iI82eU@_sob;-yug|DMp$>*pa1h;?~58({n%6LYq12nzUeMpCe)Ln=D}
z?@ewLLO7+qpok+{X4E+bDrHnFRMc3uxrYH8c-f2-cP_peaTa8H{6c2H$loI5Y{p`<
zeVH(ogW{5HZ~#d0q~uba9T}TLQ|31hHV}V<`gitx9AlMj&`-^K1?hQK<5_2XWz2lj
zKo1v@iRWR>@>rua-Va%s9rETOJ{H4}Rs<%uy_tfJ4Boo<j5;Mpo7FsjAr`9NFO*UE
zNM%H#2g8^=C=1zW7U%Jj3$^_lGB9ak%}y(=L9S_w9M3a1{#GOM3wiD|3k5#uObfGD
zI%6rv{wq#^hi|Jc^J6PpvAnc{{FUQB`Wug_M@i=9WHD%&AFg!u9@{?Ff2`HZzEPDP
z)n#Ed>WEPE!hH7T>90{+!zNi$`l{gOcH1e<pm?1vsLm<hS$6eq5!*zQ|CNx_L=5<Y
za4xh5T;yP@<d(2E^Nl_}kvVy!%v<eJwIt&q^$SKs53MskiaoAA_~k^Y>z?kOQ4<W%
zKqVIOZK_bEW<L0(B5)SOsD#vY2g8Sc7(GFgp|DKOVD|Ha)$M`NGsE+Bz%w1C|IN?C
zs$T5R(@tlNoq5*JKx3QS`uUToO>6CD(c7(f@QkIaD6bB|J%_AbNB^#@0C8lJ#t{ss
z(2qI~c)nlbu$g?D%3%@Sc7NOog&kyva>RqTz%e3Y%VaOw734emJ19Uchh7^!Azx&=
zMtRqm0`8aAwpM*kG!V}gtg;hD{O$&AE88ve1fCRRN`pUiQb6Om$O<8;#>`Xc0VYOi
zWY+o)k;yZ1@_}<Ppho|ncDGPSkGV|6`tbP_En&w_c7Wm4lB1}>iU`a$^Q<bfTt?Uj
z24U7q*vROu&Qulxf^QSs6~sI*%<}#ujGv=jC~Zf63rpSomBbnseb%SL#fGJ-j?P7&
z&SF0Mri+BA=Dxl~a@!)2B7HRWCCE*$AMnCbi+<0!$alro*s{qnAh8IIcXy3U?yer7
zAg#>O%U0eVW^ad8FTgQ^0kBNq>tR@u{Ieko1%`ryzLu%44uHY_)6%3HCvl%tVrfnN
zX&AZ~aQk{KfYtNymIVM?HelBrf*uvy^b!7>-VBI~<UI;>Hg#!tExpk3onzC5e>!)f
zYb80Or|5OPQ-MV3iCpWjobPdqkl-BL@5uzS<(6mj7EUq>di@b2+H&GlN-rk9q!GZu
z3<N{Le~};S-kY~tOdCwQd)@Kq3OMUMx#YV<okV7|xE-3&U2e9J;4iR^Pf-`fm{<YT
zU#Sw!7^KLg?#;B?(-2RumUqc~9$XoZj<?PJM(dTxOVeJJ|7OX<nkl{UyXNwHO)|31
znd0i<MgffK?-iFX4NL%UX^O43@HZNfMa<vC<Q%y;YqlGwQM0v_K`|*GkP<gGr2KSc
z)b-zYYIJ<52!t<L8~$E^Xo?0Ol=el;ZBvFYAysrl9ZZHu`*ldBtAcsSv9tYRe}+*m
z$m3`c6fJmM1XNn<@C!*a!h~Bp%AB!}%!AcDFtV~VmUUEm;6%A{ppd#mhK2c4(6ckK
zm9fSB3<E|+ls7DXa_D^Y$QSo#nwklIiAH`gT|B3x#d##gQZYCk#4ev%=Ko4h@Dat7
zY%SpV@iW=;MWJX^1S}>M#J{%60u<=G9&xwyjOD@ObT_r<;(06Ml(y|Unh6kWzAH+1
z(H!wyai43-EZaP;pEGpn$JZ*_xPcjZ@j&2Scqv0A<+^}F#p&SB%)UC?r-$1bbI_~q
z%QR60fbr5VsTYql;};TELs)8y{RddIgt|@;-#mYFF*AF%6?&sKuj)F76<u~4SbAFs
z<;Mo9xf-RzPM&iZ9f}|j9^MbO4xfi^cd}Wv4BQj9Pilr)CW}mHGPwj`OZ#>Dh^k_n
zY9uO}AvTg`FDyNzq;Re_SI&k<;di03H(eaRe0_bNH$|Jt72`e5zeC1<P*z)KM6`5~
zOF3ApC2F0WkA=8=x<?75VL89R3u<6LmR}u7<;?$1%FC?(94Km*AX3NvNjNlk0~3p*
zYi{eVB^4W1#t<9u0H!><MaJRFWi)%eyeHZgj|P-k``tOAs`$7YWmuQ!&0qPx@%+_7
z(-h}Z?7%#mUvZdoFZ*TB!9lcLDJgNJ&LVOYh-w>0r!KoLAj?%=%+sk*<mPDcS4jPC
zn17BVAL^+G)kLzs+J_=Pw5D2_?Fs`{RQp-|P^{fJ<gOeMa*LrJ4e040zsps(ZC<e}
zV8FVc{3#51*gdf&*cEj^IAcA#(v_h62W4`(rq+q(n7dECD_vom_@j5t=S$u8x4dpP
zFDgE2HqAWTA-m`27#es|sHw^jFdH$u5;PzDa)m#cZuveNqbK69g*4>v#x+VZ8-K-2
zBHJ$iRHAW^=e~pz&02zZn<^Is-NSi9B6;cTyIvt|@oeDDfa6}~=DoSyX*Z*r%Hx@;
zr@`tZz%^?-5{Ga575|2|VpTy`3NXlb*5zN3c`UMd(Phw~0ro!D1q%UZE8@Ks;ipJ-
zUszGJL=A0uMdj*&?{Zawy=&&{8B21-4!&ugAEjx_OqewEHg^bSFSUY@ZA&ZfNeX+_
z3w@ThvGPLBF|-d7(vS4_lPmOTF315$2ZAF*&5GN<AjfIPZq{J^OVSpRY8Pt&zVXik
zy`d%kk%KwLjGT3ExlB$@Fd|MUEf&L^dByUg*xQ#E1$a^8lCluWNoS<imzo_YKY>G%
zIc1Ofy8d8r_hoO4h&oLaYBz2PHatZU`cpq?Zm2QN?w@*ry3Z@0#`y0uQle+BZ7Z6k
zv2-I5mLWL&Te!E-VOB`<PSzAEKp(=|y!EO(<>s(<e0senBope`aVt$(_Xow_h(Qg|
zU;Yz@4u;-KcjqS?mK^uf$V4Y38+yx99)@9&(h?fD#gZ8A@&Tac?lgbW1v7l}o@;(H
zm&r3~n8rSyM7lsqX@=5mLL=YmLs*T1dFh$C86FKe_~=jBLuJR^C(1x2!6UAphCt@y
zNutrF0<h|0tEXeXBy#>k;K4q3sRblFak5F)d`kiaNSEdHOXy}j_)9tm%OjCpjPO;8
zX?S-ZWjyi#xn~AR?>dk{G@jwlfM?c^U}Z^F`;iW1lV%UbaKSI78IRI7+Yp`V17PJ6
z(_VSwIDG4H{IK;pGtvE{zdaS-FH(fFWKVE;gcwy`_4E@QWH;nx_%$*L9x#E@j>#l)
z#S0wuxsQW7m{brrGppxVE-;0ChuAEiX3X@9l&fj9Rv?;Po-K7&X}y0)5FC2eO;pce
zdf|NwNrv!t-bRt^5P8-G`{%Q=TN`?xNi`2fA-2gl71Nyx^z0R8vNWzaw{D^mOe=J;
z{lYnJ-l0gu!IsB1KIN=nXW4r(7V;gB4g<Um%aJB$340Gkb#|(VJl&W<Bv2Q_RvVXH
zBKA4_?%WcG`3gmVsD9So#R$g(PF?*gZI~&Nh*qbFICmt7Nsq5tIyYU21<Z@Ch%0d)
zHgE!zX^118LposfI_^E|gETSfzD*RI;x4cvk^`dd-aS|>P<BLeVlL96vdRF2&SLRo
z$Yda9RC;}5yPC)Z5k$&(5j-}1r0~9>2Hb3fnK9uA>{9iIY1yG=+<PwjhxtB^&IzD2
zc})1z;&k7_^pah1JQefxPwKjz+Pc-`7jpgj0FZyD>xBa*Cwd<vS-|bE(IzZbcj2b(
zmLEHZ=z=kK`J6^wb`0fqW;;UkRvw+Q^w2>110AV%xer%sxm(}A%O$i~9o#{?&h&;I
z9tHPqx{KP*1I!@LtEn49nX#&!(GM1D4$#}Lw;Xv&di?g!A}$AJxfWTRuN*~Oi#S<m
zBrNw2r6!LSH=t)ah~{Db=|#@>ot~YExfI#!39fg1!S@f;M_b{!jqX5e8l5J$#0Suf
z>t$;N1+B))<z=#7V-l$n#oKr*mLzh=TS*>URjp%yV@t;KA{Vhc$art9G?OMmvk%<R
zV%CO$rqsbN{<OP=)oGb>&Y=A!*x^puVct~@;CCnl*esQL6SY5BF1n(A=Vtq^)7vg%
zM7s?ry!%bRR*sJUq^dkWpVQ;S*EeBAuSQqV=}72sJbS1qgfTycbCWdVE>6g2QPX8Y
zam8#7FtxEA5R>q(bZ(fr5GzRuIF?}aWLu_R<wJOTe$Tlz++3=EKRfK`dGt1!tU9V?
zwfA~*U~RZK#vkvXDpN10^Ps8Re4v~s_V*g|kBDdfPZj*XvW1>z8(juCYq#QCJrf;{
z(udw%$NUKJ-@C(GCY?(pj6R^dKbg?DcZg1RYPzjvhlVNbNJ1Qbty>AFfkp?elSR_&
zC()Dd4R3T={N2-{ad_#scA0etu?8-aDKd4fT{~|5gY>UXi2#;0q?8UHu{^Sf1DwES
zT7_B`2DS#t>h|lQK&(t1i{}`83<_N@Z5nf+LCAaCz$K|{Fz#m_1dAhZ&FOguC(CqM
zL5T7tT1wDZFV7qq=KDxo1jf#<u#+d~0dUT3FDggCs*)R?D2_>p>4B*jSx=Ykt7-sY
zf6exUCS-md7_Aah%6{8aYqq@pMLy>VRyCqaIT35i!JY;hpaKe~hPflmDPbaUzWD)S
zphDp(1?(nb)<L3pSCw(#BVSvm7<YNq+ESfdyqXt_c3I9~Z<)a1T|cO3Rg9A0wA_$;
zy1lIFQK37>HFHZa!^LAWN0%oH+<<M?nV%naMC-|?<wSx+U{zpGd6jRC;e>Z1eKa`%
zGIx80S(?{?<@Rp4jOJvg?CuHw?NqDjz=0|mkx*hFJv^9H;^o77u0d1i^Cn^E1Ujzu
zM3CosjKDtqOcV%5e}a9;uAfLiG?09U|1ngNXfqPs$vglN(<u^@7MCrpxnhE@PhNN3
zrpp|y`z|!a1xOr^DPLo}>uSJGvYT3E%p@U~*(Nde72zfby-tw-mMB-=d}1IHD>|TN
zPO0vZ$DhgaORyhe7YfZ|MeA|gD9@5SWHM~Jzhv7n;9ifM&UkdbWxeOc>jP4UCh;sU
zD0=^wc5G`=h}X+?@vf$=O#jo9wa(7{kd04l<VC?<0J-NN7%+pMk$TfZ6C{gD%j?j5
zOF+&`<g`21EMsIk5tGeksK^g_ZJ_czD%JUNjFZ-juh6R>`EeI9(t7bz`t?$PXn!0!
zo7;{~$ae}_Od?VF1gGN-bhCKIfaZHvf$fwk;}H%DKq3mcH4dK1g~>hkk^AO%kq<^d
z(j(K!7<42c%^WW6uT}qh`^+{3Pf|P#N#>jCD-PnCP9N^Cu3_ijt9P;y>*2z6SNnRb
zF7L3v&m8M-;$J|<l#TW=H>DV7hkSA^2ag;_64V-O7!^oo<}`kHbUqIfM=@^I*ES@2
zj*-57)Z_lK5^!Jb%m1!r;Q8!~9U5@0kdU51EeNflhGt9fCf8<VynXhvFJRImUs1DS
z)@6?0mYSpRd}S+I_RDTdRcBAL6kVyES0}n<|3$O6wG6TI88&pHx*ZZtXgSD@<43J5
zbl&ahaw*!8*hyK_?CX4sdpls#zV#HK7{8Qxwl-P5zv?<qP%~su=s91OD&%DDXaj+w
zu`s^WmqrN>8LN7->rwS=cf&gwbIRs53(0VP_F4c$XmV`slS4MC$+tEK=!V4uh~8-(
zZ7h2BD>u6gM7zuw(ScBN1C*ONGln0Eh=<7xkzM-zg)NkJj#oQ$f|h;U9QS66n<sNV
zP~W{#+o?=&v}_iJ(>KodTTYIEL=Mn_0KGi%tWS<b`kgX_&Bs}?(AAWp=*G?z>U*Py
z?+<ydc!1|GM_Z#tI0*&F$60PysL;p5eun$Y_rvBpf--|LQ%RuqoePhYnb@h5hmTR`
z$z!{$y{yZMiY}FGWTyeyAi6cl%2b1!^^do`U^NJ3#kjP7UGT$B5%bo9(oU=AIbE>>
zYrsXO{zKuJ%XQGo>vL3`wGR{k+i0@e##o0BQd?oS!+9qsp~KhbT7&6UI6oT4NvTFx
zw)!0A0!qNa2N4RPMO_$o#Wfa!4s}A9E;JS%+4vL`ruzcPxZWW;P7We%rGve?T$nX7
zc|PDSi*se|Z_z7FAdq{k;-nKM^Q9{%u>W<k@bL9sM4Voc)W;bNq&DYdB_DgC>Q5J-
z!tC8(Hb(BNiPV@#F|`h~`R37JAsomBa_I2Q88~FddezE2A1@d8+5wYJboX+|!s&56
z247BdrK#X-eHB)nXi#p}pByzGnT_hF*^FLRXCi-Er4$$wC{z(k<RBDj)`rVw&He!x
zMOrvObSjU6>)3kKkTwub3M9Zo7?ee^<%Oc#aP0=EeA&QYA*+oW^*Jb0eq}xIr@Cjf
zcZT3lp4NQ}>~EW5yl$#&`h)G|8LWCp|G?NCmt--<SxOX_ZF8jk{<Ie-S%v~2S<!y1
z+wQM6)`u!|LEciO8=>;`rKIspGX79vspfw5UXZgzJYMJBOyz4I%(t{Z_Vk|EfM~m+
zWUF)I_+1`R=M-WDkCvfA6!`LPRXwBT7@<FrF%>aC?y}!Fp%!BKi^kH9--UV<Am8E2
zE4fgRTu@32NeiqZlws5kMJA)*sn(IFu6r-!Cl^-`jg=AUtwe^wFcVEI?cT_0;40QG
z0=a#k(+B_S<CRNpjK%JKereX9dUFnoYj%hI?my6JPuEl7#xW7h{ALez1ggJOEBftP
zbpzA-w@x~rp9H!QERkqc{GsvLyOJTq!-%L@(;d0b6ptsli>O`$hppos9Nr0f_^$CJ
zcDS;7UX7{FU)dGX(RRD4>j?!S{l}1VsYm(8%t@dV!CIe)l93jHS@8N+URPbVfoG#g
zp;6AX?}-{I{Vrmmr@h(|j!!9hh>eRRT`QufO;1~{qs8T3Gg}^JY?q%6jT|VsjXK(o
zgt>`lo7&hTW(RM+M_7#eGedeL?%pFQ`LXqnZkH$RRIW;RyMHqr-J8lS7xL@Zg95+p
zsZQi{=sym*-LAZE-<qVkV;m36vd>dX2i6SqQ_3RlU?^CxM}i8*ELL##?^enxT0B@i
z@sMOh*u`0di(B>GeJ>a$b4Hgwkv6*ccU{tK-S}<qjh#>a6!SQo6Tfq}WnQ|nrm@%M
zO1d3;Y^mh@df(K^X_Unz;Zq+Me|4Cz!M<&Oc<-)9a_w!SJN~-~d8_5vYEPi~F*$ws
zb__XlV)4j2;NW~r4+P2dSN@9bu*oaLIjWE#eJ!!vkt_)Auw9~cIdl--QfpLM=}A1;
zs%vjlCN2o3F!v`BWJ=@b_4?AX%W6dT{AeM{@1mXOITt+&QOX{Vt)3zPtse877825Z
z>Lk|#*%#s@VKb93P*!Y(l|RUD!$5LmEYfdf`ix<h4(~lPxg|aG1FjxH(2>@2i{)U^
z#8m@b`thz+a*NFvM**69y@jDmGr_{%_~h$kiK}`2;l{lgO$Tq+l4<97l&{U_nKHTu
z-W*LvGX10@tt}e2N}UEdDZ=1=Cy@*2a$@Tz!fOV&jaZ7KJTP+QupFgAV)=@tUu?R_
zxT3B;wWx+=j-IVHwJ9(|=Zd+(iBsyBIyh*6QgEP#l(`#)rW#oWg7J$6<wZB^cw%y1
zbr2Ad_2lCw%V!jCRoVe*C4sNHkzyOJriN5($6oVh+}|`9C_YIPQJ^^;bx)W|MWYHu
z(qUTa1r?m|ogAf^>fR4bROt>gU;~-vc>WJxcNx`I{H=?+I4w|0Deh3*rMQ*?rC1Ba
z-Jy7JhvF{5-6`(wE}^(XaCd_HUEO!wbMD#yJ<gYW${=KA+5D~fKGR@1*vfH!uV@Uu
zABizq8UbR)&TvE`0}k;GpBQ8}`~@^(O#&^0IMSqyby^TL_R%-t>dt?;7Pyi5t}DXM
zvhb?eLd{UXxUva~4f|ca4B(V;w({h?S5jO0aeB8CDl~6ELcE4sYM%IAh~f1pWFFsD
zm5ZX3*@yq23E8>UUlX!2b~<&CW2TQs3HN<9?#ujp%JyXf0~0r#DBKC&q0wv#4>wvY
zOI{EMAA9XvmfTtp`)d|_l%lTnr(AoR75lNwKkL8d*OPtWBF_Wh{^oJ{#{Wb&UL5-+
zP$9eZX0MN|%OEpBxYtFpz3Ev=9(p9;g9o>X!(*evm}Qk$gg0jk4@ySoxx??2wH#j!
zZyU0P-zVV7{w+u(mFWQsc?6b(V^5$hCFm-@UFCd+Sm5#3u3lkcGoG<y1*GyaUdo!W
zv*2^O6pcFr<!L*o7nK4$PAsg4kKHJUf<GHh<hYO&{t42Hb4qvGzI4K`F6Pee(-kC*
zbom=o;~p%yJeX`|xUmMr9ux(2J+Ee51pUwq5e$hpJ&yUESLVv_=4ALhMJtD16Cucc
z){o<?x&gYC8APxNWXEksebIcIv-@h`#*pqKge$8C=Gl+pvvHq%Sf6Cct&(OaQ9mo~
zyWcbn6X-29{9zS$Z<x7g%pk(^Q>};(nk|jD3sW63*p*+IXfu79xe5ECfjf9%qwte|
z_Wr<Ny4g89^T{Cc(y@FMwyCk;{jf*axOhIt`iJ(3#~JLU$u)B`j^SWBV2mVWtXk#_
zPH{A%3Cyx5)7{f5f4T3NIMKGe5w6t?Nk61PF`5Q7DEy@)sE??zdp<^oT&%>@+?;$A
z;1_Q_#Xa86B55gFrXHQi=q#K@mHo0;VB@y+@_`|TpLdi0Eo2rF&AikXgu%85g?XN`
zOnl=-I><StA(pLAmn!Wvw5)K#&^~<rSzO{{&rGmZ^-CE=ss@TXkyjwVe%1EqTkdTJ
zQ4G48Qc4&+cDE|-Z)!TaIbPI|e10zEe+}F3Z5qWfXd|w(Q2jLOiuZg-aB`LLmvA_2
zG}wvt^ZqxX(@X_N#^=+Q#&6}ePo59S;zr$=VcYvR?ePnveJ@wiVMdqp5}@9M3E@vm
z0zFzO-mHFO?h5kDCAmk?h6u}vn)JsVJk6$?LkSvrXGg9IT7?ciF(C>w2E(DYIF>{Y
zb_Mx|g<s~|I;rF8<eZ<udT~8=@tUa1{-}-7$t8x>+Rq4Sx&?j|_Sp4Z-G4n6U}k>g
znngYXiCv+uctE6&YM(fD+<j_&rG>o(gY6Kg&pJ&p>r$}1xHS;9P7|b^iFl~7y8Zkk
z?qbi77DL54@1uisSIu_dQY;V{Q(I|6(KzPd?xPM7yw4W>vMWgDaDER-CgdM4#<m9P
zz58fPfLT2<$VOOh8ct<UZmjaQEMVF$fdDqXN&465qi?e;5BTpl#y}=)^thU4k~wgU
z_!8=w!AHHpaj(4?o8#JgHIm-PY^p#4UuAKy6x4DfpsL2?itrPz9hK<@B9B3N4-K}C
z!_NGcW=8olKNX=N+Kr>iCv}3p#;(ButNDYECzDNKgx6=-vZU1!qbJz2c(p$;YS3}9
z3U3ZG%+?wnV)o@m@WrK!e&8_<N)g-nAMCk*`HtLH5v3|V`tefMnhi@-JDih-M9q_W
zyb#xt1AcQ{C8aEU{=kF`X}LD}dv{yLj8nKxtK6erMPM|F)0&AD5C4JI_C4~#qc75v
z#lZMxJDIEem-6)fRNS7qKX%;dS48(Y*eeXOBYN*Hp84SrNXAf8wJ0&w5l69FCDqo2
zfwaUlf}aKa=`#_=CS(+&r9!Q3u=CJIk8yK0F%YG8FQ`Ni{QZbwjAb-UN!D#$$mgWl
z;JM2-otPc!Dirwq2`X2hqK4+pLGy*#($&fe<iKgLtf1Ab;<eBbk>+h%%U~INw9t`z
zWV_e`TioR|x?njW$gp>x;lBf2zro|noy6jHFeqOvt9gi1mx0Qn>+bej<0R|teyA{W
zbSgi?2P1yc;k@|_`nrKAyT<P$7S_ja-o>D<?Hyp9R<KHIqoP~h;>x(ou=2Tne?g_a
z-NM;*tjS&EZd=f~y(N88H8LI&X;*SG@b-JI3Ql-$!HeWUUSiB)seFry-Jb&i(icxp
zX4cR01<92y(Vic+V~%$P^yLnkBIeNFIoR5hTfYw&^4V^MUlT+}H#GV#^nOHKjJ50J
z2{vl&nA&-+=rNw2EeH$BSES_(X>xjE;4?3jpKLSajDJdeiHNCz<^^3^vf$lV(Tl7$
zm$mZPH(B2Yrcqei_zX2*f^Q_{6L*Ps239s(u@XJ0JNWWyv_Q+E(B-K32lf+Adq`U3
z-pu&EM^AeypZ4-@bb6uahsSG^>S6X`5CRlAqR-r_XC^A>cZrvCeC^OkmOa&gXJjg6
z&5+>tN;tGra!^fgTSO|$DfRn#k0573J8dIJpBa3u2bXcZom76cK2t{@B2v!7(o$RO
zjvJes;a+L%D>%Q@kDwrQg4lvzgG)*8{><Am;=D!|BHdHh@1B98C;i-rRyRl0^T1}i
zM8%KXVTpX8FDb@X!)IMCJr0b3lc_Wm`uh0ASZf3OhBfMKN=~`&5XasG0Rl>H{9)=b
zf8@#d#dCGpu$!{^`vy0ya(0cT=c-UMIInOd^mp(jH@|9Il)HGp{0&)(U@47Ud}A)V
z`#z2W+>vL8mT}D@gk6gtjTnxpHNmd6*0bq9qY?h@kdb=iF>hUp^!OE(iY@!7IvmvM
z2D;23rhP24qRb;s^y9jTS+{<t#ZDg?-wmCFpKGVb;`dl@`o@Z(JeuI23FOq^%KJtI
zeZkGe$jG$_yU|dN6QFB4iy1MDxuY>D#E)V$f0-spj>Zw&a?;0Qg1|H_n<zGWwf!W0
z#fI4An%VrGOy}P3CR2UC-<FF9HmBn$7ok>eYKi>yY=G%TgNDq7+4uT4N_FE~>%)FL
z$Bnu`6vPu4+E0Q1&jQ%m!w@;%COKDT%&njzPZZSUcJ-<p!&cAG?|*~3iAt4(ad6vm
zR1I;EWt-@<cz@mMBkXi?_6ELW(bC0K*R8Y1@B8%C(m_C^Gz&cAzTP(iZx_2W!EQ<0
z(m<tMAm&q6`^Yw%{Ab+*hy{8*KCWd##=M?6n~q?Rt*bfRxp+sKufKgsyHpaP<)8n=
zqgLLBOq6zMi+tVKfR=;_ywuy!uZ}pJ8n<*OjHbIC&W`?lm_E)|rsA1M7qjT~Z<<^@
zo*w<i(~=WqUp{X+sIq4C4wqS-p0iz@=_*JvqwVNkc!<FE#Av4@kZifY4*#Xpp}|2Q
zs3kRmxaFb%6l;$$pd&R909kew(yw=LXlaVd;2mhH<%ye0E4h<?r~pe?$G@D8c{3+I
z*@vb*f-|a{mZrl4|133^h-sH-rxmdj$02+e9A;ty;f?H(UsyEve*=@5n#A;@XjQyP
zOY~J)_lizZ)RwKt=i<ehha)w81$BfTql=h3U5++4ZwM?Vqa{LEPoIC5KdRf9PmZkb
z4t3g|t`N!|4l@^8vXVd}PXhM`r7!ZTrflw8^Fi0Ew8Ogcr_6=K^E@e*cA%H*A<HV-
zqzalbaG*78`_}VeaKgPJ(S6J|sY6hQrQP{Aanl^G^}sXoWSJaQ({U|HUh)!gv<jP~
zWOydkU+v7L@~1x09o}Kw;=l1D)@jR~Ni(}q${(yZmdEGUOuBpb3ciA~4QG>X`d$l~
zWw`byH-})AdkDE*es`A8+C~-pvQ9H_QU)wAR!`bbZT62uM5R)d8v^;k_<!kCM#8&B
z77mO4@?rd=y}lj-B3i6ax9{J7pD9^k(OIlox$M)o7fla|(_srXMyH6W37Tg8K}9^G
zSTs1OtZkA>W%rxtJmhsc*<^`em(aa89J-ZT-P2!HxmrdSEbj>sAi~@kSAQ}Y1chT6
z8Gl>zVzXr$6O2+LFxrS2k_i_Ir$9&WmUs=tx5vlD?<!RRX>VPTE{2G8`XipU8_=Cu
zxm;3rNR5%Qa~Gh%k{qhnHuYS4Yf(0TAJQVfXX)BZg9s8CK^>;#U~?T$%Cv-Pj;09Q
z=%(S4TNiGxPI)#-N4_vMKira&@oT1UC5e)zmSkgN!y$%?hTF4sKV>SkEbyUbn1j~E
zoo)3v@LO%ggD^c^UNUc*z^!LkZ1)p>(0ui#?F{7EMrls=@N8c1E)Pes-6unu5PB5a
zd;9e$w0IQty)a){;jV$y>w900{-Z8yCH55pDJh5kP5AWJJD=d&b0s4t+py<-J?m2E
z^}rl7yk>awdRg}2RUIJ@T118k{EZ62uvMvPw724mLdC_Fx@7LBne4-PxQxR`1*2^#
z7wB!a)TWfA;-n<6TNNvfPnjKOG&z@}R!tx^&2J7`6iWG2e~PH7d(OS3**}M^)xr03
z83yjY`c1GDh&A@{k`-ImMRVQ=c^BoF+y}+Cy7?I~0aD|(e+pCshk5;eCv`{}7Id2g
zffC5!Pm!q((}$?3*pma{Jh$^t8LIDG+O~Su$%@h0K1=BsvO}lXe)RoXz-+Xhb*#~t
zJy={>?xwAp9hF!LH+PQhyIGLLK-;Q4`D?V3qs;GFGnXX~+eSg#4&fPqyl+qp^{-kT
z1(V3TN@X~tHeZ1x&q<f+;;2XoXbpj*-y*H_PDW+uiPdA%iPN!PpjvycUYgT&v`IcH
zjZG@5BeUh_iC5PGJxTgpg66)}>B`mfjFZ*Y)<8cb5xi?5#F*xIe+F3{B&BU<T^-Y;
zX%OTQ<8C=4ArSfNSv^_C&119jm3zC*#i`l83o6{WHxyOwCH%U+yUA9`UB4t7)I|`x
zlTjY^{+ywL(c)+0UCAVD6UK4z)(+Pi(Fxiu(NR_ACGe&dIf38#VPRC;JNcnj-CvY~
zk-oBF#)jG1@gqWNXPM(1t4el-9nlo@ewxem>B8LFUZjElL}ez^@wUTZ6&>oDIhGNU
z%ab1csk7+?5*`n`Wn3LJ*rkc0+$?;d^JDSUCG|3lh1fG*L>4ynWV{Z*DF?10W}%wQ
zx>>gy7dM?H*v1!ztI-peA%~D1YsxJ57Mi#AyT03(ga<kTw<l1f(LNzUGKVg+xdHJ=
zH_eQfGxzCnVLPZ|c6Qe1{9TGPBAE-5zmC@_LG?y!V!=c_r^xM3*GuQapBR@)ZS5{L
zy8KYynQZdWoUsjHL=w@ja#$~Z1jXo~s4S@k2dPFj{n#)V6Cb=9{d?^F;{J`S|Bqih
zp3|FtI$FI!O%mXi{TDktwTQs+=bm3EhM_1D;R)}Gk>T7scl3n_M%FesziWxRynv<o
z(HThRo9b;b3^o}_la47Cj8X(i&)6kVr+X0Yn@3Ejx4H)EEcPNNJ|TNO%hDkG%Doqb
z;BROQHTniBTW~A)VBqtdT^}z(E(xX_2TZp<s(xr1r5HfRLFWkfP?=_rW`F-0e_x8&
z)xV%9$bvnvjEvW$yL-`^oj;|#SI<W<Tv)_5y?;$`+H*J6$6YYh+^)?O<P$GZsh<=7
zx3<xQbAkV{XWcISEE@$sw-6~DRK9LPKB0;q>hjfYOq6xS*QAcHCqf!ig|Q4{&^J*J
zSs0oB29YLkfeK0Z9xN(?gL7{)rRzji-;(CAA-&kqn)|mcpvpf3->^0!qQ^uwlfr7L
zA%1_V;7}+EdIVg8h3r(ylZg$K7w2ZksP@DNo;pX5@f{nbb2+ULW%^}-#XqFnm<Lj9
zWN!MtV)hK2TI^>$sA*~E8@~kSM&L=bT2POwo5&Zk_Nh_p1LqaTLix(?9r}T!;Xi1Q
zI#Y)5&}CHel3$rV>P2t#b@}5}kh9r!UBx(`A1fJgTU%NF2)e?2^rr_Gemztv1D;Vi
z34>1TGDY_Dz?UBVn{dfT-BG063uAdmM4eS)%)IFK*w>}CQzxF+P^ssKLGL<+#z4-H
zK7Wi&_w{bG$=-vyXYzv6=pd%WL42bgUK)nW8t)VLn?{%tD;m<?AR_86C#>n3t*VPb
zWQ@!OXk^>{uv_N(;3H>+&ON(tp@X%?>%SQA%Jcc|5{JWK%4{-xc4?vWfO9*ljYH|=
zHh&3c*^}Wg!QLHWJ{J)`wUR_5<?xF#s*PRKt6GYP@0m7L2Cc!(YpF^T;?&T1xKptj
zlk2@GMoMr>gVEICV3Bt!MCF|*JgOFF$Fb#1HjL%BG;FBL8q1s_kn1q3@wCIsh1VFd
zuak70Y-tbE1noVOKnquv86F4FwpWNAAlKGCjThIepPz-?d@Wr^mq2HcR@+pn!WW)5
zAr(r^GIMe{e~Y6|W<HmDn1yQH-8$S|&QerAC6HXUTNk!8FDs~>w7({WR^T?_xDj+L
z*CivF-Ehu&9zqGOapSytId0mnzpT)X=0EiAsRs{*C+?I6)51454+XcJCX+{XoL39N
zmaMku8qSG8YMoqZ9nP5sscSl%<fWj~W60ii04WAbR1NHIfiYc5{O3T?!&SmM7<X6)
z&x@&J-X=8M`O$DyoxEYCI^ObF>qUfk_oOHnYJu8V*K@kap%>-^>$Itl1y>MJ?Y2Jo
z5zm!02|n&vLW$O<5gr;(lX>F0o-_wv^MogKU}jQmlHhWhmPMDjaVIdoe3IaHPJK%E
z>P!GF1-n)syPgg|3)ID*Tk|}Jkg-H7VV+V#1sYW|q3&lqX)R5}FG~}YEpqX?4rU(}
z+W4fd1@ioeJDg9y;@w!>T1X;2PTOIW$1)ONDlQYds&(q^bomic;i<I^@s=gYZFI#r
z?>7E8kM2em5lpfb-!8Ce8Qa*9;KO{k{LXcJ(-)(`(PVvLyuTeX{1Aqyh2y#>%8e;q
zYHf?KyIv?2MS9=ahk$_@?eTV}(EaJBbH;I-v5om`ZSvcMMK|$MpDML|N`m`KGE7y$
zwPZ9#Opu@pHtj>AVLwa%l4wJ!wOE^5^J331aS3y{uKmNv40%Z+NW~oAS<qtvS=q~#
zg@B^N5*5GS5<aM^|I?s~W{j-JRAxAWO0ml;lYj_iNtJYV;bwtN<Qq^q-kH5nPg3u=
zUfs-ro*ykbEBf2}<eQA_hV%ZU_4{5)lg4oam!2L!UJmy1=#U?V9Co^<9*ZaI_zZhp
zb&*{C$fqM5^=!e7EQB`i`3}SjhrqNY*$@lC)?7=*i}744w=9G?NiY&7GOi9{8g|0z
zT-P0Zf0`+N*Z4cNm_8_hc#z(m-q-=3;3XVmj^@=8gU)e&1b$yF300sEX@ZpIYpvC6
z61H(q%04-GKg>puw~x))TR-FL(gl9$eor5_m@a>|W-PU#eCV3=%e$m;5>B`9kz@g>
z)qukAXJe1~lhbhm+}ei^4HoxeC!~EXr?R&`uX!)-U)@uJl@*P?2eKee?seZ95>G0W
z>e-J7v4jvvvVuuxR|iFL!6?fXO)It{eG(*X1md!6V&W|r3iqj1Q648XvZHJ|1bB=p
z<<VG~5#u;sduO(XC!<I-_~Y^Sc2Lu_HEn%fHkOGobeNas71(*zRW(9ZXQDDfeSJH=
zx-Sg((7!9o*Duei<#VF*q7g^eMh(^p#iE>-Ep%JZfOry9vgg($o^ZqrA>(!0z$F1c
zJ3W%n3Nqq7F$r5nSBQO@u%YavPCw&3h2J5HKriR0ngj}2Nn+KPHsIYPUH#<M6WHna
zDE&QOz}kj;?zYC^w<R}eczbU#o0Ud6BZkI9e=S7SrC$WI_g~G7VhSYwBKTNW-ej?y
zcN3IbAY;FqBdiA*5$el+r!Acx!EYWZn_8z<zXHPE-*BSY)jk{RNT$XvV3}5)v$mdb
z+v85pi_nzoJL}zc3(bAmI8^6=HkEC{mR)P=LH&NYTYJyPTffC~F<1whahz0ERrw5^
zv1uDBmMo7_hwRhE=H<D~vD0rFt)d=(CCEB5?qEO<PE<y3iRWAVnp>uRU@yNpQ?(Ya
zC4;_cN|lM0gIe$ULER^>y{>;YaEMF{X&tJpKD5+3+JXo!YRe-n$cf551x=bAj+ei!
zR=xB$*(x}v=3*?hmvDB^?X}D<@%Pj#wU~LQ^+8mI&R7d%L>gM|Oh|I&xDxpYPQBS@
znx-2PGEU-sV%`#|3_BmKs;lnoHfwKp5Q}KemTEBzwbaLCr1GB%7W#R6d2-Ks^7ZTU
zIHZnpK&+y9CJE*Rnofrs%m|@{3%#dP?3~LVe_`+MI(?=cu?LCRugbo7MYhbf)YpKY
zoIH8#PRj*YbCDoh5W5;<iy-Af+0-IT`zN%99HCMDj!uHL)TZ_Yufx$v`kKvquAn*8
zIU7j&vtxRsU%IfCg6^|XvAi|<{*u^(Q@FkQuE1q^Nq!jT2+H;TpH&|z*2VK7RZimr
z%rFO>c0aJtw0;-kyCElWU99Xy{Jqj|x0wF3YW?I0(I6EMm%%m3VS(&Y{z}T<c4L;j
z<%j%cBt3+twvNZMGi?@=il$rYB%MCH+8m^asLMZ-llxzpUZ>o2x%h18R{#<BZ#@XF
zN-zZT)+43$a`9D!kDdg*eHg@U7wFzqf=e%v`BSAo#LbJM5HCkU^Ofy3;9{#r{BSoH
zFBj=VmxJSFH;w?JZjx)_mRTOW9tFq98Y^tiDLbk6mEHGt4CRGh^Ljy1Lf^mgoTS97
z)jgf@qcQdV<S_Ht#zzI|s2K6eLo=2pY5o%02w?!o;$F$w_=q`xI8{TZGaOFl7lorB
z$f_10k4#FO6im;OB$#^<^~AOPT$RbaqE*%YHv49IZ!s>9F@(vb%nHA?lyFl_AB-<c
zSjj*7O^9(oR(>lsGa+;2B8u6ns*cLl@~$_|PP+cES~tOFadh8Eh+*lqG?9R$g`I}B
z#JRZc2b+}Elq5X1KOt=z>{i2V*lz1haoicUU8J^u1wf-~k)V?<O|cQ_bfuw<F-^6a
z2rb>D70_kM)-LV=zTKfQBhA;Tr8mlCg%uTc8rZQMljiT`Zh~R!s!$Sz#ZNKSMr6sd
z6@(Tl#1;?BKUk6_&8m;)qo@cbdV?};_H$e!3R*W9ob4I{*}&<zmPa*{-8~qmrtFMk
zrFB-8y+`b)^g15reZSV&Ab*dzka-!|8YN;AXU(_N3pJhu>E5YXb-FwRxCccAX{#J2
zXRl6qj-vFCA_)0M;Q0cns_NO>#HLsfZu6@eO26H}vg$bPir9Tc&7$Mw2DI|1mIU^Q
z4U-?QO+@u=Zyn;g6!`2E6Lh6mQa)ySkCZt--T#p<NyHb*GL%S>CMl2dyubc&<Is<V
z9aB2E(Qd&{*kNMi6?;#>Ts<+jTUD>ALs-GOq|lR!8?6s36-+pkGe{O5#}}TZWpA5W
z*vjATGWF%+8d{qJ(;&EYc=hxerOLN!nI&Bs&%~`=d=C;~@emflAn^{&B_YBXC{QK2
zi4+>ZedOq1nVAtYAlkGmjj|?CDGBqKT1Yf@{9ch?CJ(9c0QQx({=XS>6OE=XHxn<#
zJi~y|lVP0p;CN>awz5;ii}Xy-mdT~>rT=B7sLIIW1(uOza}yeTjF$Q?tDUC8x;;xe
zi7RUz8w4tWn91_IB-N6njx6H)`9>*p=3Pg?tF*taUr&m9g`C-v-)P?#Z#rtcCk-;`
zImndBix@DfzPRlTC@~Jvw3>Y-Cy6}p)3=q5MUd4|s~L1a!OJlNrvWM&7#^hG+6^|$
zV9wzZe4Y=CqRdb(9l1mfG9pl%gbg;-n=QfhA{l+LVwi?A#t^687{H=`PUe{6>*gvL
z&dCmU5`!?*9|X|dVBRfom=&qy+Le|Fgx;6GOZ+TMt=xp=>`;0PdG+g#Sh86|o`Tdi
zn}~Y+rwTx!K?%cr|01BD1R=jCH)Oi#93qqCNUgK8ALt|LN%bKj^mRT^lMcegV_Pxy
z&%CL1iT-pWMC~Lp-Sew;Uk{Afn9cFpr-Z<IptOAxeh2~C`nEA&|1797hDy&=i#|2R
zF~vmwIySi4PAc5p5j8&Q)x4(2M~hvWt_NkWN)%g>v0M8g*u_)r(AOO+m)7-z;{C2q
z@8@)Mrua@8H>Yv%8Z=cb)suUL5`6%Tq5{+PxoXVN@!qw`%#(1^yj7a;MQ!`wm~D7_
z{<B8<-4?Ympf+5a53<Ua*D((5Lblp_-c-2;C(C9Iz&zS(YiANwRN~M(Mida(Q+M)_
zI#@)~`%dxs<dH?9*!!-I#cs+4SdMcgqZAXLl@$1tPb>H(IWeUM&ktc!L;Sq*2eFcw
zdC~&c#<URni*3aW^d?yBU3*#{dL-D9_AZQ30j!=1i@lvLs1K?KWmvmd6SDmJP7~^R
z&!w`tPAF-1;Pm`Fht-yEv9$Mz0p>yAWL}vB`W*?OBOq}%kCis$i5JOkVf{_{qTY8I
zNTZ&>80FEy?4{wFrYPMuc;h#QogU_89tV6sF`C+x9W|v11r~z1dr+)r;?{+6%IyZ5
z!fXspkCF1vH0!Ly30r!_1%FF)5d5hrjJzkLvtu-!|L_ej(fC@sbZ2@xjwQUn#`~n0
z6QIA?%Ov<_d?6UwFeP1&z0%bgor=US{jamj*UHWZ=f;sGWKNT_&0izV3U|wBB`^=S
zfU&A`^6*rF)9|VonWE1*Px-Mim`dsgLw%`?B4nPiTmYMzo>#L8%VBqAF-g<+kLT_r
z3)(2xV%K-;M$f%eWaYs?5gAi(x5P<cs<@Yp?8C`t0~&FlI#`aoR5f(zlvXv~as^>c
zSnN)XPU|cqnPf0hRf2-nF_fvUiqbz0!k2gBAC#qB6XSo7x$mt#>7U5?$6ib6$T1XU
zjAQend>bwa=ko#$!UpIvQ8vdgB@8wW4>+yD_la*U!{z7|cPDG7T$os@f4~2bA{aKM
zW*@&jtN{M;lN5w%ev|g+<BbiWjUp=$r!JJff;>6I*8i#gXyqtS{t9}mF)FY=aJrIv
zi<|f}$Ybv!n_i~|mi_e~|0yfi)ZaQ0AH}_(7z)<ba;>io6=C?C$>5FxwH|KuhCD#=
zcXu+7DbwDSDgyqSHct@+rg(~6RPddA6TzhujE(bUX1)Rt+_;DIYY6SdcR4>>68h_7
zkiq4WxRwTUhK;@9M;79*(s;|vW%>Apbh~t=22MJP)HN+nL*};V1U2rO#DTl0o72x(
zR;F57%W;5eui>1c^D0hnu6ygee=a8_-)=9;3`7||k@Bfr+yWlH4#U@$xxz|F$MIMW
zpja#SAf?5$slL{jarrTa7Z2vTD&=5$=}bXMNsWvlmf{gJMZ=1xqV4T?^#nK2`OYwF
z#OS}@T1Lfn!Ua}_DPxg^N&NF(ZbR_Xw*=XquCa?LYOogn0jzjzOHGU4Ds=a=Fol&#
z1LeW!KdDt>(c~pXHmpVl;uKdg;;}EA`fr1>yT)Uw@_2&kn)2K}E36UI(N}Qn+zzR#
zv^)IVJWR=5jG$@xc)Vgp7@uSIUuJ7zFtc(3AUw!@Ew9$PGv^2$7Jo}|oPiy`^(?n3
zcko|YZjo10ru-zXazb92^#D$`yR@+iq9^cT)bY`=%21@>S<7%`kKyycKP6WOiyw!&
z2Gw&?d)eMs<0ti9f@?MVD0}QJ1Sjh}!bQ_qVF|VH4~AMPsXNL@#ZpDjRxW03Ki*+9
z9H;PTzDv1B#Z%MQVd@U1tF4W{@w$Wop6XuwiKVcdA?gmUyQUy3^Stc=aG+o2Zs7%3
zjl1~&-8$T5AbULOjz87Q@55LC$X1xki&@g9N>^pp;lCVP&;Rb&?oU1JYr!u5j{Vy*
z)0*!WJ+4X_np36|a@d&U1R@4Da<&rlMz++``cq4oRJ~Hdb6~@iv=KbXyZ`v$|2t(n
z#GCk5GO?tU>8@{xrM#8NF)^?K*dQ2}lRp)|k*@NNO8FDao7YOj=g)u>6631i<goM#
z_CLUiS>Z3O)IY1=K@^?WF#daF9TD*tDg6FY!)&WlATAOjw`dmKSOi9}636gxCMAav
z(tk%?fc+C6fTHI%Mlt0u|MQRi%g!J8{ga{<bP?tLha^3aGta`MdWU|gv3!@kZ~P_8
zvZ6Av3f4*bpQQ0L*T0<nlp9xVA?)3mlijYT!;+ruRm!LMLh(QJ|ATSOj*5uT*6(0!
z_Ej2-?vUk3SOnP(h(6DJ)on76kXC+u`TvXR|1YshwD^(JA{!GP{u3M==Qt6U8F>l7
zp>_bVv@o0AIe({eoi+`V(KPkVMP=-Jnp%qg^K<n!|AD_m-eiHeaky|=(~vtMkjlhd
znI<ie<HkXEuy<!I3+)feUj6eUmw_kzaHfRzc(IOgI=7XLiw{?`hmmqrS*rZ8dM`Rk
z)(rJ7QUYvl*Q+huaW_1YSpHE%*?%N<IukfP?mu4DYywWQkng#QqK<KCFPZM#x6=C0
z#l<ITs$X+U)c)vO+okaTZz0*=qv90H@iMhQTq9#l>F46^)s%Eb&Twq)%yHxYcBzMt
z@TUGo6^YCx@iY%lPaG5vH_M@juzb$c%X$zEa5(-<KG#R3@L{su&$?s>Ks&LYXUnx2
z%^h4-vIE&3EtcJn(*>Lpe*Z2OGETNNTW+*#KR{{zA1DDt)St&wUavo3<N>=!plHei
zzbpR!zoa=>-`YxowEu3pNC_g5F~lrGtiU_}CXICjBJOO4K`S%x7^z^c)^GpfdH=C2
z-ds63B%a^e&8h97u&PAL)WV88mX|;f`j~@ZL{u@+GJo)GO3B!@cKn@ed0(;a&!3GT
zb4S-p$@c>^spujk*dJ&%nkUUXbrS2j3B1%>11K*PsJ9s3dS!q=%*6~geA@tJ&c0#3
zc8ORqpsL~>brzgm;cAOp0jF;9@Zl<v7IJzxS+aXdq~ku?Rk(V2Ca`>rnqR&R-i|SJ
z_{h1d`Xxy_A$~y2KsMI+QJvjiCp``;b~o1qwz+TUN;4M8Jp1VHF<iYT<f?uroGt~X
zPPOn+Fb&W-g48;2TrYhg=(-;@`^1?Wv}3%uePJyMo)|L=AM4Lg4;tQm>UC_s56!EC
z_12AV-GjdK$Y5p$QQBDEnQ#A??Y3~o<qkx+cfbFG&InmU4hh_1Pfn1M4Q{SyTQyGB
z8R89_yZt#BDAJ$06>_0JwD+0R*FAE7bE?mLio`(VuN^x@0-Sa%PqA?E(lCczsb&sY
zP4R_`rL3B(N?Ss5NEuVf+syv0dsiKeI}FZT$Epb9&_{lBU5FBKbmAVQak+$1-sE5?
zr;49MatoWgvE2C9fKR8}NL@B2^ZIM9k)q0unyZ!}Y~N4KOEE#^#0wo;m)o!tlfG4*
zKZymEb2d*9tX~Drlxg54BbnUugnFbHr3-84FqQjx{N5)avAR9n7t0>Ad3v`?4_7QT
z$7=yV28CtSykj%lQC6F({0gf{D|56;JjdCB_<MoY!4@R&PIfF-J;z<WyEn$5-|<qS
z@~%S+teysZ=5m)Xk2avn99lCdz++F;Hy*;*fN%=`g|PJ-t2Fcp+x13|Ol&5JNN*0&
ze;fPyRkC&rOn{&cd*T<=_&fT^#DiZXy=XG3`rz9nO=O*1<bn&+^>#enF{Gikk3h_h
zB*deHmc^OCYjOA!up{+vFg5$5cVJqL7*Du^m{+%G41tmw@XGpVsKU4zrv$zh3DBz+
z2{mIJoDT~Q$xn1sWe!Z*q3Z|E!q#eQ`vNBEM))WFe)3Z@Yk2i)(XrCg3bPc9CY68x
znFfOZb{yEM8svX#p34Z*%3n+F3mTD?OgK{8JmSA7xMJgI!qt4jwTaF&Gu6t-0qZ6N
zhzHIN*9;3n<Ct7!3o;X_NFee$rV%R};VkAcL&1lTv);Sc+SQA`DJ!1@<RAWV^+@ks
ztHQsuu_q77^hc8K)Kw?ZgU?`w8jP^TD!5eDge@fv1?$+v&Hp9CY!~;qOdixe%TC@7
zB-Hb&3el|YG106l5NQ*;Tg|kNRMoQw@W`p8CB_Aaii{b#d*(aR>~kbny(OR!KKT|K
zXX>ba6b#zO-K9Mk9Uj|TpEEv|&X?H(OKk3Mh5h|TYv$o4qiN}JZkJ;{yvac&zh%6h
z{70ZH9RP_g0r=O(Y%5Eht7n@?fE1a6;2qFmnx}<YBVP@gRy2)={Lp!a^Pg9mpCSNq
zE~>y|mb347S$%+FFmAR91R*yW`RG+*=;kd=C1$aV{s;S2IZPC=;GW+!=X>@HDX234
z%)U+u6jc}E3Uxou7d(H%xVIf*68#?^_$LGPXNa$L-~@n6GyO{%0>p=;h}=w`H1-qI
z%s-*M!~1-r4+sOd42q$Ue6X2>4>y-=A-HK89^y&i@)`!H^Eg!BEfZpD0~U%Z9wMJA
zQk7|$U|sV6xFTC3qE456BVmtQI8QkhxbWNt#0N1_07U!Lc^8Lm46q~LakA?~IBVsc
zId986p!bfy!ih+#6buLY$u<^4UTx+xWtTou7cEsV;_p(6v5fzR^2SYySZss@4UZsk
znioUHBrOfRDON7o0=`<@-Ni0M0#7>+_}6DThi>v-<HP!G7(<*|-L8IpIyjuAPjeom
zMo{-67@(MDK^$IuIa5_RJFK?TRd)<>`ycKdhQ+eJp*wV#n`jyO==s}Q_M`3t3WC^*
z$Aw%nuU$@aSA)%}j?;>(DS!v-9qkCXU7EQ9oV@Y<<$g85P5ip^e~q1|kSSaZqy&e7
zAoMnn_6AFXX^O)C;T(WEOws0c^+vLJm4HH<197+!UN>jBwePv|w+`hNyq)wrz0n~e
z3u=9g+W&SRVJwwpThw$^{f)XS+%62b_!mlYbpx&nZK4(^gcy(jCtF?Z_vfo}m+b~f
zs(~l65Fp1|cy&%HAbAUL(~M8|S76owFM7cHNGEoj{=s3`kFKx$G|X5Lhb_-5rcrNM
zblQ^Xv}g`Gsv0O3J5@l9D~9tex~BXd!njw5w-su|H<!Ng%%I|jk^gXSsiyJFr$QEc
zK3ZwG0ejOfEAscMUGR{e)l-IHVDYD7`$r+rqiQXzOVX8tSA(Cv;g6vUtf-KV07qE`
zt66!jUgbkzowT)}&hY7^a_ZXB-qNq9Oxy~_fd_SCgn)xS?S>p~sTB1GNRFvuAgf5?
z@<#=h=IBf^cz^`3{<T<~)wdNz#3N7Yb*tZnIzW=4XMJu-9;Eqr-0<=P<95lqMGnIS
z{X$stItGok^}9x`S+?Xe5I$v%5`+a49uLj~IO(<z0%mlPQsK8m7Hx;MrEkL96w_~k
z9c0&dyMlxc2z>8M1uZ+kvKcE9?j4nlpfpPrV+vEGx%3uGu^_pO?L^?D(@<?_A*Fus
z3H{NX9L6LfS08_OrMWmfdHX1sdvj4N(@g15f|@*oRIykp)xO!rZTNF)NoTq#!Vo{u
z+L&KB0dFN{RHCI`eJPh4S1vV5hi1$SJdkiYa5sK?NukXJKhrN7HRG^3%<np$CB|Vo
zN(@~fxw#5J!y9fnZ7<2QBpo=<jS{8-a>&JmyN!c9mh<1Qq0YLW#?^&v7Hf;jD*<^F
zTJ3QMS^##6e1In80G?1xHal9xD>qoj3%u|Biy7i0N(es`bj_6#ohSl-%@#yE3Lx2z
zr=g!LX1@SQE$j-_?+UuUU5r5%p%c5h;$M>S$V!AxnoVn4LwhU)WIuwQsvjFq&^<l0
zLj76ohvVq)2`3s>y-F%D>dY2CJFR*;uitBa|6Zy8G_49x(#Ud=G6`5eKa~z>DggKA
z;A*-|lcx_lSM))S!8u{$vI?}=E%e8Jchs$-3oZN4#muco0CQA`>Am;$(L$)gSccG*
z*Y=dgqDx~%SI?ndINq7j#==RiWG0UL(+!Y9+_%tsII4-t03vr`?e8&5y{_{BfM3<9
zFx_cVb&jcnsn|C1LaURH<mACvtor_pgXOGO-#Z5wf3bg+W`U=R!Vtx-<f%c(B`w~`
zV$2Bih!+Y!>Hw+!<NLNVuV!ng+d`f))k3zj!2V>pc}lRs1!Zo7TJ;->tdsn5D<sFm
zuerZ1HTy18I*j54@1LEkEmp6q9UD|&U8*%k2g}Bwy-{eMS6g$alr$}k&Yed}sd|gf
zTm07KdW(hPJ)Euns~X2MqNnA7RT)4xf=$7tKOIH0P{TLHu;h16R-b<dqYK`iS>OHB
zx-m<5pblU_9+KpD-Y>;75+0D$%U{OCUlCNhSo>`um<b}{1|-A#RjGWN)%N>$5-ZD1
z_VE#6E`X(R5H7lS_aptP?4)^tZ>)EfeSkQZlp7xbAWvFbFJUsE8zJWsU$=i2|75<*
z%>21-N7eO055*I9A@r)(89M8*bH6hcUGF`v*7;p{D_VToj56v_4>xlYwk-};KAFkP
z<J90>(eb)7{x@M`*|z73p(e}A*Nq8-fa8mG`<;n;HShgaKI`h@<}L*f0UfRA3!qg&
zVx&mv0c|v`6&Sw(o0MT4BIw{3KWqo`r_)of)5C-26*jM}d_61@6$}M`5YJd__gi=n
zEA>|)2}IsfL7!?Bq}q|tg->YzN$e*11iRCQ-tVXnbB@FNIL8E)ZdBJTXfC?|0htwm
z0Dd-krRS~dPj04Il*d@rFG&6iuJZw`%}kQ#W&}^%z`=4>`dbBv__OP7df^Qu&-%IQ
zjf2S9kB%qm3s2JAPlVfcm?Odw@#^RpzC&;NM}Pthn1KNt#Ybwdmoe`W5@p5xN^3^>
zWoSit<-vv?ao9&m^LtMYUvMTQ?g9i=5@-f4xd>gD#}qVOgL%HZO5??38-1<SbF*l{
zsy)9D&A&DM6_F!xL)l_>$XG!kTKCB!O%V3X<+|l(4m-Um7)Cgi1B+V|(5LF$M|#}<
zWeN}-Q%dZ1p*{BE*thhz4Vz;(xpCg(@c#ab-(HAQUR8^Z2ONU#SmkV&#*Mt?;%3%F
z(o&tzs0!ipe)yT;;vNh7la|EuOA!3NqussKgTp#9q4jQ`i|@Wy!Du1$6Bg#&s4TR0
z#=&;Tvdv89qvDD8wkF@%^^55#)2mY6SYe&Ljgw|kRO{Eh0itRCjXM5~EuJw58^A(t
z7|BE!_6>1)7ZeI5SyAn9t01k3YN&*~L6hxjPc|<qFF)B&_EvQqqgx;>SuI%&kKb)4
zZu3*n3M8S8^59gLzaLXWL!I@z7t<-%?)8#IW=yQkb)!UI>@o~Wn@I8*J@TZRjU+7^
zNIxhs?w!{tc4^gV(&}<~kF-RcvQoXXsE|>Ul}q<bF5J?Okdi2%qf+@sHbYRo+4<D^
z+#?jjpnwGC5r)egd%ii4dKGjA3{U}xR3Q_AUPEJiF0*JY_v5-RLqJN}3_(yX@u+Y3
zeSk<d@^v+GcY87?##*M`(PVxYm(}7=ZmY$Q9!LgHfmi<~$`IJ<D-8oK4Ru}PIcPBR
zN9O}AX#tkk=xrQLn<{<6*Z^z7c{qu{EK~Kzu22lV%-#zl51}%$2(j+5e~=d-Y=c`)
znj!6JzDW261{Z*GkpSpQ%QU?I0*zcC6xaJENc4Aq?brgfDg%|bZac)^!%#k8S017K
z?rQ|jqSxwjqTj}wrM;e;yp9$fNg2ntQ4=y*uRld)Sqhv-oS$<I-F8lGKH)Q=STN1<
z)A&v0>NREbLw?it+8+m<dROi$0VTQB>4$3t*Mp)$e`Ad^1tzjFL>fgGm5r-cdZv`A
zyV^iMXn>q@x}Z(Yvr8oWnRdn(b)mLhh43H|yDr-?Y^GFu#vU8IMdg>Yio!ZBH{YXW
zlp`SoKGd49`KZL#m~>TIyAqij#Ay6rvVVtu{!Mkdf}3DklyPW$Pt)#A?m)@MiRS*v
zcNrO$EnrX66@hPj(PLVymhj;NSQ(-KwjF5oir8`tL0t+(5sLPQ*0SmM-4+?`&x9a>
zIyi0muh=Asu%T!>v~B<0z#&5+8c5=eN*pCSqpfg+Jxu9TmH1*H12d0eJ964>{oGeH
zkfQq51&buXV9Ya#uJ6ul5f$(3yjw`8hn|c<BQM&4tVvcwIZWdvX2`e}@m|U2QCTrh
z?VCeR@l?^(_L1SQL&r;KwvWby--kpo1lRh{xcyP_sS3^76I#vI3)Ttl8>UQZjEvle
zHGBowu}IV6@9*ws#W%@$`B&6!1=9n}qamkKb9hDCim%v>T?trB;|y!HoztYo(trST
zT5s=R!EV7!ugh>qb){~FW~H_k3oBK$;DG!Ex@e)V%d3lBzeC_OZ&i5=@N|#vd#`MQ
z@hq6OTLry=+thk)l0<2p$0Uh>+jjnzCNht+uHQstsRQSf0ZVHD>_!bJa@>b;Lbv91
zdT0beXQtk>qSh;B>NAFmO&z5mw2KzxJ%o3guQ%TsBF8Cyl)!hcWU^jvoahe3B(rAe
z^dX>4SgTY;_6dCq=ky@?jlm)nNo?)`EO_bZ0R0VOgJDL%7%WRm0}lgPcv*H9v7@ps
zvd#B~A9Tcvii%8de$)Ddvi>AT5hM_RBlk%L(2$hYUUp%4kA*K&(93lNx7&Xn{SI}f
z7m1Jk9Ma=I`$hjT|JgLnF0e_i=7SHbURD$kT3s5JH0huD9Pp-(tT`^`>nkcFA0G2N
zgg={Yors73m@(6I#{^F&FhBdyH|>Qz?5seqyU!@SctV3cSJ4#WBiNxARFS-tCmW$x
z@%&ZUbYn<^j$gc5E5^rwQpre1d9T+Ig*>lpLOY&8xzr1I37x}~;qiH;*O|w@qA`u4
z5M<<EUbUP+_v3TD<qBo)KsYyaLcxsap7_dhD@JMyXq~ScGP`Bd@7+QIPy1Ywi%RJP
zdjQF~j9Q!3mCLZO2Y_p`5e*ok5!O8LjD?Xe!b3q{M9wJ78bq+UF7pXEP1Hp6rSUUN
z8%M!`&A9^H+Qz?;FuB`9KX=fqZcvBiTc<ZGc6fOnieF-7(q+BmqB)@GYQ?mjx~9tx
z&SQ?*)50cr8g<Q_Pa=RqN+VN@G}vto%jc-pdFGdLzQhHtV!MZ{h3+Cw`pxO;>S9KH
zkAUIwyq%NCq|Mst6MP0fQmaT8QTh~Xbb9-5`RH_8qr9;kqJveSx(xHfeCj#<agTWJ
z5fS(R-z}8tq`!bsJyjSJ|9ohy)_pd>wZ`9t9lK5NkHcv@oEQj3+wYv}3@yV#i0k|C
zhtV>Eam~fCsr{m{33eE#JYlB+*X(8k`7;z|+*@m0E#_;MOPIekSTKXWWx$7&CYRcw
zAt|fDWUp!&=f{8pPKYQO?3S<?iD;3A!f+dm54p{ujm7YvGs-9}=4=vt4e`ECHRI$J
zH%;$?^DVfCRtdR{C-~{L^RjBK)qbSpC4_x``<Ki3<QoB>YsiRhojOB2(?<-8#CVk}
z#^Fh2BBQ}1B*i)-vE(}2vP&X%DZ<$@I-GBK(n|yTCmIwqAG567+RZOi(wE0rOJ;v9
z@QY1+-%hU6i@!{5{8^mHOew`x{xP00F4~uBcj(sDA`ev54f^UAI+<nSzyz(96UuiW
z?&cS9o1|2)HmQ-P+X82Zn@G2580V|RD+)x&Xnw+M_S<1rrvjUlMf)j1ApZL@#+PX`
z8j_*4vmAuKygIy9%T{x+>3~^YkDqqBy}w^6PD);BmD<9oxBkkNO{)L$R{2Rjvk?1!
z6Bj}dG!xgX{B8J8k<778U!c`wmd0zo9Q9B1N~O^W#3WI)W($Ue%%9R#ex}bBX?)Y*
zDP{^N&35S4U;jlQY!^~z74SZwcEz~%Iez2#>!(1ArKpT{*;$JhGnJCsT64vjEzm+)
z4&Uu27@kk<OTU_uzR8O#{l+@t`Pnfry+<9@HmsMS6N2h5L-7T_+*8f>A7|MF49(g0
zMIo)Ma}ivAf*akjQ={;DT$geM3=yO`c^!V2*Q3nFsh#Tz855$_c-?|jTEEH7cW>TP
z_CS~{$j$Pvy}Hy8@6fo2egNWO5N>}GTm(=nMgl#qL@lo()Yxqr*O6;vFRCMPGVok|
z&=FCM<c{N2b?AYrrN#HR{V3nSWKASK{HvHCJcU=Yz>P7Lhbz4E?mw_|>BVd5l|_fw
z6O+&;wT@)#2tqR)))vbA6Tky-;d#p7->y?%mo+>aSHmN=3Qv=ecQpn(lH|yC_5cMK
z?VOf<L>u~MfPV|WyPL-i)Y)!y5T5lVOlt47{#ygTUD~wq(b&60zVV5P;@n7*F%DY?
z07>}<Ok0zi`*;LxX@HYyKj}&uqsb|q`!}M*s%IRyU0<eoXY1&Bm!F7X6pP6>fi(s<
z{OS>Y+>AEZ6yE1`Tb1eSE6LqgrWta3fv*j=DAI=Ffdj8w5M#Va;RK)aDLe9A^WkD$
ziSIlvvu2W|T5Iz6wUUpojA?>_{t^ju_s^OTakXg6_sKqVEKwK8%NrV}j(QuurZf+g
zuhJ%XR^AD|yo!Eza%#0$%YL_iQ%m6OqZ<j#f2o&qsaf`Oo4_0#D=6Ok7wzRxpFnF(
z7R~XXNiRQg$d^s$P-f{k0!^_xA~uTWZKe#7T}F|bHw=Mkh;-)?$&$BngkH^z1K|?`
zZ1q~qT_1HHf?3;4JyECokKjw6f8E=urpQ^z?tTw&QTz$fdeVhVE3cEt-GL2jRUHw+
z2e52G<9OHvabW=o?its+Xq=2Hk`GAa8ACK%`rRi0Nm}!oOZQsTeeyKP>@#;i!q&g>
zW5$qK=2<CS*KHh5-tVOD$K$)_4EkDPr{#Fq43AaMp@e3PG!}amOjyYruA%#kKPP+(
z;re!eSb#pysM^dkY~qzqnYzN!{~6=nD?1&4WT)Q`Y9incW9B9fhwz}sqO6k9Sj;dJ
z+X}lpe&*Y2UjcQcZW=GAeFE)Bf%8n}+9!9qd|}6{7vTe8A`0<`BKRfqYG{!WH)siE
zU@$8o9k!{|jaG7%Sy>uP)|>C?Jch&J{@ecL6_>f273-bh9`Po-tnXbH<`o!C1ONz3
zO5<ci(kX+qW;CA*Ejp$C>M)b&5V1;pxNYaP&L>{7svVhp4Ns?%mcC@)^(9!?zWqQ*
zd{GOkYx{t*mFe)X3fkNB+)m<=q96MNy3@qYyR-M(ZZ*9hqPK~NpU@c1w^4`=Oh*~y
zMXDbM>xR!&{@{?K#fkK^`F*KYDJIFZKzE!$OzlT=2h)qEV(@)hxmm~OQ@RkvHYtt1
zp2JMwR=5O%KZ)?SKDLES?sYwLn9DmXYvoi!Zx_k=?oK+kqs}2)9pb?GH5FwTn=FE@
zeG0EUralTY5wD$)iuvS_*147U2kzmQt|$bPZx9*xA#v}&@hjj2h`Gk7oXuOT*^P_n
zM<vI(+j3!0_5o<RR2ddERyg#7=Neq$uE4VZ#5QH2-q+xZRRUJ=S~->nljho{;wX>v
z4tm_EtLH15{;#PJH^_q@?zgBInY?3VgnY;VmGN=Go{oh1rA-rPO$z0y?c4olz3oE;
z?i|dz!H$=w)8NrH&7}4Z3w0WHl=qWFKiEirScrbnHKGL8M)kj#t}~|#|DihICz$Bs
z`&Z>kJU?EH1-JXm#~^Z=j0FE7(%S-vwok&p*hA^oqQ28?am-8H+IO0DE@|ufP`(MI
z{54Z4;+Y*ucN?-N^YcrWXo`rZUA1i;jpTGJ{)BW^@NYVSG5WrJh_=yv_&$xMe7s%O
zx$>HUvWg7xSmM_xQXyc9GUdTF$&XIcx%(W_32HVhq&HZThiI-}Xm6M@Z_?X6A0(%n
z34d7BL`c06rTq1pTj!%ucr!q)QT?K*_c$vM1m2(is*(+93%4Lc)Jbktsq({*cO*Z)
zfA}Fv_M^n?i|!+H#u*w*BYBPwmz-DnpKv<SnsdvwPG79LWzDAW+;<%AZAFYAy1~eS
z1!*Rd-8a7|f6<5UeH8zq>qEuQ_iK}(bsb69=zEs&d4|aOpis`C7Zra9A=%Q<DgV}r
zIPTT2Y8NEG0_Q7vHkH9_Yy>RFriO=zddjxnd%-7|dYg^EiO`ffNjq2_t=zwe8Gd1I
zjxmNLs<&$7Y;x>ryacx|OF2}}4O6gstODhIBdOyB4_a<S^i6R|dFY;hcpcK*o<!M7
zLLlt-)|H`0_Vb(3Qkx0nW`D{Y?AHlV9I+GVp4RkV!}i|$-QI2pK7D;!FPpV;7uKm5
zlRoI=1!n!c!R@b^Sb7?w%?v;60GcbY>V!22_@#}^MV>CFTy)oy(SX$~cS0G5!7E^y
z4lh~A-N~q6*!AALP>SF2z~}}p#G#rx2mBo?{W0Xbuwn7<#bEiYL$#KX+ds6QztBok
z^51>1!9J+7a=%>3%T#)6$adL{#yVf;71KA#$#T#e7eK*tL%3g5h<!t=kAQ++OAdYL
zEXOfga`{F^mEnxmZ;#q`7~72M<~xpLzK3@>Th7_GZxk7jt*{8T`<z&yrd@<!RSxD!
z2z1-?Pe^CvZFt-|Z_mem9V3B;r!MF@zLao-KTJ&ubDv}B&dejU6*}BX?rUKI)4WBv
z`F|LD>!_##w%vP%5fBhi5s)qg>FyQ;l}727l5Uux8xf?tI|N}sx<jd<yL;#yKpM{G
zyz4#Ztn-}pt?%ExhS{4vv+ud<`dv7<Tl;2Ju4@TBEBH6h>i3exqQL}2fJjW|<+iY^
z<a#%a6Ycqp++nL+OW2JF6cF1B%XBCFGmsU#b#gR`-B|jW-TEY%BHGnJwuOp=uF`C*
zrl<Y<{#mj8rd{;0le+dz%mf)7e)F%PLp%f7yR)@NYfkr#KC*0wlfsp-RIX4gE}O-^
zZ)qcRT=u7)a!!a{2%Y2DP;{u1#%#6r7@Zqni!pUip8VTWYCW0#?9%6;HX2JCuj(r@
zxF<UDhEQ;KTa3OtEq|BRajra?L7eiLR)x2mNhf{YkC^krnY{X)?w;=ZRLf_F-r{_;
zQ%qemm8@LG?LWWi{`1ParSp-YAJTg!sn?31P7r<KA+6|9Wo|OL%9~I)Rdo4G@Obx|
z|K~>U^#z(}ZmL5<t#Tx*POYHhe%LK7vFk2@R+IauM|upo!qsJo;!{&OR}pE%(GjAw
zQ?29dTy8T$IAmN74$^EFY^X)M5w~RGB9H6xQa!#MczME3kB@iq(`aof8H^tWFG)nL
zf+p7(V(-UV3#1dQ$WAG9*E~b)XzhH&*(z0tdp>2}8GruA?IFE*5A&}Hdb?TT9g%i)
zWDp(1oNFv(qp_K>M2FwP+p`Wmv0^>1vZJLdkj;EIzTHQBExVquM7N{xY@7R|srVJ5
zM?<s_pa+7-|AqK*(>Zo%MB7~}@>9+=t<9(n-t4&$yO{;kuspt^wc5P->M~FrhP!&&
zZo++C_w22v<O^$^mk0a(-;A^Ezx*R1mM_IowzT{?;L08Li(<A9RTa0!1=WO#aPYv{
zt3AF-4ke5A-r^henkevi`HR<P80~i;UB)&PD)qW)x^ko)<%BQcEGHK4=VNJf4J(pP
zl4aS$?9rzkY7OQs>bvw7Lmm3CrX10+Hd;O;HR=teb5F$>_6EVRS681H6*sMZXI?#n
zOWYod9d<8oSY>oY_U!ru4kU4QmS!FW+c%qrKBN4Q*Et$;T^BjF^}S(bmZCJ$R1p<W
z=<iSR+xZSK1KhxioFH~HV)>=XQ8D{vI|#nGN{_$NGN6h!THg(!RE=v^(0huAV>6z#
z)18RMm*Z}7*mQ-##G)t>WxFGD(GC5n!b7EHP-eLGccC@c2wm*On18q?Qd4n3$Z2t6
zz%5v1dpJ8FNJT^U&q5fa1&Y4>PwC~5(2jInEtw}xX|?9ranhU5@Tke(Rjg9WB;V9t
zE@We7#6oQhjW&8HSq5>uRPoLV?Tl#3^nW!}lO%koZEDQ0AL@GEKl*soSoH2PF%vx!
z@7|hGj%L0SyvUN+hI5eNaiF3U@M100n#F3k*VRL8Oe5=0MTEOl6(Nx&%hkcJ<*Qc_
z*Kzq&nv>D!2qq%v73)4;J+sKc*?7oZ0B@%K`svlEIF!L}7$>mw?|0d2va;R9$U3`Y
zrYwUjtH+1vi+V?8Mp_|?mdP3r8-IR`KU{IH4YlYtzX(`d#W}t6cCN7<G^e8%rj|Wg
zYx`G}NO3t@vZxL|+12u3b%fpOL!J;2ekY2GWcU>mY48%`n;}hQ1IISE$1#2VQC^>;
zb=VvF3x_RF#^KUH^3kI2wt)@px}9Vbj102mdg^zi70P|_Yy}xW5G(OWDcSX~SX0Y3
z`L4TEQ6X}6;<;}=vdK*btOSK`^L;n%KLscc!3`tq+RYypUslRz>}u-#jANz8Nhq7(
zWO%12o>S0tF(HyHV4NJsnk}cBU>C3NGo+q~N>P<vs1>ijeau)IS^xYhHb;2an1(&y
zh4~W?_DoTVnELbgkS|96uCi=rqR<CsSlpII<##k9+0HEsT;g$QYct}Qs+kht&I4G?
zI?C7r_^c-Js5h!bzAx0=PQGYU>G`t}%E@&9HQ~ye2*Iw;@zTxxZx#TwMAu`sUA0-)
zdEQ#P+`r07S9N1I28=$1GHJbZ+`Qyn2bO!^A;<p--Cww?BExcGe!3^sswNV%&Cm|%
zs>-ri5PONqr#I_L>)l08nBwg6TPMWt^ett%3fAuxIie<_&>d?wGKViYCY+3lv)In8
zCT(J7cK&imT}zHkliohB>nNY0_j#hij$zX&v$J^jC;nGnNelCf5#%!;yi|6^UvkM-
zw?8My<ZCvVJpQWZuU{MCAlEysa2jVA#{3w$Q@wM3r7QGT_H|MHX%aZvoT@vo52J!`
z;;VkcEF6qVZA{lzvsNIsg^IK}a3f!XQSEal&o-KFUkAaX-r}yS;lBCYSCX?o=6HEK
zq)Gy!+%16}W4!tKT5&wLd-4(Fk25W=l#6M{(^b=G2Shhjhv&RsW^DQIGtR-z0NMFr
znoab>w?Q~F8D!zG%}h6M@+xS3>8)uF7YR&Dr&hWh)3KiIhMXEh074!^N$d%ItB)c3
zts*}3(R?46I7*v%K$IjZa6WV$36~T5gTrU##fUQ;sl#PcVKb+W&OYMeAdl9D1OF_7
zk<f#9giXyL(3PuErl8S>soX7%Wf)%xb^%c{jFLSL^YiX#wvn%?R+)Nhhm)8YioK(-
z^x@;IO%x3;??g^BZZxLeN{50-CE7pxIgQ;a)`>6O4p*^>f1}0M+g&oMx8sHsFYqFg
zC5$SA>8!?R=s}vP5xcypePB(-v1_nJRG4?KQ+)YL_?Q?MSlt5CPHMh)WplR=yz~@A
zLQCOEq3JlFiVL8JNxb?l!6w^oRgDD3dbIBuBJLi8*z~Qt3o$7~e%DZmE*IPZ106CB
zdVIvo>AhiaLv0}yc<a>eUmn;nS7Qh9DrgCRi&5YhV$jE`TLhmN1M?%csiz38Rw65b
zebmNouf=QO28^`d^S->Oy05=QB42wvC1C6seD!$Us}0CFcu0?GXq6Q#Emk|iAW{&+
zW0+NKA1f;Y0l@v?ST0vVq_xQF7Vh<ygHQQm4kyT}P8TXdbep*Zo0#Cpe-nQ1u5q$n
znEc#Lwjwm|AFI$E`Fd+PvdPo-JF6>EHA+8Ug;6h|at%gwXmjK%U`cI@vU0xVZ_HRY
z-?Xkj?AAIKVEpNa-BP=Sw@*xApwZQ@%gFD=qqn|BLi-mbml#`ucx|)$EsnilsAh{>
zJ>523^pK_<cbH|^XSD+=u~(G!o16g5`DH=Gk!X^4M==#^PUY8E3Z57g_Mg+9dcy4P
zWzkI$mQQ>B#~(iuMt#SQNgqxX!^?y3#N<ChOueZq)9(0OBkH=8@J9Q+q8jMQfNrHe
z!RBw?YhB1rA-hI8Q;Y5CAKxmQT8C7g;$K}L)*64s;G5b_=X$+F_AUM$=eEf<Wa?`l
zzpq}x<a)9~2DSPJ_u6yoC&>*)THf{LdW7{?f86=7Dr8_b+6^i@H^0v#xV8*uiF^_j
zqX({L8n-q#;>tfnI?pJ<u(KHW>I`(GtQP7wz<s<vO(Jjv>*N#HG71qB_zPz3e*ID{
z?hwL2=XF1S&$QZif#R5X7+d&S+nDny9s#2sehXxkd+eqt$9-Rap%t%yAR<If1EhQE
z<VtguIxz8E8qHIpy#>j>-_F@{0uzcnIa=lv&FBf3aO)dqbTC}L1bGb3b>8&IiSNcu
z*vzoh`@YzDE`PY2vO8XEY|PN+&&f{$etR*BG5HzO>qsiHjCp6%<&*;oR?Yet&y-pn
z?Wt*A@g4HEI2R(+<!(@Ni0_O-+ah;%z*Qk;$pINjyo$wvS{(QL@;g@6EJ;#U8hM@Y
zxl)pgMmT|(q$%FZdH=()_ce=dr}8R`&BOxtxU@xI(O@FcR^ir=@QR<tUT3Zj$wjfX
z&-UA;pSyt4=rH#o(5|aKlVE~a&>o3ze2Ukz>FFWS{`bbdpG@2Q<@6^{xIg$0f`;Vv
z(UAp1g7w5w%IxF4lKiL`4A@r<K3OT^#j>NbP^z`$i^1IEr`tuDttQVl8jjpvE%CS(
zlz9|pa`-6$5XIj<{)Oia%YMES6n3#}>5cEx8(ic!OHIw*wuOqV!&t-Js#zN>RORVA
zJXx{~s2RNQ1;3KfGZgVtQm-HLq!D2Hm=rF!%=qr(x9Q_pdv@hLKjgOPb8GKdQzZQ+
z`7OPp)Av>lB4)TAYl{7cX*<}!g;|ERMt@CCN#y;(E~i}ZZo}3m!mQ0tS(BC8DzKBE
zM10Oi&4+KX)!W<D5QOosEw>X`h0MC!Dk6#H?^tx3-*vN5fa2=R#yAfYGGqhiq+7UX
zm1wF(IyYzVs@MNWS@mcy*#9UE!Xe`5*KGahP#sPjmuM>@Pg!S}9-dzRmjAO3y_*-m
z9OeFR<qc7%rq4`zpD`1+UcO}g?34R<&Q!!&Lxy}H<D%&mEQRA&CmUlr^pzZc;kFs3
zhI#hS&_hBvquKUmiz&n14{Y!5Vm!+d^k!c)*b8-e{>gx^xm@o)7YfynoJ>ktC%RA0
z5RW=r8)_6ljoP=Akt>T=%5!X)2D8ef`3dosrp{|~Q^C_Fn5cxY&;&LOa$0^jYZI%3
z?%kBrIqzh;q1y@x`lfMpet>6>sm92ZIX~Rjg07TGG$|%UlJAV>#~0w}F2Bg>_2uth
z?;=USv}ZBY2y5yMP1)lgYiS|E{Gi|eG!Zk)=Dn-0|JfUF8tF@fMsAK><*meiI-8?d
z3T)VCyQJ(k&rdr=6%e;Dyv2Z_4~?zG>sgGhWh$)9TscPRrxSDrOtqg_{~TkRG0}hx
zc1Jd{@ZdxrDX9hr2<ungzMB)Z2N8u+g!49uaF+=@{`=AT1@!GRs#hrYt7T|h5<MmH
zGScEXjO&hu!LtI6R^TuZ^tkh5ui2-j8;0jia6Z?a@m{{-FP&W9Jdcq0%V-jvtYpmW
zruBqeJ^0Dsp`$;vj~|1rkGln9^YBc-*`R0aVRUj>NxG)FO@M9vv<YOT3bPGs%4WWJ
zis|JoU{%!r5%(f{zOIEwbhFGDB@DKwX?j8pdJamcdB>)k?I5{Gq2g1Ty_J(_d-M(%
zK7Ud5AAmI52BOa)TsjL(@p?XV+8#BO&H4)5%=kX>0yluo*o4ne4Q1NROfAt&fYIF4
z08$HX?^pkMEaGYEp^AAa%DU`(KH!0T=wh^&gAZN>Z-Hd!Dd2CU7%OVlL^Q=SrycEZ
zuORy&sm+Bh%p5N5*a!RA-()kWU)IRCQ`zWzyZWOTP~jSJP+WNM%<wAr5FVIiHC0B`
z73>qWJK}=l8+?%<LUy_mB&joe@3rc8VdzyqxoB>C9N4%C5=KZZ;+?W4tf|P-CwO9k
zjG*z$^h3`{>7QR1GeXo|>7_0l(a8R+B)2CKq)Au4MWK{tSJ;hX+$qJ)STsb`;9f_4
z-(pWrj74beWAt_1SNuvzgo1`LP#+Wcqcx6%pc(RYhMq?vocm>~&&M{ht$4-e+!~){
zius+9GwD~stL#)i7O;diF!Rf?($Q_&#W7*eM-1!zjU1sEm?PJfyu!_->vWbq<}!6D
z)~xOrHEtufYIuuXls(eGTqmztGL*74tzG?=^Kxlap%P;m7p1-<E}8gqI~P1`pv78$
z$@D^1?#j$Ut+1551WCS76w~mNI4aUR<IPDzS?s15)$997+}@vw>}-W^zO|nC6QTB5
z2i_I@N>sDr+X>7=O<2BAO|<;xSY>Gun!_t1Cgw=d$Wa_Mp!j7*p!T#~qT|c4yJ6`Q
z?Vak+zw2TH1X?%!$zKvPVy}uQB##&_m}XvLco`^X&9%sIUh482>ioO{r%A23#5PP8
z-YLn*YyH&!`12figdM8TVo58N`9T^}_nlP(UmH8CaYG_9*y+0ZV~ZP#)B9)Mi0hBZ
zvwc1cG;5&q{*HDdPGlQ3ie38b&|oPdo<WS9jj;N5Zo4u>XUK+LT)!~Gr=+HrbV02p
z;ow5drI)_ApzKn1JL0<gYbu3mtN)FdVL^Cu(_?h%!NGmQ?Btsd)(DAzTVyiSZHl?3
z48dkYE)UTlbgDfD;;{HZ0M$YAN(g@i7U3wDR~i?qzj)elF5N4s-+pomHPuJ~-IxB0
zG@+fzdhv!%uCNzdIas)tLY`P;NOgszKTQuQNF{CtzmR8ex~q_U7De=`C2YrLYgbJx
z6>nv>G*#tc24V9<5EN(^_*)UySoR_8#W$`q%)}F9(9*BuG^tPv`^bNZ_D~pUB0Hz3
z1;seLlc(%=Y0w6@p09{&z1fr_J$nFbTi6@-aa(=@yRitcqovL+ftBgMQ?TqAuMGk_
z`ceU9D^!n?K-s4bBV16atl8%|M$zi~msSB4i)buZ5a`X){cYxc)DL||Oo?w{?7kFk
z);kzcrc>5!#0fgirU&bOm}JO?zkF}Vc7M}2qgA~4nO=7NcXgn_2nDwiGkl@7&}S10
z5sw2J>%wkpu1~}#r65H~J-$u10{A=04P3DH!J|{gSuCrQ)}_1Jcv)c(n}bH7XcUQD
z%`RS>$NX{khtSp6>&<RU++t8M4o(RB?901xoN_D)@vLVHBYg6{APk3dnO4vj5h+dl
z9~sR)qld+G*N@@X0@S3--w*;k+6;avOjHtlUPE&vf^TAZr>Y=Utrd5b#zF+7<AhI#
z*v`h|7y2D`GdPWd&vYhd4=rOJWjwWkzndb%z*=}a?$<KX?BRC(hsMC^SgE8!N4PQH
zLnvZzOEJ}>HLHaMQ^My|93*-UwviWp^^djB^bl(&xNKXlK_#vFnN85*px8A_>Xsqb
zV@mm^EV?I;w@4CQo&Q`pmnCPl>rt{}Ut!&X!h~)qhn14coJ!>NZ;v_oxKS`5kkFSG
zxHM<cC47QrhC%}MjuqJ1nvdlBh!SajU%t<j5B>vRb~zH!g0){P!@kVw{e4}qusfw<
zlx&!mXS-dOs{_mx$nA(!<XmsLf{^EeyZtY{QoMi4iDfeV$fTss)Eig+8OP4likHT!
z*C?NGC?=?iSEl+lL#5;#Qm#-lR90c;dDS%GIKm(_qVciS^%<EPsX99&4u<0AHhx9c
z6JFD!z`u?s<_!$;SY8Q5XU*UIcMm9b`r`FuLJy|sDI&}AGezfE3Y+;SCEPYkCYp7+
z$8;zu*>}gUe4J01D&5n)(W=j_)2ZJ_tL`M&mhJE4+>L$vtD1N5S+LlYu()`)%WF^S
z*0sf<QSrSrStmXHY0AimiC(k9nDp9S;)zQ`tp-OF{7hI3Fr%GR?=GENA7N;05_bry
zx`f4$FK9B9oN+sZ8pqfsFp$|ZXpAb1JE!v-xKto6hTMO22kDl)I(IH&C|u&yq%0Ho
zTqacA;`RKXWoTm9-jc+{s+{F_!qz&++2f;SCrcMnrNm%8XN`X)sqY8+ULzxoPS#g2
z8TtwjYZLl*{PAM$o6cF%lYjwK+dgVmX_o%eWM9_KJJZR^GgaMId>^OH5xx;e>yn?9
zT5e|dWrr*}`o3~^#ix<?@-eX)I}Ahm)5_vj8lT-Frp!+PwtUDvrNu3C#lbPQ*}qq_
zUa=b=@rGmR2U7jo{RG*v)u%hxj8;Ar`FUwV@JP>BnASgxROG3ZsS4VjVegQ@`({6Z
zaM*XODT4`Ps<%OCpr^hBhZGVF65oeP9iBY*gr{)QgK6Or4e>vm1J^Ki3D7_jH9}Zz
za-y5QW6~K-!?BoxpvS&XLDWQFB<LpvCP^V&%LK3b)>hC2z|^23(ChU=atYZ&#q{z0
z^N%j*<yKl)m`@szBi$NhBVjt_Sm$-i(Y?4V&GO_RlS>FoPHT;}#_FwKNF(Q!ksdEK
z&eKxiFJzz`@|}#76ejrsfoujAU=MetX9M)2gq0ds!<>&9@@*u+muJ3A52JKll5z`=
zV;dCXGJhJaq3`u~k`+SgklJlZP7?F%1_Ui_Er_trzz}Dd06CP_T%Cb-iRJyHBJQO1
zD)c{1@r6`#9?1$DQn-?U7sg`;mXbWP$W6BM)xDj+2N9)1Fh=;tSHg=-XU;X+xY4vg
zyqdFA?pritgkJGNBZY80$oL3>=Nn9tB77Tk>Mqj^ELvxrr{=7p1mcQQMPY(o5#BPl
z%kAr6-#8)YwTHX&5Cg^soRKB3%Kot|I(jhm6?WN8kNsiu`D~tOB^>-pDCB6zQ1!9b
zF$w=@Hs+Q1t$!$w*i0V^?F!xFx#4XguK(>1$Ku>`96QVl&+CQS$ti%-4j_^wL_yp0
zVV)mxeHGK|?7+({+q7Q3N?qALs}>#?4)-B(oVfNr!)%g=A>qg;pZ0f2+h#q~|Gr?f
zTIx{LA=AympFB)z<u%HePp<t)y3=KsE3~Vc5!u}{b67-7{#8Yxammv&&{=?Kf@{fL
z-PozK<vej_^xeXM<al~yIh#R*HmN~?x4*UlRUN$=<v}s1VhPQnbZO3ebM*=(oe5Kw
zmZWwb!8&{L#_h`er}MSWuiexd#Ooh&a*1*&9|+L{rnc!V8|E73@2|;kgSR|?EYS}z
z<@NYt>ZGbsSYH`+yN~V{S~w1b5ST(Ow_i$ZF0_AFCO*6wt<!#np7C8i9+VaTv+!jU
z53}>{)eTBYg<rv_8+Y~EZ+V8<5z(TGR)nDly&5Qug|1>ZQu9VosGbA5C{({4@Bwwk
z8uSI^74qa&iKhQ~R?)MZB<-Gj6&0_Dzk%b>b<>=@VDAtLp?n3_%c~h1$Hl~0<UVo8
zPDA#-qot+8<$FB@f~nlXGl{PHtFEgqiaC*uwspxRN#t&c({a&52%MP{Khhg{em9}Y
zrW^*7{MF;Z&l6+SlFmkT+{>T-4VvTSB3S(aR*wv@u;k37o64A#P;aVJJGgPFX%P83
z$Mnrkxdwjo?<iZfQPo-Drl&9>=30!BnPO?8_r+Dn<l3B4L=<;KkxgZHJXLdF^pfY5
zvh(fcdoazIG6bduxqI8AEy<$LbRp%7sedYP6$YR3v(h(#`E@8nCw|iBfzd3#=4N>O
zl+R<(x>%(-82rZUdw>KNL!XE;G)HGPgIHL<Awc@RO|>Wb-^RPtswROkOb|zvpl<^7
z+vyP&nmsBiql)~)+D*?I5-OG$%5z$4BBI9zT^)?zCC$P$%zDz-O^fpeW6N>>Hk~Vb
znb+%O30EbT3TQgJB_p`-kJ-Yv9~{oe6@C6h-ago-K_Wj?+yRfh=khX|<3J9jLeCYQ
z0p00tCSi%SI_>Jx!^;y1=W1d*dX>g;;5HlK4(T89p*65$AH}@J^xVP(DPBb3&qVe1
z?)uvb0|UF%stOZd)ceS(!~l0Yr=6GM(I+{c9ePXN`xenby=Xh3yy?(BS)Zs#1vUa?
zXP9%;HrwRVH^9G1(dUcxS_;=N!AcY&D>FRS!MK~`@n6*lMaIb2MDj8fcU1QKkJ#^u
zJm7lGY#D<tN=rz5nAVAwfN4mCD6V_q!BZF4%jnMTGsA^q_H8{SkP|?&#`%ZS7tGM4
z+Xbz!xZ7A+Z2gAm>(P1w-I35D*Yb{e{ECs-j&K&VK!>wlIrr`&whUu9wKjKGWZ{p|
zAMZjxg&iZQMzS{wMHFPYHWMzr=;L||{IZ=No8;ZJ%@??LW3&vO7-_klA0UAps*f%n
zWqF3~VJ$F<b7zD40=0MbYiR`-z7^Bu!#wMZ`gu`t%5FP9m@sXR(a~ai>e|bdcLih7
zHBD?5sy2?j8{YEAN#XUGvbsDKfs7WB(dkMecMnc`usOxx$>e3{rDKaZp75N)7iKZk
zq#DYdIF=%hhD~#S^BDCRJNaTBAF-B-kbjZoFEtKb!qe~K_!kh4-B~@4rY<H@6oM$(
ztI}=Z9!ivQ?p0->vN}mD*#zx;8ECG>UxZYVxu8tZ#fWmwkqwUwn-#ytsd>duwo(Hl
z(Aun>-@3)L8(N$ii7(=&T)UbnwewBOri4D(GA^8@FYM5;8^``579p)+qT(w*@f??$
z*$@Z<iFT`z6Gdt8F7wl{t2J?4hLPi6hJ&1SC14htrX1bi1~d+qFL!U#HC!6R5y;!u
z<rB}Q9O%Q~FTNl#l;MlX3J3Pc*DRtlV>pEojo%S$6v`MrcxA3?w(9F268N>I4|d{{
zMKThO(Q()&HA{OWX>r%tE!ykC({xChHeNNcKxcZPCVXtor<4(DWLX1TVSokdBE6fv
z0cM(k`*uFj27zq5tLsc!q`~1f=g&S0_9jU&{a`9lw&Ro|6Qc~VW@C4l|4O^lrj%yY
zWlpS8@0DbwXNI^)FzI7nxH;*2SA&_`BK}v)%vw#}A-YV?2d{^ywB8*Em1~XikS1X?
zzq328GJ3yvbDN|YcG^KOwvU+F{xQ>7;xP&}H47tylA|;gp!67LaXhW`)M;G|R+T(N
zRJ&A?-hwWa>59FK2Q*7SEYXDY>Ib8K)?LSx!qh^xdkHC=W4oVsi+|&TTXlT#3B)|X
z913;94#pgc{YyY3PauL{aBe^Od@Tb(Z&^oWjC_>#yNl%fL@)ZB8H#@2v*wVoggCJh
zFCacRCJ$%|*o=#QJliyHC<KMpovgjgGjW6_ZQ`O{^_Qnd7M@#0w8Kq2pN~B{aa5!%
zExwV|)M(aSjX6GrG7Hb%vapK__Y!y{KiP1|2;Huy9~^VU6;whLB4Hxk>7(7NqU6BR
z*zuZ6yV+NAt-JPF=zK4D^XS+W54Eva$(8$M?{wEf)PGkR#qmp>R4V}1{3H<onr`W&
z6(W<8f>9z~@IAw%6MPiSuaIjPM~2gCZh1gsqNt`TG@}K>e?P|iCx@BOIH*%9hK5#u
z!cg~AEV3YSF>%Br%Ha{pBT+;%Ej{IDn2X<AmGd(b5gy}|<Bkihq{tHYCrA90?iBG0
znVrIY=Z<<Zt%@QTyE3{!2jJk+m00E`LR;3!!Dc1&8D{}~F7Z=2vbw4<LvQEbQXv%$
zR!mc(Br(19zXw?eenzf3ipn7T7<6;ig`Dr$!Vn73(o2YAJ98iK-}`Y7v>O8>T1jMZ
z=}8?%ox_>g90SXilWSewro*q`)&1AFaKYzRw#T4$vcQ2xYD%^{iCMWEis8R#Jr>0G
zX_I$9pSTv8<BA?E*QFH}6r}LWPT&8VBEsJ#v|Mf*1=+6QsN2TT2BGB8UE=W=$0Zak
z4KtTsu`$FGpcaRCqr$k2v?kUgV9lkD*e8c}q#%%_B=fH+&PTFkq0Mf89QQJ5bBmLZ
zOH1FpySS+MQgKwL@BL>V4@uz$l1?Y`wxys*uhLH+21dacZ+$b!wDAy+d86Lp76m8}
z2?he08E%~mIme$&g0|r`5f+gH3S4O8UirRc^Yd#eqiZX#b47M+Ib7bSmWFm#hCWn?
z^BV6?l-pP8M_a<q&oY8Ydw!EXX5E6-SfIiUX{h_S6)Iw>G0~K9ua^`DC@}&bp0Z4f
zWr>%F^WXGy<|PES&ufSH^xi-dc;TJXNv7(818wFuP7P^MK1F`YkZg>%z;!<cPDed1
z@o>OH2|^>W<k5gHw>U>S9Wv=3{JX}Z@pm9H<o|rtfBjhx3>TQopNDH=oIDqhTx@!!
zH0Tz6TN~(A%-YGjMRFJt{Vd=WSE)}IcOc`#?fE}W^Y8|H?{L?JPrvp~?L3Y(EQg$i
z`z|y+%HyIv)VH34Z^?f(G_XY2<GOc|@o`!EO6z;kt&w!Z{l^6Z8j1o8+bQ<uJsaPH
zERS}7bS~#DWya4%QAf?13~iqPk^vI1Gauw$Qhm3<di7()A07L#@T-t2JFB+q<rd|z
zVgHO|_1O?U;vV_xj;jB7|LbOqe<|n-?IXZWe7-SY?$v?C@O~ucohi`KP?Z250e0)A
zd(#!8`^mPBbH8e5_|<zMu*I#QLB6e8EYbN<`{EA^Z$E0%X4N>u!+uFA%6FPbez}94
zZ1j@ZHPHauT-jBY6HIp>=ujAAjzF!cc@fLN`&2vsyi{v%apreveJt~z&HCI;c-(({
zT6-hDEv;9=|MVeS`mr5BICI-$O5&^lu^<kYjPGUA2C%$J)%T!U450*MIndQmGb7BV
zLU1cKyqU|w*Wn4YQ$)eNIRdg1$RjBxDRFoy=(;<pFetG0Bz6Yo(PZ#oLH+JM>=VTA
z-x2}XY3yfuB0Yist(Vei!WwM6@%5iA!7?P=_dl-7dQ?Kp%je8(uNiNc;sr6gzOJgO
z>RzOK0+U)FP}rPKlxXNofH!xi$`gT|*rcFl>Coq+a3iT=t=|QI%k>)73l!6t2$|L6
zkcdP?Rq<Hf*NU8nW%Od5+Om9y<tFIymt+xGr9F#IO=*6yN^Wre%um}r0tUsnor#jv
zoV&YgJINN8-N~6(9jDu4zrulKsHAG2n~P(5{L7!}znB0)F~?;yi!tflx+v}=kvIRO
z>*gN~)C-cpne~>nI=q%4-)L4?6rD9;i$vb@@lP8=nd=;KUwnN3`<DyBjQ5rbR`!bN
z|KsVIpOElgU@vYPcCf%+$U7|8Aw<0OpvY=8@jaBrMsr$CVAErIP*OMjo3(P>ub%1y
zmTy^3Hir`LZm$t4vJ|iDvew)qs&9eAal7TM`tJYs7(W%rM;mxwzEc!E`wSt<m5oez
za8e9@LPE<wZy(TfzUYBgcL%&U4-jXf4TszVd;jdQh4Kk*i{9On9RQkAo=NTJs|Nsv
zcC}?M;N{5Vb#)po$KL!|$a#x8UoNo*u4C7%M(K0o@NllLAC#=eMVxWB2C#bu)rOlA
z6*@}UR4||G_*a?@q=<}v7Y<YP92*3ANsURFEC^~GqzOF1`rpyxzn`9{Fw$kcYH3=D
zDYy$~u5$)>Ypg(ld-oD($Uk|+qFvRaI}ZqTbS=gTeiT1RJO{LP(x7RD6rSkUatS@m
z<xR92`mp>bRATd!aN{#Jjj}XAqu5ottJwp1`=|--SB;r$^d}R!0gJk1!(Gn}%JoHI
zTiGG&503Bu_UBSxr`^=eEdlyvJ{k_dKprwzn)t9XhXf4dYpOK2!JF*Or!n(@pKVRD
z9#HwOug5AS0P4Q6h-^YpVh;~n;`r1oLBssPoj)BJMOgND70JfcmJ>O|uv7jV0#o6d
z)NpLAkDVVk9qLbtgrq9|uMnDzQK<rH^w3z?1ucMqRTpA5-7K~>blj{p8N6G-@(trL
zXiswxJX%+}W~KwkB<2hwnylX>rleS^pFa2$r|{WWUV;g9#ZUUVF=icHx>X+x>mWz3
zI!GfXQgN4MB1u{Im8{t{v1Xne0Os`k2!U-ryG52B$@;pcHQS-+UUd3Lr2MulHgh$+
z#5cg_A7vWC4A%=1zmCTsuilW!M~ek(Ra}|Z7J1*B#h0HP8>?Tm(?y4uW4P<izY~w*
z5bnc+{~ilJnva(cy{U7}@O~2fS}rpgPCcZDM0^$6jE+7f0jem+wWO)_59Nc&@v)~*
z&n3)G3K$?OGrb4|R^N`%^$sV)X)Liz1;g2?h<AUPyP&u@{aWh70HGJU$Mi%@xzc>F
z4D~FL#V$v;eRmngwfMiAe*;!if;1jxunp;}epG+2>xa0%<pHvNO65Zyz%Q4BTaz1c
zCIMG|H-Xmk-t{o_0~g-|cB}_&HrR}1OLP0;hSc{a0ACDFu~=(8^Ch+WY->ac@C#nM
zK!Ai>r8LLDTTge}XqKK)!uy3>ByKBB5iDhn$Uciv(0WBnx&H526u9NV0UEM7i0O;B
zf`<<t2UI~Gkn{4^y9OTXVP!?ZvlmW3&Up_LIBt4lN#ENY^%TLuxvnz5_Z%8Z`x?5@
z59y6vdy$?ub*W#@1>7}m+z4w6(ai$QWkw$%&QHBxu|X;2Q)f=XQN3S<*v09#xnsI6
zkzJhSy|Mb~+|1@2UPmILcO<w&iu(Fyibu|i^<MLtJC)1}`yWI_BdOf}igP;*4n_6?
zbGbx)C>LcTHUc}-1V+&54-Df>xX{-EHbX7k2osa|%Jnt__mYvx1l!yQZicznDnvly
zR=^NYD90|wywbSV+;~wMm%adtg*Z!iDP~7HLm_3mTLo5%bs1|J-ixr!-cL%Rrm1B{
zYPEU`_@d*k9<n3vzQ2fO(`%USvTiY3Xe~VWO`r?8s1QNBqlv_!)ao2O*grCT>pQSU
zZ8y9;co66`ka7lK-4Vc395}G*e!k7xP3ciuzX=33WWto0bX=i|&{?F6*_xet-v@R(
z_Z1IX*C-9(2I$Yk)3=E$G(PI!c2BX!b+2j2#N64teKF%2BOJvx$7h46dtPZ?dL(qg
znTs^%qqxa7S<_LBOMjNp7{kb9Gi$K9Eeq%(EIyeZFy(r3r3}gqy}t_2bMUTr=(j@U
zX>9iQeQ^KgLH!%XYAhrUe09Sf|Ns2zD)P|ZE&=x&iLDRR5DvIqm_r0(2TW|7Hu{Z&
zSThf|del6@T(L$jhsB#hMAh5fYjF%282&%+cUqJMK8zl7Yn#ypALm0XYqZ-Qsl*eb
zU%}HnWq{Vxbx_-mW!d^preX4zj*fu1uO**VC)xqXeZ41oH0K?T4uK-+WQy@wnR12y
zFHr{T@gkKCCIh(PXLLH5aO&w*MzW{t5=-EQ*TYnpm!%emi_ML{{OlH+>W{kl7ZO?Z
z=u36FR}V6yPnxz}r=kAVLLbJR%%%$x`J0dWkN#BwGasi&B@r`V=(F_46<6AHizm4R
zRtbN?DVOj@c{8iD-3dYTKGEtHfy7PNH}Y_F7W1Abpd{)2&3p$`oR1GpEVw$j=`8%4
zM4PEXaWXK+%>Ie|&%2NKkGoGIPbH!h=_AwmJ2o*fnLs4n&6euCuwn0`n}@2>&|0_N
z@sNBPFng?Q+WwnQoDtq#)=}BPptbm4^MYjg|L>4N5V_sEygeE=^zq^VddMcmY-;Yb
zxDFCc#hD>&>~SsNKE35KGuqhNv~9=8GG;W_aGeZ-WL4;{1wX9QlS!DT3pSQR#%Jbj
z8qxM@x~bTmHu|Fh496_np|7i}dUyJrKMq^ekl1y9n^>k!z`2%rx+&QxC)U#{&4wGw
zuEG7F7t@I<IM)DU!P4*sa1-Z9bSu39zV-Rbs1^bNH<=aAaS3|fsDACt4@5?&|45`B
z4xs8VP=_{`F<$xurf6~emg7CHHr**+fQ&Kq?^WjgO(A%%6JLDZmxt_6#feZ`%D}<{
zIpy-QFw>g&Y>hC_*vses_8eE@(_*^YBIARqT^GQ<I{rm!Ob_mPs_r8-^od%2MO3FN
z{k)X+Lwu_LXgW8M{$t0s0ci%g-h0z(zw%bFWKGL)#x!xJa6Sru%JVCOn~6CQ<#`K6
zj+2y!L4>mZ|MSY<;uc|`7=7nUIM9^`0Q<_|gR-?nE?>vhr_oT%6K3_&r+4;B<(d^a
zUiv>oN+IS>eDhN?DiphdBFvwQ42huSOl@Qu?tJ07o)>6G70}pV@xGu|ySJNM6`>S9
zbz_iK!7ugZUqJeK-6$pfi&y@hn*<Nn)Z{hY=v%|A_A?Ia?<3sI{k5cNZ{0EWZRTIv
z)s?OV``b8-jEsV+j8{8A>Ygw2gH(8dPEd7OY|#0-k^TMp1pnwb?b3ej`#Y<{4+KQ|
z|53IClJ?l(?j7j4lsDEvvK)k!WPFzZ98Om*xiZ*y;WqvKlIa2gS}CZ<WoP_2i^q^e
z%ohdh2>o3Xbhz_{8`if=`@*T8(&4MS-TALK`5Udk7Dpx2r@|Qg&&hu+h*jCr%bvlz
z`=!tiDt0=;|2w1lUj<ryfgg}o6D~33>xEF3ib@cpJvrW-YUja=6B(THhRl1d2`H^K
ziP&(71;RWz3^>x9RxeJf0tH`hoE#k`x$#6zP)s>ZoW0V(K?vK#dBp@`_lxNTr~O9`
z<M<H*<gUyuY9E&x;vYK`l(V|Ug?inmrBBSf!9z=9kfZ81#)`c$p*L{1h=t<@prSA;
zXKlC7XUU69%7_vms7;lG_TFj}0zR#MIuBKwL`HtZBIV4%Ngh3ysk!;3exb=#&!A(>
z@z4*Bas$`#zkGq=0}Q6|2CjYa3-0}hN>evIDewA1@RgeSVd{J=9}A-}Z`;PO<Z5;3
z(}-)2%&Y@|1xL<djd2#8e6n@X?;+9Ul>CHA(hOyicI}tw2vn4Ns)FgEXG?b$d>03Y
z{3fp32foNPx^r-eFQj<2H<PQKllJPXTuE&6aV2pa<1d~WALJP#Q|TN>K{>kHRJM3~
zB9yE0ciLW*ixL!iTltxoBGNkMTWzYQP8+`CU2-(*ATxKeBj<j`xP`{5={t?aRkE&q
z9nziy?`pe=yS4;ApXVl9k=0ula6A9*@NgS_H%gKj2mDv4p13EoxO#_}u95xP`{!i7
z2kD`T>zi7i6p}nzr`p7L{9i#Luf49<-=g!|&VNl6nN@wckH0lTB@HC(5q9#)I5~g9
z07KCLY7P6$A3(b<<573tyD%aw<lDU=m3l|yMM1I_g%YFr#80&vjc-bi;3Krxkr!Rz
zab$5JyIw(2sS0v4Z(`UZ_pk($3huMXX8rTETO4*WwlMU`_^snycBixqP)L0bS_8#(
zt$N4&iD%8GnEBg<E9+LSMb4Qk?YH0&%*iqx=67K)7MP9C#d6`d%F3m+a#PkdN-V;f
z`6<pn;yTdt<VW7j&%{<Z+m$d6OeaVuvCh@#EXWZvzxrEM#yn*gZF7aSi+YXC@FBe-
z3u(n)LhLBUm@AUK&?}JwNNYa;wR+xX`KiF5s!h^qJ5WOjHC=koSaA14xcMiGyXU=u
z{`8UI3c(Mc3@QgF{S71TZfyY#3$1>m+j9zK)vlVRJ9_D9AzxrBp=EuI>v3@z*7-n1
z$Y?x_rd;|5M+&hqi=-8PVZUv45*77!TgytU24FXrJ0<1op$r-Da{aor^Ch2FU5CGf
zqj?`<p$oXuU45sH?=x++IHUd)q(6Xj0OPjLljR2Nsr+^|?5~b45o-~`gJMovKw*)7
zVUibaVi6MrYZf?Q6Qi?O2KM{Du}d@^b>KJ-OJeq_Y3LEMFm2Z-9=fm14tbpIyav+D
z#{P8SsTMJRZ=mKIztDIulFl808qJ6rCTtpdLdm8104=&aXgu8G&ecyqfHI{sEMSM<
zhU7C&FWfIrv7Eae4FGZ=^LOs^2YnyH&pl6(fDT?e)6{XG(}=P^Hp9Bz`Gd=sTIO_Z
zmkzI}tP?|Tx!B=1B=LStG^UD>Mg6ftrP_*crG?26&ezkJT-}`V&E;P~put}?-ec<Z
z`wK>>zy4>mWLlMkVai$V)<G|}0Uk+x-GZblOAtHDyAx&E!846}S4vq{Gydbe4|&u+
zkztj+o`O0xwuyeR3Gee&8Ast}$&Y4oha!TQ`9J=$g&<R%b`gTIlQBbgDYX1&I@DHF
zqKe;%8S`K{+c6Wo`prpMV%E(UXPU`P*14XpQz|(Edclf%mW<j|pW92S#wt#_>GFNW
z2n-stfBdbd%t1b{TGJx#Z<1o0ecjteDrddTGy7?;r`Fu2R=8e%HOb%gwFaDR2G%mw
z8jz75j|B_*ujUjtsUcnOVzUQAa5If=p}{3~FkKh{P(0|T^gVugqIwxdD^2Bq-UEt_
ziZm2?872T}z~7<Vu-NyrV1FcB$f#dhH~v!(!BU)u`N-XSv$%fMDDOi!1B+;7r5Ybg
zl3s&rG?{H(`;Msh4K$g@BIX7t-wI-Zs(5x2gK-S$lHnlu#etGiWcE=YMwDxB%xg1T
zjPUFwfK#SBTVuog0JrcWjpeh?VM!c89CIjjayIj1gwR=}-H5zA^c4AFfH?*gi#%~$
zpdH|)MDyJVk2-Pn&cJNWwC}+B*E{HQvqohWU1)H(DSlt^YF#exO*zH>VP%80=kqF`
z`@7?o^<%bxlT-W6q3S{!7N`RB%aw@*V1}OdMNL9XEP5xC?)BoOdp$V~J0h|j+L09Q
z3p52@%Tjq&vFO$fmtE3iv>H+lrYAC-S}AS9#{QHD979FVMKbs*(*8h)c4b#@DUTgH
zj5m1OUJkw@*}nrMiZizzF7GiDFrf5`25|L3rVIn`J6Xu}^>NG<Dj=gU;mw<=n<apd
zRv4=CrtG}DT*EvmbTPK~z+yF~`|x99MK&U4%Z8F7;It1x<A1a@(p^+_%(i4!9YJ+}
zqkLJm)c+NuL~?IjYB`^Pz-rR2CTf)N5~+DRR*yy3_XEIoBH$bf&wk}np3Qgv`u+|j
zH-jeJ)u+*tOr0)HMr@iiXjh$so(J`#h(20Sv8}TH()>g`GNJXPG0F*bn!pz2Bir(3
zNYiEQCg50K{WOWQ{{60q|E1s%?(8AUR(*CAJX{%4|I6>_9UC|4xsgy3gO=uadg|w<
z>8TP8_M~>c^gsTBrhP92FO~xab`Sx0#7ETVtwa@iR0Zhcf5R~Q9x78H03&m#Q!z{K
zFqPXNa$IF2jF!LHbBV+;)mXJ_q9Vobro}cNQz;ES%g75T6nAF)Gqe&S^5+vq!pDd|
z45JTCrt;Q*4L86t{b;sS2$aX=7tl!41zeI*<DZmzuMb=85)|(}Ut>i>VD_DEQSq8G
ztGrjDfc4Vqne|djv(`|><dVyj9t!9#CJ%~I*)BdnKC*T)o29Al;4=7Dlcmu>w=LQ8
zfJxycthck})l*m+I{`sc+ey7VOI;uXDrj@I+G_tK83@ozp_mSND+Jj=ID|x_kHlLo
zNYKQqu=XVcHQfbsggdC$ZvjNjdO@mfO(^X}wbhg&{88)O71?Nov23w^vwMoW^<0Yr
z?G;AwT{E7(>=h9k>W?JlfmgqaulI#?QTJHE)9t8GA@w5V_Eqq;y!5i3|6)<VxdN&U
z(maakFi_|n7(IA-@*KDQ9ymv({pzxm(CNxKI#T(b8h^BIuSX%Z5wE!{&riVJs~FQ~
zE6{rd=8v?UDXlD*XA|ZczAq*diDuT!n2NmnzBsJ(Whs}%Bt!{rmjYi=D&X}Mvj}ro
z>+YS<&|`&Cj<kexE>b3&k(y1L^Gc+$Ppy;~r;j?$KC2!ZU;AdJM%aoMcOXUF>q0>a
zEFtGKlHIIMq?-KJE@u82B$RJBZq_@!;`P|=n3t?JrBUEu%~yXFr$~#0-|^cCwar4;
zbnaSOU*3H@syCQ*&Lp{Ex$wnkZ*L4E5#D1rL3InklIpZoZ5crqg>!oa+We2ipAoIC
zZh5STI)c3AI(zxMlUWLu%?xD&#A<SGX3mCSwjmk3)$p&(FCTERA=egH5o=wyT}+NP
z8{4s%%#yRdlcvr>ktxYVr_zvHf^Qt{xxpk;MrK2HhW}5potDpM@+A6YyDvrURB!1Z
z7KjpEa+<!`0xn{fEE#N3*?*%uVg^m`PwRulrtB8!2|p4h<S>z~dkV@HR#6}m81*%=
zziJ7up7ieE>YUkyy%=di%2_TLACC{uEnUkZN8jZ|?QrljKH91;-iMb0V~Qd$70)@x
zH8_EFE#OT6@1r-qH!1Bi{As6tSI9(z8sbBsCn_e^6G_VZQQv+0HAJoH6vC$@e(Rjf
zVdM{1)E{?<Z$&2P<N=vEm-DppHc%sLeXK}Y{i2leK>TBCS^vVQUX>L7lCy(FQu7r2
zt}fG9i-OxM{N`%6JmD_MID#`nlZK|W_JLGzx<5As>R>EPqXlyQXDyZvKx0xT!*Bsa
z3hcW?&jadU$JI~<QhbTLDMGh>kAr$yV3W?;Nz!(*G+B>3+K-o<1M9uTcv19m7Rx3+
z_l5objR0nnGL<h5`_!N8HK5}=sQ>eUgab;ixd7N>NTA~5@x@bayg?z5K&8t6^aeQ6
zv*dHn3KTgp_+l4r>qc+EN}vSBhh8&A8_@Os;!B*&h(pl~z%jTC+d#2D5@GaMZ!-A+
zOd5N?jz1Q569Y(R{J4wEj)&g~N<BAa;KK^&(0y22tdlDzZRN2+CIRbb3=u7mH;}Ab
zK%<E9>PR_@J$nvFNwr%qfW7J`d%4UKwgAi8v<dAg{Uhor&Po<txH6^JsY<Fyj!(9R
zNf$F%@c6Sm_0FZ)PzLM$-FYi+|M21#kwm8>20hu`^^A|{zz(lt4^4=sfs7ke_*8Z3
z8tCF`Yz$@E!PuJDRg*<bE30f5X0IMs;*xVHFM4m<v=6+T(W~A1>#`!-SI~;)Xu;n`
zxWAy@%#<XsdY7%h1`vMgw{pTt`yZG$*DKFJ98&rsxJO(U>WKkOr=tkh!<Br$O#p<Q
zZqwmgvj?E&Sj#1-PG?f>TC_-czCLZ8O3M0T0rq;jJQVnt;Ir6vfM-t_jwk^q0370L
zm!QgH{c6k}a!dEnW`E#w*i$_hx0&U+oNB!@MRHcCR=j<;I~KQV6No3(SBEjo{5ju|
z@Ur#(THK16{?ih`kqqa?`gEW{3VLd*1}A>qynI}C`$%~@N891j{stREB`l-G{)fo<
zUrm_RD3hF<YW@q@>MFgXX6HxOp;fN?GVS@svt$OG8tFV(1)Ae|Z!wqq4wr>~d73T^
z)s8}G7@Ht>evBe__#Bi4-Hu5u?mG-GPk)*@e(mhb(bV&%<dJLe7S;+bp98ufwCVWe
zuiP@Gr27)yNAqGoT^e!B*b_ELuv2R*6F3>9N3Li4RDF*0of!RhW~8c^nMie-$<B_a
z`9^%+@f**tPm3a;(WdP;OEwh^x5MTso?K8rPw)GRO8af?%kC1X^@cUe_e)rE$n=qt
zywAO8DCb7f(JXZm*v;;TQIAWrVm*P(d2eU6`n&!v)wkdy;iVrF)q6Tfe}Y{^dV}-w
zVGvnfgEkUp;V$*&B2vb2XJrRjh`f|W`{=cg7^zfrnYjEewSIVvlYc@?fUL|dK`=lz
zdhKxdzkekeBLAddD_T;)%1f;@6n(PJ8K0flM=ex5QsJnw?P+8zc)I+Z%WMN1c6Gw(
zI8^@w8PdXytS}(z8Mc-EumG${9xKW8$>k>M%u$pPyj0Q~*PN{`HM6PxvQH1&pF2h!
z4FBblRnu1{bb&3zppM=t<m*qUR;o*0ceg4f_}tk=u&BWh7XD$oi3ymzEi!+fe=}|H
zxe9YY9lY45>Ra2he7Bw*mhzq~9+7FBz=7$LGxknUQ=&)cVJcf{MO~F;E)z0>X>aBT
zdP(E^OkIK{2=w(a;`JZ7e@(EHy?GyyuCFu-ETlPvI~`2LzFyY{frEXXvL8Oc&HQ3=
zBZT@!K~b<|DlzN2d~i5881(xHJ+70$5Y*PRvs3aU3A{ngQi(5m8U-eA>jh$`t;CPB
zVc*y5A7AkW1sqm%Q(AdO6K8>rj)@H<@B()^j)74WpK?1d^+nB`?eA)2&-b%v&pTPM
z&CdGRkjrTM$$BYY^2d65_U-5xF-bRD_U@Pfd={~OG9+FLJq=LI^!ld-85$Bf2?uL7
zQ4HhnDuD|DP#pi1*X;AQ9RzU{*;5aIT{C+J0l>4|EqQdfE)W6n`<oNh5_MLHm#@^f
z(9nk|R)i4h4P+tFz@Z*S0%8L<_C8)1aXWerlK8|h8-{!Y8cqIf_VEWPueD=Vy7mC<
zqS1v&0tyU9$zBH4?k4h_B4U-qX6Tih%Dn+PPyv|)^KFpWqxMLrD-wbywTtd0&K?D+
zX^tpH$8HL@=|6aY)JV_l>Le7h4o-vAL_bS6y!2lCF@*#e4OLi}C|q&@<p`mxH*?%8
zm&6&hcM25X^HbQmj2Xk=2;*4*TxiS=_`Pr)>jk2VMXV~oGK=9f?$#uoEPWW<(No#r
z4;N$tGfUZK1H8%)FX+EUh~Gg4Q9X3LN@9{!<Co#wgK$1`+5qoyJ%(Ot=ePH!&x4!k
z>&(Fer_T&rC0osD(MU2qS3m61rE!N!Hoo0@-VAL8-#lP>YnptI^8nEZU-2n04*ytN
z*qB^9_|UM7eYaj185k#s3(fq(71%3GFO#hNg_c{O#U#Qi$=zTQ#P=D*i9^n3r?CrP
zRNtL=EAsV*7M4AQR$rx}r4!q1G9goZ-cJp)TK#x=fa*f+PAK4NQr`7YCv$k@7cWmx
z8Y3f!8TxobKF3McQqb0>D}NI5;*iwe<5Ws0F(pcAaSnKr+BmoYCcm)z9ColSuF)ri
z?l+7N&{(=LL;8--`rfyu>h!iMma|5B@knLdn8YEkZQEGtHzz)fA_Gaelo2qkNcI*H
zDgA59D#Zq(gfbhe9zF{3T{RXaw{&H>k1v?5J;!{V-lDDTb`X-~bq~i2l}QKUu3sr?
zGHWz(>RYP4p}#$_VohXx*^JC(bX<+?TrVXeG+Cwy=@I;y>Y<;(a@)niaTiNm7Q6UK
zBU$aPw6jERta8UIukAMZUv=J>`XX~uuDzEN@45zb7$9acnQLTxHnB_B?1{_`N^xV_
zpJOE5suM7)OEEJtU$E!iPX~=2EJ~`XvR?V<&dGZ><b=xWR%>Z5jTvmP>M`i3-3@h>
zCGeF$iN6omVjD70maCC=rd=6mN2!(vq0au|u9xqo%H`9d=iPP&m|nzf30;PWQ@++)
zOa*k_sb{Ul`8T_9+Nex%qTX&F`RaySA!x$<X+IRU+{09G(ydhb+Rn03|9tQ<>nD@r
zK3vsuD<7EKDE6hwb@z}4e?o;-O`nkxUILcW{~+uwgW`&seQyW>f|C&3Lx2$6-9mx{
zhu}^ixZB_kA!vZ$?moD?```rEfk6j%xjWCfr|Q;wPSyKmK2S5mp0#&(uhrfEU#l<P
zY|7$B7-2uY9i?ukUiz1K0g9X1$ike%%X^c5Y=59)X%18lBL3jDS^S_{sGwT9-0*_|
z+LxYa&6E{JHCk@aiu=M=CJ>q&*;M)N?2V||6&VtL>qQGG7Xso0g3%WguCZ@cya^7d
zW+z)X^SYh5eyE=^fd0jyMu*8uL(Rv^kQ%h5>`)3z3xrpV*)#^#iE6s1)XS&YCKfT7
zi3Xjo5MGSDoBPqpIw$|i@Rzk5W;}y#Ki%7aR$b8qzH~~a1>9ytLnCIhoUPXL@f!QG
zc-pTCm3O(E+kOaBHjAHmbEzpwvk{k5c<pRYHDwU{{MZ)pF*xTm=p?fQEH4N4Wge;Q
zNN-f=f>P@hK?v)~L@)r;DRknClQf>xYgp6LgDuEZjJ*MX$9mpj7`i0h&dhwP@p-;F
zutPBi8XOcIY3`O14SX!rtrrvYE|c>x_7j43GdwHmP!VE=X#9BRyjyvw?r=VfT=;RP
zUVYLp(qHj#TzkCMBAeHYsM@J1rkt~)`-uRq9ioXAGCsoAgz8(A#$nVVH=7=T;jCh*
z4Hhf?o%GG^57i_2E~#^siW2eA&Y63mIYpknz$7xGGpp5Q_o+&8iPX-{cVnM1y+e3_
z-C)X!SuN7hz&56bM8n{h4#ThzyVZfmpV!E#_@6Eueukma`BNQ}w=(_pd=WF&hUP1z
z{h>>~I`F?)063e2+hhkB$!s3`Lxn+M6bSEny`@R3y@JBtUbz`g1%d9U9VZAxGTzsh
zvZMLBmLgvC69G(vwaEc*p#_^Awl1H1#5-|eJ*!nc|K(T>RLhkaD_KgX={f7)t9Tki
z$}X~lkCt^I?c{8-CX<rM51VKz9bYfpr#9EWs$6zuoXuA@sZ`pFyPpg+opo}%Y%ABH
zaQ>pGNVr`5^J`=X2a7$?{AT@3U$V<6J_k;I=*5DMo-}ePna<3&8!qEGZ(6tAXO#;n
z$tBfD*hh~SbR}gVtTSM7O4vIuv^3(cbHU<ol(gXgG*YZtU(^)DTdw?-WZwMU^>qs4
z0QU#_24{1_FaCXP@B#Gszywl>k;eLP?LU`SKbzM|-(@^X=`~&F5ah*vH2pQ4>dPFj
z0WM-}NS~h3oGDWJq_2)~xyWs~r~WbDFfGh~=2ssyB<^73C2|KNuT7%oxTOh@f&uun
zUrShqr3{!y^C?Eor8IbBM-(DNmat3owKIrYbo)oOn2#=hpX@8D<{6*Ip3Rkq%GPmA
zq8+E;|B^j7Lmly|dmv;<=>CB?m2LbBXpw18df+oG@On6SL;Nf*Xpxt?BQC{N{Bhv%
zob?v|Tp$~`XzJp%_Ac|m0I%sHK7_!xasR;piE!|Wc~T}WSayb{{F6BUX=Tb(wF3XN
zyWSD%BOPnQ#oBcyL$ycgs7HC5ye016>-Axk%P3Lkym(Ey?%m6M$xQw)#Hg^^kOq-=
zlV{ZbH^m0Apt_154$^I9I!C42Kn9EX2kh8l@wsl&P9O${@^@YzAY%{^@on}|`gO{F
zZ#sjHI({WljJ|}R*{c94RX*u6d(jshWFSFBUGQ(4mIVJLj?Ps?3X7x<2x1%984=4g
zxmj~~pDI4btk2H`dmw1qGoYlkkd?U%ypbbWer!4DmAE~FMp7c6#EdRF&0DEx9WLas
zzR^ZmMZm3?@pImn)=O$d0E7u7>4>O#?%p!d*u{2r`^pGkh}Kc5(3FgG5GTS??}5XM
zXlm;}urH#kA8pVda#;t<^lvPY0SM^xKDy=%s4GhJ!+l5yZH5dAl6iXh&9EW@)y}4a
zD4v0wUzbgQt2{+}QC2PYruA%?@yE)4RI#Lf^xm%#u>B-O{hT@*6s=#HilZ`x8h%0+
z&OzEoD*9~ejPo0N-V=4m&qa(gPOZxom2ArgXXDndju`SncPRM;fob|_;^%Lg*cTV;
zkK5P5=kF<-p(2O~)2K;lU5n8z{I>85Mj=^qou5~M2o|`_?(6=jWf=&z$27XTYf1+X
zHYJRMBbR8zCZAm4JtzDB2#Xtt(2NXPe7TnsqOd~TYs5zkeuRw!-m(T^ij$nKU(uI7
zC+l5~8_0xT+XD9&@(`h7wPw<u1&GdsX+O_+7CCK=D!jFjG$Exh?L89w@I5|KJvY6k
zLmuK<=iIWzRz02pghf~~W4>YVr>T4R!ju%;J!I~od0dP4GKNx;g?-*mt4?iPpf$wb
z$yJ3l@1O<8BI~>Sim`x#WZM+XsvUdf@<()yJV<<xBM&O@y2z`(nEN))fF4F?tCN@X
zV(kl~O9V-i{y?6Zqx06UcX0VXDA(7&LlrCNLQC`8wA#?oU&xY<xa_~cX*%%9(P{c}
zr)W*YPW<-oCxTi5W9ZEX)!LX0h9$bX9MhPJR|los%XCB+BnN_~%A#|BYbr$aHB8g3
z2Xmy|YNCfbdqd`Mru^Qf-|!X787<`LqcBn57~he&XM4LK?Z%@*exN7Kw|iBW$35#L
z;te(*IeuAMe8D%5kHBHHYWaKS?H;dj2Byk2=nCgUerCW@*}+gIWy2>5rPUR<i1rT_
zEqY(<bQ;;Wuxk}Xzl|;RktS5BgC&)f8QNz#3l}?srmgLgsdi=ebX*qt=(=KyBVieO
z+&nMpJ${pfI&Q}WqH?|M$=9;beQcc|#YN@9yk9@(_(nPa8=WKhn6HYBvC|g>)K>WO
z5mDyLT8a1vhZt@HyaHLtf5?Pmw{*SVxTOF9a+!qQOlGU}Aa+)b`n0B^?{z)sD3zOE
za34JdeAzJxj^<2v<wZo@{BUmU0|-0K>GcJ!1o;&efnJc*c3p&^Bwgt|J*`*-p<q??
z0nzdhuhoH=*PS6QxJnWWy_~n5y<#yjJz)<gE=m|m4jKBs9Xo@RP_{0F-#8N(Mh?Bg
zlv)#5$Fr>MH#u6dyQ9f?Ywsa%4x&fMG9~8NOaOhB6{xZn5FxUR%9$gSIZ$2;*!NnF
z6xb=O>JQ@J3?A&-Ao^Yr+Aj!$Da*%G*5)}|Som^u{L(|KlqtmC`i;H}(An7#C)vu6
z;J+3+`2xp1&FFt&EDB2&8Pf*M9aJ|UxR`CCvLb@%1rXl1{8@8va`QjW8<+FJRn$jd
zID)MbocOb<@AgNMde%EfpNJ8LT_bxnu2IoZoltbW7#HxM<Ei{yW8#OF`uUS6(-uhV
zI`~7*S)HnzH^Osri2>@lz9^X0<`>gyLfSNZ#8QuXs%5o}611qCD`}<6g>?(7k*i*R
z#MM22+P9ii#IBzW%K)1C7U?bmyzmZQ>zk<{`8#mN@%L=s=b@ONz(9O9N9s*E?)<mA
zw09QYu%L?Uf1J(Rl%krlGar1Gy_(tsqd1qh4jP8Qcp8Ir$RR>~zhHnwwDi|hiLC|0
zXFB8y>jD%90|~#I6sW3m#7|caBybg9Kf|9-UDjO8TfORjtp(&c6>BxMLQ{5g9VxWv
zL*9BM(%gFR<r1GS#83TIQ&u-ZHBX|qlT5#3Z=uDq`@_P!ywj0G@kX_cAGS<7DBuHP
zBp6de0aSftu+6MYKBonRSkWX1lq%T?Gow&O)_7%Vg||W?44?%rWChnOEPABZD*3uB
zRYK1BS?62!n#_HQl^OP-otKt%o26^*4}3mN*7K*@$t3oo#ahTs%H`|UY}Z|^c=^-x
zs-K7Pe;<&>$4ivp$34`!OohCNkFwC0r)%|SeSpLH&;A75X|$8%lT&pFdFabGX}MtQ
zXf>$$Bv<Oru`y=F=Xg;H)&H6)(tX#Sob_dU9TL^hyMkYBUGcfzI>D>NkRhKnu<z18
z|5w=c@(L7ZO9E?~9qT51F5_39VVcg{cy8X2vGTj4BS%oS>n54lrn(hVWUDn22$3Hq
zN@;8T)w&)9d1>H9fN08$zZe@INZ~1c>afJ~^hAR#97-}mL7>1C=||QOQjMtRLDgnC
zTjID&?v`@TW5~wM&CP;+yYUFn-8D5w2$;93Y3HWFh~P%VE&XF7lN~bsla~)vtlR4Z
zSwU6;y*t*c*qUSx`E(fqY|4IW>CFO4SBc0YWujqsxz=Sp@o4L)X-I$n+T&#OZirCC
z^ZpJ!mt=9qxD|Ps5$yuijH${gl$uElL;j%Y*4cE;%)@|`_PwgVjcnpz+Y~tdiw@+c
z%U>e&_}MXy5E^(@nbpyBy4{>?5I_WM5tu#1JAjI<T^eB+*4uMyjdxfCE=-UMJh;wG
zFxtzCCJl2ux@!C2>#P1qGBCz|;_FY*VL_|OY=}3IZ>)%O2{rv`m#ei%BQhuGSt2Z{
zfnIb;mD)8dWPXQnM1xUgVlldj6#d-Zh#7Lr@@ildlhb+$bvnVnh7kIJ<K_benc4eS
zdRe@5OpWwI1KOS@y%I=TNnXb1{m1(b-FZkh^6p`djY9ugbBg>gxGJ~AiyO+l7Xso5
zNP0d*S|;LI=zXPwnf(P#CC<oiX{$56gD-mo9VxVb`ArJBVCPJbUDymZfofm&E60o2
zbdjv!Rs<Y9VL|E+ct+BOtn$}4)Ea9e$9eqjY7K`&{$7&W<0LVusjSrhfnznhwoc1j
z+B7&aogT7?=#P|W73ayN)ajP&r5v}gSX1++)v2lP+wRys^NCa3LAdzgFl|RAmp*N6
z6v1Q(>*0P_YwlbaVcyrQzo6r67AsxzcbonhACG=lg{7}k$2MbHY)gB6D8+sgmOe8J
zW2Oz?Z}AJ*)M}2RbR2m!UZGrndd5slRVbzpcEJ*HahpW9x-B-HJ!&&VyuBU^mBC+e
z_l<i2`y_zKuFf0JNN}zVyI6ebo~<g})c)PMNASj%$2E96e2CNK7(JD!@pk!#<7M)a
z>1;VEG~K{?y79VjXm6n4EAq-%d&X6CgPowt*SF(#RvWJYQH!a{CwiM-uql)eg#!oX
z;X2AKPhk&FL7zoLr0P4|Dn1uHlFO$}z*=@<e3)?PD2yNSuLIu7#fcO~^znf&hKGp~
z*=E}-{LAV)bX*-JaJ2q#^O`IZj+`LQMT-k-OzL$~i}42uT4uI;k5-RzAD5LWnvy%S
zWuCvo`X<(3>QecIZ0@F)%a6?WmcBxI66V2l_TB%SuOd5uy>o;g;f3)u-*P0Po}VyJ
zD+gm?pQP{l7$1fof>L!9xgjqw3JFQQ*XTEDV|5gTtZ8HlH>PuCjc3lX?spg0PYT@+
zkRHU|lm1|G`yLNl1VVL(x!NrsY)a?RX3MqobT1esRUqlvqKlQhYj>dChxh{mOIAGM
zWwnKQGL6YFw6h!<9w6^hs?n-&!qz@67JcG}peKioaP_wgQ^RvJ2Frf*?)fugjp?>j
zrrjXoNxyJv`-@loE-4;HlTy3ZuGc{o4g=n#S`&CjdsH~;jndQOBJ7`=4l(?!1do@!
z2ypBn+nC#rZ?wE`5h{*rJ4TK(k=_fhCwPF`ssd_y1PvVrW2on(5E?FUUI`m}_m5Fv
z@IDS9I)Yc*M!V$&>1n3iwTE+kk;B-w^W=+I6F^r;Vz$F3aFnvg_FBIuWMxHt`3cG+
zg%-r|No3NW4v;$$6c|6TGTj3ohs6|7<GVS2uGM2b;ywI=;Ai#%DS-7Tcyw^27xfy4
zl*71bH!Te1j=?(}t@y@7m4mvwQqIaxBu<;2d-}E;^S&Cz)vwcWKj(-lmc(7|^<&v0
zx69YzPmfH5ZeI5ZYNjSYfz0W7Z~q<nYrnOKCPwJQlIIAaaG@+Ya-UQ^6YZ`KyB6Kp
ztX?a=$$1lt>r=v?Ni5|J1D*2XH3eOo@A%dd7|u>rZoa7nI7b0H9(ppOD=uo`9FDUM
z(<b|^YCqhOX|7=WYEc03SI(eS-Hk2tgW55inUk=byVKyyydgjrC>1o5JdK$*I_hVx
zR#<6p*i*pxRc^jSy&_(LWrfZ}EnBK?ejLe_x6b#&tkN)Ce&Kf%ntzT!@_H}5@B)He
zUo-Qa1<xh=#m~;F7=pAnsHZ8PMx^sL(Q~Ti2&d6vLUBspD?YPn+`;;tBUV%2M$~LQ
zqOwA&6?+%V#<;wdu69uOs<WC>w|7@nO>_6(ec9SAqR??ZreE<*eaHB>Q}JG$TTc*T
zdtuEr?{uTP$8DJ(HTJx=>W%<YdyC^W<q6NTM-57tc!7u*Jb{cHpTk8<^~GXpTT!{0
z>Z``fw}^=O*B{&5UeN~ON>ft@zn${zzK7VY@~$1Vd9JOv{SgGkEnlxLr6;GSuQasO
z2(~B+=W(SUP!Q7xVtZ<jxMmbg>)ttAu}U~xYoNBzrNB2N?gl~Sl!|Qv;)DlvK1Deq
z(_!&r(I9;a5cHf+HU*V6)r8_;zv%}5qW*40uU%|SrDpz<naKVt7?-k-CISTk-S1ws
ztrAblv$NVpfuL%Trt28jgeKQ|#ka^W83jzJC@7VVria>NZ5_|H_TE_)rDV;0l#;k8
zxGYm?D3Ke%Bg}!WygX_2tq2jy*x@gTVrtG%+mUQ=LJ}ztgAuE(_c&Za!|9oc*YQw5
z3*X;wc!a<1LMJON8tlW*MMp|kjno>8cuW71_p9d74`^af6&r<S<a<%x(#gSL`unT}
z4NNN5{zTH|@n|!3^tQ*(6!VmP%Xp#Kd}SDJCKo}v2>p2UU{3)e6|#_leg1wTN!-lz
zo>xD<WI|HL9xW@5$gjCJ0?A;1Be}ZSmRqgaezM8&s_9k1Q}N~LXf=OBPgt%XG>oXn
z!Cq}?>tBRWAFIBOKDjA)?FPZkGTcM9t-&=b?|oX$x~%-Yj1C=fH@N2?Rw5e7XD?HP
zs#(c$V-=wrbzQ%NJ-&ZQ8zNA*WRGx|-CY;oCytW{C3j6qmsYF>h9A`mbec#uevN2f
ziOFlvLRypbOXXvn>J7Yx<vQpEQ=)vp<KM<h1gY=v4`uXBPx#Nvc02HgqGpo3rz9gA
z_{f=oZ_$a~kVxomG}Uv7CF0-}8|5J<Y&5kqA%Vl~y><7zt*SU&3T{miHABp8%ggJn
zM~<Py-bTtqLovx+JtuA?mpnAYqdv)MM>1*_QsrGfN=KtvRcr_OP0H(S#k*s_e{V;0
zI-@8u*1wIGfdYH=sFKOn%OGm<PIThLXVc$LN~+!?s^1E=wWc+5hGsnM!~=R{demq(
zBQK6L<L_3$6jy#X;4Q$>@_ML*`_v64v#LSfE?2+Qn3?9it?f8S3?HI&OMfS-L<sX0
z;+L3P$;u_G?9p~TSu#b%`fbDFAv-7f<bFF=;SF7Muq--fs1)C4k3WCC_QTsol)#(0
zXWUyY>Q0~ab^zv{QgYQWtubW0@*OL!Eb~>%UNx`ji4kPq(nge6&@8$2Zri%)ECS1P
zq)Ql@w4y%byXGbPRnbd9(sp`e)4<oYp3JRNOvh+|ZPn8dm0wTMNAz*S;_T{HXLx6y
z0D|78?vAuXDq?FPGKPHT&PCkv?Yzt;b#n-o-~8NeJ&IV~TGNYpoFst_zof0+p3pqr
zJ5L<h;!K1_PPBVeu5dq-vAVTB1ueUrDJ}T6OlhKU>+3<{ob4V9o5v6;FIEk->+SS<
zU>KCiO}by_e>Q}Z*9~}_71?tBecGOv<IQAz<cuCmaC&a^w{DKgV0j*5`hA@UH7&#p
z-81r;C+lc`*l&&A?j<Z7VVyr_nuqzjP1V$qHQ#$!S~tW!9k|9Iv3eLBdwDjGYBy`^
z6E`SnU)wO7EuS`(fdxb#LIa`I8b5~b=-WP^+APTyuxL`Vw({v*Ju6cRzOw)6<8vI*
zGavt`Kuk*tXaB+jUr|u>&!kSZT~WhcX2_7HW6iK@D<?EBAf`+INZkx~{!RQm&4B%7
z?eePykI75n4?bvWQv{y}p)umG0+mQK)x(LBs0biFraQ^_j6Vt;lFz&B#aEHvtq&k1
z_k>D^;}>AI=aLN0ilW33DI9kXQ{lTQRm^nZf(p*fl=?dp5SdEp%6>J96K>A;2?q`g
zsnU)3Jt37n%_BqXIHSRKjdD4qV1~rLAM|AOW6^y4lsiZ0k}QmYX*`jLQhZ|ksugK>
z@@<A}idumqzo?F0hv0=!_A`Z0oI(zUNhHzfJv~W?c*|5awIfS;N;C3yZEz&9_Y6C$
zrX=r!x_*iyD2mYZ`Rv8ehtRvsDZZy?#x&=3WP72R4aH@PK-(__rj>R3GotXIyt8c}
zqAZh(Ma2a1?)-k;GpX^^K3A?Vmdf2=XQY_MrYspjWOOMi7wC+zO1N$M=kM+7)e;v*
z!7piEh?262h0pYIX&-Vg$n(-AQ3t!PyAe^=8yVu<)==e$Pi}d;mn+|iuB-6GQ%mwQ
z7H#LtAMjKkT{m#sy`p2d*~b4O>VCHCI>iI$6<hn<zHlsdi;yRAFQ%muLewfp5<`h;
zk2;Thgu!xO7LOOrqAC7CSy&aE6rW|6n=#RG+V0tcgk#dhM`uvh`5nY5{`oGRv|9NG
zjQBB%_`K@aiqfhTtw-AA4T6XI96K4iNfDkLy7F4lyq9Z~K=g*3dyX3f$rP264x7zV
zCR)u&9nRl@D8{nSV~WIFy0lGro32<ynkkg+1;W{YFzl{kb24;$iu*<<zT$}Uy-*|z
zVYaZYKBc?;{RWP8OKsN}f4=G<Ms`WkmczKo7E!F)+A}ELW@L;ZroKw5zVLX$r`|jH
z`ZiQiMkZ{J#rnKhr!{b$Be{DU4TMmgOS;N!63Xj-CXhZ8{n}LYi&f{P)JiVq2gQA0
zzR1`I3F~SS&3wu8X*;>tiv9Mdw#Yjj5l0E37T?`1Vh$ONs!%%)F_gXieU8V6MJq}L
zRl@V8X0X<pg>bfaixJXckQ10U6GmURN+zt=uGCXi>q*MMa70W0U{*_3l!+l|k@?qY
z=ScC#FseuQts}SJ{6yd=k2=y>kD}QSn{ebxNAY75g(k4cm^L{}qiyb9*5#R0TQ3hO
z;nlFMbHB&~Z$DOXj$P{QzA-UeQ#gB+zdtY=+w7Y-y%WA*&m51RZ8mQGbDVixNMAui
zgJr;Z#Cn@>ckZ#ivbaCok)2`u)YX{Gp={yv5Ljm#R?+&bsFB}5GNpmCf8FDM8=g3#
zN)i%bwebmDDeW$FyZD~C+7(Aj@wWA<>l>C1tsoX<-s3L{P+_L6r`f2paD+|1byolR
z9qp-6<*G*47F1c)y3ak78D(fj3q!4g)5I*lP|gqf@i`nWmyod{_;=N_d;Na+UKM(t
zyf|+qAR*f_R8M+6A9t2ILU>nP*H9Y~Vpki2sRVh!N@z-B=f$NbqLO&tBtXnwBqewW
zf`Ltm;A*?~r{KMk2bJ{qUFF_7%o^>wJ^R<!yvK}7HZzJJmfS?2RD@rs|0M`P)P7o<
zcJ|mdF?D$|6%IN+w>UQ~p8M0@jAaDes(iP^dUyVq4xShhbz$`ZfFQH9PYN5URXJ4W
zMQLVCUD#{$t!{AMTkcbTMO1rZ*aOC_O$z&wH9XXtpFg@9Z%|(RRr%X{fv>tB9negW
zBf5|S*#sN}^rv>d{cSuebi!mb5s)NUe9N_d2`)gn`_aRr>Q<5f#dO})O1v%9=cxRi
zHH?tqnk>m5=#2Gk7TEPxmv!%+te%!Sxp;IGOcwOz2lJN?(2h{{pv;@AU-0RXbWY^h
z^hjs5F}@s+11JF)kubJZcTkeSNVk<ftAzeW+xeB1wl2Go1>bnh1h-5Nr;_zO{30G1
zMS!vFi2=+e58vvpto7X0(9&h+v$zQEUJngLvfWB^jo!ejj81=?FO&PeJuT_38>?Z;
z;h!3zC{nobe!4P0S-U>WVo&`~{qzkt1L204sS8ScnXsZ|#T{gbLs~>5^Sk7!%`5_5
z-XT;03C!z*j7rH>mpa?`V6|}3x(mJ8@ZJsrjAE$-<i3IU*5f%mOfeVBlqQ&s-YAWh
z;-Po1PqGcQaD%=l7BW!~jK8V%szHg*E7o+o@lP=s8I`;l!S8zqeWRo`jlNtC?Oo-=
zx{@l(Q<mclV@<qn&uDNLzG=PPyhsO$F_UAf-sGsq*4-dJ4e~40SOTS99IivttrH?Q
zyYC3Wq~x6+!%1PmH}j^5_|%ReDvBg0&1$MwrcuJCWR@c}5vsQ>F?Rkw`9k7X-||r=
z7~s_%5U5L;!$S|gT-Q=St&BJ<?sS|{4k@knBGzcgOtB`yto0)nh2(4cpO92hh&s~3
zh&<_#kES}>^I2j?(!;ty8}hb~t_Vfbwb{vU-n`-uQpr!o%P~&3p61h?)0oo^#Gf9N
zD2^YA$p%>8?pWX&;J<hG%N%VuKXvY+9-ZFce+f%A{Ekn^=F_Yqwh};BMo6s(g14UP
zb`n8}r3+;Fj41R&@Dgb^sFemR25tu0wp!#)daO2KxzPk7&;97UjE9YbPE_QpNPb&A
zqOERCSPTFSBZsVgzwaEyL*@GEuuMo8f34H~Y~5=ssk@`1)TDVl(B7Ub+P=e$q=F?C
zkUpL+d$taxFrsW;`l7gGA<lbNTkre1^yh?`cj`8%sjr2T|BCD)Y)B`82$uh@?ONPE
z$ypS{YVWAg&8~v+eM+d%q|F?gZ(DDsTtOhS8rYJ0rY*i%i88R=f4>fHCVu#OU=h5R
zq$_DahjGDA0KE}V%pDp+R2-1P8REnH>}%XKVnVfiZtSk%gCO)NBM88oF1Wk00mC=<
zc<DK7ANYg#*w-3`>o`<NoO9nIyYrPQ&9(YC{eC*Mh{b!q4mrk$I0!LCZwYm}r0>8k
zR9IMr=IOt!iGfiqKD^hR+fzD6PM%H>-SY7OU<8``M01UlHD^QUKSHC(M^~c#kyt|W
z^YcSO89!FIH&#^L4H_nhUO_f&f29%!zr<!yZX9)>if$6CxA<QAqVaWq==kS;(uNxl
zJ|~6lY@h%~0#-hvFAeF(HtjrMDYYvHrB%7a?l*1ZI8qe46|JuM7K`2oT?B8E(=)f3
zXrA#$7KTpx7-g+F+>_Ub67%XV!xvCT^iAzBgdg(`g+d)pWDDS283m$(Hqw;+!PgeG
zMqXzkL!4d;ap0uN7%2{6Uxgl-VT#S0HiL9UP+woF_b$j6ah>iUAsC8+LTz)FVS~&w
zJoqIqKXn^_Gw8Zf3>&q>jjTf@@np(ymW<Hp&Qb(*z+!*saay5g7M|{VMzUXr>+=)I
z`;0$XQtecR7^HAU#T2RdlW;W<Cx}a}57ja{tY%GM62U`p+B=;MlEmeKl}Eh8Qd^#s
z$nAE89`fj^-FB+x%Dc08u(vV~ud6ScZE35Sy%sS{!I0;HjxU9#4Jii|BJnRz!QzaX
z@(zu;{K-x|algOcN@I~fW@Bm%rQ6;Uik*)s04@aYJkmYYmMbpX7X8$3{FYDe1BNqo
zkfIOeW>VCr7i|rdN`+xJA)vB!|KqyygjkhZ_QqtRE5a^8wr<$XU!7i&uBj=Nri~C%
zOiH(l*EEuUMc7et8^N50o%qq>y|1OnsA*|w3Hs3yN_XC(7w!d||NS1lKrsN2A4EF>
z#xwX8a-sI`ust^H39k+j4@b6x^sqgRVmnc5>PCGepX4n%*JkWKK3e7-1nyeKm=vuc
zVohXBVpr|rC%UfT9BP4WN%38^$7x#T<d7<4@A4{jg<5~;b`XD5ANH;jiqC4kWXu~o
z=EO1~8RVJ(qK6F+%AImce`wI`=VLmq?J}7w%P*>`D>LyG>fJoJ4t!dZVloBqRf9s@
z#czHvm98ARZ6!;H@!{?SoU>LUS>2GK7q*=dlSMdPnK-iHl*0dw`73`>88D8H*J;E|
z_QvV7k5GpQMZ|MD`4#BtcK5JXfBF9J`8d7%U!><y(T6TfHyC>gQI<)J1=X}zfxq2X
zq}pI89j{Y>N&hsj+Pl3y9%y^O9sUdVp?!^_--jaq-UJUqsiuB*%Sj@YptEWS(AVIX
zE<Ir=Jx|LySTi@Z_Y1$PwV2*E;C+$>BpafFZE=e0>jC5R-q?bNY=X&2j3NBT`(o!i
zr{cV2i%wq|Z4VkAcX{@fi)$)V#&^Hp<Y82V<%Lc{EUFqIHE#eMh6)d#Ok7*VMeJ#@
z2G)$L-$IP-?qG^|f&V9xS1VS<iE<QoWxmuYOsz)T6Y(QG2O8=PB=zGa#isujd5}w~
zH^7)Cjt{Re%rZ;4EQ^q&@qOeQ%zd^92&Mzon|{s-poZggX_8M)L;9~_r75>&AKM!f
z8cE*$+h6?W)(IMc`V26<Q~aI_gZ`wtV_LKn$;Sq(HH~c{ANWS`mGK&d?6A4m5W*Jm
z1JfQeR@qb188P*Sc5$egj2UnV|CYAXO%f@xTMQ@CHnVtlU(zXi$}dXI)~&;dhHNX%
z-j8k9!2De&t`kp7A!Jx!O~)H3HvgMP%jpIP5kyz94Vt%OWk*47G%K*0On4eft3J1M
z{|JPETRK+jT`~&Cf1aoRdkf`|{{4Zg3}s4hnH^={s*fd@SH|dYJ!SmK{wm}=v?{`X
zrC60de3zf=7hB8<pOlTzIUxx&NCSDoa6)32&2r|0U=sHCOyNB*#`GTOar?dP{S_nR
z(8euMw6OFnCHDEFg=K(Jsx$f|0Xn$ctM8<oF)d|K`={iA{F@Rz%2><*vwZ;<PBi>(
ziIt#hneIDKMd<~=z&emn2X#{x70vOnFbfqFE!eT&7RwdXeoHSj=t6@}oBzUMOi-)>
zW!SRc^sV!Cgf#N-xT8axnjHghl;%rit%8t!_G1(2UjwRh`AcoeJBx_sA{mkNez40Q
z*4^ECuJ+(f^x(qNA|^}`G5Na$uEjjv4JQy~q4pDR1P*-U`>ah%%(mVd<PWs8z(OO@
z%zGVpkw4eQ)Kekw#Ri`+eA^`lC)JjOtK!2;HcM0Wg*OX<?L@pbVL;xgI!zvch?wf|
z^<5J50fvXBRE^3U*8CW&hUD1P&8pWEE_e7xeMpNMaWd~UH2*%~nMAs_?n!h%iD<w)
zz_uHIv(Xcp`Z`MR9w3OAX7+EyG}S;vmadAoRGxt*`6Wc>Jgjd^P*&#W{^BTK%Qyw<
z#ZMdDuZ6eqR|z-OH24soEfe*`Mr=}~>!8GR8<(@)0lmY)^vGx@pY$(X4K()nDLdwx
zI{EwmoPwDu3eMj4)DWeyc@JDOI&66V)LFSVzI_|MyGIykfEjge_0#HmT%l4%DWlm>
zO5pnqtU-EbXIuw?h3B@gg9ZlHKYJB)x6ULT>ir!80676{QTyLZTgKxxWgce)tZOEn
zDiP%>y#r&o>?TkB?p;h<v(N!j2k=s(8(V$jbfzthGQjxOAoZjKq=9dcT6>VhvmyrG
z`dnh%U^)AhSkSdh?#?TgNvFB&g+QiT*Ux@)l!sp92-D8%$eQY#UOdHk45aIS6cRRd
zC}O)6_Kn4H8VaOg(yj?gaDPG4e-})sZw}JE-6=&{lNAN}KF`;bz=llx#;!IOBt=J-
zym$^+C|D_)fzT2}yy3Th{f5V5hyP=YfD8LCBQYT<%0?jx_HW+^kubS{O}b8bB;iCD
zMA%{f*n;OyQp#{X+6#qty&kw8Vjo7uBr-Jd!XT2MBvIc&gt1kk9RO1zgUOvn+5dIn
zZ)a|}_2Fv_78hn%g2;phcIU^1&gB+-J04AsTbl^RhaWoqk%FhU{YUwDxw#fH7ZVI5
z6nbaX=je2#lh^+j8*Y~%`LHTxS5ah1ChMdZ_H}_!2QS^o+0f8wO2AjGD@1~aDYUu^
zg%tS5fvFE32jv<p5s<uF1~`SXippk#YcK&#x+rS5{r5FcT%_;^Us@K<q_chVW?rU}
zx$fZt3a+=6i<y8E{qLptFGuyCwOO;pdTW=(I&1fnbw*+)p*XwJ(LLRwFY;nIf}9ta
z5q`AL^Dx1efe}pW&>7fpPVeMHMQ$`caJ=9${`m`vya7@jlO&iBEBj~j^3p2qnNOTW
zR_V60WI=~r_2=r^|B8P9kGBHu%8#J<V0^nQa9y}`G<EZaPULw-Rk)jtI_1dA2)({=
zcGp<GAv4fjmmT#pu#F<t1<~Hp_QakVkt}#mPmA&0s^IPQprxlwXoT*h>WdF(i5a}!
z$m(V7c&P4fQAux|;k8A5?EX~v`Q3=V*H?9O(3CxD<PID5{7_?O<F&mVOF9oGJyXPW
zBS*Wb5P38o>h6$}^ug)S$tS)j%6?Ce49T0rYlYog`(=~ns6^LGuE+ga>WXt0vW^hG
z_)jTMBU|zFap$L2)ViDGQ)gdwNpNs5!0RDG-Hj%Ejo1}PzODipj~Q04s71TUS&uR*
zYq?&b4uksK*ufm>g77>{s0GG|VHhm>Tt-B2jf3Af0=HwcZTCZlvFsGx^g93c#_4Gt
z6PP+<YheBt-+F?Ka=}27*#dYA^y<McZ`8<xU9xX2rvJ3dF=IJ&kG3bhzi0X;n9r!S
zaO;5_($n<9g3G9p8W}{fyP+#_8@n-{dGe{6Pr+1UmvkqvlI^Iru@S;-0MByLN((JK
za-as^i1Z>bl7DjVl%7iHOPrk~D)iDHHs5moM0WOe_or9<5R^}5@uZ3AF$w$}QfpzP
zu<Nif>#`?AHV=sVLu;ucx7@w7bz^xO`||M4%Zi-3h0{+~dp@@G3-vUA>tEaYcovZ_
zzlC9&L^y!vZVwmpB59;Z7db*tFN9v3QbW%-_W0V*S$kNgjgr)f*Unr7dmS>US*|VC
zogH+OBV=x4YcnK4$b~{3Ny9y9^x1zV{par<d1;@hK%;?c$N~y_LM|o9d8DxMx(LY>
zn>yc<yapa)@RQk$YGlr5k7B+Ij~DZSsiSrN+u(1ZY7C_@5y9Q1n~4OJXXx;0d+xHL
zhqqmY2=@XQEMLcsRWZ(cys<y#y<S)Q>6D|b-!{2{@oJ^nZN%-Y&P0cGo7vGo^Hp+#
z4;*iUHSmA2rbu}t>vG$b^3<?DwpsV)Lh)pQLN61hEfI=KC(zV^i&6jozZiNGgSU_v
zDu+zTOo9X|#wYec#X;Uy-XF*(zZ8pJjp~wsGM&xWvR?(*Vbe}I(YHVC#6ncY|G(Z2
z*b6aZ)y|z-aS9iaN%Kxj|DBznQvCiW*p$Q^^PkUjG9E6`uep$KX$(!4u!|&q_gHI;
z^1t||lwT;;4=JSI3GK3Y|16t2{vi3Ff`qT06Q1W(Yq+r(xPP>N-@ni7Z^>3szFPA8
zTclF1HAj`C$H>qBu~liO#ws$}_<lGl_8c-yC0qa6oa%4O%B#HMZ$bUF0s6&RkyO<S
zJ4-*){HQj*icT|ZaGp}^XmtB+J8_I|#c9xB`+N+)ZbWDsc9vK^5dq?5pz1=+04*38
zQ8Rr5JmU@u@cH+)+I&WS9ce=x;onDm(nAeKj5k}@gj(HNYuTc=zIvJB4)3-;pzBFf
z?lAN^JXsix)5y_diRNL!;~I_9)(Z8~$H8ht#~|Ge)W%Vnr`}*b3PsSXp_3DS;Oyds
z4VUl<J|SBJQ)3zV?TWT4h7cQ{;GJQ>$@_Y`O9%MjCtr1clJ)~;sYoJEw0o+%vAg~N
z!iLA48tE>!h0=m$M4m=x9eRM|&48>v<JRj<0SMcg!bn|-JqS@B+sDrT%R?hm)?J%H
z+;(s`c{Hfg)b+sx6rsl6hP&pTw-I6i5}JSSeRIfVlyopU{k1<>s0o#f-shv|m3@bY
zml{_uJyd!sNa%4D9Bkin^IqZR5ENVim=ktX=GCEtkNhSGl+AcUk=V-GJJRz=G8HNm
zyJ4g&eQ>QC2i+2SAlamYlhXs!OnSOKyUAkr-su0N(+ZVXj_G&5=Z;`(v8%tWk5a-S
zycUvijk8!^%Jx6`Je{%)OXWzhVsw)~TZ}hPvAGid-;G0F9%)uIq^%gr7RNg+=55hQ
zKfWLCJJ>)yjXwR~2c`cfRQ!8<%-OI^P&@*Q1!Rk=EVAnFo4qro1Gh17lE`M%ZFl4J
z{zrWI&xdqL1~D)C)hLp=u!{T_n~Qj17CQ^*9!PT$OvV3;(XK_Ha3DRVPI@L-(oBfi
zXmnc18j$02nP|`cX6zaJpN17<k#)=!Lkx2%9Eng>ofgB>{$o2adsm<ymE8Kj7*IPc
z$)HHh6*I#i6DjwESS7dxWd`eOY<bpvbaQy}jk)<siyYRUio(Q#jpo?wYOWg(8C6k@
zu5%4NZ+^X^`x4Mh5ZyNXLM<O}ifhJOZ$y3mf#fTWmcZ<^U!s%rpR%>S^`MLY#71r6
z&?f1*Ft!TzHL0e)ydQrew6<EU6ifKned3=pT;<Ppnw`^$6%R@=oMflo_KzC>niTty
zMVOn9xvNu*NP=hQMdFEK)nd?IwF^&;0slQ?B>=OWO-xuMJLt-Q4K&WaU1rRx3AiYu
zaBnE9JxzfKhMltQL2rLCzRgIm<<}y<QR9Hg=7OWy64y-6cNYtw50)eJxpu82AHHe4
zLM=l2vti<rcbZ`1LR(tGgfSbqUobs!^T&D8U0Dcj>RzVTbNnFWzdyLnwNU5!?;bAh
zvF6%#lSuQ3Me;Msdk0rl?ac{R0YJV%L3k_8KTRjl{;`YdG^WFqI2Eg+W!H9@XF8IA
zj0$o|W=*sJ*c4}gB69>dnz9}(HGmUqg_*ZA1zbu1fuk&k`<cmz0?;~ba!}ro5Xrl$
z35Xr^4=yGT{(<%Y{2-(>yb|CXr25=n!!zeSRXynSLbsv7PBaFX;X52cYO5LFp62=w
ztm&}%Ihofk<KlQl2L+4dJ-L9hG<1VYHi79kATrne#{$r6cK#h3`=({*k51p%l3fRL
zI3a7oQT@{Rg+8)Jo0MmJ5hcu_w7fo6#QQF9KdZUBOuO!D`_rA31wdPHk>Pxx%R&ST
z{$m3zjsb$rS4Z<%{(*T?5Q+q(f$Z_~3P7~30kn*0c-ol<3bmt|@Zwdy!D2FK(x&h=
zD`C>E|JZLFP@u)>{Y<mBk{^kw(-L}dyr^Kt&)w`=Z$+yz<NbC)vtA%J;iFS20TdZx
zs=ULjPmR-l8*I(p7+NNp72bL~#FQ*zSNvcejyR<<z`^amE0V-wVD&=KraI&|t4?1)
zf^=#SCN-R1O8$>)xy~R?$OTql%{_@_)(={Zo?eb>IiyG#BxfKFbL}slF}?p6(^!Kt
zgEcA;=cy(46n*bhZx0NfKk}}5)jR-G-{Vtln`OqIdKUg+OPhdZaMQi1{2zmC6T7T0
zZ*cp$TNN<^D>%qV`96#{I&7H${K%NS=|UY9B0nIlYWE7Q{T4)?)JPV!tHB$T>3Phr
z<+kDi?L@*E#j%GsAFwP~*NkoNmsX7L3PWndeA4Ye!6;%OOSUcYfH0W#Us7q?;Q21`
z9}Ka2T0wBg4-tt8a|<&$t>BYK3SlT))ctnN?gCh0V7BLzKs5b05S|7$_V?V~%XOPn
zY=L7-6CfzY8gn_|ilQt)!|v~<4kddB{G?)C*9Frb%YgoQ@~n^0AbvWy5N!+)!Y}~i
z*sC|5ZXv!ri<y)sPPahXm#K*jCwR&iQ02)#k0i2!#qfsrFDk-KbUB2WkJv^})4>KC
z7yuMS;|gdRyam*2hshq4Q}olQ444>j;bTA@6l(=;Q%{;Xsl<)G|8Si_6!7$Z8uOHV
zz}-EAtgr-hLY5X{1%jV0)Hz~hajv2*M#-`?_0ruxT@;EA0YDg&@C}_oM%E5v%+6#U
zo0Na3)apq|QsQH}RO@#gceCKKF&YYeVlH_s&_cQ9WgVICyyk76OD^;<bYMj5&TU5`
zbLcfrWa0|MTjwtAGNAQ&!J2bCJG+;C^+-SSQDo)?N=c~GrCEhvum$MVXa300xRB7>
zwgHE_#uwF0TJ0(dEvGh3@IExj7q-N`*XIdk@xK#&slZXm7RrjPPfZWoe)A@$)qk#9
zJzRt&#gDywoUZY=-yxyT3kfK=K25h&e@tAh!|{HvvBJCPjDQomuI}S`Pqc7+*Z7EH
z=E9-~oqB~9A9g^)nmF0J+{3rKv3KY6bIU<D4D5$T1=328<^4$?iVwr9MulBErUD*2
zT${51XoINJ`@<rdNW7ag-rk-~Jyir3;v#smS*&GOW@*1MCgw2t2{2gYv}?_c7$b=G
zJMLHDCN~5<ruVOxmMk4V@L~y?zD@{TyT|wHP~7xv+``4NuIeK=`-lDb#RMr^N0RZv
zn8|#<hRa(XB`6Xy0T9wYNKv@wr8)Eyt7vOmGHK1WZH)_V!*Zh%uE-bvF;dl7GA$hW
z6cdL`2ANg;ASEYuzV|w}J(N#W0O%J_okwfPW52kN$r}^$eBbe0(`tNC=?LGunZ4|(
z33d0_s9C$`j>l@J4&pOP3%#FpznSgX=-amj=9%XKY+vew2jNC)k?Rwq>KE`}phZ=2
zPRoqk{d|nK900+`OU0su$i39{eY`vaME4_r<edfh5O<qV)&n4vXQfB7Kp48v;ywg?
zb%t+}-N5d?JJyyS-R4wxHj>a07uhc9_k>w1p;r^)G8SQ9+}qrQDva))4vn5h@u|pq
z4(sOi<NmROx^<6ZLM(?pHywjHNR^`qucNl+V-!IKT0#y8H`I}9g5}hvqR8CqcODm$
zQcwVwK|uE%D8*BQzUVnmemN_p@iC<!27Q0HUwf{<KKjQ*1@bf_0Oo^r4N$F^24eBB
zEry)MeoRVc${`^6IiSm2YXi?684W%BtD*1=-MOpg*dS?81^ckcUo|?%>)JolpJm21
z%cdqQ;=nxrmbuHxZr%xy+}sb;@%uN~M_-%4Qc*8(&n-!1u#?TO#@Y^+2gID4oZol}
zQ5@FW=nSptXEGc(M-P0=+uPgzD}w>Muls#vqpdm7C0NefVl!#5SZ84hVRkHqHI->5
zt?hn}?9pZMZa(SEcAOI_%{Nu3kES1s1FM^_r9pK1yjHv~3buI$N|SGlJ)5|=ct$;!
zu}Ng2SlZpl3NK6h=$zpW3O(FfJnm#fxQc#^;(<aUgyf0el~^d?7JeFQz=r0sd?Uhl
zYgX*Ukw>4}DVCd7i?oen<YitJGQcUZ3rT3{HdZ<YNKB4^CU|6%HXJKd?W^vFmIDe*
zlZ}I3A<Y~~I72F$f*GK-7L=nn;)wJURzP|oY5da>9J<Nbq@q5p&Ifa2n_BX&?L?k~
z)#j7$bd>3%YDFGSB#tVVhS$3SO&>4kAzL+MCqs>j+W=VQ55PKR=%C)Y67syV?m72z
z-|a48Wo$p;2OzNdK=YG;JdtrHInNnevWQJ{Wljs=!H0n43v|V84S#tuac}s*Ggnf}
zC+oY~FBz;|n@@X+GZ7U66^(fVCa;bQSupm<9C}~ZHxKR49eW<K^ze}W2%&WU;@ene
z{IEIJDMs4aM%s39Fb^?#;TFj~#}<fhhf2r$a{!w5r+UJ^U(l#K2#p6Z(DO#ys@&8!
z9&4lQ<M-AaGu=UU5{JuwfJyAD%`l{4`_UwJ^@O}|Z13<Ow4{3nZz{YHuFDWL<FhXw
zpf~H;{t=x(!knrM!3SU|)vt6rf9=}$J^8&`EzA0cv7)iNUR12d1RIYDB$np(<Q`DU
zYVpUsiHcN=$c2!mSMMa5>a@ATe)1FX{i$YYB2nQsWe+m1p?cMj70}k3XOmiAua<2F
z$@sBdIi*PEt}W%lCU$^=!hV_Vh>`@Nw%Io-lAXum+s2~mP>1XE9mA~8%_pma|5eQE
zvn$aRGEVpm4^S%s;Ndj=DqTDx9L)teV=!bKWUva_!YXZPO^v4!*m=yg20hQ|d)j!~
z{SJ3~1zW63C2@o=Tp{hhx%3m*s_t1jKx_^`y+ot(o}$J9bneNMxRC8|MFh6jA+|B;
zZUH?0ZYiFMJxQjb_+0CE*$)-X`yW&D?oOk9tvw2(C_P8ntH<h>v=@{MPa-zkHpr3c
zXZcz|KIf@xnxs-KJ3@@_6Og%xu}0N-4|y!}+$_SgNk~gybu6g|Zr`LAj7)D4!nRN5
ze`HDBMfnIR-+z15DKR>zn3Ul$=!ov?3-RW>HJ*ESE2gnhIHtp5b!ka2F@HqEX~sO`
zRvTO<SeY7;%qMkQo{)WTp&l50t#1-mp&fl^Qh-S@Bk$pso36r&qqpG$k7G4`NDR++
zYDuWx6A(5{f6^OO$}3NLfPMnF6=ca(V)vO*<)$Z{EY)2pE2hUe(6jcb%Y$W=AER0y
zb44<!X_1;`8We15+hc1GX0Y^kX)qV-Z5hay>SEOeURT)<q2HcNYgJ!m9iR1$2A}me
z@=t-SNEf_T^ruRtP~CTP_s)iDzf9FQSuK2SK#adQB0D#qR@u|}o8a#dw=>vETYC8D
zvDIV2QE5P^l}oQvwJ9_$H96ZbKlCyYK#eu$n=HQ2vNF^lzP{Dq__6<KZ=tvj>Rs-W
z?i5EqW^#HqTaXI*!NjowUIE8h3pE0vU8_;IU-9WH56QAlfmf55KCz&V>)*W0_>1EY
znR0XO-$ra#r_?wa?WBkcOy{f0Q))jV2&A)l`$wc-{E^G-(Df)$m_M9W=Fu+H5VFWR
zRW)Nxz$v#*l)1(hP?#=9l=$lT_h+!<OG&F+A1;durrEGCEL7J7`MD$Vd2)zbgt~9X
z;s?_hi*><bL~=2$_Sr8iQ-~&cNLu&P{)QL808pvsulRczgO0xcv{Atwcl)Kkv9aHA
z3%%pUeW&MM&P%>;D4?>{WkT~Cd})AEH|<A4(J!WP2-AK?9<O0o<mtk!pyAGZoE3=u
z%lXU7aoe$yRF;Out#l+_%ku)nhoCqcw|L|2<2tHnCENb%EJnVoR)Bo^Ss*Fe*%Xq|
zaoq<0V#!7{4)`(~qvmEyqmwlNb&%8UeJ3f96*2Rn2DDG*$Mlb;7hJpq%Bx6q1($A3
zR<s<Gj3sl@NAEh)fJxGSS2QO?PnUf0Msl`mKb^!ck&Z-b(crz5j{FHr?07J(1*Fs3
zf01y6Vs`!YWdcR#+2Ml#n}Rgmc}nIroItF#Fjw-|X%p}+rl{(PS!N8UM_9&<bmYhI
z`9Egi_d|+4Z}zsuolm+@mpDx&{sIs^3Ub%KMg&&_qEGD4t3p7=ouI%IoQtQx*L6Hu
zPBPlBfqFuKBE0v!leG4iv$0uf***rb?UUQX>420arlcj&=Vn^!W&p#3HQ+1sj8~`I
z)v=Reb$03BszUEv2brW9)@jiBY?khluowDj)rFNao#^Kvq)0h80;TN*fVOEkEtH!z
zulMBsOS8x=rnksEdRba}L??Je4)qvjDAara5dIX(Zok#q$jiKQBP9laGL3#-+y{cf
zeuZtgBt&2M+=WP3tTc`)p+W)n*%GGHCcKmMohj-rFv4$l1T1iB%-4L>y;;mDP>gp}
z*%S5JIh4YqsE<ZeEV=Oz=;m$wgS2<hE0u?b^cw!wS6}}hNpW(oH~#c?1IO3=@(l@h
zCrvt24e0Xh=Zt0)C=uAzCrS%TV?JMZJbUNeCt-yOl$w3k<hzuJbZhYih#gEBPwUMG
z6*X-8h0VuHZ5~ifq3Y+m`Hp(F{f`2Y*v<#=;Df$b)tQbY_A5%Fv-b=+V=s%QJO3d^
z?(&#7$R!&7A@cifJ#*uD2rBTj369~V!X~2fl1Tc51P63`{`60*0`UHZkAv-ETUYHT
zx8{R;kk7Ijy^l*B6qKYxuGgR26x=^99*&y5TiC3CMUbvV%`h*+*2sA|V|yqgP2l&r
zkIxsz?`bH*P%`dJ-rtMh>VXFCL!1Vo;LoY)S0=TeyqoI8YbqV$I)3GCU%?jKHtW65
z%Dzn661L(FjbDEeeahAM%|1HKxRzp#__fq^Es{z;1m;Q)r1<69p|pkL^C4F6I8y)4
zQ2SyAmGj-gL08d5<G{Pyy3NB|!;87Utjy#tI*^9!I5XC$?MkVfrHseXO_nzAZ||2@
zNttz;1nv&Qn|@onyK!MIM0+tU9(j@19J(9SR){=5#3pjJh301Jm+d`mKS@XVFp&t-
z9v@&oj>t|{PG?4~CKp{u31|C8OCtULo_L?Tlo>wFPR!JIV+J|T44+dYSuf@ljW#)N
z$uTOS<UgE*6%tyI+zT0AXZpM@WH;-oh7B?rC~Lu^eC)4kCMCZzE-jPm*&np@Z`D<4
zqlkJ_HE)k!)7&c5tzjY{eC(3>EdDKd3cE$n!)5spR@^|RvoPeK_<1SwT=zZc3Ng9M
zrPbte-|7AH=Dh%cpx9+m^XjfKv!HW(PupZ0s{&g)r9v9sP*rrQP>WStrQ_;G2JDG&
zQ^YU!VYi1enaS$$n4z5a<9uaHPK_NoKC?d1DGzq*ugvgquBHak(rfZqHyG;Qk@QfF
zWRO<e=kg^PiC_+*N{KRFyP#XcQ7q>OCCu<iN*|))5r+$)?3`K^KwZLU`or27frIE}
zYucrKowNpM3^p;V&-seT`ulP$N?=bQj=*2ccQCK@SFD)$H;vQ}z=<=!I8FldgNx}H
z?EeJfDIL~zwa1KMbxAjT3^5EAUHLYAH*hg98TeBtnK;l@p$NlBS2zuiv^?LsLTrOm
zj5ol;x%a#=;h?M5k?tQiLvG)F_l<P5+<245%vm!d-4AU>C5B!h0Qb0Kk23?E0X0Ax
zo(_{^<QGFzSFodzBS%KMH`@$Wa3@Wg6diED0g<k@oAGkrefJqY1~Kn0M!)XiwlH=7
zHX{lHiTIL|5-UF@(MUsGVJ$E4RgVLmBw0JsJ@-6D>IQ2#dLBVukJJn3&=;T{1(EL0
zk3RU|Lz4x*x)(f8XGpmp*D3NdA)_n25e6EDs2h5qGC`oLfHvu|<(6CK4MnF1CpY%v
z*6v+*)S(~!=tpMkIZq~TkeU2^I65xpaPmM`>CG^Om%0TTE|Ue66KxIpfS&q?9w}p<
zsc#b|OtAL^!<UI5>LPNI2MlyQRMG0JdN4$K*qhNZu>k05azy^lJ@?#lMyTh72@2Bj
zi6?bawr31#%9y&tgc3OL<bB}&$tRyQ{^0oJ4Tc#qV9fIlPn<Z>aNJm<zPP@e5AQrc
z`NJ1Of6>B4RwrF281d+c`iKEfo_Izc>IQVw2_|y6|KI=q7p+{i(&WGW_S-ECUAb}S
zMkBIbef8BAKV$j~lf(6557|$p8bDXBFZ9pk4ilNQOa2Z#^K3kMLU{p{17+mzcKE(k
znphUus5Ic^zDN!0;io+n9d_wU(Yxd4MDKh$D|$<f>}S+iJ6nx*47>+ko@lbaHbxEK
z9q)~9cyvs(XvOMCSJ0#TUz%V>-(#<T9xYk<bu<j4{g}t2Q4{7wD|E~??W-lxiGS6$
z^+)$c*FN&G@j2~=x1vM-^g=lUef-pUigSPT>IYK|=j7{jBIxi(qW6{0Y7Ln-cS&^A
z<!Tr!?`iXvMSr<l+p(WK5Pdi)?x!*62b}U`w5J-hx;h+<pRq7%e*6>B`M10qeYs$H
zegZ+Oe7??4o`r$_Xx>1-;UTTtYV_;DdC|n_^VL9oNOkjMgkj3`!y}qU+4ufv>$9GT
zK2>>nBc6OmCwF(eC%Ws|PfhOM-|%L%*9Ff<=yk^YWzpfP*ZtIx|3dXJe@SczGZf{|
z;l#ha8XfZIm+bMS_q09N$&EuUc`=%xc_59KM^9IS`~K*rC&wAiU+-5re{gqn|H~7i
z#aa$bq+I);8peZeiQfEpM)bkwIyrEp_Q3}|82#(9kE7{x7Dq3R*0NK+SKRe}v{3Pv
zt5TnNTMhOf-WT2a<R_69o#y?qXsAv=e6D&S-DyhKQzvguzwUKQ_uBhZ=eyk-z5VHI
zd!909Nj!n@oBu^imBFhYshnHi9o_o)$I)WdAz^nt|5+s4-xEFZ>O`Yqa-zN=-Nl_|
zE4Re2cXbMyA#*<{$25^%DvlE*a6P!mT#Jyu()<^h(OU5+Bx9lQB4sLv7V--3%ORCL
z6qTPM_>^(1i#rlbKwZ8!VSdW-4I?!D;ISw@B}A^(V~}>8@Tn_)g@l2B0O$+&qwZOp
zsqVPA*=mQMcXQRjzu?k~>$CQT=JFemo9=am+JbPaQUhFW*2CsFs0hI2c848zkUQ?U
z!(6(+0d!T(TyD8ve}nZ!jbC%e<=G)V#zxGo_|{u*HCJ-ta5v|a^{MJ&4B)N{sL_x=
z&6^Y8aZkrpI}MsQi}zxaSHR`6x|Ab8H~i_q)k1JBM{alny?XXCmr*tT<mQ`hHn(y$
zM$Ofjc<9tXHWy<ZJ`+?a-~x|}H2Tw(NYgv+)3`P7yYD`_<=|)L%1Rjly4q{*_}p{a
zKJLZ3s-Jg%#w8i|RvnO=%QY_gln3tDxP32Pv{>D~Tjb>h51=e?Wd=Ab4<L^H;_Uxc
zLrcqIsD<Mm{fl4xLjLiOf8_N<gw?CuJBx1BK(ws{x(b}vHToeh$f5?N@gtovAC0lR
zxGOXfxoTs4<FaZdx{_{ZB;ZEP9&|tPpSpodFMIyiRF`hH5vY-B{3&nvlCB!$wk-fY
zxP=qW)&MnX4A<+#vZc%HKtHx2;Qr1j@YHiPD6KAnjO(@<>~`QHys4A8m0y0@<)%xW
zI5Jt-3yv(vWNyV;;d~o}zl(lFmY@Ip=XSUtZt8$~q}UZ1{rm{dQt+(?Y)j#Edfdfv
z6=xd-w4?=4_T-m%^tDX4gOCpO1Q&UL@?hJBdSgh3jvXwF{r&*u!+XH{%wc=J%>=!0
zZh(5aSUsTrjvP7CwjiKC-Z%C<Q#R-YeFM12mzI{AdprjQ?y>tG(ye<pi*x<kLsR;x
zNgM7oeyv7+sV}Z8ws&xz2xm2LNF3$8az%VlmRb_!xcV6fPFpTgA40y#xavF`9|qL9
z_Mkenw8!)V<z!6iD0r??=3;RJylTn&4I<N(_i5ikZu)4R{=H^>vo)NHwf~yETz<ZL
zKiPSUfhOU@Z%!52sH2R~=SW@D)o7LBL;A^9+jo~|Morf?tDU?%W})2v+l^$?((Y6S
z>DR5D9KY`nx$?;`_3*Q}KU<}4<!Zl`&oz&W^~(|JUOsc-3c38XS#s%~Ys%mOU1i>a
zrP8%S8#zGti2viUS7pqEIntrMwmS<}$*3_iq=UL>59-}Pp8MU_(p=NTRlh^44)V3u
zo$ei4N^f-$2goaZ0#^g8TmBFqZYHbZXN#J$LVkbrP#IdHc~RJWa8D6EBlOHY){=GB
zi2FJV>)tBAZyY;!p1O9oS6BBA`qxqSx2u!8^jAFqE`vQ#Q8kVO<<ypqg9!U_l^nb0
zx>Bm{<-oA@`pAfly2#Y8mdbM7R`BN+r^>NEA0T^fTbkdHaNthsNUvMkNPl&qpP~Ab
zSu5qj{nwPkcaOL9{qK#*B5j(>kGCxm{l<)Lky|dCZ`e-`8PP-Td3~COm&&`R4wJ50
zhJ$*xm*p!~*(vvIfABr}Z8bl#<ZF53qZ#TJ-$DlW?<!XwwS`=)+Y{RA7KtbIM0^?E
zNk(kBrmR-_)vCm8H(o=!ZazUCd1r>~G<=Za)&2`Nrj%H9cANZ3H_)v_lc*F(+)!|J
zpy4PBk1LJGrCrN4G>YK7lAUytHjkGZsZ{ptaY-Kj>@tLS9_Bb6S5>&<LFc$0mV9>J
zR+n=RNgST}OY+Fm7J1`*i$pJvR-pWA)WMYJT=o>E5#E<!l)Eb`KSl5<$F#N&Wv@Y=
zlh@;XKy7Zq{Df(R;qVWhi_%kVWJ=NoX(ywq_3V87S}o#gAUpq@K3!kuoqMkB5ylW_
zppG%do<$5}KKsO;bULgG;QWG!l9Cd8asv#bh;;OS{=LR<a9Fi+l^IMfi-(8p1O^4*
z1{T*=ty`IqrK^IXE3|pT)YD~eG)8F%o`xTe10R6)#^R7sJ6P^9Ms#J+3?Ymz_RF)s
z81OWlzGbVHqAQ=GF2Q-@LszKe-BIt#W}9wS&UiHj`r~Qp%BTflAhM?zxK#IAt4ncC
zZ)Hl>)iI+}4}x-g%a^ZMZnC+N1rH2Rj5K7$m}3t+2TJj$tF1N}z@Bv13Ha>grmP(Z
z1CY%P*L?YYtp;}U7Pg0(i2+@0cCwgfP+gE)SC1{+b%#y?CIR@00EQxUZqL2;v~ve2
zKlF=y{JFj`b}<myKg-|s*Ih4bud}xCavZ))z@b^{Nn!ii*~bqr@&PzM3|sa<UwGk#
zw$EEV5`+mST_Mk>w`lPqD_3{`x@v9)F8i_p>Iue<8^MIJKO4C4j~Ch=bqrKKUFhJt
zs;s?4_vjh}+hs?007f+X<T1oB_)k6cR4aeohGIrC<qm+)pTE~kypRudcNbkTw>{|O
zAKg2^n~4CQ6d*jT2TU3PrKP2I^>={D5dNsoyesP6VDBaTC>P#&4y>cDFww;s1Walm
z`-YmALv*5tvUk~yc~kr;dtHl{-f8?=BqbAn)Gg}MgAY6?2OM~S>6}C0n4k;6-*XS6
z-c|-l{K{}Ei>nnM)klSQB&nm|xyrPP#SQQ(%UffL(<V};EnX!@ZPHzSyWddxT2C%V
zAFK5G;7gO_oO>q85j#wioi`t-le21_&Rrqvod2@E+_{FD=J>0X(z~_xLuyEi8mAi!
zi2G>_&z5@PSvNKMx^1YFJ}cvXlQe^7K2`_1)4&$m-Yr<DeS$t;`ur3*^{FXwJOr&Z
za9JeFm#&bX{h(C$R(~bGymE|4H=U?eBl)r&`^!E*C^39>$+xRd1nA1Sw&NsH86@Hr
zzjv2-k`t<xIw=9PZB^DLTUeMGy(byc;}AM@z`ddPc)_^v<LUX8{#}DRwUEAD!*sPw
zG?>rD>FT#kS+G)u^zUTGIlO@`9a_u2+sFOc7hlcRQ*xWhRvYxTxMgb8kN-+_+P0Yt
zzUbAmvQr#A!A^P%>LARQtgZY1?|XTY{PenaWs2@W|B6W(jc>kXoZmgRDUshEv`~)y
z$2gG=<5lmi<bv(h$lsyV<~8Q)zIN~jp7Vm%gIF$|4G@{PQXbF=l|QXmZ3emtRokC3
zl~v%>vO-8D0siv))lvPK%357;TBl1sr-l0<#N)U+U0Pg-=l4lGo2ATGvcmj49rz@9
zIB&=Ccz^czv}+Q_pPfe<O%_%=4Y-bL%B8mS)k!x@t3roe9Yz^$ss>6*O3di+NS+yB
zvnqYo@6VET2d`@beK)EYlymQcbEo6#1MINO9FO-Ia6^fE@I0Jnhmby<CmnTjI~5(H
z$<uIp46TrdhjYbPRTo|R{<*qH+oS~aAwDaU0J=avjOWk*w#eKMgIx|RW5wQArU+wV
z0lw%89xkJYLC%USz$&D#igNH7>0<1VM~o%tS+SfraY8x$LCZVgMikEgdGVE2j4%vH
zR#-=l9BGp=<VCjt<muo~I$p-kgDY{KcML8IV?d#uHhGypZ@wK`XAvns<bhRM0REI0
z#w91@b9maS)vGL?6<AKB=5Ra=bgw6*PkspJGi$8kV}MhJtOVO&R0mOBeoV~Z{XxA^
zH)=DuImpcQ4?pD4vkT0i;gfBgqEDKfDzE#q^SPiL@f2|0lo9eX$pOF%@MrM69GO5s
z?-)>|hYpj*yb%}p@%($*esx0~?*-~F9tf=3qm%Q`Ki^jPnOHN~nw81O3M+=a?)x?m
z6Y>i$3})vEO`&?AobqMj2A=DZy2hb+=$VNC4Dyj$*We3ItCp?oba6a#(2wr<x6jHU
zJ96=PLk?`jJHci5{1#BpcRCoIVw}@1>9z^MlZOdaChyQadZ*4&cLARi^0vn(W&FMV
zPAjuXXI@`$1^R%QYHat{!_GGBUp4_wTn{}Y?3{<E$XlPym7UZ;Z>H_>kUnkX>R)Um
z&9wPirEPd?CJ?m^?cbxl%=t<WhFz`(v>Jp!u8Fhvc<z$b+6KgFs+Y60%C);p;1g`P
z7_xfPvQ-N+B9Gf<4cTF{HDvM9mDY~7!7x@C*6Y_%dgy^~H=nePTyW3=d2hm8J<x5o
zoN~u_Ipw)&GX28sq(^5paMiwyQ#5f3|5SqH1~A0du4uk1gcar-(^SI0BB>^zhIrV9
zY1d!}*`XslMIGJi1PXu+#!39>{Ie-@rGKxEI;pD!`qvRJ1BF&vMjdqmMU^g(A2w7t
z3~!~<w^X_ox}x1(%Xzg<K5TUUOEP`Va{1SR>&ixJb(aCU>i>&t-<B0CwJlaYy}P!P
z%YMD3oU*ST0yuH5ygzQXob~t=`NI=m$S0R<FP+s}V9j1_<d$D;qLUf=H2@`Qt#WW^
zX=jzClOFU3gn6@oI{qozK&P=|z{(6eohFqp6(~TBV$k8I?xx{EPv&$_n@m!-NM`IA
zOt1<==jJEdI^M*I6K!z9Co<^Z8GJH;L>>TFCfvqIlT{IT!Vg(V$2(sJao}+RFs^Fg
z`ZEnT_{@-V5-@{72AD1bR|YRM=+HP{I#}{S2S6OWarxtOLZpYBblz5&sEtahX@N_A
z7z`jUowN@~{lr^Vb3A>^X!cWQ8mO&ab90GxCQoIIkTrw)(|{XY-cB&ED=8_l1n142
zXYVcpJ=#EkcmNkwz}{`Uqk}GAR>UO@jT~zJ0*>>b2~9#8PalBNMq%V~S~Ny7vT<s*
z9=2rz)sPoyz>8DKN2>9Ffy2qhoPZq4$O<5<xva#p5`{71E2v3+@T5J%FySu=(1JsI
zOq&R}{y4>&Z-8TD;a2RclNd1WUJgEP$$TP-ldzlXs&x|J?>xge`U>J`Uja@@#}yvH
zXvdJ%{i^m{*$=wLu%b`U10>Cj+9x8=)r~jZXsh6bcpzI?hIl6M$vIYPY3p0Ih`VA8
zU%*%K={HE5J`sAp=}j1TY;|y8b((ncN?)Xh1IkrbUuE74xO^iwlM$3LWkoq)*s!9@
zRtP;4!CB=E8Oi{D{6XX3>CzXnwSaz%zK{9_fa@L~)Cb@0K^*Dnes?J?Ewz4+utGq(
zg@C$5`g(4I=s|I2&|=h*S60XID&ZL(csbC=0=|_%58KO6s4%vmzjvHMX%fzWmU;j{
z!#Kt3o$fccZ5NX!O|-hJXDZk@z)zfKEWj2C+~ui{=t2)pw6PE4AL;@gDlWU1rzMvC
zl+yrBtk(~Wa<&_=)q{HO;N?QQjc)mb9}`d1SKyUbUa@gp=*{G`*|jNYfc`daE845=
z#!9dCz*<T4)>eNz>8k8@L%Ybhnen9J25WYaE8m(egV*RR1JxjRxaR&3Wxk#ezFw)m
z<NaF8qw4Cs<@&wtG<0w{^<C8Uy~Qq?kV3y6?c@<X8D0+#Q+M>Z4Sz$8J;cHNy$`3j
z>xwwr<F;A9m(lPuamGUV$Ae>}+irv9&S%F<XLYkabdL?BR8LU<={9TXY2)2xpFg}S
zb9E)ZH!kIxmsV<&0!$91whgq^hLQWpwVxKo%t5(G#UUu!#)dfX3*w2wZQoEdJWU4T
zoJB&KoKU(TuG!FS<7(u#lI{DolLtSXBZut1Qo7}ga6P0>Hr??V`QP1JOW)r5H4xP!
zX#r{@+llQmTTgD+vRhkEgLk;`kulO$4d;aeyUX-X7R$qb7$!f~)B6Ed-k;HJ4nJJ0
zlgW4Eqhs`B{Z(?({u|3coe22RmIL*q_#U#?Iq%54MY?jXr^0{!$}Cw|Puh160D8cy
z?s{L=(zuP*jK?`;HP4c%^Pi#(bOxpjwCHeMS%svbP~C9GU`{JYr)8kRj4^{T20RSd
z^zarNkd%~^<Z0<d>0Ie_aTjE^nRMLc662Nz`k(_3GIusyR598a@WIb#t+{7V0nlKN
z{&2JnfH11PGJ-?r3lH`qdIQcti9rE*!a%3PWzhKEsQ09#w8T~ld|=EVi9Kup208;6
zcQYg%1~CjW=tRhu9(-g223+JAIMI1bnLNc@-WW_(rbA04`>D1D3N;|bGO8_ovFR9|
zw|dng%8;wlfVXA-?sN5fUwK)uFy51kJMg*Zo||{$#!zF05Cg=G<+Sv8*WHa9yfmOK
z@i=#-M>dx)Ndr$`!6SW)Y-GXkz_??D6+=Z&iY~~5l_ouj++1$;gmK#=+fF|>H+JmD
zwi@-kx<~5)c4o}c=CV4Nl*eWCaO86R5r#p?mA0KW8ksQsz@txLPcm&Dt6#Kp7)gA3
z41=9KkT(ozJQ=pxW*gBHv(3HP^X_^f4cwSRAPl(ypBP|OdBlhjHo?ojaq`5<D~27i
zQVwYVZ~8tA*<Yz!GIfL1Yx*x%p8@hz2<V59$AOhwCP<Dw_89$4)YVd2T5A0SEBx>$
z9=Pt>>*Vz}UbmG&^uX#cuK(zc{?2txKf<S@&<k|{UC|fP?_tzaH<$pyPyvStDGU|z
zgn>gphfz&=GigH~$>b9Ts2la37N3j5D5gxv6O$%SJn@9+N$)n9gASPlVWl6v(zlT|
zli`%>X{Vi5sWI$&N-GQ3ATl98?<09<f{Ff=J{|DI0q~f-qTXQyckC2<F<?xyAAH*M
zY4W$f{>^C6C-n`QWc@M({h=O^fAWf#2VN0uU7!weCIaQN=9(pz7W?ekmyQcQ^$$HT
zDZ>~7AWjdElRbaDr@bE-?0`>%HE920DL18`cxcS~OZ{fs0AsJGpLyE6NvIRlAIh9M
zK2qN`-YecS%8M}>b(nJ$e5(fKo<UR8Ks-3YET5_SjOjluMEi2>eJn$)v<}tph`%;{
ziG}Pwe1OPppUO`zeNF!Kv-RzBXiw|j=2QRgBf0zETgiI*$+h!#>o4d0bG&p>!*N$N
z;6B&Sb{%r-M<N|{k~7zq^J%uvUi(CDdTgxWyfb#D{PH$E6Q*km{WRBVX{WB&|J;2|
zIr4(hYFsvxAAWC;EL^-o_POZ;nKOH(9Q(6PWWIi8?1Zz%$P$(PdqexmLS1El;;m`w
zQr22mj@hE6c~Hyr`HyDnxi6Ds+YNim8r|EM>rbrBw7kdVXY#303NT3_OZ7fhg@xI@
zdK-9KYk|aDSd7ACq#nQ)Kj<$!@7KwQ9iJPod)Rx(`nsncdhZuN4ELa$49hF_iHOCg
z)tGNMS@*#2{o6O?qBTaz!Mm&@3sg7f-}SM`4jt?>a9`+Nb>B<xf^0RkpU9w3WxWeu
zkw<hDy??Jx^8Bc&a>7X;%1ze~lOei_j?esKZ%nnx4Yng(|CqY6f2^Of8`xf^smnV$
zJ^6Q|ZQIp08~2k{D_6^NZ%@^g`DW5fJur6FJ?3)#CvwoGugF>ZZ6G~#d%zQ~O_npx
z93%JKy0z7>WZqrAy1b?v=#)vqAfWN%fE_wM29gwt7hM8XT^E|mxKGE~Hz>WM%nP01
zO(%;X?S;(Xl7^MeiiXo?wds6)CK}@xBbr9nXR}%5x%uXs<;}O^SyEr&rgQe0Q*aqX
z(GX*NGkDu%(@ktOf_qk#0OA>du~Gr;&O7g%7oUz6SpWu{?<oIWbhUz&a0kNm<YP0~
z83dpY<YNF8z7XV@!zSn~82mHf@!4HElK=eYKeEAw8`$8+c^7?6=(%V*5DU>33a=LP
z9CFAjyf4J7u5beWH0ZS%1chbkY-`KY`PengGxs^D&@d$Nk}sqa*2gtWyEgotk6&xU
zt1?>n2jDB0-u?tpHmEj3MGx)TW#?VY2w=4lBaya<Nk#pXlC9$L-eQE(W+izMj+-yx
zv>y!clQ^_{zG@0StCZl-)?g$h@d;<(Nn4C-IL0h1(RzZsWdTEheZ?3KNm`z%Lrgqg
zaQ+2$NZlEypJ9d$#sMqw90;Z-W}7=O3>olZkR}JQJk$1hIK0!+qito<^ISEkn5@Jd
zn$<4!NBfGA=k*%bU+&otPMgW0S`4OHZA8!X2|j7+<%N7cp%R9<3o~xbte#`2V!Xcl
z&bxBOl~-6<z?*?QeU-Pr&cpNK;T&3q@x}pclp`ylxP~*q8v^n>z=QlDBS0U-ia5p^
z6BB#wxt9#xc&NPf=3AnlSTh;v6M+>g^)Nf_>i{NhF{0UD```cm*Xkhk0K<LrEjG7$
zf(IJkcjoGSBQj%n^Sx+frYzVaPF=(>XEK<H8YWJVlfH{T&H;c1y}0b;3;koDmXwsp
z!G|1d1`+g>B@-AvQGp>&nUFqpm@^43zUX2(@~9(C&tBfHE7!HBg^rw;DL{W%2x3wm
z8obnSX-EHh(!0qx^^2*Zr`MY&2{atchA~cE($4|Pjyvus=bUqnxwvnmee)#sqPyUN
z3#^_qnMGaZUB&PVAt@VP7w8KbCLfrrgD=kjvY}VrLA+2XPx7OmY&2c0*7FNkWFY>|
zJMJ`naJB$_K6T!pA=~jd#k%>eNgM7oeyu_h?`QF(WL!!+$Ak!Vhj*1L-Z6N$&`wg%
z8PCy%FnI(%Z6sS80I!SXk}Xn}ax`fV@BwO4o@um6xx(l0K2Ej#Zx|omm&YnR22A2L
zisv{Zv<>U)A#rV3qRm6BboP%A=+;I?>8kmsQ|HTix-$CaIa|p{HI#R~^j+Q4tTbxi
z-gd)q+57uz8TE<#47L5tr`$bJMBSg&T9uPV^pl6%&Na7ZkoYF|(R#?)N%tzw6O%>M
zHTsGl4U&`8J)Lie0|)LjSlzZGIquHS<jiN)@KzW39oFqAS02BG%G6eV{iF5OC|@PV
z-2RzJ=g&1>_dj=TCC{9`nXT^9m0bVB5;^*T@pAW{-<P-lvUSct*D95(Q|u9!j#~HC
zlM1kF$Jo4U#r|yd25`50;*G9L3w=*w{Ct@18E?TdwE}wVsVn<v7uaK~0rK~8W8{#F
z-<Lbj+&E{TYlNc56gm;ER(B`W0ni!?yjQDyoCWGn*VgKat{x4*?pv)Xw_dbDe(~Q=
z<<d9w+>&Kq>w#_U<n_~rn`{60FZFDYt}V6Qk5gdMU=8&s_~?`^<>Y^VAiG~WO5LY5
zpL+JdRaa~z2ky9z$$j-<gXM&KCdjpVV4y5oC8zGtSN=S5EjjIm39{t|6ZOEk4P@pY
zSICL?<jPIc71h`MXKX5Kss{z>z5b`I<>xnkC_7)S^+OM(WAfs^H*P6E{eG#*US~fj
z*cF&y+DscJ*MB0a{39-g3_{}la`B!jC-u&Yl0=W}@0h4YG~o=8(i%Sx!+@r98#!{M
zwC~Vf{&U-ZZ16<~$p9Dw7tqfc*-5w@0)x@asyhZPhOGlL;PBmW!wprG*!W^l=(r4I
zJzd=FSe0kciJKgqqPtXLyc0%#aV2DSo`DM!2e_><2<IeYX2e;EKn^+t=gpOL>6FPo
zD-;a$85kx3p6ml;&`KxbgLHV1PX-HgK+Zc!pFP)J19aS!gAe){fYZ5i&DZT*GCVVA
zWMIr{3+2wh8H0@hTYUgV7;cZ$1veDxBc1OePgbxvWf@}y!#D)W+4t2_z8Imbgj3#>
z2?K8EFrbL1Zg5ZC;Lm|~8+_hvu1l6Kk@mV$P2B-Flx^{n#a6b|A?hMDo@N?P<f2W0
zCxcXsw33n%%L8&l%gQd3nScA+-z+@YhJ?>dP-3*vo?)cZR#Mj-hbw8KBV^P^YwKB2
zL|)`bzAK)8uFxWv8}fvKkN$u#9m07=CitT#Wb*RCXkD><MP5J9yBw~wFpM<d!TZcg
zHdo3Ap6HgohPEF5@Cox`<)yNR^}}VO9)OF%i{1z){UpGHdK1<Q&o6YyL_Djp7_`Xh
z@q~MMA~*E{9oodA_O<kL{6}Y$3-QqUes||XIB8*A!w;E|6*~6fq6ctk0|{rZJI@&B
z$UvH2Ry<?)5Qp&tZ~8h6VO)rzB@Q4R@<$rz3Z28t<qCD>x+Wj!igdsWbxK{MjJ!;t
zr~O8L@<kfR6oS7i@T5*te>gJ$eWDL^PFb=D!FU3lkdEsIUf?1RVgBBd9(5F4XqXV8
z4uyFmZE(OP9iMn{`S3J!`H-D91qgXKZg#Dx0e>fm@wUase&~?$O)D?LdUWq$drC81
zSJZ1$4AJ7&J>Gf{m>R3?+s4}%!h9@KH+1$Ox7OA4wyop7cd3TWoWD%;d*RYk_k!Dg
zZzW>&7b8u(W|=i_iOgNRQu=gmBdfLE?0Cs5a`>h_<+p0UV^=NL724VAijMKvQQfw?
zc5EYymS|h6>9h(a3%**gOy;QjG<%tQtLu9^mBH&Cd)f7y;I`_Ht=&y|z{>%b@FnC;
zQ*5IrkhcsBYjBWvv8LHd4{IfF<ds9;I=0uoTK}0mO4m9!X+V15bDDW;_Q}IT!xm{8
zZS=W~%DzNb^5NS-SMkZGs={c%g~sW4c0m6?hjy)P5@YFd9UEv__(XuRvodO_dO<%9
za~CdG-7Hs~tM^ma_I8>*aqRzwN4qwy^15`LQD(E}FSXTp_MZ3B1N<D2b>`fqx&7&?
zZ}m7>rMxD6xlr-6%~$>6957dP&h`Y-rQC442ar>XUl!{@cJov&_PMk7ynCm3tCYtZ
zay|Z(X{d@sPzs$4e@KrJPN5~i;=xm#vbmWgR{Ar84mv>$W!xV5R0%5v7$yv2k%30I
zq@=`*R}5S3`|99o4gF(vzy~*$LW+QNq;!b5(r>!yrnXW=T0FDr?gkK@?q+I$(c$98
zPRH$o8`3~dI&?SEywdm;H!6U^1Oo#GEu_I91R3a97)X?smfDH{gBw->FviI*lK{B$
z1J2VPvY$HBKyCF3eM5l_#B+)H0HmSP{x+w>!A-3?d81t3U5mEH4IOkqd1Dy3q2hW8
zal(72q1|wx?da4oULEx?PE>a~(x46Ub_g7gb9}C$(ZX38>}$nkd*A)`wf)Sva<j6?
z<Y3Zha6Ys{{tjSd@)v@~5e6;>go8dBU`+8>6@>C8ap^OBa2?8oF&Bc%)4G*TfaqTc
zX>x>N(4q$>9zq~btf)DJdS^sp<&|5_2FGPUwotxgop63!;gKvu&nJC@8?#R1Y49BK
zP1Xy?53gYvIUlan!LS}W4ROd8%10RekAw4cei*~78nbGC`st^eQUCTkZ<}#Wox>0X
zC|B~1;p(gTggYPZLkM*ZPMCMc3w7Xpm{jn|Rmbx<p2NCFICUpk4=|iV<C$^^fw~jM
z!-qN><_mmJSEG5dw3F$3*>lApccUB%z<I<^*>7bUpj^VbTL`zR@+w4^g*P1y&?n&%
zUI;Ko?ekiS5Wk+<#%LHTrg_i^;xNvw&ghkW<bC2MP3A3GDU;P`1Ly+5X`?H}{d<)4
zjl^{d<nZ^oyB_w&pM!^UWd)pr%R<P<Yas6Noz&Pa1jyS-Ki>w>_Jn!GuIikNb9&P2
ztOjWaOl*XB?Slyd@W^jv`UMBW96$ypCt9^EpN5KAjt6*zb<@ca{v60V5$QbQvT3W6
z1Os{oWoPLsodRuZpWNr*EI@zQKhK_Wz;v!SJ=Iu`PZY6W3nyLuZ})umjz0&JOEp1!
zLqJ)|MWm)5W;er<8R7XlYn_x4r7&pepdCJ^$~Mo<))c`ykAedD!+7RIU{<<uk>m6{
z_+!{&EaRGl!OM4Z8K5$YPB?opaSvs10Dc;P&#b=>jKF7q94qG~B_&3}YD0S+jI*+S
z!womsJ~r;1Co*Eh`{2dX@pymc%D{uwDJBMdFv>k{m!+kpX5=%d!8Ly7mosf|A?Y*W
zz)C%aJn5zJ4%5l*zr`Ae+a1dw4Q>W?qXB)b6ykR)?fv4t1Bm0#pDi%>aaxb}=a9z3
z(%$8eF1#m+<a06%_hGzwaF6Cxa0d=~^K&pfEO`xODir78RmtIaX;-IBi*w`L<3gQz
z7}un%{!E<nFBF!}llLfiv0$W-=aGBO;O$}Qd}6wCv}HkdmnL5!UY-_&A)VLN1nc}`
z$d7yHlMD}ehH=g)3`^dFgHeh{8(RjLOvb=uC73<u80{Fhq(QlQTF#p*JPU#2r=<&x
z>wtLY<224QjEiZ?$~fd3;wO0!=lmTH91lz56Vk+4tk_<J^x#)aw=7?tZgwrK0k02*
zG2(Kps=O+r&*GYn2K;^U_bik=Usq^5h|sI!J05i2I>mK{n6ks>;DO;9Y1;;**DE+A
z+6s_k0k~5rd(YE(#JponxG6Y&AS1yJmKWkQdCx1nfjFOWTm^g&NZ=Ku$>BTifV^Bf
zNf{i^bq;+_yd0fll<CIvolJ{o=i^i`b39KwjLRv}f?|4Z%ld|}Oo&NK!}$~65Kt`x
z9pe%=2=<b3D2#W+2p!LsH`jww4lu*Bg7PZKjjGjRxzed)_%bs(V#Emh-YbSUMlL)_
zi&bfiR^ka`aDbtXG0&<y27TJ>JBhG@je8z5@wmSG!7MxzUQ$wGX&?tiGA{ibbi;l!
zR`Y!oop@Ggn5+Pj1?Qg|Vs!>SxVXcI2?lVHn>+y+>!e#+s^8$#cw7RJ<@x8IxAZuX
zH_6`!v!7aN!0TO2nPg>BQ_VFVI(-!t;`S#^s$>}_pOZ9c@yW2{vq=yOV(gOf$>$K?
z+Y$D;vV{gu9rw;*TMppuract+NlPP%nue1WmZamkSUjh#3_mTdn6NaQ%F4cwEa`k4
zJN;USrck(Z3FE7(tI&k_&LbI~#7RC^g-<+tyKlYiR$<S#yPUI~i+(h$Z)v=tF9ace
zc<=aWI!MEFe6Gp(<Z~KMNbmPay5zIth1aTZs)`SJW%m^|kX9cmV#Ohut_x}A+Y??{
z8K>2i%J9*O)9B)|L1#TB{N-aeH}~{7q+<ILhnF8y#KVm8VNTDrN;zVrkfg$W$iwA!
zq%f}7uwrSPAJ>p3DTCvMIF*GNKl@47TN<A-etBvSQsK&n6_T%vpnQJ9@P@koe?tNy
Uv$#Eg7XSbN07*qoM6N<$g7y%E?f?J)

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/deploy/run-on-aws/login_screen.png b/docs/python_docs/python/tutorials/deploy/run-on-aws/login_screen.png
new file mode 100644
index 0000000000000000000000000000000000000000..fdca724a0a37486a2fea14a733bf92536b1da1d4
GIT binary patch
literal 474538
zcmZU4V{|6n5^j<SCY;z4+qP}ndSly|*tTtRV%xUfaAG^T^PO|<xo550zjjyEQ`NP5
zt=)T9JsqwfCk_vT4FduK0xu~cq67j0b`1jZ4d*-LUkSBRek=$GOr3?Wu!5wpFoA-j
zovDR200cxLJUInQMcD#<q~~cKhn5hPGM`<L3$$nj1ZG}PMMwymbWp(D)x;iL3>67U
z0rh7Q1qYSr@Gp%zcrdAYgwedbsz3-FsyBbq^Yff7-nW_G9z9M@J3dcux7|*kAaPJ@
zV1h(65CX{jswY=_li%TC&31D@iTwRZ;=WN?%SH5#&CEa%XtjN+;=jggi9ErXd40TH
zeW}iGo}eOt#NqcVZlcN|_nXDHjUs(l1{EL#WtmroXw->dkPU+iyAIck$v%G6jLA5D
zb7Bqnr28HV>QCOd)~^8~aTU))9E}orPq=`{cI4#Y9i)rYay(f7`};9LCkrK|M~XaQ
z70!dakI~mWv(#qdR1DhPyVg;V+hOr{<hAs#W7l^(Qql<YoctJYPPvoQSe?_+oFx(x
zugG-#=6SaOkImn&oG~aKkhBcsL!{mTzpd4;Akay{IcXWR+uZ5tlhN9G`%U~D{CxMN
z@EyL)Q_>2RH^I<3LXM}kcB|TY;~?aK5VbnHNkJf2L)aJ&dK9-9Gq}Xg7`2R)00%Y%
zuN%#Bhk%?&=Aek<J+Y4V@Vj>chc1F8%}wRFlU5GDf>4g!1ayLKb~KMZbdH>h!`9Ui
z`A+^`v^G%ug8>l1UbwVxC<dT$s-<FPlidrwq?&P*2SdZfOj&WtC{qSEoMO^gCBtMg
zAf_|B@A~hy_LtUz&hJKaSels^C?E#EhjfzAL8dA}<Hj{7Ep9;aHbL^b!ibSerjBPD
z^)U~erNOWjK&S-1oq!1&z|aKH+KZ4qf^`!h1_d<xf7=&8M*QVmhh+WjHv#@R<eLCi
z8MwG0JO{K~Ai6!C4&+Lo%`E(DFEInSSRXY5G@rl4_h0si5CuXoaa2VRSOO)8C=H>T
z1o)%)Fo8t*8eu=e^P*>6De$R&jpq?7(B8v3BecV91$pK{%`&hcbOm?!Ql!Bx7yvYI
z^dOlH$yad@f>wI#uRS?nrvu#jitW+7!E8h52U_>|+`s!m$mfGZ1Qir6DuhswjYAwp
z!iW#$1(i*e@u)yoz%2P&3cD0s=7%Vtl+BpaWJH+e!Oyjr%R3`x1nR|ZN6`tS=3C89
zom8H9ofzJeymE*9QtZbxux6l<p&&y~j2RnBGN@pbO|welo1{4Uh8|gL>{6$uA#aVz
zhSveA?av!#GuUG6RY$x^iHlI`&)mDV=j~vvLq&(-2I2<h2E7uAGvw2scwOPH+{w*{
zJ2PSil)iR(j(pa8hka)ejK~wMBYZ|~3z88c+{4U|wohe{N+Xj(aS3xB6foj(z^PAK
z73dW6mhl!RieHpmE>b1Iv!E_da*l8gWRZE5Ng+H-)Ez}KLGr+O;rN~X?*ASmly9z#
zOSzY%GEOH&dkAw#d6)>U+Ar~ov@dy;NRe)iW{&zsD}kV#^pxbc8k2N`$Zgbt?ie)X
zucVfwNK|dhMwQ=-oaLb9lol0D;Z3#93C;n|+4r)ij96JwEm4_p>SW?aWs_*>NK_FO
zf-d=vWgF!qWh-TcEHtvA!v#YNw-)3Ux=oUrSej0225WF@j%%<D!p^9=nbQU2^RTC-
zryuw9j^mDUj&qLOhu8<6hkWDo@oq`6DKm+7Nmq$q1-%OP$`T5zg|h|MB1qL`wQW+2
zx;JtAtV7|XO{6W-9MZQbS$ek$&}#GQx9aP8&MK!d^GOX64WSJJFaEb^htO#45rtBn
zQuX7a$quR&6<t*F%^D90f22cb2EyY<DF8YEnF`8skBaccm@=1g*~+srsB*JP_6q(v
z0!s!~BbEqOE*3IYRaSL#+r_FC?=sO@Bg-L+i<Li%uVvMRKMT5qJ4C%?#Pvdp6N{B=
z?=(^i&hk3+TBV-4@zi3=Q}f&uYPIo~TW4bz=vR8IKyWm$ui+4|?`ei;n)Sx@wVIuK
z<E&3i(o8k%IIKn3ZyCqgEEzq!WSlgg8+a$UQhnH4801*^Oy5UEh6g7|Hj1VWs>{16
zN18V8yRn8Qx2z*IE7x$GzTKJM2_FTbt3;xqbJK9qbZcs9!qDQ-j8wB!6ID}HL8h^k
z;5Yhv3}72ySa%J*c0QQlNN1{KF0lEnW3Q8~F0aMVu4v+Qy|3OPal3H0Y$0_JdWHBD
zeHgwGLa{=%2qzCPi^Gf87&il9$$3lNOHt%%v!k<vr)8({b***Sem8X#=<w>?>2z$G
zZ2sCD-^^*R>=<cZ>5%u-aJRlTe;Rc6^t!ykHK1(dsM1<4N=;rNtF7ENxdORjx-#Q?
z<rB(nLA!>jMe|bU(JOCG=xG?jcy921dgZ%WxY53PI-@$L>QOzc%z4Xvw!6QA%Y&?f
zErWeVJi*n#e5R0Kmq1-aqQSyN(!qIW9Aqt`FrjePJSmyoFpMzltXsC%akqDOd0w<^
zU3ycC71Izo5WBCP(@d;CI?@_y4!5GOO4J$Ab0b4W!9v!=cuBsPs4<cktuLS|AmgOV
z`p8n(5w~S@<5{O`r+5{O(v?x1)Tq)u-(J1p2yIJfgU*4ijd~*6X6|PCH2!7Re-o5G
z7&kaf;;m#d&)E&fAK=HTNy<t5Tik$P6b-JrRkO$Lqi9xJBO9!^O435Mn9%e%Mne5Q
z(41q6ubSsV+0*_t9qts)cQ{+-V=_CPzG1V`ciVrvcWt&_(FWKOJ&>8j_Hzx+CT4xJ
zUes86I%TdJPur>msg>0Fjdp{hgW~PuG48^KbDUG#NzC{9IiF9RNIi|aZopOiF!luB
zgWvUnWy8bZ_=(4MyV@K0p734^k@ZWPKmA?hbz!n5O;<NKB{<a8*zL~k>B?bMS(!vP
zNtLC+ZD{+*NX$X+=FR8qaf^nLzI;=DlYX6F<&W~Zr@&qB<v0C_{qfiF><Jv|Bq~=m
zZ=O-!Rj<(-1>s^xfbU`IK<7Z9I8O|Vx`lc&?hu|V_v^dHb^Ua1I{%6qwc4?|ANMpr
zp9)alsd>4grA4m1rVg(LV<|Q_jAPDc{=vj5&~PAL%+vUj(q78>&mnz$&wcJA-<Cs&
zN6B8*@>&F$rLrFLV%8eZ8uAL$BKGpS)AX5+c&3f^LakU29)GQC_xrE&;Qq+LNKOnh
zI)HA5B}S9HPD~fie0>dYE_s&vskWwl-DT=+J^Ji&rPi6tMX%k^4frmbnR#r>XxF^i
z(z)e5vM;w_hu*{LqUp-I)7-wXZnL$u+}L?+R4aUS=vDJnvtzc^v(?&ATgqSNXHQ^c
zU>?WK@knsfml<mDqZHr9C(0x1N#u{@m~wHcYR}onHirqPv*D`C^K_%XS*#cbaqXwX
z3-HW*+G+eQ0i%N6aztiiI<_O$tw-m>@jiKX>0ab=r|3)VM(gQejsNFYCo>io=d0MW
z>5)wzcd*;YPG-+l+HI!iE8*$D{Ya-gOs*v#GryZJ^vkUg*9Y~b)hT<Ioy|^H4}Xqi
zMmkbm@Hr>7zYthVIOy($!ytJOsK5oJKLz7e{4w~{+bbv7SNO5deHIO9nWgw9EONJm
z3Kv*jbP=UC=(RA%EK;wj>Hc%5lF_8hu~)nnqLmU8^BsbNBnK}@H~i$JOzY&NccoZ}
zPuO$XkGpMeHKnjGejT%OqeG|K)^G2Xp9riHP$0`JM`|D-pxG8G8qONBGF--XHgtw2
zc18d?cN_b^ds`3?9(S(4LmPmzA%VM%wXGAEJ1^0{EV%xT{}Iy@5&X-<*@~A)Lso%6
z*v=6^z)HtL$3VmfLqI^l<7i^ar6eNuANb!pULtd6XL~MsdN(&WIyYuIJ4Z8mMovyn
zdIlzXCMMdy7PL+tw$6s`w6;#f|90|!{fGdZj2$iPoh|Ha3I6eGXk_Q&%u7V{PoV!^
z|9(z@yT$*DWb5>wwEiYY|Br^Ak&c1>zrO!MdH#`dDOk7ztTjX|Yyh@Se`D~mvoi7g
z%l`k<{J)6*gVg*VBq!_tBL7G8A0!X`KMDL#LjRtve@XvN7at4{{eP#P4`z79XAcBK
z07OzmP{keeybHoxMZNXwBYRAQ4269kT&QmM5@Bd-T?lqaOKW`}HA1;*dtU)mORK7_
zqXYa0A+&>A_Gsssj6YHSjL)Ud*OODOt1HgYWRB0{npZY$52+VzvLLeGQ_<~v$J6X0
zKMI5lDud!K00z@YZ<_fqjl!V7KOBq<=6yt2pirs&6Ud$KEC8?-{oSi2^I`($)%~6n
zs7^FSOT+Rw<v+4h5L+VmAIuj5#3$X^ThD@-vgUuhUde%qKGx(t^#p)#$Fvj<Gq>2m
zo;RhWVGPxKPNFVDK`0xX=Rx;=Z9z4b`;oz<QCu@Ys0?;zS&TC(_KJZ1<K;QF#2;*z
zRo=_M4)TzOpcHd+ic}kZ?&VtHv$IIUznL+ziYRPJ7>hEI;F?3|lcv}jzB*|tkZ2Ez
zYOJ|ylQ^})oHwN7H)*WmpQlE!=xg{}+@(NVxSWJkvIIxFvDUbEC?)ZtMf$O7Z}<w&
z4dkD`xll-wq%KL1wN~nUmn&c&g;tw&_e7WH=yW4R1iT)USnMnwQC`0Vs8Sw!$Ax4a
zd?DTFb%@FIV?+Jm!Mt_q7t1KerGd*s8>_DA<MVn5-giq{j=0$r<5EZ4FIFVoDjoOg
z#sTt&*lm>yZ64G?n?_z>XW)5MR7Dq1k!4PP#QfQvq2p@OVmchHcH(<_m252*`J3?a
z1*@CUf0{cbW1>WWe}YwQ5!9w@I5srmkg<jbcE!uGL>I0vtjD*%qGQ!@2NbeIYqUcB
zUK|fXARYJ#ak70h!Nxa`SNGr{St@&9vP0)KIP+)N3tf)Iyt~}V$ftvps$B{25V#^7
zmD=n{!_IYrAF<ynSMsYmx^7etONJde)r8O1@iw{LB4&li)ofOUibvt^Gn=x4WP)Wq
z`O>{0lvd#B^gCpu_3-Pj7Yuc>x%>eY+tyi&VSzYmjc2Aj%UZN+NXKpHbiniCqGHRp
zdKny5*yi+~-4z}8xLnbLD!g)Ztmdn$--PwhU45r8Kh<!iKkDPV(p_E}Z1`Z+?a25>
z2!tEsR(B_YNK%-vCx4pH7C^7xi)XcYXI>_%GrqC1tI^}mGz20&yWvb^oPMjD4m170
zK+$y78+o@wSLnKC$(7NNQHWp#Rs9*}fhhJkK_YO7D6mn27T)C%zHt*A^>(EGY9-HP
z{%)F8@w#VSU4SU<iGVLGtBPnBl_M_Vy)LanF*d7IY-_`lZOu>^HW?&A2NP(i&lYES
z2*9<1D;f%NxKYFWWVPHgsu40SBxa>U!!d(ee6ipeR{|EZ#;IEeS62RfZQ`)i`yJ_%
zw^%v0do%^bM0-(A1zd_ll`{z;9Vns8t32Z+o79!pamC|24L3QmxGvL(9`CL(kGKnm
z>zL`Pd!FC0IT{dmI#QTg@;CF~PpGlozmTMsqJLvikMv8XdUof)ddo;gmQUFB?+S)M
zZ+VQ%6x52r!kaeL>F^C~l7UyXR38`^Lr>0+yKiG4a&l3*b`c&WxGN0nHwwIG&&cC8
zO!ER)pzg)6(rje{;yDPOnA=|$xAZWlmwULsdgRZWUGJW&767&@76D}oWkhnz=qC@J
z^Txu8+{uKLG)Xkn`0p3<zdiU-cE<UFHoF4d{HqA{=+ep$(;L(_dHk{u_LYWrfS>Cf
zFN(!d9_KWys2B&xXpX5~il-_Sy7>4QI25CV=i4@T<6N2@QXuGRT1BLT<fk|H4a8(L
zLA%%d0qv5&XJ>+Rx_=h1UXg$vT8K~ZYcy~H`g=xKm1N~0LC{4U6a!_h3OgSLjdDeR
zRCnNJ122*)6gga|l7S`@M|!>%0pLgk#vLQ?tA)CPIvrDIl5uky6r#7aA^68clK&Gp
zX!vCc=CX*e9lHwHBfKlAtYYwiQ5w+$3C5h~0bI&G)r=kW5z}cUzupHjTs{~Z9M~gu
z9=iVxFy;cBH2GH<-iiT0Ia|K_HA_Gh+l{E<`l;Dq>I52h>c;e_HIgzHe@53(Z`=#}
z+4KNa2nMF^zIu4@B{$x<WMHTwtjmet<S`(VzIjaMSf(`=eNNpe!?4f{DMLTrq0uzn
z_Z{Q%$V9Q_!@$&SG12TW$j0(Jj_-B^p!G+&n=vE4Pe&?lNBgACGhV1#RbRuBwsY-4
z*zb}w8S7b5eZWYK@Uyc|XhyeOna;KWpi2;SSPz&w5q|IS;ewn357<oUcvkt-KPiu6
zkeDf9Qb9W9)oOA!4Q#4W3g;%LM@qhsFID__etr-u+V%^I_9L~m%^FF-e^h8*8Ii$W
z!<kqN6*<rZ+Wi~|`@sjesyVy;Y-+;hi^@Qt^!}QnoR<==5la5G>SdyaGa6LBMXBA4
z2jTXk{>7C?)P=Ub!KFkB$acUTisX2Ts4WF?1!Hjs&EFL=hr#0fW&nA`%8rp7Prww=
zMJcJi)(Sg0!EUl0XKJFe**}i|nKyrZwCb5!49I*O91?Zd8?Bk|>rnC$#Kwp{SFN$P
znh*4ydjE~#?9Y2F3~kO9_X6}n^gY5ptn4tPpy+Qj<fVE|Lu%p;mn$qC<(@uX{`?T>
zF)G8F$Fp~z`DXm_03}=Gq-w?XeadJ7AJI{1jJ<G-I~Um;ZGhdWo8%E~SC{tPA@;}7
zt#9t|!B9vXjZBW!BOrtZ-e|4~={fxvG1Dx#H9LSm#V<s6Cf%9VDqwv+>wu*T=^E=6
zzu`|rh663n*z}}Ot;^tc-1K(xM3^;GM(afl!~2%JN1S%d53@-@ryKQ454Td~WTrHn
zafYV}G0c05b6v(<ej=(mlPOeOV*EJ6t5BbHS@g+_@$xttWBuH>Qi`0Np7de%@D`f&
zHgLPchYvu)&C;>)u<tDFMwG-nO}4{W_e6Xc5%yx4ES9Do4Ezv@S=o)-%u%%9f<xs(
zv;*<Fu|yQX%=EVnDDE_Srr5qNtz8oeWRM!-JWl}fqtDbXeqcI_2ORu(8Fch?NoOwg
zoBEU?z*!dEZ#lK#04~CXjpGkZTZ1!t;@5Y;bJUE9wL20HR%Bvr?c||q8tcV$us*x}
zf*0bQ<yO%~nO9WyYM^Fg>fKsHx(08%xOWX2-y;@H`#YoAL-99v@gMvpG+L6<_}|>{
zhj-5pr<;wnHQba(GS<F@_xy|!U6Vo$&DxJOHamTvRsnN{z!dRCPBrgcS%}c}s{;Hv
z_Zvd}Af1!t&-m}2Csc7)9;8-c+Jz{%Cn3PFS)$|EBQTpFbmfAPqQ?h2oKTsMpC0rj
z#QS&3S1#rr<^;tdYIwUuGyDpZ;>9#-d6!ggPtw_yhLW!lNrlRqKdP7jo4V&j=;~hO
z_KyJt&B#4n&=-VT9nfnt_V?aY=$uU+?{x{a<jlpTouyImvd2<`HuBN(Z1{?^m1pl^
zRK5(ZP_jeno4z&&?;aCYbzo2*1!=k!55*NQ?DO%bt9NTwTo$epP`S?8Dk!)0+i&%n
zhKowH0)<gxR$7$!;ynVm;aL7^gQqbKJ}rjCh$%}UN)Wx%qWrS&UH)*vJYgcU3RUl8
z#eHBHnKVaq&so%a2sAIIaLrmvL=r{Y5JKF6&f3KZ4MY#jVS{x7MTa%*aKrLB^4^N7
z=#v-G!2s;D?ymqOpx8dPQ$%e`Rz3%NB5v()8B8Q410QMxypVDHE85YK@zt5!(E~g<
z;P<>@j$zyraJG*1+XiGDN>i?#?{BGWgl|~O&Dm)`({GM?5l-wb3^tF?B<N>%_YLG^
z99uf2ov`6?XQEm+9L*1f*s|Lw9R=LD?rq2ubd%Gl%j;%Q?Y-Xk=**7x^nRnuW~vSi
ziOQtSH&7lI5VD+rb;N4!nd^tte9J8GtY$^17_;tFlX<I=JzT;FM-~s(YJy4i-5AT(
zF(h(oUe%xsJn`@^b1a*jox`7-Ck>BR_9G>${vPTyvU;TnyAt7IzElibjjb|Z^r&Nd
z@^P|S!ex4E3&%wE;1Hk1XCFmTzEo^VUJ~ntf0dOPrQ47~VFLoYgA;MI?%1u758SxY
zC+25Ii*3-VqH$Tg;PbB<^(y)^zEG}6@Pl7NAi#{%@{JRA3*bJE8ph%J%PBt=@iDpA
z);Nu|0cz~H_2zCHHn#Y|w2kHGWa(8CF!rjn5hxWkw<#UblSEVDY^IS58HD$d4q@dQ
z)-nVMWkYx&Mn{&%5CbqSb<(*SPJ3ZKeNf|<JF58J<(F#Z23U8QyPoPZYG*@|w2jBk
z4i3Oy_8HleW~xE=rMl`uiZ$Uo!a!BF244}v$Ewn_2tVg+Lw(CHv^d8d!8VLZU=C80
zP+G7S@T)l3K8~dAiMQ6w9K(0b6K)(9F@IpG)kbKZ)kuGOA=={e*#)12W@M_X%v&0L
zy`@hOj(+UqETT4bW4qf<m}*=4GgZ_|J(i_#qzuDf3s>+J_8P?7Ca02u-+kdVSF20B
zA5(2Iok6T($($6crIqM<seY8R6&~G%VkfI5vUYvv-Qhsbs)r-Zi}CGDJl0UWJ;RV0
zeGYg5`hM#|a%4({$ICI?k0$Q!w9jI>R4ZX4OAX25a&-NpQL~vpCEm$QQFGYCxgh{X
zmCE??PguG%gqAA<MscBeca;ytnZ>}zg4>JB1HK6QnI!sA?V^dPw7>>o)6<kmU#DnR
z=gs%(>~AfXIdJJ5xhME)3}zdkFh1+oLn~Laibwt@nyU?$M6CK&{w|NQYT&6O;+U+;
zO#eOKnEh+bIj>%8n5)7F_l%R66c36_k9y}>fb~Td@YBo-i!O*o54s(6i2kL9+DlPk
z1T6iXH!x6MRsoHOPax3QEOF}gJ#0HML@Mpu0B<?aH>aKgng|O;pk~z^(>H3!UR=hk
zsu&3d#ju*C(tlLj>*caLy)0RpO<*9?c6Lf~(hl5&1;fRoY*AHH#1UoSdzVZ6&|T@>
z*zwL>5x}klwh5Qj3<COeJ{3br?opKO(;-K&+KS0`Psm7!?(VLjSVxL;z>b@4EJQaJ
zS{fb2<%*{gqn2=MGpcD92Y0P0F?4Oa=oobuNM++8suX@BSMN^{%p=7gffwizT@2Cc
z_Az?nQyRv;NUjUY1=EmvD}`0;9WO;mZ=mN$3vRyi?1EM*Vq|-H`KwPWRLtDx;oZWB
z8PXWbCzrWYp85yUsg<na8a2Aics304bsHY6^et0^edT((CacXM8v3rDtV~FdVq?qv
zu0{<szOTj)tL(0{nazUHZG3U&Z-(sC$VABEq@=@?UlsPJFGx<It%UB!u{1h8sotQf
z*wXA=_owpkhNCbL?%EpYjtsX{D+oKsiy0pCzOa+jO@`|Sik-~2@UDJsS;395g^UXZ
zy2UtmrLjfgJ2mGBJ)*HIP<zG8IA@9&r3%gu3~raEqm2N#q$q6SHWjVHrc%HY-Mz)d
zNX%T}ch4l-$u9a{o8<@EG;OW@#dqChSqt%r<!Td}H3oyfg-q1ZG)87(>T~3J40*)k
z<&ao1Z-HhaMieaO%rofnMGN)Zkflxv_tp4t=AZTR>+^p77#jRaiG&$=8=>5otS<1&
zhnCDw9T2M?DtS7QkN0+Z@kaUK!Ltuqig2y{M5A^$=#Bg*l7CL8yl>Z}n4-y~3OM83
zI8DzD+FX8JXv-JUW9<*~bw0<g*Wk7Ur*HPShSZr4sgU9wCOf;$#bf$q!sa6lV&XZx
z$T7gTzZxSYP84nk>bPq}w?nl$5!bN&R1HWQ<B)6fN-p#}$B^{WR4SsqjL1)C;!D0U
zLq&!KlDiz6nK?Y&YT~(Ki|95|Oy}?kCZv&+Z;mf?WiiY#slUNtQ@UvzkVV<DJsRCA
zYMO6CI_1|(nP=TxgVn3cp=95?e|E3dD|M$<Yv{=s-k)QAQ9lh74h_=D-qTV|4i-(t
zxo3VmUN~K5{ngm<mG>HUqXG@rtCl@Dqi;h(!ir%QpNCO?u4qPrm~A_(J-}C-oSHLq
z$7l95#ISvOMPMor%YlPSr@~@?sD^lP#+G!GAdtg=E3QU!*!K-*PUm1>VLCD+LyGCC
zw&9~xSOmR2E?ZwBUS=^&g>}nnaYQHxTv1Z3bGd{mqcd|*1P`!~vwUy4o=mBVjpBKM
zS&3eIBqul)@Os={)gW}Nk<uBKsndZZ;$4syPfE6HIlF&Wu~*SV%R!ja-{fQT>j52&
z_k*Db%IR4K+!x-;-s%{VNK2;l9QgZ2XRCz%pOVVI>N$Q^Fz?X2#hFy`{qeeBc;OhQ
zE!6}<r6cn{4?WWwttY|S0HNwxE12~KT?ajsd0LW2uW|}}A&Q0y`bDC4&|&yQnLaiC
zzi7%ks?ey^L2pkR@*^c<`jp10&G*7rHU6kcx<mx2NNUxuxi-|3P!mVr(}3<rEczJ+
zF`$4w?zj%m_9D=Qm&>)Y+5!eygwL~`3WVF@t4fUWB;KnIK#M`#10_6u)Mkv_;Je&o
z@`dwVcrqS+C~Utt@_Th>_T?|R-s3T*>s7xpaj`~;=2JyPm^dZ3g!lD)$4GumZc-fc
zV52<#b$8$v9kwt<YhAX|^8=Jki|%`U(#E}qCk<^=lEu7A3_9ju3s253N6P@gBXbv{
zMBcX8RZnU$BGSV<7$@*1*j!_}BVwh1b)CTuD(OjZ0!_>6I%I;X1o>v%ZPiO8YD3)d
zq~a<6nj}gIf>nc9*+sNR{wOj|svwdx0V<ptNA^M{-pZHoBrk-!*3H7%Nf>P;NAk5M
zL~d`V@uVNUdT!xaLdmNznUc8~btWq8$n+Pc+qbm0j#c%PbOho8UyFnvXaQ#nDk^y+
zGMHe}s(6y&M!%A=WTYJFRI_mv&jx+fo4#qLryHrENVZG-;f1&+3&KCz;v>(=uLuyT
zXIHD71lO^U9?pKRXl@#w*OBi|=YGIx9z6O4W#e<#xV5IVGPNO9dh}*ea%B7wL83i4
z&XxIh5q}rzsp;XjDFoAijm*MgwWavkx$t6gkh+QCM4(-dGb0&c{MABhv_pF}P8tWo
zs4NlgO=5;US*F)Qld;Ibv>@6+5>+g?-54y2dxjo;yfPF0ZSXm2pU5ze2B+?MD?mdm
z8*p2YE1_yp7yo#8aFk!k9HvI+iD0ueR%q6jEdzohu8v!!LPAO!Uz*yQkt&-Xg*9`F
zR@aPxbP>a;#zgA`A@k>4=Vn<GS<zfv^M~8yoyZ-jLAyq}1d|?<a^tL|KH8BSp{=1|
zKoq$&ZY6u4y!G%x&2V08Ib|tc5&luj;-9TdsmEm<7C9MM!=|85E!gwJs$WWtYM6yt
zGH4F!txRTPc1Ec-y;|mKX~a$}W>DnpKA$|%!OV=QjFoJSH1XG!QzDUuly~KEzxs)2
zOeQ-<?`|6xB;{s?#{?sb8QPaEsd0JUExt_YH}4JmH^-GoFVtUE34lWMcUmckEYr*8
zES}Cr>i`Ue<mE2z{0VNEfaL%=M?E9wja%IHrigE;LWiBf5HCXV(pKc&7fqWe0l~GU
z>{?w_g#9uYktH(pe&g>-M{Qb10hOzHm5q}k---&oGrJe7cclfLYgXUMncECincP16
zeoD*F39%A>4iZMdFs(M3gNYALcW>mmoi5+NhmdcYZvIIuV@=`cXv?D<M}K>8K27AI
zXNRp0{lT}LBN7(fEmm~SHlC4M+g};gA@OiX6C+L9iA!q&kG2=lB)w{y?|`I9$;m+x
zHKiPp`JdX$-%9N#CF(OuFRZ&-ktO8eoupLC)Oiwa1rDu1XW>t(dOLP=Q6aj}9=Lcg
zb8c>77e+(q#W6^VjW9XeEE+&EllEb^2BmSSFd8MnK@&*M(-tE|38$MVa_#EoRSLYM
zM~<Vrs-$D4BFq;il*{uoEc#RVpxwj~qtl^YeJh4j1o<iT;p@$lmfhXfz942nVI9?3
zY;B8wBlk*NI;|;wG07R1u*RpP)2|krL)TGZ$H6F4eq{tPHmsp1<3IK<PqLe63TZ1m
z3b&#e6gaib&FIZY#W1r-4285bx;DB=nbNb-_S=4i&{uT##aa;jRW_{OT@ewB^Q{}@
zIsw|T!#}m+e|m>S_gIhE2%v<xV2_HcLe7?1YOWPBA0Sr8i6T^5i1OMN#rX4AH#hqe
zzVyY3x2G|ym^1s|K-a9Q*_$n-OY|ExSlkSom$<s-yTIeaATx8^Z$M~0okfoX8)to=
zKwiklmE-FiSjvp$SZTrX37tCnHc@TTrdFDx4BmM3HC^MB?a_1hy}RLlzg+_3%(JMG
za550am5qce1qkH4t8YOJ_qrD~^=S-9f-@}BlYrkz8x7MEJzs6WPc;=DeB2IDo`~6B
zb>@(UOgY@I&rGsdPZtBwu01|Tf-;L994AXy=BgDZeCu1{Vm1*V=IlKU%{HdPkH(T7
zZT_I)&z5+(bu@TcbZiJZ8aBBU3KRrlX)$Kr30klqGgQciA0*+5sq|{<ZQm&=M7Lrz
zpQ)fO)ETtLV#(u!S|7ctAY8l$zciX*S2o!&<UBsCJ6Y^##ayr_2F`TD*?p8`{TwCf
zIYq2E<RxMy3K25Y0>|M`<=J4#YmdbV&Zp~SSI<w6N*~0bdA=mi5^=OvO&TcV7M$5C
ztZy-%HRES0j>jeV$r(YMDn!xNoZHpfK3Dstf&N?VFupG_A|>Cu;|r6Q@6Qy}9hnmM
zSiG&{^UEXtgkr0=wm`L5(Q?WvjJq0|W*B;3L_xoIQx4k6-3>W-i+QlL@Ao_3Z5hW%
z0o)yn(NPF}#bO~%>x&>srW&{*lcLgng~D%zJJV!qLy<glQWwuI+=0sohXdA7bRifD
zrJ}O7AKhtJu3IT)odV#@<n8x<iCjxm3PTGJ+-Ihfn05n0PH$_z=a3JOvilou+>U^=
z)ToTs0B!Za@Cem~mqrWsQtkzo+313MTx;0H{<gz$To&=CG>7Jv=DmabH}}>m;htLb
z`MlA@5XZ7r#M2n<TIi^&9i_?f1pCLDiT5EJ$|$apKMQ6O)gNS6bm?MiR+RVFXcE`m
zXa`50k?7;tu)tUH$ySDFHKwQO^A6#+)<4WT(~}yhl(DUiDI&R!n_Ss66IlV*la=RD
zW2L;+65$d3wLtf7T&0|W310%91Ee8%?=deL5&JTY)M9Ei1-?^)i|q_CEj216Ggn8y
zYa`Q{w6U7RRtM^1amN#g7CX+q{n%}@s7i8lmyX?3tiE{VREy2>fcWy}BvuLXN!0`p
zU+OLzx)PIrH>&<NjbaP@!P3pJysFx)6xeDfjLfnO7mI~+{2hlTWB~Cd)H%kkFccWI
zOgc!tMYu8#iQW*pmvQeDV;Iah$$CuVkwsnU(IB$%Wp)_a4+~&L$+tv%>SF<lqR6rR
zxH?JiIvycLd?@5QLneb1I$V`a`QOQFD~Kp34qsnTbl}yZAob$!i6EMg$8sdvKZclx
zIYF-)Q|(Aads`Ds4d3pE*Nnug*;aQi4h7b<;LC|a@A34wr^Rk6ZYgA=TU#ZLEm;d(
zMa?dCxhCy!TEm(8VQlnRFz9wjgsuB+vZ@Cg$7JascvIf_s&(li?olI;f56(gjjv%i
z+OD`VI)v@-$=(pf#}U3qWxIpSL4W6+Dska={PZP)RDA1zfZQ*UJQ!{HsS_EscNx7w
z5-!5Qd4Rn#fK17R!fl@kb`#;0{uYZDAD0oh-k05*0h(q;YVUZaS3e<(H0B|Cli{e2
zkac-%D+3nl_mR!*z`6KmIajZimArSQw;bhoD1|di8!22v^EZ-4D|!wa<S)6O#|3G6
zI-N9nY=gj%4nI+_O`C8J_ed+kx&0yHc$K*l)Wc(#-LYZ<?(jzS%Q*6cU5v!yNOl)1
z);!I($D~Ht<M#mBDjl~VnrIZ(KTAb&7uc&9V)jNWcHW;8Nbx2U9PW(J{%&O&nI)>a
zl5ntl%Yo*GB_b7BxMP5uZQ!3WU_hFOj$hidM!0dv^jc)e)Zs_Ct02Bt@{Zp}@YINH
zmqz1FN3g`NpH)*sk&@^yFCGU+2Z{uGHh96IJP#EG^^F5^$&#}V+&ri#kx&ldWx5md
zoh~?=@b&NA)~w<~fm5(`OBy}Vf#24RK)csoVl7h0&5)Ja+%IAVY2z5v(ffj(jK+zz
zYRM%@vC-UaXn>X}z45{$2j_w*4ER_&9a!PTAXf5oQSM_Mo+M<t9lrYXp;0yz%Jp2T
zgHDS}C>t-PFKqE;8;HbLj3jE4aZHy^q=Xoan=Fn)#!@K!UW|(4z_||s*Bbq??Mg+=
zLAXI71o;#vn*IKqpGKZ2l3{7in=ujujKtaAjFQl0s?g-B2<)}l2ZO=NMMvCWrD=b_
zpF0v9-?&O=a!7e3G4C)E#X8p)YsEQlFj0J;@CX?#3b2?FEs7j)*=jzM+?W$N-G4C)
zA{`8pC6FS!vhOr#6m6TV!~||eqR7q<*c<)2vKKucq@7AUxNC_bb(;R_PHn4#>zsHg
z!0_)jyMpdV*$)esBha_L!WR{ZT*)@#9EhXNNeSZIXbE^DVz;kW2+Tm+8mgS9>;C+7
z;{>-3DdftwQl~?r-S%vH)S552toQj)Tm+npVB3yl7jknregxo}`iv%@{g^u#v_Bnm
z$XAEt;%syPzBA$thIwSS!X|4s<!7|2`R<IQOnHT+HZKZMJHuU*v;Sf8lGhQZH&bIu
zFba=zt3v5`8sEnnA>vP_E=6!O_!5NcuY$lnmW0#xeM#SW(igYqR;Sw@$QvU%cvS6&
znH+=en7Zi5m#6&N_k6mCS_uB9SN_i-t9R!YSfl9_)1$E955Es`1Y?o*Pe-gnndI+>
z`tADQFAW<w3ch~zw#9hjFS0ZadaoBl4Mx^`2Y(C@t$uw8X;Kz0V^%CxThyT)v(GHk
z+Ae|PrM&ZI+F2Oe**hYx{G{hunO_#Xwb38<tKZc3Q^z{DF3P1}q{WTx8y|l!_I8rC
z3aoaqILy78aC%7B#Kg_AhW}-eyIi`ohAp!Gb^n%lYndI`w=uDh*O;OlHPbvcX>fdE
zU009ju<!I@w0E#e;XK=w^kwv0-KTYy6N{&_6Rrk<e$mFl*#0Tb`^~Shhra1bb*8z=
zjn&<w4QCMUuxIO0eaU9BwT{&thW5f{h#crwFZ(6O$*97}(XmP)Mohm{>*CDBv8utB
zYexxO`V8(&y<EbC^y`|i_x?~1v7{T%PsTX&U2<0)FNf!X^1@P|*)hv^VOZUOWxr9<
zrC<I~-<g7op_79YeR;Cx)ymc_2R^Sc&FF2cu3^#Wl>ygw^xIn$jz0amE?ghKws~g9
zyc}tQ+1MUqAib)kL~v%F)DdDaakFuYW@chEqXnt0;<F#_oc+q_=@%bNs+IY^R5cf_
z=sQC(%q<FH!}&@W4Y0p`VT|~H=S}t*e0DqlUS*mPzrDFmFEam!(tw7_0O9BfVJNfq
zpZD&^0D)Q=JhFWB)z`(qy7h0?!aohkf6*8I)tU4|gpdLG#J_Kl)n)x(#lHy-Go`)!
z{Xs|kU##h#{~=%emxb}K!D@FT^i-~%xt<^r{td>@6D1KN#}aU8Ptp9ZYHKL{47QqR
zRnsEy{1?AP%o}{&MjLXlA$2F24DjF=3?Jo<wz+OEvvCQKl(ej@ucdDgkDw2yYY(j0
zD>YJ5N?;aP#cfkfH${SpuN;4XlA|<J>f=2pyI;Fz(a+O0WZmm$5J^b6N0j>u?>$f6
zLw>GPp7137#o+Vd;XpuX@(i0&pjookBH~kai`o!LN!Z)`S7EvLA+}153Z9E4%;^`z
zx;ahN-wts(I8q}S9px0J#nY0mvU0N;FICb52|w8~Ceq^)K(^=acl;{^%o6(7a*Y|j
zWxIttCX#r)`%g=at_tw824wx`{rvN<Ii^qki)dvmd@w6;rC~Mcil#gLi*Mt16;c9q
z5llO9L&%{-%AGYIqEy=XRH_vcI-&bS34^f9l?mhZTwPu*ebaHG&IRsXUS#QGo`|b;
z!Qk0<i4XkYxZ#YqJ@HL30Q2<<Qc!@iNLXp(zTXq6)&$;$&VF8zSscN3{%0%#sLCg+
zxpp-!Cz2v^+2xE1Ssfqk*-C+u0O#cVg5=nz(~i0(x3c!NaPqse5feD`hsVa9I+arv
zsg`@8R1L|{ptN=DvEQfq&gA5l`C<tn!gqL5|7^;SJBmE_d?axUWc#I&)Y#IQUO^%X
zbO{nk(u{;;5!Ei_obfFg?QOLSU?NmKi3E86S98&A@$s(f!8XrifE5yH>_gc|9a62+
z>4X)J1YLON%Z^nn3{<P9^OaiqLPrZy#`W>NGqRg)Ab(E4#H0N058<S?xc4(YYFixf
zm`uScu7o^VNs60_L3}d6H|@C+DZ-7RYTXKvn2#@RPL;FXd&#T$yaEia@EUA-I)s73
zeSOcU@nY=S9_Eaq2g1#m@MnD56g4*0Q{FsKq|ztH*H31?7ky7l$&lde$mfpFxKBOC
zPIh0saw(E+KWa^-ma#MMTRkgpolV7wVNB9XNOj*=(p2fQ@HU#NB_C8~60NG}x)RP)
zXxy#t06V`ff$VKz%ug)<+<yBECM^wlI&r3-!A3Cu)>YSP(<?6|bDfJ#f9?5R$Afrp
z7w8rIl{Bs0MuG>C@PJUtV{MCGu|rqIA|-jiG>JTbtmXqR3vl{66~xQjlCyj@A4G}r
zrreXz1Mb>DKrFV2HvN`COK2!@0M3G(RX#CW-!!|qSDD*_Oe$DsP3;DFedbf4K8sEV
z?_X~<p_z!;qzCM&19iIS8;83?YBQly_CYBAvdb&^Uoz^*KGL^q*&rgg)h)^dH>rok
zw+t@}(Bv~8{eb=1)3t8HH0LB8)1mJ_GKxxO(kz_g(oibZYj<y}N52GhOSHdqVK}^6
z_5;fUEjIGV>fcfw=_-*I_%FphFto#KvV~8U3cX*4Vo_85WRI+=!@p73hLS2IGw~D4
zeNON1#P%TXZg?dDKk9)LiB6H}xnCk$H4w=iodaz($#ZAY!>v+A{F~4oy`LK|Q5Wi^
zBnEj(J>ZvIcOzQ9yjbs!sf#uDcn^3!8%w=&V(aOt(^I<I8qKcE`LO|(&3IQ#4doak
ztskQ;cMZNL6-?&?ttZQ!=xoT`1n=jzN0VO}VM;+VRBxV9SnU2stW<`TjY7Fd6OUMp
zFQi+UlE@2v!cVKg89dU;X_KA2l=t{^P^DFRS=%45J6}zSNh6Mk(clwSFGrZ$6-CED
z=sgl$KUU&vNAa&WPS<YGPF~MWkn4r44o`6%AXQXg3gm^C(i&Gn_1knjE$F6Ra+S^p
zCXXl8pPf?VPS%&3oR^CEudky_ZHU>ohoeFL)rlK!zr74CJnh0MoG6Ra{vU>^YgiAZ
zl?T19fob!lErTv&hIjYrJW_~9rw+_NNf|bGV@r*D08H#FLvi<1IZ|NcDK>;RB9_HW
zms)XyOfObhH$-TY)l}D)&p)6yerIS;Jas-dp1$!1fr)|S=BCQ8b{S}T>zFUqP_l)u
zthvz!wYl@6%i*1nAVvj0m$oPrfJhDY6TK*hWNO_eNkk*#D}uEp1z~<sw<jmjGj^!=
z1|lcu&=L~CBPT2VR-=q(8N%qgg$qol&VGADb-Q8bbmtv^h7-<*%m%lsr%LeWMn}#Q
z(^4dfN5gLwD@c~Q#)fWDdFL1}Y(O$kdQCny|644Sbe;hVhoOFPtwF<LsDqs|dqhqt
zli`Q(uo#t&G8u?MLM<H{ZOqM9LDp3T`=EHBu81^>&q57H$(<B$efE7dH^L7hh(C&!
zBz;J#Ui1&hzQ^TvRb#^d2qUq-A?uV%6xi~`Yf$j@psl<G3w6vVA`9t_Qy8)&kLJf-
zCELWL@U|;f%7E&3^Tk=+YAY!jm8)V#1exw*@RVwplL`79s96L)JF2r0=Lh~?tOYM)
zlJL8aCH_@GqE&LM7adTEnjvYA7wp#9a8vx$dzlr<Ig`Fi;-Pm#&8|_*=+O{HZdQ5V
z6K>}XO?1yfIg{*GJUFDZ-HaUHNYW)>>`-K0!b(p1PFIOBIaED{<9+MO{tCxI+}ASA
zUwwHRjHsELIGCTSW`D0l(?up*V3y0L!|elQ^U}_8%WO6>hI2ytr<#gA0M%opm@N7S
zaj7K9c8aP)>xu*Gv8!Z#5{;5kB1wi#_cPjz__0>FT6N{^!l}xQ%}yE>#M2t_=emp9
zvdOR%0mBWgz9sV49c28hwcZOUdjgzHE_-f%VTYsjwA9>d*>`j9teXK(C2!5xW|4K4
zM%&4EQT#o61HkDb;2<*B9Vgu6?fk_1krQT$2`Hr5{_1t_Tb=f*$G`1<#>D)C9EEz`
zw^`47`&e_K(@8qK@p=UtCYdQT>gvx;Tk3CB3YqToX9GVbI_aZs2E1`xWytm-BP@pD
z-!ykRSkB{Fio?k_*%R*cN!|dyE|w<%E;gJTQS(K>%UtL=8<R%AH3d-hj|Ufi4p!~>
zou1U?g$B=+jU`kPM$e1K`DVLf#5*-eZ4^I1hsr08v!+UT-VYc-3Qv!Yc96)aXpq$?
zZ(fK>uZaRTj$PtBpQd^nlEjBjb(T5l=9C{_e{PITVO5|YenK`A|CZ#TL2=i&stv%S
zVqsk-b&^dFx-FJi&lfRb`z}D6BGGGUVGu7Q52#k?=COUty_2$-nm8Tvta4T_@7TRr
z*HDs|)J|d*B%PV32#$g%&Q(fou%&!R?MzKQbG<bm$pS2tRYjOwiWC3-`R-5ix9OV2
zKRR5f?qIi)`vle1H;MxFyj0L2!D8ijHOXi@c#K*2F+9w#(yk~quhhL{k1liKZ--?!
zi30FZEQkR}t88487%3}}piAPtK)5!YEvuL-_XgwslpXJ=KRy63MHbORq>~hjq$;(V
zK!31g7}?@(4b%{uS-d>5h{2tROI!nl<U<OePx{*w?G!fX<o#c-gi!24VsIvQ5%8=(
zyrD-5nwXAAD0EMoBklB@7Lj{xX4((4V5NcCH%{(vA33<We(4(F0S0>}f_k?3gLh?;
zkr{j^YBDD0vs=!XEFrDpvi&+>l|EgEEMvFbsA8mq6aLlUOU#{?)gJjjO_`2a=EV=Y
zD2=O4q9_yxk{!z6rV<=<XuL8VY7=#UrBU>NTqQk8trUC2BQBQTYf5@p@Z+-#g>sb$
z&drHsJL2>Sg(5EMIfm~KFMg9IGTa**g{j}?kLK}UJyIIXsLvvpdY71Dl6|zX&Am$+
zQaZVkyUCK;LnR;3)wb_(R|E47V6)mPzvt(i8`97CKw15Kd~6|_R7u{2w~!B*428u(
zXS#O=v6D-W$rh3IO;ABDW2EPcdSDh)io+FMXNEZ@*C;osk?bK6Y7;Z+Sk*dCHlT(!
zEjK72XJM;BW4cO`ye78^;)-1Xm&UqlaeeFu*W&l+JiWu0044U9myR-<TFEuGw~ID=
z(0bnHpK{ZGkI@76Srt|)pYP;&SV8U`9tw>&1HE6y%)mWZP4C;{o1QSr3oF9zI#%l4
zm(ce*rE{&h-o(5~1X@+$!-uJ;MdhAV)v~`AI<>k6Rb!5%ju?F}<4>ZzQZwB7Ow?GD
zX28itIBT!HsZo}ODL|;F>`dcXV;OhJ94#S*w`W*za({I0=JbW%KoCynZ|Bwn)4NT)
zx?9_X&{=LW7$VP>h%=B}(qb8GVTiXuDRmqug^d2Qm^ihV)55z`lJI>u@yhzREuWd1
z>;W<LDcw4gHF6gU*-#lWSPQ*V3LcUHxIoA;v0o!n*+q|p+VlHQFFStAkkT)n4;EiC
z!VnS~n+x-$3t27JD*yF*f6%}kQeyJJrl3p~e6GyhE3=08(Z7@#faJ`n{(=t1R9pC+
z6qYCUc=MmBpt(TkI+Wh>WhZMORrluyjZ3CWzO1ZY1ZiB9{2QISsd&B3h#a({s+1Dj
zQ9n<XR?Pm&O|!#XIY+ZQUM_yONbojsI!{?*kX7p2EwUz)7y3vof5AW*CEV5K_h_14
zw&mb=CPO0ji}ATSL21w=HWi!gz_|uV0RQI5k+7TI%XH)dGxYDj|MP{4#V4v2e5$6e
zDXSOVCcd%uZEr>`&N|6tb%&J|=}@ubjz(Ds+k46YcY!!B_I*DlImlo$!CfqOmbKD;
z@G2VBs8~`-9DmM5BhxPTedmB1f;cb*6s8hzse#4ZLe$}}H)e+gu`tai9Cq<E_VZ~Q
z=1V}${4`yR)lR#=AgPMRiA0^<0o=tttr10bSIYxReCwm)3jGirKln_2xZ_?=__WzC
z^AvK0g^U^`PNj2U2eWn~Imx31rbz43k&?tl$m3FpL8IQin2lxBCzTS$lrh;rEta4j
ziz>*1^?N>l<+-DY&LNo$4)G)CJy;JUTQTCazvD31d<T-w*y+?4T7yCwAs0%*{qEbe
z*Z^QG%)e}5H6f9i-p$kNSM8p<ktSG;^d(}k<)sICQ6xln`WyGqn~n8hqSIx`x?seo
zXN+y`bf)0X1T*dd9gMIIBjpk&k~cJ2D$a(-CfMV`tXD@!fojze@3ykn+wfKE=!1C>
z3zE9*!85K~L&61!GdY`K#9lv?%yrT1pyx%Kmph+)eN;4s)Wc6Qb?b4Q$l%Flx24#c
z;ksX}$wKM={#AT!k@(dq5q5YGGgakX1X!zxFKDKyp3NOX)0G-p4@_e#q{q|Bub+IW
z6SjhWJQrX#gI~r2xFe#*REq2`nQrCHa7UJH8GT0?b{W<bqDCFl=xDKmAF0}4%2$SP
zWV(e2qll`=fIZ^_HUdwhayZg!0&<AR8Ads_hMTThl#6$u;A$!cHrG)2`P~{Wb$8F|
z7dVwq#%Jr5PZw$m-1q2-$ZwF42-WKvn@#$LF~wrA1Rm$m-aVQ9mu;GGyD-O!7=<Z_
z-TRd)3ExNA$mq3X|4@hIE!Th%^Q8-=dh*S?hSOzZ46TMYl2o{2l)zsF1Xe*;yqoa7
znINyH1Y+s*5VhpsfhiP(P0uR!{L0C{k~<Q7o=}o$u0j$v^%YSSdhwk`MbP!2(UI7i
z$<V+EN2n^1Q6%#uCCD;bKBfVYJCeDMcCzO4l)hDxknti~@P7PBxe|O3@}0QjgzixL
z(NIOXS%7a%0q%DYgpEne^*WU_&a143h@v+`hdS+0!3!J=lQE~OJw~4*rOEiF{Dg0?
zc@slG^#n#liCk|*nrBl2F9kKTtc(Oo-K?t7Z4pvZD-Aj%M{a+X_c=C8p-p4OrUrBJ
zmO6d`24V}n#w3*<siJIW@6#PURzOCVpUol{ZT%1BxzgOjP`eol-o8dtYQF4^fWS(#
zxNC+q6~ejpAt&f?+>urw84@b)M9LW<=Abk2eZNgXr_HEmXLq+mHTe$O?umZs4<Ol(
z<gj&9R9KFv_PXSFW;UhTDf<%H6AC70L%hg`tc|9uskP>pqR6z^ljfvE5M$9fp}V=n
zRch0Wmw0HA5HY3q>l($fA}8cN{bSHYXnQ0Ez2sIAVC9OmLKCpZNh-Yk(<p(roPi(q
z?9az@_nSK~1Ig8dI+9;sy2y^1Oa@m_SsvQCbo_X<gw?yt_D3=7y(pr3Ek@%oJkhM;
zuj$dWSJzJ&gdEuXr8tMEKWoU+fFBEJjTYl3beSE>wT4!P?#?RKo6&AQ-0}Dhak6ih
z2KqHW8js)$va%t9`*}q0KH{?*>QK{8&H20;12dL~$fy%?OD7GsszZ5{G!wGb30S=P
zi_#QJrZCmy{`_i@2x2yxyA}ZZpnMiQR*H#NHk+?Wh-BF8H!<5bd*yZtg5*3{e`Wlf
zz`sVv#t?(N+3EmtR6UxU+@Ic;pOUT$4+S#&F#tZP932dI-*T@#Q~#JpOxY(~BR&N~
zGZ*{OL7<+P$_zDJQ;cfGvs03RjJqAy<_U;SEkAI&ne%VdXfoC{uG+ZR{vQB&K!(3T
z<-o!8t?R&+RqM(1Ys;HG2NHMhGRqH#J*o2Pr&^+YzCUjwovWF%d*iR9I@P3KV^^H)
zGH|eU$5EnTD#=2^m%F*x<L={+k6$1TB4yW>t4eMAT(T`=v566`T%)6oNIehk-+`HD
zLov~4&6YS9CX5+~Q_3ZNSbv-LZ;xQ+h^~y>VUCwiCCWQ$IkayZ{*5}*en?xgl6)lU
zp&wccYsys(#907dOx7LtpSi{#d$crpts^dWHjjO5s>D{U0aHKwn1XA2Sb6jkW@Rei
zOcbdPZnJH_hMwJ<;vni!C27pH1k|Wa1#dURoSw?nCD?3shD14y=6p~t!-w|ZV9+fI
zK3k2x&8m=a^tAE57yq$x3*f!c?@}Y@2Frgsf>Wbj40>yrsN~e-cvQh(`ebS5NwxYa
z3R4|9dEywcS*4j3$zoY-Nt5<<IIwFU+4eQ)*uOak?Q1dW&F&Om*~`*%p)}|{lv$I8
zh%{+G9XmF|Ha~<Pf7nZb&Q<_#3eL4U6Idkxo!DKvRj4L5t$I?6!-WYFHT0|Ow+-ps
zIlP$ZM>PV_6|k(rt6DGSz1NBO)4SMo)4+f?#5Cm1b4)vs!8D<9{?*;AIC>d#&j4KX
zaYiXoq1v4KZ;ofufTjfRS;Nw!p(06fz|GB0-jT9^%I+i*{$#NwEOHMJ;Ie?G{-QY&
z2Jf_tx7#WC)K5(y$F(L4m#>qknJMgEznpJ>-bJ3JG7c?`+8IT{Sd^PZl7R2S2L<S=
zYGSTvaGt)L66q8&p8I?>n?~lL%wM~Kgp`_OrzT=orzgK{8-gk&nE5kiaNuGX)-G<?
zS}GBSWNyiVn;oA>N}fbym6hGDLIXaUHUqb)BaGU3lpI@MX3ic$)`dN+d*CADqevJK
zJ+=HCDYlYv`N1;Y_H#fb8`Y7yb6I%mCiZSl!kh?fSLUuT(rHm~q$^Tfp^SI4-b{U`
z3+d-Kv1IoxX@h9JM0*w3c?9qEYQn|!Gx+IZ^3&cucOhD&@{K$3(Xft$9^1yAvv+YW
z=Pyiw1KmdrrHML@)!*+C!{7D{>Nf<JvPZG?^~WF)R*TDe(WIWOM6E5r$<J2;FniEO
zK<&{3d$?-wr*EgO^tASn=*hzTKbBxw3bRng#ep(j<!~ui9VbhPM&?_ETG}iM6V&W?
z6#)FfP^w$UvhIgt__cq7iNkyGC~PUQ#X6y}NFkr2@ojeRx{Xhz*F{>b^!%@rlbCUQ
z_VsWq!_KpQ(=8hH?kB*)fjCDmoLq~Ei^z}&zX4pnn~kkR8TN0~k9ms+k$v?rKb;7r
z!|Q!<)>&}*>;X1sUX{g6gdg2HNp$5D!ZXyg9X5@(tLs?1Y$@p;^?AGh049nSWm~#1
z{I50P($NEK-Tr{SUF*`gTXXV@8uLz{s+`%ni5sa7^zTs@C()!iy1P-mtq=Y_-WVE-
z1|`J?36@OLmX)dH3%`87fw0s<kuoQe|L4GczVnt&44N{D-t|4$yYVNkClpcD-w93K
z{)`{g8P&CYEIkuKJy{cGP8!DXbU&Q^%9DIIf^zjLV;-GMj(}tfB`j<yNv*0QV9uJG
zM~<@f33!)sDvbrrN6g}rZZ$+J=s<eV7LG*5lW!>iIUA97Po?)q7KEJH!K?*Kxf7F7
zO6>*2M#)+ylI}`vUzbYrpU%&c@og#Qavw6RZ6)@9H-X)^GH6^Ykqn2rOc$VJClPFq
zZCWkDrp?4>mIi)=8I#GWwsxh**x3xNZN>WU*0DpDxi=<`rm<TBD?dNUJ7ZgrpOcBF
zyFK>WT=Z=};-?|@7|g9mKDC1h3%(~TqZIIJP^7HIXuSh@@6)-owGLzGuLp?AQlOql
zlRW|%I(`b}Q_nJD`#B?cSBPK`bDEpyw{vr72a(LXvM(q^Oi!|TaN-Ll-*qE2_OI-q
zRoIoU#kBe3af!des&#uM%CA43s(30i9?0AUuk+}@?;N|GNtZ#rakaBWmE%aQdiA({
z<`CPr-KT4}R<s$|kiDyp%6cEhx>YkUe;8o|*4A1ZD%Prlm%R;IwGUORn@g1LG!kxk
zNy{H7&3_@uI;7x=7PUCGe~)O8{pjDJ6CFd6$gkdw*D9#_Wz{;Y>h`2x|JONl>I`Q>
z(q5=T1Z#FRnEb&z)Ub(T!w-k?k?}Bbcy}HIAEy0lEd}mIvv&0^^5ra6RD2T7&0A5e
znwTyrnrPX>YP;r>ZWbYHln1lEc^~)GAU1C}N2SgK32=47ELOn3lPi^~rE+}lb}BXL
z!qAC>**bC#dPQUQ+@exUR`?~h?Xi%(wgb+#y2toIjhlN5dUUGF_4VsGbUq9x4=WO7
z3^<5}->XVR(oP>18V8F>Z*y9`-j=xN+Pu@ohl4-<La1dm-tN|u(Lq-@P|%!FV#0cL
z-)1g`nKS6!P9-+bFHy#n@lT<^Qvm;}4y#4Qm3(C%(`POqHov-X+OzPgSDP~BywRk^
zW93^O8<9ZSi0!zyf>uib@b+?Gr>;nzuCATJ!VM?ztlXPUBE=QOuMt!fiaoATl_fU#
zY60=5e*KQ?aphS3#}~wI{G1t^WkZytD&GflRUwyt`;Iv)4^pq=$FzOlj|$yfD63PG
znW&b@SQf2WF}}6xV|iHAusJClT)TvYTP`#5v+DFM>&kEAK4F(%Uw&RafN!UN%(+K@
znh~s%@ax(-t&qs?C7Zq+Hn9urNj38rK3uy(Bnr<<e}51ubA$GcMWUfBzoN-aiRb$%
zBe~dR3UhiP3(O*87<J`W|3|J2Sv8vW)qbPMvj-jpN$lMz3t}nBO~SEP_1Cd?=qfe_
z-No|UBUu<$;#=04Pv=iXZ~Z-QmW^iQhy|tG{d|@P0=c(mt=zYfij9WR;nUi5Y#V^1
zWd^I~d?k{GLhh!hXtVlVY#a{|8Xhl_iYhd%6T<%ErwL2S;KEN|b7ad6HvaK3$5+ng
zyCWgbKjanPQsa|Jee#<~eF8c)p^VtS#)=Bo-9^q?2)OmD*Aj;q4S{CSyf3!OXGCKH
z`%3N-X41WBU*-?*Ox(#$%v!Qe@&%k$5*7Wdx5T_lBw<3^;?yu!ji1bs)Ohx9{+YY3
zt};$r(5}Nk`U=q9v12WBLfmn1_oZgLo^*YE7%{;Sgm`ymRPP3uiDX};XasJZV(DjJ
zbJZw8Q{L#ysY=h0yj9PRoeRETe^81PxX$`zU$fuQ29JR1H0|7z3Gcm5aM)&=kN!Xy
zS9f8e3?dm-adGJ)zTbb5G>>Y0IB7Z^o#OfU{qG2lNTlD4Wwa@0!?AOM7MyBeDPqvJ
zo!emRq^L?oa+!6I&*z+`TF>Eh{qzG?y}g=`=Y2pW(W2%HfVb4-uw%+Zeh7{iW}+*z
zCyb%s#$Kk*SuN%~3Ihc;Zhnjy(i5|b+gY^X^i%L-{PEd;%<L#rqbrVar}=u>3hu?0
z=y&Y>s`Ax{2^^mCIlr8`NdCSX)M>d2kJ^nX$_r-okLT$%vJ-`OerB6xclyebP$cU0
zh*P4?nEowVxAwGdI~J{-gm6|E4ddKb%*aaS{)Me;I2-W?U;MfZ_2H$AU3=2lD%TFf
z3G{WMSR~O7!bmuYHoz;e23q@S^ndeBItKnp;|t#{<okn{NEh>x|7xn$rwe5=!}(4m
zoty9EVOzHaWkl6)ZmuWz+C{dXcVe>PENf0Xiam8D8ujafP1q&AS^OFML(TA)P1{$b
z@Tq6_u;#3WH$MFwPo17E%f4dMg>aUPugJYaKk~~TJ4oj^Ra>sdRcNL@X&~+TLFUex
z&%<Q)R1Sdv06+jqL_t(P8n&;2SM_Q%_G(3>(BiYNej%;6CT+XdAv;r#+Rulsb+nk}
zWn*2g8a+DX5Epe(v{DXanG5)DT$i}bySR4$o=EIokz5*3Gr*tZ3+q|DRxvGtgYwt$
z!?}1k<g-j!6ND+hEmm^PzaNV3k%rv3%Y6G|GM|?#V&ATV1a==#V&-7H`}u!Va`wVb
zT||^<k)BNkF~z$l;C4;IMVu?tr=0+*4||_x;txBSzu+c1tv!{RccgpkK72Cd0dLLU
z^6K$v^uO?I?IqU^q8WQ{a7QwZtTUcXK_s5{_x!}%hy&QVdJ)*VE4^g?3=6-{{H@ns
zu0YALcBAb(lbF(}BD>do&GJnr@TuF9H@ml^_~JL{ZOdaJKs}&-Q<Ba7gn4$Ll4~rh
zzFJP2a~;OM-<`^b4ia|sda3oRT>Kd+8r!};*?c+wD{cgrnDQy3we_G6>E@m$`_ekX
z6P`E0%gq<<Lr$U5WJm<F#*~qq#x?o->!iY_a!+blC$aXoRczUN^Ko%=27k2$ZOD1P
z`1}j*r#VoiQ+w<ca4KdjdO4f3c-{i8N9m~Csu4C0j!z%+@W$Pz*J;t=d^KS>nX`YO
zhr>0-eEcohuVNmJcZpe`fas{xzcXd#H)xz%)2i)6-0KFS^>v5z2y`;X9nA`{^AYCJ
z!y8Hg@Xn3uQPI-@Luxp7%9^0j;~7{R2bZI=hsDyoRVA9$d%)hqCkRbSCtlW+1A-+s
z?wZP#wW5XIf6v&O`lA+8PMu=Za{PEbo*@;Q<L{)H`-vbkD~Ua;m+;N*AVz%_h<|Mn
zI*4}e<zyf$<TUFx#A8#Zh4Bnl3BEUwlO0KTyl7JEH6;9D4CiiKm$P+xoCRAI2#pub
zolj7Vy^PKJxCK_HQl&arrKO|s^u;~j0xLT^s<iJxoW3sk7dNs{G{12<)#&n0$Hz><
z%dIix{4*%<F9Ezp&|mnK^Au&}%cd(5Pnq0WTU+u)ni3~=fp;#62}Qwqj_u4OIx4(W
zs=10f+`*xU<A;?ABR0ORn&BjNipu^f)lrl!7K3UFd>hojUcjWvT(K(?>5eQ?YQ+rf
zNm3StqC6g+KP4Ahk&cQDok)SKMe?PXR)kzW#>0n2+`V#=ymaxpV%1jEoauxsYJBWu
z$ya0J?1H7JMXj`Ahb$0XBk|XtO;%PGvJi+QR_tFDnTV1U$(~~Cr?mCOWFsa!TBQ$S
z?(n3%(l*6L%^;SL<^oW~?#Eb5_{~EcI^CB(AAU?TtFpuf?PABR*cYobem4_ouwt5&
zFH&1&tyYT(O<|EdNC2&pECgIAHh0Qzf4Nr5?`l=CNVQJNm7HVHSfR195?~@y2x+li
z(XC1n>u~{@7Gm2P#?qOSxgbnh=K(|b<jXg>zi9~*mv1FsD^gMUPc8KMZzXA{wMde4
zGm^P?=@<_Z%tWfRm-N(R3{{3n%`elqO!@B2pZhLXfB8z7wyknxR7nPsqOOQEGegXL
z6edsryx6rq5Rgn(JcqY!Drw%9NE+-KN9{KC`Dwu>P9)qh=I=aSOzT}k@d>QJ-u=5+
z8G8wpqKcGVQJ;|@x|JtM&N)c!&zWDi*5$(iEr`Fgi#7W$JT92Z?Q3UCu8$t%YN!KS
z=U2tvRn6(0zYumxz^XJ&Ev8o~4<E^arwY6`Z6>W9BN#n=2A85rfR3j&TMi;8;MYoQ
zY-OCP4bBW4<iy%La?T?&Q4U=H%+5n$#CtWQnSj57ya=}butKDYk0tO5XiCqL=eA+q
z{INLPJHYVq3rKk~Fx56xY1^OX_AyLZx?T=W>7R78luu>MEAP4F{Kk_4=0YPyC!v>d
zS(0Sj6iS{l-bLz`m@d*akuHT?xQ;{DKJuyxP>T(zGX4}AXv$T_PUd)FzJO7g8bxBt
zplo!$jcVc`cCado$8%qXy6WjO<otzb;}lc2@Sv02yOGY_t7qAqmcaeUIC6LX%EObQ
z%zjMcWRVP(K%Zif5Q-*I>R0U9|0-4Sb0RAxhKG03(D=&+?j!2!2f4Clh#8jH9jEKP
zh`4?mb$(4Otu#~-tw>VnCBj0pv8>vWaxRV(=(AB-+0$k8490h=%y)}E<)@wJu@dtb
zg=S(9CI2c(p_ww1M4c|?Vvnw0LZ2pZTTJ@YqWSRf70IQCmf$t_#f%F4I<>}2Xmu_*
zRqpYmSrs#OkN=&3333p+YA(BOhHyxjv8WqIXxHm?h6w|z3OT{}PiAsG`cL7+pZ%BC
zDDPI*g=dqp<|u1I{$qfmGAL_;v{AHB#px*|<;s!mgal&mKA`^V$~yG?{ZHyLhzWt1
zXUp1JvQ|CGmxC~bp4l&dlA}6@+0upd?AVdQm4`TYBEneg@PS8^Su5VgYSfhVZ>zNP
z$vAsj%i{~h+(V&ZiNxHPsd1!s$9~i@58?ggzX`Bbatp;n%Uw?Vf9!n+TouRK{&VQP
z_uf0w6hr|F7O?l;dt$GNv6mPVjfqB$DH>y9nwqGwMvX1@h9ICQ0#c;+-uD0OIijGb
z$<3GV|J{4f`11gJc4ubaot=5-EzfgWrf?oQT7uA{Ga0hw$?uv|;589GY%Ug!@WNM%
z7veL98j3|nr4B`pA3(_@9HYh<N)`B4ord5}4CCpG%29P~FTOr_0ve_d5I($*)DR`T
z@c9pkEk?{=j=s*Nm^)!1&eB-YP*hyHVs<ZAT->`6zg&!MIQ;adB2B*HJHUu1-t}KA
z(lxA(pN*e6^F7??(aX7e0eOWCGxQFHy}mATX-E^})C(RxL>U!dfQj>5{4{73d3n*~
zHR33*qjyw-vX_MrJ*hxCCtN%)%aw6(gLWNr)3}C<8>962%Ur{g`R-oE(I5D(-%V<^
zS#>JFVlGI^ZE;R0l+Fozx8>j-^U`jvS%h21e7J^<#akZ@M&jO&@YZKPGOtaXUnqx0
znvWX|12q)|NQk0wPR|-n4t7wYr$QWC<wbc&x+FdeGwA9Y&_c&^7@5#e#CfG*dKHhU
z(sQWK^|A8laRFILzhmB*i8RJ^NAEGSF+{Zp->qMb^IW$IQW8;?CwiJ}rMi_WbNn>R
zHIS2h8F5#yAdhae%cs64){H7TO$<TC<@=~%vYkQOAegE!)Tf~%@}K{e5@<AxW%n;8
zp*_**WCu9WD^YQiNR>7!Gkmd&9xw*3ARVoPy`Zw^Bx16efgaWZ$`$EIY-U6i7L}o}
zi0;!#gOQi1jp57!*C>fYGF_NOL}+mhjYNNU3&z^Eg<_6~FCtD)R0^nQI?)o=`i>&(
zy-aXAm7xTjl@&F_myk+7*Bf!txPRyda)Vr8ZfB32!v~<|6$WoJ1tg~PUO3??iW;`K
z*l-dO6IsO~;Y+A$XofaEfv7N+z}2b_KkhxrZ#V_jG1SAaB|0;i=)|RHl*L^@igGx{
zjTsB&U(P~e;skS~;G6yDrRUev{B9TA79BM-@cSih7TAQq7g*mV03BPjL=vGK4QrFJ
z|3nmyoIQuuGls!VF%MsTx2O5Ib=I1ZRe<D2b%>lY95*(81G9)pa1#m<^FU@b5Fb)Q
z!XQt7Wf9L)BHD>q#Jd-hn4)4*U}w~dp&-MM@#`&w4;cpaq?;%+2*kK>3#6Psjvv>o
zMfsvN=;iK2#hVE_l)Z|!KD+`#(6Z15ZjNgB_24NKmCDp5X#=nNpv+?r-b7Wm9<VSq
z!oHK2VG|M#56x(tiT?wSRN7<w*s)Og<s1}D9ccm0N0w;>d=*OYRfQ_L3>*wKhC^gA
z7Wnt;i8xJVws8?5J-ZI?2i1p1ar9CiBKn6yyP^b1DS2>>7=a*_4BSj8M~7jZphD=j
zoJaSNdOuqFzi~a{2F%mP7bfPe(BXzo-NX)^+lC+}mPv3fzA)2NLiB}S@zm~R9)O`e
z{ZVo!3Tbub7&0sfx%aMeqmqjFN3nQjtCF>%AJ~&JHDeOoC71_vz|b&N)aE_JFQ0yl
zeWISJV2Cz@=3=_<L%g?c2ck<NFsc1W_)}?jyIq6!ZCgS&KMhr7>9}&HsSNl=pyi0}
z2-akNNOBHB`wV8-6Bpy88+5xD!rrGPW8@Fgpq1N1{nE5_!K~TyU{QPxZ?F3rB}CsS
zjJt&LvEwm)QYv;Iy~!{NN2p0EaPfKuhL0VDYxzY`Xf+f*%#%otkA;qnu~fm}aHFS;
zNz;5m#oq-g4hC-i2#s_`uL-?T`RFE296yDw&K=<t)B$}>dSKLuj!-I%=lGN@j$*Q^
zJuO)wUggmDw8foUSqu{>gtN07%J=O=zOEZWJnZp^Av<x`FF>|6Yy1?A5fthT3kOGT
z@)XdbMGzNNbzHd}iwfqSL{4}ML%db7emxV3A7mmatpM8X?GZU{1S0$*5$wo(pU3)q
z7EEeo!mg;(GX0T}^1LCw2M-?=qMfrRdW;?g-H=}JFw;S-p$%;N24ViNc6fO81YP%5
z7(93oa&ppe@md;YhP6lM-_9Y|t`l0DYvJ~9w;8VJ%MCRpCC<dGS5(l-$}5nPT855;
zBGKD17+nYTf`M8oE%C}I$}K||`+Dq)J&LZ6yTir00$GI$=rLjp^z&kH_M^4ju&u>{
zFj_A4Gyp2_ZV{s~vrlWpGCySVPv-!F6g*7Ngir7w%pOsIvxybb5Sm9+MDt5DIVTK9
z+Ar78ap-VpaQs%5ax+7NjVJ>#!B<(7i71Dc6e0H#l1k}q^YDY8+9BLxh@x0LsPKlG
z7GnRt<1h*eL4czPGH(0~-IjeY!A~96&)k7Y>)!COWAMY7IEsTxrI9VB&3hYe_3>D|
zZaXpy%Aup@#PAL`*jdq_#C(g6oI_HQVsPcL66`#Ek$IWcYlgx}TUDWTykk!UGxX|u
zOd$gMM=}?RVL12iN&93O*aOo?_D1c!qgcH8XXY(gpliF9KuR3S^%&YQ)EP>I6i&Ju
z%`nZW90T+=yN|}Ux7*<N-S6R-Q~GdRIS1|hJ0jffJj&XSfWHnmRfI?si(lV2F^%C9
z7k=G^9lu_Mc8j*??PHJ3JBOj?6N;g2n9o5A@`>}8q2<#W+T}U8bNys9>w*HT&Ai}j
zQG;La6r$>x4k>Nt)gR0qi@$&*#R!h22}mgV5j6%5aL_JB(xU>jclYM_naJe%NVu42
zqeRb;h7dk$Cbr59XrkiJ^_WJiR~69+V(8Th9lQfjK$IYjLMHW}jc)c5*__~qwh<2K
zF|IFa9^b*KFZUr}Mk~T=dO_H88p%~=QXL~ZJpn|`eW7HYrw%c^2re!jsMxmydD<=r
zakaw38dD5v=fQDs3VYYQhq6WQV*rgc+MEY-i7Kb8txL#H>J^W4Veh8T%|<m8H60K!
zaRl$b72124qr5~<R4%AHXb}l51icvs(<#6LSGKH2mAE@);eHh6J^|>=<?r`PH<)}(
zorL2<)E_MTTcI;I>AziJeoyQE80N2nGrPBAQ(85a%^m<3R~wwo=ld?oSbX=XsEfKv
zWmYkbTG5y|&mB5-$8kBn1TE<OFfL9<NqHr#n1>co=76^S`Z7v@o4F^C#45NqW)AO;
z(mRK+V%tHnd-C#MAc3X|JYB#gB}FJ;Y^HSf;|p6u1&HZJTvoZX(jrq(T!ylud-$3m
z6i$v)F=6pKjABN0eZeE_jy>M2$Z}%iaOz|f26pa?F~!Ar9Q6k(Y)w!X6@@5U2Mq2o
z3}XumkR5#y`5K&d1x5b9gJUP6F}`azEcK+TDEB6Ae4UI!x|S-%40raZhF1JsCOeg+
zAHe!=^sr)7AFSRS!6YViWc>aGetlGlB4V*t@kJ6#d6|elAWp0`$jiBa<L7%Ka`GI6
z(aKf&;1u@!e3CCjEsAM%=-i<@90I*jc4q-06Mx65k36tya%U{?YKMAm1k$hXl`8N8
z5mpLws?f66064eyMDEo)h-NszgZpO@n-_+Wi<a_*uEEXUzNP|y6VZqMKulyOx{GgN
zSIjf(i?oA{KC05=@XgMz7=t_*3)eH*iHTnq_J4<6Ka1uo@m@-aHQY>$WurQzzw()u
z78fzmRQ9IVF;={Q9*=v6w_s=CNKEZN4DYrdOv`))Hm%r(ghU%mnbHZp84^%Um#vCM
z4Gz;nR!~sIc=78vayk(MIu6B(5KdH&FXP0qvkVFNvxU~ixNorGXFE*oItd?#jo{>^
zgj+w}$M+{rVb#YTSTV5+7I?Rp4!`u+lQ_zWyQo45od*n-Zori2mALnZqEvx@zEuTP
zHCS+g98IK}aYUt1qi5#MsXh4m=Q#L`<*~tp)Z}p$CZU}^z(mTUFIu^zb*h`8>o8hO
zyiFNzUIU3u5a!Ku#JkH=aG%zt%8~-4r8Qbn6c{hsrW*nV^k=-cA``g^@!rbs5tr7S
z^^>rzmKrE8ErnT&FpO_OzYrG+rM#QCIBzY|vw4lAl$LZR+HvEXMrhEY{BQBeFCLiL
zZ7enf4<~9tDZZWeA<COe!sVoHWU7QB`mjCQ_kagiU{UGHj@HD9_`}dwt4DHD78jc*
zGyfAIs_7cS*{cOy=p9jFC=s}*-M#oLw*MM{VeR^1abR~Q2G`P~@Hw_@S&fkQS77%0
z8>EX|-0weO=i$4U`PM+blf@`zE=C1CQu(EY4Evzgr(O$ZDsOYz)-kp=8DD?(1<sty
z<idCdew_zkymtw5VlJXopH_6DCP8?J-%eh~)V}So=>1SAF&E<PTZ^%Nhc?#D9EwFB
z1u`;%vBo#{;nY#tyZ!uFkk|)f?BtO!*HuS#K{eX;Aaq}_8z#+Pi^?)3SbO-w&zqZU
zhVqEz>=*mc(~lu}BgP;vAr4V-cbP=5gEGEn_2h%7VNzZx&EZ8=WysB9Sl+ht2wOK6
z-N%eVPHZ%8-LHoRy(S|6fTXwpYE~W?GTf74CP2c)AFyKUa(FLYhRLhfGeK5~I`naD
z+Poh&L*2PBbAFZH2In5mQ8~8>eHfF6POJN2f=3lH<8C0<h88+jmXpoR#+`J!({ph0
zLM%~`G?0;+0rPGHFlbm03>;a?_-zeDT|S2U4`j)s#`Jo4F@d-y$xJ4u>I`h#v>nDv
z-bA<2)2L_=$%V(}zP$|Fxj~-B`22T62XIcP#3zeCLsgrRuyLXs=s79m@GPgLw17#I
zqNk;~Qde7ot>2u(ck?=6<hXmdzvCAs{8dAfISu^=2cgHH$u!PrG0F5Y-rstRAPGYe
zIj{%gtm&<x(WW@@6c%keDy>=s1#t56fj`$CHEwjoWMi|o{V{J^C>2TxifS}5a?%9c
zzr?uU?U!jOuSHU978A*z^o<?_L-Q6GGkP=zm(|cCD#48td+_luvan1&YkRnQdQm}@
z%>}Aj+@c9(sMEE_fKik=pOspD5e|M|h)ds}mG())j0jdm49UnTMNw81=NCKJO&f%_
zw(gRm%-r4cJ)*D43@xV49`N>ZLuQPL^jbrDRw}us8n`mY!KDq~ePyT?M&mZuNOcVf
z@^W*KnOPw{x8XzFKpNY*Vf^e#sHDg8Xu`v0;A0!kZvJOM(s``>@Fy&t9D#X@T%~JR
z7Q^e7Zrh6vACBR38jPGcj^l^4AW*@rt>nFxRMMU<mO2!3-4>dQ>3`Z>87^HsOV7zL
zOj)s#;c=BXxBrV~75I9tNribi@DE{9Z7`?1ibwc*8|Q??c-;Bv8`#bqiy5mo(y~p|
zwrn0dzV~S>jen^wzJO15{e+c7*?MO~0Otf%<X`&<$8TrB!8Z*3yLLep-&qySIvm-4
z0@=A$OiI1QkeYimnhe0wpnedtu44M?Be>x>9&Lw>L5qwuM918Lsk$~1?n>y%v!RZ#
zfQq&v5|4b2j}FpX#&=ak)DvjHaBmk0`!;?MViRywRN#4U#SA&^*r^xuWW13#ekpQP
z%n{bB3)+QsXZtkPWX0n6v5OG=11MvTHK~wA8J9w(D3z#bzu;JJZaQYJLJx*MN(j#I
z%O~&S?00+7edS1u9bJWNZb0J{tvQxt#xnzJ2j(SuQ8&8XSVZ#Q{|h7_E*<~ke?!5`
zKU8Sls)b-ekNfND@$hXWv~X2_;XLnvAi>K~`Gw;&PYGJCK0;W#mV%+qi`O-6Pa<e&
zI}2?(gbSV~MzpcLzCVnm5gdbB3vFAu3+gIO+o#1rkp6E}kDiUA(57QMA+Uv$K(}&}
z{x+Q7xK9!zYr!`(SnzeX5eR!JSlHMJ9Y-$|j+{9!EbQwfFdCsr9gVMj`n!pfK=K|T
zz{~3O=Qir=)9YV(Od_b-`3dbpf&^E4!>9EVs!DKZ(Mo8?dv&%oe|lV6r?Q%0<P{)<
z1bGSC8mdjddu3aV?^Q)GF!dHfJA?_&TAI@BT0?*JY@f*3+fg8V*^~O8Jd1{BB7&H|
zq%AdVL!nKlZbJWoeT6n&I%1%J>|AwS!8^2z(7$(gA;?YKB{uHQi@!HKPwDkf+I{6U
zW_ChommWgj$PmHRvgz1(*>z9b_%roW?q<QAgkb}F3uY#|ukF9s=3lBG>VkJrdm-4<
zSde<c8_CDW*<A?j7|M6|*%*`<*$XWKLxf;Id$#kU{2K0A%dwTP^Y~R^$IL;3t2?iM
z?%9cRf{%MM&x7$Z>W-d5X!}6H&ivWfR8p}K0>e5A0d7tLgX~@*hi7A^u`Fc$QV{e^
zJ%#X&{(_zuN%y24bxj?ib(;XeNblw2tKnSnr;fg<5FG3y=xM%qepA^$X+y(MaBCSN
zgtc-Ol$Bn*rg7ck?+$LhLZGjS!2Q(IK0bd=<G)SqT!r8c9Rxp5$0p}XB<4bBr!c|Q
zxaqhQ-*czHHbTd+R)TFK87tfR3SpcR-0Y2-)YG`F*Zr=nsV9tDx=pxn;SXW=mbF43
zF9(58#DcS5sL*>re<32oLC{ryA_E@R^#~Ap_U<QiZEY<uuenjXDuR1Jh!E;)A*eOT
z;mP|V8+T2gbF=+Y(-J&_!-f8RdI=$%6PnJu`a;Wg?FB!(=f>dEHX7=0+@}|RS8(<d
z`t<83wB@|mxDQIIHbT43Z3Rbzrq8?S_-OiF<LjYmB8-^6MA&=cf^hE8ZXt+rZ1Vjp
z-+>qVsb*j-wC21X>?e*v?$&s{uA!L_+{#YSX=WV%<+e<1g_dE#f|s-9Yx`+pVkfw{
z*$G5idG1_O!IyJUkdKEzfTHKVd+~2YWoscYw4>0{-BI9nTy{R6pB>k!b{*RbE_TnY
zVM?lkftQ~U78)SvYN|=kCxC_!7~D#5uxR>@DDhk;XAi;B^4T2JSSC-OV?(=7el{F$
zI9E+eSMU$%AcVCI5Y$weH2B>4FaE77IC-=Y+VVZLQX*{Clk@GV4+Z*}3N+k5Jtq6k
zKnUbG_OX`TPs920pU=zbKj&|T7mE6g^oICDUj9i5SUbDm{ZDtmQMCs5PXB~A*M5ak
zy2Ru~E`k4K38>rq<D<<Vqw4#4SokgRq5qR_R(@|yB%q|BgYZG)(60kMCUHz+-gp4T
zl`@-dlN05Cyj%jV1MumBZcOcAGUkDwar(-=m!B*DMlJ!l1paFzAm#_j75M+9$E`uq
zg;s6aLa8JZH?Q1A0rBkRMJ|E=bO}h*wBhdQg4(qENMwAceANG^Kk*muPm!T*8cNkP
zSTvC4ym*fM$jc<43N0-Kl$JIPt9jYk^5b#|$R+UKApyAp|KIVP<##EUfLsD{3CJZN
zmw;RXatX*K@E;-pQGsWMnY_p)AeVq#0&)q+B_Nl8Tmo_l$R!|`z`u(G{=5QDqzrYU
z8z?iw?e&EOJ?a`zS5qbZ`s>er@eE$Cp#=?MOaIeFU0V-EMuyPWCHtJ%7)fW;n1!!J
zj9Ka5|8xib#rhS<BCDsXLrjK$q7W)hLmPTpn#2M4n}wMEnjAN(BC^TfD5~OkQkSva
z{+Vh-q7V(XtMWJ7eO+4$5>+S>N#}J%iO5JA8ZY)=iD(Cg9DfFS<S8KIth%Na)c)Z<
z%lb+_2ezgD+INfyB~RYv#`h>8UbC*AE@37Ar5F5JRYc*C&MQP&`a4BJhG0$7(i0Y1
zRuomp_Nht4o|n{0S!-%A4fcPzh=?NMxKw2pT0>D_-!&-X=iia1GJeTSBoXg{?5wK9
zgZ?{3k?0+wte7<Zm#R_VyRA)55FIjiYH+UqYgaeZl=#e=c}JQ#^Oc8`)ifwiktd{4
zk2tSO=Mci-%8OhAatX*K@Sh+7ad}%#trc3j7I61=gRQkStodKW>8>xy0-4-N{p*N`
z0Yfpoe{Xm@86!KV5Ni5XaCdcp4Jp0NjET=q5|h%38fY4r!j*85lST|fmyiJHD3?pw
z$B5KO+;wYsx!95Ts0xLz#11#b(3vw4W?F*FaT))NoC&;a>=uN#R<6L5F(c5mts4#=
zJ_!|LJB(ko3Ec%^hCd)7jF{{Ff3SG<pMllHaIZRj3<<9V1y+X{pOcm%Atgm-nAa2u
z7xW(UH(*Y<D^mZsfQ)}3a9LbB&E5S;zdafgdUuD9hY23VJ%EatBb+UDQA4P`3gU9x
zxVXaFNFCXE1fx_|hoh?}@#)nF4_ZP<PYE=Q?9q<IU&BU@LdP}%(BgF&k8`17>Hsef
zCs<oq!P3$amgZ)Jd#gYJ$Ca^-8-|P-jR9=e&DMa>m9daAmA~e`oBLWzY{$tGio}90
zeh#)#Nm&nF2Tq5Eay~L2li;J-q7G*te+(Qk0Rtk#prKfaw2W+2GY3M;+7qK^y^Xh~
zPei}20XRqO`e9Q?!9_?#RC0c^wY~Nrp*f80+=&;hOH!%=DfYRoYa<yqR3Nd$=y@{{
z?yinv!eH82S;EZJ6y_F|u(h#<j=mAf1x@to(-Zv%4MaPVtf><EEh{IFpt68n_;9RR
zHV1|`&*5f4SwpRV>8F~WDZ&Ph!nk4m;AO9e<cE(?t7QyNcQ>*@mLac%5OBKI@N40K
z3c@Qku*p8(h_<;M@z^J0RR6B9F;qe9-G`{P48y`1!=ajV0S^kA!X0XvJEL9y5tuY2
zlFy?WIVq0`!>k22Uy@8I*ApuDHL14Tw3raq&&gZ|)r1rk@#V#FY-LD_bSWvz^F5UP
zt!7|@p@f|r)W0u$Tr7w!9uEZzKa3bZ4g<P{!<)x)v$II5LlABj`_3b=icHaZH_jl6
zaJkPHZG%>rw_+j;DszxqrvqmjL*x<KPUOlEi-o;C+&w&C#dpKh)C?9@*03j2b$NL?
zDk>Ucsuf}59*MaN1|Tgyn(tl1@n@}BIXc6E%+Mv-c?94ksTP%XH`Z%L%=US2j6u(?
z?cks(K~(fT>G#jBZ2FtKpC24;ZDAqy$J|o7c34`N<1xvhbZop4F?b}#_X;HvUm3Df
zvLPX^ehUw0>3FoVG$)RCJ^LrhMgny$Q&<{n5=O5Abx-bBkz`*s<PI1)eggW1hr!N5
z840%^pqd!=Eqe~dm=Qw|=4X$`X=zVo+_Z=CzvL2-OF%Axe-8;PU%tG-z^V)v{~`Et
z8!?UJ9-_FGtbxQM-Mwrc&Z>D}-r99&XIX_yw-V5@ZCBJ&ae;FLhWn}`CA$nJ7AA;0
zwhODb`~?4DGcmt^Ansp_LV=k*f=P&U<M<6E5$q;t$aH)>p(C#Uyb;q@eIr!_niZ8q
z3*G?%FjW>YO=V1Y&7Su+4k~CGVCd{c@K;I2*^_rjq*5$pM+Dg<%hMksQ>jV{NB@LZ
z=5Mt0&s-`LrQuPUE=mo#2P=AwQZBK^<aZXL_Vg!s_a_<t@vC|z5G%emCkc<LNf@F{
zRlMr2?_$(RI<olPweS`)aUn4uPOU@X_tPmTPgsGuz9m>SV+sx@n`6R~^$1ZWY0QwN
zT*RHQ{F7Y>kX%9kAqx;se0{sHF<3xykGQM1p<?NecRp+h{b@@P6Y>Ujy%~V?j9k>x
zx~5Q7f}>j(V@I_!mTX=R4`d+fZUKA)+ThX2z4$rtH8N^WAZivEy?8y|XsL^xYv*G9
zfv9I^(Y54Etl#<b`e4<x1J8~(`Ar!f!K1P2z0pufi$QEoEhdc`fP{nu#3kjS`;>(k
z)k*`$N%gIuMvUS*Z3F}dA~Q)Ow!hz`ra%1$<Kj9@S&ca!xmY=4DvqRBVA8S=5hPqf
zr{OCaRU^RF*AK0g)M44l1$MLu8=9Fxm6*esRFYzovhmID>6kRq3$awpv_0BlSzCW>
zeRCSVd6)}H$z!Caq=D?~RHyz0C@ZqUMoxSi^Lw`-n{y2O2G2wb3j=&`q8tmC&w)<D
zw-`U=Q-sX<2+O)CVf^q3xI$d~7mA)O#>{^gLtAJe>JAB}-dF^Us$zWcz#6Um1Cjf^
zXrp-c>?Mi@7`9+JMz|^A%AGvSTecehN(=GPIaRFvVhtYc`xw*L{`f*OO%71T;cvc&
zS<a8KeCBi<O|`=0<r@%C`v)RMt!i>=!ylS@J{Y%rDf&4JxEY-RH<I?WbvTZP-UHCf
z-wg2&3lR`GmJGXYm@)kwB#T`1#TiI{luj9o5`MAN)N7$l`!=Y(9S?Pq8O`oki-^9n
zkd-gn0JM(ij?sPlkcdbD7G@-@$tP<qsfgZPwGwB}{PCp1Iuc14p<Q@89Q*nk#5c2X
z95v@{3<$8o>H!1rb6O>w{Q6?&mv3RkhVP{tSFg}e1QaDhbvKDBUolk-nK}~zX2#qA
z7{Exq4v%xW`5~_U*r}^9b>%9AYZu{mrUF*33B#^=b8*aKBzCSONmD{Hl(^Yb%#Ff)
zl8r^DSHr=tAKvb(f~BjrAW6DGBeuV91U`6oE|d~)<6fmA{93og*>6vyr2PPVFn=<#
zqOYLVr5`?YbHQ7SRv<QwMgw_~OF%9Gxdi@ACGgarC=!m>X2syWWy^6rF^l9cWN)iC
zK=+xm5MhypB@0*L@U;ifqJ>?V6aJKCU2*4!Pq6IA%P{ioiT9Qc#=u+!sJOO(N<l2v
z%$tw<5*3DFs1Q1iv%16);eO5pr&fZEt1sG^eupzzFEj0us=d0FRPiE7u`;Ye24Q*+
z2Yj~mb3Cjz!^Dxj;A*Ois)7`3-0~&7A_gNszY@DhpDj`&X}PuL!qpLbzT8G{!Hbqm
z1z2+7Yl|RP9i0E{D2_*wo<98!S@L>e#=syb*JR^dTsBI@b)U+iNz3jS7-&k?SQWUs
z*^)Ig3BRuY95-qCF}7-hF%u(Utg1+>Rw)v1oxrYxQLqW?g^Ar;L5<s#yH|h1_I>AI
z;^09ts{o`GNZ?6w4{f0oF>$x?SlJ5J<%Q6&v4<TQ9aC?d#qK@FVcNbsy1MA%VTlF;
zTR20dDx2gjQBbw?MJpRMRAwjO{zDlQr9|Hi0|tjfLWV^P(vVx|NRV*y7*5=L03CZ;
zwe2lXm*S3Aff}eUzmL23vp_zl7mImoOdAscBa#I@PDny(-ZSfEi%x^kGt3Wq8VoP0
z%E8gkzQ&nHnav`LsJZq;kc}!%eDVzzetiNedU{YH!(=!&2W17ND9d8KLWz`It4dYu
zh$?K1gJ5H%KniU)^zhb4!tW_aI{yXUy!AOAB&ETv?`(WBvmYE<nc_AX^$O$9W5udZ
z5T98l4V9^^k)ZpG)o@Qggau1K#?71>h}6^y)iMRnv_&{Z^lp!;hi9>Qx5&CH7G?N$
z?1LWR0np_$tSimIsUyGO#MLbHp1KI5+S;+dsZxu%?0ng$hHe;U&zpe6qdRf^Ry+@y
zp~tBHXzgZA6{()&V$t~dt}+(P9tug!&v;|WCZrWs!Hm|&f^1cIw`z}eewIkOc>qVq
zW-HRY=N5R8JfsdaRiZpd={}rfQf?|(`~DV`*V<v!)ZTD0re&Sq7d*O-%{z}kNz)r6
zr}Ti6A+3>>Rixm)iN}<2Nj?eJvSl)^AQ@4GL>wm)lG?PvxXF<)R3jUqfhIEIQ?Y#2
zda@JRVAk@L@XkAenH%?^kSxR%bt;&2u@Whda}Y3UIX)f}LgrX!I6L;nh!86%<daM!
zuH-2Wty@?mI+NKjxmX<m-p){_W%xmAGPJC`Q6GB*+YVlZnon=c>E?iR$|Af+OI%*J
z6z?234UdSa*tlv4%=ga3@-Oyc%hX=@@B=egg__`(&sQK(*%h-EPlq819_q+^Qb#4a
zB;gVsm~}vRe<S=b{S9o1Nk-?fE3jtT6#TaC7K$s%q3hiT3y1kbN65q3Z98!$!xW+Z
zw8CE8h3_^UgMshQaB$Ox^2G{NmzSaP$ykv+W!a~uu0Mv0GB(iQJ1&!PHtFU|`MZqE
zh_h!Et=c4(6J=~<(FS8Db%Qa1%gf74k$gW2*=GGQ)S(b>O`L}FX%%Ef*5t^eFC%#a
zMtq-xVv^GhTmA*6w01#&p&nF8q%=I(91_a-ZoEt(m-R*$L0x*FOFM7qX_`TcL@wpE
zB!Xhw<>j>y6Pp^!@!j9z3ZVxToO|K3MSXDl`*qm(+g%hF<na_!MAGvS>}3Z7V|yqR
zk*=>^HXp0owZVWeGu%A1AF=uMB*UR>lnf9)xE+4}JQ^)K^n{jBg}cYU#;-}K7_k0F
z^e2=z33sJM!f?wTBPYVVIvqti?g(%&K+&Tc_+rH(d`7QMz~Fh9@0)}7)^Eps8i14)
z{Snf_3W;Cu#I9?Kuv#@APOcgx#HvBn<9qn%o#}Y&IUXBk^+VqlMy%5m{yui}C@909
z8+4Q63T`AG!o<4=ybQ`QZ`?8*NhPx`L%bACtTA=!6r}I{6tg})ilHBG!-TM4Odi9z
ze)TsEqLTlVOF%9Gxdi_0B=A&$7s$F=r>u|Gr295?uRux8100Pjgp-FO?jPHT%eNj#
zZ>}_-Dl+4X6-18Zx?<$#WFei5jSnhDLrbR?w_>g$(x(enY<>^BcYaExKSg?-mR~TO
zshq}~xD5LM2lxfK;>^LgmkLaQYC$bo&CJ_R#7C<~L+RQM)D&o8;*xn78fZX^YyxcB
z55wZ32e_i+Lj}y8tf`l=^?Wiy!h2%mfL1uNdwa9>Yqsu&`O^kNpYi<0qu)U27$w|4
z@-g1MrU!F-M>yJ3$-8(F7ZQ@m2B*MKhd_)PPgb{_Or(;?)wWF#)E~wm<x&k6e6SKh
z21ST_kdJ_Nbla*H;CP%FmM)(Gy$3guq2i1Y<6Kda7=sH6Mu-?b6}k#!f=x|_se?I8
z4ODSM!4IQ-3{hU3g&c69^mju=b_(v<doVPhFO+J@0GXLZHpEtBmMz1BdR@4-3W1p<
z1!p#WhcrGrLCqSi+xJ3{ITib|JmkX=fo7V}49J6jP<zs5Q*rM!3M)G*Q%=8P>H04a
zm(f@;cp@qlEf`bD^zraQt$`v=>>&%QsG=#jU^Lz3zRFaDE<J>kyFE-9H`}~z5U5Vr
zi<^%l$Zq#07BM7e%kIM{q`p#LR}U2<TXdZ;9?lio@L)JieI_?*8oF@q*q%&!H;`{)
ziq>7jpmhEll;@;EB$U+i?~2KTyO0|G61E<Wh3&ur(6IBzoAZ|<pO(do2fxAIyLIsN
zWGF_8J_gNSk7;zVKe~1r-+lTW5_k{ho~_Wm7iqhr%bqIm0X>Fd&eS(xQgRPhAJS9c
z<qzlBvv_PkmegKexcJLC__lLslI<;0UiX_Z5l%&S@xf=iQCy{tK5xv$8^c0So)n8b
zC38fyGD6ZH70_0!!IeFHMpRhElKD6j5~tpnHGcveb(q1>DgvwgdLj4X4lMX2kqTH1
zDj8Sr(rX%)%pZhDzpRC_#0WFqTY<g-hDg149~$<aF!EN%x4%Bb#FZ=2%|iq6F%MxD
z=)ujaDz+xnL(SL@UB{1yOQkkk8R}7+DnQ>X0Q1+aL|fw$#NMY+mB^C9P@Y_}d!mR8
zk=1;fRQ@wKHc413E#e3<4(^31ecB-J{yFS8o(StMZQ<w}0tdqi+}^Pdaa5GWV(vmp
z_ECMwQb=~<^ekAp1OeafL%zN@2EXNl@}eDZoi-ibIhS$Z<}Gdnt8wi1J+d8AkBvQp
zq^i--FjK|d{abN5+z0);bU@OXpYhF4=TP7f4s8pDfrt!k5?%NN`ylg30W>6fyoY>R
z-yd-!S&xJ}f1pr{43{eRAW_pnr?KOxSeHRfFBERInYi)cFIcztGCt|w535|dK$3F~
zs}Ed5VWp?^9-jZu&~d?>b*s?TRSofX9>P7)6B=&H_#*MekuRl(H*bjrJioP}Y)<eC
z34yPpI?mP^A-gaQ5pS%<SWA6m$6dj<AAF8Dbrp<Sum*k%^|B-@b^O`S@mq$3tj>CH
z^x=GBScaQl?m;}2Xt9L!8;@C2heEG70rzs)2Zq0k5%AB{cIj!-kFsJi;qpK9c`Z+<
zC@n^2x)_qEjvf<dV(Q3nR6n|d$Mwomw%&$2(r{~sk$s#|a_(2D0xwo35ok#93PHGW
zGJ^R&2;68YGIpO_^U&b?&_L-fJ(nRyEjsl<Z+jJb1E`>D>A=WS9b0znMPa!HY)grr
zAaLAeJ;r0Qjw-6%BEw}gmi3y5<>4HYiAS;JSUl=m_atkrD%^s)AtcBjika82Cqcl_
zw-=*}BMkyvy(Z0dXWKP6|Mg1jzRj^%yb^D|{{d`%`V!mrUxA^%B^-@ZaqW5(->Ko~
z<V%A{PBj#bt;Md&i(CS73CJbzZ!3YP*-rwkFV(6h=-90vMvNGa!QI-yQ^YE#MW>uG
ziHeM^eOkoKRB^>1wYQiwMtz<ZN(Cli2{`uEI&A*oD0JN0;H|Y=v3yd0Xwo7R9O4cm
zjXYY#Hz8Tc1nyp~LDsaFmKvIK%zgUcla)iDc!MM$?`%hjUl@A&+mUMgH1?c2kLx!R
zNKxDaQ9qwXOqn7=`}JlLlobMkTB6|0VOmZLnytSq>Ick!XDLz`Fa7PdH5ff=1eSkw
z2HDqs#QY^|@oOS!qnT7EvpsV`p%_%^O0Z?aDohzS8y9KaG4XMO|IkSYH!HyEcV=M@
zDcYmcNkgxv55EC@(2{Y{Coi4D(Zgp^scT9Xbr>zHw3Ml8Am!Lv%$>Iqm+z(_tAK7?
zK?#aQahNlA4yI08jZ7_l*wfNqLyM|N8Gq-8w=s9|CPb&?qb&0-4s3f5OMbin_L8*D
zPnK>f@znEClXVTlMohx0wcn!J#s+bxzQ^3j%W#)@3o*ZbiZLUH;*Hfia6jwCm|Cf@
zQqSW3CGX&~E2)%$1T|0VSBDl8%oMrl$+>eLix(`#frJ!paGEb=kMCpooT<2!DIjv(
zLhRhO9>Lb8qWKi6x`woRM<6016gGOAw7AzpPhSt6I@;mv-p_I3+8yZmg`<UvNS#h~
z!`2VW=Fdhe%~Y&hxDJ^)xyU%VA3J_LgoK=OSW%grFlQO&MLI!CO%vY4+qYCL#@*yH
zv>!SSV`mJI))Tw`JFH&41n+PBQu;Uf?hzi@4~`X=F?H?&tX#eb6UI%(e(o!0O&y2Y
z%bT$CU=q)*hmr~tdzsiQ7Ojp`Ffzmlr}un?lW8T;4q}{m&tT+J`5OJ^Y<#-oFe<pg
zRi%ffo`a{Nx~Z&%M<=j&-V8)#Q;|ORBgTvxhS?;!$t|VzT~LF6=TTVq7RU9bMOe6Y
zAN0D6L|;E$oZP+)lV>i#@7zq+Q)zM;FdjYKmGJA<MVK{j8P3P1kRqM$Dce=mGlE~Z
zDC00#>oQrBNzZ;9ubnN6v1ajXj@S1Pmst#D)*~%U#FgeS(&MD0+_0~gz?^jBtL9FJ
zLv0*ZEZT_N;%vkm+>H63AI2l*Uer9DLD+0<EyL^75x;K<=Df23x0CablM#i@8@J$G
zng%8=e+z+X>G<-acX2AWjMIk#<tme@q7v(3=$`2DLU}%tGKyJW6*oTF3@52ZUF=D$
zn>!P4FMki|N;;@xNYeVR_8>Z~RGLJrBx-YFQQ&(Z<ya6P=G#BIwI3r!j=|ouu`u%S
zf|{lhbokz=5T9Pp)C$_X*V0<$C-1tfif5k^%6I~LxJqzf>pPV3a$LCkh#QDTH~r*f
zfptolknBSlcd{saD&yOn6I8iTGG?Nug|jyt^{WwoKNmicgBjK~90EN_3Y=?{RG2W%
zzS#M+L5OuZqW6D-`ClACI%Qn@bbVz|9o@F=7Xra0xD(vnJ-EAXAh^3b1c!~g+s579
zZ6m?m-66Q^<J@!Zt@~cp``=Zox~qE4ImaAge3F#PlRwmx&Gf0!;xrrU8+CId(NoMF
zI-D2tPiPQ*eIQ!~l$#*mUJ!)L$gDD3j?!7LJD?Z7%F9wLOwZ?2N(ZYuK$)9cu_*n@
z2$~-q4r~8v;-nX8)D@y6M(F57ll!CUtDy%A)W~}*K?$XVzSuecnGO%89#I>xV9Y!A
zDG9EdB?sOvj78Phy6x6_5OBbEnPz^}zcd`?0V|ELt7>ZPMF45zbW6yGPoTXteUmos
zCP<XSkm-*saM+RVOF(<r8P04(sehV>U9iFDj}eiQE4ul;|FB}P31imaPtc{Mt*>)H
zsNv|A<R1PrT=VtdI0~i9{~!%f<Wn`uPAn8W!WMi7%o|rL>q9fz${bEYLp-nukqBH@
z*Rgb{{!T<{+DZz!ADTL}R#q0r`#d_mf|gub=TCdO6hZI)!8b*uhQs<~#fv{DS6~gP
zidD=d=G3TJwWcxdJ%M5L*1jZ_<taZm2Lpjl#P?ZzzeU?_v#_lcp3SJV-3@M06g<E5
zxX&1)V`TQngsvKxOmb-6isR@Ipx1t&0itEhuWgFvG?IUCa;A(b^H-0Hs3&8)lhUfm
zUZUa@4i4t$nE`bmuhFpkz0Y~1X$Tbi#TJLbNCzGiRFc5n{!|Gn4f_<DpTM$FCiYno
zdW>3>kRHf++8?LC_|63x4ps^w?&94mmi_RMXJ?sGM(Qn-t5REwC_%`tK?9G*=y+{P
zA0Qq@y+2SX@D}ej)=NY&dqct4iyr~bBIH#mmWhpXGSgEI@91|`8JpG;uD%QnG9^@2
z5oK^_-HwimVuP)qSgEk6^Tz4IN#8>D@{R@sK%)Oqv|m{+d~<D7^4ZlJFKF@11Y0pP
z=weehP?z(3`tH3CjzM>p^?b8edlw8kP1Q=}APKnI5xMlKrGYz+T4M6~CXbo1oi!;S
z9ve@wTTMC@(6Nj`4<MfGfdr;=z#y{;&(YK0X0)Sp#e1c4re$O!Our!)P~i7dzB1CE
zlRDVnP|=**pd|e%XzHuieA)<Y$&p&Ex-sx%$Sf$HSLN%wfF>NN`Yvd3tgfOaVc|)_
zk|s$hUKO?tF=5(<Z*{9l6*ctoC0*1&sV^6bXjgrjm&=JBD}wS~SZa;g`e{2}RUjk;
zJf*5MP?;d1qyq>52$;ov>5%o7BO7I6+I|rc1kVZw|9X~ve4k6fx=NdZm>^LC^$C+9
zv0^cT1t|?q3*nWasw&f{ErE{TUtY))nIdI`gJlz2f=gj(99dA@4tPc_$DWY>JT_vq
zPE&cK1b@aKf=dmZ=-zjXAj_%y`{E7ssUKq9dD9GR7(@w_+<Cx-1m@So{$**5rehf}
zLv`Az{8r>3oM*CRK!_F9;v5sC=voYW2f5*gTCOsBRE%G^*i1g3EIyMW$FC%I`|yQR
zMZnd~g@FE&8vE@VdT!5>wa^+tJq*I)cDcwOTO2l<cG9}SuRtf-ZK5@t{M9Tlmf3l}
zxD?h`v#NulR`eXd>n{@<Bchw<s`6UFKjP%%*G#3RCCF4!PN`P^w7cfkgzc6IuW?z5
z2<oFB+*$Z*Z`ut$3;~&3{_sY|5C+1Q+lDv6$y-jIhc@;_)-g`XORQNafs>Ud^IK|D
z6k##UU8}G-3Gkr|NeWTzdPe)up++1M9pDZjHL|OJ1JikoQ#4rDs9}k`AHUdAVCGJF
zBMYb2(4+fBD5Nmz2WJ|71@BCS#2xYF@<vy!LeKO3>fdctFs=ZPcl&2!5}xq}-_GGv
zaii|boUwfT0k(K}JcBv#y|;_~^S(69Cw(76X(w^$xNe<SOD44*kd3Ub3k)x!Q4N=8
zvwb6stirs(uTXl-IFiDN4pU^zY{w6j{(~tO$)k*HD<ZQdT82`xbv_Qi<Q9>gt$lO#
z^+E)9gLGrcX|U<9yX=K^I9i54&%Ux(WX?L#sadhXzMKiwHNWOaW{UKouO@#tRx&bo
zAWn{+ut0*RRXLQgnaK}Yje~X6@~GBJybwgW!6RxrL*ta})S|pFh;IiHx5yInZ>-q0
zHwZ=FqKYN92YvY#>7R1`y}j=Fuz+P_ArCLhhNJ&+P(xA|npB8`E_3a0iYxMq2U)~k
zh6aNUAa2TJ_}YpMmzP_HOp6EIaLk0W0SF$J0fv@l@sA<Q6*_?1yLo_?E^;j)%yhle
zDBQ=vH4m=Bh-mc<*vPhL`Y+1cVc?0-?vF?`oD*y&C+0qaM#Y|%zb9<~QKFTgQllZm
zzQkqCRtqf0>j(#vhliH(u_-7(PXkDPTUPpH_D$R=N>WsYT&90A#7R^kuqC$q<fK0+
zqEM9<Yth`H@DreGo4HoW{22V8u>?eVG)fFVZeOxY7iOeyGh6J7{v*|QKt9Xldd5XD
zTG<;jRIMnK7a}*Plh~j+02Sp0bA?4!VkNXWfM59%^63o=5kpikEi5G#b;yZ+{G{-X
z!pVqwfOdq7CmaD^)iAr`c48>8Tub>t`68tN3sol4BCOkFWFsSri<owID&->Q8^)6N
zKEVXZj*WeE1>h($2?2T)*BY*nRcL)+z`5`-8w!)%Hj1&I4}wa}zDPjBQ!Qd_b0zHB
zUN7TlX(?tlbkw%Pt&PK~HlZ(Rkkg=b`2^&uYQosih8!?)w19vHkXg_y#s*ZT!Prc_
z&%9)_cXo1Y1R`H`%`;Z}H)gO;5T0s_XJ{3FJmGLlcBDvDckv*EGk~m(zpB`Tc+C%U
zIO2*gn2!u_2y)_Sg<9cIPCVN4Yxq5S@Ca#35G}agqX6q_jczc#UuI*t9(i@2=Eu~y
zT>g1DKOnUpGlpbw-pjDbnSOrS>!P-WqH$jNwor2oEA)^THwA*GJ_nl^PkG`m`JqlL
z8NX9F(K^}U4*0M#q6*?=C)~m$t$2pW;=jYD(EdG+^FZ@28sp3jDI1XU&#r~SUvU#T
z#f*-fv&X+uqMa4VH#3CI5fYgKUOmih73@kYsX;*LrbUXqlsl(hF_)ECN)K6`GWT!U
zR4G7pY*Q4h$N7XwH-az&`a<#}w3r2U!StaEN_Zsm*e6rzlO!TXq@GE&%(u>Lg+m1i
zBMJ9|U+b=<3!D0}5ze;4_hgz4c5wU$^Lg^44Sp@}r<<c4z}jS=Zq0sWup~#QR~GpQ
z=hrVx4IjU-6$NG!kdSyy18k#)Eo21{n|gwGq3w?V-{_vY_zSS|)km;xR=8LqgfY&$
zbPY76+3*hXCB-W4MJKHDqk#=hHCWD8G$?OFzM0)_5E>w5{WEigCmJU{xQ40Io&ZU{
z1&$^ra!`~xn>vMYk$L3<0UsoNRwdQ0r|QkRiWz=;JnklGpVG+`ui7Ms1xhGf<*|73
zy*a-tSBKo4B0xFVt5#Hv7@^F+02eNuf@xYQ0njREjc-sibk#GYKbCp{=psS|FGh$J
z8L<S#uq@20e|mwCdo#1U$mv^L1Z1mx>km*+KWW)|LWLrqSRE^_ZSrpn{=O|)IN$Nu
zD*(hpvH6;PiV;cJ-`*eeS3HL&Qh~E#Fqz0MmqhR@bhB>{*PTLKda_u#2g5y4Yvd!I
zhQslU3AzJc9h{cIW>RY_6UR(Gr+<dzdu30|O0iSQ81|*1gt5e$a7W%4Q&mZMsQu-S
ze%GT`W~T<Dw;jF#51DGrG^fBQjQ{omfVEh|ho73B*%!lK_1>I6YGftuZ>(}2X9*4|
zMZ~v`B1Q-AwpWgYg7U<)B#>c`lVsEB2Nm!H{Of*MJ*Jj}C|I>t-1{@P6k>}1o3QgC
zkBY($YoU~T3Rs?tASSa)?T|L&Rg&-0dHg2-amkGY<r^+mz9DhBZ%)Id9@gdnm6VC^
z6FM<4w0@#*35veiv;n(O&N90WdN#1rfd6`>UFv7LdfZ-=jCu$Cj;>_#yY$ZV(}dJ5
z*Z20=k~vC?NI2-H<Trm68Hh9UW>28kU;(!pztL2@7_x>$>V!^4KN(&%$SqBCd}|DG
zo$~R9m`$-2lHQi{?z*A_RGtQPc4w3E$V=_Q>AmVAIG~`7hzh<qiS=z|!+_ad?xF_Z
zNXQbIL$n7dle0!eb$=Br>I9c;PJk>2QRp_$5!Kp~Xf3GAiwkeZ&K166)<IY`pAY!7
zJ<?~=Ir!etok^3v;?$PmR7@2N0zwwJ?yw`+ZH9W%VD%2WH-kFO&U?*h9NJ1F1$3$K
zbjc}z$2jdq+xIptzSop=%SmEquJ;ie5DvPZ{YAO-ps>{3&!C~O;(0scO`N4OW)yJS
zVC1x3M7f(X-u%+7MtA^Rrnc+g3$M=Qew_)ICTbXaPUjbY!qB~2$D>l!AKMGMs!R@5
zt)Rbx=-L|=p#w*}CG$-Zu_+7_wj0Rq6Ge34Sq!?bI6ObcOuzcrAe`+4J#ID`jK)_N
z0N)A_+ouE@@H!HuOSdeHmO2Kpi+-Nc&{sAy;143>Ff1`m0i`+yYb>`B3v_xgDVPtB
z$&H+2f;HdIL)}ZP;r#{?tXJOa8zLs+NH3>5m`l1^;kS)8_kT<dWPk32cW1a8B5g&z
zEn$;{+3`0!<F;}1q^p(eY{HoN2~b>4D9zg+dR~=?TWTB`kOuhSBStzL5U(scr+g<~
z>=`k03l&3z!5CI#**)NghT8Z3@JH)<;;KURA5&F?PFP(LEpy5OjEFQhQH^voYGZ1W
z@~vSH_hkB~i&mG@#RsX62Iwr$-wftLSEs8hLPj;9Xi6IgbGRZpT+x^ec3tPD;sNwb
z%dy$CN@+8D-ztT`J1tw7(9GRgBIHA-@sr&_G@bvTHoc*HWx{2;X9LpAF|bOyPkY))
z^GGeS2-@A*!P6qeedc$6B$|w_D53LuQiRmTQPCDFC0<hNHJ|UlIk^2G*o?Xw7|-;+
z+6IkGg{=Uoow@=a8jx#kah2397q;7T{D$hJF7)%gurwh<w<huk6df+Y;$+{up6@f3
z+^#SkIU5JTP(D+w3m788sgY&$J@qz%XJ<T^qpgcq2W|XZ-s`ZWDb~MT9>`i6ZGyA#
z4z`ubzH%C`L?&QD<8p>uo(%9*55DdC{b04n|Hkl)F8^?Yf<4Mq&)=TL*%ynVCIX2D
zd*BqJU4pX6cibFvwS8e0x9I{$VY?FD7H?=vPutpBAwG7MPX(J1M15f^Tr92Iz+OIB
zT>r4ur1hEhh%{Ko*qIApn1Dr~rI{Fd;^)1TI5cth8sxbxV-c5`mG*w<wL4<AqJPfV
z-%=O;t^)`a=02wJU2#(9xko>;QO%DmwqE*u&*|4+$G*Aa>WnImlpcMT1$pF<sm5e1
z6?I5`xjxl!+s{K&3UOHeyw)D{q!T3b0AKivfL+efol3FZbR@bhynV!H+ToeTnp*jH
z7l5FwrF5*r58h9-Sh%~3-SmMu=^!R^q(tlpW<p_%vgP@SrX#O^P-qj)$4pVnZpnKl
zVIOl1F@syfTCd?-_l8H09Cz(fMx>3!>}&3IC5U04$o%qSjoTTCT$jRd45%<&q=@5h
z+tyO^m#Uc}PLb8EZ;l)<&ssKh>9t+clMm=JM@&hJIZxqJYdjx1tTyx0cSp?IV-f*R
zf@g<jky?b(k89?%(c(oub5?qQDd)<Z-xG7Cy&*t)RL;BgCAL9iLN|1~|7n+4)GzR!
zQ3%EIsP(n@Kg2#iDTtjDiWQmES_K_91w7m|vD1(sbL@S0=fiKxGT|Q9e*^B<d5DL{
z30tYBm)wr%jJ#d+t;$H%Fs*Rn<Kt6*BczjM_45$3(EWzB<r$Xj@)v=Y|BeU>N%jQc
zG8xE;rYI?^1m=ed!YQMLp(d<G^384U{Y7ewXIqn+2x)8N4akgYb!eGru>&zZy8O&&
ziY_U{*gtt<ZAp(ErPlraT}7~e=Nxs+(}<_4m`|l9-Qsub?k$3Q8|mTpdU1`_jt7ZQ
zd}9Ldv|<TDpzT(<5*4v(R&goGIT)N@knCf%ja2z;h>l*?zdjMRU=6ia-O%?6MXKl+
zUlWL@Fx9>lGaUWbz;*z#Q;=cD@s-|Ky&Uay85j9{!wVQ#Z7Q_XGM|A0@c0UCw=Qli
z3vrc~rU{Nz5V4ZrvUhSmM8eoFD+1OvwUVw`o4d^DBiNbJVEp#x^5PbW$BAjzGWnz_
z285>I#E1E3Yp8w^p~ZS^unYaEZS&~|0&7+!npYzl31w5Hn8sQdIuM@sbM%&{V52Ll
zK$%^br41zZ=cndLIr04p)LMr-RB45F5hh!u#Paa>zh2Hl&=f-Da|heE$FnW(LoQ&S
zmR|98B&mmUl4fB<q-<LzWon76G9=(kLO4J;g*T$MleZ$WAx=#d%FBpkB#Z$Xb2?ck
zF(+z(#RdU)i6EOIec0C?NkU$X_&_M+2-Tl8!2{YECV&v*wo~vua%o94B$Da-0?R*N
zq1{B~)(p=0Fexh)WRL1y9VqI=vH8LUbFO|6nlb(oF|=&D^)j(V^&vordtxGVCGa9$
z(}L&a@D7$x4kFXSXal=m&Y8V3(C_Y#XiXsy06lD^nRhGMTb^o?@wAAXfnmSv{b?pX
z-f5$=+kmKDXcGdyV?L7o>ttFmDM2G?+3)ft((Oa|!Em4Yq@G^YugD29+aQS@GA}3N
zqD0V8B=;pPe<Ti3b|^udv2#UKY-(PJQa!j|zz5mX+X2xus8CkAg)*r&AM0v7*l=L&
zkA!0Jz3Aw4&p5nl^l8|wQg-lB``R$IC6mEAJkLB%SXWL6j~M$B7n(@sFfAf5JryH9
zCn*3MATobEZohB(E40>Wp4=m>6$f687Md&F;>Y;}e<J@j-V_T{KuQYl^=>uG=jH(^
zJ=uKxGIec^tYCi;q+6YLbY>Q)Gl8-BZXf2s6=Gkv{if3bq_ZVcogagtKK-59$3m07
z_Je6bHx91+s8z9ozPD0aDHfXxq}{!3{<k1Fs6?=4x4qwc<J)bSCh<eBjm1uDa6WGZ
z;)%nH`nTON{;5`4`uAJgOkN*|#mTIFF7?3nZr)n3mf)da?fZ2mx^ePRPoPxKih79q
zPn?)6R>B3TO`~B@=OqHX&>_-a1CVOKt_0HV4iU3x;dKx+nld{(Mx3HSxW|nIXlG0!
zwd0CwEH#m*LyuFF27M>Rr+e!@s-4Mp{>xi?aDV0{Pg>-SpyM^iIhm>CCbbI%plt=n
zqMj$g2tD1rQ!>Q^k}-x{l?4-FrQTMQ2KP0ns+M8*foFc_(Pqww4W{3?=~%PX-j)+@
z>ueobhJYF%$yhFgoGH_JTZmceSmT1Kc1QnumzbAr6FVHkb5WLHFZZqj;4R#Y;?#(H
zHrCO2*n`7-V;TOY1QZcoEw5u=V+EqxdXHscuiAmeQAqq@0Mn8bpeS8<zmjBraUBpH
z{>{iAa2Iny<Y{<<E%^>=yb@qg@h3AiHysQCuX9M0I+QQ3FRD@s1Bu>xvip-M*)E7u
zxkSuC9@lo$hpbXMyp@5WcN+%L*&onrlqui_i%ffqG973Yn1DzFcfN~)$nC(!lISQ8
z6Nw)(j6y_}M85N~E=U?;q(j%Xc8h>L2YdGry;V+HJ7aW>+zR<G^5)h@8>Fbn>Sy4o
zf^!l=06^!bRT-_K7-Ybwym+OMfdcvaZZpsU_;C}=oO~9cs5Zjosz0@70dspB+}%u2
zb@q{{x%1MqR_)hRGG+NXA&yX2Dr5*iYOvwovek;IE{+*mykhY-m<G}x9H~tkKGcE0
z<0BP~Ila51FR6bD8u1xYKQ!}F#MF{j;{}BdZtDcX93~8vvthIzSA<i#j#^_;v<e%v
z#zq$xE=SP@P>AGcbl8&$8zeaek_hnu_!!H!S{}2$WuL$xQ%FXMj~x6^GwwxsSzX&Q
zVz_%9O((ImDj9C#YzW+0n_OSO``_avgARL)Jn=w?*v7-J6_2kd!~`ajDruNf?BJ|K
zrtYok<Q^$2ruIf0LbS$w`Sl>F6?v|9$8QX+kzjWs{sQKZU(>^|0@)#4($&k*W<(uz
zunw{ufys}fR`0cpI0}V>C}o1I)g5KXsJ0!AI2@|-qM0j{EPnG(JKug~O}4iL<#AL~
z5t#W!Qf~kx#|t8(sEv@n;(K_pEMzIf|04I?RmI*>1%PX?C~EJ`ss>P20mqrSCc8*0
zqBT}4qTU#kf%R&{{S9RW$-rtlS@8<dU?Y(v!YIp-Exu+2yCyTksz8#jme}8Kn}OKL
z<R}4ktyE>*YFFLvwKOLAePny_Ga7h$xEj^NLE2EJ7ZJJ8Gks4|GFet4Hsqr%Qoeu!
zVuf2%)c3l`Czz-|GKWE6$`QYm|By$JrGB1JoB1VwlnO8#l((fP`1FfD0V71&;Fepf
zxsL68)rL55oojj~;>-=oW6Gi3jBZk(6<;ZzA2~Bpp6P++N^bThUH^<o1@)1=Hh7Tx
z8qTPTbc;TwgQa}O8>y#A3T5oXl={@xS~BiNTiy;n)dNGRnY3T4-mOyD1u~?3SrTKw
z(0bRrUtg`bOv^s1imvKTaI$|y*JIg*Uo}U1Ra+G49=BN7#tdA&wY{KLS9;VkQwT4z
z;E&<oH<?aRUI^{9=F~VW`W9&L5x~m&lo;sYQKK`q79SuTrcm`PEUTygD6LeLV@sd^
z^keHw#iVw6U5*c4Krc(u;<_JfU+OiUJTV*R(8ywg?p$Vk_ru@gy*4{c(r~SgPTfvq
zB-+iP*|{m%S@O^wp;gI?v>7I(Nw`u9iSu@eKFd~EDJ|c2G7$)9P|U+LdL%eDK|6#*
zFVde(PI|l&Uzp|2i{8Edgs5<<6QU6(D*fs%4ExMkJ$9?xld!s~58Kz)$}juEKV+Z_
z>YgGl!hVF%b9BLOByeT_@>Y2A{g~(8_B-y{AODYL#ZL|5PYUA)JmzCJp#9bR-~ao6
z5jFqK+Wus0?x8*sCH*${w@a6M5(IR1u>5}>%1Q1~K~MiU2JYXD{97gS5smsIcujD{
z1~yyf>{UWLL4)Cc#4|ZmUs|Ew1+PlqQ`QpNjA$dX1qia_BKN8a4zpVWny7gqz0uy*
z@rC+c{3_fVPos}nH${svF+UXXPHLR270v^?OH?x;jdsSgj>M3FhY_fswyAwyxpsD-
zR_ySWookZ+KIvaVoD|4(s>{!4oy!r<ms+d8#?(!h-HVmOUN6e9l4ne|@2HVTZhptq
zQQeBlci^$Xh-t>Ds;lh}g>KsZLBAi#W7r5z>qUDi3>)-QjfeUTHc|)b-xZF#Yf@KS
z!MuMC`qyQ}i{^en*wop$9`j@WU0w3`X#Cg}lNy9Y!}JSqkvYiKe&MKLCp%siRHW<h
z`Lr9L5td1RYg}=O+9FzkWliY{&AONjN=UXYQ&GGfY+vmj<aOHUd6Z(Uu{u<bymIyp
zNc&h!kIkX~UvKrn2+IbSrz9#v#OU6}E7VU1N#7{Rr>rS4LqeZ=Vgc$9|I*e52s$Vy
zKN07syD87LvXpO9-Vt5YAWf0=B-wCx5);veHf?Y=DJsa<2@3y#klqyzbpotc)RDRW
z>BMRt&ruUylaLlelrIZ{qOI#pHYq0V?#Pb)9YRJF|AUt|hRJGNgx&loEtXqJLt2(}
zPK}t7odBb(jVW&?<%5fWoE>PL@K<t4+!pp$Zai1Cgc%BvU-&y}Uu6Z#i1Bq|30@C)
zEtV;@K#y@kL|XipQ!}L4Mqptjdl|e_TpA;3l=3udaCkDw%nVDeoU5!8Dtb5yP>efm
zn6Bive=GT1SaD{S#Jyy^m==n5TNzWR=J@1~eOz1>%C7p_DjLE=^@JyNqDEtA`l{(5
z3Jcns`kOp6b9r%P0puM?RW$J{hhI3(ON3u`^Qy`zO$_KpMh;l1Nm)z31A-yrGEb4u
zIy1#Nmf%!T+-hMx`x8Qv)YL}2&4C<$15o@fsG^4s;t-p3q{XTWYz7Vue+0&n$=FGI
zPFN|@B^pQ{sPa>*SQG4P{hs?6+R*;3;bL>T*-h{%{1ite35wP4U8h#mi9n|Z0NWeI
zXcL7aRH>9RNe0)metdzDf`Mh;vGwvZvg-(HDt=h=ntbz1n@lh^``dVsZdE}ovcbI(
zRE(|3y+!sWq>)FavG>45O~B7;P|P-;d>g$YykJyt3@<gK2P9w5sV`<kv^uKM=7OpV
zE^LxiPCH5EiYIaavgfQP>P*J49gD`PI)LNd)<k6-W#H4|N@H;_hWQiFX35`LSUrWC
zk}{KNMLCPOc(h`3(tiS~gf%$`*xodUk=fq3=*!Q1Uw771!_REUonq5-n^cvR!Ti3W
zT}{+g@udTrFb>jiPVVTmHba`4{Pa|#)I9?$$_chiYS;m@iQl+*(q(dJ2L?tzW46AR
zI{LO5vnd^CNH^i|$_7CeQ*lO_H%78EN<;G+NUHx)9<K(ZCR81(H1$cY<_fVh*fsx9
z=fX=I<W4+VHYnE^tqLZUit9=(lWGE?xEg3%7x*Q2#MWMNl7T7A9rvtGQzKGVk$h@{
zqzeeRuYD7;1!O{>cUtvc=b38^rf#`!E9@q+)nqhZ|M)tSyHtdlK`9eaN(v@f^TtQ4
z%}t1bwFSb%aacA)O<6QL7>xKyS*?RjRI|jDiI>eCS(QEF6Dcy0CL?NTD?=5)+)t<u
z$c7GyU8;1)SW)DMU0nruvOf3{Z3V1qlD{0Wyx3{0G09mlK}I=>xUCs6p7@_eZp7Yf
z9eA@L<g7QRHheS_``7-POP=R24~;rnt(N2jJoCmw{ZuUQzc!x4;Ef|s^M)tMh`B`)
zq{LST;3%2Z2?c=|;%XzE=jMe{$muC}iAzCj#LMv>GhiHE8JBl&Vw)sxdSZ%f{P}M4
z-yPOMPoa*mi88r55c{Z9DLt#G?u9+4+*=m}bOS63)X9_MxUEi}5@c1qjaqyLzft<I
zcTNohgQci>yS0bwK5?cE7|V3{oNT%cw-{^v3DAYqu!q<8+meOf`#HX2MM{BM9@NSu
zy`5<d*LcxYPe54}JyT>qqO1<@NLlo=4_{`ycwMba2ds&)lZkkhG{}YH$+K*V$efNa
zURaZT8;<;;$JX02-MF&~2>gnq3LK7*%dM`lzsKWc--&9T@`(Atbh)(~I0DbF8X-Pi
znfkg)^7Ne!PE5q76NCVt7NEEh1xu5fNkh`LPM;~4OCLIefVViqZQCQlSjA@|4UcuT
zt;temwL2g;_i0$`7GcVCYc401*$<9AI7o`vy2Qckf$8*xfDNxXDE&c$W&6*j<P6gO
zEXHnYv*)**zpC}{U0M)Ui`%mT^Vn}95W7QnKXxBXD!R@jG8WoiTMi)64rKGPW8wD8
zgM>Kg1HQ{V%)SR!xrYbr;BN)i<`Kp`RG6(lnZH_c_72Wrn`BLlU3%mMqy|>3H{?ht
zY|2|{R@!i`1kZzY7K3^YeA0fYM7*}IJ~=*!tVFP|mxy5iZ-CPiQHK$e2el8v1LgQ*
z)%*bfkJ6b2!30Y0-<?e2$w0$&CpSj^!@ovzi6ae&&IOv+GgzW}CZiQl#@`UPPLV~1
zCzbRm_t@LCm|t0ZX{e*xX6Z^$;bgG6JnIVHX}5ObqShKO`Z+&Y=^E&hbsI#rN@2c<
zE9!G+{7HH9mCUGO8`8P#P;T;9<9#kIKG)PqjP!d67f33`S~29C$UC=(XiJ$VlLg%U
zgZdcj%GVv60xi)`GK-Re46BL_F69%$rL*RQ`owTKu_6}5%-(CCN`X~oBYmrepe6Ac
z&w9@ZN^AG6flP9dDbH*4>ZzZR&T2Z3)^M}^^pDn3g3^TigYDubN;fxLSI4rZe>(!)
zE@INwo4-(;pr;N}(`EBV1!57x2mu(2<UY4~{Upqo13lq9V=Gp{bN5%@n{;J({2rO*
z1|_5TKhWA8GwKZi&|?ko%ePn9>cemF=-*nJ@keIfh5C4C_N*~P!N=3yobK4&WF_Y7
zW+c0U6{JnNc~rYAI&W{h0_pYVjhxRr`Iv-ir`NtJcG2Ff>Ex#qmgu$`IW!eIwZB=N
zhU)b{Jfv@JaN72G&p^?&=DRi&ejFhvMi?}44v!atEEf@Xk1n`lkMDnDg=j+}shM9m
zSxn#EjE5`dz9i+YyglXuk?>64ei~aa{Yi-dOQL&Emc3)c_Ac!+9x!=4sGaz6h16!9
zfYA3h6uVRR>nUpA_Sml@;JG_JprmkU7U;2s%W0dneAqJS@koMe<w7}#5Q+l~c<>Z-
z(SHRtAJX5UA-~^hmmTWF`SN94jwzS95RH%7`a2(SMKA19u!6#?v5ES`V%-C;h!hT(
z31tCnIhN{xK685R^N9S^1(O!C4UI+hU!&xT4F3PQTRsS3JA$S(J$RHWL+IPF{6>?O
z5Fj$TV$#+`vS>9|xv?KI6X;SKgO1LWoh$i{*joe<Wp}jR=MB4__R)pLS7w`C=2^5c
zsIA=(FR*4^bo45M?^p6nB~Gx5chIrHNADNkpZ1Fp@c0tR1@-a<dyq>B&gl2}a!HL9
zPl8S@4Tbo1uV#|8+VSVE3x#a_5=$SJYup9Cev2}5lZ60$+kv8;7t_Y{@<4SS(3w{G
zP=1LToIT&s2Tw4X)|~1jrprNXCcmXF2*ifH2{O67<L=ybrtzG0rpP8%ZwfH-X55u4
z>@IY$yLe{?^E7Z|m3hSxd4&p~nJJe40OE**GFi{D+y-PYS-4Y50~^>PL6eeVYS*$W
zAn($gtH1G-aKX|YV^M%l7hkIFPEpwEUxOG{UYy_La1>k;USwa~*;p~HXP6LAN`0>S
zDvtF=5(j4~Slx~p=&aO+rkHXvG|p9i>}j_u?6!Jep=(@_C8%6p&v!fA?4?pw=|x~T
zrYO$@AKwF?U%iflT=B#{+6d2ZIOBT*#T`jTQmJP{jjXgdocor#+3SlR$X|9$LlALB
zr?&GoYs4c93=+)Ew1-#X`f*r{k&qF6(zt888+0`i4qw5?m5~I@;g@t@g;!O#<7%YD
z!N$X0W4q_$<(b{?;Y*MBg!r1hLHoQOs%4CCq7rZ7>|$CVg;=Jk^VL`Sd@vpHpN%Zb
z$^b<vNGTa@nc0-N3%$R+^G#R#fq7p`Y%as9yL85Ya)+=s({aD(12$tvBK*)Xld!ow
zAQ1m5r}CKwHTY5GQ~CjY6|Dr$SL3ze%~(azcNjS~w(`S?wn@hZ3kyM9yeUjC2}<5i
z{jd<cb4iksOs2r0jS-3r93w%)msYNTmC)_w2Htv4cCx!Z4vNWNqfvBL6u^fi;sSP7
zGlT*vZ!FRfAYHJ9`$Thwhdr$hh-@U_gAoIBJVU3$KBS%5qd;eExCICP@y#JFR-v<R
zl8Ss~|KzOaP9;MOtkRNYCV5vCRkC8kmH-`6vmJ4afX5vJ6TVV1Vn50tzN8tXuV$Zs
zNJ&B-e_Jsi2L4Rj#I+f^9b8?3=<nfZaP^108h*fI4gz>id*2Njs@rx+MlJmH=(Z(*
zpoQMm&m7#2+GH(z2p!YwPQf~89c{)*5VRPpBoM55nv!FWNEZE&Ejh~5KM!K2dwyra
z$k52&*FTP_6yav|2^jW;+<d$L?Jz68vu%CTVKT@U%>`~0B`tdakO~o^KYwE*V#$Ji
zJH7rBKwZJ}_LD2+4+EY$VQ+yYKaX1lC0!k;g|k9PXaENSaA)L*XU8nQ!17k#Sdb3Q
zWT7ZgQ6XHaJs$pI=6A??yxUJnzf*JxotW~!n~iQ2h)MoSPp)Nktczb1FLf-#3Us-3
z3jeU%G`2<{eQQO2w4DET|4Wvfg8o}@I8@+}tFXit;>FzwWf(TR?0$Wm+%n!ckOj2l
zbwD>iP`}ikM&$7qFV5LT5Xk037j?7bc*OO<S~tSg6ez!bm`90%l9Uc9b*%%`Q%%hE
zezLwgKU5dDKN^q|u*Q+SFaYbcI|6mBIED)?>nP`)P?MCe(BY>)z2s_PZqdbpa-Omf
zp`(p_1cTR2$+?<xe*5>p(=3>eZ%M<gRYp<QV$c^vf@nq#!L9U}9xJgN!qd9BW#Li2
zNZoIq0W=YwC{OKF84sk?Khn!%+F?lZIm?DlUIeXTl==1tub+Z<o`$rI576UHwxlA;
z0DA)h;F;b~a2~ZWPRb~l*GQi3JzNB<%JA@<YGa<MRVR;Gv8qaYqRWV~1my^5Xl#Re
zuDd(%m$%_)IWoX%XsPaLjmYi}{DEAZd64Jsy7~Z1dVS{G^h%ib)MZy5hi)dgGnH1(
z7ptDr=A0G+S~8DdA!C^1JW>3}HzEqMvEYGLAlRDt${ZWE)AgFo4UVT|mf&*SFOsCl
zbZXwnml>A^1r5bhH$1HR=}cB4X!9JPZ=0C*J-wAv(YfT;W-zxmTce~7(}u76U`xcs
zx+Zlrh>3pd8++E@q-d(#6jQcPQ-Wz^#<24S)rt0Vm39HtNDz7<23Z_x&;y6jb^TYI
z&(?`_ePm=KBbgc)@Xm8t9MLor*+;f{xm=|ZrKy>bW8LO3S=VKw@%e-bMB^P}F39pX
zo&BLkG^s~+Z&wQO`7wml-f)ka?}QZeNCctM@by~5c8iT1%GM>!H*79Tj(>+^<2=73
zV(%$tz{ww^8pF>0p+@Ib<7ij7IJ69Mk16DP@kw~pMB6}<dpbHF*NOBR)W`7^$tmg+
zr8drId`VrI8tL%MA`rCRQcorwf!Mxsei#_aqZw36S?+#dQfi$rhV2;LNlImZ@OqLN
z`sWR90ll~gzI>l{uz`$IY2(h}WbsU>`I|t{qPdXotz6&aM!pZPKP=Aal`m0Tt$)(6
zCXtGlE>ae+Og+HIeD>xmAFbZuSl2oqFg!tfdCG0-A8SXF$JZgB0?QT9gopOn)4ZO;
z<DMEsV((GY9p&&Ou9y-@C~{7mFEykD<^5FmqG)T*%G)KKqk5TYE`^S=5nxUQ81BL3
z<JGv=TS}zW{t|Fooucrt5L!a3&5gU7j5~ii3u2S0dHja|as3a&Uy9u7#5WxFRhD5r
z|MEiSwEO#lg0uaj!3T?(J^n}={#J)4t_;#S{_c+dnJIpLN_~A$-HUqgw0Be}5x(|P
zJ7+LqBz?pBB{|>rMRb6AnnsqCMyzub!{KE`4L$lD)PQ#>*>|T`%`4)WM^(6EGAir$
z9G#rx=-W?d%T^{;{2h&&Mcq+<(&8L19D)jmcudqn7~P!}t+)R~O7{dxo47k@M0s^B
zI~VN2MAKEQ)HB-QPZC<GH?i}ojs>{G7EYN6ykZ_R(9O%+3Joq*NEEQ4Wb}t=%|mli
zLdP9sU2_1mg*>q_KXZgDJIrFnro5+iZjNtXer3ilARhEc!!2yDcsZchFnT!&^uC`)
z(HZ1T_1fjUA1Fj6ljCGU>1d-;fC5Eoh6R+MhX^H2719FtWb!;8ysD`&oCkh-N@x<3
z(n{3)p_sc?tn)o#gq*;OY}O`pBOKDVJdoN*z`r6nMVo0QKnmDrj5}1-NV>WWe@#A|
zoyA%{F~4UD<Dq{++G~Diwd)opn0Z9o?ft1Qc+$YY6L6@Gb@y~487Vx3;xyi<Z($k|
ztq)ea$n8>P)unL>GFbovR6<wGmAg+-^D^etc4z0KFw(T(8YGmcxJ;kAm;`0`_Gc#1
zi|VM=5*_kNHJWwxF1Jo}%R)ep#34~durt1_N((0CeRyIrU1PE`bH;_!aWCdlEh0mj
zCrVzM^S5a<Q15>~;|bnQ%Q~PPhERQ;p}W@H{pmb7Ph8E41XW%)N;KbMs450$e2uK|
z5N;vgNtF<z=n5sM8w-f=BGyFxIhh;9^t@-wQ15t^MtP-TBkrb?4i~=%O5{~WClC85
z&p$Au({+j(A|S9GV!<-J?M*r@d+J82AN!4h=;C$Tq&7V${+LZxxifr?3-_M_|7o9D
zbRWB6`>TY;hkbNxK*r)l(3)p!)zv0wMI{);&a!|Z86lsid=H178(WfM@@DFuiW3J~
zaQ;Js3QH~N84w+e<>`6->lY^ZwimK}n~?4bLi$fc<`CPrXVzjB$77Jq&$-eIG8PA=
zdypiZtZ`;sGT9ASD@5-p>zP2{6YgvB-uo*mI;UAD+&;@b5G|r7yYD$ln=cymHW^EW
zdIX~V&P}jfok#cw)v#aZ?e*y?byNV+r!bQJxSA0d9m&goOl#>edYbZv;jqTl6%z`L
z{84&c^$8{X$gce3s`60p>U~E0b~x?fDn5utW?$G*taPUJL_GvbOmB;DoE?!L(LWv%
z;nFGplGS!s^yl|z>Y+>qXZfn`|J$LPv$0ue!vmVGcQ4D*K*O0NGR1J4)^Cw;^Cuur
zX9Y|dekFllZQM%a6b{F0HX+Vu!U<srRpfX|<^EJJUh4J7d#f9oE-DdF5^cv1e}a7H
z;&)z(xo=*`tDd<~ZZxS|j%tl<xrCxN0&8SiCYPN!VH0lM*xQi}WNRt2@LrVccblk;
zF5Jy~T_*9?CG|0QTf0oqdV7-`8ku$%aFq4{-)79GG+!$NO4XG3YNrQr_a(H^|Di_|
zJrv7=tFja5n~m-_r{cN{#Z%awu4!#JL~&Zg_cc}R;JSO#Q?o$YUUYTht^eM*mgwDV
z3m;zh<@$0vL)LB@xY0~9QnHko9uT_Td<Wwut36XK&+3^vSMaNoNfrSzyTE;a(7W?l
zXZZ*>MJr6nfMPX1S`lS2;-5v-^Iu&Q8(p|;_<}+4L*fm#B-z7#?ziJ~Q*lv^o^?2y
zx($$eRkVZ}?3lW9C`wGurE4u{{HSLR)jK^^dd>EfG+J-_+G6CUq0)<q>Epb!jRVMb
zf=1D69zTWdFcDH&A$IFwoh_9U#^Bqj$9%qcy4R<JF2CxyuS6eof@6WSt(b)FcmqzC
zOP^MmNJhCo>E+FMXIO@#hC+SO#ds9?W+4(hqp6g;iTIV;<_im$kl{bXo$aQxB}F8x
zfh3R5Ltn3BEhQtF+1s5(91dB!-(9t<7BPbLE<E(U+ckMljA8auGN)A`uFfoWagXDb
zM$N0%5e}f3xrWv}v`_EZVs7J@&Q)E=JXWz=a}!jyKHznuGCJLfL(cOIZ$whVA&Kpw
z?ir1Q52F)G3ZAq3;kRZ?qAa-mGsrm+mzGHf22vfRyOH1-i773j<;%&up8x|Y%d33_
z3bDm`AY7E~-Cle_Rxc<ERmgD)Zbs-ck<zGzn$NS}C&-)f4IY?f+3gdCgVEk8V8^uD
zl=%MCNiCN_5Tv{HG=&Isap2HBS|$vu*yJChPJ!s(OcKu9{toTG&j^Z6`Y=a3&wYL`
zjz9sqew}BfN$Q)cO5DgwYZ^I*dFM<{LcN<9nwa7%EJ>ErpE>%BrhKvogUOpS9WbtE
zf<oiJvo-f69Gq9bz$sTiHo1g;p>LnnH1?)l1@ahRaJik~GZ@&|kG4=}KUCtEpbW=K
zd#0*d%RJcHP<1s$R6lgzBcU6HmA(Y6q$Yq+aBw4Le9t2zL!0D|Uyd~syEluJJ~sCB
z_{CfS=?8p`KWLujD0>7#bE?SVXgxdQ9LW7Sp$y)SD7@swyA1aDx{v0Gm>~(pU%?wz
zayWBS>Ekj@v5DCuN#hbyUUvT=t~wKY-gtE2a`x{{D>VsJjumh;;cIp!Z@RXlGBreD
zyYA#tG5!nv`>fReHMINXL(ZMd(5X1FT0*tKbQl)9uc=TmM2oDhXtU(-?T==Ky0>dO
z9^87&=$Rf4Q<5+%@nzV8F4+piOBg*Z0;(|)&inT?2<ru#SlN4l3xCT|T)YFf<S&%s
zont7|23u6C<odrg)=+tjg)~}mbv&>Y$;&@}3)a`bpA4=)l#-R&3FyIOfyBI2pKdHx
zul!NAXeSzI>Qb=5YyIRwpb@<}#4;87kDuRdwz$2(4zUz-TxR?C;LenopBz<AR}1+m
z^ZWbasA(qBiu)&Q=J+A?RBB2fgv{>pd&?AExLFw(7^`Tr5tE{~mYj}EYgCeI*jsRp
zIDN+VE$iXJer?S*YqU{1a^Jht?L+H<_1(~xjI^PAtCW_jl1N$3!WFJoja;^SxbB=5
zS;KJhs=8y>iy{KPF}5Dg0yqrWn-=#+s@mYepiOV8(I`x<hY2TYp08h3B-Y5*{N4vF
zP?ZfwlmT1kdT9U`5oSjD-MJ^)f?@Dv9Z)$<G`sK11x@c<2}Y!W5b&pV7kN=n<Zr-T
zsa`u=8nQzD(Ty6&JlUou3BABktLDa5;McWn+3umK%=T<(g<d_-nw2rYYOz?HoOugU
z`tGUkuC8_t134c-KBtRnS(7YZik#DN=feKA&*qv8!uSB6%;Jj`4EhLg?+8X-FG_TC
z21$dK7GDRy=6j0o<Mb!;7j1H{JFy-eSDAM(6S_xOtl)UJBND#HHXxyIz!^<=VBks|
z!$4#_Tlf+`KX9WZh;$|PVu7#0keLvsqE$z|w@|5hOIJ~XjxVigPhN2+v()^KS0ykS
zH=g8TK2<|u8XWU@ePg2IsrmDF3_G(%v;%D^3@fc!W(6FS?~SCgA&tBJF_#+@k>TM%
za4k!e2sEc7M^n(~`+UiIivUJtl)K{%#89=)&e=FRjk_1BB75Q}AyT3#7|wXV?#v|i
zs_a6VK3rK1BMpN;DtUL2yyl0O96l|~UOyqUnbT)#g^<B{P^f>$3v0?S3Qqe=yTuRo
zm!_=G2v-O~4sp><%)oUaeJ#||6{l-;t@Xf$ND{hE%b7+ek$BScxWqKMY|+#?^1tE4
zF4`fVS$KoR?Ny_3d?cvx<X@wq8}T?m;%asc@kXuo0{Hy?!NK#0`SKZ<?zo4J`Y+Yk
ztzO?Tjln<#fy0f{<+R(%E0<=H_h;BD!8oeWLbNI*xck?dOkULC%hF#l-|or`Qqe~G
z6uP&;MkB7qmU83{!jWnB%*#WIH*Xh1l9RN(OZk|(D^T!ke+A462&yHh=O&rpRqlx7
z(&&Fw#IKP=(ur8?#q|_hxy8lCNkp$v(hMci2+fx=e#@)Rj`vEDm(rFk(W{1Q1T>S%
za##+n@r)PcaIi8xB%xMU)&Jk!d?}>qczR*0Dgo_BsGuherJG+;b8)$J?9@d?{|s1N
zjlUw>@^<E~%&7aV#B#6n=AgBRp-j4KgGNF-kTbg7Lrs-iv$U4&qUz=D!kR`^6-xD9
z<M`CF+d)o@NFga{WV2dAVpD6M@^0yU#K*<?PE}~$subW#RL0|el4z-XI7%bpB0rR}
zcvO1E|ElJj%~4GtVq$VyR(AA$;MCQ2aCiFgS|1ouJUXs)8rAkuN2qO<<o7#8TH)nl
zM0j;tZvW!2O5D7oEha0atS%>1I&-u-y7T>wNYLuEj7A+v@u_XK+qFx$@M5pB>}>b(
z(>bO@VN!J;Fe0&kZv6C6{UoAojZag;r~5JWz;<|4uEPhRx^d1x<DmFa`CQ58qbfuq
z<7Cw9-Pr=}Q`^@l1&wYzcR%`KxYBHn3eiZc3pS|bRXW}wD?WK}b+i>9Y``UDH8+nA
z?YiK6bRME)#+OMAy_dbyr<bMAmsFL_+O(Z)g_D+9(06>;$8erH!C981cbjIMoD{N_
zU6@8`G*;~t!c{3ru<L^z=fg?8RCR^NKkPfZM(0@-)uzXv5OMGT+{5+X%5?B6*bX%=
zZ&c<HZWWJT#|55+@8M+J)PCorj4tdvz0};PM`hh4oeV#fm%ZVobP}^Jk5ybFz^J$K
zB(J9gg{%D|I_djb9#V$ec$)X@@S!yy(2J<1ZaFs*@j&nAf~c$N>+efr#ol$e??9iF
zp_e<lIkdZ9i*;A}(ZzFdXEiFA@~5x^Iq|{&T0n6F`u!IXN=XIo_cCs+_t%Fc0R774
z)2jy+k?z5&0#>bB9q3h$@gN}ZSC<KvK%2+e%exGwtpHi}j^Ok4d$pj6$IFLa%5UMx
z%$j%Bv<*_#smD5nJgzLG)s%X}J7QGeeA_{LL|hhaZ_?M&lhZIEDVC_0aWKiQK(}{o
zB1R~~pI|@Vw|1*{PH!WX3<G}Qu;`ZFEN#y4B%NEo=CJs;>p7EEb(Q|!urN~IN8@SB
zZEJV8psRf`6L0rUd8iZq@B4-S-kSYa-MpM6%iTD=7X<ZWXlm|IkkJJTi=z`{6pY}#
zO{Tx*I3;-omV_#hj{sCYZS&E&)1&-9xc}1HNqtVo*^r_ZoOR%Nsk(ki%|b50P*LKd
z(N`>MC7Wd{1F~ZpT_`wJ8a(d&d@|mt&Gsk1U9Gy!pRQb37%81Ydy}@WZFO&Ehi|hN
zqZp5VG_BXC|AV~$r1$?|hU(H;`%iU$ua?Z!@&D+)E*f3c`ZqYg^^t?BNPBrPdV2k#
z5S}RL@}C~yf8lih8sPiMF9jj;Jcz&Q!b^-e=P3jfT+s5mjYP2i`RI~?0C;{|f;Wit
zXSz+qJV>}DJI<nuzbM<iAWGrerC88}r<7NAq!~QvI8Fz}zMc#76lhZo&5}32MC@La
zX+g_a)aTJiu<RIH4m*$XsAy{tc}Yk(Cq3~=#h%f8pd=TNd-PUXS17_{*-5YH8RU=?
z<i)b4NxSx~;g9TQX^1E1*Wtc&+mFe@lp>ISco!8oBqjd!(<QqD0kWluqok!?PRA%e
zg-v~~0AWk%lMghe=59>IC!7yv)G5xPjXueKuh{rs>fcH}B9qIw>FDUk$v&giESCNF
zi{>hu>YPw^dX(c;W`n+NV+aaqDHt2t2G?GO?8)HHRdg?@6vj66s=G#~9+ykXN|NZ+
z#l2;LO!TFpII+;R!bK?yKAy<?9t>&6Mhk<N<9$x5HzJS@nmDkj6OB!Z<1;>@&D5|h
zSt@X7>%+WnySs@*4fKgCmc)(81&LpB3_pU5`Uh4BwN<tul<HF{$>Y-W9xbtAtB5hy
zbg7~uD2W9X;-Nn#F>F+LJ9PdsP0^4`)j?D6)1PJuYUd*cRWK;A{CS%PzHhF35=`^r
zkGV)HB+qsGRcn8^aUKg+`8GoTN`T%Nm;tV^2ex?M#T8tX2WcJHjQq8g`khi+5X1Lk
zXXQRlnEzN1hbD+XG+b5u!NYN<XA{;}6wk!}tWVO~LtYN$p+&vM=a{b9JNRzrHr4$=
z7k{GgCLo>Aa<>&ufz{cSfSg|>3!X%cH?q5$Z<%a)+vFfP+9r4=G-_L6ahho6llL@m
z#hEIZ)n9ouVKrHRlahEML3?b^9!+oZkx*V|i&r>!{TWx2o2({Fu#Us#L6emDfX#bj
zH7=^W;Bg^f8Wv~=qr18|)C@|A72w}Lo3O#>@(5?420M?z*i5HIk6Uh;7mx^?V6M8I
z?X(qiR{*M!&Rr*A3dxSS%3hr(vXc`t{jMtrdks`e^`*}GwRJiPBKq1$<Sd~K^W+uC
z2;>8`Xh*i~-BH!NvuaUyC<$CZzNOtv+g~IYgA=qL?^~v7ZwqZAEa%E_LK-!3ktguC
zo1?lNntxv3MKCoWkem6fhiD#%I^A9qzyXu6xglqc-@tR#--Qi4`A)ladRis4Ov2=$
ztzuACIFJhgl2klmX*%l<c)SleBD9h-?awQg!_9M2>WO{UnTd~@%8b8g^RquXMd0XZ
zAp-b^{ZL2x2kNq~E}G<o>;n(tCsdc5<}K7`KbAx0yS<vPZ?dI&YJ?FzZ%=;8DR=={
zcDX%Nv2&4s^$k$}7SAI*vUnsk&K&+B9o{h-etF~ulpk8kd`85TKGELJ<Z=)Tdq%J?
z(~0mD;7IqSOEQ<7Bcv=u$tVK3nqV;l=8Y76fj40t4*qz|CS#ZY3e`%L6jQ|hOJ^qv
zLF$kM?WWof&jA@i$o3wdIal_*jY?WkK1_0{KhZ1W{qA~_qZMK!0&Q1bt_1C}3#%eJ
z!?0^Xot%l^Tu!&mt%OHA?VBoJ<Lq@M(iag2el9S&pvlO{1Ygp}1coXof{cVS3=%9Z
z)ce*ZF&*D**Le4@ci@fS?b?u!MNYwu;p?r57N?{zR_Kwf_8{}pLy~QNcY9l+#T|Gu
zwQ|NR!6VVA-$3+YiF&I;!YcCPnIB7P!-oxuzz)NLEDaJuBPu>J_SFZ_|KaMKf-`Nx
zF#S*LWa2NjZD*n{w(VqM+qP}nwr$(S#CA5jRa=XrK6;-%=&F9Zy07QHoYbJ2*F}Uk
zIVRU_TFj2Mg&=LrA>S459_YyF`2XYOZjVfpggvqJR&NPg2th>2cvq#ahu#+GJ}u-&
zUQ539*5TRx0Zf+b`gB`Ge-7X%pPsx6cjlun0!|zskEl<=pF&ph9Bk&!_*0|9e2J*W
zwbJoUmaskV_VlrB8Tg7lT2gO!;*&e{fzHPCelwaiQ4J;dPY`XJ(VoJ2lPBMer|Yis
zPP=mLEtP&vohen=E47Y8HO5M&((hjdw_`lxk=IYKj7ETx4@5jB*t5|^X9P>FHvl=x
zwm#HX)ze=?{eRI?v_Ea(WV+JFFW+)=6gO<s)srudM7lmM;bc`C2cM3_0|>1QrBQ1=
z%6>oEm}n>QE3H?0$59`Vr*=H)RHiOO$`x7r%QEOGEU-9vAJEU^Dl+Elob>f?3D~cQ
z6ROHnjzg64qU-$Y%&GKon1qAX&L_<eji<c;27G;>vPJOs(0k|o-H&aoh`rDpX}I5g
zY@${CS2Du1z1CEGIp3Emj3*;owY8EHngM0n%hI8U@y~C%*6C@ug#}z&z4q%dKSWc^
z(DTyi)$1QwC)T6hMWvbAB+kg4V!iE0-D&#X$v>dD5=X}M(d=2lugN%``-J1_l7m^w
z#)g#4v)^`t?M7na<Jwm`oTM-x_|$PtJ2a^R#O?ECPQ*MUJ@3;w7#66m%5}s`7C)}s
zKHq*G!lQVtXLzB=zx?+s1;H#MOrLx!DD^?Hqp6D%W|>{(`84<`(GJQQEEUvlS26bW
zgOPK@Bl1InW4*?jWAv=~3KROCoHj_D<lP4bt|VM!OKA=EYyui}p6^rE7Xcr34NJd&
z?oyizjD5BBK(Ulu_FAfX478hA1B#`F6gV@i+LFxXn#)~xU`V<0gwz(yH~HHx`DOe>
zEAl%}#D#0J`Z}{(#$88y_zJJ*;yF>c<vZM^FJP<}z6-|kdq+MVgN{r;Fh~jH`Aqpi
zCc`<}o#>z7*Cn>UI8RC8cRM{?j-_pcUxp+L(P1`fC!t&JYa&2&mucZ|&1VxR@tX(+
z>0|RxK{MNt=rZb!W$NJbj6445FVPavtEO-XYB*m#a5ir;=~*M1P6zVm3g?kS_<#(>
z%!GpI0zciC7tNO;xRr6Zn=MPcSMsDX4u%qb8@3@)C-_Sm9((hJaG|exKCf}eGdZ2|
zm|zjND<8j{vt~}#4+`1GVoCledW}i$o8HwT8p@m;6l!t~9@-4rAMq!(4tV&q5`?RQ
zrR`Ye$aJ;hrjw{pPStvoBvZmBemAU72K9zZ42s@hh`?&D0DriJG`%g+O10+L&<yrk
zd-LIk8UDT7KA}y?@%XlCVGfih!vn1x=m-;e?dxCEJ|Acc;Y(#&MfD^Wr2~uG)i@mX
zfEDvcTgouwGt-Dr2lJtPLikl`Fca)RpMv<Jy5OewGED8IAWXaEo9x8RE%BLbMX}gO
z<qlPPk~RU0H|cbdf9_v2nNO^H;6C-S(DdTpw~7pWudD^CSuYW$Ue7D*Z{AX=+`%_T
zifWD5+YKm>mp6}3zsEa$>W_JPQZV^k5Ulqy$o)FmOZlyxk8O4n8t`_EPtI$^d>#V%
zJtj??&SneW#o9S`^k)N&Nxp<K1hrMOt^zPuxngOVN_0^U=vg0ee<KF{qRZZXDTD&-
ze@j^>ocL4!ivi(9F|{LrEt%+m0-tEFoHW#USv;Mbe3A%``u1P_#z1@V_g4^aP$4N~
zo(74K%eOg@p~ZKAxx)(=gEQN3V8PW;9+vC(eLaJ|_}Y`i<6_B`rNzKANkdW@f-IOX
zvE1>&p<Nm0fJN;CiUn+1!Ug>?A)oN!kgPaMfOcRdAg9Z;;Lb)=>Nt-CeDaN-lDopJ
z#S}nqZZ{>>40fazUCzu>ewJMx6w1rpBiGd3RN%W}=ExifG+?UiBx_@<-cInbs!Oej
zkm3#GSF%*&uZ`w!3GOQ~#RyEL=QS9iI4@~3wb?k}L_LZ{wD(Se?<A0}UGU?u<Nd4T
zfZG(hZDb{Q%|;qYbVsJKVonfn`{#~>M&+jPaY%G<{1GafB;(1`jAjyF-$6j1vCzS8
z5Bxm{xnwn}m^L=JD=4$bx-6wVYV3u*j)*<>zC@j0L3Q{xM@#c|i__!|U+AYy38c$L
zKCdU++m~na=|Rh?g>dE6)H9b0dtP?|Drfdv$rIIN@ry)z2(_!;=bpAvV7pw(KAZfq
zOnsspNth4JT1eYABtvrAH6^_I?O(tYo-Csbl>PQalGU^Wp%!m<_ARntPl&yoNmc(W
z)96DdcHkbvBe6`f$M=$l8=1TyhYdwjdHUH|p(?=m41X^NnSp8?7?}S7PLdweYD;U`
zCsQ0q0~qV5(W+=_o$X2Z3GZaBq71|zQ;;$wB+O8Uz-OXBv|&L4(;gC@Ey6G)#s(tZ
zQ(uc1VkMm4&T08`Wm|?<F-Omp<0sa<GSr(q04_e%1k5eICciEzD4K*e$BlWqaHz!e
z-pffTz`_$XD<!9Nc<nHbl~Bx#;{t0EuCk5yI`PM7snCWOK<w|wA*xI5lTeFE7%cAW
zhi5LyXS3o>pC`Cx>SqR6=kOKD>4B3Pldn+|;?eU3t+jM5GFL{^?I>8yoBN)GLR2&x
z3v#YL?27!aS#bLQvEG)6=Mdp0x)1F~DefxOaiRpbh}IBD3NGg#K^fYM)#xnFh8?_{
z9jtltqID(o6e!;z5KE>_1QhJtk5Lu{jy9B8>qcxMYdbzLhjKOs$Jj&|5MOtPMYi>l
zBXWFAJR46acq!=%2FQG#-s)hQ#QzHRXK}Vs8_MQT1j2v${s)PYC^+79Qlg9_xon_R
zQ=KZ#r82Ou)L$S0Rbf8%cJLgywd~h9D$LVgu5Y8(pG3=F$0{*=9Lu)(<xPfNZF-<s
zMGcy9UiaOvopw>7r*G*Wp)0+f_$D9k9=4ETK=EiZ@IZKdVD_^|PQ#;nb)~=88GBqe
zGKYtCx~B<2UNPB7QH1vd28Gp~zT*)OH}|axhk%?3J<D2?4pl+-^@AFlSV+7mr>#(V
z!02y^7t^eU>i$&)#+du1JsfmiIMU&4j~SkzKa^kn$rIK|M0Qv8@RP0nPKSS1iUjb-
z&BCBl`~+c{jL>^_+_QC&M-DQoraJIa?d=%>Kh_33doDzYZgWRw5&!CR<oB`$YO}v3
z*PbQor2+=(K}8|~>vE~ZEA|zI!Kq_=M&}&Nm8zmRQjdYSYN5N%HYQ_lUf1jgNKOAc
z3xH*)>5(cFgf%?)mkgGy)eo338QYkitgj826VK;of>^-U+P|++*;_HApxvBgbaW)6
zW(!6(B+rCL#$^4`iF5i~gitY6TN^*og0F#yn-TpU?i^04#VFQJq7VtH-?r3fh-7|_
zFT^h&M4I+A{a!%#uNvTf3cu6(!#y)^8Xnrqa)qNgwmRV-m*Bl=@WKOvCu(c(N#!Uw
z693yP>qxSD&rYMeS2KKchI%3}SrdKeeqtU_3S&PSr+++uClqeEq&$94p#@;IhU#l>
zH<HALxexkFQ~3M31rAN*7A4sdHO8Wj4Je2xFZbfv|8<~9l5Nzt7;$J)Sb*%`z_k@@
zP(xp?bgqv+%@LHIxV~ON<$xY+`&JTUR0NGGKe~!1w#c(J;rmg^!~<KiHPE{mj94s_
z8!V2)OeS@_nq@TAQr%sIYFbij_|El|9fMl`H|^bs&XT2^eM;LQmCgE>M<AH2<TD8b
z#Ie$)Vu+G{*O%FtX<_<>{@(d3%k^w<6e4vl`Ax+A$+Rs6S=vE=YGvXxxO1RVB}wfl
z2rAn)3pB%F%EqhkR;QHGpw;xYgyE9ukICULQ9{p0lN4ldK@9#^Hfs>r3@Y!8-g<L`
zPz6JB0y({b#-$hV(K;lejc+K;&a>`CvYI#4=W<Nf>jA197;B?E87XClSSgKtNf4eO
z<DjB0oQ<h$O&i>hFGD*ilCs8aN|=E+Y#`qPsrc3u`(wBCIwERFsU*vzxf4u^4}4c0
z>PwIoJF&J`40|ZGp$*QHOc0p7!U(q3;nB3wR&_CyW~_HEUI-~L`%Pj?#>q|p1+g>z
zg{~Y01as9Wuu<|esp5*)PNlx%lKOdGb~;)+qW8h(dS|eo98t1kOz}=1tF!8<HF-9s
zf)+6OM8cO?Fc4>D7^r`tjL9DC?!1~pq>_*n){SqfP}=~@VZ&!CsG!gJ^b8($>LO$V
zzvf~!=A%1GErix_VZ<3h@4{Rv0&gDXLM<L*aG3@<_h!KCc!{u#EF&okyHsN`rCw_9
zoEdu;&-ZAHbNr?hfVa?Bxl7z?z^G+?s}mmErYp|}vBSVpX!?iAwOHoo>`aqEHhts^
z*J6T9d8#rZt2ao>)9#wOHnE(?)O_@bI5JDs>MvYQ@~$?H+nx1=Mw<6nrZ}ZMijl-(
zHs{AON1b1DeIk&Djqmjl1~h@O^EVOJ*^N-&x!E0>aO)8NjuU0Udx_sSm>+DwIJ}lH
z(}9qX#3*;8qHrxA?D57Qjy9i3xEF1Uvlg3x@N1p$PyKuLM{Xf|aV+NGt?94lKgfMI
z?r@Nl@rTL-n!M48!NQFBQbUh^QYRy=2krUI48=D4|CZ<d3KsWgn}!EE(gSZC+fclt
z0(o3CoUJC<^v$Z69f`EfIqg3e?d+$h=bC{?=uDxDL&J0MF6yh6RbZK)sv<g}f)FWn
zWw6$GI*BBksFM!3CNj8@(s%IDT2m$R5!REJ*nNk(Hgb6qpy`~+wvQLajgvx92sdPk
z=v{LE1n-hyJiR{MGUWoJofxeJ0G{oZje{mK4tvfBPMV8-D{IlzZQG5WHF&aDIw$F_
zrsqrM!V3r|U~|6*Q@0<XcV4k?O#~Xxnf@?b0E9E#7OHfW^{qPlamzZgXX`O00!uLF
zgZ@C)9adQ^j1Yn;193F#@leuGgYsx>^NG4>Vh**_+!js$^xS7^)WVauXdtsy45#D=
zCMh>QbfvZ$QB6@pD_+zkB(?_1P3(Z86oh;7SWs0Ry&Nf)P}ai^E~)xe>x+<0rwW2%
zkYdPY5o_*}tMq%D8>y!N6hrpY(fZ;RT*_ARm>u|CM+>85Q=c9kKOB)c-0vWC$c|gb
zdfmIGY?>-4qP72>*mK=N1Js!+(YRd0{8(%RUE&cgiL}mu|JXhO9^Xv-BdbhyA9{`X
z({?vvfi%|f^9y3R6r2E5^RwTpRy6o66;dy8PL*7*m{z!6aHLClg1mo<ep97Y-rd78
zApD&yB;*akU4ft$B861FO!ltMcax_p7N}D1s^%OjLws?%lwiZ{4$Jl<6htLUsC}R=
z6QzVJbPH-x>v>{pAAXbzPq|rQ(Ug*DroaI@BWHgEGJH}De|Dw_Z!II2smQl|LN#|j
z|CX)(oQ}8g-hVpydGl8(SoT_S=xqPm-{gTQq%|`Lz;stKJ|$1E6XzZ9g*TmTb~G8B
z6fk(y*{$>jryBjMLaF@nx)w{T_GAu<u`sYA9vKm0naPkb1zm;-0EC*JuI=aRu)x^c
zAK|4Hj(v6-&1cUxKvSyv7>;#wCrW4o&VpQ<v88fM1hh7pQgWvGHD7kzU0|%R9=~E^
zAf1W-4gZebL_Jz60cpP?ggcv!ZmxE-e7)pAXxd<UDcA$k=@2QZ9ZuzoLP4}7l@Erc
zh!^h=^!<g#y9HfZuv<;zEc@u?>hni;KblNojylv6J1;V!Y-@5nj*1UR0&rDWlkI<k
z0IDrs_x5VUI&Ec!o^r!wFl6c#2qWNj3r9V3F}mpS5Mz8(?OFIOc99(r#o2kk*?FsZ
zVeO=YvV0y?op>Dhf_Utxcs3j0EQ7#u*q{Ac%2>t-)-uiiv_LVSd@O6ceS#*C!E~*H
z6TE?<G}%remfPkU3rZElVy*Gpcof3%#t~_hW(z7pU$j!dxc4W|jDKyB@9B0#WHUX9
zCO!pc$$!s{5pH{hR_yrCoy9Q@{1fImUYW&wA$()STA@foH`uXk{1fFTy5&glbKF}k
zyw6PAD3pJB5dmI+e~(f9xl8W&T)xB%Q@<N%`E~RkjHIR=U*~y~4Nv(SKCTeNbhh}j
z9625C+6b?H61?57dv|;$3pwgWB6|B5ODyRQXOlW-HdR^+B!#i$&5|^=<_AJLK17eT
zo;-3n;I_hUk$2j}Iegkc?eKOgbcg-ZW7WqS0|2)~5ay8v!0Q4iNOIfcX$^DR5nntD
z^7{Y^-Mb-mr;Xa81cS5w4hihkla?Rb&eA#?x#Q#V7QO~1adcT@anq5_aWwvCHM35m
zB@eG`P2}ODK6b8sa#((XwS14o;*+?ahJK_$8!=p9a;snZ+he)v$UGnYItmjQA)#TZ
z;pY3)HMl^3F9D9o%ElX#82iieln>X+&O7Vdosk->KaB{%uSLR+@615>RJ_%vH;}Wj
zNz&x-V}NH&e&A5}wdpZ`rk62w=Q~Pv8J}2EQIzgEhR(nwk$BFI(?<FA?H5WyI!n1Z
z#g8S0KFQ^Gr<9&VI4ZF5bQbiw))VzLM*a4lPm5RE&0RJ3&t7_x#xB$AVbb`!qHOkh
z{zD3CY_e-uNZDOwvBqHbXWaW%x85;ge@3kx=>!{&NHGH7+Zrr%&c*nij!G;C)=DYo
zu?3MQp_T&;FH2*hMm2q|D!sC<S(7vEuGw<a|KGe~;vYn2Vx_z(z-~z)%NG`+@-`gK
zY((!qh4VNjqWMzT%<%Kc0jI7V`4{-+eG=-&c~fCXBqKF1aU{0$p!{)aq-5|WS8+^q
zEgSpAYT$+oslPg-upV%VvyLYCOc7!))jOlWl&Ey48}|O4S8q_&W7GpuG}tdXo8ttv
zhO%h>n<*P9Ar`o`PUt%J1s&Ip>Nn=fauSgJ*xF@9y@G$V_g6bSI*oP8{i&cmr3!N&
z-SPZLz)XNmbY&<4#OHo#fakl=ii~T)x6Bb?(e&SwVQQG?8$3Y9?<wX~_3V$3jmJac
zu_HDks<8$t_L59Rgt1Unb`++13Rk4b*~@Za)=N+#dD#nu=bt7rjr@Ww0`Un+J!M)B
zW9_KAfDZi>oat*(Je2igFZ9TNBF;r}a3twoTOEW^18{5S34*1%%GLZ`qJN{_QXxt#
zj4G*_uGm^x-%lz@dB3gDbSN!i(jp)qHUHV)I`e!fmoFIGIvDH+q|FtOxkTe1=L@%L
zHe-)=b}DuA7)$i<MYZ-gaI4}O9UPVhCC34#JRS<_7+fo&P7rvtz_$}87#WSi8w5`m
zH)9ktTqF*~DLpXkDPQ@EJ1aVTfqtqUS9s(nuv2ZMDNowKl5~8(YK_gQ<vf9y=%0*N
zzgyUqSOsJ$+bs3;S83iMv$4VYYfV3LoD~Ba&{mABKO!#LOg2|11A968hnwN!{P6wD
z3?WhcwB&+kgQ~byI{W=B#ihBF`_ooMJFQ1AOL$^M>hL#zH<E(;u7odACq{N~$ZEgz
z(p4>PJQY;xEpFj%FEVZqhiM;~Kg8wb^DP9k@R&>4dT|`8P?zc75JJcTpphi+k}1N+
zDEHw}K#L^;hEFYfh{LndV`{FA-wKNsFZjbWn(Jf1!0nZvKnBMYR@Z;>yE0o$mAJ{z
zpU$EU=Z%9WGsmk)s4prY#|B9T{#^1(M3@XGQffsp85ZfmUs9{)hs%RbDj0;JAxf(}
zp*Ui%5Z9W};*ASxQRuYK9hf8}>Za*B%aW6y;oZN73pQO~3obm(SscsOPzKxZ-whas
zaICfxerh`94F@-zHvJ1!pM>AP=Jk9x;@saBEZzRJ<RrefS#O>QWC6$|WvlF~pG$p8
zENiLp2aWM(VbDKQq^sPexXeejTfgCGviiAcTF;u6WA`~V-I(u0{<-Dgi;N`KN$lJ7
zCj!A{$!7v8v5Oy+3@NJHF$BfQbh~nVD`%<>RXJejG^^?x5lL91@I)}R(m3%?i3uB{
zN*xtv*|lH!dQ6KzcyZ-e%$3pC{8s(#cNx-PD<-Ny7iwzLt#>s?n!EfSiMI^1wNIp!
zZ*&6O(VOZw-js|wSua{B<h38{&~P$uxe^{=j##ZT;cVGD%z{C$0~V0|bY7%uwj3WU
zwnJtO5$8o;orbP3?oA3LS8q~m%NQlZCcj<wDEo6o8K_A+u+gXw7m1q3Y=NuL()X`#
z;>6_2rL$d4c~<cRPjic(gbhG9F(-zG2=eHVW@>cC;@9vOs=*z^kk8}=h_W4o(32?G
z=63OarWHrGS#ExDtH2NM8=6Su_8M18OV>596TgqyEmw026ijQMHryCS%WRIcrhVGY
zQYDW`_$9k`vrGPU@&QsN_tC1e9D_FE;}~2t9-;Cf-e<hC8cycYod#YTFKhH|CFgDY
z#Z#M!GuM}{mF60+<-8A*Ogc$aAT0c~gW#lZ^X=`4<_&rO>7rJrPlm^8L^#ni5l>Ig
zY-xaUmAyTzT!GF3s30vRc$%#7WVq$_6!eg6?TNtL1?&@26{`{Jk&+Gt?Qhoa?~b?8
zNI#iwWv^}O@+P!x<cJ(<aYq-VCzQFu(j%v6hW1dLHJwj@)W6~J@}Em8hgI_?*fEl$
z(;b@1D}^UAomuc}>aa%njL-*R2Iw<AZ7~t6G5H{G*d#J3>S1p)T|GM4bNF1Ko&lBr
zUf3qnSGzNmN6|jIy9{03y76T)CV9wa*}}pKie{3|aX&G<=b~*HU$h_(7~P+fUbH1l
z^@kDSWHH9q#AFrC9<tzd^u$vbYa<&Qx@>nHz~QrIXa%e*X?*|2b273KuF~6Y@tG<u
z(qyQaO{dy<AfPMyk9bXWo=)jzXQ-$(M5Q39AwFv(er#-9XhMp}`>B5PYJK|iW#syy
z`(QhKSa~yy=Ytjn!+Jr?xa#AI?Vl!%vaC9fJJ<VB7B8!lkz>g*=1BUDFpmbmHKp+r
z2e-;qRG!N_!R=-Wt(>x1-K3%_M_Ec>mnxbdNIFAlpEo-G+AzrF4>!=UtJ;9z#2sVc
z?hJxo!zFJbk`5K@S@5ag=Hrwu@<(zQ;X-Vz2p9R0i+)?*%li5yF6b7m*3q84Pyodp
z0asRhn7ool;VCoBvaxE%l*fT9#NJ@Rg+{#zvgZjz{&YQk6Xm`&PA*C22MpUD2na`T
z{%fJB*rdR<!S*FwZ3rJcoyeFPFE)PUQG@mLMh=Vd7W1A+s0^>xWK`!`V#u;%9wwBp
zQxkKBksHrxN$i4hpRHR&#7dV8dUt;!(uzkztw#ft1KN*cmxRko<fqjm4$p}kFAFBF
zkt!u(6p_l%7YQ2=h1`{o>flPpSu9S2HxbDZ83I*hW5|CayM2B*xIdf<E%-4*$GchW
zFVfGVpBjn0gLH4+G7~Bfg3@K%*lY!C`jq{LfkMt8sEy4V3D1(m*D+$zGh?GO^h!IT
z*6B_8M&6@;I3AF0lYx*S5Zl4m)0jOTa|UqTkvKZJhA=p5MccpbRvBoBc~71>7?pi|
zb=z>Qu1+2H-<qja(-8P%{q4`Zk<2V|vx7gaE^QN5|L%NC-ivv)lhh;)wP|mhU>kc1
z-A;Y@#q|AAl}|->c873GHh3ewTS6g{O<z14pKrOwQW0P(!cK>`F<KLXe<K#pu7$Oj
zFbLRz3Ovr^utZOwxIWY#+MW`4q6fFt+eb>y>iTgHk#;_NOXv15Cz!yKS3Fp}xs@yw
zT~E6Fr>eW8MH5`-lr-(RG#)(S(PR3xnIq<GS!G8dlBG7|TV_!@FSfj#hjzNTtcry4
zT6xKpW@WFPR7#UPWh1KO|N9GB#DdU^Ux;X&mP=`1f>M=el}Q)lD762BbwLH+<fe1U
zv5Y_*WidOkKKwx-t6!<PM!O$FlQ{L|5(eu{Ip5=v0l-hL#%K6zr52Ughtmn0wcuft
zupYMY=E44?zYgA#4^KSOlBAnrx=ryq%2Wl?DlDG%FmAmZwTYczGR2ctv9pV6^c9;E
z2W9G@e*G5TTZ)+{0-O$sJda;-E$Z`TQ#lc>tJVeC#>JZsjXK@dY71@6PB+}l7qWQ%
zGts|Dp_Eoyy>~fFoRFC{Ks`rFDPv>OqlVw*>9R=pd~tqWbViN<dKuXw(V&DHB_NPm
z=Q>)0Nqlp?L9$z!n!nw@jjeBcS>ubE^zFlqvpOoQfQvhLQ&l@GvA<JPi|790KIhZh
z#E8OpKK$^mi^CdkYjO{JY_ZC``quaS6o7J3L7d?nZTlLBg^>S8H%Y|x#R{3Eu*>0y
z$9bHYW_2|@GLhtt=7)2A>U15+Fd!fl)OB&d+u@u(5WbCFZPU-IAiqJBzX)6<JF;%w
z-7>0IY87*t8(tp*zefg51GBencIX4rOb%=~+l$<!0K5G*x$R>+6D=EQBInT7(#3#C
zVVY(p5=HedM-4r%oybq_c7GlNKRXZ?^52kv-itw<av0UPAKH-t^+NqRGAjGx)BF9+
z7777)=z@(!ikXDibW=mz+ES4w1^I--^+;u1EXn%-y5FgPCWoNR5}-}cO(+u0)YPD8
z1Utvpx%QJPjDQbEhJmnB<AZs;ZE0sb-B_T**{Nxmk~40MPF;T=a=w?4gGLq3>1n9?
zrq`iSU^zNmTBJ%{W*hEu)fg&X>kGag4bIkLWrKS}*VkwxGnyk!Cz1m9?-#;iFbc|)
zwAPq74^2(-6Tyl3?~r>&W7<;}@dn~-)+5-PU?97>M=wo7)|~Sag>>0_r>DX?BaQZR
zFe_XV@@N72^Dra@zSUYZOXy+oh2Hq*v)>W<#%E)v+fHFz4FOpl(|p`C9P0;=``KRG
zAJ}O*H71r@8X&6e6ke=xt;k;LO2#awWut8IIyF|Z?{g?-|KjQNpJq7lUN6?qyNj6w
zuoo2amB~!zi~EnIvXld)U5w(#BafbSdkiP$M2*@Y7+dQCD0<sS$Oje6DdrYy<V+MT
z%LB^1De$B539~=EBC{JqQ~ghfl_o-+voz$k=we>+zivEHb|c8&4|J{MGf5KD6UZ3+
zRxsEfOOq}C?Dr{37ReDgm$_-E!CvW7fBt41laNl_%Z{WD*uD-!ieRy=&PeAJcxwrB
z%@@u#4-)uPr1F4Xl*}zlfaf}=2z~e+)kl+~yie*SKPiHDE=R@WUGdg_b9f9`CKriK
z%OW*YH`1znRv6|SrZ++NBjw%cQYJch(H7B6^yFukjL)|o*NP)D1Nzr7FeMJ76%fZY
zD6J8sos^3np4}&6aq9ie4T<Rh#L(>=iH8ec<7^o%<VN>W!5GmpBB^aD1+&yl_Blno
z*x##PP(#3OYu=QLAbZ6D6H$i?qSr0Xu*32R<HCgADMlwTK<I=Tji>GI?S*#oQ}4Oy
z6bWMX_xGa1dhHeg`C26A@>3#1tk6Lo;*Le(0!-WvHnp!M6pRGxiap*Q$=y2<8Qk7_
z^D7qO<CF(&;1Ic_cT+Q-DBgwCM$Sbb*W$~O7Jhoh#R|DMDX)_GE&fej@ds430TlHL
zL(oL>p)P33@_Z{UH3|4`Bgd>S4;!5Ml`eYA(Q6lL-kJ_+`zhEon(VmO8cln+Fdq$X
z5iD`Uk6MZ}(E#Z<@69|1SpO=fw5}UbX{P_q!g#{EdJ$gbQ<@;>Ioqn)@sta*jHzzi
zU7K)vTeq7y8oa@oRjd~{^8I-x8Icb*7#xunurVhvXY)T}(~N>j;wXU%cVkYTj|)1J
zn>KUh2QJ7(33eJLzk!FpP)-%Kb8L<$jW6S<>1UXb)y_++^R(95{4-qex_G|my?$CG
zwIh4x<5e9J-rQ;B$A7xGJlpg@M5{@8D&~7(T0BXQr<al4lE!F7$=IFZzN+vPsM6lU
zS^ijSRz?}=Dh@ZW9uLp)ZgMyTMB~F5I_|tZT>T!Xi=<i|bziQ1{zEfKTg&`hsABAJ
zrR(+URX$%~GzL@KKl~$s=s?TMMjcic6*tWWl`5`I_-c`qo;_QuDDTB~5ygH@gx7g#
zPVq7Q&7Bf`)n?GIz<yMCnRO0y5MW?Ts~%@`xiH6=+%%O*;n%z!`u)Aijo4cnUKR7*
za&5xi^&A_BBzR5h>#GDl7;GRQCxH5>-*+c>TPDlzacyv|UFS#Cg>pvv2YKFO?ZVo*
z;qrZF3yh8M*3aL6veQ(|<a#>my~AOK*Wq}QbEB=SkyL2MIo-#_E@<T$4&t28UB~~x
zwhJZX$ty$@53YA{%=BP=BxfKpGs^Yt%E!=-?h$$$<J#l<c_&RGnw_;^LabgeJ`zTg
zkgAPcu=p{W*)3r=v3g_Z{o5mm3R)@nKa-{A0uCGe@uO{riL$y&tq=H~^1IW&!Lf3u
z*q|JE=aeg?=$^8-+LlQ_r;q-?>@w}SFgl$Kz8Qg&TgMU>>jMcVV(q5#I@2+GJlVC$
zupA#F)Wh-O6mko%h(^`w>?^)^)tlEs1k!`u*}2^!{jR0HP%*qT#i((v=q^lJ345Qx
z0mR^a1uRrko;wy3bnBsexTjmU<~ioPlx=vr^X%>Y-fk=1y)sil+oxKA*0Zz}{q;xc
z?A90*o@)ObnW2JSZcHxvmR$5ZAT1l7+Q?r7)CYb2QW`!mQJC2P?9*u&slHXf3W3Uz
zZ>FG_Lg}kdso4~d){8=X@(mh<Sy@$?YxKQ;LUhUkQTBER<RlvSsKvTmZZ5uwLmXZ;
zaQdqV!{II#8gqa&m%4@%Qi9sBN{l#CsB~r!z|rGEq4IUZ>b;)2^|4g&m4w5F`arqW
z`6A8J?Ygr!gZLE>zmdq2c&Q#Vva{_A1w=f{rP_bwc+KAjc+!barPL@99z-Yr1rF@T
z%`}rU5jRU2*M3WXck3S%6BynVodPRPV5rC#<m==VTg40${ADzzSyPq>J$(33Q`+n9
z8dmCvHo~j56dLT4G3V7~OguSU*$b14i0op&;w#1V&$Q=SgqSwPaZgd)VzJTg3x!Hs
zUhD#ZHj64LgwKBn!`VHfYS^L%J8u{Cc@u+7dtR!wD>QJ))jTx~MjIXGN+kv=D@KGm
zo)uSL6;y8WByic#4*-29<7WkVrJ5rXvww=_2(4b$I7HV#Pv#+|b-F>`ldpsZYDtt#
zY6c=~s(tZkY0TPTR{JlrkrK~{hY33W2}fQeLp^_KORJuoeSLrI9gfRAjpMYwIrR=-
zZx20ZP{(n({^w}C#vnrWA1-}=l48T{SByGTWC8MlrLRwonOF<nAhX32q2c1+S>|~Z
zZIhK|d^`T{d)n~czf!48NX8Fg8}SvrYg^p2Aj%QtTo@2p%mD8qNZVY60BF;qg{D|Z
zOL)k-kNc&MnHID&7hVN!QeqOWu!-zW-Mw4X7qiuz6wt^2=sw3IUyfH~i_TJ8grEBl
z32!NK{{@KeKzk_^$Z6MRVqZP&W|b5|D^DP&a3Z!Kgb%gXY>U)UAs390^r|F9oM6!S
z;_Jd$i?CF;zp6M|Xhpy4ej+S=Vd%>oBT=PgDgpn^)<`lvY|aqTy79YR7bizCw-1+A
zlo)E{!_LeF(OA#gIvz$8Gr4d#tAG;`7m$}H_xLwG@IIk^>jSiwvxQZI{GD8%0!SZ5
zsszpKel<FtrP}GUb?an=v=*vam%{IQLxB(LF#WdB`3Y7zkrHKx|GX+&H48YKCZ^oQ
z`3D2ZT;d+mEm{p$>?~xn_MaA$3(<vRf<{lle`z@X#3Lh_nJ57(Gh7&V7c-ocu{s2e
zAaAdPbq<Jyrgq{v)RG=P)Pk2s&WmW4L)0@qkj5H5MuB&vrW+wG*{-#upk?6JnAh{f
z;AM`Vv3P^iW?QW=dHqipJfoij4Cf!zaR7y|l%02E(j~T;2K?;UeLMX$hI54nSvXMv
zx*RWj^}aOZhs;pUGO)$@aP*0!N`1qU+!Ya30$#b0&0P-1R3_vW9WGSx3Jk+XZ`f%(
zM${!OlhwAIuU!nO-ti9wig#`Q?mj)Gq|aE=L61Y~o$G8<ab#r`s&cvv2(KCJu!aWQ
z*2A|6q_gS0cn?w{=FHKXn*Mxx{{G1OVT081NOx%_ruSfjYuRi4qmUf^5A7I(<Ip{^
zmls8<8`e{<V<stFR9XB_n_AZ?>s(jza9YO_94|_;YgT>LB!B8g^Tgy8q01e36mrvi
zb4f}d;#6Rs5k5JNxF@6HSqVeZU}`eV?`x(Vc)C*1e%t9Jhgnh0P}S}4_^Z&SQ=UT{
z$N8k)lAMOWezGE-wZD`%?*78#1j*z2M*XvoWyr-hXk)A|rUZ#1qrpp&&GEzJW1i`J
zWR3I;dBtw6k4uiMMT(}zLVbI_h=`957;R>_6HCD>ReQo&o`X51JSQkbm=sR0fZ|4A
zvkh=Y?Ec1**Yq5{w{x74Z~e%WTD;N;Ug_2n8Gsqt6geTv4bAxbtYmn4T8Go{xY~Yb
zu~~0fHo|~5|H#C{(SVZNeGkT`jS&?2ttA?v=VAm#>*u@+HaHPw%Lc${XNKw<0yU_O
zuJ+rh+JgO#bg(-K%w{3;ioSq=(86R!m-!)gY_=jwCnJyyCx==rIM$w@zBqjDvf95@
zQK@T$TOy~S`hW1lnR$5oS>J_@9Wf42iQIj>){82O+M|sYx$lRrR_8aC&G!e**^(y_
zgHZo!mdrv3VZ+{2JE*H^=^c5dk3Z}+slN<o)#E~RmrV@cmw9Wo=V2K)71?RZA)CFw
zX_qah{X9~jS9X~2a6@7s>iHgZKvj>JJ46SRGG@juuPKKcN6VE%_n5a?`S61i)v#bL
z7&HPHZtooGp8vsPa+zWEo7?LrXl1R_D!G2bM>enEo^fkCZov+bMMNh+QT0Hanccw<
zimz<+uw+~}3o9Ab3`f{h1^Zn$D`~fhmf5tpdF$5i1Qs~~`GGVWt;`t^j6j5a43aHE
ze`+T$PQvv}ZU0oAg1B<Jerom^;6q_Xo$ZrrBwzzKLRUvN3<7M9woz7r3Z|_d?Si6j
zJWA9t_4r<OG4&<8ZvP^J%)r<T-alAIls*8L{|Mib?=baSd4WK={>jTzf%(7Z?K>a2
z3JiljL=`Nq(P2?hMY-WiKDprOxhzJzb<9JDo%ywgbf^;RdUr4IX$f3thg;{)kDtY7
zvEtEcd-ZIw6HyT$M%=1Ok03!@r{yV$+(v?FA7a}<frNL9m%^T_!XHLB3r>m(!<<W^
z^9xBP=p5lFyx;C*vq9(|p6c^rm+5PT?ker2fA-{MNV`@yfk7057pff&_7r6?di{6P
zFKtsWP~@`PJprkAsGfBTt9sJk;ksjWC1mkxt+{TEAsd9q1H(f~N2uzcQWX+LWN=N$
z_n$WWYJ(##JMTl^>Z_vNyDgn&%5knz+U{EHsb!(uv3=eH5vPJ0Ii+uBpMsqv{*OyM
zC;bcJLr8(~c`l^(b)$vxJIfwsop>a`yAh@e{k4Zi6rFZGfi643h?v&p;l296-}hOG
z$r0ND&kG==Prdcp0n@q2$%$se-@WN2Ikz11cknE_ZQ)wv{02FAGeTr6M%LhyPvc_^
z-=+}tnw^KYB%bhKQhivxGW~l_5vlnN;-m2|F&PgV+Wpme@bJFYu!|ExAidf}UakBc
zw*rdM-Z65)q(<-~KkZ1gWKJ*bw-;WD!-AHaZ$^f)t}vZxibOLt8f8mzh8F?Hmue8r
z8%#`_IUG8lzyvzI;h8ACE1E0Q5;)0TdkB7StZq+V7h<<mo@GBfJx$-DKJFP;Y)}q@
zillV2db9UX_bk5M>Zayw!i2;VleJR#rEk0{hN0+8H73_5x?{ybD}y|Md>|7)Sx@%)
zUz=Ylb{<74Nsk%!mNJ!#SGcN=OrKTCR@4b|i4d;%&X{h_NSEWkQCvG}vK?1)EULe8
zjj5%Z$f60pmJL53SeA(qt!|*~$W{#JGj9|n8rd)ap+8;q_@GDECu)W*x_>A|C&Ze#
zJa5j9)OZE!qj+~*P>i5D-6g}+v)nB2^>HG_ZU;6(vjxG)mSnok5@Ei&9qBd6n#3u8
zV>!lVzo96o_39h1uM~`wDk^h#1k!NGw8r3I`>tU9MoIW-Xi~HK#AKHN%nS|+-<uIn
zQgWoqg&)dP7>PUxK?(h}^%cxmZ+%HsU`H=d8zoZA37mbxI~bXA5r|FNEsDdA^_F*X
z8Zm23^V!15SIH@9*LMMF7PX6Eq}(WY`&ygPI_AzTl@8(ASVT?NeJ3**eWDX(3*hU1
zO=8nHrdY6ui~TJkNw>vO-IHdhHAbpP-u(Fh^Y!>-UG4djXMn4nQc0^@J-79CunAi%
zm_b|6^G|`wl<Z3P1!`>U-foYzMeoLqOb&728NVy=xwLeP{auh})0o2XxBRHi9eVd@
z`M{Ker~=C@0YQ{YG(2_AFWw|oj8DfmnT(L>%|TQW>VbTbYyA@#c7!Ys1YjLs+IVf`
zZv!k;tZ^(p^68(uxuhmZ^2uJ}PdZ@kJ3)2VnHkTwG}cwmg$d8by(a}{S-qt<2KI_+
zUN?M>q*Y5;>-8_|@*^;<G4c1Km7VbWJC*%;Y&?G=0?Yt%ZGY;5=Y#v}<owVB+c9i@
zgL!7v{qWbazZ6u5OfBGkVbB!GM&MP?@s)D7{II%$%jNDLFURPS9*2VM0+0I;FF2?h
z{z$S#_O%7{LU2@i()f|w%)xg3W<z&2+r1g|d_fKPSuyW9XG836BvcLEBOcc@L&k^;
zsdF@ruQduKB7~~9;}QmTkBfj*o5OIR2wr)<&|gYp9%~u@DUtWu6&=BpNCh~Vs(`u5
zfkJUr`PX)kp%96^oQs%%4b|MjF&hrULt!aycIfbQULEl5PHb65l3f(o>i(*4ozYMR
ztU%7E-5;|+Zl*0aU8nJ{cGXoRG=q`+Ae%o9eYj$YEH3DzBMH*TR)_n=`oMx`Q8KTj
z8OciF>ANV`6d2FRTnF2Y7hx@1GsX`CE~3G1{BP|>xL?|)d&L{Uxs|!;grsIc)NB47
z+gkf(s`!8q#uHtCyks=j%YtjyEOU~WF7%j500@7UANF=Zo!%aZrS==T^*06fUL>Qg
zjxel4!kn0fh}0Q7z$FJeB;KBy2qnMH<#j~u;ebu{{kDtQZW|mUkEwmSp!Z*Y+eyfb
ze@{2Lwa)CoJ)Ai}X*av$s<)nY`}&8b+f_@h>j|Qm*^Yen8Se#UZd2VKf~#&P(ch?;
z+1riAiFV>_k4z6VJ8+X;^5B8-lv?4CEv(7?bznE+D{iR^?luH5nfeU1??!*g25T;n
zPKKY2hqr@B{4Cqm<jRki_wO(^CS||F>oo-1mLh<`ZG8!I;M8_gBya}ZXz%f{XIH$M
zi!PNHrN$XsrieEXq9)1Jum-m;3t}($6>x*7MEozK<#rtre6)36W7CC?T)C~tQ9Qq{
zi~#nX-BkaO7Uia2QFpIHI)nq0ZT``cNOh4Lm+v_=t)cXAqV8iOr*>r@mpO$0;d3Y}
z1YQ#%e<zUg<$8hm)Y`C*_nF^6<}YQ>Svd9$$MZo@+``Dr2V{qXgjdhn{xfg`q4MN>
z6#QhWpIGSBNpB-c{aVEx4tfe9HXbf9acVvbF1(fo{GLwN_ap@UehWQh+<0aI-#f(g
zu}EZK%Exy8+@8X2*?1*)WDtkPeu$^C^hh%B+M-1$hLQO=cjX1Q)nWKEmK=U4a|5El
za+40ro`|r5Kk%`Fl;4Hh`Qih<si1go8S*>k{g2XW!$lPv+6zSRz(=_JS?~@!1|ow(
zEE5Y1xJA#9Xim+;!zeA~)S}3j%N|UWe;=$7{L69GNM5)b6zs{Sp9^)eJI!<W0F&ta
zoa5@_<-!9^YCzh`R`4MuS)eHR?$-BqSmlTCDm{MmdVJQZj7y9EJOqgn`drXVZxZLZ
z$`r0y{Oi=Xn}gGRPy8=c_nA5Gv3~qca=L1@uliGYBfgveis?NuQE)b&7gGZuv){n{
zv`zNgUy0>pl7YC-8)`6h&Or}a^S&uPfTM5++XWxI;GoMZ^&V9GLlXV3&25UcA#p72
zmC7T?zZ9!yoL-L4zfwn|Mt?eB6m|SkZf?r-SMlDS!6C=n;({F<qWDt+lNeMO++xP@
zKbb>_Bvb>Pu<u%h(k9-~iHR#qU4KGic(&&{S%fB(^~4xb>>?ZG)_7E6W92~E#{u#)
zr3B`~r|@hHPps^}*y^kMS3~uvH0glKr3H5t`FAKdlhXcvc4GU5f<JuQdMB1+>x;>x
z<V>ICThVnfQiK>zB;!DT?dJ-q<4Uc2Zp))k@sp?;lF*BtVS!QJEv-Zk0~Ize0EQ9y
zNL{m*`5TD}purS7Aw*}5RaW3nhh|1eO~4Yk4o;4&6~5FO$sjlZFA>=;x8R!?3HKl-
z&se#lv&cGsc-3e(Brh&99cB)Mw`)yPqR6zSbj&Ja)YzL@-Sv8V+E*|jwpXbd!P9`v
zl<--%oibYP6^}vB^Hg7*x&ZxFv8SXVDPRwFiWYwCv$R}m+fl69hltYip)0%JYh<@w
z7XcR>{<Y(B4not(0*cSefKpTQ#VZ-;k3B3H%jrp>s<muDtqP_TFA2q}!V7>qm{-vL
z!n&BulSCp$QL=;Qr(nVoVt&uv-^IEZGe!PI<*vNM6G8@t%N0wy<y_tpca(>dxMb&)
zF9ZYeC_QjBWg(|B1G%#mDnaSqe?4lp<FV)n`Z8bQ!|JuOZ+OTd+!DCu$Xr%{TwEk8
zEm5<5nvD9(a8AH-bvy|zTGEGBl{uX(ZvLh@JxXi)L_xEHsLhYm^kJm;YYR!h_`Wy!
zy4D^R#a0amO6p(qZ_sxl0UOaL8_8O8P11r+IZ5jZxi$pYqdPp^?QBwW@t4_ZWqI|X
zv5da^%v5tKz*+9ugal~5`(t10_Lg2PhQaF{+G^Iw)Il~{ae-{^Bcfn#<7?tX$W+SF
zF<E_mIF?NzB%jN#zP?$edU-Tm^!OoR?6|;eVp09SU^kb~l5K(s*XjmVTkVln=b%EV
z)B@>|>^=Q|>kba{<7Z68phmofXw6@F-q$4;-@qa>8Nlv@sBZ>__lwN{#8zNn`HJ6u
zksJl0;xcS(HXA~!zc~fD;QyGEP`qK{_l`Gl1J**a$@{!FwNC&1hxSVx*!l?)$M9f4
zlj#8{$LRU9^X)ad;e^!W)HI3POS+P)2M6HGOqhCnvBLLieO0KeD8NQKib<BJK0Mf>
zRtU4gW@8qB)5U@zs|@=W_7#8$ZL(ssn&g30gae73vn(QUj65{;I!|V5c3*u6OYfjj
zwrPh<vyx{x(=bckj>`X4AC*zvDmZ$=Ql?1$aD^3ciM^ax??=zyP4hP=Fk+dEeI#&P
zKQL^E)mGKsstOIa!|6yZ$?zc93vNUdJc<sQZ5*--)hf^)h}AF;*3vg9J@mNgo6h$i
zC=+sC!NhL;lBWCs7iC(?xX$$}E)1^Vz=u9-{4ebw2&4ItobbnX+9R!BN<S`#yyd@~
zl(lx>Pyvltf;{$R;V8CVdjApso#SVvLsizI+mRCTBM~a}z(L+ZJP;kxpQ+&|s_3PL
zEc%P3UpIHk_m^QG)x>hW75v>HqV<&?$Bs8dPY$lu>YvHQnyMJJ2UH`IhA1{>K-!hK
z6TD+j;bu+QK1p)Im$zZ@?k%Lr-sPIZ?eQ_MV@Sks?qu9)JQg&ps-yQ&a37ELRSGud
zw#^Mv<oV!VHt>J96YX9h4)UYQewbX&IQ`hodmU(6;EuDGef(hhkw79+$@pp^rf@NN
zi$Xgb3BVPT1C9fK;K`H2e#t?h_3G$vF+}A8Jw$d?8;aw@;dj0{%+`ILI({kc6uq@*
zsu)5F4vJ6|Gnv_RJV6=O*Y3*Tpm*=We7(fv+W%N5BcTlI=eGjsWak2=hljeja(8zT
zXkPV6r5-Snr}42i@DG4PdKds3<3*ero)Sq^W6^tNIzz|AyKyWHLeM@Fd0oY++PZAT
zPd|Jn2+mW#S0zuNaO%y%{?N<E)(nq?0|WD8St+Gkb;QArmSQxQ%<561;#M%65u6KF
zX}SH))boVAMUve$!j{sixEX$I8g()dhK*JoA?e9+D6pW~liqk53wXI7PI08bdX$^>
zEZj~98bt>478FA6)OT1G>BwBm3~J;#HuW;Y)IM{qn_0*&{Cp82;3!JH*#U`dc9wEz
zA>5LOnLG)93GPvwEMHbBZEbrgFdrnG2Wl)5!@${Gy%YFZWD>1NHj^Kbng#*prN{7~
zg-}^pzQ+rfmpu#H<vzkRcw#csQI$EP?{UolNYVq<4k~m(O8P~%RgtS1)0pCGU7o0`
zK*VQ$rv+aXsEF;`I;iGo`UTGqJke+};iVd<qD0|HUS!d6yzm#8FSn&cd#}{C>K-Vx
zNK|Ztm%$GeICBhOJ<;PN|J5X&G4xqzA>Zyw>v`P_IjBf~2HKZR60V#W$J9e0tcX#O
z|9eJO=o#Aqek2f<s_GXZPUJjq(wQs2JDiA#f4YE#v;HSpw-2S2AZNCgr_W;QE8fw&
zP<#NZ3K{PGO8@+znuOYMN%Gs$lZUK(vLHN+HO@)u$$8}>dsJl{G;|b$Fd1=^DgISg
zMPEAnMy#q_pD8_*9W>+Vatq?erIT8hGiVf+l)bnBLE9Nvk>fzG&R8t--~M&!j@IWt
zek|2Mr;E+s=86E7>g`dB@>{9NMfYPP4pWBBwu#$=VzQR{oga~|Cxx3C3+0WEEaW=3
z#YZ6a%_w|hA#0*s)e<oqFoZN_C*Y(<E986!Kj@P6HKvB&W|)SIq5Z$xUrd&tw^o}E
zr|;hfexD=6oYAm}Iy#9JR$|6BpLQ%xJR$XV;`%&kLT`mn->%h<m@!efyeKg{cJNR9
zPNeAGF1bU6dzcwWAzA%kAH*nj8wWK6q~EaZCY@0@8dn(+g@2kq;hME~KDzkdZ9Gms
z8!p*=zKvRTJhKg7*-7$Hs^3?vPDNX~KJWpGpF`i5M7BJi{KbY&VQ`|J_&{UKTB{?2
zkCU@!PJwT)qffPJX&hpxOmQ$7b#T03qewne)Hj#tfar$RPOFKVv;C!9dDTQ`D7Z9+
zG(tYed=vVVqMeU=vwIV6$Ov<RT~yNC1BvOwxd&|%u=pfM0LnY+Wy$Fe>Y57+Gjt<A
zkOsO(4jkijSH6g8qUv_)1S8r%$g`pm@K(RC-@R?VF^yiCrN9_M8wa^kf7V`kXSNEz
zh5<1}_fu5$<8QbMXw?w-gM<VI_UnHSyQxrOFduuo;3E(4cCiEmP*5QLzW-<Vr~(TF
z`EJ?AAjkN>KKh>}qGNpJc4J~vI5hsBdVi2!G@+ruzifu+W$6CDVmSD}@_O;{>FpZ-
z&jkN#x|<ecB$$t#aNSI;|F0MaAIKyC4h%SKIBfNtt4WZ`xrptiKAq`r{FwBUYnA8H
z+Fqsu0q$UhwyexJp-;l~LhX5uZCl!$3>YDb?R76H!3EqSYs4xV1_Du{|MD`syQQ0U
zZ3Iq9>Eq6@9PN2`KR&LinTSe=AvHiqK=Tyh-MOlgE~ik=a?;Eutj%snK8+|xI~kil
zgzAw^4gdnu&{x%iosPI3!X6+S(%8|+rtK2-y@K#Q3m&s+RJIXQ6=TYV*_9y-2oiv-
zgg2Qh*3>{1c*QbNW+JZz$P2WF>m&nHZ72v4GeqVDrh5Rb@hDr6!Joq6l5Bva^wFpd
zl+aG7N%ABTaJXn#B9X1x#FF&jyZ}>@=HC2YDjEaG?WBTpU>!i+2rv|xC10gxO=ly_
z!#oglc1WvcO_S0xpcV#a;=jaPQA&T%WQ=9f2IspTQj}7M{{{r4&VNA}u1eN*&-?lr
zc@{EAQUYwq4g`!YG8`M{RQz|=^(?I^hO7;tAes%dEa(OV95CNR@$@MTj~65q!C^EQ
zjyd$KS;Oqoc;UMG(_>PG>(IcRCXLEN|HRL5Bx9a1Wt1rl!6j5-+ECj3Y4owph(t-f
zrBhn5@3bcPALPB|TOG^tHXH~LG`L%EcXxujySoGltl;jtu;A_lcXwU5ySuwPJna2D
z=e%d1f8qT!*EQ4CJ=0xXb63~h)!Vfb-^>(m$|^QoRI_QYpC6f>5{zw{pxOpCc6%0h
z^j;^TfNhrfWI5JRKn}I1*s4tVQU@gi>wP*^ah7^b>B=&8EPt&CV^r+(BE-H<jP2Pz
zV7xd>g7l*4F^z&m@<hKlxdc!;$#9%JN(3M%og6(*TWrfLXHAC63!F76y~pn-x8CH8
zG!z?^5a5>`9dB<TNac*=VT?7boJ%w%V~?Ym2whBj)gFLl*tSImo3J-vkJ>g%o=9ch
z)WB*PaEa~Z3<9=AxGXFWa+ah32diE2q<cDifzGu=s7F+&cXdRlKPXWFTzb!SzhNk(
z&dlPqku0Gx01bgMoL)^Orl0}z4zDQDa}N?ekDR~N-cAbXg7V_)7yZsYNa$kotMvlR
z`1=oPG@!tmp|U7)h6MpYHBGS`FM}f&xZ=)>PF!HmP*jl%z7Fjg{sa|=C96<^yLsbE
zMYCcSr+Tds3(4Ldm#ROP@g&7li$I=~<k$MRTjDTx;ZopVKii-}pMGK-k0L6=uqM>x
zpmOPg5Qg`pHbfTRq=g@r=Ecw9vu{Z&jy+wC$z9a>>|xZPmX+p7YsW0wF#csO2oRTw
zt8fq<Cv=XPw>GCutpHLv3Upd`qxc~+OIa6dnu{nMCDi-Iqs_AS`c0YwyJTQ$xxIHG
z%qSNbP#qAJQ*NiNpl1_UvIZ}1m0(iJm}+>dYL}vM6ltU*YX%GPyK0PuIqx_JMJ8c)
zRwmJ!quzk_+VG@Ar&|uH&dX%+ch#^M2oDaDzAQ=zg0=o5Py8<Kf*dX|0>zjnc`>ST
z=jPL_iH;MRG0)Jkl>!x0Jpq^AnXL#Db_=H-I{&t+B#6wU^qaAB_~no9n&>rRPud?1
zYb_3-&6%-Egf*K}+I27XYRH2?7~4trslf07jb#kug7m#jS<w_bK&zS+-?qk3@e$oi
zUr%G?b~Tx?IqJ3$^BsQ0C=iY-GBwk_sKG3hNjakduY!Ii*;P+l8uVMHu#{qZj`@4a
z35%ykZrb*&ORbDugV73aq-sac!kQaiY*h+y)P|8=<Jj;b8J>y?b%+vG2<KQ^?kE*x
zAJg-Rz$+;~*d76!=i|;Q!N-A}CVo9)$p)-7?fEj5nq2M4-K}*pjCCJ4Ly}oh44+Le
zUvermre${)A+8C7$a&e=0|-zr17wF$t&1Z6Wxh95h&L38Sh)pmjs&n?yDg-DNhD7e
z(hj9l7EyO*gDBy%`)Vf-%hZQ(V)QkEVvf91esODV$#~SP8wRda=)*2~5R`_Ak>+hU
zW+yZy0dpT2_I@){TcR^V&e)PD7>G!@)b=m!@#@IuQ?CPo2NDq1rl-GU^Z}5pV<;cI
z;kGT}hw6l>wVG4_*-S)WB?c7}CzGG2#n2JREuM}Lp-;<%JdjEEHO4LQ7Rfr_&BO`X
zclej=nv)5Fs|77$=S1E&&ad;++!kYjE!1c|zh6=pBogxl{h2U_n(g3aXL|Mw$!CWR
zN=N<I@O-l`#gBFY9MmP(F*Xss6PyAc!3#8X3?<}<?hQ5IHFenL3@Fz)i@_M~Yh0qe
zU!0GL_tH6;>`2c7gs36@mQaqxr1fYcK@qZINS4q})q*rFe!d-A^F%z*f98VPE#t{(
z0e0sAm$2*6cew<}&Qy<H#7N4NMnf&GAq6ax1#S*E`T~dHv6hIi#(HR7r)gbhcs>s@
zPw?>PQTXmVCIN^_Y)3Vq!kG|{xT|jrX(_g~nD01ZO+8qo?OL0Qu=clo*usEZda0l)
zr9DBzk29=X6bXhq%<+^_)h59+l@(zzvH$$OjJf>L^;XSH`GOklkVMwcqz*(Abqqla
z6$56_B1Wl`tUP{X6$lf;qL2*kr@>x^TR9-W_5Cy(b6ISU)+Ggc-6qM7ZSlauw8Jam
z((B<;<muL+VxpfUVquCiev@chVTsgcQR|ciMTQwvBl@&i>PP`kZ%uSiL6<fc*!|2&
z0~%JO+YsxQuro7wLRtB2#+c~a;5!RB(Tjdn_>l3f^p1GkkVPpbeu?((%tS4vct3=}
zQQ+IR>j<j-xJebsMSflF3@?a+(g!(v6YXPV4kVK|&Ph;NWtj~Hg&=B~2|?`4^moh&
z&y_dKch>_D4dX7i0|CrK0bl7%{f5)NsGBkd#Z)<`xtjXL1JUbD(kv`K%<=J6R*680
zJsb9yIs*@*-SV7DjH);op|dxxrjmK^f+JoJx<MMnzflmUD71tVWdjJwN6()Hx!mbG
z|F9@$iY9ng>^NH{oU&Qp<+$i)lPP1(TG13ybb*k3Tb^-Ewi}JIv6X{IvQkXU(q<Jb
zwTGh`9GaGZ0%49H$?UM^xnNOWkf4-!#0<<}wrH_4kHVCJaL9LQ4&2b+4SKsjvLI(8
zp#7l$MK*8x+<Lp!-`1&ft^D&srQ(&^{OCE%<UOr@aBTNKCPFNRK`OZYOtDV;ZKPY2
zD2H!~PtPBM+K_~a6#Cia)T32v!dkJ!CRPtFC9W&3wM7e&eC=&}1dH-SjU@|&(m;&K
zG7YVPDbjgjrRA`ySqOES1P4}PV>lBne0UX;Hd-=}oR*>u<AWtxj|UuxtQ7G_pT=%Z
zc;sxT0^+tAJBl<RXv*`d@?{;kJ<td_Ob)s<Ik_<;q1YDE8eKLSi3dWAjie+?-cN{v
zc5N7)qV40g!?E<NuN!&kL9*Mky_ZL}!BuIuZq52Hnjk}yQj4ysVa}<{+>Gs6;pF+^
zA2=10CNP9%quZPrw{#l6?TPyBtw7Ao^iR{kB9&jQYO(eXa3JPLW7H$gNbMmA1@fdQ
zeR2y*)CbB(<^&U=PyI}UU*t3-K7RfXv1Fn%hMGhX+u$W|n{hDx-5N%kf||(FTI4QA
zyW&+UoK&cdPgwH)0SxY;_DtCQ&jYYgGFhdRzZA$PZik%lc(@{IJ^+SgEBkMISvXLf
zU#Wm1pkc%)FoQO5<@FapJwH14+fRmSr?dMZkxS*jkg9J-vQJ#7>J%aGkE`_+Ov^Ru
z&41?tuy^}6#QqQ9ejtP(h16qC3n%^`U<?iZ2j&ABXOeSBIs9Gkf8e<wQZzI)q#p3|
z|5UcYO8gD&Vfw%<v>f&yfE^lK$QRT2KTZA%>T^awr-8xp#tJ}^XE~p<9$-g3SI|)B
zncN7v3HiuFjiTB)nNkMHHKSny@d4mCsIvd`0@7?D-X39Fb)t4AYwYJ$F^R%Lv)`(7
zq&rP(&OtYCk}^0B;Nj#*!sL2k+L*Mx(orj25DM^lbYcI%`JLFp8<`cI*J{Q7J;)e4
zh<m$_$6-PdJ|T<{7OHC|d=y6`2#+q0<yK7Zq4G!*=DcSv21G$jB;}O<?U6u(n?w1s
z2>Cj<oY*y{dE0}i&FUPvH%uyDQMGiw&Zs3A-l|s^kF_Qe9kb%8ud$*wcK`qYAqELn
z@_+QY9WTV#ho9o&lg8iQY&i4=n`w$26P@BG(l)UnpXpBFM*UyKI|(U1x?5oE?B{y{
zF6_*lMpgn%7LxPT&kn+z&;R{)FX#{fOZVyu{FeK6Liq_jRIy!j@D~&j)fO86<=OZu
zhjwD;bPfyR-!m{UXWW=%sO8G_FM1LyC@O`)PQ%>Qy$@T4qW*Ja@!))?sXZj`C-;qI
z#v`M9W^0L5p#p1gmWx#fpxFeyT)2t-r*wnW^}GpZ9c>#=S7d%T`s(wX<tP*}?NuL7
z@Cf;f5#leJl?g_2LGyL11Rw9t%+W~SZALhEfHk_#S~_<3F*H5!d0LQ;C3E8!L2;bO
zo>F3iU2U|}dR*-?^v^P2>zX;ZeWq`6Cuklnxt#mmSLi^Xj=E*=59Rcoj_b6{?+otk
zC9eAg#cB~oNfIkOsS%k1OQuBfIomQ4b<nM@1c#b>-ERJy^B|3jbt2R*1|h-=*3YCo
z+WrD35v?>ZIZ0IW>>gO!J;8+|;R!0SerS$0H%PVzp|aWKYA!;;jwf#g^Zo8I9DzEF
z(DbPo0!ki_)|8vmJ*D%@{wL+b;E`YS-SO&rzCa|N_U8-L)_RliDlct3ivJo^pKo?4
zsL{N<k@_WkqYO{AXYKsh9OE63huIj`T5q2*JnxPzJ-KqO|9gC)!TaSpWf>4Z{8*j}
z<sAvg8!xRSx;^DQfgId_mh(pcn9=X5d3~<`pR@h<wDd(46ja(k{Q1lab{b0lKMy}J
z8Y$|7IMoFp8HKw!QDV~I%|Wpc2@NO6z<V{*_NbRF%UA6WpZ#dMA>H7N7c>ll?^zzy
zJ@2E!XD)hS@RudN7@0rfS!;;ir(Ye(;J;@>BV7G#(D#1*!d!^(-Ne*jsHUU-0sAF;
z9)S8<g=QN!&s-drDd$Kl9&Y@fR~dJ=*%a{iN14<mH@NF-i*7Lf0#rIk6wpvvf|h9o
zK<%xvBfh>+S2<k!3M@>8KBveOfHY%mK|bv<3spAvnLjm9Bgb@kNXm?1f1_+6W9Nvg
zs@`r&@OZyL*feBK1?9S*k6rkGX1h(ON92~QE7~px3B&CPb!?hjmso{iIDn7u5~llQ
z97-xQmlm>U#zZf{SV~S7GEj+=jOX4W3N{e%86-SsYwycRNCL%G9B6<4Rz|`t6f%`v
z-Gmmkag;8H@7b{5H+b{BH#Dh5U#2!Wz`}%e_e7_4!bh3nCLyE80wbyDY(>VV99;q`
ziEGp0TczU>#lVEXZf3yM^r3#OTm4y_V>jz3&UC&dGo!AaQ(g;^unuE`gGb;?psQ?9
zT<AM`7$8&j-DqELq7k>LW4rgm51_DCKJ&u34sz^Fh__OF{?a{A@7|Vyh$eA3hih5o
zTUfr^_N(z*EbfH&45yC!DlPCN#xG@v7)>2#*+#5<CTl&<GRd`%HEosxd$+HTvcmTL
z!3h_I@L#q#jR^vSUgrT3kR4NmH04f9j)3FSDPU-;{G7j0ol4kyI!%#t*C}T$paGDA
z4;k<xvG4C-5CU|DP70YY^;B+D?O$x9H*D<job}W&w1ymRf9dKRDW|A`5c}G%9In?n
zLiJgG&vMCnSv59z<ug~|SBvXy0bSR*k(P_QoJ70r8RU%mf;C3Oincr2F-h3Xb!t-Q
zOdtop2QJksefJFZ{Kgz4;`Xh2M*nKVy4>9>(!o5xdlU0l%&{g+#I<XN1-Lt6D|xUd
zJtqo1mmOOZvB^35@Ltmf-hh3AW&+O^#P9E3tH_({Uhe%)Rj|M`H&hHg0c_ut_e+Ep
z<S))PLhXEb_k3?yd*fLC1M8YF#|!4rdS1t-GGie(rNIzldE00EYn_!{ZEyXdk*~(;
zxsV8=&vseWu#u?EctpYc^<N6sT~IUJPi0$gXFPgEAp}9HE#G6!w%;JHk+)R7HJ_Wj
z+^;<zATN&<2MC2fY+`Q4XqSIane$yB*x#BAY{+#NnyAvx<5c=TOTGalwG+~}^5*!S
zm<xuXh4*fuBU86Vn7IB3U5;PSJ?*R?6{UXu-oh~G?xpFx;?Pf5xc<PU=e>W7I!%%2
z&vbBHd2{&aV)~OLD3b%%d6gCSBe7PGnicV$OlANpH5of~+j!dzM%d8tH~GQdh{TV4
z<66hZSKd9petLlGHo!ERD8OG;M;!cKgyUA`=l9-Yvo2<<b0svP=P5N~!NJu|4%+=8
z@|6?w*Ek`(|5U`wDb2-9YPf5eln7ZwZ(Yr}7~}Eio}2aKrRR%QVJCWQaCY8O;0@3L
zqNH@8)gHedOE?;v-k+M6peS^+aH)>qobCyx3$>!N7A(dN`#L&(6|GAgJyKF#7ucC1
z1(BaeLe-7<c&xT@J>D)(B*b*(cg<Aojl-%UQU*ftc<6dwNLQX?_twNrTctr(ON7Gb
zaTc$=qDjKRneh1rY<7U)ceDNc6qyFyHVnSso@w{a@EpVaM&IgT!<_Lv-cjXdPET83
zFFOZ_N5LFvA$(6k^FPmM!AJD2oQsXMwtj-wD%q7)bH_|m(V)Yi!K-&;ZLB@ux8)u?
zT0|JDZhN>`yf;W<#m^O@sXDloSKJHf>Z@V!^LVE&t@~pDXb?=of76Kx4a_&dY<Jt_
zOJaV?k#?iad9}e}G{7pRyDcoF7QE7&j!~&ZBcxk}3NL^wZ})<S^?HO@?|29IdOY!T
zAoS+TXg{_caW1|%pU228tBYLyGF-{0@B3>EKLpWP^&6=%tJ|T}B^A0YghOw>x~@+<
zzDtKp{=-`;!^LsIE0#x>>H(pNP&+NMaC2{Vl?R8O-VIpXAPJ+R2YL5PokHX2!()Ep
zg{Lm9ZL2Lx_l_9a`G~k`4{}j7_ZG1Zdx3!8D_Y?~3o=9NE93~1Fr&E=Cffc?mbn>W
zAuaEDY=r?j-LbfQ^6mwPgRp}SfxgGh=W;%Zk=RDrY`D%q7K&_>+0-QWah{1k`hE==
z>4B-{z@CvW0(?JK9>jaM!kXr@53VsUEB<)*lF7G+SdHQMXtER-oW1@eaBMC7&#H0?
z%u6oX!(RAP?)%f@eP6F))xyPS_`{2e;+iYd_TEV6r_U_I{Ljo%M_N?v-Zw^k>&EpO
z=5)ow)K#X_4279>p8&dqI?k`Vjg{)3oJz;VrZ$Qi`_qEm^~OLWEw+6JE|^Xx_*>QQ
z*0dIspr|9FB)}%pLa~)WttJu@JJKL5+{Xcx$VX-UNByMYA}bOr@0s32#NIRjYeUZa
zYFtB_g{bjgzwOqQ;iAu|d&h`TgWz!>M!WPY3)XyV7*Yc5R7s=&SICQ0FgVoxO|F+o
zzZ?ZLio@*-^&&QPV-=J2!y=^EwwR%^g$rSh^*somaSms4Zp823w(V>aku-itDVki1
zn`yyI-I?dsUiT$&?DGzUIc-FfeaaQT?QYihwovy)#0OxYAao{>;Z79EW0Lkg_8e9U
z@BJpKKUM&SF8FtmDDft{W;6C!v*tuD`l8ngq#l<T8qKZUg(=N`-j6&D;TvA>XUTMn
zzrTx&FQ}jo{_G25nZ!tkW~zq0^?d&wV%1GeG^#%!$=c(U0No{404fUSb8-O|Psx{!
zA*-NnB{g8H7PgX`<99(}gdb3&y@he#23C<9?Y(|-(y40PPVZO3r-cx?FOF58T4WuM
z9KaY~YL5XtI_jV+XN{ic$G4QJ2b34Q&c{3YwdAU`FqLij@6Kqtd%H+BF&ptdi<##T
zd4IX8v1zoM^U(SA-Q0v>9in3mm5E`<$KU--ROh~{@!-T^2Ai%j88>=~13uHM9yhn5
z8Qd6U^<j5DJ>&!_*zT!ySXK)nFfdCRq<L_#-${{4w`&sC(E1#x;X>0N(S)SQb&JJx
zN-kn$<6@wVGS>27G+<?=pfc}skmMUuKGqe4+3qvD*8DD~a`p}`FHkN9x+oz^#oKJ+
zrRqu*qWipGnC7&E{2K*6LJEqmhOt4F6^E@q7b?|jIaDr|q5nYd`v6tVcVvv^xy>B8
zf8%#fnT_%CDni?Xn|DztvQ=|%bn-Yf^eN%z8OoIQM5CwPvxUoBVvnkhvjAtQf}63;
z9Y8}rH363`JjkRs%j22aqwNPty^Vm6Z?0JOuxX-!Nw6TAt#;SbEvLqh-ssD1%TU%%
z**?#n-=d8#SJ)3fR=s*}Prs6tf%<h+a7=VKAwN5sm9Dj72+(T=Qa3>5GBhJ4Pge;2
zH2r$kbWOhEG;F-k7ypUsvTXU#PdiuiOg?J6Ui-XfsO*cPj-sroGFSu^uJf8bu5II4
z$3T9nkPd65)n$0&b{QF^jYF5$LY+S<Rxo_n(P@(7s?Lh%y)@<hG?DFqLK@GZHKnsh
z0QRr9K#Gh<G}W2SC2tKhEH>)TAM_c1y&L8*c*%qG=n#c?T+tTi?-&WbCAJy<7eP{S
ziJIU3lmx2ejkHa@?_8~$lg*R;3hWB1&H+z)+fVyQ3W{EP_Q4MvglpWZ#;tr2BtzjU
zvg0|eTuk3c^Xrq$wBfnujn0it<-;wxxOjcv+u5f-Vdcu6Tf8`FwYpe5`pDa>6R2I0
z=}S|d96s;81XnjA*>OvrKeu>b)9>2bjnDw&4k*r2`eBLwGdLv|y5n>iOj^)<1U?D`
zO})LS-H^@R&HzF<2it#hd~kXtx7O<TeA)Iy%+8MIl!|@_a`SjzkfC+iLOr3Q6!({f
z`pgkd&4#0aXw!dvzaA{lWp<ixbuqvPcd?gX^kaI&i&mF;I1a9#cu6%h(M&Ju`K$#q
zvwR_<U3tvh7Lsqr$r7(4P?P0nFJi|oQK2y&T6K?q-?GpCpVSfp3=JE0i=qIIibu&;
z?G`u8DRG%TZ9<QLKNYyapPC7pQkmJFSvOwe4LkAbp&$@5f)<tgb552yD)UoHgm-BE
z<8oiPLoB(rysfFfHDwNNIVi9%ZC2LT9lX!$H%tkjbLe|A2_3U<49+JIs0qoL$fw<r
zM@*a|vc*?jgnTPRgMASHW4r{p9gSktzZBta2o00dT_j?s!=dB!reSd$UTHY&rBO7H
zTq0e0naiN~6OcXGhXDN>rdsw(3JIU{WZ=GC_8BHJIz+H`=-R9J=I>BX1$R4+JG|mN
zIB^KT2Xjx|3DTNcOf7rr*h#S~8>KWq=WVHhCtBSL={<uDA5q{L;pMZ#LfeBh-cr0j
zRCD40+7Ad(u`20^iOV$B5T#9Pehzf?2)t$f=TBsiWR-yPJfSjCnc%M%^H$MqTjnY=
zvpw0<lS*>Kcbi`uUPM8v;rxU6NR+Z7Vrio=5wM@1@`;I*zD#E$ELcuv^+%T%4lD<L
z9OR+rufFIgF~kzjxxe{VYu)h3V5w8=Vr^V9n$C;}*F5H8o86NO9Nqy8#KWrWoqV55
zls2?g=sMdo9<$mPH`(_SUQ!$mq4+^vKZB92M5EaLvA%4*D2s{5RiPZ&IUq4JuhwO4
z+!8^qqLz@`TmV()FsVqnb5g?!W+#kYnD0fqn4yi4n0g;_yEZ5^Jl^WvRQ2Q4Hg0Z!
zackZA8#>!UlOu~xjN!#RH4z7?!BFqzL2_Q#PpcXQ<0|1nFP!VS?R?X|g}O6g9oAE|
zlR^Cm=QR()lDj#B2eyW-2*kDC*&a8c3cGG8%z{r%;i&w2zmzfkZnPFkcR3B}0o_{R
zD>)t-Gue1@)U0vnM$;7fQ*@F+VyST7R}5(PYbLzxJ9m>C?X2xc=sCe2P6i(z?o0GH
z6ehiBy{_@C$Nn5^x}&ltJDj6ovFy_JHrE?&YL{fGQa1e{=><HG^illFM@I2o=Xgz5
z1X>_0w?NLONFxyvBC^f769>O|LbGBq-ogS;F?<luy}%&v%+Pm4?j)dO1>w2#9P=O_
z0S}3`plk&41$R>8bLa7GU+4PnP0X+dXmpdJ=?%PT$kYxO@F=f(%&-Y8k)w2pq&P7$
z3W%i*!Ct8Q?-k{tg)42~U$xwp>20H`F_Dn`9vWRJ3$3+|XbSCcHb1O|cGQYr=KU-Q
zXUe3!UtOcegabri01*)tg8!c}k@@IPrDh9!QDMTOYs*^G$)r|Ad6H6c$0~BE5r62=
ze{zB6<RGi)g)NB@(BfC*=ZsfK9F?k&<}1_xBKby&L3Id_Npj`yPXDT<+m_kzhRxB+
zk=uc#kl^kUV{T-?ofH-4+oMPj?E`9^efV*S(|aE~&7B(PHT@)fHbcV63BOq@C;gO3
zp<}5?2SiXg$a|}$E<d3w?4Avg-XaAXo^1VUybZERmxGDO$d;?)hM1yQwn-pPV(j{?
z8|0AYkbjFE0SsV7du_&@4Pv$6`VJFu=#%&BPp-QNBfBIj)nOV6!(h@%E$`-OP%U>S
zQgC)x$PHq^YF4nf31=}E#^)QNkR855j$>HTixxV3cZVHhh?X`y@?W@^GY+16>vP6G
zCj*ms89%eRs~Pu$tdp9wCJlb&+D(fmy)WDsD%<JwL1Y(~V!U#y@y0e3oV&kGiVG>W
zf0kdr{QTQ3(|_1DDO|chzH<wo$38115j0Ix=Oz+3#g{)8@|zkCUUz$fgbTJQpWQ+J
z9Ns^FW!s4qfjU|3$1D#@UPi;bJ0v1JJe;^-We_R>WcMMtat};tC~85veupnQ;Sw^N
z1$}|Y!?otp(P6O?VZhmHOV7Ak<n8iZm-Ua{b`)H(KJLq%SO!6K-cNkSbpz`O+gy4t
z{{gj2Bz9U<7fk7H-_oTIYmeF5G!E~GYm8olXt5rz;%FQvrft6OO|C4t-_{3%7gnr?
zIw!^(cX92LTu<+6s3}aobx}OV9z^mnogZBec)fzPHWbwAif$n~`>TCNM-PL!&g`XB
z{p1&S>#}u*SY@k8<;U&ViO<pQvt!7!2`lTDz2QEhc>#k_HrUzvSdo3busT8N6hw1u
z)2c2BXKR2)-~LXfLxAzwv`=j1eX5_aVgFqY6Em3|g`{9aY+g~_RmaB3>kaAc6^4W0
z>pKGm|2=)d<STKhG4q}G9VfrXEhfA^Cqa}04pc-FteRF+E{{`y`!U!-VI#G+h6ESE
z4EF&mb(9}N?p2W)uS3L;m8j~3?tpxzPt!PmL=A^mFy#9qm{dBUL+yzab*hY9ASdIe
zJ>lT!W6DldAocahBmDvXEM=||)z5u;;Fyf?Z+)urG;cU3bsnS`CXDlrUjjn4T3B^Z
zCT+KrdtPHOvVs&c8OL9o`zJL9pvj9uVJ3nP?Y3XXH(K@Ad;Bo@MJ?M*2!3jwL_A;2
z{T6m{I3d+(B8D5MTi@d{xR+ngHd?W1-0RwpmdrfQ#%FW6Kdaazn8x0;Y_q$4ZwrX-
z6;b)AJ;DcnvwJ3`e?q~~)yClHU*KCVfkVCD>J%si*EV*Rj;4sM8XKxb*YwC~ztO2T
z0MhpfCX5K~8%K1XdkJA^*N<9s9_c3OsSx!k3&Pc8^Eivth-it-3@!4^sl30wudK>x
z3&l%yChT_B{<_f$MnyR`*8L>)v2Vs3WTo%W_j!IjSTvo%M-n$sLKS9jwEe4GdBK4L
zUC-t~H?#dcDhE*>J?6(oq!5_jMDDA$GxPSELgm@C3M4k2E+62{M;((lAU`|<q?u0G
z7s1dGQWK67oq+?CCv3~~x=o(IeaYG?4%(s3pLzOTvfDdbGVJa`_?RuYlZEZ_B-b2$
zi9PkD#D3jBYI9<2*TuRFOkQ5TFVf%a25&GvZ#Hw|QC`ih9VcxG6)w{g0l&nndn4Rd
z6hXbb-Sr(oMlLn5OMNvAxu`9R?g4?JjmBmTL-gMe*&@^D#*N!cw&C?o?ucT4%>5Hb
znzU#n+*XF%@Xkf7s^Jscb+l(C(j+#j{q84kkBhS}lp9xXmza=#+=zX-QT!pK+fE)=
zkMj>_!<mZ2O`l)-$f~3fbsW9O`Q0~dPKw#nA8M?+qt~c1V7!wa!swyYKq%{V444rP
z5eiU!9~r;1u~<nVBl@@&YR%B=chS4><9y!uf^yz{L}pIcf=W+Qkr_9gRs^^{A7gj}
z;~z~2MwbFSA5~B9)cdZTj+Vf@q_}hetx}mqte8As?YEGY(Q|{NM2ymKM2|U)dNNK2
zDin<`2D%R7bGkvQ4*5O;CT7ioaVgQ0OklxZ2?T9CV~0`W>n0x1L~>D{;b<l(;Y6U+
zQLWKMIR(8fbv1aoLE>E-f42070VhkUK80h46SMe2PWE&=K*h|-cts7Y?-ZTcq7G4!
zb_sFWRmUbP-mb7`ZI<*2r?h-WOIymyU7y$omXf#a6%{8ln~k-&DJW_y)iI2m^+Nkm
zXwBn(1LcZ1RWze!EXuX)^bG%K>gTKV7}EAddb+MKF3xFeiwy*<J#_rZ5b^fJUh{s|
z^>|*4;xtLjz4iga;&Wm(Vnfilo0ivTIpQEl4Y~=iAa#@w(!BHva$X7bC#zE#5}y1i
zLjL49V|4vP>sHr|v(dF-2H9uE0Q2sYXJFwJD$h}VmwbZxWR%L6oe_M{6Uq?FIIcw+
z(Qxs~k#eVC#(rO32rm~@s@a6guGKgy%2jD$Y&X}0x#4#WA}O>cDNlArge}f1%RR+3
zmEdhA3+mFyi1HVe-4-L1n=b;x;%~)`0~wvHRT5a;pZI%wP>29}4)6F}#c@;ubABb7
zIGisNN-Dllt~H?2B)o>@P_v|^b&8TRH7hg<s8D0VI-iK?rNbcOH|MzTLEnTskXUX1
zDAj92KzPz{I7jl?dUaP=%M$bWAs%3fTj}Xs-p_V80F%FNauI$Ml2Np2_lvE2%oH_V
z%U4Vp9e@kp`0^4e5i+?wF_r(|^~#M`dc)uGLzuKcCN-#Xsm^74ST<v{3RX_SJt{d2
zCYcZGk^Bpx=L5PE!G@W^|HYyIiY7WiOPn?Qi3dL?M2Y<7-9av$eP3)+i-I97h2c4$
zFH?%sE@88xGZ)z)%tINeB0cxqNKkvU>WMkk!@6*JX#6lgEb!)ql1id4VAvr!XM~`O
zO3SW=@;;!mBYz)|;AlJXi(}~^^=vt*VQsP3WdcEdv+J_T<uqaVQt@tb&UpPN!SuNU
zoMH(X;ZA~$q`Q%P7^Wg_`jq2IRPb3^l$|@WRD><*{Slbx-FV}zKH~O<BC_O?#RjRW
zsQFC~8^@YoOuBLObI*0s^NzX9hRANr84LB3n>KAdhIxX$-|^l~spBIFZG7!OOOt-i
zVxKs<@<igg<ZfM&qgZZ}*QRh1J2OIXJ#C*ZB7fA2(o^M2XpRYhV$g7Xa*bSJy}l_Y
zZ!chQ%zoOa@rhvjnWsNFsKl{;Zd1lH>e0W0=CFWtFk37(xynKdBVS6E@E%06!b|vz
zOl0WWD5+XP(A>JkqvvJfzhjbiOCMy^UAkDk+9F-%tfFy4W{T}9DoAhBFY+<J$Rb;@
z67LcFK32Q^XsJ|&1+oiCm^Q;o7~8#JIfRE46576IdSB4jqb<GP*=H#fPxyK*`<@9|
zFDtK>>%ZxH<2oGl&H|sB!Byhs?W$)>7vFFBkj>Hl&7pi7{EJp8-`=vd^q;89-$ArD
z`;QRu+rU_j_sVW49Lm4T|EtiO>tAByEq}Jc{zt3}V(b&7UgQL&7zg$&rms?(RRZ!h
z$WY-=fk3M&#pa){ZAs)_g50sawb)^2;~C6&Jz`NJZabFfFgN3RwGZw8?v%gypXR*2
z-tFKO6C|!D)+<hjt|2B4s73Ic-sF4hUL4I*o1AiLhOL+36;1flZe)yAuacDb<DiI|
zpS>3c3HDX;-N_=c{|trwLHeu0bIHC!1JF3h;aaBo1E1*-qf91S2DZAqk|C!Qm++wE
z;hDUYA-8)|N<+n|3=73>T#n*D<BA=bodWv4_l!;r(C_Ap?~r<r<hcG6vZCu6J(8_(
z{b(^yh?yO`s;`@7y_k^y6+uUWkRt&%4*_4lifl~#`f~H6{7}h>!lCC*MVy+xtxZ7w
z_P_ls#|3Vl2hOkx*>d=)UH68X7^LH8>8X^>vuhi1@V_KkJ62y7HQ(Un&~C?$jS^M#
z#s~dhNch+etoVu~|A;nzgHAJokTb$c5b(;Eiono{FW!BVX@GOW%!(UcE>*80Xnz)U
zZCx}1>*$Wiqozb(QCKRLmJ?^=f4U*LX+Bq@O=E-N;J^N?Q?@d<s&a!*&!tjk^u1pF
z9I)i7SK^>Xi6%vv!d^ABAW$YZ{`TzDjgUCp$3LWbzRbgsR=UrwF`zxWd>T=yg}d15
zH=nn&U&ej1-1?yV@4xonSa*X^d?QX}!(F}+A~AyxS}VDyL}9paV`;dmT*&v#uuJ7^
zb2aX$h@oc*fc_)`si<k&EKsW6^0DF_7?s^R6bVWJs3d3!&fn)s_3c$$j3rt&p818_
zwR?Pn68&MANIdTn27ebsY+Z)X6|I4flUnO8Ia{w0RyB+jM#QAGka9$3+D7@YsrtgZ
z0_=m|XNqo7THQ6c_d;T1Kik?eL5CE)tk@a(dkHp<r+bn|XjMhNk5CP5PNk<TOvLC@
z)Kx8mzuIZ{kbX!1GNA`za?@AUd{qlZ?$jshx4$WH|6xXOqC%(rhHYK_A!O=^?!ZjJ
z+srCVMvW(?apWTv2<J6QM3nEwa~uJyQkbT|p*^Ts0hpZ(8`VCj|7Lzz;^LBulRszH
z*ScG#`?i@{xY!0e=oy@=W4ghW8kGzFvn+UUM@%%H5Z(S~8(MUAb{BMwW)PqpgLQt`
zb*-7Zrr|OOy!HL6M`3qgQT9u1?autXT6Q-P51@4!%(x9%4D-1{fn)YMWhJ7p#QZ&8
z>fm&!=N1QD9bM*QcjwuQ{>6;4E8|&W>FfUyN&myzPNRY#H3rnwyA|lvG|K&B`g}x|
zYe~Ohr=+IK$qmCHrDOb$Z5d<zV2%h(pEWk>{X@=oI^~1tEq}aB!Oidwb+#)&{vd59
zJN5V7{*$PXHuqtrytUT(Ctd$p6HCNLP2;sZ^yTXR)VW&nzsM|4ccEi&{~)uZSwirJ
zrlC=z3w7saDSsTw?A)^nV1z3S3yqDOk(LDF<i`~-qtB?kr}_`LA19-$6j-aC`$b^K
z@06-P$zYn;b#2)Rld0EgR;W=<q$Nr=Q!#(6a<JeQDRtFHt`zLK;U6c95B}jas2a{z
z9(>fz?B0>Y2*&d&{i(QA<jhi}<gbj-hWPo=oqkSxx!8FzIk}FECG>YJE$(umb9Dad
z>ea&?cFRD<V$qVpieSQ!vDqQ%XU5A}duFv5dpL{!{smbOf=%VmL?%tG!BRGNisNOY
zM|U8;N$GV{a0iFp2Sd|idv;hu7-SPM@AMb*=@ckktL$Bk-9Uz#cS#pvjA`n(hh^Fw
zYABQ3*=w=ngq6vg+-ZN32Y-of;Xxny6Yo7zhVwh1p=&co1_{tWr)kd%P<Qi^B_}B^
zPL@<a`+ET7kiiKc*93^a{vz;zeH9iATrK%clO#}ESP%bLF99E_#xXmb6@cEvhHjxE
zLc%9;sQk0*t@y{ljG_dBiqd1^lA)MNQe{K+S1nmI6!6`(lR|ib3c$RfV->n&S9Zu`
z&DzJNuy%~$@Y4f43_nJ?*_S;xb1E+?#VmCbXmFL^Ph{v^abrd=N~K*s2L{yl8B+3X
z-HHL3(l622xC4^iF>c9iNFN*U?9^Jj#Ia4CRJ6g=)F6yiTH6oPrkqVIOvGmEX0uDp
z$>+$~rxs-sgsh8T%-hZwSuB;5tf;%c8bgTnS|ZL5b3IbBBy9MQq4XHB1|O4w0!?)6
z(6*s=W>mVrgVH^)N(?~l#W<9#lM*lrtW4xASXK3nU{$`^Asi3Yr9H()xW9#kkaj5q
zx~EiwcF6!H(e?+5!1x1vY<O=%5NxP@D29KIf7T}Hxb{wT>?H%48+-)Zr`T<Ra&cp@
zg!nH;CTZDCzuy7HqM(vUBlraz*s5{_(Cwa($!xvJNQtnjCzDeuvAx;}asr^5mMwue
z7#K`oHd0z&nVnUSCmIohg}2KSiE9_V=#@TH$c{}j$k~Lx&1Qlp53(=AsvzB-iB=GK
zdW@WBe$k>9Z@55$u;euQ8nunWtelOAjq8EUGtj6K-epp1+tnD|kIM)IM#)(0+QnX8
zk%QLEzYEcsex>g{8Zt<ui<l=N?34oGreL{e${t#908L(S`;QAX)-R7l$gs~X`!sMW
z`s(rrYD9^q|6<i&TMZe;E0aSs#KC+iF4<HQ%{I15R#1gw!*;gtw--{NG}0aj;4b~P
zEtj1QJ^#o|Pi73+9BXet0}{Y2nard`G`A@zjW+3Kh=X|_UvvGWrfB{C{>#b@@en3&
z!u?z{j93B{T`_FR{aja}v`|A40?w%_iysbFNrXG(Mo!|qZH1KM-usSR&}T#5qrKy5
ztyIW%@9b>EYwslaJdLZRwrqx?agwRGtlLETEHOu;{s*v+Lg%eNnGKYn;RL<?kx&H7
zYA=(a-ALXI-O#&EegGx&LOb#ypad&1nwWnxZ^5d6_%3YWSbyKynS2NN;Hc!~tmDu*
z9bfA4PCxVFWth-{BY6o8OjHp0K^&=4dJRRI2veT0e^gjRt?6R!*u-a0il~aze_M0!
z>Z%%yoQNjf$e5kI0f1bDN@PQWmK{d~&L564FTG3rU<0W$FaYXe3im*cQL`bfJ<pW3
zRw7axXRWUNu36I1%1giKN3|M`*3wDE&`^M!2wtwKXNs&Tn`ZHxn1_;T)XCGY9MG($
zp=L{oF`01GS;ULXzYEY`)>n@c(P?J+bERvl&|cj!`ddOLh=$^d70kOx#|4cPIW?Ag
zS1u8<72}9uME)B)NBXvFWa8q113Lg<`VMZGaIBEs8V5WvF$!nq%J2-#*E6Rxwr<O?
zZ)L1csitZR%fC@JBX<k74%0E2BI<G}qZ8BSXBvFeSZN#l)tq9V##ry_8cPM<@1^Tx
zX4zXH389BYkvgQVSPQT!W@O(;*rh6AO2i7eppqlvpY1KOwqbV2*U|T_l91wg3IodT
z>5ewmDqxwzZhIK&@MBLc>FY#Gc-++iv%l%zRRq}Ik%72AMv->9S~-NKJ*DdV2`3dz
zyvb-J%kX0O67vA8l#UuyenIImwz-Ls-3W--Z-w5EfrUP6fiD8DB_!^6Hfa$MDornv
zxF_4J8QZ(NYgEBRg@P}4A2&EL3uJS5!~{j6V<(1q<|T2(Zxh94)6m}b8C4X?8TH#b
zb=d^#bVbVR^ekv4eFLrrUdF@hlYy#sfedgio6(6B!0W!qU(`$XRGM6XcZ~qkvDv#6
zYJMU;W1UF~=<VJSR$8Nq`jV4bV$>OU>n+q$^>|FA^q4?N525T;ijSjl#Iy;RB)c^{
zgz7zwTm$V(8QGz?XRNUQ`28w)2#_ubc6!F(kzj(APPS94R3D8rX5$&VjUJp}15O9n
zx2Ix=by)YuJqN_jKR}c$*q4Gh&wqdtzzs8hmB}N4nyNeYP^lM6<S1f24e?5t+D}j&
zn0+6PU(Wh=cwtwj;?`d|Ud#Ol#XLL!$-0Fd=zptuFj6I)OWXIT;NaAkn2?%0Ba75}
zYYl5U()GQt;1>TpTZ>&Sk!h*#mdK)Ji~3Nz(z{}Kmy}du`zJxEHP9~4Z*C(>XH?Qn
zU(1!~iKpnfcWKR1k?;L<T3dDOV=ruyGiYe;DF=F{r_t46xR&~r)4{%QHHiC;>Ycer
z4oOgl3Wq}83w_SeL*Z+og(YkI(}?684#ObNVVx)xQ{DTs8f`<5)+Qcc^;q}gP!LL)
z|4hx?-90Rf0bz^W8pO*R;G6h|C1H41EbYd%@pv_lFEVl1aY~Nl(IrW@f_gsuQ*As(
zv?+Q5LHCqiMI{=tA}J8A+|U{g^f*T>m9}Dsnb5aOJB*cRU}xXAs<`!(;*&U#qxtrW
z0SO8qJJ0MiiV5nzVm9U(M&YGZb8ot4`%sFi#6RI_im16-iE!In#4EIJh9tySP(W!K
zMs`?ZM8glBNglXy8>n#xk^wPs*r}kZ0sG2s`v(X|`omhYLmNAfzR}mroT#QkVEp!^
zGj4(npB3741d_dWYXMcU)N9paS0kkv^@bPBFrHaLR-vD*P8P~m^i~-b?YWFA{7_o>
zxCTd6%=%N>Fr@Wij14VYX)(xd^A`NXoA8gRD_Jo^@iCM0<$LvL;&6vfHF)IhAzNmW
zd+DXwo>4*QL(+j&EsS@i2K_k3v2rKNE@k*umgay=PL<ku=1c>0kSor1iHA^8UaV%N
z#j|w#G<xQS$vsT{v4@|3oHkQS<2Jca+XzJnzI2LmGJbelm;yuHvNL4nxAtz`lUZB8
z#FNIXnKcTzB|bA}MVxKoOBn8!S5*`1IV|lVkG|bqm{E!aY@UVtjv<PqK+Vwb2aI`G
zpeegqoO`Q^@ty~|F-1g`obic6-G@q_6<B%tC0MBlije`|sXLApvl^cJK5kvLaO^v$
zlc~?lPAXmp{ZNMZFxeY~!h+h5b%zLII()oCfcfFNlDY0Fr4k8AYz;OrK+)h;_KaCy
zfoOB>_nKB6IE#1*E;p(U(zl%!>0R!@@%FUAocLivI>b53*e|Eo`LfIF#3T)e&49uD
zM6lJ;-E_TTmB<;%bPj5UxL17Y#g0mq;d}<di&ON(^gj~?yVH8Q<-0hTi#NZy0kTaA
z)AZ^NHchg_`fcA`LqS(5*KtE{YKE5WLHVl29>NJI=p8}!F^nwGHCEVG<AgfnN+M;2
zS;xb}*u$>n*6$p~;)WiQ;Xn9FZbL!sQ&&S+>po;0l<$0?`KrVxseHtD8xXii&HNCS
zx^4e1FfhVw+I0E6=7o=^JkFF1Xj&hZ&kXVo#UC!C;P0~SfB&9ohrf;c6wvYe62U`=
ztX;@hhZI=RcK>sS#&$@rPvMQa!<{5kp{b3!qh$3@{FCmbWkR<H;OdHDb>Qd^aTYWs
zxHk<LoqsR=#`~OF;n3fvL*cJp7exO1vVn*^0pZc7a7@T8ahms0Jj~~BsefI(3BkxV
z-udr}&HgR$Z@B^(N-*!~ff=xW3WRm8iwnX$bVRikYyMvg{}K>HZnOlK>Re}KcKk;R
zAL1AH$O_<Z=f)L3)c)(u{*tpz@j<!Zh>*em>qGv#p%+hxIG^|Dg_4y2`6Ay`@+^oK
zSrX+h{}lM>hyR~{O6NCKhNi_Mgz~l8GdtW-oSA&LLc-+9s+~n^4)+voGw(z(n*)?R
zaU~sa(FVa1+*)hl(F+6Xe2nVqYww1>wK|;XVp~pbZLnlb$c~ot^}GBvncIRgOEg9F
znrxrik@b!O>C;l`*STD6o)tQ(-n`w#aL<yt9LZNLPMmPb;eoc=<II;sAAP~kD=TF?
zdn;M8-1ymLXL;tYj8kK2`|D+w^6WPZ0wX7cV%uU7&}Bx%^hB@nnd|vlbXrp0joj$5
zzwf-Ki_~)I9gk<bQE>+~7rmR;-cNkRirFm0VkHE|`bqUvRV7RiaM-PV8p3^h<A5dY
zB`1j?+z5sGf5^x7P?V;BQ7`X;Ovhpz&K6_Iv+3~!Fx|IbXE^?)TMcNwWYP%+Gg^2^
z1bsJ>h}C=P_zoxbm|ganHBS<3P!4yS$$v81&T>ZdzOS%&pp*%x4UTe;8uQ#mr~PcR
z?Rd|G{7UbL&6&rC+2Gx^N1~3H;$4W(&1VxlW-6#cbl&!W*An~UVz2~};s&ZoW(Qt|
z?{-&xP8)c2Ca3;d+y3j*?W)nEL5~`M);mKp^{vMxOB;^<Y`IhUr-xOCNV>Z)NX9#f
zfX-^z=jYy5xp$SxB5V#E_0M0i{3J=dd5GW7zV1+@n@&#c;&G!J3nZ>B#<qx!|M_VE
z6;+`zc$miw=0PWIT&Bm%|47dp;pRZB<_JYX)goYixKuH~K;7o#%=@~;@?Pc6b$9#R
z+GLpc>P~@)xmW6vp^*F4%Cbm;1s}@acP9@kf&Us-_z!)a`85vbkei~})K6MM?r{I=
z^#~4uCwSbh&#yelI|iGvGyX3C=G2xT86&~<`iCZNr8M->0-KgfNY<|F-hSTOH?<M|
z^rW^zimHD3ExaY@;XO<j(`_n`;?=Q}M?TH>?q4r}hwj}UB>{mf`-i(8%~x#PYxB`&
z^q5Jv3(He)u&E2GQC-GO^E8n_e{D&LmupYG%~J1!TxAQJ0@>uS{YCGphlru)+^tY>
ztvjQw&(4aK`=1Fse@gdNls}zdYx!ft5oEgtFyuofTd!U4%U3+U6nmDx=9{h)!5^&!
z1?YE}Jr-A->We!zrVi$QQJfx+$SLLI*L$WuKcO}}s?BDFcgpVlQB&hEvw!`N+H$Z^
z#I%|+-@Q2b{YZBI*fgrjcHPicX-;psw&TXxUmLwyfHhu`FJ+^Y#&9DBzclR;Lfo2u
z=9E84blZK4?sQx}YX9^x_u&MXfAw_Be}gL^n#9((+9k91Nb<2qf?EI-zbHSN0XTQ6
z=wBPx-ya?Arxpm-{$}&MGLFA#*6g%4z_T^)1bTn3=@`Pzm-?4^e1ipd0z2Q>5t*K&
z;{+(m*X+@Sl*V-SlfBIp8&=O7I@om&c9AMBDbXef!EVV_TW=|JpW_UZ_#bD@AD90K
z@7nQ+&Bbnm=GHDkmo@|w&F4<bBZ&RZU`mFzgQ@U?QId4#TaG*ixJd`fV!!p&u(K<(
z%dx$KQ!nkh)${%yx<SkLIQ@$G?H!zMU1w+WpCjz`84{4G=q1pf+G_inTIuGD;9Ma&
zuoy*I-PB8DbKM|4DyT%$Nn+}bCkLi|Kkuj8*-v6|ZWhGeX@n1sGxTm805#3?1;;eQ
zz{DxOJeG%`Bx5G;{?2ofnR=W0Z3GJkTZN5hF;ULq+3K}>9Kk~s?I|5tG<g#6ogWh;
zUZPEmD+=36kw}y*K19VB+8C2=qg)O7!L@lat!aHs&pgEBW~2z-VjtApk5R)l7fN0i
zu&c`VYZudax&>Rn;~PCiyXj?1i;LZ34KLT}cMyYDXpJPK$}w-)p>XpyS_>iOA7-9L
zz)ie7R0CtmOo{BvS%kGfW@u_GT#<RyQs6^;Te$}_*V9y^CCSxS5AUYRyxyV7wr5Fz
zL$2&SfeH;*kNNUlEG6<)r5fV3-My9vrxiV6n2V*3KuBo`+STs#g1`a1?Fxq>7|9pb
zor@snq?bLY&`8XQ!fOTcsN?a+pKB&3Ex1MVtq5ahP~0;%GS6SvtluvwgJsuPE$2Q3
z6T{iEr7GCX$d-cR>AdW=TlAXA@6*I{Jx(~y=xA~FRUM5$a>L^b3GZ9s6mV?PvbWmr
z!pl82RZ`UH@Jcqt=1QJ)hbi_Z>$Ox|T~+<K_xF#<@$(3)bMNXx&Rs}!%EanRp-mi0
zPqSx?OISx$Pf@vy9mA6dZZX}mGv}fAs3YuQySM1`tQ&Vrlpt_(!QxvM0emyuuG80l
z)7>W!2{-)JM6%F*g{=%tz?%6Tkypm+N>lIcsA2e7_ssy>Xy+yH#)eQ59no2C7r)v&
zAFi-pI9FV;<AyRy#x1x!T#ec5MB;0v*6?5tvoG~cR^f!7xt*|^V`S&ASN<zzRE>@&
z9xwjnkxDLk7CV`r^|ZX>RROr7XKaKyk9enMwE1}&bK@p{ma&<l?4RY#ngn*%*tj?q
zNbgg-4!*s}I(6hfMhRVViN?#JIdTr=O&6Y$w3p?noOvFae^zT1US{6hjC`rIAa@@v
z(_k**cy)Uf^wKd-U~uzcA#592k&jfWk)@lmW%ZqJnkYT)k-=FBv#DtBT#|)hwZ#?z
zNG5wGlbo3hh4|LOv;MvaPpw_HxF2`jiap_1dMVfRh^IULM)OCYw;<A)#qh;{<l}%c
zmKuWXeVI#^|3lRucq<=f0n#J8PfVsb!cFlz%4tm3U;|FNm9z@&J2ZL!4;i;Jm^0uF
zcxW}j;ti3sS}7R`8_asCj+CiS2_;K9U-9L}4%dpeHiDE%r+u~Q#as!^Vgljd6qWxa
z%L1bmPH&L9y4^q-e@{z&&+_^gT{+l4D8O3F6HtI+EZ6Bw^zJW0;vvx#i(%v4YDkZd
zq(ayV75l4=&#Ml$D=6l%G>+V>Ju}Vh0D14e=20exM9Qm!Gua7g9y)t*jrteKyEGu`
zrpK$RH5nNiW@4PTc@5mT5mgPwut^UekcwswNr?qq`^8W%<C+wd4d>dqPdfdFZePbx
z7NeDwaQN<iL18MaiLbj-Ll*`Pu^V73V1>aa{5{d#LUS53wv4_<Tpn35b7&Iv8Tz=*
z=P6j<Y%^sPt=Vy_Z?<RQeTk&?s%P*RSMzuOq^$nMBF*AR%m>?<fiz2)XQ=_W)tL(S
zz{NdEPw(^+Cv$|5bFRC@N;1NexR22$=XAUo0Eq>2WfLAg-_nS?B&mNZmY5PIDzZ0A
zv|Eyz7hBjQN3J1>DStVA|2#M@M@0YVtb{8ffsTne>~Sh3jxn5fvCUS}$%NM6h7V24
z2l`^APeC*+@%mOH;=`qMv?nDwCD+d7xN!ufbDJ7j8B~VlpwAQOT^rq3@+Mk8GVky<
zYdPD2S>T^}aWHJvlw`KK(p1A4z?P$o2p2fUhQ(fv38(&Nd0CCvQ;*n&mdf0!CS1Vs
z7i4Z|AxkujF8Je9V1AmEBu2JPu7<Iy;dG$m_GN#+()f5xl72g}TYp8`w1L=a-;ns1
zXiw_UX8e>*+u^-s$m>U@EVvq|x!qSy5>0QAuP;kRLHM;7yV;!C0zR@w69DGtc^%Vn
z-=9#<M=nGLcYhCQQ=<{VYkL1A7k#*^DP~h<)zB(+>pyGB^qvGcR&+nFs;hUnX*^s%
z{&A}RkGXejt}JTXc01^fosMlAE4H1EZQD*qovaug+fF*RZQC|h?7VsRgWCK41^eq&
zv*sK%YgCO3$2d=)ldTp8eMbDTML0WN<vzlW3JcN3^KXeT9@Ydy{x5;mdQ(QL&bQFi
z0`8)sRC3l_w;#Wc03zChAzV8KRq}!hsGTTruTk@7C@Yd`242pmL-(Se_0gHN(*-ap
zF{i!bV~NK>j{C#?#Wxdz7877}o<7iV1Qe-w*uaf9ih+BcxTn6*_Ux5ZOsF;%yZ*Mj
zRE|K`YF?=rlwZwFV!@C|E*>ef8eBd&*?O|glW+GFtPo}$!^4Z>i*)*fOPcqdE-O@Y
zTkV6!TL&n~l?n60{!ThRCLt_wx7exmgzLD)Xgp7EuN_#r_js?Sv{InN`^@zpA3ZM3
zpNPBs-U-!iczr@XH=UJ7lClf~=V}qytm4XZUq&A2$|>mzCUXz%H_KQU3TdN^L%Yl^
zp)9v8i5X8fru^@_5`J-1?;~)YPEp3#Yz%*VW2y_UALa6#3d&Uey96W@>Ha{rc2q*K
z9AKC)$FDe`Qr!RIVyqZ6Zlc&uK**2mjt8$ui?x?+#%w2EoPGAynr>hmKm|2CX!yxH
z0#0C$Zh76oUIQ;^-nspD#ht8Gl-YD#kvGP6@0l{|aLkwMj*vM+9>IDK2TypkCIavp
zicDa%?-BStv@A1~-wk=E@JRE0)XI<B-ryz7PxM7S?6;cW@g=ULq$Ja%IBS;BLJ1Cr
z<Ue~X_7_=0<;Nd*uNDXj0uABlR}{S5#!Q!!hp-Z#`h;S`5zBXQKRH^=pPo9VhGW-f
zdwyN@&CI~LOQ@rym>ykUJM?|^9a4rx^Mrh*=VERqTdV)QNX%FY;sy$r?~<YjK_LkQ
z_6EWr!p2c~rBujT965Fq0U8z{85P{UVPy~K#|+jMuOy{MdxX?zN8{m%=!VfahlG7v
z751VQ$~E2}yxTh+Sat{)60-(NDue%O{8O_Z4yN9q=5ENwia<%u!HLwrV_FpX+`V6j
zHzFy4y|qW*`5Th3PKP?{RI+4EN9glHZLLO^qg(mywLwJ-bcJzhIw7p=C~8AoO8MnT
zvjO@+XfH4nc0lW4x#F_Q78x&j6vZL&)}D@L!!v2Ef7KM>ZmS?s2eGMVm$BNFye`Y?
zn%<C?C^}!R4i^j5W#RGTi?JW;yo+)wRAhTvIFnjhD!hd?U(e_?xqQo-TD*$I$E(_s
zq8;I#;5=KZIne<lP;IBFUlV!(q$JUcEVqB?BKB*XTzA_-s{O>}XxrP((QQM(G{^KY
zNi^L%y|<BaD;Vl?*5GiZqwm=Dyb8HTSEJY@f5Dk8fk30!7_=t@9`mIT9mT7%Ui8we
zk+tf#87qh*+K#^}RPG(O$ks&Q0*wqw-;Ltgi^%dR9~B6NCT8o-V;CVd#hgHQlq59a
z!(q@Qq@<-7`!TGVZ|(2is0p?GdZzbf>xLF?cK@^_A7PE}0-+qjxrVDPIp7a(76>$z
zaCv>1qsW9mN7D&WDbbYV+Id12MZUa-{znVI{H<N$c;6$bn15MPwlR*TM->?VLPA0U
zEF%DRRRp&@HyDmM8$;9a_VH9#8f}HR)PT9Wn5C+ySIc)Sy28LAlGYmH=DqQTxc<64
z-Hh!`foOkunrpuv^pYPQl{3{Y6}|5AIDtU?`^RxA%#)YhnQ4Je;`zD(fn?PEc5={o
zo#P#b0^vmLp{M$Jy9uuhiboMuF}-D~eZ0JUYbR4Zd#5waFfo&WQ@xH?J7;Lw<=dFP
zM~JHRmgxm+M036xI-fXFv4}=my(1rXh*)h}tjyg28>)*^WLr6~!T<-R#F0n$%+pXR
zqRZ!FJV77yT>Ts@@dxen&R_HCpl=@PznVJ%{#0OXzo!xkPgDAN!F}7qHhq6T>995k
z>zG<L276`V7&1&Nm5Xq$F2MBkt%c^`+|zX74H=v+mc$%PAMw_`==#0vSZ`|F|Dc>A
zSEiL68k(xwJ)79NeW0Ck)O)xup+Hh7x;#u(?HWP#f!}I>WZ4?PQ0}mHCLv>7Jl7CY
zrn$`V%Ypk?IFoi+ZkzS;_c{_QlJicS_Zb<|ZOZ`e4b@!VGg>Y(2|KmL+&+}KKYzT&
zJl}=drkpk<sD3Y&p8DmPJDK$-!5IWio5Vk8%~-i3DU4%=1QP&^tqCWJvw>QHCA-Mw
z_1C_cs*AZYdhV6*JiEW||2ejV3e@Q^LB(~`Cwkpi7*7KPP&;GXJ|r2(ix(uYCVu&k
z0AhA=3t%l9bXKYef*aW2Gbclt`YaD+v$`ux83kY4N>=h75=CfyScav;Hn#`foezJJ
z+tJdhbtrVszjoJa-dP~iu;5rVlm$u6N{B0@|H;m_56Q3gq2q54@MJAw^_(T6p!bL-
z>x!n!WZrk5v#Y{;_xB%_j5Hot2gz!X%p4A1^1hz4!e3|<-#{S5AF8m&?#`RoB!$P|
z%BXe7a%}kUlIc>1bsC%9%tk2OI9x5FN5rl-U8+7vV9vN-LU?GC-=Ctc$_Own6-}sR
z9<y5#%i$Yqwm2aahtp0PFkV@F8lZuFz84eL?b*N<g4gpx2><Fa7}<F}!o0qNtFFCu
zV2ch2z`6ieSLQpv#~8CpNHQXCRH~0C#-KlFmym09s~)2ndH<l{x-UztmSM*w=jJs8
zk(_d4?44hT8H6$Fv9GZ>-VDpM>D<q43?+}i9U>bJlQ#<?`1tZc+iqrt610Any)(S$
z%_U2FB`3PYANl&jG3t3Zg)BEE?vkL{dPVIgaxUJqLLV-vcTGA+P|l9eT^j9QmmA`^
z)H<o#6ddh8*Ska?jF{__F71<@(3-Mu@Ox1_E>Imrg(!@&(o>s;pj2w!rBwWA6=Rjl
zig>!HhgQ)g>O0~mcI_N2Q?0H!oLG=O)=GBsZACSb+{<in)_es~;w`Fe$Wly_p`&Qj
zk-MuUn2VxHAN$+W3U{zj5A%#qLO4DxSxaoSaqpPZ5DRct`?NZqoHD-G$0gRr4JGl)
zh&K$fAu%2R#bQ7avUlb4r@bkYsw=gpB8?YZGJ**Nyz`aBi>Oe6I#E8IW?aTYk$cI#
zH0#XfPlafD_rEZl`}K5zPE*?msmL*zfP}g%SFGDv<v({PfyQn>lqzwzNqT}7AEOJ5
zI}AEs3~6_K|IE$yCGZ<x4&JOGM&mWt2C7@lB_t-A`!|Zp>e>EJuL}U?4J%i(ACM(G
zF*tHOI->jfZ)A<UcOzpsJZ4CzFZGFZ3v}?u*F?)iaAJ&{i}|5!j3Kce?UlkvM7xSp
z3UZt(?=IeG#%bRcwS`p@HoDQ0q>qY3yc$MTN?{}+pq)w}dmaw-<jSg?kuR3CvwZ0O
z?2=7)A~WeJ<c34Z@E2+Sp-$&LxsGnn`sP^~|G{32EKIrs@0o4vTh(i(3wTG;R#oAQ
z#o1V+Fv_eN3}iB2Mrv}FxKP=?aWm(@n(8dqz8sa8oP*`-(XxZ>d@!=Q`Q&J>7fj(%
zA*CTbG6VOBQld3ytG9FlS+eHXNwOOk0NdnQ-zlfz77kN8x4<Us5dLBj)gNJFAz6LI
zo`kJ%o~BoK&4}EVyjU@d9#9*F9~!-vVY3qmIq4ctsJ)^_<!i_4p~y;>luY0qighIa
z#Pc_n#2%e1At{op1SLvr+!AH3^*()+rsp*=Q@nG6;zh@EbQ}cGayymF^fG*C*Ew02
zj+x0DD=g%ExqJ}`zja=2{cx4Xk32k4FPdwS;90b*-0$H_-{&wdGIT&hombzz8+aDe
zksK{BzxZ=JApp7@b*=aDWTP;&PvBf{Hpw)fc+su*xdUszG1^~&shldj_e9<Kd-yjz
zN~^G&Lf~ENF$qSLtZgf8^x=5C9M^iT$Z*NV=vP*)a^8WdWkQEc`a^~2XvpnN`M$*%
zzZtL*7r$^~HhVyGw)hfER0W~uucJnP*S<#R5}6`~d$>%d#Y+3nt&0xHMxCXG^!oVt
zdbo^nX2n8zV@)7S-V{=<p>NHGG)G6$P^L%Pui30tmX+K4SPm?b{Rzs%F5&?iw2XG2
zpmwgo<ZOMfV7V@w<EuTvR`$@X#*gk<xV8Px#Uy5v-MHi)O{po`wS8>NXAAo53=FMs
z@`3TFAFeh&yxu=0P=ivA$la8vX8fy*;Ez>8mpjg$cQssAc3R8evAL2sI;qET>w+&Z
z6UIhuM|?g8>)y_3rm}~h8@w6c-#pV0ACuhB<5B66^vA3u5P*6-1^5TT!Ze6cX*-}Z
z=0;mynYNY3T@OdNq>6<0b_a?sHrw%>oxOq>9;LaO->LTRM3yGWRk?4<^@J1>>t(8#
zxs2_gJl*mn8-b?GMqemoC5J4j@@$ILLVAVDEV(J3)qg^2<I)+%Po78e^5huKB+Ti8
z_9xTQjpu<zMssk(6I(+VprMgnefdI6r3GDuyAx9Mv>?kbpHek8SrwR4iKy$o_Bv0>
zj<!aF<z<*Bv9#sS%b5N!|CV~o2b&})XPY$qG4lrM{~D33uR@-mR;eWI*6j;1Ws;|p
zo(xgRjpz8PN}$GM9X3UDF?%S(fp@Nrq<Y}eb2kn7U#Vxr$TaA;Y;FrD@OMxF`6ecH
zDY?84*)qK!4L_PyI#-iLqi_ODFp``O0~6bd5%%VB_2&$>smcxq9Qj+YI;!*Ei8XAc
zbWOVUd(r?cAmp7ay?Og(@V!Cz;k!~&2$SD*3i^MAE`D^AznHq*<_VoK)K5g@nFYrs
zQ0_U}Ob3IJzUK0z$WJ{?P;ot=X-(eWr?)3DP$ErrLtE9`jOIRl!%;yX^1W(W#w*)7
zzEIfXxyoCzeVis5?k=|O+|~%9YUdG~_I$!Rb-ff)RgjQh(`Tbj_*tjaUpG$@)Wrew
zuN>}K745C_Ud!<-J1%(n6PClcBk4k;o7T8j6?cC!)5Xc~1Ev237e<E?hr^oU1ki9D
z2aGQ-IJ!HXjq8v|;9^+vmWa4q*L3E5#t)qtXOCf=t36L%wdqzV+EPuf%&s<FFz@>3
zyM_Jdj-f!h7bUs?2kWLE*{0a--{A<mj%<x&`i8~2JEF{Ms)tjL(e5tl3=d$=`Ob*I
z>aZ7WjL`fF2#9p`W&%Yc`@NogE6km++yVq2`)-Yx?2BJ4so~(2Xf`1r@gs)?@fzUy
zKe3_|m<B4z%usn#Y5g*e4&HF%>>!1Ex_(FS2=&+_e_`P!2fbX*NlV`jhpovKU6H&1
zH!}IMNk&L#zhh$pT0y#WGG?_7740jBnULOoZ4_hmoJT2S@1$OHxHj1HQQqa)`#ZZv
zP6uFpFS_1)jqCwQzHPg4I0|W%7)bmcgm#!sH|IxC4LFSUH)P)!I^*E60UB!b8N(6p
zA%yOD<f?jcc@x-~@4_cO=IWhWP3e#|suw-B`3Yu3rbg;>PeVp?GY#;Cnq9A^h2=k+
z77$1-ihXo8+F13~*lXQ=dRTetu=xE+>QE8u&ih120vS1bK7__L{LLO@J;StA(x@|1
z#|ZQ7UXtlNUw*wl$_K=ar!}RVCh4cX9G4f2^xwsepfwC>ivrwDo^Koh_&(`FdLJ{L
z{swiIvINY~)&2CcS`R-?s8?+G(d8b$QF7REjpLJk-ARib`r)}IJv)H*An*Xe_<11u
zPB8(U#JTj;%C)_RDPJv*&!ugZbT&aWPnl+mYDfq$X8sbBlI-&Mal{RAod7lRc>Z>H
zgKMUPTVG_p2d%8|F6fZX<I$Yn4y4%Q-Ll*48PR`qB45-|tEnp(KjvO6Jo<|wI=YtH
zIuBQHoWuG0NVfLMPkn%Vl>rcC?K6<xn>4I)=FD${toe*UsPOZ|pEHXgWud8m+u5@I
z-VXNp&s&qf0qKaNVo%~1f)_%%xZ5a~{21Bn`MHc>Eba*^v3pWvPUNx*qRp1VNO<&}
zm5K<3h0?m-SaRUI_Qiucw(2vKj1za4-zP2vrAv|hyFaK-!n{=s;C#NOpz-m-`Os2I
z?DNf@&kOT)(_eWO9cjd5G6X{6Wkfs4E6&-AGoAlUv#w$+JwnE|mE)e=(wn!`b4hRN
zVB>!a3+>?Fy4}`F*Xzc`T+SQ{!7?17NZI78U9rb~-lm#>Ap2IjB=t1<ykx4l_&Y5A
zm98E?&&4-*<zE#p1Z-g?LFork&=OAu_c-mmgO0Bk5SG(&1R4zmHy^8*8XbqWW|7Uw
zEeiE<VIIWUUGFSVcdjre`@~~cmSlxRapWz9jrDA>zdFqi(t&i@Pv7PU{nbbYC{W#f
z$Ny}3wpJ1OGwS;}r@XG-o9Q`^D2A<2TVm3BtXU&I-r<grzLR;S`h`gldIwdkOXtO*
zKJ&ixSozuT#_*#h&Hj?nmE;<BuJ#SReMcBB*AEiPml#3q3aLEHGQCF{0<}H;9O8~N
z*#6DmkH0$gF{|k0)G(aq#+Q1abVm}OfJRQJM-qS<w^{Qpk+$F&OYD3oWuiVy#s3jz
z-}#2=IYwZhXu)3->UU_Mn9~kC`Vmx-K#2c>SY!<$Abnd$eP243!W>}2*BZIB=^yhC
zTO_sdM$Xe&s#J7mb7%U4#d7bbN}}S(9@6#WzenAkP9#rnUo5m2*X@xeZKI^nsARh5
zScoB`vAzD58zwyx7^$ioN?ig;hBzr8E5G563LL9smYwFEqWxX2W-<Tz%!M7$?{tK}
z4cNlyCsbAOL#0tO;wB6@LH19J^b*mU3S4}Ox=#${TEL<Xmt{5qh~}q;(&B{D+$=F`
zEa>fZM_{L4I?Ue{%E-T|u~J$%K6rvgxzfxjc2j3w`??P9COR~BMi=pHyQk2uTa5qc
zLO8sNxE-I(kgy=@A~kZut>7uxKo-GX?JHErbs>o!*KuaT9Eaq#v*AvDFlzACqtef}
z+TeDvv@pAvUOHErp^c&<)}2hiMwZO>ep&rh<rFi#7J-<B36@P+&P@Fm%X-#-9a;lt
zthJd=IljSbj7&}5jT<w;*2J$Xu4AHnwsy&&cW<&z-$puFO|lEfcP-D`+g8mt*)<8Z
zy{M&Eyll^Qh%i^Pua{X|c>pvoN|dWIO$~jT7xmeH-;tuWd^k+Y)CDD*Gn0g)F-dj0
zls5dno0p;0mg*nbA7*DycJaJcUeob;gZcXoA*XsiMWIn=ymG(eY*Sei<~UW|C{XtA
ztWCk=BMm0%-Qa>Tl&1ddm1qO^yQB+WveA@QBmrUf5n=me+u3IR>M<Cd-fa;R!*2fL
zmeJ!1UYROqY!d2JND3`2o*ut=ZAiYpB*bNlx|;IcO;K@%G2YV!)kx}+!tq1bEBRWw
zYVn2cqcS^j+D6+WGToH0#=dLmk?8^HOFtM45t;MxG<ExARxa6AddVQZ#X?>}#s0{t
z!KIYvH<i4rL|%6Lznlx}0w?m)kDtR5_Ejd{hO`-hLvw<9XwNg@3d?H_kbBwM)&eo)
z_heiCcNh}C-aOvhkO?wCbeX2g+0Jg5SDn{CA7wLS@Hs*72Tg5s(~d9042$-jQBm#;
z#@(dVvkGr^H_=xfpQ(?MUG=LcnN&y-#j#Yh#VcH^tTZh)PmZ_~ffJ(kpMU6gL+j-j
zrxKc*y>F*`Emau9;-L1r$UAIiCO<k}Wd6J7xk#2nU$iMmt=3MYPGYq%IM(tT4k|{H
ztajHu(f=XA|2NsF$mW~F2OcKc7}PF}5*v&&rV!f1$gOE$-cA0Pv}^p<s^TWBp<K%_
z(3zQ2cDUIGQA*4qrc920i3E|5PbOghRGR;r{0@SJju^>?ghFhld;YM)il*#Mc;`#y
z>aIPVL0kJ4e-p1<kA+J?*}nSFz`$_%URBsHPEVo+FI9-4Xi6eYCV!h-=o~+KPaZiV
zy5hVsWiVk<>#^5dG=R%pxW%(EWt&-bnQKd(o~rWR)W65=(lPy0gn)V*OyRfc*RZN<
zg29|0_!dg4%KnphJJ+D)u~+pEq0n;kf_AI9)IM)N(sJIQK6JGp#P~b|y5{a^z9AD-
zjprHvG8GDYJtbEQUB18Z>lz%kpQ`=uSJ~9<u8c);I5KC7XxjXre~~tQv)`pmE%|ee
zk{b>Xs0Khs0Jm*`RF`wvHurrgE`hYyxYWbDTM~|3nxd-HSko0BjqiK5l8{%+=8ve;
zSRC(^<$_O9e2TKMvcKjaO~g#u%DYN=b<#Mf+Vq9wy983a*4SSke<)BMh#M@G6bPso
znulYwk#v2B<=zf)7a}p(-=$;#J(m#X_PfIZwdGkq<ZxoKA8|21p=DChMdz)9yYf{7
zv5bb|%^u7-pg0M&nrwq;Q+>KlFg@#(T6#viDyIunX#?T8eBS#IHTmF7_bWo?A7khH
zV8K6zdu%H_9Pyu23vK(j0h;m8N_yp9bLs7;_14uN@C0u}O7faMH;d)GpH=On68~FO
zvw_iTJVJecqxa1$1APWTg5%a7WVN-<0oD_Avn%%Rqs%{`s7qu%vMGq{cT@Q-i3}%K
z&Ntr}H$PvJKP~Z2cmJP>{gXZM7sli-qfmC%ObkZp`ES{5<a4i_l#@5%MHr=d0b24U
z@-5%N^9Jd6soZ~W>8{W(Sn89{x3o(1zasyGYWe^EO|jkJu2AydPyR2F^#xd={9h)E
z1ivt?^gjqIi!Z=R-R(cQt99Y8a90So47=+8=Nn(I1lL>nPq51Y?*D>a7^id#D*u6x
z3Bq`oLj1pht}j}vn_l#b9MdkTQ~C8DP}diOmWc45JXaI)SJ*yxQdv&*Kd7;<g>(G>
zWZ^+X8b(7obGdV8Gu8qJNXtykFVm-!A9okCNrSF^BfO4Dk*MXq?)OXj3f*0X=6_;5
z!u&SY_RJccc4fz}RPyeXLMB)<E<3!HCX%{C{`#DB_}kC%G|#p_8i*sdBu`FQJKa4|
z-@wu>WwI4`a$a7!@>FWEQFPnv`N)zK)~b7?6RdC5(bA`L_WsQh78Y;pcxy#_3jnXP
z2Z#*C6lwXenX`Vrd4340e&*<ovvpP?>88IrKj^>@kaKS3n?8d4PviR)-pGqPN9vY+
ze}#nmT06UU@i>eD<aFGmLKr_h2`4_{;Bf`wV<V0L37N0<Tlw;K?8gGf^1R`{fKrps
zQ+oe41>ZOOu-9oa3by29LD`iTTima5S}KLEo}8_jN<m(EYwo`_$`5B4Y?m_Lr(QO!
zFPf!wFE5r5j_T3+k5nqIlsh;d=Z<$2x(xTQ=06$I7GRAYd-DG#ZMGw|v}k>9bl_WT
zRuW$1;{nhz`&E}e`%7IZDAVrn`b%;jXBtdx<umz3<n?;80dt>2B6pK2*3Oi=y$Jn7
zMF!77x`&!w<vQl}kx>5RMf=$oP@e*`Z`D;+Bk|;a#kQmP>#<(Y(>Z;|j<S6RllA@k
z(qpEIGLsn_v|RI-HXfNRp1z$n13uLuo_QE2i#7d|=(EJ8)DTLQ>WCXom!2{YAz2)5
zjEayfoJ|QIsV<wnsranbpPNF!xFKvq$M-<gNYf==a%uH8(dF6)zE@wOh#TJP105nz
zbLGESGe))1Rod9yAI=AUheFNOh^yjN)!!8fc)mPOR~fsfae(^n1nSM^MXNmD!oZrj
z^z*)^Stl@+3(19JW=b8ST_PXi_gs!gB6XU)jht0Q){qlXh@}-enBzGoGAXNFrlwnZ
z`bFiF!QaQQ77G^>uYArNr}%;_2=_e-&k0{f#;Rd$x!0u<20Ooy)bz|=Y?ebqJNva`
z0snzUWv#|5(Xpfn=-bcY&0w?BdV=xV(#Kx|su6PU&y{L@KkDt)Qn_7qXfpO0Z`xY5
zWcAu3i8IUextjbTF54c#w4mO;Kb9vjx+~}m_Iq=qY0AC1pA})jf6nu_1%8vQZ~3~{
z<z3%9^K^Be?Y4LbVSd|x*TYt&88OK6r^UNfwc<ScS@TG+A?Y1!xsl*<N&d#Kc;M@o
z=exFj4Ka^ic)UxiKh~hJ#uy|+^CKt^=SeF(-*N9g8n9V!2regN40_jdedc_587I!A
zEyW0l{v4vA3jeF-@hT7LZ^lBMtEY-ZA^b!Jf9GR5wb8h7mXdCkM7#|C4z+u^`+HJN
z*v*Lqb2t7=ARkSK{x8kt<Z}2|CJZFzYrD~`4Jz(}C%7~j%n%D4Z}$3uylqKJ!2y;a
zft(wl>cE2gFA*Kt{}NoP#fgBDuq+g6_s}Abi8gibXs_m7o~(C4AkqqKlchT;K0U4f
zB!vvG9RddAk~3`8{VPMz(vX9haw?8q-TDT)z>M8rwpF=y78QV*;AD&&N2O-aK=sBa
z3a=d@DHTS{K#rR~-n!;)DHp6xKueg?opde>YllqDgcSwg_WD(AN!qJG*f-ZFQ&k*m
zlj^!A9YNjT`K#QG4{tm+HAPTX8AmOjmRMEf+SMI(uqA`nIY3wjgs^Hk*f%60>M5h_
z?%x+rUhS9*hzT^pMeOAvnJ1)Fnt8?^$!J9$3V0;gyJURx@EE{#abnYpuD#Nzi|xz~
zVgAZNL5@s13`;bw_ti9!9p{nG!60lPo2VKRB`q^dro=abR#}EY?f%ZU;c@z&EscJt
zsma6sT-JT-9_mzsW;K}JHI?wm^T1FYbtQRjOmby<vw-sCW#KSJ#2@$rNy043Xh4OL
z*|I7Z=i9jVpz`6I++;gyB4&=X0&s|hGPQxRg~$aGH_sjL?}!A!9xb`<4!w$W<8U?}
z-YLUWRD*QjL;L%I^3hWb$JcJ04wrA9PPg~L!Rm=ynFE)VZgw-NYJvM7|H3l8U(|a{
z5?+tyxs=1hO>PcEHSWtixZBXSFN90k(e5~S--Ka6kAppAbP>0}#P?tHVoUll$CeUY
z98?u)VU;g$`$sah>+0kosrw_~;ob&-9k47XX<E$@P)3A8#|Pd4xmN-(XIj#SFT<pW
z@v)XEnjbS_VI)Wk;dHJy_nSVUJ^Xhu4Ok~Cy7<>V&XjGU6YYyl#$i&f4-yO%bg(Kf
zr|>U5%I2VdGRdl`k$8+aY}Rz|ue1b4L(rk^>ps#nFoJ1#_>L~ug-T<CCPSaU+q~n~
zMONI}nocW>pukv8D#P=uf`jLF31MNWU9ZVVc(IVo>+1Oue;w^owFqe_6mO)@Zkg4%
zc0^P^!3E(Z3<7T*w{=3&c13jhFK<~AxTyI>*Ic}idaZ-E89ECKn!|Wk1j^^X>(_-?
zv~bcD%FjObEIS=&fqE5^r=X@L4v#;aZ*;Lu>LPG+{q**w)V(}C!-J_&yrU>7nBl6o
z9fZq0au^qU#44U(C;r@fEMt4`k#ZQK>zloHaQ%Hs`quIY!K?cZxY0i#nviBqLbo0h
zp8>Ap>+8U=Dj5;gW92rW`*PBMqER~ZXOhf_gg0Y9(g)mU9q_c7kin8%OA8vMsqkNb
z&XBpKV(|+1cPs!11kIT<8ePMakSrq!iTHW3P%`xAJC_QJ=HNcJ3_QKOhT-p{xbNZ}
zPBX0^Y_bBl;?v`T^3%)L^|>+txIsM=*;*(8DF+0@athlnF0k^QLXfT4)HSL7pDmJ^
zn*CXwf;Ewbxp7MMRx14xoBZh5u+%ie;^Vii6C(>zv4VsQdgEW`FFY5hU>~r1N!jiy
z?)2b4mR_U%eBiJ$I+%UawzL3iR%1+AOoNJUDLeaYKM;NJF?Zg)Ov9%izKKWWb{-kD
z?6_OXN`|TR57-q0kJQ^IHTdsbrKSr|!C7kzlNc=i$Ju-7#m38wzxusCw4zr>+J!=x
zlw?NxhPmZ6jQ(tRN`B`qMD$MF-fA8e<&*M5*+;SJ=>b(jL8sq84KLC}`qO&1iHb9^
z4zmHo6ozByhcysdKt6z)peN~y4Z)toAE2C-Eoqd5%}AJV97q;OYOO5h6IR*$yI?tj
z%xoVh0GAgYafl}CmYqXOO~y=|y{nuVX}lX+i7Ms>D%L9y05M}qCjVR?<)D`#;$?&8
zq^yAg2^Gtab*Sd`Eo{gP{!`n>TkX%|Vsw~Hqx>knad30+AY(vx(rdQL&(q^6UKSls
zMKg87Q~BN_n6(U&sxek?xeDaFBhw3`C|?fv&19&aAysjl-5smw(!1;fbWCE3manKd
zd0^=ni7LU_X&%P1q@^aI&ks`uor#Ihi0v$Z;<uy7c`{ROSsH;cu<<Mc*jFZsBwUvy
zAj|J=0&(t7P@D3bJ(K#T?Ir(OfYo0z-yJNC14FTY^@If*nBF=>&#n1|UdY(%WSv{o
zU<%8!aW!{?j1sE&)kEgWV!jTZ18}_`q|eoCoWLzN`$OSL;!B@v0+iy;2Q%)GD7sJe
z<vYlx<B7<Ye2ZP1fz$KRl}f^z;~910W}Z3>g*@8=G|$2xc+QO1H>45G$P3xeecp;0
zalmx=-J>qw7+_47m_`#ZFUy{v@cxg*Hm(}y_j2Bco6vogU6(#P+<v$OyQ6aKcTdx<
z#6S3<tkv$+Xl7rkR`n8C>)e5N<39U-dlx`f&vyelTk%P=9zJ^JpBTnea>fJko<4O)
zHsmBiq09xubA^@elIUwjtVU5hy}J@g35wRx)sdD$LRE62rc8*Qj_q7#yw-boSpOV|
zNz)*Q4R%Bg$Mwu6GNzavMVD+@c0l${JW~Sf5#P#n%G+>f*7(hLZm)KA@;Tqv*!wl&
z<(g8RTRNCRs<*rYu442Ux!?0nnQV@+PQQC?Z!c$(kJ$y?k@7y?d*0Av<Xz(RmCuoy
z#ySw0-g*s&-?F&lj?bPvqLHED1~GjY4Dc@cb_A_UW9@AOGACRWMyG>0d)SIIc*j@t
zoq0ZdIkRwj67+E|p&$+b&?#?s_r{)#h72pbG6CM$H`SmR3g8PsE#nRtl`5Rh<a`3&
zx=<n<2MyXy+0p>L!A0slK&LOY18e$3FWol!-&rSFOiv8-K6lmiQWhYVW>xeE`GK5s
zwWhO2((fO0sjk{gW+>$ZB!TxJ2~B1*)X&j-qxIh6VDo>nf#Hsh3<sf)*~Xir2urR&
zwQbzR-#c>{vtKe-vKZmlPBcavXN;hTLW&r<PNdKbyo~mYm4}Dh^Bf$c$9CQRNC1Co
zf2P#*43qTGqj?<+Cl4~-c?3IW&@npNgjEug#Wg%j<b=l^5JknAre}9scBc~w``z(}
zjQK_oJws4fQNu;h(3ApyZ{Yn}*fDRK*zFZ*w<4XqPM#w*kEtS(QU9C7F}+)Ak}2_U
zaY`~lTK_AHVT;<?X%vN5RNCkTf|U`I7pQxA<*xA*@H|)E?%E&mEORtV(G63c5BFT`
zUSlxAf|#LD9vqeRyQGn~!wH6eV8v*Q;TY}VX8s%#h{qb_*6_xZTyHYie#)|40K12|
zlDU~O0#`I!Sw;CAf3v5N(R(VEz@>Qxc6e_oDjdh@8nhv5Vt#WQoefCG7q!Kvi;TM3
zjWcx@T!c~Us0~Mh30Z0=7HaBT_tiEJ)X^=OH6)V3;T7yTnbK?-qYCQG`hzrCtWdWK
z0ODWznSsIauS+!qBxrI}uukS}N50_cX?BK9HR1O-iTUeQ4N)c3Dvf0or*Z|M#yIqX
zuaEG65H>=)<c~`0Qk}f<u?_BzohEBc9%S9J7>d?{oKslJNab0i=KIaxcKkt_Y9iu4
zvdqR)JTpck3{G!`YV5od8crrW9JM%gq~krGb3qkj<097L1{Z_84HmGTA>zVcDg=9$
z26Jn)8rO^1hZR2Yiv%o#+^wih<`B1-0wTHYP=gbh;!_lzCP$j4T8ah&QwMM@r$G=4
z+=vD}=ft9hLPv+aA5(fpGn6B<RhB9vlO*+at{I}48LvL{LlY=2Ha~~=iTeO_zT3E)
zX~|o9<MGLjOwC`WziPMSUSnRnk9d2s&lCI(lY8qs=?b+XkoV<YIT35}G1T_6_WNW6
zHu@WWNNKld8lYv=#x(n*5HY^tu*m|eJ}p49$<4xgDmoui%%$NZ$v7XtokmmC;M<>Z
zUl)Ie@4iP2COYMc;rECd2}rEQlljZ5vIYaOFDydyd-!CpMcI2D-(zAIT$yywByu0A
z49_D&&?5jVw+pxJP2@W?G^>nC2sohic*AiIC)#`p_yNZ)nylq*i7pshNx6}O9lIzh
zIf235Y>C^1h0%%U4TaR^C#lIJ-R#ln5^w((*Yn^m*#|xoUhP>?g`JUm-mWDg@J-RV
z+=%94O?*6r!gC)Z2DqiG!olrQUY2IkXp+My6HxEhKE65HL)Z1b9dqUSv(-}o^A276
z)8N#V^R{h$Vr8QGW-<$&LI6a<E-7-Q`Q`eoj{4vuxdl2q=CeVZBe0TJ@*}25b<BJ^
zWZyl5&x^=Lq=eDI=ZljPRUyKB@SnI|A!M}x8U2~R9W-(xzHf?ZZ`gq<n#36kEcV;~
zh(kj0Ih8xCayA}*T5YdC#MSdMB+g3Gk!67a+;4=^CFaO`Y~_wOm!kj&2<BmKx;Kbp
z78JU~qcQ$YkCAPbQ#?jxPpxMOjbpJ!%e~T|Tt|bgbJ?KI{ctN%QJ&RVmusg_g|NS>
zk)k>6V7rMMiDwQsjNzBM1XX)H=Mzdzwsb0Z$!QF~@2Dk35=<ri6+YeM3N|l(G&&v@
zTj%s)du)g#643Xe6!=q|#4s>xi37KK;%W8<=!}LlIX<T>O@E^Pwe&Xrw^%Q)^%y=#
zT^0OYM{@a{m`{SwHS^u_JE9SU?q&AKy3&Sdfw{4hqseCx<I{G#j?5YX-8n#UkRw$`
zkGCH!!Muhx=O?Y0z|#nz>d|5EErzh>X@IeG(H(W{PkmC@hE&o8-pJ#C>5@jFm58{k
zzM#HTpX?O%;96nWW43>qLjJqxQe<AM{4Gos-jfwV4DTaGD(bf7?X-`cNAu6EYVVAZ
z7=IPZHq{-0&~8&tRV}N5ZQV@2APLsT5X{o6q_McD?8F8YXY`)~spetpL`ZXs%xReV
z9)B>mR)jAenr>tF)1p#%0*d5^Qg|GBZ<8xY*9>Bzm4Bvj*o9S#aHI=(WWpfvkJq|)
z_2Mi9V3R^^XEz5GD)9ETZDr=kba|K1&g71A)b&sWm1HR7#jX}5Q0cD>rPW(&2F2I^
z@@d~?{=?6fI5nx$-*oU!*IE6Od2P#la`p)D_o6Sd2ExHT>EASadeY~X_nXb-^`!az
zvQi{o%WvbCvhj3w_{;^--=dKZA9rAA5^_g^X<37hE;%zB`6;tK*)dD}Q}L8bhA<Yr
zk)fhtDzz<7Axt)dvUvV)!7HQ^EN-K;9#UCP&AA(ra(w+OSin=M0^6fYTbuR9h?I(Q
zGuh;+l;o_@EPcfIB<oxvdbQcCPUD2I<8~cj+$b4w*&m3Q$*jrU;>Eqqda#A7_H>}g
z>t4dYObG(aL0kn^o8|ZUQY|jyO#UngZ$A=km1qvQt=e}eHv*Zwfo>RhJM2+&v_Tf5
zCxBnOh0$T4BME@4wpa{|o!!YcfjzyHDybjiMLt7>Ge$>`kS8966vJs2t0w~+DK5i4
z&#q2f1qsy8Q;TBD(?qmPoRC6qozi^FB_Gz!EFHXM_lJXC*{(oM&c0K-E8H=kka)2t
z^pCH7{zPo)`dm(MVni{k*(_`Suqz<llq;`x;(V+F&3w8mOEpE#@bBH&G4<_A`elD4
z=rQ5xrLGp0Jzi|ePo^9ybh=ZDPjjXQRPCxgl0bzYLiuE&04wgP*G*Smr{CF;s4T%6
z!Zq~t?1Kn)ab>zWn~N3gKe}9F?CCclekYIG(S+S^r%P^>I-22_|FnMuH-EAc7{)_j
z0&4#-x%7&0to=-GQviB!8gVS->PA4{>>}P?qRC*f_)u&OQR=(O=(X~uiyq1oE>8sX
z!JwAY?_PJ}8<FC2VVG|RSl(JFe1U90-xw|FJbjTvdB5w~+@&`*RIKGl9AfckyXSE7
zUc=p4b%1j0R1IHSSRy%-wjK>nqwVg{1dmB#5ludZY_YQ-{Fs@X!RfkcfwTbEFj<v~
zT4<!u{Wb3+K?FvZaII+3?OZ&d{)p&=#d1_3s<HUWcJ+Z4^}O6ghTl6py9qDOqD-`8
zPod8Aw&~4>D4<t(f)KECmEE@vlka&LB{zSylvTp>gXJ?|(&X|rvPDMyALR&;dhf?B
zV>wQuy-^<p61Y$1(|&17#g-pz`H1YqWl*ZSx^1*iQ<7)Qs1>^5IF2{r{Z&?XZ%yU%
zMI9y<yBQ+S6y**biC%?@nw;qI+SPYjaa&Z{nGQICDpaMW_&KAdAGV4A+EF+_ZC;<;
z!TWYcz~?bQlJal)H|sVb<KxrRVQa?fZEVe-2)3K4p`q)fzJRz(RaOlSZx+_0YLf3k
z^BT`)s9^k-^Fb<8e8YPR0u!qUp%zQU0<LFj`8nN^D(v_XMRL0}iTb-sMc6#@{f;xJ
z{ePkC&|G)qZr84cX#E6Qyq7@dc`NPhq-y8upfag^x!?x(--JhtjE9-_{)s2SSyg=$
ztT~;Sgwcegokk>U$~vj6{_t6~tJ{~Cz{+btf`pvO9>@mg(K;lkj@6PH=+p7>Z&{lm
z`_tzydJqr7OQeC>5)(&-cR<M4Qv9J_sAiw{XjxjowxE8a|0Bnq#Kb}HK*)=(m4nX}
zBOX^G4a&6a!{r-LF1^Wtw+J<nw)v)?(IlyP_T<3~au|}YLQ_Jkp(@0GhO5)(g4wL2
z1t^>U6jhrmo>%R^e&tnfBV8ivMze-GnPKv}n2$cp7l+|JYd5rpNFjACrbdL9X(C1&
z#I+{@dP~=~T<;261_jZzcR^6M@taMOPSr9lL|L-@@sts5tF0{9$9sJ|FOW<!oy_1L
zFQON4I5zUr{AjEA4LKTquN^bFvlhfWJH$}$;Q(7lP&L+CY{u$!4*oDy7-7$1h@l<L
z@0`WgWv0l=l<AtTpuCSOef#?5cv}c{RpA$XFjYNNsKz6He<dkgl^U&^j&gn>?XdSy
zZ-XLbqN4goE<OA{qA7*L3H{emC8<tUDeihP{j=vY7R1?(>xVH0qZjZAHGxmE<ODfW
z8@-Oe`!DrHrY4UuNgHBD*|~D5^U=3K#OA1=m`U?}=$6N=&9P%_v=Us?!|qGDU77IV
z5y?0Ib6+SAm4(Fz9{SX~P4DVpK~yd1%xUf8cWE@p?@Ee-=rzB?VNX@j;gnUquE>MQ
z$RJU{i3=VDy{=3@9%aFQK!<N$ZGQS~B}|Bz9ge5^K4_X>HK=<oX+a+nU|=M}tzNI%
zzBXow0|UOait$IT?opPz2+1B9@~{3rV_oG1=tg#>30#>5|2#8XNW;uk$Zbj;ZBHS9
zvs-0DM0OWEYgR5+<)nzh1?|0XIzFf|q_rG1tchCvBNT<$@AZ2!z2zRoUH8`0iY9#2
z&jZf~H+P4~c)PG}7v=ZOCg{$+4PrV=R43+ZNM@Pyq4Rv_NrA@-#VmI1yZ24Merc?u
ziZ2ls;u2pm5l;~%J=9ndZ*R*pwbyRGqsLRQ=-wrF-d1*oBeY!&zCse~%MQ<qDG#|Z
z2bs%nAL+ZrnJum2yAAbxGQjfgzK0)*m!!E3hSj*0&`xwSrgIoA9o)G|&@Rn{z%<B|
zw*I6TPn!Z(d{>>sbw*;e&?wu!8N_dPAjJ@O*;rJXj+26;g4PquW|(7kTI6kZjp<8-
z=pxct2%iR5wK$!IZJmw-Sd*5Nz%EuiaB6-xgk3kADbS&gf_ExC@OF)6Mx>nF=+9Hy
z_^KY2pVndfKJ5L(>yq&+eT_6&i7c7fZQy>%4TndDbtBuo0vE>dmo~(a9$Bl2QiQZh
z!##qxu%W#D%_U7Nsbr!DYVf;A8pl(#eCT;AZ4D}I<;pwpAO<JIk?h7Pg0<&QQyXPi
zU9FbnF|3+DYyoyf#rgal-<odB1gGX9J)4?H?=BU!#MvF6y8T!l-P#lh=qS&3)@zK9
znZ2lDoggG)>a~)hEG<>OG%Oz&9Y3tFY`D2{<O^}X_{`jDYI3sQt53p|7ALWTH+Zwn
z?+AiQDWe~A)9$CdB{1)Vb|?Oe8oF9AEDqGC93fcWh3z-HzisiO>jC-vRt;7^5Uw?w
z*N%7nUOdU8<^*wuzV{S%k+RI0LZ9!83@b3)s5CIy<e-r)JSu8uL_Iy3uqiUxmcx~l
z497ZI2}FN5VPU)4QQ2A)4J`kRwz3<Bc?LV03MK8DNzls)y)DcnR+3~|WEC3dK!c8M
z&b@mvF=P7H)JDu)&%W$T4hhtJjKg$79=UxLnMg^ORiZ=8ni6^hTWt2SV3hvR4(>dQ
zAJXJ_EKK}@G})V&|GLe#_p{mc{&%M|_LopMT3+38biL1SaeuzTto#!Cz5{!HF$)t6
zo7Y6)iJsU+4_b?b<n8^Qi?9~$@73|ry^9v9<q<s~ICrKx1<v;CGUi>Qs@<i^HbudR
z^#nO&XyZTy7L74*V{in=lX>F)!<jO%wCMU7@t28TVY2eX1@zyS5{e9CO>yxqjWgMa
zX!l_AlqI^|mqoEw-Fqj>&Hs5n!USh;#>jVs_Q>xMAYKR*_`F$hY&tm43N0UQ09@JA
z7CBsDodY)&loIv897phNmc70yoe=So{V3!rZPqV{`bZe;B|G6h0+kL`ee8!;v~kA$
z)R(i~F+d;E*?C?{EDk$AhaoK$8(vg!WGDHq+wdyLF^S!{!InFcsXA3jh{5;}TKb(h
zaN&o}s{-v6Jw6a-u0%w3+49Yc7@Zr&6Ogw8IR+{x&ZnGd8KP=G?~$+&yb>$?j@Q<l
z!}ZD#h;-4{q0;+8MaRY(rrm>}`?N-qvoy;yE$X34Y86qdb;LZS2>bZeX!@Y4mg-R#
zxEx9?$ClKJM@UWL_%n_aoC156a^hT?Vl5~_6VOSTNJNWPp#KI8#J<v3NOZe5=M?d_
z#yTw(s@e_Lyrs{n%2kkr>u_+g9W9IsdopAV?5pl8tw9s@>MS@@3U&weE@$N<|Kr3C
z*lS0ry`bMGD@MhEZ2pUuM0WJF*1Xyuj8inE(|yR7Ow2Oe&E<>ogJI=&Sv;FgtE0pS
zZLi&6x4$^%O}Nwirb1t;zg^En8|ocHm7u$fb0Gg|9D#2FZx4*_!XydV2+T*P-ak-n
zNP*OMuza%|xS#BBmp?sUiei<)Xj;AThukiQI%sJBDq?XO_^8c4XSQ>y!`DN~6L1H&
zuZCL9&asshs2}=u5P<taV@gi_9uAlK_EsJnhY=Nc?ozUk?qpwc_?fiz@w0QSWHC8Q
z$m?X)Qqw&z_NRIT$oU5eD|gB*p!I<6h5;tmu1DXVL}6GoNXTDO0_Ad1j__0ke1Y$W
zL5(S4iuSQ!VLXAv0WXHy@?ItJ8Dex+N$)mr&yjDRPF|vbT49<kuSr=Y<e$tPsxFL}
z<RPrE#_vuU0g0U`MLd3&?~gylsYrEQs3FT}q+#yEg@W!4EC#Q78d`IquQ>vu(ix7T
zx&O%efZazq9ha99NmPYq;LyY0&4@|4{lKZHVCwI}x+!u3Ot<=7lq>d|6hI(7kXD({
zsL-DWz%v(^IzCyP`H2>=k=*vs|J`8hqe{-5zT!pBZ^2;vxOKH@!}}b+82G~^#TD+z
z1%S5<9$VDq3*W^Q_{|;ETCIa1lzqb3OTw4ty}UC7yz4K{D=-H5(L(>?dF-1<Ik>(f
z+R|}{#}J}b(u$3g;oOTI{E3f&7IBHjH!Onndy<`~!UgPMNpPjqbRYuSx2%65g3^(R
z3_Vw~S}6IbaqZZ5k;yGoT$5W^xSE8%Ej;N#HmeNyyVHp*dp$JYJDP=PA;>#D@S0yc
zOjZ^7Sr?E;Ex=}m-+d8?J<Y@}Vd_UQTX(~yp4lwX$bwml7<nJ1<ab5&*qENfn)a5l
z_S@K<1f31K&$TDg^HA{@rbl34t&WV3pxQZ%ja1ujabawpReyo+1TfdAKv=7IMx%|v
zE0k>AKjF2t#X2q|^4!cjsB+_?`M+UVdi}a31oXs-Yq}w~yZa}gu56BDR9=kcEq|VI
zxZ>@Q^xvZzWBVtm4OmNT3ph~}$>K#}POvFDamxW^BKX>{2??b|Au4~=SCR+_bm@+=
zQ#WYpl_z-^|I&V9;fdA~Aw%!y&RTsT&O^i^cDRX1;qc)&H!XakQ25hYrkwHW$L<%A
z_cIW@pri(;5WRcN`W|wtti)0%`$i)z5Kb`JW0+g3|EapOF<h&ONVsfHx!VzyWk$AK
zYT&(X`Q_usI_*B<&>lEJT}P3H9mO9#NL^ce<d;x0S5_>mvb+Ed-Q3dAg144nIB5j-
zsW32)dBz5J=G4}|i<+j3M6-pdz2OG;w3LVPOd&FseCVClCAr%&VLg?k**giOBkPS^
z4`#bZu&Y6G^*Tc0GO@u^nj{)ITH2)a;;6e52w@NyaoAWVCuqVw2$`1$5@s|341wQ&
zhbMKf5pbBg<*SVe^3`OO1$P|Jc*SkfK~JyN?<;4FSA+z_@KxD8qonfr%<xRZaj7%V
zv?=EQW5D9kdf9o~Q~F*_ZV-JEyn~5X?Vw*)##@cuK<+{~$8ZAh1K1qrjram$i}jvm
zJ@ik-2dBv!J6O7;vCql(_&A*W&TciO90*srF{V|?&FUiqnRF2lg4Nk!HI$4X@2O0v
zalf+%)`zuJimCcx??H+xau%5CX@wT$aZjp4-Qh_=i^(xzR|;lRsQksaDYlY6iweg1
z7alzN<4D6-3Bu%%p#CqnP$6}eL$&goc)7zhA{>0~cbl7Iu7Bu14SR~A6gRR6Ganh$
ze^!LzK^!GW$}&PaZr`s%&r5m}1Inarx|`81w;Q+=ln!<R^3F9XQaSOf{N9;7Pi1A(
zjFB8QVfp#8dRm0^1cju3iHtUY?NYPnNQu={HD{$*{q&eh`6jbzPRhv%bJS3h1ds<)
zUeN(QEw9lhBd0pD797+}@M56HnwyUyUxqNz&FZAA8BM+xEk3K|W;~YNsVQo1-{X{d
zT)f}}qk-4*)r+<~49G=BQQHjtoSK1N%TfuOpr$>jgR0CQfy6~N8t}7{adtQ_X9xHt
zw@2~@3qc>ZSg<lT-CGoxlyA@?GxVo&U~ZB!?smW`6Y_;5K|u9MWjhGb#0mdWx2<8}
z&lu|U4PVJ_&U#u=?#Y>socDG_#$7cI6aKyu*R?ZuB~RKUy<PoQZ62kH8>(1ZNpUXr
zgJ;s-nc-Xb%|+v&R$kf9V9^XxIuNGT5D)>wIq7iBhv`eb`)?v_d!QoMtgJA(3`An^
zqL{&c8t@LI9j0#(zM^=V1p3NHQY9={Y?vPs$DyJZCmGaG+YV_8K65jxm^QR6B+OjU
z--_!H9oDRYaF2~9^j1f7$A-eMA#z45N|Ax-^T76PI$_Fu@@U{l?Is5jCj$aJhTL07
z&XA?iw;9p>hKd1Z=oH2rMrVXef;ha(b2x_FDv&sIlja6?I4!3~gO)iL(rC6=X{71d
zHC;_f-jJ^g<vnvAbc?&T({9Gl3OFWW&ih_nA=gQ~UdXf+%%{LbnVoP2`x+9D-PEGD
z)?_s33uFDOaDvxvpH~f<H=x;tTw(hb`61}#l$+*S75dqd-c5`N0@IRWmv^$*>9e9p
z=e8g*+(5_DmgGZzumezCK^~#5=LLSFI?ueIfrda0-Y@$jLdbZ>(B^lzs4(rY1a64u
zm^b4n{f`U&JHLb^HkCxv!R@g}{v-Y=EHc9#3#{q77h(j{G-$m#h{9^7=YTqXzHiJW
zO${Lg2!8u)k@iOG@vV>p4Jsr9Ui*CD3ft`H3(fAYh*LyeWRqRTZs;uZ>)Tu=s6TXK
zdP<-EG_FaV;X4A^Z<>RpA?bHXV6R(S(ItPX6oeoI%8k}EZS-kHH$FDO4s|Yv#tG09
z`^;m=A>~~^cc4EhGppq8<eL&iG#{M^k9yMa(#9Fc?z3^6ZbD^o93memyByw|fOBM~
z&IbSd{LN;UDtDoqut&LvUYahL?I^m7H;TO<LQ;xsFN%a>?Dlso(pik_6HpBKETj-i
zD0!<z?~G+N4DD?hN5}@uwNI_~G3oU=V0gB$pIazMuG1Q+Gi8HT@)GWCu?Kx@rU!Gw
zp0!|yRI((eST_Q#Ww(vtv8?>J@&6A1nm}d0NB?Um&?<d!qEp~g<wGkeEM_3Z<IF+W
zC}k@87_?w6^ipE5kNDECtFd@_$}Eh}YC?2@I=T^mo)UcnNy&8>u<|8{veYoh-;KEL
ze8?R8V!;q!SXJD?)syFO)w4TZaL`2d`6xv4{j{K$FgkAs9%wkDM<;E3|LG>&$$DB4
zPDsu{wL%R6b5_7u>Vn}t?NAYU1tRAld^kG*nHP>C_Us8{_UZ-`T4e6-zlh|KT`_R-
z6680Wgr@HhIMznsoh|zqfcUtXTi6WFo(#vBm-=JDs)e{(YJ%aNZIF6$8&^bUYOEB9
zg$;}gNw7MLOKWlc*V8B;HV5O!j>hS0IdBggh$gz5H}1Li)B`T0z<lmZG*&NJK)8oG
znmdm|CrLSe3vW4Bz%HwD^ATV{f8H8US6dH<zT>bUQ59M)mN;{xl!75GC*0VDCKgMf
zPP}mT*=xA2Z%G<2#;E)q$k%d(zg-QsezP5M*)0KD?Ojhw1x3ci{IFfR7=bB0W@6rk
z*P$DB9}@?4L&4R3sI8*qKqLtIGFW=_z``Z9*!SyUq~#U2Y+@83JOW2yk(CYh>^y|i
zC&Do5rG9vE<pP9O5X0EX3W>+J;YP6^)I@d&Ee%0oiV^(9ekjdIYPmdj35iG(-6l<i
z#Qi#c+8d^1%&NjO5x5pvm*Dbk<OY>rPym}D<FUYO0J?T{qt%Je>@kyPOP9*OHJOA9
zMWFA3vDmb>8ON^Yz{SaoX&X}zd9wiH-+c{5KOKkDn7J^ze-E)aBDgpU3{}EsrONd}
zL6X9%jEaK_VLB2Xm-AK5<7)J9+_;_rb01d(ZF(2(%{Ca^g8@$I=1|dS;(Aiy<{%87
zfooiQas~&M6&9db!wrL$7_}G=002M$Nkl<ZuZNq&4*gt=X*RhFx%X_mH{Aob5AH|&
znNzsmvr8*1NSKf9H3jPHDj+s+z?9LG;Z&;yTg^h8K6wp;XE9aj>_lW6_@ck94x)Bn
zZm$FGetPIS`8CY#Qjg0!4&ox$yYD1#(z9hw)bY6+_Zy12V~bI4W(g@h2l4kR(fj4O
zSUOCBzP;Q@lX_^IP)O%PQWcad<@#xRxjN3{M!58wfcdF}S9sbZ_}~si-p*`$Qk(ZR
zG%Co@b;wlAu5^UGhZcScqX100t55-kfb$k;Pj6T9k&}q?aYDe9myuO<5USn-;U<d1
z2Vd+)UMUr^cA9d|EBpEV%a~u!B4@%f44e8Q@_)Gst3HEZly?yq^9!NY#}%Vje~4+e
zonWsor-iEl(RZ`3e0nELUGo9lI(31WzB;lbEgPXAL`evp>N#ZwWbrq!W9Knpp>iH@
zt+h;K<%OFI3lVnkBBpGdit*Wm)uk!m*T)kXH$qTQP=WrVr(?0>1oURyj0*dfDtG$d
z$NcbJLD)^E4@@9_eF4ViY)87t3OzgP;M+r7R}|P(g~&Xikyj|^uZkSi!TWJz-dIcw
z=z?4Ow&QL>TVsk8#tvSXv1Bd|96OG?>Gvtu3v>5zuf_%PoGEWk!u<8Gz&N@D<NGrH
z;mS_VNd}gacQOv2t;NV7r=TpQvLfR_39ZWNTnoZ0by-CHat8Us=QB-c5ac1La2zlQ
zl7!PZN##f`r-0tN_GQ9aRPrhYPBS*ez?1mpG1<u0wSq?Rb!^^#R=D6}t%flO@wuhY
zVBFC0;w$h=3xc1KHp;Tnl;<ggwM;m~h?f?^<X#jG9}A(M;Q9EjFxLeG$g`RPRqRO(
zzMRLi=sk2~rkn4>$V)hPl?n*ImGHf~x*SP|P9wgDGx|+gg50{JQ0p=XF3q>_-sZgw
zMivy9r<;&IR+v+gHgk&PZsAjH=C_~}UcZ)x!9)ESgY+>R+}&YeK-{*aEh-tnEIgBD
zDtGFfD`Gz9rm`wT?hC{0Z8I=|9^ebNO3`ItFJwiXrdPQTnl#RwA}yWM0@2;d6mel!
z86<0j)vqr`E`vHX117<tIu~be+<?^Cv4tlHg5skl@KrUa)CS{~b&=S(X%S=6K0?Og
zwTLU=KJ!Ei4~s$Spgx$f@eR};xP~6n=AxQwq$rPKGuK4RdT!hQ6CJeqSd?=Pzg-xI
z)stLtV*BUx5|*RvLL{cG7>D`qEQa}=N{kDjQ7QN*MBQPqe6JoDzI+XW?7P9uQU@tw
zn#M?XVJ{Gjaevxq$S8<Bf$KBg$Er8qg!Ad^(6V+yW5GQ{3Hvxb8_LH=)*+`6rj+u-
z`Pf@nzwAY1(^@X|9S7IuV(gA+ksm~)qg-x?p^Fy6Fg_B8j)ZV;i$ve~qwwk53LJ~N
z4|g{UMAI{UIW!h8k|&JI`4OqjmgwnchTU5}MpQ~Mde3_o8>aUl)cJF){hIu#qy1M?
zK$zX@sP;jFk^TA&X$ZLyhcX6iv~8*kW;Nj?(qoX7Msp1kL&I@3H<x_hgej_e!@H9$
z8go-|@$gy1=QqL5(;en&CAf0(EK(|}ka9PPE_)4h?dFH>LkGf2T8uMC4&qYiMO@D<
zfxey{y7di!r@b+uDt8bZCPdD+qy0T7qT9)U49^2<#qkKa8iAbLY(&K5!_LVDoqP3x
z|G*xo%8bTuKkq?w@(tYL{^MxxiEe#*5mViS<QrEB+eoK%q3s<C?-eLa;&s)m;Og5G
zT^;n16mc9MeYO|1n#M4f6(KY{ita2`nAw@4B1?cZoIqN+I(qg9fL{+UXjT>>Iy@M+
zGby}2*_d%5Hz^isT=3od_`_DG9_J73!q#6dl3fE@%-o~VSWN8MO<GFZx%bEVD@PHv
ze?3b_`1R^SWu_WeuE&#MH=#Iz_~(o=x^NAM3zi}(_!peLodqLfN75jG@dZZ6xfOx1
z8+VmB_s6<z{U8WjLUxH#Dr-@3Ey7eKU6FZB+HmOH1HJs*kazbYHhuCFQkhXeP(F0&
zqBW*PK%c?2#i7?3phzW<m^^(WX7FKV2Yp(4^6Bb~qT9Gs-MW>=dbk%(8h^SMb#g7<
zM+rjCpGV9C1uQg0hzmcBTMT#;taN6EX3T29fK_J6h`)3l^#-QUu1rG2?F_<X)M4Xn
zh;(9LgQ+B$aC0z}H6Zp@67*~>P;fgK=dUNXbE^uZ#Al#_0=^A1_xO1^Lx<j+n^%tE
zWN19}9J-=+KzHWZ&c@y!w;?2<6gtvMgoUTW$fypn(Tqh<*Fyud6BMPy;o6N{m>AXI
zW;6qP4Ga<b^Klf>O61J-pkAJUOBcgXNDobTY!>w8^+=4mip1h((#(<;$~+v{dJuKG
z25@tAWbkSnLZa{Ces&733W|lREnzGD;or9_N>Z-l*X?_7FYj5o#1+Jb7obLL0-wG;
z;BIYzg5((dx@#vYHSN%AzyNqysU!N*A$<1b0g4mtG;SiAdRFM=-vvHHhQZsK3Q5Rm
z?EmdFv)VMEm?>@K2G;26?*kicG5flTP+~r9ygXs8Q;qObXOK`<jm&$o%-zqqO4st0
zkSNsDwdF14C}x%(0~=@f_Vk9aq=suctTmfeJ7Yp2O6rNRb#{XfGwjI9vT->&1G@4i
zq(+AzzMv5rI>v-k)FAlsZ-io0z|O{lyrnm+X|_s_2}4+H3TkOde6k5yXiRNwQB14h
zjk~`q5OuZ5^sdRM*fFhOA72>Lp&u7^5vPtvLDSKR*=Y!AxfX^B=H1qycjC7nPs6a2
zGdfx8Ac+u$n>hu@x)Y74Bxd`dmDt?C1bPhCWKtr|;|P><GZuuVNb<Zw2H>}}I%bz(
zpuMUMy887(S8qp%@-lJd#0kV?R>R8O6E?cFh&+E0iA5zyjEThU<oi$F5<eGGNzaG6
zmNmKs^oAFe)2h@sT#QO&_7+t<xD$q$>`JJSzZn`ZyUgWK6qFR;0kPr6^$&3R+)-Rl
z6D-1P6i#$Zthf)TK$jVtN@HS>oL>xmTL;t<N)#TG(sCaEmZwIcKx_!7E<MrH$C<&s
z=kUoV+mXY5?R07p6A_M#3Mq^z<TVg#BN!42IZu-A*Fd8o0oRJSCd3sub#fPyi{<Fi
zy|<Fj=~5|;jR-+PDdCEuJcM40W)>njEFB1IOumiqXrWd`F`;g*@aokSjs~hoi;qR*
zofe_|(Ex=v<o}wwgbx(O;lkDHNUvlZg{T<e!RL`$$hoaYytpR!JZ3giWd^6a*Y9wh
zJTKoBAtLxH=keo)ut(Qd6ri+T8<wta@a^WwfZZ%yA|~BH-hd<upb14y92>oYYE`(z
zc^sBp4SVik-TV5(QcZ-^o1x5(lS+&1)ALxkhJ-?}iMa_9BEyhPZ=muR1#%N6V7MO_
zg<FX&K1l_=rw<Z}VdQQP4;v<;xOyJf((_SXn2p4oQsmsZ%}iI!{*!eB(KTw&txd;?
z6Z?@}tqp(we(>c!pvuf^krdRkD<#k>OTfjD2-Y}|bHAk5;P@_FiM$0J=bq@n{nwlj
zs-wUBj2}*1XErQmZ&H+Ty+@*&N_<Nb!^qMKO$F&VbLKEErZvLb#TxM`smNwFn<ra+
zUIIN=buf1ILvQjaRr1)+KK=$#j7JfqUsKM_iuBuvh;8TjKhc?j?jEUXR}AeX$JgKQ
zL_!`j10_eGh%%0oPcQW7;)Lv&Gx+$E9mu_#3w?J7IN94EH|hc+Q?ikp7>9_2To_qc
zAcr)LZKd%OmwD`C1!)(@z1qdg3thT+!dy#?wB&diFDs!_m4b*n+}jiyuyHg*TErO~
zIeHRWPCb?KgSAFAPVL`;ZTqe$<p;U0k<w_X#|(P%hDzLxxJ6?^I(=$V*pOfNb#kKT
zwFdFGFJaH+SZHb6z%L*GUe4yIN{h!8%5=rmP24;E(3t_{X}813&odqyZ^wuKE()w$
zw@x`AfeZZ2|1yQCwX?$B)`B*a-}^8!vr$-?h@a|HP0vK(<nE?0GtznDI}L@cle@y!
z#DL*_zxVU3aar5jD-5)irs~J~(YLfyxVgJ1RHRQ_TV$l5(62Cl^7u2ZMYw~)-O=Q+
zzMnM~;kb^0y~5emiJ+T5A3LNv3R7EWg`+j2iJs^DKRQRtNa5sar_j?Ri0zNgQ+}tZ
zuyuA<*qG=)$GOkG50Rm*!rj@1G=BCue?At*3;snKKTn_3f)pbr%~#ACH%c+IM=!<j
zmp)g7Ts)#!)ZOe)?^)f<TH)^IpfJ!Pkoh4k<;DtoM@NN)2_sh@9{U@w)^_#^Bi*Ox
z>$Cc_bF@|HX*_+tp|zvJ&COXs@B4F(N2D-zbWqq^8MZlJqA=u|b+b2abL`puiRD@f
z8y9zlqis7hl<O#LT^$rA`s$DRGBB}H*qReF_gCnoW5V@gr_j}SzTb%q%@r=LHVPW_
zpW{0E3P(2&g^BF>`hM29B~nv`GkJ?)oBI+=R22@60$q$1>wiA1IPv3Z<#;GSDI9Ed
zgzS#bF}^=KN2)L~b5c0kTPx&k`Z6%JQrP?@8mlR+9NZMPW<~^m{n7RQXTK90*^^gT
zD~Rs<m*}9SZ>w<Tyw`iK=gn)WXeq3m9Tb)(TL02@pEVcom-y}<+`F-b!j<#*`PQ?c
zau54g=W%=YB$3Mh@_f}ZaZorp+bf9l|8p9v=qW7hofLK!+JAoT^Yr)RIa(G9XD9Ml
zy*9l2$#eeTvuo!rivB~#DZV)ztk}P0xx!5Cao#I3X>T4&xc03SL|gvL_axEbKJMW{
zndv!X0-3JD%+^WaVD%j5w%)U*g|N5TEA+M6$_Z);OM7>Pow@N7JV9IG>gB31eu4(=
z&6|#6|N684S7=B`qOiv(2f>$))<J>)-4qbh<=ubYTC579Rg171MP)XQZ~qM258eO`
z-5sri0{<oom@$3a+i<Rm!b|V|@Nc?CN4Fgm=%B#Ag92}T{R82$&PWYAj*mBQBUG{-
z5Bk63!X15fP~cynfDlvEA;A9&1nl_fe;Wmedt{KNnXw+R!3@`9VUL0b`CuGxN9&-#
zzmWn$0V`HlCA_epxcwm6f8)hFy6&Jr2L=8G3K&}m#n>2}%NV2r;`}>W2L=8&P@qGA
z|KISGI>y*Rfes3EP@sbX9Te!GKnDdnD9}NHRtg9LyfTizgCZRi=%7Fc1v)6uL4ghm
zbWos!0v#0SpuqnQ3Op^qtLekZ(;L3NU0}zeA^&%p820X7aJM&8=7;@nyDup-2bj{8
zXKJY5GBmL|teiaI;bi?kGE_BVTX?wGqorKKA9Sgz3u`-D7U&SrRsX5R0vNicu(LK(
z76kiOud1eR0!wBCFwkM>-(P6DX3X@`$&Hot)E@mx&IHJoEH<OT>>K~7rps&}))pqu
ztzByvoAZ2zSN<EDNTd!6TRT=VW%`u=4^752bhZwTaCUNnt(h^Cmj2&px@OMsa<%)v
zaWfrv(LsR@3jCj>fVzntWf8kSmmmI5-u>UXDQ4#5zHDh`3QaYp4tSy&npsdrF=eV)
z)hE9BYad(ENVfh)X6}V~%U0lx<qHrrVkB%#RdMoM=wIuxowJ=f`(WwfS*VJRLvC#?
zgZND_ZP9W#_;$zO0fW&=qZl_+%3#)IJl<F|3{Cou7&&4%tm{$`&(vbp?n795doVIn
zQl43aNkz>8!^aJTPF(?VpQy*KW#EU!Ygb^!t1qLwP8m*x-9p_{spME>$!jc@P40<m
zR$aXhCrnr{599m0AcXm5|7}gA1CIeyv3||Vm^yhfh7axs7kMKRZxvy}2cKj0+_7-B
z&_`Kfl(L}9-yKKLny*-ryC3qIA~B&gf3Qem!D@>`VJ0d;W<hJpGpC`<iM|AyNoNuL
zp#6N=ZO0(B8-Ukdo`qq7fe0Kt7(Kc=qb@d{RSw%PzQO!nOq<vh!-x5yESD)9g%n~e
z9c?%AReUnD8*T=(Fxa18w(Sspf6vOQ9>eD2qj#2J(%4ZL*tZu;gH+(wy^P<_Y4@E0
zhR>Xj6)Trx*6<!EPYP${KoN_Ay?|xQmSD!@@fg(21?8%y7%*%k>>HACH@6BVcKxyB
zl_2C-YGLr0ahN)O6b26(2>&h)NKHvZc@=Mj>Gzxm&A_YE0wJx<N6H@tNNHl+{AGB3
z=^{)TH<CqFBuoXC$kdDNrxg{7X7!!99BY;@#zf|2_O#N%)u=lxmh(Rk`j<#Ou>PBM
z@Ybos#mK~`sVFzVm^n+ZV9IEwnCp!JgE$|0`#@Hnh2#fM*K}y<zySWkhQnR20VymN
z^GD6P-*kNV_9{#t7l_U_I*4UH<3IYv=-KlS)W0(_uf;&Px^u6=m_K(WQdz}v-o*Y;
z$-Ie-Ds2SLUW|F;eQ_(U5&u{=8;+W_xRczHX7AbKVO3P`u~_l)bOiD~2lej_dp+Q0
zavplK=ESVNPE1dFo0WadF!7}Y7}(hia<^fa?r)1k7HX<$u*S?~^Wkk{j7E=%*!u1~
zcsp64A?+s9vI<FMJ6Z<?Iw;UVf&VlGjL>h?2so%$A?*pCJ^9`5uzGw?+z&p1L>9OE
zJ55c?1tZ4vV==>g+%K*9J6-$-exnBOKEu(&K?7-7c`Sk{BpLdPO>Tkd%T{B>k{1v}
zdiG<A@mf~6OJ=oPs5xWC%M0=9;)R$rVH~_nR1tGM8ddFY@qc(>A&ul?7I?3|12Ar=
z7cQ-T3kw!3#24Fs`@;^N_e*mdTa05#@QFj*ctIHqOh#eBqG=G9)4J*26El}ALO)g`
zTe$W$^mP)dVwz#tn2DIXU^a{xXKQHL854t8K!w#FpVg#tTTGub6#-7%N!v6H-R@ZR
z`FaF(v&FU#)?nQaCs9?`-UMXTN^4(?9@3ln$XP0S+CnT{G!`m16PTyxZ#={{WB9|j
zVpZrcCvIhSJhpta0l{fa81s+W7~!vn{hzPFh8-8-K5Q~pu6+YO8jn4$KkW9&FR%Wz
z5nF!z5#bfT&lH8i1VOW=VX$XgPm4M$!x=#9899cZ>{n{+iBThm!a;}C+-lX(f7EoW
zSvv>vr;7GG{G|d0mOgmlg&<gJw`!)$!!A`3D&Mk9_um!Xh3;99MyS%6bom56djC75
z!V<G)Oh-Qo!q0Ao7`<QxpY;?}$DGE}mFsaTrWk{#FTt{<FF>Ak4I4Inf#}o{=-Ihr
z$f)t?ZYRt!=EXL0L(qg_Ft+Q2fI-9Itj?;GOsv+Og`d{Wn*=Ks_YvmKknvM6W!hwn
z4Cu!CuFr1WCoaPLsl$;Kd>mixzR9xPi?DoLFEG7+yG^72T&$Wi24J4;1DBF`-T8QD
zcK?5ihpCA<qxFo9VQldD`g$}l%AM84rFc-Ufq=oo(Va!23JXeD=<5%}Y!L<Mi7Zys
z%kfb+&w7)0;|xApw+cIB%VA_@_&DQg=SO~h`eWjhnF#b@F)V1KSD(q4HG2ZIS%+ch
z&;hVwT?;$!$#`SMbm%2!Kvb`XAuPz&qqBWGy*+t|%D#P1jAV{|HRknKxAn!0MQ>o~
zIDgpt4Z=Sb%)tQWiH1mn6@`Z(uwOSgb`Qkd7w4j{gEeQ4Hu{ekg+adNNI3ol-u>zj
z4EzGI{KNM!$oLr#`^h16eD0t?2L(DP@IOfbkv>LDor+O@)=%{s{QdjbvV8|4Du0(B
zo;vAI4yb54VdlKa=w+|_Cujcu{9242L&szGh^|Bfv?nJ%$JkihQB_fiYX^SCrY{aa
z?A!+nCJ%!lrP&FKU%|Y|0}yj?2R40m5NSoltp(|xqu+K;X_cVVU~Jx%7BddIA{S-a
zEW$xyC_3a8RL$M++M6rU)j}Jh=4yl;-h(ZBPoc}W7cir{B?~pNvY?kE)LE3_#IN7u
zy8~fQU8aG>Y$~W?cn1!E*69RvA3q3&teRPvdK{Y`B;&_7XJGYr|77useC+;o7jD*f
zSC$zz_vnuq!!O`$c{OUPS;CkFj-;N$@&2+YaMoi9=*k>?_}=>{*S13sFJEZLHDTYi
zA13PdKuyjST#CxUwAHU6z_=XSKU{^q7gK*9n^=>A@hptcy@MUUg&>b*=D{-G#rdpo
z+G#Kb&gqR?->kys3!zX*wctKx4pvX@%`)*K<lec6mG6B6xtS9dzOsmgd`e*F%;Hr#
zV#HnEi#1=IK^K2oU0(@=I%|p7Rh8kwj!*Fmt$Zt1zXeZoX3r%;r7STF8`o?>)WOY&
zX|6+cT?6#`{{yugOJj2hauOdPCpnSDii+{$XR|PWNiLE;{u$-%=o1qbEk<91VtjbC
z0jpl_hlriuA+FIE&6b83=xvK`EYwk$5RTa6my|TH_4Z}C>s~O_Wcg~3-WWg03r%?;
zI7bU$4NH)l4R{$puI&MRSq;vte-A&$++`Kq-^uszouauB4dum%y}S>bk0zr;yb*H;
z_QM-rcZGIZ7(U&&4cYvi)!><UW12s9|9S>KEGnTws7^rOIM|XQJV?BVyt-;s$ZRot
z?WeHzbU<@{B7S`3EnHGJ!o&s3Fu~8B#_R?pTt0$#zC8(#vGk5O$|2D;gQKGbs*<Cy
z_00{q!OFQQl-Fx$+4q_}1<jhQX!x*B$iqRmdG#GT33FxyBJJQhtor9El+;iXHS?sk
zVK7v0AH-XmzD7=Q4I)B=VMQzO_@S)E*jfcl(M(!#UN$w>E6bh79sU8EuH~U~znR#w
zW(2H`o<mj{X>YHMl1dRAT%2L8En=aQb_mHadG_Qp$he62zS@Rru|2vE@W-%m!|=t?
z2vj}cRT3u9q@ZD})Orn@KiUqbB^x1O_1E4bM?<{*A`8{DchhQc^Pw`hVg$4$&8VxZ
zLHLoMajL!(7WXkjWrGHsU2IU78H?|hzk^T~$O@eD5~hyzg9b~Mo2iI#qp`hyIryex
zTs*WLmt%&*&BF~1QO9WjIEQ8x6D(N&HcU#3VBqQjYhzkA&K+h!sB_ThH4xTDTF|od
z#-z!{5Y=Vk()lYWHuT49%VwgRl>wS-i*RJm57>Po0=EjYpf{ixje1Q&9iBgIH7wWO
zAkl#LzyL@yVp)}!#gHn>$U|yT&f+hImb}IrbCJDwBR2ec4CN{TEL&fV`ub<w-(y!}
zLUILpT4n;y|L_@ttAD}r&pxL&YdChMRIuQWB__=nfso`YsI9ExT&!c?<*1T4W7^E&
z2>&1owY-*UZ8a+MQgLeUw+Lquu+KNYjD@R~;@0v_C}@9*JT{7s4;>Wfpum5g0#X(o
z9J}aEjAMmdp@OosHXC2R`Zg|BOE7Z5Yna-b1+K_nv!l-Aov(j|->f$=(If{Stp5_}
zb#_?#!BR*gci^Y!O1$*)i>NIwgQJT(Oj))d=E5$l`}zcWk9i5Rr}TrKOaxIA3)~(0
z0bBORVam!kFpm4Vm{q55p8pLWZ#&XXQOXvYNTkk~zIYCX^>(D$RDtTe6zt#rC633i
zs_cU07(c*+m9@oauE@mR<L6OPV*)eERbnSUOqgT`aYX`7rl?@aqB-bnEN0=itB5&q
zS=ssY*H@vxWiwt`xe3+vEIelIh4<ILhOHm2L;3?Ny!GJ=a5d3Fv!WR};rnsmP6=$R
zyrHhD1t;GDm}umW>dcEc6Zrr}{b%9r*@Iz9I7w+nB;MZmHL_Sp>&Ye*bbMjWhv=>=
zhMK7n5^kolsGl9A6^RHhH^&f@LcIUZ`-s=-gZEcXL;3aN*!RF33;UR&x={<x&eo_-
zkH&YeuSdAd5aXA<jtRc@kck=*dvPB=*>xUWdJcuVjULpRoG@;}4Aj-Lh+EVt#HK&3
zxX3Nsv1;{VG$v)iq?0Es^i`2?_5i-#b^v?6-3)0{14M+RkE&h(Ez-!gS09WR#0uA6
zufVqlVo=#A!ZlXXuCDp}%)Z5>%M#jF{rjVzn-x^$#^^gf2tnh=pjW>E=>Ly5FxE~+
z!Tks#(-ast=Oru{<OGRa8%_b^F{zIOauQNt=s@9wRlwS7LRIO;${J`ob;m>-8w~Kb
z=7!b?8L>h6XTHNJRthz+HOIY>Q`mJPmZ<<Wps3Gb6+;mM#!NvER;{dU;>IfPinm{1
zh%N^8xN!13a#h^$&Kz2}h7QB9p}nE2q5&s9fC(J?h+h809`!<RZv)&3JB1(H3h%-o
zDFNtlqWaN|Emp#5C^S<zQFCy|=oQOQ7rYG{cL$@0sq!t|#$e-$$*8z@4W~nsVb^;y
z-kIW~EZc6;sSl<QcF~YYSOKds+F9DbsmC;||70FS*>SjZH4ZvFW=XGtt;;Zs=wprK
zYnO23z-io0dw@Dl@cNocR5!`cW8T|%d7v#T*#3$g*B-Qxvbi34Q9H3WvIzE_Srzjc
z)?@*e)XB{S9)W$~<KYP(|3R>!rQ1TC6+iDpA)`T$)oSetDdEFlv+?rB=y8K#Org)(
zcMv8{9D|X4$S?%jt7yW1OdnRUyMwe6bp$OK4g<lO{EQ|DQ!<wRHZ*l(#ZXr?X5C?B
z$Od#9G!ml+(Yi{Q&?rJ|JWWOD%436idqQ2Vhk&7@5Jaoz0Pn7(eKU+4UEt=Ri95Fv
zp>5j*qZSQ^&(u|TZMY|5P938{_5gn4=3>q40f4FnMok(6HyT)DSdG=TYXAn1?u14i
zOT78z&p3189A@>jMMC&d1eZR&_*!m76P-LsqsCZ#xbsSj@Xjx^b#z#%QjXYjXHZDt
zU1$w;waPKJl23W;sXr=>sH>^M{oGiTv8=U7t_s^R{;Y6ZjB|UBLBq8(tgX$T`q{$+
z4YdtWH?~E03v<q6S2)_}a*nC8DtOB>eR$r(SA8`Zipp6I-nly_53)jPYBnlWSx$_w
zou`^e0=uq3_;AZhkQXH4QgAd>SUuO?#DG<Mt<i6AAiOMPh>cB!nMV%{o)iH4p5w55
z$#}@9Oa)(#L<yle?T<vbnu5@s1}feP=HM3`4@~39_0+@?L`EiV7(Zhy^qcY!n^Fni
zz(E)>YZ!))9nQ*}8ZdP8$N2H1F@_K<LjzwdTD%CuI_o3s^cf`9TVnnTv(U-BW&W|i
z_*1vm-T@`gsiWfP>W>-DR<N<qqHs<|Nsov`-v~qBcoQz=5%~CrV_X}I=9jcXGj=Q@
zyh~e8kJX~}peMZQ>q5>=PuaLhD`=&uDoaONZa$igtRa;)Ae+^5MP9=((ZdcZibg1e
zvhI>PWMrj4#cvFPTr44JZsJsGy(Z5oO*o9bm+nE=xhs0vbXeird!`-7Iw<hpKmiS{
zUYIexKh(-naCq-wgvKPHl;x0IgH~d7kROU}Ucs5GNpKiA9Um_l#frn^<yKZ`38Rw1
z#@+!otWGYLsKCOjFQyLfj@pbwNDR!C@ja%U0<dD)3}}+?9y@RZVXP!vMf7=R3c8EN
zc0ua3i@2Iphao}#!2AKW?UF<!)xqpHSJ2wq69so7SbhFJY+M{+K+ogg`OC3raz7N@
z3dQ~-rxBa_0G6J97(H<WEH%|(+NmEVP#zdfxynqX3HM?nk=0}f2V&qK$`6?(s<7!Y
z6fgFs_g<t5ckf~7<*Wu(b$7h`^_%EnBgd`KE35^r2^U*4c=Zm%@S#1RCF5uR78#Aj
z7GZ2P1RuRI8>WrfICJU>G(AV-{eSd_oQlhnP2iX2_TEYvLtCVYkz)g(sbK&E3u~O+
z6Ap>@K)g8fReZd1F1i@kB6!agsO#FHf8Y>wvXCH#dz)pao)|d3AKWOTy*$Ae*^!rU
z`A#VUM$W)08W?+z4uZS6E;NYx7(p2B*x|#`)sEiOcA8YK0SjwyjGH<Vnl;(DcfSdP
zCQZf2As&!>jlh;2zvA%W{phP#!7Aiu&}?A_Lp9dM?XeJh4jsmclZWy7!f7y4{rmjB
z#R6Ynf*ZTv#|D-pUpvm0aDt`CEv%(b-WeaQ=!EmDU&7Wa5vWy*fa|I6DDd?{WNH&6
zbQ2u@as{?t7GsIH5vhsU?c63<qF9Q8gj-NCvA}3r^U+v@yLYAB)P#*t2Z|;!BvJ_)
z<ub_B`QQknP>36F`upE7f8}J1oo|GuvPxJE2|y<^ZB%F9K?U6uO%z_-`V7Pyla^px
zacBH?>~(~Gxf-9Jy{8<9)MO%bWlgwWkVo<2_hw6>&C<Xao+fH6Gwy_*{d(g8Eq3V?
zl*BBlE|*gn5rlb~oJArTt?vV1-M}*5lvEpPs#!)<1s**E0G5`QN)!m)_X*zqDjcJh
z)Wf*B7=5Qtg?7>vEPZz!Qp;_KarfoaYeG#`5h_KxaQAdUY~(LEk&;ZTJ)2Y&zLQ?X
zhS`G<cj6~}^yU61ER4-~kY5g&g$!z{Pfu*2i>&-Q)Ua&%=&pLGuWf>zuOHN`6*zh*
z3@6S;B3;KDANZ5B!p}*J;2m2Kyz>%%IQjvycf5yp_T3P=0URe$D{sWj!&|ZRi=*f^
z@-=+^l08(Z{eorWpK4S<nq*q&J>wnpn6C|aO$N4a`W~laOfhWuXH)_MagZgWy*zDk
zXYV@vbH@do_L+}=Zs>>imd?YC96>;q!f|{6L@X)(@%nWLiEqRQySAc}yE}r+J0tDF
zer)~zCo%&=n0S5<8~^^mg(lP{hvMzE>v22F5Oyav6Vk#!&Wcz>Tt1DWdkkdt?gpO$
z;}H}bik(q~PYoSnE$Ha#pspyH!rkxKW1--qP|H`rxucZT{><F_uxCMP6dGwlIS~!@
zS{TyPnI4+EEJ1!0){{q}m$e11#pSf?%?Lev1_PXD;r%_kP*GOHIZ%aK3c~Hl<1N=|
zgu1y0#?SXd;hCQirb$7Vmh_f_mC7?7{Sm9Gqu2C_FwBj@BGULyfiBE@c2f$YVu=LJ
zg(+D7_S?9TMh3qB3tG$F;nQ^(RO1d|-TF_FRA__FqiD^3)Z;VWJer=5-jIe86i1)L
zqNN*H=6(qL_P&II<Qv%X=}El5eJhM(zQ=;iCzKuP_L+fho;LIbMWIYp4LT*oF!XVQ
z5cGrGlxJM@>BC}f068&HkW(O@^r{*15>laIrlVAblxtijq35U`s=^w7`sa_1sOxmb
zo1eS_J5>ehsm!ZNt8i$|ySV-k8`=8fCc?mkv|bY4Yv#RJ<kVPW#B1YFKto4!rUEjl
z6lu4jQ6h7~*p*{Ztfj_H|IuamMxk8eV69gT`5%p_?Qc5XcTk{%0{>|WG&Pl?u(XlJ
zCRZ3WU%~lfSCCR(iC0JYAn(RiZ2NEvZb1Wbn{R0S9st}D3>gZLMX=FZOXZzngVN9<
zWr64`dp6+h?`~qkvIa=F&s%q&0*m5s%>LwK#ATH!Z%?FWjrs2_fJRX+iYfav)9Woa
zb%$$LKj3`2@|?DRG;CeaJD?LXD4VR<d;pqegAvecF5?S5(cQ-ninzmA`QhhyEWAqO
zfY0`R4)KN0v25F=N8Kge2}6dq7vAaeyZjJx>=cql^hEd36QLh@0DZ;}LGppGVdCdU
zYrP1^2v^<`oQj$1&ak!9zyZpAKWhYH_b-cZZu2Vq^CAtNY@HW;1Q*pN#Fv$!ibf^@
zs_E|E7t$|J3s$p7V^F@~Mkte3BPKK)!S{^u%8+z?am^G<2WuiLVISW4)CAuU&7nb$
z$Y-xE!PTl7_()`EDoDaRYu`ppx)v-BZh?`z6FhwUp+#C2(eT(*UPg<47Z~YlVeLya
z)Na^;K-FuQ{rcB`T0=%KuH|1pfQ3sxhn86&e2%Pyj<FG{;u3KA{7IM^Snz)QF=)g%
zG^aOn4j97At_XX6{05a~epontCfvfpk#{Zr@q2yFPmh_iWE7$WiA6=$YVG1@skn)j
zH@bk?VRK_XN`yKG{9YvBjmr}7OGF7O%Tus%^=kZfF6J4RWnIkDy9m!Ifw`*#Dky+o
zFQ9PLPz5ue(FpXmL`G~DI`s;~%m5cC=yDc`WhlRO6u;b};O^EL>Y%Vf?NH2zSVv2x
zJ+-9r>=^v8;}}Y-X}P3&D{P=pRzYqZh886+9#U|@v~2^YF2tK}EJ2W;DYRS%V$N%;
zu;R73P*YPyWkwQCZ$AL5kqhzi)M4C^X_4i1>nPx<(=u&qB0}h~gV=ZGIyGo1R0$n8
z9+87G!f$qMc^_ZyILn2tgUq-j<d<>dwsN8pL1jf;fwbF4vFXzvk=$&Ag`2*_m+!Ac
zfSD2W90y_fyn#qKxgD!E{)E!DgH^sKt%SwTXG3|e-IiZaifVOxbg@>)tvy#Eqb1kV
zN)7dN;S0@}@COm;)0Pq}1+V;$tVQ|S`hnh$YTP@2mLJt16%o&+sX=)8vqU!$h22IJ
zX5PY?!+Y_;o6GU@l`K?N$KcmrV`1$RfK@LJhpoze>_06i2_nuf37=Int;?Km%GRt@
z;7T)-QItlNhDwf@H>yfYxC-yPrip^A^1LFHRMb(h=D0XF)304cMQ#bt*Wq)LL7NfZ
zsLsdH9pB@#k3YcjwZEW-jIZlJGST*$z(uM2ipew@nt!K3J>6YZl;<rS?9n#XnL}1S
zhlX0^YCtpL7|nbpBK{XzV@(Ug5s56J*+hlQxHq2xwQeH^jF>$eItmr|^mK!fR{I)v
z%H?12#=9S4+pfLX@!fa05S>NqEosDIKHUT*Qvowe2Xu3)#FpJ5sHl@dL#aeOEda_`
z&)(Tu2ltawkW)z4o{0kt)Z|bQ{v*(`C^;TwS%Q*7%MkC!z|0J~To1W-<55|_^DS(l
z!@A+Wt2s}PNI0SptnSKJfkwg`K!vcggo|f5uNooYvs00)F_4|E6Dr3o6Z?1CYtW6V
z%K1@MT?b1;Jv5ajVAl_O>6UHT$Xfbr=5uM6D9W#eKY~HMI{zkOs#W1+Z-AJTbTA%X
z86VJ4nvZka_MpPHKbFm%3@sWM8d?K?n+c0*+nO6Ikdc&xdkKk1N=!ify)=}naqjYw
zEBKp+I$<0-FyI=I)-n)Wz&}=I<0w6ZIXZ4|)YnjM-pb<@88|=}bdjM4?6p<V@Z>!R
zS|dAFJX8eR4y5CK2L(DP@LxfJ%3^x%)_;y;5h)n@+B$6g=jV7q37es8(p<%$d4VTX
zGT}xwnxz`t+qkBMJ%(Gc$iR?AqXa+;`G+7s(3p8NG8Yw%RrqPsdVEj$L5;?;hT00H
z*Z0v4Q-;veR6}J|HS{%fpf4}S;T^kiE`l+7?KFKF2n_U_5PdR+UVcE=#R-~}gPOR{
z3Hw!Dbs2@_Cx6HYEn(|WJoSZAeh>^(kN9qW1b&N1hMs*dEF3lfeOyF1bUF+gYU+gi
z6eA`$oxD&BhQ@}FP!JbOWl&XlD5L$(?{z3!H8v6wNq~g|y`-^>U;BCA1=JHR{bUm?
z@KQxRDvAnGT&ITAh#P38Y#<ioR!RsJCE2`J6-XOvkl&=ML|F(jZBb%8WzHg=qeFRy
z2?)$Bpsk@sSa&US)pcR4RfXfb_TY55Kua|marwMR1B$qR(nNf=BHTTHhWiI;tIuQw
z{GIYyr{BcRZ@1!;57%PNmzQANnTnRKF`r*8GD7y?n_mv$(Ego>DVIa4_UzcsKN-Lu
z8`O#_s&F^40)v@ZA}pr>(r#1WtE<3;^aNU-eRz{ZGVlieyosPjAo7uK_Mpn&9LeEP
zuv*X=)pq^x!sM<{&k4gT>vrQGUv9<7Ni%WpN+s%r4VBjJlRs_25Z}+>!A&;x{2df`
zaf75Z9eck#glc0~MhnY00qP;5fU2Uc14qv;aLppT$6f=WA=i*zy%^3OUEv}*hlJL^
z%>AFeg;T^Pbq`vBNqS-U_@|S|qu?R(UI@9k649r=MLGrVr7y3-t9&r0&nKX1KzAfw
zJ&FS%nXskx-LT>TrxPbDm!h<d`(5xP6qVM)$Fma#jGO|$iPPYuEk}}pJ#4!>QiG1c
z_se$S+|<>0XSz4~3=e>g-dNaD8QKz)iyq9%AtN3>BQX|t(h6EgtFT0$E@rgKMj_|H
z(-T`bDIqBbIps~TlH}q0W4~bj#Nh~#)uNh{U1-9yl4|Ipldm^QQz<AHRdD3gXbGXQ
z^Xv-Gs9IRN86l!rivkA)AT43`wq8qk7EcNr%3k@0k6+SIjhiQT<BOARm`P&;!Vhl4
zwIN?&cyDz?e6bccO9WgS1zvWiW@w78PVT5OP=PIFxinr=q(ViHJVf9)!mO&PszVHg
z$|c<b(A)JK^343v$KDwCBO-XR1H^=o2r?kKCV5LE@6Qn3hEC*sx{S-WOVH2P1*#48
zD9%ddN$s?T0%T_-qPeFZ2AQbiS6aP=R+DuXQPDRscf>Nxk1fDA$F9S>S5JtU4XB#e
za`X)V&g8(gk1sUKOHe~>HPsku_I)tQSQi87j;oB=2^U{Wx=O`3_tOr9Jy0OMP>lKG
zi9g?Y9w^UwYCs~99c*P~*!}Hy(CjiDKlSoJ%C2_^(Wv3QYGcsk7cta9gs->$faIJa
zoQ}%G?Cz$BIC&6_MuZ&o^+R3UE;2Q8!zY>$O<z=22U90kj&C9yM@&R#GhIYzXhR`b
zp;e@UCzM)<@E}BJCZn$-B*xgSD|(JpL8tLkVMir7htID4rV2Y-XBc_XRZQ!*%-9-T
zyquApTh}t5GzkTI_(NeXDd!UzAT?xD_paz_n?i_@4RSKekeOQs=cXbY`~C;qSFwg~
z7YlkwS`<;?XEIF#bnfPhvNXnAP;qU)@g9g@{P~mbP~%pKE4kY6W)LxFADLbwQo??~
ziCP7|d3!0|W(Cy^y9$Zd6fu>nD+Ud$g9??GvWyUf-?@ixKU)99B?w1yqL+s5TW3Pm
ztZav4lrJ+69a;-mzX&-Qa$MLxoC@s=+d-X`ItqtP&z?GN2mgYzk$o{Y!1;+j_0ZeP
zmP$@3GBY1nBAz(0<6{Q}Iw<h}kph|?Ll9su#py5J$GJ<)ocG281Tw4I&U*z2^y`KZ
z1G?gJwH32ZF(X^@B_tL)L5usA_t4Q8KWqxT$#+A!#|vg3F&Ubyt->y<MQKtr6tgE`
z?#vYIJ{=7M8#^?Yl;KWrGJ+;GBl7qzgrqj2b8jDdkK6Gqv)^hO9ILBT;1@I)L*x5l
z^1|sb5a&QzS4N(EA4B>EVZo#<T!<?rHrx_%gp#D@HbW|t!JG(%&bE2bb+bcS^fhEY
zC?#)f<-1HTUPk$$q__f?w^L^8{}IN$u^71_CvY|SK5Y9oL#E1XHuN^@8Tw#!FE?Ub
zV<{w1o)A2kOjP0Q;SJXZOjmBBg1C@mR8Me6DGh+zw_kx)=KwgVKH$0S%Q=F9Lqr6G
zSP&xU(opCB=0*wh%tmAFTZ<60?ORB?^u}B3--YzeWyt5iMU)+ta)Pk8k=9jtWk`ri
zf%FAAZk*bSOZRHv$-O~Q#4IrcfQo1YHgM@g+0q3S=C)8JR4ODY`KcKvcnbuhp0+)`
z)x9k-^2JGLO1p=Qj3UC3CNcxsQOYm+80zbYrpi)89f*L1i7UFe_`=KJJY)f*;i*}N
zebp^{+fx^KjsuVB7y`-K?$31((r{?oW&|)e;q5K-pJ?eJ``RgNKM)S<sXZS%_m8gC
zED%wmiSon?`0j24#Bx(Cq}6up=wYy<_H*Xt?{F_Q1N*idL9h3R!1O^nH(y%o2<0fb
z7mZy9uVZ77It8m!_;SA<Hq4)c&)h~r!Hv8wb|1n+ui<`mG_Kw#z_`)#@NxepsFlay
z#`PF%-FFi^77oDlNjLG%-m^;0x2!0ST5=ULX3$F5P>Os`=v={?=_Lp<G71$9IJ9Le
zoXkH!;M6(TGUHAB80LuCD?i52ITcVR4)(^+YY>@N`4kOmOG<G1htufu?pQ2+ok|Lo
zos4QZv`nm^TV{z@miVGDB?EO@CMe5{L+oARvS#W*W?+dImM!PCCCUiqGrxX~JKvvC
zl6CNch3I1{!TBR6ahF0zd#$dZ2<0_g{AF=Ss?WpKm@N1?m7=PmMZjsMfU9HN8A~_3
z3$u+MV*B}PbPwG{NJJ(V;dE^94x;PnE*AZiMFEhrp!G6bb4u&9;^Bd(F3tr+flo$j
z?vt&eAQ{JYUc-<#9Iz++4ud3x*(E}LUIeZuj>U|X>oBrWjB+x$_d*}PFTt{1&D2Y$
zzB_?lAFRWQO`B1xqC*(Pb$s*X0cZrRe(Z9BWr6rBxbz!`xt*sVtDuh9Lkr|Y9>?CJ
zw;nt9_YZ<{7Ks6U{W15|mk{>y2go88AB0Q&_Sqg-8LhyyrEj6vlnMqYH{$~a<X#GO
zz}%rPV3*r)3VSBFef&qn$30-6vkX3yUW6At7P7hrSiA8kTxYxvjq-SWweKW~_}t8x
zmUH~{4>?TU2ig(fJp=Ii+Y_KDtAL7yB^qxX#%BjFGuWjGDmt3z%=m$EZnZeL|2L%o
zpM3O79O>~X`V5<mkB1OTu1Ll*Dz=U7WDshssllmj#}T+`Iu^g~imLP^WRzAy%iJ6g
z_*R=H(7rM+50Mv6V&;Y!SnSdtwYlkdP(uicz_HqEYD~q+VB?@3#C@w$6X-J)?>YM5
z=bZ;p*Fb@^&EstpEIDMnP&`0*L>zid48(hzdqS>SjdicTg)esR$L2R@;)AalTO!k8
zD$01Q<OY+?>k7(=F4NsE+qe#vAH0X3E<~WZo$&}fN34(_C3H77UvGwC&uPr&!@0_V
z2%aAmbupjaZ`kA47c-|W!k5RsWbn2MoVpFeO0U6?sHh_2j0$cf971jVllM$OobXJ{
z958<I>yXphl@@jwAO9AOzAJcJ^d4z|7P6gZFr<qRKqlO4%W)yte-Eu}L%Z1UD37-`
zzX=Se_qN8pBOl><Yns=G-#vWQG%{z}%E8c<HYxQiVJ%5ekAkWydQo&~m21(zT8`5Q
zP*x8;HBHdiuT-L1j@KfstN}*qn$V!KOfs<v{nQ|}v<YVPByhiLeOx@=T+X0t39Pl$
zX{cy>ybMXDGN#W|6Mo*>sKz$&c)1E3_2}Jscvv{0v6Kl9<Z#hbrTX-+(frX+g0yM|
zPwUbU^ZO6b#N(OOn#$u0QFzoE2=~mXWgwgmy(X>4h0E8}mM{^4HXOAHje2yPS)po5
zQQV-zSPMclTaSxrm?*6&r`1yjHrnDxv=oz;#oSCqVtQ0q2czZuze(fL|Hs~Sz*SlH
z?duFUlf9=hoG9P~6>+cJTB&84nW>qXdt{DsG&CDFY`j@ny=J9ZTJD7d1sBRtL1ga@
zhjD=Kx(^^ISXOVn^}Xl*{glJQbDnWM_jBL>`#-LWi(ie_koSBWV`3RsAcZqA=6d-~
zjPvDiw_s#ZM0N~!ODwDMaia;0CB*m!F>XPO>w9J$#wucLdS4jpiE&~R7?+pjBBktZ
zj9CO(nuknc>~$B$g}Qv?aeMQ-F=pX*VP#{Cv)Nx|GJ264VO+u#2@CMH8em_Fc=lHt
z_E$6Z*P5ZNzP~zXG>tHhWq);{XZKEwiCI4L&xx^!s{b0svg!hSvuhuU=`p>#nUD_g
zp=l3c%&@V@(<@+)bnM!D42MYC5YYS;Ojx!CgY`<NORnRkwI3tVW-zjcPUX1kjgni8
zfEE?ec$XWVT_ZD<9_Hv1-{apQK^QmoNwn*oPltdB!gp`M3;*7N;iFz-5$t9>R;-6s
zQI4H&MBv1gyRQ)RI^V$YqtSSD<V4Ktr$<S03{sfBX>TXNmMx#6wbxQoc`w3Xl8aa=
zim@s=0&&UlI7yHGtf3>Y_Kp5@#1vrplIR)({`R=L8V+knYH{=&%9!T({n<El80du1
z@Ds?O<1OpU{fO$_3H@i!Ltu73(vuTlPd;>0g(>T`!f^6JK8B5%0gr)IFwHqjy2UMc
z=ciVf_wYiz9sD><tnHD!?@j!4^n9b663<M}mq}7qM(miCKopB<=qt_5@YJdq@XkDs
zXFq2F?4bm_vH4k2_|C<fr|U->allI2Dw5JPoIS7~=f=H`XI^|06AFl>rV4vEN8;$E
zG@K6)$KWnpn7z6IRZQiMIP)`$bi2YqQ$JS7s!KU-nj1(gIt-tRl>?<5m-F!X#&@wd
zNrerh33i(}7TLunaB;OFBh3d0JG&djUT$c|jD&St=!mv)K*mqI5OcZl;-JyZ-(!Oq
z`)Y?Z+CL0!tLOzSV%mk607Vnw7EN<MbZPAd6<H#VosLAYuKf1C#oYJTxH3%`I#Ka$
z65$`iVVNhYVyMN0p>kmm=<*9VbaHV-_p8=iZ3v&3Fq|wH9K>KOqsWn&N?cbV^b!`u
z*|tU3?p`QoPVB{qNMx5Vm4W-2n%TmwgFja_<+yPE3X(E%=}C6P)2lY&k<Rok(;(UL
z(KbdC$nQzZ)PzL{N<N@srap-8OKAXVl~PjQF#<pXLqSiPlCxQUejd}*t>NR_70ulg
zNIr88(ODTN<_NFh08-9Dznl|#U6Ya;vRR9ktY!wn&c%}BQ_j?a3Y4<yxLJ!<Xwiz3
zdQmz~FcKjK2onxY3eJw~TZSgQ`XXh=y<Imv^vERi;=~?z;v39f{Vo}5>WA#Q^BI0J
z-lJA?lH~wU!1M!gZ))b(6>+c>_PF79hVEosftGPwNm;q!!J9CI<mB&<PUQT*Og_?h
zR)4ElRH0{Kt)OU^v%p!StmzX?J!grWixYZgD@w_>!PJwwrqP-~!=k>a%ie%r>c6mP
z!*(PT)LgshQwkd=bO`i^HCaZ^gk9x?T1kU~t7FlhXn3j3n6|^zts>rw%B~sw`CJ{P
z#OzE2^AC%8KU%}HE8(-NWC4-rH`VkQ%e4*&?i~zgR)i;C-G`&BaxXHZywqLUT|c=r
z4;(ie%by$xS!N8r+xBnlJ#>ObLM01w)acZwKm5q#b3G~?$4=6dz<LG0;NI}_<l^A^
zMH~;gh!S>E4JQl{t`pC$iqE=$(JKWDf)z~D$!924G?8+pA?uHh{Yw-~H)`yEJ$m&)
zOK*1;MMu-Sa}w!%*F+O3Wv<?V2=I~O=)MqSmDFIZwsk`w>kL}6P-fr$V~D+#-srG(
z8zfv@xO#fQm+U>4F2t~Cm!1hO>_op*n^dzemKa36V{~P~+O^%WtsUDoI_lWAZ6_V0
zW3yv+Y`bIaBput>v9r^$-#*Xz&iH<>T4PlGs9Lq|dtP%ktf2eNp8<+cE(N}a=2-F!
z4({pNOfAwzh>yI^SFm$c9`(>?S()p6;XtN8>@pmbEDf`z<|xa=;k;KI9AvK53XA%F
z(0+Vt_SxjHF(AsE?siGQ|GM8{lG>cq0RtJj&T-Fzz>Y^@GkLTAloAtIV=96xS#BB5
z^l{`jsNJrLwL)PTL|4_LRT&)uGNg`2FB_z{c}T}5_$#8QLr6Bvg*fg}y3Ce4{wS{Y
zN}-j`$t0gnF5a+P$0?(@6cNa(_7X-|mitI~rTnc}5xWjI`r0*Rc@~UAZ-sNQ;Ks3n
z?OwBI*w8l*J}jwdaUH^HJ}P!zX`_qgkD|X_p)5!)FU#h1)qCC}=xEY;X>-*z3|XyV
z^3BTKl`U?Y?oZZE1~RpxD`8W(bDGD8LQqMO+DPOp&<97h5)<*0Z*40TgS}%=YqR=n
zi%sCyTp%|4C{U!;N>6re<e}v5oaSsq)5`Z;vJoJ2YYA0=ZgR}ZAW!NLK3fFymNJau
zCJQ3Pm0x%Z9d?oYL#_<pqxq686MC;|aBRS26ERq>G%JYjn=P%63<<-+dYMsRNd3F-
zmM1-)L5)lk6Uu{Wm|ySQw76I6q@|n?%_c4|4obRv4F9lm@YBY}euwio?$AywO=xk#
zpUwHFJLu<&2R#k{`qyneB}=Z%J8`j#awS(P#~m7(Pag^LGAp|gg!!Kr08xqoB&+^c
zK9@po!u=QzB4sF~Ye&;9^nEw8T&XzOL_46Qm3SN)#=cYB8@<!NFnRW?Q!7guoUV9w
z_n<_J%tExr8fSy3R4NLp4aoOR`=aXaL5z$PK%eDd9d|;)U{{P*LKf`x6w4BFVA|NY
zvJ^HuYA@72V##sY9u51ID;&^L=8vp22m6&L9?&)SYc)30qIHmC)D5?pLy;LdEgt;+
zdk1=XMJLG0QogvG9QX`x`kvaJM;r~f8h!#avu*IN4mObkaW3jB-8|fyf*(-h)LrNl
zgRfY$*c^W}8Wbdqma|G&Y3=_zaBm$P@B=7+=3pW43{K}s+t~>?!b+_%(O0eXQu<Hb
zx||t6t8oJI4vF^nPN!V8M_hRcd`es3{(R@YjfgnyY$0VJSVh}=j&hxzS|RP7a!4M9
zcUwIKB+%oi*lvM#))MLcN~2KtV{=Ae9$OF;e2_ouwoHw1LT8cg7CBMcQYOu*b2n03
z^z}T?gS8C$w=L{;4^O;9Q792P9r2UVT&#_JG_Wbg8Yt>6A37K>;8;Y@fQF5I>3kU+
z`bCRupA#llw&6}&Un!55=_R(ze3?dr1-`19nkHHizt2kP#(=@_RGc9UrYL#UIjnC2
zeB`fU0B)_ZurZ(dhx@wox<LT<B>OM%ilUeVC0l-v+*j_Bgt~MWmg-r2H4T6?Tiic*
zjJ^;<U8LRh>m{_3Ff5@~rE93ObkM)t+ufsal}$Qq9ggo2B!S%Y#i8q+6p59k_#A{x
zdTp3aF)ywwsZPbSIj0TwM@Unr$~R6jd}b7TfcxF%gXf&~%eXq?jU=5?<hkQO;2IrC
zj*>0HC0w=AfOmzsJJpEc$ej7HjQz(eMID8uL0oCPOO^D+=O}Gy8dT?!wW)@3P-;X_
zFn@iJxbyd(#^?5K`7}Z+kv_;CeR|hqQ04cW_Rll1(`ygX>g^r5UJtL#H0qQH4}LL9
ztWTwkl@mPyWIyDeQmA6?k|dsi-53SLU28U^`>>RdpQ_LbnLu7si1dU9(aBCqoyTSC
zF;=^ao7cqr1#dP=NGa$zcmT%J5WG#d!2Xoiw%B;val=z=vw^}JNc^J=>TL!9{$7#U
z>@uyEpLpQm>v#<stJ`?&`&@udKus9?qg2%Xz<P!M8JttVcFymsQ=v3&wkl*z`N>`_
z;f02WZP_s2_f!!19JJerk9LzmA?lpAoItOvJA0ZPHTG7zVkVe{0rB5yY_dXfAV~|d
znUQ%MKkfq-Y=R0%-veisz571-p?cY&fz1D%EE|Q1M1Rze|GxIY4-XIZ;hDZCa@-WJ
zZTJ5__|LNbGjcZwFPH||8}~9vZfvn1I6qwQIiNE>hHbX{`!i=d6g@M!30zdxsB$6V
z-6N(T9W}m4{GN~Apd!Hr+U$+<yLu{k4@H+iPYvnw4S4if|JvBy$BNHJu~SDE*tw<S
z!dQ1+@31FeF}&ZK(|^@|5wrv6)M4(}XA`A3B=R|4APH)Yd)J;03i{~z80THs1s63u
zUklQ3cWCTO|7Y1B(ijzuq|maVjawp+OnkhnYC$A+RIo!;8+cSUPrmWMkdfP);SN;|
zM(zPyj<C16GAhncESOk^aUl7yJhb-j+EB(t+@sW*cB=2fS^t_=G_*K*IkE9{0}(jS
zVv3CNGM7?yKCRrj#FOK9DnS3avY#u|YJsDFauKa-RB$zZpyWuzr{_Vjc|9W{JJ1ZA
z%CC%b?(mK3Dj8YR>T@E>CTo_-LD(Pt+E}w07ZVRo7pYC|35EU~u=t8EYzO_j?MA|9
zklEst>E>iUgDpS@?r)45fS&i7M`-=JGc_TRX>i_kU<{Ez`rx-8En_qcJv+>)jt{X_
zU2vvQfFPGGs3Yt3JiHgPl-l6Qp|+OZR-w|THlhaA!sNNspRF?*Ha%TWBL%2Vx7f%<
zIQ?y(&98&hjFLj;`oZr?#Xs^ws>TiNY%5YHLCoY8cb1l#yekTsrq5f3o*UPI{iu@P
z`)F<;sYIGwpcMV6#OI9D7m|c%6Ao3&t;8xLskxwraLGc2{P0I~2(qvR2p#7peR;X$
zgrW<Y9lb_y#6q#sd`Tq*VR&siB{$&E0`KLJvC;j0_#|4LJy*lPlD~wP8fS%-TmDfC
zz!AtH1^b%BDokjb)z|&WN_?L2L6hbK8V^@4ETtjE;LSGqO)j5MTa1)@;b>wd6|T?t
z0EfgG<C{ePW9d<mH|8f;NIRLi@*@9r(SM=$R#fnIQ6mnph;&J}hwH^2Bhm2awETeD
zK(jAOU(f?UNwvatacL;G`0)?Z91Bx3lE5WU3tAI!b!a4mQpUN+yZn`!%pd8%(YJYW
zu&V>wb8tR+)JnIqkofh42gm5HCbXX1u4?W5QP$w<@q_xApwy0WH3d#tT41Ki4(5VR
zpbW%wJ<trESAS2=OONn(6HvG6U;Xc<0vn6|FpDBe#8I>Ru}NKm&E|uT8;>dq99Nfl
zKBT|MUtvzGG@*CkiaA_DINowU{#++5vFLB6A|%iHb2Z@dP@N&w^=EkwKL8G8q`_32
z?#L!_Z&<?O{Lsb4HKZpV=aN+ZqxlC6J==tAEd6jzpuK*p9S(CoENr)VJ6z+@b2&&N
zIcs%M@ZjhtD`yQt%G{23nNw&a=(aXT)#n9$`ljPy%5OK1xL|O-6pKcL9j7>eiqC}%
zG2o~#k{Jn(TzUf+36EKVSCJwRHJ%dhjLHB0<0l_&+>8P_>|zH!7lgO#zQL-M)UKJB
z_!+G|;_l?A`~iX{j`jZX(k-?%xu#(rhTrQyMjyWcuwhITUjpTqbS*^U?B5MBWN5MK
z3cf4Q4x|4rIy0JXy7m*wMn-<N>7ShIazFG5%UbnxD`rs+!rN?5M(0pEg;O|9K5u7b
z-ILlfwI>D=c2xFQr^`FJWuOUVVz%jxV0ya(C(PwKo?1CUN_K1oUh$P%JR(AMl=kM5
z_MFX1l=Y6_FFK7f0P&T|Z5GIV#9!a#mcfIzH-dwvE*rnuC^^{ibExzYo9W~{CSd0+
ztY*i4SjKVoGPzWT-#rRKz8+P`*56C&=msmQleQ}oSt&oX@ho1Oy&{b}#Tf`X^O^Un
zf^6_#kP=*M<-c=thxWRUtnran2=Wy6zg6Bfu|Vw>5KfqBXO^<4!nb153Dez=_{B>7
zlQf47lUjBzO3}m=36E|Fw<or?x=c)5pnV_D?A*lWZlO(Axn}!L;Kf^SaJY^ch<2S|
zaYnX@DMk?gKx0=Ytke1NUGU=ii+bZ{)OJ)XUnI0z#(TLww*h!F+j`gNTj_>59wDg_
zC`$HL+vI9;Jd?|ogM>%`T}a@yNZ?SAMK!l_oW^xPol(o=M*vBJ!SAI5mhXg9TkC~g
zL4@8d*J0^xntYw-Y<8B@XP%w=$3=?QrfZAIQ5)|<dveqOK1+mqC@s*x#?-bhI|h#R
zSfBn92CMTcfb%z>$qqg}Hx18#a$alzE#)<z*1qw9i=PR7ua7gr%l%38T%r<iS82+X
zTL3p8-w)4u<{~nx`1L8jbFZ{kpRKYT&H`fL2Ke(EyvQ4;&o6z`g$L%79kJ=a)cD<9
zGKJhHvhEW44s@mUH6SgQ6Z`*t?D`90f0Xd&8+L_wYI7(VD0c1}TtbC{6eFE9B;YZT
z2)~M4uRfFX**N-vO0^3je9ov1l<d|Mx<<7>2dCuXvRW|{y%zSsr`0GYnn6R<bV7~)
z@>l`l<=Jtm#fEUGBk1e6krWo0t-dfzeWUxqa5kH0q_Jx=3cwLI#DsaRr&>h-IQ1ub
zJVO~aBm&~!$YudjeIvhreqNlm{a6-q1+Vi@w++0ML00T$^(y3C!YHWQj(o`sNt#t;
zyuYCi?PD>#E*a{8JiX}Ty=rQX3Hd`^W)o>1T+SkhwT_nF=Ns4PX`93oBm|)_iRpu0
z4ve|acEoiO`X-b8Mqob^ijY18&WaK(HPvP{_`wQd4D=H?YkBT_PYzcqs?_ctf{w1Q
z0}~U(;n>%%uBDUREGSt%mS9po6nR`^i9zK8lY!rf$>OLQ;>9Z~>$TLIX`NApA(=`#
zG6(i7(~RUh^|VJU`H06g`~QNT^kJ2&23t{HJZunfe0rLHnz;xvMHmUmx7hEb8wux`
zj#yghg)0y^S+i`10p|&~{91yu<xA8I*)hi`Nfc#YQ1iOf$;KvsQEBBSM>Sj5fuv4&
zb#T(-lWvnw-tVNkAK21*AT_)lZ)5qM4E3ujn1@@AjU>LU3;vB)<8L`ay?WZo`BSeH
zqZT%DHdh%+IA6>{scn|X@VC*APj83{M1)G6PR7)D^syA@ucM3Jj9cRC@jaqh9--5A
zA?aJbGx2l%;qv8d5M58$fnGenXUZN^xT0@kb-19rDvy#w5K=*8+U_|2jM?fuK3{Q5
zAqHV=Lo9oQWMoJYF9a2^&+Sk^P%syC3W~70no6Q%r3uuH^G%(-b&Ak3^zHv?WDxL;
zhJ}Pb+@tJg$Fqkd9INzS+YHJ>pk{7QHvB7YuZ#9ixU?df1W#PLafLycUYEw>-l4Mz
zAAezObT&(r?OdWCo<f`8_jVM#@AtE7Jl6?6=eWhS2berH-h_Tbb?Pep5b``zG(oPy
zMnR_3o?#M&;fbV7|77aQiDxe(q_44s#!W%;QTk(WgkVVnZdgG+9L{oOse|~O4s>g=
za7Eq|#i`kpKTbR}ugTvNzpH-Imj6l<B$+s_2R;*dIc&c$94X=_G-N)~!OyM6ZkY}e
z-8HfHA4yK2A9EuzW(X$W<>N=f<!`Hys#f-C4$+pbLkao5_QxUqDLr6OPKXr~Yen(D
zNQ)DoANRMg1-}_1^INkeU2bg*v>UqnQe_Xs#U>k6k?^jK9rxXC-5W%~?T>{2R3Z6|
zW8WT5R$wqI9K2<!rUM@z%IVPRX0aXh&0|q<jA+d8Y76;+dfLUcutD`Z1_x)8Dlk0L
z8BZjEAGc!paPRM-d=%O{3F8y1$#$2H+(4@vM%r7r&+xD5ntKWG>;vDEqQINptGImH
z5Mtq|$!xL9fDU4SM~d+XBeu;1v%|y}+2;U1F$o$~(+eE`mp}G0o(RA@e5^(fJIr2I
z`jEj&YN`A4Xs}~r#a@Tzw5z<0WW|<0+ix!~gSBMYx;M8mdSQ>-eyvHtAixmK)WF4P
z1_+;J*=XJ80gqBganE_v=HKPJ-#Fw)7;B$?4h#6kB_stAtm&V~N+cW(eHPF6<nfKx
zE{x71ZLBD2KRT9o_XzKc^LO@ee-^^9+Be7m{KI%DjkbJqd%_bNkyU<varxl3yJ0|X
zkN?j<Ma&fe^VtRu*!~oKohnv+-ps^Ti$yp0zj`z^W1j3iw1Ar%s!~1f-YvF?%i9Sa
zHtbD0N%4ep7#*?@a_w5gpoNrmVQl*fx^YPf4dm}kMEoRO;`vM$!B()UZ5nxqdE;p;
z(L&LoymBsu%kSH5>Uexre_Z(vIM}VVU^KU(pt{J;-tZ3&{>+tHOY`a3+Nz~dvYrYQ
zRR-EgUpMmwMl{S}(#L3|$e{$g$vE`PR;%0Vu)7&%lEe;K*DcyaZ{Bl-e=*LP4sPJZ
z$QIGFP>bIj+|JI%?i@?j9ST0%qOGh|r*oGuX@5w<rQWwoAQJXOJVUMo5VwV~e1*YN
zjv$YHD9Ay3e(A|I73-q7k%Ati{AsE<NADVMlw#q{WuFkzq=tP^rmkOpV?k+--R6!*
znreA45H~XulZ|G}p4{OfL@W|?Px4T*BkHXoID+nF5_tUJk`WX-b4n+8CR&SVmEwUf
zjAEWuWt6(TYSnwbeEn=658-Tv&=+}Rwj4!PR-3xl81KC2>iOqV!xV-4Jds&Gg7V`{
zGt>7)R37aSn2|2F;>c%|DVPd(M2_eV5><`fBCQ!kx_(<d7_XuiU934~gYFB0ekZ+#
zq>cxq(ZGlV{bdOG+LH)}JN0ew=K5xOS!nEBI6jY-Fsp2e&Gp56wYTdB7QqrBqXBrf
z+cuMZm2h0!X`rbU?RUU$<dxhPQ;SEIh9YuOE?as5Wi^V|Ni8PdSI9C!PUtN19kcNl
zG}~@oZ+H#=TYOGDWqVYU@$;`Pu~hkHg{qEgMCM1Vk$HERTC6?@<hlOL7rrJp`qi_I
z95~||_qQ!S_x5oX8d(<zlxsJm(ivD(GWX05&wf0K>(jru;>#Q9hLeqQ3|pP8Vq7!?
zE!?gW9EYIPi-Zn?Z|RcM_!|XLqiKyL7E7^Y$n4q-BhBVTjW=TiBZ5d1Id?1MqlcF>
z(g)V|u>{#JWy2(x21ee1G;jR~SZsB=?D<>ez?X8r-4j|36vZ=m-DbCzQox`7A4*9y
z;beiIx(0hlEAJrNOdhi@xpY*Zw_QPhHmTquBx93-L=B(RN_La0SU^>wFL@f<?M6@h
zT27yso_Z(qWL5?TReFIx4lj!}`=wN*@Kn{@Y2g~cDLFwqeRAaL1QNL{xXH!C{o=Pu
z2t+;)(G#|J#OmDL$P($!V5BX!0T?{my<{|I`I1tM`vf{1dFp6E+BRl~DCK&QbIcji
zO$qlh-igxR{t14e3PK#lrmR*&!1KWn)@R3aE<g?j6YF>oMahbHkqJE@w-LwLHYvwI
z)a?wRk+0T%i7n8JE!MVI6K^3gvf229uiehv??ukNy?U<AIv6k4_Ba_?{^n+T^xXhO
zUW(^+9z0_d28<YFN)MP_uOZM@G7j~NL<M^^OK2G^#3CROv<t*kON`R|+Ds3{A7Q%C
zZwWD|#FhhiZG}R=uXpZhO|pmWRQ1QDL8+t{AaRFNXL^qY5I$nachzNn{*^!3=|*(5
zg~9&rHWJRt3)+8k@(%Fy(l|J#jQkTajZOQ!_ssuvkxp&oZ%_HgTw#`}A)$(xRamh7
zoxHU#om_}JAF0k$L3zK3TLg=DZy%wNH$T4@`8``zM^+7JT^%lY=5xXC0y!0_6r32#
zUsX%9A4;OG%DkNe<2>ZL=)vSo0pE#7S!i<@DaSBBt$%}viO=6u^0X1a-FwE709X4P
zRcyC%>tuB)&zAGfQQf0b%{081Grdq3j@!tP4Nt`CU~sW}Im6W$?pW#KaTbr9_C^3N
zb`A`G5)#?Tml2^&#z~8o2M7mvcp!zx)5%b5W6<A>(~GuOs?QxiNgIHpVX}0>zZ|ad
z-fWenv3dEz{ml0!*rG#p6Kr0dGg%#aD)>+JB?u3C5jU^l?LK3!n+hglKW29;SGEGA
zt-8w#e_a!RiTf|lb}mQ#y&ciG?-TK_FViq`#<};|_;VDK8!#1SMm%tryGA%Q{Ir^<
zUtbe}codmYK&<|9E3SNjvC)73_2fz=W`aGs!P9Hx{(U1!>gLzVwlO3QajB11zRRrP
zkUb;bzKMeRwgY~M)a3ByF-?El6D~|^OUZYZk@U1?*LiEY^y#4VxkU+yl8mCP9wJb5
zmP?~^@G{R)1(6^WDe|xJ0-_ig(p<4?jADA4)6hSwLs4J7QMGMEJODfxoUcCL`0vRt
zdW;a2cM4aZfF6SPvlPhLe*tBQmr;XZIZ0hU-^4ES3gq>R+Le(q7urrZE%|>76-vBB
zqc!YaR(tOqas$>CBJ_FPLDQ0wk-T_&@rED;#h%;f2?d24a4t9CXxhMk8p=n(FI64T
z(9M>eyvWquT}3FY3#Q=EzaLWH7ucUD#}iwzr!XqO{~|QP*IbcsUNj48wnS<aTEMFB
z?Zeg+XlQD6aw%bc7-_2o6TJ$)`09!kk%pi+qe07YG~9+RJ%L5zg_glF8Xj`OJsv`W
z7KafxJ*%mw#rPQww(kNLe7>^{_DBS$`%o9IM^711N?pNk`n)djvdqHf1>!*>gl}*4
z8JQ_^hmAXHc2>22#0<XvW%WO;9iS8<0xtvGE6IQjJ%1n*XYk|gbBsr0yB>=ND|xry
zErfmV(SUUdM>y4vN%!w+4sVUf6TD0&fz}HMBph{W&CbAiM#n_ya>dA2U20M-9-R6L
zsmzR!s3#_5JIB!TO5RR0Gwa>6iYfs#k*lZyN^$(6bfBoV3gSjS6xbwjc?@|Y-m5oO
zYL^V3CV@Cw$X<9Td#F*T$BC%!;tk$7-zrAo8}8qUuS2S}(t_V6I&v%vu(}bJFcb-~
z=@9RAYbkHHw+vh#hNwHRwPno5uxq8_N6^a|9Nu^Ja}}EgId5r9p`S&9)-hw`I$=w-
zcSfrerP&^yTN{#XXU*r;PcBnKapcqY47!<(-Yxhw#S~4-Tz5t25mV|x3qhA+n0WI1
ztxgj9pKxGEV}`^SIwqYS{OHug-mN=Z_}J;eFCr)g6}ii)5Cy5aF-=;i0QBAdZaRFF
zm@7K3B{ke9B!?}Mgj@9$^I%e(1v@l?kA2UQysq%WdcokhfP(#@(BvA`F{yM0KpB%6
ziyZR6Aopj8@(yjIMF}a6wU=9=XFJAt=p5NR7Sty>=Z#vBKJ>jq@T_wB-j1{(diQsl
z7u3`EEq?lwxk8kAROitLWigBeTdpI8+tFoS-d#I8h!}S_veR`e+%tx5#ZcGlaRsLB
z7TCadzg>(j3>Aso!Fe23%B2?O<)CiFP-(Owee3%Mj;TY=AbeVu3Gn*p%us17lFMI#
znhmsrFWh*6G&edOdy1hYZ;njYPrt%zJBh2@op<Hj-pS|`BoKP0e8PQx*%;R{khQ<d
z6;?>t%r<9@>8h(p4m?yb+~MzqR(0F>--A}X|GsB{&Nq>dZTop2%Au0Aoy`W#zpZxr
zV7Vz=?p_y4!r<{d+WGqatY$WtwHM@nR8QQuG2pzjDKFDqH7V44wM0l0hoVs&La$e=
z(eH()t;^bb_F8sPKGV^XIeDiansBZi0)aWY@3s*;zF$X8Z@m;13t}>6I9XVX5)k$o
zD5A_6J7M~INIci7B-Gl*>C75BP2VMzA9aUeYT`NQh(v5(Svcsu3*_rR6EQho47}8f
zRs7Wp;c8d%wt`0i*-JNAO2c>mK4EvKqWp0+Ez_OC^!wl8PETr}H;Izz1G_t3X@FwI
zQKT~+Trkp5W_4t<GXrBA=N~RzMwl+ZUFNYb&bVj<xET}WQ>knVIh!hLQp^2tpmmc7
zh7xk!6D&H_6<3qx*6Y+rTs>i=OZPGP8z(%*r7o7j{35}f_QF!EcOFmA8LBEXgVd!H
zny&1u$3XMK6`6zL_iOHl^N;wOVvOKEG)Nkn8rvpo6ugC<hoC1Go;kkB8CGDiI&gsJ
zkb#HAC|+HYC4%2!HH`g9uny9)=4Kx}0F2`9nO937UCJe8P^_2?I3?}|AJGenrCCNB
zK_wcg&u)-IRcqIdMn^$sHbtN#BHd-TAR8%^2E0c)C)=OJO3t8u`xFl5+I{4O93Ae&
z0V)xoOyr2{;!p3VwGDVXUdm`>wh!+Ppbd$8p)TNY_-1PV(IbMP(imQ@3TUvoWRjT<
zidF2IF3g^8EGa>D(DlQ$F7Sn!X-zFs*n}Fbz4po<XmAPya9WW)^!gAO!lFpC6H}*u
z<TgjPi4P}aG=5B|#N~IPiq~rJz0CQ+w_Zl$X~l`Iwv(ES>N4~lS@xIhV8=N*h9kWS
zkCaO{vHMIrAQQfS+1~KW3aLbKAR8k7dys-CVVKbW48Rh65ydguU>&MR(Bx~nb*#B>
z9csSC#bTr%v~hPVHZT4DmHxO*ttP;oI3a(<d@KFXW&gWZ3YzG|o=x}tKI$@b8*uPg
zieh#YroN{MbDrM=GDpgE%5O*Yy$xH}R&mik`W*DdNR>Ri>MxBIlR=`~1X=g}vPH|C
zJw$G7sC2=&&g~>6UE*e>_Rb6BbL&xK(H31dn>kgAlJjWJPOg-AUlWKYXefvqHxm47
z-aA2YstCCoS2Y<!<_}$jcr0q_UizNg1@~rAWM|V8T@mLIThAcE(wbZ5k<s2CT4?S4
zTXst(b=^O9RQuP$eHskXl6yW9CvWI%4rD1#22witj6<T9Y>n5k5WQ0>Wj|--m*LPn
zS3^1dT`;o{_8xm<vA*3+qg}-QS!`~&w-}3oY0$gA%DnX;Q@~-cuqbDDeuh<xol*tj
zQBvmr?Mo%bRlG6>h78Hc)h(bq&yob43dP;HhgYuW(o-n!=1nL5YN3LN?foP6O%0NT
zW2$^1OgGbo0fN*hE}T(ZOH+aMgxdoW@}9}9f=+@$JIKKE24o2us!!a#;Li72-e+rW
z8?9N1B!3v@<qKfJ^>O3wV+@Xti!C|ndAhM6gY=Cb%ytSzci)3^*uF#9b67W<9@GWb
zCIBIijKke;Jfw@~R3&x2AB-BV>c-R4b)oZol<%6;cY&33tIzF{ytfXy!kN=t5e{V)
zQQMSp$RE2%nK{`%Hn|9~n^nZUF(_GLRRB4>LP3Y&e2s_9n+kLxlz4WC_Pk?HAyEG+
zaC=|<wG#3jy=RHu8cb1zcd(|H>hODo?#XC8ufns+d~85!@y;2a!z!8_hh<&tX@$@$
zl&s_wvKDWDW@BI#QEz)@F8ISzQ;wMImKP{b;jL>CQi`3(c)n(I4$!(DYVr7A8km?B
ziQgse>@uBWsonP?C(D|AJ9W$I<h5eXjTb#sWb*fgQKGeuW_0*XuTa>eRY+0aZ_==G
zN#$RF{Uf3?4QR5t<XF$yQ3d$GB$MO&f^=<Xhy~STv&5v9+HBG)M-t`4y!q<@sjiHc
zqFyCDKX2<seATjq_hvc&<-)RtE-MD%{p&9dc1&sh6F6S&d8HZ=>8(!W<8tovz0=5>
zX-S>FC*rZt^5XhFYA@UFJ`?AgL%(!DAU3U-SrYxd2_R9hshb{)MR1-qe<16fHc4%n
zKp=pdNU$4ZbeN!DE(|<$xiY?#XUfdh3W87!tir|m`6Le|uRj3u_UuRs`ddnu+1VbE
zij|Uk-0jH?$xp37Jsu?u9j`xU01GoDXOCF=TPQz!Ic7$81ad}!MUc?bUupu`Z|7B{
zk!7aKl|a&vL@M!v;X=?m&k4<0N`=Z`^LTd=bbBImxHalaGC+7C9wGf_NRzk2$<+fZ
zjrjK?DYUa>+Q1Lq<Z~7OEA#~RJqlPbgE(`1YCai<pr+P5XNf3Gv6YBn{K%v=sia@o
zuMtZOYuqDkDx<;P5)DBibL7pAcQX{VcIQEKhyzo813=FIUCz~^5pR`tjW<yD=K_fv
zcH?<Za|IKXjn<H@?Ws91_gwh7q1MLw4_75x&aceim!n<7Ap(`Yx4J`%Xt^tBopRo6
zKTyJV1)?M`kQDEWhoUCdezTS1GBc|q&w%Cm`<Kl0Us*l}rAkv1Aav!LFch~uPoJ#4
z?P5>^H<?|6=%j>6<a_^BVpAbRNN8q{pz6SiP1`=nZ=a%Wl$J1x9nev0>ydlJi6WCt
zpzn+ZW~Cl!Dfk1BJ*d#<{lJs-4;4=qXaBvAF1Sho1qH`7gcfTY7CuwVMz1`A=KR@j
zrX3yJ@@?>$k(njClHy9?l1chyN`~Y!0+cH>8ndW_FZdM|xY>rhnw?J0TpDL>CkQXs
zq4<rFj9vgS!|A#3ymzZ3om7S>Pel_qQC;MXF!6rpht&w^$@^Lp$jBGCP$$#nLST&+
zM%Eg4I-R>g)YG%8V;as-3OPjTpX+nwcH%DkW#I1g=|p~PY^89J3nzocNMgL_-CFRs
zi9_n{<}oQ~Z|S_@?yofd<y2J@BHg=`>GP&EdF*;3f2^@M<R!lNPX_Q^jkHM~R`itp
zXL$F#an871uV?+|brj*sZ1YW#O;WVhI6~zQ7?b>|O;ap_U<N!9Z@nyvh<sW8EW=-{
z+zrF<?&V3KM?c<V^MYuhQPx4(qxcEhMDjIjOt~x|ho`Rym--=S=zD?5Q#L<9*aF(g
z5Zy?bpxTH3>EM<Wqx%XWvk~in7H{rkmQy*9d)YBD#K;tukduE&F3ZcV@<*yvaJpuX
zTq&*|r*HCp{k*yr<X3HuZSdY%Iv!r&2MJ7u<80G}i-+cRe;t;BEj{axUIzH45GtbQ
zGh%GtzF1l#-3ZQ^I*ThGe8+YD)$R-YToaPaf3`S({!p-307xj#6%p`dB^GdSlJW*I
zadGV`QVCX9`3FCf$89Xj#A3t{@Xw*!m<pmV6NCZJ!wzvI9I&!0doF4MU&J$kH-j}^
zuIj9z@vS&`*pid7)rP?-{6pj0agRn(^Bkc*7JSA-pL<yI$?A1tUo}yM{cgV^Kf4jX
zIk>ADy&5Q-FsHhZmugm%n;U6USLc`dPh+A|5V)ggaIr1oG92Xmc`jN|`5EoLK!;&u
z?drF=Y_Y#5rls{GfK(`P#Z#JhwG>^X=Mg5t!SKg8Gu4=ID-;~ZX-o+@O&l@5F^N2Z
zn;c5LZ0YVzIA{<wkVH;?!a}W(RV5WDi?g0Y4pbe-Q$0m-mX2uWz<R!->jZ$rl<#)s
zZb)y7EK$MN{0Njn@{Ly0U!$xRM}R;AOX_esOb;oq_9h~W@-nfx<xr^H3s2ROG1*u}
zrJ-3+;*k3_w*;+~xa0JhKR2b6gIp>9XMIFSJ`6vzh$<XewuF_GXDRN<qN^Yi=h{k_
z_h2Y8Ee;G1fUBE#@tNNR{tT)5I&-6dtf8Y>vx(Gj^_`XIvk6)aDX9BJDd=SXF)1M0
z=XiK>d7Sux06{kz6FFdC$_-(%>o>*Ts&)suZg{y>O9aPb_CT}yaq@_sIwF!h4mJ$K
z>bk(MwJ}++d&|J#x;@@1VFMG^s=KQgk`9x~ff@%Ko7>UDE}!?DI+kam6Ip(BN~|DO
z8N^K~Tds1k*1S38WFRWeJmOF>PK%z|?oV)_&865Mn9h{znuv({-1mmxqROz)WJkLj
zI(JvD*zUQFZEBPv)x8#)OjoVBVI?(76;(FAVL0#QMHjf9#9fH21(3N%R7VFJ5i6qg
zKDOk8eF4PBE$0p1OB7*vOarcawAu7K>WmyHvOb@_M>}{H1p}Km#mGU`pEJ<;#iiY>
z{MZCY9bDI;`4)-uJ5TvtcRwNmY<ChP=S*FU;~cI<i6otqMTf#C^YdOY_)73#ee`(@
z_Q8H?d`S@Uay*1vTXTaiScD?@|D-A49VyX5VCSXt-obHy5`8RqtN|iZm>IT^3s~b|
zu=_2dHBN3FuZ~LziBrYE5eaL$$F7!H0EGa~7>-e*Ay6)fCiYI<kojKYpU{Y1<}CJa
zq34ZMEG`KXAilg<RbD!T0EU0En)Ml@dc{c+IS&%IO{;>Xb7D<9Ha_LHRID_N`k0y4
zj~6-XgD$t)qh^hw+1LlaraK^W+BVX=?CGDZU3IXtvcy118Y*iu)41-)+rg|(62HDO
z6{=|<@Sm8<PR3S5gZ<_8)JvMpZiz~;^T;Q&%m7#&=k5DIMTzH#ps#;a()C0c_54w`
zk{KcQbx6YrF>63-zq`ID9@yE(mIvfG2KD3|@9k5bCh4vY=91j1u>i6iTA-@4DCC-<
zsKYI1-Q1FAZK;;Li&w(NXG?}DhBl$9^K(M*tA*KCIU+iz8m2*;z-#SP?Vn5am}|)*
z|A2kcJvm>TwV@hvkT>hR9ZYL|Wftr*L120n|7ZqTz39q`TUY>-9!^0?XDP}$@&&@g
z9#M&91)KeRv5jf~)J#tk&WIk;YK5&l9$R(kl+2xtQ~ZgLK03wGy~t|Vp?lYOW=0Jz
zo?+M1xSZmUvcHVIwNSiI!j#D#FS=4E@6E`~0^21Qz8wLVmy6Yo@O&OAbU|lnDY4nd
zJ8O+VndmoP7?KS^;`PR6Z%%^E7hkA05|Nm_>nte_H>P!Z%u^q8xEhvkRwpZ~SUix*
zuX^N%v+aBr`@zwi#*(dDB&h<=ZaX$iMz1XM9{6L{+q!*`W#T&wgv`YMJU6GTh~mbE
zkbQ%tTb>X;d-F{@8suq%tVsB>Bpu+$`>zmD18#{^)-ZA-@2ynBa2VX2%+CEjLyrGQ
zdRwkJ2>yLj8W!w;o?$jb35p053<)Xx`oUxLHFZ=l5M^lpzIxY8jV^zcH2ha%Xya@l
zR<~iz8vY{N;~6w**<LrBOe~i1l|-Nsxx1U9%5*nNpD;1`5{~H|ClZ)UJFbjtG4UPD
zFZ}XR1M($aM;^?aPLL67!PA~UTkiKVV?q@8dq;$hhy@m3gXy42FZ#)B42@eCMFa97
z5?nV~j-6=OpP0#Fz?hQ6>{F)@Z%<xm=;z1`FJ02<lD1|uvr~)hm_CkWLT)VU=@(>i
zpXFjo)w~VwZq_8HyoC0AV<TWBu}^U9dSIWV!}Knh#VP+*ZkJH0JSisHiBDAGx{66Z
zEgqV{nmGU{3Dz1(11IiWkMwsAv3UiOG(}rW-@28NUPjtBN2@S)^nps$=xn^RIyQ=X
z3~9$~YS8k8;f7YP71kn^KHp%rH(bxACph2AhDIuEN`%1hR;U*t{2K|&-hjV*;M)XJ
zP@fH~k)J8X_UkERi0{)rI=+|_)nPI3?X&brki?EO@Rz^~cFO)j;k;ha6qfw%eONqs
z(8ue&a-mP;jSJ_*K(}lnC08>d^Ka#Wgc+-E=8i~Qrv36nuqLOuby30usFy!(PC%+5
zi97)llhjgGOnGeKv77uKX#f#dL@}0~!H<H6-EpHS{*Dckw?S+6l01|^fBSM%P{899
zVpWy7h@okqne*h%EG7%;vcW->$?+QGybU#1Rg4~!t%^N&Qt4T;3o{7|nsAtPPsA@t
zPQL^cJJbs=Wc>T`WPS4!N_Bpd_Rx%C90ot3xMN+3m)n_g<VrUN7G#tmM0NW`J#IK3
zlE@jFujPRESD_f{@cafF%NdG-yr=xfKJZ+c_qRqY{F;-;$8^xr;e^YFl`tNDnakbT
zSOeJY;%SeQ2qo4cKw-a3rRrBQz01Wtw~QxMnk8r1Ktq)5;P}YopAanCH4mP8@oUe5
z{X@JAv%9;zTmz=L&XrJ|oo-gjFLqQUesK1}LPKNQTh+K2mPrHa8)#VRy)jv}{)O~P
z!>`8IAy$K;6{m-4=>`L6eqytu#~9o7m}LSiXG^)8F)X`XJ=U{++jL51W$mn+o2`T-
z7pI{iVeblE_Gbxva2$IlC%gSEb-&@mbjsKUE6UmXHAJv_k-4??ntL*3OAs4%dVBJ&
z8Ow7vHf)&|YbQorWB3{?pA61`L9VWt1O$>q+KUD@(U;$4WOSZ1bp}Rw@u*Ssw>&wW
z>Ni%vURTD$?^B&UR+Ut3e}BXc;wBqS<(fJtr)jKI;SVs(abC`97`O&h?|J6QNS7Q}
zJ9V9C`{Kf6%;^?8HW(Vz<|j}5?P`mKvEy$)ujcn`BwQvchi!HnDw%W@H!<hC>|y($
zW{PIV+m|C?HtX>vG!LdGt@VyX9x}LeM3{u;u@mlp9msTmig>>!+_y7hYWRc##LCyc
z!Nf5}HI_j<z{c4goazeu@B%FdeTGNAI*xR)9eF08-M>F&u+?4GE1LTM`$b}#1@A(>
z{;u<^|B!pcNI4m{joF@Qx~1ywV*+G&1xCt9<uRTPPncDWlawVP8*z{{udqlZQb-@E
z<(TfgUhg;c@^amFnF1&3hw@Y{K{Bc-5~7O?TaF^Xn!X^Ace(h?(+KwYQwuc1PPF;B
zm*j58vbG0=Mi)#C&&x?K3!Q>idYD-PdvS9-jVmu@??vXZ)G@F$Lrj)&?bJ0-PDb=7
zv4rfm|G3^A7sDI<<=fUN2dXZYKex1uj5^nhG~V)$jWdoyiPM;4P}a36jKBNeN`)E8
zR*-sWvkpBt7VMpj*jEU45|+=?m@wqYz_t6whbiFJko3jueS`2msEt1{z6|53ITWj;
zRXA;6{MVDCL%@O+7Q~wOe6}&o2`@c=0Bv?C0s3sPYV=4WeaeL!qIaULmxI1<u<HeB
zE0`XLMMxopejp~Gc<^X71kJVin+%#UU70Jsy5h=pC5+BXCYr6Ow}UMqE)iKQ!bnqq
zh<LOm2-ahu#e{3N{c3)l?nRP9-k7=kTfe)NbMP7O+}QYZNWhM1rigfgn?TSl1={Y<
zEj%5tKJSr>!fqE6MjM6DISJILxv4bF%iWFm6R$Hl6E|_}JK&O;X|!d@{$!Pd-1M7(
zQII9GT29ws1lPajcf``pGU*c)u=jT_@@RUm&2`zpsXFLRXSoJdf&Ua*kPYJ7WzInA
z2)f9%&=?#6Tx9ws=-euTv*;|t6l`pVYC&jwZ5F)%%6LZi5h%r9e_<EdiU6{DL_MFf
z?eYJyofMqyQPv;+8M-lEgSa9-O_uiDnQk!%Z&y4}sa{Ao%QgC=KM)??1X0uqb1>mO
zZXbCxb++#Q?mGS|07{ojC%oEP*>t5)MT7r4P~W*PrtL-NpS>3r(GxxNDMk7S!c6aX
z=@uT@q37zGT#1ecw;RbBTsw*IaEit;Xy&)B!J%>FSF{Ujc2nLsD~80bpY-$7#{HG<
z(yrnDk$F%8<R5=^sjOY@9c=Devi!_;#{$9baAJvJ<i?gLLbv_S2Xr_6yI0^l2Us6d
zfdpdVC`ODG(+w7MBTs^%GZmtc(qfsocoTLe^i^y4+alWj)?G}iDL<0AN=feW&etFq
z_p+|1!HCWgX_n!eZOGYyG9qiS8Hx-Np~&T$gh#iMIWqCLH5Cb}5&~pW-U6qSWfAy~
z1UrdHlA$a2Q=8+<sG4f>sH69_&SZ@8R@`zW4mnk|oZ+S2O}8$sll|+M#Y`82&d^@<
z%_x47>`!5<)}AQa*lpl_T()M7L8a7r-8<W_AZwilotL|4bG|$}$zNUA*2)FqM&bMd
zcNfUaws_(!z+yu%8}jZo&k{bLCm0<yKOw~Nyw0XrM*Jx)hpwuLT<IeTo83d#F@9O;
z;JGf3JS#c@JK{o&b(Fi~43;!40l*tXeT3#1FTNo%I!%hUOCerxI9p^17le$@4!AUQ
zOh$J{B@Mh|W;_yNyBNSY>G!B>S&e-%9ev~rip4qn*6TvWnZl+lm$l*bcEI29;u%t|
zl^8(FLSW^J1DD<J6x=~XAU4#>;-nkIj2X$3DRgMs@|VJ1@$dIH@RTP}e4MpKlnTc>
zHs75q@?-5_<aI!d4hKRM`*_!=N*w;jm+r|}*MnnH@DduWEgKjqo?s|Zju#oQMVi->
z#up@rjgKim_C2d3yHvt@6frEqNHlmG(XjJZss*qawgeeXNH|}$T`-KKFAUS|Bcse{
zbdB*QJ^>Yr^9jaN9LbMcXiULokIa6Mt8%R3oXr&+hA$p*Tvz(7&N4sHMZRRzz|_u&
z;UfF>*ZGe?_&YP9sr%}um)9+Y=%#NfqCt0vc1Zk4gJxpNDM~!ZKELJoS}$V~6l~Q~
zw&_16bq$mhZ0CPGUM>?`!O-)Kd+Bqjx?s<4=lC4qpX{1Ogdx$QPy8y>pP07US%w)!
zGYoSUCDf_53okm^qnC>tY(!!o{(L~U<x6K{<f`k1r`h-!O$Vs6Sbem@o|Y;b%n7ei
zN;`!YA{~iJ5`qjp4W73oubs#AL}o7N!5#wFVEMwf??2&;8cb{40XR^LY2&2v%DRQV
zzVheRe&<@uzj#2iE|}K&{8R0s9YuiTsu-89g}1L~g=@ip=dI7C+349NtBE0|vjVa@
z{Nm-FKxg*P@V$hCbQCat*XFuwG2K4o0%#OMi~Q+fb8G@fXj#k7{IF8KE1H6An0sMU
zv!aB%$-*iR{LoN`?pz1N84gLhb=7cTyw2-XNU1?312x(W)v|v;*;)Z+<0i#gb4Kp9
z&zwLuWjL_h=3zGvq*QdQ3DPoc%&@#G%iGnfIkNt7Vfhoy<@u4n2Ob#82pY)hS0Uav
zHQL)bBSKnoEeu#o^F!_5Jvjs^*}vjM<0PD%GOcDHlSP$44_lRTvaaFO))1J;#uZ05
zMz4qe%MkK3Q`Z!Y8$TInk@aF`$4&Po1}dO9m|CO3eTQOVRCDE%40%9ZjJ1h!#b;Ma
zE3A&q8(8J;H|gg?cvZtW@j;7w{jI;m+SVZwbvWRGWM&@d=W5>H5eBf<#WHt<ip#=L
z^+2L^zXEm;gu@`f&$J&mSB>h#73=!QMo|F+t_S(j0HYZDM(ylnCxo4&m`yS-zq}-7
zi2)hAQc|#0X>~TXx{=iC3em3E>{=Cupz#mAg+v(OGhebnoYnB^C2HKQOX_=TS|0Jm
z_283AQ`l`5F;l966o7;|(fp*I2HvCb(5&T~2n#jPRt~x=r82(0sW95QLV4tA+&w9I
z%{!N;)i(U2=Y2MN(&B;XsZYWWboUY3Az9YIvlU6v;P>Mj>;B#N{;Jn5QRqnB$*952
zv=PHlN6yq_=wIl0vGR;U_mzYWA9iedU6cj?@uc}S%8(b>WbsXcaC%<X129BblTmp<
zKhz27Cc^R=$8erN_)om)T|Hm*;bu23=cRx4b&rvyAFyI#b>epBp_?7>2E9hm1$B9|
z#m;QqOcpXU%^SQmt_tImk)hgf9)zoedC&P}(vrL9qz{cxy%m9*rSIs3zOdSuogE+~
z+ZCEG!7cdpl7<orVyK|zUsxqyVehUcx$@8rqE>GCPi(v9%1Ro~w<=jfUXs!xQH$lR
z#Ts76@aZ<+*em>;Tbz$Y&G{isO{HBqI4Z3issgC?BBRz{zF*727EqO>>kY^!{}_{7
z7h-l$k<%B?`=`;hu4h{qL^bq#{Rz>4nc<9Egmv7;S%^38W*z>cH;-)mcLJxj+8oP|
zmYHvPl^cblwVc*2f8T5+N^#k~uV4}TK8mw8jp6_$_w+pRrIdXMj0jlX>_-t9dUhA@
zEi(CALiB+?M7<cX=NVC#&&@KG4N^1xO587`uwNzO7HaYYh(jRLzC!BplY`#a4QP?(
zNNp+9_fVlew&98+f0EGqXd;L|D&oUEejF9t;?c)ja2_3ScAIAg6Xb@Q`qy^HjKFcn
zBD#i>7{ZxMcl(^k@uWu)JDY63cByDc)2{7S_dTc%BHE$Q_N0Uq!9pGM^o^Lnju>8j
zbPYEkrgFSjba*CvJd#B0ls+Is)DEPA43k_B+0y$uqVFVR6|Y_*d1IECXj2CVrSM+6
z(1ZeCIBk}?seR#EN3G9~_NoK$T)kG3IeiO%-KZP9oD=IRq4mXZ{Rr>NO?~L9({?<M
zxM{}Si!KyW!44oO=x&DC5Z^}`<(SJ%CXpLB`F7=qL6apnB<P3}lO<Qym1zITjA!}B
zJcr$1dxXvI$`FYLVIg92l8VlXuilhMYFx|!3C10X|I3c@LWNHpjZI_rOI<1kZknwR
z)_X@~PtY-s?^X)|R)mDTM8;V{(L9G{bhyUvE(yuAkB^zp%5TTg$DmrDV%2PK2>WH{
ze$Z@%-*)=1TF3o`^y_bqc@U4-`L|{mPU-A1Vra<&PgomSu~|ddg48>X_rx3bn@T`h
zDgCq>xQZQPWw~9s%tn^smS7|!F*G0Cl}lX2GQK7JgT1HC{P7qbM(UwGv!q%_%=rz=
zRM}4WU*4Lbmj~<nAJ|}ZcE@@r>yMhcMe^7}zpqZtt5tUb2O4be$w*bd?XiPa-2F&;
zoIJUx&e~(mIOcma)kMP~nNZp@*r%#8>7#g1+K@!DWhiIopq!^X%74F)JYYK2@A=UP
zKHqPgUQz>7qyQ@>^nlhyFVif=IPT3ln=eYl>5qwx{R+O$argtaV404jMe=3@CVMuX
z;CDlv53RU~XjfCpIQkdy$t%+R><>LfrdE_!>em}q(;71oT$0(1nJ|)BhX?KWO52}+
zoMzJ4mc-v$E5^*7@6&+>p^f*3@0x=Zl26a@+xiQ0Y_0}a?6h;sbh{njrOi#N_}x2^
zVy@q=obkO-M$P}k;1_!xj)ccGEfsuos<MRs$;ZRPa^_X_PQ|N<pIf4sMSv+VKl!|l
zv(0_gv>R_0=EjmP%e-|G_1OMx#Kw_-<l>5F26@MdN|n)^tEz-tI2USCa}r>}aQmLO
zC(@K*!k|u{sU|+q_caIIW3Q;MA`^R-z@vmySvLoj;Q=c_Ag}bRl~6nSq4CsENWB;z
z>W)vC$ZPwKN&n|s#@}O7WBbc5JE(yT3|$GK)_Xbdv7gT?;GAhPZfVg(r^y~=2{kdu
z%F`>5&!-~X`?;sl{eU&Riw4ETX#LG!+AcubC!_JiPGA8IlZ~gFXGmWnp;Nv{aMW<$
zljGZEhQk|Bgc#!Jv8(k25>JY6&wM{Q{d*8nL(q#OVg1g3xxG&hXsQPzofZd{n1>|T
z*M4r`{m1%)h-Lq0>ny>HmDo%a7!{#vNKSlpJSzHseBl4b^L;`<DkLU1kByL7JsUT;
z#=*XQh6-DB!~BnB{2v||{7DQQ1`#BUxb-<*OL)EbKVtBIVBkNovIrK0s#i}HsFcxg
z!)&-h^+b6YP2TQtc>Cp@qaG#kb?1YLc4`L-P1!`UiHH|d=i9q6yw6(}fNs1AW3!o7
z;H51*vImC4nR-Xd@I5h4yH2!PxOmLeG4!b;tdHw>Vz?28@yzFHJJ_W~g^Y&fM#NJ;
z=t*>a86e;Zu&l7`c#z(~>%???Nf`X1%(%HH^r2>yH$RmTr@*Y5o*oqvaW5M56cGse
zaxn7!{yYKop#)*dVqosN)(x{<WxcRg<Wy6L{*1<S&g^3=T%yl~82EpM<$uhhbHon`
z^VmAHII*t3f>f$x4diS6I~Qu7!WqT0gU+)ycW(0(`JNNT+-dKXU;Y`ap7wW64W${m
z5xXH><!%T(oP;lvupBI`kbhoLoV<27XjW?_^H+aU_kl6}=+*^@`f?%ghq3<7GXJ~*
z!}`7TCiV*Xns47lf=Bi=OD6t=mBzwZvG|f?`8<PL+J0PJSbN**1N0tpA`Y=O!S|Ke
z9*>T}C0q6TTjIQPO}9fjg0<Y#t2<H}{yo3shpJWWHdOy~;5iY$Cv0A$;1R3w`63T?
z<I#{e)Y7H|`l5-D^nJnP;##x7RRRb(LPUI}0)?ZHQj+&1kTi{NeIgNZ1MU|MkkOoA
z@R7tz4<=@!1%mVN+}S<<sDs^qH7rfJ%Lseo`t&tN7}Pn0N2|mAb5WyxFqG>JNGv;8
zCQ*c_GLkGu_xwwRZyH#APZMr<P4De!$ExwO>vy)9p_mEX!>m^K-E;EBap!C3KXVT&
zR)d7(EkPI<4kyX4rjNlty{;d2UbhBudZZJiwYyva*iCTyJ3e@(_<f1B<Ed|t#pTmd
z=u01iGFV!XjsFjlKyAOd9l3QUbU@_3?{J({h@xqI24VJNUU;9(If<k&`^6?Pg<GE|
zvHFQXNCm44snT+9UB(wLy@@bBi>8|b-AN(qZ<&HGb{<B)j{A{eq3ys)m{`6O(NRgz
z{oaMJx;bVoTZUm>Jt$6CN%nzMe7o&qe7ZZN=}R$gH6ma{z=*)#0s#qZU4rn?nASM3
z{Y%nf*3&pzJ33?B^m#D<E(_tGC6dMF_gruj<L>V{%s=Ei8cQoYG;J<?Z|yeFUKig(
ztn#n99TkE{K8iM`*YVk|6J$X99rL)_1GArBh6lPd<J?AC>HHMz;N12(DV|}|1@o6Y
zfxhiMp(G<xe8~4$wf0RUHlfUZKlgjvfEUcIUHkS#a3_0wF=GaHr(~d%;zEL`OwhTh
z49XT%2`nb9Te%)4H0FdDK?QZc#f1pkvQnLns$z{TV+$)wwCma*qekq+p$|^L-sM4z
z0TB=1002M$Nkl<Z85V#SW^aZ002hpUd^%#o^3ikYWLRAJ0wqP|XiE<50Q+g!cYFm@
zmd@zl+tMKPLxO1v&}gXY#o_^qapaNGQ5Yj?tt&aZ2T}y(D2_#Ev#@~0ljJb6_ZWut
z?=OUNUIZ3BIT2A*WGtdYy|^zKDJK<Dy+LWL&JM99Kk~pqeGy=tf^MJgGVr^bl1h-Q
zVg_}_a`HLJDIfrfS`-xN$Tw~Za|;UJFd3<eA|T~LrHm9=2E1rMR~ZgWnloK7DatBq
zvE=v3)m>V`Z9JY3Cy<ktNJ^0!Aqu7EF-l7AqzbHmm@>F{v?Ry89PhsIJ&H(!R-Swl
z(dQzt?9?InPFjJ*y(xBZN0S-^BZQ!UV8GRrTCIf7%0N9wF)q2_)#CN&3JWQ4QU#UD
z8H0#rWa<eVj?ApVlJ}`1QSPpFPa{Mt!Oc6=A}RDjfdnDWBZ$5#Nh?-BiYt+$b!V$a
zNkjy`{i%YS;Uh4_H4|HRp2YRAOXN9M@%Vx>nUtl(w~EiUn9sJ7{nDd#YxEEDzz>TS
z;>)Nwl$O^Fm?orL)KJA$%4;txCB-SZvV>@unksanUn&fRv^9S#;fhTt+G$7^Ci<$5
z*z4F2R1__e@VQATbY;SaSW(J8tfNvbFWiKRdo?0f0v^&{6&0dFPb~QzAxtE;74v;4
zPd$zor-!4{;Kwj)CPgHyG+4B7BVrqJe>DI?t<^$8NogHbceUg*FJ?b7Q7R$qYW|9y
zYiHsZDM;gMF&2usmNw*{AK4%BsKYoN$E6tsZ6s8nRq%dHscB$oMao6?`I3A-!v^Q7
z|0sjT&J%tfHWb48Hwv{a@X*Min6tVNPrtgIEPuKhIzoOy#`{*YU-8!96W9|Y9HX#z
z|4D<av`|QvRa5AOqSjjCSx$vPLx1A)t`us6qy|;8pNPU9U2zc?%2j+fH9~2J?;9!H
zNUK*-%Kk_C(3@9I;k`W{BP>aRMVnv6xS<0P!t=!b&cUqyUX7oO2pAFgJ0KvGyw#jP
z6}2Smm7IG7W#64+g&rjwY=r5cr5bZpY0X?ImDPy*37bR0`9Ue=Jj$6;MFGERYPl41
ze5s&FhJtjCYEo(n(#R@~t3}*jU&Zl`c7meTPRJM3&yD7zh6vRtk_)?nlQzdR9cd2B
zIGYJN!J6wAbDLgJW|AV*%CRLzjO>f(1G{i5x18f$1?PRvy9H_3w&F>YWTqgO_apR3
z%t(1{m{)3TBN?e3)k><67EsJls(`sU)zLWx3uRBSU#VVk*SJf9Q_hE+nrSO=4ion(
z><3{}+{+VbxHSqNkt$MPBXXtn#6}2H38hh?h%Js$&Je0njP;z`%Bg&=qyx!d&xv`w
zqMX+v{!Xe`QgVv<-0+XKkD#!u;`1@1D4!V9tGKVgxSDg3R-@o|g<V4dB{A1>dnqwB
z&{J}MVdIE?VDLQ4RWPL&di&~W_)%Jp6rmGzuSP5l!dLt$l){CGlCZ@joQL&70F2L|
zfI^y8wKk%J0$YMjTxFnymBN(viRd3fY*V<b1P!S6HkKvCQrHhl_y_gI03P?dpF_Ey
zp3g-ux<sQUl<hU7ldhsYEUTs2E!Xil<b02dIEi<E_z>q)<ygG=6-;=b4|Q6SF>1+N
z3~gVD_g62*>1^uVv><hDJ=<xcbNt$d8UtRcMe}w;Fs4sem=@*1edtI$sVl(YU7z8K
zOJ{6+WjVabe=enx+xgwwu;!gl(T0krvj?_8qRs-HS~!!_wHV)gv=Og-a<I`kD|z>2
zlnZX#XCNH6pGWVBl$_<nuGgKyyI<Tu*VfNr_6M6$nR69ye)TP)%?GnLmqO*<1&>eY
zg>PeuY1|M<%Dg-Dos13h#=%u6{Z$p>mFE{A+ujcYx&%>WO@nrQ$6>x71wXERkI(mB
z$Kxwz!Yw}nudQB+Gm#Bw+DQEG|L{CKY7vT!AN_=6vFekkVDCN*EvWXHTW*5^eVSu`
zVmhY1`Vsor$zWz@hAWrT;NHR=#kY=Q{LHt|XY?E_d8RjHWLv2wH}Z*ZKfqbrVOTiG
z1BJO%HDw1;P#*o~x1Yxw5hmF5+G@0-7>I<t#8>xziIwmE1pmqNFrlRyT4y&nIa(wC
z!V$c-_$3^_2QPaSjia)Hd>juyi9AaCm2$u<5(YW{m6meQsg^Ye-O=mqJP1qIKaI}r
z6!PH9TX5+|EPind+(tcvIS=-tB5VcK;pF&w(R_p}+|j*9pZ{a;J>a9Ny6)jMlT3Q=
zh4e~#LINZZLJ7V1-m6GaiYQ1C5kyc#rGuaXN(bq^69|w1DI}yPr1#z@-?|e50fLYE
zKF{;Me*c``Pcq)QbIZEtp0m&1YwyK6wyJRKFa+})>rftd0{i0a@XgXmu+R|W>FNCx
z%8EdP#D$f6Uo2WS3(h)XRB}KnkG+J^bH1bz|7I!1I+W%H<K)3)R2ospNsYoIXAdEP
zf>Abu7Gmva7Y^<cSP)-PZXCAEScoes-k30|FO*91V9>EECbcfZ{nI;erN)NJb}KQ^
zQ6F`@r{o8xv2ghYs5<#!^}KP&ELMQCtr=^$Q%LEj4Oq1|2J<(rMxZ$r={SHqIrkG*
zY`%&qYgWU7%C~wpcF@<Q1oo{n_-fgg2vf4aqy<Z;j%kG|4gxBoJS?935yC{ySiWo;
z+%&0bBVU7y`!{35?o%i(q{7{SO;l>F#82OiN58H&@x$pm;F$PU`*7`Am?mDvH_tq=
zW2PzQP2Gin$>VYQ38Oya{9sOTjmVS4_<kg#C_uYGqtV6Fhk`lkl;R$Z`K^K6umd<<
zUX4m6J#?M50q^;FLa96*`)1F@*2qLI2b*ZZ08FXpyO$0kI7tj+w_gz0Isjk%G#ILP
z4`Lo8PK8wG2%NhbQ~Y$W`{)Dswlw6RCdQ;0^Y{)I;?~UzvWY|F+6!xTO`u%1K1#x_
zWA#U$Bh6<3md=>~OD;ZGN*MPJ?ZBF!gE4vaXRxm>hQ7T$^trgnxN!<!eZC&&Po0Dr
z6=C(LXs0M*UFWQ9E@+zotchD`-qeghGXnqD5fGz=vp+und@+m^1S3ZYxgqDUaN)O5
z>M;gO-yg$yypBd#9j^ZJJvQG=!^-umk+)|)mL3U%k>gN&y($pvX3c=sz>hG)ht$p5
z7VvYV8gW$yKA8PJ#gKIH!Sdzk=AuW1UK(ln;g~aj4KgeOv3B7oSkPvn8VkN%@d?gR
z0FVN?O*9GYy~bhr%26<*Mn)~8+UZyK;QiG<Ld&rqK3O&sj@n{6D-==o>;iV(&4;&x
z3k_E-^c^(=ZCI;6J>&o`OZ#HS;(k!AE5U(9i;$3%1qDqlY&>uP*_VFA(yxC*CFkbO
z(^p`kuLj1BTY|tL^YGEk0ElT=N~_Bed~6GDRkeJ5+~wH$;LJ{3O=*ESUwwvNR@$7y
z<PdfJ5SD$l9l2#q-W0U-{V?;B+34QNi1pY-s7MV*aM%+*lP)kbp#U3cK4A$`__x-@
zf>|FTG(i&|ubzjt)|#{vD8-c!jCB_hFlb;eI8Y?6g7b~4t~Sa-PGHWLKf$c$G%On3
zm0B)*w$cKuTk;Xk7r9{L>d`2U%|y#~-q5Hk#Dx{hvG&SiSPY$w&nI<*E*AsUg>hK9
z>@z%2?v7n+#z0R}jfV&KAUq@*?<$xr&;B!O{Z4eIEv9U!i|EHGFs0~WT|op+ChB3L
zS2aF)e*vNly5s9*W07!V2YxGa!LokVEE~>}y;MQ1NqLNKr_IB8kulz1vl4yn^f-^p
zA@<f0tln`HK0PN;=uj7m)n1r6$B@p{90Z;J30K12zA7biSFHVJ86=76Fmkeop0)}S
zE*!#|bz5+bbDOy^x9Rg(Tm)o3Po@3h4?l`t-re~z!l-zwE|)<jL#j+@vx!dmr#96a
z@O)UR*7`71rH71OGF?+6lod!|>f{6KF0;{+hV`cJPC?GOCuR&Eg~{<DxJGZXx?Ov;
zNxP0?2Sd<*(g0|vk~H5$6IO2(<bx@hx?;4a7hIe<=@(XTl2=8=gA+I(J_zr3vPH<f
zt2o9Q?dAiNDUq9ooAHzY?lu^CB~KX2sG_2yA3l9=IJ65M<NM$4!>#XFES%IAd!s3K
zp`%Yx1&RUa8^cgs4&_W!dg*nkEGt38rJFe4*mxJNk*i1r9dnqeKBm{a-e?f3NZ>ux
z8#%!@kWA%7&yKyJcQBkDS3N%CBBZBP!^hi@%C7~i-E4xEoyOtQPkJEq(2uzOv<kzf
z4uh(wnyPFEup?0m5)FF{9MT&~#m^8~tcZT^eFWE%7;M>c0#!DhFmv=!Or?>3vqA~J
z0Uo$WY38$+)G%R4E41$7fE&lD;Mqj0<5FCt=Yr2ZTTM$z9$`0*<C`5v>OHT5>$p)<
z&~wMUwR7R6lY@QR_n^+Q1BOx6R!&5(c4uE`R3~G{=0nJ%Pc@V3tm>`3VPIfL@fQhd
zh^3LJ3`M!-kjsw3fqg&0uisc$>uSin9l~iO26kBR`G+tsip16p$B`v(414WjI$WC`
zRAiUN{=nrIk3mrvLCI1uH>E1Dv3KOXoWY)5x5z2)3;$7m2zsOr6K$^271W_)Vg&i3
zV(8NQ;cC|lV|=u6^2gnHCU(Zu;XUzQk2Ba2OOYDuwrHP!7Y7d>L*V$qP|-AlcOO6W
zcaq1io42FTGyqe&4aBgxYzn}rp<NeSoH~36XG=^luD2JQr~ulc)kF+$-vTFp+KRhW
zbF;HIMwZwP@6DJ9Pql39+;R#A{=+eJ$S_>Fas&6`1*%movZ7AnRK#d_wKRg^MG>Ai
zVUP+Ffr+IR+znh|s;>ka&vvk(I-?}#Br%SK-+=M3)K#bVhKFR=RMR$q9#!;|n1i6M
z&*REhLd1JQnHZWzMi5nGW8bcy;oqk}I*;qXNhIk-<VS|~%YOt1R4vbK?cfGEuMY6F
zGeq2_ow!`m37_`s4%dT+aVxzA2KcswH0u@&%*>(12#1n_4s;C*si`CnT@^!Qaq;ca
z&IQ*_9mA<hiWon{3mwPygJRnWu*rCY?|(fDwN?W#eb`tel~8T5N|g%9HdL)8#^q)h
z+shm7txfT)raiv+cmixGFRn&4%HLu_kyrmJzP#q<56uWPBk)f}KoK@}0dThz<M5_^
zxF4Ov=v*<%^lUM2+9b3{x`po!U4p*fFiadh9#Z`q9PSuZWdv0i`XnMbXlkfYkcrMi
zdOP);+CwS)9F87~z|i4-5UHAA1S!9PHgedvX#*nj#c;D#L9wbQ7S9|Fqml@0-hLY`
zdyT{VDFg8|CV^_;6>ov5X3-hT*G-0c${p-Dc?Yij$HSSC0A&*=Eaco~%IN*JJtvUO
zxy??i2r4?BFwoKAy(mG)(2$O^8dPhVp!oiE?EbYH9eVYJI&C3g=^#YxuSZs5diwx4
z>^+XK5-0Qx@I-COc{m47!s3NpX}HpUcvg#%QwKqd(MkhGz+WGC>DAVBf{*h640Th*
zp=~>nq1_hK`qI(*=qA=4k9g~z#LD`ZwD<$`^;X8QJ=^eHLRxGuYdYpsplxP`tjCcs
z@w9`Tc@9G3OQ2%e9#cnmqEc-+e%-zg)n>jJ?XQd6gd?PXWWrD)g}rZo3@|rFXmk`5
zS`EVoBf28<)IRJFNrm4~(w`ShMAWwD^vWB;&XbYk{kver-}nvphiSGMR!r>!>9foD
z_Dl@=Pn(G)Q+i{<rU&?CuN>O<8wPD{&UtU$Nuv#%8<f@cAulOHQe+mqy^U})jJ6na
z;tp^81s*?tg6T6Bqt?<6ni;pTZ^wBkj%`Q9TqoSW_&au8zKh|*1JPzs0MZM5Fw#pC
z$9L>RqLK$D59osr@~AYdVMygzI$f3Zpl4WyYTE4@Dh9lWw+(nX+FlCs2593?JLJd_
zWXL<ve&~U&kMH1lmIGFkQDS3g1l6ik?1>76vVI3>P;k+7z+!BFPlXFb6%^e$fw`-`
zLqg#z8djq_{+A!?4fyIpgdg9GO$2cHU@I(Var&%+8tB-%W5Z%ooLR6OhweN=&Gjct
z&^QEL7au+dhG)v+@#V^8IP<uQFJ~z76T+K3p&Y{zc@aUVG`2^t#UD}CG7<OV)oB#d
zaFe%(wZ0CGc@=WiZQ*9Bh^%sMr(JiIFQw6!D=_eV3y6ziVBV!Qy_2M;RG6VtA6=N3
zsKHdD6>fg^9`@wA<M5e9xVdL7HeQOAU5C8gXlRM0NXmJgH{WgIT=?6VK;Om~x{Ceq
z@fV&bEX%~!JyGcGp@Y;zA-Gj$N7>@e=w(a#kbDV3u3tr(QadQd9KoIhH!L1%iw;A&
zq2k_Ie6@ZX(yR5+Ze)K-FjpWi_%aSe8)NbMkua!?!=j~25Spiojh~s}!qPQ3bTt^2
zlvwue_8W9vTSG2fjD%Br@Y%|(D3J3-pW$txp{L&@ma>h4WmlGg)8|gh!afB_vG45o
zwjDac*+L1w^BHanj)PrmH4JoZNj1781U-mEn_>PKGNKj_L+|7CgIZiWwj0+@gk#^C
zWq7oAJ=UF#dU+jZlB1yFGZ5B7xbKCbC1=$W&T5r7yL$)r{a(M|xcKrHfBu6Uy&h%p
zkMIrEXCGy2!M<ZBSnFva`Qi>N&56UFHIuP^)hCEbp`om5fXQpU=|K>~zI|7i(FoVp
z)PRYj8>EysR^|({@vG0V?`k@3Mul-EAU$koKgwy>!L3VA_BmZ>=xD>x)(NHTXU~po
z!P1q#K-s7V1`c$Ds*Nv9tsPJjas(T89gro}+_*y7=+5KdY(wu4l`#E!_l6epH?)lO
zphnsiWClEO;R33d$(ZsyC+D3eTU=^6rK5e&uTm9>&r8r}U>g`|GlZOU7pJe6K(*~)
zaL#*)@!9QP;f_gv?E85f&Tjt#yKX1Tj;q(%1J#-F*uQEqehICDmRmdYXko=g5r51m
z&6`aK^Fq@c(-y5`*s++=w2k<F>roWy#p7Y$uQ7xY-6BZ<EY-@fd)Bu&m|6_!l5H5#
zA{L*{TPF(%69shhP(jMMpRsc3W@OfRquU@KxO%ujY%a#B4}QdvhmjE9jfP*hpP@rZ
z@G@c?eP}b5t=b0_lipm^&Vja}35w2K!rG-zNr}^i^T0`%GHNPC)}A5gSv|-9&FgRe
z|7HZ55%?z|P=(~=5G0iJMqvLz(7YXlJ6F%iqRb9vVjN$x6UQUsp!6sSZ98p;PD^*@
zMpc4AKv}D)E_;<EPXoR>>k$?&UV_LXBRqU`2~R8RFvivbN!K@F>%o(<-`wEjW7dB*
zYz?SZT55`peR{#jL>-2j&QR9S2C^FG$JL+5tA7s|R;J*GPnO|$dKo6Tc0)@&dh>02
z!&$Wyr*>}V-1bDa^99+z$8ptR6n1~#51UsmL@>wmhIXhRA0~cXNo$n(UPvS;#hITs
zVc5=<@U^g{P1T*n4l1#K^*MB!y%D8%PGjxY+mKmnf=+aTRAxWN?w{tN_Kk5@q-Tj)
z<2<0iA>6rR4>&>-8oD|#vUldG-!|YiE$ra!*#ft}S%9xdQ`IsGME^c^kfXC!Qv4Vj
zc09q76`Htn<Od|Qnhw{l9pGZBfio*-W6ParSbIt^Adr#o=V7>hCkU>+Ct{SlB_b~T
zj1@b7N5K2*pf1kD_g^y_P6u2(c_3Z3%!Y~<Da7RkIJ0>%He86o7!6N&v@t--zUD9@
z<+_y4x4_<(Fx66oK}!!vGk?Uf-;$y2*AMOGNI7l*vWdSUQl%B;N)USE8ZO7$VRp`O
z?2dK9N4@otmwpy&R;gmgH=n_{^a-ZU{|ZlOMEa06To`#5UoKybm?Cqu?b889E-m3C
zcBB0uBFmsVtZJ#YuCGTrsv>r*_yBTW{DdA#L6|pxt8DIX;!<90VjXzS^I-?S!P0d{
zpk>?(y$38{Khs3f#fw<8G?I%MJ-7^>jIm>;0bx{guhE2Y^*wwz^CyU{+G6SQ8L&Qi
z1_?JFzxy<Q@J+n||6&J$BQ4^pSB2al6ci9Ffs-tRxFlqru|rCfWt_57dROJ>ITh20
ze3Hs!#M&a<y!1zA8#_&P9L~h3V;Yk$pFO&OhmuaHBiFn2;QnZBrHUK-FTj(=<gm`|
zu$wBFb-a$s<R{p1`YFDh-VRcFu}N-{ao--D-G!r%$)h7}rJ^_&X$6%~*HYovb&z|(
zwUgaaK_>Y~T(t=t+(u1BNu69f6)2P1QdO%6f#a@0Bi+QW3rxvn((5?}7PXXmX2lKf
zF22a%aVyB9kSdFj8BYU|-fJc11XyqZQ4~@j<B@5#?E))u;S>16LfsI<Km3gJ4l$N|
z@+Cd?snE5cSCiKuaI^(1E5+pB^I9tf{xv<)InU$4wFVT)kCrF4^5hb~tBJUOT~RU)
zA2^IADPsav;>C|!7^p*8Ns64vOcLnS;oPMIR5(~cl!H^-zCs+m_q|6>MX!<X!9}AM
z3%}osv<gLj{|1&ko|qvl4Zeg@8o~|cVI6sNQZ6Wz$OZe;jXb;>a^qyYL774;z45%K
zilSUf7n61)(qM#u11PzNLQ^J}PI!5hnJYk&AzvDKF8&me8F3BA4_!wlInz7J%1FUT
zg^H;M<jS*ND5+NB-t{Yhp*=c`RYB^bBRG8gAqp5r-9-LmMxhcWE$NJ`<T%Cxh&{Q9
z@?2F*RJc0kb5RiT0~8qUdjYbT*C6oTVs0G3{wvQJWvHQCbTMKYRm2hQL!wB2{2#ay
zQc`m0LHCA!Vk~a_5(58~W1*QA#GsJS0mzAb17iXL6%|E3Q@)GjD>gJ?9amLWgo4am
z?qbSHF7#A!A@r^$T)U{4-hgkvK}c*CGPv-|;R_<J3d8ZAZ!>{>DlDLf3VKP-(0C{E
z<SVkbifW-qX>Iar_?aAdavpP^C-Qx)BSlPz=8%)f^9uP3^6cNj6^OYYS0weOjvQ67
zvWOVRGRDKgGvu-g`^b}PDJ@LLlZW*aYJ8~^rtInqEdyPysG1FULe<=w5%`BAP>cA`
zbNFC#G6H%H#*n^4(Td4?m#^QFDWS_V?}&CTqpOUt3S$L_KM}{4x-wd+98;`2w1E!C
zVPUS3qb*Wfl!k~bQe0}&aP?vuVA4yrpCZTU#!Zg4Lmg?mPtNbgp^zdOf4rhNkMzcz
z#=E`zY-OxL9y8~U^a@6{t>9>H4f(VXj_-1^xlQq{xsAMd8hgTcEa6|e;9nj~k$iDA
z-a{qNUq44mkrx+=pMo+ldek&@G`2-)<Rx5A$;W(C+7CDLX@fAg(Jv5|s>*oEIfawJ
z+v6^sGIET-raeB5qet%|uR@9~vN?}9G|{xx6rrUl!sDoH!50dL-hnV6P3JlJ;-cyb
z<~xyQOPeMqAr%_!<zb?y$~i`g>?n@C<bgYN?Fe1+)T{WPvHwtfI(s5&NFVxm?RFG1
zh#}z|CFCP2@VN-&d|~bs3wDR_9~WUcDakx5$9mc_3bbY9xnFffG437Tic7Il^4BX-
zQId;%(tjlCv?~N2Jv%^yUD3oX$u`j<<$PR4wt)m0ymn>SbN=C13UuPxa9KktX05~k
zo&4LACVEd+B%_RWpj4KRrcS$+ivTq`k7?s%gkQt4!#9!5T(fP&GA~!aQjIvX|A?fV
ztHA4g*Ok<7=ah@g*ce$qmN>M87IPIUnL{U=+aA=<ZHX1mnDBik6ov_T6~sRL1|CE{
zgI1{oDRk@$^YY(qul-U;xDcv>G_;(&LfpHbiZRp2;YnEyl>A1*RZ@l{kxw9}-jVN^
z5JLC^OXbCZX!g*ER>sZm_MjwK4=JI~;Jr8yb*-4p>S2efC+D#Gr^8(N`k*gY>i4eX
z$<#Z=Ol}A}yAAhxwt~N!0^-gDBgx$XwyoMj`POeJH756+tIYJY6cn@L%d2U_vt2g?
zR5+lets;&by^iQS5j+_&XsdP-cS|@zu$|rg4K7J^(0<HHOwhTC?>8Sq8khZ(H|#@U
zY!EiC+=!?$V;Xl0;oq(+vPd0}C4LCT8Nn7z@EQ&*(a*Bs@8yG`Gv>o3pfk+1l#ycQ
z1m{shFn6Fgo?br(LrSGjacG6))aSTyD*-d6jz&0>cxpTbk&01^Q<0Bo7zaSY!A$53
zLKB|BYY(P=^UHsQ=OnLdi>Z^RqK2KVBr_K0&Rs?>J0eXfzA*X-7}O66v0;c!NGBIv
z8C@4nW5mQ1!~1(cl8_6jLx0R@>wtogE4cXGH;}5X#UPgR6B1RW<xDtKR)@P^H+0D|
zgsq!CjvqdS%+gv}GKZF#9s2j|gbVkA8Cki8h@v6zA2b6qDs_+mLpW&`V*4+@Guej&
z@oP;4RSkD^>E;3#OTH)?^pG&z7#(z-6dv|HE)<!I0?;5AyvTfw>VN&BUvf<$Yc)kh
z2t9cp<34Q-+ZzuM_ox6KT?0}2;4toSaUtfQq1dQjigNSu=t2kvPHzv3%b`enE<u~l
zK8Sx5F5?DDghUouH|90)IVj{L;#pk`dJY|g<+BTM=1~ruTAJWGCylVE6m<0tfZnBT
zNKvpv`*y8R@@p~^n(MDkU)KX&+|?0wCX0h?!!oM=yM})sF-lNI!?rjt61$^r!c#gG
zido615f*<UC1Naswz>TajPD~Sj|b<#E73NDw{KTOiL>EoCdHX7Ch&5>A+IQ0d;?|;
zXL<XtKv$Bd7yemz7{U_-%1?chRu$sG-Q!3Y`4wFCD)7tl-(Cg`rCcQIxG*Z@6M{6I
z7F>uIql60$c@<8+d>@1+Pa{7)BNqj=<>)zk0>Y&~L%Z_?dWi}U6devNUk5IVI2y{D
zun4b)MhhoQc+Vc0_m3hdK^x=8c3@aJg`SkACnRA3SO1J&9OlIz0pa?q_|I(@UduP1
zr11ZLWxH^7^-oFo^nbJcQ?Ps&_5!NS82|P6p{aiSM_9g%aYM0&H~O`LA?XTZ&`u)-
zLBDRrZjm0A^dujf3;*bta`c@$5l_GW6{dZr!?8LWJ01kX%-0;A9eQAd`CyD3><_u(
z7#@^P0Kqvy@3CNSaC)k5nGac&E**o~kHIHXt8pu_20jj?jGRp(Rfe=jZ|23_+>2s;
zHw1c_BP)aB?pt#b{RMGZkZ<FSe)DF)gY=jV4u(v?*F#;*RnBdF(Q(Lo$gb8y5{%GN
zGY>!fc$#AvDY*JA(4|cW$lFTbU|4~jKcALem$X(43qODKC&lK>)w?J!$-xs+ET*rY
z2Xl_yyOy0mCJQb^Kg%U0w+)6(SA<6wIuA8fkfbBfHHc^9aaY$2iUrvSJ`s%J3;key
zB?QTFRq*K&h`f6zkeK%N!X&q#1ex^NwVyr$S#e?v7#zTeL?V>5^|(k9qQjhD`JV!9
zI5BOA<kTFfiOkVu-efvz3^0747Zf-@)wLUsIYWlgIaz`8(Rt`PU@XK1&+vqf%%Vy!
zjQDsmiq3|k>zGjxM?GM|YGJ*TlFm+{3F9{XHHbQP7g_$caP(}AtM^Yq-764I3JHjh
zPvmq@!@ZW3eXO0)zgGax-3*rH&%N0%g=-e(Fk#LUXxYNG(_$c6O0Z-^WYzoKag-5!
zxi&q}XUd1jPM?d%H2LHN$8`PW$nLcwo%TS`1591k2G-XeAR(d@UV*e9LOBQ0q88>S
zLmw7zX!8^$=C%-LJ;sSkZ_}BD=}$^3g1&n@^py9(fQkK(P2)T!u@KWf_z=lAe#6~l
zJ&YUU4=MXz@GqBPq;H2VZTp~`LpVe|C&Ejq2>S~QWmx^4e_pc_iwg6QlBUcI?)o>R
zIy(WsZ&?LbQb49HU&VgKm*m7=>^SiN4&;ibq-Qb<{LQKD?aL6Vccf*q%0*T%4j<v#
zNxKD{(~{81#T<I#EbL#i8=2|3IJW*rxP8`_-scLWB|c}uM;!{|VzFlr69NWmq9W%4
z*6ozX=j5|}xVja4hnOJkrGt<aR4O0f=<lHz-k~Qx^5U8}@eWt2=dkI=^EkA;KPJqa
zh}dlhSZlgOR<WeAiscsRjm#A1qPT=fpw%K6%je?W&8s*YnZTP!z{T5lVMc>9Gn=(Z
znP8V!lZq@RmgXTF36b(Rzx5H?tsRd+13Dv_6qFk!EE_~`zFnO%>#!$6-p~d;d)m=6
zT7yTI_u{)v%i*(n6(%lS#ZFg)hvyDp%Z{^XHQ5yzyaoaFI%ye_Q=TIu@0Ee|I-*?B
zXQm@LUmG2}b*0g(fP&~C1Ye{9&mAiYGm%$hg|6dA!}Q`|e7^Y%&Um|HP(XL|$;&|4
zm2)sKWY4RXXQ03a-RHCw#z($rT$t@Rhm33?yF3&}`HaVO>5BPoqzOI0hchS6bH6I&
zrKO^lhW^m0V^EQpgkw*`@%b0quyWoo^qcrTSKy?O#9qZNzHDzd8t^`CF=j*{Z?p~>
z91wf;9}a7!a{QE@gVYT97fD9d9Bk8|Tp-*LFO}K$DXAIEh<`D1OH1(J-VPiJT7hn3
z=A*+XauAEMas2)P*|U}HE0lD3(duflaqY?uoc8+^ecxMxJ{&O0)1$HbA&ukAG^iKw
zp1E&LO$ic{(`byR;rNkVFl87vFmM>$c)8+oMw_lK#do{wV91GQ%BQRO3o(k~gK>e2
zM>&Zi`H-fVwsZ~*Ga~Ti<0mM2$E#73o{YHoI21m9fYiJ!Ja`xe%Y+z|GXa@lF65^@
zht$H9ITfQ(9-n{{G~n}dZ{z6c?&#+~5Fh!m4@6zY=`;CAO;={$6;8w?(Cl<1R<UZ3
z@DSc=^js8YbFgQXqqj`nI^;w>Lqt?2Y|Nwab7Z~eK=3TxX40xx%dr?de<8gXMYw$7
z9B$>MBcnp75X3GaEk{~<77`xa!s+A)7|xu8_c(|O^*%2i`Wd_T-ox~k0mvlA!d2Ch
zRG5^M%>G^lOZN^KJ+L=%7k20S|I1$TF`FLDoV)ZxUq3HaqTnJ@c6*Ej)a2q(o*Z*7
zG{{Hg!a#P<!uA}5<VxVIC(!f+b3<rcoY6=K$y0*6KC1w|?4DfmR%emorUEYmMg<yn
z=l;^FOgt%2hqnR8#QKBCj#E>S$%$PP-iC^=B`H^yXX06jHjkt5ayySguM2T6(}stB
z<L$LfK+i!M7rkzJteW)Vk%>9R=FnT1Q$t#jE@=ZVw^t#vr~p#Zzgnotz1Yrstt-d#
z!Xii%^kBq71NFl(w{skc%&mZdnkMvA=)G*fOj?TY+)9|Llm5j?_9d2lryl24!<tky
zRmT5cVVO&8<e0>#!HD787`qT-AtF=XUSaG(jAbhv*%$o2DhrSDRnW>%wUL*N-qgxW
zgcmY7+mIN)xL4sP)x`K|kruZ%#+Z>%M%QAR!nl+em+KJYS6K4s$S=-8dez$)XA|Sx
z*D&U_vwB1Z4G@81XF`l$V)=J4&Z$HTO-=G?8)3};`j|Xz8)B^T8ph-yv%fkr??rGt
zG+-}gKMu`RfQufT9gQ#+u^)%BAA53qdKKwwz*v=-F@jkCK=^M1#v0tN^cu!h#5k%6
zjLXZi5LZfP*lYc`mPS<$i&p6H{dtA4;DAkx4Z)AUoP2r4mmlb)kYhBp<G}Ig)O$25
zlmMakuHp1AhagI%*Lm_N%>H~8gS{0v&9UOAT^G?`YytP)T`|l&4{^6HBANFt1Zj(C
zz&=lxzCO0qW@6KZt<d~r8rlt<0Dr#JwWQgcKm85X?NGz2>2##6S|_s;Qt0rL@f%-W
z+^Y}wem;g<UKVK6qdzQC;&6*p9}`A-D~iIga`kq6K5r=cznI$!V=mKSQA>N}29BTa
zjNapz#7g@k<H=d<5)R4Y8(Ar7Xyer$!yPS<9u$f@teO`8>>i6wM8mCuHc&<eBh589
zxAj-}d_Iy%q8*VOau+v?ZBRv)17DWc$6fyZ=CK_(eta8_b@?3Kr+$pCbm&y(q~Pe?
zH=Shl_f;783}=pA!<a$sG1#gI@i)&S#nh11ziN=2ltylT7U$ax<g@aMG@s+v>1#-#
zCdcSLUD2;F8xgb<RE@P@V`>29@;uHnO!RF_9zQGPDHdDc9-W+9Rpl|2j>08={Yfdv
z#@glE5nt$xtSn~~%A8js6!6%Tj6p%hF|66CjU^+yV9{5dWYLyq7q`po0HI=84jr6g
zPfHA+IFg>~XAlh<3O^?6HXajdt8<Y}+oz7USSjb|ObKl<&g}&yx)}0aC*)r}jw2Tj
zAt@*m>PA!H+<QFjxpbsvvU;5CrK+T}Hyz^aI6NXH{BZCp=EyBXXP&>RARWJl9wT_=
zxPIj#+^vUW%qO3qhJ8Ndfdu-ly<x7-D1z)b_2X+?v6R(#%FxQcKPG!sqCDdvc5K;<
zvl)8$a=Jf;_UcwYw=og+$lil^!UbXe5j~i_Hw+8D+r{_13b%gz5fP8$>L1$Q_*WQt
z8x5Q9ID#6JH>`}IQk90s(HZa9>A$yGBxeHmR_3ToPegP|hRpb?Kf4%e45K)xRw9f>
zcohc_iIOV%&svE&gM5)29fBj9H{(FaQ&zS4*SGy=?nqT#54PrVcp9C_^Zkims2akC
z#r-v;=?IN%+S~Xij`Ejxv2%7qZ9zEVQw3A?RWr19f*B`*tfY7((};f67&WnWgtImf
z&KIgei1YnXBQ?U#)fE~{sC*QeDD$%X(eD4A@03|m(*cG$QY1fog2q?h^zl_;<-jnT
zLMal%qUay~*Uiw;1y-!QlT1T8EvNX!4|L$f1RybMmnXy}qg2@dgGP<T$Pq(eRFi~V
zTQ_0PiJSlW;D2=wh9@*_9AKkZipLQNFFEpm^)QY1mnZeboE3PC?7W$TGz0gx%)r`1
zPf*eLajP3JtZahXoK(alWjFr$+n*U#akMmqeED;P$NVuBMqS?m&d#>bQm8`QW0n)m
ze7$crK6sl!<1xHzdsYJy^aJ<}-SP4q(C_hb9Q3rIPO8((?Oep%&5?tPffkf$_|%WN
zG|*^7-px^fr=cbzX)lh><A`WP-py5pkC8e(eGO-&>0Ob5`vt0KOM#JBYa&vrSnVgY
zP=niP@W1#%_*r>rI-_e^XltaxiKl*i5e)d^l1xO?fNvu+QX96@xGdtrhX%ZlfnZqG
zZx;;sOj60xt99U|&-w+j?Si3RiBv9Vs~H})QfCD4<@Rze=Gl`K^<l0i&psg>huF}7
zf0SJUedZS!t1_x4Y!@)AC5JSt941;i&~Gr{Uu?h61V3x$hG?;JNBv<0jB{`|TSQK<
zHq;snc<wJJ!&n|}6pd4CFfO@Wz&MB)w<5-5MbtNLZ@{><apOca0poOHtoBZfeTi}X
zJqdTkGPI=`h@b)A)<`h;8?cnJd|W1CDzwpBSZuxAUW;5BBgs|TT!g)WaUl)+T3vYQ
zy^is7VqEhE#_}}a6N#~y7@NI@aVbKw%V4BIE;8%CHpV!F7`GtCy2SVumbtjkLCRW3
z>vfE)h;fbxPWswVZ7|?v7z<cR;6W@E8aiepjJ=IC>*)gm%G?h2*L%#hYQz5es?mTa
z#t*m<6|k&du+@KCMGEXAE<}BeY4E?qjFrNOaby!17ZBrkV(ilhV>)jc(F?-5?|y!D
z$~Qi#>6yUFn$<$+Tndj)eD$rC8JsNW=`KmZ)3{fow-tHa3RTY#o!GQ-`sx6$KAE|B
z!IT8?@bD;<y!5gg!QICaRhdb6#sUqEhqPC}tp6lXhh5xTk{TGnalXD^z1*i^ZYvk&
zwnpPFOKq8vPpw;BfX9(Zf5MP@dBm3=)HHQr(?X0$Qe7JPoa(=Ca@<vAl{N=x)5)2{
zMAZB@F7@SsUwkmPb%Clh7Ej~fPTqZUU*Z4lTDtLhgd;ZP-D7d%--(P{!pB4o>8XiG
z$#@ZlZoI=ApCzpB=HhOQf`})~jVOEL+lK!YVfaU@(8*et@6L-GWdE)xNW_O9f7!6p
zzu~Vk%xL52X;zaJ68^gVpk)LX7I`R2ibIjIDdsO&03(S&{S~f<Obk&4!ZJ(IlEomz
zXVziW**i#1eAS254S1hUw$PMRA@<2_#5SJWEL_mj$rNd%l|E(;$lq-OHh<25|GQ`S
zKk_@VqACIgjzNEx$rL~P9ZNQ`?B8n^%KwoIZN7|uCIZGCvGju>P_0hKt&68{>iXk<
z%zfw>8e_vR2jIZ+X)#xS!w2j4(C8tRr@1vF(2PJc0?i0CBhZXMlL*MQaK)yr-=k#j
zJbe0F$iH9tzR9z`+W7yB0pD=TB9gI`S#Oj%28}#w4c|8ZZARelB0vEnWqFEY{W&kk
z-#yEJ;&%#6Om_2W4P|;}!yi0hrI|kyM*2@&N%JwA5okuB8G&X5ni2SCARwY+*v8I^
zmEaPPl1%}r|MF%3KWD)Im+@^rT{8mB2s9(mj6gF2%?LCj(2PJc0{>P71OvW-rTA|>
zaPz^M5okuB8G&X5nh|J5pc#Q?1ey_OM&Lh#fc#sBl~;y7g_V?<uvEf~{JesO#J#un
z{Ksw3H8PQv!OLP*kA~#4f80&H<AO!3!>XmudaF!!lCpSdVL?%&${FwY(O=!nEPrJ!
zJ=R*SLUvxkA0`F;)kFWw{aN>2S5FtJtb<-&QpEGKTEzdACa1=ChP8yt3;*Z*abm2Z
zr4J1z$q4uOM~PQrMKx$KL9vu&s4D+x$A2eIygo%$ikmT8zlb&a|EFO?p?Z(1iV_p;
zSf#46>fd>#&Btm+pc#Q?1pd1S$Vs5CtpiC(0SZ`E?0-5w{&z3_f9Y&YAXd}i+|CL<
zIka2;TT|J8`fmRla~pHWv7+HdL@N4d&uR~g7thD|p#xy1Cyy&PA2xjbxBm)>a;?2x
zQIVE~QkLuy1ah3b+CtCF4Az#`EG5aBr3F<`Hn4|(YkSlw>%z{~25PKykXtH+rk*ug
zds(9>FQ3&=n&#-r$!n93WC6vxGL%r-xN#Fx?lz!be=J(I3|&mBaV0ohCM3~#pO>Fm
z#>u>;7v-Ndk;TdbHLPUwf2+yE!odx0ZcebbvxTjl9ZYpppkU#F_vXyU=zd+`X>W*U
zH|`_<Z(g`qP7~JlR!}CE#pSOm0m!LnV#M-InAO$<!8h)p@GnEd$~I=s=-k#5mgZ(K
zv$TR8uU}888mZauDx1lhkf_0@&scoCWC11&9}H(}O`N%O=WpKKf8b|^9q{Se6&T`c
zC#yAFn3c;CsDJM~a;lmbwPG`7`WO-8pqCg+lvwS^wgsz#l+w_x@824&hhfFUE-bqh
zgyhnHbwXBXGI2y(zK3?UcC3VC2?OdD6lG-N-C7lL&^NW_IH1f*Dl8i*JZ}Y7p%^*~
zqwT8jBs%$3P3FIPyJ9ug1ooyR`fv;$JOHhoY^aElj<kQVX!)zBY2LpXfo24n5%~8a
zAfm9H0c@z|UPWh;Q0=avshZm3z12UUdsRGwV^ai4lfTiL7<d21QT{1EQP830&KFxS
zv86h0-@cE3IhFlKZ%iHrX0|ZVrh;p6>EFwh<lNSh`ar{mkHFwT6e?|L4T{eoxtt}8
z+fcRK!4_6l*8I;BRu(N#L`Qjf`MYP~cirxa6)R*ST`L-PaOyn)E9Q+r!Y@1V^Vusb
z`4o-k&;RFTBU^Xrj!j#>gIdf@Tz{4g6YIg)e(-B#rK+LV@Szyn%?eM>JjBp%_hD{F
zBP2;p@xi?L=*;^*cm4rv{ik8)*CTQ3%8hp#dP-_-@%5IEp!GZ$_u^lft_sS|_+af<
zm^s83R}SyT`D;OlXU+M><+K{^U-+wKW`zaocjJ@c9u)t%g@pV+Ff1GY@W1w19pe{$
ziDmEihKf`f*3OPFVfn1+&?iXG%|Q*7NZUDS;?}RH(NN9%uU?v1(F<Q~T?``%Wdz06
z*J2lrQAY(gd1)nbb5mF%F#h#YgMaEIVrZCk!ieFW5zu`Y#!;Y4Rb3yFyl~u)d3Sk2
z9SUK6_2tJXJh2bESdaek(<mgdUi<%<#xiroInSYL)dq9tj)g^C79NJg{;jNZ0b>a<
z&S8yT0prRSbAzsFcYM9^J;=obAvC$ZPingK$K0L{czp7AL~)_}hm8VJ)_p$0o>gO^
zuBZZsHa#(6L?4*4+~bvq#CQBuf`KzvVs<xuT)GmBQeoX7)`B~gihC->;pFXT`r-d(
zQ)+>+vuD9ky%M+YJ%(MtU`*)lg5X;>kWt+1iT|6ZHvg;{fo25$3kVq74#fI(<EhGh
zi8>6g#;fY`5;_qpk(?1rJ0p)}2mj8+G%@b}ox}V?ep6dsN|kQvTx2C7A~xxT7R>+F
z#Vcau{4X%Its+ie4Mp|8m{yy-N+mOlpEC=#DrLBH{|W3n4aK-Xd)&BNfR00{DbUpy
z&Mlqb>C+iAXN|&xTi210{MYSl!GM23p<#V#4O;~Gx<j>sYLkgn*(%IOY*-lNG>xgm
z`aXOtb*L{;ggd8?V*kZpxON?g-Yz<bXPxBMR8v*1DZtfpl&Zh@wqjKcRmBQf^3%IV
z2Pj@ofP0VbFcw!K>ETh_$xO!9IeoC?t5q<vuf@sl_aP*wB}P*&*UY;o2KimaJ%-oI
zSTMh~nlDO=)|fkG5FA+>w=zEw-+Z?X<tm17cVvBU3TIf@d%(ZSiXsP(@Hi$1eP@4&
zf!=C3^}}L(f8hSB@Qg?WR_*&BKs^>GFNGji_6|y*t3hy0B`B6_flh9^2u{mH_sJi?
zSxp3WJq<h!OGOKNOVngO!WZ8igsF9RyhjBvZ3R$Vpb%lV&QW}#0|t2;QiivN9{g&V
zYFo%j$~uRYVa$wKXltWS-5rV%ojry<XT#t%Xe7MV7=bXcf~}b@GD2_R*Ifq@-%tbf
zwO&$(Y8rV(DUa~w`qjvzqMM)=P)=!LX+kV=P1+*ROr5Voqb9P#$mzqm5;Uc#loXkf
zx3K%Sqfm0`jWK;X!GKc6@)UA9w`mhXibcfG3EITc+|C{CO3YB3AA-k~?)Y$GALxjS
z`P}ZHG21~w*$Sg)PeLp95uxsNPShQIx$Q8o>6Q1s{%WMG|N1-DeDMHXCN71!Qw+9#
zy94o5k~3~U9AkZrP*|z}M<-@>XUF2eXPXeIVu6kU?V(wdiHiv-jKbs~=_&h|oI3nR
zOu#^@d@Av7Bkr8Rru~;-Xzzw`y?t35TMTzsM@lr8;~vEwemN7v=Qs_$Jgp#4?C+dA
zgneh@Fl5$5Xy#=@+tvw|6hKM3eFX>ipFk2xBJBr^LBGy!P-C&<+LCl^-?|y$d3qQ%
zc?5iI3@Q1YiBm@o;CxUNg0H0G(Zg_rr7Xs(sdEq<9E&55Vj4Y!S09JZoC)o;hd7pK
zh1r7)@Z-kQaPHXy50Z*8sfR1H<tQli;0~%@z-Qs)0XG*Ls8f{3+Q}RKa`q_AypPms
zE*xs*;n00N)>BDPzT`QMuiA*~^p-a{Z&1Xef`jCNUEkm_Rr(gJ{{d|$jKzm8fhZ3?
zk6kBkLO7*87xDu=baC+bZP;1rLQUNizU}GhFE2)1aw4kuw@AYUlNWlT1?xCxhuy`#
zZTpdK?Saulhrq*J8+8@=xPIaQ4qs2fP-3i+n++ZID+_%UB;LM^gTJ1|zHe6&R|@JB
zl|k8O2z(t>DHhb^mA?Y7`BO6j%?SKs5KuR^!q|z^;M78s33s(fe|!_$w;e<YbtlFS
z8w^)dO-RdfapCYjoQx{RoDZg=@WM{)y^{zX^Ujzw&K*a$`~bCf!_nPLg!md`_`6v`
zRGNm(Tel%Hz6^cFPeT_U8|o2=QTjX#TXyY5yx1P^Pi3^$NE_9K$rMrCfw+`cb-LdK
zP@>cd?=2VvJ3|fGoSpjcEVdlDOkpKUjN{zqX`wBf+p;)!9tcXLeuV|cV{N#3w}Zs8
z3Pox6k*?hb^GCIXq#_3we+-5ooua6!h7Xr4Lh_wc*njGt?6eMnqtM@5pH8XoVP@A0
zGv4b4EslGo#RYhB?HGbGC<fVh+^sCd^Qe1>&C^7eu@lg@l`Wk-Wq5G)1a=?0_2#WK
z`hO#IA2tMC+c`jy^M3yGC`2VBLeI1%v<(cQT%Cu=#2U0|Wq@6qe?Z97S`3>!25nu<
zSm(SJc?nN&GCUt0JNUwcRLvR+DGEY&WpTmSwf88b&OI@0P*)ggD4;w$9vi>kie!0b
z%$*p3Eb0Y0`L}_VG#8gQZpW#l=a6#_#D`;hz(PwEg^3UF_166;(C>t0)4M{YCLhmk
zU;jfb9XSO>445_tE~+3~L!b546JTs-2}x-JB5O_It&oNvcnyil-k3XM0BYldaO$}x
z2772zz)BsCb}dkn6oy~dZ9}+30|6r^U;r5(igdL-y8b)1Q|Z>pwJpUAb)hLWhku7a
zGCqos^o(k)S#MkN3KYVeI&}mpVp5>*>;f}wc|5&(2EQG<j=dW`LroFqw&Dt?GE&~w
zP7~@ikFfXK2pp$ogq*Slg`&Peiw9Sc_N-|;q0xhU=f`>qjRL)DLuX*#z}8TZqacq@
z8+7#bLTz~tByA^Q#ZY%7P&7c!(i=VfJy1zt%B?zJ!r1ZfbF-r5S_xNQPn0BwQ6~4<
zTPG8Q2fFxogsQeGvgy_CIA;p%)Filg<qYm$I04hvUE$x>83|O<TDRp0>KxmnXDfYV
zR%yVRB15_1IcObdj#IxUW9Zyj?EHp^k4T1rcXzZ?O@oF{UyK~k1-h!LFtB!kuYUks
zHOq1Hu_C6<ABOyU$FOYkiPu+l<Sn}md>^AN@^SS}B#H#$m6AHzj-3f#<wB%TNzb~4
z2xo6cpx5-}=<Ug{N(-vQ`M5)0w*^d$RB$z<2CIJf6i&)i3Z;sn5FQa{M&brVTiV%Z
zp|V^NZf)Db)!Ky0txr(hYCJw4)e1>ZC{b+UjvoGAs4mDrwPiPaJgpDRwd7DF(nUaP
zXA~!g(x7|g{eA_iB78dcfV-UmjWlJrxVl44sRpr0_5OBCV$#u0g3AZaqFzSF5EB-E
zib;dop*ABKmD<*5YX)4ol7;@nvPVlbs+vYXtg40FC--64aX6+<=tl)dRp?te!M8&I
z+_kB|8&?fa4?zmK9ZaOzxODZN%=;>*pu~{jMvQD{hupX*WP(&CgF2k&_mxeEKM4x7
z)fEb`;4=>}$isotSCL&Jf|1V%MimCZ-bf8aH41R|Yz6hYG{l<);<Kqeq0HP2D-SOO
zbnt^}ejHL2+i@W<0dmRF$l@T|HJ~kOpIt(-f(73H@MCoJvV(|15ss}rQ1v_+iEzW`
z%SNC)DgtHt?&#HqbeEI_*awcqcq-wUsIwc(o1l}AEmEU`pwez67QH_KS|!Pdd|nI_
z3nPR*NQdvxiI_UrlgfKhP;u&vZZ3Lwz=0>XjB?OaDox=r&55B?btLYGJR@c7)st)Z
zG;HSkXlp1%tZ`Q?9^j0dcgoSJdr!nreb&iZ4~|~#VJs|It_905?l)^GCieA(hO!!r
zZCz<hcYwV@Apo^NO22Mp>0o$2U(%>7P+l#9or^atl~Qo}e$3kuCXobs&YdyPQw3*^
zA4E*GBZds>3`Js@sOW={t&MPpD#Y1TV4S)1WB5C&L5=s<)yW8o8pd#I<%4!U9+2iz
z*-qXLqlX2+OjiL#(1E|V1M;Z2=hSZ|-V3xrOlTyO?S0YP*Ae-7xv=TLXv@F=7^_i;
zN5KRgy={>de;+|lNx|VFug#>T_^78nuAli8zg-Ibw+K`<VBP$;8G&X5{<jcNhDV1{
zSTwT>5<)_eQ$`w}s2H~&WMcH<l~N|@6#xK007*naRG8S#l#$~oXgGI3k5*QY6W5_l
zpD{2@zJl{l(xGeK5p!pCL-48N&}}~y6DgGEZes*djR;<CoN?paS=jZNhPh+=LX;Vc
zXW2Edx6(k+y+RCme-;M&nISPe9=cxL(Mi7m!4&H$Zi3q=me^y-_g|oQs}{&lO^3dV
z51iz4aOU1qj9ak*qx@`;6BiA-d1P~&x@TVu>*xwqWg6Pfp73k$4+~KaZlx=r1ML`l
zcOO(eyoLLTX{b|FLH8+3(8F4SJD0DqOs^@XE?$V%Mzy#ZA&*txErEk#F_Lp@;n~Iu
zS`^(f?KuU3js~xdyS2{~kf+@VU#u8~>e#0!&}@lb?c7iidkYb1P3U%rl+l0MBFvdS
z0IG$l$P??Mt&1tusB7pz9Z3N;+Ey;^aCEeXxw$DFPRa1-GYs#I2_%*AIm)#x(Y~cV
z&OM2T5vk7lN@6IfnV@}O2WaI-;X<w{7S5dxA3Z4|C}L^n-y2;_D-o1!fEkmAqoa=#
zlqkAqXyXMBqjFq*BF4PcAESer1aaKoHE<w2BuTiNAwm1rPH^z_g--EvT)GwVPD-a7
zRbYqAUV&b%>|mmA1W!_N4RlRmY@~{VN769mqY==~uOZ!bAo}{7<I;v-QQ<idOQ!dQ
zoxTzZYAA9h%wd_&kg7`iVbypbJ_<R?)(G_VfW9ajUZWNuz(xm(TBh)9-4^Y<oKc+?
zj)!q?+hWSbc9=YEHhOkw1w)k@)N0uxu#F>%GoIqs<5-lF?(I8eDHisy!_`y2;n3BG
zC@m{Rad8<uMlQq@A9Wnw@eMA7rv9y5iB}%@s(d{B^>eIKug1JyW>_%mQzT^+K-;1v
zmV6k1J8M3|7Z<oX()PiQV_(75zXO%JDpAK3@4a0Mu;GFTpRArs$=X-cmBvGcn2Cou
z&my42B*bA07Sb3i!Lz7R$gwUv=sC{FE2Fe-4YDhWkjoBcC>5l<%W&eyAWWGOfFWvz
zs4OgpakoGOI2a>0>H@BYCBsM40>k?B!NzIJab&wYcK$MxN|K-9yYmtBdei%@(i?h;
zHHb+|L0KbOye1#MhN>P6Si4(+hO0@77W5caqksl?k4{#Iy|feeD=o>l?g(%7L-bBl
zaW5zYsWmp};Sh!OH?%Odhd%r#On`GuH0FLX9}f~VvHrk!5c3|Q_J57JhZWFs#uE65
zrMPn72kd{Cg{3<?<No?(Sab3M@>Sii<LElLb?Jhn7%Am_A7kzZ%aK~*h+PL4LC1&@
zk9RaFRb=Z_jo{xokOF*lFjpu-(DjFqr5nHDz%=gMoA>03m;-Atd+P<X^q+{eQ!FT7
zO*KWTo5`uDL5UuUU%vSjiJ6%wfBH49p16-KzkGm*9jmbZ^kW&8m6VBF8*>+ZKih_3
zOmJ=p4N6?-O=x9=yT5(S`#aB$D3&QOR#m@nA2)RmlxSqGEgS#J32R8f%BAWfJs0cO
zEW?HSC0O;-_b_asfw-^M<AcI5eEabTto(Qh9>->)R!I~6<}ZYCek{&KMIx=N7~Y<J
z;M>t37tV6oq9BJSzplW_z0Wac)yK${S3&CmfzVEm#Bb42s7aB7-vnQH`n5p`vp?(T
zJ()6nC2BO<VdstsP;>M{$M#)G$2yOp3)Ufz$(nNP>ng1VU~+&5>f&$X+S6!Q!U#V7
zr~t?TE}9+)q1BWZAnthy0@ameiQq4Rl6_nMPh5Hh+F046yS@QRI2i@@^I_zr3f0jk
zvF36K`mfr7!HqEfZS?}&u^Wu<zZ;Bm8&_cGwHQ8=YN+~s2t{cr&K}*2wLgVm-nZYu
z-^r2Kp5=+&UO7mm)sWJgKK;`zn9sZvjk-+i-0&@e<@%xLu8C-8Z;eFD*6^m}`fYk-
zHe5(V;o|k^Z=8VHOMgIGVFhZWI%q$2I1~yZuwecQ-nRvQJoqvEy0n9Wmkq9ewg_L4
zrY3KE6FU#CfO7|blra)jn;MKsGd_bzrxSMUm;@btO-4}^;XP~~R*&n1@N>Ul-KN9;
zy=2@*&!hQcGXl*B{AUoLP0sOH1=@<WII(LRq8W)(c5p}6_SU%c!Tb2)?n9_q24nj#
zi{RR>4wa0wSH6fmOKYm=^{t<{rBXUbYRhoyn`!v{xeJyo>W_T=08HrI9u<!cVA7(k
zvK$?Gssd~Hk4INeOB6<*$F;C{c&J-qRA)N)_FY735krq}G^e4X(9u>2XEuI>Z%#*H
z;>u0vt3mI%>u3z{(#Ng+>#^d;i!bN4I<W#Sc#Oi=tGZ+TM<3!gjXP;gHS_AESVSe)
z8LPj9$_qu^D&Ege`_7^3B7Zp0kyoJF6~1N~2-|-V0h6Y|u`&{K7A-(XvK}@b*?{tl
zBz(PLBJ$rDccp5^7_o@<OjZ<*L{V*4OyiKA>(=dkap3-mH|~fYY70m7>=%HtYdbJ~
z&0Z)Nb;ZVShjW(YoL`%S4POUg+}ut`cz6a6mD<C$cW-!FTO;Y{*O>ABY1p+HgLO-3
zUqoER=F^9vZEA!0Uu{Np!b5zv<yTmCoeL)uF?RQ#jbBN5Z1pGun?CCUt^4;Nt*Sue
zoxPa*)j^D1yb;3~t+pN55ACT0oOu5du06_yj%zFU_2>cd-o;q9{4qwa*@OVjd4eDH
zolUS;#N~K&HxzfCYG8WrTzq|96O#fI@aWbKe6mFsU(q=xsV&5T)iZD+B?F#hiOW-2
z__Jl7;C8qK-yix4MxLavD0hQl&NG~hjzA9c%39CpC1XE)IAbh|KK~va6(3^yiXDtP
z(~cw?q{+s9T307VMdCF~nEfRb3_4@a{y8wVG~wJ9gVrM!;ES;x5qjn)tlxa(<=m#<
zaS9d>^Fq+Co3Qmv_#fYMlP7+)QNI3T9cl@BC5`90Iw=YDq^y*cGmHSJDo7Dln1@1!
zPihOJ@H}4%IU2pRzM+sGhRtUak)EE9IpesRc}EkA<RPVj`k<N~d_OP5QZ_xj$R2Vv
zWze$khhAQmlrSztt9IQnuH8ADDXWBt+1xp|e!=n19WceWEvk}kK~rA~5*mLZN-+29
zN;h&{5iVbgKrSogl-E}B1+JynyNK6bFVQP+r2u)}Pfg|P0z1POeu<%8ro073P)Hu1
zw;D=$^mxo#84fo+339DnVOhzQzQhonJD8(ds0i{r83kI7h`Dl=SkQB+WQvw{I!JmH
zgy^_@NHwjXM5;x3HIorIXgPEmidlncn4I5^kG?w&Eptbxk!BN9l!JoWTC$O%kdjx$
z5Sun6)OC+MbqraF^u}wsKwhE%bt9v9ph>e=z#u&cW4VeeYsyY0!N0+LvuR-fO;s+2
ze!9l+R23Aov?1pAVo@aaY~Ka>k$ur>)cfc;Wjao*UWQE<gHT*rK`f=HtSEkorR-w3
zDAK0AT}xuv%k>ZwOyz1Sk;g?+HTwv^{ns6mgL18qguv%jE2G)e@mX+@7ZZWRhfhfn
zvZaA4f{uxatfEdiv-yR7Ss_rV_-rLg+R)Qe=Sp1!4-Y>$@n)m$+`*$~nUH5hK&dDi
zJI=-+JtqyH%$N!ZLx%I#3`Lcw8f{y*mw9*}T)oBcYYrUj<niSAA>=XfR8?6H63S+)
z8!?1Pne5W+6j>sRki5x927L{BrIjS8tJJ5*-=B-1S}L#JWCHdpNoc;(vTNlUROkjx
zw)D&*D75#6JF6juo=V`v<pJ%=Sk&;H6T*}-jER2(#?|b<WffJB60Zu<R~q`anwTNu
zv_w4l{UYf$#EL&qV#rvwI%;wxq|`kJ!K%`1gkHOkyQhA}#mFKk>RrHzEB!EFXiv(K
zdqBP{4#!S}5?_*&I4Ma5st%L2%F2X;vd=2XBkIgyq{pYCR@s(oFFDf7^!PN&kW!dS
zT2C$H@}DBLkk3_x?<@!AC&vz=AiIEJXJV|Z3>6JS7`FGpCsVp0;`bl%@i%+<>*m&s
zKr;gW|3QGpR`4P0`qdB}TL<9FeFG44j#QZ&F*K`6kzA0AQjQhU+^0w@q60@w3nDta
zq~s0?W1pg~8U#k2Fpi37Z&as0#8y%jtE!)4>bP^H)AnXmQW1rWELYKX5t=YgYig@Q
zk@JaEqXqhOr7=}k%!o0))?^Aa)f}u1QB|CZo4+$kE>?xIiV8%W>mX6(+*W}+9{=^Z
zjZ{QB{G=RkOG{tb8^W>W_&ZImS}J%UvMZ{qLPpR&daV1Phnp|FomxY&G72ZpMWF8h
zJsQwg@hq_bQZ0KZ(@9h+ECk32e{<XwsR6n=YN!-d!pGZB<`;T&{ie*J)zm%dYO2Ff
zUy57%t|O<U0_HmODsxT|<|nzLWJDxZ@}5iZ<nk3rhj)UNjy`F#75H7S6G&B7(9(ed
z_m@_bK+Fj2q>tC4gH|@aUB8z3YB?A<h&+~>LL?QIay}((HYWzf;sFpUuhf<m;NhVI
zjKGU&{PKAi7{Ry&4F@_6H4JPppocxZ+R?a}_Cywel`7Ogi}R&mztum*rtU~^@rl?U
zSgD4#SaLxtDu-V~L1hQpLwv@yc}NPogCaVkOv~fAsH%`fq2(p=$P5ob3L`Pq)oz@#
z<zQfC37slc&TnG0BBP*+9W3JZZ9IxfCHCc{K5?;FRfeKs&b7S$cQ-C5xKIl{_8W5Y
z3!zE&fS5KxEu-;<9me64Ndb6#bSD;X+%Joy%Z{U@iw~yuK~-!p_HI4?uWhAwpIr9M
z3j@CCy~;^+&MK&byO$fx9!EoD*9lI#5`^Yua3y3dGgR2;{zMZ&NudO1Pv3-InKi<%
zUq*QcN0f18JakG&=vI--zUDU!+prQ{dk#RvJ;B3EBb2MxW4|6mAMbfE<jOkhX%;G5
zOObqS50>u?Mzxv&dCLmOFD!vXLmu+1ZlkMj2rcabmi1FcTFPT&)hfW$(gr%BOUQd+
zQC<F--0IUhaP2r2omIo})9G8t=Zosvdln?*E#(wfLy`Qj{JaV#AoRjh4jv+gZ$#vg
z@%;k3E@(T^z?VRa=d-bMLJLwWT*#3RVig<B)_pN|_H@W41!3p$U|3sQLZqgG^t>9l
zdbq)yRd7npJHpvO1<ztgeG!>ML|zovIWL<~r2mgvokSfD_6*n1yIEaXjC6AK8yG9X
zwHg>3BAJ~0I^rfLrvf{VmawnX!LV_CpjvPVb-MP@u1du&hPBQ(cEPAQ^DtmkH=GC!
zMRXot6lDcsX#gE{9q1Zs;(3ygQ7;&$b;Ma6rYtoY$MS`>Ox`S2)IiI&{;&(pLcWwV
zCe_;ikG;2mkLudqK);zxX5#Kj6nA$CR;0L7q-gP$;!>a#DPEwswNL|EC{P?i5(0$a
zgg}V9yJwPl-<}8oLR)%o@B6>s%f7c{vUBFhI(si)-$GDG6qIV%lGmR?Lt+4PJ4+br
zi2W=h%83m!3S!k<T1rJvLL-MeOFfF;L4#seY$t}&Xw!a-gX^}!m(0l8nYry`!V0H0
zGz=IAsI)Fi$hecEa*&;S3)`3PLqM_=cFxu)D%3{(UVOKL_97ynmK6Lz29(J>a+5l(
z(5itK9<ylFoF3!6<b0%O(;|O-24Ac@g)|m)I<RITCH_@-O;f`NF7`|V3MimQrt~@9
z{5K#dmW)^%%ESja{_}Or9@G`GXTCHvMM<azX)I(%DUCw~tQMmme2WC*(S@0*IgCv6
zV9qM}Ouk!&QF#C2=1Yf)(HZq~luuOpDV6eBGo!F;-4fi3ekIHbXy{x$aS{VIPe*T=
z5_fm60ApDECL<(L4QQL2!Pd~2Mk=E<ay<%36|X7d-%775XqQc+PgS6UMok;SHZTm@
z4LnebTsbzC9xq$2Z(Uxy>|R9lph(vR69#vOS=v*q|IHUxu5OSLMl3Ea4mtVnAY7$q
zss30^focl;AEy8#pN3QfZyenU&mS%D!6%C_x?NW!(Sehir9cy=qgsc?!mN=8Y8t5M
zRZW1Nt^<rrtl`=~2O}m8gh^2nl^iOaWF9K=b&WEfqki(-L}XBrZrG?D8o1s^1f7ev
zOqIxr$)@6^L<}8(OLyNy0nzoGtQAO3tr`+r%80ENqq=T&T#@eB8l9Wgg53Wm4U1T&
z{28K7%Z`+7DM+VmGpB5O{xXJ_CqE`^vo#?jjm!umY$QQ&aJ2figl|g6%pA5BMu<$#
zKt@U`PMy4tEfZ%!q9wt-Z<Zl|ihh5}X%kB;>JN2MW5mbgmwNC~N2G;*QHk$zUYAbO
zxNs_zCH~mO2*JZR6>MD`fGp|{RSnWfN*Uu+*9|pG4A6hta9EVYBbW*;wQL$nLe9`L
zMFo|j7!qblNLd%?)}R*LGPN;md_R~L1tHJW8GXjj#K6YJ@H~DHH-mFf!`2KLGzuh|
z<}@Y<q7V>+x{cbyB#-Z^kTQ%=!&2Q!0+=7;o+Uxq))5cR?8K&%&!K7Q0Bfe5h!jex
zf|YYZ=II%bW+n(DM763CvQPQr6^WuSo?GZQ(sEslMuCdrLeroL22Yuc{QJHLVco?S
z^QU3f!mp_cP*%yscX$@z?Bu=VGm@Mb&o!d=82i?2hhJ17^@ket)C*dY4y28)g$3*=
z^D=a4$P}a^l1}P3M{!wnNN!}#G$R+Z9P9y|w0J}%NHAp>qY)`jaqRSM*ig1f=wObG
zrjxUFZ|cz6cz*m;nT&n&tnc@GC6$r1q4lIqOy=@Ek{%t3A9n1*H_NABYfBGEtehbY
z^uo`_ufSx$Txe_a*z$w_<hoLs40;9@h&sOsi!RZ+VczWY-g4CWcqSjF2sanZ#96;U
zWNz(%txJZWwni8<scb0d`A)oZ28Yh}!dJb`kaFi+>|v3~swrFXU3;cNm^eTu;tx#!
zg5mw#Km<gTpeOUqT>2EiOcjK2b5>*j*&yr~+#4TvzlV)Pv=JlZ>0N|C50ZK#&>dlT
z;N^oDE};EiIwA7@Pnfe{7t0zX7`kjX<}g26Gd54{(U!68yq;XfH7{p<fD)(oUWUh4
z1F&{aJ7}aQAcu>~zF9l;sM8c(-3<{MQWxttZe>PCF>YTxf&&{j<KX%^*wU;6lx9xQ
zrGkHK-zhkZS_bVBq2O>aa;0g~5K+*}UiA<Pxg5HLjoi4IDLYyUq&&NUr3=2m^GxB%
zma#~s5tbd^3%@^pz?!3%aldm{^db^ZOL{r9iqjxtF2B5n2gXn62RCUFLYbCgX{Lea
z=bzGOVKJ;S2v0%_@Zq?HaPY{4nLHX(=Bz|WG9xs6_S$WFW5+>ve7kTZeh)Z@b%$GE
z^^^(NRj)HrVFc4`KMb1uC6(RRBOhY-m~|M7Jqvn4kKPtTxeHcqXpToGx8wT)IoFdW
z*Pc-Gq-rm%BDZXL(ty$;O*8}-=P1cb#bt({`W#w|b-SA(JU$Or4raKrc?OPg^Qezi
zpGSQ-iqoU;`wx52W6u}ZcA!1tvZSywDaLnu4nxH#jEJ0wGpo4f<YXfH1<}~O|0wEj
zSd0x@zClQ00iileuww2?Y(MrJw$5LSL$wDYTVW1!X)G4b`4s-)rJ<1C<7dOeGy`jW
zA0w$qD8X-!SXP`1!#*SaAuePFVQ_K@G+A6)#QZqWz+uc8b=vmD&Vx0vb3SQIg*ZF=
z37*GkkrA&z{h><e=iJAv-(}F&5(UWO8cbc%;<`~VE$tl|yf8R*^z=!4Cj6IMN#d*I
zY(WpM!P9>b2Gq~Nu2bIXS+Y6=F~nHXb11&r<cNG;>xthDT<3ey7J@)MDE#zk;DtQF
zg*(|;Ie7-YY5xJ_wywxyc=ng?&tvSm?yu2UM3}5<*}%xj68em4tk}I9a?WWL#ADgA
zCAjV%Qg-fFKdjvAqkauL3UGRn)MTZAV){@_juaN;S$u_MPc7!N5tXCC!YeJo@5=^v
zS8*1ia}}^-{SEp4Tl4n%niY{Nz6eHvGRar8;J|qidng?gHp)!I<!Qr_PJ{APGVwQM
zQ5KSD#MtYz;H&J_^DOj4Wg(RwAG<f^^9r&V&C`XA9?SbGjxVMGlgru(U06{FR-Vt|
zJP1){NP$;6&u68FFFl{=P^{_Ee`7wLutECdS-SF40V$qIN`#D9&Kon*q{aCPT5>)n
zyBKDqrJVb$^ma;V98DTqabD;aujWf>Y^$dwPFptKO=FPuF{J&Qv{aFnv81H~X<7cP
z;$+2~Psr0z&zI9u%>Ox!^+;oS{VHf#!t|{i*3`UB<80D0_ca;|?puURM)@S~ps@yN
zZ15J1i%4Vbw`d$kL*JN@CIy`m6*N|_R|(8XOU>74oQ-I%S1Ybp;SejQv4rb5I!6Xu
zJ;iG@E~lkE*Rd!nEFJvtU!gHU61ZNIxnAE~$N7cXNawt5B^nFypGw56JO0ngZ{*ca
zi3yr~_!*WCv_&wHrj#tqEJzK(<)6+$k!p{{Q$E9vF8wIW+>qgS3J15I#D|}{VC2|E
z*gTL?y<$32NLvP|sf1}!KD=Srr{2TPy~nYR`pLIDYtb7_00!-7%$WTRwx4#!R}(hk
zNNef|Mt0DRzJ%E;wv(5?+R5cNdg<ULLI8h`iMw`Vc%}%q=0MM_Hi{#@#JcYqP_|9P
zuKHcnvdt*-Hu_Ikh4j=%c$qR5v%c7f!G+{qDYvLVu2H`yiAo2(XUrqqZXi}pTmXN<
z1ctCkGHP63G|ah!L+1&Vn8B2y{a4X;`3G2AyAxy?@yMnuvSz&?;QKn?)z-oDD@U>R
z&^PGw{d#QP-v-fi23lFOo{4EcTh0WQKT5^VxEJ(7`@~?#{6%n1XaAPG1Z3$m>gimd
zE>IQkO^fSOu8vf&4(_x!(wnwI-!*H{B$GV7C=FRmGp;jWGDdi`pkY;lMxBP@P+vy0
zll_V8_an|Y*kI)5Eoeg2KYKTOoZ7M&5#i1RJz(TSEvp$-A^*|RQPJtT6$k5E<I{!v
z(XdYt6b>~}c=r${eX$?x%ELvkK*KJB@onw8*f4V$V(F=OGShvHCrG44YP~=_q?GB`
zdXz!rd@!IBb^4cI;Uk+Qd@|@$BsdMkwjDDlOY*Vpp$t0IkxCtU)GJt23>tpde%Og2
zKWxC)ds`qfv5@JyhPd|ae5^b941v!BF}On?qP}qraPI}(Ig1nCkuasBuY&KE6d;q}
zDS32UH5fhz+xxMu&q{({HZQ}qm=g4Hw}XLH8>{y0p={HnY>UIf&p$<kBOT0oGDHVf
zv0p0Q-rxTqwtpGB1H&7(#J^M^EHr}YG^Gn&gMzh2^X_O{zXr5(!*TJ-EriFVR2&-~
z7WqzW_VU}%XZXg}NC%;j(F_f7mWG10ts@Lc94S?q_<$E`@3>rU0_R$`NTr2jpeR97
zNEo=z**wSwt~Od!h(nPpGe!N{t<k2fD?@6bxO>|d&tsBUpsFISd~MWg*8=tQi}B#@
zUHCl-Vcf|Lt2S*z7xP@4`1uE%{`o9kvba$w%K8@eu#&NWBrch7eG<6Vsfi*M<3*HN
zTjKAwY|xWK%TGmAo+3>{=qeKtuhNILz8v8Z=}h}+fhMkU-1U8k)M6dTn5UOntj!`&
zx{_(h358VTsb|xg4~|KNbITqq{3W7DK?E*cy~WD-6d2KBq<NyQJ18awyNkOG@>nbw
zoseEm!b(3G0-^$LZmg$n+9cu1ROX~3jI_!jt#r-p;YiD0q&egz$KyprBFyY+pli1-
zP$l2Nok!{1P@0I0&4!t!DH^g+qXs?f2``_*>&8uWI*1SoCbn*<+pY~7S;+8^=_z*~
zzeGM2Qd2t@*cxj?CQ~8qWiXXtDisP7)M;27O<FXCVSXa~{rqt+Ahb#kyxiOYb?r=<
zJws16wJ1$$1-Yb6B!#<^u>=ue5v&;3hP|r;qaYb9&W(qH3Zj#(IikWNsN9q)%7V6*
z4o%RuO*7b-mLSy27x%+oAVsM~4>*m*h)CoJgr4%j4R7m61ef+rsl4P6{>umVW8<M^
zVgs$*I7Be5MZ)KBakWHNVl-lt3Si~vh{l~d(lergh!@Xr&F2=<i*$+q+Z-J`)q@7T
zAAxuLa6c>@wsqT}*MMOd(5(rco<D;5>krTiM8;Ir;B{P`ETE*d9-f>BdnXrY7bGAo
zCWR`51`RM5?n6Q262t`sBR-p64N7(k$NFf{wkh1T^5A#t4jxD6!^uJm2@#=4E8w20
z;h;v{2?~uaQyMB?oJB~cb}Yh@BQh)uS@~Qnm4>!$Lu}suEsP$l#mDRZU@BPYz037X
zV9%ng_Uyw@5+9Bmx46!Wv=|XfSML+0)Br9uZIRDNOahCM9h-JW`?~g|O$aWLS41Xc
z!Oq2vjZI=m<1D7Vo5IP(jLo>R$=qoy>Cl_P`%rP8XsC)1#(PU6D$Sdtt0>4CO2dz7
zP~7Silw`(5akv~KMH6dtDz!XE`8*>_7h^TihMpppxs)<E9<Nalg^O|8=;UBhX7rSv
zN0J|j%kg^X>d4f<iorUZya-%PGDLSrHtDM1l2r3@BH)!`f*y|gT*IY*5R5c8i$z)K
zW}Mek^CNL8p$ez7#^hnP1-jYmRLl!Rj>NQRJkMjAo}G5Zyarc5H1|V9p%psVGKH!1
zVJW8a5t_slJGmWd6YZd4p1m4^l9Fi1+rrI=X$ED-YpSy06P*b=V@nzuG$_Qppjk-<
zZbaw6)zlK!x@Crdn5VLIH98OVEG^iVp-kZy^M$dv6sv*eHs+umQ$b5vQ4B7{Xris1
z8PPw=h*m*k9dsrVQJKB1zHT{<yOG8fq$G8UN#pBD#%fx=Qs70JS2(;=OwqH3e%Uh#
zS~Ai@8vCW2^LX6~nh6@GrAOj%js+38D$`iW`l=Yb%(q16H)tHis8ghpG^Sjspd|y{
zAxQ~PlE$@2<5#rI!SkeK$h2+YYFIu`+AxLFoAoXZCKhmDYM6R0l+oCe5kGe-7M6^T
zmA(=l&-HpWR)YGL<}j^-#x2O>DBEArxG<XQSb_HBmvj=Bj!HD{LK;{2&o~d#@^V7y
zdVP(?xukKjF?y=kYuS1DqaZs1K19{(SwpW9jaA5EiiLlMIoJIw8jJOsoF0t;(%7RC
zjfEZmhzM4*=5Kg);)+k2YCWN8ize(@;sjM{B(8h<ATlBj>~;i)CY{is4paZqUgGj~
zKefCwv#x=T9v(!)dxD#H<JAtT_^=R2$-As|RS>*QcvUL~y~Yl#-D%pP1Dx1T<e~o^
z_&t1rY=S}5tHmP3cJ4%s3xoeH>Iv*aQ&4W*Rh&S4(1uIh+N^tOg2bR3coIQ)2+d*y
zhtR3Qh-#fibr>yZ0>ivS7JJ^s{eTb}PQ(jms%;04mN28Td)xO89tMWf(9nh@BaW7a
z+FY+h+IvdfMx>T%(h*R@S#6XAM#j+zpn|5{6iwPTM<Ztq!sk9lI%E6Nyfh+xCD4&n
zneRrx`{pf##bwe{OkUikDa`cp5$wgZkEk#tX1<E1zIrSvOe`GGp=%cyDWAjVb`)Lh
z+Vob_<IQ&&P1#|~8ncXeMiZ##6eOgmJ>cDXc7-&Z00R$F8EI!UgE=JnOeqo$Md2J2
zLxVcbix)xkHQAzOn{H_5D5nGGF)m&9LzaY*3@3AB!~`QT8<b^^M7aZL83vV(4I0&N
zi{{Pkk@n;P9un9ih`MBnRFhG8Cs>hYGFdS}BtkHB?S2fbXGMj%>|Ikn<fjK=#*%NC
zEt5jWhYrj&QxR7}qurc}KC=vDS#(CSaG2F$MAD=FprJx^QXZWE#Rw0NMt+GlYSd)?
zUv?70V$)znP!^#-SQ#l1>g`K-X3E<1d=?Lz!J%<WbZX>)45sx)P_|C{YzdsSnfhGD
zUll~{^LY^kJqvf31zp7PM*(>LAP~`!VaQbJQNGi;EEf(*?&mVu78*imX_*OXSQ#KS
zTF~cp8T{(q{k=~j8$$5JO1t6RUzCb862>a3$clLPm;S}Shyqx#7~fKnsL;x&Fe_h4
z1DMsQgRwI|N6*I2h`YQ8Gq(PM%)I}Mc$FVbc&0?|-QOJ|pHG9dtRhPEcTe%&$BAN5
z6>svbmrcem<K`?D^$%~JRs4<5_II0EiAamc0jo^=H&6Lr|DE~QrT6&vUVwyNb`gFn
z{<{mAhJx?_mF7Ucx_fm2HjNN9gR$#^uUC9Rs4;EaJaY^i_MLp)czE@-|NIm0M?iu8
zw`go=YKG5Ntz(tGC2Tb!Sx~hCkAsumS_@J^WUimevh!UJ^KbA9Ek;K4C{Tp5Uop7W
zgdHNMaZ?((Df`P`%qzGT$Wvbp?#29Kc9`XPD$k3;`T`a+XmNj39`BkLIkAOr9nVxi
z=5|&!ug>{oPNt$l)8$pjluL6#-=0_UJSwht%?hne9M84D)B;h5Ai6MD49*vj2Wkj@
zSjD`I^RJ~NlpQZ44McH!#rO+azKiznq%mn(>FjFog1=AWchgc$V-}QY@%~>8(o&s^
z{5CEB-=Q&Uw76d1m&T>*vJ%zG-pT)(#-;1Ebf3JUF?l1?Cem1k_Gd%QFS$R|wNDiX
zifFP(<xq;O$BH4+J4Aia-{CbCU!qLaq};EHPgkiYRDAK(2XQZAy;L>cDckrRR`~r#
z{FtW@thLHf72g#yOhc{XyyHui&h);t6zL;W6yAgOLVsY&v%3DR(uLpokzRRPeZ>U1
zcuU`?^c_88Q|M^&UMh_erd}qeyq(HY>5%vTNJC6UMZc>17j=0hd@d20H#IeR<&aQc
zg$9^iJ*-;04*AFCVBWD?bUeQQ<^Pj!yq5z1pIqqw%6IKu-B~P=h`<nbSp6Rp`~M0J
zs_*09pnxfA6K*jyB#59&Wf6jZ<1+ukISovN0?!@{MTz)v`yP0)F!j$E>%VXd)u*hc
zKs5!bDNs#;Y6|>+iUJy@4(xF-964S;;WS}5E5-Z&C(rT!I|crKLe=VvR8ydu0@W0#
zra(0Xswq%Sfocj=Q{Z2sfKcEo#~c5p%T%AXngZ1nsHQ+Q1*$1fO@V3(R8ydu0@W1w
zZ&9FHf&Xt&vidyL6sV>^H3h0EP)&hq3RF{|ngZ1n_#aR}R0*tjbsa)n)$4!>lO|$l
zUk_Ffv!i8T=&L{f!%tFCNJS_eQBWm5i((NO3m!!XK>YW=A1t2X^<}&dQK+E7!VPh6
zZXNq!RNroF_QU>NiSdYIVNRvmn)kj<Q9#n5Qy+}|Xb7y8v3M3$RRIQ}+ISzL2(OeD
zf>xy%+pksIzxUPu>I<dUY0<tb+7d~}ny5ALL}e+mXp5P%$DyX71kZ@&Uh*HjAE}-h
zJjTsHCutUf1x|hWEwMOO4L@3bZWiE~H|~Mqo!k*kWShTJ3?gVN){t0B;y+P$_g8C4
zAoi%M!poPO+!!7MhoOO9I+2x#X<t5HexHB$rzrNdt%L5Y_-|g<l|_{Wh-1^DzaFBo
zg)K%;{}kQb^$_?nOpX4cLtNRm-TI<+eS0J&vO6vT{zWHPKA<{3U91=Jy9l7E*Kn0I
z5bKKc;RFAjcW7!`13mi<Ktp!R3ybDD_=_T6)4EO3t#cRDAxcQFK!*6IwEI&c5qZbH
z1qKiAgfv3hX5>^+UqISxa=(Z*P|jP#GynNoAZ@r7{(4B@)~P>63!c!jF=Rw2i6bum
zpYa5-CPlen`SsO%oA)L#R7Bxu>3k{w5j;n10Z~L=z7_ubo=V@TOz<r6&c!pZ_*7sK
z2!1A3g!(*UJ&K}ZZVL7F%hrvgeBQ7TMt?E^9b65G1QGH^Vd0<OLG@p&DNs#;f1d)S
zvg&O<W=N>!sgs7o;OP^Dvccg$8>RfORA&COGn9U->^%JCUt$06eQOdJy0yUQ2@^1)
ze-C!PwML;u9SrZ?1uYuaL>>|D67$}Kwtnx6{EaV=A6vPjGv#Az;_B-tiV&BORzV{<
z;h4MkAB>g_YO>L2Arg|R(hL9kGAmZBU;%ZdVbrJ(mM@u(np(L?VSC)1I?=$dmn$9d
z&ptNm(iKY<E`TN*L&=F#UCeH!GKB(Pu38CCPw#(r(*NLF+6L{g{l_ohD9u+ljgb;6
zGsgP>PUSR5m#+0-Y|Tz?fsgPoJmyb3gZ<ep$YktZwrB;0baq8favZY#w5vW`ZjJ8K
zd0g>He7^lEbbHLk{skWJ{C+pq9sIrO@BBAr%{$?%jnh%zOirw1BJ-Q-!f)LQta+?S
zEZVy0>(L1V`+8yLx=pwmmR+&<-sjQ^Eje1W?f?rs#reli*mO#4a|vwOeRulDb@AQi
z&3K+bh(scBnAGczF+HTXed;`t{^}xx+H5*%DK<@LjEwXgLKA8ulby|WE&K{z0&=m+
zp^JVK=b{n(v3CC-L>J*fnkMMbdlVdQhT*bbM3vwATa(sZux8a%!ow+$r<4-^S&7YG
zF2VVmPb)B~-#(8fTpJC;@-N52lAY+nJj03LBZIa3AgtZ^F|4Hp$mQ>&d$+{C7v}i5
zu>uE{OvC9A1;#Jif<ZNjd3!hwJLh!=*zin)n7(Pxy|8r64ur-ND%8>m6WNY?sAUi)
z%vpz*oU3XGL&&Yk04!cU6=o$w)z#HS_x2|+=gVDuUsb58G`6f7w8zHPOZi^2pk%kg
zZY|GX;o4n9c6tM+xvC4k*IWtoE$d>!)ZvKqzfDNWkhi`qaqfvt%f=E;(~z)T1Ysn!
zQ<C3rShD3HBGW2N#@D{5Z`u}f7xcixpLgTbJ<*iqT|>q9A$USV@&p5DKYxq|VbSm8
z3Hn4%*mdGK@~-`i1#9;pwM33dYraKOJ3Z_;7lBnBvM_tb8l*KGjXhrtN7%Vvao~>V
zKhYYW&smOpfq!-{yhkm2OkceYgPNGK(HJ}8YASJ+c*c8tQ?Tai&Cv7Tg_-M4K)XhN
zY~M8;SG=C1Zi5!ky1fH4xBdZ}x+Ab{+W>sMg`J_T9nrgYXY}gj!FySba{-y}e6Dv<
zr+U7c0@W1wHz*)S_mQ8Xqj@+s?q-+pyjOhK+07MAn|FY!-Z5P9W~0V`a(RgjKKlF<
zn1!9irsKE&$r;q&k!aM#=U<LO-1+_Z-urFd{Etuk55K5s;fe*zmZ7yp31X6n<!?xM
zO9MF_O>A0eMtD@}ngJ>41kwDLhn^c2Em=tii2<_t?u^*y<MJ<i@b&()&~fXHuhvW@
z6f_a3G_;ZZ^b(e`mq1wFKYEkp3Op?acI|4{sX1!d6yv~{&+tclBHIKLkC_lU_O7*v
zm8nD6#(cy_g%evd6NZ+y#6p)Lvxs=8X2hfA-xA}atHdr32?)Tq8;BEY*$i{Xc0%xt
z<M{1S9D;(L)BU6e2Ny@;rs}ejV;1o`!;qY#ARf0B3fQLC(9#k*Y=jj1EQrlha-nTz
zOH6Riv8BDFq!7_TFA*m?v6|Yz-N9Nd#B<px_E~V08sg8+#T{D8EEE!d+QLAAf~;gj
zrO08V&(3LAPGiT3JJ4nWjokDAJbQT(mws}=>@C}Q9_eeIHX}Bsaj_wk5XD?Nu~UO$
zl2NOc3)}j}@!ACKFA><HN@BDdvny&EdW@cru|4f@cHbtfKm5n5bFmqSHF3(39z$=Q
zu%L;BP6brTE^^kceX)GO0HpdJ!*2)AkoLsec5o!lsUe%t<g>GEIPu9dsZ7|zk{HgJ
zMG9E)Z(ytYu&|fJ&n{s@BPUpMjsQr=XD8M$V(2QhE#Tx}%eK?(CQGdCxR95KVMM^^
z>`yrH$Pagk3q5z!Q4AU3fy4K|#^UjwXxL*MX3Uv@`Ckw>e8y@7y+bi}awpDidLojQ
zhH$pjK<LYOnAqFW093-=-WXo{ma_|L3=-7JI}vx;z{$qm0nM7$#dQmF#Ird_5u2^B
z@c^Orv|;VU_r)%x#km;>fA$g?Rqi!<UA6TgO?-s?d(W^@hbyLjG71w{O+nJ3D@Y;U
zYGN)eZMs5@h^cNtT166<xmLrvXj+&Eb2Bqy`{yH@uzbbEg}kmIYSwK)C7A0uA&$q!
zAzz0bRckoHRG+P(*-ko=bWF<DL=87v6lan-n47bC3y>BSh4Ac9{B~qJ_B?uo2$c;s
z|8f{3o3lgR?Eqp;SG_T9YEe=DW(H)>&tv+sZ3rgjzovP6?A|&9mTC9#)vC3)pJjxW
zZidio_7QZ5#aoJIC?%Y)7XMQ~gP6neoMfEXx&?6#9WZ)ie|$9~79*A(h6S6PwQkG~
z#B5yA&dNxw)K|s0w2Un9(YzTjPV~lv*?ZyCdm>hUG8l9F-^Plc@4ZERIV<lyCe4Li
z!X3<-y#dXZY{!hQ{qSk%W2`)U<E`UMC)iw!3ZtEkF&Pfw4cUG?mbk;(gs_z6=0S%)
zMH8a5m>5Pv61l@<uyk~S4f#rq+BRI@gf)AIs8WSK#O`eguxmCHUyP`Y8z&Cp*DKyg
zDmH*yEfeHs<iUhpiRC4Ehz@wk?zOxZOB*z8Q6G6e>}ngzmfMAF7*pCr0kvK2crO~r
zV`H`_FQeeKcL}^tI$`FPozQ=SC%pNv!lR|o<vllT4oBNV$jIu3Hg#MeBmdEmE7;0B
z3kFv0@Y&+=(2u);pSK@|PUq=-x1xa$zx<bj5gUC2#CZOOJy%|!e$SB@Jdz!sO)e2i
zl5N2Uj75(VzVM>qXQ;;pIPABfkZGaC;PLP{?uP(&U^Fx`f^On%Y@dG=XIu8cw7IkJ
zDc4%b+%Iq@Jozv0p!$Gn3jAN9fR5;MU}ppUO0q4Q9ap7>mT<3O3q393_UEMGDP@UD
z-wN(_Mo0<^LK0OQ0~7YWF)C(%ns}HwxIm{k8->JXw_sC=!t5041Cd0llfkj3JIoDr
z=wK;94*7zJOsjM(P=~xik31ol{XCw&q_gu+hK8mYYS(pC_oXN-Wb?IzDE7rjXH!%|
zxRXcdE65osi_((f5R;;U8*%BISx8`IW&ss@t>m)tm^QnjBHRx@?tBctyNPO!sB6H!
zF;2F4`63v3`E*#2k675dL06hf*&jo`>s-Ni^T~IU8AZ%0G(o*44PYs~4<jQ}ib={s
z%CsVh9_*=$Ss4jkESr69sEegn?GTf*xs-u{IT|%<0OJ4)@)bIWlo`lkleiN0IdONj
zhmkJzt&Btj2s*OIkGrD@8(8I&A6u{=1^Mw)z6U~qi#|9G)~0kw5K^=#8xee0iF_7)
z&b!!{F``|<-Yo37LX3C8N9`$>CHXl}7+R=ha5lSrzYI%*d%fCd?5>03>?;3LKrnfF
zj@l`4?qn<M-}eP|O-3}z-w_>G_Aa$y$(Hb<OKN;n1Y$A@QHx&t(C`#CcCumD@@T|o
zX~C4eR${^<%fDIiQ>xH~v$H*lsHbY1n1XGMkQN(_2=?W;NQM1ZHbH!rqJ_1`kHMp5
zGaNq=iqVUv!Y%Ismd@M&{}eqmtglB|RdH7p=l;7NWa<Mo^x@uiB!+cq2|2?u9cIr(
zhk^{8IkpS;<Snr6>(61#7b-JichT#=V%5fNu<bJv^9Qs*a)ANdt@L52tHkl$YuQ!t
z{9D&fiDP3G{)al|m^G@1O@kic%J~O8P7e286R~{mP}mYnR%$>m@bPU}aViJ%*3E)G
zF+c6=xwEN)6c2Z-#^Ph%=ssf+CU&Zg)Rb&kSnA^Byw9-haT+bk<rvqwF7h(-+0`@^
zyS`qHlipA9(UPyx!5{-ET283L#sx*;S1@??b#*AaNLk1h@EOR@&p;ZxZHl2_C|XNI
zrq#cRe~2yY(h3V#Ek+wFEhx2gQ4n?y+xH&8Co7iV?C!%b>M;jBCFd}DNj&B(?24~9
ze@A$sj_^D*A6vh>@=AFZr!#MYY0GDz@Z@5AbuL)_H-T>|F>K1NZml7Y^u_w^d+~zX
z%Aw@|e7<-h8p;cyq*5Dt<7cc~y92ccO~7Y8Y9o#tv97H?H-9$fOrC|nY*#G(@>4Xj
zBvX-6c&7zo+T=yB8ZZrWhju`AavGaE7~&E=Zrd*S;LLFvP#li!`=g<Y0epXdg5<1H
zIy_?M*e~;B*t>ZGdbj-zVVC?-Rb$b)>%K&x=a2ZIun+c)(8P!l$MDho$vANFF;>s+
zrdFO$FPV>RQS4O9uz&B#q!q(T8XCqJ@yP=8EtVk2>i{nDT}Uh%V9uJ&FtoFVR!%Gq
zO`D2c&)&rtsqo|^A<+K@UZy=jj}e2Yur<Y;4TF(+WF=OexQr|+aU+(lM|b-|oVpQ-
zmTVxTo!=gdmNz6*jKs-5E+a25kKJKMVdF<M{@9^3=iWsuTe%IYZsW0HN<VhU%Y=@J
z9`63K6U$G=W7>+@u%|+AQ_Bfjj3Omn`xVodeuW1lWwXXj(9p&XYy|~BDu3zkP~Isz
zF#7WmFiLoc6>Ha1d8e_#;mjr)F7j-gIlLWSFXA~9&&Dr6+q%Qme}D5IiO?9?H8K9#
zeVkz<6ayz`eB81%q?*6Nx>hgPN-~j>!=^gjYLTHuFg@qhsxCj5NI$VQWxAB-EhOYb
zA%%=Duec)|8a4*1P~eS?4bioc70%6@hh&R3=+MlbkpfK^k@moi^3%Td)4t{hn6q>y
z+B<8}Xkr&vHhJ2%=NGv48;klHMX+GgE^T&^P4hW}Pd;CRLYEJ)_{*7W9;8VlRRem3
zDev~0sF<4eAQmYWy=k1XDO}iNJS780oe@~QWh@G#Qc<H}P3W?#^NrO@aMP?I+Ba<q
zEq1(Z-hVP|duSr%{`c5;G!X5kFT=dP4JbrtR2Rfz{j!C)=pVsOdhF&$p73`@F&khR
zv8}cf9ST|<>$9nvK4PNT8k`u|MKW7VS~LkRnvwW+%{n~d9&hw_PFndG3h1J^7r5_x
z9k-rDp~;Eeu;sf_7G@xw#^ccG0};4662)8_Dq$3;a*(BA$Gz4c8;@j?j$8vG^@byi
z9R^o@A;pn{i_w4FAY5IwhuujlU#$9bH3j}JQ$P<r$A5}x{aPW5X$U6l(tcsp60E-w
z&W_a!F|%Jw6fi=o&qii@SA2&1Cf%`qaz~u}Y#{bMl%R3v30Tmx08<xk!;l4Q(bY5u
zDGKTd4hATW@<N}F7QlwilO^-V!djL?`K1R$Ud7-~4xr<l1(?&l4ob)?WNhrVdhr5W
zdlFkoZ+JyFIU{ESCoab9(T$K#hlPU8>uw)dh6P9OpvS0rSUAB0c^OpZb#)N?@Ek5g
zo1qiCmTIs`)vN^$P!^=&${)WVrp8!op4<VNIbm4$U@}foSF@|t0AKIii0|f&!*S0*
zHZ--vjIVb<`{iY9K6nqGuUuHccQw<4arx3Y<XLt=EjFl>HSU8|t9wF{5r}OY52Co~
zFsxcQ5_WV5s&%p7_TbCy$52(DsHS~}VPN}qFjeS4r`uSpY|Vy@FMh+8{kPyad=^%W
z@65)Z`E1mfi`{D$<JXK9SigP*vLnflnL47&rfFAJEW}q=LosE=*Z6>rs$@DeZA^=>
zZOjauEFiq`>J{iw%MAIHFGzTf)n6>dy%ZVdtosHo)bXT-_OQ0lLBy?-n6@hlGbi*_
zU$e=$NvKPQ;N4SOaF}vmbPZRx+9(%)5;-iI^uVUC7r;_UOU}>$5jRfZ*PHk7$@EcJ
zvcVH$KN$zZNBgk)qy@%Iw!zX_^IpBsvQOG}wXt~fIv6A-!;D6hu7N%hZ~l(eUv7jS
zn|(E}R}X#7Okt-eM8K1W&}-8Yb=Vy3?g1|tHEN6Ak^)3Nddd{pw@&@HCrTA~wpC9_
zc|fmqV>mQ0#qHZSkVIuKRo4lV=X?g$@84nWiSsCS?1qh-#$jye1DwgsW1bJwO}sW@
z*^Ug%TQf&EJpXiv_P5MJX16k$DjZsFo$%?zp|FVa#>#!3aBV*fQ~C{p>$7VpEMVtM
zi(D+6F$I<l2Vqvv<}f*zgqF-@lCT@!Z#xg*?#mdY(;{v)aydq~bik?UQ}9b-5ymW9
zgHIPv!_Bcv*r7|CApFJf+_eY~TYiH6?!pVLV&fKVH0wSFdpnMTHe3CkUq2n|&jy#?
zf0+;y?_0_i&O_%cL<d<smQP-dP`MlCubYp#Ge;pbO9Kr>ZY&KLQ-daG*Ch@nG&T(N
zo5E2m4x4X2N2Taj>A96Mn6S%Q20h{G%M=!{J$Mo75l>W=pNjA2Zp58<`bv!)F=65u
zwA2X3`fbMn`_A}$TyG4%eHH#>0-9EJpnYKtmhP2f!SYcsuGbCIJo>;n+Xpkg_yIap
znwCsy!e@1)Vp$)0C5bq3crR`|q*psDjoYVmxOj}k`X!T?GIS1ej$A-$Zuu%_r`2F@
zTzNJIjoeLOcpYr7UG}9)KjPBaV!2iwv{g7m$DjzUdvt`esR}s{e!%DRz477f1<+;Z
zSM^z$DE<8QO&GJAsk5UF9{72(wRH~CB3WzT#(ZaMBmA&u8~*T#$BdN=(0WK)>{|JA
zrK?r?Nbk9Zi5vQV^a<py?a|b{7(cJsiU9L5*zn0mIOY2QzIq<`(9Hl(e&59nRDx#p
z>%vXNZitsX*#enu%HvAVR3%5v+EOfCwj7D7+E}rmEBZ_v4%-eNBIer-SnGKgj;%*y
z_53kt7;zT4*-}P_Busgj4ISsMSTLzEtgQ4<;NBI}rhmvaYX>t~Dtx&))f=LmoRw}J
z(gtv|)JD|9+qfB4s=SLo8?bX*VL>ip*xk7D_*3UBl`$#1J&KV-hFL(5jy_vZcOTUh
zAy-ZyNa}zOJcht|mlpzx$gJKN1@v^hjLOA;zGJXRuL10vw}%y5ZYHXesoywQ{L1Gk
zDauE_a|e7jz84Ii{Q+;g?qrfx9Lp&Y)||flQ?yV##=@Cwn{C`0tLAs1>CUDp>~QB^
z517C3bCj63!QyGn;L^Pxx(=TJ_xyX9KIJeB+<IcgqVDCP!m2Mqo+6qR3Em>5;i?{N
zCoGrg(l7|X=TpB%=a1&YqklV``u-NYa&=L=wk4uoJiwKJIOMWE!>Z$GEa~3}0hbQr
z(2YEdoiP#frVfGU)J=@!(f|K96kB0X71Pm?kdceteMUpWy$&+tQ`w}3sXa^|v})c3
zc9KGzTQvdaUW)h55KfgtbmOhVj*B#O+2KVf;zg;^@bQc(Z5*b}`VXeK7qg2r0}O2P
zsLv)gsrda&6gs!<iAf(`Wh1c?QdrbLq~O$<NPN(~FUBW%A)6X{Wm_aS`WF6pn2n(}
zR<L3eB=jGKa;tod>d9&f{F@YzSffRg`p_v!z|V*F!s{`;u30H)Fkm_+_i6&q`Jduo
zXfj$2oPl}sr(yH;0u<%5a!70*N?#FU7xiyqlxWb?YOV?IU5jzA$s`P^rvaVXy|HZ3
zC}iIH8Ium4rUI`IbDbjCbRL04y=%e$%t0KzrNrdf<P}o}!h7~k6u#+AWdyACN1tKt
zXjkl8I~UgiB>42}g><s$!;uEWqDdWabmMaTaVrpVLsNF3XPu9c8y#vcFg8wsx90_<
z(E}eD$}XdiwqQ<CIwRzCmXtM@3lE9IFJ6iGWK3UNdl&(&X2a0QJRhenM`6O^`Dmg2
ztb*@;F~1uMlM~<<<cGSf)zV80#f1wG*>O4zIgH>gnmioViMOz7?-e+=8G@<Q#ZH~O
zf}4Q}Y8qAkCz@SUuNTsJTh{>zVNY=RUIYpgBO!0t7fVNXLHgYjSatFRhI}#yvzY>U
zYhNlIUV2O^^~c<)8_;vYBDDUn6$~C2piMIe<OJNru^q?om`>Z&gj9S0i$HY0xNIYO
znC4;c)JgCvw8qR;i?DFPM2uK?7%FBISV>~3(9c7gPnN=+jhJ$C0^mSk6TAQbKmbWZ
zK~#6g3N;6`$HND=5E`9=#Mm;0MaW?BpYUh!hc4^RCVW00ipxJ^{tu^-Zr&DaH%!2U
z8div7l)Ym&c6T?@hDnnSXcbpXciY>~FK8j*|4NkX%<h=ZG?)d*)oads>W_|Hui|O2
z6(-FZ2^VX7m}}+W4;l(ib`H>0XrkG;MQEBDg$x}BxD*Ft3cI{M46FJY;yQmeN)>p9
z5E29K;O5iL=;{t!y6`(&P#40?wGpPV)wJIowr!7O7$ojjJW2Wp3WjA=R9@v70r+z7
zZ}5GdgNTm<kjwVe?=#Hkc{A0Q({gEyNz-aVk429%jWkGU)oCyv_vY@+@Vy&|20Cq_
zJ+d)VT_SPrqA%QsdtlVeIcOUafFCz(g|m$jQ)43GdG9GA`HF7c_h(*i7iOTaH7^C`
zGr!B&dEzEyH)3$spahw@ZtSjDh)3SPV$boLd=qSU9#ysVEM>7^Eqen*1zyA5kZ`7S
zgv0N}a5%MeW=m)}x;AWzsOTsp%Z<>h3)^@r!ytENY5+r0)XXc$A#zS9&i{fr=LWE~
z)I!O`HTZsLHf{xkA~2@_p?97@-`*S@hK$EWpRW;WWDE<o2$t#9z_9U?$#nD?8DQb9
zzL~l+m?ZTfR_{KAfH)0?x^BU|SziouRN&(5Be+X1Y7JRK6iQmq0;bh{)f>%R24K+S
zX=oZ8h+n_mgR3mYD{eLb%jb-N{KaL=`(is%v&z-_(%V+Cvte;jp<00u3%{xnl~jms
zjhmuHb^%_#%z{Vf`Y@rflb;ff7by(=6)4%Lx^&|RE$FH5B`O#6Nc6FS&&4x%5GNEF
z4TceA$Ow9fb4QQkX_ggInB!a1%(ALWs^>)4OSxqObnC-5(CJb5Wz#wwx%de3S_TN4
zyaB_zHiVs14@lC(aeDt5_~%H_y5|tslmz1R>9eICT}?-(FKFV)PkV9x;tgn9(JvsA
z!@<dsq0vnIe$yAhOp!{uc?!XKJ)tn-`jjeh_ux11y5k3@ygK9t?Vw|1!edTg!HbJ*
zfo+La<37X8aiih+GzO2t-cC!9*qK0w;k@+JH}cYjGR`Q4Hgo3QYgD`(dR>`=Z=ll#
ztsOKG?3;$jVk;;OT+p?yJ_7Dkp}<q%9oe`7m^c}oy0xP><0c+!b>w|it&vNl>|#r=
zQ`eEhV1gLTo&Pg>t>^(Au9d2WA>W~sgBGr@_#W5pKZU8C9STI`fcsQaF2VgH-{P|O
zE!fm_gi_HKH5xUCwXrUqeE&WCpFV@LW@CnS|H?H`)s@LybJF3pa|7<&OMrW~4CvLe
z!OMF-xWhdDkH)vaOTVi)cRf`7<u-F@2vVdwH|vdAHNn(0@}MGym#coPsxHW5Sw>1>
z82025EW7oC6?w%O&uBQ)i=;t;o60b30W{Hk@Hn(PzYIR_>;^4`ty^nMnK=du8cQmk
zHz&dm2`_e(cET=u-=#lD8x|Z(^>q<-aR+>C=A-wpK1>TrLlF&TDbuXN&u+z?nk&&|
zNN=VAFlCMN<wF!g7tx8J;wF7HTmIMTpVbujKTH8tBra06)gIdigQrYK^N>LNyk#p)
zm@+6)Ccx9j4-stfD)Bv!^wFQOzK1fNjvsaRY)Sy7is^I0yG#C}LB1Yz?+Uj6;sfdR
zNSrrdI)v3=m?9fD?_6ctOoW;c;@{3a9GL>53HMeXVip|`Hl|uAPN!Th<7<^ECX>t2
zuvKj&`upJY@4iS-Ss^pOh~1a9s7Tg=Hs#aJTfT^38Y`m9qs@V+*Sa_AmW1N`nctCG
z8WdNbHZnR<U36Z)g$q(5aF>ec_^&3SlgW>;8rlwNFYe;;t#FJVqmSz=U-4Zffke{c
z!f}J1`mV$K!!r0D{y2ADeO{~X<6&XM)J%=~nD~hcbahSH@tICqW*k&Cq5|$wCsJY9
z<gSQ#;DvK%pQsP3HM&3a>G9UD(-|{u+QZV?kh+i&`4H2S(h_iG=O*|)$V7t=v!LJ5
znjKJo#_3CsF}g!@eEhjJ0w4L{r(MTT)6NhHbS`)^%_da!0=J(IM6LF&Y&b20f{|sf
ztLN~$?`_<Uo&$YJHZmgOap8szhV^a$@9P)wDEMu;D{fHmIBlk&x=^q6-|vS95p2K|
zk%UKaBhbLi4zY;}bZcr#ULA%KrZ0ccCI?aBkJQih_CH)Vnv`Kef4J@oV|z2M7Qu5B
z$bNJl%RjpXEp21eVLii=zQd4sKar_Wj6jE+!nBo#q37BPD>f`<`j!pDq5p{jUv_5-
z5?)9wEh^BA&8x!>1x#@;5qXo86G{VnrbNgP#e6K8l*bgLBQ`_0rV0=o82I+{{l!dC
zZV@*Vk#5dx#@Z8sD55ZtNt8&^Z38V9q-AF^6i$JsMjK(FsfM-$K0j<jz_}ynJa{q&
z_vwYtW=F!CAt}goV9wlCNui24`Q}h4^VI!`Su%_4taOI%d8&kP=AHq1%$b(59er9t
zBpyC|Smj*`VJwgylzbrt42x^)8o`JgG)uz-R>lTUW@b>i)5h>-PPo2qH=??CMEj04
z5pec0@*_)7sNaO1XRV5JD+}@wNvpNc#2Q8ntE2@5(-=xdcvK2o^136_=NOJi8{)GG
zeK37qB<3A`Oy(j-xX)g!-ggbj)N^Hw17&BVpyl{(YDFO~i>EKi#>?kHux=%V9^Xw#
zW-)a1Ezov&KUh-ubG75~xkH%B^qY45KF08Yz47Us6ojt6Lxp|}tg;?p&f+Zy&dgP#
zc_@{9;xb9D$<3n6Jhmb}|GR|y%djLUJZeJv*q4T1C_daU8JYo4L^oz=^1W!$dss?K
zYC>vc6Q(FB7zLq1qoc#ze+_0l(@P<&*}}qHeowq8<{OgMsxt3L1raNKkW$gk3%<ry
z#(VKJG99^DWDvYb$%}`$dLsa%N6$qa4IKpiz7w~Zm&Z7n8gWR<*2y9-GKcG$i;PzV
z*9YGLf6BS>M5A|+`BEZPz|5FA+hOrAFs7l&S6R&5Md4*FU6)*|^q7m*(;CbN)F_NW
zR75Jz5(gvCASl{8GqUsQ{qqkMLlql_3>r3umKHQ!c;2$VJPHa$u~thoA_9%<@o{xt
zvnC@t<Py9u-jmuM<UjN{NXlHoluv3}Yw5H^V`n1Zq&$Rk_YN?nLaotxAnZ#NkgF7E
z+`S8M=f{eBsQ5rbC_ca++m{7m7uUPY?gQ*?(S>y!aq2@v)J%(2^HN@1c=!u>-^naY
zXj4m$6N|RsWt=_gT4*AgX>3&u1Fivmz0!55FV{h<F@s>kbSWi0%i<kpXD6$@>zdr(
zQW~h5WQf9`E=*^UCi7Su_Ui`oVkP(Cy9Svk?^`ORU>3rf3r~bg1x;1_oI@p^FPZ|H
z^dL_W^(G3wTS0gQ>j=dA6DTl6%z-cDy`0-I4<{bd&=E8*&Q+%!iB%znDEJlEdRO0b
zu}9>RJOsuBAS2ZS1}S&plWGe`nDcbpM`8Zh;}whXvnDcCYzF-2?+|C@e<kFnyf@N-
ztDdJXLg1|*@X_E?>c8>;zT-lMvJK(E{iH19+S5?bW7rS%P4e*Qc_?Ic8$-7+75l!w
zg!Wse!lH<%2V!mTyo%gJ?Ed}=+N>W3)4bOU(#7615Op4fCA^F3QB8sW4^e=7-tX7F
zn0o0LW!rcRAJhwTX2sx!Qk(ojn+mA`BxxB0Xs}~`JH78^JX4Y{fx^xW%{|)DK=x6u
zdrkhElf@ivu?eUbWKd5~80Ip{#eHUD%V^$n@)Yt4sR~7mVijb>;`H|UIQ>`%X4c~s
zG2glh<Sv00W^F;w^9v=@a}{vxF%WglP4JSkS?B~(T^pEL(7=z)Q!A$;Do|KNork)Z
zl)PV$^?4cOl^TL47Ac{}JaHj2q~a&{ZZY}tvnP+?A5nr)i&sF)Nef=P&*2&MGb1`j
zg+5Thcj=5{V3I6{@|O3dL2s}I^*ssCC1s6TF=fV68v9@U5<pohqM9n^%qPBcL1<E5
zit{K~e<0;~WIRuCE_pT6p>G~qh6CQo<UhOy^WBqd2az``k((!+NZRV_N-0-L=ukMZ
zaWSqRwnooklc<1?gAI~-|D?Z;A<TIGFqIxGO^l&Iy;JZ@P4dFHj4ZWIk`NhO;U5<%
ztpaIFfM--SB;?)VeJE+fQH;UZ&=>}kLFqbnur|~}W@0QN5-rfLu@SD!I|P@7<I$W*
zT`%vv%P~zkOnPRgrcsrU!>mO!rkYb`P#2LG=OHFCleZBCjVsSk(z6}n(_@*JUx32s
z7l@2cf|*GYvZ=HP6tRCgN<Hvp-xk7JA(X4K^CcuC;mU;vSn%mI#Os?t&YW!5f<#>P
zy9=4iKpv}53%|d6aTNu$LQ6r*uJi&4UVga!VGj%)G7H1fjv`*y9W85VV#~=eE(|&3
zRJ6np-azrO#HkNf%x;2=yH{}E=PDjIY=+ic1UD|<LMykCn6zObjD2D;v|Do|cpXPh
zW+nx;7PP5oy*B89lXG4ul2r|EZ#3#N2s7Q?nIHNqtzaK~Fq!b0lSd#@zX7_snKD=Z
z1Rg0n&|-CjSB4J?LY&c|Q+;GcJV0hr0iI+|#+bGZ@%V8BSd+q+oPb~UZ3RqwpiYaH
zNcZ^xThG2gvD^;*=J$i4wI=*Fug5_apv)OG1v8@d;?m{YSorbB7#XjDN2z*f)y@sR
zht?sLn@df5bz)cP69fga2qRXCo-60VDA@#ETRB4`HwviP4|8TRR26*}w>+;iJlF<3
zct3WXTB3!E4fFZKG32vF>d=lVnZ?FOP7>`yEIgoIgRbzXDaElZF)Vy3z0RBe-hUa#
z_aj9D^U#mQhM=pG#g#F%UTFx3b;LvlH0U(|Gi~kh{n3+%W>HKLYYrsF&KNOZBx>gA
z!(J~DzeH)$GUjHdXPR%E?@hsXE?8AngGw8jp*EGGZ04w^XJ%I%q$*Cs*$Y?Ey3u5~
z>m=fjAI_91$i+;X$$>4?jHV39XF8Z2Vj_qrqNb(r!tnoG7kYZS^qNN?B;OH(R(_7O
zL+9Yu|6`cN1~OF05G5h01mhD!A{0(4(++J0V@W?9gxomEIs`Z7(RD?b*Y`-`{;6^(
zpTIvf17qv8#Q;YZ5=WHMyU=?-&iZ%7h`v)XkCBb5$%g3DP>xeEDUcdBLWkjF5OgaE
z9!*^6RXt6Erxx^B=<~t!FVMecOSqZK@yFJm(CedbknrRp7S36X=ehQnzjG_Pw&})O
zsX~Ego~2WxK^WY$2!Gto#uye@8s<l0|IdG@Z{4xuFsxZP6Bf~DS--HF;mu^6_r8yQ
zEc%Kr(8KtpQ(>E*fHU5nRX%E5JdMRLB|0;NEs0d1b1OHdg`~j1+J?MC@S9RvYLJ$~
zGm^uoL?#WcrsHR0=?D7g%rp{-DwRU3lxMtk5v>Lo^HC=_vgk{j(W#bwKY~*B3w}-F
zyWtwrlndosMd3nYlAqPQO-aNRhG9D~%_Yqy1+{H+vGt%gf`>LiFGf$2f4By#)?Hzj
zdJT&<pI|<D={j!Mdmv`Iy5akSKeFIS2$R=`Fz!TASzbsOzV%)Nleou_4_%>tE(sNG
zLrXn8qcOeL+!;$g9)eX<({Sj%5}F2%7&m?fdK1M*qc{t<&wh(je(8h|eYLLDSJI*<
zi)+-S>rl*N!F&Byt&sQp0&ZTvf`KhNQpu1b{mv2me)mJnXvyd!`I|6G<>V8|zK3w`
zes4@|PT?txkh0<E>Ve)3^bmBEg=6nXpONe6srf_scho;=92HO-P;l?RHvV*;&LQ55
zFm%d?FtQ3+fcy45YX+Elq^RQfA|C(dd<liJdN03ryo%{dB~{J~W3PmJU(n{&{ZNS!
z7BN@b>yM?P(5G>zR^Y^HDj!s;m@*;YLCZv1*&nEc2?qw}%Vnwh59(*70`ea7Y8vzS
z*Ovc#(pXsd1@A*+(y~bK#W!g4PiXnRG^SdqrhPe$#oNgy-!3eC70oWEK5V;S@zPPi
z3vb-^qHJs02HhET_c?bDHHoA)ZsS78&OJr@(W8;!_d9NS7hzztA?Q4H5u6mY(2c1Z
z8Fv`HqH-)y@#Jsl2_;W2!ZWNNdJg{-UmChok+eo_ohYn5copG;XQS`fIY|G>8+L?Z
zwv0KCg*ynS@J0$v5%1gYW(Yh!Xpd=MSu+~v4lCAFrCKm*%Lw%CG3&8-;bPRjco%v#
z2rC$U9bfHXZB}*`OdEB@!~uy&v~Ykd&KFw`UnXE1*Q}iKeZpjzyzs}5KV3jx%ro5f
zdy3X0Jy7t{6Bmv@K$2a37O@t+&Ufh_*0-d$R~5?IuOigyJQPzSjbT{V9)B<&-}CNs
zj2}#0EaiK|>A0e0eFN<KnzcF+RYOUrRJn|%(kat>G(MI#V&SbWzTN#0_XoE_r;p|$
zL3s-DMqN;=_#W1IYf<MSKUT|k+C1b-TCzOoH5rHXW9s42uO|?A<2sT$HY2p61Q*Zw
zp!>A$n7V8_>ZY2ZPh&@fp8iH%yC`%wQJ3`Q5DpfhUvf9T_9x+_&}8~kyHR7I9sCGK
zj@`h;^Zxi^`XnT1GyS!0Pu3ac;Ly2C2yz@iM~Do6WO$(=-+e0<*Ji~<LZ8aM@THZn
zOW_?+&_Smpz=A{jKA7m#AH4^3#S5>axEGp>MQb-A!S`p}jxfc<&w8UUg!<Q^-(h6#
zicwugVd_1mR(Aaa4V5w2755H3;kBoz`j^+J5F7Fg*J(B7FvO!CnK8I@Xbl_<7oz3h
zQIO^*<LII9@cs4YsHF(Tb+2rC<=&y$Rdv1eFkHWJ13}?sH>?W8fdhLW&l`%S{YF89
zc|wtQuOd4k6#ln}B%v<uQ&DMnf%DgL8P*6!NVpAJcJ7ZReH4Uee2%?bz>obx@r{lK
zhW6@=A;TIX=I(FUx8*w|q-NuZ-%Z5CM7(x4MTH6QxpJK$>6q8%#V^HqnF#Rpf=z7}
zAJlM!3v<=){<0pMje)*h2BDrL2e%jj*s|*swA;(^+bJTf#3dle=O!-LO7Y@FIFiDZ
zIDGUX_D!FIF|@2t`rbu!V!GPW2jbv=_XdiR%RFJUPGbWZ3Mz@GAOn6_y9rjhQ;;fG
z;q-|;L=l;Qu5J6ljj2HK0e2CTn1ytv4qf-rM}d$MWh4HMCw@L=OZ@)UsG$hQ@q?bw
zbFe@*k%1yYB3T998Drb`V$dW4`v^}NQ{s%fx2#}JC>0ZZEr$POkh4fxSo*p~mZ;gJ
z1!jFd3c3Cla4{s5(UGc02@RI>=V_(h@<&Q^F3z7m4Q)bjid!q8H5z!^i^`P+p(9<H
z)<ak)<WO<Giv5RcqhH6)=vkP9CpROI8Tkm;Z)qSyxY1bz`rx(?;xnIDeNh%GCWrXr
zs#Yp<fZtw!F%PJW&`>ZAxr8%u<?+XCT=@Ms%=8AK*}%R~CPyIj@gqF*_oC4ykWYa8
zyck?(DC~*ndECo>1dVZHSY$L3MVZ0azT*f2{OMi2=gtBrp>R{Mq=m95$P1wnQLwS;
zh#@n-q$j@s385FUY42%-B$r?N?Z^1;AX87aeNKgWAzp6SjiBI2)=QLN_p(jUTs{nK
z224UnSuw+tmk{yWK^!E)Pp?+}u&I*<!tbBKo`aXEd=|mW&ji}cEzA$Qj}s?6vG061
zrZo0JkUzt|*@BjlxP0*}G#Zj|S2f6#6!ykwLr=HIPz|Jo5ZUMGK3qsBedEg9Y!+cg
zq9{I&PfW!xKM{|QZ^co^Wf(Ab8Pik}aO}u_eCHWb)!C63{SqgR9HS!J0j?(TIDO;*
z6b`lwRmRg0_lACCDL!saW;m{S`r`hn<HVs)!N-F-!CotjIqQDVV@N!OJsGMTmik1j
zv4=7JFjswu2t@=0XBP^>sCeA;@<vFic(W3O1p4DVJxaOE`OnI|g9H1{V1P$M^y%)*
z=)pB)r(eWZ8w@dHU{?$rHwt;lN$~fJE)5#xW#Jk5ku7<GjWhEW=~a50a}X>iKcC@S
z=3g?;G~{t41V!qwsMe1#kLlqI>HFoOfb_n+e>dz+RE#sR7AF1yE?j@eB3Ty7>e7=(
zCZMIT<Tpj+;ElnEz|*^4u&8fE#D-!7c>apZzx{~I5i&S?dm)50Dx^p5$K40vm{%X6
zEUMEeAgU7UmGX0QaBSCp*k#wFVUS9+93#}ISqC$gPk}u6E>2#$T2UAC##Na^JPso}
zHesYhT>>jCSICOu;gcXoGrpS&JDc+wg>mpn)J7{CdOyo;F3w9>6zrQsL?m0iGJ{%N
zg=tRtF}Razgbubkm5St<^Pd-udufDuwPRXhS-yZcU2aYco@AM!JwxCH74s_QKF8uk
zF8e1~6ZxWSp8iQji{cScV2+knL>4KVXGH=Lz!DL!v>?)lylh^m=lO_Aqt{Sog&KxL
zFHs-QB1I}HVf3<TYFWXay-`Z%NdtNh{S$IwXJAU6P0yYPT?*Qiu!ub_7tSVTFlE$O
zt<$M#mWbPl64Wv?VXm|2&mc%3Xqf=t1Q{Ay8mk>uVp_~=kj6I?6ll&ggbIU#^HS3I
zCTZN7G_FwWIgfnOI9VU<ZFOIxaWQG^_ZE#AV<*46pJqxLYgf=lD9kz8u?WaEL%T{e
z7M|g>tT=>_#;sqYF(c-5S4WY?Eh=a%XjzEZG%BCP7HIkgjU!W2SO-V+7=zbo97^L|
zK^oiXGa{xY4x_Y$*xk8v2YmfXQ#1r=Dn@ENf?r0UT~iN?>(~eRX<>9coX11&XV}gA
z9Z8S;pi!>KO1O=cn~yT(Rf==<&C$JiZJ35Xz>i0~pheo`<(D9UdO})qc*Uv3hoby2
zZ2EEoil+`ho#vh3Or*WYM?uI)IfvC>8)MpsoiX_1k;q9%z#Xq>>U8Qi_u4s13i9B2
z@BsU3j7Gg0x(K}T3%r@hphf3SVi*A{R&U1ak9(tY?+>6P@{R8cA<v57dFdokxR@{9
zyBFLulW{eOo_>zp_#pUPze8j?b2w(xd(XKjO0SC-&%s4E4|##-aaX9BRq@>#H4vRD
z&J&N{e)<`D!+N3fu)fHT55c*j`#5-rC|Rn}Xx4WmnlpnT>Y*3Xvr4ZkuKngHE}}Ag
z;3S6kX^jtt_C(S%Z+hRouz8yTpN?QH)97)Gq^2O?Y7}Xd1Roy-!ZS+w@yn-oaFKOr
zS&@kdi?4ykLnolUf{wsPmvQQsOL!EXi7iG_^lslCeF)<5<mxeO+i?ncdZxHTXHjZM
z{F}Ff>=g1?&)Y=fWA;U*QJ9^Go7ZmA!DRqPdpp<?{Nn17HE=avgy#K5a{r~`1ZCdA
zA3ag$Lra`EPL!bZ3|u>Z0rhh%5mT&-d8;?UQHO?k8Bfq8IKbohOyuU~!nH;{G%-xW
z{c}Iy^bdz|JIfkvRXX_K<E7}wC{a>`H+KJY62ZxIa{hFXk&#jO@YAK##bR-I(_TCa
zimG(C@Apyg#J5-7Tjx+f-$WmBdahD3-&UC3I{dvSWH2`8Qp`<9TF$!{O!;Z$ECMxR
zSUxi)1^KjqDux!*Tl9+&pOjHC_22xUFeL1pEEfqW%vbx1A=fv?&}oY>X;2q75O{)<
zJJ(^K*NeaSR@Hyjqe86!vH9Pn#8mp4o}mfDd1+KqUVp7J9eo%Z=pZL8m3is!Qpb5B
zyEfRcViwvrcExkg6Ij3FFanuI@^1_sQ&VUwS@}<BssF|>CR6MF;ULs+)&X7qANIZj
zK8ou5{%lR}z4wrwKqv_%^xiv26Qm<ZF9L#80TEHa0wRJa2uKl-UZuB$5)w#ALK;a(
z@5yHKKeH(W38+}U`up$mBfFWMc{6k8&3*UXa*msvmE)svIy~d~0nYRZV^bQu*_o7s
z;B9+FV;IudSR0Ch4AxD&dS8jA0W8c7`J9T7l9=+FWK_b7=i0O@rca&3x(Eqw?c9I`
z-|WZJyozUIYGV&QNikv`Kg*31;6n|W{Sg!U`Y(KKcgv|ChAiwcAZBhBy~ml)4IK%+
z1`P2s4I?AJf=Ei$hlT-cEKMLT%|?8Zkm~m8rmbhlR30Lws1_Bk-dFflk97qg`a<qY
zyu!cT!|&LG*JLIuMOsG9_gM#KtcA!YjP8p*_TSOxe_<CQ@ysA}XipYvCExfNOICkQ
z6s5;6y>jQe!SE%@f&fjcYP8J7gxrhEXL^7RJ^jJ}5*oLQ6ogzYkibTlX;;D^7n*8&
zF1O2QoH5K@u~qeNIUzRV3z=HLaR0MmN29ltP@_efH1e1RP?^&w99KfrrIZp)*bC_a
zRk?mVvY5rgPnkMlr^{rB>iv~mu(3$KOdGaB8dr_&d4>7N;UsG#q#agn7YzPvhWN`Q
zIxyDY-Bxc`Ad|oWA{Ip(R&S>a=txM)FCl!KHuHW}G+L^A6)TI8$oJTQsdYN^II8YT
zB{SyJa;TYTYY|S6+l3!0w?Af$js;~Yc==RKwY@^d)`SG5GrW3xwT$U_soGt&S}LWC
z3uTlIl|+rEq>K|@kTIQA-2eB=Sh$Nq$~e6i8Rt{RS<lH>NQhMM-7P>KWo-SNj0wX`
z<gaqd*r-~TLd;b`00p9-5LVidhFpd08Lf~gaaolk{!9<nxHZ*RskT1nHZ9I=>8hHm
z>fc$HW==xFT<ZFoejXzeV-)3NFg>n%&)@u_XKn#aj=i+3np#1Zx<Z>yvecI@exG$L
zhKZ3B>9uiFXlOBAQxBy~_08sZs%~_u8JHT-Ns`0){104gbq7pFeU;v2N~4((Q*_cZ
zSi4tskc61&W*{sMZL#MK;i^4e_|!3AYBi9WTGQ_0Jy{r{kg&`(wd)JVR&LNXB9t&|
zqJ*rE=bE-DEVT>q<mvC}<+&q&#~-Y}G%|!1=OqCF=VeW9XpWXGLO35YqF1RCB%(5;
z%S<rn%`qtbVHRfoaGu;8&umeVOdA#!M7^UEE-fjMsWLTs*QS%nQlB+&X~-Z-((5*X
z6i`qL&-CW&_M|?pRtd=UP2t9ndr(7fJh^cIhlt(!Z*Kye<&Zvph`E0U(GMS}aOeN+
z*Iadd4j$fwvDyN&><9RM?I+l9{2E91s}1j}WB*s57A`($5?mkVC6U;9@CprufA{tN
zujHgY?_VeZ9WV6l8H6mtQAS5Z;t@G5)J-h`wFJ}>P)pzsNWj7!%U3Q!?yf~xeK4G{
z_kXsj4fuaZ-U4iuh<Syz)VTaZSMWzp!;mgBC2A|k`6IpjFZLCRYAmd+7_QI5ljNr_
zP5b#@T-$G*Q%a9q8LMa2tLfj8l=}BSF9G2lr;n7yaq6a)fLa1-38*FT$0Z<QZKjcl
zA<FVtx5Ws_KRtJ=4fsF)%+yDyC7_moS^{bbs3oA5fLa1-38*FTKa+r9z*ncx{m-sK
zePXo))DlojKrI2a1k@5xOF%6FwFJ}>_}vm<vIG`Bx3{(lJUqSN;%Gx?uX1E&{|d_N
zxt;%S{h?=J2WLB0cAFSML_~=~GNk_5re$mik2)kqC@(}7F+l%qO^?MR7Um>vAnc>+
zezh!BEN$A%=*s>b1;wwtA3{A^xOl>fhy*!>B+Yo~&V-{gwuU2N_6i6I`70!@mmc+}
ze-O)INwNk*R=MY~qUq0uz=Q%~a~7`1nJ!gO8|>LjF4);S0QN>KAkE2TnpMSB7;!9`
z8E6n)qh`_6OFH;VHj82H;KTwlB6|Fu=dP(s23kw#6J;a!_w4^#*T`|9L$n14a}*ck
zqLe8{|8#p!ClI%}@DDuKfBIT~_xZ#|gnaXWnHE?;{{N4D|K0LY@2Hl*zf=MeJqxl(
zJ3;z1lLf*rpHcs%m;d))7Qq#qJv`y=Yzuv2=$Fa$;b?2cB4Kj(5RSCyA50t6(j~A3
zxn8vBCeJVW>utY<3m2+5RH}t4vkt(>@ng`pO<m}+y!G1oJ(w_kRrT+$`^B?<Lkt;A
zxE=!W7ZWu`z;zc03cmjOYZ57iy>7R!eVm527qK}9st^dI0wyxCm7j(kLAZ4f%<SyV
zy2o7Xn7065U${w3k=Jc91a%mMp+mdD-_8(6KADNdyRN_3nba8dIt@f)<t1$W`6hDh
z+u*%1&GG2^8Eo5q^2PoCqAenQf4nuO2U^tkRiV=qq{U&+qGi}q6o474X23G<F;4FP
z4nGi<D~|NjuhRtM=bhDCV0&~4<`7mfu~g`X)%ezdXw}RH7mpr6ViqM#SU$TpW3aNN
z1n<tAkBHpgIG<P-E+M@!vXd8)AlM~wj*|!zxc8eM@%W_|s)hQkI>HVyICM6&g7RA1
z8bhZqhxN6OF@O6d<i2i7u#OLgj_!#zjeKFEuZu#`B>%YVL+lTK_{s`=4IMv>ojnZQ
z+JvCs@^*|~xDHS9yf9(@U^MmfA+kjY9$h<(?e`>T?Igj8Z@<8mCvvnNFak|%a`4%y
zJdEvApGX%Z9U>w|0_mtf`~D~mU1A&Snxl2^p=fTDh>yS7ho_k@M-<RE^TG%+k+unR
zhcfF4j_%ov^}Ei#RFXPob{I8%9$NX@qm0mUKkeLx%|D)g>He?(g9Hv99kF702dtVh
z6Bq9#yx5g}5T=re(Nb5Uf>o6f2f8Tw5@EZ3B5_9TN>fYE6CFDHApYuU;_<(tfRjid
z4d0l71wH(U7?Oh@<}bwN^EY0KIH9GhhjBA!py0+CY}tQGr8`=_F&3Vdx;T724R3fC
z;;T)&kfL87?~Es;Yg{<?ku7(^U{AuaZAVnRKm_)RCNo9nL42k@R>Wu}p$y^B&K%l}
z<53EX8#flB&|UcQNGNp8gE4AsGlWrI4o-ZgH+SHxlaVkdUHV%i8)5IsWDFYh2AY`^
z;^e{I*tPZh-+t#_aaro0)Dloj;IEVbxl@{SKpp)wLd;%8Zqc)esb2SCSUA2fE>7=_
zWfukB`PXSCXx6DUbkhj|edXcnbf7v$EVW0+o{f=x=L~*{_<NA*{?NtgSYYtP*%;cb
z5p;>qlKdnbyY5QyPD=+8NG0Kmv9qxIQT!_$iGS#9FW*^WiN2%WM3;~Nn2=1YI6E1a
zzWoNDUdX^3qx!&$o)f`QC#1kg=_vh*c5uXlxR>|$ieH{fq496g3f<fK;MjX}Fk-|g
zeDv9lR~-8Le^Mw|z)25msfIP$wr>Y-D^27PAC;w&zrT~ea8H@s8tuBaho7}J5*{ZZ
zow&45a}#iN=O>u9_+#RDYSR$YdJgXFkDXqQIt>P3_S|tW&kD!1$y2d~*!i_KLKWKk
zH%AL!E6By3m^JquwDomB-i`aU?*3P8EZ5S9g_$9)9o>V)E7u`OqYg%m?*m)q4J?^5
z3H$Ekp!=lRm_E8Uj0LQs*JycUwEJQCWRlv1;%N=Hy0L9ty!mDi*cw!j_z3iHQkfo1
z4fKh9{5wu0lEcC)1RWbXqaZsQN<ByPA2AvuhqQjh!7>DQ?1{nc{0Y@osrJewBxo>!
z5e;Cj0baim#?0K@2)9ok#NrP=Mx59gqsH}uy@k$8`;=<<Vb+>O=+o34Tb9qo%ujYA
zJ>L$KKA4aGEdvmCU=u#ta**4~VC@}*PGnMbG!O!2RPK#hpmW<s(08bZ_8~!#lcE}C
zp6ESt24?kdt~#fgr3JbV9E|>h`k}rtp+#SEC352G_Mb{j_dqLrwQ3X2#>>%n#5)+!
z(7N^`w9MfD#xx9VXpST6R^e)@9!88Bi$Tp@Yv2D*Z6zx(*?`R~EXfA@JTzWax1tQf
zK&GW2S8k6k?L%NCD@H;h3A)Id`?98`=Ze82`XSia;AOj1Y^Q<^-MtRe7JY<E*@PCP
zPIy%lh)P;_?~DF@y1>zxs#O$#9=-daRoiwjb`L_wX8zDn+F{tFiRjm%KJqW!hN+!D
zdUS08GeW%nW+NF%U`PklC3IanIW8O;b;abjC!$a5`l#Q$14g|w7QQ51($aTCr*7@w
zNiK)rmL2id#4&Iu+p(^xJG%Ai0tfK}d`ZH$FOG(z#hc?XW6E%N2@JUErj~$O0{;RD
zNKmg;7rfam2)YC}sM#XPI=gQ9JnW24e@)|^y@?Farf+}r40fs6^&kE&5?iCsumNc0
zY4(SY{W}h@aCSlO9znSE$qI}cISTXUuf*xES70igh&hbp>S<Tn8-K^iYxXJE<eX)K
z=xb-Nbm<C&7g%86=r`a-Xk>v!TZ=HVg5y}v#2H<CcZQWI`5a!eNma**v|wS|03Dju
zgPb0<LNgoq>FDCd*+bB@^`=od8+EPp$cR{oi+i_Y&GthG=rsbvg6$EVtBYViSEh($
z<G}Y{;PYJ<UbvL#JE2&5{~p9X0}<e5f!oKv$I`u_ycq@Lby{KOJ3~<4$`IwG0^hgw
zGi<#o$Ab6XKyg$e+?xh5)T_Yh6(8WsUvAQPcpIa-G=mOV6}327?_TgewuMF`klQEr
zY_7r*&3SYaE7p9DdymsFYSC(VmL(#?&=*1OrpS4C3e#4FtJpvv+&YVmUmYPcA&Emu
z7#$#qP#UR#TjUgTW%LXYL_$jUUSnrqpui$2RU+>4QGB@VAZ9L@ij&)ZfI-M`G*X_x
z!Y|S>U|dst^Yw0wo;@Cl%U@&msx630edfm(8Tw=7#Qsp8+CUF~0#}1%|0^v-da@pd
zy*ml592M9+cN%_*NrTL`K1NI!f%YCmRw3cjx&2$Q?%N~u3QoYFx+aLqC!tAzBk7>i
zux!!$xDug*akD3&xxWo5;u*F{xQE4ymO>WT7ZV3{g^4B^4>goHylOeN{Bjv<-oJqo
z5)sHO-O#CAhCGrtl;<ZSDk%xE%NHO#ZYEYSBGcxVJJ@wIvQ~xh?#CaZ=)!&+Dh|Pd
z?i!dob1yoM8-xQS!C5l08T3fXbok>n2o<7zyr{s|T}iy*L555twCLCuF2qiK5^)@f
zBz?-0JL9c|YhmZ@g7UNn*gR<-j^x#blgejPra)f&9TNR~g79=XR(|_E>JJ!#m92&$
za^os&y>thq#C-2Cc@f$<DzRh#4S3ocL95UM{RR$)qA(ek??j`pfUKNO9kBAN(Xchw
zL;TspSiANs<TmVr>7xe1MF3te$-==c8?fba8K%q|291PdI5Y@^6-iwp4*Y-*zTSiN
zOTtJ#lMk_pBid4yxkSP#CGkh?O^m*?-l11rgzd|x;M-$SD%sj}oQ!S_%yD`9a;)BZ
zjHD4_{GuU6qf!1O8<CR@QdoG96|#h>970G=NowjRF*r{d{G7cVW0!t}u06Y8>33I&
z!P}OEF~qfoCE5jB;!JEJ!-p?kjfRdk0_$3HA@?yovwI<n*Ff_PqtLoTOYFG1{e=Te
ztt`=>WML;6QQCei1+#|eL5Bnxfh}8O``M@$_7^tD<enHZaU?o6b>VxZMB$T2eDlK*
zIJM~pb1t5(>juEaP>$%I_hQ-ltt2t?!l-vS?)=F>$PlO&si{k7OxM=R^Fs06raQ1{
zZ;TFp8o2WFH~9Ma1F|;rna=2pr}vZL+oCZH%hPda;Zj`Fwt{a!J?aiUxHoKvH!O>h
z7k2_bUrL5$n>X>^o9$phMV}pa9rKp1LtOj=LVK1XB&P_ZJFE2_sJbq}qmr9j2pb1C
z5_?&oFt91=m}(*Y=@XQ5ETq#()noifbZBLV1ItEX$HPbPw&gP{ATw}PC$IceqO7bG
ziRbszYjYN|Js0rF=d;kPc?q&fK!iGh82MI9tT<GPVq$G8ib>ne)FTatMqJ!B#b;NG
zQN(dmo}Z5Rhf&zEemTOT^RaQkPz*^<$J~wk|9<b(EALPJqgn!gL;_;e?>-!(`*wh_
zYHlmTp%u&V)rC87>oy!y2X-WpmIP%v30OuuA=R-1<_`(N5$fC@A8;;eGaMtDlwk4N
zZ_#1W5(G$|AWhE)jcJhPJvz_m=*JN0`Qx2=ZzI4)k7S2TIlFff6Bciwt{8&(gImFj
z6!96+m+>xjMH0ywYilBr4!j$_fmt&;L0_z77D5rO?OKZu_gsTp{hpXLeK1T&>{U+w
zfO{8y!v5PO@OL#OahMaj_ZiIeiyVaAz5-d@(U{ZQ2g;mBSa<#+1a0dY&;ax1PQ+&m
z=HhC2Jj5D?7&K=otnx##`r~65I%!h1-px;n#K{u}kx=FZ2Xdk4IS1m6LDmo#$Kk}!
zr;%<Mf|*l>p@EHHi)CWpcbl;7z(pb0_d;{2-xv)%f?=qo0|);W7@*w%r70J1?A$|$
z>h;0m@jdBqA~t{WJ$$h0W89VoVa?Qbcyd35`ZyT+iZtw>zX;nS647bWd+68HL&a#U
zp-jOS@4Sb@>BZ<gb|!|j@PVeR94RD~n!9oX3N(!I_MAB|BFU((V?Evn=Z(t;Fz>T_
z=-I6^^a@gtO=2}~PZ=Vw?#KQ3Qe=zu=`dE*xDOGutXwf=&O9`<rxS-HnOFC3$L<?(
zm_DHoR;@cvGQWP%y7Mi*{>2;}d)eUg6(7AI1Hm?)H-8e!9>&AM*9SJn8i*tT*SgPl
z;_FolQAV4lRLcZGbQI>3b4FZv9Xmd|ialJE=~_5p^73_vI<p_KcVFLGTj7BxCB03v
zrfA}BO(rxmG^QXrb#9M_O<SSu@Y(2OrNpV7J8|xQ5xNc^i_vY}$fTwVr{Esw9b}6~
zVd3-!I@16$df_I84NB%66{+$^Nq)iqgvqLy6Nys84bvu$M{74toH}q2ck`_<V%QsG
zQnZDMr3ZTSZH4UUP~43#N9Q-&!`w6w-Fvi$twuTye!m0z4xU3ICwGJ3H!*K`Hz48?
ze)#?nBz4+h)vUp=G9mGXod-fXc7vD3QzY~Ka&~p#>W55k<#Kp8>V*08=3~hsGG^7Y
zRCy^i=<#Ff|FcFT(6|2_%;@ZkTZewcfs^;)-1-fyn9vy(c8>51ss}HBUo>vo6jt^P
z;N##9V-pT;hkUI2{`hmoyU=MlJ(zxe4dG%$!VzHub81T|8`mLY(4(~{E`7BWo3Esi
z4&DGmhmXUMR!+Ei{2(sI0=)(fMc=xH5J-dEn)X8103$?_8P3ST5eCNo7&dn%`m`be
z(}gp*a4!XRE_Tq8c%fsruJE=f!HGS)@Z<iIcuX=1B^etF@(Um~b;5|b%h1Iv8OygF
zAyZn#%`1x1aP#!HI3BOVG#eWj3eMkJnz5BFeCm3`t64A_`1`@GPE+{1xI&a2gDW>~
zAxp=etdj;6xmsik^l#oC%^P__o3!waTD3*zj_uIQ*B=VzH=Db<!^K91>sP{{W$TY_
z!<*D{#;Q#;rbu*QXk-kV#{J=8A|XTR4Lp2G1va7^ocJ<{h<DwO>0lN#w3~JA3U?Zs
zy0(63$8)xC-4;H4eg%a}1a@f&Z8ASzxtE53*3A&ozZ=Gm9)m#XQykd-1H#e`@YdTC
z;1}RQf*}|57|;QwiT82yQ6Yl6cVv#84horHC(^gYklD-7%PJioa-kKK{t~lH#PJef
zgZSGg@Oh>2F5H!sttHCxvk-dn42^eT3zdCwi7M14{8ek?8(hw1cu^i1CzFy<VrT^o
zBV8`A8zb-L72F7mfp0r1HVt{LhYJg#B0e1lJ9oHq9JTLW4@TNjh)7pmTa%EkT8}Kj
z(OD(vI%O;Z4Ki>msfZ~%dbRBik<1Xo=1j-%-t`cF{VdK<N!mC$kYL4-?};0F4D19^
z>O<Ukkb{PuTf?Ca(~;&)L;vO;h`4+j=dRsHiIneTt-T8y8QoKqa&ZIhLjo8^b5!9;
znK4}50?@5nu!_Mrz1)hrs~sA&?}*kd0(lQQaBbKMox5~EtH6d#FX@2gGlsx8KN@?F
z-k@Wl6P6Bd1#-$j%JWNW>V%hG3x80QK*33-xw9_@g}8C@5h!q|Z}^TIHEc$v=+=nc
zwF+yGMiJ(;!hiB&cO<aU(19t5$xKa6RsWk9=#k-*V_vupxsr>+Vq~V>MM54mDjhB|
zWh$;lq`<ZP8)#=D1<55T5I#pvh*uwE!neyHv@+!4iIFgYT3l%5rTK`uuoe3v#qbI6
zh7FAfbyG{=KO+H=Bf9kH3Qx0Q9N)DIdzrQtmzfO>-`;qa9#5mBJJ`K_A0A7H*FE!1
z)Mr|vwUra+gNpY~&(IXE_Lh*-9x$?XN7D}7;j5E@<QyejTpXZhZinfs-Y2`S2<Hy$
z#-5{>nev$jGlz~?HT@0fXGT(2+=8BOH!L0+@{G`|rU03q>s+7dE^9_X6n`6sk6jX2
z^I*$4(afs}<}RH9?bQ3&Lwn}v#XBgGX`x9+&et}^&^E7wmhIc2L)*3pBrAMM)J<HA
z&4Urs;>_d~`aC<AOt`W-n9w-@26SrbS~fxNHr|jIm!t3Gna}B6fw9`%#hqk_?crd+
zh@yKVbm`I_?VGoNu~`F*A3qjtJ+*O~w%DCq3%oUK2z<%LTU)dDX@s^Nn{(kr^0dGf
z=-Rad+BOP?kwpj=O?wM2f{pg$&rsMk$Kp|)$Q)}8ch5j{>KKTGyVsB{u|xN_xbWis
z0|&H#R!%gXab%VLB@C&|*l_7F9q;yPgtW^iu<uA1%!B*j!*_Z>MqO;~-Vp6Nv_mOV
z5c3$7bfTSO64VgwgZ!XH$6z2Mo}IgQMe{&k)uqb?Jt*kSO7$kuHbd*T-a`+jrJmcp
z3ny--qgDTrm^Z8?%t)Z<+olP;yu8qeO!3w>wD0W9YZC#aByh8|_CYtY&`L9-5E@f}
z7Jd42&Z$qwV7_V|=skTI2D>Y;bN5c%CTnvkoo@w7ZL}LR7tM6yuxaxa#N;Sy?f>^|
ztQg7kFy8!b89vY~$9ugTF?rl1Bxe-E)WshwXL{kpoN?HA{wj+0UWfP3q~osF7?&TF
zL88pT?oVgpi?d=(5SJn*BL0=<WJ+metN3C}q5Ha8p)CCdiuFhrotF(W8cD_$ajJ<h
z{P0#xUbX}NEhivosyn^MMR-ECFYS7+@OKKsPbbdPh~f%vP+d|YC*k9Ht8kJ=Pl1Iq
zW)5iuO}!mt11zV9_Y77oSP0D~qtMo^n4WfFHO5JdgHKaSlZ%luXwopLzP!q-6rQ<6
zLx$cR{gHV2IKJHU4GG)zVd(xDIyPv5>!~u-_47l4oE|S-NA&Bh%kxoza6*+A=a9bq
zSplM4M-QG{Et^>QaL|jO?H!CZ`Sy5{Lp)~I1;`jukxENA&?KZ-=K{yd9)4s)m8U&I
z0gEBzWMk8}^MkWb0Ajfy5*0tix2vb(hj0tB;AKF{?admv;`Y}IFn7x-*!lED^M<|1
z;79hj)O3jJdn2HZEq?sz7;Z*Bs>tm(w8gM#OYoMD1fQ*3MUQlB<(*f26p?m5J);oz
zS~5l{xUCj@H2R4GJ}$m!Db_&-7iC?0)`PWiHuA!c;In(zFv2wmb^^w^&?Sw<&GW|N
zOudm<zq}=uOnwVDY5WPMmusI8lxIA~$|djPLSz{XNNwz7>mVFeYg5u_({O5vsY@Hc
zm|pVuuw&T#$rs3UEx^#v-(*;CH_GgL!HM_3`J)f9?KauwR(^vP%F{HmH&rBW$sO=+
zUoB+bJcNl;KY+xf6&h?Ghv3G6Fmlz#zM(6zIXVtz*An3W!(tfQJE=VX@n?5n{EW}w
zTz3e9*7k-Ly)e|a7%*iq#s}(R<C+g~o>8HfHeD-HsY{T@)Q_4?LhDQ<Xc{uo)!LrD
zK+peNxqBInS0xQMeQg`Gt8a<uT|02C+zW048=<kWCiWM+m{wAdn~Ouc4`cFxcKC8@
zCzNKS(pykU>h{`AW)VF|N~Wi{b{P%tI+i%Gc?o3w7Q#ZMA)mi2;V&I7Xqz<gM$FC?
zn7wu{bgkQ>WynaCLKTs!^T~zX7(4Z2*n0Fu!}WdO*scef1h^sg&<B|N;UPHt52Q1}
zl_v13n!x;6)vCTxj->^-_~R#-`Q<STd3OUw)U(CD_vYdoS!;a%!#EsUI~yBLS3IYH
zF)QFkJrE%<ZVFa13&iRKH=_-_mt#e+hJU*WhH*(jCLTq^!T-%k(AQTY;$|$g%=t#M
zODQLFv80R(Pcc1ETy(v(ODWs~x?|?Fo{X+>0mleoLF`R@wB&mzglpq1s)m9{!GdpM
zy3??iay*^?W*0Pu_DA1oTwoUQ1RM_%8s(=p|A3z3-$0+qZRrJKT2hVkDoPbdrRPwS
z?|3x}XpP_1U)2)$*GhnN_GxJukodU4zm7Hb9y&osKpa{y%4MOOkM%PbW3NJi8%f2~
z$pfKFeXHagRI1WdWt?~EL=?OqY$Cp6X;Hu6!})U|4H|)tp8VapJ8O-AV_&St>hEcI
zSGGoz)^c7c#_hu53SGheJgF<hpB&*k_q?tU)5r^cgK4PoS226(VuaJd<UnVarlt`r
z0=pnU{um=ZUX2^k8CBi$b2GB$eU5&%QJ64g6`s~$qh3#t34gziyoZY2(_-Ur@Zbr2
zG-M#0zc~l9{ykxkbRV$~<IsHKaO&L;pXuFJ;}OUDoX<b}8H>OE3>KjqF?#J`RY!&)
zgVDgt4$7yj`_ngonAql~jXYptMFAwfIG3H?vIeKOp2yao79eud`&hdFwyGcd-ZS9C
z9Pi8r*(jyWQ%;A5Yr`hcI{OgiWOqIN?MGO+?L0;;-h{r6W>jU@@g%(n^<BJJD|8Qg
zj~_!sS{BB4tc&#UE7-JR9d6MglG@v%Pvh1QZ@EG{35dS(Bi?_11DcJP3O7V>zBq`t
z6GE|h<rplUJsF|VwbwQYPshZ}2wmHH;>^;i*m&$5az$t1vhNExdo{wNOes3ouLJ$^
zB52dY-n%P}?!sFY!mj0C5f^{Smv>>p)HTqv?0|+p&4fN3XwWo4=Wz=$v$Z{TefAMH
z{&2SX=sI2L1aD%310OENzRL;K+h6BP#pta(QZD19&B;ZB)V?$!x!U1`!wEaToD+qx
zPgyQe7&vlRgenxG%CaM|>smGnNCCX+z4t16`TZZ-%xe|ATgeY^BP}5VsgI*@e(zH}
zOt*(7U0xBVkFzs5jR`q)jm<d8Bx3!-IS38r3ar;aEbaFuZfsnL7@~D37=FsHoSFo0
zKa+D*_abL_<WXceZl?0l?fn85*`u~WD^lDR<Hm_?n7^JcT`f)ZqI0`IkS(R5QOY7g
zh49i)X%rThu?SL%m=py%w)Df94-cZCi9dpZT{*B0ao()(x(T0EO<SKK{=P#{$5@L6
zcXH$hbipw1d|bNs5RpY<M0~#y;kJ!2;O!+u{+WX{H}h#2Y9jT~eWb+1qEHcyIp=pE
zI`SUuyS0KSg9REl@{pUCh7T6rMU%l(VPygQa_Fw=lune95m9uclE>Kg(ffFKA&@c~
zgaw}sK-8W!n6vIO8oV<HgZ#=cXY3>#jd*b&3C5(D1C(~aOIu8Q79${aVRJJArw=8d
zE?3(c3FlN^P?@G2#`HjzaYbEP-GdZzxsag5VrJn@RJC$05G2T<qR5P+Q7dwz+{KXV
z@kMw^6Vt#{JdMP`orm!tCJ_&A-$HVJIb=mgapFo(3}EOmMAsdOcMsyg?Ns(cr9>rN
z&a@LQ$m#LtCR#FbE-oIN<+6{9IxQI&V7w+dy~!dz@4U*y_%c$F7njn6q=6=7cqZc1
zF&-@DHETjcTLa}{S9BOV03#b~V$s|0VE^5imt1w_W+_+aM5>Vrj9E2WOnN>fT6%B}
zpy%NJ^%}oOc-@pCqXo1|QOYz5k!nT7TOd1FiHg0|ijfX8XcT0_s$NePK*?a!j)mIg
zdh`Mtplx&?&_DnHKmbWZK~$GO?En09jXo>BDw*nYeA80gX1biWy*`rDj4*xi1e9|z
zR$EhYf+`?gdz*G%xVdE&*8P-<PsYem5cTW{*F4UYo*r`msz6o0({h|X!jvZdV9S&^
zld=+~f+(n8#E3k1T;*LCOSK@^BK<arF*xK9cP*5Ai(yvR7X~z@Ym`vb?7s3lJ<{w?
z$^DeoX-izp6zAi?xwG6yhv$$`hnVtt>9W>@H4ogE_H=dA)z(G{DdFQI!jPRJ#<y#4
zATJ{k#f%J5+o|@e(h1cE{rU^vcex@952LR!4J-sMIY;qnjEvDO2YS@yczWX;j?(eb
zci_92l?zPUe26^=FBhLJ4fSWO45<lW*uA5eP7-0Da*>~(iVRKO1<#=%Vp0hea#?u*
z9QBz}lgP>6&JnVh(^#|H8S7?tgHBFNMamWBiigLsW}hF{PHF?Ky!eXk)hFY-$LFBZ
zS+8CtRUe`LqL#qFLjoz-_`!P!I~j-$v<DV`_6F{5TZ;RpnicjyrP5F+n9iuA6H3k;
zXukJ~9KJjnda(6!g{&-^3u?Z%qEbALx<{uRbu8<Tn3Vy|;K|&-oJRlix=@#ChsA}t
zh>xVMNR!~})z^{xl<!q7N=mSyyqt}YeD^#JRRuIW8^Bgi52?cY&IqzF9|;)>&o>R#
zd{|DqOU!pu&<|D3j474$|EW^xRNLshyLFx(^MN0+82&Bjw|2m-!>rkPl!tzn5}d86
zcj@$^hU9sf7D}6@TqMjV6-}_kWHQ=f(YNt5Aswl4QMm9^CT>SF@9!l|MtO1yt#CS3
z?xU;CL_^M1i4Ve%lTwB~8&~7o%p{~J8$+3&jfcNn;hvml>8R8*GpD2B78Y>MY|^+9
z+IQ=Z4MX0<=?#le$Y??-Qy23E^;llO3=Ym2Ohpn=X=^I8aqBKq8#xyp*z{rLnfXYm
zV@1NV)T%w&tW-_<!u4|QW*uAsWiMz~lne8_2zks(NXXK`kS0brI)4xB-|CEDe?6Q%
z{Gx4CaZ)-+Xj4R-I$ohu?OmbAv67c1M+oc0rggK&C$nbYTSiu@n^|4V7~Fym;r-Zr
z;Kg+F>ivG_mshIn%<O#Ji^xXHf!)x^*$yrH3}tS78S-NvPym8yOPDtrK(E`LT#KT?
zkP8D#6U1FVkL&l6xo|OJNQ;@_>{g;JJP2WkuyG|<#&Ar}&LJp%a2a1SpY^A(ECe?4
z!K3(e$aL(`mEmbOCm*z-k)RPDj<O<p)A*m5gSBQ8{uHiVDyuC#B)qVIxrYy$HuC31
zNO0p$3Y`2KAjHoNe$9HKnTI`c?uX(=R0<awBAmW=49A%VC8sf$8y$v%#H&crv_%Vh
zGYCr|q2;C}VbiiXn7w!x(`pKFZ_lTgJ$pXB`{pM`7PJZ3nS?!`e}FTI<@A<KKnF()
zJh-0(Jv(b;MBc!~s}CS1<fA^3Nd%Ep$iAX);or=>LgeMi5j?01kt!xL=e;?!sC*QT
z%^BWm2d(r6`2PL(@xzrT@bYtqbJN!76X=5Edv{T0<&7@gx}!5w3k-Fh4Sq92H`Mpg
z!J~&<q}6Vp;d*2uCx5O&lbMTr{Q{Jl+9*tpV|ZRTfMz-kAk#YD%zI~8kI@Wa8`p%;
zHnm25rb~F&Z_YHDrZkxOldxb^oxb9ki)u^0<8G=(1HLRX8E23EjI)<+s*HDGdns!;
zj-0=MLYpAC8<ykxjy=o+uV{k3X6jiFP24;Y+=eM)!5%8)5@DS2y=UWMjbD<VWFqCM
z44sDef}gED+V&d?S7j!O)6=N<XhhYJQ8|^Oo=+3>3ie^X^<CDFxK+rq6{9VB&tzF~
z1(pA1-b6x9DXiTC=z)1QHq*{*Lqwhw&3lbQOK+w{SbLy-`^HenCD73~rBUcb-De69
zh7611VpORkVr@gzY1R(Ir_4a7#-2#Lv=2=?dL#GIEo@)^2|oYmD}=^oz@E>$_9ml8
zqAnaoPjTwx9XPe>09VQ_?C=TI?xtQ%F>T>0ywBV~UIsGL^KdCL71~9~*t_Q>8ulCj
z53LepM%=8m`#ct}XXi*!zh8TF>N6VCMs|P>CoYNLo#FVDXmK*BY{D2Qp;A_IyG!#<
z9LKC{8PXCOj0ilNYO40G`m{jZx<P2jg4A*rH(R^;!{1()@1@}Brh?TLR9xlJoV<l5
zq{wL)8ljGNAm4>x7LhhbB6BwjX~c7Fjmsy_BAzt|rUo1rs-t;LoSe-(d{sI@sv5_t
zu3q>@#tAm{!2@jnVk0)J_!>$1)Iq$Ca!x!N@&X*)z7G4YJx1`bv3R3-eP+-RzEq-%
zCP6J3UhaY}9b3c4p5DO3d-&<cJ=nKzKmXr{gU2r-IhWdu%1O_;9y)aIk2wn_!J?4$
zD%VcZ2$ymK)<F27uW&6shx2pAxDuiXQtIm4hc+_W#gw^U=|UYXCp7UfMB0;dWHaop
zZfXhqXCz=AfS%pkL4zqM-*Rr-bu9$}Oq)ndW%OHNg03UG!_D3e?Yj4Ye#!%c$0pGT
z)PQ@lHt05TB8GHnz&vHveNjIMZ;N2q)okUDZXiKvfX1B%qkHoPsMoAB`gd!AxKoiR
zA=;RZRDp}f&mpN)le&Uw8qZXDbvI?yXGtmfuypr8o9+WJeewv@VXc+m#LA7ljx>FD
z3=@C`7|Czmu@^cw7t%g>J$!GC+?i_W>WTU-JJ6=_R`n@V;RsO!XQr$8dpW7L<<QA@
z>sAyz`wxVr@+r=LcNR~mO!IOiFX&w<0T+ZOq!C+tH$Z)74>a%89-0Z+h)c|bzNHx+
zU;hQy?+Uh<2_2m;*9k&upj=xAE`AN*=fYHy?k$<N8ISbRa_H-6;vOAikFv^P!sxF|
zSS--km1x%x5_OrTM`!eI?T_TM`|;W0kMJ;02mXO|a62MZrOP|FZ3v&B&S>H90c8xG
zlaz}vPe^{H$5c1<s(;GFu&Kvl|GFN?%+1H``<V#o*9DE;9MHH2YcQEhA0K*?>DpOz
z_vGVX*w46@m_?nfMAqY&T6-?z!b_M^44i$?pm}FZnbZZvu@S5x%tH6pjZhR7jw~%3
zl|9g{eM^`#bxWsCeOS;aynm4Z6)zWUt>>$4W0eb3^_xOiozkOM)rwLdVc(al(PYs~
z%=<Wijx2pVzIX^*_FjTR{~)e9p6y$8%qu@B{1QGiMWwf?^yvkx-@(GQiT$v2eJ~9b
zeTbjjK-h+B^hlLeN27#^zKG$jGMJ%BC%%+@dLxT@-1?ScoMTAk%>H|@Hd~JVEPC*L
zqm=0hnOMJQ6OvQ1=%wO-|CJ#q6pofM$yn(3g+{MyaX}dx^&A9mqFSsPIvJ-oE=I89
za!mYSH42z2BF(&w1*^6pr|CH4Fy$b%@G(8Wk1)7xU1U&Z$#KQ_?#Ls|etSG~(ci<x
zgcOyrm7dDgZ242<awVuJOhsZ6|5oV?%8|x@!IL<A^zP?y-8>V`TGhvj?VI8L{$xyN
z@kV|rSDs}l_$vA|l7-`cCHu&u=W!u8gmnXNViF7Pa$@h|iIqO&Iu7s;>4*@9)-zJG
zpiKme^V`osspHCpg9N62Eiuin4c93$qM3vucII+q#iT;V#0rBajfF#I1lHVpfErP}
z=R}tE<Oy=}a*&>I7q`=&;&BpFoM^yTcq7VHvTV?`KfGE6Vb#bPIG+3zDNiop^8KzD
zIAZ}ibLE@G!r~<tgy$_R3M-m$f5nw6=I2gW`G?>^Rq`bkQlJ19H&nMWT;F>JVQoUt
zQ1dRn39VW^=i$n=YiL=w2WBx{NGvYEfy1Xz!gP?T@gXS3GO-jX^p^Z|GZXVh_r!AF
z*3dN6#pQipF*iS%`EFgSvDyWLzmT2*b7y0|=nTzyHp7%U%`0S?bq7=D6XEE7V)d0h
z`@+J(-IvjFdK(NI(F@^U{HQ9@EYFO?ig&kT#p=O$_k(rFP{?6Y5Qb4pcH?UBO!S((
z3{6HVh~ATlFPHp;Ji~4f8Cs(A=qZdEXd>z60etZNbxi&89o#>A7F&;=RrziVgBs&a
zqTSTm7)7vc(+?jE_eFAgDJ<>uapn7U*mAw%x=mfZ(WG@dSSOd@gH8KTn3aHEc5c8O
z@0l3=;TMn_n<M7(A$+>$a;@E`vsm)%$$J>oX#m~{DMfDlJtWu=5x~@h?*zS1)#I4&
zBqz|glcyL2G{%s2gE29u5Cy3*NMcA<#OGaG6R{9$)YOr1v7`Y{k5I^fiEy_y#;hak
zkLe**hn4Y5QK`^}l7*(%aqfBs)4fJxMLQ)VPp@Fe<S+5bK{rhAJq4dN8wR<d3F42e
z#r|WAgiucuGs@Ah`#^ZLZ-zC)rs62m(q3pByb2{%YGEc$9NGXe^VA!7@|@)~(uMov
z^(nLX4!^e^9-9{;q~|bP-%40YsSSD!d5cj#Iva|!ad_hs+<koSh3*9_L-0h|hjhod
z21?eq6ym_fZP<2&5zzizB=C3z;a|V{<`f!Ep}yuhl`IYr-l?LLhuHk>Sw`LbDi+J!
zCS|0x@8n7FQY7N*^QVxha!0**TKAyVEbMdO>8svOs;#nYJjj-^$eUiV8g`FJh(P3O
za1qE^s>WQ6(o96<X`zl0v6*X}LdjyeM+MsOU>aP_(<=yQ5Lckf?eZGYJ1+2wGZ0^(
z2M^W|J@YB>J*3Q^6zg-*F0HY>40()5r7QH|qA#uPpHd<xM-Fln25=&>Q`LDzV9H=#
z9wRe`FeP|%)pZIGt!b=rkxE&&LHFmg?Zt>?Er=1LItENtsv4nUhTk7$mBNBR5ZYXz
zS4mA=mV>Aq8WOrX&|qY;stK}uNLdPb$u)y=qRQ>RmN6*fD9YH4GUg+H)&v<7L9Gt!
zf2yug_)#U}d>zV+h49bz<{X%T$1lj3W4$O7NkuI3HmoUQA@Y`mG|JfZS2E6}j5AC1
z;Y=g8s((SonVc7L%W9Xgm@+o`m5fs<V>xAPR71uxI`dAQI)Nyr%D&LbO;NvfZ`2*o
z3mIuS(6Q9SrJdJsX2%sq+VnAU(0HuwTm*eS<4w!f;N~R}9yRkr)1lMgpFtx(A%eo8
z;m!X_g?Hswc0x(Y6)au*BbHC;fp^}mg95taBuQuR^Ay7D9&y9O{xk7;%M8vB#)#gx
z68ldIQN!n(qObrze|Z4yRu93rDeiQv(7;RAhrX3D5^ns6)m!}V?tme15AM!_eHkN$
zTM2V~mj?M2BsCq3x0ieX1>di*<6q;%FVqRtB?^Te=YlB+yn7Z4rz}ToX%>F|<tkd&
zb4FemuP?2DDkvIfeyw+zhN5fAeD3lHTp$8ggMO{C=wmuNrFmF5V;(;F_6Mw)H55zM
zH9{$i{3RLp5Pk6k;-9~z>c7eqw{hlj90s=@jAae_5+LF}Mo;+&Yj!!ZCSW8!^6LSK
zfhjUCe1%_*a&Dz<S*4E!TeFz;e_{<sI%>MpG1~(<gsZj?r{M6<=WzP&aRl10M6X#3
z(YjO)t)eKbSh<z8Kbm}2v=wTx-Gpr_yXd)>f|o1UbacqHe`_r2CDC>pIvaaGUxAQi
zb1{33Hx!I|XWTxH4WI0W;}ADw(czU?nvColG3aV*i(-z8?gNLzoTf(A_!cXf&aEV5
zG*d3?b{&rS?L>4;WnkOt&v8Cg7rm)(^eh80VP+uh0V%XfQ!pnn0&xaRr=*wuNdldL
ze?b#o|0?~$0i?l)DW)<c=}wh^xUAH&^Fd%8J9^z7<J^@<6jAWi`;zeJ*8~5h*Cmm&
zcu|HjCh`k-@Tx|GNmti}1+$(^3yEh@=3OMwxYnSTOIcE=3Smp6a+R@JL>LVXdhA^3
z4R<itMbg9jxN|?6;bbMGB$hGtY=j`|a@@Xs8?o_ERSH!@OPe0wQW{#%)V+uvKrI@~
zN-iXXdwH&LHj*=SMT44DP`yP&K@kuz<OU=-s#_0sEY7%e^$yZ9>CK|YON(yFA|gJN
ziDVV`#udE~b~pD5#s|w6!<~zXBYXBT9KMHG%SD{|7y@Isiun+g1_jeH2tOyN%A#T+
zKb7b@4ABXpH6aIGL(>j{LB0$+QE6YjNzY&*l$4|3`7dA&-m^uBQ23_Fh(r_C5`^Mr
zxG)hhCzpLnWHRQ?*JFXGG1B55BJ@rK2clFZV==vRRdaxd13jOqL2bGXK(9W%;A>oh
z?>DW(x*f*^8>aR~!(W@JGOB0F`_^O{gop#QkmnU<b~&#}gQ;0UI!;kufy%=wxGLmC
z_VEw$hf!fVuHFpi%S(fV4*DXFJt41GFwDgaAr?>uFE*~^3Du-Y<*o{?RVrS=3#rHa
zJ(uQ#Fn>Zn+}=0}@9lh6;VmXyq=us>ntECiW#kdUA|LY_r5qo^IN<d%wL**Dcm<zf
zDHju(j&;%0%>;$%4{<FtmSdJ-RfY(wo}E~D4$Oa7&`1(efFzW6O<5N3UTQ9gYu<;5
zA>5&JR%1*Xcig*p5_>jn!>On^%0~=Cl0O7BZv|_5Y)_s#&qYx>bc{?`^xFmw`eG{P
zlZboFd`{tgVz^M{Z55Ok7rDYX*VfVD*e2qLa8GPf%1TI&E8qnAVq-<Ti8&fKX%0Ip
zZNwfqj*E#zq7te?*(5S^G-??Ht+cB+6Z%Az$|87_&Al3<MW81n`H!&o;Q8m?uV0@-
zIKLlJ3-px{xEe~sm$DMls)T5Uh(>NvB{zf?@1;oaSj*&S5Znx+oSV3HpT)t#IHsPd
zRhE>24t0W1>mm#Tv0zOyMJJy+M3Y`F;aLjlCc?YHKdEplbel}i1a|a#d1x2m=FPiE
z$zV>4L>pE%zVzsuBlT7&BDgrs6&`vGogi4h#ez=w)nqT+vmh4%BCL!le8rXW6YgC|
z6)NH4K`50d6!ax07?ob)U=fOxwN%$%Idb?Mg+iyQXZO4;D|D0aEQP&=P<1i&fiPCJ
zIW86aF6crbjHRPRLsm5|#oSH^Um7SPm8@j+PKxL;>V&>;3_?Q(O&s0vC6;gev2xM<
z{27J6+K-)$H`_69mu8_#4NxAEl4P8IB1J<QB7!bbHEtIdC*pL9CUb|4o)z_RyQnAu
zXHs<#VrNkOj!<HR7v_SH22D#lJ=OE8><A_K@wl92gjROCoRlk+kD!n9@)B?}+l2e;
z)YwihS#~1s6__H#M!R}D?;NR&(nS~3@nfy^Y&#9|#EcZ8ahM~RMqfq$)G0K)W9g7l
z$gF4_%c{3anKt?GDRZqgEaAlM6=Z8d?LiJMC+8DE$&^Mp@s$K6$$OR3@V>xkhqH+p
z5sjFhBwW6*y(9(aQveS$6BQ!Zv+YlC=82f_peE3zVgF3VNtCe+jjZV)p!f3EGHz;X
zBsevzx1%r~XVUc0f{XBHjiz)#0xo12sAO3)%cGdGypsK*jB|4n5SnAcexAu#Sk9zp
zC$UC^$Y;;VxD+XwPY^>HH?Ed37oz2abES-vDC3~#WL$_CMlzKeR&XVZaFy%@89$(m
zW!&z_1wxgKwKWvvfmn{yzub6sOI6>fyEXal2y>e==eE?C2MA+oMKN`Ygmbf@XAm0O
z$&eHmiBNJP2=}g|r;kQVFU^d-h=|9mC8M;3IYi)(__dv&It|ZZU}*)PKo*G>q~hAG
zyLg&Kog~$PwXGlg{Y{W^{U#z(Q;}DxZ>uh^>Qk^ctn8gw@282|p%0N+$oE8;+X`9p
z#&^=z+Ydg@mMBP$#WjLb2y>o@MxUmw8yfnMYa=C|SpbX>QWuF;wvdok%qypjP)L2L
zvZN~K;d1Iz)!fUIzo>U9SI!GU4WM9C>3jMk*vSw{j2Pa1_?Y!IV(8EoYt+OGB06dw
zgx*7<U~i}fWYyg~|0$*MYw5>GvY8CGuZJQbHG>g(4Hn>gp?)JrWZ$J8O-w)*9d%la
zPL`4jsFXRmf{i7>ITsS0MbFRx?zHKw^h5}~dX0|PH15OHMLjdhCJ=sx*|>Ux4oPNf
z2=9?#V<>r_!hO_iV!=?ReOW}iNQit?H4*0-O`cDPIta#mA?*-_psQ`XSQkhDkg^1v
zzj7b>d>&G{7HvPlAwb)TGSX&YkSU`Sv*y1~@Dw_qD_&YAXcyvYYzi!#1E76=Gd?|k
z4Uu=_XtN2=gLRC8{ZiAm;y5c{O{7w=F=#WEs&w@$1Y33SulN*fKh-?&iha~S{Rs&;
z)Nh4B{dyt5!2s8P_z)}ig#C%${+UBXuHCVIUO&hSlkxM9JFw??&0Oq%<{Do2EcWho
zuxQN&xao?Kc=Z5AE&S}yv&p{h#r%%rr1ac%96}s#rrSs!{Dc{+w<DPzJ#|w{;BS?H
zbxSOo^Cld1^KkC)9&Flm>Tf-rx^uMz)Dloj;QyKgv|Ix5>H1HR|Lr@Nz5UutvlRZW
z`&Jw9f0tx`)vqP<*voQ~kyZ)O_^Y4)Wq<0z#zu#QadZ{^$C^+vKXAlb&`68KzU>DP
zP4C2iq3I9`(^!jPHReiI1*ZNB*R8(B|EdJ|jx)V3F*)hKdIRd~R!cxF0ks7FrxFnO
zfjV?)gTh-Ua4tNhI=$wfx+t{)|4&I;eIB(0)DlojKrI2a1k@5xOF%6FwFLeG2?z!}
zlY-PuEdjLz)DlojKrI2a1k@5xOF%6FwFLev5@1=?uNsR4<Yf60ienT+i70&q63DNP
z{7?QV6yr*HvjU#BQ0OH<t_gL$0zyVbA<dt>Ef!G7iLY8t>~5i`?EhL5ifv><aR_Cm
zqzn}=VA}o#cdQzd#GEB!OsP;P_D?nz9c!@qQNYJ3VZn3F;+TK(GHN@Qhy^hkEV3yn
z5eP|Y+v5*xtCX=o(xf5_dg-AoP-77)OY<sF1zx(}U;G2FPr%I=m}CEn_fmBaEMyYi
z^FJ%PzxYno-Kiy@mcU;i0Rgl?ASWxO-u}Ng^Zo?_s-+8&fJR5S;Qy}72<9Q!uPpLa
z3W$fC7*#eM?FPY~tp;bQ<=+4DO`Kn(oGZ&|6A5UDe@A=XfG3fSSBtKAYhY(M>WOjr
z%uiUg{`)_DUL7KXSX)^U!<g`gH1dS1U;&vSF)@jdZmcZ+Uo_$}8#;Jn%%lkjbTC0~
z;$3XMmV<tQc4QCB$MLVe#xWw?{0gJy4_&FQp#@s>=!Y&LehB+vEj~L^L9$d^Ct@u_
zSeqM>t&9*xWSjl>H7!=sTM!maU}Yun76)g3T0Ha{^}~oZ{-l~OMEKd`*m~gfD;OqU
zbys}xH1&u>FD6824s*eusazd<AIzRV4QU&e;)`2E8Tg~`0u5b#Gb>md5`v1*f+{R`
z){1AQCLxWGn=fl(O+z&8JsiWj2E&-JldD#)#Kr63FWdbe->zS8DAv5w7}tN^huuFN
zL2B;nk5(NAU%WeiDw020j4#8Y2&gACLSyL*Sir=2&&sVZoJk7h*L5&L$}VH;q3g(c
z9thw?84wZ0)WQ-5IwU8lL{7+gnnolC_V?o6m0M&mB%zb2IG0d#mCwf_0HcO=!jr@6
zu=h^ZOOO1mA4GCPxVYM)S&KHXGS<d})5mc3@+~|SFnxdPVD;~638*FTFOz^AMy7<2
zEzBV_Xbr+8F8G^`ejfuYGO%eWnNCYy@9maIbzwy4(;|{p<_YY^|Hqqu|0x(tHt}t9
z7vchu;a=H9S}-;xN)!p2a%y4ue&w-$$B&%nor61L_~7oSW1@-Js4yIUs6?LtD-@Fm
zZ~xj&M1e~C`}I8eNGxou(YR>{+{xLJ`}iTw9Xf)0tbw$)GlvdIsswvpNP(eskEG;e
zBGkOx4tbTRo-^RBg1TYujIq!p2~0#}0rLJLkFhsN2Nt|L8&U%No0{mOC?_2yT<I-Z
zumGn|pL^By{cXRAv`sKz+WTm0k&P>ao)XIn2|-8WRLg+$?G2E6>@ZHhwgE4Kw!R0Z
ztyqD!?sA0Q3`P1Y3`OB4-P#Sp$1_Kw?9R97J9afeivDFy?1jaf*1}J_m;){wG6PNA
z{B#a}qAyk?(}q*s1~ANt$IjO_-~mV9KA7E4A0Mn(PuR9+%0paMK>R&=QOqmJGXAJ8
zmYX54>v&A>S05%uMgZyVlO8`JhW1iyIT!a*L3?x?gL$Lc<ME{{cqn*s{$&|wisBR`
zXDiTk^ejR;I$-(oO}O>+H5GxPfRi9S6c#c8sY`%MXOp7c;OXdO`4F=|*o<^O22g3+
zdNxIi;&^N?`yB{H8ul2!bOU<($`O;4&&k#VS=WzX<+6`(AtgsR$%{>7htYFqL4IWu
zmJrujXd+!Z1UG4em@6Bx_r)WA_ZG2JXDnMY3}wmj$S9GZ%bNCR{Qf-5J8}U9H5QG(
zyF>MkY6++%@V}FQ$O#h{Ohe(x^;os92Fe?8#`Q!+D5RrFV#IWUls`IYRNRt*MF<uz
z?S!)%mSe}&TCnK<cX$4`oLNsynJIK3Go{QDe(Mw-n7MR3;&-pY`eP4%Yv;fHcMVH-
z%$z+Lb{P?bA5J3EDghm7Ow07m;osC3_rKlAIVbHEj-$Wm5;Yt!X8s2VvMMLZXc`>I
zqtd;;D~7MUiZSyhpt+kN;k`*rS}uo+lRn10H43NB*G`1^TUS(Rz%y+|YJ{du0${0?
zi7(cCiqolS$jqcCSgwt_LCxSq_Ar#@BkE2lZa+$frJD~t%r%f!D2J21C8YFPM@8Nt
zo@nw5=Zv^<9q-Sa0g-J886Df>{(;Z&?Ui`MMf0I+n6du=IGY(jNivSGTQ~41T@yjU
zbx=qmCNmc&7|0Z)Tqka8dIpS~y;0ZG75V~uCc*T>&tJrSlB(!B`XJbs+X=Rull%y0
zuY{@arG0qq1|+yB)V7ABnGOol<8bCeEF68j5aefp)2lzlH@}1-BP*Q`95}zv9+TF6
z23>}cs!d2L0c#(!J-XY_q#^EY^nDUE#UZ45L&V*UfCh2$EtGM%7G4BbUmM&Hk429$
zGtu5PA6q_JhA)4rL2@HEfv2A<K&Hir<h;t&pcWc<2=^p`(#{u+48*u1F+rfW2MH;3
zh`&rs-IQWDIvOE1;x@v_Qs(9ph&rwo#3NNAk2t)M?90x<5hg+(LMjUfud*;3*Dqf}
zj*dMV2l~NGUxQ4gPjU9#1!O5r5EMjm3d-2R)d~7!m5V%k0TCIQwO3P9A(r#`Klo)I
z=6-yT20RH?ViOTp9EPhu@5ZoCx1kkfR@190mfE37D_@xFYN!a4p2pn4)dz`iAU>~`
zvjsF6-z>{d$MsVek*jA8Cnsw-Iq36$XB24_BA=|P<)-z})XSX2L_2Yn3bdwEz}UtE
z^~kJdNX%=2Ga&BaUEBz-#UL+A$BiG>V`5};yg&U-l-<~hC0oxSn>fY3jhjK9n*(ig
zYs!ao_fdTL??jRy$qFr+`azi%j!h)+dPq9w%p6*<8m91X7yuUwLzEX~;dWRkqRBbn
zL^e8mBFUt~1a2e}QV@0M;+5+pkum024uFlZF3R%Ac1juNh%C^gp)+!x<dBWk8CuFb
z%Kj1}b8`7Kbr8_BF`O(6h$dTx+|)Rnz7mQOLnpMV?+p{Oz~-hz<HW_=$iH(CbKgFU
zfcC@j-uzLRJw6YUmw$)kN}{xyFQSZG1HnxjA~8G?QsUoQk)Gq~om{xo^T6ZOB7}Hb
zFoLp^WLHTfu3(UYnEHk$c5rgGfum`@>Y3=|7SbbLMi~5j<cdtuuz4G3^1iYYVsPtP
z7&2>}BT0`%?{@0VLwIl9aX9!k!jy@FFmNgfzfN4o-E&uvOl3y?Yq-}9f;c|~>8V^q
zyV^mLU=3S4M`FciA(`0jMI3)hJx{dh5?rAZ&R;^rQ?f7<FS&`o7Ywy!D9%p7sWTUm
zFSkaM22P~&ri|SL8RsJ6%q2u;T*nuy5)ppo5}t6MP1}BeUpsGToTdRUEKt-<ErI`o
z5)d=}%+ae6^{!=w-YrPSwPWXyKqs9Y*@)}AT2tR}ZX<i-`LGzc1U81FMi#DIxsALs
zBLp_{ha&zCBD2H@@O4LiUI{r9tf4JdASvb^bxABt?YvMgz+E*5D~t0HO}6r|hiU99
z7`|i{m(UUOFgy&mIS0JFQ4jdlLmkc!G7))6veIzl#tl5pDTWj0U>`SYV&aQwilien
zoCJd<#O5bUd|{*wZ0u;S(5dmH#1xGi`a|^SBAswqxRX*bllzg-&P<YrlNWBQPA?@F
zM3dkK2)%rsY|%OhZdDJKB-$yXuE~sx!2J}?shpD>ooo?nX9rti@+<Na@g##hI2Lu#
zqP_=7rzFTuiNf(;?$qF9dGYQvbzBg_xlm8VbyA45ct(;9>|sXSe@*K2L^7dT*qS1b
zy8V~y5wIo)iJzYXbU3FL<YglvE(Qu?SJbi8QKc>_$N*kWzLmH;*KnWCu0YOX4rXMx
zX4En=>?Xn==fJOVL&!L{=~&pnlqXJm7)hM`2;^uOt8nez$S)uwaopX|8+ec`N26wS
zq5m`-znm$+wS<~8`HS}>Ag7rc6SLph77`m<%APu$JO?Rhg;40*qgf+gSm<k2+FQ3!
zpk;?<b#0MFncBEHk(4qA_fB3wG&yu^yc)vK)kcLAUtXMvTipJL2y*7!-q78SJQGS}
zJdVVvtKpDo2~4ddM=HpImYD;Lv_!~AjH1kvx!o5|CdJsh>`Uy6h($(bhANVL@_2o0
z*|L}<jY6VtWg}03U&9c%S{X1`ybzD>-a>e4A)3|q#I+l-wByKel6nu3DLSxo)yKUX
zx2le;{*>sNpkYHll%!@t&)NpY<a|hu49BgA6#V?%di<0ejav`mFm(AYObzscopdL*
zeN0XRb8=OXGbgC~7|vs<v;$tWnX6Co8(%66c$p#m`%S@wu1z5;E5y*%YcZT`Y&+<2
zy{gw1pUv!usB3qi;o=L!jBu=8G6#8$I^o?nyO7s}Mp$+RESZM#)0fL>z#n|!lsOqs
zah3lg-&18=W+uhq#ub5>!3f>nnT-hp>LcyeJ!pB<!yBPHG2yFdjF>tZt_02(@!=Io
z4NzKdD<*t+90O*(hptXiT)G|uTNi5>$3-Dr=8gB4&qScH0^!k_Dg(LgcdM}Un`7uc
zVJ^BlNXeu}7J7Xh=w@C)A6Yb(PVEX)Ng1rhPDRskazq{e2vgS`A(x95l~N^{hU!kF
zFb#PVpD!E&MeGA)>)OCczYNEAtVh$~<8b1;A7IdY9J*?pM9-;DFm7x!diZG2_Vd6O
z^Ty(vqcyw&LVslE8Z&bm3cj0&`THKI3|^6`3+noLBl*fr7`j=aWe0EE3)jbtC2L_z
zg+q-+;wZq%OoBbzw?fNRg4xrDLO<&fl9Xg0)Xu=Hua6+0NlP@gBBN`b44w^rA&R+-
zVGCs#IA9b81)1aatynnJtBX$iKf=P}3XGmU4i3^XMjyzECNV&9-K}_M&1S@A*6zg>
zXah72E#c|!OA@6L61JpLVHQD3Vg<eDWJY^T(;ZV*u0Z#CCWwiShnX80&W^3a#7(hS
z@bO|;q{kpEwg9en%;64=M4h>-(5a3Y>EK0}y2yvQYD!%BaSgsn_QT*&?chky@Hdg;
zNVJlzT3{I4I%EEa8xUk#fYAG~FtT?<(#37H8}M8J<uE#s!?96Z#@skAVxnSW$;#x6
zv2V>nJ6AoDPZi)P8Sbod!?9%bH*n}O5`6-lAt}wlyzjpQ((YpRw6(D8H5SuHG(kGa
zf8=%QVo3NwOq{t44SJ2h#8&ReDV9QBRs?I^Gz=U5F<SH)j&YsqAc1KqhF$?^?z9j~
zE*jyTdE;PAk|8<>Ak#5Ks?Qgg@aZ=QdTSvj_G*k=fk{_l0Y`BJZqxlgXz9D?<RC#z
zR63kIol$SY46NRLjrXHO=+Vvih)Q(PxRK~~>?FRt9REyFsm3s%rK^Wo3uofoci%%!
z2H`$>XVLBLI82z_9V@;*hQXsdk{nHnE#YGk!-cz^nKj<|U@foL5E7*o7OiefGM-HQ
z`0YmAr*d`ZKNNF2<s+?B8#d*s_+r6Se08;UmyJNKq{vN0Bp3ftQWs2A6hYIf4#v#s
ziv-JeF>Tu!7+crFoaGCUnUusyOBbGu)G0fT#TV^J5|(fYQ#RirxsizP>Rhy>UMP?l
zp)6n{M(=)v(UZobor@;!5X#HBo*&xnT!zIL^)YqcTQH!EK{iYc9Rob^-hgqRY{khF
zkrap_I*)l9{<=x@@}EI&VfAvoQex_l|Hs~UfJJq!U9TA!VCcOGf;2%uMGz6Og1yJs
zlh{i%w%B{`J+URWXks+6#~OP<MN~wT-UI~cz1M%80Td8#a&zy^f4}czKaav}&Ybd|
zUEaD@g9QHfNI+fJo$KyeuDc|#<GO3zSd6SkFHqck5cY6yD~=AO6U78g)Qa)T_uEj|
zX%R+i`C)v(ON8Z_V#3^o$T_tVoBT>KY1tMuQQ{m-O@)rR5&TbYMQFM_mT%t+XZ1`x
z4~>U`c_Re;b`%+$dz*L727>&Nt7i-SoR`?VauIHZabNqONzi4;Ow5?_3HRwpBvL=H
zR?EiBabKWOyHQv)a}soNBk?M^6z2LOJd4kQg|QXcfJp+@?<;)YM-h21PGbJ)cy#GI
zfFwxGP#SavVYi+klq;fh-%%LTRu|&2Lvj6e1*}{3!=^1?;EMqn7(8n-Ms>46YIGC{
z{F<WT@ey1{b9gqh;CiHlZWETF;{*k~xwRYf_P@sTZEMk)%3?%fDXh%2VZU`InVetB
z$fD{#mX<*q%-y&eolHs)k&q2jrd7PTeh3MU6PN+Ov_}#grDvDP2oWQLZXz`KDmLuh
z2upD?B2tU_t~7D=r#-l(?151ojFFyN2CL?_P%BBp_nTKjtkea2=5$902_{u-TR<hw
z2OIXBg4^i1=xsydwDNqUm8ntoA=of~12Wye!2Bs)kmdIRVk;+n5q1&Nciu&t?)~6J
zN1tM<FQTtJg5L+b$onVr82%+D^zH<G?*011=HUCGipcRlj@j$(qT9ly_?-I2>%dqv
zcCbT-BTKRIl@%7Q8ck<eF3P#bE30ZE+HwoNJ{nGD^tG@pO~TWl6f|wBiE{rSq}cSs
z?hTVk^q7R$LKRpUD52H2i*Vp^GDa<24;OVh%hK|oVXTMDS62{ost{{t_k=R{!+uLv
zp+B8Q-ba^X?J0jcMoHqtfWo^buMYcuQ?Pe_Z^ZhM-L^3evCJ@d``kju0e!LN;4^$a
zaxnD5k7MCsEsP(piCOh1@KQrdOrF067V;(3jPjAApbM?kSJ<{;9e%xgOLn=Y{U>9X
zn;stAK89c_^w}|y2<KlM2G5393oTqew5ndG_q%)jgI_8Ycv?fjH@?S_CQA6Cy&<-4
z*nx!POopv>Fm`%>1n=C0RTplc)UX3~ADM^tp6ziiIUi+GEhL?phpiWiv1HpE<o!V&
zkKol4Xp2Q;1{IpLMF$M<Y>o1eTR3pW2Tff?n9$1|jjof;G`|EzWMZ5&e;2IXM`6iO
zTBAz3FwkZspp5j}4=&-mok2*;Oh>mV6VOt(0zZtJgwtFXzF52oqecwH?hB9UiRR&X
zZ*XQk2YSbO__SFOas$s{{l-wtW|;o&u|2rs7lk5P`ZS8@g<z{P(}W*lvW$)x{V7zL
zRy23s3Pg#`v3S=Sczx0jL8%pR;35H1NQ*U^qDL<ca0wwBBfaSrsbqSi(ekc|Dw(;M
zd#}iU#jFDm*gJ(hC{qg~*jmct*Qwjka=}_ywdxGB7ryj@rsK%IJ8&OJcGa}U_~p3`
z9GbPjfR?migHB@7+9Oaf?T*c>`=LDO9`;|qjGvjKK5XH3*rmnd*JB5vZ0(9(?JRJ4
z>Lh#{{ss=7<FRwWKr{;XK_O*aSnw2IF4_&7_G7SUP!qCvQltEc0$)-t2V?t=nAxNq
zq@pw^#zf;JbD0G>3F%xlo5SGI=-*NWm-j4W?tdaClTFV-C`6QKLtlqpj)Y9yIIssh
zGc%FJ$jb9As}SWe4Ac5+lhXJw!f2`IWiUk~#~0I*gZRE?apDW5q-<hUSacqX4tj}L
zxqLnD2E@oLbGfqmHia5@#kvb2JEVflc(O)TO~F~MRvw25Ggff|x?sn)VQ6eniqqe%
zfmCfJI_V@}&g#P~pe;bEnG>e=bcZDT9uA)MhIy+pOc~e~W~Mr1U?amOjhIK@e}(HA
zu2?<I8R~62;S+Z|WIexv?@zyii<bmr`t-#2k>O<PEJa%M9n4vGghu8X474ZHaq~~{
zd2eS#-#o}Mb_PY%?o|~lpxSmE1~k<so%T^2xs!&^znY3({d;5Ag%_MBd;m%%JUR}E
zne?*hlTna8b^Rwo?MXDptDSu_l#%IBq-9METUD~7#v$(RVN6dA$A%3v$ShvTCYzg<
zgafOWqF~BgbkTc*_22wN9s_#dGkK1|%xhOl!E7#GFW(qo_u;Q$Zf^yAM1e0O6SAsh
zCoEa#2rDONMxUPIm$iFgH*q6|d^QZ`=U+gf;{e!|Bx1$xH3&~L!9u=o(bE&ye8!hv
zr932voye|8Wj>Qi#k`%cX!9AC54XW!rB-NTsg4s9CgLcqbT=-{>!%EcPNW}M2g$@4
zaSQV|{D=Y5)?$EzMI{SzsRTX8FU8_Pt#E9^a$I;!L%PoRtsEc?|2IhBe}@DVgmqW9
z#=4u8i5%v1j~g=>Dq&BtZ2l^|mbbvNJ!{ZoaDSYN7nm*?b(K9nivh{tDKM^Akma;Y
zqJpd^Td;JuKgO?Ljhs?-Sau$RcABx6w{R_dL*FuTOg3?4UGyBc1g2^J*nIIkJ<bW3
zJGm1&c5tNvU#Z~Kl&&H@_@9pG4auutFmdfqP`2oSP0PBW)VL#h5BvmrDR(h$-ZrG>
zmC6{XMPxZvRcel<Tc;xL-cMAZpL4Hgv@RtFJ68C>ZQ6EBHm0{;P~P|;C(n4JkLwr=
z7|;#Z_Xkj6?S;(SXJO#(iNT%B@#x4(Y&q@E{c|(ADJtOdx7*;`xGR<|?vDFE@4_kH
zSQKZcz@*1?^fp(-!;8P*#N#qdm^KEz2lT}5D}gMEr@~RAiI~CQHfkceHqOJ^#dC2l
zOc6_2%cafx5z7_g$-`&JSGItwNhG%2q(jri2p*qJfMxbWEL-;to+mWIjw6d9&Ps&O
z&ucO1XE}I`oP}w2=6LYiVO)+Z#f&X|5kMt))wR2jH|vUh`=_F9*AzPPxbBw6V%)Tq
zC{l65mh}T**0BxRc5Vyh=!e*O?iLK16=K>j57=m)#o>*M;q%2REO8gp<@Jx-<I_JN
z&N~7tw~RsTZwIjdRxo9sfpVAr7~I+%agTq+cNe4a>3Cx@yZ6HPar811fFS18Wg3#F
zF?7yGcsW{-kva>8%%d-l3CGJTC-ChzuaQZ8V94wt&`pcLj#-OvuUsE9`FuTw55{5N
zJt&hSt8(&Vte&?Eo?owpU2!H7AD_aeU2n1Q%YHbxdnW=T$&8y`#d9GH;>v+6%c{`O
z)5FLy0}%cFH&}J@5;8Qn?;lz~rt{8Z2^OPE8(ZioQmwOgf|nN)XNzL$7Y3D{k`gf#
zOTsZ>_6C$|dtleDF|c;D1Rh7g(rYZ%P92K;fGgOv^BA&)t(r}#`5-Kt)CaG<f5y&J
z)Wa&f|I+`Jnhc9ebCY5blf)Om$ruqHf;cKr1~%#pgNX3zbtK}lvPgS<A7Lq9Lte`e
z6{++zX1u`0pM4M<lZ7z@ePlGf|IA(JHPVMF(;t)#95G>BQx>)=;ce_2nU_?a!H<XE
z??!BVBD7QUpsH_2t1SvY{d5-EpY_4OnTs(XHxWmdEQX4vGL_M{@J~oa7T;80STJ%u
zyVAQ!gc(s00-xN$(R<Ml`FxENq{$Tu2Fm2<siu{d5Eq4z;E)e)yMl-`!c-KzhlZMp
zND7HYL>iS%_~TV{BJAA^@Fq$^CDjF?PlJ(Qp$jh$OBP_hg2Y?{;#^V?v+B9J=`^;$
z*PM{8bR?v6Cf)kvZMZP02={M4#?|Ly7>RS~eYYo#vOM(6Y|y@CFe>PUc;J_U7G6aN
zJ$DKz^or=|X+fk^fg798BAjGPrn<D?xj=~vbC8>r2hZti(BDxD7ms~|t;fCL`K1>W
zDl*_7&56N+;i&KjNR#WqNP{ZGjJ7=3zl$<XhHh#;)byLm7G9v9++?N^DTqrDa`zaf
zt^b9ORgU5UF6#WPN*(4TArej113NmJBa<mJmw$eUG_g??1#uZNQ|{ovQ8x_h+#K_E
z?M8aU6Gk^S;&yZhqRi45%@QM$VW^O#_f-Hn>6st~jVyF1J3kd$MO8x-CWIk8B7)&h
z9-CdyJy#3A`X}G1Y#!6QFKYs&y`mV;e?E-BmtjygwWDlRm?y7}w=qoZpgc>NQWg>%
zEIZt!dlwdCDlprl8ODvZBr1v`5@X(?fL?teM4ih;antFS9J#?5_~>Vt_W2STs!H_e
zxMJ$0R*WtvAvPgSHohyE-sHW1H{#+8k(E^hH3No>wF;T)QG^!^Jty;-g~p&T+OhD3
z<n=Ib(G@eA@}A}L`Ob1{DV-HenG%daAxv0bE9J%;mnl!<kK@%Z7;%i4dA7GE!5EF)
zf*fRLWOA;sQom~4R}k4IE+QDonWUXoPDN;NAd+(_HxX0bqzVW+a~%E;{gJBRfl?y%
zNOiR9yR58Fm`jS<40W4|@b=Lu?4e=#AhHl~ckaT|iALLJ?UCi_f~e=e!Ph4U>5Q>t
z3$)=(AF7r;8adFBUIrh^EauHyXjtY$Uel6<L@fMf*xWxl7FoPbc$g0|7288zk;)1;
zl>35=6G}KdC}TrIMw>LyWW*FK{?s0~7-9V8{Jjs1osT|9!yg(X@IO=n>8ZDI;7Hr|
z>u$I&4sHFOdz%6hUqvI5^FNzD*H@&6w`|@BqIm9qTqmh>ae-&-mFvH3?PTqmA`R``
z+*<-7qcCsu2yQ-1zn!oa`ENoH!M<!kQ_0SPys-w;Y&bue20CcyD3sH2k`ezFsrgmf
zR2^CBvOdPxpaR#IoJHi@M3@;7A&U1<uBZbwB`MM(U$BlQMfR;6VPO=SNENvyCCEvP
ze!o_*yO3#q5~i8Ro}tRSl<RjK*Teduw^KI^v&)B9Cu5wQaT&Vprl2A_2A96Sfn@G9
zC4wi9j+3O<5y&WZAfQ4ql4Bzf5)vu9q!-hH=uLvPOLt6nqykr-jkr|q_p-m%{!gAx
zOGgJ&#QELEgGUiam$M`oK;<!t<(Y^IWc^)JD+FFVgIJXzP*F98gQGScoj3yD7ct18
z5hUBSN{J4Zla406X5fpS9x~-`)|L~{ZRW-lRp1%#HJ&<}T-1Gpr%$1d#`IeH;{1L(
zh~U5osF^iqs8<n<cr7*RToM%<j2g`}ML7i|#c~g$gDQt9Ct{+I2?|BE(Np_r-hawW
zibr;OG)vP;_)a6=$=<$`K2dDsVA#|H(@i{}s8q^5kGdoG+7deC?(hE&ZxX}=FDZsb
z(?%#Q+K>G|T)~)LZ7^!tS`2y<fWxb|5^zEh>FKZWGC7s0nJIV`8H3jCjai>14<+g|
zcW&RpV~(}wv&V7YD&zYuk~y);82x@79nKog#P>H_^_#E{2~9%dOkYieVCuColvz=h
zH$oB%&|Fs=iCN05Co`afH~>;QN<Cc)C_kU7Gt~V-lo>`7o*X@ZSD}&6Br|IXi&D$E
zcPY0Tge5bFqx8jP%wD(^@zsVMqdC*24u=LK`a9Mgrcz)3T&R1ck9~fpdkF^!C=tTT
zlcSJ?CP1uZ6^LME!QAS{uK?3lu+&ySNKOWYrp(--A{5JX)N<tKF-PT}Y<V;iic7@^
zx%MsQ?Y@URIThwrDI-(4Euj~g0nGFct`0KKwd~aD`bhP=f_Zm-LG#YTF>>q|7(ZnI
z&Qs}?C~2dqu@(YT3Snbm4YgwCoNxjYz>!u`QRODhLCSd`Yl8P%cv>+%;U6^0RHCX1
zO*IvwQ_vxuR|Z#oQ#6t?hg{JFwx)(COOHiRj2cEUS3lsFeNgP#6McI%N9r%ZC<re_
zfu=i*)Rkq|5MEJUdK#|&_&pwJTjR6gJ&|_zEUrf-Q0Y*FbI;F_mt2IFUY-<s0Thf(
zVdv3-3M-W|8W7+jR>T3%F~<oZp%S9RVqiTO8CRg+#9=Tj$wj`RDq4-4!t@I_MBn=b
zyG}obwzdX}Il;=r8nA6*it9NMXl&66+RV8wr^i@$*Of9RETC{5F498Z$C?m5p%0y~
zic0i`3MwU0h4QLaBR-^<29t)GDh;H3sM4rXr1wGJP#=z+hQrOS2}%-qtfCZ8iOA!9
zv;sZGEWl^|yfA*8FK(~9M93w+M>S<=tExb`Knhg_=1!+_Q4*j%d42^obt=$OWEK>o
zm}5}c)CRUqEfJd?4Fwf7$kPL#l3t$|Ua0Y)BF#KNk*Hku&Q(>VM6(f;UW#0<l+Vxm
zrwoLruaH5B3ZMB&CX-5u<Rx>wa%GFSvkzZ?a}zmGhAzDaS**cmMySv-&W942I7*R6
zFHu@tC7Mpq>GfFg<8zcMXh2U>4#_$+pqR&5#uP3g<|DffuUSEbvzYg3?$81TH^Pux
zE`^4)f;n>ej1*+z+P*p1cRgOVIBQCZP{4(vy6I@DLzM_V`8oB&A`0j`K~ZTK#kz;U
zllvj+*aiItx+3agoa_|UGFDVmtCX>f#g^}w??i#AXWI0r<<nqMP-O1EU?>QwR}`8s
zj|3r7tRHQHp_BIPB&IGpfUFwshLDRF;N80e292D<VV{m)Z{0+?Fj-hkF6R7KR##)i
zz7nsm${H30*=AnGS`C@ZC@<O@$RtXQ*hGip+ZetvDKNKe39XVWlylAtgQmP{4G|O&
z;j@ZiZ>;*fJwk8)ioJ}YXzS~<euDK4G}iuBh%w$xLu-)0-y;F8++2%mt-F1@V(geO
zJm=o#EZf@{-BL7zt%)9x8_PIyBRXv8Y|_w&NB<FMrlp35TrY)HUycsH!YYNNFrWKA
zmB8ZMOsJUI!j?5X$uHs<y;LP2gp|?MQl@{N!_qZBA~I71Z3AtT=cd*B*uvhX!1=DH
zs|zC&ITq&+g;7~5%JZ|3PsLK#rUk4T-Nc)WB4}`L&B;tfK2sLuxb~`OsLR$0W!`5_
zW(L#kxXug9x4Z(>crRk+S_=-fqbF}+z_Q^OyRs?7ul;Z{@-<raRU;fPQ$dXMU~JwR
z9xnEf1h9Bs&=IJB%P|VC#x+pNG(2@CrDrElk0?yWg>Poz;GGmG>F7gUu0ZBss{3GK
zu2X6nQb?I&ug^Lw%K@X%+)x_{!CbtAHA%Pz7qSBG?^HSH6cR&~d!Uwv7WA3B-fq-r
z*wA4TN(ETWsVC-ezgJGUhgo}ngC_IPm5FGQMW>gQMH6Vh2!qtj85Z1Q5{a4vC9}$P
znl~j9)=Ta=1=%G~px5}d_aV&MdKu}Z)FqfZpF?CYp~>-{1SMIGH>1Qgw6L&BVUqo|
z_J49T=HwJ5P^IjZS;M5NMMr89_bTp#kA7Uv=qYQ-sIzHNiBB^bEZZ+=_Xt<vGxOPb
zPkeC*xx63Kf87h$-lH&O!f;HS{0g@Tc%r4#2v*uEcw4Lu+r}nPVEuvY+jLfyP_GeM
zabZ3kW|d9YQ~9J6sqnLQ6ZQ&4>JMt%_Y0^O3VN+YlO`}we1>dYCp0xsK_-o$uvAkF
zX|0N*2Tr5e@)_vdL<P^USMHIu&q%$2`kByY1w5?)06+jqL_t(4#0t>R)`vsS4$$D(
zew(a>sh<yrWoam8Z2JktTDs7ype~(}fik-RXv37Kkc+$Vuu^%hd)j~Mb8Q8l3YZEN
zB$*!1)1(sK=JM$qSU>qo%r!KJq`4P!=pq@$e*}frV=9lWKKM^uwt^E~s-i**P1sDq
zJmLqq`lvI$`g|o;sQiXhWlOX$Ex?k4FS#a2nHpAkoE#MZ6)DqAOuVpUQ75L0Jb<5n
z0`jQ96l7%M`lSnSviJ()x2=ZhlO%NSXp4Yz`zWw%=Dw;znS-ou6QC@WcrOxqPU|+Y
zSOp%#Mng=UV{j&2*sWvRwryi#OzcdoiEZ1qZQIrp+nLz5c~Ym&`+eu%{?UDRSJ&QM
zweP*wx>~DgU23-ie^XB9uLR_$wcAnbsanr%i4vH)W{b4_6+D_q-PH^yWGTofv=y^F
zAnOw5i4F_3Ey7ypB+Ypd+oy+=C&p}h@69#UE}?ycyNvCc&E#@rMkzt4{_#41%T42k
z+tzdy9qPvhs`rLUO|(21iaH3-?G@b>O};0U3MZnZj!IFAt$;V?rB4P(IATboObpf?
zlRaEqfGgu;D4B5%9{q?NZ80Kkr}+Sb$JI<`pGuN7Fa7l^KwV#-P$;^X;}p8hQNvBs
z!LE$IM3=XrKB-kD7F<5-gjUeUL13nCqgN-o1PwsKv6dUm7tnvJS?=x}@wfTjVuvtX
zL__~;QK05D_AdkDmbJ>;o`&%(<sJN<)C@W>QNE=Ww-Dx;QHaU90HaDn1PnnhAQR6A
zs9mv6YJUb=?fpJzp0nOV`#}p0w=8)gPZ+Kr>s<_eh)v9$2@y4Eykwx;VMMy-eBM00
zZl>rCGC9QIR+%5DI$=c>SERIgyb_>`G1LsXW9IznQ%#+BLyD5fA6qytlqsz4FnJLi
zQoWW-WA>s}V{*zIg0Cf4&Rw3&00bx8Df{?V-DD?8wA%CMvG$~O0Z%wF5^whb$0Ptv
z@~TVoic1$3C@l{-tF$wR4cP832DZFZjF4H)H3&JC6Idmu+qQr35WqY4BbCIMv3E`c
zcd<GdPVZGVkHpdHTPT$i%r>*aMT*#u2v+OLD}ZAT?Yua}tT^y7x=#<V;>M^WV&sXB
z6UZw7z_DFTb={0Y7F(+~L(y`m+1atrK06JU4_JyE<E10vgY<$AKC@5&l*jQTCK|Sd
zibZ)vlSwSH;zV4Cu-mx!Wnul{My_-2-Eo1qVdt$}UncaZ<r_us4|vhwdnvQc3L^wK
zICpr3AXnY9LrJY6ArM|QdVV=)3SK?V<W1M&1<u<7P(5{s<zq?I(ecvQut>!No(%r7
zjOuB3KB(rS44W4ep3lR~h-rl$VaOGTpd&ou(VApa19lPZ4RfetR8j;Bc5G<oK)vV3
zP)8cmroBAxm9dyxcGT~%6J!cT0(MhA{f+9A__EDvg7^prQB(}3Z)p5x*Rhm{z?=m6
zW&zZVs-FQ;Iw2%96oxh=AU=+Vx5=OGDu!rfeo%w$^U#`WxxZ<Z!$Z>R9=fkh(NeaE
zsFR5epv%1I2=o-A@NlXtE-;@q)<0D^f+Bk?lSfRIg%nJ`J?EhG==R(X@`Zx;yQ@ch
z##vfba#)PM-x(DE!z|b<NyT>QvEykCbVYQQ6K#J|2*=yfSQv_<f}{F>Lj8@Y-&{x2
z8d71#^<=S?Q`(b?Sxl&{GM!Dz2QXFjzH^i7!&qX%J+)#<eoh+y-M-k=(B3coa7+o_
zIqu*Y__<>V97FXno?F10|C*VSg(hxi0ceYL6>_ga%5hr;10t$fU{T8T9J5msFLyzD
z9y<X^%cGvhw+=5DbXqNVD+IxVtMh0EoIZo;`=v~Kv&MAQOa{#5e1971KKk?7SH0U=
zDqgSfKFEoA2zGbHPMYixoRl=GR)g9vA2cj5>&?E;_}gX({Ik!OZip&?p44zwr@HP!
zYn9?tR6`iN0fsqE@RL<Zb&mfSj&><Wm!XvZaioo9y&Huh?ru#$CAsB}6eby9T^7au
z-o_L1@}Q2efza13ow<39nK~h52`a|e^!b}IglalxAw)uo>T$wtMN>5GUZ)T_(iM$)
zYjtAi#o<fIhDR{CS`C^68f!3KvJf;@!yNW1Gb|?7=1C2-IpREUK`(1iRXXMz4hSe_
zj|k%TyH}WeRE#zP(JqX*izufAGk8s#OWSq_d!}U%(@Zs;1;Fb3bI*$orh%1lu6KL2
zpqfQ?eZF$e;&sQ@Dv=+=Q|4jNdiMM4g+#FJ1r;lWzkO-FaZ;vlnHML0Hi#x$mDUAB
zdteQ1q7O%@g?^#ILx?yyPb?8A5nC>!yh0cJyh+RD_zBMD%+Ae_E<o<d#&au5r!6Nb
zYOl#Me@AxnVoY*`<2Eo*079#_ITUTj>BZwsizZVe++R<sXkerNrD`H+GBe>nL$S+R
zNrf&nY=G4=<0P?~3=8Ek;iNqszxbk}-Q$C?i4hG@KTpk*rFj1OyWZC78cP>_ZuB1A
zCskNgAam>xT|Z_C4Ty{rUN#zMV~g>Mxy<wd*izOSzQ5R~6;E6Moj=tXtC&yJV#c5G
zVax}xZ7_OIKTJ5eWndFA(ql*Cuv)htb^H#_dflL+rU@2n-Ay~L3X{vmyFEz7ce~@j
zj!Y9Loe{nst6UKZe=99w3a6H_jQb^#h|n+U1(G{<a8vt5DS5vc##-m#*BuT+JM$SQ
zu_};H&9pSo{;tsE$`)v-3s<At9Zu<A8?LS7UR8VTcrbovXA=57_$~Sqxn^e(wJ7Ne
z2ACI_rdX!n)22NdZnlN6mm?7-*c@4kQ(3K7U=;jd1_|#eFwqCagmmVEmJDQ%l+My}
zL|s0nz#|C5?bR{v5W|SN{4-eXsLq8OetWj&t=;*CRIY-7x4|9OsJA~>nfB+oB(Gq4
zcqllfju2UwW&z#!S%l8PQo>XNch<u6O%~{W+)xtC;CKsFho`MyG6CV*)75T&8um|T
zjG1LMu;BKHq^fapb0GHofEO~>3`t_mpq<%NYM5cn=IsLJ?i7i>W|VM6tlIR`<f{jS
zuZJ1?iM5@x{ZONXfTU?meHcbtw6f=kB^8Po1$aO3T3rLDxY{D@0lN)CbQYVT?eDo(
zUsrK#VY_c3Hf3nGvYUfhTA^Zf>1%RMg#Ll#TB;$^YhEff_CU)OpPM=bk@ZY<uE%@w
zAxw*bsZETvd;utFnvj-w_^eFxz4BT?76*$qsmfyU;Pql}+LH>XA@!ysZfd0ff_+V6
z6zNm;7CWclYU1u4m|`g60tvSil=a5Z;_gWDfG*nxq+l3y?`3(Ch`Ii3C~<_Csy@cP
zg%JU_0hI_YkAQ>X3bVC#M%q>Dyo5}+>wm5Gq3EwDsy~4r7T};b@FrsEZXTBcBH0dR
z#tSHyeE<6Ula}6X3+<1m@e7DW)wgfobMGpc>TSc9MQ^E*_+TFJpoZ7;^LOKn$UAvp
zHFAO}h2)0OY%Z(fg~>wls6y;AI|pPkLOo+ax3hm2nZt1|Q#oDeBf1+-guL<fUB~AB
z8Z2R_Q-KYmo|aUNrkJx0K&bBQ>#jy0SK8h=&91zOp_TaUsk}U_B-f-fc^P~Q!RIyP
zPB~BcM+i6OR&AC>ZY0t)*6y^0@c<rwuGyNh$;=>ZG7pH)`z(4S-`?zO8I=DGacDFq
zW%6CyFSkZ;AL7eKdZi7OMyupUa8MD9DNjhMIgLdyDKr^Ih9ZM!AhhHZezHGgFM-{t
z@i^veOLOBCcamPvE@A3qwgiSQW8UsF0tx-Y+D!P-X+bS)4!xQE8v}M1RlPy<R^Ine
z?Hw=g-x>ygE4ICf3+@sVcIyylwPD5HuKfg)@5ziwrWD!w6x%M~UUzdo;x)RZhTW0d
zKnMv=XND=v*-<qlW^1Qt`dhJ@2q~bzW&xo-vt?&l6$Cb&sNAb9jsmwUWJ1im15Cmq
zYGycdlCO)sD}}AdHeXU2yG2^#1A^U5B?!$)@RA=FX+K3#aLa7+t_bJARF+>Bd1CDq
z?Rm;`VjvE8fD@ZI<9uYFLeRmfCgg0KM8n(tUhfuP7+X^^&i<C3oBIWkA58bhR@5h;
z;y$e1zuVyIuS`8M4;Jg5`VynMP!Sl8a&dxqU2%Zo+6=dDPU!3+ng{f0wIRLS;V0B5
z_f3px|Bo&br;;^;nm2YfH@MT*!Jya(igd}MIQ`Qkz3s6C2m#`4R<WXjJ>Q*3FT?NL
z_Fy(+*f79B+nx7<F=pf%8s<j-p>Q3#uR5XJ%l?I55jz{#GVxdOpA0l71G*Z@iu}s<
zzs{7t^B<#><0$A6S4U^;#*^!A<Ey+n>J<AH*ig2#spSar4G+}&84YC8V&ZflcW_ij
z=JeQ<iU5PhQ7#60V0&_Lvu`=T!-nbsX#xww`!7H{l$2DkXgf09lI1v8CJTj9c!~f{
z<x;%8Hi6XxoPJ-KMzcSW-W^6Oil`miY@mxQHl{KF+QH7cNJ8YaJL*uHYTpgqNk3Y)
zd7-i-il5r3KU{Ooj1PTiZ(*i{l(o|aEnx@LLw2s++i`WhSI^bkc2nFd9?HeqbD*&h
z7w%BW{`*R2qw9Dktc03}m1n&o9IQv%m*td<%Gki0=~$^!?J4ZRH{bwnEk@1R+dYlL
zQMgl%z>aB76apW#)ppyDFj-h)h2Z957A7Mi={gY7?IuCeu`2()Zfvm2oc7^jA37PN
zAfM0B8ddqe0K{u7dSF116SIc>vVB^Oh=UHvltEVlg|sF_t$Js<nKVmQ1^MV5DD_ld
zW?$FB>N)}=+WGVs66yFzDh$as{j-=iuX-ESTqXV;O8o7>RxR+#^wrISov`!?|I3Kl
zEEnV7@d-z4l2mehiYt5_{c;tkUdU(rv3G10oL1r17W^Hv+Q3CWpcjGenI_YM64j}d
z=NZ7F^04^L*)}-CvJzkCd8BbXgbEj8W7?PU3x$7}sp$O}lxWi;UW!+XpFYNt&n@&M
zJIn3K6YBw$Idec<3>%LSU32k9uNl_-JSiK{RuE)gF7@P<xA?%|RuGR0T8$Eek3qSe
zlVfO7IgU>}b)35OWcL(|R6Zk8*1+q<UidUVex6E_c=kE44WD<!!w1Vrec963e4Miv
z*q1&{gU7i~8eajU5Dgd#*>1U+-?)jvdR6dE;mG#l7Q9VWxRp(wpFW|jcGqDv2>Wlh
z=EopQOV)!B<mBON0fKJy!e;#X8C||#PAPZ`P1>`fl~}O>8m)@}1O)oFUe@8A!+pq`
zc4yeyMvboXvtW+P^?o^i$e!3P`^yYQ=`i7i23cIRE@%Mlc;{c`-HfC40TAGsoQW;p
z$89nT54=YD{91++MC?Vvu6O|2#PXu$zgxGZJ<c|tM~LTAGb}b|aoBDYJ`apAC4SyE
zPQ~OU(5TEt2-%{C5B0HM`6P&l1N&*~n`qIAIeULJzy?0`_6{RWCF$l!U=mOj=g^Gr
zl~;|xa`5yB2@j-YcdU%UxO{C%3YNBPE3ThszR*Z^o6%lked96Uswv&oSdYIK=y(VP
zEvI^^n85?k-QlKGL&z<@s;{m|O5)09@n}Y?vmqzb@Es3n;u6(kLH<0G`R+DqC<ddS
zK9C)L*Y6yr(ab;IpQb$x|DrM--W}LdU3F<+AAG@N&^@NT6UKo>f56d1bB-x{kKdy3
zaF2w+{(3h;UirM!`>K+ub@KTN&5;F0kdEKl(blXGzg(DL-6JAak^=75@Lg?7gU+!9
zUccE!ihmoNP$m4?ZQcGExczfy8bqzK$*QCjH}Ua|sP=NEy!=LOa3JZ=RPH|X*LVBG
zAn&;(tr<?Zw&dOHqs;-%30kk+_1*%n_aSa~LeNq0dgjH{SGmZvFd&R!n4NuD@cqyu
zrqRJHMjiq_RVbjm^yMd)_fT@YniQ8fXeOF0_mRx`>uwi&n9hu}NJB_KWX;#@CRDGs
zS0V`D5+p(jS5c>-#zCXO_QfJiHXA>${PPc{D-!-Pq2Rya(ayAZO0}aqPNG&YucZ5f
zIZaw-J`Bfx+@Kfwxpc}a?3<6&K_Om;F;`0SPRoA`Ue(5cNv$-7nk2mIL-QW|01}3N
zx`qxV{4<{74}=IO;kQt-m~Zf*VKnrJnb+(pqZ4I4QLNz0`lMr%BlRhxK{W+0eS7yQ
z;J16LV75)_Fgf(xaTk5aDph=4C~}p$!I{XIm0v4D(bLPkT0gVtch*~DWK5J@n)~P{
z9fyrmlPO|Mr>p!HZVWvhtC9c|C)qCx67Q3Yv*hNu2t7HQX#c{t{D#JcZI~=X4SfZP
z3i6>cA|KZ%@8~Uw!k#-i6^uI^F|Lxsh85yolS~{-YoI3CB{F%{m9I@P`DA%Qg!&4#
z>>F9$jJ>o5yV2KCd-Qxo6+FdLEZ$Q}${mrMf8QtH;3Zc9xlgbBkdPhdmYr;_jp3(%
z&j`!WQaw62Ue-ay+Eh=DSo@yYu)5@ppI^`!dMw=134JaaP?=aiuUD8UkxZaGBW{*g
zy{62`nbEt>6*xh|QcNbBWO`YM6|sY(J``tb4zRfHhcvC;%%@uHAA4HM@RYeUa+1kJ
zS`>=iPe^}hC=L!`I2N&!#A<M?fa9tmuZZGVPT^N3N{3My=&qPY*M9GE794AQk_o#x
zP|Iz60}4mV){h1S=KT7U4VzS2Av%}yk@Y&`!AwZ3$k$}$l^m8CtJ2j=chxI0i8*b=
z%sV;OOmoa)8d_@SbME=heWR*69l)X0V*FCZeC_qpC8ifsR)&>(s56z|8%n&%Ac*{)
zNHBMM`q8lz{h^<o*2J#`35d%YSWN#GVqGg2F)bOKlJGeZ@#?(NdTH?M$!C|$69R!p
zF^l{4BkQD;{8eP@&wXD?dOUA~*Ma`Ufcbsh@eBd}b)Dce=!`=)OXU9t1xcyPn5jY(
zf~ry&vdsC{U?>DM+kaL1e5=8*+LO?$RXmTrw{Wc$cD{ApIkl_*=NbRE#{awFI{^#~
z{7c7Tv)8F4gpu>V8b5FH|6k=B2?o}cjm}<6#_rtbCzP9~C80i0>YsEE&Hnvy{i!33
z22?9IODjd>_qDAJtnf&dF@$pcI!46Oo<e-!JnySRQ12fOo|Tn7E2#{Q9X>`CzY=&%
zIXF8l4GWw<_r3S7AD`o?2WQvv>yM<U_k-;9G^1g&^K;BJQmJ@7)RoS&?-S;p|2ey5
z<aCU<WK3i#G04XuY(w8#MKSK5wT82-wkD}^-+-6$Gei1LgkQM`^l=1)(UTR1IQK_3
zcJ;Qx7p@pfCg1SQ@Ie5Jxh+(BI=DEQgv31yj<{H8QAKDD1_Ha5WeQCdlm!(i^5faa
zrd=C1RBZo^47(UYUI`K3|M?srx$%BI%IaRyD)krY25zxa&)at|L8Z+W(KQT5qw-#$
z!t3A9942?$O&0JHzfmzhk5CQjIL;DaE&DF$WT3)+?F9Cg307&;@9~G15J8taLr!M$
zqwAD0siLL!_r{~CU&qbv=vm3(hh_6$(vG467k5m{NlP}c9}moqRDwsyf%h|(Fgz^v
zy3GlGbOhaaf7W>|neA<y5q)SwOrzj5WH6m?u9S;rxcP)1>Ll0M5C0V{^VFZ)W7Lm3
zjm4gD>E*rH{)$`$A~l`q-;r|>Wg$|d+dU-l6%3x@wJqQ#zK7sO@~GA_#arvZ;Vk&>
zxLt^ba}RV@j{kUp*y^gT5>Hic7%Y?8ym{I(*Z%R?z-t)HW1s>ahK5&Mk>#SU1T8A6
z1qPoqH^(vnoN|U?0S{DJf11%(lmykPB(Yfj)w~ReL@byxpi0N8fyA@RY=r7(SRxE~
z<sf+%cszW}EuDYp82(tGdI$DqHp4;`<xpbiHUxapv=H>F9QCtf3rb!rmefx+Yrr)_
z2a)ZbS#(SP>`ss?&qX^;j0c@lNx=_){hKpeD4;Hgp&#^@D6g|%2m4*RZtyvW<TYOl
znt`04@@tPuE_&$5dOM*#4JcZ)#!S!xxx~}VXd)%r4G&B2c<)+poVH=zd&$tPnT<3d
z5X&}4cc_5={g$KJXHcl1BoMh#&7drleYbL)>0;R0tJ#5Yu}aHebXE7{atV9|!DgV}
zvi}8&dhb1w^JAIF|G%{sNv0q6Wfby@^ot=z*HiJlod*spB7RCW?%WZ4uJ$`4q$=;i
zPZ5<L+4CLE$M^QuyLvfT@P#PEXCDEJV-zDb7GFrT0ccy~pjhpbsk#-WrQ0KllkE3L
zt<^ZjB0OecX=Ay}Hr1FE;M54x?q!4j*G%*V`~KYAbka=$jNAPUC!J0=sC}@nY%6h`
zh6!l{qy6GPR7_<^LAi{mdoW#ky2*HIf(m2s#CYO(e|knrT=$19>No<;^ftrc0~^dy
z8&MUsyL$)d#)o{Cbi7<YPxV@q>n3V}>+6UWM8+LMHxg(sD16bALKYS|i$qP87$*MU
zppZ#o7bS4wgGIJQNl_vp@ZIHkR>=|Cz_qwQ98JpPRYs<yl(=x((Ag9aCspcb1oDAR
zXa)23q5?*J`>d>lzA|Y$UkX@gg}_r5%h0yj7?HZGf8~DPVqWam5~6dzHNzEkpy`XK
zXowF`4gNtbk}k3MC#U!Qk`BH)H$PbeS>=CFPu~H1p|^%dN1)&0H2`UJfPq?&cB9^s
zz)ltuBOl<uqZ12uAEg*d2Dz4qR`3IXXzp{;`o%)pUtU>LBh}~YuMl=X>Q!>_;b2@E
zxAj{3-e$K?+*M*VwBr3%_-{gDmDd!_rsENigpJDNc?l8rmmcs~^mbdG1kQpxAYgJo
zgF}WyMOKBXM{p}6H7y*{{;w00L|Yza`(S*GNCM8iyn*<KoK3=Y#b5)bv_@7gZBBhC
z9L60k3C`nz(`>BGA7b&9REFI@y@4cf9NW*SOqh#s5weC`x;wnr&9Yi;H6Q^aq9((q
z25&fV949(5TZR4Rv^+GJ=^B?stKbf6=S)^tz^6L4S|af`^KMVr*fe1`X<Kz}mLiL4
z1UP*S#-WkhCPHeX7y(>Z7aGnhLZ$AvIolCZ2Oaqpc#obpxNB}{g0=;!$Kyq$dTQ;C
zh0-UGK1w<L-zu$WftWk4Zd>>mZ3-E*Tb&U`y`X&EXnW_Gzj$r9z|S1?ig_z=94$|y
z>gqc>;d#PO-0vkvWS!EXCa8{Nn;*ym;2tM)wm{2M<ABNLPUOULNzf`%`=Da886%K@
zJ;j@N?hV%?z6YC{;6dD#9rGS^4%@Ua6Vk!k$D9e{<jQ+;oZm+x$3_vQ;0zb*VaF9$
zY3yIhR=GI-jILJwOudO%ysqE^I_;nl!wdEIE+@GGuX};)P5-UOQnI{XZTjpec;k&4
z^#KZ%+^3pUM4+X7@9f}vA}Dz1%C)1TW2u+?G${U+M4_Tq4jYh*MCyNjHFUEyR8=St
zQWegZsFX+A#m*eKg5CX+wU!lbMafBe*D&y5Xn+o+zgaXZqq66umN3voc>k(jkcn3a
z1?g<F6ErcVo;r4i2^%c`WrNPHD)fz<ry2a^g@$g4)Y%OS_gQGaCkIt4Bq=Ixwlt>l
zb}-6QlxenWuU&jsBm61kmnlm*6IB%tq%0zm=<ypP0$ROX97D~&tn3da>L`2iQuJK4
zJSJakPw>C~a+Xga2tgSoh97iKPVBpbBEdY1;8hbkRIyWZ)m)^^Cb0T65l13Z%HpwT
zu^|cwOkK6o$?5O0n%@Q7XC48|)CCMB%EgiBikK5c3>0kj8q(O6;|IFpX@|nV4jIPQ
z(u<tnZE|7)`qb&CjxSr%r)HJ%+sWBw)4l_)OJlQI;@lI;GpIYRu;rk~NaL&zr`ADP
za6|W^&={AS5ybpFdw@eOuLe7e4)&>$D{{Skg9;p}Ceh@NU(VB?rh;lTH$NKhrjtM+
z9c?M~Oz;W+z*|)z44W+e0HAQ*^vaYz;T=Sxl&gE#kvflOgH?o#Fl_z{b8z#%vGp*G
zY<Dhqeb{BfuCM;)ebPa@u<xB0aDme!zZ28en?WhTzB1e)2Xv;3C-fM42Qup}yn>nU
zu=I0;gfnk2j!6OtN0x{1gNU)P4j@xo$o^3mDm7~?P+D1~m3wkiKAsTs<NNxvbAi!V
z>0s+lHHFI2f$QI?`kVVJSPzd%QRmXpnT$1T)M%E=Re^}<8vw>^sOmO}w0aj*VPy*q
z2jdHePb&n7vF)LdK+brX10RFEqoZ=8)u?bg^U82Nb-AIjAv8&q$=Sg1CAygsJaQOG
z`0i-zd$mO77H|wk9U$g<i_A3kkem=GM7isMjmxdHHamU@*LvfVAk-o*Sjtwqfp9Eq
ztL-klx4YpW9E#6}iE|AmOHhW7y5AJP-7m#|SaTA8|L_$UB^n2k?Hn<tJ$3*I5@ZC%
zF93FT8+1l1A^Th&0)kJfu!7sw+3H;Vai^`;!(l>+Z}x-m*2gexWSwpRKYm`q9#Jq=
zi_$C5xv61%92E3bXM%`!1oY8Zu1Hd3?Xs6BhQgJ(#^2b9VS9H8!K+@^*mvjPTP22?
zNs(sXnJ<q3tujpwLm6Jk$$y?vR%V=aZfj~m@mvG$2Y7V&%P5d?{G!JgQ^d<j`X{T{
zCsGQ}d7-@|QLFpKJj-dV_efD#4g>%6t#Y~WSJ*NQ&H?Ckr0&sS2dav#az_16%5~UJ
z?11K3Kb2Tfc4MSTc%Pu^MvR@1XL7WR%>GfCz7-5odTN5PwMQr!3VUw1m{+)NZ8&?I
zeKIdkX36DBDU9_e>kN|Hc?2u&ug)m8<5t#qvw4x&VX|x5Ia)If7}(&tog~yyp50sY
zEU%21zA$6FRX&hE9h7lor@y4F!HbHL@~b=1r{m+OND6(71~!xs%MDy_7wG@oMBGAo
z@xxv6I{O{$z|-6`W4iPM;hr4^Y4lcd`8-iWSmjQF$CfKucg((U2Ogv$S*3Nt4CAI6
z%?^mQeR`qfn0)DQD-zPd!{h(Ad7vdHD$R?opWxV6P6Z4ZF$Ts+BlKYNqV|@IDf()J
z8`!9^K$_|<D%0sSg1_Z53{L*lT;($bwxLKj0E9d;c{xMTGKd>7`>K!4_#2S2Vx7~F
z92^lxX?lR!be9^gRSCr<p(S|h{t)-PL)|;QZ<m(gPbMBlV0dZ`y%`O^D)1%8%kxVY
z<I+@?a28T(h4na5(--2ZLSF38h4Fk9W9AxD>~mX%k+}(y#ux0azhp&Cu>1bRMzjZF
z9x+<MDjv~Z^d?5#Dwp(4$YGibIpuBM57J(l=gR4YF>b&!n?In5#u?}iPGY|!){H#0
zJ0fTQjcNYZBdTmo(>Js$@BP5)13eu|4o#^lm1B0zjd!2zmsNMq<#vXS<I96@3Gfh#
zoSslZX{grhL3!2#c#ZxOB42kL;&MySP>`z%2F{fidpAVh%gO2MZXii2;}qU*=l#Ko
zkdpDu_(SXMl^XL5{hb=IlINwVKq(5{9+A0n40k6B#1#N_q-fd~D?d_h@3C{^yprNI
z%*bSrnuOOkw(t{1v9n2Q29t-+t}_y`_IAD8B9*M59M&3JM8<8c^&@+thr6iCuPlyQ
zS&=u`1n9JVy<eLdR>3MKyp=;uHZ#}Hl0v8BHfni2F$!a~Gr}!4+|E;Cr2EdjGr$4A
z6|YG~RvA|aDZ&2GmS*=BmjaSfQUx^2-{jN4&qaMNG#ep2eV&4qdkwJuZN`HJ7!zSw
z?^Y!THU8vShi)sE3-L1Y+Gc+b73pBYrWWndTY4#yldYLu*@SDA#w3ItflvNx4Kbf!
zc=5PE!omzPeCG7L^|XU7i9UmUPti?lwc(3`NpGohgp}Lzf>w1_^$12k(c$n5?53Fh
zu0s@v7c;vz=WY8j7KCSK4qV?bR3Q(bjZf7%JV|1iwTQ45vhex7<+a=~Y^Xqw$v4Ru
z$i!TdU|*_1)2}O;@id9H_7H^ecH=`{_lRRBYw{u@%nH%lT|XFjIlj;&Rr4B56PXgo
z|JItpvBpW=U#=ZN&K!aA?;tke1kQbA0(>UE*E_FJA3(7^7+SN{n;}X#B6rPvMn(T#
z*$a()j_)5MWpjsDhRf`R7__3fxt}AnqyO-}a#6gv=6dJD1+byu(89)j5w;Alp!lC~
z1}K+ec-iQzHl2dO8i|-41?>#n2DTw30h`c2R`~xq?oz=&i)y2S&T*5)H>*by7Fi3s
zs5E9Q#@txf1wBT*9yBVsbb6^`EreFRZn!E==TV+MUSO^F_q+~2xi|}B>&<L)AVw;&
z$)2r*rOYT|_VDBqNkE}%^jK0+!OGuxSvU~%pj#{sdH7hEZe$bDO%Qy#e#cI~IA`fW
z?1;^QAK75S)9A(c*V7A}i12y4bgZjwhQ4!l6;`z4x$3&ZDLI)jP+W*E3L(pE`YDIU
z<ALXW;fyRVeOMNYqHER%SVWQ5oT>Z%d(cU_u=7bBpHBXO9X*o~bNwdvedS4FlD`Pn
z)hc>0UhdcvK6j^#8J0LeJ+VLQ_!yL)Ki9c`{*@4~3Py^{8FjJae>LclADoN;{|hI@
z8L{e(P6Cu9m4GO7y2`{{DeRcop6USPy(a$fW_<8?G|3i>6Zg*%{QtHnETn!F%%bq~
zUB*i+KMZPd7!6Jry2YnMQu2fY=Gz^=JMmZR4ke6FI`x;}N{Y(N<~!+vLGURCAc*6z
zV0hwp#8OW*`j&)&1x#a=kE!;y7xhmT@$Zjgew{??)!QuLSbbkn@<B-jD#t2fofSn`
zjyxM}L))gcT=|!KXRLIzo*|NyBXmS%Mai$5?~KoNzdX&7qtVg`lgKacGcPa|B&JvS
zE}cEDzAtbG^-hJ<^awzYr77ms$aaT=snT??F(lYMrh>jaO7Wqk?JBez0mpuBlc*14
zD0^`1_mGRR9&a&;&<$>@Y1FyH$Vd;|esc2Ko&5z;SyFLlfIkCUBusyzJwe1dQz~O6
z)E=yWOo_b?PpIQv?f3*TJg}<M*Jey#XnE7D@P(OIrl{=VDMXSc6Hz8z_!&W+DS;-X
zM&=n=fbee$<83^HVpp=4g}M!tY%<Y(EJAOY`u4?$sOL#$TU<fOb3NfjyR-5RJbxHi
zt=UH*Xd+`5C!e&dm$0^Q&yx`7Z}+fLaWa`^STjCgv)+SxyE=SxEVZhgmjVUSQI<w7
zUvb<N?)f}KG1Bq62CKJXC^(*lFY&q<T<2Wx$o*YWU^${WH%~yS#VX0A8``pMF&OC)
zS#5QKz;;!&$bQa!RXFu$91|v@@9pNv&=4qtR-?}jJeX86(`xQ}5(BjHxnfWA-AJ5%
zs@t7AqO3nvD-wuD?0isFX?5eN>!S5@9$>Q=1$?JlPQ;)qY31N)#L7b24$hm<y}3Vz
z=;Tf_EhvU0jAG?{z6;GL;QvvAZNj$!b~H!u$>~OuC}iEq+Q#w@Eo1IZ+pYeP(O$2|
z+TMy7!sMx*+gL;5=UFA8B#zTP)4LT;Q}~gUnTl#jUPR<eQ}lPSm4yD}1B!@vQ_hHK
zDSMx2or}n`fzdwj#bsoN=?RNcO->3kpnE8%KG`K~1<QxyU3Bt@WQRn_cG@t}C<Ji~
zQj5!K<xRd*C&dL{|Ac&h{mwVLrGc_APP*VRz}6*LjhT9%Nfckh03CVEGt3HIJt+;F
z!~?4J56rXPi5z*ZRiF3A<<^Izj-+f=8z^~=ygA?RJNuXXS*34aB&SAj;B1benqk=d
z#o*=k$w6pRooQ6pg0uAls92tYcNku4^E#p~uLvZzU`D!PL#VKR4=qL5+f4_;>+FL2
z60{vWq9sl~ZXy)pIhN7h=tA1wa*tMgvz}cagu8<?om<j%mx=xc4=ZG0V8|BK3y7f-
zUH$xAZAy()7c$!TJV4@ce+tO8AikhqvUh(2{*b{j5_~RSeJv(#q1W@Db?V2Z7n@KF
zj6*$(q^DgbChJ7j_Hgwtrcd@ICEeKQz%RAoBZ^%o6-Us{+h2u97(sXQex#~?=a;0h
z4&NH&6<$aiCQ<Lcy{veJ^Q<UPY@K0l-Ny35xW(yYSRRb290C_nhkum<mZ9^!E<R>5
zBBiZAlVE#_b?U{0Rki8o)%zRiV@;<SCbE8#ht#A_7<`35aRcuinDw0M`P>}&-tx<K
zTpg>|1-`+k@RouB1A`12Refu=bDzc+xkeGfC=?;hxZ1DL^N4uL9dGjAqAr<(BMdfq
zp1<VNbjbg<dz#4=l<=ZF<#qR!kDstOl+XnPT#+YLU~5}aB%yAH=Zs8|roH6``w37x
zngdUSpj~0FJO>1Rr97aN;7v66w9C_m-`|ibQ@iANg_QUREehW0&l-Z@9ETc??TSFT
zkuI`s78_Q@hG&}o^0xv^@jM2Yl*25@EgH-&#`~7;m&qx!a{LH&INa_o9jpX=5|hSk
zuz2Rtiz{UHEjQuB7<~pg1+(IcPkX-4=*tMUKm(6Qn2s|muR90z3i=9QJJLscNy%Z_
zpJ2N@TLaa%na!NdQQu-tLnI(me@#uIFRu@C38Z&ua7D-9p+3A$BDMOi1`)z++)Nw>
z-@KZ`w&=m8X7|3nG&3m?Dc98FdpxuTl&6q9py3hEB#|8H_Vz-4QJf$gyHxk1cFx%A
zn=)SLbOOnv(i9FIf$}M0y~7CB=td|DIn-HggI8Bs&&f<-SS$`#ZFUai`6;@TP;0eZ
zO3rknI$Y`WrCdVBYuiNF6uyntkk5SFw{6E7e2&|u``XCs$GOxsxBB}xGcYh}>Hgkc
za<bcXy;|uQ8Ap`iJO0A9+tZ@k_JF_<@1C`d15H|Fi}Sw3S!O5*)KtYp$d&6mnV~4y
zGvQfiV$aii;4P8=0=|>osB;=dV>7o$R(!TtAz5^+_voZSf2k0Jc0VLR;4nFoC1y<=
zPE1bPgi)DKJ0zRPPLbA7{@q%JhpubyL!wKrFh?^pJovBmyt|LkIU=nw83lK2kZaW3
zI=nm_#<)Ac^Y-u>(r%G5`wi+orD0*#;l;}1*F@YGPbBoFxZTMK!mu!*97+idQ!)|#
zF8mDXLFE|plvcV2xntQWw^~&PH1&Pb&?GCZyyx#?c?6~Te=AnfcTL*KSiw8YVh`FP
zdFZA-NomJaG48?pu7$T_sfA3QQYhk8ui?)Xepy3yS^O{jhUwF)LmQtLQDh+|bVSrs
zVmG+Nkd=6h1e<|>NG-kQB;ilz%vsh=?sfU~r8Pq$BjfNKU>gB3V*heLnUf$QD;wc|
z^n*kb&T!(+vqLepm^-OF<DQ4!>Z5l@!CjUSm0K8uxdC3`$T+k`yYnI2yB*FfQA&x5
z<9JT_=1?$rlrrp7ZL_6hb^`a^qxWB1oNvfsZ3!tiDLTRe>3H{(ua@1?WSy@BhcFBF
z$3qNRZJ7NYp^{|BHw=fm`-@wat4*`8j#iqaaW`<s1AFcyN)oJ;y#Xt#Dkxn8$*=Yx
zio!k=n&O(<eM?d$O%Y@$fA&5gMvC<X(0*YnL7$#uQzNsMGVv3qaKEX@Y1$HvyelZE
zCKlfZLA>0X`7lnSDZj}D)EAQrE*18r9uUPgW5H0IRSmn<?od#ekVLn?JrU@Zap4l<
z7B--&K^dhq!@dS!lOds^-*UG_sE?J9=`q<A%__MDy7<7c{YZPHwLqhzx^Eu$5N7j<
z@c(rR{ZP~njMaKD*W4?=en&ry$YIpya4rj*`@@J&>wx@j#?y?Do7xZBex->|)PnK$
z{Mn;)M=h*u>ASgxz(gur;#c(9RZEUWRxSOz$^W0DA28@7^xokf6G!`87>`^7BW9<8
zmAZHgB7XuGMe0YGwUSC86LJfzMMcpE3s~a)`3smbE}p{h8V<{;G*f!REf-A!VbnY0
zbL$&r+Z>$;HF`IYD-&rYgWgINL$OK3R8lvQy-Hjz_dSFJ4J=g^1;5D$(cKgQGaMyw
ztW(-uXVDD0h^;0)M65Y~WkA!m(((@VtYdW<kvw?My4Z+vSdxiluO!t_<pNufx4)-m
zG4g<53fcaRv!GMfJiOjiMhJk0dUfX<GT253%bH3|oU7hqG3|4<96u>SmkB(r{czH`
zwo-7Ed88+aJvmm+J92w)LP1G8YOf2fNd2&Ur5)a<NMO_yb#D?T!0QwY?G4P-+CYxN
zVctzAp4;(Dszek!0*WIc6ED0m^ocm+^eupaqTq|e!<*80d#r#N{KWMH!f_d$Oj#XG
zdFHn-?%*>y70e=P*}1gFiYDM$RegKPgy(t$M~_~qfaLUKAGf(N^si?9g?F;4BBzh;
z>-1I<+en)&FX_D)fkT<=A^4W53Li*Duyok5C1$<NWo^r!?Ida)ueh@mVPby6m><JJ
zA+qRoqv1aTyExMtPa?gf5=wNf5Q76jJg}wh@10U2s}TGxz5KhzAV%qBDtK@VQBj>+
z5jRxY5DGKdS3I$j9MFy?hF|&)I4~2Lg);@GOv+8e`ixqmp+SvE4FSQ0bS?+Kkuz@a
zgcV0WA%*PTi_ir!Iq)K87G`S$VPQaO@|eCbgc~XI-5oL6;yWYEi1B}NL0_*qcMH0J
z`420ZMZY*NRz0D|R1Rd{=nNz!5N4(ce|5-_PT~sSO-K~8#BSpbcX}*DZa~c;?$32u
z5{3m%@54MQ3UMUov#rtR2L+So6Q7=N8xo1HI}3*r=;RfvK-5e9Zo*o{=Z9Cm*_lM-
zqA86f_m-JmokLg`6{L=Y4(#yOlCfC>zGH*Yec2fJ^y6BSN?!Vt7g{N>^|mEpSYyRr
z-lY-!O<JHqr>y{!-LNug)C=AnCB-CJ3M>LDYlbe&OgkvS5AigJzAX`!Kfw61KP-J_
zw$?-0QOX(690(y7%KRAUkgSff#feG2>&hTO==t6t7U?TfJi<klG;ZU(4}|FM1JPQb
zVQ^LD-`E*T+8)<#uCfD8Didg~)xE*nU#ATlo#c3<ou-F}+JtE=o8vGwH9p+EBRPrB
z3*z7YI%>I&TC?uZ=6;SXn()*m5mV~g0!hl~@%M!L$a0xayFYp46^qaRgzL0C4BAW8
zTfNygrI?t81IEN!%P1@u1EH%bD=fH{VK=`PlH%zN@3;7{(R*_0&4ZH!-#K_gTMySG
z4%}27?8)jj>hSJNd}e$<xGZ$bPn?N3r?S(JJg2X6VrR7g7QU3ti}%=)PckKMrY#t2
zW;8(M9t{2%5#0@ZX&Gx)3Kx-4Fu0#GkQ)=uE>|%k00bk^5(l~33?2ffdw4_jA2Lp=
zKkSy!%YYcHfe&!qIXje2ChLj&>r1C8S~%R!@Q0AIJ&bNo)#YcIecYsEZG+I3Gwc@f
zbe+Y&A{Rl#8eRh9k?+LM0TP{7=;PNqJtjVGIzcYoljHTfe)F&U&jGKu9hV^MvK${x
z6cz8!Z*gp2DprH(70QX&$8S>X@9%hT?*_ExqPMqi1Z-Hwt}BH~J-`6J!;=@&udn<4
zyWDKV$FaNb(kQ{CH9hRw$69D%`*N%CB~7QK3`D@*u8W(?5gP*&`hdg2pENd>zFI_)
zS))2uflMYOP9^8r%4U3)kLf<ad6)|N$uI^Eg6?e3g7kVGT@@Aa(!(}esUW97*XTaH
zuT~@h7vi2A0rU|DffDu1G6N~(8}3h_+}b-o{`2`gy}yOduJQ+hf{`n?&Dl%?<t$L+
z$jI@*Py3<zDxLl(bFHxbVh#CpG{4Lgh{k(PLX#UC?4~(^U4r;y?%(lyHz&|R`s?h@
zV|=cH6&&jT6TFy6NEBShQ0w^yVKsqoBCIS>@;RZzkMq=E2=~S?_L!nNywRzO*b2Ux
zazLoo54t?~6n7k08%qcYVO(d1(q(y++ErE1lxK)J!&CaE<o&OfjPXp9=ubBIw>yVo
zWld)=yi#C>M*H!fzz=%Kab>hrCv%otF`h=O<Qk{wduj-ed^ltdqReQ3!Z?7ExE<0r
zE23gorulLTMVJQ?B;-tvg1o1{I@m7OLJ56{ylSvx>NJy*(Rf^q|0Ds@1K3-oxfirc
z5?DDkwi_C5XsyL*XZs61mET)25R+2p7&HRYS&WC=<($Mi#;3%ih(n)jkhtmo1h*ry
z>ou5{p)<3GlSC5bQ2+C&GuFwU!c0%!hbBb-wh^}`rO6?A)eeoz#_x9d9~o8EPup-<
zQegm?zG;4OrLE2u{vwqQa1u;QRV%!~?FjZh^WN`L2OAqL!5sPcWmQQvDY|%WM)p_b
z&|aBXHp*KT3`NMla49J_=B5nnz(ji_22@Pr^UzMlP_B)s%&<Flcf{NDmiNBZv*{Z6
zvUSeQ>yib|$qb*FoGe8Q1e=ZxnXR=csTC@gdUzsnd(UqV5AKu)1lFT&o%0r1t%g5n
zD2eTY-wS>O<Ke~k!W+^}W%&JZZOX>uy89W)<N7C%`h-YH1xe^sQkka!iY5SU7($XU
zz|RSa90`OtE+}rx^V&M`dP9~nf7K_}=R5n@pRCdKI@|MVBi(h&ecO=<aL&-vdombx
z!pAZ^>!vg=^2Y@Zg*=^#idqy|#6;Rb2`N#s1urBb>t9RcF8{jO(K3eobJfW4I~=po
z30xEYpP2bBx-4HWxWmbZnDCgC#z&}U@}#{*w5)&5K$q&#fI9!-u!rs-T(zf-sT~H1
zax{F17?EROBR6AkukM+_<1f+&t4u#QM{g&C5mXm(7246B1oRlKe(E30;tzlQen%-G
z%62+i{K!$eyrIcWG$^!y><|NGttT?oE3X_ySUjtu>MkpZYdy;5<QZUwMn1el1pkt-
z_s-?<fk5$e9*)T8!OHytAqP%D9D-$v35$tB%*u`|Px@P7-0izW+Fgi;9n8?pYpJo?
zSB8N9K%j^T&!A>fG!rXMG^`k|K0ej%P>9pX_nqI#<nC<$-?1Gl8T8!k@CGt+47Iqh
z*H7jKsNL--<4KnfD1fyqvc{FK%L_~I`U~F1wvDKVER_ICE%F7gV02S%+Ew-*BaP$h
zx6B@_x?(~&ti;p)?4buN?CwKzF-obNuF!LgHM9LI3tv_%LH{ZCgCW{(D*U!cC~O3<
z(cirrx2tKiAtq!q+L-ckqq8_6uA2q@EoqD%El-eLMGDECm|(~Inv(%3Ud|V~<%{<5
zcgx7McXj(0p{Na@$EWrHHJib<H`^PY<reXaBk{Kp=Cn5kOcrB_yH9u67G1<7PuIm0
zli5X9zaCu#%%~3miKA7$R{hdQ;1Kxq7<9ZmCBod{(2Yl=2%LQa&nREqLXWonAOB2S
z_BPjt6qlSx?xc|IR0!^h^_>dRa`-ER@j-$^lMLY$9JkzPZl;1>$YPdEl5%06bfWLr
zd9XK|0Aol_c(FK>>8c{S0YWQ2E$kRg+z37<DHgM9U~G_B8Dro614^3ZsjyzP4?kv&
zxk;S~T~P`Q<W$Cf`>aP?4HzaU{*l$qa_|DwaeVP0qYjL`2*6>P;eg9MwRG1fgys*n
z<8ieMTgj~$<3gYed=s-`Z7Zpepo%#R!C%XK{56C#FD5H>dl-V_v%KzbY1OjKkB)#9
zN^tr6$Ju3#_7>fnzlWG5Bn=9P)=_6G>O{PP@X+LMW^+Ij4k4E&v%igj<UytA0c3Py
zD9kl(7)FNJxrrlEQ&4|#;F%F*7(>EK7~pdIUyzww$N(KfZ5R{dUN5Ldko-Sf@<EMI
z6NoERBThrQI>p6BTIBurp|*D9dsB<cgkvK*x4VyUdo4q4PQ#lwa%#7v!e|$YisomU
zK25o(KnG{dZKQoeL%ek~0QgXwWG<FtYg0wM*APoXU=eA36Enbkh^rbHAna>0;ErH!
z6J8JgZvXj(BN0_R9>TO_rtz&ErTkmJxDpnhhtGofukYgw(qKFmESwFy2F#~w-+c6E
zGBro@qbe4wL=<$WtieXcXWDu1*?^vOxP1x*vzjM))I^xQ9cdJ;;?Nh4Zr|5>9$_MS
zH4%LMP}>qKmMO}<vEjx?x=&U{qQ6W!FrUS{Jvsn-O>@aeScJ|fydH*`!`#n)DNP^n
zWidOHrcXK~A;?O>XY7ma!Zl{C5NQGrxEe~6J6iWJ3F)v|d&5^U>td%d4p?Zs?aQQH
zpHCM8bXSdVIbDDZ5+LqE4#H#cUC^@~$Jo0Zs9I@1lPV=?4YnRzT@@jUb@eiKMJmPT
z3PEt9U_(9q@&TnthZzFX9(tRhuYaiaN#ic%FRd5e;3W*s_5H$t>@shQc?Z$~q!gwh
zgO*+=vs>gS#LGjjrSnV7T8!Dju}+AjJzq=KIpz`TjKA?Su`3Mb#RS&ZvA#oYXB*AA
zy4@3Vz5f$il{tyUrp3r-VFZqMot~J{ixYAPrDJ`_VZsqNja`Vuix`-f#CyBs8Fasp
zg0RO1vDX?OV{MyVhyLt<^Rsbav#w`lV&0?X+DSos-8+%ei@IEbyyrJ}725>Nk^ZYQ
zNJUPGxsG%{@0{1998?ZjfeJ#D#l%iPt=V1G#2G=|64$!28hXnedCjB_i<l7U`5<Q$
zF%-sQi!?-fI-wKzXBm{9b-xZrErH$rfa%i}wC#E&@$QVubnrMG)^i+fdFn!ySJ)Q!
zjIAf-DeVT$9Re5{CT871nAUwq$6v0gX?A5K{3MX50T@i*H8Z8$b={@5SRlYo#OLSb
z0WoCjjbu)ZaiCoey1n7I#aRTwU*Sux&O<hGw<n{M?T^lc-csG+u*BjF>~+_tJrCR9
zd<VC^UAjWSgjwMPsF$JZexL1LflE|k5B6`tS!^i5!2R~__xF945Wg|AhSmH-C%jM9
zpyKVJZexB}3_(i#cqoh6L(=?^r=ZLoa8k{fgqs%?Gq)JJQWaaTuL3#5e>XU1+p>$q
zYl*IUNZ5{uEJL?p@V6jH)PqkiBqltVAC7$i`JsscTeU5tBM188qS{|uPTQb<#ulix
z_btBDd;B20PI-Dr2+&}?`TTw#`wg30w#qY@O}#G^E7Gpu$zG-B#N8ihc)SNeN~_+*
z?@D|(O*g`W)0F;bEvc%r%B3X~m?&l^ntc4;eyqYC)SONJK&yPqM0VRA3QZ`9Ew?{c
z!5-B_7Nyq}lefyly63Lvbj^u8v6Ty9nysY$VM5rm=8Fcs1X1f<>0-yRC~9WLgxNrS
zr>wzg2~Nq%4@1tsyC;70_k&-|P3n^WPrvwRN-Tm=kCZ5Xpc0%8sl+PoA<=<347Pto
ztyG0Vs#>-|ZE_&3F&fpbpXts#!Tqt1pYA~-Vz)h><FZ#E9t%0UYE#tY&Cy1&S;>S{
z%Q%REMnV*TJbK!}07bpLrgTnCQ<}I1E{Q3BL{8$5R_zeDFJ3ona#m7xxaU%*D*KCA
zB8-YK=$3FG0ov{do(g#stasWUC}j#o+)C(OPpfnV#)(013ZJ#J(|Dn>bt06r30AwG
z7(>IW7z}<E2gVEqy_As7tf9>KE0yGbLw7k79?bA~+i=u>GEVYb_TwH&aN>K?E!e!+
z&fRCmXorUD^;#g-Gc6YHXXhjtqydB4*2@b!wJw{`Gi4f3)RQYhkjpFj6x#fZg5U;X
z9uB0<CAu1LCWpnu84zB$`#5y%SR~*!G?-FjLv1GfQOPG`zl(qwq1x9h5+Czn@D}yq
z46PpF0Kn$p7uRoWJW|G5k@f-vMd<wru-t<<pt^lgSntS2o#I%fgPgKp>LNO3NKH6&
z$zk=j9YI*I;-z-abW2#<Qwrj=sFx+o9OM9130S>6;9MCDGFihCMeCDsdC~E0V=$zB
zguiSrhtD*4vf^|U|G<yW@7x$F&Xy*a87Kg7lyt}mzy8YtC@Zi1V^=~P128!+VJd;F
zC&v~#tD_SVeiUvJ03?i)48;L7CT{^sjU|MrtPEVB^DqXJMf|7~6%+3Pe&iz3hggY6
z%XI)h%(FGU&v{w{`6%b}=Y1B#EV-!kgA&EsUK;y3M|BM{?1hu6HMxpVdrL!Y&~88e
zuwvU1o_BK0vSu+2JBodwn3OI(H4%#MrX+~X!V)G1$^JwTot)C2*%z&C=MXO%^{)qQ
zzk-=qOd||sA7e%zFLx@K3Nwr2qLzIX7E1!v=rt`*lb>Riok*W93YtR3!YYi})wEr*
zfh+~$u|6pl8vUn@oQa3J!A()NGN@6^f?K)l>pye=*t*1ls@BdNOoQ#%k?^OgfG^dD
zVla=*Je_U7A&ZEqswhlWW#8Ge3aO*qbyKb8b4I1*(!?#GVkgKC5D)7ri_aQ>7VltL
zrxY4Z>UCQpWWOEU0ATYZaPftk@#WxhbqwTGc|$YLNy=2h$R_6wtm4pDr{Q9vM<-1l
zUkq{BSHG#&5D<*Y!-{*Mwxnfb!)mihBR`O3$n93(Dk!xCNWdx^LkjE})_o1>eOLV-
zw$3p)lc??16We;?iEZ1qZQHhOJDJ#dW@1|tYhv3rPCmR<=Tx2F-POIjdiSo~_geS0
z9{epNYtka%TxI^;WO5lZd{eOIF<Cg$exCUMs4_R>F6LZX$(fJ$8dqG;*rpWtVtK3s
zhVYnA`4g6nn=+>s1g5V3LIuCs_(mp8EwbPO!&&EVCYWldaKbwkIG`>YGl<`B@I^}(
zHS2l%#}Pg@XcGar;k}8O+4b)`1-E+q{%30TCrAbFJ=YLq_IL1kj1U`Zo`t;E(CX*y
znv)P;4E9~^bekktT_NuL@AkUALGvx2LveP?tYr2oeqP()+Bv7h9QW+zbYJL6QrHMc
zuNfZ)3vS5aPZ#o4HK@LKL*~5OkIMnSBUKI8EFjfm-GsL{&YpL+t#GdD83R8l9Moqm
zAwXfl(V;1k6@Ok&6BU~7MA`B52!Hx38xi{cOqlyRs#|)V_&P#(#*qvqg~o8gj<%Y9
zf74(wu3%CssR$MN_XQ*#ob+k5hqEhX9uyP(8DB*AmG*0RKSP;(7`lk-YqI~sX08@C
z8}=Yzz<xXc4NR;W<G*iLC@2`zYak(5@@uLB1~;{I=qFKE1H15keZkA(R3YQG0RqL4
z(tpPGUnd8Gg8bKDKQW-8A${#$XM28$MT`H}!2hp<|NA7u;5fL~%5IsO+5?+Ykm3{j
zpB6Exzi-87)N4zlpDh^vEv$@v*xO1SsF{~@7(ZE`K7TpoWmYwD%P!0=DOR^La~UH{
zF6)!?e&CCV{R5<vhok3vOBpGb1SNs2py@lWZ@n@N^@C#~JPxdu4D>kq3!w?dleIa0
zNzTr21f`Lsv_iZFpAwfQ=ku?S;wXnK`o2!Tt6C`M@{955H&sB}(zoL*Mxe>uK4|OY
zP^n+k!xrvx{?D6uClnT}8WOc*G5Njlg$oCRoY`V2ItLn5d$OfZI$kwXdQnDo1pKnl
zrs-{{I$R)@-h2eGVaQ}mj^q@F_su1)uWtIY5q_N%45z0buXgd|wo*1U1H(d6Sa@~w
zl1eYaIm%^2U3N&GQ*e$0^h&5P0WXg_nH`4w?Z-*($CoMO#<@f2M%Ll+mxrVBQf2I^
zAlk0yKRA5AL`UBJWJ%&K^K4nBYPk*Xmz4l))}-x?t|6fdaWGcao<0NS6L?DmURv0r
z@{&JR>OM_TzU@zRn%pHAyLARawrpS7no>~lb%?1F)S~uAZr)w<KWe-K@I;F?q5pcn
zK%~Xx&NQjed^;L?SH?gvhubNmWvbGvEvwp$EsqmjHpP*`(3O%cYE{}-tY+Y8@y??q
zx}V0d))t=hOXU&B5$PD6b~R%OSYS6Y8*9>&qD#=3%R8^nQseEnFs>9OZHBMuJ)N&`
zP%|wr*Q@x36>{R8MBWBgq;ba}q#d1|%20K2=HN_Ko!kVE+!06V)k1R0DY7|SC?Hh}
zNOHd8Q^XoWV|`KAaCw@Aj}F~OC*>)4=3?yQC#W`F_iD3PvJ}@~sKrh^adMOsuuFI3
zplJya^&pK;?@s_L<#eJYS_pB{As03-mDU_!+NKd>6bLrnbY^k`S2Ul>|2u_)z6?+l
zL(+;_Vi%}KLl+a9%GP2RH6P3aN(|ocQHBEONfc>kLsF{wtm>(BMQf7<G(;5_8N*Ui
z$WGe+NYN87<Dq6g=E{C2AMNY(#Dw+>{OcC@moIQl2PDsKDki>cy#a7JbFOxzEU|Eb
z%)V4vZnTkTw|DgR^#y&M?-*Ld<>uO=GxuC3q^nh{C%rc3gIqCr!m3Z<#xz`cY`?K&
zTpvq8qoP`mdVU_CoW-}eJq?!Jplxq<!jUWBgs49bGXk504BH`J-R<D=djGTnq}&$2
zv`}U>Z9~L#_y;^;iSoESe_(a`q2!2<cUZiIO2V!Q#Q;s*p>#Q2ar4ozOy<Kq3Vr|L
zW{4DjwPDJ{F*y(WSlST>w>;San*0$Qkgg&dx7V&=V8q(e`8Gec>6Bg5h(d#rruTvZ
zoWQ*P47H7pNec^l0-UR<)`qBSZ&s$v7{6iucH^XfW6Y3f0sjzgjuH}CI>M{3Uo8He
zGJ^RC6hJ`vsR7y_2TNc`X0<zzdp2RbFEesiyQ1O-SFV=yN-$;$YgNPS5@0gtgb~W)
z^@Z`J=3NwK@1aUKY<X;9_3vgvpPF*ehfm{LM(zbSQ+%K$z4d$M2Ld@TjTfV_6{D~F
z5}*{4Z>jJv!n+gHY>oyv`H;)~^81q*ekI%CPGmFTiz`_;($)O1$1mGmlPh*4k;Za4
zoD!vglLNH654Mgu^ea&zfb{L?;zZp{g%dXTA0n6VUJn%?mmHBRwh%HQ*$DXJh9&q(
zDU?TKUCD)d!(w%ntk{aih;eh}>8mtYOyV6=@CzCSQI*NzKsB8kuKNQbV{3TfbJ)@J
z{c?yljxhUe2)yS<sjUCAHgA7XfQzPQ{&AvvJ)zjEaWD<xsivF~LOHSf5GnZiHndp_
zxmEoYq1pw;exV8e5$e;5mZbGnEO6fu7K6oZ=L<%(i6wPK4WKXvG~vf(ZOA!Y-E)Pi
zls+10sn#-DJ^D1Lj19OIKsjD=;W#|HgtsVQwkKToZ}VU5obE;++Ic(D6Gr%O)DuxZ
zlBKT5tydWK%2b_Vmld6b=I7}Mb}<qVMWNpn6^h*#k0c)JC<~giuP&edg-Eggaz_{V
z@`sv)iXBu`5E0$4Di(pWs2KbG9hhf?$A7hTtXzcjeys#ZD}NUB*JVRRQn=_l{1Y@f
ze+fH#jEKzD8NXQH)+hT1RbeW(-=mzKXu68eT<%WTMaFN`c^BxmCLb!AVy3qM&w`a8
z#)2zS^)Q~e|Hb3JPBmPzW0ypb4VyrKUntTS8<W%1carJJ*~1T^VsHkH?@Uk*vDJ6z
zfJ79sImHBrWm4!u)=X;<UEET9x;n9#VpDH)T!I&gmfAc`ZX#jkg))a~YWekQ37@x#
zRP?646SGyP)EKBW+9hEWuVGOUV;0#N0D|lB1xuoX6^qRS-*)2V=;T3x<;Vp(+R?SQ
zt>=o*2{8cG6kLWX_QQG3>EgJh6`7{*-ov7AKsiD$_38eY+&#bre5AR4z-b(*T*a%g
zW(E1aX)dtY93<F5e+VrgU>zna#EwvYD7Dqq_kO;O{7)g2LO!g56mNG|W$+V4OK+_K
zA}GBdaz4dCA)>tFOr_xJ{9CJP?Y{NpE~3Vo`OM-{c#{i_D+4J~KpElt&=6%I-XE<a
zsb$W3eAn@Lr_Z|!PP7cksH|ZzQL3Zl8q;<D55Df6tZx_NKa+(>6ivf99BBpn?X|8R
zqL`}!ttX|-Pi+=FUUwLLZ7rcZBEk#q$51JI7_n~9pET5h@tRmBhjbxfF>KL$!9a%%
zb>1bW{y`5)*Bw*%q+UY*8;x_$%LvQ?io$9TvXZ#5S{s|-`(B>q7CVN^g7xU_y)g8X
z@d3dIf55RNC|m6j(vL|4+%RkhB=Os;oQCr=gCyB7)k+zG*eq6L>mrNmJ_;S=JpDkp
z)%J$d2gu?3(opB7U%FyqFtAivw8eoER{rdu0h_UUM5V{<uH0R?Bu?A?bf&{?g<Zv%
z&eCc#ee<A4e(y=wcnK06{&|Wx{sc5FzG1ely~P{qu{d|!{++0z-RI%QF=WcIcpRwI
zPW;cl7w($!S_n))9DZ*~_xE<t@<)OGk8R%?-8TY$%^QA#=~wt0^f5*B|7`3kL<B{Z
ze$|m!7>aTzSTdT6sOE--ob?PhrP4MM@ZqERG>4WsL!g4%8_qvbY;*71d9GmY%+y`o
zP_BWSm@9-CO?aJm<wV28vQkcthNh)J(N@%=_+QboUvNR8b<lezNZ*)es}h$drhFZk
zEW51}@|8~nYDi#JD1YtTOwK=he+{*Vv%Sip`E`{UDmoyw;U^d52gXqN_j`S@x)N|>
z>w~Qd%2b6q!?JI5I^p--tlr`~@yTG_pNsj$?s7hpN;3KPgL^8+QZdjYYq(S$4hyAh
z3M=@S)x5hRMN^Jf2&>&89ZvPPeej?>iw0v8$_u+VFT*u!5{IQvfs-=Qu41%nb2k)K
zc~3J#Z9a)SLzD`hbN_(*#xTakbN?>B+PlMl8xt@{$ml-o2K+tWUO}{5TNM%siLSe7
z7uhI^orUanWuB`xunJ8durM@?NUxZP985|p*i#QfXYUd>%GbcUeb@Lo{zc4RbWI`n
zYT6Q%$@NFTl?;QjF!NyYu7_B%iR(2hrbzT{X(g!hp2+|C<S$?3OiOPgcHG5AcG==r
zrS)#$2@Az9QdUIqNnWE;ioO&Yi-xaJjQ376vYRP!b$_b+c3D9QC_2lux4k0<LX~?Z
z7-a<f&bD_M&5e%O!E%MOf=8qeeY0K~n|`xp&)Ac5p`B3XSgi)+Gze_Y?kgT%;C25!
zNPMgJC3)&b$9Aoji&9d@k0f&TV{T*K*9<cfZdOeCymsP;lUj0w=ilFJkEtV9hR!e)
z-LE+dCT5mn1vUDj)jagP;RGRTWAxvon1GM6Nav@qW1(H&`p4B)pQPIx{5O*W9VX4j
z7f5ks40lmnk<j(Kkcr>e-c4>Jn+>|IXAj))KIlLBxnQ!*Hy97{B%k#lmKe`PsEDAu
zQ6P@x!A@DyjA=JgB0o1KuD@#_xfKqba5GVCh8y-jUsaa(&%=JGs|t#PNh}6c<z6+5
ze|B{(HyqJI<P0{rV#huZP74lw*g$7w6<-~DBoUq@3`A*Lu6VHWF5aPF0$^h>Ykx|^
z1l!TT(Q19NQ<J=3bE?7QwdEzt(mzRFq-TyojK-xLCF$3J;JYEbxQt4o2)l}2<8dTZ
z%fqdAU$Y=?)~=$UsM-;JHsg;(xQcBN=o$Gvdx&xXiJOXNgb7JZbpJIDZ+F)vDpCGg
zXyk|{fp97iKIVD|%`?ZA_w~trQjJpokT6*dtYcJGir6e1a|HYpdh9Z2cm^#EWxJm}
zic3Q8k_=DxLX!330OjC#pJ+%r0t8|V%4^OJfO<oUm*~I><wv8H#RCOx8we3!zdyJJ
zqvMrMR0wZE!$6X6u*x`-e>*+|+PKrzvpk*=bV8C2-2LLLI5gQd_q{)Un^KUf`Dwya
z1p7!W+$Lp{eH?K9=$dB@#c~x@Wy?OK=Gl?A)Ka0xh$IPRH>RE*QUpM)D?ZVG?k|7c
z<MiXro}!_wQrIYKD$hv$5&F&}FCF))Ux}QZopC{_FN83jIDEK6@z&*!l)$*ZUb#F;
zef^t4q7;Lmy*Okrr~`>c2NHx4t07AT);*=V+ZqMp<wpC!I|m`DC_5cFabyK$rB%oo
zDrK8utEdJ<vhktd!WhevzC7~Z-?J)Wx~8<$a=eU(ZW@C_-$=llD>kyeFM$7NEtk6d
zagaX!S_8*%kI0dh3sQ8!|NJ&SQ=lYPH5D;&K$27SwL0Fx*y_a+m991kU3mbFmMHhZ
zw<HwP5xEnxqY#pf*I7^FFX^pZlRv)0S=kybw_pydggEahu_k?difY|K#QgdV1<Uh=
zl%VhJbb-h7O_uQh-S6x?J6MYBY@NQkQhfg0gRoSa43zpy_<D{_F`;ub-vE;Cn+xTw
zVzsL%%~nTL*pN%&Mn$xZb~o|;rUA<%HiQ^+IUxv-M285Afu-<Y+IsDU9cmVyiW@D;
zHqe+&g_+v7CMEU)A-lW2maDE2)nTymhJ>h=NU2pizb*p1GsVN^F8-xZ4K8EpgXIjo
zRe){kNQ|gixbQyQiL|vrwKus66^Sso-daL}9Zpzv@ncVgl#JeQBp{{a8$L#JSr(_q
zcXTsZu2?XuW04xpa`X_@dmr^29#+J~b*Gpci@MlUq%g19tkO~!Mx(sG$@}8#!RYb8
z62;AlND9-dDH@0z;C4s0S5mYLiGFSl_M-cVqx=~~&Xv6H&1S&tWn@b<S{1Y00+O&<
zUXsW!M8}^Xj;5}`NFTFVQlgp|7dkLUKGvy#`Dj$`le{~!)s%TP36Nz&TCVv2$bP66
zwHD@_8#H&N&L$S1z}_0Olc4{xr58n2yXg2h<KUT^8>S4&uzw&qYcXYf&oMm`RO^E*
zZPX>8Qw9G#NBewX5U0R4-eB0kcG%Sd&IC_4({k54a@R2@sS~L{?_Dg~g3$fu#=_g~
zY5`BLWbtEaQV;HWkqm`j_X%WIpLm?4tdD4!i#Q6ojpSW;h&>7Qckr~)`J2J5k?e3r
zwa>tq4P|?~&aa>Axcob_*9&oj%kp`%UfwMC_l$#+c{B~TqhPLpTM&K%VS5{#=)xA3
z?bC$JmKV-4-$n!yJ6R_G8Vc6C-G-NqmsV00q3A09O&nJ6)AFvOV%5-@{Xy<AtLC9V
zh6KdDI`9Kw3?P#&c5#U|t%;@_8QF2um;t@WeH-Jx#SK-3<*%XRzH39nk<C%YP#3q2
zvOE^<$J4kF0sehHX=G1p67_S>&@saLK<P^Gt~S^Ve$H5=B|8btcsPH~HgSLZaZ+LO
z#ZV>;%z!U<hTyprqJJhS75y&P9nY@ZrCAR6L*WWfMTPnI4Uwq5J5F2x>vB-~ZK^a{
ziKe~)rLlc_kH(?xrC2f=w1FlW37j6{XWgDyzPAsQ#$`x2e5zdvUI2Z2z!ngM1Icxy
z?&A*S{=(YVo%=@`26JkgeCY2AJS2wqz%YZ{!G*LfLsXNt)G+Wka`<QIO_jDP0`IOi
zOg;g|ux@Goc>DDr1CfoJRp7y~eC`zbesOIz1_7aN0>jg0xW}{y&_9}qUmFOM@a(Lv
zS~XxN8$gLwZv0VYc8!R<6Ek4Y`$t8mV#R%^s}{H6W|^ymhR0~_rha>+oDh^?ZduN6
z6vPfAIEC(pg=vXnac`2UFHO^*-G=q|yMobVJKt!%dW6l7?~H9tC~xj|N+}CVW&gYg
zov4q%@s+}Qz2jFm>xy$825REcDqW|f$my)33PS_Dg*6N>7=KRZYt(e#9*~Pkxtl~t
z-;Kr7sbiN7D6lew{;3oRDk3^_VHb$;TblhV<qajb9<|gA;l(+$fvwZn-!^aC#cLts
z4i9&WR5E#qLJ85RSfpR`HT&)2)cz{}T{!6N!3G(ofED%2_I^b!58pkT@A=6@gyG<~
zfQ6w1vi$mqx*p7L9`MN}-pP;7|75C4TdrO1%S~ClDXx_k&6t-S%y)hNu1>q=j3C0v
zk);f>*7;ZUp6PF2?i2s;WFdIANMk}{ER*+`2K+4i$Vvrt$=umS+r`hutIasb_aJf0
z%1oYL)1gX~JvSug0k>eVX*~T_SGK7qt%WxA{|s}Tv5V9FgHvmW^9wGJZRvq%Oa3h=
z;z0r?hZ;Rjn@7I=?oa});Spl5&m~xSHH#YUzOU5bk0-JI#Ur~BK5w&T3yvjU{_xuQ
zbhGt&Y`)!k;TTXKfILQA!i0sPtfuPg+8Pr7=L4|yEg-7S6vX%iRP|2G%#bWXQa94h
z9VuFw9rrJh9)HER8(4(re#j@avBz_*M*{oi{7|~HSL3~4u2y`h2mNfl!;n?MHeR%d
zC;y;&B)*|4!e$kxPK@j<uU?hZ5Wl1%*2wwU_Baync$`W6r*y|rgx(r5I22?DaULTy
zzuyb4=7`yp7bxlU`Y>OVCcYlV7~0J)5q{P2jUE~74IViU#~`dmy>O5!WLTIGI~hy|
z9|}drb5H{KnP6x(D%rS2$M?g=H90d79swU92IrhX%0#gRLnYHZ&yJ=p>+5eA?geo6
z%+9|~M>D<*%zFP2VVv8VF%yekOIO0_DpS+Oh??S83W5*(xCIZPWq^-b!9cD)YY0N6
zwBQ@Ai*b^a8t;F%a_D-umct{$)Y#rh3ON@%SQl0fPW}xWOB-7#OJY!CAL_pWXcuYQ
zbHO4=dMM^2EB&+WcT9lbK(+AjV$9?!L3t2%FG<MLml2-4)EMk4D_bNIbvZZZIhdh_
zTp~<bQc@^si~!7{E*<a^AY<D|^%#eG9ZsHRT%P=a$~;f0CO^3PXp&_Jh&PL1NyNq(
ztSwh|Ex08rgDN-Lcrg53Y<UxgF)$Ry{B(|P<YE7l_9u?9nOQ|zDRPb?=@I$PeAu5>
zn(e>`0r~JPix!leaYuL=6MK&+BDHbx#4V=t_Z4_QZnwegE|EmXJyL$HCj?ZT_(N%5
z=;S*5{$Ms9Z2lWtqlE1xH8KNV{qC{k?CgPzHk#G5bmHw$!kP&8Aa{9iG?Cj@`-;GB
z!>HS=jQIxi=-7|KP@EYIVF5~dK`8|B&Qw|fIHZIE(&2u0!hekI?Gr;a`mtj@dQS$W
zjEW5gX&7d^O1*yIy4-<(kdF5>6h?OD(XRE&^`B>|@Gz^ap7%46#@l2_;E6GQE2Cpz
z+_npUsRWBs3u#OKKn*VD1hoIDJx>XToJmwC&bk;g<1r)KksPHhT4+Z6w#9`^e}ykl
z4yV$vc!f@|;CPeIG#<wyZ9NIsT{%tYG&flXss39J?@6)~@3*UiB~M=%{S60&&Xil#
zC@h;!&VqBb<F-ZQ`psmyAB5v!KQOcHXwW2s6h^s}qa;RrwEis|Uiu(TLql?$fl>HT
z*oUlfT4ZBEf#-D%UMWYK{)+z!dY4SXRFlI4`^&g+%^vwx%V}qIpXI1yV#@GncZ~F2
zvXkWiRJ(I64{q)ilGn(z2SuCI=JjT5t@>Hve~S2D8Yq->oe>P|7sH`Md@~kH&au8U
z1Om0%F!&fSDTpjdf-%2;F=N0~x^`A9&yrFfscOsXgrb3Z%r1K7`l^@O6^%D+4rt^P
z=M&-as811<SX<sLVO7vhh`D3NsZ0&^uM<rSK|*R%*3n9^Jh4+A_&!~)s_`Pm+uSl8
zTUqtailg!KI1rI_0KD^u;X8J6N4=z_dCD01P};m8f)g6*A}J3?Ub=I`T`u1BKPm~1
zz_ME?F%B`=yP0QkDXzSB1~xYsyHqIKT^9z%xHO2|@R8W8*gFH-WOQUf+5A@Jn8j6R
zyj0c6myvWQ7}rY=-(Mjcv8_-nk9ABG8F<h|p>}QiMbI-~?iBZkCyLD8Yl1n^7ypQW
z`;hRQQswKm3Rz^<8h#3foO_0wt=`*ftQaqS@2x2}guRXNc-x=E7d~VnWA{FJihnGe
z$fD&ZsiwhI#aJYwN>S7;N6$Bbzhb6S?y<!B4sWWVo^O!D4WW?v@>3-Iq~So~#$0XG
zG)+uW8?5<fN}DB1u~)RA3H(sJ2##cu%zS(Ni5|h+__Vh5nS4XfTDcnN@Y_xGjn*ty
z8A7|s4&*FQ(d)c1A_)D7CApB)J<%tD4B;ZUOyNFR8)&Y^0v;mzoPn1bF06pfe2G6K
zVPGJXl+AQ5TXK!Jhlqip)7tDPQd0!gDgF><eQs1&whZmBRHL=^t5Vcgo1dFU>u*=*
zO%$2s#g2S}FY&mDZz4Sb42=#CbFJrmqs7i&Px~j#^?8=<^x2$aT`wd7-PV|zWMWnA
zIHN`docK>)=(aK&M%x$S^-=AR!9?0oUQ*6RH?lh3$*`n6l7*NQBBM1?U!l}fHi=)f
zW;~@0?oNR!!XT?%eAvyg)pzr(!AalXK?(QXqezg~$!BB-r<`huEr~)msF7PqTAo7$
zFbfO7y!Di{jIg%6+OV@(8+)#u8&_s%vE=vaPPwB*)d>Y-D|W;A`Pad7z~)En_;1B@
z9SnA&<qKmBgv=})7FE(5@eW7g?sscYy;5xWrW7N?vM^6Jvp#q8P^As9`VuG|enB=T
zJ*I*P)B)eR^R3uCjWY!PuovAxd_&G@*iQTUP(_?_|FHh0pa!0Sp!nf7hyo$h*uwHk
z;wuy4;j5@ndgZQ7YI9AwUp07k+RcF*c|T0CJ%Y<CF_S;8{t#x0)QHzJ8_;^~{7|g3
zjgR;H_Q!H!65_YR(os+x-%L)+D+p7Ytl_5ZvX(HT%QWTS;w_4Ks^<O7$Jvj#einrX
zyd$pWA&qoJl4feJ3F_&@F#G~|m$CDz!M=g4DJD`R1Raed<Agst+SSE$gHbROJwl%I
zX!VPtMf!|>n+ae{K2(ghni#${8!*^EIBXKK0|ElaIQ%`IG^rClI~`Gvqn&Q?Ir0a-
z?xr->vml7sVXX(afw{!k1mdP9!ZLiid;!5;kW0tAC*Y*{)gJmV=Mz#GWzz6-*|#tM
zcvNqy|5qeLBL2zb9O@h`&`ke^ftV+5+!t|WvhYm6nXdsVBIDE8Y;n%Yw^XZ#Pc->m
zfYl|eg2!iSY%Jb$VqVqE`Ptu+dWTa^Cf{gm^wwO~@SBtm@c{B;rcH&XL>Hk%WCEFb
z!blq4M6*o#r~m6a&9i7m@A@pk+<Qs31N*a<RESOk&G~xXb~b}h&kCAhnJQvZ4K!C9
z*Gy4)htAnqs|+(N37Q|e1$i*IIr6+vm7O)BiZTLC4i1q38$lM={T$rmkGu-<GIB|p
z2@(-$kh<K?z?N6vNR+==g}kRIQFOnImetdyB<NZjog`A@X~e%p*L2rxyPY_uD&b#s
zkRm3Row)nGqlyqKJB?hKxR1wA8_Uk50A)0t;g5{5y;IzE#V&!+4J0!<I?ZET;T}PW
zZXH@Qd4*z?iCl4`Frs$@ai5<jRCd7h34ua7cdpRp%vvy6fHimBv@0MxyDtfmt~^R*
z=T~u%<z@Ix!!{QHlXe-Se>eP5TF7Yr(cVy-7|BvMbjG@<;u!Z^!H2;gxexL-M>S3n
z%j7p_Y@8?9yIy&lAamWhqZAj`rIqG`?2j8HAevb5xz#<ZylKkUP%-2B=S!^?UX@MF
zqpc?9?hF3tuohoSvo)lF|0ZlY5h{*@3Wa14yfccasCEEkgZh`d2O$s^Up8?k1Caj7
z;5$=QWldR!$YeMj1e-$mn~<4ENiBNX+`d=gLRhKH4zX$Zk7n~cE>Z28@%p13Aag(w
zrbJ|A38qRri8le(?^X>#gZQf~o12R9$u@j3*UZ!5&Al^S8P-Pz<6Q3#3KdlX@JN@H
z9SJX}Tb7d(1C@dUU1aPQ1s2S*DFAGPG|6+@FD73bC2pUi&`wl&s3Kzd@#ctetDQh5
zV`}93#9&9Z1wpgRkFad`sJC|s@5!BR@Lz%$Yv5w*=+2s&o^x`tllL^6t>!ZUdtB8v
zTtOh78NxAFW6+;;G<G@n!@mT9&feyCs0GP&j96Xkw1D>Fh#{Ts5(;hD#amjutv9yj
z^Db%un<ownlZp^<4fi|=U9_94exa-zs?p*iy$L`=%#gwkaxR1pDf4{iYQZ#t?ZqZO
zuD@&V^x4u*uUtS~uE?mgO6X1sf2iCQFCcP&X|EifvKrZhYml|++cJ0uQ`I5;pttl^
zG-y#-1VdMA;q{j$5#0^<&HsL&Y=wQ<Zx<&zte5fIvX7YFmR$@q9mtTZR~47vN3exU
zo|{pvyiD;eI$5ENmG2E2(3x?#Gq$zgfEeOdpON+pRLFX^VKj$;Qi3t?D{_>Or~>uV
zA!+621oByDzO9&FHb@pK_u)l=tzC~jVI4@Yzt=;TGi-=$(a$&7F5!CmTs+-AdpT<u
zJ+%`WwRxeiJAB|g6|?MESB5Ke^M<bVU2H#WYWptIWy8rLd0uJ8MKa1&BzO4D{GvOa
z=0%H(hWCB{RJiuFU~7a8Ve*<C8Ql+Dahqed)#eWW$fWVRH0RS%)lTSt#Q12fP6VYx
zQaCQt9T~s!7enDguhKP*GkE3NWJellsD#_b!b_le>+YY#Jlk1!<K(|Gm)zq=fa6~^
z@X<a)3}G|_-2DVa+FMs4VkvY|BA@n)iQh1+l2(v==p>O9gosU<Fv$;ZD{Iy7igh&#
z!|P-#`imD{wHJ2X>b#Gh><|Ig#ZFRNMVua*m!)iqk>;2Ymr3dJo?2i~!62mE4d0H3
z2Hq9az9*A7T4Dnx59?~Zh(Em^{OrplvKi`8YtA%wr+P>dpOV!UPW?||G0s8(Lt~$$
zIWWeO?!Y@A$)4s9!{9+RW1F<LpBxC%qpcSGF5GBn30*B*hOwWS7d<N6qEU76;PM3b
zxv80#B$ZM`oVs4uwSQ^@{~uL+_3Vc;nv<Vn5{SS?%Rn=T+Y_hp3=|Xk^s|Nxq71;i
z%q+4Aq@EcT22g=TDq3^NNPQf?%6Yere5BRikNE5%iYa25n>NZ1l!={qSeHRVfetjC
zVR%_sB~5WLP%#3CfvcjT6J|yLBpn|7J#{2b8|7pigagc2*sKn;6!RiTjsnVo#jdoB
zJ12d6b4#QrY3E6YcULX?kM+^h8NW(kV(%iIh?v-`hcfN3(wsnKNx}hV4TS%U9w`{7
zNQMWK<GZ<Iqs)Z6E56TMNsvmC_;!$)Vc3H{s(n0b#fqJeILY0cFglzi7}AmqQ^hfs
z2@QT`%g5H@-kVGjmA;Hp&wE1dNgJ;(WGePC1pzd8zZun;g=5_-$a}Pxp3+?8tmh+Y
zCc%hr)#7L-!YTkblVBKN1|{2|a_@310B%ncVHkBdPrt(n$_w7gt68!J%5Z?4oGP48
zNZ6eVzQn85Z;g;*6lZBSB~J63>>>W=O55hZ<ZaI3x}t_98d7#-Vv<Km-dYeF!HNpO
z+2~fejPGYH&?OX34U=eh+~0R`72^)8PULG=N^y2cXd<plv*Abvex(Jw;L<uGbHNyp
zGmrhpkBOg)et(~h)@o;#^X)JG6wMuwD`FPd7BxD{92S>_+Wq+`;mKLsn?2nvXev!K
z(YoOuUPMvwaU3!tG<(eE3fm3D>BYB3VP_J3NOk~FV`~}NE<)M_tVgYtBN~4^hET$G
z1e!bzn+!No<Z`)#>|`B8a(iPK-H#`{oZ8$^*BVagLzXPbE6$rPh<pbiNTeIhG@3O8
zbcMF*6u~pCL~U^3GX>n(H{zipV#rvazg_m-vkCl^Sq56FQYYr21kT(`W&W{d8BUcZ
zlJ5w?(6CyYC8)TaW`a?Z%Zte&UgvK|DDQCSrCv2w+5MemU>Ni*KJSiB#T>VLBeQ$o
z2cu#!v`Opc&ID1!7uU-02-){IVw6;sMlC#-FMrAWc(lz`vAIeY=#Di|SQnZGsTYYE
z12(zfro_0MXwp+tir?M1+&n;=X=1DGZXaT_CTm6&$sf*`L*HlX@nB;JuGNyl5YlJH
z0BFz-Ii-;aZ%bzJzp`M9KAMiLaF{=JB(Q-%nAnUu9CHYDxU(g{nU#4=Yq8l@#~1qD
z%rHH-8rWUqJuaa|gfRx&&}<Mq+Iz)bTC1Q%FlBt$eSpyLqKz^8o;zEtp;cvENv0%v
z<guQ+Wdi+%u2K?a9F&LeH7P$<IQ!D^d<TuQiT$pTB?^KE)9#$Kp?P6^=f0)04vS*n
zAZoZNASyaCB)k$#m*-KC2Mzo3?R7gS0MJqnn77|Rtrqye!(TwXS*zS~yzRA`L!Did
z4^dxR&gGxhwU0A%VMZ1b8$n+>f4}McM?8!N(9#siK_UuXh{+a5Jl<@hsG&<m_Blf&
z5RD+t0}f%r{~48y6(i7aQww7NR)KimUm?g`us*N*jyn>#r5+s-S8HP1aW@91{Nba!
z<kanQN2@$J55;6h2QeHuI8~%H_KssYVN+*z92Bg-0c_TjFkcq*L(#M_s=skXl_=L4
z(#f@kB!*S&<5}it_DS|jaZ#n!P;GB5G5(tmRMm=&UAE-MvTwO@=Ke0{-rUl~k$N7x
z;35&bP`Q`J>l)_@#Uf15S}CIJWrBxyt1^sM*{go`B!@GdPqHPYWCt$fwkwkDh^T?_
z$Ql3<{LCU4O)eO|nM>9knQ)SK=J>NwZQ$8nRIN^H3E~jIg=C_9yxSoR>xRabz*fc(
z{ZKs3mJ>NhEQ#a4sE#M6dZL4+H!E5Lfk4L_U-tuIInA}4!eDU=pk#=jFrc%#VShmg
z$(MOo1qX_J%pnkRwgE}qXg0DLy2~#JcuL_oB#>zy&kV&WbB}k=%{%Fae0z*2pWh8a
zhA(pO?Z>FSj6*`boZ)v$O~Heqxt5*WCzv%%C6e5#htoF{QIy8kC`uNItV;w+zzxaB
z!6t0%ijHQCYfR=#Kg$;J@4CpBkX8}>2GEoDg0C=N<*qoWjKF$!&&{nvy7%nhkvJav
za|cmQckP$KSCHe<RDY$x^zP=KWW@~S6_+h4c`PA}h83zgOVXjf<@_du9-I#C%5U5}
zcq@Ai7q#S(A5qvak+f`LNN|g%-bPy`p;BGG{K{~xXHwN?L+hI~f|@vgxKwGkYiNp~
z!~xc=9V%H76js=}idy~g;=#PKQ9KxP(Yl@^M}1Sh;OW6iG*7V&`9{tfdokPQw(usG
zk&Lf#>_lgOlO3sqV%5#XS*@!P$cnmIL9kG>@Eok5#f8-n9BVX;IXA*Y)$SXX>Ng5e
zrby_c2g1;qxt1M4)O1=Gwb5h#TCE;C6zkcA!Vu00(nf}Rlz~7xp0$$?O80g$GC+Db
z$1?V67h|#z#nRGHU?SbcVLz7W*XxHb&@%gD<P^4qY1{G1`3_o;5|>S&Z%m#sV#3Fb
z-lL~V(d~#ghg4Yz$6BF>mz6tFgyX<Y8~Ubrk*2qkvu2-}<c6HxKsb^Po38_FhLQmW
zM8gE+!={tJPH1?mtYaBa_nHsU*U{kpK|q&e#v`4AncFpS7NV+DO?>&45V~p}PThZM
zF;K@344KN^lMFYZ#b2wV4{b|l(?c#>t2K#n3ivo$3BlVV@BNE(QtLsn&SI61^~>qy
zWZ}U~fQ_5|_nT;#YLGJ(@Z=4-$7zhGBOi+`uN6b~WK1$wIp6DlE**RJYQrxK(4bNJ
z6G(!E<Mz<jnw6;V2xHO))cKbX<)jOe8!Ow|S`)$ac9tfJlAQ(UE94(8jcU!T2z7_Y
zUqprX=K@y)6i^Ir)W8F{sa1b34774E)jbpd3C`=WRH|NW{9?_*m|C;zBL3NieQLYm
z?0hA^YpzdtW9~`s9}M?1{Dc0$z2Ad1H68h;4n2MMx^N|*#KKYx^Vu-f;h-6Fkq`cr
zoxQ-09hg^EEHZ}4fc#)9FagDPwoW${-_ud#QCE$FX%5cR4*?S6{>w}~?q}-T&tlqf
z4u+=VBtLcvQd~=@9tlOn3`b^vw_#MTmK`Z+IZPg|2S(^jysVCm!4lN24dh-We&y+S
z-f}``O~kM?-UQ;NsOlUa?p)#<@cQC~!2&VYv1@1Y$1*@#>gP$OhwS_z<GzeFHFbz7
zxCppj7T4Mq6NIFlgO>Doib{*IVUvG3-aYb(&PSQpuAt)cG#Aavxp(BWpnNAkt1|y#
zqu-aYsBo$e`Y^0PkIUgtO01lVoRSwZ)-LkRw;y}<;vg{_bRlv+`ZOUeKT5io1aHRY
zF3Lgk=nx5ZoIE0Owu-d|X!p(u=1g&uty)z^O39`%w5lOY7**GY%ZmRfF^6M<8)w|R
zba7ZU_HyFb`BH5dvLs1LtBWiuX~&eGNV7#!u)Bob>PUd<GpbPJ2rIXtXtZ$%tC2vu
zzP3Wf!o1J@=e7jdLa}SyAIa%)vVjTNXyEJXi;_>CvSv(mbF(k16oif7rbcgBEV)o9
z^pSR{=iNK39Nn4|Ooth+%8j7yX8qSoE4HJBV7^L4f-LKUS%xz~=GJPD3D}rif{JKP
ziN<JB7igE$-Sl|*^4CML$RsAtz#j%T?RFI?ET$T$Q*<-K>K@*g@`v;I3qXWaz$`(G
z`NCUJ%a&IkOp^2%?&u>O3mzF2B_0)8<0hUxDvf?#q0yLwTd4dLk(fgj8#NtNe<`@b
zK_srr=#4X|%*MM<_#NRFG1_}xg;n<oE6i-GHnCi-pBzF&aqIkItM-IX;-fI3)1Hf-
z6(0HK$7AmIU_*ytxS>>kiyH?v{C6}IFE|6!Tm2Zr7FOb=YfiF0F@bxUE2$kHvtB3U
zlMenmGHZv>?~?@fc=BB$no-M9KanqFt)MF%m2WFX-~)A%w$`v(Bl;i)tMARoHN=7@
zSEuj_b!-pLz@Mzh0CpiKKm`p~S5Zm}p<XU0fl9<%n1%+tTHRk(e~tEqsehaJfCX#!
z<C#Eh5lDw>e>Jwx6o?@Iknx3lsnRd6JBAmAQw+Q;>32chB{;JV-2%rjfH{+qX%i&Y
z2^yGrS!QcNnj5&<nV;m@CPZDkhI{x#%@2a~P)OwxUVumLHzYFoN}V+!0-z(DzI0ND
z)y_kX%=<9hn>`i}KiJx$jld13q9)X{$$H9p$(pA;U{LPRP<S?nHZP+0-M#+uK6uS%
ze94GJc`{Es8|j335GH`ex*mVPy$kjxsuwhrQQWy<TOLIUkncjKpx(Rbf3b-VKp8$5
zY^9pWkGT2UfNtM%*)Hr41w2|Fm(bDC4>y}%<_IDy82j!DR0E<nFa$u#?2zZjFacAY
zPF`#UK0BD|w%ovJ!$jF(m_A899@3aFUun7+0vZXw9GEbNL(8^4juv}r2s!#Q>!bG5
zMna{-v-i6_<M)<nI%(n#J&fLWpQ*79tNlzYYi<lz?rP?|M;5=-qyTGH0?-3yb~`U&
zc|#i60bxujAJu(NHu9vgJ-(F%O#z`vO;xqivUqG;K4Aq6jYFP#MiNqwvc=#$g~2-%
z4-OAu8M}W}lSdwUt}9NFylU{i!kaT0_XT(ax{qxiF7*1MYKIgF_={HA;jZd`%emBi
z-Ls%y6od@DT{%iziCMhA3A~9wzHbG*1WaSo;o}vN$qdhDzLyyU1}Lbkpt5mtF8|ER
zzXq+#{E`uyz<LTv!`-e>3f3u?-@R^nyIuU~`5;JL#J0G!3~Fs~v<LRwHrz3E(EX;e
z)MRM}!lR(TeEn{w$}axDoclb`lqWhy{W---o_EmlFGJs+&;T8D$oDTyhoQy)vF`sr
zUjBD7aB#5hPx})C?yTLv|DRp}bm;dxXiE6!00NUCjQ`&S?4Y4}BdV>ZhfBaxFuNH2
z4qan$mk7xxAd#S>D?Xm5zl6=9qj6zexs^ZBl5fEE7oxMzdo1K>@v8d+l!s+$V8((M
zx6n{E!t6qNw7++BDdP2D(#H8eYoF`$6jla7=8vYs&`jn;SS0b)HaNZJh*&77ZBRK!
zt#=np0FcWu$TH2T$r*=nk&^gQ6QhI%Vc1j&`hi-DW?8AkhQshNIJRPt^w&5%pqOGP
zkrU5wV|D1<Gv<&l4WC^z<BOPFD<^Txr#b%g#+;2O(H!N|f)sno&rD9ggU8GvG2H>2
zrhsf793p<AEEr4KZsW4J!fon(b(t|S8d72y)1oR<r^*YC_eS=yj4`qCLCJnvaxE3D
zz6adF?u(^5npdP_C@4EvzC`^$kj1YNi`$gCJz^d7$1Bx5c<@r+kVZl2#-1e&90Q4p
zq+f1?4DgpmGD{_v%1vyuOqAkeiWXyAQ2Yive5jXgPRhy~7^1?#X^DWL{Q@iuA+W-Y
zZJ12PRwV8wHxVaO+R6hQm8!*DkwI`?41OdOmSF2CS|W96S#J!usAgA~(E_CUdgAgg
zQn})HE+!`_$^IHD(;|R?4}rdeS^S7Xe-`{ZRC2cURMB(ge<swlP$7^{(WOxAiRsx;
zmk>R9h3rH@!A3Uzz?ZaS`wPKbOKx8O^-k@mIV1mg1#EQrXvqfrRr4=0T7f)hhy{}8
zVvp68c59KtmjChGT%2sMsRPNHeA)$d1EiJV_9*apVLVQkpG;DItVS;0Z;=Liq*l8r
zTGrtqTECh|vul@cIVqfhtR;1UoBe_R1FpxKTSeHD<dwUcC`NbSl++7Rzym-S@It4Y
zV<?cz8Nej|bn*>8fyJRODWxHw21cXb9Zrb|3T_)%N-k=o*>M}cBP*C1a0eCE;IQmZ
z=c(s(mQU(5N=VO8Nsf(an*~ViY`!4Ko+yXQts(tPTVE2st+ab90!W3NjxYY4!|p1n
z(BAuq%VDgF<7fUY{XC<V<ao#0Y)|YX+2;c9l!URV&kT1eBylqo<)el*<34C3GE72u
z14_?FrgUCFM_&vy834CbR~(Z26;z29v9otjy^SUs^3HXHo&?*t*L(78IeS(y`d3J>
z-wkIn+j7H)JG~wNDy0rdU1EKGx&}G&C&%Qs7wh4)R(v8a6z$s~+A_xah_{2BFGoUS
z5w@2idA-N|MT+L@iv65<dSB-stR>}xCE5qP;mOTd-5|TtC}MIf-#32;lLKD{^dod(
z84En_yiZIU4?H1%6^F{-bo|e*_>i8*L^+Mw?dTcV$1adD>V9J0J!SQ?gJuT6D$vUd
zZ=+#B1l_LlzfUAxvrnqp3cW~wy9!II5alqWIk3`*9&bG|pB9y>2H3fCF^Dx=5k}rr
zHf}!3<UFP6caAVn7GG;5xmKF0`C?@&ojq1?6Xsb?Z}K_b%kq3&d_O+qbq)BT3{q%%
zM6Dy;1U^EO7fk2Ml77=%N=$6I;|~vDnLNEuaW+3ll&rcA`D_H;sHBr{{mJ&ok72=w
z^>elPhyT%9@w$^z%DI~(9PTNm^q<a;p7=nj;cfxk$E*xaaRWO?ZY&LoRxi%&s1WAM
z2)>bg%j>w|fXGzd2Fs5eN!|`L0lfrE-(CS_lHjHYtWBSJ2p`Utuw>zP2bz)q*K<~Q
zeTcADU%nFQw}a_i-FXz5bpKd;cbo;G%Fv=sRbBqya0Tbw5OV*Dp7&F-8Z@YdlQi)^
z`0N^t56DG7q?}`|!SXltfspNwmQWHY&c1Jl#<?=z<Zy>jY0d_>n?UTgekJwL&`e2Z
zM+=eZ3D`q){;{I*@+=^K+=VeI+2GG+7$Tg$!teiI|EWC}&4)X%#*<kn6^Xck!V=PF
z0YtI=J;!~ReBny!8ob<_=Q0k|_e<dcAQEQmP3Trufg>3!z7rFQ;u1$hHD8#|D3`Xh
z)^0?Qf|9t%*7z6(&Tmk3nzI2_1q3tJPy&HC;({(zwej_e@lv94E$RtrA>B53GF|2>
zmYCMOFc{HNugKeSj(k<qX-uK^e~TMo!IC$~Ct6Ailwd})VvJ-I9^&rUT-9oh-cQ!x
z8%Po$4no(8RW>+@0qcmh>whETm`umNed~X_g7LP7(5oEpEfIC-*wD>Nh>2OFZCB(l
zZl~vYv{85B6g&(<-2NbXdW<v55?{Ftjp;<fA(lGA)N&}}Wv#XeDksZHne(+a-3O}A
z${nBK&SFUKAOyK9s>5at!qcs0D*r4;g$Y70`)<aLe1Eh@+;1#*wz3k!Bmj1cN+H$B
zm!X5x{Y2`*UZAu`#YGO?hYvV^<wZ@f-;gNv!#d~dL{QPzn)ShfJLP1h21PJB>t+F(
zH(`My`Ng^MAQBfB;vLeF8`5EchWYmx4@clCny<r8+ASEeH`K1@(9f{Ie2B@Vbs&|Z
z>;9V6#>5J?GdK_O{ww5Zv#wbq8Q=TvK#y#ov8;tHEYm{Aof7c=luBN!1xTiMovVR?
z=DlN6_w$4L*ffY16CvM@D|}n_s;ms6K=RK>*y#&m)O|o03}t(3Xm#<Sw`@W|OI|tF
zul}x!?U^E>IUmFNen9MJAr_BUVtD<QX@#C);V6z};pzs9<b&{PWew}fL5p>DH8lAp
zr**d5o)>0Or|ADhy;O@7W^rZpT$lmb{b`K_O1L2s#_sj6GFc~42hs%2<T^}{we1v+
z_1;jfD^$H!*GMdEgH?;jU<2M7RaMy9FbZ>@U7d(e%Uzl_m>AXReE!MlR?)||#ez*#
z_0&6QFDO2MvhuWe<P87jZ^+x|Km4f2`0&=Wq_Lu4(VhLd-;aQ>COAmq^eUh7_*}sr
zwR$Di5HP#Nf!g+te$8`Tel&`lhi$|q(9VummefhT*a3y=)|-ML!JG2`6)Bm(T_?{e
z@4s6OZVNQhF_i!A!6e^B7ug#_X;^zzBtvls&=G(X0cU`YqzKF&b0PzYYE_Ul)TLD!
zWy$lr8F$@$Y)G&bRo22n)cz5>LP171CHIR#K5$saGelZ_FPt=7h(xnvicG(E=o=xP
zjQ%EN;e{YaKXiSIa%%(W+9dtYdcs9DsiBgjC0NRqb2x~qKQuCuhg(Y$^*2OI%v|Ul
zb`UkD3dAA9JMEdAsXmR(uHtojSeCKq`yQ<KHrAKoaRim0p~Oc|XvJK;wN^R`195n<
zvxXX++`}uoF^hkgmNY8}3yUKT;l=H&tm3aG$Q70sh(zz<?H{Hk?QQ+AUoZVvK2Xa^
zft?`ys}f^=xf%6RC8AYlK74$VDuigUZ>W?DIsgD_l3=dNq99?Z`ir+pNV;P>%*7Ag
zO?6^SfMLQ8^IImZrz}LZAr~8<HZt4;rMP{Mq4}Gbgd9^29MlkjW8XJrD4Ul_i04!T
z!fUDKA;ifCmF08c;nfyObw9$cHb+&O<x<2{pbc8WOk<k<RXJc$5H}Tt#~{YRVtRSZ
z%e5UNEI?x0trpt|ns^30hM-5=*p2MW{N$Zf911sqn*C#EgU5{*B_(6t%f1FBgB5`I
z<toQ&Q#bbVBF19BceYet$-JGmylwAbsC(s}I5&FE=78djakRbgiGQtf8OXpf8NzPn
zM&{if+3gQ%!{G<>M`-V+NW+J^*{W|g^E5D<xk}$JSdC|TQ6S*r3RK5K${e0xy<=)I
z9DE?$Lqw<EgVDYUd<1KH_!3HKro}cMsl~o;%+1B=CYhsAI>!WruNy{zV0*6AQE&j(
zfnGP*1F~5dWC*;mY4F*9KpC%f8?OGvhH<n6V5N-Psx=!a{n(c%6=OK#%bU)S1q(-3
zHKb%Il<kV(@FDxvQpN;1wG!Bp!zB{Z&s?4{)z@B)gj)du8>g{2)7#GQukY=R@aHKz
zZn0aP@x;1fXV0$OIq&BWTr7m(cuu&j?mLiflXCUh#gWjbjKYWxx<T!O%%*FT)Y*o1
z=f@w)%Uq2<2ty-&6v7+5KYlI$u-&QaziBPw`+l*1Xd_~B*EU-*@<LGdp!%!q7-}w<
zFk3ii2g_uTsP=3m6h6!&S$-wvslrnD$wjUv^x{MqRmhsW$&VCg=2pw=-)DL+{~aI<
z_NW~YZtfcS*2Z~$kwpK#JxNP9-U=UjrkcZ{VedjvGA6I#%R3;Z^Vd-~9!g??mb8z5
z1bgl62V#?26}4id3p<srN~~{VVIbhjX7S*?;8F5#01JkBkW7Nn|HA^{p&BUCH7MT}
z6^HJA-q9h&x-N}}?Og<5#1hVIpEU(Q!~K$m<q&&*BU*n#(dB6py-T-ib-dXVE`|_A
zPpAAPsCnft|LnI_yn40xB_^haExua1?6QdqaS+uyq6c>1B+uw__amEmTV~$rqh;?_
z<+$BVSOQ5!RAOCJR(XC}3OdBeiid!(HXjr!&Y7N=?I#>>kjE35;m*6$jA>6p*>)=I
zo(yPn-9U0z&p<47J&lHJV3;#iTIAx-ai4450WEcum|h=jMLT_Ywprs)$QNSLO6YJu
zbK|?)&rzc<CJ&c=P<Gt%bUJa(oSoyf+!$_o9JDQ+;GKO=q>fgE4*CqE^V<Zb+Ax%`
zT1v|#0!jK$GS$?{qGNE8CQHK}TN+h>wW%(rcJn1HPuvG%{tRSzE-QHWdUP~1<%vgc
zZbG~8<34rs#byL2tOfM97)~=5^bfdWLdJQe7qT-?zWroo@U!Iv_~})yxIpvYec;>E
zBtO{)_sMLw7uQ2Uqs5SYiF`J55(p{l*lZ50;T^1`EX=u7rIT-lUKc4(*^&OXN8h~E
zWO3p<1V=UU@g^r2UwX7K+H7Dsierkp)@9DpOSj*_iBG)Xbd5}o{PuV)BISrN9_Nni
z=*D6Z=nCTC{{%R+o;V{GKI^>m(Q0*q3Q5Uj#h~l4S_XO9{4)`B;oHZ^5E0Q048c7z
z&QM_A)O+OW<j$O}9K|;ew*}`d%u}-rOUG<i#lHCN3~dMFBI0y?gj|g)iuUD!N*&w~
zI!t@JK3LwcyJuYmt-aYvmAXcpEE%Z8P`6zR9k5AP>RQF2`PT*K^T3>{sMI<7MHxSP
z?a}ZM3Ub8#@R<T^{{udxkYF-@$<vU-2t`Bj6zD93qWVebvju{=I2-nHN#Ah42_e=H
z*cE6jg6wmpWO79Zd;;jmvPKPOn$rRY2P>E!gZ2Io0Kz~$zX2(NqM(QhNbkKYb@zYo
zE>$u8`@iphekM<3xy;VYeec|vbI!cy9Q7jX+x{U=-h2$^zj~vz8nqBxN3~)S!#vov
zbzsKQ44mGw5gPBVL+9d0SiAB=1f`e2b=(4Me78LgUkZhXV`JssHi>)ND9RsCxwjp8
zW`PejuS5qU3CfwUQ1&tq>pt9wBwYtA|70t4OG{vGWB^kWO$49*67xPghp8*JVS<MP
za<X!utEYh*m(Jn_4VQ*)Be7}F3?{oWgq%53K3%Z_7nor1^`b?Z%&Jef!GI^$CXHyI
z)7M4Dy^GlL(WeNAh(YHz9WdO~6b+>%2z&GpI$c~So3j5O{9)e714Eh2G3Ft0`3J|&
z6;7<h)cykTm=<fAiQ(;i4W}-7AziB>=B`+bvfmD3>CBm!zhn<A-3Mdx$ev6g%M|Gr
zEnpD!3qDx81F^E2=C#*vRh0ln->wVhEn0<7K3Wg`v=IF8!7c>FOE7B5B&Mll*32HX
z=BXtkCn;9hfRqBCnA~MCE%QQg<|Lcq*%DV4Ex^p_bFllwV=P#;5H?mOOb($&s)G^2
ze_De>7ayXC3WK!S01EFDQ5bvzGiS`itQj--Fb^V^*-`b4^iZ7l2=iym$CW3U=rqQi
zJnITLl)b6`5t0xjF&k(r=Qh;t3}9&27CriOMgwUU;%@H6{B@t<2~#(#Nd+z#4=I>B
zKKS-^1cfrQn=B1?u3yC23zzXMs+gLj8k#oO!|?+<F?-e)q#N77xo^7~z199$&IDFc
z>$aG=XeoAnz6}kEqjB=%&v7@j07Dj2DWP_0(rF|-jAVFx`6PVr1R^7kDSnv}`21PM
zCcPenf;^6jNqlT=im`IxJZ#x{2KF5P@OcX`t$%ymUb&dq%)ySML725-KI|IXPy?63
zP&*k*XHUoHKiov~fv(Uu)P{YJ;dp<^63m!98Rir`nF_PERkg<OrRp8g#IO*LLhe&g
zuh200L!VTroSZDACZtu>7OU4{a#dYdec+cU_%Z`BOEK~aN>M`Ehb~uH7gsZ!|6(id
zy-0#%uTijg?VFXq=4J7OB^gXvy$Wkqe~3?)_JfosK2`n+l@(w6Y1(!GKHj<ot+<eE
z-ug9Sa_U%t_#MO61F(A2bm&E&$DBEHu<e_3P)IeQ#-u7@g(l1mv#@N=Ol;e86%Ktm
zz`oyjtX(@6B_Ss;d-iOcx*NtvBxGy-og(;xumX`F-&_d>lVONqY@knTe-P$RUw~^b
z^3Y~b2cDALhVT7Xe3xYi^uEeWtfz7Bc`@3KnvF%>E%58%-Po}G5ONG#VCmF>s!j>B
zQh5LU*O-<hD&l;8IdUqhqo-grv+5<sW<bOY#-+@j)OzALX1&wE_5EKUDD^Gc-D|g5
zp5})O!CA1RMWun(9}M8N@7DiXk3c;F^$7eE1VrK{aP44^;<Ohy$^B%~ltnljm4Lo;
zmSJdP4IG+16EnEJdZ+1P#hSS=(4~Q<l)gQJvl-7)g=9q%`uqqSXN8s-%ykO!{rcs&
z{4gFRVog|gpNbFW_rl4qHetrhS(rI%9zOlW2c0I)!~9N$ICkJme6-_d$Za}e(WE|g
z-ip}33WFD}MBfI9*t&8)<}TWagd!#plv<(9umu>~R)LRKFT$)@Gco(URoMU2MFiaT
zM`ET7g=tYZfBsjT_3}epR1ALnbQRVf^Mgz(Ra&=&4!yh{AuPiHizW{cQiG#yzjx7E
z{t7T{j%6RthW5)d+^go$7<V2b8Z{F_gK_op1IU=PFzLlZTs(IYmwlt5(QYu-O>jq6
z;3=%#@&y8tG_Y{)c$k}z!BIz%DAbsrgaUU~AQn!ahKpf&=r?O5e%*Tw>aAUwf!vKu
zf%X{E-37S;7xD1cb?$AH4;4iS^il0?4`aocxNH?%C?9<=a|UM2*oai~Hdwx349b|~
zQlZ-b)_FeIzG@wUV{@r2>7z;Cd6?E+AKz_Rg;{(iGv+SA(bGQkyVFE7M_nBL<`c|Y
z@EP)~8>6E~>pDiR5`E-ll#6VDZ67Sh@^wEzzf(8#@N5t5rmmPie<2nvT8{RX#92`c
zdm5Itxz9UKU4@13j>6bEYq0m*kI~BBvd;GZ%te*tR9=jfXF>3P+8ccxMYwtGGLkZj
zVb-J_9IT`WydQ+H_;{41oJDBrRA^W>h9Z{^ggH;JY2O+6M-(7>awue(6k6X>1P3<#
zCe9dQE<<+YP3$<h2O;5U(Dv(v&SSe_*3g@f40c6yz*!!XR10@Co3pbvhN3tTcLN?F
zo-fY*fUC$M7;4(Obf7CkRLEU?dGa1a*J5zlK!jxT5ik+S5bS>&;n8u*ri6d>x+_q`
z<Em@leuc0IDN-^Epk+rY4V~?T7qVs%vY^FgNie9j3t|)EkVp-5fM;_^3Z7DsNk-(0
zSIW$Cm7-l~Q6V1Oxd;vY)=XWk!1c=)X?dq~1Y>v(mg4!jW4L_A3rXcXMWExRwkg&9
zw-f<0ZJX8@(BA=h36HV++ub<lmjEX_AN1-l9McE2Md|<#q=(<b%`-QUf`%A8Z8Y>=
z2J?lyu4>h)AGG+C{rB!-8s$fDRCnPE;f!XagNRF0aQ9v?DMhsG`d>lTjMY%r;Sm~7
zUv7W%IqrufL1di<o#9QW+(_}{w{J1;#zE3Bnqc0#b(lVTGA@VhK#DT8@@qHYz!d8x
z6t<b=uBvdolT#s6qrk4EEr_cs)c(loTlIZ@5Q*rpTBZXF)2^_S=HfQ5#b%VkrHvEZ
z?A38H>dkZtIq}c&-S#b5^4@H8>1KmSpUVjM=tHO3-%%Wz_rj9p3(=x994nT7ga^rQ
zqs{<!q*$5j72)*GBX}H@f^zdTl#3b&qouQF5$^8Ufxw4RC^PYd=12$Fw=;$T7oaO=
zjv(xL7Fv^HBNyqi{$DE+^2iI~U%>0zZxHrU8);dEXh+IcnBQJ5to9f)$P&IcuHbPp
zIq+PV#!RuL1z!r!5mV8H^dt){F%or+|5B$ywqSWqJf6~0Wj|spGz(th?n7>1Og*no
zde)13AuzDFg6FuId`~~a(;BT;=dr7CQTQm*pc88e?Lfl(c<pcXzt$sAk3c;F|3L&4
z<)JupDG)PA<D9l)C3-yZ$ItZ7Yi4bLthAT#4tvg%L<IsKg`&&YRutUxmBP9ZZUGAV
zgee3Ed|~w>RTz2jzHu4n{R43?=6&d@%c0qIF|>2z5fI=<8A8|(6Tzc;OqpfFjN_W<
zN5f>#GIbb}a+9WGQs<p&8yJzU(g=QsH{zQ2BNS@V;?2Mfa;X6=^LEIO420i<he*p;
zAdzy4s-&aG=;6?b55tu!UbWga&LrhUA+3^jvcs;x`=KYMbQyyFCVuEWvLl|~-Ajv+
zA*`7-{q*9)2z`|fG@xuomP73006a<0!_@Z&Anvg*ysm^Q+jp8clJ`uZyrl;g+w_1b
z>7mHeV>_f*)>Wtk<&(;J_4{w}AfgzZ>FXvprOXv}3YWtB!?Tk+MoIE0{7P_X^Ere*
zizB1J0iy<(!}t0Xgr?9`t8IwUHhOp!5QdPLI6i?m1ihSs?p95p5C=k2o`yX~&fwbp
z2s~LhhnP#yWY}s3F?k5T`}Ywq@aPj0`1s7wbcht;zx@cWv)7R+?LwZq1iB`qBiC8z
zB+O&$;A4An^=<$RZ7s>0=UzxzDL>>AK3MDvsTQBx_@!7sWGs?{Q&3o>2F(}0VeZC5
zq=<LM=Fiu|)Yb}!qJ{Bqlv)yuhF}S$fF+e`v8iHZI`g735zLH@p-b{>nPDSX>Z#G%
zUO=O$Hml?yIaiptC3yAX#qV~nPA+|-?_nNYoTe3(1b$hGa`N~cK8>(GE*Ld`4N67X
zIDOO`8Kpv-Z<Znogd%zhEBWj>1u!A+OPf|kIeFgd1g?;dl1hKkj0EImaGvQFko&Cx
z^G3Gh;6CAtCSfEG&Lx4GLZzjMbRmcgmFdGpyzjb7d6^u~W3n-^Q&ZgDxfhYn&hQxG
zjO05i0a=rlcL78p%cv0bqfE*5(xfoMXfPTI=<8@9gDH|FYMOk#Nf9n2cTA|y$Q|rl
z_{9I~F6tb6cF1pd@1viPOqz?Vut+KLK00+C{+?aYj~ZNwM1h;%-$JT#{U8;C7E*Qc
z$Axs=VsetpHH6+2%-WjGpTZc`*(<H6g2ShpAuLS{AWqGKDY<w$q-V&fQ41;L3uOXb
zgjQq<Y!ZG)om_NDdJ5v>_!7qzB0f43+U+f&rfmPs@t4QEK%PVchGyiX@y|-Nh^HAL
zTnu&lc0+6TUN|4cMTOCAG|3C3S()`HoD1?dd1@lgHw6_Kion8f_{^lx)9;0j4Gn-7
z$#Ck^9g<RQ44C~M%GGtDrK65^z1%3k-*{s%in3zFKJdZ1c>$2KKP#8nIMk;E!HFr#
zx)Kd7=xTE@E0K{WF3bsY%%3s??p7ICJ!2alb5Ri@uapv-st7nWdJ(J(QYkItl#{hB
z?9pTFAm|kP@mX=;0+xjW_Kd2-IU?pWRg-Y;3yW`V88jQv2cUx!q-D`ni)oFfD?X}}
z9uaXVCnf^5N@@Jz^eN}P6)Wel39qpp=M}vtIN$l4ITv)bwfOwx5VFaV+DT4h1is<1
zZAelX=cfi0Vqrt6E~<H~#&$VB>MU$soXb<77f`pfgYHvr1Qd2f4<kM!o|NVJ?8A}!
zE?7BZ2<EI#!m1s=Dz`SSnv5*PacP!w<6zo*VoBXtRj9}s>T4>a0{sIzV4b6_zgUkz
zJp%Oz{4N6YP4e3NF?^31!gI_lOddWC%jGEu(4;J+X$T7n%Mm4FSecp8FN>UOuCM}W
zP*fs;wOenr;i<JKp-j1Fsgd%Vn<t2IKzh<Eq*F3!>MWzxS`X${7Ep^V;JVEHl>F)v
zvKI1_o?zFyEqI#7^Lehv<usnvRdl$|ni*@8IwOZR_u>xI-hmx?`97t^Jozk!j=42?
z?#9UEzO5$sB5@qWrJCGUxhGH_5a^q7?#Ci|8T|sw=#RzSmc}sQ^jpF`rMx^Jzg)hD
zL7k>!&gP9U6er=w+c#0#Vv5o)K+L^En-TYVj2Z|X*?lMkL5e5ABDSNc#VBMHbk)fv
zFC*2cjQgnX59_e^b}AG+Emx=YIXSKNK2!4^%efbn(BDAA$Pso8EqM|WhQgc_ocZck
zcziht3mp}Zg<r?{gj9Zw(apFQiz#dAFtCTPx9JI7dqxk@(=&o0H%6J*64oYE8tCUI
z;XSI+h?c^=Rw#)vk=)-6(3n1F+NPE;Hz%(?GlfDo85A{TG>T19o}vr#2cN9+yRZKv
zoZz#06y)R*BTZP+PtK^g0EM}E&=i%?KOuqhAPHJ`B2YfKD~;mu$So*BY2r&Hq-Ma(
zG@C&v1l6zZ_FwO>uHOaOv#N+0l24PN--JO4C54Wx*DeQP?UDsZGvwrT83u=<WLymh
zgnH94tV=<a6XTDI7#h4mEl%7V-mI*KfL>no06+bDA1=%LBa=rYcdiC0C%@39h(f<G
z9yLx>q4?hN!#KD0n6_gbbgn&z+l28*y>pHnrYxvyX%R$D+#01kT!_3f48un)!pC||
z5Gl7rOPzRZ_tWKzDU4lcmk(GFEI9e<DhdkUkD^mK;hVxg?;et(%-}BShU}P^s_Fzw
zLLY*z6DC8eLooL5IZk?k9K{sc3|o)H%&}=m;PICt=M^5y#n95?OQeL1f-5l*UaYzc
zLn#WF(PEmHpNo9bekzN+gj}}YywPRNScu{RaP6#51!jU`RE}=F$6<b&HH;iB@v9Fb
zy-;YCRtVZ+VydUB4gb6MV4^z+lRsYzwF{5YeZnMU_?)GK2Tvn(wCPZ)61xTAU4t)$
z)8ILn+lUc#PM=1ui8}^&vqR|V<D}?RL=~wVT*c4Q{QC%u7sJz~2b?r7;t4r><mMpr
z+PAnkuqTF2d=JlK^O0-W5nXKZa5Xv>hCN--ZD1eR8n~d7l`d|b37`%!0!B?cVD<-}
zz^0uGOiJQ$inNg7%f{pCz7_cN&<*5>obc_%@6nKbVgH)~Jj5nw-)juIXugDBVM8ok
zKN6|5INZMAuN-Zw5eu;M{jo^6y%&?$?T0#@WL(-eLwVLC44AtJ!zc`^kr#jHz<z|h
zR0;hxZ#OI?5@|yRVCc5>Xk2Ioj}F!lCp94Vuz+DerTkw|qA1`Jdu=UE<j}#WM6E4-
zO}4_m%o^PtdHRzUt5jfWwp;mG-K;wnPIE(}77d|ktc5YN-b3qazu+1dYAM^RoX5g^
zQO#qXFwv<Q#txV`W)dkt2Cxyw;=5xPkUDTaCQY6KbN@Ir@6`+Ppd;9L@D3Fuz6WA`
z^q4pmx?Mu?!`DYq*H$7Zzj{U{h(5m)JAAw$dKr#3>w9yqase+DL*JSwcDuLXhlb6u
zXv9p+iK4?+X*smaoUnW`6;6&>o*f0R)BA8ERHnQ|OM4FtXSAiLGtZDr3sPN0gQskI
zyjMXPtt^5=RS`}08XQyQnkD>=Y9OsNR5ZA`)LI)^znK02ntbP~Tj3X!*SQgKJ*a)X
zjP)7D^iN(_&=OZd>y9ShmzwR!xoHZ<1L>c-u23pz<vdy@q&3<Rt_vEEDpY4>`$A8p
zN=28-Id8Y-^<rvBYTQ`aZH?<(o2VrT*ZIh+iiql?@QP|EH}<N_N~bjeV<|DNiDebW
zudiPf7{9Tu{Y8wGSk{64U&C1N%b>FO2KFk9<-}NcH7hX_KK*MLb7NMgQKHtoBnCyC
ze{W#?w)x8I#lk#(3&#I+9={c1LC7O!e{LRE&ezxHcjfgr=W!Y5F?Y>3=4&ahD=C7t
z=5h6WeSIGP-h6#?O^_p-vWLpRRz1IkPhzLB*txDhUR*wd&;TE#_UQp#ZqnB-+(EBJ
zqcLa8GBgUy$B6DN5PfYgeW)5k>gI+aD>h<mqjqR+t%pbn?}-mvgK~wmmMl?<?7;K5
zo92NP<hM8T@`Ji{Q)nj#<G|7L7=LIcrc9oQ8^Nh))w?Hh?tG7J#{z2)kiTVR=Hfor
z?5T66VQH_4aA`|_HXSifens%|y@ce68}aET5zgLwg{Cd+5$Ssz-=Ab)4H`=ulhgg)
zc<v2O_J|8OkHcsEDR46^G%fezmoI@sh%bJ-?28h=bGR2Z471$ZBK*=%Je?P;*|A8^
z)xogOHlnG_3ho`bx24$7H;@9dJdadmZFCtt8MD-#(X^!&zWVwB?x%D^uW4(LDLw;@
zww`D%e~1mAA3<_9jbpDB5e-~w`X+Fl^d36ebz<ZeIS#KpL8sRu#6(`f8SnmBG}sac
ze>sh8(u0Ndue69h1||-eG<p)+mKnlEnuv>M?x6QfPtts1kZkG#H)}0i`|bu0cezIj
zvYxPhRTf1K;{7fla?)08UjG43-wK6=y*-j6L-9gbzp3yjUtH3ngiy%Bo~=*cMnMj$
zS}Qni4Sg*dq8PM+;lbxL8HItvJP>~KAcCS~Si9|Wr1_nMf2;|nFCUDO7k;>R=nNyT
zIbbrM--3`_C_Lw&4dt1fL_sn6d!?4tn(+KFuHH;yn7RsKF)tC<4}XG&`m5kPWDLZz
zR2<%a3P<leM|0^j*3G6hy;`t*>k#Oj9OQeQkxU|JWqY$$WKa|Z)NDHHT}Mb!b%mnj
zC%AOA6whL5^<~KlK84-iYh%pNei$;^9=WgXV8f2XNX#h2(?D-(Le#Em6nSYpz9eTH
zb4H+L>#ne&CKeMKj-sSw+_;sA7{Oex;CF+4phfR*fiCk#;i8*Qipq&o=}3r8!s8@A
z^2>FROiyW{l;@?v_u6eVv(SdQr4_9C;^*-6BI3~{9KF~SojiNNHYW{Ny@EM`{cu;8
z9?b$*PM%7B{59l;M!(Ue@<Lew9{Jzo_`;}3tF*_O6{GG)BAa~s%pfoL7gmg05sQ;Y
zk3nNhKXf0_7qa*#D9w0<pu0C9OB4ic1qJlyxOiQG=UxYRoR>raZx{xT8HBueA8aB8
zB(A_7e%>Z{mM-*JhEyKEoxhrhm>6p5Eqh_cnr$?Y=OOgU0UV_8oc)&YN5zaKzJK3w
z^!j)jX3U<51Lv>dMRWr4voi7JnvYSuaww)O`2eEQJUqB@5~0Vw#sTa37&dJ&HRb{Y
zUi}#dPWnOI-3B)UOSs@D@GAHw&fY$cvky{X*7X9;1w>Q`nc+D7>p3*l6fE6!6mEc;
zRz~PQVHpO~eiG+<7W<AKM4(KCn=CUOk<l-ao~)vPsg-0S*xL)4ba+uIGCUTe^;|;D
zzpj#a?;4K%Y>pBAoMBQNiXZo0fvKfB3escXclRa|m7%WW4CVP07jNX?;jdRHbQWVo
z&o<DGdw`=y17WI>g?t%J_;nRVnlU#wL}_X`qeD^1(qg0~8#@#~3CE4wnRGG|=5YZ+
z@86}RhtZOFB9<6+9eaOhieWvwqgQc09^VZ?Qp^o(`BWeChPYw)_zqM;qjBS9g0jcb
z+!XkD-9}RjEts2`fHoy!Tm3Fp4l5@k9oMeiLl&dRJc|zJda41F5FaqO5;BXqxTYsk
z=m37^0%oo;jrIo}#6R@KMdN&>04vNvF?pd9krAW}C)==z6J{}Vt$tz{c3r&k*3exY
z293ky{%+7FRZg{rP~j*`g>Q-kt<8*~!$+eUEw7Ur=AEJrXG<gHiJNNd6d6jA;GL=k
zS00U5ujjO}lqA9@RTtfP3|oE26lseS;FqqCo(**QCR7#?Pcq_hKbw(w8Zg>b^%qc7
zz$g{Drs!c!>srNiK`_qcI{K`@4BbdatiE233_7Ss7n!4rrAF0tB8a5y6eN~dppylo
zLshgR;hGZ1d0MV!2?qm4;8I@ay>k7E%FLt>jujfxsh@Hq3$HUeLvTs~thhN^>GH&}
z>UtgmlDVlHnZit)VtCc{T=*pw!_m~3ekzp;jtXNRV%(0A#u%*X4UAp*UeOp<QB)Y;
zCB|KevC2@P8V^W)^d*P52QjW*W!ZmG0s_drb+gf_7`v*e5@H;jZKA}u5(D8cg~T|t
z4vceh6A@API>u#4C(SX27`wiPaU!FUC6`&i#k>+@L8L4~EHPH7TcWW(pF|}#6&R}%
zW7}F7=M&>XMq@L9C55SKEc4(`C-Wu@RA5Y+TFv?Dm%@A$^wrX<i81G^Pl_7aSrTLJ
z$jWX75w|p%^OzxUtqf~nEUuWx-RP6WY88balo;y~OI?com4z6~h;gPNdfM<5PSv>b
zkHy6J!5bL!v^GBhPx4IBz1BP~M|N%!UJ&E1wdQdd(ui>!G45OoV+NndOhroh>ln)r
zlR=*u4NKU)hH+$O7OkF^u&IS{9s)UE8ycFxq87%v#JHFk8^3|^J!0IFYl1E*DV11i
zNpi4$<7aruJ?QnKNPUHHu7gf}$G}-z4Jl#Q@bfQM5%werI~d$yxTh<I4hF(+{fuvS
zokUt`Bm7G4dMEovc<KEc0t3yUpdVIIVFB*lxPjb=#9CdX#XrDyM%h|CdnjCcd!jr)
z3qg@-jAG-Dt)J>*_TcUq-c*6isOPv(3T9oUgwbrS?B9o$tHz<RsSMYDK8O;V#>iqQ
z=BSXT*!@i-4D0QJetjD-x?LQC^8~pp4d+k(0)5F)xD4-ylC)@q<<OMQk{<mKckYN_
zU`@ZHSgysqR>l?o^od_#nb8~}w=dzPz-{%tietyjFr1;Djb)FOdz+b+a1y}3$-{AI
z-wC`kq$@m!dm#0RH%dx;uxpnTigCl}82c_tveNM6W+Y^SwpB+Fx{#=Aqe<&FG~T4)
z$Y)>Suz##_jVQ^?Ky-LA3R*<t{0j!-P=3E0v5~^w=76C+d%%Nx+e13y2Aw#G9s0|e
zPh~h98I<Jesr}e}+!vyT&2aa|O@=S7?S%R&^bWq*^*u%p>;jKI?u-=2^&=<)iE+O0
zmBukokAf!&loKyq;i997<y$_7J@>fEbyuDHZAfAcPuC04#HJZqYGuOj%we2A_9Frd
ztkAVs9i9_cVgUE6)aaY|=J;>S!}AC~A0tm*=Uoh>KSNPgJVRj~!BgJb-z~!HubSh(
z+kysk4H?0uJeP5*1&+hNQ#!i!MB5ha(Y4P2m}mK8(Xx$*&iOOLF=SK`F!WG1h1)+j
z79CSFXqDw7JzHh<`*Yv=r?n)|V-g_7`D2))x+m~o?ZL+00loVUMu+a6Xeo`sym^c8
zJiU5LRJNy~4O1>WIgEZ%`r4n~=uh2%Sc;IBm;Hy{(CRwI<eoE>RA~<465k>$*XTe@
z>wSAVY;Iy~dZUso+&%ai)*rjUu$HPyLO3W16_vQe^r~-J*fxSxUP$GlZa;{sihuEg
zgyu#QOB+U8Q6TMA{A*#MBrvfs;bM~t<+}G5+pesxXK2LDG7AC==g$;FV<QwXD#2UU
zwm;LvAJ)+#{Y{e?XXRE8N{#$WLvEfqX&Dq3|4T*F8VlB~$Gd~NAmhnRY}&pXL1C}n
zIvCrQ9niW-BPPC3GEZ5L$G@B>ag4!g%A-`lx~(e0b<U{*5jT8Nz^cs5f<Hn&*<TV0
zw1NUr*%d!1udBgExBB!^XkSs5hj<ws!1ZdM?8&KgCKfZ~t*$`at1e}XHkDGs>#SdG
zs8DcI%jc%A&H9q6>w;h}<8f;iouC@jyiUtbKCkD=bzn__rLr9%G-(d~YvhbpVMO7t
z^15)qo5Sc<;B^B*D6ANe5K1kH#uXi@wlbWZ>N>IEadsk)x%D-*pi_0Spt>HPUj}1R
zuC%HwP1RVM5lfAlSSm3VQ*h-8Y0c}zGL{%OAjVaf1u3%<V?APA4H+@cuM^{PVw@yk
zOe`z!N!Y4NiDhaX7#DLR&mfjIHL)bdg$(JJUH;E7rdmXWl{5(Ezo^Doz^sHAml9(w
z=#dMnf?kDX0<kpUsc<cfIbY-H(Em1!&55yk6=(wX#Iit`ucRo@x~nRxd91`(j~G{B
z$-i^H#?>`niE#q2Hx#g}s?SGKC&moKz<K;S#=_mnh;e2e7#9%ZoHsD0;fvu6-<-#s
zugvT6`aG`0SWHWRKEv@<&sQ2Fh_N0q)~y3$Lt?C5a~@Y<Y{Ge5b4^fV#E)Bde36;;
zb}EOaJ{2p5r%p?)-j9@U(O_b(Q_2W6wS_=qh78Wkczb&_pjLe(H`>HlpRYm=vTGPV
z1%r&f4v?LmS;J89#<!IhG_-Vizns%~HJ_xau87fB%(y1yl95nUQ&S@~#F(MA3wT{H
zuKb;%+}jGdx7B5@WxuBMX-iM9d1fnxF^x_|spNhCPKWNxzQaa$V|=%A3XVUch%Vd~
z=lYQG6VaHbxPu0|;mpn@ID9@>h)`UsDEGEfuAxfHyy{O<eKRU7TyL}fq?|`OlBvG7
zav#pGD@+cXRimGnN3Wiwk(2gX0Xt*eAX{pRcc;%o>G73Val)I%>}p|4q((!Sg(*+5
zXhcY&QM~3xt7Ql~3j;Do(vVf5P}RDzKlw{#Qd40_|CRqIE?cd*=^vmt|HJ1BkH(h&
zl`;Ic?)~rf+tIzZ;<|s3;_C4|iV<xb{`WBxi4~1ov{Q_pJXJBVzoUXD^#A)~{x7``
ze|Jv&Klwd13&oJplNEh?cUL&rn%B>X`Z-rW2kPfQ{T%rJY!0-2ca`GccONU<&FRcn
zaqkQrt=M()oWl3^RmC@}W-1J{q*Z^btn)wd)1sMT>%pHDs|U4E@C4)k`WPv|(|b(S
zF@OBuFM?L5Pd;`DE!1fY^;cj17y+pkHAWsAmdWyvMhknj&iBXP{jb)bRxQwA=u%K$
z>JcCUoDbCI%IhaY{cNa5pdNvG1pdE7K%LfmeL4&0XOnnbLzpoo9a&0?cP?pFdH*C(
z`hOBt`m`As>O)TXHD3tn`Je2oj<y90{C`z|uVYO0m+BFyN1z^odIahbs7Ih4fqDe$
z5vWJtEeHq#d^LscEtTpo*CSAmKs^HW2-G7`k3c;F^$64>P>;a>4gzlo@FE>kv}o^w
zu3b8ziCu&Loss^FBa!Huqe&Ax7}On9<gfK4)kX7m?U+_x`}egWW~$V;XaqY;LuJbP
zzt+h=sI6hZNFPmMNvGC-qZpf8p;6;T(5rH)`r}sBm}tSVy)#-f<5-oC77u@{8#Y#E
z(f#8V{+$|9T|?M5c7T;JJ%;{7(PGML+eXZNU~Kp&YX99T%%*B(hbByZr15&VxWC)v
zU-+JgasEwg?a{Jj3$%1>0^_$t+W8Aj{<*3Qz1pNzN3?Eg`{(M{*Q!UL9)WrU>Jj)?
z5RfwOOS?8LU|i`N{jaq8|9B%zHf6xQ*pA7k=okHui#FrZ3-W`75d)e0hf1w5lIk|?
zF?;?zjP`89;BUZvhDchl{L_ElwWbZm&6tdi_Cm55hP7fwnI~5-;=~QVIz5z!Oqm9^
z4(%D<O~!bjj15a6Lq}VK;fZ$R<;%!_JRWW5vDh}%35Pc<!ugoL>6TqF3d`p0u=U&T
zkhF6a*8Jv=d?k-d9k$K8p?kA(T)ytdP>bwC%?KUFuEa#~6|CQLfe{w|fH9?Jj-gYh
z!mR~`ULKW{mzUs$_cfe7e-m{Fni2+NhF-&ZLz;LWm+!w+jnLW!@2{JOjH64i?MCe1
z=V_UD!^%a&(9x+G^!Y3@q95Vct-EnCKIwNOnV8wQW5uSK=-j$FUY-67(>^%~LrXWz
zUpW?Cnm18Kw0Y!x9#>K=(91Xl2lgIiR0>Ose{TvbieKVHs3sPTX~%GR^nPVDm2f{V
z95~~nyf<B@E1o=ZFpRSUu<eJ7jLBMqM5ns@s(-B=x?=g-_uyn}jKZWS96!7VCvHBf
z`g^SpVhfC!GXq0<wuOm-K1y>_5pePt_MBq2+CO+~wc7bpzev$%+ybU`7Gv&$EqImu
zW^^C*HiNKvk_RzhOl!uf(_jRV^amI5#V^+xZJ@4uX3`HE7kKut#$9FtPOKyTZspJ<
zRIUcj{|F0uF#K3iBKFQ(gA*@a)d)jZSyT1XvY|cZESiI$y?fvl5e)@n9*>#78sb7m
zs47-Nlk}T7csT^c4g;}qZZ|x-c@J@FOatCVgwJ>V$cO=qG$0f$D@<7N5&GH6ao6h{
zjvv^Ekk_LrscP2$TaQ3J0`&;gBk-3IFh<t_ePCAb1n0fOsv@We&3MjOfn^?zaA@u*
z{1{zVXqi9mOsv)jgGP5p&f}Z7`&1Qz`H#Q#PishE-mnvTb+h2!cKLVrw!hqyh6y^2
zTa7WrXYj#3FQnFt<nvb=<9_$foaq?cqb-aL^q7r41^z!B!k2e4Fmc{kv@~b@cvZ|<
zrq(RN-u(v<9`Wk0^iWl|mIc1OdoK*}bj0PgD=~#a^k-lFSXJ>KeNZz%N4LJ{VyB6y
zxJ($?b-;p^@1tD{n>w2<$SY(fu3VIA+M=&#4~7ye=f<A}xiU>>onQG&7sX6IU~FK(
zu#*2=&B}cF2;Xg9jJ*${P#qFo4Go>TV)9T|7|=mOS%hRLn))zgC@IOGapYvkvNm1%
zz}?9ZaS7?rG<U?TMGF}Nzw_Jgm04x`j2VL=E{&?|lxxt@!U(!NANVJ-r$|SiVbMgm
zbohI0`0RUVJ9=Q&oIx<qsigx}_e%@AnN2@z_-Z3sYh`2GiubXRapQGNI$`b34d~y(
z4EsObfUkeKfjr{TxPv?T^l(v~iiq^#?B;<Uo!h{qNhi3sYmW5TIEby=VdC7CnANwH
zvY*y2oiS|mD2yD<*ssiR`nF<V(-c!yu0wNC9Ftufz;i_-j(r~bIlV<QX7VLNBYiv%
zxPgruHX$g}0Ar?(M{_%)w{`Iku9q_&x(?&Kn=ymlTd+*ZLu7nB$_$#LKNFc1rZcv6
z+$&`^*SB?S+ORznHVuW1{_mf>lrclE*Pcyy?~{GV&@zP{m9yU~dPYX@95xQ)hr7ar
z^Fgf98{;R8KszSUYT?`y-JHys$fPY}F29HFb|zFbLgD1>hCz&_tXVVBnk)wUH!i~V
zpS;m#=p?LIy$J2uUVW)YpdNvG1nLp^7Z4EX!fog%jOo$*jZh^4M|WVyt{w17qjB+X
z7BL0YQIp2N-O=oCw($49X4bGHCQTX!D~3h=SBoH{X;IE$LQ|0{p~l~9p0bFsu^z&G
zy|7{97Ul*q!PuGO(bUKgg?U28UIuSqrgN#DE&31b%M*Sk#rm5?B{7J!VcF0X1A26T
zG%p!t4IR+g#1w(I&OxhT8?0TwhDj!LDUuc8-szvQ^T-*r9XS)zyW1fuR}Y=r*i%5v
z#o3>~!#BTp*D(~vZZ0WBTBI+&|MEB*-0p^5Uv0&Nb=xqfG!s9r-iYhTnb5LqhB@ok
zBjotk*!t0F7`rV(H%Dy*oI8T=ZoFicLlK<cc@K-;=>tOzf{+u>dLJS9Sv)3h_yR7{
z6r}33LRU^GS&T1culyV(nys;fmUd?$xdk(8<vhENwO2(bkZ8eq(ua7*tuv&uL>ynX
z4nMt0cq6Kdrmij(nNQ&V^D&4zPeC7TU#$8(3KN)6;q2WQd^)!$)QjTr<Jy;aLJ3)e
z^<6zjz@w`@jqarsFef9Aah)H9++(7bJd`x;hBdnflFDI)+$Z<&_1cf&Us9Ju&ty+b
zF`OO6L_hn!huowYcK`4#+!=SY!<gx~#QQ!K5eEsgyiQrS3DWduh<cs}TTTXz=Cn*t
zZ31y#1bz!w#hcdeJPk+YcVIkQ;Lhit;G5e)upK-ND<%$v3FAkX=O^K_O`CA9&<UTe
zAC2trM6`780weVzcx~K>eZe>I(YjkGVzMDaDkmk(Vpv#^Lrt=-u@eVvV@%(;46X12
zYu2xWU!)KtR)kJ-$HCLO07rLi$Jsl<ymTM8b#yUe-7pl<+gufARnGL#MJO&7ECcjJ
zj}F6`179I}|5<GQ;cK`L?1g>TUXha14tk1wlxR1?z-}f8yPi<T+ygVtnW1$vEqu3n
zFKz|A#4|N}oZa3VoqKeKSNPR8zf&BKBRe-S8?_AOS|;d9`cna8a2N6aw-rrYTKG55
zL?;JR#^e?uJ@^bZUP!~tY5h_1G8s-iDe!8P;KtUEu*WAD3bUSAy=4a4S{pHAs}V}`
z9x)4K-6~dTnhy@{2}9>8&Ea7y#hGJ!5E7jQ{Vvn+<#cBxz05{CHy3CW#NpWT4-x6;
zite2qncAAd_K@*#Vxq6)(9^j3NP!L$=3&i9&Ur?FNql?_E4F-#)R+k5@`R|6L4wOw
z0hr%*o65|VdAWsX-rWtRKV61>zuvG_W42<(d@g0ev8)th%-S#xU5s<Ecg=F#PAbO?
zD&Df9V#ao^aew8EM;`qg=ML?_!-yOl+A*G429pu?<*{1HX5KoG`ajeoP>(=80{;dA
zB6SR&w-%E;+biSnYl$+M4Rj+;My8<m+%=fhuOs7mGm+%;0Brp9Ynb+bAMbTm!~P8$
zaXZxtGuN*|)ATF&<kUmVUArD;c?r;I)E;(bnurNJg}ECJ!lC_etX(@5W{h#q2>f_@
z<7ce?`X*ebF2IT*9oa7s6?74+Ki<zw^>v-9a$Av@W6bPj7&D+H^I<ScFD<(l_HV}#
z?^q0)x*C&)c7mpekx7a(;C<@~9wz9csg*wCm$%21sU}QF6a(+jDD<DQ2ECdaB0nY=
zuP)z#kOy?+yd{{>y&;w@{SYxJdC<0KiiPVxq}=lb0zyPsxpg62$e<ARw&cKb`0ZgV
z^o`okK&gX9?R#O8UKf<6c;nK|2#8(AV(WsDXv91Pg(*+5?b9z2@*=U;2|(pzYya*_
zEb7$=krBCQ<IxdXIng-u!B#X|v;nrM_wn&3-y@E~bd#ZTux4Cm96Ej#uI-vB_qIOV
z+gdU6d}7FNxD}(13Gc1IXy?XcY?LF)=Mq+bz8`vxT43R#xhT%eN7LrbVWFo$(3OK&
z|MdlUzOxk5#`c6BBUnn9bK%lOFFeV~L}}xm*zo;m*xH)X0CfjnZ~7d;b;xL1wyp5t
zh9!`{jDtn%R%l=>MTFO{*!|rx{P3Adevs=}z@76eU&bgkg+Vy6HxR$^S?gIh!LsdN
z<HfBrhz@;P3CCalsWJ~!<FM+=^!4N@YcM#yOCL{oc=kkxuI}hNeKmTU$Z+|{5nOwi
zgMpK0VRm0?bj+S*+i3`fI-4T=eh9Q1G=!DOTP&I!mauh;)V0vSz8PBd8V4JB68wFi
zpv<TprcC#wXeWkEv+fw`V#BaJnM{6CLgA2!l}VZw5-bb98+LjZYi5js`ZGTqJ$MpP
zu*de5)6vjW8#-pT%z8Q)P8!T^n8wU)jcuW8YKvvG@^-V3<NB#%`1#BYL?+XcPeDn~
z+y)(cG)16~FLKozV)!gia**G&0E(DS(Ae4*tva=VW9yc1c6Eidy(?Nbv_N)j2yWef
z42fl9rZm>(JrEYD4xByupo60=v`p;L#l0sy+<KsGqb3xP%F)WRJB-ybaLfBC8g%Hw
z3}&t0`p}f(j+zvf4H}^t)9*S+a}ahf2)R;6OrA9mx=ht=+^{o-yV}9R$_gI+Jed4N
z6Q)g_(VJ<Rdv@&(Gd%;8l&PWfknTu-b{~Ne`RF&n15U$dV#V~a(0}y+KOH=Q7o`oc
zb=5R9ZfpljYex(i(h0E-eegI&jzQB0kV+zkjKaH)gA3l{B4wR?3!8twjqIWtLRpPF
zkxE_A#YqDJcfBfvcm9#%wPfbE?8F#6@e5JaCM~3{OtaNZv3;tUaxPd!1yP7&G7Y(b
zIV8qrXywun4}Lj;mq`U^*LwiorjM7Fm7-XzgRV^+(afPKh74~HO<~6IG^EbLWIDx6
zHd3x>f|1KV#$-nYj-U7iA<X<&S5eb-#EKm&(Yv)F0<K+zPgn{X*;>=$YzQkWCZibM
z6$y`g@gTaCi)TMrIdr5FxB{*X)ZugG93H+(VUid%<@~9uedXOV`;{R7O8B3g$8W+3
zoJidiZQ6B0&u;ec_r8aGt;QHSvoG58=!MR0nXX%Ez=U5t(aWPJIyP;O#_h)7<AsBn
zPWvi;z4{0?okwB)JI=~3)x=EuE<DONUx8Xf60&Q`DU6G)T3}G)78vekk1}O8H8qs*
zy5rP&7~IVcxAtzuPa*kyZru5PuTZ#8d~ggWAIZ?1?^h$uKN!I8wN?MOdIahbs7K&0
zA|R5q$Cxp_peKvQsUHsGtQTp2`7$&Zun23%bVtTLFJ{X=4{6H**s+4Li>(71m|Me;
zS$09$MA&<6O%0jdSprl0_UP5S7i2FVBE6hR;JDwJHFv=W+g3s=DGa|HK7`+{2O=S-
z9L+jUz~;IA5b1jfr*B8l$TJqJN4BYZ^lIAZJah&YOzn!~hqpP#2heAzMMEYcvhOhr
z@6%9}9&#Ip4j;p{fG3da8o|APFEldLfuUU+r99`}tp}QOoR0&pBRHW9=HzEHxu~*4
zCY3?GVK*%7-GsNK20N#I7|_X*pKY=D)79{7p%4G7=i&P_6%8Ah(SOMqJ>6Yltf>i`
zHr>#tZ%_1S=S+HRPi$FAnM{<3Q@@-=p=CF0o--Kc`rO3pD0)WLaBA%WHyVQ@0=<#1
z+Z?l3O~%uxQnd3JfFT~jcuX*qnA+(SpoOak4aN@8)-<I2#=Xtm4ISHbfy=}-c)yne
zd{6v@Q(jNdtk1jHGTmLdPntFFf)Q?xNO}~65*=oyX0~aocAi)^e-uhy_~XYzM{wCU
zlnL>QP)1fp+rB-ZU!H{9_g=uEOLuq-bgE<I+>14stclEsK_i)6mzl)<!?VzR<QNR4
zLZ*}-w9GJO<;NJ`q!`Ce97kBHj5Ob36p>*taQ=F9)r`Xa14r;GziuOFot?gRQH3f~
z$3Hx_9ou<?y<ucyESdivlCw)-+@vi&U)dZtR?o!`-uF>_?jePP1F&xIit@;EQbDqD
zV)tt7y~`|Iq7uA%8v9!F)xVZ;qqgWc7GHn;E=+WaaOd=1ocs0`#7sjw-D3p091DQS
zkf9KVKVTa8y4&$^YXw8KEbLsj9TzENg{7Bc|CdvsZFEpb-i7o?KkV4B3X1lVkl0oP
zS;Ik0=`F$CLwoSWk3Lm{(e1VvVp%%Q?fncp&qiR;&L$XaYX=bpOg_P?dcub?xtu9*
zndZ86TjXe!F{`c(#*AWyJ;Wm;^cucqy5Cma+B379O7<zpNWsp<li@pfH8u?wVexz0
zkfP+)7{mR&@yKV^$q&|TL1eleI(Y7Yk%bL=sSsvs^(+jf1o~|T;NzYBnJr9?V6US%
zy89QjT-XfGV}`@MIXT%pA~Q^h#)&Pf@FdF$+m3t#y?fta!FShHty%S>AgP9zUWc)C
z;}JNz&P4Zh?J%T&Gnny(II!kp{6<TEa8e1neKUobV~JBqAp$RcgVo<&MyEl`(0Qgc
zC|?4WEwN<v1`IXJz>)>K@H{r{wGmhUDmJx-4zoVy5ud6e$l`(&%5_h~G}o1-`cvhV
zxBo1cGiP6!7^Y@)ytFFA54X<3z@iz3HSdg$+CRXb*5=ns2I(gkPT#<ydCSpd7L~!w
zWTex$uTCrUTZ^_C`p^CVD|_4F*F9fg?}<Bat!m@mhXS$*e0ML#)-%Cy>QV}i`F6r>
zQray=$eHi4a_4Du==(lgXE%VupfTuRDTVj`)!2SA6z?o^LeEC}s`_v3<;_2csoW}f
zVs_^H?fB|K6qbF}3_XnWuy^4C+;E<SZ$9E;x@0CECzE$BmSXV6X|O8FMX;70w3w()
zO-mDwow@;s{NHT+Emy>x2(qjM#4z1#xA#}_-IE|JBtaR|UBs7APy!^(KBvy?e}W$k
z6Q%xP5ftjw^a5x!P*csbKm100g?a?)5vWJtUqV2UfyCr|G_z}gb`Ie<ee*V+B&A_O
z-*zaBe~JU!zD6Lsk~p-)oB_R{c1t)ZE(T>iS|&?ST3!Z4#U>`~lP?2)!{*K3!gX>U
znyM$jsO=cE*G$5^Z@$F6XM(+(g~27pe6$}HvK$oYkj_gUsk*K{nmM}y$KR9(q~;AM
z9QH%%`2*PS`C+6Nw?fas?NDye7<MfNK{w_aHtpPvl$@F=KTmTH{~9ghPhj%LCsq9_
z|GkeW8UZ_vv#9c4dwlCAf(ClPeb~FOI`awK#t%kmus5_BXu_qr8SZ|y7$2VtLCfw6
zxX*LI&3)_fbw*1ZzVHzu4s7P%LX|By8odHeCYs28Q3$ayeIA(++QGRiv~<{iPF9UE
zRDPs^wjdoB4t$C)&pyPwPwX+ig(>_#JcjTQpP;LoC(bcjc`HY2JlOjszW?<eZo16D
z?#)yT7SBd#N)g1&Y`Xo_2Z+A&8xH(<4C%N8Ytn98b?ynpwG-Uu%W?hZPq2CaV~k=-
zR7FZQ8g?2Dvs51}{^CnSB;~6HBQitZCH;}hlj&7!K0-oKYq<9L2u8#n2&)`$?VoBA
zB-}fR_g3tpRlPsDo?iuBL#joZrWmqdBbK>a<Ji|baNv}8)%To+&Be^l1~|i$iF5bs
zW(`zT{(}#KSXNb}0tc5SHcciA0tI*}1zNF)>8NQXRf<JCW+*D<Ocks1m_i-uInQw_
zI2W?~Y<#hKb5%QaeUMPA%Zc{EcPD(2nh=R+;jbX1ZaTa76#BGVigl!0lr_@B-5+n@
zRn}W9o#YNFI3WrJOCd{nc_C8|tH!J@;!#S}Q`}GCNUt2`F$o2fj`NjLwL&gXF{+DT
zRWHm+LG*n(F^a6{Br4&{qeCU2;w`MMO0E(G_(U0o^lgh*yDlNPO>=bW+6-BtXO*>d
zHPs;~;^m|dREz#v(4cdlf-kw6l#;#R#L6=Wbw(M9zVC~otOCkY2E<m?Z;kG1eU#97
zSC$Zr!$03ZTx<-UKaD^Z-_N99enTJE^|XX9MV_?{g3f)5=b5~35k)2;%nP4c&7#Jq
z7jfqXF9`yY6zUY*G^DgXbMcW?FyrOr1t?R{hD~e~r7}9q-eP?%rb*X;uCWmxd3(%R
zw-oOBQCKj03j%&eco+I+LU?Mws$@OF@0I0cS{@90Si;uP9*BFcyeubnN?sTh36+~e
z5or{1r9wrK5OP|di-pX{qNZrf#X*uQ;-WAZW*U0ba5s`(WWyBaGI;cHg8$hkm0eZ+
zEX_&6sV_gk<J&H<*5^@Zc?)dY`YuXoF|NDlqW6?lSUJib->zMSL*8#Ls5QUW%*GhH
z+C>Px#Fw1Ztj4a+&@7Hq(osYd;Gf?WuoqI!OQAy!xru>rpCyP7ir_+S3~M_ZDmK}w
zcdTZ^H9ymdxkS$QkpGvLsKzZ|R+yfIS3&Go)PRengeObpL`+H^x{x-#oV<F@KV=a?
zkMDJUZaU(^pCGwVgfBMSLT2)-iodIV$f>~oo;^UsIV&wm!u4kv=-YP?)FOX_S3)DS
zx8b~^q8R@39z1jK#Dw=hLQcVY?7H-T<KdiCl#yan`#r4gn8pBgDkH-C{pKrJU9J98
zJp%Oz)FbdOBcLdc#K%h)<5m|J3>Y~cUyh>z?)yzh)l%acUINu>t3WYX0OcgFNYqIQ
zCUvwln|l-^cr`FHf+(5P6|P^J@?1QA`jWv8<hXqJOI)T|#&KLXDC89Kxpr3-?$dgV
zT$h)bg4k!_NRx@MZS6IrB}P|OsrkWBTMNw^>Eh|$r%Y_5K!aZ0VL@w2nbP3Mb(1oV
z;&lq9NRy`mZ^{EgyXKTj#2jO#xUa~0f<s=ua9J=2>t@e@LrV#+?>vt(y#X-LEkWo-
z?k2zg%>V#E07*naRI5A%Xxv%Y+Y)*DOSwy;I>Bb*)T*KC&>}C~YuwWyWW<%@*ta1#
zn4N-TQdsIN9A80Z8X~<P^Us#hr@x+v!4&ecgRpP+ZERaO2%ohmg{kZ*KEEpL!x9E@
z(BR(2vt8~PLJ>F`WcE;IAZtT;g>=_aVohd=AggMYrQ_oLR20e!@#B}<S;K(F)L|Sa
zg?VC7T|`P5Ei%9xr8zQa>C<zNrz+}9Hd=QP<QpL3<_+bZDQeUddZdOHWJ%#ZaT%5l
zw!y9y%kh(c4VlEO1D3z*hIs!oIC#;o?q>hO)mj4l>)%Vy$w%nZYz%yN1bkC6pw1(o
zHrnOzijE+)#f2bJRH%b?@_IYM?_vsu1qm;4(Mu3!s!PtR8@TvzG*%3BBk$T9{#WkS
z5QMAi37<m36Hwl#J%-I3iliG6=s#>UG~>fjlAo&-zJ&-kwMuj_VoS!M^T0_Ml`O%l
za#J`O7vg4~N>G(3o#r_Clykg#64q=(5FUeGBtbgB20jrtA){clv|9`0#70$A6C%Wr
z{?ox3-T1J5A3hUgbxN=b7&dFmNHnbwrELK{@`Ot`sU(bDNf}6}ptQJ(D!h>^8msj~
zm^>wEvABBWDsn5|G}b7H_rpcM$7uJC8#GhGaQv)S#fvDSfG&Ye>&|d#ngadi4H2GZ
zjAAY}k_zWUVQ6Z^DU?{+o3+Q#S)&krDFnSoj)iVw1U;s6l!CKFTbLTE1xHT7+QQWn
z!<(7n<?Y+hbL@oS9i=GEipI@*k0GmrA6A~oW609QaB^$~3*1me&fx+UbM*kC$L+ze
z33K5UxCZ&=ZQ$BW8`-2`nA$i{05xQG-qvVhY=E4nQGChBOEj`V_aP%Nq}5<_wARK0
z-|MtKtiz)Vzu?%F2gp%4VDYADXz1!jjj+bcQiY}#!#TBuM&twBk=bJYXG5Wz8;`JS
zsu#%CbtvYHa!1<3i};etlGvmXBhYk2Y1DmW>oma7;r5V}<{}`#4~euCzh06v^H5Z*
zgC4WS5{rhIG1`@VrE`Iia)BVfPKkYmvcLyWB0LW+E-eO)#OQL;m7MJ%$_QZH*IQQo
zE)h3^OV_4o(LfV&H7!~ex-&$S4^jk0ji(4z^H`|P6C-6aY)s3{8l9cmpp2N=8IgB+
zH4F;(P7voNA@E)zn)Gmkm0_g?fJ-9hal?)PoIqp0q(0Fn!7or{QK@T08d~aj$T^o3
zl>oIz*HAEGo$v~=aIusm;@5AtkPdf%yvliamb8nDX(>dS#^~RBFbX(ic~J_&ANt{i
zplq{fs5ORr8#APb-a}TUcCP$O{Xg{x)FV)jz<(A2k-a;{_ic=@t6ta_8Uuq}JJHVB
z2|Hh;U{F6N3?AWz;_w3W?##$|(f9Bq(F9tpP0_0V0JI?A+r{1tks*SGiJ}A-UxD^p
zvlNg!pQODUQzlLT7(q?l!X8Ft83-fQL;OKYATu6;!Rc^x>kg}en3`X$`dmo=u*4J@
z+7Ic40T!*$XTnIBXqG~gM#7BeG(?UYhWV2t@taQ+?3y_s@6~<W3My8P#iCUw$|2!U
zYu*%P31RSg5UG?;BvO5}>rU=^avUPa<Bs#XfQV6^aGNk1S&#3+_j(vINW&>9)q&fb
zcNh_{F{Y2|1dXVx6yP<LV^e1Uh9+%Y(e8;Dja!=|C^#1BljJZWo%;Dt=b+Z2D_UC_
zK;toe@v82o`h$r3l$bnvn~u)tNBWIN2P-`IekV#~GSY>Ah5x%fFqnRjcej6wR|P@~
z0@YIiZCb@UJGO<wMh6a765J0-!cv#6@MwDlv1Xmoy|E>-?w(afJrp)X!FXC#M1}Nc
zAf0CNc+8nR4nKK?z^t)7N^-JE=jGn4MHx*g*HXe2Hq&y|K2`PoiVs2-aY6n!bZmpp
z`J}JT9s*fZ7@j9(V(6&Oki7^%o`D^Pjc5*WaSrbJ-a)>jGt3EH;JpjTRBqI-xBAz9
zuO+~jazMo;)D$ZkE=`TX8PZI;Zd!$PJIT$`(xs+!j>iFha2W5Z<Stj%{rzqgoCMTf
zDL86t&}vvx6A&3L?YWLA9`<<gC=fRisx>vi+9)e7uCm;{yu25`I5ohe2@CMq&=Oib
z71*=+Q^YXzUK#6_QP{3o$})rT(HAGNec336EnH6*Tq(p!-taxn@RW3_rL4okv5_NP
zAUI6c1t=?JCnqB_EfuK+(FhES!n>(CNRE$EbwzDcmM`X{pNdw!T#z?q1s<0d;T0Wr
zUln>{_NJ{UWu5yzUbvLbd!a>N)s2GpM(v*CsJo~z6EsRqZFS_<IvOj+^#j*2kw<*;
zr-3*hovUg>5skY(kLh6ChsS9>AeE<L#a@3}ugUGIP!trrpQ3URA};K~&-qiZVDwCU
z)}L7N)aUyxJMi+AJtrltycJN&dHRxHD1(K)AuZtw*moI71xAgUC$*3ye*}lVfGnf>
znN{A4tSAlNp9sd@se{nJ%PssA5T>Lf<-K@>tskDmhwJ;}n}a{0jJ^~lk=L+f?-7JK
zFURJu4xos9b^XF;_=@*f?$H8jmQ68f?p%6^i}CQ%QS7@BgHOiG@e^r6mwW^V5P!6P
zHVCbFoLFa}@aQyn0#*-L&7(&((veE>>+bJxAv#A{-I79Xx9&YC5I<K6@Y+T?(9)3e
z6s!$qFKWYkRihT4hD~WP6yRUH(Zv&n_Pp1%3;K<lfyUW+$bR+^QX^wVm!U(rV6~~h
zzMQxgaWQyy?g9cEH9)U1bKsPjjr7Dgw#j)=%NkJKjao+!EL%AmdbDno@co%QZv|dC
z7+@=9>{7n30tH1mk9mAiNJWSwA*3YSgKyYCyhEqfK|JftczhPqw;sU0+l?@F=_lwn
zqYzU1OZXqzfV1Ah*UM0pFM-FT8EBmxhL0z$zym6ouNOJzPf4j7N>d-<%uyeRIG08a
zr*)n0UU@0``LvD{B*ftJwR`aGz9neyJ`69ICR?W25>sZ)hC+=e1DTOHT97~iJ_Zt=
zIgecWKH95i;^*65NK=_mUhfu7I=Q1q8%Jn}c?zL?foT0G&%yI-Djf#8R01krU*UQ-
zo@J}Sf&5SEz!il)4`o@1$dRI{v6fP4SN(zK17(?bo}-24Jl(0jW1a|<X5eL>4xH$m
zR6Q()!7a%^RK6}+m}t~;xGkmgSZtv_jRfjd*VzgRi!zZ^q>tuCHLuH&SD1zLQUf$G
zRO9KFYBVH>A)C(O+2w|?H;~l0UW$zTJSZfFFxO_SN({Jxr{w2DUEK)AG)SnX8Zl55
zA&TKcjhU+4fPMv4m?;YJGP?{GRMK?mP*?N%^UN|vEz*Uia2lc<jtb*v#JDjr7L0Dy
z*Hsud;k!cHOhu{2xEV37K24!O!(at3a<$+{jH_2^_D_r>^K^Oau2FTbLStpbI67bN
z4U9{Oaa>&(%ZPEY0Ws#>tiY0JAh$3Z8FgTsMT~Q5VJvuIl^~rMi-@r)F|Nk)_b?{a
zR9p8=jI+vMsizAaQU$9pR?b&Z9T=;yY|Qy89GF#NEap6p$WiC~pnp{jj76Ns&pD6l
z##oyeOaBzcrNsEv8yM5l%=sEGdwm|uQ6S4g3Ndb02gaGi*x|K#oSC14TzSoTtim`g
zFOToNAxvJwI60p*I(5U>=5Zk+IbTgVU-e&`uP=zPRh<}%Vas_e1ih-nQe9ktQ@>q8
zN?IK>4zU$_4x4}x1BY_2D}%8{8m@c!;LhD&;AFi60~fA^t4t1Uc`Uy8kOru%7Kk3W
z97AR=MfR&mhW<^YkxatnTcI>ol+_aW<O~*Y6`$;BgN;*1V?)P&6n-@j=XVTqckIO>
zkBykPayy3PQ#xTViCg=^ak27ss_`=~DFK%cULpN;5EeR@A?L+YywtXbv4sZ0emIHK
z{ai7a`-$_wV(2nh!sTQ1v%7x_KE6TdGPy54*wGqlk^-Fj@f)Q8FQ>&`tYM9*8`hxx
zl~dTT;}G)mBXIieBe+_3L~!6u1k!3K=icVmqYL_sp9zPoT<&cl&@|!R#?u3oh2o}f
z6ef9$$5xk7(8zy^xl0+{<`-LRnLGubw<aZ;T>OlChjH!NJ>E_An_qcrf(2Y^Xo(R^
zKBOS8jfC4ru<J|!S3$0i$uDvFm;30y*ak-f!XZ<>kmX2;_r<-iei*-U1BP&KOM7$%
zEB0-H^X4sBxNRr>-L$Ew_+!`ZQ=m1Ia;jiyuUHg>j>+P9>U|bRZ@OaK;K}qo7)3*L
zIc{ISjNAE?(P$VGY718Wf<o?-CRP|eVFs*pYplC+8It7MC@3gI#{pBYt{3IB@-!UX
z`5k;x^f8j~XwyJCcX4~=9;aEBinQ3Lh|!aB%`2q*Sc6(z3C7>}DX5B7cP8QHs>ZX3
z5(<^oH_T%NIyH7|ZVT<AIQZOuPJx$NN}YAzHUcq^*Gapf08`UpQAdwlTrC~+oVF1Q
zyX)ecbu;nn<D#lYM5O6RsW}N;@Jazx%n&^W<U4n1AVGX=1j3#rDK}kphW-;#*eoF(
zqgJ80TbB`aT6F9HBUu`P9)}|{uSDsvsuZrc%cyDaSd0&?Sm}&c>)Xm-l;cnrQ&<v?
z(8OFA)G5pg+&6CbTv;`Q?<=)#3f&ZzmD7~Uin_HuoLkvJlopR5dViO(FM;YKSmK2w
zxEiG2kfWpgy^daa0>pONnt)m5{1d1z`UZyRFmef2jBJ5p8z<q1dn!RxXhWbj=}{we
zB^@dL*>i-)q$;iQ3gvj&E2&Hx8no<FF%piK^qMhgm#q{@aS?d@jGp4`QA%k@pm>yV
zK@!>%oO`KvaBhfe(~S7?6;TPPo-4KQLes1fz4EtVLPrw>k-~F)?;+fcOX0msU}9qf
z_nvN0m!;tHHE-lFKDeQ!HF|h>K#$f5uj@X@%;ILs=dHnAO_-<x2xS~sDaWNr97;$f
zQ3<ac8?D`9s>_12^BaZF!kE4WUAy&wkxm(&p1FjOJb~h)swtLOqv!u&?>pe5D%!8l
zZj#;f-g}3TMgk$BCILd|NRi$}0RfTT5kXK<6zqzkVg;r59$E-3kN^oOkluUm+3(y9
z2?+#LUVQcae>?sp$=!SB&OLW_=9y=n=bXNtD2WQewS)@!R;UUG+2)=U!K9IIp|%*D
z8}GzE#6Honb63>lUc;@pO4cEgFV=(|5!r(_%dw<!R8?6+mL3iG`uU(B=@Md6xYC!m
zz4`a9v2On4J5UoWRJK9r2SNy#jK(^1qFeN1p}z<rZ}NW3XI9oWf`^Ygbjow-p@`<>
z#6eqG3(oC2z}iv;sh6(OKrQC?nOniEQ{LYm?Z?fUA^a-lHn9qywMM}H0e%AZyd7J|
z&$(C?jCo;qP9on<t(U(e72+VABQL^xB%@UO9{n(S{6zTJYT(?SZKNFi^@&V8JtoY;
zOM`nuL-gRgAUt1nHqK>=;AUdL5GuYQp#k#anFz^LMh7!}sI%|No5|zistlaXk}`Q%
zPtN)vZwrqLTefGj)zQsT2y1I-A}d0DMH(*TX~Ex0tD)|O3s6J0pRhtb_*-fcg9Z?U
z%gReraJ`thE*6^nt~Va9M^SMaZkHLN2X{gq9<N1SVFuzVjnLJMyuc667jqjpo#B0{
zHOBBWRef;0!5~k{&qj@k8JsA})^NUXyqq~YdCVy>rID?|J}n0U$_#hB%(z}NZ9`aU
zF^En$E?`qvf=f*Lw$U?!sfM`W9SX;5i*X@?iNr>ROiW|{l2;>OoQ1QQVt5i`EgGZp
zr{eia)tNlbK5s@ArN&)Y9v3hUA;w*Zag)=F#~U&3Zl&{Jw?+VyCU!+CF6L<?fEYLS
zC*gQCF}_TU1Bh`WGm>!k($ZAiAT3*fg+>!*d{ZbaN<&nIA-bEj#5kuQ1NW<p(4{qu
zYl*Q*ON<kVaqS}*E6d6dpPNt5oGENcUH1^<5@K8ia~(riXtu;SG`)=Ben#@e)<Z0_
zIk^LEjSQgI3dZeC4WQY$c#>mGEHfqOWUj|>)(0K4ImVX6xUs+TIO8pGHb;~Fm2rYi
zg=2SlDndzx8(<~$*QO6pRYuQmD;SrQq#~lk0RAm7u0wuNI%0@%w-y-JAiE$FiNv^b
z6UOWZTv(>DAEy&z-xe5`BOxyr^-?o9lH#_p9}5`A5n~nVL)fstHpe)O{nb*}5aul~
zE=DLrTkQ=EVMNDHbBxbt0`1seb!iYc9#<0;VZo9&>1j@W2tea+WEoS}P=k}F4=nU(
z5Qkr9Onk0<{!?PUkg<yoybY^y<7Ol>vWwXZ#n3Y{g_oxt?nYciE<-xGeJLOJgfUdc
zIb9e7TNKW_rfpo{=3q(ZM>4KN#*&$f&K6Q^ySR6Ng^3u+p_g&5tgOMVXmN4FA0oEX
zg>5uePsjBeTr>!YcMJoO@9cBFvhnPIwxpTMNQlGj_*6PR>$n)@v)Q|%vy(ou@5Lkf
zUb?((V%l#Kj>*D!UN4Y52sR&Y(`f6|(dd=iH~cOtv+j<a$vl^HmGzRqfOdc|&kA#|
z5=lFZ+&s|UNEwmWB9WO_gnF{B=o!#v^s(hGdIVz97!o7cFs(dO!0Vr2O^_LLDc53G
zY&HsL9|-f2t^vmz69-IRz7f`Uw_wTUy~rqiPy_qAvbHfiecb5i%fS^!J(N{cLs`oN
zt{$E+m*yb!N;HbNC=$Y&g!xLaXC7sFQ=)BQ-p(DaWSc2Sy@wl7N$ii!Y&6=AQig?^
zIJ#od@>Q^u&`I3Tp%_S2fvZV5Fn9EZ>bbA5Ipiv0?<6(gBG`z+T>Nk{A&RsgXmi!`
z9SVYtjrLV5ko@UGjdquS%0Ke|*w^OE_Mf^S3p;g4_QsUH|41ADsb_!I`+9W`knKHn
zQFbZhglx;a(f@kP)bveduPlE@wtdI%vQOs?l&L7mH>Ljd?S9txqqy!*jwM=-vLze8
zmTmv`Q`xKIf))MY9~y%d{q;}wSH=5Ld`A_1;JNpKuCq7Gj_v+VHpEt?scstGvUk7#
zRd(w1Ioa7=U&%Zj&6+;@H;q?PTQAwxqZehH$N9*(`T1XOBMo@@CZ}-Yf28>J>5uLm
zJ&+rH6~B`uL`Z5?l&2$L?c5F{$v{(@5Q$wsA4Ja6Pyg3^s%>ZlO;I5-avE0p|C)v=
z8uAYzAb}~pWSLnR|Iqyvw^bmZKtO?j0)c-L0h9KOdv{dB<s-+CP}X3I3S=T9r_YA1
zdM(0FpTdcovHxTfpLlO2di?tg2t>ur)3`u(p+{mro_O8!`atl&3mb6%m;wL19z;=(
z0s#dA3Ir4gC=gH}pg=%@fC7R49}o}>_@>mD{~sP$@r()t6bL8~P#~Z{K!Jb)0R;jI
z1pe0%c-(+z;J&~Euc4tK=aPQj4=5y6NSIV0<lOvSMauT8kqzVTyvMV*T}TQchrV1H
zNvK84Bw95pi;=VRITRu3Q;j_DP2?L-s*~?oNQhN#mT2*$Q=jK?d6JBf(EJ?Ue`8+~
zl577;5fY)*$g3+PlQb29@0IVRdE!aaXMfW#@<bxu^UoHMka#3dX8p5QJj;1PGAlXV
zn`84V)jZ=hf|#1nFIAZUD{$FAhi>wWEqT6|DUo(fUA=*N?fKTMs7--@0s#dA{|o|x
znz>+WHJeHQnT9;4yUK|sILAH@+fg8T5p3PoY#qS{Y_Nm>vl&jX^VT#yJB4xFgI>gv
z<%{srn4z#VqSX6kfnfi66v_;n*`WW(i5M}kC(KD3mO+{`Lt|5@GXuS}lC*sqM&#=@
zhJm30Ovp%L%%6Hx^D;6vftjf>N~=})(-4yCVw970PpV59UPm*O6qh`pkkp{mZdc4-
z{U!!GOK~YY3KcaEiQyWbLP^6I_Lhc_l~V<+P7Ov@*03RKLOv<|<VnVjCqCDo%4EZ_
zK(F9F7&?47x_G%jl%7K6G$}exT#B_X^+Zl)28wfYKq#KMc#fKnH>M6iamZ;THgeOE
zKGVS1h=Nn3yd>?QyqNo7_N>V;$-RNQIS)9>pLSPLG1^gZ$kfD?{~JSBn=BAT#i)7;
zV9M-IMijZIqY9J2Xd|#o82G)2g(EwoDE0<2o|W^Piw`Z!b{INtB8CO`gqxEovU9Rf
zSX}<JC*Wsdve8&!*ei4K`ji(S&AEeH$wgFJvmy`v7*gp5!`4KD>>y>(F(7xpxB{gV
z;ZhNv-Oh~cH_Wa#GJ&;~IZTPCp0+w__-q1qtWZ>S&0ue3LS_((IgnxCX~o#e5id@D
z1w(_nKvbH8l<fQ#3=xlBt8dd5gC@R&k)#W&Ezd_%TJEE#JfFWs<gs5g3*N?+xE_`I
zc%5V}(lNC`8(RzLQk2S&c$gXKL#e)!SUu^Pc?}skv}wNz%nMH|M&3b~GixSB^zViC
zcH~#S5`hOKsjXgSL?JT^Y6nyl6v<-{C0xAQ+S{SBRsu_NBeJLzajXzQ&&(DMwniK)
z>S1PM0d0v4#bpl}uUf5~2bDFA;b3P4Q&SU;M`X3(daH;s>$<GZ%1{$E<z(@xlfr~!
zh>0HQDXHLVX`n&2loB#&NO><C=xgi3JYWVkEgysO>`W9=d`lR=6h(o60s#dA|9%7>
z*big?YqB4NM$;MFYxV{#3U)`@rAx?o#vpJd6*B!8Q!G*tD-`JN{wqZcEnQ>ik=dw{
z9QzGsj0%--sR=XG1+g)4a+~y-nj%((RmWZ!J$fYi`}@J%kSu>x_7%AN1#wI(N77=y
zJRSW4J3~)Wi-d$!vfKRiJFQu>M((Ix(|D<Q*B7yF*>vcp#o$&#26FO9S41QDKQAI!
zI1a)aRKe=kP7g&jQgrL+23U2#%sKPX$Fu+k&ql$a(@XgB%enCO>4?7lhGWR!VVE>w
zG=h6}=ij|>G2a^Nm%RkTyZdpiuq~!<`UtCoop3DV5(-Ks<kg;!b!%oqDK{Chx2}`3
z{T`}XK}M(RZ0vYvJhD&iN51Wg*tqpQ47XCj@25gZSNS(}Ah8~NMlQlK8tSzaB(QSp
zj28#`BJ;vUq?78cL&r{-G;ugAYm;&FW+MK`6n)Y=v88a5gOLW}?wms+jTLzj*<-=G
ztKp@TfD6&&*b<6qXS_abBt%z#C$(d15(^EdHT~-{4!>;MfZ@Hnpi{s=Oc*l+wxn(g
zq1e)s5HqwHhz)N|MDmrhNS7;7iD5`Futj|x5pno1Vg*&7XDQ0s@EklF^ZK_V@3}JE
zy7$FEA8TAVd8&orRm-ZyFn0(hAMQr<bJ3-$Z4_>WUqzX^FP3b452O98QACA1A1_Z>
zJK1B%^ri5tx{Biw=`d?M5<mR18u3}G7(L^4%pKhaodf$}+_>Q|6PMt6WDF@CWeA$P
z3ES3Ahhc37PF;y<dHZI68QOHg%J()Qz)lm@YPJ|P)SoKFcMzYNL!+<RiH*OSw#CZz
z?_;#55w#3-F>1sh<lVo;^K+m7f>UIOS60kI8%Ys%ow(ZAf`>m<4AF7ut9WDnOBg$j
z0%`pM(ZSQ5`U>F)PiTc>S$?GnCM{i!7woHW>}veOx?1^7!_)zu&Qv#S;{g{-2`>J2
zv_S-{m2-tdv)8?cHFGB*?eIR_EvbjI?=bwd<5OHrHK4NLM5u=EL1cv|HvG61Gdr5#
z=(!61z6srp%CSF$0w_-{A{YkD!*8E2g?~pM^dCI|gS)!mK9v)_UVa~+y*`k_9anHY
zQ3Fdq{R}huIiSj8GCrEo6SqQ+A*sj$>$ZJ~;oj=le>stI;7%AfX&gLsN)UN9iag{`
zy}9B;1p*2L6bStD2#B1q^u3jEt&2lwO!mX}kVd+lw=Yapt8nMmWu!kyLVb9`pZiVU
zsxQ`Wm<r{jaNJ4zZ&8`3V9>Ocn9<V+=R$9SemWK@TsjTHw7!-&e&QnX%ARpWqUnfv
z3+7TxN{_U^h6sLPD4caF5pp3KR^2CJ(?@IIph7YaEgQ5m5>c2k43s+f%SABYl^d>7
zhK9No`Ud&IL@5i~w*QQq`T0nWzYh_;;zP!aLwg$ofND}#PaUBO=p8t94Py4NHj+xz
z;pJ)%X>Ae0LXP6trMr(cL~Q7Q*WR2Bx4H;E*ju=MKL>{Nu$%e~MNrqyXxn!(#vP$b
zCb`N5mcgB$e~OzinK0-y5g)(W35U0C#Lr=g$kGg;z=s<-sI4*mt@RjfpNnN<S0IVp
ztN|0=#_E|}aP`MG@y3@&P~J2-s=(C4AO7}rIC=OC@`ObYxl@a&sHvvwjyK+(4Wqli
zV#&gf<np^(?!7U2AaS4)X;$nNeEQQ7NNHgAA3B)IqXjUvYXfsFF;b}-`}J?<VPxJF
z<0l0|mk}`KWu>@%b`Oq57b2klNOW_v;tGj^1Zh#^K;MHqCqBo_QzeKe*QxJI>+!*a
zPH^>B#s2+YVfyLa=s0jD-kHA!OX{UqNzsPZ{NfJo9_T|AxE&$I81zCXq+dLQm@+%4
zSQ?|9HUBqOMtXb%?xZyI2t9KPN<F*4!QKkB$!*Zyi#)Yuv52D3OGQ;Flx=!rj(=Mi
zQvBi4_MdPjA*I#(iYR_iS5klrJKn?lhmtV<^~D%BZzf)L=zzMU2<-UvC<+O!X4}pf
z+qV<WoVfr82YXmqTfxnP=g^~Do^y+<&@vR)nZmK_P%Nk6Co4+9k&nN{O?t=M2am$w
zPEO=Du14au^VqgO6qcR)p-&qvzE3K?Ihdm&Jr4V~{)(u>TQF{C4)W>I9kKjV%<1Kh
zo(3xTzN{4<c(%*X#to}KTm##j16a7?YpUyVA){%DRht*UUq2a37p=wV$Rv8`Eud%T
zidEkIxcF;WU<lICf~UQfe7UHtLg?ok@Lp&pIt5R};+Ll3RA?ANA|wdvU`G)M3aPa7
zgpJw(Bv!O$jROIbC&E<{i<#3`ATHk&Z-4y>7R(-t%Xi;Ka?ayQuRUK{fbPbHczebI
zMC<zF^Y2z;`NC;9J#HyV%3A9sfR?^5Mo$hP2e_18?{dUlK7k9R78vNEPbqFyI5}I9
z*Y`dSeYzb{+!-;l@Wt4Pz1WZSVXewOM4tCnOLZQu{rny#T?@p9HM5`<`3>fMa$GJT
z)oaWo=$95k-NFvGrdmi3zl2i<&Oyo56>XfY$tvK0jvVl16sSo}j73(R4T6XDho7S<
zL=_Ysp%UQP@Z;Ec<2XE~zmIi2l_{<B^ioq($wATxL8E(M@0Sti;6DV~^(Ev$u0nZ9
zIWzPf@#==R;F^CDOI~{isrfD(L-<+aPSn$@r?v#Yzg%2xTd^2dC9asYbSYLYn22r3
zT=eR?VaVu-*nQ<^a{E)ho8l<dWhD?BwZ+tt190op2=W3~kn6k{Y3F~&g7Z7!-uG3!
zx$HHpqnBp(`t48MUHF5hDLz&ppg`bXgn*J%m2<)<1dtzIjan<UMakIp@prgGg=??A
zqcF(Fkp@>a?q5BN?~Y!B@6d7RC`rSwJC7i*)(XR?^hd>w!#GcUg^|HNj8KrlxvevF
zl&f*)%1QiwJRD9Gf*KI)OR+2J4pbK5%ISkRd?^{-UL1=+Hyi3Xl;BDTZM5^ZpMs1b
z0*4HMpNFkn7_T@p5xe*7rX5v-E(1m)praGD9mw@wkb=YKF40b~hrNRpN*&w6(@UN9
zLkyzJZ7^<FUzkb?aW6Us@!1V?Wxuf#V5^je&wn^9zrPOcgHeP0Df;&<vhz(aVP=1F
z_iJ-5twKgb2twiuVef1UYa1)HZR-xNIxAG>L?Ak~5}NILV#>fS6rEE>cKlU*_S4Y^
zkru5#nilPxaZ`rTKBEG9O*x{%!{j<1g8fiYuMH=AQ<P9l>Bx6`P~qf>b}rVi)~tlL
zcPA9mE=h@tW&MJ-4Yf;zx!$5PUK<~PjF_wVZSTp)ZmMEniIJlQqVj$Q4BF5xq>x$s
z#nU)&G8|v5Ux1Pf?zBP^>-QW*CtEwXxOB%W3n!xD;5&G2<372)q^-}kRkv;@Ja)rY
z4m23>D%x-zHVqSdb$|+0M903d7UQYLw&UxMa7DWhmQL@3*susl9R1PFy$$M1XgIWK
zj}cRbP_Tj0@cFqgw=;xtaeS)=yoRwkEKRj>YunEVr|~Y7T&kC;am!t)p5XxHx(FIS
z3aMKYP{SD5d{WD$rlz8Z9y>}gprjxN$w_Gt*;i7@QHl-|Ux!tD5q{sYnF1`uRCu+=
zprIX+7s1K@yQ9sFciu=z6_#CwVybT;!cT_KfR~ZbpTafzX6Q3>9t=t@W77xQ5J{l{
z4V_L{x@Ix_)Qb_9DWf`PS0Lvee!5+afEQmvdu^&DGSorG(iDZJ)s!u+!;<&kL}vqv
zP^6c@u8j?ha&IBBN*A-{O+-of6=YYb!=b%0j>~o;IW1oP()4}$V`~47$c+!h4`&nP
zcz|L%*LHq{J^o&Jq2C~Q?mLbv`Hy?3%^e&u?uGWaM&+<EqkAJxqYA%VcZR+@U_>=l
zKmRV!DvreYuXiJx*m(pE$IKCfDK?^p`k+bJ&`XN4h@Duxr-(uj5_Fv~je1U0;Z)X#
zmvRB_e()_43tBgu1Y@S6AWi;$N@c1PXDNn;LuU-}?|_oK;rRJxHvGABGHFyB=utvB
zz(xo;P{r8UOEHq-4)>3LjQ8$R2HDbytKX?8tQW&fzbEvvVv&*Qg7u5XqMQnI#afo|
z_w7Vcpc-7(^T+f#UD?O-kX0#xbzoPh+`EByF6Cg@%w=eAtw)c!Gwxmd1qU-)Z#U(R
zVC&KY-X?ii`NsDR#ye40H8Ft=MI7$#+l)&z-i4B%m4*r$_D_t3XUJL|)gZId5)gLw
zAW~oM2MI$YRNcK{r>%tB=OSU_>;fBG10+Vb9_B*(n0=NCx7h^{>1h*x5i|^qp{&Xf
z3_NbYyPE4FFY7K6i>O+<Vl0(!b0N`T8|8Tvj~m)rdR|z}e%Z@Wg4la$u%v{xqbwb_
zl05M8tU!ul3Ro(`O1B5pDE<6Vgcz@{Uxz@eD#Roe%H!Xk9GNGmHB)k$3k@MUt&WCr
zHtU1L8XW_MVX&({WR)ezs!``c(+)-kdNAwv5<Jbd0OP?}=l%j@1u^*M{g06EJ|3^n
z3`G95aEKilGDFPLmcNPkygFzJ!cX!y^7Ma%b0{kWE06BzZV-<yJ)JmsS8~}*VqWH!
z_)X<l(XJXFFZzOlK^3U~OF0CM18X%ZxOh!S55eWAq44PK3UPWRg~qZWYD1Mm>kE)l
z*hEG#DX~%wa`SRv-ESCr?xKhq1yO{yG?ZH0I{FQM?C6R)frHWcyPt6^?HODz4{B5V
zP#~Z{;NOFQuAv`hFPH}X%vjt^tbmJ`K0?&AVAgRo)-M{3(%2}J>R6+PuMafTHls3d
zBt~jo!*LpTd9?;~W(-F`)lFQZy6E63uR&Ldwog_zjO@)}Qg<IG@0McziWP8I%f<EB
zG?;t1qdFr2QRQA(NQX{ML^#S#yTiAw6{@nA<7CX^3hBaJ7&Pux%$+_6l59Fr#0F^7
z%>$Q??1I7I$(TQT3bZ&r-_DW2t&Jr_w(fAWaD)w|+UxpG#oNJBl-}HlCBLV^nxP<_
z+}fe$_F<g<{Uiz*hhpZ|17kYrA^!YvoJf?xulI1gG{qOE_g%z@d9yLb-xk^T??KJZ
z1v332==AbNkc$m8Xeaa-I~RT;36f8Ij3wLBv0(LLbk!<DY(^crbngzOf+TD{aJ$uA
zXKf=hys~ONhP9I-@=hEiW*%JRR3kdt5;JCZq4PBlnY14)x^;wcZ8^#UUO{h1hV9fF
zV!^6TbWZ1E_m+*gP6J+C!}-m;2NutngJG^p_~zQLt#&f0o;msqnLuwkMXD%}S1d6?
zKW`^g7p}p9QX9Pb@+?>x8=$RvAx@q;gxWSfFxM`}oqA_1+x!vKMHPtLy9Ym?2@`bK
zpQ$t$@KoWubM#YeV;IR4e>1$Z?tP?Z6+%hJgi`W@aOZ=!vG%|zRI_t@@#8Xd>qT!@
z7ClQ+ZKV9R5StEFGc;i#N*Ej3T2bbU736!0D@v$DN`wEwyr{&9S1c~Y{v8n*Gqe*`
z88s;9Ue}c1DWx$laDs}J8WKEZ{9Qx?-lc~ZiegUTCKnaL^e0lXry3iOm3$95&El$s
zBmyO#ugZ0=gmtM<3!DO3B+jIgr>;I+x|rhT4{LGfzTi6+!Rduz2sRVp?1BB*bzOvM
zb0(qR;J!5A_jBQ(hT@xBvF8252wu1z&XkbvGWlir(<ofMWHBzqYhv9u8>mjkBw#6J
z%^C1pP?UkaU+l!aic;j$m==nW*Fbvk=AbR&v2f*Pq!u-Qbw!AX%fSc>by!kHy)^?q
zzqlHu%Esv9s|6{uBP_gp5vVCa^zGm9>(TQF&wCSVgCtF!%j*YrVCjWedY|W_>fHBu
zfA2LaOco<Utt*o#lu?m+k!q=*qHXsnST~8vW|W{tLB71LkN#7uB!ye>Ol%BfIFOGs
zF8{a+KOazs&-TR#92kt9F=ye|(G$7ncH?JyWkX#CVbziVeDmI0xRz9a>XJg#d3#dA
zy%xvM{D2R?zlPV}e;01ud@x=s7}Dq~Shi#>l9cW62EE*UMhwBlBb2b_I(E|s>u~yd
zCDw1<05fNE;CczY<wh{Kw1NR+(Y2l3;AN(QlgZ8TY<vO@XldF&RG7fQ;NcdXkN_fP
zUl97GJZJO4dP(IEl43=Iro3>iSWwj!6jx>F(W@PEGm^0NtIsiY+sE*<w8teH@U1N=
zqHf2czUv@-`pp-}7Ey`MUK3T61s9~aTPw9x_5)+FyN_FlAni09y_SSPUyCNQu*Efu
zOYAUy8iMVs@yiD*@a>r*Ec$pOxKjri+bIkh8$Vu;qhUo@{ps7V=;#jbc3siewuJ9$
zF}Fz7u;GVysd(CGbhfDd;U5G{z)w&I6cU)~`32_&8no(CFn!@#q*prQ<Ii6KYSOWh
z3W#&oEQMY6DJ=i$5I4Z8QKW2!)dRXiEg=*?9t?qrOD$%P?gV=~TN?29{xrF*n4Ydo
zDh^9cJu!9WRA|>0BA1hopfD(wXuzeL1@dnn!EJ_)37c|4n_DZ&Qd@e!hQOMtjzSNT
za*Olwx4$5Ui+v%ZNmyhFMNsn-)R=|-k{lm}8}~FZZe|eDjQPExQlK&?-T1q=Q6jR%
zq}jcYt)mKYR`d5-hC5V4tm<yT7<vvy3Ikq_tKuI80{`m>D68l~pLsC#DWy2`?Wg#N
zJNBhC2xm+g4oOrP-dg$=l4z^E`PBvl4Ihj>bOKayE~F?mORZd;Q&2aRF^sofs)O8+
zx3Kz)JDBm#S{nE|a0;9XU(NfNx0q23@mY|n@m}O2AN<NHm}lR>dq)qUPA?k^rqj{d
ztrHFS(~m)_#+~R9qk|#4{sUgx@H2Fs2Vvu?-l(+*#DKwrp__gZlNWDBK}8*tQ%NGg
zBvo~__IT@~*(f}*4Vw?%Kt1z*((_BPc}*BPzrGo-aB``6FvRWHbq<3%O~H^MK{)yK
zZ3J}pMc&C>Fz*qJp<T>z?weKkVDD{=U-<!g(LUS1`E7*R^~0K#eQ;*`X8d-UYNn+H
zupY1geJz!7{@{N6c1?z9vjtnMA3i=5^>~=GNDA|AQ!%QO7B((ljAK{gAeO3eEM_cv
zzeSw$O7X^fZ{pmIdc3=BE!1`E@X?z~P&;=e0u2(e=7S%prdxrc++x^wuSc!62Uf4@
z0Y|+`d^u|YcHByT3`h9^+72}}oC~U=@zVTv=oIdbPd<MM_O1>PMXJHr%oygT*3i|}
zgcCjV-bHK^`$*^heUL!sZh@`~rmtRz$eabZd^@T2d;G!C21`U%i?ZBwq~%fuw@w#H
z3HOkkRSFYlCXi~1a62UtX+ly~awy_*>Y&Wve_4()qy-V!xZ^5fk_$0qXjsFxLn}8F
zCeAt;<%bQlTg3RvrEOKzkahZN9QE0P(Y<>?sXVP!qo=9bnlhaK<s!UB1!D5lX}Iz3
zS17X40F|kcSMty}6V4yCd=tj_Ia3aoj3b;YH~qAq-;)%~+?s;2%rxA%nt(2&Ud5Cf
z>DYAUDih9(Xhf;N$-56;bt4-Gz2b?)K}3cURqhmQ-+z(uu8D}4KNafQdU);QpOAFr
z2rgewM4@t9c@veyl!}f$h_8Qk!HBNC@#eNZNWFFfAH2B*QQFR!vuGwfWOuP(<{P+{
zEi4>bmO6T!Wn3k5eb8ztVxnfLh7)>vh>&nL0%o1tp_`-t5f`!<GEhv3^J*G)%JPMc
zP(f*a7D@|}QO1yg%A6G3i;tJrE3&SWCtF_n<}=Dh=fR9#cZseZH!M_Jy|4WH$>dR8
zQwtPl<ze^6x%l;1IC3f^*l}hgW_$T#$x9Nr+K90C?c+${VhY-M{IXRsTp|wl?ib1H
zwd6vo=-ypi`f(?6a?KdtQAgX<7{+`-S;<NCG8Z$fAOcaz*_0_ZXNS*4R^%1j47rTl
z%C<}x1oW+Kfx?scXvI8yF33T9kH_zCkHetRgK=`>uE*QYjBjQ0hNN4WOz4dbMHnn=
z9`sAOq*=4$g8f?GD2H8F2l?Xhfl*t}zZ;4gqzw0|SMfzBCkJkp<-^3SJ<3WY=-kl`
z`$MlGv6VRg8^3SC(z0xf@9T#;#*UvTC`XVhzuc{*tMf(_yN028TZ|fBhJ9SvL>L5N
zKo7eIZD{#J%y;C@(5=kd;W)lGoc+><{fXXa?w%-f;gNHPvFgV|kz3^;Pw=+1wS!f$
zISS%$AUd2oL^2n-@!Z_xY<Y(Vf6=g&pTm0P>mC9qp)nn?^9MxVyo)*`E||EZpr)yT
zl!TioWY%V7VJ70@*e_T#>D|%QlCY<8M`^qTl$2DE5}$+;;RQDp8q31>;3<t!rlDSz
z5Qappp$P2N5n<P(P-WyK-$4<!n9u)u8QvT#ru6NFclJ^&?m=Aw9?grASQ{Q4ec{aU
zPLR`<FuADs(jmkN7C#5-dU<jJodC*YTo4vibSl*6CE(J&8|eDk`)Dt%L)sns+PTn|
zWk%z0L_B8Cn+y+04Y<SHq6kv!LY;NO_NAgI5KtiSe*%HLtkd{%n-4|>dSdaXU!d^b
z6>NL&OBiXZAoG3_5;(V&FjzD?IS01w%pl6(xWaK%aC|_*eMH(Y@)3b!p<3x(yu0%f
z?j$B*!K5*eDH$PfW&q05qLGl4#3cC|d7`|E)DWg-dXSOfYS@TzTns27i}%VZ;TY3O
zF*GoMsbL*XEZmQjoLs2b3(nO#E*SKnMfR`UxZ6l&lz~tf4cJgb1}LkoMM-vYll>rk
zP*qt4Rbg)ASjvCp=az+YS21=72DA^tIQLq3IM-wMrlT<K`8w(f6LI9*v&f@Mm1`7X
zRzgl{B1)BVXeU%7Hzfi0?j^|U5yF9GG8H(I)L@n?Z83&eCeZoD<&FIFmjBQdO!5G6
zNj4Jh$D_EEI}gJDOc-?_<6Y*BFhrcY$~lkW*PKnYh(Tu3G2|6jp-wdq_u^uZTQ1B8
zDzNACs9L+ggD)d$=bQMA#=HEZmN$Ixm%u0}!nN<V;ck2)bRB&;cht%4#qt}6uzb#O
zP7@kvJ7zV$nJ^epS5i^R$||^cdS&HLFl^HidY^v?Et0hWNv+=Dj}A5%@DI*v2pgcc
zN!)O_mM^WaTm)xF2N+%D)`fLPIO?k)x;US4%9dOyRug#PVi{lNlNlX~GtzJ`Jqy7@
zro#DX9HLY57^7<lO?Ad&b0Sl!D8~=q??E5$xiD?0?m>$l{~?5NB%b&TA<8;fH*!1%
zN8Z4WBN519+>(in9k@~7r05!c=yw#}Fo0c$p&05?gCBq1%>>#~#vJ)UsWJ~?yFNmJ
z^XC||b}25uz6=G)rKqXQ!huib<D0YD3~MkW6G=JUaYhXFsiUX0!MG4&Xsh|Vh8F1`
z)wRe_VF~vUli_Bli*T-31**Rb+uud#ug2&*X)eYE^uve|kvLc!fB{xzSU2x$zAmYp
z&84EW2t`eC%*wDd(V?-xc=3vs21X+id0ZuCmvNE7u$`|y-;E&O4)8G|6GmY}0-^jJ
z^8IP)XhV(hw-pt_=%5TG8gCjpy7YdlK|@ayMQRP$*Q*Hk5zBWv<TNOJAFUO^j#B2<
z)5V|O!rI?%H#?;oXTJIgLw{X^p%Yz^8@3mxGZ<ekEF(CWD05)Z*V2NzrWiV!4fLTh
zF^xYis04qgP;VWLqkIlH9ZYRtsVPOW)D|uRn@dT9<s+k^!>U*WUoU$bG1fP=g19Ul
zxg}N5ODW`{Pq<&}Z3nl+O1WrI9@QP@YAQ`%m5Q8f#3m+VT>pu1I~0wu=wxDJNaiDL
zdDw%xroP-8t7fFd&@#Szj<iC1#S&eZS~$U{^CZ~UC&R?k431X%C@n66Uw{vkv$J8-
z*&q7!MkK~Qt~4sElOXB*J}f%;BcS1icfRNgS>g$XX|S(xv1nrL3`<=x;^X4weIq8d
z1nNE15qI%-9Ncvi!#`aJjlv{))LR*(xyaCDzf`N>)~%T-H2O_I2OCqQL^kv%0ekLJ
za`fdp7qFxOEWoNlZ-t(r1q=s`L0k5Z`%l;n5KfjTk)~Bg!U;$4baPS1U7K<m-U9md
z%G`qF@F5d5yepo-qa0xAvR2d8;kG6XT=oYQve}eZijW-sJ?6cC0Qog!gJRz=$$gk4
zB@!5KbabFvU5U!_*2foNae{j8bTRJG&buC$!>+??s4M#dTgo`bn4=%zJXwU+Et!wV
zl057UAr5S)s;WASs1B^eFr=D_Tof?uY{j&(Y-`J6(ueJ5_?J{mAGS_Z4fJS-y0|@D
z==;($rGmKI(Kr`*ALBba$m<oZR^y~`>hNiF=-&&&+gmgpZ>X7JV`R-yR;ou?W0HBx
zTPpriAfQ0te+~iKO;Sxn?)e0leyB&EDN8VMNKZ_fbO$#|YT#vREuY&`r1ogbd^K6|
zJ#N@pLCOdL9RpJYju?-&IvO}b`$j%8*VUlr0i!{66~{j1GE~#<(zJDfYa2)8-AbVo
zQ5#B}&kJ*^8OFE|tJZDD{X!B&m>HtJG_O?zC90(9WoV%ZS+y)GHSogAqZpo(L9e>N
z)>Q+Ow(a3$6@pYc^mN&tqP#5TH*x%?{h+C<P5Xfq<NBH?&M%+?s6j|bppw+q)`FOJ
zTzyR)w(mTJp>K@Eq&2RnIClhR?kA(;2-1?PsKb=u$JVxf=+VIiDmO04=Q}0NA<AmR
zf#Ia;n!0j3p)j=^wUya8L|bfYNH)}Ii|Hsa1hQ3@hdR^}8=0Ov+O;EtOimOHdPXSF
z!!OvEGVY2tOh*lKp6J1xB&LxrrJZC*%Jgc^OA<~EVmkYZZ=J=tGGhb|pO5)T$@uWM
zTTM1)b5QFV9efP`(c?Sx7~l^b?j9886~V&F5=EJU!Cnsa%wp7XPDs3)gEkJ;(5Ee_
z#{X5c4539<Gnuf<`;4Wf0WaZpiH2GeZ)ILaIu7qTj(4U{$0Ac(sJQoJjCVY?ow)?5
zFTK#@(tcvBaPtNgGFk4|gYZ}~9q%%(>DyD$=<L%Lw+qNuE*QM@7Z=^!hkc<Vu`p1Z
zlUu{&FNDjetE-c*`$1)#&^0*uOBthnYU?}vB7B8q%U0s@t!X%RJp;?ScEbqwz4-Q4
zs{Dex*U#bZbv1O*?}yROg}8L+Fzz#qKqsIxbPV;#X_twgKHmz5H&<iBszn$)^EccW
zy&MykZGl1i>#+3ePL`Ifcw<*ADK0hWeUn>}^17?bD-d!p61}JN!lL)wU}2!eb5yws
z^MpfiKYTFB4rjmlkump-<F6^9XR#jcy}jrmufmX-uVV;3c}y?F<)c4h%P;5Tbs9SL
z!7yJNWL;$vG2^~kE0u+%DBu@Ixg-rIY9pYRR0co0YLpi?#9J$~UIVjsnDN$Xgk3m>
zZ+0A~5t@%2ej$SgPQem&AAW&=cMlXmS(#khf~CVEd<iw!ekG-bzvTy;|3i<Xnx+;s
zHFcUF<?k{S6<o%zzum$5S?zK3;7P^+3vt{cRFtQqSY(PxFTDn@5-F@yld&<30|5Jt
zirm{wV^CcK%G?1u^y?l3FCK+?OXnj~+XbCXfE}9;LB;Szp3nBlo2A5ULse)SFn?kC
z8|bBa3rCqu>Nj!{j7l>YLUi$wwl)4)jpWpuNEA)LjDg(|`N_V9Nb}NEoZ7h;UdtC?
z{mLcye19l9_;o;1+$}_Ni*o1~#_Uy?!)M4yh~us!AugToSPR}`=i~j6ns9M8L+B6h
z!NjN?oJ}jR{MA<xl2Qfh_9L<Tw`I^K#Uf*N8XNKOr(qAqEcQYgjZLMlgVDpT9N#Vc
zo%EIEP~xKD#Z{kRxt|1+r%c6hZU>$@d;oDh+tP?ML9dywVxYG%4t>6b$u`2`tYxXE
zcQ=IFoP+0f!}Jf{=l8`3IvVV0_Vh}VMMqtoo*wy50tYW*ufiRWq}y?jPV>W@jUU3w
zM4#_i9jYA2S}j~SyY(E$B+brbiP2}={tH;e<kaH_kFgK2eknP0d7%(mqb_Jk$m=O5
z&1V5@0$;@P*Ug}6Z-Vgshp_wTIlM7rDb}g9Lki5$zKt3_<a-E>&8J~4A-!umyt@7k
zgk3y=Z+|}A>du@*RSHd2O<eh66P8%ip}5{0{io}45uwTjt0osFML6-*JLuK^7pz{n
zjQI~gaLmzyN0%XZ+q5fGwKS0#x)pD3rFWhzL@f(_M0^e%8yAdP`Yxt9x<acm17Cc+
z7vdrQP*vBYk0*zHJ_JEtZ5XD-Nq{*o>dHJ`8IAo1FQB)_1b(liP1WjHcESjMZ`7on
zN7%i`^B05@1ziXeCZDS-(?j>5NrJbkN=U5Y>PyI>XgJQ0Li)ZcxsVg{IH7NGyp&^(
zlnWFU4qFZDa^d`1((y8ckjI7LtoiX09#`i?Eert-x8?b=YBD>CIMFn4Bey(GZx(l5
z9+t-Q#iZ#}dQgY(sZe1RN@|FuMA%PiEIeM%<F!04<?&|l%XqxBRsvlqpRn2SdL{~&
z*K%Rk?6}b11P@*XcTaWb$!t7dz^sObP!*4B@pxnX!trXsKw=IMqYqnPRz)mDtzh{q
z7^@LuZa6-~m{^t&OATTvjAxHxsmT}B+VTGw<3{ZNEXKsLysmYOD~YA{pT(H{wT7OW
zzk;z6u@~n1hyD8x`s-iBSc@17!(S8jq--dzR(h<ziilZpYyFkS6Pb@!C%BNC7g~tM
zU0%i)3p?WG?tO@%^Gu0E5y`PBICNzn`YwJE^Owv+rmib|jq35!2fMf<KM>llb;j&>
z?O|uC2MNay`UL2#62?yTCOb%eo7#NbJGT#!qu<A>)o<X$m3y$Xw?Oo<z1Y2D7sh@)
z18=|vcXCzG-rE}IzFv#Zjy$sU$BNSKry@440)fjH!6;7?p4RGgx>!QLG7iTMpM~#&
zG1$C88<!HR;cBmiEgRM#EGmcgLp=fqO~VrHZm@As!KU{<Mnr5T$63zn?ffur-3t7;
zbt}%1`L8B)KQ85t!6Z)ueDv;lq!-m9>_h~n_85nWAH0VlA`@6KL|A6yK+O>)R9B><
zP-Kk$V`f34Qw7NmlCk(*G7XMefeA}KhQ8-jnEQ5zB=UQ_x^cJMd*2*-VQk61vKRZK
zdtmmWm2mUB35lr<YSXS_N2Y}K1H%{vlagLQDd!}KRPbQcBR4w>S_6YHZx(GBLsR5j
zJ&iNjD%>$C!nSvIAxmWmrY+utlDY;pKKB3sKmbWZK~y)f^J+Thw#G3^80Q%BYHEP$
zsv6pRW3i;86g_&h!{uK-L}+3ymb~>I%5EJ&c&aW&PVbL`h=aKF>n^A<pI~m^30N|N
z<7wwnu*r@_PGWkC$N4LN2xDK<@Mvt`4sFfVas66E6OXlung+bc4d0y^%hIw$9HnO|
zJSG{&4h`pqUyCIF^W%oZ<BiaSqlX)U2lt0NIoI#s3dOFocVVEbinQz3kVf)zO;cz1
zxf$Z-)o|n%l|jX%ExNYRN9x@OW;xR+WOAXer!DgDh9NFbNIJDc=dSH=<-&Ost65{y
z#~(52AqU@l{Vk3iJcHyaPLP~{_3Yi?W>AY8;kP*{aJR~c_i@s}{m5%bEmej!DeD}V
zaC!F3X=qt{qBjk9cNcwR-ME1(H!k8<W(C|kcq20=0%_R|p&-JfBvrL!>~{b<I7o2y
z{8i*Jo>}1Zj$wF_e^)oSdU((inu~L+_jcr6XmiWN-cW<9L17}LQOe0TD<uX|Nks@4
zJRT#*jDUT03|^VN5HT6T7EmjN-cJ`VZy3{X2#vT8En82tH<t0)!Wp+Fg8`FHJRD3Z
z=A?{5hOu0YjFo$ljqN?qsh2-GT5BQp@&z2ep2lZ3McKV;<bkh&o{1y8TyzkA?G_44
zTk&@4+M=t66$%q?<4$sex4)@%WL_Ea`WINzyBx2)G9S0o=t*V~t4Ltqz6XN(_`<NR
z7*{TyN9g@pcv~~M@+NuUEBGuOJfSDcMcB=FICbiSUgS+RRm#QrvuAPU;%#W#wuhUs
z@b0(@t|fhBXAc@l`N&tX!q_2wU}m6-?3fT7J$?;WZzmAZCln<u44nN2R*dz-^#ePx
zbN69fiHavJqZ(}5x*}+BFgn<oBP}i*yM8}}T+%fL4;_R~ZETU75Q%-e_9KFWnww8M
znCofq-PNP2I1jgO-GaJR8^+6t5fXNr9)dbZw6sYJ>WlbW3^B@m+!G=)u*a0KBiP66
zA<MdlgFAQNd~7m}4FNM%wC_0{LwzOq;j8V4&147{AIXLxN<)Uc09O+k&K%u?od-iX
ziM0N-N=l{(7|;X0?v{u+dm0&anq-M8L{ui3e#}M0lHBSwnsE2?glcIfZpM{xa1Mr#
zgEIRu!(E6~U4AmI-${HNOooV<w)KRMjUL0F_`T<LuY?2WJ?<9N>smsuECDy;Gr7ZJ
z3ZJfR*k9Pc?`J~C%mH43z0l3Y7zOvDaq3J6`<<8!009ULaEB^)#G)@<#2J3)3P>Sk
z$o}eKXF|a(WfUad!?jznts3xc+IvH<wt$qe339_wiCzsKFE`vx%;f@K3pvcIxW~!I
z)Ry1Jc2>B2{uZ3w?V(SjTJ9*|#9Ntp523f>TIC>CHE9DsRJ~(#CSlXIJF$&BzGK_A
zor!H56JuiAwr$%JPi)(nm|vdv-Ov8^{?}{ORja$}Pp_`3^ElnCaR!=(ELIMfP(HL=
zaH@28`hK=!#lr)r(M|^kTk-e5;NsPaA<!O=9hTG!MLjioUGIh;qtjUVzu>S0elfq_
z%iO+ZEjuuEJov8<%|)ZFV7e?|2zTM=`6pC?J~=mzwHd<z_^xoqVx5=Ed*GScx!`>o
ze-|Q(9B0I&?g(o!p(U4V_Z^%;7OHXfLYpg3>{u9pI$uSw;hV23l&bYb;~+s+fd2Ze
z=<aLFEnh_4ADZ<VfU@Dz#f>ofm+ZvMa`pfmd0M?fx_=J-7r2(Q<MK{u{BqzQldQ1?
z;kfFWuA>k?3!7AmJ(m~)XGX0Mb+u&jJ?|W%*VP2;-rbTe(GlGmQXQ(jX>x?v5gqD`
zBUws$ag%hIfU1QI{8}+JeuNZQPfj}3%rm6|qHNiSg999vd&X3D!*WuZQNF7mi{%;)
zfC{dW;)}}mdsO}iN2cl0<N>W2tR*cvCPXQ!Ko-DF2@Vp4&tV39mOP+8yPfBCim8Tt
z<W%~^#1LH0Zk)rYN+nx5s`$iCEGiR^=Kdaa-*~7Ez?6bZ?!?F?okX$k-ohSW`-@<K
z$h(gW_xO^f{dMSPCB(K18B<T(=n#)~G`S7<U`fyg)pA5M$_D5@_W?%H4m;D;Cg?tn
z$$@idHWpM<UmL8{0*_=ho5+&{V@#=8sH{~uyUC^dN~{cuTvYK{N}ai%&q{2hV+>DR
z%PZ@vmVr(fOOb%x#ElHvLkd=|EE*H=PgrEH*+<&PgL?<;J1u<sZ>tbT45n$tU;#Hp
z)eJ-03c?i4j1#$pAjoT5u#|=fQuspS(xN8Am_!kVF9~qfHH=T)v9`k_7?NaZoo*Kp
z^%*@uGq!){?*S-7?ha_vw&o(j4(#{r(NZ$^dE_yz)y(uPLxmdYo|xfo$l8-VJEy--
z#-I-{)p)xg-2PUtxfeLkTsg2UiwjBfPAguM?dgH&jLXkQ#(>~UVed)F`B0rfqB>{j
zuWLjzOJVZPxe3~rC2x)l*P)l4oD=Up{xUg;*jXFT>>S%PC?(q8+h>2o;}_fS#v@16
zySbDjhL%AX(&-5+G{+v){RzU78skt%A~Eop<?w=I)ZmPbLm3dR4Ao>Vzt)_|ahis@
zmM@YJIDN9Xh^9Grj2urL*dmQFP$Sxa6lQR|3YPt036!F7jCLJh`pILZ)hxl#OK{_`
zC?P*2r-O(z{NQ!L6CL|^uN3;M$uQvcsp!Kv#wz43s~b7w#2bb;+6tL|#rGHYjnP0&
zn(^=hk5p7$U4XSZGrH$`K0gZzg?tu8WdS$=JL*Q8Bd%%2V-b?;@050oX0MTc|1{P|
zbr{{>fOgLL9iG<7wgBUU=-6%#IHf4)@rpxtb`x+<7b^+1R_YDYw1;{YH{`?wx%GL-
zp1=@e((|c&x?c{Z2I>w%D|?!s8^D3900Z2n7>P)3QS?T`C_6q8W*mtr%-pXRfglKf
z%tR7t>B#gSH)GU~GwT0V(gy<5+WtniIlzEsQ~hwhp_zPp1{^S}UDN_F><-Hq^Ha2i
z{q~HmKI5!TZ_$eHXQ^NaNVxpJ_H!p<!ya}8ei3bD&q1}j%@_}NAR(h9-UTIo4GwPf
zE1D<hc_@!f`)L3ffPBX68~8f2#ffmjeqYem0-G|i>Jy&B0Ttwylr$BeZJ80p@H5ED
z(BV?7ekv+#*yN;DJMFd);E!XNs*=soOcrO>X}3nmCgrWSXo;M?JOBm(iIs>2wN%th
z)P^e74ed#MDmqW$iV2yON3{V}t%c9!PsNm3k;x9zqFQR-s()p(a5X_r^hFsW<pue0
zw&n01FVT@>rQ6EUS6LAh5j1?hmx-^p`{w-TC}d@pWqA>x(o?xI1584)TJ_S*LLb|-
zvY)<=ApGeZ(9N1|b-UR}4zErNXh7Az!L|NxH7633ITsjuqTl)O2GxFz=6)XCP%W(X
z_C{~$THq#YzR|8b%0E5Qjd4X=X4#;9_{f7N#pKBIP;tY-TTov|Hd2*v%#A*zh_HQi
z{$)!o-Ys5<!riv852c$G0bc!^vSal~OJ2SLK}vxda6UUAD^I3jTJnaHN{P@!sI6KM
z;{Y)M;Jeak9DTk&Wv}acn%YBiVHSAx{_K0k;?<JNB<!Rw+xli1!m7fDRu;Kwr1F3w
z)th*qu^7-|xGHP+YeZwoWB@5rHlRP?us96LMWTlNAdQc350~E|zLk?qZY=j$YPWT~
z*!ws}I*(W*53&dLD<97^SQ9Jyi`u87#02U-ML>Ylg>7{&W<zg9y}mTTX|2MbM&>b1
zGw0VS)_FaUBUDd3%3flaf6jw=r)=_umr4X<f&k{t>+i;6>ZmTa9IJ(hDw9btSA(3G
zX2J9|{cuW-nLRJ3cADQ1`u0BZtp<uyDl3-j&)2e9{Od}2kq?1-pvp_#V%BvA+uQC3
z*LFpGcJ-mo#3r=euF4$dn{niCRmbMy5z2(u3MW`vcHc(eRzdT2Q519;g<R8)pmp<X
zIeE~LBzD2NM&S-?Sw4x~*4|;+s|NulGecunJ1{fGSdQ+>p%Ivb8NbwaE4&N2fxon@
zmbi|S{N=BE3xI!C1D@-9C?*pT*y`vSl1}#xTZi9qR{NQX;Nps1HzN$%v8p=7r3_S)
z5+T0&mC?HU!F8-UEg!7ddEXBD{zc=T2srJ1>XXK6yGJ--dT<aP$yO9C{_I?T+ZGXb
zEbT=A34utP%{;Ksj4b1+H!1ZTxLVs@`XM&_UuoJ6FA6gD{BQjmYUvnn|7(#w?M))z
zoF}iQny*C2_d=xd=eKJy!Mqprh}rc23S|D9``z38j@7kiTq}HbyD2Yxe(URkcR&5^
znyCq-|5r=%zek?|B(q6o_Y8K01(yFeS@8cJ;T04d)6<;CLqNbqg7>=>yGOVW&dGpw
z{I-!2_>A>yeYg>!7y$;tphFaidT)sZ^A>=O$_oQ<*)~Y{6dnp)6lUH+{Ob8hSghUO
zTm$giwIY@070ZXxKCVQVx-#)0^d<Wj#{3@v=eE3?V>Dp_8e5d@7;<-GrRWWR?yolS
zGl~OF1```Na-2R`d_-fMpn&)17eRMXCV$=dl*!}(*l8O1VD&C_jN+C~)DK!;I+n}T
z40_O4Bed@>MPpP9AkF}@ZLfAlqKE+0muOf_znUI%zKLpQ1(&=i7bRD6Gy|=1aw|Qi
zcBe}pu-SB3YR#fvE7>Mn-#`CeHh*lsLG^1Df{v&A(Q7`wY_#TM%AL2IU}OEreY~pU
zSQ1wgrcMv|T!wqV_YN@{FaVN{41L~qe(L+_h`nJzQzAe2#{NJPzx*h#7g_t0)%ir(
z_uQ*j)^Bti-G_KI_Gi+p;LQ&c(`Nqw_8RfhP<Sfao0;~bAO0(~U%SyvZUPxc?)V3-
z9xEK*8csnB_7<i%HK)t#oqkPQGo0uiySwSIERMbHScD*?e}KEU&m%u~n^)fbJ*Lg`
z2l)k`{?NekhoaP{kuIGx>N}HqI@6MHS_qDZU#bnl;DLdcsJ(U!uwY@%bkAA?uPEF@
zW=kBD%2U?n&3L?n>V{mvY9!s>G0wJhdd66+a?r$^aTU9_yBn6Gg4^IRA$7MhKFTs_
zu$r{?c>AX$(<xh;PBqJzxwIiJ6HA8kfWq#k>U#eEQ7FuQ*KDi3{>8fkn`_W9n<>xN
zVvEZq2RMn3<!s26$omJ>3~@t$o4Q2a)e^|NCw)u4w6Q`|_A_h&+yHWV&cD%CM{oZ>
z-vZMjYDR;&ni^U#sPU3|9{v{?d}VNvNP;xRCH&BDZd4krKW>gy*A>{!8MKH^cv~2{
z|NOu72y&e_g;DKB(6md15XdDZWc-L&Ku_?jc*C*U8AdpjeXj$i`2E$7hYH$vEH})2
z$0aNtH_VVHR|*=?<;GzuP83(L3|Ia#-dB2d{3!C`UBN1C4rJ|CeZf%rY?KotLO^Z>
z31p|=JEE5`@!|DEqAUqbNZZarP3_HL4o!7a-fo<vO_U{#nvrBGy9tbvxSk92lI?4j
zO96t;XRIEj5$5|%`#B@qm@>T@8`my3X7??B0&{MMs8>^lz!L|Tr+(HW>Vo}ce{kmZ
z_g`J|*SmOcgjnFl7(Ws1`wH;SKgYdMy5~pP$^GMrSS;h6p?XG!j-c8nUAt`mo&ID`
zMEu*mAs7edB;v0v(+vUqkbzQfTylC1e|~z;XM@L|LF!8bf4W(gsyy+peVtKi?^{Qc
zXE2Sw#a`cjGr{>W#qPGJM6%j^AkDjPbypLC3KsVJhA8)CZuK6%$SRUgf}UnYVp#(n
zGp+^u&x>grEwJ0)wTKx#Gk`2^-z0l-AYBG2r2BWXq<!wlF@yS#ojydVU(P1BweLo%
zd$v_w0q=E>&W7h<Tfx)Hn|fH`|4|uPf{VJQae8r54?OL)M&2SZ6xuGCAArNp$%;#_
zNEPu5bsp_PRDYFNuOTE-xbGk3>!YAPD2~-WH0CZVvAF=5QTRQ_ovO2lxz4khFLc3}
zoYRufbKGVfF0ijkjiS?O0ENA=eApdBCf60>_cfOq?K~JPsS1IyWaW-rxzMC>8Gp6b
zFwdo0J)W@bM1?a$D#z$1^nJWsNsv0}TNE<GeN^PDtMVuk5N0t|l%=#qZ9SZZFbj&Z
zP&)$3kf_?I&xcf0R1@bDlatX4pOwytb|KOI(mRZpMjq>j<7c*p0Th%62Qo}(iLP~x
zZUNN~E_ht5z(X8Ji{rf@U4hS#i;eTy=IbaapY@&B@$R;aq`>!|#w#mpB+(7Wmtdtv
zm*;$rc3Z*+C8i?Vo`hR3PE@l0><ox3v9D*}^fc+P>JFGoW+TZ@pQIyY4&NJ$5JXnA
zekzI=g^o9x8N1{Ww8VPhvIX4IF9yZuq-1?ob`)T7y%eL5s)VqslAcRBQ=p)CS}>`p
zrX&DT@ivX9xpB_N%9qb53zYSKh8f|YW~_P6mM|v7?P;p(hP-=YTY}6xJeS&2-Z<eA
zpi^n^v>-tu@P|k1GYl71@-f;@+jKP;!kIW98Axo9=$wPb(Ei-gk5rL|AbXgdBlBZO
z)~Pf(s3$c|CoFK@PerP`4Imguz=?7bvizwj1=I}yRn-24;8jy}!}n7v1xRx%RJICB
zqU_Hd^WJ;lKvx_mWqmFmpFw|7JsI!$XnxT_GT3%Y!stfAwN2%jJav8tCw&l-9>yB?
ziatw4wDd~;2z|AV-->iCY=pu?OVwu5d=O!lsiMkiC_V*(T|N%m`0qeZtvrPQzG*RB
zlaG2e^)6z9aBg<D6HQ}TN~nyQC<TLZvYHsfkUQq{jt&uzAF;ImU%ju>j{tq%fLZ)y
zoZp@Y`eflCCDl5S_5R|VkC&J>FAtnlO-W3iTkGzoMHKYDtq_^Mz0lY4J&MJ98+Cc?
zlz%bSgg1ZicQ!WCC0`d9+S*1M;rKzJTFoVbDmafF`v@2k*^ws~y4BWL$ke>wfdk(g
zo?s)i!4e)IWHUvWfD#w0cl3x`^P^DOm_`pW<KH@p*fDw^Z;l~@8{SEro$y&(pphGE
zo!JDWjb2*RElrR3YNAEin4O71k(@NUlTRW;PeT=!X#g}F)&7}D(He2Kx{n;0>iZp)
z=|FLE0uFq18M3sN@-HfgZpqdqE22*+B8nSRTc(m2ACQWt*!{}x)OZ&T5G~Jx;v+Xf
zgI6W8ZgW*jn21#*N;jx$g3USl-~r1~5~?AgL*Ab=i+@($Tz=(?y~_I=cbC;6Qyse+
zJRb7h<a{EgxT^V7)H5{AjBS8V>-Au*)FKQ#nfCR2V7dLoD|Z4Q(OAofQl0iwxI>oo
z?gWWAr{pmz!}CaZC4GAZy}HK|sLYi(<+XrA^}Ij^$M|0Wly6*hA>_-oZ_+Q6vRnYA
z?AgKT&1^QwGWleMH|n)9SPpxPdoc%tf0qz3sprBr+dvBGzalJUwuaI;IFvOU5&7Y)
zcWV6Zb0T{G=d%tJ{hb)hjgcAbp^q$ze$*0B#j7$4LbbRQ3WCuP++JN!1pP9MF*|#x
zK~p0n1d_}RA*~EX{aYEOxhjRVidTIlNwv_nm{doEdVgTg>92>iaO}Ut2*LjG`j)CT
z#hRt+8si_ja%;Fva}mH53k7jScm&&+PF{UNC5)s)IIc&v`7)J#ysE-tdVq5!;OJ-%
zbs7M9f_V_Tm!No2>KnD;n>S@B`1w2!WDrWqL050UIikb&xjRjaB};WpR2)JtMqATP
zQb2N{qmqxlr&JSzqF<D#wA9B`Ul872!Ss3kC1HEOq|>24a9k0$PLcI)D5~n>AJIIA
z>2=3)i`L+SjibyW6by-ToOaj1yvBl_X>BJEr`Nz(i~NvPxWWCBADd6^3hWWYt-#6p
z?nq3OyA6Ar?_^mFan;8x9{_lJaOsGBst6X1&n4H(3&i`CUcaa<TG8*WFAgZ*U48*#
zg2fCk8WfmpR|GkMUs%h+)A<A9k&nh~lWVSnae{Db`LQ!E3O>_j!0+uFBZL(Xg#IwS
zY(gR=*Y!El>e5?J?`3w;GIzrXFB$`xY)vMQViKf}s9!)Igi;g+V?6=4qU7_w`MDrz
zpTlRLbFXi+ms_(%j(6(fcYA<7zy$aL4SW|K43Bvrf%<8%IGToEhRO`aA)B)Vr9Hz8
z9iTUhdn~^%Y{Gp1F2|IEgynn+-jW*@Jm$;UeM_9i4P|_L(J#6B%@yYah9r2h(Q4c>
zS4m$R?ROluVb0Tn3s;4(v)*97CL3QyJjDyvllYUg7f6HF<`nLrnS_hr9_@ubza?$3
z3D0Ow)LGO4gj9u1#Yx>r)d-9AkZ88QkUNselBimarh{;<a@S)VBmG2fP9QXR3vm^7
z<&1j}&3m+>k!IA5PD9vE49WtuT%jH1!Be8Of8Qtz2wxR0u#Wujl4(a)RMCO8O_kp0
z^~9SvPTrZfwPrHg)d`yRMWdn{6Tz*!SfPQQ2<0@qLK@cSe1;JHYQPRf<;&m98Q_v9
z$6A;p5UU5&z3On<-AtVB0i%3%JqHr9ihlWu)J=N+s6a(BJV#(^4LjTA^oH@hwN_Aq
zz^3nNgvCY760W%kAfUULCC1q~odY-qs1>yYvK;l!LNeGs<4q^!`x#%s1_74)Om`;S
z-Q<DqT>|Kr)i&epm61e=M|sxCSlG*cLrVP)^DTXO!I@0UquC#H2;0-{7~3i5#{Ctl
zC+j0|#DW`65%B#154nPye(YN~yF+m(RFgKezb8ANO<wUXc;Da-dVbK4fL>O=kWu&1
z!A3Rf4LO`FfDH!fAm6R(*t|2T^u#GQ+zF-<=#_E4@ol}?F$C9HIquEsS#ZDX4ol%#
z1v4;3)`5{?^)uf|z<nJ`<P7%=GC}ud|9NW08Nn<O>_Ny?2STi3)Y-`xQqV^Xu;5@S
z#kPzOjDSC^a@;)l&zBX0>bg&wL|y(39x%CwPUWY6XWiIFGd<<Fokt?7^Yq{YOslNi
zp9x`KUg}6WCHWPF4SU}B?f#<mt_HA<_cH9coxM&^J@ZcB<BoRb7sz6NTUBHMV;M@i
zBlH`^PS~snQl{X4N8vqJY+QCsfwU|V(};-wB{Na6Hr|QzpOxti94wWelWZM&m$NVI
zF2dnXSz=^e5ZWp(T<Q4*bq^I4nh60gI{Vb<n2aQN1IK&2q!_Yz>i?rUKbr$ew;|mh
zyxlvno_kH|$LxrPnebjCV2H4=(Byg52A%bne$%smXT$d%>j(@(I(Fh29{_2B+X`~>
zPfH+cy$e}iuiYQjkz&Ew0Z{X{Kfphe-LRIRGn6Y3^3j|7Tk?%1>h?N%e)+u2f;vRI
z*1+X`m)^9uQn6Hia_m9(XY~Mns3E7%FC@U!0WW#VfJs^+dUPwGD)6s_z0u0wVa}=S
zsIt)o{jWCF2$uh#V4k0d@@gcs`rXQ2Z4`va?{3ct5v)IfUfUo9s?Dz5X5@*JMNfKC
zgdoDT`Jq8umA?ZB!-)TiW<;=JK!A%1o$6NPx%?+22zuK6BsEj?aXv{(gs}e5{?z$2
zqn0q9qrALoe*N%R58O$~hvWZzXSsJ~G6bQKD|*o#F2!}t--ohhl3xb0nEUyE!{ML3
z|1nd>`1u0^i=MCkX^#C#tXnBSNY*0O5$Ga_^lkgiaqJT~u5y%wj`?%$XUxCOp5YGt
z{krGS+S&O4emIpba2@WjWL#Hbw$@Lsjg2L^f8RiY8S)UtgkwTBEVv`zupMIaNyFr2
z5IQ-o)^I|Gh*AHE9+i=mfuHJ7<htU9j)l95;yL}Uek8Q|?*v+!=ftl^Qfe4Tf}G~(
zZYjP9_7gU;=?y=qvetFFDU2=?I2ezop7KmLp=i`&LNS(Wf6~zn>955YJ93}wtOZ1n
z__*M4LYQreNqRH@*Qdz~)}zQYSiA85)hc5r;r%9yOdVh-xO%0G0!L7LzhZQNOql_K
z$x)|^PHqGH{xl18v-VdgMVH^ED32ROC#;mNbl>A1c*RPjmS8qn$lQEh469I@?!MN8
zRYeMlk(`sDE8fm~PkKEY)&Od*J}@hN->av8C$6^dpGVW=OARLlXTn8AH=#Jcd@m~e
z1M^*N7i;dOhqGQ=$04C2f`=Ve=H-9DQh$k#*EaX&^G4On*^Zq3fst75{4Ovrq!b)Y
zPp74}HoLEn^D{*NKq`g^j+V-f#czw4Wdz)P!=Skt{Oq*gvVbRlvX_G?z+xViw<rcg
z8o&Aa3yXx>(Pup5C}#%AjtJv?YXikTl^4OMTH0tKmUBr5qI*=pvP@~ml&8;}FjC3d
zByJiBPob-I97^N=%sFq#YLIV^_VX=hz>=zOWSDLqh9P!=<)$u<b=ILZPoaXzaN=O+
zrG3vi$;gW15_KNaUl5t;Z3Z7BLB<%+`b#dMz1e$MP_J?aI_O5@O&1^|DLgBRRKkgT
zmM_U}_<Ddd`_3A)eb7@~+am=Y-8VZ2^xlnj0HYIB;?$GDza2E5E_Fj_x%zc6THDu8
zxx1BB{H+?xs3QE&@bjITd+#$mH`;=19APf{@2&!fuSc#Hw&Q@xoRT6)Z95Bp_U;pH
zvCCqa&?^U5Bh&E!#%H|kU@*4<qNQb3%b4IzvLs?tC%&;0k?LZRXj)wZ*5Z;TV#Q9X
zgnUbFnXIih5=4Dy(NHEnL%R~?8O=rA;sppSO%CznE0Vd`#Wh$>rOW#{o;VJx(A~i`
zXA-WiPESDrk~HCjGzNY+t()P$35{`Rm7G>mQ<7$)j4dri_j<pp1#FBLxv)LuPT^+8
z;TYGX0(>fa`_Al<3O9;*_%r#V2BOiUad<>f-jKC@S0Y`~A-ItM)5D`RgI50Y0OJxj
z5*?l{w9(w2>Gu)@=fWjMCH~QxiGJ!k`U?hhkhy$CoU?0%^9dSaokEL<I>XD7YF~C~
zQ&{H6BFaQXL9AIE@dEg)jSgH>*QwdDe5FN}vTiTTY7Fj4IR4E@wLr(g+4f?L=H1%V
zffvZxzYr^~_yOf4nS_Ll=n+o&ePIT`vPBCf=PPc+Tp#8i>9I3LQytaN(i4F-d5-3m
zo}=Q?ak=m40-tqoFoz>2tQ}Ap?$M(Inm#ut_r6uyonX7Pgbz3$Z<UGlSP{~%0L$AC
zWSrVy4E^<QkG9!OmS@Lhh?BYj+w~q7IU_?f!*@%ob0O(nLF)Vv>0Yiak7K$f^TPvO
z?UtSAOHs8!{O}g}ku&Em0>p<W41wXUnD=|UG)}i6l~{HQyVAfeUR0Mo!aet=EFQ3u
zIga}M<#kDl{8}~j(j!L=pQU;!<u(rMkdpV~t1C#jZ?|qOZ~K=vF>iayViJ~2h?6FS
z7ro?0I}yel(+rlQA~qUACtqSn92~N+7+B#9Lnzsk)hIj7fFBx{DiAY{OHX7;3p=fD
zC5QR>Y=pDF`@X<IUm@{#HoH+i`mYW4?=YUtCWL~1HeN|F^Zfa?#2r{``J@!J<NL;!
z5FM~PS^Yg#tjv(q6qOqxR?EPlzHCa-)oyD2GujRYg(A^iqB8Ol4(@pthuP+1L!m~i
zAB?b$IQzFoba^*XbG!{)!tEoB&m3%a$(j-)SMF?BrNBDyRK`3%)EMN^e(U#TXia|9
zIZIPDlhvcn{jYd~>yl=T9w#<m-e=su*uc_i+dL<)r3JHHQ4BxEKr=I}n5$2iQc#w0
z3BB8BYb3Nh%S9aJZahwVoIE!qg|vg71J7k_nO<+WgcI$R`^(lgKn6U%E|&+$GUP7M
zakeNfZ<xA0<EQgV3*LCesczi!4(pN~e+c}Cng;K9r8r8BItQh{>3NUF{VjNq4u1qg
z*hhvOkK;cj<e|g0rav8oBxeM^&$~Yw9&F}3G<cFtMN0aK4QMk;#ja7qgT#ss(CffW
zza3Rdm00TRS~!TOFGj{mO7*iV)+k_>Sz#(2S}#|idyWwepW-_mE>0n8LGV?y$LTU>
z_d4*l#HCj1p9lw-X)xJD%w@If(COP_>piw%NS6!Cs$Da-Kgw{pDb;p{(=S)7%%V&S
zvu+OWh9$=fEC-9(*nO>n|M9NYy5$rcT*0|ud*X>ttrgG)2_6UUP9#@4+<FiZfb#_q
zAAOfr=WXa+L0Lir#)B^z4(D0|8U4!U3sZbT&0ysM1sMbIZZ}MqpJ1S40G_pRG1ub(
zwGq(;R-BD-&n6nCP<=Xa$>zb{mB}?Qw1x5$4VPB4H3U?cx~Fp-b(*VvOj4fQ@;X3X
zeA$6t0K3MAyDu(7;NJ*7<AN+W4H#WgM@x~}2N#Sa|D)|2VrC4lU?CI;<1&pzt(zLr
z%oFdD?Z)_BJLXLmYXpNS3aX~oGKT|J2O<uPPospp_cab%b(eiRmAKzK8ULwqv~6XI
z;X1@NHxdPf#0|!BNZOOKzKk+Dq4qL@y~|P%F+X;S+`3_r5&<H<FRWh*BE8K5AW-+d
zA_DgL4KMVom|{f~!De?AU&DJR=e5pN<mD&k{?XIK%_f7wTth1T*2$dzj3nU=*DrZG
z-UenhWNQv@0<CCDiTiG_XPb53Z`sH003nk56HbWG?Hc@A-QWYm((bWLti$RH!o`av
zsjK%t>?^oN<jko)0?H_afqn*o!Fa*G`bq$AcGhn7{%_=rtQg%cP>7oTa&0g7zqy)s
z%=i(hPxU!y(uyr~ydh74`ROoZaz5~xt+2UBP6IvmY6kymIaEe#pF#<B)-nqzgUOC3
ztSpDAEG!&#DojSK228t0%P*?%dWoga7yi&BPh*WWjQ`Do=7oU`u<jwM-Gt!)hL%B0
z{`sKx-R5iyKI}C&y7)VjFB?-R&I2!g|5BqzRQF16l(7PDyTRlmq)ocH5#0!DB~8h?
z$RR9Rm9lKAqMrn@(U+oX?Z_bmRV@(}#AmM<h^A(@f@Y)ff&t1rHQUh3k%iM)&@6(~
zF|a{ez8!JHOXJ@A1{oax8l(47VdY28bi9Y=>4f{y`;<492N;pP7ZlRMW3SPNt)Al9
zA^4)9Qyx|2k=Erlj)>%fkV4Rc6K}A}=Gw-fJIT=cL9`gOwpLpH&<c5(BIUcRv{V2H
znaPa_D@zUAtuGn?Gi{z~11LFI?M*U2#DuAe7A(!za%YrmAi$j*{T0@qtnfhRZ;o(q
zx|PK%fb8)s#7|%VO@<%h;u}CZo*v#WG@a)x4v)tr7<f`cqhj#6rCf!#_u&jrR%Sz<
zY1m!Vrk9CR9>{ADrF&KRs@Zi014V#-cO#BV{D;m#K#{M{V;`k{wo|@s3$E0^vp5dY
z{}j8EHiH4_sTb({^msB~!14zyQdLdwp+Te-B|##&PQi#$d1Mbti}gt_VyGDa;*v}k
zV4SNN7c}SNIO98Qs=~Q>lwp1v5lhh_5;)Gljfgk>5?a$aLeBWhg)HpXOPV9e^wX5A
zgU1Wh^KFDc6;7(*2C_twre|JxH$Lk1)yPtzxhm3*-JvmZ`@rV`cTw(E#7sqd8M!E=
z@R}<r_v&A1&HQ5bKQG(%?i5X@90Zr|7ATjezbEvoLPbJh7^h;1b9KTSsl;Ya8Nuzu
zGoCL)?H&4E!4WTf(Ex5${dU_Li<*1@X9QFo0o^s@wDOF|a0q&G)TP*re^iT?j4qdO
zP^RE&m+Qf!O6?m*>|f(^j%&y!^#=PL7}m%s$G>xoU!6+SjJ`Nz2sJzKjcN4_UJ4Kt
z%i5aJ@!y_;&E`Qg{%Qr}C2kvh0#ptvYRY<mE_}y$SAOnjYSmvDkZNE|Syye$hI*|w
z(d{%T#%Xfom@}F&G7N>4h>SKzpzzI8!$?5yz|LJi-|9+k!Db{YQ2jX~l61_uqCC1~
zi0lH3Ma*)7D9n#waQrq&nOM_2&An_=^*H_^2rBpZNq?1;$TtpqH6EL(Sgv~v1+|00
zZgXImf`j7s0=BA}O^!kqYDBI)@3$i}lweI*UP{Z+=i(RYT-U9|H6(b~7sSX^PB0wN
z?u|lNaV;8|2H3}0rtnW`RN*^i@z873nhh~tLoT@=0cyRhY?%B!hy}-YwM>4)vb;KN
zDNp^({pq)D9e#9nsP<$}G?OTx(!cVgdaJ^Uc#}bhF0cd7%Yt!Dt3x+JSA{Kp$aX#j
zHs%u$pDI;_dG!65aGPtMA&eprD{uFak;F)Ei(AHKfm$<Mj!34y`;<O@J8LjqfQHN9
z^+Z}&ZwM<cn8dnD5##LHW7VqlK$5%ASmKb7>p*z+K67HQl~x=uP_~W<2CHmpCC#-A
z|F#&sm$5BOOTj{6OMPJ5bt3(q>a}0jKL{f;qY<Zfi?;>pWIcbfzS>{~t~zJu$KS%^
zf(@vK#?803XMsy5uIZ~BgrQ7pCzT4ybNl=eb*&&oLJ2pe%8iB$WGjaw^iQp19XT8p
zj`D|wSjF}Z8OWPePDW=~W-iS9BRPq47L?no7Y&r$(wCT20sKq}Ex1>ae3&BK<5O$l
zeI*)U*_UPL(6=D)o&2HpYseY9AHYa!NE>KH$!z=+n<t1LB-BVN7reQuMh}V8vREXS
zTu^;P1#9(hv`H}yeA8*;%mwGsu}?he=v&pI6Y5~S`3IoQDQ?ZI7;n(r2i9#<M+^51
z7X`i4Dd`@AL&W2Z&gS`)7thQW!e>jl{S2bQ!$FAT(x5mpph(BWJXyFl5TH{%sfesm
zHoz>vgJ$Z2fm(+SYQy^LSGk<w>h+}eE?bCvsh-1ZDoJC=TH6tqil5_bWdWIFf-07y
zJjQf(JeG(vrGWLETjF(kNe%%=sRCIwI&noW1bnav+~Lfyk(PiR-q52n(LH#<1l)w?
z=+Mvt=jydA1jhPYB#QueDv9<!$&*u12!EhQrLUEL6(v$nC&%7lRV}0Ssm(LPCoF@}
zA{HDy`(kDTVOZpZck+mi`-|wFlgSWSK@(l<j9es~I~g$tYjaC5D@n$68Jml!n7ijO
zJppNV;3qw|%i(^sBR|P2r^g+&7Gp_SX*c<v^=43ttVl3e>iJpf>`gm}KP32PB+8f!
z(uk+p5%2XDYGTREKu`?+oMb3zQu+-mYq{=mx2ZEddR4xTs7g)rPM5lLcD80Jy+fWt
zM=xV6Y*rZ~zEF7ZMj`s=)74EYw#{pjh%`i|&TN{xigU_OABA;xBOpP4jxkc)@BzsH
zZ$^BE3PVDOr`Ej>fJI0Ua)v}KlIX)gPxh(zuhAk7EeG<F#?O2l<wTw48!2!jt!~Fp
zcEtyns)_;((w)-CnK~aAj>!y#r`(}X&W8ht4c>c|8md()9UdwdRyPv$Fke}nqBzwX
z@bY&Ie2i2X#kl0(G*Dy11q(V=VOPb)wM3z&q;I55vr9y7WE4tldu4k_uuhzNI_*bN
z*u7GjkQiDApj~9xQ$?^Csreb{?}QxWc<BeQBUhWJX3jXrdKDi2uX${Kur5?|)IUw5
zmn^peV0XMn#LtX|f|Rf>w8Cae83v2vtgJI^6{4>80>ZrB9JzVxkNQ7H^lOp#r-|v?
zQ2mEg@hHjo(A=&>4c9`Ik%gn<OGY_a0mnNx|4QSqy#AT7<vnm`x->^pNqL5Jx_Qre
z-n-L!ae$ordIjX<5KuBV?SbVpC)1du&8ThQ%^`@`P>Mp6NLq+WwB`H}2<~M&iBZl1
z3huSQ%Ug5ooCmWo`HXXelcF2^Xl%b{=MSmGw&HesxKU{H&;ed<YXl#5qp#c^GK-=!
z70Q*a#<4Kc(Y<4~Tyupe2&Db~^-sC(K92$YUdC!Z*p6x8qSFH*$m0y*qBpys1F^hM
zmOA6+)I$)p!}X>w6X+9iq}w^Lq!`0_USh%9MpEKs@y*c-<oto=&EHOAlff#iJ+U<_
z9TlopsFucGiErtQ5&+a4_Si;VR3g)|`|i%7aFstHqbm!fPe>~h#)vj%3o#>#>HCvf
zGV7eiB&6n)^e{7%VsE;<Mgb85>wawbJSVXF2c|MuH(muYkqQH1u1YvD@}^Rgw3J^X
zVB`p|aNeJ^YK?s<$Y3URhmg_nD)`lhXWH`g9oPH_x1L=3i)&kmxi#Z7rMyp`XxLiB
zz6gBsH)X+UQlzt_3>E40S;PBIgR7rWVTK;ovIX6d_IC)pflT6;vz_&10}PkPSk~2R
zId=t~UNK7P@kCk(r|_2tqG7g~#%G~)7DQA+&xzKYK5xaIgJ%L#uJijq;)#2-D0{O&
zdsJrCpk7`&a3nh&|IveZjTa%}l<Nnf<E@;5-=flHAX-SCU}{K>v(6iYC#9ssiRHyX
zOxLsL2$Qzu(KX%O@rP~+{Ry(S7BmAf3TAug`wSUPxUmoVkAeP&D0u{+NZ)LEycKZ&
zV|&3pHq^lK;<naZ36m0P!h+8Ev={3eJsvK8)1xFW|9Z|}^FU><c0zVWWa+Kzuq|5@
zH}gj7=dxE@*j$+xVF++?Bf(a7tkxS6td=tfb!WbyG)<u-+WZt9<byV%Wys{d5f~+C
zR<`>c$74Ew?Pa?<V?syr=Gg~!$Wd<AGtu29w5n4MV(SG=;}8Jxj%C;%Td_Za#)uf~
zkTmMuovboW0nO(NNXA}fN`rIIRQ^ov2Y`@zKFGq;z+7ZKt7!Uc{Iq%to}6;fU9nmA
zI`J<UV?)$i(4ME9Wf`|&yu_c=LdR-;ky$0h(7RqNy~mQ%*cZnu^ka8};cPT>S3Hnz
z4Dy3s6IVZf8?=DD683ORrTzPj*!bfkWIt5|1?RbHnl96sXr8%6T>aI-)&6hfmHbj$
z@1=F#sW*XssIVR@y>54SOp}Ea6(L-jw5iK}cxQMd$5r8F&=wo(B{70F{K*DhLIF5G
zg`t1VIyjNKaDI!`ma?tzVW5?gDx6U5<KEu5?_)(!fL~WG1qP<Mp&9aLt<?n}MKqi+
zUe(^OQ2*Dv)+h?Cb^vi<3r{9;EOFn>@#|vR;$}~SKBsSVq#xY~FWq`?7!wllhIO{N
z>fF~C&&byp=Vv7rTqQ~Fr9SrPA0xKjGJYrU{>_3>6<R!-@XpYf{l3p_1CDDttOR6W
z$76uCHbWG_J+fm7{@lbWJdwJuxS$~b>*?2E+$!7&>N;+UCo`|HVkL4E)h>6TX0-k9
ztMu?DR<r2b@M5?I$7iP>7bkAnm^Y|By_>^h!5_0{zMKT4Rs~NxYGGZT&=hSs3wD+V
zNK)fQ?mnyt!{MG-{B3;3Ax4A>Ug5_KgR`cy;NA~PFf~Ewd8W!8F9hqM0Hu3AldSm8
zKA=(b?!q6`WBD9$4ccTO9SZm=9Y&K2=&WvMxVc-)FrM+}raA4|;#{t2zfUURbpIM&
zV(h;)*-Uqd?Kg~e!8K7(V-rmKuL`L|ci;#l*6t2;VN2t`Gn-K<<X?yrb*gkzx7@KD
z;cB{}2FND>=SYg>1)hM)O-&CFbOnZe(^c5XY!)cY(ksD<B?#`9dUxU*nHsPngbMP>
zndF&{r2fv5>JDR-*i=b&b6E{<O5%w$+QL3084e1gr35!7^B1cmdd&zLCh6pWinA&_
zb5J1lzNhtE%#!}*6&AlEbV80^Ut}9;RQ6C?^B9!FBVNEN*b6Tl%guTC;FyaoqH+h(
zW}DltLTPsmw>cls3i)}mi8LrkSwtG4b5?r3(7|k-n)U5KIm(=|H0*Ei%q`!F;~C6`
zc<W)y9QBkY-)Qx!nC<4=gIB%+@I@$aYh-3{`>;N+{=*Y~AmC&};gphr4W+XcKX{N-
z{eWd6dRE#g;g*>ceq0lS9v@yM3LZ%Ul7WG0hlssqB}xH+5De+uYrpvm_G4LoIOzpQ
z?6Pl1T4JVjt|#i}X7)S(l$Vtq*bYxgV`302ZpS=8j1V^y>S_H2a>2?t&?kQxhu-=_
z->$i@s=vGbAaYCbXs*E_N~kr-f@E+ojx4Q*Mhm8Jz{i*ScZ_~lu&=BP*6j`?JsG!P
z`V2hoB&pHi5(-Q2c?oP9hGfUgLueh|A{^5TxyAU2xnVqJL31(JZkq!`k{!_!7W+Ag
z3p>@QzwRjVT?Ss!p+|ZctVj48%1nGtE=+DD-bS@F<wJSn(n0AkG5t7fwgi00=b;lX
zng0xpUpN{CJqv0F?w0lfD#YBA<1IFnwL4|PX)8lo(OtNdpabU}>{)tNkQcqAgacNq
zx}vUPB^CC8TWpV8BKHr9_rUma)a`U~mQ%=>_ci=MaXC>L@ySuXhyL+6(k09txKRzL
zHBn?BT6pRqQCs5^=wk-0Nbi$CiCrf;6Oy0t?=i=dM-~wc75_g_Spa`5y+&dZ>;ht+
z5j>5-ZV?KM6j>XX?1Gb57~X4!Spno!V6nkufx->rr}MM;2(z{#io89aY-Iwy_!EB2
zNB3yJTmffOps&4zLZ6e7-^EH#wtx02r;iCm&I?(ggbwM)SZ03UFRr@)`u)*MsJwk9
zc&g83hXbV|Y7)WQ$bW7By7+1}>F^c<;0%6P<{IuZ{W|N!0}i`;o_N`G^|^!($px}R
zWBLr2_m%h+c`l9jbYW+1R^MXQURM$jn}odX?8}v=`HSZ%Hsi2SEuZ49ZXW73{ks>v
zH`dpYZ8lt+MAahNL&gz{4Cx~t+c7RA#wz<jb@S~E<e9$Ck9C6*<!=u?w{#to^OoZU
z`M{`_ypy=M3kgWVLO<g+UiqQbb7wIX2JNpW-6BoNKsPP!pa^Z080^LYaXgb1x?e<;
z)@DHe#~IZjDRDj4t94M;o?6_yEr>JewufLBJ+MDGB@;&^m6OF~my`XDYM2G}8MZs(
z?ClQ_hG~w`s3kM1?VOW9q_Ervjx-pGM1X`K9_44UC2P*p{r-}18fyav8XT|S%7jD5
zLVAIhf|dzZVZ0ha;C77|^G#LD?BoShglJGH<&K~_Pk7r8x7g3$Y)!<jo;*15YvZIX
zrfr_jc-%jCn*Jz=b_j=*p=x<?63hTO5s=JDB#o7&)+{y|8w7KySeVL^5dt^U<9FD4
zVqa4bu{eG~X3WafI-xN&l9Ezeg#XQZ2p8%-^F{Tc*Q;+zv9ib*%*bGSXzKTct)5Ya
zlZ1^jC=rP?h5$*Po!nDEYsvN}iUOBWQuRZ8{~5VEsX;{4ndTXN;3)m~hEXoyi2OdV
z*zc_JQ-xPHxd6lY0S=pEAv&myGkASsc1gHaI1a66_vw!bul%yOgjVP`eikKg25mRj
zw9nT=)h41&!cmAA6kYrxRn?GiQW@e!3VNDNTmp`+tOR*+E?p?{pSq|&%vz?k+Bt(^
zORUNejoj==R0Qy0c%GpJi)z(Ul8#ACh8(AC=%<ce7`T?SP?T-BBSy;M%aFz`P83{t
zy`gTDa&jF9<L~?~oROiw47%_Dqbg-EW44s3sh}#QT1HG~JU;0~(vhYV)j<u>C?Lwd
zYsvztnlOX6fz`(7_nw^Wn}T5#>4^V>L00Z47xxSnzLkNFbg?~bLAYb57FH?Revl`l
zyjl@abrb!_t!BCkv0|YP-2O?G&8ui;f5iyr^e{_|x^;}Ya?G;&J}1;CUhU%~qB&({
z_-&#KOa`*CEj5i*th$>~l`5FAQF=x2(vXG@*uIHTRrF&@$70nkubvXBnol>x3q*5M
z1F6elyEN8X=YF;6r4*_qSM<$re4)96wj-FATx$MT6huWc-PvcYMx!TiNX|WGTA8t~
z7V2e?P3PDvSN5>5fe?^y7(Z6sgQ+}@?eGTg%RP%_+{Mc{SPhpt2(KgC7P}8<-4}hD
z+2wL1=n|VmADh%Lgs5$cxZziAncNY~;_?W$W4T{$mF!P+?iCJ&VI#%CetvnYW?c3I
zZhA%MJ;bF^7dTw9ve_Oo+)Oa2-%c|k-G#bRhIxSJDi5p;1tH$<N;=y{<rIbzMgH>x
zh3R7Efew||57m?~bN6`3I>*^*y$0ymnnq7=b{UAPpGT_K3`&bgzV~|wJg~=P-4Df0
zf7}>$GLs7QhS}D?-5x3&rLi;tF#YBtrPq0Rc^#{T`)ax}|7=G|z;^g2SGw)ZpMo)Z
zh{M&azPLLb^V)@olhAUfZUF{btO!a`Qm1EJfIY4RQ#f*cf@Z|DG-ClX@!-7Ca-tep
z>}aZZqoMki1{1Oj_h~OM8AYjsbH8hrD?r4aMjO1r&8e{MlaZEVJ{W5>`5Q!@vs&$f
z?a499!VSk5StU~Kj=Cf|M3YPQA0HwQCb+`j;lwp$eJV8%Ki*%q2hR1Gsz0XXnOQiw
zw1jJ9ha$sZ<*u)7KE3q0+FFH-<zIIVg~FnwKD*_<{<;}iRlUl7pu>?riaF{a`>uCD
zyx9o|`PQF}e-s;ATJrQQP9c|n&cta*Y*czsVuSJGtV*HaC8#MgFU$(>A59U_{2BO{
z7nZ@Z_$-4K-T63Z9w{J>7D%ka*#Jhv4y>*p7I+99`vXEpX95iaPonA39Q8>v>gMz-
zusj%yeCiFJy`F-}c^s1TNEP@Kp3(9M7Oeyy9zOgnP+7|G^ly^V8TO6Wt;G6gfBxCa
zTK+uYWG)NXCeG>3NccYI(2sm57%L<W8tD-^+eu^qMpPVhv*kpW5?UMg8X?Q~7*fuL
zPvZSew<UUG=fonC8Ff)&Mx??BGYCVo###Zb5WZg_Fu&mfrZiuCHGU10V;*(m)+a2B
zmu=2x6GKv}373UN$(=E~!|LB(PuB!xXx9dmxenoKX};|*65XaYHaBKbPZ#(EW@-o_
zT}}>bcuOJbf=`MF_QlwwU*}tRUMzEqQ}D6VUr`?Vw&rF*uiHY#+7z;BOi(pVkm~XR
z$>2R{2Y>yCj#d4L_9W@^En&5F$+IfmA4qi!a!L;eRhFSsqHf*A7XC@@z!{iI8ZjUP
z7Y`H4Qjhjv<{P>Hc9?1e>Xamoh0P8PA#;=7oRK5{LiIu=b7Kj#Qqy4b_lu)PCiY3D
zQ9XP#=zA)JXFILpjkwXZG-Q59?;wrjX2UGl3ov;$KhG~LHlR`giiz#~D_a<6Ie2WK
zh6g8(PfQHB5f(|9Q2#(jQUT{zvlIoLaW9G3hX&_QH+wMYAZ}qcv|6k1C4=#Xl>Ro@
zgqC8GeV|B5A1J|0nV}?2KgVs*h>W{Xu&~-arFye1!(t{HIU9r<P~_Iw1Ct+G$>Saw
z9^%vmS63G4a8NBEF-VVqI>oV&iV9ZoJX1q3hBMMx)fL(SnpH@dt8%zi47HIk)qL8~
zU_U+3hOP?GrhhNjjJynSreU?#hSdKqCZ@oMA8NBjh$`nTU_@7l(q0a9{I=xO1T&_#
zmE4(-T)5=Yqp1fl#*mNT46Srvjtsar6Z+SNJJRQrV9uS{LK~aaRm9vH|3o*BQSYWs
zMKaQ7(#vG#N$G-RH5yNAgTnr4R3)#j5ndm*|3EZqjSE|#I*YdN1S^&M4aWo3?|qQ1
zQ(bLuA5>|C{+tP{+^raIo~oW+BcC_)G}E#V6b83-9VYn<)bC(Nbl7d235ykr`m;uN
zK^WTYY`o=6Uogf&ZN9vW|EDINhV42(tn6}o6rVQDyH8Cmpe&4%e_cHwV(d@&yT4B8
zd;X364fU>82D_W-+BI^K*<z1o35opysDv2{JhX4~qqXIFVk9=v19p3wR)Oy(UHL1U
z9NpYdKkA<*_aM>I&#RUShV+h&U`=!x3&U7x6&fMyYZHrg=*A--aA$JLGQ8TK)Ij|r
zK!GU*!_K``6{i;zjAQLnO0CbN-=2Lx4prB2bNo9+%m<J=1bW)Gv%rl*Eq<y*;Uiw#
zPXPO?_xzhLK77FiijS8Jedia2i=_A7IsG1+bsVV+wLAQKuk8(RgD@Y-AMt)1)Zljj
zYK=cM+rq@%WE<={Rkd^N41JvYZVhT#J;?FC`dd&JT!Us{*qBw^{bcXdM_;G?@~ISX
z-T`GzeS)_6UlzcLrC{EFnua*Y&k7`j4KBu@iM8?0CRA#<-ty94*S24Q;60#%8MFr(
zhgB6?D2pH0!w(EO(KW278E)yuAHDVMj1GfoLWJWk!AfsEUv3-0m|*6APJTWhVH1Dy
z*mdkue~CcP9!U2tF++CukVBpexacC3+Wx;c<$(^fICRa!=uWen2*-wCtJ3y6cK7?0
z`=tx|r5<mw((}I$XYt>J|GD@A1qTPMjY)W8Xj=)9vHAbS{_`sTIRrAYz6>Iy47H?&
z8I@o@{nLzHO1^TtzIWQ!uR-LGl^!Jg^Z2xy;0pyPQIp26g%OopYzM5W<hUT>BQrq8
z<Sgov*`!O~fI|JP6z|eG$%D&%nvlPknbY6-N(i{mak!d|D4}x9q2jEUq4_(%QJnz;
z5js9&NGc4H!1n1c(G9Uf?9b+H+6EIFn?9W_Y{feCk6xS=LURlm+oK`|70S3Lo|UC8
z3l!Q*{5;JemDF<hKjmR*e>s>9%|?h#Og>oRm)T}CpBR{&*Kq5|1wH%&4$J%mR+O7t
zOYp=y)?_Er25m4ma#XFRH0jh5(^d8ozM6&Me%03Gm;QfB_T>dF`H(&(tsk_|cHl=k
zwcGoFI374jN2iL%l8eW}-%dOtCxR$bbLt82K5bf3K9savdoIi*>M!v7l&&$&=;fN7
zN_qS=K|P5%Q2x<6Fz4%PZt-oA@P-&4?jGj>a|?eNsYQ0x<>`z;qw8+e?-E|sYhtcQ
z&Ke$z9dD}o9{J%8<Pne8KWO2ig3iisw%IzI3dnq^bzCw_C))@0kAJuku*>Lh_1lug
zJkMSmaC8whynuws%x36)xk<F-9MEcR?Q;1S|45uUXsauzkz$%8$v`c3q_)wR55J>D
zuFhrO^WgFsPe13MSj*WDqZ91)et%VMlC%^?{qZVPRQxu?tQgDba*b{~DAaVfPd8k!
z4O(0d30$?}1p}*bW;Iy3Xrm>xt<nxxOCK+my{>u|mJL#t>tA9;&6aRod|s&1^tGe0
z>LlqySN81fbREB}!}a*Q`a{Z_f9mlLGj#fj8@S3hFPQv_ttM&?SuXobW3}XsxW4Fz
zvPNXKOvLa+dK&86>dqT3`=kL(?=U&fcAQ}D9wt&q($0#{KiV2lEA3DLb0uW7&9ryo
zOn3#ITo?%;#Vh4W6IlKUb-FUDkneQpGfMkO+$1`}$7Z~<^RBfOP@Z8w0{H_<4Ico_
z$cQzzW}#aoqlNaF;$w2qef=M!C|JeeRJk>Qp?0XQS7yzt+DK>BWfdK5AwUFZ*6$S+
z!T*`4E)x=lf>kP8V>0Sm*i4Zk>QXQaT_AF=u%C<Q7!dXK&XdYc&b5^zD5FRa(qG2j
z?Y?KTbeRFm3Ahn_;I5f~or(VNyPxWewpXc>v&EryB&7zgnPBNJl7Eth%o#IR@6On{
zXmvK2oE#L|*c^p4T~Y0T-{AjPFyB%f>&-&Js)-9$e9i10+${X6cr-vnrG!To<<j-(
z8tM@@?UIxh<wD1gm#smG*!Witj6-3Mti$Bg9ex+slqu}Uol60OH%s6%RFCMu`JIsZ
z)qSb2%$(1VPL&255c71#aqpFdlSNet)j55XY4q-VZRP(^^^Reh1<ST>mu-Dz+qUiM
zvTfVwve{+Zwr$(Cx@?|W=iasVz5g?3{+LfjWJHd9#|-=a6j^U~4g2n7mpCLmoG6e}
zf!voRbBNZV{>-{8hub%!P(P7^vNF40Tj6g#?~iQ)k~jX|gHhNYU=bPD#DYUMiGoHN
z!l?^+%~Fl=wwt5_9&nlwD-~r0QC1vas$Fgv<GN{hE2Dc95!pIrVy^W$BXw4JaPU)2
z!{Z-9`XVsz_A8E6v1@>$ahRO$0HcM3S|S<feY)P?NG;=N%nDV*S`#d}_mFaV{oNS$
z9W0Q8+<Oje?fasSK<d)hCKZgyY|ftxexMr@q`c_?a>H6-%IY^ThFITE`bL=&R&;Ci
z$g)7q0~%?}oDNq6sI=nL*CkJHj%H@tZ&z3(8UE$5CBy`Hl-o0VH)tg@r1IK6YgI*`
ziVif}la`@m(So#Txu|B4VZ+Nm*)kAsI5Pzc`dgi}%eLzw>|WgaWcO?27|S&(DGj^R
zgQ7e8|K!LxW;DT?X<!+r8^H2EZ5iRTdEb8yF4`Zg=y3K!ReJJ$l^a^Ahi4Hlh%qU>
z1gK}M{qGGW`DjY);5$Ac4?R?mOCrsCHUACR|GBwpfDE%{^m?RvV3ZqNgFs@fE=pXI
zb81lB9bwst=t)x(F5g13^7{Y`J0Lj3qUn9>Otm^#d4E0{8fR~gzy|f~83+&<rmbvs
zYM6OH9%+-{tj1K4Pwz|i!VHeA{H2vQjA9EUuO}$QtJdiTr%ky^<?F+KJp_^8{}zgn
zNTc0OXT*KN(rER3N6Pey89wBzZavEgsTK|~5U6=F-=7Cu*?8`LVjA7tEV~pd`*kJU
zy1dZpb$8DHc*2H=+1@;ugL&{pb`}10ML#BZ(jZ`wx%`1=TNiV(m@>v#Z*uy;cBI|c
zFa2QzY>&h4LE}2xZh_v-(`fVg079qvtDX45O;DnIoqjnFOdDLC!-YwV4WZ!^t1=#X
zh|s07T*$h!v=o82!)h?j+<qqC&$>AZY>1JKO0-57@}M7%LW<aiuyHwG`>VPx!iK&Q
zarS^(SO8IsP6vuL{K&1Q^^g6QL|rTKm+jVKQz`IiwA|s&B+KGfn|i~-uZ2bwD0sC~
z!l2REPU!W5>~j_%=WhoGj~Q26c~N~z;EO<$1BOtz1f-~YF?-(2g;)*C$TruVMN8OR
zD*<KxbX)KJj48H!@iXc{BoNTym}In?KpkYwqtWgWpL$=vzCCfZQYk}|wwQ!J>U<XD
z4lOixGi><tccKt>XZbPN{WigKMnT8HJWs!;wON+-a&tqrrUQc2p|v%<L+vq!_G}Fq
zgR#L_aNi7;-ZUl!X~brO7hY~xIY+r+d!rlz4eV&d9ZYGu^?IQEU*S`ky%-z=4(5U^
z*r%Ez9BdBAK9C);t@yn9BPu8<m|R6*(6{U7&5o4wvb}j_M|=1^zr0}!dmX($g+3lC
z-srFq^a32VWF3sIHLchD(2I~U<uFI*tW?bok1+B@L_;*5+8FR(O(x5<=!8{QnJ=|k
zpdIeW)EjMC#iGwb1KIQS&6dH6?}$((aAsSY2($HO$}l8-P@J5fYX`dR`1`XPd^zyR
zhv=<B^8IqATbmvgvntr#oCUwLqaBVeNLO8{pmJgpdy5^koX^(4D||WpQz}91`OVpy
zY0?5(%F5K+pEvcIHy1fAgvI|8$^R45d%rzEMGirEM|}Up^KNRON`dU>i{NNC0j-qw
zUfc}Bb{?%oR1Y6XxO<}LUB7_IhIWO{$-Qzs9JunBFy^qE$p`*k^tkLw2?|%*31Q+5
zOuj#^Wke;u?8B_wc12gE<GtCtgrd<UEn-R@3Z=szNH&t+WqkyRI=_UbB_sFty+CEV
z>bT){KP%Wtbz>N<;A|W!8kLP47&_=kBjQYoSfB)K_3%1tkhYU><F(l82<vA8UDCql
z=B%M^Z&zkcNmaHOv6TMuMya9^8IAFc(SmL|>5Us(*gMM)p6T8k-+Y!Doy1|hK3G#;
zw8>)+LS^Pp3rdDBrWsU=cp=BIU*^XXE1uHNtw0DZ{<o*H9K^YXzt`u4-etvy2vY)U
zEJ$KwEXq5aKJ^}#s`|UIA-jW_!7iAxfxIA?b<02lmSuS+l+{tLZ@i9vJA?I?t7m27
z6j8<uJzjrz1@yMGJOGvaz}1m!{PYX?c@v@Cb82s?1L8r0fiBgyt1=-tsQ08Y!90ks
zvjr(DT_6%{^TRH?zxXrd_HYpa@{JYWB0~GY;VO`D>R(=&P7g-#WIZEaDkoBKULAv*
z9T2w>rLHb#uumwx(M-Du#n^<Q!3b08V`dng9M>%6(?Iw2hl6%85aorFjK&-8<&~Z(
zQhBcD)z^&6oSuJl5=90q?f}q&vWlSn;pJLoWvc&Q{%fJIEpwTB=RY%P*t6E#xJste
zf@MVDdCt5y04|{Iap$0}FtLT{EU3w7&zW*7YhWc}&JXx30hY0j*}HnE;#KHt-);a(
zrw*d)GdwDp?CxA0+S8^?{|gkc#*{n;(ZG*ARws2mK7L<<6Lcr13r_kRE;ua@qnC%B
zG2aJfjy3$?!%)MnL1OSmi_mQQj(er?P<L!McT6EkawqAm(s4t&>Kux<{S7$lm<zqq
zVEQ^Ox9cBFYKMraHv{7gNE{yVlzjLshu1HI!<2~GZX-7qZdd3O;R5x!-x$KhQzAbk
z2_v(*dWVI~)Aip$86zzNCApx<v|I%xf{?&}C(2atVTU3!Zurmio(C_H%Il%4nN3K+
zC>+lR4G~@;^AXY=Q0!!Kkb^_@{)yHZILQFcpd2(IGd0}{z+gCgYxzw(9z`|oU`Vd+
zf)#q3-&cQ2gh2~Nj6xF+!}WWp@_QTYZ=9M6ObkS)*x%y?)f}7YZ_lx?SZ$$Pb^S>_
zg+v|IVYuzuyU}^Y?N3l7V5=PricK~swLwR>c@Q*|`vld^p9A;tS|z+W2D0XSloF|P
zvI@!{q<G2hDn(C`f{9Pc^OhxL4It<w-bbd8JXKowyh%uCC<sIm!8w(E_aK-*p<<zI
zay?za-oNm=K<W!JO3Y6TNJ^6c2PqD>Ufu?B_6P)rsajlVJW8kW&I{rPD-^_^5t9C-
z>6Bm&7#x*p^6)S~&(8WrA`fv1RdYjF#pjHrP`$G;|DQAB_Xec-_=drFM}dC&3l4@Y
zPv}n&3rovipS%F^<}?C@;oTP3Z|9S|^Li00__Y$<(L2Bj#;*SQVo?k`wcZ?>w1^*?
zZJMX?oOfy-7nxfk$Y8S%ADH<Iz|A_6U!&Gvl}d+>kS)B^2RCf0T4}<J1+#-6&^o~v
zw&Xfg3w2FM&xW{Bv!o=WP#EmGZD;T$eh~lo03YD=u)EfV5<rhDR>HV&a|??+nXA=`
zk_bgEj>2Mu_Ci<CQBRPIH@<<;kMr!9ND4;aFo9{@;8-mtEsuV_&#O3vIU>p_ZyBzd
z?ZfbI9Ll;q8MLI>hhcFedZ`3^&XW&{m^+nlcN-*ey;&dyoo>|iG-i5g)1Y5nstd@j
zudN20bDc1VJV<}Kq+wPneNZUqxB1F7ZRKS6kT>$3WmA4Rcw(kX#ffhNR(A3a;m8|z
z$ds<OnI+JKnBKe10}3b6q4k~xi?+ipfMp#cg(N1GtDGJt&qCph3s<-D)aU;6nWO45
z#4W_DE}{`2ypuDwIM>$t)1sL+YZ?eR3q<k>2VArp3}y^+Q2pYz8(MJDfNeoyX6*1&
z;^}Y#c-gK5a@Qyek3e48bj|ih3TZ1NDh@8YA7r`Zj>_~u!T6H6Dn5MJa~3zo?_JK6
z+(ZTB?0#lsR0MaU@C)NpgFV@;hR?E4F?^dUp}3oX<6Y^+EUin?;9UM`V@4;U5(HvC
zNml=ghp_d%MOxNv4;LD+TJ`WGPM<INFk#U_7Ab1&{v`Hx6O9U{HYJKMalWt;%3?wC
zkgqFMS6Ql@_ZljG7I1g7ipb4T+Y8L`o?SFzO84EyzDuQk@jjNMS$BY)hB9OLyze40
z!!hR>7W#%!Z@z$h06|d9n29L5Y{BG0(E0Zmf9Z$qTiI=r6%T_zPk(yJ7b=76)Y)?*
zEn{0H_?%nhj+3siLs}kYLQzF!lhOP~SqlSf-4(zm1|p_8CeiH$nKRtk8)8$TF|MtX
zt&x`H-^5I|XZo$7&Zvt4gHbgwQK+35`8aDkuOM;1fBRYz>F`|y-<-1r87U^mWA_Q#
zoyfQ22tSF2&7*H(6!CzOQfK!_wkpds5Wqg1d*F*N@&o`m=_j!+4tX0zk)_%AqQw!4
z+0AMiQF&G}-G)Ii(H!+X3%q4s?$4XTUVV5Zh{BYt-;ux$9g!Vc9#n4SjT)(zwd;Mj
zvT0AJMsj&3A@glU`S(86&%S_5@K+L(uriYUq-uE6)|BI6Kup^URkvnX%f%4H3t*1A
zutb%YuU&_w`%`P5v792^mxot;-<Efrqkv(?i+rkCq=jj|Ii{v+_Du``vtv~LDg0)M
zvfhEQX?of-;H?8ov#@CO9Ya{apb>t4mQp9ImvNCr#%t*&)0V}o!jB2$7fG3hZ*6mf
zc^LK?Ur{r`Q@HMrNRGc<i(9k!4Kq;P^Ig5<1_kHyzkvh-39+$TC<~ES$!QdgZ&fT<
zU)vjcTjw}@|EA={cN+PnAC$T06P@@Sp|2{msIpo+1bN9(DJo$end(?Vb6n$ArQ9Fm
zgxzo~<O0BChe>mHr@v*w<e6r+JAgUiKprQk-~U!(7w(Ybi?GlLnJFIz5jmS*GV-hO
zdOZvD>x7G9%o?kJJt-@A#dV(66yeS|Ywn@fIuX#GZ#C1+AlVx9JI6cvuo8IhhRP%_
zOcz1#QWxaj%!|=yfO3g(BGrNDW(*FLg0{B3rL>%4Zi!iqVX_0JQ!I91`u%;*=E1%&
z^BwGsR&$^sn$^}$C#pysuV6s`E_Y6hqvOuWn)WKnNBnqy?|7!}>XI2v+zF=vkVShK
z=8X#3Q!2B%V3pt1!fxC_|F?G~<_Sfxg;qxcEAsZ@*yZJT)`pc$5fl8!OjA~xsP=4v
zl;a^6K>cLx?~;ZbU$U}4ut>hz=?$$)r!o1`=I+Sl$-YZ=T=;6Vkie+UWJd^03}7xb
z3%<>H43*U6m?jw{)S)bXmgH}|SWW`{;@AYA=}k{F%5(RGrdGWa;Nvv?tjZk0ASNFT
zO%7m<({MMqj`v__VI}Wa5o?i@{;~3uYxVJlV6*;&8$t`Md&-Up9!TFGzJgnIxV^U8
z3AR#1ardR5r&d8=(^aH|FtD&FJ2#kM9Y;*A`IE~|KNcyFpAT!u+0?^AZ(;hIsuMYu
zMG75e$4iexw@Y?RnMTU8miC{uI|_Yyb6tymGkO<rCYQSwSaVD;m~kiko@UTJOe++E
z;EVR!`2{<hK{A%9-&~=*lN|!}p&0K_*`UDTVMedhHo5y>#pB^4YKLL|$~y)Z-;{gN
zNO>hg`xQ0{PxR<q97?Xa&VWX*LZ_oRCEz@LS?d=?wM(7h3#ubulcJ%?7^Tu~x$Z{I
zbYv2UH$u+DK#9c=tw=&<rgEogHC%58>HBz8v@nPi5QoPHdPoUc;fE$^z@`;Yu`I4X
z$4YXTw*&l&M6a}{9F;+FqUJv;YpDqVYW(a^Pt<W_(wcBvdd>9NrIpaus5RxgI}eN_
z1qz}#rD~A)V)S>I+lRy7d~V4=^zrujOu_1gP|uXt`(1cqt8H)6)&{*-*Q|AA;LMYW
z%=#V08pOgH;BC!7QM8Y}SZn9P_ClzYa7VzlQU)y4lJgT1>tpMjZHf0zb>k{J5O1hr
zBt?tG+)u(NK8*DcF#_FHyoa7#r%PMt2=psS5lI^1&N&)Ejiu~!^O}E?Y?K%ruUI64
z0#{bXUuhyA8O)8vMRYfV;ZBHZk91@Q72VyJK>Ol@vJWEf?S-xW+k$=h{sm<6WmNfn
zEFOhF53{wS7qxdyqW>LXGP^P_Am~d5T61)F$MPdR$rYK1Pb?KVI{%$8(<Cd7v926a
z#(~}RR6p;G(}5l_xM^)|L_<Iz-cg3FAm+AVEt>4fU0u-cH!RjpQK(o01ww_}_P|q^
z%(1JX+!cn+G!fp=1%GAtBx)IhJRDs~h{1uf!T^UJMl&iSX4{=kQ1Oad6BeN+SWRJK
z+C%lF;NVQ`2q`F9hPuqPw~unYdl;CaK3{^peT@zs3lIKcGY!{H6C0{s-usU@wlxja
z)6{g9f>r^yCQ4gGomo1Su=eg{SPZ<Jd6Xj8-K9=c^%UhJ`34qDXg}67Jq%Rc-fc`u
zT2UuN0DK}1t|sj-6tb}0-rN9a7~r`?I-3AA0D4n!x2j(>CEiRqTkS<_7tn8)C;w6+
z=|)5>Ol2{avx(L6s|@<;{be^z4;A*Z-Jq}X_o$CMGCu8E*op|Ds&-x2j5es>9jho^
zpv%ALFwkI*_Nmp!>?lzb?t|$WeuFdlkqQoC<KxtOIln`QodJK|lZ&?do@Eu$-HQ!c
z>Y879b6__(>j*2vD1^4`;QyiqTahrp;T)(9$HmECja2_)d;-hscJ>A#;qSrqpyG7n
zLAJQs7+4k$>?(2o!0!WJw3$tlF(rGh1QM+v%cuEwi0p!hpR4dBd_8b?=2tWt7z06J
z>kSqiIruIX00=WU0up@TIVI$Y4?unZ#Jq&WiX1_}FSnc#Lpgj1k)H=IT{zN{Z}zbB
zAG+~hiqM2)(K>S>jKK0j1+U;Ykj5^aPtmgp-y#o7Or#SSh4x62oe~0Sqr{Y*kmY?U
zpHVe@(l7Z(;xC4@pBD)*LD5UJueYS#4_kHam)y0}(S6xD2*kf1lK{x!faB<lzO7@*
zMs+xv?%u>Q7U{gacW7?^))29x{^5Ew@R)y*f0Ukg8TD}{QRfsK=MfjT440nHF*>6L
z^;Uc+j^Qn08FqGVNMsB9D--8dV?l(@@|D<IWHoMN2dT_@i!`KJkaa|Z7^G{#GB+X%
z8Fvg`7K3k~USQ8Wr%zYa)l9OP80vz;vsfW|;4Y`4e<O++>7;>x38OULoCd2-e=_DK
z@fyzHid?hv=jMDd^LRV~L-R{rIp13>+i}>xdM$Zae$57i$KdMXd_tMQL^B*7u-Y4j
zWL#j_m9sJ1EQ1-nxdbxJV{rR*1rHutixgoL)REwAuwMzU7*Nm35Tp3-En+FNFfyMx
z+Zuq2HuBdmGN|}2-E{o-=3v(k6{t)JgN1!^$iUJocEfXa%LGiUdk&;y9~H1zP5S2Q
z5-Oie$+OWWjQR{fN>iP6U=45zt+v17L&B3ohwRiVRKyQ&rE?tfT@oR6UUQGJUXTQE
zTT)0(^93{~@fweNgT4>vdK(jZKiTy!<?^(;Si(pe9#;5N_)rzsGuH@+u*cAy8Y}_=
zcQV9UaLG0)ri2w8i5|KjcB=>lZ*Jv7jEJ!*4BL{yf7?|O68f3Pm@d=j{o5BX`EemY
zX;98aM%LWj+RVr!;KjU!YP7uO4`}i94W6S$C#K$Is|FM6EhGg4ts~?a-HLF@IQSd`
zA%OXg-i#h^HKbB{eApH6ou?3yQd76w#vdWH^M)Yd>4>s((+QnyBF4HfV|H=Ukt0MT
zBV`^fnt;eViF0Vh_d*qYTefFWCaNigC1aC29p2@qzlJ)!G>?fRzq1VcxE(Pp4vBJ?
zm}X49V)5!3c#IQjb+rbH&j-ye*b!tl;aJwY6aS0XHJiD}&BudC_y}4DJ+AG4b-Lb`
z>)}QGki!R!Y?=ubP1_%O4IfJL#z@C6D}-?;7tfRe+>e6Vh!s#QmVU!6D5u$%^z{;<
z*CQ$mTtgU)PMR(i8yHYXk-6T5wmre$>o}r{n6^$#zRMMpeHXk|N>^?(^nm+4pql7U
zyn`!x&=h&Imw<N4MTbkLs;S;{MugYiIIxT9G)IyF!o!Z&M&j1~*3M&$HK@sC4sCg)
zDckRhDXW+yi{y&MP470i?+{sA_75OYb>`>ehw)U%J^xP-gVr;45ZaDb;tXE}*$3^w
z{AzTv*rr^JTHG=M@grBnbXZzDg0gSJ<7iZl@HYmQof6Ibj|U3#tfxK#O-AQ0`MV?u
znFryJVaQ4CH?aEC*knX^IIn`jxZm$pLLnp?fvL$$?p{lw%HM?^(ZG?~;@In+Obp0P
z>nR?s^&=1k0A-70tUoC7OAok5Q0{L$nWj~OFZ&y=v&$-mM^|2*0+b635h^EWk*ov-
zPrMIzg2r=xK39nv>Ia<Y$0YA&%{0R1aX`+cQ^~44{75-n1gIR%E`Rc}=_<LNM($#q
z!3=vij_|hgq-BkTg2}qp(UKEM4X$7Ht?~W^&w=>V9n;hVR#80Qb$>oFsnR_Szg<Vi
zS4e(05%Ev1xnk92-!r`aWj)m?4O9)`gq>{*Qe1H}INgM1=5zUhlTgido7EkBIC<ZN
zPy)kW_fH!IRK$T-zPab87+EcGo`1w)<-W$b)s?mo_B*l6m1p5zUFpXDomp&MYr6Ko
zwUZB-mlqb@g+Uf}xY|c+%$@~N>i&RFcFuurcLEOe{wRmvvYKacW~S>K8d%ZIkeV--
zv}&|4tby)FXTk2*M@l&&Vv`4!w!vg2w8X5g=O;_)t8Zs|Q&PR25tOM;^yk!EV)<;G
zob^QhTQJm%FPqd^+RYGCId469Jibal`jHe}hf2C8K@ry%?mv=5#=W<JeB3BcmpD+T
z*~7pW2}sQ@Ui1&lF0}L#1X1e!k}1Ng5!Y6k5U{5mr=1j)D}%4e5&Uhb5&cx8c-&SL
z9D3FeW$f%zKKSRa9{NZ_s;$Z7xk`Ln`0DIk!kB<5<$^ENoe`QT%O=@jVz0umTb|gD
zOvH)9VdBX9y23^;ybhkR+TVn(PT5FiKvEPXTM|B>lkPIyWT@!AgTI#}N1aLie2M8^
z9B_enaIQXo5ut91X!NcLIKeOx{+$oAfu){I7z#L94y+3Rz)+4`8p_b5p{oh!i%;px
zMxi)Hgm*NT7mK?@9fa30zGDI_!g+!3q8KsTaGu1SA0((d;%K2589*x?6yxmx4>$K`
z7Nwtprs~Dkd3G-2jvV3C+~U;qw^riF_s0i0WU4DTbwYohJ7{AV8Q_#qyM!~MQJ^8D
z$VW&)kfFN64SW0uGN8Q-w9$q8ZmEcd!Er_`)#UTO9|7dZMxQJC)<i4S%(@46rS62o
z%D~Hg^XX1PI#H})jQG~9QKC2_j5s6kI3oeJzqfSx$vfc>6b&<#5q{JqOif`<LeUP|
z(+-&fKWiWGyrG>WE@q>QbC4f02@2{&EjbB&0;D-(f;W7EeTFgxXqqZ|-XBa*hbzu5
zj)V9)uPqgdoFCtGj89bc+?K+mqBM=pKUx%ccv7<=ifWTin1-(ytNGd@ZRvJ){~hV|
zQ_F*6<AQbTb^fRvbm9jDmf6qx%O)oU@N~F?9+e1`71L_rm1V`eUxvck{lNLN&i}-c
zPX>8ClO*W+h-_`fzH++~xNmw+ep{k!OV9?D74cQu>O$^doV!%sg~RkP7Z^D_4ua6U
z>m#E-7a57D;xIK;>6?)bJ-@>jRK1&}a+xu9D&84L9$ln;V&5DV(SPKK=dRQmyg2%p
zohop4o30_F`O}xMZY~y+9{atcUH!NY)BAEn1tf-YytAO|gtGVH_HZ{Irx4yg;07_^
zRm}vDo=R8`!p1Gtx)d(yU`vtV+`>!`?9MUkFDXG4BoN4CH-T~2v>5^U^x$%JLD6EH
z9qw#|zax|e*En00x*BmBtkmua(0hM9*?SvoU`eGR1s7Vq7~|`BF3a^E%3~LBkb<QJ
z4@HsMD_=`WhxY1~C0DwV^v`%9zYU=cHg{6Mw9sAkjWSj+`aM1aQmTW(!xPg^p9lw*
zkh#blWwaWDpv5Q)Ni}Y|JdsQAiyUn$weLB|YpZ3GlQE&&Vw!Nc;40oE4UDuSa(cGH
zJIaWnnixak8+rM3mvmXdjmJr7()l46kHuz_tO#h?@`hQzw)lfA@+@f=7})~TvdNHx
z`Xxv$H3^I{>ui_)Y8c|AI`KlMRP!1h)&i?+)VC(&zJ4B%6>$fXdOqNXB{z#fLbb*!
zo!|+K{}PtJ-WC`J;c+ki15PZo@E4g<g^DxoGLKZ|W^55%jinE4AgUS_85}@XOhwf}
zIL8`TA?@Y-U@yd05StU+Eh1G0OZLV^QtVxrghHSs7&1aKpLl0^UcnO<5<_`8=@R!h
zSwfMx<aiy_gfm=Xk^suUfdGcu>NH+3?B$L$NSruS(~A&0+T<So#(V{!nFiH*bP>7;
z45xmcSgvSKHE~$$TKmh~QM%N%E9nGv@u8hem0^KOz#ADxRs|gLhA$ZCWIp3d9Y(y*
z?!vk9g|yO=J*}J4dc#!&K~0GSqNn$f{5O>l-L$lrqADTF(Qs|CC;=%MR*|Myt<`mS
z_H;WAhkvW%R4l0fvzb+Y3N2n)TT$FadVj(dLmJCBra~%W5H72!<#RR#coO;&HLa7Q
zVcgAp4SRl%52f6<BP=<5<{%6h^H7-c-g-0V+ro;hZf1y+*$goO3Dnb6MnOsnJ1bb?
zANpAyiAagIY);~X$JRe{D#5lAoN1Kh@Al61!nU5!^Yx3b#*b4r63PI56l}F%(Ip6I
z2G!{)Mr(Wi)bpf+p>U;EbUd+uf?_>KVl`N-ZMdPnl$(t$z>;k`4b5`p2EoJGZdB*s
zFC1<Uu(C8xd&lcA!GfJZ#F=J^)_0iWsyBbE7d4jZ^URM~uXFE7EN+0OAvh6?9g0Rl
zBL|Y&zNq<7FSc+3P*N@J1NFh)Ux8;DY?M>wK_$oyD^w;Yfdp9I7h7eGrd4{=ERY;y
z+=pg%Z`P1CJzc~=>an-ei(EY;B>J(~zGxQV2O~d8R;Zph^vsSlvda|{0$EbHf<NiF
zf#jT~r$!~hO8M=21czm54(`7NQdqQ1>B>)2W{)WR4@iZ^o>#?G=j54*3SJ5p{wh#3
z<+=I?1Y&`<s~#~6gz|-IvYAg_$_vQ^_KFmeY|}5y=M@=AJ5EaeKE<fbQ*fl_!-yd}
z)<*R}E1wGfqS20|7Ep4UmUoOmO8OjpaqI-xRLU6iGXBOkLNKKHk>dpc-VqP7U5Baq
zYdk}EYDyW<QxcqaMWd*X-F=7d5f3WL&;T$e%B)DTg>$WG22s9Go+<#C1!-S7mMdzR
zxfR_OXlZ{DQ%b|V94mM`ZaUlYNQ@ZW2vkEHWpg0;jB@}6F&#0`+)(nV?I*MmoFR^2
zv^pcBYe7~A*D&Frx=fUgrGYm&VFo5WjlMh)S?gOFqv|dhqbpn89~IiD8svjxbW?^T
zm4Q|gpp<<Qb32>yKX!tRM#QFALpUS1>8F-flj?sX#*#jPhcW)9+Kf+^{@_k<VO1!~
z{o+Va4lc?<a7H}Q3T0h@DZ+1UfQ+8hV)*wi+c+8BOBslVYlbWSIGC!^WFMgzZJYyA
z2+|H%3&X}BEe&v_qm0h=wg1>pvywJ3BqyH`QX~QSI%0D$AEbKXIB6@kc4x)Ga1%SN
z{zjxg1JAX@p*Am?7B*sbQm@UHdc5^y6$EvIQA$-FNqwZHBbAu-zm;!a`dRK))NnHk
zVD*#E$z`9Cw)#B;Y3QBgP6yn2EL-n<sE;!3by&$E4<6AzL^DyY7MaNp?d9Avr#SPl
zesqws#GJ4s!@5ljazw)hgRN$0!~K~GL8%WSgGuW78{bCP+`9#(>sK>R58GZ6kdg$A
zvtHK(1oItXEb%d64@9|t(9lp}aDmPs=Y<hS7vtiBe#UKm=Bzc!D&4B|Tz~p`eM-7s
zPj9AiI-N|n&G=;BE1!*cKuXH}QQZ3jepz$$sou*G2bg%oQHcFi`8ij}*t0b#$~#jt
z%8i&jsUgHbEiVlRsbRXgoB=cYj%$y<ERl>51!JDikq%{CYjC$afN!?@=b8yhlw{VY
z1wvponu~)dli6;yO?1-6d+(g(2{sIy?;V7Rvwy}V${WepQXw<Jq=}3skJ~zoPL`cp
zQW2k+Z+6qFW!c1Gzay{MZmCFHWo*3Vx=`z_rl$~0UW4Nei4l-?pchZPSL}9lGt<r|
zGhO*H2dv&<j>-rLN%6^4*~}FhLAa+cuqI}n%JnLd6dljDnn9H$SIy0B!PXrj_`>E-
z6+?$)r@@7H1HqLb9~Kvvq7Xt|%1M--3Eajx^Wjrk9+beUKEa=^H-XgWR}&IY<dA3t
zaNPBI-+>xwsj--EVDX8GLf-VmiVM=`6~2eD(_YhRauWXA<K=;d%P*DuDQZl5Xue4l
zQ+vA%K$>d9Q#a3#|Gm2-r9sUU0}nsByAL3Q52_DyG>xW`j8f1T%DZ$Aa**ZC$DyTM
zU^AVS)V*y|alnpsx5)}OS}XuBlZirhcm@KEh4Y9b+i50^k9Fr(8x4k!MR{Bgh^EkW
z7a=gC=4)#s#DwI@f~4|t`tv^Hm4PIpfL@i)2L^V*kzWH})8JxTfD1c2vp^RO*Rn1M
zn|hOw<6|Khy^8kq>-B+%nSaVR5fRlP&()M&e%?cembI77#t6(yNz9MG2JiOB;YaYh
z5vgQU+{((-)4ddbZ-}<iJ+gjKvV6Z>@}#p2bc*8VW+y>0<zx*FisCErmK+xfPaZ%6
zQTS<d5E2l7J8gV9SS__MIF!aRE}Z1+!61<6ZTY9+<Co9=TzN7PJY*S0Oa3L;pfuGM
zbXXIq^Yr>2d!BE$7(z;JEoUWpK<I<$vE+q<l5IU-g~<!VZa2C;M>{>XiGqmg)_Wbp
zgO_T8%PxwZXeBC)gQ2ENs)Ei54s(KHq%UAUkG?1`nTn^UHPBFWvNFIhm?b#-Y<L`O
z3$xW>ZN|7U^Lcz)VkC1}M!|5kn#$kKe)!8%AoY6$s<!*hVp)}5Ki3yYZKE3%o9^0?
zXV$ueZc?}D>w2)2jVAb{r}<4W>Y`Usr9rn>Mnk+y;FGlu$yP`Q07IX#URPQ1rSev>
zt2NGAcTRp^{@GOh&ktjw{mns^=AvIW`u6(4$RCixKjl}|15JlgRFQXtF#c`q_c<~W
z#PvoktL+kl_RTr$*WL4l_p}y;@V@nIxaO|xgzZ0JPg5ZlA+^(R)K&6v_5Jnp-r%09
z8^7Xe$Smu?Zt7rp8GVP@J15zxoJuQB1X>85wCN^@e+Iw2QT<kQmp9<up(=O;H3Y(i
zpk^4nW_%9RdQ}4L>Q#+lnFB037I-*d$6Mu)l}$l@91gZLtkl6RXeB2g`xiFf7o>;R
zvHo1PmNHdk{R;orQ4-__zc1T5h_YbsPz4gJSV>+SP#eE*6E?l$7x$sOb*7F!jg**#
zNOOp2)p*e-hWA`>MFq%KL4W)VDVNP0QLd;!<R5e(T3}7kM#w~B0!F7^NUC2)n;ZcP
z&jn|Hp0Ckq{lDFAm+@r(cbGZS?EjtD>lgG#V7G!c?pyqyh1>G~|6Bjhpj~`10qNau
zgFj7ER{!s;|5?o(>4h69Hx*4)l|^Dqxa0T-=)4RYg*gw=Ooq&p`}Mq|1W*@mt5W3!
z&vPqDW>A2nq8)>kDdlz-s5A;|7B10$YJ>u;wozt7fDvb5c9OiAp0a$<PG)n#e{!=8
z7m)wp5KTXKev=G012~Kbxu}(pQ2z=5)y)%)GK~XNXh^&jbL!OL-`N5giW|6z0?OjQ
zLV;jmN#ZXA<WrDSp}`6|gC)!e+XQfd2+jwX)FY|b17HnAb76}j!J{xk#qLCchD{W)
zcj?i~cOd7JaN8dRQ|SIt#*+$r!zP*fp7<u>g}>F&JyKG)A$DC{{0F@THe{H(#zfS$
zqu^}5A2m1#ddQ5M1y3pvZ0#x}ke$h-3B4~~<jD#S?VB<|sWWCC7qP1f4nw(EuOGGc
zRUlvtJ~|2j+U}CqbGEsRs)ZDD|CY-wBcL}t*+2sEd=6qyq7~{-MT~?h2%XXli|-7b
z2^3DtED#`;IilY$-}?Xj!H*R}0Ta?YWGD~G$K2_Vz!D?X98-yQ*`%%>cO9ZT={8N>
zjDO#RGcS=4Wj5@0&=<p5x62P_fOk*7RtDlqzhBI`Uq;?)yNsUZYt=CAF4w^9Fh+$+
ztn?W?61&;~gRQ;ai{BFzAXC3|`f!g`CdQMfshR7bxijA3`|l7;d)qj|+e^S|9qPT7
z*e*7=M>|2#Z8AffW0*+l|IG=%8%~WQ*uUDEXT3LKbpfo%8^wUfl1CTg6+;(sOwU*>
zC{;V>QWaz75Ysq>#1UD(G9m&rma!BOHaQ-loq_*Yvj$=e44DD8F|DKMdp`V`)Dr^~
zB@K=iPzu(SDn3t?{5H#q(9O9m!1%IPA;-qn;?6J$m~XK7^qPUx7J_(Mn$WB|8J*ZV
z9jo<^Vj32$^y4$~gs+hp)V4;TzDw6#-<({Mb!vStxF;RQFDu(bYtuO3x0<U*-u_}x
z<Y}xF3Sr1{%l6M74Ny|1$Lub8Wol3;?6|=$+u*<zWGWj|p#j#rD19N_`WJ_XUcC2Y
zGNWpT7FqH0b9_ilFUJO+r9FxGX#?kTz$Ls~p`@3+Ll{i&mx@?cimUVn)LgI&%qm6Q
z6;-oV40l&SFW75>J81Ad7Hxcu^yMql+8|db^E3(+bm#L_=)Idgft>48wWjPz76kD7
z=41W*tK%85n1k=W@VaXK2E1Or6ai=<O``w1xj*oQ1P+5*_}n2P-aG0Z%}wY~fBML9
zn|o4e=Gfef*~1^K4S7JEy9p+r_I}SN9D^cK->Sb?SxL+^faN8*i2gbmTv*reIm2~N
z947cl|86zh7QdKX+Cro}bUkiP25j9z7W+NIURP#^!`lwCutA5&LRnWGrt(^`9gX0B
z6$wjAKO-NuJj``F!L(|j>yNxVJCN}q1#}`$4ci+f%X%^U9OI1&JFmiYM#%@Jb;012
z9i;0Lgl08i4$4SlV?QF(8!1|5Lq78F=rV?4fLd!k@;zz83pKFW!~sZ2%9ny0#C=w^
zFqQt~gtIgKz5T!pZ_CE>d@zrR8d0lt?IVYzo)*)sLoHT~IbG|5+P}{1D?idB&Swa<
z2n{x+*;|-}GX;lE7~t6S!y1n&ZE|C&A>$j3j5RE$pl;1LA*SGfV-sSle-L7+HuLB9
zVke+_Aw>zS)zP$ljGi>Oz68a$W9C>_C(atfzq_a8P0|yX;2!HLL8y%KP^wS3B_fFH
zS<x+I!X-;+51whp4PEU=xt_!emBg13Joe=ugy0kIjFSmymrkzUuFXF-7XjU!8%2p$
z6w)^`iOx^ZHlEGb(asF@jt5I`HiyHttSJ^1B??17MYi1YVzn;-ZwDS~!-Rx_77}X4
zfh*n9ldy3@;XYrTNDT9Q0Q`R4<M4_btImEoFmUEOw%v-1uW1S%ioTGDso^~`|5P=q
zb6pTQ+~NvqQS*&Sec%CbJocT(k_#ibVooeu_b+%$zbM*mmo7M0<0vrayg-T8c27`1
zknTk(r2%3-Gg=7xieSj1@k5ptbPX@Zd23rrL*J$sg~W`UYnenqp}hayD?({!ZRB@_
z!e)re3_8s~t=`%4J6qa?UX=7({;8>T6y9Oq@fV!+m`r~rZTEYiEq=s{I5Uv1v)$6p
zbhh$`YQQj@yptW3&k4lGW02O0R&7N4PiFg1HoEb{1;TPRI64GVJAa}!PING>uifp4
zjaL{f6q?pG-!+G7OKpQP9Jr+*0bjm?GpPA1u8UGGXMMCii~D0fA5@akJ2iy~rv1v=
zR2!5n5}p_Y2^uoo_XnCB4=yupQ3~4RH{U+-K<XYd=JKS|{`3brGAW9-Xb=&}`Sax-
z6PCf&-o7zVw@Z9ZmjCIff{QM;v^YX5BY!mf&Y0+H8YEP#h^{{=7TZ%0%TRK7v^k4O
zH#{LwRxjl~{q+!?r>r(L@ikqxE<+01J|2n`v}k2PYzSP}S@aEPng+5vo5e2}VqP+1
zY9Uuemir)MM#3ao?{s&fiv-q5J#^$2)I!EFl2ntXG>oN-Q?R(d$Z&qg!DTuK4UH$^
zsh1UkM)c~~7v)aFg+t=Iw~^-6cnbI1EqD*54%+H*u_XSn!$$)p8pQ7F6}8V*M;vcV
z>6#y$haDlTFSaqgZev1KTb)!4G2xJc6b?02VJ>icXBx3>&h@!7nFwN&&j~oU$b^h#
ztqz{}ABKVUv;>d`EBL*g%{IU7PQX4oeXy_A&+@`Rw@WT4I=81GgU`yESieiorJ#OV
z4tQC^2F5GNaTut%8y}*R32FWZ9X<UzqE^;x91!50uZqmfBCogu>9Nf}ej)ZWc<brx
ztBBR;o|D^UgLyUlgX1+biyXCd+`kLJpWHpHX`ghcWR<>mkI`(&O_+k}-2H}(-2te?
zYQ+dyg}*wRJ|Np+FTwQ4ROT0K{#fwnPB##*zFE<cb#lVOr8UXx&OkJ<`ScgchSna1
zcHE?LAWVog1+!4cN~2~5S!>4xV&MC8Vpt}EU8oZ5J<w$)!Vi>e5iwZ4!O7^{pf_i=
zhML@aG9MH`_Ryq9&KUkxAW+jEje0#@E9Dz>ro`g6g1T!xygmJpdqG1Nu-pN4TDJY;
z&x#*%NpYF`aL_VyV1=VpiuiDq)JEy<`%Oj!Gr;J4C!y}eGV;J$L;U%Urtb4XbVq`o
zT6-zn$T-vg=p}jM!WLZZ;YOpi1V0Kp-2GQAwX*4#?iRV>f>yIkw*H???h*!%#}<2h
z9!zQSCm~5GE?AoAT|a$nRUGNzsH5v%E^cgBIPFDS>Y1KTz=R)1m|koWSxEVc2|#n1
zeK3g_NTNJGulZ6@#1wFU#ULSEZE!H6CAlpv`uF_t416SC<nr^D`Xv-5O><#)Wa4@`
zxz{PY+!G11D+2Q~Aprki<pROr<REZwN)x0WrS|y>&cl*N5$)}CeF5b?XJjs^n2oAR
z$Z%eoM{Zg0JHB9cG#@IV|HEMq&y1%D)rrjEbafVo-0Vu&UyI(^(lVT8+81sU%ju#*
z0;VfcUey;gb}NHHf1E@+y9b^Adw>L{DKn&5>c1K9YXV6D&}wjejxaYS*Y{k6+$^^C
z;(jUfQLcp_x!fHa6BEL6%SbQr!R*~?!CWa;*}w6Wm&)hD2<()}@GsCMPWJ_HQC(<o
z)gG&_C<81Hl3Nr^2OQ%U(!!22PzZLxSTD_MTxcpVBZBb7?-#!0HBc9asi<|1N~pI2
zu=oM*zLrNr$|2K4FGWJq!9nO>y)(}ZOO6-*l>ulVhT1Jp4xDjlb#@qYwtJis#e=Sk
zB!{E0UooeI6Q5%)C~Sm?vCbGMkZuo!9K-|er%|1)^&<}<N|2l$3A5)qm#q3_qAYBq
z(hkV>Bn=s@dAJNNo{CcWUC}vzvsbFWflnrK%FLPvIQLvTa4bW1<89BPVdfvjmD!(C
z3y*K*)LN(!w0Ab3I=#6wTbL4(^I@*m8#2mmd1zF?Htp^&xD4`izMJBYI!aW+$;5kK
z6Tsz-FdDuo%HS^}wym{2EGzWdorrlO=?7vSybH(cMLjW}<k|?r`*OE}0L|N~ywWuo
z=B%}Wr6{I?m`+%-q+sL<%m65X%xVq)k#$OxQzDmHorHs3u=kRXqM6Dq&$6<Joc~);
z@3CdB&g3iZCPa7MvRIjg&J)By{QH43x99IZsaL7>Fa&7FhRFN?rWg8ZCl{skH(PEj
zOvP^0bShrm1jeAxBomYpPVPrqz?c^7o7pi~+heA1I1i`I=?bpPdv@UA#W1v@Kh=9f
zytbR}H*6od>0Z`!PUR6FrNhKSFMnF2ytW<Y^QpBTLQNIT>D9_W=qq1i=wu(mh8Edh
zE-JWrDk9}##p%K62*^K_FomWbT-u503v3X{dQn*35ixKCb=K?(DH_-yK3fLzc3=r&
zcZ4^U!wr{45JH?H{d-PIrUQm>Eq1DZ3Mp(Fdoim9jPVz^aO(rF0b{cl`yQRS0z-+Q
zp)?it^lPX=!C0EHIh3+Gi$6GJ(l{9_3&?~S%Gx@**5`sOvm?oWC}UgLO0%xV=S%ug
zxV(qjf;UCe-C%LUN&j#OY5PrMwe8MGq+PBrB<;ZAa9x<E9zV!ej!Ao~2YV&wA}>3X
zJF{WH{$bPvm5J3w#2Sh#!Z@?7g2C_B`w+XSzx*|e@#SBc`9jl=uT@Pi**E#k=?B>|
zJr>0E4YSNMbx5EJ{IOgH^n_TA!QL!-?Z+bxkzR3s!L!zCqcN~D&`8}?iIa0l*#TL^
zkA_1iYL?o~p1r-E8GAlIdt@oB)iUi;q=`sDb*!6U8Kqf)FtjWThJ=fuFh~6wN!DR6
z1CHYM--JpsyMo;YD`-mMGx`*G(nEt^f0_f^uzl~%*mQsNXNd!Ock&vRTY)rwV(=@)
z(|Pbv*SoOe35IqJe^><&mQC@_2<5j3%}sqkN-!vWfz?cVd0psJ!GDY54}4((@evr>
zjCJDoFB5C677+SZTgj3=tCs}PQv%7s6Q%8;Xk;&%)fXW2$(*pQIT`J-)LO+U{^I6+
z7)gm-lEKh2SZf2cHW;hrSA&;&6@~EV-EAu@vPR}Xax093{ZEF|^wc`t5Ino?$ZAi<
z)p{FxMdu8o+sf_Kn{@nSU$3@d+&GPukthVyg0dI9<C#r=?S&`xXF+@lIPJ4!ZjdZm
zBSyF3k;$t}2$Td@qyv<xDDyJgLvRtE;k_Nag;fdo(Wv_zTE76RzZ_5wCZ?zJD{S!i
z9ih6kWCqKnwy94`AdMH?$aiKZ`nyY*FsAT=`$aXi_RlU48Fe_kkB>;+7gPxYk!|Jr
z!L^|)4sQlDcPIt=3obP1&vrp0W`4cX_u)UdZo1=cUK|WOTQPV^w<RKflZ`rct_p~&
z&rUe4=MV7sZc5lgD(BQz#0EXL!IKKJgK=g`bX%QqnNlg^;<tl{9LjSz+ej<5F^_%4
zw0KsSviXd|4!$IBc23;6SM*c9Jsaoc=)n=aE8!497@G7okOPP}gSF`j75U&DP>RGM
zPx~h6;eOKlIJ;FW>=k(75nZ*t=PixZMw}>@39|vpN3vtz7t;I&>9(ZNDv0G84ba<$
z1NGa---W94WmqFG1rdtCGe@a~yAU@BF+w{b$l6^W*!zmm`e)my)yqtqU4Hm8h8A$L
zT+?gI#-mj_x#Ri*LT@NEJHE(Y^o}=<lfs8)kaUc!krAqDgv1$qI^2tDI>Sg=*o$WC
z+eDe^J=pTCi-#6TQHI5>Ner}4TO<Cb@yRe?DTH*{{Cabzv~-M*S#NYv1=*^L?3o-9
z7H6chHj5xmSsne8iuglko|g|rDQb02YoTF<hR%-`P$ru2{Rf4ob5%&NDo7L((S6cy
z&KT&)iQ71~HHO2hW-auBq__Py%TKK?k389A(7N-EEVjhr&^ij!xEwLLJZi?Mr~?Vo
zlrCs_w82T_Ty8Bt?(Y<?)$ge9)j|CPj{u_W&9FLH<hGr#uRRArj@yNrR&lq)n1PT1
zrX$YjFW0<A4P5rq^U)jqm6BHXE&SwuWE`_1Tick~l)}BkuJ^DAd9m%RzmQWGP;K#)
z@$~&u|8jc2_l(@nn}cIKd?)}C{^$d>=Ued<p7y|zYQFwaJJRSXvD82~?#T1Z<=WYC
zlWP5y<(}X!kXU7b%$8}WiEV2z70Bhtlo0VTGNg3F8t~SV{q^bL?4B%40ym%Q$tkVc
zD+aJ{?&UY1+*Wxz75H4vnBkc6!mSaGE#t3HdaYOf2}F!<G5A0OohPYH#}vahnBy50
z4%3%zqN2g)N!*^I7Lotm`Q}Ka{sMA8RXZ2vpWnEe2Gd*+BB0xS+4Oz^8C8Ua#5D)1
z_5j&9gB!mu<SQE;e+t5Za>Q0j78QXBM<|khvjj&S6Q=Gp7aWXh-jTl=N~aN%Zm)6A
z3V=sMx2tVYYoddH`v(F&ny`*7{t{{dga^fU(PF<Ln?W@9t*FVI#~d6I59a52Tc0Q(
zkLheDXQ%EJQWu(rKq)7$uD&?{z@l<tv{r(N{ss%7f;f&jVw==5z}Zj#WdWc~nHG{0
zM`pvFTTxWV-tq@o(ZA)2SA3m%5aIE@dF)@VS1~x?f^X$>;B3`H2(FQuX9$9n$Ac0+
z+Y(6c{8B1z5?>9xXmzJl-co$OigCV<@2xueHF>Yv*RSoL&jWHdxUSnz6!L4igk1s$
z?^RD&Idk`{_uC}ow4;>I@LIbsjoB@p!cl<k;01MD$T)~^Fo}B^urh<qUadPAnL1fd
z9ah!nCdy_D*tM078glpfP%af|S5S}<F0)c>mVe|w<YPj*x_S@MzNC}M#EF~YCZ?hb
zD)nWj(PV&&vB(sd`bLybn24qBKRiR()x1H*UhximVUiCyr~O{LRsmFN$8i(dL$}dA
z(-YkOCX5{%S)T_Ed6=SOa|+CgN8M`#l^*gGFd$2C3KB*mqWUn&8kv-k5p!1LT7SX^
zTKL;X;@^fiKeI7EqmdUUFm41;Z_A$;6|AL@5t3Y+QYy(K=Uk*gZf}IN@E^iDUI=Iu
zdP6<hyKA{NcrOCoRsg1_6A_a7*Ow<q7(RQp4nDL(zw4NQ%DZz3cl62KsX6bcnRs-`
zZCIB0Wur({SsPiQE}lT6Dq&Ds0zzr#?b!Yf$y~d?|54vvEZiA{wfDUqDN-yTH_J(*
z^T~>4TdyF1@5vW1>gS1%QO!S)dJ8ibhVU53%fIKp+usLIg7h)`c=(`m(gw+Q^p*))
zcQ`*J3AvRdWs*4;87H4pjvSA7Ni#`!M6;0SyOYeN+?JOSdqZKoFe7WPk#N^Os)FgY
z4o|&}_XA7vo78w^H9WH_tRNN_6#V@UX`fz?AWI=tbK}NeSai8g++VPFwD{)VCl)*y
zo(NFNxwA{*V9<#uO%P*<IB_-)Owk+6MHrGA609s&ev%fiL_&3MgPnLPYT2yAp$O_-
zd(lUa#Wh~<XP^*>O2pzI;2*?;$obGGhoc4iR=Z*C#S0UQ)g0sx-?;W03-j)aL6v}&
z7^2N1Si)Fe$O>WiT|+BQc{e;nm567rSNXU5HrYd&Qu(~Qd~kLy?SG5^T+v)AU{Yr|
z&h|&2%<pO7fnvJBKX}B99USofLs9@h`alSHvw0CE4h<%DYECp!9n_$A(d;1PGZ4t0
zjYWihHvsG)sj%?Zm_`xV>fotFH%7F?)m@&E34|86RrQZ>%!jPRuLbti!*FnmJIW;o
zN6Z}@iVE#tgd=FnQo3kPKi=CR9I_)diVuo&HVd%pgE&2z{5W|DqpN*LQZQkgIuK_n
ztSFdAR9(Ra!fS}O8W5&rK&qt&Y5zED(w%dgB^@Fz=WD8jIw!?ge~i(}oY$UyJ4zQq
zL6m^^=OJgN7Qhi3%Pq+fuxJ;gO6#2)2D`5vTuyB*FY@h->qrr<ESF6wCTPkCeFp7x
z*U+|lA|KQ5)}oy>qVl;uH7`~DRPhk4{{u#a7h6FYHpfZ_%-feE2ug~`tKq|eNA13E
zfzpo~5`Z;vKepY!q!)4R6!m56!W+tg!W&MRi6f<z5e>7Pny$puMN}^q>W)r~`{SuG
zTyiyrP<p5u)&mvG0b*3e7*oHGO*?a~;^?kb41D>dTJLryHqZqz_=oJun(mdRoIVO>
zR95SL)!P#|y0}P&$@jJ?wbWTp(-fv^3NJe%YC9d+pC8+d@`%Tk#+yU2KOsa=y<+-u
zd%(+@Mhz#Emc1M<3OgJ0Uf!qj1UY$F?1oD3C0dB?7;{TNi#(7r8AhfhW)&Xh|MG1j
zI&eU{8&^W$bO3r5cN4iTn~1M)1Ll(L80gxC-`;s3MzgbQ*9YyC(I-bCCOhDR7gb28
ze_=%(3@RHdwr3?u7(o{nGqeRk@P2=^9W=(c9WEBC`MOADa75NjOzjmy4%XY3RHeQ_
zE<|=|vsUuZRgu#oom_U#ox#?LOOXejPIJ-tO}Sv7xiZ^5re%*Vx_dZd62V;VZQS6*
z;f538HmoF9pUoQ(IFMIp*9K3HB7}5E3l|-AvwN&@h@P<<{rG>FddKKWqIL_nV_Q46
zxntW&CmmZm>Daby8y$9xj?=Ntj&0k%`R;emJ?DRoRd3a(wcZ+Q%{iaDnN2q|{h%Q+
zA@*-@h%3{DcIryF`n~;9Juu^~X87zbjUjZr&={@GznR~1-_m`pVNew$((#I)NNqz+
zj6~~^-6!^O2;iC5sV+y62|Csx3d7|4$XuUJwNj7Pg(Kd#f$>^yQjZQv2^c@aZ3U5G
zH*U#C9CQFYUZAUX4#44ZVvoQxcl$jo^u$U;>0ha<1wLYOC|BJ-E3vLNf3Mjs(VsvH
zvIPd>y++;N?#{7dF%NBPRcO#KAC!Go?mc4%hAt=k^{=Lm*!IPW`YCrR^Ms&z+sNmp
zapyGT!z^y(*6#mmJ7kzn<^U@j89~+ALl|we^Xhx5$gBm3$t-n@zn^+n5^HCv@2Iz1
z%PN_TrW?aGo$v~;!^62V&`w-};bJ6dYZia{c;KJpsSO~`9!yUKT&kUf2<QHhh@CZE
zQUF^m(`RmR!2%Kq1AcKdGgqat4$ItTavSYGP_F6soR3XOJ*igaW;+B9kIhyx7eI-L
zkIZJ?>n82Wi2?-ansI7XXV$BmURHqEd$Lf`9ETJ8nhV|O%oF?yM4vLz2xAv&SPT(9
z2{%(9-N>JCYSree)7=O2z)9--Qo^y#p_YY@7iQzi^1?3o`$kaZ#SlSUL<ZFZWhFCJ
zs@ki=Ism7RSf#z$)UT@W<B<hBT>c&?a#Yi+sYE~M5)C*1T6a}2`icMIkORP|7tQZd
z#}^wN3SMEUuIO4&H#kfuK_#1AD>ojfh=cPRYN3xNrH!V<p!g`qN#eBIKIN&hzuW@j
zM{tikThOQQc;@!GBlDcP=u5A6bKqM=?%=PoH`@e*+-nHCZYW7_I3RCn_3DlcBw>ca
z;Md54S~kfr<Yr_pm+xQWj>W;?1nvLX=N{x1l;h%h@h})H>!4<NIK6h~)5B@j$L?!J
zC~ict8%U$hOTE;)<_fs>%Fhou`tw9MT;MXDUq+6Sh9D<FWUJR2Q5|jeY8?aKB{1P1
zmxlD<Q;)kyY(`407?IjB1Tw`&@<$@H-+QgGk1HSx>?$jcLfO!LV?zGCL9k^b;ktYy
zeNrTfPGH2}T6r`mhmf2rrPG}LizVsP1ea6HWWH@8&4ppMqH593AY0h%kJW~f5xnME
zcJ*hXH_Sj+bnEH>!0*GOnu=q%_6O$--FF~xLWsPk_HE*auhzhDix)Uf!Pm!c()KGo
zCt!9KX+ff=#hDa#J<PS|Eav`SKo7Pvf~<q9(k0E;;rqngXoQWH)BLk7>3+>S@@J${
zp8ViyhGz>#zG##}%=Oz++L8~Wq|Fke5c#$Z?Jh9#xN3XpC#)$uAhr{Nm+WfItwoXN
z!cE&fpL>@ybW$&RX3sM5P2BtanOfS}DIf%_Lz_sqf>j~KV7ua@s+Y4c4`%DG)!bwT
zRK~P?-EuW2$&Je>j-ahM;1Tg;`$~Ftk01guz-F7(8@?e+_TmN8RqIAPB8CQiZyPo*
z*G9&6fEMf5Mf}S8r_6U2jEV>4x=(}J@W?!*A$xXLsKpZu&Z0*xQ_X?ozRMdqG@7NF
zM%K@I`3A`p`Bahk&gd>mvAdA3Cwl-6L7YQQ=7^Ah7m>g9{u^mR3HQS-SSgW^D$MuN
z8`i6<S*>)h%)WSflqL#XASU(~{vF?y$UHoDLZ?tqpXx#vPS|qTCc9NRkp12SNXW%B
z3&S8-djK}ip?d6b5Qfp7fcwlrjpd+77N4p2O=}oeSDuzS9**0`oH@EEXr@I`19Kp(
zJMUBvr8-wkTspB0V((}0_P;U&cUL16TjkXTlQSA;ynn>cBW`s+r<mH^fAz1^D-1`X
zQ{bve%fa?D|8`7xVujIQ-cuF`Q}<msoB?N~o+(f7n0Lhb+qD|?k;l;Q{_@hX)&>!v
zOEyj^@@l$I6^_#t+w?w|A5#?mVM~qXx~F5^4E@q@5%z2&d-XOd;$8m=KfAv7bPqoQ
zXJHTT1IxVD5v+G;%(DK-ZoAeJjCqfN9>q2kOR}n$K0L2{l;!63iNd|l54K)1`8$Oa
z;wv_Ns4mI(BnCmb4El}?^@Vv^3i4)L<}fHvsE=vr5omtkjrmh@H;Yhcd%DVe_#Sb4
z?;_?J!L)~op1>+D``e<uC8MA~sNw9EAWJGNMJlyyRIqABfKDBhDjRDUZcyI*SdplY
z5_mOr_)NGu00N4(+Fno8$ASNC$Hd&$SQgzta$1v_Haf^45WMPKp2VKg#i*l&CG;Np
z$k1+>vSZX!&6nMl0yjS?2+V+EKcvFMW2Pm?GO`-^g+`UQ;jZ~D+nj%xMam3Dpfh`(
z1OpH2&mZF}_Em7gb7%S!sI<a=<C?*H+WCYKjib$J_B|Ye0z@+*>gF~o`poT)Jo&QU
zMH9xMF9v7-oI~Lyho#ZdqN)&f52Q267H<ickBZP3#PYUr)^eFth<5nLup+qaIBr{8
zBGU`J#H1RlCkA9WKwXgxmT<77c838F5LD)Ywj$9X9%V0<^MIip%Rr`;6?_d>r-dkZ
zkUe7pfDd)oktW1K8w{wxNoER75wI_Wy1Qn+Cc`F%12)DQ9io#Y6%E14I33}@v7ZhH
zFf$!sai2gZJTniW?aS2&+l%Igmea|vvf)V$OsR%-IED*nY>=eK_kc=8j}=imTclLR
zZ&7-$XdncAf&4Tr>5AZWxSh(hF^oT-2$obt#!>+lx6-A+$^P>e{+3Mb?nDQgOu#0|
zV6JWtTYjeKaTVS9&>}j8x#dr2Qif6jxqM=SpF*}jt;#1=@+GApc3t~h0*l}QhZwsQ
z5DyxCbnj{-zTRvD!I~#dWLgmpMz)w2#dTf0mJb>vVF00WsNLvs0OY=Qfe%)6R6;UZ
z+?6&<>~}d~?>Y``ZH)o$L;!t+g8)c3;-?J&178jMt!!!EP`;u<5QzOE$}?E!QEFzJ
zk1amke*--PX@|hp2$GNxsB6cjmQcY5RW2JGM{eQN2-C;HVXft!M<IZ=(@LWr`ZWbQ
z;cDxMETfU;-_@MtX|~htql^Qm4d{Ufczmzd?|6WU_zB>)4E7LYj4wkpoCN`fKI*RP
zJc|Ow;8)DycL^aTLJW{HM#2we!d-s4#3Ese=I!(!Si95z*tLUfwlrtr<bc?NkklAX
zni@S%d4z7o190i}_DG2;V|v7+uT&0Ei2a2)3ReIY=*~arkOo4Lg<+Gs4!suMCS;O%
zuAN6@Q(>FemsdPK*-?yz_NB+}><71RUvNWRmO8P?D9kp3!yR<v84!8}5jpELicYh4
z#NunCn9hMY?OysT89iPPO7&LKIrh<F3E{d_RQwaeHxXtF*XrWEfzX|;$4#*}Nt~ro
zG6%y~+TolWw3LMCF)2U)2z^0MSL;xs9KCofe9_4vqQc0558_ABJ5R-i<4s(inJL^t
z^-(ntv0mR#F_g%6tyxV;W3o7{(&)ZUJ+FKW^H{kwCK3DU32r|`ulDGf`WWhd<{HDH
zb<om@3T-@(_8+GU!`H>IE4-g2f14Ub-Mb}B%yNfbVkFZATc}r1h-6*5HQN-!=1KK?
z9+GmmcGd9Xc{)+{^Y}zFk^*2fxU2J?kh_-R0^?#3Kd6006B8iztmSBq9hA6z9^kR;
z%HhQqxBfc&dpNtH2(H@7Z_E<;v`3=d9x=_sf5*+{M`3y?k95!&bkh`OxQD%U+%^@v
z=htGberv~|M!x8I=hs>5@P>z@;^pUU#quZ(gs2K90!SnFJlC7q3$>rLmMY#czmd1Q
z(S=U5J*J6lE}{NOlrJ({RERv?Xg*=~?2YRC5vF|<ft4ecmoQo`8hCwp0Ir5#dGj4i
zg)VlFnlh314I^ZMPsMmR$}wJ~;%!rj;rwhSEGd?Xk*^>|Pw?QoAHJJ(bp<9>3@dO>
zjOcv|;fpAm@|~`8x^d)RdNoU{A51P*Rk;vsPO)#8$lY;f3>D&Cd)KE^`{%nu(P8QX
z92>sZ(VPoyWc}w22qbnai@s5szOqX`+Wac#cTD%w%4Xo_Fo8H5t>#a(zH}259+_+g
zwFOaI>1IY17lSAq=<j+u@>*AdR7l6+XYu%&jkXB3{D?D4`6p63agye$T|~~}fr`c!
zXl3wdLuS3{shB+=HGT0>{{Dkf!;+zSvKTcwB^jmyCyCnG#ckH&!G~UsJyx1bc~yJD
z3Yvk+sifXmrTffzy3z8%J*7?kaQYdV(Sk3R&zKf7x29SuXn`8@sJq91q>-+ihXl)%
zyj2?J;e|EEQ@aCDm8V{b!1q3SE^(JXZsb#v*m7#IkVX<Emqm&0lWwoo(C&j)RgC-W
zjjm;<TigkItQ&$phcpq+YMru*Ln`<?9D|4uV7@Sy2ITw=plUQE=#v`AmisdgjHT_<
zEC<UhB``innr@-gRi_zP$^~e|D}YoZza)Edk$+GPej#{)%CsWOs*`_|>R{}Ge>rx}
zIchP_`WuaP1J43=O)!kJECFWd#{2Y!*}`JsxuR(oZkYfa^j*;C+_7lhM;6vnnvwPB
zM|mMwtaL_HIsqC(c%5#&{(9W8{>X<m;V#X?f8@#)b!m>hlF=N(Du0NF4(3B0IDys6
z22o1}7*;(k{(#@=r#0Y%9?t|7a!UprwKy|@@0I`@(x7*&+01v%qnekqn)&~%Jme1=
zMgtY?$#HNTEL5qYevMn{BsDVzP|?RtT2=bpOR)ee)p}wxr^@*G{+3u^e(=4EQMT$Z
zFd((}=0i!t59`D!!r>rqumzAofMNNt`v({Rz*obkw<N^)+LP62(Kuw*H~uWkN<IS2
zag*<h^A}s{0-PuQrCv~sGFV|_C}_^I=?4g1<8u7QH1<{Jh2W+CZNmF<kS&i<b0;73
zTIf+UI>qbEmEcg2p%%p*(mxF_WoIh>UE=gdyA`EMo4*?IxSi=9(qQ+u(CbXNZS}Jj
zt}bK6Pn6uGjE4A&s!V1xJx4BxJxa<M)$7AZIHby7Jscf@Q4vSb^d211r`)QvgCWv#
zQ-S}&B{PuEhi&LKU*$O>*a@xVy{)JsADFv)A$%J_x>C1(a*gqCG5|fgyqT_IEgSNh
zK!2~@gQcf^&U7k)zQ<&<HJ9Cy&lergzAkZ<cC)|7hZBL|Ydo{q#P3hF>JLA6oiZNJ
zhhvFTL-yXlpjJGUwDWhrC;rB;#s8~I{CFf0F3xK_O(Va(^7QuPjX$0Kw4FhJ^252*
zj-&dy7C?J{kLwwuYT3EKX;kGh^a+*Xd<`jJ{t<<c=4sJeJ~4IO^;FRCyZZmiCf|}^
zOLtL_eifC<F=4@e+Q_w}c7G;uX{<K=?@sc66_fth0bRIY0o`d~WA78+t+M9+Z*Bio
zsr-`I$`k%&iHRLYGTSUJrpVQ}A|`5$8Q53wxv-0;xzscIBebZ<ozC#BZZW=2%*VY|
zQ$;@!t#>LgK1Br&0NUAuVy1SYlQtpSa`=IzHTZcHj0-r{>;k*n=|daciscnk0NmG@
zZFh%zP?FKir+?bD2C!L`1w~=>t13q*fPSp{Io6?zEiG&PKnvwPR=pS*skpVQAPQkk
zMatw16U1`K{V`cr%TT*8y8#6aqKgxM*Xi72O<!K=71#E!6>^gIF>d6np%X#^2C9}U
zeY<ZLFcLj~d{q>y&@X(*`oC}Fn-~m=dwMwP4TW?S=dA%uL@rPJuoWW>VmyJOT5UY+
zJ>{FRcbf7ZwyXNMQL(tJHb3iV!^fxXS~;qFMj>;_RAwxx9$r8s&sCQzBpZ(POIEB!
z6sgMKHpi&+3f?hUmSdFFqF*qK2+*bJa>*A`ohdsUnj#~#+(h2@Rc{ReHq`4eLi6Lo
zyhSTZG_Sy~aBxD{(B35HynPg95V<<N<Jn7-=>1!iTdriFwwoNdRT}5faYYycPPOtu
z3<gRa^4iNpf2O*JW`jFc;MnZmf(Jtcu-z-RSLeMw9X4P~8IF{7>r0L59;~%`LT4(k
zw1)2*lI+_qo*`!><O3tG-2g_FwqUf}<2C$Vfw)nQbinM6k%O;f8*!M3`3$h)@{Rp<
zrF>l6HaR?9$zjTr<1aWan&!IC*WT*L=jfcdyN+c%liBv{nJ60klGpn|B5>zQI~b9(
zYERvR2tlPNE*C{%{a4k$wNOWCBn0_R6+IlWC0NldN68?oKX{STg_l1$;K}}VDw(mu
zE0CJAsb?tz;ac0v?kL4XrRkWx$?lm*$tAm`t_t6jNh=f?$(oiNH_F({6?8a*1K_pd
z3FY`Va6V>M_gPWZq7?@1O*mm$(fI#rt^Ok`?*4><)ht}B)sGj~9m4&rrCVfk-r$vY
z9vw_ww%Zmz%VWP&vj~t$jm86QZI!@wUE|xbWgK*=iBo-LD1G`wA1z~gj&~9P?D3LH
zYI=nR-CPKO9v|R{mWs>YGc{F=4Gcf4FYV84;o@GQy$R$=(J@U*s|*q`BW+gQNZ7Y)
zC|$}$x3b)hBYpKwcJ~TAz5o(D#4ZmCz4A633BPIvgW!F+ibGtSS8+Pa{B}1-H%B?k
zo(iLS<_?SogOS}`?{utNuLubF8uAzHd0sJBYL;3m3DI8S2*HNdP%Ci-@VB!^#3dvv
z*gQKpeYqJDi9rMHcBliGW@rhbvg@<AsFM<uMTks76Qic|f+@BtZ2|V0_rL#O3>WVE
zOqE-p77vg#ii$-j>rZw9`1--8Y&RJwuhwM9fc_C+9PP__azNCpGH&@s6!;}}^PtHM
z<ug7H6v^s|tHGjXZs17&i5|{qb?esz;a6o5{_9(lFF}V9txpwz<EtK-Z)YfPA!+&(
z>qBJ|J0|Fl=<qn*TGY*}l5a=xp{8pd4O|+=E5Y9>qCLo7WT(`r-n)ym+KVtvig~p3
za=TgVu`b;Z^%C5rwmUt3Z`mBpVi;_nKi~qVSi4(XI#(o9c;|Y_wGU_Ewv6H}Zd>?)
zg)nxTu1A){v3jIXNB_nV(qeP{XY1LZ6|zwTMl-6`y|C0{AC9=(VcQ3gu1*7-TGF)^
z#h5~Mf^5@yi_nm3R{f>_ZlnuYb0?N2y)GA*L&V`JiFq|-*P_;V(bc+~%W}u)N7H80
zU>4Q%#94U|ekDhq{M*(vF%FNzuI`PwyYVgC(0Psf-_@lw(Em!1(XTnR%PIuv-~C&K
zFHD+x^5}a?5`>Y^_Iu@q`ciea5G=liBkaUONX+QwjVScu?D?ow@cRjqDBphZpogU+
zR06IiiWlLZC^R+L_S(-7n=#Z&P$VC1@m@#6#Hf>6Gc$qIE#4T4w(Iw{P+I`d7M5vf
zy-Z|_zUKb1RYxL;|4Zp4>rloYw^BVn(uWb``Mk9=TFm`K1`m~7fF+ybo!0fP6&<n*
zm!3+j5yRZ7=4(lzU~3jQqX{&v)DIsKOOGPmA<Aqx4m~Kx;eO;!jQEjJK}n7#G4A@R
zAx9fP-SfxEChjB0JAYW#zeR+c%Ez!E__m8=xfM6H#+&$i<(+$CNQ;Xzm*C3>EMAu&
zmJ08D-<Y*ngY6N#j}lev`3HF3Ipg<|LJv_^Gv>SFMQA84SEB(lAti`5SVf47A7-t=
zS{kb$A~>hN(EsjCO#cw*@4Z@~^bIt_Wv=3rGE+h#+wk^qzwm+za)4X+)#m19L2E~%
z+|9>`Nzy{%VaeTpP9ZCE1OW>F94F<xLtTgy<)MdCsVHXnaeei>-km72`NE?LIyF~-
zcv0%lyZ0U#SM{56*10n=6(||pZLWm5w1pWj8llMVo5E5CiqeSAGiJlwPqXuT`-KK*
zitnqs0#f5E4G;?YOf(Tbku_#agEix$>V@G;Bk93()VzS92iv|VYw~pBrKr@tAtS3i
zTK9)%ZEwF*xTI~sPRt|MuR<3hg&Z$rW+@E75!XOJ*Tk>UnC^Xlmc2t$(K7+a)d$AV
zm<%Se$@jqGypL}|lXhDj&7pk(aLnQo$J{UHhf5w((Zv<iqUb~@k4=muCIp=?Gtkz5
z-64d$hfJhc;l{b^Cx4?>CN8SU?X>=pO8}k0I8BdoL-8#z^v|5moOm{KmE@;|5gfXC
zhu0wM{kE-yZO+iDKFwB;-ymV0OHj)%D*XlpB@ZuRbET`0XnV05TaPQ`)VS+(N3aC5
z<c9LVDPTlfTv9ulitUT9yPj8?AS{KUgzTR^ycdpW$Ve*;&ymaaM;R?o^YtVkrI1no
zat@JtaiV`@m8>$?4pNW4Ewa%nOdzeR&km+D^s`>|?P;G~K9?}2yOA%xWdADD-%99z
z%p2d(;*}14?gFs@Yk!C$S^>NRz$fPwQ1)1gasOH|V0j@3H(o9LuOFD4&@<WuHPaAa
z)!e(*1f%z6{c2<3k)*W_!r;}+Xrq9KrX<%(pzVAGZ)VHImD*1zG`A4j@Md_@Sw^iK
zV_)z28Q;6%5ss?rzyW_bDGF<MI|M@W_StvO!t`*qMU1yC259lYerI(E4wNw!Q3gEt
zHlhs?VS)bfT^eboL1#-}!8;n)9`4zxDjR_WRD8nQ`9L?xgU)k4p?Sm*PNs?>H67sB
z^6hs!y^KW(@rvGl9G<P^EblpLdrp)rt~LP^#LW3dmjeAotw#HUTr=IuDDr@4K-c@z
zvljN6t~QHDK3Mze<4{)hz%Vjj5V=Od_CG&5b@?Nab8p-~1((LhnG>)P=&{Q6u;Q)y
zbBk9OZxQxfKbg?WheP!E{FhJT;^P$u6Q6y$3k$-_eHR~nBhD8G0GusC&Q9pRobQFr
z5b)P_Eqywjrpmn+fe;^Egp!p3^c%WOkP!GQ4uL<SUk@LQ-kXBH4h~L_g4C?Ag|X0~
z@bf~y%w-~1%VJM^QF`2WbI1oP@xc3N9!J_xgZ{l7*)}|S+SN4ldsv|h{@{W@a1&G0
zQY>pr{o}j}S|m_$a=zMFiTQyESZgo%citwcEZ3@;^#0bV<bfkglK37whc|U}&)AH>
zvW^beY;f0!*Mht_QX<{I5Uq_6mFat>O$9Y}-^6@)a&<f9g-&N7r6~s_Q1KUr+P_Cl
zSJa`sDG>EM|3#~9Xbg?55b$2*46CmL4|PSF&NtI=HlIP1+_<>1Sx1b8mbczJs;aiz
zETEMq{0o{`yRKz4fq6ehWo2A3oc{+Vuch-dAb;oI-9**)@)G{Wd1k&An*q0HgA_S6
zALgJyZ|j(Ec#3AvO20n}vKdIeZ}pdi7yG?yXmx}DUbAITlZ{T_P)eDjjHeC1fT2Zv
z$mXPe*mx7^OSF}aL-q-A@JN|#xqnp$M*C|g9J2}*xZPU!9F1OdCMztmB>eDrCP)(o
zMSJOXro>9>W$CnMdIP~`wNcc{G{B|a(J%}(Ut$w^7}?Jsp_zQ9)QXQCmldqOL_XNc
zlEl@g1EU#q$C7dJV<ToQzveQ$v!+qX*`K&tKvtp|=sZ@J+|x8<a%NI~NbKqzM72Yc
z?el_o+$g}&8xrm_GdqUJe33VF!Ft1_s-vS*K*9#4xCA4M!;oImo{j;wJ=7K%FPZrq
z7I~9|nv)()Mn1@$j#uy;ukP--hx1Y>RbVs)#zOMYh-2D?&>FZ&R07&sv12h4mlEs`
zv%j)n#GvLRG3M0Ma{!Vd>;axXR<AH6c0_bZZxI|g$uZkdi-iP&-`$kA&W>YDQ+sn3
zvH$BE`;WT*2Nsx5AE&#qxfPAEOn>K6fS8%NE=83Eza`wk!<Fz=Mqb_Nl}mavUi9~R
zn7;m5G|utSiaz;4(dwV{_*R<uNi<2`nXS#;-4C|mZBB$*tmK+m8NE$fDC1e203O8}
zqeHH77S9jt0yYZs4KH*mH@AS#V=-=DHth4)n88(nwenouWv+xZ%p^fg|2iLmtEXN#
zg045r5?4-^qWR{jhUfHuU9?0qSC13Tf9)>@$&cm+8uWfQ)y~cbwCb=qpDp24`RMe4
zYqbni7Cd4cOP(1b<gof)G5q;vde3L%X$F%>A;3<7V{M%QlM%qB?gJ#=2-V{D+CvST
zlilh@?7PQn5~eq>1F*;>ixsLLO?Ps53uJW<HWG8T^AWT~pwyW?0x|hq`c`BFBCs<2
zV#oq%r)-Wx2kea(mh?7lhc<R`&=TWAUd|<Fpxg;q^PaZ5^PPqlfdL#zoSDt$(VQFA
zKZYl!O9ea_F<7739|ECHE3VcbY-WD>8b3V%!esPvw*5}bvReq_CLyGDt8DKrvm<_x
zCScIBx!w6zu~Cyh+TV-tue5Vw`gpwx*DMQr-}LDd@VbA?nQ+|lyQG^<PvD>?hmxFt
zHvZf$q<GFVw6?>W_=IRv8jVkf;@IkzdXiQQ+^_iy`rxaNkJs;?oQKT9nIE!?+8k6|
zyde9$BY$#ThRdR=QbI2eZlFMV74#wAe2IuBV-oJ4yV7P>ZNUOf-igB-l?51#@&<_g
zwr!6{ZMom)x8`<+vF!{w+9*eH?qfFI^`g#ydtf<{BJTN@;zhTjx9x{V@0RC(y_?74
zw#$6txripWWyBz62|{zOsuGzZYk6r3i2V@p*BUQRn-<4<;WQ6aK!lw@)!A;$|8Ab4
zATc=S^u+lFMG?c>n>|JX4yT5_x4rt4&7tw!*;{Z9tm3e`zuv?Moh)oBz4HfEu;K0r
zM+;6qL>lJ=uIjPsyFJ!s3gvk8(Fz>GLQ_aa9Y93B=WX|Jv*4En+@PNz*m?)bcGDE<
zQsR<tn+F>*Oa#m4oQ-*cBRGaen$a(Nu~mm`Bm<$~<*4S~@p}$uYsI{~3r3|W{pda?
zE@MAyGV4IrX~!wnbo)Qwexe2Xt@ovbu3uTdQnh(cgSWN4guDA92wf`fe%l4#w9UC%
z!1qCWa%3j6x)tDRHd>G;;nZT=rM;}M7xGY%xLF};iIl6k4N|5(s*N(V-3qk*o{{+6
zjcbC1w>2SBH9aXGITukZ=lZp$+dDo9(HYz3zWw+O9mi~`t$ELKKn+r|Sam&lGnhZQ
zya3%DB_osyN&b3F!^()mgZ!?APFZUyOvRC;G400a(`r$9IrSmwe}1~(KEs8Tl)wvi
zZfBAq|E=$Uz1gk{i@P!%+*t>wdud#~AM#ye^0M4k$AK~Ofq_xpLH@c|d)KBlT$n>|
zbofDX%PIrsyV?O~<;ve}HBLkKT_&{f5>=Bzzi(|yP|VP}>@SRbHjB->JdLu-uZD&I
zpvIs3%E}*c5nnLdBIxm#Nh0(0$fSsl)1wDZaA;0}>E=Vs<aEzMkHSG3Pj5_mq4%Z}
z_~v1(tVdt?>*pz!$G;U=Hlffj<UJ*XnVb;je5$w2tG^XV&40kKUkV|rFQOF8oeEsN
z9WvHhZV==Sens+m^E9nT4r*E%xVN3`rcI1pCF!G_32|eXbv=DE&_h;f-n3q!;vR`a
zlUL~=VyhUnSaY&i##UYiYCn|^b;}s;-STE0Q*BiU7=Siw6y>Ir3c_S`kVQWST&$N`
z0+OJiD<3W<ms$vdi9RuJcXN0C&KuWS_b>zxrkJ&MMZ`&)vp-i47Z8<HS$ugUSb<F;
zz!={bbxwPWaRecquwkeUq=I*3!^y-cLz6`q!oSR+V7D)OYktATVGq5d<H$&VoyGF<
zjqTrdOwX2^pfoDw4R<$C&tKX@xqf%X@PP%uhMSl^b^Rz$=f-lRZB5twev$q=jSZp=
z6u-BZ^o@48MLI=p0Bu7AektlWh;hO_pb+Ixd|p!Q)IwFw?bWg~t;`r>pjkx|jUa($
zT~0*|Eh5s9!Y%!&xMPcxx~a0}kNh{XqB!ii5Q%!&dZK)NvJ?$feEWq`23pCd!mMx~
zt-w6N`ynn|IB9wME-kFAa-m>}lD^8H7$z?Qy%W@A?8nOlJGHm%vS`lEdn~)8a!dNh
z+5xpuh`jJTe#`;MfI73XRW!K)=_o`Ab#J^buUiL9G0R`SKdPGi`unR$wt>iw;5UIi
z1^EF}@yWjf&$hL6JGn584<{t-j8THuc>U`|tX0Gtap7=e#3`+{q_r6_v5K;F+gp5b
zdozZ)3sY(_WL$zV_d^VwP6|NqcfIHe=k7w3WR!t5`X(L8uP4HrOR60{2XpvKgzjQ`
zml*BvUTJrM&(-x5S9QfP<{uT~2i<+fauv?F!IY*VM$_N!Gua{a`Ae6?7^Y1@n%P73
z>ibRx=nkZ}7<DSx^hz|@rNd}U2dVLggFX5ChApQfhF6p5d#9OVic&ZN&<)+kIg4~F
z44))3Tw<GW^+vd^*a(N0!l5cc-Z%H`?R+0_B~`0BomvB%qD(sbtOM?D+EUeXVSa}L
z1ZSEw5|vxCDoWg?@MZhU&49y#|0|b+2IsMOtlc}uHJ4%zNia6sMpB_<nx&y=w}o2x
z@r$Z8gr;alno_xRbBeB77ALXr*wejd@&_?(;3m69x=W<8{eeFC=IZlOmknFo&tPOW
zgF*w2%{0qi16TJ9NKa2sY51;)+M&1vr6<%&J)ZET(v^tflsY1Yi2%7SsB+^&-8N@`
zo(`f&xyXO~QWAyw69wd&f2DERk#vf6mWQ7qj1i)sxWMy^_`7iq!LKOwGSep5{eK%{
zb28or&BRZl(zBttM-hnTVIXqhNaGICq>TL{QN&0u($w}adTDFh17C*q8eS58iL%>t
zN8qujbEYple}jA=C*=hHUCRCyBF5_8ic9|C3z>K<`s;KqHW%SVnvTw=>lAh^oL^c>
zW6LxpyOwt|<(A;S<vZw!1M~))A%Rl&H0vwA7*J;)1SyN*{S2ZNa=rFPyJlo!5LNnT
zti5#X*AMdf4g0(E0>%u*=gsq$Di<N;aB@rX7ZI}GEZAUbD#<arr9tZ(lbX!r@L(S`
z?UA4_%{<@QwADWo_60^sQ%w>!O?cPuXqb3hh1G>P1A=|;LnubeM*OCT7vj5(q$|#s
zP!Me`MP{SZ31Gou8`|L+eXF8U3_+UJIvOgC3t`zF(W(k|j7N(EzEi|9bjB^pAZ4bj
z0qs#ex=mOvcGd$XUk_|p%D6Pb+r(g%qwJ#NGki|LiRs4g80aSXXbW>ZYqhyJD;(CL
z2j^Z!)Beb3Q{w(zNoi@eeSQO20X3p}^JE3|VXOi~zG+N53-7-zZ<wr=l5vAnH0*wL
zcVcDl@w9ks!O9-42ZrUqFU05+%83y><FkCLTJ^~HhX@i^)SqdIek6<k_>dWaN6Z!k
z|9;w9xLFEkKk>zo=oS8v`%64dke;Czi!KVGMSc;ZPzog|jgs2hG=`-%3`IF8kDQrY
ztV1uxPuZ4~Ob*VBxsC8xRnY$$r%TK*7tAv@J>o(zcG}H0G5~^)JWh&r?`*DBhWu#Z
zu>`YJAT2}mpP->K1Ooe+pno~bWucjqnfL~O<Rk<h`SUW8ccTY_p!PEq(V$6BnOHO4
zZ&KA#ZimF9aMDG#pjc8yyc<yURkFSPPS6Bsy#XfG?uEc=1Ggmisfg<5&fqQN(O&;n
z|A@|!pe^`Xd_;1$*U5Q^pE#L@vyEv~^d5!y;3C?9PF>?e(Es064aB>DjgUki!aJPA
zs4TVJQwJZO^i?F#UkFBrBq_=vo~)*REcHznD+!puKy7J>LPs;4&3G<JGZfrpg@p(*
z-lPnYw;C4FGZ*NZ@OOz3pHHT9I<RYV*adXf#ECOAYe2p|KuX%$QcR(_1Fq1aW2<Yq
z$}E4-+fkA8p;#P)8i?4w1r2ouNvL=NH@?HbPm+<5k>_uEvm8Vd2M=}<vVg|T-MKRy
zH1KFFemU9B#zs2ESa1*_YIse82OCHe><y}$OI)ML$r-!ovFMqQrAV4{wF~`efxo6<
zjp1jHdz(NPph6ldwLq9ItRpvuMzpZl^GJLf`#!$4UWAnS<l1|+_?P?zJ%r`%&d3N9
z9MAVL9Iw?qKbbBm6;P(iOgZf53!&sleTT~lm!sV%Jk;68l6ldyJ(E#;`yi6HjdA_a
zE@RKN7`8Ut;0e+7+QCAL6rhoZVeD248BHA8*Bt@Cw^$>N!B*W1?$zz>ug6`vCy0DJ
zKooU7!q~|Q-QK1C)rh}dyFcmP;pc=oBuOsO3z)3iosP@JL(01AZP~VoC(aY5M(g?Z
zwkVtO*I*b|j*0Meo{gYMBN;Vxchg!lOYL&IV<IMBd0OW4hWY7Yn6V|!x^O`sCpGk=
z7SqglDpaqQBR7ncE%n!!d)whKYpwS@sV=+Iz*W!gl{z%syoz5kce===R>qGT_#0fH
zF3%jDQ0$Ky6;-XgG<)!@M-SuYBjD0McanZjN$Hjs0fn4a^-j5zt_+7dX$1vw&>hAT
zl$NoH3A$`S??13?5R=`$>0Z88UjEh5QB=VQf&(Q7L|ww-6-Bj>B!g~f03u$5w=;4c
z%pN&&9`;X{Q`wz_xU=??F~R+?5<7RaX0xqoQKtAyWr5dOgOnDpBi7veW5~Gnzf13x
z<KHE)f=(B0^Vj#lG#e}+ibkE!gnV&ZmzOE7i>1_N{^ay+g~vMy?Py68Y<S&rRN5`z
z!?%gOH+@<`u1)lZC-~4ctc0XQMprU~Gw99K`#41t0wd`e+E`{7yWJ{~$=T~1L$!Sm
ziv66f?l(@CnoIEq6^TL*`FxEGVSLt;dh2{=;xr#;q!~7@DF385(j?k~PQtt#>i0p2
zS&yWiSW!mG=rZ)rw0o`5;&lmFo^g4DuM7iYr>|Et+rKqebV6Run702-=$!QNY@CLY
z#^hAwiY{~gG{t*E$?YF@DXe?T@}b#=l+OeNR}3kZHM2O~3(qas7@6P>wU`4VmGyfs
zyE*x9w8PyxmquV#VIRNXr#3^aAmK-1T4Q18&R66W0M7Q3A~l-o53juHuj~#zTzayn
zxwOeSZivHjReNn<6&>{S?^C2hXNF1*(=8sDA7<nb3XXEr3dQ%wu<}X0F+HN$VWrfn
zq8;fM<^ji!>@0;Tzd&DorzlaisSv>qdKeZ=q&TDka)1_ga#~8vNn#)}rM55;J7<dl
zf}Jz1@W(D|B)l?esud(L6LZR9J=Vjfs`#PGPBNHC=KZoqnKTW{SAHklsBuyM+3_)t
zg{m2=h>!8a3#iD{Y!-Z=SVUk)FE$|Fz~ARP0|&%z#%QV))c6hB6s1|n8e~Z}qB<xe
zEdEYZ^BBKZI$z5mvQ7ZFPyuC(V+?U`(sAbqbvAb$4OpZeY=6f}PXSf>Q$ute&uAza
z1k~{WnQEv~!3RY$Ui#(p>+qz$K-0jWNBk@e5bYV2AwjNHbg1+DY(HxnU<lo~h7LfV
zkdhTz1j1<hv;d8`BUYQ2(0<>tEz&ZG#spQ84K8E?D&XV0uZ8K@LPDxb;Ro}R|8gv{
zz4@K_z__cbE#7C2B!2~U!HA-Z!Ub=5G0S4a$Ho*mA4`CVY%W4a`iKA0L<OUrg%c1)
zPmD*EmcHyrlQ}SOQ<j!cz560$X57+bWUsY8uDLu4nC1CAXd#3mvG|HdGe*=g0jnD|
zR<6ZX>Il!(l0-6oYaYxp4cJo+B7!fJ)$}gq0NAjV^(@a?fP+6wwvDQ>&(-R4JLoKG
zP%LWn8y4~SFcj29q6=GQHtWF#fA(`X(MDU;$P~u_C&>qKzZ+(Mk9ImSVt|wzjaQ1F
zN)Z)H$JnLwK<Lr`1Rq@fSvxyt^^cl=9E~HuK`n1fo}t*~6>`PFy$SQ;3`4c-H(hdw
zAZeCQAVHP+keUT<FR}SFU35m=nta|gsxcHW);1W(#W$<J8?{W$Ub`y_$`*~*;jA?V
z+m#3Db}5z`=EaMNf{V5MTXDb|JT4dPiT4rpiB^G!Cr!fZEy^X-xQ8=s&<LV>3HB@B
z#Co}Whl%@9QF6gu<@wEfv2w<2T<*)K0BMm;In5k5Fx8dY7m^-jZuS?e52s49OO`en
zS7fQyh&gfmh1{1!Mo2#^=4_Znyrh1U28Ye1A@SV~?zM}Tkb`_|Tf=YQgN+dA&@Enh
z$t&AP`fyOEw{-Q6b4A<+#inuI4SAp<N9TBbJG5AiH@~%6ITUBVEsv^?D+qo2CL53I
zfiv?j4#=(!TAcK;Z_*1SRoN7^i*;!$u&(rRM@Vp(X|d-4*hQbkHS&3i=w}4D5wG_p
z^~)#Y;E38zL)R}dgl!*uU-ElKmSqdNPWpssxG)Q;0Mh_mtCQV!%Rsu`OD<|07JS#n
zBLdQx7n?!S5<V5It#Z3D!WD)p40-v;WF~kM9%5hi#^&YJIzQ=JU-F{iIX@ICe&7gk
zwB(f=q)5lL2Y)AiHJl$8ba%_aq04|vM0Y2^cl`(yBv$CD^iUY~1w_J(pi8%23e#Zq
z!h6fmzW?RruJ6q6s$ft&j2y7NyXXR|*RUC)aNPlwu;r=s_Y|E=Yg*Fg#oCO5F1)72
zjf~J%P;vwJJ2GUHa>L(=eM2{Q?(5#G;O-Kn$ZNzQLbJbRe_pUb3Y7P^X&G;ExuDCo
z=oZp54s5@Np`za|61p1!Dmv(a<o%!$RM=y37aLiSOxW-Q_6p^7iz}DxPj^r?AuPKf
zQXJMdt7_O|7Mr&F(6B~QGGfRbg=u3Za*OUT9>vk@zb&7os*QDpi3-&$!09dP9~2C$
zs#5A!ldC2j;mnEod@C-_!nWq9WIXk>)>j>wmk8vs!5c}5DC&ma6JSqCyd5wDse8O;
zw=R3(gTBRx+ACM|#HX+HB-LB4_p(nm5f>(xkpyCt7<z@w!;+>Hmp!QKrRAjA>!VGL
zoGxt97qi)ki&@S4@uqgIpd2<S_QfVSC}z<$%;!V3wPca8@n-Uz(3W#Xa?kvLrT_N!
z*Iv&^rIw_UzVUEu*TkRyCJI_WS`}#;{X@^N8_j)BV$T3a**;jIZ?KVY#e+{INdY9?
zO?eP^+q$UVdJTibC?>D}2g?bjiEQx|>+Nk{KRePrkad#s&K;$5-7^uZ<eM3@zL=z|
zU_*NOT)vc@=2(=&^NmYU30s>YeiF;h53w!^d75BXnHEH<qP5bN3lEDPCiW+WV5GY5
zIKWC=%B!CzvF!{<)CvYhX2T=Duk`BRyuF&jxFej^P@F6;M=GJXrWzb1B=j+HeYv$v
zWR+g^#efbc?9R@!edn9VhQ}}l8hjivH7gcp73n}%K(s1yyCHpl)>z15QskGo7M_h)
zwp>uOlv%M)?L@0_hqr{~sx-Ixhe2xKm<9TiCLm~!N6p5#LtKqnOp0D?o}{{s_jI6G
zrX{gDgozWnd_FkSGnrz?GL)%p^=N*B7{5QcSbS}qRajAH-4jBLR)hFr53f#N>L*vP
zM{ru#FA}szgy@+*ZJiV{%6|y4p6a%&WODK|^Ro3#`a#tivI=xNqvKm<GQXQnOj}E}
zB7-ZPlVusX!{w|_DYJ_NN5+Gd=g=oT6HiVfNz|}+luS9P?bT}+ceO%pVV6`7cCno>
zZDQn&272`Nmk{o;JX%t95xGFtv)_zARnfG{<d3c}#>&GCq8#{ab7#vPcs+<>W=%k}
zQc@2>jb25X%w&Tf$16v&3l-X3xinvin2quR&eW+Kmlgin9!lW0hk$~x36<!OODv3C
zudl3U$=}#)Jy?@tilUnNaBVtP^9#2Z@flfxL&YUWAT69-$Ju`+qyYf8@*rT+C8}KX
zSaE8<rZUQ|If#L5PyvahGfDTG6NJhVy79c1TFf;P=*-Y$a76u{q6~TPVqA=)+Tkq3
z^Warq2CHv%Ef_*U%qyh;VvfX$%jnu2Y<H}GDGsp6H2J3!de^r9$zuCeG`hy@q!0$E
zo{@ACCl5_A2-dGzYn<X!tKZ!c4}jUn#n}D*Kxdde*3fkY!f>uHk8|MF;4d7*GtN%V
z2^EI|Ho034O&Bpqpb?pw%(NN`S~CvzOJ>Ba(uN?)XY_`S8_-F~cSpN=lb8KSAu?Am
zYp~6;kMR*jSgBCbiyjMi$OPCh$Y&m56kRp42FCaNqH|%nur?lcyR_(-rhckdJev9S
zpUz`1Hcx$|+MzOUKuewqHgOO4e1>siSb2hRvqX)$TYdj!5BwSx5qP0%HAH}5Q4_Iz
zEKM~?cxt2@2srT2nAX>e`1zff%<wH7Ah%4-{qGL0*IhC?M6hRY8gt>E<-_b%uZ#@o
z7Q0eZ(iDS!mO!l0#bx_sohUW4xWz3F_uH~Zxo~cJMXx$R8vnhMJv~bxE;MC>&c*eg
zKVnv9--FlUMD>A;sgr*VnFB9LS(5%e0ThX;x&AiYQM88AI${){VG>o`1ocxanV-*P
zdJW2=`Urf3>vhZP<mNE=;>^Fbs@w31DxyXFH6nnq;G+}r_f1GY=2PhOwV%WIGJ^P;
zAZlqZ5s_9&O-&$lKJbP|xd)d%v8#31w?q4g8Ld_CrB@ohhLD+HmA*x+xQ3G$zgUIE
zp2rhT$%wRXrA$keKhO?jde&N=M__AH!Xo;HpS_VGbF-Q%HUygC%b7s07ZRGx_Ehn9
zosc1YZu=Z%=;Z6^ps1S8h6WKQTq}U7(0hRx$OmVJDF;QnNS;jTh$95UwuTprXRIeT
z=i^n4tSj}R!?-TKjcXV*IbvD=G)XG+%pk=`*<yNJU2b#pf7?+wYq3X&BkEUs{zBLF
zjwE%eApk~Yudgn&ASIl@d#+_byU`bdHio1VeL-7eurVGd2Au5^LN9s}1|vbiMv8hL
zVW{@^H$9;!LIL&d1lxyQeQS?hlDJN#AI;bisZ7xFnNdI=UeUKJK&t(%G!@t8TOPv-
zvb*uKqw^o*yU$UXH?z%%&!pyj+Xg%^4sc7zf3T%!Kgh2xNlAXTT^;@vl-O=YsN31f
z@#Mk!l%)TM{MHq4L#*33zCdJ5?RZQhfIig87w}}tY4_G+@o_J73c&06F{v7U2a<gJ
zxF75Kg&=EWYZ-Vqp3IB3fa7d=Z}Hz7{E`%o@y84JWJa5wt{3hSS-W)mM6VVqGM(u1
zPP2shFo4$e;rZWs{(JdXfqpPxJpnZ%m9Kn<N`4w&L;Rnj|GzQ)-~tAb`DtMqU0Qc_
z3{bT9#QkO@`t|x89m}1BAfwXPerd<gU$}a9R#*%-lG=AI+HQGt*1WSxIfzAJb2XSd
zvJKTEa6Js~O=Gq^<oRqxz0<E!9+sfva#eSR7xcsoJGQ<}O}wcB5)uL)RGYDsbET-J
z<=%B9LPDtA?iXza*qE3Ira0lVoIgg$(9T4=5Ar(X=bF2tWJj1N?GdeRbI{qbb1Te;
ze-+A=S9>bnwRS!)BJW62=Nrrx@He*o=l%cZQMwJJgeg+QG^BKjDQ_32Zl-)x#B+!l
zZ<;^L@|y>7b_cgUktCM3=nFqd-eFeB*N)p(vQqH7&9>Yb3|b-CsA5A2Y1`lIpcg-$
zFMZ;>*CuiMsM{m2`(Arf4`Z2F`;xWTw$yLF<ws1+;4E~Nvw#6w?D02_F9U*47ZdL6
zsEOw7kevxT>TF+K-6=!2ZIwM;h%j5{UA`OI7Db@XikERRB@vC8?ODrPmx=f9LShQ-
zS6JB(o)`=`YA*Bjjr1TdsH?M3^97ek2Nd+~SgwqgbW(*yQ0^yEV$o1^6qjo@^tE%D
z_O+5gl8&rhziULTK@#Z`0r-4bLy?aW@PS(FJ4rJ5&u92(a?#UfYwir1?SS<Oc>4<l
z3X7HKt+@`qlJ7oP0^aLN@kt@9-!Zt;kAJZ+$KBaz*E+aT1%)CGfXHPD_)w&2_Wvgf
zpnGr>58HGwK58^UN3);}!Dy(fu#lW#*fc&v@zrC{)w*mA6=LXnZ$I4b^nyCE;?Azj
z^K)`kGbsbBfXY)$X0Qb_QW%Vu56Bo@=_z^_-}m#`FD?IYi6@<>oI^BlBtqwQ?Ic^+
z+7PNny}Fj28X>BTr_#oHc#up|U0;N)^%We$AIQLfsHCr~I<}*9nA}Lr(SS5#HwbEH
zvq5}ueiTuXhJ60;*jORY{4m_i!ZfSE>7lry3v>(@2w7qv$lgBF{8?i<_@dgyNanMM
z;3+cU1=@eD09IBJ=JxmJ(@=jJOz~Lb!MRlZcV>KWkqL+LIU!uAsc_=`ZP(WhMyiRC
z+#aY8FW+u|PiiJXx9IC#A#im3LN*GlqC6%24mApM&=hRtyCL2X0c{h<NUE7F);&_Z
z?;}4Ih3?w+cI74Md&QA4F17s#aoaPG#^cCt<L9LL9!?EafjD_#YhPOz2=&g6fZ+7v
zLPf(=aCBfm!ld<u#(jrNuiCU{@biKe&u_M;FZfp0I(4YJ$l1P(ve#(?1i!S^`R1mC
z4xVRXjF{8L65QSQ8(}o?Vau?7v+5IuGNrm7YR~?QN?IgX7(zMqXn&fwKk#0{FiH8-
z82SeBxDU2RPR*&`{Fn>v_BF+LV31k31XxZzVsx<pr$@hA*X7Z5ZP91KnO=9mCV!Ve
z&dM3bT)-pS%VhXEXv3X1jc8Xo49}L@iO|vQqv^EOcA`u6eU{PXEZ}a{U~77={_|O}
z>HX@$Yc5|tRzy1?{Lf_8jK?Z81D_|+^L+Lll43b&V^2DdT%BP74sDVsYG%C13at}j
zP80a$_`TR&jt<S@aqLfIXl|_qD5tXmpxGdJ@cy##cB<l*vOPw{6#mv$;$(P?Y%o*K
zQ~UI-W}PcNS)$YSKOaaz_eUGe-#PtPG8G9M<8mbP`L}`&w5bZVanteQPYQaFiW*jx
zD=$3gH<?H7H0+#)3(A#%MYOoUi^7i0RrAvw|Kcp6R~UfXsNwlpKt2Rgt=pwikttWM
z$w>B#@kn_B3$VdICIK^I1UH=b6)M%+u@c$NL+>u7Xo(<{Bg*?~`z8gp_{_~oV%nRA
znJHf|2Ycn=EOyH$%$^dskDJtA&L%TFl#`Mg0#Z?i6wF^9V*tLdD_$vE;qJ^L$Q=I+
zoK}Ik`k*!-XQ~ILIXazNG<A{@*2R%;&$^IwF0aCQaaH$rM#YV+RWbcf1Db{fOQ6;F
zb>h%2BJ5ku%PoI2vjF;(%0nb)0Yi1B`vIDlM<sqrk=mf^5-zmIcXjWZ*S->zm#9Wo
z1eCiQ7vXLqQklJHO;$R&qo|+LICD5hf1VpZX%~Ok$_ZJM^JdZ>{Vnb_dy_}JcId9S
z-0W{?HV{*E6SHJYzOLT0cjNfl?_qQ3`P?uNE$8(U0oSwfLOi>KadM!0Ab0`mS=I`V
ziVl<>ik8(^nc3)p9G%sL%;t+QUKHKhFeZ6vJ0Yi1#6K6%BhlE~c_~)mcDWZR3=JRG
zy<S*Z+*ZO4hmzwYWuby7NnN1%XMqx393Su#W!zvgyuy}}D3>oPk9l1@VHmmacT8||
z5|qkMy7-4~;>~sXo>C(LLz%|er6Dx;MD=$=8*iYos!oxRf>hMxIjvb%$e009ckw^x
zaJ|1E&VAo=#Kr_dFO<K?1X4s&@7qbgd&2o!l(RVPS?9$Ul2)IbKV{&F=DDylN~lUE
zas$H=(nu`laW-aI2>5dYs|&rhnRo`xkRSZn*&szok7>3wIEGJc4|kA=nWdJNQwsH7
zC#A`k{XmXnqqO~rR(sJ7^_!rE+2}wB5pK^dSP&j5mFkcED1y5(HwEL7gTCoTi-`Nx
zh3)z8=<W$YA2(cKXz3XcXc84|ak+>dlNkISmHtr(^FN$M!Zri&35G`qbTvoSzDcT9
zd4fLRH6puJcMbxAxbbn3Y%X)t`CD)*5%+2GN#-;R{GupYw&o-B;-6`-E_~08`XdrQ
z(_Za?&)^HpZBZuWW!Oi2Mvu)xJymQxdx{Z9-CSRp>r8|(IxV;eVLDr5NC<VGkFVv6
zwe!PRn)li;x_nQhsh@{Zs^(w96<wV5gC;Q``C9orJdu`?`nYQvaK0S<SmZ;q%bWg&
zfzNQ?ORm;?L%O}~frYox5<(6lpt%bvv~hZ{GoFguYndH`?x>T$jt}|G49@ivFM5Go
z-SG0ywtEQ1JmcoN?LYVW)N`PVF4x-2ly>DOEp$cPMc>s&-^_KhQ^V!7<WL2jI41z;
zL9x=NRd&W9@P^mfl@;ad<Nf1_0*fhUr7t<VcJbkC=s6v7)P`@TSc`mSWfGjJDn}{#
zu_zUy>;GZuoPtFO)?~exZQHhO+qP}n_FlGa+qP}n_T4jQ=El5tM0eCfS5^L*`R#*9
z2F31Gv(XlQw;iuBD?@3i)`;B|qKj)j^=IkIipNZbp{WE7gGjJHGK2_OYtPM$BDA=+
z+`Zg~a2G<qm%AaP;|KmzRi<!Hz;I$DheEXbYi?)vzj|oc{aSEdu6Ls$Us7kUUWq@<
zm%XS*TpNN3Nzh*h9j)Tf6qKp2?o!p=-aw@{p$A7;YV}36fmd+A#dhxt=L?f*Y#70Q
z%;FATu@IDD+zKZ4=$&1ggUg7KPGf(gt=3D@cq^C~2@wr;XEL`wj=_1{FvZsTeY$yo
z`FuA42hFPYxax84nNSamg{A*VaJ`lPeQ?^7BiLIvqYhQ{7uSoD2q->hZ$8!1HbP!)
zUsdl~niM0UPQ~=a()0F%WZf1ua)*NO<$J;zaFi(%<i%9=c)fd!A-kF#5lF<u?10^=
z%O@3e98&T2J;Q#Q;-)t_VPCtj`YYCr9tSvM^n3qD-3=oUBgiLzI{8@amDX@Kfoj&g
z75lm)Zor?nI)Cz~(_a&`^`B!lieTJ)+XOiD>6YII;KCBwc+a+vC3xsOFQgL(yGduh
zJ_zqS0-9TPTqw>!sXQCNcsOLQKd}N{yfvR_etP>)uJ=6<WR&E;;r3u(eImTuC$QiE
zB)#PYdy_-Ga*PdyvUKkQJ}RP&Xd;d^zSArJrnHeVDz~t(GOA=Xr~b<1cX>=fSXc1p
z6ImP)Pv^UST`=rzS~}96iP6x~7G#lP#0y%iMmw^<^93tt<ya=|jNA}KtPQ}FIX(S#
zpv0orplBS`6LMN)n*-`<q&9;UxKI6)Jh1AgA-vT<{U!X9)1pAVBAo5a3VFDne_?0u
zpiw=ncUauu9;~gl0J*?f%<ld6xK^G0^UxV?H9{&?fSZ*YJ=q_CpHllPL-E2>LZ;@?
z;D|v5>}24@l<G2-ynwOF)BDR?xBB0p<Tpw#6p1B-w3ctQsaN};<})ep22%vsGYNvf
z=7v*&uX+EnFHdD<qNlz)k(6M%6>xD3jr7fOToqo)`U@K{X@bkmgI>I%{y+E?UN8MY
zmjUq#hOJShT=Bio8qSJha(E)ioD~tprGq2G*iYh_&U-G?Rr*HxVV_>G1mQEC@{Edq
z|47MoSYn>B->a-36UbRd>&k|<InW{OEbfe49)8Qi<HQ0Afju=S?MYjsTVrSMT^>g?
ze3IG!lsb*=%W9)H4~(Uj3dX@TN^8Kj_fT+CGCKSwcBb_hGJvi}-Q5X5dXv%YF;J}e
zGNWgQ$_o2gktkZsacWP@Sr$j=fXy;H?vXf;g~eb2K()xNCs3f1vfXVyi=p#c6W@^K
z3P-VC?^{;JJSC>W$0vc>K*~yM^~7E;o7^~)WSioVQ|Ngg%%OqjNmeq=9!ryvmJ1`v
zW_Kx|I>F_Bj^6!Xp$RbO7ZAf)IR7L@f9FlJ8q;?V*^MzbQAQ7b)yfL24goEi^+aza
z8K3N5_6O0$G<*tM-w+CzEZ|UY$@nUAuHopV%O;c~9$BFmRx<<jhv6MAubvp>MIpNd
zA<mgTQLjln)?Wky1Eq5sYG_CXlrSbSxs<qwfZp7$1&xKB{~68-oz?t<SIutywT`zg
zA~U<g((FU;SjIIlb354Is4~NaQo6UbgA}<2*7i18t8$Sdk{%vs_j9aTtlqfo!Et`&
zEvs1buHxL}2Gk@oztBmdXuA_oQV7EdcP0(?<7@=4K~#n%N%2P+n{G{)p=qkzT|9xF
zMyZlCj%{f%G(nZa7rHIZwwA6Ck!)D(!6aTjt>WA;xW2YG#sjIdfSnZE!?WegL@!`B
z@o>)X4VYcwkD!7^AYwU0SGlK~-{tHXs~yHkwDmv7e;D~;9)WM@YLzfRS#ltE<d@=K
zC=Lw^<O=zLr42NtlDGguCaCnQFd7*Sg=rc7wo`X#TLTCg$v#=Fy1%z+-~@cOgDp&k
zRhqr%vKR^BN@@Duqb=l8zR}&O6d2Xj&&pe6=b2R%C=&TbhT{-Iers4L_*a*O;B*zS
zbxareMQ4hpmW+m9aR(0bRCDxpLAHh8cQaoGW9=?j$FkdXbL+_O_gr~g?y#s+7okmB
zZh7-HWT+bEf=9U#yo03urtTDE%<tre#=UY>UctjXlK>P*Arx@;mp^jz)uU$UbA>3j
zqQm%=5fDd>wgTU5|GA(q{iDCwk5<}E33FMn*6hRtc}=d=8TqzVEsIy_F~XrDLwd){
zM=CU;4Q2_XX{ib^)FdJ@kog^1*3)u6<ABx{A%3A|jr7=Ja$+SDv2Z|nwy2Uf(~bn}
z3enqblSVrx5|Td=7^_z6E2f7ozQWX5Zg18}c=`+1vy?Qw7ND;Fqq0-Amsq&?D#EOL
zfv41Zft`dfC|8yE>)g_k$FYaiAwqRE8e(WC-GjR)fr{0jW~!eT=30m~)tgxQzuQ;6
zVoH=!VRyd%0{fKM{X<A2j5y=gUVR~yV=Fz8|KV_cfH&T5_ow?t(zlpJxnCta;M+Gq
z=V-;-$y$gCC<7Z~t1G?JrfAxHGTB=R6IA7-$6^4xjfp3^-FbCC{iZ_iFh>|^W#Kor
zr{3uOLY*qN>@?W5IATtqApmEW^>O1Vd3HWFCPP2ZSTVP#>8me9nQJkec%D*A(4h@1
z)}Tu(4nCYO7o`5R$LZLNK)5GIfW^JBCyx{0kH^^R-t3PVuuJdNGli6)oJd=-SSk4D
zX<mUaNw*NEOzE%J0wX8H-Lvn2N}<aHQc%ZVaL%Tmj4JP{&u^=>3i((3$}go607yBm
z*{&|odFHSP+eI*1lApLQ3WF!2LF-s-dUh+6KQ=JU?oOlle5Kjj^e2%E{b1v^_W)p6
z1Y|r5+R~vVy9@AJXDEyOM#NZgF<_Jvu6I371(v(;l%V$;O>Zrnp|h2RwR$~rC<%K^
zy6XNTFu(1$pVXS&1J~qayY6w0()nT$NK7d$ie^=0>NTqFFc`4XwJS(lK&3K*5COU8
zEboX3k(wNTr9>NILU-z*e5xl(ZD#}E^X0JK#DdH{CO3FKXoKKnElmAmSi}7(BVzfm
zOf=OV1h1wi`rmPXn7R;i-Cj4|Knr2H|KMziZv6#kr1ud(v~6?1rHKdBDS~j}*UQ_9
z0SAdDd(Q4n-rM(t;M8L&QmQwOMrtQ)G)V>Bd{DfA9>ZLok{s-vT_-}dnn&Jns0FiA
z|BPG)9Umb=t!LzG3EcYAo`TZDsEY-8q5Z@Am@pV7XZ(yDon==_q=OI{Ida~!3p@0H
zm|C+H;k6)IS$%`46=RzH4zi;1qU~uUqgtCJ&r?I>%LaSn4MN+@X7DKjY0s7Tk4g7v
z9!c`oigy#J0R^hN+bE<t2;NAC9Y-P^oj^qukV&*jnRssy1r%Rk6kYbGMvc40Uoy^)
z^TSUJraCs3ILAJx`}u`=dMLQO3^NYJoUd3AtX|R0fLf)KHH3_fqz>xkzPk3yI>#ck
z7dzb;B(`v)Z;&X>C*~ts`V`~?1MjQFQyKSSZ&i}6+72olL&{;^MtPLfp56*_WMzva
zN&{n)Sk^4(Z0#?GDN<VDQ`hf3Le8Kv(i{WS<n2JgvZ_`CCQo^m<&ANHV)0dJK#p#C
z5=u!a{T(}4&?h&Mu~|JKqdD#5$1P>9>5c{=y#25urC${ql@7PeP?y6P3J+Nl_K77^
zGTjlY+Z(}4UZ=Yob1HTiKM=ZCtBAWot~$?GkLavlT;0oWFsz_<1ktrin(zcGd%SS@
zBw#&BiPK9oSpCZ!EO)0%)+UF>qehL0Jz1o>uNc=i<3Z3Zxv9gyIuM$2x^cLmB5R+A
z7Qm(Hdoduj`g3!>&U?LzA={b$HTTykW<+miqwb|;$XuvTz$pEfXuMMU8so;v<P@s>
z7e|plwzBl%Z9#OixnZ#~6gwGL5xj|`Lb;Nm8FIFJpG<1{2vAcv*HCV-rXnJcNk`j5
zORc{^Ubzl$=9A=U84eG^V7lZc?-bw>wETFIQ<wIsN~k1V%Od~LIQ<|15^$rRUC=ir
zgtn#^*Se{&*Ol3Dn|*nzq?GRNtJv<n%1KXbe&CHpdV}*aG0FGe^5C;DJrR>58DoRA
z8BS}HF&&Xfw1+!}bMsCEW$uiPx4SN(yS8-EwA84F&$SY^XoU$uID4{nxBY82Jl0&!
zp>a>v^!Equ(do<BaNdMsY=sOB!+aM@2MK?ecXr+|a*&&cW_+U30x!Z*eZu;rrX&V7
zGs{Y3dt-vZ2jVH0tpnb%&elq^ha0j-6%=%1v$C|4^g6>f$1Ceif(>jS4?-4O>FeC&
zqOti<^>%suUx-Hf*<WajU}@-0h0%s`-@yL`js>D}7VG~3f@|=$A_hfr)_N{shQ7Wi
zc)5@wCl0(pIYy0=;&2dezJl#!hYh16fIeXHqT}MU3xRT1&w7k^kQmBSnE-u%=vc3L
zgrrU8j^1c(x!^V!L1?r1{NY}<hqUm#{a>;-`)9jAKGe|;GT~PCGpRt-dz`qYC@0UO
zt_K5`=&jf5u_a-XSZpCc^v|PD_q%DV97w>|ex5<soF4?eH2m04ME4q_%*s%&LR-<I
z`QvbP;O(;`%N==!ekYc1ZWZgDM=gy{QJF1UD+1xP@5F?M^V1s5p@d1d>n|rc?5&T1
zwf&-1g--Ur+Y8b2K2Wsyesqt!pmrB6_z<j2U&MyizlV-#S)zVD%N?WGH_1)g_*l4d
z8+wxXW8;LhEqRMuX!Hd;VGW2G+Uwv^B3j{-1pqvOL^mnG=q6zYUhse@QZr-Djck=U
zL}Z#o;)QGFBMq!g)#WBM>fB)*NNjK4{|53~&`%gZhA2bPU>vU|9`CK4lEtgY2Rh!+
zqTZ_)7a;#@WXxM|KyY+JX^whLx}X^wc<$~@$RQ}$a^)y?r7^giIHuQK?GRDxI^iD;
zD`(lCl2ELZrKKF&&Q!DK$yj*I+!^rY6g$oWW~Ls9N{<I|etr-css?jcGz{F)(aK|d
zMP1~MlvnJNfY?;+XN9OYCz(yiK@UrkHq=lIXiGCEK7lwr29=hf!2S9F8n6o^klKnE
zkp#s;N(@b{dhA40)B#GnK*N6+aAS9H$Fm(uB05>{fIeeJvXi-vd8)y=BYtDQ+46uU
zVCt#MsveUS(5B`g%vYx%AJmD^`t0GSRFjl;nr!1X?Snz~gm;jjiGH66nZk+laRlSQ
z1Ztx$k$~_h5xRe4rZqagclUt;Gq*>oc}((9=bgYIbGzv)?bWFb@?9>z_@Q!~3yHR&
zDBWH{HPqIo2t-|%b%t3RBv#$05toQ!Pp?Gg6j;twBcY)wIHX11*LZ|S#W)&(t_yUW
zTKU$eqB$m%<?5;9oMTxPqr>A724TG+hD0K|e;+9x%Vof!ks)Cp{|4ADmC-luNsj<x
zJ5yCYGuo;IO7J`cvwnZab&XE0<TTGmfr*N0q=9yUwD*6>NY9*=O?a8`La-IlR*1&(
zitrKrhX*c(P2IlAjwVn^+WT4GjZdERD%PTf1{wIBMg`2v%Z7+4L-r&=Wyt>cdL!TB
zegi7*qG``khXA@k8bemKR&?@!D;AIR3v2`C1%jb*^(MtOKRVm6`xk9c6HUMjTRZyl
zOjVY!S*^5qgK|2Nf=quHFi~w$k1ws!N)qH+=ME9Xa-^ji<G5cuqC|DRqIbP=&z}#6
z@C){KU3%#-#)#)M8UUsWANNH;!VST`xk-^sl79S%^bw?VhIH=@R8YncT`qRlC1EHl
zn&w7o?0`}7Q~mGB2|p4zIf~o;@eLk>le1k{lQqzYiKrnT(MbxZ;}P1>o`5PY5``ne
zJu&DoPB%tgp3vw|wl7z3StRJMnxi_R`ZIGfN1Nv}<;^e|zWF<KiT}htrz_DS7L#%h
zKL8swqo6?TU!lgB`pCL`vtNcYMG8>^z(xJV4A{Vn%ku&iDJ1RhGw6YWHf*c&{D%li
z3D^8`o1t4!B3_D!(i-J#U6;%PVB~n=JZ3tWfsx-15l!6@h0+N#gbS(Mtl@Mmpgo2r
zx{{{009v4IgttX7Y^$tuKji()?144aF%>769TK%5CMASi(@;g%m-lms<{wcjDkYT5
z$n-|K6WQCb2w*h}qD1s2tBz7stMmmqa$!Mbc1K`BNJ5&nNmN3H2P3@w6E{9b>qbLP
z8xm(4n!RF^u%fxkw!2NAqwy>cIN+S0$_X^0_dscTxHoG{QIc}$$fdqa@NUd8ab702
zGe7kXdY#UxKajN3uXv2Hm4}ALqGCk6ok@oY$Qd36|8_~P@MziLuGCSM%T>sIW{B^E
zPOu(PWod5cqOO|1hlmWTr^2SonS}{1-)7Xhj}#RTvhGHRI@IppB<7?b2EQqG^2E{S
zeuc`TER{3(rW2v^L(8!-dvm5}k#h|pSk!=m3j0eCG-J3(8JPl}cwWo+nn(dE_O7ei
z<5CiRMx2pBqEo_^N^$U*sjxsyPDu{-Xazfx>Gnv5x~X)4{i`bp43^TQh>Ue>lGO~3
z5BBD#+10)h!RW+sq|Z4C`Mikd{A!T+;Ijz=hWTYTf<bV{5ZP%pA#GiLEWEt^Su{pc
z4Q@$JVyt8Y+%<}K%)z;VOnDzmZ@G}1`2-3WY0J|{`J+Al1FcxeN?!Q;%3T-wFfAJ7
z8K9x``_#Nda`b}xy5a=JWl&6tyT?p`H6)9tF7CCO%p4yvfGd=*%4yroO2kEq6A-+S
zt*O2M8>%)Dyk3y0<G@Vs#UW+nGedHxvjke~G2(y(8P!I{;6REtlH<UNWs_N4|6rY~
z?fdboD1cxZ?b2R#jiG8#U|a5Ke^yg^=uaf2NpP49z{IDS5Mt4=&1ok4)miefaCP1>
z^m03-z&0?Gi$kVtCJ&(&-*72$K?$W8K~!B`ojWmDP-th(u-q5%4ic2+ecptzz3y0$
zGeYYaSCwQIm@$&&dB8Z|0FRADKygG!h-$BjqNl9)Z0V78vZdp3>_K@4K?~xsG;Gob
zlt-HANFL9)Jzs$U<G|G{<<ro{m=n9Pc10kH%pVs>r5DKup26ajM=dfp`nUT5i#4SA
zJpKk|T^SmqNf}fphhJ~H4z8TKG|f>6iqM)l$|O;GEvv3qHdjMDf`|!{#hr7jlqkSS
zp|lJ<o*oIy%|f<ZZ_O>wLNzdo(r2)@$`4b(K}$!che?E`(^lr>6Fe;h+s5v~XKCcG
zBIZ<I=;upSHt<EnqDntK<s0R}2>H+$lOj6^ssv|S?F=1{hS1m5g}NRuLd4x+S}@9t
zt;xe_7<+iACd|<Wr<|i_^5<siKL(qy<nR+S`4`9VL~G3oXcZD>aXP_p`re4cd|*(q
z`Njd$8fmIyIsI|!)9kGmZ+oUewfjcFK-r*YWAk_D+(+jT283ycji%n3GmvL9Hz_5`
zbyZa&Hq=?uJTIWnqR?gGahb+j=3D;Htey-W{#+CP@CKra{Trt1Q+nVlq>;hsMjqQJ
zwZv}BA>>;{+Z9B!vxC2rJ}5R1em{jjuDs*gONL*}&Fg4*j^M<<hJZbe#TUd+wLww9
zPr_miRd=TvAsqpd;kDX=Nj@cAJ(}@U*}KaTpk_3sZ*2ngp`iqVk{P{fp7^+hcG1Vi
zI$iW@gPDDT**>E3io|1m&h0t0v=ho!0YQ0p?hlHzjLhT-J3fNY<{CI@5d^3YGQ%rF
zi5W_THw{7%spk_)lhios2+Fcjug*~L&NaQmBB;1y;oUljAF_+ZV4g-Z9ce?~n3^a*
z*)O*m=GyP#=zRQS-mKG^<%QsGQv1oc^8<!?x5pcg^*aH!AtGJcg>psKZwao1h%Kj*
z`1Ah82-*r;;#n7yk4ht=Ng&-=eKlpR(s2pYAXItq@to=z5)c+TY<l3!3u<`hAYWbC
zmzr-<r8k4koR9wgo48qA4FXXUHqm?ZMB6}SqC%wh^yQoWHSLy^Dm~6Jq9Lyq&#Z11
zK+<9<LeC`s64TcPq{#9f(TZ{Abk%v{Q=qReD58>yyU)X99S!X0fQ}eJajo&RN}#lD
zUG*J$zzgd~IQs__U@+E4rbt`$1|j?K2KG~>kD{@o=<B9jX}!w|Hg&yo@K7V0^a7#7
z_W{h<H}@FMw2>8T4UZHOUR(dekQ>=vca{{SRf^$nF}U;P66ELX$Xp+L5c1z74Pob{
zr<f-M_jgw6Xl#1#Nzf(=#?UYt7?N3u;a1us`lxXO%iRttRGXy%wp^b;sw@=<?iCg9
z2Sf2A&ACMqnd9`qwyjo@k*-IE#UnbQ(SPZ~1K(k;UmnnMR1W!Lm2z;IrnVNzG<j~>
zDMMcm62XZ;jz!0zGD!?aq{7PBd?KP`^uph)RRcT^w9QqYNaGoBr^dtl>j~^#HuQv5
zCIMSP@|qOM{AnxI?P}l*PEJbQw~eUfPy7?bG}b2*(W42p1+@$Jy9wei?>oSc_bGpN
zR_s!`U{#;Q#^0rAz^5}d5UQjcddH>FnZtR?F6MVaD3Ly`T%=sC(^!7FN0Y9pVv_I4
zggXpobc0b>0+tA@jB)_{L*Vd~DU1?$f#g0Hey_FoTi(%Po)zoY!(d@RkpUU%oi7kZ
z$MYvP^5S9xb}Y?swazC2tinTZc-DG*!g#-DEpIPZ_>!26SP%`AF*X|*yKmqth$Gyn
z9ih92t99}D*TC9z^f1?VpcW;AoBgPe%$EKN6R0Fnu)R^KReI0KO!jsY+LmwkMB6Ho
zmS0cw72WK$NK$Nf7}usjQ==jmOhCx-6vkFI+^ly{Gup6n&1(j4PF^i$fCx0+(X^0U
zLLO2`wV;9dibC40P}x1uwfZsGo`I~+3$vZQe>T8SeA_X7=<P048mQ<!3o$U}RRm2?
zk|Vd(z$^sJeDJl)zg#>-54etBUGskTUU#%Cft|&$!s}B(wT}H*S=pDcu-|o#^Z4pc
z97?0wvFTB&hx_H-?CM3uo?w-_cpFeQnp$-zysd(^ALFSTMl)F<XEk#{67xP)>)${F
zqSeO2dp^#vl7UWWd`DT|bSd5r0?)XU=A%KU-4LZ~pKo{DzxeMM_cjIvW#uLj^-`$?
z!{1VGcZpP2#3tq}&X_CCX~1f5WJB6N0KMK`=qV&LbI;Z9YkB*TWrRXa_j9B3*La6x
z=auSQZWyfNT0{V<-m_E=Po~&~G}zRmQ~etVre`-+VNstghYr>@&_LIoJ;6P&klrqI
z73)tJKu}!j_6q|yotSb42NSZL(m{z@24n&<9-4Mz9~Zuven(75FS<!m2INSqm1>TD
z;yr>BFh*O24^P5bxhVzm1VlEQi=O-ARBecg+60F#m#e9bJxM+hxMcb)PWrG~(sWct
z8|hikSp_P&dS!Gb7wP<~<>R`Y6}sqal<3=vb+acI%0VB1ik;)-n|%&FGxyPH4AoLm
z&jXBJ%jkqzV)+@t=klon=$VzYRQiJ|>?e5_4Z3F+mn)|!5$xqvT*2JK4Fn^`{Cn>#
z;W9wc5gIvy1D=N3U{U~wfuQP;a#ynkRY$<J(MN#Jj2V4xS?HH5hT0*;@Csj}^fe<5
z3?nLQXAc4_Bmdaw?|GJ!cm~=rCfSD=79OA@O?HNQv#}PbAx*2NjYb<(4764Qu~HVJ
zk)-stGrE~IwGC?1q}S>aTq#Sfhnil;?33auG2M|8{n%(?AEt;z%+!RZ`D+jTQsVb~
zlS|wL=E|a{oH(VmKmNG4JrV6%t9dTQ2%t@kBQdqD1b6jZ!r$%fF3+D^c4jb4LbfHw
zcm!4GlN=?J9IPv-IaEA*YS<9xA?;9Vj?xgJOIrZpJ>k>4esN+#Df;Ujul7vTNH^JB
z^e#qJE8b3pFPAKM4GRnVzh1yQIXp;IU=;hN`5}@%HftG@4o85m1TsJ1m#a0yA$h{=
z2?p0c={##aeDLMv8g;i^u(z}gp(Twd12eb-X1jW(Nm<{iB2ZKNX)d217=IPB1TBU@
zv>|h+k~HMDzVF#<8f1nG{+&{>gZFQ%!^lQkR$Qfzr`!s1&HGXrKrG%3oiTXOCaZJ$
z#uLhytmF=zg~g=PdVw@!Pw^kmKr^eL)Y}Z${yJT}(J!;9;{I^K>S0A{5S<uQhL9n$
zOV}gx?CEmroPmEfDBDUcuaOe<p!q=z<*{`d-neRA#G^l=V@PM~D4QXv-WL~@-LN^~
zUU}~Hr^tkzeVPk|0SNde-?EHSgDNM#YqY&Q%B6jN8+LW>fVve%&Q&N`ZGBsU#M&<u
zPJhF(1ZeCJDbnDq{q4{gfe^9jofPl<+caZRd~A=w4l(J&G9QzU!3O6O?w{mb^<W>I
zlvu#V+B@29t|%_0Sa0*_Nz9?%pO!uIe5bFH`Fq1R@TRzFlflvA-<iw`2qZMVtBs<N
zx)AYn!-cxtjYB{e*?*phHtwJBhlvc=syqfCcN5vo<uLTTZ+#I6C=@P$Ej6~FEz#qb
z&J8_=DTt9F3F0SYj>$E+H5EkAmWR|SoG29bCTO7u3mkkpAhrkE;1$n4y%<>a#%BI6
zSBlA28ak3aSX`v@T>mDj#nf)>I7)v{2phIEC4&7c@8jRC`NdYUjpjxXTXp&yM~-7%
zYfNn)PW~*EsJT&zm`u&uP`IT96r8PS1cWIVI76nl7RMvolTUEDv7lh&keq+iNhH_Q
z)YHSCV=eL1GV<m5FtyqKv-y6IgC_Q@PDiAO;ST$hB7&sO6e-~m^V3zx;&QnR+n`X^
zt?rFJ>GVi0137m0RbEn+%POwvk(ro7gsxMWDh_OgO+zFtx3{PBn3)+-aUXj;zE8ej
z7zR~)Hg|VRh9~K)y+ecOc>hlL%#H$`T!OQUyqKdj{jY~djUu(QzSe3Y#_?Xv>AD^U
zw!iM|mINvNW?oLRq;13`(2RjWM1;wE%bNt`B?xS^(e$lX0dc(EaI6+?_HSbfS)9HY
z<<18rj#$oES7H6#o`RZLk-M?qfs+6b=DV$Ap>S7VI3l33x$FT{(wd2Do_Rh{PBqGo
z{$@=C=i@@cj_LJQr!C>&kX75;wzua{NER42tr2KK1u$%i7P44OY5xisZt&hwdO`l`
zwRHTKuRd}d$rJzc2Emryv#bL}$I0snlN0xQW1WbK6&?&OmtWai^Kesn*}Q+fkOdUx
zA}Eqsu@2N^Ubh(h5Hdco8snkx_Q1gu1e!*B@46(cv$a6~a2-?V(@jM7iN*GcHms!x
zT0j6U!#kCkdV6khF~EXDT5eyQDqIM;SeSrb28j1%!yOSZ_{dPE&C_cj8Y{tWCuXl8
z1T|~JS>)HZhklQ8x|u3<5AO}UME07Y$xd%gOA6x~wy!!~Yb!5#Fqy~5-NSQ0S#IWd
zj62v7G5e*-%G`N(<0Lj(oTD#3Tfu_X=4nN!gsC~8u$Y&Oa0<ql*Z8Rre5jbgvBoHJ
z1y^UT)lKV6`DAbZ*{basA3nll#Qbm@r`yX*sF4^bfAtH&XdQw|FE?_cFY%3sGn7K1
zKO8)c-_!u)>TTy0hZi$(6ju0P*p9((@pQ~eRNmmJp*n4|muOWhm<0-TBTm$UdxqE|
zKQ*Yn?m<UIosKf%Fw}_W$?YglS<4-5xSl=L)L$SAoX^ExSbI3UKUrD{+Au@i!v$G{
zT}Qze+V&*PC@%;WC!^w3#lFkQ(Fh^QF5t&omE{?5QxKEv9btG2+@w-PtG2(nB6x~;
zC`BVbEXLHOyH2SLc>bJ%JaxSQ<@G>0JM4KvP?(WLw#ovEO$cH_W%Zc9gt|}VMXp5@
z)qd_2GD&{cE7?(K-K0dhJwv@uveZ+MU`g$0&)b`!i+Z|UQiCTS{$ld)ofl#RXw{VR
zj4e#c-v{9=iTHxdYzoV7DO@^zZ%zp;AMEIy$hx|J?tOrvHb@KrbKP8YJ!vMw5l`nA
z%f3@yG{Y;oXfUpe6+<F@ZT@IGwg3lLS?U`n$DP?5ij-&{!jMYKI7#aHPJj^y42f_x
zc%#b>bTpz!3#~{{bg2jjV1^WP2fbtgn318-_U0c5kUBP2Gfs2{RBM60Xmq_us$edT
zHk#&3eeIm+#|T1qt&ILQf{}=!G@lS!pI*)ANkn^6g&7zx(h7+*$ZSsZSCSDGpZY%}
zJjjL~tiykRENWfI3Q$Cb*t-cvq9IU*4lGrLqLasFqlUA!ouJE%MgzM|kA{e%(O+%+
z9T*v62}imu8B8@Hn7oW490(b1cK#fWT9lU+b+;6CudLPkJGgs-2En)38+e)|<3ORG
z9zZg<GPn5a@I*V=Gt{8dU?jBcRkU1uBiE6QoQ6AS8Cvs4tJIQRx(uzSJ#QEWi*T1S
zG;HJ&+Gsf&^3QkLfGs>tAV`9ya{Z_w3?{2<$??6p-iEN2KyGz<V4Th-APbhm^#*j$
zRc9j!e$(NXYexa337euC=c{PpB{kT^;y<n}5(bu(BYiC7S{zyiJv_0$ed-NNf7ToG
z+z-$B`iu>cif+YVwK?RLV-c^IYY$0tx%7pnh7GEcfvC}i6ey2^n^??)dn7@2rtqF^
zb%JqIHzlAx!lTt{3%NT(F6w3PZCA2&v^MawE-KS%x64`%GNW&_2A2}CnOjQ5r=mxB
z5hPToIO2T3UE|0kU}E)uTVX%7e_RfHy9I&1h<Z%OY{8D7tdWie#nT%zWv?%E^KibL
zgW^GP4A{OOx_xkvY$0zH3eh=VodpMqHw*yJhebu(9StsR6^gHd>U6r{8|h)4B^MFx
zt+;5%Q67V8%Ls=7SB|i@vgXhuobJ>5^w$7+<t3J~1k&b;NrH$lPd5CU7zoZW-#<MB
za`-S56XFG^-3VB$Lej)c!ee94NR$I?yfvFfIzaoVfB5hC%ynkcO~Igqw~tuu!0|g4
z18g)mh?v`xKR{NJVzcJp)I9^;%qLM<UaQO~=T>%+;V`+a5%2Vshl_JVLy8Em`FzNW
zp3RK_ePn4Xn>63~=>H+Q+rdFjSlOIh#~#lQtkp~gPz)N({iZOk1@|ea=yiWd<&dAV
znA?-?d9t%bPqY`zn=F$NFaUTo&UIyy(b1E?sG=sq8g^=`E5GHdKAeg==RBU``D|6&
zD$e@OkM{VYI~%Cc-AQBi0M&F-s&?Igk@mB~VT{E5LwvMi<uG;%UDvOdBdGWoQAMa3
z-Iv5Tpm*)G^mT0ai@ac$wedPq!E(#^EduoFw-o@gC9M<`QWF5+G=#BFrOzqnGH>A9
z1DYQEkg*Q0<Fxhfv(Y<JYIx1gkel^$JO|ap5*u$O3?V6(SR@pAFwVv}CTWe2O7__>
zTlptYs10&Q@X}na(T<ffsOSi8npx<6f<RL(Nru{*Xw~@CI-1CVRTP!KMqlm$Gl`QO
zhGS=2lAg+3qLR*a4GA!(NhC4>8t-#a8WG<2m8*CW?w3gal|O85u+2{oVgr=I8a$Tn
z64F%W-B?`su+}^1k5*}QZ>W5eHBcTiW>l$hLHKCq4j!ujy+{RSFCiCSyj7Gr$YkFs
zjkiE57!gcpw}O9$J*dndbs+c}6e?jvQWn$eC*7QXj-An8MrD?v5#AM7t=2kq#n);M
z9BY#hU26)?*gS`Xd>9&}r%E<!LT9h8IHAzTA-H1@r8*K6={kA4bZ52aG-%$&{p8kJ
zjvY3u(^ad@#@In?ULZDdmMVx)IH3pFp-PDosA#CNppmA?7)_}pI1*TXajQGpM4+Q}
zfk&qUX-}MI^9p%4r+te=SE_Pg8da?Wr)+@&+0>XWsc@s-XTLF_M}?cXBE3)wZ#YeW
zcvT|-kp2XApQswCKC%br%4}Xn4WwDXH%*!{*qYZayHXo=n<BYxA&*S2TPdMKA;pt}
z0!OCZtZE}jv82P`8*+{>-iy0Xvz1sfO}CZ>#OIQ}HYGC?bggc^he)K5eglwD{JjJ{
z66WqDRSS>Ioo~r-f)q?)te*>8Sz7wXG||bdWGCU4`>xMgT~1+iIjW6g`KO<2>_pRz
zuv(OF=()KFwUwYEi-g3CY;e-lyecH42x$4>DwOfX_;ge8e2Yvm=#KEHkzj{qQZd@-
zu(LDz>vR?n1>>esig-Q^Q>hj{%?VQTI^&4Q@FbqdegIzl1_S*9)|BuLLkaXXD*eL>
z*8`Kp*&Z}jb2-ovorYw+=5j~uvA60wtA#mBvyFi&^*X(i)mHJo{;~i=cX=A!907#C
z-w=I?YqnsXLLy?lxi}ZC2mm{d7s)HC`XS!=UGpwqtNizb<G-Rao#5fqS@eGW+DG3#
zSi8Criq-mGnQn!yd%rSG%d3k2^W6W*$K0_Yz+<~F|Eu2iz<pl*k9he1gEjv#UcaTn
z%X8iRBF<e*{%;KB|3qxu5x~K@!d8}f5^9vRx|j_BLtoCJiEi@ZqGiiIPkX=DhfArx
zyfx)Hn*V5!gq|_A;Z2R3#}i*2c;pPgWY~V|j;yu3h{O3sm%Lc<&06R#F<^qSqO;jB
zUMV3VB#Oy?oXAQO;C#Xwht&i8Rcaj9o!<(eYt!9D@M#O$s!E!upN(>GlF0*h&Ye-7
zY+@4<HRY6SKfm^fOn0)n$;tI^*&6@#=e`t#?UG&v*714?jf%m|86x@js-G8zm71J8
zB9{V~;<&?whTl7H&>P;f{msxOxn%JI#(O>`V)ONSxQ^ClVMRg7@9C1?DUi!-m7$p8
zoNZ7;{a4u{H)lZ$Z^niT#grAUk8?UrS{w$8FZeTV?{Ig>fbT^S<Gm1v+GrswCJ0>N
z?ls=?T-(l%um4%q-C+L+IlnH(A|yzlLHT04@7{73;w=07;XB)E`M=188++f})kuqI
zSW<dZ1T+}+9>@#cIdtDjqw%^;I%0fpZ3(i9+R@QhAHh)BpRmEHoj`8Usq#+6Kf5`t
z5|=C0O6M5MxV4)NbgI-oKswnl<lVXgzDj#<>myl3&3bQ0c+AAr^l^ZULEhQpru-eH
zfn@3*GVr0%IFh)B`dgk4*86E0QV`m?-=cDT@G=!CI-fZA6tkzoE8f2%zs$|8gJt(j
z@qdxREBQzY`At|D+uYCt6t0oiyzYQOK-%_YtH4@wkQiV_5wM?vlSKXY`X_A|tcE+$
zcUA&BCniCrfrWLrV|hAZ4-}j_`9)aKe>aTH`FLY|dUw^fE5_PTMr1Rhq7f&^M!E4u
zWDJj3G4*V-Ai01VF6<Five1K<GkJ+s3?&o8)Kn(s3%;e)J8c=O$VriXiDAKJgZsy=
zuv2P%AYeJS669R{DG1E&r9z=bG>GFTU9k!vaq8?h9oNu8_8PS|)>vDaEFdBPP!x&2
z;tU5*`TmZJLL|Bh)kO!Qo}8cFZ*=fX-{_*v?{IU*3|AyG8Ay-dB%$nG?a$uN_d89~
zV@K?9xI?1O=KQh3X;4l=+iSgjJy;AP`wf6;!#tbw0@l5(jM(#nOArHy4Hc2qeWu&N
zl}$l-5MKOA?#eBq0;@MXr8CC2n22T8zh?cDING*%%Hsu}J@2oQKLf&S%z1Yb?5^qt
zCVuQOhmWGFY`cTU2<Gl`6{tWLGgy7c)aKA0Z>Xr6)?<w#+R{o~m;hK138>B9fY%xK
z#zyg^QVKEqtRO)-0ox6hpZH7;jpL5n$+r<}1@YT>`{Tr-?Gyh$sUR`<yt{=bHT^^{
z9Im)6cS-QR7Uv5#cp@+M{JCE=Zto>-rrE{}nv|xYD-ogu-*@`htyqthe7_GnPa3ej
zW{H<K<}*e^$eg&kzNHn><EY|Ou)~XeVa0@>^U0nxI`d9>D-#&druqU>WS5mmNBgHa
zwYP&XCwQ%Ae6=6!w}c9%AWSZbRyqCWP&fpwttIxK(dh(b971z8$^F$%D2^GIdb<&B
zY&l7yY6Ki!PAE~+6iQhsfqY6fDkP2&lDP%8qCVjAGxG78QYp+fNyvaB)5Owl2r~gg
z|8%Pq18+Pm?C&F=D_*QvS{?6%4B3AC1>9uHXMGzZHIe(&>=<gvi@!A3q9rABMBhXZ
zwPR3Or3F&V46J>{v#R*YazL$0m0<SixKq*1;z$y_Hb6d(M2QX5955b%Q(QR6P)tS(
zg$Y_M>|hZVg2c(MLrzxl4dT?RY6@b>sYePOJgHsv&jH9o7*pbFdRdv176dAj!XaZ*
zv|%+70PPRSb5nA|H!ya45<^7D3;Mtp!V%I~FE!Qky-pahIK4w3ilSTZ_#r2eEC!O?
z#YN~;bW^3AYOaf&sT4S-rpJE@3-qnIkpGa~&ZMzhP);yM)?bqO8JC*I2gafqe?CP|
zi4Up;8b%&DU#-V1nG?M2@I`(ZAT#6_Qi=0ClB=&Pq9hvvx0hRt(HuvDEiY0_N4x)G
zQc@fc8>$klgr;M>S69|2DgGhiqCj~UabTArV?C1mBYDkbxO(%PcKQmXI#Pn)8-XTb
zpP}PZsy`X4G1l&h-pI${uP)jxqaC`}9d{~3cY}#Ky&KF(h1b-)>aUxMtGr5W_6Z!7
zq-eSUy5bQr$H*8eA%g4b>w_NJBSi1I#9cKZF|q-qHeVBiw(-#vYS07N!@0XB$wUQ6
zO1qQRb0mWdhBFc^XQzLsb*q0-v@RY4I~s5qF#vLjQeMqgGMxolsq9Cs^$Qc~cZZ^z
z&6<q;7;pz?W!vnp>|R3Jq!uYUrz-{GMdH>@Kt>C>5N9z@Yf>P&VL}<=APzR>#ayCu
zu1GwP2HCSjR4&)4Rw{x*5U}QjkA|DAH}W*I@#Z)y5WL+P3|aA+fi*d%s0qOXv|X|8
zZHPHxoKe^0B+}c1<8XNfCr(Rzo?IX<!b3``R?}P#;4zm>PCF)QX$tDQMvq0N<vcu^
zOHHJ6rKH)-DdYZ;kjQ2Oq*S!a89J+2L+~bRx!lL^)lBSCc(8AR-SvQ;1Ef0=tO1oj
zCNZdXimTw)hb-!OBz3}-nw_jNTrUNw1Y~GB7J;o!N4|`rEMLAX9CzzQ!TjuE0f+p1
zaIYbeTIV^!@&=t1$+nv@0g;D_I_rlA7nNE1>$ze6O!goDE_0lJ3GT9FIX_MDUOs<k
zdiGxd%oRs*4Lex}2?6=CCVa4QjCN!@Mv!fo3J*u<8vJZ(l?D@Nbz_vI{<*cZ$Xt^p
z=s6{b%4F1I1?_~MAaU7gET?3=0%8*@hGnHi^yKBd*yuSts0Y&!4~^yy(&=VpsZrpO
zk$-M%ToDb7hjQ2)HDyr%3d(*Z4;^^@Dq<KLUte03)93jTEj0Hbvi;1(e&`}{dC3R`
zBFBp`ymxFoQ1pFM<>bejSqIg)rGW?vXnY7>c%sly(vz#OM)At>y){!wJb5LNT=ju^
zn%c5p%BqvIomwu#1G+ur_2Z;u67A&L;J=uxCnqCCO+}`FK4V8u)R1+{uay_OxuDTB
z)On>OL_9R3GX;PP3`mRu)bT&*+1)vo)|1lGA_%?Yi06G<l6P9-u#)@DggaINiddQ<
z=ahQXDLv{c_{jsBPf9TCB?^BPNj{3mOMX*QBs`TO{V+57gAbXy@s3W7<!M^b18)-Q
zb4Y_GmVIfk{^q6%&3NrkJV%8OM3<C=E;W!B-oS})aD<|r%=yubk9JG-9<1cZ1DB51
zo8FqCZ?{s(;>8V1-O}>2MJZpod$H7*T_iZ^MsArID*a}__*FXY4Ran{;s&LDxCB<!
z!S5)U_7xJNnfY!7@mp|tH~5fTF(Wgx{(a8<g1!9t1lbyy8Y(cbiPtv#V<*P+0DyKl
zO4n+zc*6u!3(#&v_4eLDbbKWCb`%Cx@sz+s77L&X#Lib1e7*P1UN;dm6AgM0i#L>c
z=_WYn!_3;l2Uaa@4xCkvyz>oU=ball45cT4WyLe3{Q)7-iia<0kGB=N&$mgMhffJa
z^W~jaIW@Z8Tv>N7P0w9IJJK=ElP_h~+CUGGxmp$IXH0rxw+mwDz?no!voEaivIRy)
zZ+#-ldHihVrKybfePmV9eX|aw@XAIndP}IQff>QpDm&P7Xz*rut}ppna!~1UtFLDX
zYn)Vo$KwOw*<t}w9|72iR+Xo4dU)}!N6x7PoW3h>eZc@i>YYAGXlL{};AYR*9U%dy
zQ1k^MxtP1RU?+B`#m~<9`i)%;7BeVI8J45fikz$C_Xh*T3eEeAffyq$LGm8n3`s|P
z18%s}>O&w2BRFo>jEPYPw9^3U{#sLRSqk%R-m{9+73{jSE5ao4Loi4-URyI#Zk2ju
zo><L#lL6;gZed@P+U0GF&Q3dGx>u)fU2EOmsX?F;)s)|s5tr2dVLteg7TsTa6w^sn
zHtcI&#Cq)ok2{|{X^z4@8V8_tf$Yus)ej*@I6u4yDjafUU?|q<{VyL+dY(U3*V90x
z6o&}ojShQ^Sn0RH%?Ay4hbstdX#Zd}4fA?`l4Lb~UM*0~*Hc}>H3UkAC#OAq<v&1=
z-!U*P!su+6;f()}y~QT1`9MHxYinR@BXn5mm0-;}f78GFc&RarkED8`1eh)mD{Wz&
z>7}-N`lS2}S||A(S=YM`;*Ksotm00o7Mb0dIF&>8MC+v=C!EhM#9EyP=oWc{k}R(N
z(Uu9?j<Z5i=;1`UJMzD{*b^*Do6|LP;31U3f*C-M_5xTsa<t?19z^v5^Vi)Am-|$5
z1g}juhXd2_WaG{VzhkbL-aFu~wyOTwLbCeWB24m8AYFKSfLlt{U?H>P^MWYwYIpxv
zvqKOusX3nm$xh}8n1U{Hi>x=!s7fH(QU8fw$b3NC!zM@8y`MkosO#W*Cy(?2PQCXd
zpmPzbzoJs8SMT-k2^^e2;k9{{E(4S<=NF|{FV(J3^!C1{yB!2sg#v}W_3Gc3ihwbr
z-Ggg5SHff<mA-o(^^}r4z(;LpJbPG%(Hh1-b8YDtnE(0ta-#tn`gAoIQjxxMDCL%5
zpMaXIeQ#{8p(#aURI(l<-_We{spb(9A!e@F1-I=QR$Y<yhdu5Nj1y~3={ADkmW$OL
zU1F)X94fI>E)X!e1y#4RBG%vyw1wp6EzYsk-DEq&%PR>B)4rWKeKOchdPHlsL)tmJ
z@`H><Pu)eio4u0`nXqNcRux}{wyxL?l-0PBm!^b@da%OU=>^h#DKQwW!%APQ3KoAC
zkkAfXTbUu`7zxZv3vt$T*Ey<q;nN|C(ShcjCMBZ_7)%C|vDJM%@N60M@;R?~ckwPE
zLFr{jbTYKl*S(B%%(fg3#j=1)G4G@E;^mkg{Warq0`eV-gLBlMG>R_YC%EMSpz0PO
zkKm2f*#+&h9=de5+u8da-EsRPs$yg1x47U{TNED;OAHK7(gg2$3mHgw9SKQD9tc@L
z7)e-0PL7HUf*>DHD#Mvi+(caTcgO3N>DSFvkrbwT$7{E<$FF65WmWWly8!;Cub*c5
zyu8nBc-1z)P|_b_)|5^6huL)pAQ{x-SPr!t_gU>3sW+MuXg^;n$ei?2-mp>gsK<E%
zVMS})6QqEC2&AS<_v(E>^G48$j6I>%AEf8c73Pr;BKbM(s#?$94WdcvQlsh7=#(D~
zUl4=dWxg=#W-cYWpZ9Idqef1NTkEtzk6v8YeGRrUEX>D8(%TqsaYm-<dckq}0^R*g
zkR?n~pX$o4(9c(n&TroyF30)Vh)HtB_PCpEX3mx98yxRLDk1q4Wpjnc+*%KYK&kER
zTZY|iQ`jejNy>`*pXULb9a{uk9{D`LRPEcSM<t181pGpAk{9<Vjm_nYY%1bNhAO7x
zhKMjIc~I#eE#M@g@`8<q8LYMV^0fH6AnX*D9*igOV7%oh<{II5H6zx)UVi`E{1`C;
zs<wq7Q9L!JA7x~`2D(3!>Xu~lTRLJX(tJEO_g8OS4PWy!>n>N3YJ3Fiy}uXF7U09K
zwRQ&GdUZ>un-H3y2<vYr<s8^6;5kR6`F59*1R;TVpxWG)`*&tr4iu#^085{OjQZTG
z(wU%55_<o6u5%Cb$3QA`Fb7i!i3asy)!59ocphkIMNAoiu-n5^P%|}4l*fVH9BoXm
z9|9ADK&&mqwx{X-#D2hxwL2l>`8^XcVmjwd7r|BxRB0{MNR{a<EtCvN0*J`cup=ok
ziEu`+%)!9jX7#tk`3iUIs6FAVcfae}^pd+fMdZT-hC=sOiBRp6PLCFUhm8qc7#;*d
z&?nccy!N9Xj18%`pGza=&whrNixih6ZoGnq&^Gr>!B^ssbYIyUzrvdK!i4Rn@tz4a
z=87PJ5nPYzxF0^ZI=rb;=&Jt^V`p1aY-0@$PJ(~B*$)0F!OdAc@S|s5^m`2uTCcaj
zT&mU<tZ)I$zk6h7Xo@!j?wP0rg@@ClJkoMw@3vwfk)oiT5b&^ezdH;sVLI!I(<`&l
z_ku}rL{7A2=1-qCF^AaES!sWKNC`6-WtrWXgWJ^=DoWW12*lu;#^Ofp3klU5`_elv
zpVv)`1nvcYSSqi*>)%(D!;@(bW(yjS6}mK}mrOcadm0RHM(vYIjF@S$A{|!7pPws?
z(wh7QM|yip-|s`iGq}Ace?phT`pOZicic{_Baav$!|MuTYDG%#&c?9K#JoC7JYf=i
zkw57;Dv63*K@M!$n25!^guGgNp(yr2_>j4TgCHX|5R(yKAwvE8Gj=H+#%d3j6QY9T
z?eNp5_O|z^muC+?Ffsa3c_YdVta+;s-gE=QA_I}?J5<^moS)XI)zFeA500ipgO8|C
z*k3*C31v5x#s$`YGW_=Pc>VUHjQs(F7e$dDMDND>P59J!cl>g_=Es0TU_@qE7e_Q2
zLGb0NxqoL1i4$fw=yW9RPS4}6BnE{vvnT9~<LTNM9P%f!#2kJOKTlwfju0R56W=%<
z?z;Nc;Po@}exzg;UR0)OcIlM9wibxi=Ye;n)(ek9^Tz!-WVF1QMPa6vk<<sV3aF{v
zVr5^mN?99gBR?~Wrsnz=bYCJQ-a)420yi>Mbw+T?&a2*Fo3pFY1WY!Vtl#Vnn;P#r
zR*BCyz(y}SSfEuvowr&pOpaiIp+7G@iowO7AlBy*VdvYNPZC)h?Vt9>YE5by*0y(a
zbFKdR0cySe&NFb>?o~}yC7=y4W=z#KyPKy!Ku&2Xd-Dygmm3u#xSQwE(Tk7}EA4?s
zjd3~qtO}-64h-2;CEPs)F#E56L%WP}d#3rKbI@SUFp#u_@DRIp8b=Xg6^rx?Y1#3c
zJ~-!*zKS3&BrNsr2P}7AAQ?w>3U9W^Gmu2&v%q8*h)PcfaIh-#ZLb<;t<Hi9HVfA1
zFBb<>s+Lo0zG8dJ_H3$t-=WYRhcT<J21a03UG6>X<KEDb8a+c9xQAQpej(i}K{X29
zNUKsU%xcZdm`q!AT#IiIArBU?G_+`3{3ye?Ql#GNRbT1uzcx;O@)IaP_;<~6p+BDJ
zrU#|9e*CsU@n;PYtybH95Y>3XN0**Y7lBe@J%ea`+QO*;LJY)3T+m*zeO9+!aWr_=
zLV8i4#0-tq`NarG2g`#V8&qoz=Q3U2{iSO^8;)&h4q^LrsI<-0E++2wUsP8OEkhmU
zv};c%nn$RsgHzRHEj^Y&NK|^m`;~5U_B5D4WH`YQYJZL#j6bLj?pHGH4yV2?OGJAk
z{yx3McIF#%w`KP$k4Z)<807?4=UsBK8D8E<oneyyW0DeQjUVors>bD}bO+oTHSB8%
zp91TdiANsVM`#Ij_>>mLp)g@rltZB`4dHHnLAvUS4{LA!fM40b%reZ>9nnx_c^WP-
z>#=8E^bJjClG1WAua=7p-G1KR#L^jfuwui84!p=&U3cEmKPM3$J`{n&T*XcO^*&nB
zFnW<TGG8SwV5Y$C#ZEPvdO}z-ufVOYX#2C<-3nT7q&E^ePH63Ut#d@F5^M^7ULJLH
zxO>nYlS@$9!bQr<0XWhE+co(N>rh!+8-xI{pR5*E&_j>A+trHe6)V~=b``P{HDa)H
zw;Rvq%Q=mDqrY21Bc8gcfVc%aNX4gr9otyJY9mJ3SfulLMPt4B|A;#0=sKeB?Kie<
zr*Up<+qP{djg!W@QDfU~bQ>FOY}>Z+=BM}j?-{JK*36uJX76V|AGefDV?0ZM!*zWS
zB{kYOLCfV3R3<)^mK?xxQT8Zf8W`EmTqdX+GpRUOG-+)oud{$9O5IX!^keH!edkn*
z`|z8E-1Xnz-k{H!AA268qOvspCoc4Lzo_wR;4{47jM`EKqsZ&#6NT<W!llctPHg$I
zNWHRBcVh-<Mt(t?SWXJ!T26vxOn(p^d>`IQQn=hLIn3>&H#3(;>W<=$2r#eJ*xP&<
z3Z9x*E(?SEXBf_5di8h6ApNnINm11{wCYFV7ckCiN%rct(P^vw_4w+{D(rYY#~V6N
z#W2iG&uI98BJqmEF|LByol1-stkwsK`*u3(Y1<FkvMo&b!l9TD-*)_CTTA4sViY*M
z!DTeKA<bj|8fqL`0)^c5hV2N$&{IYLvpef)C}GZ&BhRy}=zHjmtDLka;Dtz7WnK@M
z30qr_f8=?Oc(fH1F&t%|(z?zHBcVnr8<oEe8e&#JRl&C|*XRiaF9;g=)h7i3t&puK
zvYcH3vN6PB#e8`fTDzI=tFM^-g6ctuA!PPs;MCXMgIZ3B>{tEqCe{f6-a8oT3{4$X
zK7S%t$2DYm3nfqv>soTA-WI;rOW}~7JTc*E)DSl<Q-G&5>H)t)s2&#+`MoJpIAmKs
z;GVeaa~nrI@vGlup5$K{j7YWw2FeXF68L8=>hJ2s+j78lRQf$An*T3x$LGEwcldh9
zpC)L@4Ry%{{sFzsz?iHx#{Cv%1>abf$IcQ!d1BFP{q>P<Q4xDdQ(-VFXe_WMCbxtU
zIu1#60Qp9ClUOROn2%Vm$D_X|sI&osES@JQXY<@HDdnBqy@K$1oEz9ofKtwZ8&=6A
z1{hsz^ZromaE9%UKZ?)TJuKSn(gqp=@bRp=BgV$C1>;x=^<SVL#4Y;Td$85o+qYkW
z6e5}Kf`_MLBTINDhKwju&ONg_vA@{CH1-_;lT!L@Mh_Fqvl2{l>(Jr_n0c*?Q)l;C
zrrzFuqus4z8jF4Jk7mSsDFsQwhpy&SI|AcPCvM3$kjd8EMR~g~@#>}@3D;jB8n+pK
z8|-pPl4BxH6$LXVygqTVlbE%<=)MctVilv4qoimUTX=$2<NbAT*$B*UA4W)EHe|Pt
zKQ6&EVtW-1CauKWgV2C)7Uxb{J-B}CpV&(KObSnJ>rDBqO+F#}{#CvBo;gQ`(uorp
z!A01T*1-OkG)J+#F1;=`Y_rj932TttED>GV*uVJQ46Ux9R6A5dUpG8`KNO1VogC!L
zE2$L5D)LE8_9OD%A&s<$ra^WXv@sm(q5QHc1d)KsB24R%<VDdFN4Rmv0S)iSe<dcZ
zh2X8VhR1h?B~AB#nQB8XzifTq<k?_&|8QWTWfb<EVSW^MVq}Q>9Q96Tw&?3N`BOE-
zfa)I!2M4=&qchD-2Bb|Wo!=Q0I)bkZCfu(F|M0v?9w(?4B&!xmGZi3d!7=%LGQ|m$
zQ|xL@&fQZrZPyi)wh@OtL&?700-yLsQ1OrR*#Vuts**fozJInBYDp62tN6epp2?Zz
z?>k=D;f%3OO8!X61xBWkHb<Qn$T<j6K?vrG+aRlEr^y6@5zmy~$yuiAOT&0-Xvmc4
z(!>hJHIopDLOX2STBm*2u3qQ$LPYBi23(U=DO(iBUJ2kN$}kF96HupTOGAcqmiBFm
z2H*E(t1n1bq?E+vK8^5z(Uy;L<gerXtumG-;LYd=dI<E(Yf7R2D|7ff@as6kg170k
zv`Hs73HS;=AT4S<q7Dh=Eh-mrSq%Z*6{O-#XM@A|M9GdbGxJ`|fQxjWpXBr{?)#=P
zvFZy^0v^_df&~jgGgRLym0(|>>&m%H9Hq7Yww>8G8BM%4WZ3YoB}6vOPH3L<HY8k~
z(!~Vfw-^8P-wO9au8w+=xdM?`(~CC0dPS`Gu=5^E&-D`l87<5l^6>@+WhiyE_fL+`
zKPXe$`q$Ni)N+>eR%fk*rKEyX9R;GfjeX<bf&#|<ZTV>&mA^6k#jc+#ZPAVSi$rF?
zl^5UQi*<ZA3~&i;ZzX8j)A2iy!c<hffBUZ0MKS+`Hd5A7N@&sNHWj2J+YLd>@u@Gl
zL}DAt-Qw<%LXn05J<RD@`PU7-{xYdXI$tKK9!XC_GF+zy_OdW6ZXUVm*>=qRJou))
zW>Q)VsWo$1pu!QhW6Tx}BNy2Phl|o+U1Bp9HaV%HuBt+`CXB$)-yd=(U^D8xTW~9x
z)XJZVa-)dF+*qiXvg|6u-Q?^Xf@0gxHZC(dlrNmOjkQGVI`7zS)+1912^=4i9kxkK
zSS3^-Yhmj|$r1PQiCn(e#)c~ge&7hMJ&HG$uiX^a<xyL-B(px%O+^$Ur~B?5H)XsR
zztDc-Uf-_yC?+*gt2p~#Qc+)`eAiHa2nIQx3`C~u%!9hOZ^Mjk(5deM#M$$anxq2S
z!7i`5vdoeSOMX;AP72cv$kKVGg!Y7++cl$G*QiKet_dkNzf7tBO_oE?0qymlhsHou
zrh(c-nt}ttG`XmC*|L3O7pm{%ny_^b1B3rF)e<h&?%_YH=`8@nOW(>veH0FXq~RHu
zCvdbC@H=-ZGTDIxYbYcCkII+$?(>__dWG>O$Z@(Gz}DS#oUEA#&xGyjxQy?tCDsYs
zcCNefD-GwmZKzp6bfW}eJg4f{KbuFzedQ@wFI~MUeGMwyBfHs|karcM{*jja%q}SS
zTIPI`{CtkInF#}RMzMwU&HC5&**i3f2Yn-1c(?q^4V0Y|@PPwZJ9{KYqoVrzNb>4l
zD4UA4<8L+veDcqNw$I3W`+p*1$21Hxm@mSpR>SkJG~xWaL5(DCM2T>t08QLk^$2?0
z=Z0=wU6*+BpzHlq&=hUK-T~GQYYrKX&?EZ})Ir^*yjP5@%j<@Nv<(460koRjpV(sF
z?#6B;oyVmKi@R(V3@4_EzJva4SU94{(<pXklTQnC&g3w}$eyR6ApuH^%bzX;hM^82
zY}8>s!l~Is=8Ed7+39}qFx2C@aTHcc6j8C#HcX`4TW}KcrP^|GK}R7tiGokGcgPu`
zBXec|Si&I38{egmT?=&1JT--?vX1%?Ay@RR@lb}N$Q&2iLw@*OBvf4dpM`r~&5a&Y
z{$pNaC#LZb-Psx|dkc%2y<?s<rART7bYk!}i`b+FxWmxk5H)S;Q{UVuWNV1->)gZl
zU07hmMD_WpPO%?qYwtLb-+mAUO#;K%DZmd=a%>1A&97+oKq!O}$J4CoK72d+l;gON
zK$*3_qCk(Q_n~{5rA)-$Gp<}IsBz42egPb$$9`qQ9tR-@2lPL}>PWJdMxsO0ZhLC!
zB5JX-Fv$61rnsW7ZCl$ZMCG;0S%A4VBO`Yt6$$3)(I%9(zl!84k*R$@T$KWVc1aJW
zH)|H4>n)Dt={Iv@Egn;8JM4F#9>$(8o_)ZPjRUY8!IK+T%y?@n>Ugp`IOo&Y3vYh+
zj`=&l68U`WhtrUhFLNPo?$X=)>d}l9s>ALo+Z8Haf4de9|H;L!Yzc;;w+pYW+U3?s
zgWTV*-Kjw+7q)`<!v1Bw->qPAsd=Iy{FZ52><B+pZh#F7@b^waR%ZD_D5KFX2CIr{
zZ9L1V;ZN^o2<rHJ<D_gEwoG)YoKbgz2)yAmY@5c8YFPNp%6mN!IyDj#?)q$E2xFs~
zY{JQ_*-G*^&}*icJhcB)^00~LHO+;yYpN?*0z5ipjPPS0Bvt*^Jv-RUZ`a;X6EhPv
z8(1rY3*~d6_-JNJW8pgDk?vpclh*}Pu|m-fLN4^;)}jR=xn|#uOQkT8Kkl+rK^mNE
zy(D3zG_r`O>}Jpd4cpv4Oi7EgH6@eQ46G;d7ZA%oJ`A#7cM!BE-6fTeDDR?2MAa*3
zUtrqF-wqK#-h5z<5sD%`IXTkFt3`F)ROD{lF0&1NEM5!;PgCK656YD5PjIwr=#F#`
z4<fi5VklZDmIdV)5MCy~(>P2E+7xNwRm)AgpR3Po&5k<^wKqbq=N{e;eB#WS6@**L
zlymAd>q77Is>}9iR`>l^n<(ZGm}Rwr8jn~nOlwK^*-fhpT=j-yJ|==%LfyAMGpLgq
z_c}kzpt2@TR!zlR+QoobEEq53>@^I1IruD^zMAt5586L8>rR|rZ#8N><1Fle1Py11
zyNV}#!@nucG?v`MEv)VxSR99{*%<h$KAn0&7(Q4>dR_>h?49tFRg{%dQ}ii#!RYxE
zR<enY>Jy*-XdZV~`fmqzUOrtAe0FYrF2{nBV+5hz%oAaw@2>K&4^n7{g;&-vM-SJ%
z31O3_#gEGFo~&ODM68KtpdO57PWt2`${oy$#m?CMe_A~Ui~pv6yG5wT@GG9b65X~a
z33q=ZSxGtUV|J%$Zh~ppm9{I17Je)#Nr{a9I~@AVcI|%Yg3Dhmgouc5EH7bY`Q)8E
z9zaZBNH^Rohq5ghro51YvE<3BH@e0L6+2|s|F7VD9!X*v=jt|t?~LyGo0uKYd`Y75
zsU75eFDN@fEIClElFn%hP(2Fu-nQqL6+0T!Gx^CvpxM*bf<PU6^;1)p7>gZK(z>*2
zidDF$UM5|82lDZvbSaPq{+ca#n}io?I|`;?-XGV`BkAh8R&1ooZreoNEMqKSPbU;d
zUUa1TP({Km$2^gUAIbG<U5u_Hw7Dj+8*_mZ&FE&m9S@h26t!`&I^5<l$hnbIoP1n5
zMIzscuFg*?w%_c5^XdA?L9k4G)QIz1zt2Z!7_hP3Z8TIkUT_rn9m=Zc-!#4ZH`fXO
zF#0(RI9WN5E=^eT!ohoSWzN<qyz#=YEfP#bd+-R8%xh)&Y&K*(v|+U8dPL@q7v34>
z^TB3w1O{DpLZss;*w+I}2F5iOCNH|uTyI#QE_k2~*pEXT&faUKz!uXjgbI&Xpp9`8
zyvWNIyGnBKu2BbEeA=Sv%S~E&F!S&=Nq1kG)Md{XUTqJ;LERJmxky9lXw=tvwbk7{
z#YF#T_vy!)S^~}x8S6DUx>=!$5#><|R&u4Q_a~(O>$X+6V)gJ`0_oj^Z={<L$<J6u
zj|2U=(Ayhp^u+VMxUOAEvcmw#83b2U3=BCN$)i@x<zp0=2%w$IhKc>#04nn&;$R;K
z*d2Cl&pSp_U19Mn;38<|PrZf^--`&N?O?yOekZl4q9S!)%;olr2Tt+ytPTwlM@eLR
z+BYW@A;gB6GKF_<aZYtIrW3C0=&RxUX9TiJn!e@P@6PcD$SzoGk}A=i7Ljq$qAqQ+
zvUZ+!lC?4x$;sB<(5<1Nhnmc;Yl1!q%Wlv`JIFb52o+eU(d1|mD8v!-U;Q%eJ0#X4
zF~ceCCSJG%v6Yg*s=uzz%+z=;!$SRWXZj4L43~$RNa7AU_!}EnQ0Ou1iOJFtyRIhn
z9QoqTq@9!NNL^N1Lvj9QN0bYTXpcop4ZFDTbm~pyYlfZbTCA>PX~S3VRTx)KKv=)~
zRSEN1f^K$t2&cL?)GEW0*~8RHuNYlQeSy$UBl}_ITKcQA(@Qlwg9u>z_v^t{oGHKn
z7Toi})!9Y=YUyrT-NWvy+_JXN4e^6fSBSVI__x}vr3`?V9et3-W%vo7KX~=~dqXm3
zs2r$W`IjF7pU@}dA-p<BhKC!<p;^4tlo8V#<!bk$&iHxGO#6QNyLI_kzt-lNq_*EM
z#QFBI8u!^OR#N!Ah_VQL!1qqwWoPF_Ofr@SFjhGzAm*L&<Uo!#<5~&Zxrsk5H8fO#
zlqUE@E)N2|4%4u~uCM(qq^c4ZA`vg@%8spgsd|6iqp|kF{?@}fA%A3IVWf(6ez}N`
zEa3%C=}%&Y5qaJi|87X*wI-e;L%oovDwu14CCbM08ljxj<UpkfP*lt)bd%z5il^Py
zg!)>ixYx_wfU+%5VIXg8d#tHR)&))A!i|n;q3Sl_T%M&Cd1%fD8VW|?Hi0pW5$lEr
z9@o`ikN`Jv5_HIWyRii~3K`~#WI|~)`o)&yKE&CJBM!rT-avX7^Y6(!+CPDC+Zx#j
z+;Tt8g`g!hG)6%3D@&aOHhz*Baf0?a>kW7JB>*4XprX49G)!EglzdtxQ&lIW-w+Dy
zBG75+>m!R2%!)7i-S`MN?``%Ar$x-3o}uSj8rvxxI<U;FqCy0c=(d%XG4~oeAU)Gy
zQoS6_XTnPQ#;g1>;JNi|MEM`AWQ{!ldnKNaUezK-#EgFNd^&}&v!5fsQFXbPV})_0
zRS>Z|zFkF~1U7{tyENnW0IYtc4=Z9cU9oGcdZh)FFGPc?HV&>+R0sIuO{Fn~__Q&N
zx=DzlhLNIClx=tjhea-mWVLlIbAQbmJ}4{0H5WUyXXy6y)+OnoOc2;s(m&W#B8b9u
zV}4{~Audu2$Bs3$80BJ20946}&4PLv82jneRuPE{&4bVy6RWKR)QF`=SwP$Mr7As=
z0%p}?4k>F`aGLV=fRb-l`sv(E$I9hn&#x2;>XSb>iHx(wfVU@64H6hbXME#itavgC
zB0!?3hDssup_BnoC}J4a*$2ZA&-{_8Ej)$+2QX^C#*d{f18){vlW3QJtvI7Jo{FWk
zr3uc=#h>Z4If{iN!WK6M{}|L7TW}>yfY3dMx+@Al-||9aY(M6+u8F&dzWYjKtgn+8
z3vy+<vym9qM*s<a$Jp)R#Kl4V0^*!)hQ~ElC@|UA|1=^1&c?)$rs~YtH**Zp4wf{1
zZw+;z2g=lW&)2iDhEW|ihsEI?huocz8;>2)j=Qf#Hk9E}45>6shnBQ}FiGs!gZ9+-
zv0X8&D<{zpoTeLko=U1llcQ8J0ttMPvxjQ*XrIRRkmT`Lu_jTV;CB^wa}+ko8)uLX
zL(<e_&_vzF?6@=Xz<I`PPeQ#p55PR@=G}la2@0>LT$>wF95KT?*HFaST68X-e&D8;
z0U?iF?3Th|JXZuFf+6j<-3GPR>#ajZ8hZk9=W8K97cGswgf5WZ0w5dKmaEY<C=PB+
z{60cps8LSh2Mr;nl@nu%uFdNcV|S(E`mUqk(wqHg_o(o=6TB3bO}!1_(Ua__&E}ks
zP5|8B@Sa>Rz)84W;T>I#^o2ap6Z^h#d!S3b>9d$P8{Lg69jyF7U1(V$)KS0Um~wv{
zZuByhC&lhSjl79itnYpSY^*zmHs4Ykf1E6Wpnu`;5|qphc-|RvluUcs^$%;mzNsT-
z^TULI1g)f4I&RMq#fKMEMP@-QRD;`!mgE7O9@zfbdBK}MNsHG&k!E-gfO6&YiNAQ)
z)TtfmMqEOO%?n~r4bI8?)Y~FoBq*w1s~CPl4X9-T!h7R<Oa3!xMML8epHI;0=2Y9l
z-c%3)m=q@>{B(FNZ58RBF2g&6a>ga|Q~Z~*Jl%pc)!X4xRALq=gL{rCG;N;Hub$^U
z%iwm9d_E>~nM(CPPn9f0SRed4aa1a_ltG(_3v$H$I|?LjR)x63EtlfvUw(2mAIC~2
zPgT}$DrvTQJyFDe+Vqc1f33(x#J4q_vq(AF>hO>W$VW=Z`WUYE*95eKeDh0b;#Qdh
z*9J9lTAs;%E%p7Igr-8dpb#7PD;u}@{K#YHasAP_Ju)fcTKbazaoURBC-CRTdAMK_
z&d&8W|Blx_3~sbpQG3yU<Ose`OUGpNv`A1E!OnBuXgZ*tN!@V$%q8<fp%;C(2*;}<
zTe;Z^21Q4g*pR~877O-&86a~;Lmzv<@1lMe<5#IAx7{GuFS-o@eZ8mCi0zHbe)!)9
z1$m50;?7r5BSRql;>9~$-`U%SL;R7d+xj@yPvkKqY5qW@;~GW4PpzK>bV(QF5|OF1
z+K5CQxIshk5`UsVpZWJ#3Ww!^yh(<qaytpRt25y**Dagt0s_jJL=o9LLY@Jur=L$;
zyS=`D`s>?Z8)O&}(lR`_mTq#aHm%U(Kj&&zJrr>iFD|BRXbO%0M)HmiBW>**!{d!#
z|5E*J%wbB_()k+|#gn0b^{^c4uNoXKB3LMol3=XP>`-q$H-4Vq8(dC~BWLT(gWQ!F
z0!s^y12az$9Z8~SJ%|&f6~1{YNB;XWrG`modHB5quemT{GXBvJL~@iXno6NmcwUSN
zo1!@a9@@5H1sNWmK*~cF4(zyw)&Xf-A8Zg%kj&aLjJzIoUtbxcQXbW~qDXVIC!zgW
zx`snS3E}*}hb4u`o89hj1y@*}w_0b9_yTE6!Qlxd_K_)#1HVyuon#y8j;19tOh~E^
zE#B*^`_*p%AdEu+EVJpZua5#C-XdvPzl99?bxj666Amx#{Kl7+&U%LL%jo`fF?~-B
zIBSmi)_qpG+=5*4J&?|#cMDu$jOr8$=!ES`=oWVgyZh}Fdzc4|$Quc28r|P?R9eaQ
z%W!@7bpXW0qB;D#k`)Jq&iYl>4gCIv)lo;t+*J7UYmG$PbvIorLK^y00_Co37w<yN
zQtK4b@Y8}WL$0BP=iGOmg_>fB5CJ=!y1w+bg?Gp^vOyq>k}dPB*;pqM_T*c(Lg>_w
zno}pN$So1zy{?J;c7^cYBNBF6thrlJm45BfrK)Kb>AG!+*t<$zJ<v~4Rpg6ge3}Mz
zmbS`}W)1c96ugEmj?|Rm#U)v@JKL}Z!jgT$qH{oLg1FDPbyx!_;=C#<;~DBMBD{=i
z1{C4yR8>!p+^F5svD$M1cq~}~eM)dSRzH}o$Ik|c<M}3})a~rKHae(jY@mnuzL(Kx
zj}-^S>_bd*3{G`yNn}i%V@Z1QvUlHbQyij2AuDQ_b57(z8F6mhmuXZv;^b;eO<aK7
zcufZ6G`<H^Mx>GhOG-oieQ0wSY(}E_N7k^g6yrDS7RBNU{Q;`xv3vz9d|qxg_#7tJ
zS+zusd}>(SRX|~M!^|8-Yx%o}!lKZL+SZ`$aY%YE(Q%7}?WL%j3=0--lx?DA_11hp
zv4x9@v{m|`5Cb}1c1NeNa!NeSg|$#6tV!ECi0S(f)$He%iq*$73tlzRn#qL0``r;p
zF)3KL&C9s=S748<Y-5FFTXK|bGGa$yH=2Cv9p{3J6%}9OpuF^AC?8xd6XLN!OvH3+
zLTT5OD~x4%1S9M0FVw7r2dSL1JFleY$}36s&kt%`NMt09>~?P{m+KMFXz$+j&)3Z?
zak*)?9_PWGeP+5;#e1zO|LyMX$v_O<r*3Y?8fbYf`^`Y*2EY1{`ppG-dh(=igR_$i
zTJl-^y4&Zrv-+QbXb|IU%^jXd*!T?W?D+*jpOW~f%U*PXOOK7F>y$!6kI#95Qs!F(
z4yuuHrEdbL0Ctv;tSp2Q6I&UY&Sv}bPtQjq@l|0)-h23uj?6-ld^SkR7fbmgi|ER1
z_Px{!j2Lme=oZQGjOIp8_WS?JXZ~*mTqx>i<Te`-;koi8QA+ksrKQ>U=bpg)DmMF7
zA9Qy9_zdR%q(A@Xbm5Sn&|v>H@bM1~75B+ftM>Dz%eJlTt1|Sz2mbHI!Xe+#tS5Wy
z@cMe^i41ey-5Xh7AAeHLhyTf8ln+xD{>;VxgwaD2u{2gJ6LQP^`F7fhHLcS>*N1*U
zJ@lz=_Hw{^v@)N8tfo7@IC=*(wy~M0PpAoo;~@D|@|)&r&hH=3D?@O=kjve?sb+;B
zD1l2L+g-7Z{-5dn&n&J2VZp+mSnFLwV`@@Z1z$ElM$;#U=Kh99p@bnGe)>#X=29WZ
z$>AQ)xPBNhh}K`;D>y`s=p*Glvuk$W@e}9q6C)p>XaAY<E{-TY{WwxP*b1?z>i};B
z@_}*;-h?daDLKnpFG;CCudjnp1hR9^##ORX!&s>ok3o_7!(*0)h6cEA-O?HQ;X0_s
zLiU1&IU5KnCMGntL{}xdmb}&T;mvx?V>w(1vt>%d38aDPETig{KB>CmadAv@;Z}7)
z+8{qFY$by5l|9%sg$nbrc~VPLy`D#Q>;MDC1U1_5_B3XOeW($pilCrB1kgwqqXRrE
zz^)l0f85vAEEOgFND)OvPTNy;T<x~UzNrnv1omXIywMh3W>xn~Awq<YYsAtx`0&K+
zZ8kMlmAdHD`3qJN<<l<%xC3S#;4#H3D}fc`#c)Zl=@$B#IW#ZIkoTlqoTOq!TaH`1
zB|FxC-lMO|U?3hic>`_Ht9Rd*T~OTe?>M=pdw|<|nU~{F3dfHCIEg@7RUmBJ-e5=P
zXbg*OV#3j7U}?gp+kG%wktP;)TE!QtQrGO^w-%~tc1YxsyhUza_Z&_J5;zQU&lyi6
zi}wuV-Sv;K^bT!Zkp|tRMWM}`m-|ya1k8Y<(%mqsB-GFQAJsbT#IJF0ABTFr0~yeW
z77OcKsE!jrvDe7kkM)S+T8f5?@p~`ZK4aEAv+KX%p4Zz|w<*hQyj^MFoCZ<=%hQ;v
zdh0p&*-5S8IlKUO!g<F$IMCOVs!TcRuXwb+b7$!fE9xfwvjNTA@dvpP8sy>r!z_md
zgRr^}hNKhWv5nH+a_tUb8tO>sn#X>l3#-?X<7t0Mr}SH+R8M4uc8ep%!9(j|Gx<_m
z_|ExwT_2k@!vS+3l2SZDw4m6K*`S`@^#U84iBsXP4{D)Dx)%Q@rrZqPY_vN*W`Q9F
z(~gBjW0izRwQUXU`Uj%wY~F8UnQJD~7oWI-UT#P}EXKPzm@L?}E0J1<$Rgf%%znA+
zQ#VAh@jv`SbqqB{#dQ>>|GSL)e_t^ZY~=bixp(lgh=J}sC@5o+0y!Ys-+bdX#RNx>
z=p#u8v)&bsjJX9pSCcpJCNpr@h85^<3$?&VIJ%b;dr<&n**!XeP;wKaWB3g7#cd~Z
zzrn;^{6fNBvjNUnun9Ojdd$@k)!uOAC?-oK8j+n4Pd6dju#TlI&zv2oPi25nL8)PE
z*!<muof!^@9u^=UbD=J7*F-!5E0angGZ$<&o`^uMBM!{nx$Vn_!=;)Z252+gi)`D8
z)u-I{FJ5>_CXvpUPh@^W_VHMSj_IF$`ggZzs=fbIvazFT^n`+HKh4MT2;t={nNnmb
zon22g-!RcaRvi1U{_fCTncJ_e>|l00_Dan`9yYg^DHm9miz&!}^(h6$qr_2@+1(R(
z(Ya$9vOk3VP{nNtyn_fmT3UdhHe0^?7$g4yzwWP}Uu6}%F-B>ls~-`ZY)50V6Na8h
zRa)akge>Toa5!u>dE)7;v5H%Cz|eb_?+7F@2w6fHZg6?D@rut0ITiq_&~k-Gv<qMm
z0&IH19YCJ;e3~Rxrml(bh%Ut8qn!?9v*+35Z<30Z*mV^&f~`=@fnKN?VRH$3K3mJf
z!@UgqTnt{XVgvh_Y%-W%{0}SRwJTk9F3W&KHW5_~38<4(TNO2fINw*H{?dB=r--rs
zS7!Ope-#KLG|eKLwWTx_sXfb`1ldo9=>Pa0Y%X|;QOuWduIR3rYTbgBHVxCc9T0!K
zV}B))n$sI&olI|<Mga^RG0jeXh%w7`Y9oNYBif{T@5x(kUvA=7N5KI*J<c7HQ?2cZ
z*;CYhO8Box^#3En2Qyi|!vLkqZSLUFH!F+WsJ1t4zu!I><Q|MHJ{Rcnr{|I>dK`t5
zL_-H5Cb->RP=3D^jTe>D63ec7qdnruZkG<Z^{m6KtdJyR3ot(fI*b`=dj>Itj2C<D
zj)Y9mEMqTWD9GqNJ;)R<KA~S7HFq1R+|W+Wd(1tk^gRQ6a*G!EA~g5nzUKvkL$vYH
zc{luGKUiJrB7*zllYV{H2#aLig?vC!E)6&Qf#8|(yoy8}z9+yBj>h|&9{<K^OZN|o
zp&=^~r!JOsmkZc;d`4_+Kt+<61Nef|a#csS11W~IAX@bJDJS6jPodPhTzD%rgCP&A
zU*mE@+uHg_J`YFm1_n2#?_$O~jFiUw@%11dceso&3pJR&U*2G><blI4H9~+k1K;`j
z^u;(oTUuy9cp$C(_VhS$7z)$w)I}2R{fe#l`AWDLT0^;2%R-k@$InDZ*0axjUbFiw
zHk?>CQK{JPm+FzPQFvq5D#5Zb0bUjujB56=i2>&fT^r93@ctnU))R<cY!#W=EN4m=
z>#^@zH}zL3g9+1ZeFbGggz^@VeBO|&k9mC)u?^*njFLQe+nhhhz{k=B4CZ)HmY)Bq
z5~;9U$w!=WTEbI5SZ3;Ta$aXx<<l_tFu%Gd?D0b=A=O(*)@>H-3*aj8-}0Y~au263
z7e1!*;P#0VvOFejPu#W8dR8D^)C0oYb6`8a$BEgRVn#O^08WqpfSl`KH)e6RqR<?@
zoR7)j#{^hN>-pHgUX0hqj$uz!V?rXBdV3a9N!gAg#*plc0*FJ->0CbM47ic;Ly!>A
zulinn(@W|xP$A*!3o>;$Gr<0Bz}>8A7pLf$U4v|-+ufjy^v8zNr_ETVv=E+WzO!V*
zf!{TG03NQN|5od<d>>fKAdC2u^&NT!_9H+S>&*vwa4u|{rUG{8)D7I_cmyt4`5S!3
zJIctZowMp*5Zr8VVj61LYliC`5kG#I8q@YefgWKgx_j0WN(X@Do6F*Z+2M0CG8)A&
z!spKb=S2RVj>-71zwBHaV@qf`H~+QQ6Ij)e4uq1nLPQP@57D@I#h73ne)@E63YJ{(
zs15T-k9%Vbd_+6PKm%U)5MU?Cm(Vofs~yLLAlm1^knOjqRt6{QT8+tYb&6`Dp!l!2
z`@<hiIREHJ!q@&1G*Z8ivT;CQ`(UH8oK__GH&fnHcL}*-Z%hIutUb*J^ydLrMBsH5
zbN?F?g1TwJO|_R%`rn=@5?$YDE-1C~V+7cyY%>ZD$I`A>s)<3)d$@mVe2wQY<O-_l
zVhA6*V5d3}1S|z5iJpIMbmo&d0%MvbVR1QNDDZF4@X%lm!9GU1Jn@mi`s;0<)ym^L
zfw!KGTRYp3L=;l;891_ydPZyf?&wIwA=h_rQ01navmt1VGR2v~Sfgk4&gZuL*gMhM
zK{#yZ;X$3g0JC~d>aHtR?6}idP*oYK;&$(3oXn-9yeP94iZ&Zrk<JJrKc6&k29|z+
zNLnO7gem(Ky1$zoV+>0Q8G>fkr7xi%ApOS;DKdWox%r$pA{?L(C$+P|Z}#8SEK|>T
zR-*G67ox}pQ~Aj<3Pn1N+}7X_jW-M0PCR{rYE_u0p~1J#Vm(kMr3oLqlVbdkoluh&
zVa&1b@l*}U5X>t1t_dZ2H06<)928LlBwS&w*UP92C?^y6qzc0?NQH@fA64E8FPz`V
zgAoa}T_GV*K=W^)iv&5p<2RT9H84a&lyc|aJsPhG`;Oa<3Gr{(B!g4uy(Nw%MBG@b
zG)9S<3$!I37}GOt49a0iay`eOhDPe$xn=}VqPRt43)oPd7e=GN$<=Ocf28HYLAS0t
zqMrkqW56pf=Sf%eJVy4_TcL*Z1YNjCYp$1@1WER0Jt0YUClJ2~Vaemp%>5K+EcpVn
zM%;tD@m9jmPct;-G*86T*75s0YSMHP`R4@Lm`)eg!-m@R;Gx@;*?~NI6klQYYGjE~
zcDR;M>4*VW@(%%z^4i*<u>t%CUJmZ7cdkeonV{$TL0EE7$#pAQjvj1IlRP8}K75kF
z@Mw=`ahO)Vq2<pHHlv77ehf=uhW-ymqpd3qdPKfAWa#j?fuJ{c$h)I(kXTm3{VWFe
z$}6qk-4ECe-4DcCYF39_X@pvg5K@bUwsEJr;bXmhuhVv=?bRYa%jG+%gJ-j)n+=!|
z^ktFD*U2p`R`g+|e`JLTxA^BDPD2D=GGw99C?rq7lR2>&&TglbRRP@%?n2Ecp+?T~
zk0?arvAgMei3%Exf9BNKrh%%|6=w1t4O=SH=J*dZ7Y1JRf0LfMJ3v9tJEte^EaXc`
z$0<cFN5dq>93KdG=R1)A)+?gK*?WXEz)G4Wi_oPM-tFS)(R@KA5byDei;XECRqZg3
zgn|81uze$_pYSkH9Q06=oz9lw+7=wOEsw<8(2w>~L;If%_Lfuh&yFtq`FmAvUhUmq
z`7A=Ik){rK3yP=lca1_O{|-<RDO^Lh<5#x@+__mBt5oEK1omeXW8DQ9aYGT?-tu&v
zpqK(1u?Yglh8s`QD4LZ~h#`i1nLfcSt9=n@|5T36qYqy!y@U!iu@+{qG{Hi{$pA8z
zF*d-!%(s-9#bz9*tPu&0!j=RG>dg<Gjusx_sF)YFSfTraeG0E$9<^Uq(rRjxj_>-l
zA{NL$s<`U6d&UpU@&@#KkGS}+DB1Qnj|WlC1cG%!414lb%v@N%<A=Pwl6~~FU*U2g
zf4qFDyK6L-Ja{lBoeD0Ay)z~a^?^knZVm72XiHChY@lpqVNY>JM4fAvj=_D<z?YeK
zl&U)9D}}Q)>=YP8-5F~0=}jSJwO;#I5|l}h?>CVyD@(xgJ%yzo2TbpUl(H=+oS=_7
zcK0*bm&<YNZ%<ES&;!z#?>-x*)-@cBUk7{m!6vO%{oWt0uO3VsqjR0;t%7&swORb(
zT%FMI$3l}I<mg5hzDTuW)JBs;L4~j10al}y+%F2lWh4p_UBoGAx<j~~<^l_G=ix<0
z4#Q=e9}j6~n^`>p$;)gSucp>=`y&-hA^9XVb+RTt<~#$u(8ZM02efg0T8E9w=~_Sz
zxjYBk#(pI<HSx{&dsJJQ1dldLRJEd0dkb)i5W4IQg3^wmsR?A>V)ZC)=*^2YFo|vM
zI}$dWyoro>qfH;UvRI_ipTEqK@l1bq!|csha65D3OY9RhWXD2InkZ{#$3;E?*7zE6
z%E*6b#PP$uB1@W#kBDfSsUmXqi`u_a0j=P5{cK;b*q=>=QY2J{rLc-9X_0jjqxi?p
znOJ*#<mapVt>~&4B{Q50$uXAQ!JCV6lGQci){fdo0;L^VM)KrqF<(l50O~JdQYIB7
z?$7(Zwk=rS`y$V(>m8zqdG0dt9qBn-j?LFjvCLn<+L@3Em(a$w=)I!9Dfl;U$rt|Y
zsD*az5<>fgE6F{liCM#ciDs3SrzPWC?H?QXCT2Nd8tuz?hg2x;PYX`Fuh6gP5p~7&
z(7LXjkry5n;+xMzfguT21Q^v?h$eI5$r9M|ZSpvSmMc={znyb(&Yn(sxN-0UrRYh?
zd#*R28+G!Gd;TZ8am6Jj99O-U<mFj}MVyBk43#C+J8QH3^w3W<wbQ1ht0w9Ok1c`!
z9D&chivR1A3`JEhVucRMT>b4>^|^E_kh}qLrKDlWpNUAKWLdqI={t#Kd=#^i`Bbd7
zx_Zl*sayuPijq`JDT(USk6TJBwO9<H_vowiG30Bv7LJf85stWbFNGsS;Mv(I##~ZF
zf`}w>VWBdB)6pnYdoOrC$OkP6t%1~vwhVP<L_&nv7ZRp0cfh{8K%3AsxR!c($#oC^
z3`bE!$B@p^P?5^W3Z5c!FVa<y&~)CLr5C<uZZ6@kRL{r&^t+EVn(sd0Z_~@l&8~UR
zDEZ)On8ySnDMNWlHshNba!d-wXY@YP1Cy7t(JR*Dh?}uj?trK_A_Ld3JLzCJSE4*4
zofG_giPIRE?|~8$V1ESkh~QOOZ`@1o2&%<3ITTX7O)b1xZ|Dd1;_u0JZ#RWpBmF4z
zBzhl9>tLeh;A_pKcEe<HpYCT->aQczIu^S%iAQ^IQZ&e!{-F0$$M%Ko*-QJ53p1;s
zO_Wgy(_Mh|@=D0fM8UT>bdMP0^Z4fb3VF)=Tasf|JSLzTePbq3y364N*V)(9z8a%U
zPu>(0OEbP}X6tVz5$tu^{0JSj<23zSjmG`pLI+=IO9Qvw_uwF<Y_So$%1t?ATj|0_
zL*4}|V?b3)@<Dh>66?SdGWMK8A~ZZc6%Vj2C%*9e;H<^Lyaq-0&T+Jc<~0()-%&dM
zP9{|_R)ac7LbpglaP_cYBxj3mSQ3I2F^DIh{mmTQzLLLp0p~hnP!39)(hpV8Od^DY
z)7^xBr@ohZ3^%tI@I|Q&iRjC!6QF+vD@?l8ds(=vVM58Ey;i=v3^SH9AY@QQny9?f
z7GG-@E1R=O_vM2z$^}8y^=wR08)`HKdR}b6aHm2pk_zFfCl|JKBr8#cSNM`3mMxFf
zH?SkMy|b->kMrnQ@7@GaM2@8t6>s3W4FSVOQMxIpI6EtF#`DM%AsFmf>&^c09C<7D
zE`DA~yf9}e8|s6xw1z?}dV!PGnx1`J`0|f23}vk1rQ?{#SA9ozejUTug?Zd2{m6h2
zL;V#>E`q|PrH#8tes3~-spOG+Q1_n<+!F!0Fm=xw7bH`mx?h_;T(uO_knRl+8HMkb
zsy$jR{8@Zg?e_#b0-!Mt!k$#FgsaCl>ecoF-w|9$ftGKzKCI#iTBuKWK_=8nE2`op
zG>TC{7Xyi2rZ*68K8zVAJw(cxMiI$#<lJ~x+dWo!q@{D;1gy`MlVv1ZLR3X+LA4?%
z{$)U6$tb)7AvQcqjYQOaD)Po+aI%mx6Im~0M=Oq=;Xzogx=mqXZ=H->;x@0R!YnZ=
zLIDTo*8@gOmk7fto)c|tvM)`3pPK6E+QQb=p)bJg)ppFMg6ra9322w$joU}_Bb@pH
zm)qydJQA9sDS|C}@{}gSnpJg*qxC1VcLToxi#H1kIx}@aKsg+Gf56}{4EgpHA1o&a
z3ZECbMoO?~=-ddRq5wF+_>AA3Lw#$pYLS##y1!tzY^%|WI$TE9#roc{n1-3GRh)Bj
zZX5y3Kwrhy0!AeyS>+X<)N%k>goF;Tjt)=z$Gc>TW4gB-&&>J$R?Hbs2uE-Q3zU)}
zZ&AZXdADg#_a(t-UW+hWPTnq$>I|okx(h*s3ySP2XuVu3LT_tEopWC&@&8iJV5!RL
zt?0@s)H<7JD6%34Hbam(@yBpwzk|8?Vs7$9bVk(mS^5ag|H)ZaE??J47%6T_xez$z
z0m)B?rv%`2y&Iz-8>05kTLB~)yooQU*!gH>Br-C1n#TQDOcx-ZjweO%iPXpS@0}k<
z40t8<2Wl~hy>6op8|Hoa_lABX43G@HuX^GQm3J;^FHt2N9|;>78P#xA)>%MZKkcuc
z!uk(F2pM+8{Knt*L5sd|qF{Yz8S_0x0#+my2=1~Pc1wThiWaPyL<@fY&_Noc;(p%8
zM)W`Q=y>ampjm7Qb+JJdT)Sp#lB(0pRpK>HB6R3j=#fWJd}*0tVe3m`t_=>Qme7s1
z)#vSiuoDt2JW;ReM&kBm0q>81QLFccPfP2ra|e|iG9mlFQLLRUZXM$oNo^$)f|ia<
z{z+|a-;Cq3KEz!8Idw*^boBCaL@6v{Bb%R<%3J=o#XM7L;m^X!zz1ZSP3!&DH_*8G
z7KteP(y}fGuz2vDVqrI7USYd}pCt2oj;$ykoe1G{*t}Dn+#ZLpQr2DlR*?Y~TsqbZ
z-mUu<@KF3KDA3s)MiIqT>XK}>!6lzeAIY`hh3%rpnG|+#U=(9^7+GZf-N=Pg!s1^Y
zfBu71WlhXaW5F1(*h+i}4Mmf3>S>%gyuz3Q+_eN{?7w5><+Nh7r$longVoG6bYfv^
zs1Np19)b#i<peaPL!t~Up$&1cmDcH^{*3VSRkR`m!Ip2f_Z%ZMRS|FwAE7SGt^ph~
zf9Nd=$`FX)?fW##gr!Wqc~t)m))y!g0k$jKXy?+%N`M8Gh#uuOHhRorI2*}9@%RX3
z_Ti`Uw6ozc4XG=d1WML4Ca-PPY0Z^si)o}qsxY<u-EeFL4L-aq78<Ah^U^E&%xk5k
zEs);L(9WF6<@0W75x6)lz1ls$v)f%t{!1N5I)s%FgJdGfkOi=e$sU6O#zr>qTG9cV
zK}pob9M-+6VGXn_3r-rJ)<6d&_X=g}n381o{Gx1U4dcpsC8i`76^fxs*^hS!Abn&>
zqUNn;y>nN#wx#+p+AcOj*a0N4u?lI+v^b8+;z@VLj<HC&xfk>c7YD;^nDb6o68H~4
z(uJ)PzA_9t8P!aJ4+zCjMVf-&T(GEXo;r{A*m9>E;y%;BbLtWGb(*SJnj~)vc_(~k
zFSZZkVO(~)nN%xQr>ykwXt9|TMVB_mm%y=|0ywQK>*Js_3_oM%Q$l>Ky~3Xg#Q+6l
ztGY|MKXKO|e#wpllv1muV`N8K%swl)iORs>rlIe`o31%3mREiWeO<G^ES{o2bbxZF
zJl~I+voL1<O4OHfUgTw95QarbBbg<-A}97{xXTMxG{CwA|7=if=Mf2OP*&n~B=dVI
zaZx;iHJgC@!;y`lJMHqXV-;=ab91ttT}<#{mC80}{+nhO9_?bAF@Nh1n%|O}A`Qs?
zXZWtp*GqWW_9J~;NLAPH&k^w9bnFbE_*lIzkra}Qm3y`N-`{J9My#=;fdyg#A|eVM
zJ$WuhoyR4&Ica1&V`SNQ1i)dOx`7@Ui!m08-Y6%*A)METey4uZj)r-H!TTnSMY<u+
z%fX$A{@~P8f^Lbtw&+Nc0^C1GI-ry*ypfm`?d3lCUvbQyACQ-xxr5Ohr->3sjX9K1
zli!EQ%4pzQ3*iB7Z{YUu*>Gwu@E?h&A$_hFw9b0#e_oBNKYP&f>Nb9cc$%u@c;S_q
z#x!)K!AdzMT2-lR?X;G$e1GB@^RZy0i^h%nT?`p+pwFGBQjty)^jgu#;xz9Atfi{T
zbRlkB42bw*1YSSQcy960VGJQ7M}FEsOsq*;Kgx?rKtN%zg<l%}>BVSq7wnV?I9)}c
z3;#UWc7rA1{}xg+bLF7zw|k!gBEgG_g7|Ja-kwDp_U&*Y_fRo3^tKS*o2C07BBC;C
zAcDzbWxeC{uGg>=0P&14@#>ZMO<rmbE2LEU@LyE2M6XhB=-S32F?K0*ebagTyc1N?
zaDr$%FA;OC58fih>aBh(k<VU)Fx)t1nUy^<8wBlc=;5@w7zanBw;BCr5D<MAa{aIQ
zP#B7oqY4idid(uGxk}DzvBvq6p(&$*wvKMkU<E;|^a-l}CD_gFZ3PQ<fBMSBR)N^h
zzATV;HzvBPNEj3rfSof~$(V#10w95$Bhp_hl^|0W6r4NAWbiia^pn7rjf9+^oUr1V
ziv7Dy0PsLD(p}!zsUV(iWf&*9+5Vq2<d*yyAn?2lHsuqze=9ZL_kUghPJtoCqVsI9
ziWx&+MFRG6E(Ti!XDSA8yg@{p0a!+Fo<HS@YK1Kg!J1+`zi(uXFV12%{C#>ge}y2V
z^<M)R<CnDEf~uty9l=iaFtyJZNo%W2J3NWfXOLxWpa%@$$E+=M(0(s}BFAW>gdEoN
zR4K&?kA|E+9Y^FjADl2^&c&FBk2ZZqs$6;g7T*cc<NmP&zsD<i#O25R*3Lstf5EAk
z(-rL(M<qw#qrv9#%enjIM0RCqpH-0`18xLU`X4$p+j#0xwW)(yNZ*=$$)`yB$5DpK
z-BIGz*#YonPCGmQE>oHpRT)R7X{!0v8S$8Gs39+~NGRBLcMM3)9wG&dXw|_}%;k^0
zdX<@);TX88P%?efepqjUr8m@%shXz!Y6^F?5>TVBUTZ@TJ7QN`d<uY)q|T#OQNg-1
zbDmSHsuR;Rp+>K&VOa{=IQx4`Yp)_LTf*kR+y4XE-chQ&PTbUxiKQ_jBC7h?ucDBU
z#m`9>ahw*=PS=JmM#tQ{kH|}!q0^UWRxGj<P{_?>aaV$HtZ_6nfVjZ!t-79i->k!(
zMk|U!fWQi|2Ce}<_;P}nJ&{@%p96(>R9sxF+d{D|ESOUh8-#4h*sfcRqgArw9w!+7
zgT>*R%eb?U73$D67O*y|Js*zF`NFAQx?|b}C&`Uh@v;PKrQuE27d?h{Vm`&M8QPwf
zT1s9xhTczfKVNSzY^cIpNM94>f{{AMJC)54g8O}5*DS$btrLDX*>5H`m8e6WmYdy_
zSwo#vFuq-m_lNWMAvy-yu_UmlQTk%$cmaJx0v1ivioT^NTs~ZBbzrBmkrD6~PveSw
zDAfTVC<lCZpy5y2wvBN?9nK+EnAQa-_E*>wA-b|_IhI$D?iS0BP3++tYvUwY?uYfT
z{<C3y^37#o;Z$v2k92X6fM;SO7q`agw*-boH5D>Iamg}hJ52#gdc?8Qinp0Oam93}
z?F0^|5eL+D%&flx%h!Ip6A&|@%L&`!ROpM<NxgqqsQ8aK7igOw=R(MWeY*0+6wT%x
z(qj8(Y-I}dea0o^N)ex>D_I3ui;cHXO;{OTDHiur5$Ipda7<sUIGn+qL@UXxH-V$e
zs15J#N2eG5F-A|8j?QU9TY(K&7&Mk*D^Z?T-wbt$Ov)#<rZfK)_5SQ<HdCTf1Rx{C
zC<w<jA{(N}#%)uOQ4*~I-?vzEWNx4wV!Wd>)>qFC_szywG0iqe5AvFT6I(IP)=UUf
zpMdLiI0%gx0Cf=!?MLoslg_t_X%$feWzsNf>&#R8dk22~g}mn}fW(=l-;zWsj_u-2
z)p^Q+S2i-WuZ?rzV3|lIO>he~`3h195QOD(wkyX6?dV!xacXKXfyr_XlGAk5hvqo_
zmi{m_RWvB`9w*r?Sh$W+s*>ls{(d%~a6DUih=;7>YZQMd(>o1>wu^jdKu1#*v%}&r
z!J6sWq(RAwR-DVDUygM-k>e^>i=$N=Ivq-Zq%k*_&i7pCwE0atsL6$vkTyf#H(kE*
zaf?iy9h3Y_moS!{yDW9jElnSsfLdbg-?$xyhXkTn?O~VyYyYYyiH=MI2n}ttIWL+@
z&Iu8BZ?c>Y3Nh0KqoMfn_eqOCrgg9%%Ar9J4hGg{U#u_H-e8kTc3SLTTizYsm7?LE
zAm@eJFYcaRWVBp?X(90o_Ji~3EX>CO>l#9P&`2n|MI4?ObuK&CTxlUX&BU0bi3SIj
z8u?MyGQbBhAtNhBm+n)TORLP$V7faVhm}}gL>~G+XNT9^a-SA&6Wg#Fb+NB6F+CT-
zfr9VJ>)z7ZRn1(#J;>JvB}1vS6R&x03SX)F7Ryq{-1xAI#F>H@D-5q<kD|goS@sC<
zio2oT2uT%TG|<S`pLyto=Z%Sw_YKjKJr`bAM8A^==8rngihAzBj6A+7#L`kmekBcx
zg$H=xSE%c#R*@^Y3X}6p=_oC!AyUrmH;C4SHyEn_JyRB?#^~-2DM8}RvJWazjgqac
zW&kt8nxyQA64DD@$1T8XCVP*R^OizmescRRRG1To45w72@4rfdsnXB9q}IwPyXuG^
z&?+?ZF!s=G6c9)NZm<sesj+j<QRQ->Y5Mvj^w7fL`?~G=k&v6?DgEbF8tEUT^tNxe
zWpUxn{4g*-9QzgzHPDNNq0nXbk;W})?c+R57ig_pZp&UiA_SEA3K|?%ddnGG7>5Ug
z&ka{SC63j|xhLq(p9brRyAX});3af^fN!kd36c{Rx3EtgWRV_?fL06UYB{^Cv3h?C
zC<Zv3LfIyGs@E0kfmLC%Vyq-0e1n@TmJA?Ttg;p{UI`Oy$dL3=nO|%)T(Ur~)xg?r
z*RME7Z&}g(YgA*kIiIDn5k*Ykx3F;Em!rPqu2~nCQEBu!tBvlu0TOulD$jEPES-3J
zRG0rDpL`19a+!@vp-CGE8>Y`@p6V3gYgWEc^ExVQ28PA0&g=(b%WaQl9vS9zX?VVG
zW?*CjuwU}&7|o2*=|8vt*n8DOHcbsm=fhnm!mmEtJfZVOY?nXSwP4F#7Jtc~6h;3f
zQP`r@?S?WzI*D9FO^s_Q6Rh;#){d~NpAsanbz6+f@|jccme1GQUt2%0H8HS%)OVQh
zo|}|1WWF=X@8U$6!gE@3CkYA)Jn39dr$+AoweLh4l?pA|aJ&PYC)kW=O~Knc)@s<=
zSCF1m^C4$9sC@G?&(qDJtw;M@xox$%P<nqm#C;N)-`m_RF4L?DLfLiNcss{M&PLH<
z6M1635Go1uRQjH5NlhWOwDY2ejiwM|Z(_FkGdVdf$MhpLzT~*+KY5HP(kr8opVE3H
zokNNd-Rwj{MEoH;l458GdcF+04d_14EK8sB@t|Q+hQQ&QPezDOPd}YZLA(F982SI@
z@Bfk{q2hX(Nc}&u-T^qXZfP5ynRsH`wyl}iwvCCcJ9h5)jybVy+jcS&+cy7v?|Yte
z&i{SYReP=4dskQO)wLI{zPejP`9B<~{~}F2rW5@(-29+ml$w+M4=DM6V($OvrElMa
zGScsQF-;9~zl5%nUY*`d8BNwRM;Z``K6W%0=J`G;o9soV%Jah<+=jYq3@TXowiNhY
z3l+|?n=}*h4+<_Vn7XrhevDdK9n~&+Ab-0(Vfrdmhe{<3sd<;>_ml^SyC;Ig-{m@P
zuTc}n`Zl_Z5jZH*TfEt6U(5bOOU%_tV?eE_@hK<IE@+w?SmSO+RsS6oIa?RfM%Vim
zAv3I|@3|*Ik%bi-q>@fB85!5@Ub(8ZRfmgpII|@JhcD2EB&Dpb4)rHeUu|ewO7`MM
zZUq#ncgWv7VfzlP-eL}2g?BcC`e0;bbJ7InecJ1~*r~_r7@AfLSLiG*Fynw#EY0FC
zqI?_eOFHd$*K}C(iKN{8AY=-Hg@FB(|DUZkLj8i;AwW1VKflZesE6hAK>Hk%)4@3k
zIY09ArAZ&Ymh|t6YhM{<W<T5U^>$-x@l59qHPKu`rZ2Me4G(3D=!2BV3YlvSP;YLh
z)>X?P0xQ2_37WA5fB_7D&-q!K<=GjOKX)lDR_Sk~`8S>NW7~DL!mU5wQB{~xeWs@F
z0NmZ{TedB*Q%v$>G1#-yvC41n2DYmWee!8c`1f~;>^KeY^Bu5w_n5~s*&)pkjgga*
z){bn>T4l{l(ljO7yy08+ge(ZDPeUe$(q_)NoF2iic_O;!Ev<e*<?kOpEYp-{bAp>;
zAYdW!APBe1IK20&s&MkC7iX?Ls=~3J7?iJF3z#8X{;<&`HnI&RF6zN#WPZDz%k%1)
zAN@)U_Kn!(dMMYl3`qk$qz>`>=BzQn{i!Z6h!2%c=ZZzo(-U$~HZ`~<CQ4Y*ku9F(
zI$;Dutg{+^qoV;lq~WL;z**vriDLyv2Uh*wXOl8)f}}>)Yf1VVhvpr#{+E2B*Y9~i
zN!q*|V1<hTf8(EM^~EguG-lXqi)#KXpe6^5$#IqJwxRUL{23P7+a7X`kNAJX@Sj<E
zK5E<u{1jEro<D5lFFKTjU^*Vw3)~WNi9}V9&=nLDay5Rw(Jgl~<dP8Smdtv=)5lKF
zdacz@?0-yB_=A5te(<>X@VvkyU&%<#<h~hCb<1kX`oI8pj>=biru((4zx}vSuU&*D
z^vC;A<G4ke3t-UIzF}wcOq&hpgJkwSw=k0XjtOF?fA>n`4<t+-6SmP^ak>SR_|NPP
zEn7}aHy*;_#^}T)8of{KZzKjY3P@(}H(XWeW&aF>+$<a0jkW*Xrqu0~Aw_)oJbR`z
zr*=YiCzOtQY`8yHq_?eJf}WL0%g$IZ=vEOj0h8o;OkP`hbSI4&9<No9)x!>*=-Gb+
zAr|mjFxfI7Gu~h^qi{}kNG_GyXT&<7MA_=XE034|?rwBz$^aeLD1|?bv*iMF?Sg94
z8=-lenZ@sxgtCNm9IV<WT9VGt5LcDSBkUYuLF=l=X03uthzUj4Qi2!nS@Z<q*3zQ%
zM_k`xe>1HB;6|4nK1X0WA8>@{{bp5bRE6xYRjRU;u+ZNgV5!3DH<oOQUKji0cnOiT
ze?vdaF`CXi<ENz0*L|ZBb)0o@Mozbgy>?8*Y!cDv3++1MJ0b7sQ7llU-oA`89|5ro
zbkYDCoukcq*5qyhx&4+3S~BAMu(EcQ^x7==Aj4}0b;Y003UNi1&5C!AYu~gk>*>%I
z7Efp{x?Ty&T(gsUJg?`|S&z^w_vV5buLl<A=B@s~E%DeK&PuA8QCqRFX;)Z@Y9^@q
zao;ZR0(=XC*82$ek1P?L_|1@q@+zEv=_#wx+~oUIc)B0D#kyib6fDhu=2(?MJ$VvB
zAcMHt!mMIG*!mjjb);Ov>2>gUBESUlZ(yVI+QOcEzTX}=puB#Ds~MEn?M;AEoAG%x
z9?#?lVVXeHY7*KK=}IY=%8LH@mu&i?e$&!)qo_6m`!$*dBb7RryUIp_kV&N4zEkkp
zqm6M&OLiqiW^Sr_9}5}fFa{)M;ZaOdcPjFk!$~n_K+-o5Z?=zuD;Yayw9?0t{vOtb
zEV{cuw)$qJ)T9<h=SOK;0PEO-;i8D4GESMV$Hi_!k1wmpFw?tMIY3K0xlu9F>*o^O
z_Sgc8NsGadH24IIX%Z&@+v9@sNk_6vNE`FOhS6!w3KP4Kbr1Z*m2sKud7~7fxHuu1
z9C0suvb1|#HP)<^GPba)<mBQGDpV5Zj{=RRG0sNXzO%)adLj?SNb$R?<2029hUUQl
z3&O@9Eh-B!P#i%(h**P>q&v8HlK~~o@WnQsiQ|xA5<e{#lq!3+?b5Rpx7TT0m`e2M
z<aME(!LN++;^QMVBD&2#Cpab~!nh`);INzQ$q|X?iBTLJkCeU}ZW+$cQJ_-cG0_U@
zW0WNnL;mFvAgE2V!HtwtBGf+~5f{_zIo%a?*CpPyML;_+cX;aMHZnn9<&ix$oERS*
znA&I*laz8sJ5Xl4?klpp+De7CMf0jLnLu>5T@1Q#=|a+V2yWnI*geND0ADBig5VP{
z1nJse$(8*w(8^08m`RxY1`LeW)qLNEj-S-+#iAMO)8p+;2ksl5ab>jZcmhL=`sFA>
zKLaXXuaB(SAGZc+CMg=+O(7;J^33NJW!3u7P)gg{(<SBUdfIL*vb-I!<Z72CLt<~f
zNV9<`I)%!PrkFczz7!46oT~Htd0%vnhPLHmUEY2p=&GtL4?qV>PcTITo=`3TUAh!5
zM<OKv#}aWJBjUF~%SrsG9i3MTo*=CLjvKxXd?Syy)S_i~?sS43v&+|Fdo@DIymUHq
z>-gXz0x!#&STFp8ruM08ci2Lt8qcnK8V^WuK3NPO4-bs7Yn&SAb$2)zX0&FgZAGLp
zFJLkfu0WQrtTlzmhD@Ujo}K2~Ezq3sAkS&0zU{;3c`U-<4(G|~X_}^SXhYIRx~fOQ
zZKcVJ9+pu)d#ccwxzYCJSA&I$U=VO)YVU8wkd60&93I7RLuI+Ewu_W-_`Km1P;e;R
zn^Qt!tmuFf*8cuJAvWz^Np7xtAWt3IW;UoANgY94y`wmxh^`<t8J(22N|$0|K*&)n
ziw>ki7#MV*0O5ZzN0xva2ephF*VA9*r5sNSD;&0`N4X^_nu~Pya^$@pH}V!h=i{(3
z3058^Q3^Pj?@|H(N8V?kEiMs`#%T#Vf_9>$$x@<%n$2%U9}k!saR)-3hH`*ia&hKN
zwkzt7?oTX$P{#a|1^JYgN6Jdg<Lgzcn{XW-;2yJH`|BdGSL(Kw$bdo5-`4u&)fD3m
z5a$6DEJ#!*IH)Q@CIWsve>^sBG@Um&&p<xRBdGzO4;lOG{<gr$<w#ofUBvXNA&1r7
z^BGB})>x9$14RRnS%OMwaK`7)Eic!vjbRC#oJIXbI2MhoK>3^JeKc2`)cpF036-0&
z(|B_K+n@kXe8@te<oXSmIYO3b()CU!_0%jQ6VRMee$OI4WHPU?&Oi!Q>^75LtfR=I
z+Krw%4nL5(nrEd=z%Voioj*7~9qyj*d0Sg1-m0_x$OfZwbU)k;b9${!17msq`$f&=
zQ2;iet4oEs*xiuF%kA9Dni!PUVu-<@Mg~;mIT)<<7<g!o%!rzbc1wLbl0vCnOJVtN
z;dxG7@#qn{2>Dv;ZeWMewSix2{AfFXxVyWH%wxb(6_db_;ULdFF;>+Xn)70kg}st-
zVxDVXr5%$&YUOculL?o}t$rT*)e8#0#R#vd%h9id)y$EaHE0Gz*t4;e%+reIda)t<
z+9kQ3XhlzcIgnd}uiaPZy$)x)HQ}c3Ef+sOINrc>J$)P3@B0J;>$@I6o)7i$Y)`Z1
zRd4Lwid|MG=%KB@wcx@%?qO9(NABZB`zt(SRFiN>S-w3`*8)+`yAvuZbWMIppo4f@
zoWSeF{978rL5t+w!1lRl6p~=gvAM%>Q?`e?TJPw%>`<kp@1To~$lw)w`<pSi%}bQw
zYHTOq;RIRptc+%xnl|S6w0Oybt}e}MwoO}@L~UYcsKTfI!~NUZ#Yummo$a*VcHAtY
zjnUZBkfg<GsH8D&jqQNBj>IrA?K|Y+rCjsKuMmCjHyB)3N+$0a9h`@wZi%ILcU}Gg
zC&m$>FRpi*mpynPk;hZ<!UN4b-EAM9>AFKe&C7K<AzaA7c>%lmR{Ap$<@+S^;i^H8
zor_X(=7v%%D)TFr$K0{j{+UB<tvb4SbM1CYZh8n)!|3ZnR8z%3FZQT%`!cqu&e#AH
z(>rNe{O5V`WT#X@0nHI%!gji{=3sleXPLzF4C!QJ2b;AJ@+t<R6=J`}hP7%kH0n{S
z#ENC3p5sm7bi<fAssnJZ9d8Tla}7gZSaU`(P{Q9Vty&xsWK{yOGZkcof5~Gg$?u*X
z;;>)-lOpH5LlLG{&BTlMcwXWJSzM4YV?*w)??&7BI5mfTD{!FIK75bh#>6DkOaaSU
zBIBBY#;~|+?Jb=&FzJ+!1y!?l=1{y2M3>fvoIp1cr-M!ky*o6WRbA@o*6ZQAIT#84
zp4^KiBmE92Rzeg;@3s)Ur>#qTjaK&K$rKo{=}i3zW1w$u(-l%y_G;|5UvwnE0IL$F
z%>(eRqy)?6PEDJTzIu80Yh?T&6t&3>Ul1=WS3>Pbt}AI$8+LNq^p6!B+sJYMD0`UA
z-AjY`;R}Ee^4@xIreVa{W+}d07#HSDAdHz`n7etKT|mz_8Y;^-)2tS0FB)AxZoU{%
znc!FNGx``8qlfPb%u>nP1#(D8>T>6?lN$aKtM_6b9O0@k^y@Wmr6hMlg9Z>ZGCK63
z)w|A$v)^$8_p?^%t=3dqlgEn)e(8*iIG+^%O*TXCg*iQDcjsl84d<BjX*Ydh7y7CT
zIeRKrfZ~<^#@%(&s(*KB|1KG7&eWc<owt7f^{?IK=w#p{8~(`OD!AWo3joc;zX4tb
z6mwYsFk=PIeZjS_ZiwP21In|7#$Ey5LYJUmkv#9!8cYlZxoF?AL=^W_1?rh1ZfY`{
zAHrZz_iu7KQUU^ovM_gwD4OyP%*@Er&tj7lvb|)Kj3d*A<6M2Krs>=miG8~-@(QsP
zhgz!aH9@%bc<%seDETak;_4i_pV}f(o`#Pt7_~<&=sIE1!aLc~qVZxbe_+f1R!99o
zL^SEYO?8d}n(g6Mq3Y5r^xBa*<jPTI0dn!>CT&p!K%(J6Pbv%693GPU<%`Xv=lggF
zblGFqgs%4VOQ|lDAx!n6=7VUIicM+ZvUawcuc;Q-^bPLzD)7-4ADMZzc#XCBo)co8
zEF|(ZcSHKk3HW5ABE&AQZ^ir8Qw_ogT&TG6(R9Ip>(M^8hq93MSmEt(Z)^RVbUI$b
z=<-#d*AJ?Eyxlc!*2iUw?vgq1B-xX}>Kt*dr<Hr&P)6F=KVIAlDHt}|BlQ5NG6jRO
z!%ex|FU!l9HqoK!WjT^j(^6+<@!<#+4HErAuzwa3YxYIsE_TI~lE|{y;LUEfpi#ye
z0=`2nf1IJ{F?s~ebfo1Z7XT6Q1*K^<vvf4?8cGWzXPdNy2P3KX=hU;v+KvM2tyCPa
zUC1oe{Nj8Ewuu$eBmDj)-5(6#<z#~&lcln2*sD2&Q;U|e^Wpffmv>dY6{jt=J9Nkn
z4)KTBQ{h&KbAR;Rh3b)9n5z>zYlFwf+EyE*OVO6Ar#gu+)}u~&Q-}9L85B$$a^4=Z
zc0~wxmQ(A<4m2dCB8L<zs5?<ua^1e~^hhet9O2hQ7%5oWSE8&>YZ*Sa!D6Q0;UDA~
zQ{BsgkPN~z?7#Q)t&6PYc<VH4;XuUV>3fDrYw;_aArPIvD!P6!Y;~AUWTeX){`{&2
zlgLnnf7Qi!(4D_NZ@CESM1HYAvo(0z5!!s=lm0o*y$EQHD;DBkF?-6&czC_??nUmy
z5^TkwM0lOt!bLeIX*b_&CW7H)nfbk=5TI>w#gZ6X?nWbchu`I*)J%3h|F!X>Hq$ED
zWf+OHu7)TWB<|{vvx$1%RTUgWdMB3dlz2DB>BONge;XTGY}!6LdDd19;d&TYfE3{$
zmY9A05*?V}-V#swKvJy*eLrZ;k<Hb!&<`FTV~6I$1()YW1nCVU-udVkNGXR*0>Bif
z#N%(5_}dk+V$Nx7f0;Y*Xs*d+KxTOI;KWr6H_uzSx&FDA?X)Hg)QgxMj>r}Zj1>;=
zR7%3)#QX(wc{Ly!&{Uq)0OQQ;IFLbKqhn;mFitVCA4N?fAO@g$u!!JDwVNE8oB=76
z^lsB}uKd)G%nXE44oAkEk!g)02y**LLOzSxVgoVfTK-#h0}9*a!@f|Su;)ZPN@y9D
z>;4Yd-myh9z=Ik+3ZJn2SejP*rAiL^{eDW{V1!3ff{Ko+%rm~{`9XcwYsHukMJ?dz
zRE<MQ6f3wKHxk9k4$aO#kKZ#da>}IkYIl1(pjMg67nVN}c9gz)n<7z@?~{-_4<(u9
zR~7^!8>wwYp)eUy|D9kKpEt^xFBI9K5<opHMXX$3k-?8S;*(1$S9zUTs`WFZ-;La`
z9Mht&^YuPWS-BY}Tc+yed`Q0XNJ<D&j28;KY((tP1!r$P@wZcK9SxaY=@``n4Hj?9
zqcWv6bQbo(rKK8>DcptCqWAM=t4c8Q&a57awQn5R3NAW|lJ!QCb|z>f+HT$Wr0B^)
zx6i}y%7_idi&`luCJENX@upWaB$j5u?E5Zpxa8zeLn3Xmo6pp_@Jb{;a)_~<P{Cf(
zS_VFujwCH&o7Co*ftqg#bn*YlYt_#m!Z|^tRR9}`zk$ar2IFYYci8klu{6oj=uzZ$
zNL$RcA|e-H;sD%MmAiYEYcs&)RjT~9J1}9GI!+Yo3+G^Qi7)&mjm?gVa7MujpNYR2
z#`_e*eEs?d%)=m7EzINXwK`Pt=A2BYF!>&O?D8cPQJ}U|pH@_>c#gMh?aCgFitL#;
zj-3fv!Lxh0Mn1;kNN~x)RbW5sSu0jzJ>d1%+Hy$y<i{FE*^7A{aYE;?k8s^wUsJ`U
z_<}^hE$WS|*|CPvroQ%?3+Q!Z+flk4%(PTY6TBr(C@vjsozX2NMhDJ(jd`}Q)U2Xt
z?dyV3<ur)B_!Zg8!eCaT+CDNxM<K|cM!4Y_vOqbG6@+y{|GlNEmAb_bqlC|5I?dj0
zC7MW_bjC6qtv~Ca$z~{(?^?2#jA!?C>cW9Ye@jZtnFmHGepOmM+=UStuE9t~6)w*s
zn7a`#1O+=;?p)L`&Pb{u`Bj$ilyR+ltck%#3Hee+FWUY`O(~|ySOYZ^j9fsdUMUPu
zYW%Q7(l?UPH$l&++@JI|&8qy*g69<?n+=e874rrjY+_1)Z(Wsw-R+cw=4nNQAsArA
zNw|H_-l5F%U4&l51)iCaw}@$ruB7B&^DZ>Z8eI#kEclTHyQZdiYoRL{OkMP{r7e;$
z@o}DW(cxv(&8eo~Pa>(Gzd${Fmy{hDke_tWT*Zg_(_}Ke3$Y!bC9L;DIju!!tAJB*
zCk<)vZ@L&oVqmOUUIOW8a`TCHMNrVmUyZwyAN!GThHs_t`Gq4vXw9%U%2ckE6bo7u
zGu@{W)!um7;(tbVlN}{F4C+(x@Tib&Tz3XDZ+*%sV+4~V;$t-GI~vOMI^Y9`6lRvk
zhfQHy5}{^H`VUdZe^X-2<Qov;-sev{T?@OLP+a$A2{(nP9w{>*#~nx>3#OIm;xNAq
zz2zdqBFlG>E!cC+oQ_D+d2C6p>p8_DWNJG-D&-=*SpSLf&R|F_JG|Pohf_2UOEy9E
zO6A%z&f2el;<F%u>HfQsE*4eo6XdyHZ7>gJ-KF@$J#+-n1wfh9%N9ll*V~y%EGlf=
zTx|^Gy2F-!2;otie2p6~2i#V6$^dR~vn;fFg%RZqFOICNSKFh)GfQE(m!>af#1A(}
zQoOT$=?v9OnLi*oAzjb>n%L7iD9bQtBEHz8SExELCt6*PTGKX4DTS-XPz@wL)6d!=
z_qe^hOZnE^{$1mXmW4q5s!oTP){i>sP2-}mTBZ*k+Fy#jyQQWCZ+oV1?c|%9uk0YU
zdfX(@1*zQy*Qz3_7_I-uj#5awB}w~Dw6;rau#)~nnIf>(7ugIbm!K<K;4?x+S)~!0
zcf0YuTS-oZ?3?<1vPPNXcmipn1cOA6&D)*^Mp6Pn7vSUiT`;C5s8kZA7KMWHy79FA
zp?CIIpB^nn#^0J(H>OThX`n%2jbVw7mnH3x60OF&x=}gi=@@hM(LBpT!V^udUW>YE
z>#=O)1&jVg#t^w9+?B=~MC46@n3d|y9U`+@VH|wD*Tj-iWZB@gT;#_wK$Pia>_)?+
z_iXvYG?fTztn6NX6ufLWsLj|KP1B)B1_=~B`$5U9W+Jh`vL!gwOq2S1^&TZI8G=Qh
zb~HBqMm4d`Ev`+epM<+Y=KU{0)-0hAUfDzH$TpN!wfB)@E~AJd-9@hobFYn79gqOz
zb+v39pMkv`t!6t={^-n)oE4bLVZjsw{*Wt~d3^njAw-9v8x`y51w}kYBK|oIeM^3@
zC2{TNTh}Qz?(4Cd|BkplzrV5kiRJCfc?2A_=;;jK{Z<_#x(C#xFuLh=KHXg-n#=2u
zQ2Z3%5S>Cz(HtDeUyV@q1n0fa%w1__1~d-`zHv%fs0#PRHwkWzFF1!YR2QxNhT%J(
zPttP1cYn!@36GcpnGcId^fitH&Y3$u?kU6N!bpuzVlsS+3EyzrYMr-9w&%WcH6MK2
zF}pSB{cFcEn}gHfjW-<kjhbgGQS_+<jW?L(-amYP%z<d0A*88OZ)$;<)x+Qq0PX}q
zxx)z&uBg-KKC~-QU#ehZpqDeFw-@_q9xu)9B82%4t*d;60@R*DuiZGCW2%eem0xGO
zSICm8F5{|3;iLO<(;s+^2M&Qk3RA4+p}r!GRU6Q*#uW$xvYJ=v5_7Zq)i6#_ocxUf
zzw`Ohglo?2wu!Zd<BYOnhuwieP?}HxXG?1+Da<FqRi89xnIq#_8xs_0N6NubIh*n^
zN|*@!8s#*kY3{bRKUj@V#u<md(MhfLkXnE2nH_tgA@KHS9?sb+W7Ec3{x?G+Nd@^S
zFDaW~sPRrvS&%LEmmUU(cSAyym73FY8a{2;ds@!hpY2$WAXCECZtm&8aa>Gphxy=1
z`gGQOOL0e#+|?E*uEc2A_D(_eZ+xCuNPZ{@BNIr<rmRF}72V~bG(fb=38y2oR!yb4
zEk+I1kXS)1D*9NG^pe71Tf1xU<@=;K;gV5x|5y{U&BJ^r$V8;5#-~h?=6u+yf(#VM
z>$9gdX(?AOFs{uoaBH)aX<$XsRsI+hqh?F~yA2UHf@MVMH?^eML1B~yh%|g@(RjG~
zC3Q}hQght=NvSu+M*z)=*GW|{`n55N-sTU}PZZCgg7(Qpka?KwnAyc~wrDXXYra0z
z{`jw-uBY4_fX-la2g{VfTl0P8xe>Yc?oj=z&Cw^m`-^26F+ZTjE@;Kx2sXQdN5(zd
z01qTrD~;luh~eu(sb2+6PM(Jr&O5kcIMAlWg}U#C0*&(Y-VhA%5JAy06&H3uX2zUy
znpGilDq{Od;rp@F-ZBoVd2lh(=n#r7*B<P(yI+=4e-2vAiCXO<wh>;gy@iP0SJS0l
zoE%9E&GJfYR4OUDq`0D{;HS-!HnaO9z51uZ2RI%BZj0tVH+Z4e^(Yo5DNQo7EnSvA
zgggc35r33Lg3~FupqtOpdG>gH%rLU&_^Wk({(*8?laG4uZP6Nma#zxYSbnDVGfRh=
zF9y^$EcDH7_ncn4%HCTN**%4Y<>8J$s3rq2b_GOP23h=0%?|xclXb>m^iv&e>Rg2P
z&!aRuh3HisJF$Wh#0qFvV(15^lrc#nykmO+);)RKEcbAU&}2X{lv2m0LK?1a0G{2x
zIMv25=D>@4A*JrOGg<>Rp++(~(Ovf$$!jEALrobiGrr^-8sqYLRVfL~NLccwo+)>y
z564KLvrmvDh3}CA8cxCx`qi)n+`9@$Cq`RiA9@gb+xv!cxWvepdXPAv#tt282_tf?
zth+fbrbtCUR$R74p+~I8y~$uzhzY}7OOtls@j=963%vPHnCel8V`Szb)~=2#oGCE&
z>=o<2sP(&<9LHV@;Df{oE_7}U!Z?(dNq`v3tum-4ar~5P5_S;Zu8&KVDSGmuP`BWT
zW_|ot#0Utm^mgi`ncVqSSron}b|IfqMx-Y&B}Ep^LN6N;{DgX}ggl0qTGbN2yBFML
z0I=#bbCTG1aOqio;fQ<~<EuQ`^DisW=|QHW4?m5=^g`7y@YFDk^p$6um7>uJ1FJ1x
z>uv5Z&^h*3;TW;1B8u)9k#k0gq@~Fi2)OD|;Y=NhHoeKaRR|8ansG+N9o)N7S}{uU
z07yZL9dt+Bs4`7P>;w!4AvAsNIpSc{MJeh#;CD27xPCHq>Qd-{R&!A@1=;f>G(=O8
z$XR-bScvoEWj^CvU<b3@3;+reGP3ehye7TxBJf)QHPWJyK=2(Aep%B_n!(U(kMg<+
z<Gdi%Fww?ugIek3)kVU;-R<lgxgmBm?#bGq%@R$8YKDUXRfmJV^*m~{4uiu4w+B0f
z_g@S;QfQk9kw>bwl=*pcwrHo|Pvo&;NJ#*5P1G%N;Y`H{k}pln)8yUZK<vr<={^Pv
zY4|$TSq3%6#NL1J^R*lx%R1kJOa-muBdE+8IXz>4n6qk=zw=J<`lXa<niK)ARD^)a
zv$T{sVgzf0UF&y-KVlZ^?Ut~dMqOrWbC)0M$kcGI4=HkbGet0lvUr9EPD;=r-A;^W
zYjgn*1)O~^k`|7O%t^lX=tTyCwXn)u^{2+G&PVljdf5y<<o)_wKR&5_0|S_kub<QN
zdKZ61TNBy)xNS{0&|j{S`+C#vlqOaI1rrk>a-~2w(-NQmGI3osTvlLMp>nERNn&*O
z0KhCP(tRVUWw74f0fTI3zq+oZCD|HeNb*q$C0;{kFxx-vG$y04{#=$SWn%IAZNa?a
zJkDs^rgS=7h?~5X=nf#g)PF79?)Sb=;H_2%=++cGP-%Y8$v4G}2MCX*QzqUl^EFm#
zhqtXMq`Cc)cerMkizp&>eZUA0i!~x2?X$!H+hf!YTYaY$XNk_m=Zr4wcrh5^n`jdR
z3?*eUH!r~?3(=P}nsf8+k#v=>i-?FsVg>B!MS8|(X_XK;|6<?xsFo|GU9X58vB6Xx
z$R6F%r(rBzb}y5r!)1^Yny)2)e`$pAv?>z3lB9DMD@%ZHCGSmS<mQ6mrXEMZ6x|a(
zJjJuIja-d%{!U|heC%-NoX5qRnbF%NkoDD=d{9z~|2|H#`hHCl@HA#fjjH*yDh6Xy
zlRTT8s;Z(<(q6=+n-P?6>9m{Ea`w%3L-F19=6XOf-`Td543P4H<!KH>kHcxhfp~8z
zIy$ljXmN%qi(ae7UzuFMXAJL~icOY_Zn<5B?3w*Lk(v>iAXuQI%G}@Py29JGy?-3Q
z&YRdjJ}RP5ORP#jkeiY((NgqX-2N7>e@<>#ygZ(e82br-u*UjxSh7EJo6_B`l%6T&
zmTYs{dTTOPmW9~xY*VJL79`oELzC-|Di>&<LL-g!(*p~GywFodP=J12d1z$xfoh~_
zu5v#K_EEe5dtB%*bp;!xPTjJ-?xQck-{Oc3e`-1C^U(JHb;XPNtp69FS?>X&iy;5w
ze6%I~vg1VGybMehD~IHKsW96HCviLCT)R~od7mY;N)jP*Fvl6y)eQiONhJ;LV+S=y
zc*$LLt$Z#Vf|Pl(&`A%ZFGC~mUuX4eH0fK&c5z{xSyVi$fxucx8np3*cNXh}3-oYp
zrUHqhrHxbdN)mmkxB}RuuZh(vLdW^F@eAv&@}tbOL(Llde(6Xc?YG)25&vTMfEu8I
zj*ni2hB|9L-;(~ovTAjG7npGrz1W-v#Nc{BO{Y}kfUFimO|Rc*^(`j-CdKXOn3hi6
z)sw1WhOm%-_>UHK^xtR~N*p6@20A3y49RRg%UvjgY7R9KfEMFUtr7G{r_%0a4t={J
zjr}sEW4NQHxZc{Y)L4`eGWM|%S{x9wi@sI_ykmTwdkZb>F=f5YqffumH?H|MAdCfj
zt|@NOnUkJ8Q9V<A$yxM2pK4Ugt1wlv=?~12!giBmj<%Qk6<mL>Go)kb(Kiu#;!_4F
zyA5bmPa&uN9!}D}Q7yNNkZFcD7Nszu$q9IlxM@bK6N!UC2q>7dVId}Nf;PWWQ18`X
z0%E3aQrs+`H+taFzUgi6Rgj?uoercG%>PbUdZ~(s3IE)M8v_ti6)4<3za+7ZL@21s
zdc)<1(GsmxP(-|D`O5jH7kE^*$kasrkAb7>kRmBd?l<4*F{cxYam;szhaFX(&-{gC
zgPsvdnV?i+z|o=_lsUCNb%alniqcbFp*{1i*65)~!CF+MZSx^re*PLpEeYR*FiGj$
zY$->-D$lU)yS<AO2`6h&Jc@p&6*Z=EH5KK`D2CT6|Jg9|z_abGdN^Fl>!KUn9KrU?
zic?F1UPM89GA5W<be`f|qjPcP))Th!ic4dHb#k`ym2<1atfiqV#*^wU`1&9DCzIZR
z1-(%>sV&TbT#uMSW~x29HKt#mhi91{j0UHew~GczML9IhXH2u<4uprPmHXplT1&#y
z-V*vU_oG0&;OaN#tM?BN7gt+>poL<wImn+P=O2l}$scb}dmjfbRK!x1#F>YG*P~b|
z<(vdq{+PvmeDG4-ozi^!k9?#fP4jp%84U7y;*w}wj+)n2${#I2EEW%Jo9b_akVhgO
z)(>tr<RfPFSBhWdySF~*K5H8er1TR>=*dM}q`A#)ydSIyRe-}KOSpQr1RwdXLw~r7
z7%5}0w(ib<WGmx-VBxwTqJRCj_;-E%>QIC;Kx;TJ(HWgGI7ZznC*-NKx;m}#eRzDl
z5-H%BGvlV~3hBcf=GB*U@VMk32k3v=cMfp0Z&kY(#s48O{6l8=A-YKz;3u{DLCh(I
zU-)lK_#bT8@Bcho@c2#gf6FDJ&=2y<kKgG92&(^PM*LeT`Q<U*Ki;1Iw2x54zfm52
zrzey`OaC8*zfs&>5dIex`|s;(Ad&*itKgwoiLmOw9Z3IH3DeF4^`FoDr}zF7x)}VO
z(7A3tr}SSy+kdG9*UJR^k3s+GeGq}GeEUFb)jU>E{kIGGAB8I4swV$qk$-yrzDVC_
z{6C=9=v<2bQS)D|zxu(yNAcg|+ARp}KMJmwH(+t$mnrFcKz<m>)z-STbsiGfakS{#
z0fWiurrp({YuPnu5Ca6V4r+U?-GCN%{mSZ4L%hx*v;twoDJe5{vja1vZ=hb(SFM>=
z4DBAw#Z|7-n|v3IYTf!fX(DYm?tGUwczUMR*U#mnr0gz&fO%l5<KR!7ka?@DrUVx6
zqq5CIsSS|4c=)H&@3KQ}LnZGOvW^3XcW${kGb(EzcA5FJ$?w9E>^!F#I`#?7A1)Fu
zDW|sClsu>S_{`WYiDFM~?+%=iACpe66+^S{$b(v^cz9(=MQ-?Edew}eJQD3pUEN2k
zc)8vDMZdu7)8qH&%qm9>(;?RP(4jdXoSI2f-7D+S$H~58;?i%||FzRAqwtTo+?zzJ
z*<*ONBi{Hc#OMO2n_pzBVZ`L5;wwRepRsa*guB59<}8NuMD6$$r@ScsgX<tUQVPir
z<w(LALP}wQQ2C6a_gKsJ>s^fqSDP)P&IQ*<uQ!XEOW-)}T;}fD-xf6u$0glB-WY?)
zNO7#yBG?8#8J&2iy>*w1Yj_4W>!l=QW6J3&Wo4=FwPBJH1HU?6JVv`T@9YEz`Pl<)
zUEof62~_VGRBwL;{f1mD2X^HJpJwnyurth;s>5=&q0H8es{(c|PFYZtiU@><<V-}m
zD!EQz!=)t(e@`pzNwNvyJx^)%j-R_UclruCxSg8i4uQom8~(6~#upUV`{|H4^fZ@=
z1Lm(wXmTG*Aws|_XI5}yS>tv+xH0Dc+75`YTj!*&)O&wyP8%K@+cA5{7C6e@P%LuF
zJ=q+(avUM&pxD~Syo2QQg!jUO66S%qlH`2($U?lo#d^cqc?ov7UDmvY{dnXqb=ULy
zV4`3IB<qPTar=;m1avX23FAf^?S?Jl@jIXH6E{3-$#_}vY{{kKbs@RceOx^Mp5vaA
z-3@6|MsTQUVtQ|j{?K>8d-2nf(R@VUhpn=w<B1OVE1wIqo3p}th@{oqqpWVbp|!Vh
zr!6S5(FxDxa7(t(8nd(4!fMZZ&r8YrULHBUi9MZ_${0;~GRnQJBxUNJNcmEi+2h!L
zgt&#{77L_KB#^6j-1rvo#)NSIQg^rGp0-#v9{0Ze`FwloN4(FIyaQ@^;Cw&*vheo5
zzwEmmF_Z5*K#Km72lXd@=*?nX%*^t_5BI-Y?M;W=#}F;cNHAw?3Fd^pU*7hh?@tBA
ztUU`Wy3Ar`_;^j3M3IM9yf(>TOyAN@xr{`)(mT%UHPj$#C|E;qsV&i(SN|HBFsDmO
z%MPhjAk(J@@4SZY>r?W;c}9@ib;%v1opV95ZI?*)Ik-N`(_N9z+b%{<+N7zwP`jP6
zHbXJl_7t^0D{!{M<J1ohwm+{F@v(L;>2}>bSTiZq%}D5Ze;A83z<`0EK$MZWuCXV&
zi26p<-Dy67Ddlu@ld5Z=Z{L1}%(Z<wiq6h1JTnA4e)0)UI@<BDTZZY*oNdf1)UDWK
zOaQ(J4n-i4;;)JGkBqT~Y`tf%PX3j5d)aV&>cRJ9JqT8G53hBpJE{+IA?&W3g6r#h
zTpGTcFkZb8Ch#}BJ6x;Cb5FNz3fZ^;Hb!AMQSA4hko|?(Duw<_2vFTo2bp8Y7Pz<+
zNqK1j;n^;xqd8=8RE|Hi-;pAPc^{A?#kWgnU$qgQHXM+oZMg^4q)OSStco{k$uqUs
zpOpKUgE@9rL_wC@`p3ZeeTrD3QJEw!)v3)j1Wlou#V9#`9@Ntducpj*tuAFGdhPnW
zq`u4@re)5pC(#?MS&~h}7|oPS;a{!<S@2p)g3AYp{FT1$KU|>xXW8`s=!0IyYRpsr
zl2^npb(}VpdN+x=t9~&VnmxIrNbFg+{+KAhueGD)9%bjN(MRq?GlN;ReX!y8;#uFH
z;FP*RyJSx)7!179giCpZlYMhZL7jBka`b_)h^j}2^{u+U8!##vh<C`8=gi7e{i`;A
zR~_%VL!#(_e#)GN6e5o$w3F7^D;Ock)0BO&SZ+R(<8rHc$&T7lt|3K(RQlbV=Hqb9
z2aS<`4p^2hV0f;rA7|n~);*Rrz6v<LZ_h@~#M!Z~jkmu?BCK2vs*&RybU`2{9+b&j
zj3PdP#NBw0Bn^gW_G~iS&hrlLZ&BPBgK^3?LY~{iolm_2c`i%jb5o|Xwk=kh`I<Z-
z*}gH<C{ZPC^VN7J&+@|tKbei^CLHQ|W4~?o1wpmsfUIN_t^hB0jAT3zbKaL<TG^pR
zwG&1<D^8ZHc>^H%A!o_w`_b#%#c))GlaqTR&^M4Bp5(>~bSoyuwU}Tome2U!Pt$ee
zMtc~i3B3)33#SPBS{Aaa`AcJe!F#g&)`X!&+-GuDzy!dz^y{>aFYIdXs^nD+9EwAs
z5^TywJ?EsbGAfuf7!)sri;KIG4o~>s(uzM_mbsX~40=+^w<36@ObSo~p6_rfDa}@U
z^R_8lq88inSjyLLaX1ra8iZ@e#Oto!sw65F{LExak+Jd%G=UtCrhA|`yq`JV#>Wfr
zB;9L5K(#Z;5oAXqDRGIvao`69Mz^qqp0T_z<_MQ>54a<DFlM`8JH@!0zxud{w~02q
z>!L-l#edugM0QlMmmD<K0q)i#NQLc|A}F&{#kMEyR@^S<Cu|TZ@jZUso8&eDMrKeC
z@;Bma`^4u8Jm6)7&YgB9uLd}2N8l~Sgu&Xj4$MddpJ=iH&*%=%e`IjhqIZWyjvKA~
zf$W+5+1`Ek%kFfwHsd>iB_})NSfK`!JfQ<hkv;Q4Fmo?T6Mf;ix!-K@UZScv@$;u0
zUs!(+bc_%c!rO%8FeT|COYSCM%d(5{raj>H#V$6Gvz5jc_whbC-$kb{1yT4x6;9=K
z?xQH{zeqgav03o?`FN-8m|geFqxrM$)5V$_EC*577oS5wq~fi&#<91_C!KHP2)=nW
z^sdR7)ecAK;;K7QO@t$ZJ)`bl&@F2>yY9OAXx!nBn3{8NH6lk6Qkspi$mMf0x!hZC
zbNKeOhOxa`xteNh{Cd5<!_=^HHWbO83@GS`q>EY8_nk<pQ$&M==0LnHsI0;jB~cg9
zx`(fkOm!yYk7J6La4i-tW0D`gGJo6zvge!bC1$o<Gv;}N4a%I!;}c?D-@r<?xkhd%
z^B-ax8QwI}a-|<hX3vmbPVh|@1`!e9&x3m#!ts)#FmJHQSPgtz7c&te<bx>s!{C~)
ztM(cs766_u>8)*4RfFW=j63H0{<io<WL-Ap4#j5#O5^ff>-ZtHKWxz_)-$UPbF};r
z67d|D6|ZSrB}2Hkc>h4=bjG{<&4ab3dA4(dsI{pCm~MqsXw4cd>X?a;1Bb_&&}itB
zkOEq1zo)ER8F2#WLmBz#_cZZVAx)c-Df2#EPGIUg9*I3yFeiG??qbHNn+)dM|1d+e
zC6iJkXcN#HY(XLIaj`<h!~0>I30cnsr4Z}*y(%fta+e_|rPF73aL~Zvwr47#*9w@-
zY#vIfoa_@`HC~_{zZb1sivQ34)7Q^0s+-nf#&G>UBbrOT_R|0L@GV1rvWGk$`7RWa
zrO+6xK8cT_^O@$)DORV>J2!&O5Nocgjk9PAU*{uz{gEP__WLJ#<#ya)8Q68s<F&3W
zDI<B5UNKP(!^iK!CpTiwjhrm+K9}^EHQwL1lrCnYwUyx+bT+m$Dy>UQrWfh+j7@=H
zDK=P8x7)0mCu{(t?RH8$kP(xIlrK0ZguB$2lG>e>?L$N@@1bjgikThdeZ$<&Vnre+
zG|gzb!fbB@#diw<K-q|AMuk*Jh?fdjk@_8TJEXfin?$B)qE{%bAaKAyF4-IxvQ0v-
z*OTe~o(aL$Aio8W;i%vV-AZ+=|Mg9O&#wYya9iT;**o=G!A|aMIaKrFr-lYQF_n=a
zfk0|%j0@#iH}Un!t$`=JzN6i^h;|#c@X&33vw~iEiDl5d^;phElZxbK$FGHWCR9@K
z<f`g8H&ZMaA+$2~w(L%@&@Fe7FyjP>uIWO!BU6*H0nj5j#BA<Q;qUzL*4hnGaOt_b
z1G^AT*lPul;mHgNMhVNm!nK(Xaj(Z$HJxwhw_jVp)1uR&>$v?P^v}c&4yDpxLRdN2
z(p=J;)-t=tQX+!$TUDy2E){+H)7XO=t)kQyUADCZg)Y);v0TXcc*;6Av!+(?_2bqr
z4Luhf#nrdRP@U2Y*^BDj#t==|j)oq^Y<Ep5K>@rSS_?%)ijA5ltftnCa_7rH&0!0T
ziDRzi+&*b&+tuhE;n3JkkPnpq#mBM;4sxHD5D&|>^hm8(ndxQy$oypMaC)|ncv8Qd
zGwuVId=z0M`#!k2vP=E1g|O@?bY_oTrdDt(E>D(_=QqmB2#&x5H>@M}O`rA(6j!3D
z#F2r}XL8F7cQ4q$ERQ&5T_MS4XGN}Tgs6dt9)^t6s`eirA+|bg?l4QOQ5L^2r$+S=
zVg0Lq-zH5kcjWQrG@K~Oh9!DT=^=>dQpy|M1-1%~TFE04AI-zm>KwD|&zNmrN$hmo
zeSZoO^M+)p6?SoLzrw;qnE+orpDa(#Dkz-pt6OpuiEC6cIgsw~pBr$Rk7D(T$+KIH
zvPAhVcF&sOhH2`suN)*LF_JS(_KVG0tT*^(x#4KI50UL)bMeh$?X6goG$Fz#-9JVy
z*KkI4{n-E_i6`%n-ySV4&dLUSeXdJD7_!OqqV%mE_XVjht@-+;Qn^_jk$8AcRcv>t
zA6uTeH3|_kMyJ2+FP0uvnDM%}Rq;{{qcM(WY=$OL3;+G2X2;|HkgO_-dmwetQ5C&`
zJ*zxJW?Q8CnP+nm@93j2)+|$~G_KFCbrNbt+g*-adFt6;_y7m4929~0n->BEk)h?@
zpL}5HtlB1`UfFn)x^kkF4m)bVuN%i59+o}I?)v<qk$NV_{hqr+V%KXu?zs2#kTE8|
zBVFcTS;M6or)MWXe<s?LCip`^V`>hH3BJw@Y?gns{o(nj#wE!sKTA8nmw`y8b{cSm
zQ=WFgNW=$_;w|HK;*Gw4e?8)F<(#8{(cJh9W<KfRL*49lvODc-lN0xBg3c{A0n6N%
z&HJ;m@km#3%#?{TaA|9wKPHT*Y%!5Hy(>z8gYKG&Ggl#sx=MafQV=x6hGR=&_+Tyq
ziI}Ue>+8WWpkWvb*6Z|7E&#T8(q~|iFv^8*W56~F)ageNMeFi;(6jLWU=f?y{-4ty
z>7rp-EL%qpQN3bXCp1tso*fpIyUCK8aLL)s2-iF-^{rKd+i4its%ZZ+_@<tbxz#_B
z(u@H^+L3@;a|P{Oa3U_~!2QKv;d_ISJDSgy2B`B5YCEwysz#oXLRBV!0_5NR`b`cv
z@I2hlnX;iK+|se$HTEOq-co8lttsVggyYyPeY`y`t113U%4438#2P8UlDbjBpv97)
z&f9!9<=x#x$8dy{b=mT2zh((z#`lpa$rmjqXL&q&BD?N!ERXMP66F^sbY9Mh!SnpE
z`Bp~JibHJ*cQ##4Cy@4RP`<FjiEe5lZ6JrrT<^t`*>@AI*?63s9A(EQ9$XKEEGtJ+
zmgFl^uffd<D7_K6xX=BOu?ghw%6phJ9_(+dw_b_#mKtxG?7phXHh4m2-0$KEUTx<k
zv9~#38SYSpOia(F>~F_pY`>vezk+(YilA*l0qWyu(LZ!GEAZjC9@?E6jjXOYr`~);
zB?A-|p9Z{o#TdGWDnwTT5|g|B427)#Q<tU08@_@as!oL8bbWL`cqw6{>HsUj&UYrV
z5#m!(#+rb7n?edKiYTOjDm4<xWI0dPs*V?ybi_i|3sBP)YOv`$th{~Yl8M=Eo%vR7
zOt{=g0hM5~(T?S~{KRLR9-Nc@i%f4TIO@nqb64uuK=<G*Paot1qc_d_Ilj_`s=l1M
zd!Zos1QW{39WO$Mg$&_VS<N7jAYfojlX8T&t8Nx4%7(xMaDjvOzNN~cM-7jw>}r3?
z7YxPgQFj7_ux?|(u>6pNJKg|eDBsID5FpbpOz)5sA9I+RX>6pWCBPa|FX65~<xO<U
z!Vg$#$wR|LbKH4_%mUouit)Ws^!0v&#w7u7&?SOWr`ukzLtr^x8Hlti%$q58SXH0O
zg1->#o1IXU(Yh7D7YQ$J)s*CK51>5AGMs%4PU-(!z!%^N<uxe4_T^-xqn+gdz0#_*
zX7i@$kk90IRJRrXiB#4wQe=_(&DA7yiv;1H=v0}^)xhzrt`t;tfcr&Ja8M99bE_t$
z9D@%i7`HM0VkJ$!(>Huym|~N9RLA>7l48my7`6sG{?Iz%WS;do_3NR?R$Za)PWnT#
zW8ROR&id3pQRaXth5IpfJM=v*JTEj1G(`=Li*mKp<ZC!>Q`=T4qe8VxO3Gud4WHVC
z=5e+RUcMi`SDyKqdPAcd3^TREiBK=MJtsq%XyJ~^R9gVSP8Lx6zC2T9=Fe(qlLRVL
zmwwV*5v$ZljA*)-h_VU-6MJRMMsI-_azQ<_S=uVr{8px=Z4%;Mj4NC!A#oq=2oW7E
z39d^p`EXI++4iSH6B!Px+L^e)2SK5#ZaO`8SBf@LXSzp682|X)4HFS%E!?>$iwlI@
z+HWQAyU?w7$28zR5Fvj$u=!gN_rW&mUZ-8SW0Fv<*3DgQkuSy%gZ9jJBZgVA>oYa^
z$mGW<EAp8M0c=^D_xNN}MT`u|ODtExG1H!IJ9+XHn+t{b6Mq}Kv0VIAKhM1>1_sB&
zu8%z46Vkw>TIUf|zM4zRTGW29r=FAkT{U{)1m1!~rl$O1S()??Zdp(M(SVtWW!?kt
z#VU)*E&)Kwj(*30IWMCTvizS|%$GPc+@8_~&2e|WAXD5b=HFf}&Z8Ns6g$}o8>1cz
zh5rIk71+OVe5*p<DhIhcT1h$_0f&uxsSH#87|}jyTIPrIE^1km2{0t746GK{>#!aP
zeW@@ajs!+kBy^joo78BYMC`pZ7RG&N+U}fc#*h?pER+p_&2C@80|G@0fiChMisANE
znxsgS$`rK8wiL=?C8~_Oi!?7PXs_2pnT<o~q4nOwApBdglK7pYFUjBSY;WxwK_atb
zI^*L5IwXq?R-9DUI^uf$BPMzsR6#cMshVGf$jaRzfdx~?@{>^(43e|I<Q(8j+H)I_
z&mp%ub@*FTLC6lfX*k2MQhHa5wN{-TjoO54-v=WGR9tID52eA$d6hZLW9$xIWVf8H
z*J3z74Y{3!PN(gqL&K4>`{xad$sp{sjP!Y)+lb}+OpCN`M8Xc8rc)M~q%AJ5dzpdq
zod;~is5d(jl2()qF_d~`!Ca%F>}r3XQDO}SCN-N_d3!VqIvOCDDYXukoG4!;nmQ=Q
z`tki;U^^`iDW`&W7%iq#tO&Fd@y5EWeLuae-v$p7YOt6H&xk;7YDqbhihyl)G)sS_
z<sIhtIfD08V$7g0T0*X|SV~}uLkXX^C*`M3>UXX~`bjM7z}#Py2!eyv+!u<tsS8UC
zI&oM0-jJ2+C*ks&6;}<}V;kit`m50uYJ8#W-uRVG`KnGb0t!lP^ML|M(|bSf1;c@k
z95F<TqO~5ns-?4M(VzGY=5M;=1nW;a!v-sA9m-()0xV&xpI(BVhZpIWJBH;G)7K@2
z*f`xZwTf{&UPUGLfs%~mpe;V)oTaL4BIPCA3dwUTJqoBUEio-m7>|~1@5?JvDi!(g
zTXE|(YOJxo-v)FK#O-oO+%U=gxQ>x@QF&wtqaCf{+tsS=Xre7GTozJf{Uyuy8C1I?
zeUkFm1sX<<<MFTF(ZkDZr4XK@XLGZSFZQhvg)A2ZiQ5$@QxRn0P>_#~XP5Fi+_-_7
z)$_x?qu!2~Hy$cRaN)TUS}4)s1I^GC;!O7t_b5Eh8O#0Pl3W-oaq7Qj)~fG$+jWMf
zOAn~t*$vQ&mVAf01m4rAWa;<$Sl~|XblV>m7;uxWEk<t0ry7TmO!ba4!G)PAu$&Lb
zcaL~i32}r%VdNb*`gMHL9`K;v_)XKPdg6td4eQ;%L)3wgF-H<j+&d;m7wWv8_WgpA
z@H57a3mhdXg0ki-g>57=6!wc1g%0DbxQ$`%3#jPBS$Xe4%0y0DG~-k;fxX;`=3}{J
zOSsNT;SyFS>U=+gO|cFV4a<=Wc&MghZp+MD0M`))bOcg+91+*ssGAH+=Hf-RU>2lH
zAOiG}f@U^0lE0so7X8%DIa8k2f7RL`W@J=#M=A;x+y(@T?izcrMi{w!F1YD*xWh;3
zgRv=T()<>~5;ONA@qJURmhT%A=i_8Ca}z|&IE~t7E*~7;^5H=7QAGKu6Qv&MxW$Fa
z6Pv3ShAwi`-+U;`O-abkFWm@k!F#=CW!^-Kr46jVx$MpWiRMx%in27-DeZ&<Y1L<e
zBhf9N&U-=ZIP(@qenq;g?ShhNDYO;kExWtCf8q-Cg}cQzoeElqo|=XJG}-=`P6Eph
zxzDf323wNXt;3fk&XSs=v8(%DQLm4}V)NG2Ut{AIr6&=NDYtJ;+@Wdob#jQe9Rl9!
zV%ISM+YJ$uC4PH^19MplnTC<Y4UzNIv5xuA5jk6*e{A)yAgpiLbQn%U;~Uj2j)^z0
z>I)G<o9A0{<?dkRC{jwQK(6-CRj9wS4u{|lLtXOmNReM)(4{Z~j@3wmApHr*_M2yk
z$|+n|lte{{HzHtrlh8ChC&;hAsXpH-i4OKLCSd4_&v6aI+Cuj@*Y>Rp1pfYw!$!#M
z52<JS|1tHBL7GHu)^2xIb=l~$ZQHhO+qP|+Pub|QZQHhOotl|<zBzw0GIm5p?ug8N
zueGlABgd{`_md--C=h*TX?G4XkZnJ@_V$XvJ}9V6<}BKF_lPWGK)V_JeSXH^a}D2>
zmaUIV_!s{Q)x9gxgwu_O8!?Aurf!Nx96?-rkz4_P+upcRPuiN3A<<9s39j~y!Ry-@
zh;Z$anA)OMy+MYGsvD5OY&ui=UOWD#T;4HKSE$=+^~bQt%1le8_<|A*+}`Q-fw)21
zaILX460idgkvdOqlRJK8dvxEWwg#Q^l|YII;Ldj=KOkVM<l%=6&SJ+Ue|}3rbRKp?
zdBUrgC*TgsO7S;qr;?{J_Cep&x^;0O5k8Np2l%DEcqPgG<#bi`Dl#flm(cKdb6-tD
ztjY$a+2R3tIP=df4(qosYZ>W$GatRTvV$_*Sy<iJuIK%fx!&Dq1IOwV1g}Bh^BHp{
z$VWyr$o~x7Kh;p2{<n%7=a#tIGGjf~-|#ebtZUFKMIEYbaOD_m7qVb0UbQ0_kgD95
zLNwMP#>e6|+oHo9^Mj6d>11(3E;6KsCl+cIO1vvLNHG;pH`U;-&hb&Yw-I<ha-Pk6
zHbfMjnwm<Fv*<Niy5b8JX`H!IOk$D$ZL9;jTrQfY?XTw*e$k;2Wj$RSwSI6G(_Fzg
zx)&{xa>p|XRDnccbR8my2167-li`K%eunL4=b-E2OP|@lEGfv51R+O&!vrP@ce|lf
zvh`U|M!nyHWr#10+~Tq8e3L4nBn-GLJ+~Ge7_~gW(6TpQOC#BBWOL&|Ky^ipIM8TZ
zt@HX^lk&N>?7JB@Wo-EV6_d*rra4*XgjjSvua3JlpcYQ7T6sOC;7-=kMJqTA(^tPk
z@pqZeDI9?+r!#qm<eC1c?ldZgN$x85AU<sH>ny}I?Rn5X;$jp0fgwIEg*YNpZ75i4
zEdyS|pO3_w$d-$}sG+8BT1MLW$BowF@iL$H2*1Wu3TwVBu4)5FnsJVw$yMnTndhJi
zYd9&Vb0Od#6^-*WW8xUmf*#!OqF;D+CZz|6Ie6WXl_yWUJLVqrGy;Vurk*1NuN3gp
zhB!m*FFzMMa*$W`g&CP7K`LZ*bl9N?b1KRcBz+&E%OhhOw=)C^;ntFK$I$p7%BZH=
zB!=wL3NRdMki0SrD`l(oR<bU~t6T(iANUc8vnie2m_OL=h!6BiGF`Dml+Z%aY+t|&
zD+2wTFcmZp6#E{@@()+*nAg3O54u&Rs>qJVkX61C^?2nGH;A0(MpqU0u8W|H5#N+c
zxh4!|;!JypG#U;rQ^hG?sh9PT;fkAs_y&!PB9*&7_$j!P-xU9&Wbecx)FQY!T0my{
z>Wl+V#~j`=)vd5xM`{i+^Q^VZyMScFvxAk*R;lGsvScqjI@^({95@vfqflm7h{E9b
zyb@#Ju8o=I))<Z#UoyE{P}$Nr#ktLDoh{kz_Ot4mJl|3ZAV<H11TktqZFf2|^ImiH
zMR#?c(9)MhPh2fLCN2jlI+K%@Dh-Vw%@G(QoHy^E3Qu`G&kkR3VY|;OXxZZ}DR$p1
zo_$TU_!8Zlieov%aAF76yF2!KfKGU&iRz#%`~E}9KKJ}*y6RAu;~J-Nkbq|zIZIU$
zPWu>9lg%Y7K3vW9wuV?o)Fw9R;h7N3bg||hz!U4Fe}wsXtBQDj(9Xkr5G_t_gEFi@
zghEM^!!`+mFR(v=XI8$9<crUedmWK~JEJ~vWwqCHMlEm`JR=^)|KLAA@b@DBlgeJz
z(o&+b3m68|t^Uhe{08Z;wESKE){B*Q7|Up}Srfw7gvxeuzV^O+;Uv}LWwwMiey$QC
z8`s@`#sC*16m7IquSK0xqktJ<trl3e`(gz3x_fw#(R37TJ-KVTga4es)xv`_PqOKF
ztcG%BkupoaeyhKtR|gBzdEj%*qL}6-!NAoQC+izuQJ|XMYK|%<jQH7RQe}U;7f(gN
zm?c?1*EDWg<==dbAvku=ek`waRyJx|%jJZJH;Q=p?F!7<RFt`au=5jNBFdm~nFGQ~
z7jG=6OM&q_iPm;~LL(>byuk(Kgjo$zlrM_0&<A%bdIFRj#t6ceFBdM4GjAPd3d6%F
z+zq5T)hnuYnD#dhFHSoOhhpa>9(nd^#t81V(PjFbA1%7U82ysXmgvg@e@Ev?prJi6
z)6-8@D>;<i0{~l~lNq|cMFzorGFaA#n}AHqd@YPlFF_ML=S1R(Rm5c3w_0!HQYyQB
zyp<O%?0`?StAT23a2;akl)V*tYum{mvm`mzA^|J%n88vhrLcJdc$G{?b_(uAGIjXq
z`1!d^6trxFbxN26d|7AK8|{i(eSDF6@G@qU=%xwhOR`OBL#aJ(;)lq3B!nEzM{Rqh
zR5EC{s8yb;9<<(J^JJmxdqX@X-*RxoXCe=WWMito4fIbu!SJg#Gv=7fW9d-pz)d5!
zHReu9(sm70E7~j79QAg2hZ3=OaB6qVvp4vpMy*n~W7;Q$g&L04PHBA~di!wD=!+Rs
zzj`6xk_R`qpO7Do-LEk%<a{_m0N>e+?<<PzWh3_11;#sg4;qCvvOC;gTbXYLjVf;K
zB>#dqy{#72-b6Q<{N_vI?|qc5{<PaS0$~>%qQ59=Ml*?PB)K5^5b5AMh84ntfQrg^
zFIQ{zwnGibAWU)7L}CZbbIjRq5CN@04AIi=M-VYtmNXsTE~oBLia?^0f+-LmoX<@2
zBC?;LA5pnQ3m(rwST4q`WN$k*9=SD$F5etJNwVB&feN1P4ptbNUQdr4=MaA!$ZUP-
z(SoX%^y`fB#&b7BV8uN$Ql+12b;l3wASk*4I#WFG6EO8`@%IOQFGbQ?_b!;aFIFSi
zul?!^jC|Q0zE`l)!}~Mboru_ziQekOUQog{6ylA5m5h};m8fepTFc?}y(@6!RVyQ7
z)U(ygFFsn!oSl@ed=RTr-93UR=}^tBEy@#7%-=>9dOHw7U>&s>ez;AB^Q$b6D56{3
z0p4o5$xF1NuH^Ej?xn$eR21raaC9Ik^PuYi`b_peE+l_m0X}!QFm53#Ux7KzCv3rO
z*l7BU0`*n{7*ZOodhhKL5A1T!Gj{tkfimUUC8#dubA&U<;h>$Z@@bf<FXl4UN!^iJ
ze=PgP+~}V~rIDxwQ>kR8jW&4V%Gr55g4Ie>CNf0q4Kz%NO>L?g`eUJF>DX;#f)V;w
z+`ETsG2YcLQ38hG8#H#`r+}cp$UB%zVpKxai1U@iPXpvMC1EHnR#L8CclpY(f1S=L
zlKBe%jXhCkt`O*B_&96MR|=A1v>)0X-0-_xa)d9olSm6(w0K-q27#@}*Xy?w@|)P-
zc~Mk(Isvq~O=YmyWEA2D_Kt`gSvX>2Iyq;#CPw17V-w1q`Auc^4q7W*VFf)dusVvC
z9{Ij-J-2`3C^x!v789itC2o0MM>{3N@qxp8Tz5hMMU*<h%Yi6a;jL7Ykx}4bx8^<Q
zAcA89K@`Sqc}{<vLxUiOg&lt7KEIpr!WkV+kbBnDkGF3(t^Iiu;K%*A)O01-72$wx
zaSyjbg*HX?41=7vf4Pz0=*kIVpcZUt4bC?V{Z%|cHF&95Vi#jW()bnw&7tCX=H2in
zcgWAak(N(iEJ~Fn-AK-M(LeMFG2_%A`n(cT+T{y*%Q{|4Tp_q;-3Br1JhCC&qlRm7
zMo>tiC7|$|Rhu7hvWB0=??`G-TovDIrrhH6NU}+)g@BF-@y(+0gNgp`E4mkJk(91(
z!wKMo;cu}1+qLcm{|uG+D&1IGiRK{5>XsR9_jv}>hG16Tg)>t=Ny<YGFIL1wz;TaG
zBS$-71ix?pG|6O!k=D!1hA7_~V>ZzdSs%{6kToq>`2zZ+Bh|zF)ZWgD&ZL&-7hXk4
zKoCw}#X%<4kMP?NGSc|sGsHz7jfDwO_;&raA3(Ilh+N<avae|}*`ocWCKrpt05nVE
zQ0eK5t?)<m`6&2$tA864Up~HexqNu>g^|rIia%3i>gIX|1p!%z@mhPR8auSCl~us6
z1_ttq$WKDSxJ``H<DXWPtbbrPuK_0rgWx2D7D^@8o`hCgk|eGvzH=EWgALYjBQzgP
z+0dUhL9zws<Z5#4N`^Z9aekw}Jf$^iB%$2%a%&itiYpa;(TVmWMaA><wS@VEg8DS0
z{qhEO)a1CdG+r>|o-F#~;pYMN=koF9>G|(HUv^Aned;E><#MS79LF>^o3z2YQob5M
zEoIeGgI~Hadlrb4gELB$pwi9JF##p%EDl#&1AzHd`Qg0Xoj5+WpYJ9KCweF>PCy%g
z*uxd7*A>?*m?-9SI5^W0G<0al#X!%CYl`GRpl>CZ)AeqoKrgaqMvhGl4UhGe1NPei
zo_xY;LF_rZ$wxjc%ud8xhGI_!2Y{C`M1KX>`R1i%qWjuuV6f}Y;^a76&pNybO%E!Y
z`71X+m)_}##7#7WaAO;1Sq4ksXV{f1$zab;p7@y1Ukza>OkADY;V1AESWAOFyCjW-
zDx)LhFOd1Wi|gBkL<$>dmYO6(g>PpoJh4SpK0-zibbn-Rabb*?dccC5T9AcENzwFQ
zE*6oNSV&PU?t!tZec-WRHs&=S81hDEq(V5)epIQOx!6D%<0<^P86ov8LsbmN#!IuH
z6Bur(>fyf_QoC50$G3;^=~f4fX9)~|{y`8tFXl#v7y7Hzyyf*aSbzIEL5_cWw~k2;
z2{qJ<rv@W{FW}D0d+6AW<ELaHS+k*Xsc()Ntrxp*r)=I&(p1(FNqUafWT-8bGO%5l
z8}Z&^N6$1uGpoX~tghMlF(fcNOGn~Sj0ZcGNy9HWa_0IcCV5Q(q6A(FFNdEe#8=zH
zi@1|43KAUyPgY#2f8=)*dA1#f)_9z;aG+$r_uZK<GU;WA{h9S0vZ@`pCg^N&34&N8
z8vJ_AT=Y0CEi{nEWfJY~^jHAb?ffwdG08hqSu@xcc2<Gzw)SE2FpScm{Mbm%BHmYo
z*d{J!ZdfY+_J_5575AO0$TFN{(X3IRmYx<x@3R06%0Jv0WcO5+fNp_-P7b`*T7?=x
zJcDT=4Iyy$pRDfu#lZ0k*<#)`uj2w#4*6uHl3uk@`LqZyCn%0D)c1G=-TgfFNItFW
z_#M3AQyFP}GdM<{2uHQz>Dc5&AkW%C%$yTkx?VP*+p%>9BB$fAKT#;EHE#B?Q23h!
zl%Z{W;f|vSQ*<82cmLW}zfvamjYGpH$Lj%iPz?`d9)3AL1CzO29#Bvfo-qd5Fw{?&
zPur3qG$X>RuC6f{mMGMV2K2k(C>TQ(kh=(eOBox(dx9I?_%MC!mvW;)Jq2vs$8|Gs
z*hj1|g(OukRIyTk$@S9XIbP~tT_mX^NhN&Y`%m=xCcMJu*>WWb#f^Oi4SiL8BaWkw
zw+C`Kr6bDty@tHX6C<h$K0rOU`C};yPpxUiX@Sl!cSkUEsROdu+<Z0V*jr-!p2+Dp
zBIQ{Q25z6aMZ;YfTB5=`avt^d?GC0P<VdpoaFp)!ror3yIGUr}rUt3SB|^F3|89tz
z5(GC5zwh~G1?^IS9bcT^bus7$!yUg$l6?2qf7;jABJ^H(btRQyzQV!H+@1O>_S^dh
zH~iLu2?nk|Au*q|`RjIEt113>xjca#+#o**zi+TkXl3R9>^T9xU_Z#;C_w*p(+3XF
z7}4Ku*Mu|I|GDtryYm0;_+kfdkfOYwE=w&Z`M*W~e+x7O^ynh(OkyJpB3p8ExCT)w
z4wa1=f-FQ`(mSj%Ik7M!C<s=J@gn?Y_6~Bz^^@^Yk^8nJ+~u9;h$rqNGz3g41yM(g
zU|ezx2<)>f5;e7`JRZKn!>{IPJ5d441S=K$)`c<Y?QWw3w}k|%ljTyZ$>5`X&M65c
zf(edL+_<8!*LyyqF##D4M-A=Z%+kGa+J12+Bb%EM+Vy~R%+wP)`5tc^f2kix5md%Z
zdwbj?F=q?X9~F_C$?}Brf(BwpN%g}cW6XTGlu!T91HRyj<^``9V9a4wuviVh9VY&e
z-lIybfogZx;dW72e%bo`L!cye4X`X(P2=??*;sq-SIw>L3qV)Ay}M9Pd)#AVBX@Lb
zo+Gg~o%qn>cDY`Q$!)UkZ@T6k9<k}20NWUA_1(uW3*qH?+4ZDKUwXIgIKST<OM<TE
zev9T)Dosy_zJ0Cs_^QuplkS7dY}Ol%+)P4!%za>R`TQXCNR(F~8*<5cal5B!z^2En
z-+qVYbg??_#$Ks&L|y9sMBJE2->OdDn=_<fvD)xM@aenG06$@ZX=kYkB2>Kitf7>$
zU2*Rbf;PsGcqFaQj)OFvNoq}-BW*FRC+CXwGUg!}dTZkPa`oL;JLB@9`rGx$;256x
z)=+li7E7K;=h{nTV)%*~G+z8({>3BWm+tBzogKyQNSd%wHi-QClEr%`KCzINB}n>w
z4oUdKV-DXNa&I!-Mj*O+^JIN*W8pd3@<khp$@QZzc<;MHK-xk=d4cm%HXW*1>FS?h
zCczTPkZpd2<4piDHT*k0lq3%PeN)u;C+Ew5?3FDbJ>+WybCJG&08^LB^lh5-$e&jk
zYEk$<ukIb*tQal<uQr<2t3xaOsTqqOqtM;df=*IY6r{1jKQRy+!Vd_rZ3dxjsqJ(k
zFeIGdGJ(yEN$Ok$uU8n7zjW6N;cSe-?Fc1#ki8Z($x1QPb!{h=!GYauD?99-z=5ur
z#;>ahDy)s-;CP>OQCavLSk?;R)X0=qS|xP$DaB-Q(%UBowRqS#!(xQvRxC9xJ*~HO
zLwX0vvP+oIyRm^~;ZX%%!QicyXrZMj9hKpL2r%L{!ZPaekkmvnqleKE^ya3JxRd8Q
zQXxb#oNG^n*47L?o%54GWJFOC*DI7xKo3TCSn39}VX>ndIW4l1)?1QvX*@wXQkKN2
zEA;RIQ3Er~M~Hg~2cY}!brg4ixD(MMAdZ=zX7miGsgA0tO*4fS{nX4*m6=(LBKb1I
zqyPOys){-y*{?^v3TN%DoyOj<OMv3HX&KWyrw^YWfK_)qo;xDoiKZI(Zg4fCH*o#>
zoA;78AS-|Ax~h2cJx!fYQhZxS6s8bM#S2aW@kZ=0WJ2Hi7Yri&ViGD+z1ImwCVwhg
zMEY1=4mlBj0;;n-i~^%WT0sZIe(^mNz*vj{D?N-H8f9iaH8HsXYxUU?&xjT^{IFKP
z%gh;){c)fzp9s!<kmxGmq$lE9NYK3_2l?vH2sN20E?r<509Fy5f!oX;$H7gC%tGUh
zj+nB#v<dmSUh|(Og{;|I>?yRcm8xL@3zgM{*fR~kF@ilURft$3k-)CCzBf0*HcrQ=
zQmBAO7pM&f48jTxv_>@QO@DYhmrkI^ld_>Vwt>Q=YvuG)B7f><V^Aw9vlFzMC@|#u
z1ry}IG!qj*-03UGUukSYsI(E{k<n3iilWjCYqAj7vyk`q<-Gis7yW4A8K;<m86RwW
z*J;nOg;=v$T}(M;m0xuv>E@9Ua)h(N023I8y9$6P7v%j&jD^`$8v_`tae0V_L2gR4
zgg?eTYXk|pytH(%PZ=M!qO(c@dHO=_1+W5QPP|b?M~u}#Mk&?gF=v^d)->{vh{F@=
zA4P5zKxKXa8k#_)w-9)nxe<oUpH+FuJ4d-WJ|I>{#uK_}Pr_jz9DA~gc)JbEe1CE>
zW@l%93-F}F<3!=6N_B}bQp)j;qv8}gBW{q_mg;iopI#p`cr|tDXq?Aorzh3cI`Rjx
z0U{HLInX89UZsC$dmM#O$CT*&!gSFX{s3=IJojbDlM~EdK3y@-GXL^B2}JT+^k`+V
zeKyqagIXfr;5df*97kc|cRd?>;`h2J$1vh`D*ybt9JIyyce-eb`3H=;oWhJFHF7~0
z6C5HVAL>pAcwMS?Kg(NmxH*<PE^A;yGy2OaA(A-xGC*6Dqp;0y3-;7A49Du7EE0=K
zVSOJONXg8iA!|_SrLw9t9~aJ!Ve0;M?gtGAW4i33y4KVAdzyt20?DW}4JDzcH4DU?
z!vs^oNJHt!QBzCewHZ=ETR~@XhDZy2F`)Lf2#yhUT*-8W20Ch_A_eR;%u|Ey(3%~(
zv$W$+{%=G_tqe?Xa>?^mIB~o_rM6e|tnTDb*+jz`<Ta_~)t=Xm;(uuAt<s-5keNA(
zba-RvB|O;$R)93`^|h2B`HPW0FZI#ZhIq&1C~Us5E|n>EO-<1di(*mgDrLdZBHi1=
ze{Ndrxv$`0^z$|tihRq)V^R*|`NdHOHmk2=$n9`?Nd=pckuxF*5y|gjW<~$`0r<(<
z+^G4fvWAw_;}U6woikC*6eXf`wJiHxAd;`Os0qm60&6ph4z!H5-jw83PemEw|E(n@
zpM9)1wc~g#6Pl&Y5I1m1BFgEn;nf{J%v8U5@nMH{=cQC+rPAj59fNE&n4y5R+VPii
z>B3(<)n4uP3{On~ZBHX`I<+GJ>JC=z)q(ba_^6M1AyY`e`Neq<)6H}mA*tbKYygke
zV#kA@7p4~mOFK2Z@zpCw2-g|_og;ZP*`J=7Y{qN%*Q9#`B120H1n}^df|B+;uRQU2
zgM_x2@U}4#p>&zi;_bn?#Z?ykEb3m65d|>OsvUf7zMWkNZb9eyZK-nwu5SHzDaIRO
zJJGXC-oh^~{+^yWEl3&pAbM0qEfG2Nxp;D57ei4>y8{TAz$%Ti{kH}N^GfF~aL4Ch
z*IzWAw|vwXmYAo~1E3Py!yqv@YhxPEF-W_}3+Q&+X+jSn%@c;>da<Ms&D8z)a`YJW
z3Hsc0HyqQN67Du-OWdHW1rLuHJ0~l@qF|E4DUXFwYkIdnqmC77zdliHB9v3N>&d}d
z3<li5&DNU?Oh8;fi@-dH$(ieH*)}FP7B9Br+aK<pmKWH+)M_A`IrldnV3I}~HU5%u
zU_^tiw>9YXwO=CWZLqn)>_&A;bi>VQ0<*7=l60Q^%1+q5`-y}Ay)Cb@5VX`MAAeO|
zo={aDt?=rmwbInFIM2slCbU}q4fz~wP8c!DP&xrBb0z~(K2VR-c!*}D75+Lac3up9
zDb^{3*Do|)-SM9_%V=(CrwqCtRDY>$V&4KR#YV{dvK2kqFf0VIHo$DvTtx+KxL}Pe
z_3uV}N?S95OZX7w=%=5<V^a-IQxuKbvLnlgg3Bq0_0#L1*N-QM*7i~5Cbcer|9bhe
za$@RXFrV?}w%nd5i!D0MR=tC?C}$0~z;x#|y!C=AOGNU=;_U0G+5tIs^G2O{`~!c^
z3~#M*3GM)7WWu5=_`X7)>2uqkWjeQ5@nG44>h#|%R~x>~0o1w<v^5M<2nW<6EP^?l
z&J=Hldd%5$?$avI<cVMSi!Z~<Y^jEO$TSxm7zwPxXt1_IG;#sp%}M-<DzW2Uw!gnp
z*lHhj-JZ`a-OPKnoS^9VVcv6EsIW7|R7@81iQ3r4<Q24lwG{<RQ8xS6P$caJ3n{mJ
zjOh-L3z?k?*}C+Zx3)BeQZ`fdH58yVbzRLIWkz0}|4QjTAzZ08<|J>-QG+^eXGH5s
zlC$1h%+Rz3#XItd+I5Ggt&t)}k9}qMKW}S*ApOIEyRQq_SU?+0|G{TU=~X4itbEZD
z)Z_)d&PCJv%;2pY;Py}8#^~S7IiHBm5lABm5+xokjf^2;7(<+JolraB++pZsZ{RTK
zSLf}d-+U@`ydCNbJ!HoSGrkFEDc|bB>gT>?ML?x_>VK8f>RTE9mOoPNc%L&ca`J$v
z(1{9Ypn$=J%?+tCPL?Z8$1~UflDe4>Yzk+Ge!VC^C$0NfWVKsu0yd#e3!nprz%XXm
zV0!^h>G~%AH<rtQZ@*<(NVg#tpVi%%25gRSLI)&i5^F|(O`}_nM3NI&>of>u#|mGN
z;@xmJN7K<@M@*or8{+Q*Gos`wl9x?0&}vF&G-*xcRfl9TAF+dY2HcvD1(rI|-=03D
z*6P4j=}gqp^k}*96Q8|H=I68{;?YEl4DcMZgE;umR`s*4ne6%LxB)-Er#PB80qqp2
zCX~w0&8>w2*749y$JHGsLYoJD4taUA>FDN;*+5Y_VK@0}5(6hJC=GMm0;;w74FlO}
zcFTldk5m;yxg4=`YVQB?g7k9d1iU134I-{g!Gp$vvYw=cXRz`jwBPA^6n`P<Cc9SJ
zVh>(lLffAms+R5vJ<x#WiS@dyxsu3dz@aRUpZ9ab`mY$E=#r2%CX(Ab=Ke}sjU8nP
z*41U3J)m5GKbwV1dqO@tGZ?g$xI?^?VzeuypnZ8?C<45uS}UB@R9d*U<g-3g7$8?j
zto#;8DHpaUVZm#?roXq+TU#SOu(oJfcdvzG;OP|Vsz)k?y9gE{4Ds?((757RhMf4i
zIounyM$HYGV)1hIQ_%U^Lp>ma1>&e^;rPWze|s6;D%cn2?ZdkDejr*Xe!9olM!At6
zqJK3pH3C?&c#V*?BQFSBW0o+?ni1Zpbjw`aI~l66qi;;hFo&^#S=2MMjLa%jd^riE
zjVEUiaFc~A%-sx$?b-^U3XTHm1zvM3-=0ycfW`GC-3VU~edc}p%{;_%6KNgk$5TL$
zxA3cdxu&<hf(4*PR7?_4DAO&ERnVztO5D38%PSh+#Lg8UfM=OEltC4tpa>TQ4JTNe
z$P)oo22*&XONMAsP89H5Qj6&!f>2jwVDYbQLNmm5TRR3qFn>mjy4*1q0&RN>=9t$T
zH<D~YyR2y^fD`g4WN0HREY9wMMXK^KJ}tvPivvat?5Q`a7cj!rt{83~(ne_#eF;QO
zlSHxsVqRgaKN*@!P^SgvT>>2<Iw3ZZu?1Vw>=WLs>J_r?zOXUow1dHjRU>U4JHNiH
zzj`-qpBhkGTut857R9H_YH;(Js`h!wkLK>$lce)CmZH2REBf)u*y8r8A=!FZ=AtO6
zMH}oI#w~OGiD22a28NA``y$+45U;`an-&5hxZIq$v{FtEEk8M!p-_oG5}0|$d1FR-
zSpi8px<IWE$`y{=>dOJ8Yu)<~w#c)8l`(u@U?)H)Cu%acdJoj(5A3FQo-vBOVY<&D
zflR`mmRdP@Tb#dLo&|X_X>1$7>s>2;6BnTxViv~Kgl+m4zeug#oVd{$$MumI(C&|2
z1o<~)s5I*^P!;aWPM7(FJ2|pCxOYyCVHiDSTi)LKvnoeGMfk&@n+6{DJ#S|*C0pdb
za7G`iY&(R{ILtVA{y;}8^wVzp1Q=iYL|P@-n@23>Rab<1re5Q5%l=k{bPlCSNM4wL
z`7D;=XXQm`^7_KxB8^ue{=@f2UD8*h`!D6THyCej``|65$lGLOiy-cO**;kQ?TrhS
zb_?3}MQe1Le*f<2zE}cbOd`wQmAWrSug*g%DZvkY^Ro3Ix$$m}rZlE+V>gLw!^OKI
zH^bZYm$MRGy*V2+k~s;t)5zFbvt0(Qa;CvbRnh(o3^xWv{M{94G<8h8^uHZaF_IAP
zbYXSM?N=vfl!I`V%lDm0l+<#M11EE3Ms6+U@$!#65`}FyQRx1wjBAAV3$g<~)HVl6
z{MnbxmPnDyrqBb)o!k1On31&?pR)El6UtNg25M~TdtX%rFE4@4WJK-eO15we|7?ax
zHDd{73n7>-VlpKRt3bVgvqrw!FmbECARsNlUS_b3cXj|_ER@5~x;_!MF1jL_Cs$sg
zS9Emsp}LtYH}{XinJhj=eN96J@n1py3Knyqw6UHZTaR#;0qBuKz2!%sD&5bpEEimY
zmVeI&c_6x6kwj_5Y%Y6-szgt<OD1o7ClRTYW1bMbnva07I@JUGeBv8;bbdG*@6ce^
zDgwnnjlqJmd{7|NcEutnYVW%g14YY(rM_%feQ)|#l{pIu*TZ**6?XJhzcQrlaT{!$
z4=`q@im|pUH^nYnF}&Ji53}GS3^RH6wQh;8l2sDpWU<d2w3F;$J2EwZX;C3!w5LMa
znm|sEoAwf|N#*#Wik!mtGEHXvvb8n;Nwe-fT;bLkToHJ^&Hv`w>tAf_4@&>cYjK3+
zwc@Dm5O~b}pDEXuI5^irPeo!*2ld9|m>nB2!fM$ea*V*t(gAqc7$w4`URcXysK1Z)
zjs%q5+^oVzryF9VPG$fu0hr#@gZxWRKNxPc8z$$zf=|0Q9Q9*>NJkEdab^=wE$gy>
zND6Zs9JUk;nYOAE^Uf9{=n^P{SvdBrCmOSyh{%kpV-Qtq07k0A#rFw}mpvZ(SGE)#
z8vz>PC@}tGGw#M6>T(($S}kle4FJt5-!Hq1gGs9c0dNR1ST+CRb^6YxO%ZDQ&3}W;
zMbheL9zj{VJI<_juZU%}UoD`j1mpZIPS)xUn@i;h$2wjKPoD?&lgcF%-wN#1s(gcs
zk<S>wWyxGKbl&)M=ibu&`t$GXbH-JPsd5bj5N$*B@5wi2`s7L>ALpmW@_k<Cwn5~Q
zpYq`3WEr$Lf=WwLJjC7~8HdMzVP_U)e~o-W(;eSssbXZhJ+Q?<kVefH8nahg#ZH~0
z{R-(LWBJ!}oDLdlfj_K&Qz^MbU+nf}&z~ikdCnht-<klUcEE&X=*=SylADIfUTD91
z8Vd)aO?EiF@+*vuHT?>7?d-DOf<MNy<{6oEdrb5~&y`eC4@V^-$2@h3Hm|JGAnw4K
z@_D?xZP-YBpgzU2AfG8HB1v{3Uu_KX<<SY3IT;lU8DHbQzeXVR@4C+&<r49F9#+tp
zoS+k~E8P$D@oTKWmSSXyny{3b?p0QXdWS&g{b6Re<9_NHrt=Vaq(1qaTz}>TFHH98
zz{y2xwU67a;pH%y-nn}uGptX9$S-Fh_YD>ohJ~VNwy#x9wfA}9mLM|&w^W%9SIC$w
zF~9E-G-%8ce@s@yTJGC~yGe=>GS8Dt<FIvr$a3WHl@!LBP8!d#@q+G3p!0A|!6|C1
z{4bq!W{AUvk4j!w(Nh<crDmjo(c@KJ)jgv+5-yL&P<BfvQq?FKf9N!-8h({Vgb=tT
zf0KP}<b%OoF)vsHgLi@H4G2{ltYPJ%iiQ0nX77Yq=R)mRaIM!jL9dTR85@f1+3R2&
zC)0dNHA4~>FJGL^;S79keH53Bn88SW7N;Znp;cvDL*_qRbvkGwvQ}`>5ds@r4v5JJ
z-m|O=sIRE5`;Ui~cn%Q(1?s?E-xh?hC8qlZBIx<;-ay!nmXINjY|n%3FGipCY=6S&
z3$K5TMLMWdp}3OO%ekSQ?+i^>)R}JwKHJBb!g|(&2`yNBxA;&_)pc9Af%LuygPU!p
z5*!~Pc(rQ7;jp;B-0D$~#76tyC^$s{h2KK4b?<U}dGeq$V?ATcSr83n_oT3asi}Tq
zZtFJpcf@}!{Bsq$JS<SVe-aI&T^1xeriEg*K4}f=DW*WT{kp-XDFmm{Hhqyr!l!)@
zzFnMuaa#owt1cvV6p}@ogOp2Zhz06meRFXZ{RAFOIEKnGvk+Trg{9n94+S#Ughtzh
zZEA2?TA$61rZJ5F1ht<Bz+r1dH%xXQu;alqhBODJ*~l0$EFO`5M5x!N;J3D!Kgn;Y
zyylW$8=A_TBjQZAGhjP|z@12*D`5M0oKk<dae{3bv!ESV)^v!>02^(x`MpXm=c!F1
zmcU`)Nh1KsJJb94*z$C7#K+94#G7I2|FO3xfW2q~yFbydC8e~Vmm<kW1dy=)(M+Kk
zD5i9#I}}-59RuB}qoEsV_pjmc275;|zBywAuimI(c3@y|4euTJ@?ZJ>61eEdhAS&Z
zAb5A;TRrtwdmL>ZF{cxqSn;Pj9FkK^icJKI5$x}TDwnMpRJr{nbz3eZe<zH^b{y-Y
zt(}a<%U1~ce3jih`C)Uyq(?@DMYFpR@6A^8As@4HTLWOEj-w>;2^b(v55W~Idn^2J
z@xcF^9s~#cW)0LX8az<S$PZADI)|SL71)3U3;1%vB+JM22d&PI2s9anwUe{tyZv;6
zY~&M{;h3`4j&x@V_xT!RdjWI*EdC{%s=Kf}lO2qK?A?jhGAe1u(G3qt%Br>Aj9Bqp
zG?$u1vYkO8w)P<U?P48YyKm@aPZWyePPj=d3BcrmofCsYF4`^;89^4gx`Izdo)yk_
zo__;Y3Px`=hj6zmtc!sXSEup$Jd*xMSm`-G1=H5_#~)t;q+o1WD7c+9UjoVYE_k;`
zXjst)4_Xu+DN|f03Ghe7RNg=#&Wk2>rEWyoy_S4erB%PfG0ChUFD+(JkMt!Df&dEf
zG|`2KNg7+zB4hZzt3`D8?m84`CZMsdX3<q1_NBaP$G}q?$q<n|TiRCyM0lgiY`cD^
z<JZ_a6><LgbW+@}doG48o>OI_rpH-+Z2O@xpWb=wNlF4SKTcCPX%{cG;gC1W?jAg9
zEQ|yKPWJL1BvIyRRgM8)61t*b3JRNvJvwDo)djXT!uG!x$ob6V#*0deay`viU@JYL
zx;NbY?7gM`cnx<3V!X+#&Wr)iyF4T>3VMr^TTL94wh4Nz!X}np*)h@!%qCmBsZ*YO
zOQOb7@UiA2VUg7;A%iC?2|gu%sx^OYiygls?xBoH-s7-H^XuNsnKIwc+9r|9C8hQ@
zi)Uk1;Rw7FKgbuihFyya4HB~2QA2tMzWNYN#y~B>sDync><4tUis<OXrKIH70YJc(
zf3U@ZnKQ;GMpwR}{mS)-HI*NrWx)amz=qfnmYJK{QmL7!2mpKBT;KOq!ew*`#}lDT
z7ddYNmCqYmB?qh@p8t|o%-R^V5-O_zZ3|<^rFC9dMV?A4NML|gT-z!9lmzr_lP6xp
zo=gGLGYOBIGa_uTsA12}E*euW+}k3$S_8X`I#@iNn0gRQ6G<?iB_nl`Vm%d>%h@I-
ztdm&xxNG&$ty)zQ3(AP(cyL!Vn!}SQL=~bt^1z?Tn&Uj4+QMW&#PtSQDeBRr*B=+<
zRC<?L(x17fgMEQ8q{4|AHyW)*R81TsNpqb{7~>A-=pov%n0&z>AqidMy0v~kZIvT^
z>f|L91n;x+7WpJhbVy}iC6HzInQm>-Qm*xa;ji+cF4}yOj(G2)oO?NxeGnoi+_|0h
zAI%Fe|3VI4+8Mt5{1qUffhh!Sf>I~AP$$#(`Um|r3Z_oUV$8bKtyHE=NV^p%q#?>p
zEiLcFf%$s!R(P3HTZ8-;zn`f-RlHm@#Za^z{GDG`CUR5YaB%*(^ufM;Iags8<xeyr
ziRUvTV4M3G62j2*O!!QUyZN_eH*X-pQ=mxOAc_M{l`3a&W@uNjF4wyBuRpl&QQLa)
zC?B_&>VcN;Jl6ErP3TN89BWUdVw8uV6F~l(hI!iF$;vizOR_h&(+Dge7mUXzHE^*k
zeNcv6f;5!Umn^pl418ij)-7Qvc<jUBGi${J_~4I9ut})%dqfPXQ>h9~(%J&w_hT54
z&kHs0!o9&=HxYdmGc&A$x#qNaw;Qo5UN;R6j5Gt7YK*_S?*xeJK&T8n`hsssg$MX2
zeAGo#@nH;-DIX+J0VO}v5?RBIuqe#oo3*rE@R-wR{J=NEgk~g+)468dIUGVyklnMC
zR<^7SrV^$`fk)N<(Ne#mL6U$v;_Gk>+THhVRBa6wkC31R?gr0rw*!hq!DV!4f(vD0
zEK!>CrUyH+0m#BI8YkS&ELj#d2E(+s;Md6D+5ZTT!2`T4gENqOll$wCw3XIM2^+k@
zl1Q(LhZ4T(iWmM3$MCHW*v1td2Gp{&t=yP%(KuViK5dHN(VcQ4qrc)!l!KR0E+&yE
zHQV&3&-P*hqt&|K)U%I_Q&?P<af$qvA_%N-MK+OY_D_jnxvRDss?HXTW4=LYIA=j)
zb%5*7*G64Q&FwGx!Sy1}<MyM{`fU_YqZ(@$W@_5wcMtZEHKw4Gr=jo*CAM??ajw=J
zBS4svhjbhhRs5I{(ChYJ5EwQb&pFqsb>~t1w)fTt{LVOLWl*b+gK8H{{X#RHL)ew)
zmqV=t4xs2rHn=wWtqaG_()r4;ge@6RCn(#|?LrVm4z+Ng-JgE=EwD?kZL~EelIr?S
zD7Y68lkL2VaP(VRnw=}(!@!Psa@@2AzeEBaMDy<-%z~i{5(fSZ11J!HN@7>ju&h?V
zcit1)^AWOH_Eeu=S`!&}H+JLDW%5cGv4I6(v4zYLK{avjOja}=i5ml96Le4HOTLZ7
z$sdNkYe-lno4@Nfa0mcc`j|fj@}tEJ>FyL1ex3&TrxQM~-uKr<bfcW`_jW)4(20<z
z;Ye2V4hH-$DDAiAxhkTo)B?BL#OkoF<lw;6$lYyr$V1bv;bYI5zL<<|R$X*B^*NiU
zw9XJHUq||UcTe)L`l&un`Re;Vk?8Fr2|%m>X7KNlku5ZLWoT@gEBkj={4b6`6-?n9
zvu)I$ddwN;dcLL+AZFy33r^UcAUg}{P@$cJWhgt@UGZ%z3!UlD2+7RuQ2%78HhSf}
zj2Zp3^uq6r#kAEeG$_=G-rX0Yw(F>#g)5t|^*j0|60R-3G|-sA12jsk&m-=OwmUhp
zLNljR1&+7J$H5z7sJv4QNZYn_BXmu?D*+bP{i&8jR~#@3EqDT0QT{dWmEozLESw>>
zn^g}JB8I^ChAwy(1A$p*i$K>cb%^(TLo}9s?4)Gn9R_XqkU2z#-exEP!T__w9hSu`
zdko}-qJ;q{p!TSJzLVj>1_C415d?Z?CK6X1f0^0dxY>L4KpJAhPEW!)VW?qORD+(h
z(V5aw(T`3qt)lM_zrp$V*Tv}@G^Rg)Uq{Jyj<lqgVtO;-f;9EF;_ld`3jq5IC~6DC
zz`VZi^jq6*AQ7IV2&r7)z#N@^#!kZ=xzxh7-h@6~i4k{AS7_}{91F2UdU~ngNrTry
zjXogn&2*rvy1};;8@b-g1a4d;qM7?Q%ar!?k}F!=WCECrFR~NTWl=!QNtZ>yC^W<y
zqQfJ4QEDCVAvyYXO!50o0Q`ujtKq{86t~OA^x7>)NQ|ZGnaY+(#tpVo#fmSz3n+$8
zyPD9dJ^ng1Oh8}=pA#j{`j_`{uNFQ4uafJySEQSZ9XNH~7SF-VS4ewYRG|9rTGMl1
zmG`#Z_-%Wh*QN#F1df_p(kYkeFNrIn-NA~x)=xZjGh?zHprgSii(1*|!0thv3!KT}
z`EYguWUc`V?23Nh`&>2jS|Kbyg`%WHM<YyT@Kr6M-*Ku<zGA}I{42`DYb3@S484*<
z;Rywn-WZ`)ne=J!&)`g$ozTVk{p>u?FCF9=D96R!C3{Gb=J=gURbPIEQD9<eYia7b
zX+9${kYh+-H_$(`1jB4-P?igGYh({75Z_~LtdXzP8B}pVF$SQc&Dab{Dm`vi+rF6A
zqFq5bD(-Au?zAo#?FKfgjzf1Z25J-VD@RzveO0LF0TutNWtkG-h2=;(rDL*gO#jtv
zy6AD1Sz%tN6`R~d%TtZ2uy58kREpAt%jPF<+!}gRXRCb&j|yxnkbSl}Suer;1RrGU
zv;}MWb^`xXMc!YuW@q;=5Ta2jWCB_)3oG_<`@`?$AQL%HGJVvaWG5{K{K>_Sfb-9A
ztaaZk@|!m2D?nQc8i?CkenkA)B^tl)V?$rx96}LEnkaW8I<|W~a3f*~M<QLS5X_Vo
zj0O>7!bQIiPXK8ot@mIj-MdAjG@PZY_hg0~F~(_GyxR$Jy-CO47?ah_gDrn&&VHce
z2vGjQ6Ej3w_a)OJt>_^!Z{iQt4Z8_{!PN3AD@s(nlO@ahLO#I(Wq2R)l(QU4loRw+
zA=0*7*8fS5>r)I%Un@RrUR5FOHIT&1*K&i~8AudPC)Ci7w(`(`Xj3etI+~rK2CIJ-
zYE<?S1IiN04IMc5vjtB5^=Fhg9{3m7AG7pyqRj_|bPBgOInz~sphpJPlXIiUg){88
zVeb?2l>-#sT%%u+3NT{zwg7&;D3|)dLs28+uj*WmAkEk48TvZ^Y;(dU?^X<QW{(?)
zbH7q>-=uBM=;sq(4Ztvrj12<~sq(Be3GVRqL0=S&c&P6eWa|Cm?)7>KT5}*Uy-d3o
zhC8Uw$&NLQpyZK`Em>bY?(*J!ZVlc!OXE#%&W$TGzsrWb)&z~P`VjWTmII{7^QyAc
z|FQsNVC_u!%+I2Q!qV|ecpllhR-2HbL2=A5ae{ALe$$oH4ktSf+f#R<4PCGTe&ax=
zbogU~AthlPlCGD1$b4&vs*);gbH_4<ufmT??e5uxY2F^6o@a&4c}T0xH&Rr3gJZqe
z$Yk>bMr|l1F`xA1&=~5=JEiLN+%|TIxc&28*U)4*kB6O`n(gS$p@%0P0*8GTHA|{R
zj}9SssI%^8WpgA%a!xYX=x@X8(Fj5kjoHI_UxcbJ>B~R+%cJo@oH72xf0PR4y5~~q
zZbt~X1(X?Wk`+V!eXl4XjHr=|>r4$C`)8Gv=eJ*~8u-%hG%Z#hl30f2I(60m)50m_
z3eKn2yaoQ;m=Agf%~G`;rDP%=kFK7tt%UqwnNYXi!@gCLfG$wJi-Zj-3kpe!%^PG%
ztXxVqCg9=2Zj;s7%O{6^XOlgMO$};Cy4e#xKWYa95fqt@rV5KIpkR!>8R!tsTg6ng
zVPttq&lyKfCH=VC9jkY2K1JPy5OyUd8O4OV-u(*6n=57NSsCwp2{No(rf}2HoVfcC
zZ#YuC-7P2wEr1U1d5jTQS5u4_0h!gE0O82lW&i?8xR46uD2VT3^MX8zvUUSY_;d**
zpY8#AUGoAo7=<oRDb*W10J(zxcjx5N3T?KNvL%}r!xp>uESZ-sb19HOji{2lLq6RL
zxcMK(37!*}DP($)$dX@`<#P`~4>nG<%SfjiH0l-VEMh{yCNFN!;)6s31dM=~9bOjo
zqg<RjDulUmt>Sopo#KzdE)N)Ws`-yA&90IFVwKrZEh9@vayD^PtGEQ47lHyODO%Fj
zK>B%E>0ivg&E*8gh2LJDWI3y;SqQT{7}o4MM#QUPY_5>v?9x*z(w+Oay4aFnY_rmn
zV}m$rhruoIu1T2L3TmvGuJNvLnO$p<8XAV&z@K_jtY!7X)A<c`pe3@op&98&Or_a8
zea~A;LJm+bD;B#)tAb)wg|$~ZK-j`KuviA)fTG@sC6uRzT6NA$!fPk2In0Ni76q+?
ztW<^KsW&!0g_MBH^PigUMwH5`iTH7Q)gr;v(KaVFr0x#G^;-RL1mhHn-Bw5nE{8;l
z6Pbh214xkvTi$^2)+k}}DIp1dW`=4lQFiK~WOA&2x&V7=SedMjUoeD@6n}6GDhDU8
zcR@<mJsgmb!fUU%cPxuS>cLzIZ*^Eaprd+Xp|*R|leCHk8_PP&yZ__ND#5EY8bvf#
z28rK^{hp7bwCeOik(ZB$#C5&r_3@509Yc}9>4GGcL?SV=(m~}8j!ez%o1GAI_ig5P
zM0tn9mG9V^RR>g)zDG_QsiiL{?4F)i(HS;1lRyt3ItORAXA6w{T6VhNeO0Zr1)OZT
zCK-sustJpJ;|X6ZEJn<SHj$HnYJT{-f8p9>rq}l^&x`XHJvuJR<}V@c``CWvu{Ako
z8{}OFx~_e^DmYUISJeCO0S>cy_!)$qw&^42vkOqN0|Jrtfse?aa5*d07B(+;>x&=_
zp;UuIOCkek>L<?%%Iu7`ofwiKU7}2ezXwdc9fL!g6Z^zUjD3;NHOaX;vr(W|<<y3|
zN>|(uDh<qaeS=dFiULV<%4`qwZl3N{(lE@LU!<iswRVrM^W!9O7seUM<aUqsHukV~
zb$IakA#E`OOJbn9XCVi^ENb$GB`dml)ltvHQJ!*mAZcBPNvBdNF0?vPY*;j6Z;wA=
zq^yvuW2nzRAGmRO*9Ycwc8v(x0=apfCZ^Z^LaMT|21mc%a|?nH2HX0D_9-daxmdhH
zJG#mS=avXYzV$DN@AiL{m)$<RPS!k=4CyRQpuP@p=RRuZ7nj*N)+>YK7|Q05F0v}O
z+-GxYaBoiMay`fTF3YL0yOC>UTcMMDKh)=b`&3Q0KTXj<LKT?qKYmQVe7*%gV3eUP
zlWWWpb0{DzIA<8#0~8}@Nrfo|2n3L)imRTXoU3`OW!`+<y(-Sp$hA7WQD|J?jx@26
z7=_*ZG6ggPi-Wp@F0Y$4oqbV6%6an6fPI_bW)_uCOYo(XzEK97&V@RdSs=T+*wJ}6
z3Kbu|0|;gt`F|i?Ke`B%H-~uyjqGp^46b4S65GK~X+rw37XnkAtJ^8Ilndo>2J2Q|
zSwM-lTgJBdNDv~24a8qKsQggsd(LIl{eh|J+M~$Nkr%K(Dd~)!vzha6_`Ch)FInkH
zO2R(}LH}bRKO0LL;3GrgdHFfHQL#V^Esh85z}05Fx(9QpUB@kD=Zyb|5~_rF1LY7`
z5#P)P`ANmfl!n$xM#Tj!N8QD1-Sgo@Gc(K~osIP25opMoIziBHl&W0uZ<nq%ce;1=
z;A=xibhB1MHYA(eKF!e&^2;m%Rmt?DJ`3VAgz#{)+$VLBcf9$Vh}QmeVzK_M&+<3#
z(Ql#)>dFu(K;LK}Ob`0QJ<8hD=!p<<6W2b7cW79Z|Iac!!vXZ|4CHk_DTl56-!k?8
z^9ui$HTcFC_&2|wFN5gTcXUdHM%n*4h5y%Fywi>R+XMUSI5N$>QCeF1lz_mXDM{kM
z<d`1#iMIePEsZ|bgV{m6;A^6<l4i+e$!ug)Nai)r>xOz-HI%;oBCI?zPIUll@H;mm
z$!D+FgoA9VNK#_*`mON8rfR_%TF(OSpusfd?oMHD9F-BQshkVxH#2n_{kW*LJxjz_
zJPi{D9n+F=*A40CJ#%`xcuc_G;YED|b0A@3{JY3+*MW4d`_0dqkN9d+14L$XdMNng
z^5Xnr0GOenbQ0rnTT_bG=(3z_bAy&i$=m$a(Sts!LS#lnMSM#9J?1n1bErShS$f2r
zoMxZ~;U9^+yOAO7N4Wd#r2ZZ`7~x=P_pr39M5^&(qRw&ymZH5o-e`q+O1CFn6Yhk}
zwH~cbSoBkMpvOTZVGwOX#yjE#JklC87zr7FQW^KN3J&zN(J}2LutCLBCvhXuXK5sB
zJlaspyL!QdoVNRW9C*vjo~;B+B?St|QSuwr$Tlfl5fKJs-W1Doy78<Y&l}bEI*_)D
zVUm1`!r9Jybz6;mYj8*I;;F-ocT9*IpYCv}-f~N$vD+Txp@TZ|kttzxMyj$1NPpe{
zKZa<cfx=&|FapQ0fSbDq0{J*>Bgi9XleMPq0<-mZgROjGG?@@(Usc8pF0W!{nZfRT
z`Dt&sx(sFmu;=~(`~6w=2=@!(#g=5R+#jREb7#`J`(9P-GC^1w#PpW<up-S1fo4a`
ziL;Oz>yFw+_Hp&FE~h*QQl=S|76;C2xN6)~QkLvrX(-eND7e7xT-pw9?%h4J;IR&^
z1dztA;D52`;e$2O#x_0-#%B@spC5}P92umxpSa&N1pe`scA`OS==h|RNR3j0sEC_5
zGD8Z5CVxMM?u%+2?t&82NgyM}eD1x!h6a7FYK_Tsyqzg7AouKUea4-92<YeUUfmOZ
zRR9SaiC9x0mLYU}aOD+|iP{UFU+150biXar+kxw(tpE?hVw>Aj#@^6R%x>Zg$7Z>C
zX&v~T+D-|@nKR31N%?qbJ)U-h{w~XaeUr(86Pz?C+-U!<-7<)YoJwogBg-?5^%opA
z5V}TlTR+`Ds$+{Y_=(p+v=WC<e<9DWY3x$HBd`zVP^8#s(_JRh!?QOGZa@gaud`zr
z{1YSHn*PE*`~}hXaJh466#$7?u$Yj#JDnWr7|==r8L4S%YLenM0Gk_GDknLRE$jAx
z#+^t<rS(Msou=_RcUdVq{EdV=V1a>Ft4FP4ejS#CG#sb%exOkpUR_XPh^SsMoh6gk
zcl4}G*|gP3_gI9_=6!+o=ZiGR^oJMZl;QUW_{0A4LLHeq^@Gg+cdI7Xifu%=@uy;D
zKuP@V#V1h8lqW>S@zcJ*?)-W}NsZh8N7gw7SMr7Zdg5fqwr%d%nP_6$wryK8|8Qd4
zws&kzY}?MscX8@FRp)Ni>Z`8Ry}Ek6&-?tAP|>{F+=epSAZAOCLU#s0#!@x$*NtTO
zcW}in{!2yE&vJBJy`?RJ1N%SFGX>ns7yZ{=+w_kWJ9(<fqnUGl{)*8=WNmz~r4{>^
zUtfp<oIb8WV}!W(yg{767eXP-_f;bBfE%SW`>I8>Q;AUv_=<vswW1(qb3E>CSi_hg
z;QJBO{fWb3Mx@T<gWm;i`<ddsykBfGzMNyC;mnx%4T#e}JG5QobVp^hv+MhL0I}ro
z`-W_|kPJp60&?FVum-kTZM=r<&OZ;)&wsUj(NH^$tKb`+?Lrf49Hm5j6>N<N)BWyS
z>p_%7c+mFyT^4{nq5IA`><SlQph?}y?LezOK{CVx#HmVtuK?XVXc&-Iufh@$Jr+`9
z?`D2kL~9m@AQe~`O_pOGpBjx1eqyDu_<-pXExzDlDvO<$;%f4@;Wriqxc3DNU+BC@
zQ?LdW#8BIgioTdC^`f$;FbqbrbYKZdbMUkJoPZ+$8f~54uRh5htP`|isQcs=C`JT4
zgC}af<(_yD9jgn}ME_uQ*a&=Bg}Te%vB`RcJ)cPDSzKI63MnU{<m+pGHEm;~L0b8v
zHQPUf_n1cRobDme8GgB1k?h&59|BiAk8y5wP=$U{soHk3LR`p>IX+!0nBsiGJvE;1
z7}`HO-grG0*me0Bo=oiQCnXBL4O)Dbhcl7u_x;B95sd&$FGagOMWwL=@!G|u8U3+v
zJOw`db}UU6cb@}xWlZ!CnQSRjV35em)GoX+5hkio2VX4{b+4YH7k~uwshtP`Rk{m_
zKhDbXOG74li8hhk6$2`DJWm@_U<xUq2x43GFX18Lj&$(ZrY?sM5zibucWVGwqH?Wp
zN`DIE99af=T56*{o)wX#dpa|v2%dZ&to~)gY@LoKI6i*&17`o73EooN1?rVBwS>2@
zd*MkPFW1u;o~&XzbO9llB#{4&X=h&_>xhY?GQGb^`Zj;Sa%6QGR@Ho9TYnulmm`0H
zP&~Qm*$CtEiEyc<<i1Xr{=<Xb{-{aGx3PtMq&OGr!DXMN?gg70rpsLKK=rA?GTw>Z
zdEW0*YfZ_NgX$)FRrm{kcWa}0XF@RC$q&X#MDbk4*cU{Ie{+$|9?9p1kzAcMv$Y|G
z6Pyt3=IJ1dyBSeP$F`7ax=sDNEwj>PICGZJ@$1%)Eh4;EstQf0=L@V^PyT0@qy<A7
z#}E5r-covLPzSwGzZ}{Mg0~&2?@z6fBp=fL0gPm;BUT3<IHzdhzlnc|td~t#phHjh
zMqa-uQ@NhRN-@MQbJ457qz0D$E}|{JR)AC3A&#_MqxhqeZRb@a7wAu49UXdyP&Z^y
z)w_sNM2LrnwE<81Y+V1CB79j(sPr$SoS;3H0tHxHursH-1)|Q6e~-m6+AkFmmjhG<
z4wc9pLx`m#3IkJFK6&U1P|a#XGal|o!IJ7LDm}&<7Whe!^!GX3&>Q6Ic|~O~7tziO
z9Q@^~F>f(a#0GkD>XSKwLu2fV$K3X~o0dngesX}nNJ(jLbBfPtX9d*^5XAo1sMYIj
zaetm121@5GAx}9HB|dKKW!8GQ4i(NPhg8vvPT)K}7P(p_<yyO~5X>j=A*spP$Zox8
zsk!z-<fu`BzPzttKElI=ue+yjnp_T0NJnb@&Ev40oP`3iS`p`eL3PI=h#V2k&Ao}n
zUHKTv%Ge0_OPD1pdx~mG;n9F@l{VVqiHrnC>UPH2mCRZPKJ_bpq^(?}>qf57l$GrB
z{trbho#=fTNyma242Y7hQ2o!xL=M&Mh3?5`+j*lED7WFX9f%#<eDRhLiNfsF2nE3n
zNRfM|GhU(^Wc!FUP{82Z8FFK9(+f5fP^ZZaGw}tXPm+AKH}j`fgwQ)mN%X1F0VX6w
z(8YoP<VG4@W6F6c55Qzat0jnOvd3NDZdPwM>~un9q$zeN4avUMr|XBmDcfTC{;<aj
z=$R6TPdMBiFQOzksElBx$Jwp-O!R1RC>r<Q6GX}HzBxAZn5{Q(>65U@a`Ql=^9Vsa
z4_2eaLl8(j9*4)5;M}I#c%ekcOBX`EyNhKnIeSKBKAf>AbC6COZQneN*GI6QAdW{!
zb*KOH;`#V3;LaDW-#d2T8ca!dunc0G`^#;7c_pVfD#}E%cSBd60dpjhFL04KAR<L4
z0<;)70!LmFCjl~)tl#3yCwW1;l}aqmPK{JZkYaUlrjd7855#z2;ZOzTse_>YKtF{>
z>1I{-09T%FB367qUma{qzXb%``aUK&xXBMZ=4VO%=6v=io&|B)2&?K#G%|9ePDwnr
z94;(=n-M5?@5@KXfnD7QZr@@6f~{Z!8$Z^%2AGC|1^O$I96%xAC(_kLdvrW`iIEiF
zZ(8ZIB=653ST3<TEyVFrN?0^ibD<p(5Y`P2yK8OZPUW91jYY<x?t~#bA^AoW<SA}u
z+|4Es-MraBCk=-rQif`Kb$e^w1I;#Jo?h=XI5ULeOl2gvIy|f4|F(~-CpB9RM85vB
zY5~Qv*0nktlFRGK?-Va1i516-q#9ydvKIU?7Y7379mumMdUgZ-kL_ny=JbSjV^WV?
z&iVc(eOq{*rZ4&s%Md$DNoMNmMfBwH*nW+&Txf>0ZBg!q^<5azR!H2Qh&?~hB34uR
zenjLq_)%hYHWGPA8G1z6V-S5vCSl0T%C4vO_jmKHt>JU>TAKo^hqB1GOs)iI`^5;;
zie%!ik1uN($)i+u!7_Vf&>D%zSzUy=IVj^=@q36JeDBE{rFppw*Ew8o>%!Bl&Sg}X
zcJIu-g1H+Z_zAeFr7mW8nwjaWThm*;A(!WgtAFnP*j**&aq75vSwcV0#X9D+#&{ii
z7M2miepOQ|EiVg}+6u8*^K_+nt1oTF@$r8~<Dn?VpWOtDN%j3{+{IQet7E)C-;n5O
z2qB+M+uNGXoZr>Lctium4*4hPO&il#RM*jMW9|9G8^|vTV`A%L_`ocVBh-w2e*E|!
zQ2;_o`8U~-!At6CwL{~ngVP@g?&~&6In@9-4nyh<cG@til5rmVkGu0@XL)48p}zn0
zx2Y2XS*-KTYW}3=^ClX_C8bibzD4X^dyI!*M~J=(^scEh<u8gQ98MqFN?J&+{q<pw
zyObTzxKL9QKUNb7u4B}_UsD6A{vTi<mwVJx2Z(fkps}$}L18-@?K1L?^)LDlok>VL
z7`gsJd8AV2?uh?3-iQ!CEhak><gGtN_5+jVL1e32sH@K8BdxeJQaxd<p{k`3NTS$&
z)oQ`~A1Iu4gTyrqcyXLarULtbfUk9?byb%@t_`t+bI_ue)kyh5c#j_Dp}lN>qmdFz
zQKr+;pa_9w5$$9}erXj|MJ=@Nn}TFQBuXjk!<~XdXN*rg#S^?spzM4g!sv#f{Nr|g
z<(=atC!xPHsx<0Bt2ZqK(qP;BChkPQlg&DF6=>#0KxxZ1%kGn5(E<~ET6DWkTkQCG
zj(5R1e@uoirUNSZ{_jJ$MJQNtLge59VTY0-UTDz3u$Y1aOe1Y<4~7qHL-$0`3OB~U
zTLih03?6iRPWVk1zvF93K*20K^d2Zd(u@iyh<#f+ncTQyR^H(XI-4))LoQqKI%eQE
zdE05ykP7gt|53e(dc18Awbo*vtGh-xe;6y)AMAImkjFb$PXO~s-P`?LqkIrWmJRTL
zn9y6ztq8DKSLhD;<>m+Z>?0CW1_K80QNX~r&{^0o+<X5NQ(Ru!^Fmv2nPqXg56O{)
zLb7xrWmJv$6M>!CXrH&EPQ-Tm3w4PTc1l8rru)akf~<~@Ti<WwLs21WB8%!G;qkdh
zmoNmRppcu#iEj+K7QcmJ>UhZk9OfL;w^QgOD&v}dBQ>}^w(qRwUUI2zpjDqiG8LA1
zB`2O*g#<f7%BFB#iX@rarS$0}>TfD@--ZK5F;a}MH38K8FY=W&KIOj;g`Bzn3ZCuV
z7_*Fsjbdg$2ptU<m!-yleVJ<PGoxi#Fi(ShbmiD7vZry&>Xl-@SXFfD{U#k~mk|1O
z?n?Spe#eVRGLtuI1at}rA~tl(4`eRh?Z`>2x>!{A@^+$wuV3U<EJxPuc?<6?@tl7G
z;hN=NOY?^N7dWx57}9?H>$>B1Qu!|T71VO?@m{&KYlRW}n-WsYX|GR{G#mNNf++v?
zdoZ2?<xu&31&75~c2YNBl<e?>FAMZT;Y`4Ot;3MdY=-Kv5h4(u#Sf2xpO@2+#`$6B
z<6*_etUczg&>@IP#*1xEa+JEAMi5<`*FL>%&?wI^x1Qdd+Zmcez`*z{P~LP2Rw!zg
z+AIhUZf~}_>;e8o60#b~T38;QkoHnTnzOxTv|L3<8wvM$5$gWeN*bKPMDE5o!iN;9
zRtOtr(Nx<t2h(Rqj(VOP!{?zw@*QqJT#5tdkUex<Cy<wu5-0#u-S?XbN^Z#wm=4Ww
zuDfj{c2`y3!=<JQxt7?BGxrboZ8JN1zmjUN9%o1FtstP4(tX+>^gbAmyYWP3CjiTJ
z^wIm!o7c69mcu%o+;!hK7O`dMQx8Vz3?*6+BOWh$z7{x-(_oMj`MO5!c)Tv4siISp
zXBJ*Ml{MQ^!2am49+s(%yYKeE$ay9QME{JYXvg7m`2JCG0ackqBvQEuhvs0{r*Cb|
zWM~Vz*|f8J<_S`UoKIZRj6;FH4Uw#9g;uS5TBT*=-QHAK``a8?L`|T_EIHm8sY@R8
zd9g`Kk|%<@#f1xr@_R9A#Xk77l9M+8%%Kli>MsJ*#|=t~$lxUvwt{t3IaayX3^$@=
zAB7kfobYaTI-Hv3#BW~g953(iuo7Z>tI#8o6fNSX{PcV4UjdclP7=%dSxKp`n5oLq
zWsDbr{^SXJ3$FzVi%B8GkDp0tKpV6{kLm5+IzAQ;=kWG8zI;%8-v|l%`KA+&^(G<9
zd6<<Ww-=<lQI?TX3S3kjSmyevk{WX`c~!;_D9<Q_fg|}qyBo$z%{#`Hrf?iVA1bPQ
z%aB6$2q!TmL?0a+`)cl<x{+sJkya}@V>g)2$4l`0>v{41y1mjm+Yn&@{$f+LEer_l
zW68}=Kyji;#GvH&#|39S+q@y>CaNIOQ)wj$^QB1}x<s{L8b=rM)Ta$A6YO6&tZ$@y
z@vi~{zqu`b+=0PFklTSCf#6@ro_NA-1+J;`ZTaJ0Mp`j<PSd33>#9I9BONGb(<Mzo
z4+=p1du)swXXFJdQVsQH?^GvAED>s0g-*+kgao1N1Kp^zP4vnvI;t#~KB6}c?=U8S
zPTXHBM;z>v*C2@ZwTlxAY<1{)e?TEUTQj&@rP$;&($bV#aYSJ@JS&7g$8Msj3VHa!
zacGqBjpX(+7JE2u;8CWH<@O}VYL>6o@eJ5bJ7nlME!^p2i7YYU2UcJ#;Iy}Ziec?U
z2z%4#1?bGq-YjLAGaRqTvoM|H5k<c>P@t)6q)ge&e##qYV~!Bb(mV`pBkl>3qKDd5
z$PzCK2aBaaus(^{i-S@z)bPTWoT4Dj$6n5K^0>JLbtoxO2ZuTQka)Zuo?UEkypX5T
zzqf<P%6FBLEtt(>=-oPJ71J>gB9Q4sFtI=mO>8+;wnlwl`<Z_=LQ^=5Z}M`Ao476*
z_8k#{y2*<1dD6J#dJ;D!KRmNwIhd;yZ&^tTnvMH6cuU1$HrY58hge!|BkW}aeT(;w
zdQSWU9(O3tn)5GbPTD1ecDjeS;{41vX`pGOwUUF;q$&(NHQcwI!mQJ8^?PMerfib!
zutlQfniox?CAzmb!2YCOa_rAU#?FoTOT|PW6a;<tBi>JP6NE3)Jykl+97y!$q~wqJ
z=ET#ufEMKk&CtI(u*Mi=V=(@G5VBR=BYGaxYrmeC%Dd}e);GxqIY15Ct~Bx(Y}ZjP
zD-N;`k(@thKz)iY%bTM{6OmB!?GF9xZBXyEEMogC%#>7C_D4Ix<B(lVJgk>ke7o)k
zv+k&L2peaWDq?BDpO)B=LOF#wlcUXnja$HiYLOT<TdaQ+6}RJer`V+s!jjI^fbX?1
zjs+#z$)jB(p%Bj8giaxAO|ZQ-FG6tF&gL+2>-E`}$ray^{PyOi9^CMtyeIu4+WcHH
z^}$V_1j-EK7zsIJuAyqyAuQnVAXeP);7Z3ZQ?M&a;%4EKzQyMi0?O?1#Z#BZ7vI|^
zNhVLjw4hB**Av~A_L+jo*}pY-JDpKS<3Z3f6E(Jcp4Li}50k7i+JE3~=NrT~kFVs{
z=K{!Y%dTd938crSHi2^suoS5&10?h?T|TcOUu!OdyyHs0QKKEy10r9iu8edg;mY+x
z?yy`;R2>J(i)uFlUac8Sol46`?zn$7*a$e;@T32up_#L$-VJE$E2-n82jhEmD8koX
z@Z6t0n{k>$@Z{hhTAx<maf(K2c0c`rPcGJozley`1m&rz9K3#ukSkQ>FW?PcgR%`|
zAegndrTK5`8BB(gSU9K@ebqxxKBh#uKy-0lCyhM-;<V9cduEoW_H+(HU#}x~cL!Os
z_7vs*JiOmy1_puG&dIs^rxnkcn_KoPn0MOW0*ZICc-<ZtrD>1ZF!koKM!rHb%0{+^
znI0czI=iDp<!9$wazmla8~Eakk!UFiXD8fM7Vc`3Y5ilWd`i&{NDvnj$m5Bf#UTbB
z8rDqYAep8Fb98Q%=(H`>an~N<jZwS=H)K}33hh&CIx(F|0yFG2zUxioC)lTgL$8wz
z&T|R@=2*TuMs#YtXm&Lghs`~Uk*Wp%dK1>#w4N9kQ}6D(e_AcZNAy2*#l{_y@Uw`W
zivzx7uYcl)OtA{b5Er&V3Allo8PHYPu}{bAsC|?Oa|e0Hrgotw5dmVV?!3+!$4|%C
zL<|b8vA3<csWh)gLSeaE_b@^x!_j`0M_e#I8a@L7%fHUo9kC*qD<yC9nl$gm_k_AV
zg+a&>3XCEnn+Z2BZH@<S4s--$zoI>kCBE+8&OF~#jM;p6g!BE0%@E?<8tX%FiMIFq
z=%q%jGYftlW((c_i|{nm4~0w#a<E5(;6g~dA;L(A-;0pE*9H2K03Ujvr3aKoo7_ma
z;mFcaJE`R&d@j%@UR^0g=i_Q8BND;b#bQgIw)^6zYd`uCF`}9KPS^Dt@ouTk2bv6Z
z-hZep*$L~KORG%d;q=TPH=+4)ul?=+>aB?^bX=AlKXd|qRa~qi`9*%n3bsMrd1Qta
zy>WZm62&*hIzx-|qIFwDX-h1&x!wf%=YdnlAI#q=BSv+TpCb-(yB%8mq%Q7UdfoIs
z=XPyY0v%MzTn{yAPCTzbn=met76VzSSz(1TOZ!(TQ=Tu4*`Sz^s}s1kNhd)&593a~
zJ&;#_`34fQ3;NGMwTFPPr=UOET&}R9(*Y5TJ<le6-9<F8-4PoVT%@7F_0MY#GgViH
z+n#Vgi%wQr57F0Se(zP}Jh5@h*MfKFOCuPq#a$=X|Cm^>1xqT6+eBx#P_X5{7WAin
zH+s85R;qGAe)E4rV_HAMXo7n_#1B9S@cDrO*RvCA3hP@GSJHD5*p@akguvDFg4c3;
zmb>Lf^7?cuv2)?Tay6f0D2Dg;kRt~6xi}ZR5x8@0=MWX>H(tAYBzb+xhkWUPBcfU(
ztmaSQel7)ionGBNEn0KD<U*LMd|SDDp+#7mBbpBr?#F`Hq+k_$!X@sj;6%I9M;4Zj
znbyyRL?*~NQQ1-d8=8fmy89D_@vUmLx2qeE4{Lhxs%vvV!=>uxW-boaM_s~7j+k7d
z3xXQU4W5ok#GMqKe5BO!{&r~81c9#dPkwR%;(N_kD1O<f)cBa>_L>8U#|BX>M|^+f
z0biO)w-@k-#74<ZoX7>Lw=dyTv4+@WhA&SoHb$Rho##-C<;;WlvcHVc_9{RSsVYx`
z#tdzm6C&HrykL#|a?pRoR)y90og+<eGxpLUHpIM9s9Y|KHJ4gWCAHbOZ^@;xar)<$
zTo+Br>>shdYIPncew$Mp&+KF2%ny{U04MxWn>FL=Bv`kx58_&ULELU6wso?eDk0qH
zlZvO_h7uh40_gm#ZrG@M%R-FgJU=zWPVc9%*Gqbo3Y|HV7TCGkt?P{q#s6kGvi<}4
zEp-W)YO|?Y{xd#@bUQ9PQduqPt7L=EsrkZa>9?spp;>OVfVsDwhfwE`oqX}c%y*Wn
zty19|X_&trDyD@c=m4jW7-J0>O&y`Z;xDmwykx;6W+DKKd(|GHm0VCJ;_lqB!5c*~
ziV-D#6FmLbKTj^HW;T@bxWQ1aDOSSJpO#%k8?qoGM<)^grS=Vczk7R=9u0F&=zPIv
z@>37gOzZr5xP#KSejRYSmb$mJ3x4Ym>Zk63DwxlGnl;Zv>Y}<2-VZfd-XySnUcpRd
z-U{*gB2^r34av&M0i{)4umm0X$O!tYeUZ`iS_8wlJ@##Uy@H2J93FEMvIyO${9%?m
zCi@?O0(FNUUa-ID!&E^(@ZZhJ6Qh52x5K=yLGGTB@J(DjoC2TAkIJgcXs&qy{2F?r
z3eMUS*5z&UW0OXOl;O}}8h!$H(BmUJhH~rqZ3G$$S(3d2KAzVj#8#mdvS*hOWgi5n
z*#0n2{kOR_1yOYE${PfPa7i@7|Mp5yapXvw+nh|SHwBAMPZdpSZRO*X870NFprgwv
z*8>ZKPvau*N}d_v_O<&-mB;H~Szr!Xau?hGbdW1%1n%|dOB#7$lFy#hC%_L}xgKnM
zm{DWRQf9vao?}^JgXwr08xaf-48wEzQc?dX!9ui0E_xZ#Tk@1vP$0S<vlUZqod21p
zYF3RHlY`LP2`<rOKpO~cq@9qG6agS(`OzO+OOY58OT4Z&3XT|=*&};M%B>3p5G_Mw
z0}?(}*k@AuM%D5Au!9wQPvzPN$xoI4v)krgSQL-!_C{SS!fgC$bL)URQCXEYdQ=F>
zF9Adbm$Q*kr&Z&i!wfj>x(QCp5#$#3-OA$7=DIMHERCs?1;E+#E~5+11~sbINDbfo
zSxzLMsXPhPP^7iarWb1`3ZtSL>@TT;s3#-dJz{S$5~ooybD|2qESc>rMuzYch~&}0
z6i06Tchw(PY8(4u3h%$tg6`mM1A*?R2VDS6{0!$;j79bP;+@ik|7^If_Qg9Akpn#Z
z#>njUL=QXEcVt~CuBRgV?`IIWc0g!ZFd0M}r5lPBtO*4<d?!N<`FqK1J54cK?!Jlr
zc6@WZ@0Ygj=K(>mNx##>J@T{pm(>EVYhSxqM6KUHepilVCBdvVTolnWJKaqedb4>L
z=Ffqf(kMI>uN{t>KhhYdp$?WJ`67acMpTdz<6b09w#EQ{Q6dc<0l+jqso0>ggx-5r
zYGYn;o}Bg^z50|~z&MjGoOHxf>~~+y2Un<-r()YqjB_}^B_9<f=|NHg5_PNPZkvmx
zAq2Lgn=kHsS0b<?Qp8yxTtNg|i@@F2BVJ2*f1m!A{-u>f05Qx8L7N&<IQ2}q&y7m;
z4|Az~z7vGi#8?`coIt6Qy!9BMjD$-&2@j+oHC{$s>#|it!~Qf_B1F&|zj#%^{(TSD
z=C8agoRyNjWx_?t`dgdv#<CF3&M`E#?Y(RA;f{c$)q8@b1`}d6BkuM9;`l7TALIUi
z>h3@Tnw;^q=>4_&i<x$@umYo!y-nO~RY>=Uh<WN7@T{cPz(#-|J>@wG5AKjxdgF@p
zwz)&-Tqg(Qn@i1HLugPoQ*s&JB~0L-E1XL0SFr320a5q|Fz72aQHk#dLQfj~vg1da
z_(38&`G0sd*n=Q4F8!6e`_jl5+FqS*>&Q+C5n;KIJDoxd%M1s!>@rd-VL^6xxWLZ#
z4A*_<7^Qi#3;Oz`-+FQok`USJZODjzv=|ufi*g&au7`>XgZjPQ^Q2?d_gXm8Ay+By
zG=Y3S5ZTnR8rypsH4<TxXCV9sM#E9?t#FAx2nKs7W?JgP+kcy{KSi@8Y8S=>=r*PE
zjP7*-OX|18PXP4qc(Fmu=6%PKOOv|R95|*d{@^rOq|YKM>YvhHLLH7SEcwe^a62MA
zp=Fu_!a9F#tJckU4A#3^Qw}Eo$+RBfItNtcIa^qnhdeU1d-^`0Rlh}$2psYYZrw8~
z52dIAo_5!!g+v1QLWZf~&lFeCv-gO#DB@W<aZL>W^ML8_@2u!lkk||1?9!$c5h*R?
zlDD=iS~pnq!Wuc=DKB;dRV9<l0^yw=_q|LZ4f=u5UFtgCCMgEYvB%c2phmQ#gT;Dc
zIJ6I9+PB5V#%(R?lAzdbWjr0cv>-{#_E;!I6Oz(;ns&!xMO^e)rOCHyxThrVTM_3U
zT~K6p-kfcq8$yIu;=9Ogq5{yz3eicPhtux5Kz0|v^FBSYZKLUW1G$HcCv|#<Kw!Kk
zEcq|?gynbONqHz2soL*b{@WZJacQR!udxpuit7s4SMy;<w;4%oRfaj2a?(N8W9h{g
zej`;3Tu5zaA)8HuQNn$Y^8Orb>Yu;kl@eb2%g!uEf%Uj|1b>_n9s+tt${jEFpO`N?
z8fG_jf8pk);krAoz`Tu4iIHLwK%nE<c~(=g&!}@J0QE;f&haHTUh+%Bjk~G6UtFX(
zS^$WtyxK<|+l4;~^U;MgM{QYXxNvS&RDx85p-$Zspg4i<XO-<y0G{`*|B)wT#%P-c
z9xSs=X{mFM!*htindcJR%wB$_t~WxFROeIT9tD96j%*ASwVk$TjE-;NtD1Pwva;~p
zdsN`wB8!LkGFXVwDG*HX5Rkd%4mnzUC2Xn^jt_e56;g|L-{?}*(Tog@wDH7Qficmj
zrs`&d@<GZh;It3Uv?y|%85=sea^JIRW8!sn7YD;Gn1Lj((=119k=N&EU_V|R7u9M4
z3s)eF9#MIkv0G4W9f5bMicFy~=0eot%|+LXowq<1mDmP@B&rz{zH%=OzzVu*&JszE
z!tcx~4E<O@5nFqw3i#Msk%37PG{Tmc@Wnsc{6gAc)v`Qd5(P&#w^LtqJz0g9-zdp1
zslY@lz)OY~3ANdlw%A<?Pq(B)$<&vH7lg8Br+39+K^O>^4J~OC%+EoS#QqH~PL0o>
z>*&dtk72*TCq~{8hBj{;xv2X4*RhAaP%g|nf-cK`mR9ZW|HLYbsxYn>BA*VrHy2uO
zTwGG`+Lj2ta~<^fV!b>7v8SH7Uqy@4lllD21QE@4K}X1ZwU&LdmE03@&4qxBw%O;M
zm_IZJ4Su>DWxa4P@W~V+kuzN4d^PSEu4svl0$2ws-nlHSC>=dr&{k(mj`Q^4-Go+$
zwopAg5JqP+=pnp3dAr?Ird2Sx1>aza-zO`RM_MGMjD^u)X|<*sIqE~mL?WIyB3}Sf
zh}zL*ZM6*6&!A=4o9(``uv$K9PsPXKH32;(5gQd%U)o`0;?T7^S{fI;NPEt9sWvhi
z>3%8z&K%?V=qB9Du=-4H8~vdYARLKixQV<2C9Mp4n(2~;=}&G&3pF5wdG5C}LKe1d
z@0_5(?8X~-Fq6c<k_Bur(s5+tcE$5aG|9sIL2)rXjqli9+=QsP1`*AmtBp-xcJ=&H
zGiI%wNz_a)h^B;uWN|Ub<jmbM{tw!!Fi<w(YqrQ?%vNNB&us`rs9$EvPP8iwnRh<S
z|FHtdZF3odX<*ws;v`_Dm5?t!hvOGXdugQ``5}}g0Zl1DxU8i4s!!Y>=Dw>hYCfM!
z$zsO^M%=wd2;yY^Y3N-5WgtvO|B|+{iUc8Ph`{J?m#)pmQ~;PQGQ;6>M!6g;$zL~v
zZD+&O9c=r7QGzFY`v^OMLmXpZ59-bA_i(cUqmX!{iD?h7Ngxbg?tIW>ZG`c56Nm|9
zPpUoT{-FZnA!5HTM_?{13xsOSz>{+&`aY33QI^dSLNy20!77Y}nD9ZJFR*>5(F&O$
zb=m6;nb9npau%sez<$~Z*%Jwf)1PB~XTM@oml|RgruNz#BgCGWM!#uH?^qiWjHTs?
z=?y@37r=;u7Vrzao5jU`93^5&N`(R`VdW@@1%yv^i76X-kF{6xv0qwUmYt5bHo=Bk
z%w{MDZ6NXhOUXAY?#$VnHG+dA7G$?pV9=hghKk6@yI~izpaI<5#=nJ4q(7B!b4jo{
zO3JOj6+z}mX}oTDvSazRdm#AI{27(s1aLR^sA+;pU|!4QhNd#&;bmCldt);m=P2t&
z?;(ReWSwysBL}<-EWedXF`Mmp1`&-doxW%^(pdRM!6`SrMt{-a$g%5Fvyd3fEp=eO
zO8Xf0S4OdvGQN?!L|%WQ(V2=S7AuOkMuwyC7*TDMAc%#nz`1M^{<67cR%FqMk`ldw
zvZCsYZWl#F%d<If!K)dKotPaRK<rbErYj=6nXQK#>3o3i7CPJF$T<2MY2!rDY0GWU
z00ZAsQ5<BDm3WLl2D+knKV_G$d(7`P;q~-J9IrM7mlUv<(~fA(pAz^`gWWP!!}15<
zU=tnXt1a8lFfoS%y(Y39CpO@Zdfm?@pU<-dakzU4H3DG$j1)##YeRXRu9MY2STb1(
z5#gW6vWDsN;KrMS#)2{P9J<A9S)a3TS+JMMxR{9XPKz9YBYh5Amm<EDMj2T=RDgOX
z>?soKNapZ>dNj-^(&wF)4w}pB;@kB2thc<JHlpH5A~-EM?&&n^Vfv_@<C7Rcdwb36
zDVEZq&q|Edup@ZUHs>w7tDyYQlG5F_%4*UOnHql_x|_l4Y`fePA8ZAO!MyfV2xpT^
zljXd%8S6C&rVOvdmB+q0<nX<duq!KmY0UP0_|Y2{I>MN@wHy8pF`NWMtgdA|@r1om
zqN#n2x<Cr$+lpFbE|gbgWj$p2)TLt{M-2X9`LKv6A|kej(wXCu2oZX&T~L-`r5PN#
z7uF>kwCQB@+1RwfCuwFDKzo^x*FD(Ao9rn?``Oj@Yp#0@->;L|1XS~O9OPREbLW3`
z?R%6xQ-=IH8~r$(=`>Trs63VISTfTSdgQKi>m-<*l>aOH9~s1bCI<pyhJD({lA)zU
z_V#SAo7i4R?ITn{w0Bopf%V~@#Oin~<F=gC>fV1Gsv0xo<n!V+v+Og|O0f_y2_&>K
zntHLgg*P&la@GgHD)711x+oHMRxzLc>M;bIiXGvwZ*-*Azx2`3AI@z*n|$ym2N%x)
zAf}n<dcPehA=aV0@>N4TsOJ~h4uxTXyDWWR$QBL5J)S6|K~}^iDM9Bw3eZ&yq_7fr
zpyNSbB`neIoVke=A<25v`pSY3;<v7dcJ~7t>WS(?JVHJzcr7|zv!jdf*8v+&<VngV
zUfj%}Z_!ej#d-8oi~Y}$gFl*GZ(Nz3`{6Gthr2R8H!RJZV%s%7DAE%*k=+~HZ&SaX
zStv8~#drnz{NbEf&7MDvVJg!FiTTzgb48{J@>kel;Q>LqaWJy%?(#44HD5}92|}y>
z))?Nc8m)9~L1i4OqoAWDIwhkXlOGw+N#^8+^XSLmj!+ke$^L5%qS@y?{@$iKTeSji
z4}XPbw#B~7v9ksw!{Y(!6-Cb?yafuz9d!Ht8R6GDe#pzBIJzU~w>5b1TuKyvSt%Bs
zOgoCfKEE3RQXY1|r{zSd+j*>v!tk_}DHk5YB=DIj8cUNYDA1@KLbL`-jJz~1A2DA-
z6OZTd-b$*Iu}>k-ScDd4e&@20|A2AN9`2sr3f-B8ulu_h!;IA#V>bn%G#x1Jv@1Rs
z8cxy^do@;laV(+tm`u;0UNxKmhH%}!8<M=2YOHZ2t;p3P*7k+0`LG8>xVje{X4=~k
zr#DqXl@z1zNlE84XTl50P(n|X;cpD{My=lT4qbscc)oGMpU7+=A_}R`Gck)BRKa&#
z{8A$V#An^n?F6hwi@rPGLx+2HC>l)+hoP^rc&||-=(|Y9#pC{@1>^SIX4`5bN=n+n
zS+wC#vHJ`rg+qhP+)QAe`UtEuX*L{6^*KI48BlBhk9hP&hR}AQ_BtcmlP1Gd!#AZC
zlrWX5|Lq>X@oF?T2o^av`Z5mA4`hI=TAKV-C`C&FK<`J?CkhF?)&<nC*A?w~PN~uz
zQV-^nFoFju4dJkRBQ?J+iMEg-YzdOmX_*J2659`2>X9!{==KfoEi0OHL(P}D(n=&q
z>RUnBno_p9ys=tC`m=KiKh3IKr>K!l>2*cLcK({sV}s{C0ppe^Ma+^LE0(S}^pV*L
z@;)cb?82S&3>`j0Os0h8@xdIjDKXxm<Ko!2$V-FyB`3l=_4SBS(EXXlVL$#u*=6Jp
z(avC!L878X)E(*v>upuoF-KvjJ{PLJep@g<y1~RXkdkqaq{UI#h|Xy{^aOTt3WU5z
z9WUB$m82Xv9UmnoY{+}F(@@v=-=h&8SoEZ<rDu`wnCE|E6D5)5qY~SH_&s%!b1S2{
zS8f`19v)TsWyUQGTd+i@pvah0^&t*b?S&i}HqRY~Z$LCqo?j+hi7OFxs50vPEv0~>
zFrLQ2e<Ip7P)eu<*(&4*dD>!j3WIGOe#dm)zoF4bLCF)Nx|*s#`)0wi-ZTrc(y@L;
z+p&7=NdET$RD9m&-`x73^mfopNVnlMvCKk>J^fQ>1cHn7!}^Vl7kc>*?@j%e^Scaa
zj!2AJK15~~c>b2|A~tPu{=v>oxEoP7kJVcd0M_URym_8OUU|AeLNL|sUnLABMk_GD
zaRvi1Nf>|D-*AL-IMx(A{Wf7PQfur=Kwh_s>UvnDhLx&T6x4c7B1Q|$y(YyGy0>FE
zlLl)l5A<q1`DfKGRtt`Y&UV{}jlUCc2+R5INw@>?*6-05VW}YtZ$oqxADvbCi<r#Y
z22$jwo~S25PQ^E$Z}gwQC~YEl<I})u&H44@uG$RI8<w_Aw$b?G-iQ@yY+0uk1uQGt
zJ<b!=D&k2@&6Za5R!>i;)7BKJxi4jTJ=^G>StvB`^`m!j0-~t-24rW4HN@~-gBqS2
zQiGsSq(OwkVCHnQiK#r#{UqYe_6Rmj<z*a{Ogov4B?X;p6)Cj>lwhp65O7Q&FLSJK
zPEJQjZdzG?6&+*kX?XABuM;2J6S7k|6+Na1*lg@%T5M4kcR~>ljWM0(!!0+#a}$ET
zjfKfa{38t|A!1^TLe48WP!ReE+<0cK3MM;zYv7}kZUJgj((&97fmjH6{1+U3*@$oy
zG$=p_MH*x3jkP01M@wLk(yBCR<iBjQ9lyU%8wUoweU=BmGJBkH>rDJ+9Uh%sY4h@m
z>k-^AMDKFmgRlHO`q4y%v%I_E5aE3=);Hrg4^52JULkFI1mc`8cg7@)fI=mK;nlw|
zu!gtI!@=2K&hyVz0S#3r|Lo8pMrwkeI_P=`G`u(wK79E@+69zyl7nsSafaR9&#RHF
ztomr?aDtJS9Ylav2|ah@P|q3{TX6~42#U&X@DtDXCa%bxkq5*%q98C8mg24@_&58X
z^WAIDRYyYbA;e_d*02(B-l;xNMpcV1G`qZ3hbBSHa&`hYI~w?gEl!9?Ic?D+VE)Hl
zmT<gSsDQtc5U9y6lh)o5ZOgDRDp_X{;dfMm_PSL7z_L8yYhnW7^`vCZ?!+I8?6&q}
zw%?l*&5NP&Sq>Q6ZCkJ$HiGG4A7Zs*G17nEGYx+hCk(97FBz!{n0x|)6Dd6PO4Fl4
zUDCePW?fcew~k=mdx*lPbs(=MU@QN&L+<=a-hX+k@K;gVcEdp7PCcc-jqy^HOUn}R
z3Q`YaGY*l_x)$Rmc%XHCb2XaDa>qoweBHBB5@bAD_vLUiG#1t%3JJconpEQy6^Qi=
z;M#hesiyzQP^BM*aZ0CYDkTPry!6J!PQYoJp!z*dCSt8ia!4dFJ1&h3rN!zw5;s#h
zZR`6nYfAB6PYB8>Bq65~(Xx!<h1Pp+ul>#lUM89<^-GCNtW`8cfhe6cq_h&lynT7w
zS~7KX^bpzl^vk1X&ci&Qt1iOqpGv;;VV3*}wJxa-_2AOaaVtaD`drAu#%8V#Zto7j
z()Ub>v)=8=T5JAR0br0Om?$aB9D_0HzKFZlovr1{EML~Lsgb;73+3O{D=BL|Md#fp
z{&$)ilS4cKoO}MXV({!|z-bJnFXb<tmov~D^D+c*tFY(gO2Jvfk)Cc=aGi{2-jjMY
zW=kna;AyCTwFLm@##e+RqC_>h!<dYKz<$R{S;fH{)$v86N#Ul;?rO^sc@Zh8$loal
z5Qd5W(IRM>ipD8h;R?_UHG0R;=<=?56o$<a4JGA8=8Ioz88Na;t+4Ba!qjIA3ZJvv
z$A{AHr*ibpjbYgFKKF{M#~5m7sr*)kyHiBLwnWlMjlaI<kT4e}$!D8O)L1$`@eC4P
ze{qYGwW2f|4$4=yw_!`085enneyb_u8jMgE!p1@0ym{+VG-YQ|vP6@!!)b4poPI=5
z_fL_<6Hg3pavf@xNVO+4{4Uc!za@--hVETw_WE<#u%S7Z=em4zs-KMoWpWFt8suXM
zPQuQ6Wm@sqVjt86cZY~G@zKcz7E%xX$Rj2KIJgRPH57kp1PGWyAAaL8xSLqYp)LZ0
zR01}ksJJ?LY8DQS%QnIz1;WQj&p-ZBd+jXkTyUo*##OF754(v|E@g~W3Cqy?vG&6n
z$o!HO;W5%SCi<Mx_uDo}jv40?RnjCM{`T;TO3%pY`e#0alyXH9ruJJ(NYDB(b&=&h
z#0*jOf6Ds1+*l$jPy-<aiXSq38ETkBM67^T>rk7GO!&%C-ELf=O{Ad@LnOR@<gMOs
zxmj^ww<>!#G#p57`!YwImzg?}bd6T6PuHw5yBxF1)0kqC?h;BXnCsDWijR3!7P0nS
z-XaBT;9z-Bq4iQq+QYqK=~z2Hp-Z&ONpJ`YXX$d$tiXwGvYFiECa`*RW0aBgQ8P7+
zcy1#0H+!5p5Wx;t4GDwZO3Z?m<|Md)&ucEri(m+9x=r-+6J8sn^#jH+Jtd2AN|ER=
z*71o|USu}B3=E&^#X5AI2m%em0V!iXTc4Pw0_;>lA_>7<Ft*ZEtk|?OAga+RI7Y*w
zagmXtkOe{7Y638bfGfc!ng9Ucu2dqCvx`#TtY>Iw%J1cnz9Qg*hwOs5J-C8I?9XFO
zQj?ITHnX;^#@*88wV}oHb1TEM)9jCT?G?ayF}b45n952`NU8iVu+`CbBrT!xxSA$B
zcb10X^fnkAv!fugn@WMXnbZPe2l}K{2%tU3TRx}+WpC~Z>oiftRFfhS3exiui9jX{
zb>W1wDh&q%^OI}I<9Rv&-Fxis`1Ve${w3?5pY-$<BntSsm4Tw9RZXkm#Vj+s`XYU;
z6x7&VR9$^(8L*k{eYUi|11WNf_HGDqc?z&OX<9kaS{x;n*-Khn7SLmymmO^fs^PdH
z|NYSzEQs;~Wvv#tP!|(3O$P-C(}etx;dYmCeLR8Vqc?~0euho@7n(?WeB#_F-VskI
z-Qc_Kx1G>=?ps9uQ<OV{G{oA+n~j^3F}w8!@A>gGT8BEGJV>OC@o%h!(GgKB3>tO+
zmlGAL)BSA<rjy;>M_YvJ(?e(_C$d6YO3rU+(QXIeCAs+ow2HWbu#*>1mT(>SY@lmn
z7Tk;$iiMb!c)Y9O`m^3ns{A*~z(vHE1u`m;f!K$<{D34Ygqgv*hfMMt)6sf#c3yT?
zV$SoASt|e@08i5}!6uT3>PVe%rf&^8%t&Mc^3R~~>!@X<f27NkiGHAIsnZoF`;0Ku
z-JjGYE0&8|rc-*rNf+sVax#5g<%!wcZa5iuBK;3ay$E#G7yL7u(!qw=qeBl2y+hBL
z{M8mBUL4jNU>gF2d4}p_0k<~(te7Xu9BzyP*>AjwbL@+~v0h{KaR1i@VA<9-G((od
z(__B%;lf1*>m#19>90tSD56LK5*B2lWiSkM6X4K_9g~t28wFv$a9FA>2<NC@>-!5R
zk<M_(qcl(nri|Dedll`LOV;4;<u#6giVDpLj}#*>*hvZ{D);yJ|6_S5j<l#H4;k?j
z_}SVY3df`B3#pxP>P(1vh>j&znqG&>F(m55INXzH@W6n>yt9T1o$Td#&mA0%A+S1O
zN}(~I50(d6M27ofAiD?g#NUq>7aI$!hrT;(O{=j_W8@N09CR8T4bH4A9JtL1xMtS>
z!v1(j@VH=c2J7|@;D29M=*RXjK!*lFsGUH3QA@7Hilax~I9Ppg<9;#@c$t+1Ff)El
zy%~6P2)_m3Z-ilg_*i~rh<wt6o--uhSt$uOHv<{>Hg5JJ9JVbPlAARedzK=uF)0=P
z<QN<t2OM;8AYUZ!c^!x!^nRMzJgWKLwSzdnMHvrQTLb0+QM6y_FE3l(Pa3nT6oxC7
zc3$aU`fgW3eiv$kUowN1P66clFT}WJG4lF%;7bq|Mg#9<ELG!$sXw9db^O-<wWZYd
z@lBS-Ec@OOx32H{8MHip1?hiTbk7-|f3YLKgR*al*(L(c4^+2a6AA+zp#l1(z^`Qq
zX3aqH#ecBy2<F)fLORJ`HE|k1B21`HObitMO~fLr#$ZVj4Ol7-;P)Il?KN?K``46F
z%B+}*x|)1sGyx{mzt(_D$#=(BPC$kc<5$#OIbK6FI33_UC$BdHX!FU0fAEzaQ~{lz
zANW7hr8K$!JLnA{hk}In{qE2C4&$lV)Dj27gi^&&R1*H*LC}{lpcL}?t5;eD@&CWR
z-IEGF`BD(MXfm9zw23oQ#9mqS5b5)Z!sQ@I*YMrFl_&|G&N&{>0%}9C`b^-6QemFJ
z--Z4%@E2A@3VLA7NGod%otp+SNT}3IK+itsC2}ecFN6TqI++T)UbwzZuzA-f6vOET
z%-^BaE|4umQO)*?t&WcXm2%78t}D+8oJDx^Ic?n@S7@QE?ti=fi#1<8%EEdLh!U(@
zZx1olX~VSyZ0wQ<W{V4bgy)Lsm@&!t?)57Z6(M?Gl9pukLp~EpOfo(3HAq{p*aFIN
zxYYME6;~+S>;rPz#u)xRgd^qMi(|P`q!6K3&n+yQHG~}WIXicDTV#UiWeJ~hatx*z
z#_p(4=(+GvgP{v?4-<t-+VtUqcb^q-Jhz%0dEvAcGU;5ei*f&?((1<Zk|DgGUquV5
z|KpjcA)#Y7XzQ`IJnL)u5^nvc!?x^$!KB+gPR8AQ%$`fEi&!)w9}qm=^9Neg-$qU4
ze^(_C!xC+K(Gc`rvc^>tY_7!VoNis^Vxhj)*%i$r{naIUZet;`AC1g8AvU_$wc;l}
za`5)V1&n#bVXZ}uW+s$Cng88nOT`IMXWd2|Z>t}Zcz;A&%b+V^JoH9*L+HXUY1Wmd
zUwQEK^>3@F`0-mKb5I(?{Y_kYIjp+dYc}}s)H@It{+NFM*R?emZl<D`LjWpwMJw(y
zHiA`q`^o{A)#1G?;7Jo&K}D`6v_P`*i~TYbIDE#bU6G`V={jad%t=Jt3uwUY8(cua
zqYVFQ7m2K7wRzx0u(XMM??*$x7sCD1kv+UC$ER7R70)!EVlv6$Q24Vh4LPQ=w1M}q
zrvdj_7UV%6&gxgmbYFx>V{AQ1js0Zk5zZCw$N!^fqK0vnOW5iDe>hkmTK(T~nkF1f
zddqfXgZLUOH*t4m=)%{-G+yr??%hqWN97Z>rbnsA4mIHx=?63SRtc-7#mE=UI5Hq^
zHa#<_iD8BQZ6b@>P_OCqCGExk=l%Fzg&}B1!zLkWE8Y}FUt|pc#YBu<I5;9>qxBwG
z*KdlSCHr|IWU7aal3thWmT9X$D9sokt;Pr{(e_IQ$vNwORO_wQ1$)6YPBI$y>Xx)g
z-$WkfE%u64botn%osDb8=EK&_&N5u-Lepy?Yud1ZYJ*K`IRZQDaq32C8I(<+mpP|s
zqMHNLy;vD<!f1>!JG>7mnq41)JCYG-qZE?TvcFcX{?k~lS0=i1{M+8UJe%RQJC5>2
zf)mC@sJW(<k9bGDR%fUzp@)lC?woG0f%EvN-nj{QR97D{VLEL;<^Pru8G(#}$%uF?
zT<pSjR-AWU>^=1G+$-&#{b7_N>8sgo!FMQ^#L?i|my{DQ7mL~IqF4o97+8|@0W!#e
zNUsz6ah$gtwy!syF*j!>Go5O+^ihL-IRQp$n&^k{myjE)uC}_}czF}j%$hHQ47&R}
zvv?`c+TV?^*~5b*S#94W|60F92T>SeyJI688Dq>fM+b&ERXB|dtT_0%)fpaN|D<-D
zcyr;5iN8r%wjc&3Wo0LIP*D<2LWr0q;VKeQHPTEn>lg6ukO~rtJnAJtr&}aO<W2+K
z^>_)IikFRJVVSLkJ%$W7qkw`D>ctqoHC>hLR+`jym79!~Gy0`jVB2*3{c>V*O;Ig0
zGJ<rZ%*U^p6p@k7)%-wwDPP%J0^qEy<r#f&nJ%NPq8D^-3*bo!$0$}t!TJMW$3HZ5
zZ!ovl=ec7`C5ss{!hm*nG^PCZkd(RkrUX;A=M0IHwf*|r=o-<K#OZ>ON=izFM#}@V
z+?Wj!*B#3<v4<05JUlm>dZUK3VlE3}EF%flvtj1G>nfc1($&ihnVDfxJzdE~r+<Tc
z3A0I~{@f`*Ip%BaOLC!}MRf+Te>LF6^jj{GkkACHnM1+FZPu#$vG^NWtP8Y44|yTh
z9^rhn^OC*Z`rEGf0K&|Q7!(FN7ve>$&FL-KKwe7bY~W<*dBbCIRcq~Hx|Ed)Hpe!#
z+X^DkY}SoN%oU<PJsIfmTxV7*5M635@+8*4!x_k{`(CC-nL=2{J2Vb0NJ59I^cNe`
z2mX!Eqi5u6>vp*y?rslT|IL{2xH=u{Hz+Jh!?gOvolULKurt#~_yAQj!^uufC?#`=
zISYqdNNK~`+7wQHmm@Qxr&f80_dtP<JVg}t^#lhiMblRg(PQk($91T;Bt|*@@9AO^
z5U+5#Gfp{CGU=uxeUu5Eyc2&pHBluytz{+X;t`RTA>Bl{<`$UOiGZohzT$Lg^Q->4
zQSql>A{me$LrcxN)`C(O9e6mLQY!&vLsWXlL)jKK!B_>FO(vcB0#B(y5=q^0xv@WE
zu-&@DH{Q$7T^fjZ88o@s!!{sa>*g|_&WKg80PRe=Ng9aM^O}P62Ob|ZoK98+TI2L%
z1!6$C^CL_nl|+H0OI|Bo&V=zo@)1=w^a~hlUoRU4nx0Hhvpg@JPvbe)=6uBWT)($d
z&jTj#U!q0In9Ba|cl#YiC3h6upzrn=S&8m%$mn>Ai1TmCa)@2hI%C}3B7;79S9mPD
zo<OuLB+V`(!F=X-K!_mwPSL>FAVa+}BQV^2*o5Hyfz#J~BBY`)d&qXbbjdI%WI<B1
z;|?<RRlYADl}IX5Dp+f+nFf(R23cqW_6n}yTI7q9raV<dVGtalGSQ2Z0;;(f$#G#e
z>>_RG*`5Mkk)J0_p+_)wP?sm1VI>kVB<^OTxk<C(Y1d}7CU$O-eIPAahjmmKUz-g#
z|1lsuQGI~5wTs=9p_kof5j$;Ec?Y-UX}fF@2f^NC;P<}_&#U<wMTIBN_lGXn#<b`n
zzY8tM0)!@p6(f@B!58KwV}nM7zmJvS-p&Zl<v^nfonXd<el6T4Fo~(`XIqdzH>U2-
z7AG04&;y=@zOJjknk>DT%R&Z$HnVY*7h$QELZ+PN9_*kt1@j&Ed<+*M{Rp^FfYgbz
zf$KKOpg7!0%hz{M)K=f`8-1y0sX(W{&7}x!p$UTe@K3W2#wXYD46*regyO&$iv`2w
zBG3D$2Z?u$`RmaG3&&f65I2X@U8g9h_Qry1GW650Q6<g%aP>p8G!8%V*QxZlS$kgC
zn6W@0eA0Su>aL+-p|YWG`U;u0?A8IlJn`V%EH3U$t=rFouB+J@Gss=}uSlErKeh8*
zxF(x^v0)Pq-A)_szRhRoD@u;BUg{9k^ib{+zL7TKHBA4RT5dwcd`C@?U;_&ek{h`H
z4^ih7U0D~k=`XfzR*Vx{6;_-IE4FRhwr#s&o!GWdY@1y@y8rI0HO5|JU+s&v=UnrB
zo@AE2IwA>?Ft@px=%q{cK?Ww38ADapU?J`})eYZ~Q~|yGm}*#yAC9Y8Quc2rzK~fD
zz&&C$n!WB=hOc6sgb=%yYSESr%Ie%b(EujoH`{7%d<VR&Cq3Y72+6=dKSO3_K5iL8
zPBbaQi#b$3&2CSn2Z~Wj-?LeNGac3~1NcE@THJH14zP~}gJpUH{8qt9{}=@S{h7C$
zF@Y<4b_8Yvzs$jqqWh-?%qj>xdQUUlEP<Ml?w^lH57J_n<>KQM+j2v`{|HH0pVmTH
zG>>I<#R)vI_Np&4jMcl_R`BA-A*Muzm8M=m(uVrJxgxP?-BzeP?~=+3zDC77Zptl}
z19vCmk>-%{s&?Rsqt@LQe)>@EPl*jTKwtKf036W-ZFvwF0zrx5ZuZCWnR+)}banR4
z@}@Yz6v)IzfHAnm1Nn^wGiNvNrE#ZbR~#(%v%m_Ta%<?x?%EKvUbicIl>qAn{tW-e
z6t%_s4y%zURDWo`0#Gq2oGV4*diMHa59HtUa~FNxyhZ381WdoDTq}^m*)%vVfIGLc
z`D?nERr5KZACghmU!*~H@z{Y$G|^~idox{Z>YzRBQ+fNI@Q3!c;wNo3D=C%@1&ZU(
z(jpBXh2f_kTSY@4Q16f6x<q?>;XzxzQD~HrK=a}K{P`oltvWXTC$?4sWzlm!Z8!pB
z*k9%-(<L=hkOl5P#RVbkEFK-|tM)q=M4|pl1H`cF$*@E3Pk;6LQJF;@?QlZ}zzS13
zYb6T4W4{CI0Uk87Ra_ac)nLwuoHKeE*i<jcJ>oWA?FZc(vA{C@6NlJVBHZ(a!uGf#
z_*tE`_^j~%)EPH=QUMZRy1FrCZVHCiV`}MqE|jTlokf>3lJ>is7^COsDOeC#)yAZz
zP``N?*TE}GYvsWqERa)uKX@7m*7vHIOY?PF`BhYDG^Utg{)Js*H9u`v8*M2=74YDU
z+!P>d-x0Gc+Fh?$AhcGH!<4Hi%4>~>lr#r1kZb$eA2>C-4JpZOvxh<m5g4@iFovFI
z_GT|}7t@Z&vz7!b(1$OByc#&xV1cb9pDDCOstJcxP38?7%@>%%4BBzN-Moe?u|=b>
z=TRZ|9s3t^c4!Goft6j($rE?+TL!pC(>H?^1D$9|;^#y%^__BPqNPkDz#={e#(Y!+
zE}!AwvjPQ4$xwU<HiMfnLkc^25}u++@D?B}O$Tpw<f}F4rr&-lO=lpIsY(Q@8A9a0
z=}|9V!%0F+1GO-wlNWL0`aRd5D)<I}?LQX<PY4(X{|nQ!-g%;k>Q8Mf?#5D!K8viM
z5gHig6RAIA$J1*%_#1U@FyDg?PlsC}slI!R%U7+*lUdS!t~5f|<|1c~5R(04{uHU4
znw9|n&cXf~LpEar?Y03lTIo{o(-ck;-UQwg`DaA;_L$?mcuDoki+3^e#}U6Zd?}*+
z;C>#b(MpDttZoC8&Yy0snEue5CWMX?0#WOH|Lc6{bW8R6;+7xX#Gd88gux3NJ)(wk
zSnqzw29|5>;q@W53uXrHS)SIP#<W+|=KD!GRb*K8qlvuQypR7;*$C_!q(<A-;ufZ5
z5qZkyge)VGX|!766v7kTF6Ba@VhIj(FJz(m$wt>EVebi;X}#!u!MQmuHrSdl587^o
zyKoT;@Ax3P`tqO&B=THi?jaJ51HOZ%6w_Y}_s_rp?4`qRbYR<y1Q=z}A->Z(lb^<A
zjrf%fxxCfTT~Wa8mbUsEA3`Z+v9vjade1XTZ3QE&i955GLq+(~8H+H4XVJNer0&ME
z8by&s%UQnkO_6|_sA=*%tT-&5=iARO_(|PnR1cArn}tJEmBUiQ6GHgmzkgP{%{gpW
zGLx8B^d}$SxX~JM9?@;eRr!G}OPzB@Uz+Z}#)4F^EiDg8Usqq4J%X@${)jXYqltjZ
z<JbB(I5E6xPYd(*NkYc8<Rk8$FM?sK|GWuCNv-NHUBwm1{ZXS)G&Zq?@=<e{j7IK@
zNaCs(UCx)7AimR7qh6dI+B?i!h`b77=86k?Zkvm5!Gql@zSpQfl^o56Hu`Mrsdlcs
zK+N68+1+BvK^wH3<n!xRkm)3o_x<K@PW6M%+9UP$Ie<QkVw)SjW_);V?#p;S-d8lm
z>qr{T>*l}<V-h$Z9)i!<)7;$XME`Mlq@RhhLL~t4cL~Y+eVD2mhDX5EvGibkT6*xZ
z(<EI`3B}7!O<!1Dv_H8`yt2WX*MbVYA(Gz6Y*w2-0cU7_I7C&ZLwfZ5x_T|P7CW=t
z%<m^gB$SCzRG-=Tv+&j+T9lwhC|T@Zjow}Q!G(p_mICbTf4L%Z3glmhyeH4TIO%|8
zlYtn-W)l~+JT=#G_)Kyc8I7|!FQr9(N0aADlK<EA_%6XT$J~9rTS`gcZm?pj)${Qe
zCz%=vueeqW<&Tht&qG3-M@Qy?LJ<Y!V=!#|e7&4af^qov1wauhtK1P6yya#l1ETYu
zKxCl*-jJvN$x8d;u?1}YqH9No8@In}FNWxd{~mHLZ<=gXEbOAYMB^mv2`lUvamV+>
zhT>T(S9Z^D?tpHO0NwcEKJ-6OhU5;^O0lFw^?Iknsh(OQG~K+n@|`@DrnHyCYyN{A
zVq(df^yMC2@a_JZ6@8)R(DGX#!<mEU8^S}2BO#^f4xCYyB#6Mj+v|5YpTwe6SuHGY
z#CF}YG#2cP$YXvpaE>$3IGls{1>|bDzW)rzPR@<A&KB%S1BVL<IRbzuKuaiOf(wmU
zT6><;7*NQOgf{~m*4f>&F!%Q3Vlm_gM>ti;S?C}eX71)js-d5+xA*z~i`9V>oh|Rf
znr97aW+)iqF0h7Q{IlMZ8|b8CN^0Se<ygvVDxn*gFvE@bbRG$PNLub4L5tc*!qE?P
z=2U1eP)=x+Te1r*aXM)+$C?<C1f?x->2tUMwqMMDgc?)tmrZFoMBF4Cfm*VoTXbv7
zr#vDT-kHclq;+_L=nf5`pk8>L?VrJ5%cRFJi}bErW-X+ImA;4wg%%JnI=zfe)u%_p
zJ%T^ATq(-M@*kt*DrtkvBqfUlyT<*`Xbz$@3nDYalJDtkXa*nsjh!1F{P#d`{2aNV
zAn0LpNtufx3N3rC8(I*3NBLoA<aw1+QXbSN+O?sz5D7m5cLdgkUudotN@{oAV0$=h
z@xjqgH9tIsC~8qlL&i~SSixqTFEX@X>%U+Wl?CD&Q1Op@q#?cRa3W+~6caln4i+(a
z$H)KK7HRl)3F3UAJ<KX<#KOK4iQD->BEcJ~p1RpnBy+oI+CvPG&vis4Iewntx((m0
zL7@5UOKL}sS~G;@=0!?^Im(&>+~h40R|Z56aiKHL>IvOQ!IRkHOQa+fWeIs7+)y_S
zXH}ASudY|@1^D6P)FwkXK@t$y*L#}?R)1c2q0~jfam*`77wInG#F$!U;rRkIrXo(t
zy!)&C7Fllif^KNS+41z5!a{$a)Q((kr0t4W`KBo?fVQ1T1E;@(^{z}r1_j7YUVL9*
zf<>c<GaYMD*kqJXH2zx=<P8`t##}wc=1TF!Np?nvCfr8Bx>dhj>v5tf>q9yqW2HpF
zt2V34O9TIgK}ilG5YP_$vF&sdUZ1bsqagOeh&iU#(A<q3@(f)Z{2gmI9RkKBH0;6H
zXEDhNe>g06E`oaSDs&evR)g#T2XaTXLFxUM=s-6^?!DToNQE7={HF@P@L|5<iE-qG
z^5967sjCr{m&azs!XN)jhZuh#?rVxHwyt&}V8%nmy<%_=lecTFLI?U2+x(RDw~}~2
zX-bcRvI3pN*A1~npyQ{5HCvDNlVEoMgWZlFG4eEtzfw^Us>4moY)$3=ugKDr^XE3j
z-Re~&^Kkb)4<vMkN)I;@<PhqMItieh;AJrlO*)$Gr?da#+w+PbJsK06bhBY>Zg$yS
zZH0->U@WDf4ht(v$KK&J$Jp7ZbaTU9QX%IG3Jk|@0%&B4jL*o`toJ9T^724|!F}73
zwscmf5^<``!ApR2xG9#5Q7fvHY!lIxtC0nKpsSVQu`^o=@TwUY^2<cM2lOo9fqN1^
zexeS((v;lL8TjWXzt}aXfa^Tf$xqhz<Z@mo2$LWBbk$0N3Fol<2R-~YPQ9pnqKIMD
zKU+jVD2Jq&m=YAa?)cH@LW}KvgLidzbIE>!)Wud*b?4jA0p0MPS`Xm(t$9r&(LdWw
zzl|$iwU<zLOIq>=l~SO~)9|kEsm*U!hJTtt#kco0EI1JC^EKX4_NUcu=i=|(*4AeK
zXl7&6HEcD)j%++S$}R;FE<X#7UeP*fc~|5nllb&(Hd=Ol{RMoVVWa?8#t>_G^1wjR
zUC86AP>l=AX`y5P^n`SJNr2rt=EZBs8lajN^eMg{Zndl^P0=B7RNvGPC|l&fPc?{L
zHOE>ZBM3M>31hn4A~o=v`_(?X*S(}v(B>h4jD5u!pL~#?+y79alV`ZB)nxyJm87<3
zAK<5^ci0)h&0Lz=Qr|#Im3dt=>_9*;pUCh~XXcO0R$5q-KnykO`9toI{0_q5$|M8}
z)$~^oJ1b?2wkX4)H-5qMg@yT$TV>%*^+GL4DziJn@M~N{bh;tW^<42|vjkaCP09~v
zDH#j|x7Cw!5nZKH%&tlTx>1e;_z1CI^gkeO?c{WfR##X1H1u}yTHfI4upQG|9Di8<
z+9kk49Ifluj-Y(AuSYecq^u|Zbiq?6t1A);YEE<NsjMiLrcJk+&)guL8wme#zpH^8
zkQYlt*%f+TUGZxvgyqHsYU<~j7^d~BZgBY8nK^2s>2MniLOC1NPfD8AVkHyh<;2FH
zo~d`zN>j+e)wE}4Z{~cyh?{u+l?M<{WxMb96h9a%^giY&Cvy!AOc3aYePKWD46RyZ
z5!9FB-w(hzySRwxIj<ys#-It<EwGrT^VNI>;)HtzwlHZvo<awmIeI2V7EmluM$17{
zU}zz_Tah!ILH1z2jZ0eZ3T$?{5@1P#blR3zgBbvZbd^&zkq%6TJ#j%M6ful7Tlp)B
z@>PCB>P>-vO6e4*ekkgw`CCJ0r#jH1>MH0^9%??3MNeX#^?TRTwpGxs`g#1qVy~d7
zL|_MX%oRIQnM4Pm3Kfh!k_~d-$W!H^EyR-rMZ79HyEQ<t7S7fdp?G^e3AfsAC4ajd
zU;je@*x!3X<i)xSUB&3_YkG4!ySTjiK%px~De0+3-+frCEGo8RO8`IRM+cSlbb~*-
zv@r#x#h_L~Wy>OVl8aP7|KFv`i(RR35j!bI;PJtb`$i3<8Iyo;R!@>XbLHTa)-#%s
zRTxhYwUNPj#Epf`gi&UI13DD`*_pvPAH36LSkd<`u~e4_u6mu&zxa-_!IB*~wwLUX
zKTt5dyNv_}MSFyMp9#0g0L#+%i->8UOqfCt7mpV22Dehix`R}<haCw?oTf%8XePY1
zL2&>^zUJ@kRIHDCVse-geGeJ*Hu`&O-M~@Mir(Aay^y%Go0SC|y#Fi@6b}5sdLzD|
zj7R`E&Qj)bI<E=mW@L#y=V<?)>JEz(Ya9RzW-^_VAQ#czdC@?T$N^dtJ2t)E9c$C`
zA+VhsDof#CRK5Pki(v<Z?Zqn?-dOXtTZ;%3WAINl=ga8sovRR=X_JSHmPFKlY^m%H
zRMXp)`a5mRn)vYzQa1a(^osie93QYw<`KG(m|+Ecze)@9M8TQ5Vrmve763$%kXC5q
zzPbUIIeK#vMSN-^{D>}Zm-=QgfZdmxjS0)61pXoYmCU)40kkrWE>31>UXHd?Bl06u
z7mJKjSQ@CPqlm5<)7JrYRSx?~1Vp<XJ$T1eLai>N)haFsqCH2^H$fRjp3IH03b=K5
zr^jDNMH-o`>+RFXAkv;PE^l%B>FkbH9DOmx05?!)VS2KXA8|K{nf{a=q;u^7x}BlE
zW#_^A+r3^`S<JE#*WyFq{VTdx;gbR}vj@>aJf+R=p4Kz&G7hD+C4dbg+U3!ihNhD=
z&N)Fb9;5n!H_(5|t-Kh0?>aBI#AyfE_{Fw+Y6%v~(^ov9h<I@xMLfOko?bYWKgbck
zjI+Ni>bKc}5yz$O6U^!bE&YUtGGI0r-bDi2`=^`K>UZX#p$lClA}>}lZHuj4-EeHH
zC+JB*tHJ*K7ycm$X!sMrka-9FWX;+?&<yRqv1D-lXg*l{56EScC-Sa|*ujz|_4X)A
z({M5I#a@A0Ne>aS#O>=+fv<^@9+#mbXK%D&yD4jJ7(Z-LY+nP7#2T1i%+3i_o88m@
z5=)!j>ww3LiEEIahSVQtqpF$4QSNWaPWxy4o!|@=I}^4WDhFV-kY?Z98U_a<_z{S{
z!2WGWN6g#&@{_Lkw~cp?PhP~!**=Ovt}nKaS+$6&3^#6wTtNhlyQpzwMpf@m^-se2
zL;l2y@Hae!fQsNzrFMrCHtcIB6g=)W`aP!0k%l~CeNX!7i82}e<gz&1tHDVpyE)GM
zG*QDC<ZXdn?V2c=UO}<QoA}bw>DOgShT3+4HV2qrJwz-??91JFVORT9q?d0qG?UE1
zDl#zZi2QbIh5hJCLfzD%%v-^>tkmwSXRLk6b+RTu>s_l@Vyc-^+|m42cAFkFkp}jD
z<>;TA!Vpmyb^W!+E36|NVx)_MmcIlzteJLglu|@Paj#!dz$zn1Ml%T*$Sp{wz;SE}
z@y2FC6<Ov_v{{^R2lzN`h3NJMS5Y$=KA|kI*g5iFMe^y_W03{+3K~HdPTkP-MooN`
z;PSfhHa9Axd4G#57*9O^{;d6<JKvifaWG)=-%wL(V)>Sv$Mb+5-%s;7#*ss(GW)AC
z85$;<AUu}aBTJ9A+l@b^+LL0Aj>v@kodc<XCX94wuFt&kgaV=Q@XFEnoVcZDNabqD
z6fZ~O*)Ol^!S`Rzn#+fwHM@sPnd#fGguM2+D2BoQ0uU<4l>16j)$_bx*&iXJv}(dx
z%e|a7uv)(&fI1z!GUHbIhR4YCHRzB6T~Xn|o5y+{r(6lVj3;~O!D0Lo5*gvTkT#fR
zLu@ioY@?`qiFySAYDDY(Krx641@aqG?#MsFj`4c6V8^jG24!`j!6!7BryT{7^rFP$
zSW+fOi3pNUAa8qY0dQW#N_H`;ou0`^3h}<{H2bsGiP0J*#nnQX{<VmkeBta~END?J
zdfBXHoU)$PQ2#+HbI1{^%L{)lD(8V)Ip>11V_KpoMm<q!1qmcFOfV6a7+XJ6n_pAj
zp_&4c?yOPaZ2){pWa7b`QRh*qiFN*d#=ycXQt*q4a{pPJy_ribkwlzO8Js$b*nVGG
zOJc|LOjb(Pm_S$LKnf#L(m5l%cxtlkodO#ecbEHMaz1!TWTK&@2XY#kgRHfV0a->E
zF1u|BQ(v9}@jruYR=`MLNeD};{jDy|0;q%(@Io7e<b>+@j7>a(B@z^$pPsK3E?XO1
z5<+&--O<0W_McLm?e6iu*-dp|z|3z?CvEMyc%smWR#9%rhi%HEpF!BxUIUu^hR7dY
z4i=-bMiS{Sh=U=hty7afGa?GYnUn#F_i(nky$JNr%YdcPk?Y<Z34KfKyLpU+ya5IY
zi!mB~>^%G<%(@aY8pd&wG+QBwoF0hvB$VQkTW;4A)amU=@@*Z08AI4VQp}<0n*J)%
zzP`Br+5L_u89cyk4d8Qoj6XGAv8F(-#E$|AF<!nlz(gFF<9zPk+o=((3~gYG4T1Ab
zz2vVw3h%#{FvX75;4@)jX0!cy5t7kt<p#sJGqDG0t9+{iveYGv8UDRdQeGLUREhmT
zx9lYe$5Bb5xUKmRY&a;*oXVo3mkX=6m$aH0cugWRza1&bG_<|OC5RP`v4poGQ$5L7
z0_(Def>w<w#f3!p*|2_ug(<Jmk#!HCD#^+KJeVjcGx<%%qGg9^<dFLgy%J_#rB76h
z0}L==@~_5-w2fywkqxx5TR62*dg>D}LFD&EU&M)_S3#k`pF!>OSXi*_E*W0^zaWRO
z0CgTj(dMM_s)M`aD{{`ROocxK<CROoY6UjHu<&krZ($iR*evmI$@1DkqD2eIb*fou
z{>g#T=4<i8HkXQtyK&5|Bx2-j+7<-(g`}r&O{c|l*Q&@o^l^BJ%?3s`5x?i(#)D8P
z1!GKvU07I+C^BrKnrozmXW9DCDx+?L0~U0$<rFigq5;Jv*<L2%Uu?ltIEoI&@qm<#
z&0_Pb7)gf{Plm?y8k#2y{=I#I@2DuhNJ@OV=##N3)6i^3PwmPKww}cJp?*T>zPBC2
zyZ@}3uh(#hs+Sx-4-~jvcfpINA5veD{PpbY!kt9z@5(T^eVAL|Odm$h%YBvpXR6_a
zx%hvNt0dborq3mR|9V8kR8v!fSEkt0%B+R^mM+$W*I~@8$*QgR)2i(4$Ee|O(=7sD
zguU{p-ARYMgQ#|qb6Q2(aTxcW)hihv^ix4WL4^B?UeIfbLvW){@wnuj15<*k1D@*E
z{q$|;yH!kGp`dO+JD71>{Y7Wo;miVIWeF3b_e0L%yvz>sQV#CQ_f!yMX9GbI5|I0p
zC%CZ8rE)Tce_G14q^O~hP8SaE2@<R*E$yG*6f1a|e^eJZ9<ur!!ZcD|a%<?vynX+{
zmJwSU=u)NY{&aqYRV!o63ol9`3Ket0>>Xwq-#@A$DZ+Pyu=4oeg6ZtsWq-;1SXW?`
z4`6rq&<$}ng$U_s$@H)EEnpVNIsSM~dsB_op;JDw{Jwm~P%M~TUQnN<`RZrW`zGWF
zDTpWtoQ@URs;jN419-l6FG|VGA0M{+wfjLRV}#t&yWbwZe?7E9=T(&5mNk5w_E2S>
zotA%2J^z+gKY{g@U7w8;w~p=k_Xoj+MdDMh(ct{3J2b`DikTN{_1z_uAXZ>yUOb!d
zD`mx4UNCZiD-i2_`&Rks_AZ^h*;#jNAhRPB-bZKc-dArgEbSe*Bej2YJ(pBO+Hq!g
z{aq*Uo%gC;T>DSqtfG<58lxEH`w%mQskWR7-`(pF)A~_+_WVuZv|%Wy>QtDCfrV1;
zzN<F`5iO2&P=8$0lB`Da_3T(xS3@cB`Kp?Ls*gtk58$rq_|5HvuO(*!m$)~em@0=0
zzcu7%%R$3f()oF9;qB@WWya;3vZj0YJ?*P|Kk0e2V2f;E;~{hT5AVePUlm=W;Flj`
z_j4|)l$6v@QaDO7ILLqD)3og<OmSgtC@2J1U<)i~Jt?@Mh<J(gbvFh&Y4`J2;``^d
z3<{XTie1W9duO8YwvW^C*HydObT$tgbsk^OC{(n7ToKN-v<I{!55?@JQt}43ZTC}l
zVORFLo)WO_Ud7SEGdV4lJmmZHwulhw)y==%isMJfl%1b@E3>%_JdmT;jS$^!cXYa=
z2=~=v>YCE&IAiID$>w)gJGDmt>WL{+)JOl(8kc%+OILA17pI&2ej_~QAs01N%uMBX
z6r2z1d7h0=dsmY_@l`zZu<?#b+GMESq3?A@%vaB~(75~5k^Xi`;Kxr7xzSHzbfbQP
z?JDRa*PfFFq2#F1Q>E`GpRR9uBSlM;D;3pvWd&ia#5j$!V06!(tOYoep{WTu3lZe6
zSuKaWXsfJ0_GvkBmO|fgU&(awS-PE6rC;M7f3%Brzsp|aP*EWv!9;{n%PEA*GHu0F
z)a~_p?f+(h8A;$tO$W<lXV!h9?>i$QJ+w>Jb#%47h<sO=TGtcf$fS<*Rr8;4QLHn=
zerQ2r5-#97GQ#c8wxf1LgGfs{RFB;%l6istydY#9hqMLp22xU3Snxpqf6Hc;e;2_)
zuW8>-r|xaV|9}2}w?BkX<B?v@Y&gD1=x|iw|Ia)BckkcQm#2{LiIXYRyDd~Bk(O_Z
zi@87q6ttC+l^0<d(8wfK%k>S1KmSiJPpy7Vo&1M@49=P1<rcCIFZ4jG3%!PeoQ|c$
zwBH}t=)kcMO;Yj>0cofCbg_!t<sdnp&yjCaswMxc&)HO^OIn`I>o=iof4|#6eC6i2
z@W3WIoKugIM>0ddHeL)!39<g1vBJd0D_NDtu&Q^xx4;~}{tf8<wbZe;@%z{9Z;tt*
zks4Dz7r0Qg*0d=GW6q7<eGw0&3rsjJIk0)BsP9nTSh(f&zv8;KRCI@!vo2wbZhr8>
zYE*8|6=V83&j~Mw3gr3|TQwgU3Z&v`Zo3MV4?EQoF{w5KXeYM8ix1Wx@`A&D_nLd>
zfqj0|U>-P`nc~&n1s_HLFL}i1gUh2f+RngVk;s1EXv^1KA8A|Nl4%a^wjd3iJxUBJ
zQz55YR@TNV?d8KwhyRWg&XJTq9kX|OpwgJN7fje}@OU5*MkLq_6lmpbxBO*r>^2(&
zcJy4G{KiAD>YGve_rGz&`~)+C^WwDMl`TJ;=vZ1CM$$bL22Rs(>|Z~%q%4_I&huOv
zgqnzrBoQ+KC{%o@puL*xj_ORm7xc|{zmsBGfWYHF&wAC`^eu|IV;wVKF2#rjdeGhA
z+D(r_u*g&B)5}NH<}x<Ji8w@kh=;)|?T_F0N#UKB>KC;S_UI<`J~1)4pRHbn688S=
zGV{%yP;Y-83uI>VjYaeF#PAtb6`36q-l7|(#A8g~giIPh`97y(XtW=ti8v=5;+kq-
z`poa{lU2+;ui$2W(BS@wf~I|i7lP^*A7D#t{ZzNBmX<NQ%qX2m5-#8w6CL1iXJEax
zBb-K`oo-%*g4y8JaCs6VbUMbNC%2lf<nC<gHkkv+i1>R(1W=-&)?Xd~{u^9F$LRXx
zfM`C_`q)ukKLq_s7bHhjCeED%Dhs^#J|T0QZ2GYqS~KtO9dg$eyk;Rse``wZodxz?
zqnESnrfhDkW9aa<8J&07B}mm3X{tu9HlKB;E=_7y$`}r$6EHSmTV{wTPQ#8dLR)Ow
zD-k>k8=ooA!7?_dxal2L{I+sF9*;;ij*CiNldse`&VhkJ)utg}^Mt){WCE?N(W8wJ
z#V;HjFnD^(lB?FTRVGi`(o&mse=UTiJKXUNGI-<7sZqJSkLA6;>B#bYAcgX`gNtmg
zbUw}2BLmu{#cR025iSoH>aZpmHZ}`Pf8+2hOOcsP9DJU#ia6#g*G??=GgQbj6dmMC
z?g^-}o>R~0ClY&j1Y{fL98Qk@0XqF7wQW?-nJgva-3U>w&av*By0lBD%}P(t(yUN4
zC1;$n&N>3~wIxkr%A}9#KxS{+vgYHTJCEt!M^`X;Xx9aAc#bT#cunf#kE|?8KX3>q
zh*5doi-@0>lIkjqH-5Cs$pVTGwvz9?x0IJmZjBoWUY$(elG&`s!?ariFO$+YOW)YG
znR^w1lT~{Bd`RPg&c$5Kbndy?f7DE=cKmp7eonk+R~>r($&*f{etiXGnf_&GNvh6v
zmQK5jvZgV+Yf^5U!oQlL@vHi+1#CQPa*26^_+Avk@cnf(=};3;z=HUy9Eay}m_qKj
z*9b2t`5G6>FtPSJI-?}$d?&{<&&Xi#K0&X6@_fscdLEA==mkM#oydl};#>9>1pR$S
z$a7xFAgG!Q_Gj3b6MsBGtp|IDi|01!L)-ZFul>!?O@aVw>oxV}_?~sZ2VtgHi6s+g
zx4*iq+Y5+byZUq*x2hWdN15b08fR&&wX2M_j*(66<Umz*i#Vt2uFzYr@-Qpq&hYNA
za#U}4CteO}Lu>4M)Wk7T73w_2$>8(>^4zvI1HOqAk%$GKD0(k_$%S^_=M$8Wr|ni-
z9i>t&HqEJ3;uZF)+faBkk<k@%EiUTuhy?!=$4pdv=Pwiq5d=9el2{$Fp!+`0>!;F<
z_cHTDfwmA{d3wXr>LMa^GRLb^$_Mf7J*z1}Dvyd#6||!)UJBdu*&c>RZzIF#<Kij)
z*Ve=ADa#gH-4fiLSFMP>GxNhDpFfE!@rub|%6anjV{8zf0a6TY9emF~^51cMkEQXu
zO7?%=*G~g4ez+atuJO8Ym(diBy#f)4f|o;HUw&e}Pr_H<szV<mmuta1o|%*#pmZ)X
zHG0yc+9hAPNqdU~j_SFK>51(P(_)JfXfG~|8Zqc-o+_DZ)nSyfjbu3bGHzU%gsteW
zwFalVS~7@EXM^^zG5&si>5fTBO||1WU!Hl~ZBa^nQMdhe=FP1ul%;ndYkn*4BifiJ
z<xslJ&11^)lg>;ibtdhR_mO-@^tC%V&N9@|kLH|)r_&)Od+begS<74D@WA?!A%ijS
zy1Qi!Mnk9Uqpq^FE`^z5N1f+9(b<vendOmJr04tWBiLK|&MAL$Y(~b-SI&is3<6*(
zE2KiG_0JitcC7x)!YeTHq(bdwI~&&U`S|G`<fvQp=982crkSQFzubBEIq|9Pl1F7m
zQ)~8z{@Ja5@%ISJSY|{4+QwwiXFztoyt>e1&ZO{Fma|Ru92sv8^-iy~-Dr;BhO$VG
zle&QBfba(@<X0Blq74NtJ*ABSG&0e8jB-PTw+CH@c?smvwt>de=E6-=F<43EV8gZG
z$b=PFzuzk2<i*iYF}vx>?(wwB0~Gt$PJdS3rA2C5gb6pX4lltR*b^^`-=Di5{7nyd
z*`{>2e8@f|i1_PRz-d#Onbg_R8GY`NtG=OK+({CGn4F0+gU@z4$mi@3NiQ`}#o;x`
z)f){y(jxj!(DJ=)zRQ_%wvxuM!{t@Hj?wR83T3Y60zFmWldhI!rbGQ3PeZVstu!uo
z^gtwrt8QU7KI{F=QtS`Cfu&g_g4yrg<@F%A%qOGGO*gbTGi+?3H&nMavobBEKpnlF
zxXa%(*pJQz&=Sh-?z{yIU+mkS#w<@N8ipT53PxhroSQni(4G#QJ>SQy-u2W7M#n-y
zN&<hTpXe&@G8oQmss-|gHp`B8$xOEsl2fwL{)DNk+kuAf1Ja)cwI2{3^^Xez@Z7*@
zk3Tv5%Jqr)|C?dLMe&m{-8TH^-<rr3j<n(8XS2ht_50-2eYu0@ozV-;a9z1Yc7q{W
zSRfh0VMCqefGm~0ez<Hb(dfPgw|<u$j$(TUCg2H9G}eMbaCG}`t4`D3-|!(-N=P^Q
zNr<``65L#1Ol>9y#$P}63+RTQnFZs{SI{kRCpy*%iKqlahX(o$B=-5JuV9JwI?yv`
z$J~952{19sL8lAgqaCq-*+@pX)}UZd_=#uuS@27Z^LMe2%zR&96?)uYlXSh22f%aI
zQ0URz4!rnZ_k<GEJ-Lvl7UsBIhj0EM07`K%U=j*?IEnJC*9Tk=`Z&$m1#!CxT~ndk
z9w*d3jE3H69<D>ma?_*rwUA*;3_l)*-rcY7^$J>UfEPc${{ent3A<(iqx>O~c^?5<
z)TvFPWdVj40bflxMj|*qo_%Qm;P1N}Btsgp$!cnrDm~l>%t^yccjB4e8@fvJ(!JAd
z<QMZ>qZe8rEJmIuR)Ok!!(j(L(K=*<D4S$m0S204weZoa+-mc2i#NR+u`~u1;0(@6
zoXo6{aE^W)Y{Zoga2?W$+Rr#DR}*9Z=8W$Rp|yJyam{kQHUsdya>CtOiM!MM^%);Q
zv|}@96DN|6cN+flim}Sn5zNJdxTU8XQ7HKZbo#2iYa?$Ysu~MHAT)Tcmpi<OlUfLs
zM84MY6aoP=Kj!7vm&M#0qC6EmBn2%2m_HsqW=}_mbaL=`SIb_C!N97oznnG$U;wX7
z%L^T{eJlxbeLaxoRY4kW;}o#2EYx1eKj-6&t_r!74pvC&CJDgJS{fMsK_~_838keY
z3s@b4NkrvkF%0K=y7@5K8UAVtxWmZzCms%f%%y^|A9q%JpqZ_Yrj^+-4@A;_P2_%f
zGx0~s>o0b>%_TIkM?t#<(;`J)tU{!J$6?X5ng>A5Fa+>2lhiV50@r>AMRl$iE#E0z
zdlNG#SrGzUuat4uBu!;S^P->qp5f$E|4O{4F3;A%_8pww=QB?9-*)S<yy~!JRgZUc
zR>OJ*3^aI-D%ZEN_D|_?8ht+P+n?N^YObaW@078b&LD}IT$yN(jfOSJP-aiXkU_bQ
zP&oA~X1L%}OJ}nf88aK}$cEY->!(*u+$>B?++M{hhiA9IvKjXC{Ohscs)DNgJ|IX)
zI{o2&jH0#Qz_2CojDUe74ab8q9q%f?w+s0c#3TnaWc{wK4p#W>O2*;$!(zn!_xOiv
zzK~5cAs{8>fqO%r1|y1+nbmFWRzwfH4}CzFJdrsI{5SmTfovRG{c#~^!@~*s+tK;s
z*}o-Nk0dc?+GlsS2oADbeYKt5Tg;X*6vI<qmGalAhoyUq1$jz`3n-UFat4L!!_^C~
zun0~=v5X{pF)qg}CLnOclLHaaoKV9z4)1Os9PQ3nk>V<}$jI@0Gebm^WhDO3#oq@W
zBeP@d_{a|kv=w^2-#3iaf;0wek+h0C@?q*4fEP=HZ!7wn<tKsBwx}Xte*pwGlV0#m
z>JiN`^33l$C0RdDj(^q#U>8|J`~p}qrB2`dOmE<_5w?*%y$mi3*`H}SQ!p+E|Dq%8
zP<f5uu0X@})#Y@6H^WEewEn~U4n%fI#@`3=2PFGK+g)3Sb;vIMFO~}?#X`~NQ;o)Q
zF2n=Dh}evM0$pGuvphxfvGd3NZ4cl;xG*v==k?Ku|MAM;A-(m#xQ4pK$E!yOz5U0S
zcfe!B;Y};2KP?+~9(Ps@a={gM+odvt-!YD?)&n@Sc<UmMNAoM3#`9SxO^I%Dj^rU7
z+e=J(ObXjT{bf+=&20fXgSRO%`HMC#w7*W4P~W(MuW)OIf<bmKoUIBgs5p;;-Go-7
z`Q#U&pj*`yPm~^Hz&8hY2UxxLLZjs0{#htCQ|3YXx~{;q)Jci-UYIv|o#@%|jn$iF
zt(UtM4QFB&v<1)iIYl+#@Hd&W%?|^Tl;0^KjMb}T>~1s_{H8*!s6OjihR3Bd4JIQz
z>8opf;n3PmVL3m#J~e@J8W2WWC@*2nA-#3X);2l%w$>1A17{9lf_z?l^lUjJGQ>kp
zWg~pZKT;4c1?hs`;+{yPg0;fT5f6NXEuwmKigtzn_4Xetglew1*sRpk=Md8Jsw9k6
zWQrY8JY%}~G$mt(nr4uOE(e76iWGXpYsXPJC!Z=+H1c(%0Ep6i^(GTc<vQN~q;Lr9
zL$bro31Dqq?^fO)Lv`h>oFN$vMBwl8gNwjR2ZQHUjprpMQ%;VckQf{Z+3ZMQ!0^lI
z#N*4AIj`pfNvx92y1~nbBuiHlAF}mEcwNs{oT2B(34NuN-GE-QDVPSk7yNpLnbCmc
z<f^A~5H&GgNMe!X&E+k-v$wlo>P$w^$eWkp#yaDqh9<O^B(s>9=Pv9(!jm4;@W4IZ
z@M4S53<2`Oq|=N3C5{w_O_*8PT$5QXijRz0^EoD1RWt^-A0LV<vg<5W4v=fZw{PjU
zvPM?cVzWHhu|Ju+uLLuu?sgPM-gR)JxH>;y0YdlJkvYaJa_Zz=hsy*C4ZEY{L%4js
z9YM^G2~)D;Nv5t&M=ZhZW+I%#NYIWCc&S+D-pMn;%~}iFO(bFPE5tfy*W=E&j)i}E
zh7*g8B0&E80ZXmM6D7-!CwlRM-*fW>>~anauIzcQQ%4;{L==i0pp-7?g3h~!wO<t{
z1gt$rmx#uP%fn`JcfTcnu(Jvv5qkg6uBQo`FHX;JRW#(PXQ}iF_hyw%gLjia$mfOg
zcg9p(`#{4pg$AG)1HHv=aHC34BGyK-tJ$59Uwf10ITgTC?yED!IB8&I0AH5cb93hm
z@-jw5!5{Ip>6)Ei%bIRGG7+({h}{JOxjQW|<aOFYIJNWiOJ~b)b|8kKLka5%Na@BQ
zT9u^@b`PL>uasGwR7h(tU$IGiI*2qe5JFc|_GY)QGX$QUJ{(WLA119=Gmds!`oY-D
zgaTblxaxB|3@7ZyPB>gyhmqmR)(k>yeaPc|Pyg%rd<5qw=PfWwR(FE+clONg-3es4
zf70lF#o!kdgsxrwhhuy<xXnVCovVn(D_jAU1+C~C86i_-wP^giZG>&48)((59+?TN
z1p>#Qsit)rysykPj&%CNtOP|fm9!W#wn=Q4Lur`GgToXcsbKzw)?^S5@HP`_KMY6N
z$pv^!i&wuN&}&aD&!cj#OogjD43De|z<#?sfbnLOfX<(HLcID&`GqdY4)iu-{t~!~
z?o`3#;$eu0!>9i%Iwj*NYCoS45+_vZn_A4jbTdG$`~DT_^26#+)<YwCUq6Yg$3<c}
zy4IolfQzW1ivl$p?nQsqt&pMrwb{Z-4zcJU1s1_*kDvFmh;Tr7YY~27{b_$9YT9GU
zGT8FU@^HE`Xjg}(P-;HC^@lP<5pe8Y-n|CON=T|}2n`HCq0?L5_Ld`Dt4^FS6vwdz
zAL99LGg9ekJN?5;--BLNLL;xy+W0e#nk7mZSp#cx?4Cvhp+Fk_g&sCj*|@$Iw;jn%
zM$@(w6wmIQfkKwHCV`e(?U>?4ZEh&!;i$T}FyA}cvpG4b17Vd^I|w$b7s&Ub6gPxs
z3minQI%IPTBgs$97%&0N{qY`>o0*mEew+<o(aehw+pF1hQtR2pVb8QC!{x>9RHhom
zj4^K#X?o`~^SJXdlgX3wIFQrvKh6jj;o8#=PH#8d(3U5~w4>c)l9t^GX2GE^Cpf?{
z8{LeIc)Oe3WF8M(%dKR+=ON$fzj*;8Gx@MQKN-)G%efkyM_!of4HRlpM`Jf`z6_2P
z=^OI&`#oh?xAMW%I5nfCP7;Ib3WWMIPAr5laWap0s;=%kQDd#J-NT{%x&9@H)Y#Yu
zC*uNDExTmJ(7&ZE*#+?~!RWOh9Tv)YXf3s#8SZ;;4NY-?j3;U2UYx;{RaL$1eO=oS
z+_4%6W&brzg8cQwwecH2W6?{k6#FI`H=txjFgh8dcdsa}*I=hKpe6^wyJ%#Oq==7m
zzB4Km5*Yr;?g+2=+hqFDd^%i?DWpJ#Aow9G_V!Z?gt^h{J0Zx+TdhAN8=F0we?WuA
z?o7Px+XaK}YS$l|r=Wkf*?w{g=Y4<5Yu&3DEiynNkx$WDs&+cSQw~FfRI&PZpN739
zcJa?$bvpOI14{=SiT_4rD4S_=d`84n7jFDCpxKD-t`_tl&HoDC!|#r65*>ze-i_=E
zMphnqxaclUCFpmK=j|vQU~NKcn-^i1TI^B9347<(KZb&{pThhEY=1<3xMrywF1I?O
zhYPzIUkFot1!B8X+YC*b5I9r6BH98i<U`Txa~$71c}nYk7h3J35L5STg)byg%#t<2
z@GoG1c80Ocggo`Q9zD!d5qA`EKo=kgsoyPOf8Oj~PLlbsksgIt;{~(vx5HL;G(y_E
zweTgo%agfN#!eR-2cz6iU$NS<P2*eze(LWG<A7Y}qzc_G54nWIJw2=*aIwKCsJS5j
zo<{76=Iy6&t^5vX8S%f>(LVyg>Ph01J1(4b*+B6I@T50{DC`u>?(bCa?EvrX>_Y#_
z*hOdJPK$^-F{$@;7@>A)p8#j%{Ke^+HLB=$4>pKh<<D<UE4k*j{eE~xUj-O#EF12>
zmQ`KP{|^g*p#FI|?59>F1i^Mc!nv=g6iWVxa)3D&1{7B*@l=V~XbY5?_9D7A1fTHE
z2|*~KloWR>L|vUboCK%{?OZps%j&%zwCsWl`50WqzfT)fqP|e&#(hB-nJ@`eE8hZ-
zJDYV4^h|b;HeWDmxGPb&XO{<CnFVljd-i1xhqPkGjgeFX$GCY4W@jF#*7QFn77At#
zWbmm`xKqytW;e4XR8i|sFLL5;Y=1z@6XLc6-L1QJb-jrmOe8UHyyof3IOFkR!Fh2S
zf*RdGvyK2yA$H>2?vB2RDSVnPWT(bvRn@+K1|cfoKpW`<-WZI~!1!a9i~0uHcU6AE
zcs+F8iG%yNT2ObD-XPx&pd#Tan(zA*>lDI9o>0(C(kDK#_!fC^pMoO)*Tk_NBF6mM
zAtILHYQm(`XC(D))3dqv-IZ{)CM(Ja`ZX5BjJpS$g4lbO(TG{6;$%RTTOO*|s*Pc6
z5I^Cz$A4JJpc8i=0>Q}Rj!0YrXZ(YZMH0cG33?%e#)f~Mwn#R)5)Y|Y8<^!hH{fK6
zkN)pOf36)bgc0Q_z{m=s(98>}UJf_PP!G2le{*Kg2Pvi!XVSA@%#h5^ZeKo%=VQji
z(25(2vAdoZmrr<NKmXtZNoYZwAd20H-(JwU>5T*HVD5@;|AvsBaF&$lL;4<K+KJ%B
zZj6KVCwf;C2obz9L*G~hmESWIxFr^oNQh-EAYf2l8)&tmk7*Tc|Nfwq4jEeb9gQ8{
zZBJy$MGuK|3bWDW-t~MIwd&oDc|<m6pWo$38OAE$^+3cyBz=k0#o@=&qCF)#^8>A(
zk@;s}03OczIGH;BxP#>G$Tjz8-B2BY8YqP$Y*wHXd^u!lqBysNs2|2v^6vS(9AlZR
zruxUuknL!X2h_LsJ-teI8*w)hve8rEb5+v#7A>pG54zA+%I_9D1^y=ZQd<_|XBsA_
z-M^oM>h1=gF!j7oWW*lVD;@U*tFb8rJ3kK<t_?tM5FL(jJxzG2FxelT9I4&+5Fg~y
zsP~@gp>>D*!!;_wp$lkpAZXw1?mTEs&&R@tMs#bhcOLDxUnsv;(9h1)Pdf{D4%X@w
z_`_%7bSt%b^I0rHzLLyH&7LK^9qxF?xmOX1)=`dr9p8*wG4F+G^!;^0X)Ys<nfOgX
zLyF3i$%<*74X_ReL&T(|p_3B!&cwD69pJgoj2yt3Ci`S6pyeHg=-`^~+~Sc+9e7~h
zwcdCOIuU3<K{ON^It~Z6Au}3~^Xzq0e{X*xd#dHk`PuW~9+d5^#&)-f9S<kf#Rfbr
z!&Q6QAsw2tYj#%DTO4`4P%#ocISj$>+%QZM>Qp??*_&*5^55S{xQEG!T$`0M^K>p=
zfR>3wrjOcQ2JoxD5_w}sm_O_;qDX}Qq=(P&Sas#ub&u1dA`o9e4roD3wL3zR_!fm>
z$^X&lrAJeigmidH^(c|+2I${88(*#aBb6Dsz}=5aC!ywPNBeV<-8gke2jNVU#~8#w
zggu4;A1U1LW4IhRRsx&0y7kRW1Nzdz>gD(%LM|wnO0+19-ATALHT#~)gfuX57SDBh
zMjF3j(|$L6HHn7fE@L2D5Y+dyQJ0E4kabjW>~YorqpCOy#Cj>-P*9d1-!-SD@!g++
zPDFC{RwI(`px4K?tw1)^8lr%YbHL+6U`Z*uDbt<skO<CR@l@1+7xO1XuZcT_URLBr
z1ro;OX?%p=zhj6N+y%NT5nitMfOFgL-6T7%YP#6`e2W-jElhRs<uoOGettf3c8Hga
zSaAyHGR#R__9!vxS<mmYcGsFQ#=t-~r2D~mTrf**t5ARFv`O0Y^V$~>8t-5)ZThoD
zAUQfINe(E@13}9tANSM)DDMKB&Rik7OEz=*JMIwp_i+90{^ce{&-~$Guu8Y2-k5vp
zL#NEeVZ#M$>4V&h4UZ^}Ts|UAs~pYmjvuR+2+_j*%{`Oi(HNWvC0h;o43SHjVskhD
zXY9gchX+x~ANKN-81*J-Ep_VTbv%lvsB;>Iq}^3UeEEk|%HLyIxb^n}E<~E4eyeUM
zK}0aL5solLl0SSNahV9aqscGHK00_{La3(QFCuiM1l`1OgicV`629a@xd5E??E}I%
z5Y_!PO}(JKF@|g**Z;JZjrR<_p)nsC@lW>P@N)I{q1j|SdClup=KbTg+|rja2KxR&
zC5wm5?%$`D@z;^;L|8efwz*p~OW(7_19g}mQn1?a+>p{7*SxI138^jd3nj0+u=dXm
zbEus!c!mLmWv!G#80xVXgDLD-(}Z7OuRLptZweA3<BXV;W~YdH90D#>oSgAY6M}*g
z8Jt}Lj%=*#ViF&}8hN58(cvUreIufqJs}Etu-%dqGME;FyL0)KmDwhD9}hM3p==4)
zM$D#=m3G2|Fgc=Q<Y<yCHw9ZsaN^4LHqktjS4{Q|8x&j+%WW&m##A+Il(5})^34_&
zQ<b+dkI^IToWhufQwr#?I7r4>3hta+f9F;t?w6bNV9VRPwS>_XM25D8Ml3uSFXWD;
z_>K^Wqoit`2)oa}bC`eqPR7%`{Zl^MttS}@NX`ee{zvD<VM{24AUfij{3|rMEsOjq
zO9L~$&PGMO4N%He5RB);nB8zv;>I#q1s;{FZ;m@al?~oM8pG1bF;k(Y4!{m&z}s_v
z_)k>qW&I3qft209U`VHHp5xi~p=^q~<Nd!_Cr_W-<{sSD#&9d{NP81%m+)NLE{XvI
zMNh2Z2g8Z%L<K#Ab|~#YhypTt0p!BNM=UP+i~gl^tTuaVlt{eaTt}yev&XV?W7YER
zgK`lHPn-TdMI0aeHHyhz7!GMp1r}H#YTkM&>e8dBzkx5Hzzb_!hnAV)G04hQaH=v^
zVFz1iCX%&~iBr04!54u$)+AhBWALCz!VAH)SVO@yoAVu;E2Z=CNyNvoRmg_(3XB6c
zp^hqf>cLyzlf<P{wAZz98CP7O(@ap3eH+j%E3x0*&?qOMQj)oei8GV6e3!KI)?k!%
zm|+5?1W3lBLhialoQm7uH-&MQ_*-yUqx#PiWh_jq=uScsH^v>nkgGR#$f|{uBaUVE
z(4x(=LgdE>hj+iW>>VSJT4rbPGWv|HstdaCJb45#EP$cFu;Z^%x|+J;_5u(CP(MQ3
z4&ULu0}e*!uv2Ko!)54({I|i{Zr9zoiBF<-?d?M4Y$P;4<QdY_18sfYjb;y^a4V*P
z>degjl{v`)RAKE6V47pmLblaz`!p1xlQg**BW}^5))x(IfJ@&0QnMcc%gVc(+g6k)
z?oZL70`C=$<K1ur@u@<ckYvzYfS`hSbz8F{Ey>sJqD^J92^e+BdcCB8rk#A<2!XD=
zlIT}8<s-SNf`V<!uXvY)F8Siy`*@eY;@!@=B(ZVB82R`dDii0%L$x*vE;2ODuiBAk
z<$|)ft9{}DsbgmbPg9EdQ4PVn`8dPdWQ!AA{74oIQ-je5>5H$MA1WT{6#JIojp-(v
z`8JsR+%oF@f|JorY&^8dAm`!Y_mKXvLD&?Y&By4b?bp-R^M@-C5kWp{>+qn5@>t?|
zulsew$aFt@GY-RO&kxbq+M4!Z)EUcE<i$hHo;3I;zLLosZ4sS(Gs~eHZYXpbk~_`R
z;)O7K(4bth70TWS(rcSD2a?hjQo&SL;HCOWS2=%Js~*n^zl0JU><0>3d`;?`C?{-N
zeeu(1(VXX*EWUtF(JV(k$D3SwY|G~9kLPu9SCtDo+?GNG(c|aX%`Wl-)(=X{Bbw;!
zcF4H6$b&)v6Sk4}Yd7+m`_cD#Y+Ez`F!UL)cSUjdNHAcGMdX72Kt6Rn0LI2Gou{qH
zERIG)bH(fBK)m2+n$hLZ6!AE|rp1c7cQ_YP;Kj3j?<{qyEkjwuEvK}16v5I3f%lgT
z=kS^%U4iuA)?|Q;mgnTi)F4}&lV%bGmX8wwXD~vcxgzbsEwo=6PDm<1@cZZzyqRE~
zAZU07=yV_2pGnlVK8ZdwRAPU*i1hEf?1y*+8HfZ?ziaZ=>d6PIW(nhBmV#V=qw#}-
z@0$LqGqix>?|dA%p+&fPSj=U|U4=Vkm@8$3nSavhpGwx|eu2&>KZX7c|A30Ri%j8y
zYC4k}l#12hZZqdKYbEqwl!UW?6fKMtyVjw_mG6F;!SsuL_SP#xY|Tc5wP%u!X0!1|
zZGL}0Q9yS4?rIPO+%)eA;x1pEf~-9vqDb8dGW#}r+szhSvgQUbLpLRB6H97l2<I#M
zF@+ix^NyTn%RPoDV-t+Emg{;?gc8@&;An9y{Av-7g<{x9!&obO+ZCQ_`vICZ(tckP
z-r5I($4H5>v&JiS@PD!PjzN|MS-WtTy38)ywry9JZJe@g+jf_2SC?(uwr$^<nfuN=
zcjEi`MVyF?b9U@?GBYxFK5OmuJPXT$Zvaa+dZD(fUYUi&NCMlnUvOs)KB6c_RBZDu
z^=1zN<75o1l{wtVqU})5;o8+=ill#I5`XM(iDZPBLKV`c0L<G6cYsA0hHWHzl$#ba
zD)tN(6DfR9LKMl0((dC3jjP>;I%td+c0IWBcB}8I2G$q1J7w06cosTu3m>o<!qW-%
zPos0Qj3_C;!C|BeaJYd)F*N+V#t!Qau%L)tWk@e9GU2~xp>XnE^v?Z4*p|%Rl?Ua=
z43T-7Uw_59p0IRu|6)&3yTcYIL~NtZXrsz!dMb`nY%#MwOPQsi-Zb6LSnJWjVDn*(
z;o<;!xQ(uM-~fy91nciivdaRt%0wVUW6(oUAB~%FHN!WVMDrtbExs$ngLIY&?r${z
zX6cDeqqfjBdMW0nws@?ui6BTI%M%+?Nfea%SaTr5#FVwWkszmF5_lRS)gLF4)j!O^
zKgLL(wv7jqi}LN+W3YZCOb(e=q}@4<F^TJmi-qQKYgww!Ss#Z5s?5~uwKArHx~)AZ
zRCdj*Z8oVUwiU?a-8G9$TlQcywB^onwM6aJU}PSij~8WjBCM;uTYFee(u)oo)9pKU
z{@eWiA)&O)(Y>+>cZ0TPdj8jsFN)yh@BXshgxB#v-Yg?ykqr851gAc0@^=(f^RLL3
ze?9lD0AM27{u*H`J0v(#c<b2r^w#(@DFwcagWaNE&AWzsU*zesN4xox9P|SZH)s*p
z{v)ZK?UHx!R=mY?8GqwEz8ztmpa9FAhdYVuyUOs(x1w0xDxP9-w7+x@jivXA(Yr0_
zuSze?UODFyTfJq6L3_`eo<9&bPP5vw7q|gp!C*V@)w+PW8AI)~_ZYt=gmKK{fc&-2
z!5d*XP`BY$j!PBR3T_P|<6BT_H&4vf7Bzk|!|1)GgcI!T$nhxcWoH&RNz5emuu|Ko
zEZ>m;A;h8QW0ytm0KrBQ4Y!u#Q>GmUSLcNgSC<oHbn`^=JnIe;ChI&G*zwBe9K#Ng
z{3eylm-X8S$=Ec=z-YWs{RG@0MMd}C)X~z}@?W@FpAfAtwwc2>W7m*pVId*+qPQ-!
zlHw``)A@S{tF5qGNh8<fyVL@rW!AO|<wA~3^{pX_AHO-HbV6nMJi+%~Z5QQut#O8&
zXa%3n7=Y%2ANfNj8B!U%f2?Ie7S>{Y7c>dw|B~x>gbdkzHLG$XFI=vV(a)6J^bMYb
zc<~|S6=L`<nBC{Yx@Tx$m0h&|`&_)r<~Te)q3s&ZC0mk~NpMVs)-XGN&IDv|s=Gc$
zR?vcrlVZ3i^x;oi<G!9Ar;ZvkVwM)4a$<VS9bdLz??Q*n)v7Ca_tHqH!4z-&`~Ktn
zDT15yYGojyqiZu>@NZ9;sqxSqcSKq_mik@242I9rx0>^bD}5x+z-V8y>rwH3BlVOf
zGic>SD`G>XJmofiwNr;4t~ckGj#fO6e9V$(RfOj&GJaSsFQr5jA}*@zh?cS_{gwu9
z9^9=eH^`nT-k=w{t{yhUu(>YlUhpmKfTv+O!=uBqqQB;4&1W%@5oomzM`kcr3<!HG
zZlu|(F@(XPB_{)|OL0>OVeh@Ot%{QH@4<6R`O__1#Le;oo4gL_l!NsuL#VbhRx)rf
zAfEIWH)-@b@7S!j-i9D#d{p2?`k$!`Te$8qya6<BLF6Ot6Rz(G=;w!#bh26^e%Ogi
z;{<e<6JrwxyVY9A_PW==n5=H({HGI!mbwl<XR4!yo*{+e+;?DPO4orDf0-uS#ii)>
z(<<;b3?J|mYljcWiYZLr6(XiCw_=wb$?kCTXc@J*EqD1axzphcz~#cENvOsdY+|}I
zS1frsfn?Db14iRjF<1p;on@$bz3*_|U(bNX(HB8}X`m8boSJFb*B&^OKdiBKhTZlG
zxxURsseKNMQ+rq9;N-cB^RxqnoArd=PwTCXnp_WL8T2!WI<NH2+1mOb^6;8FVtkSJ
za`01a`_HqnUI56H!<YE(dg4Pc-ri`NvggpZA1`CBSzZngj|4@GyoLnmRK1O9_vSo$
zI|BGdjj0DGBeHiUs(I+!hL9^FF)I~EhuEt75#e&|@8&ZkVl7IEySNws^rZ!7*X~{{
zsxg+rXIU;~YAd18o@>NZS}bi3c~|RQpBo*IWn%7I$@=_WEw=E-;1Q%*Nn36D(W|o+
zD&+F#q?B?%6|WL-euaG>g%R`EQPylq50dTE6oTV`YEn>kWJyW&qP5&PTfw7MlMeqR
zl5qVjWpkpIOq$g@W5c%dk%ZUn{bC7cf0W}w!t?BAQtKm&Y2W<S5$^eD?FlAjnKc-X
z0bP4C{)?H?X2b7nc5&$@D1{|B${my{xNeNDGg?*m6@ty0Yj0iXi~mSoF)k(1_fC}#
zWZ<@2E@FH>;Q57L_ddPlhc*P=(JkGx`I&WRN>usM^~M7$oxxBic-z$IX1T+%T?JPK
zmj_`A6MoQsCHX!~4E0vwi+H<2Xf|(z*woUXUY-ls!5g!T8As;DJ}BP^+zW<!%`M!Z
z>sGW@_r$=O=F;w7qs7gB2Bg~kDHe7+rQQD9)XhjSIKx~QI2ygm8$!8Z^1bm98Y>Z<
zeF8?zI%@+Rp=eZhMxEpab<YS+_Rd+8>}<<+=qCfwPCrJ}+CA&qYC3`blFPEiP$g3S
zPk^2=D!{+Gdp+2}_+;&Rwb<Cz+klU@z(bs@`lhQ&Kwk$JtuC2pXB^ejRmNY@gp3w@
zV&Jmz&&=h@%rU1sHe<EUJ|++(You6pJY}q|oPS=Tl<U_*liuz5Zx~NU0#@EzOciUV
zzR1nFns~dO+e>P(PWG|TKc1Xa_ANwG4{KY`6wIIH#6&oJvp=G=ctI0}*@GgdR2u!m
z1Ke|v!Aq}tS0lSCj<WWs<Rde6s|RE~rr4^4KO?U62#m&a2mq(;NVL--iNYHm=$%BZ
zdN))(a!DozF9-R}?o6nBSL^}-oaxYhTtcHUeXojcobcw$!|GazRl*|^UasDKvf@<H
zzN&5Cxg?8GTncug-8KGaM$Y&VKa1lxy^j_@A+8s?6`Z55QwRM*%^uy_Tg;iDEQ)#5
zc|AUDw+|63_VOfGh|0N^AGIx5^N+ZgW-Qj#suI{^6eEe)4!5cKIDne0S7+-zu!bge
z`owA9qhn$Ui(9A&U!9KJ3%sWlvQbE~dg!{aXOvCEowInoP2-Y&+f*Teos>S`1lS<d
zTH0oA@*EuxmhuxBnjBtx-^Qk-L=eI)-$_BUpE*OD%vtZm*>%uU_~v>a!SA$35Mi(L
zpcfT~WQ+^CIv$5hWZpL;F#nq3d*WX72BuDDgqZ4_NdjS``Rnz_JBdrzd#w+D<5iXn
z;ua9#$eY0Eriz2>tfN)Gdlp$f>G(Vhz7*Yldn)E?c0vr3{mK4Q)$Wkyj@X#S4J%uN
zw*PpCsn&pg>jf#R$-mY3{ujM5yQWAB!CMN_{@hAjW@bcM44lZpLx5V-qZnsfS$Xo8
zGG*mV?GeA@A%3>hRPIPyxbz?I0U((RN3VncgG=N@?;Gkq<Web)La+UL*yihZWEdO{
zgfzCj*jSsvM#B?!2U{xM<&mHz7Wbw)axAhZ@1cv#dU=74I58XriR3!C7B@nWHJ78D
zHHSX>k!oZLw3uzd2vjXLz$iWWW1ewRbL$((c#(_G#J#{e3I)iOcLQpb>|}q&kqV<y
z@`u2w2=*3bZcfP$j{EDSNQpC>-|wUl?=in(b?-bri)$WZfjJ%H1=m`)?%KD?huG+a
zp{`k<n@+dMwam9_^XEsE*~s4M7L2aW3Ei4v5_LXK*-rAwz@;Wh>%N{gVJ;tAf*SLY
zG0!RO{$6*U+S(k@Ia%7!&A1hEZA}kK<S8V*I)=q_y1hx_h*ihJoqwvNZw%xY@mh?~
zkt;yK-lkY$HPkxs*B~ET*y}DOu2m^<NcAAjDAdFh6JLoqG`*QI-BO$!X}1*)FkBE*
zsjiV8Q4kaTqNAYZ)VOVIOg@gWx_$Zhtc;_!`Y0Ga7B4LfixUQ4zFGwA?2tHxN5`)=
znUMz^#uwY9&Qs*&?^n!D@SL48{H;sLdOk{-ovbV;wt4s%2@CM?plFpMKN!7qS<91S
z7Z=#kE<oU;3-iNwI&vuIDODblQE)V@F*Gt(Q}t$BoJgixh!0Z*r{E-1tE(;XQN4$i
zE{NU{i2ozR^ptNy?G_R&sV}e@pH-A4RR6$cdiCLXNjhc`n=gEH<h2pgJE#an91!~>
z<&Cpzg>|`AQh&okTqyWH#sAOz9BXs_{$39lI?K}z>Z~&6GOzHd<O+o8f_(b@=+f+^
z3tkd8bxS7X6a&3AMUc}8Wn*r;J#XQqN8^^w^IZ3^($Mnix%Y#gXkFb?3SpbDWL9wS
zz29crXYi8wvqz(ht!Y+mM;kH{3h9eG=X(7G?3u--OH@b<@=Qy>{*LKJ4sMX)a{QIP
zQq8`l6}emgcwFa}7r(o;TC?HN=04B1cQ~$hkAl(?d1=xguK>=(_|nFh`9{qeLo>Mv
zn)V?fg~Tq40Ee;`;fG6-peE<`d@bwh+O#BOOk8sH^<r99`r6o4F;S^F{fm*x+;J?)
z&)m#HIXtQ`c^aoB^D>t|fccsV<v))Kv0W`0NQG6=!P#3kZTZ#6sHmhbZY(>_Xlc`I
zx3PJ6Pzl`QSYzo5Tl}{d$Rkf(9frmgx?Wc5WDQWIi;&4ri&XMn?vnbO6Jz0n-NLu9
zVX>TIf#QR~EKc=a#`7t;1efup@=luUOp`7}*~~RUHOMDdWG~LyR90&pQW^~H+OW`l
z#t)Y`zs)Z<=E&rrtSv}p{Jedr^~oA9meo(E7gg(8bw6EpJeyd<Zr{96ciX$lE2dkh
zyJWvS;7tcw=?b^JxyUaz8Z^w;>R#Xu_y$DwwrQljj>Jxm{WfN&_^uM8aaSoizRnFx
zql#(`zGNGRES3dN*0TRfD1t;?DXx5e?2{Rs17;6xy5bJuAQDZg@Rr}6WqIzvUpObd
z4kiCM<HS8)F4JDD*UNG?xRf*VjEWi9X1QMa#QJ%RQ=83AbxGb4T~0xG#U6+@d|Y0I
z%nnOpUcZZ;495x0K3;T*hrnMZb-I6<mF9~uO$P9h1N`4MTA*3|C-CBcR(y-+#KEKZ
zK3$?85i|A0#0D$JRP9JgjXmnS&L3GKJDh~tSnai1p+r_Fnnz>vf76=3QNej>sy5iw
z@56zk#cC*VO?{{~?CW+FCDn7C!!(2RN4-9;`E;<}OmwjTMS*F&H29ZyknH(34vRO3
zJ1oUp^e%la(tNG4$V`nwc4j>ew>krby-bAce_UYFVBUZ6`$A?^Xw57x;izP2&{B;L
zMmdnhsXZ^<OMJnzk(VKZ;<^IYfqeSR`uc2VXEgtV1oVHoK;MnumjrMf(9|`+?ths#
z|KRTYgMbqt$d7&vKbnAaf&4G-(7$gJ|Gppy^G`m}zkdVu0}u73-^R-cAxWqF9}3X_
z7zNQ8{=ZiL-CX}bMELm*a$h5?2=@Q>38*o$yLSb|A-O9W!T7qWq-^snX=FGqBRe`!
zfh&;fcp;H0HLwM_$(~8R%HGi2IjfLMmzJ&vwn$vn6nlHb-b%XVr$}>p5;zD5cyl_X
zxk5Wti-`j-Tw!8w-z+AaDD{%I3hh+iXQF+>-@pEie*UH72dH)Jl&~I(N)Qe8fu%*9
zTrpZzRg(HGyqxT6{JJFBRCtmllnJ$D0}z=R6FJFjWAiwY<P8$fGz-X?+GTUzswsDz
z2NLYD`hKQp`RNv;+2nFM>7n^6i^r!1Y7VmY6mm-Ofr>4x$ETKZL%*$+b>xv~57+V>
z0_;g#3T38b1+>C&2(cf|CQ~rT4ho5C&t(SLMw;^=0kGkAJY3+EUSbXO%8LylmU6yC
z6ePBcO~Da%faH+jx}YSMrw%b5DT>TUlxqS&ITw2QOtgpjA34S{lHBy0EMyQzN9GXk
zjo9ksS_Z-l>7I%Z7I4`T{5?lSM?35B=J-hXcso_bD(}q&T?#7c-dZknDROF=-ZXAa
zVKxKxpz~fsEs6%AL`5E{S^Pbv{sg;X&OxXWx#0eG7~+&Qg7MB;?4cvWT!o7t)j#d2
znL0%WF&Q6Cd!Oks+G1-Rehq?;EZ0-)l{ubEDR)l|LLI&)Msjk<6l$3x2oDazVyG7B
z$(8{Lj;K`w0Z0y<Y|0=BN7PmVZ#o^B7+I}jhXsJF)RDduEKm0vDfadZu{Xq{)ABMR
zr&#;gBO<F=^~akqOAS5i=W#PJk3rMy1g(EcCPkZC&8U&}9~cS|?j=h*_OcXtEzLOu
zwy6toOb+Rvf~rDWu(QlSf)6i8+?vk`>-b11_iPQ;VGO!M<jT+&$dn`j)XB~ZWgzlp
za-d2RmdrEt{K?T2H`=s6faXLMqMybhwTGKqk-Q7|!7%Vf>I$?l7e{Kfv>^|kg=OwI
zcYvJ)Q$+2x!p8lPwdno|8D_To-@YE5Y~+`U*wQy5x<75rw~4+gKfApxxtctvvM>^P
z|8t*QFa4UvxJD@?Y4+ZMnKx$sVGd)H<S!u)RTrxh2i3<c?Zv^BI{%K1nQ}tTTFEev
z!hBnxK>9VD50o7Yycc{h(446X!Slgvh&7X#bhebK8@N2qh@7TD7&U0rcZ>Bcq^*(K
zm(gLFwC*r^q-sn=8-b&h4)zPTRxz1NsYUBgP_rcq%<_!6W7uxlm@-Nl^-_pLG9=eP
z`ew=z)hH7}iUmZ3Ws71JUCQVzVu}gO81;&8H4RyeLYA)SsC~dG>*xYgm$R82=57Ry
zURtTH%`%glxjy0)owT2NfPG;e6h}~+IJIn$U8@d{RGJ+xhAOL(R&?UKmXi5!GvXK$
z@&18OX?AqGD0PG#Q8}m^De5qz<@mF^C`R}MO{^eMU6v6XxTc}Zpm928pUzM!b-j{Y
zaoXH<Tv=(YR^f!OXEH8~St6TZMTNel<lusOUHQsb<le|KV>!bfB@W=1nlV;hnY$rr
zpRkA-=PvYkI(?CV(zuC225^KZHOYR$&BUxys_}f?eyp^-L?3TNusdetEY@&&o`0|x
zE91yAeq&L_l9I#$cFtj5_^rk~3;+e=XtSpo8H_Y}*pjfi)=<k_V9?!#F?o17F!F4_
zkuic=d720iKpMF>yiDGg2r{Yhq9~{?;5N$Yi-EBOr)ktuov=q5Ni{Z0K2%gg2-x5n
z5X%E-2_^?EM3!&aX)}Zy4U$M>sM_JDhiOWqmk+Zia1u27M`E3VGRlmN?d0&{kT`Mr
z1-LWH5CXy?q5sf#N^>m6+D&Ssn=tEp21JgHEaxEtP6I8$kdqG=0thhvP!F+i3S|HD
zBc_-P$ZS9DVOPYpW!29B{J67(V?W#Uiq3Ov?LCIelq*fxE4nS?SBG3dV_AU2K1YjV
zaPoH};;0g&brJ-2@|RG|ye2!@_$H)c1E6zVzN9<Kt4n(?G%0TX5X1fRnE7M{b^O`h
znM(jHB#3C4FYFeS;g#?+x(>=r9kRL5DF=M>$3fD<fw}WynED9$oI~!Bg0}YLq`VNF
zmX;_bvgqVJT)8|!_LM!3`(foAlb-1*jEVCi=bnH7$j_iyq`T?fML%^Wqbct11)P!P
zW+jN|iF2W!%S5YQrPSjK62?fnXnAD`PPB3z_oiF;^Y#7|b&d(|&{!%Um(+oL>|ykm
zTt-EV4DMos+}YT|L6f{Bot7)Kl#=<`SW+RI{3KjgLI?-t>F7cUWoH<r`x+tlZ}}ns
z(N;315!j8~!3uuw26~~>fh6+bMxZ_-&qbB!_m@>*^_3=ldE*#ccm6YsZ*D3TVwfTJ
zqHk?*q>T}&kj1vYiZLukN;fMJ4kZB1HI;ls&6t#lHkgnBjh6M%HV;U5s`-a9B$%n0
z4ZJ2MZxSPOS|Mwu%&p{eTy2YJ+2i;Ug9`re0BSI^tzmy-q*S~zgiBN5j^@;FV=aX~
zweQVzk19F$d%83Ttig$zMK0Dzq=-X3J-7Wk%WpK#ReVK78l6gB!S)xXdk7Yz3X-wT
zr8N4S1BT4t0yRes(H;zAr@V|?VR3zOZMvw5y)$9_FndEX1FS3Ts2vj%Sd!s-d?eOr
zxs^NTJSnX$8|hv;Q1Ljr;ko(V53#<Vx1%dDqibN-_BA0+m3HcV&D^C>R+;DQf8^<^
zf1-?#ORMArMBv<W)RdcKh$mwPdMxv=_!btOvq~z!&Wvn?8)!}GD2oo>kFyz%FQr8`
zrlC@k?R{4VI#AXWALLj}v`P4BUm<MwDSeC*TdUcDO?7fQHPHg5=)QV(plnP{z{3S}
z!w4W_T;vhLJjD(rH6qCso=U5?CocDRD#sX7Bom*9%#wEWhs8MimftVL$lMkfe1yf2
z0-J{fyUuObScE%ux=k0&#LvZX^0x!Y)Z@>TOV1MqS7j8vi$sA{Q(>xFgj9C2FZHYV
zH9A}JOM`h45(z`qKA9*_<X0yD;HZ|xXt~5jTXjx5syx}T11nK)Wv+}Ien{j5Cc~%0
zLlq$d_~hcF;MM0J_odViMQ`_v3fKXHe<H4}U|;S#s?z)Wo$X!0zUIxvLfwc#RgO_)
zb4>g?6M5@;tx<@uQG0P_sQZ<frR1^t&jW5ebC!Ek{Dl-L@;P%ep%SND>IFF3ph<vc
zzr9yf`nLe-5ZaiVAi)^y!#sC!r`7=T$%{s}nk?9gTaiUHHYJfVe#}8pWOivIjhWMC
zqn5gI^gAWVIC`<%TFBVZQu3dAEjE>%#shn>DY^=dE`5&SoKPCJd;xh)+AU>eBa9Gg
zbL~{wzFW!Ul)80cR(?lkH5MtFGYT>QU@&K%soa<%F6|SCnkvUM*iJqO)!mRH1e(QA
zYuFy7&T?3dL^<9FCGwMa(AaPbdyKEQ**6laZb<?n4JA(9Au_@OeMv`^_p2J9B{5jW
zg^m_$2jHkmmdeB)yU}5JbggV3Js_PJu_Ga`ei~9sUMixtIA}I&iMB(*keXmhK4{j_
zDc|~(9JRn0i~4g6svcdw55%IoT4cZ8E)#p8mJe-YMqFJdV_HV#$eHUl_@WVJU$Hw^
zo%F^xsV0FFuUpz&E@MhL<gx&1sH-HBi4UJ_kyR-}7Hen66nT}nWIpJVyxH+b2EiTe
znaEvL1ScbVD6JSqMl)RK9|+s83kZnAU)ZGk3wD(-{okzk|AEB7T|)e<+eSl4?Ei(M
zfdsaJ2=HN^!S@BijQ^V!&j_U74bAbtp!;9Q85bY8*Vo4vH`%o8ziIX`t^^?eWaIz)
zfBm~bg#Q8Tr~8nkQT_|q2LjO<{-=BJ5A&|R5D*}rK<;XJrT=B<8}0|e{IA`AL3?<F
z?=JH3!%HzM|A(LP&wl=FkNq#C|2<T~e+>0lKdk7#huZ6l#`xc}#?22J2=w)Xhk8co
z|8Zl!&3=0%KmThc|Nrwyz9${y|6|gh%(^Y~jI7|LKn6e@ExvM#%p<pFT3Iq+VQ*4q
z^|{Wqwe&5@o|gXZRV_d0TUo#d)lrZLC4XmuuH#xyI#0L3hbfiJ*Ere+6&I6nZ)oe4
z$!|TBea(Vnf-JYR^r&22jo#|EQr(dJ?^vJd2y%A4sN0r0)Kbs#=UJhkakC@v7o^uK
z=!03=&`>74Bmg0u08+sCwf8Y5PSkKLYYFA=p^Jw0!^uzRF=YD2N3kp3TyjZSe($hH
zm(@J~N-27G=UZWC4a)FQs<X)^i#bG_st<H4rfB`IMq(v#C%g|6A3La_-r{Ug@f9=d
z$O%E^{PMM2so!+XCr5kE?gATRP(i<1;g5m9Mc|N_pwC9X_`D%cd0->5(PMD<tV2{Z
zr|2xVvB&?gA*GbJU!N|^Wqe++876JDfnhRnMY`@fakE(vyFZ-!R&2rTX?PQSQr`#D
z0N>n)FCnHnK#A=#$!)EAG?ll=qEMRL;{&)1i5SXDS`>xT{&TTEN$aiD88tU`De6bF
z@)&=oY~M}uaQ~Ru%jym<M$3)q61qd1spnU^>IuDW2r6I{6(4uyX1GFhys5SNeDA7I
z@(+vREBcKUgqr;NGy?_s7gs;^(bk1Mbr_KKO2qUCv)FM%-PxSsTgNx%Us3(}>5~AX
zCnWnfN(0Pl&Y9ej-u9jS9^T$cDI3%S<_|Mm2k<#o($|WQsfL)rcmrb1RLhUjI`8ky
ziHN~KiU7SVZ(K|sDRvEYLFLys)g{y;gDI2p>s2-}dFu;M2cd~<<MuICRmz8*(|uq~
zsfR8e`~-G!Oa?b>_t%dv#!oNvU6}t^6*dI6SIZ!!=n^-o+m?J8qVZ4%xH`itA0(3-
zlV+cvg_Q29V|IY06t%_|<ny)1o-$<zL|oKRhlT{Sar6KcmEaO;nIV_#>Y>98GBcVL
zgXfHvW3)rCC<REXM;6a-#Tk&Fcv|nK9G%>npj*-T^tsTsjiW%FDu?tA&LVEBR>TKb
zM9s=1s5}o)v*&#`=M_EI4fLV{vl#7l_AFIc!g>Ql^5QKSe!j=_spf7Qg;cgPlfMK6
zc<Jfs#Y~SjYhX%*dnPuP9XR19X~4H@$$oY(2nR53jx}m$?+m%Z6838HPmP1hZ@l%@
z*8oc_JrT?q!DQj!{d6)TWX<9MD;>05tat9|mT;hFF8~u0FJyl@Q)z!c(P_Zr1@p>C
zlv``=U7N$@_zn{H9c}fRdC#!ev!lx3!dxU-sa>C@Ox$&-ni-LdjzQ%d@gS5h@<Nh9
zqtJRaLF(eQ0&{iA)UdG$6spp{!aItI95NJ`O7EEB{lxly`{&uGb{TLxA0XZk-e1t!
z)9l2G0<<BhPjCP@3s1Rz7+`nAd?O#chw!Tv1?woIw$%Eh?CkKyqW{|)%Q42Vs@q#X
zrn;?VX&#WA4jA5sJT^><6hrm$yA5A$0g{TuWrehQu+e+h#L9*7Kz=c2i*!3BX?|@K
zHX3B{Y}Mm<RKn=-*#@P?M1t}%LfMVB8JcD5>Yjiek~2IAhznQ~!bKWBRz8eIXSKj>
zm#WXX+V@MoWEJOVgud!{C9t07&G<>fWP9EFUW_XEcGl*46mmyz?+FpZhZ|;5Oq-j7
zk7Zu{=?b_2*fOf_y}Rh@^{v?dPGtg?&}|QG1drdoWCL{|{X6)E6`l7reHm+An5SGE
zZA)L)d%2`L;J5x=sno@{FbpyWaIV?M4y9<|U+Zz8_`LJ2O+-2=#`Xr<{y9}0t1|t5
zACP8+Z_LX8eX%j_<Pz(q&xYF72$|c5*Yj-cM?Whr&eH}@SF!2K%Y>GnUkDWp=iKbg
z2^YPY*f-9U+O~i3(}m9ae@`+$@TBezSl1v5tqwssWu-uaD2iK!U;5jZF^`*z&EAA9
zFCNLTBu-$Xl2CD;cc4g6WjUr;0UVj!2xf6X6KhX7%1=G1Z_Aci9ko9$g;4c0zP{6x
zj=(b{tNmj_D5x{Jcjj7gV3|R5@*#>;qD|K{0`mM(P0{uv!snUQoG+r2(?samX#7e6
zs^dD$h(PWV&qhaxfv}jtXrx2>pVm|jSJN1X-f8SEH<;9#A$t7{yvwCfy+T&p`V}UT
z$3R2~Duu$m)|h8tD6Vb~g3AXw1CvB4)1SGQ5T+;L{*N2e<&D20kG-x~=6@cnQf7SQ
zB3RIo;<J{baEdhqganmZa|+Ei2`(=e*P8M}zc=4tA#;V$SD2EHnl@j~J@wbv<u-fm
z5Kpd8iY?LdOz(~BWPE<Y{HC_t-aZBu$x^=4y2@`5EEk-Q?QFI`B(^?i%{|M}8NM?O
zSo}n^Wnd6i4%ti<C*nG4ppa2vCoHfcKgVdZLBS0OyYO3A6kSeV0JZ`qL^%BuA(cW$
zutfW#K070jNcOyYI0Ue*VhgL;$p^<J%Z03>9HXXWUKOYJyR}@AA~Nb8vq1@{*qH!*
zt_jw;$`f04ch-XY0IE4C7$6KU`+om!*-vKFlV1nLTIo5XBkz}BM*#r|_f7GbX?B|h
zqoc+4`;Ms&luEMo-)Dvn*cI3A{*j{BqxS53L5S)kz~))iR|_Sv)?)WNJ3$FOWrA^|
zV5@nb+kjnF{wN=vhS8}Ekay|<HV;0ufT5md?FJXQxxt13(r4hYJ9vjAMr-8%ss+Ry
zBfzu8{6JsDIH9CBAeYtiWs8)y>8UhA;1oIAAq2OVP@tN0z4zmsfVg-U(lgP67#sFZ
zMHd_-gRNy*!s6ZZZnn<V=TWwGwg}^gC%I>tkPR)Bwxs`RM4K6C1?{?64`cR1bHym%
z#$>`98x$jO{-VVuZ{V-i0o(+)LfCEQnwsc_dBXjC7j@k=R>1h3`I*U|!!;cS{2yll
zH01i4Z%Un={t*_s=QC(BH!D=QHr37JfuP}5bHt5~2N6_|H9?`EdRTlN<?~B(&-5~k
zCfRtO2ivc0&u3QNXHN#|)^XwD?jOjRuTU|(T}6*;rWj0EdvAfcf&^Hh_ou3f0-A-v
z{)-wCgX$HYT#$6M76I8l`+E4T;2N;U(SQF254WU;&d)+2qom{kl$ax<hwh+XmfsF2
zx!*=`$30KJmbu`c$s>wmzN)qkF6~n`JxKfz1o7`FJDxT{kwxUgmy?P@OZ0kxOOUZb
z^X@kZ5)mLE35fmfH{Tl9?<O5`!Q^V&Q9s+f<JT{Erg+=7G80x-R??;z`Zpl4il%yF
zHv4V{$ogt6Hv#Nf8j@3pIa^d`+nOoiPbu6n6L#+H2+J@8qBy*%JQ(0yll|cs)~-gI
zSj~wpYr38i`mDTGoe=Xp5!DT&DnUCS8oRHTu+P^CQ^R*|;9A~VuwCCuSLZO`eGdt#
zd_Dy6x7@YfTbeb0n`$H9Mx$+N@acZ~w?{Owzi*-e$3X-!@agZ)QSGc5P!sZE1VBfi
z0tWtU?e$%$3U=*TjcdBRJJ<W8K6|bi82U7#SKDx!l-e-vx`nd;nzVXDcHmtOUi8Wu
z7@}+R{cSAlmV~=VyLqi7wihD($Cig?i1F|>+`zNL5SR8D((V9CecuK-ZWdabY|5YB
z8G13RIj|M$!N3aNT_!#3RdH+Qj!D%lI0zx0k+x#8r%t4L&$Jy#s_PY5N4gK;R3?Jx
zJ3zXynA@HQQ4=nF_n7%ZVf#we_2<IK(DgdB&8~(YStmyWu*Q<Ez3xtTnl$}%-W;JB
zsm`Qni0>~sA7XO$FD6_sW3Y2C@a?gvx!jt`kQGDWfT#?jmid_nN3t%{!t8s%mfoq9
z#dPHUYe0*%-EK$9jeaZJ;)N@SnJ#sIFP`a8nB;It3iVcu=B>7k@JH0Z^v58v87I<4
zI|=*q3nTTatH-CY&nJ_4JmBVpO3Dt-9Nkhy&vI#Vn1XRz!I2q`WLaOO%d&#oET-6l
z<r@O6nm9%NAnJyo2mWrZw+L;061H#v<aAX+UW0ZO>W!clE`tfT-WSobS9kG=PwpK+
zhTVZJo3N~P?x8TEp`e_|3I6?P+S+iVr@{#0iAr?%IBoq3K6hwhRm{K&n`ag;XJ9qX
zjUlPX#JQoXv{ND~VCs8!_Hno_kOowc%!`s^mDmNQsN3P^5kuDthXExOEZyQ(x*Xk9
zJmwJN^T`QJ{R$ZU`&8DXDR^qAknb1Rz#W!y8A-v<&4dt^+#?Tut1P!eP6G%D2BxF~
z$(TEIoytmvEXoKX2{Xds<Xf5q1%z-5MdtH28U!gJyOQQY#$XIBSp~RUfo1;o5Jr@(
zGQbw$<Fj&<wg>MV#D}+2Z-Ah5vE9oHM<%g%(?}#pVyI1x-+0sP91Z{~>T2e@rCZ7K
ziC)AbcK{8N^JOk&ctw6)KrGxhvyZ((;}gi6+ZltP&sc9_RrXrxN<~hsV(_7aR$^eK
zek4aaGFTAx!gxb;r<Rqm;$;`y5SgXqs@)%F#e$l&ar^oPO*2kc#(^3^Q20(hD>*VR
z(bBy-_Yx|nVNOuWp`0yM&t@+WAO2g4><f}Bnq>XF>cFPs#Q7@-l;GtUDDf8nN*RQM
zZYP<;(lO!y77tNOoX@ypX7|kc;1U9CA}!5Off!|*v6dO3-o372?3gYLPg@qckyD43
z!~VeDH^cR2jv95INPmg-6J^8~lu}c3ILRC{BGl2NHMngeD+K_wVN_Pg`Knc^Jd;<N
ziz793`AUK?TlI57-pUBD#6rO^mynU9(B&(csHD_OtRfE|Swc?9FQ18RRy@^XSW@Td
za%rC2VFZc}B<hQ(2a4|ZMee8D)V+nN1Z%ITXw&i5h=#3BzpzO&U7w9c*e2_jy~EkL
z)dD{eG9mDF17hd>K^q906{%;35_vNeyEcv|e<7ylnawS1a*Vid6+orJI6oaGmwg?5
zjJq9^Huomrs*f{XfDTQpppZ9S@H=4)gv>t@xi>LDReoU}ENKO;_Hb___GhC&=wE+i
zc`}HB0_-wZnks>t!?z(c9Ra1~Ribe^h!+K>_YIPg#WcOM%ZN#k*Bs5GI8)gOzlw6$
zOQiPXqQ_gIW}6={GLo+5=CxPi0gdawY1t=pClGSk2xw(P0{5XJZRQZIJ|6Su-w+$f
zx(@d9#g21(5GW~_bAw)^KCdGnD5Ish^w)DP>9zTy;k$VBO5cdOgipEP4sO#6MLLjY
z3FI-(g?bTB)j^jK*tDW~V%iN1#A%szq|@`mOkmf6FZAmae`z9~msVNHDgMq_hb;}^
z6>KTuXl0w(-ZmMly(=emz3ezR<}3J>sTvw2V;V%;SCp5M4Pnvs9KRkuihpw)<Te%0
zHT9Q;-In8SP)?iz9zFDhsg2NiEQCGTb4Uj6K^Y&#Z%LV^*iRyCF=Yv{$%4KKmVf-R
zy7o+q;eD+ssFnCk)PuR)7;(KfJs%@#PZF_~SQWdl93GfxmB8{|m>qqr_M;zi>>*k|
zlfj!`^;j`Rf1yaIA(lC>U=gCrm);RSrC5HBwD&J>l;^3m<1%W^Bdmb12Of;K0ebWK
z7`qbzqO?W>>DCjuqw-TfAO=35j4;@l0Q0bh!2=V4;^2K(qO3qJ|63ow2sKOKQ5-Q`
zufY4Q@jOuHLQ`LS$hd^hBYIHqBJ|*x-V*aC=-ACctCRRBxu};0_AMAuY0O@n+%rzg
z@p7MRxzKDQ63hicbf>nao=qB0t5~o=q8n-kJE-8NhhT(smCg_Qkzz0qs{eR&)*!g@
zgMA8wQ2oFopl{v+9Y3S>l~jnRv}oELqY;~yMvY#{C*8gn<~0O!;Xj{*6>qjhmvFaW
zR)1&CN`0aXUVzxfry3kjVI`s{$L1ND2<Eftpp_895&4N2J1}$mLqSrcT_mFcsa`F-
z%wp~4295<a(hU_)IMR2y2ZyD2arYgl?lz&$k?Ou#&Qjh44EWdR7y!Y+30QQ>8-)jE
z-g;Al`)NhzUY7w3knMrn$gv--r~;H!j0U?I_`Y60JPSKCpOT`NnwI#{f5C?AIvf34
z@;?2OnDAR6Z{X&x*6M-F^kMbR)o$QznhG0VJZ3OmwyDSLH@mu>nM;qqqIjJ9aZ55T
z5bb`<W{l@kT+UYj6t{X*DNi)AuGHH-wl`S-t{@*qC~vmk5FSQYp`TX{lHp#Es$!gG
zYUo^TOX@U$aMPRtMx~XwBI;M)?+TA`Enl%=F)a-zFgmY}04={)gX6?BIar+NJVqXz
z+seU_zG8W0fWMlR7-C599Dxdm^VRD}Ubk#E8|>u4joS5zY)Om0Cu;Zr^VD3<D0e`0
z=W<Z%&yFR*fNZkPFW4Ya{x%R`MHE`xmGFs=6um(UDu#lRU}tJ(_QppM?1G!?HfBTI
zp;&c?>C;z?Z=NZQ`Wfu?DJM4FUu<BpaKd_@J+5bhq8+(P3pFeaN0^YCyve-oKf@ms
zFS9HSXDmQ>Yw=(is6T@m@`(;A4-0;W%*pia0vRkzQe7{MJ~TI1|2)xtGZ>B~qwflL
zCK;WRBaDxK$lQ;7^>#3JE9bNsXeyaF6c!#j)c35Ga4kQu<rL5KA40?JYXrKOHd>7^
zRnHZYHUf%L{Yh}uN|Zp9_aql-M$z5Vu|@;cPH@GX?A3~8GkF?wqzf;Ck|>9aJ5(@0
zWjpSJIQU(TcjL^k2IX)spN*B!a#KP4MH}+eKec%=9vz}ow&I^);GdYFyHCJ(iP4p5
zM^|yUp<>Zgk0d4bvb)EUd0L>A@^~0b@HZ_nYmfXfvR*`4uVDtK3(A61J6kRKqVr#f
zi)kQl`(FxGq&tG1oWznSoQ@};UOLRX3XbzxY1knB`x$zIBtdhF@yZ>oMR4{OEaszQ
zdU-WTxR(<%Wl`{yy8t^5BG@2KAZ;=sZ4nA{X~U1k#rUxhjKabs)IX)OJpt4eG&@SV
zGM9KMvC49=E|PG_7?)9FM>4rJ>2MPEhJ`fc4b-3&UV;cKgg?x~t?;~P;lN0g8%pre
zaHCM|H+_P%EJvZjbIA^*FED*oX!`VHw6HnsfPxBHVoNP^k>_*s(%0jMH--i$F^#pO
z4m$crf5qHeguKr8ExY(3w{B^Q1I?Qb8|7C4qZq2ACV0dziuzH8uS~V&h$m?@{|yyR
z42d5^jo6RW3WrdHPiy4iCe$FD6$yqmEKUCFgFRNn<DdF4RvX5r7@HA2Yad=qtI*xk
z^Row0{<<cK;B#I5)^Dn&zaI^P<QnEOG+Li9{LVX-ur!t4b(A<YK?Weke6_bJ=bDna
zgRD?X`H23QMK<<Y$pD3~Vwywpl@xvpRVS11Tdsk?t^=+Rg=OA_tif^t4wqKYFoQjy
ze-~J323>?$adH2Um}rbg3^CvoRO7eqE5<6?RbM)YBRe|*ND|3F2@5l~g!w^`*z4R%
z_<rBAPi<_LOdGu;D^Z>m*|M#wSzLnb9x*s_=H%?(6chXg%wI@*3G(Xd0+*ESFlN(5
zLR0vEhyF(kfDB>`fas96@9*qdR`wuAc@j$>)KoFQAS8GxuVDchRXE$LHpyhYf<%*9
z5X$9&OK~!g7}AY;!POw3z6z-|S)lmC*xn;o?eQTa7B1RF_(+@s?vmTLJsj&&fRH1j
zqXPjI14!BmXfi8C$k-YZ*%`M%aRF-QI}i}o650?+sJ5~!pf@cb3oT8hk5k4$F%QKc
zDTUd#z1};e^B=3;C-}!z`r8ua^;cSMee@@iUryZA-bSf*?cM<5GxMYKY_V?Z=av#(
z;dPEIt!7lH)YV=~jC0O5-dNd#O<wP7@4xW$KeiD3FCZPiuLx~ZTvG0@KY)=KzUZs2
zDN(IQ5JRQc6aq$-6hikk_kyFj%SoP<ich0VqYeFk10*2PZFnR-)Mq}3=#9buwfWCu
z=Y0dz6`C6_D+uLZVCw&3s(&=kF$m8%P~0{ZCI?%;`E8kiC%XP7bP)?FO+9@KPU)_A
zbxNyX{(7#rw|B@wUmMg~;rbsF+gF6$L&=)g7V!1LgvhT3+t&m+1?S>a`6cLV2BlkG
z*@=SND~iVQ_0fzPw`7JC`caFMkP6}RxyAc3UmJo1m?Tgo6udmy%*U$n#-9#C4Ho_k
z31kGRQhf_r;h1wJWCr95iR|IKM_AHLfG#;?*OHPfSwX{s=aa8&k*dh<63CA@#}_1$
z=hhGEAWo=!vZs*VE*9fxj7ZvxKYbZ-VHLM!ZG+cu9%RX2dZ!s9*YSx2VT4^Zf-n8#
zoU`j9(1<D-!GZ4v;V1WR;c9!rJKEXZ4f-83xytn6bULC0B<6sie<06)P1YwaFf=Ol
zGYuLhg}+9Fz|jqW!Tg;)5|a~Y@7RV3>&43heKVKJ8D}+YNCW(?g)i66GFtZU)li0s
z@%q#2m%_Ia#g5wJZT@v*!aG-6<-KrQ70C3AkQ4#s6(z6@ccUJK@jaB;yB|J+!=_p|
zh4u&5($~UOg(ZVq5A|=1nay~&CzU%%u5mR^EC6^z(RIFz?Zczkk-!JSrC^$ijrRSJ
zZt7{{F-HksYlGR%5SH8UWun5lCsrfPe3lBrNAyS2AN1des{DX6n5kHCZpIRL;0k0m
zJ_8?&e=mk@8^Y@|_jrCu)V4)eheN&L5tZUAHk2v~lUTeC=#<gRD23kQi0Xi3#<ahK
z=@Cmii-zd_7oed{1+6LFF^qn%?zf}rJmzCih#r*?S)JDODat*IeIS@=7%vhMp|WE|
zsaKSN!ht<~@<O$=XYy&;(NYp4i8RVV(<&1?)?yFHE**cl@s9S8<cORF&yopj<;LX2
zAl;&~rF<Rqm|V-qXQCTf?v0GL4b4kLSlE$pBHU>x+9yEt^AY5=USY7KB>ekl;1M?$
zOQ_j;H->PutSG9mxk4zhz+K;(38tq|v#fDTkd_2gGehbqq4tDs7WycnTN_Ie$N)&W
z#mlE(;<;*9q5#;_Lfqr!fy`ZRkQZb{K9)9g1uT<i@R#1yLnxCYg;ZFPX*&b*3M~Zw
zhjghbTl*@YVme=elW5Y-X!5b@;QDHCWGx)p>`+nTOx$SRT!@<|UrABO+DK(_PMnA{
z6l8M+>Hv5ON1#Xk-@mCPDbFtX@kRNo#TaTT;hkx-g}mC$DwFUHyJ7ClE`6`ic%new
ze%a|+{PMhA@bJ8y<ud38u`guHTLlxKK$Ux(*Vr#0$rm?;pA{geneCp@iE7>D?jP#U
zjr@y?y)J~X+Mi(1|0W=_=-rVE2ON&E#hnMMWmRdqpb-<^4x<$*T~HwIo1h9I=--i(
z2%$f#O!#nWRA8*!N5&50tpgW34JRV4j;H`dxpR8@LtCH3k~^%n0GC4|Oni7aK;1gT
zm_~4?$E2>3rmvK#HnDnp^J>_NzJWbDG%%(fmd1R%gl1yk<nE?~X<5$N-`KXHsQH$Z
zX!K42L6=;RiH|GXOcxvtPgb0~&2sjpewaC3?+7dS`iIN#fN`RU?$AiHA-NVX!l79d
zWjMi=jJ>9=qJJQOq2$IgyhKkdW9<=RH?Mk2vS#2(LJ)FDqsc;`_L+pa$$h_wY=`gw
z$kwr#1OCPr#YkXrVG*C|1&Ng=JAy|WukTd#)gP&YSI+JhPQ)~ptiqaJrUg63LRmpL
zkS!oW1?U$;8@3DLf{F};b8Y@r5+<P+OL#e`txIF?OFaX5(U)w^e>}B4Q44ix1nsMH
zB2x><<qlnr#R+z_&7U(wy3}AEu8*hTttwseCg#c2;%=*|f8|Se4PE!POH8XM{*}r_
z-R!02eth(f%x1vdSu_}~08M02fCVdT$-dujgFt^_K9o5FLN3bHxBz}z7fR)IL1`D8
z8eA{k@XLcy8pEb|QG^PXeTT5h^86X&Y%63fiK_+TWqZAcCO79yD*E?2Pnb_|KJF}Z
zlRPMs8ml9?GpbhV5RelClLjX0+FEwA*LHWEUrLuucMLDYK*ATV(W67sd!j5AyJ!yl
z3d*JEkE=JChk^?2vK}LKGumpZ<`J}8c#xgja!J1mpB?bN>mxHlKZD*!HYuc;^kV_o
zLOlKvFm|BOaDET*-dLdam>ss@RX`r|0cQehZHlvrnpHsF0{bV5MaZy(y$Nv(<!$dI
zZi66aUmB`15tRoM`SKk`w#x^b3AOXPBDZM2&ZHQB4vzP@w(tEjWhlA8!Z6xqUp>t|
zI+H#W+>llq2`(lY-?*0}Ii-tF-~t!zcF|L0r3w9^G_G$uUdoP?!sbg}O}rxN{9xLE
zW#*dRBNp^@pP28xpk#vx)I+TkRc&D>(`Gh;OkF8ap*E0-0LS8Zb`)NCM$L1;0Yp=2
z??b!B@@#OJ4qbrFvtET32Z&kX`EY@wZ^JgQ4iVU9%O3xD8V|g_O2NLda_^W_gto3{
z<Z<|q_Um=w<%RfmK@fNWINsHyP#uuho(apG0xfJ91<g<i9XzjBqUT627>A)oftLeh
za^UnlvM7reG(Ljr)>@`HY5JJo-f#}24*|aM=?A@Vo4XdS_am}1X1CbC@#bNYR?=cy
z-+Dcbd5W&$^+x>le~=*mqCAjOSKpaE9Z2=j2}ZHuhJ;rl6f5>ki9-0ps}*|jz;k^`
z#gj`_<{Q1gEI`r!c~GtIB+YKjy#|g8XW%Kdl@b#%+RgP>ovYPg^p1-BtsJ&WZkINx
z9Nb>kJ7NH<3UD$shG4Gj*PsB>zQMBm)N|+h(N<rRYiQMw<(@NJFsEn0jV8S>B0gO8
z{dxyV)8`oo(b<Z@#ngdVS3C$%Z%oe92^m7gjJ{q^mQf%og|w5$NYJhg=6YJ1#_mY{
z=$AD_Yw$nEEv_?gS5>zIXQBztz`9G2tG%ags*F#x&74!Sf}EEwt)N1p6cba9%9`GO
z;)$&-5)lyxp7|FlI;t7?$^st4-b8gS+-VGn!<wM@pn1N|kF*m^-0Vl%jNahwgOgkc
zOm>)jF=yeCS{B!nJ?-l&cEqWN`0lC={NI=xxL`wF-4=LP{CY<eu7Gf|jJErMV{%=?
ztj$2>wOM-m!MS6$2D--ua(N|JoVTY$gIOiKimimODl6=rH5>9^wpxMn%ZFv#S>F*^
zp70)_H@!F1I>`i*JHc_*isxHoV`>eU>>_KmmP4=oCJg_f*A@M}Q=3=?rGPSjN9cgE
zdqdC98T;ll+MKeo$4;%!DoLiwp^5<x*Y_Kvn#Z#sktr9!&lsD%fL|xyr3nDj*)NdU
z7cHPdB0!Yv95R<3(P(J0!ZSNpwU@R?kxF#d7mz@ei<EbH3>K53pf29X(4>F;jD=8*
z1Q&8TDN&A^e4>&l(n_dlYSl(tPVFsqA85`0I5a0GJ9soJ(D?t97Gp;XIqF)AX#KO=
zHjXO|E-O}ZiJi4kw^fH<x88r&>T=u$-175+lslLg$i4mHHiA8_tp6;#-MnSc5}|vt
z4AX|%8*7LTvi3f~?b;kKY%x45G?sL?5lv-VAq!fi$|uX&+;G|)=L7~Mq&HE`^J78>
zc?MTDZXSavBuc1&8)a%-H@qOj-u@srD0&vfK^G75;o&0nWzc>2y9VctL#8RfH(&-o
za6UuuwiOtz=i}y3YQrp2h(5=H_;(oi^~~TL#D$h@-7MDDC2v?eGVk7*_CP)~Y4maQ
z=rWD#XJ%AM?gtIc1?jM6On;zvPw2)y>gL-gckqN}_@x1&DTuE#Q0PSxh}z@G6#B!u
zg0Kjvp70&qb=Ab6*i=U((N8k&dPkt2F?iA1XXR#1!0b|YAD_YXuCEg!*Dx+Zn`>A%
zmg-14J^<D&JrMI;6mBl*!RE(DSD&7-Wf$6@I{dj^Sz58B(&{{k4-snb7q5&d=bqcf
zI|)|IL4y~MA*~IT-ZdK|+G8G1?A`*^8<+<!j%p}#i_uYRglN@b;hl4Tw41{dcGo71
zn8NBu;pqjaR8zLIvBp|-&Ptj(g~=ULV{)1-p9yo@H@kC`e+Tfp__vo(T<fN^+gx!=
z(d>H`wEV8m^)t%wn|yWI`zetn^@clrT^sRw!5_s$oPqP^lx*Rh{gaY_Jd&*i6^v0m
zvB{0NXTAf43}!bo<$S^hl2E_@9&8V{qN8H`jA{Vhs$LJg&y!qETFUFx0YkIFE0MV2
zY{)#Vr`lFihbDR$VCjZMTE(=5zKG`7#Ksv<jD-%x0Iusy*6j-X6*h@(&jN=Qe>?rR
z1DYFn1D_ZT>1*+Wp*YPKnTu%@D#-CT8F{IJ7+dF!qw&f&+?<D5Mn+uf8A)Z89KFp}
zJd`Km*?*0eaB!9I{N0?T<>fm6#UvsyvCbjeCy?D<=r-=JpKrxkl89R~vSz4#CQxf6
z6}!vjd8JGdG;#?ex?Ey1{{!se#P*?R>Il!_@F~14OC!>vfHJeHpzTBLv+<n(H%G-U
z(#~tn*P@}FHW%Lh%QKDw+qgdyBNO70MI0SoyePEZ+6*>=Se|Y792w#ka*rb?L8u}f
z*W;<SmYz<mlSW%!+sUgrAk&^qNNF(oh&sdEjo@O@WO-`aHXMN#BmATZ+o#c2cJ@yU
zhUw`7Vqt$WK0tOTZUkR%Vh}xO5rYi9SnUdIBdB80@gjumE@tw!)B+vPqaxb6`bMMB
za!Juj!Y#;RZCSvQ+;bWVAmF5Vwhts#&e&LY>@`Ta=iA$7-*3U|2e4%-b%Mr5My^$q
zr}O%St3+w#C`@k(qWj0w^kb<K$*X?{g>}OJANJm{JF~D|7VVDFv28o)*iVd(ZQHhO
zqoa;(+qP}<iH)7T&wkfh=j-`=e!v`Kjyc9Xuez(Qs(PZ6WG|7psvgP_71fD9ME(r3
z1`_Fz)|@`OFS=kSBC`HA#Z`X=2mCyPskeKckBxWG_G5m9w-68MJg<~jKn=aaYNb4I
zK+H&r2+Ph!lu(pQ!H(_M^$L%|rBGVJMmo5FA!(gMJvf{X7>e@1mI~~EA*z4$D<Q^#
zhQ6VsF8wJfN!WNNe20$Dad+u~%byq}SqlQe)j7%1Qor%>IW<jN9V}_4jIC6FFABJ2
zRZ1MqS@K3~|Lxz{fX%NhBkW_&^y}s=UW4}sv!*6g1j9~GI`ch#QtZhkKY)WPv#a{4
zVS@U<)dqEt?%bZcG1O^tgyN?wuOH$db$S^3vQDfQl>;MDd;uJx{!n!Zam?g?jR0_N
z3f=o`W?Y#NKqPW6un%Ws`md{3N*oBB%#Y|SWQA}r>|pGs&(9MklOGjjuAl%e9x3*)
z$qsOaAD56$Bp6vvN(YGnvXTe0)4Dy7?aUJPjsB7q!!@dWLDgyRjS@fS5)qmgln}5w
zt4P5&)Kr5m2`2hdhb4*RE%nKM15h~=lduy-)JYzY0DNG%VPe8pAO^3wl;4UOc<Wo(
zXlg2nNEtTZw%p#qg{a($uI?^kROA32?(t{XM{?@#nuCujaKwbel;xRc5op4~Cxeki
z$=l>2N4Nt*T~+NrW8cV(lUS{^f-v8sRT4g3SX$mi+@IVPH(3Y~WWC9ch$1<~0YG}8
z7D>@mCAj`m@p>f*$?fb^I@8A)F2Z7BX05$1<^AgJ_D-(2c!k_(5TC`DOGy>MaQJ4v
zS<7_5g%6Y66{aBd(E#IRX1fqU<CQj^Mz1igUz`t{O@nM;Rl)4{feEu055T(8K4MDa
zTG8<Lxp;2T?&4j9Lw}kGs=1gPLvUjw-uqCLAA-6K%g7gAW3bxi1ZoR-)8KWf?7!ub
zn-R(?QFmq5-=u~*S5qyLkpOVs<!BddqXq%FapfA<YvJlrl3gp>+7TMwFmzfIHFGVb
z6*8E!zc8o;NBMk!u$G*Zc+Ew3Oc59}C4;2ol$%r`<6~*Q%Y!`O$UtNbCm1JXMySDt
zYby}V^T?9QVoE`Uo>lojuo?-TYv46cR>6;SLPXL0Qn%vF(vr=i29GJ*aZ*HOWq;&m
zcXb&oIqRY|o?BZ18k+hqb90N)2Wj;qKP5m$!F^qnD+q@isOY{cZY)Px#rDB%bs0!8
zv;)K3I62I77byvIPoy*|c{QXn1-1>Tm~BcvQv8t|OXnicX<41v{8jjs3K|F<9R7(b
zIr)$l$GrlsM4df&8@BwfT^s=pHid)AV-Z?*zcSHU=UV{T+EAWPj4iY95|UV#2D1hm
z6JK#}S+xKJ$b`U+5Xidi{}wR>z<d<_zj%`d3P>%i@Z#GJS!nqlSuE-#neO|=;!{O-
z&x9@R-9FqolFSg%jKnnJen@J1j)<%8CyRGM1r@l(SG#PKM%p!=2loQ$1zECChyyE#
zD6d5xZj_QT3fzL4_CTH4%#O+hwM(IvU9JX8+5AtQ?$;4HE(1E;Ra-C!gyHchlTyMY
z+>_&jmEhyW0g%MW;u~#0Q``6hXB|_7_MAbctlRs@FHLD>Nee^rEW)VX$;^@Bs4M;v
zl7;dssB0?zNC!em=e&b)*|FI%4;fA+JjlKM9*$_#{iptb<b?AD0wW0o>I}pJzZ&Dn
zX_^%R64|6n<w%3ks8kJzrc+QCEm+zfGon`ej;`Sq_Un~%;s%>Albw+p@P5P9D_`y&
z!r@ZZkPOO3j6gf?7Mll5#nV6krM(jQJEit<WOjhN6&^{2RYEr2*^mc8PQrObdC7EG
z`H+U!tG{tj%HhL=7cZ3EYsXKr)Y*l#OpQHJ400yLecMd1qZOk}@D&!-{@x#4&=5YT
z;q;dgtqE`c=B9mnZ{?Hbgli9J!kYrs2S28urfg^D5>g&MI`*iDC@umK1}4<}Uw`VV
zx%%}PKg2WyJfNhThH9_ioqA)&;a)0jQ6<c9+e*wvpHn#7;bdOXzSrwltT1ncYpQrH
zkoe{CZEat&4{d;ADQ}(TyIepi*BLQuiZvm@T2UO5=NxIn7e?pICNz5!fONMhiMwit
zEA!`Xf)#C_*D3<#JVd;&E8muvC)&%RYA$f&>3T`PVfR&!2)$|xl>P&%Nep2CrL)*D
zIGG~B!g_Xr3&V2SNMO4SgS^TM<Adx9%0=$q)_m+i&EEqBpZ$w)zYR^dL(Py*-7C>%
z(~$jAc3t6d1DtzUSHX#)<OT>ESTgPD>Rp?$+PR5wrtW_z06-MCY#My~5|f+ns>VUi
zEA$%`VCIgYE96Lw9&maEO3x1u=}NITXr9Q_J&wt>Vueu3T6-J}#smio2quA;&Vt=O
zxKq39Ri!MJ1v&lqCU#f+?`+~2lhF|zPheyqTu5xz<q@po4T33#MmR2qcPfWFI-g>m
z$zk_y#+L8b7as|c6kp^9F<ReWDv8iy5$4OGQL|m5g29j$ea3q*mH&*NZ%Xcq!lzd^
zSdcWnPg))@MKM6gOGA)+A6p%Qx@S2%jO57l6FyrfPHDh!J+|GRlbzruwMsx&JK%nt
zNu0W*aX<rOMt{It7bq+v!kNjtG5U%VZM_apTv>uR`zDjek>eGC#*8Y*b3@9tb7ZO*
zXwA*OXU6lgMw3Z(VXQbZeuwDz&ba4D9hd5bm8y6v@pD|@yEx~}a@_uNL1mywB`$at
z?N2}G386lO3;}<DRDqSmxr03)8$7r(K&^Ceq}v6{ltD+tTARoM%*77&Ca``ZXdGi|
zU+djj{7};E<y9aRFHl>IK|n0j?JJy^5VkD>{<DxGQ^<fGkG4=GQcli4?ZlC?NSqhk
z2}<4q7_dX=nxrK=5MF=dM9z!>erPh1Mb#(c2MbfBGq9+E(*Tz%PC?G~C6^UzsM*~c
zqCHzumMq_ZK@&lX8dH<<C(iklvg^eIwhCQo7DO5luM6^z)pWn7H~+puL)yy#HDdZ=
z=vWsxJel~Dcx^E4b^xk?;1vnSheH%85*tIOH#Kq3q>MeTT$*(bK3foAL0@15=c2g0
zyD*hw_b$oBT%{|r$6Du+O1ck_gjcU2BkX5Dmd)&$4LsgKM1=B`K!Uek_jfGCYC*<{
zw8SdBhQ0q*6d&!-QVoV>`Srf^6WYs9DJv@c<_19pQHdmEk)mQQI3w;<ne}Aj;|~_Y
zNvpXfQf}iE$%N+#`B$K#4m^|s6x*Bj*6G&c71gDy^bh_TG>Pg(GICH!hjF_=neHi$
z8VB{UwSo1j3p(1LVeTown$37<Mabs=y8tMJee70&FiT7|A(vZ3Jnrr7`OOssuE>0h
zPe=$&6#fo#enDlWKyPJrG6{0=m#j;uF&$?YuTJitq5%5|-_t6jdDCm5!7&d|Q%Ma>
zgT0~bAR6YRy5Vp={c9{HDjLbF*qM#FyvHRnK1w-W3>1fNu(`U<9}!~qrkcX%_XqVf
z*ugx3^)!c$dGA1*U9O9l*)D!QuyUA>mrugPtL#6G^AJH&Fhmx3bfG-qK0GZDo;uv;
zaAF%|?oFpMrz|U}!Ht2tZ3wLT4Ot9-(bL6}+H3ATQ=v{M{en?@b@#xLr39s@%x5rF
zET=1FccZ`qV<uEgNQ55j)ROsM3doLc)(-;6Y=#c^d)t?Isw7;jKLpR}Foh9`w-k`J
z_tU=DC>x%(9h0568&)<;6{;=Ho-%glCU6J11?ULsZ88(3jciUvH;=sRz|JpSD$~kE
zy8}P_bF8QCtH)!7#=j)K$D+$LO4w5xM1;pV>&qW1++QQd2AsvSlsFHYa|opYXQRIU
zuLQquMQo;fg*FuBT@Jjwfo=mZXvJhEu1~szHG5W86_k>0{k$$Q@R_1AYbSWpl#Udp
zz=NJrWA@|otw&(T=hU(VS=L@hex<G_UBU=w^5RDohCOv<iXwHvQxjQpo_#6j?NZAF
z7TpxbAl~iS+=u+tTR+oA9nmDmZAF{&=4BTYN8wP2q9k@~hE@$nWhJYaj?vQwN#l|o
zc5;P$r(~toLhSnGwrBStOX=VFGMwg{jHiv*Lh(NJbu*4~?xRrjfbO1Kj8_2uqlJOA
zj!E>8(P?aeX+@i*m4b}OcIo!rS1|wYrnl(S#Z7LpC}_hzBfrrfiT=Kwzh+)oxJF;t
z(Y^8T;@<EItBPv5C1v)Ulb*~%mnMa1<vHsNy9YhDuY|&Pi?&;#l@H;3h7leyK?l9Q
zys-@6ln=VSRQH`1oziZq+I$Z+>&g;|?zaH;b2K);$|Cw4$+bHLLPNuO8Twq&6!BX3
zH%x1ARbgQoean+j*>I;ilm!`{L1PEOpO&`WCmA}c%35VjeRlUeVCdAnSvI`#Ezjh&
zKVen0wCV8ft(};DwGG{t(ToczDEwlEp-lPoR!nm!DQyAmt30C2_B{3G1_M?90;Uw7
zl?_dIE>19XGAix5MtrBzm$B#7Rn+<|WZV;~zHQ5MyuyEi7_McttGfIuscOsU0lML~
zC;dlhdL|(4;r8TjF}aTgCH~S*PlDf*xAHh^8vPe|l_QyXA6x(Yb?WCQJ<iOPsK<OF
zXv(VI8tCV@;aT7`)Fa*XIQ058vwRf8RFnGOSnYb7yaz}4%Mj+>7l-4V_dg84e>V9{
zp{`-Pgb-u-=V97Y|MMRIEqt+oAe8i2Qfhps>~pXN<-JXAnQPCKS(`RkqW#1Wi|C#F
zcB%X<MA2|Q^@n0O_dP9}K-!JBrsSI1Bt8y6kC#VX&f5OfRxW3Rw>}bcf;5Xe*3ReA
zvb9FopsqP4C%J3pQ!kE)yNac~KF^8BWj_w{-|zPhL~7B@Xbbhr&xg1r*Jk>>F=cF$
zk_rk6mdEV$q;FTAEf^Q*c({V0)wrfcr_&C2nIG!=SSt4Yf-U?5E1k}Sh8KYQ^%?q=
zCEEZySTkDBm<Mj0-uc!F;`%H5o&%!k#_~Q~;2=6)kw7d5YFhN+i@>%m#us(En>jLR
zYG_r$Q`QsLG21f=-@RJ4Rjvr|6hEzZR3M1@_eM47%Ac!3VEAVule>VXq{f5ky2n3A
zzH1S>H@HE|aos{Naa7nl_Q+q_5V8I5>4rq9Y$h)m&7XVV$Zo-peTIe}y*dx7jW3Z1
zh6MjZP0k^`K9@HG!+v)4L}rk=1lJq`ls$Q%>7y#XmmgN^-34!W641GbY!ji(EEa;;
z^4(D*YkSQKE5QZ&?RhF@a(rIr_{)gGP^}jzQ5UE!B!Pt?H(aapi=awriN}9MB{j~P
z6{#&J`5jMrcw6Hc!;_K4CNNQ*5SE8Am;I%R_O{j*IzbGNLS~f@kzv96og`O>9m6+|
zL#0@_gcBJ0G?;rnjFhDTh?hIT{<I@k_aak}j1fYDbH0~mDGdLT#mJ9iHlXg2@52Wt
z>mdqqLQLK(y$kgUZaU#BA&};#H#}~2noFKUcUGsg7oH~ikdyPwM!q<Z^X4_KENsmc
zs(AQKfvkWC?o^qLd3PaVgp?|u6}NI{ef=PcfAl8>RY;+FUVdqDh<33uRBvOYtAzIk
z`YgJcB*ttlcHnteTfXUu_pgc%eaW9J|2Y$RWV*kGOrt?535ul$4hkxVa<{9TR0<I>
zRY;mg8AmI6d9`7bL}@_>gW~(>LK9+f1!=bi37}mv)y1X?=-Vlq9(huoh?)pPGj3)T
zzsYrMpioC)ayFCUug3;Uj$7lcJ{6D@AGJRjX>v+aS(X*i=eQO$jkH9=>#>buQ>A_$
zio_`>F!FWrI%hJsCQQtZb8T9?qi}%h9TYIhkpcmKpbt6z)s9n6|J;Umjyv*ywPl>c
zv|Xa1gKl%P<jrx4`r^C!^-TN^7!vh5_!K&AMcdpUw2jh*w`X$@Mngc6_}c3W<bWyc
zH>Er}-XQr$7V?0+vyzKKSx)lu@AGz^GA~GvN7F~%tf-Z3^Bq2sc7#fkt{Zjl*~7qW
zC(%RR%dHDR2^#cEyXa#0e=hRqO8swU5C$$S!yz6u&PPs8z{P(FBG}&3{>EeU!&7ip
z|DeDLS{!RW(IC^)fnR}gAb+l!b<5ZGr^E8ZP#)gswG9^p?6H`^co*sY)jD)r63=t9
z^-r8uER3#~UzQ0*X?IG#Nu&qFErwvf4M1HOwEfURQD!VQ6w|oNa8Q#}bpFu{X;yaz
zkYFg@oFpL3%Bu%}lp{9eN>@8C$LcNiIz6|8B@QOnal7DztLvL6X?;NJ0wuwUU3&Lz
zt9ns$tfVvQ5o=Yi>IqxFD8I5A3kuSsoNLU#D3|z=U_33M6MQ%Ps%6Rhi<Mnl%92ZB
zPlAiB!(4}c7GPWN9B<;o!ze)IzwVnaXAf9m(2uR}rz7FTC(4BtHSFh!!ue=iGxPDe
zV10zm7uZbpq;r@ZE<W#lFNhKmp@B4l7SZcOQjkozy@#uZ$&7Riijx-6R+0=6t=Qok
zE?;z@b&8O>3>oGR^;N!;n%Hv0@J;)MmEv=zeO&-ZmtXWhBbNXDdBsXSE7Nm<?ztoI
zfp+DWJ*mp-eS_umpHGCHWqQ-3)e;F$I_v-LGPf!KuK1aq)2A&-!v*K#Y;5AmBe|mL
z((A%GKIua}HTieYgRo>f1=VuFrdE;VKQ%1(T5AR^ck{s(iurcjZRz2D>M<?hwYymU
za_y2^1Jhf(W6;;LM8J;x;b6#@?R~|X7U;3xI2-~HibRZWMYipJ#+`9NC2zKbvBaRj
zjC6>XuM)@*Kq=zk%R#0vo(&L;X}T=>m(K?3sJkA;BUyI1=){9gj}Oa$Mn@_>I>lLs
zU%#~m7D`TXJ=a%zt)lR#-ij90x~f*=0y<&O&FDLey)e^9>UE;%X6|Jfh4qL^+$KHb
z==j!?qJW-u+I`1g(YMxzNQ;dW^;ZMB1JuhekF+AA`^VrVWS(a^VHjn{SIH=gypTQz
z1A%&|cWMQr{bM=iJOvm);c>D}^5XfxxneCR7J8cg->Y2Mb1;JO>t_3nPz9GlXnMo=
zTz{wwvRST!&$4L8%j|N=!nQ<Qr^$aLDNKKUDL?j#4+o@@owIqc^wU<#>hDCsCk%O;
z{YX86jJNIl@qsRD-Q9>SxN-KXK^obZfSh``0TZMoNl!IR3A$Z^mx|mbHj9$Qr5c6r
zpWQM!N3$!eOZJ%P!VD3VEgDQ~GVGf3z0iHkpA{U%FrwI4fraPQG}b&E$tT-h&v6wZ
zE?Eg!|I%wGIYA@i$baGr;!;z4R^Ne{k1lo&cmAoxI28j9D<02%+si0AGF^x_pG!vm
zbkoHbf8AS+9=^2wtAPBEzJistI#FRZllCts@K(HWK|NKMvkgKVUS6HKMB}ZEkq98~
zyt}&>pkk>4Zl0b(e;%;eb)`C+Er0x8ikVq+%XLnoiTSvPgC9&byszKiJ=^%{?b#Sm
zasb2D@x{ld)sA&Tri?Dt<wsj{2=1Q5`a6<#)pdZIqP)TL?Fw^a3L-<*E2J-P3_{ZA
z^pVWctvf~1>+Z_5q@==sjF`Z}q1L=bIKQ@tw#%VTz|rpEE~NE>x@)@wCsPV@h@Usf
z#mtH}hh2*yC_0*T!Kf6&Zs}x(7p83^^4VfukP<W__b$xk`uZV|x3Rd!Igp(JGMmq?
z=^-GwFJ^7Ge(6@!lj*{`v4)uZiH=z_Dw`dlUxQQsX=u^yZ8NsU{a8QjFG6o-DG4Ro
znNEe<n>Vfu+Fo$0A|e{@4@-6gR5FuZ|GI}Z?r{0OQo=&%GB0`X#=eQ-c7v}o20Jn;
z9GFykwYoj~6;%rT{WaFG@zMZZZ&|Ak?%l;^f>0q5=B`gKxG8N0zqq6~MG3B;#<M6v
zeQZhpvsj<ab^+}~i0+cygDy6hjR(c&`+D0r_3_>qbi03tYKbQLHOwaZ^X!`Pxz_^C
zwRpQnLaTasmxRqOeiWk-?8eeA4X}cdKCuR`*NCJSn?bVt+c8{g;PRTQx$3~tG&a&-
zsgKsZrn)nEQ7&E;m)=ZPeM-Mz>-(yzy9bU)PES^B!N{E-nxm3G{!4+y^zV-8u~pj{
zM{IT_lfknIG|k3`-|ba?CT+!T1L4Da{}^xO)vW0#L;eVf8tMtZ+ugs2l64`ULvIhC
zt8zpSo|Z=E+sg8D)!%vAL22@i5gG3e^?e3&0&;91pJ)AMAFz4>lTx_^n0b<TFxUiU
zXKCd!Vi-=Y=NKTUqs==zIDOes7uq&Awrr#7@cO%Md7&Jc^v7XQ4~0y36)IhNUvD<Y
z>Q9?SUa!{PuV)qA1Gl^1uw~Yl32DmhZ~v}id^GFaKnho1uKTHOzk*~>4jBmKK!5uW
z!Erm^QTSFmVA^t2^*>c-n<2@d29No>gC~Gn{e1-V8fSr_Z*epU-<?bD1X9ZcVnzr^
zuN~+TRj6d*GBl!g;+R~GQv;9vVsYwPfO>%{v_-P#c-o`dY+|FrqfHClGU$PSREqL;
zJ2+mhH(WMH5_NU9Aw-GfM3%!`UO8!h!lRxyS`X?3=X%>e<4E9JkK!U>JCP%dOD5Z*
zTvIy(gS5~<xEcaN8?kYXG4@im(Zw)gwA9Rydb^LbYb6a`3~%y`N6LPQHWSI0(2WjJ
z04IV@E~{r2y)b3Fu3HPO!^Iq(GHCs-LbR6V;UR$31J%GQ%?sk$iD`nvB!P=826jz)
zPT_7(o*OQnizu~^rO*{+#Za0-5T}=W`;xjKM)d6AGUpeyD~7(VOKv0%JKWac>t>6Y
zyIJ*X+VH`E{RYOOza_n$J0|pfuZOmJABDuyMfDV)O@QYo>>vE7yX6cidv_%6X<H54
z8lf=ucH^#?oA&S;4X06N$q;k_mv%OXYz>%<kSgZKwGJ=qD~a^NdO*HwQdeRmI=QHq
zQMH#P=1i&7zHUcI4w$o>!9G)@WlQ`mT(V(H$1MT)oZnLCA)LS`fvZP2wa;FzgHx}Y
zkrH}2j`my;J=ulq7kFUBI~aId5hi0L<O047l*7GWNgn2skKjxKZ01}qbX9|2U$&A@
z&;JYZ2>)-$<01YBtr|V6_sn&l=pStXdvcAgr%&Igw>XD4M6rkLFb>0$0DmprOJ9Po
z1KbNSY|yoh*cJ1`0g7}q)W{*>!vK?q$Y*<(znnY>QT||rWnW{WSgFp>)>MK4R@Q?y
zK_u%D*5jg1Ey91+-nZw6XuQx=#4qWp_N76uo(G<!xVe(lF_Ku`Q*XB0ovV5wRpb~{
z-7KfZLgimm+wX|gIedayB)XR20jO+91u9-fD1S|unGyv%<dGu6D5%OiA|EROz;i0Y
z^9K~uQc2KYic7VHr376!<gJV^+8E)W{I3%#HD)tZdhgC(cPh5ubAcT53?|O&L4~Hk
zO=`yQ)hfzqbhtycOK3M*shYSI**hmK`X`&Y+fgKenQ1XTApHk`3#E599o!IY(oNja
z&1{jdpQ({V*jxp>%<&YoopL3?wj;L*g4p47?_L!1oP$GHq$hn!tw7DIJ$NLHgupd{
zwRYq0``Mrz=QU)SJO2Hz+ez|GWG-al&rUzDg~>)y4LK0}OA>vtoW$8L3>cuS4brY?
z&fPK7b74?;#a;@)LEV--IQ?n0np(s@3)OjWaHKsU;Cfae!4MP>%IlwAdGKsA%3Of@
z8%n<>lup{&xW%Ou6J!k>`u@qE1IV+0OmtReDHkW8Zq=xBeQ!s^o~59q%^j31Cbm0R
zMN&&B3F5qK@8!$`O(JS8|2I*9+mtnq{Ss8St}286+$=k|WMovpnU4+w%>Uq!IB2ae
z#Oo`Z253b>*6o7G&Dx1@udx5RyeuygmDT1H%<WIZhI1+%fce6-tT$(01KdETU+7jq
zCZ>AnKGx<9gr(S%6RC3aY*Hd&V6R7_c?|jQ>-Q&*|F+T&4!(RWm%^5BeTf#6p0MtS
z!;~S$i!`?zmtbyrU*m+;bIUSGjSxfBJ$7)oA(x;UqM<ydTVlE6IQ_|`d93|>8_Cz4
z%0b~GV5JaFOCvr*MA0@XXASFSeXA!%njvTkGbY|~IM>S-*&`Ys&i+i~WMq$l)HwXB
zwK&dLrL0lRO-Z$Z6@p|%Gi>r8A~$u-U?q`3KTeYgJsw9)LG*^V`E!~2SuV=JLD7P;
zpyqCkQ~n6n_Xq1gXLhF;P@eux``ewLTGMO^w^qS=WxzB8a3CT#>WZ0!1X_QmHT$o#
z4O|rRMv;l5<x%G{yli~bkOAKHTJzO*J1?L%7=-(LuDC)Sn#M2pTeK(`B!im_TcS@5
zBBfpuru=RK8EcL3J5F|03BU>~LFBMU+wOI$?Y@G3Wufi+?#Oe6f>{9Xtoz(=FsG|;
zI2+j}!2Pphh_=~P<WA><u@1=bTyn40?F?Dv`fGS)M%*dqBFN<fqE}uJrqgf3)y$ug
z+&KHG_h@*E1?ifb*NdpUOHs}9-X<L?Fcc$ep3u0v21dRECqfxA3Ud`@?$xTTycdqL
zWZ7r}XAyc-tZVQV?#;h1Qe0vhP!3OWGj|VzD_tgP=7(3MyhzwB9J1?3PGWf{;grb;
z9T5e#u+Wmw&DHt<b>HBV2U^e&hivaexLC<<5_KAOfx@rfKf4Zd)i_}wo~Q8-I3b2)
zv5lOS!I57yhwcpCa8lDGa&Divn`bWbK+mWf8CtsJ7c`k6_jzLprK)1MUJoR$?|>;i
zEj&R%kMNI`4Q_MR4dT)y3GV_ODGo+{!G(55nq{v++S2Qv-PN<UL-lBg>QgR7>A(YP
zNze-L5%M%F@#NsT2TDci|14OD{rgwLEzau~BSdb0&u2|Cj9iLB*u~zjhVvz4wsl$J
zLh*nSdij}5Du72!FyGtYe=no^BKboq?GRj>n_zhfaq0Iw@21^1gjcwO4B&qvi7i<P
zMpr;n=?5|rwcv7^#`<*-Q39Y!)d~cH;4**Nl86M3a>U3kRS8AhF-B|=0nT^l5do0k
zmJ0AYA9SIk!GKsediXTqh>boQBjsv_;al(=BHnHg<z!>ZYBM<~;Dxowi@DWyPC&0y
z*h6xR$wCocKLY<na9Ca}N8%v=ini$I%R%3bE`tg0n3Oq^f@NLFCAlG-11t>H9`vQS
z-ExiNllcVZ!gmheB~%p${>>~!DIY$!OU`i87Vpho2Cx1^%+PB&DqR?HtP?IfO63SZ
z6R8^oXvG(VEFt^MY8y*`$rZ>ZOL)Xw<9zO;6h{*|q5&ahfVERhL2wH3bw!bl_se_E
zzCyP%GB4+M4_f@~^8!nt9CAH^t~H0<`rM!RKex?50fQ$BMvn`|0}3r+D+@U4^5_!e
zd8}YWS!vHR=(9g~k5#(i(PC9S#TFQ2+qkje`^0fLu0Jt;Rh4{KBMcN$lBHs6pvXC)
zfks3`%4zkA8PD#X#P^3?6U1P_>Oe*vuC#q}58!eJs~CD1BoN$0aED0NsVH8fO4Zum
z1;oa(-%~Zgp%sBte3-#qEXwBX+rj*ijFb(h$6)BVrB+c)N>$%~-F+D0D=1pt30zjm
zCpRGF`J3RptrnT5fJAMcxysp!$x=wb6Qq7{)rh3b-kf*>J$vrkmoCoVfX{{9yNbnu
zfdY$B3X<AvHFOhaipXk*3gXBdZ}$Szz<SsqntM~2)yt+q8|ysPQ?#J8FnfUe>V$%?
z{(|Wdg&Y3<9LeF>8uOif8Q-Vtzo*a(cSW9*k|H2KmLtWu`?}cAku!_bf7~U0W#f^#
zeFwVaU5BN;jUNf!1z|K_C0fyo!Fq_s;j_bi?_o_F0uLpuKZJH*0WjN542jk>)A2Gf
znCKwGlt9s=9qVa6Zf319?EW7c^bBM@<*L4$u|wgomfPt#ziaq0&~`UVHhYTbS`d9l
zE4ik1V(m3!GLN?K*V7&E9XgbO>~`qQmov~dyE99fS?!yZuNfanZXx!U`5Jb7HmGd-
zg0J)m_Dw|k3poNZ^k|zcVgsvOp$?ZCZg!~G`@L)Kai$-U)$uQ2j@IkJjvC4DF*h)r
zg#`9y%bAMt7$VnU7%jYz2=>zt(EGounq0MpS&;3v{%AUU+hh){DwAEk$PXG~{<O&8
zW!NuuP(U!5ZP#-zuK^!3Z=C_TVdop|kQlq(z|7m6^55`UMzF3jRJJ?>3h4>3zwQqy
z8&2@1NrjqR5to@K!t=HLL?61TQQ)(+#$D(*eGx)6pPnH??I#1TaHl69FIr2cE?a(h
z-mbS<>TLFtw8mU&*xAH18_Vrr_cey+HSt<swSG>$Ww@U7I72_R0$Cdr`$QTndfmBL
zeTtM!1{<yLhFZCAnV4-KfvI2Ai)>m(<%lwsZZ+q0)^o1xq|^zP=qEnsST=lr!fHvx
zb{XiL6)I7d3(tR4>88eLy~QC`J)Dqj-1M)vnzp%Zw}|zFvsnw>VUDfNGNnSE(zJ)z
z8!-3$A93O*IVb`>sL}h;m$&yd&gYrsCVu*acjGbQ(=ADM^k?qV1$W?jgD1n2Rr|W+
zlJ1Aq($>c@uilE5&H0q|#zM^-Rl`$vXzly<E^*`2E_wCJP!^wi+Isub57(oGpI7xF
z^DT(0KsCzuz^`fjy4P-!a;vIUEfy?2=IffVAJskEE(t~QpPM2Txg%&@QzaeQK@|}=
zKClbW;Y;9xeDIVKodF$X5WWZ9?Y3X#(}}B|gd;+M0R_USasUDqO#dOUftoxs7Jb2N
zxdipDqK+S>;o+1;pq2j<@P2dlTR*;HzimrjLPoOx#rFSS1sWe8qyxk^r-lw;`p2sH
z|B$8sH$L$XjsBsyL(qg}y#GJ#M}A0dCD4rEpSjsOY#jeF3H?9b?f?IQ|1#P9fBQ;d
zW=^o56Hp(-T>mWBQ)*H&;uCdmP5-`5dR_w7^j7?~%$B)8vazWg=Ie65Cfj|^LC`dw
zaKl`3sY#2EdME6=o%@CETYva>QWz)<6QT~0<z1+XlG~Zb#Dj7P%Eblst?`eD6Z#c-
z6GT~k$1ok7mpmsQe|Ylc@o}^D$Hi~TDaOUzTJ(1pTl&^e$oTx3ZFQu0ZZC_=*7dKs
z3S=x06B7;r2c*VF=W%TkSiL-2J0uf8#Gb<b@rJI0Ew}Nt%Tdj>CJ;Pc`a^_-Ehnes
zlS8BSUz?~sP#dH*-w`EYwp}lszJ(Ep(&^tNgIv#ja>b#^CGhQR-Vsl1xNW|QAnZ{&
zEO=`Rc+V%tX>YoBIE@<BcK2uN7&cSb2RW<3QlZoT_GD=`BpWVy5d(Q0YaO@2)6OQl
zXCr<4hv_kSExELA;q?Zeia&)zIT}tk&@L~>?ahG*Eh(C|=2En<{q+oRLPJ*!Hy@`?
zQh3MM;r98jyT_61adP+T<3&>}sr%`a(0rc8_>U%>`mcS<v+KMV+XrmDq&WttL%0AF
zpZ>egl^;eYd=}(foTmGMto;899%Gvi=RLW$JiHJcWGaU{4!84b!R%`0e)k&6{t+2W
zRx3Cp`>MU#a!wV-mx&4`v_8gIaUkEXlKt}Cf&rs$IN^eW)wzI_*T&;m^TqKvq;yGr
zH56C%E%^<BDU&0jV9qJ+TL+8Wia<V@7O(=B{LMLc+FCM;aG21gM~nUC%AJx!U>&PH
zdHC)+X1nZK{@Ri(Wd@n<Vni%m@9v2;W$^`GUU>ZHxbA#Y|FKlp$Z$hbS%Kjn@3rUo
z#%`wb@<EM25W!Ve0m8t*cAQLWB|nMKjtyGW)bm`0HKI(~W0wH6lFCLzJCB8>h28}<
zO$)HGtw9`_$0fwXJpB?Xv9(^-v>H>oS9+aLDmLP@E4i&g4GOn*E&(E>ys8c;DlGRn
zi>zI91~GYZbs0F~m3B7)x(ZQI873-oNw?Q_p?tfLPK}>eF-_Rc0b5W(AS)d1y~(e{
zd0lBviz96*;PHvv7Xw^I#z%tFR6E~IbPGxUWXgg?jY1?S0~;mby^WEWSt9@4+y_kj
z3l+}|(x4Kt7GYwphtWe0-`zWf^|TBQR@aw-hZ!r+ZE_6>RPeLN2YYP7h6*!PS4Wa8
zZ!r6|iUJR4W)P)fSP;k8-s+s}oC78QiK$TS9NI+JZD>I$!S6h1Q{a%%d%wXmpJEl;
zPy!WNL9lU58@{3X**JF%MJ;Y-DF|0!c$$6ONEQ;Rq@%%7E+H!(nJCHPmL{pn(u{@_
z5gPP~5YcaJ%M~+f!l`^n*kHNUHsS$6SVwI#W<?f4=bM1eiJ6|c3ctuy*q}x-N>-ZP
zg1uu6fHmcJ4ev;6Hh7{!pr6T~*{MGvzzZw=0`P+P=Mvlwy+Q_nj6T_!g#N?5%fMHy
zEB>`S#_5+Y(1tBzO9BWk9*hX`Eu9x)h}8Op1sLS3Bi0a7tL-47&7z<QA_<B_1bhby
z1>t)jgAjFIUOjzX5KG>DK5@$e;Z1cI5;j+Xu3ap^bhsS%qjb|ThD?&`n)e~eHOEYI
z1^(b`C@k1QI0;7Y-YrSZ$osit=9rHTsBq{Yi^!;RMvI{&`{!r%-{rrwAW44qA}OI}
z=hxIE^wijS6~=fFDXX|;D}tMRGt#B>^uwJY1Tr3xjETvRks+zFgm6K%<5z>U+=P}$
zM+TB(baSawLBm12y9R*zG^)f}uUjoXbWI<tv?x^+^!oI%xk%i^+I-G8Bpf8a@+i-B
z7KEIUR(Eb^h`c$^yZ3Qd`?GUb%&(<wCXphPW1WGg^P-xAJ86@yJ6aE_<J8ZEQ)sBz
z-tBqt3S})0*T3}`V>hv3J*@!DV;IB0cE&2fLGbmo%D#T~upI#cuXn&8*FT~84~4bH
z2Z#H^&tuWKN`Hi$4z74t)eDMza}tMfC*i?C`E1vK%sz`uElWh)9qFl46lN3c18(wd
zPIl9ZWHMYnmjFquRQlUZ1nB$zK~mqT1`CIn^)8@FhZm?hWmJ|6or#D_eAK>yq;-@t
z{Ig^?c-tw5{Vge(@z+~9^>Uvw5cQ{s0FoS-jWfvCcYilnQ2Y{Mb{~NWi{X~5QiPz(
z`52jtihjB)<5B6GIP>4x4XxGM6Ffex)>IkZP`UYOJRuELa_I}G;5YZ<%O{RB$GgMV
zoi%GrULRB}B6w6K&K>pm!5OjP+Vg97En#8Z#Erg|yHV4Y>OaiMdwV-->BAWe(fdYy
z0^|=7M_Id|>AnNuKCvC*mh&E0-}a7!m{`Wg@T!)K!dr5bMT$CM9&N=l2HTAf6iiNI
z=2Jc{NxGQllPTt#D<&`=resg=C#L&eQ%)`XeiVl8FrRTPmmnuoJvxdp5vP}62zh5G
zlzAN=f^AQ++1~M}(P2oS08)y&Wjcoo^V|D5ll3j-`-CxvTbsSzDi66#mY~s7JC?g9
zuR|G+_^-#lRk?wXhi%6l1H^E_Y>sp;W*-A7O1?f%;Dp}SDUSNq%}IWPJ?Bsu(96wN
z8bY_@ojhfr>pe+VAP1dwGzxZaqevukvmsUpgz%$j{d9>Eg#l7Efoo&(SsJwP?U>0s
z7QcQrct}WRXX-Va`Ge{(G~6)#decL4f<2)APgqqps<yjrqmur0Ou_UCRZ#K5kXqVg
zsI2UcSE~H4Xr9rUH!=HvzX7!MiA>fnVXNQ|?w3=fWz=xbV32wIzG0mEI{#WOwHu_N
zQ=pS+`g6Qe#%@7eY}7+MLyygz%(zIl-$T5Hf9s_2C3v&s932j}`<zaf>z;JXNkoJf
z@=y>g`-%TGeXezVa1St8yFO_VGgqGv%!9#`DKs@>+VQ!<KI;hz4XL@B5#a$<#C)1k
zLhpX3RX?_5+&v5}y)4Y1JCR<j0_S-?#me@oAeeFo7cL~<q1|qZP8_NmLzE1RlI97r
zmmKy?pIH#N@n>-R2%H>X!x?jwed<%q?60V@IjHu?@yz-7PL@EpnK-#x5G^j`{7)9Z
zjPmMR?(K?#$1m?x@_w;9;bJ2vbl$c+LupKw5Z1Tp59&T&n~JH~@B$k==#kaYdY~`$
zp4!kym9tju2loi+qTrE6w-=;S%=4o8<au=rnNoJCqwD@cn+VTWZ*zmM7mT^*_D*<n
zS7A@#Pli9fNtd=Ud;OhTJilE~wdX-7>Dl)s2A#{Xx)a!ghvlnnew%i=N4sL!_B8p_
z`MkI;n6brSp@i7&G`3?sS+mVLzuuhH!?_)04^v5B_D7PuAt`D5Z%xK?R!8mb+dj=-
z<`!da(5LSOD7?RGr+qy`)dif*78yHkz_ATItzuzZeg&}eYly`6M*iI2HgzJw0|nv@
zAZW(CYcM@Xejn%mbE=;S2<<viKa0yQj2L$g%m_7Ud(=o;&TE-?9-igaoGu2wBj6;;
zT;fU<b@#JgG)GS6GVtO0R@&^jg9{gvj&lqq&ko;P^cld7yKgwQQAK&)FdHY^vH0$P
zhHUT$4O2|*1U;XxRW$#-^nEK=c)dhmVq}S^Ccqln>Ft01Op1?}MId3Wfk?5ygUG)$
zpn(Vs5v+)aw-!l@x#$T;1K!LC$=-f!B^pShdFD>4c?lsCn8hkF&j)Ze8FN;Tu|V*?
z^6)DwMNU`A)7C<0CX=e$BU;wWSDc#3fI+_0T#RG=!r{u+dZM&xuO?Yc7PT1isJq^0
z$7J(j9{kCQbzKnFw;TKK>(MY9lQ~Fi*hg6DEqh}=ANtTh*%eBrXsDC{i~o~uN?lqB
z^`PE%m>$L`PeI2nWU*ZJdcQ48i?-GLaFnKQ=Kw_}gU$Kc?vXTCnHxF3({56Iayy%#
zABP{mpO6Mo6&)}onPaozq(-}iek+S_RGHLmKMg)s=9H$jVRT3s%G6Jwx0_O5@MIgE
zk#P!P@I@(9e|CNIu%j@AF3NMwk>T*m31>-DMZF+VI7r9D94W?N2R$Xl0umtfuGW4J
zWJb?E3R{d!Qeox1pEr>?P8pk%CR%5!)_-XGGlCA`nX>|0`xIV*8Ai>jMSowkC~2XZ
z`piL+)5YO$Rd69{J@|^(7r2jK96u~E6y=t7OzsN^u4j?*U77xr;^T2q<+gJmX`RGd
zV96fb!|P9+Mi_qDfWgOqM68~ChdA(nEXN0>4}wU#n+St+fA~lyb+Hr`xxr$q;*T1%
zNLkDctaz~p&v3~aW^nw0l70Cd)Un=zJH(!Wu&?Jp+E929d)n8kOmMvu*stF9K}oKP
zVPK5QKTj79I?i`cdc1weu^Z)H6I#=5PkUnZhA`lvYq!fomKwDFAaSJUa$|#XbDLhT
zRt?1B#C~ejWpWw$_~mVcO>b`PzV~^>9CHYxZ$YC-L4zck1)e26W>xN>Z4i-uf1l68
z^k{u+MA!GZ*#4=0yEWSDmrq-XH_b(8Na;^xn5};i_Ev&|L7d1+wbgV%&N_Q~;8_@+
z%v(wqi*OY@JgOY!dCJPCnLZiU>m3F!St$6i#*vh**L$zp@-;;?vF)3*zZ$w)_Y%g~
zWT!)Z>@w6ot!O?Q{?L^at?M%cdB?YZU)qteAY)7g65&Vu4~i1xYZL5~47S6J@nw3;
zuerM$Y8y6ap-ie;$(Co?df<S}2IXq@zTK8FhTK+5J31+Og!yAR!)&!3c1u0u#bUS4
zS6qbR33812Wt2d*&Ust^t&t-3!z;4)CdAWH>^1!Gqd;xWB_eEA?P*>`qkv7&cC1;`
zztRP?HrT?l(L2$?DC@U0c(HoD9{gJ!uDOm<$dPCMN$We|pA8kZdM9nREX|D}A;HGg
zW^cH=BihtDI%2QJoI%^*BwIlS>Jh?3|IE$|wf9v68Kr>)RjMj*Vz*y;k_bNRu_AY=
zah5Vf-F4Um4D1G=f2_Pb@wj6MhCLn~A9XHb70rIF$p@;HATZh=9b0jbUXi&&QgV3;
zvw=sDb-Y;%<E@g@Vr=&8pG7$yOEJ9_%r9xW;q!>O0hr;@z?F14WFB_|g(K4DEws!*
zakpFJ_dV(EyPBcJn!Tn~rHm3xq{1I&Bw^J!_gR;0r_gTPpo=+t%@hCRZJNC_Z#yOu
zK-hCr@gxr>JekxftB7cPXBW*6lP^`U*ZhW&f)pdcc(OLW$(-h-OnC{Ayh(PkHyOW`
zElFO=2D`-Erd8{4M@r@bLNPzgp|?)65!I?uU)ZSJj{dPQ7<b6`(IWm_x^wU)8#YF8
z(NOw>O@W8r495>o=M|r<>4J!eN3CK@aoa?1&{<4?&~zb6GyTwTx*2!j>?mv<ffJsS
zY@3rkxm4ju>Sm;8gF<u|*^|sw;q6Op$%aq5*vM4Ho5<Pa4XWM>zQI=lE!I@AQx2@V
zc(8mpA(R#&7T(V6pORWE&_TpNvkj|AjF7tFT%v#G=6+J1{a1r36?U*3-78NXR}06*
zLW|$`u2q8$IcjKq*@)ob*_!IRa%=P}!p_AmY57Z!q%JzEJ`$x7puPII3(<tfEpu2~
zf5EYyin-px<ND%hM6+x`hazu<luBvu91ca~c}S}AUXxC3?}?1}xjw2R_JV^<XgE?E
zRMLj!^s70c`*&oSh5d2@UqdwXq=HQ0DnQPlpk)g4=DK2dC--LW=rDFvg8b){soG;d
zI`Wm-<Rt>1@M|xx7vG)t(ijJyLyy3AJC42EyhE3U&%;)*Llb@aq<`a{jq3Y!?B=#j
z)T{7+jW|c>cPWsrIytYNJNh|JR#1#)f|s2|&flMQY0qZs2NmHY<=cjH!x>a%=yXKH
z%_j<79Z-T4DT0uRz2ue$jbUuHTbs0JktU@@l1j~D0)A@x4+bJa6~|miMoqWXq*Ou`
zrv@Mx+ottU5)57gzN$*UyL9-{Xp{Fqtv$Oo6zNk|YBWffqxyD7$nDW;^s36xkt_#G
zZD98XkJHXrQt9GUBf8IA%>~(PW8K4&ov~;4AN`%$3@N)_6?_to&#j!9aiFX>|8m&-
za?<zpAF=4_Xa&iv0p;5H1otvBTx)-Hre^QGd)?2m6)(JkL49<*!zw|Z+&J8!YE}~3
z!}e)<(M6CUhY21Ntv!QKFhKvPjQZeq)u~FJBIvYZ8letb5ZHNV8&}-()Gd1=5l<a{
zU+yY;nV^6k<NxD)YD`m+u{mdcjwI1U0J0+SzGH0`EhDNayP8ut3MP#5l)6W(nS`iX
zn-&?q;nJxcHVQmXkl|Zi4RT6GrSuv73uWoK>maM_j)jIXZtP4Hh{$hCJ!P9Zy??Pk
ztWinPqE2Jqe0x+7U6`aRuS2_O)xHhh*}>kjBF;H-AfHWy?xK2wF^!??ML{8UGm1{!
zr_IpER;tR~SK!@Vjm@g6s;Ej5wbn>7C<z?#mgNu5zYx5(i%tH(eHfV5Vsko5-P=0=
z>{(x4r`j9Uq1KqKl6$b>^v#%N4^6*-+%5aaIt%k_eS0TIk96`?8R6Po2O1qRo-5a1
zCnaYGdE6@`vJI2?90w>L*dv0s*kq!}^=WG!Zn=17v?2Vk>MQ<YlAOsgsB1-i|7HlP
z_X2g3ZF`AmMO1zfGm1N<mcdM3`F>5V%hn$Fg;|^;X4twlUVh)h>&9G-mUmH}*`g?Z
z)BpX@9JChBw#4CT0xl*S?OT^eDt9&4RV%r<jo`z)cyK`Mom=!#fA0NdY-CFD7jt-m
z50W4X9aN}V)}-oIBQJM^AGkjgs7dPFm+W2spl?$XkCB!;Te1y$$v>UQ=8An)d>h#Y
z$5LfhF<OTpf5Kk4TtMcR3U3h~HBz6i_1?AYXgvsO*%%o~0=miQ@ToiMn10R0ny<S;
z@p9kmC6&gZByN#ZYmmRG4%J52zQfgy8TGTVGjgbvqWY+;KQ|he6nqb_|1-nh5s;o!
z!8#guFwawbq=sq)rQOTIM(-R&PB<jYdJ=JYyHmy9HvTN5px+ojlCiKPROWO~zCWNT
zjSkhV(RtW!Gp){~;ZDZtPU)|ADYEZzb;@k8dDzLGd#1l8;o!)eni1N-vuwY_2~jYN
z`fkE6gvprn2nDKNKd8Rf7@#z>w}lEa@cO^C&EH$63ib}YSgq}iA=-zV-R+nubSRo3
z?vJ;Un7|1C=6+@^=r4hGLc~bsir=&M68|jjR-eOywqy?o_wG|;GGybY)fC%IyUJs7
zy5O?I-u|Sz_dNqg9+br1x$L6~go3NZY!e-tHJ>3Va6XH$97XDSJJSRJtz&IB$(`Ey
zV}>@{`d;w7c+OG0JcpRh!XY{7a9Fglp9)Qs>G^H8Lzv1aV%U0371fsusF4Vi_ZT*_
zdLg4)$OdclZ;JOq;=L!8o7lx-5AEbz-c<2SWu`m=ruk*A2XT>n_F{~rJRrUL_FMOR
z1!=V&@!n#xk%%a94DfhHPG)krONAzdO}91=Oq!c;qS+R0Py|FX4;sxnGm2PD9qr)F
zWb(5+IT9}g=Y#BI=+*s>@FG0*z_465#D=5ROK{{yD@t=}dHFD>?}cAuz5Q1Xlxoe|
z9Yk%>XN^&%%14iCi6p?PuNha}&6HA-_cG=p+HJT8IgzH64zB076z|QK!rQ$yW(wqa
zS69^N5_`_Qs(h;Kjg0)lj1Z1uai+FYfc?BJbigURKwYh$!F9~?-9SS8YTJLxcS`YP
zB6WHDfWweME6%k<e>#?t&s|uRFS{ryx+(kGndB;l@NPGCMm%h>J-gTM1yX}_Bg{cb
zuNTWAj3sjQtlB?-3UqZW`+L&wyQM3y$R!jDj_lacSqR+e*O;(I+ftEWQmQnV5ETz~
zqF}Y#TI^r(zmBM<yZCk0ePdTIG#v9jl$UStM;FDG(B?}iJDF%v8Lh&3RN54hlZTlf
zF4+7{Z(*}_40*2)=_iI6341;X%iC{Dd`K)K0~Pu?pC@NmM=0So4F)eV+41)Fk}92L
zd)x^}8j~Ht<EsRAIIir#30_u>S!uus7dPs-aAlNK>R8EkA-@nw+iZ(DwM^gUQJqVD
z!QqwhVnp&nYcso|SnBPkN7j9Eyby(vGsP4@1Ni1vVU5_#Dl3rM9Ec*tUPD3_z3w6I
z>Bx5!;7T0bPk;M*iyZRHOk{WI&`P%{jIJ@koGOu|vxQ+*1(LQ7W~F8S^8SfK2#^$W
z>iZVf0RssklQv3n8pHEGVpy^6y=S7V<o<v0Xhe@M-A^W0rcd2c?Z++_ROPd<KeEM^
zZ*2xWd{d8&kuPS8@R^$=W-|p?rQt|vvUvLSK}F+J(}n-E@?3<M$$1qAs)TLTN$A-*
zF>KID+7j^EW%sCOMfMcV;!_Zz$xH{}1id;3HwS4r5(vYu_E4^5SEn{FR$f6(>F@`N
zOy*kjkzdsLnGYKg24r6`w;J<Gw9C=kGB<At9*Dx_?9-}`D+ic}C27;+4}jX!DMG~~
zyv}m(x2}<mU#gY|#`GRH?C@l~yxI4US>{Q&(vR8B6W&0>@DPV^>^dYCvcAZEOO+a_
zy`3ChFEB?tUhKhF31N6w$tiq;UC8;DGd(mcI_`LUWl)B2X@$7XR0h7xQCEKBL`N(B
zD^{HN)e7j{Dq}D1FW`hmg9Ml{$CkmVZoNyS@N`WEkMC6(F@4JU4Q#b}tP%2o+TiO0
z4yWg*sl3mh&<F(Qn&JWXulPbu1o_jQXd+ig%ciU3&qU>z9&x~c_9z99UjnZuhtpMu
znvNq?(<^dvn)Kf=3-4YDl`9URe^4!;d$v9Y2|V1S(pvUY*ZpVKNveOV-SaO0?Dmpi
z0!YkP3ed8!mrzsll%;(g`N;5Dp-`n<<)WVHQnU15hFPBV27lV)Q5T6i&?yjij&5Cp
zu1{wk-+y*qU^J%)IH+}-LyDMS^jSjKeO}|UUGR{4Qc~OB|0=E$#tuh$iZ@(us&T*M
z+sj}hpW5!30;Z0JY!lZRGdB#Qha9&l32hh|@X}4_e6M}9R;_vA8%ATw*nWh=al<1f
zCP>yuA!Vpm?XDC8nrt_E)N7x!E0bQ&Bh^1X04lhAVx!@AbsReYX$6q2#npuFm^<WB
zv-xaAxA7OK)%E3C{^~J&y222#)(g#*zCkI9ICPZsBw-K9-yy`%wJ%o=_n&+VF|qw|
z%^Aw;1IA^~JW_mUu5_z2X9R?^fie<7Xh@%Yixkgq$MvgO=Y-`@rN3>mW#W1#dMhng
z63EIWmKzrzo1|5y9<BIgD@`6w)*30Ikpo)Y4EXu~7kh6R6xX)33nzp?2*C+%L7L$1
z7Tn$4-Q6JscXvy0cX!v|791Ma#%ZMK%X#0k_de(T{QiAaw`x}P`mxsPS!<3t#vIQx
z#BMZUL;_e2B94mf=GZugi=o%-jp^rch_;gut1lXHFq`0TPJelUS!T7Rps$Z-T&fku
zG1sNu{~mTZG5m2rlqUGAKFg|l6);X?Irem=?_Aj2UJ0MGV+V5qn2{8hE;+1*kd(_g
zQisw-u1`8Xuvki2PIREr?8LULG#L}Ee*x)@3<^Wk4zZDwjkr2EJ&&%~Oqcf1!MtV8
zoG#F1>7bTp0tKQhyK$yUS<_Qw5Tl`2e%1X1$L*M@-TFc~HsSOl*ttJ{L%SVH?6>>-
zArpesW|^#7^gpiW>yE!5rZk9rXD{6ZJ%R4|cWuvT1^yP(T2C{OCE)j?A1(+Mp|F@S
z6wL6D(ARD<|E@;EuXXL@+QANeJn{3>Cu>Sc(V!7ZTmV5qx7^)d>1xdi03jXLRnalg
z^BuZpm#0-@OO~~cXc->Abdv95^+Ok=rh$de<(^!eZ5FlhIo<nQOVc6U_nmi;icu}6
zmQ{v_m>0zr41cI%=KcJ)O0~2nhKKH?y~E02ktIws_2gT88Nb%yb^UTGTHm!Oj+A?7
z`+Y802OG6&q3SceH4-#!oZ+^0)OE)7*c$g}G{feivnYnm3f96XtP3!;wN)@E<_X!%
zIXJ8eUWix_g7Gsw-owrl4du~Qz7r_hvG>J7MK2A8)47DSHdS%7Q?fE^A>On$WplKj
zAM*+;w5ZVl4l~%rhs7*6?7CADXCLHjC5Dl;4(@ClhGf01{q?&PpFBh;CkD5jvTu+D
zwJHZzG7gXY%O>Rb*%}(6Wa!wq_M!frfuUHan#$$4N6r(C8*D+2f1;y&-}N<&poUsk
zb+&bKcoX8_SDUg<PAdsP*p|_4yE!yos$icWV_Qp4YYm4h;aWk^#@owJ5z|179X5TG
zCH$PT3zi-I@o7uIfdL&Al~Q7u8ix+_#PyhEP$>E>8{9mOg7H?j{U-?%flRv^bs4|X
z8xYCQ)Ru+LVY$4-fp%gZyAY9>R-H0I9bUL7-|$^wVkXZ!sVmz$K?>2WToTkjW;n`3
zXM#25BCcq;g}&wdCMyk|r|k;Ax;mh+0<PhINt6Q(T21i28qCY)dVZbfoGz(T%h5Pj
zt~&T6JOv0n-98B|ZBtmI96NKYhOWn|?XBm*60CiQ>yKZf_*5Xr-m1szE+m)Jxv_A9
zW!r9*^4DvBe$WyAh5)v9DB<|aQ2j4{@@lU*-e!L9_6!Syo_m$A{0*%9`!;_~82^~>
z9BCgu#gg|(>%4i%FOdZ=en>zKYue&br!?pK!6zZ~`Gt$K%C9b~EYMqmQxN_AuE52O
zb=lQ$S1l8A%2$S75jgl2;|=J>YX@2K2pJ(>!6E2;%dB*oynxQSjpGq?i=1-dQFO6D
zO(PCg5t5%s2bArRCC*E{JjO>C)OoEAqG;H%3~YkpC*&t$Z!2!2%R@JnH@bgGm3{ah
zmgN<e-Re&1$<4!cJ&)(Tn5X$<O++?ar9ah|i>2!|LrQVFg5uKKaI}#LKtDd;Ojgpm
zH@~_m_3#`E;8HL2A_VR4ap*M>!@8*6<zCGmVhe(1s#(gs-*xz%tUdU*0sL6c<|7wN
z6`?nT{)@qu+2@9{nGAEgFOD6|cWAS|{@n`FcAK&46frOTbjG_i`2yt>-TcG-3`z0I
z=5cBamrK6|4wLKcN&1VPv#I1ikq+Dpvp1c*n_|8y-R7_jAEiw4BP+fL$SUADi#{0X
zIB$2E%Y5lh_kzh=VKyBy!5i^>Oa0Hn^aubhia&T%AbX4t$r0Bss_GN_iXI53@_-7g
zS6pF;{Jqxsz9WwYKwI8JPmA9w-r)0ouL1gMP>=bwj%&12y@R_Q1og?4jlH}ZE*mde
zLe>0SvVB5M+4iDT>3<eH8Ho%5{S+75?RJ9_yQDI8;kqLq2kO7j!>sutBq5~><l5e}
z=Xki>3T=3s5;({1j+GX7mzO}FCIITkNH~H^<dkJsaxa@8sKAC|j(<pXQH<GRp6PBn
zv}MT4<YqUgD|(uE*=G(<O^0(l;U2(bxy{a+uF@h!u(+99ahIt2PUwfDtJ@=FuoV5@
zEE;Bjc2*B@Hy*<qHZFqFHYvHnisfj_`e9VsqWy}&>s_2jUoa&b*~lCc9es9AXI@PT
z!M=#&XTi78m#*&pNz-9OoxsvD&0&Nou}{Yty`{Pb5?0Ho47yDEU10QF5Wht7%xkfI
zePBQceFyj%(PquNvBas+uk&rgM=FEeKw>5G;a+{YoSSn$j`S{bPjKl2FO3?w-%0C`
zs2Q18<DPS;m}qOV_8r1anbGyy!{PC!p!0#OOj^psJB;(8afP1asjj}xqi=W`YFEJ1
z$Q$nxt~r2Qhv6R_-oQf^AQgIj_tr#fd8<XqFbl)Rq$k&IamyiT%GN&bwVHfiU?*3r
z9aH^&MgCJz@l0jfB)<OH0Mk!4dA{|K1${s1=%VZo)Ey+JN+0fB_b+A{((fT|K0&4>
z1VpA0lKJW&gNngWP77ocgUu=eFDR~=ed{+FEq|i({lV2afn5W=Bc$5~Q_i*lW|Px=
z)^a*KO!SJ*=K-vtj%Rv7IylA-ac5o`FM7iDA!%i^gn4^T!d(-EgE&-rQ*&ci6s**g
zxlg{&6cJM6dw%kdH!Y!`7)J3SRND~r5=M`i({uBdaz*N0qODHHYU4wmAN;+o9na6K
zbu^@&7JX!#pp5ZNKh6ole(~AY+=#Vm7nS`};2>r3w!^A0?*ip}-DOMuwT5)xrU)MJ
z^iQY(H{AHP_S@}&G0Af5yNZMLaQm7$iZmV~->9m(S>qbDn(cOZ&+Gg`5i=K~3tdQ9
z3PMt#;Lbpsz2{yuSIan6Gs>q!$+TjG{GerlhG%I&CyV~1ac9=^5AJJ;O;)w3+{rs(
z&jL5Rk)Io9mx)`a0An_3J?~^Le0M{+<DQn#$Am3VK<h4TwM@qEm_YMGmMc#@&g_E<
z=O0;0_}^w`8ncp=)v_ngOl}ZC#-F2!R!Vrt;N4A**L0)W%9%FW(C_HAe;?-xC$3m2
zk<liGiml5#{iIPt$n-ONa_7BUfdO)F3?}+!04eFIxZu@T_6yxh+-?d%HSsQG7y$Cy
zGzPQF$1nt&#K1%w$>Lb^k#%>VmmML)Z!J<?dy@PU$l6>r1%C|lQtF)X65XT35J-wz
zXqKw7%y(fxGSq)Ph1X#-n;d@hV`^(!a_+=_r|f};*R!$;t!Fkz_eP|*h4)aD<wP6G
zhQXyAS+l^^cY>SGk)yj*lvt@_+tSo+F`X>)D1Yl*VpOm)Y{%Jy>H?hBjm1<>W#$Mq
zO8%v-?I=pj7jFJU1ulenrD`^awdPlzuH<N^8dEDLxBw?|FIO)fJNY`Lmi`fSY|`-Z
z_3w{t47J`%^wcoN7t?8iHa@2mF?uC^bM0_`equUVgw>XH`aZfuI5Xj|!MvvTU2{4q
zAGm)U7dd+$tW=~u!otU9ZU1V~YGEshEEF@=T142+e$QVpJZ$Vx{*#&Pv-J97L(-Kw
z{YO$=x%G;=U1drZq~->%1_w44>j%wQD>41+QyTkEF><it01Wk3bjt8OI&pU;mM(zY
zG7l#mW%{D!>W{#7cBjJz3_70syfsZZC#2G7?Vps$dD&3_6d(q0L~Ae7-T=n8?V;X&
z6%IKIA<y!_E$bdBQJE1RV|)8Y%379R{DMmM#^AGWvWN(U0+o<HIi4MnANgQ-;oJ?r
zuD#1n0QS%vS#M&aboahPsYXVB`K29ykYnJgVwW|W^qqd*&iB>yt6hpFuX(aBlaYNL
z((`ufKTa_QVINz=t-F)-%gD$Z-!{8fjMk|W?@Z4)2St3lMvBbq3fr@VmrtNAK0wCz
z%?aKYu?pqdHghH`V??LpUo5JB3oAC@T&xk~?*D?jiu@T3vm{@tUcpZeK-t=8nUEq9
zS6*q`wEMl>{1YGi{LaYa#3^r@_$q*IH)xWMR1W|8TQlb3gsz041c2`mDQV6yHs9V8
z-*y)AV08!MR4&7rjI9vB!pC#}Q?m{`cS=tpeuEG)2+SRh<{es2?Vb8G(Be;++YK$8
zr%ZS~y(v=`{AhLV5<J+G_7i<?cA&-kG{A_r&@#disfIpNM!7?LICOyk+WL$qULU2F
zHMSxw7#y%Wz=?L3<6^m%&ZB&AIfESCbk;UYA|^*c!#x;K5m>-xDXF9Kfn9`|t#6Pa
zOHmn+VD;N{8)#p07yU&@ju$cdtZz5|9V1A)KZ0<&Ras!@cC}wSQ>dlfAEU18IZO6w
zPi0O4!*nrs-O&ZPfoLuF4x!5D@gmd7idm$H&HTpLUAY!2o?<g5gsdveGG>spOAFla
z8%Ty^$K6MmTwDwv0{z2V5xWLiD#_eGd46b~-h&MR7{nn&O{jrLy3P?UnFt&Z3zl~7
znOm$sdaKjz6|g03$uTv9`Qvg8OZERcv-N<;FYu|)-BY|0S8w6qubOCgyF&FTd0UB*
zHt<I#?E#Fgv%DK=97yA{RJ=^yx7F^)3F(a&6i)o!@#g%iMpLmME+vug>A78Anc~I1
zvJf{E7H1tYf^3l*b$wuDYA*0wULwJ!-)^dt%h0!s&3jISL7o=(g(iL461db4TANQF
z+A0w}{MYRw*tq!l^~J?}J_0DsrDVR`9FzT+PMgZ{kxxia{_51IbdD#N%MbXJLJ))1
zqQFpyY$}IU#+!Gzx~hET?C*Kk$S_bq-B+(A1pV`=M9rDP46VMYzTw6x%V*{V<Y_Zc
z?lhhNwmE|Bfbn6}{!NO^klSEdS~XzQttSK@zB(~@rqAN>hsHyELu_RezSeqFqe$i@
zY1H{8WHki$Q)cbj3%JJZo<)IGy-~MVlWBD<p8MG>;#lN5hy<C|JMmyc!>0Sx_`!4d
z8U)lz?{83KxC$g7R@ud$%<o(QI?V>Nz;j&Q2Xg~>-E!Q!cqZ2Q63F#hKjZJP-}Av}
z@3VOK6OFO)!3C0OU2^gJn=^yxxF-LkgQk_(&79U~d4r-mbJ*F<WJuF#Xg;=RRHcr}
z(Ye{BjRT`YIc@2GdG*5-9j=ARokof#Z6{+L4ak-Qr}4z1Ge{t~vJ>`{=xG_>BK+Yd
zJ&U;*`jp$_Vy!KFtrp7r_n)upyb{HXCLG+RG<(jKm+zRAR7jYfMZZ%H!LfkqMNj!$
zf1vXqy<_Sm$~P_u?iPT4S(UPx>vj70F~UJ2CP6ZcL*K7GOBX(7Yt;zVwZGH!y)gr}
zC<**$odPp43|?RURKKMb7cWYC-lq=>RQEIaUGc4vQ*lg3Xn2jFv8Clz%Y^iAi`Wdx
z7jsK8oAv2$l@6MKr~ZR)KnYGfvhT{l&y^extJtaCWMQy&OjZEJq`K0YxH_f43rCVd
zOO~=Fo1HwFGN6^d*~4v0AOV~Ds`eg%PHq>UmgD}a<!`<?csP3@ygRa+S+VnU-t<mN
zhUMy~O5zv8x2zTTJ+s?89^P}vE|G4i_X{fNu&e_*ns)k%HMj9*Rj);R(eE>;<|kw*
z{P#9-nEdDi9M>(E8dG$4p^GdkUH{<;0UVaI?kJ}n;!67h_M}DhESxe5T4->haddU}
zM#VJCuonQpy9^Ulp8R>acIr)lSH;5Ksjd}9@#Nu}wE?OiH9z;AHjR}0MPgV&9}N+6
z0{&71M}LOL#qDz}XVP{3co;8zsjI-b{o%+HG5ywnXJ&b5O#J~30;ZuN8Hn@A9XnA=
z0J*<VXL2_ap)y-&FvQlxRCT0i5Qp|)%Bks_xQGxbg@7IK(2?fomUNo_CFFbR6%rhu
znxi_Sx)o>W)3~8L>26^RTz)`cc~|kS5w2RU0a{f8EmkewNsXGCG97lS80?mjb+^wh
z3F&!H1n6|>kTB~M0~wovVU_aEO=n_Oko4qmmn(&NoBXs>=$%8H6MgPBh2|0^S_U`T
z>}QIlkL>VhLu%3p@7+o(6g8A23MVy>N#U*|xptGd9FM?`Qo*S+<fNJa+)>*{mah{5
z_xw5|qdv>}2IY|&VGP+^y)%vNg;j!S1cP3Bx^(dYCMFV@<+iIBK0c1l36crFGMvS`
zjb{fePwCwDs<4Z$?vf6U&`<2E8>_+kJkqPb&FVYVlzv9hybE2b%A?hXZOm_P8RV3h
zPeNVOkxBX(_=Z8iN9MiH!;BC6`SRpFqWSe>lswfZiR~kIGp5&E{&|#n@Ks{EjQ_oJ
znGtguqcwXqKYtpdIicLI^r)lU(aXl%%cEl&jKNo+#+<u;pKiF^P+iX#>MM3<Ayv_B
zTi?(EQPB)Xy3HqbXQ&r=%;@NIvOy%_8g{F{D+rd|>=-`0WEolS3VUe34wtd$otB#j
zcj24(QK<bjzB(kZVPQ#eC{QMfUfPaI3mHBUH~mKSSN8B8@$7KjM`eng-z4$YKp}$V
zHW##923_&V@Xvi$bZ%IjPETO<^oJqxwk52TzEagG9tlSKgG!WCMZ?t+(jC=e7Ab#B
zWmUua`%PX=znnLJhSJ|9V`@;J-PWt@DUj%MbS9R&JncE!JcYCe!*7w>GD>=9?QVwW
zHmwdfSc{}pNa_-8a`-2Vss3*$(`!Ui01d3j%d?~;Ye4S=y)1*kv{_6&*JFf~y8|nF
zhDPjeSO%;0X3q2*H2yplBsn<|@}dMB#YnfaOjPn8BSvfrWK_w&C!1M36s(aU;CI-A
zzVF~MqMwit8V`o!FkLmbwl|aF<?nXqQLfW(Z$Bdz>fkNrD3Wm}+};jX{Bgf>@)c33
z*mPF%ag-(aoV)Fbe-BjYm-gwbCx>Fv8|^_ICxozUCi-O8-d-V}o9?{Squ))A)T!#%
zSS&fN`K*8Cb^D{2BmM1D|4>&?J<465q!1`gRDlpf6a(|Kd`8Pil>f_@K9QcVEbMKo
zK|0cupb$Ebo6@%)DZ6!EgRwg3*WM*EUo%{~1I=^W;EAOhAzrKRKAo%AV+zvetxe7z
zesd6_b|nQ`%(hW*qd5ngn&lm{7focNT>qI~WgG$MMJAa@yYg&~?_%d{Hx(h=-Dhn~
z95F}l-nq}#t8zCg;mx#5J5TOo8eK*0HxW&Q#Yp`0of~S&T8=HOgUPO0ii9>+egrs<
z*0ic|in?PC5&miRK;8rixVG9nShf#s9Sb_FbKej#rTz90X!2`L5`Lg)@m~BH(-O^l
zxrR{??4exfPX4u$Wt0f}VQX|d%HQ}cEctYiZ$u}rcBJjjmj*j(*Bvr?Y&)7wQ8klO
zLrarJA%JW!C4-a4wIr^5a)s^%zr8Fpj4m`LQU6G22av8p^c>$D!Z*@TeUFx!LSIE4
zJt`x=2b?sO@wLDKRdLEG<$r=PVmTBzeG4qRd(E%(1X^7?m&FT}hg=e9@~5VT@Qtli
zzHd0$NGJ<z&0@fcbT(wq?YhMSP3Vrz%oUr$s9$EL?C;g{=kfBYu1Ui=s*s-A@Mk|=
z?~1HYXR7Ny3y(^a+UmQOan>ahXc}5AT+`z%NRl>M!nZZs|K3gxWo$n-_3riB$a;p8
zZaO=VITwXd;z>MK{w<zQWVlxDOm!cTui7pHV`@Jfwwma9aEp7&54|?&QIffw$+lqA
zb^?9pC#NKz=@8~vF`4h0wK|f9C!L#<jEW^!Kj%p_|Eb$;R}c(8*Kt5{+^X)(Z7qOJ
zQyH{SC9Pa~pqvlt8`lvnRHI8oi@vzQa*4AR=bM3-Pp^0hcyjhHZT9;0H?dodfr_z_
zn`oov*6tp}J4tOv#%`T8;R|~Pi{C`F-FwI9NmWEhA2Z0yxBsTt)OqKzlCrgVkWfbI
zD8_mDt=SnCO-)!EJ4#f3q2zM~;{D|f<z|9D{h}DQxssN9p?e4!O=|df|0VA^kt4&5
z7CS>1le$lWtrN%zkM!3ojB(FwJfxnqLTx0EhwXelu=i5~0Q>Z?ZSvV&w_@u#JwJi?
zo8~2j(^zc1DO3^;OD)d(it)XwW70U>{Mwie^7(|BLFnUdE{`3<1l`vtcHK6NbP#PO
z1-7!HOMvnC()P2CdyWL!Qret2m-=hwINR-?HDe>K&E8iO2HckZ;I*jj1A)l5EC@)Q
z$hkAt1!(9x*K-~0h2^(=e>Fg6+l?QzW!IFaCu<y~v5wZ;q%t#iPBlgj5B6(84ly>c
zM3XypTz{Ni7zgW)w+*y6;#A5t;l12G6l$@D((ASBKQG>2uQWW!c<$SYMd3~rT8Vei
z+%bcX+%*Z<BlO6Il!|+za<vgya(#(oSBKHbtN0I12EQGv{i+wme2Z_qiP)aTe@!#y
zXGp7>BpIfyKzB}X4O6v2M4$;_n!~nBC`{0xcWKQvBs2ah;G31D<ft(t;{m(0(>0ti
z*J*4omG%7dg7d_0&91s#LQ6B=F+VeG|Aq`7hs&icJJ?rLysGB5MYUpH%aYwX#g4rE
z-XtfBH?Z&HGUCtf0r)xd?d2ApW{!^3H8SSW4<A0~-YnRxd!?w6I<=F3BEJV~M)k6B
z*4Q$Ir>Ec8eIDuy)!&_dzp6DdC5bJONbjEDq^^H8sW-s4mQIK0NBUH(`WwugASrxq
zW~Jj@QPrga(jRELea$+R1aM!09##B|FgC{kXOws&IkAN8Eeei%Tp2PjUjUsHI{<N}
z+g>JGzS<B;5dF@>FI!xw(MPl8ZiVd5te1R&eaJ0aE2!&0;y8|>>#FPY;aL?qyJDD+
zY-P4mSYC>tlW8<!A;))_m-`Vxs@bQ1)>Yj9^R(7yP3vx>myn{HGnSy;lwi*B0gHnx
zO&^Ari>4Pi`{gK}h>)kHt5dS-i=*OP7B>YHKv!~1?_8>lqx0jIeeR5-2c4FCOJsbs
z$;#Jq?S~v-p4;Jv%;&n6>i%rr4TU>v%p3}JfLooLGave`-d}M`3b0jV2Fj@Z&(Jhr
z$~FMaw1oOsfzUuxi*JiJXZY8Yy$35qF?084<=K%B&f7sYi>t}WvW?56GYY&2QkC)$
z5;Wu!4kN3_H`Pd%Q;R5o%cFp~T_S!#L&xPkxG;}TQ8p6Sa8;y>YK~RYGEXlM2mX5X
zpd<b#W=L|Ad#m%Cm2Eno=7$Tm2QP7KUR}acTAE+KZF>6G-&>1@gYEDy9hj7?${Auj
z#)`C^MVUUc2T&=BOGQMKEN1TNQI?HIQk6G^xd&24uQvAP8{m%*K5Ek~iosjDOL+l$
z&s%MlGwAMl!Xl%<1(EN0csVn^zY(uPCdjMVLQgh|VC8C8EEX&pDIl}i_>!W5man?^
z`A~N9uv{wGw6Udtb9>v;Sd)}?q7HRCLf?sxIiGjnF?|p@-QkZKGaj35423@;PyN^_
zJzHU6A<(#b8#hj>J*C&A@*#)M+}4ggVZgO-yc=%R$Q5}ux$d>5tz6b5#&|1=U|nmA
zVcgJp_9~Z;%$n&MuR{_z)l?PE(YscO^qjOwc7V<lUD4M7L%NxuHKcZQfsh!5Aum7w
zwdjAzhYg*lwCNK9(`!=#S76q1BVVNS9%)DUnCLSPDh|4c65ledj&9E*l6I@ZuO6b9
z`6i}6ssOtfC&pA;BLxkGsEt9{ta559`s$<uscuh67c5Q3uT<4Ts&`ZcY!!j2rAcHb
z%?>hoIfpXzB8yVw?Cd&yf@b5e7T6SppE+B-$KXy(jPiuPCQGjE=ej9NKQCeU-qNNZ
zmA+S1pO7jzw<D?=7{!S}zWf6EBj~r%8tbI#I%DE5jMOc{j5n<0dyd274x*?Q(Xm+L
zWr~oXPgq(aN4t%=@^!#)PbbB-H+zk*piDpHLgc}|?3@`z7cT`LrjEPAa*@-KkY?RH
zx-w$sBzm1w90>ig!MdfO<__K^x1Y3I8G7j;NUy-VMEOXfsfN2&nfj;2kEZYnaxz<t
zPq|cOV7u6AU1sFH^VOKKPL5g<jqPia<iTP#JVjev1xeURVrC6T#7Uy68J1{jL=E1a
zLfuFkYX#vC;UmXIn>B{Da)$)w<BvQj&RCi5`_kD+Dk(`4E}G_etac%#HXPdbdI`w{
z@MF^SP}!DWq}0OyY_3yS8$Ik-Ita?~HueNUt{l>^*Iw)E`BYpE@q@~uC=C8Pr0MS;
z=pd{O%!|D9uLpOBk@e0H&)gi-&C-*K6lhGJD(+vX#$QYn^i2Si3h^2GMqX9)FO2bD
zNi_eWcTmU->U*G`iWV<5W+gv9|8I89-}FE?@>_~6D7SB=R@q<H_kZT#fQYI9^MZ9K
zvH<&EFAVs95BwEC{kjPMU%nC;1<^x20k83w=DLJSoA&p80jnRQ!COO>mIPQo<^<Z#
zQ6Mt0?gpf1D`r~<mXGgpObXl}SPhmbLHYgu9u|<dmCB;j|1pP)&2L+9$)6W17?g{r
zb}w^~py0<NpF@|Y^|jSj{zq+{9{hFd>7&Wl6OuIb#t>A+_%=wyoY`#`G7$0zHG^`e
zSutJohFYYeUu7<>J-@@4Xj`8w*?#BX=+yB*L9PCe5dTY>+|e9b9_@@R5JQl=GE+w1
zR&do^9LHAqR&s0sPlL>wesbVBG9H#xmHh0e`}o?;7GR#06?G-mZ^ficm8+Uwi9@le
z8I-N}`z&KYMlw8bx7C0-W17`7C=i3F3NSV<RZ`Ui+%<aottN^jt@_zC`LjkP_o#ca
zPye2eGn?t&o`z%3!ovG}!PvsK1mLWbcQzpbgTNdK3#@Ftf})aC;h+`tbYI|xvt9B5
zS*c59Oh;ExQnOIUYG9%tH?Z?ut*pF2HsdX2EvvSzZHkvxA^l#VX_~!Kja>2Y`0~&o
z0(NOPrPpuOWTks4%j~4{-5-&o053WfDR1gBJGGC8L7nxHELy5ud#Zh#TVG`eOUjxI
zT#~p)?BT`5ON+&lE9J||3S|fZ=7vWWeYz`K$3TFuS!TQL_^ev)m{q+FzDVdfA!l5D
zPV&=rP#|u-s8uP)N8<y0oWLk}4t#G8Z{n6VwT(v_Y9V=w|J-*k7_fe57hdr4A;`os
zVqu7dgA`$lkcnMPl}PL&nE{9m{YLE!?Bd1l&5CFTt0CTS_VLBYT<mm{9toCg=xraG
znLOcC9z+Z-7FT>KN`iU<kA6{yi0}^i^Ji63ZkpOQ(dnb!T3%o64`d;{LT-YxL!I1N
z+=5kUgalvLy22>+(*%D>(+qWAk+{O>ihhgqm|XoGho(IJnumB*V+R}dvx=U3vfx19
zWIg9!EPn7R{t{-d?ci!_Sh}z{^Zf-v{3r)tmHOM67{t@*##f|Gs}_%Nbor6uo%qC;
z`Z;0VYY`q9tkUYqv7(2$bY+E9gib3ZHA7NT_D^&ik==Y3IQlu=F2>uWa}&uFoP+Mb
zU`=;)0=#d%=gA$sR}@Y7{hltfuq)zTqvT@4=b!`T%UcwxuicJ$+n9=>RQ7Ut7dtb&
zJpPn^wS1ngLWo%O>$2l9blV=t@wbVWjtkBPGn7$&XTHDrWpQqS{j4#!g<9qV3XT$w
zZ=QefFuyb!lEZVZ;vU#%3_o|i4}vS)Iy#i9G~@GZFmtD_OTCy;_r5scOiCp>4C+?I
z!6w=%3}>iwTcR>LSs$F+0g=p3gaI%B&XZP81-k%LmH!AXcvZHq(6qyHuo&&8E6+`V
zvMyphrLT~iT)0Muv|qhNcJJHQwlHE7$|En9T)BRY=_9|fTuzswd2$HE4j%1j(^(=>
zx-}wu`w8=B66yPi^X2=$bv-<-cCbDmp#{-U(K6I)@7&65tg)X`<(z0oMmulpgFQLK
z<UD_f-23qR*~95qtp$b_(^LgmMrAxRHWEy+9IWH|G+LqX`{j6uG?_gIb~<u52OrIC
z92ES*lHc4+|F$XSVv`&3a4N^2#htul?l*J}lb-b~HolgB!eR#O2kZTrI7!sszimFK
zF<WA?Vn3P!Bdl{h?!Xj5&Wz<!tUN}KzZbPC@i3wJUVh<!ba*>hWypFmx5XiqstTLl
zGElr!v^4_Q_qltQbBT25<xV`SwJBJQX`bT49LXh(61UMrdEZp(Ip_2V8K4uKnpxrS
zhkJ@75uX8tV@+q>f%&~e?!4K!dv5(!sPO#JT6|DQCGO2BBeO6YKhyw_>)VMINlTyR
zGv^6ReoX|->L2cN+8*=u@bcQ)!T6@2!1&t7j~Axz17fA%cYDs9ZQJw<1NW$64i?)L
z8#%6l>zki)B7ny2Bgy+6U5D=|W|Icj@y>VL>kU`b%Kw+P#Hi8Ude_&7%k=2DcBO`v
zoD<aifvY;Dv!!z_x7FGHTxy4;iUl6Kd!hw_*znduE^{(JT%ufB$K}IGG4CKvBs&m)
zC1;~~(CD^IQb~sHW7DMZ82t4ajMweQ(sp{8EKZV!hi^Hq6(&bX@iH0h(P3TrlE{no
zTk+<e_V<?PnVxe;Ip6ZMB1mpsO&0KXJVEUk1x=;BgcHZ8tMq}W=I=7(G3mJ9qaE9l
zj&#z)?R;dn;gRbONxwO=HtH{0IHjlioSK?RK{(mihH5{u2WbUe+(rZn=$jZ!Hw0U>
zt6h$b&v7aqiApuogTrZ}12qn%k?nVq9jEH6dvcT9CNA$#S~Ry4D3^G+9eoBVc?B+Q
zCsoO(Ta@{pt;FMTQlpH6bAnRNrf>K?z>jE{sbB9+)(dFMFY1l3uyI#K2DB*Xd4R|H
z%RX@fUeG!5YNpFG)1ZZO`Ow98VRq~B+=_2)peC?$0vm!d8S(8rjpT*O_*So7fq>!W
z9`>({jTU&;(*d@e-zWV(Vh(IF>U^4UO<nEd-lG=^ZqFrK8<c3(XQktY!#Fl2?MV`G
zigh71W!nSI<?i)Yihh!^6ECXn-Kgd$?HE(~8~6Ly2@0GIdkUjht*Y$v_=a*qkG$|{
z>&U9-7+ZRxrM_FUqs1lBeYZxpgljQscuq<7mmUVOHJ>K@)#>7P2BDUsu#VjCtMtLL
z?L%M?9cOq;Gc?QPP}D_lj9<*sg%wTG^}{_lJPyx}lRJZ}n*lq^N5WzTwm4!P7<^8$
zr_ti_I|U>z5~xB@=#vdL&@LNazMOcX<x`PWEd)EI>g?*AqitkzrqH>bOgBP1*B-Vc
z(AUd6pV#fFKBs2jC#Vk8f<rP3a?}&#XlLTToMRGGp%VKeEzR!_fFB(seRf=d&dP37
zxyM)3t!u*1lee60?fR{^A)DNmJ}<S#8iQ<N*xBE!PYF4{bUli>Q%;_0j`dYHDQZ~m
zt=B0zIm>N2bkhk}FO55kwxp`9zV~sNg9CrW$c${GOOQYRvN|B_vubP&q_2_`tDhj&
z&ENWiiw2$)VU-(J^tly=SsS$P&gk$3?wkXqeXfT%)d>a+m<>yk?#B5Z%h^}CM}*61
z#{b++(54Wx>znELyahFpk#7d~VVG`xmNXGzgtJcRwBxY-KBn^DE2jIpV*H>RsvKXB
zG#m%f_m9oyE4xo$pZ(zMeQLHj5Sz!u6mo-Hv|M6;?euKC^39W1)m??AB8VCM)4A8f
z1c%GTt>CEK-esMEUu5Ez>xCRs$TTL)Inv}5u-A}2T_dhVMGqG$dmqB7%&AX?1vbae
zU<YK;z4HcqK1_LQRtQh=R+=mNs++v*!MByOdzJ#`gdE-DJl!>!#k|~+(J-<iIp2m`
zL`P{$NwQSsEXUG*2%+(x+gnop+$%t@vi=ut!07=}))Jye^p$Tpm&LhA=xS(!E_W{V
zriGCXgA-VSzY^nQ)_Npggu_yD;tj{0hcsp?O#1hcA;o2NYRM*0M9bo;`_2YICV+ai
zT#b;KyM49>l--hiP~)?x^HCYnYMoX=O1nB1e(i<VWFxn+KbXboy&du+B64h`Q$^Hg
z$L$8`R0_&Z$Ao!`)oU9c-HRHx7&XcK_E?~VkITCYAPo?l$YB3c8Y=Q+c_<3nh@QrN
z9+*jdnE#0U3z7zZNs;oBoxnsiA!wg4F<OVTv~?;cmEY7ba!E<QoLN|2&$-l%P)lV=
ztK0pdxs7g=G20+j=4aIThRG)anK*4`I$&Up2`F`9s4jhx<+~qBY`(Vx(NxDX2l5z;
z>i@MtePL~8ojU+22N8A5V>VC%$f!i@9`FT;cGU}9RERH{)@Z#M0B*qnCR%OlIAu+l
z$s50*h&x<GkT!b4Np9<9{pkuuL^lQ8PXqXKnl;%w7K;?kj59baC5&2lP8K8Vy;aDG
z*r7+Q`ZeX;VMN)?5BqaSDr06NaCFS!cQp<kOZ3aTR;bViP>si9q6$N@fjHv9HR6R!
zsnPZUsE%nW-fUuJwoTX@Zy{RT-;Sdkg=@Of+*_;h<lA^tv|}ajxH|D~vy++UOgp1W
z>M_H6O+w4)<GDIbW4wZa9WB=w&3g94`LZv#rH&f0XS)XRBBoRmhU1datldg}8m&1$
ztQcms!nz@?zO?$y_i%Pzl<L5QV?_?89B!c1Zh3o89?Ax=K3X%Y)g$41(0+sUQjA(<
zCh8g0xuig)+2&Cm{+J-`T~~l&LD*M$<ZIwfRpoAkDCF=6-Dt;3vE9uU9X-D`XZdyW
zgX*j%1lNR(By5ueC-7pjxNCBqtf{UyUy$ol1&}g1NTt*$kk6I_2PKDB8Z$@71wsP#
z(T}3>W(o@D#UHoY3v+93^DrhdUw*g9@wPd8YcRSK`nk?o#OcL;6J`pX+E?~GBHyG>
z9nGyz4NC3^H2bCM)9mz)3)|0Ie=r(JmFvH~;Ux?l0lyBu@qH<X|KkCHqPRDxoo+=u
z_I?Ry^lab4QD&Bqg1X}(+;zqkAnDrpJH)lYK;S;p&(BpCsC;ZO(+f<!c7~8LuUK{B
zn^{$Av(FvR+g0!}y2dvFZ$ld#<i(%Z#FSgqsuhjt7Uy(<b2^rZa*+};3wZ2OOKZ9^
z-AS&6zcV3j?k93@-Y|4XiU_K(w|H5(vfx{lUZQpDfPnjzg+;K|^fBsVkR`wN)(2}Q
zrtJ(myuHRLx4As)rKRHq*=Zj;@ad!VOr8~m_2o=AGqSaB_1*Fq-Abl=^CC<?q>2W=
zQca1Fj{Q_UMLj25p*RNXvx{-pMCcG3K+!rbr-Uo>Q+JZNO9bjrx>gh;RxF`+S8uOk
zd1eZ^ua$ORYg-lE#HxUv_185W!m3I2>gv4$kFP{lpGug14t>Xa_pghKspz-M_ZD*o
zcaeKb0=Zdz2_B}8tT&N4rl+T_n9%Fymns@zZr<M~Wbj+|o#bDL2IckEoXz;IS+CiN
zww>nhH&vgZHxs>da2I_gy;&JdLE)POT}L1Lp)T)U0k;3bAx;CNUnt&IJG*=-GW)dh
zfMIfg1@SaeG+GcJ5*-7m%M%O?KlY{fpYr6~`{xF%{m2GwZOhA;yfH0~b$B#tHF83{
ztF=n_hp7Gv(=-zJT0TVm@Gqd_@4wrcqBOv`08&@|*X{o!LeJfI=R7qL)V^Tw{oUHl
zhD4$Jhfb-~ybtu$)YJx`+vf7TujMW6Rh=g*u4kQ<q(5e1!hXTQkOz?`#ciC}f}Gz4
zedu*vch#@=6ce`Fm*YBc-j6k(<UNqlMgu#(v1i@EN0s=Vd!|m;ExAvIf{Zi-%*ND9
zg_e9D<wz?DV~m+FffDmQGw`M%Z1x!ZfH{?o_3_&lbm91{@b>S4W)bsK7K>Qycg8T`
z{l;*gb~T{T8(0l%LQZW-iwN!b&Yw*x=Z7395T4yW^JNXXyL!k$`QcN#Gh(cO2l392
zOhB=~RVOjR6<`fKlGtr>9P|z%t{&w0_?)TXunuC(y(yX?5%l|{%QLiXZL4_)wGg~9
zp#BN-?=ijq9vS{(@l9K{D_QIYBfjvy7=a?0C-`MBi~Z}1ddW}xW~#U^WTEJUSXUUs
zpb@%A(B5oqwx1C`<k%wsVlA*kJf4zhX>*xJEzG<0a;aC9HNOL%fJ*ywpstqEknK&h
znt3iSjHVQHb?;Oo?k8Z{a4iGc^xms{`}bJs{5F@V@ve`Q>tyZd)Z@vK$mbY;pO-s$
z3tT0W%^%L-=Wzr}E^WbkUEKjYbq~6@H^s#fhI2kDB>R(?A~ydWt;HD`eWskl@r#+8
z-#rD$ry~ko9#eTgYXTgg`{CpH_r|&qG535Kj*S3kdcNFWd^@|cHFi?YvkykMUcCE6
zB0=wsWzPpM6b(JfnE#~2%gf#{R%QFct&;^>(2j4<6zh>NbVntXI;XMO)pYzB{ZU&R
zH%06pP{;+po1It5;MYsm0vtLb5B_6mV?*M7M%mHC8<>u*D`QaD!N<-NT)>F`z|*xS
zKjklZbHUQ$xM%@GOLS+e-3yuWC$;CdMI&$KjOHVckTjn#721TAQ7RtmsO$juM4mFi
zGw-jpW-+xOs<{(ps{0N_5?3VfA>H76BiG9j3zo#9nY(@|wal!gyzjs2e|q+PkfSy1
zZ@GzxxAB9%c2<co_Hq}vn-5~H^B4ck_Q3nJZ06a!U;sCeQA#tio2dj!Iz^37lTixg
z7YT;nkvi=SQ+#2?wEyCF$O<<Af4KsgO4xHS3YP(vl`|NQ;xp9IuoPQvra%W`9|@g@
zDQcewhb8zdOxKpNS$euc_b3cyj_@Vr%qV=tP<o2FQ}2nrV&-Q*KL3DszPx2QB)}3B
zYR0jObe}t9M8#u^k*>-X68ah4_Q+kg==0l&Yp<uv5f=7|$zbGbA2lA;gluxErluqg
zOX7(ca=)ER{B8ddFEztWU8tDz`7_N?F6M@bPJDejHjz(yCpT1X+-z>@Z@GpB!pOeu
zm;eYS+<4i^Y>MV;I;YQbe6J98O%pQKuB=-Yi2AitWL%{&=#74p;lsn|2eg2(rU@fr
ziWX$~oivV_I9P<U?;icDt|m%Y*UX`RP`PB{F`z>%lpNGzX%n_zaWVyrs1H@D*@GYZ
z!OcY9E2pc$?ECl$FgX>Qrp#cpErPh9EwGDnUv2U-!Qo^^jE}Ewj>=*Jh}<`IUQNW!
z@;@R_iVF7WBn{j>pMibM;YD1dWDIW4(HXe-3Xk7xGGn<Vk#OvGA$P3zD9f4X7*Aiv
z;(RGDQRHpF#o)JRa+!z!VWmD`z44b!mM>dXV7tdwJz*s{E$oCjn5H^SaHFnpH%0pp
z6%X*GEuH?Ind8ZAJ(XtT4`IRebQY);b$wIq>_VWxnje{A;ZengV_ZuCtk;brYPu7i
zpK7`1eS5YBgop8R-f_Ol3tQa@1(55`J+G?h@-wW8%<Ez>1VRFOZBEVg<4={L&*h&@
zE|OlGsakV~g=O>UFFo-0;OkcBs0P^;Z(6EY<1Mf<qe+G7HoPUdJK<m}i&)KhzPfg4
z;@Srn+}}a9zN1jECBl~MjRfvckO9py7a^DLkOAJf^w-idQckNkk}A#*J2b6#Q~oAC
zdGWIN?n)r19m;CL4<N=L?_)Paq2H-zoA#!SM=a1!+|W2(@s!&{NK#3$_2zI>*9dGq
zPTuBd6Hj)!>qpyqm%$zoavD#ns+^CSh(aXxb2(DZq`S$g$An}o!1lDDZdEbe(!Z4B
zPZ(&^H{~}BEPXpR6_hqN?{zlHDWCWs@SouI<$m<KCSbHUa^YqEptUD%m{|y$I~q8<
z+e&c%xp+`zxN+;>i^mj$t=APLrz}TSIQ|u{+dtjYy+cZ6D*eXmiZ_8Wdx$DuY2;z+
zZI$1#bQ^N+IHWnZuWSkXYQA@p_hB&`(-+Km-v#9(N__{7zfN0ge@DU7WpFg41NSt@
z;yqg$)S*e<c@JlUe$kF>bI|LqzpWs6De1kbystA*a`;;CZ%ZxbsVL}#*f;6R#yqlP
zk4X`$H5a12SK7Av+C<z>X0OSNNs?Tv4Vle>roaG~qr~5bqlt3pJ@|3vZ_?=4f1HW&
z`|Y{v(T^4XVTfAp&J<Ibq32(&O;%Dz3am;S=&?L-u4&*|KjvTr-FYv!c7Mtr4bm4P
ze&MzNAQ`R9r_#&QWJFPXaFb>{)os6OJ@N1~#&loz4o@q-nkcrfFXWDcFIp)Tn02z=
z@QP_bS-k2wai3|~r8@U9W+N&VhB=NQzds$X(L6w{zj>1_$CsU<D*biC|K|exeG4Hc
zUbX~$8!bIm<h#^n0wsPtE6wYNO^xS14xPK<Vy<R?6tBti$z5_3kU-;iO8(_xHWdE8
z8&2Yo$;@F?sE}S96KZv3nQ8APQL(WwlKh4fm-VpTWF8`!M#CXm6JgvvV%n)p$BW=2
z{<sI1FN>z>!_B!fAfGbopk4liQNxt6fx+iy#oC6BTjbKPRBgPPM)fa~rn2QPx+Ni0
z$3ep3c#NALW&;io=xW2#kHigZr_$p2RV9Xy5-o;6PH`(_#QFP|A9@2`MjvIOsF#j_
zPh?*Lv!`%*H9>!HcWk4jS>`%TcVU^J9`i0SiC}l;E?D9R<OF<|Y;PIIiuD?Uhqc^Q
z+m-TK54#mNPVcepV_AWDb6Zo+&zf(zu$Fy9DFf-s!xAXIY@1apVyd@rIetLXMH5xV
z^RiWs+d|4#4-<PInab2|@#3#zC?>U5qx1x7{ysMRI64V7#*EuLr6&FN_<vNDCTaZJ
z!ppIFh0{7X$F`Yg%0UihGRLxpLuQM%<9xk)7au9pdHA=thc^rPiv$q$eA3O&TZ3!E
zx0$bF3aW7+xXd1(FhU07YuyM}?^3pnQ3|R|1Sm{RWd*&Ah2J&aMjO%os>9*w9QvYX
zZ2y%Z@jg?u-2_A4FL}7D;<Jw1hG>PvYM}Iw<8OgJ3<Bbb-W?of3wqK=Gj<i_yf&KZ
z3>-E*9-u^(PiT-9NXF5#oe6=y&lMfKw3dHy<>+pw%C1=T`9-{Km)b5X&MtQe)db}k
zgrTIGV~bg0d4L%}o3t_u!>n#!ICB`eu-Zt!BpW*!k7;sFa@%f0z#jnCsC07PFX(Ct
z!7Hs_)s$B?<yu%+NYI;;@GxOSZVvXN%IEa8QNLm@uBD=hJdK(Hub$}7{V{yw-gQ%k
z1I;l+^P{xH(5?Ir#liz4%Xh)X7JbUAFg2io&A|8g>23t=CieD=xw2b1n`;~Iyp#$q
z8m6F6^M*{<BX1s`D<`?l4V>%ZPsThTH}g#e0$3HZ-d~>mS0_NqOB6lZJJg|Qw<eT}
zA})1VOa|atZVj??sY2S#@-#<1{yjh!@LK7QARpGfH$Fb-@jV7Qnc#UB^)VrGT}tOj
z9yZ7HYqpnCL9miB=C8q3kKHB)iIj$nO#Ak9XZnnRCvBDyhE(3C9jy9^AJzIFblY43
z7N-wUr|}}|U$}~&?UVF8V%InubUwPL?%=(4g{%8s?Vf~zo+=Yp+_483=EtW42a7rz
zOz^BhV()QT?#-&w+&&h^tPW|l_Li^}OV&Ja3J^G;tunnW!#i>s`u5C?WTGKu-?ds7
z`*RvKYTHnSqwNFsPSANL*IAi!c!Hh-f+NL&wvc5iV&V+`bZ!fdj9OnlDS2UO##ouw
z^0zEHwI8JUAFdHezXF0@>zg$BKhxurBblyLjF+VFLd@Mw+3wef{pW#V&1(uc2;otM
z(tV6WNT)GWw#i9iV<hr)KK;UBkvIe2HxMxGbj1R#G1)V-YrrOO@;QUkL<l&@rW}oy
z-xN4?+uQ(VSG;f(!*RLiw)Xas<48%cFHC}82pmGuNay5;&{@nmu&bYM)znN>F}_&J
zx)m23iwxDgtQTuMgb*7atk0V8LT(r|Aa!d#Vu0ChbL^;O&x+-S_M_gh%iEdmf3(Wa
z%8#t?FI;B>AD8i`Sn5|*qgqeh0IsKzY#h<WdXSC<FE&g9ax!0HvdPvQcmO_v&DUQ_
zv$#H{5#PiE0|O2=Mle_^$OqHF7o*-D*K>q_yr)d|DG)tuu)z2y78keX{lof(X=oD-
zPat!LLNtB%_qWVFY$W6aPW6&g)CV2)_WMV@&ItE(yCIo5z$1SbqnYH`%j3gOS-%j#
zdp><^_Dj-uqdKP#?V}UbU)G9K&hTeTD=qonA6_4%GF~6iw45)STUL~$<h;NMwYem6
zy1d8I*B|ZY&(fS%@swQGgl@C=FuzRE`aHYt?<S!xQl;~O*86Ye8LS6~Q>%8W_G_)5
z5*taA$7(b_xdz+lHANKOQe@qZ`SOCFQJoM82L|5P?rvv#ocF7P_va~Fce+A3DVu{F
zaCzHrc4Eohc{8mCu6j$2)-<Sxy1-KMExlI#^jt4+{qgW$>7)#qGZA7F381ODrjS%n
zEjYZn<a#%*t3B+2rFy5ds~+K=W-q4w+}PKPeF}O@GsG*98GWLj_~nR)QroFZuJ9rc
z;dj?PSJ6D)MLoL`C5OfaK9RM<r+ScQ2x7kx<8HCjQaz}Kp(Z2`frAMYVD6~qX}`FO
z5It~|3$aDOI2TCG%smM%?+~(+&=GMvqc4V}<@M32z-RZL-SY|kGHF;kdrYt<^7}zS
zhFuRn>Q3ye{xPzvvDo>7n%h=%HJ{A4T<=Dvy3M4JJ=GMgDLV+_Ja=DnUR{7S>l_br
z#s=Q-17=14alBr{VLNnlW!GB(^~qg3=1it40u_zR9gw@?(=jfW1S`A`ky1%Iy9gpU
z^_tlk(c@<QTlnK3kY*HgaSCj3;^t;IX`p|uH)g#aG)T^}JueBKw==ZW`eKIg?cQ|E
zhGF}a!Qg!>d~{#2suMD}nHtEotfIDrK`<AtmoLuzb#n5~^+coHG4sYCbWR@5Dh6i(
zSNuhfy`aiWJicJZCA=8aV>lpvk=bQPr#;86sIcJB;zT*$m%9<DjKfxd&!d!DUp@3=
zT|vHRo}<c`5`p$Q)2zvILpBK}uZ`(EsW#!xc&DcgBssn|xzSg)FB{_<_k8;b-rbzZ
zY$jor+N05^RginDv#Y|f)Jg{8@;RZ#2SlH0HipQyw(n5|DE~M&Q4)*Ja}RwJad&Ps
zcr43|IiJdu2tbP|c&eD_-7zi(_UIk^kcOkw*6`54hjMfm4FVZDr$%RbXP<0~#IE{S
zoROynFmRG(rWDC=GY;j49$hOWSLvx}kj19F(*_U3u4jp>w-*{IQMP*N<>kG?dJQ7n
zDh*coQTB(Z^W@uuMa^BzZ-@QOJ?-AMW^%;)PJ0vZ@WCSilfLLK*ZRGOFQ-e>T`)PX
zEZD3G_;i*!lESv>RI(8>p(`u4&wrHN96#^R`r1*B>}T0`RbV~eQ=AUG(*~2VWEguw
zyBQsR9ShZU5VMPgf13-4bST*8ex16`-CUWDz%QOJ-ESYZ6#){`&9EH4%F`r>{WoIq
z8BC0NK@e`;F*6ZQoIh+;?@bQ2u020=r+i_@ks&%lwcA+Su&g=QE5zrO*JWMXvF2}j
zIlMG=C=ypH%4`;B+E_b&ECL~k#SW6rm9wT^Uc8t^4w99U3+UOijyEw+?KrOHoZ62T
zLzh>H_(Mi__&}pE?$2I{HZ%<M+=kwPN%afP919DlXC-zU!v@c_mzM|N)R)(O@y;)M
zIg~m6>-9FvN>gP1oKri-+-{S(+(nYVwNmWCjVRt-56^^Cr}T^a%*|`v3tmzXEoZK_
z++LfP8P|n|4HB~yjOy`3+-5J|1_wSbdI;otMK^n1vifZ&+zsBe^|$JP%^^0Dw;jcz
zRbi}=CafG|By+j#9=!CD0$fdv^?7ls(gHudmIQI5JZ&H|@^+)EcD9b*@Nqdd@jfn+
z-U5b>O--|O^##u@!jC}^tttm@Z3g*qTfb*bGkwSBXJ4JlWJTn|cqgJIM?WyDtSiLv
zJuh>~{Xy>Ka3SAzHC77IsCKgy_1u;F%h2^yx43Vl8?RwE7LCqu`q^e%21OGibA+~i
ze<CiA_Ipkr(pVi{%K2q7vq~L!N*24@M+-$1NXb8E5L?xsT){<^YTw5H<6!dYLZ6BD
zWC}S0zmD7~c~=&HEcyP=Xb^mhw6$uGZyEoOS^D;VrTRPE#aZ)N<Nn_c`0ItO!nb!|
z_CFR{CjW?zLckr(&$m8Lw%(Pk|Gc&28$Rarn=T~w3@pum^pna@$VZDen|`Z$)>Z#Z
zntB@UJ?nS-z~~}TwSQ2w&lcS<cs<RVO-mW&|8KhgKl-``cHOG4Yu3{ZV07M>2|*^5
z5*8M|j^XF$@3>pBKK)ah@0soQ4_q3culy=h`pW9D5CIBCi)oTct^<Oy^771+&#0(9
zb*AI5qJcGo^0ydv|DUR^Ix4EJdxIcIqoBk{NQfW}G9am-#1N7a(j_G=H4LeMbayDN
zG&saCbPgq;NOv<ZFbps-#E<v;t?$+E{<YUx_n!5fyVf~p@8{X)Y^dB0e)DK(w`0ey
z+j39f=x)#2!TH93g&@Lb(0f)ri~mGGwxvIO)Uj=RaryP5HCevYQTq<F{YCq?n#B3|
zi9?~q^B-`(t0a|6$sZCB9mq+|RRk10aB?`Z(e!bVEI?j49wuhh+I=4l@3){P_^sh|
z#Bx<p+Op1m<avoEO)Jw=<tL7oHDBNLsVpu14241)nO~OH4;(F&yC3!+h5UCC$iZmE
z9|WFI#aJNA4#oYI6t!B<0(U>l)7R}jo}aCX{%Xq1@;wG17QEvOkK06-OP!9v?jr=2
z_1&KD!0MnA;3d|VLot?DNR7U)=O@A3`E$g6N$DTY4YT+N&=cy8g4E)LBGMIV58CuQ
z*I{C>zE@K_lRGeEb#bHA@((&rl#LBofpX<ci@B8|ir8fuo3JyYukHmKzwO&|r~|)q
z@26bAfy8KS?bWtrTehv5O9cvwVsm*JKb9E<bMx}q(C;;qyI75#XSrkv2STE5o8RV;
z5GSe%L)-Blnf~@k$Cr#M4y6KR&TDvzdJJu}bT2VCFE*cWhej!mJvwKLC2HJqFywuR
zRDL$eAzm@~t>;4f8Fbbg>qXff8u=BT+yID8o~5?x6+py%u88^S@2M6B?I9f9`iQSU
z6_NM;5CY{h)YxsEzO(_D-;RF+brFj2Yh7%4ovQgtKX^sy2$TBXKOIPVK0#<mZt;YA
z)LgV)274nw+*vr<+PZ-p5+%K++7lJti6JBSky}<(oG_MP@svD$c&1XeeO5bRAJ@-h
zcqI(hzY(9m9KrZFXs@-A5CdrYNnuMfKXj7I%)O1qkMi3QWn=fR;^-0P_oNIFP<Naj
zL6&rKJ}b#D*7fbW+WM?M+h)b-@|LZ$MLd(?`eMDML+-G8l8C&k%nkRR<H&TV4}YSz
zz_J(GKg_5o9zEfBW-?*y+cDHNE0|D?Qs<N!^j)roJDeR`tX{B(tCF_eXX*~LwHb3z
z7hbPo{i2TQz%r#GTm|y4j0p@EuKEl^e&x=yWDS)VH7yC93ZciR$XoMh&}J|05gbSO
zr_~+W+wkYI;bWB%Y%>OT@;Bda+$!2_{wU&=qY;?&%||FB0*=!u+H1-v4PfCk0#tqM
z6|vX?6PZkr)wZh-k~cD(rp_#=#2&bNZ>Ddgk5xYA7OAv2@T8*_0NOPD**w~ub@LjD
z=b33c*U0DuWvW9rSin)QTc!ga)X)To%ln#9Xp%DlZyV)h*UYMpiJ=PaNk$m^jn^9S
z%De<VNw&7y!v=wE3ArzPX_%0ax4F8fU{eheAv_L`;KWD8?jC}oI&m$YA_h1p8f^+i
zzc`5>Y~7+7G;*Q(wMORS?(puL$$Cq5*D+-gMEBtb+$`2Jg0qQE-0@@4yrGenRR7@+
zJq15veooCPmHrECy7WzsvR!<{ezh_d_uJhpHf&M`UmOIe=0xy?^s22bcxfh5Ki6Mi
z1$XX-QpO~&=q>T^bWS}9x&;f7H$@dzs0Vj%kxX7>c|bgF^wdcqM7|w_VizZM<=e`O
zmlL5`Y4b8kyff|XR3I0XVrct9*y2Gy7AS_WB}<Vb8xH2#Dj!~Q2|&B*iV^?VJtn~>
zJuTs@=vh;9J>b8|X|-^~SLg<|zaKin@Vu9nI45d%)h0v2B`<Ea@U)=77=+k&LBb0m
zj~S^@Gw6lp`_QYFU_|9%NpfazKW=?oq&s$2Hv)4paLYbrbtUcgp~kpN)H;0=1!dkD
zJQ}H=rJnmemD*2}w#TbR@brD&h7<cOv8K0W_d*lr2oX%`ZE?>$q+1Do&5%|fiz4K&
z>plbaw|=I#Y<NBkEv-lt<(9Pz*-@ln(N{lPGp*g4dmgM2(Ua(pRSpW!;UYRt1f(P2
zU>HPkT>evvuJfg1^cs@EcDysY{}Yl@VeLAHQ$w3u((c*y)lGkv^6NT5YKuyE&JP<;
zO0#5sAZY3@(}AhZ&OC8yT8vo~o*ptz%wAb0^540m!q4tAx9(^lZ>7wi8ExD)PE|C4
zM_1hD&oT7|o?Z;EiIMQsS4@RIVI%4tcAjdKNc`;XytEGLebs0OC>p*Hx{kAq_L^hi
z*8HSi6-!GmBE$XG7;J{6BRT7(6_g)Kkhjy3+f1Th+M-uJq^6}0EY9(I<}EGfUXs1*
zH;-xdBCQS*<Ay~vF#slQqAh>CH9gL94?MaG<i7&ORKQiRtFBb+%zHdr^pvyj`~6d{
z?8E#)qa`L?53!@0f}^-4J4}ky@fk`i;cjY2T=<=oXoR)tJNgS|spS10c!B_5kn7c3
z9=$RyH#wjqRrwb|S2hTZpU~>%yR@|iNbX*KS>8QMRo3iW@$9`(Evc>W_Xt@V#k(+P
zla4bkuWWC8(WFGzPDLHmq3!becooQiQh<UM{M{JRhT7J|BrG;=4(X6Cs25*`8*CcW
zY)$qKUvj3?7U*VqJEAhqEf8Q1{Qy^C8O;_##3>Y_tu!>OH8DIX03?s-8VXOIy`1op
zZJO8>?>fo7E8_SZNOTg_sb|`ECTqRuLa%iCmeknKuq1`+_Iprdn6#po%-PIJ0lGGs
z*T^aV(23?2ipCWKa3pCb_jSGlIlcQ+Spx=yLc69T;CjIqgHqt)V!`zrWE8MZQ#jm(
zw?n3=FOe62!>2ZBlrVbs0~d?wQlG8q5<a7!XJJ@wQ1$?@+O0EX-F=7nj~9yNBfU?u
z=!8A>?9$?c*Z6SC@)x(O#0ljZ?O{dIIZ32WJ1EG-4@IB8jOBE@N}cr{aZj2eKHSWw
zQ&xmVhyQSKyhrr{HtxNlvG59Mm&Xg06e*7C%7JGn%3xC{{Q(W@S>_%pF^PDWF3zxT
z+jyD6k0k_t<gzlv87_-Y6Zn;hzpN*}Rc_bi&v6eU`qcI30IPm1;gTLfGQeHmeahbV
z(v`;;^85B7BUOz3R(48Iw(wV)dP3y<vy_}7N;|qRJ*)jrz8NK-mp86RGz)SMIZs2*
zgwW|623HheVj%HH1hn}%Tha1vopWlv@%V5pqEyVZWis`gro7r)L4Mh-tr*IVUSo~Z
z!vQSY6<()l#-z?fQHD}%P4Rrh)V9Ui3sbGS{^6t{n(ACOIQkoBzO}hm6s2-bs=K&I
z<W#L0--|};F;5g1w~*G-isWb*#(Z@y#I*orxyB;)gc$Vz7-iWNO#22<qYQK8Jo@$i
z>AXzZWRjeeaMiC>hcD;VqVM?@VoSBfM-vmPu``17(p?y;2PfaaUr&D0{K&86_*$n=
z{s033KYDQ6h9T*3H|EK!W-|&HG*!!Vt&5g1Fjt&@0L_hXU|k;>-xM2F9p3%sa>S%s
zuQ^7kCGD<Hd?q7?CtFLNmApb<=yln)Hqc*D(viLpK6TQ^$=xzve2*zNP2KIcik`;2
z8hSa1kStV}Xn+V8!p9lZfOSJWR_#(pMQH>E1wVdWy^%TMJ*yXkYEN!}H)5gQ%S3ZS
zRm1Q{L%s=j9b`EKx83g0EM4@X+6>$Y+-;+5e;5E=L33nMC&yYbLn7lHa<X6ljY!&@
z<rZD)tb{F7g5oPkm2V!b`_<_Wiaj=x^-YZ4foiSOsg!p<Q+iAN%+;m^wqXfHO30aM
z$!^g<QIRt0QZxn2#jc)`r*p~{k1XT7Ojo71ZXTc1T2O4Zcc*fgCj^Cb<H$-pzi%cd
zSD4h3dtior$7%PWkK_+~^KV;^FKR^)U08wY9Z_@jUvfUl@)xqf<IhM>mMr^@g(b+}
zp^oQA#twI*VesoS-zUjht_yBFUf7kSpxB&nGoCS0!D$C8ZMx(c(nlAc->|9DjLsjb
z)NR=%g&3^i&1bzYE=yW$sUB8EH1D~+NguUd;%<0<Z<*)YXQ9eFwU`W;6=r+%j2?N<
z-qZ-xk2BC|YEl`gBCMuJV7qfy^=F^AM9ZX~?fBNBypM*+3(1(e4<~qvRLk9Qgoi<q
zxI6Yvtp2lH!Y<=8NAJ48OSeAG|6=L<$g8J*o-|g(VE+6T3%z_-$zl;2uv*k9Bau3L
zL|lSB)ROUAmLy+l_rQ*CMZugnjARn=Ao8VzGm7eySqO_%S0bic<n^M--G28EyF89L
zkIb-xE(nu_;Y%vSCVE}zVT%y6*N7F#@Q_D?zc^7QMR6fJ&F_)5Ol|E5W&!CV;j~|V
zM5yQx2Qc0g3lnn-7~>%<q&c29Yhv4lX!}ic)+$R43XF~Frf*B*^nWC|$|0N=x<<#h
zWM<DTFH+HFs#VquGWL0x3y+_|OyM-FJg9nNo2a!th44dit<<mlx&>o`;m%b)bC%7Q
zKc0h^?=-l`N!9YVBs6Vn<y=aY`vhG4o6C`Rqm?tL6TkUeN=6wu`KWe83aedaQk8Y{
zX37@xMKDKS76*K9%pYF9NPQdbPeMuZ;Hhwq>+AkDS>+Iy83Il!scd5BrnClT%QXM)
z@2ZlhH=k5ZwRBe=O72R$gJ2vG#tD|p>l(VsnzbKpXWj>mz2@*LN*+GB_7)FnLr2oU
z;Dg*d>;yo+#nKsfBz*#<B?`cK`ZFOoK3mb!KaTJu!Fey+b*+r>dC!ZbDQ(`Ff#{HP
z)WN~LTwg3uqYDQrV#lZ}a<f!{)M0!G)3HFXmCbzyF>WF;5;S`yfLElDaB+SwUI)NE
z1ogi^=@K5&D8rF^Ph~06AroPlWa;C~%qs+$q|zBn%HG|*&Lbx_ijsXG(qxf{x1bLW
zU*^ojKJxW<myUr{^$k8Q!czvsGuwvrhB_y)3~&1~YM;?eODFU=87VuH&KLGQU}AvS
zv5;(kGw|=`VO$|NNaebBM9hP|EUS-Ut~sse?ugAJ1bl~qTy=h$O>+SGZ9kA!DQ_dg
z_$DSiDAEeN=3kR<7ux#!AR>owEEm*=myf`V0CCKeJ=A}>Ex=mismct}m&>T<Wq9?k
zu&U1o`c6-hu3X}#v&b)RXF@zq))!vp-*ku~FF8s)_cGW4@M(yW?)3RPgcU6GMaQ&G
zQYGTI|GG;HHc}}JxId@ZAozNKS5iid7oD`cX=d&qM}JTqpTMSz$Z5XOh+k4H?k`jd
z+T(1v^2cn6ne256KN|FjO<Psh2iO;sQ63jnu!0BQvRTFy?u*jBlKtCnS6EbP2@VN9
zkG6VRu{c&*T;)2G>D~DzRzbn0_QUXst($dNMAUuR(2H;L>&dCItAkhu)s_Rsy+M)j
z%6vkE9x)_aQE=m@X4EYntD&%g(h|Ei+6g(6Z9TW(aecuP%01ze)j*+`q`mjd2Ht=j
zVNNxL6qC+jEBn`EJ6F);j<b|{Dm7E!h4!WO?Ft9qJEw~t>aN5}O)(gv4W*IVTn136
z>{4Wr(8EVbo8DBg^-60~Pasi?Y#QZA?u!P_z#n+ZS5+V#yTthwIc3-Z(axQ$%+;;H
z7ruuSMCp>oM3WOLUtZ>30o0<}!}3aq^x&F-ry9zbvvV-=v+z)Xn(r7O3yH0X3Sxg=
z0(r<3%T8%&cawo-6`1_XPsB{vKnZNYhM##{y#!Trb5#D!AW&QAfmHdqPtOQN+Mrg9
zVs)$5xQy_O@2Rnxg9+Q+I?WP9KOe9Rv!aerQ|9Pi0h8cUV9Z?eqzwp#EfM5Bvc}-H
z9eIU`;mSA0f0D^;v>_~kDvBDnUAB~ZE?ORc7%9-wqEpNDgK3@Zxoz)s&|?t_!IC!z
zLWsy*Mf_+I?st6xrGWTx5fRsAxE_TX94IV^2hI22RM3cMYcv<h<i{$GFB<oko9kcG
z*LOahUXE2-RaHw#NI>Fu2ECB1A7y`Lx2b=26R@_~6wxl7t_wpAUt#tqA}+)|k7zrN
z*W=!g@;rZ;`+PS&e=FdS^#-3%SGRt@q;N{!N~pUJGG=Fg=7Q2v;EcVSSPxBgdceOX
zJNs;yDvT;ZI({Y>@=<xoGbuw}iF{XiY40CO9AZUqQTT`BYn2XS$8T1Bm(ZB_dLH}f
z1)b?g?uCW-PQCzEMmm;?r8TknopHpidi0Oy>2#3sr!{1Z+=l0V$Js&=E|!$e(v374
zF1HGCREzs+#THn(1jX1d4;)B-UpO($yhjz)EDF#%GYO1q9j64v;INxhPmuGk)pabW
z)YGN18|enw0@f6zj@p4-1%ANV@=3%Ao!$>S2-TLqI;X!Sd^H+0>=T`ipoD}{ZL#{S
zOCa@|!a^QXEtOT8%~@M><fJR?HvMv(+U?Tklh~Oeny{KFnO#<!*^hG_0j^3vhBqg4
zXtzjbI}5lZf+1Wt>^~_5@F!-B3@9IYu)hyy@spVWsg*PQzSKeo#4-53+XmIcdPYZe
zUwj;dy^EU%qsV!pjVSj!8(EfD9!nz<M4S`$yhAVzeh9{rp{HHbbYibA{Fy)a8Iin&
z-xU+*Hf+i4@kq~vbKiaWPP707voN~(5zRBUPP;s-^Fwtd9(>71N6WMqaXg>e$A+T+
zB6Tte3Ztx)K6gIa*g}#6Bh^UFhZ)g(r{Ph(UZ6cMYTUx?%g+S>>pm&uya#(?0mYM)
zdf~77l)v`J81-OO@h%aAf*#g_23~ymo8^2KKC6!FhBbSaS!{mNF)N4IOtRJZ({-{b
zoD~H>$-Z<#g;f%Ox>$<gt;tvPr0=yT>cCO1oYR@)WH5j>dV_h|N=EY0P;2Bg?aN6`
zhM2%?bC8A%7~l{yE9gzLs{!o`obh5Y7)<FBEE<B{>4kbV?3Bj#4JK#aG5$BhP-hXy
zvq*82!G7Q96-VO6oBr?a>b*K~{Wy)ePLTo1kvNNZI<$Cm2(J;ZP2^y}yOQEPY@4dq
zt7}4@GqB?GQiDGjOu!*lp6JifFJU7dxz`+1+SydK0z1*iEJm)bvjD@P-KQ~-y8bfm
z3SpnoG1rYJ$}x?-+&-Az?hKjH_xHPn1#t06==|Q((uI2xVN**5>O~n+O;`o}l2p2f
zOs$iWh=H)x0yP1{r<Vil0f45L!kaUv9;(LOHCMQt<=KGGf?N{ot*~jcR7@Hr+Koe5
zyfQUaK>~iN-0E^Ld~d}vHK+@A8JQ@Up^EJSO-HK&4B_?Lik$Vh0Vy<vZePP%?so;w
z0ny}NKCAbbhP$1eia(IixgAg00^!j|n<tl-m$NRN6uhP_=bJnif5`!@8G-fr4cEMl
zU&_Z%&t(FZ@FyVWcL<0vU&Og8Pgk!C+|3JQjp63(>3ObC`|BnT;h^WQOlR1B@JRG|
zv9xpms8C<3@&ZZwQI`^+5Y>Un>E)-&b$S6MztSg+Boz*yhGnf_Co8`%&cWo3kC#OA
z*0YUpuYSp`O~@x9`HT5mb-zy>pXvVg*=DptcJ>oY1gx!6_^h>V@-<x{B-AJMh;IMh
z@b+8Pc(eW0=#4EG#J?jW7@p2mCJFhV$!wRlTGWnn_c(wY%Xp6dt$iBZkR@G&b9n+M
zG(B8oP5VbGTSV8<b2YQ37ox2nXuK(Pt8h`j`;(Gl)-eeS&)e(BWsUMmO2UyyM|+3Z
zIk~g5v&$02OIsFIpw7QCM3CvD{e0RCQ*ZJ)wZC+X>$IT7R@Oze&++Cle-UE;i3;lO
z{v)WUfb1mx7F2ZP2t@;b5B!@?^dDn7?pkGWDygn3`Ip}QkAdH8LhwH=KR#Z6?JOT%
z_(JzzX#W@#i${!qsPyZWATf?>F$Q4I%lGvUJ9@qIzs0Nk{cp_j|1cl?1M`)oo#y|-
zB=Ck4{h4RiF!Lx#t~HyFjG)M`f7bJBbc}5~PyTXLuUnjy{*i}%>Kp3(3Cyq2{lGJ+
z{2$bJ-^$;xiR~T#`03mKjwk4tFHd@Z7k2Ue`0wF6ss7XNgqh)+zxus?cmE3St+Y4I
UvRSev-u0vWLS3Qaxmoc40iFZwr2qf`

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/deploy/run-on-aws/mxnet.png b/docs/python_docs/python/tutorials/deploy/run-on-aws/mxnet.png
new file mode 100644
index 0000000000000000000000000000000000000000..5a196d15132b556702e70180b54f769e6d94db44
GIT binary patch
literal 32270
zcmbrlW3X<!w)Z=1TXWd9ZQHhO+qP}nwr$UWIc$55cdoV0*?XV+o?CURZvW6LJxNP?
z@;sHE{C}-Q$jgesLSaDx006*BhzlzM000sHNqazm{kcPWbJ_y{K-F6a3CT+c3E|5-
z+L>Bdn*acaN2H{JD=S%`jrF}OV$%?yP!zBUZ~_*u0YEJZC<_Wgk__{kyBgbrh@v1O
z%A@=!CTFJ<84XmghXs;sfE&-xuMPsmru^_HxxCEX;rW>B@aS`L`swrXao_9o8z3Hh
z14w|78k8T2Pv!Jxe;NW7+H5ZukkH?sI3AeNS~jwOVr~u`U$gzU3f_Byrtk}lnb+6H
z&9}<p_9+S+Ks?@{!ZwO5(x6#F`#2(m5+FYTAoHRUXp?p%y-YYv_-%wnY|hEEMr`KE
zhZ9S{Z(4{rK!38PjX`w)@tXv0q8Q|;M}j2;)?+6R?_eFo)|26e4u}){Ze|J!k5oB=
zYV0RDAH(lOCduujnOM|^PtD_Cx1$mWq>YU46W32W5|T)?+=5sT4%ySQIPJ6X+!bPC
zuc!>XmPNM!kL`|kj#y+5FdBNY5fbl!4r{d=P&5({4jOu`c6U0u6x8<qL1RA$Ki>mM
zJcn=d)bt{yZ6GxE(34rsz3TS<cu?6x&^qnC<Y0iC5iE2EUGjVMIUFKq^g0F#69-l}
zuRDzjhk)EDrr^kvebKJYh=)&nhaUVDjcuj)(>8X$!Z7x{L^S+fHdK!RH1^!9qqg-i
zxo*CG)OJ9;Uqb)_{V?gk$ohcsDrKT()4famBpUG)zeYw&7_;M*kZ1JoI7Fo|OGini
z0ZixiKJ`9r?XRr`oIed|F*UNTkOA~NMzoXB0A{KH<0mzyE$#sFw*m5d!ikVdXHMpu
z^e}!oO95fY15ok<p8^T#Ls19N*b9?B1NGt~1P8SE10V1sAp|<tBU%G@;Nx9_eeh$J
zgNO;hvO~%Sq1oeVgRKqN%)`F-6VZc+4p7lU^7>mq1ll8j779YeQx=0_@|PkYH->HF
z<Bj7%1rZjghr>tY$IQEu<531q<`c=&JVHCeb;9ffd**}B(=)^Mg!J~4r^77io2X;!
zf-xD8tz*Lluk|<Fda^^$2DlBB*rR#_*@n^$we9n|L->Nq6#zsA7Zxqchmw;{f*wUd
ziH+n3m(P@QD??U7t@v9CxfEU(gvukA&zVzaMw;fsF0`7<IU{5S>Bj9w)AFYkSk2Fz
zR-JmC8axuebA<*f3}Wb8(^E^6lcFWXPK+e$S2D<?Tcz_(lOF@4MU@!2)T^q?S!1x`
zb^&Vn^Mu<Bw;FlX6RlI=z?Jzk^>6HZJ6LN|(xSToxB<CAu0>&w_zWi9R=O*7bMfNL
zjhP)v-MYL+z3P5Ke=-Y1=8M!5ydt#+OA8Y0V-&>Lr_oEMlS(4Hgu4#&8*)2fHzcp~
zcZ+&Udy5e!EK95ws}SQ_P*o&5M>+>FOTSB}5?mzdj3XK&dZ53tcjSEff5r+Hm@DB>
z>?bQv(n``CK^;*XC4s06iU*Pmq^uJv&@NCfP~B-J;#ZKI5qGFEN;L}KNB`29fTRda
zZcUCt(Xwn(?pWri0IZ;}sBDgCu5(Uw4sgzSlsRL-%#Lo2&Vo@R6+14UM$JH^jI0!J
zDR3;`su(L@D=%WEmI)gz99g=zAhXbEme9b|aN5w{fZ1@|fNm6WM$yTdEhJlnJ}W!>
zdZcrlbd+^maO66|`sI1VJ4u(|mK>Klmt>cGlk{EKFK@3TF27zhUwA8wSW{ltF3F&C
z7k|Jq5<${T(kjI+b)TB8doK^Ex~O)qwwdp&d=|Ty+!)yy*2w?ne~)?uiP{-iB-t(5
zFe#Gapi)`cLn+sy{*-tw6-qr6kuXkfqHQ8wNm1cZ8L=E&?ouIBbx{spVOGUf$+v)S
zNzY=)9Ld7TOv<9dqGoQpT)pO9E;4UuIbw0OcE0>xUQ_g=ut%s%#7kOCH>@P7M5*pU
zJ+1H}ze~4G@}(D7HLfBp-%Y+w3vab;K5mI_t<MSoTOI2b1`g|ydW5<~cT!KY#koJ;
z`qVhxRNao<TA1ygVUpF7!Lvu&N#nJVXNoh;hpm-fmYLV|b6j|Ic#3$dc;;74MK8rz
z^Y&ve=E(Gpb(BWc2DTIMgZYEdaS)nv6e=1QH79kihNcD-4L0>y4RZ})4P`Z0I&&#r
zlfTChmeG}U&&YfClNq*DmU7k-tKTNpCh7X>Ml8*m25!&i`aL3-3s>t7Vi$o|s88{i
z!3P023wWzg$`F$ntXQp4%ONxwPnmlevTR*WOiswG%q*UcwKi)<b624@kM@Ih*S7I?
z;P&KpZf8~3Sm#=ooTs|G^{x5Ku)C+%^&O5rMH73q=4x?T${J~1)voakzzyS#8Sgu<
zU|uWgEmR$<mm0TjMN48|;|Tg|qxZ`@@7>a!*3HWW<t1gG%28GBN7k#|;|)wcST%Gx
z^ee(CjylFGxj36R$}%D~CJv%D_9w$IOEI}IxwFP;>HL;Kq(OK6s=c<my}QfnvSr)K
zhiaUty6`X2$GQcLq=w^T&5@P}E4u0=?J-?9QY2(dBn|Ypl&h&)LphO#Ldrr?4%+Om
zY&C5$TLw4oP1;WKcadlvX@zO^YMsm7^*i>k_QZC`T<E&!7t&p(UdG=>fp&v;!5PEx
z!=uFBipGl^y)b+Mek>X!97G*r`uO9hFg0x&eRf~P^IGaTKqb`@7BVFSrYEuDYL7wY
z>@&PI+*eAT_U{=mXE45_InrO#IT>_~+fBZ^{=5Af^9>3%hpjO~S=p>VHehUGH@6!^
zjHG5$7iw^|tXdJ<NUT3-w%EJKKfa#huWUFbIkcQaeQ#e2c-08i(z)t~T*Z#!PVqeW
zT(6k7JoHaqxNUc9ybnJUKkGoV1GV@vK9%2>rfbu6bV5=?!d#8q9^77T9M+YTh;@=x
zm>b<jc8?839dz$Ld@i1Ms2S)gw&k|zHu;p#l{P*3ANsF>^`;Ic-zRgXu&I(MT~)og
z$9dMh#_!~XN*qmmkJ5&^hl0eoW0}<~)KYLpaAmmOKh<v=X7e)m)>NrfPt^RlX8CxP
z5A~c{Rx4XuWh-jyacj|6;_|}T7kn0<jIDwUh7v?Qjeb+uOFEw)(Ixaf<~{T7ID~qX
z?pLpFM3P!6=`t;6Z*XrQtuZcRt!_HaUTBMD*=Q}*iT2_0)w%Y52402?Mh!=CpqtT}
z=ww==H_K_q_HZvY)E+LR%u~J8)pl;W%zSLdTwJf!Idi(`b{e=He#&HJo!BziwQRR`
z?|6?L$S&ET^|82UxU&3g>D=11+1Xib>OL{76S_I_s(q>bX|~h1)7Ds5##ioVk8h}N
z9?!-8jDI(f6=nfnhG*jw?UDT=d@eDeR8pqWck#8$Zp`6qu<r6Y+vIN+C(2G#_gnn!
z@WOo7Y4RZvy^_vyOnPiKt}D*1Py5U9F=cP%QTY02@we)o=F8It-;eKZCQMF_chOhV
zW19i45Vx_PS$#L@_gS9r1ZP8!W8HF4d6v9Pd~UvwZ}*0rUsTstXKX!oHa~m%_;MvO
zGZ5=TE;*?D1%YBC0QasOhRK2f`LDqI$r)}EPC#Zp-Z_B2BTjrCv#9~gEycE>k$T0I
zIf3$HiYc@JZ-v<B5&KO|4_?C*4X33~yb?4KtP~lU9^f1#*m(eYVW+31+oq?zt3*S6
z!e7(jA9lS}6~n*zw9PIJkDTh-fIq8#!?B2i1FSM1s{#N3=2$4JJFCk`a~j#%&>9%q
z8Jf_#+x*$o{<*l_IsYVWOq>ny-EFLGojBcj2>-6Z`6vBXHXR}U-&LHgcnH;H<ne{<
z98K_9Xqjp033;LL@$tDGjZHZfg+>3%{Ld2)p}DiOJtrNVo0}W08xyUaqZu6o2L}fo
zJtG|>Bh8;0G)^A2&Iax@woXL<H1gl=2%9(=Ia=5|TiDs+|JAO6p`D8}4<X@S9sTS2
z=RHl_E&kn;t<!%E>(2n`{wkqkprxn#SKEI~x&O-Ll(%p<u~rwhuraZ9`qKw53o|?S
z-}V2ml7IL3AC?;bwq#&r_)p9KDETi-Zo0n)@E-&EXSV*%{WD#>P~3F?ntEO+re&RW
z004df31I<ccfd;>aCK#|HQx$F6=ejJGeb3gc|_r1AlG{Ta{cI?@Obq^j3S1JhG7gA
zN>SxuU0{Yc6cK82!y8n@7(-xa@^EVEk36rormi#<hIgOuoRf_nC$r;h4yWs6CbP)}
zK>R|y8+W){oIQ?yC;t<GIlx?CylCJ~JZdvuqg-Ha@PC*22LpHW52}IwXYO8*lRvsR
z?<?6J<bO244gMGo`qQDIK<L$7SOz1&wX{RL>M-gIF<@STE7q^wzmjs61*mTp8=j6z
zs5qxbl|{YibHPOcTUpBoRjv~3kjtnceHYTE&pyaT{>QddXH{_B=(g`E?hR~_-rQk=
z;&k8lqP-`0d#qIi*l$+ytp|9h7J>pLXm{T=u-F0F@||gjxY%Eoz$zYCgIl(;*zB)b
z2iLh?DP?|h6q>bHplS4K>Rzg2LCfGOiCf7rUWiwZ5L~e)YLLBdqJ@Xi<8*vMbyrr|
zosQo*3o0x?W$_c~$V=EJet5!vR;1d>4XUxVdSI|8xZpM|=yeV3y?2_GcU*336wYP~
zST!yu?cU#Y(P)nE?mzu#z#mx@YcHbSvid~8yUvq^!=uor_b%4adAv9UPxSxnUHg_H
zG))j$ruWv{Q8!tvLUMsWh<;ZksC(4D=FD$BSp{<Hmd@wNW3Q#E7KjVxxb)A^(r-Hf
zO(>kSlrG%t3NN>@28*l1CFDX6f_v=g%Hzc1BBeEL)PvF28UbJzzIdZ9Y(2lbv$JRM
zpS%fKH*AuZvQ**?qHzj{n2=Hr9A9T(wAX>6P(=-ur?wk4qhM<P@Ke_dwlOdFZ{om;
zaT!_#!#4g@kcy&K?J2uHHn_#prx?QKZA6{L9y`bcqIsSun(uXjO~&I0ssRLFA*w{s
z#H6uwa6J$H?ZvHMUPd&qis5qebKvLAZB2TB%kTEyaywZZw|77>V8J_?I0p7=`Ch2v
zZ>~B{)>EuSWLUckgN@asZ`s_)j#E$l$w3AB$?!3F%X|29)Noaq8T+4OC=)6Rd@6}~
zgcf);eU(#tESO<wk6{IZ0b*d^5xR%RkwWH39lj&%?HE#9!1K&?*rv)OFY@C_FTd>^
z`5mGY(&B>?NV-mjszebN*S@1CroQzLy~$up$^Zfh3%IX79&;i^>g}kx1q&ID9t1FY
zy`d3sBU9WYcRAH^kV0KK)swZ}SJ{_=u?7ZrVZdglSH!9yZ9I6Oo-Tkkgno-E2uYo0
zVdt;bqnX#cfymoXKs!vEGF*po?&aC2;B&h`p+KV%-VIbb@`wtMI!M{=>B{LS7peU~
z;+M+BF5LA8o2NsU7YDqj+a615LASR(Q(>0Hf_uqrF-Y5m#N-7p#!$}dc!_Tk@-OSt
z8+aNrx$WZe52mAXhOY_W@wj4MDvXrC6%5RC>*JiKnHUpjQla1dgxrKJ4>FC&ZAWvI
z%%-hI-%O+*WUq;hceoRYMwAaz8vF5;D1jk>dF}Tbub9Rwq_mGk<jthUSxj+yQZ5LB
zI(HMVRD;Z+vK>|1Demx;kAqxM_9<XjcfW-nL;zEXj5nA`V9M2&e!_rMqSE#)yq0~T
zlVCQxqH+Ss&9@T8JDNa6JM=SZy`B`F4X&7oMk_cS6&mq)6;`j4W}sV&H(XC-Fq|Cx
zPN{cf@{tiF)T7{ap-YC4Pqg-)xd#uf993MD_AN6Q@kh2gZqUR<o$|OwI2Lc7(bORR
zI`+lkQI1ji4_QT~rKby*kx!Pxh1HErP76TYl;pk5*p74rFMu7=X(8os`1AB|Z~=o|
z4euqhK#W-`poXQe!I{W2bC7Y~Mogj7+V~?cRtJW`I+TOD-~^0bV_vw&h<)XWMy6BW
zmRn*&7R9!ne8g7FcNCYPf!!qb8ADV7J9{@6bv1Y}&rWhW>VA<Z757#Sq~BP3uC@$g
z{lTN;;@xrQ1hB*QBz5*v)M#;~AA_5naL(bNgj`zgw6MXw8F0E+yrE+R1XvE|oY;lG
zlyNUvp96V`i06wBEY^Gwik*$X`vWet3#OvW0}0cuwJ4SV>W`o+B2FUW!c*TRP2`u6
z#xcmBa3%+Weh?PJ_lES24A7^BGRZBV;y@?*U94&-6eCiMJydW71j)r8cxb0;j;X>>
zuz#qdm99G7QZK?!8a+OaG?5M%Va+eHn<oU7Kc%E8)dyf=y<rpeWl;1a7tuepveWon
z>;;R?8zG&YGg8-VJKTi?toko8Hn3DCNS+;?U?-3+glp&ISNA^0XvTH~?c6;sb3W!^
zXmQZ+!uB@@rg^m4*JU@;ust7+fqqbh87aosqdi>2L0DYiXAD5ur)?@?w2-{WnHri3
zt1X*s=`qy)?!hH89Rt=qjAw3xj<w-{i<{@IXImPp5=*ORHl{|3sLP+2aC2BVT^>i1
zo-#33-?$xI*yVsc4qTEC^#I2mY$+v*Hs>wSt3w5Yt&FoI=HcsX2K9OxJ*Wc(Hc(Ur
z+Haal=b;xZmC?L^@ohgYAvAx*!IbDlMpME9cfMR#Gs<f@ZtXb!oLvkqPvn(K%?LF6
zyxCfid108$=8^AvWJOZSBcvaOZg;|fe7U0U%ps8-^NUJfSzi4tN?@+S3noLF_w!=8
zwkr-mi_7obHj&SlS-9XmMyG~o8QG;lUoe$-TQ1`yx;U}B7TT8aumtuog(4DG=ta0b
z1TSn5;7}xu!#5D7M!DW`l*&zixx*<B2ByUFECsgQS_b0es0dd;6rF^~Vc>+l3ek+C
z+dEwmqjxipqj=ygiJbzL`ppFWLz~9oX5H3$%L_~p*-1TfaZiMOXy{A6j2PJpjczm(
z*6(n;M(jWXZ)D^*b8K*eVsZ8FAAZ>$P-SpK^~{ACjAt2Dh;FY0`Wq+$wxW{`sJqeB
z(dN{PAp}=E37o(_IvC6PyBUbO=*K)2K{UQ0fn+7~EL8!jYafBwwOG!Nj|yJ?JdYqF
z?$2@z&Q%%xZ~lBZWUZn@472p}3jwu0KKE_@hw;bejIAtWvOA6h!7`RYeV3rD*uMi4
z9T6c(HQZIDnU8q~9<b%)9}96hMOV~kLYk>X(N^-S%=nv`<tc)gV>Zbkf1K@qkWWH1
zqJptN(j@VQRQa$##AORQhLnIyEylLDFb15y84-(4fMT$iuj7llLwS9H3QCa<WTxf@
zlg$x5(ZgD3E*1u=IYW1$o$!}e#d%WX6<8n}LC|TZESx#DK}SCcyEoHkbxP3akTjbQ
zS}diXx0b_@<UNY1pBV=)(Ote_xKoA;v{awh19yao$XtbUbfbbdMage*;e2UJFf;tB
zy@(VyQl$~XfFz1zbTt}@WO<#7uuf)Fg8Q~mXC{SkiDYarxP!tO_l_y;5hWt%WD?07
z+%Coy<h=r)`m9=aBZMYhrJ>h%bW`om=<$IA%Gx~G-$1$Cb_w2b8<{n1-|Qk(K>bST
zx1bEtdj$~|3mZuFq^7z7{Dl|QU^GU^m=7KAhfTJq<iJn{bIh8$EHp7PA5|<<v(Qe}
z`^bQ-B={Eg{1j-gz?bcVX6C#3tbsadTdoK0lGPSE(gcp6>?SB44xand4=@SLRsCfJ
zZJU-&WGi8bp+JcKz%;m^cGt){y)(7m-kS(clYL4)fOSMoi3vJAeO)2vG~~SFB;~_H
zavXx%>-nn4H9Npp#tQ$VZbV(u+xApG%ov$cYflhocAOvV>)Bd28e!w0(vY@0@5G-H
zT$zQ~^GSY57@W?oe9N0$R@dGGJy3KZIta_{v!JBeVFK+suf?Q=GDFMMaQ#4;wbmq_
zO`6!FF}}2AUs(#gD(x$nJ%Vw^K^<aSJS#ryj_vpKbf|?a)Mi_(@RUC;KWM<|?w9wV
zDmGear7;zH3?k=nVVEQP1L<|OupF*Y)r@EB($xzmMyQL#h+i1AY;?t^2yW}DEW%I<
z$WXo~3r>Tk@XgG8A29@&*qotMzko*T^8~C$A4U_k`X%C7f4@3<As4(siwDR|tBbt)
z8tzIJVhnhkk>Aweyc||c08dPTZGxl?rt^8hOaQ<#!<xZyTlEr2&63TW4I^w6vHBMg
zHAIf*YGC+e2hCo%BGyGq!l}3vOW}bxP9xziLv7-x?srn$ng=m{CWb&+C*UKuR-x0W
zMZ>XVw7^pzQnt$#WExPitV{#c&j1h#(<Gg<XJMS|>*)CjWpMGRkxof_%6z4J$Ng%X
z3bCU#u^iQR1nu@9a5}5}fI3iZU6`@6b1QZ3E>rKAL@FA2?{hnj-Qzw3`x9}DD5!WZ
z52)Xq^(m>Ryh7u91LDyy18zc{_$MFi!QjkcIyZK=W<kg4%(l2?stCFZr!ql80~0O~
zAA*Gu65&P<IDMQM%%q^<H%wSm79NmWv;?u6HV^!Z2sm9+r4+N0q9@@R)?F=JjW8<~
zg!``(!XV2_V>`d}=*?H*SZgil^p*)BUgky}TirYiQhc)QaZCVI^$}rvFXBF7hKBJF
zTle@R!~L>bq2UxD%}*@(v2Z-Bv{LHf3u?d%rsm}s=zJiv!C(W>pV-jVY!0Z03Jr8v
zj!)P>(Q7`aNeDA+t4@(hWx2vQAQKR>wW27>x*IH*AzMwl5EzC~y^bHp;@|WX2L&&4
z86>Mq67dy2VnRX9i%o#UFq2OgZwqVl%o5Rl?TV<j5=75fFQhl`%LWWF7`RbJ*HrvC
z&nPro>um3ILykpf8_wTjP4E8lB8JPZB|zs*Kk?57t|aQxvPoC)@_l;D;7&=D^ADOr
zlM<9pzmCC50;qkG;2aeqrb&e#O%yIx;EPOwtVm}rX8I%|UA$r9&WH1y9rmEFv>G67
zx+kp(Z-W5}F}ar&!JRx79jfYN_n=hyc>#q$h$4KGNX&!;zg?Uo?=or68GLlqPK=2Y
zq&&7Di5kr$9IGS@6n<NR05%&lzo7x6oRw;afm*)2!lsDhSyC{qq=Xr5u(Jx4n8E<*
z5Ah^8;}E0KTRH3!V<pM-&bVFCBg=8LP4A3j2%0l^!fxE4xjzi=<_oI#6~Wh+n4h_e
zSHSecpd;P%=axCb>M6VZaZ3PRQ$b60IBVl|(9Xkdxrtrayiox81IijnPdqP#Psxxa
zsW2dKalzfF>S5n*Bpogf^I><c2GqrQbG^$Il&dFcQwid3RA^JnATQzKsj{*b{3$2c
zv8M?_<awd)#S_NLy;(QHvZ##dlf;4Qg1)hSUZA*|k$T6|2p!*X{>>xoqB)Bku<Me8
ztZS)cHbIdpT}KS|qNj>4nwl+oK?l>4=?zjgzy~^YeE$N9AbDVml+ZGr@g(i=(U(_>
zJ!4B!Qx8>%s_%a2k~WwL_4!Y0*p=oKrKp6&AJM+HIToe8C^8>y1iIQor7L86oNkl^
zi}Pj`bAhlm-=aRLI0=AHtw->6d(%3=i|$Yk?uV-3n{N}yIGX-H)_@oqBQQZY$Zmxs
z1E^&W*(ed$<gVW3Fga<s&p$lC=(AGii;zgjJLQDVqjyg~Hq5I#rHn$8C~_FJA`ySu
zm?0-~kr)|Ekx3Z-9&qx{0<Hv_$dGbZrX)=90k#z<ZFR9)$Bt(`(%`+sqK7>MwX8qy
zaLn|H7B*6>GfnF?y~EMq_%U54<PH-G;wDB_*Zb;yPeDzN1dSPX{Mf(NmD^;&@|BTk
zS=Nij0R6$Ne<99Vt7&uYZ`qtpxPn2x5APC8014)P2SunESOFtzY?2RB`$~vrF?^H|
z>a;*u?9sbFLj5aUYw&YdXS>}lc2vjmVC3CM(f%qI$^@XP*r2gqk-eomKPC$;dh~cz
zhCC;C!6pb$Z(XqMkpzwsk|;SQ^{oNq%NhQNIQ|ri@nY7oOb{AewkwF1**{UA-Gs}+
zJ9kgBW(Y$b3E+!#b-e2p295axmBCBpR4Y?MEvUR6W5lDbxwMIpMw=ba7`t9dwD4p5
zNmW;|K~C)zrvv;d-n|#(>RpeY30!SCzw+h$*|4P8RP?C5)0QI=OqL(pEMmAaS@12S
z!owv8CmDEyEwVxt`)R#Z;sE4o$-Yu-QEtJ;LzAg=W;{ooICc3*kU<S6EMy>MlM7i>
z!z;~5@mU3|tOLf7Tblc-TTxVVVr!0TX~9j>3`U~?>CdQ7>iE`qNR=qhnlPHr@2Nb4
zc%?#^eM~pf3-X?I=Jx?gLb`pK7}A3gBLfR;%&fCrafN&dXOfpI-b6R&3u|E%lQnrz
z-#t<uDbf<Sn%K^<Gw>e(iw11{2fO2L#M_~Ey!*u_0968ln=v)uLCr4@f*|2m)>{Nj
zCZF_|cp(C0F6q6M$)_byyBU0GX5F3z`N~L7dHJx8<Z>7Y*K<Ibs!sZCv$e3VLY4eT
z4nS%se%mesOo_FX1&lzPs9uWA#-rWlwZjzYeMd6M(TJNv2G~5ia_%_s=d#ei!=7CV
z+HgRREk*s>xAb<C>-LYU!>p9Mfq8L2vnOHcTxXC`x~vTGDy+!k$c%L^&2Z*$?As3O
z!tJ39NgAy3Vtiowaf4=#of(GClQ}r>4*G0A@A!0J)&92FuwF}5pT%)3RmN(a%H6-P
zfoj&#S^lkMmX3OSxV%<oqEST^M6H+;mP(_!K!Gr?W;AM@V>x=8iOjDVr=6YcgE_im
zg-9n%F3DX5V$)iT*u^KBt^!b2{M(#xVKX^rYB-^ej<Q6>yFr5!LD$!FJUd;u%-V8*
ztceE0p_cgy%Vbt2iV#=CZ<mi)d%le&XBl->ifxFv7t&c`UDy{f)}7LMH^-CESr7R%
zn>$KpM~=Ku{>f?HahORrx0g_MCzhP*F0i)`ee+GPza?MyaRzq%h?WAODZueMp4B}w
z!M|58oXyfsOHmCg=8#J(pp||y=i8WA=3!(enOV80M<_;+HvsoX!RLu5$%l(9*@3jJ
zUgw7ayQ*JVTrjZP-sxG4=n;B@<&8*iN9$O9yPdt?H7Pqx)+nvkuhLS5-qrhVRPSbt
z5JJEu-N!vvLa3o@bppqHwl0m<^gt%spR3!usJF_GrrAgShvY-eB7XJd>2OoAu>xUu
z<6}#yy|)z}=u9LI2i?;9h-hG0NSuqg&gwYH7;#B?uEB0pn`xq6IElWSL!nNKgrUW}
zk}|$i&6iEY?94nc789z+FoF#piU`t?wFH$@)-a_qW4k>?^e2v#y`?oy=Qs~B&1ZZo
zsR9vZ=M7aCCp?2?1ch%_#I+iNW!9Grz60{?^U{~+4MOZ~l;Ck-ZT?n!;=wnZG04|R
z4EZLo$)+adQ~vOW9QlKQrL~pq8x0=_^kr;=e!n3nfXS$;b)vY+TM?D!Qv;B=MI<lb
z)jfML+M6*?xMuvd!T>SE6x`y9_d>kTnJdjiHp9G^oy{W(7^dw=v+`~|NE2O4=TC|I
z)1)%>H(v2!@>)QKMjF_iXab%jz6sG~&DJFAY$f!~*cR<u=IZG7RGL|&GuaCmNDy`>
zgZdlbU_+%HDxTs^2iw!QG#UDj1W~Nnsdty^bD(1-sxQ|)isZS|j9D#o<?P~)ds+5|
zX}jcQ<!}LZSPr|7Qy{X&3NDYQ1)Sj{;BsC8cVGj{lb|pe9?u67uW#y$iP}T-@&I8c
zelaHp#4O#SpNEJVVIi?bRGGr1P2d)bj2nR>QCW!si;QvJP|fjMb%nDidCFKD{1k?g
zrsy%(oFj-&-pCwkmY``bllE8VCUNES3@)0!)9x$^jzaUGo$Ni>H@j`rV@uy!6(ide
zp5!#3ppvI7X-;&0u3nX*5Pd2^>KzB5JfP-_ZvFvxnL{3{EGkB7uwk{WtbSc-Vl{NX
zHK<65GF4yk!tEY?a>TN~WtY#Md%hM!lywW)6c#j<plWfR{1c(gp8E>+4del6BqFK~
z-C*-pBHX4M?6A{ytQ&S`BmDDXBAwZXNCu6xLf=Hw;}0UArO^r?`VGAh(41k^<Nr~%
zc$e?twM^DCupokSr2|^T?T;K=1Vas$Nqx2M0;v?JrGL=)b3K1>${9-f7-O;q0Sj3M
zv^^qB!s4Je@V54+7v%1=MHzBeLxzGt{9$p3L_S5|BB<t*1`L#lnk3!e8jvMBkm|$W
z@DUSl0dHyb4_NRWLE@QgaWX(?hxe$JbzDBA-+I)*rQ~u580=?kf@iF@nty(lj@!j7
z=5QDpN!eS7WzLB+0Uf@Z-2t>KpzSlVio9gTm(RR(<f-r#%vawUY&4%mTx7N%rt$KA
zuCNb8z6c+0e!0LY_Woxr8HjQtPhYM-de0Z)DV14~+AjO?E)tCcBch_fEtlOTPQL6a
z``<vpfIYg-OjRu1QM08k;OuSXvE5UHdMxfUc(bI(FnxD4=i%N?+FRIUQq%Ot#X;aP
zAneM{$xCIK^<S%@)LHr%*`m%(a>t%*=v0tap4>tbQv9!9+KZ6JK+6)_4gLLun2WI0
zLd@_KdmXp6*{#Ih@*TXOA+F`k^78>J!ZqJ*^SCo<zZl1&Kq=Tgu0iin?h{Ji)N@k{
za_est!eY%A7j+FeX)PP#M6fq)&Rn&;IPbN7|KeaQTx<dK@Us}?-bc2s$wjMN8%WS~
zcXb9|v}G6yf3}?*-7)vQ7uzKMZ7StH`=hHMB!o71rz?bcgmtqX7$|4Gh2WA$Q31Lv
zgSM7+2DJ>6NFFnLl#yN>+Y`S&gCz3CgeonLN+`B{(tII3FO)ZmsOkk>1(OEV9W5zd
zTC)kO@)2ccIGM-f)x)1j@mP9*Q!{p2v04aZ|6l_)cg9*@wo1-zZoa|l7^Atc=vCqR
z8KJv7ESTn?M<sIM&jA+z9ULP-_^NIzc%!g**@%#{mt>QpCT){BQ@@IM+b{E!{3s{E
zfxp@EIo*%X57xUPv9Sl(wkya;WWOXyc!z9x-{T(6`*Z$h!knH@-_XO+(EZ3umCN!8
zE~?Lk(d-MI_xF9>DqxEg>oP&T?axKKdOM0Mou>yPpR25y#bWl|GYL0F>7t$=7N{*0
zH^Gm6c<p<`LMUvl#Y9CR-(Mm7Qh%YLezB1NaJJdK!4z$6Uvl2~TLr{1<OjYPz!)jk
z@1>Gs=&}FiM}I*hZ)F^L-isH7gi++7|IGjN+XtYF|MYuX^ycvR7k26wn+DXye+%b>
zlf(LpK>dTAUcUzcXJu+&kkcQ|eK=c(qk^n%hg3&C7=A}-2#?``x7bOq1wqEYoI)i&
ztFi}5i$LSDLZv!aUzzBZz6O%4L40zqfXl_*48>Iqr!n_1ffs%*uxvwdTxDp9Wz0<D
z?P|<BY^LV2$ftgToaN!D5SIT1nV5@7ubIQg-r%L_oT58jo3Q3R?vdJx;Hb1s>VzBb
zrOME5aZKgbT+<3pP2pyjOzc$-Andvpp6APm#>My^5sfM3r&@aSbz^EF7w<7TSh>?1
z9q72{zNA@=pRJO*&zG1lLn{ny+uJP&5}STrzB;cU4;lUXL*Mp%?D-!`RT+(0{2tm)
z_0@9jQ4bC}F)@&<?48a(@<o5hb{4q&!$az@0>-Tv;poohcG)Rrt|@dbm#nt6@2DVg
zFVv!gpm$;^Tqu&d3_|tE6MbWdp|erm39;|=VumN3oC&tM*t>!bPfgX8e);vHc~Ppb
z_<%P%n`JTq*}4Yo=OT;8a@Mg>_IV*I{M<w!>@1cMPHR4c0&Pb}2`ieWY5cXx7ahu@
zaakkjxuuH-4{nBhIv--MRef4WJ1X)dv@S@df?Y6(WPP0*5%8f|ueKe5yu=sslR>l6
zd#8t5@t`1TeUNp&rX6a3n#4v6+d4j^yhH@8YIjJT>DkQsLcC~7Hz6iRodj@+*8^n-
zTM76`Y*yc@U%*0g*36{2d}N~n`Tge3zaWs}Bu%;Pw{KYI5eg&b!`PFEkJH_@$m=Ik
z?-@hs{S+jJ)EHt#MNiVm0ZeCgf=v|C(ld_wEBRl4uHVoc`MN@agX4)i4-=VYe^DT5
zwRW`x(Y+cB<G|FbZT-X{2vL>2dpg6ROzv&=cv&^PD1;_Mg2C<1eW^B*bhiocyDD)E
z#3Ov)$u{*2-Ozq|IuyP_z&BOuKp&%hM5GvsJov%eBQ5`1`D)^(ExQY6$#iTY2`0jZ
zj)zuv<^5=V13R!F)8k47EJ01(IKi(@V=wdZpX8S2kUmbpe|#!{V4q|0xyYq7%8gi~
z9Qc+4Xp_7O0jID*-2TQ#4W{MVa;|>V^^aiJPs=1SU`}{72qbf`3U+^7372GAjIB`K
z(60|CHlf+u9x1WNV8ggH@LQ#Bf?@FB0yti99bF-AAKf|?UhFYHui^0br=WI(tn}q{
zxn`Q}Q6oV3ZhT$xwG!ikOsn*}-;kTJ<-uo=cpPFnVp1K9@ycl+iK3Sz!AD2X(V)L7
zh9peE-$|0_1vm3P^1i_>rgRG^G9bzQ(u(%M5Y@R`_@tX;50o9K+K+L^7W^FLiAxT9
zxLKNC2m#NSK*>|qS3Kx69>o7z3|{kb<f-DLweMN1G(U&$F{E|<L`3&qf3_86T^l8H
zc;)+^*$|iVis*-<+n>-QU2hpUb4g{#exor|onaY#n-kBRgYQRxJpR;$m2m3XTTeZ_
zg}4ItV)E(sbcU1?8jYN>@)E&!eV1?7U=R!g*brki8ec}eg*b;^%Z#NjbkOS4rGmjy
z&a?dl!PQZ8;>wSfWQ_SotS6zW`bVrMTWX`b!5O-=UyR+}4r332HigBIDrY`LJ%Zln
z^~J}+PF|w627x3x*wUfWwB(UWr5*YoFezb&vWh=&dTKWRV`VTYeC6Qc%q6oxPJs>M
zl1D;Km`N6-XJCgxUxx=HUbV2h9sr$2b${bn{ME{Tt78NQ7#XVw|BkmZjQNlB*gZ(o
zq|Tjr4Q+M7IbX0KXKV&NR}wH!7UWdtiZ^r^j}XJ|k~3kvQNg{w(DL#g9nTL{Sf=wL
z5Wg7n0|30%DTJ1(5Gqo?(WXQixIdMym^7D+mr(PZDkZ=P^IRSd_Y#4?=a%M?2Cfv_
z6BS2i$d6iF_eRr9Btf(oXRhoT1ddlAlFUh_W&LbDk^s~UPbz4c4?b7qUBDw5*JwH{
zdyfH<!vd#yTwwWq67vHP%+&msU9@*6N9r}Un8CS?gXa6{0Ax)581dqYiMnRT;Q=&o
z4Pcpxp%pzViJosSq6JdS_zEO^^8LeKJZbvrtZcR5W>~$1y@l>CpfvtBQ0itl-(8Qx
zYf*39xAfa-B`wOiHu07qrI3{pW_~FjQD^Q4%$w6F>gZ_#Nfr2M#obCRe<ifdml2VH
zsH2d^;-nK@WejaPeCfU#i)Qw-sCCa9|8F0NKww6hCTgd}*k&6`z?^L5VR8r}>FxsG
zAImnoSZH&&{PH`H1O&M5zTcqISnCEmYFhSP+bA|-vSYz;1L0YSqEpy$4F>0$fy^Nt
z5<J1)oA9Y`Y7IBSXwubM`u)a@RRVASD{sO493;c%<b-JzE=hR-xQ`oIZ!ki{oDU%o
zD`H}4@{fr4akK`#;xu>oZ_;C8kPhcG*XtwB^Dj-k8gU>L&PANHpb?aIct09u9=2s&
zXcR!`7nFxX?tk(etTTPW|Kd3Xs5asW?EeGk*c7OJLV?Oj7FekQ*VMk$fYf4b^el*7
z0Y1+oqa1GVjm(o|(aeH>?z1<rV8uF^#_XSMu95jcv%^`$k{W3_9W2wJ3$7QlK2GWC
zH|^7rQ=lj4Cx)1f2pe4^bNAhWh|o<INP+#tK$Ja?rmhrFoAWof$X5ijz-p2Eb3z<?
zQAkEaQN>VB)FkzhOf8#G|42|0udRR7m)JyXQzb><@rxcN?*J$Y6h`WYkTa<3<2Rom
zR$p37y8Y!*30(ZOn23hjZA@gcc7Oxn@}%J<ER%&H<i1}SEt}V+qhC!nSQN5Bd~bP1
zSs;Qen>9+D9x*9@FX0!Q5R+&B9>RkMEC<R^o~rVL-jf_UDsvN22pt)BnpV)}#{SWX
zWMMeIt5zz(v87Mw1b1k-l}9wjoJv+!vot(cm!A5)Z1Gta4A~i5p#lC(I4SS{h3^~<
zEWC(A9_RhNWkSJ`Ux`0m5t2b}<Z>iZOW`MVSs;<lafNe2A|zmMLsOFVFj_J{hnaFE
zFbt)7o<55sc<3t*4q4|hN>&FZ98k_qiJnDlGKYEg-$^hjx3z>BGXSh!=-!U*yE8Lo
zm3C5`?{y*81bgfcHqdDZ2T6Sr^9?mPh-)sm=Bs3O2u9J~4arw<TK_zNrwO65fWc6U
z_d?g$2&HFj5-~EfVh4pA3*M=qZ!MKon?vF0S{+&G$+GWmCjbL&8~AnglgMGuxg`5~
zXRhpJvz}hXq(D({f;t|NM>)rRmfG_kL(68h$&2s32QJh=NAd0_qELAlvJs_ULRd`;
z@*g@rKwqv4D^`ux&~*BnM1Y>%6HN;UZnCjOj&w`-*YW}JR1w%{C&G>gMAZzDd23D3
zVCQqWEUUTwE&FpDH{4h80>=81dd;0%iwQnerwtrPeryNa8SJoZ8nb>IPum1Db)x=L
zTs^+?xkgk=ZrA@WiXvJDz#flpB9`hW)kb!(=WG@%OWK=+7Uhz%heD?Kbuy^2r5HC^
z7spxiNYHK%4zIh)58UW@b#KTntEM+ZxW#g4m8@+MaG>NjmB9858^k3xsIJV<_ANW0
z2j04fv||Q=$&C?l$NOq4j{eIVFYMO;xc=e`_P6ysPSWVi;WCtDLY0*t#nP&geVm3l
zCKhDc3RyfVGm4Gn=^o|Aq$UcNf|J*a<zQjWdf56pyS{fY9*qhre(#)lwej?<O;<3x
z<;@K62hiAcWr)!`?(W?m@2^`22Ywf3mnv8$tOi{kpHjgJz!F+NcTlryta)mU@FDCr
z!R1-G-0OLu%XYNz1et04agG2yMJQv3Wf{ZoK+r@t##<7Ufz1+v^R)1qd`*ue)nTnv
zoDdVsFd2_PNgp5_?N;?vJQpQmW(F?=^E?omE@6^J-NsQa*a{}kq8qcKhI87KjR_QZ
z#WcfVx2+A3BiRXzjYWw>^}7MXV#OYcoH{E0aCw9tslat{Zu=xx!4-<3GlbN%!$f0y
z7{woAjn$u`vi}oTL>IGKwW#y9)!7xZKT?_&SHO(c+ggFDM$t;q3iil5X)g_q>#ntz
zG8fSe{Q$k_pw8?tIAtG(;tZK{7&mMggv7r7gz9}q_VXnnF!uBdo^=jBI=E)`U%;<k
zaM+u+0_HOnveH1bG2Q|1I_XiE+J!3^15!Aqs9p0U@IiQ&3|o;72l5sdI*h3q_3J^^
z<MuQk_26kjSzWO(xY$I!d_=)!M*a_0p&f`b?*cRZG+EBED$=uh!BBZL=T1-_lR15!
zG+a{{5fb17MW_>FaI}iv_n98lyTm45#PVk^#b*G;dKK6Iva`cL&<siIa!0beJ@mXT
zgmpU}wDLr7{B%Aj&(GPFxYbUuB9xLhlCmXYel3k9w_S3?`{Wac!*p}_*8#+6&%cu2
z@fZm=B~n~wm!HSyeOeK?DIw`u2L-aA`16VlIY<J-;2l(GYj&OeiaYePStxcadQ}$%
zhiMrVH3Ixu$jNi>LT3)u@JN>w^vXUSyt|_AZ>X?(f?2duzNbh_<YsEO%*H5i3{2V!
z2=#ESCSLq*X<3=`t~pG7nbe$5w9#(aQUP3fHqHVjnJ)ChKs*pow>vOARi82lSp$<i
zfT}lK1dGA0WM8`(!XnqsodKHTIF-K74V~>etH>cO)4kz$I*N8znIL9AwM9C0HHvH{
zkAkE)HX98_%uG$zWSN=smVQ5a>pYE5M3Celgn(5BUe=u4i@YpQL~X$V5K0qpao}OU
zh1+}x7#9*u8U0vm-v5&sG`oonU#b+?S%{YIiiC`i&l1r<g3FF<Q!NgLJP5P`rjrmM
zYMA*Tmr;4gZU?!FbL$1Ve$nM;1W{SauXwp=QBFrSWMHNwp&9*~8rX=UZt6{5K83@4
z=BB!x6oqtP=9bzBGy<J)Y<Z3F2IsUR2)AsOkI~jB))gMYlz9>>pzfq9sUynb4t@OP
zr1>h&o=+K{j3=@x1c=r3svjuC;%XN{stGGGL+1omu+B{ZK`OF_uwudrwp7Y^v#XHq
z#DS{Emt3NFAD@W%e~a^{*GUNLrgmjZJmBk(FbS@J1rU7kab+|<`$`YArnK)ukZI>p
zxYnEC1oaU=LgS!HxWu69|4;$$|3!U0^?6z+$ks{;5)0DmXR#+9bjulqe3`(QZv>NU
zVoY9GN?eMcr6o=?*?0F$aMTWI>mt*gU(jVz&fCSjKBV50v~+Grf`(oh<XKpr!;ul=
zT(WM!=`fbRtre@2+@~}yd7Sjd{Qs=|XGpqd@X$T^UBEOAdS}0DcGILu<k!NW0;K3!
zY!ssA00H`7noh4)n8MU?02V)uk4SXl4YzyI9E3oKM3|-QO{X#&zKlUMLG2|k<H-<f
zW;Hrxu-`w4lr%@uu}XmtPRYS%ASazB%h0?Uw}KMQrj+UwO?Ox*3>TfweS`%=zqghq
zbq;!-nWhi31<*ofjnfSx%7F@WE6D|4TLI-HnKe{>K#HJL`od|(x{ppRsSQ_bA)1ir
z&cD0}a(zR$qb*1Eb^vy5lYrjm)MGe>H4iLBgFJp2f?{{uNa$EF$KF_jzPTDeJ?BLi
zTlBVPB!*fxyOk@9lg$?$-hOM>dH2E9_*B~XS(_G<XtcSpTS(wosEGq28L^F<>vLOf
zd37oxZ9p%DU2b|I8q{7fP+wzj=XF8C4V4Z=1rKa5Ge@?hmZ&_^+jsxHY8)N_EIq4i
zU#|N=<THKm^L+;!Z^Gt@00SRNo{@RWOyLp&HYrYZdXJJVk0rMh?dNNS7iS<_ym%AH
z`ZZu}9f}QpN161a90`N~8t+L>p7uFo_i8VKOpSsYyjGK7%o?H|Ot*lnf+NMdUUmjk
z4A9U!Y}i?=?;qt{hS=;fUl^M!ItGfdkAR&P*bJjIn|ubAiAQ_^O3k4U^#svug0?T&
z<-<^?T@z#=PdShLr&NkI#@846hR8R*$e24Gvb5ntr`ATS?29+FhJH)AiA5@tJ)f>0
z(sqw;Uo8|wFyt9p$4P&E6s!76r~?5M8!qaF+4>~V2#Jczm6gWWS@xH?(XXe+XY*?<
zVA;z?Xiy#x{wbwQ2i*tB;F&{tSEpnA&M@IH*-!s#Fo9(W23ZEf_qCuSbvE@y#O=Yl
zIsZ^&N36;IQHxjTC&3~HSjenl6{fH$WB5hFUkyMJDJ(?+NCNhH8ceENA&_L0nd^hS
zkQXN3!n({gCkWz`)-wj1#c10~r2RTTquWA`$sD;-ZprowgmWSrYTTbNy3ZG2PFwG%
z!dc=PWYi|0k|oKi#KrWnE1nTOSl?9pQ82ZHv-d;dTl@XH5eMQX&c;ZoJ)<%qN41)*
ztkiup)FpxqFj<~+L1XT^y2!m>#5N)SDDe~2paxPr#37Y-ev7{-oD%6f$4<(<n1nnQ
ziwgd5Cw{^!p++_tLF+>njI|c7W1D;ucE`81BnFFjQBD&kiOiBqijXAGTim~t8D1zj
zdY%&X&2oO(pLT2eRk*Mg@{^(msXtzWSaT#mlb|H3j#d7VqVMMZmZDkeLUAFiO(PDx
zHpID{kQpE+n5ilYGTjzrq&Jj8^T64OOlDE4iBMh9O4OYH$i90U(_qKV@6IpLnMA`V
ze3Lu4foU)C^&;K4bNwG7(ZaX>0k^J|v4-cjK%sMr!c%8vwrDF;+;JCM!($Ps$ZP+W
zwK1mOJNA^3)L?Gy!c7HS@VP(Y?|X=%js`WjCc$I2wD`BQc(0PgmP+)F-5c;h$>w_a
za|L-DVfc{ZOv=eFy_O4#4x7Ex(qp4EFO3#LowYi5R&!%NQ<r3FF!A0?SLx+mLA0vM
zLN?Xyi_vp`>;yDxP1jfVWHmalW#)Y%4xy>_2A^<Qz?yv+IbOZ5aC-k-gA6l2C~~c6
z74%w*Tnznza5`MeW^j`bnaNW_e;e`lSE3(B9|WnT&lcG<Dp4^m;`Fu3{G?gea>+dK
zD}KLMuZAcyF0%KDp^h{2t1KcG36GHxE?jj6SINbl<@UGG{f*3hD;K*eiY}+bvX9`8
zT(a+@pZ7AoTxy!N&o4VScZDt3NE^H)B({utX0Mz>Yk<9Q<LCaHB-rQH>TPysZ0l_(
z=2s`++jZWi#33K^bN^D5Upvpm3%D>#7j|QtncsRNDusUkjH>t#9l9rEkD^Wy&c1~&
zDYN;l*^{FdXFa&%r~Jtv*FS>spYNZ;MCb{^zXjyKLK60V9{AsLZ2M8E@#6iru>4P`
zf<F+rdw_5NW_Q{@VGsW$I@9CqfhNKax$&y~XLbGn;As|wpA_AFcy>d!>72cM(`TG-
z)hDPodER^)fw2H^=RY9q+R&<H5-!?Kabwx+2ZlrogtAtRzejP>>b@Vq;_VWWV~yaj
zR=``JYWMQ|qWJ6tN>%ZdQ~w=pphp$|4IjZ794EFMjA(9_8O03RtE|Cuff4#o=meZh
z%L;`t<MHsL=*CZQmAI9v4~SaCSC?IP_XLCS<|gNWY|fWh7d2$P_3GV1S`P4-;pT}$
z24j9aXXmD&U7^UA({d{Rclwijac|agwoh}RzPvDGT%10Cv-70iYGLo7U_*TQz}wna
z3eIn#ikM%5gbOuIbei)mEa<!W-@gXWa8N=4b2f`WF|E4o-(sJ;i++2nfAcq6n1?i}
zH8zKNg(a)3vi=MJv*A}cT`t|R40;7FR^Tx?WWoGMjla$3pH8YaQWs5&rqI2qMxc3F
zge_G<iw#r|<@Q~#oJU-K5f-1@rRTzk`enW1#r_MTa$L-WUD_nyO4Qia_%`p9#kTfR
zrzni~_#O3dPmoPfQfH~#Ha%fhpiPbWnHsa$JBAm~?B)|usF?xc6gBcx?gRhm9M73A
z*pc_z2+in?%qBjoA<}kDn}+1<(C2(T-%4DLxw|IJB+_NiC|_Oa%R8?*!D0NrgEgWM
z(CN}6oR$i6-()o<UObxTD{<dEUqt3|W9K<A!iKQUE#>R}a2d<c58hI7Rq3J#y8kVX
zLp?(a$3>09AcRfETS59c)MX+<TCF-;uuUZCD$~0WXklES0bE{KyW?kR2H%~M<3sK(
zt1O*n8TGKmfyPlehtj9jRnG$ZrwAD31-U$vt1(2XGiK}zC)}&;l_LlvTCN5g3J>Qf
z!DSXpq8ltcZMO2uhi_Ntb!*~JQ;b~?cuc-8-B9aaflsf)V<Fe>n-R4`=;MQ!7njo}
zDT{S)XZ60pX3|G#ij(x^FXof*n$0p#7=SVCBmj1cr3xGEg%Pp=XjiIAfV}qYmHdJr
z43<gYVo3GuPaK*buSD%uUeZx{!N2zT)C6lGHCvs6Bx0Khr4rF0%cBtTn{V|NV}I!l
zhq8FIJK*QkhM^OfjtB;KxD2J)O3~kRzuGFafG%7_zN!#MhdUzh9cfid(+TOPqetSY
zoCd4-!>*6sKQ7~1_dOz*h8~nY;2ZTxO11zNDl+&j3%gqUOonZ-Tuqgps4LL2&mh{u
zYw`+N1L`zTF&w`=EL0Zy^41ex%<nRO@pfM!7kq!un?M=FJIHRXFk8l)!8ZtY(9|(d
zoy2h{Qx$zKVeU90>O2s>2xWH?)i?!(-M8Gjs!#{{7#$=c#!}+N3*Mh<1K2N6_Ym8E
z){gD0ihwgeh=S_RS}EcE`Z5p{eKviAukpSwPE2#`U(n+Xd7<;)Ds~C~ksoK6;#wA9
zHfg^5dZlM|bM)e1sE0&*BPX($uyiVF3Tm355wK^mqoDhxgd`dr-}??Ot@{AMAXQ34
zt&f5hj63$rMvWyc0{G<k;)*^bI3e(%i#~uf^a}lQm_6@$o%4!%(3KSdT1>DLG&?)D
zH-H-+>2nnRPP9eL0t4$F)q0eA&^t3<B-Scyr~y^lbG)G4M{z(x$9J<?#$~<3UUI3=
zBCGVHJ0n5(s(n!8gob9bCb=~3vO*Q#07&m-S9o(*`Q3&j>39rbJ|EK5ZYRpMGwqJ#
z|1|c`(Uoo67bsj&#kOr*72CFLt70b=r()Z7c5K_Wor)`bse8`3_x#%Tz1H6TbGEtH
znp$i3(fjCQ>yXpqVr=B28O?kF_u@5!)wbg5{qZdH=26LDF7Mrni2U6-%vR6@TIZ{G
z^{?f+9QgBzyI-d>q%^RY5yEvFyYC~?<B`YVtrZ%}hT5du9G{8)bB$B~%k-C5;fQK)
zZ-IA!O0LH`MkXZ5ML)2*tiaU%Kbi6C7HSRAWsGv+6dul~kPfTb<rBdogwf|9jSjh(
ztU|6X=yxZIxW$j?%9H426}L-~Vp?WaWz)dzRFpl9xK50O4_`KJE+bR)e1*lez(*p5
z{My9Wh^<e<n?K-Ky1mfRqhzD3H4Ic^NHkL+hD0I+6;#@>jx#*V3DIC&1q|;97E!)%
z11_G?7T%fRjY_=!v6+ZV!$99o?xyo1Uo(}&96q}@2^*~peRSe(IOFG!uYlZ3*%33|
zQvwUM`*DZPaK2Kb)}ey@UyI9d9#y4T{}jO8fB{_|T&A~jRg|i)=om3CWa?<SjsfCf
z@cFy@5r%1KeABcQ(Yq!C0+|UZ0&iR73W$+$z0{}OAj8X`CY$tZc}ETEms+nlj2;s{
z_;Wvn6*P*(bZ>PSJq?AL&P_P~`CYDky&>5*U}L2YT)}%Z7IU!B*;$%<>fWOmd1elj
zd`soNsC?`J_F2o4!16Iq$3@Z*Jm%oqpM^2+4LhkU7uY<k4Q`@BH3aiXG^~2U=>i)H
z*{4nCYjHU&?~lTuNd-oB3Q{l+X5)+yzwTbyW+CWjGTtV1o*atiM>+JBm*Qkz7^tyb
z-_H1tWSPjSS0_SfpDz$Rs$oOL9<z)w(}f6jx;Z<|SY}+{yJFHqX*%ehX8Y0xY?f-_
z8QgGPf7;Xn7T6Kmd?__mb6THJH*i$Pz<}UFDC}!;mko_|+m#6yu-iA=3mCg3*T_uv
zup4olz}w<;YT&%d2g8{UyGO`M3S`O$C@JGw9R_&HZfeWQ)+&QC9-pTHrHvKJOa-y>
z+XAl4lK7kbxlS<#hHH5Fq7A6XB7vg9a`YHzZy;UQkUyf~0%;8zZt(cuYcaT8X^22t
zYb?gh(H3ol`P2{f20D>77;0_(f#SJ;0#*ONu)#g4e`WhuLXbD9^$cJZAzrOC!<U9K
zPgh&O8h<pZub75lZHK@WbffV45!e&LnHl@pmJDP~L?8-&J>bYJ{XSiD#uFCI*=|RE
z7+Fx)k?(|aD)fr7J-sTtKUesSr{gKb;CLO(bwv7+8VpyJ1aPxvZ@S<C$qnPAk%`h+
zPEi>{5bYh#kK9nNHH3Y4BEl{l|MA$g<$v0Je&K|$JdXxHRiuAZMQr@2YE2M@NdYkb
zi6VL-gq&Gqn#90Z0ST@2o599F0hcxPMj;;_{!u71PP#&)9m9(4HOi?OFZYMzc11Rh
z3Hy9?VC#fd-!A4t#HlO<<gf1A7kd5KZduxmI{v&u8zt!D{Q@FU0Stp(>T<I^qvHIQ
zU(nW1tca*Cm`tx!c%m_ek+MRbxr=+y93J<G<G8CvqwR7dHDfjz;&KB68B8f_>WWmq
z9ZAUbP;Q5{qX2#IKx0J^j>v1sijj+}Q$6US>WkXRUWhMq97v*)KkJ^|UL6i8Gq_;-
zW`CI8Ie=Vq-m`D2K2O&_h~6d7JS9A5A$D-p^@bO54QnJ9fGIL2sYvL=^>z#>DS5L0
zCCl6gcF6NQw%;@tdcj1E>mMxOBfdWXysioGpznug8%h^Z7~)5(+c;(wN9g()fW=Y~
zh}H&=DN4z8MsR?1hUKC(HxH?VbP<aQL8O`zFMobvHT+G&!tV8BZe^ZH#-dl`A!2uz
z4OtQyLxHK#!sxe#v~~-6HO!g8)1lChUv_@y`v)n>?{Mghj{9mFYsRBGV`XrVYJQ(a
zltYzs4tpj@Dyun52ggY2)#(u=Rh0`>?a{~g>ZTAzophNt#HUIT6iqfG(^;_P_ZK_y
zk?X;2y!vS-79_fIi$Ke1`G#D~ZIwEwdJ0z}GzGKz_u(l9=~qO9a8L5~{<Dw)jgey<
z#)u1&^JtpDs3?#@v8URny_d*ANm0&@3#45*T9MoC&hFYa8W5^pm1LMI?RCoGj))3t
zA&m5uJVpzs4B$|s-b>|Q6`CM99jp`WchWQURyx`nZID9|*oN}<*wUNtJV@ZOOJ78K
zEJj$KP`e`~6}t+-SDT#uKYrlXY!8^*q86J*T1)HsYF1IcfOD(8!E^xi9B&_rjj>@%
zaFe>8KJC7*sJp6WwV05t;7XXdZ!isOt>?y$*04jGfsro}o<oNjXCn*|VRv!;uIvol
zBS%J*m&139_w6AVJjQ|~l=1wXhh}h4AfouS5gv}qo4nCL0F;W#nUSnXFnuvZi59m)
z_W^mA<U|(!tgO~2S`W9d_WbAu!tVc%h*RrVoa93$UyWy(7CVuF-9jY+IMvYAq!o@0
zK8Yv~sI``Zc=nc1$+>I60Eb=vsYE=TaxFO|0b-f%Y4$Fe2>?){Yj292-8d~@A;?_}
zFHE8VTCn(J*T6zcKpUJ`<u!fO`+JhZgSOh_u$kqxBrqycie}y5I_(!ohYScS1jhb<
zG@&o%lr8y>z|t$<5qerFuOUAqL_<91B_}Qf>M)8*=|*CeuiGJ~sQer|EiJDU2^zOl
zu_Una@AO_2^i+_g-cP^035vp0ueZQ|fOWyzf(XcBiNd2_)s77Ve)!1RFtD?Rv8`bD
zbn@o^jFV=f5`%Q%DAaEv<kKfn>Pq$vz0LITEB=9L1ITPMabe)HEoClOkUOosAy->V
zhz9zX@1*j^tN|ZutpPMpGXrODbdMdQysTiuQf1anuHX}|OsA={`t_Bo0lcZeJokKi
z51pzhS-{dC#9~+M-J~IG0;F`jBoy02FKR<E5M+zxU`_u>7c68XUnBH&tLb3Clo!l5
z9z&Gr$ikjRFNj#&Oq;Ye3T;(UCs%_b-&J<&BqneYDLzAAEJd?B(XA;_qmz;WEW)cz
z@2?!Gy8@?*kx#bV=~e5LsaUb`q8JW&_2PeyT!`s<sWD51j2%)LKsmz^LM7V=zoGCT
z?FQVvWTO@2##}YJi0=5(C}gf+WT_v0=>=vBkI3SefJ(-WG-$w=FO0^2Y5^c&_i?VI
zQaOZ-*DY#!g+2$P1xblp&FltqQW}~j?P+F5pjk+|$koysyEvB>`3%5OpI70g6;?=#
z4-^n)?%*^2^vT`<a=~&Un~bMrEbE<viO)a(N&=n0*B-#^23w+pvU@#PiO_hf6e`L&
zUdc9Y-ZoWUtXxdRQRTsA7)zYKYSOKF9>HP;CUNA$N6;w8%jK2{I|cWC&9L;d3C+^?
z_xEkIh+CS3FW|`%XU$>X0V>O5MphI{k;()TV`{&)e-(iMkR0JU=c@}_=oYHS`WaW%
zX2ZXaF`^|Q#&fQxF9-y;_WR#<2s<>mIY=EoH5GVIk8p~|XBP{vDoLL&2yi1=AYbRr
zTqwwn#Sr&)CDHcb>R?4pWew^(=a5@s@d(W3bt$C7u|6F~JPtY#E&0vyXM2FI7JMTs
zA{>>l;xK)4=y%TD5fj@(AekI=0qrb?t@N0hniXAKIBs&`1#a)Fbyx*~`5i{auW}Ap
zEux{Gq1EJVBlWBtXEu!GP8YUGzg*XYse&Vq3QikLG_hk*RS{^btcX=XscvFp@NahY
zE?%rN`-{fMvk{vVgLY#6CbO2;;2%lZN+>xWZ&uWPM`+BA98e<qK)Y;$STxJkSBh7P
z9br4>pd1uGWPT*o61kq*p|JG>ln^fYw_hp{8^6IOH=dfzR4?=C%bDZ8ibjHxxflz9
z>&YzAiksBhSX`S6IpLAqNI6#LGOIq<C3;EEP5i@=7aLL(q#Rq@$xqWY6s@HZfdKwr
zzdnD<d&Fhy3|1V}F|=Wwm%XU+MPN$a^@uaSN?#OsD3WXvT<}_^0KAY`#8hR$Z_g0$
z0ttIIW=6~Dd9%I3Ww{IMC^6d0?(5^~>TwyBiY@SwY_L@1=OrCgZaJJFV<biF8|`wC
zMpUL`3-JVaoq8iEi)uku8|wzf+B5*~$BYu91)Y}%)d4Y(*CwhQs5)nhHN{ugH<(Jr
z)NZH0^I*&@pW&of6_s(k5SL^nON!`FQ5jJJDnK6P?Qm0~RNnR~0vo16%IQiCmEdMV
z%tjH?wg9Uy0!ZPB+QD{X)=r325LuEItR1mm8a^||VQyuK1O+gDlJT{<BM#aowX>%E
zn8(2V))hw_rTVeo{hK3(g-uqqq%>Iy{Hsq+^aOe(js?<Pq=LX`<`ebUmiLQGK|H@;
z!T5Xl15Zc)PS0%(=Y=OngSlwfrVc@A3jXK&NonAs{6~Dfj8nd1v<imWX3!*+Me}Gv
zdoz2HNSVv?*Impo$pQwjvj;eG+A!jYO{iFmHUUb=z-c-HlpXM!Uk!!yUY45!<IaU7
zb?BzJwo9845KcBR1`b=>@9Q%57;Lb*rd-1^GWqJi2FWq~`un;aIAg9Yewe>wg*7Np
z(XQd`p~D1&$P`rJag%`D7U~>0S*ZQ~lmxhB+X7(uns*`aU~Pexx_?4!xnPm6lN)_0
zMVG2);@H{#tid6=y7#JA3V5v`d-mo9Zj$yXmK)vPoJXE-!JP2HHyqg(SoTU+CYtx#
zdzcb)qDPW_A6Jct-jNzySu3!$3Ant?S9qVtBn}vuZPDNX-*zUN+<z#1Q3)}+cY7A+
zum_LM+B8@8K`h_A;YNW>IV=WmMpVFX9fJA^(qYkEY-5I!;gA}LCYe9uFwIGOOX$xU
zG}J8~1jg3$T~iU6dv<%qr?_#(mg8QHNOpQNdn*b{pL62NJt4mM!on12RUJ2#)gFw9
zrX{AhR{XuZ-yj`t%C@|W#Rrlaj~1^{QU>ifKYu!8cBX9iQAEs0AM5koziceH8}!|S
zPmQRG>Al1>Fl?mowWKFCG_tsQf7C6sDVl*lh7`>P8XUPLTTXtsitW8^Xf++KW<x&5
zkOsexeiMwhXSED>O94SM2<!UhxsyT=C`<~^meoE5CYaJ%I?CLi*V5$pT1_NlhJf#A
zt@_x!j~4vtQtVE4bcy<V)>khn!mD0dm==j17(m1RsU_O25wh6>InBbq6;fngIN!@c
z6!x_3Z8d;p+u(%PcLgk8p2Jh^ch^19h*!S~@Evi%ts@dR_6T$F=LDX!tg1`JPwR6{
zK;T*-sXOcIo61NX1d4ZjhbG5k29^?PfWihzzSGl3o<UR<ejHoZ)_AZRwW@k;YQMnf
zRtPy@E*dot4q>ab{kDsxHd#4b>Re&STv_HyP2b~r&uAn0SqARg={74Mfl~rwW6x>6
zBQ<w^hABP%U!n|+^QS1|hvgqMl3!2(vyx6m*z^l4@@y6Nmd)AB;HH|~X!b6OrCVAt
zmmjJaDHnmD%qYSY=<*Jm0(yeygZ!``FS%o>t}&NP7CW8>6|{Qx{!d=J=b`s5nS|Nf
zE7qadYd0)7bi>_wxa(bfD|oT1K4^WY5O#4N&J|Mu7;1}?oq8r{Zl9q+5G2_qUQ0^o
zp$+zIL>WKP!PtyQx`mDF*L5zKZVpNJM{y`!fSW+RhiZe$8(NX<Z&-&<-w|G>90Pr<
zfOK!)J&`o_yX)4@dL1gqZWjWsI5K0>bHaa(0=SUmw7V4xA5PN}(EA*6f+1Sn4E+c!
zO1uJXbp1R?T;?j}MM6?AQ3gKD8tc7Bsp6}tM3Z7dZy7D(PAi@^I6ymANDv~xzmOmd
zM$Nng8)d{YV)lJq&v`+oXLX*Y_y|*Fi%cXQJIocP)KL~lrI^#mz$IH4S^42@BpN4e
zInn=PL_vL!X0)jI<3)(NBEusqoT-Q>F@yC|5XNt@=V}YxJ52&*jAT)i*+>+%@UYzd
zJiDL10<V?=$fT&x<WvnQh71w9{!}Z-I_rWLOJkt@or*vS92bcYVSlsUwc)KgxeCOp
zAhU%i@iD0?*LYWmUPV&cI+(_Q(6)}P{Dicw%2r3}9M!W*Go#56ZZH_Xc){>l8sdJ`
zWBujn%_rw$3<+r<MW4*l^(E?V@s11*i>qx%JFcCj1^6@O{$3Zt``e1cSu3WzyqEg1
zcpADhwg$!)SXxq2qp?_F<2y?*yVZs^UZJ{n1!pyhX&)usK4xJjpBK}P4UF!v+k^xW
zXH7-#bX9IV6{ad>HdV${OY3IGa9%F_dvR;I();=|wKHK%OfeCsXZ4a$i(^747&w&Q
zjkYDcx;j3v+&_RpKt%j;wHrefLdB2{cUNRs%$FHvN)-Bp{qYu*9ZoqS0zzStesi9c
z4<eqIDC7(GeFZBWv<S#&0*B9gg(zNq<uE4=%PdTEZ7e?=0vkdUYbFhU{RYFj>vXv+
zgMx!o@ErB|*0VntLRHh)m=h*PmrY(&P7VkZ@%PC(9jh+&=`>ks8J&LADylU8!Q^U(
zA`=CJ*h1lBROK=F%ckYV$>A45qffkRaY_tU=TmSb3hg=|nYPdWDYfj?Ypiu_0KLj?
ztO1pbm!T$~i5_(<cW^2=o;o^W$vOioOTxhP!(BR|*0*q2=~sGru1uQl+??Ai*6xSF
z%%k_}5ADr|wN8W1%D0qvCzSiZoh#`s!<G#05Q%41--XtGfqb!86j_;UX?v9cE6hr5
z8>KmnZh<bC>LQjjdsVh7^PKuw*YdDvc>JmkhMRwMpGitm+Z$|^5=p+{P%}_Hym##O
zWUgtOUTXzKDp*;Pl=F<mvyS~=x~s__EHetCDWe<&nyjAEiF0pyW9YQ268ix*=a28+
z8IRnz6KYIAuZu&knGPMv9(iDVn`WIVBWI5iM_nN=Jk4v5+He<Oalf_P!X7Rp@+K)y
zqfE)KTE2J@zjfmRe|R@q301zm3Rf;z?DH;O4Ve?OSfrN49)56jnkzhX>UoJ_)yk^O
zvU3ItN2pe45<odHl^}V2SY0$yxxexCr2Cd`bi(VNh{w(by8WdJ{Zrh^AHEN0wR-$-
z#p$1_S>2%rpQZ*c_4nJB+kY3*>i;w~?34rjTadc$;mmnw0$Y-Q<!S_{F|K`{rk@;~
zc3?IJonxeFu$9N9zhz3KQ#V9vx5<EUM|D5uz{k0uIYG-L7vG8_=>pF~dYx2m`a+ey
zeIP?(&DmcRt$_kQ#q~9(T&Z8S3udY5Ex^64N&@)3Bh*Qrq?hNS9~13Re=H9)v5qrX
zdT_pbRUGR@rqGSx)idFi^H1W}-(|n<abm-8Bxml=w`VF4S5n=>$mxfzlX-OrRfm@)
z2#S|brQV;AbX9jFd{c5t-rKg38ILaZFi1{uR)RRC(WNBl5EF_iS|P9Ke%?HWcZ%*^
zFRhBVI;vj|M#J>P(e>48n1Tu86(66yTwv9uP79-<{1GOoU+|KWTntpk{Bk6SW{A(~
z4vhiTEzCNsFIUhcY!k0D^~FJ=traJCc`j7I693X#WPK^A;jec78T@C>GGg#~Xc7@a
zMdptZ{i%?uT%D&Qn(qrfQFjY``Zr}81+ZblR^@FCPx*vZqNEPm+7eQIAvOcXBFPTG
zC}@kq_)I-IM=BzFaHz2fTSH;q62{lGs1jnb`Pe$hd=XV)@L7wv>Aufxf`-9MK758y
zki=j|ZU()87V|EB^1<KELyHz>h-?Kh-*We#x0ryOpsu#u#C(6FY<7LIjb9{m_3Mng
zSEXbf6LTI2TZDBugrScVckku<#{#pxci`vYFDz&$;>i}>7t-C<1cygLZx)P5wz9JC
z!XYIm#$@!y<`7pg&v)!`2+)fe-6cYQlhBO$bdkM?{wd?*gzl3e!G=UKSwcs<7VDW(
z=yA|W0{McmRZpuk?e%Zo6t?+Hy!S^QfsxVhDz8$|!A+Yl6EZV#YhD$kMVh!5;Q7%b
zG6i>PRaYWy4Q!|hCsfQFe>weRZy{ew9g7JqjN(XeWH6O-la*fP0yU}&4JJrJ12Sle
z&gh{V<G0F|@tKH{kK5pDBv_zMEC!Fb*9OPRq(<GDmh#Kgw*u~%{$tl94%`n~L)E$a
zm-iX*v<3KHWXNMamnasq_mTg3g*MYb>>5fNdAVz#?Qf<ws4zQiGN6cHfi4xs7^^B#
zncOA^+V2EmOO*pZMTosKx@hBS6LiBS9Tk}z1VeGtc7=yDVI)LRn(Uy%5O@c!^m&mY
zL)<8uQsF=n&!d~^y16_?ffA*JFxUvSZT>?mmJ3!a$eB#YQItcndxRT{|4Z7oIT<7j
zl`<Rt{CD{jhObTwk@FUlQ}g3UWFf2u22{xkv^3@xqw^SNovQ(B2Svs^?p$Qf5Tfgm
z4D$<~k+4cjl8Co*|I?MPWpTa}`-2w7;bs<r4vy!1Y{tybdoQ=z(<JOt#T7uPG0LX9
zrLBChEN4bekQ$}*N4B%+Ulug}wz0>CaGYiEc+$4vUuO9akD~Fr<LPCJLr-vwiReUD
zwNhaiDLUplpN9cVeaVa^>8C-RU5MO}$smolzMe*!A(h&ZzY)W2gq}kt3b7~&XnUhk
zgV3C*;ja_1hNa+{p%Oak(kVG@QUA0%inZV$n7*+JEvpzzA5tvrbiJ1v`3<wX?Jk_(
z`Tv>Blkb2(JF+-T%1S+<_37FSfy`<kGCCmxV<@Z<V{+77U@2ogjY5yZfQoq8<$YWA
z*L*TOBDP&ImzmSP=ZSuzIw&olBL-2JS4oq(VlDy@Z^G2>rk<3PNs1|nft33pgDNqT
z#h+HN^F0p1n5SyQV`cF6LanJyuQXNbWT;Xuzml}PSFxS!dw?OKkn4!30~(=?W*#>(
zDs%Hpg`=fjUUf$**6OB1(e#H{YfU9%oq+iYP=<fr@cuJ_&lK3`KfAGi9ISO-b2>X2
z0~)8L&i?Z8>CJz_n!JqpAVS1a(2^o8AlV&|GPXd7lP3*RL?X3_EwFSCqb1G#G$oc1
zskG0c*quvRmdf8t3L#3Jk|yOXhIDCaVi|@>l3ad_udkocF~_G<BmQL0p6YNr)R=Ud
zeEi1jbV)N+kCc3})rk-Q;qKmWh|1J2PFnaazc&Q(PC6(i6SZ!(lllf0(?+E{OP!hS
z%Od-G=%XMX3nd2P>AQO%Fqo9qd_!ln31fD+n!hG{k>BniY~}H8^>kt=m5Y8c=FLDa
zWVGvDh+_1l4Y`&#yS%z`YlO0=;j}pI(KIQR=6Z;54rXa#2b(aEZr7pnS$McQ`*)CX
zvIj~XTn#Q{ePC%nNM#D(XS3cs>5nUA>#l?dE?q=*wo<;tCPe&86uF#)=J*?Ht<I?a
z6rVhj#~ks<7%yb`QR=*jL0a6BL|~k)$3SBVJCDE+Q<4F)ohF5yU5s_asNuTtz2xiX
z9~A8$?=QP%;`DMdh~f)cS&k_IRMQJT=blas@p{doMe9e=hNV4U{VG)b!l=`gd~)Ml
z5NpkmyfV`WyiQ5JuWD{T_poR{A}ysDk3mi40z!kfGu?nmKA&bV*Mwou43e)2X4MM4
z(Dql@(Xo(Dj7J-6!C-jP3)T9J-Ss1!!3vuou~~%yds_Jn9ppNgd%=(lKxEyZDg2NK
z6uY{hnUOF_ly7GFn<2IK7mP?JJoBk(X4Jv$tJtD_^^X(<b7JavnOl*mdUnBzS(@Q@
z&E7UU1<9f3G&0oB2bSz5^t!pFr0Z-{+UKWg=u0@<kN){&KYj4Cs>4On;le2u?}NHe
ztmw&wUG4|MKQsASJd2xiyZW7|SE<3dZVFYI)nbGM>jyA48?j9*l=&*sWSE9zbVck|
zBALsGh9?%HJ53X8VhIweBDPByoey?_>*9UpF+pX>=^t^bNQ-CI*ajn-I`*#|oXtG}
zuF?D`sBI18sO}f7)3B{&KQy=!fWLXPQM$6g&1lJn4w4ye#PuAruw@MzD1c`V#&Q0$
z7=9<91!gbzk%Q6hc6-$}w}>|*Q`h#@kn5dMk+(;UaT_2Wxhj3FEKsLc9H|L{<Yhzf
za!~Gg3FJKC<z88;-yKws(Zuxu<hGYOF%4TmbLC+Xf;`31EQ(f2Z$C9>3Lw8(&VQ+k
zA#6MOPV@dmtG#)R2}V7uW*gaH|KQ(6%tP$>0j&6t!8+=riWD?s<6uNR3kSUv{@viu
z>HOTUYiEezuOw}h1T*-A_io?a!T4}1vwBp~3NOT@TD|kzjM2ymW-5oz{_6d?yMA~;
zCw@Uycu&u)x#UpJ$utE8II}V(@@KNRZ_@>3KKyZsH~E{1xlmw1Q6_)iL_Pqt03;5{
z+E0Y>KPTkNpNAud0(ii4tcb!QV+u6oKP`U-#0+82Z}^p^s`tNb2{b@(;*v?^QbvDY
zts8WlfFLrAbs*A1t}>hfuv#OJU*f7GHUvq$z15EOfLpW_llf*o-Po*<8N$ftE5*}V
zLMh1!7G%`VZP&B3gx+}>4S2p~TxTgHO8mJn(EqGBASqO&%uV|95CbLZM8S^GcT9Fp
zw2O64ETF2er;x=sZ9EYV1-|(GY-1Y;E~}kLm6~me2#Z?j1;#mkZXn^Ux}XK4M3pGr
z@gbFJol>8HOjIn-H#_V2_|z{!=YzS?vW4!Eq>~nYJgdLc@nRWCaq}2z1j(a=HB*$A
z_5GYKmn?#Ou*C-b>G!=Tx0+`=-aKx{76p#L{5vbC3%uaV^YaxjDrv_%A9t?j<K?Lw
zzK4^j9h>&SYF#UZn$kEeY-%_xuYf5(yl;c;24ohl`LU-`Ql6Lc2ri=&(zU`5Q|09$
zL8(9~i(#({2Sh6tF-5i0E(8D5o?k(;b>C0{6pe~S5115W#SxS**)-^ZY@?1!le|jK
zjs+GNXoGM8ZdPXV<wudOnUODDl#ZiDm`>g=;|$b>TP#bRH^*bM(<|l$iv`u_-Tp;(
zBjs~gSNHdV4?QyK@eZ&Hm1Cwk71ZoOoO)$@ix)WGhLv&&y5wUrzF~E-!oy#s`nU*x
z0X&X9Zr_t`bJ=ntJ*!JhhERiRs-m|*krgC|NF%4QmJcrlw(k0cGBYt<rm1#`<F9lV
zW>Q<zb(xb?vj7r8q=^SikS`<N*yZMYOEbR7xzp9{Qs!G<%q>aFqANY{xzjz{Bpf7V
zco!Yd-(JYpjCA;wW_~@6tgx1xxX~Nb-s0sRPk5AZx}g5ZqQ!)c1ddB6FtHqADb?Ws
zBYE`U{!w7U-QQrbRg|Msh@3LbuF3r#vPO~iSRh9%T*(^)*#0Um@eF3OdGgg0TWY-n
zfIlagZ!zqMHL$=80~rtA-B@LRGa_`D#d^&5o=#M`{BmXo@MOmirN;@G2)1tjeh84w
z^ip>hC8De%*KKU<edCKZ@|k9^1_h%OX)x2$5R=|1TkGmnZV=EwH*zm3pcpL}dsWc$
z4#{bXf&woc%O~7eA_xMsbYOp$3#}><%MdVFsMb=|pKc(Puj{#5v#EPOTdqYXLbnd`
zz2q&}2ju9Ho2k#@2x^Ee<qa7^d*Lskv;A(N0JsCsJ&Trlmoe7Fu@})`1ulKe_}$TT
zzpZP(x>(~1E<%76c;EQ~+2vYH43<bD6Kbq97PWjoR|p2);fp3GJg}!jX)#}=J*>`?
z23xGt4w+KAG~?0+rPr8U+6tI#up9Y>BFd?{yUXtQRTRvnTPknMV$csel(F<kd<H-K
zfwCs<OiB<{WDV6AtuSK#y|`b*TCzv1?xpR>p2T+s>?PhO#Ixp?gJ4*3g|uQ3#S6*I
zbk|w;wyJ%OkxEY`pFA@IhLELlPijYbze17JD*R9A#h&Wf>|r;@(6>J5WdJesy8uh}
z(!j=d;yaKPqaQhKd0P=2O_Hls(?_}c`I1$nlh#(G_#$*Q=i#Xl#50R0s9aeGVMFyw
zXI<U9&<h8TGGhjHFAd#7i_MZv&_}gUO&DM#F&EV|vKgVHDvc6t(XU~X9jiveIZYf1
zPt5$_KWZ+sY9={@ESnnjjrRO<9&soTM!Gj&9<FU?@EDml`!3<92zG9F>>E46=KH|_
zXRF8^-b4(1*6^ZL9Hoi$ErPdWPj70OP80!Tr*j%N7OOm60v!$?H}p@xNGh9cf_Z1E
zjrPvh^VZ^1u*7CUAj5V^{2g15OH|rbz+Up+kz-=fSnVmjshjX#Kwvjk^HaBLWj`ZP
zJ)oL)2PkUvix3@ltkcdRd73si$cT#lHwLvH-+<Xg6&r=61Dl{3+3(gzvhBMsT)It#
zYs@R;4*FYg^R;%NuCnxVe(s;?s`B_LO}1QIbCKg8ZVag4bZWtRvGAu6PS89B_QI=T
z;960_;!0^e>gw&!`jN>VtWJG;D6!M_uTa|aq!Ul7v6N$JX+IAHfkLgjbbojMkTTm9
zjE6^<g0B1KErbfd>4=(^*K2ovK|!N`Ml9I_O(si>%@@ek&o25gQ!S_$p3)|?=}Ii{
z6;kAcsP==>0kL50S$qV(xIDKt3KusSCI)%qm?O7aNutPpoNAO81ZTI%&a9nJlo_}Q
zeNbB7?YW+np7=stYsGt2n@?S<BglgmYx_Nsdbr+ASbn-3;CKLcj?G1ZCxo&_9UhUe
zA($qZjZOxUct4YSNIE&rCFc(f7Io{B0yTvdbG?>TISkN(-q^hB9b3@#+wE+T@X1!?
z(iJ;btJ#>hkl0U2?sE?I>t9Zr*Px!hnkco0gcFMw_DEsT|FSw?(_K9lZ?U@tWEi|g
z&PJ?1?CgirX1wIZ;$rSMh)7F;k&9_N*Q`IcBCXt(5&nR;V}BwIB&q-uR*U69>6^V3
z!rogHe!+PjCieCml?C9T#J<Dw#=^df*`<XNwf(T;6dWWg(b8@be~pq}7ybESLvzB3
z0)3JY(>PT7L%sR#3ao_QRA4k@J`C!8ZZ#p)Tkl@<t~D*?u>-#1a^~c6-PDWCff*}G
zGsIPf`fTIS^7Yc35u>HyLaLqFrQ<jhji-G^kUFLcO8m)OR#<I3H*hVSr(VC~7jsa;
z!DMmwb1mkeHkKIZ$ivy7DlAFMJQ^s~wer4Tdp~ZICN9Tf6Tji>k$M4h=)94Rj9vuH
zZQ_*_N^A+b(VC21E(R$o076Y}{~>e`q$}YbM7I2Bndv-`eikx>>^SbAbYH7JMx$+a
zdWIT~vD}rYMv;mu$fqh8ITb_c#s`5*YSJMx`#b1daxn4Luwp;XUGHfweYbX3`h>_x
zb1({tw=&ka^?nN=3PZH#2iPRpfFQ(p&~1v8ANpG*Fv_FMXfn4bRo$kqM1$_ED`T;t
zH!3xOfB==M9ef}8#ySHNfHT_dR|u)kP-~D*HwPiXNZ;gBOE9QSkP@j*?Np#Q&%Jc}
z=NgrX@9-HAp6R~tLH-W9*rijQ-+TZ7dM)Qopp;8&)!-l4vkG`Du*1vO;*-_%a$7Up
z7sV4AQ8(6dJg3c$AASJ7aYe?XJ5JA@kglSbnkUr?jQ9o+P*baGZ-yN=YBc-|p5gHd
zQofk&)&wx+b(NSI(RGJiP<mhnr_BKAt%`XBk9i4YHI}5opkKkGMXV^kwNlUticn_d
z@wZfqdDh0-W=EfWeB}y`Dw=}O<^h1siA_y!3MQ6QC0DH{mn21AINnGS>7+%p$Aoir
zir@FBrE$zD@1L2ttI`%4CE;YxUJIFUweq#PB4H*|<wN08yH-D=mAQ82K^-;~&mSFF
z&&eb|(7G^(?FAhZZ;XCDJ6uJmrxH(+Qu5rv@PHk0a>HscT`fxR9`(>=8grd%p!6*a
zM~{v5E}c^)SpPt7!2O0?S++(*O)KHWFt#@B3J>2d89%epks6hO!^qN8bz-;MH_;3u
z#k<l}`5qw%hO(ca?#adWEj8|N0j*@g$(lqV)v;{e9~X4(WL~aW2It~<QF0P&mCe4?
zAJ*|584D4we1?~Wh9sq-#5<xlGy+>JD2MWQ2ZIW&K(83qj3B4gz9rk(x!SZ)TH`nL
zLZ3q(S=<T=M}->I=&`KectS5P!Tjm$&1H*=73eE5`~r<)Hwtm3(Yz$%m$>!|NhB$1
zxk)iepDz>IbaWx!HS!XiSq_En<8h$Ml0G#^MdGLr)xcbIXnuz_^xETAaz7hI8(O-U
zc;nE`vS9PRVba0D2J_Xs9WR#ITYG~xBL>bysxsq4`QFV(Ov6XGjVmrHC1_f&K_|LT
z`rJ(&7Ga2e)Fl^BhgEyvwnn;OuOX?P&esXQJpf%72b#=AB6zSLvNh3YgcEOB51BkW
zZCE&e3wd_$?dyYAaDe;~abBU+Y^)j_3NB+;jkPW$c<ev=C3CRQW}1?q4GX7P)l0`i
zs@HsliOdXV^+)8{?~)l)0;J;Ch(VcOV*SgMPv6Se*@|BU4Nr*-XGos7SOuQuC9Y9y
z!*HdkQL6O=^FE|!s$w<t#cJ(kvuZpwdtIg}X6@MCnUw)x{1LG<^$@(XG=fk2pSk0?
zm(@L28x+!{FVY%WXjY^;3D2n~+IQ}jtMU7S@ZGdJEnkQQfv+FaTGVSUg4EZcVzO^%
z=h^R@k2{UaNSsC`E_|^#ziOjAfRpl>7ht}Jd&~P5b27oMQA|F}T?aOv{z8>?TBF9K
z``VcZROp!zOJ!IE8&c=oQh<;4KEEK+oO-7?6jaSvoSm+|zF76MDQ&;AbOKfN?t)oN
zPG_w{Y;r&sE8J-A+`yyqTMRa^)`|#6@&1``NLoPYTs-65#G}HMHrJuv{F|FqSd#&x
zIW@}on4o2Eaf+|<_iBAwvT>{Er{OO!8C>unA_8E;9F5Z2)TQ(STkBrWKIW31f+wvI
z(O5#hcyt8ZCt~Z!s6Xp-&1v;2%J8!DWp7;}x?jmX3+3*y2Zvs<BTU6+>j|$6L%Leu
zy5x@Zce@?L&6_a#+Fbi06Hgsz>*VbE03+qsqa1|8N=*WjzKT<rr&^VLH2;Zx4cuPY
zm**_lypFS<8RR_f@3!f*ZB`H;m=MDj#RBwO!D@YO9r{6=dXJxcBV;zXYX!O_QY(uG
zG|hFBA_V!k>-uBIcz0(v{USi+c2W^I={_a|CPe8l5J<fYMVP>vCyCsJ%3uof;+fUP
z9!YbXDlSAK=<<T1FM?mT*e<ldxZKlxifZ__cPb2V^%}gd;r88m$ElOXi6R%qk_<Jm
zd=OSCzw`Hzl-x9U8a36Z>Vws<DIMQ>h>qHdKdI(bEQ$rYla5G-jcsyT+27sA?3~Vl
z_my5qc`7;#RYJcTsHs(kt#_6-3iR?8dXGD0G!>Z+Oh2r`p3WiSmXLN*<WSzfzRx1c
zdofM!Z&l&bs1+7psmNSuNNI)ADJZSlE|BxAm85;=u$sF!*5WAjdIM2;f5%l}AxL#j
zH9XBD->R`?Tw@;j+Q$adRBW?aExFYxf3FF-dNe;Hhl+ubbHloSdM-rsrt;1!(?}yz
zD=_LCjst5wPrKINC)Usy78<%*oHbL1dva1{m)~aYGD}O}a(P~L1AoWfB$ulrFsWox
zS@5~g$j@!nZH*^1&TBt+h!`H>3*LqwS#S4Gn{qXhkEP-|XA|iA^j6*kdf+9SC~jDk
ziWEJ=-R0Kc51SqvS}5+>oowV|HTHd|>+O=l?ZRC2`?;y~$jjr_wot9r8glQ>Qt=|C
zHEM(#oexBzzRjwXM&#XZe@FjHiSJTUm8?5Bh$@v#-bnb(wsP3C$G+OIQ7bMo!CL+%
zyM^n!Z$%TQyGh<DpH#eZTd7?^v7I8UjdGzf<~Qhy36sHlX}&`SYSRJloZUO10WG$9
z#5=U~v^#=>h#32u;)N%}S+9=vax*6T^L>{Lz{ArHeh2V@{+@&l!24d!T3Wn&{NTjI
zv@#A>@|{_2r^8BhH+0pmC-Ey{9XVka{zfo9n(P2phnD-HWODT*O-<5Lw9{V{Lk<8m
zs5m3Yx32v!uIeu)VjKeHgEpN>DG}TBzZ^rB8Zb`mx9TjDwtvWp1me$@1Y)z=vVX{s
z&puPq{JWo|fn8^+?4H{<W-6X)UbFvx<C_y`R%*TPf5Y<mz=%Xl$~0+8Q(0hMC<`T1
zs+4(q_nA2oBLKqRe4#V&fX78Mi-9*!2{qhz4r1axTMIU$0BdYOgH!Ot0lEx@R+7kQ
zM3%|OkF~!B>whEIO+f(pVUDHyY(i>k$7Nz1*y1i4;EZgp`Ri)$xtEL&9gJO0UvrD%
zoX!iG)3Jx^N#z;&x9nw&?{^?<H{%|@AA{sw0PLAIDq=bSkYb#VK)PFN&*mVK8~x=_
z!?byA=UQ9s57@JCj|8~aD81&2?i>rL{v_i66HQ_zHGlbxCU^H_K0=FFvGb`W)XYql
z(hdQ&VyJ2Ak1=}^;+z%S!ktw>esNi2RUG=}ZOIPiBVs=!UsK8MFa!5XI~45oF*kM9
zz((|(d)^DOcktGct5-F5-tmVR`OGocp!&=`LkJCSz5o_c1M<HaUup!t?{)cm^3&Jf
zp)@xA6|f}@!q$?dJ~}x$SiYfSq+TPxicWTy<iLHGFcu}2-r|Uq;1RQY%e8Ct7_(j*
zLYL(wCrz09`)u?gB1|qSIagjH{2~+#d?q5vfz<_g!(nzoD$SImJKeI}llqn~1S(~M
z1EidmxH#4Iw|1jA^e|$bA@Y!q_;B@I-l&x6yTX$uKL2Bc`cZyp6RVAhI^Xk*%=~f@
z1x+n_dwW6B-qjY49ZSU9#fROCJD1zKeoFt|mu>qqgMWH(47;-l{ioM9FxMS!WD{ML
z`9=v>^p$jAa3JyNdY4~D5+TO?1}^>VG0g&26Mvg)^-klGZeU;F*puxCr^J1*Fe#51
zym;@~PUg<;Nf`R%<`6;}#Z>JLD@l;-pv6lx#%w*U_Ig_1^E2f6Hy8AYG*+=U&86#&
zsr%R(XCiH@0|HhDu;@~p>CQ~{^&212dtLnWLh@s_srJhHZ4<k|h?gdVm+I#2`CiDx
z$zmg(Mt1iH1djcTp55Sbhy7PQd5!&TeMzjUQO$SIrrT?FC}qxwKP034F9-}CA)<Az
zFrBynZWqlN;qOwv_FBO(RwAk-rwwhhh!&mFf35VZkMrDxGLYlwj7?(}%K&`hopAQZ
zFA>P6JKc;a4SfJKNpr*P-iFMtf?`Cb!^IqL^1+&Lun3rB;~bvYW1es#Ub9TnSR9z-
z6xF4^^fIi^6Mdc49!ARr!rG&gLzSMKDabRZy10h+S#+}6tJj$A?d|<?9{_ykQ;7<y
zlhiIu1AO05>v@5XROIQKU>xgAE@5x50{Qu^qvoBc&JV%!<!bx1)hf*kFqUYDfO3XF
zyJs|OuJkYvEk%K#?b+Wl_4m%$j4)ZOH1=$iXdr{W;nhWz{iv35^$?~x|A}zAC!eMm
zPv7Jm#Fy~X0$RJ|%-gCcbj|+r*6|OQ`h1WP<zE5zP|wb`F~6i{_2;_5-uO~NajiNL
zC{xqbP<FpA_U6<+OPO%3@ce_ZH&i9`?rD`9sn1YG^O29%du_1qBa?o+*EHq5(6p#I
zgq;2u%kSfGCj|fgi}b!J5tWASO88E*^;xL?NA%@RAYR`Q>L}&A+)t0o@V;lI)0UuH
zI{fb9(&ok}bp`zfX1Xf-wUEpBl50t1v*o37(xQd?@a|VkW(Hz5oN9SV+fLc;qC~@0
zNhQyocPsI%wmAUsgkg*Q3f!v%Gl3fvb`<J~XN|!oc#Z^c)Yxgkx3CK?!|#cIi3L;~
zKqG#_fCl{EkR29Cm(;v^4hP4sub!%TScxs}hXGA>bQKzY85zLy3GsV7{%X`(+m{p8
zR07BxYPs%iEY%2F96dA9o0}sF;9!E{O8ov2>qOEY(#1Y4)+f%SJf1^&dQMuFC^fd%
zUsYw!KTDRVZovv-B*y128ZO<jH>z0%QM2iUng^iINK#p}W_@C7f*~YUs6TOFr0Kl#
zyU_iskSCg~sJ<9|WcDL%w9cr1rWt)Z{)UF0T4^PDt&SvK@n4)ae=sqDZsAg6>rzk)
z2KI7@t@{^20w&KzW$Q8JY@CUSxy0&rUD!)P!jf!HSc#FgsBq14n2$=WJfD{@PwZ~p
z-Gn_QvdtE%v>M@+v}ptd7M{X1X+hL_6{7h+!afnR@3}a09AzQ_zr!(3<aBo3&x744
zI97T>*GV@;5z@K4y9%60);(bTcqgd*R%szV55VGCr9z62AvkY4nT8I)Dfkb<KX|X4
z{)6y&pP(=`8n=<Lg1)AnA-vto84m28rusYqdvLRfHK!U~Q;b0d0&fU&Hn~O}1Fi0;
za}A<`cWwOuu{tyqSO|IYF!xl?EmI$yr;oSU@)}eAsU7|IhJFFc_bae~He2Vc#r-vh
z4^D&ycX()VO8o&PAg2Q=7M08Qc4SY>2@6kIxDBK;B7ab7H#F_~iQ;k;){t!a45w8Q
zGLnPmgX9=Lj<>pJ{)JmNg0zjZH$xoa)xypKv{!;{bt<;Ja2Wk78MxlJ3~N2f*@n^3
znG)_IHMmfss5XQg&&@sX_VD1MWcpi417|F(zacm+i&yazvS)YVR}BQYPaxuB@=x82
z_wm;n12M_cDlIVzO?5aeZXciF_bn8|hr}oR4>O_z;-k`{v>6B_pZd$R+FZ~^uoV|B
zM!eE(UJiYjkT_HSo<9-LPF&z`c+vzy#w&*i#_||s#o{6T--%ir*$$KH*k<@o+_O%Y
zcVA^jv+4(bOHxi=UVj{oJ-s|i7f6h#mTASgJGYH2Rio;5)4qz@wVQA9&$1bWWOcs#
zcSlu<b4&MR_00D7fy=_4ePO33CcTIiptEE8TUDXJ1wLRW_^r=rdhgi3cx?ORtfv8A
zq{=mEQ2GyKp@IUM=!kJNyINqz<Z!*=lGRq?i`BS<(GO1juLeWw@8}sUYz0*PKa6yc
z#h<E2C?O}898A%EI12q2TKS_Ny&_NJf7$!poJ?!{KkOv%2RjAI8YKRMos?NVTa;O1
z>O1}!NY?+iKB7PpPhxWnO=?aODiag&D1%}DT;TK46eRj6vCC|hft~m?gE41l`XyU@
zcl@d8#pFTk|J?a=jQafiDeA7_wyQl8)Yok$LJih)c%3?d2uRakS#RF^KPI(<Tijc3
zV3)w*@vKt{He{ux=c{g^{xwG=d7RP(vtt-({9|ZB%NAb;_DzU4eyQH|Z<T(%sCXC?
zu@H95e{Vv`chCYc%gX)eVM-x{<(0&MpTOFjt;p2qv}$vV?A5!qCK3NzYGlYy`nZE^
zd|<hot%eLpE!uYITtlgrmRS$Q%r+~m)t>Q>qWTv~4M1Xm5>IQgAIwBvL*#pNAjmT?
zvdt>u{rG45KSeeEaKBw<5=B%=uBdE6N{c+2n7@u?3jc3n{kv7W-^~83|HHVW+@I3g
zXdAeO?>~2$0{U#p0wJ%1{AZaz`$Tki^Q$;II=VLp-v9Me^=GQ1jQ$_sT2o)h@^S2b
QfBF1Kh{y_83F-&@Kj*=o1^@s6

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/deploy/run-on-aws/os.png b/docs/python_docs/python/tutorials/deploy/run-on-aws/os.png
new file mode 100644
index 0000000000000000000000000000000000000000..d8c3ccc654e7ed36ecca2e402d8babd627c06609
GIT binary patch
literal 210080
zcmZU4b9iKLlXj9(Jh3yeZQHgxwrx!`LC3ajJL%ZAZQJ<fx4Yl&zVG(^&*`eC>UpZJ
zbM>iHb$6(|tQZ_L7Bm<b7@UN-up$^3#55Qf_ypwlzY?7sl{qjl=xTEzA$bWQA$)lU
zTN86DV=yrB(1gSv%1Y*FgP_M5Y#IU-iX1ipPVoFiFz6WpWkEqGl0JSj7bCl`q9}-n
z@+i3Z<m{9p1ODpOa1fF;@I%?z<pJNYDPMn+oSkK^^Sn;BxPu%WH@zQUZ#o@6z@mRF
zLkJL3f8$5uQ#rca9)pB~G2O}pC;a`JIQk2vm26no@Z{tVe9h($6}*=iP2opaQ_uI;
z%TJY=wIdXGuxPv<g*6meq#o0l<{?B#C2)QMaON4MZ*_n$dYKT|kgHIQ$c)1WjmXr)
zS4S4V4_e45@ZV&0%RTB~;+HYpL=niqJAzpR)&oa(uRv|Y#>2jv7RW>Vc4i6+_e42@
za_oCKZ-dVnCdsw9@krF$H_d}U*Zl%Wq~(;)Lzg#O5|S{q%$&%t9I{8pQGny2%z0vB
zPhbjO!;Gt+`&!EjM<lZQcN%)KeiARg7Av*OZ)hZ6IcVs$n%(H=5>T7FdW?MSeSLN$
z@$5g%5|i_k)*#T>{~S(eZk0E8MSqjs{Z<9oiVp<4?8id4*CD?_pTr?@La$<=Ft%rf
z_q^69vG>aaG6jYmZi}|HhTgv6+jrp4Ypf|nA2qT2<_5E8#iHSNvZ1<nqp@e6?>8+C
z%C+-#p*Dl#?e&5QbipQnLDmD0Rw)uS9qXL!BGHJZ*y|rCU`&rzLLS$<<`9*_EF2(}
z1~Zx5deeQgvAeJmaC$SK#nebUM+Vbt=?BE4fsL1eM~`TXnO}otuYqNEgb*PWjvr3d
z>0<0TNkL%AgHiH-If4+<gQoVQu@feJfat_W2=r_C{bh$A3Bli~8qw-Y3qIc2_g8+*
z;;&)?aO_aB0cdu=0N)q8t*78#x`^n%igr`cL-GDLhxE5Y_?9aO9Zi}44U@kR0l7AK
z4Igg^4?2J_M?K_cXm-Su3ppO8|425GJk1@96MQS|dZ0)4k12X)_>Q2?F7jm9Sv_NQ
zY@P2+`eaMk@PUh6HCG<&FcW^R-34~2UJy2a=z5#BdEFp=zRBf)g$3s3&B^~ECms2=
z4}=!$&kigeFXmQ;Dutf^Z6V~Gdy(@;9=UkZj5;;UBpYtJ(M-+>AvHiJY9pMMKPks@
zYW%3|$n!}5j`)S^kH10>hMpBYwKO>?T3qCCf4p8PgG{nzGVd7q!51`OfuVD?s=AyN
z2J5djaIN1wA=Z73hMv_#OB6WpMZcN4mbbm^tpJp?=&oR{5Ux;*K<s|+p17-0H>Gwi
zUYyB6(_N`6=O^Hk&Kt}dvp`t3NHxI|QgfiRAi*|9PJ~?&y<{?}B(igeOCP@hw>@@E
z{1Sh=sF$>t7-7tu#6rFb@h@|#l6a>urvPT@7wJTT(>U!RL?c9Z^k?>#jJMx!k%Bp9
zN;nkT@ya8#k~I6!`xN_eUsZa<{YknLmIxJSr>Uo@t~F!vOGu81TT~gPYK3pY_q2zh
zDE#9a<AEqz7In%ka~vh$B^2hR^`Z4uPO(mYP8oMH#|)V1;f>*Guxg}Y2gPHkDTtI|
zr2@`54#le_gT;%*dCb%@!2`Mdvp42s=GyfV8kic6%X-VO%MQyhwL(rP+G!KHWHT_w
zMaS=VbPgj9vJTS@T>Dsi9{ao_bTO{+QHhgrw(*y7pSfM~c1q&%OL<edSHg%D#Z}Fc
z4BFSxJ1qU7B=sbXQtVPUiRn5w@=&TXYBy>t*-pyGku&kNVYR`v{LjB{Q1_uwTf_1s
z+a+s8L=x;(N=rK^<r>uQV^5_1Q1^z$43QfHjHOE{O596B=OT-pOJvGUi+_}uma&!c
zP2*e8vluXkv2ZezvZ%1Anc2*hFM1V=Oc_}8o1ZV9%)Jy><l*La2(^iLN{i_P7sM4P
zRo$v5<(_7@=`=|`cK%Y0DoM(Am9NsmTWFe!nx$I=S%P7!V_m_*W8G2rQ#a_0=xR1N
zbwyhp86}&j+p=2;v)wR^uv##9bVxgDJk|1yawd7RHPXv6^P0R32@mv*60hcu?^Tp^
zQViCw-F0I2kF8q)HOiK;9lzX~-3lE9peX}U(YUBNsXH|^HK1v*sRt{VD+ntn%fBZx
z7vk0ZcJIY9Jh$rTe`&ur#g<A_PMc-*UBOx*U0PU<q*>JX)$z7;gUIE~)wqt>M&S9!
zJO5q(mEZ@<k4B+{UM4X(u}Z^+T^KT+BDW%B*{Y0)jGzgb2|R5p0BcKqTP}bHa0_T#
zGg|Xs8(GV2Eo&QWU2K!{P<OMsGJEWE^YFa5#?hmwV=vcS$WKaGB&{mjFuDZ0WV|%x
zec=_%YDB$)u0r)x<JKu@hy~U5qd(PpJ-+Z>&t7X?KAuvZQG!(V%Q9cno^0<fVY9!N
z!xY0jAspeTV?2?Ivx%e3AyQ-FAOf)882VW9$&JXJG>!_VR`tX5+p8Ds0B&|}&QEg|
zP4lm+QKIU?d!lz$(;9I#2M3z{4WX8F<#B*P9amB$WK1Lt^yh^0(Mkh3k(yk}Tv86&
z^!Ic%fS3(~EB6X*EBT8^xVE&yn0mSP*~Zc}dvJ4XGgKx_Rrn+622&^FhoQf1&vjr*
zUv%F9v6rIJ3`Zv{pPw&_1_=jIi<ln%5Grg%lLpB4J%36|Jp-bkT*6$YfWYK1Qe5pW
zz>IyIw}Sgz$;0j?1@;)$XCOoReJmq|u6C`?XXE!q*YZ@2g7t1=L~mL;EABF^b>zxg
zjfkPtMB;SCFD=VP#3mA}SDID!HuBf^hv;)_ju8$mM^T@vryO22LbYVB>RuPI{iq{6
zcRrVM=2dsS!$)qLjS8>bx7fF;Z|VM8d?|0rFSBEn$=cdMi9x|GhOW1+kC*mKN=n4q
z@hZ%<uKgPa2BP*l*RS5E59`znbR}zYYji7o$|p)I9{jgm7hiNocSc@DGDfkf;wfEJ
zy|{;XmOO{9<%J3yjD7Z#dfR&g#JD4w)y&lraQc7AaJ{^#U)4-xrSL7PQmG!Q`EpJ0
z@hb1?IyNknHa5zZR9640M4yk!3Spo2p1C)&4AAe55%n<qps<s4I@za-0o`Ri@UGkc
zaWC91Usw(!wNTPwnoD2iUPfAEoWojJahx~>h^1L;%~pwme(_bgbiVnY1@!>?fE?(i
zw8q-07U=bIfXEK+nVQPo>4Yh&$EwQK73cBSm59@e#VRLGXPs7k*WEXnw6sGT2HS?U
z#`blu!5!IITQm@hvxW=HW<%@hiuL;XLS6fzL6y+uzGvlQ<)-O6XuYYns)(=H*ACx6
z&n%jY{Q>{FJ1yA!XAz#Yces1{qwtBuuu?&h3h4BGgWZV3Nq@=tX`=48X_P2CQPqd|
z^X{qHgyYC<EP5%O#h~=yL{wXpD+uuJaF?((e<ysgng6MJt@(Jr%!m8g&V<Ry@gn+U
za$w!f734a&nFhK{zDe_VAvo^68*G<@&a&WT;&b(ZdcHB>e5bmwJZ9^#wchLi@nuS+
zrXW@aopDh87KDfl1>ZWi?;{HY=Rg1co1Eb?=J4zI>k9|OXXv5#T{<;*v4z+g3{t1K
zGABfKL_UQU_>~a*6k?Z&$<9--qQRK-p=XRHf~6uO(=EKc1UnB{C*0VWbko?FSDEM^
z?~teDpSK%cs)`|>d;rrkgMG)UrY~=0AMh;VKfo544^;mavKi*e>Q3r1(wv62*0lOY
zwg$$uZq{~xYg;fdZa2=qm)6Ek`uJ|vRyK~DZajqlv4ZpO^*>@dLj3<&;$+D~s4gRq
zFJ$XrjL$;LOiNG53yqJD&+TAj!l@`M`d{XMzwr>7IXT&J($TrPy3)Eb(b_tg(lKyw
zaL~~+(lIj9{9Qrg=x*br??z+eNc5jh{=Xk#V@E>=b2}$<TO0g;{OTLnIy><Y68^K%
ze}De-oW^eE|Fe^g<9~(qH$b|7G;|EK^mPC2`>!eYKT=M4b2novbzyUBV;jf6d+@R`
zGI0OL`v0T(pB?{~rN;kQGP5%NZ_EFs`7cXux_<)rUjhARw*Eu<H(k8Y+;sn)dR}N{
z{6JYSFn%xzVF6_~@Ux6>Uh#tq1D|OTZLtV`U&)%=8u%$R^Tqud>*Za+S8nDznmjB~
z$Y&B2^9!2W+JcDre-rm<yVP}Vy1ODn0xaZP&b<$l%G<eK!ZX|t#xNtaaY~gC<^K<!
z<{_I^A2k2~!279j77ZG3*VD}G$ar#|juPVE@k#tuQcB9+*|~pp^_2{`e-j5P<|~H$
zzW|_utAun?QlAK+g8v)*vPAh&bA7R}@6rEF1CHufMrfBJd#B>9?1_Q3(7wWUpBMJ~
z-_W&*0`7U>{I{&gt%BiYss4BMCm}>B!aGR))FMUYtMb2~62@;cf5Kiva#6xh7cJ>U
zM1=p}uoWfBkNn(Lkj*#^!j88P{yzzzf-Dxi`;?R^+gJX#Og^n6$Oj#5!b!3a{##sr
zpQv9ewaH$5zY#Q~|EoveFJEJsoa2>~r^($_bk2MOq;<v68Z_bViM+0E-z@u<L8SAi
zVA61<gxUWBI}V7;N=f4f_!`f0XMwV5_C3>JdheDd-#NCtWkIh%HoQn%Y3b_0EFD&r
zkdIjcHROLi?$bMhx(9jGu@+MZ{LijR`(9GmdhOyP-%5n@{H1k(Rr(0s{y6I4^W)Q>
z=C|Q*s(%Bkg8Mv34)?(Q9;eX=h}T@)y4Vg%Dq6LTJVgJpWPNnMuWn8I^z!_XlQCj3
zr(jdRyL%dN>fC4otO4xQZtq@3A)|7=)H+DBV%z9^JT-H90u`#zab%i3p0~~mbK2W+
z*nZk_^Q_SYh^o2yjTz815WX{{c1>;$`UR~61X4Bvi<-QeFsHBqkdRlM)MuA3{T75Y
z_c&OWb_RqHbfVTQm=uf0c8@oSE?dWLHZ1W9Hr2bQmt=cAws^R(`^L%4nqD0pRi1V=
zTI0zDa4l`-8Saf5<aDz+_Kt&ttEUDWwBFT{+DFyqYuAtViH9?`pi8p0@vwf2!SVAG
zq0}0|pOwGdl=rn`o021vE3mp>6uB6Vr5Kjhi9w$lztZL@k(Ks0itjWESON7b!1rOB
z8oi`T+Q<)T<m4?C{o0-VefR08x;dBOGB~yCvb|M6%4OMnWw*Q^u#D@XG9g*5+a$@3
z70#xIU1N;#PqP_Gr><|G2nA4^$V#PwwLmW)F`Rax%0K3gs%~sRCsX#%ph1kafrQlQ
z&FO6ENbO7I-y7s=kf8chIY-EcUK22tV$BFfA$`ZEmL!BpmoU>}c^>%PqmHZqBTr;t
z)xz)%FZiJ#c|PU}3C;J0j0&a*)N4&7#$a+sE;{;|yn1Q6@h2c;?dg?MoA**KWhig*
z9eA_JcJLWOV?X`_7W)IAOfl?Zw`udH%Z74$x9Gy8uUTSj)2Wlu4AEBBJuoJ3;A-VA
zVPd;mb%d-+&UkMqhWq;D5hZhDG+<YZ!L|QkG`0>C_;eN|_Iws>UH<WaJFxjlUL;ix
z&zItf#;8Ar;j`xOTP2HcG=GpcZ^z@LOS9{Zj@S5kqp$3zKH*Pmb>_H39WNjN@t_RQ
zA4J5>V@^Ej^F)FgSu@%5riN!arX9zjWmbIikhY=L!4#wr)h_CBd=FRXB1CH|moenX
z#L6*KMLrsP8Rc*qvc1OX0CB5&E-2groUozRpZ@#2GHAYewQgrKWg;ED-`_UaQ+tkf
zdHSLAY4%G^HLW2x9+hcRZzdmV?RLwE6_@=K(%a*c@>j>@uZ#9qpF%nfu^?_5_PV4W
zq#L8?Fo}v4nv_aqdLM_Ec9R{Cw?jlxSAED)*K1n9Vmnp>t5&NGUwtet*(A=lqO7I<
zQKer7JAG|#AM4-tlCY?DYigW7#M(h-O}n0cPoDedwo@ttO2<&Z%HJeR8;6+^j}Di;
zam$&>Iu$gS>UYO!UbR=hdtZ0xHjO_b_e!%tPd035SKUClnj53Y-YOa0zNoryd2JUv
zoxzzVtD(w+w|zWiZa03RYxal4yvZQMj^UmY+YQ5ezj$3~q5FS+d#(-%vmp~IYnNCx
z5qe+;{WocWAC~V;!#m5AfeE*pA92?l@57fnpPI9+iFVLAId7xY#D;mmXmm!<o4wMQ
zoN6JZ@Fy{|@7vGBFbBzs%sVXB$Zj^LXbtWdOxup(kGht~K5I0hpl{#e6Lo3%F#~C+
z$jOl&<|!D-!&7zCi90F*Z|Izy)5a}H1IB2gQgpG~^DD`f`5`^I2Kwo=L5!Gpd+2*g
zii$dz1EL}#-%RvZQ5x$7F`V|>hRw3`o1<e#xECzg(oKst#Ln$(!($7(&H501&7H5;
zN%h0XQPF0p&n$y-TA%jEg!D&fS;_WuM_anKaV;r>B#-HPIMw2l91fmX8H27_^Rq}f
zuD@~y&<}|e%#;vkc?>V2D#~v!XO#1r>>XgIN<f%&EAPZi9NM{{cgxb9)uYBCMa3`4
z=j2sa?-7!Uy6Y%xhtH8(rSEJKT4f5*(@SEGN&?9#5gV1#6#`@DrO;~5pUf!(Z&cqf
z!Y3Ft$4Qq_tEcOP3~!he*_{Yi5k=jvBO;1y#_b|Qt}-<6rS&Bs(vT*Qkv21r<mv7d
zZ3;FL1cdC-;`StV*s$*-Z>T1nj0^S-5A&*|s{$aK<H$B7lcVAe=aLfS<&}j+jE$+J
z&4TK23t$UG6r(sr(#tQb&a@v$e)8QBQ$6H|@R5BF-dmpGkyv9lfvDJ7OTJ!qip-#E
z!=S(4Vm0xt3w7xbW2`oY+?;&=?OoE3?)zEr^Sd^MyZdvznn5LfF6lF!!b<*JhBP7y
zI+{GSwYYswjWy;>(<y2=UAo-T7<!pv^3@$M{P=8gtmRgGa@c6%mC0=8leUn@1LhRX
zXLMs%BV9_SucJF69ETerD2-K;Pw6I`jS{Ky2d-^<O4~(i9G5L#1c(+j3d_%pT~waR
z5^a2Yu-8AzFh_o*-JOYH?syVNCp|!7PLPH?LoU8Ccq+%&)pqNXJ(OZyt_ox904ZJ-
z&j!k7<W67j@WWdqy?t_=#n_;T_4RE*(l+kM+UN|YL$`^epRgC8^R5<LI*RS_f(Fvf
z`}qSq%hg}(5feu`JFJh@xL>*q%a^@jfoJmM7f%!WV;ZU5UnV<;LTI<!U$2{t3bPK&
zFm(~uUAgIaSt0{;C%3vvlvCu5VXKeH6FtZ&OQ-{h(C+^!Otz{-yHqLeR`#`@y(&Ic
zV|vDKF84{>Y8i*zEvy(nMr7AicIHC7wn?ym(=w9ko<ElH)Ik~oY^sOc$QXxJcG@+Q
z%XpM}C_hz~$J<KnZpoB~<{x=K0|V!Y^Cv{M9PfeIm%kb6L*`}*ZRnLYbcg=*nwsYi
zn=af%skiZAeq!CHTs_Ug9;-*?X(e3`-z}$)%*<vx%VnlL&=;vr?x!`p)`ZDt#%K!y
zd4=5HrbFj$K>0>h3XNGA4s#?&6`YOKpNYqgXt8CpGnI>Wg}khh;mBN1XLZZ@`-ysv
z_xlI-QI4i;zB<0AU#N8m(mUmcx_s6-8cgfx8#QG!cq2&JGmEl(Q(C{hK3@da1qS`7
z)4-_x8uvrQyojRqmGNUZ31gEb@qW{rK(Lz;vRq_WT|o)*xZiRT{&40`LxM~hYmVz}
zkwAGwoLH0{Ymq-GBCX<gINI88-rH;Lv2vzJF<Ii0i3%<!BHMYjzr*mBhg-)oR=rVV
z%wQR|W73h<aNU$|6@sD$`r!;81TuZh2#XY9%zS4v+OMt<mRZ~a0{z7|Jnq32e^wn<
zL?to2bVHq~vjf+l*=X}K1ulQ}2So9RZg<)aBRrGtzH+oDW%-L_g{rxc6>_tOzgvQg
zA5MfbeN3oxR)W&mGrD9dh|+tqfOs8kgLON3<HRP1`Vy;RqM1R{+NrViHi(zL3`zA~
zOcfq*+cm)#J9GZ78tKX*SrQoi3jvr|`uo7DM0w(#23<F%Up{Y$ln>TM5~V-KQ(1TV
zB7?k`Z?$G%!spD_Glt{v2<wk<Y&&mlDTNbA&Yc)(3GUr@3e=7?K!1>NyG!fwBOI0q
z@0=61bCGuYgrJ`0PJ8pA3>o@Ym-(KlCNkvmWUP>(Oo=#15T)d4Y}y`e2y3xx+$Wh`
zGR2E5*Rw5(b>9tp9#qDjPQ=11b2|-Tx1L=K73zIiZdEUONx4u+JlIwmEBnn6PaH<h
z!a=rJ8A3`+wJcc&<5&Pv@gh>Z!sV`<JBw)y&kXyw=HpEqp7!9oY&7mE5SM}u*7rrN
z$sAN&$&WaRVb+>^`5zvECf6U~w&V@&I~TYEU*fU~HS*Br;;4(PJ~O#LW!u?Xqi{C-
z9JxTI_ubGoW}1^Zg?{9CEp6`GxEKP;Mlx^h?M8yNI|tZ;?`XJF!-4t5O60OsrSzzW
zjkhcHc}K!#t}vFvFiMw$GRo2@3B{?13hKS@c032%*19p?;kJoqdxh95hE>E(-oi9S
z^Zs%Y>J~{sC4Mdem-~L(xE!6v6inX+86_y&1#C0jtzcsqYMhQ{paR@s8YPEUs80yJ
zN!LXNO(N)nFg2=AuqTSrbGz2!fBbkL9ntEg>G0ipYH>0EXsdG`DzS>&&JP>3KH|zT
zp8P=@0+ez7ebev*VSoosqjn1hE|*OkgJ>P0+fGT}z03|F2kNiL1P6xd>m5w2M+}52
z9Z9N)^VmgPB5ctHPPN}sK%@ylhcuB4)7N?75s}ZD*J?C;l1tHGa^Dy32}wG_^PbVx
z%1RPAdOi}-B?eQnEa`4-B`zdECsVE@guV~1h>9pCO7=1y4+L|)0XJhXUTnKEtYld3
zW;aYu<pqbmO&bP_8@OKCkhLsr4Ew`v3}lsC>pww6OmtDR6fI6|=K?yZjFGda=vVt}
zTE6!Bi4JB=wmw&I3fi{2p>4qqUduSVA2w8CkGKNZSd&Uhq_eDm!7vgnavBN(wZQQA
z!C4Ge3!I8C<LR=JeR--{I>|R+;39+SbuVD8Gl1B*2?ZU1<OD}bh;ujh^9)S=9l7Ch
zYDYR}GjOtqj>KoGqy6rVJS-M@Xo#|1`icQP(|EOex394^W>{|RkIdJ78ZlWI!Q4*@
zWZ!qz@el_Z{lU%*oTVDfq~w9Vpk^sC=PtJdbf#cgQv^bPZ?_1rL5r4FvIs(`Vci6#
zWvU~N1O39aW#E@llbQ!JLXrMuqy0U0dKRpamgoctdkKmZis5+6-sG1tqZ9Lq>)o06
zJ1ovXaXhuu2dp9M-nO``6v4>q-mux@F!7{n(M|E4_BDy&v0gyM<>KjFQtE{XtM$g{
zK;E#r;3;y2%wdMFx1?}dx3v2nE8+^;)$t;5czkkqZ~M!VVJm&1mUrjNjnoXlu=#TP
zhZ)9&<V}eqic!8JD@)p3{(JlV1-0Nx#BSVAoxayM7aec4Io-f<9^Vt@l`U27$2H`q
z!}PsiXR@+v1OT8#|E+H3Lnl|I`e8>;or@MiXysR8mB!#qgqScn6I7XeL9HGt?k!=v
zS3t;}Yj^Lg?n=XVDHdEzU`_bl9rHAt90?alzVAYkjQVin%MWw5yI;S2pLkDRP@?S)
z{Er5b<c~DdOm*CW3b<d`0bAWnbR84<mD^Ujb*Tn%)psx4%kSg~;G3AXmDRPz&m?_8
z&s>CyX5OLGspQgf-A#oc$fgO&DnwGw-TPHWFEhNA2TajV)=sa7e(sYy&k)~^_wU}=
z?sfW}u|)7v3Y2vRw1t}$O$xJWjk33L46e_#12R7T10OnsC{!!28_Y1!2AA!w?$^)g
z8gVBA%EIg)3eGLgYhOJYtV0#14bjL>`j-M_E}1^;H{4I1cX(v4yJNkDnTw+;cTgrl
zPY`&rpMFmR_RTOb+_$45bH84|*qY!INVQtdP*_(BBaClgY_Dz%n;ean(aSd)gO=+$
z^VUON-Opk|mnN$WHX?~n8jc5++HA-wWkb#=Ileu!^4RSA`BhL%%4~Nm^5#y!;X=WJ
z2?@jUIt=jVxOrL3MQcOn1#qx^Z`f{8(>+Z2AzJ-X_s9`*s;qC1BJQp5w{P(@iXb0o
z7F1JVU{<xSonmM?53`@JK_F}~!r@dIa&Ya}&PR)d7o*0_Q{VTs1rhfs4Cv+RxH!YS
zl7=A{V;pEyf_ZfCq>XuN+|@;n?3i6u#w7ilYNU9!)CSD<C(y)8iKB>cvx+23MZa|9
zRn3ofZia7i*;tXGh*Uq;x6R7gBB1xWcu{(T=;IZ-4Clh$`B&O3x=|Rvv4|y;qOvOK
z2^N|U;D*PBm2IbIU4lEwPePe(MA7y7{YLLBC*o>8WF)|<E^u&Q#&!yBZswrI>)GLE
zRs<~1cHfWNw1%a2rA%jABuJ!|$9DfM6OJ0@fjHk@n{wv1cr}l=h_UhJd{H+hu5^%U
zM*A)T5GZq-@A<ik$(Bv^>`6?QD&OkV85*hG@vPt7q#U2#tLNy7ldQO8P;q2Be@O~z
zIuSNWOC`>1=GTnG=2WxwfX5WXR`_zX;DdNtTP6_`J3l)JZ}(zFAz(vMzMKkFX#~1d
ztdD8GU-Z%XYbtx8bN<kJehI$3qNU53%y`sWXASO65>u+E6zLb@tc%4%M@+_jQJY!T
zru=D;@=YlXhkllPb=3vtZ)XK#wux!MA0cr}W3Xm+`K#(eoPXB1K@Z`NG8AM%R{f}5
ztqtKK+ViEBH!AqOB1AMU1mBzkLCg6(*?0DOL0}u?ZZUfqb+Q#w5*6<py#2m-WO4ma
z@s5rL!-qcH(KM?+o*I<lnTnuQ0C=Cr(bVvi<I55)v?2hi95Dh|0owstxxs+z{+9D<
z#E*;`f8A7T*87Y1d66t|&PMj|h<`*Dl8$npdPp|6>tuA+@W(XsK$buU_VvXHaacJ^
z5CL4a%w@OJ$w_zQAD?s-(U#U(XeG?8%hRRWGR2`I_8s0o2NOo4Gk90`^;?ubUYHeW
zi;&-$5wGHDxZ>tQ+qUv?GMUu077|Qx4g!9n1EC{AF_snF&1TF5gwxNyGTW}ETDPgz
z*i|LK^ow$a=#^44uWT+w6j#gFT8NGA(CY98XGcNz(rD2)PzA5AkvH4LKk@>|&C|?X
zX32Zjxu+>4{lk#IHw(HzuB~+lXD>ooQcvcOjOWVk?QBcQN5fD42IHm@Zh(^ees3!k
zjiZC>u~~<X{&QH2HTqyawdCAVN{Alj3$+FOvrj#Dy##66?UM~aPN+#;11!VjX?M~=
zD$Nef>xB}idYSv$`vu36B@TzWLbD-7_<Vq*k&3OcEWjbY#&q$QSN$7C*_w3o{6+t#
zOiNsBpmY1{F=OI$L9M+QXC4+=jfXE`k>q{)m{o`h@wMug7erh%Sbgo{MLoDvHZpk~
z_3j$$Q4T`ywF6B5j&?;AG5ASJXQKHXJ1q0jK{i_Bh1~r>mec+?*PnT3dsxf`xmY_#
zN;4Dt(fQgd#f<vv)NS%HIkkBRGZ~~cx^b}z!ex+$g8i6zFaPu*uhj6fafqEx&~-ge
zppj{m>J7_BDCTM|_-QdkJT#0>i?O#xh4KuJe(lo!D5@A+cJpJY#U>~ELYiWFaikkZ
z#`SfFN=d-O11f4lwt${E1amjH7!Uua2<z*C9tTe<#~@J4rg%Ihp2^FlQE=&U1pFAQ
zwsNnFL0Ma9=@d?p!&%ps`IBe+{s*gK`_>ofq9{Eo^bW<GzeD}r{zbSfY%xS`8^)Ec
zsn0N2D#74&`JU}BToiXhhp*ps<D&R}x?1;q%o%_EI$*!~WtW`CGjLpN#9W&!-r3+d
zTjx~^W3>%jtjU@meK{t(yaL5&zt(cq-bu$iKZ_2(QD=PgtBJ9eh<>Q+#S%$Rm~=KF
zbMjY=pA7OF7m2IKvt4QD*s6$g?9u&9pW_;w*Kf0~)d#oeOB4$PXM{s5+JI}Pdq=$@
zC+33r6=5+={g4KDDU4bORj{gqvxK|qqNu7F-;l@<;t)oTGZti-LHxu^>E0^)MJa+a
zQkN<%KVJH<Oq$an)~F1ZYiIW8`ZSus?;+0LBCKv-XG<r+&?>znV!KS7tXIM+PC>$Z
znOSz4Uc$ds&m<@z%Bq_9Ue4ajbz8qzRWeZPHl!h?;UVU1$BXkVpyC>z#xS{=+K{L~
z_UbU?>}A#fH{3YI0@r1@9l+}P4wnQS^s?iZBNs+{8Gchhgb`*N7ZgTk&;oJ0X}Jz@
zBurx^#MvXq$Ato887EMZLy+5z34Eu`x!=q;I=MD4pZ?%<{LsEUmQlZwK)eGSQ;a=C
zSQ{=iTA}hz!$DDVg#||i!}21nsrGdnqMI#wV;By$UQL5GOfDd7J)T=6&iVQ6XuO#J
zj%`4}scgb^rd5z$4K_V;Xm%r8hDwx!7m-CpQC^+x@Cjoas;`m>RGIw+;(S~LR#&)j
z1F<dk;9_!^AyBuwuNrvz=5}B14Y6}4`MOT-e}vMm3WQB$q+tP0T>Waa-R>h?km4}(
zXC%3t`X~*px;Z+ZcbGKg+){kT1)pC!l7fb#kr%_{rZ-j7MV7J3-vHieQ=|)mgeNZB
z5lt(UoyOC96QMN2)H;qKJp$S#;+*2OkW)fht_HU!leodUtBcfzYW|TE>qN%-4gw*_
z3T;={$cd_h6GDd}7TEoo3VfUgGnuA?PI{Xs)=nopP`(8d#CX%!85rAIl$<>+D5w1^
z!G8;+Qz8*v%oCPyVtn%7L#c>o3H`AU+VjlYOD+kQHW&WBF9PtS`_r9~=!3}c$OvSm
zSg3<nkv2kZ{P?`v4OeLQwAS)CO_A3F@`~iGTc{4K*<Z`3p^Z@p^jCSqJlO^c$V0<#
zpPiy_E_hJ;WDH4XY75D@RH+B*8X=eG)3woD!=yGozSw#<XF}11X-eN}HityxVc-PW
zb_3uuM+-ceaW*mp^J<9fxytlsFN9#tb<vepBot@r@5+r{%0w!B{+UHQ!64X$&oY9z
zApI?6bibE64$f+5E=GKe=eidOFuQ*<E=R1*@dTw3^0!sn_1jx8my8B(urS!5hKUXI
z48-_3ouP;?)G&9B*lic2<)y6g#kXJ4pSWWtzg$yz+*L|NK{C}P8ejQC?gG_*nLd~k
zpc?&Bq(;*QDzU~IFLg3=XiE_<A|Bw82q#%TMZP^|b&(2Y1sEe93lI#h<ys)opZSEL
z`$`{7;|jNP<29w$EP}6IA}bRKmK0J4wBWVlGx}v@4}Ir;J0tdJu#`wQL87p2pB$ad
zM2@qmy!&;JA*1DN(i6Dt#pw8JmUVUNW#OQN?Sj~Lo)sVx^73*{A&nkql0z6DiL-jS
z6IijQN+hT&Aj(+vx#>7DaBw`TWa~ZDhdB7;bkSyomeU7PWzxY~RD>4{y|_>0X@6DC
zJu+7ffvUri3>tUP^RRCq=aEs(#F`$ACp}{)DRaK==~d(OPfoWNx48iViwJupVjZH#
zNlPiGWmbW$-CsMk8YAwxc8WEql6Ne4Hj+I9?}HhGM7#n^qJBCDj&oP1zGivFb!f2~
zI9>Bojd}L|u{Hxxg~Se#GFzYwc!(4$YSuxP6P^P%>f_d3h4K07&2Wa<U?v<0gv8i5
zs97ilzK1T`-t6LBZ|_W2JZ=iTsy1lUZIC7+GCrHFbPyIVg;=iy`{8Ou7h6ugWau)f
zI2F7HRoo$&EQ`Sd$Q?ok2>M3|h15AK_9jPpl3A^>89j%kKS&k`I3JIC!c+Ke`tm%l
z>>m5*ve8y*on}U@okHOUYYZ>OirX#BHfl)}SkU53Y=+=y3?QMSwku=EQaY}bSm{s;
z`HB%I*B0W_18u)ql6;ZGY!87-_~N7M)2FIWEG5m-Y}tj+{c|EQK#$d6hwFAKS;xS5
z+nRN<ZWkA69C=n0Nzr4BV6RPF10@5x4fao^!OLY)dT^8DQ1N;#)5T^-4~!g7sOo9&
z(qQ2X=fcE#6x~3PFLm!FC^V+cmFaeVFrSnAn01f$J4-|>N=0>%jv52-a7Q~}z&UsM
zYF&+(Hn_fBae_u$ILV~jj7}3*c_rR)f1B`pWnN{Dd@n}3SkYKtlV{_}CBVGai&BY9
zZXn*-?^oVMkw{!(IIcQya?|UNNG_UD*=9tg^=@;uCpwlsU_IcJ!D1`I>*In6ryN(-
zPhHe~FX%d!AIg;0ZCb(U6nCOO!-Wl2NCTRD;#}~1cR=uLerWYlZroe<90hgM-L_z4
zf|zcK@;u3&_T8v=#_0Eg6Xsu{2OW>xK%R4ycsKfcUGL(OvdK<#o&Diqu0U`}qK5&`
zJ$Wl#BeMBz_*c|gjyl}&9wBxL+`79S+03j|aSe@MEZ9G{7r?!t7K)f2@6|NW_oXCK
zu;t)N=N!?)>t}2e9?I_41M^m!^(c%hj#TK<v?GhC{R`K*LUMXnua>DMYi2g31@zD_
zZ0D1;DWY8sXp{<nbrX^j8UB#Yb>5>pZQ*jP=q&v^o_>pgB*#gV*AVm=;&zLWg<7G}
zae1o+Hv2AWy!BIG5Pdmi>a^9al?t?qd9MG2i!j=n(L<PzQWdCGBGHEkno<~wWKXfo
zDh<Z*XhK5Wm8k|%nbPeskts~l&T?qJE)O|7B2l`Fveg<d(#w|(AuJ~AV+x-eT-<5~
z=o*%y2bc#Uwj{gQ+s~Vv_ixhqR739m9dEzwxSpjX@qVIvF%q8<c&?aD(>ZDWv;zW3
zt{TZi8n3$3?a{Jm-zlEl828iu4t^Z@2s1NC{DT=o-<&G}<p%Mm#=gr086%BNMR?tQ
zNnev|djL1kQq!%kQikPzLO!U@0c+BT7I36q{({ArXOrBBnIL4L(@D@fDPm9dbI6LM
zS@ujiRYq|Lc_J)jU#gg~;!VJxu4+5(^Y7CV6KJQ1gnf&UN#8FcG`@n)$|1%y`*kXF
zEz1eWr=n(dko1rJA>}hed5+!;aDDs;+<@Hj#Hs>kg!@mPtHX4R>~E(9Ua=DX`Hsg@
z(ByT^+bTYZNxB|i9tYkrXGp?=@Hzy{-@K)LKU9OG^_fnGXvq7y@G*S#U2tWS8Tu7N
zM8C=hNxC35G7n7xcXA;R!8(X(p3~ES!}Gxi`Z_SsVb?b>J#sS@4z1zu;UI75_e>Nf
z<M&m_Ix3HGOpV-8DwkprS2oJlKSI>AsE10VMSsp~RjL-XAdwH&bP?UkMICBu`=I^H
zmXgq%!9*shx}q4TQC$soL__xmjhxA-=kSz7QubQD%5Cyv0suo3V`!F;I!{mJqF37N
zAa~Z;gA3yoPo|><eiZpKu997dnh}FqPemf)E&}|KP|jLfqfiU~h~HCKf#04X4QUss
zjQmisaE`dg2z+WuqBuL~n|#29g#+~{X%PH0h0KZ}a|rPd_KZSUAVZ1Bl`(95tp=b=
z29eqWdah8-JC%2shi!z4<F6Tu>ItShnmm6X?VaYoIroGdh-0qv?12H|w@lgMvXt;2
zs@|buT#OK58EYMJ1)YSQh%>{wKB>|rs8x(iOmYylPr$m%>XpT!b9wP$Q25NP!scP~
zst>}>&Fk`Wm_p?#gEvR|P9&nO*mRD=Tag%Pjq(uxaBx!2)R@&!sfWro&XD#J<v~X8
z?vt7$d(^@B`GnQm5MB0stI5^Jmxs?=fK=XTP>iOsmRi!kq%?2TG~ldZ&x>EzTGoty
zh$As3Pq^wv;vGZWIBpRfF`aQkz&EZLCYUm)u`*;z3N?;^78L%;LEIJE(a9cF-Y&=h
zi^N}%95RZg6~=Q44a_wmI-xY~hX>LaOTHol3=9k@gREgRjxpoI;O?qZ8D^Tw8ZFWn
z?78!;JzB$LG>+54I_qi!2xnuusDnc-z7F>QRse}ezX)@`5!t%dJP<7C8+Gd{Du*oh
zCoO3rPS}@V!Jmlz(^Tcd^P_-?)$nQOKR|y3s`^xtpodY+1n)n9N-EyeJBcEq9?-6C
ztgoG*7h{tx^6l#r=|G9SANL0C#2azT!8FpH3?bufj3N~be|E(bliRcgd-JgD1Td`I
zp4tey#+alOk5`x?S%0Cnx3~Bo8xm?XP$f`|-jbv0w~usw*pAnkMIuoz6rr$;;?8nH
zhN=uRzX60(honFs;nZ51nv}#5R2vSHJ#sC+fd~~x{D<ufQ11r~VOHW>OF!%)ib){y
z=z|E~+MrLZ*s#e{UpWVqOobbSr1rQ*ug#`zitL`Xcyw7F4IA^5-ClyoqL~yw*Ja^<
zS7>!cKW|o%mQ4xgJ*+ohc6*vpAM||4lWDzMnsiPqgrT*+97Rk1y8S70ZA!>?J3eR|
zS7_a}`?&cst4Yp>9PkqN-*g;)RERKyj<aHQ=U66XS*0`;&|9Och{`P8Sitcvb||G<
z!4)~<!i#fgyZh3B7BcoM>6o@K4b|#G(Uo~8+pTEe?J`@ioh9*)Rjvgm$Ub8O*2if&
zk&ZF4=;hrLWD18Q!SCL6>@ihL+9^CG1c-t@?bi|zcv%afFY8rA8kN*C2?!N296p1e
z&dNbYRNyZxn=+=zLtYjLXbb@d%dHd&mzDURal`SnTEu4wR&`;&RSj3HS`cyq)6vQF
z;?k)7$+!il&Xs&+?mq8*s2#eb^?^JnOt!wrY6NWLlvFx&(+Mu039$S3Xyde2rPF%-
zLgfYkE!U0>9Oa;<Q9oIZD@v28KuG0`|ISF4GeI`wG~X{oOSxJu_YQO|ibE;xJ}miN
zNWPOW)JT{V5gghFggh|Xuja7z5is%EpGXg3^j6MwyH#r{+ZSY|MW}NzT!beuOzmtU
z7Zr9fOsozQC4b69E|4Md`Kpo)gEAe&mt}wB*hRwM;R{^~#)v5Nw>9plK~2*#f<93N
z>y^5%S8hIZkw?<qW#Nx7p*$bp6gHy+*bFA5nygE#q$DebvN+n3dzPz^m><jmUipgs
zH$jB*pGxUvM~5Hllry7R<vef0sM+e9S9dSA*(V9#CbP6(_%N+lIR1<D<j02!F2be8
z0dV8toSuMf8+s<vg*Vb#N}@mu+*<=W$ov+ZO<3*UJ3NLCrYz{qErQf|c2M6CnTB0(
zi%YjAQQ)OCHI5gWq{U)$sIV!QjJ;LKy`dOXN2y?dX70?DB>&Nc+f!!{zlD_(d!?G@
z5FCiKaqSR+sI65EN;`ADR~&&B)=yvWhc6q?MlRI2NO#0pEWe{|UfCx^DqDLxu=7P+
zQk!glQT`X^*VYSy-HNPg;K(Vkh_arL(j2u^O_rJxy-b;7aNo$mCf|c|Bs7fj5Y_-S
zC`w9}!PC>2d!%Z9w`1>>&C5abG#cDjsCaYylut0k1+}eZ1cF$J_VVg@N=BLOU~9hi
z$9@_s$M5-<VYEJLhj$zCW}zX~uQm~{1wy7E?9_j!nfZQ$2&2&aflY%p@)36TxyOv_
z&><*5rP}gA_!xzdyKcC9jWTQg!s9uOw<P<VNb+=t;cwZTJQrI57bR*CDEWW@ZQ2ni
zL$?ZJK+`Qn>n8s{x8Ofyx-LF&@Hq7rJM<3Y00*vw+siEZ^0LGni<m9am-(<dc3SDS
z(nY?z?Z2@sg!q(vZh6w<ER!v;_br@=DP5VZ#coN^<=tNoMtfl-VG+txiJ}zPyVu(U
zo7Tztl2=>o{TsvWJ46T8k5QVc6u0ITNJL=z2#=XBF5l~5k~(58YMtAKLv>B_+L(L`
z>hdlfd&y^14cyl3HYtP{&z2brYmK{AbQ-8O6KgE!)gvq+lCBzcPj4}48;f)RL?#_K
zDwe)T|9P!aQTVU)xW7E8sVWf+xix?o62d8t!>3>F!glj_VUC)|pKod-Ca4DWJ>@x;
zd05hz%7>{mUI4j@H=;9P-LL#$G};_dNG&N-P6D(6i29oLBV`M^%IuoXBxXI1ZgUkb
z=Hb1;FS&o)VE==wM^XH|A@LRf#sg>vu;03pj4-)rLaEkawSSlvtYVYFl)e;Ep=|St
zWSW!)wQh*Dg-O>s?Enru`B!?P^4E_ah)=jdN4|lwcL_55*cf4QivnuAs-Uv}L$Q3L
zg)FW=yM)+f5zalLQu*n!jNU)7>x(7bw2FdTbmB+*`Q`}HzPRP=e3yO!ku2y^j@pKf
zmQ>%a3X*r8J5a?<-)+{MJBwI8od-R$5ajy-K!`r3f)kB}doPR3Az3$h^@pEtGBwT?
z$g7t#YjloiK<>7koIG6ZgVQ9?5^7peWHT{t4Vi7sXW9O4lHrRx-bld@)fU=CLNc#-
zW*m9#f_6T<QQr@Xy3lu(&{@rOaZ6|0Skd{*Nba}v&6Dj?Z&kZ@5wc@|Do*!2b@PY9
z1Zh+5v6I6Oeq#v9jCTXp1n!UABHb)QU=2h@=MMT8b?FhB+8vpblS6uil>s`glF1R5
z6DuXf&ztoJrTfoO#19iZ$*eaHQ^uodY-#ika_=*T!?ROKU&p<UT3d4Kr;(N}8x|?w
zlG6QA!<GeG*5hE4I)|By$A~6MJ`k;{saE-gnt89Ud}3*BVdrNz<Qq}S5g$g~CW*&-
z5HVW0tlnNA;*%y!^7RGv$_A_7g1tVD`ZKP%iJGJoY&ZtrpYUC>lIdcG!$#`dd{JzV
zhEiXVUsh+jH3*JIuAEJg?K^BtVsgZYi`5h#HY%d}l~;=AmJi2y$?vC#$QMfI=JR4i
zZ3{Mp)SXQCmfw_l+W)vxd8+yVNg83+Q~R}ro}>iAWN~-e0)p093fVyW#l;dyy@QwD
zftCS2+>E8G1(2WqawMivHBbEDyP|hJyFUR7sW-?eO-_7yWb1x78z1hb(~jiFI1xK(
zTizm>cPU~TjTIqiF{n7K{dQWHg?z|K5$Bt91cj532Jf`CzU~sQ$<Mv2-#1>rj{9c_
zP1m66R#%cI#+-X;v_6pU;x%XCvr(<0c}d#vOWhScBCk0Uf6)>H7GwybO=t<e>hFfY
zw7z{8chq2jGXK^$xY+-GW5iMI#Y^=wBVl;dADG-(zwC`+cY0oL3)nRsxnA{QJis}8
z$CRSC<hGw5iqQ2@sJHzUv_X^zj*I=#I8^#fE44ZKL$|W4e;rvVC^ETF8<V9R&u3or
zCM^By2ah)eDLRuVhf^LZL9Iz|;~Iw#0%(M@lOoFfc45kLjNM}RzJqUn;!Im2m)Bo_
zt-c?SI#G^!Te5Bl%hptJuOv`;-ICFlVX1M|>i`YsLb#*JcyO_L%?XNemH=O4Xg!r1
zupHu@Sfq+#aM@`-Z6}!T@oV6#b~{INa#$c09q3veh$3G3<P*zKdR4vce+<TC@_~F!
zniy>AwHvs%dB=0FPt2Flw#jq7bbGK_37NQ%dIaw^)8cz`03`|@cyR;68F|n+=nqJf
zRGSwdduLCiE~lHOsn7Us?cBdEKAyjI&wDj_uytSaV6tSRe~id<B}`aQo8HDQm#=t2
z$3b>Rw8Cyb=yweB)~3#jjkY{yD!7Gh#+@eBkqv&+dSIBjuLY}4ws&1bLYej-AlQFm
zy^shNokmGt9yJBJFx^dfnkmk<QKp=FupMOW?M59c=82e67ofsoFr{LFh>T1hD)5IJ
z8p0*iCOXuN+6aR=9Ft~0BUgucJj0qr$A^~gi75JJKw6_z+-16-w2%M>o)7KX_s7Yi
zU~$r-2V265IcfI;#Kdxqfkb4}d_A;A+s!CTGjGktutCN2(Kk7h>QiZ6ca9?Q@o_>r
zx~LoITVwomzSgucoQz!!4SR5omT$?%H_S)1$xN2tgPGEsSg=N=_L{s}b~iBB3}#}|
z;sPf0&~AT!Z=^{dKXD#p&>e7CqM#Wg0~P<SpAAJxWS@26rD{w58Mg*q(r4{d4H?_G
zhNGdQC~o6JVBlVzS{E0S!fz78Bxj6dtD}D`@@JT^N%lZIQ}SL^3<C(;jHdL>c;6T!
zqDM3#F*@xqe3pnlJuzaZI!y)x4i#nZKv5A>77CyW5ckoK3FvMevI)6z;<TrOGWNMU
z!=XDXL=9LoF-OVmiE(kEHTEDYGhoD;N<xj%m8XUc3ySpzgh}3`kLEhYSBYQPKm<iF
zaM;Dw!e|w6*GX-_iIVAf^xGPD8dr7O`)gLwkx{WE^omV#%-z$7;udgHG>uDl>d6xa
z_&A#;IGo&jgxwJ18Bc7YEGX!6O6_xc_8;#_^JnfoG7jyxERcmKTBmN4`oN)nm(0)D
zL)R|lEBF=vMlr9ZXqSq~)Ja^@l_Fa5R#;C_u&%t9MRt8eF(XGBn;d&6KC5Bsj?pJ;
zo<K!8l-FDzvKK%`!620yYug|BS2j6nv^rv3uI)PYJd{4_Uxi+GyZ22n7fYYBDXWF=
zjOg{r<G_YXsO|}40(Z5m2+v!(P?qu7fcSX~Q8HZip{|_%(!l*D7XJ2f65YOXN<Lw&
z|8eO67nAc04Zy^71!p~cl@kyG<4Q)yc4z=%#DY~8U*D7TtVHM*u~#+Ul+>k2>Qvkx
z!BOWr)n&*jskRc;#td>VF``6n%x7|1vioy)EFq!F7@8synf>R6u$xo&rP!+>tw1)z
zdYu}!umy=o47)WKX%sj+yr6|a??&IWHWSr{3RJHnf$ErXLnX6b-uWf@sV{W2!JN$W
zZGVm43&Y#9et`TwBaQBS>3F((4AYs@NwI|0ai2X~uY|>pn<OT^2kGV`?~)UOOD7Ck
zkzE)6P&S;y>uTaOznLvtY1VAemL&4JXBjk3S_dgZY7R5ulXo{PwHsI-1ZzCA=}4Eb
zt%x+?G;y}OJ83d|llba>v1}SbDYX~pOP8a*vi-bkN)cNhHj-|bg~Wdt!YB7qhx_;&
zQnTLNADC|&eVrqI3?d9y<p{Vt2YM^I@S&7_=PUu0a#r0WHST(LK6!j&TYqMsl=a4{
zs^fa{8gLk#B&~Ugr+HRe31XD30MtQ!4xC?6aSadS@No_dhVpR*J&!EeQ)CrIT@IcV
zn0A}$Dk8209>l1ccwaF*hed3j$X4oJxX*%pf3PMx=h6B)-wk`R9#LK{JX64Xn~zHY
zQQj+o32kIWP77kTA>U2%ZxVRPo)Jx8s4o{Ea4gv@jX#UM<fafR`A}SS=`*D@?H(%-
zu0QGtiU6|5C+qT<;%IXPJPU$_`x-GS;iZXpKP*$PI)?A$a-&VWrB^Kwv%ei?NE|44
zMqIpqpH^BKYC$GhbGwy)xib@0J+O!V+P1^Q<#zp^64-Pk(Hl|*Gldo~#t=IRJ=>o?
zx<>roY4H)wup&=zPi>vsEdA1X#+td@5_apZ?_|09r*J%Ns|a!mE$`0N*o!i$c}%_F
zO{Fb1l9#-^!SKg0K%nK!XNX_6ld*~1(6L~4bSY5_e<_n|;`pr8LuJ56<Ba@W^Ti;j
z`c_glK^rAt9hi8owPP@}Gm6Q?PH^j-bC(l;277aRYqSRH+fysZ>HK^ol8*pAT~40y
zQ3O{K)Cey=YzuoZ{OL2SZ)u@>CR3t!f+1S$j}u@}P4or@x5n7uaAXNDkK8*&IO*TN
zRf%t$1#fhq$;o*K9!aK7sU%aNtHB*Uj2+J6!EY048L;fO&T=+}<o6k|R<25BVCN>Y
zxW+(giCQdNjy>g^YY=B}h&AfLln8uY$SnIXYb^a{Sz~V|asGk=-`%6C9|1ztCv7hz
zGsjdV{W;}eH0pIw!R@a?`dKM-u`@8>Vlt6RhTIObK~Ujl1|aM7I3<~A3Es7)E-+d3
zSz~%g1cs*o7rv;~9J@UXS@33nFzY7x+tLOYsw2>KeHef^W#}dyWIy>hLj%Vhmwno!
zThCJ!6O)A_|NccaB|6U*V>08~1)g|MZ<KGWrvUr<=9X*{r7!+4tMB~jr`OXvd=1gD
zq>U&PvW|Pyg>#?)TT->hv#5h2aH+|Wjcf9J-|0`#hc}nq+med|<r+QqyW3k$+fwI~
zw~L9zG}<Aea$6(-PnYmZV`N*FkKa|8p%;00MnR9@ZDs)EFqfdF+0S2R=e=_!x(<uC
zRa>cdxNTB~M+&23{||pafWIfxSBao(Cal$sOd1%+r_)zbYBkcdK@$Q3eJRgP<FmIv
zra}J^*weOi@Jul#5qyR}@DyF+>o}VKNntMDpS6mxnl&kt33=$Ghp5ddW-r<WawBJ#
z=JnXNc9UFtZ=iAC(TwZg^&&%WElp$H#-l{nu=CbybEKYpDJs%2_}-_fZ%ttOqHSn}
zS*qQv75>&7&L*E^<N95=g=a8N7FI4Y@q{-X%<Y{$(7L(HzZMUzJG!g{zL-9X^a`=r
z>D!d29vaD+&0p~Kwp3xQ+$b)#(A}kiIj_7()bJ-5)Hcdd7MtCL@H(|gJGqr&TL*Oe
zH<KnDd6<VxJHEl(^Vq$-^1)1AUF|{NJD#9nq$h4Z4XAG{X4b+jOuMs%<Uvkli?RJZ
zYZnPyw%5V>wiz;v^4%LqJ)6XujfZJCvXdjds+X|XdW6ZBqe1?7#5JOMqngZl=Uq+~
zmtyve<lYA+uy@W!9Fuw$$paVZM^BCI#Md8wPLbfMa}VUfCmtn#{|e?U*eYeV5!Uur
z?&%fB;;&|NI@3b0uJ!Qu_ah=A0x#LR_6`gpEzuQE!7TmsUfz9YE?RF3ZgDqocfUC1
zF4)JEXYXg@=P!^NH=2ifdhyXaE1CGh;{<D~a<06)5E4?8mQ9*C9`21;M#}cZyqi#i
z$&cPcv0yoUa~{(k9z~(%6ut*5Xx*v>c?Xv9f3I&8VZcDGE`zvxSS!AK=X0_u>@@7q
zn~DR=Nwv9PbdBcW`)|gxG>f?(ydm;*1(Dsy@T6cU!^>sP#noE`^K?G?*E({8`|#>R
zL-EkqII`wTzTT1`!m*YfLvP1DeGdnYCs7g6nb9qbEKn9rBGd-Q-N?kD@yz~ww$#6k
z0!>YxeEdG@1y|?G?HoC@8{f|37~0jj<1zk;C)s>3TaY-xnUr<B{r*Wxiz~Qy>ht*L
z?&Z_3w&Lby#J6r|?i*N_O<PXlt54yR1=|Sl)yg6p9(eX8g33?x;b-58oTVeO!vMzi
zs38llRg?;uF#O)<=qBT{(^=WL`w+1M?xmOXFU8$0g7l6A5n#{l;)@yIQ6?O}-5$p1
z;cYm0{4}n)c4i+bAtT+uv9sq$S-Y1_Cr)9O{?hlhN9otH277iNr%jJrNtZEe&~5#x
zQF4N{8>Ulcv=J)p<f3!&pl)NSZy67Le0NZ{X=|F+G!beoW639Put{shyZK<Ij_%0z
zg&(mdxkz>?ycl)w!_+R?&-8^Gu_^3vXlouD*_to^{UHVJ!Bohi_{PysFtCF=pZ@DJ
z&KHUh=jO$oPfey(RG>3?`QD&@1V;~_z~WVx*yw6Ikaj&AH=bqi81Y{>M*PQc|6@;(
zZt+EzYNutF793pu1--k!gR8qMKAu4|XxfZ0O#y+vsWffX9Df%J$2R+teR>x!ZrDQO
zfe$jN|J7O9(ySAF^6_l)MK)H*lY1Yyn+rR?Xa33rv{cY+&>i%R)AQzgO9_qiA+OAh
zQR7FjXTx63UZ}yTvne9HrxI5$(t*LLd?Qxbk@Sxr%8;5D*sE>BBf~qgWBFW40-NEP
zvxCh?@`!C3PnfopT}LiRyLDs2<Bt-iKf!CC{eX|ZKP575w;59<N?BR?`5X9lzn`|j
z=a}}d^}P1U|FM7mO!C6wDVN24@+nVFB`33P)n<A|c*-2Jh|I$>23q`i{MpGg2>cys
z@{5f~{qRR2pfb^a6tce<uLrGTB1k)QkX>m8ZW(hcLFO|YJa`m$pN7=*FbJ!bMdtw{
z=p+}%+#*}H0Ww&|cka&%FTF}#YbI-sWM4`}S?(%Kn6MUo28?24-`1Smvyr%N{b}E(
z6@v$KXXDIS*dtmq@$R9VJF<#JYqxPasen%XhH`VehQu}L%+1|ebMt^9#5n|LkvH>_
zShjSpLm+(i(FxS`bE7oljIi4qXx(Qhqi$}>@@2Dm@3Z+7__Sc+_~BU3t!3T*<K&gP
zF?_;Py!zU+GD)vt&+)vA`PGQUl!}5h=6$@7#&QUB?2tCBTsEKNJUw+=_T;Xy!-UC6
z<mj1mWXpgP-C+o0Zn=rvVqw;9f1Ed_-bU`WjhsK9!v$g4b-H3)VmtE8OE1$WqL}?#
zcJa-sUEF-nG@gI<VM0SR6jd^oxQ8%&#LYD6+?SrMqB(ddTT}`jrcILwVjB10uG?<G
zv+y{_QqEG6sTU^iAzpfXu&_-9+&A@Q2Dc92uuSY<f3kq6R(-j1R4+EJTq4+pk)3#u
zbH@*ol9J5%<Rc`XI7g_b2j9+I!3`6h=JhvU#3N}lYel%rEX-u^xcj)VVHkO*xADwl
zFB8-2Hb!&~XZN858n*Avuz@$?D~o_Cr>iJK6WN-nFTKQLW4p5a+wV!uOefzxjC+UP
z$j(LcY1sW1Mh@)6hOZWK@aS=NZQsg{T?Zw-A}T7fF_}s*y1Q}j6VLMSkj`w{w1X1~
z`ziN|Vd$;HiK|(gfg^{}tVJ6J-P9FVHy`>A9L0z}tvI!3jR;;gnY0uqLbJPtHH#LI
z;?j_bw~xjxYYPikFJi%#OzwN;HJ*B4EIvN6D6xAoc;fwZZWvls6k&g?#J0bQCP8{m
zrKhugR|4I7_a|H~3s6+m{bSou-@Ax}0xy|}h0QY=gw+XV;{6X$a^PE8@MYrd?MX#O
z0*e+bqn)(-!95$ZZ0<^0^cX<1=G_?DukFS3F8?5X#i6%pC3)Xswq$7-dB-H`$>!>}
zU#}t8Y@ku!J9+h$#|Tc_!;!;B*`HBP*8#)n*Rc(j>|~ZK+{w79ukqURPhmar1G{7q
zeDr88eeZmb?j7rpntYspz3~RGzWNI54kwHJ>n1m*=CX3`w{#ry2(P{N1~H|(*_B*Q
zh%8zT9X-!UN$2pPGn_vxf?-fC>GPN8+Ev=Znd5tS@s(G3`T6Hauy`@_zT45JC2{a<
zF~|4sC8ACp5io@yR`hK}b}GxwWY+Yx40+%=-gxsF4EtBHZC?_ngca{T@(v=)wo_KS
zr{w2#M)j&MEM+-%Pk*VeCwTqo(PVAjK%xH94nkFq+(p*ivT`|FB;P?o9BL&kWAQ1b
z&)-IyKBF1gC!Y07z7Y03ja=UrJau<JJl(yx;pUMtZnWq8p$#OSOyKmHGI|Ue&P{C_
ziiKN=<T7WIg<dwb>eamXRO=s2_s-3671ll|vIS31et{ufW7)jtC<eUgK4=8D4(!h9
zV>?O7FA+8{2YpEqTK71fdhT^@?hwtUZM#@CZ-y|&-557^ASYHVW9#7*vI=v#^^S+=
z6jLQYE6Extl<_65Uc|*bc!$-cN5`hJCb816-A&vzdI0u}0~mtpabuU}bQy6EPfr?5
zo3_K~5>>=En-9}_%tUT%;?LrlODGhzoRsnqI<#xT-D5{f{T<`&m)@sVw?V}CTv6$j
zshWitQFM(hvKyt48)l6=orm4cD=$2V@z8d5@7T!v9eE6rI_=fwp;E;F06+jqL_t&l
z=@Va5qVcA~O~V;8s4KbqcXB>8mkXu-jC=Gsp1J=94)0yZ>Ted3;oFJVWjEorE;X1x
z{fElk3aQV6bDYg8WZ&M+_%&-nwg`bGRjjuN@mcuDf?ipYI$e$WBH)WaZ88Zt&}S5*
zI!818i?z5p?qzVcA(OOAr5t7l>21SB7TAjy{J{B~5`un4eQ3ki^D))BnK#~gjhkCb
z|2n*%ua=$T-f6G$>ht$=UdWz3N0QKnh4a*u=NZz#i%m&JX$!q+)4VGkn}?E|lg0iG
zi#cnE=C#*fVnV+Tm~x7QkT6py|B5pjl$}V~T}VnIs@*`kMf<UG!&ye&KAu+5CcgOY
zN4}iCf=)vp;pJzZq(wvxDN9xVaHVd87>)`~XU--{+m<;X?HtF?=MrB(klex?+TSGY
zqID|<Nxr-_M(W6X_kYj7Naw%`R_r<J1X6wd+aPdhcK+LB`Ja=~_)g9E`i%v+M0Q|8
z?RsSTnwdL)4V^|jBzkYkHnf=L^Y9R+%?)oaA4=@5)URI~PY-Vbg@H3xJIJA+q8yaF
za5{_K=TivvlkGfjg)x+w&eauf9}n4564jBMbaw16BBpj-YSnK?$J#;cJ-CyzC(q#)
zR!@W?@$2dBn1q!$ZL)pluJNNmbRb^Bh%~HQ1EXFidZeO~k$4oHheZZWVG~cK$t0C6
z!kU5L+6`sPOxW9iC>q3y?w$;cbpy4O$(9=q4p50%w#S8yvdOkt)@flmgw1Q(urB#$
zb4fUKjLhsT%Cy3SYh=Q3twE>eE%5U+qVW$TwpI;%4B5hB$TqaZ<0iAWOfN(>Xduj|
z2O)kQWD8?lE{tKV=rDYQ=?M=FL{sf>j3SI^1y>K5v;#z6v~9CU@^>EN_<_T+g&&4q
z@I`c4HNv*l4-XSeLI@5HlAEA}LHG5SxMbU0B)x+3$?PuH(jYFDm?oV_JvyB^%e@&j
z?iLbvtf1WF!}!55>{{eS-8zwYie7O2$RINF%887wPlpCklpapPUalh`*q7YYOst;G
znKXJF+YcUL^@UWTtT8mLUrPiGnL1_isogl9AWt{E>NY{EKS{CPpSEq<5F6<y477&a
zbH_O3X`^AYy697rNJ`DX;wctu+SU+3NAwcy8bU(Di4685%ga|}gmSvvcss_`>)Eks
z1L-c&G;UW5t%th^salefGue}uMu?9NjklXjfRd3)r;+KlM1*RSK93zc4iOMjA8$9=
z3iS`Crs&lN(}+fXz9O({iL6_%Dp=*a91L>_73|$uI(HUULl7g|H{@(;qA-b&ylDyh
z^P}n5u#B0qTk+J$Mh=xD&xm*?4(!RhAAf;|pC{dgITR6&EYXiIlS!$OsP}kj?YK(&
z@$r&~e$Gi@OtRH#qnv{~c3}32qfV3$7gD{5t6vW<?_vVnY}BaLibqD5i;%pHgflw2
zc5IBNs~_?8g7MJ=&@jr6^5PQw8nmHJix^zeWdg3(l4qw)7BNGbk}MiGq;HY-E-Edu
zCEP^n)6>VBrgdt_0z^aWHnmy5YykoBBWP(!;;SttI`q4va<Ou$0Lp>LaAD6UOu7em
zY0m*QLh+XV-Mw=hyJYdA&opw&z!sMR$S1R?rs(bd)RDgE?XRI;xGyC+r3A(bwr%Q*
z;9;On(>ik7mmAHR#Zx5e6*j*>YS)&2rq94%*kzmTO5s)^lg%{iGnOeMI`P(&C-_?G
z{<bb|qSAw~?uRJVnrPTigz~a{I`ryDn7;=HPi3+<Qx=wi2CPDD*KFCHapP<p-n~^8
zof(AJZ%%A*WVQUe5D*m3_7fS<*SR1nKedsM7bkIJE7{2iszb1!7v2E@7=%eJJfFtl
zgc4GNq-9F|E80~WY4dgwI%|g1CCFF&`itM93S49nr_WAdw=6_zHI{ZOJ1tRS3bk#+
zNJn40lnD<4y#q)-E_rC{+!4w>_#?Bo<Vqf*WXw27Zgvm`xj9!ObtwajjH9w^;w|H-
zo9O<^IAYeCNs@8a)#OEsCgE&awN(VPJ_LyObNiR^?<Kow-*Xb9hjhG@?9YCZ!-iho
z)M?m2+GrMmvKyl-7feO%AVGv>7qcG?V?#yFAcMolGRO?I<L0lGIl_idcpSAuC2zU_
z>EjnDEH)7?W2cXou+EJequH9|7*`che$UqnD2*Jz;%DM`{l%%gv$Y*_x^{5pOxk~h
zaPXPwSx`I9@$BHbS?o8|p`MGYsJCe7IsA6^zWf5;oq?c^S7(^5(q0BloW|hrOkR5X
zUFyZYN4q+g7TeCmL>;C`bo*O3lKIe+22GliAu1wPpPJN(@s<6n#?<l?f=AT1YDPuL
zj)M<@o<4%N4Np%OnF8G%I@n4-G>95u%^EH6_4FhvHU{s*1v0LPuDK8k4ku%*Mq`zF
z45dR0*_F#)<4{LBvTG+6mk?@6+_}kGx<*Ig?(IeM2H_m4Hn-Gk){*Z%UCf3H-b}dT
zM%K>#3T<Fx8rKM9sm!cyRdce;C1tYv6I-{IW6llo^b(f+3UwN1j?~xxVgyts`Y)`U
zAUPo8CWE-IY-#%nOVz4<9LEx6#oDlr2s>gqLUh;cBIMbP`Rv_&#G!L%l!;4mimtA?
ziV=|SW)YB0qEEYL`yoObwk9sr*P-*3l`q@Tx;%CslH&_)8{r|7P3zVTm0dfBAi8?#
z5!!U=L6oM9Q%PAe=-WATcsJ#hI+pI?GV#<j^6qDg*zn_Q=50JqP_1a{Hw?r(vJOpS
z{m}XaF|d0*yk$UW-MR@La)P~PL?|v<2beW`5u1LT$tGbx>NkmU<Xw5O3e)E%TlVp7
zWAKWIp?TdvJOe!`7Xj+LA&_=0>Y*zvAU#`jl|`pmVJ{Tv&8#;#t}|8i%?yezn><(E
zZSrrfP#78~Hi#NY$1pQL%$d#NuV=CI@EMugF0C1D!s-}}BE(BPI?1mLnVi}u`nQDB
zzF8g0@+EB%G)yAoD|*g~e}iD6$d4AuJ089?X&)=3b_8vjioofqkqNk-sLyz?{-lw<
zeS467HUYg~Q|d*B)2fk;d0)+8?b3OyI%lL|s~Gg9rH*usqCf3kx2Lew&ysUu1+$lk
zp772cM78cp(>O0qo=p`I!0Eh1jbi`zU$AK77QUJNIRTC9P(xTieR;X$DU9d_wb2B|
z(o7ENl)3xTwBM~f`0!&qI%ynD!#zajSJ+$`@C{1Ym9oe}#wz--NA}2ez6i8EdbAPM
zms8}6s`US}cNO4KU1|HB$V}Ybow$bx1PJa@r!L!-cH8c5yHfY9Y@Je}xVt+6LLfj0
z5TZcb-EAZz|9d9{sBX8V-TvEqo(E=f=iVdlk^A|l>8Qi;>;jxPo(JdPIC$If<w4uZ
z25Jii`Q}qo+SIR<B0kKU#DIkM){!vf8Vm}ILy)QvYgcc_mSyi^Uu7i;2^F?{{60<;
zUelgvNwoCPZZSF`5tS!Tz$PjdZgi6BCvnqy=`0qm-h(^;@*H0M`=e;wwG`U~69Ezl
zaybcQN;4;n&>;U@C9FLB>1^hvX97o%D3zPr!P?#$-SwA{UtEoYo0nko$?_`*Ui%QF
zWrJiWT)d)St(GB(l6OljO)jilX~sZeSMcVijk6G4p`oS-8%{SOa@0gP($TBB--dSE
zgLrSjdhFV`2)j#U2=#Pj<c5B|Ev{CG{K9o>Xh>#~|Ah3k`gKEQ%!g96PZzI1`ku8|
zdaMm20;3R1DPJiivlj7FkYwA2tkOOthPYw-l233%Al|MN4Q=cV4b0(A>6fdUi=KcM
zeu__>hWeZx=x|QB>S(4B)~teN4P=<&0+m?0YAyECLGx&{5kjIpq3i9_+hqIrhvMLh
z4M@*CidSD-h{EcAnwzVJ_L*Sg&2vW8R*KI*`UG`kn1+jrOr(=7i4|uLJpSav*t_aO
zI{K-R>}O7gF`f;c){v6%wv`e?!7o6kL=Mj<+d!I%Ia-n^>O{`bY#cjMhNLm$V5{Js
zBNMrA`R6!E6Rn{lLDJ|+kd*Ghi=VAW&WQu~=O=5R@r{Fnl=jP4&M6HUp+RbC@=s^O
z!8Z!Qu9mbxrwuE$FG5rXVV}zd^#+R&VT0Tk{Af;~HgiH;v_Irhb67juP(U!i_0>Q}
zv_+`y7ET4B7-81*NodSkg$3(&Bm3xfys=~ltind3{_JULfjkix;)!nBCfBqOQ?4uF
zyb|V0u*0@+3W2YQHO=e7;bUuu0ghX+3`iRp#w@xOc=?@GWN3u_B}c}oOEi&d!WRoZ
z#l_a4^K7UO!tqCDm38X`LY+lT+uuR%6@gO_B4ar7JPxqZ@(hTAmzfq8)Y4qazW@`z
zXasxO(658OCp;(Q!n)@)3U=xP)G)-24o1cC<=A;(KNhXr4%aawuQCyqB>dXSvv7v{
z)|$5V7$~EodhL}inPJW?W3Xf4JJ_^4gW9;&_+Zs}L<Ww4fsVHQ)Er4noN>=>b5N9*
zj{!Ql>I=^fg|N5ug=>praJ4t%c{d&R-<C*8^FkDpKwd)Mhl=iD8)V^paYF9?ML2SF
zAKrieU+A!Op{D90K3TC1+n0WbJc%>>0vxE76qsth6Zwu*aqsHqf$s+$aT3m2zxWhX
zlpeu`t-G*f`9`!zWaI&C(0nGHW{=ykdgCcFLq=RTD*8px{4T(cW?3@$MDTpF(ZVgj
z8{<>0v2DXr9HqnLlI>?;z<1wpvG59nlcX616*Li?H4ztR66YNm4pUPJ&jbb6BAL3L
z7MwhHiSJoq{6koL^_@Ok-!I~mFTQV#V04{8<(vDdp-nmW+Yc%lGT=jV`GX$+nI1BH
ziDkGg1tk{WR)o+xX8-#1+RraNdF{LS?S~;?Xa-*&FG`_e;b3hBGg}XR6pfg0f0&X;
zv$k`EmzNhj-6%Psd1gpN1iZYxVQ*zlX^IuRDFGIwJ3=wFvgP490<lyhH&<7{*v1V}
zw1u>B_2)~?4qZwa#*Ci=D^&|ht6LG8GzH_S616s`lO2gP*Ql|WJ$npX+&#F_`{3f^
z2MZaMtA^%?jvRr3mRd@)%rR>8D1=8uBOx*v9d+es9gre*!gM63jD+Q2CrVg%AZ;uo
zDa|{6vJ`=#0g!8qF=fu}i1EH+XGd<_%)%b7KEX&%NI*|rDH?kXkUVY%#*U^FkfxKS
zAcGuB5+!Zkh@>-!r=J%k2gb0sv4=N_cvB-8ES+2^E%b$(yBmVYh<H%~;cRP3`qq|2
zl^+~!%z0Sr^pfya+W#e|O@)W`5MD-<G&?!C@EI4lyE((l%?UoCzLZY2qrIOmmgGcO
zQDPGv5dwE_4@zlduygR?{j~9B-<?RfB`2jo)lx}kPdP?Tn28`qD_FbvP)ZU?A~p~%
zuAWFBE>4`=VA>h$XlF|L6I18Tr?ZhKENwjW?Hf~S<>>0ngPiRJQZ*{GMtpb>s!A@w
z$ZiDY&zJ-|N`Qq@dHF1ACG0DuE+(!axcR1eaIrRr6HPFj?ClU49!TP`64Z=h(uBz*
zR<5a~4U8;cLwi~uZ$CIVdccE3n6b<R4({HFkB;ITSD~&=jVV)a!pITcaAO;CBYRAm
z8jaT43P|nT5fv3d6}msdiIor6pR0!lVn?OGUSPs1n@Avz$LPcugoTHpy}A-AOK&74
zry#`716tbRJ8}KmT2ac+VnmIK6Y=+EfBd`x5H~U&&NL&kcXon>sR10r(=b7h>XOh2
zj_^XS!VJ;z5yZ(2rqq}Ogh#`Va}y92OI58eCGcf5*)heG@e?S`a-uedFDsoH2I|h>
zg4}}+P1j2}$wZQ%@uH;ElWhk&$!Moo3UmJmOr63x6Eq&)Ua&He@;)boM#oSBr9ydW
z6-{_jaQoZ|7#ST5d21bdv{smR+a1)%IKqKZ6MQ|LA>;b8bMS^I$2Y8;H>G6E)ja^C
zQxah!Z$)Fb4ijeGiqT=Nr0Hzn=^KcIm~eP-tp|rjBGlIvIr}%E!#*4_zC)5ZLvp_k
zPM%z|PFJ->dMs&HlapPDaktJ#fWy$73-`Lac@qn7gi({DY_CI?#uPJex{Effj<B|N
zVE^3NUNGD&G^na=Lini3m^?ZZc05mneP+UaX651rPx?db+r0y!sgq%EGBhHoi3OaT
zz2HoS#gfc+w4VhA&78UJMxnj37%d8Oq)nd*KN9@DoOe5Fg#05sxRxrJLDm&f(UI_q
zjHQH5hf9^U@JnLbX)!pzZx<9aX$uV;8ne{W2_s{I=rhoO#?~H$j-G?NXT(#&XaxsP
zU%0!H0VlQ|Bwpg;W6@k&4h;#cIBINs-Gksy4VRB!5Sb+(xVc!9m@$V73C>Z8@jzD{
zsv3HcJbEHxgWO<i&-2znhMhb2!z*SS+$p^kin)^?62d&uQd>=vG9}_C+=5%i@jMRp
zhL(1<JxXK!nPKVRfk;13czJq}!Qgq|;KcRigDCFzs*A;B@SHJrVgkH;gAhjZ5D5ud
z8Rx;CLXB=RHCnADHCEOXJnV#JOd9-bu1H6PPDqT1w<|)ZjWlIc89OUG_)wB6Xx}XD
z9S|HBPXg8%)g_my3G&0FY15Gy;Evv=N(>rNsE`~6XRh_=5xzWkoG1}Bg)RFVmz)NF
zTLaF23&)&>JEqb8{R&O!Drmh76{v3PMEF=b$&L-Ad5j@E_`E<8J9vj9+{+TRbyet5
z7-Q<KcOscM$!NCY;qIwFkF8vM5gO)?fPg@FI8f+9Nvs3UXMZ2=4KkOe<~DHSI<>Vh
zf#-+_cv?{dXl8+PNA|#bWExy4u@#Ejh-kP<Rj8=%K-|Qekd_clT!YZnTmyyF3Afxf
zA7Ru|+H!w*P>3UC9d6viUhb~2_wwXgcjEiQfoqZdqXQ;+5;BW}=(h01o%5$NddmP>
zct*wXyo!#9qLi9z%iA4xtS`jF4wo-orf@otre$Lg$=^-L>@;?25gVV3*r;H>!-x6_
zi7}&Fxg$6#hVLaGcvC|Y8y5{rO0BJ}o#4%PYalfN-GkKZlHu^wuN6y}SXlB5@P><E
z8}$<w?La#=ulO7rckD;*#a7y>2UB}EG~peUXYf9K2ZBSxVSZg$BO$WW+7<f|7#l?`
z`L)jN-Ms--2Gm$JUZ78j6z=r1xxSnrqmfx>f|wC5*Iy<FCrA1g`dV$;j+41X)L;g~
znY2vK{;gQLdIK`)gJAC9h{M}yCdv1lo3-@I&;QuJUVisl4c|e_3W7`nYA=ih)#$gB
z|E;v&-!e$?^3RWeyrC5DezF(8dFctbToVBQtQ|>T@)|OmrLd4H(W3Lg3xB>FR$ujw
z`dNGQ;~P7jz79D`e>^s0<d3iHS5iYx`VrWM$0NYS>i^e>b#k1@I1jII67Y`S^<wSD
z^0(hafk7l*e&P<=&|i`G-N4amx@wS7)QC|@BVi&KKmSyPQEAR*9D{4z7`Wf)7w7lv
zv$D6Drb#78nKYWj>Q6Y<Z|e^k`E*93NgPYT#7{7n-`4Ce>N>;09X?xzk%`GP4ZMzO
zy8)f-Wr)sK-rov8GWM-BeT$#}7fg@wy#Y1<f2cx-%++sTSDg#~aC;Iqkzn<i{(IaW
z=StJfcD(zyzu-`pE0%mre+`;`4HsqYX}s{-R?L6=L1@T$c$4^du#_S<D-#-LKa{Uu
zfKw(3c=n;GaI_sFqzBtC<E1wiW9}cGgo6?NU&xT?sP(L@p?^Y030{4BCmwkAw`j^(
z!m}#^kIsw1tIw>&ZFDYHT-=4ieh-ZH?7;3KTfFep?N@~n!+rWupEodLp-4pksF@HS
z{#FF2&hF~&q4%;KZI-|FuAle^O4lwHp_P)8P)f7E#b4(q?!%3(evl3?{b~c)P#Yl@
z5rH8DlqkDY0S8JMZLVl6L}Ww+L<GJa0Yz6c3QMY}IrK%Khts#;_&@hc7v6m0C71=r
z>Ybe3BSvG|*aT{wfrG2xLw>6<&7gYmz%$QL3v!K!RyP*luTMRV4odBvWpXqdj=<Zm
zJcg_dpQ2XgjEg%qqH@3<e|zJ3Y7c$%sO9fpiB-9lc;(?xJo@AZxaZIRfN{|>>~0BR
z1S=oB`SCi;f9wxvIJAawwqr0gpb0xKFt7mqrxdJn+H5-68n@z^7nb6F8cc-P-ata)
z1`^T#B^lxW+{zRO^i>3CQ)WYT`G2<<!Y71|O8?zX+|bsI%`7o&6mvr>7AyOIjDQRQ
zOvWS@5djf_?}va1L*&JebpC!f|1aI89-wV}GurY?7!K712UmRzx$I@6S(RdMjSZfA
za2h^;XF1KIfO^J?uc&KbVkZlXaMsd1#tDx-`#PeX)cEA}zazc4j84maFds1!cceAo
zghdj@MS5RFtEHz4PC<0eb{K)j?@YnlZ$1f~nLk5q&W5c~Ii2aNuzKkdbXAog^5$vK
zwC6LNWf|-k16>H!`7xuGz4Gi$u<GeRQ(Y&C=ppm2s}1}@zKBHhFJvObZiono2#5%X
z2#5%X2#5&$WC+j^aF8Kh?|$UZm>6M?a0?|49Xf=ys&ll-x5KLCi;-VajGcQ=B2-$1
z1DSa+wD5w%&5WCE8HgZvD@Hv6f}^8R+TG8{M9iK^^DYVPr;SN@3pVb<MM1|}-IWx)
z;V}uY{^T<Z*pESoizzZU?}l>394vVHUS_x4iszRfpbfelqh`&=U+$d_w4cYLZ*HWo
zlp*cN>Ck`G5p}rsU(TmUME`Q;LhOl%fQW#IfQW#IfQW#Iz)ypKu>%wJFj-n&F-CfY
zApcS|j6G&xMtm58>sldaQZZwrDp)a!SL%d2kvii}_Jbj0nN_*BqZr4|RWk;;1gA63
zA@u(7=q(wF)+2<f?QUwo0HdLp2~;+V6@w$lh3G2u?O3sDDO@JZhq|-~%bB<<hzaEk
z8Kp%@$JzmA-c^(M_N3E#&S9p7?;M4W^SO+2B-4jWZ67dWBrkg2{z8jLME^o3M(mb|
zfQW#IfQW#IfQW#Iz>Pydiklz06>ogF7B8J=^eroIJUV~8J_<~D0;46i*P^LOiL}&k
zee{)~{}{!DvAL}+jc|Z=)5j=zHnd5_^oRiL%d>!*anS=4ne*a<%{Y-4gvoKi`tFYK
z^}z0J7g2s*hx}?wJoDD0Feu%Re=ggA<Uc)x&{GTW>Ps&%K~o3j-~VTf8tKCn?XTir
zOeCi0)nVR4kHSvLNK;mpjP&x$bqW_93vb-&{10o=F<epw)C?ByW;f({-&S7>CDXzx
z7;xR2i4cZ3oIzBV*KiRVGg!VOQz9#Hsk|0yh8&a_F@eX35isYCeQnILq?5tbaVRZo
z2NN0$Fs`Q%Qs}EiGtj|sSZC0#FhF=*GQ6EE(b-bR=dIx7JhWJQ7*0yg?1xq+gQ%>i
zgG$Tf6^wNnz>soRB3!XrHEPQ%8Ryv>4(2~R1kE55qm-9bL&-6L$xZwi3dvF?K_4^v
z2_f`+J%((d`a4_D$*@2UvO(0gD-b~MYC(CV>S<>-J^?d^jQY}GzX9XA8fYvW;cR0H
z6}|A!<>aD+9B)+INO;%}IqNC=T9AE)nYmOZh>DMg7ej{iG&7W$gqzvvy0o{~wbjq8
zYLzYh@DF5o!Y?_KYtd3u3n@d&*_eKR#|Rx`QP;M1!kHnhWctUnXs)S(!q6Jt?)F11
zGfY!`c{z+68S;v;#Cw=>V7Py^<%~CLY{!Hga+om=w+#~xe629%a$Q9!+WS-t_atFL
z5rzx2=X%j7IUlufV&*$bA)b*|iJHo0IJ!H~Hm#9it^9eNwP_jpir1YOLhY+VQ!`;l
zIYTEZ7?4)Nh}1e`YXk@RFrmknXXtBv`ZpKUicaKSxD2<z2!>O--no*or5P94l%eV5
zw8b-HSUU4-3-#aB`t$#+Va&?*J~@-#INVsT^XrGgNEZza4Ga_M^`&rC*WYrZzw0zA
z)YUY=j$sq850Uln+R-X|QP-+ua-?s!dmJ3-L=9sn`*}NmWuO1M+W-CU@(%7_#-AL)
z82y6`dv~$H05PFo4vF~hYx!TjLxW2fifG>&ay8WE&o)xE9JLKhj>1^`!(sP+wzmI=
z8`2HRnFvh7@PZasV}cJ2S0|)nV6vI7p3}Te8bC;preIon3(FyzMTlK4B!v@h*9cj2
z_}$R(%DaXkZ{KBh<&fK-#<Vc|p+rQyLrXIcfu5m=8geEXGhmoi*>zcY`xxrcn2C#I
zOxSjG<y$zVWYVgKr||w8SZ<(W;WxZlZ(v8os=fmOE%vNlgqL3b09|UXT9zX#*I~zr
zb6BzDV-#KqwBKKog-=(l!LBu%usN#+ef;~a4?e=~y$7&i)nfepug{{SxfhpCt;b%b
zcGefBbzbt?Q+VN{Yw25uexN;6YYG1O_rGB~v&&T!oY8+jSkQ=z2bW{}j{M=<kbisw
zQ+MZ~v@i>=y}Jne_V2~=&)=t~>>H?JRw$wL)?eoHk7MD+(^sGSAzxZ*a`D=~KEcj?
z`x%S!b3Fa@b0}))M#Y5;{Nu@gps0B$;C{vNBUrYl2%Sagc;?v`k$JgY-&}L)QLNmw
z<!Y+hYkl}S#)ns|!jAK;7;G<QIGv}l^JF1P&!*$WXP?9I(hew^itxg7FJgBV$DV%(
zFZ|<qoU89f(Vp#CzPIpN)z^I+WDL!B-hPMaZ9icay-m!Xczsb|`Rb)OUB^J*Un{*>
z{l&+q>ihB&!fpKx<@jjfcC^Wd)`q~aY+p*w{ev&ic_mLl4^vOS_T*DYFQ=#Zu5DPk
zzgXWw?!NVS^Piuihs5yOHOp|m>6_!fYZcvC{pMR(y?Gb*Y~6_0pZhyL-Ic@4ft^_X
z?lX92?QwlV7ATvsX4R*tZ>_?Dw_d@*RT(_~cm(qLqU9@5(?8rtee?SN`fBp9@SV4?
zW8XgPV`}K#`?Ar;nB70N9ND%8pKU!3`EcTfs}=TP_varnjdU4yef|+s2!A<I#nsz>
zAz$Q-vA%WtG5pXp1m9~IBusa2Jd8%hJ^#6iwyzD#KU#&Ff&Z+<uF~UJ@!8IA*u&TA
z>8(74RlMi=*L&Bc3;1BgLH$jKmoCNj!l5(a+Ed@}TS4Xy94)BU$KF1+ZZ$H>I=<h7
zzk<7zEyY;7_8^qHpI>pUVEo~wAFkxt@hbrOXB?u0F~TjsMnDT4pndb#2<R)<886w0
zISqyhX#G{m5CJWGH>5=RDgiBAFt)`l^X8CdslG};3zvroXyF@Ugj-mAiGUWaS}?<{
zKtMy)iDsQY?zlBgxcrMPKmV@y#X|A(cTC50x|c4T#`**2@%tHZ%#213%+4P{#z#?@
zSBmI~VaPpp8YXrO>TghjQra5{DT=*<qww@!A7fT1C04)w47MD|L^8v_sF_(zC=C}g
z833ILNxJfIs&;^}7O%ya&DgpgT~4uh_4!92T+LYiAx>vzBiY7)nd`2-Y?<dtFYQ*S
z1`sk~4xYYuGKr>cEPdl2Sb6X~UbrKL>28lf<{QTBa+#<dNQI}_^{+R5S4e58kdtv3
zC6p!v#ilT$pE+`h8xa%3Y+aNDm+)m}B2~dNVJx0{Y(4|ttFZl}=dfZ|2Id7w(9u#w
zLTwBF_4hkTxG@WuAb}@or)?|8hSh5k_wr-hpgP)5UF-Xmmh~TB#aK<LDtTs{+O`(j
zkXd-~k$DhOe;i%*DvI-S(A}4VL7%C3_fK=!jv9y8e~66S3XG&wPs_i*sq}Z&F$<Ut
zp1d;{3)kn7FuD~2pFLEVWs#Y$n5zmG&r$l{-;3IMCN4-xgJo|HGBS^$pBc>_m^}`c
zPaQ#Cbr-WZrecH%M@xIL>x<BSIh{TV%UX~c?1ZxHgUD~|#Au2R)LOQ|<Tzy~_u#W-
zM{pNaVv)YgswHH|5=0B%P;`P+!OR`=AN>bn8O*yPdlO#%d<&%p55tfl3UhWY!PzlU
zw4F0#<}YF{OfQqFWTvmc#0gB5?jd1{XJ%0Qrs~zbw5v-Z6Y}V_mNg2TNk5FT<~{_*
zj7CDJD=rsaWCpoj9+oZ`AMc~TcaSN>k7pcb;sY%Lqf-#aG|WQjY%0P2taES*oeoKV
z4bB#sF<YV+TB}Q$dR(Q?FsNo$G9evxE3-wO?XqDKgaCShHzTK@iAfZgD1h~}*A%0;
zx&`e`l%o5Ez(U!CqS_wBrA|bUn>q4N9z#J@8=SpEF>YiKDlX-tsihSSZG8w!nurK*
zdsO9}W)`;!m@*MV+PG9^9F^nvfrDs}8^YGc3?rhF;BBQu`k^BXv1o$S@e|=;D?>&8
z3FKU?hY4{TP?~UETr0gT6-duGLlvth@g0fF*(YEfkiazE28<V-%h;?Q)ErcxyN6l_
zl^IgTPk;xre3s>&z*%NybPr2JDl-vYyjTNye+&AliArUvY-bBYCg8b@!za$djEM^-
zjfq8N0n^)eb)l}d8P1_2F*e!{a%QMZKbpmq_jX7fHx6!AM!1}F3}?%lm=-)4$r0Yy
zI(zk-4j0cHLvDE!Tmxf}!c3-v>NcE8--&#lS23v*=!|5Irt%9omVFU6o}rkS8U;gD
zFOD5PjQTDOqDPO1GcyV*Dd7^7F2yI0LTMiWlinKW=-D62^wmN!kQk!Bs~UUvZo`1a
z9OD?8EBnYHoIKl3ZJG@ES;x@aW{GI?IMi2Ep|!UYT4FGM!dRxo?!&2L8K`QNb4`sx
zEEB_=KAww!k)xUMvmd7}R3rJf_fw@;gOiOaJb0%+O3ok0`O9^%atlOitQQIj>M$}T
z7HwsPXx7>xD#!^1+4*o`me69(Rb!VLk;$nDWxDj?(#P{F<HQ;C=}eJ0Zakbc9o)ZF
zXrYF{$T0x!ru`@`ZA5V5SVRXnqcry<&RwoymP3Dx=ibXdnvUAuLE45I!;R;DjK2d;
zr>CQW;|NX~gIHe+<eV;`Q&=k#Q~{&YCNNpdwew!tRgWV_PM}X>j>OSvj5lu1In6+B
zX&vl+A~7K)lI!Oz``*Ph-UI*e2=sD)w5lyJe*73}7>!VH<~&^E<Cx*L3On~_L&fzm
zIxP(@rb=XG97Rpr025V>L^2Z|^){8_aOPPUnp!g{l@n6Z;?ZASh$ANpVCgynlg7rP
zq2wHsx3r^)8KDB1lu6N0g4%v5(k4!37Rr8P9LYd^2eXngV{4chvkWRU5CoUi+;de*
z+UmypA}jp>YP)nuN}B|C;{j%uW#W^Hdichs!o&G`4b|{Wh@bxr1ia(M;G0g3@6{Kn
zE$+DQmhW}zfAc0$68&F8pM)P%&EEIBJ5aLoGhArszhaPYit&@<keinWB?FV6Dr!LT
z)KseFNhI=(s}s0HQZsszk?Be&+&mQ(7i-bUgH}SWkUwJoo>NG?<?pyV-Vw)k9@R??
z^~2Ql;ygpvButyFC!mFE<L-Y9PyA*EBtlB->o`z>8y)^9q=^>jBndO=jTt*0Z8=3G
zX4{Z{tQz<H@ns~LcOaXA@xNYlIJxa}tUgpk!omPMR(#0pL^;^8Y%3iQm@JGE+@;(1
z@qjnx&~!s%f)u02PlqzE2+cimOuqYGc(fhHj%=omX3A?J<E@4g)~J+w;A32g&$geV
zSFlW<R{5LS{~E@885wB0aJo!|v8kzg0$O-(+<njBfk}L6oovADi78iz8zW4(?N9jQ
zln7=LBk_J6KG(Np;T8o+`#C)F@@`DJ^?n$WpcC*KF2c*-h(y-5<)2~;y`81n4t(^<
zCUmw5M1z*9D=AKH`V6ZNpM!&)1S>xH7<mjxZ~UbI+f=1l*kCME!{=mOAPC(!kz0q<
zw9(|)2O%ZrUV3aLo_Xm##69>Yj0tgN^Xj2F9PY^QC#hb&8jhXR6LG@YV)0|PvP7p&
zR5aDW#5Nvx&P>8*i`Sr6Ipl#YU|{bNir-9+$BG53(5*J&xb!s)*L?NUz`zu}-E<;Z
zyAI2juf_$M>!fe|1gj2R<~mnl&4Sl(sIUxMmwt@5mhNCS%;86M*u8ZtnLiEeW!?B-
z(ekU3ZWEa?Xyqeh;%11ZgV?d@g#Mw+1)12GRX|Y)=azFMq<7a*^}qbn)y$fw#l`f^
z*qwe+FJ%-Ss5-F??=9VpL5U7)-hUYza@%0omWSobR-^pf9_%3z;OS|FLrXux`GPW}
zuUddjr-+HF4l9-}M<b<Ut9KoRt)~YH53j_5vlp0Jd@<H%SHVozghlTy#QD53Z2s&`
z<hBk%Hc-hl#j8+s_7E0)zM7&I8x&<9LklH4!_1bdtqkvT-aBNrXg#|f3zzSLdY}p)
zE?$cD)L88)Y=peIf|`JLkl$_$ZR1&{9RC7mPaebvYcpxGVTbh9A7Cfbv~OAQFDzKK
zhfY16Sig7+E)<@{2d{nt9W!m_A6<h*+p>_kdo7l1Jq#W}Si5K$E?lm}lJ{OjS-%l9
zmFKX0(MCG?tis~$N8v!CWaGjG$hz1xJV{qS3lA*C!gb89XKRh)D;6V@(p*`8BQ8^W
zV<4}_l7$;lSDb-&K3~hM^bWYNlPT+uoWbtZ3$QP@8P+B}Sj*JrB|SRGIJqabe2mrm
zjxhDQIV$tcpsb<uYFiSC6s;ZQBq|Nil(!$-GfqRzy`wYKas4xuJ<mTAMhR#y&%o>N
ze}aAkDe7_#;KPNRkg=NQ(2fE)@ElslOl_5o9k_7fBs!_2Y0gbYyHOb8-DJ3U_7toF
zlVMJVZOeueaQ1ZPvnz4rOgRp2+kk@lK4dXj#hdFg(N9g#mW^AnbIU?(zR(Cej^)7C
z)g+j&beT-!p3hgJLu&)W=3;#K$v#vSWaGn+modYhF^(*G7i*7RgjCmoMgLmGwD0Gz
zb^S3od$=QO-Ab%KSOznu?lw2*#ma>r<3edI_7S&r$0}fE*pC(O{}TtZ&tusaA2C&X
z4+dK<;N#5)p}5k%R@sX8-g_U}EovBbmr{eY0jE#w!kdc@=+B%ZTR*4f=@|BGT7fMY
zBz)CPc=P#}Q7hM=By%S|-Fcn^k>e!Sauk`-<uAO3s(v%b+e)x5BOhn74r6~|CtPiH
z_-x5C+Pzj_A<v%{W^Zh%Jb_OZtwvR8F5dfO1q_){vG~9;tlfVKXLhc{;seDnP_uup
zzk*ZEoU;qNvF+e7<Q-m%Tr#xoHUn7xKDA%HYDi6uP<&)FzF4ykS}P0eU;Gv|1?{jl
z?8N(TEka>YF5djd3tUfiB(}f5$=ub2C4LbR_z?(*lIR}+-j97$O-YS&=mgv@>%#KI
zD-g?<h?>%A*YG45tz3fa)6Qs8d*U~K4mfp%(9yRk5V%(w*9d8?EvQD};UTC=lpU2g
zcd-C6&3e@ImEvq?FPeI%!IdeHNrFSe`VEF{mUu-;6@AXdcB<+~d|a0q%8-&F-4zj|
zo|wMUo=!#EXnO88p3OjUT?q^}FF{X3EjluDFgG<2Up9GV_OYxQOuP3POpUTdGPzPE
zJ$#L*7Nl>2K9#0aFm&UgFA(s<W71J|CowT4K}5pUE)aMB&JQmyTnT9`Q$Vu;9s33Y
zTRi^zyYckjmLW@$K*H&p`N6MZ97o$=Ezen+cR^=x?ZEA6DW}w_ff85#puJXCcT)xO
z8-@M}YOCQMUI9b+&@g@Jx&^RLn2NZ@W*pkR3XLt}arewIda2s*o%%jA<@PKxE#n>P
zQJD1w9D`;dC@2u4lY+7L?Ja0i+M|*ZBHdsmC8hNQ;aWe0dQ@5?#Ewrx5nndBClzS7
zjKC-#1v&-=JSe5_GsK*`=U~tFO_&(>4E!92o|lkV8!nXv2e5SAF=(WYxMl7XJ)u6t
zd<l0@ic7-SghZ&COx3s<^CAE6MJ!);f|+ckoF7Wnxn`4Zx)&{_Ph;aACzvpW?>C^r
zx-?9eDuX#w-5PLROKCrymDz-OPrQin!A!fXYCz_Jv(VVtGj;i2F*9`7)?9+fuo0*}
zQHZ>vCJeMG(c4Fk%iI{Iw!R1n_JcMy3GRwL`gJ7q+L#jf>xp?arRJ_ovmRBLj;y=_
zv=r7N^`={{&Ifr8q{Pm|t+%A2p*S1SQ>P%s)EyTVpN5rJEIh30kbk}q9RoeITW;q#
zobb>+_afT61IM3Pgl+?-KlgORrTkpl;`hV2x)LS@Rru}0uONz=7G){Zop;sZcwQT9
z0-8}T?ZrTQ74~L#!X@n?+&&`#tv*&*kx`>J{SmwgS}V#>Ufe|gf>O?b2Khyo@cW0J
zM)b))VDTn7mTh<frk4-HK4K>Rbl+S^)uYh-)GNr`O51lzr7OxR(bp}<*%JjYGqu8l
ze|#3H)?GNc)d20ac_>o!z^bZ}4m-W*q(;P%_T&@qei(D(?9qKL2bc5C;*voyUc2{Z
z#vvahqtJ@&%RfSM7c~^cmC$MGQB+xpQ6Vk@<X4K0W2`{L+&^Fz(?L&2N<<f><e7{G
zd)NGX5NT10qW9L~Y-SJrKe|v`dYOI~8XTqO%Z}QJ+n@hCV%!Wda%2+}%>}5uxC2=^
z1NhDBOAz8}hmeQ<Jlv4}Q%fz9yGJs9cl#V%ItQ%I9mLf5k;rN+!}JMhP@FlAuGnPQ
z8y`aS_!+p1$r>;r5Py7QEex1G+NoeK&g4}hf{dVxn-3m&;c3>ZA`x1IxT*J$$x<OZ
zyBN{4pMag023~$PC_H~2oqese1GT`|C`Vj4o`o)zD{M54$ULruMR*EQV~kO};t<Yr
zop^;N>lr<Lhw0zqpB#y-!po?o&q;ru3FJywc*f4ft+Oe}C_4jX*i_s!W&nBF+hF4C
zMs0&V3UleF(bs{lrfx(|pMdHWZy@Zp-{H3Lp?LF;n{nIIuVa+I32gfrn|J>?xC92^
zp5NXDS;-mXEIq?yOyIuc*<ODU-PFcB`^sIg<latg<eppkJ|@n-7dOv}LRzpXKG=E$
zhL#SPa`PjY;n0rUQ!PlFa}xy^wb;copsR$^eMunV16`pQ8Hs(xXQ84Ue^MON+uKUv
zT)?v^zZ3HIUR37OWXop+p1S8281+S?p>q{AHbtnfY=;ZyzK7>_S#B{Bl%}|S=Ixk1
zN{fQx3f#>39L#2By6S#7QS)TVHuDPGsTFLbUy2day`RF1Z|%g4C*EV9ZSmK4YXA>(
zRP)?ZbQR-LturixCgQngesfhg^reL({vjgp6C!Z!?fMfoB3Av)2nf<ZrCg1~nfD?@
zm4l^6@?b)#rG#-A!+h0P^u;Q8C5~iZa)Ich^pMq1ct%OwAk7!lRBzW{CnX_4p#iX^
zXP2Qwjr_BjFbbZI2X2~zId}aQBQ2YdSyVaH0XoJ-xqBde(_XX-qF_xw_N@5?=@)8X
zX(K~rZLwZL3{?ltWM5*oK_}KpvkoB?&EO!~s1n|GG!sFIJ~%{aUD%ZSa5E42`42n@
zS#2iH)wZIhsul`?Le&>RGDPR57J&*?B4h6+l+d19OO>69;>4!X3iJ~LT4O*fv_a~o
zn=hMf`*y=FE}Tw{M#M)Bmw-{YB~pivm#wEKFje3LK|{v~6Pl&m#Z;l6Q?eshN$K=3
zfSQ^b5}Q|==f_tuUUK+6Or=gp^fN)mp8e>N^JbbJY+LmOE;K3-m*hYd{BHD+Ink>6
zuzA%YoUHGHAtkOVm71m*gHS1z`tSNy`0S;^IwXvpkLUh41qGQ$`2rfeO0Ws{49nOB
zLli?{0y%d&-XS<*C<g~HFlYcPrc?BbjK%D^b1`|GU{+v1#4P9=8TwC4QyxS6U_?<$
z^TG!k5i^#|7KvvysaO@I#Q_s;$6fb6j+?`}@zLUykn0Rc7*OM(Qb0|U5~WfFC*MT;
z?vY3E$isIsQ<oK=V;?vk<v<rs?AwW)F1{RH9q9bQYX;W1>(Sq$;P7giE_5*T5hc8w
zw?RV-%zyAcoLapE`E_mj`Dm`IMrZ#uJ7gWrcr2`ga4W}i^UZS*8sq~j(*e{o43d^(
zF6Ua2k{L55^JC5Qu~&<x6<c<!gLQBs=1fR|ouMH|aE(k?F_e;qbD-mOr9qD#!-n!I
z43bfxm$beb9SOBTi8W?U9EpRpBQG=ZMlx09*9s-{#72<R)B1z`;meq7&xjtJ$JT5_
zagQBl&6)*oH*2mRGLuBnKrof!JqE@ms64)j8lqOrnKc`s{_bQ-rPPw>P+Z#xRd*c<
zs@q{GGlQL#46$P;WA2<8NK2W3KuUBwYcFzt(B!J_l3rptd;ps=)1aw~JH}6&g?Tfl
zVdm&)DC&z)(+iBCdVb%L0y<|(p=_%~eK*%&TQgcUhOlsRg1u)nX3U<0DRU+xA=Xz|
zaIhugqSc!?QA#Vbf~}=9rp%aynN#K>Au$F<9Js*9>M<Y#Ev1vPVRswadsXPK$;a;9
zSxmSfqv==}X3m?3Y2zm$+}nj_2z6vEu9+EGu#%42S~jV{nS*;N(Qc#zGGx7Dlx)wE
z|9#rFZQHhO+qP}nHcs2NZR@mcpLRd}duHbT@10rkqV}r2D>Eu8SMAK)--u7i;9CWV
zsJ0+QGju9$Ksu`x?y+=M@Hm73J;SMxkrh@BGpD`&jjOU7T;H-CweHu$pE2z(jPrF-
zFcv}@oz-_l^ESj#<G+}kWDwj}_&69Sj@M!>zu{Nf0(Zu+S{Jknqre29a_U^RutJH6
z$hN)tj^$*ALTIR-O4(yvnwdciAAiMwAPRMHTsx5~s1uQ_!LE}%6ErGJe6j-(=&HUw
zq2?xrD|PY-wVpUnJ)I3M;EW=m;Xo>7kl41L2yAgu0D3*w2*Jt(SW;P*M^KESZOY*r
ze^^UnqJSdJMEFMyxtsG?c|2Q~vkt>d_*9A5&JbW%r|7R_pMl@in||ofI6l*k_CRWV
z7<^gZCxFtksTJ0=CzvVmQJ!<*H`WIV*EJdr4|o^~_xp=(hn-_?4>ngQyXF_@fOr^D
zwBU^S2M$DFA}$7Pjg~m8t6|O8N1G+qO$IZm5Bi#ui_1Iuq+?JySnqHMD{c-iqL4lt
zxbN<TAzV}pAIG8HXY0byOa~oHV}PM4UWG~nv+yI8hvot-C79ci09o70NOW}KmCj^F
zwJ(wxIePyB!5t`FbW{H>DA5<~_nAgQ)#BUv<xXn2@yNWU>40;mV`hjGnWpQBdc>7R
z(At3(>#Y_-7B`sP-pCL@s-EkNn5M3Nz7sRhfD{$pHyhzk9`VI7|0Stqs)#`X$}KIP
zymfbr7q9b|A%f*=I_>!-+5B?WO3>Rn`inxShrc8lA1f;}|9ZirOEhbBd&QJJIJTcC
zNm0S!k`T^_gI1e-xG5u!kZ?-pJDaf@{AWEkNF(vZO-_>Tc;Zv0ev6RnaSgI@Fg`Cy
zx4iS|zzHam74LgZ#65a}s!Zpq88`5UK|nbRtzdr)wM*L<@sG{bZFpAfCSmp6Hi6KV
z(~feqJu-EqP`t3#@*|8EK`mY1XRWXQFDU~H41<*%rMsC0x!WxTilwIB&&lbCGIkeu
zyPikxLz!r51ZL1uQd4s-FJQYKMZpMBDC!Tz+Y^oQvZ-x;I6nbR>i)G&KRK;)%dyv<
zc=Sv`$xfw$A-Qf8C)qqfpd3t3F3H>b&8)kvi`s!z&r^{n8FVPS?4?fJ{_>NZVj%+p
zYKg68OP|{<AtM7b_<+gUp5PNb@(_-aDM$AGjEID?bqTMoZmV0#D+I|eWc%ohn9&>Y
zlE-aS3Z(tAFJFK#j$;QWv%>0Acv+QU!u3(r9kBwqP(-4U>EwG7GHu4-r9cNp8S0mN
zhM^IcPKTYTcJ)qo7E}bYm<?n%k>I2kgemPB6er$UvIKSt5UU3{0*a})mM(21Z8TC*
z#V@Q5x93m4;o;tH|0k{weSs0n_tDye<dg{A3Ery$J_Bq2(YYA!{!9z=!rOVPHQPD4
zFcGVdz4EX<%c7E&{=~m!2Ir`;eA`@5H&t!?g~R@oZd5#rMprmtVb|%wbc_`S#W;PT
zZfN+A*S+$9*QK>uf)q^*7+e8ciD$KA_7~kucQf}O<Qq~Mc^PzHj#`K<v{MF6Xa>`I
z6vmI<WzT#r?qCxnkdIHx1u@Ym**=eK>4-oq5L#(%_3>BkP;@<tL7E%XquYpJModnY
zl+aIk#f-ZX&F9>87`zA(L%wYQHIhzP=u8t(JBLGSp+<~_9R45>o=EPY97-g9m{u#u
z1x@u}iH?$SG(Tz|JL2eIqrS(DetEpDNEIv9qM9mP)O*;EuT&59wnHfGEiwTICq6{&
zKzct(Y*ag9CsEw~6+V!^ayc;*t-3=|bgKE?HQ=;G*ZZ<~l&(aghL)ESRoX4+rsuRj
z#r@#4R_sE*r}z%a$ysu}oItW&itsIHhs0{T1jl``E){JDtCfImueqRZhtWuk4U5}O
z_Q*Mp;K2e04*7@>b$JmE7*Y{rRK0t`6Qq>Wy=Q^yYLay#k_7>ieiB!F=hGKe!a?Zx
zfP!*;g{8ak9!g)0`g?gt_vvT_)+_r5jeZ-<7OLs<Jju0DSO9a;!h1K~ZNEp|ZXG#~
zq_d0+#>EIjU;uVEL|r)z<9koYl$Cw_(xd(Y)bv0r<$FQC&fbXtncV54pdbQ%Db;Vv
z;gF^o-;&LVtB|TSTwW=<wc!!19`v4)i&_dICQu+RSxl(Yj7~>b4hAktA<8H!ZC-1j
zib#mWzQb6A1CwhJl@g)d_Iz-8wD%qP^8nT+eqO)Z_{aX%&CrX%o=wf*H7jZt^(ZvI
zj<_vd%HDpE0+?7Xh@!yB%{i>e2u_u{Rt!Z(^03;ajfEzFb-2|2NBkcxz*8lq0uJ*e
zY&IiNwUIbE#>&|CKGX+fqTc4t=li~d6z<5#|8}f0)RcQHx56yr7+C!Zzi2+@Le6r+
z$y_=f7=p6-=r3`CTN6O4ycjk#|Ax*x+XijqMCSdr0#yf{8|dLCTl6{w)>fC(nKX2C
z(E9V_+>}R%z<g4AT4r8Dq3<a^;$+7UFlkYi!FcU~Ic7g2#UAN+T5@31?v~q^6{z}?
z)ctg}SLAMm<SiHju+iMWqVU^xJX|63Uh!}q^tFK8&K89Q6H?A_GNGvHTa8fq7O#$C
zuw^PpJ(o(r$_>T2wAIM5m<-k42lI5J4L1iqIXkrY<3YX4ouNG&R=VQ>LEPH9oC5`x
z3h|gU8ejNWaN0>^#N`y@XX&faIp?VxD{84alhTre;U;)kMj^kXJp{?g&dY8DsA{Jb
z5($mrgD41(VGN<f6oL!%DTwfmgz$oa057!l8LjWvOo5WqLrm-oOejt+iP(=Qgi#_m
zu#+51VR12WOE;hwl#FWmx*wmodwPMFFRVO`*2$35O8wMZNl~(-*il+QsV*w$wWh2h
zb{42DU^w={Nk)o@UOf$6Rg=&+zYbFBJr5}&c4nkx1Vxg?$ll4`fQPuk79Q~BD@jY<
z&Y0Mr*%FBir|&<5Yg)QW9FB?dQ?ahnqO}F7_4^v4r#O^YRInSe^y>0bZD6EdV<(kA
zxTV$QVOW4p8!?hGvk?06=}ZO$xKs6l%4k8pVMsyuX?s~p%p!__sHmXuaafpBm<1A1
z6J@mUNZ@OI!l&)?Z`|M?$MBC2+31kF7RN44qy1fPmXAz-At@&;WlkaX_Ks7bDT}qx
zY(<Cuep&Ls;`z*USQH#j@ee9<TZ0>OIjt34$$%+#)c}BeWibJW24%?(+2Ip$?j-lc
zSsD@_&6)t;nMM>NgNnC~x&FQf)@See#U@eN@=R&cH7~ek!sy{X^-{8-kP{i;K*e;o
zjbyBaJB8^?6E%7Ts0EYf6lM|<7fACOaf@l{>hU&+PejOf);T$p#$9HL0;2e%IlF8<
z5R#>ZN*g7Y7l@4isRf|Oj31W#iBfzw@Y{D7{BM1nqG^SqqYS3P1c`X+6U#$MWijzw
zu;TbdR97vMPO|#u21f%8kYQ0iu)02D4Rj(K)Wg>H1_02{o?STv0zFc=SphU65YB3N
zGLX`<%OV;-2vdy&hK)1{vj^g30f<Di{<cy=UIblg@xgYQo=DqZ^HZ6gTDBGj11fQv
zXf~FB5PtM}aJIM`*>qOh3^j#L(&FP-;3qTjb!hSe#6gNf*Jl&k^&W0r3uDQl9sKM1
zS=*LT06m<MwZ>0^>DL{q=zDrXz14~p=4B4{Q>m=w(XWlokY+oP<Ai9qcb{hYKYzBj
zA!!D=Ie#WXa?o>Lf2_^FnYwLZa9+08H~mlYx-@3z*w+_VQtQf7KF!Zg3y#Nftr8b3
z4K1tq*lHfrP6C22ikiz|K#x`HT3J^Q#sVHL3qIu3^9DMKUzNR_n4FU(HydpYM3wJx
zot>Zi7h(y3OgJx#ecC)8xux@w*6kIZ7B5RG;Z&bvp{dNL-hw$$W~vLArh?re2(Yxc
zIMMqTKwZ@ajGo+@&RKY$V`YtDmX|=aHRzTM9~Fg&kG3u#%nT43PlH)V(rfRg<`f0T
z!FAr4__udPpJ+<X;sHF*#1vAyQ8y#~d%5kY)Y=d(&1-3r0XQ*TpC}Dv-nE@9aX>x6
z)G6Fza{Z0Or)zY_%$=;NcI-Jh8CnYB6s@Hgy9PLnO>9S=W_iY(=EBt}+R=hPDUx|5
z??9?kb8>**i$(Z838iJmPpRJLM%o$b_3@ksJZ4^r>#2@-aSk)ag94oTnn#&5(N}*>
z8+iW62gVN(gSb0kEDFi0%=Wa|;xqe47LXkcuz`Z=h_(;EYpNukgZ@&TVbfj;5J*>G
zT<k)`!q!lr;9%uoS3Oxts0UT&Xw#)WV=vBmW5my+5idyY<qUq(u;W1Oq<9Zx2EWc-
zD?916AV7s;xDXRey_(xu6q(LvD_XJET8_4)aP8%$^V9WVUrs0O#jyE_EfSGvZgQPk
zk2Q~WQ?g56)Wl}-0ttFucG@>#DwQp*2qIDL6AK?<ANrJL!xhK{7P;Zg!a~gm@g)U5
z%9kAM9!mcKUd0%t*(Ljt2;YSC!9Y8ZA<pkWIoy@r1o^`=R@?aBg<e(l%wDm5;F3hF
z&e%#)=-Al0|1k{to`sipHC!jl>tpoBw9P<%Pjc(2r;L*|Jv+spM`p1^EG;RC2k)w%
zeqK)9!?zJJlacB&T(UR1_-<`Zc(t|rZ9Heo?)uv6v)}6|WON4+nw+e^eW>uA^6b|f
zvtY{Owc~1hoWj)HL{hr#NoI&KE=o-Z*vk$qO!$VzMq(KDkAT@%0eo&G$<wb}V{mqb
zj?+|;)6!X_@v)JGij|n~7D|x6u6jkmiqc$Sh>-9$N_h82HZ1gZt9M8~EF1Cvi)cUO
zfz9LI8+(T`qV?AlkHcKGyk&)Vzc~OwALS5!Rt&Wu0R%uJdXwhE4w3(%y8TCiyY=fI
zXF&bQRS5|3|HE!C1o?>z{d68^VpWCt|HF1c@$nJo*?tMvL;i@7|J?%g&;SV$J})xu
z9GVCL|I^rh0VIU?0N_We{JZq`pW}}Xc=(7@y;bk1A`}Gp|FAXy@E^%A%rAv1!T)f9
zUwC&l#Q#Uz+HtIfUHu>0)*p2%4F8YocE7?6QU23~pWxH}^L=|+cYM^FPia05+S6_~
zCh7L{Zs^3cdf#@QOF8-QV0<=ecUJXcRI=O(q@JGgLTevaQ;lZ(7v*8~US{M#B59Eg
zZn<t=XZ^bDE%{<|dq48{UhHyD$$10f)Gk9r+vNU)Ea&-#vQVWF^nPuaV@*cQEqGkr
z*wrWoJXNmlDjKk|;!V6BYt?99*;EKg=%67#d90=TesSG&e~n&`AS$yxYWtmmdEfL`
z98ujFS-r|((sH*W`Mpo>+`U=V?w9!aL}ojY-L7=n6*cLolLW2RIkHf8hS$3Oy4Zf_
za?LJ(QG5#d-LgC0%Nw(*=Z90<{YiVj+2iGsVq)7@vz1!uvXpkBfAMvo_WgC9cI-4-
zf8xHKMhoYvYS4dibD(%JH^ScJ0O5M%-L9wS$^Rkruh{Q;5#p1!f9LZ5x!r7XTvBE;
zI1cg?Dy)RQ;B^Nfdq_$+FYVu}(q@kiA6{#NZ&E|SC*s0u;jI^cY#r)|+X-^r(sBP-
zk*K35?JhI#uNTTNx3UqGZNxCO0Cl)NRY-Qbpl77ObkzMD6)tFJL16g~x_4>}LjIoM
zaQI6z*%Y1201!L+Jt5Z@IEc|qS{w^RsV4Hd;^F1AdpVuk3f6lN5CQ2tEVe_c%yL<9
z{*Qm@vFqBKh=I2tz2*c|g2yxA7GY96wt1A38Eqe*DYCT;Nw7@NKvD*|^{L4I3xxbQ
zSA2p-^zi~<v^aVUIvtfU4o76DuMlw@4iFqyBOY*bOd6G@8aDrMpK@DEYC)MwM0r92
zJ=^8r@K#irRfXV608kx7Za@Z7kb&V5v>O$OqZAnYwOQ<eSZtA$QRq?x(Y&%6kcl@m
zH+FMda#~2>g6kM`418{O_*W!1@~Sf-F)Qp7*owYgC5W%rsBjyX20ziwp|)|picKhl
z5~<*-a%7atvbm)SzQ;bY`$l^F%H?ouvy?+S6}*#KXg`%Pakfl{eN7!eX`*hj3Nycn
zaR7t&w!&&u^dq$ML^i#NB4Nc$cCZCM^~dFOL;@<ykp1EcjeYa|k64NY6O0-GV>dW5
zF-raddTwPac%=om4Iyh|!t!M_Rv@|e0d7Fey{x1JwWS{E?q$mu+LDC+k|zvKHak!d
z)&FN}6D;N_CBD+-w1Rf1y|J1;K705v!FQK&Iqf*p@g-V%P>4xXh=qOZB4><GFLEN0
zb1jU_$T^*82Ae0k0v5$kOfInDH-;D^tB}Sy@Xne-9oD6glNKIx1+Fo!xLEBJE)rR6
zNMQ~7Xam%|cRO+@1CqSkD`1E87X0-Uu#pxW@H=2-%k>CS(w4)EFqADUPTz41)*eD)
zcrmzH_9S;?q$x?E;v)Q^p-)Nce%QbMH2U0#^j{DsZ)qSy3|}r*d7Cw9rpSFdG<7}i
z&(-cG;rRJ~_eB)F&&^o{Uq=dJcQ3^RzwldYHsRvl)su_w_>cJF#L9PE;-Z8VM(32J
zwGZa+AsQ1QCcq!U3Pr%a^Z8<-i2B+yUJSf~60uiG)!hifXwOI6fZfk&Y5{ZIAc*fY
zV&w2xE-kkfHNT-lZqV#n6%J)gz%FHPMMm;MJ?insT*^`rhqMX&w(gIpq8uUx+rNzk
zx|NC@+%vJ7BT(3#>v0~6l^iL@;hMJsx8Cf*sL9P`Z?a&j>LSD^t3TSs0%tOUket09
zz7Im%wJI4X?e@5JO(3i+gF8U%s<&H(eQatrxG9&qZAmEV8U{`v()Rn6?k{W^7I05}
z=+@EK#RNwAPLAH3CmfZ)(&EzsoT?iz=h8aBK#r;dV>Vaj;NL4jlUm&ip4AVkeV!!f
z@yb4vT0y|1+Bcp#C9QykNEA4yl_OVpK@BLx9j~SggLQy=xsd=S$A+N^or7?-6jzj%
zrI6u+UJ~?tn~-d{6XE!%J0aj21r4Bj#qQjJP=qLmpJ1+#>W2!+<fl#<rLbfISy`iq
z&8-&m=CnC)iv)n?LGpaaoX=@TCkZNDK*14-q%va9dL6X1js|<IXjt5r0E9tL=2X(=
z4g8ejXq-1C<X%O#LbnZ2T?j4q@V0BU6U!n7$-p><!4-E*5I7akp3t}TaZ=Jl4Hw3E
z&u-?@raccXW`^YK;xV{ghpbW;&=L~tDNQ(IqXo5I45qJ?4j`cqxK$nU1^84#R?l?7
z=$Uz;pg9jB)+kG@)eeUcF)zO3^-EQeWG2;lP*N$tXFUbnbs=&5jNdVu&ee7cj!Dgn
z{D^ZYdn0H-S8MD-j<B->M1w1?pN12&+htw8U1Tsy+Mu@pXX}b)tpfO~V6Tj;4IR&x
zZloGcda#^kQ~&BLAo>byZc{B*CyaJra}%&cr8^w*;Q+}H;lJ;xFL@%nM{cz+S6t>A
zC5jM0KtSkNa8ZFZJggrw%qQrg`)1!u0*2<$U|Lk_TuPLdRs#Dq071Aj=^VDyrJ%1w
z@9wlqxp(Q=te~yM?uw4p{;iE<fr5hg%d=bf=UK$!nM^2)qj3W9Ign4wEdH{P<uOQr
zU0nzW=RrkaZu)&oNC*PH=*nXXn{zR3O<D8~+cxQ9hAlOT=Qc!2Yn@*G$~?t}Y;Mj0
zRGW~$5A6k$QCg}tV@Wspqn}#?Wge-cWiVpK$8F2lvqEUw8K6xRU)<aJ$A*xNe$V;`
z=|aLdD*#q4OBz_4!1-|@Ia7i%*u0@fOXdXgSHt4O_M~Iq1csVQq8~Y)`VMgq6~_un
z&A1llS@3HcLOVm!fJ!)fgbwXrYncx2jA97@5bH{8O%{)AVf)t$0}0IYl_~R^8c7ny
z9D@V4a6*(+liIhi2#FXG;d<S02zzMo*X))Smg!pYhYl;0(Sh5#qFVn(i-EzM`pJ_|
zI@tk`v18eYkV5>H#Yh5|(fbo$9uYv{?d`LR1s~ZYVWglGAM*%R5*`~DTAsu=BApp&
z(Ha{hpn^Mbv0*UfYR&zjKl(d~Y?j!7LcAcPFeoF2=y(P*D*fjvYaX8{J7vAm1udgS
z`E5PPJdY!yqGICi&3=f(fmr{PaLkw8Nm5wFXY4l^3+BrRvB7iIdOE8Ib_N8$NHlKS
zjcT%;dhlf;?kT5Kyo6{`H9637bqu@D{pB);ijpJ_Ql9<ME3&#cr~x83q>)&h2z^Gq
z=LTU$83&f4m3eq0w&<-?ClrOzZ~Pf4K?ygm3rt}Z53uMd=)J>35$(=7Bclk715pD(
zYs_LG%II(`A=1U84RL512Gc3y;MnYNce4B%r(dF?q__FS8w(tmNu!}f<rNr+F@eod
zBL8pN@lKFWHr%sQy|2TD3XljS2~n6hiqB$$N!TSl_ebunpc_c*`Eqb&vKgI)0zy=U
zW39!FZw0qpL_e8M<q$GcDqAE^5}z$*?}Sd|Ei-0K*5d9+v~O|;ZA1~Gm{{`70ySQk
z@cg46THQ-{-dg~Sg+f@43@dxH7gj5kZU9jL5{*4@2-CzBlSA6t!Vw=ihe1XQ%bv6I
zmjf{{-OVO&@0j^oMpnVolIkUIcI+ToB|IV@W(}*gG+{_;jHEK;<?r*Za=pshk^Vn$
zr9WFM65`33nS+tRtIss=_Ak4#$P&={QclSYxK3@Fl?wK++z`L<bhRzc^_TO)x_Iq`
zJL2(6O+|_l<}QbI(BYxUKWC@M15$%%c>S=c((FVg`+h;vnHI?v1y=MTU3J5pF8$Sk
z5>jDXp?Q<1%Sl_+6EwAjwG>AetQ3%QgE40anvg;>NBaYTt{KnH+PC7fS(k(8RZaw<
zXGDS=WLcI%30+ZZ!$Jgv1L-8oZ-4@br$cg2`Idl=^ov=0<m*bS-sjVIjC>yM$t85A
zPhRd6Ya{(&ZBJ+Hx5kwGyg<p4{|c9U#S(TB%RbUay~&k8v}6>iP@#X~RfURV88T%i
zf7$EbU6sIRu!5?U!s;4q5ws`;dF@EoOr0|J9mP%jsZ2IZ92gb$j0}*~;C1)Qp3d77
zzYhQtY%_`Xc$^N^N>L#b8d}|s;Qd4?^D8Amo-)I{HdW`KGFtL|O?^BSAoJ@}0Y5dm
z$Y<)of}l+Wc^+^#Gp(|vP;hXkpa4-YobUFH_Wtr-xC=L&f#<29(=-QLr#OPfwreS&
z<T4WbCg#xcAS{_sYJvh|0d=^E7E9#SiM<2cfU3ZOn@5Tl#gKix|5;h|dIAeO#eQ!f
z(gzkrv2E#Sn?LJ=TX|`*zk?bnTf<{kUcfU(B2E{xN9SYqlovqs+#xa0Y@^BM*34C~
z*gH9eK`h^I8z9WN%*C#3VU(9sh)an3rwHI}yhStk?whkGnu9|^3ko}fTTjdE9qt93
z0wbRI#GJA@3}0d#AmAWCnuyUXrTV6Glfsq)irDDS^k@hOB_VK0No`cK>QBiy>906I
zXfy}iI@gKOmn<2H4HL{Q0<gHqpbK!F{-j)=-ry|#T?}q&uCBzxmid`l{!}g-PzQeV
z>|b91|D=QwmCKB(uP4gCwg}36USl$y4M^Hn-V2k73|<iV>kmIfGQ5C22+is$d;9np
z*!r8uT7sPe(F%qnhXG=o{`_}N<0QCbM1+Pj`THD*)sDSz*eek`Gbn)X_N}8sXjUT>
zH@0;FzQQ>kDjc5_(3k}$A{v%tDJf`8Dq=eO3n68?qUTInO>Ioek!?DM8EKbx&hGvm
zAeI!>Od46XdYnF`{=q4v(Vt(|TikO=ez>G}jTSeyp-v~VgMA}-&&l?)=~8D_#^B0L
zP%G)~`iwTs%gQwC@<RGHhC`a<5iYJ~-<JjkHehIOYVPfwM8w<x22EZq`G-4;e_&>`
z)oN<4n?lU=vdivvKLLZDgd(e}iL9Yr<Q26D*0ej{Lh*^mkYHg|v+ewZLqX=gHsB!Y
zzel${eu=7>_<W>ECraFOBjEExS_E$+)Zwo*S9efeoL5}DwO`kCbUZiz?MOvYKmaIl
z1JB_HggEQ|yS-^l%Jh(v=AR!3qOsXiIoLJjb<^|y)td9bce3?E3`0f3-r~Wa4Xh1I
zBl{8MWtZ?;v!tR6*Req?-mnn{-tGlP8OGc_5}IzfYYWFPc7h@yp^S{p?=xMW2%5Em
zEY9?Xr-vI1@V+DQIdFqIU@*4B8hv#+p`+nQ>!`5!5*4L0@QUUVOq4ZJ<;J?|b(N}a
z)Wzk+wdDgdnFf=&Ea(Ce{busJCboXxJnKscfubWA`W)iX+p5AX1OG4k@PucNy2eH|
zl^9L1u(RXyeQCZ42ms*jE|mMQt>JN~!u_m3P=D|Da^@7h>!c!E)Nu@tA$k6I-b&Yd
z;`&CXQ`7TKAI>wr3wuZ1vss>QfyqK=`im);jiR6>^Y832GaLY@ZzK)hAnMKab&484
z)wRt$-+myKa9)5CPiIYFWF9G$OIfH{j9mFgH7Exsh5yGc{DGv!1@~zn7s|0)eu<l?
z;XOzR^P@a|VKtXR&V9ouFLSE^{|~#%7aLfa;BB;D!zc1zq`!YKYd>w&f&-|8f1#+9
zzES?WWB(lU!2(LAe`k~=`AE)CgFVsyH$4yFU#|gr#DJ7YpHWq;&2#_f!z6$e@SjSB
zwY*yYL9yc(*+q-!`|EgOZaa}2gTn2XKv#jP`M)mtxybsTlH6uc5-fQC>h8Zf4*13Z
z?m<KH;Z%Mz;W7;Bx9?ayzxm(2|2U5YOie@6>%cZ}qd3<{D7?A0rf*~OsgwI#x9H!v
zX#Yh>yG<grXJzH)q7rOUjBiv(&qJ1Z+WWt4?jQgxlf7{PmgtuLpC;_Ff8xj+8~j%N
ze@@-vAjpTD-hd~2X8f<{|HR+npI?R@@x1l_o^li3tpY~%9W^UYEC26-`L6+r25)|Y
zv-?LUWvS_JQT+=Y_wGq*2}aeS2wlWqgAMc6ykh5{@F(@v@cu7|;3a-O_*^UP3`u1T
zoEccPafSGNuc^cab>%>Stew7;D^{&8-U!DYUk`pY<h<GM&x-aRG_fN*{MDx#Ps^91
zr65iDe+?WBf!!b<7UQKy#M1-8)@{T_2;r#@6u2kTDMH~+_Kk&|ok^!Gs&s}8lncC!
zz*TO_Gk2JC=Z=Fgy?54(Vo~Dt%}u|n?6<)TeEm{lvYe$R>4}L6atfNFntCWTJiVq&
zrturBYmB)BkHcRg%QP=wZx1`p!FF_VMs|`E*V{d7>mUAT2?+Vz55LAeP}bamcMDY{
ztI4kB2pAtA;frsb)HVt;1|ExwO^p|p%z|2V_#(hy=W6Rm2ba%?<qtcGV=l)|4KL08
z;H4X2ZwQLIB<SIWzRZecSyfRScI{bRalhC%aR=v?M&}2?$}P`_XJ;?KV4{6wK&sR`
zfxO0d{=B;Tu_op?|74+QdVl!T*S8ecb|(mRB3aC@@1GoaBc|?L)wMQ8@OZ)MpPdI~
zbq_>;_%H1cxMatq1d+i8j`Q15g(blyCFu#~h2ZeolC3s#i``C0^NNe=66APNeROI2
z^$qqOsTRd1eh5-u{wbdJYj(pRpW&2r6(CWSy=LEBhc;bNR2iF(rtSbOp6-B4=`N~C
z9})_i?`U7t&>Bm)H0<!|n4f%aUxWMOR*`tBSusv$(YH7n6mV^-2>V-peJiLn4o!75
zyzG2HI`$a(2V(7vVO(B(a>^yn<sF>ZA<O;gUB?S!vAna~7cdm$tl6=9U??&(PYgMU
zW$*m(RT!s+W|owz<KI7VSIk<%U6dA>NZGz}71cm~Bd?PjH?si&c6G+3>t`0!HK}mU
z>Fn<5<$Ly}w3q)?N_g8F-FHs(r0jypkXe0}1XCPC3I!@`{od>wJ-Q<Ui+#!5<nnC4
z=<J73*vko;=TRx|dZ6mc@=-bstU~ZMb@v(Imih8=b>eYX4JRQhWAJ^AW4+N36o?9h
zv_HCM-+^%@ox8C)RSCB<{kDWk+jXgJJxd&S<1Xj1;F#-agoac)Jv92~nrr*%X%f`4
z!u_S@hrlpROPw>2j*3N$)_k|s{kPHmh|`j@io<Rjn7M;9*XsjdTdO1VgH!D|mcem{
z=P%PP&Uo_a93A8J4f#=j@0OU}obXHpmV}oK>EEwcTvb~M1NTGYfj~cRra82b_X&af
zmbll^MLYzC13}SOTqH-CtFG^n$>Nm1_A^xyeBuZVEsXVt<6z<pz>Iza>2$#NhbXnT
zb5@h+JP+QYo?Hn@ZGn6{-`p!irc6W|H&i7b&wQJfP9AS}XmwGUBF=pn`&1Lkc5+}4
zmD>jaXuPINhd(R)^OxNeF^}t*5@M1oqs?pP(9vkK^i>D}K7+f(kB8ym%bngloK9E+
z9L9jM-S0&FYCDrZUQzXU8~}Q)Nhu&io4npRX=t?t<bT1lkQg!8TtGifz}!#g%_Qi^
zjW<he)<DGQIp>n(bp2AF&aR{*?oJLy^@@k5rI53LGjT)_*rq<4{98+EPg3CA7<>)s
zXZgO^y_6c+8=R=bEUVJlIHH57^o}yz)Rfc{2TQBD?xHdz<Bia2c^|>C5*fueTZ4p9
zNKNG;xJ&Xq4;S6sFCK_TqOm3wEQoGuc*S+!1P~RW!gCmNI)v~ZZlUnx<>M!hWjg0U
zhZ#uw5^|qepKE=i&{okH-ByzO8LE3*qZTgzY<<shK}TiI*z=7l^`!Mw2alF`zcV?B
zueYWYHMdNq)4%x*&ORiEm+@7AdouxYIcbvH%tKWhlK?i6*O@YKeEG+%8<*-4+kg6F
zBpn)+ldu0Co23S}>eVjFZi)Y$cb@q@Vn^1Sw=xkupKmrF?T@Du&dURHJN;CW%L4_z
zrmj)=gfso;+Z*1`2V-)_FH0y(wa)C?x+a@hat$+MH#ow@9?ozhxs4!H6TPMBkeD*-
z4IC3?y6u)9lPapr+<bItM@-3zZLo+89%J97FnFK6xVdpu6}`GPQD$Tt6_yOARKsi>
zz1_|^U-~DjrG4N!Rhg~O#h%D@!MLmW)A$(K@Z?a?M+m70;OnuOwJ5=@th~B^fg%2E
z(GWbkGu;dClz473vT#b-liK>!wyI!pAV4CCbZ<{TsHQn}=#Y8pIT!eSZPCn;h?DSE
z%7luqE68xZ0;l@X*z~GEgbxJ{3(DHY#sQhMx`Q!me+A&<JhH6Zysv*8L5tx)-Mp&K
z$v)oE#{=uF^b!d9@7rysgb|nn1$$deC;$y?Qym{o2*{#nIR7@~r9Q;-j4QLUs=*P0
zpmI2q1%=qKDRcCAQei!sVOtM#R&*dEbl^!@*<JLr|6`TlqoGy<BF!56ufc($TZa~W
zx+D6K=IXZhSmXO4@T!<@<B}7DmimICo><(=baofYYgjUMFDi=8pzf=*X~h9RU56{Y
zv(~%9)@IGMZczgO8D(X*T~u~a;9B`iDo1kLc|<&{ND)0GF(#^#T<!8|%RV<{<Ut*1
z!&mYl7IWeg+I0NmS|H{vcLLw%$Ya~_7bw)-X4Vj$h}Uecm9*?XVD*2o$4k(nOIp~I
znm!z@bpkUOSx~(!!Z<Y6)jK`sb=4x}UWlnNp~h)zh7~U)w~F1lKLp#~?f&lLo%4iy
zNXa0S-(Q1bnHi4Ck@iie-giN3J~94sZCO||A$I&A7VMG(AMEI`{&KhhDpqyQcYwq;
z9^<0AUu&QUp-}t4Yo)v0OYr&{Z@8kEV&4vZvwE@iLhMnhIV`v!y&C}f)?e*t)@FUO
zz3c&!nAc7blT<|zjX`oq`n_vsM@-in_|>CIiRBrN&Gt|AM~~%d#cw2VyyKDR%9@es
zclJuYrKc$!luKmS?*YEvzGkFs_oJ`PwTz4iv1Nd&Lf^qkzuv^pJl^sB7%&L8OZ>`?
zoV6ds<>YjcYhP~EV4<)Eb!lTkGS6m6`*It-Lq`+Ju(HYdk+~vcb6`ee^5l$$)Ol$$
z^I5n1*EAV@8usBA*$$G_q`K<mjPb|~?tbcPK-cG~k9Q9(%J&$fV+W7NbG4X5)%4+?
z60FmB*9#kK6F);n98atL-_nCMt)as8)E;882I(d=-`k55pS4}mU2<@K?FRyhaKDxA
z&tOe;z2N-R>0?!9w?=!awK(XW9k+dMWnGh(^wE5W(Ij`S<@ENiV#3T%9}_#U2P9rw
zYuJN|+K<{mgBeJCp^wI`%E`jfcD$jLMo@9ReMwk)d*k6dYh(4v?&aftc)F_y^1xC@
zMu-3}VZ=pSc4RBH5CMiDNxmBkRYcu2c#95;D-s!8>=Q1zD}+Ih3v++3W>q}v6uG<S
zqeY3G7(6Kp|7MVl+s`TNgPAxU$Ek&!GL2q??Ub9lVOn_!28ux_p~?D(k+6NfQox?5
zFm=&Jtnz7x%&h$InBcfhc7}-j*u&!Vd}3q39_mmU?R&xTqQ35cI|e5`oQ83?y1D6{
z-Sk~ycWcCQagl?2%H!Xj9d$jgHDFPgVLF(g(^qf%8sQYO>#|8IpAiue$pI}9dg|G3
zYqbVE9y0tC|CoiX&*xFiL>S(Ts5o!B>#w*h%mJ`{JgFRO+U(A+I(h6DaIJmZ2VtK%
zAuIJnoS&1D?tIY4>2ijyem%i!#YS%W*OHCYsA`MpP5bG7eL@gyY)!uf3#q?`Io*lF
zXu*{xXw6SWD+o<5ivmy0R6s)B-=CVx78H((24C2n3z!-_-rL{)9aNiIN!L_QiVu|A
zvfghxBDUU<d4);a+sU0lisK`K3`mdl(zIYwm-^#AzoIPX6qXV7^~kLDdUQKinHRW8
zKQ*25=o!0rt%cfPareIDifL|RRa7|}+1q<ZIx=+B^{oNO&Tcq|714R>if}G^cMcV&
z@OyFJ+9JQAC&#I_CkpJQBOA%^k-~C%ZDirQOE_(mV~c*hPl(Ptw6bmLv4wTc6k}|0
zmRIgO!0*915R57<Vpkz)iVx2_Z1cK1rwYYRsW3)=0h~(Jj%xR2wpUtwNSIk_hp4j$
zsuzJa2<{OUbFzxzy14YZq{IomvvCrG_l~n;f%SGsYmz<^aB*k7f7{7#5(iCge<yBR
zE+d7@m9Z{XPhMo?WU`+M-A0dy>hJW?tlH4+rBR&YYok=UPD`Gco_83gLNxU>P}^JB
z-7eC%{$gisJxwcs9UGUWB_=w{CwK1aYLGiF(BQ^+6jV(|YF{#Q3l1%wF{h^&*BPr8
z0<GkFI6YqBM>a>iM)VB0;c`-og0Rx>j7ifOVrt6FmltqEOG;q`CM5IsX+d?XJAfY`
zj?jSN$h=y{aBQ~=x7QB4uJ<znc3_bM6Z8Gus1!IvOy6=th8%0@{nlWw*@;*bM(e2o
zw<wH3`L7KvJvqNLI;Vr6I%E)f*AeM@&?Ch1+T*P#+jxkf#PBwaHJf!wSJ&RQ^Co@C
zow)va{-U;l!;_R<(nYBOv71F6D?pw??yAj{VW&76!%3^z27=!i4n|@T!}@5MyA+QR
z{8xwEh-|~z7}T_qGpg9rh;HN#B@h@m_@)wCqHXVr^0|VE(L57}PkTvl3stRVdn~QH
z7V869ay(K`{!lY3Is=-&9saN=B(tq3)lQc5vxoBWy^t9S4t*B?h7tj7S>R;+{_D1n
zhd}oV^=VQ*y0IcU!lG8=cdJo~MsmNO2`bEsC%s799doOZ7GZMf!oURgv_4S0+*Rk!
zMl=en*y+JjzU+5+jG=Zn%TQ<^wK=)c4K@!FJ$j;be!WJtM28aatpN-gVg|K{kK{9g
zFW&2%aQ9M96_kNcCUPgg_!qj)(~cyL#09Ql1{A|8#h7A9<prH>n|CLtRTcTx^0_Y_
z-tRJ-k&V23M<3IfN`chF#2gRjaKZ?RdyU+83-9>v!HW>}&1lFe9%&?8>Vxzbg8R~A
z5+QvV3v9DETCYKyRh0;PrOA5@%TmUl_LoR2bhPuSg6X_E*HK&7@llzn5j9;00NOXt
z74OH4I#D3=%kG!Shnu>`Pcr+9a^ua+oU!ERX6FX*1h>cd^Z_UEn7mOt+Vq$j)-X6;
z4*ym=`jaWXv=B5M{@~iw%n47>;#G8)PxcqlyM3lOhZ=Aw97c=T_I=vap1<4*xSz;J
z#)V82(p3;p3NX{9zJNxw26qi5+Po`4g+(C7P)wb4?TJ?3ub6qX9s%g%JN8{-xKuV~
z9=sW6A;Hjb`-M^`h6ZIuT81sz&F}_G{8LjHLoZqlECyU$t;G9yd@<yHSMp?^8(36)
zrxfPagCgF=Jb!2`rX&e^^J63KlG2^7ot>{dtof8fMU7HFz|!5%@H9SVlz`1U>mVXe
zKH;_rXT;<zL=jM7`=fI><0k2J#I--jIV+^--Q^B5{Ca0^P=^XG&Pf~x1PQ4#*V+uA
z2u`*qPAXL$CQBHI?cUM{Q*zQlAA_e~VWRlH{GCQ#6ZO(XM(YL64)>`NM#&O2wr9yP
z|Ncq^62%y^ZvV>t{d!vB<*>Lcuc{ECFy$^p*$!=sn(AmC{|0%I+WQr<9f_Q3w>JB_
z`jHDY0<v#R6qq>t%VgO`k6Vxk@M4W3$=sIQ@X4&w;xrWFAk4x3ifmw`)8URqjnTG^
z$V;`FptefWQYiNnx4&q%XNq~%_I%?V9RX!FyZwaT_R+pRoBRlcDZf4_CW}p}<l*>;
zgzDus<GZMpIjH#pkzw554h`StoDZ+zbYNT=Iow1*J2RFnCJFCE&znYVCMU2c#J4W?
z50DTB(&13(svi8_vxKY4lRk-!toE<4#ND89_m)h8QIhHs(Fsa->{;aqgtgX)lsq3=
zmqrC|+xOVKe)@ca?QD=K47~B%UwV%|SLj1c`Zri(dDq`$abH()!E2Tx23>hzcwKo0
zGAc{U&63vxW#J%XJRW3Js=g!V%@^ZkHiO1jv8po^5=Is=J!)|Z*r+*GVaS41<;GSN
zFI78ReUEMQs9$%wU%s0(!Xcs$n-{bqR4%w2@0z$Dn>)h&UhrYhMAmEVdBp>YwjZO5
zA4ha<P8TT;bt`|77)Uao8?L&Z=N-2_&vY6bvFe+ih!!>zj_A0jD`t5OUkilq>aR;G
zI<=4w83hf^=xx(czCE5;5RI)lGIb=T4)2CWMwsRt<Ef3<dowsPEYNhwj<WhLAsCpC
z@b<>R@J30QV>N!}wz3=1mF1amdLGd-DE8~iX%U+tB)VS$!0(YL`^29s<AY%a1OfxL
z8seMs_VbON<ETy7gW)leB$A}7OVxP`L@TX(gFfO2YORmj<4ZIAVmlO^<M<8deE;mL
z34ud1%%c*jW&a2ZZGFNznb_2&JRDHsYNkqLL1!m_zwXkkth~sp+2&nM<NfxcpBh&j
zh>|uh;nNydKRTm%Sb(OxPQMx+E@)w-ozx~NI`}~U+wWzYaVZ*~h66RyPA_ttIC-EG
z?zEzh_?^T=bAx7!!>54s^29igcq!zU1ii~BzZq#2bZo3QoCkiT#G^{8kVLO&YQ(oL
z_Pk(>j=!-CYgE1f@kT*KJ2XNSjj@lZZ^0}+=MSFP?SibfM(?=Ko$U=s?}Bo$doHP;
z4-B&ooz_Qh#=tWPOgsCYm|8rgK}Is@e=CHF>4|AoUgu)?;n7HfKbM%;0KKq?dA95V
zMPwmN)Gr*#Z`POlG7bd}cVAqtDNIg<7Wf%_hxo1Gria(zi8WjzKm?erUG-5bT}&ht
zg41N?TucWsPV)%sUbyCvlao{5)G)d{h~P9U5t2Cgn5>iBoMHb19DiLU=2M_{e6}up
zJ2z)0MAv*5Vc_bUYK&q_v}!nseLs=^)SzV1n>m*9%Sds>KUx_3<v?!%Hw_9C)>=a>
zwIxzTGhKYOA^wo=plD5lK=fh%jP@4|jg4D}+uq5we|bFtm5Qh&1O(Xx8^yE(E2i!%
zb>Gu~irVPBrljk(pOw%|ce+t-8<s>O5RVXC*q~@09+_P`c*k3_<E;dHCHU<%qJ1jn
zmz-WhCFdz?x=27Gqi@1cr}XVRAw;J+!I2toq_Uv_*+6(JT8(YtS~x}T?^>l`9uAX|
ze$7lzH>1IPew);{Tgcn~?RiagKOR{Dp&ArbMN+E*U{92{=P2Xhmo@b*wJZIj-!A4`
zVu&~#qS^$HgukBUHONunztu}S(}xiY5fC;KPczc!UEP1-w4mLeXjzY^TD9O%j|?>D
zmc?5XricHc@~Q8x)!M=VM8CEm8*stbomqK|gbGsyrq26Cw}o?sL6r(Y<pZOGC?486
z;FK#Kd7n?*2)(2VyRGfDI9-goEcxz0vT#C=LK_SHcwv5#^rSHCX;eNxk6$Axd48G%
zYPwfghOSr4s{?nHqcf0;DICp3aP;KLEK#SExTJ)R>#9jn`K0gV+Tw=mJj?L+XGB;4
z=SIR)w{oy!R}z(@NkQEaTwW9v6(Lox<nr6$HKDRJg|9E@Xr>azbFu&At>}xHOv)Fn
zVmUiwR)^#;6rl|-nj;SnTEm+NC<vkq_W|A{5Ck2qh~wuqhGo$|#{}8Z2>1#CMDzpT
znpb1qfoBcnT{{+Z)u5hGG=^8C?G#^=MQaY(XFzL#tbizuIy8~|lB={NB-I^2LCd3a
zv_?NOk&C2~CBtllxq?}4IwSDh#~x|ZoTpo(YLf~_1WWUqYWPz=q~{2<N|(+#Cc0{J
zbNKXK)#tc`o$GN2>3t=)lr!(`_|6I18_iW%E2+5A5MXPPj2Kec(STj_Zf+`$MI*f?
zQSU_#Mc0`c0%dyt``atM+Tv-8nPyj=^gQjK#~Nw~@>QD?Z3QpFc@~P{aNUfi`vU&G
zU=L5Mmok(FQjSOkdj(zYC2XP@-%b019xA~!wTF^sscR@)30k??Xj9f|MewBbg5nus
zJ9H%~fO~qKLpit@t2x@%8cS<rksoty9F$`L=A)>NoXLQB+}}Qb7ox<*^1a3)6h1n4
zyjW|vJ#;y+UO$^WoKYd8vclVmpqx6rJ2H{mArhEP6yrt)G(HTv%?$R??-x@a))rCv
zWp}hk_S5i?ROMCi#HWWCh(oPtb7yMac+``dcdxEwwLEB-ullzTu|{b1=s1~e-NX4T
zY%OSE=TCM^YxAosqE~VKVEts6G)i!?y{=!9f<Z;NSQON9$CKcAqGPbN)}s85is=Kp
zs^{7|_9&@vlOw*LPG2&NYuN=m84}mV_Lz?@hw?85@&7`jLpyR0AyQU0qrW}fD+FV4
zutE-&J^~>)!Z_wEAf#OUyqPEwJd~@;WHdl10^VwL^zcn=70g5-b`BoPK^i{hVyZ*5
zxpF<^`NI6=J8S2V=pT^%iREszF2H&d&xz7sJF71coc`(a)tLL-r(QTXKyi6x#BYV|
zEf-SFD)}l)Z1o{LcWH^5<lz9Y$wsn|<3{jz+Y?}gah#0WD{YxVj6Iv6hJq0DeE11)
zJ|AY_8bi2T!PC6=Frv#yKmr%UrF)Bd%JC?`(!+^qcwevk><`@Ob?b=i=CoFAmt-`s
zcE|)JencMk<R%T0gxpqaksUNrDAz*<{o7|GAkjE=WP(I+SyB=sJf>Hs`s=e2v1EQG
zFgOwqtFJsBvt&DRz{ng~RaN>JJnK5WzWgBvHuu*2GTFoZL+zYiVzey%ja}{T)9B9c
zLE@DVb*9Q(FU8=^*4y(NWIS~#18$LW4vN><7^1c5l%ajkeM3SFu7R|LaCzv62HZ$;
z<Uh+ChAVLWS{u;{bo;;COvx$8OqlFyxyM-8K0w73?p4i_7gkW{p6JjuJ?L#N&0QnM
zzaq@OVwA*iO>{7S$P2=Ii8lKuH0a{Ekc~FV*b{OL>6A!83pG=Pj~N{oug-UyO|P;>
zhJH*LDJxq|=8P6vJ4ZP#!&`qJjij;mW>Qn4&L*};3YH8<bl=#z;yQuU*BQ^YD$|RR
zKd30I#0tT)TC~}WxX-ZAl(eX-Y@Aw`=KJ(9QpOGId?tMsDuE9O9!eC>^LL6(1};PH
zIa15X@3KNZbvle$)F7=MUPF~>JN`myM`n3^FUCdObtUphsqHg41vEKTtJ@!9NQy{)
z!j08CniE_R8LWd1flv=<dxdue>f&}8bpYdE>yK|rwS9e{uT-xI7uh1`AlALSg}tjk
zj%G2WTo*1MP97q_dVlq0HAUwR+nGl14XO-Bzg*p<*UA`<c0v$iW5XueiBvPG>EVf3
zoC9nD5k)+++}&|WWq6k$#RQqmhqD~~lIv{@n7sbr8+qMKNXQ+(HQ3%ie!?tWi+Z`5
z60dM#I=jOi8QU#zEs5p=lFZ@(jksqC&r1BAo%EEOj(Rp~mZB=1&XJY)Q@S&QR*vwh
z!Q<)^#(t;zv@+5B8dtwK9@tV5K^}pf2XLZ`8h4&CHh1el*6*`?23$o*#1^L2Z(;Jr
z3}kYR1}kIiBpaRY{z+52dYU-3^#H+IYE7ICmDw3a6MT>-JAKzqrZQW~Vv(M4CMOHc
zeSsQS&&&xbjW=hMSs~CU`)ysl;;4uZ)@=P4aGNHz4OXEm#*F-x>LY!gyf`U3i%;au
zrD~?2P8qRIM33jVf`xG%8=gSZdLEI1a)}(ZK^pn<h>>W#UJDvE6_SePWT@*+sWi00
z(R0YBk9$tcu2&<yz^VO$&TS$R8uy2cGPfBoXK5NxC+MpE)oB%hrb=&EOea!4j-0Gw
zKoxyic!8>z(h--PX{w<qDJ3L?rQlS1ymB#v1gbudPaKia-q4loE5((3`httV?)C1O
z;W;le1>v+84ose+wsgSfirU+GFQ^@oQ(azJ*j0fGW&qdC(}m7v3mxMk1D!PXbt+E>
z-EgVmV8mCiWJW(V8oE%(XwY340p(Vz4c=F-WO!;((NAQ`c}LW3fwD8=8ip{HheGxx
zC)vCPPh0ATW(4x|22a~d?<?=T(2Cl?)g4%#raO7mxIb*S5traAX|GKot9y-TZXUE6
z4nC8B-wk^Q1`Cll1L8DLJ~1gm1v%D4jwd;Z1%byGbi23z&7DqNH?LA>X~k+lFmUK{
z%5k%iWnvO(?;VT)>(H1l8A#+K3L1fLB(*hEj<{7?BA-eE1)0OIt^Dh!8p0Zw?0$pC
zkB+P?zoN;Io_}U%7LSh105Ha<?Hz1xbzV?Mh$$Drsoh~u`25~!jG}sIgDaZr+Yt?c
zSM~5%je*jXaF3v8DHR23@n&+$0*Uz6sjLZy#)X*`N{SNOLd}itkD^}97m*1CvsE^?
z5C+yI{;|7N>1-~kbvbYV>Jo$N05fS^RDMZex*`OzGZggDB2_R#ji)1}X8Uc6SKCpc
zL-NFfCe5EZTk(DVawI?XCixFNIdX;uSS;5L%?;_;DRuXpHbf5uO5fqob8kh)2MZ)`
zR9%nmwqNYfD;|AA7(*fM6Ze+9kIc#Osi{I)AxsZ79Y4hzW8q6m5*d_^?PsFP+2O=@
zO<hr|1s$nh{#<GE(1k*3)Xeef11pV+M8G+a$Pk9h=I}ttkpw;z7bhJ(S8Tq4f>i&~
z=&)&f5jYXwwREsSW1`g>x<QjI=wSXQpCt*!E&2sP`PfWR`;YYLqg30)@y;$aYLJ~&
zZbPKF!LvfFIQ*4=4wj}t1xxrvR40{@(NIPHO;J}*Sp9*KJ^c61`G%<A!3e|La;Tqs
zv^uS<X5`F7fWp2~UXxSj3n>~Ryc6!KHhat-0YPClDP(7oXr9im)~`L!EfW12*^QL%
zbalW>P-6Y2n0h3mGn=YJ14$u?4TA-#O%441lExy@advp|kuRGcR<?q^4FoKV(Y8oh
zz72)p;tbEB1pJ(eUt3W9*EKM3TRi0|E@xuI&d@7$hc{?0fq}Va<ywmspNLAgw(B3f
z3<SPZd^xC=>P^ASB^UdT_5tm<lIjjXsor^D@XA~ofGHkCsQ9E5k_k^B00Ca*iMIS3
zTQW8^KwGAJ1`EJ&NIcDH5jv_$=8LKfY`oB`dQ($^33C-09-^U~iB*-!v3i51$c0uT
zIm9CzRc+5{yL4tA*)0i3@wGH*Uk9oaJr}BzI4I5#&nFv}id;2>mmPk5g3geEP~HS<
zV~9Bcn~$k&Ed*1$UT@@8QTE$`6O9ii9~1`!Uf~Hz1zN-AX0-Og%EmlYgQOhl80Ho@
z_<b{}py+M@!~QF!F{ZnMmiorT*GjnRMxfGXT3z0?|BtP6?9!x(+H7~(HoI)wwr$(C
zx@_CFZQHhOTX#**hne+0vu2%t5SbCVPUJq<CU{7tp>4nrNCa8ZL%SB1RN&&G0uMox
zVQ)Nui-B|{R3blGxHSU<oK-r7VNXEbcXxMxH`V6T6G=Wi;TU4bdhN?O2$BLDO>7Ua
zh8pbq5uhD_i@Vi>s8yq%EZ{}&dhD@lZh=XP^zDFDi`9jB=~6NSO3AM^{7w?gZjK+P
zWOv@qYy^EzT0nJV!2A&X7F_G0hTvMsuKRooYP^!bYqH<<vj0SHFMoZHWY%@x`R06l
zcQxvQ_m;uH%6wgKSk-5_vCVFI=C=`i6-d^iy=WoIW2D!rU6lKMuS_jL|8Jbf_cz2=
zYu8`ce_q1}ibyzekONH8z`C{58TkD`Xm9i6>p`~^X9lM#vYHNB*3uV;bsTs&9P&~T
z?O{`n^LMp~aCF)`<HHz1$Q-<25h-ZMU{2bci^-O1P-bvYr&D$@xT6*%))C<7w)E{z
zty!;CaK+`h@oxO2204g!rfp)8^xiGppzsv+LST3qfZ_EAllM2>_R9KtcyjVG+fD2P
z6MB2k6YuU_Xc}mz6vJ{Po$0-(!=^n#YxKLiDEA-gDmbNiuQ_Kv()k=}6m*{aFOUbj
zvD;J^O4kYQCL$Rmou5a#)@qRW??=7aI@4`1$_!`fu5UzIJ^F7HCBV85dNg&v+ve_%
zq+@nBJ1mR{MD8}(m!sq5?Zuyq0zP`PeruJ#fMey&SMu{!Q1jyxrJoO;1{FG$f8X0T
z4Wc~zf(WV6JxW|Nb;a;Gq)K_jSgBtR!-d;vxYTGj3s^fF3jN?n&?=;8ZIOdMcd06a
zJK7543WdOsx}2<L%ncQ)qt{G<=~QUvM}sC^zD3zDQ+QoqK<VsL;TZ*#J34AYp1mQn
zi8K(Xmle4~Sh%r2?1hAreI+0iKgx%jwf`0_8S+IhqFrr>Xc$uS9vq1cZhDH0#8L7~
zHTDZT{0?zkf;ZxdI=(1n5%5@PP%vAzU(WVF;h!%`1o^Zxm-A?(8KdAEaQgYt{<GWj
zehF<x?&0Ms9f#dIXAJG&LGppd)VAt$V$uqQtxxNr;zBu~6C30DUnc$_R`=;INSKw1
zXyA3pe;uG9lUSe5!E{<ZJwMMI>hNJsR>La`5{C>iiIH{&1oxe0qt=wu%-JCCR0X$@
z@iQDFbc|wq=U;ySy9Nc|h2U~PXKNcl<fufxv!=u7iUt;_v#7;6J)YkZtremFC8&Ct
zL6X$B^OJpMr2p`|``BC!_i7lEt`n;bk!u7?4v~{|Y)s4u9N-Z9X_!cIVW^h$%L45s
z|4UXv+t<8_kAu0wL0xbhyRovl<qzC4%6E`wmwwk_l}%jcAx4pYft43#I__YSnvhJN
zD4ad8-QBJH(+lSYC&I3pK!`(yT7&}!@Va{KH+udrQ0A{k21FWPkbK=SYo8yf6u@{!
zLMeE$I;4$Q+w-znkAw0qrF!|Lh?O)h>oBWE`?<igdkh~Oj)(HwkI>;(jX)T_rj!b(
ziMq!@+mqK60|TiT^?Ll^O%m12obin|x%~g3&F+kd=!Zz4HmM9}=M>ZVTp@kGPGKje
z8{!g!A^b>9sylQ4d{YLU3DD{(bfdr|$Nu4hDE-Anu7*GQvc}PPKvzT^SxWjJBF<lr
z9VkX~vj4gnmCw|Ks5u{3z;B=KnP!+Ra%AsMWdx<w%;wmL8DFr}XsuvaOWZaDO;b&p
zr)KC@cJMC#(!XU>I64uimVOrlv?BOfNa<-Lt`Qt*RfzHYmt?zp6x$QkAo8fBc37QT
zrt)`fphzC$7UFsad`<d_lAv7ZuzKM=_1~N^IP4}D3daHNQ^`M7Am-ORCoQq5>dd!d
z+ifGSG}EAT2Bf8w%dA;gha|l~4bGYHp?BXK0{6x5P1RVn{{uMw>*MLL{S9(lse4#5
z&|8${7Z;ew^#KjLXPZ*BZFS~0Rqc)m_-b-bfER24wAvSEO4}zTy^jl_IN7-8yaA!`
zrAQjuDr_rze!ibK&2goO(vRxDbKrm0n+MTdZ{yIm+GYO?d}Q8a&qw9Q{|Cj>5a2I1
zorQ|BB>nF@O$qcP1@Xn{UEt@>V!HG^x3wLcTG~m?YFkuZ^4p6JAEyb+lQZM|3Lk+E
z1(p|}+kiYPe_?GqV15{u*|@#B)7IGyTbK+?8-rBwp(jPkxyf1I;x;7vDU=bD9#cX(
z<vP>GxdF&q$avymb~(R)u*cq#JN-0P)k=};91jkQ_uV~hY4oj@*yVbHyQ%qEwrNAH
zy1@3jgM0!M$YhUCrh$K$TQcn#niVaY6LqR_cpI_m*vP1Jc^G^qlQQL97PYh^@AmP>
zv})>@Ay0RsAz=VlZ>NB~x5J-oug(*G4+o7H_btz&dy)JV-fF_K%yf9UAGz@YNpfag
zo}b%^9+8A1R@ep>h3@(WDx%5hW`_j593+1){6AR$mgkEf{h(UI4zn9*c4}nOXsm6V
zn|?&PyUq(`mn9V~0<6k$+$0-=n|>HGFM41r<ZUTxtYbI883MiHg$Dc|RaE}F%{$Yc
zSeBX&h*>5=^@}AvF{++L0G@4J@LG#Xn$pdQ=s$L;i#tvyWMJge<9QYBq5Jy|AYNSa
zyW5gdxu^i$nbPT%rCk{xrP?Nj@Jx<tmEHRw2{BzytZ1YmW=cmKz>OY$XmXZx2<^iI
z)g2~UM0tCFtrooKn^(>OfoDX-B_tH&z})&DY^}BZYX_*SB3{bL%JbB0_iE$!--!w*
zVC!FkR6p&X<5>oefxA~ICS2!a=Vr^O%l}#!DJ`xYCAloNm*)87bfMJsocj6sHVLn*
zYx4r9J~+MD{FeF69qH5^AR##}p9cE62Ru;^c&ch9+J?-!k$zA!@Zz8Ja#{+}OCA`y
z(ptXDA*J5g`1pwepeI(@wlzzK=Z6&B$DY~mUGM6e&TQQ)ho6>qHP^?=N}EBRK6DLn
zBl_p2=Da~Pc#}S*MLMM4(}f8!soJHBxO`t5tqu5{30EfQ*eu`nAf8l>9`C`)!qXWc
zEv*5cw_W(xXY8WyosHLgwedQOqP_y8hz<_c-hP;@g@hDaA9fGUH$mW^-o?L2k<fbi
zMSkc-;qj3;WwrAT3r!~PF;Iga@3V2_vc5JHK1yQAuOG?AUX`;q#$u9l3tHj(XD>sW
zfQh)&dPLL5Gwbs3(O~A1vL`|}Bh$HkmtHf7y)qs>j>`Rv9&lu|%WhZg-x&PN$2o_n
za!Z+PHrDdDvj9@?Y)xnl*4Jo+WYTYf<~-MhV4Ci?i!MRJA_jim;bTVVZYbBsfpUxW
zRkOxE{_*GY0;SWU9QFIVw*}#8L9N#KUXB&A>%)UTo~WH+98nKgiX|;wFYr4Y6=sfq
z!&h_s5P#kt?^~{qzDsWlmudfYOX->VHIg+N2K3L3dDZqtcpag1CA-APN`611de_mW
z*PYGPc3;j<Q>piuz-z76l5p^3eswu^i3%f=`*WC4onOK|?kUwK>S>DPG|g`nkZx>5
zikCMUf06$%`AU0@+v0iO;7(9hipy>dxA=%*@)&^K1a5}Inpj<s?_ay6#Ah{St7N{&
zE6WQD#iobX8wd%k?7ln!ICVV_i3CaI27T<k>Xmlm8r>+kpgY52F(gzrMDcvDnScmM
zkq&r>*|R@tnBJDxrpf4dh9j~=&#lXi9#5_4-=c@>9g%ZwZA~|p7)qy`dMQFpuw9XO
ze7OTNE3;oF?05}ss!y-Ua`mk|396S#tkt^_GYuG`p1wPN^;#`|cJ)@rr6IuDb@yOg
z{)ND7?CyE_Ynu+N`$O*EbE3WTbH@aCBazoO8h;-@*rP26Y(wq$1N?%sF-qxS>+8!P
z;<9|TlNqHp<qU*7TOj<ltQhetyY7f1#d>R|r-@Mk??70bH($eL`S^aIH|I}#Q+h|2
zP?ZF`r1_LPR*dc5`KlTMo7GNUTvmr8#UxykQgTmZ(D$cCrz!L?s)_Q-%c62uHfg55
z;J3Df_9t3x&8*SGQh3`&w)>1)<g*XphH`6$iOz@c627gGk0Kh8p`JZEy9zuqzS9YE
z^Rhb(fB$8An31*dDiJj*EZT6|jOrdTw#54t2~IgP9FyiSJ;T=(;_3Aj8qt74D?Qiu
z=^yKzTeeYUBXhOcj2P?SG_QJNcpgI><r_a0{$v^q!C8N*Hv)mL*KsMSe!uU?nH5lX
zNmW!PBIhMTQq5@v<P+nj$&?O>P5&<ahkGa^(CgKnxe^1LjE(b>QKP|~!?f1EAjBgf
z*YWRAl6IB0w}ab)qDI_APoS9j>gFClxE0^m-z!S!bmxJ(R!l65sBW68-S*6Z3QI~T
zsXJ_*-+pGepKiAVokfUreiWI~EyI|hYi{hwiP_+~jIk#sMUaaLQl6@iEjPSvzWs)2
zW0Dg3eMZ#r{<8lfej}d(G`YQjkJD3<Oz=%_`%w(=rr*3e7u2-`bX+)+pP!wZTNE^t
z#4J5|HEVwv*y1?6x{5CDiiFc+q*I8MUoayQPsJYd!-s-IW*snUVrkBGc)~J%!#FpZ
z9G(`fW^(*wg_}F$4mg@H<vA1-Ge+`&D_LAN#+h*`sU%1_2+&$Qz_B*?c+bQOLtS#-
zK|>(;tBFkjwsFR3N2mGXUK(&RWLnzRC4GvH4NtEp`K3TjrhVL@Dib>F*eKFP{)uh(
zO&6sUG>A}9-<6eD<JFc&-y4l6CMEI<gj%F=%?)2`zLl9>4=-;+pX;XsHn1tjCTNKD
zFzS&wPSMXoYpP=|I!GTRC+yK$m@fNd7oaK}o?$;2rAYjnQ#9?ob*hBxsPgLQBeM!T
zyMP1gSYG40<ThkvA>nFjl1^_<6HPMf)IdPpU?mh^nyw71biC^>X-)hrv3=pPld!te
zd1#HxX5fk;HxC11Gl@QKz+S{N%TXeQ_UE}pr86b9d^vqsYueV}Va2OaIlOypnSfmZ
zQ#lwR<vgM2dK!YIR_tur%?LgsSeJ@cb1TJhb7!;owdv#e(Nq%L2?A?L9w2Rz4{b*1
zg{0G?qZOe;I$UEERw*xMGOh!nDYo2yy~IHZzj~%_-ebc}U#>KmN$~WCu0*z6HjmAZ
zvtMrb#I1T4YPTpRC(&42lTbWTWK(9JP)o`f4<SAiV&*DZvARM4tcc#O@fz~}*<!zD
zQaCLwt;)t`OM7}EpbQp%-sga)GJW6{$%YjYQ^l*-b14jVipXwG>_utW-46|yB}{E;
ze0%Vg&`uYrO%Nc8e^3sNu7}4v?6j(vG#XLi`RwUVXEJ5xKc16TF6&XP{>Ij&johF)
zR(@!>50gYar|@c;9OV1)OjU{dDZX4Mf6$~hd_WSBP1)89-=`)o5J%<b(`NF7Q#hwx
zLEhCHKdx*CQeh;Ypcg!|x8LSq&{skwmZ6Ebx5?1NcJ*X%tZt2qAO|p<Z<tuGiMUz-
z`wjwGR9EE5to=M4sGirh6p`M<`^tv}cZ289x+WbFJN-E?qfg!F2LMd|`K1$E>a5iW
zUU{E^&~v%a`J8yPT+t`h!Nn9W>zXV}+gfmc_|FqV@M6ZiN=#3U8eWG+bw<Fd;b~WV
zP{!7hQ_~sO?7o1%Kv#b=!e1kbRQ%$SE9aA86FHViN;xTLeH@Tk+2qNAtM!b%Hn|(?
z#XJ4-aB{Gu(qE0|Jei7$fANX?9zXww^II8jXgrKdYKw^)Z03lmmXn<7u|um(+MdI6
z71wk?!aRtb&xpNL37uXEF3b>VJDvt=b1kOMrdAnWeLWU+kwK1NaT$`ym}q{{w_MX6
zg!J-`EI%5FZ|DAjDNE@X(4I0pzjtmZR!h6p>3mg~-=dqn7a%gyzv%y7Sb3P&;@USb
zE0NzYGjIh<e_w8!T&s?1cX_wCTK7vU(X9u(d1WCSM0Zzt+rci@d=mK-BymJ#Nr&w^
z1PpVN>!-F&AIy8*>hEkh&7J=H4}28ERqfqk$>bvHb|St@`)_Zhe`5eVPjACsT1Y9%
zm6H0!(pWr*IVim9U}N%mo|$&m6efSRpUnn(-AC@RFg|D}B~#i7V6#UQ)NRecIx^Vv
zri6xF+TKA8wpWEsWf48v&y!Zpha^phZ)SP_6prLh(q&FU@bQJbd(06UJ=c7Tm06f4
zcs34IbcSTe-o^PY$!myu<%aXt!!3<#Ypu`41e(xsA-^bY8W5BBK}%`W{So=NoBxu$
zplUdC4mUjz5AI`BNJyqN%0PD=VO?;lynDJYDy&d{ScP~$l5x|5oaEJ}kh&H%TvZY_
zl>zUTd0uLs>_Xl10Q#is?f6)g`}>zt_y;7<Z&gfFol7y<BMoa)-g6OtpUVb}=UqiO
zX325S92dI8tKsPI`Aw=+62*VPNf;z2!*a!Lnv8fXJQ}-o4~r*I-%R8WgVLYCq+~mo
zYz_SS(ygFNe$dO6B1Z2ZqEEOYM0QRzr*T;W;E4N{Tcg7bTtgN52K6<d+j)VjtwizK
zXmFQw@UuEN{fR6G_K5I*gNrlRbiKlk!D+X!Ce|Bj<L!@RjV8ld#;$AnHPWVXrob&a
znBEN>m~wJ7R=nE;0qNkm+>Z$9oO0SO!&N&u!fS69g!+_;ff7M|z~);a3LQU8FDjUI
zwqyEUj-Z1$ml`deT)d3GiM<~nX$1_5s(f?cS<w;cCo-ddJTkjzp{uy4G`!l<=Qmt=
zH_~H`{1|Gx8j)rV09V?b8epI55vyH&CkM3FpBElK-Ld!Cz66EbK{iRn@Jxv>i8|}F
zC1z@>T`xEk&1fL%NevEu5fIgeuy5{|x`U&2w~43g+6K`&g9g0XonG>JW9tBl?;D~r
z3C-b>E;#MaN)yFY|3==}YYkv&?3E##*DmQZcf=({UXpe+tl)Hw?4>Yh*sAR=Y(771
zT@L_BL<TH~r-%GD!{5lG$Nf+)NPP5vQj1+f{yPiqh7?yWJyCGj_AFzPn&bG>lsyhd
zlwdE_aDJ1K^|;Z&Sz5I5P5-MvFNs>FgSqtK5FwGMOvao;5PLr#C_8GIKb%u3q4=JV
zRAzFPj<4~hZY4}+Jh`Q<h<O%-=XZ!#xK->~n*8SK)Y+k_j%B)mPJWP=tf&as$D*dX
zbp5uT7tQ@|Zg@x8EME6Wm%|-$E&DV9N&^RE<2NoPwd~PF6Q(CQe5ph5yM?oKq8MbQ
z{k99*333v(Z8&2?=)5*)B3gVzm5WepYWN1nhvDJT#9o7!-mZDEe%!A30<O5kcx5EM
zUl1$!<oY*D;)-y1NL>f&uYgVC=FNg~K4KU7;FUEoO$F5b`NMFCcSh(kdT6Y8Lq<1S
zWawD>QdgF)yC+5MAeyaK8y=k(0FLfE+NPtW$ohP}r&LB8Odqse6`^?$yqK^=J-X8O
z35i`jpV&rfeq8ZpBLuATVKx~aX}#g!GKCr(iAh8?L@7xTbB1>Jd4*KD(q#1Q_fGW&
zS}l}m*A&~e$j(xxTpBI0w;@PtpYvI3bKDu~TT)#QH?GB*%oz2~UU43o;441O!LvC7
zXEwIFAAU++qO7%cC-{1%Q+YRcTzs-+COb<Qo@kD%<QI?rtMRM1I}+AnxA7vKu@2nv
zs4$45X|W)Is&gPjv=utJK9@9btyNp(5!$qfA&?5MApi3U{pEhq6Y1!W49G4C1@>2x
z2E%3613Ca7z#gD}u9;2kckSTlTp+$2rk}II@6@lm^C6z5wJRUxQOWV=dwW`ax&syy
zgx&3aB=geP`@zUakPwf^2*SSp{lF)={$Ourc0O2VZ1|oau=g`q1Q8#fU^4xr(fd>&
z%lm6=y%Vl*6+&UDrJkh_8b4E1)kccgy9gKGuO;<QB`4td!>lbrF`?ZHH^p~<snphV
z9YE<_eW{*lAN0#lD#zo&LsF{<a6ooQVs#By?7LF)jkD`yYrJAU>}V<=?#x}!P-r2m
zH9V@N1bNqJwm3?44~;D{voG{ywQbMLtVA2IRaWm?<#X-hnI$YSu{qo?DOMuFb^qRQ
zJ=JvuR$m}OPhf~a6I@Pg*2Huf;ADKoQ@(KQk&>c_CPanpR>b5m!+q|BhO<={vNeHL
zkM>mOXhy+6l&$e_BK6evuGHkl1&Uk1eR5+e6+u4XI&ebAC!VHGx$AiWX^9hXHnA-v
znvUA<I?PQX85bCdEY{*HS$z#*7=qvEs;RlC;gz(yqv5~W7TNyQg<)OABJ|0Sw()L%
zj(4KxgIw5cqTO6)=)4uxUtgj4(QPA6j0Va@ADtGVV}oac<~tSNv$?8F8f6c9s?7>7
zhK&p=*h7rBHQEp@vi06)B#rBmg~JK<Q(qeZJn8X%xc&CVUgvZOb@dgv{x#n#QQ0`4
zoH})Kgv5QF>G5zrd~k9!y}ylJ?gk;Mmm=v~7;>f#uUfrL;#84xs;e25-8O4-qdt@4
zZPle2+t8yunYCtcrS}8pQLU<cqcQb~{b=t%UuSaZO0Cm9z6ofrJLzdEsWlzXf%3-n
zTF<dh+ee-;+Ok)pC{O>E(j&0z(;IoTzUg_jInnju)iUv}sINb~J9#c(>vVV$FPSns
zylbv737%T)@5dvy-uY{CMl6`jAt}QL4bl^d;FHkt*+~BQBTqn$iH^try{}W$lEER?
z<j!Wp)A>@ezlR>2N#AL@?!aJrp-mcvGg!s3Sf2>z+3&LI^zFjNG{2k@6)=MA9v)e=
zE-0jbL@gQ?QiGm*VQL`oZ7d%d0bw)}#lFxQwr6P&+FeL=jf+~O{Iqv!DwADB!9hTs
zm7X}l<m1i0STZ&Ang(3iQxlhgg!{^SXIAq$nA;`}(pB=n2zN_2tnoFOCH!lOj(f<f
zQgL3x%c!;<6}Br(nSnsdeZ>A0x7Fb=`$xa3K4$9af^Nc!qRKor%@kRL-(g`xg7@YK
zy;#8vaiDDsW2(a!=EK!;6MODi{eF|{W<OyanPC#d;knw);W8T9p?12oxDh_uy21$I
zr(-Ob&GE5$)yX0Wclmt$s$$vx+vc_Itc5F$nE@qGAnQJ<$U%Q2iy|r_bHW&r^y8Ya
z?RCGGULq*&Zht@R<757E11vXsJ~7kp72NiFuowj5((5sLmB~(&9d>zO#8z^+U-Qn{
zp`?u%{`1bBSw{nhH!U!=GNmGb7)r7O^Z0NW(EUu^@%W?uRNBilU7btx=_Dcmaa{Yl
zJ$QtGei2)B5>K{cx-#9$=wKc92Fy9HO-1du1``6FP5+E(TsmC_%4Yw0TZ1-o0-r=|
zCO<zXaMZPHzlz#eJ}EvTgYgd?fuB%v6yItJY2EdXW&<?-dzQQg2Z(-}!2O6u?awWl
z?Y01pr10f|eid)lVZPegiNrdGQ^wORCkz(OtaGMAOgxrEgkYVOOmjOLw3Ze3DHqWs
zOqpDIA<U+9p8D3N;rWKBY>7<qbKG$ozmDL|qwfP@#HH}w(*m*Fqpvn(z^Umyx+`pU
zUz@zxLhvz9n%3-BI+H`(E^4e^qjkm-n|_`TKS)G>ykv`OGUJrio7Mr01@*2y2o;x?
zX9d*QBvSs8NMv*%)jK#eWONq)(AoV|1`@COap|Ynk}zcia`}R0`1s6kHYc)kFSrFV
zckQ|}TeNL&7KVZ@*GT^JW5bg6?o<W^rn|G6qNcjkUcMJK+xJJlw0%VpQw0jnPjSG@
z-0m><rI4IjY+ZLS3L5(S(hfy!9^&q*XTxiW<0Fro(;>~Pqe`2Ex-u?950(N1V;QQG
zub&EJd3mt=kj%)tv4*5Y2Vvim>dC`6S?<GqY2|$;d-7y$cTD_16P_#$DGy03Br=&}
zmQXVSOeeP0dg|J^joS}?>({e^VYMblhW6iaFLt-@YdmlAKL-<+Z`;eO_ha8|&`!0(
zM~FksZsL5M2ET2WJ)EJKUYCpNaq`MKGV9|DDY&}ALwK1!W*l2enj%1l5YfZ8%V>5)
zG)iO6BJGZAD$Nj)II<B5yI@+1Qw*J3X4*D0%k3_WM4eFvw|nu3^4Xrk72Ogzmh%eh
zJ5p@VOZO2U+rLY&h3j+4-&POtaYobl6?UG0nVXpvc1`+JsfEA-bHv+URzxDxxZJog
zG1rZ+WcoxtB1YyDOSX{e(^U32xTAV^U)2b^WYLbI4`R1s2N|Fv0s=L>tFeayjxbQ<
zl+Vl!cAw9LF_}9)kg<6(93E*8J9e{(jx)uo#Hc9#WmHYHT^2Q$Ff+s#9O*cZAyvf-
z)Ch+Ji}QV_q3>>v1Z>(R)W1?C`{KOOeOnKloy~}}0#r86P(j?165>6Bk}Rpt12ylJ
zLbIMga0*5kBvJSV|HAo5H9I>nIK9iA%C&Cw(L~M^7BVG*oQwG@)f}#9`OaoD)|gi9
zMqky@r(G-{4Mi6wQXq#&;a3^GFU*=F?_Tv28^!CM;83N5$CLtgR4uW!FyhpKL-+2r
zy}nJVG`}da8DA(p`&JXDoKAEnG_BLvC8ZW_Y@YnS3;OjET8a@FZeJOHwukfpPv}{h
zl@gUtCw8xZcrHBmU%ewhj7(HvSq#~`yG>zI1)_?oEPd@v#?%nFVp|w?p2gZw`zXwo
zysZ+Q?S~<icP$uw<}GM$x%ohrRmO=(?=q_v{mZSpgUWhj5sw?f_48xw7K1C@;Z?iZ
z(=2R&aI4zR<Vw?#f<=6>sf2o^QhDt(i5_(@9+6W?DXDx_Gpd!mUoeWHMx|4vgwiKR
z8Vhp4^En>ts9WyeGE>m^#6+zp%yzYaFDDUx)B?YO49INm_=CX8VK#RDAQZikN#T$F
z(aW$b42pr9IertQ+$P_2gs6!dp@s2lV<eS?E7x#$^ek~l>x*0C=}#--IFi5f@kaJ>
z{NRzliFm*Q?>I=WNDxMN^g)aUj5zE@lnu^A=^qy=+3hT;Ne2Fzs=LKNsMl$g!7>iV
z2Sg*|e<P}zblV8gRr_=Zw1Q!~p@&4n5OLZ9L6^!QiH=K32l_s2nU~0BkDmR)<RNaW
z&<P+Dp)6LL_y^7>jwUw9l!~U=YPpdCiAEV%UxCnOa|J(u+;>mj=s%^^zr9iR2N1q#
zvLBSIc-KXzk*HhFO%9O<HWVlC{pnPk=%mVQ+nfL?CT^r>!-|Gw{h;OK^c-r=3wDJJ
zYht%RVVh_v3)CvSh+YfZ3~mer4bQD5Yi~<cFfbySn-a;PVHZD<tN}}`6Ciw}8YBPB
ziv<}~kJ`T=w{~$?=1!Z1M^|-9Et)~;;iE&rw(HAt&ZXwmv>9Di-;X3-SlHOARZhI&
z^-Kt3K_Wg1r#7xl_k4&!@9zb8!Zx5zQCh4q*ocB|{|dmw$XN7H!my8*14|4}64+hl
zWFJ58Fcxe|LK^pam(Z{#hMFSAh6jyUKxMHdbfV3g5_ix^V==2q%O=|=Dv>E{;Or*H
zU1<=hSa^?az>II+JH|yAc2U!jRgOz3&ZSEpFL$yh#F=xE$kddorB^c=5QBwcIzTD-
z%biBNB(YIMk^>k>`PUC@&yqN%b=mn*ZF8odc@f)-)~4=hG=dbfE*2_TUbr(FoV6NR
zYES(%zM|eK=$voLnI7ES2nr$r{SN5tRLVn<gtnnTm9)9k+U<ar$BF0!gG2}j3gSnC
zfOK$My?@W)A^;)ZF8-Wf;PZm#GbLqu&2hSMx_U<E>itCW0K-K%I1ES25ewlSdhz~2
z&Bz`-d`_o*+keDYkZP{PYleUV=Hh;NS`6FlMlwsJG%&P);V~j}PdK~X>ShaxIu|$*
zjYh+yI@mHVQc}`+yv3b<dMCQKmY^^g?c*1I!jzye1IN44#9#{JW0wPg(zP@UWLPXQ
zSnHcf^294DA2Z27EK?YC$v@_<v_0T9G!j)_%}9Vye2{m!zX?l4<BKJs>k0ea&#SHG
z3{i-ZMMhS^PRH?3R+Tz9wg9&M0b=yCQcNG47V2-RR-ZI=yr+wM{#Q%FFy89cQ^A4m
zE+6(S)2=6`_2(TfCz}s>CCMHZdmDY8t9LjZOX}(TV$+o)S11kdgPvK@9?*QYVvPGc
zMI2UNRtd_A5ig)qN(h%^fqI={e2n~0|7^2xPXKa}OSZ)_lyV$9A-q!|`4JWj9viM*
z_aov!6xH3hS#N=cA($03u)E6{|G1tT%oz*9$r5jS2js%A>6LCf^0Vjw@4SBoJrZrn
z3<ie{T8L}u%Ko0HaCZAtkFvu}(=;-qcX@E9_-}p(BPzI5_}cyjJ)(SqYtui7FQBqw
z2E3FPh}}l88Bf+6YJvKO1JW%6yp&%hqxbPGx|RDM$#X#hvrA|(HvkC8>k$9L&%wbR
z7_^a+I%ehk4B0IkZZ;Ke2*<|(3rkom6hRZLtj%VW%NMc3rySrm<+A({(j?h~lnx#t
zSyK5my(3D_YDXUl7-<8+l7ZepzGm`3dUJmIm>g-rRso;3m!!4suYXrTmUk1QdWP<1
zd@re=HX0<xl=;<Kk#|`(yA>R%($NxE&>)oI8WiDXR8~xHZZO7NJ`8nQ2(GuAp@hF<
z_4PPw9r%O4r-Cknlh3l0G(yA6L1-Q{b-JHjnk@2=tvBZryxGA*bVd$yZ+$>I7Y^6(
zvPH;tE_*LF3mUtDN@GX)DV!y#25Xvk58jfkvlnZk%tS)*jOe#m@ON*xM?CzH1B<Zf
z_t%@U48h#p0ZeaugRS!@eqlW@=x;39*OOE(UN+c3NaZP3{$Avh%|}k;C?Mp_hZUuz
z1tt0ZrkGo)&+WHPw$LK#g9Di52RW4|#H%toyuAdU3XAeWYs}6~XgHi6y7CZ(D~JP@
zNy#fsC9x-44aaSm^_u|_N`F9Qcq~+zxVe9wkV-1ALR*E*EX&Z~ys{4cy3MndFHW4}
z)e#aHWxjg%#3a_;w?Fno{>VGtGB0SX4f$SNOrU2oH}Y7Iu}=#>{y_n{E`0T?*n}4~
zi?-&<LuTVKvdtRc2YEUN9rR&V$m7(eL{&^vvJo|$8}ESJRJ-NJhu0;+8E~T{CMO9A
zsdHo$*0x4rn}Ir7NV!yO!Px<8THM5n9vz7hS$HX2Q?L%v<dNduP>H0`tMOOJqbv3~
zfq=dXlo?jB-sr{a{^%7+&c}qw4xo1rgXi`6v;o;*JFu>RD$G3#a|rCJf1Q)nPFd+M
zJEWh=<iVOuwe6SP*_E&E%L=QZubS+CkmDN?nB-hyf9|`Yy;=3zkvziM^f25S(pC3z
zCToaFGZ?SM4{>uIs@HK~ch3_xQ(AYg2D<s+J`0Z4k{U?1>+lK5l}Nt`BR2R!6Cc2q
z28EO<Pv^qJTgR>Rbw?j9!|ha6jucSdZ$;f#x;#Q>)s<v_Z>_Fmk)+e@&j)^Qs-_9$
z>_(CWy7%=lN{r)2So`Huj`%c<%iw$Qi+ZyY+$5YBp8A{KAL+bqr++PDHH>zD8==_>
z`-<}=UwH#fsnmIRcGvH(Q3++!oUiTtAHAv#oZ8Enkj!>OCQG<nUff}iTm58xrS+^>
zLyl{X%nXFB)E3{>&VLLl+x3!aS2bl&!5x;Tv=^Rj1~JO=J>(d7G|qA`(#E!UqiW%N
z2^HSMbK!J#4JNq<eWLfC|D;fe<wgr3fsnjLTo6nA>YkLB2)LM?``Q9&69u}JTXt|G
zPcz<W456M<$d)3Bjn*Z_Z?w2M{(Q!vvim~&<4cz{fzE@F22pOn1vLGb%uuUcFPM=8
zZi8h}TFq(zEWR(yZK!TvT279Q%;J`ROJ+7$zwVD((DMbHSj8(S7|HR|Z=53K>uh*D
z|4s*@8q%J?Tvp-5khP@HSfrAseC?+(`mP@%80qqMOh$IR&lbDTND6rASpj={x8Nd6
z65KRHeQj5q;Tz=p*l``(rpa-TYI?sh;*X%{pv@M#dY@4^Wpp-!udjCc!=eI&)YSXo
zzot@45!Ra>uy9f83d7{Ug&|QP2L4#Ny&{ll#X!?=J`9PJ?`x5u9UK&0=H?UgQ<NLj
z;>E@|u+tVlGG4c{egK&I7$lU6u6x^7T6?8*#)C0a_m``PxGD9k+PtBOO*AWBU(gew
zoc=6d!0M-Li2~8stQ`$t`(w~~P*nZZ9cN}Wxa_FYfIPoFwvehs7NY;5cBkKV<GVuV
zL*G;nU%U7yp^IP|m~{WntMYEvicCShBy^Z=>X#0yZ50!bkrDZX()}QO<^7tNUjJ@^
z&2ikHMTSq$rbQpn+pb4EU!LpOao{g|{Z%w#NzJ__342-M!%8g!usX*Q8Rp`;;swf$
z$yEYc2H^Klw|?R|a9Bky_f8F?%hKFFhowafzPw~ih)XHKs|z;w<7BqE;lUtTm+7>w
zJC-%&>);S0A{2iKrawlOmKLFr4)f$P7Q!B;BomF5e~x>|_w5GQTXdzscGmEONTWwm
zE8z=S7ZFT6eZwFc$@Wdeclcr-ZBcW)A9*%zp=K+@1b+;Sf1Y@By{~EBv?|$4pBZl7
zOAYKDprOnS5!?A-Ets_$WGeY974_V(M<qa|Rv}=XID>sMTLmP;+nO`sGn(+yB*6t;
zrcJCl9YJY-BUSwzOMT!tk2*OC-94Y~8a*Av&aD<^-=Unz5{0-R0HA%4eDiVCTIrxb
zn&r&pp4yQDyEk3=6?LNSAY=dZaRQGf$_nLd{QcU1`dCtC8pJ;Brf+>OP`xl3BT9_>
zIEHusu0}SmC?6~mvD4kf_Ln94Ab9v(cCY<HSz3^Pa-JxB@3)xB+=_LRV({Hxd|TDh
zJDjd!-+*1s<3EXkKXnxvX;)@szruDqnRi0pcORfK?hEQ&uekf8J=*bH{2B7eLKN3W
z{fn;q9YnXNJtNcxn$~(ZYTV8zrtVVqil_vBG<7m{f15c&G#=mEb^H-StaK`N*9W@A
zd86@+6B03>@hGc&d3obwpe5)#CNz*<rI=VhFjOW#$(>@y_fv=&O(&RCpR)t*sqaRv
zWiN1nsLdGwg$I`}2Ucz-@OUbHI&DifV=y_RbHo}s_nANJZY=BP-Sc9^==&12<_Jr~
zskdvqUbZxXn~&ELR%1Vx4UEJA4B-q#0N=l@=2nt9fSRn{c}yQNFZLIEN9Oz6iWh1i
z{-FYFE$*C+!TPqdu5@*IrGIJx>*E?&n5h~~S(R-d=0u*&r`m}1c^XqgME~SQKEo;^
zUZp~`G{()5SFJFC^<2@(O4P_Q^Yp8xD+_rW%(X<4c)p01ojUGw4VUsK)B9Q9l=>_4
zuNT|4c?nc@0kb3u%$PTog32a{RR!0x1o_eun?eKsG8sv1a&uF9@sfC~BAJt{IzdtP
z%344|;tJ@T_H6XvSXQVea(9lDfwk#)bQ}nw4MX)(GkM{4IW}&|#U!-6<|Yuhr0Ugl
zwE91TfFzYg0zV`vF6|ObtEwa!H<#HewxXRP0{Pfvs9>dBcs6L2%~cx<rMz-ltY|YY
zH+Q$huU{_5!Q|Bkf=<r?8b<#NO>Ve&nh(q*CRm&Y9(@m_c9$t5o_K#tnCbGApm8}a
z{^gOq-j!gf2NwhYF+bY%Q|D_)okOX`)wh$77{Nr3%Iqv{fPpbflvUe>Kss2JIFAke
zrPSiB4=!iWB+p-#Cv>>)Gfl$q>L326EpHvwzvtr<oqL9Wv9yJPRaVH%#K?<Rl$6>4
zmTYoFK|>Q9F>7FD5sb`X_>?^-tY@Ap1WB(jz~;Oe&%n-GU^?jL_KtLv7osSNEQQZ*
zE|OPR*50ft7);{0xU{6`Z`xym%q&V-J$Yb*#m(y~Ur}-JKCWwN3DVptNGw@X&r~v(
z3km#28fmU83p28Wl;af8QRCk`>N#dtya^%;aX#tSpW4mxkOMZx0#=8>Mm&`<@Xe>q
zN=hvylXd~Wc}^FfvJ6WH9fV?)7s|8JTA8wsF#bawEw7n03n<?g2FJzb%w|<dEf5{P
zlS1<x3U-Px>Ld7zXHzTFevd8RK}P0bJnc|XOD+gGxV+BpF)+Up<)jhwb;VsqK3-4H
z<{ur6ibZv{WnogCc_|p1L?e@g2WgWTe_s*Y6O0N8e6S^q?U4b+3{B8mrOBqofh$&N
zDZ#D8Z8^bVE*>I2d0Jjm<xk>L0@g*BYtW|29CG*I3|<OqNj353bXN_oRwQImoEity
z8#ipFH6#N|Jm{Wa#j&RTYk=`)JIS1Dv%vpy@^5G$L$Tg7^Dw<huL;biinb_W5L|_>
zGZ4y(2;!#op7;GzQDBNP#v&T0o2DZ0Qb31j$RRSXo}J3S%$DcYdRl396NlfFZ=H3t
zRW2n^mP;!u13^d*<sA}a!>uJ8Loc`HojXpPq|Z<D%Y;?4o-y$(rv_Tx0nAn8-`qW#
z9r#XqUdY+FTH~&$ECSD2k*q7K#DADMPqq8QC#I(pFJ%KWGDKgNHBXOJ^;9IuDmB+Q
zI~OryX-8O^S69;mW{HXXm7{C@v_sQpyTRx<$Du@KJjhPF8oPXdO<<GXml{<ApUOJu
zvJUQI_HV9c`gRTM3~>mZw*+i7e0?EhY^yt-#QEl}M}=2mi5x4ZB73QkNnL&HTcx@A
zKoEU2F=^#-2>SZ+vOr04#(4Ip8$2sFh%!$UCzWLK&#+KTi6Dn(%yfWqrv<kc?F0Bf
zy=FlBJ!&-it;(C@SXLi3jj{u+I7ag$66c1u6X*YcOa~yDe!#xn{OeE9x}p#mlWjx_
z&4q1ke;0@8k8#^&yw3H>5(x<i5WM0+(YN7`Jz)kY=VF1+cq#di6enrE%qA%nW43d=
z%P)ElCu6mwkfK_b2P5ZH7F2*SX4FDz68iX9p5Q#!JvHhf)ayeMW^MjcRlMsC&PAaO
z0K|Zra~_B11~Q^3PvJ%vwnC1$R>SA;{UPq3oI~)!i@yd4bFfumq$=`51lWm)>*`Q8
zPH>I~*X(0j>}^j|RR_0bFl}E{ZDOmuYAI-hHDs8sbhZrg=}vV)+1Z;mX{gATrwy<z
z4KHpEWdbDiLhA$4)E)WIB#nGflTZZL>~9sQG|TdhJ6V&q5*us|e5=X!ij{okgmXV*
z0zOAOP%9Zyj1bIs@ve*gKcx}F`M7fQ;Lws{%8tnQIEaJ7ADv(S=BKzg`K;OhB(p3D
zkzOt0go?J~$+*E-jI4p!2>oWomljqp{biiX8z#Le;QkZdUq((_Ae5hFDt@wbpy@!B
ztfa2r-z^M;dMZsT6IR5We_znu+&7}?a9fKsvYK$pHahh6p+U`E8*r+H{0fSFqvw+B
zLVt2Ax}?3l!rYf-C99$r;}~MrRM?^}^gGSomgTWa{$9FE2}2-%#!Kbmk#4Tnh({Sk
zHhHo3Je^Iqa9oFKL_%z3?=Z0V?V{UnjxFCOG#-6;KhjQ4f4uakjYM;CN2hn-k2A_w
z1KeSO2K?03t)CKG^4i<_r@XTd%45j(7tbr;!|?0vewDlkGy3m!pT?|#Av|5x`)cWv
zO}s*&MmXLfzCR%m7%O5UL%Q_OH@RD4y*zQj?daGSd{cQ9MKQ1rHJDsB2o&S3lmuTZ
zdS?NKg%Cl0_LlGz*2-~;IQVO^(ob%*sV--55*n9uky5t$4cKp*2PWwKJ&6+e(W$Lz
z9=}v8lrgHZ5~5@x87V+Ec?npr_Xkap0rK5hSq*2wn=YS!2%=}iMYFvdAh8f!K3Y#&
zaCv1@tJI}qb=HCHnL@%HO@V0t+ACL3kI_@!RRY?Z$JQmRJF|z6es%0JPLC!qlZhy*
zY0>fZ=pH4yc&)(z+)=VZU)<F2qzm+}g-Ge1PeFXm_ldLOt5m-~NIr|l14wbV2Lr`C
z_G2s3{dK!m-u56emd__v0D8k;4TWSc`&DhA(+k5Aqq2MRjsjzT8$2}zUxY>`uRx7X
zC}IS=w&#nOkh8@yt8!8Hpm5p2{pNf;j3ZNWHyV=v+~kT^C1eK9(Yu*9JR_J*-hNNY
zTAXDlU>=~}LIF$nb4WoBedtgeHIpa%{N^dex#RN#s{YkUb0>$@*-219!H4osq0}wb
z_m_2N(M+!E74^p-i|Z-pZ)zu8sj-IOwLz!wDZKK1Yv&T5_pdKdmooJmq~AR5AG;FC
z?y0T2tCQp^uWi_ZGf+-??tzv7u1e+GVIpyin3=-mqBmXT*H0Ju?dyxf@6X;J&|VCA
z&+F+hve#<)jO)vJTAyya@3vf6Y>ocMmbs+Y3a*fonKU7@$C43Ij0{cr3cp%Q0G~WO
zQH3^DK}qGwFElaQP^nXYb@snlFd>Ipoi1D-^VSqBr2YW<R^CW`JG#PlU0D-|jt=p<
z6YLCapASUEg_*)7+AK7R5al;<1Ls8!FB%$P(k9SXW9nxrGdOx?a+@V5_&gGKxGtb=
z1CsTy<jU2&2!%YHPwtQoYfk6ZqeCaoceXyf#~kkz1+Ov>YQFHM12o|_rN=uq>VGCE
zHk>5zQmN~#t8Y(I3L?Lz^`f0x-mOPhk<&ykxbvTDC{V6-zSzHj-stExFGPPXf;_Rr
z=+80o?Q$u<VYetSMX^Wb%{&5kM}YN}_6LhiqtD#n9+D7(Yk!oyo^}+sn3y!^4Bl4s
zpjkq?%?d0NnR7s}3_5qCaXhVORL?_hnAB|XJB*B1mmmU0JSb6Q55%M8YR=Na*5pRA
zX@c{I?_)8j`^$T3o^x&hs@<~~sE~pxD>k7#y^IM&f5HX#Nl%$T@(7aD8S_q(H$tWF
zJLvCRYJz$lF((1e`AFd>JqDp6*8i>X`k&<+`IkVa6lczEO<c6DeyA>ofIZ0(HB|;m
zJ~cB0o{w+h$WVlS%@GCu^-;i$Bi)IJD-BAP<B2Ij_mz^lz+Q5ioK0|k;a#!BFsWd(
zXjOA#XOT}s&3Kx{`GchxUYaHY!-kye9L5@Kx^IA^aE^Tq`*D{mYypAC`F1hYz%1|=
z8;9$HMXUeIs~`&9`L4P>v1CCle8U^*e+6HCgnit<1_`O_J-#W$#ppo{QH<>5{9qeB
zr}GlaVHy&&y}!R*qwtpTe^2QlAZ$bgRgfd1f;zl8th2&EFPoLDGjl(`<7jsi8lezo
zSkQYbZ<LE0!@dOO5={%KXJIz}ILpGKG;_S|eO6;M?Foq^vmGw#xf+U|%9iQih8Omy
zjK%H%VYB%?VXUL-b@hUE;zTRBy7Iw9dGDE*@VAd-X{gd@aXqkkC-+P)tk9)Pue{u|
z-C_}>kw=!FcQZvPu`o6>0H+SF!LdGN0H~ydENBgp6EIh%psV*Io5u{%y0gH1+5pku
zSG7ap*8d{D|8MOH2=K=`RB;squL~zGJkICux^r{00+mQ>nv9bZ2|f@&;(~TM!<#In
zzL>2r?z9Ko(-*^#q@^eX?-ZIE8qe~V#F!h#{Ni(lb1{3$wa&0}XMm<>`mR&QYTjEK
zcZzyB8Yp9;bvK7Xc7OlOC**<MFC-h3>V-p3dlI)e3E_z4oe(wy*t42&J_iYt%lI(d
zmbcSWM+gunYRcg9#m&cVd_JoAbYw;azb1#t^425M_1vLdTmjAXTKyUP#lisLiJXD?
zP$D2Mo?j}=VYGRHoyjni8Z_@^DX*?5DaOEq^e760tJF`gDAO^euZh)yt#B-_qZ%~9
z8NzGiN=^}2rN%TaD&>S*k!~?H6y@Msn|W?J@^7ll7l67wA{|&rE0hFQ>Q*J(v4!bh
zb7F327?3eRS@t}Q@k^(qaxqEZLhHh6ZYauzW{RIA=$6qqt@2?3>TFTKUo(_Xo;i%G
zBhw=DdGB<_+JRa^L7{CVPDesw-2VR2?pan%<)9B>L_S8z4{5}~#gIOl5zU2=Yma!3
zo|tc9%84FAOlf?vR9WokPS2f+@NluPe=S}{Zw#dxa?nw2tQR#Bp_g*lw|_XrYXXLk
z?`GP{6)^-v%Bp{1m=CdPtjutfPz8~>VrK+p)RO8P?I4})ykG2$&d}cf@77n|;tCGF
z^Czd>vTST%#6=5Xl+}<<o3y3{X4GsNrTx_Y&6^d7%blqRK|a9|@zhC6pJ;Y?rcmPF
zP*5mBe>E)~CfSVH!0N7&TXv0IN9wL&7KRH(J0*4-Q?sS#l|YqxI~;h7NMX*rjt_VH
zTKW+Y%|5xWb`Mx+Opa*v29SU+?HCt+bdVvew~GYPhL*WaNPVJFtl_4@h-9-%XyNlT
z9D-yPhhvaK5s&_?@<uC4NFg$kx_GpVZTL8ac40*=zp*r-5Ta)Ri(>*-eY}@FMJXcg
z*Ov4CbWoLrSc|o2b1_-c`Mo%~!H+hFZ-wX8dRx4aRm|0pJ2__T=1s|60NY0&g4$?n
zt=Q2yJMqv6(|9BlE$s{p+H%-G#8%MY5|~V?;t_>~-Ej|u8aGYXbC<x><S__X=)mIN
zP|i>gvg(d6476+Ix0jR&ZHwJ@6q7@$Jp}<Ax*{b4!r7$RyHMnjsM;oPl8N5X0;-n5
z0nRV<&CC-VWFBpH4<t)c*>Q1pc=a|$>m?iaR+R@fcRPc!n#ghvnAMZ1z9>K0`H2hr
zqd5kQ&bNF1&!53>Td%VC<-;`qd5Q6h3C3urosw>Pa9JA8+k58@O2_SrDz=o8134!$
zE~ooen`l3X=zy=?2!cVg$#L8x4!vz8xX0!7&r2;Mr#Nnm(f02haw$W__%=_|wodHQ
z89%M-!280M-&$(6p&ZCk8kt`jjn6ak7Fdz?N90PzpNy)2>E98z-tBMKI)DBUY$I(n
zqk}!&#?lJ#whC7z(4(0AQd-@W=7(01#K>Ss3t#RKdlLBh?UN3>qOzL6bs7|k`zSFd
zTx6EZ%LZY-FD7J#u1uOM+@oSQmAR2=8`=cH&?u4`X1j%r0Z2#>|9rU&-RUOOpRG$1
zbbyBt<$I~LyR|oET-M*rsJ=U60__IUv>@IV@!Ox=MwoZ&o!q{RVVYchKO=&`W(6r9
zO>=Ga7id9#Js3VkXIT45bU#Qx{U<k+x*O^eyPt3ER8UBZg5PzA#P<PEpwanaiW$o7
zU$*LwHqP?Kf<B%Y430;(*;~`(cv$|<@o+`t1$Y1-K9C+XqvsALq^8+=?08qjNOs+$
zd^D{x?DMyvhYXV`5E|huc2Dw+6rAP^Gs)5QW#&=~eY^+09Yp2|JmTPJ+|&B*ctOQ$
z`3)tk@{P*v_QbCCto1()wPJ!at9^0hHi>g$N6+$ofv8DpHqz_xq6SWWc~e~cihi+j
zenln25meTud`LJ%%$&F~kv4VYWr+-x`NfSZQ<nfqLwLA=Ir@&1sBe6Eqp);juzZ24
z-W>yya<OtEHFG;ss`m}_^ZSs&X#qof>y1d&nlH4W(#)QM_jY9XUE|cAge~=1SHxQZ
z8ouTidwP@p`zPH#J#Zd$F+I=k$}+T#w(CEIqCcO6#<3sWiYh7LN|?t{1qi?B$dIkS
zPgtyDWkf6-SdAon|7MpuIk%?hbOs=&M*N}b32>R@9W^E8K#-(i;1C%&Z>8D#eR#Gg
zuFshCAjNe@N2&>d#f```@-+HBt*5_PJ~`IojBxwq41y>`s(&(s$KLzt^@Wu=&3u54
zkgc9)b^DS!vbCt*2q4CRrK~$B&7F6b5<_8kJpp0FtSPVF`YWguoy_MQP?;=vQn|A5
zq9vumkY4&|zEli^?5jjX`$6xk-T1rR#d3SNc>VN#TW&I5b$G2)It$UMI4O16XkF`e
z-@7Hiq7&EtVGHtzzULu3BE#uh*_it2+MAr<PVl4L#r^nl+Put8_$r^}?Wj{_j-|MK
zRD%(6IcnR({Fc-E;o6hgUibX|v(U^)-Xqlb@52Mk&`41~+n-;!UtE2=lga4=nj(-k
zv+kw5+h5z+HIZAr=qe|=cdk7wDKx;5gn+$&p)fQ8IJkxrRzHhT#!_r7_LFhvz8&A#
zy1q`QzPA~hbVxOwgFZ6F!|Yx+7j~=DGCm#GEtvq6hSVL+o2H&7X%r*tv9~6heefIk
zSG(KJZqwf-yS{E#muxzNKHo?COi}(`tv=S(<^G>u&c>fuJxZAm5inVNShHq!w{j|u
z)c=dOcZ{y>*}g@0>}1EblO1ctwy|T|wr$(yj%^z|wr$(FIp_TEZRdAiYp=EU<+b`)
zYgW}9Rimn}S)=#C^KOS=u<T%?{Yt!;MPqrjiu>vnQtjG_W>*r;KPd!w#fkec1h(wR
zbbMGZ!@q!Ae?6IWf>~N$-#dF+cf66tcjDb#FTQ>`?5^JOx|_FpJ0(-H_H^14<kYX!
z*|5s$TR2dm+cs~_ti5v6-rQ9C{9J3Zwvl#;lUtxE`uOuGYlYsxLR>vDaML-A8S~DO
zu=xl=_kN*e<q~n_g8+KZuu~g!e6rYf^|ixOmUzslPgV?99Cg3d^4@59Ak8<QOu`fq
zhvSfq9WnBIi-4*X6dA)+n~<enk*<3bR-LY|VhSdex%4M>s#UeSF@`7S10RBlhvk<y
z>g{-H?Qvyu?Pq<nUG5Af;Y48LQ$*D9!*YA-wz7Xp7l$v5H>@Kq%di2Aj=;srRqd50
z2O8{^eMUoFr()f!1X){bZ+;UfP(NO;RE~&e*XK`rs#igs6^Y|Nlnd7SeSz|d#)PLY
zq|3>>q0QZ@u-F9}q;?mLpPS@l0t{>c0a|dZmYZP+DHBFiretunw?nZ65F}bML)W=}
z04G-5%N^wH-o*0nFG^C`e+_ZFFjx(1s`Sw*k;5`OeRnp8V43DPGF4=7{iUC!Tv>~V
z8Jl9^fh+B>gW8E@GlQm<OX%I?Y&Jd~rKHiaD*Yw&2Hw`l+hfsG`y6KlXnC(>w9*i*
zLoeO2gGW}7(|qFyX+t^jqI#LK`T1{23sFWfYCf9D*l8A4RA1+?P!<|`m@3!|SRnMq
zyGSOq=wVLdNxn0F1>tp8Zi~P>4#X02ST77Wfbq@>F6X5nDzdllTsI|QOuu;<;~5+>
z5jx@5JGe2xtes?GPluS3Yk+_+#mVWvX54B-KB$EtB7+NE@P>3HWcLgJWjVU6V3x&c
zYD6W3L>oLe3^N$>rCxe?7!VII5c(yj|0L`k&I;$5gIJrMS&M`cz_GkYo7i3~hh?W7
znl-lka${jU!;+U*j_E<H|5@D1mykbhy4i-ET@S8z6`UkSo7_uhi835n>qp7m)k-A@
zvl?th73<25%<}=9ru>efBn6y56(AflMVch1fBzR<Eai3kVJXMIlOLph5=sWM`LX1J
zj?f`BJBi610WVsPlN<K<G^knU3v*>KD-9_fDZCG0$?J@IC%Vii-4H9F2CO0KG4f|p
zcj~6s)ez7~H*gT0*^(U$W%<r-1HKX4z^YQXGk&#Lka{x2)dn}@FDkN4QGcGqfZNjN
zMW3Csn58vG^fqQzvEli{z6jiUV|csFEWmMvLXv4}TWo?f6<m#w)R|X-+VXl}!f(Rb
z6;ka1Tq;s`vaDAORSI0u^8_H16T2G*wwE`-9>Vf=TdT{!bW<C2pIT|yR?WYr1Oa1W
zvutZQ%Lp@V(dQWn)jTlt+agwGvZs&i#}sX9AQQymjsk6?0n6`+ZB_^b1h|3A#&TQ2
z&Pa=8th*d7SPvt@t`}p{^fcV4jiLS42t@tFVEn}^QKtyPU@5fIrv6K(t!+zZn29uz
zwSnp-ILlP;A7Lerx_h61V}`6-V%bSyk$Ex-5Hfvza+WSNJEVeO=b;(y_A-_@z9CRv
z5EEM6VrCahtsCTcMhqBEdSKbVxPJZZG5Mt=AyC`y{o=}blI?gtuGG$1t&mXlg|w>7
z5KF0{6oe(AVKi8}vw8&DUY@_gcU=q)vK>f>|I+J^;lRQ(^gpt66J%3vYk9sI%yihb
zp=?^+iMpz=q8QAL>BLU_@>h=TP384~WN`^nee3s_wlP%w6Bo0ahv*Gxi@=JhPpipw
zHrUx6*3%TUOt#|Bl8T!4+hZ6AFdItq6_ymSdXVMq$=h-)rNj3pGX-WMaGqu<o0_(1
zTnKah3h-g%O2b0ny@8y(+>jJ41^4_!O}HQ-XB3}+zo&=o-|-^P%_YlMGoAqSz#!_Y
z`6pR46$Fk3TM7gl>?{*hd^*F4@Z4efPu#`EmVd7pkm(5_lvZ^%DW??uy`?V!dpGX;
z39g&xb09;4DZR=1yEunfgX)Vw@*$_5p`0e7TpbJ<yig&QTG-2}Mov4|a>p2k>V%l{
zfhJ@qQ+df5ms8JsM(UyJxBTe=sX|5&^k<GU!Vh!2cMsXV%$ckjmkWE6<i*pv7@o&_
zOA;&6rX-r!-%15A39^w_=0QeVmBMNPusofjdYjL36`y(_$Yd;e`7*)s2y_Fb);vog
zViEJt{i?$guIk0N(HI|)A`iC1$@tyF3w`z&tsGVQfd8!p5V%_wgg|V!rcQ#YlApZ{
zcy$W&mad{5KdvB0%d_#tMJknsKIKMfHF4Hki*se12FKv>ASAN1sJTzkNvn_m%7_NJ
zd0($>aW+L}Wn%44M6zh?Lf3Y);HeV&4DXyV<62v<wVm<YAF3FR8$XC;P>b{kiJo3W
zA*#*?tIzg^`kcjiZ*WF1PbC>CnKPZh)Saj?gB2{%8+$I=%3rVL#XB_%(owb&vg4k%
znAe1TP~J1W7dO-@@7pKO5e?PxAWA*s3$=NI0#YQCgMP6I2~hr_mj!aAICa&v!kO*k
z;`8R?W3=8YmN;Hs<GvtKxHy97b4+glBB5t(CVU5suX;@Ydx8<jXTyd>fu*#+PxyIn
zMvH^TGXZNfz+9zv<U+)u6{f=Liv9tNGp$*m>nUsV{QMT1J1xr1hUGy90eOM*1A<%w
zqXGKb`lKPaObpD!W=Bh`p2kpawhuB(0NBj97ZgtFgL1#c#_-2G2Xym1G$|Bz@GwKY
zZ(QzOuKVh%J9xDQRaXRWI;NW4Yu}Mx5_-U2^19TMQQtfxU)lhO?bE^X(n4lNns)ep
z2xouwBI9oLi0fAAo^!NmM3x|ywkRnvN@(Vt>lGA740<d<fr`@IXv-95d3)7axpC%n
zAU5|StVR5KvWE~MnQyH#G1gA(%^G)-nxHN92sS9!eRMwUoZw>%mSc8Tlr|35?fVyJ
z%CD#~@rEmnS{$rqAlC~gB&=I~!^6WoX7&WvISIbOhW(({i6-)$JIXYtKaDNz5Iqfm
zocJl@`&0n>9>_zD<!@%#R6X(38goVso-DG2P=`YHp=!C~x#%+&<69esVrJn{rFq!u
zy|W{yf^xhK5q-<<pZi9oKljg8KE0N8r7olu3(k0e*t}a#1QVnp#?}qrEiPE@@mVl}
z*h$$s;x*_Dj!e@Nv(sdNZRxeunZZ(?a03H$bOK%O`>*Y|a+hcYgGnGGa?<^FmuW(~
z;``ZuMVe^L2v{5X%@lYL2T3LPQ!daYW9sid6Hbqy5LCttTx=Qq&115Kzmx%1Z>hGL
zUp@U<H?cs(Mh&B;DGMhJtwqKe)bU%vN_P*Kmn~h6!6R*88GVZT4lsH3A&jmvADZb`
zBO_(mQL~>$j@Tc-R;ei-yno2k)J&X|nww7oUfuCZ#Ufh0y`2X7E<q+x-7n_-^s7My
zULOWCZ9D}zw&-Cfu@|>`t75pq_TZTLU5|^y=*J{Zl#sLZS)bG6WjN1{ALI$c&*(8C
zlwy>V%Q8a36itP)e$8$4z{d0a13~1Hy^(y(rPhLB!g}Wg#OXe`M)ng5o0{S8IAE;V
zDL!0gq^YUXsON-`k!_T3*<oyLRc7q;31qR{#S#K`u;`t=o85XQ+pwkm---F7%OJ9y
zHIP;AZUE2AU(QylU~AdPHHI_4#3WN&ndsnnX*VFJyyxhB^i^<HpP3%<SA&^?&{W@d
zC68rj_;Jxa)*32Xoq;5mGIhiE$>{-d0r)C$7=_8S-d|6yF9A6_n+88JVfR}y)%|vu
z%1AR7@a4~x4W81A#3)#X5+ZT}yoV@}dT&~iJwct&a2Z(V;m~D@w#UGNBUW>K%VcAF
z=tqaf4m?uzXklm`vud6fO7c?Nkr+%Sb=kuz5o5Z2kBT1)D1ReKyDJlQEh0|Qxx@aY
ze6ywJ`5of&7J(MhT)|hj3R2zm3f)(UB1WuNC6AGN7Q3SQK0rZ|3GiJGSQ8UN+rBx<
z4o+c49%s8MAT#4$_mCdu)po~OjklmMn=c5{<lyo7Vqtc39^MX(hCGpJtd04II&?&m
zHNMYaexr;$I}liBqSsx9bOj4E*jx`gM#Bij3kdE52NNr^?p-HOqxH(=WVKcm6!Ao#
zmH114RJ#H@Q)Ikq<#3P92pKJ<m+g9uA?5YjljR>9*aCB1^hcw+49h$MUiE%Lk)Bd&
zAc*=e32nXYjXh<&4Bmhr^630#ku4lE0gW~Otr-1e7AIWtx<Qha6!nFeWH=W6>kfIE
z-7Id1_cs?icMt_2c5_@$*Hga(jV0ZtJL;fV8pS;*RWR{tav4&R<As%ML<z8^2QfJO
z)$<O8IPqus2LhXh<+CwnBxlqxH65J*Q(ATu=gF{QW*86h;dU7mcWsJ*Z`)Z&K;0>p
zas`hs(DxmzgTs(ROj{(fhRUvaJ$7=(E!A*2B=w@Tu(>z<T-DQ{KBKrwflR^E#0$n{
z{@`W;<$7U+nZS~-b?@b}Wbts(IK>3UQVv4^_OqqY^(h08>;z`WH;r=F@}2VAi>~ub
zbOtJBc9<(CL{?WQEV*3a9MEWlV$~3nFCvAsCBW}DUUyzhig(P{zv<=IB*hOkLut#Z
ziHXVib3Nj&hdX&<>}_!DEGW7Z<2COIY;k|=hV~h<Y7aw-=%FyfOpFowM`?KX1dPej
zid`MC*pjfS*O-;qjANepg|?^obFC1*9L&G6)ED3yQn=i}xQDqzipIT%N~UCmMp<b4
z#z}ZEa^(K@s}IfR+T&eHAlce~R##F9GAt}2A@K=2O}tHlTqdx*VQ9!jEPs^O?ut>h
zA;9&&PVx5gZa$+8Jz7Iv=)z!<jw2xjLKiDr6XrP=(8F2(zWn`eu!$n|o$Du}I~S{6
zc=YkN-0Y#6K%WfhPiLCHiLt5$T+|>r9WMwKoC(tIrQqGPAK<^&e~3CV4w-zZ2ix0E
z`ZSm>B>u>7-M%5m7kJ;qH|^I%FxS;PS%9*xv$W2SGj+ZM_9=cxhJ0zt7Y(nCaxB~P
zvpUj)Vk)j)B<5F4O9-FHm$3ip*0oW9x}8Ig8$4HOBO+9(w(-~Z3+mSX<GTIX2cjVo
zStwFyS&c63h5I(k!F^95ZpDfIr#$r_OiDuHo>FO=$8&4d#x0bM_;Frs{tFzK#cJ7a
zveUhHa}(EqOeUZNm2{{<1I3gM#LG*flBBKeL5#kMThZz_7>X2wg~W+@!I+p!Y-!a+
zPZf-cnW?ZDXKn0wA_mWa9RN+UFMR_&_xp@$cKR&-Ch}^+aIbo)%>*}9wc&<z3<NwI
z9f_-|LX|nKbx+9vf)6|lC_jpoQX^Mvpamb#HQG@Gb3V~GQ@22LeD2ZU<A7eJbpR!e
z%Ff5DC3GxMleUFfoj{l(ip7S``p|o0SeWJ#^|c;0%#oH>xk~o5O$e=3YN*1QmpdQ2
zEB0U+%~wJ5)#RR;Qj{<A@r3gI+>XDxzD@)SMJoI92JANlh1jc*cGs09>UO^c_S7pr
zobc@>GLEnL*Q+h)Q<t#74y4vBt;Oexja$GGdd_Xx-rp~(OU=!aOv_uM&-=l*^LUJx
z`2@s<7`l6H8d>^WqKd&5AGOTVAAq4MNRLH_6Z(kY-0v?26Gyu+=DB|)m4QvW)C@15
zB$yR!qW#t2HzN-lO#N7v4NK<vt2Q5gb{Frk6iMk;d2#OKT&)nGd=WsHb7kxy?zhb5
z@Fcor>mej8LX>^y^Bh{m=1wkT41EtDkiu!zzR@E4%&^cHf?Z$A1Xj7|4{FHBY853E
z6l}Ksf`bI&0eg%jVpc&{z$HH7X_`y>x93rMEmnT?exJk^>rWzvfCevl%+Gu12jBEE
z%W+efgd|%mQ3Iy!b4&m8S*jLc*nG@nTyR({cu_`nD^3NbBKzB5V@*I^4w;q&<Ge_X
zGeQY-hQe*4{rweMsic$5Tpr!4i(`<>63}t3PR#l|Y$<lQt?m&X^DO^bY23+}bS8t-
zs%x<gYY>k#I|>Qpndy;t{2}cN9FC4+F+Z8$YLP5&3!s*^_^GnkO}yTVo(Onjd&7W2
z1pZ1~Cx&ePH>w^26h1AiR{8@rf%aFdVkMtxx1Q~ODs0BhPmbvne#!dKOFci^C*xH&
z3w8+@>XGKf-I1yd3i;HT-BO?+)Z*h!rp&U!0D9|l48Y8F_aBZdP%St?+m)FiZQ!q}
zPQ+t9j{@;ZWq8i^Xvin00H(PLwwcJ0V2i={Z$>n{OiiP%x|u{5=n!p-1E`lq=8V(w
zkGk8&Yiu@b5P`^=hb5~KN$I7cv`d~E-t$P)ey>*8j}SZWQuCiKtv?a91oKE0)z#hM
zGyp?MnlK8Nm|m~o+XD0ON~%wS2oieDc<f+T)?V~>vz~$C)HPniJestM`D&X@wn?T8
zN;}J4xPUCrs4i}aU@(LGsvhfWXFDSzT=M5>=k<l8^j{8?ivE)F5_3&7*v<JBMPX9R
zy4Lx<#UCXM`a5SB7^egG#|rnS$Ur6tKCtJz0rpjd8kb30?a%#DSHV$r*kO!`ETkx`
zt$L>9HZ5RJz4LiqGn%m>o80bwf0N!AzLMwT(-><7R9zSmC#^~5xp(+2wOZ)Z<W!R!
z4k=(W-3fa};pfol_`HU`l`R}#X-J&mU=uU9L~8>g+p`4_9*lzMAMXUwqh-ZhWnJ4C
zy*!0c_nn*oT^?+=lr8jff83hRm?G6EE;2h)A2(66fY*<=uO#>(!C0vjNpiyboL^H$
zTCB>Lr3g1uBMK+<>4iYfkGqwWDuwtZxE^y_KsnQ$SQuzJ4VLsUmUt5le00Rpu^p7&
zue(F|-cDZ8dD_tqUEg2OcW{<L;2%Y7F_<8}Y|s0-ZigUof3&1#?Cz*+2BuY0pSxZp
zOt}{rnCL8X0_JYy!q7K810ZU--GiTk!Uuw{w}z-wADMP+3zSNgi(&{q4{a7AgB)7<
zUJ`c#@buuF&#(ugES~u~Ui@J8)oD2-V87$48dM+z^bWuSs<P=F9dfzzFwwX>A^my@
zYYv9ARk!@b4Mh7kH(;b&m5+IOfve(0G+Qz!2REDSHo0R3y1zA-cIVvyLm}CN*wnIn
zWr!r*u;)J!ErX^O&wC82eZYIArNyCI6v$g=bL%>Oj+T{`j*0b6ZiF!0hc)ZUd;1QE
zAWvoiz`tf>w+}D_UNmWz?=RMtQ5_I*@lVIqrRe|Iuz5C^VR~Aj$qlaf=J>3DW&p`5
zRxXR}p8nW<n+B3eiijeIVQ>oLg~iM+=Ar``RmqeWIBKkfpwl19hs4=zgV;ms3QFtz
z$Tg)Mc(d^qOVavPVKF|66BQ6JStf?>osE^5&pb9o0EwMk&DVW`5Vt&K@Kv1`8*QHi
z?SXrvsgXI(S|yO2<Nucvq2MZWkluoQ>KZ^p-*Da65%zI?U%r+5*2h4SKYC0-*=*dB
z4K^k7jas#XmU@O~egX1uS_AV8hI`XPa^)@qZ}J%;7~EYkHZ?wpap3SnBE(6%-t5TT
zx!<|kVr+V3sd<k4-hpuzP*p$o%pEoEc}R??68E+H7s?l>V_RpN9SI2!M_vjE6GTs}
zoS1?WTH+Y-jJ@?RvBaO%Vz<rDKtiGe%lu)nM#+$qCYAX6=}@JAi%pdWb!VWOCvB!O
zG@?h_tbwB!=b=KH`IVVd1M8K~y9jr~AG#Ww!FFl0fFwbkG(aryk<Rsy)iAwt+z*Y{
zD_p<0T=gF5*zm`gZ$>6c+FIB6`h%90f5~;cZH6vVWsKJ(!AhPck(i^OiiKVXR60Pu
zi?@t`6(<bz^ti#;ULKH_db11*WS<J;JhxylpHbp?I1?gaUl8OuT!d+PY)?CZ3UpRD
zIl<HlHYR?w-Pq1U4VH199G4tCp9cu~Ae?h}!2uz^=3h%er{w5%dwNkTEbZ!&9F0G#
z%=~r(vkPOejDgdx7Oo;V5W<l@x0Kt6jkGu+1T;NCDs7PS_n(0Rs~0IvH}m5Y55o>8
zO!+`I6UGoMmqm%8LwYg}bDVJQWhjJK%gNOf%iEIt2DDr7hQk8i>=wd5$9qFM0m~C{
z)%&jlpP>#h@P7Ujv)D;eAv5@B#?K8B@?*<+Tlx^CPxyvoIa7U!)(JoN4S&EOwFj)H
z+Y(Tr>fu_|7%hR5%;Fr}X0uSxA8$O)*Erh|5bA}6$|vY)*ufu#N5;CFBhSKqFnHvn
z<al>u(nUK83yvY4v(F)S-I9<vxZto&LkxyNe@oWmnIK#R*H*KUUevC`?0e!((y{+l
zIQQDg%?a<_D=y%<1l$&0mKHE;_nM$fb%6dl2jp0VN6+v-F=k3k>gmMey}qsm7$O$`
z#R*~3Sn8kag{>8RJT6Sm>I8+V#p75_+xrnV@N1Z1x!IDG%XE^jDFhhiQ)=B%nyTf=
zImAerTSO)&tgdCFwBcURX@eAp8xL&mCrHKS2)88$l;9JgtC2Y9rsn}f=DmG@uo<u+
z?Zyn-KtK{y2UCtH1_T;vj%+0>K|vWINNfmjdW6|L^U26)Fp*ruz}!qI?kvp1(W64O
zX~@_e`n2IP5DflB2%XLgN=h|sK9nH1B~Ijw)P)53<V(k0hMWOnIK7Er=Bfvh3hLyu
zuC~qlb1&&U>-cn@jjZQWzd#G`JuaEW<KBXn+Oy?t!id;9TtO_5>w1p*Of~C{gfhC)
z98~+#G{HGXcA#5z2axnEI5ZyCOrcVq@F@F4{x;JHdr8k#AU7$g9}mOd$7kK0AHUuJ
zY4CSJ&pQP)6iWz0jwY1166rsW3x;-xA#@k|p*(i&5dpR&j3-L8Ei%V2cL~73#qpLx
zgT)5-KXoP_xXuA~n7yk?6%jPOHMs=m05=prZFfK|6ZC7eR<4d+nnmQDPNh(SKUvcK
zmHchJvV35`jN0+nvFc+jVhI9C-qOAr{bcyS>xPFMB3X#^u^`5ULJWz-Ae<$4?gUqi
zV98$Is6ldTCR!xs5i~Wr25bfg^26wxyy-rq8|jmj-iLfBNd+${XZ5akl>#%d{hi(N
zoZ+8tNvL>a)`F_MY#9r^=hJ}J=fee;n&d!la#;svaA92}>FURG4lonzN9i%L?1M-=
z=Q-~(5H(Lk#ODOW*nh>$4C$cm%IF(HyU5b1&KXOq+)MAAoWMhvn6zAX1CdrfT5H$n
zgTjL-O18MQOrtHPK0Ol=^x$_TLb9)N_K%N86ytX}))Avy+wcRGKy|!x5X3Gmr_BY=
z^<=KIcW$-TOjKrVUz$1vgtF$-6r@z=RMG0#Pg%pW*4Wt>+y#<uY3Z4nL-r~zM8#7P
z3ktTlx}Tjs+!zl4YiP98BQzNceEr%!xRCZQla5{uKJ;-f34^ra$My2x)-!<4$%W@4
zF@-?Gf!7gb0<75?#HQ<ygvQkrSy>u5p+;(b{2YP%JYB#sEH)vCX>tH5o)~W7fmKsf
z0pV8kD5dx~<#q*SL+ce;g=94@rl890v`6R7I$fTjL!lK}2+e<`Kas#9HQ2j(PW-fe
zgbobq03Ck*IGEQAgwF^Q|M1T>HxoiD2r@J@$*)oHW2ny=(Di|lXePyA>foJ-EKHnK
z>rc$Eby8#bzHCv8DJF4F`~w+MAv+8|A{jbVb~mc@5!Lrh2%?)$A*eLZK0n>WMNa~&
z1>7m0m>C#30lSa#yI|^>J(Y(ZHayt;Sc6_YoV-X%8`ei~jS3n4k6Tt~R2Ymvz`EYy
zHXQn_sId&s@itZ7-Ug8430vX}Mc~H$B!-8}xd5dqohr-G-oOb4(!JySITK>N$ise1
zaG$1f=-!M;TxNVwk9WfLNR9Qr=pR=;ox#QmOHIknaOg_zzJPU#T(Z1lW6_dsVlk=e
zffiBY{sx{5vrPZ(;)vP2BIar=yoMK6a{jX!1{C;U!@$#h$n!<8A?e1Jbd4bSx#HVc
z_Gn_01l_|Ku<!gE)|QJz4T=yA90L|JJ?z_u=>9)_Oh({v>FrJFO3(i2>R-XRNCfHb
zNjx-z{1p-)G)hd57n#(Mz^t#<Bx_0|0ellln~9G!sQD>tyrM|W#70MijFo9EJw0~P
zf|ztGs|CP{Dscq@D4>{(9Fau6<iw_nL^1ZI)j@-TZP))I?wTRc6!q_DaS&cSW2(M_
z_9xBC=9#);Ed7Z@L^|}*>L=jm8|K@mKOJ8Qq`uBS=_lT6D~O&Ef2T;Qv$%Urp`VWz
z3JXkSYQSGI&?&kuOyB%l^;>#PNec=aoi{KTtbm6Ts$_TWhs1Nc<B7~d2W48|5A(SB
z#7C#X#4)&~eEpw8wo-)Il>?pAZ0tpz?yQ<q-ixKhon<b=x`1@7+&X@Le&TlGH~U`-
zvv<qUF!RaU*CqJDy6t9G5*NRXkcq(Ig}ED5P2%Ttg+@!#X5srq9t3s{0GiarD=NQn
z{X?XCTQ)ZVF0OR?2{4H3E&%YkGtF*}0C@`9-1I|GTuv8ZjH2y635EuHzmy;(h%zsU
zlKqaSSpIWSwAW}Kl)YdVvVNQh5u~V>#1TrYG27N$@cxp?{?gvgX;oW9(HN1r^<iw}
zV3(jse{dgt^CDTMmQxW&Y{qowZkpxhs~`3n$5RY$0I3t9i+$<|d1CXI)9no}0(Vfg
z-Q`}v7UlfGR7@s%TRxY0XheG%`8fmhP(6^g9cJ7=XB2SoJLxFl2-26Q^|S=16QruH
zF8xAPgieaYo&tf7n604-{apqnz5LpIiUAaf@<`nJ6QK9-cl`Pjh?%mJ0?(nC$9opi
zWV4_f9&`O$GU6r{V3Fnyd9`=JF|@m}J)DXj1E%bGi4}hg;g~aO1YB$I8*N5QlQ?rl
zO&H11&0MT1_{jcxq?b#cOyv(&fW;91>cK%Th}nf@csSw467+Z_yfFlB2<DR$;{|nH
zB{&g)_I!m8i0Q<fLXcE49@^0qK(j1mr+2#HYnCA_+|>9vYw}aT>sKzTQ_r>^x9%tq
zm&Y#sF5OX{$CfyTMA*~HJ}jscE~rhXeP*O_V$OFOt94AR(G7@66mTd+A1rJv%oVcK
zqj<z85UAJt%i!b=&~)L#xvq7@L)P_fz~SL4Sz$2=arFxL968)fWu8My)hwC^wDDYY
zn?DAG3v@6~V{;>w;4=VYM?n+5F9cRZ)CC!b*Av5xqz5AEo9=K7Lrm8ZnZR%qW`%BC
znE}duNlgnx7^wg@uZX=?D+w8SlDobv^&NLg2YWJ?hNSH!Mxg{4Yhx_;Ww^r=MBpXY
z438FpE)ZzF?zn}yjGP=`pryrYoUfQ?OVxe#ce7ne)}xa)hR%RxuzZG0o$Z-kttT!x
z6D#$aCTfwShb^;G#$@UfltsvlVfXW&<xu6kTQ?ygVB@q1ypQCoX{JPCq-HRig)9`t
z?<s@9ypF)|r`?2?!${3njD33O6k%f-L`U@dvH`s$<ACsrSiJxZ4EypBE;3L0vAhO@
z1X&vjxenx4?D?IQF>uCUxUY_es0Ww!t-i~gVqCyb30g!p<A(!YwEhde2;W9%7Il}P
zR541m&pm=U&Zp+DeCNu(FlM;E&Ut_j1H|3d^G`p2=ySZ}x)zG}GdcFI=c5X2q;n6|
z51%TDg~(%rH$8j~V8YAAub0<1$H4_;>RRfBA2zh<9y#vU-76K!%0r=QIz7pUMCpBo
z``AxCYY{_Xffrwgj;;jRZCzd2WHGN_ti0i3LcBi(KiJ$L|I9Yd6Rp!lrRQE2-Q40@
zIew8rhHX5T|IB@5e-&cR<aiX09nO)xE<=3?HQ6owmHu##1pxK-U){$;H&TSV_>-}H
zC3*7TrDiD5!qN-=*~RR=WXhU&K-jE|kmr~EWJT9@HpwH0u5hND9;8@8Ct!5xuh;+D
zyW8}9gD}4rj&RpQOn@|l@cuIbf#M$#!30FM)rc+FMCy)4olB(=4`Gm@|0rD7kfB2N
zAiM*+AmRV0_?{07P$8mMZDOiL^8c&J>;Ko@euf=;M3t)Mg!=xY^nk?pejDss@E1?+
zKPr5`gYiZ9zBv&r`TAZ&!2c`=T)^KU=RF9u&%XbxAOe2E0*VXmS_%#S|M*h(S586t
zUM>;7;0s9yrGKhI)L~Rt0lf<|r@WgL%H_Ew<UvCDF5`{rNjNM~jH+sC{O)J6Y*mUZ
zY<X?sKpqg-?<SYxFpcVPVM_SjiW^bxUn3#O#qJc1;5E!_FT~9s!xl*u%_4+;1yStP
z3;9iTNHoxx6PDk=>DR(WHKDU7f!O`$2C|u)q9TNWAoqOJkIBcy?zeaKC@d_oyriYP
z4k+8kuc<w9$HE65ZZXm?Hxx(Q?!5@Ch2M=eHMIe~U$QJN6>pO|W9g@I>I(2d>L8W#
zkTTKEa+Ihwwe`f%1C?#?#mbBHMC)*k2eGNkeG?<wB1UzG94P22e8TGlGa>aQ2*2Qb
zy6?h=utrFXYnWN)7xEI>3dF1Y8jI+Mx@p1>!5d5;^o$mHnwiIpm6VwKMi=B=0xDX;
zqa*~EHPSwsfN!u7f&QoX@%r{$<>>n4BHhe>0RvglB>OxOvf=}x10Bd~&+BF55;ZEz
z#mZ;a74nqVK3ygbX#hHMKI2!wGo_T>;W=#Qe3khR<Zw`TYmyF5nDJGIz9g^_$zRq~
zt81UiD*D5(8>JOVTaNN?!)tG)t(8+3Dox(AHQsq71LuQz4(NHUMFHnuG+AbyM=>2u
zDB1%mTNV7oHnWT9+iP?_eWQxC6n{u}DyX3|dFwK!beun~);0r=mLB+{8^=@j)RoC-
zi}veu1h%X%G&Qzhko0SL51|bggvG@Ec5Clg0dCjUtVIul#4<TDoHQU-t-CM0H$5%R
zteO`L24{^Z6dR(A*P5HVn;R>w-fZ8%e73J(hQFiYb65#zY2i(F6Xa68;J;(NaiX=6
zvb6%7##0E()xo7D*}GKlF}l_gY$Y$yqpNyVs9AUWf?xnHJn#p9mrQo897^D}=J(EO
zXp5a-1H+-2CiUmC_bQ$2rPm`PYDDa1@%pO_nw!evmJAM#a`e`M^qp%lC3)Y0kLvya
zzE+nK<#sL%)>lKHss4}eUcW(x#vgADA5MJ;my;pt1|lN-<&zZ^toVB5-bX{rC%qwf
z9Mtkll7&2+$_%s^ZX#kN{1#x$qW3Zt#aV8L3Rht9&(4CuCeHnJL9%EaEg&sP93I6f
zD6bgEqS2h2D;{vo)0syxF_eqx5bNNIZ2udrrlv;lH$OCM#hQ@+8cIM3kH2m`mF2n{
zBo#NcR2maHkPI4yRM>Fx#hRSDJT#LF{zMuJnwdOjtfpD$ZKSHiUEy>y!cgBZIl;lQ
z^#F-cKUEEcaSW{B{$uAc=LeLl>Iwg%s$Uj1D{;bX&$Ybbj<J5I*BL7@=^=dwF}S|H
zijC!Z19DQ7{XNzlq{!p-XSHqf`84VzATsbB4U|PYz%PS0Gc$5#Uey<lS17Nv3r9_b
zwx6d3==UHky~|l3WC}+`OvS+UB>1z_5>%-Y5^OdrDmeHn4mK)EI@$o<TJW9;qnwsz
z43sR7f8U+_NTsQ-tQdl+!s8wvCPNlC5*9ZMO3|RfMe&EJf&~5J&I|oGZw#>tlbp_4
zD3D@K-?c3QpM03sn#+!QC4vOgIU@bFo@4DVecoAJ(kVUxXOKqNAq*ZDAdcK0YtDGW
zQ3Jq=N0UO<`>f0GnSPs_tOH`eZdOn{U;74tbb#IjA3~v$y53`pb?AC4HWIN{VbNiJ
zZ5OmLZMG=|t*__+U;=Lo8~jV==a<d2O8ifkvvrvM;E*$tQR6?%%a|*B(~r222<nht
zWx@X5l()3mL3$%3ruB4r5(_ZSOD>p20#G?MRTv!1{?AxF6_mIZtat+A;<%Ci5W1f~
zW#8MZIlrlxfv}m;1cF{9mhZs$S$4r*$GWi4EYnfaYMZ2%8=e?&;AE+f*Tkh^oc;k&
zW~Jp60@M2?b`0j^5;XnvWPSLr-+U-GpKs4-^55HuL~D<(DU}%*#;*=4kVW{;#~84I
z?NX3x9+K;qz~Cged}WUJ3CFxE`@heIuK-A~EhDq<+{gc6mRQAOD=N-yy_0$0=n&*4
z5t5nn?$R9)(mVoW_9f^`Q|KP?yL{0%UE#uEAnYQ$wZ_c`5~_ds@h>tAu(}gZsDfW1
z&LLisl{Wldet2Mfo^nT2?5DA%%!oe^2i5$;@*Qu<EhKEzGv9&fK12q{@r0X9b{-j}
z6V`skTz>LIaR_!2aFS-3jP02QxP%yavC14`>zTUXtE!$6Q#WEFa6kN1Abt|uJqxK1
z+5L&3AG8m=eUOsFFTKm4IXN*@`|Ns;IgK9X;nLsNjZee@(X%=EtwZ2Yn7JC*sbaxE
zA2_gBb!ThK;y2HOLRY(a$Js<s--VgR4tBgkj6r5xb!~?ZIq;Q#jLEN_5WLLH4m+Np
zi^mMQ`mR{Y;@nqMU&7$kJ6EHyNL=xSLGdSk+VmLQXWSfzLmAQs0R{u``a?KOY)qkl
zLJ5l8f_tE_S@*GFw1%iwWqLP~u#+=aTki**25L0U-`?D=EZt+&Xg@qgoL0@idk6HN
zjK;Khr#4a28Zmq#2}s`;%?uVbOpmycf?r)5x#TQ<+6I)CAi)Id1(+stfAT76h-$_C
zyO~k5$hv(g^}v@q4p0;nqXoqeM~fbz(@<E*WbvdD@I5e~dh)>-N{|^|MoDe0ByvHQ
zp?{l=*4#A>jv}*PH&qHm^Seyo?F_jo1*hbs`wM<6PcE|qD~o*LqG~1DK@I0u^KB?Z
zRrFCUubdSW&LS7|9{^l=&=tOQ&D#R?zlx*iVex-l1euO#^-uRMD6r)Qos=ND@xY7)
zt20Kz)iBxg#0|Lr@^+i~GPvUUQBF1c`LTak{Ip3x*dSNv5=%tIDpwN$S9AWS{qnN;
z?Q4;ys*@Vcl0Yl`e$zvYl(6t=)5C_X`D4q07&^3yV9fgx6K<6IDU)()g8hi?s)I@J
zwCb+{#OHF@@k3MD?*57XE0}rNU3yPWPD|GF#+umzhq?&;?9*j2i!Dj^D;Ip>L2R~K
zfBn~+$vfv*X~V6_X=)zm^UL;gBXw9rb?8Yv1sbmP4#J&Cz21rPH-?tj-Pzl%n-epa
zfg>gpduw(nlBM(cEu=GaQKFrXBnMRr>TLz;$dQH7$gJP-vPg5&$4F&kz@mu?Y>cQB
zz3#oW!F8XpWv`eDf$DZ(s)>8!WY;&okW^fZW_$k=UydBOBl9?R$Hs!1X3N=dVpqp9
zs^GcMy*=WqY{<82JTfJMm;a+$?|C^+lheLNm8@C!XfdOP$2)aks#Q|+2Uo|t-IHU#
z_Pb*H1lD>d2qwT2QjfchQ(Rdc+fJhqO@43KiVeZAKYuC?8RPgMw6Q$rxr}4y%1G42
zfZMsr`}q}u!Tr`%Q|say1<1f)fqU8dm~orX4)hUkWVu!kO$7<xMi%kumRRswng65p
zTRh*4nvUq*74$Vn4*g?Tx`^8gyFI=ggEt?+J#1pyNLuU|0R8|iKy6f)0w1*7U5;qs
z;9-yux#toW?;hN7YU~b)?-3DG1^W=O)!NutsTv_$%h{e}^7TToXeFE}G1|*Af*>mk
zaZ}mU+dGNp!HJ0!TZb{=Ft9X-EUTwyRg_S-`V(apnOw!CL7~nX0+_m?iRjr12NPfn
z9MxJPv~SF)emW*{<{R=1@PKgFotyEMv5Be9K=>GvGUUUzS@-g?o%W{owYtE8ym9Fl
zUJcFik`310n+Ts$M&vpW;TkMio9QOEj&I7T$G+U9wjo|L>qqeLc_;Xg0pf6d{maE+
zgBP>T2E&WEXLW%q)#5};8-$3%^;<%apQwmNol67S4*rpZhb%t2nw$LIooPuXr}teH
ze`n{7Bt}24>EVj>q907BETv+=u+E<92KqQEfSysj-O=$5-8<WrJ#EIxG0Y+|j5aEY
z{#I|gC+^<Gp;tocjK@BvrS)0NE4>ipI4r`e8UJAg_~DYtAG<`BNG`pZ-}U7gjKNwT
z1Bg4s_DHL~ri^A+DA2LZ8#t%ltgQPtFcnaeuf%qDfG<^kwLbUSYRiZaAAapI7~*_C
zP(i4#UzK|H*Ak2+4a@bk|J!j1&30vqwxOFL1ltsbzl|@*Tl1)`Q+d9WWvb&G{bT2w
zthTEUDJlp0kaW5~|F1d-1d-f&7rpJ$u<2=m>uq`0wX|CFUPkA0K)!PK#FoyxZ=wi|
zVs(y)*)2n~P*KE>^K9RsBu;JC-EhJxOu{|u_-}><I08<1LMQIR47S_N1KV;+;a+k3
z5hKxjI*12Cyvm$kh7-DucU`dlAiJ8tmR^D#b7GB(XZM<=7SyfdPR|fa$IU8C;!6m_
zWI@7(M~tF^N{mYAlbd(d2E+Ta&j#@89NRR!^oS{NCi5~?zM3YmCN4KA5)+R$JkD|#
zlnK6%eiPlnLk!#W;jwQ)XZGs4D-lv%sTp1V=yqUM7ERTm@uRh>sEi#AHP3r&=sOm3
z<x5~rXhB`AFB&-;G*YxJbCCoG!soiH0S2~8=P(SNx5)B9#_<W2y;ho#NfT1s*?Uyb
zoJPf{-P@9!KIHBy0r%fvJlNzqi&GfYu4^!DR|jMe>(sv&Y?1i{#KG4ihFtE?HC}t>
z`Vzv=KDd@vq%uX-L<bFMd0ghpEJ`V7t<Fja;)?55LPOM5(~Gm1j1hCKS<2L%r}Zj2
zuQqJ8^TznA0~t1*gJ&=)@ygbHD{2FOhSEH8GKYWYAm2cl!FE4*<7K}Q$bOwrR#4lI
zR3%R4f_muTHkv?u3+2Jv(5XwAXW@~bpoA{9Iu0}d*w+*_a;CykLCp}Nm_q}6hO%W{
zAClMxP|L{s!5<SRL#P!^q;i!+Y@)Lz$Vn`jkv&?jtI@UhA~rl~U0g`WJkdSk@2048
z8Pn#)Es&Oy`K8W}`of4I#Hr1Pms%qNIG@7X<&jxu<L0x^Oue*WPa(dSEFWp^_a2)m
z+ap1S<8`-cYH7*!wdTNsiNcftYGZTd5dFtTn+K@2!s)cFGQMs$n13s5W(-Y(nwVvQ
zW!d9-<hl;d*noVY5l$cRv2SBkna5>@<g<U0mKMzgiu9v6oyt)^%K^==0#>iNd46u*
zxJUF?ih7tvHLQ4OqY`ns;;|9sPYn%hb;EeQj9z7B)$5;jCdFk<@iO(Qy<Y2Gh#s$#
z&T2dPl7YLF&bX8NY;?R4tVZNl!j3oX?ImHyc7wvVhVdOLON)Ewhe7oPQO`Thx^#>8
z^&)eVO(Lfm9D}b%)M)C+Rx~sXXKl83*Mr&>p)~q`FOL~_D6PhK%WPld2C>#jKS$Lq
zD_Qh|lEvfd+CoPaj1QW#lark_HMLC5O8M!!hGk@AmMtRZ#Xni#iL!;0)K@rk$tATh
z1}rU-EgU(x-D_<*T3hsgssze}ZD}X-)jHUVY|YTR|2~xt_OP_7Ff%U|F`3U-U6bzH
zC~QQ9*XLfqrs-Q(V7>{lv5x;O(Wb8YwupZ3b^f%zIweAu-X(e*)ol>i(&0KPfiKa?
zytou<(*)PGiPzBBn1>osnUyGgf<p09hgxgHNTF<PLE?rPCO2E#Te=h$)4nfma4Dl9
z^hZVn%+k_o(BIq5H_1_5L!IW=gnpTev$H^c3YQ~-(@8sr;Q`!>r3D>zj3fE%!V00X
z59dwFqHZFpp}NM}AQmkyOzY-oYrBCS`Gve$!D7=Q^Ad|#Ck0nf_5MQcS#FVZOh^Nq
zhkT0`xLG%2__V54n2d<MXQyT@<4M_{s=>ug`AZlRvzS@%3Zq26NlxquiM5pIKm}Q2
z!H{(WxaG0;A1>&es~Om)i=&}oB`sr~TeGYt#0oy;pi2MRP<wp9{0<L|F?)Gj?!Vdx
zm4)wJ(n>`qiFNlKg-BSvK`>UQmKg0A+85I;USD!tl3#abp_r8p?KwKr%9g^YLt2vd
zO^o(7E$FkOQ_Ae=xM5CF|GiEBXU%H{e(%+Ez{1>x{{|WV?(skV@JtBIEApMf17Q?j
zLj6PL-yTx>pCSG02iIq$x#7n;HXDCP6~EERe?OQ8SxWS30$eUq{?9i5e|zNmB3YX^
zFLJCn<jB(V8ChR}?)N{3C<pca4$7RKV=UQ4MARnP6ufx$!0ThSfbw6j@h{A!1k4-f
zOO%U?i|eECOGQX%ZDl3zy_Em|VT&!k?UI$+FKoy%Bc=blCrv&+)EV?2^1Ofk+erUv
z{Sz1Hx6l`-!0FR}O~}7Z=?C*K69N;QivFL?>3>Yz(@ktEOosN^E;N7jKj-a#Ch{8~
zY6Fki_k$<A97*r<QaDprOsi{XHR6;Jz5dBlL010naoHL{3K_E84pn)cu05z)R2Y82
zhdrI1j8iL?;)e@U=AhUwis^j1DndT8c6T@gQRVxT`p;ke!#M9D$N<Ipv8yG+oo&OA
zg7OSgNyM4>Bc1Z#ro_JzW2Uq9zw&`Kuq{51innz-l9doYB8XLx-y4zXkDV~2vD7#>
z4*#GnSf1w>D8^I<Ag}6l1$h4+uOh4Z_nP_4fV2!N&y@_UFOTAmx<CV|6c+kY$E3&t
zS)s<}M4STBrnQW$+)X?4(0zjGKCjS8v_-QwF*#b{?Y>(xWK@g(nBUOqLca02078Ca
z`(<)dN5^h^O#lIpL$a%cR@LhTd2QQK*Et*wu1x2i)mi%ziNzbAqqus2`<*uc#%0cC
z1hPYu&ITkqaYVY~p%)tda%+6^0|H=w50F;2hJ}O^5Gk!Pon}j{z2888p&|;I?j^pl
zpPyb)8u9q%02YIg&u~7GRg`cZTRiVyxTtk|(Xk8>cxvdo9b}QV+`|@x#CU!=(X%Gq
z+-T4tr!0xD?i>gmG|<}*5TL+$SiV4b*1ezvyh79&ym(}aF)&#x9j{2zy|{c|#xTf_
zF26qST6)BN+N$7Qb-!SZ9&m^tz`Yeq3tMRo_QkO6pfbi1=%72&6nVZ^tFR}}3=Y_R
z(71ILPGE4V-rYJ_jxEu<x#D}rd#G!W=J?zK4c_bP%Cb53Ijjuh*O`AP@5V=*u({G#
z!EZI`DvGjTr~J|%O@|vOMnmM%g~W?S*RvA`d;Ls6mbE-|Yz_P5_L|}xU0xXIdKg!2
zbcQg(ZMwfgj{Lh6)mXrlw4fo&peyFX4n9<6{5ak^zGPj}cRZH6$*+pJ2B<M(8DF$1
z$la9M^6sobkNy2(qWb!R<cu%bLs~=br(N~^rL7f{{zQYl$qScy$(pEqh;OTAGx=<a
z`>3y*iP#14_q2#=^$(7W2(@l^+&H@%A<@CS2Po2M!SPy}vt=)?@VOazltxZ^iutH0
zizU5#{Lq}am(1~5nY!mWIsj5uxTqM0xAkMg{9&E*gM0p-EHbhK)^LyabL*;3D(Z_R
zk>@xgk4AgKCULN02FheSX*nP*RWxSv<Jz{X!(>z#Ndch@)H@0Ek$gd6)^b31F`vn~
z-JaqSr+X29@M7(j_zSSCo;T;jc(uxBSy7!iC{{9;cW!QKaCgCZ)HmdCpwLcl{BYMz
z_)R$Dj7nfIDZQGWQ&60^;kI~Qx~q=WqS+Xs#*E~sOV|yS9oswF%-rZD_RR+JURKl4
zafUjjatIBL&Kn%Vb8(YDA{VKBj}RtV)P7k<KN_fUvmqIFxg?-v=#MuE=n7-1We~#W
z6nh>uz}PqSaz_!B!69TMN^fXD;y4Wt3lA+)IJwP1wigmR2V{?L^J|j~4##E6|GLfP
zoIfN-#>^11kc2r2vnxzo$Z<F*`cg~4oxG##5|P&$7p7~fdm^1E3fO><RUkuHswW)S
ziY^=oIOm<y+B~hGXp&&(+|X>PP@glsLAdnG31MPvb9UpeI6S$D^Q=1tQMTd{({`&L
zFfbercTwwPLc$ic$VaHs+X?^MK#i&oW&BwAoGQzTQ>+;7;=ZJ={pET#wK{Z>g=1UC
ztHVY0!c}WUNe#ar>UuGHlXO-4AO>Ar#j4-;bUXBRRr|q*mAn~N77?K1O`x2hU`E`@
z-f&L?pE*Wieq{1%Ek`Uh^eE6O;@S0qhlBIa^L3I6C6nv!mFlpCO$Qxt>zU=w)EjR5
zg#p@C6e)8B9j1rM-AYRBk(bX7gq9Nr{x^ob8Ej0hrc#WlzfxjcWNlI%9~UnZQPe=x
zx!{z6X<9Z-I^mR%TueyJ775t#1lNk9?6|)jZfJcP>ZA^F(RR;pZ`G2V9N|v<9r~E*
zN;e_-bY%R9BdOoBzc!6Jc+;mrU3;>EU7ik#=G_*EW^}F$_b*iNgd~^UIaVl8kpJtT
z`SwZng#km#@ifZO8FS`DI@y&DEoWxgD?9#ju&aBSVUVI+(}i+L^A}!GCYkt>!r>L^
z-JQ4HXTtT7v}Du=Fz{?|?`AWdEcX*xfTsEiDc$^(^FS>dPXy20YmLpE1fpS>>pjNU
zo-D73sxM3w=}CoZmw#ctaj;BVRLRj4$;UH1{P0n(jagx7hu=)|<}k5Cv7S#;1c^&Z
zR3%N5GAp`jtyE$tK(kAqX7KN#RObZ{UOG$M;TSM$g_fkg45f1w(E8`}p46q0@j@HT
za_dWcn#RO4ltI~;4AP6Pu(=3XleeJTtBdS*K5E>~Nx0h92y1OFYQ}l#q-4p=Y63)#
zYjD_y=Low~>|lA5o+F&Gs*h}&W%bSt1i6m^&_XChy_5aHByY#aUqZ6Qt~--t(dN-J
z4MBrV%;aiKSs7_2+Re@upI+eL2m8J0<b`tt-T;}i0cnRrMvF1Xbm#=NI!DyBBCE(w
zJNTGQP7v}8hu_)RBVG^#tLI_OwP}Lnd#z`$g!&hYp*~5^N$khs;^HM$oe_bhDCz33
zyZJkzjrWd~6^+T2mr2^Et$6@e=re6(Or-t4Je4$L88$Wy7AJpK!<=R$#`J9Hl-u&>
zI<8|ds5GovT|Tc;(elx0Gfvp`;p>ZoYi#b!q18Rpxc|odUF(aZB4OvoZk~pk==Or;
zWW)K{I5#*ZoTHcOl?C|)8m#RDp&0zI+tX%*lGc&R_~Cx<k&+;<#6LyI{YwoFhdWB%
zPsje$6bBbUmfYC~*4T=Ii5xEfdIFJ0yEaTDi>V$jeab4K572PS6mIp-kW9AAaiTr6
zrc|pxr@fPJGE%6<W&fna94*trxthy@ITFd+cj}~3cG|Glwsy7Gr9q~E<&bJ6FTQMk
zUuc3l?Jytp{y4*VQ*)7gaTu-MRHZtyb5FK&N{}zzg2h4ctqwmjLGF6xV{*Gvf48Eg
zBq^DKDT?T1ti=*%rUnX^Gt_?dPWOiP5p_WRl(1lnu;l95eb58kYe_<BUhSAGGh1A*
zKXC8idYv4HvsLGp*(M@U$5ea85LoMl3-H{9bp{G>eQb^i%}kUh+I!K0tFH=+bT#9Y
z?d}6Xer24l45Je^oy0tn6mSGZeb53(n8>+}o?wTE!c85;b$3j}{va2*hwB}hN>1f=
zTYq}~vVAJ_*2JFf<CHH$L1|IQ#U1MF{>=WdMrMBW<yUd8|2}xztv?D*mA-a;${AgP
z*PaqQOB5{A9^{6w6>VchNDgyXn2JmRXiPC$i^*eJX0kifve$u4;V?HYi}b||2-*E(
zC?O#1jQ8;-t2bG3W@rC2G^#8pDZ>Td#ohWgndTZU8QYvupScp4Fl)A46Wae<*p#c^
z^`7l+W<XdMnalU<X&4OPgmP2z<rQ}9Po}xA#^5{;YdnP!TpxlEo%|*{d}DRC>~Ra*
zgxBe6x+$0_>?tlH4)wSq3|q%AeaYcpl9L+3GniDOLNZ;*w5RWL*=aG_^cwU12f!8{
zC}P+*iAZjW-4)X(T__X&bZ>G)aQ)iFSKy|3^QVez90vWQ*B+*D^%JC>L9ec8^uYLC
z%j$E12odU+kqx=UDhDj{vDDF^TiMEQ@b_QQNbyJIh?56)EVEzLvWjs^O+;8(+#jhg
z*Mcn(N#zYK{ueo2le-T)_t+efRkyaEH)A1*$@|x<ygCGJ(<9ftgag*@F~Sxx@p1OS
zl^IWRq;zB1U96U>_f*u6WFHz$PHuMZdoOI1^skJ7<*4dSucWB|Mb$kAR}%Ga0-u@8
zB$?R8#I`-LZQHzYa)XI&+qP{dH@0ot$>!bP?$*Bl->SY<-Bo=~pU?Ms$dHz+?Kxfz
z!Z5gO+98WDcs*>voRp3GaaIO*3Up_zkO<`C#&WdNIAWB<qMNWwg4k+J6n{kgy0%(r
z&Z^z9H+VePoBUAI0`n{8_4Jix!!KfFi(%8%72^*g^10Bq((FhKP6plHQqnosJ)zpZ
zJke};-da109&Yb4Cj0O*`fQ!Ui`h)kJHN?6Aw^Xi44~QXHr+mmmQ6j~8D=Npwy{>C
zLtkNTxLvq0Vq0PGl8XuL3-D?=EEy>CrX^QW`n>;s4$ojodafIi!*GhO$HHBy3J9>R
z!owJ26dM&Iq~Sx3E2CpH6+C{^;ZD{J=28^M()YA;u5Eg<%mD7RAa#$9N-0Z&W2t&u
zrOvn=Cb)>xQWGCseLs#@8a5VDK$f*9B=u?Ck%GHe5popa;AzJRS#67~Y9!k|I43+b
zx3hcrYgoK99OJo%raHpFL}4(`gOn89u=(e$Tw|F0vXPzgxu*8s@TK0;j56vlJf5nO
zJnJ7~-_%0p!E;x8zz9z~uMQ`#+pRF%U}22&4SiXnmxvUA>)-%M2e|Dtqj84GLY*Ak
zp~-=mCcYDsu|pd#s*`Jg>#bQ{#Cr$tsCTdFZTg&}R9e96Y`Z4mt{ya3P%$@T-<Ym2
zx{O<%?xB9Q$mVuGDZ68BeAGy7mht?ayWjrsWX218lzu2wVFU<~XGX3QM@Zi@FR~#c
zvHwuz(i8xP>0Mc(Wt#1(@|`FIZ!Kl!)os|EId0bWzsxjFGdr>(W1&bTDshrZal@t=
z0jeYGbkj6#%7~sI!r@Bh=YEwj5k5@77CxX^P^M!XT^e$nA#?s~jRn7x`QUzETzp=P
zgWokit6u`FreaYfPKV)2Ztk{xx+PA!6zN+Zj+dM28Z9(?VWd(m1G$lPKfMT>Z?AD@
zR5`IE>S=c*^hFH?zo#YAid^!=0xwz<+Kc<XHggPSqs)YCwtCwTm$j_4e!R0}(>iEl
zWwuqMc6T(Ob}t03{&SKV0nwWaI*KN&o;(+A(IkV0*oW9m<&op!^Y+(4N$!uQS)B38
zJZhG#cBUGShYjG{HCxp6MQNOUA)2Qr7zfAr8vUtEd#ZxkQ;y>dRBC|^L`Zky)>c4S
z9-FZ->dbcKR&f2Ii;i37P}Un5LjY|4nRZ{THl|b>N7{!Bi-EZgNrun%le_zrshuvs
z_*hczkU_`&+*3!VS$@R><D>MS%5W(^$(7&Fc$YYX8fBrY{SR`qqi(zlkB`6b7x*XL
z#R#HkYdFKXZJzmHv?}J$ou{W+a5YN6XU?lFSGqyhRP)Q12d;gbykgBbwe-o%syCY$
z{XQ?AwWZgfP7r=^fpTTbT07-pMuXhvpX99TG23w?K0`uPA`3+z)nK5-G@<*=aevJJ
z%>pP-*P3z%4|Zo)9n^}@(k7N3iAzx9O~^*nH>S&7DJ_Tu^-{0wxe5z6z$k|a*U|cU
z#{7;?O47e_%GC-j1lXaEZU0X9>t?Ixo9w*e_OzBL0iL<!hr#FR7eeMa#KqT7oU@?r
znJ5zW9r)0)D2@H?%t%W-5~Se%iizheC!zq2<>t%>4@y#3*%yrHQK78|x!qPdnDaLH
z43N6uWxP_m$U5Z~ZUL~Un9Sg7(W!D5YTF|t)G&jjfbm_tOc@P~Yp=>GyynO55?Cy6
z9~r7C^OI;%i5`(G>1iVuT`X9Z;GyJLczcjEum%~j2i-LSg_9lFWyQ=;Ob*m+g)`7j
z4_}(_dq>F#hhSP5RbJtSkF_}xhcv9&nFe{;f;uVwp6QUhZ8M@SGzybEu&<>2NG}?6
zabE3Gl*beD4k5#2)L69kCPF%qEI$8nPyK9!x>Cw%({2bC09+h%*&MIh4=xcduX5*J
z$2DG;AfP!^57Qh|Z!6$02Q$$(Omt_#?7wsx#?g5?<*p?Xc;1B`3z4eo;isNcnUPfW
zZAfS!!L!V<t$*0xaPvNB%;5~hcaxyLz_Q77<2ilUV|u(E!aR>A?f{gKjO9Zysr1U0
zL0W0LxVKpj({Q5KqUswK?bNVj?q^Zw<>W^&xXAQtp$Uhm#af@75Z}w36)4(~a;@$d
z9`IbLYydB#T!YoB_%KC;Y8*><(vJ8%xWQhYV{I3<jA#;H=ghDmw-VTN^#3`4ofq5&
zuOW|QGw6{KLEL^m5fXu}_js?_N*P19R%}k$RWyG1y>44r#e^`73Mx~oXyIFwS=Kl;
z6^Y8hyMwoUrAkDcEH@$U(W=<ih)~_z8n0^;n6!9P(;-54TzKcQd4mFrJ>1~TS`2$^
zn`JoJC90i&Z|T&u9vl)!%cn;Ts_$jR=I1dzu00boi9F53V9N|e16yo7yrsmshL5d!
zjlNPL)D%&>-V4%lX^>e(H|fDT76u7pX<~Gff7fyQ?LqGsN%lKx*JY}^@QmCMd|0X9
z;ZVU1UY)dK?)6_7UYla;UEOm(Ug0VY(I&!-P`_S??d!TRSfnR2@7nAr-mp6pB4fft
zqid2C!4J#=uP{WS@cmj!|F!!Q^((XNa(E!gZgQ+6^$jzyDe5}<sy2xN4izV-qk-Xp
z8mglftIZ!Hd=42IWIP?|OYb9=Rv&v*Iv=YU?Mzo_?)S&y3C*AFcRlF&;~wx8YNeMR
zTbPA#_XZYxWO&}ER0{cq*49PwSEqW3oXl!Ww~wD&Xk>hE+VxiMk0j3JYya?{F<>f)
z2Zd%xZA1!RkU%-NkjJQ?{NH`W!SB8zNnW6I`arlWnUXFXz~wP9^_4;AoGG%>|Nb0%
z;~zawx#za@F4j){C)CnEEji0`VDd@I{x^PR@xjIE%MOVzhmweyZ6;#3H5d+qv*Yz~
z4&x0cN?nt>V`xf=q36?WgU-`17ppYqPSsgy?#OZqn+XK{=w{+XqfBCdgZ(A3Ed!oK
zGSHBI0Ck5|Wd^Wp$unko$u;>tM)_o{<Cr<v3S$4rqh(S`nyPoe%=kStorD@JJo~Sq
zjEa>2%&zm9U#qh1m5;Pbh0tOg91+TCK0f5NFR_7&{Bp+bwfj#$c}N+M-UXB?uBuE6
zIEKButvSq=7aWcr0cCRj<GyyVaX5`KG-f`p$RK3rgmktl>?qJ)Lpz&521#hRdvYl!
zu+)bo%#JSLbr5b<ST~!D(nw)%NhoOL!)T866I?S%+n!BcW~K&cTca^B`ujnm<PE~{
zZSu}M?c*u?`!)xep}K%mX#!sD3g-VTG(y|xU?bv6TwUD@Wfcu6uQB6DSrcc3?_qI^
zfy$D^r4HLYXiUQ}VqIUQlR>cW>r<==)w{!Jp#y9$e|u@uU#0dcQ<~&-Q;o077}F|#
zW3xtdz5`>ifzhyG=y@&H`H<9Hm6$A<r$cu!GQvMOYs^}_D$K@lo&1MH4V@qJbN_^B
z99uneYQ|v!lY37u5VtLRdCk7<7DW-gg?X}rOK&IU_&qz8RkzW%C2~`JlJ_dJOWJD6
zPpA-rhlPd>SJ;pUX$m`f{bIH81K7zW2ji=2!dl<Fna5A)Bte>xaa9CE<7Tv~z%a^J
zi1?QdDkFOKY8d)O%zfWE(L3A==Rc{<Q|xbE{%fcR4Q^RNTo>8e2p66)AJ3+wpO|Cc
z(JaL5n0lQsQhFGu^A0g8B+Yg`%^TncQzx6ufM>1MtOk3Q;;{?s@0JqGjx+Q(<t`D`
zrkg9Djh9hh){%IlL*%M^uG63(%5=FkVd=Hafb3b3)9>Ly=4b%Dxwbao<3pO6#o1tY
zH+*o->bj{R);X`CZfImcW@0uYjbv;S+^>fat0MnnJR{((>sfXOxTx#;=@P~7_QUp)
zJhwGBIhU!$^&Vz$yf5O6FhzBW)h`yr9Nt)IfQb5}8^o`8cr{0Vt%)vwfs5k$K)+8>
zQ?Biu4w2;+Pm$NBb~i0<PPbs68V;krfW;D=upq(Nu)doskwEzvt$;3peaU3RIq3@D
zRR4>O4FW5&g0!MG^{X4QkR94(^DkiiSVpErqJ;AqpNtINy}21-g-z+^LM&J+6O_mS
zT@+PrG!n^;_jK+ch0WJ(pEJ=#hiYkPZWb59BvWCuKjz<~59?85&-Px`CaV=yIHgmg
zl&XL$zf80CwAUepi9h_eP2s~un-kXuufDNqn)r8{`Z2rf+0S!WnmecyVNhknTs*ow
zAraIzVeGk7!d<2-I;#R(PRCmfT`>vwb-g1cIKAG##6O75l@p{jSeV8JM(9qcJCk&T
ztpzzc{fz$ik2_`xSP`)fj}MfoT&b7`bEL>nvzSb~URM20)b&YG%|s0n$c+PCj~E9a
zys(^x$R&SD-!mz46`BsU0;tJ;WY%*Vn-nh^sk4D?ZSSJO9C6g?M3tP6JftVDAEDmW
z>VU)MEuOb`RgXvPx_)_WIblE?7g6AY$v#U?`b4>rUpNjND#JU-mFxjG;n4O|ryZaQ
zzY%&CZJH}C@>9FzRKpu#u}2ye>w}r)-va40+eaq|G3>VRy&yal0Yo)hv6Q(qtfsr~
zCMV$Ajz6BOLc3U{?nbTY7?dksDe$BmVy3N6%3{myLrW$r@3y%!Hw@igk<uFV;Y&+`
zRkhh}fk-quc5VUg*Ur2$HtY7<LRQ0iY&LJ6Ow5~KXPYV|<v>|8_E0UNMWumlaSfR^
zK0IxR$!E?U)caUR_=&;7k^%adb;LO}3T~+JXUp_Nspu5eF~Z~aJiXrxH8a;5t+fnD
zVR-wfc_uWIQsZd7UdqZeJ`uC+eY7UNOjajlPjx-{9M4wK`M8fTIOeQQsGrf`v?|-*
zFDa_i_D=(;DEq1lnF4`9kPZQLK9P|st^LLddB?dOivH=0XGDFwFI5?Jch=`O0&Fub
z{TL?Wsr?)0S+T&GFcVC7m$q!e@R=I3q?{Moj~jL>;hLs~21Ir%X2T-5JZd-W=VcBY
zM&)RY8KG)f<cJ1ToQ;mmSul~UH4W-SN!!3pJFW^sq+WR2BI0qsn^4vRZf9gu(EzGj
zBz8>I{%8o@gL|^dI{M}JY3TM2n$)L$f<$}gU?6BX$Iy<^Mt^JX_mdmf%+f+=9VAa&
ztmv_Fmdmat5P>xD*7LX^S=+|=We!_hvn|LJojW-1i8ib!s_j6wFsR8-YGEoSp);ch
zjaZJRZ=<ur)NZU^e-9U7vo^vu4!9N(Ix6x5oGS5`K8-mh3>0}ys!M$iV$Js68BK<-
ziO$RJ=hQVH^wH{A7;aXF$PApS#2x_#`-YS%kfy*ItMKtgsq{Q88*n-j&D+85%n85Q
z*~D0w1crN{iE)-R+zDIu8(m&;^|-Gd_&yxndLErJIqI;Sjjr!#hmI=4b98DgVCq~b
z8_oAC&^eW}0)xe!GS3-zFM*FYuqr}_G`r>M?tL*QLR}<nh8Bf|;%x=BhHmVe4hCq(
zhEw<nGoJMXq4&Hga%)!_pZ@TO5wLEyw*vGIDkS-8xP2sK`>_7)GM+qmi(j?74Iz_f
zX|2EG=WW=%oEa;0aez$Bhh9h+Xs%ykS@K77O+9xeGK|lLXDDx$=3K+p4J<`?YJ`{<
zlR?f`zA6bW&5;;Z0iAJVaC$!M<HmvYYX~s{o=+>7!4mS-C3E|OZ}P=N#<z!jKcT1{
z<+o{<oJ~7NGMkQT0%o2kyIpan=;zkh!z6Dmlid&D<rohPhiG0RGP~PieHa(5o-}B_
z!Yn7+lnH`0miDYCBDPs2?fz@r>2x$UOz9a|sm!ghMAS?9ESBVAqM+ZnF7Sr7t$|=|
zpFNuPo(QNyCJi@}svA4sWseC*nFv0rK}6&a`<a@g)jJJcHR2*Lq)`{LZzE=;)UF}X
z3%l(EowLFvi_`H=p_TtI!>?RSY%OV4jqUSm9LYoE$$rD64cxng?;hnGK5u120nEl?
z<{e+?J*DVWKF$>%L`azmqtI)nf3IELrGf0kwr#V{cBUxYY!dS27I^(3GMF7l>VyY7
zFzB5~%XD=6AX=d`m}IA>z3DsLy~bXs9qya{^Qno~r>+t<H2H&~M<&yy%0~JS3vi1=
z!8>UP4o+|XFABsazwu)s90xDFE>DWRk;$P$6+P6}F}jZEJ0q4c%#~4Ol);vdIMJG!
zT}@GbbLydUA|r880s*aiZEJf!0@&|gH@xQbqv>I{B&!8`9jjy-FtJJY)2Q64FoHMC
z@0`r6l+ONP5Ca1F|Ir{}S#}9gSf!yQZ--!x`)I+-qW$SjOfriT0)}*G>~218JkN~E
zRTD+~-2~iOZ#<b%AMcchOHwuxKio({bpt`bBn`XU-zIy*{OfeO4(KC{Ybiv83oAWo
zHE~18<Y-lu2bAgrgqbVOxEDShDXai??9Eqf%e+vxy^vo^O)T59z~ZOK2W5Ikh#C>n
zp&_}2C6b}eN*l}zd{zJ_?KvT{VVv+&D85owj}#6j>6irxa#niS#tI(1dgQ2{y<R?y
zUlCcs78c`|6zw=Jr~Sj&67AJD7!o~i$H|IT=UJ^51|vigCTz3xO+s;(;Sp;@tmbb*
zp>W(ZF`Hp9tYsoi!Mr7Vlg?_Cc2<W_YM*wwoA(A=ZdWOLf`7fjm|VwAZBxvM2UuVv
z()F>FxJWu=5Mh0&u;!X@eI#BuE>rByo>?q3B{lMgz)OT)e|M%MYdkU{rf8m&&ea*>
zU;}bxOo;w^P6=gNG1IeuM2eIxX0M8XrW>5R_%b2Xi+q-@|77+wOmuBP7PqwAr@p_d
zMQ})P4}mL&z735K=_nMm;ZW#g;ZE8bE|N3dP82lJfT|})-;Z)}G-y{7eOQx};3IU-
zU~xlmeIk{;{$`9)P2uEYF8_`%cqzgAL#t$%n<x+NS&gf0+lmYs2iFgS5R_}IW_>jb
z`01V+oWQu;dT-Fr@;zTfG?t2rH5`Kp@img$pkKi9aEHRucyx~opwF0Zy>qFXaPu4s
zk3o*SJOL}sD9*xK@$E4hF&uZvx<L7@DsOl8h<P;__-}6g-(Lk;yqx7SK4kC!wUu~#
z8BmI2N{w}dY80;&22*nG>`-z2If>-jeW6YW5rZ^!n1Lq_bF4g=VDY#(X-Rv)_AcqR
zaV6P_5+SX@G#=6pz$P)@a}mkqgjvUrjPWn&Oe!a~F=iLW=&2^)8pdd*0eQ+<34ykk
zdMgzG^V^w#f>Y}v&OLqaBgMYL(Y=3scN2Wz7a?P+Z>b13*pC_{<hBZjYPFhy2ok+-
zJv+f#$q9tWhXMtjZ3>xAfilPJ?258^BYqz}orh!uaqk9%gcKz<-9@6_VVb?mB@yQh
zB>89!3>I_WjQ<H7NU{PL3DZ|Zc=$a78N7txccL=$WjIUM>lPw@1_m;?izPo*m?GjP
z5tb<~F}cnWgWum}vr}a@#|xaTy<f$QR4I$tRw{~U?#M{IShKf60M6sDn<Wfw5z1Wi
zwW<_F(nVxR%ZHjWO2AY8YEi~%1l_^*&e1v{;fX6t7CDy2ABraL&5I2l#~F#e&WCzG
zHYk#fj7<oQV(JrQG>6V00TQ6~MxBrWaY?2Xb3h?2EK)a{@OEdYj($_!O)Y6xSLTSF
z0~7>S-`|2SYAbU38s%zD68-C=8Gtylu{v0!0_gx4JObV=(pqE#u`#N+iB4#uA(<FX
zc5mW+kEau||M<u%UE><60-1XMiYFRxqElMTa0BY6RYN~Fi)gT_O6l!gMd|YATQwWV
z#yY83I0VDxROu0vyOR-QFr0ofqpcY6i=7SCv8Ng@;=<wHp8#u6TL-+f@chz<eU_*3
zKp7X`nDOX+juzczHEM`^A}_*lD3AKvyBO1^`qdGVeMPXDEZMZ2!BXhcA$u5Yv;Ua)
z)!Y{_0$|VG2ugB&S_nC=#~zxmRD-_YOJLNX&5=-g;hO$;BMY1CGIGB{Y5cG-QB(~?
z9)5g$K`Oz@&qu$Mstt7AzHrtJ?Ke1nJ^?<haYk%xDD|fcP5&b0(Vx7SD@`u@J$#>G
z%XzOl<5t==^;|~1`+T2w=x?T<VV^FK!&GRO`@(aBR9&CA=blxE0jBsbgHYaXfD+Y`
z|4}pD-jv_Nf@LaE^ODA8k*3`dr{62D4aSJxuh1m_F<wuqi};5zNjwuv41F~r5#{+X
zj#7_#ItPdCjHzoRu5FLCR*3(MI3Gf}_M}Z{f#X?YZg1fXK%(>J?ckZJ)ynJuJ&K2n
z?M4WxJqqFTo9{den^TWj)=}rVaac`;ni+p+_TTP)ZQeK#?v+WD-h@4Zpi66hU*`;w
ziJC8el0>ATspBA?LqraqK3-1IN@~n|euNsU&A+C2Z?hgYCB?0Iudz7olNcP=<f>lc
zp-C=!Yk|`ZN6UM7F)=dsv-JM`Cl!2#{A7t$%A%;QAXb!s-7!(~qWy0x>6x4e!7ZR*
zCu>!bwUfgV{oOcLMN$5LFprOJ<Uhh(7vhC%Hgn^d_kGRW1e`P@M#tnTmRWb;j3}!%
zB~6*3w+Y#Z_Ch#(?Y7A>M9<pV@Pt1cr7`B>HDh;zvT5%6$}p^;#}V0uddx9nRj$GT
zq<Od$Y@_K{m7_)J>2h3dkxKmK(>EGVW0s1gHMm#}X2zJ0n;Q=mAxop>nKFXhhAu7C
zPY;UYFh+*Cxh-ZV#s|MagRjUh^C9$PBZatJ%2k=se1gX&yS7uzl;Zv190?CvsxYrk
znD<@=;ID_h&sdm-Mu&scZ9^}8i7q93m8VCT_X4EXZAYW=i?3?g_bZN*rvD?PuBZt9
zP%(u-?{Vm~CMnZWr4VLmSo~4OY(WQ<fpi(dB$NG)N}7M9Kx!+6CP=ljjxu0p#8ljq
zE{6$_CWXQ2;OWq)j-BDgl|L1f3;a1fxtu)PvP+~k|AX9K(a$4r-MpGb)b{;gH0nm3
z$U%fwR!|d1DPcan6=J*Xv!h%<_dvJ{3eh?-L{>#Xo%i!Lr`d9EI)Ti~jYhhb)AyTl
zawwuql3QwJI<fkTWTK^Fr20pTl*NhhfBCKtKgi^M)V@D)&#*VIMc@&}uU094$Pq_+
zg$WC!kgBA%YtKZesW3+v_BBZ;xKgr=&?Kvo%8fUnNt}!eRE&MTk1@1}aG0b>&@z=L
zSX%A<46{p|lxR=FHu_{#S01H4*0F)%J8vzEKa(B13sALOGot2qW}r&KytXED(Wm>E
zTKR{dZldCD#8|G6);*zk*)LN*XOf~_rF5`3@20PGV)|d`>=WWQIhyvqsM3V&2jX)%
zHO1Zd<T85~5LC;0&vxwO?OkKI4saTpeebZpSazj*Z&Hvk4xrxW3ppyCTde9uvR(y3
zvgULggN6s@?ocNdH^WxfC#Txq9YD2G3A;~efOwkPv?h<x67@iMqamg?uf9qsI~Mu>
z`VXqH|7v3s<h{yS2r2fP-agCVB@nHJ2PiF{s1~dSMz)gR{&QQ-PeR7Vqk`K8p?3yz
zieC2Jmf+KZKg+PE*;}Slu>3j+b%h=%Y9yfVbO_jGnxB(QDhiAKf1$V)ij0AA6wW&s
z9z@Mw6fAC>0BamqQkF|y<$6qulvB{B7f|b>ZkpsnP@7jV9;Lo<M=1XuFZgxoif?u;
z*WWpj`^<TqF*>ZKt+-n>S+&J$p8l@>yzhOD!XIAg|6a?a#f$g&DPBIkS$Y1d_^ovD
zzeHej>dyli7-H6{7|#)w+L?gqT7@L}uQb#?&et%z>hc49;j{@TzGcDPOP4!Dsy8~_
z72R>pX^8?%W1b^-b-LF5EfuNSxD7U{Lb1#Km3e+v8bJU1bk{bc;RT(-kOkp?J%e7q
ze~kL~`jy-(u{fz|;dq5GSD9diu7-9zIY^xx@>f$QxfJwy&zn%0Zl@TBtxx1bYDC%e
zs%g>&8AtJ|$Mku1n`zmkhAq%Not2!}{@1<w4^DA`d{YfQx|EZH|1TN(AM{A;(;bC4
z-4#l5LF*IZUiyFN_5b_X78!E2mwfNC8Yh==P}k*e(zx<NvYzSbYv6<&ye^c_#EJsp
zPTlV#%;1G3nWqcEEzIDyI2z%RntW80*bH^zRRf6G`IXSY!NguBhWLinty79w+ev5>
zsMy`&3%mZ+_3d`s_>SW=O^xct2H{&G*5v61mb|3*Kr7l-`<BeJeC9h=T6V6Fc<X#=
zqajiG3YYGo^<&>Rot2@7(jSpzNeDB2vZIf?RV>&m%T2~KfB})fJLtD-^bZ(Q=eb0L
z1Puwf>?$e9XH29gP6<Ilh9KE=f?`{`u}B5J<XCFM*|`<cHo0Zbpt8=A_$8+OCC9`V
zt&*ams?|qP5?;&Y-!;kHzIF<%e4J$UN_*9xuFMy;_ic65pep~HIlJE3Q^4%p*O6@6
zhNmDk^c{LSuZV#@lQ4zpkt1yX>aq$ce?5{DGZI%99{Y&F=OK7pImG5n<>`j-=^EYM
z<(lHq5|BCc4g@nZ6Duw@+PlH?%{C?7zQ8x9#y_L&Auc*yLc;>d{25g(jf>otdFRsU
zIWCpU@^+c_nzW!W#*s9}IRe_p3-3@GZ)P67+UZs&mT5*@Httr2OipW+ziNYrIxgbF
z8WVQhA3tP(LQI9>#i!&jVcB$;^0GRg4*DLs#)WHulg-yt`_mdJ$z8zG>AU6vwdrbS
z-<<Qxbek-^M(6}W+_aRL?B!)dWm#u5(e}t)wDTR9@bkv$<a=%>eMCdqm%h?tr%|c&
z2Y=PQ^RgzF-W|IKR)0+o-CwCNYMvi0_8qPjw|*`x*0}+RFL*2S`|;vf6(Wjs|G<sX
z+8%-%GQB#V#RF+WKzEL@aTC!hN@DslBye8*&C6Mv50f2t5^w9I);60LV9d!GkayQl
z6Svvy&p*pltM=uDxQ9EIXD2g4&s<B=uwEdENa71JNeRk)K~HuDu?i!@Vk9IKF}}5|
zcTNM$>KwZyw~65u^Jhz)@DWMW;9GBs8w{I?Yx!lyi-AsibB%KHYQCbA2xoME-_Dnl
zK2(jp2sxyke|j9Q1NpUD-6L_ryNUAA10z}MS)G;gvq+m%WdB9I{vs>G|3rp7a;zn;
zZ3bIi^6=Jd_mw{8%NhQ3xzW2QHaxs>QmV%~E+5<Lc-%Npvxxi(H;$oMB`>!sQpBrO
zDWvUmbt3pZ&v~N=AFZI^93zm*-T}ldxtV&gG24HKM~_{8+eM3j(d~%#9D?e?j@S>5
zi61sZNoew#eB^tm4vBx#&PUNMr{-ggytw5|)hH>q=-PuNx0FC$b|g&duEM`bN<2y6
zY2zh}h~V0x&#YpovLckQ!Xi4?zUQ+Mch|<WZU00hVdQ`8ol=VL?$IU_=b$uTCcNye
zn*6jfCUhSn80Bo`+CKDBPm3F!3Ba#?H1c}XcOcW`d;PZ`gk+r9Ktr}bXYg18<u+1y
zdt6&kOGS~s^gvNXqaoXdlu?pGB+4qkakD<*4U`9`>uSs7>}#QL{;aUHEgG8|7{Og-
zPtXd@kzDAg|0qE^eRJf*W?XhVqCW>mCN*B9?Fi}b-^G!j;r;BFe4Eos0afOaO<_vc
zH_cZ+RV;=K+i=pMRV-lnIr?IS?W%X(BEWL`{HZJN`ed=&*w-_QW`<Ls!d8BlBOl@A
zSJDVfH`wYvad~>PW98jY)q!zjLA!b2``~Y!@cRlCMEaa3+|YGOUs;PwU(0$DRIlHw
zkY0I{g{}Bi)BaMWY6dtR_eTWdXn&&N-Nf;nHhZ*Ml~M_!sysKh;z|ri8XDkALB$j&
z-Z!Z+zlQK|+JXt0N!0apQFmGG{9{tr39YS3aoV{eA}?K39<n;{febNh`@YTD`X^QL
z)9lP>z1M|9TMN`WkQE)h>Y;-(@SDr^_+6Pj5YAMo7`klZ9X&`vf_zN+;2iy9peCGi
z?AZ;GYza3eq*77T6F`csa#_$KNcX5iX1e1XtxhmmumHvTxuq<Vn0kHNY>&HgGTX+1
zqGN_wx<6hqqfuo=F3H`0eYfI`UvlTP(u%dGVr8=Z3O#a3ubAi=uDJYpfcbK1s503c
z`m^#ZZI=>l?^9z2?rUKN&k!R|!gIZ*_yhw-L{)(>>*{@TXLnhxXODctUSmikKT<^g
zX+ehEZXLYT6;N)d3HXgSrg(eVJv{>(J1<(_n8<US7Ajj~G_HfSA{Cy&8TL(@^fv>*
z%pHf&bpo=nQeE5~z+_msIe@G9>fpMHC1V_~x9RYc4ek+@Kkl!fFD%jEM~WOg5PD&!
zo?OMF1-qh%YJA@ME=^ywqFpJ`vn7eNhII4!YBwClho(#wL3OJC9|M!y){C6;@L?}2
zPenvVc#0d}m#=#sL^3I85TtUmshBXjo~bG}pS6E|hsKy43d}fFLDpURqmuHS`(ui_
z#t4s;s0s-iAKyw-qolZ^K<kF#qVg9AvUX^;E+SlUhyye3uctMb`(@AD$MI_omF(Uv
zUDJ$7Z4VsKM)NAZoP<K<tx{v5Vp+n_feH0j3(jcRAFdAnn7AVO7-ShO8J-QF#jAe7
zCIlBFF!5jabMy5KRv`M(x;QgtVC`0k=~Z6xfNnYZX>N+<1rRdr&CKG*pu>=zD6ej9
zTG}B`r1Uo84mMA-%|9wCUR_D!G#~k<wXyTO^yONvn3Y0CZmkZ0L&MO@)JULl*fd;o
zq)w{G0}ijBdijR~zIVFU$NCO2-!+Ej!@$h^G=EVB89ZO-x1mBune@>Dh5i}X#mMa^
z((RI2`(3cxWJdBQY^nnFel5))Xx#bbaxIr=XHOz0YgUlwjkzy{@%~E19P>%CUElrD
zD6$w2Dv`>h*hPoP*b2=njkN^`)*<D9EDwutj=A<EB15FnW}c<iaNR!F{^W?s3LN?(
zRls=Vd!jY3k6#cQ&G>nE-HF~9ndEhgP37K5)2p*G>09ecG?GK~#HqJrQgCi@tV%k!
zbn_kLZphHZcHVeabQQ}M$3>GACYn%w1-y1j(31U`243fe=t{fYtVO}bj(UR$7cQ2m
z39kp;@@i9TYIIMbwxFB)MDi^jykF7!;Ffvqxs?f&bKIfb<@kH>N^9()Y|-w5C5|kw
zJN9(iVw|R`teZIx59x$K&v+j@@uQ4~7ege0psUA*=TWF^o&A7Hjf4fJ!z#B8oC6{E
z+s|i?eSiOE#2MRf(0G}v9rG1?TUQIR#WC&9%t<v?(@3M5Tt{rrIwr6#dZ#{CXrQd|
z65O(q`>ft`BAjQ7dwHA8&u<rp7+A2iT&iZD`XFF6Fo0mSw+rcdAL&LEW_Oy)bV?(=
z_uxq4fbRwp_IQ<JHg>?53_ZJKLN6ycRl^M+o6M7>De_utdi%Z9qM!mkEz+=ufY81l
zX~mTL_7XSH+LY&%*8{p^4*)t7O<}5hWII2imZGXCcQvv;cv={+lBzr0_x_07$aJ1F
z60Nn`<xPdPS>y8N%}`(~*eBULalh(Dv2C<k7p%a557g!g;;K3wzkszAi+^2ZaE)KP
zY}$45F=nCIfsEiOs=ZqNg=Tqk)X7`Ur67~kK$TDs)BdbNiraj>H!Hh46F+d1uv&~<
z!)kVe3&PwLH!Vu5AF^}CMIQ4sB*At5{&tX3bWN{PXK-yVak7HxJtVkB|95PmV?=2Y
zee94dc}g)8HBs+iDzjrLsxyd>$31C(I6D*XEVwGCq9xv1*GoJV1MP=^DB#wW4^D(+
zcd2hUl;vJ2>}v$}P)bG$3;O|Qa1W^;>^3VqD+;WJfdK}SH*zl-&FitMYi5%(>eKUq
zhjJzS4!}_~Q;^=Y@%rZoDmb+2FtH_y3AFgvO^X<q*fF^siS{yvR0S`CE#|z(x`4|W
zkswKa`(-#)6-mn$tqY`fU~oD7Ude*0fv?V^7HP$nj0~n5ml&&?nVy!aNSR8$z^iWu
ze9s7-rl)_^38i->)GoJCQ1AtT8<5865UltHZ#>?6HJU*h%|C(cv5RUnPI&{`Dw1kV
zH_#%T_vvdzTWAO%TzHv63ZnFMW=lMGD_$8!g;v0|xD>83^|KlFFIq*{`{TBjrN2e;
z)Ge!>S_8*x8UlggyE#DH90n$ZOyeN4B(k^gAbi7OMO0lI&JdYQYwHy}P=`ZpK}OQt
z&zTLIeo4cixWVq}ab0`%90UkVi{BXB86g*_M(XfJ6Y)Li9F0k=i*je0bz);Qf~TmU
zzq(Y77m<S_ZSYZ_2l{KR5>H0gNzBPOQ;q1mZ0}Z{nC$J&9GSb9rvDU6xWSN9K0#VH
zQD@npN(iiu#H;sJc4Q#bYDG{od7iHL(BpNj+U-}SpX*B#{1%`mIOA9O%cM8;_jx4G
zhEkD0Fo+Ox5ZbAOlzSdG2(Q0^H7=c6eRZW3g5cr4pKNdnxTod|CWyi2OfI95r#8ji
z{MqwrF2y&PS?@qieN#OfJkQC+5E{+Elnk8x23PS3HR&aIjEdEpy#3D6XphKopm#9Y
z;S+4FTWx$O(`YMs@P(rL`btwMrDX#j;q{k}7@k^O=+F(VXbuz+z4;X{wkAsS=MvfU
ztSEMOqg1w;F3@anFQ^4}98|)d!pa;>J_MP>Hy=qvcV|PmF#Qy}uw9QPEIC@8Yan+Y
zY|S*)&FLFdYH#u;?V^L0(tmrEYcvW4cFM|I+Z}`qUFK<1U)GFp<m0Xn9wWgLiNGBw
z39+y68oF&Q=;N%_$ED9%MT&;}qn;$;EKAhH_kt{{RZI^SGkl*JVApowaj?N6yB<{d
zgMF>$TcK7@3PVQkh>+9C>On%-No3dDu0(>@0U^ul%r<WNfI#OIhl?y-^!f8Z>c;eR
z0xZ1!oIqfFIXxKWa7>oBsO(VfV1rSp{}QQG11p3FP1IALThsZBkc%tn&L1NcJ5Bxe
z*z`z{^!BQQ4WVw^A+kSR|I;Tz&F#@t>>tGW_&zo8Qn2>4BZYG*vf#9d;VF<q>Zl;6
zxcLa%hGnM{DMeur)&XC?UY2Pru`;KbI6zu77XxJ_u0oQx&wUHVMD~KhJhZvgwlJqM
z@}-qj16x8&oJ&~qORfR{Fj*ehalqAT^G|uW3th=fX}XIPmYCExYVZh5X*oOcJcxi;
z?*rl-g+=(ar6?lo1;uq^B>koLqw1KP!??60yLPp{H&N3#Z^t|}hA6R4R>rdfh~$+`
zS*>Ud;eY1WH`jgR+nT^)9n;)T|FkQnc!i~@(d)cxf9YhIXg~FzyoB_J^jc}-AC6$M
zyxf@5N&SY4X=ItM{zR)PifZ!&W7x}jHq=|Q!c<qdd}-K0?6p$nRg2+hswLmUvGHDD
z+ipMU^`m71#M6@`;+$(ObZH&%QiBTj*d^IAQP`qpl`MWFzjnje!(9@-?dhxF&}|jA
zU6P5dO4;ZX=C@!?A~LmHHp-uAKT_1F2Uux0#DGWnz~JGC=?`M+c<x&3c<-Y|7NY6R
zECqipfHV{7au%G};rZy}_Q0svGgSn|HPT@Y7ZKV_X;%B{ipM0-PIZw@8$iqLme%$0
zho-fFF*MdHU16z);I-AkP*={sDt?*a{&tj(B4cKPz$CahtL4>&wPDjs&+zwdY`3jQ
z1|02o28NVhXX_nFoQO_)_<MGxDWZ5;QP$e5MR8QsvHGCyo-dMHNN}dx6i2%5LX+9(
ztU>5fzEd#i!er8`6!wp`KH+*Kx8SFA^1wJzyV~<J`pyi}xa!btGCON{Y&@i|$i}c>
zla65jLqtC=-*6?^^P2N!2Yc|Zb~&NL=N=(-H#|>~sHZ{PzKTiqOm};@2@xV15!hLq
z&Y+FP1f0-NBEjYs_&C!E++B_uTOy7Gl6>QVuzRMoqbGsF^$!G|3JDG*w@_^=zM@_?
zNO%zD*oU%Yh|HwH<wnHPD=aDKRg0}o2FlVBgZ*Ju+XgI1dq^>AUB>!EJoOBCljc@e
zjPhO6_Pj5+TF*TybI^J=5P1d2oI~5-ad(V1IPQ_WZ9nOCb1c)kaw|^D$mDh7)PA*=
zb!59a@j<t!f6j%XJ5=X;=7Hp1z*{hW!r8VLMhmm!b-ab!`|hGEy4Sd#&Vf5nbQ(#&
zKLH^eomB1kj5f0)GzFnMmRCoLQ0K<=b(klM!yz0+QP*Pb58YNE=wLC4)ks?J@Yx=8
zZ7I1`rwp;z$&L-f4phJG(X7frMZ`Or3Y6irPt-FbE}M_>0ei{hN$FwZ9X^i}6(1__
zGk+X6&8<_LbRjdOy9kbT8*|I34XYk07k%MC>UWdl9699$Cb0~SC36_A2>+_LH|Zaw
zW0yA?@+HITSF#TsE*9f``C5$>ZH#L#-W!aW;v*&{3OU-7E^fM$@wZYplCm*4?|oqx
zATo~LPIrl7zvzX*c#X$}+mV<1DqC0#MN;S*VNI=z<~WrE@^BS7(fbnIPG~t~{rOhd
z{ZPw&PY~pp6rz56l#^sv_Pi-`6ev@|GJ!iOURs)(KdiA#L`wzf`SVO0!b3^<;e#}8
zgY;|j4$boSC>RA1Rp=M$s)qCYCuQTj6OCmRq_Z5Na5SkHbccT`!VK`zSb@~@<*kcN
zoIJE`l(6-*tqY$x#qDw(*yS?Ta_DiG9y)4erPhVlkda>qu<DJ~cq~W0iU<wjd)(Uw
zOs~zB7%6D%2LAdw`|`AOa6jL2=*R!0feaS7vwbQ=5)H~>bt0<es-UOzVsE!@t+AOU
zXci{x)u1F1=7)00`u4=mp;)&MU$XHt&Qw-VNR7Jp+}O;PW3qnGNg8tB^bjTVMp3l-
z-H!I9Ktm1q)H5*f%j-Ly8AX+-i!_Dvd6CJ=3KW()du3N*8Zp~mAr{9j^-AC3>aUL<
z=&=GVa?I9F(WSU{x8>8pI6P@9dkBuq4cQer*esZ$0N<YVacOg`$#_MvIK#z>JbD*;
zVW21%b$j6Mpt|^+)MP)q>hF`{94^hLw~q@MW_dfaKM>pv)xS*8km}2{^j8G=4d3CI
z6)}z$odxNxyRGNSg`a`+LOFDWGFib-OLDFyT+@>gb2A`+$(rwg*+W7GW}H61v*|@S
zUq%bULfNc;&GL#2gakjq3FwMz#3HmDMd57?ETPr+l-7W+%wA}}D%!tT;u)c?=49)L
zFq>1~+V9<Hc1}csp6?jw=y0Fae=K}Qs;De|+R34TvZ!%&v;mfH<m^Y#)yBZ@h0>T2
zwX<z8+F(LKPuZmtNDfsQzQx;dcFOr&EgdXZQCI=M16^2rQ3vvyFXnM#{*$!-+Vht@
zeHRu-J!gL6eT<)tx3l8xR5+i<gER&%VtC5pCXACGj#Gt&R2q=Ugf!sqI&J+iJ6uei
z6$Oi+*Qh(S?gQM0b`92mHRTPBbj~g*1`1}xh<HdabmU+BV|wv8wC<*|@YH6*qv|Wa
zpe+1!Ev;?zy<aH~^b)dHQ;DgvuyIHd3Mk+rL4UBQ<P}y!WQ4Jut+`;BQYcQapr=ma
zgDG}*q8(vbCO0nRFhLCFJv71aj@(>7|9+pGBS<#f#D8tH7E)=zn+=3FeWB=x(Vo`H
z^a&@0W?yzv_?tTt*Up?#itlr+2mq$z@@h4rWoR(X<X}xNidO(G2-3+<{-uaOiKZa0
zWpr>PdZ*G+G5h=6OpA;gHmS40ok{RFt>DaDh}?~7F#smGg@yu&K&|)_c!0S(H-pCo
zSaP%2zq3lPAsT_2hh#)vp!;N8bD!1~jU{2Jo(`7@X8T55t#`X|CkMNAZr-8c5SFQ~
z99Z11N?R>@=okQUwO%?MN{2;wq|-o7ytZ7c^uMZ7NKFL?1TKP<hd6wcMs%FZuasQ&
z)|Z_e(?mUs86n~Tj-90sF!qG`;Oh~?35$pT$7WAl>*5%$omR^@M79eJsjDVYWgQkH
zM22S5>wEgeCIe6e6CxglF9cqDn1dL2Mj1S*?zkHo`VZOE>;%8V$y-gssS-V8W;|Qo
zM~oX*x_um*03Ve48#J&{S^U0|UiYQdyF7LC?(o%%ToMAv2brE?G1-B~pxS9knKsK_
zS5WF+l=UfFPF(>qoCzPw0>>bAg&V>dpJqc&Lp0U|Y<*)fB4TWK{u8;skZ5*E2fj=r
zty!VUWt(Twv|yWORVp)@hP0!*2{Uw7uslE>G7_ptZzDKy#o})9@J~Px8U+p3`8^F}
zC~g>+Z8{uT89|O%@j|~D3s)Dcga$!pVS?l(Mo*2LAMED2DAi%%#tf89rOeeGyr7X0
zy*IpXnj4u${`)X{EBY^|xfDfxfYlJKS^!zkq{a_rAcc<SK1u!<&-D?L%dQ8>-LlN*
z5jt2sXpReT(TxEc@0{1zibm6cWOTL~fkg=eBDS6a0s#@yeGBhH*-540Xw<9OfMf<z
z@IKLjXu^5l^l~&I>GZItWO7iN3+{`3C_}x1iPtMh?TX*kNSowT<|k~X_bt^OT!XO1
zcgskh&JPEOtdn$}CD*N=@ZqF#sjYfFp57wMKFaLM>py=FokxZJ<WwjVAY1qG-bnCw
zy6gyivDuEk>NEIj&QFF^q!Tr@MNZv$iTE`8F{MAsIw{tFbsQFLmM82~f>T2zs_*JS
zz0={Dz1PFIG>J<anR$icf!JKt7>`0kU_*(NN627#;Mg3;k-I1f@zYbqlK&G5IhOa%
zZlI!R(?<^*`Oe-dbE&~E-5a`|jcs=GTeHQAqrXtZ;9JS{oJST>NI$W(04~QCnf(ca
zp$QC}&H9gk0KP$Rx4)7J?PV_C(lc5n?XBk>XmDNTjrxUzhz8eDamPdPoweRxT_U>&
zMD8!!M?Sg6Z^Q9=0*={VN~G-?s(Xgb$nwv|{#to71HRfY(}z)*UtU|Mrie|kWKYKD
zz}iw$aawM}vU8^HD66;YE)lt}MJ)IO1*-_FQty1cIH6gG+qTelWM=0fsXO0EVZ!bL
zq20;<E$>dk$D~fF2V1YoC~y^t7)pZq|2Ys~aJccGC<jY}-_NUj!r&++vCG~K&WnNp
zC{_;iwjd**A_W6t;830f0k1o0F<Xaz-|*QmH0*20SfY9l#M8~@`^LS+vKju$%4D@P
zz^?gbAaZz-Hq0wVBM9rqrIz^O(NXI#!^hK1%WkC0ETuQ=LvgG&obfGc&CoX>6qkcT
z`D=Jp<^3x!c#tBuu;7#ewTPKx$G8W}q?*4LYf}ZqYqaO&=Iy3POa<E^D$0LDcO(v4
zv=J&G#t1{#fdDe?$c*q0HSby!qRgAQc1Q^7XG|9TF%>96ctkM$8I|h0imnrutjG2<
z09LTwldV%CVRtaCl3)CVUy0zKKs{S~`mvJz9SS^B%91X1kY+R&%qXv6hlGuQ^Yr3Q
zp7G??aFec{tvhR}nL+0>9Xt^S=wAa+k)jk11$yQ?)T6Bkzai5MoJ}6-Dq7@^qM%Ex
z1LtbHYk{LB%j&B_^(&ZL!JYKB>nL=cLB1?^NI)jqo={l`<`Cj55%nvUFU2&KynL)*
zcgW(d&w%Rgp8U<cWeH%Xxul|`Akh<|w~)HK{QCEtSB!`Nf$C6SSmsjko#TytollV}
zf=wQav066#D>n2{BMi<Ywcz!LG&_Gqq*AW%OJ@doDc8^@>Jf=&J}!y;X-4Y>mD!IH
zh8yQUQA^*XEqQp|4?V+Mp2ev@S7DdB-e8SX>H%8z+=^73U+d4UwIrWBGIAJq_uk$S
zXofi3ZeQ#|c|WY5nwpZ1jV!bA8$s=HuftqcSVC88@VG<K(U^-p(@_)ZI>}vT41>Z$
zeSKUA3JLalmDgAVKKu@bsFc#acAK9(FhdUl6JYj#D%z^uylE+K;SF4L@Y^%evkyF!
zV?--SSWVCxgG0lGl$20txEgURvBap)^(7^y3weg7<P!YGMa`ie;0A)X8Q2>wVar7)
zhFsI2tk_lj_MW~OG(xz|gJCh|?DwW_?O_F%k;?YZnJXgTa&Nh&c+A5Xoj*Yam~3O9
zGI#f&F2KRExlpBm!H3zO@g#;=SjZ@rwQb{V!6`<Jl2dbBbpsrS5Si;|@;@#h*adPV
zMuASfdWY{=V8Wpnq-V=D=+^ZDFsP{BQ8`zf25U-CnaI_54NdY(CINPAiZJL4fgH#P
zN81plP!vcL+8FLNfPIyp#GC&(;zR{{S+XgM!sF@jxu3ScstJR*?uBS;qb>xmmjQ%5
z!-%_bEw*5+cw>TW@b_8XF9b6hZqe0yIYMeb)#|n2@88esWjqDTMsPrBB2e(pjaW`y
zfO4o^kvz<1l|C0s6XRbgy5H9FNOvMv4+#EnV%Xrx_&qx6*+Ow_i|lk<IYLWGVnr&Z
zschTXkhG?bU0su9WlI@G)Wc#=TaAG&ye(I6V_aUMFF?c2rRxrQYsFC;cjhCq>p{V{
zE=s3E!l$rA?l<VqJn36C<5YFDqnPk3+HI~oWS{P1iR^cOoCD=HS*jPHX)wrfR6RZg
zhhT6|r=feX`4n%ew}$<)l0d$R1H#GD1RTll%bkW0XMwtYZgnElh)EE7`aH7^G`rSV
z28HsH2sjtPwIJ={;<5r^3qC0U--($EPVq<iwA5}o!mq|(-uU^soV0j4!<b=u&vf_&
z&pQSvN=>ndv3}rKS3pPrkCZffP>ak;v+(vq$@Wt9?Vm*%+9PW4NSbr?0Pw0=i5O;X
zv^X2DZ^*vV*W8|%oG2AbO@?f#FM<Awzdbypj{db^)6g_LopmJ1<um8Z5aI)8B<0*K
z$QU`Lw@&uE@7?V15gx=Y46$h7;rqqN=Efd(Ci)DVw;A^0!rCoOp;Da`*q0@zl;s6p
zR45)UCX1L-vaPl<SBe6|Bio!EUph^uag!5pLi(5f2nAUH$nPOFPR6P;Mt{xL4XJbo
z2y{je;zLZ{Z8Z)o8sNiw(8>d<n+j@!EdOPCUZOtinP@oOb4NQ!qlI4aegxe@W4%6g
zJ!RdwCFVqiOmhTAH#72)6z;7xFcW&53Hd)ach5|qNoy}8d(Ho3Pj>J#KN0@H;t(5&
zlyBTX7%fm7A&TJ3lQFHO{7Z8Nv6uVTM&|dM7mZ=xT|}Jhp`LdLx!Gaq4GC~eH+TIP
zC>+W~!wZ&KIWK$k2Rz>2u7h?&*dmo1I%gctNEWN#)~c_-n^$7#oS9(>$`_|Y*Hob)
zo`9)D#;49G@$vMxdGMq%Sph=$6bjB4LhdRIZz4Jo&rGp$UXBm}2TW5dzx3XM<?P2I
zW4oxoszu;@$<~4`hXUc;ozwmj*ak-E{vd^g?%%N9+}=z1;Yuns00Ml!<K^=Kt^G*t
zvdXS~C<si~*_QupmynX3lj=$W;|JD&Fh7?K4gt&+wGEMz#TK((FR5@A6*Z&*b?n5X
zZ$&5u!R5rK`L*Xk=M4wII$}+omjJn@2F1waKWxk{<%xKCf7nzi7-euorneI&t1Syy
zA3}JGV!MhCDrR4jMn(7X8=$za?$Hy#?w$Y`*lR7JTb5GnLUDp>P&UdjOmZ`<jx7tL
z_>0<MbHry6vA05_|Ehk?FMyHBni9mTf%Xp+SIhgVza}D9i~=c8SsLQvw)Hi_+F$To
z)yk^<a7G7Zm^7dd2>lW1f3B-}3bV3pLD{GkkncVvy9{+)<)W2tf^cJc_#y8avl|_;
zHzg6r19z86FY6G0N|B?^*OMb6V+vi=;e}jJPdPSX9y9gzrB|DBUFJn2@ECB?zyXeE
zq(x2IXkw9!*`1&MSfm~uS)4;sk=e5U_Du=<^<wR5cYelDOj5<MFUGmL9kRTr<0ogT
zH{`M!tv)Y;mqR!B^?DJHtD|Um|G1cgtht-DCm<`bJ4@C-vOLS*VgVyyggxP^-;-#}
zUL(fvZZ$_b(-zb#uoTj0iH-S_jv&BPOmrxrmd&-2%EjP52&>0dV|}Y)bf8K=?1ePg
z|D4^qMU2G-npScDR)U+Ej5QQCfd+;p76*Wo%~zpBI~k=uTWW(xT|a1czd$*n_y-j2
z?+fb*J~`ck+)pkW=kR}rko{|Y8L@hy5x$+XuynirIINdZGmED0o(qAs@*3UEU?7v}
zs-5QFCF<aZOP+DVDRH))%t3-!q|ms{4lhXyr~}+OPOxbHFUsCANRz1D((bZt+qP}n
zwrzFUR+nvCU4F`Dmu=geI`bjUdwxvB#Qkq)L}ny*#>QIfa*jm1#B};+8^T#JYjYW#
zF$VSW`yWqv+R(g}&CajND?1UFG^!Ds11%v5&%bCn%Z*Y@(0NWY;xBNP?`nL`*5oCU
z_#!rl0_#mGm>6IYB}?{Cm<Ef>twh{JNd19A&D15v{%1^-W=8F|nM#%pmb<(%Au&i|
zl?RJ2;H!J*GwOsvE~91%Ci9zHl6FNng+ZQ@u<?ObKIGI7Tj?zG7(OS>Ueuk?O#5Yk
zRB{^%{Q6*CUC(;O9mZo*x$cd2ey-cT5)zExGm);6l#Y5HpQgCvn<Wd}K|$k!d3F_Z
z2(A|+Pj;P7N3Pu7rgQDr3LFFS|H9O}akldayjRe>V;PP!ao{hv!;^;D(`{-H4-O$z
zbaX;qItn!Q<n5{u{;r)BG{F^s^Wu-V`^OhOl+@p2$;)-nYzI;q157=yKOGPmovFJn
zA<EHe1Cbn4*;)3+^-WE)!F`8!de)Bt6GH(HDkG94RQs7m5PZvE{+XATT_i}O4IAZ*
zMZ+GP8>E{nt!6ZID$t-K;D8ttOXOt)D-SsMTzV3*+EhQD$U<hgsgF3#7Gp45lv?40
z0j{@XM5XdD*S|(Z62h9pnN+iH^VX+U6(VYKI6p)(AO{h?*2{e2phVl@>A(25ap2j~
zd;xon?wTqzL`4j7MX9k$N+fD-jxo|q$HO?Y2HwEy&n1N-XHwLgQ;wLvBe{!z)Rmwb
zo++82rH6|d`Nm(*j7flF&Jz(rMU1X4_&p)ZSnhYYoZ;<rn4)Y<@nG3=haXq?NMXIr
zXg)pnn0N<cdEea^ops~nqe8ht?YNe0aN-$XnC>K4(Im}QC1xG0!i#WvNhcR}sYLgT
zZed_VO`eD@j715mera>nWyLZ=(6!oahgJiuWwZSehQW1;I%bdujD_d^X-Y&VF}zNa
zHzRZ2Y<4P9<@a&!na(N2iHuC=cSr%XxR9c6^TLCTAnbUmvo|Ztkj7zx&|Fo~_s`uR
zK!wREfU(=H2P@B)4KM6*S$Pu5Cm&~Ls)Gg(f;;`7!53v#qP2=2&`wSX)3|XL$Vu}{
zqyrl?W&+Z<E~bb7zbt@cDr;+PB(bPcFA@4UjdMg{0}Pc63}Q<&yPBH7MHbMx;Cqi9
zqh7!_@9ZDvc)oGJDjBJ&>hSwjl7Lo)xFRevNB8@HSFeIXS6<Pw<mKzL^8t|g4tBHT
z+Lhr^>ehpNnnm*Z23`*R(5gx)Fka;qIhwTfj;xqor_UN%z^zG{;^vavPyhw(sma2Y
zq(J>*5v9XPaa{tI`!%<0?r+A6$(c6T&mE=Sy*vR5?vZcRwQB<;ncKNu#K4|4;E(-(
za-J_Wy*P4zdT*X496Ny4|9pd#s3+buoENqhf-rQyaE2}rB_1)W-s14?%|4}amcov(
z+>d;Nw|sM`TlMWeZVM8yVI`Y%eNAxZ55~1@511UFLKBFnXGgmgAapKQ6Mn0NV|?8*
z^4ijWV=lX?@0{nHwm*fvIySd+T09%my$|InnRLZ>`LxX~6Vu;}DA9pdrKoyu(SJqL
zygYN1pXAj$4G770o}#y__;?yIDf&oqjU8xgES|n}xlaj^&h+B<xTk&V>96EXJf`JJ
z8zdXxlATi6T-)?%=)mt)+Bk%a<RgA2FMk0TP}RzA_QKzp%;p_a@ILYXjCt&TX*ENC
z2uY6>sA>MK$(BD!l$7EMtNQ-HJsN@$u0`MOxkcrlW*a(tHVCb|*ud2Ihd<&{cQKg6
z@dd;)KKADi2t?LmFjWG0Y}oG^>EgAx5fm8loUFV`^sc83nh1jz*3yx7$7$F}GP)Xy
zoPVpHyBYC_azpw)Px|Q;lLMd2gS&YMJ<z=s$@#Pbps_QegPIz%@iw$ye?0*XckaLC
z8_|?dKC`+qzdxl*(A96?2@$5IVzW<Ol5fW!bpf8EL?`y;f--q*_U0)ef@R-dn8(aI
z5T2cX`=^`)vwRM3KCWxVNH~+U3nvJ?FP9}m8H*&8ML5cL-^QR*DNQDKq+E`O7cZBw
zFpybvxMhYJm!9h=1##Xbn*20L*}B*<E^Ef>sJh^nERD@WBhocT>Cbi~Ag+Y`6QkL0
zoP9@c;*6N)4$QvSG!5d0;rW6^LnhiMab#(c&`>1Crw;{0HCH+p0F+`(r?|P1$@PZZ
zV7(KoFGP~R(*%4Pj)EN!2O~s~9+IQva04a%72@1nPzhE8@T}OH#n(+9iX)rFvwaG}
zw%jk)oABP_@P?kQ*-4sKVT<;Il?HHsvAJ%2=Bf|NigVmW#E3<tB{v+WQm8oQ&m+N%
z@a(Ln@e8etGM!B8$Cu#TBMJh2T52)i>pBbQzSB7V)rRtD;!ZiqwhD|6b+AMwSso;&
zUqsQtEdANX0bTh9cOIOX6E*lkdh5jHG%l)T1t$N~8W9=aG5=3rF^^6JMXzb+35NgO
z;d>x)WB*?F{%gI{1#InSi|RlaWgo5lfmy8|F+iZ|)IFWaq2H|XelI?8R_f7$?QpN{
z`$K_hGF=Du$8`QBm-Z|jh+xBnn0<1`|8mS9J8glk0eX?Qo=@YyS;w_S8k2;=<=l0<
zoqE0Mpj#9Ne|ve!*<ps)c=@PkIq5ip!T<=}xSPQeI*xn49zhp(+jR}E?2Z4TIIv=g
zso?U9e*h;-s_Vqr_hr@mTI%FvWDojyfPM&@99A1ZPB2FbPgLr$;L((WkX??@BuTY#
z4FIj{eW5M>gw}QW7C=NU4w<sJ-me%TkkC*g8{T;1eUK}TVa}W!9~O3HRJ}Z*^4OVa
zBNr6cHq~;Y=o;|Pi$YY2;C~+<G7ts*@AV~tM5tGph7?h&^?|X$%Z01+hd*3o`${<S
z2({=?G{2_u*Q4AcC4G%~>q5>-{W~hxdZ6b?u2j7nW?;tte*Z7obY?r$fnTkFOd>>E
zEVAK=fkf<!Uz}QbJbq8JtDB4IZu7t}_F5sIZFE7*C-9TSb@Qj)G;_uo0TlRuK^`f3
z`b#JIZrrbfzRNQ_cN29AsHOY2)r<3=-jE)8PlH=EBBn{8=e~=IlrR4L2kxlv8W%`B
z^42x~#AeuEnJUCm0XY>PAJ$_PfY=P7Orx|sA9Aq)y&>R1gj~AO2N{1<ucXe%twv(4
ztrqcUSUS`w?K}qIIS$TE4UN>m8my!ghFEXNOulKznr9N;+E49_iPw6cpuGs}X9JRL
zEJ4K23dx^j>YMnNz<kcW-kKv2Y`vt2N*r=#3{g=Q(Hw~7etU_VZLuu+6P0ZaLqzOD
zR#fo6)s8<DkhyF3ZWw6S!@u=O&(nTUQMz`sv4P2@(-5u;3x7W<eeZo6T@DXs+Y8bU
zj#)1|5D}c#KgOpM3`Em2s8*}!o|ip_7Xn4*kWyif_wK?=XETnj#T-c_pRpWZfB!P}
z_`@BPDEoGGV8-`+P|LdX4$dp6@9Yj~^xv$Fc<Sjy{<%<v&GMufo{{d049^>?a$qfL
z$%jNjIbyks0iIfNcf5INNWQ&3Y>AlT`aQr=aAvXM?Shlr1&&={4y#Xuk(~fLFzii;
znsp=Rk5E@65{sC%vLPCuyT!lHhOELeY72qu*nB{5<yA-46aghRfmy-r+)JBoxic~p
zpdR*pIw!evdTCN^Ftt*l4Zh!q-(?U{GOmirn0%H&-;#G*1a}~%)jxfd-68H-+Za!d
zGpx(@iGa%j3){Uk!f&M+E;`OA>ka}Rpgrt~W*n`}^wO>98e>ys1i)em6GJw>)kesS
zdx&wXHoY)BCuo*yKOVt+?(ZW7LKlai!aIO_l!dYX2{1<>3Li3@E9fW)jMSanIh;}y
z_|Tn5F9YWPBOs=3${dbA+}zq?q_{zV%GP0q>_TJgHLbJ{k@oSTFwCmP3}4;RF=VJI
z_>?g-CqrxKmfI9t8E+DNEoP0l^<xNR49-(r{N$63G6pb;#gGb8%tSBS?Y!^134iFC
zZ#1Tkz2JMXoL^n40oInh2_KNYuS`C)VZwM-nWoZ9e(aSv?X`K!8TZ!&?o)!i-c~^i
z&@|PGoamRAw&3r~H*+PQbtcx&?&%Bt^H0D4MiX$BxHc#@8t>tdRA+lh|CMr?svC_C
znTW(1qb^OMfgh325QA+rqJ)+dEhqnU+@--wgn>8Lh_t|dZWfEsI74OfTJ>-1!D@QX
zG7R-vj3p@RR@)kS-C+8zv-z)ilrs8hgNzvE;M%;~p+`q^PniZ-!r6aYKrZAjjl2Rq
zt7t$H5zBRle<!@|&gD{Q=99M-$h?neMn&|(k?nhkAn$M;K~dMrj&s*ZuLGjX(ww$&
zteb=uW)$-_P*V9UuNMIvoVfa~p7u7i;r(kF4Lfd>R5ix%Pz3EfaKjbXm4nfBz9AKD
z0<Z!!DCSnENfYIYa&)Z{mY69q&w5(xuqr$fAz@*Fs6rVGw#A{tAF7v=kf$u;xJo)4
z*E;yO?;blj0=D%JX`M>^xZPRPKv<63w{7o-lA1YFtkf`^Ue~GN8`t~?9(S!1PT$D{
zGb=rCg~$2!%3`~axd}5=n*3Q1KG*lv&op}s(6dQ3T5Bs#$~Q(6S3NXGJ^Txvs3A}2
zak={olpQU@Xgp#EX1(-h6jkD2I5q7z%H@>;CFq<^?8SM8P(AoNtW_!oFQO!?<5(u!
zp1H5N3U#Kbr#c&Um~Ja{&wQyr#sEE~b#e0H>4?Q&q=5*O1fa`MeT?xzRyW<Zo#%s5
z)-*%-BXuq{FpJtev0OB}=+hx^5ZZgWm5rwsI_=k6N#6|rrH201;7byOB0dnh+m(6$
zZ20)Z8iR=>K)8`p^FD%E#waEHXBwix-rD!}DO$5Li>V@>N!{I>ET|~FAIiD<V@J-n
z<rA`TAswEl1@^WmyukOJ`-lEusJa+GP<-lx)3BFp_F}tWPi|Q+CR}{&6_ki<;QI#k
zwqb(Em@fTkdk@RE(rbfIov|_S1fhiRhQ;7=is6GKj$o#<e^xN$-D+c_jg~Y_ik;Jy
z5~bbsd9YnzNb86u!e&PUA};QIci@j**V_yWKPle+-RTC4hKgV?yzybgRG!ZM@!D&0
zpU9fc+fLvUZ`!v=_A3zHy6bt*g)$M8ged|j&uUob_SQmHc_&WcRB_(&#>g2<gFM=*
z&xfEk5QBvc+T`(LUL`ehS>Fe&qRb_|@{=*nw3+eAnzlL=Wz1i3-lJ~7hQ^(7iGIPG
zCzr=ENb~peoJ0)TMU+WaPgsTru&$}V)xE#sqgi)LS(FwOg1OUVC~Rqddv!d4r{)g<
zUH^#<w=k?<Omfr(EUKprEU#<8^~9;Dqd}jO{7daI=TPEyXxSp2pZBb*!(|sQ>W)P7
z)r@;ME}HLMOaqOj-dvR_t_6cxIlW?L5{)1JI5xUT<Llf2T7|<zLcNKp$ML9?p}t}f
zcmL~LnE30QU;nHvY>S%KFIdV}Jl1?LjD{sHY^~CMdYFRXN?3KizG=L);Ce7GEh+%?
zY5MBs_Q|~K6DE~ENTe*Mdow<T7;yX)C-3O~XgH6<K)nEdg0>WMKt#DkTI}1-Xq{5?
zuj41+!!g9J7q?>yJ)N6<V?`4x#~m+2Xf;M!h=xa5W(C|s<G2Z$7REZK+mAA$ldE5K
zJ#uQDnaOA?77}yv+OdMKXYqaT1ouZ~f%dnqsT*zmIahRJ<u;vVGVN6Ke2#z>+MKR4
z-L@D#Q0j}4-y8ArlNnUItx&$-)~WWg-c))E;<`5)=mPEK<4~i1i~=;#@D9-}zTq&(
zErW9s){gJ-U1^aE4XM#(?PSu<fT*PlBfk5FxQh-}^Z6+H#eU(pY`sQ)QPH*SjL*CF
zTtBP{bOkPa1Io>;97=|83x3s>4~~tmh|_noWy%qrgp>ruS^TxoNDL*T^{kkTj2ssW
z$rB*FOoIqmBe*L(c@PWSjbTBoYIdscLAh6R!(+y661A*v_^@v8e)D(wUcRY%kDewH
zAHKpiKhXFUJS)wKuHbGRtsh2@K0z=BL08~fD}CvQU9aV)z+ii@Z=Jn0`<<60=fBHH
zigfek&6w~wkG!F)q7gz4hQ0!DjIMsw;U{yxm!sge8#!wvajG>!j)J9U;*w7G<Vefr
zmtLJ8zy8}b)AbvZ>?MzD0X*E~uT!yUO&=MSmI>)moz4tSLfE8QWZUoGe1EChr_G?Z
z;=-s^TW)yBDe8@lL%nn7GrAn=8TneefWq%+oIZna5_e3Zxl1|Z$3}w)*p@bNWiB6D
zuWBa-&S&T)9;Tj+P(uUw&d+xRMrClk=Vuj!&Sz(>qOknGVD@xLN4am%)P3LB3iI?q
z#Y~vs{ceHCV?Coy6rzuo$jvEC*(btyadqyohGTvQOOZ9p3J%Obq)v&<p#463)3Jnu
z>LZ!ikO?%1sIeIaK~T9;Pb=)vgSyUVY-j4!hG5er_qE4Ko&xnk7{Z$?Y^?^hn>0VU
zB!JZj4-D<8(~0AgkY}cvaV77cEr=9)<4YfJ>um})$;WXfr@1*hHJJ~xc11ilw;_7u
zXd&iRWoPy1&(RtQAzItsgcsAOYp{n<FwkVSlCbN<1|r%`V5-*iF=4C3=Gi&ejs6uj
zd~RvSX8xJtQ5j>hvN{a=c_~L(V;vA$#DjwCg~Rp&cPN(d%o3eB8HE!{aZ%w{K}}^U
z&hHW;5hk1<;LVM#q_kiwdo(|Y+G}CJuue7^UPzo9VyhP`Y$W1_c9C(k700opvE09u
zczWTJt{HxxuVE28dt+GYZee}C#GUeQe0S38bl*7kDNdjK`9g@Jh9%itHl&QN<vyDv
zH1x3E^-ie1js~@c?nI-C(7`@DnZYZPJ_uYrHmQ)9T(LpK)06Q2A+Vy|MoDQKex^^+
zGeO3yq0q8c#7u<d93hOsKn-QNA?i+;@;u;IoQsdP{NkpaEU7Y#4u?RGh0wj3pq>}g
z!6r9om;_4((L!qzLQurgswhoHBwTg+$&le8q{VxGY;K%%4I3LJ)mT~RcqabW7NwE*
zZEScZ;=%r)>Y7f*>OX9x<sqaZdskOCMv-C=K(ODdF1bfJwUA3$X2r$8&g9DsoZHk7
z9;SayHd-YH8j<r@NwVc~B}*l?ia>YpA_9)`xU<m=eO6F`=cx)}YfE+wfO`Kqxp=x~
zDHolK>rNkMX?{myjwfNb(uc_%jncA4HdJ!Lp-D&@13@lF(6V8h)rDp*D|TtQ7OzeI
zaRX(ZbwSG&uVul8qQvEcjWW2%EY=cqznpnGc$vNVgKnP?)tRg=Q^aWr1lZWvoBF(=
zo?N6U7GVe%9PtWijEZaP2H^*j0bb}qNz@f==*Deyi0*GBezhb3bWcmrn&tYdv%Y?x
z*w=~?+`_>;a#(*$&twN$2}172S1G1vL3DS-%oqbOBY|T7ky)N8_76}%=1%5X&$<`2
z*8?M)pEnx=s2L1A7_WJuWh#vhFM<(&5CLw@`E6woH5(3HAASW#FLn!%moV(s0yd#u
zgSN5!LPYf%?)TkH!So|u{ys37-Vcxu_d6?8g#bXn>?kC;vGTlD+&g3u=SutAeA4Ut
z;Y=<bhJS)V*o=NaeQrN&T5t0VIjo$|j9xSRXAhk)Nb9%38rSXHM8GeR;hROa_e|0Y
zy-$9X-@Wn+-B7YqSQJjeoJ&XMyKkffxcn-yAy^0lRCaXOmbvVb>1d<l`1n^>8<-nd
zNk`sYf4d=bk-C;`ciix|dJuk~@c{@T!^Ib)f`b4hkjukTSIRbA67&zSf?AN=+UQ(Q
zY_%F~R=nxrqW+E{4NfL$tu(=_wp6Z0euRMUqp6%o1q5>dBON4!yM@uN2t(0HXMAGr
za?I(iE~CF9wY<YnrvaWwKpm`H7Q5kYtcKD38Y`{WDO345OT)#v?PM=el-tTcj$fm3
z9{PqeivH^vtad4<5pa_-mAr3cvz1eQ6Jt0P)9k_kXJCabPc(wzX9ZLGq~V8h>+{GV
zF=ld{QJ6h0=Uha_vOo}_hR>FHe5k)4KHa2syVt5W?cYbM(o=L<W#)~qJ{3#F%%WC#
z<LdVjw}6cH^8P=zgHddBm~5Zy3DWLDOeA7MK~yfizV~P;4fGDsMs%%_?Np5rk8o3z
zqclR49Y!{71R<0E&>pu}cD}?+jL5QYW~8->7Z_y>Wd#Z5=bfoU<NJ`T<3#rM+i#OC
z0XFN@aeHFmGtW8CkXK#ywCl4&4=wX~#fpkpf|cQH8|gXY1Hpg?xah>ZFJS5Ob1Kf5
zK1?0FKXuamS^ij1D)S_ukuy^az(5BCehtPvxqhyoR>bqs*6^W>%HY9K02&oZmX^O3
z`2x`Kbt8BGPH*m;MMdI4p9O9apXmHiUhjUxW_?m@SfCcdt$c1;U+`{*C{n;hspfj_
zHu&sL`YDM``KY<^We`2!3Mhv!Dr4}z5Bi!CY=^OSz|q!#LIY9JqH?05t$O;|!R7jV
z8a-scz!Q`_?02MHF+<M{`DpB#k>g<EA4RT3L9alF4wN>b&i9SA+XaE}e_CTK7(+0+
zXyaY^AIxyJUb*33J4%%MQM~1Z?I*5_o4j@!^u-BFifFN$Z$ERCa?K2OZsSTc*MYHU
z{@KeNF$mO|cz&Vp#cAb6Ywe61>2B)2ottkXx|Itdenzd9FqmoJRD_*8X|*1LpuBDi
zFD^h<(%1&oI#<e86gLveMyFB1c6y$ar1dwUx-d!JUD1I`jyP@qr2gIdK~q}&r$DO*
zpJUqt1HSeZ7tBrX0COu~DF#-y2<z)%Jbw1-WHdq5S+?s`-xcoz8Dg9Var!63#o31U
zc~Eb3>jgS)w1Ut0iTAyJtlg;#PQ7*uT8F_wwPx*SD*r?G6VX})U*F7bgSf6;fjsV$
zWJUK2&ieAjsa88&`PdXMjl2pI`V%8@9n}XxP!qJY_A>yb)_p(j1(g1{WUeU12%E;-
zF5LS+pn$}=l|-KSyLzozYI}~QST9~c*Fr+u?cM~-Wh*M+uyy7$LIzOR-jHK5+aT?R
zhE6PP29WRr?x|oarCk&qE5WaQZ}5=G<3)JRqa}O5#^_UDd?kp0fvazJ)ipjo<03U5
z*C5j0FLWRmvIR2$ARNgU+A|N!*w3ObPm4R<9vXnajO(bzO2+Tn^9F}|`Hk0yZKa+!
zG20Q=E<X!ceuFnIVDZ;IQ+pa<TAzB8JesdJ%6E!tAaAtRu19ibS9iv12XpQBjCQVS
zlsc5U`gTfH)3pA?prY<ln3^*~-bPPYb^jNxiy~pg6s^FjpHP!@Gy*Xv9SZp?-iTX&
zZ`?8asTWT>@{bL<x#CuH_kNp(yHpRuOElPuzhI3>_qYD{!^xiHDZhQYow?*yA8vo3
z=5V=En!I!&r>hzOn!Zph10JULX^7dEPl`!d>i`0l1ax%|A|mo%heK)+y;ltF*JqB|
zvNK|n#bPgCo6AbD80DSVyPQYnvMyGA6j}4>bc?WD&NRIDm?K;3b$8g-%xrFy<t-yT
z*L)FlxIUXlSF%-8{F|?c$NSLLbj)JeEG%?)zjm=O!PVsG!=`)?nMMI=;paP-mO*C@
zIrRs%o+fK=TW7kyH@nO)Q@=6wLU|glK$~K<CF_~U!u*^^Eb^*F(^wJXzb=)OBG`N0
zw@3VgaE)d}KFH9nh#I#W*qq80j*CkR6bAD&V1ZJn12{`h<?h&ahr82!1zoK1A0e@s
z@M~7Z7TZx7<(k0A2j7yCs|D8Sg98D(I|k;m1JxQy2r^d{qSn%O>M(JKM<Qk?LAjX=
zF)R=jP&i%bPWbN+pNqgMeoUH^STn1UflH6J6R+&JL<Iqg@rtmv3w;g&a9kUu2jm{i
z=L;Ec;I1k>md5i@AhJYn`-S~IvEfWtf8QGp8p@p3voq>Tmz0RJWkVs7wl^i{>l7bf
z{Ti8@f=e1PtYfjSn3C%qw?wPIELptAZqW{WyK^^8jxl-Nk4bUg-b@ysZhqaOd@K_f
z$+{#5u4TwaF|@)TJ2E!4aww~6^T--n_|bQSV%;{7Xn7+>5qKm5`+ZgTDPqbhts99I
zw86c{TkMCAYxM_r<gLhc`$2s@t>D#DPlPCJpvGXMhuhT23>Tz)p0vgSR*Y^LFL?v^
z7mpGaeT4j|$qCaXr;J_nxwWZ(vPbU+Vj?3KU#yMnOGT!nN+(JvB)yc-I*Kqhyq^%f
z?BIJo&Eb}+x5LJV?V(3_Q($B1R9seWLG$O55bD~73x_=VOI>N6va8vh`$L_6sn~2h
zltJWI%JzR<fLnLd*FG{O-joAqJ+hYkqaAuTM^(>;R^BeDemEcBH;V$fBdQ8;%IpN8
zLA);^9sd1%O~w%Gqoz19p*g(tP()u_md2H@<e^J1IYrlur15bB<rPgxr2Duh_%FH+
zg(C6lE>C!;ocX@{$h-F1aoL&TxLk?x^%Y|lVCrTb1jHn@!}|*SVZSg~Vsbhm<G;=D
zxupu2+@J?<kWdmak$|ln;Kp&z3(lBE0G;@N<e87uiV&H(>IRFyPo!V_yYcEV4;!|)
zn=Y7bAE$MicY`eHNJoMEUQ|n^2TZ#&kfcmhLTXEGEA0*h%|E?$vIt@SYUti@oQsDw
zJ`1MN@kEjh83hFi?I_|}8QyrZ%g0FLmM{0W2akQ{vAd@-5sedG*hroytODPo|7<pY
z#I=Bq)KmkVIBD#zP(Bt;Tj@e{4~hzgK$Ol%ctO@$&)EkE=V<fU9LWf=?ozl2jz@kD
zML813MmR<82aS08zrBY@k`+Cpupi_J^bCI!sL4LB*RSnpaZqDemE85JMkh|5?+Z@s
zm!W_ZzHmaB9=Q1LM<ia?ZMpgagwXLtg0}5@&fMR&OeuO=oLz1lcN2@4Y|fZoDcaSm
zNkMemhlXF9GW7=)Wlnx~l-8$ovx=EPWDH%gAz5Ehk#k?|aS$u7C?wNM6-A{RLwrE-
z0zV5`ykHq6c<KBu?8L+)dt03t#@4p9!}6Wr`cKsDL-GB{t*4=69K;k9U}kW72b1TB
zh-|nn;$nSsvB1kT41~iXQNi1J!aMDzCYi?%&9<U&1n>~lSjI`oREl8(+N+XqZD7cJ
zatQ;hGbO2)mef_kyQjXCe8Rhghd<H$M%FJ*m>HbVDVN~T#{mk$z~ao89tml9l?yC$
zt6~Ph0al)_W6AB**-*B7wa4C$G?Qqtv&ud+i&z9GU_7z7VisqNCUbd?Zb1;bVH74y
zFP6T&N0{bYuMtl0u`SntxL64z5xYCSc)mahhfX9jEG~;>A2YijOU!!x!K%gW%D#fJ
zd=P?jTaAR|9q-3w#_^56OOd1VGWlr6kvE_QVZY-2Dj<3qdU^Yq(w+b*WE`7|)sBnu
zqH$ytQh_p4$0*<2e=^;a6znfxYz$_9Bvwe%kr1^R*er)TKsOfc5AQQ<uJ4U}$V+<R
z$%<3<*m-}%Zsg4~bN^en#OxCEWY-Zq@9Tq~qoHsX&iACqch_8bRapsu1Kvb}oQQlB
z<A=2Axl+0<zK+HFI*3v)(*x2b4?p}?GqSsbGt-g;(bh%Bsj@0)*Uf<cew<vbq8Jq+
zCb~0DL<l{n0e?=gELu>kU?*O1pik(5*2AwSZJ&T@URx01_-8+wp*N6@!$#o_>k*Yb
zgxHdtY~xp~1rM}&hXexCKpF%dEL{w<hL$|{+T_PB*)H%2E)awm6W1^uh+Ec5f3&d2
z+OA8M(e#tT&D=7NA>nnagjXE$oUXP|O*<Uhoa++`U<Xnm|JQGF0i5XpK*HZ^(2g!8
zK?ywE;Qd)&ZvyI{)$DmK>--WBwWlgML$NqC9XXY}Byv+YSKBVc2JvkLqgVj<C#cu2
z1nicO57dqhE5=z_3k$QbEq*lQvdIz2bJ?z@E1#1o+E}|e#nC4mcaTyrt(`}Yo`Qh_
z!pTH-!J3QLM;Li$48v@FS-BD#VY?K{NqS66(r*DNT=mNtamRE}uF&HfuC7ZM-$sUI
zOnAr*RDab)yp0e9!~TKmoqhjFK&6?C(riBF#e~<7a*V*-(b36?xph0K42ZAcUt@0&
z`leuKP)!b41=2>fghF6la`)|x^A?I_((x{lAt|uj`OTh>!2kyp)>CsmOrfd344D$k
z^~fE<R-na<ewPAJDkX|$KhdLZXUSsJIu~bxGhr~6*NS&{$iDfGkCzz!yv^m#@rAJ~
zBa;(szhB!lNr@TOUFCa>wgn%8BC>@4ht;lWwg0)DYRe-g+om_3kMow4NZoaC^oVBH
z0|3VJF!h+}XZ9ZIhi41AkSz-BqeC<NrMjRshON{9G-#-JH|ue?;}L*eiEeBUMgjeF
z+^a};j#!IY$qZSSEhaOf0mU{e5}n~_7+6DLE*}GUV$dLr=6$q6g$|1nDzpfrmnd3V
zTO1DyC#5<C%J$%uMaPvZQ`D8YucvCcjpHS>Ue%>(UxkLC!QY#tn^ScJwve|-+$#tx
z>8^5;$6f@_tfM--(}TVjv&@59x2JOKv)iDwl?_Q<6bzT7G2y|p^g>z*77=EpfS9zs
zFq&@Vc@y2Gz8jGPv(WExS@=<ncD}_zNV3pid4*?xFS%Be!U>JxF0%i5n!O{$6B2SX
zg<Pj?kq|C#bArvhag_2wrd{En+Rm{!(R?!uxv!@u3RjXOleZ_P)70iRYXlm$$eJA}
zi@%qb8!_Rzi%$KZHa{Q*_2FJXF|cs8#s@UUZ~wd`4hB^D1Ab#-GXc|La!74T73mc4
zrzwEA_)s-^U?K?<_h|4;N)ifHx>W@yVXT;AVM~IS@pwAu0sfi-H;@Vpic7Rmnep!J
zj!t8vBhge8Ngt;<SQRS5OHdP&PX);dU{?+khJC47_J$e9$A=p71TvKs1O{U|B=`K^
zSBs6J_}{pi9nlpn5t=Y`6bT8TWwkh#7HU!o@cd@hWOW|$q!W#SxeTJ#lS$4Ph$WJ3
z{}}i@u5^^yPz8=_p^bVYNo*fFba@;oI|;U><TSlS=`#CL_9exaEhkzKIA4!HgWG?A
z+(R2|&SiVbZsa}l+nDJ?xW7_n$CKhkv<|CPpdct<_W9^aBpQ4*;P7A0kwM$Fgr^qQ
zTAAg&$xRdIBDL15jF^dd_NKTdy9e62oi`w*(Q?G4RB2-}5ac4#w58TSj-z&VXqrVd
zL1ZO4=A5#*c^vhK?U%CSgG6fU=_e$6xx4RweIS$fudYF>*!PC>IuR@$<__k%;Upw_
zAeOW|sxCR|6)kkeamuU^?51Wk_<>OWinF;hCmS5#U`5&o_;TgIIkq&gO{2hQ$Db?#
zv$e!2<M+v5^2Y%(;mc1V?Bw1OX5}M0@W|zJqB2$9=O%$DI1zN5W2|NI80|k)Ii0@N
z=Dr8ktrDIFd<Kb$9ZL`~*F0)f{fzmJiSj8F)D<qH7Wom|Ci9|~j>NyF?9_}SZ=rxB
zc@l$-2E?&3NZ?u;u``ECHX<hOSxyY^$@JexR8|r;<GGT{`hsw^)f_0|t~SG=mbb$~
zIW|MGFt%cChQkeQ#&7$l)=-zHdeNgU+&phT)mA<ZnQ3LH#4ZM{BzMRS61(9hdNsq{
zXbeV%oqb5f#ruQsZ-*q@jSr9W_VZ?VV^g5P<q1wh_O<@CA6~sX?Z?76u5bE_v*JB2
z)olit)%Ap(^$_t@V+||ZkuZ2*^}L>$`1TmV$ZiBvzNqaq&GZgJTvrDKW%Xi#jsIPc
zxEVUGx;eV{A2^oKuD6`_)PzQ&VBIJgi~YO1)M@x{Ap-vnBy<#f(EvpXWm3-<rVH-!
z#3(1nF7#z&Vq#YEa!B!&X&4UmK}t{46=m91Khi_s1KOrnR~p3ut~4emAcKQmw7>NE
zX8PEs-g}7Ix0?*5DXFOaw4z*XHAu~%7GU9ux=}A+@k_<p+%Fif;dXt-@y3+htMWJH
zG<Q^zx)3ZPG?))G12<k9h`8b$)OA``w>IJv7*Q}p+D}p#CKEGyW~HzzmA0ams&s69
zDAU?tN3P<|UTOK<x)Goq@c`Z3h?tGZTO4lgCIi{JsAOrw6%*ni@cl~UpT|V~r6^JH
zf%KL3JyehGBbfTEQ;Ymwv{sY3$<sfSopU1yH61SY8m&NuZGRAjt|v5cU7l!ODI91E
zY?BLxyyB_`!)%I<cxR!lJqrLhTxo1K`q<pGB{83%aG=*=G{cUg{JmgWsJ;t{M#HuR
z+8(lvuA3TSYxQr0Ds}pSH(RjbX)Gv38e2q0Eb771mV#VA<B*g!=0%P)kpfPS_5~Mw
zNG7SAfuh;fE`+M(>fy70+f2x@kw_|dq112<YRS*|9EaEs$~M>N?8v$98x;`Ij`(UY
z-?wnqqd29!R=kjwm?g!XB5~%>HJ?%7DJlO7+SQG^)Fb3Y<{7t3z{hjxS=%1!<K!2U
z<Y!eE;s?h_>b(y~est)|4J>8o?6}7oVmt)}h0sylZ=kGC#2IUQwIVdPr_1N{5Btwb
z3bC(>Rk|X?8NZhU=1bj;bK-wjps)A7LRPkedESPKy<F@gNNFe*^)iB8)9Zzz{cP`8
z#~Y<P-NPxmvM1_n#9r)iOX})M*{LJL;G0L5B<X6j?I=MKvsKC03QEaSdBZWMqlo0(
z5OeXXeQb`A;6f#F`+j_tX+GWANKRFcq(DV7Rxp%4sRoV%i1B$zS3de>4q%sIiK+S*
zcbO<(+_`%6-^sQpS_-o6Rq~k_J>mV&Mt9XoY(F&H*P9dlw?iu%c~{=R4A(Sys^5*N
zdR{N^ss$XAuHzHAYzJ=2$l?bY`C$zF{BgJaAE4RsxjbgksMX-K<zSn6Ejf>-xQ^Co
z<{cS};>=A5?Y8ZkXN41eTp9RvyW#8jdx3#~Nb)E7WvfPs@PjZbLR^%@u5KGG3;;(t
zK7=q4B80EPr=6_>+^)<URDLvQ(3Ec}ib+*^s_P-pUNFAGVM6P=zZM|1MFF%*{plS}
zPC}Hj>p^x{V!Ew#vE%S^Ht_nd$2#R?+nv$Y8b+73)wN=FGJI@RsP$Z}p~Rvj^U%t~
z6n}20RdXI>3KFU=E(MkJV2X&c`|iJe4LA*mJifhc`IJMYbQPBUdr%E(HKXWCq+{|}
zg66VRd1Y_plp_w&1KGLJdvBf*^4+3?@*D*8{cOP0eIMZ2bIHX_o@jA5?&<j7gU(-%
zD<I`E!FceES$#FUTV<xa7$(Jy@|4(7jU1F1$mR0c1UJL^@@mBNBR4Gl=I_BgpdPTM
z!`b7m&%%%;O}}oIpA1*m33jFwGzy!Aq7CtxeewQUmYfU2A)J;TxMnRhQat1cBI1}l
zdm;XF(Oa6oE&}7@oo!j3XDBFz8d?mKneuf!|3n~~7Y;0;K`@-ZA#7GpY5LMOCM~OX
zus?Wq;g0+nF-MUQhP8L@B_OC-5)w3ln$!QjLBzF?Y@wv24Xp8jWD8uHeH=@i8N~rI
z`f;Pm$e3Yy*YNGwERRs4mu$RX&c<o4&W>_=a%vZJS3vW5TT^qR9#Dh<63v+l9$M!T
zD}+KVt>44sWuY@`Se&IX0dhh4l9};XZbFoF|HL>&rkmq3LvPxRSYx^7=1P!WU#sJz
za~C(z6K`)FOQCq`+OZ0d`iliI9YVVrap^9dZ8y4*+W23ogTf1iFj*-%{gD6Jb+eYh
zixlVKXj9T2p)<wACWGMSY*03H<ulSC_yLZD$AukLX<ML|Ts<oSg)KzN@P!t{DE?#$
zQ_7wkm~2L4a}S2XwRAQ@UUJ*K%&C6P&pS9Ro5<3fQC`RkggQ|&wF=XakeZ^Wx(skg
zILZ<OP9KO-z1)Zzp{Xk#nM5ov7(-sU7h((og~PU#paG06q3o>t@X<F+#+6rABId(b
z4=0mILzdWjaEJAGQDFq{gpm5FC1&;acM=LBY^J|`Iat5{{8>_AK?!khEhv=<e+j`{
zG|Lm@%1QGTF-E!~5s^y86)FG{KV}lzsiJ2HO_e~@m3`-A*OrokECn${OF^%#zqAvo
z5;ga=5GJsOhKZThLO<~5U~dtUm`Wn-f*6Vos4L0`G9{ubCBBBmQ&$6MI6gv&tJZUP
zJnN^Zvi4Ij&SJ`m$O#pu18Xa)B4W2UyoQBf<{YrPCec(nh$wo3p1IuZ3Wn2h#EBJH
zMoM}4At0cAS&ZQQTOXZ~mY~a#{Tx6>!rF<6npft04;5rbJx+XfUrZySse>hl$FE;_
z2GEg0^VfpNhd;Vj&aZp-r@^}(u38O7XR=xFwEMi>_059$c*lFNar6bo$(Lr5cRjXw
zFdfbvC*nF!Pgd>dS}k)DjxNVtmnJR04fKb=8@62DP+Vn{v7B<6gH!O(l+)_%Sd|8Y
zkd&|JP-cu(;qwFWQ%>*(m-5r;>Xn~fm|==1vmY$E_swk~`*7F}JV+P}tMv;GkEvKv
zl<|ANNP6<DCp0Dmf83FClg^*cEkG2wCShl$1qB;=%LCD&!%OaXTGG}9NI0Tq+dWXy
zjF#3bhyUY=cHG}!;+>a5N*jeFi+NGjONtGO66f>^Gf~avV5%`DO*LG(fmG2}2FZ(X
zB<Cm>U&{ba=kzvlRmKIIL6(fQiN~^z-DgRM*RJ7oqlVG8enA#zQAuy1qP~~Ma&mf(
z7xpdYV5jwmKPp1s{Y$D{5EB~it=%hqzP5{m7}*ZVz+abSqljrM=Z#rgiPXPap!zaT
ztgdS0N{_=u`zQQM-?TKR&|E2eG=%A<K{?TRF5J<Yy7TZySvwC#0Yb+;4dnaGY$CU|
z#m>z|eHw5St1pJa)}`CaYt~F@CSrUZFQ~zGOozhdT5KYtPEni19p;>g7Dg5uf`|9w
zJkP9Hr#NpE3xOWjopga5W>HMO#RR#P+gd(46Hb;x()|Jt2YZ;BEVZUu2c-)JSC+%G
zC+w%Bmx)00>^LoiFL}njx}`Y$dLfKH5}Xb25!3_*Na>7H*%Xr)vA&HyP8lP^WRTFI
z<4Vv^KhPy=5+_XOOu4qRue|n&lpgvd4WymRTIqvhcb@p{*iJkX(z3+_k%oL96!ZhQ
zK#PcZSGHNhD9$Ae2OVosmT;5ZTrFXV<Z6Sl-MIz+Y8E6R#TAN+0%kWulCiiHr7sZk
zIB~C-T^3PYx>VWw=U8Bi_CQVtb!-n*d1TZm%TV$V-iuQ;7O=9zA)!X>Lv}<dIZ2m@
zNUDBWN5bui#|CuQ;+35yR2YfkLTsJy97RAcktvQB+m%Ri6%1Ap@B+ad-=BUkr}KCx
zTp@?oa&zO4f9xFP?{lyG>YX(_dEo~u)1|<Ho0m#Ps%BPS<jfk|Fcbk22#Bi<y8<`W
z@E{4hF5uiJb($v$dcabkf&27Mfh;LHoz=z0&E7*m3jBr)%tP6eM=ks&i5SXa5DX~T
zny2}jAc;>@(W=q)(9D*fz&nOiR0M_|tobzxnP7fDSH(~`Km{d(|91@2YHvA&Wd8=;
zx05ekn03FD2Yjd22O^t@!6GN>!_`-oW@;Z{5}!5IV7lx(#$s+wO)~)^ZD*Ht1Oa#6
ze^^iBBPl8wr=e;R)xYEMFOB{)!CzrL3ia+=_%qB^5eXR0n}g7HxABC!0Rn8ShWqc5
z|H|m_KaEDoq9Z0Gz9AqMr1O!n;kn4hyfuqpxh3Gg+uq!oa`bn0R`vVzjm_-z_xNP^
z)Zg*08l`Ei|KZJx`JaCJr<}V{bH620+zt#X`loz-w|<&RDky6_xC%Zv5k&lYTyK@A
z))U>CM?wrekr|QiQu%K()c=yC?jS_<%^($uLC+DmI!0`wBO(4rwY!3f61~kgpj@N=
zM>`_YOEB@zGL!!-27|lK|2*ga>1qL#qTfOWT`P7-K>tyJG7^HQg$#H+xAOnd4ulja
zKnYxk_zB^6YuqOGAA)63P<lKdrHCHai$R|MdE_hqzn(9_!%+19<xTnJnw4KmmpGp+
z6MQb7KMv1S*VOnw<h=Bk5ati|r_immnh4C#&nx~b7M0ZeB}?}GrgbS7JDa}Nw-=7_
z#J+3j4YbkKW%Pw2QM46CojqFd1LB2{{dQyfP!B#5=c2u|y+w(CZ&PSr0YekRDz{qh
zAJB4dsAG6-nvT-Fn6eVp41gd#k+2@uKfpK=q<{&Gu33E9sBI-LCqu7i#Cdt&ORY%s
zKA|LiqtB+a6pF}uA7a=qtF#B>Rz*&CO))g6#VzQm74W)zHK6P~I8l}|GP}Lr2E0D&
zz4#EoU-1B^1rTa}WHGty`u(KV${AtzyqcrTGtWCQcfu|$A!l>CA=7{D*0cX3e(Xci
z{Odiiq#K`1bxxS(4Oo~vvJGt^qp0_uQqShkiCb9ps)vaQjQ*$48(sRLit4)Bzdknp
z64N@YY?{j2zdUhy!7}q*QbUXJI^}oc?#BDxP}&5)&Os2MqX}9K=`nZuNqNB-+Pi*;
z=WjuS>W}pQeq%3rf`TQ={ey#`=H})Ct9cVt1fQm4dl(o(g4@B1!4Nyh84(pI5(T%5
z=C~8JVYR&=9Tv=ro7Ie4#iuqB^6KD~*l4rQ9>(Vo6AiDT{HlKoEUXokQlhI>MjFmF
z(OG%VS=CZ@WbK)}y(q06QbpQTODx4~>X1v1N)ZSDoF)01Vx$P$Am$c{NeQt-k6VWb
z#FVtvpb{|X1um39&u(m)eaQY>4-|)9dTjYOIyT9>F0N)l!_300w7xZR&T3g+Bh+iY
z!v*-RX+awu9hYbu4j4Z3#pTb9nw-QP?Ns|vk6)VyHa$u$G_bIp?7;EwcR*LCF1Fte
zGj~_t)Qz+YpYAgx-!Bk_6SJYAmUd|{`UicnmK+%e59a;0$Y}XKyFvXBi-~?fm}RGk
z#medS0J-Ls+svMpU(tiCwfag)M<-#?3vKs!AtTOykh3|u^@_)@(+PK{4@Dab+yJj8
z<~2IbaEt<EW&vSsL-}%uUg+`5Qm_n=cxh8_e<`%M{I5u%eKucMB^lR_E|zk9Ld@O>
zYjL>ee5F?RAY}>bJ84<1DCZKNk{Xw%8R4{Jm9o|{DCt^!Yt`YSQf4mf>B+sPjQZdh
zt+1p6sbiJP^78Fe<Zegg`!N0vpi(|R&geC78biPXJY6dH@|GOm@cZ3qi-O2LeC~&V
z+}MJAE}$FSLUqdd9s9==Sp&sKHiH>#KS})VH=6b#+~~xlu%1nP!&PY|arW4_SebJC
zpLteW9!KO=EiTJb^}?!dXe?g|KzhZF=M+N?GrvJP%j>EZXUjL{H0rVtCv6L*ckE2E
zHjkiwYS@f>>CX~caGk(50^hdNQU=TMSv9>d`G`fDx1f??m(qvA|NnE_*(M~kF8qcQ
z>|I+9<HwcZgDtyBylGwBhXtd^n?=O^xT8U(LgjQfMX<%yYlsgru+l+@!{vnyo&*w;
z@F?0G#*lI{Vk~3L?%4&g<x`4$D|JS7|4TSr0fXa#8&+70DA7o*<jjqht2E%`xi5{%
z@s!_LMR4{cYrByXwlt1x+J>D^%M<m9byz;i8A$FzAo|8xP(?(5)0!QSoJ<9eD=V_m
zzALHMja+7v)!=uDp_n1rV6kq*`S#aj<gIRYV=ginB7R_D1fdM!B2-3`_@aXN)BkB2
zgn!)ld|<|&y<0<4G9cj>(Zg1WmZDOB)k>#s=gLI)Q<a9&U{F3GVSWT9F<!~@mJBjs
z_j6;GT<f*R)@BH?w*U}L&j7>4l}_o$@#LhHODUyG8e`@JQ;K=xtt1zn@TG5ysQ?f@
zTlHqckNuv`g_J<#OGsG;<YbNhD4TE@OkBy=5Y8)i3{?H9c#<Vo(GJ}^hv1%Embkk)
zC@99&7HF!Ric7G7EX1>pmjCMm5h#om{d%(4^V@r##xJC-hMapodQ&S^)JtcDME21F
zrI)1I3}xxFKCrTpmc3datdu_4kBl9;kQJA{wuIab=ANTeG2?GW3UT>XXOdUe{Os}7
zzG49d-x>$T*UaeIQ1oc^&7gK`malOnYble@sy#EujFE{rotcEYOZ^(ez+6kU+v5bd
zbM~$(?n(qzCEZkzA1DcoO<K*4W=ZhHCJy70TD|pzlDDhFbauRjigpTM56!27dSl=N
z?!7_nsL9?RN$;;m-!ZP(an}O~?kFqbRb7dEaWQ$KHWG7CIlWwenVL=|OMJqt-7l@G
zW0NZ<5F)Kk$kUB1M}oFiuJ09h_roQWqZ%}znP^Boq&#0%kYLGo0b2pdYy~tI42P1|
z2SlAX^*QeLxPLSpyHtNd533N6H<@v}`y;s=AhxeZ$v#KTrSWoYbJ_x$$Fbu|?fN;q
zetnflj8-EMGLfUkhGXP#4>j%EU{T<}&;DLvq(-nOFRRX`JAc}_RO-{XUs9=>F>>&x
z<Krfa;>;&U1Nz?;*vkW4N&coF2h^y_LsxvUG8`6*L&fM_#H5jQemJv_z}r{;Cy_Sy
z8$|5u^7R<x*~MpD@u_V6Bx57=cQ8G1pP630qoD^?+U7H5{Pu6UOJcHKKb`V=D=4U^
z0aa^KVs6Riv8u6#&{=Z!Kbe)(Pk53=*8=#51r!7(rfrr_4!Xg^G$Dcn3~{(lHhd(=
zYqJG*81$W2;z=OZT1{pgR4x6mmKY)a$B7$0&)vGKEa`YO=uVWqF@{V9HWSgJ>z8A0
z9$R8fgd8-=gliLp+&D%^{fqcK;suPiMdI-Xxl9Oi%B(FAHj|>S|8}vE<)X=tO^lc4
z${Zvbf5@35xvoi_&wW}d!39e(9t)KC*zc6V-fkFh)(*gYc7^?}g>zg^5v^MsyqO_?
z8az)$le6_HW6~htqb+m9q&6FL1dN4kbstYaU{7X~j&5&7L48yd%PE9F^ZEV2lNJZl
z&Z3Z?uIS8v^tX4TE+&-7VN7L{(%V%IyVE?ig}g3xC+}pZL8q%tIDQbYhkHbPT<(Sx
zSqSDyE9^)!H=5#`uaNQl&Txn8%k@sj_O?A=AyFJ6TVJsBorn0|Yz~8?feMza2)w=w
zX-|m#eW)TK6$y6h9)-_R#a`vfqrZ#VUrVbZplycJ{kMprpP?&b%Fe=O>;owID$z<=
zDH$Pq`vkN1_73q-XGA!jjHVu}P}O<nw4k+GKNBngO=B~h^p8?JC5R9`{JEfDU?_+?
z6qq;-Bai6XFBDRbhp5`0r?cEZ0$!z*JUGEB5_jbi9p^poCzz^3w-3tA##$>K)&%ao
zg2Er&D-8}573_HNn;r;lu${Ow+9-PAx&tMfIysaY7#mT$!Eefk8G|KbU-JqQP*Aab
z*5!ry^CF;P4i~vb&99+XoD|IJz2%VqPkq!NaZ2PYZeuqYRNTT}xHE`v{iZO%_sW!6
z2Up58i0v-@^8hf0|D%D3M-W@WnH2;4>erPN5P?W*j$&4_SGe2AdkzVWt^w<`IXXUB
z&<u$4SESrUY9RHHM4z(a4Ss*vdGFXp#+>`<kUc90eEte>C0Y_=D@|(6E)_3gtR+_W
zqH7c*bF|`f&eF)s&?<gxp?fCW7nwrU=?_4OdYyB!=>#TEMt9@Vqto<6tkhvU0gA}l
zZAES)OMCH`h|3A6x36w2Hd|jrfMU@F`VPJZyFJ~n&Tx1OCGtyZL=R|$Im@;)FRCd&
zYt8l(9@Oxkv<^CMIKkwm?9@}C2Rj_8?$szc(5TU!r<Z7ij#Xuej2&wi&5CMjlj|cs
zOi`2``%UJkP$V0CnwSDjNLlsJG4T!k>grE>Zh@2#$J+q*V*D+@QnT$I^8U?jHA)&9
zPAZxirYOxhqJho#FBGbbQ_~2ZAdwIv0i*F2?y(``fnCmu?dEm56jTh9wMvLBvUklk
zj^rLc?xG4>A{ISb;1OoWH&7I0OyT6x+ve*eS<xoOMiynta(mY?L?A(Kl?~rS>-_t;
ze2HyLa)osHwh3@37MuRsIpee}eE72S1@gRUm*WDa47!lPAt<{R3MwLe0;=BDCBcOQ
znbWM3si(~mQOb=d30IEV)6_|}Hr=tFnsd<z=T0e2&~S1&o!J5tiKMk*sqPC-SN;D*
z)>((O6>NLEEzn}c9SX(W-HW@syA><$P-v0h?yjX+ae_M~xCVFkpg{s3=iYnnx#xV(
z`eW}W^JI@@W>03`^;;p0a4MLFXqCo8o&MO&7FkHf>NZIkiOC(w$(9<L+EHCKX%$sH
z6a+kum@2N~_DhJICq*^8vQ9`cSDe3+2&>xZlA^_%1vM#|5xm2vn1TGk!O!qNr7_o=
zK7Cpa-8O5)&Y2A#SK=BqXo$xe=#W-oc=u21dh>QJXB+?f^|FvTe$cLdCT$b#=md6s
z`8%U}PxqJ`vrXQp&z&E<)`cPwW!@^J1*ka6+MKlnz7f-hGeEbVDmC(3>@K0^5qTAC
z!~errlz!mZ(Q;e&7N^t^`=b$y(R)M5p<B81{xaaqhFQO_-mj~RvY;1Z68#(ItT-+9
zjk-<LgO$Wp;-Jq;h8ST+9Lc{vn-w<(>W*-RveOrNb%_)^uPfJ7IMaYK`yy|aWmP7m
ziKl{zonap~lOeF*RZ!{1(jai^t8A<SL4{>Ve;Hq$9m(#Z-(mxEjAzG(>1rPg`Z{*U
zmja1~6R(fp+ej<ekrs~TA_12<j-z?+#A5c@=BMxBwhR&i*JTFnY*XC0KP870g#B$Z
zCUI0`KDBw0mFm3Yk|f-gM))a2vA`tTNI_~42n)L%q8)`|kS4|Hj!z791wXj;pIQJq
z1|x3eSf@U}p)2nj9l}-g1n7Y&P=v?Cg(})P3RMDO$_P8Wi4?e`g*s1!NA(fl(Vn0k
z14VEiBCXOB_g3eITw4>Qy6IlF>vXXWut53)k$Fu*PBtj)&S|_yX=EWIQz5lQD7xho
z?Y&p5^<7g8$pk0oNhCRmXbX1X763^T{!>97GKyp3@&=ArH)R8Yk-n6wBkD$-LhCk{
zX5^!a0v3gkr0V2UwMTYCpQ5>Wh(d$_e&&3{{PTe_L`}p$zJs1$vY#4b17C8vSa)lg
zYJ>W84s>1(3!AJ8us6rnH^u2XiEq*V`N7+QU8~+J&EZ+FA@7GB;TH0&{2F?xQ3XLX
z#Y(@IvrH&-v@8w0iHjr0xcEgkHH(6nFm9^wu>|#G3M&h$iGH*W2w+TetJLqKrj(v0
z*Y68CPgOT>kY7jZ)=U*E;QX@UAg;_c!$P3f|2o8g*qckP+BM4|b6p>GKAwYmJSyav
zd8h3Uw5}tZDVEN#YsyVD;s4v7OA(*2ywGAa)G0;gwxS;0hOONQivST3P@v50F+Sb9
zC>zCui1(Y(0ha<t=ryJf<f6H)c9RH8OY1dy)P!w5=Q+454-Xpdh&+({=tFndP?CFu
zDzIg}27fm3pP6XFk4t{30T<02Qen+a$dqVg9B{g5)MST!PhtQ#ZDM1wkoPuVP4z|&
z7xsWvT&PrM?mwGW^QtoHhJE-D5!8+yCHXSx`}mpk^H+6D)&Dgp943@nu>phx3;uKs
zrqh?O?H`5?5#^LIuAP|_oduog`#!$kIz<a+!rbEWzUPJ(y#On!=9Q(s{L3&@dGUT*
z=2QHqeM?3BrFgCPu2iJ@|4Yl~m}2<&&zIJ}uG!S@7_b9Uzn8~V{Lc&RUunM-8|2~s
zIhXwFY6JKVlFXwvJ=*@)Xa9SLT{ZnZ@;|LxkK&@DAJC*<wQ%Z*<WZ5^;F$`o`2TWz
z8b_oPsz^%%`=mTnuuaKJHRx-V2N(+%bXNXHbuQXIU)a+H2I11^^BWlcpBe9;<p{!u
z*SvwOmz2$U{{D}8J$?O0Bh|bw$xQf<f_ASXyiEZ-w_P_ZDgV!s^uHITZ*hZ7KK>(V
zE?<-L|90j-x)s{?IsXjt=|8^}j6xLLp~tOBScT;Z4RI=cWmfLjB`EqlqEV=Xy06gf
z{(vN3$p2J?{(JeJ1Q6*^cZpFrGwO@r(R1+7WAJvDLUTv8iIFhZ{N4RHswe^i@AiHx
zUZ0}@_4EI&2HiE`-MoSd>5Yx)ds96NIk}hNY*R${UIjEI9vi!xpSwWaL1~ukH`P=P
zUG>PuR)sHL9ysPTH4L149lfSDPPbKL|Gd<gP?qIn9?4gtq4)On1<r_BacisT?Nf0v
zNk%-b7@m#YG-}Sx=JLIr>6=N)-%uSs=S@e;M!VM9O~0T}JgP)REbEiD@<GrRLPSKP
z?S2)YR`TJ$apfor`bH2)gt9x=2p=A{<+cQn;ko3DRjY|9_VA}tuE+pI$_qWcLqtM;
zx_P`6a^*r+BB&&?YYuUgkVZqX!kvRYe8Aq=4<H3f$XO#>#hNDSb4%b*Ru`KZ80(`6
z1W{0~crP3u|H5E`Nr*%4g#D7T7d2%XClz!hsdD$_DA5#zrK8|p-UKO^7%LbtTNlK>
z5zXTqjO(mJO_HGGA_|M!FL%zt_qXX}S|C*7@ec^FEa1CpQZr%ZI;MB`bVaMph$c9y
zf_|XihKzbWcQMiw1^W`ifB4XP@kf7j;RFqAU)?Cb9URx34c*POxb}uZF*g|9x82PX
zY);XdSrqj^+^)IboA-yQhp620(k2caYcrMjgoPl}Kt_9RVyoB&tj{;1Z>we)uJo#s
zcUi6E7SRN0>a_^o(<?BPeAzCz>esmZ_7_@FUe!nYnP458@JmhsQIrJjmE-pcX63fY
zl4zfa@2cWYtF-C-j@E}d=>8qb^Y<@(3LP~R7c%631dE77MOhWKtL)K|dNxS0gET_}
zw_Q_Qsz+IJNtJs;o)*%Ih8#TpdJBgG&V-JRAd$s`cS<y72Z1#FBxit`iHUQYTvgV*
zPwzZCv!U;6I`2hL@B)XPocHU$Extc`S0tw#AqX_hHYyc`HPlACOgtq4k2!$e{i@?}
z+z*EoL^Y)>#1pF%{$kC6KLBW=y!wkxClaKAdycP>E~+*tg4UTncl64G&x|hSVDNX7
zx=?<9qD%Cv6}US#t3#2*R&)xM(X7@;oWZpl42>@1$VJiMwNne1gSg8b%D7iN*9XS{
z>oxlO$ZA}tvSqkP10Y67txn{eZ9?>(Z+A1`Aa5m^l6dcG5~lKpy)7&l|6~8TN<FDT
z`t#mZyNDlU#|Jb@hDl)v-|v~0yR*mgTSw`MTO`^g4;GWl+kg>^mt46ru^$m=w;ooT
z4O;9Fz=!nqjW-h2t?&aej#pbMJ{!a6wwYYto0!GNTYq)f??WzToH2N9sj9@sw-I&9
zGA3Rgagw^$71j$R5N*?=PQ%_kKSBvQ7fdDw;99p=`D6qu9dbkgWhUN96Z_*9CZ57Z
ztsc~W_nXa%nGyVaw#RA?xc-nm=a$97F)rhcZ^d|tSWa#2;w%>)tQGL%(i0Qh4Q7o#
z`-%vnrA;@=&|)G6(Z5|<>@vfw=@N;p*<+=ynNL%8%zAZo<0ZI*7;4w(kAcq9YODT=
zHP5U0^ByV<s@!Mq+Bah_G~hxoi9~!3=}Z0Vv$gECFJaTvIGBpfo)8x6b`C#5*3KvT
z({MDJmSA9G27}MbKpB19iDFwiMr5q>?v0Mgz5?NkUu;UkWGA+mzA)!d*I>WO{zYF{
z4*Z}e#Zt!j%tR+>YnKta8BV5q341>(0x)0Vz8F_v<!~J5by!Lg8@V+CAYYo^)e*My
z1Y)7!Reh<k69@0`-7;s6cCF{cP__~q>4$-i42ST~yM|jGL?1k21?@96O<w2Y4ouyE
z;*b*tVo%KCDvH@WXV=Kjm54oP_wGo)M3c<6-9+6UdD|-4CaXowlmaaE>jB;V;O<vx
z{}<n@nnefCoWWE2$o4#iE@L}Uoe*ExK4!c!uTPlkO%A&2kSGh~Wb)Vvl5;)GJK^|~
zLPv1OlfOLUb=ht~4Fk1k7h;D4scNDWSM%K5oTirM-z$XK%#$+VbM>(Z49ohL*=|==
zJ$*?rGxi&b0dAV2&dBz5p-2Ugm8!ftml8MkY*fCM8a>fyt1tL`NgwW2E<HK9?RcT^
zJf{8OQg^Eerp9IAE5VFQo!DhdMx3l($X}<ft|@EhgX`h({wwND%s{J%)k37S(ujwJ
ziTO%UZke<An`K^DcY?fE+cmBgj{GWUC%_7We%ShV$uG9JkVK9tHee0?i_S>>{e5WR
z7?#J9O7ZrG`6TtPC7o<3pC0dkd6TjqCASp=sKe15CCZI%o5m8ofT)_pd#wUee}|m3
zXwqlGCn0e5{Mw(!v?P*tm{A0ID~r{a5`i|N;q^ohT1Wa?j@`DuD7y0h&YA2jebq7H
zy)9Qy@ICw?Zl_UTy8kt4Oj1j$v7#pBxw?AZ!rs<i3n-3_Kx^A;R<CMApNe*vm)I*l
z#R8_&%S}z@`6?OZ^;=tmcp-!~Hy~l;!E=y8fAok*_#qP+zk{AgiF$5sUe)m%$_kI0
zuvQB9EYXdorY3njVwJAsqp_Igt1r<7$P7~I4)y~D=}&y~{dg*7w}?4U#*!4v`OSL>
z&)*_*x=P3G=vrPcRCB(?u9yB)^*|Z(;&m%Mlq0{XRoqtNpv=s$;glmH&BQx5R?P`A
zsa{hiJE&A^JG*+l*{!M(S}?B^B_%J)gQYAnrasTSS^GG|=C>NyqtC@hdj_tzou|_N
z#1b#CU^hwKvi7@1algpUWE;FIKe*=0P0!e6JunEPwXGvR&V#YdX(y&-NvLIH#o|`k
zRYj+Prp+%=6iS>|EjVlbz*oc<=~%uia{eT?5w`=cFw9aK*p|8Z;-8^2`26G3mFR}5
z3t_#@L<!HC`C!Z=I%b%of78*Q3KpIWol7@2dKN)BY~_NgttZ1v<j7h?zVS@|J;j&k
zTd;6H6iiBWvz+0e#GJpr+k7#L9S1p(xWiI6Q8G*-sPpt61DER?2R<^Nk&ujCPgo~8
zBU$$6phNmCEPO7SFbNpOxMG}Gnm)b8DPMjhOEfm5hAr~Z@-MdA3NYT0rq!}C78a#W
zJPYQ=pA~r@kAWe>D?K_W^GP12<RhFN-e*<K!0IG?lktb2OOx<temj_hv7bhm(>w<>
zbtLS~qP+qkZ-LK_KKrLGRQ#4sIl$!Ky{GMub39G~u0rP`L7X3&1BViNk*9CTb_*4p
zOf4sL@ukDTOs3;oIl5APRW!K4UC8eu>t<Mjl<ze<^N#b3=HFK^EfhSHa$zDw{mnJk
zt82S_cFEP(`yw9D=>0X&$A>4&h{=*?Dsy?FqB`c(U9Vkvbg|3X8K&OYe{N9GC1;1`
zLOppe!(n^C?H&oqx48P8OLB)`k8U{=izNbsO5Eqleeo7*jtj^<X8k%+owCm1v$SuS
zIzRK<BIp@5ja^&=jb0q_ia7ov96B&Zg$~Rt%k}^MQ?>c_+-9?Z2x`{jP#RDhAL+}S
z^Jp@LqOvZv)_J4&Y>788C+$Gw%mReC45Fg!b~#56oqUqEuFbU%Gm!+%&&>klxF{*%
zY`zia`rIq&>o{6JWV`1gBAJfOx{%a)7qN~OP9F_1t8@*n;t$lHM9C>;Hj>2s;nU~6
zlEC*ta_$mZ>gZ-jN$J_Z;jA^WbkiS{aJc%`UeUiw@ns|zfinX-@Q%R}V+m=aDLi-c
zw@8Ab|1opZr(v)A;2R>5%vuYl4d|WQ{kM7WPHKkiQ2<h0OhCM6ZCQ&ZU&VdkJ<XFj
zHlP`|HgK2cjD-7dRk_#K3yhD8kRRo}(OR{qlP-%8CdD$@VxqK921ko6&v|ba#0GES
z;G2%6XTFdf$$Qqj+Em+4E}6+rY=kq16K3O#tqmkWzd_hTI*$3Xjm5!%_}ITx%w~d_
z^zYij@Slb8j0WWggRc1rt!@?^iNe;NF?1yHIh{Zpo2C6FUu=gCv4^*vpTZxMOdvhc
zK09Dt*#n{UN78@@<u!Vt=an*Vg!@FV>-1|C3RYy!X;%IRNynh0sG`oFKJ_*;$4l%x
zn$=cIbrw)tRFV;fl2fVXi(3tFtvRS`!HEbYQ)8%dbX#+Oo*b~^Y46+#a}ZgTS(gd9
zN7P6yzB(C+OIYYcc;##Q-4$4gn*AFT&ZHUw_+^?Lhb9>knA?zTIUR5YXK$kYoH;(Y
zjo2;KamIB$2|*eOpZ0U)yk(>`47CO#62Ns<YfP>;H@5A)EzeVB2U{6qHZy5%LCd+`
zIHx->KwiWigf7>Nxdl?~*J9`Gx8`%Jhg|&r^@jmE5$CnY+lpoU(7NJ<LsGegR(N$m
zK4~&IN}mKtz%^mFSQMk^`(bFT6Ju1<uHozOK?;wszNp6HC!2eu=dJCnn#<#XOJNnr
zO*92me|I_msKFl9_QkFwbjnM(?@ubill_X$mi)GqnhP~xIEPION0my^BRnCF(^Hm>
zv0ooIDfwi&oog?v`5ckl49U<(4J(kVEFe7Qm0SDq74v;{hZPmG1+2yLud4N_P*|te
zn<UX}S^W&9!K|>iydtAx0Y%WSOOL%&Iu^~E*rL~@PUmL|xH&%Nx}t?JE<=yuG!hW=
zR^1uQZu(uU(=&2_p^8&mD=Uy#1GqhYEa$YCnA#esh#TB*J2k}utk;|xPK@g;zUi*8
z%BBH13y$nh<O?)$=D<Z$L@<~qq;63XLp{3yQ4cp*grgKSi-?Y**=9P%Vk0yH4_=Yx
z6`fyf_vrdqI--@U^e3MOr$&S9&=B4SwYn$PWxrp0ai#g;?2}FELK-dTuvwQ?0#mOc
zb|UB|R^V9Zs7-IdLplJVe;$BzFAUKw)kTyntFxE!A9xI7B-*2KI3JQ$cUGcXU(sXv
zP;MNRMRw=myp>|Cw0%QD%cX5&q9%29vU}L0gwMTOsw~C@nTyY|v3$rBPsNQ~PR5x^
z@8HmCQb4ol?KZV$9oZXE%+Rqk(yV55E9o`0-$jsGEHczcG^C;EPn(hpEVepa)H26C
z&UL+6Ni2j{KXR}8={B-&xGrE6<5>;-b)F;mt`~LD(aBL}x$_6$;<B`pF3)FX-OLS7
z=wSr~RrU{u-4oxu^V(k-Y?=Z`k)Qhm!at(56un;*BBeO94Qw|qA9MFRz-0N~M!ZI!
z{7;bzVL;HQ<@!`F(TA|qAI`hw>|mFZaMpX%ae>&fQtatEK^4(E)DCl|O2bx!A8TK%
z0%>IGezzR2Jsy7ArD7CLdKDXQf6TM7)elP6V)RmD6`2BNgceHS#^{?h*}5299{q$|
zqj1-o?<!AwI$AG{?q0Xv)6=x`ypP(^(>Hb#5M?M*vEZ&fNow}qzG>F$q=pkcxBmGx
zhD5+Mx~Z>vA4p4<JkXK)@wh1w=!F}leO~ZL4R%{sDw-s+|GNT=zM~!cTzHkTW$ePM
z6~22{mS^EWGW<?{xYOf=Pk*!oyb2O-{#dlN@SdkQ(xe#G)=JfrXfkTDl_|BxSV3N&
z0aGr=mVqVtBTIoz?-7&D9mF+pX&@3n<jJHbE#~;<Zsnd$$1`f9JF}#)02@0)VZ`Li
zivj7@6r*3Q&gkkCoYQt_xO0MUjTtO~1NkRpj^=tlV?VFhZR8D4jbUWz4fWH0j8037
z1^nR<Cj`wY)N7OFuurd{?J{pP`!%X9j78+ZGAx<#ot7Obc@B&96EtYsX4tx~8pfh@
zmVqnkGhkOksnW1MH@$vSct6HUw}15bJl5rO(o@UaOJ$9t@#_DTVylv3$sW}x$@CGj
z8t&o}CyA#_I3&m!n7^FA>29C(#Z<OfmzblRD+gYLrX0?3iHDrqjg^?Ux>Odvv2(RI
zL*Psa*<DWPrFW)(mdtdeA}{idhMe8J3kSo-n%_CO#f;a<c+K`h8=3j6lq20ZeX%?f
z>3!%u!7(HBqz`>Ti=-P`p64?0-Foz4!y=8otGywas7BH_^P<|elDpEd9uG!82p9)P
z?~UEjvi-6MVe{Pj$a;hjaOSiIyB2(i^NrY~@Yj{JceXbaE<U~Koh^#VFVBO1j*9b&
zh^t{6$iyZP9Ox3@-KHxqFDFr%5E&Cs^tt$q)KR^a@PTw^8XmI0oI#UGCOju#au#sL
z{mi-N>Z&o*vLCABbDChl->~sPUNW5%Ptz3tSjp2jlW~2t(jW9Y`k-AO8Lo;-<*DOc
zd3@j%PSBlokJG9DubNpE_+r+shiInrwf4In_-9x*qO0dwDIKek;g>D%t@5Fk-J=4M
zRllDrAO;6^mn|74#^h0|PE5>$<I!rYQQX5(s5>F0Q|pY>{-^M@u|_ySwNzjf{(8D{
zI7@&msv9da_0T+<S6L3R6B#u(cGP%mN#z<@KQ{WhRD2mz?$-Lwq|N2P{`za&TJ)qz
zCXBKz-c!t8I2BkD4<=6&OYY!G$L@6-lmm5Z9`|UuUkm*y1cLE0AyCA&nt00uO@58H
z#A`Gy7f474zQ@Gb^hCczioyu}o)di{j~VS%?{p{T>G+WQs;o|FcHj|BvXr7$bY5an
zoDx^YlD`^bK{(GYxmS|MyWNVc^`e0Z#U{^Zn~E&eotn%&&Qs!B+pD7VHd7@C-Xqc`
zWg;)|z}R*MP|HG7Od&n?_@_#)$!Q9YCx@G2$EBT;eo;npI4S_84x_tm$-`X?t2DX2
z(R4OeaHHpCtaQtovEvUPD@oL5FRX!3MckOBE4Okt3%{3NPO98fK{%H&yYkTzR@>Q$
zzawL#tifSv^I<)@adYY;mjh9dsrNJVJ!vHD5dF~2`F%&5!#kx5$0-SaC8b#a5AAOs
zxA!J1S$j+HiJi}3zqb@|NH=GxzmTtK2(ElC^{qfnUoj$vipaR@3JY$R)R6yb6y1Z-
zkHR>@FIRm1i#iMijjNPJ-+%1NQIlxZ=rkIwUi#*H*AfvM0JEEK2RO_RC%oMTghQ$5
zt?aD64crkb4)NstJz0!~dqk$IyzZewm8mi7la;?N)B*Epr?q4TU5aKPa^;Fd2SR;V
z<WMcabv4ilT7`bmR@O27fjZ|IyggEv2m`q)R8MB+R-^GEi|fkuhM%9b3O&yJ5FX6g
z!;iCfNLGgnr{7Q*Pz`V356V!P*Q(0Z)#*R+GNPiPRq07LDK~tZ7ijYDiZr|PN<#W8
ztv*rEz_EKZW>bIRanu6qQCQ&P-rWzyUd*;kd<y93sGZaLV|{1lppSxrKZ?C4vtkm-
zq1Sly8d{t5KwQwpr7N%wzz|bjQFl1sh%QI-adS)M*sMAK2Jpq8HHNiI5!;Kv?4-yX
zF(!8Kj|7*eJzC<d1^l+LA0ljzf7Vxu!}I`Azxk%XOC8Ik1Xu01jwk&i=Pzfn#L!@8
zLPAM3j}(Xe;^0NszSCJ>L|9HSGWA}m_A`!STOIR&U>!=|gU29Aa_HSt`RwfBfP8+^
z1MmjqY1U+PlBff(6Wf`rSJAY6fo^p%m)7b(BOdThs!b0-Hw>`TltRJgYKYR+N<FbG
zwPo?QXpJZN)cd}ug*PMikEIKBW<+fN2isq`iaDKTs9l?8Vb_jw?cXBb29M}+xpCmt
zn7%ieAJ~m8Op39*T{4eN(~2_qTXJm+4x=(~-mByjSCeB$vs1Cbj1*`(7JtE0O3$Gs
zMwBW<M$t}}lp{hHlImMOP@%Pd`*?RoQMK@$#qpEi4C61CN{b~XQJ2S-5v!`ndv9d%
z_ecE!QloIaU!o>L4XC$3(=2|NohE!~ie^vm#LWq>pfE1AS(?ONi!9ZLG3~wmGIiE!
z;WeZ+3Po@d#(JiwlK7;KBnG%LjV*zEc3u+mcRjE{lt_iufm@2{<xJtSzk|L|>cayM
z1kbqw*@aa7Yk_URPkvmp6((Y*-^V4&=CO@`Nz5?uE0H^LpLITxb4}(==2Ci>lp&%Z
zl*8MPWZVz%{`Ad&T*M}POf>dR)&Yf7&T7NMxo}T$wIq-jKYp~D`b3z$(mnchlqrk%
zM<`?S^!ac06}HR5FrT6EqeX>7iiSlN!>`u#7Du$4<#A`Dl`1yh_)c5cZldh;TVj-e
zjZmfxiAc7K`SyEvJAp9mKq<v%Q;H8Um_ir5#Me%r`c@F?fa-?dlXuLTk!jE}oGrf?
zDCqqm4<*$Rt>Dsm5aSV=AEA-Oq#>8AbT9lBZ<9lj-FzexA0!Op6R<(@61pI=3$+%v
zlt`80!Ly09vT8&;?W0V)@;MT@jP`L*wjcgpY%QPgdVB=cpxn4LNfmpnkIXeu^^=OK
zx^e&LKiW%%lYEfTI4!#bA=2kY+7Bh(KENRVp}5y-wH%plP)H|Rtho4H@OH_11x(+~
z{agWx5qd7yEhy@RUhmmvMoJqPn|sJt=_amZ<<+#%#U>XGWn}BIrgnz`q4=|qS(#*d
zttrQB&6#Z9yXKcb*PEx)k%v=-KanQY&}IH(R~PNGe!Whm^tX2Ak|tbc!c)*;$pcF4
zuJI1a$L5;J7Zx!}7m`nd@=cD0a=A7LC|x<>#{SZ*WU~pM_@k_3e6i2kkMVwU-qxLc
z&u{{$`PW<(NP#g|gqVT@h;_{D)<XXj-XupkpSI#I+np!LSX&cTu{sA5^;NP-h5Z)S
zhW*h`rz0jZa}u)Dy1tp4+KoI0@=c}1RHR|kW9=W^a}I-V_e*GNtnX=S{%G+@K%C-5
z%`Pl6v=gIjtuPjuojh`-J$o6||7hs_Qf9;Y*g0_7UN*aIJvG^G$An4R$n@Zim6}V=
zw3Mx&`xsm;r1DgTifm?%A}Nttg0#^B?aCa;Gcd>-12f`bEbsfCwF+g>Z~Z8Lm1lPp
z%tF^;dkD5EUujE5GGU^i-*GLkVEyEazFikswOFUf1;j-9*-BtLl^>bm4?gRFr}Zqf
znLzVK+SX@XYNmXYG{^-@6L1dmwGbLT825V-mu)ZJ)3go@T>gNDB5+5~C!IR{0?r#W
z*y~@L$BKA*^M<P{98PK|PMl_$I}$1$CgPr~)l2(Jgfb^UCDe;6k3%xpB6*^;{MB$v
zzohFzJ{YyXx)4kFT|9N@)I;m<WZCUy*W0rJ+ht}rr5mwoeUd1CBu1+6olWvhHAd3e
zMf`1Xr3#V>Ym)Fjtz1~1!v=C|(_Zt~u&wHvsAp6w2jk|D6;rbY2V-*q{L-Z=N%!T|
zpZ8eJmcw5L6Rx8uNa$}{kJ@owgTVLSo`xGKwsbUoS&8cuJI0OeYC#%<c_RxO`@|y`
zI6LR57C!BEXsCk=U>A>0jG?}a0UxNQC|E$$ieZgJ{hxTRX#(!_r?U(1@l6Cn6>ilE
z*$`Kq7Fz-S_8Pf<E;tn;J{i>OEQR;09SAaQbU(5Z2@WU10v;DVTF@8G3Cri8nDpQ6
z`-dh9*no*olB)NI^g0DbK+2$L2Q0zctsZs}zDDI+oJi?KLb<GYK;@G)(IPkb>++8k
zGt-C3pzUo937@uZ-=B&slsRA$3s-F0Q0E3II>)m+EziJ`u7D4Vu~E(L__1%jSf#Se
zS0xrv$1-&lpH-IVKMoS#m{B8B2dh!P-0$KLK_V4*^5VJ<ewWD#&6d@eKP38){H-XB
zVF|a$@QEhzpcUkpuNCueST@S&Z-V(Do{$sEj(`?hT~R2PO%x!vR-^i<GF{KcPoG7`
z&O4q}zN*iEdsWXjOJIj5DywH^&MO$paad23x;#auw6j<p^cT2p#q0{JbvL~sp<x->
z83^i72IHwk$bEsQH)-$)bzo>@KYFNU5gRGLv7G$7JcXj9e<ROiuz3y#lho~q7R06^
ziiKK{rG@J5pR&4XGIKsZGL+QfUp@3u4x}g)qWv|TY1QDGxji*4w#~X~cI+odj@T>2
ztq}5+q<G*1R+tnL`zx`XT8y16)F+9ZvRW@MIZ_bxbLMxo{0#8Q4q*^}8|Gh0!x@N$
zS*l&jA^W@uFd}p_iN6ko(<mMFv<?a>j{V1M$oP4~I0$!r=c>YSufR~m`K+gLmBL4o
zXB_-`L@7W>Yn7nZ`=t-8fIcJ1lWRg>Z8~slUpwNPy26z%()(qfB-sn4v7JLGwN@ug
zgGMchf?JxDG$@%ojcSs*yWj7s!ux7|&mVa3Mk5Y{MhEy-Vw*fcJv@8lbRIX~>^6LC
zxRzjd@=hMt?@w7f^{89z=uI6KLvP}A`-(+jVMYv1JK<6+D-!3N3=4yovdKb<Owhy*
z`I5G?0>ZkopAMc9EoSZT>$zXYD;yJy=Oy%IKI{DQVT5(KZg=d$AonxS&3Q1Q=DeTY
z&D-aPZKVX3&)$+J4oiZR;gdGL|K<C7shQd0ZBr-UZlZ=hElP_mnmwmmI&|D%+TE^=
zNmHc*Mt--R4eIA7`y)2WVuobob7~9_iJ!{tv-f0sKE)aB<?{7OMX{g@R2M%Gup+Lc
z3VnNZ3BlXa2^6#vo0a=j@y0)7rxleah7FZefR(;<(??I*qYxJ9w4M_r;?H}P=4f`8
z;H^(Bty+4LDL`%4h=r^G&8ALTH!nbCa_+*Y1|B;}8wtW&T1?iBt#fWJ(15I_9B5Fp
zypjrj>g{owJ!M6p<tYDP-99onrx76#d3fS%dj4!(7SAJeAIE|$*P^aAZVW;3V0!|5
z0KS0Gs_k<^6rb!t3}^t$6Y^4@licfrrJVG?QrGP*Y0v?AJ{G+OkKP5~(mD0>%Jdnw
z9I-SQP0hcRIW(XSSS6BAcqank>#)FSk|zjnM@=1mUy5ah*e-QNje#6rr;1|SYl2vF
z0BEi$#Qa#e9=~?-<co5y{5Rf~eAgQ(3Fx&;gS5Mg<5-?R?<^87$2E!GC6pU}PB<Js
zLsM&4U`m$JpJZ`HG5^A*pVK_TkbfPg&DGe0Rd<%W)XG&OkYtNhs`!oF_h$Y}HJLE-
zE9HnJw@?0*F)IbZOf@S+L=|x1zCe#FyxWM<d$`3HdRcf&krgq#!kY-kXkBk@!qfcp
zD7-|-0~z&;x;NMJtBt_f1n<a;69qfJVPxvWhhd59C2Wh8DgQtOU{sQJ#Cfgd3@flQ
zd)RHAKZD1x<S)`^H7bd#*?#CJCT3o(*73J{xYs*;zB2E3Y?jN1slOIO1oQ26?uWCo
zi>hIvtA!%(S5^D|RdhGCG~Lw5Z(Ak5(o(2SkrA&`Zd-zKAG);p1cPdEY^_A%<POHG
zF3YIUA&bnAqk$AS!OMBKB!1QP#Hr@E9Y~wZ<z8jnA`2{WAqWR-GQEH8a;FiBi0j$;
zi*Y7U_pd=l)*m8y`YGp)FZK|R)IW~ZRPU<85nImAiQ0SL6|<IzN~M3(OD+~APO@1R
zGn}_zCF_7zO4eXjeO=z{<!d6}JR0$_2ek<`(<}14^Mjm!2**;xl^R?oyI8qutJO?%
zn-ZRw7H-Xa9e3tie%k{d6<Dve2SCvZEzg5*{V&EDD^dTb;d3U-v{6U8tBIA58suQ<
z^WvO;_875t`Q%c!F#{M)QT?uV>U(ui<h5Co7KjO`*K-4SlFJpLY}VOJ1J~>E0eM!K
zI;dpwC~2xQ-+)5IMtPY12%3MMb(zHmR~5b8L>oPb!VClPDZ|(07JlRVK!E$dl4BBd
zR__VS>9*4yIGPR<?e?ZUf$|nmBd^-qMip-stHuo!i`<UXSU|43(H%pL&=smaQW@B)
z8~0if(X6Be2MIVyRY3d_FG~ICA~^snboFp{)F=onbt8%!EXw|l;t_xm*eNpgO%7bT
z5Zx%mC&d)cK`ZRxh(-CK2?M2!&Z-~8>Hp2(q_<;!B=+u>LkQ=VSM}DF?sqI+-T8iP
zs`8N~rDqFCAlB!kpr)3r@d+NcZ;mX&mdbQfp>3*_wSF4p?;Ij*QO7erXwTDHaYD&v
zS&8&WRFbPspf5nndLXwx<`(f?nL;TRNk;u-m<KMm2Fdt?dG>&4%S-aWufu`K6NafT
z9{{OT*r28_PgSq}A1{-o=||DM#gQ3mm?g#d?rXipEnH4#Uo)t-g0TjAB>YJz2S`{|
z<Xm5=ugOL%)_GPwUC)UJHoukn(Pf<;VzN$|=+y{k)fSBFVJU4o#i#f7Mr~&)g+xgv
zs(;drsA%dW)!C4eNJwb(CE3-M2X^$R6FxfhF4DXBeJVt_N`toCn4{fZ++x32LM(8u
zs>Mc2L;~NkP?yxs(~Sabo2+mMkC?D(rgcV#T$R#2f_#htpViOfs}!xu+b?>9E|f`o
zv;wl~OzRx9Pi_&D^15!}ugq{w)Qg}dJWd-kE$O*$(s)K#Tr5VYkKlW|86clSg>jVB
z-gqXdnSYF`2xi;pAvgP2OYuol;Y6osk!ER;&1L`FV>rWUv)+-VS(EIOVb7PTYI*go
z-lG_gX><|3Q9QF$7&TcH^apV$@Ft0`&AdJB07<{6TjpImIFhxrrXSlW+OA?J0cc+U
zbyeQ$bRlDB;t$Ku|DJS;B@}HfxJE&)D_KuUcP_7s$!q-fZSTes^@M<(emo+kHfV&Q
z*vq|;J_*5isr+YG1BlN(JY9!Qj52HT9YRo9e`9qrl2ViY`*4htn)Ey71F5#d<Jaw}
ztuP|gUQ6F7yj!aGG-j`d4aSt?@_}ov>okx6;SR~912%<dQv?%Hoxt~izniSJ)g^c3
ziRp_pJp!r%hKtaHLvl3*l1m5G_&*WyRQSSHi*4SQ1uoIh^Py<Wmt8X2sFtwV;kLlH
z!CSpMod%4xGxo)6YrTJEWZNyM1;V$Mc22*RJ(5!&Da3Rt6Ue*FBzz2t{N1jd3;Bqc
z=xh7)>?>7kct2)ppt`v!;|)7qR{+k^P4WQ)2j0)}+q*lrsoC<I-)S!9*uLoTo%~k6
zOn=W=V3N~La-${NG*mxpl%D;rQwNY$CyWJbLSfScC3UvR^a+-hV#psMX^3<l>ZP+X
zhS-KMW}<z8LlvtP{sG4VWdJ&}Mg5xPfX<*c+;DDXt3yAdnqhjev6@m=E@FZm#>)*+
z5uiJQE`)dw55da{)h<B7j_7R7)}Bif(c1c*#K4W%!R#5fqGZ1EUa&gAfpW0@m+W17
zqA#PaA9B+gOF=*VLc~k^WP%Z%gcUagqd6S|z*v>wOH7T|5O=+bMD1@8lfo~zhizr(
zlRZsP-;e4Qd9lyDt-uP&aoh?k(9MFkuFXV1mR`eR?~|1)*X4WqDcy_PWfcmqI2-l3
z2vMf&ChKI1u7V4BH4Jd9{6kuA5QtSEMJXO=nIxNLT9wQQXR_q3zEnRfWLe9&eVz<#
zsD1vg0}0e%n=S!Rq6997aet$t8bz)}T?EQd0B`el38`4+s?pz_oD?BHBVyk$JHB+g
zLXCEO&ixbG3J(QR<x6XjF)^zl2`F<6;hJzETN5da&Au%`M!K2C-h`#8Kh+S^Z2yMZ
z{Dx>DF;;+*wYuB}q{LpimNMZMB~OPPX0Z6vn?=rtS(8VU(SB_Y-<P)%wx2kYaeg3G
zo=*pQ*0J43`2pauwlLvEE;}inhX=ek?U-^YvEWY8$)z*AEq%bisp(g1Qrofs%xmE3
zpGxJm8VZg7JgxDv+RZL^05!4QE`|dz96@;kdiIMgUoqV}^~3{#8Mgu9uIn(bM2tP-
z(U)0|7lafy*~(?Y^NuL)Ljh6(YVp-|zLM&kfqlUfp?m1*)-lY6cC^byrd&zqA3Wg*
z<?Ehi6ngS-E}|Mv_6O`LsMces&*i3)BjJ3ni6MLA0+sRQC9Yk2z~p$AS96gGC)r|>
zX_7AF&|iOd;#y5j@&2nQsbeO@w9C5MmKc}fK!iuZuuL){*Hgn@Z(GWWc0O;SQ*Jv;
zJtv!GA<Vk&{vwB-UQvKWK7o_!)MTC-8T%8>SqPEK+tnv?vRCTS>JRLI9unN-Oho_I
zVfDGC4#}Vxi(M2QkJBT7{PuKvfL<qFo8YU|0_Mj^nHM`-@zv5LvfbOX$HS#5hLEz`
zpPa{eT}V6?1GtCX3Ot*@o2Z~?ftP6_hCn;C0#S4b_KP5GxOoQ_{j>P-dY4<?Z^!>?
z2RDj;w8L%Ma3EUkQ|@a!G$=2MZjmF`{QUcczZ)b`NT%i1Pd9J1ne*=C#@z|?XJ5XJ
zNk)3E6?UOc4pK$XpZ@w9TJ0550~C!m&Jo!ho$t1T%&wz}p~_gj&4v^%pSYm_Tv>|e
z2yQ;<!Vf9B9(A;0UYQ7o4j4|Bg^lLakCf>LC=$Z68%3X&oqi_aGzP-epCnU853rnD
z=M&!(4uA^Srtwv$>85a=%}=L>&t6b|K*!;YA!*t?O0d<WTUuD~%h|7Ar~jzUYri^T
zsJ6SUh8JYF35K`5{;9;mk^C+BM!=Az8gC1^;wl-&=Q?wO**#AZ1y6**+;}mGETM&;
zpKbIREtoVAi*!cWGgOEXwP7o~<m84uqWSv4Ve!kal<)6*zc4mdBz1_&97`_lm>c7$
zoVh7pb{ZKXtvYO+Paqi)D0FN^&<^h|FGiSc@0-MS2+4@6n`r!3q5hjohAvZV#MW1`
zrF4<PR|}&QW{7w^cJ<oRV?u(eNu&fTPZIdJ>B0R{-5#Hp%E1G+et~kAT0EN1YBUol
zApmXh8erly;m1)oID^wpmN)h*Td_|f>!fZr<<B+$2JT+ih;`E!=&ddt44&=>rDno-
zZoav=MI!4X$vu83d3}^fun`<w)sts{7NY!pAjo4k7b^?Q+)CFqTm0`20!w|Fx@HJx
zpjUHCmQcqzJs}&?f4=2oaL6gd)G5@UKP*-VKi)sTT}SAVWbEXtZkfa?t#W}YK+)!H
zqMds}zN)k>p(X+4?RKZESC!3${0sWlglCb3XE`P40SiV_jh6M>q|O)<7h;*jJ32Zf
zYp2_)m=#uhty-C-)h0Mv4<dei&0u>5$8qBB8y-{ev<F`@n<ugab78tP+VQu)dJ{za
z2};<x(KNn%nSX;_J-<~92C9l`{SU|fKQwzCBB~tP{%O_{yOSOsu4`|8VTSf~MC9PD
zxDr-|F%RESOQ#CzgvqC%FmTWL)ENq;@^!ha-tV3M-c!D-w0t|Cq-<rg_!-gZss7mb
zAG(=Q+7G?)#LA9!4@UoD#$WI9L3FgypiLQkp2wAi39MC#!XzpB{U1;7r+e4;|D;F@
z1wGOCG#eEHNUAFT>67<Q0Qw&YbVxU=Ss|-KW94s;XKMFCk)nU=lYEL-{M>Nil|K)G
z_=!~ayJ;5E9rfcBP=zZ6`gpg%(y3AlwcfFP-I*oGDTBJU<;Jg;fjB{8q_n*^+$-_u
z)LRiJ88|c2+cUK1u*`K5c~e<>36}%Wyq&L64X~%<<76Ws+VK8K)khu$VVbP$?w$Pr
z5v6<FHQ_s+Y^}{YnQj_dJ)dlG4V#;1KzPnVGPYQwx^vl}m9n7AF>NPbig$teM}Z%f
z#UjW#e!0GMQR*_Gx72m9D+x**F@yr_8ICNzkZXtRread<-Oq3MaUo29r_Aa>0OQMC
zj@#ruq<)u44dsu}a+f*X2fZK$$NDA14`g(K0QGbS-zoZmob}<tYX7Rl8cQm-;O6Gz
zh95J(%QhTqb&c+DM^k{($O9uISN(>rODHx^-?op)Z-k2`lzrySno&H7_>=3*IOE1b
zhO2vq4EPf_!78Z&aPN~+@+aBA&n~-~+@a4xFJQT<X;1vBT&m)$-?|+IVFG~f7+829
zr0FLB$#Hb!f&aV7MWu8r(mNO+P+ds-SP9QRAo*VA_DnW^`8`!oauIgD9$IsJ1*p9u
zJK{Gy+m-74JC%w0LKvCVQ&iB>&s=wLQPH%o_P=)&1yp)NQs8)sxm~FmG?H~=6jG*j
z)E!Si9>5w&E!CxhQIu-#i{!>`>*}GiO<08!R(<ACLXpUYnytQaqHxq<?_BQ7P2TZO
zeXtPpWryL1Nk)xOyK?S$Sv0wh$uld_vq@;h-SaniA>)KuU6iEZS?Eo)$}SEG_5J;Q
zj*Cjiw_8L4Hx1}X6nDenJSM9GAz26eTSr5zv0k`^T3a+h_36b%dvr$uudRdXfCGv`
zSv~G>t{<#b<`jXN`9DrkL}`Zz#G<UlL|bjlz-s|enb$GOR|Go%Xawz%ElPzlr%!fs
z;@d1%4VUhYu>0^*Q}pl8g2yl0a+ss66C6L5TFv~a_rJRyVw|~U+kV7p^-zZ9MJ*jV
z%PD+CA$(bH<}>{CB@c}`(Erg3W|Y&Jm%$i2SuXLe^~Hu%Brj4Aq@K=hLoX-XHhi!4
zqEa_k=lEjQn<>40QzSrmb$fZUg(JkE4&7`CWdSHX%UGk$Pdp;qM4|ok&C6Aj18LLv
z3uSGf!6I(G>Z$zj{QE~gX*FCj+6<05+szcQ1_}^Y*eIEr0FsOp_7lrvDEWCLZv{0k
zcN;R`sXVDh@2(Nn3TsU75)3JdD98B--N1zt_wH-v^Obr7gUnfrEq1JLryKz>*DqwJ
zd~F7MiE!=Qfmp=Nr{<6IA00&FLxG*?eFJETnKHRX)@4Ff6RSNz>#(1Wf?oC3b9cpf
zM0QEpR2Gjr5_^f1ir#k`^UkjvvDWcoof-B})bYVI^<i2JrW^fC*#s>G5QSXy>?vvB
zoBy&o&!?IKBXOoZ5GqaJ1yW-$QbR0~$ZomGcEt~vJsxCgC`k|K%Pe?jgOxLjc|3(X
zYGS}((X~5)XU-t<H@YV9Gf)N}Y-ca}xbfoG*P9w@!eKXv`U)}kbv96B(o$nTP_}B+
z@z+_gZ;R+jy4S0A!gk>5ven#`BSebe4Ybg(Mf4)$;sEz1qvo(ZVO#MnThf~%{uS+>
zXmnBY!_2u#V_;ZS7H|EwErVe5CvsWaF1x-3{-W<DyCE76KID0$&=83Mo+T=pEjN&A
z{hUty$rV8H@ludiI90$dZ8mddRpI`m?UVcI+yHkFiC%4ds6kx4^V~bU8a-BmYU{__
zDiS$x(Zr%y22V)KU(-kp-pldXfqp6va)yiKkntks(rGwF?_=T1W2Tdp+O(G297H~=
z_H3E?Y8_mEo;3KWv*^Y<8UC!O4E9q6b3xCYgH1Si>4A9Qp@HX)p6&9b;sbPQ`DjA)
z&D;9L6ZIzV$UhHmhh+$_F~9UXo-4>VC}S`9!+Dc*ETr<f(yWNJYsCm`Hc=PaVQ-#m
zhp$f-aW(WrOaeG^Vq}oZfBmH?YxusBO~UZ?NTotS3}h@1S_OlmE{8$m_%VQ(r6VDc
z7l-$4bqq!mVC~eu9YVnUz*WmiDnZ65h99yod-^=1c>|Zbe(ZpzUCTNR{dk2ywYT>f
zL*?vrS$HI_%yBEd9QS+59^ANzUM!7xQ$mrstDlzu$mPz;PUP0R(}11g;%h!?ZfP)X
zNhd!-39Ygu2jTBm7umwf-`ZzUc+w4sH-khB42-|3s!HlAB`=vUs?xC6Bdo7a6C<iR
z(SCG>z3VBRy~%C+bGh~I5*fMtH;xjZf83oqJcaT@bD|TP)wgx3silmmv@~@aPg4%f
z#Ec9jpGI|FS`TUAxm~H9J$$pK^x`pooYdhxEiYxw6r98ygP$R?9R;ms65((X9x=z6
zMh&a)iwyXxv~$Pk^NoEL>`JQF#63I^eE5BeOLrMT@<;X+bQRToW>u6r4<)}UOADy(
z!sj$)JhX5_r^kE4{CLXK<lg()NrhIhGDKbd9cJ}R2|5}f4OTjsDx=LU1aQwD(5wAX
zdzhFbpp_T3R4~zl2VZV!ie!mINl4YP>!>dxWhOrVV)#tS9o4&iSiG;<a+Y<SGJVH?
zpd`PYY*|Jp8g71AvHf9&9q~5oh~dbnVKcC>x-R+DUc2rxJm(B0ReUd?F{0yeK(guJ
zr<V^AXXhdn%BAbx8AE~1zW3!!6I;O6hn=I){041~t7$gbF8XU-8m^<SaZd~a?cs5v
zITN)Mp<&7@RetEZx697d#lI7O;lY4cv01VXh19$To{hZGj72?Pl(Z1|#U~zo?@ZZI
z0&Gx>7zEc>hso2C=6$4E!lF)kRB4PPtoTa?_%#=~R_JzDJRfsbIF68yK%8={E_Y?d
zwhRUj9m)U-swL3TS1(~D=^|TNBX|n$!wJ6CkzcHmtQ3<UdDod7TY{e&74$kU-*7j6
zM8;^FOktw}D*$z_W3dN&dgi>=dVVQ9=Ch`{&@>!{C_I#PR_E&X<m)3&tLyCMe)u|8
znA+^hb;pMw^>JIV5??gs>u)#uO7;!5J;-fKb9wYhPq(tm<*#Vx4C3TY)kHZ^?JRL9
z-dDrzQt<=jj2~Z#X+i}g`1SM_HF=wQZsvzyKr>AoR)b*F)>A^`fE(^aVdQj<L*kyw
z_@`OLNaPaIvRFBtXJBDt(>5-N#KhX_6vMXa-iJ_{C{Tq;w27A}PRl-R27WTTo|YC1
zm&ejislBC|jQiF1)j?1MW4{}#Ol{Jw8M`Q>6fW(wwmsBPmeHH{sa5(al8?FG!<<8m
z5{Jto#<}%s>1j%4c5{HRPr`rT;!KIJO2-E)=H9$-L^mHVmP^(rvY8E&5e@-vk@0@J
zYQ#@4P?fkFaoQ}cU@dCp$RR0-`xzS@vkh{h5v4U)@^)SO#lq-1HsWiSl`dq3#@#aL
zn>z}ODVn3XvX8wM)AOKo<V|+u<_OwD#(Ff7-rbX;5NdgBsKxY4RkE5=Bfb-T#Hx8Z
zcBy>aul0TmynbN7o5<*~QMd(?R{b6RJhU;q3tsQn>PVs+|KmD#ungRbBu!2uEs{Pm
zNv(@a6U~;G?jDhge73X7vCkbIEhw!n*fVd`nlD`myXmt_0k)Thi5GMga&&Nk6_3Jp
zaWse6e(9+AdY;pS6m*+%{PmtFX=w-{Dw?#(A+_k3?4;5#${7(i<r!&b05e*#c*%?S
zEG{N%=4Pwl7`j;+(ShWs%i70ps>L5ct^3CBm}d`SSoG_WU|@a!gT*dhGzpgaQWYB$
z<HpH9qhuT=`@4JU`iI8o#2o6P&lHGUOHwZsa~<D)m!mj}8D70X+tpS}bNwkoS)DIH
z{i8KFLDLpyiNy{%1slkI-JIBbLRGZqVf!pS63MzBKShyDWP_di490G7z$h1P_wTQ)
zBDc6=5;69LXAKXXOkcZp(vW$8pz=5`$lJ@n&vfZV$fbu&E}X4A$E|S$t4OfYRIA(5
zrJ*17-%=84?o}oYSL0WO;5yc)Xsri~w_82h;hjoJSLf`@*e(EFc}%L5`|K)<_HmnE
z<;oSaM8X-`(g$G6SAS1o={I{LJJ114hbr=hZB^2CH?E$K?{c*FV|3^ETHP=;x>k=s
zWqt9)1dAhib{aP7+n5m-cS2847>S;6&Q16Y)!nh;ZYNwaKzUf(KxXHAR!bS2!-`RK
zxhcL0i>{KaB>aGMP_ECRo%@GQ>#am1DBzxnwIbVZJ^Swl{8ZxNb)^9?V>(;j(`j|^
zp<&E2^(&!^9XcAKn~~$)fNs%t?a<5cpz~mT<H85$0Q$fw7jJ>F<TOp!p_v*itCb-c
zH*gWVf!)l$dZFdiC^B*X!lwH3D~xyE__CEEkQNh$hZ<SFzfD*H!Fo^qYxT{ukCHu&
zlCfk}3$wf2(HGS}qFM-q{!imW?k(0_9)$LMiS~o)YW>`IqGS270G2Hk%+qy2jWo>V
zI_d|Ie0BRHIhIiD)#1vRw>-?3mfL2e)zO>ps~z`&z!?euPiY%m0}nifSBEggpBZ42
z3}spObT(Aqu=Oh{$J6liR#U-F9?B_4L5$KWD%x$nEbw_htxy*KaqPBT4J}n5pV!A!
z5$zSSe#tXt*sqD@3ECNJHS4SX!-T&Yach4eT1n8aHuU6J(608S9t<U6llFU}TCnhd
zmT(qu$sC{4OYs=~H6o<i?m?W{kZ+`r1mGGEA0_Ye8L1!H3!!KRul57S*0YT*Zb{!v
z7xQTVv*P2|k2NdHaOm{w3R{Iv`W$J_VA6gAkX_KgoUW%cl_+nDM;7`N<e{@niKKvi
z3@L?iDg@|N?~Rp(z~;E(XBo++UWT;4_TkJLTzSW@YB#7bTGjL##nK~d^2#sIG!a2J
zjul<#o`Bi_|8lo^8G=%Wx!^*DZa5w0!&#cBR=PVaRt34a!D23A6)^{)MMU@N98Ahw
z{8e1+_rvb&r&U#ivcrIPp!w0<ww!%q|MWSsE?MhdM<MpvG{8C2?lU--;)JUzBXuJm
zafzk@E2$x-B|`O5>X?_@17$+^=JQ-Q_zLpiYE0^f9b5{7euygNEB_d3&}p`H!o2lf
zO+_<K0f@wOP4rJK0B3jSQjj-Y?Sbr>Fnr&5N%CJ3^FK0|7e6R3%IVF=ckcS-wPn1|
zr-ZzYyE!YVNOL)qT{#Ivosc|SbRc=`X+=o_r~eOhK#RX#JdLlmWhk-%y|1_#-+~?N
zIGBnfZZOxiH}Lh^!>)1U*K{Cb{?VT4lV@V`(NSy-=ZVK3qHRPY#VXzA<iP%2gj{qh
z!@DQA`f>ClkJId6DLT_$j%Tc5;*{ee=;|3Gj9gIZc0T!Xo48My@sv64!E4&EV$~sh
z4JmvuX9JPpGS2j7?tlJOLiA~T_~}Bavyq5S7jx^d7~cPAk^FKn`oR|%(7e$yVQ1a$
zLnMwEL!Z{Mt~%WI)Kj7juA%h!2GP=fi^bDV7_K00x}gt;Q!>$(uVU^&VdnBp9L&rh
zZSht%XXZ-eRn7mg_Z4t)o>}(?1|NpO-Gc;o5+DRZ+>KaMZz@~c+P2-=CR<5U+cat8
z+IWb&hY%qKL4!+zyUQ>P^FQxEbhG=l-@n`Le#`SqkYVP1pXa`hTsik1M9+K;lR~U;
z<?>AgjeHCh#wHjO8x5lxL+m|})@xYZA1YGQgssy6?vXvv6&t|T#T^5zyJ0AA#=ceG
z;({h2S_8-6gSp{2vu!aFZV{%|EW)&TufnPE8dmR4fJjdro%)`5>#;#tz4%)+i7g?k
zu14haH!*3j29|!a5|yolXA$x+_wDyF$iYN4PH<cv9@a=acM&4}fpBqP1n5pvw&83-
z2_`@OK0IvA`KmddfAc+*cS+EguS9TkC~lrui~N{xp`on-Jsk-gTwFOXOJN|+!vK1$
zr6M`5Yl?9@^DI5o7ZEz^EzBL}apw*Xd~aX`Y{?Is(556DLRX3xUVZ_$6XFneDxYR$
z8(c>}hOsU(eE!WI*f<)avO$U1CuSn~{0$V9n;|DZ4>>6Xu;$pdd{0BXMF}~TSUrzv
zm}XsyL{Tu_ni-CZhc=?o$PYT@=Wsfu4le%QFjUqfIj5EeV-FsG;|=cpsrZ8Xu#vF=
zjrU@NK7L1)>cR2VUqff;%NT52i1!vA!&j?6#g(mVP;2Rpri|+_b2LCfK>^Njyiu0g
zh~K#fmFbD7ZPUgdKl(E~P5ytPybp-1|CirD0{x!o-$2NZzJ(To7{a-I{R%FX$}o9$
zEQ}NdxOz1O+6Er5(vhQ)9-4>=Gcm*~08^)rhk?2R!6T;OuT;)F)oXDgrSjgT1!oa4
zX6kwt7%^%J#t-#IZqj);4V{2NAwifjF$!ntSyVUo!`#QlbJIG3-KVdhthfojk)!Cf
z^29*@NQ{pP#`p<S;iOWa345rziDQRv;FZr7;-h!|0B<v0G?wIJ^M-i%MvcSdkzVv%
zZ^Adrcc4Myi@C8ZC0Ii5;`IzPb{XLDm)^%0ixxmF;|xRERd@TVMm%48-EC}L&XE0}
zQJ6k1fcM#f+<FB(1BYSOlra#u<srR*-Vu5pJcrN0<I~2Vo*trc&%cMSK70~QiD&6q
zt3^$<;5lf4*lRdG{`50Uux~?B{6%a%c>_~l{VV4G<!#v6>eAcM=e^J}!StEq;5}q4
zh6gzyBfAx|-h2mx0-fOO6@{6TM^TB$Mg~2>Z8Zvvf9y?s_HL}otMJ-~pJ8g41=3Qk
zVC%A749!Jh*5o0GKfWJo_BN<5NJ3spGQDHPD9%nsc4i4|^~Kn+{v;K_5Ao&KpTqFh
zWt=>H19ctEnDfFL7&Xuw)mf*gd@g|7_-8SFh!xT^3*qP!j)~((!&t0)S8naHHi%s#
z@zJLX@b0|PIKE>)y;0@pFm}f4Ge_g<-W_laAA?Dw2I1^Bh6=A<!>Qv(aQqab9(9dS
z5CYM~vX5#~1HAU$$9Qvk6wb#dQW?34cEWT<Pn-l-M-NPz6ARyfV0wy%LQi6b5u+z#
zT%;EYl24(6b=Q0KIyD*vT{yBk4#jG2m^*zkbZXCI=g}RAyWEV|KUsu7zcmjg2HFGy
z>0;&|-=VinIBa`c58XBPROoyxx{yUL@X5q13>!HP<{CXJPmb8e8xA^6$Zpg@L?}I+
zj2QGlif3PX1>Na!IDP#V;bpq$C{4%S-A52KY7$0=yW_})WAKlj06)KIOdlJ1=XMW$
zrvgiiq8hK5eSHru)QK>0#^Z2ND?;4nW2ocb0w%wNFFt<@R+ZP0nURGoMtXvx#_%3t
zP^-$t!F|{9{DQBr@Y4^WTW|!I7)i>?Ys1u6-a=HEGfMKZux!cq_-^rch)*w|=bT1H
zTNRFNjzi3|f5O5=-@rq01DE+3D;h0H$yBy(=HX^)9_5^gPD?i!KA=D|hk}ZDe#T||
zW&U6BmybR{A&rjMSDuBqitsNU@@ZGE!f{|PY(qf<Kmawj&;z|;-8syDb3PV*y8t4t
z#dDYGJ!_C*+|w_@ru`BWUSm1dFEDRXAiC*^Q|cM>y6@x5_a8_7#WSd<NBiMMI|>OW
zP9lW{s;;t$3K$rr$iu3giSUn_ipf##h^IoG#RyEB<q-V&#c?n&FyS+vj0sWxC`pg!
zyemLyX&Zuvjo}{TP3X?UM^X=(nyRQo-kUe-VhaQk`lLpju(^E*KA=K8J%;zXnvNbV
zV+<WP5#xu4py1|J6xKB}51<|$&CSp^@Wh`#`2rIoopJVjB975hYUDl`)259^=FuZK
zNBK8&w&AIl-a)wC9R*q#H6sP>O|W+|yDI`6BO3&H35JX|yhBG}=ENAFDg|A}E*L(<
zACa?O#=9>~MM&rjMA|f9`=vAtojeyq2O41ix;@;}5>X_x#GoK|JRUn0T{&0q`TXzU
zI%*p1{sGpYo5q+x>8wz(XviTK=^%Q}EBJiD>rhL-gv9e_5SQ41Q9LsuUDdEBZVTFV
zjWL+(V$z6U)TLg-?b|h|=6#=k=M#MN`WR%~IEkZMcB9cO248-=2+xjmM%=2scRkeY
z71_vQv?)I~3Ht7i$WIoW41IG!(TZv{F^nzlLGp}k-Qi?o&OJs3_vlzW7UPEH%MUT%
zK(GpWBN}o62h6jomkfk!P`v{O_MJq1OB*zM{;@aq)aISgaG#1V7cE3=R3H`4WNbfP
zi9h~zAr^9-R-M|7%PBdKI5^_{4;Emimm$vQc45|}k&I#uMR<TYiWp_MetI`5)m*XY
z%TMvrv|&)=9BHF++`|7tlXH)3=RBw>D1v*;EJWKI;P}~Fm=qfeAG>Zs!S)lDaRM=O
z-o~dNzK_9<wv1*9vtMW;xO=-IBP$uXh1rbeaNj7(qa&=5&Jt6Ou^xlROr}RX2$Lp_
z0!=Rr96Xxq`BOw&^x(*qqTb8&fBlb<z{5M%e@w^!FLcx^A`rRpTXD11jF!AJycpVB
zwSF&LLVW4<QR5JW#6l)bF)2t}o7O2UaC<vTdT_NM(a?h09i^~O7)plnQZHsuDJ()Y
z4?S%IdZ$GiP!K1eO;3*ywpV9jM@e}*f&)V_&?6L2zcv@@jTJ~ddH^ZOS-c<LtEJ7u
zp|>gNozqm)gRQ9;no<kcS{Xn{{D8JD6b#w5&~rB|*cWmtRvPpmmXtSPU{EBze0osV
zHG!RlAvAO?V9kS}jhp2CP07%+l8QwSArtk)VGj=vMrCR)l5XUq=vFPXjHJ{77=qL=
zLx7t*blEpGu>{t<rjR71&)G}I4&|Xnf4LGC4))Mxa;AYEH*AI>J2b>FHIYJFPYMHl
zT@vryySpl>jB?TBYX&a9@OCxDt@J!3r{ohN&;U)<z2(e%(Sg0OAvE<RkW%q9&}LGK
z4#&nrpod8@N?AK9T0|HU6aZIl)Ye^jShgn{PtBW!v@_h)Yjxo5WseTICakSYm~3PS
zTVpZW8#-ZW<p4i>Qziz{_)+S@&({&zSJKcavchwZKL=ZlW?a3Vhyz!1RNO&A?*+xr
z!rmVGR5r}59T}$XV%Qq+_x6IdnGWT_zR4O<FBfCTupnsBxM*o@<9phB4@S-_C52{W
zWCBx(E-7L;M`UUUjhchl-~lMjOT)f|%P1vWLY;F@iwbS(g$!io5MI-vgbo#ARfbTX
zD0wvqQBYxemXy`QEg%AxQf>6;8N%2|3;LFJu+h_Dztv%4ZNq1CKekLrt~ExOpC@+z
z_&xFdmI!foCp4vp$~ka-_j;tad0}d3H<t2Xmuv7Q46ACGxZ>r<CS&W;ACOVp#<|Lr
zA(1|m9fbRcOfiHCnph+xCu%au?IA7n-V{*%B=o<Vhq^ZBXI*It%v=V;&&vW`Twk{K
zj64tvr%xx7iM0>jd~O2N+DdTp#94aa(&<&!gM+m`v?W%sqxYz@r4<rqA9%S^;iHCN
z>*|jM3%|fuUw?xMRF;@+MFmJk=usCO>ABO;k}xNLkr53|!fXcMP~19b`#g?Nt(#bR
z>>7qf`u|;`nSzN)Hg5iS^{qGY);sTE(ZUbl%zTHKVO}_Qa3d0myD?<2&jX`W$eBn9
zF|2qmEqWlW4d_8`Xor-0i9hG2Lf#20Co5H=nl*D2gd|O+gzL=Gko!*WI_pb`dEmNo
zCVCE<@Fdp#_ydq}6e~7g;Cfa^kBCN7D<KguK-i#A&Pg={g!@BErr4Ce{i(8D0S!G7
z%sj&I%uBH})^F1TxEI-Zw;#BNpp=-J+adFE{$0hcKJ@^;A?CZ8>&8@K3w?rgwZvj{
zF{h`9N?vYO4$28R6KU(I_B_F0SE_Y0kHa2fjz?RU@1>F_S1{R9*@4P>!D~7QCfp1x
zEgj+QX!209o(}Q3CP+=pyW6*t3tREG<(qIbqX;Q!1t=_Uffl`zoaRvQei}lOxF**M
z_i7DceG@y~R#yr&i8G>yxZwDaa~KdY1VS2K>dE!^X6q>oed5n}AvUVdK)rjbfA~X>
zjt=Y`9iU0WO2Q~)C+C$W*IILJ8MMq@F_>8KYd15H#z<sEQ!Av#LSkzV^vrBwF4cmr
zz8<^RP34hZZ5y(^alPBv*@G5CpSlZN-ntu|Zqu=I6QA+TpRjn#Wk!Db)CLVhIEb21
zn0rt0yL9kJY&w<5adW&p6NXHB5{|7Wu=@06sA&m`^uvwokICILUj76tzkD5+)-S>7
z)RKqar23u6AsE}(^Vw*?#KaUzLUfdRCa~hZr)z2tb0!9~H_JI+%<1*kVgyo$$Rs7#
zFwZ@84OIfHFc;*_?M#3)S0(yNjhUp%Eka2=mWXGi(0aIb)#<Q;$Qb@^p2Sacu4?I_
zv8)Ud8&3o|SfIUK9j2z7CtCV&wlP#0H>wZwi>vQoG^Jg@;fpnR`Nau1@Z)O8%zWTZ
zth?ap7g09CnXIGB`}K%mZ)?eYmg|h?uIQmN{mwP}zyJG6pl>z&`*!gEa?d2TUXV5s
z|LN)gT`FYWp{^({Ws<=F2Oek&6~0o*MXxTeWr+R?T8Leg$$P!2UBq4rl@0E+ZXT3!
z9t?Ro1rSM$iMwhfPOK8QtGb}oUX3$}*>DZ>qJ`ZA1Bp7STk4?MQI1{fk3nK)18I*8
zb;NaPFp=o;m9uDK*0|8LEM2Lb_TbCK8*pITN^FkLhLM8<yxqlU<i?||s9_?aCMGci
z*Hc*z2@?`)Zxu7lWdx0?8(6jKCmh_i1n1k0;o;pI#uu)uU|%)p;q(u+MI+%5dLTAa
z(ZzmOqO^rdo>+~T;4&UgO~fL1(KE?SjS6Hp4@wqR;O0a8GB+H-E7Qr$@gDvxXXd`p
zx`<_imKCDxMA);MoBHaN3?~<>;0LNV@JkOB_%3>~gsky)zF)XxWnm$zWExBs>LEUm
zxXp@YRTT$8&Po~|GAeh1FQr>f*${3J80JW9UOOZV9oNuPWolsye|HJ4-%?Tm3P)2V
z6#`>FI9Qrvu)h}8uU~;ZTbAQQxjMW<9MH~FLAbA+W74n<#2fFvj_&FNtlhB(2TvYG
zo7fx{MjFH^Hc?^c8`*T|{!7}o3L6g|#=4cuVCFjz7K9JB5lbsDcRG98qO3{5L^Bx*
zntR|J7Q-mU48%?tg#pIe#C*zB8-6EcF3cVU@zLGg9Z0);02fP|VC!H->}U;TN~4RB
zg3}jMk(pf#4FhAMU<mOjt;X$2X1Wtw+0AFzCeuV%fGaeJ@ztktR$Ej^k6i$~)pD?5
z^%iVewg{(-ZgD=#u=VSOh)*q7jYIg-!)gvKgynb!g`y@a3s(OAuryYu!O??qhBwz8
zzRb|+oA~3aFQeo9UhF(^19GK8h5xn<V-zGrN^Eus4>mX0xtN2G3nGS?Z+B`!Vq%Eq
zvJ70KQFM6ga%?%5d-uL~e(2)7>g1tj?Gy~1`gW-4F*I9Tg>DUFLIpbc?77xB&*c1G
zRhor0hm)aW>IiXn3p(^1Vd<!bMc=H&jtxKH*li>DI9hXkwR5f#jw0ZriEX~OLWMcc
zd8um(w=hpETfQ3m<JMrqrB-+i^n<_0Kvd*khe7aA1e>?05)l1eEwFR>cQ|(Ao>C!H
z7C{fIAQ;f7Gn5jF(A#{8^KN8Fywf@iZqnXY6`bP&MqA(10%6pvSFc@zU2DI=g%&Y<
z0_~t|Zlyc{7mom(-nbR}j~-&u;%1cA(Rk$gS8Xp`cLF?ER+EDzi@w8cD!+G{oO_Oh
z%ApSD#w%~XfGbDW;Yhj+L!5QcDj4xH8rg(S7--OV;C$mELP2%`)FlMdv@(C8vJThc
zui)U01Q=V{K~GNHGUxif6@(<DR@}J_LL){%T6-2Be76qqC-yLmz8_-Wp)lcf+h|k>
zt%q|_sJ2r~g;7IH#i+d=HMh#qNUXH*{DgJh+4o){hd@o+0PdjyC~xQ>v_Of%`Yz~E
z36^nvbhMY^>#vuhu<_1VMU2jkL2KqNELyu0XAW(}qAl^T35h~$Q4VUmbqG_FbI&M8
zF_mcrBPK%i7h#X-WIl(4VSFb-Upf@pTth7=B_2GhN(V#y-5Ht4!&fU0Lu%>_MN=-0
zo=$_JtsFmm{Wqjl_hNMK2trVRyM(=`uM~}<qmz3+`^Y)k&gW%qN0^6Z6XZ0c<yFOe
zerl*GDS|?u^O@17;>s#iwlq@?T@cbkgt^qt=M*~37q>60$F@DYv3%13IFF9Fn}liV
z$S7IL5gd(AWwj_X=*n&)F{!Xm$=1fiIb(5R&G*>2^DqwVT#0473lYkMOHmK^X+9HZ
z8e-<`*{CWhByL;SJ=wUIcB(e<-e2Y1zYUojX*eJpFFrF2KP*{+l&nlF|LSYxF<Nq`
zg@oH7?bvc0I<yPR)~trY$_ZYE`S{zaE!g_Q*T~j$fSa2c^H_R6)6M4~<9&oZu9I_B
z$q6kZKHp_<6v~RuF|mIq*8UWSI*~30xDy6==`f>9Td{FRqRIf5^K%`X4;JnNVQIuY
zPgjCY<^kwgT4L02bL`%{0*CkQz&b(()dlORuj#N~*4iy7tW-nv@FB=!RCa*32OSM+
zJaZULqvEXIQiU@Y(}=xij!EB{F6TKU=ic#~HJ+!BZjX3fh=2%Z@Iy_gF2aTV(5v@;
zRT5U(NCWPF*W{?bFTd%3ca4Ajr$qhLIsWxR{)cx^*M*ClD*^-jSPes$o46f3eEr}v
z(19K_3Cu0+VN36wgS|POf12?0^@k%p4@UH=8W8*IJirbn`n}e@#KaPIyjD=S8=9&s
zxN$onG&G3S6`T<6ZUq@VQ4vE&!a~%FlIm9Y4;hchP;Xe8YooNhl414X7(YG?mbO&j
zX@y(bJ3veihZ;AS06#B!;Ofz%ZHkzfp~T2}A(*dq5U<+;Lkt@+7D2&5Fk@(6xV8@y
zL%ap7*QG-Eh4^tJHNdzjGvIYMds&@jsw|0Hb3-T<sBR{zHFS#+HGDiGsOalycA%Nc
zY<Lt8O7`DBz?T?c8+wtnU}<Ja%bk@*cqp0J+QP}z2@Z~Sa2wzRCp%l166Y#5qUYP$
zfyylvK@B|^7;B-rx*icRV_;*@D=#f=F}><Gl&>Wm9r)eC1dd)V&{Jmu2oJmPp+o8Q
za6*8;FTFEXs;V>=)-JHIG=;>F-aI>JgocDe)K*P{M2R6IC&8H>W~q$}y#2i4?&1hL
z2N#5d1@al`z|A|5c;jZY_UK{!)Y%AjvxTv_Bd=!$Eqcam?5ttK@dOOCC05pyO1BTH
zONt@p0X%z53=A1b5L#i*byR56h%uMK(AghTCr*F`l__f*M|#=}S>eM2-HnwrOmr}Q
z)Mz*u--Cjv^EsGX+QQ!68P?X0^yu^6diul&Iw2?^5TaI&rBxl{#!f{5=a!?H1U<Th
za*PQgtfc}59DitVAWUst;qNm5j;_wUmMx9HFj(m+P*T|d_ps4as=eUp=Z}t>N~oK;
zBWg$_22#P)VkE_i(E_PHJ#=hZ0>0DUntivYygU#X$c%hLdQ5GsXy|FdE?@+Pcv#Z&
zZvw9%7b@-&1P$`%J%#5lf|F+eJlF>(cOMM$b46P<VJ&J#m@slU9PP|tV<x6YP=b!K
z>$oYmCzQlmb>BN*q&y6r+y<y*=iwkFhMJyeuV9QG5eO?2b6&%Sb4VM8mNpzu06j?}
zl$BJ$DI^lp#}7e>zbDk&8quX~f!LX`9HSYpWkv6_8KXJ+u&{Q5EuZh5<5geZkgy_W
z3?38$W3@KbGa5U2210o7Yf`Zs;Kp_3>kb>|0r2HM;A&@%lv4-LY~~GrM^ja|HMI39
zA17E@nBI*Rss2E$^OY-E7(Qz@=V5PBmIg7XP7ZudoL3$`e7+s^#B`fr^7Ps8axjIN
znHBqI$!FpYXA3PR`!Z>bYb1t=Ults{J;$O=Z@Vcy@-`OQ^uQm3*N~Ag=_>`ID>j0a
zwFAtIB+xT)K=42_s2P}JSWF~3t8b~IR3j!#Ab!^x&W`pl)YoUE!ik>JN{Bi4LkTl;
z^bh3NbO}kShPwc-8tQ}8Q^(P5Fc2Oty|`Qru_<8>Onzu>U^rZkz?i9+HG+w!ViOub
zPTa!`AQsar>gbB#;9xY>lta_Zi3!JkaI$rSyQe!G2eA5zvm-1xr-kQWW9!6g20@ph
z`>KX+L=79oDl^txA6y$onmBgs3>*TYxL?ps$);=SpqgW?Oh9h#R71p=nV1~r4m-~Q
z#Ld&tSCb$jDw4{u4g7>TNtu}%5#MiRK^fU0z}Fcyw~L`Pu}2J{R!;2CK*B@RsN|Vg
z(KBqIp-Oz#qSEQ$D8+>%$6)9m4JSjoQTv*jI@biFQ0~q)EY&9BSU7(PA=Bp^6{0+D
z9Iv0J12MX{plRWWkz>Xn#MK_%bkvCS9Wit$jZ_E9i2Izar3th+mn<wD;Tsge92^M~
z)0^Q&Xv*~Afp_$DSPb-nmEd%#?|?t$K6RKk6>uS{);s@<Y}^s#W`UZzN-FPCOk%=o
zpey%FVwN4~)i&dPtZ!xyKdu#bcNbV0aZNHpVB_w_{o0JiwHo(RMmV_#NcFT~<>Cc5
z3r%QnuH3kI3eF+Xu<Y9}EE(Bx;Pa@c>%ic#Gcbe^EMG?8<ju7JqYN`*XTaUw66TEX
zS(zI{S84<YC!V3UcCg}kU~O$quQj7a+{5WyX3l~xG^xNh={sTetZ^{nvT2s9BVt%I
zqftJvCw|+8`;*kv8s5&<OaQ;lgnu6lkBoqar!S1uS_uEqLU_bb1p2zdn0uD0(Npfg
zbJQmwNHq`KI3_=TFX;1rW{d*Z+BsmLwXw?Bai^!-kxnTSmIl-(Hr~;R>!}Y)dFQ$R
z+TRteRoJrn2gDsXMu=rO+<e(Lt=?U#I`;w=Z9WH2Z&om&L+wrzDm(qO_AoqXcxv8z
z8HW$<K#|A-dL`G9)}Rf0uCM#sxy`FsxkQ3McZ>U9rXkgaBU?9MbKE{8TuXt(b|9<`
zby)#t8`f;zfiveXL&wOR(KY59n0vs^sQ1ix@FM^E-+oSYj{keK-~W2K_TS-WNT9Q#
z0N<}Vj;B9(8Qc?pmv;6idO;c9*3sxfyVx5aygK7oEp7CB4C>n1I9%^^#2?0n{2sUe
zkGyA6`~{eK2f&_?r2nW!sKMn+nQ-v%hLtg)EdQ`IV$Bx|k*_}ppS(PcxZ;2K#{cyn
z6wM{Llv78=(+^sGp3wjG#s1j`>A_FAco9~C5wH;d3kLGf?%nq{SxYr@35qa?iE-MZ
z`!t51@!4PHEnW4*tlvT?AuzgxY5Z5Vj%u7wyG_G75Msjke$CcenSsy$wi6as=4hzM
zW7U_p5#w$DYu@7D_*y+Uz3x+-Zgj<KkA*S+V;d}oJ%gD+R@7^&u<)I?aiPNwtCxHM
zJA-?3udDPjKK^VsranIha+V5o_VR-b9W<$z&O>f$hpa72aak)6AHFz|iR%Wc@yn_+
zF#oH~nDz1>q0K0hySp>tkIZq&uVbQb0Y3fiI9~hwRg|A%$<9s}ygJn%pZ_HePyhKf
z$g&P0xt`EGhbHX4u8sNcKYlmr_FD?~=P(xbE78B@6Zvnt7sIEmgc#^BL@rd}_`O<r
zVGim#MR4<QVdaM3s~5lCeY*)4=upsmb6>LIuXpwSEB$9mpbNJPOBp$FpjYyDTx|cD
z!t3w#f1w1r8ClKFFM)xj1MHb&^WW1N@Wt!%q2uBW87uJE1q{QKQ4#by_u$a_uTao#
zi2B>Lc=FF5V}Rkk15;6vfw!51(Ij<aj!P$sJ8iIt$()HhmZL<+8u^6S6n2{6-OoS7
zFdqk%I8%>o#HMsZeD=I2UU~Ct%=p7Q(9GI^Q!RmbZG;29`2HtMp7$KePwzyDK^R6k
z-p0P<R@mw>&xT3vQ)k4&QKudse7FivESL{3V)}pU_H&fzU)RL@tw-2@zkgo|Fd0%`
z^7|^#f^e*z-QnZ;e?x&5#-qWc#(o7_kVJpGCjl+kJ3IF)(Dww=|5LvN{;326+`PA+
z-+!3`O_4DFrM|ujnRz4&a<Ie29V@WoTpkK9AHk_gOFR)fnLGkadSu|MI4gw-ut!))
z>LTix`=g_8jW<78h|d;&jR^BnoMAPhR`MB0okKA@CIXQ&pFwn=0xbx?y0sCOOtRK7
zcE#JzjKGF(|AbZN)$rV7lOQ$`%G8!KFJuQ!BxQk^9(32Tx==cQCgi1y5WMfdXLT7S
z;v3N5sV;5!r_b~E{Nr!0+W(%T?Z0=w1o|b=FM)mu^h=;$0{s&BZ<GLY0K2u#F!v9y
zBgWGZE}}*pk7sGb?qm|b7+~d!Jf!60V)y<81WT)M^>POB^vsX3a%T>LGaOh6MJOLL
zz|$LfBxn;eGecdSiPZcra0lG;T{9V3Na*bg|GK!7-BVtNHXSoKnQGxY6Hyi3Q?ZmV
zjjFW$cz5MVma&u}a?)(PJa-Jx;Eh)n?m$(m8uMdV67jx7)PJ|f)~`hWyFHQq{`5<r
zUjqFS=$Amh1o|b=FM(fM0-9#<vlWqY{W^TjJegRS4{g_JBsmL$e?=WS>7~v}uVxaa
zCBh@45Y7Zzp~=fqn6R6Mlca8saaTja=_CZbFcOV9Ld|cX@J=`CON!Cb<p&815(rJo
z#AczCD_Bo_&H8n)8~ZdASxH!ZC>g$5EC<=rL^jAqRFZ9y^#|b`<cTY+bW$)P2=!MJ
znP{s+n27?l9eU7`FzfT*wEezHH0v5ykakmIXwJ;l-?l6Aj#g6qw!*~R5_j?znptf^
zreUDUDE>EnUj5zbmq5P+`X$gWfqn_}OQ2r@{|gCdV*0$t@%7S8SeSVb+U8#P!<=Xp
z!H+u&Y4tReqP&tG;X&>XTw9G*dMquCkhp(2uCP3!*nTv|kwy1-rU^aUS{N7+jGas4
za5=*ZBLfGjKI<{Sp0)2Yk$Z#XY8$Qar+4PDywm`Ev+e+5{`ee1l9%D@g$tRe*oG&b
zeuGt=Y*Bn|5tc6c5?viynEm`8AY}>2YN-@<HV;<)c%XB?^Dn<^VqwwcgQU;>5@Y}P
zIbNF?eYX_UZy}THQ`_<B`lI-ml(Pe+LJ&g1qUfKX)OI4NVh8^QT~MO<W-??3&TzLC
zNQg;RkwexvPS$pzcCN1{xv+%RfpirK3h9l_?BJl+hD*uWEHlKJOuAIxpb(gnw4k2#
zK<kzIaIrVzXOt+*q2;J+2TziDJls0DP>vrvPI_z+2FHwppR+kCi?dMQV+K#>-uKrO
z=b=S|C7SePNJ+_tjOL~$$y5AUhD^$Om#Rhz?3+oX7IyZ1pp@Ff_xqoIm$9^35^HI8
z2y8GK`tV`dD{}))G?eF|xKRrMEZZcsrix;e%fzr3cObK*4MBk(Dq?~Tk{INb%iztj
zYY&3_A0gxH49JZfS<*`%t=0KBbus}}avem*jHax5OKP>$6yoTKc+|=C`1(i{`9f_`
zHfl&L<H0g!k7{*gWLLc1O!)-BQ2YK~C{UDn6T0@SH7a?yOo@_Kyc%ng{{0nuWUXZJ
zt7IuGmfcg?mpv%VOhuc>j3p3ldq2=4M^<t&v>kki8!JUE%exJ95c=1H%<Gq-)VC#V
zb2Bk(9<b1VM8X+ApM8U@l}%kj315~-WNAJJfheRM$=5Ss=NrK1&zmdSk(tW+&+hgp
z%*ul`88ppwNnpV;MHwkYaP;-Y!$Xs|)*&e+gA~RjJ>l9?YMGIE!$(D!^Y^3u`LA>~
zm*e!=>u_U@URP@pmE3LJxRqDPn&+%@T~h%mYqc8cKP>ONZ~onXkh3&iSzU{Y(B)s|
zKIra5PF@}?9KB%3=k;4OCA09eGV@?VDtoEU&%dXly$;zV&hhlLe;_1$f1hQ|mB_r+
zjsPElDX_N*Ua&jsM_}_)yYtKaAN(0Vw^S4&y|k6=uD)m|%iNV>EsO{o@H0O7-|!YW
z5>H)()1YwfVSQ!Xeyd?~|H{m*Wn#D!Nm1@R_225||CjHhV9jxX)an=Rkvu5mEG{n)
z{QQrN9_Szcx&Rgv-bQSQ<s;uN5J9Q25BKjBT`W7OA-u-NeQ3pJ$dyFnt+_)U`T2kK
zi$5o^@b{||O$F@qo;66i)qu)oJ;V$TM@P;He7kxJjvd&6gYh>|mURg~{ICu!TC9y}
zBf@qP4=mff9r4$*VB=(p9p8R~?MF}I)S)A2k+M#1_bq%m|08VLcN#rXYdDJQ@!hw}
zaO%WfY}|eVQdeI%_0=(~&AEYdsRbA^ekz%cgvF^OrRhZ~wT`GrI*iSG4r2R;pHR?Z
z2q%p~QrRXTAk-fhw=KrmA`)4g*^1+NR6Z)NVcEubc(eAa{{00M|Hr4>gWaqChRsKl
zF?0l}mB~PNbj2E^cZqQA)Lxj7e%hK;X)T3kv1I#cH0Bp0r`sAHn(6p##V!_XSCZED
z2KMhg3TMAiRNvT%%VkOgd9aL3X9Jde{08>jRFdwMH9zm`i>y2YpMCr(sx*yZL}H44
zTebuC0gxqaMM9PsgGmA+wB%Fkky=l7ueJg#CLxPDX(`gLpT{w7rmjJQVM6A)rjna1
zJ@Y<t<qn7mcE0Z}zwF1#!mIe4RxD{Pke88!Lx)em+B+C6Ip@guwvzQ@$H0n8b?%uX
z*qx~$kw-e-|9TzFJ%_;AT#U-B<JfYXavU<~fw2n8<ij#PvU~+@YPrEjub5P;U!h*h
z0tT{j#O*o=iAyNVy7KVhr;E`{!VLpuId*P41atqvs5`wEhl({370@f6`$yPSorXo<
zt>I>W6G`bchzR#({nI<|=){UuD*?w4xRTZG!PbKHWP3C42!`=PRTbJwbMWn&Gh})T
zfq|-CrySdseubTvauGQ^3bdQhSdodpy!jC{SmRr9;Sdhr)<KlN9WL$OfVJBbFlb~1
z_HW+^4cEbNHvId;ucxySs}?LoR&zI+$_sJ)@IGXB7$ek44?lkXXI!ci*eGe3P(G{I
ztbv246IOn`2<g=l3=MH1?Q#YFwsI>5ghs*ij$zd|z?P!xSiEEnT4^NImY1QlMoHty
zgG%&W!8|C6pZVMIoolc!wFyxXgGk78|3=<|U4Q!<G+58~($4jedPK42^e;Rher6wj
zjkmQIXJgmNG<XGj^%={*##JA>Mpp|C>^cW?*1R?T1q$s$SNTQ1_Rt_+{@oThL`K8x
zp*iu-E>xd=8XLBxaxV;eXbs={RC8WDHf%~k%*f!oFE(Z|0cr0IGKRY0_{!x-)^voI
z<u5N~m~?zA(pn7>;^Bak8&)At*A0VQA8jz-z4?F14;>ZhShw{O9WD_pH~0Uyet)UR
zgc|QFemIKY;gKv=`b$6E|Neh00f9O35ssCA{DI!Tko-+_hq3a1dN1pm+n7mFS>1tw
zE>@3xw>taq@Qf1rrLu*)8jw-cg^?4YA${n4eB{Oc1uy=bI?=z|wCt!Y!pXBGc>T>k
z;n=DlajEE845}+bO6pCFiJgLLKdnNVcq&3Yt#NqMc9K)fCNq>Crc4=6<#{<S`1l}n
z_-HijSdXJ=bqJl>h>hQUjSj~Nm}*{!jcb>~lr;;l-ONSI_*m$4?Z&3VR}k$nRrSyW
zkHv!zPl@Kre3a-00{Mxk)C$GOp%Uypy9GXya8wl)P$gr=X?`*aoxJd5XdvP~`3lE-
z^bz~od{~mw_Lnt!O-L9~T9|@$`!C{8Gl!C*wu43e-4G}}hqUCI2pSiF%V(}Z--gBc
zyHk;^Vo#I9kql#hdg~S5t_KHyT!6R}=P=Yvp!M$U!|jxlsFV7^qAM3?a+@*At+#HY
zU??2hy%XK;qcH!ixvIL0{`)o{J~<V^Voegd+#7?6zLpY_k!8&@W)j|hVgx_liS6Hg
zj132=^ga{KdakEn9Xt%p1sBPVHw*&|?pN3Qq|x1mqq}w@t+*b(L&su-j}eY179wUu
z1eEo~xPGIO^w-_gF{AOutFx$3C~<1T7x-z<8O-n|!&7G+mao}_#q(dJ%Sm7i6ueC&
z=Of9<b~3O9e)Rz<pVjD@zt{JB&*AT7Ox`7K0t%dq+YY0_bMf9&BURq)fZa=xp2Osd
zs`D@ooPqhzalCAShu7dr)-Cv}xCQC<dPI}8l8A;B$Inf4!Nvmx=wesysjTcV@nRwx
zA(u~FL^-L_3kquyIw2OO4d=NA=<(Kfz^jjQt{&cji@Eg(ik^rFE6S3d;`>LX#sjR3
z9`V$yG7R^%L3aFM<hHb9I6ZLW<s#*;29ghM!p41ROv?BRJZ&`*M;1tm?2%1LMlLy%
zMrObC4*bp4QI&ZDpM1Rs#Q`s}KCULNlYuUNh#!V~XsF~X&^K#pcOf}}^wKfGh;~)y
z>tywNRO@LYYh@VTd+X(Uf0Z}m2wB21N}J#lH34J8TyX7tB3Z;5P%0XL*&{qv@9k({
zsiQsnQAWl%-^lS8MJ7+7RTm{=->ECm^&Ekg@>HD2kzmviPn0mCQ`1HocB)^3XHpn%
zMJB1s3#Ax2ED*|uA{;w^8zU!0sT9_78t0cXOPKLp3Tuxb7|Kd;Ew2vI6Xzh<#Q^6H
z?8cScO4zyuBX&#((yyK)lV}Cm=o%3^ZWe}ePfDlkPm<h3Y&`%oW=w;zdNX!y-ilkT
zBA6KKBb3k3!>9#Yx9viCyB;RYm<<mreWaY*gVWcG8LiMnhsaVj_f&)9m)fEfY~Ond
zYDSdB?6F8Wz8{9ZBS|x?jy<~);2q$C?9>$E2y4*LA;#Dlvs5G*Ni@3Sujg<LjUZ8m
zClV8HqNS;XjF<NC4t62~rwCQqSFrQ&S?D_k;j!r>kd<%-#Wgi3Dk$R`9)Vdgeq?ye
z!nWN<(W+^NNmHl8)lv&rj_=0Vv{I5RjK<_B5}y2B>qg>{-AJH3?R_FKIl_tay&6Zz
zZhE9mj;M*V5bSP_f*WVB??e(zU4k)Z`Y>p;*JAhfI24f`ame`T5Q4{w9v;yAe(At=
zblH%csO~0uY(fy`Wuv;Qy1F*n8gsF3?T={b*2nbeqY=MvFA_7FutKX7`3YxHQ>qCs
z^-$!bW|Ez+4&5}8XOIZTO1l9Ecke+?c^e61#$%+PF%IuNjli+fU?*w9h4gZadHqGs
zl{{RoR>!MPJK*XGjw`K*JRUxnI@|*nFXUqM<Pj)OPC!wQH5~_bNI0ATM~1Jj96y9x
zwQ^!eCnC(->F&C%D@wzjL#NS1lBJQ;XTecXi3>L}P)_0wk(C#mC9SxgT8zLEQ!p&h
z5lLqb;2c>tB{p7|IV~EAyS5>>Q3hSoR=b2oBih#rhqrA*Rz({EMoh*?e`6dybe?mf
zg6xY*OyE5{?CzCpYpKjZT--rYq#Izw#2N5*FvR6!akxMSlKH@J%$^*H)YB(X#JNyX
z)&Q?iQdZ|Dp{z@a8M7!GLv17{UWHTmV5rxnVI}8hC+ElbnKR(bh~WNRaVV&0Wn^e1
zCPn$8maLmQ51eH(ni0gLWS=s74C*t_WA~BEklOp<vDi^4zHt&6g%v0%t%Gl9C^~MX
zBCkmYv5(D#jaCD8#qC8=btn9XPC%3m<5(=|$(%K0oIQY?E=x=vHjvJNEy%A`V$`%b
zaMxqhuCRa+zhd}}m<m^i`y*6;H(~o<_zfjs<2?vAkJ>hVP9L<4Fm3k4pYzV&^*xW!
zdVZV!<Rl(LPF({x(w)euuftg`GO9N)BBR>43DYn@YJjM56ETI#mQ=G9-AZnt6}NEZ
zdOEtOEb8i6V1QW<ZkIP<_KWXgYOn*U3mPzL@>EQs;5Df6-Y%|z5!rzzOrL_m!7k`-
zqv!a(;i=K8(z#3MMtg$P+k%3oREo$zM_OSrZy6A)O=eCg)kzV}&uWwNzK8vE^$CN$
zwGL#uRu~b{ivhat(m(4*8QJundg&=tpW2Aa#m!{u)8M@|5ivXjX&Hn!(36zR!#r{%
zJ*)x;uh2JkP)TagUe!e%A+*p-ax2i-*3G^Pk5hr;CzBC4<yFiIG~tGMM%8ashk9C&
zMGSi2u;{ym%Y&vojX%yCucG<w)4}`t&a7MhBp4e#R036UQ`13k&>*y=<&Y|xw53-o
zG3}YRFxa30*OGGkK6n2&B@*J6<78GV1`qbY)x+zNn3jr*`_C|Q7d+^Tu{+@++Q?+b
zLG_JE4FMqx-DKyI39tjh=R5^Z*>&tDZL>CaQX$0Ct>}dJ;K$)>-hdUmE|CFCc>R8L
z^IjkRUdEba32Q`hsS+V%S-fM5hfIGBPmc~DJf$8{VF7n95ITMyUK#CA&p%-akG=(T
z$f(p)c>}M1b`qm!Jw;}<j)&%%h%AsYoH-bW#6l{NZbsO=ZV#W7DNIO}ZB53-3)_Fh
zp^REY406Gdb*qq6Si@Y4JG~RSLq%CnikKk-aU(I6*~;}u%&w<bE{Z(oUC<_k<mQPr
z_;m3)1kHT`zP7?as^FA)xYN82Ludk}tx^~huiGQgTeDfXGP%UM6}QOzr!5V@Q!__m
z(@$H_&`#tK4HZEKM%JEqar$u5QEx_lmpW^JKY9yMCmUyDeG2J$*J9bS)wq^jKm~dk
zPUdtWz}pe0e)<+il5=ok&t|MXd>!Uy_ZfPJkD^3_jB;M4*tB&!id$8)4^m??j*%Tt
zVrYi0;ww0R@`kG4x2RknVS<d3Wfg_ds`0zvxH!Ch4=Otp$T)QnS2JtwQW=*gpTV}H
zDR6Li#i>n8aHdiW593zuMcYu2d>mH`YA}3w5Hb$_gyf6@CcA7yVwomkPkPw5Z41iE
z({S)|E(Q&!0hxUaC$A;p@QyV&UnxR>i$0F=zS&s?q&)u?HF{KJt$MIx$p)m5R(O5f
z8Mp<8qM<kgHC%9<`Ks1gm5smsxB*7qp^(&E!H?_q!N{lw>wj8{wQE-*wMk9Ij=62a
zHb&h%;UcNUie;;CiHgO#6S)YA3`6FTwTQcrj#I=f?l_eOV@(>_+fE^!6zWU9-2&%8
zQqgA}#q#a(NV<3oN3P_-JJ1#9cJ08mj9jc)x(Gei9?aOUr9rS8myWE*&hxp5pdpp8
zbuHr4%ldkI@7s-otFZZWGC~H2;P&yoB*-X)W=9>G)CqmE*T9bTyHT8S0!ud^hxd>$
z)Lq*}*<ZlkjX#j#OpH)J3mlKzfSg8#h(&Uo-1N2TS=u>RqPeOZO??#3!X4Gf1XoW;
zhXx%D`pq|S;CwPn$Wp2+HH4e1HN*ynu(Yv2SM_zQ*}0$Pwa9*2mx&*L+=i1st-;aD
z4UDw8A>qPB+^%6xOi39j&DBwM^)#yn_`=sj1DVNJVd60u8s)i&KUajvVNobJc@XE4
z%8+{T1X2mvy1sWAR_wou7DkIsojs2e2UjDZS`(q(=2U|Bb3f~o5BI54TMj_#6asfK
zldD!8#qGRwY>7JoD=#nPAK8Etna%KVH%8owION?(Mtpoe8S{qW_SwVOajXDN13ln7
zz?gf-elqnn;^6uvh%e$E>1oS-;#(ZLkc@--w^2!E)dLy>YxbQaEUx$dJ)M>KZt-%|
zNL}Dz-h(Z>cjNr&-B`Il1Kwm}EliBV54%qDwL?hEuZ5?R7;C=$8<dug=(%+bYYuXK
zF<N=`W(mB!9PrbE@1QXCB8#I8M=ssOwZ!vCZ#F_?h%<I^4kZ`lVA-NIkT`hLrBH_b
zWW+1ZzKW&m_rpCr9Bmngu<1ww($5^k(ad&uI2&T)vL(2s>k6ah6dXQ9=TX8w)DW^3
z9c+n%+&fB|<TTvXkaXxLY(J3z8#g-~T=^w(Wm3|M8)D7kRdjyb#1CICMrN}nj0_)+
z_VqoB{%`#f_!mjw&K~|Ry8NRrt0dLx_I+0g3mA!66M|5hbOZ;J58#HSHx)J+I>;g4
zM#VtU*@dni8F$ZaWM8|4tJ!6E^@B+$NVq^K!8LreX&=k1SYU*>5ecccNEvFZf+y6O
zR70+z342FlsNdisB<oXWXD1auf#<!iDSL>qEyB_T3t*t$O-3_9DyZ3YE99Vl0YfSW
zU0pK%@8aeyqC#GXjZ`R7l5UYES%!Fe{PL^C^dyvH>B{~1;_d0bnBA(I%8S@J0*_6t
z$9iV32Ix>ZR#U>lgG^cLc|dZruZ9x>9gJ{^3e24*D7JT0m+nqE#8NG2YIZ}N8Ll0*
zg)C8X6WWT6C~VIq>rW#pnkV+s*HXh!^7{8(3Z0eNIG<k6OH(1b56deO6laxNPdL2P
z@2X;o&TeR!n4s)hJaWqNabU|b9wud|Pq>EZ(E}f#1Z7SyZd|Lzm}fpjl#dktUZLo$
zO~*+!O)8aC%(OIB^tHmc1mob&n0jQy(27Y9%d$BNi36T^aUSNgFuRT~@sgUh^w84l
zsMNztFFc74-dTk+>i!T>;e1$K6t4Ry87p~ns)V2|a<?^<W^jI0@-@ELeeVwqx3h4)
zoP8B!pfXbKex&yY;)RcGS`6!`>4>Q)#Xi>EEGB*NQxl`^D(J!+l_Z}5>0`$~{vx7{
zOX%fV4I`&%aB_AgeY7)<oY;nXnK4pt<WS(1$S<qn_d62ly;T<veBbC8WF9+(s|g)w
zGV(*HT^nkNZ4=BYQI8>>d}$7L?%s@{{(r$h%ihnc(eS&|iqei_&DMA*w9GMc>KM)&
z5la;H{axA3b)w0TtrogvGK7tcMSJc7tle~$^y$6ji{!+s1x}cQ>YVqvh7+JgkM*PE
zPA#9;HY7#64b&MWkW!&La-|+q-ufe=+;m~sRERUDZ$N2khMBQ{V#c@roi4Jjw<`+I
zr6KWl4LTd-XltdCPkghs)D|wJG4AmVghfXMpGmL3UrS4i{kYc)1r1IMPd@~vpTLEr
z8>q=HN7T&6?#?fHw;I9&#$x=~2ozk8r$;XugGnu(xb_MR>;hn}c?%cguP`f|)ZFxv
zi%4tx)U28CwXetVq*YW{tl&*d{nd-->3wX6T3HEnvdS^<rBC5wuZPC;n~*n^aXvLc
zDlg)kZADLeDNf$#fbH;SFmYHABMn+uf2xQpq&I3&S6qxzD(}+F8-%2HBe&`nT>kJL
z0?)pPE&JOUnRyr5S*Ky<Hvxa3aigIKLh(C)!KH%*6t$R<t2DH<DRA>D*S?+!<~{!|
z!u0DAPncLkc_#9^TVb3|Onq|)<XLG@kY0WGoOu`-V1$bE=ZLpYK_L~%FJi}10UC|L
zRn^$Db}?!iL`b=s&h=S?{L&KEf$jAQ_dYBo5*ZcopYkGNBApT8?~nS1YFrSz(2E)i
zAN_oK__=2$wzCh7$WBUP-US1FS5sh6A;+BgkHe31)YqpP?Uku0&N+g_qz*j$*(wYm
z>)C)O?u%mZhCMtBY;42u<m8daNp8oc<Zes~Ar<s335G?4p)uhk8c5r2q<MnW;NvlE
zL<|sNhu6ON33|iEz^>pRPG3lci>;T68v5CH-{e(gSbZiNA>*FrB9)P;Fb4tC$HT~0
z21{EVTuMkl9r68gg@jNpD=K*jXjYIuyr+`X#2t(jL?9+m7x^0w<9sq{-Mxn3E?cs?
zCY(Y&xxZfHzFf?RizZ|;OV|fZ!h{i{p`IQOnO6)(N46vJ>|W?tSi{Orj6~j}h7{)}
z^(`1Uc@WZ9pMl?Fud4Rf&;GCzu^;~lVUAkRtt~+OsZ?0HxMJ3mQ=yf64d>QfK|8k^
z0Rk^8%Ry(mIsWwI6zFlJp)^X)uKyaNCp?QWqkRzO0DKph0CkBeM$UK+V~uNRl$9WI
z)EHD$<=_z4QA5^Q)M*Sv-~da=M}*-(%0)EMSP36(3?~{L($up!f0?xB4IL=SOoH6i
zgOQH05VgCbpmHN$qcfzW0Xa$6(b(OCI@T5rWVMAEW2RtCxB^!gah=TPDZ5;X!?$EG
zBlHZs=S9{9ZebL%MXrI?n#=fdX+6fi_7w&@N$^fuHDFVQ4DJhx&V25bb};f7gZJK?
zi+&ZF54ykorV_Y!y8ec@l{ePY#qW>V&pnS3BSzrKdCy{S5Pkj{CKwVH3S(UzdP}2V
zXCi@~sVxFS1~WVwjS<lSxN;^DjzPh&v0#Fk_YlMkbwW<cb=+#w#uIOT2nS^;(ki-m
z%~ufWP6gD*4}!DV%E=F5{*HGKd;@E4`VryK<HjkY(oRhRPJZDCcC~|xe>iN7#1Ki$
z5gHr}PeKhw4-Q6oehzH?B8j=vK|x*<S$#gi$A5Z5g~cvxVA%W@HYFALE-JwRW9A}2
zEdy(*ROkq*m)I6Q1C&^`Y7?9Xhf<*z4iF&;jd~TobP;0FN~56aRxaYU?uLiAJB)~V
zROgYLa`h~9eW&7;Cu8yW^KW2?Ne#}>OQ&jT5(GIq;^g*yC~xAJWsTVL({h~8t%jkA
z9&!tqjMzz!a2rF8m$K>cv!bVr2?$*sXl1eW^1@VXKa~J4?!G6^T}QyA7pPoJ#k`mQ
z0R8gwxLjC{{G5Dl;&+r#60YcVp@>P33V9oj(BpKowi7*?O=$0wp_GNyTd5FJS4KAt
zme%$T)Rg37+kpeH9OMfL-HBbD9WZkogxNv5Sh0RP6b$LA6ksMzX_@%oiP?j&e)$Hp
z%UHZ!-io~3e0rDeJ&3=TaT0Vi%@N@)!KuCbQP<ps?#_Db*sumU4ZxsCOB^HnQf-GE
zU2P56wt-&2`VK1nG{ibO(82%hWMXXZ=zHFTs}wL=;2As~Z$CdA1y?Vlnu&V%FD$_r
zA+SWHpsNYWv`{~j?@-WCk#)85u%OY#gdJzU5X_!4n-B#dY0j9>{Jw#3NIl&&=nUNv
zXwrpG7VkoEG()U30I6c4OQr_r=xJnmdI58T6<D%j6+_;f)|_MQ?XB#4J6f5rYVHz=
zx8Hpi@4oRIlOzox<2YJd+tAunjT3vI+-ZgY06+jqL_t*JklbboXB$&KFRlyDm)Wnp
zgu)BkSqiCErEn_=-;mSG9Q*7%B=7nOsU<ax0`#DSUZ~n;8oYf?Nhpe*#1Rw5jKR2Z
zqv1Bd0Y;Jz6ciKg($R#Bf(q!G8LKMehy{T6ou;V4&OKXU9uUogHWE?_@}Bv~amqPf
zAsK}RSO@ATzfO_@<giaPa)bpU{G$RdQ%W#lR2WXKTZ$rWXAEKv#hoS?9KD95U^Mq!
zonv5KUAOPsByEz$w%KSmwi<KCwr#UPW7|d>+qP|I$9A&go9CSS-g}<&X?<E_jJ4KW
z6Tdme{M(&RyXDvu=iNJc%(Cq|sp;>J9obbS{K@jOQwOOi5&E*?qWy;Dw)<ZC+0kSo
zu(1~7ci9(SkZ=5T*tBT>zJ?BlaLZHKb^f^cG}KrcXX-|a8GId^fVB>jMl1Rnf85m2
z=te443##>@`ikD%98L;nDihp;pp-r8H%->9!ONElAqlyurX#%?Ye5<Am624V)p9|h
zG0cH`zmUvvcV>GIh=8v@KIQ&WBNxUt)3VQ5X&Lyj(AXs$^h6b%75Gcf*X)|N{U@?f
z7fgB*9}7$|ZE0~&dh!syA2agFN!M^MG&50p0Hb{G)}EOk6JKXZQ51JxAc+*7@*X3T
z$id(;FE?y9Z9aapB@EL}E$YuJ1!h#J<yM1{vcVtLR`8-hEwxU{%wc9;u8pb&ljr5s
zR%QWrzqZwGyB#?i_n|4#8}pxB_(m)=&o`t(RK9=7NVPNP1CWG~9;6{JkR>B??e<K<
zr`vHQVqxSJX-CyW;O9-jI9sZUfdi?20Fp!xJI0&hlA=NWJPBAvTS;joli#>mu(SmQ
zlfU<@!MKZaM8M5IX+_WenA-UC4fj!JJHYO7>KhAZL_c~ln-wuUU}#c^^@4INGnlFv
zexTZL)vmtk+oSrUnFS5;%QghW;P}8&?#9&27ISWSvC>86^t#sO`WhrhXmb#{c#8x6
z!AL!0_`|K;kDO@+v7YmJ4ow@sgW^af!M=j8nbb|nfeg5=?BOLOZ0!3t=e&66fi~tp
zzRj*L^aaisEF5CwE2>0#{p8oMDb~MvGmJUH`{7qBxA&(dHcPB2obkc!TsDL1XP^9p
z&(M)@jJ?lc$cp{`5&{%_^e@Uq$HpMuEbl7}c!HeY>-;aj)^8ER@gW&4XT(!1QNo0J
zBG~w5^HwO)XY@+TP6PYIk)9eRQkWbOi-6dEC>hzf@Mv(3iYdk4-jbQ;R)<+5vt&;-
z8W2_(<WrcO$qNin(ASv`#l5fglHug)Z0Q*eSbNf%DSToBiO0kGtGcJF*k^t#B)|XW
zT-<!3xWtp%a=j9^Ol{t(Z`^3-R&qwOHSi*WnviyXZ13pQ-2U>gAsn~3@pyP{s>II&
z!Em$j;+gBV%?sc=%J(SUp5nYze4TslAF+kmICzI%4e8Jodg@5u?#JR);^)7)e@1F|
zR@=OQyfW>~MS43keWRS84Nl3W`Ez72a8n8L*o)3ljFidLe<seDC=r7x)rXOh0>bms
zBx<}!%ji>L`rPBJzywrZ5d{>B1(LmMVY0YY85t*`Al{&A1@>lI8%eV?=;j@KpUE^0
z#|@hF!EID!=+`}%4E~LnXwQq)20rEBI8tJscKE*UdyR!DXZy4G&aO=W-o#mhawMhh
zp0oFW9$_3CnGK_QBS_6l(!Y6fuxncQu!@Ydt#W&&L&&Vf8FJFA0lngD&m5}$44PsJ
zT?--+g_*$Y5>7q@sa!06v@KsIlDaqS?HVb`LVOOzD@4a5Pj=RzN04Iv<qDXDx=HJ^
zQ2WMil^soRyu;{JkC3MJ>aXD7EzQp_21N$<d$iPr+w&_iK$_`H@L~8QEOegMG9iT_
z3PwZ~Ctznqx3kCehEvCSL~s?-mk<M1$XR#SvG*;Z^29+gxB_h>{7UdDxAYGOYVhRf
z#9wsjn_m)c-Y(=AU&(~0^P6p6i$hHFR)?0Wl@caMtEpkOWqG!!9shQu{2)e1IX)56
zM27SGVqgfV!p-=r+z;E+g|M4h(V*IU-oq<9UZAptasLT=>eDouRcnXK0aqFf4lBCL
z@(R2u7wHWf1_wRLt&_VN3Nw9OHLxx6^rO%jtNJ8}r2?^1Es$1eCa~9j%+VHk)T+WG
zTVX+n;?LLBB;U{gi!(D-_QCWEUT8;itlKdew<=em@Z6il?DXFkK9c(vxKoSlXin(j
zW!{%F@$pqpRw%iO1RPaf1Yh~?#o&?ZRJ8E|X+ja4Nm6jV-weh)OmIi>%Dcs=66M&h
zyV9Az8ctpt@x&1QVfbAfz>6AnF3=G2x9IxY+#;%|-qD|yia?Yw8x8Fwzg|5%GZaiO
zjSu-bFDMzjyl*F#a$PM7hWGM_%(@;#7~JZ-M#zZ`8#b`I^@S!GJO1Y7fmt0C#>K<V
zRE4B;e(9YUB;v4Ytx;LiPYfYpX?oTc-2vH1{@*f44pI{}iG>e9RbeSFX>l5dv)GXo
zB4^jm>6i2B&NPAA*ak;o&1k(<4$n+l`N?Zxp*=vv@b2=0NAQV1DvJtMm&p)*BHeF3
zoYvn4pe;+aPLS9{s|q+OEeO&xs>}3vHDx!yOBb_TBu@ofIP$mJ;~p0BcRW43S8S6g
zhXIgnvfgl<be%Ka*53n2d1*GbMANKTcx^@ueO;Q0+fNnh*Sx&(UEtzuKl8tVH##X=
z$7S6##^{qo)dZYscaMW|WPzk^AjcH!({N*5K<~^2GtA^-9~`4WhT1(NdH$w=RZ|t9
zL?2Xlq*mkhh3_rp<fqB616`b`nG=sd6Bu7i!QheRk5c-CHJu2eBp;w*0b2Zid%&QK
zX0_2tV^c})uj;`clwMNGg|goHCI-SPBrf>kzZBQM{P{B06LGk~t5rZ2)Deo3jhmfY
zgcxasfIZ_`y55B5iB!<LK8Q;jb?SFu2g#DO+2^VYC?HXYjGb(Q@LX4*qkxhyFop1r
zw&TE|sdgOxs`mJ!M8GVI(@{e<qxJJ-?v7HEyBHppIPQaC|FwbALW6xJO>i!TK8`GA
zhUfXEVrAgCHAQLYQXb<Gjms$y44NnubkofWZex|ztNobgiE|+LA<97AH(Y{<5`nN9
zL`yZ?`*ZCTx-%NmjBs3<KG7n(24}xpkWZPGj(>>;PW4@K)t$m;7N<xURWKB)hi0)O
z|EvAzw*n#sfnNXH;o6>R`;TlN2am8iJw57e!VHl$@E5Pkvy&7K6Jj5&reC4z4of0q
zeM|bivGs>yUe}DgBkKxn;C-Hg3k|VBf4w+lS@Rz6&>Lk7hTk4<^t(gjS7nU^6cu{6
z$v=wV4pQgJ^g1S>n!;|XPi_Uv+KNIkDRD}V)bxJ-!BYWf4AHXYsGEFitlAD4lkAV?
zr~A!CfVhLLEyvCroD@}6`5-`Ay^p`$j>C)MnC*nk^XYklF?y>cBbJjw0mhe^sW#A3
z58WiHGn8Y0gooW6o~^%J>>kz{n-ko-IfNV(1d$P&eEJV1i=?=0O=^0M9DK5MZ6)l+
z6Q(~x%u>DNQD~QQ=lu<-BY5#EB<o8q?1l2R96CzWv`DG;ucANljI;Ac^N32>6axwp
z(e**cP$zf{<2N>(>{=ed=k<g%eF%r-JY7TJ_i9(nOJ@voBA<%Bb>2u;a|MZb$+VWO
z^U)f*j-j5X8VGc?H7rbeS^E_TjN=VTXCR*K+PdSb=cEkW8ANO0ZGJ-L&2xS8Ft4e}
zgya6$<0SWcDjM;0c<=LynWYVeR4A>W;7o`GLTy4fX%eQj1aCY1dTXnBWH#n!RHU6`
z=#lK*=eK(YllIOSTAQ}a5I^VT4A|8t&%=a})@V1Zes!{EjU%gzSd6D#eOhzuKtJW{
zsv%Ab9~C55_5=I0FFzg6EH;s>P+8H9W(K(P<8|M(^UB#cZR6F{H^6$F$?Lb4q+VY&
z=HADaL|g5uRw(kY4&Ud)a8a);u<#<3+phe&-yB3(5LagSdbZ^fv;im*4`eC#uC5`f
zo5Z%N*DhgJt?+v-*k4D=MKm*#zUdcaX3JLOns82;P+Cr7#|xjX?PXY_UR@j?4X)a5
zrI{^)#nVS(518cG`?3LhGl4O(*h_og9a2JtX6nTWzxoww^LOv8X|1hw(k0(Rrwd=#
z9ac2VWu*%{vNULji%Go?rOvX2W?6Pxb{yuvF-+68XT;0QOq~jQl+T#0=2~7~Fn5*j
z>$H%MT<n}2l)>Sqlg;9BEQqRl7V{*u8<}a5s(j6->t~Vk&4l0Oo4T%+KyQ(vtaNwt
zgm<egTuWo^R~HRsBU-BQZ`_#ND}pA{6W>z3(=DBaypx=ny5<L^neCmOoH^-80fuXJ
zo7_T_4@bSp{fo5G;{Qw&9tDEkhMnWZK`K}7`>x@}5?)<5yM#?(+vs?L6F-SO|JQ3=
zZC!q%zr+3_Zq6=F+sAwKu@YA!L?vVsH%advEi2VHYgczp$tc9(A<urY1ws{ykNe6z
z%}r6zZ$5<~=$XRz^-;f)ihkcu5U#a*UB!fKsY>s05z<tiQapII#{X>$F0JK_jdy6^
z)jb2aydj20hhyK|6C!)KptNf<xzG1f2?RF(79kqz;eLfELd4r0(|n#}$j2nm=AH03
zxt)Mh%(R;`T26i(<t?PgGLDG~TND5)h<b2|fThG#_dACY<PLZl<#bOSfa2+e6bwiB
z6(6rgp&;wWtZ2$E6BoZGg>SmH*X7LjAE94<=u&q0cnC~x+B8evGmBGs>&0vu#7$#|
z%6dK{!idnPl~>P>*5oNTAH{$CF<b<}nt0)J0XpG`Kw$HL17ve+Gv@{S0WaO2^8)i<
z?iG{s3zr3GT_WA@m365O7r6Zjf8r1AOidf1_`P<MkS%jd)8iincoRKUxo|y9J%8Bg
zBy}*TV=8dDDwtSr_{)nD)p5d2-2Ys`M>6*<rwpYV4au#{2zKez_}wVaVeXZ2R#yj!
z>smg(c&r6<ZJjek$p}6UUn{E{zPpZj0x<}N`niaV<Z46um1si-YXM5vW;NP29f@mp
zcqVP!1%qVv&%loscfEqHp3sO`NX0OXLw|aLYxj}4&3O`Rjh8<I2;C1Uwe~ceU0lR3
zg}4N-+Z-Cy-(5CV%rdp#%o~N0=g=}sTWgsOV@)&CGj;{qr?R|fs+0F7S!7pU4^9A+
z-#KMl`QCj1oN#4ticlTUeunBG+0es_&uRo7ET3EdhP$Ze0WQhgwTI{o+NCISzkKoC
ztNf^dJ8pc(fhc0z@Z6Dk3C`|!j}Ln5%yv<f@uQ<^^#58t5s~IV$vj|+7}AB7;t15{
zJTY>T)U@B7%ISrbe#MSMMOm7j_AB<i93Fnh4*PR9qt^7>y53_V)5T5r3L8nO)tesz
zk=b*B!Fc>0g6{lthUYRyfe6^UlVyH7tV@DCNBCAEu(C!4>O(_a07dS<FC46}_~9XZ
zJPytlS-CXzq&YCu5MjUmbFo8(p+@mW%_*hB_#gMkLCIP}ND61KECLgC{$uFB>}`=F
z%aJ~j3#h8o{0}QRFc~XQ2_by-mDx1y|1tevj=Cg>|B32XMUB$GqpFAf@N1#Vuk7*v
zL;djJ7cU?XdKmvn+xYLl<Fr8g@H^MxQ*!(Nga5|^mESMaX{ygHBMSeHbB^*Kza2h^
z!33@AZvlxt+jcK{?wPj{yIP%-%Y@vt!w1hsPBVhu$q*bFcgm%;gRUc+58w+073<9%
z+CA5Ck+aJGj^MIWtjn&`Dj3+>+4+7sSoz9Bcgw@e+j*b))O&?|2)a8UI>?_?tz|VO
zz+=aV%ix2sW$*QsE;CwXYw_;6a(r%l11<_~fSnMohX1-@cXEbim7peBGZ@Gzz;D)!
zqCnr*SV|H4$)jL9i@iL0`mA;6lRSS&+KK-5nM{b{YooU%0QfY7SLZ8EYihXfJT4B=
zu0=DrQEzuZnrbRnh3mXrT{&?+ZRPu%nBJ$?GU7g~Hx@_bhRTb1d+q`V=JyinmM)LJ
zgJ#QF;iV#Yu1{=V*HoIb(_FA1UU@s5v)|uDpl!!Ik^`sn?cOiAvtxK&D$_^vft}qG
znX78;tVDoC9Ubo*`ko*oG|<`B`r^g_H%X+xqR)m87>JG=hmDDMYrNsP;M>(Z*)eLv
z>GQg8{ZYd!)O66e_>eb$QB%YjQAcfSimNP=t`?`+Q<VS)%<^0dttYXT(byP^zo;&D
z&!|i|MtArZCnZPw{k~`u?%+%zk^d}4Si(p9pJpb8@*`FH@XkX^t3#XlqwS#A5M5$<
zQb}Q!gW6WlcS{_L6>6;+5Gw4!E|^#Q+(Zh~rBtkpL22G6?gy{Y->qlS_Lc`}k$Ah3
z*dX&~gFy^Wy>sdlR2O3={VnvTmi9ZhWlC&bZzOPRrvp@7(E;wBFxco~y%UkAw#Ngb
ztLb*W?0lZ@2uZel0&ZU4bQ&->d@l$Z2>(!gr@(QQA9sE{c^nL=oLXLyD(x54lIE!Y
zqPbMl80NjYP~)&nBguUWFB4Yub0+I`+jMSQOd$oXv_mb(Rq5MCMP3I1CL3MGV4E?g
zn+TPj?S$z@59{e>Yc$^^G6?dP4t(jIWV06Ts|3;AVKeBAvL-@ZnbrAgi&4?ObaND>
zEn^fRJpkwmpP^q>YxTjo;d=^vp`q=_QfZ;Hfx=ExZ!Z)TcHwv2@XmJDyoQyEFrFO1
zH&C4Y0kgS3b2gURnWU}hx`T-;x*gQ-)nbNVf(5)E_l<8<y&&PT|6!B0;owaNxoF_X
zCol2W3G-C1e?XZ|vmW+c@GmKgcK6x<-X!mE-n=n3f%PFID$EpXmBFhH?x@R6Ki25*
zy1KZUB+W(<<^BXFNvf`xf(B1(@`nsMZP*W@$k<Q{$Ag;ks+2eon?Eo2lJ+!ED-ht>
zV4YXgkYt_ipw{XEDPuiOr(2hz)A-}@4cx!uw{Ce9mN7(R+vI;`$PD9LzSHZ9sW@Y1
zJQBEVrB<EL6ajl!(pamf&KT*<Mi~Koujy^@y-{$2jc?jn$&P3BU=eJfbZ}g#y1nUF
zT7JbwJsOw+ZtR7#Ha7*5Zz(ipBJJC5>AmT2X$4%mS{wShBlV)aras)S?(VC`Q7`_j
zCssY))N*Jz6~$tP%8o+)wu;af2-d&69Gps@q&m5gxVF-NEAbd8g8p$4-^`-vg^j<{
zJp$y{n+_Yt7W#^UbLgyf2P7!?zdg$==|?9O{pi8<wA^IESa^qtd(&8bEx^808<%>s
z;5DFQ$FPTbM{CJg{TA*V!FQuY!%m;q!%NtCPXi%3u4iR{?U9+;PddbvNA-hlrf0$e
zhGnnzUJtt0%k$A|S*L{p6^j_;JU-H|Hukl|Qu7Z$Q!J7%w2T_bC4pVO#)qMtbPHj=
zb=dscq-V-Z%Sqd6Q+_hRaiGe6n#o|mq~?AdSp9fBwA>V5%h3F*T?%X3fU(x@2n&8x
z+;nG+6WnJ4GC8AUVd%y|0j#zBLUNG8;T_6shI2ti+aB1!B~#5{4pWBXD{TIoc;`NE
zMD8ZHe%xQRd%4qM!Ickq-AiAd#SgSSK`hHn#ZGfHcY!s!Y9)&KVZlRKg!nZwL)>*o
zHqAMgv_u%nM@2fOBK2#sot8yIb^DZmYkH_0Kc3}%Bcq|g2Xs|XDdQOmxfMiLZ9v01
z*1PiOQGkY?J;MX47y&H5;ZiTR1ub;tMj+T2Y#&43Chp0bg&KMJXYS$Arlhc<XUYO!
zyLsSAjso#Umo`uRRR;yxFj!~3^f+5~XR*(x$6g}$@YmV{zeZ!Xl{%{H8WD5$5M+mN
zKxD?=`Ye<!b>&o>f;3wFj$NSLS09+jdDDQKnk3UlG!4p4OfFTK&72e&?B>|IW$XP@
z^7)r8oE^Ep)8f`Y&L|0382ENAX#oT4T3k;brIzjR?TN>}PANWtGiW^9C-C{U^m4fs
zQw(Hg0P&Sew4S2btHp)CydGmWxlX(|FCK}1Tz}xvG_KZ(QOAZYEFD4ElT8x(g7~{W
z;&uA1mfaI|(u8`1ra1Dx5A+@$ru<P{h}1I!Tarqx`?8B@rgze<o69U7Q^f8k9QR9j
zN-ZwA95M?#8kv*Lo$Q<N=BJ=p^_wdQRIw<%*&gFM*Mw>dMFcWtgWZio<0dDSsOSlM
zWliUp{~qtVq7%BRLk@j!pkBl$cd69rpt;&<?d@ax$cx3H7K0a#HSihrrHPo+%q3t^
zU)Q3AgpUf!<TLmgf>uiSNCU{zQc`|RyYH2;)Zq?u7&0l=)QW%mF)ODCxQBDqg6NTu
zku=z?f>Hsz;@2J!qx`v2Gj_}Cs7^=OnIw50yWuc3iojP4nAnoFvuYDIg??E+_v?m&
z7Wv`k##S7SXmIZ7X!cD?->VJK(K~4*?eTSXKvb^Y6pz;gcw13HPC)_M-xIu#z=0yX
z#&%+x&uMHmyhR{k9mz(FPsCxa!A|A$g9`SW2<gs<=M6bdWAj*9?MMz_^#+k^xOffJ
zoFPOIazg~eFqFQ8<8cK^P4a3(d6C;!SG(ajHA%VOaX~XI6}(`!w6@q`O&d3Ah#ZDR
zFXRp~D9ix^*E_;Ad>$q}#SvW5rsse6&WCjOM|S;!kpM(heInXk5ppEUU^V}S#?S_%
z?Tc_`qAChZ9~ojKv%j5}lC3#f4~YmPrE&QYUj)pxc*j&ZXP`86`bvoEJ)XvqI(!g~
zCFuK$rdd`&5=TOuWKC%D&-db3Y_BB<P**P?rPd^Zgq{R5z!E};)XM8=_`nSzU9zkb
zjwCgHcxa56kuI%TJ=JX_*Slz^GnI}FFQB&gvX<6opb#rVA_i(fh7D3x*PDX%W;1Tc
zj(cGrf(>h7szH~_Vx8V<xlWk_(Fzl1dMJ&f+x>+COS%_m+e=1~UY(jR@zF{v3z4*e
z(U*5ZRPL#}$M7Q^YG4^sG8Leqcj+7w-2D7{QwRb%i&ozJzK#cETPh=E0BkQMXvLJo
zTp05RR&43FQ<f2sBU2u2(#R+$Ac!zLf~g6GpF@KyC7<)*7XDZy;^DzbPXY6Ir;_+s
z{J-XyZ-f8Lb!QlwhCbIu)kV<N3;@aKS2AVeKLb(XF2+*_slKpE^V>4nC~H3IZ4|e)
z=Y{3w$N#{Uw%SOceYOC4s>Ug{tKC0wdr?o<_i*vT3Vk)4O0B+qnECraO3R(zR^JST
zh%1p0O;9=^&<2ieWN-^`)%A&OTtp97CYI5{^1gLx+}ZaXx;eKKT2$?QLg2=BUs+Zv
z)1da}3ix!@$8PhI!WoVk@+ChG1+54T1E!r+`bn4phwjy25}dGd{`m-<P5hL^Mn$^a
z`?lE~bWwz++3$rp@t*Q3iuF?l;q-4k+NpI>0gdhq#~VG5KKbW;2*eaLaX9BeE4JZ4
zeqNmc7>PL&0wgp|M@Qt&Z=P(GTl1>?xzlsNt8Q(laF}y5^Mb#%X$yo$TV@9aHU|Of
z?*^LTRL;-rwJr|~#~Kz#Xt|3kI-=ZPC2tyHRTm;KVS%#t+lJLMUf}C=H{l}GU4x=c
zt9}bMLyT&7j-I3aJX7GgWJ|k(IHQVvP7fVs0F+bxm$COlZ$2+e5}nX|>{QgyVT*2q
zW6_I?@ezufCFON@m>3mbtGEgn_3$sP;Y5Z5@Fvr3chYHG_~~P|KOUqydb*653~SYX
z2k(KmM`F2#ODo)vIMW^bdpqNwC?z&61`e?%`}w&eH}+{977tLF-%x#AXPws`8#m>u
z$&!2a{hBcmgCl2NzgVr*q-tH=FFM^zHIF$M7gTVR&0%6VRw*9qo8r}vpE)%+*SL0<
zj=jH--79g1M8fwNFvq14hRak4+5(ACx~qj#pWRO4zj(E2(9weIS6aO>N*|O7oh!Id
zF8#7Rczzm&z}@HRrljtsX=;ot$8HJ>hhVizO7`zN&gmax{b%8`r3rgz^u-@w2sk@C
z+ib~KCUsiD#K(UrfBpmLfczYsIs*!T>L=d@Fn>-zkvwAkx?_2Pj3+|)Cee$Xi7(}8
zjk0<pdHL-_sx!pR18;O$d0Ox(1O*&IT%X>$6kGcn5yeSxdN@KFey=O59JQZ?(ZC8b
z7FC;`maYfDhQe=2#Y$j8+T%66S`#Z;%WgPmx?QZ!&L)L>|7jqSh~fzGw%j7@x^J<B
zjB+ZmQRgXU)MSFZ$_f@K^b6>=3<c`<AhU%VemSd%)w-snn-)!PT+jD4*O;Ykb1+9c
z*d^?yf!i9ncfbf`>tm|b0fACZX<%HV8m9J-27;(&V<JiFPRKV7eDhaCy6C0)+@b{8
zZ4QpH=z(Jee4TDJVH}acue0+?^lbhfLGUs;c9XIm<TLm*V+K5)OU;ci?a_b_agZht
zsIcq>nYKRCw-&s=D7sBDHt|c{!%j(ivnE(@LFstYSD`G*iQC6`5yi=X0RIT9^$sB}
z4u%LEenQI+QOYed2<@joWha!3d@Je-j=)ms3T}b55T}N=H+*^=RlD^BJ00Jr&!<<W
zvFN8l``~Ku3D<IsHS{_r@W{+qK~p6HsDz5Lk;dE8BUr5@Of?YdhM`{xN8B^`)5pBl
zDGc?{mO5XANHW1mmgdA$Jmv;aZbeQ*d9O*8Md){h$M3>AU-K@2;_ady*>@-s7Gag*
zY1Ky+@<r@a0y<o(8b|92DT=%gSgWA+otAn4cq;6tu-`DibdRCLrTQ?{>=u)P9xTBX
z7s{ABmk2XR43xYl$Qj%UO%E=f;|3m|SeTzx#l)n^TK$O~aKV!ow6RK#GD`lCi!@63
z`cdq-Z2t+1lhRzRMYuNo$Q6lHkY_r$@r~_>SOSTjl4;Hzcoy??j226inQVq=wJz!G
z+G&R;@)N8&h|j0;`hVB;;7zir<>l|;@Ys()I16f&*G^SH#|{p++K*>?kJs7eYkzsC
zWLv{!L05)Mz8CJIe3y4uh&u{RcUKz{_LnOQ|J!wgR&`O(t`rV=hhzQs|1A5$x`xR<
zCdtEwl@9-lYl!^AHB_<=6>mW$2LErF_+NA&gXbT5!E;Yz`k#O7zi^13V!{V3C_enH
z*#Ez5x&K2i+)tG!{tXw3sXtTX0jJ@L!^;0nBd-4$Nv@YlGyWGfk^jeU9+oVI=D%sg
zF3JbJ*sWIO{9keAK}AsIt-Zd!s?lLOaorWwqjf75=Kjmq<-3?b<>LFGst1wO1eLIG
zYjbn%t77`UD9bO>%T9r<zz{PGzc<}KG{^Y=4hs=YF6*$Dd=B`pxA9qI3;Q1k6Ol7#
z^Y6zF>(c*t4kF52pMOIhYKTGc4?1HARp_GlFPb9<g^0#Mf(C~~RrJ4ZdAWp2;~H<P
z%8P5c_qp}>*bN~u2E$hJoW|TROHzYD(<m8ibeF-<44gD5-U?Nv(*HAZ4o0Z7R=PFt
z(`U)mF|d29R2~;g?4zQOcklxLA-krUwptgnYN6fzWeytT4R^tHJlZs(B2q(`@lv>}
z-j#I!M_SJ9IpOB#&=uwX@j3hygS#|OEU7Jysc7b+ZE6WywIQbSa-6Skikr=Op)RkT
zy2nK(nWbnr>FJTNdXexv-iJdE-<03`rW&9u&8ady4`y1*VJbC!WpZr6$#+8*jYG)4
zMw2_UGmsF!x!4rtv|&Dk=x4-c`kTmA0swle6R(GmXxPcP{T1V!V1(?klt7}2U}a}L
ztks9)ST?rwB>~rF5(FsdM;}farR?m{{XEK=qAc0KZ0<+AB>9!S!HC*{9g8!A+3R|z
zXQZCiGh+A&15ixJdstKB`KJ>X<v2DE)PSGuZzpeqwL((HpDi6-W@grdO$D%*(J14w
zw>^0z2p$*<pBt+itG=pc6Aze}qJgXnivPxCjB_8=;^Ab(%4nRbF18r0a&f)v3}N#O
zsq<^ld>dL<|FwSj(cfM5L3z&$o|=^qQ<<`>{1w-LwD^G?DvmvJm}@_%y>a1_lPGht
zFtY=i+_ld?vb65n2TA2bXWor2Y^8Ddm+dYAPG&lbZP}fNu1-EGjXq(qqt1vjnV*JS
z@EDoY-cPU1McSGn69(C4J;ip~d?^f)D~gsHyg@gI^<1SHt~@0p^Ui7Kh|`sF{@i+>
zw2cuvloDZdyI}L}9&_qsijaoO^c6NLii(YHYpC~a=RoIgdb$f?*A|h){UJ&d4*B)$
z`jDT$WSwbk@Al8@%Up0X-e21F-i<umpN5LV*tot*Ey};$mmqdR;I3TW+SQM*?q8u{
z2@`1U9Q8K$!@OJw|Bw@V{J`l&%UE0^xzk^4&}K=EEQrPxYRbk#l){3(mO4s=Nmwr;
zqx}ARh?vN<<^G)V9{*IYFND0{kC-)mJq0@Z5l(WJfT9S>UL?UGzKhmNk3{^lR95dq
zIkhc&7Nbx|QiIHDn(^lPBlK0IxSo>7_2P{4dBF#NmSyrAnDF7@)jDJv=C$C&_fN{^
z-<Aol)3io3_ti2#IZc>!;KzG=op^u$Vi(546@}nmp8rMLo6tiQXW)?0z~&;qcW&~z
z^n!|wDWYIVsb!3wW#PeIXQdnrMq)V}OnM64kP`0C1G=5fvVsR9sm=nhLhh<+^tVfS
z-u7|VdQ2}tkezyWFVV1b#V>lKD+}_YQtx266Fjd+$4g?iagWS${ezpCi-|{60V!|3
zXdaI6^w-%Y^E?26=M{MGgbhn{ha>d#aM?wM6z_6m8$zceeUb3T5OvxcW7Yk5`l0#k
zjr{Al_!Cnl+FfoXXSV0N54$@;d*BFUV{=K6yHvx?$B|1UY(qD~#y3Q>--lm+aT9q6
z9p_~6#fMH8>yC`^>yBG8Ul>T}(^qf>^Rxvs-b_>BY}*!Dy^9hy9en-?0HYmU$6Wl~
z7|?yYMrJ)TDNEubO_DvhDmFzu;+wju;BE?_BJ@HR(}NM@W2j?i&x4cLt3=`*#`&uY
zd-JX~bn#jkbykY$IK`YgHV5@~m%ORZVM@sifZH86C8WA7X~m`@eZpfG!@A&cA+n`k
z_ILf`<l(7#Sl4_t%1v~T<jB-{6=VBsD6?Vkc73RD?!Bj5@WpF>n#188v_>&@LL_;g
zq9`Wp%6e+*EDK5YCL_$AWWBf1`WDK^5>fG_F_86uXVc6Q=4#q59iXjaU0Q2j)#f+)
zeu-4U?|B=G-ErsWt4Qd&`HbCOA((=9tj_!B@pg4myVQAWot2d?=43_vN=amsoho^=
z5bX|5`~rr8R^0rNKI?nuYQsgpp6UX;!+i63sLgmdfeuOMO9``vb3bfu1_wbYsJ^mc
zy*$dxK2#oTi0SrVqY7G{aZ5Kpxvp^-{s8b@ec~>d-73B~*y&$&yZy)mrqi0>&whgH
z48fLew|lE+monW*+~4O)@?r|_cSjlXxogx#YfXe+xLK2g+z}PNo+t%WmG%n+rsG_Y
z#D@Yy3M<?D-6wfpE7_Y)yO(8Av>Oa5qF-<5*8+OY%&oRgPkaWIO(R|xWVaf7lys>f
z-Y21MgT3(BP!iLc@WB^gW@m9y%2LSI0^^v&iqaQ6w;BA*A|aO_%U$!-1b4P8+lT8w
zu&oQHZfnA}(Xxt<0%_XQ&(MZBK~=#0g3eK5({w62L!KcVFX5NErA7%rdGKXNt)1(;
ze&DQc31dZtjhp+U-buOM*||mqj=2#~mQd%jPm{P>A1`%;l<gDiMgVE%aNRt5t^Eyy
zhqF!uE`Mmj4>C=*vu-Ntn4mZs5Yr%vfti8zIhzMvR)JK3qj+5iReES9CmyHTVxXxU
z%TCQ8%As-JoJ#LXq^5a1^mI(bRfII6{$S8uLS2e@w#(P<x>~Nzl$E5+-O~s|CsAA*
zhxk77@I$1Oai36p*Kop;`lackv(vRr@hWR}6GNB-=?QWN1-hURaxM#`I8_xY_T0>p
z5)BBD*bieT8V@o4R)+=o87q>ZnXuqussSPS-aPH*)9)31^Xy^6ii7!<-%UK_;qnVk
z33=<S)^$ZjLZl%g+fSF8qo+vnFHg!TT?hHbxKx9pa3^B~NpwLM<}^h)MOn-?K7p;3
zX(KgQCf7@*o_{PZP*ALmRL?y^eHtWpT{skL6ppOu4g~BWKl|AzCC_bB{9I)wSMRfe
zl1Y3#tvn?2RG_pj^jvNtBg!|$o|R3H$d;v@WLSIYHLLheYBC3pnn^k_2I7=5W9_?~
zlX9)T$Tv-GCyR~@$EuN-=>#u6*n7A!`=&giS}s}@wwQc>*J?rxhRG4MsgM<nVYVg2
z(AMF(dP>KZQoUTsYiIK76E*@1^ht;Fv^IG%?+u7GAB2(fd7pgG%4kodIbw;kFv*Uv
zKv!YayM!?^T5tnV&@5?a3s2m8--;L54h>B!K<5RfCi3Df#_7)Q?lO{CE+xn6N!ik_
z79u`TMjDxwHJ&zNcpIViqW$EoGUR@(=oo9-wV=daquyxn^B=+GaoMkN1;ea)zf9e+
zRNE~7)Ogay<~C=V_S0Ptmf=?Y;k$zg63reCc-RcB@Zx<~yFrFC^&LqxH3dz}1MXOp
z7YGT4aSVqOYVI49s+e`J?S3;2kFt|SUXk@IH<(kgI<xLpsEbX_{l(rT99>Tm%XZTH
zih%QdddN*w#|}f^#W%t`(rP0Pptbgg9xzBx?l+7!K$;&|r;Jr&KWjs0;}en1mT3pz
z_S}5}p$KRiviiWL2Hq)Ex%*ZNPt-p<ghdqKYaXo4%=Q$m;U5<&qGxer45xmdOG2NT
zS{9K}|H42L8WD15^gPjy4QY_d!wL5u63b*U|H|s8mcdQ*8p7;;ez7Rua!Wy|`Shin
z-!nl>$9>&AtZbt_FxA$^q(=CzP*Cq;-x*zn1qGiw@M`&}X;^m@$;!}kL$FN<l)}Oq
ze$6U%#7ce%u0@(^HDz?=XG$)?0~Q|&;GtE#;5F(dxE#JQ-OSbClMZZl^}3Gfog4se
z#xj(($VgY_*M_+#eZ^g~>r6f9e;S!=n9_=K?WnwY$gs68dQT)JsW?bA7>c(lacL)4
zx$ar6b%H)&6*nn5RAsDYXN+Pu;`;dRt<Ooe+HlK>9RAV093ld6B=Dyk-`|W0Mc3Tu
z@#ab-dp}k+IxW`v$S`EO0LC)UnmmPf4lVQMw<ofR+yf)JdZPunvwg3>UeIfZH3~(C
z+Y_fLYdHNnpQ=r{8v`tVXwbA(v1dyO3;Q}Yo{ob4AbsrplH|bZo@G<gbWWPY@3ecb
zrJ$b({;CUzl%Z@;zSGCZ_1Y~8b=JipNJBsVX>QFv&pWu^y%05fJr#P4Y5n%f#<}6%
zvU!mZ-3lM98M7n1HRzO6(flc1idbb4-N}anHPJr5rxseQ6Nl*H6HbL=Hyy~qPCO|j
z(v}ePnmakzKmMh3zRw3GzWy`csaeC$QOO{U5GpUiUfaj!Ro^-oqLZjOoB!qP`wHFH
z2vWYySFoecL-0xJ%{7`5tERnkGzjZJxz?o@wPf||x{K*5H>6_q+#_(y|Dow*PnkR#
zU)1zY%fTh1_o5=ZZ`O5V{LKY!BTjc!Ec+^a)uzbtL}i8G>(j$t%^wrMyf>Ezxn!G>
z47Sj$i127_ziJ+ngitRv&E`my%sEFHo0b#9-04PN!-m6JzGCPt(+|&m??Zrl#^`A?
z&*iZeguzZ>FEQC?O28X<_Xcd8*`H5j)y2<(nhC$!aEBvo#=Z2`PdoN<;N_kIZGg(%
z6&HEna2G*M*m{9c;d@VD@ZdGV3-B4c1M*B_Pdc1?URpCe&(rTt`u=9p#D?|b+Mm!K
z$&BWgHW}-R&}Pq;tG;!sx{Akolsa-LGL`{#Q&p0Z&BQ-sd8cq%Uc<uP<=(w6qi=X&
z^=gMTtL6>p=nM9VKpEQA&E^nIjJ~&|{|0PK9*n1s)hS<H*V^jG4pp|-h8lBAf_M5;
z+n&0lM<U<cJfAO1n<RpT>oRi=(I;f^CQ^3ECGT`VI~E6eIw&gHg3@CnCTea30r|Wl
zO^I<XPrqMC7_yKyAH{>l??}ISEa|L=Y!pTx5WTh91s-APida-p`gW|qdqCIsbJA(4
zN$U6z_$U-JDUvOL?_YTs-#@{IhALfbj{C38rnnD^@H{7XD##)a&n%|h^gC3%o^9H#
zKXO&lk5WxdrUxatTXkHry*aJJXPgx`r&H7t=5lQ(x48}KWqF(udMH+I*Kf-17K)t@
zdahBpv0LrrNJ0IraQQ36;B=}vypmaQsKvY74uaEYz@w)5YoxRdaJvLF+N(L(OpK%1
z6ZDKPu8-yDRo_Bok1GQDX7my#loe4PHlB$+OIxmPW@hiYpSA9a2eEB+M}?3&XhEEq
z-nMwG>wH@7)4!F@>HExBY7M3>ewN+Qy%;c3Ho|zFA)t3WeD8DzOFZ$r`yoAx+AkV^
z=kdDeJrpvyoOV@ISsW$+wsk4Jvk}Ho`=s+B=hp`Slg{^Y`7?T<0epS;Tmfs)-Q-C0
z#f+wi-ue<u26NpRBCYPH)%=o=@8M%L&X+T@(F$e=`aj#S0Q_wbo5APBE~{c~HFZHa
z>b=Xn2i|=5AIGtU2;YxAN90v_J{mia5KZulYYY$-sCd;Uo>~~sM8uj=*7uzbFY%|#
zv^&Z6l4)1FC|y03AvIkF1xvs?==TmzUdPQve@B~Dt35SuAZiMN3vRgSstw2C)`y=z
zNv_owV-zYtfW`5_eY=ji21-znZU{)oAUv|p<5RoG*3^5vfNGm|8fZD5O;vXo#qsFv
zrlwa&^1ZsnJA+uWJ0&$=I4U9;`WGsBIW^5rF8bsf{rETE_z0)Tn(o+5LB?UmWz&&)
z+NF<HZyKVJ?NMES%-UXG)xmF`N{{t3$T(6;RkTu;Cri~n(7u*x!NY`I^uBMr-$??R
zZ~S}}n)Dej=xrStB=yez?lePR6#ijPE7SKW)2>I2F4HtCEy;6vC)Du-+eGZ^Fio^%
zSx$`XmX+EX_P8Hhk1<aUJN6t0;$h)M`Aj?P)4><%J?aESbtd^Sl-^k0Ws_d!lWw6;
z8u(+X_FmcMi9=GD-QG?b4u5lW7EL}wwa}@{4ePA9=rZ~ou2?U*tB!8BqUWo_K^20<
zPqX#c8x!RUG8ON3#@;3Nu%3FKuR3{&WseTXRPLFEi`9jO_2p){gvR8IeVgo=;Grng
z%ruEgOyu22xK7<-++*>IWk80iQUv!QLW7}2%|i}>DEFZ*FEm`oGn-}Chg=ka^Zdk_
z6sb;QkA$5=B(KGz;aqdDjD}X{%G?NCU)!J-FN?Ys$M_AYJ(a?dzjy+_OR=&|IM0%I
zRQ0TT(<U)pBu~vezWM#4F^>5}P*RiE?RHNc{()vY3D0LT41oFLdlgZsu&FoX&rrl~
zB=nx-UkoUgXSTMosB{fQ!vgp>-5^UBtoEl@ePlK!(nDHKk;dAX5~(WNQ(glqLEZe?
zSH@0aJ!SAwQWUQJq9*emXuMwE{+_>DEpa77)P~MQ)P_V|4iK|yazh8mo^v(o*}SaQ
zM%b@Uj(0vn|2|=tqe5v&nLY^r1*U)A=j+V`RP?TCP8MI#6Ei)3)A6|Upi$|Zs62lT
z36k(NRmfv+)-B7GRi&IZp-pc*>+N@@8mN*Upm?gv%_oXR<|jqV3weO0TH+LP-wJya
zHoY1WLaMS@r)=2WzXo4_fuca%DY66@_l_uo1SiQI$Mjt2w7Y7M_({Ptxg56@(j6ez
zHIHF8wvHQ)XkSYC)$w$-*3R(R%F!IR8>)ocLr%wIwK0t5GEM$qXQ2RNa}wy>@5NVB
zx)n*m9h<o{I}?C6C#0gHfR~dFZuKe?K=S()VVp1w-R1!)A44-nJ$2j59Bx;_7~NxX
z*ONP3M(mfqL0eR~;N|_{K=XqsGNIXvj(Z5f??)NgE_H+TwS{RT7I2xblvcdn2?R@%
zA-S_D(?n9g%x2G9P**fH4^Pf=OKYJg4>?f8<pVtj;|_It(P$e$mmm9Ujz7{D=?(iw
zzGV1rpwIjypP}Dal1H`QY-LNPC|x-`W}eRO%tT0EBQLQ%SYa-fzfwVD%g0iEf1;sr
zweGr@;eI@u%x`*)j}F1}MF6b1;{e_sRa7t&rdN!ULxNSL@ayjHK$q9ahwrKfXvjFf
zr^#cl5VC9Yt0KAI?-)$s866kT8f%<3s0uRCNaSH`D_V90C!)-EThiZUTPK3?Y+7Q<
zT_+AMqSprXEX?A~D!UH_WsVp^yD&%HM}KteTA+7UrF4=b@S3iB0UAy4ny+Tkms%^a
z*>`MQR2~@5#$w$b5~^5CIkPK(KLJQFzYd+Uybd3nE_C~~X{DjU2YE@D5VME2KhjWz
z8SCM6J8kJ5RWA}Oi^<IUI2bs{Jh%AbP=coOiy4);rdf?CQ194g4Oyjvhr87~UoblP
zJn2Tu5)=A$`pT1mMuj2g^R0+_GR}k4Z#xYu?Or^l^-f*oeItLQYS%M4dP%k`n2FC3
zidE%kg(lV-&0)qsr}EX;x21*~d0swm&(BSIPUr*&<1xeHnPcg%UyiUgNEg4uDN0dz
zN}eotw7^7P3Xy%`O8rBAl=g%#&G$}P1YL}bWwyOMn7!NY{&(%hu^D=W*8MJ2b-AuG
zk)QXC7Nz6_tY?hHOw=0Fw)`&e<jv97xU@X=i_8gkaovt9o!>WDEc+?p;NtxYmm~BS
zQaew4(K2ItmiamZo0Gx)O~H2MKzkshbEI{FiOlx>_M4HUn9r5w<jqpqqe1A?ZyvA^
z3_^^{;PkPT>#@{*O(9c;scvgBwe2>M(O$b3noFbIMCS{of|1gwMnfu<uABedTry5F
zGI8uNay3ZG+lepwjOov^pZdyjLy$(xvvr+{a|o6uCG!V({b2t5Ob|11!-s`D0vUPN
z;BTfVp2B8r{L9-FGl+z{{c9@cO->xe#$QqDE7A+b1d0Y)N6CgqpwrU|WU4~fg|6fD
zR&oS4UYKTyd>>!+)VYaHk8`B<8??CTQ&@1W>cs_adfVGyycCFg$#||(kbl~HL#7*x
z#O_3juZjp;KE3RUA7)RXx*F4akI^QEit7`emX#EH0l8tZJnVDfRnKcgLK};<Dce(r
zzY#EGElur|YEyx!trf!ivAgPSn1<aiDO8dUIbKK7B~CWFj{PbU!CrN+f+{U7V|m)I
zu<-VS@d5@r<dQr31EjsPhDQF&IeNY-n&hOXCqe=tr6HqoZEd`Lo~xrFvw$5{)Zeu6
zxgY{2!J-1)L8wWaH6AZmMEE$E+{8~nKnWdYXx9(|$$`1+J^*O&H;v8xlxQ88FwEHt
z0)xYq6I^fOSk4e9WS0Qb;|Na6UfA4jbZ?@i`0ij1=&zw8PtVWW7bK(-rwHC4-Nuk#
zS2tHKvWKt;x1tcBxXMtSc!JJD<O5_!akoIlKs@plZVyQ|VEZFOVjdKr`RQ8&!&{1L
zd3(guBX2<g{DB5NuC~HYpKhHJjYgCNzN{tttBKME!wuTDSk2x_n`d73bqDBG6-lSJ
z&}F(wf)8O%6jgWXmGZAaQQX?1Ob~&72uPiuYKnnL&_!9H%#>FPdGu{Jb7(BBVoW|@
zyRe(hc(Y=`x{nbtQ(yWjjI3`^xi2Vo&VJTNZS(yP?yOo>)F7cp*w3$5jR!h=brEj&
zzAXrfVrbR`+Q?H>@eNP?@89E#cf~WEV(JzI@v?+2!aKv+JGT8^PEE0<6L<6^CM;Rl
z!&o?f3a<*oN5u`ToS7mNeYfT8e0zno+H8>}?HOm9TNltcM*fSpGVip7k<o?FxG~Z(
z6M(<e7(KGzZXlLQx6g(J&w$!phci4jMmdZev6y^g7s=F26aT=kJ4#D5&*b(((Zp_j
zAkxfW)14|!fH{CS17rEdQjTaY(#RR<Ao{p2s+KU8m4uRu9{L0*jejZ<p~`gZaVnq;
z++p%XOYe7yOSHrFs8%0qd4FJPK^bpG`x@%zL`D5GrA6&3vBl`<48xBM2&d}%)7#q~
zZhq(NfXqDXa=N5~M<Sx)oaJ<dh5*0XncN8ZkTMe;B`|FeNL!#HYt1YUO-U+Q(F*>l
zK(3)l(eSx7grQxYCg_w1IjavR{#As-W7dd0CWb2(d`aALx4h;d#V=xPd^))*xH>@^
z6iw6*4*X0To8Fi}PpZ&YA?<_WchFW5m#v&vt;s%IAa6*XKS+Uk)T-K-E$9{sT=W%a
zI_BlMioX|Jt+O<l!xZMNYi@Z)LBaG>PWk!_rT49BW@?puvnXWAYLMZc2FZG9Xr?bO
zbKXejk{Ks6uCg$G6_6SFsnZX(p6|_1Sc<A$RU>t&OV9M!W}~C8Z|RRcqa6Lg7f>!+
zXxGJ=t4l<DFgyjDIWe+*mhc#sL~uUuXqRGioB!aSP#&g}Z=`*2wMHU{nFNVE?}zJi
z1a#dU&z#kS+5x6|#Oldf3@-RUuifW$+jIT(62PTYYEJ@Og|U+djqAoE{%E6<eD0{;
zy?1B)VI|Ikn2l+Fg+T>2YOh`MW_`#d1y*)%OR$6Sb*+<CpJh?}ligvQL9Kdq%kz%A
z*~G6(U1T;`t(3xYsZ<ZP-n>G*MC_5FLdPLnGuJRPPO~kaSycPm8uRgQMcVxF>66&9
zs|n~fuRB^AKF(2fu#zI&wBb(|Vj^AU2QexGIMjR-apvZ?&d#IL-39PTTfxOd?w&Mm
z)Qx_DD8oFC<}lXIXCN~;1A_$lyh#miTP)4LR7ja<v<tLo{p(y6L-^!mb%k}JLD$jD
zuM|1ULS||?W0Ob8C}QOl2`CIlYe6{H4y(i7o*6V6mJ4+UW5X*xOtFXUtm|NLvK2Cj
zG4jZE6KA-iO+Uj6ItCnCB)kU}*zQo}ZVFkbp6H!AYf=r**0VswOj(aEIu#8bY)*Tw
zg~{ZRm0%6w83r`hr=!{y$L%_4J|D0*Vdn{Bk91+#i0$@1|9VufNi1}Y^kAYjs88Oj
z&7KXo{z`0rUcS4i(Y$;87eD0zm+1UxKhCjOQCVlK``823mIvYDZt`+w$2Au9uc?eZ
z+-!w3m0H&3Yh0EHTyR(dOB3$*(bPf53!J9&gXFE^?^eqOqTzD)uYoNcK0(p4q+An1
zDuQ4+p6c_6G1*>|3bgmXut*Yv=T#%MtK@moGw6)^v4;2t{5WV>1E+U4&6`VtnZk5<
zt>vT%q$ym^9KcEU@L+DlsiaOE?L#>QP`t5PvqE05;}Mfl2*94J{sgP4fv>B%<)`T|
zAx@1?ViQg>GZSmldUlxIDZy4=BD)r8xiO$)FyP(H$VsD3_&RL&)<2ccnY<O(;g97!
z9h%8iA7Pi{SRz!jiX^l!*LTz-w8S9QpAEu2-foW8ut&4Sag;jG=LNags<v@ER!;Tv
ziYwSdB7$!Y6(EB6;5+&B`o!cc?^pzp$UekK!`YrjI(nDZ|C|M&UdxjqH9-ihIgs1{
zFM8A2pXHVe{&p;qeo}x*`JFpPiq84RbfC{EsjVu+^NkHH9{e8pnK1(YdLH5i5k<+x
zk%-e#C&#js2w@NUZ!P$Tj~rbg2+q1D5DgDQP_RN`Vve+T<=M?;VYNV-E4mB?cGsIc
zPZmqMCFGr+>H8wdLfZ9=!WF+5>iPRTFB&~57t>VqHAtmM#|t)d6i+Q)ky__)*udP*
z)qz=RP?{=MsvVx*CYwL}L1?l-H73Pa<cqbVxY4>n{QgB1D7Mo$!<n(TTzhhDF{{vH
z^Xo0IGH9`4%YW6@=*i5M?^*Bjd&`53sOZU3Q)$>(X$4i^-Y0L~SGBt3!<l6kSirO(
zs61KrSF;2i+9YGs9WlndbwmngfKJ;0W^=JeVbsaq#&pmbt9|7>uvS=sCD9ViGg0I~
z)_=*?|IWd?e{_?AAQKdDmf>hm%o!J4VVYxuak1PrS~;PAJXt|ZU*QK_Od6fq`-LIE
zJ^2e9t3~qu=6zygWdyYOeDLzB(Dr0(y*Q!~Mq6^3U|7yHD6m?FnJQ3{8DT~FH+aP?
zy_Zo=WwJ|9%Vm%Fz;(sfJrn0;XPh%4#KG_(4(Sq3hEh-zu?1;gD&5TRrKub8xg|Ir
z!*YL}eWT}O#GcOk0mCC-)(|vA{81*(8j7hcyK(KhG@sVDS%EvyW{P>!|0I}VlxI>-
zmN@o{;r#8ny${$D6&kP(HX65~Tw_UO*Y(H>7$AAEk!C97W}ik*B3=kd$L3N#j-B&M
ztj+35unQHA4wfg6hfeOegV4tU#&CXygTedA14(Rkx7=nsZ@M!0mNZ_-%aXYr8lbk`
znKX%cAnB<x8sy!s#k3xPwCNT>^8ot-T8k}ndw|R<(h8}pHT;9Xyc(DYqtCfIwPXYu
zySRQy&vvv40{kDg-Z47TZd)5}<Ow>gq+=%)+fF*RZQHh0)v=v)Y}>XvwryKq&OZB`
zv)?hkU-kE1W6ZkdnsZ&S4T}(E+tt8&C_i{lLcbYZvFU2HM_;iJOu^*(do)Gqr@U-j
zZW}V|PY=`MRc*zP+f&}N=eUYu7>Q+$;aBpm3gLqT!ORtk7BNqXj(1VOGRzWu_iEun
z*O`vd+tl<+1PCb(<c$69P3txyW|;{=6S(Rv7Jj#ro?78QHS-I9KrC&BG?&E~7g7YI
ze4S+a&&=rwS)7Ar)8W*2BU4fOp24we3B>hImP`&RN%xmO($pTbGKAjT#))d_IHl!a
z@6Wv=_Y)rPz<)_je2*?J_+GCp`Jjm6HYwsdeXP>%?VhY;!-Ph)ip~$?^n=ilxlhw^
ztChLi_O_c*+l|TwR}Bm2&XSOe-;TE&87K3FL!jY(^clhC-?DYy7LA1iODmG4bgfBD
zJ}|C^3#K<__-FsxOQGqbo|xwTDN<Xl#?$h8E7#&>Nx8lzg)EFBB4L#xvK9m<5ylxT
z6-sAb&Cs>SoY0MtieSC7V)oEd$Avom0;=jd63+~n#$oLVit7WU*H}fe>@gd49P!TY
zbu5g(8CdPRg&_V&0A-=$(wt#<eaB2lp`DNYYQPB&np*Xfw4+4VW*2^~-4?bj>F4vH
zM$ceFV*`+nOEV6eF1kU!sD1KtrRRgLATVEwEi8y_#*SW3SM6_EmE@NGircBd1tRbC
z$8mto3uS+8$cx&mvp@o1Km8Fj2S%h%Q8p#x9%<qRqz_KTF`e$6xA6JWYh}}f)Qp;h
zI$u5wZF7Wk?&Bt(I2O{`x;b*ysH$y)q<%>K$gOUPeJ*fOccB4M*}lW0%isw=R~cy_
z;{6fgL_Y@<Yq{Q$xGl9DGGh*<GRGj9LQjHx|L}YKJ(uE94e}76uNIuXiWSq<;it$j
zk)>@uPYVBiQ`pe~4yuk53*#h0ra>vYraXp0h#*pX-R7wo%Tx~8s4dj(i_Zqci#2;1
z)yzCCm@POLIkIb@1heejO}|X)sR_UVD2Sn*FY?7mwOT-T+XGxalJ}dvh`s=g_6+96
zyR9AbKeAS6cr|4^|EO;C%^k>+N#vB)qb6+I+mMk{E96wzho`xb_7VyEovq8ZJL!o?
zwSrD6n}e=$70jq<bA(jr?P0~YN-QghrZ1qQDyPu#Ic&p3zoF|op~*XqLI>faYdhgF
z7!;aUmM0p|8#$y(P23>q@;3^xc;y(t++W&dj$S^Bah_Nj4cB6jhL1+Sxcm~w2Ig?9
zB=*ldgf%u-@KbZGk2O7N*fbx*y$0rrg(2?h$rDODM46`FJKA*aQ{QGp7&u<*tp?eP
z*`<w)m>??-())}-msyW9*Q+NxtfxS<Y8J&iaA>^(N3f(FUgZp4g@;BJVsjOU4$aVQ
zPx~nP9$Ws(sSE$Tw3>*E)M#o=X?uIB6W23&-dmt|b^|&)coR<##1cxfg$RjknOKl^
zRt3?f*xFA_+U&N$m@|4f2wiMqV_~xK`Km|cYmzeC<Tq%_UPp^*V4VU<LI4;kQe}3h
zHr4~uq}BuCJja%{eE?Q9Qb&*9XZ?z&9D#^)G5&pmLiC)eh`-BCxYk<iDq8nEa*V(Y
zne!s4E6xrV5&R{=RwC2Wp>p1ORGO}5=$gWiY#VO%t1HD0A86J3qqNHrflZ&=I5A8{
z96nLv(goXshJ}o*H76c(7%*^@I}n!xn>>DajM!9bn9d~6p(;lTq73{4%x-xm+iJ;-
zVw}Rmhskr31i+<U3j#)v@-Ao1>Vs!@hhJgF8}WGKHGS@2r$Z64+ufkjTu$@VkEib%
z*LsSw&1(cg#6<uOB$b>YGR^5!#+c*Tf<i?X%RQNI!EVCF4lmM4JH)xd%O)p)u$VH^
z*py6AsVR~m%J;?0)|i~Y1h;=eVq-J=-3w*oCgf?b9bJbG!xBbqS_D<XVK`>9YG-B_
zN1_3_&?sDL2G>X$pK=%X1)XK9_f@BrO3(rkyoDl!j}e5*e!bi*L52#9k9wylxrQd=
zIa^YpU9HonF-L0@-()F)V>0!3%wxj9keYz_I^sKLZKdHNQ@?Hz=QQQS-_C^<>hnct
zTdPwmqKdWLzHQn^ho=esU)%nUGHv$LWEddckSffhSR=hG2@@M3sEKoxQkG9Ns|<!I
zTACXh^TCWQHt9RmsSzWB<9DPlK|4~&T2o<-E5dbqjn>;w?-Cu2jg7u5J~9^>_r_IH
z1y9x1d*j|?I*L7ZDPm@^GwN-{{@Fe}TAS~6pjs0FWZ^H+3n7qT1$Z4A!QY~wH2js}
zqU-+@VP4`wzux5vpq*PM!gy6o<R5kPW{aMcxaSnggzdfE5FaLN^+urbgvSgAp5x+r
zHMB>SPQamt>7kYb2pI>9qo7<l-S$uQ&}gZQ7W2B@oa<{VX2;YG1D?F}d8LtmJsBqA
zw@R##h!GIiC)~X{X9O?!VhRdcBKmr_2ch1*4Zg+x9H+rJB#AMrKCwxsD0OYd!=mLo
zE{nWKl1b^+XU6(4ynHNWSl$`k5w(xM7l`iECfAX@g0sNe4jUkuT33@;G0}A|7EarD
zO(L&L(AinKR`{n{d<dzy`zYu`{--Xgz<=`ZE`)!Dfh{WhFKYWTbaKWm7m_YjHQCUC
zQ1pGOB8?Bq^rp_J7Yj!e33J`F@$T#~YvrH9>=0Z|S>A_pldTF;-lEezlWM;_(P2tX
z%P@C+&(OA8T6|wcpn9O!Vq<IgLT8a_QW@j@y#`m{cWqz`4V8zP@jG3v>&6cep*n37
z^!J16PFtR5Pv5IJkdQC-KXGN^D@r7mq+u*wn=ERH7}(&f%0?#c59`6A&i^EA+ED`j
z5>4H>vT>3HJ&dmAUbNng_F2oS7n92Er#d^0TF64GOAD5{l0JF*HHM9K#<;T>@H+uU
zbvoNfr<BoHqKD;bPBU<)24-DAZwrHhq%R{{s3}mW)GEy#yeUux4|cRfe;3xm8l`+1
zhJo4{QUk6sVjPhImNAE4)bEuv;RsAk8iaCW(=&E_Ns-^ISJ&DEKv)iuj{61k!yM{7
z3AR*O&#nK-3?&QjqmCu~%xFw$y`alkGeQUxCR%Z3?35rAP+f=Kl3Gw!fj^!lbr_|T
zoCvn<!#>-)U?v%jhXpLu=!T8qKg1@mA!XI<=R3zWJDt@8M9OryCS25y_YO)<C)tu9
zTKC*u*(@WwE(>j<m<7Mf2tSE0a=g!ATJ}7-iNd@qwj6W)C*{<K8sd~lgym~FO^|aA
ztLdeD-}!>kIosQrT){F!C>k1P2}Vv*XX3=dG|w^U<2?)wJU`8oH~#VRmCP+Fs}024
z-KOO5pH$V?B4{%K1<g&F!kKxn%lmaM<;I7oKJ!^wJBvJ*X<Ebln)u=;u@~m=Co24}
z<oQ#qO#aQM=007W8l@wX^MDJpg96zr=lZ!iFP8?mbyTn<b$UJHyg3f$v)hUXz5Dx@
zp8h|A>KKTMES%nJ?{TG}8vBlG0jTlFGGPnfku;ONzub!%-Uuc-7-Sd0?Dla#q7qhQ
zJ)9#Aga6$BCu4R~iByh|j{E7LMxsQsWzV{rY;gTj@Omck*3s2*>$*BdD;nU`Q_Gt;
z$MO03TZ_s8|M~gk-wGN14FOYpUZ*cgq$ML2{|{gI4>a^AX<3=&-}3MO{ple7+WS_6
z%Ukeo74xhPNwIZijoCe7&2TA+tId*xV<(mVwZ=t&HFi<`6Y~B+&qO`;QS|lCGm5@V
z?C_|;#__1Sm`;*VgbX_W4m>`w>L?x*_2{;S*nOGSHsLF|rM`_BIdze!hB9|mCT*4P
znddd2C-6EB+K(HTnMF$=u<O&-RTJyvu)ayGL*$t&t+?W%Z*l`W14mI?M_RWN?2}Tp
zd35eEYcKj?kXu{<$W3A!KrtGvxpS3Q*ADC+M`ug&@6dJCM2VomZE}Aiu=R4aL2$&+
zcvD%`hHBz?_9UmP?Atmfv1NQN$yAfp#-XOoZpe-qn5S*?`fjYyGi7RD@@jH?mRH+~
z@94^P*6z~Qz6z<P=2=`~KDe?V`D#zLX@!t~KzxMTu);DYnNiiuc{0kjuXm>Za6f*!
zsQP=o_oHt$6oF}OPP$1#(<pu~LT1<G+{S3_*xBiDt=g4YL0(Z-QIQy4x;!?kayI)z
zN1Rg=>!|*C&!cWwmKe`QcaF;%OUhB2#w3^Poc6o<+@3A5t1X8=A)w~MSN20Fvr<Cx
zzp-*N4JKKN^Z>g;`%~~7`sOCgY_jhD$m>8^Zd4e0y-Cyf8TcD=!{q$B{~(KmgwlJ@
zVhJN5GdS5Furw>_SvVx?o)$@p!Kon}<l=gLKh|mngF8{QOWq>nRPS;NI=t@DKTl9q
zPrXEnSzcNvw4DaXn;qrm&IFak3kZ7Su6d^t*ngO`Y`j#u?6=)Q)Fz!7^n_Ck9W&q7
zC)t1C`sR@F2JXM&{I=%L6NdD<NPp+?PYuAq^RoYXB_3m?-W-3ql`^Esl(EwAL|2tP
zKXgbKxXAvZ%YCyXGLXT5TbcOQ_Bi5McO)JE;6YDK`nVCtv+c@M+4+oBowDR$-O)L8
zR8F^fC7NYmJ)tsRwjy*}RlQikdVOd}S7UjhqH<=V-?PZPoOYsiJ)GvHqo=n#q$X94
z`g)T>rq*JPSeisl=+-Bc^9I@HYxv}H?!K#hoO-g9*czE%>Hv}wCtLkE$tZuS>=1WV
z!irfJ{hduL^U&(UJa=<1=?KE%M5bG~m@KTJPnv6`x;muGEs{=rF{d*!7pE)ElpcE{
z57pn9-kU4isE^j&*qE+LlI2|*DLU<Ld%w4$@P%Ai_X)QUr5v{R(dFt|pBbu+;u)&p
zVfm{)+{O|1H<%;_2iFVE7SZ`~>&4dKhb+nT$sy#}x^n+&BRUcn5sK{t3Rm*HLQ4qx
z`f6WM8E~Kbo?UbFq?lc;Z=I)qdOr^t#lflY#*&Q2SM6?pN9kU4roj|o9l~xvwCmiY
zYtv5c^}zAm6T(FPcjakeqQzj{``y_qwniLu{pKKh$N%pF+Xb+r0lWj0x<7iwkGWGg
zX(JCF=H%33MXSX{jm~Vqysp~-v|rbMA%4v7{+-e45}a(8F720*mZnO2KL->*&Rq{F
z(#=?278XwR&~d{l?UrwDiDnk+<WznPuDf@MDjUPU^3fPidtUGa1qC5Nx2s|=YCE6V
zOgTG;oi9(#t<5Pf)Fjrk3GthMo96NdA_+hf!W^|fNSQD)ZKQk^a=oi9`*YGUN@v7)
zv+n)ye&KyH!SfJ*>PV|E5%k-20o8ued*SjBH!Q__CIf=G{Gi@TwE&8_`dJUPL?5a^
z=g!iHlj#c1>2G4@jM!4O1VSP+qoonj+zVTr?uOKKRd8LrJjSZkw_7AA7n}UH;;hGT
zFfjC}n7TZ>HWy`~=qHZm4-U7sl&VLk8qt2nzw*8J+Ao+&p3yw+Klo!S-__P>5tNw$
z$VoCPIp~(WOF{FaD3ZP;*lf0bpr5qqtSZy?0(MR~RIV!G>BCaLEi{g-G4l#q@Fg`-
zGa{$7;xcJz913<X^;=q2imk24I7!l`#Jg`<5~__C_;G~U&5js`e#rFur8hdi_=L;o
zNTu*b_&*~<;sk&Z9Z7vR069#IQ_jkY3+3IPruH&63?7}(q_Q}H?|B369~IWj?1eY0
zS(OHJD6Y}9uK2vp_$XX28aBzOBY_WmLN@AXQ0u<n3@8auW|9qlWzT9v-J{X<tann?
zQ7;KIo2%d=Ot<mStgPGs(C4N!Ym+i{JYjNu)RL;<dU0}JzRD3>8TWf%K@^GHOn*ap
znh^t%bWgrI&g7b~xnfdYoUEyU2F}1aWP|mJbk<13XGF}-!TGPHZtb5qrDTlO+=j8Q
z9Pz3`AHSgLdl(H@fwKuKd+BHFqWKQP&E`0()&AEi#79dr;OiPWpSH}%2835pBjgiT
zfg(1iVdsVmlcA|7^O=I}bF(e83AD)ByAf^+<7m6r9))Trr&{w~GMvLp(w*l|A`2Hb
zR16cBrNm;UB4~g(9<z3UX;nqgT$!i*E&|Rogu(k5L+do4Atm-zi`#(lYM~)cbG7HN
zEXGL%C%^x&yN@FUhpR3uBaL<ECkGaKZp!oaTG`=hsI3(wis*osM`iqO+2&t4YFXY#
z$F_9kpEe%BYz~M_wp!GK_cr-W1sPR&)HRB^MHIYZ8D^eoshpq$_w!tHqrn0irD7tG
zF1vq8U@Jw%8IaN)PJQ^@Xd;8QwkoYBK*D1=&Fr)~mct!Vp4Wrx<JHgsKO#j+qY1^B
zW&E~&ej6v<&(IdvJ+$-^EM|1wH0!%U!61pSl)O8fC;C`5Mv{Kw>q-ZMN2Wrc;)oyI
z{9>HFCT>^=o>D9T5DPfJRewK4m2M3!Wc%7J4~KxY*D5GI_K3N8E-tzUa1rZt?%0yb
z;#OdENn!WWBO8YzbP!@qsc9Mw*Bz|tb5s5;@Qd&dk{tBw1JdJH%N*+hVw1#*RcTRe
zj<4A;YQldgxU>QXI8wvV@~_sZEWx87{f$HcwD(ihyhnr@=Ry1B<NLq+Oi7~WYQNSC
zw1Xlgj5k(3y3D5|Ubl`Z+8!np(gS0Q$~k^Z)J=Le*ys;YMK`oryA??1`Mg-PyoxZA
zFhSCN&?6JSPIiGbh7zo8Y<+Dxxs;`bpi#=VLYsnwkdQNk=GNzwECP>KpLy}E%1K%0
zrR)4OuK?M{_&EvX%%d)jf-siz`^b1Fy(NuFhV~DJ3#F6t2N?c4zfifbX5E<2cly~H
zkpLHygOZ7cPVjxJ^8ChWDQ32SkT6MT*|c90PUH<4_UKa}Tg}F-CVDmmCAjE}EzwZn
zi0pcd(8VbXDi_B;{XH=>wzUkMfkARC_+@K?V<0)5<`25$Z$ubY>?7UNv?kKn-6*~u
zv0nkNV>C}~jg0_XLwG6#7l!4=(AEp}@Lo_e+(CP9#rBVRt9OX3B0$}4g<cnNz##!2
zm#gZ<uNtz5{y4mbvUiA!N2yd{B#G4e=wv<oRK#7yqvSF8qzu6Tr$2cMV0CqS9F30(
zYPk|}B*i}#flvoyKgXNmoa7{AHUDYedy|@w>6Irjp}~`}(r%V@A;em=w~7w>lH!S9
zLBP(N8L;+p87|#D>6VQ8k}4kg_ojO~UnsBO^4?KaQUAWra+AF@jLQzYXtRIM9Z2?A
zyVd(p1sPOzolwZWz7-x-;veM@iGsZ=GtptNJ!qm(@6CY?+X8}NzMj9vg@8PEcev6m
z!g4VlJ9KK(u1ki(cH8e5#Jr8{Bj0I$x8t)}3udg)1WSVrR*vf-NF!p-P#DsRP6rql
zWehtrm5zEtb2#u)4#y0mIzfT95aq#^i^_)Gj!mh9nI3U}V~E+Cd{~^9*+mB3U@=iW
z6M03e8NRUf>Yq2AkeKLjM#<`UrX0WPI4s}+X@^6#IYL2L3<!;pf?i%_p#Z>n+1nv8
zQEkC?VWW8@O_{GXOm!UkNY7c{8wwK3Z}H-(pH@0OOc&_UNP7b#QM`F8G$E;bvb8x5
z$zps|R2wU#VY>tlj)<Ftu-`u-dCJyxDF!_+-$(J00q8?Dsw&91JCjthZwVI4<TgCP
zr{op`Yj)vD3%hX`do&MyFHKM{wWrOZi;k;OO|oQ@9kr2<pTx~ZU%$B1M~JZEX;VpE
z%M@xqB`F;2F|;GuIOx9md|{kYs{uN-$&CwdKa+zmS{_tSX1-*^+??*u_?4TuaboYH
zw$U30LSNe1pU4zy9bxL<u+}}^aNBZ>3wIxFmT>~<S#k6{b<l@nI1|0wGIq=CXD?R5
zc}=((wtka7*Y0GusX&uM{QS0o?q8V;M#G&$Y;=Ptj4dwNit~oQrtb(~zWbti6LvY7
z9vM17bc8n-%d{3C1F;q_e-s4uGTcxPCX9p0qF)k;5GR$ha;51tf#eDWMTQYFa!lBS
znYo0KDD#bZN(v!mv4`FBlrG7gbw7^5?QdK<769F1$gnoGxgs;G{q{%!GNRIH{>@ik
z{^3|DM&~ziFv02V?M<%Nm=(;}RaLD$Y|yoPe^R~ti(f&g6hCuZ1z2@|k!FX()qkyL
z8w%Jadp%vYa)`tw<K&4*{M}pURGNbGDJh?Bzc-}Sar&pT&4&o8G!669e4uFdO3Uub
zy>DQU!Mq1UMje$M{1^RnZnP!C(?0CCvBH4Z2;!bYQ(15fLv>AbwVr(G)FDpOW5J2K
z+tHkm8XOKXzcW2$EOte6`iYMZadGumX58_(VbqaIUwam|^=GPYTnL+Jm{E>`46WBC
zSFpeTcx<qK=2&9#IO3V}^ZxvVp+lYkf@(w_Wv}mo@+`Y{588+VBi#FwV*PqJ10D*B
z<p6B!I5Q;e)z$u2k<jy95IiEnHYQJ`rjIihIFY#RZlXd_IrEVwq@BzuoQN|ONZ&pI
zrk@&tI(!b&OjtuuK}{ZMdQ_{asTKc?DW1IE7ZbbVbbqJlboiy6N9XlCX~Awux^pYh
zabUX1mzY$rkos@xk@boGMP#oV1cY6E_?9s&U0-uP%txL)P?Ut8gGI>&@IyF@^Ov$W
z;y?*;yo%B`4qGah?+4SAKo&g-mKiN+XRbJ!=D3p!w%KxR%3A0H<CVvj@5IyVbwO~E
zv8rHxLk?oNf!96x_RJu->H;iNp1=JI0J%}T>NVc~DzQA(Nf(+rZ-czAGocKN?l8sf
zyDS{%f&8Nwg;de!mO!|NfOS10orlXTHn^v5_g6S64LD^792Q}``smbL5fXf$-Q5#b
zK}7vG*bv@3Yu+#s7d7GORyDl1F`@0v9cQ&INz#Y%=04W4>F)JsB>2nHInu}zy6z}R
zlCD6sEqdxDPyyLcEQ{X%F}@n&sGn~8(7we3+x+5!pNvAs+U1#c;%e35W=_kkpQvR8
zd#S@DE7=-;s6vTpEs*AvoE+WriQeaJkSm3JU-(K#24{p$2%Qm9S9UVa`t3ot;kKDG
z0wLm3(X#GPD5J6lVFxLu%XmR|RF*DAJ9OXhl!~&?u_r;R{qmbl#~B&>u<JC;1)y2B
z+@#w6-f#@`D-v~orV@r^U_rB^)=A5v)s?m*M6B7>Lerzuapm0iBIY{s-)-D$0YZjv
ztK%fKGw_+2HWL^L=BMgWaPZ(R2K!tiEkq|^!tCoqzJ$^vrWkX1E*AVU9HJ*Lfb>8{
zi6ejB#pk~5T01Q>7dktX7#L$#X?w&$TzkXdK|`+@j6gcGC3lWk8`F0Bs?*{F7<dCi
zJDI%HpfaZ_J$`QK*Oom8g3Q8}f<~w!;rL(?xu4L$JA)&F3zTW?3Vp*!?rxUUq*y1M
zyYSz+Sgo?__-g=Q;+$OW*?LN-RAKVOva8u7HqcWLab`D#miL)+I6}j9H>~Y6nT)RW
zob<IG+YG^=S^xbC(P_vY)J!xrI~?3F(asE!34!>#hYB^ySv@oyK-~T|{v25N)u6Y%
zDpeK)P037@E{2`&-5|cZdpaOziI@>sqi2r#wZQUAMqH*2fLs$Yq`xdTCQ%3WYxQtY
z^X%1p$am_tT?<GdSZ_0e<T@nN9K}Mr-n*n78_J!hC|9vC*?6?(cf${@?mjA;t%A~t
z!8!gq@62sxRSg<ehBch?Qv%+J1)r+^fit72PiEH6j(=q%Uw4i<D<VS1^AIh!rqB>(
zV#fp6uf?QSV^vtv!)_cqnFl4A#}F&?+ygU2AFwE0GiPHo`CIj9K7`S9pBm7$`WJMw
z9i1&HrMjEEzf-o9GU%t%aGNW_Wcu!9qDvn(MJ#xhl3U04t+$tS>OL%*B#b*QJ4(Nv
zeYZG6Tr8t2;GcuvDgECByw~gj(&wgc7<Do>O&sowIu)MhtBxt`igoP<FB{Y*+zr!8
zQv_v3KaKb){5Sgfx_}eef{b)Z(>_!a+h%4sKe2rQ7Ax4tN2(kW9!e>iWVKu=dl>b{
zK_TlZx^6vU)TkU~qeol&X%mLyOqE+m86lmm>}?|lXh(y7x83vjD=c*?E6pY^@jPc;
zMMTbCL5pdXsO3#lE@&&No#WT($WDyDmVMbM`kLx9L=S~wW5&(?XJm+m)igFh)3eQJ
zYEt8M-|GP=r8L209#6D)g9?4yAa98A?M?Dx9b*ZEG^>rTvESxLF+ZB*dp>X`vRK9|
zv>P?FW2Bnym6}o%C-z!OX}kU=_ol7<IF<3qXxH78qdDr3lJOto^(73WZHsm^k?obV
zYz<Sof3nd`)-;CdRZfTGvZU%?Qh8Qz8uPgrnbvvs%t<zq5)hT%Z#k(LSWp_gskDxl
z6((0*ij46TUA8)TIbxE@D1QC#&icb2pVJ?|Lb@&@o%^&<>(%m=ad{Zpq0ejLjvL2=
zcPS<(5>@|lzSW1{%?qR3XA1%}p1-o#y!tcS#HxhOC!53RKCM5~5l=i<6p4Z7pGwdf
z^RVXuxZEPOGqOW{;SzM<G(bwa{V@uKDfa}b+UuWlef^D7U0xrgMd?zqbA~w1#1K89
z0Z$ngdF428v;3LsS4bj9g{4;xjS^{~sqb{T)%Fjp+bh1TgIgd~hLEFVB3T7i+G3f>
zar&tHX*YV)%U_G-w9n;K2Mmtp{SQsmyR9vHp~u7Gu`KlAT14*NcG3Bn`MiC$u*z<w
zdU@taB4yBcQ=yQSoA1#No6dLSRT<jiX;Cs{jYSD3FZP^kVhZ|irrzIlh^W8_8aZla
zLW<{<Se(VZtWpj-zgCS%@pyZPUDs%2V%uk3n^BS(W6J8h&_1^CaLV3-WIEEt5b?`W
zV5}^x35Bh!EnULy*?A<lW0Hqgs#6m)uH8524bl73cWnsyZ0U@hzeCt)j6|Yhp=tZz
zI##epg=W1#<!hFiy(%VSITC5cj1-XYQQ(-2cgD{}wGCyEa2;Yf?3i|@+#b<$vUAR)
zHU^90lbaT`V``g`Hhws<8e5hwCDno(7y;~-TtysmG|T2>DxGhH2V}tt8I5ViG@Pk@
zTV=U(ffR9pZoBKASN4a*UdwY*Sy-~`hIBX2*rMjBwXr9*GNOfZu*CW#W7gI);6G=(
z%cERWtoG-x!P|L_=?k!HNA*FD<4s7P)AZ1Ac2(m72SJ3pfp7bD!h+B!uaww45p9MC
zrf>TL=6UI2_f?O9iB3jjE;U-YNraihTM-Ibhq6rEU-7!o#VbV&#Y=YIwgVkz<U8Z-
zgib<-rf5TNvO!rQVKd&HKZW<u`btV)S~&&804rYTYm6e`rO;fFoGmdRPW?avv^AM+
zN2kA2MC)S3$`~8aF@6j-!dfSTnt1PBVxl#w|KZi20MYaqD}2@EL-`R)(S7rZt=aVB
zg&EGneg*fyem9~sjCFqAGVB!%zHKB$P5x)4k1fZ{_ct^0#nGB`EsGV;VXk?w$^}TH
zRPcmyAHzX>3MEWEGO`%`GYGbF!Ts&7_jar2&YZ$*wGUISq#+e@vBQUAg(u+f#8sca
z4RLhC?Z;bQT9bvsX|c(ufl}B=x4cx2#M4Xf!{%9D5B=^~Gc0MCd&JCeb&PS0SqT&A
zSpNIa40oLy1FnJVIm7<9rTiGfzPOnZN3?(Z5?-~lszlQ5S-yLRr^NUL3cbo@!I9<0
zK;v30IZvmzCGlDZT?_ncVooRg7NW!O6t^ap<)^pGht939mIH9!K02e~vM=l$Eg$f~
z(rkf&hG|Ii&a_aGj3eI%)+8j=2DXumL``~qtVAAJmlu=`OAHK&tz>0Uq1F0}EHTVB
zxuYC9Pd_bl3VCbin(cY@Wl45$!CGQ)a0X9uXY#5JB5om)7;ZhV{|M>!QM&$vYK-=l
zLo&z3#l+1FQV;r?utZ*f5{X<QEhsBWmC-KZ75I%L^*bCK$TBPFp+@?O-`>IE>~yeq
z5+AYMi(GS+MtU4WnEe6qM-^8Brbnw;0=>;Sk(T=qykUcRmT}q|TEWEM1uFd};8<p1
zC}@x@%<TRJUgU+v${@nr&QISwjwTj7yv|!r4v;p(%{XOS{<Yy3^igAuBF)SUa+&3)
z(}&8ivriDCn9d}Hab+u;LLoxPzF$$OTX;hbnGn9PiYm#O=9J(eN=Ce!nO&y+mI3v(
z1CJ~cb<1jqp1>O~UK={iMk6AvHK}TPnhe1hH|Oad9!|hlfgF?dx62xJ0q%X8h6~v<
zm$?NxZ%)L$Q~lZ@*>B<JMJjo9D-E{<Xu-^NoqK3BRvJskTC)dXu9Ai2+cH`MA_D-~
z2;nM^mXMu|vD)&<G5NDd7@n-UdL1*%QU;CSH}BJLiP@cD`>b0u3%3rAMt^+2h+@*M
z+;@1NYTd%Y-!HM0uWxTIt<dhQnD&vei$SQA;usoUc-4H6VN+|YpaD9<iS!`<d0HN{
z_{&<scwd2MbKjh+3~pE{L31QY;MopWE<|@kNVD~?m4bchu`ov^RO=D)@C32GLXerJ
zd~e_|t+O!ZO^*!FZ#X*!g{WVY8CKcKLHNRukr|p}v$cnfDZ-R4@Bp2FKXXLf*oT7Q
zzP}aWrrWb~pxq~irHCsEqPz*BF>J)pS*_qCFfLMY{WNgd+B>1U0E=w6ROHsFqJh4@
z1+ueCV({3%pB}RfyNyQ5g^m%bE440H5OdMe64`M{c!mmTo&g=lO)MA)?nh}^8yNS;
z88>+PfI}wVH*9w(1{dcU2K;SONvLz;yzF1VJhy(gf(8sZe3&g2#b&6oZQ6s@oiE^1
z8TGui9BXuzYUQSu=GNil<a}$7DW6duq6O?SzL61YC@^l!Q1zQ7L(tgnzGE%PaufzC
zJoXuPok7ji;wog#5X|*W54n{xr#>lSF@Fm7cWvjk$HSTMj#t`!Dd^f8`FF*C4YH;I
zzd|2PUhkS8{(`^d6bz3|3O6&-z6K$sH6H(ir?W9q8?{axQzipDCFuO}c!QGmc(&u}
zi#0qt-=IvjF3e_t03azHb{yV6K7uJsqt^ee>%Gfgw`03~Y%X88k)$e^!NPsFxl_mC
z33BvdT1tBT9Y!W}wf*LsvznI(zwa@<uMIA6L7X6_JD4^vOu)%1J%szXa%M!M6u8C2
zgUI&?_7m9b4E4*tqMz)%Vjwor9y^_4&|R`KbVWH)>6q-kR;lwJe!Z=Mg+>WovsD<m
zuQ+%+L)G$nLhTE~ZB_XB?Z?;Pt`tJO-SK|csU$BL)k*1SX!V!ANkh1$YF?+rgQ`c_
zk3WL={en@vznWgm376G;{a*0A#9fk5fbmv*xhA?}|Df!>N}n7d^$*W4=xi5pgl~j7
z|8l80)sr;mssW3xQ|{hp(dn%fe4}l>SGcksIS90-F$z`_QkNf6tbFxfo|C;6iAg-!
z7rGt^O<1)btWU;*r8xV`An<C4=UWPT!Rz>rm?m;Tom5FIlqK&N`Tp*MfWxkbsN$XY
z@*dcfu71iX8i@l=N=nWm7ybm($8Hq6A6Ua@t_2|Z;RPxLJpXjxyUJub)*B6|(8&ut
zuXaihE?X#{NtTPlTWTRiD8=W>;)lPjNY}Kmd+zCFJ>5Zra>Qr+bQfK%$&ia9cBN0%
z?-W%nnyI+^k;wgnQsq)-jOWrLdefMMoP+j*4+*o!TpXT6$*>O$Zj!B(H03ctp1!to
zBMO@GE*8cJIT#E%)w&FH9c0t9W$D8j8zAK{EaLj^QDIhL`zC$%jE{)z9Kw=w<z`H)
zFO83-&g<=fF5o9hM29gkiNOR1>nk<MGuNDd<<m>}kEsUhD^Wamq4cGRL*ot;)ya4L
zKxEwQDN%gmF$@^Kf}j7Yh(N!^J8#AOP#K6rTg~^m$jg=a-1NfF#Vk3*yY4pwi6t7C
z1A&6fUgKKPs@J7Kx05yN?G*<<PG`y_VX@@ZOn&<Ue7%E`s7JRK{%UuGo#QY@yPJv4
zL|vWGPOmw5Nt1=wYb+dL>rL!LF#!?ACK(__9~$YM&=J~!`vU>PsN>}xEtvHFCTrt2
z#VfK7mn(HWvPHde&qP5OOm2m`q_rcAb%pjV*FQI5nw5E-mxek%JE*ZAubK>{Cx97g
zhSnF|pE+~eyL;3w9Ur9vb!S^e08QQe|Jp5m7OdaD#?wb181@|38{^x@VIi@^C2;Px
zny$ixeG<YmCDr3iTp9&k`Jnx$iwCDSkcmWy3_&M$RWb(vHt9B~E-tc02tqa<*{3Gz
zr+g=bjJulYJhNS1PXfu__wG5Hdg4y#oNxQi2&6rt&*8)bnJd!$-10*3Uh>!a=)zhS
zDG|rb;MY(Bx<wvjtl@(YBei<bsJqbGH=#5-R8g<0*x6G0iqC}Pw>HVM8?Dy)bZZyW
zyR76Xtz?8vV6gYdGtYHfL2(PweOL`JEvyd=qA;p1EsH%t!+BNGXznvpiBPLL)EIE2
zU`#6g`VM|B+03$6g?|CkzfXVphV=KD(KP_kw(TBKYG(V%ZIp08%bhOC<Y|Vi8v+uW
z5(u-PFu~=DC7(?zM3T3(!l9-6%hH-wmgkLhCa#`ZQ@h(*HbhR&3<=5tJO)Hq?N3v&
zb5a0`$9@@C5diL7PfWAIxNDAVo2t@Z9Qg7pULyFq>%*u<;7c#SmZ||N5<y0zS4l!z
zJNr@ll@HvLTsA7h*bQGz*@l-k8HHeNnjYWm;^e&f1nSE4q+*RYIXroH6kq!-bs+S`
zi(B2-EF$Ne!}c`<I>Ba^num*miQq1ahJND&fB7(z1vJe$YRUAjAbSXP;CcBVc@BGD
z$Saom6KK(L=F8}Dzd^MaF7h|&iE7x<>qd1St1G8Dys30HE*&CXI1mQy(j4n~1HK__
zwQ+S5li@oK42o$q^be8w$_+vQ4v;xT5IRYVG30wIn|)jLpbvb##&Rz7?0uTZlkC5)
zV*jc#e|%d1;iUE7bDrOoalO+$dvV7w@Tn-pz?5YBV`%M(RJKj=ZVbJTR`Q-f4tvs#
z)5g(quCZs21zaD$OoZqw6w&qWZ-`m%h|rE7^e{Pb9r^f?>x*5=^YS=C!e~1pk5Kv+
zjCQTrt;R__#-w~oU9#l_`kxCY4YzJNV#OxF8}9>M@5HDO`Qv-PpK1@O-o+U2LSix8
zh^`h*S8fH}-`=37reE}7ic!;e(_pUVs5cadi0T2Vbhg{+{UQU?lhMFzyCk)$hFzov
zBx8WReXN~;w4gDnHuCuxB}05|q4og#8Lx{o)TVuzmVnL^0ENoj`GvFi^S(ykB`e@0
zE)W|6+0x=+cLhVB)zhK=tXahH$Ygx{(}EHf7_{IgMIf6)+<Tg`!Kpbe1Su>A$r{lo
ze_N;$n}PANY;JL5FyDqbi#sv6ixZGk8WPf?!RZP4bNm+3ScK~AzhLO!f8<SAm?9jW
z6pslK-NzJ-3-S5KGT#@ev*S^q`0ZUK2?Ni|u4ZPe|K0SY_vz30f#gl+-8u7S`*XJ!
zQ>=$y`{}+pcv<4L819_Tfp0fK3~^O&i(xcE>jSw)lg}_V8(l-2eZ!^~9~aV~2Inaw
zW-*0tWUt=dM@3ddp4-k+X?TKy&NPw8lT8J}$dc^LwH1Hj+~X9@;+5tz*Z$!KV$MS1
zekTdchImqmA}kjqF(LF>Wxs*^rj+xF_@hwoML{IX6PC6;pZoohi5V-S>1cifkbgX&
ztZ-qoA`gVTZ%3A0)MhLSt~nf_9tf5YK)=v~yg{6yve&`YhO~nP?M;W#5;M;RE@Wwn
zh8hiyraA+rroK91-myEMAJEBm%3NH6+3QSg596u+)ua9w`UsKbm@W$0J}rGX6))K=
z@`cz&c^@QlP;xt`%uQc5FV-|Psg_o?NDdoofp&i-VnM5SA4Z<)rNHWFByw5{Zh@W@
z4%=@wL`sqkdI529SN5)^RkcCmFP6p2t*Jc5Jdgk6MaN+I)wUJ_ee6h`m0H}Q=dU-j
zGV>nYi23zn!a?BZKDY`qqxyuODddFez_SJn@FPTM4A34qiTjqo_r;BllmTi=ya!q|
zBQa!J3(xKFD{nEriI);{Dk0Rg$lw+UMYc8&Tg%OfsDoF>WhSPU$8F^9KhXX24G{^3
zz4-+hAN%?&l~lYcHa0h=&4lmp`i(`FhT*$5t~u?-IEpOyge87izHV-Q`8D}jZ0gG5
zM%R0W`@NL++M2=T`sd3jkMQjsJov3{kwtQpCiZHSj40#_{_qf~dN(AAr~1)4w?vu;
zz4}<PXX@e6Q9qGDwLIgu)V_{pjcu$%Wi|<sPW`C>E>Kr_H+f2b%X4|3baGeTqTO&C
z8D_OGN;zQV)Gr!Zfk+Yjb8?#Y>ermO2Y=wOfUY1-t7GI8nKQyl&H6A)e9pgG@Bbd}
zvXg()lj-2Em{a&btQNxYGVnPVMFRjTm4wY+`z5nJ2z;allJF#MYoDk$)`A^r`I2^b
zf4Nku%fvwsXgn=MH)gbSF>}K!Tk+K;wK#k~5Ni`qRHeYTT^C;j*;%FJSbwwkwFcnR
zeng8Kiu^gT)_bcUA`<Wd&N(3_?nuLh<?6e5{75CREgMYb@XFKLCPvso$<d!mKxZT`
zW@Otxpx<Qc%DxsM4Id|>>5j|5w=KvQRzMs5|2;@}!COU1N$k%^S1<E)GffZ|6ISa|
z5PYLJb9)Y+NNI!cklfDpzEz{<yu>YRLfge@S#F{0=FAR_Nu1CEd-gvnVQ88p%f{`=
z9?8Jr;b^nca^&t_eu0IIX+WA3a^}Pw?%6LsNWiviA9o#n+4au8?wU-xD4WHG-H0po
zPOvMRc(fgE&fkIYacO~-yCvdaxY0YHTqv_*XldMc?e@T&@%Qa|P`BQuKNKDTG4YPC
zAuC0@(*HrwFTzV=g3I>8w|B}Ajf`BLx{=}?Cj%Y(95W%gdPV|yCLCe4deqXOd~6(y
zXC=Re_QLr?yV$H5;ofMW^Qzz`$HxF_FlX^7=VFw2kVE<LtvjbB-9_82>GYuVuTRlR
zTYw>Zi$p<29BP_WU=0$xeVfSXAM1WJ&Pv;^$NX=%!-@&I|9h$VZ)oHb_@-MX@D|Bw
z5jJBb>HW45Q*GyG{pn@%O>t#M$rl~kD%GskE!oHZ=wMA%XH!fWKSxjW0<~ARvJ(gY
z?E`-_|9%MI6f+Fbl8$^OJHM{m8w)Qyrc1B8t?4_AY8h4Ao)_^+c#vqBsHB9HKu`rR
zF~-b{MpSYhl=AV_*?!kXh>;SH`Zx~k>;~ZWx2k(oMWy*LLPa#EJ?OlBDXmQe%+Qjt
z><ZbYl}h||6WKa>`nX%~sv)7mNWqep&rO9jHSsa0de*=)!*U^+)fhfThDDzg`<OQ5
zd*Qdev2Q7RM;4emtF`s*U1+Cvw?u`lc!n{hf_sZek8<~XqFv?IrssHO^!1?Ray{D#
zD77J39v!K1O^j*Q1H_!0B=S6v%j{Ghq))|U-Iuy;{LuLzN0dzqtz!SkbX+UXd-i%d
zEm>_3&#h2Q&LUN>&0|*Dt<{xkpiSo4n&ISo$=TbqL4a#vaP>Mav`ynIeLM&&O>v%?
z1l`P6-}NpW!DjU|r^sjF<+lBAqv~oTv=a$7la-Z4qtUqKOM6~6;YG&4_za#f!2FYw
zleon9hPxAh>wa;8SbOjngEUxusipPHMvxO}gv6iRhU7DjU^h`?deV_#lF3*2(b+7!
z(O@P!)TdtV*#VjA4;xEsU}1NR`%MVNVs*r|4VB?w0mI*AfnCtg=SXzL1<O8F#*d=#
zz|++R|3pF_#RA!0O|Gte8cli*2zqae2D68L=Jsa-U*A`2lPuS-^2FO>lHqx^+409J
zu!8Lo%q$ICs7${pFzxG<ag0TM7`lshFtAUCyH^ErW@@f}st+f&6|kG4i(+{Ur?N(5
zcd$0#k{9H^g8Bl}e_14BbF4HdY*Z3^UIPPoI<@7TC+U5$Hq>PIa+V+(5*>>=qBxjp
z?WXSKHPj@u6-ip>eFIFlzfGjXfZ|&5`E3M$;^6~1mV?(cU8h-2dxQX~mZel+Nm-J<
zg^}u>b{vj%|F}~jVAObC=QVx*94^o}yx=^}Rc+yTBqC{oU~v)<maREpz*XMsQ81Yr
zsT{Ny`%Iat5sJa)jYfz^)Q0#_vz?*JK94(^<0yih%8un*X!GMYwtY_2P69keeT7u)
zjkob<cTGHV;)JBP9Ka}XbhP`O*eAg2v1L(^ljpZbr48*8MZh$bAp#&!%y}!Kq>Euc
zwOH0nH8(#Y1X6>RODqhg$zqZBKKgI2EZbIAm4Y>om2BDxG;v&wtC!Ly<EA@!t$#ke
zZfhNvjEX0oDzCBhpDt+!y09m45D8@=$6=RBz>FvNIp2_hj^^nRj%g!tO8(^3p=|(m
zhGg!EeP7VTzv%(Jd*C3sj+g501VcvNz!LaTgK})}+rE0NcQuwfCeK3AS-jRng7AZj
z)gScZB*tp_-gm~qPF#>NIOm}gOyP%m#J1Eg$twpz*TeH3XlvXA?|@NgNvzT2-7h~}
zY<Jvlka9_7NCrOm)EB+rK_i6!Gfd?EtGI1#ZEf1F2Jqp_E*gj}VCW49_)PHwX#;l~
zI32-;e3N|5>3z*JL+VeAxZ{o#D-GbsVy30ed+SuPtkBY+d8XDT&=&*Ch=d3_9?51;
zNPUD}CwNYKvq1+|d<&v}%Vg6m4ymeA=s2DCQ|(5`3{mzFecZEzGzyCw(*D{kRBYF+
zm5wM%M`#2ZD7WT~7|n^%Xy(IMk+ms!L^{;c4R^%OR$Ie^Smk75f#H>guz%2Xhaw*~
zy8p7pd8aKz$ES9OPJZCt!FT30=A*{ZtmR?!;qYC;*xFD7sir?;GTqaj3~!ZKF;W<p
zW1`l5VwUqDSPv4IWP-YAxV)OL+bRpk-xjLnTKgGD1(+jNMlmaSgjS2Bo{t|Cs3}$&
zJ8vl|m`pZ_R=K@-$hn(Pr4NnvMH*@>gmL$$5aON1_lJLvx6PPIw(X3_iDGI>j%!gY
ztGsOzu50>)pLn+map?wjhA*T|(wQm*1O_SH2An%MQ2yaOU}q_z7vW62IxeLVLR^s3
zgD0eF3Uy8N>bdZ7$+U8URu#v-Abk)~Ji?RkNKZao>YHC>O;2rL#HjUCQ-t~Oa*T%l
zUEBz{wcHkno~Lc-?Q!{|(Vj<MDz?F=>r~>bo8`8yUruL9^sxNkW@_3wmR+pB<A-UD
zA$;3q_g7lHtgcr4vi<v&3env+TV*GWtvX*e{N&>)_7{4N(ZE}VB<L(6mED0Kx@5pb
zcrLyrscrbq-WF%f(Bu6$iX%hoetuHx_X_Q%TNV%zCrG?Fvh8l)!-O`uf7w{lE;VIj
zJ=WzL?y=ihh6}C4EGW+I`}%oz+sE{YR7#S?M!ovWz)gHIksY4e<fwJT?MM!_{l3vv
z;9`jKSkq?~-;w<d&y;9FNVBSz>`tz$<ER1df|hMs;{_l#aYVGTO=2fVui>nO*i-B0
zO2awv@pMW<7oUXu|FrflRb;tLbY1YF6s}%<A@YNrGje$m*H^rXTV@sm=quB$vXn7d
zkp5(YmK0&iSQb;DBw2{Ox-kJMdq5t5*se;`JGrR5k#VcXk;C_132eqFq-~sk=!0u_
zdrQp79uF9fv0AB$<t>yG84wmP>ik+_$u*SjPH{uq5g@D%n2k@$p61-a{rUUeXjx{#
zPfs9#NF19l?ub}h{r#QtnA;%b=YhZJz|&YdV}$!o;Af3#3m{tvkvr6w1}brJGVQe8
zT+U?CvmKeSPgJRg28Kpi-B=!BFxyWw&m?nt^V%C6O&_C7gipQ4!Yf!UMQkvVzYG(O
zBy;=kuSP94b!JYfV;X$WW+TI+3B*n}@7KVJher}o@Zzz7MQv)MR3(KlGDgO#@@&`;
z(}Gf%0^h#hUxJa*0l7<HBtAq7;atyAl!s_MLYY$5K?U&$PHKeeuV1Ht17v{X9a1gN
zqEZ(7?lwm?yE|EWXVt%~e@Hyfy^c;GLOe@^U8#i`?Fae>kg)k;k7^6$yxs7zVMj1o
zJbpg=sTE9eMcq^+`<L;Om)iiyG;wtZW11viNXNLccKsBO)XELM?M@2An#QK!_$%W|
zw@rR2RrLy|wIPZzXhCAPL5M4qA|Ry?S9dJ<<H|h6=B5GtQNXl9t<wM?W=dgmrDe*O
zWlZnsPWhzNfD9#d2pZeaEmbjBV5!V#*7=Pc+s*QBjEY@zlKYojjXYXT6Bj4&!%8KR
z$z<Xen8at*icc^Hluar}#6O=g81?1=TsIa4V;a8-sPT?Zmh*^E2BYdnFw8D*U=6DZ
zBV?Lg(Z*8;(ixcS4%uRvO;V{x0%TofsW4j#CP--wISs$h&DP!jo(y8*C+uK<i8LC>
zGfK5w>Er7Ke*E81;9n{F3y{-^SIA@+uWTg3f>{&H?L6O{7tAG|hX}jefS@w0a`$6t
z8;{EyuH=||erQ9oaSoY>Q6PRY@Fx!WpA9n<X(u5e^QD$sV%~Q**!&12WK4b64k!C*
zhQm#f`^D<r@{IsbG0Ipc`5=h;WQrz7vmHj2_BYB(YjRR%Qke-rx29p=Q#?noaH#vA
z7%hgGp>&qC?YRp;Mk*=miigHS`EDHU%2KN0x>lz8|9Alix8=RTUCxT-;Mn)YTb2kR
ztv6ZVj=v#~YW{A7-u3{vrQ-sz^VU(>0BtJei$js+Cqk1J-G)^O$NY2xJkmJS%b}pE
zjTVSOlg)WYzwc}Md5|ZVH7j|geW(3sr)!i`8j!-U-bl4|*+OH?iEZ@(D&Sd%!%Mtp
z;a_!PO}!KApZhV|?F#mqk_aAZ?<aGI7VfGB)#{T?A4*NEvn`Al4~wH1T9pL6g0>3S
z(t%(*%*+Jr`OB-7i|jjapQNdgSdfjTmqXmqZFxb=AyYpTNpX}Y<JSJp&-jOE5)%gI
z#2Wi?3*OJd(iUTc0lXLWJ<Tu4lQgW-o-Q}2oYr~DUdKEcMynkX-N9o@t2yC?<Drbd
z*p?f=);UoJLWYD$!Hv2t`lJb4TEBQp{CQ)IrV)YFrm4rNEXjLyqtVWo=2QR7=L6Pk
zu2$F0D#g}1vb|#-T!vB6?KNy{NIku4$A`IxlqZk(*3Ghf3SFdwXQxORHbVP%hb>Uj
zX-Fwye^8vTf~8wy8Y=LfTRkXrJ{rtG`5SYB5KrzBQm*h*;{G*{Q#p#IFxPNBjg?7z
zCymt}4<^sTE7}T8VQVCJStmIbYBaFQ`bQ{YRgKsP9!uj>&}j`=BWdG(ztyKE0{@8(
zzI805@cqAijU<d25Mt$fA#XiLkGFa?2h8$sogngjFLVgz$Hx#*0~>UEQNmi=+EtW{
zL0jKI1w|==&#TqG^bis6fT)#|C5&WNcR+RZV_1R5kRFhoS?SbCSqNCDXH|jrB4<k~
zg*Dk%MtwU^o7$ozmBk@nm!B;pDjb)}@XdDk#}W2VDG?Rc`3Z}bdY|-KJ<g-z;ae6}
zJ-bM~`*?;!YtL(oc4!)psR4~k^qIhsSPfDd4N$4k?^SIcF~3%9``kiCLkYBMLLDWQ
zd6cA7X%7Q}HQd0*&|t9_D*-2F9R$eZRCuX$-r&a}`nHTd8<(zM_hZ3Q__&k5LV=wz
z4P%{~-@XCAPBzj|mZNk}!GvS6X#`R6oM*1bR`8@>%JH>%uacDJ<3mpMnT8LCRFKZ{
zPKBeM-38qW!Qq}Yg^(~PFak+{5k#z<0dgF?T??TEbco{DGonTgq%j^;Zx)Cm`6NhO
zl!B)z<Lr+jv8hOtz~+kT+RU%gUk-Ha@V35oKm<W<k?a$G^(TaQ^jsnCaC`op(FT2}
zqYr??;qY^rb^m;n8}V;zHz+|NJww<_hdqc(NF}#A4wgo$?Z=$`yAc(tOORQkR$V;m
z?G<43{<w9#PjN$}ww(!ulEkdxf)mhJ*Ausi)LandMcVq~>(}>K^%>dcT~7z}oixS!
z=rsuR(P*<;s2KQv0&<W^u$9Su-ti!W`eKP(ZoX#A8yQE&o_K7LF2FuG_peN<)_5NG
z5l%ssj}`K(H(`+Q%j+q{QvVd#Q$uWZmpl%V(xPdHYlVzsB=S#g0;*qf%uE@Ztfo7P
z;{zyI-}sGwTeoC)1Q4=KRgG3i|9_NyV|Zl2*6z$C6HF$yZQGdGww;b`^u$gkwmq?J
zJ007$jhpk`@7#lb_gR0spW4;6_pW-XSFQD~_Z=9L|4U7E6)tV4I8U(@%$VnV*Ee}~
z>zd%7eEkUR|3zcAU(8)eNkz<V8{BjpC%mvR!6iKNJIn<6vW@Ng_!5QL|E}V$=<u$o
zDFh3J`9+tC&a!k62T0FFVejpO?i;nPcc`p)O|r0bz8aHs1=WJ%-m0xx>+8nc4fD!!
z;kEbcO{CE3;~+K^3gl2dwOS0z3%PLfhtzHY^UBWxm;cR7{7;%CuxlX{xf}}g@lEFG
zW&?WB(5aN<HcPQX>)Vfoi^L+uwExA}QGj<)f725SQ6<T{2)mDN!V~$;$LwEf$IGmp
zpo#j=bcY=q6ougJQg)fP(7$5T{|E<eihUMR<^q*_OPoaitHlaIKH#wSmqv5Y{~PLm
zB2Zn?pGAYhAH_jRM-l(kvP6&#1v2xmae)1|NBQjJi~lLV5%R78TQrUNUoEx2KF6Pg
zFxkWU?;r-%x7g$-e)@*l5VE-4ud4s74fxky0x8>-IIZ=mVIo%kDjNWRgbe;*Z+ryK
z3ho*I=OCXsO8AH_S0dq3Whx1n@@HRX8S{T)6#w;eV+itAxsyND3RM5Iw=X{9KW-io
zzG9m7zgn1u{&DmFpItV&?P++JWt1#b{^@D{Z$gR-+yf%m-NX_Xb`4_o1FRdbOm)g!
zIh}nutC}%W4-(|+6G0>lJP=R#@&D%aAH@>syx(~JO?}=ypdl?@>>TZd4GRtGKY;hk
zc5Ic#^$(wlhh+0O=bI^|NGUkL`|su=A$)0h71~n&KHXie(wp_Tce^SNNLYswoeGgM
zQsFv(-lOPm!Ad3?gsV_(&CGrVeSNPf47+M9$Uz;yU7|^a%ujF0?$+nHyWa58qCtD{
z{4jS(0+v1k?_sG`NJUUk(3EtPgq&4a$F0HKo}ad|pQ3VpVskh!1dj~Je)9+8j0?J4
zs}Ek%lDEDHz5z}nK0y@IP7rq1!DYBIoWnJ!VsoaajmFgM+?Mb_T{<N`l;O}*?aC`K
zo<Tr*WkjA%4tE#3^sMxrF%#@HwaG0iEJl{Lgs*}HI;FBcKDin8t}SVIufIW>pPu&4
zji<b}dopmkZ=YGT4lGfqVoAZrdFKC2NW+&k1%oKXf;=l3*56y)^2edF>{+3QB0nQ|
zYJMp>!sz4gLVwm8T1pzszT{9<7VaFu)Pql|v~mM#AwRi8D!7Aho;#V&&+^jFoi4Tq
zzxoFkn*(}>*U;hHd(|4wT6wZ_aznhD7mhVst}bD^Bsu_|TBVD??B9P|*tFZdK-`&z
zykj6@u|p=V%^e@ZS5Hl<r7U2`$g|5TPdmk=hLCJvXh887cj=5~MK${f-BRT9iI0iW
zoU)MIX~{Y|z9!BSSr)@4z4JSNWwyIb<jvp-9&r;BRN8WDCnu*NId0K}IGhUs9d||%
zF=_?Q<}JEZYOSMU?tzY1BJIGK^)2CyJ5-Zo9PK?@S}+}%)0K?T-*d2f7r^0L6U_Xw
zx^H5xwoh;`G^o^PcPh&4EHmrNl9dX9MXm3Ss}a1`@hXe^4d@si6ciNM74`VGKs~3d
z@?&9o{x@J#X9gHkkW4$->)z_5Ph<Up%Cw0~T`ePQUA657UyFVB$F4Wy6iC>6@J~B!
zmTK{5yzDgzNj-B#+&cPut>j=f$saZ?<!i8B{%L%z4e18_qwMh7)Tr~X6)6}r`?y(e
zXpfT+Gl`KaiF5Kr+)r=M-{&X=Z_x!fxotJvKAt+S7i%kPxXtLb*sS%(TO#9KG%?~B
zf4qG>>^>I4#zPJ%J**Fmyp@mXaljPWdc3o9bT~X{t$yr6)R4)McD<~?WcPwO^SGT<
zx|uJL&7Mm{$iXD2I$a-ZtPN93B`R&44V_}90Dj8wG(6Jjy6u34Ve_Qm*=|3$u7-lx
zQ%+|W<+S)_b^6yDKk$SAKZ34VS09FMPI=k7Zq1Re?4~b=VSu~q<ho`H)ojzEqBqCH
z@H6;)sb$Gsawi;d^bzI3Pj}XxW_y{2f`+*o56f{Z#joO#)psw#ORc-Mt1pki`7$X|
z#eXCC-|rW^K2H<h55{sG4^~z^7G|2nM5?#WfYZBpy?whON<}_LpKyBa<{S8M1=0xv
zr})7nya#K33=7)rEy#<5@jYw`N;<>dmbu+{*SLg)z@qo~sb_1;503+t3*8C)3-1v4
z1sl_wAUTfp<FP|Gv3Hjbd^h)Diw0ZGNnb@`C9-HyBSw23GhEkK7e?Gv@Iq4&rYYsI
zZO@M(Z2Yn$qXh+dgD$y^p4!QFxdFeMD_1_Hx2l@$wWMNDP#o6zP4mQlrCVljrh9m=
z7~JLX0MGVdME{2KZ*+A2u4!7%UF(&h4i8ujM5@f!(Y&(tD*v_ChwAG*c+4oQ$g52Y
zJkN7n4Vv8=mowem=|P0uy&=bFp1#b#KGTDyAwh#N6MuQmn{RAxF>fR;XKG*a$e#uW
z#Uw&x$*5Le9IXwDeaW-jQj4z~FP&e<{Kr-Yj2`%h{$@O(oUV!XXTvOEhTiv|l>B%e
zak*T)p&rU)@-zFmC7^CRHTsyiJEg}r6m+4wXT2@)mwRV(iND?igRj;>>ug={k*?<o
zupc&X*;-vy>b~%~`C{71(j4r?S#_Jm^E7Xo%QT3DPT|*5@+pe;>^VRG!r&=A);U}D
zT7vL+L0t7D&W(yIs*ndb>#0>iQE2$&+IZG_ZO|;k5D{R7)v6wPvRE;^hdJhSPCqkh
zb$Nt|ddj_dpLyV0VHX2^B+H0aWS}_A2Ocu#c|2dF0XL+=t(UPNKR9e&c67jDRj$`d
z>8pWztyXDVDKNE$fHDrjt!m9%UBZZQEcfkShOBlR^hwKXU^$GG`2&z3RlQ&3fJGWn
zFl$_I;lc<q!c%5=4toYWCx-zA7Wt3Av(*?a!g(p=n%b;alt;pl!(amzxwn^uR9Kq!
zPWOhp$<kiVLO7f*IA&__AdC8Kbc}$UQrL2UBsLbdLMu44%_&rmDE;>Nizn2>jDr2{
zAAm3}2Ea03tbm-^kwrI`5xd5`_8QJ;?gi=iU?{sk4iUOL%Ti0`oJ_JM$X)@Pl4eyh
z*c@lQrpnI2sZZoW+}fFNkb4}QHBmurE?|8Qb;VJu0JObZCYJa1I#PW$BrEyy+T^n;
zt{IS3Tgp9<s{O+v71|Y4WexNPWZx+UW3_8BG`^lkyY-1K9f)||F~TJ(=i1+s$OQ1)
zLK2gTNr8@e4*}La%pNF+EpSIlg#JAol&o=fdTD5j!tULKBiLG?@IrQCW?VH$)Lw6E
z7g)eUcIslxQQ!9?&N^Fk72MvjP8&jyY&i`rP0k>9R_q}Zl99DvTbRAl;7&79*6{>{
zU#sQOQH-l6HQ?Oh;1MKR_EmwTNY2<woPZ9|-ibw3VQ$es&osH^S(PY`Sv=&nw((@q
z_iffWZR*ZtC)-ujs2(V!sNnQ_Iu)(WFAsy4DrUW9Cq7h^fzTd&qR=KPaRC8={Tu%p
zfk<#P;vWVi?@mCih`&vZMF=lz@Ge!uw1%p<f&wf0YE|-vHfLag-0J8Ce>~r;GxFA1
zH{lX10EIkxD4zw1_}ig*X<k`{m<y&;JihIqLM1T|UnT-Rd+KP^G*N0_jQ!SZ5~|&9
zQ$&hw{#dCo&e$7H!ac{!;}j(~_b*~5=(;my_2(zRxaQwTU!L2?i;~61Zx;uZh7tn&
z%_3*3PLs&s>^?o)YHC%bZa-8?^|t-Tzvph*4Ls$G5}U0+I8blW(|1O9ToY&N+5hZp
zau49LHSJ9_%GZu}$gS18>a1=|cx(QCn^GoBI?Ri8E@G{b0*DMMF0t&QX9^@J`U;0f
zH^|Ks%1E4-%ypx^RQdDKYkc2jeIs4&S;W<D<+}vW)u3LrlNKgBBznxs=}=bY3wB4j
znA`ar{NaOk*LG(+0lX?}J*4W@?#a_w#O(FH-aO-uS+qm{BSYf*oh!{!6@0WrX1p^7
zMkfI6mrYH$(fM{esqK`$Q#>vrYx!Q_E^Y~-^XX3{MdID}O7!wO0|=TZlE@K~X{GSh
z%-MuJr5T##w=TdyXqDAjajp`6*R~is@|Lk@m7}1M6>hHldH+DB!Rpwx&d#y$UIi<T
zdCle2%;e5oN7ly11Nh4cIuot7oQeXUnC0>%GFu8I;;DK^JluhDrsp-9CY*IbpeE)3
z%-{|M)>T4fo2+x3uLe`l+IYh01oa@l36VMByGvs00_~3ft11&6j}lDwBz($slHjHl
z0tYFFmhhK%xiF0h*LX8$k1FQ#&r~`9d)w{?A(3AG{745!{9#^kg-g72tF+Sc#<k9T
z(7LC2{8o^>G5d^rOAEbZ(uU$HYxb&~K8i*o{<<Bps{EX#Mg|KvLD*tQT+q8-+jAvr
z$+a#qIq{9>ZCSBpj%!RvbQ*27i+4eKZ_Nzftp8rfAyZW~7;e>TQt0;FI|<m<`5E?y
z5AyJwdKR@`c>@}XPKQDcBsovb>hx}W+$Se<%!X!@e*47oJiI^IjIhz~&A~Sy;NS=-
zd86hSRqW-QY`gEvJ%IG>1`c;8`<cb6E!P}8=5&*vTLIX1o68$~{mSE1cy9L;;TKPw
zCz&m2t6zC-h@9A1To|tkUz#so!9gx-lgH_jUM8&?q*{%c_DPqM<P~ob2y2snBR!|U
z$*Kq>$H@?R!Yr3lFdVmiiyR%YOAAwDW09UUZ%U45hKaUDJ<L5fBo!tUZhv)ygFnw0
zvW@=Lq+VfxaIv8@eU-;gX!i!pDt5kNc>HzW+JeNmY3;PvRpJiEe}|cCw@|M6eHEEH
z=--*rEfu{sSWPEZ2Io;Y(HZXxCbg(D9VA<d)J^BN!GBG$uOsDtJD1=V6g;)D^vh~q
zW!1Zv;{E<IYf(v=+>ejrDZueAAxW{xr&N1oWW9d0cxYyZ*?52R{;nnxWyHUeZ*rZp
z@(|P)c7HE#kJ_P&T9<am!F^|!-4ro#@9|BjClnrQ`&^G-yr;gCe5oqB^VO02>(@*t
z)`gv2?nE=)_ULO?+w0lV3+A$i0hzkXvl{lTY}VSHj6t{KcG{mQa~LKzGTa{?Xk@PT
z58I@xS4l<Cha+3}p6i>jD|lP4`4&FFg;8zymot?12c&+8c7R#%VYR6JhQDhpmbW3|
z!QsAztHaXURqK=r?Y9e_cQyqy83nF&&+TZMH5FgkXX_~lyFvW(fl^1>PH(qBZ}eUU
z6q;`y&^`xmiJcovQRE+9{6drm4)&?YlWmW9y+n`L&*Rc9o0=7R@m+cs9ceb`yuDyT
zucF|C0&<}3(jfcmOS9!ul)^|Eqbg}MQBT+K-zrg#0Ge7elWoO3qSv)?6t#N>_mQ8o
z6&+<doiO1#v)H?C!v;9%GG%UWleZ5VDPG+w#)Z#UzFE|)ft<;ohc-s$v)<dlj}(uz
z$3b3Kg#%_kgh;6F6PL?Rv7y@MrQpStJvqgus2*TPkgFXD;W`|S`<~AE>&p&~W-E^6
zfQdG9|C>|$TBJG)mgh=6(P|VvV{F1;7{}@yMYtw;B7bx8{x8UVT_@)5>)&w&T(_*3
zBQI$wZ?!L>56qu}2ttO^A#aa|h{XAw{WuQ}ISOcf!6sX-S7XX-9rtn;cS~b<YGD%h
zGM-ve9Ezade>+2#8HZnYB9pqve>8S`B+~KsF$3E}aysC2-S%0TkcB9L56;(g9pO^n
zOmfr|hr*-zemi5BYC93vPJ=y(cZ??DBjoUKM(sQq^6l~dh%TnFY4M>!wBdT@#!)y)
zw~sBddjE^=W@yeQL2Ov8`%LE!V~e)s)zvZdu>$tMu!}K9%Kk2l^4(5<2mAEIWhd2f
zT7QVh`fpBtgDhHF6w7VarxGNbs#VGMfE2oY6Y!8lZ7<;y4W0g5$4xa<A{eS<qhS%;
z9^RE4uZ<)9$UxR*Nnd`fAkn&XFXqh~V>~y9P3p{{vP6V8BYA6B%CX(u$DC3Q9e$;L
zo-JpQjDUOOW=b_^_obJNV`ZWmI-@!VZ<To=*tr$Ut<?!?A#QLf>TN*en8(2S^-Ua<
zwdD9RAfqPxNC%}(t1etzi&{BF;U(;EVQ#IzHefQF;@xx^mc>N2o`FlfuD>@xTZ7wy
zSlMJ%(YEcy^V6B89(q~G@XOw2uQkv+##PrH>LGKdOs7L4;5U$-yzn;kAsqg6YbP=|
z1e-g0bTVsLU*;=)XDt|g7D3@y`gkC^j(}tpz?=G*aaf0$hvT!_ohW*=*Ulv!J41(z
zOwTP``^H|e4T1vktiGEjRW*-?a@>I#79NR4aI*BGgSaOdoz$HaW$)Z*^!x{1>x+3}
z+dBra$(DW{?nlbBmP${KpJJ}zw7Sz3j`Dy9+bJKBSaCjXui*qkin79l4FYm<B+)5&
zL9|(Z-HcD1PhsW^y@=!Ls&7$J+M>h><0yMX7Vw9L`PD2++3tAseMkd;NZq3PY;P}-
z!{~j23C4{8cN)KYG?pa~J?+yUbiB@R#A>b<56F+`_M0yed0fwr7qgz+ck8qoHL0Gq
z7z~TI>z?F){sjC1=M#kC6=2|1Hkw(rzi2RS9!fO6Tlc(ZnMtr!t?7ni!wCN3>rIeV
z*Z4B>(Y}_!ev^eM?GL8j5^(LB<1LI;yQopTQ_Y@qJ$wS}RSk%gOSH-prU4%pQYDd2
z9dB!{4Go`juv>srgr9eO*!eJgidDO0SFW~MF{tRgP_+seb=kSVGXu>4dluF*ndv*j
z#e8ZvOAXXHpTaR$gWGVig~3{)i<=J|_jkXrdAFpu^+5RL6>gar#pAyJ(I?CjlwnwD
zcDP`!yxQ(v0D)*85B6$JP9U@m4{olCvg{8;n1v@$4C>Aqq*SeYT~`Zew3_|WOhsx8
z&X_dUVndxU4F_|q!fu!kF;8=AxctFZ9`v`EeeKAR1Lt7fgh}k~m&};yI5pW=^eUMi
z7-8HtT^|_8W>a|6IzB0R@OJKi-oNg1BHKFOzq~`wH1CJqRG(69_R=>_FO%;J>EPQt
zRwUlMM+ce(DAbrSyDz=;GffY=F)mc<U4OSm^U-#{V~x0~H<~-3?l+tX&={~hK%UH_
zZ|~m~??hY;;06w6y-(=bri@;@W^OIHHZWM<?~wosq7z*>hLEQOh4nq9_6RTf@|O-A
zSLR@@oa<vd6M}2tbkxW_KzQ8E-!}mk`)|NDVTGjcx^#3-ckI)SW09)q^;xK+iM3vP
z7EHo%K6Mz89&Jkd_FJ4aa7dyCSjRbPW&&n*`?=ibJM_`~qGj^Iv1p(2o*x&86B*|d
zd|Oc1BI5Jn_%QGV@C&Vm;~w{W?E2K2zyo4ULda@}r5~pC-rlpyXgQIRgxwo{?3wPF
z-<rh|WOm<asgG(x4c^s(OU=#5nA_c@^lg13CgB=1Zg5yu$<9lx5mFMn&nuM$oyN%6
zKe(R8t&>6u#dSv%#U97PIe{)e(6t?LLw%wmqS8hJ&E$y5c%<2St*>)g43P+Zwq)S1
zHnZM)vMxGe+hFk(mXAjADIJ?PbEPow0pr}0?c$;8NG`j(_T&C?H-r4InVvRn2x&RG
zbs%Wz6sDkD?=<(@VKsYvAZ{EF*OOf@p{d>n`TO%h@Z*VXMeo9*td?4>mE8yis|`~}
zp<(97V5@SH*-ho#oVLqNh9NI@Jm>5tE@NDHp<L|vJZ!`r?)F_jb+X4OE(v7Q5X{F(
zy@yl9tQ@NEhYq+X1rAn>q1?kDg<y?dK;l!5%k_EX-qqgs>$dvMzZ!gQqud<706=!R
zo9#l0b8t@R3r#c8D_{*9_SMv~eg7wWb;ka%hJlMZR>U><90T`yhSu&P?9STd!m^`k
z4K_3J-cr)3pkG|UkU-(g`Gs0tR2E>`CngneBaeUUs^8$;FPLuXGcQtY9il3+{ujlx
z5X5CnFfoT_)ZCUL^M<mwr+<PrTnAk<)puITbL*T-IZn@p%w<DY;nN`qPcbNQ@~b%c
zc&&U70?N*)s#8bTE9|$ObWCBCx5-b~VHnl*sWoR*6s@K|Ui;E@c!u}o$iaDKTppfW
z=I0FhM>c`e<AgXE{3e7~Iec|2>O(N=kyF1B7+1LR?rw65Iwe?=6ML^HSsi>ukME<x
z(0=$c=M6koTkBD`d%%O%HS_Lf!_bDmmN&u_v=81Y_>A*4W&FrtYuYQi@@lWMHkUcS
zcb?Vb6XhuNuzDS2n3?Qz{3NNE1Pq=5(`DXjq>rO3_nSJ$Mn)Ci)%eD0tm_fJ0=Ci#
zblvudRaKW5y}QQ)j+r>qm-xLhMDei5?5PfIY=lm@!x6T^Q*p^1bF7Qo(i7I2U?q^z
zLpE`xujc7a*XsHLns*GL$c7d|6BF4?H|*P?mRC630OScWxWXhbdgFR{Z0C5weNRRQ
zWrOrn!G7H?5PK&4uUUW|S`mN(IKI^9Svd-y+21e$DvcxZy*zvvLE5I-6z;vgCk^%2
z#xyA9$#Gz4Uj$F1cX3ppfP)hds#?q|#Mq?nBWa}7Xk`=OW|3!)9_i!uXDqs(4Z_od
zMETEmqu1Q3I2SR<NBx}Ap6n~*Zp=J)mnBFPjU~q|QZq;W3$p9hmSnd*+rV6=&=;U5
z`%8_3daVOL7@AE)26vjmyC6(#jaEP%?GmiE$7CMQrvBzsVL#8)GSmDyb{~c2fb0^(
zJ{mle>T&fIGNgo3Ai&$Z`En%iu{kuwZp4dk<((?JQ$Bi_^*7~C?$D$B{H{LyQ?R35
z2uA<x7cr0<1vWl1GP<^~WIj~bVEla;PNw(u#k3i-EB`f3(wKx{?@M&JYICMbJ7#(E
zN2{=sA)jCy>&S(kCEKa}<^1;a`G64ckv1|oaa8m`<uBJAQJ>~$kOEuMh!bu~q3=3f
zDi$_lxc&(q<D&|Fgg?<P8gzY8K?cYdP96H<7Yb|c-8}+i*C*X>UL0{Z;g1K6`Jy%P
z5GoTH3Mu`nM$abQQyh?m5Jn9XXwif~`H(Kd+rvq<0*W$taW_!g`$|v)a@ZK1U%Vt)
z-{_2MsoI2`x#IaHgg>C38m6KpIAmK<6M`m%&lL~5lvhN^I59G*EubIKee^(2amIXm
zZV)f0EFVE;4m-l<5N=E`nqjzbwD_V(dO&GmS@KU~vQN2n(8AwH^&!ziF$4@GiVobh
zo^ILQza?f{r1YzyS9~~hZ@id$f*Vy01T+8=5|uw?>|()Q)i?LG=Q9f;N~=RX*uY6Y
z^DBUdlnFCHMA+(*A(JqVd<St>Qg$C=AOi@79y;Jou^#tVOwaf(h_AaOF~OFXv@Z6W
zJ={Baw}hNw&7Y(?p6FxvGBVyu-wD$2#btFFk>Kytd%A7<rafwo1@E~;mz`n14cAoH
zZ9hWEG(o2D5_K?)%%L+a+)AGPSm~-Gg%M~yVj`EXCVc95cSdKsU3+<F?giq%Mk&zv
zx$)AF$5Jv)10+O#`?JG$Dv7pMqF3tXn-r_jvq)mwSl|fc*>U9e1#&Y_6l4h6hw$if
zVZJ}dzbiB?hjr`}frLK39pMP6U?xlPb;YMiYzT1NAFPH8Z9gidM&Q4Yh*ZtED&IUo
zR@RK?W}L0K8l7Q}_nXP+d!CV59gmTaN9UB&g!`+|MTEnc#kICM@rcDRko_r}%>a1q
zZ!;<jdCH*T(|KeuoQPhJE~k*uXH{x+kDSj6JL(YHw!Kp2)`}2&t&1Z*xA6*RN8-tN
z{bu!ax#+S?hY99i6N)zFx!IRC)p1U>BXTz6O2SsE>k{%0GnLY)%WaN(j0iJTcR#`w
zZo)<55%#x~uWNQ-#@=%jXZzkOLhsl0J7p*YK-6BYem||GJlI_8u7~?eLJua))SH{T
zV>QS`pQ&-x2bzuu2Q}YzH_v%HzM)p!K#L(eIhTA~=;b`g>A9boj4Q|?l*Xtk4qIWF
zHH1uc%gI}(>=LQg#WpSK-3K%=66Ky<nWVs6wAJdy8hIxqoyngPMBkTR2BlPkj2~OA
z!PNpK#}ld|HGef=_VpJ1Twlckdy<E;Cm{DoMLWAdI5=`(@g1>}_BjBm6sv~XgY(9Z
z#D(S~9(KFU?TvMzN)4x55gwhv@%8)SefeIm+J>oQc8pN7voLKFPI=_7x=#+R(pxRv
z`$R!|j^viWNIt%FR?FL%96$(ENYBPZ$R8^VToe+lCjBh{JZKL=L^7o4w%ruNF;6;3
z;wW#n1j=2N$+Y&>=49WuvJxH}&SzkIPrllfIFiY<C>tsT9a8auhH$pXUndQ2^Awo`
zON##4)70>0i;nA?8S_WF6hL~V`<>8r>$GrchQRb0SdK!wRj!S6JaS`Okt4|VQ!G3<
z!`E9&O@3iza!CmpKoj?_(ag+66gYoNv+5@p5)N;=5#$^odle6>c6W&b00%NwBBaC%
zWOpKa-cVz>PFz6EwIHJ%ifgozGeQqLq2$j{>XNQ+)3A}$k>{0l?u2#30ckyA%7eos
z(i^|%7~x5E6n72sL$tnUw7p%gxcxMq0o?Db=Ob=uX|#@DsnVbgtua|Be|uY5xE@Kv
z4^uj3i-<A^SU#3GJO6dE7b)-CNfkP?pY!_aX<j)@P_H|<Vbd-kup#8C1(~74I~@eD
z4PJ>c>$XcHR->AyG@(uI`z%5tGQZa#8Rt+eZ4R#6%c^pDN^+ZwHdynO_M?BNR_Z5d
zIqGfCKZroJ93r$x!-2Yvciu>sGA&0(7yXV?(v}s?=LY|>=-BKgN^sF$6SI5d7{qQb
zYAy#_pJ=(J%k_i+B||(}*+VWv&s?I5ZP9?(R>C0KsHl@-YrejFy$Gf<%})`6_)|%0
z#(&$L^1nSg)}hM~nXPd-!R3{#-}Bkx;BCu2=t$^+WbJ(%c2Wb>!xl6zk75(F+37QJ
zE$2b=7s6W&a_+#puQpF>#KM4bO1g3anptFicZtepH%tRHX@i?Idif*1@FP&SS8PXS
zIth6y+>ql4ncvAoo1b}H3yGj_3a`{g_M|IXo|lezTx6aDJz!^I9~2yC(rQrXw4my7
zVkI5|{rqsG_@RpUm?bRWK1t2rd$B!(<D1)EF;ck)su!(DKO9WojXBt!#i0MuM=oM%
zWN2-HyAC2WBz@4=b%b~DL_9g_2@t?OhB?*%S#^6edpDbRB&xc)US%EbP8VG#dy7&~
zPH^!}%wMkK8qny*O=9<xg~AyVe`=Cucpqn=XMD;@C%7KS$@?gPILEG$rYm2t6WdcT
zP9TqDsF5Pg$(!G|1xWWHq(HepF<MB<6q*C1ZxDQ~jM;CIH~ChLy+ttB+QDvJ*k)$u
zrHrp}&XYo5?7KS*sUneon3JtM7>i>t*8-v{j2#r)pzzmshkE&U?C3-nC~TTc3F}iz
zb{AMe$x8Hexmf!qS|Ye@wSVs@WX2@Nj*f;MV%KDsmt+nUl3}H#rFgx|jA8CkAVM}c
za1^QI9FaM!nZVdrAEL*3Lq7$QsyBH^6tk2Aei#*(qWZGiQ%NYS|AhWv>g-6`EnyZp
zj7`emSZ;nIjYT>Uw$F<(SBgFSTiHcx&b31<;;0iNR_jf1b0C9qASmSl2@4+7ziCmC
zCxPE1auB70(toH0qu$sUFpR$5#d=eOh=e6t22d=Ogb8%m3d<<otINp?EW)I}4bT)c
zEUFgJ?Ll2y0{9^(XKu<UNboZ56f$#ZbKv*(e_QEx;M2<*-8EOL2C>hqU-V$!pl{CW
zNVHlqvFq{Z;1rRhC)ab&6p?{+Rb{8#5V1r`EkKI>=e$QO_sb4*89@C^d8O;{k*m<O
zz9x{*BZSzF(HgnIYM*;`jmonRZ=()d5(7W`NX@hae>`!jEIVEEJsTZ|V+{wcW|iv$
zokPC_y~i`@s_Ti!A}m{s&g{UW`3&?GY*%4pq~A7@{W6lp`0gR`4#Id%Ev>T*>taxp
zN%yKbxSNKewt~!5z|&Y3lV*=EtU|1gu>3X2w^P_U@vKh}Iclk|%a0!?$t)j46R8+-
z<x^a6`v=Sb;(b<nIYm?2-RtJiSyba#k4*;C1t=fK5AQz6=54ZN|9uE^qcM&NeowUh
z%pYURc680RI9sMt>WuadtL>hRQ)(?DzuSIe)1{DO4aInlax;m71B<q*EK>|SDlZc2
zz0P#BYwh@k74S~iI{YI}waea+n~rN7tI#Y(H8Olt%*Ih<nlI-AJ@jYR1qT1nCapWL
zZs}?w#qIzo#y%CuzGAh~s@px?kw7fRdNu_VOE8DPphVus7@k{hO>Ie6Lp?%6=)@<+
zhuJL+j7iy&iIf_tS^VW-TXwj^?c7O^mj|hifN8irf8S#TCzng$^FYSH^PdqQ=AM~8
zVz6x9d*^4Es1Kgx(-WGSElo*r6XxJuK7{7O-Q_!`9W{N4;#F?A+oZy7|0TwrZrqE)
zMTW?aE@d$~a*3TF{Rgm_W<lNGY{wGS;)3%g3ccVhY{acyFG}-0XWbY|gjj`0$*oWN
zAW`aI6CIOUb460Md`((af!R#Yh1_x5`^cP*ymK(Md1f$L_zlraPo!zR-4wNzgeJHB
zg5<pA`6sj#yReA_Ys@wVHWeBYKR^HLtN)>;x&CY&bLeGFQFS2_WEKX}j$g?i&c%+0
z?8^ufWHQyDcRtTHURsfzS=r+X9pyVtySKH6p?0p+juEGMTN(7hE*rYGCqA;MAB23X
zS>D)5afuq@j-{D@k`)Y5K_RF`{>!P`2EhkyJyb}A6DFF13{OXvOVtNobSey1-Ck6r
z#ls3(2@>kLrztGi?D{i04PJ}&z2080lJFyiNw4u(GZqo-gDPlx4>2beo2s_#DG!>o
zjzO&=3})K9(5BM7`JdinU{~TQTUc6v1;xC+KZPHPN!QUU#gF2ZiD-SF5Lh{2V(*US
z$)^rbj9D3RW}+#MaMY(<IM{WBE6TgG*u$T;+S8zmtuMeFsS(a7qDjqA2P1uf;P!xc
zmXObE*Q_kWX?Yb=!LM*|tR{x;6fja9t8%uVHx+V4l_RWSaGJWPy<z2P!jzXr!A)^8
zgA3c9MW<-8swm?TiaflXKEE<fSF?_rsw=>I2O#@umi^xC=w*VPowQxR!ZTcU&Pxba
z*^_O&u?ObmZPH(p`+|7i5Ulv;F;HhB1Zf{1a;=t8n1dA8fzQ!iK22$A{B9WxB6fTZ
zUb`zwFKTR+y{n^2+%LD2SjFSpjqtCbc=kkC*ykjx%jJE#Pe2~8v$C0^drH^7MWtS_
zg|**1J^WX@G8?lfK4u~#^Y7p{cCXI4-~Y@#z&=K2lo`><+kDujdyYMPoav#;9E2+N
zW?GGVn*lqF1b(QkNOF99@R<e%<@9a8uJ6U~@3vaJMaklOHeVMj=UY*lkA8G-tPkxj
z71+k*<AzpyotBV)ym{8SUQ2d7i@JMJ-)8KLmNXj3^Xx|3G(=bK#=k4}XqiXxghW~2
zEpBxFJtTooL5|)kEsc8F3pmKkU*Dlq{*Xs}3?#nXBo`^;Tn$r5?{Rj}%d){l`s($r
zVbJE7$g$N3TUAmQ+zg!w2!3921N;tT=3Q*AO-Bp;<{XsfTLg#Yu9^<MU_(jju1NwB
zx?V5g6+oAMPEIHi4uFT-k0KFkz_%f0o0lpx9_MM2nbcYUmpl)n!}IF&yG6^O;QV;w
zbg>|o$K%I~LeicjnC}iIhrOwlI~%#+E7cOz*U-?ovVKyCVifjq4dj1e#&QT?rXEwz
zf&Ce3x=;q1-ZmPP7h^}ekJ|oCUKiZ)bsGmgEN*I2@3-nDU(A<s9DXVR>P~X$<Uo@U
zXt}-3qw-#Sk>9G%7Wf=N{QURedS&V|&O-1vHg7OVwCO%AYpQ>F3Ji~M`f%Y_#XR!b
zF@gevd?eLy9j7Uw-N_NM6R4s-r|~b(cnMcw(8uWlGW3*WB)OL3#?rFu&mm3lL*#@R
zr0j$+S#w$0+3o7Vj=Y({43ArFWVR^2)#t&N&Y~~n$}V+LM$~+78=aH_z)rSz<gXr0
z1xEAt)x_NQ50#?inh(ecpVK64_QPXikzvT4yyT&ewOC9A+@~3eB28L^(!OOD>N_Tg
zxz^qjT22ejPB$JKCKtD#X@0CVjmoV^vsl}(S`!0b;8EZc>ZCAE=nvNV7POeW*7sKw
z*LD1->93jq>x6vEa5$&w%$exuDY!vM?r8;;$u#QHVZ18CyI;_uR8G^M`@-iIoOQX*
zmrxQL94SEoAoto&AG!P3;_M`lsY}W9FD*7lDMB3m(uM_7U3#JKeoIgQW*#8wVMvda
z^vs5w6wKF~j4>Cp%dgFSyhruzU&apxjv{mWx6CxhL0zIQV`f1(`lm?GqC_1&y1n+j
z!_*Y00QpA{hg~>c-y*$W^)`UNIL|=5Cr=ZjpHU~cp5r8~zj|#RqHV9JM&^~gPeYT~
zo&4u^?JoVot+@EG4YYiA0P9?+V{R;|nk^~07KK;D%t>ucw5zMKAq}R4r&|pmSFTNi
zKZEjF#XOfA;%D0$pRD2pSFGl=gkC#5{P+W5u^H$1T8{<_+sJQSoIfE1ef+mhm&%n2
zi1Pntj~YbT3~4`e=Trcy9~v<v6mYV)l7VZyf-ki$i9>vfMYFb~s&9opPj4ae<%`Z|
z!UFRYky?x5FQyidB(a5$(?8>iSQC2}W*;bL>BX*A6g<cn^E1)|SqPk|bx_5QXJ~T+
z@LXt6{@%+e`Y<|0wFUA0B?;H7;?Q(<ZB)`*7nM?|?!y>2vzk?INx+@ahn|RqTV8rf
z?)%OUeQJ^SulN0rPy9IFBxh+v5_$Q!=76dnRaR6Ao>8cd!F&3MKGD1`R@5U8A9U#O
zdU>~Ji^vH#?`IbmFGabbc&difJ&&K>ETf=P2NXyXU27Rq`qug6GU3a5cZ169LJ{Io
z99S&pxbb1mL^2Z&wVRtD@LSg$nF_e_znPCXxacB6?ip(2m0hv2NU<__MwWQfE8(C^
zPfOH~NuoVVlKefn1dbzgLg7Y9gp0OR8^4URVpYL``Ugoe9%#ry>ew%zq#LB@Wh8V*
zcz@KJzbXZARHC7N{WtT~gZKv5^Idh?l6y%*oQWaE870$SG0H3A^$O-i4JYypbw1>j
zpp4Y`O77RuqX-m8GIgZ3_YB&xabQ`q=Ga|*E5EXPQQa>t>8Ap82GPN7YFm}VC3=@w
zjoMpJVjLIFe~d-=ElKX)$k`ORJvL)_?HALJ@UgtZ0${TUG>FO*oKVH}4ztPe7X_Ot
ze@ECYI!eaQV}ar91L)5UqpE)?Nq>I1F_>e|p$qBqpCyiw`MtVc3*3!}=cKUT|7Kls
zQ3A3u^poYK`3>zC0i0iauHoLhaOE4IUFPStsZvlYQ%=L@%m-5WG#X_|xd!P~L->A3
zs;On==UOAnDcNeTojyXdQHizY`uv3|{ttVFC$RfuSfclvxUzQKqM{^Ez_0($EB^8A
z^%L&+2p)_7_rQYpKiAX$tSQ8AiP4gr%lFyF37i@ViV{?ND-I!9r<!+<p$H5!YxB}c
z;IytzRlE*<DN*~P&;oS6qmV6!?3&{DSsy{wUBWW%jMiX@P2Bid?`^HM8NY_zjLnRO
z<$>(9J>?D#v5sS+npj#>C&3s<l^eDfNud?@a5QJ9!QNbQEYBC7V+>Og>s!6Eb+_AF
z1*n=6z)6GLqP!Ho_I!#)<i?FH!~DE@+`NGw*e8|9LD;uo7-L~#m*RR5fo!v@oCF{g
zTv>BBZ(f~P4at<97%rJ-A1qRx_B{XgfK$$nu#O2;ixaG)^Rt}R(m1xG=;0AKNYcPr
zcy*_C>v+}KA*ZzJ8r0_*;PJWDypm?JkdFkdYLpGfN>)?nK)3D>80u<Q=fZ-*2%Efe
z<58}#4<MUBt-1KT5j-25i`c4_v=hs)cTl;T3x0+iF>F0-EXQb2R3e#1tL*j}Z5+W#
zM|DFCp?_2dAv2uVe4l_^(P}FM4$8vig+fc(C}*+;1t+xCp_OGpUTs{|z02@!!QihR
zJ0J1_x%6D_*5yEGfCSL2k%X~|pRTk`<9TUuIq>b$-UJboVu(jv=YSwH6K^o%g}0?V
z^Dj~Mr(Ko`2^7u1zD#PsTs#|#pytX2jRD`9Lh0AtGNT)DOs`K%7Q<x(7Ia4A9^Nu5
zHo>uK8e%IQ$@!*sATK10B)FnGE*Y1<;QzQU+QvoZU+tKde;!d=TG5wLbRu|3?3j?E
zo=N6#g@D5iY<qtWX8Gx&Fl{uNtY2w+v_Npt@kZwxifo4|A$LoA;Par?GOwuXcJ*D+
z@h`>j;>2?q8Cir(SNC%;TYmCl?LM!r#|iVai&eq;sgPnmw$%F-%P5<T;m(ARTVQ1O
zV1H~ykfAQ{*&0U(`SPZ&h|H2%uI-WU#l?!Gbv!JdF-DAuIm64#JgZH%lnlpFj&6kt
z0;;xbcTSC++4}s^Ho$;pc(Jb)ztV8wUd3uLMaEv1E%Cit_^CCc&G+JMoS<~WExj&`
zkjn9XY8yeYuJYueVy_eig8ONKTCUp3k`33{okp)2HEB~J;K`?Ua%ICSlm5N|@sxQ;
z>f`lGbO;L3+q-{5=W&2=<7w&ES+r^_i#<+nw4gV9*6YqB&1m&@86~LK>|XhF)3=kR
zho32CJHxg%XF9dSo)9IUZfkBYcuG_GdUiBTJd+WVom&d7sKQu9mOXAqsB`NhvwEL6
zR19DE_revnazW19sIIBK2l-btk+$1&ixDZQp)n2tdl^V*<k+g0#_<`(UW_N_sz{N^
z&S;I3v80uIgsgPCRo0++?f0i1rdU%Mkrv8+;r)G;wsvE7<cVbe@Nrkx%Sg02u>ZRq
zbV`EDeik0s7IOj<^s^?iAt616Ca8!?n^Kml*Jp87$tXuhp>bIlxZS^Tk(Vo5W?xR}
zj-)<^NT!Q7krY&x+T^z8A-8r>oN|Q?Q`!<SQ2Bo1$er3hA>OV~83ouoWU^gr>#=|R
zq_3&4phKhy_-147v>FVzj)F@~nDHxgA}JYyzQ@G{egE0nB`|+V-^;-tI2KDa{K_F9
zGEOj%=zt~tuFdGfBZLO0{CV)aFY`|ga_KP~M8y6pvY;Tf{NB6R#QEuP5K{`U{=8$U
zBIiYJFXvV&l+JI=!O4hBf|7DU*^NiU;KrinEUI4kfnI6@_X7`3myn2LkkEiWSgODK
zClyz(``KP=^>O)5iC9))%?;A(R9s?W_n-F0*$ltxb_fYg2{Ro^GBIFoUY=bIqk@9^
zZ#J)=EzjEb2lEIB8N!0akTSXUr&d;7>)feR1g^I_dqh^Jd(WM`zariV<T{(vb2l}N
z<DIWs$?=Qq=5DN(J-;;ic)nk)mZH=zGV~X#1Dw2S+=y!{JM;u;;jZVkxWTc(?k7}l
z8y6IoEDW8T5S&S$4%7Xi7c}Ly*TtuHpK_mY^iP&yKczxQuTP;MR!){$%^=^ns0ZXS
z{aX88l_gJizrR|qE?MbbbG($B-7JinF)eCLCf=P?-Tg2-Zo*i$kOWv<<mcpkuPio>
zC3-EU;}X`@Am%f$&+iM;N9uO5efBkwxp*8_0%_hUt}Roa%};~m2QvM~$J)3;TiQ(O
z9<W8eSQG0^T+B2v*&LJ5ERO)#JP>eCg>_{qeL;+iCux-C3<%`dl}1va_XMlThs~wV
zweZeT(wF(RfoWXGWTXQ*^8sC|mFEy~jBrR?t0g_Zi&b#Aixq!=y)U4g%-ee9*u<FV
zI9rss*u1DEbqq?*CcR~=LA7~r$vXD_@2Ee2y2vl^2&Aua6Y|N{sqDwb==tZhhBnv?
z3%#f?GEs2sngAu1$`25>at$6tm+BJP83-&rCzjBwu52%=i6?yTq-K&mdWGDM*cDXj
zd>i?3dv_W$=s`a5O*j6%-<)(L$d*W91ENzxvI+yOu^Y4DT}Y^2c@UeTfqlocXDL;e
ziaef4F-#9u=Pie)Te_6?vM(!thpkUP!*p;k(fVXXeMTmy2UD5tb%*6wcCJKIE0LK|
za8^2RoSrqbQ8hf0HTR0zE5C*gQ4G^R_f!@KyGghpVaH4cb1buT@2JhkuPq-etcz-o
zC7u8LZ>NANPCd-B!c89HNRILkCvJapk(Pxs?T?jX@O(tXiW3-Lr!T3xH`3q0TGvbT
zEe#Hp>lE)73z%HU^a!GfL5rkiEqga4Zm>Zsdvt@1;`^HKT@}*C2Z^ZaC}CaGU&I`&
zhUOjRKc6}$rDn9VC1lxeFp~BR3j3|`M=iw67%4QbrwWNQAs2C@;hn&Ks{)7O*ihRq
zQCCXKiS`L06>9Ef$cizx{fJogjAC#mCmdCaIaKE7j{k?JpJpvD?cUOE{Jq0`$}r9I
z3ru_?G;XdTyyf+?-8q%Mi3|KryQkYBJ+RkiYzc!2t(LVmATqa~mD^pR;YR5;6EWZ=
z641(8NOzxaO|oP6UC5<EI+Zm`Tn_ny&LdPYgZC^YjZ>LA$0pU~x{p68yks>tf*1Sn
zLDn>0h*z%tnev=Yl<(CkGI{K5vEC3<6kUza`{=@I)RLUggr-ANY}nXbM1Kr`cr9p7
zKfW!0L_kP4ubd`o-<|G;BU0N)SRTea$s2bxGniLN*F|nhMu=za)#5`tP78Y~J4)uT
z%!7F96(sZ%cGl$Tx|BMF!Iq}-_34*G3l8QEdlz=rS#Bwbzid14IIO#JeTw)`cp{?<
zGK<IxCp4=(!>*Lw<Ua<#WQ+`a6B0-Zf*WT5=)Dp8u=F1{tX$<fluHeMXd2Yx=J?l>
z^f07aMVYW^t|4E{HZv9yw-8T<ldxwlq2Ns(Ea_u)SHysph}83F5^1V!uVYH5)aL{J
z!vZ$CoabBifal|?yGzrHYhZQfbf3VS4V%f_wqeEgO-6r2<kfbi>uUJU?Ds=O{;eDB
zWM;nY<ksQ7{Re(Br)Lpq{GCeh8xjJd&?};$q%*yVqSn%@h7;4L<#7j|_=vOCsU~;*
z!?K*}4L^A~F;%ZeG;g|09PQRbk2v;Fg^u1kHPec%4A!N`mK2R@uRy3uP}|G4lCjEd
z-Vv>Vw+mR_Yq|STOp>rs>E2X@wNok&hvhj+kccKo*zA0=M-`w<_JXkeEl9nF2G%tv
zR(L>~gpUx-_604|>sTdrqBZ7ZQZlE7089#TKbVP`A&_j#xHzX~BjJlTYq+kSe`_Ud
zY3FJ{WIJ~M($TFtWJn;T#{Ae2*&zi3YVOvB9i9+Fztw2*z<_afzA{^4V#Tc)uv-t!
zJG3<z`Gu`~fY#zCrk+n9`B6d>)?fFxNK|k!@@ZWqU$Z|jmJ1g>qs_r;i8ccZYl*Xw
zrO+W*G6NO$D6kxcf7b@rr)>tJ;EW&6X%tN-V`WnQhVpTt9~b@4Dd8WSJyfZOJ6V?-
zs%g(_@KL|lX_g0?YcY#?gqOD3l|!N3&g0!8goF<Fbj;AkH8pkNV0;|$7xY{qfgKNm
zh?<dyJVcK>V6H~&b|Jf#wg-u6=i3d`gH^2Dke;sP`Mei>qx505MytbP>yp>3jrOCF
zt`38&!}$qpOg;lBxr38i%h(mhPTDkP6KQ<uvkI8md6oR{bhuV>{2@Z};4=dQN{PT^
z!Dc!_rf+$H3Qjv%G{V_!kN4b7TMeGjv#yQ=-rVEsKC6x!xT~%g<q>cT)A5bb=X|t4
zk3)8z%a<4X;?+QJ0glwaf^f#F;qr$R-{yRnL^knw&51GvCl67|0f1{BL9e%to)uaz
z8u*X9U)z2-L#r*40n-ahP44)?_j6l&+X2Cq+@4JL3c54BhxzWS&M#{;Auv1Hf}Y|s
zgS>7;yn<Th=T(9y$6@trU$F0YhxQ3#H~3x=LUVzvkFQ_8kUK~S|56@S4ofJ9K}I58
zY(DmWzdA>oXx+1@OEd*JMcd6=3xhNNJgjPP?PDkSLW;R>$KaV4^%b-{KsL6w3{zC~
z)DBcwo#xjrl2T{lv*Efw<^Qc)7c10WU~FuLjEWtdR1fEA&0;o|DZbjrNg~>`xtrQ`
z0d(b7({=0sl#*J%+0lx9U=D9skhYy#Yz3pEIE4}%1(eRK2E6>#Tw&FA+q%;Ro_K>d
zSyG&_gijfxMb>=keJEaH(h%k8P3pkEN4)?)zW79;o+i52*I({5emtPlwcauf1O_<{
zl-aBQxX8(&$Qr}Kf4^O5ZT_eP4&+gV|0=ah@7u0=up^w$)Rc5|SkeL!e%ndT%iy^#
z$<bS`udjPKZt7XEwzd&FK^IeuhnlZp1en`&9r6Z2Gv+0`zq6(T&$Ug*QvJDMPb=$1
zEH5IWUkHMk8bUjO`Asjv%xr8ZQlV-~3p0`uO7IhSHmjFi7tL>}meu8zh9B$WP2(Ap
zI_w)={tr6-s(@oXlg_!2yDt67I?UQ(N{&8Xp_E0D2!jy-y@Wi&_RE&l>=dmf$O>hm
zn#Ga{ZVN5GYrHXI_-_He{DR5AdKoddD>rTh-8S{MuZ|S-xPQeMO6kNHX6@yBn5}pI
z&Y+k2Fl_!K|L!gh8996ONDEw$6GSZ1Cv1<lk=au88)V<NxQw}>_Rc3<_VqhRw1FqX
zsp#T387wgWFk3%=WETkD_S_OY<|mw0+`VLP@P{==boO-@*73W9?H7Vl<%EprUwUsU
zA_AYb8T?BCH5Z71BUg$RX8>iH#Yh5xbbEYyfI_}$*^VRQv%a35iFge$n#qRZi*<;p
zY5Qie3}N@+&^@;V5h*e4bsv#j0@)HT$8A_NagZB$V|QArUt*Ws+RtfU>?(|Mf?mWP
z$w7h{0r3=*R1L-I;r<m{bT=r5QQe(kWF@meY90{@Ps+*b=UH>wl8zA>lX8FzB2)ZB
zwWtxXtTmBD_)KB~0Mok6njy?F-jIpy2c}+S+zknAk?t}E1BDDMlt_2TWU-syDYcti
z-CB~KD-7?;I%(cYTxf1iI^V%3F;;?LIG}t(y-`e*M{&B*t6OVDvT1i&>v4xzF7|MF
zGBLv8R3rojI+B3T90#V5#DHG#ldU17fk~vU|I^3Rs4EG<yeZnIDA~Ym`vwIUX}mmp
zB>nTMXJAzAlJ9tyQZUi|k4Yte|GkCO`{i!9H1=L}_+^GO>h~TRkz(q-#b?im%Rw1I
zX9VQ7*f%6<GkY>8a+g*0+_=F_^0xK!UnUdz>RgVZieD~1cEFG(n!`APUHG&zC8h&V
z80X+&i>Ou7S)FrZW&sh~`G*9x6c{7<6w3$5sOD%|TK3|z9*AUuwj)i$V^U!lvhEiA
zG<9&pZ6QY4j$dvnn^G}D6?SbN<Ig;Q2y@d>8kpdX3HjGlNN;DG$2Q;mDg{uO=wS3C
zP7y3neJd&ebkNd3nyil{Kkq5o_E1xVEfNrCh3xIbdS2vMzd#z_H#0M;OSQ^N+&Y|z
zgAn*9(#F76j#wgaRyxfo!xOEEoX^Nv?$wcz$eggn`k`kiCjR;r{qo6&H*Iek4^`=s
zIT3bK=NL9BV&-I&P(2qP*6s|b5yk!W3xw1$tumiN6dfLy4fQ^<+>pqqIqEU%dJjLD
zR^ciih2O0H^v83wcmWv)Em};AjJ~ryz1oO;hlPc}sKKCW3&A#&zKeL0PqKfa-^;z&
zV)4llARu?R=$Nv#-koMkqrfBA|1<?xuI}2?OPs=Sqn+E!v*&YyX@ef8iv!u3U|h06
zF_JFFbHa1p`Qh_^+4V+~&M0|^?qOA)5j?4Kh?_VfE1HRD?G2Xfd?x5VJ<1U+*?UcS
zu-FRj_(Yn-U?NS>+TcBL4Q8UQ#c4pVhiyD%p;%%oP>J_{W3-Rm2pm^h&yc0M<%cT>
zRVdxv`c<aO=_DS)#mVUi{t0a?2}yZ@P%31W@TStKJ%2?is{kN=^_FrJbTh;nVY`F{
zV&wk#%qP%F8_kAyT}#AG5qV6j3umdCZJunddedVx(sH-^E}ggSWBNl9YL)%~2M%l4
zAJU$zKxDQzTrWy8XJ{mce1JF}a#&n6H(`t*DMAj*&=Qz!v;ZibDk%~QM8x^y&0rTP
z?^XkwF;D@GOV?}rdnRzpIgEPwGBi!v?oU+F?qb|zd#LH`Qb1@IqsIT~?YqC>`o8`X
zQ6q>EBx*3DL@&{YVDv7*=rwxpT}Ti;Mi9|k5G6+MeK7hU+UULaUgD9@`}us=`}qgH
z_pEi-U3cAeez<GzefHU}*LiJ0IN0LRAxZq9D|NewN#lVH8rrhQ%a5O5FU4mmF}K&z
zUO&>lQ2@Of6<=DkyhG}7ic2*g<U7_ciZaGjzn8UnRVY$3%y#SSUf$u<8Heh9&$sM%
zGG=`vPv~LRujA^x*T<kKSW}2Yw3zE2cbL*l@o{9(SdoL)o*BXrM?cOHXtY6VeZefM
zeGNzt*d$FXo$N{;WsI`g#p0~MwY7MG!~dtBT)01fNUV!DmNUE0`4zXAk;ss=aw^;4
z$0kT;Oxrw~GOa{B=Qho}BdZOFkroz@pRk$oNta$e2zd38hm=CPn|*ts$XZ>P8Nqg;
z*W1-Az!Bn=A;ur^!{2k7Bdbj?oPX?3(IGVAf!MI_y*9F#v60q8DGQNcz@Tw(l1L<t
zFRVrUFx#qYIhkxV#E`GPqx_c@zj6fRXI%9uc4PK(GVxT$=n%Nubx5?sc!w|B6eFCx
z_XFJX^F=2dQ{G~@Y!WgP<=%L6G0(g&T<-m)nK9j+n&Z^hYpF2b`+7jzYu$B9Fiqgm
zy)-RK4McHyGRvpMr<P)Ldm1XyRZN~GLhr+tQ($TNJ<9Q#Fj@PW_*Lkbt^G1Y(I7!^
zW%oyEV|x9JO|oJ*%-IdxE5j+g%65|M>$R6BEqc5Z=5~hs@$R7-OSw~+v-`2d2=kO%
zvVpt;uB^EEGgdYH{ia*)KgSL${hO|}r@@f%=1bxC_CCZmq;_m|3*#wx<UYe6AaMEb
zTto)WG`?rM?=5|Z`D046Nj*)kV_rYA*j?O|oqyIlC|4nSSM|lm4x*UW?2xDCVz`P}
z&saC=;~xo!ycbfq`=VT$5HIe!#)P!E&@>F#1MzJ6!d=Q}3?KFKN`^lkWvEP5)habM
z3ShCA>c{dD?#rJ^8TrPefS(MFwrXmjxJIcIWc;lVwdi6hg-T4Lr=JU(dW(>K{?lwI
zt?i-D9a&ydlw-RBcJ3Dd&jO^Co?$StDpckl{alOpV<+wv76B?>!YBH;!^Otf<6=8^
z6y@ckA+5s5Rq^<amW!wmagtQkVFKj&++1khr(2hD{!uo%+RAjA4Uk*nTsvW8mRMAF
zf{SY<Sx`>!TB*d1^!CQ8orJ9((O!0lCZ8)c!SAtZx13X=uK7`EKD&FZR088+b16B9
z;9xfUo?UU?k2(^fmN((g7eZAeCr-E6haZz>Dk}A+Eq!>lurJUbt|e?k7Ok(C{|R<{
zLLoLjG1r^f^{}Mj(d|eJ9WhiXTc_IH1tHORH~k&$bn<kdv@prkSq8E}SQ?|^b?p_w
zHLhr_^BNl!BQ1kTB!d5a1!@~h<5+?NHYl<BB(n%?Uw)&FPYST;@0C!5%01}Do>wbZ
z(b62}0p^?$0H`{>QMubW+2w$=5U|POBD5zob7<#7Sl?$4or!t**tW;2S$@oUH6=k2
zS7fP87<k0?0Vz=^Wvd7G{CpP1w#H^SX_^Wr5pe~su*0W_GlPj)dZJ+LMciuu#RAxF
zE~#P@7WSpeDwEod)Uf+0>5|=)h`BQKs~Sy=kMt|j$YJ#OSZI6_IT?MX=N8++kyEa$
z)9=ZKB`f>f=4RsPk0}t{vQ`63znrL&x6Jf3dmOUUZ(qT<ab>U-nPz}H3u=z}YEYY8
zLLz#}N1Z4&$&4x7L_$f@FU?bU-O%~DH_AEvu_-6u9?EEEwJRNt6h_KTOCX^n%qs4{
zQdf0iF<LLoDxP{4{CvkD>5tb!@rzu$=Vl`9!5tuA>JwSX>#FcX?d=lMZX}tKM|mhB
zYj28tZegi`mVV@RyWm-9n?rBk&lL&lcJ0!0a=M|H&%gFk{KnOks(+<xCQzu&MlrYt
zO}TpUeB^T;Lj7u3;k|O&r><j0wy}Ly<x2U=jeFsW@*g`O^K`2|<+I7~u(bFS^mGBa
zZsZKnT>6H&eZsw_@}wxS9d+8_NPRSJJP&P}{qAI)kE!iWUeg5gGI<y{naFNuBheQn
z(yCh7|9A<oR9a4KOOAJLx%`r{Dh_^HBQWB)N^jd2RLWS_LV<Qp@a2-LYw0wpHRPJ;
zz1rIzmp3pp3MoY&1qp^urz2+9Wk%*kgiMJ6L6f>y`3dz;WGH85cS3&9pAe$DEbV6H
z@HsFehMVm+u}kk;ctAw9%1GihTc9|ixzRD93R`-<PREbwd%D%riF)fj$n~+qCcFae
zxrdsY6R$J8gP8?1Q?)EK+3($13)9D@;-Wj72-KMm+Vzi_IUS~zJE4@9ORBNwRxPn#
zn34@p{jfbT^w{9`;h)1H>Y4K~+RzexS-4)+l3DTc5dBXV)+xf}T63FD)UOrS&#LKx
zu%J&xb%*^0W-*a|Gow8zL(Py76$Y)XfHi%0PW&z`_{G#Z@~dUCy5zKq(4EcI<Ia7v
z5%*L9mW|r&z+!O~kXhC%$Q`sIt2&4N0AHqcY+l7ZObvMB{~Ovl&ogw1PCB|QwJ2@8
zCd)>6AE6_Y*_`<dYW7|VE7Lm1uQ|_6Rm9+1pYKkv&Tr|otSA#!`nsGeYvT&sY?Cj0
z8Wp8y{RK$+B;R17Tf`x*8(1?QCKwIXc5T8g!W9Vh#(d#OSz7OO>;|XwfHOx-#j)%h
zX(P`svNXO)J@QBxZ&obaQon}^FZ4^DKfb}4%8uVY!BM}7=tV1g6g4TApN?#t;8jb?
zAlTEef9@8MdVlt2ICbdzY;>phlY^x&9ntf(j6#ER48_Oxb*rh&bI{c8ecJl?tndEj
z0#MindVtmF>+yzJ3$gS7YVO7>BcC2ZOB=Y^y`3@*yRQ<oa^gP3N9l&WI<ctoW5s~;
za;|3;VlNuv3v7u*hUUs!;;jdM2l|*mMefp){FX6o*9?3_EFb4}9EtGO`7N8C{skmk
zIq@F4GKIXpg%wznoI83aeOM8i;n#Lim%}WM6}wB7CpVNqrQ02<3k3`(F1hkd>d8ph
zKSN5!`&}dQ4*`8zhHYXLV8DtGKHH~Y3vZ>PU+uxBJ0CTGGX=bnz!r%H=yUsCZPW{I
z&~Z)&f;*5UQru8-J4i#6Nng?OQp_#%J~ujf=)=}ncR1caybSUfXkaO(HP>&QP}%A}
z-^;&Myx&gg-uxc_3p)o$<1!Cs^TSFGuAi}rWXYzTT}ypT5Y+i8{h!U(n~QWeg~WH-
zC^4hJ0YtIac81@9H5Z9}Im>I1i;l~aup0BvQXxT2lv?FNV9!Z`<#N|$Cl|$)()szY
z7B6=6cUs1#S`5mpZ10dLlP#e0iD$YD4B)pQw@LLZLML;Wc-Q}O`;ma_z9VhtLZFx<
zSWr>2$KglG1PnK8Jh=qNYO9n)*BXcY<JpXFxAF;%AyWXKBjC^ZHINp<G%G0!G6q_;
zqOJltGJZb2bxp>rqW!<f>)mV2Vz~2>#rO?YCqkH)ODNsFu8yjlJ*PrAi79<n(%FQ4
zJi~73NIs|&5w4w4!sM}(nM=Ixkisy|hI6=R6}KVYd#3MPIw9}$FfKoqb!nU}W{V;0
ziByxI@YJx`jzqZ9yTm%miDYf3A?4%E!tjN}?(w(c6nHHnAFc5YpPDZ9rRKR2Fe^N;
z)}uNIEWJky150^x&J_RoyZ?J%2$O8>jTB;u@vB~Wv@;m<J{hPGS-(M~FSH2|_QiQ_
zRSa9P>r3_xg`gvxEZ;4yEG81b_=>AM#2s&JNiAdG=2N|*4t`e(5w7ll#CKFV(l9o8
zv?OANxgFRVq|penlC;djq~G|N2(-%J+U@Et>;DsFIcI<X<;aeLk_|j_z5cdZE)9@~
z65$OY&$#+l1*rS!JY<7hqkQSUVa+e!qY@h@9LD-5`S$pD0LskmOSFsTv`)1@I4sLK
zXm!|%=D4WUb+wXxWdPVJ5^TU#L|U!c1(r8`hzs_&cC*$8ns!VOKMJsL&GfhzDErq~
zXo>nD!$aRaVi1SSx^ZdGPwo4?E+6HGlND80CqJ%*7jI+VR~sHaNlo%#itX_H8ua3;
z&OEcImHZ_CzRO@5kM7Ysx8+4zX^lql-=xyDmtk^Vx{FXnXZWV&#9-6Kg^yyfpJQqL
zj*^W0%1<AZUw*19aZqLXzA^tT|D3+>EkUPr4D6Gw|IV!Emw;_!huT~0os_e(=7OY1
z@<EE2l@lZEb;{ov3SI3Pj&6jm;;QrBA_GCLUMF+B7t0`R<J3#pxq9*MiX7MC$V5+u
zJl`{({jP@a<M>VnfJ?DayOlK<3<|;oh?SBy9RwOStP|e+^8cns9+jV;BQ0s4jJlHh
z#^Wyze#D5!w8{*B+yFHHik}GhQ`9g7#G|saa)3>S0ul!%uD88f=HEcUe-Y?^j{`^C
z=NA{3-KhMTo@iIeyWNJjm#&{<(*qwbe^abmWLxyP!CVa#5f1AFrN3tR8n`y5{_eYC
zcqhrpuOYy$tH2oy7bwh;+P-UjQbl&u*|#6Lo*!Oc@4?Z$&thS{?Y(t5v03Am^UM2l
z5h&3v!F<;Ds4#G3(h;$OmBh^p>42Tq-JHfzUR>|fEcc^nI`f?$?Nhs%#;7vb^00>J
zId&A=?yq0*ng#@KzD;@eFGkQkna&8(8WA!v2MIV`*mMsl$T$ecm;3h2*Jt8RPMbQX
z>@H6ChGqbwnk?aI4`LNFQ0~p$6r8R(TD08ss+E39YNe=|02KYwNkBk=O=jrHDWK04
zRQcOhr`h5K#HimjcK&1R$J{$=tMyTBqmH{MwAU)~8aZ1RlWL*pCberfUmLx(r7^;L
z9$)WIyU6`#Ww-CmGI3jfGO~7xCWZ6D*Ub?8PYLS32D_jg@gY(bH@+)YxCy1#z~g=e
zO^r3D6KhiVbFF<_9Ac$?huLNQbE@`Ndd~~(I4ji9*~dDs*p!b&d3gBpakl&^WoLvi
z=uaeUqYd9y-nxVm#y2LBg()4?rOmUL+!^VXDJARm4l^q!r*m`+kmCx(Bg7WdsgEhJ
zKDI(vgeq&6D!oW*7pl3hF{|&H@66p3VT0qIwcXv;sp<WjZT0v0f>vU|k{!az0ypcD
zeG(@4B8^Hp*!(w&NQcKnzrcL?HP>){{7H|@6GmhO2eF#l$49Wv$)M+SgX<1{(cQGS
z_zgvrWo7R4t5FcG=DN?_cSlsSjIxZX5oR_DFRKU)QvFd_wfFrgrqL-+jeI=<;4o~w
zC#4BdrUvZb`(P|y{_VHJ|0UAgKyh#UsPSW84nqwf0Xmc<mMU$N8tv)DT0aKAf2h!;
zwJyZMl*Liko`u{@0<X3snXN00O@o;Vt$jB)cuj5J%kI=3ev+fP0E>8zFn4DI^1nRa
zlXCPWu*UhB%<p^OA9e}!6U9RZf+a?Xp3%JOG~ZP^T4BNT6}p8U|4n%Q?Kk&n3JHVL
ztC$dRU%D%%E;W0@e?_JL)E%`$TMKbf;`4&2mJ8&T<to$u;bpIKZhjt2dzz70R>s5^
z$S{lyorfBR4h~X>1u~(}C*U@=*bnJUG`hoo?G=?6`^vCLm^@5nD?gji-YR4(X+w4>
z9}(U<<-K$ze{U~?(KH)atC062Ci~VeU6<o)##6pE!*RbOETPM(Z=y1byP`bPXIBbY
z*Mg>=JRd@RbIo>ty7R2jc&zgQNY*?@0?uk`KEIdJ$n+p9Uo&QaXXPg*R;seSq`n;o
zq>NtWx(A+<Hx=tQCw`cO+O=G-(5^%+N$GhnM<aG-TawaVga}Sj>eQG#5+g>|PEtS{
zOxXz^ngD3WT0;?>Z*55>3(hAbjugxJ54ZFbr&=62eF>@BU%$d~42x&e+I0RMR7JHl
z0$5w+nz{S2)sg#t;^~W}!1lw+HEQEPWr3_EO6STa?ppARv{kPzMg`i}JM%YK-m`>|
zi^43{Skv79N3-%H33_|4jrkZg-?`f@WV*F-3^L;SlT9h)C<8#~C5`Molg>0CLf7p7
z7`2OS$!7%*knYSRFw))Z98|toxor`bu0|z%DtKxKseet`AXMhd6vj&gugd(G91kn(
zSF#K=dCjrMQ8_-bw6Q2*WH0nO%^x~Bxx@+oRWah}R9j5!c=v|59a|1vm39<LgMs0%
zzH%{Guaq9JMa`EOBWVlo8XZX98@K5#LR%HEB%GCRa!nJQ!;4B&sT<BmEVpBRMTXvV
zcu&?}K2ET}>^<6fB-z2zg%J4gV+m|0{zX+)4@b}%C9L<Qi7BB!TUgZZ#;HuGPVLm5
z#LRvQL9RIQRLqR5IvUHbk}k5A`_M~%;a<p2_c1<?4%~}7kDdR*#(p@t80h6oi-4&k
zJEqv4@6p#3<NbQWo}yhs$K~;aUrP&SU#NxAPClYG@u8}Sl07d>`awUUy)C@)H*E#5
z>T}s=HTiHG*|PjJ!JIvMy{$un$EIcQ@9iU^<t~b0ygWh*R~k?Ji~RzLQ_lHv+$&nA
zr|^B1IYU5IlAJa#^_<vC9TEdDO>DQ;TT9bc1Wo|YR+KeW5{-94Bh%>p&JPi$I=qa$
z)Fo22Qv_-gCa_#dkFkk&BU8VnHKkd+>ku12MxV7dC8vWg@brL@tevLqg7WaiaaJ-I
zmj&OPR#oFsdjnsx=(lt#WKy3P1DEwHLTc&8;}H#o=)L}g1Bk4EiH=Bf=IrbA6b_M$
zS`@d})c1mf9zmUJLKOQVv;XWa^Osx&92Z5WVeH|Anz*6SZLV`Z6IgRfecxo#eAg%L
zteyF~grzGEjE^@j>`F|}_uP0!35U&>LFdB{lwllNFN#8<AFU=+>KF<42+m8)`MKhR
z$D8$lYcn1y%FnGhs4iyl5t=70E^IPQ2ArQAp~thX(*Boz*vk3nq2}j5LPI$^m=m*l
zDD;&nN?p~)guxk4z1aKO?1^DI)l4;@DS;t{oU?6ZPpa#Tz)j?GSl#J?)Eg=ivyQTA
zu8QMXa(Txx2=ADjN)d?(zk9LDKwD`|#AyS50egR|h;WcSt9W*`{g<^hj(W<GsdRLx
zlo9^DLZ}tEF|PRgL*1h@je>J}p>|3GOHfMn3Nd$Y3j4MvK*XI&WX?C%1*ukPg5dG%
zXrF@HfD`uyi*?WNw9W|aZe76B_qqy4<p&WF9Y`65!@8Q|y{%cnl<7+iK7@%OS+QaX
z9b;$E0J_>_YsdVuTI$Fj9HJZpM5O8;U)zsbo9xS5{vnJwHL3NWd0K2*wm+0TJCZzP
z=q*)FFNB)Vh|RfUuBRYE86Uiysg9^gT3MVilJIe-1z$EG3sJ`MoXs#dYi$#JZVBBN
zLl5tD_7XCg#n1M@Q3+Wg(UeCAP8&I<!*QrN;Sttp>z--kUPJ&Dx_mNzUd40JfuGE6
z$*uqhZplE>TPgH15$5e>&Tf3v^dwvTs5|2u0c~c;*z(U%2L|d1tW*@)eOQ}`rYhdd
zXZ6IH+Kqu$Cn<3JW}YfwuD1lE#~rgK=3>tpsVmPuuZgn9=EB?jZq}Gi+{xhFRTQ#s
zg);2Kl``#~jprx9_&D(JQUY}Is@YOzaeBGVYTk|xht~XhZC?@qJs}@nChmM=&l1NH
zkJLM7A>;kk-6TkWMv)x2QxGd{E1Z=JRbWdwh6$b!E?7^rqa6c7Wj}DH)Z{POk9Kr5
ztwK+e%Git2)zf{Z%6b?P^C7>B-h!55$Zu~grd_IGhSs@N^wHTKb+L&hmOUNuT@h|<
z^o&bFYOIB(vv{Y@(m8M-zgXzaZ^whZE2=d^AN~+M0WM$)a`wKb@vwHe-GGFC>PdO{
z7UO%RPfIz)|0M<o83|$h-4AO5ZhNz?SVDghPX~n${_r7|qu|n+&f2!g>Qn3^NHS~>
zLDAV6)T9{BJL@}7A@oJSAF^G*&RHmW+-Ni4=;c!oNO;XKkYK`qF9SM6*Bj}wL0jJ1
zg?vG-=qt(KH`U+^E=g?e3<=>GMzbPqw?K>i1NzR#_WInQsFC82uzeH_6S=_jmb4Tb
zLQ@hmG%UunH<_G^TMl!cAiJ;{*SL#WTff`z{&<{GtM}*)XzfhqCmOrVqYR<>=}ID=
zecdnst2u@tTh8`9$1DsqGCqYROPu~4nB5yfKC~VjSqfATfP|QbP_etX#-h|KgFQOz
zGv>r8x*{@-S^}vX2;cF@NRraq6A(T>fVpLR&iFiHafast8F6PHu(7klD@Tm!)D3qa
z4@T<+Kl8ts)~g?e%56YC%5&5(^6&tb)yOBnS2&E_*h^XN-7;1W4VtinC70xsl<4%S
zRB?QSnd44!llp2)>(?!OD37c*#<}blL8+d9M6XgJzdYv|DI;+{gh$Dz_ssDAP*1GL
zpbqN`SB#1WO2(zSZobxc?W@~38bNZfb=X;hj+gG`*2n9}4W%_c920vf3D#b_bP$m9
zg1Ir9SUwr25LVmI1|bDPxJq6(j}Dr(1$%$G!g4))j}NZ^1nGYo9%W~Yx2g{*jZ6Io
z@za0eK~dP(`%&Z<I5LW8F1uEO6T>|ZxA$&-F8Pm)w}JD;3-q$Rg{YaYg=qS3%C68!
z#;;x-#SZ~n-$4=zR-~^rQYvYu@3I1=@(L1uGHbNF+azO&Q=okLDacp^9v$?2`7mY`
zt4z6ddk6gJb9Mj^i*tdyA@Yy$yysriP#WrH&pYru^$YHdgA9oNkLLC7?pe?ho*(F8
zwY}o3k%R@klFTZHp~xR$JwQ=89GS<bN%wBeFVG=9FE?(Wq-CWiJbuSX_Bn0*tL#u*
ze*-gMC(JD2eEVX+<VUXgE}ALExp7sKhrDB~n#w{odf&OlTtuks?)?#l^3EA0UbIpi
z3-Q)YF<<*8%})WLZ!H;}+dc6^L52!%@R%QhGBL+x-bkNJ3;N@f03ZyTR6e%^b2pm}
zI~)Se>t{U3sRnwC1|cv%a{P)N*af<p_}x($BOlQh8II7xoyCPyD2CH9Aoeh+^9P+b
zmy)9TJK+s8D>}!AzLDJ1vpd!f3o}OI#3F6@m^+)0cx);Fl4?U#>R`F$pslb70OrVR
zUF=-uS^n}Ykf_v)A}$YoavEE)-6O$qdq{LqefJ=rC8?pwa--2FWH8;?FU8gwsw^1M
zDCh?;Q0}UA<LH^`m4aI&azhc=i>I~l`>MLU+R;g}2mE6tNkpWSjzUSkFR;5to_;1H
z-Sjf><t}<eZmr}PGv8n=Nmob50Q8FTv8_ikBq<(7>YUs*{E}NL6&@#cco5KgbA-{h
zApR}<W$YaRTgjk;&_zL|iX5W%z1Y=4jUT@)gE7hAtVF$J&a((<AaPTNB;Vl5<jeSq
zNZ^Qz6PdKoEX~RiQ$80|AIeLw;bS=N{hDq2tW#E5gNN|cr~|4W=6@)>Ycw?cu7%Vg
zx+ioU!zeZ3vyI7vjL{gxTIV)r`%*dTcGX+bGm(Z^0f5&n*Hha;9Jii~)i|c#e!YJi
z-lhFrPNBa0^9`6Hu{qB`pP#MJW8P3Ql0<qhX{ty6?F&W{h&cnivHTNaqP1;y^~8ww
z=`|9oIOY7%1O0IB@>Ea1$t?!uOH@q+F>BZCN}0|D!c17cKBFUx6*sq+b22DjKT#@O
zC|iY>Rkm-n(|5GRZZ_S7K=l(@`UMmt`uJu@J&um4pP{M0l9Khshh3*`*0x{H5t6MI
zxAwddm^Cf*KI;8CU=Y&HNEs`+6A~f_Hf)qm29mFbuU-{8Q(mIQ?-0dNs+{*$`1J!o
z4a1f$Gkv2<?Mh`DDE;R7A$G6y%KOjfg27XI${iLWZ50w!?$?4-9#4O<vsK3uy7cM^
zUT#IML=NN2LrU%5f*84_uV3{&-xC~K5a2ePM0FCK@2~t>nb5}@_}eZcoG~e)gg)~7
z8e{4bhFrzUXjw%#vM*y)JV|C7u0^p*eD-Dti;>KpD&Rd)H5DH6$104prEhEo{IjSC
zh_&EO0s3j9N3&=*^b+55-{hCw+67#yR+%`M+?V<1q>5`vEM*fz?JHKU$V_;H!smX1
z$aS~iQ2*<~BXpg>>%rj*O!sS5r%HHgbGph8avni8$_QAyV}=UXz&F-x-q_a2(|5gX
z+*0%$riGXdzk|CiyV=A-7qhsN19{&99yXKmsxu^Tc~1B|-`_WrW}^!blG`AB_f~Tc
z0ZWi7{oIuMc@>W0p3&53bFu!8x-2M8iBv`#VH_&SDm%PVU=<wG=+4BgLy0)ubc@GH
zieuaT0Z^?e9j>h*b-ieySW0BP%a8nPs~wnZcX;}O9_^ICN70ALGn8b+86EJNx!k<!
zuftWpzTL=VU)%d8Q&9u;UcDhr*6<*VyF6q&G{fPF;Ncf(Y#*0bNzbq-e40QfIct8t
z%-NsV%>ET=BFdr&v2H!zVCfodDXe(%q96HO#vz7R__eCDH7N)26$m8lg_w)?ff>JI
zHkI{fSPc-sLM%llMPy?wHPpzj4)cZUt466fx!QdVPZkk;drmIQd@2k&qRszVsN%OG
zr4&}-<bC@U2cul-+p3IJ=ya1kpF@1z+Oy$uR}rb*_2j5k=L=ccLe|$LH#ZpJseT^S
zJ=ms;{E^98$=88|uPHPtYM?AEMO|jmsJS`mYD*khJ<4y*<Dlv<xH!71YTN$RIEESN
z<#Sp8SnANDpsGnBKE&*Cna0rNb1|R0bEUU!oB<vmLV{dxyjwdPWVbSo)y3~RQeJGW
zG|g8XAJ5-Jyg35|-W^EUpy#qAUA}MU{WI5nAodHy9EUBbNS~b7h}{W*Wf@GO00RL|
zc{dLVGni)Zi0-mlG7{g2a-4-$KH+&13vuoJQly_jmQ$^b@lO~(=*KIThKP&u-}N_)
zlh4Ru(%^55<!bn-KFS+1_Z}U+V^ytuykXg{X$1Kv>VIwZc{==S_cXKE|39n1fBf%$
yE<0w7SJ?t%E?egRee=&E@$o~%#{b`s@33uWFRc!9<98n1U-Hr_kWxwG;Qs-f*M)ij

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/deploy/run-on-aws/p2x.png b/docs/python_docs/python/tutorials/deploy/run-on-aws/p2x.png
new file mode 100644
index 0000000000000000000000000000000000000000..ee7dd859bf43a9eb8935595801039bf6cdee7957
GIT binary patch
literal 77498
zcmZ^K19T?MwrDW1&53Q>wrz7_eX(s%%rDl&ww+8ev29y#{&Vho_pWvB>%F?Wx;AUq
z>fKelsw0&YBoSb7VL?DZ5TvEVR6sz$iT;*%pdtTWnds*BK|o*|twlwZq(w!El$;$b
ztbdq;fJjBAra`HxT4Rp%Jk8_LBS)poW)bCp%-d4USH>oQz>tT9<eP|^IZ~Mli+`i#
zQG*x(8_g#v)WZITUZ<EKEUpg)$D#HJD!aVG-{JjuxV~;^-~IG;>3ijSqz5wUU-vDD
zO$#x=)vttUaw(Vq4Nr0e0i1-0s0Qv{`$u9gKN%S^5eL?n4Iv~b+joa7dE1Y-udl^@
z?nn|25GCk-7<?4o?>np!TSjmp86f%237waxa(!%sqFTxk=n;++Y?L$3Pi&Nv^=20G
zVm~SSuRwc~wdssmK=x0%j<wIt#KPqymxE?@ZcuVJmqMq^osc&Julw)s)qk&t)hYK2
zo{)WsvkF&?nUKqRHmBnZc;AQxvHe;7x^sVbkX1yRxhRO{%Amc66D6^G?FQDsCa?$^
zOJC#Paj~9R{-k}3&NgixWwnpvrmGF$#w?X+pJA5e^0(vQB=L$1t?Vy;eQzWWRQV7q
zNkJ3e+;Pbsyfc<L8#9xbbi!n`8N-iO82~?gx=lF@-DR?#Iw^edN(vE}_iN)TarLF(
z>{nbRwe1cQQ;uomP$*1qVVj9hsX;OGl46IjCcf(`ev!E=ta3bQ${3yC7@?;7O7P4<
zwvO+G0|X20i$W3i!Y+fz8o`Nxj>_pJb8o~P(e&vMGiGE-dq^rMk>re7W-%7)YHO{6
zGx-u&5<YcwupYq|?i{(`K!7lH6Fd3{wA7;nG;4vFz1(qs1cM--fwsz-M~7-RaNpl!
zL2@6|3!#*O$?yBJfLTcSy7$7g5@Wmt8zF<5^uhK*>LnnC7_n<1aRmD)eRB`s&&S6H
zlN3ha0#yjKn1y5rtlXwJh5+9NO$boFMvVzZGC~a^!WRoHOQ0!+I1w#Lz!(hBBXXcZ
zh7C3;08ruVikevQH$u<^$>cj9L#x4kfaL@;2rm@~922m?#|k?XFfzeX3`EsI{s3VN
z!l~oM2a)WZ*fHY*pANg*wsXYMgCPvh+LpbR2Y{sYGVZ{j3nePCrErYH4+(h|1}Qt0
zk)<Fvp{PgDh^rUkDpi*;EZC)ddw_kwi4|9!b8W;~g}Dfi6Kj~mIgULxapi7@+z7c8
z+mb*k8kyT-MvvhcM&FAtHZfru`?>hjM~=D#)&<iAf;9kV$fwuj7h4taAA+;+*r5`m
zCFAH{P<64(?mr0KzwrhR?5H`Fa9Qdx#f44`9_&5t#O*QczB(1PDgsaq;2c7q`bKtM
zp7ngFyAl0Cx8nW7^2kO}?qT9WjKIR{(&};?V`gPq#j(ldP!A%*#4Js|F^9HhXi5Aa
zG9VTp>n2T<;vs`lp^B6oCr(XNNUBK;lg=B1HAAbX^u*DY+mxuGXi2_G+)}1O2am!d
zk4utXkthf1mGG7LBo&PRoM1BNV#zY2G0QZ2H8+?*HG44MFxMQlm`EGXP3t0mlZhj;
zBs!uVNm`@QB=%6Sm$DbhrE#Dpq|0M4q%)-S*Z*t-Q|J@w(+w<2D0NiZsqcrz5MKm(
zsjMu}FPJXSE+~sv6&8@FGK>F`x(P9f-)&6_N{UrUna2gf0`Fu{aic7ROy}pV)~rjd
zf*TpfamO#mBgY}e5yn5p3Dq&nTFc2Q&@0#~;LCiJft6mBB^8%yb{f9LVH&K;aO%3%
z?n)O?tCbK@aM_r3Sm+E~P21f(RrZAhCEW@iWnL|>Az>w9wP8Z0dBf(hnwf0!oKxsi
z8<|*aBaLM`3_5((8#-7`2D7Qhm-lR5I9<sCHPtm2rSyLU3O9=Q%T3GB%9AU9m)%LF
zmrcyd&u>((74X)S0<nr)3~CLox&?&u3T76!Dz|`NbG@@ft45W=4Sg=67@{S{&Bk3j
zqNGuYmC9ktz`5_HRm=lii_Za0wJ<+SZ2Q^EncB=@vsN>1<&b_p(ht(PYK<CdwK<N)
z{5Z5vGuLq7_@Tgx#5Tk3z~SE|<)FM?m=oQO>DyA*B-OxWanq~PH^0TQRKIvzof?U;
zR?&YN$3C<=;+5<c{eXw3nkAMM%;8~I-Ynd#YsdSeXJus#<^uP8`268~@8W3<i5VS-
zIL2g{9V#X`e1IaHBYY;#JuV}z6#vVW#YM)g!o{n5+0kit*ECu(+bvsrS~8oQ!8IL_
zKEQygX{VWH3p_*P)aQ)TO<iO4FnvOQ+Pkdfr{fDDu;dr;;_<S-{_!N@`QnA)^#Gs+
zNP4Av3qH=>NI#Z8;XE}x=pU_JhTp(lubprXE*RBkG)+4Bdw(H(=78=8TnF3+PzF*2
z_V@lF;u;mS(wrS2f0ccf%?uW1Vg+bk7a<QM#3UGs{wg3Yz$j20pd0uT?iQ*#pc%Fl
z>K}0(u}pA7G*6|1zk))J*F;yracbJA=_=hwW}!S8_A~33&9BboG%h_oH9ht9L6s|@
zcUl}ngMM1i568BhTlR_CS|#1Y=G}LhmDH-#(mm#zxf{0|L+!pQnLlD+tj7NRTx-c)
z=^sgZ!z)ubvaSkr(mrYX*=kAmd@J0Znzz_Xsk-C7LwEbBcbmn}Kzx~(pSamNIXtPD
zIe&h}DaI=bj%aR|-tuZ%*XT4<C>qg>Vo9;!vp$-|z=4OTlSnn{EhLpkzzb}cklrBe
z?$D)IMO7(Q7HQ-ekoJW2lz3N<tp+vC)ce-^8-yE-7%crV@cuY_7{FWS#9x87lUrW?
zCGDZQSu%&em7O^?I8~nIKhZQwKjzy*?j>uRD`B9pIf~RRxNtPF4|f;d%wxxYPx>mk
z8P5;#2jUI38_OBi0bLtC71NjA0&DAe?6$N%ucVpW?rg=r<zpN19Pu-*FQ<mrR%Tze
zURJ^#;=*QCeoMdg?ISnUyC!`#g<w^7opzI!8jj9(rL@Iiq`UTD{WcIbK5F>m^bPLO
zpRbpD)_?ZGXn;6YR6M*J|L}Xa_u!M1Dp1(`MEQKKsz@<j{QKE!#*K?zs*bwyVvCJm
zQ!zzVNyDG+=4OR1dvAU{#u`H2?sLEuOf4Ecf(3#ku63+9WuNrT7*@~V7Yl%9TW!R8
z_#}+=)$%b9m`nd7DDw|TET>BrIsGqs-=p^#w?>|(+>gcgrBjZJ=BPWFqZUIVgV7Eq
zH|Dopv5x$YMc}?H>33RQ0=>94pHkMwh9jBYR9j8o8iPyct9HQDi_V(c$k~xSlRev3
zW5>#Z-J0(s=i1iv<MIRBMt#rX{Mqc<YD?>tNRRCoaLu5lcGGyXjl0e)-+{^b`f|3O
zcuaqK<LGJrva2o6&Hq+-T6A)tU(z{VhOknw=6&P&Ng^_5|GjR>@hSP55=mm2l<^Ds
z<Hn8ciu%C%gtgkTX{I{2CH5uep<C<K<Q}+vuwVJ!GxlZoWm~jl`RV%B;LT^@b02#>
zwXnA9QE^qVwbps{6FLL%PW&;jKhm#Qk!K^oD&XN8`FZ=p`9X7isor6DOQ#3Q$Db=z
zfSue3Mw_(=1lEa*8YZA+5kx6rC2%!*5>D3g=m00!!txyl>?_jJ@)V~5Jip|7z$iXQ
zP(q$$qSZDe2+R&LnM5n-@~Y117n5>Q(_$57IS!7zOlip`@g2o9c7TJZs%r9-s_Irn
zg2@&5?5DB2LLNuk9teK7k?{a@=ag`;e^U?m9wyY3w+gt>-(f6_qm;G_2nZ(mKPRZP
z3fVOX2pEL5nwG1Uyd00IgB^phnS+Tr1HjJlFEt1VAAsj?(azk}m<V9^!`_7lz)$io
z3ZB2^f2tWti2g<5YRgZeC9gyz>fmfn#LmFRz(gVdOGHG(=WJ%dqar5pU+jOM_(`l>
zT^)HC89h8a7(7@R9Goo~nYp>S8JSoZSy<@*Qqa42+PfM9=<Qud|1ISI$`LbnF?F_f
zbhUP{C;CUOv5AA5D?bUzKZ^d}@oziL0oMPm$=>C^n)SDVjQ?O5nHiWE|2H;QYm5I6
z+dr6pv;C`G|5nHMk1!r3Yk>I=Z82**b9<M+rU|fevGDy%&3|D2+tB~PsrBES%&hDj
z|C9B9p#BHzKc?^~I$N9n_0m7S5MbtG{Exc-qUU4$$5H>oasTd>f7Sl&76Dj3#{bvF
z0<dEQ@Y*0CLLky&!fF7}b6*G}wUNgsZMIf4vXJe|OT<u`h1BLo-zLq{8YLh@?QM5A
zEU*?Rk~*)lzOlh?MI;3#{P^~9c=>~Z(>r;JBINp$HDSZsayr}PaB{uvXp)=t?2a@t
z18~I33bthMLM-&#%yW_TZt9FJb*4X7dZJSRu6P<}{Jfbq`!&QEmR%z|DMoOhAdxw2
zRCb`?0%9+fkejo>Hq*7UMs(?YYm|Kr-#WU}5pW<WLrMG45B$!MjdgMU=czv{=RJF$
znp6)b)jahpdoWdfHH7oTTVFDW6Lp>9L(0mcrCXY{qqSalYoENdMn1Q{EiR=dl2-*E
zXYsE8DwPmcQ=-nm+@jm_JFGvKCk5i&YNfpe(+d4lVHVy?0RSmAUEILu1U`X|q;6XS
z?0<tD86v~h0L*Eb-v0%NI#BB%rQFG_&U9h4{sThjL2Uy<=xN}r$c_F367*rTz{xsL
z8y~8_Rs9F_rv<PJ^<$<&G@{o12aMKys{&2Zhgp67-wZaE?ZqWBjvfco`45A%V9YeZ
zP7shkTcslQKfC&oi3%F}`;I`-aE&=!5Oh#lsI%y@A5JCkNDDC(eEUPdkA{TYPrY{F
zIR)`o4VVmGcIFY$7^arltTUI4cX=55oC|XZxaLPW(nHZW?2tAG%sGGX?2t^twEk!w
zGb3(UP1kjuYzDS*m>;_3l!##3^Idy3uY}BiWe{1ppFyGQkYu&;ty#^FUf4Fs4>yff
zdVXj2hP;e$GE<v_>(TV1(!=TVO3ipVbfbFT+0Wpbw5@H==re0skG8kp58vfiGr;uZ
zmJ}3|nkIdRv0FlFiJln*?}Tm2NEeUHhI8B~VY7o_>StNRkLPHcVC{EfShcYJ$17&i
zU@N{MIj>%E52jJ~KY8m1)pd;IgIlE|2`vNVbPA2vxjVCSIKqWLzsMp8tP|M;Dot~)
zabj5)zl_L=P-;hN!p5Nu$?RTx3@GPs6822{OsmS63!!Anru(<YtlmQ^ZhNvz4=~1D
z3eO<XUC1fWX1G~|NRAn5GbCGPf2e;uwoAQJ1k*#{!5d$Ep@f;Z`3m}We_BHD`|{0k
z_4q*!a1b8+8zDKlb-nd&o_}p@Nuk?E<NM7iR=3{_)(I5`NeFP#uS9*o{aE0L)k+<<
z)<AJe^Mf!U&r6GnZN1WmwpUF!CtUN=bJI6ZJGpav>ir%vVku^K+GERdd<sXXOj2)u
zQabTHRMpljOcu%U50{f188?bU=sSrrxfm>l{E!O`M5`ikE%Ys4iFk6FXHFD(dTsCE
z7&>1=>5ye}s9rXAY(1I}coceZr=Fe2cXfs3AZ3M@vAx}+{qjp~WMNoIh7e=tU;AcZ
zpK8C8YAge+LMKLHJ;wqoleHy}&iJ0XYS7*hTYL*vF%>q53V!JQ{2L2cg%!E4XFM~)
zY~(1QoeQ+?BvmeL4^IRR+|rV%IUDLVyJ1|KYxu3Q>=e1>iR4UQas*gQ1sR*s<%HT~
zxZNa@;z~!yep?nx6@aNUN)a1ZRZ9=RDO$YXLOGQdXD9Xd&Sd1ggu)pqOjCI_@)yzM
zniI6DDCDH6CK##}yeBc@!6w+3Qhuz{Fs+@qeWzwLc%Jk@YoRrqI9|Y+W(EK)DI7F?
z3l$Xlypi1CuqN|PD)-@smm`x)7rE|OU={PfUKY@uW5uUvttoQ2sK+Q3%5Rj59fjF7
zemeArv|Ad~kgIAKEG4p-?na|SotlSHY;>kdhmze(2fn||dc4d(gj%Q;aRoDvbjR(s
z-e7x-{mhG~CHkCng>}I?V*EoIfBv)$==TMk?5IjF3CygE9QDn?k#nEexS!tbdZEki
zeIpaZG=^=(GGVcbubJ@}_*IPNj6Xx4CoSM$@m0bpG;LB8vD)JlHd?Q|i+2rak+Xa5
zYlE|?=Js+kuxof5Jv!eauA&8{4r`py=%wN#|0W8`Nb~8r6;<`3=A-#erP7G*2o*zs
zCV119tUb-b8Ka$B6N3T&`8yTQ{M%xr;~<>r_}5V~i6lpK?G`V|oSQKqzybUFC%uap
zg&>7WeeUyXyik@56u?wJ#let-<wkh)7ufg~I<<<-yWkeN3W<UbRwoEPgi!3o!?y5U
z65N)TP#8(7vNjdqFefnPSu-DG?YLO#`ndgZO=eq9S}1;PK$`mYcUA1|)GfaXF5{nx
zED71IKTawYR>TiU_g-_EK8$)Tk_OLi!1v}8<jvxrgv_b%XZWO|uN(<9=Ft#02_+eF
z>z6DLBW<v5&@E51%$|PtCNEaE3NJgG8ufNpC@X;d;!eTV>P+kTrK9(~1-DV2rOfUy
z4|Fh>Zs(fs^AtUsmA|*9mWiI^R_WlbE{T2}L{C^#(a5wht-*zONi4iu87$~UiljMg
z=1_&l<s?wB(|j}RF4!I;O#Y3y3An3RtDxB?tzOTtU0{D+xb{5AayIIC3G>}M0;)6v
zh})_?g6o!lyCI1*Bl&SFKIU=Jf3*Rt#nKsQ*jO5O%FJUK)L>^zo<*GpG^5QHrtFF>
zhEPF0c~MQj<BG)luq$Q`rKIV4EwT=`574IO&;9;0F}3%{lo$;zl}T+)5-atH(Vogz
zXE3M++Eb^CUKn>cEp>hf)@8KP-D2HAm}t?@68{eMMPj_JHiwdui@<d)c=2voYt~s>
zP9bsY&_2;OJZVA8DET8{FEz(T`T<sfFyjKTnbJ|Xt=?zva)9Oe&U3!T2F7#eR70iH
zG0^A>MQtK@%O}^eYL%Q&ag``<eN#CK!iXJzBAy~ICF_e{_VPksvYoR66<>2iF*lJ?
zf-%KI^D=o=99Bwh=UoNUYd_4Ck{jPl+$74!Nm(Cfy^G-<?)H$$rdjxIH|086sN6+I
z!j!y8VY6%Ax88Hc7$^8?4(SX@Cc0jkrPW2=TC+!`B<Adu7ZE>I@k@`KadLHR5vC8E
zMstCIRMBu%Ds!=-?7G;-A){~#5~K@Ntr6)8E#oavs$T~iW#l_J8ENCWbzDHQ^lsjD
z{d0OenqiTw_^yuW;lSzFaQQBwpxpj3!Hrvo?#}(y8e?4rewEVvZ?afMc?$cWmntYP
zuLWx^{hA)P?$19juewKfylS~L9#$`Vnvp*;s6Ufi4LQYq*o0hp(uk6v9Ta!@Zm#qw
zx_R`K!MGL~<UY<QBk{TZ&f)9%-Bf&1#&+020$V~{@1*9zlg_KOeu-$*=uwZv6Q<Ls
zS4dTAR2TnPsW;+$0G*DwxL9kMNTF6W+uHQ~sWvUu7X$&+Zm^i|_Vtd8ib_%~RTx|T
zWlAm}V8GD*Y*Mb%RKcRg75Fn5+WPN1vVLIyb8;N!Dva5>b%M?B&|$ewcsLf1l9`#=
zc`g)7rcb0?rZke26qn-jcd4SRh)7V9WcfU>4%Qw38#p*<{fE>qhfS``SzltgDy@Ox
z?0l_7@$-2PX)I5$XEz*@TvY&ji%wBmIx(7|U5Q2`29aA}*%w3<$Cld{Ckg98gG|oK
zAScP?PP%4{HMLZ;N0+KLW`m~I&_EV(7s6|?1Mc7^Fx+g}&90cpprXCYUZIJvlwpHm
zBYY-dQ+|B(*fvXwo641z<2)70Z-wZtg>pRI!!3<v2(A*#)^TV?sN^OjiMX!}T4l3A
z<038I;v&f($(qKkGBr{DCQ_wV59Np*|5z8?ZW|RNQHwC|9hxFJ*Ry(u&9D!)mFX@8
zUj6HBp9y{R!xm8nHbtJIX1#nL*}dkbd?ZGckzR06UV@Twlfq*%I^A^U9;2J8Jb}wu
z7D<t>yfd7E(^J9xn<j|j#oDY}@U`cD(B_X!r({gK)DZz);ugd*2F|B@T2cBY`htu_
zXDtxZmkFGX6cd)7-vn)CcCp)|vrOm;y(m<a+igF)IE1Tx&{$9E1RDpR`Q1f>22)Jf
z0y45uZifchotH`{_HSfuq@GID$zf)eOVO4lARH#ss1*;>?PDv)<G`ioPgZd1FE0#0
z!9(1*@!2q-YH>;PC(n&CK=<@YYDKjg><~FVPI|plEh#i($vL~(<8Pqi#$xjrQnNDC
z(rnD5sb=~URsFaVex73!8BtWDi@0!_kGW@X``1qD<q&bYR}fDX<nwe{2%N=rMj8`d
z&-7)xXNm}`6bU_qwh#3W;Ji5b2Cc8d%yG!!;xJ@tQWHCf9Sqp`fM#^t)cQ2s=EGm%
z&>qN2j0KWX7IVbtQn<t|Tr6&#=%%jL_TA*y{hV<pB1Q5)fe5gBi<g-~QN_5sa<8FK
z{d1B2C`vhYj<Nyt4OR=D-aw}(#3kh4%3wJ2z)y{5sHJq<8<&0mB)$E!g;)G?U0(m3
zVP6qHQv+I<_mk9nQ1I2{hO(Dm2VFw=2G+-BqtkoyZAl^c<|h1^XA?nmZ(6NY;zP{O
z8*F$?@;XQlEVu+)BtGDYtv~J2Axx9lpjgiifNW%EZBSNBxDG1*T|;~Bmc;z;ppsl)
z;at*)iag&Bl<L;pwzolPzCbz`WtS|R$(mp-*j){iS{1{gEvWopEL_-|^pbgZBDlHc
zOsLq;X3y#&j|3<aRSO?efv^?Th)<>h??e1@Yl@K%Vx__u{EOVJohe;>`vxTKcsL&+
zKL^Q<#NnM*>DxQHv(fEnsh_ygiqG3c%VLP{{joB&YDvsOxvC_A-?MSKMs+4LbE&x=
z(y4ef=2(_PH??FOzOsL4ezB_dcR|0-`>QQO`<@SX^8`ABPA$K$508`8rb-qe=z-#!
z)>?Jr2<OAnJURRW?Y&zX%csipM%(N&ugsFq%?^*+&inZZ=LvdF*{yEhRM0}FGY5o9
zr(=hP^%-@%&J^oy-}^a97RzaT&zFOApy%D8HT*9&H++lVP3#Epxk#JI6v^7d-czy`
z2O*1*kfR?@Ta3Sc{d}KD4=>oMiaOuy(nTZ`ptJo`m3X<)L29@p^;Ru@<^z=r7Z9>f
z-o&Mqi@Da;_Pdi|t5eb7#U%Ls(?+tb&X;~j4hI`qbi_UZiovEzBb>XC=!Grj^tIzg
z<Wj&UQg6MY)5p_LlR9`&x5PlcLWUn3<^xJT;pU=ZY*GKL0*h7?dJl;pn8ibaCY^!g
zK&C$+fCwko@f*V(i8!n*iw0-Qj<`yNlyJFDcL=QcHqEVjXEsBPp$@~9k}3|Hxr~^!
za)=2tH9mImP1(ree#Lb=dMr(G%19EEUz}*9Tpx{@^mVD>^+=rQ1Bz>gBzPfFr{K%Y
z<UF`=cNsQ5pO>CHIawf7-e5p_q0M6QeWMtD8<USnR9&}=KM}LE3??H9On9<6XAL#h
zACD;G{lbs8?Kx?#G!pW;X_@Wx0yFq6F5lY}SH|<?52|=bB#4BW$1907L_$G`88zWW
zMrEsxG+pM=3#$EQ@F;DVnmWk+k~eCU`dCR`t4Xz>^@&2?xb<x7+}0;i7#f6H%|+ND
zb*<zV8<pOpXHi<}Gmq~{0$=(JD%BAwQLvrh>ZYAIX1dE)lyjekNA{^_YZ@uwN?JWR
zZQ8HR6>)U~3)gtK2OUg*xZ-vTZxAgkvFNv{vWSHFU?k&Y`82j<jrdaNwbd&6&`iUk
zxatRdBK+R0Ki%8fT*y5SN*;wF*OsEwXgod`1gAC&;S&ZVlM7hU|K5@g$V3&Ha=A*-
z`b6J_@9~f|7*x^sHkqBV$CJO!n8Waa!vG+kOtRS{s4KVKDFZxGZdS?y{1}V@EZmpw
zBE-BwPUxzLGteTcDk=&xP6Al@;U#cqE_5l>VZV^E8@%u#@Eqe9<AfAcn(I2<96YEc
z^#@ZMIVV!9?cH<{cUHW;A31QmjI$?KoV5IgTk}Et+i}ARJa}(@Fv-!@s8B!9Nep-w
zpmi~LGv{4M^C4<u4da7lV9`b}?+Q5{GclXlI?lXKBM3}j>e6q~DI|0-+LM^ea?Oxf
zuGo1a$&wsOd703d5HN(@RD14dtp;|RJ{z$Ev~dIgs4n7TCeuH;pfeJX=Bq;5fwQGY
zvhyG8Hn%(n<m;~@hLJ<hohW;NrLi#<j}HO!fU<7kA}|y6DOCr`N1RK658I}yEoZSq
z!8LuIC28M4kI+QIx9hyy1rOPR_9taHUTgEBy5cFBAp#P?>?*1dQ8!PhVNf2EMF$ez
zFOXy%`UjzWOMP54Jc|AZp(?|PiSI_ZC_i}mYl<hpccU-D5<?wpep9BtDGm#KL-WL7
zN4aqUJ9!f5gvuGpi>V8-CJYEC%r6sFy5@PNVMjiBs(2n(AHmyBdB%zhOFu-|CZS#k
z_nuyQ<1hW}%OUX>p!npcWqJz~IOA-UDjrd~VScv;=fm;epcU9>eVx$6FY@nA-;K5|
z1SpfL6qplJtcFxu;q{NN630~N(PLYqiMdxpRx^ZvA@<&Y(!de4e10oBrqBykOtZ^?
zszrSqP#ZRD_pcdufgR=IWljKC$D!ur&3SOSu|>)`{+3NdvI8FNju85*(Y|Augy!YK
z;KHaSl*(sOEE)RyO2yra_E<g<yxjj*TqK(<mPTvaPm(QmJ(*{5*yPLLaw>fPi<xhA
zG6mLQG8*VrXx7F(ySC4M*Z{l4B2kqpqPW<4J5*A(=?e-c)auQ~jJVQE#(ZdQ+0tOK
z7(&wF;l)i=77nDPr7P^2XMwxLXD%Tw7+=s<vqi$Vm#?^gZuZ$QZWXX8YIc#ga60!9
z$qD++G8LP&)9>E)$C91Tv4=rX&rTEAF68q)gbmR)s|MZ+#iMCvP)49Vck(!vQFnZc
zC_|ksat~7*laiA$^l<|VR^4_7Va&-e8T7E7X%lNU29?{K&DkNL8{Ez`Ue0QJ&})(S
z{w`j{0kfrGaLGd~NstaXUrcxrzw;FHQ3GXCWh7}KysP5y1q#j3D)fjatC8X@qn&=G
z>uHPXELEcK7el97cWSeG4N@T@rXS!v8X54?DRyqkP_L*RuPmv}x3fhi_V^uB&ncPw
z#B^At6z$d`iuN#B7uzAvb(ZMvY%f?3LB<VJQS<byU)Qd!irMgyY9{P|_ufzG1W41j
zofT5t?k~aZCv)=xmD~DNE(v=^TP0T~Di&|*->;=#-lwNUOm{;au*T1ln~jC~sRB_Q
z^_!Ad{QT>y{0r0HneD3ezMeF=xvFYa>c?gpw&()-JwycBYCHJ&oO4e{3cFcvAF;Cb
zpOCzyxUZLYd?%e^61n_zc$TQi^N_45n`lj^)%OBfV4;I<hu(FeW%_uiMjs6FDB?M&
z8A;Pjm0=15oX;D50OtISz;dSjixcI$gAeYTajV1-bkAasn0FT=I8RVM-)5tDwn}Vr
z=C5aY%<cvKQ#D%JB|j$pVTO@iMQXZRd_H1Mw=%k`tyST;!VPu9EE1$e8frlYg%o+J
z<l^7AYFApGki_Kcw5)i}r(Xvm(sDxD!gA+EmhTtz#fp_&(qSrhwOeTuvw7FMj4<Me
zot^HUb}vW3DIoN!iGX)XM&>RnE;cMxGSaxr-gUcs0TH2^fP#?^!uKX2gCF1NM~sBd
zrT7Yl3&56w=hf3&ei{qSQ?3<k`(7_8yxf&PCJ~0|69O&?y6A*6|4`KqUl89<>6&6r
zT;E_zk~75-)pjsZ6h(0ta1TB__0!Kk2}W~Mm;G|`-E(Gn*>-A+xjHm*#vQf{G#EsH
zN-~GD8c}z}#GoD6&VKX3qK4Tsy1##Lo|23%7H=w?e{IUR1#IEovUJA(PP0q5|1yYh
z8uFlZ9O(Vn3$vK-am%h+aZ+mcVUjgC#iV@t`~@1F4_M;gkQMmyj^%q>ljy81xWnhC
zrmsv-m}@be)SxHrxQRh{vncRkb7=AUv?1vP>c5Zx^n7lK(LKGcHYK#yl6$}mjp#Z(
z&Gp;fk}Oa|JxMmSpqtqz;TmcOs!W2)PeD-}mR;1^QyJ-KTL=<VN0NYm^1^v|;^<J(
zo0Gyy2Tg|!M%wi6Ni+6V<m^jVi5+#v&XQjeDiC1=Z?Tm(@y44k?l8H=*bmBpM@S7C
zii0An7@qk4qdm>B1c!pV0*7yVH*tp;W`vkLBIM(Wv8J88DW&K!WCO_Pa`A6QLIO`0
zBrjHwuKWUf;i|>{!S%Oa_O_)ly>@SLqqDV#6CT;%Mj%b)dTJH@*EU)VGGQC5(tbHz
z(vYz_r<3YTEZkpH?G4qgR!Z79K%G9MA<a0x-^3{4>E$CB2vy3m9usT6wkDudGKYQ9
z1_Z+^4oHtmGdi<qRlG_?NPJHnXmMk3s?B59pn$x-XUF}E&QhzoDZi><odgp=Rl^S;
z$~7-j-6WmcMQR@6>}c<{@&LFuzYjut1=yFtprJiZC1e`tiLd7_l|ewI5tY!~pUrPx
zND+7lkLqQ_{el$O$lYeH?MP_P7>36Ey<&oo9OrmA2v?w|jPDBX(cNf<Z)-zK=wSnh
z@F~=cy=4Hg{dR|;9_zSg$~a`hi)oUa%f2!`vTkd}$S)*p49<_J1WIgd-L&YdUClww
zF5q)|f5Ph?{mo*R()%3y(AOLPZuA-Dyc2W`@@(#X{ivZwq#j&Rr4`Ol?XE7I=Nk*Q
zxRq#i%`UCFK`}|Y>4R3{YR)WVV<Kr%vNb-yuwLhVEiEb@GJ<8CqHkaxSx1lc<|?;{
zSp{}Qkto9&7Ai~%EZB7<?z>E1=T6w);fzb7ir3TC7Gs0ORMLQwYk8S!#v)Q+*5|yr
zthyG)Kr<u~0iD~~A9W&j+dpC{I)$+8t4+4)DnH8;RB-axz0+deZHiSCJpEV9)1|XG
z>=v*M>&+rzzaqCF4#4dq<Kog}8N1Z1)*a_fsj&EISuLm43gm{OFp_neY{tyf4JcA8
z)rP@cMQ{Yza<Nav_QGJ<truzy4Vz3~?@wT%G+)b)(Vlt(!BdY$HWW5hA=RF(#S(gy
zizLdCoA|vSNB<h5o)xiIQCXWtMRhSKXx#s;0AIUDBovm#YL@6g8Su2>5qg{N5ty4`
zztK)j>M$IG)5nv778~t;v0C2juu?BB@Uo9~kXi_jNvB2hgL9z2rt4A19A1gxZ*n75
zI++4uO`Z|ce59Lr`tNjy3Kt3yU+Kbrr5;FAT2Qz)<>1xJ<1yS2Hh7lj|EZ0`@twz2
zX=}^(DThv{Q>v!plIMVUrOl<(2Cj=d7MEScS9H@VpjxL%21f(|k3)^cqT}b`Odey;
z$77aK7`~x=7Dp;=O)H5RVHvFD+rY#6F_y}Q&B6W~FK_wvTqHE`)myu>lfy2{hpDbc
zC(vctSzV$e)8xh%p0-h?CPD2;hWU=!p-i1-G=q8GckPebO9vA9gn8a{vYpc5k-#5q
zniK|#JW>UZYg;&=#z9hqFC$d&exnbcph0&}Tbtw1DQ!p%-|dtR&TM@qpDT<8>`i}8
z!%jfwml|HVA+YhC!?Y+ns3pys%*gP|JlW?>$oAw9oy0KXGEK1H06}0T8)i0j0&Gww
zmKuUsBEjF)0xC1^Uq0}A2Zbd}0h<>k8h>~@nMHF?QD~p~(Hg<WM^m@`pOIwu_jo&m
zxINJ8K`I&z(Gg?fP~e_Mi+OcO^{T2mzs^|N-bR|7rAt@qohSS}{ns^KE)$#QJ3Oo8
z!q*wQ9NO6;=t&1^97ut1j7cjUapPMvO-Zd4=H`Pl6K3b@T`aZ$59^SGAi*Ptv`Rxm
zB9EtaN_JBsPPWtPlaBpxULC2@VdmS5l&0HltjhgUh9)4a1`2Ptpd^p+OdqA9TYzR+
ze&^$W$;sO~o%WZtwl)OP^6b^0QAVWZREDxQPLZq{KZk+^;XL?rHA=e<K)N4QarhYB
z$l#9KMc|(8{T*Xg+^GNMB}v}bFTFqgM8;p-J{hC)B(vnn>6g95x>7jnG)Uu{jR(A`
zp#8X8iu#lTUme2TfHL5{t-gMm5X|Wpbqq*M7gdZUE|ah1Wa1i+#KbJEPMK%uTap<m
zrg!_z#tGT9X|`<{vSB>-<C%aK6?<+T5ho`VB|omX?A@t3Q2N5kd><oRb5Bq-b1Z>S
zV@xwROZw?>(d=V7c>ZE>g{4)U8^_BW?m*ic7xpxO^Kd-OW4tUhL+sqY{mJrrts(Q?
z9*y$1=I}%VVO7eN!ATjwu+cd09^e@^InWJ(OZ4?(8tTJl9RB{P<!!A^sdFLgc^!@6
z?2w1u6!1z4UqFe%@$&Xz2Q(cRXR-gpZ2L5f2HK}4IV<<_R;*#owOoco(3Fm+8f1Fv
zB91RCCd^4{5lIDIy0M89bQ5XhcGsITg~a;iCq>ufz70>V&|Dz*i<>v0V~PDuJ<1wI
zs0mALI#Jy?eAdf#>(^=eeOTgI>vHOR{_Ih=|K29YV5bFnHU7?m750(ffjPmtw3xW|
z%C6?UK=1);YOYwX)9b>?I*J5aX5hpyPxlE1zFSnwIlj!<vCCaW!>Vzf1j;y*gu(wv
z1HONemp1Mpi!@psz9y>=Xcxfi(aHVrX=>~NcAtmeupCzcoh1kAZ}|@6gC!NEN|7wa
z!Hd$z7fOp*ISKS{o$hjUpS*X*_I&vKCx~Bco&nm8{4j7JB#LeoH1UOoZDqi%y#l~$
zygRE+skHoYqf|DgmZUzo!&_JcE^P4vrTWqSvqye->Jr$f;@)5>-RyP4rnFYpeWBSO
zVWD$LQD6Q%OvBV+CRCliw^_W0U-KT&Xq)ZKyOmp#J{woV6YXKonfes-1e;0vXLSX5
z;O-M52Tj)xz7jsEcu}<$l8LzkN`#Cf@~nY;5dK6-t<fQ3nfi1u_AelB95(%283l9>
z*6yLtG9&#={jx<TP1g$efpaWdR?Md^jimvl=6R8|61G1|Mrb0p`=Z;>yL);Dd$4E;
z1NTdX)!Ra)@H+)31jHCl31hInUSUXLx7n$5`U)!Zi1d~HEh$(LJkq)!1{B0^La_`I
z1bpA9_JYSbFl5rO0-Y5O5~K(2bda2By4Z{q@CAt<PKk4ohoL6Q&gWemC+J(sX6b19
z<OchA{6AkxS>TT$jJRg}Kko>ERW$}(I=Nn_)WChs*z$`0o-_viircbLmXFu7+9*Ss
z8Z{ehbR}Ven^)-8^<=Y+b~oq@FKJIDD<yWTS)no}!H@gjH1S~*$%+EhFi@z)fAd@8
za6-Ym(Tv?nVUV6?c<a(fb7%bt4a}y)RH8w|HtS3M994q?xVTtXop*7&!7_XUQx-G1
z^nbrad31)|eAr$R+_TSx@8vNbC-dTI^tu#wYt5+&IXu)u?+57yX;WF8v>M0Cg_J?V
zl6rdCESJj+xEOM5d|?Q9HLt;TaWtsphW;N-?`MWzD$W^vp7nfpldR)!!UMr!OO|=Q
zB}Svs_AQ?!eM5scy?l=tU9|fdK5+#6I?Z-#B}T3O5g+}2rOXYEJ0goC=Ww_d_ZG?m
zg74SCL6&#~{_jrYw6`k1M8sbXvmDUj5u~Pq!30JK;c<4$HT4qTz7s=ddGzW=OZpSS
zhmm1_Q;-ofU|WMfNJs*N)m2zhG|$P=a$F|bO`w_Y*!g5=CgDbS_(3zKnp97Wh79rU
zLw9|<X7O>A{@w;A<29rrUW|k;<R!E0?^Yx$wO2t7q`UMM;x>8KV1bXSSAs<%&r4Ev
zX_+tuw;fyDIIXx2#@kl+Y*ciU7Q)e1KL2JQky$IE^#Ww+X9;6Tji#p{{47d_2ifqn
zeKNxM6###!EGe^XRtdRm^e)r0%HrJ5u+ogjBBBznylPS5lj?S#;^QxGdNNm)2@BPM
zZfv?Of_j~#R^PYY?fb=i4keYCOK)m^^Moa>7*ee+-m$tw7hK*J;Od|UQGgp-Y<P#A
z!P6Pu7%;EZd!$)lyq>>(G$w0CK)Z1TUqPGhQB%}k`Pxz$C|=l418@0<hR*ezmzZ#C
zT?bd4k&@krN=4e!Ns3fj46xEeX_w;Nu9;f1%%qF_eG6`QO@D)CZ1x84fT~~NwqNUD
zobp8rbii)(snUEC`~ryIe~M^2i5{9x!G2GD&$W2>LLAcU6X|ptZ-G%vgg)x?>|LUZ
z@p814MUIjiPfFDTzNDz9uIhP!J?t!e#`SV5L)S{4qp~CYn@TYzdSqL_Lbs`LA)1=B
zb_e-epk-X{UgH=J{*rqosjS3(Qo8q`F~U_bJVAp*=Ehq~BmQIhq*fbTfzlAr6dXXL
z80A`{sccdQCP*+7l0ru`v$3p!Yi#P}JL_rd$U8gDh=yrD<P&;?V0s=4%6@QiwkqRI
zHCJt;p787HDaB7IAI`YSCAb@%F{1e}Ss##_ms_VV(A5>G7vUV{vf*ircCrXbENPTU
z91$P8MHGJ@kvOfzF1a0lKLq(goKtB291{Bz?zUG8+LiIG0XjEW(px<lA>JeT!{H&F
z(ZQC)A#RgOaAxyoGn}3Wyt(OW5IEhYR;zA}oxXagC?-8F`3H|$!WXAqaulCn!1c(}
zu&BWjXG%0NBY~QuY#!vTRPCwFx>NVF@ieCOd_@eOt0!Ef__B#DUmm0377No#D|C4K
z(6!PG<M?WCFV2@A<H>3xZm=108pH<tUQF(z{PshRbcfmG#S{=~9LK<G3pkYzmDbm9
zY&2P*&5PpK1hr%2v~9i_tbfp;yZViS8hM*9aRhAub|v$RVTJa|qnx2*>zHy&Z%NDG
z)i~J)dN0F#+6TRz7~b$ebUZ<nqG3p9dU6Y@)&tZa`(uy*u@id^ltMJBxjdqcN9rP&
z3O<_J$<~IlJ}Jtn|8z+J{+?Y`9M@IVn|XmCVK?po$WfuHCqVVYW~M-CBDUy$iBdDv
zh1VU`bO0}rd4;|1%}3B`1`mm^(E#@B3#27QdPqi$s$G0YSjGJLK{%Rm0)0jUu>@cR
zNE)3>p>p&Oe9-J&Ca_Xthdy5oNKjG-_LxGcvo1odMRB}()C4Gh(-V0Acp|9_ANwxO
z=5EHSfmVmJA=!=Nu2lC^s+wWf@TTkeu?poYE|seui*JN2$O*hC^5NEM9y|{AK7m*i
zJzWpSa&%$K|EpS~xeaVaY$)lvJ#KsuDpk#k`_{OA$yg0HE}2O4<Yh!mO969T)#-2C
ztz2}(#b@k(E>y`cD^@`{5<vAL9Ys4)k+z6fUT<?r)eL~;?4pH7IGZa9cQowRb?5@7
zY_i)fNzMNv!}uITxc{|inwls)1BGFUOLdRRv~D?j5M1nWwP};g%}>lG#}JxcY!Qna
zW$2;%Fd!TpF5`~Ss~l;8jfQ}TD+Yx`pcciGYUQQ>cY(FIe`a@gH-%(I{^gzoIZXlv
zf&3D*08H^9dy@c{a6f!l=7a^9$}Dz}mkaKtLf~^vT&~}d7K6{NvRIT&&1;{+XxN>7
z8=>de2Qf}15?{{5j_br`bUf91J5HObEViQRe*b4yX^(3*($3P5*_!)0*H?-W>ZyO$
zD4$s5Gcx)^Zc|%9tJ(K>wt!|oZu7gBqZMFwwvbtD;PG~XQPVrb%8VRePU)jI7ISK%
z?ej1?g@qxiE~w?|xQaxGaR~PH!GS*I&qL7x36&Ae<7PtY80!<`sQqBI%dO-t+kiDF
ziAH6_-q8=CEzAMd0M9xR<H+HO<Ve$=Z>ItaWkKhIaKsSxmO~Zh(G|{qW$Cm7ePEF{
z%dUAYZb`(%jg)2K-@z~3ppV+&D#zuDhcL!D7G;g62j8VEe)i&jmCGJ+0Q`~1&*hsY
zq}T!QKT+E<A@_g`oMHq=dU35bVL&y;!v0)z<hhFBijGD?D+L5>d7c;U+F(tZa(*ML
znAY-KzV|ms&*wGvYay&e|EEggbk{0iZ~VaZo<N8x)1^#-p$*s!c6R;Z#~JzVh>Sz1
z4-u@mS|SC~&!;nW1)z9nPRv}Z-0&t6eoK+UA&K*NO4NfYcviW)lKYoVo8jlfF&BCv
z0pLf7`!Gp}=mRx;flCHgZflgZ=u@Ht*2F{XxO7H6xfy9nJ@|1EKXjjNcyf%5$jL5B
z$*R_zzQSf?@DuIMMMn2cgj~F0l434SZGQnX0NLGL<j~aAOOk4{(>`dbAN@=^h7^~k
zx?ovWpz|vr!kd(52ouQ*Dq|!eXIed)SS89?QNDQWrk(IjJd=bkf>`8y39C-g@}Z>5
zd53eNcp^P|fBSA0Z`rbJhI#t$Ol%*6UACf2xVVp5560D`A^<T4n?3I4M<cTWRFuR^
zp<4&=4KLaJrrV4<kC4l09QCM@)#nq@ly9J6Xz(a^GoyqVxX<-Sm1qxsjN(AI0zUK}
z->Y>k4UR4Bw7lk+<U^eOcSpi_GU{QgpOd4vIhVCx&9R2zFkM_a&Kc4u`$o<S@!uUG
zJuFj(!Ay<dl>31&SqFjEYdLap5R>$_D4QCYcernu!^Fu1a5+qr)5|IOx2QLC?tzwx
zb?271>PWd1k#Q<BZD8a3!M+oN37x>}ZnkG@;JFZ&A=?|G@9;1G%z8;J^J*D|EvL(U
z<|9pGKUq1R^2Zms_J*eQw+$BSn{zYrV%~POG+wVob#KM{=dqAf7sD7t1Blv9w2g`}
zd0(S@QvJm)0={9G+x=aB=ciE0ML%vyi-o26dcEwoJ|d1waI;;~p`~#9FFF9&Q$dxp
z+{-$P&^*h}%jpd;uww}fVJh*JLnanK^si5P=poo2MDvhSB`dpx(M)Bh@^dnlbv$8E
zPF7?IAZW3F6L~pjJ^|vldmiG*_A!wN;{s4S<pY!A`jX5^s!TFg=+~T_KF?<WR*nIr
zuq88kvY0W)*A9U2E-?)}IikAHhG7U6IW8L1X-;2oqvfyXQ&%!-O6$(+sAuN67oCmx
z_6Xgh=(%_v+a6I}$v*TRou+WQm)YQM8|bDW;XOlsh>q&CJ&1J#C)&kd;b!f74|C6V
z+bskDvsf9LE)X>}97LCq?;ccFpc9QO<F>5I%q3vcP`TbLKO>U;s|IF?Ym!tw4=eZ#
zQT3EdLHpEd^td5Lx**vQT}M+8TS)wB5?p|J-Y{j`zl9146$mm~g)>{*PN(bW`~OB&
z{hZ?=CHgRTX0hn3opPbyx&=>Mv&$rTgVLfll%3qGAk5~XJOY;`aAf`U2x`a5Bp5}s
zG^7D|QzsCo%fE_llV-C-XOLftc7(O(qI<>K_roI#RNysD2;cw4xN)f>;nJsA<S+?H
zgr61amDd{7ukbg8F;w2SgFHV3(bl+rEHkNhJMBp<BEb(89))ef&0F%1XB*)C22q9<
z@u|zp7=`Mhtr|)G-KrQOL2w`^U^P0=weLql@rV&ic{?FM29JR5)!(`m5$RmO8N12(
z*rWRiOT{U;0DhUAXJMa^hq-4Z9fFZ6_Ji4iw5?7aAM(n1CAig;;u6ZaRLCs`1sfh<
zJ)_^D8)|$*F30`Kv~?~#`2vK-@VaXro-ad1-#66~Khy<{{18nJt#ltko*r}>9z<xh
zP6Dr~ZzL`drP2};c;-b`Lwg(uoe|p$wjTH-qmqZbQfgh`exrhmdj#>@wB6K7C*?%E
zSv0*o51{3{Dsw^)>#G^yst%KZOzKAwE3TjSQL>(k^r?r;BbJh^Ib9U8@6Kn|ct9G(
zr7H5+7AtMK(MtV3uWSdPyd}BrR3cdN<CE3cODQ(2{XsW%rq<u8kjR4+L#g{-i@kHW
z%SNpvq2mnJlLHo-97%g5S+^?a!JZ2PC|HJi;;b%vT)`J`*Q4gpMWU#j%~!c*DMR<k
z<8Ent8^he)=LyMpZ^HY45#2vCM#G1|!Fwn-U%(TP-VJ{Wt2e;k8$UrVO&g%T1AM&&
zU8uB}7<-mD*fot$&YO*1{MlNXPz$kOuR;{fxAJT1pJItKFUd&=XPNGW@?SG%?j+W$
zHdk>GIBYvmlBjj;Rqx(p>VX~q@tgDS-j_8UX^->B-BWi2Y}xlW94>}8qeK@Fun{_?
z)2+CCO-srcs7HSkI#_1qv5mk-THbI3laA~_;OY~P$b-SFZL*J>Dw?%!Pd)m8jN{})
za=srI?SzjO;=sf}aUQtWPN6a2+7BjoMp9QJiFg@G_zF99eaecLa}`Q6b!JD)NL4!b
z%@wmcHa*M<BSm@G9b*t56<pLB>ov^T1U4>Ih@byxKI`sETi0BuXr2q7NC8I^f{Ak$
z+<mQVkp<6ynm|tILzX@pB>l4*=Mrw`T*P2HAY<Ssnv{Gz1_tCea-A7vs#^u2!ZU;+
zgXF}C#|qVN)@m#%O~Wo|;CB#8`SOx=ThWm&rAWT*KdzX|L-_@M*z?D1P{%)MKtWe+
zb{ckzzv&?5QJfiEkh&q)RE!jfBuEO5&$<NqQ6XJ%qCG%$e-+KuE@->KKGL$pNGi~z
ziFM6Gm+Sq~tvS_Eyy)8Ph`2Su<#0!MG$9{5?x4*D2M>X5y)#gmEKAIeNGQyaMSlFI
zD3W0_UA7C21RyT9SIiE20Dm;?WcW0Ij^7P)nneA4c7v9E{bsEGH_HKQPU2V8aFXrj
z28)f03Q^evW?*vGjp{$(M>8lBcRA!d>3}VbYy>RXRg^>iGHHF#R75r0^BdQ?sm&$&
zBn9}YiRpdaKh=#x2V2fcmGU8upCDHK{rN5antEFxEaMEGLt>~|W8s7L##x60l~v!+
z6}tXDIa;;PY|3)C(k<}0eLW^f&F`Z$F4F(c8<S(*Rj43^)bGS$ct7yLrEQ&aj*pgp
z`j1{Xv|;e?<b5efQ4#hoOn2)BR-vJ^^XpkcGre6;o3!AsNXP^zmB|`>FtuM?+RDNu
zD!OGx#2C(v*1su$KheUn>Uv&JX`591_F_3@e5rP-L`}uQ-I!fNJiT`Jf`uSLUU*QD
z&^Ra27>%E|+l;LSXIpn*`S|JB*sR`}2BS9-YPt<$`El=Ker$GXSO58)K+LdP9Ha=`
zpgl=7PE&O%y%F=%`eH+;4$IEr@s+^={o#-&;F}^M|Ef0s^|tj@G4-l8Osz;SxLZ_k
zzib*WJCn<gZm70ip6%ifgT{|jtV%|+NVUUHNk~C2YQUNo$#yKi%^Ay!OUbrtm9Hc*
zN%_UreNU}fVFF1QVp(`Iq{RHl$xocZJ}?!LzL{0luUx^%Rzc){Y`YPjLL7@eo_HX)
zAL?!Tla1cZ1yO*>v<zbqRg3-gu=K*&GG2fDLs3I5`@J9;RexGr-7sWo?}vyf02`o!
zri+H0$|i~bHuRl#e9^Er;Z(_LP3UF^ut&|8_tmCS&0;uDqf!073r+HZbJX#*w`)No
zX7-s07-YFhn7Oh)tl|oy&i$KJNJ4>!rl;)&9KXy)F7Yjv@%3YPjie+%mHrTJ70cG4
z)`QK>`}s@Xhl@M{yU39w?Oa}?6zkU%Z3|%v<4qR%)1pgEWWQ4Hg0u$czTKHx0@l|$
z3AL7;J=Su*p#8K&8|3(DoQ`?UmK12!Em&}X3YQ})i8b{~fWt>2nT$H(MFVn5yYe*?
zKbv)Om3JeQ2u+2I_n?pBMiLcmfzs@X2r$GTmuz$*zRhG+A;jpu@_WnxAurY)7kXY$
zFAyDjRF0OV7)T~H0GERabs&mZOyuVqz5cBz{DkqGxb60Hh2`n(bG`u)Jv|l%dWjmI
zQAS#<@8w_u#`gNH9s)?K;oTRNdG@`y!mGybxH~@3^2d=XzgiX-W;s*$%WswyNOIpn
zc6zdQIp!S+eVPaa<Qg@^k4rxHY1XklZo`}g+0Encl$_r*G~Gl2^~>NrMx=n+*IT)8
z8fVIsXM^kao4UXOrB}a<E}TC^B3(pB98Kf0^J}tZr~ULS(3Bm_AF&@2kvC&DP1<{E
zz5#f-#luh+Iln4_5(o1t-?NM}j=M5C><LLQU@HivWy8|Nko6s@^3B}Kb%hOZ+cYTX
z*XjNbXKxi8N4KnLTg+fFGqc5vmSizAv&C#NGfQqUTFlJMj21Jun3?tWH)r<VCuYun
zGf_7c(b3&ixpHM@t(EUnDNql)(HSs;>cq*=OPoltTpP^AEc)gqW*-;7lJ-M|g_sY(
zzVg+~MAYhm-B_*_G<3Wyd0J|rkVb2AZ*Qg$C!eRltV-@R6pA`&P0rTgWwg__w`tlI
zI}UBB(8SdAfK<{^5v&W-U1K8)D5#P_mNa*sj!pbdMhyI<gf-Oo8E?&m@qUx*l;lj6
z<b#GS_PzE}X9Gq1(A*K#3=hlk_LHx4Z<}MmW-z7`ot0*WJ*Ok)SCg!^b0W{)51hyA
z>=K^Mbo8HTZBnbom%{9^nw!uj=fhBZg=8JS3H&<o)DlmHbjg~lmrEZiYCj+!zDASa
zz2H)EaFy_YJL5!0B%C6TIcFK~(*?(~PU_Z*ol{=t+N!ySOK$AmTnFFJl2qlvS7y|}
zUVdQ06w3BHotReO^;3<;Epa4>z~0*gDvsAF?)s&3$|3j1tB!una?V;3gn%exKlsCU
zl9<4Ja4yVE>pKlKJ*Lh-Z9g5C(wn;F;r&ekZVej6SnRBPFB5oIdfRZV^;)AK*ZSID
zm|9;8Q>7!ofz@(<dFhd{U(MsSF9a)MgRApGfI~tnnR`TTo;)N@^4!df`5A^1vHq6$
zw7U7H{@YMi?_T+WRKY7?H|M7ANkWh6!&Ac~G25SOn>tf41ly$gtGkY25)E8vj}f8}
zjX-ZA28xg%KXl0UtUcfNH<O|W`#aYjN1ntt$7TC~h}`Kp?(0ABh`6#9*4;zqH^Vh)
zwq)!*5W5D=!rqK9k=1r69aoOv!{BG+xC;hv>wsD}<daJ*gVWa?;FmZgX0VT37AD^z
zG-<f&>K7I7#<l(+l?@|m7W|i`p1jR~)JMC{_&8Zl^VFk|QaCa5-F6mTUw<VCh(nsy
zJ^=<<4!#J#51KRPfCLrFZ8+&(B7j&twW9+Dft)`>6U|k}l}KS6Gjmle+eC!)PtX=(
zfn)wqo!gfFN`nW0^~VT35=Ip_A|Ce-TvTl%Q(TSye0uTnB->D^YYN7yUu*;6^!s;T
z6#p_nLS;$w$Dq8=?sFl*;GrWXkf97K5NPP*bi&K?2bU|>2Htop8c|O37e;kc_iY^;
z!lJ8ymY(5jjk)J~L?DuSd)4ky1fPU)$?t^Yp*#86IdRIqqkQ3tc3#NNRpU+Lzj2dI
z<N_B9d|nq4p;V^Uug{bgd@kHsDA!<ayv{PxzwG|(`VA#kU-HDJgnS1(b1OnbuTfRd
z2lz<=D)PJ2MAJ$j+Xf~TxhAnKPbT*e{qiQ3stu~68SUIHG%+3@X6morZl}v1l-2KC
zYJ`5$u*}DTmQEC^zk9M*Jylw&#Il<%Z=DI@p}%!vQ#Dn8A4MB`%H0tUit-tp;q#29
z@q5@==W$-(c=aD5er%rM%M*L(cp9zr4%aRGg;YM$n4+3wH!YiDq%p7RMYZ|tlcBo>
z<DnQ^>fh*T!KoiQZ1&jAJQUReOXPO=oy<2SIPYawjnVXM*x5n<)wI=1IC{Q190&M(
zHB}|R@OfEfu8bhfvgK!wVn_4n(;#Xtwo|E+TY~sXIIQ19g8)&K%sOG_`unBu=!^9f
z-R+6VRK&NDefaJ9_bG&=r$x$byg7P}q|5G>TJM)t_R@C(&KI(wPlQZe6&iRb_bQJv
z1Eh|c5**#<4@BJXm>z|xFsCM9>VO7gRZ+DEtk+MEv1@sNFUna+OXCKo%|Zr6oL(%d
z7>$X&U%R;eyQDzymOssJ6T5iyn^5tP`%$YxrOKWvvaQlfh!4-Y3ec7tUtf#Vn|s7}
zApw2;V@)mfa~`Vv;<M!G%U(+{_p>XVl{$wL{Rp{doguwB)r+98-GGF0QxyG<_L|ub
zN`*e!Y~oj?#jzNO9=2%lZ1AEvDZ!wO-wR!#ADW{+4u+YCzFxHq8!o~g8v6M4YT14j
z2@T>Cy!}|Aayev}#Q4<qE^!G4i$w9q2xz7^HB(mhA=#1~7tZ&S25eQzq^=)i#I#Rq
zuu+Vhs~%l`nO!18pw_y#=U4Grl=tCh&A3}fXD+dFnZ&L*brm^78a%I9`p+ypBcp?7
z%BT0FbH&Zm(W^AR!YKrR>GwFRo4xe+s|!lwKsx{02n(>+6rz^0P+dXqDt>pM{sb<W
z{p5h=mK&Ocn-#GGQ|=iZ`MT|_9Am*@hVy>P@bGxOUU^S`yg}%$KlqBn_pYvLO3|Nr
ziD6+AO?cl_1Y65;b;BSaiCW3zp}SZ#h*|=Tg57<6SLvD!Fz5vw9KaO(e&0*=F8I=Q
z2%ILR6iZf#loyc}uy%KRLqW)TdJdrX)1$ZUeUx0{N>QJd1JGTOZ20%%N*JlIA(dm`
zWbv!+d?4UU0d`ah-uCwPC#$}(dIAO0n(zL|5^r+I(L|wb^E}Pmvh`DRca)dn&D}XQ
z#W7OGeb$1Oh1g?Zm|1+-pzu$KwMfv1^cXs8QluxfQW2iM$EJ4>RWaL0n~xCwUTqKR
z6~@*7TZAVMl}U5T6fEN4p>ljPkAavGaii%>z+3a>y#rXHS{lLwF^%&$dlyhUd)|6{
z^H%6sM@y<D!4$}p>Mk8?^r<QlA(6-+7%DEnK;eHOAV)WXV8U6-!vqyYN6ZlD7<ttk
z=Bv-xtyWDkXKzWWcg6QD|JNs@h6Wu~zJ~Bt-wttR%Gh>6Nrt277La+eHvpHf5HU8I
zNYFOr?ovsrtpCVt_i3~|qN4Nz4DfhoPxI+(SvJtgE~P(g+%wZUlaEq3;wGj|A2+0d
zzBD=)$Mid5uf`(wy)4`DP;2k0C09~FtDRmcv?GMLBCAo)&Q@FGz=n^3{BxPK*#M0`
zsLe16?5OaQ(q#%dH+VlOCym`zKo&f)%a1QQKmZ?=8I1WmX~iP>b8V^iqHI{;1~`7(
zX+io(*-U9M6W3(FW5d%L8$=6yxax6iCvoS1Mi+Ec?!1^tv;ni=c5+`2Qu;?@PeveH
z8vd_A1{ef>Uk-hE=daXX1S5wd2O7~0Dj&+aUV5ONKSOzX>QS$HlnM)<PF%qR=`4Gi
zzuq-7_GQ?StIJu@lJ{zq6YAG&A96>7T}Vb4L^oqchnDqBwheA8tDjRtjTj1GM103#
z;>VRe=gN)OgzU`;G-@;^`?(jQ4$piluqJNB9=1e{96I9Y9{4c?p`_&9D1ts32WT+m
zT3xS!NyfewuCvP}V=40IGwh-5sqj7$950dg2gaLFD#it+ML_(1r7tO&Rs7S}V`6qJ
z^`}l+0etVpzVlukO33<v!OTW+v;bU|e?X-0wbN}25EO=7=1te2dX(1Ey-oc26sFvg
z<MW{P{E%S}EY8Ww_rSI52h_`F@l%@k;^nKK<hbv`6;|A|=+FMC6)AjfJ<L{lh5Lz^
zVBo2ossIs~jU1{e@N$&j=R@MQ?i;3@Uiy>GagJMnm|P-SF|>y~jz16Mu%(5&Q8F-+
z-4C$W3q!1mf4#zV-xKt~kO?h;%YQc`23kw6rr8U5#nBAQM)o8S{CF|}^6XxgD()1m
zw{+)uJzhjA%odaObkez=&L<i5hn33hW$MCgxd2!xM)0`?m#Xx1JOr1SW{aqUZOsJ5
z+Gt?#AkVn6@di6F5mQWvkK)VIIjrf-vU8Q1*PWtX^0C8REoEyr5QBCPygoh<o(n}l
z^$qLX68pswW`061C)+@DL<<Kq`;QTPBm_LdY5w=<#OiH#v7b1O3^mmr(U%-cy7>>W
zHB16Th#R{@z86LyyLbirh(RdKX$&~+kq^`tZrr@05T}eHG>^2oXYK=t9k-SpHfpsh
zR*%NFPE%`LGAKlP&lie9Uu<nUm|OqF0s!MEd?F}v<!id&qpMG`PoZ2x9_6%&v{R}r
z;f;wLezj6bE`O;{zU&zleVy-$@l=zR!lqHtjR88=-LbF_o#;UfjIo{f!5_?v>l7ex
z>JM!{%@*Q6j@ecC-&<+O&<>*?+V?3?VFu@_%Jo)%ZyATS`6c8(Q|);*A#2d8<2w`=
z_<rwz(wJh*tB#3Xv63Gh5{s1gl_#ndy*ps0(-jF<$PIuIiniEkrdwl=Oh)C6cU0Xo
z<Q;0*&~xI(bv4fR?%M8{d!TT^tQj#A21aO2UwFmbP$<Cp%NpoT|CAln<YuR&XG^9B
zjWP)yEOHHcP*%h_$=%38%@bR1uSswbbQcJGhwiM&T@QoGb6nPKC+vn%g2Ru};fPF_
zd~&@w*8~!VW6mXo6IR1%aSH!{M^^x(wHem4+bfEP7+kqQnKP_VjW<zkdpwvhi)tMQ
z{K6WRW}0iu3Nxj8aYye%&kN{g+h56iC5oR#%5cGjXRs!wLaw&PmIzcjpxcR}A7OKl
zWsLiJ0hY#U8c`g@7{<Vx7iSz}&@TZXeLaU&;3;|u%N57NK6DG+mmPqoz4dCYq<zau
zD2-|!#Tv=icX7;QSC<%!b?adhfp~*OMotZn*`{wI9H4sgW|7VVqVnUZ)f$gq6A|D`
z;Nw_v`7Ow6b)1GPKk2A4F)@C%5(^5QhSBJ?F!ml8CQh{CUa4Rk0*@Js;7+pPIJyeE
zOdm>0l9w}CxOBd1rZvx1;LAN%1Ai?$QcDZ=HHZ`V7Ba(BwM*UmGd&t`UUjL3T}+*n
zU>5&ZBKVE4x9GO5F%P&gieE_gfYIrSy}9;-f71<X7(DTKi~xZ<A<9c5b>dLp%=A8J
z13Q(gPFt<tdS&)@wAFOyrSqiO@q~1s-fR5CiW$o2`BkEv#XwPTeZTsUm#E^~SJvIj
zXBS(2y-BwN&26Q@JG;SME+Jm_GP<|FW39`4HTbzHa<xwSUyXc6xgVN!%JHl^P_vy6
zS2rZD2(S|ma1cPde5l>j%GkN-=>}Y%<ufq+M(3jL%}-v0Z<Q&usn(q50+G%TH!{>X
zGp_+&$Q`CZUP~!f-2BGhwz=##>8WQz(>dtIX13HvLzdC+=3~yU`B;X{4t`hGnJ*cc
zF{m>eAo_&0LJy^9z)Z?q@xi=MhST$5doqP^Sws7h{FbyCPG(}rx?bn#5kZvA<ZAVk
zek@<%+!$v6C^AB1?hhPCBk0%9|4w?WS`B`1abh*4;(FC<ekc!rzaKp2?3}MTfSyl`
z_r}^L{YHpNwE@Y0R!aNbKE0tuh@7|&ED-LhO!^DVqX`X^U^j$FTukXq`)M()5Rz_i
z*w<duS&r%~QE#g*v8xJ0U2d06k2^Walyac%=%Nvi>RYM9&>*QUgyTE^Y%(+1#}G^m
zg0tdjk<)#aG>gV!>+JM`2VFVhWUi=`c$PF(CmB0d!*!1U%D|+F6DT0_N8;zV8cz+>
z5P71*R#!el-C~ctLEU1;-SJ;hlx7QfL3`}!!`o>eg0@XvcY%)0s=I0KiA)+mON^RI
z8*0m$y+3P3-MB;FQQ!oqR`>e6JioLo1+iwZnnm*3CBO1NA>1TFap}+X_d1P56M@?;
zo0G{W&w)B*;$BsMcKmd23iDQi-hhxm1P;tpfI&bTQqpy=*2sCxK@2v1OFQ2cNb*`N
zehjwk?So>qzu;dG8c^_1v}0I0SMz_Gd1Sv>*53p)A*+yIR(C$6vtGCdvmqf+#Zuzo
zA&Ts6V&Tyh49?Jb261Dhx|iX;rup=!3N*Fd9j%DnClyt~PhCq&bOh5OaHym@xVX(h
z^whv+ao@A-e1oMzkMngr65ZWfPlP7&@Q&@6jmpRFGxE|U>T0T7E%~umNT==@##v5N
zy}mBenb>(!G>XDv-b5?MV>g?W7UKwJ(a3o0fd9lwlK%(X7-8yUVgl9Rk4b|rTfcpc
z(-iY)8xO(yxyx1tVutI6E$3<Lcz8cSdG6TcOL|e7+wY_+n0CrkSznhT5O3kw-c9Qd
z{@jRI|B3xisGGA99xI_?JqVrcZQjfoSd;wTTXf((SmS<z$W%h#VZWxs`1l#jSg2HU
z(2}-36~XO>R6o=$l4$Np`c+yIjN@)!#jy9SAVxNN%^%`tKemFe$cQ-W-Ef)ef?9}s
zWk3MH!P^<Vwb7m&-6!1|-7P(k-_ztKLjvmv!PTDsPT~<#XRk`(l1PjiV%0@)vGow_
zZ$*O6WUbxz5`HA&x5fJj+@O$9qd(WeU*V_-PYQqT43=J8PkKquR!r;c8qRiz)T%7K
z=r6$db_KcmY|?m7^yF}=uD67zL~z57@#0m}DTdIfga`3_f$t$TNGZ$}NjHV{+fNxy
z3;>{i6z?*kJ4}*|9*FMxo6n--Az_EArWr{e{8?f!OO|9t1Z*;+!DqjUl|-xbnmP5)
zpc=Qr=fEpvYx!^qiVwWO{v|di+&ygQ5n$y_1M({P&HXUZX<%^edR>bV!#Br^b%_{W
zCpX?T{j6D+vBQhQXTG&m{QDC~t9KVW^QYi39RED6<uD{0G220W8r}n#gUxs&y4m5b
z_QQFP{JViwT^Uk~(ksRc)-p<@h2Uz%kdKV)K{j-fk#|Aw*OB5niR%1pD~Hn&X*t}t
z1vBdJp*MeBV9Q^X`%n2L7fZbz=D0r{0dMRTp})M4i1gpw*OBhWM`(gYRm;&R(Jzh*
z)P1F*qy(gdOD@u&JZ+jc{q35*F?RavYK9=T{vH}ul&2b#y{)_u$zeF^V7FZ|D;Zx+
zXFBC+AlOXGWM}vi5ydUnfy@XKR#bR>+*DgRFz8g|(m^tP#<jo$w#>d%_6w32thGE{
ze~C&PeQ0Efb?JQeeEytMIOyHBNhgJmmEx;~O(6gzF4)IBgXsH>N(Vy}XV@yb|H9%}
z_v1R!US-M?@#I_S^XxgB;}L6pC9gs+%#$*ZB0MU`+%{gso~g&`$G29_reN=&t>oj?
zmT2#oAgm+GAHaxhcdCFOIgN9Q*fXj)-)P86EBY3;91CQno5_OVP%A$Y1<aagLg*UB
zbEi1c-EWpO1Qi#a>a@;!DJ;21Wo-c{NY=ZKCxv5rjl-h@mjso`y_XC-UtOJ`I>v=2
zHt99?!uKETY>PP!PHO>Gm!m(-ou(_+P5H5_=3;LfOlA@?vb!_8e$^M4mFM6mw;y(Z
zm@D`)+3k|dwfU*K5UZnzw*ZCM-#R~cj4&*8v&X)}28uw7CBAP>>>46+9gosubPSpF
zwcOqbye=tGW?~~<AA90at$QsZsYx!y6=b@)%(i$aWL|h3wBO!!X2AGdGCj5}vCHAv
z5!m7AU8qs#J1sg=b95So$4laUW&aI&w13?4tLDs~qwgaYGsSgegN!0dz_)Kv0ueog
zl3*ga#TvhrceHqnpL@L*xKGF$)$Wam2z;4hKU9ips1u{cSe=BG(vF&mnu9Gj{;4h#
zOrq8^O0!QZXJDG}OMZkBqkCdJl3=~&!iyP?SE&A1iH3r_TO^e)k*@{S*2I_?hn5hr
zmJ%uJDHj(Zm%+i40aEqw;yKJ!bf)OOOCtMlsLMxDWcy{k^xUMF-DbOo5Pri3tLxkd
zEz!EmM3+t|OIKDq+EqDHaiJzi0|V}(0WbL_gXK`AyVdNd%Wb?*FMFl)xA-5E^|@7#
zpZ=zcx;h_zBH&X91D2(w^QMjGcnT8S6A_dc9}^BGPK5|<#NCR^b=la+KJ`A@D_pmA
z@*JIYmqQ6@>+4qs0;jC((HTpvMwz5qcltjw*OzL2UVpjnvxto@W9gai{#{~VLsHJN
z?{-!P*Z0{@%Bi1c<fIR{)c}9Mt@UyHkS1ZBssuwzTeo4P2cr+rP4@Q@Ijrj;rx^Cl
z>nujsNUOI#v(o*CS^~O6UUsSGkZDQvj&E(*?^8Z-%U$C<X*v)Tho+X_v_O2K{XdW;
zBPaXnhHP|mu4m0j{{KFNHxvEq%NO?<@3ZU=ctz?go%`AmU~;f!-7)H*&1PAv>q9B+
z#VnfjcIK&NxgGR>kICyXXYL$?iLD_F(F^IW?)0UBKs)_|u7Z8JlT&3R8qjlLRc5MK
zHdVCEhEz-)H*%@<u7phI4m%xB6Lvn#9V?^LQrWD1AO?&2^R$$%R-v3bplivM!kxU=
zb~Y~9{MTs)dwmpr`@NjL)5+>SZRY8Z<MbBt!2~4%<~RN4Tn97AQ#JF0jHTSGFo4A_
zOLgaoIW%CyiMg#cfjII}(L-y0&{MdMh6Tay|6*hrQlhp(R=`j=tpGRDR~lveOBGTb
zwmS+Ev)c5z`zKMtny{lad|Mj&L)ebp^}?b~y@T1-2xvd`xjSyi3js-<AYK*rRMf9u
zr(Qdg^1kvniC?Rt$LhxX<afvDZ2L0qI;+Tg@!{+Mz7*{BI$H&4db%r>_BX#(mZ%hV
zbS0&i1_)_mqfb0`9>-8Z=*cy!S=qKakVAwr`O(8X&egcsQqPjT{!YfcDRzX7jgv7?
zDb%DV%}Vld7gl+@mPFG}f6uS>*o_s0S3A63o8YPj+1Ak(G)U9XmChk-Yv=I!23cI>
z5`S`fzQzu~mIK3e{|k<mLmrL$LzY*63Hs@Cy>R8=2mWD5KfMqkp+MI)AsGe(p*t1s
zj$p=4pGU&@KVy_|z_;ch+KoWgo2K;2N;ghd`QK4#8VlbJo*f)ASQ2JHBMLD8<!IL2
zH7X;om%$(6O!!SNe~Wx5K|~zjhCV^R75sPUe>1llhtd}8|GCc)>VMs*_l>;Xv^SLb
zVz$Fm9jWHdkI}vs3gPo}hG}D_$cIJg?MxYxb+r`dsS>mQ(9%fkWk@<<DA0X9UM6Zy
zhG%nHt6i1f346qz6f=HIdf)O=c<h=6PL)?MRkc~aFNrHK4xO?VtDjICs=fQ;79XJ{
zV0Tm<7v<hUY>{5KoD^7+kH^dEi{6~~?aYt8sm#r8WpjW(!3`6;?^Q>Jos_fRLrAB7
z{tO)H%MpkFp36P+hu!~S2sH16!3DpUW^>cc?t@<^Rfp!r*4i;ac(8w?rImV5&GXET
zKIHDVU?t~HeD=Y0N7YP;aK<&dX5Of`+198z==7+BC*)6?ioxT|zx-l1-IUSbF_}1;
znF*az?wj6p%IXbs+n@WjBqcPqUn_l@9hk0;2fH^!GhLC*w6ry_>pJ<dcta+;(-%^x
zm@5js6U__&0DvIiLhrTlEZi=1%%3-Q-k_?n*~d3?_-JuJ^LFZdO)%T`+>CizS~TzR
zwj$QlZSGopmEbYXlU&`&s#?)Wy%Eu#>fdWRkaA{}#bfM>7ov^IYBxMG68_^yxfTey
zX3lWLd3uSO6zGk|(#zjd+&&fV#)XWciC(;7VcymDP*R;PqAh!X!*NtkLnt`$YEER-
zanwGJLGwbs@x=ABsJ}A404TOMdcl?*E0n^2=Zt1Z>eH@dcw?=9%6s6PMTNq;boCXN
zgrMNN;oN5^<Z{Ye0v8@hT5%6Lz~%d!Uk9Z!IQ(kbgtZndC{!wgKlk1T%<Yyh>QW#;
zk6iB!13`M@Qs&Ity6fcQTRCtLUr@=J2MP84_jCmkwR|ROtOk?l%3U`(m;e?$mh3D2
z25#d_Y)Etg03_$AhJxoV^J<xXa^&BmzS(9YY68n+^wf3`kHn~j{;`P@#WN&6!#a&&
zVmMm-1Md31Q?h`BpJWkdQq>A;n?DHz(k1vxi+RpYf_Mo7hJYQb%p7r*Q+}%|n0(Gp
z2}Vbu%`<9qW(f=_FJ%{G4VqR#YMd~o3#f>Oiir*_Cq&T3^SI4;AA>R}a>!ZfJ6d}4
zxWaAr623XW8Q;^z_3h`MDm9yyCK;v>7UPqJpcwwC{rx|%zRw^u*fogXbGA!FNtr_#
znBE=%VHH;yZiW#0s6r;y6C`5}Jo7tVVEjAk<5-z%Kb^L$_jwKc*o5XA(~3_I_PMUv
zT7lLFe5km7=9rrU$#b@dFb3Q#RB^dg9LVqIq1R*7x+Sdpf3HTs_Q;zLGS~C=l5OlZ
z4%J_sqU7yH83}klu~)qX8~^SvbRZWqGe#!~>n96)qWma%WY*aRLE^j`XA!1A0ju+B
zU$qN_bDW}m^8i!$we}AckVV`|ELf~RH~uzfXXYXBdTiQ~Ne&dNc;wiacm4^#2LDs|
zjbYDuV-e>y;BHhAVzV-kFe}CBYaf%+-BFT7Fq?gBlh+}C<74(V?~0fN1)<R+UQj|=
zUl~l=c%fqEakag{m~t&}ky}sL9VyAi_N{(XY+((&r~3yvsrb}C%I285r0>cWG-}t1
zy|F$*eJOkJmZjyDiHGY8qMorrHi!Jj`2YPZL8gbu>uc+ojT|Ux|DQ(ygEWYwsD+UV
z)%<@U7;DhNb-@?EMCkr%D*AVF{%>g-i!y%m%ij#S_3-uQ3oovYuE#*owOZ?4+W2HN
zOq|w>U+Qj8+O_xnS3+CO`F}`g|5$XNLIHPYtc0Qb?U16XVwS2yYU`92ts`NZ^H@@p
z4P?Uf|92DGeQZ2_y)xp;l_X);8T_B%m#lCFkN)37hZpPq2m92Zji`bwG^+X?>p!{R
zKW8Mx6oyFgnZFALP5y_pA7A<p;j%QG;Xfw*&zZ2i6iFx8Ze#sM)@9e@?#TbIr-JyE
zlyPVC_kFl_zT6F+h8;Kk|BywchEI8Qbq~oiL5cPGv2Q1dm^Q0o9wFsdi-Udxs0=``
zFL>UkruG<v)sQgL0t!((nk8}W$i0BbecLgR%~C>W>7XiMc7mzuY>~@jwA30<>`+Bs
zXuTVYAt`a+apeUJ>r47tp^=tmuOHS&ET0|G-|<kE@eio4^XNcA(8KNavMbIX#S2~?
zQnX&}|Av27K#~FenT@9I|Erk%Km8I^UdmE}j3+B@3idyJ*-084$_YMUXz#HY1@ZRT
zyKUL`unyA3Z@;dGk>~&L@&H82cyj1)WhW!Ir<-Sr#M<1XZACL&CpRLjlcrE(f#y~F
z8`Mbi7GSW+yCSz}<McsRH`<Iz-^26sMp)aYs80rCze<ci$dqw{Z<oikw~u|_n)Sc`
zBOo(TxQQ$G6g$hD?%OvdWo6~y=MZ?z-@hqkq`SQ33sUgamy85IQ>QFKmdbtFvb?v-
zQ{J@hU|T)7f1RaLP*T!9*AYiz(o=zgx|A8Mq3ID~W$=-gZ@Xi<*~80Qim?8L!Of5m
zl|WqPC))Hg*8Wdo<@H6&tlKQpZ~vm7LQ<mEf*E&Rp`oFf9!#Xw8TCW2wAq%Qt#nG#
zA_2#e04EFOG9VRifyr>JG6-5BzFdJXy@Ld@fZh>j7LT*U!}%KZT9YlHKMY0Y<L$Z7
zcra4A(MpY0txUD;;m{PF0|k_-;(_DI<9UNornd)^ktraxvDDu@aY{~3bz@`W0+8f-
zw!u<mFsG`x`|)gr-0$<fxkf&x%csC9scaE;bToEMti>g#TCZIlbcOjZtsqg_Y=suv
z+M!2D4y&>F<4&$v*nV~Q2ilYf`(kP}z(bBXY^`Uz59Kh%_x(l6#iilgi;?@)_J7YL
z2XzWmN7&?Fb1Ig&-;9GNQoo7OZrU-Gx~N=NYE)+<;&aNjxg6(p5WIx^;MXlQXtGHo
z5b_04<6KYX9rH)$9<{-iJH48{Zzs8KK<o~90g)(dX7%&jK7&GO%+{S3{QbZWY#Nl{
zDu4>o+!LqmDh09cQ|>?AxOL8l@>25h@{)n@64$$fK#=S#S;wwb99ObU2v&B}zT;X#
z&3^PZ<>D79>fX;RL;<@+P+!0hgw6wq<3ta!uiK=2+i0~ww*ulseHRy(a>kKR3PIr0
z|K1Gk!;~g4+|XFjg6cBKv1A&R0n)H@L;oNlpw}6(G5fd6fuX>CH}yZl-VB67n2*#N
zPi8DsE|hlKa9#RG(Vpluj>%1j3>xYrMxCbrMZmLOs_NqfjlYxcJjM1wao_7vejFY7
z!R@pVHXccOD9n}zmCkJ|Xb27Q=~PQdiWPE_`7T<-aKY*=rqy?T1~Cy+fIK55jM$f3
zG6r?BALOq4n_ib{(0a$Sg-L(eK+1J9LyXW}35YoOq2?{$@($L|_e;N|l>PU|O%0a)
zG$Q<udk{{`qX8bnAv9c@W0Fm|IeNbV64XphK2#&6f0%Uldf7d05+^_f(h{abpi|R9
z1m{LCe*D-HgF?VeBozxSfg(<J(l^}Im5pN%Gf5juW~9H9zx7y(U<pGe!k9vreehWN
zUAy(K%x5764%*sisFTUx$#UCNl131~ndZNZ)^*<r=}pNof5?ks`u#Uwava;hn>35h
zEm=YE`TM5#tsJdVUa%*a@=76gu#xud;WWyH!VI&*x#i!dr%6`5s#`d0c=i4a2j1zJ
zAm*J4ibZO<P^ON@O|Lp9pmeEHeyFbZ^`O)Jf(Am2KY&^RUc=GA>zo6Z#nAt^N)R;C
zKFEI*#KtD0G<0TF`(h3Pa@9f07v0L7IDy|BwuOO%hOn7wIO>C}H{%WF_jn$k?~g#x
zz8Jz-_9AHvrl$+L0b<f^jug_d;YPISlv7r?*xp}{okdFTUngN@GMPKGC~89Gd%<_>
zv>5t*?$_e(S+M;W@L{~LWZ>tSEy$u^zz>pGVzv-DK2W!4l)*v!TE!}+X22o8$R+LP
z#II9pt{kfBwuu;n!;BidoIKl+^qQTqc}6ZZF7&k2j?_2PkSe0MBziS<=klRZ``!KB
z>1m-)hU$)+$hPkLMV$rR0gtHjMVDu}=@MxoFJX2DBiS^bq)h(4R0lluerwI@%YT5^
z*0=w{LEpqel5q+jEo?_pJi0n0y+_hlDKb-Z1>eiWY#~0jzCWz}gKG2;0x@@JqPY%<
zAm4WjzV<C}zl#>QyQJx;4ouf6GPAwUI;W&P2Jdu!IPWb1FWwX%@qU3EvoA@zitgcf
zPV%DtQcx@n`*^W3L)*3EylE}*E2rH$_wh!1^D26tV=8}W9wwc_QsG(G$BXsOCK4U$
zlD=2vy7RPv2+@RKu4KOB-q6ii{q)#Ou2>T3)Aca%xLJxG6$mw{VM;=lq)ME+M4CFo
zA4~&JM~XX|e&4Ywzuv}=5v^7AB?hIQK~*o%e5n0kO>fI+99I&0w1%l47&XGaq3C_W
zk7R<jWM;14vIL)#>)qyfogLw@Gdop*e4XZkapC`U&>OFhJ(EX)rS&khVprqGmC;QO
zE;2w!&%p1LmCK;l3GX7lREk*>D}dis?~yCVwsU$yZ>V4R2y90;7bTAcC7)%-)oX&^
zhp-IET%Y2$ekdzTaEANe%Pl?6qyY^0Brj(Dm`-%-?B8+PdcEo3dgBS#*{(x|u{WTw
zbxEqbUdqPSYE8meg^zfQcFk@3zFiMc9n3lRD54S~=NUwLj{>GZ2?UeiHce72JQqq9
zPkm#1@91xS8M4Pb^UsU8?IO9#%sHLjPP|~pr6u?EEp4pmoWt=f)?Ct|hdP0MncJln
zM|8|QiD`%!$RTTMeOTxHk*`qW)}bQqv@mu=?$eW>LLXQ8Cn$ET7C51!LF&;iYs=<M
z_vU9{M%^qR2!&k8v?XO+=Wy5d2{U>%En8DU1iyN@`XoR>CDevKhWr8FV;g5D8;4F@
zma1aR0cg3(t_UYXgnIx=tZqKH***$;Dc%51vtBh<BKSYRym&~#qvIXFjpp`W=gL0f
znX&{X6*4ii1^Mu7&n!DU2ejx5RHze&<He@Ztq#)|fKY1Y_3?hrc#E+Zk2}p8+v4d=
zXfDA*&DlM196c`dWL8E_OO=Z@5RZ}_k$VbGEX|3(#WKUY2I&BYjA2dM!zoFhIu`u_
z|907Ymx{<!Pb+LM#iPJ__waV2M!^+6hg4-3<(}U%E&ZI=VcY*CWv;k2p6o=cP<EqX
zgx=D+Kak&1-ng4J?p=y#CC5{q-DBZCA|=B6a;sa28%23a-$gA1m3Ru(b*PkyN^UHg
zb-^e9P+Q>f_&K>FzA7;2jk{=lG*ia4n<cJ=TSH}vU@miy)?l{S8#nKiLkJK`i|=ex
z8+}W8$g!(LrZA1wYxA#am^+Rl-VEY<a||<Lm&=oKE?M6IHskI6crY`2*F<Vd_1>I<
zkQ#IG@<}b-B`8#=W)zmb-41SbdbvIC#R-`{-vFg+cYUF=v&DJwQ2HBMdObHgJWdLz
z$ntjQK%ID)>OEJ~U9gk9>p1c#P>y;|qm-;5VhXU6lqf58t5k*BOn4qtL@Yz=U3@Is
zQV`zAi&a=mXDb(4VM0m!uK0dfYTk@?lJR5844c$v!atlYkK}v_`5g({iJcfyby#%X
zmBV43OhF{C>KDg<6Al8$N;Kv$+qkj5279^TNJ#D#dxNyc&wpK~{t!0?H2nNa_Iy&F
z5WhmPz%?-H!;e_n4r0<IezUwOtER=a8v%8kx5Z0(cBSG>Z&m5>9k1jUBeppBE3>!|
zCZAf5g%`hEeP{BV3J;2i#I>v!I&RJVU3_|EPghkj{Ic%G?slQ8mSR#V8AmjR-~vq2
zkOol6srp`#8+zNUwaNKDUBjqdwqJH3U;&7R@x+dP-dTBye2GIqk~E8Z&7+b_r;tfu
zNaArmgtWf$hrSV8Cy+T<Hd<TJu({Q9+bDdHajbtDNg!AA>8le|__hlMPGFsG9xsf=
z?BXM6tswN?u)59fer0(7#!_%N&fJY?4C2>T=PB%IjE98mM~yKLe;EcJMvKv-5+rB$
z$LFx3I77G~Hre&=i<gfzxzH=oYj48K=RXTXKt~+jW$SyV8<@TxKlHlUA3fQId1#hj
zcN>EGU0&5DeLd{GQB|r^JO)GT{bvZG6L1C~_Ip$EK!+Rl2-X)NB5<6G;euK-dqCK6
zR}9JqaQht(@Pup*{X^ziZL-ZM65>E%7afkq%^%`Cq_rh*K*16y)(~cD>`RUNfu6Tb
zEi_AcGf{ASqkRZ!K$Xri{t<LpTAZ!CQ^VfJL8liM3f<qr9kx)Zqmd12&Ea@-G_o8&
zV04=9d@N}_M+ja^f1mi+y`8F+(wc$Dcl?dw<3n>D|MqZdUq-xwGL|aF(>Zn)R$kqz
zjmKd#K-bHAGUWTO7b9f)q4n(N6b54#1coJS57V^}GiwRyGx^e}`Br!r_CrB!RG+pW
z7{y<491cr25yLkVkA)Kt`<3w@^b0Ggg$KdGr*td!iu!i+yuqH|h&|tnk=}>}T<=j9
zp9p-u-TKnpS)S}bm5%)he#$+9ye3y2jdTC$7lusu#$h#owN$CG6i5E!-QA<8wAxOW
zIGx(uM5UP+KIjveV(=h_s~q39{w7fZARbE#9JEvuiFU7jb;L^oUE?5rVk$d#p#M<F
z5JJdeoFbhN2BA=*x{!J*Tcykwy)9wfi6F^A6cgDUuea;rcg1ZCgEL~A3XQk^@T+F4
z4Q(JfB;ya2J+s$kAx)+G;laV@+Dq|!%v+k%m;2LCzlRHmnYMBoj4Sy7NKpV)mQbz+
z0u2p(!XqjwrZ<KNMi3P0Ahoz~wy|)eav_ZzFAcmjIx6b#;ywsS4B7-Vv~*Rtg0Hhv
z8TJ=hx7F>RK2rkCTdx+?pF(bzor@J9z;k1o7UN#%Zyq?(Gyf-}^X!4H#NgKJ7<+y=
zgcFK~1=(0oJ<E`fT0`wiIxlN{>oZjCi6sj9q|tPJStrp_L#y24+bbJfh&x*31ckf-
zAIUj)rwv-*2Sr6|1o^{+tY}SkurN2A%-R0fmKRWp1`aTt_0np&?wxjB?4FlNuV9p<
zfG4UoQkN2kdQ!(&`bsKG-843rvGsOPK$#df%9isy;SEzPA9j`Ls`(C=>*Lh@x?zvD
zco%J8IZa}_%DHv@;6@?Tn6HSTUO*@V&DObW!VX9IGyPX)OQQE*-?tg(t=COAG`6pE
z4TZ$ucA>OUiWdO$bNTE>6IYFOMOjw;GmG@1Tzfbm)hQW{kSj~YDO`E>0M-4ShU7fk
z^HR2`!=x`MMh0SBexJrZeen8S93=p-oRWNbrO{QO_v|?nM8qC}TWr~NL-}!(KL9uA
zh?e|fgMSodR;u+_ZlLT-;H#QuWl<|>hgKgQAx50X0Mro5jm_(<%#Scd@+*5Y_<UKS
zX0GzeIyE;y6922@5f5@Bbg#i%rOTrh?fEq<ro;p(2I@)9D%;&whxZ^>b>c*i`_nNx
zbM7KbjEmH7qpavlDDoXW#2v7dE_olpto$(<%zHTr5acit$k7DL00PZh#2;Fdnv}#h
z@nJ3sUaNCXbFQ#umQtp0W<HINL50NLgAu({L&=om_(HEFnxu?b?xQmLzp<Pa*A4|P
zJ$UGQ#?-GWGAat1->=tx<<b-YH_YwSRb}GO0$3)A;eTL*+hL22u<IoY)Z>}sF#y6v
zzNOHH7_Nng&iU*_8H}W17mL-t2%Q<BoMxh`C9mfRMQDA@y=eV1GvVsiwo}@<u~F}x
zfIck&k|YZ)I0g4vwg%!tFj*;sRt*9ZLe02A?>h?e;b3@+;&yy2&W}#l93LG=uolQp
zg=#UI@YE$WmtJ@7dm$24(=Oh+j&&<Y!i9EEI@}|AY$-s9n^a$>HpM~|${ZN<tm@E=
zWvo@Sa|K69#qK;3j}Rm5rN^>Lz9En0W;M@{m$GiVOF9-KewoRH^N)L-FBVL4RnY}j
z$(L3$9Q@Qkw*PqLa@*p0<=ZGXxIejoQ}7l|{<^BQp44qdQE@2GS`OG?7-qE|R`!{d
zqIR8ro3r?(;Ju6PjH{F+iN>;rg231HliWP#!gHxTr13db7JvGzbQU>y#}c+%CA&ln
z*{UWO?sAfU<5S~eYvT%<OE&6elsYGqzh9=F3|YkCsF%!zvQ_T+khOsd<s`X~BNxYX
zHP&uO<vgzY;qb^J+4g*h>M*^xcUrM6*&N2C=%hvS0ej5iW<nYNT)TbFfcsD1MEWm_
z<nJCO#7}vX%Y0^|$u4DCX**6mVp^k<{#Kv6R`^dNeV6*^Y&m0f$zGTWllomxl+5yN
zlUF6hvr2I@!NttpI|$Cd=Fc=5Exo!Ywx3EhUKq{HE&piYN&@H=TxTW%KPFIYZ|st(
zE#&=nA65)3sZV%yF9mltHg0ns-zE;xv#)~pt0}7yQY=(_&#k*{LN;!ag%Ukfy3AAW
z%^Ex4D}fWD`V23SY=b|6!8%PL7Rh?0VMgJjZ5AtuTC(o|t*_p_3iXw%Hj_<O{pQW{
zP{8WRv_i;g#L4Vql>H6a%(T17)SYtgnCXAd0x;z7#a-su5Dr5Tg=herAG5}oefHX8
zTIHHfld}8=-{PdA@DN<JOvN#}9iEVTz537yR&r{j9|*@y2(I}edTA?9D$IOfK7)wg
zw&8d+1I4b=0)u}iF6u3|YKpQ&1pLBNXRbJ?6(XNA1o{H$4bnsyv!L2XR66QdhDi9~
zNNFt*dd<<m&*0Z<znh^!eC`jRD`W=8^TJVDia#lOhUn;PT4`xd-u;NuLD|5herS*t
z?fD_YfM5gr1t#9G2vp|EkDC&(#+zvhld>1xV<WrzC$(M96JpJxvQv?w%8&6PO`WBC
z4#1E7CJ@yYdZ9?xluInX;mrREK7}Fi!mv{@>j-%>->rW|gAq_+pKB`n98)~)xmfRH
z4tZW{Q=TWAV<;LfUH)De3zEjC^z3e6I6N1=O^$^a11!@=;X)cvc$HxGQV%lEjRsLf
zT;NSY9StTZ(txb5tOJ1QOm8Rc?McXi**sb~@qP@gTjhHi$opf&5&W&Yq2Q7l)Ej^u
zo1r;O=xi*|6@WTL%@vu($i@zww*>Zh+EST~gxvgo66?thu4x0O2!6@5M56RFZmM=Q
zPn>`~=DI3pIAx61T0zyOu9daV%Wh&ZhZ5BqUD04O6?>Pa8Mj;*o<j{RE@@m5dU_Wc
z!G&kzuD>Z-7Y&fXTYGx>!hLU9zngx&kP^r?J~l}|PU|=+(>t9321XRAyy*4*WAZ26
zXQaJ4eR8Ln;QZh8^Tp|*x-y$`^EeR2xY%^f=EsPJ!$~J^9@9WXfC}n8JtDQ1LYaLg
zG*fplTDq;?mJOAM%YOx19G;qH9)}}O7b5lQkzd-3TJ54EBln#Q^9*5I{O1|1MQI#~
z_fwwc)JD(df9cA#5$)1C?q9ww1Np|;s4M#Pjx3NmI7S{qQpJz>7Dd=tK|!~w0#I3>
zy=H#B<$V>@j=GQ5%>N{nA-+JV?=bA2ldQsqmb8SW@!5Hm*lmBU8EebHAv3T$odd)X
z(jG?p`9PNZQ~-=?OraNSta*21F<apR2{tywcl*>Dyqc<>+~bzx6c^=zU{e4!oXLu<
zrvgMCFM<iH+VJ!RBP0vb!9D45zNqG#K4L7#dI<5L7t=*b$PIpN?qq2~MSXLb32+*E
zFX4f(D89Fvet_GP@m7Qn1h9bAzT2hSK>Cz-oSl0LQ4-cl%Z}lG@%%B1)pbjb#%IHf
z$tz8l59N^T%^WVSkUuNGRLd<YCn=P7WQz!v9b1TwAZu|ezj7L#WhvS-Uv|JEz`{pA
zF6y}kB9FFzdWbT`Ja4t3Jf`j!{Vj7arY<4PDiF?oUXBg^<a=viZS~McyyDB;4O7Pp
z#H-{y$FVA5Ig5ii2GLUw%)!^~#>WUh9{zq6vkd-8OhL?5Gd;uBVHMl%6HB0s{s!XK
zVX5c+1^Q;vGdr6#bZ9wi<<rAi&1#s4x#x1|<lO`JcFc+j8;#hMY;YQbPo99Rdsxa~
zb1rxJb0Zsx17?=F-9Bdb`SZ^Fy|heDS@hK!;*UmL?zby-o{dNBi;VhTgOb;s8MQNR
z8Khu-#tn}0n-j{V2JSzLTt?EA*qkeE*VfFSBj{g3ccXp(?NwepH3yWMw(5uskY0iH
zK)Phy5OF~mFX^DQNk-fq#5QPDh}xLw#tx}ebdxdM%oa~i>Zj!jOV7>OFF`0fV_!yb
zgJG|egHSgMEy?*rsxzO4iM(tceQLqdgSuzA?`jGIlK&V33dX^a29iY?>dtbn{`6NL
zvBrZK38P<k0%oLfNOgnC-H)y}+Bhrji9I!tlR37(iTL|(3=3hzZ7K3n`l22AvA9n)
zK1;_v1xe~M7G5zSZ>mVc5=ag^;UFu}RqiTi>NtxvQ!1UrnEe<_sS#r##n9sIk7yU_
znsZ3TFa#gJuJxALToCeZdFZk}tV013FFsYEG`F9h-}rwSg9ieSd?XK<6PP~}s#uG^
zmrEXz-eGkPswhaKn}uA#$6o2|Fiz!ZZA9a?SrYa1(8sdV9IPUe#`P7;zIuMiF3HF6
zsd5L0p_cEY2T1clEL|02D$}PU^(KdYRUU7ADrdrwgA%(ca6ckvK$=5_OMjBt9>(?w
zC{(GW&+gaopzHS3js#w0B*!Kfzrc-$&NGX$eSBxM0!ZaU4~{B}$iHE2F=USoQwusO
zFH&ZFUT3fvG+=IEFw0byb%Phj9?#9Q!Mk*4=Rz=@<JdYV-O-AyqWd<m2O7-Z-x|0x
zTmn#J7#aPXQ3HAPc3xQTr_CKQUUaAx#+Hj*o_Dkbu@vTYqy@~zlr=jT^-F|S6FrlG
z7#>Vv*sB$7`*q^f-8+7MxYoUK|4Di81e(p@#)hm{4jd$PR<DkWIp`JgNc5ahE%;M#
zxX@gugBxN-{&LNJ6<RO)AM{8;UyYKt#ko21jalf%^~7m3gDCJ|3!vZY=5d!bEoo(a
zEEHqKjd+&PLSdP1BvK9LF;QkjVBMbc(0|QX)tFjZ@NoUavWe2^h-o1gi)RxvCCRyQ
z#SA_w;TDE-c~UDw-9#5PZ_UHacB<GKBS3*e435iv3mnE5jSxU89_Wf?o8l)=Qi3nw
zuBbG8K`u8AS!?J>Y<)3g<dKZ8Rp|Gjkxz2EyWS)EY`G}DyqS|i&%MABa*w*;UFwuy
zEYFKeT91o2JMZd*6T3(3z=iHh#D(+7D)h%VPYR_Lm&|f-q>N$#o9Feg-uAoW6j~*8
zyav8>=2S*V2x>*bRf$!NiKV(Mo|?mzuEz29HCisX(K01zeC%=qM;XF!NnXEAq<^^F
zK@A79blPuqE$g_9U$iZ?+tHEjGDv$B=0{&8`V)-{)PE=)(eyo_BnHk5)1x3lykJJo
zSXIST+h5l=hbI`1V8%_jJSC9}pXLm>rl+p>W4ZN4RA(z?&!b%8y8_lPyNILM3o55;
zEXJ~gF^rC-G`O|Tk#e^)I?wUV?vif4^JFb;-gjaMNONYsXt-|C8p|W()1)6?H1o{o
ziEZU6{OORICMx(FLx97>;$*nrv$@~%q2!2khfjhS3aJWSUg|a_d|*+-vi!v3v=CQ=
z^$K<aE0=82XQV|~jCT12X_lKh7!o|tW$W&M%he*Q)x~e*i#vQ`g;hl(aHjt&nv7*x
z&4-FFEJbjq_RZh0V?T}TXLNUCViBTuE5+vHg`zR-q4~at>k44|jmvyt?3hSek0D91
zVg;2fCKBYAJ|2-&;BP2Eo<=OI3cSu>X^qx&po%)BOOk;QvQbE2z%+IyG1g~%+J<ES
z(=&^TD))=yDLyiI>&<WE;b|w{$Z(_y^FLRiBokkQ;VL*Wgl-bz&SGQIzP%+@c{0ic
z#;?R^$qOiRwR%nkR=%n5GO*=E(^os0qcn!_ea>gKPiMcaG@v#E;~kVj3iIVF)x!`j
z&4m`qm5;(49x+n_NgL9U8jW<j7Mi=`ARt?Oa{U#zH5Uj;Cml%R)RIH;KN+%>aTx9I
z>KV#1Sqr^+;Zt0*p}JRPd)?`rJy;Ri!s!WqE^<ydsN!oe`u^4RiSjfzhY&j8fyQ*T
zX!PVnkqjYZ)jB~qZTHz-(z|d<$t=xtSU(A~Um*0Y?FUpR3mo^qbDy`L7$qe+WgK4N
zb~h+I|GgOGcpXI*sZ~?}{Set03{kAze$Pn2Keg=x6U<_P|NIv#&fZbZU|Ke`MZ+aS
zQ->WZ#hZN#9;95BSjoxWaSU%-5;LCjdL`YU#iU(!#=eloWqYsFHRS{nf)tjSp?ZmB
zo1yrc?>{wdQ7sk{g}|S!0=<FtM=YnLaf!qbh4_Er44^jzZDejGHme<;=?nh6cRhA8
zTj-xTlXj?UP4>$z|I+V%M^DyGbZw7O0WQBxdTF32qr&loISga^y<Ee7GiNN&i43I<
zK)%$HN^%al-+RTX>gdtny$3xHx1y_R4W1*B!Pf7h8PkxuTX+x%VR^w<sKj-Qi|v%(
zU4Iu`ur@OHY!M{rrh{43N%n&$E=;UD)Q7Z8UtnRANTB{vFRHQTPp)^QezmuvJzk(_
zbJv>Cz}S4NQly4uPM;}jhV|n)jnZ)pr|Sq^I`{Yy<tFa&q}q3+^oP5ZdJ<+%Zoo}s
z@kufuB{Odxk1`T_OsVStR|LMvr)=+p7U9FKo6oNr$+LVSF?SA(5tBXN&^+;`QCmQ4
zuS;>UCD$_<4B_MW7G3bJG-^0>w+J!Qx0rbj<%FCD=Ak^baYq~Pq;vI^f%UG^y#6{<
zdGP6vw;k~-UnX4`Hb$n1{&z5~vU28`1M8giiUx_#e?PNq5NHCxm9@S-;ZraXLtx3I
z`4B>*1!*EloX>63#)=G+P*gyha?8xnyuDk{f<umEZ0;9rJnH&B)RBl5f}NmAUKl8o
zGC~OMWRm<0%`aTwd*PO`npuG#^zjVs(hD~4;q*sq&|GAs-`+RKj-nR3R)N(<)<nY6
zyq*Dk5sQ~>HbV?PMysGRuuV^Tj0^oT!fPXiX5^zJV;5EaSyfSS-rNAiOzZu#lwHT&
zY>-SIwH7*SCiQfeoEVZH^s-jVoGL7AP)d;x*Qg@U??DQ4q6mL+OipS^oI+#74>+f<
zgTtDh|9U?(0Yh5*plv#^NerYUgs(G@Q9)rBVubmf4HQl$TSW<M>aso+5t?}H;AMyG
zR`2d|SRFD@SRDh)nFj+FM89)OM0jnUW^dqcT@9(CCQj<~_ZT+S0Ojsc+}C+!LKuv|
z6#|-iyA3pYruQs~Eu#F6e9&Y*x$mXAaFn~ACkyxLU$4}t6Wg`89SMd+MEb^6OiFjn
znB7)d?-Awe15TV6C3O{e8X7Q#bh<s8E%o_W&;!iRrC+zt_C&;PD$;)Z9eNCLDqv1q
z+Ps$Gn^CYVUl45MkU+gR(a#VI`q0rUbK0a~-X_@iuPG~mY$F7NrQ=p3tKs?pO^H<^
zGwd${7D29x9#~^t^T?*S<OSCYO#@zJ02hq=4Z6NhPa#Mb4yMA6p)Yu$jb~L@^6wSH
zct$5SXCZIWL1VSoIlLX-9iyA!`9XooorhC0RL9P}{Rl3ks1IWhZ8}=XGkG?P`^IGt
z{P@_3KKPjAPW4k#@t8&s9VUtQ4TKQnTwn9$EQiBhDbSW7bv^P1))!0QZ4$nNtm6FU
zC(myml((f*y0~BjQ3299&rg8DhiqQO|HsyMhc)#zTdN?wNC!cRihy)MIz&Z5x=62~
z2#828p(Y?rx=I&G1O!B?5;`PwB!DylsiF50LJK5>FTU^n-TQvez5k{>XYX_7%&a|Y
z%?w;s)H8|+UQ0toyXLPhaP!kwEB~8}x5XJY3|Tjc95j#J!|x}|*Lrae4Jt!i*;kua
z8m}-$^lVs<r@{_$eSKEnI=sHKbl0p!d)&Z}E!SjF<upjiI=pL++GPz2`utoxcbrLX
zWkBufKGmO1_c6z+DE|yQzx~QXD;0IHJ5B?=MqRwsR<8C@U`&lF#d^ic7P01oPA$$h
zRO_eu#4fbHpuUwBQbSAZB2y8BLy0@1sl`zv2ZL6dENM)dq>=y`8sqcc@FSuHd-Irp
z!WdV3wMyQ1)PT{AF`>y&&s&X!>QOE-`3z=58gr+~ApOi>(;H8eEftTZ+c=$rIqm9E
z5OCQ(<5geihXzSjYSgD6QtD1DNbqz&^Q|Whwb0Ke>wgB-OC~bCy4(hzPaUFbI%%4Z
zKM#H=TKT|w|D4&(pM3YO4yZjVdbqacmdCB;r{w>UvSVT>vsf%+K!-oZ$OS6gN6k^%
zQ!KEuGuSA_j4MOj-dc##D~!)i7$dAX^!F${Dlmb?bk|;4jJw;paLK>pZdIW!{p3XE
zc@bN;7VT6cc86?{@nKY}T~I<qy4N$;)`#oT;n3sqQKdIYPM0%oe=d*OYBw?1SL}N_
zWQ-RULoU)Fapy1$N`4_TeR#EhqGi`#L@J`vt4h7X&Rom_A})TjxqDzemy0XV>iTBx
zv%^Fem0Qs^eM#GfC8I@O$UM>Xr!`V+`c!_4tXr}IJBour9yBt6)*B(r#zS^XcSU~`
zK3hpvJ^sV0^qWN|gX$f|EVN!M>^oc|OyG7LFvH);RgvfFdI#T`SH6FQ<#20c_*G3^
zWZ@HbFpASYrL{ms`+aHfDs{5Mi^-I8!6>av3(IyRm9L?PuyuK>=Z`)HC)^24ufYmc
zl{R5bOl<>AJ7x{sTF|9q4K<5`PnBNGcS(Z1IY2*N9HQjGUYiAc5T|g>@($Q02X2Wg
zcD#cjWwHimrc&dQ7kF1+WciGt-K%f(@sDnR34=5JfOB5bymdAC2*r{}7(N*;eV*HQ
zw48l}rgqP`HR%=wuq#b&r~Wl}P3Q6r{qWsy=qDZ5wP0`RQ0hVLae&0j&PodKlC?9r
z>Hz(BOEDvwmGu&iUxa%BNJ1C!I;D;Z-eT}qw*F4(ai@r{W1QD{c)`>F$@<!80rnY$
zO|z)6*DlPmM6Q3syPpxNJrVxmZ69=K+V)eF5h0x&nR3Z$%XNGsvukrFy-R@ua-4}&
zt>dA}r}6xDtC2(94o{h+=ea)9y}U1MXiVk5$PK6d>TI2y;Cn`$P2pY5L+)WJ<CR7$
z<Zj4M#lM?b1^nVq5$MWfES7T9Px(ogoik&ERffn@il&?y+?^mRe{ST3KJH;%+i|Bt
zS2u<JI7B;JFC8l<`z-U?i=r=40*|hD#Yd;ydW)L2{M2$s4!K0XuxgkRZ2apOBlNqs
zIO6>GvkLPUI(9Pemdd@jTN#!8wCX!znFF1N4Nq^qhVS-x;+HHaum#q`4%tu(nb2zJ
zCp+~^x2#o(`zb(s8^M`|Bozs@zFSr;qMkIjBM#hv=a$fCPVS9imz<ktBy5xIewF|j
z)SmHSQtc9WQIz%?4J_Dx{$4;VNHS!ykD0*+*QJzVu=w0Ioelc2tIGA`EpkTc04`35
zuu;@&xQLMHf@6iR?l_Bk=fFe?VODeXxm2^6>2c{-vneu!mVx<xi&ve2|0m_!beG)x
zGX_m^dE%|=W7~1mW-eu8mB;qZo9k5pOGt}rDt6Blu*{mPZDeT=uGFsti!CRY$dwxI
z*I31uO|p2gzLsq-g?=Jrr&iPTKaCgzzI4Pqr`Z;*`b~?TZ#AlpHmN%jKR=sZmHZgE
zT8cHZKf5uGD^@!yX>hqZi;a;uEXmAJ-#q!U_4zUgo^~+sGY?$Cn88fym+%M)jv9&a
zX!a7KBC-Q%2J;R4DF!eDJ|>l%x4smoJX?cXoh~{Tra0H9Gm(Ykbq-_aMHuzd9cP9w
zdZJ52^{d`YV@+1NUOuodz;J3R_`OU$v#kE@y+NuSO~^?64`ZAih*<BO*mkJuRgnIv
z?_hL#n>?zyOHS;9{ps2c!uuoY_ObdhNSHe)!IzG}B2Shnsv;0>;6Ucu-YpAJTZp@?
z@~|!1OPbnkaXH?s-eK-cTt$xBU>B1L%Cg3OEx4yoHy32&OC>WJXgiZdKj+>X<Qt+Z
zVNAGo1*<CdMa%JZc<vmz^`YR8IT(%dQJRqqn2ICy+7IlF-5}&OHPab7KDTC}JaXMf
z&rpHxh>%_eFuQmjM(@!}Egl@_l|V!jmm2w2^9>#w0Xtm>ufgdx$6kZQr~*vqhXgIL
zpuT#6DklYErfB^&8s={L1AzzS;k1GRp4ypEpA))=kj2^;dmbqa5rTcHQC*4CBi9~T
z&1o~pMPU+I+Sk%F^AbDhCplm<7sM0Tryrr!=g}NuV>&+_Hnh8`pGd+4LRXR|JnUl*
z&L*0`YK}2Z!KJ<{hEVm5tSG4*sri-Roz1z0XXAN?`WwG8BLH2ce^_MVlGeD8C<{E*
zg4@TKoJPmvyWVbqk#CLHP~PArpzt%%ky%*xB+~^G^~9X1%>vo7Q`6#}oX5tgtBdu1
zLHi;vY-49Tx*EFaWnF-kNBz?a;79hpV}oILC16UW*?oGP;#)FFv?JMZbUBaJ8Lo|w
zVlntxwW%}zKDjZu*n3DlrgW)YWlUxd?4b=JYr)buNuRf0`rz7`%W)dE{k=#35tlv^
zWuUqu%K9gY*6EgKDW&;>^r!;$iXXGKN&7nOEGM7#(rt1~bA)QthL$j^5rHieC<fA2
z*kNk1x63xprN1hA-p>Etd*YyY<Xf}$EjltIOXr2(wMTrLEnu0nWj612^Y`dHYh4E|
z`;o*!Z^f4*VmF)&SBeG~iam_E3d+|<A%x9J;^uAE_TI5d@1rPsr?9Cx^#}vxvu8y|
zKNf$XI@SSykIR1{T2m%j^?<#!WxtP@)B8fWapP+V3Jm->`&*q{=25cK;PcyCv*LOV
z`=HAfRNQf<g!T>}=iK_j$7MEU$7OK{#i}w$nNJzinaX_F0%e{8xrfmvyP-cMlHhG|
zxJivJ;5%Q3%UV_lB4B;n=F<*Ux_wsSdEWxHIuilo7dd+N0-8d7vt@yV!P8yES(8ei
zv68MEmh9#s8bbr^R^yXgTD<f0m2ivw9e2B7`P3IJHL0I4+SyeveoV1fiiN#1wPJr)
zown#`;d@F?N?m0138uSRm?mV1+MH@P$Qop4?cclA6<oqIOOZm+SK2ReDDPd+^E$h+
zP<M_;v-YF6k!e}!u92awSM~L32N&M<O<>UFf{Lm~I@42FPZ)7$`Cx~{>ddlL`gQde
z6!POFZ7s4a*2NcoBo6GiIKTQL4hww4E+dF=*(c!+gZ)So>scd2hNvZTr}p5KB+SX5
zA@A;%T7p93n5h2jvs$cbp9AD+zlt^968`~^(MOkVanp^+N~bjAvpc=5zctjF@80bd
zFcteEP&jJTsEsDp;--uyYFI2hwAxuYkU#48CwdbO@$<IgyXyOpZ8<>3&a9eDHOx6?
zD8j&QCrL`@MGyyclW~Z)QmyBMlJ_?9UC|H6u{8{H%jRNPXbFpR<!@j1^F_0pZvn~W
zVI;bk?`T`R!T1?c5VFbE?A*KzM31iaH4&_sUzNaGTsN~P6$uj>hty{*rz=yqjqfpl
zm%|3xB{>tw`BIfF_5Qcn0<4}>-T7dzo!78qH@T5S(wvXC2u+eQCc5^e;uZ@G*J<rh
z2ys1Fw1K$|vfE}4bZa(WO5g}wdFtf;#%-y%2j}wN=X+L4hPdQUPOdf;04h#`2!BQ`
z=TE9m3gt-Jh!1`A@7_BozpD%E6s0Vhx8z))2zpfLBt0U)qdWRm%tWQ?___*VfpIY6
zaKiu+OcNHxOomvoigJlm6G+@DPKWvu;j)95t(bnV>V?am*MrT2B{0PX^1K#A{uD3f
zA5>RR9j(7CXnq4Ls)fbf5qQ};xbUqj(M}>f_7pb?;qWTzYY<Do#x>rRo+h6=LwW~8
zqI+@`prhx6Z=#!D_#P&>E#T3CCrOo%0{m`+4tK#A3+h2z5DNdQLa*Zk4^5I_eAG*Y
ztuOL{aK0<mp?5_?nb9etjRyrM#p0<!UM(+0F^uU>g|m!q`?{kH0p>AXHK)hoo~TE2
zD>^pM*wy{>l9an{D8$Fe&1e~JayAFX6$e^XoZUgiMpE!hh$HyS6lf7)wXSt4?xU2F
z^kMPA6#6h@8q$EX(5+&?^Ff5~FA#a5k1+)&TD)xUs2<hx%yz+mgYo<_SxjLP-KAsy
zXO}uF2^^{nZ>k=TjEIKVbNVW{-;flOv?*_SBN;l!#g$fA+XD<Sg08Px{yF5lNc<=5
z23=}xbbfF*lY%@l0v1t1-bAwpC?+#EuoYE--fulxI;P}{d`|u5Y63%rT^|xkjxnEl
z4~t@9A4OOV&dVjNs_h1$UqYXpGzYq8;oO7h_P@>-H`CY`?6DU**&-6y>!~xvs=(zr
zBiH;+Y`B3+tnA3~fp~QHky-*{X%|N(#;@EBSgI$7?qWlHb#uK*p2>zt0v=vNom&bK
zZ*B~Trm_Ps!gz^6_Ttbyc*5%9#Iw2iugx4PfV~v}Eoi|>MVRbOy>-`-+HZOH+dwb&
z@+Te7u0K(4i#MwnfDzZPPc&;>)q_Qc2oqUfwq*+Nn*J6cejRak9k?g6t&jmC4e+Iw
zlT-_Dk8qAR69IB562(xoG0_%bzB&2}H(>wGYn&)l30!>~K(DmZ+EGdIk*0F3`Er?8
zc<vfag<iqDXc;0FdNoy3{dif^o=uGvlmQ$b>0VjO(PvFr){2fr)jCjjh&6BRXLifp
zh>I0HYc?}iJst2#k3EX1bA`*YBIKG0Q!d<yaa?Iv(v>W1zFzg5D6z~>w0WK*$n?{g
z7tIM7(3=KwI&l)9D*w@Ag$#<|M!B>ZtDx0)irN`lOGB&QWl9}&Oa~s>x-zJ~FTcUq
z5RWz>dmT;R<<{ZSWg|`$YgQ+YWJM45zn9YP0bGod#5EVK_=9O?&6u1ZZ<oTc6Igmc
zn+fikq^VnFv}0)UKQ=Nq-`dFf&LYx~p8rUUosM2uz5Q{U<k@zyjHqo}x%pa4{3RvY
ziFc3%&kj^&RK^)a>Kj89D+GKYx|k1&CeMqoNQgHwr~+e-*#Jc6f++ezTFq`xRzb8i
zAHrn$ZVP)6UnR@~pu!Rd5VvlpTW(6)nyrKtzY%FR+51L^tCJeVth|ZzWZ!LX7<cJ|
z<<+07z@^yguv0Pa$N2tGO^bP!iILP$Yj#2((!dCf9C_(|w1m^2fwuTfOV!iDu;o9*
zI2W!OReyhCuu$pgUEj%(wfRC49hN2dvk-aEZdp&n-`u~qqfxTCWWEz3oP<hArMV&L
z4xDhaJ@~krI6C-~R(ZM!;Ji?|;ww%(4l`9D_M&Z@%In+f&wiyu&Kv9#_<y3}#f&Xh
zv3*>qVD1GwQS_{3g^9$}@J;uUXfxY`MR-+My@JC{ryIoi7_R{=<#bOuU{G*L20PsD
z;_%^<z*<e!!8&(-0YL>Hv|7ia4-TLeck8`~p!w;OcO(4EKG01gcHZhTCLGR=5&o1Q
zHK+0PSCIxrc_)4qMHY;z4CA8%mOFZ0x9Nh#HqnbB(tuVP(WQ(95t*V{@xVcv6EUZ+
z?RWLt(ja^ldP4TMVD8p?M>YzZ7WJy423+hwkuMrEKBODnA{||j(llSUJqfSlzo$GR
zSDs&dG`)TD5K1Avfo6RQ({#&obXE%v+U+p;IP$FuTe_E#3=X@S735uH-xtJIyjW8|
zaX8mE(O<7xpYL=h6!4h?$@d9umywGYen0k!jE*Oa@R?FgIFp~D&;}euhwoPik#}y?
zc7qt@)V7cXHtOOp6UDJXr#T$si{{H=EZ%H`tE;63MmfZ?6Y!@eNS6h-b1Um=0$CBP
z?(CTK8De_xjcaNSUM)7GVgIgI!9K0Q!Rv$3KuO}%2>d7hN|sxw=#=S_Z!|~qZB!-9
z`n0Y}JamU_*-VP9*ee|ZmJgDsr-6Ybxq&jk(xiuaK%<Q@mbvzU$}maJLD$Bl#8${P
zL>^!NWsiadO8%KW>8FFl`6Ogsu3J!}B{;*O|Ga5cZ=I$yMr+H@?HMfdicUd<YuAdy
z!@&EALEgpoEs6PUtKCmry*?x;Q<RAY&?E<%d@pi+eUOm!P0rOv&U{huqe6ObQxb{-
zrvuy^X>e)1W5zAxu3uy9kkR3rqoBI5E16eiKXJ#<4UXShr7ec1`MO6%4=VX@&6MsJ
zbQL*-h;5fgjH=PY*&yybb<JeZ{5~S3YvgwU3+w%FYWtw9%L*YBUj)kAZDf_A9$42e
z7!I0yO=AZ_JKfhZTV|XN9`nNCgl;$73Y*^C{^P0iSX6OyT~1d*a&90>bjl1~s(0tH
z>b?;Ig}~+QEU4Q4woQM-R`h8*R|I!c=hWBa*WAK;4tqKG%=<RxTR+6f={||8BF6Om
zf0dZZ;Y8Jst_+}_pHGYr{T>%O(g+^Wpakcu-o?QSOo|uCX^VsJ=<5^*V!w#}ZYlTK
zl&H&gvUOVieV2Q1MO_uN_-9y?PgO>g&0w;D=Nj;n(%Nn=O%9hu>%A1N*h3~%;Veh=
z1WMO7{iJPiS%=$Do>8tC_8WbSpGUb^Nq!S|4VjvWAERcoXH*6GHkfjc0&33#VJ#+V
zCdp^_&tDzKh0U*c*4Ko&8(&y501iveST<huq0DE$`l;wpETWTXG3$F#z||ka@}#4k
z%8WX&(%?i?CEZzcqF!=)8hsXU?4<5kt46sqQojEX7&-0}4SUU>5@@!7==BO5EpA%$
zoM6dyd*54^P!UZkljiUlHoR@^2TjfT2CFZQ5WOJ0B&I>==2IC%=gES8fsKELY7~Av
zB|jLruzkb*W{o3V%3CA4k5#n%&M6yF`l-G{crL}@8`zcC3p#~2lH7bqf=%uiA#dsV
z+t<%!O;iFnBBhXxo{LU>Cmx-KUDQ_fcOAksJF+_Wzi^%SSBA|V?q2Z{_^G$3@P{sh
z)H)kxSWqp@Ix6&0!gr%j7o&WAx>J!L8MdKg*4i^%94ExutX0M}2m)!OscTx3XA-e%
z9@kFeXm7FHRtl#>*Lk9E>xX@NnObk5wlHJU+V$ZbK_Uy~V*Og)8%SXWGWl%PXiXOr
z#26Lwc)4JA<ZB3jYgE<86P`|{I+hdI=RE`BvOy6jGCYn2JOWf*jlrc~p2ieoc{Obf
z)Iggkyoa3VsrFJ-y(Agn8}w_n^^7iz=xyu`)^vfM{0a9rAcOi~SSt?ANnD?|?A~o9
zjMi&EC@ByS`ruI0=0#tg{f`@6hApR|C?n{Hlkf+{d_H@z_m*riZ#Zrz{s`cE=jG4q
zz8kZ!$c!QorzSFYjow!-E%Ne$%#C>yx)ttA+Alqw)Z&!J&d&0kN$p7KN1&9wq9P_~
z%;29wvAv?ZER*qY%TI>EJ8MM<v&nB(s5{9;`h>9nQaMstvsLG;sP#TS;CLRE4+OB%
zO!M})z5Y-tR%7pUT4E3uOjD+}zPH88<H)TkF$8Mr=}~B9OTjv(5JWzL92-m<j4v$i
zJv*%vs|bCUPQ-lI`Jv8}phkVG^D$tcW`A?`Pi6mBMgA+~ix|fxupkK77E~EB>&Bc(
zZP4S4`=x|0i}F4(=~CBRARZP2a_g6}eC9=NJr$L>^2-y_YC5BE53m3v^<Qz~Cd1dR
z)X!Va>`pKfhf~0^c%MCQT^yW&9q`2v6#t$~VWaE)QffW>VmSUivt7=G*xgi584Fs$
zIzLlqFUWmOZVAmLH)J1Vow~wv_T>@-LQ;rLk@HbD*ey?hM!06<s{&V@p0D#2D^`B@
zQwIw#^*XEerTj{jEh1ZGV9*>nPx~`oms4frfwI;US!gJVQa(X4nj1OmIlqbvnK|GM
zQVy2QZ?-tiT+;B7XHxb>j)NHY)h&x*-e<BcgWWbayO4*8fZ?YHY}-j5fd$Quoi`*5
z0^3=zrolnJ#}KMmSMfSfSNU(kt}hJl2Rs|e#3EvhmVRYD%Tkinh)oAwHNS$zW+Izu
z3+-OH@2=$>r3+qx<X89OWicb=zsJ8f0XvxTy+weQ5;JN^l65KEUtyRifHr4cc@r06
zm%feLPq~&$^(Ba>1QUiob^^!#wPT#n)&<t@&{@bT6fuPr@!oZ!YE#)NQ<}Hs(7&-d
zv&V$;p1Zn$NK`tfg2kI{#`^^D(f2qkF(lT*D-$l?+KVi)h>jOZ5i>CWV^a?h94pHK
z7)>?o``I`lXjUM>+gLgwD;Sf<81^-Rb_XjV0Gh8tMbAU4?CL*0{3>__Yw!G9e5UM_
z=X$5FW^C{+^oX~ZJDsJgMbqm2%IxZIVlgR!owu;RIK2}fbMm7Lv1n5T*%OFiFs<NN
zx0UcCGUIWr>fe~(=H4S(u%Bnf{v~lgih*OL<##)BopsY~ascNs2;1oqN@H%2OIB0;
z%$QO2!!fgMKeh2xqKCvpp3lbG_dvdG<gRxL#5!X@#}@mcIWwuu&eWpeRe^Pz^5n?*
zYx_&OS=w7mFkImA@b1nqx-nA=!q;8uI>L9Jl5$6PR`M!n(PlT`cS60FLdq8oE#R*H
zXQ_KD=Z^_P#;zX>6=dp{DUtGs1^R{$<kso$m9#46zwN%)Nn}iyGzcTcN&g~EJrP^c
z7y6~Z(p_H9x4FJy{^7DRZf!`NF=qr3`Jzm5gX|={|ABNlk4(EuP=Xoq&AyFC4T$Zs
zAAPi_*N&#qIt9$@x92sx990>EHnLHOw~QB!g2eNX9_6|0v`+dNi~Xgd^=9&<`HqH8
zi87*Z=4Cby1*#z>ip;1M;!)*b%;rdKl~Q4Ag=4FMH);%f*R?^WEBx~A<X}*MM5VBT
z!69H@%K)`u_T~-V)LK|`h%iqH$m%nkVf0#4o>eTc=w@agX!aZMD@pEHNh8-G`{2Z{
z#C2$CmQ1V?TNz0fdZKi`A|BOkiGBDk?`1u?o{)>W`~Le&z1eE?TR{Q0GnOX<x4>kX
ztql*73gGpD4C3qjpPoBdQ@L!I!hl>G{CK0I%bwQkB?xoNhvuzj+Lq!uRVuwsDLDPW
z9+?|;!H)6V4B8?BvriH!;;Pb|Pt{Hu26q;ZPrjb)W$p4^Gq9BSB7<w5kCyzYe5Z4m
z!CC_}7&0Tn#!M<iD*D-8sm9gMdo~oiDhOUTsSNdH72zPy2>*Soips=>TrBOPRks%y
z`yzV7wWz(N3m^8)QGsk{@N*}>9u5dbx#apg3m-tHa=qHA6{S4<O<uar^~1mQPel5v
zdW$CClWUFjm(3iJzFlt>HBxe8lTV{gUf~%<39Y2{y{wW_^52{4R30d!)9(Jj>u>%t
zIsG$F@_@4?&u4%VIusphH@ZSJE`?e4KlMX(nQwdWWok7EXMV3pyS8Jda_>^X9nPf#
zo4tUE-M)*Xl{BM7>g4^^-N0hyfZ2{lS=oE5aizBFMsA@JjVO%0mPBx(h_rHUpRA2}
zIb-aSJa|c7=^iuLS8uJ0flC?^QsaOvi$U#`38LHy3zsM-Jdy0L#Qix!0Kf@Y?o%+<
zn0tQDp;qwVfyYBem$JOp`cIixj0mD<pw))YQq3}*1G@eLArGiHqGZTa^E}xP`hfOK
zRI}}R_47!k;>Ci8LIEAzb5h(|g=0Nd#FzYUuie(R+U?lw1Pu$h?~Uj9)>NngGA`g%
z<rifq?9iIYl!6se%8F>Ml&i$FHzO(2;4KUR(@35q7b={+d0R(;+%cx0(vPldWn%H9
zg3NWaxa-bNA1cEuSI^?XnL=o(%eu(NDcPvKDADUw=)evdAUH?M04n!GrDIVekX<*v
zP0zz8)!3H$t@Fozs%OU0h8k%F_sn{>m%JSbf7Qv`aElRPD7pTP`bj~zSdr}BrJ+d4
z09;pNs=ks)Izq=9%RK9;hLiUVm#OmdGV&A7aW=piq39p?boJ-a^3Y#D6NbOkq4;@0
z7))(kB6%2wdu2gsC=LWBTJc##s#-AckOMLtCPg9RFPTAq+p{gZu+7c?&GU;)$Z4jC
z)KbgkL2>NygV$OXkFR6(zH5rMg^G?$_McT#RiGlVZ&xyAt%yYN*`PJa0qngrVfipZ
z+x*>1^3UdtnU2G4?<9l1BHny{d5kBqhWfwgOq)hl(YS8;BBp+YFZ4{h7S$=qVH#aI
zEk9H8!{jsE_JI%-uHa$}lm^MLNsK24JLdVwG~xgBsLAy`2^=u)VeU<4de`<DYi)o_
zWIB5sa>Oad1v!j!KkfGE`*c;>^l8eYOGTy*@D*UaW47nvbWd?a@h+2$FIK+tJZHCM
zFU<3}ZKN7G5$JW&gjS`G_TpL^cE3*^Sun=j&0TdC5F>q0>}vE62V@*U+X~7#DXa<b
z+2Y%%2(BsBKkcqZ*@*zUk@+=+5M^{t@sk(WoiDD#aCXxN@x5*XW$_x9rNuRHCRKuI
zwU61s;JLn<Dgt%-T@2T7$*eF%F>?_qfyMso`J0#fO%E@-9+>S3F6YC5rzZ!JpMa+v
zJdk9_=~#6ay#|-p3eanV3*z@Q%AXh~Id@*^M~i?)7pHI_u%f}DNg}%|KO}evqGgM4
zFdJawmEGxJ!bE#y`C)UN?6LP@M;M3AT-`KVm{m!*@pbZ!l+fi{iU-|iM%uZTlUgsJ
zC(S}vsl%>7>GlhcB-NH$WH&=hn`tWLto#-ls?I803ZwPip#@*KJ`1yAJMK}yaO+>;
zB9G}VkpnR+{aH+9y7;XQR2wC)1ks&zq$SarL<YI5<dD0bb0nR<DAacIt2x8>CpvV6
zHM14i2JWk41B+LmbmobS*)EiahD_WUxb1u@AqjHrXxqMTcDT3d^Q3@SA)cuQYg?B;
zavTFh4g*X>(P2F45c?@fC!>pAjFER~%GH9fQnE!Rq8nFb)i|4NjB^aE4tc(#xMg!*
zE2Q`grcd$3{I>x6qEEU6m+|RW2wk#WUzNp1tJ$8;`z6oMCE%73w(h_#el|Q^aqNb%
zB^M~?qRiwD+8Migrw+qhb|hsU@y(7R?BjP4)vMp68p?!CFT}<&U9Ud9h%!IboH>wG
z0NkGxm4szg=Z}<;1NWbNouf~-Lt}r0RW@TpFR<z%r$Lez<CRAzb1^maU$2|+&4C)!
z?>y}Iap_&N+aI&QEekk2aKUv{tQk5_TkDmE+#651c!z!mP^j;#*^lp5+s(*m->|^U
zC7AA6XDmpR80fIN@1jvnZBbW$%GQgx=?uXgP4#nIEc6&4@tTVnZb9V|zd;M~Sp#o`
z`r!(q9!ED2qSnC}9Cd8ntg@Zmec^nhUDtrZh~oAToPTo55p8IL=xKKbT<hZ(EYO?2
zG|u3l7|6#=Gw|@XtWb3=F2%=~0ZPt{^jQI-mwZ9K4`u;k#F`3JCTt&FIb)9KiYpJh
z8;MvPGGNkm>pvPW;Uc9eGxlEXa8%TyU+=p(kU<iqA;&I|@s=}_%3t5#UdiJdhop+0
zTbdnW4}QIWSh{DBf-|yD9}Ex^;ZzUh`z6-Eb^gZ>DVmldQXX>XWPkOA>S<|vO+Ze~
zkfkzrBo!H(X<+HiiBt%_;^q)^$vBDgi^QajQ;@1HpTUCi{3O*7Wt4krtjgyTo4sJO
z3UQ&%-c<jz@me?Xc74T_v?!S;t_D{*bL{m5Mgly8t8)VC5M$*BIRJW?Nt?!9ofo?M
z)52w={G79DayHZN+>(l0?%*m7=SX!&{qUWLx2Y0E8RSOU*Bc!bce)<PTfXib!RZx?
z8u*<*(5)d3iF;=Ed6sdcQ@#;xEUzSB%@Ch-MCTm^zTegitGfM*myU=*A60<#1O_ej
zO+kkXjDflAh@Tw9>_;^jxTZC5UC5Tz+(-C)Li6-?_msUhXF1NH5!N+_<>_;o@0L+8
zH(P`!eOergX!itMib!2i)<>X{DfkwedQ^Ac%LJKZOdI>x-sf@>hK#R$`~VqFu>Gy7
zFg|Uof{GAC5$XIjau;cM_Xw6|p;ty5v3ng8^CKL~E^T#0@rw3a0|6MZK%#i_Mj$G-
z<#`5kR(zf5AG)WX-chI=Y*1@n*UPA;G036F8W#T*aW{5(%SRTxf>bDC@+nr7dhw~m
zho=v;rtR|8nmw-w<-z;9t;`fbqAf^P$OmZ)wv;FY%CXD5HTfi9%~pE_6fl9OcNA?u
zqn3m$ne|X{Z}_IOpSJywzBjfm_bVcr?V97Rk42e?$yFDxTD7y^<{i3Jw=W&I(bmlg
z0PntzMy92CUYTz|tIF^B^3x>@B9r7(ssukB@Ykx9y{yT2e{EX`D2U5{U@r2qh(@uU
z`{Cprk2hm%m0n$k9O#ew3WQg2RH7_k9Roz)<_pW;*Y%qtjg)wr38#65QBUIEcq*Vq
zE(sA?hQ0(vKZ7cav(UtPJ%Z#a_$@Q(2w6}S-+rc`!2ZfLW9*5o0?K^&&p1nHNqzcY
z$KrBqzTheI>HPeUQ?A*UWvcfVr>!L~2nmueYsQ;jel1p+-il(VC&bU`uJaQMcrK2}
zE~dH+<7Uet%j1e;?s{eZt*C5=SEIa8VeUTe7Vg`^#VE_kc^D{uuVF2BIv2DXWI?eh
z9-W4S{}u{?3ui7Km@%YThlrV1ZL>wd9(_xmT>hF=rQOApxKU{>vY2KjlpebH#lTO~
zI&|aL&G@fB;4>)aK-`#TV5?3n;7M+~_Wnq6S>>2#-sp3<t1bE>VuSe-jX1GYy79>S
z5=67EPVrj7aX($+igN0GD_*HeYdO*VCnyk8Rz78!baW`QX7O6Rc$wGl>UkHn0_}?X
z7u0Cu73~D3V-EnRjz@CHlqfUGOSbA`M|zc`dp#vk=@B$<<t(n;U3e4yk)N4n^+UoN
zYahey6%O9!t(>X{f4&y7xrq~IpBoMhJO}h0b+q<1>DiPo8F<}MsYymsY)vQ;(>Rgi
z`$Esoj%M{dgGUi&a`lLJS1I$V^qapvB~b7ChuUlnHttR+VOS}V2=4r=z?hc;y3CKP
z%-{+S3;YQpmf-Rw$l<W&8p$_Hysje^0=nm%hE3J!_t6JWed4+suQ8_1eInM=PD_v|
zcF!p(;QtMm=LDzGX={!>BuZqo>umXoaPtUW@$ioaEI<o>KFuV}wNprTaCBz9VJBR`
z6Y71U+is-P2eb~Hmwdw2jL=dwD(?UNTumW|WmqxI%s%aUkP%<q%>DDFjN<!u{-x#q
z8F6Dd{QE<s+x9~9-tr%-YNE1U4+DE|(VdnUy>%T__+_xF@~(V!D|?rTR}?4r0%+jQ
zp*aX@6sl)9UKUxi$EHDBStu;15|@A3v?Hy@Cuao+rz;dGufY{~-jZb(Z;Ft_g5UZR
zm4(SobLUWc>sZA;5&Sn7aoypuvmkUI(QRPsIXLsISB*~&Yonra#IJnS?Uu@<n|CRl
z$U5b;h<Ql4m|W`IcWe37oNDg{8JF!ztvajqRx+`+<RjFwa&M1zNT17tyLsOfOu|=?
z<7@9Q)l|va5T-xnF?q%~`DN$mbpE;S?5>0XET6W~ncF^E%rbxlICW1U3teLZa55jf
zi+|O#!Wfft7JEInruis^!Pn>MAb{0FFJff2Jre^Yh+p4q(whkB;;{Ais6d8l{ki!3
z9m8>sm~O5mw%2ze`(cM~0c@tEX!Q@tD%Tw`J5ssJuH@w##5u+32vRs+r1?3L)nxAj
zD!Y`)x=ud|xXG#LWw;PtYq?VcO+PuSSi4NTjqeuZu}AKqq44q6Obj|u&vjv<Xsacq
z&d&La@o+BAg@s`CvFGF;J?~#d{`>=mGL?_3t(bGQ5SwtI2;<J+1WxYX%7d2ni0^#U
zqJsBF6#tPT_$pM@U->aXiD;>KFd)(?{qpaU6R0S=!P_^V4>`(dk_voD3-j`|^S=6`
zk})*ICPJxxYay%YKd<>OSp(@$>}C&+71Up`Z14_tKPCPnM*VNfk|ZS?Ai33d0i$cS
z{rk4X5J_z4m!N;{(ce<yf0q<IfRWLP@`r>`*jJ9CCF}P?{`XH>ZI@fyX@1)FTk9Ms
zkd62_hVuQDs~Py%&Ggj@CAi-X{^viY|9b0L=gsbU5U&3tY5V^czr$mWyZJ~b%Tn-*
z&B$*=Wo}e?N(3UK`#5aNh|h>ra2Le$3{=z6j8~rPOY(12@W!ZBC!TPe#;fhj4;;1S
zRDU1fRdJg3>vC*T35@rjSbI^4TJHKo+NfN?O}>CX_HhM!z_*}Lmh$x(9ofA*#3cAO
zFN1N2^5Hx6rbh8*LI7j>EkUcc%PV0E%k7U<Gfqvt0=K+PbJ9o5W2^@EcC;6&I*ujp
zaP|2&)vFank_k?X`$~=>Nf-FhjKT=)@Ab<JZcEh9zu9dk9iKO!TCA2C?5a)&x^vQc
zNZ+-;hrO2^_{FOBSg;I(gIs!DIMHokD@1=hw9bj}JqF#I+bD)XqfHbnn~kQ<0yA8*
zif+XBvj!;cv}alf2L}&EF=bkHE3UL*Ph{0NKSy_*h)uM^16dwEezi{*-C-rQA76C%
zEmQ(;V!@_|9gTI{e_?k)Dx^?%6%*KcVqR2-;cczF;<--Ql&{iC4owvGQv6gGybV!u
z6_4%Rf;ZpYy$2`?e9R0|v;`y`@`W#qrA!p(d5K3z3<SJrUICDrTLpsVg4Ueyiff;I
z8%e#fAX6FPhpub#18(YjG)kAe+cRfnD_cUHz53*N#ru5-onvPwqZDRf+X8*I+#Nh?
zQH1+O|1!l$Ha1TymM?Q-*$Q0t6BM*o4I7lJBF!;7oJ-+GkA|{A<!mF1S6UQM1X7Ek
z0h}nRIgokK^;2u=-dIeCE8I&<BYL8GvCo)vAj<QW5@TW@#NpIe-oMl1HSH{urG%>2
z<I?+x-_%4At@*d&fHCa06@`U%R%&nD>_bm51|8hyaHea&)bQKO9TqORf_}Y6`_Z21
zz?{L*OmiI$IyU5-D!$_7YbfQs2fiL9qtfRcU*sCBzQInsWBY-bD1L6lb7jFIKxWJN
z^5Mdl9=7B&TkPy$4pnT<=rQHN4<460^yziM6e@r6d{p@>`|4fPuB^dTGF;20L7S2-
zUo}S&rP#Yx^m?NwG?kC7c#Tg@n~$l%aVBiOf<z~@2<)!JMIda%N+Bv%Is7<dSAZw`
z{o8sT<V#D#fNPRH?i*aXrFN=TQ<h5Cv*h^Fowv+3#(h767lfPpHrSF~rwH7j2ZR-p
zx^Z7MjBzG9h36MNYMJW?p%`ZlRy{*aS9=MSGgaz=q>wkoGdm7@Q?~FN2r7l!*KRqQ
znDq;6hi*Ia>2yWjcWGNMT|~dS6#v{UY-;mRgK&$o_5)cVZi2ri-8HEmL`nIr@<v}M
zD6J>>>$4c}k~y4rcV|w)v0-=aM}zRQTC<huXqN}tEYGv<g+y}CkfsWLEx?A!b5tcm
zYc^70gcqRdoY~h(WIu9Td0n{`r4q>KF2~*oHa{V0SQ?cV-`;9;v)Qs#WKpCktX2r{
z!fo~ZMeVG%DT=FEHt0QG9&7ZBwQ$F%zH&L9z=0T=Rq4)uE-Wvf&NN-?!{1+LY9Tx!
z`MBYObhpp!ICK3|A?F!+5ez9g4{su66_G+Ou(houPpMlVTOp=Enui!LTlxsA?$)xW
zbW`(<218i{nY(I54b1JW+MC^n48jLKA$e$V%bqnb!=I)?*6HIWfM4izDbLqh#f&Xm
z@wOHnP}0@&HQS%V#9X6NLuJRj@o!VaoBPhKWf~)|7R24>wsP3AOZUbw+YOi`tnYiA
z9eYGhS94NlZnUrA#gh@}Rhd4>Fq|XG%1}N>)(j`PbRBCYl|4&4z8p0B_*d)qRDz|#
zPRy~Ke#?X}yQe8FjLlQ$JV(V{Hn-t)y25Y#U0i-$H#EASHYocDA@zfIo-Xu>8)LBJ
zx#|n|U`l>qK@WxZK>w0uuleh%cn9-omHT_*<vVzXr<9+s&yeMDpL~plWL!LpKd^I&
zt|9K=uMwXREO~bDW%Z?PuVtGvJ;tJgO}#Ff2=a$K`C&gjlj6kG$bM*a8ez^gno2S=
ze{g<TqswArlEV!bndQ`5BI)zdHpB9$!6%E7S7cH|xsw2lC$H=izNpeVXI#8;t&k3V
zGCNZwJMnaJ{Eepu)MehhZMuA*>+&BC{Sb;BO8!p^-@TM%7CqxmC=Py)$jU>e)Ae<R
zOk3*yZg39nGN02s%??&|^^gVUtp44bV@`JfvM%V=oR#JDiGS+Eysdn45R=OSYy}YI
z#=BisHI5A9Ye&8XCKQCh*g*HQl44FqX44vnIus9l-yRDfv#Dap*kPBsl94W<b&o}7
zWE%LWZ~X6QRi*r|a7{h+OW!%js-`Qqe?9B?|A@0T{SjSk@AS;~YEnZvT^<B{z&lR;
zqlo!0p!YWfX8b$&yF9>dzyJQ{-=Y3LzW!Hp4=o0<itz0tu6KH>ZybdZUhDo}(Rgm9
zm4s*J1{|NO?OvW-x~lNuuh#Ja51+z4`+owl2E%q3!|QYDW-=AaJ#b=@Fw>TvZ#ZGt
z?cYC8?ubxTV`-7m<u>}Sjkk4OHXD)!jqdjLi&uodezTIq{2=bn*PGv=xUINqAsxT}
z3G)A7hxyMdt+r-rto@5LRDIJFY44t^-3}(6u7Nmg1M?=LbKO=M0R)V(t)(^XEzZmT
z_lCXyZus>(W0`9%u&{uW^vcje<)uWKMp}t_J3USjzD&enVN<E~%H3H(3GX-$<45D2
z2&TaQ+2ubdrnO@J?@dv10caa4?7&AGjr0`@n|>EcjM<RJRqaTCzb8mGv(hG$y4lr5
zE`3SW-%KVs7*fSv)67)bI~znm@bh9niwT!+uDOIcwwhPB9sZAN(EVL(nIpBS2%ocx
zzS`;Xr<VGW^oq3j$f_rwvRSG$d4pbnCi`q_@lz9sjCv2RNbWE9j9km&-7;woyJ9zZ
z)18zUF+jn{40xM#S2YbGzm4_)^f1b8VB*b7Ngq2}IamS@u$wOBA4$>Ah{#;B7vU`k
z#up?&c%#sw9g~S9tbF=jzsOdZg$ImByt*EJ<sf%2v^esB)P!WNh<0W)s&=7}Z5JQn
zupP8unH6-qoDIxdA*ahFp{>sCIUX_=H_)jlS{^wGkq=No!S}v3<B3NtPzAO+XtZMu
zoCa(F0F?+hiK~=jEt!O{=`zTqHJW-SvOiDq6S;Y}(<j)RYIOaqOxw>bk;h?`%f9lX
z4VGN5ukNI;KC>`2&+*{%V9JsMx0p4zo1xj})M#VSDhdl%n}n?ssu!`-40YU2;Weuv
zeXQ56etqzZgfeFE-UDv7GV;*O3rp&&k`fQkNepA?Nugv|34RxZoW9}R$wcY*TD{(^
z^qIPM?MNk#pYdYv;vBi_?ipfraykf63(_!b>bS8xbdF28z>Pewrj5*u<sE4ub-US6
z!?iIwLkE@jnyrhRM5alVBKy0}@=X>eVd#^v*)EBI@5V#6=bL70&uga9#N*M|#vh&E
z+n#<i3+S2K{j<wnSKq~dK|Cc5omM{jZPXMpv&0)2;z2XPdU{0?_3Jff@XpfYn1{vu
z<e2O(^WrxNI{#KSxyPHq9cT41BRd^DvS`Z9z>vFc328xXWx^BA&VkVvP}F&w=C+-M
zxJz~WrlPS2TL8w}8Si7PGUGusZS=XGZ}C$?CrILAY+a;Yt=RdLW9Av|nPnI*FYv0-
z;G67NMll5j>o$`7o%2Gh^DoACz&<kP{(K;5uCyQj**e;EkmB`%nKyNn-1kbSTlnA&
zU{=-rOQuf*6aR86o5XoW;^(Hlu)XPX4C95DsWYBbW9R~@#n%AwHM{#MkWkQAsqOvm
zkh4+PE_e+D7CM&<>eaae5V;B2=YYJI(UA-!(l!F|M?f9H9TGqP>ckgt;yY_V=rrIN
zdhko-pnsQ%5%TzCBoiUa_D*1q&;uc8o>(D|tY-hEWBBnP=&J>L=i81!62LQCNrIjD
zAdsYJ4PqqqUus0-lF(0^uPU<+LbKEvCjqp^Rp|j;uCC1EBWY6f(ELJ1BLzk(#+>ak
zKbqrw;_JR>!}$at!J)RV6nMUw3+uRLnYCzks>rl=0K|o!q)S2s(_Qc#72ujum)yHq
z@iK?37k^WQF3yLP5DX>d==FH{Gj{;xH<cPdcgDq8<;5lgU>mdhx0(%<<Ys*fpZVrt
zE}2g2#?oPg-Ql@-|M8lG?stWujL3k!V%59FZb1B$=v3ABGZJZTov~pbdt+SssqM}y
zGpox+fjw)BJTLoNzrA}U8JN&R_d&dWF)LPJ;cjTRAH{H=_1V4IBTuh$*QgJa2x3wt
z2zD{EHhO6X!+(^o`haSNs~9#+>ab<h<QcYQ)zC>&UNi=fJd91i$8OdQtWM$i`;^cx
ze<{{sRZIlqwuEnX--}rX*|B1;<pQfYY=#ILu96iceZnFXb~CBrn6+Rb1hE#<$cO*V
zx8>niz`at3BgvPA8vioqlj!wIg~7jQY}UWRH&-{64~!(anRW1KCPeYn=8&3u_6iSG
zOt~LN1);3c4vgrwlR6!WRg;ZlDaSSuXe*#Ae2)U!Vc@e3mObbx_-q7EgJfI4FPupk
zS_m2L3kf`xeEb)Sr^`NGt0(8Hc_(`Pz%hqt{1N&$VXW~8){N_t)Q7TCOS{RQHX-p%
zboeCzafur0OJbrR*<fjz_h{*md;1C9_f7}UgaJDIwhKTxjvk+?OiI_L3aKnmkE&=(
zQbZ(j#9qF={a2MpI4&;i1NDE<C6(0}=pjOOL1DlA7flHQt&1VDINg&Z&(I~-vX>u&
z4{cW$+#jwDKU3J8>@GbCvUiIR`IDq(HZ%(F#a@u|MXGVXBvVC@NkxjHY6MitMcEG{
z#O8;_P*PIKpaOEfwT6cgdSSX-{yL5=ZW9JUC?n?MZh6H1ZHCOBpn94*mllq&MYCL`
zgEAQ*X)j$U>8|Ss8T!S`hova2kaUT`dyu;Q=ybOQiF(X~Ga8Te$we+2*x8PO$5icK
zM69)4sPk?NQ^u~Q=aOR^&wD<00J|WFt>w%e7LDFxbo>dgY&(%}+0di*jTW`GXRdBK
zC6ZXP0PKJ#*!PGa-#q>DIDU7ay?~K!!PT2IUeCEjGTB{lnJ!f3V5XHLac#@?q)`m^
zCsVOu*S|7nuQGH+wAlSO4nMP|CV%b;KFBd>H-EPW!Ph~Q%cr9~1tWYB*DY5c4A5En
z2FpmXLBS0gp7(Bb^X$oKAK1FSRXnU{X<nfBd<yLr0I;Zxb8NA=)NllLjzW&SO_3AD
z@MxCf?7`4GqXI}TcO1!^U&!N~{0Kvg=g->D|9W>J`!Cw&koM0?4JR#ox%L3;Ex?Dt
zrkNFqR1%YUp_|3dmvW|dK3!};?76`4rMjpcWK*Baogn0wv}qAb$;uwgeFvj^e_2P=
z(@QH`q-v4Mk&8R>4ZX{m;El{XNDM7fDhYoAxj2a%gqc75K&)#RaKH1NSrF1S0Xv<9
zWq%@(>2ZS!hi^4i9IzqClaNvGZ|k3KmSz^|_PKTW)?nCL@u%Ov>dqw~Y6k_TVhH0g
z_iHAj^I45J241(Z+!Gcf#gn>Xl%|M4sT$>CtLEhh(!(Fro)RqwYoLIXxCKT?;FQ4e
zLhV7*%aK6(io2fMu3F4L)Zc2fm;7Ob;5w?AqMSs|ZI8a0mE$C=ub|I&K`wttoIm2V
z*Tn&ZIH=VR)#VoF#3{Z>?AFc`;W!_rIf<4Gwa0!>2@^}_J8U7fpB@ZcL>$*t4%91g
ztynVfbPa`*pO9WEfPVaOCHSZ`wGMGqo?7+X0J@*(avQP9BMQB&)o~FE!NnSbY4%*y
zOFj@uR-B|Irk)ekCBOzxfe@Ci-V57orjoRqXUY_{4T`Tz8!x?cacFP>zsYFlNO9Mj
z^YO6t4Tpptyna0@_E(59>pN{vTif)cvqRFNFSNk1g}zvLv8wpFAI>_Vcy8?0KJ<Ls
z#o)Jm(Ahcq;`}gytG?~FsK?cNQJuQz+s@3RrF^zt(Dv2nx~l3qmo5^{ryaB;(AL$~
zeZBI7oiKe_XK3LY>}(WpKDy0)7}>)I3C(Dz9bg(7ONdoj7MNtbW=Wzv*9y_JBt2V{
z2z0rJ7iV;Me05Cq!5M#CI-gTf87GRf8%~<Ay=k~{62TbCzqF~|rgFYvFnG`9PyFQ3
z;lB(es^q(?)OVY0i_kxDKIQOUIUIf(c|JWEd)-&37eWGlax1oN6Ta802qPz+$gNyL
z*SzcY#Tf8>Ot+3M+av~-1itHh?>QH4T@#=reVqB=qP|B}0&k}=9;<ek#|6lMt^Lur
zeVhGDtL$CYyUBX(x-d#1r1bRr7);aLQAH~3tOTar_)~9P3MyqDev8F>Gj~rDa5ikg
z*cKtQ*7^LPx11CwN}3<2UD9a~SbC>=sD4)Y_XXxEvh`hzOgF2*z2lq4xEN-KazC~3
zWiD!D_c38&Cs}Lel}E16-l+rPsV;&U+S2$qlfqmnmRq%_oQqzr4~D~~rq6BDtHs!*
z-Lr1LqzV&)MfHDl|E4ix$#hp=SJU~{&_+(px+s63p)1P^*{pP$pV|xk4;W)UU`US>
zJt^JFHJXxoafBpJER(#9S<J-(AJP7<inEROIyr~Y2QDxkRv*To{nyJJC}(8oIlPiZ
z-#7O&X@(IuU{C@nBnQW6;ei)NK$#FFL$p)*(8DfP-?K4($|~n1hYbT7$weOj--eTM
zRLFS(?VU6lqVroY393k=`7q&>%ZZGfrq>qaN$$vO=#k;VH~p0!213bUo%-bxINavZ
z0=4~-q4U(|hr4dT3rnAQEra0amTJ?%B`tTi{_4n#x{#Q$gVVtfFoQ8Bz4YOu9*9Rj
zp?8e~S^wgGY2f^6PGLdwH+d)eNKLD=*TT^6b+7Wn@+@vhxJV6Gz$ve!{undxMo^n9
z_NKt+Kj<r(60)GMPl1bZEJu<VG$unCDx&BRlglD!>9hq;#o8<kc~OsYZoTz#5-<Lg
zrYwZ3^u(nDPf1l@taEQB2L|)~g~-PSR&`dQ#U8xpf0y1WEFPvV#-?BJ{vPB{2d`XQ
z%iB>kcHJ7^n41zY5rmZ<-ZP`-e;F+BFrUK-KWvab3genc%oYTMS1O#{?@lzYO*cfL
z11n!2mRp}rR6DD}&1AT{Eq$oXuulfw8YZIR3{369;XZ{^p<Jvq72RtHDd5IdY%hBn
z?JmhEmzPH}8~rM7its$4RKKMJJdHPV9wI4Ak;|Z7HWgaWB>eqfncj&%#z43-HKWi(
z(M=qwE&heI_ekt4%F{i@%J>fO&72J+lYLTC#J!l7T{rcBH=Q3@AXkQgfn_D#NxXAi
z)fTMkhgYW_MVTtCz-Pf#^B*4!h{LLW!_t23gfbhr+V@3P5f`Yp^IU?1q^9go3FpYh
zzh@<`*X4!H>;|Ix<$Yy)WH9`R4^?%=J+Uhdcb;44Om=)5>PmCwxS_wROr2Lro1n3~
zQ)R2{?5hMG&T3j`ZF-5BZ88W1RgBRUeyOg!Ehc1RF6S%mCDznSk6X+Gu;%XuL5R%~
zO0e65*ZcFkMrS3HeKjJFV1L_jar8AXcO$Z&B{o(e1b<~YX_@0+IW)?ho-IJ|UL2zp
z*`|I{*|FEYHT{+1*z|w&!*Shk;E31S6~*x0@BkGn&z8^%icI7$J+Y#c*^k7esG!`a
z&KlN!UZ#W`YC_UtZ$v$R@4uA07wED&^KqAR-6t2k-~D}Zvm)3W942=o0B1kDrgqV=
z(P*<Ea42NHdk}xLoD$UU=&*wsQ+5LnvqZXb=+I)xn`hLoh3Mv!L5mhtDZZ<O3yAq~
zp)b=P8+En5uTS@<<D4adE%dVG(e&JLt&Omtg=e{q8Sxkp{o&*Otg^9Nf@5p1(jF&<
zliU|=s=EftlM>vu2pVWj!11kcJksH~qFuY<plSZ}pyoKV#NhiE*$HFXXR#9CP9`oP
z3Vghr8YqkGEcCnzY!(6_$;W;m{U$PrW@zp=UL2-N5*g!u(n|pMVp7A|y#dvDf$dfM
zdxrPhcQR=M6x^EBPB&p&T-DU0E%}5)7{k^ukl{#+fpUaWN$WRfvHJ&A2N6RKotM74
zp4t4Lswr<eH~RcPvd%Ipj-cuHSa1stL4yT%_dswB?!LIYI|O%khv4pRfyIKmFYYc2
zT=G88d+&$4U-s;oIWto|)xBNSzkl`A(2f2jcy00T2-*U%niplOB0NUVRc0rYo*(dt
zug!At-<XxVnGX+Ycpq^WQB!Q;_WE-wgdnOoLs6BGx<{_Y<m~o1$|q~s6&z4L8+)Qo
ztA9Th+CMYIS`o}Zc<I0qlb=0~1MK{H&#ZS;W9!zj3VO_!v-im<I-q*jWK577Ztpt8
z*$D3bT>tYdD$q(-TlmZ?Y0bCR-L88^KSUF<hs5)IH6U$2&`yf9{cp`rD+A6+-l53d
z_hKgB@<@)ba`&aq6Lxxg7?aE*cBu2#7m`#WF`G*U5rGtQP+rw+erM$bgF_h>hefu_
zz2>M@qtb`|Y|6{kt@|b$vkLM+;=_M#1nBqL0>;Cl_j$iXtLS|rvaN-)Q)V3+6Ta=*
zeCfhZ-TW602S+bQ+*S<NV1j;k+RRZSJMuD0N6U-4(?d(Yzr0?*mINYcM2KoMDI2nB
zY@FG;0r~T!N~zHlyk`>IHL8=d;aB!liF*&ljBDgk6L<+wg-`+NcS)D%UhMS)s8)Wo
zB*DlLk{Uctg(QNC--fO#>I<3izV&AaIFYv}Zuq_(_z9vJPUnYVH%0o8>(Y;oiYR8s
zFRZ?8+jwXML4Sc~Gthf~UOXtbgJnO<gGJ2zic(ouNe5gh8L4%5Vfm43QI5&^tpIJ^
z@8e1vL(jV^jlTP4zEsJGjpzm6Xk$I+$l0pdTQx4$hN2V}a5D)}glA?Lzx8qY;k>i<
zKbo!R&7q9(9~f!cs0&1ZzOMhn@jfyFoj*did)`Xj8FS?FSuj_Ic&|@oAc#-TnA7W!
z-c?=~7Edq{bWw3w<d4{Bz{qe(s31aM<<ESTm>ox4p`9JwaaIMis-=+SwRsBF^#>E?
z?Q8$gxU|Q31S3tI`%Ow?|Iav~H%S7w6EL=*;W)estQm;XSX07EN1PhMwvPh3#zmFN
zl0LX{Each4UtYNgk0r<5i+ayZ6kcl*xp|m%h8XZk0qN?GiSmD_sQvc$Zka&C;$E);
zL4W_D#pOzeW9r?GABu$TnEbLe6kGS8b4awuJN`dmpH+oKV@SjLsHZ>?Irn@XEtQQ}
z8wG6l4K8(abf$3PTVxy9)mLq~(5nUhjXPS}%x8RZ<aC<sagOb@%y5wv(p4h$u!p?}
ziXirG4eHGAS$(PK*1cypTfPq+`dX<#ONg47K^oMozK&o}zrZcMCoBDu-q;8hr-r^S
zJrx1F#8Obt;rEgG0qH8}Uz8X$c}i;RwooG|cFqG)>e0!`8KR|=vedUJEuEBKn1R2H
z!`u4NVw@si4^|CAeT6o8*;BYn&Fy!ddGJTkqOp(=r!U^lFZO%kQFC2l008)AUe*W{
zQ1~Z8g=`pc$Vy1(Pn)DXe+aur$%iHotN`g6?%Zk|Y@{4FgEf9dtVoHoleR7u+Ab_|
zC??Z1Zb50wtca*T)YkJTQZCC2{U-FgO4!QUe$7>8^wnc+7pN}V8yNYC<VZttsYA;&
zA=Dn@2XL0$U=CMC@M9DqlV-UBNV%B5nA3a#ERD~Ve<2LvNJ*n$RO4=pwM~N_lh#&6
zeTEv#eI82xRK?1aUsS6|OLF5Cd&Jne)z8S8UgDPQ)fY8&wkv$+Hx_>>9v|DK?eP_3
zH<fi>Z(_Wm)@iK5CD@d$2y6PUO%!xhAS7l`WID3X!TN>6!xG9`%Kz2^IMUDs0c6#Q
zv4(oyPI}}AqFkR^YF<`#bPYHSaw1=K)(yiLn+&g><(E^g6!8X-O(jX}0J87e^)fwr
zI;T4Wp|c61<2v^0tzhhHDKggCs+@AWsC0R3P!`l!nHY|t;#Vhur%^w`-4~)6W@#xZ
zH2Wkxdo`J&uZtBm2y@`_tCB<Y)VA3aGuwzoxg<HiA4bTtUMmch{*|fuJ4c?s^X#n-
zv#<Z6$;57uIIKQ)ZKda)6#G&2yTF>)($*}a#9+taD3qy(uXiX-UXxbSdOiIMi>6|F
zKBT3|^4ETCEZCcf`aY5RD4nrZj(-wBkU1F0&z)i&j|40gocYnyy$S!MK&1u><_wUv
zGs1BB(RtE&Np5O&OR|eY#EDpHhzkw8E1a*t(QJJCuETP7_9R+)ZfiyPQa*oYac8#w
ztP=Y^u&aD&Le?@4obmq`5ChY(7jE>y#rmf?SGQ&qn{+wOsN(A2DpLtcF+BFmtB-w#
z<cBH&L=g`uGkDYAChFd};Y6g3fi<4$R4FGPC63tB!g(*&ZrZuJp5#0lzxKhRHYyr1
zbdVpWeKKaaSPc0+n}l*EwOp5>EQ=ytB?_$$U6r1yY~+DMFy{Gbcx%|`K+@{bN)uu&
z3c9l6Q6y~`30mk=(c@wXPxlvkwDfyO7$|R!Op_;q?z$XIMCs3c7fZ+)cUyWwel_O;
z7d{GFv=yk){Oe?>=7Pv-N;xY5Z(421WZyI0*pJ`@FW+?P*~E$kMPKmOhj|oVnYP#J
zf4&xK#&fsk^X(rRtKIhej5I>(DRv&k0Hh<F)s0y4X8z<3E2}6@Xn1amu##fijRv)~
zM1p4OsEb|#NFD>kG=1zzgvbZ@sV6>kKZv>&WpOfi2>7Tp<S7Y0*Fr+eUHdiqiLgrE
zh7>blY<WuTe;}gd4F2BzODLMP8{3^r7cL`1_F$C}xJhrf8witSrshTm(~NYTe~`G2
zuHS1K#9OdF{yZxY`6{mO27De2kpR&qrv#S;n_3bl`TF-WsUXdHnCgW(*pZUs+v6Mm
z%VCSd5GH}0Si9&z(}kqGhQ1Tx_@5B)OFEjrwl8+2{5MFBs_cOb!n<sd!s)HEL>EbF
zFexxlyI1-CY3s}libtvbkR%nq(lLmH8Gti18pom#d-n%^ZHv~w&|z!9TH4iY#e*s%
z!mXz3%_aw)i(PsTcQrl&Rf{D7K+PD;On4_Wc*A*LxEFn}^Uv#kg15L$&p$uDL(+85
zo$8>dfnpDAbbGC`A3xxKoA(&UJodD|(CVg2S4pWNeNmW{!A*_FxdOZnza<xRAHNX<
zd!?wxd^g;$#d<6#Kdjq}O(u+jOirFc#*@*QFZ5h+?rA+xE`amA4ZN##dr~?Oc+x!@
zQy*IvvWk&#BZa$!x0|rlj43>gL8VOjkwA{je<Y<4L+Ql?EaqKd9YD_StC#Zfocnxf
z<HO5b{MSsId-@mTvrp>sPr)%^L;M2H$N3sxhG?M_+LXG2<3N^iqR#;Ou$A@Zy<JjC
z7@<2+0De@<MFd|wJySgx#ZKLdDT;c2DiQnJ>pbJ)Ku&=~SKgeW7=s}|#{^90skJQ1
zyW>!tlu{X&^C|=>UA=dzMB(;_sl9j`cSOXk<*9C!0*$_g(^okqnlGy8+3s#Gw$mVr
z`O91E#q-Fi)pI@EGp*gse5^yU^MM2y|HRS3sP15<J>-W0TGA^{cyWm=ojP{GuQM;Q
z<CiB}-H?Y@?KI7clW;bqMRBmIVq-1FP+jr5{`-W!Tdd#O?yQ#8L>I}Yz7;Pkb{ZOx
zbX<k>`SN=JrH)k`XV9voTv}MFfn5y@uuw#AU^mnqnu)GB9YtQW;&zt&ZH~LBA&<F^
zzVq@2(>REsa;$_4frx{7p45aUk|d*uJnL>PuL!e|Dw!@H^Q}T>C?{T;B`6iZQE(G7
zK#wS)Q8y<&A|2NxsDEH|Lx|KeagY)rb#Z!;mCc+aGDfV$up43gT#vjb(k;DRfuC1A
zhxXRfgmv_h7?%b#V@a5rr66tcWa1%^r7$laPW)Eu_t7@%eSpKctG(<k&Phj5!2wN0
z0FDxiRWAvakSVetC$>?3WID26W&{}P#$tMw07oKH2s(dnkhqK%)(fQF&4?Ev;n<#q
z)Q(%A5n5>z#%Ig?olAxTKSMhrm{dhGf)V)N(jA4h>pj@zJ2IZ<f&Z3uPMP3g@39mO
z5HL97>-SR>RZKOmh7Tqk{QpMPl>lHXP%Jyf6ty`khI+*e$Pd+?j#f3x7#ymW$FqT>
z8?u`3#WnRRYF`Y^=WsI}rr{iiE;z%mV@ZtTeXswCg<?ihBx$}}{r0ooo%CYrd<HaZ
z4Lddr0ZbZAs}IXBGE8gQ$(gb|2ktDUsR!(uv)F(P7tiO8*b=#Y-v(RD<XIWDCZ=t%
z!eVDfDk3*|&$P?w7nhvs22!K)4jEUXefALHGM5I%lC703N)H~*Tu@;Ka2%Bs5pX}H
zyV;9TPF!4Hkkj}{REID#KMni&k;l?Ujy2?)IlFpLv1O5wGKxWSKZFM7tx`KlbVqV2
zS=@Aon^Uk?@lP@nD0h#@ekV=@3Z<}8X2(?C5Jfu!x{<iL`K(Af^)(=_A9XHb`l3O)
zY!2|hTsrX;Jbo08yO~8g9qtpVBI-M{J`q{9-|z*$^3&GL#DwN<ldZ{3`Qh$^Pw~=N
zV=grG5-(yNU0R@fFn7yldF#1(D+ZN4qPhq@T-`L}W_lP0_^yAEmmZBENO)YpLAY>n
zic@cV)XEyi64(0cKppchd{FjG`r$17bT;!xQ4FJGW-8X^P1%Akl(SM+`@2GMz-GRN
zNtg32>01aZUpOSC2S$T*dN&`05sxzQW*PnWt1RLQT^n|+{!Zl~rP0eWq%ppaUJZAg
zbYI??{PIDI><toS`}mGv#PYK$X``Q)smZ4YfAT3f@7w+^?_N<IoWNIZOl$1%eb`~p
z2)=zewnzdTkL$kPL_X;TWjDSi*RRNo5UH6%$7I(-C?Oo`m^%W2JK-`VGJmt9zrAtT
zaJlV+M~$KTWP|nKI>@9IjUQt&Uvi5No)4)tkiRGDVv3zqgS!=YMtAFjw>wGnwP9-C
z>60g1Y+nZd?)*kKk{^ogacxKP)?)3q@{6;YVJIh1mKRy@smcypU0?ClUgH$AWH@P}
z#HHd2o2j`&XW)=^LTf!9vmtajtoiK6yEeO8@UyFIwWFgD5^iV?*cHpREwIXN>N;}y
zkbly5fu*yq(Q#$5P%8IgB%ZE6E|FDgT(&58H}kG$I6)C(re@}MsUZHu_1Mt3f*#-)
z>bt}~0opBoj>EC(7;ntE+JQWLx`d94n$|I`#_La_fiG;=;VmYpZolx*q-|Z^rN&FB
zbAESxO(NWdceCX?c49Afz83D*mHSTu#v%==?Keaupm}SG`zA_7*hoqY$H@9E3kQI0
z`Q&lG39QN4>1|T`v^PMqLWKJnry)|P8zF%pOnx~Qy#(@j%LU_lQi-4a7eTN#Awn&<
z6fD3Y%@vE;zosEAIrFIIWZrL#qG|k<IST^{UH#ETI2bOQsWkq5MB<H{iF8g09Y@AM
z+W(PU|B^Q{lXdh+j2+O(Q=v_bjT4O!pmd;el*Fuk)ImD!v29~aU7K@xj`?$GXg2NW
z4?ALs`v>KOqEs+fzL5Vkh$$~i2s?oh+^e#}0Cw$GfBulcG(OF3A2+_P5uO~OVoNu8
z7TboZ^1UMLPlm>(mvoln(1U_sT-Wrx5;NDhxBD@2d>NUM=~SPUn2iubx<`Ed`E&JN
z;ZH0O3yZDs*}qZSV}j>-F6!o(98xZKVYPi*kUoX=o-Ei{SfJG@Q0|tggXXHzG^O4(
zWU_Fz`@S@Ih6h0nTf{N~G4^K$H40KMc81Fx5R^@mNHQ-=FD?A$bH?m2r|pm;3e>%-
zzJy#&`FwWwI6aG*=sa4TBloR4wZve{#B8C!l!k9-?5J$+WC*tV`8E0d3`d1491K0J
z;@cR>T_6CI`QrKzT<^53eRsh^F3@(+b)NplNJEbYQfS>p+=N6pbBrzU>Hs#)*u3aJ
zFdjwT^{gOK=G0bA?G2W@KVyLQQZ5L{jYDZKY<BefUHV#v6n$9+;<@pV$NKu<VZvN1
z&)T;XgeuoZ>Q~CQxl`1s*1COOSl5?G5;jfXs%I=V=eb&GkU?AF_(`|1n&E^_-&nW)
z2?_~&`gTNVtorAMG;##aPIkAF%C{phU;&3_V5d<B84}GQyuxZH8x2qE?c5)x*9;Vf
z>t$%4+0r_HH&pgIW}!#iz(CHbmT3H_Ir@zw^_1z#5pC-^efu0Z+Ei9@v6BHC8Id&W
zWnN2PGLr$t*puXE9@<88w7=<g1O0(7?plwy<-&=APi8-3d-8r-3a#qJrZch}ouhw@
zpwnqjzDMMTRC)WPKNTG5%szn=>i431nZs}{INs^b5aY?DBkJCJ`}%~<v|p?WP75g<
z+Vv^b0rjbL6eDr!q;3Lc=60gwg%t1*hH4}GzCQG&?YH<1d~VJ^1Yx;-k>Ck-jFUmh
zCfyfc^cZ_1he<iAM|y$}VK1F$m}2G7&+HRjnk4tnz67M}Bp1>+x6*GW_#&q~G3tFA
z$HzgGnK7Bo$DF9dZeCul>-lSVknmB`x#dOW)<)hSvX95ZQZu~^KIOYsySmKGwCQ>s
zCF}xX=6<R~s}`}fng^D=RT=}=mlch6O$!^!oH4k!m0G)}kRY;ZhdZl13~3<#cgUmu
zZ_K)>dbec?V-s)j=+&^R9)0J-W}tOeb{FOBjg#tY3L*J!<(y}VlZz&xD_S6Dg$~2!
z++=JFCDTfjDQk!s({-Ww=DA~)a}#b@*WnjYmd#B@qo|ljw}u+mIB^D_D)(oOQgNSm
zG;cq*o6L3Zl@_h;%B(+DJDLQl4!^yR`I1H$Z7HXU*GyRCg_yeQ0_ij^%p3XF09Q$$
zn6jMT-{SqIy3=IC#J%q6XXiFnTklXOeSgX<F$`5*ZD7#9p#LR_u!|udrj=<pyD58g
zH_=Q3YoCYcZKG<{9W>mR{Er@j<mGUrUa+M;;A$}8yRib#ZYX8jarbPuWw~02-JR3N
znn=Zw2HG2gvTy`mh}lF|fL4CCU*8iAh1n27z6;qIb)0I5ij?Td)bM^<+i(A5y_ZVq
zmQ}2{lwnUMt&@%%)u#hcg8|nKkJwaPF1y*&)CW9Af?AzBrTJzMHbc}efRrNi=o<!E
zqrXW4mhTw5QKKbI4)tNZM_Lvxb7GX(;`*gX2!B-3*1s5*2|}wTQQN#b-6e=%gPDDx
z^JXDWfv&$B3;sH}wMW4iiZhmCrmatFyM5}H`YxUu!i61IRwHjo*Au<7asGI7n1kEA
z?riJoncVVkfZ!wX@lIxXJM(&3f3R~HN3z;>J@NY6g9*|S-)_1tF|T&N)1A832Rc<X
zR;DbVcSnf!Ie5?yr9xJpCDP!<AT+xbnuJHFPmSUBW$W8;A6#Tsv_7$1!ex=*2RKB2
zj=-3NANFf{g#Na8)|84J7oyO2OqKt0<h`ktmbCOs9RaaNI62=JLDdA>Ud>M&D!-Cq
zHE82Av2_W_&(YV=qiJt#9S*!7viN7a5984~w>9iJzR82C@pCS+Fu8t(4*44=8;C$i
zNcKa^i(V^}X(EmVU>~g2lID;I$!D;;TKHp^f{ST7i$7iT$wc}UFVFM~eYG5t%4If<
z9(J_v9m+UTm(RE{4}fN@e%x6{UPWgB1F`j=8?p2!`Gm|hH|D4&(HTN2`j1}>eD!wz
z$e2)1WgJ<*`xq+ma#UV&ZZ4aY&4MmfeefMW1eaBd22bkC*s?EY>or444v6x){w)S$
zJ+U=2H`Z6r`x;h6h<ynF%&vCBn+|22jW;yA+{t_Cnq2YBH;m@13wl(QClK;+zKlx=
zNFU4Xvc+vh4=tS)Z~c6$w~;?6%hJilqrmZV+IoCu*N-2!l~T;UQ_he-NA_`Klf!(+
zy^>cE{Qx_ld(ErRoY#6-w$vQ>MPIs!W~K>W9$wg6wT;jj_KNLbQMeequoV`6>+O-^
zPOPVz#$!$PM1`?r{XPl?-a<%q<{6HA9#6p(Zw8a7FTyRhOnVb}u8Yx59pvXs0jkPs
zcrw_&s?(+rpNdK4v_Hek{q<~7`QYNwGS#ARa<Nz`QH-C6P@(FNhBd6xsnO<!m{jq#
z2t_Y$Xm8QMYLBCEp6};&?~JkdLr93&3>%FFuotZwKt2`QQm(cKe}fBHz-%OpLuy_d
z*MMxC7gpB3^S!2-n~>i{?uD&Sh4z7z7*n*d4F~i0ZXhH=BuNa7dh`(cH!H@IYDy;h
zm6#3+=kyil6iz&96Yc^va^s`s)8UT9CZkvHoQ!RFxdr<t`^w+LOygHgDfq4CV@E$q
zIE~4*9{#Z^OeD@aR;O-9j84ly6N$b}<a8uRV;jGjgUlZp)3G?z<y*Ph5=4s;Oy-8=
zBe)|c+mSRHK!B{s2ys!-Y9r&@RVr75(Z3QjPRDn<BropQ>e+#tZTB;Pzr9Q{c5^}=
zZpWVuo5mdJjz{Hp2ce&oIptq^)xDWd3MO~jWOA}ppsIPn^M`7sFY;PdRy|!Uo1YdR
z?5>(G8^+3_(L&h<b_eOGZ_-xYcT&5T*3YWlfFPYpt*VpKza^Xf(H8@Rl5aoiQj;4%
zxySaB@T7&Se1v89if2^}Q)5G4nLqIfE)EBf`PPJrc&MZ|);55()9!t~osso?JjTXM
zoW)c;OGt(B9@cfBX&oSETtj{Bc&eC1CV=)?Yw4P91tWLTJhY<KgvfECkodK%JV0&j
z%zia#JOyBSMX8~5i6?{ZwD4x>C0wGy<tbx*KS4gNu~q?5YG2VQsXauFjDd>lFoPaj
zYe5o04vmYQg45Tdhi657_1VT;4AXFui?RX$`wbmzaR3(R+l8jAPGWVwq%KpYzf{iw
zer_Gghrw7W9^g?^AxH|#VnMLu%DP@8jg+q~VQ0dYFqIy8mRAsBBe*QA(tQ|JNbAh|
zs7=~}kKmkFY96oPEzD&a*hBJ;-3SvMM$(+iWKORY!G!hcEsL4IFH?}g{aXxv`9+pM
zZi*O=U&Q3|#(61t3#i7}_nTi;t2>#jTe@Rmw0RrbtVXB_+C_O|4#g#Gb+l7$1(4g=
z(A5cJRkIj-H=}RbiYeo=AajFf=YkpqYUhn@M*OZXm%>bSmc&N&j8FHQ(VC0FDrK}K
zvzO<n$`IBhJ7i6)kh_)@lNEbKi*P$hadlB;G0?cB7sJS`t=%dQCG`N~c}(B5lz-0i
zZb9Gp(u&BvUj;!0dpMW$e9=wR+49t7*c~Z$m`uwVokLsoa;6CV@h+S7{-lFUtB6M|
zvb~;4D+XR`XOWkEvY<GGFKsiKL(u*H#Po?o_9?a0ceUL6e2>(P`O1A9_#b$gGYHxT
z-;B+^8XHb?pk_F{X}G6spyor5Uc$OC$`K-Feu~6qCE~`6)yRLVi@^O_d-wR@Ac>UI
zE!3m=wu9VPGw(-JVdL8(IHAdYe*<l+_7(=l+`w`uRJq|xBNDXt*+_oxOM1v>6~epm
zU`}36OZ|sGGiDHev{LAsN6T~_u2mlaR$R1M4XuoRnkH7mV~Fuz?vBs4dc=T`sv$py
z+Q)|EOPTAF0E`#458z56GkM>(QdwJWmVU#=rF}K}y&z%gZ2u%}<G%(aLU$C`Y%n-m
zQ`xm3Eq+Lq0IVKt4h&;uin=(Th_?R&EZ6e!FuNLeH0X%`{$GOGK<-10)Ea*`sZ|-C
z!2q)%H*#rf$SE=RkKQI6Cp1@QBOZSuy!o%7D;^q-1MVZic+ZPFj33vf7fAm4|6$(H
z+Tjm`${&J*;qrF@^;Yt}|GAV2A>PY&P^Iw5>3@0U4;7Rk3E=Frn^M#VI^HFCCxp7z
zQMM9Y^zV2rBkJj$ue)wKG5P=9_oZ+6ALp69#NC}fzH#EE!I_9lUj46G@4vx`i;fX9
zw8o$Q`}xl@a~}IVDAs}gW?M*K*zW2(yG`xm=4YSMnf0?Ltzf2qf`sJ7|4)z*Vl`{A
zi5uOsxXnpE)nGq<y@{H`ka17ns`=)#f*W!&vO0D#QDi|}{lUgfYyx()tX8&GZ_T=D
z;4VDvTaId|C!&Y#X4k$|e|%XiS3aba{McB`w2k!Y;@%(1{HHlUulA^I=+}C`_kn-^
z>dO%GQ6X?`-hh)}L&j~L)dOwnd#-3B+7m;twa|3H$mL{x?ZowFtkt89TIWBYf4JUo
zvrq1DHSRnVK_4wJdfzJkF|v2O(5LX~U|WyDI=<1YZ~R)HVWKIt<*4z(YN$bdo9llq
z(fhEZz`ntJ!yK&X&b8B%&OR}~!DL-^Ye!YxRD#ki=eO|Ud07qTmigv6vt|zFoQU38
zF$brlOnBN^XzlrQ9W}QuMjGpNYCf7hp>1ct@zGU&v}@QU)6U|K`Q}IANEATfc9(OD
zi|P#enW(P-f%*%-J*<gZl-#r{Ab$hrUCv;kd76ic^?#*+|Ic%_NcVf+?Jj5`DtPPf
z$zoswJCq~l*KJPZqiJ1Qs=Qk2ZCV($-&fDon$ORRq3(X>2}kn5pim6HU+_Il0QqG$
zWc2hlP}t(!4kn3sZnEpxoB&jgpD5G-1F)C(*ReLS*qt=%$%2TAkYu#$M>f)xIx9!f
z5I{yV!AEw`68~(bqlB){=UNZj9eaK*$;Vv_cXQ<vck{w+g^ult37OUNDIK2QuYvwu
zh(@V=t2vV%;rbBUTk9C#>+3u7I3bQQl!~z%%>bsf;l6o1x^>RZ91Kt;rvMWZN%0c(
z91FLSr>_P#YtHmB2X8wq+lGL=$pp{c{hBOpOB)zuG&bm6GG8{^W+5{-cy=5p0KILF
zFRkAd5-E=7_!ijXhJu<}`!_eal~3d%0oByt7kV;3S#s#cl;l(yRn_G2bT;ixIg0YU
zz9Q_i<+&q?LK<va-#>23v*x8FMk6xffKV*ln#7sA6H^hFWR(Kzk>5eCODdNOXcp|0
zRCiLDhTrV5u(A8p7?Ad-Dv}TOM0{opTZdUkOQ@HxhQ{3LXAJpIBNqxM)zs9+I@_F#
zD3|xnN5`BJ%_tBdsqFUghh)CXkEzd1BqSt=?^eiF*eLu`JXmglAKYnksQG=}NVZ?7
z|7W!NJ*|_`U)Iy*De|BrobD$Ci~jL1)3ay9(aK8|k>ksi#@zDipPKpXSlGMaZf1l~
zzT*Cv5ZKA{yzshB)%;}<paXuM6)OpUex{nTp9(PtO~%R9`lAD5*V5UI+7sP>(g6Yf
zyH*Vbg??<GTQPjrpQUMNKH|`|@4P=D!p*#6kloIg6|S}RjZDeJ*nko3M^#D1W!d%J
z?p^d5cVW&aZ14>u_Iq$2cNP@$|G|aX<hnhMpOBqMr|Wr_fH*eav`2Y!U`Kf|x@vW`
zEy(c+3VZdGv6xy%(QGF4KxThj$*@7Y$G4v+dBt7NtY}hH`m-W&!3B+0lXwbK27|ye
ziu0?YG|x15c*so?^&<fu8mD|9qgt4MGz4$JScGLd*DLPPT4Bvz$y3@IRrb9kWH1M9
zOimR>Va^yLBI!n@j{D%zwH^vW(NA?@W@DYc<sEu1<1EH;2j@9xNR{bh*`#gSDz9%d
z?YH{c#S<FcV)ee@E@F!|(&%tu(}Qz#;#~_NfgJUK1HyHc;l7*UqSYZ?5|}cocp6p3
zo*fuGI23@8f{&4t;moH1;dSK8rv57qUAzX7>HG>iyvRbV0=cV^v^9Wi5d=oqWgmQ+
z19@55dJKo$=}IqM`ZyMYH>|mO=8FUTBNAk`?oC9UMC*zJP%h6}0w_1fXJ6pqY)d1a
z=h}^LbUfgMxganL4S79X-FzDPePcFAR^mq>eDItcb7QkBcVAhyTbS?}ym?{2AjMX6
zivKzji1{Z(xd8C9zT5`6U{QeRH=Cr-*$(j5OYli{4Q)wT-bbM*-!b<EUq)(*K)YyM
zWS<x<!u&-^%i%Rdk$@R<6gnoT0W0Y6L?;XTY+c<P8W4sdGK=G-Y&J)#$b=kU5TR5*
zWfvRmEqsx7on_3$2jUbH$$ek|AD7^Px2N9=j@vUhGAc(3$cS&z2d=?72MD7y92-@<
zoTJ;rbHflqp+f#U_cIKb7hRSjcG9k$bnATvhsK{Gp;QdKFx+4=q^wO~t5=}rye9E+
zW-r+my5wglnxmVaNw}#|VGth2kH^xYn50{aM|YBt6!BRUJ1v|g6m&T&Gw`$##T0c~
zG!7(0|GA9;w0N(J=tS64XsS1X*Ue})P^$ea`kK$VejC?M2r2Ya=W4sZ7Ea1o2=z-I
zZvT0_028v_m{Awe(eG?kJK=)+l5`kEEEAs~1p+64QTjk#Fx0PXQDDyGnYr@{{Z6Kg
zi1X8b4GXvx?{yY9O^XufqH&$$`LOjU!oLYBp_*dhhdQ{_U^yCa8}FlQ@u_F=0jgv#
z%Vte@C#cOH=f76k!&Ch7#}veXp`YmLAp4QQ_9s5I8eR;Mv^h`d%lvIvkJ!pjL}CVe
zHV**ocS?*nXVNNKu<Gu<3ra@qtwi7eayCXWIekQ<w@vkvG|m!AUn!X(X8xx9NHaCY
zNa#5ij&0qVav(0ITS*^$QAF6N>_}~ng0TY3FO!*m=d(wnWz75<#vWG!m#4c~H?~lN
zH-Z87j~RcYDm}lo+p6BC^Qrx4%v8+k5~duv{b#ZuwlWgAA_yZ|W!<*?9`{$+ow4!p
zxa)dWP4!MSTH3MsWTmC8ke*r0i>Qh5v4qYA8wx%?iQnJC2MxcEBKgd2DP`^a;(YC5
zU-YI#dJ5f|jjIixwktJW#SAwVefWaZqH#{sS0RO|Acd-&Ao<%s7QjozDENe{&!q-k
zilDzc!<<Q^PwzGG6CTB;^z(DEM<<8c8v&hOPXRF!+Uj~UHF@r63=@G|c!{B0|Eh?M
zSEI?yD8t79_d^%Mu?Zv99=l41Rx_P}qVR{o3&IYEQiDLrx^G4*ZKZdmjuwLrpL0Ao
zW}TlldIXO>%y<6_%1_17zg|u~|Kq>^<CXYOC3Mo7fsFa$KYFhkl(t>s7o+`kVJx_e
zkK08#t70EN#o@I@Y!k*;^oKMQJFiP0EjN@lTF#5t*RxV?2oR~F<I&R67P+tUYPQ-#
zswqhqD?fDKiQ!|FvbdF%050Om#Q)mYzXk`{yl}l)bFKR6Zrb!r{%=3J6Z6lelp}Z$
zA`Up`B7-3m!VCm@$YVe)Y^~P(VD^QifCV&}W(8^g)~(X(R#~XgC3}5Fi%Y4}k_uHZ
zjMJsWPF}(+sA7pj4#dYNZ;qBkg-JQRKcl3kmH<p-(EMP~mhlxaE3X{PLARnAqhe#H
zx*0A#La^SHv*HRAcICYPW3n*)Q<LVWo?bC3RjxO{G#%{ji{gE&AD)Zs54fcE#m4@7
zla-td2{!Vp$uII)!a9J^fSuZpZ@8WK#G*j8@bPwh;#ciytQPoVHn3iDmRFj;;=0Vc
zfEZq0ry0i|o*rCa6{%S6G_7#{s1NL#6DMc03>$54AJdZm-Ld<XnbL;W$U(6|g<>_I
zyTY?rq_U9V9`p$vu-2bXRo9yrM2GvMB1P(eQI~?LrdUN?P14YzxbyXdAiXh9g|!e1
zY_KgxnEZ{s&=WKYnc19t&%j1mV=8mBR^Cvl(I&gHhnrUy>62jp%_j86zNV<9@~Q^U
zw=yL~je7TTEbJ;J{O~XLH)PH(=Q<Gifq_4g5phhHB$OKgD5o38@J?a@L6(+P+AYKS
z)k(>!6{{;vR>jUf<&789WR@efmh+a_8rL_g60aL`OB5Wh5R+J!$T@xWDRaR(CStwL
zyyAF(CyEV}p)4254IhIT%r{Yg&~Os|yAlcY(9t5wkd%87SJlBEg-Bt)K~H(irW`Jb
z4Nhpi1ULL9<mo<6BK=xD@f$#Q;a{|EJ|HYGApOv!EpJm=Rxs1f*~)sB{skEdKN>$8
z1%R;|izTd)ULcKSSUyAYZiMH%1JOnpw0RuCiX5>@s&%^itvsn>L+k6*xDHc7WN^W-
z2ow~F`D=rQe762qE|OobBn;Y;C!99L_D_zgLt_(G#n|MQ#<)Svd2ETlsB8KlMq6qz
z>K!W?wCL=5t|GGB_Lt(s#BG|3vz*?B>M!2lPL4m{?*J>)oj^q_VItf|q&;uo!ru)p
z$7`zJ#H_nPoQ``eBS3(*b;x`H&h=R<lj-;&v<bLo*7T2f5V@N2hW8z%2WZF)R9E^=
zE#T=2SCZ`cQxpDk0PdN2APGd)gV5pk$TMnM1*kNM8{7#7itF~B*dYajafLw-p0SD3
zbhe^R|A3h~V+rhIYu=LrGF6w2k1;cBH@^B}FuuZr-SnICY3ns}KyL%JrFK&#oe<$#
zQZ-Xz*4kQJ45MXh8VIsGvvotFc+oNHM?0ukQ%loULjee+YaCy4nVqyBI~FTs3H(eA
zIOV4fq327`xaXB*2ak#MsKiJyRd*lk0}2%Oe|wg>LcJ#B-VT`2Z!E+Y`8JP6kNDAo
z<AjRnmWTJf>UTM>l2cP<D5)ZOj>?U-=*S!EUneIg#k(58>Ai$-24A_kxU>g;8V7Y!
z_RzS;z0FrFlKPGp^w;{f0H@T2woM1cG*{Vhh?~92y0oMt7DpD%2Bp(T*<Ewv4G0iC
zW0M=GTs}Nu2Qu9k%CE`efQvX2xUEm6^(~3-$!LtgW532|x!k7&>qHE}kev!$8>zJp
zhV1iOb7<c6V53X@V%}FCXCUe?B;_T=?Q4R@B~St93i}YR0A4%6fd#>Z%hIajA`ur5
z+HI53EZeP)(EieJrrwlJv&piU|HdzN6yJE{&zYML1$B-$Z*T;CdB60ZB&i;t=E1w}
zB=3Wf4c4#G+}V;b1};6y_lG6X>%Xa+z!*L|%8!pUc%|U04dLTXd6$O=Eh9~eUC*2J
zn6%7YM_BvA1UNySfbFGEJsb3Miuf1HD{0pH-?DH1_SV5m(?_!KUUZ$oBP7USFan4L
z(*{Ed`mP(2Y$yYs8j<7a9Lo0_E?+z@X6=e)7SB{TCfHMMW!lZT2PT<C2<B8rk(US_
z^0S2_W-Aq9kT-3z4u@mR?kjm%-!dIojfb5rR-3b}`Mry*8_rM<y;sUet;5RcY=+Yl
z6UlnO>`KIbkC1Jr$0x;hJ>6tiV$&FfzG11Dp%sZZSYJ`h*h&}+zFZ0r967Vy<v}Wy
z+9(d}UUWan3%=iq@)({TrC}X?W{ER}{W%CrktEiNr+$fslhh#=m3>eYXDWdK)I$o`
z!==VotI{FYkA2Z8`B)hr4P{++Uy{5P=#VO%BuaK0XIHIuB2BB*>DI~-dvDwf2>VmJ
zJ@Eb5jzQ{&cl~JeW13|c1j`;;Rk%Zwt&vUl;u#+cNy%jNyK86X<*72BTjQX~w_!wd
z?zDK>vhEs<I-Tah!F>;0eMU^bSIr#H9m8xzJtR341p^B*-FD~XH)z<=n2u8_XjoJj
zeY(GTX42dB*w-6;c7LK5b!h3-D;0M>w%%q?*K=c|Qbp^du<R(i<KxFIN(?=7wsD`%
zy($D@`V8-dEARDrUBvb1J5(VP@R#|TGGc#X>|;L_1fp854milRxY;!(e^U*gs};5|
zJdjgvg`y*wPyQJp;#~JMARE3yMuuzbTBMO{KhOZ<cMGIR2GM=SIrI!+QA&&UyOCv|
zjo!^=lgRY}=OdDEZTi;e_zW<V4e}XK8nZ1M*Q|ri;fRxGCwcW5dmag&pi%g#$QlFV
zGqPQmby4|t40xPx&3O)|ML|2jnk24`cO8URUocnMQb7Zbvw<kagh@Qa$NtX+?xYKZ
zNeBCmYV|tmmXCV|apz#-2{hFUNA%%GvRzh(XM8^TPzD-JCaZ<K{DGdVPFWXYylNPS
z&?w)i(LsZhZAs`)B+rHtX0yHShhY`Aq>@1ij30u<DCkIA50B?)6dTkHn%il86kW=C
z+cRF>7yuZ&FN*>-7{osv{%I&RkYa)JDAX+o)L5=;dSm#ycRCxhm|im_^eP#{YeLd?
za-YfaMa?8R@|(kIn)Xq^kVYTvTul8hQ|eyJ#AfPVPc}QwP2{TK=r{AI4v&@_>>|X|
zQlS!?F=>~q5r4Q{$%JB_R57EeXHLJ3?>sL_yL<XA8^1hT)jYB&9y0_9Sf{}p0vsp>
z{x0E($dy1YJ+BV|-5goHc^?!qcj_q32niXNu3yNcXTz&m$0kNaF342%8b|HPuItJY
zo`EhC&CW-MWmKZMutKdm4)^T^%RPfZG%VU5okar{mG~<(sY(=HU3lYA#HZJ4%`PoH
z?{j=2COkhZ{{6{pa@=gMj3^?fR(h>g$)_&BomSiK!k4}N28DP`1s>%b@CEStk&S80
zmG7MDAj`FY|LxCfJ#ocyiEgwS=^5wBxmlF{j0w2bF{BanIK15}y>~Wr@Lt%+mUYA=
zOX$s-V}9QJkp&I{7+YHFVH&T^zT&q*VMH1+vzl9bP1m_b;$T561m+or$Bd&MJJ5MX
z%>~8xP$8^TXo!;jGS@)5T~S7UKOQ1FUs1Uj3_^LlHm%rOHp_|A>v|bGER0F!w7Irl
z!KHx1Q>%<2x&gH&$|vjlzKPIb4DXvW^E*q+EEYm34~4k>Rxys1i?N1EW+3=YAR=G(
zncjL0xs`yHld&kCNK(on-1~Av`}$~vB+o&URJX$=HJf?BrMEB47|_8mBMcqdMRCz}
z*Y5o?p<eI4{rxCFGe*~SUG#p%%x~PWaVRI*<vO>NIQ4wps|;~H=wXA#rdHo{>6en@
zUK*xJUnftRrUwjrs??NT7s%eA?#z|XeE0E<&kmS0+5?=94x>Y9Fngn1_q<J7zhC;o
zW&tPD;aX*KlmK$P&}|vrcp-*>w8d4WETrwNOm6_?znmu|DhO%6Jv(LaKa9WA2%@dK
zm3rTgO7ajdxt?q&?T_nytLeHHdP1!b(TN~tu~g>SN!yLvpMIW5GpC(62?L3Q@Ql@e
z<0MS=I#ylE{6v=U5~IPnTwcpo_;j0@YdSl@ndov70rH!Am0;|+q+hmJTnE{`fKj-~
z6NEK69n1=T^mDQdWo<9#=V=rM`4fRSGkJe8>s$pT!gA4)i2F=q5_WOp3Bm=D3?tA5
zYqxtH3uMKccA6GrCnRTwK-N+h%~o};VV1CabE=FaQNhn^1eoxf;>o6amcbNnh0UjU
z*xC+TnX$6aC^4c5*~gCO2w<M|Q}y+UHH<Rho4;j9m2CLjxOnW3N`QvAHd1w6S7lBH
z7gnunFwtH}oXZHE|M*_t#3t?)qHQFn`@Paky&gzSv{|ySV<HCzVUz4rPlF3|N303Q
zM@w^qlFR!PoqlLf!g3jKs{uo(BJj6zy;n3@9p{G4PvkQ7zrLJ$<z;z{)v;Ra)M%g_
z#Ux({fx;z-<GkKqiwJ#A!+!??Z<i$8PBwJko?@*`r+Ut)GwqsqRtyq<P9&Z|t(s@d
z%=i;9J>xeIiAOJH>fAoV*#Ps;f%P9{n97n?xUzz)j@ax&KVQcUZBsl46R5JpIKe}{
zgdsM*c5l)2vJ<i@7@4j<<xEofLI!8C^E7yhtnvWFx5GoE-VJF|$Wwp+mNsq19Ue5w
z$!95tfO^j<(#A}^!WKwc)clfQ^@}S%<NnB{ePeu}l%$KvP3Vz3rIIhdKvhH)h1$2S
z9`4ts^-cWsRq7_KUL`4DIwdnRj6B*(jprNucqW?+n|PnUF^3V3p@y;?5cBG<6RD52
zHMfS>;akUEb7pn2ag30ryx^Jj_A%na@9}g28OE+-Pnr;4kWh6O6wzODRveUB2kPvv
zB;60qS-vwYj-%L%hNk-j(@G2%#xyBjKh4j*9^XwowpoKabk3g^8IE0nL=Q(LFHQ<V
zN;#fj!zYh1diwPH#3SYRX-zgy-ird@UXk)tlG}!-+$6t8l#b6UgX0ypdcM-{I6u)c
zJXj8ro&CR4%dyGV9}Zwtd}ywT=F&J0QFcPscBNs4`W2T+i!qF(HdQv4^T(UZ<^X&y
znV--*a<SFNOxmO{Uo^v{w>+<C6z$5y_<fbBXKYT0Dv0UJ=*6Kq0L-C_h{*f>a{YVr
zsv)%iM85s9rN>qd-`=&AZPG-`lB~<XYKtAE*I8HYKH!rYOQS|TvSkM;ZPYt>cIx^D
z4b!B;ra4h5)AqOTLJd<N##<P#u{81Z_n!nam}SJ*0}n!@v73IS{SIGF(N18=+)~%?
zlLURdwuQrtJa*^Z&^G}qquuw{k?8kcqDv&u`S)n}4<NHLHYddgUf?8pS}KEXfmc=Y
zrUNd+7kc$g-&d3!k{Vi=u9w?nUDr(+STy`7)>JLB;4>X34wI9UnocUFo=5#QM3;<)
zy$Qyi@wlfO7IMu8ZUgPe<Q)m(c6HT&hh;w!CcCnvuoyvU-tFmhU+%kvF(jR;x4nUf
zqW4~rrH^gyI&b^^8sDDr0u^Vf0xdN~UyF{uXq1QY7ncvpqKx1ro9Ah(Wa2DX*;Q2V
z7#f2O!6l+RF-*T%lvXeT2NwuN+Fj<1(34`n`Wzri`F6;PIQ;eHCGqL&3nL=4>jVqE
z5?2&7^rZ7!M94ngTcvZkajt+C6feHyzsib85RM123_a{p|LOm9GymFQH!`X}Te{O*
zSY1EAusWKeZ5Jt6u-}WJXY@K7+XGZE2lB8;-^u@uFf|UeI7%qKW~5T&T6MI_W|nbT
zJ%^0nVscj^G9{EiowsM~<5oUhKbhE-WH)J^8p}4V;=>^=JVv7RH((<etH#YKKtQ47
zodO8&HALiWrH5?3nDw*BmaRKo`C=AKpr%3bU76@|CMN|^+1=yD;8DD@c+jRaI9QOU
zh-vn<U67MdYeo}tWK{+H<VJz+L)fgtNUoThf_x=v*(lrO4!V%KP15L>t(_MCYSLk^
z$b7XJPU~&2XpefI)o7bY_d;loe*3s;47*DJPibu02X6Z~dg;3sUTVb2FLS?fL8|+s
z^?a-cxXHZB;=M<u!E=-!?0xroQMN}O_rQ9V^=+;5sSg*A-Eo?FCO=O&hRBDMAcT_D
z#9YV%+Av~&n>sgnAeNAPc}b^dWb=NA9%yjt(WZ45-r;gl?(062!4gcPeKo)`$<wBB
z#w`wuO7Qif=Osnw@`26-Xwy8WnNV7H{e9aQ%rtAVS0c*(SnYL6sf|pM7d_)2ka}d0
z1Y#;8P7BVsw799L8{Haz-;=@&f%=NVb(I?+AUP%Y+Pdbvj_vp{7K|O4>F0S4zIUEL
z5(d{c;mjjCTh7lf(_XR=hQK2NE&tdLVS9Deesx{`VODXohacIyX-n&!<$jy*xLf76
z<0a#4t>*#Y>wUgHGt(c-e>tl*tnR$0nioCTa=BF+5&7eT!RCAdYQ&8FnSYm>>+273
zm$>HrB5*wrd{uvOzG~4*SN#;`eK%A(t=uiU$<%cWdsu6XjgpONX3fO&F3T`b$hoeg
zN_KQ~LPBMj;>!&#l7}a&>B^`v;fy4y4tBb;J|>xEF?48bh6bc?tRHIyLAKCVY<h%C
z0g%Bs2F)+HZQ$cCdsF3XAg)t1gD+BKJ_ovPBmOK%6$h%Uj)@a{^KOEF5g-<X5J~$O
z%lh(4L|_E>t++|*r#YS06jg`3RxE2#*gN|AaMbZzYZVdZZBs(}O^2?B%yP~BH+-Hg
zR@YEp-ppIy11o4mLy?nXxcibEY`TYVSPge_@s79hzE*NCxn!IEs#w~LdeK5%vZ9XO
z-2qmojP|^j7{?UG@=4jYKI!8rI0p)|h*WpqLfwgO?<vauOm_um$Bu}|7)c~aAxhz?
ziJ0ptH)=5oUO0O^zUL$w#}E}J8W+8vFbSo~FmfwDOd7r6CM3X0U62@<)qRt+VsF_R
zVQ&~@@DE1ruGEWLaAzIAysZd<q^w2!`9tv&9TXOt24kKD-U1A^uwq=N<ZYk9bA%Z?
z!6U;=cJ-05G_+N0<)bv+wR9!%%fst6JEzz3{u&Zf@@Mjk=?iDGA02=1A-vqms06NZ
zKLp%FZaDOMFsZViO{2GwSSUToNcj0t0Jqm8dr}1Fy`tix=zo%Tj1*8pXyvko4p*8o
zJHb6>^HXnd#1W=yuDl?{i7{1d21L@?68ZROPGYJG^{OPcx>4Vy%g^o3KtA1e*OL;%
zFi}t-3g+zT-@?@Py1u-8{~rx6IWKt*!4YUXQ&V=7VbOwG=iq~A#j1?zhK<`o&#Xom
z<hsUak5VDqNKMsFRJNM;#KPgMU#825){Z#^8_T4`NQM#6CoI%iQQoc#p))abM5p;}
zR$>uQ+|5IX&=Wrn(Z9uAyC2kP#6Leky8-7@b>Z`E`IZ44RiLPbsZUJ&9A#=%^~JUJ
zO}2U_8oy^g(J^m?bHH5>h+7whL7%pa+%FwmHJ(N!!y=NHWjV!XdG32CCSE@Rxm(@J
z-F1uC9DsM;$uw-Z#^M!pJD<nn&gfIW&!3d6p3fvIl+j9(CDO4@^<2j+<SHw0BeK;8
zfr_~V{kv}<l1}cpFSnK2U_A_S(iVd4sLL*|;CU-Vl@?mzERfY8WBINP1lV_cD|Z~2
z;+5bOzY|<hn|9g`@-HbhFgo5l+#S1&)yz({DSqmS6>{Ia!BKNNtL<*oMsBQp14{cd
zQJV8v=y^STvJrg|zTDD-ARsPr98F%#&z@X-&WF}6>ls{$EDHcX8?(&TKY!&aiAeXo
z>gKbX4VM@AjCAG*E55rX>M;0sWK8hoEFfIb4(el4W1AWnlifHqKD4)Ss>!yO&80s-
znmy4nxXm>bDCpN}$*W_(FnZ0Rav6$DR1$`19T=X)(7Yo4kiNHfWm1(PMK~iee>)VJ
zI{ImeZ>H4F&*NwU&3Pw|&H}E0K?nsCTid#+D$8>ie}<SkU<SOK>o$tOT@(hFWnWpp
z4iLQyp8klRS-4?$EbmJ+$~Qe01wgwGoAEv@Qqu2Mkqba-lAapN&8$|CRCG6U@7M($
zDS)`%wv&Z93Sh)7;rV02Wcw#13_0d+ScmjjzpV9#`(T)j@|+MM7bNc>>KH+0+pD*9
zm^9xtcJsVJ?@Z@}R|*_L>rBK(ldG@uHta__jzdk?vwP{+Cyi~as0~%xb{zsR{;mYW
zhky-_O>#x$3<gZ!OvX(GuuS)h$8T@ebBVGqMCEL6Sm23qC^PK%BC|C6UK2%dtRVy!
zEL_E{)rfqyv;9H2sj76TJdsJFT&QHGt4BT5i(G5ba<eX9wo)F+oJ&`ODLQ98LmC%u
zb{w^?qJa``BfmTFrpxng#?kQ5STrBjSCR%Oa=^Hrx0rr{K=Uj2uLE{y3G$1$MvS=z
zueI^D-P*X@>{d#$X9Y8p;Cs|*E9i1-6IB~IkxA%9@{3YQPhX~RI*Z1~gk1`qn9qMI
z*&XLk+U_u|n2@1q0H_-4h4znvD^((ud<xPe?~l8HT#s!R`c$;o-!tM}yHzdgsBldD
zR9yOg#it)4%=&>35S0fPOa%Q(c!q~8G%7UNzkaiYd3`e1c8JVpL^7lGv5?U{KHyvM
z;Y~{SUHdjq*QTEQ93g3t8mil&z$=aFo^z5G!vWAoCy_oSA4?QP6Oz8^tw+?vda3rE
zb8~goDc524V*y<9<`?qG>4iN*nE6|k3raDuax`%sDRD^!ho*iQs*nFLWafBPtR67M
z;gUvYT6R1!!j20Iy8_h^8-&R{B~ts2_&Rsn7UH<7i)O?$wa%>rf}Zn>^|9t!Vq~9(
zM2*(^Mc=KpATGzM86P&=0ZXmT5VQ}es`RAZY8`$))yRRAcCS^Y>9Lt=>1Cx>vgy04
zTKU~8(Vd4I1X{uz9z*My@<+eQ|Go87H6?H;MmxH9`%`LRmtLh$@KNlONqRo*JhCkP
zhU)_~tM?!}5p<(vGWiR!J3LI(0LUq+iX*L+*);CEgowx&BC7kNSp`&n=f;_l{NN)v
zT4uiaW}6s7p?ekVi~|vkP#nJC{pI$$H~3RNn`Kt*Symh6dVAX>_euwy!?IGPpH@kd
zyLylaQUaZGq25t%MzFq6T>>%06~FT`!+_?2GteXMQK^!lL;7+=^qdBSe=qsocbE<T
z4u1aXqNmZzQ*E<Y-Q|3-EHkfhFR*1$*3>w1EF7{i+IRZ>MC>h%7p^^Kb1cW}q6Q`-
zq=gu-)vnP&f4z68{*MsqI8UK)rB-9_zBX@fl3{U35al_`U!<_Dbk9!y`F=LyGETrL
zxJ_DQz}WL?vbC)|k_~INM0U9HP7)sK%-(ZC2cE=q29X^8Six~WxPek9drLMPQTPwD
znc5r-##dDSv(V66um985Sw_XtbZZ-Tch}(V?gR)hIDz02++lF{K+q7}9Rk7KVQ{wu
zcXwxS&OGlq&pPXq{#&)GtGa9T-c@~Hcd2Hg*cLTPn%C)ya6Fk>4R$1_`id;HSb+=*
zzLI>H%hL`6SP`A3VO;I)n`5v~^_-r3IWP<p9ODa^T&%4xs#(DHCG$$^;2@#<SaTH+
z1J#!fFieB_X3~ylVxm%C^_6_J)fBtM?0rDLx|{VN$K^-B&X0#Ck<+rfb?3#)kqS&!
z{d6C+JKT8ju)>nSxry(uPvCy~dlVoB-vmAbqgNhr>TNeMIK1O&_QoeZjy{Z1^%YeV
zB!L1R$_0`f>vfp~ijJPs-2E+elKA<B1MWszyZc<%pOy_TZ?SjQp3gP!fa$c^I<{QI
zl#=9#Z_=_lQA5?SVOPYxBk=8tK`d=^DRm>n$BMM_C%$<#fy&>YEw0=F-7{Wy;Z{6e
z2Yc4{fP>QfcgS|*z-h~ZfQ2d6Fi5NF;kott*{S>3p7+t}DL<oYtpN%!hM3wG8FaK#
z)pky=jD`O3`C#pj+2xjJQ{w4@O;wNW^|wS&?H7okAR(0vI1ntqy5ao{xSlqjl1%8a
zbh-tH3XlZh$3XYtpJ|l!PiFJzuX~++6g#Ry92B?8@l9-ME#WYE=@3mXk+_fNGz>8B
z?h{;Ua=Wmut?KdKE2HBF`Mh=b)1R*D2Yrqsj0sNaa*Fe(;N9sajqLmmQdbF_0b>{P
z&y-@Iod)GpI0gJ>Eft$6&onl(8^8$~L*g?09V%-0x|xS}AtkadG`yADmWhvrX4~ph
zFry1-22+q*1t{t4Xdg0<Q+TG>Ikw>Y3Uwu>8hd?a0kRxU;xHe#aOgJZQTRRtOvn+C
zxQ25kFtL9!i?2dqJz3ZpHZ!=kPT?Vzc=bRo6YP9H&m^)=HJcL#in=VTj(zSZHBDJ{
z1!>oT-w;jd2HPFPiUw)Oa}++xK!1Zl1AD5;|0Hd~SaK2&BS;=g2~>VF6gW1^O`W8v
zdDA$^i5PlJSBjyDe|8A|f)w+?&W9e|JYZO+-u_+WHa4B#iizkQ6Sqs#36e^M{{m2u
zwD$UpP+2YOHSf=|&s%prFUuCI(jsZr6kym@eE#i@iF~!iI`s`3cBTenkR12*ZkGci
zZ*1n<wV-i(Fj^E)#)tZn69IMm6GN5R?B$4<@EEGjXz}8d)YgvWDSA4$9r8SkXFoFn
zMv_S{T0e+3g$ZdcP35wblD_OxsyWr@p*lxRE*JqIJzXPOv01$a1x0Q5Nmfehw^5_f
zAcb#>(dbB0qxcifR$lbo^v=7~(zEzsGf};}fz8*Kq8w&eUW2tlwKL=fysEL<ME|aU
zojj)axhu}<Zg6_X`;zDNz5Gs?^n9uD`v+q`a0MDk9k%eIE}c$jTdp1!m4tU2<%GZ5
z(PH2fx%R<xL<PqH{eD&5SX1U9G!CAYvUdfYu(7{c`@W~Sib?_1l#iC4UfdgMDOc0@
z+(_M@3Z3I3t-N1|btjFKZ-m`1lbwgRLmJ;PQ4)epENMpVUZ<-<Mj+9>Q4Nq)3edRp
zI!|ZN4sW$~k5buGKMD7L9R~3K?F<pw{4kMQ8yOk`7jKKf6|AFT>q@;cA6TLZ^I8=k
zxEbUC#24`;;(;#jy)4}M@N&q5RcjOz%LT1UCicF~vJ25^Ps3WTd*ASFQQ)3~{=71>
zj*Ii~`vlnPbHdpf>_CG|QQq@SY8HAabB(A=Fax+$GbyHGpFa{s3|-nvp~<9m6{PE)
z&%E#_lM;rtT)Rxdrw%8lW<{Etzt0kzRFODqZ$(8?Ca8-0sE+6xA5WlVj4D9n*)457
z(Zr5|f+WEc8w^`tfBBxb!26^RkO)|Xl>c&lo)EB3*ijLM_qlMOoi}TNoa7iKxZ`~7
zQm8JJyMP|v5p#%>h4V4n0fmrk)g6RzL+dgH0;!5eq8}6j`FH)Yel`+*cHm#}W66fz
zDgHJZ_+1Sp`8y#;gc7=D=MQOB=L(mU(ED|GtElcG1|v1G{j52a>522zU4>vXaOb`D
zA956$(AuroAq3;VKXe%xHj)!4!|hJpRi##!4Mj7Hivbtijsqh`3}Z`+3zF_RQxcDA
zf>=JAGo{o;yZO++XEgsH2~1O_@MTEU%C1*<5^9avn_P6xb?Q`$kMdA-58rH;<Frh8
z=;!&l2B|`))aM(N-%J{E7)2(Ha8?ZL`B0RK+{H`mrGdbys2r_|9_tR6LiX=sTG>0y
z1_polB{&wbugvD?lnBNajQ!c;&8OyEhA5d#5=+cK&jW+PfL{b+qA4;dX@6>>+oi5~
zAO_ZJxA60rvbu@7{>Zek@pA5!3-v{ze}*f)96i92^I|5rOG1t;sSsaNEkL>?noQkz
zQt<A73%W~7;u785&WWTLvi$SAu|w8w0|&su_JWvUBVJj;#-^YYhV<7IW6>u+?j^b@
zZK%@K>|2th5<zuOxz5FC^1?<;R8@*KZNVNdlq^k@aChvtprLqCB&2CzMDMYb3E3pa
zyl!nI5#EQ8x6R<Br0;~kCA&ER40j!xv(dYWh!->3HQ63(6n$j^!J}SfhCLo_s47aR
z)hl=;!jdOJmSoG253$^Fv@m#YBD+&pM@2>Wj<wA1z+4(N8zqLxbR8Et!J59m)PhJr
zpoP-;GT-%mt@-Gc%4x?j15^B6MY~&3>9zU@E5(8Zd}I}C3`K1Pm_%#d8CIQUVI>pP
z@hMj2b|yB3N1=}9Ls6Yhr8iwqBO65hF|xCHn2X<sMD0>UzV<yz>}f%(?YIZ2F!jC#
z{U_<YmnN?&k<aM{7=C2YBQkrJXSMi}njfHk7X?dEAKjx!!6HB+U|G1*;J~J1@QO(+
z>cz;9Y@CN466H4xG5l+9f6nneQLb`1NZJxQJYOt_N=57pqP21QL%I}8iK2X9(iA$h
z>~Zw><NDL3QtQE_{vK-<tRotSnkZ=|HmYzFs5o>$dTrc3?lT)G_=ApGNGz7F1CW2h
zl><zyHgPOx&nylP3ou_qGKA~AYI0by)_3b$oC98R-!IM_Ta+4t6GScowZiEz>;*qf
zCC4T?GtC+{uN4K*zX?&wH()F*o!v~4e8)}p;F&p578Y2ppK+xRl7x!EjT2JN2{^~^
zY_MfNcW7aD#zEGhDO>Or;g773sVRIi?c-n$avhB3F<<k&@89e~Jcm!?AT$fzJ~WSu
zjGq4_xZk93-f>2mF&$N!1x;NSzv^<)onPSA<R~m^JARnq3S(>#SNe$0;*e?I1}|K0
zK$iYooKy6tc?0T8gLUw_NK*VCvB?B)cj*Q)Bj=Ii#h!rB$%w0m%_SbIVQj>U-&g7P
z)-0=5l5Xq7=6g?>P7AOUg6*E08y<gsnN|8r<aPM*3c>=1Q8ARC?@wqU>C?<*rDdx%
zMN9_$#Un9iS#wSio;@Xw3R5f$XXuN$XNufcI>qQM@;{s$!B2m}53+vY8v0&%LRpq7
z?aoAYyxBdId&CjVQ=iz{($C-k)*e-vVag(Nt`tASFCH9acEo@8+wVqE$h6mQUiBf3
z5fgnE9reI><OhFdY+<O^h+lfg0*7CH^Ix}WdNz7S!pvWS#G2i1j?VB-vO-`;aOz0c
z)AkLPLEyCc-BRERuHR24{=NwJ^-q1cRgit~t=T<8ZNnE+Bwk*`%{XFPH#O8Ehs?i5
z1eL}V8U;UT^m|!-*oE`CkZ^w@QhF2__zn!zy-P)oB*aFPx8sM`mp}zWgkjERx<*$+
zkxYGCMh}xtx1c3955Y{dz8QN;w!{HC{8{)PWu;p9mtm2F#M0{1J)v-uy(3Ck!xr;F
z9z?QzVwDT=A7xm<!cp|(Ox%^bTAACO<npn-G2-kuOsodKJU{Q6TjXmVuw&8aNw1lg
z?PosSclv-6y9p}i<$_c9`;W7ZjDlN4JdeUw=BMUpk5Td$RJ3t_r8*;2_&@ty4(zCU
ziAM0}-~fljFpsiH?VVd(vVnV&Y!O#;Xe8VUZ|)bMmXX)bYtOyCmXR3nL_zz9UAVsl
z)?POYnH7lMuDl`p3r9Aef9o~N!9vBETE-3?aRstrN5o=Vt+mektRk0g;uX{`-Wru1
zYZ%aN?|;=6k?AGoHw){4b|=n}fvgs0L0;bM{$nY>ITOnNrYC1Qqn$Jgtd)R&I|s#_
zvHhaBeH7>(9@S<6eh&9_Yr*LoWYI1qD=r)U!$J(=slS~>_l-Pl*H<tQRZM^8gS=2X
z_XCUg7gPaNiT4aNGOsfB&cJYA!KU6Q2Rsa8uX7u3c!pnBMa&q=-Xr^_h4H-_kC{I0
z*EL(3Tyy(;Upm5)eo^jEA|+ug7K0~OV@VgLM0P%SpOzEeTCckY<N5+q!~{r{E>rD@
zx0$oK4BT4@rFma39jBmS5l7o|{eJ(FYQMOw{Y$(~S5GP2o%!Z1V7aO1Yf)Vi$)nzC
zm4#GuP6FbN$hK+gO?D)MOBWL!RCV*R1rJP)3=P_0>J3Y!<$VeACGrY8qFc8q%V5dr
zZ=pLkGYNzog{Us%Jxdjv<2*b{QlIqM>Fwc)iv!R;87w-nR7ydK?`A}#{6~{VNb)Sy
zIs)+<nXBOE!ruMZNc1^P^j71Y2U^VNOO>;bPOu1;Md3c~5Se|Te&eWiBcrXtVp7KK
z<Dur%*uBWvFBsS(4!!Sli>C@ExqLUOr-f(5xJ~<bfOVI?k_^vG_rqzGBfp!*R4tYw
zvGobTf`|;>X0EB=uAz`=9c>gn0RMp*tcnvWSyu0h|F-PWtY_J-nVQZ-q6`$A2=A-g
zrfx)V-)!q#5fA&)PGYjX%leyXZPx81Od|f_9<`b*k#PE8R0T!0>+TY)??{(E@DJvI
z8XYsM0lT4;4F7Gb_HPCPbX_{eflc#~)uUNAEcWuTO(=TB3}-+iwH!h~D2Kon3_o9v
z$)>}vS<<{<WJD0J!E2tRRo{}!Rl9m}z6cBN262Q^F|YGqluC4zn)+8BH1x*_i6jJf
zMlnwm)|~0SZ-<!yg0v3X;4bq4R36%x%qy>F#x(XKG@`addXoLIBBiZxA~mXf$3t4o
zRR$g7fc$ZT77`Zjvj!L1sBwv%f<Th}BoEn&z(LHp#+|9Y$M##$MUzOa!1>EyGNI3Q
zG_DN*Q6{Fv$?PgiqBdAuNBHhAR3+czt|n`0{U`r|LFI1hd3$qZ`~3#8Rk(Xh^B81e
zkGYN_%a8US-SEXd-W(iD=EyU69xWq#p*VILhZd5i!q!VlVHC+BjnJa}B^Pd3SeZ$c
zj*U)5)QEMODF}ZsDkn$0=vEJ)sb*1I@W%T9W^$qXUaIGQzN!r$w7soipus1<Lj3E?
z-RTl(B+c$l*N=_cvUO;+Ts`Rbj)o-Czc#v-ExqY)OIKC!ouWRedqC@Mb|2!sa;vOz
z=BU-c8hab+c~pYqo-}DT?Rtm|Qj=q=P`rav)L1w$C1r@c)1_CRTohCCyaMrr?Btl4
zY+(Y#B`EbH6kuV5x>Hdpi&Y^itKaM~q4=fME9aIBVrv;h<M%Wa?7hw8dE!zBd$Jjg
zA3n<QOV9&zb*x{w(t*KiY{~7{8Xf_A=rKU2%*ymdvgCO&t*w>7!eejNd)986(Rgp<
z=a@E&&e2iOet}LV9UhEVJb`M>F4Gqlh&KjrpnYb059W&ZeZ{Uw{Dg6>Ut^VA{qKW(
zPV$<9ncw=Jm5ku&uQ40byqz-4f2BUALX4gtO#QNeyV|gWm$D$5>3-iHuLHsrloX++
z1qw04eE(WG1~ws^<u-THJyk~|)LV;c$;KC?N9<A{I+>{C$}{lj0Ptq(?}J{V1w9AP
z6R>c_IkdYPd6eqsH5b*m&pkRMIA{hJ6rPzG>ZaEu6<MbUkA*>kxn9!lx6%F^kDX(`
zPfh_5L?<z~#Ywy#vfgTLM?BxjXQ?Vm{mo^s!S)21@+-`XEYFN#;iGaVX|3rFpi}Pa
zA`eyCLdd1A8b(oTe&&O7aqxLE5tduRIHxskLqc-0HxB|C<IyhXwi(c3+CuB4cC@G-
z;4IrYuejgpdvzdLp3Q9{^rCsVV~B{ta4P!PQaR2tF0`myDNVh-{~-m1ATN;_*Y+|U
zQ3cW#Oyof>44Td-!L-t#8JJiYe_EZ(sU-l$H51luW4}pq7--Ez(sQ)(cdc;4t4jM3
zR3a{4k*bSk$Tt90^`eZVcf<@9<*Z5)G<C$obh}wlQjc{RDhY6*V;eEY^&7PP7wF*r
z!Qidgk%Y;gX_DkTA+`kk!QitM_|U7IQA{=&=#HCw4P7XynM)o8%I`!-`=>->GAwA4
z2$;PlBsDibYu{j>7~^%>$9uwI&s4HS6c^6P8&_-F2!ZzBh5LM@^BD<w9ArX1C3MF)
zjs0$t^u9SgTfS58nv;Mx@~{o{^_*AYZLM3VHpVoB_q|VN;H5aHq{*&iKm*~+8u<*k
ze(WIbo=kz~M5bRKCMW@rWK>XR?fObdAtI%b{~X$ts^^Lb_9Cgsy%`kGxf3MJ?wO87
zEVReCzlKk7;o<NmBtwGtT0uDGCwJB$IN=zAndD`I*~Nqp!hk<>xrB{tWKiluDgCQ-
z-B78=M=$I?y3oX@AoL2VnWEbm(E)kh_nx6%b3KM~(ngd^3=Y|ntIOS1fI*k`=0Y}|
zS2eLA#?pPgjd_~T%LgGQ-e#+(g-`bvzr9VL9tDe&Y&XdwIjeb53Gqh#JvT5W5^mfn
zai2fLiTPWkeaX#yLZ-TEB4uTRV(Qiu_#)~{V#%Ib=*FZbWoA9q^karJ6pvesBu<%j
zb5dG!5}GyZv&nrCT&V*PGwZyFP6&avl982vrqKqS)N;~{V`%*RrL~?5sbn{9G+1xi
zsfTKOuqm{^3x%}G(!@C@R#6k#@G#c-K#nDA#_-!S(teqU-C~fGjC-r=OvuL;AwOya
zUES~=MYvbZmNXIHD#iE8pIQP1B8aB-mgD`(jh->qcH@#@nYOPBz*Pl<7)^S?oiz$(
zE&r!GnZ2t1a@o1de(c7)a2{kaAMlUaAKYXEBIsMcrm>}(DeE^OI;lU<>y(#Myh1xO
z6{>+@Og-fv?JQ=;kA!&u@5=RmJGRR<My7Y1kol?#<A_5=eOXvh30dJVz8CDf)6LhP
zcDTy9|25{}Lao#Lt_d)zuy~q3@+pw>GI~$r{phYQ_QV2&z){e!?v_&WB>;UtX$@6v
z8_T7<t%+@3Rzxks9hb)bwB$cPVX~h!Zc!G8@qv{K`(EZ<Hy;E@Zq^o;u#@v|3PsR&
z_yYu2>cyR9>%=y<ETyY?_NYd#<0ljAO+?3oo}-K&E;N7Hi9Ujj-QTC_bd0K>0fLg;
zHk|&r&E6z>KlAWj`{j5)!WB^wZ`+Q>=h)+JdZYYseEEO|3;VJD3fKMxZ<wQ<g6HF3
z?D~Vz5~@`v&3Wof3M(*H&4~f4@?j}^;4*67RP*C6)9HYeLS?wMMjk1@LxB)Sb)5a?
zgt~C&N@<|LRvFA>%aRUA$XanGl_;=8Y^#WUh!ed2sM^_+mUA&6MJSZk!Wx-pwv8tD
z<D6jI+DdrsC|25d6-EKcja5!Qs(ui8(^-y1pt=!wB&L8uINncHD67pLtxgnV)Kg7R
zk>RX2{-Af0{p4%+J^HpkoxKy`lL|$u6ZOR3J}BJfXhAH7IXmgl5K#KlpG7CmfYnkg
z)@%2g21mn03W=q1B@uj#!T4vi`u9h{<r-a|+vCtL(VrK~^uKZDLW4Y(r@`mncMj`Z
zU1+AI-4AN(ydO>$0uwN%4|X(~q9jzOlNnJkUJ)^7pm$D*1GnpPnQS3aVl22MsZD&i
z1e+v_s}OHyL3>d%9wg*f-fX7scbbp7-rE|71S{uAy+x^&p;%Wds<>LtG-o0gU6^ra
zXVLgk8jmh7f@{ty+Ns@VfoZ@K^&l~#lig>huArWI1f?sxTi4#<juc&QL-q~7t3)8&
znztZbRG+ft=N#4nF^rw=H?*sUL2mJD6sd@i1sEhZ+`vZ)?{Hg<1IOtt@U^hrATXWl
z6_ca7XJE_@_0f&_x{WhoA(6gXyRV}v1V!CZg(9q+hFI{c`H_S*)sz%c$}l~v81*N4
zjvZ>FAJ=`hgX!<;nGWpL9j~=0rK}uAAWFq>n}-uc!BxtgXv@CWt2LtM%(JR26tDU<
zcjqvmm&>0836;NvS>+^>{#z%GT{5cnJ5dIw56UfzHbl&;Jn2Y_UJN|z^zmbO`ZxBj
zAwOg!1-q3yqX>LkHmn1(^pCN>rE*+7)w^L~588G+1kRjNn_{zf9EbTe)6_iU+~Fd9
zqW)Sx(fsqov5lO8>O2aB>vhvgNA|^~sFM$cqDTm`=9`p%@s*^)OL)CHhen>_EnY)8
z{9E}Y3O}Z?!hKqAXNUj^Dfd;yc#$P2Iq0*)b+JR_q*`pS5od9C8`?`lkAeg_I4B{Y
zO4ekR?1y2@k+$`m)6W^05_ZpDZs}K~iKfM7B-G~*JxeATm8_FAW%zFe?QY>&JZP0Y
zU#9nX;WJ@XCz+?Sg>wc?#u2)Fo&hk3ct;Ev210q}g(mH8-{GxlPDWXd3aQVqy$gH`
zyg%lod6S0fdRw&di~Bh#7VaT*@cT*%gAZ|cKeDzR*I!V5Hdswc3U(dYv!C6Tmub^C
z2}B{F5Hrsp6HVtReEUP>U4hAgHkE3MiF7vMj;Ub2na%D(LQyraM#Pf(;8ZQywwdM#
zgnK-m6Qeqcuwp)(;Zn+*sCFn8$8X@}MUU%cp0Qc-l{7mWbF|ir#TI|VPuRS~{W>2b
zv{Mq5=R9XGwP2*NR*oo(W_KxVNABYKpkx}-OX3!U<rzV8m<TT~psuOi&ke*3*eO?o
z)=U)<uqWLLk&1b@K7UbZ#4*S7DN@2cpY)m+%iB<GkIM9@pX&3lDpXD;HjjDNJ&%^5
z@eWUb6=Jhie!&aqTRR$=#N7z>MgdVBI_@RE*^CQZIDlS@H$Nr3#vs}p9G45_Gh}5R
zWi)4y;H%X7g3)21>4lA+BFy8aEh8i!(u&h9rgWB~^dX!w*CJQ_opJ-bMd?(z8_D7$
z#23PDnk_n98Z8NZ7JEQ-O@&~K<jWd0$(+1{chuc)6;Bq((RCT`){eC=CE4vMlp6S!
zW-ao&d_7<!uq3c{_0H|rXiRpT1LhdFMDgr=G=Ld7is83zOYfS_BLt5*I%go@I9Bqk
z+xh-co_o&J0G(^3C>*$WHBcuIwtiXTL9(^6>T3^2=9s_O-v{z&KFiNPS-4-oG1k4k
z3k2|@$BZPVf4jwrrKbtO6NX4iWPyu`k5!qcj3$4`hp(~CgKQql1{9m76E1@~#l5Em
zXJ1NN_gTKw2*Bk=gaN_=M95bt2;|vv`}`b(s%v|q_Hy>I67A=ri-pP)cj-uPz77$7
zsy+BI4kXf{5*NqWx8SxhJwJuOf=3)*232UkxHuwR39d@FlKqFdi;Sq+(1(mg?qh+C
zi744`^iRAQ)<s35Y)@5FjOA|d{TqWd0~e2%0)%9D5ROjr70a1T=hay$Q|+rsP*hy1
z4G3TUn@mqLQkwd2V;-z#C>(D6?W9#Xr`^{{?%be@VP#~PnK3WLA0AOfM9>Sa#YC51
z8RQYJr?i*9(w=O)A=xJ&$=WFvv=*}nLZI+~QEe!P1=CWyS~uW7IA#>hCWu~ehLzvx
z{VUbG!O(oi<EiVxKX~a(#t&;pCK|}5`8J{Csln2HSP~HWUshEe&Y(V%(adH)7j2g^
z3~OkNcPEy1h<Z~7?nRslpDCz+H1SNO{WYf~29<86Bi$+k=WN*ZKQO8KKjXtwd)bvm
zKu9}$W=js}6AN!>6V8yX5x6Zf<E05;4o!qnh$&A`(5x9(j;lrq8cFkhiuRJPgn+L#
z!L*xt?74(c?Hj=HTOpppq>Duoa>9dUBqZI^(Wg&`HHqJziPpdQO2X7~?!1)&A)uNK
zX!8^!7#%kF6xsOG$_>UkCwFE<>$q-h<+@gDO2ztgvAiVrG#8hc*NlRe;5|Q14g0bJ
zTn&sF0m2IxLj1UN5mX-uo~k1Z9~(t=;)pOI1WwIk!W!09ry*vc=leLz4)_OGrP`GH
zg%GuW1E(rP;ace+&I$l$rPW#H9H}@7`lEmDy;dE}F2kGj1OH1a0)m20;xeFa<y3G)
z@20%U5^J4j1jJ!JlbU@Ym00^wf(1rVV8yQlPI@sqRM{w)wYsQ3A_ZShc>d&z@7kn|
z7C_UeSoU1y<UV#iqQ*K*Fi@RWMuj6%A`p4w*L*86Y_gSW31D-BJWN2&ahN_!W+QgV
zBvko5)Us`zjL6&7(-ToCVo`=@YK{z*I-KsB?I`c5Uo&}utZr<uV>s!loYWwtO2ole
z9uX4plkHXs*QPBs;$1@4h}-~jUVA}<3XxJizwiy=<*hbzH*v^RqTa+8inE7(g~{!e
zPS-U6Y_-E9KytHCOZcLK0%>|c#s_@uKR;$@XwQoMx#jA04VivmGdc0WA9`dc=AP9+
zFJ;nP01_<=YZ_4Yh|_ImXzC}07wxZC872Rc+X!!#@+KT`vT+~pd#|-B=EQqlMh&T6
zGb&D{;e{SE0~H>WKwV{BWe$fOcMs8~c!NZ2Jqs-eT6gg6!CU-5BR;GyA9yF_@y-_Y
zZJ=mEd7r#+YPU1g2_MZtP?yt%5d^);(bCT6(tiBD#IGjr%WboQxp5Rf7%|=w-hQ*z
zuOt%d9ZybAODm(OsQ7ig<B>Zr(0HWZ*ImJ<alm{V6Ks9}x|a#$9b{Wh+5p|yt;fD!
z3O=Zq&)%(f7_Ut9n+FQGWR$*6EEf8sdhUqCewr$>c&<OoxtQL#yqH-(!@4Rl9l~|A
z@}s9%KD*e@RuwTGjQ1u9^bl>zJc~bX;@TKb+K5>Tm|ZIgzEDT&_-bh}y4nhHcM#EU
zbH89uPQ#nF(Y~WlDI|}~&dDZNi;W@>7bSbm;2i8e8;TqAh)*eTF;BZ}IV51~I7{`&
zuzu@<B*Yz9eBHIHfDS*BWn1Y#MK&+t&!!!`gJOEZTaN1MGwYU?79%0>s6g&wooXY3
zDF6k4#3*{bI%-Vx%&BS3PhhI8i#TvgRm6Z*1T;<5=HTl^(YF(SPr=f0HFi;Z`XUFq
z7$j;l6FZuE8w6WlWeUQ2_l4eh+#U1P0gp6@1#1FL=+?vdR0pkMTu{HWq~)ABt^mBA
zZH6r7oFaJwh^pAz9~~p(84LsgewZd-15uU%!cvi(N)5LT93Dmvp38vMed4#Zj?9ZK
zRlnwkHZ(u-JU<J;BnI5Ue*6=gmvn~L85Q$ulhfmGxtAAPHW?R#-H(%=Nh%Et-75hO
z?+uZpxQJ`9Yof8FxgJ^jz7KN`m|#*CKi2oHtqGC2!rBcR0pMlk8N=$@y<Jy21Bk?Y
z^`Qt^b;>RF)vhnSW`9kE@~2s2VO@cS<zxn3r!V++a5tGK>4f>)r@11tZO#jN_M`|G
z0o5mgfYX+k_A9HL!8K9y)&;-nW~f<q31e3`S7j8N*YVLwXbA#^=lip{3>En-87d<<
zS2eJ!n_IE_$=@pkmp8vSsK<#B$|1LR?mC>%(7rzDe^|Oeo-M|B$(}1d7@UIy8aOQw
zqD`Y+B$s!U#NaMMh5~dylIWm(OzC8s8P->BN|1h%h%b&=`0S^<TGsIoB_b05j0{>T
z2W*q2y<ZeM6rF$ME4!qNL31zw{W@|}bjcu=CM{38ZotZ(*E*DBCL`lBji%M7kSz<`
z#GM}i7?={r&!2sc6Jutt_>g<r7`LS*hnj=ybiN=1kUqV0wPx2sdhn+AJ)2lyu!*!8
z=#>%CGfnuWj7UO`juHGs)l)~tI)x`u`J|isV35WuM^*C#pg>&V9lGJQ)cs%+0Z~iq
zSUK?e*)|`Sj0EpgNcD-4;s#&x53qYSb#mEQTd%roz|KyGWe9g<QajpWwA?~G4uTPA
z)p#!EHp>j){>9Q&;a+II&PO;xyeB}OZo~F5l|okkZ7_fdciI{Bmba*W^sf^`FcRE0
zfn;B*FO8Wi#=i~xTlkMnLmtLI6;@Fc$Q9bA{@)6c*+`I-<G~;P!}WQ*aIFi)|3u7x
z==gs<5kAmD!uXF}pe9AOe(xutU3RJe?#;gyAXxw$3~3D1HW)t(X;KLB|1J%}#4ZDi
zQQeKOoU4V_#<_6GD=w@2^+!+-M+kF~KmLp8PdNC$xBI8lJ=D{;NJUyuS6@+S-F4IB
zxBIJW=SvvV`kyQZjw}B^qsX96(10$2@FbA`6?7~*RX^+M>caAdM@AAc$b}DcA(jjw
zlinWxYhLls@T}>P)Cz|+h^{TFNDQI4D)5Voi@ELovNEx=r@XxQ+02yyRx5~N3M(6w
zEvItW#!}dl-d>+k;eW`+;OgN>jXk>mpY18g!@aQkVa>fGt~Mye@`)5;LqV=jA5~<l
Iq)kHp3m2<WApigX

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/deploy/run-on-aws/sagemaker.png b/docs/python_docs/python/tutorials/deploy/run-on-aws/sagemaker.png
new file mode 100644
index 0000000000000000000000000000000000000000..2f4e01d5efaf25ab50ac12bbdee52316977e6294
GIT binary patch
literal 117671
zcmZsBb6{r4@^)<7wllFa@x+<fn%K5&+qP}n=ESy*FT3~N-|oHpz5kr|ba!>tQ{8>K
z>*)%Sl@^7C!h!+-0D%1=CiD{k0GI#(05AXo?5hNnHwgy-0IJ$VP*C=Vpdf*)t(BpP
zxd8xxSV%%5xPrV1+ECZyEH*763S|!4cTT|kB><?|?+O9}kfi<m#*V+NK}1jx5oJ+u
z^C{S=ga>_9s$qe|Yv6{nv&;QJv8i6YNzcwQfAhRfwYYTI*=>3}zTR}&eE>v*uK;}~
zq5<Vc;!`}j+#ZL3g*MvC1SInICW!{7GM5hR9+{c~Cs1qtP{e<UQ4@NEF>-r<z5G<1
zT|YvB1Bk}&m0L%VM(Q<+X&y#|kO$-^1Z19-2d&cxWsnMn3BC$Zjm$WFP>oDIe6?fo
z`JjV{0`w-YTj^B+5W9@wCXPT3yCa-KU_G#Narf6mY&`6*X@NK-XlJIRbV-yUEXTf=
z@zDF6WfEVHn}|fceN#K|ciJz2Kw3%pJal}sA|(w)%gl)c;gCK$j?y?D&RifNaSKbq
zZ<uxRaanJ9;fO?b0i$IgA0T!2X)#y61VtkS;h<$uZ+51qPe5(%?)~Lu<K?*{j&Jj6
zoS2*^zYc`P9(XvZwpHHT9SthI3tFYI74HvlIe>+3qeXFpK7~VUk6y({X<)+&=XR}H
zV&jt;#^fJ*xGmDw8glzaVADacpt>#}ebmJ6l^evK6^lmD$%g9EgT|hDzTdPwB-75<
zjoJ)|zt;!wy&EPO7+D7}TCqsPXuNZ-n^ZNLa&KU;;9GjMJo1FjHHU}<X5k>2B!J=6
z)|>X5rS*mRcl$RzI!x8Hb7TOWmH~};G=PaR!01ucag%F+>~(<bj$mS>!imG_I&F+S
zdkG*cSpX`2;3FVG9Vi+fT5BP)2cS*@1b?3fZ{Qt%Bm`gkYD9D376SY;uvdP}Vi3{q
zu<Vf1erVQs8emI37SpgV-NXzaB0ba$ki6a|5Wdz3pt%B2(Ny`MnEZtZ$hAT11o*@F
zP<})?D#7p}*%8x@6!=uWquIo=w0F?<aIG-E{av%crx}>xIs!VoDUxC4bPQCmwZNEk
z$(OO={Fl0Gu3XunCw-iH3anAxfh+^*`<k|Sogq9yWpV&Q{d4o?WdkY5MnU((phO3<
z{fj4xxfLKwp%%PN1s!rPasp+Mi>HifQbP^1VP_hRW$Y1B{j{Ps!s+;va?GYDj>?YQ
zj&$!xUbq5%<$5u6%o%7TDag>`B1Z<|bxIkflFgEN$0-hg(ZUM!9jcX7WXv&G@!9~@
zy?KHy`WyA#s)?5=ao~!)nYvfD-EGV@sOZq00GxoFAeX|h2RwS?u1cNd+qrmgriP4m
zC9WKv!k)C=px>Ckhh_^`6Fwm|`%4NCZe!#`SSK-vCzFXII|MuS^XqZjVAsSi^S6t*
zOS+2^#mxU$%vU7AGodbtw-2@VW0ri8Oe8#w(;P<ph3JC*%-)jm=KU5akYg;5L%AKV
zFiIy*yAQQbxgQ6j*em8s+LN$MBu6(xGedo?7E4e<dQ8%y^i85x=q7wma|DvoH@-1G
z3`O0vPN8L<qXe*o(xkLLq`t~N*51cH<4)?B5i>o!F+2@MnN0MccpNnakt($GyF-p`
z@mk4H@ltUfGmTWxVD7-&jS0DlX8jLUOjWxTofViB+ZE_qL3<R<w8>oZS?J@U<M%sy
z+fiF-+ZkJ~eXKp#ecn;}7^nEC#Hl!|_{+G@+-_NGc`@1Lyy@I4A;gN}s%CLU&FknL
zmVpq`deTM-c8QzBbgdg%NTpfj8|Bq(dxhi3+4$Pf+MrtgXYU)-eMr>S&^+;W@tRTL
z1RKTD(he$_29^8R6Nx~YzL1z<3Ih!T$x_M^m(q~=$YO^Qsj|~z@Digkwo<+s0#gPS
zJ?2mrPG&L|MHXda%lYyp_hR8`J<|b`^QDvdm*R>%+}sYqHeokOQLUhYxB~gATa~2T
z)9f~_Ch^BkJf*0Tq--bIDs}wDrs=3T`lT*20BjYkD;PMeJDLHS2CY$TwFdj{X!E0A
z$%ZOc?B+sjH;kjKri`u~l6I<3wLD{-NgiyC4ARWJhHt|{gZ*P9Yxxs<6(yaNL-p%-
zotOjTzs<u`%T}=MfNza&1rPkt6v9x^xM(<OI#tzFp=hybhANmVh$^Vc!IGH^@$0-@
z`mprR%{vBO+V739CDIhq=2*Q}u~x~J7gr)_msIgO-j;6=xg59}e<QXLx&?aVzw5pd
zg0p}(3MTY1iNcCj>No5{lk*fg7a>bmWkh5IOiE4SYnp4Yw$!)fYVc^>YP7BYTK8Qa
zUC(SSYa41^YLjtQaW=m)e(ZO4b-TF6(V?tkFIQX4PfA!Kt18?0bqR3!?b3+%g;yY}
z5%mhH3e`=STdSlYwySmk{i)Xd@rCz#?ppow@s#R}s!MUdEb}$($?EPBCL63Ax)}Ni
z;Rr_s<B39yO$=ookp>e7Q3Lypv7aTM;unRz>QUkJnr^6Wd-bBVhO@P^!_&NJ)55D#
zl!%JZp2%I*jA~rX!GYR9Lx>rDd7Q?OmJ=BgGA5EL`g6khSf!qfa7`{%E*S@1`g^*v
zhNvZ@6Za}zE5(a&xTd7sxJtR^*~aoUdr)(1Gh`-oRrn*>22<y^4}D*&-fRDq{^<Te
z68E3KW;r@x_<X!rR7p9ATSRpThEZWEnpC^2-t(u`RWg7I%72(h6%ZO8Mv5ul`5CiM
z@K$i2%ez{?q`(}*cn)SrzK>_5(ATcld2V=bbgxX;$XV<*M)aknv*ND6SVXR_*9hxN
zOeW4$;HjH6A~unlztXO;w^6*lKSZBfaEx-O+lhEyJ>~E!6DcQiRrfiH?nfQryYM-l
zGq1Vm96oYeZdADMzQw*(fu{Sa^QF8gyv&VPCTnU2BnAXI>O0*!Jzm-@%gd8!#w#+{
zIt^?b=!w{9UB7yqKK!O(q%T>QS*Ks+Q#g@db>+Y9z5v!9+ZlZs%^1U`j;C@|a_1iA
zS#}$~mK7|pHSpX|>TB=w6XlL%RyI*iz!|`k;(B>gxvH7WO5s~lqE<Ro_Trl4<5k$z
zwrf}{ZETb-sjSAUL|=%?3TB`2n7#jH=BL{iBjT$6L1`^+f3i;>({-2i!28=K(4}y@
zd~qd|%v4^BX+C|0dj)Cf+dS6ds@>$NhG?3F`dpPr7am`gW9OUiSwL@Ce;5b45uJf%
zswsNCj7DS!_iRn&?o7fo^<!0K>#D=V>uSX5#Zr|$r-N3juG8+DR9f1hC8JftdSm-<
z_n{r>IV-d-76(;Fmd%FNwN;DXzZdJ;5A~`9FZbOlA1gPFes}$Ds;w&GEB3M`(9<!F
z=3;*!xb8^{GJ!9`x9|veNq-bN`7t72P^8#(`o6*bi^E=b+2Luj&f6$Tgq^tRL+p9?
z)OgZv^fnf~l-_hma%eKDEy}4&<K6ZyVQb+|=wdVfQ|Vgm@qUF5_p_Y|lau2`<jL^B
zqK7NMX=pR8>oWN!&Gm)wxbJSLT?Q)4l$VLm$rJMVMvwEI`oip(t;5P<v!jbI^G9k5
zVs*e72er2VP-F<;*11hTxj!KPIhZ#E<7Lbt$i(Xl2heB8p~qc14PddU=sGk~r<ejK
zP<BK<r8?l1Ap108x1r(AQ_xSnamhos7&Qd5pWm2n;cR}e^8j?hj*m+=jgPyRi3EBC
zKPAK8Zn!J`4F2TPFgnxQx2tLbek=QcV-W)fSY$p>`Z~yFm?)^&t4K+5>RVaR>Hf0P
zGoW*}u>LyR0swG3bAJ7`FtFDpaJDeFwBvN<A^Nig=hy!~is^|6{%m4z#zUkcB}*V^
zWotmdLdQ(UK*S40KtRB4`^%8?r;y0M#lPNoh>Y#+tvTuGot&KLoS5jWY>ns{IXF1z
z8NShf`$qfKg4WK((q7k@*3ypnU!DA?A0Y!feOnW2dlM^5f<OAz)w6Q2=OH5cL(%{J
z{$;0uv&mnYEbaac>kA<LA2sxhbPV+W*Y}qw_aCL4vL?<3<|;xa76z7fUpjc%85p?#
zZ2ym%zcl_$QuQxMCU%a$Oa86q-;&()e*pL!pnrMm&(be<@j`La|DWr5p-?sXfB*pa
z0e%R5S8xVA%K+C=P+T1POp$cG>`->SR74o??KoX^aQeN_P_Ev2-j-<5`Vi_%L?kYH
z+>sO=9gQ&Sb~!?l5}V@wdD|-_v<Qlhu>Q(CKJNDT`=-rlBi(L_<B{|3#+wrbP?#T4
z7Ub^_c6bo`!yFhzf1+#%3ZPK$zdx`53%rYc{#kw);r)|;65CWU`tPm(B?~GP@&8d`
z1riTryCGZV`*+p<GK7-A6y|>whjzOVXQM>l2CAXv|HCSAKso*YMIj3^iT!14I@^3H
z!9O6%LIq<1|2ML)H|q@u3edgw93@c7|A8I)g>D|>zuolp=5s5;Pf2hH+dw7uKR%TG
zLYF)EU#|O8T{;`k%dJxESNrB*h<P^F-h}~BI5;?Txe7%^)W3P@=@P=(#r4ApGkx&)
zD3C2yv;xvUK(KTD;X+6DQjLGOaEtj5B+LXQB>x2I_(#>5wL;53U2K9N3pH%Q|MSN`
zJ3RZNYGu3FR_?#%${$XcBlsGaIq&?~e|ES+_*GTcC%2#bk1;Fy{^hzNz~e5dT9+}_
za@D$zu89Nn-&WsJs_j>xxL@BkYpiGVRA>^d5@b|WIS#z40cdGySKl=?HJ^;P$th^4
zKGZxbDk`2<kY)0YP?kCqpK#KT&d&!9D!68*m*=zGcs8rF+7Ok>8a~rq^ZrY}^#|ma
z4{(^Q%&dm=qxb@Xyx!#J=jV+JLVixq&H0&nUa(@#tgM8boOGHIakH_o<&~CV-Du4=
zIN2MU2puK$8P?_0*Tek&{d?6YtUy#^X?8ZW^~Lo({XL6qGc#=S{bP#39wR!z0YtNX
z$eh(|pM}+fGMkNsB`+rjQDkw5E}zYs&DyQwTV3sSw;*578$7zHD!qCql+)%b0vwLm
z9Iu30NgWPZ9Ohc%xwp@GZEdB6g~jUgOCZ>{;)M-~wtSqDu_ndP`f%y%s531~-4FUM
zUUb?D3uCe*Wi2msX;&Wm*2}|aK6I|eQ+v3l#@FsKkIjb-dHZAK*NFGB*F>&)=pwtG
z69osQ!@tIQYaZmTh__-glNN}}@k~I69{>pGtwwhjZQbrM!}+4>lV4hz&h+$T>+H;b
zIvOFrpkOy*Njoqtjaut%scf}DHEFVcL3v_u^YfYB9*c8()XdB41M}^eeu|K1m!Vfc
z@cO0fQ{cxBN-fWO*dt0E^xOBK+s%Pt#J&bH1$oNpQ(Q))Nkt4cM`j#c-0P;UF7J-l
zhrGr{Jo?Tn<>r=Fxt1r!-rm0Ab(QPYF?yoMWybcqQEUb|LYKWEJ|S6IfcAFoYMq{;
z=Ig-I>aDyeL>v?dcz7rC)p}5?&iBef?{({U2j{I1x2?(BT<5o~OmGI*c`+ejk*#y5
z%_3X9UU?aAFg`v$ZW%Zp1%XVjhbasu*S!^i%6%rcMbc@GldD;?OXI8}nKXZiA{mY{
zgLF&7@<6ii$K}p;LH}E=j}`6x(U8mix_9>Hw~?&-@vb@Jz_>VM(A%m*J6l(`PU&`;
zGGyeVXY$K)hgjx`ir?0c_CC-|4OvM`R-8gTWqEpQ{G_h@0=|V5!9?#;K@m&$k*-wh
zN*@_i2J~uctJ~YhfIPRCHOVuUwcTl1pA)b}qeAlBG-$-(2U^Z9%_dMEl*pxVf310I
zX-IdzBEKohTBb4g=g<WVHcJ>o$*f4yu|zXAbkroBEpLUlmn6QTw4acfAJ51b+`kYa
z5AX!VZILcoZ&@WX*#S?Ntb~5*0exJxea@l8J~gZ0uqOwG_ob*byfH~Vd0A})rEgo!
z(2#t(URx=UX_zFiFKdqk;kCkFLtZz34657oNj1qI7x$so(f3(2ZAkhkM&WWf>Cfy;
zG1%@|x4+y#*E`SW#$1S3bzQ^dw?93x(`eVkCL%Hg!eNH@_dBn<EOlJn-}j&@YXnwS
zTEbu?hjLvu!}bNi4lJIp=@zCX4Vd@H%Y3FLCc&)_m|1n+)BTJ^DhU?STR5Sg79vzE
zXX$c(`B5?Pj>;QmHCQX<a1UEtWMFhRtZT%0I5#z#68ruFZ18m6NX0HMA5EVQTyTa5
zwaOWKPjtui^QmdYRk!iM^s>U8UZ9NvP8#K7LMe@GILvl?*p7@gL&E^C5pR6$r~e@v
z`tEL@Luxebv9P}N9_-J|l;@E$_r!J+X?{_CE<9~be`)MnM$GFtgO*_vCNqb)JCvy!
zV`WIHR*P)YZr7R8e%t-%_H^g95)^2T!^8amOR7}S8|ar+8z{2?KR-A$d2&|E6<?_~
zC;hn(!K0IybvF~tI+Jq*>YxZH)2a#I_t)9!MFU^p(~T7cKVZ?w>>z)V2nwE#4%Uw!
zbI$M;mU=F)r3HCYH*(7AfgKzLzX&U2gZ|pB-O)kfzrKk-tBS7N7|x@~P(t=|5-#|6
zIQMtX!mZV?fq;RTNr_m=urf0<Qw6G@=1A4r#l(ym(v5Kuzn`P3v4Q0mZsB{m!QoGX
zj!rEp%fs~c_0345({qqiJvFm8x3(GsrFW=?qz7o#*fTnwtwyL@c0jK+UiQZpj3HuS
z<*E`3IiKeAZtd<?ytlMod8&QyXDQ8u>dp26W;9<$I9LJ3PkcR+@d^jWdt>~#dfn6+
z7+p|f#P@t+TuT8HRntkolU{AXE3B%h9E$D%-abEvMZQH2lc8E;G`ypjIH&Tx&vH11
zaZ{W+DWkxx>U_qt?9`1UTkpEMNpn6Mf+q@2pIrOCy(~Upp)JfsMdPaA@VKQSP1=~?
znCsK)7@kB-#by396EBv3I8Rhp0L}ykn%&Tjni3)6KLoDhodi1;Sx-dS3%@kfs>_=Y
z?wAMr<GRP$+?|3`y|ua^8}@Nryt|&lN<o=bZ#xH!`8fS*IbPPiz6@wWQwb#Kd28{P
z%=nrzG%~G8(}0_fwj41lDtK>K9jv-Im$jP_8(KkvJYvn(U`>p5_HkiuBPO*tCj;Xw
ztc0C9BIZ^A^^PXPCqM9xY7xG4@HB`gm%JQ*KN1rW3u@5qO^m#XzXDZ_UdxfZ_8BFh
zSyaHTKv0VOAR)B{ek5vqZ$lF(sJWoC0{2_G<S<?3cMW$WWb7KdDQR|gH@#o(AgQON
zSI?~u5K+<s;>FmGg$6x^b_4!$g0f2KK_`W9Bn5DQtJty%Yhq@a2x?fT)2k-l+DG2>
z)Hxe}9i+Nl$g>LprwDHvPFWcqwaQf*F~*YIfn?y}%T*d$=^uOqy2j^2C1TN_ZMf<3
zb8FS=jKy#QB1eZp6UrDKJK(3+*L_7jUz!WqF^(ch(IO^bEVJzH6T&~MdyS`p62aYI
zf|$=e)WrI4u4g5xOddr<0`v0g6{wenzvaUzYkGh-n*a6zKMo`n_hmDxCYQ#kN}#mC
z$z>mb>1bAvN@l=2HgU5#9+#M&*~p<JLt2|1HB(v?)Z=nZDaxBftm?P}nuWt1w1e@+
zm!XcX@6ll}ehxlO=&w#UMSJf$M=Ck3RWvge#tKTYZ%bv-ry`5M50l|lzeZ*KwBYzW
z_N-w?QDn}Oy9`xug|?uv5Oe{j{Xw~nahNA4E)D?(dL%n<Dr{Q}{$@PEzHHocA4fbR
zUN>Bgpj<v?I%HKP<)dE)h#j`Jb#@p*J{Xi!?R!Rf*5=x_dz>Pt(%J>o^;-=R-ANe+
z%FzHvj(pKzvCrrD)5;Y^^QpClrU{M*F0z^xBU;|7zXBMznx_NCPU6TgX-5ih15vKW
z(%Rvu=RglQK^8i5Lm@ySC`fN9hRnb1RBmQDuso3NsF-1sg-qe}G>+qrlF$_<I82x&
z;fjn-oFFkw9yd}Uo}0S^0SN;_zA2>A1@OvoF0Z{2%z@V8XrYaeraP?=7F}`=!**`%
z2+NS4A0lCCWOqhI(c3SDBN&$%s*jjEM)9W~|0hB7shgYtdf&kTTt8|4bm$ytS1^hs
zUQDj4gtQ{MgY;kL>q&S(13-?T^nr@w3^r{+4fArP(YDL>$^jk84DJ-)MEW;O^qWcb
zDl;_SqAXVdF7id+9Q&bI49FkE*Mwt<?&XjV-|E~)?n<7Vx82WH>d<ArCF+gt{fI^_
z{rNMt9wQq4<GI~Xvxd#=B>qReB)h5a&aXn$q{^mEXVhoVfado`bp%{oP%kgOxA&^4
z?9x`O6%J9Bq{Pklho#g40iXrZ=?1FUN_jm4#3fW28Sy!8JhwAM|Db^FpK6S(78|Sh
zr#qLIQC@HF9Fp17^pyq!!UM_7CccIqCWhwS!!g7N-OMv`a^x)|%VzJJ9iiBh@Rn^q
z>gZPNVw0n<^+inI&JQQ=%aF@e5|&`#+1Nm48S0C&Aq9WCA|8~A!lT0QL9JTA^cR<#
z^`xfKkvPk8=-~9Hag8CMY=`|&08^43IXe2*$Dc*()XL-1#@-v}wG+B9Hf6|Ef&G#B
zbPFTO-O>nkJDzd0c#J9E(+t<@#o~Ywv$h36jLbDdw0m+8&N0GkotvwoZ`b#(->z%#
zC~9d58#<a=UDS$QGZ~q>mbhHoyY4Vt#3B-}OAn8}j(r&hX2<rXnXx_PYOgL0Va@@k
zu%6p*a0xM_VlIT?yS$WplzCE}98p|WA@W_A^sR3vCNTzf696BJyEvW{F0hjTy$#-=
zyG0q?N$oJ-O1Otn-sR#fQ5X})#9{85NuP^G$-a?g*IJVts9jU%S4xL@Q&Sr_=E?N0
zGl8PWFq&lzX+_lknnTEdt$DXg8n~ZxAP?t!{L}+{?CR^`#|qXY2SV`~pZj^4Wv(Gy
ztIE3&li|UUmd4OWH}(@x6#ks5SG=2Ca@!avsQgpeXwE^eIR%xdoE=P#Ne$g%P37|l
z@Ow+4NPyvC*TM`mDyf^H$q4uwm=~dW8c*i7(M+8b%v%pNIo0-9j8-zBrIgi0Lf|^p
zX|P$SO@dYn+lw`X!&6q>HBi8zCBNQW#0d@x7rq}}gj4tO$mu$E-wOh5^M}o>hEqK~
zwdg5(G9{yeiM;kjjD8nrMn~Ic*4L|sZk#(o+B**o*|sdcu<c&}et+2&IQ!+Fq_X=B
zV`@*-KyAIM7}R~@U3^wmQj?t@T9pkIRQnSFMkZ3wP>GYXSfR!Fse5Sw(4oHh2^=&p
zX)$Im-Weq+K`!S?K-Gn@6*CSAFC`d*PZVc(G+<!{w(lphtVX(wK|k1KHiWZ^i6Uf(
zchiQyM~bL!jsOx8R?zkjM78wbj*Sz=zt->zg!9bUwg$d9AjgtFee9co7rbh#lKcJw
zU^*<Q5c}v^JBXtEU=Wi&mC)K!IUo}9_nx)R^zwsjp&Sh%hu0Rojebb4!d>dkJJ3od
z9N&~-C?%wNY1kj?J>mz5{reK5+qCs}N7GV|Eud{>*7Xa%kR^o84#wFBzJpihC8=nV
zmND4QM+KJSf+vKdKJ$XTTl?vm&!)nR(`}zxhPv?LOW)W(qWV!5<YO!f+_aGN(>h%|
z{Tz&19t5FzlfZ5fz*s4x7UCX%lH1UtK&Q{E(0sGi{Ap(4cvNP!`8(E+hLGk^==0bt
zZ(fS)N!53LA?L67Q=9xNCaz+VGy0+r1h&k-_=AGl&q4&mZgq^O4Z&@m`QIVbmiLf5
zpJdOfJQb9TL!h5^sWqdLe|c|`(&skEyBH(dZZ&|)PI#-0m;eoij|xu@l|5@TsD9bf
z;psxHN?hsxB6T0=gf9YvZa!1){wK&J5Fn#KI$zt6YH{_bz+b&~K7w@GcYkcKDYD4^
zRr>Gfu+t1k9Pqi)jX$C4FC>4)8E^22U(CR#tWjAZ)&H<2n=D@hjQbK#_Nemznes1=
z<0A;7nBb1->}>JjfvH`Pv<m}rIFrJ%vJzc?K{4a+L`ZY?AE7!-+t6I!F3hFu2)zgp
z*~I~^#K@>8+R1+tGVE@D@F9UT_mPoC&Ozdyj7YMfLj%iZrIUXPh#3FiF$Sh3N+Z!}
z0dZj|#8X;~+hi0q{)4!=8Pb}Gq0Ai0(Fa!?u%(+*Nb&GhkiWetwasmM*(3+;=G`R8
zG~};yvjzxAv6FKvJK?y5Ow$c?S?8Bj^^4%bftvt-JiOmZD5$79;`#32O3>wxJ_V+~
zMccUq*@tRP`xfWT-xhI404<MdG}(KDzs4z)FrUhW5$T9|ZR_yDzb!Dpn-#&~@8rrC
zQN>=9zW_>Ge9BD3GJW2j;Km6`3A$%bQNkSB-9$j3rL1>@vY)4JLa^zZ-75SaaIzqs
zw1C(J?$i}{NKvjixTeIt&=sNz9)B72CyVBD$@m9z);D>6&X`j~1)UiCespgBG8kJP
zLCS#t+|DN@BMe6Ky!OJEb&PH10q7d~n0ahL)$x~8{xtS0`22%q*=bln@2%bf#Bk~e
zi*&M_jYq6##=A`_SS_!QQq?J&2sqv%>byLb_}Lx=HG;m{@X^0BC0J1Sx#eMhbNspd
zndzcd@|0}1m}xE5q4+=ZsM8II3TzMO;d|8r%+T=mRqLsyebpznq)AqG5e;l&JMFX!
zNVmg{;E~!ZjJ+1&^pFfcd_wd?OH(f+325YZNQOg;!21mBZEkDU^dOCh$%IdEbtCBY
z!gma7`@-H@gzA+o&QnT^rcNmE%(<b6APzH7TDl2q@eBtz^`hFp7TCXL^<=ttt#>*{
z9aDSWlt4}tZrH|yD?1pu;2}D!**zi^&T}<uLLZ|m3QjstcG1qZ%a}wpE1DUd`eaE`
z?`>q0ZmyZq<DeJ+lzk@K8|)yimB8UK{7^T@9cxlfQw~B$`_Oyn=Co-omL*e`|7m^J
zEP)<ZUu)^!U$gs9Ubef1^y>p-H-^qCNKBIuq`Q90i_-_5>vkJvU-nI&?Z}5$4`JPq
z8;q&yKK>#PX$LIB34#`rS#l;~bg&uGOdgWYj*x|lqC?Z7+HrpFW!v9UjoaK}q59f`
z9yLO%gBmqWjQ<iy-L2vQJWb8@7{{q<RO_z^_BEBZVE$aPJ{do3GmH>$j<pQdKfvx{
z1dna+8Pa15bCzadJ$4kNe5z5-ee~Da*-5np*vfy5Z}?Opo1kI>f@k0jT2%Ep^uAkn
zotZ4Rrt0R_?y&CKG{dw>mxUtVI=}?`*^R=S*S(S|7-MkhRZQxo^iAcrQk{be@Ut@7
z+k&hYJGoxTXzkaf^JNIm9#3Agsoywp`*E(EdgyiRy^mPTGRnN0a0Z5EUf8FaobP8n
zopj%1j68I4<ii%34t0&m&vIsH99(~J&b3R^2&!0*rBq#WGe)@n``c%IjTpTdi0p;F
z?<CJV_8j(XeY9`G$i}~*iO7x)a)OP$eX=9qbHQBj5l2PXw17>cqP!6hG$@q*z)L`f
z(%t6f8Y6GiT{9?MZ~sW(9cy434Gsq8CQxbTcW=e*Dyhk!io10@({vm?FZ{8WMpspP
z0eG)*zD=s-dIJ!BYs-*dtexHXE#qD5)gx93CF<aXv%Ve^5eE-WW>Kb6;)8r+^E2hR
zAQ~?W6gPwH>B(6jGcB+Y?nvYJ_QlDvwx@?MV=9d$Ml?d93xYV_O>~0$esvOY73#C7
zoRwqGQI5Uvo<l|CPp`zHhOoy`4i4#Y(T%X~jfL6Z^j=pOd=>=P%-v64&|10jdbzT$
zc0R36jY}_jd=)WKQLb<iXm?=X6}EQukHf6NZRa+Yjd8&Xi;}YM!hVQ?f&Dp2r&407
zN_8<bT-im2MB&`+?_1js1%?JwQKY6WV@4HL$yw#+bXgL&wbd5dh?O13YELdl^*84p
zgpVumKd8@Z4=FGIvzuD$B1LcJ_WB|*Gc)VV?$sP^Uk1LBS*mnlj*N^1heVuwdT=X3
zYdp!<vUXu+GU$rev+W1XxgEK=xMotd)wxDa{7HalK!EIhuc;{pRrW}Z9JxFj@(cTR
z{YC3qe+~sP9#_0K8-C|>Olau2msq(oTX5W`dBW~`Mzm~sG_k+s&}U4U-dK%XJt4z@
z0h^y^HW9FLG6EkREuA(w^J%v-HW!hwYa{b_VrI!{Z43Zn)H%Bl5LM?YEyE_9qOD=y
zD?IP!d3?vH>iQsKy%-Nzm=Cc%STJNTsrwd~s5C#%n%9vhfqaKuYBs&9D`Y1ON0(&-
z+W1RpuAv%(O`8`y>yp~9p-r<atY9dV*iA`q`{mvGC`Wc<HUzH@64ij^C`XUb;h5Pz
zw~rs1C`)5sG>U+@Df}?PTpxRFQ7og|AJoeY^fd~l?-%no54P3SRtkqlhAb)!+72~r
z&NX#w<0-PYeM~xpo^LRkWDyBIP_f)sr)H)spkp;lp`kaOulDw7<vXvAesU7}rWCz-
zO`2h!ulMv<b)uhva8zLt4i3dc`l1=ISzYhEyqnJ6U1;TPIlvD0QWUFQ*q5DmkSAuR
zGZyux;iot7+w@(oNNgI8n0Ov%MJioE64vq|=4PitX8OofRHm<Mn;VOT_W=}~g>=B9
zV`ei8i=kd050q!abkp%+t)W!)_SN*uk&}~j7@RQ1W~6LX9N*ylbE=UFQwQ%G<npEe
zy@7s>_OICCwIOJ1Hp{`nh_&tU!<X49r7bZf);D1HdZUj3*(odeAS(oePASI|ysulA
zCsq)lXXjIXRL|!DMm2i#<OC%D6i7gbTifr7ZHU-Vsnr$Ph)as~+XH3tHdDh4$uCOg
z7xdDGAL^xH(aI}f{C2?K!qm2tpAk3I65#aBkTnGLrj?XJ-MsF}pOlRBdj=z{uW=$z
z=^+PZQMI$1djiQb+ktNn>E>xzoA~ar*$v8jR>P6zmlSswwLg&IC&ff0EFh^&)vOQU
z>HE%1O2pJ_VDQVe!Sh(qx<2n|oiCES0te*5v-TL9Ei`a8GMg~qDnFVpd(@}(&3k8s
zv$9EBp<5cH?3wEI=57j9S`CqL<mvkyN-nIADtj(HZ<vt`wSoo(&1|kzC<`@gsO>WX
zmRq^Nm481Xzg?w5POGLKIqn7RSMm8(xJVUKne|IKt@}UYcZBuVl4)?Qf`=*Lb|;%C
zcjqwTy48*%FzSHdwH;z{-xe87n>5M+iuJ@yoYt@{`G&}`EU#es_Eg?GTdlxpc)oU6
z7mxl*!3@UXz?GA>SZ=0;=5xL}Y~CG=IokiWjF$S7Od2CXCNV#+u@XvR(-Rmxtd#Nf
zp{?dQuI1=TJRnLRo7er4r+Elw2sIC>nEkw;9$zYGv-tG7x9WnwKRU#DJ4^0qMi72`
zB#DoI<-nzR_aTYld6as4M$zY7{ZV%?<FTh@kHQg;T2M-p2k&FyGP>qS73G0s>j883
zSzp0tnM7ycm0oY6kjMH4asc1+Q@#Bw=h67WM$opH32F>$dS)Kra0{b8DQWA0+%ssc
z5(~Nx3ib38u7vH+U0Op7q(2rH2`N|<&jsW5fR^9&MfZyt-4cB}`%VvD6R2GtRO_u#
z&Qo;q-a$x!7NOV@;e2ze;e6AV%N3}<pV4>y)K9U_O@SdJ6*_q66xIEvj(I7Vm6lb(
z87dV(o65Bqg3wPD-C)6U1LNxKhVjMPRUDaD3<Mu1=|6<$K;UwKFGWNMYHc$%gMt$C
zl0g-4tYFye!v@1%_l%oai7qrYU^wpMJ}qe0RqL<l%_}?X#FkwSEF|R<Ftx#aGvWQX
zQNQZFyd2E+`T(11zs1z>6^KT_4?377I$LXk%m`t+8WOei&dGtvqEijg%0hT*nGXHA
z8BADr5hyyR)!xMjudHQ<Dw*-y8XAV*+#CbSuH_}o4^Kkd1x{F#Fqwl0V2t%b^kj>@
z-dZLjRt=a;`%`e;;n_I&g7le9Hrw*>l*Q>2Ax~aFcd-UnuxO`7`weI#5sXA8J*2G?
zH*^ZVX)EG!^Sintua<`sB)6X7PS(_CY$6PJ^4i+;Z&Uw*GDz5a1C^&^IPT}ArmHv(
z!5YJRTOl$yA>xDrt<{c<47cs$LlD>InTQcU9?jqNohsXh(xAl!xZ9A|)=QYjo`<1h
zBGNs?QM>_#JCf`mGTrN4A5h!&QQ|m`9GRXcmN;Ca69SM5H!-e9#;!ASXY<SJf$=6y
z+e2)cp#Yl%{x5C0#rY6YsOQaZg+YvV8ZfpuW?Ki_lLO@Gh<thoVDM3WnoYlh&rjiZ
zO1e4%N$9azEjfcvEXmhDm#r8`vcZALCc6`oBKfDUsBLzig!a(UD1e7Uv<dG2Jcao{
zM|)FX<`4n~2M2d=_XWkor-SfSg{eOyjDH0t0N`n*v!8H)*V|SXwL4L1(A$#V?>ex9
zwi3U8SDSu*yTc;kYeUU4U^5;agbxhZiZ}(!3F?&f(s_ZK+!}jd?NQDc2rnpO)xR3<
z!QgPknPn>%alzGxE>2N77M~w0KGkaFxKCBP8a2C>of|cq<Sh5N%z84uC4_1daTf6>
zvU_xOueD?Y%8SKp?&&bc!8vYRfTjjU+w+lAQt%EXGlB_?^lhlav+Cf17hae^DU=Eh
z7Z;ZKfE)j`=NX5?!$3e5DGP!eA*TCY9mS;9X77CBG_~E(^@-@C#c411{VO}dX{O&f
zRx2#QQvNrF#{kPw$=G1yop}FCi`~dE0}MW6Y7I4-^9y)SUjTJi4IV3)@}0Ho@aO~=
z?y(gKokquW-@$Zbz->})X90F5FCIih@b~HgZ>LRX@N*u{aLm-;lX8dT&ut`or!*7L
z8N%HwwDJ|Z<g!s0LY8$%i48B<w8sNN3Mw!|BjxdAUWmS+sKG4+bei;6pF)6{DrlG`
zpS(0K?&j}D+vn6H7UVdztT(paab>U)XterN@XUKwL$A<Dxs?GII%j;xO?Yap^?}v2
z9lxk8&<D}99wD}NP$LSGUmu1T*2FT-&vd!&&ji!jn>cj$?#gs3x&5Ehm@xZh1^0)c
zf%EQEZhF>+D~$#0H5lGKaJSHpM3Y#Q;&;aeZ3v<~uvORc-TxC+YD|OtDwvZ&s}kbT
zr_b^@fM>VALRg%^l~@m;Kj^!T;-)(QD{iF*I`ri?Qbq@L>R{!1x40=PFKT&0X;fvP
z>QzrW=2hgKtoJOZHy%vmlCVB47USmOz|u_LNYc4oAGAE9H|Z&`9}<VOHL~VpAejdi
z#cHi4tpb$a1&+oTRz-**R?Q5kfpI0bTDRToX#TY-1eU`^tlN!1QV1hpt^u^8ryq@i
z9<c1w$Kd#j8;1v&ad*}vI1Ex)(w(S42YkH!TKb6tBL`k;;QjG;B!M=)5E;2_4TPw>
zEfEn=`}E32c=<RE2uWo3`{PDPPdzt#^Q+EFfjpz-x)4ZPT1;FFaGE-Z=liQDX8ANA
zH7&BBXav82IfzI&j(;_QSF@`#p-VSEOc;!x89zqA+Xx0b0Uq(-RB!l?5(bXftyF5V
zo18V@=_3*tY)*h5*E3g-j&nOI>D=vzh;iZUi#$|xn20dJGSayIIeEyKk2<|qHTu!f
z$>CSkV&ND_ezl{V>(+-I$xN?EPtA9=?}W{Fv6wVg5YKWhxzzLVA;>lh2~0Bz@}O2!
z@=H^hA&oCsApRo0S_WzvmYuJT{&SCLu53>)FWF(&#LDZ}2nUi`AWn|Lqs2AJ46f`V
z;h5f$57LrZcDS)b9n;e$ebF&_2x$mG%l#OOJXk*zrtA5wU#6ZxgX|2cF+c7eBXVrf
zvC(0zv}OE7U<dOlk)z|2gOBo^g@l9&2noRgQ^i5Zq6RCLv_i!ic^C~C$fR)sT1TnE
zJKi|QTkL7vJ3L}=@IkGt@cjo!2ouZoIN<g)ByFJte1Ygp@pt4sism`~%x3AN2l>@d
zMyx6t^OY(A&*P5YE8zFU#Ri~Vt|hTljsODo?6J$F!A%U6*18Qtt>>;$uQEBOySsDd
z&N=c?#%zjO{t31D+_BQT4?kK~T71t*lnN9iohYzH$ds$JdX(PT*m?L|vH4h1f7<|+
zNR#n0b}#H~V}^*f$pf_n3o)(KVtQ6f5JV@c=OVU$qP(r;?rx2wjwY&R-HZnB9vi}^
z72~@p83h{mfU6)tlIR}@KwHu5IO5(!wsn&#>AUtc`q|)@)Bx%HqtDbhC<Oo4h&HBb
zxD2?m?}dc@n`a1Dj4FCkXHL;V0$A+FD=bW^=X=)Fj&T?s{QHrOB*vtTYr7;CSpAj_
z9acetgkYfL855kp%L8haDhi3&iNGoc+7LMTn*CdAe$vB853O}Q>kmI;n3)q{VBbal
z7SX=mDZ>pKZCE#6q+0?5369*h8I0-6Nix+i^>}J}@s!^6lxK-XTPf?{g+3y57cC44
z-kX_Bco{R3J*|<U{<l3*{#iz_kLG*7=h~TG0WRmbyNo^Raoorx!Z0Y~XIvSw^QF#*
zq1za414ZUzWK~>YsF==egXG92`Bj-I)`*1kq>pGviB1iB3EY2^5a<kmbt~l~Zj<RX
z@tXiH8`#a=p~Jp(bd#IK-ouxp0j*GIWrdM;QrUiG)6Ub{Yn2S?mM*mdw#1;EUl%GC
z*$oX%yDD;wy8tii48H<5h7&v`ancv=VKL(4hZEdZ5wLK=3A{e%XhjNM;UI$%RK`|Z
zW5D#?HD6J{c{Lmp8@Do_gN}Bq`qskVo4pj03yp(ivCoJ09iG+nw*-&-TW`}LG9L1@
zn;Wv&kMI5SJ$hfRPsv>m(%X{?=9T^5En^1H!SREMUPrf&u)Ci^&>LJsz7h0Zh6HK4
zM7zKEWu+{IIMft1WpPnLzZy&vOUOf=kxQ39hCGU?@<_Vvv><bgy#FSO$+DiNuKYZ@
z@N)Xev(g!diTx?XTLgvjCccXNav^j;en_v#p{Vt4!B=ASa>*C604ep;>ffv<oeog$
zuVKQ`Ae<ZQt&5GO@lMvBWv7?i*GKA`F=6Fb==R=;iHWC$&v!T(o_FeUwf6zcrN$un
zRQirLEF;QSA#ypL)@BqFwgkA8oK3eQ1V1_GX)-7{@I2t6*fC>54mDZpL4y+PPHdof
zu5H@6Blo#kiy087Kt~;wWO6!uw865D_2B86O#y;RO<KL1tupu`R_R!$h7?4^=Ka?3
z*z`xga|B)OcxVfe4;*FwM5x4fmJdz!fmWiinap!RC7f3$15*Q&j4Oi@HJ(o0R?$^L
zkJp6Vy(#UMr+avfXo))0<*ML?!#$dQSE9qNnu`aRC)ZL(adi^-*UhC9kKXqpSL*wD
z8ZL6>3xyDw+w}SG8mN;)g!fD3vg9aa@8pT39R5+^Bl5UEr>y#<Ubd;9vzQC_UreIx
z7JD{6e=fzX*_OIyP1qIf7)@y}2Rq#G;XW{uA4lnb$~0+2FUbt-@bOabxp|UB@bVsX
zLV7>&@R^K%6uG*~dv4lKXgSr~WnR`iO5L|cSB1pd)aKfh+M(NGODi_T1+JIG7Tawm
z@;g4rO$hvv&D6V~teI0W!uR<86(MG{xNnBQYAijc9DW4)nFKY{dC?})s76YqHEj_9
zhkx9hY0Igo_#Y&qwR1gsfW1r_bkq2m@^B+yAOHX_J)#GYM0W1veE*&by%^K3zxo^p
ze$rIY)q1&(j6mHpElV$Lai?cyQ+H5G+(oOD$|<=6E%d|i!UyWt0u!0Azn0eeU^4;-
z*bIw=9mg#1HU5RXoT^r7#q=*GOj%XAl9pR`{Lh%gpbqZ0^QwonJyBVIwiYtJBck+S
zH<yGd#~#sHGheu=l>popWn`epqNdFvO8%iNx(B;=zDDx-9<9Lq7(t0K?gq}=bh7ap
zyq`vHs<F~1+vB9}@Mj}JE)KXix7f@%GV)@POWG$3i?V)s`w?pDBr=FF=l3;DQlrsk
z<c+t~2G^}uGpjD`XQOX8dx@+jZSxWf1-xpWlGM*vc}hklO%Mu!FH|^UQ<rw{I~=cs
z24m8XL63z@{F|i<f)8pjoZ_87;eQajtObZfoL{p(JSO;og0_faR!_Em>&Tl3=4peo
zNzvID<KBzrRv+G-_OXvwSTu83WEWp^iOL9TA9ayKGKGpHYO0nl%d3~Dbg<p839px&
zpywdYldZFb#2?A)%9J#^RHwDlLLRj3cr#}GN>}4yc6c!6-qYeZ3(b$GJ#Tz0==g{g
zQ4z!4Q`{Y|?`_Jf02Pf4b~{P(AU!^RpPjn!xBE%|s#{eSZdMY-(=q@3geEASTJmOY
zPHsVGdQu{dq?=Fw(UaMBSI$Jx%z|>X6uaJi&(rNSf72Wd`5C`|16XuMc$2$@>{(*N
zR&!&F<9U6P&7Gd{2UzNP{zRcP@F-R4tIHUV!+^{1IOWnslH-r}0!!OvzO}JxEH1eL
zcEXX(<wb9`3mIaU^yJKeyC_IEJT*;Kw)5NTw{w@ZsSyubu}?A22S)uvYL66;D}d6p
zi0CIaj<fRp)ej5otB1FRY6jwC<#Vh}Gksr!QC)ms*Hx*<Gs#^&^gY9=u&M_~V-pvt
zn`pk0G7YYniuR_Zg^$k9gyZw`IJ<{yipz$^>(+OzA4c>t8xmM930)uGWSr@j8fxNG
zMr!)sW1XeoeyqtU`v{7@yu^;wbCYz`f4^J~8}AD^Vzp_B*tI|%B#*)!kthScJT`#Y
z*uXVO#V&krc~7vJsKthskdwRLxBGcxSwSZ(VtBno1}$wKH$S4q=F(snb3+RM%#{+P
z5qGVWkP#JVDCott=ecd-^)PDW;NAtv{t+|u*cP&>dHVL|ZgVuPTdr}>kt7S<liBeq
ztEM*EtpH4m`5OW{PaW1T<MHcu7@ZcrT(u2Q5DdPTJd3X9E9bEY6vJYr6;)Y_5gQz?
zkdT)SE=D78B5pA<r=$ep>UPd>lDJpk8DQ?dMGN$anDradg=OWYW?vULX;u}1{rCOL
zgK!yJQ=523eC5JGf_3?*3DWgTeR)E!fP0Fv%kDEkLJXw6z`IoGH&+WhcOK2FA_-)c
z(_qbQ`yBp{gRT!C{catF3-xg+*a-}b;-QbTpZ@+qJ=3$}MQuz;2NnmWGLt9(_N!59
zl&DLkuE4tl<uG(b2xG>OG9Ub3iLTiMRTz^S9~v&=?lLVic*@%ploTRdq-z9T@bL!+
z1YPYu)W(d%tu?@OFv-Sya2G@hODvdM`^Tk_lK1P`Rh60hk0&g;IU&>v94c*ceNs?i
zp;X(L2*b`$xa0L#3-QVXhboNjegZRdBT}$sW|XGvc`bz~F2eT^VGuh}`l#P#AN9{e
z&mlr!zgtvbj@0lDOHa-FU$JD-@!Q1VT{NPhG_Vs84nw+8keu)UZ4Wwz_HHgU0<haR
z=*g&|t$P49!_>wV)+fmI3{#gIs1hD+k?;!(bA(c*A@-B{2{VkY)<UVnC-%(^0Dk23
z7?)BBIyw?+440A_p>JrmLzW0O>#2$&*S4tYiQ0iGS*(G3!F$r*Yk9N{-3~RDwS<gO
z74F^+P)D<yGMiG0DX}>+W&mq>9V>#}i1w}x_?8QDR3B+zW?#Uq9CucB;#!uQdFW$n
z-y0Vf#*X6Kt2+@N(5sDgzcJ9LHz!-%fIlkj)%D7UF{;++;6atPR$IIuUPa<U6;*QL
zaHv69d1>P;H{)oZQSC5MK;kweP*7Bf%xnuT$7sXTXM|Wldvhs^8XH`_M#5(8`pGPI
zPw@eLbY86Y*UF_4jEr%B<xM0g@rcgXS?$;574bbfn(PZnpbh!4p|#&qacbl-y4(Tm
z8R6~34k&@Vy2&9}Zh11nGB6>OG}eQ#;5=$tNKsT-%mL8~PoJ1u_zVo9PBla3)hxxl
zZlDksWyi*LKn_LPm|MqyxeO-G*Avt9<4-8=n`<NLTispVJWQiJoy_Xt?8i=3Y5`sm
zv}wJdEZT(}`L=Uo6!$E|C)ol*@&L5}see}@gQ9|kh`I`zFwTyR^V<(g>Sap+{OF)%
z7XziCA;QR-R1e<zu8%N6nmQJRD<TX|8;?Uy`uuEc-bBDKEgFWd_A#Sj&Z_~d&F1OV
zOEj%8;^Ee86^>6HgR>GS2wpoq2zR@c3wVB?FuWC*8Y8FsbTq}+f6|Vp!Q}#R@Ud;u
zUY7@`EFCKR;$wct*_Ow72mq&aPe3}4T5*SIeyGL60SXHN3%m7L6SLcaM|ZJ-)5gi3
ztj^wZkMmPRrpM@#JIe8rq}geaD98;)I1oHL;eG39Cs0Z43O8K?vuw{>$rD^Wlro<c
z3jysc=mg1jiaMY!40^Riw-9~EKu`)8$2lu(12BUYcG>O<f*V+H>laH-V=0!4=<W8z
z<#Qq_w61+rU{V`#7kFiHMTsA^lV)T6!cNwwGp1Z0nXZl`!tBGja8+E@18!MW+5T+-
zZs*5_aTQf}mzH|>!g=r2fKgU&!ftKcM|h8qRx!~falzZh4HxC^z;Fn!i5qo-Ah;d%
z?Cj0f)aLJp7pPyqP>BfqUnv{rZEUw7-UND|+mF3Nc1QJ$i_GdG(n?0bieRmG#hnH?
zM5j$1{gar;c^!-T2I&3Gl<TjXQeM9~gZ9S3^B`ZcS)(EbX+f`Y<mg-&^Q*bSp(h7>
z*Mx)$yD|c)Si$N1GBniP&z0GZV+XUz(fFCx1?n<XTjO?)WnHBN6S-eEG7|7U-Q;3U
zJY+0LobY+aJqnj|7RTwzAK#s^yPP01EC@cw;Yg#g-iLev!#no74fZz4Tv(u<k5{D{
zi1&;wOnAuV+^4rM<t@_Qcvmn!R?k(wz8AJ99eS<hvU<^~LOA8=DeOJ{&VKKfF!_D7
zrmx<jVS(M6Bx?#q+p>6dT<V6qenL(Y$9&m|K+sM4$(fxn3%S~XmQ3y5qJ0Wd^Soj`
zx&ig_^OWW5E;pNl9z0URy+35v#z_PZXa&8utF=9r%WElGl^~t@T-=j%;o1VSdW~+G
z!2T=uVNHuC;#K3xx^pkq1VV><(CwDjo0@M7*zIh2MnTNCMsLCN>weB|=#W)NRBn3f
zgbkTITy!ypxcPK)t);w>$?<GC;`O2fyP&`zxUhkkgVBfYsS^d~S$0cMJsyEvn(68J
zse5Ql;<Y?JYVIigGkVXRl0dHq;%1u3cm=Sm)-+mLSVugaPsHD4yER9**~^{;4-Y?L
zCT3&!uzcG?qE4=t*{hWsGl}d9eBWOMlu?4jA9Sl?X_MbG1KE?oi2I=P1x-y2+qyWI
zDdQP@YYRW<ZVEj+OhEK!GB})-dPEbSN)$g7m9!ynfIm(7T6F+5uY=x&TUOt)MtFTF
zog`+0q;4zT)aSvJ8qH|nxf{Zc?k-ZR83l+T(V;P}qukS@t@)s~wpOG+02Av)+2A~5
z$nbXL?o<~;78n-OtZivD*1o??;(1`@HUe7ZIKRa<pn}>vBQI(CYwX$3SQV{rNduON
zu!Hf`MmQ+-L(lISZ&I_zJZ|y`rU{X9?KezL8j(p5YzBf2{0ca_Vbw=Q#{(tPO+VaI
zrQCrXNPM=s#+u#8g)!^!Ht)-|dfbai^R+f=T~q45oLWO1&9>OrNOof=Pl#Q!Q25K<
zFno8P#a?0xvrA#sRrKW;)lfe!wmwpG3NU}S9HK{p#$WNd1thoy7AUPxw;49pe5~h`
z0Yw#1V{C@{`~naM9iM=H(;3ZbiP()7%YDZaXlKjnk;h3e1zBp~S{@HB^A0I;*5yFE
z<l+1_hMbM&8nAVFT&$HEfKglQRDMc(p|Q25$RZQTP;7L0Z>ti}6ys}GZ=0LnsZgsN
zP9Nm}t8<oA!uHMJb)JS1hgZ7-cKbpN%9?c7<4vv#YU6(d^!6vrV%^lnaOz^gpnUuG
z?O{tt8dQxsk}%>u8RtBprK$rqja+}KmCKr-Ef`w`PoSm;F%R4(3mGb=MByd$)Z%G{
zm~H!flQ**d8?e*<J->}7ol^OR&#(?#`^{)jwYB_+2d1jnW)n0l7`VT@*!s8>H84@^
z%%taLGgFrMB&!Op$JO;Zy4=!nu=yfaQ#o2%GrW|81U;O&w(qj;9K!ag?|E5LZv7fG
zuExpp^bAtDMki3_I`x2{k%*!?o2?VQsH-lIqs5A30V{^YrUyJ26(6<IS@-<P-1dcz
zFej!_!FgX|z3F%|XY#n(RZ|ytP^2_58l6fwUJ%}yMiwA_lahO_EpjM@bEE3Ijq~ZZ
zd?wa1UjfULZW0=`C(KfDXjuiL?0hr+@n|`SqQ#i3lt9Bg<VE@?08|Y^i!pQwM&lhC
z5ZV7n*EdF2+H6~Q(n-g*Z6_Vu>e#lej@7YkyJOq7ZQFM8?LOz;`}R3weEGG<*rT4*
zuBU2Yt~F;JlMi$M8p}&aX0t+!&3%uqHb4VCA1Ak2%rTt&b~aJo`w>AkmG{^;l8}|x
z@`NsdSq^@?k4{d$YgtWXQosbC9zUi^UlA;7F}6b2K*Ys?oL~f62X1YI$s@`36Y@4H
zw|)@x6tz8&=nXasn>`)N`J~AujqJ@)>j^ArX8{blyN->UN3{u3Y2WqtZec>R25T1+
zEy^lS2AFe27wY-tuYJ6>UpPU(%W&5~N^OwcsBV9=T@GripUytu4RjHFrGG4NMQ1c3
zTq`ek*>5&^=4QUah>qaP0IHnS^1UFX1e^cn@{qc{cM+MOV>yuRuiJLJA^)Sz5PMJu
zH?dMZXihJ$7`n1AW<&&gsmtr`sNZ6;JzRSr2?j4cJTzU<B?{PS#)qGrt0pUP2e(Xp
zkO6HkB;YnNCzl?IhQmAj$6oBtE9-WOcQwvN=2?)8oSIU|yJ%>Y>pCWz%M1S6B&f~X
zE=^tiq8(fwAtgk>ZwA*K1`{&K)Ns}J?NV~nGk&cHTxmA!0B>FVhKl5<9(!x`)Wirs
z&W7B;#J1!nmy5;S$hSJ2rD*Li-9h!T&CHPVH08Y6gC3Hh`?c^pD@Dabg2qCufYyZ_
zdYan)fM;}eD;C6OPw`!luC@d-&D1vZmn}tPLrVd%9{jRbQ(Wrr*KJolRE}?tIrfXd
zDuW_zkKK?LzKZ_63EZY5Yh9Pt%s(sCASRc#(c!oKGO(0&_al1&>>={OIc3!h%V-EG
zYbt*k-p%m5I{zNJNJ-~c!q+M3x(7Fd)1u26j`>I@PB<cVN_36J3t<^|nup=!eRJQ!
zMDw<{x3u5IG=g$cNRSU_g9};!ZrglFI1HAoti11CnWk#0MAcOo_gly7F8nJW8hTJ!
zeO7kvcqolzlAQ<40po7}_ph$H!|pb@^?4|qwQTGfF#+?OjB;+ebkMZh=&{IajU&6>
zPwmP{pWMDsYCt*G`H>F7*|Emfb_MD+mh32DcFD{Zq$49E6lMD}P9nxC9CgchbTV+?
zkeeMImOz2(bdOAf-u5fK!`feA@!6Ut+a-V_=O|SUONu&HJ=nLy21$i>8>;1&2G=L(
z+&|$D3YE-$39&f~Bt@KPZ>sn%-_Asf108*YFu0ih*jxN5aL9rXj7k4ZupNiqaP0?M
zeG-vJpXJ-ZaaL|AWf(9&>pD2w?x_EYxuoS!grtWw*VC^}W)F&x<~Y$VPcI8Bb1cpT
zMl(iD_Y3StW~Vd8heE!#FjC#?ye(ik*ip@*H4G1_t5;9G&JGK8C<qmrEH1O!ef?_H
zh8D1aQF+nhy+ixHgIW$?7fpJQG~P9rfnUqG$be>cD?1PzXwyO~P$BKO4C|wwu9>cH
zb>2Y|T#bI8&uu4)dWEpDUc^kB^WS6}wnQlQFq^|?dfdt76NBW=PK7)qio{n>z<s`%
zBy)Rm1%~7Jo?bK|Y)EsbD^>1M*HN)^1%iA`nd~bQIn7Y7dA-&vmW-j((_)PG8<}pO
z(ULvMjxAp^Fxea!`#uu!hB3XMnv69+18FjFp=RJ@?40v8!l8||Y-_*mb!*6pK^a>}
z(s(*r0fxHWOvNN~Td?fBVQ-=GKt`>5BlR?ajcirVUiq~ibPeYyKxJN<mj+GKz{_yG
z82muT7{%L$ox`w9A3y&!&5UkPB-K^F7G719am<Fav>^w1qHTLPW}#UX06b0mBr391
z+t1lNIU?(5U-;c<2waoH6O}X~(octv(Cez}tJt+OUlR9DMr~_%_q1jeVS3N1o}vT6
z*Xui~ZN`YNqEUTHYvLQL3&BGSwO3Q)Dh>j@IJ1>au!}lO8V*<qJh6OYOs_HzIt-~1
zRc@lYMKavLcp6M<YPw~Vf)*NJtYYcG?w9Yrg_>k!WO)@O$f)YxB2wrfR5neB<8}J$
zH%kgl`!^Ir`2}oj_UK*@y_}})PF)F&?_YL<igrp?tp~f}K^$eH(JoNGp{pI|ZFh-z
zlBv)N;^6$6si9O);G7z;;|pS0b9w#R<Y-F_I?Z*#!~2ET{Y<XU7Q=!niLQ2K?8vgr
zSVwJISz<JJArn{nLuf1V^+L$J=gZo?-)a|kr|q69LgB_8ex}<IrlARrkeVMXuj?Do
z*q;2L)c*6DR0oDQMab3m4nmKR_gd7z540lMi8fBVpO2YfxO;o@<eeJ1;9qMmo8Wle
zkL*2O4tTFY1A4zz9PrslBC9CMv03-@%JBG)l0l1TP0OK|Xw<*8w`DYsIiXjydQfV=
z4VEU`kB?t%tDc%#1d0y`n_whuU3Bj(egqs9Hxew(`%6GhQ&1?P7TdbI3~Ysv;R=8R
z=vF6=ID4;!mm*l2_Ux<Z_+ch8AHM`$)xK8b2{Yk*N97~qVSMYbNFXAzh{9dmbf>#W
z;f4lPtSTXQ)M&cl*NQ?*0*0F_v%(dp0N+)gw~9!pa_Qe*lzD^hW|=Tnkj3FJLcM6Z
zRYsRu*X;WRsr2?msMS&TIg~j#cYi1@0}C_FpMj`wCp5)iYD%}C?%5NkyI^3pRw9~f
zpM%TgRA~hmok7#w{mp3B_PNaN>Nc!f%aDsW9tljinZ8LqCN^HoLSa*zpVTX<P{*06
zA|>AcOq~ibwmF{7<W_W0G^eZ%Q)w-Jo`f2-b7!ESWdKrfMuO@U_x^^LC`)*w!_H-=
z+I?z8F^UuK9kgQSX3I=i3Oq_CzXxe(Tn1@ssA)8D;ir|yh<W^hHl?d$!pXUlh`Br$
zO_LHi6@>tQDN?LF(zc9+zEuQJ6!(YV7%pox5>s7rw4L|`gBRU}5ohH#NOS-yI#K#j
zk@0@=<w0PPAeqiDQR`03bE)y8Jr_)NFP4ku4@R~rd*b_!N1qnakpI;JD9+Md=;N=l
z-y}&%!esP7rp@pq63G`KBDRCaZM)Q_HQfz4>#e(Ef70*jVr#N9P^xZAmdfyuPRto)
z$L{pHF$tbq%<*|=`FViN^?pZGcao6jXRtP<7(l76h7^a$c0pt}pH84!=f+9;BRP2I
zL{o^gpCDAy-NZP4Yv2{HJ6#P)m&tjiJoJ2vi3pz%A+LGu1;&B2NB=B)CLONvh~a5I
zPNt`rIVui3BiZhKyy@j++oNJOSDJnVya)Cr6Bp3&AB2M?-I#>O{r1J{V{f<V@S^qm
zV@c=R@nh3|Ar`&<lP%Y467P;EuN%A!&ow$}I9^biO^bG|<=b&oXMbE2Z=hXWJlFl)
zZk<2d_JW4>4$l{e!>R_#)5}mFa8NfMqs=zsFJ7FSpySXYpZ%fO;jUJ6h}vr*b~b1I
zHchXtXp7aJKPSGlIU0pN_>u5pli|HZ@L{h%5dz2+Xqb_CYOn0QPYj^Rs!uacy03vw
zOP*!1q;_EEL1e#Lb1L886fD=1eAhJ=?VYFw2<YRqQ)k_q#^U=8XFyIn?1g;twz~0*
zq4LIh4fJE0O<)eZ2h<vY1Q|Fez|-uhlch<vueJ%HvH0Rz*2MINON8@J<+M`6n5h5=
zazGEvXqBw){0JMa^rg_K_Qb(?^5@5E@Scd<j)ZRE)D9BrIEEyamRAqG;S_y53SR$f
zdO58DSGe}mw37dZw(8Z3K>ogpotiGwPs`=spbwnQ&t03%-=I)w4$6!<b9+3Qw|gve
zucQd5h-2Lg{FAHByR6GoVjMq*R@<TRsMUYzUU2S^-xpnW4C^m|u!--9Jyw5R<>~F&
zKSdb8dy=Ah%t=y)QoVnJJ&m4Jdp|)XQkQf#U@*b5ILagiZcXueQ--vjakj3bM_<8W
zc+4vp;#Q9eHE@>`19VnRr_?}KH`$!6b%fP^)Cmo%cW`VBb`$pW2X7BAwKStQO3U<p
z^olZsGxd@}=5QoxxjDkY&N_+CjBL;wtGWFk#>Q2)KwD=sZweuK-CPS?Z*8#05LG!+
zC9~^iYx3h<RuCUruFq(ly5#qCC~5!?3@I~gzWnfWeY7nb(lg*4DWJ)^+*#WH1>xu%
zTjRdeNE(Uf&<J5S{1riZv<EG%Rt?OWZMfc#h|sU<=P>ZZOrc$63LB&}Hq_YU$XNac
z>O(|06Vq)q-UK#Ykm{ip=TWQ(T8i8*S$2T6oEbaw%1tke#4RI_RNE#QG33CBW!u9+
z7&+x3Th7j^AX+rtLZJe_zRq;QsMlR|Ot@g_T#8ZXtJDu!^anb%P~osed=3K89O?U~
zCqLnI{>cEe+lTzAQs-M?a}TcYa~emr(s|UF=!pckU|logEU|Y`-Uv9c&3Y`7`t_?r
z-0v9?Buj%X;Lg%q>_?jL!nn8E$zjY`wOcp{P>+1-W8A>eh)5yU6Y!#u$&t@c8-qit
z-jPAHXYcyVXv8rcBr*%1FL*qmJusK@wrBo$C?J<zrhOuyHJ|7*1L!H89gY69LY`?M
zCr2xBTpr}Wo)`+~2qEWnEuK%qZY=>M4uZ3=mg=>rqKp`1gtJ9lZtgIS&Tk-iHaV|w
z<&W+^XE2BC4EEVl8BD&HJ6JI2%^=g17un#iXjoir3})rF^kU^WPR3pvFk-ywFC2NL
zt4#T0%!=qAhjJ(LSk)DRj6TgEBU3(hXO62rP3s#f&k?Y21Y0L~8VEw#ZlRmz)*GX(
zdkVQ;oZ2vX029>@14rpzSRmv6=9V**&kh1^iwgtmF+@LZZ(fup))$Egkd$J%359WN
zr0i~s;uV0>O6mEkX?nco<LQWYR-jQEAyKND@=o65ee>|TG;pZSl|@FH2U}bW0bYng
z+C3Xwo_>xdPb)o<UdQGGWAWh7NsP2kAJiRo_Gm0ue294zN$Z6^bXeP%hJrVy^Q)Y_
zKt0-@3$7=Zu<ALiU$xDS6`F-|IedMo${1#*_~?WUf&?oj_4&<d%Q*ojmz&cleHB{^
z0r$)=O4}0yt3kX>bTd@kjZ#k(?o!a0KY`_#xYQ{1=`Cb0`Kpe^;f63V6Wt4w49DA>
z%L7>N%(|x)&pYtOm{kbWg~;U9*n@m>=SkNJkJ#>Uruiv%aU3f|&RTW+a04bOYVd__
z!Je*&4c!ephK)V}>8rKs?XkxhsVLY(cVxtARhY6i#1lP>qQPw<^*F~V5_Slty7s2V
z6#{g7PO61|l*i}qa?Y#gyPd&NGn9Jsia4SC$V&{Mdbpv-GC?JW30U7-z~_+qglx`b
zEW*YPEUcr>47pWbk(aw~4uahXj<3zsAr*9MF0#h*&#w+6g2rJdrBu_WCo;E<oc2tx
z1eGl~NQKgV3ZkG7MhCq~*J2dnU$-QA*R?OQJ#{awRnt9VkWZvGp9COjw}V5$vl(~7
zWo<+i9e#2p(&DnTaG@v+g@#242#d25?ikp9x1uvS_Z?Os>zQ7YZcLZ%du`2$y{3ks
zkSil^j|A|R7k2%RWOee-mm_}0A?50`3r@m7(MW$>36ZHEIpCokN4<G!Tpj#sZ+dxw
zKxZ)~)T$<*a}6Zyn?y-*OrtQ-5V310Z*2w+AVGvfi~ygo#==?KI@Is2(0WCPXN0!v
z;I4CwC!i&O<uH+%G6Um$%qPmUW}HT5G(T2H;eUjZgKojV*Iro5fvELe+ne$QzK?l#
zmlfW8FP<%$=`TaXH6*+MYro(J&r`|{s684C4Mm488GI7oJ$Co<w_JfoL<rz7-WJ<z
zj<RO-%VwRGX{`#RrTogCQkLhl+u!byijjw~j20!UUDmG)GA+s#!+Rro6b6GPLkxox
znK?ih&zi!XcjTA%91tD?(Y@8(Tc-*u*$5O+7f90g5-ZM)xU(xeqMAx>ZJFF|!^<UL
zP=~)6d}OPbNdJwT6c-n{t5o9nn?o5$2l9eQ6s~{nk8dDM#$?I0lh@b=L9o~YBSRsk
z1*bB4Naea^MQKTRT=%5kA{5Bs;1)nosYdBa&2m=NK_tQ?;SrL29#Z8gdB#?GkwZcV
z(o>3JG0(<d=4U=z5fbLL&{@rZTUJYs4JK4s+Cf`?>$1Q28>|H*E5<L_`VE*WCNRK3
zlAgn$TtS9jxEpzTHkn2C`JWZkoNXh8FD=O;qsby1-w#&t%R$c+wu~~IU?Bnt3bLnr
z3d+Sf`zN+|vdNnE*G}>RNk7SxONtt4q4f~uu|r{(kKZNPvT#w7V$U1RuT?Spa{4Xu
zoppR{t=X@F0ymk_6Pw0YX(=lc3hw0<4xJ^QR<PNzh;mjlkc+Kj#c2Z_9>t$@Kpdmk
zKLvvt7ZYimD-1y>Kww9o5;Ai8Y8v?ITi@)y^_QCByN2l!l!SOlnL+FPBAkJj`BS^c
z%a715>zjPZ?JpCC?(8W?FR1+8N?-<}oRntew_67(YR#bG{E))mle8N--1#@vd|A@K
zsKZmp&CUC(V)BkuCYxXeq;NRY!?&@6tR1IgkHMZ{UjwJ_7eU2VrO?>Juo8T$!>h@K
zM1HdWLPv^WB!}<{2TFddG?_k&@sH{KX7G08OFpvIWOicshEh(wKJVgr&GPm<*?Eyw
zT|q%3q^=C~Ej;5Fy}BXh@eaAQSUruFV%71?+(63Xzz^yb!N!Tt)~rcXN>mTj1Y{-r
z*iAJ_4Va(jJpRFZ1Z#b>LFdebnUnag7spNF6V+p$^fOkIgBe%_65b5P<is4Ac3@`M
zCgbQF#$5X3@vN-#GfE0kOI3XFMu+%ui4)0lzM9Cnh*};8Kav@7$B})QeuIane@6j2
z^ILa^>mH>GHekrKb5MMHN<Tfn=-bF}-$G3YN7f;$;7jrKX(Db?h5lV(F`tRU?^qlE
zROZ>rj?uk@@*K&!fRa<uEt1p<;dpHLT!8Xs9VMJ^Lx)B<<R8R4h{y_mUw~CHZhlbf
zLf7IX9bDAk(=Qv*8x98v8KwK~$4sSbX-Ga2HhL^SV1m0U93?VmU#lULZ0f}3e}Brd
z^H7x8dnAOHYz86D=>qSCaBW+7Lzv!|z{$drY8~k=yKbF_VuYQG-dMMVHz~;aCZfqc
zhqQXclEh`ssQ><?5uY-8=?PZ89Jh5zGAWY%8$YQNsUWz(NmT!P?C>%9^D|iMEDZBk
zemMwqb~aeBV$0Juza!K$zq%g-H1c*hT~c-$l(%xrg$AEZJz<-tktJM)drEq9h94pr
zr|fE|;O)g3SeG2x>DOXko7Kw644^*OZ8j*p!RLp)fN3?sw*=O~gedUe*Yh{Xfprj{
zXp!OFj~S0$Ja(qNB-|60d7fWog38eI#(NlP$hzysZ`lw=buK#8RHwE2@bz@~C6#Go
z1W6b2HP1N`u%D}AQmn42w?RsO+5$_qs%Sr_74A=tzQ1}7QMb&+#Ko`Jx7o*)SrUfE
z{74Qn3Cc%nM554PSu?OdZ;4-3jSu>z4n><HfTwpr_c#I<H79tHP&ho`Zl7tdm_<Kk
z@!%ok8|P5~EA2sNHKTeZTp`|UANdXcJRvS!T|2gN%u#~fe(98AjTnaP`i0V~jn-az
zWyG?sLDHQ>Mw}Gsp+}J_?Jz&{!D|+ssoWS|U%go-dDOE<70}etmc(LyZ<rDbrc>9p
zuk<pofre>u9JcF8<ka}7D#@;{!@$cfdP&mabDuC6B2xaDFBXv!@683LMyN0WMQ7H!
z;IZMnc9g@cd1~SQ;W5vWkE{$33F^#0jEI%yV;Jq|*mZZ$d`O7yqZ!(OEzUb*CJHa$
zJ$tLa?s1rjk`G*ndK){d>p&vC;crbc!)DWH`+1DGNB?nLgJsh*C8WNWA2B$XT{mp4
zSzSA;Dz4?<YllmnYcrXOG?Mr-^7unlVl%W~Vjz2s=>EKN^t#PIEu*uYE}(NxxP-<C
zo3jnAbm3Cuu8X1R0TR++OlM}~Tjq?m9;vI?MvsI`uo7S;aq>NigZ4;1>BV(;ui78h
z4bt-4W3^#9O@?PDgiZ`EMU9Ii6rMjWH*28yzlsns_b7z{#T|z3ZA#`m=Q;E8^<9lJ
zcxu)iY#2>yoX~m_8;Es$KA42eO^ZCHHQRXln>mt6MnKJ-P&_U_OTNE$jv6}eFo{Vz
zr>D)m_c72uO1AH{QX8$l3am`BnOZ@woB~=gH?`b(%{RR>YoqAgbtFD|Gy%<SGAx8B
z7d@h|9q%{G-Hs0oR&+Y8w60fP!#xxEo!HuO9r3>Sj}q-?1|uI@2`^BB#c_9O;h@EF
z^a<`auy}nX%$x|4`^FPz!z-#)heh18d)kS4*G9PS=`P;R?noQ%CIqyd_qh23Pp118
zPJ7Jaj|mUe*7njb%4|afPH_(Mk?(4xn{b?9W?`P`E1j$lhm{MkKa=Yeo<^6d;AG9R
z?y}}IUes_lBLPh94jfjeB*Lo<geO{*=*zNcG`{Xs{cb$sR$Uh$MRBEVLQ-9{GU05*
z?Q;R1@BGQYNki(n;F@AV<k-+@)BcBXrzGJ8GlZI%!+I(+!ahoapS8{?S4zs4pRKSg
z-~1SKt0FF7&CFzvMfd>BCOy_AZzLgM5IJZ*3JCt=i)h+oY#1WZ@cYl_OxJD3i&!h_
z%%I7p#}2!VTdzs1$w#hxU2v|cdZJLC1jP-!HW|elRA7u0pCO~7f2q9j4e<Fe>8D>S
z$$`j!^&Jy3G;7XrHxfaH$O~4H5495^QoGL7B~`uVTa1hrg@BQh1DU}AQeRa3FB0+}
zO05_g$Q*DiE&VC~e|{;C1vC(LMb`>pV8H0$5snyr_Ot)w`EUApv@m>d{FnT#FnNZ|
zn5$*qVDM|*7H7QTZiC5i<P^ntAHx~nS!=4l2<y=q2)f@YBCr~|)MmP>jny0I1A>=#
z8|G*@oOX!K=ZiDV;u^?-h9{gWGF$&9I;Wuf0+?jkT44jR0vxaY%t4ZR!_T7&hC6a$
zQrGFlGJKr>$(WDkMyQJW5`7h4XfT=|Ej;vm)5cW$8<dzSD)T|>KX|nGvxK($C(L6h
zo#=~|n|yI1_jcNpU3eTG$hf%dQ#t3m@=FWsvzZzKIFmdNCH}HvyH10VSzR6PV0OC)
z<%;YxAAUi0PeTl|3lV9oc6ToD(wQ872K;2Ua=_JyNH4GzTAkBR@T3jp6@Sz7@oR~y
zjJtnOYjx7~W(*Y1np8WzTE~+sgJ`pJ$Ba^uDVMMNsMVt36LO`X@Gz|{_iqx^`}9Rg
z<yu67X7nvP9B$zoiRI-Pj0r0gI)-q*3X6#B9NNY0^{j-fHMdPQ>3MQ3v|VT1zadf>
z>Ea*_>%)_*vb{A*#m7@>wdx_Qt!v6@(k>8*Mj7nHV>IFS5fv4Ir4BKy)O(#}8mM|%
zu6IBUl9(2&?TRv39bi{g(oRgIY=oPz^S&$oMGo&m1?C5P+;aQiG7V)6hHj5^^?na&
zH?hf+Z2ZYq@65Dd%^fVSbed-;jQ7UvO|q5#HQ?3_|7c7t9i}4^6{jUv<Qh1*#TCMP
zA~V7vNkH%N0I0R9)b^StK-&sa_qq&Y$#DxC*J2d`=%TU8WnP5MeP1x529&}1!~6-A
zE^4AYt!H_=;#DymBms?9@ymD`3&?$YO6_5u-aCg#XV}D;>rX3~U-OJ4_{zG(kEa`+
zVDu+JI@^0Y;Z;?(bzW`FawjzBAQ@Z{QW_1r<tkaC9UiU`xiW}V9`ou1#LcpGVqXqu
zRiU%ET(2A_E0SAHDN>=>@o&rJi@6#u-_Q;xbl_thaOrqAb!Z3h$C9i}3oVw*V%UE?
z+Z$b7tXA%<M|JR3lC|OowPJN(f3y<_Hv8W_p$x&fySt~uLqU|&*TpwMxaNev*ri{m
zdAt_@T}@<+QqO+=eGZ4r!P(e2x>L+*g<#LL{amr&viV>+sn@E$MkP}E0B?v^>p#22
z)2TvfIO7U6W@gxVoMNsy-iqoi>XxZ>j>1PDRbeeP9I|1uxPo}Uw?k%VF!!VzaaJ*P
zA|+mO_`ya{>c5P2PIoAazqxavueU1p%@unSG6um#mTUe}XOz>VRw=_Z?oKB5x5LRB
z2=^;8fxSDP9|zzEmJ=npw5o9QHFd;u<+tFugn50K2*J2oE1R?GVna?(2e<qcn(PdB
zX>!R=gIP`R9tbKGn*DkA7TcH2VN(SLSWP3K4KB}=AfOqDgh+u%0-8<Z9q&Q1IxCaA
z=Mh!{ny55dzPMVP*&_8L!SJV${rp3q5TN3LrD0GN+E5Jn0iIB>9sZIt6aAwq{&kw7
z!-)NI#R6(`Wy<}AC8v~wurM&7<KC2i6ZZ|3K=gBo?2hf}r9&qi_D`gCo8{X)gcT|F
zD1`KBX#@RxrGxqER0%vhpmMbDOKk0GL<u?t$dl>F^m~Z(b7HL0wYX*bDt(T0tjTwD
z&?kaLntllNPJ?E$uj$cosOQ6n3XqIuy9Ja4C#sW7T;Tp*&E~OhUE_U}(TXI_&;3Ek
zJKGvGeLNtQ9;%VkSbJj22Ql;&n#Mtz_ui09kkQTQ)PK#c3>>uo#rF8d=&jZai*iWV
z>3IajK>Kz$P1NH(R6;j~Xd;u_h8mMr09RGW<kDBLW?jjECdNVza(s_p`Whl1Bhp&6
zPFE9AKo9cbZSfJ^?h`Tz%de2L#c}_7!jI7RqdGAPsi+GbnN05^6LHG%dWPh=)$(C1
zYjd$lSFQ2>3xJ^dAB!di@)m|RxUcX^f3PTSdqo@gf&vWhc*12p$d;qZ&%4APYT*vy
zy?1pdz^BLS0L$|XMG%z)W{*Y=;_a<dJ2o+_<X9$P>o6IDC@h@hFzQPv43+0tpZfdk
zGF;q2Ip=tH+&>xFM@QQ&i7opbX&k{}7jr_VCG@CuL&e#agUt8v6%Lo*7r0~T{!-g+
zeba~<pO{2V>7qza?f%E?QQI37T2FM;W<Zoo=4<2@m9+}pM`c!%6itw<22xfx8;m*?
zId`jb-{vhU2Jmc2!4n7v`}S5&9=TQx+s*5N;5_BN6so^GNvN-3p{ec{R9ZFISQJF`
zFg6a3Z0Yh70WNzi3$f>#g0Yg+`BL5Mngr61e4!A+E4sZPZuiICF5lkYO0}uch@y(9
zDs)brHTcC@|Eyj#3c~M4Ro1y$$e3~(R9MO<(hRHe)W`IjS>chiO&GjJmxf@H-A)>8
zhXc{9%=Pg~-}kVT$<KSZ?^%FKuTc0~?Q12mdyIrJ1LcAhrMJ}H;QLvg&yhQ5fD5&H
z&CZ0mG_*>&B9L{3+9IfAnUq?xc7J5+M<qBhmM;_<9cEHP{K2xZ+XWOWVbxV1&0Xbo
z(9|b&<M2iB#b4^eEC{+{gF6AESE_k#wsua{%~HymE((m<v|6<Y;~Ff|Rfrtz?Wovo
z&~W}a9p2LZq9o(FJIC8G2UW#T%Bg~7VE?HQBL)S+79|}Oc}5&*-;uKJ72PgE&8d~)
z_Me-|4n(v6rrPT8;>-XDfoOn69DncPBC}Xj8A7ZOm*>3gZ|}d11T@_0XU=TCHCH?f
z9@i9NNwBkXewG5IG>Y2e4I#(0Z$<?)9N$}*@wZqAzB^GDxp5*PxfTZtIu~aS|4l+l
zUQ>dPVNt2l<X<vL$iCG6UlOjyEFp#j1Y^n+rt$9#y9==X4qN^$`QeA34bU){k~Gw*
zxp}HVUhDA0LZj0TZ47{tk0%u<F|Cnz^|0z&PgxZiNcpyP357;W+#Jzlbz<{hqQdyh
z2(rMjsip&FrvPQNWEM~3^nz+$Ko7LfCfSdM=13^fh!2D!>8x%U-l+ZvEY{ui3AcX#
z5Cz-4A-PlGLd4hLOOZj;m1dKOkN;eGS&$h#pa>vw!Li=nUb~02WlmAm4t*3B%cbCz
zdW#-18*R(g1{}eTH_ZA1V3YP1r>n=aW;SY&T#Ka0>O^RUoOmjYZ*_^(S`FBPkzhH|
zI{bAOtC+-C1Ox=+3WeX*zuTCDi-hd-N6S!}Uo164F&U4=?sDm`@Yvsx2YNDN3x}aH
ztbQ;!eZ$5+o8BF^w*C3DW0v>*zh#N@nSmwvX5jUdr+?boHaUP)re~6Cw~-&vsfN>L
zaN?_6Xu6aMGrciBu&%YaLQ$!d1Bv&=Q_OA=_Tfv&V*~a*^oHw=?!?-&%W=PNGKbYS
zE-r4ngE6)5=5t?=$fSgCjisQ0)SBHQhW)s;h6WY5v-<jmy}QQeVQ}TAYXYzjA0gu4
zu{{tz-)a%Qe!B~5Z^!JqfR@JeK!D@IK<f&zO8cDI!e7b%dkeC_XP<2A+60*nCnrn`
zL5N{Si8(6Pg>*LIpa!aWkBcBw3kXS8n14Kn*5}F>gxWegFxg?!($fBx&Ec|#luO|u
zT5uZG{=Sr)Y>G^&2%ccHsw5yUP@&})C8HfrEvGpY@S5=i3#hRB58I{h*GeQvsJ3_n
z*maL2P@z#uv~DyzV1<QbW#J7(WPSPU36QJO_5Ch_|H^W?edbfzNJy`D$SyPhjs%GF
z{|O%OGvIA&!n%i}>A7%^d`%}pjq&AxV*~xR3Fq0J&z1rfH?jQ*eSL!wXY4M{*!PBa
zVWO~c+3cTwmtlqP^oM2_sFMFY-|%C$Sj^#nZyBO7+|H6pGg{@sXSjZtuyQ*gH4L~=
z-}TN4j>+@tyFXpLRAoHVH4&4FA@p_GuWyXzOiHqQRBtk6*ucHR<qBAG3B5P;Q{{U)
zJ}U!UqQd<4gD<*w8JBw$&@e<mFn}DZ3^x1oZ&j+`FduTF`rAYQ${-{{CiJh>anO<&
z;Yj_$bhjU*bz?tJP*JV*Y0}fNrTExsupP(s6H8B7t?{E88`;GN!ZlkQm!n}sMMbYz
z%P}7wVD%Y7fRh+XQC2mp?dwC(asu`el61PBZ_((VGT^QzIBg(uC@4`AbOrTYemJC`
zvPRERku`CzSBZ(3y2@2YbOnT=ZBzC|Sv$fJ>OXsIvEc4KwN(DIMGSR7e(#HtDa=3K
zU5Pn##rx#5_Z548X;CZJYQ;qm5HY2(b9PSBeRN=Rxr(liR2|I7|Cp{74J#Jrs{vDY
zOZ>}cD8U2FgOZ5+B+)EEHVE-7Ov+Eye{vRiAm2ND{M2t0DBmQ~{`>qR`{w@{V#ao?
z{Lcgb$J9qdf|CP@gTmKC{Fglfq=1-!6B!#AXI=1Lx?|+P#Xdd@e?}?HfGFGzh)El}
zgRNpmD@~<%v686is_jJ|8{mBpu#}jPd~8MoVw!lQozVgO>*x`1dbNgwI2j|^`ad+(
zAx7{j5J3?n5_~f@!m{IPn@_JeOx6oMSZuan0CQ`nSX*X5%gF2VX_n4(wU8M==n9?4
z;E?FQ#b#-&t+yRbv;vRAB)mVJZ>~)Zy*i#X$<J-s%rFY^Yz8PU&rwweB_*lor=7g8
zfk(fsCpr!T3P<vt{R9*0@5O+hR2<@i7(FMXuqqEwQZMuWa^I;T&l_1k&KL_=k=|)=
z2nYsZzl2eWAUM2#@l%@Rh4a#_H)>FLcwA+x1wfykQTjI8UlAO4KrngK@%Z`4;0cI(
zHan}33Bp-~!2nvzfxypD*&fd|OD#^E6`Jklp|Ib+J*7#k=HxV1yWOi}b2)<d=c8{I
zD->0G4ob&UD6C+AhS3G;U2U=EljlM;*lhaN)U*kO>=ZI`Kw&Rbs-U(0JZE7Re)8<;
z8?K6zaFgMkM{50C_xn9WK9}G?Kp`iN4WwR;t%<Wm2ukeqlf|yh5$d1!Dy9V@##a%>
z6Z<b)6&c*#-mb{l33Yv)ji1i$pe#0w>UcIKO&JTeyViWGzpomJ%iR*v!wU+qGrLRG
zyM;~oC`-)pw)8q39_SorSl?|{074pcJ#p);Ta6HLavhc!W9j#-A`#fI^1tu~-iQ+>
z&c_+Vs7<fgyg#@SD>Va*Yc5l2O^nvr6%sN=7t6GQ1~nSG{KXZf>Ltl6*Xl543n%@H
zR5heC0Lk*8Uw*0^Ewx4mA`}`8|Lf240z`Lz_~`Kd+*w<2g#xo!I1>C~-~yJv{KzmL
ze%=WlM?yw8|BvCqNLc4}n_trD-%&x;DSP@`<nlzE?p$9enp&Fm01i(;IX&FTPZ=&2
z?!x`G(<%&whVnw48<5>)D{}FMi=Hm_`k1_S&(0!|PVk{$G}j(p-$+j8Wxxj<9g!O_
zha~RDARv%yw0tj7D%Ynh!sB*Bj<hcWWRUUHYQRP#i36G3?h&8^YKciyD$<E)qV?uV
zQ%%!(rZY_lz+Zu@91c4RHz~ULAOAMZe-z+?0ekX$8!Wo+1Y6XlXjJze#cY*cFYiDG
zprbnyHgf4{k~BQl>0#$UG0#nD$|%h4E9lK3Yv{*AMr*hQ$}WT$Bl)%f=}5@F!F_7|
zlm3xP6JY<Ta_fJc@bomJc*d;#NF_=PB|lqgRu~e^U!GL+_%sDd;Wp_wYlUsJ-eB}>
zCLN?E+}r8u7QA|x2uN$RCLkolc5Q3lzBr&=on*4sERaa(@(M9&cDqkCllfHm%eDNO
zzx*$s4Dtqyd5*Z)+hN&PA#k;&c6T`IwYCs0*S77G6GB=!G~$h3I3CyAt*2`w@D3LQ
zl#wK)ecnb@7TE2kKCft9zFbP1P1vtAh#NJRN+FL|RH{==D$_LPX??b5tImA-!oRMD
z2k}yGVcR{iG)TT=@bCHx1KXajFe6F!yaN+U`=5)7l?4H?3+Z$=qaA5`*L~#Nn$c+7
zfzY#6?=NIBnXx<xXpdBM1bD5{I>F-%?2(mGQL3UN#^m9%B}{0Bn&?#tGe7j#q6{aP
zf`Y<x27L18+00u1F`e+dz>2qYVX!H^>)am^J7w2*SHZQ!_uiC1;+{Wd+xnwV7a8Ps
zgsmZ~kEe2Ijxq~kTmBeXEv|azzYe1}2*4fi9=HLB{r!KwW^ioD`^$5{gD_G8O`}Ic
z+xwcbBKy|y;TO;vnoXlwDg0ygk%0(pSw+is{zzQH!2pSh7P*&fFgYxL1v3%HhO`)3
z5;r%N0(qMXRE8`2UmH%00Yr+b|4rc;zB*1wFxuHQd>VqS_y`}0FfTW5O<QDL7D7w}
z2jg6&fxk-*66M?ZW?+g#($)zL=UmAq^f;z+pl3Lj?jAuO7=pz}^5rkcS*F3E7IN1w
z%kPbE6$OF~Nni=>mb>scq0X7^nAR{aJMTPEDdz@wn$%)_V`iULR91c9axnuJ)T1n0
zry>7`9Aba~CCWD8B7BuWBO4CH3?X;@)M>&EZPUgbI^OY-6}NV{6JV17bMY0%HLPDI
z^>gG^TbD&E`*%=Y9OmN}9X0E|n4=X`MDKi$pb57T)#2%`KUQ|%<atVJ9T=GMeVmo#
znr|p#)wU|Er~BRA_GS%*jeVr|XaGh$4Lc;-Ld8z0CR3{u!&prGDB`rqo(37^gqiO-
z_xVRrr4CP!mi#5jkEg-&v_+#;Sv6cg3xdTxpl|*G8(s6!X*FX%TJtYL%q{-fC3L{B
zj2JE=jJA=gt|Q3R8_lQR3$5aI=D{X+C4|1yj&gdgZ#z6$iPB^5Gkb2WIi+_ZSW7VU
zt(tz*&Owca=3-l^FN^vjRen1l!&m2w@gsxf8;|wRtB!euTC$ICgGA5OHy*TaAL2G`
zj9VCrCr`jV&Xa@b^RNEFOS^vCz11euAU|}T;IP}na)n8FE!mI*hdVp`7IiR#yG}Cn
z@I1g>Jx6wzA`@KnuHZG%9^_ioq2?f)BAQ>%>CXxlTnaZWo^1Qb&pvLk((kN&2rBP|
z;uYIe{xMj-cY=Ix2)1WCFw|_esGQP(rH?9#mkAcRN|Agrd^g*(g`W)4+(>S`2LIr(
z|0=z6er0`HbLCX9s{C@##2lHA8a>CykjY{lQcNv&;<gk!TQmu1MO11~PBDT)r5z}c
zOsY1&$1Dc3=iE4#Z~67EUhj6Eu|Ap39v1a+sbIO;aXZ2I9=iZOH3RP_d1xaMfyTRs
za>Gx7vE_G_jP+Zy9{8bL`&a!btM1^21$hb+^llLiU&g@rmMsZ*t@1@u3uBS*n-Trp
zF6&gGniTd*J>sZJWp5--kZh+MpaRo`X5vSucafiEw__~*y;uciRGC^}q78_xLl5Rd
zt>epTzV3dyQd*VcIe$F4g0`^&<Fdk7cH!s$39qN%0C6f*o{NZ)3mEl8t9OG^P`BTY
zp(s))pyM?{`Zu}=Sgv@(^snJ;8R11}Y=_^6&*L%9E>r!9K3Z%E(7neq9S*^XiOB#?
zOle{=gEwk_R3-IfFGe0ay63)3Sz4{dGJt~E<zmfMVLn|L=J|a9re)~MVn0ccFidkg
zA_|Jp%D%It`}X$sTgcXyu4adQzsDg5EwAGdJ&X04VPf?-iDVK%5i&isa#^t2q`p7$
zHwK5SDcP3OFGZ8-{K<+IhxVCa`>MqBjzIF_u)vCU=UX~!q5}qau$eK{5p5y$t&t(_
zN?wW;tJp@j!dvSEM6$VN7V&UbeO4G@VfwjNQ_X1V^RxtTCFCH9<vu6-p?Qw?E5EhI
zu<+VmQEuX`f<^cAP;h#`ut2CnwMHDRGV(}kqc2sGop7yy3v&rxjV~FwG+f>;G3r4_
za_ouTib@cuSCF9mWkz*C2!$Fc_cD+&ERcoceA0taPeq&h++UhF^t@&1QO{rpP~<Q?
zxia8)w*yfNZtxq!GWvcQc7zBzbxZVGgoV$6z<oN02ZSZ+9#*W&hpZuur?L!Gtbgj?
z9c4um1#!CFdmtKkz09j}p3Iki1yDl9K%Jg%P|~@bQ8y#rfLw167=*T(O|rVHDuale
zD>QrD&aaOL@zC{eX6X#3^W0TD-j>mxj{KY%b|=-Y=rqwK6KM{GI5Uq|HH>FJyQd8X
zWlIbpnwp!p7wtL;ygNLM6W6D^rxo?HxQt2&P=&*g`PW<O8SLlaek-&l!l#6>d2e&I
zIG_Ilz|+a0$3Pz+<f>I)768E%04U7XT3s*;HeImbkicare}U<W-=EACJbfUKU4(6w
z+3acL<$+RRrK>a7#A*K)(O-J$74#P1kS9XHi_PhQ2#bJtX}8}Ju7FJF507a#5>jD>
zwYBJlXV8~+**(lN5IrnbJ-T2y^KDn<Js7EIvgh6n8oS-&+O!-}^<nU5xMu~o^4KRv
z|7o4zHkWnqOVpe(cinyX|K^^);UQm;Cid<}0<4v|$MWr`b6vNH;^J9*9dZ;mu&-=k
z2AZ$X9m{tD7_}dgo9WPt^3LScYV?f+K61=lW?vW`t4uO~sSamwqWzwW^PkF<Es~~z
zb~|-y<38f9=A%yX+12>1n8P&@isHW);5`_LBMJbV2`JdFwqGx|PHjYVdjlg(Wh-$x
z>TLX+H(rM{e`{;A?=ylPjAhV$yvnIo=~ff)60_TMK66Y2@9P(<YFdj;*=KFEhwtaF
z#<gFcF?mi^%Ggv}rVH!x`53B#2UJ$lX?s3{wlN=RJv}{%72FfL?1UuRpIu&!s|ea{
zd(d}$>OreFx?uxS2>uR_HsP~qnFb|sT;MvEJ!)0NxFVVE)O#!hL`=VtdGnkf*T8`w
z4Hxf(i3_HF=JR1a+BN!b^gp?jE7rro*>c?azr@qrar9)^uU8ArTQ6ckLCp)ypUfMo
zg~KQ1*xu9n$NGX^r`g~?;BrQBJ%`~J)pEDMxF99qwT900OYXLA7WR%dlI!Q{m)i|<
zLanR+6Gz~J0I4%@t*y(Ae+`B&4a%}9(-zf=Mhc-RL5)2So{bBUB#k`QP+^Im6swLk
z<arHR0CJ7CAi4J3=U@k*JPS5YqBks@x^Ff}(`9OLOYucDkKKMjRnl*-$0ADEe1OVP
z3l0DVLJ{Vn8+dywwR7HqW~Y<T_Cfx5h`GN#q>DGA_wT8xkVqJDQ2`%j)q(4`sO*f;
z@xry*`LR&0VY4HC<Kn*C5{<#Cq5?pF6sS?_5vVV?+#k<b&u5vblA?)Qbjs0Ud(R}S
zt!ZuHN5N?rRVuZa-^VFXu~p1^XM20o;a&wLCzq}}^V|Rk{?DH*p7Wovfqd~zO0F)4
zqNNSz2F}sQtq3*YJDIItFj=qSz<&61Irx`7A&b050vY2BhiP?tR57g%wn#O*xa7IU
zxd{jmNxf`H2pA{pCnd&?)Z-@b)|NIO+&rNP`te<}CY)AEpL5*cBUyxU4gFPM#eYZ0
z?m)ZFLUW9dj#LeK50AcUTj+^@O;N+<K^z_<i;j7nc!5PD`H~FTo)<?%sZ?UB&uTvR
zo0e9V#DE5S&~K`69x3+=58;T8f5`Zs*tk#sYKKy*B|bWqZ~#%R$(Eo>R|`h&X1(7k
z%=c_jK;TMs^x)gK7E=@|<+(bg-_dyH-14(c>Q)<V^YDQcTAUa&@bFu3tj?^8C8|J9
z{eTPA^w|Lb)7Q<nXW1GdJEX5w27}U$CrcY|q;?R{(0smc&k^?luZz(b4>YR{-RtA)
zmYv?8{KukD)wxfb1d<r8mWZdTQXzxePijM2`m2LllkdNmTEQ_KY{j_dLg8=*=KDU>
zxD`J%tj#vvT3a_SH#chf>d35QFB<S98C50yM=vUB&x}xyz5fZOqCw%o6AVQrq@~4q
zkWOgLXnyAQ{a%P!ATP3iRp~<nXo+3w1EkCJ#W7jkz9&Rwk88n?Mwf<-eno(fMW4jR
zwV&lQmbm?`4f@w3n-Q{8z18jpuy-~R6mx<k)x!{@>qOWhRvPmU3aH{GPKW@Y$vwk7
z>Ob%kz9+)aZxEqc-GWP}Xme48ofvajbHRU}`3F=v4(8jLI6hv&;Ew+%oc|vMR6{F3
z&~JEtJzx9Zc@rZt0Bm8Lvo$sOUvv3CSJL^GMfeQ-dyM-KcvTIB!n%1$a}XE2+f!M@
zmoP#zfx$BF0k@xkNkkKZFq+qBkl<5bO0SC}h(QAX6yWmrAvRK27x|N#g?|~Xqa2oD
z@c37vVW<c4WH}a048Sc>JC5$=aCv~sNOp)pVU+%!feSjgW~<k`9yUw2fjP9;ud5>o
z;jM1?q2O9rxSt{|jsN^aLsSsP65L!6;#y7q6`6lL^uN$3d?)}+PV5Iuh+!clBoyn8
z*6L{(+qekx&(i)|{yfO{PyQ?Pt}X^XAd?!p6_zx%*%lyq$%!dGP`yibq2WdFL@4s{
zL`9evD?&Mu+36$48!7DKG9y0f2{!-N+x-{3#fM3hJzWj=<*KKDv>H&pW#i-|B4k7~
z0%WkW+^rjC*9+saW0|e!i-Hw-Mz^F(bw-?-rpw6gk9a)+P-wDwZYXEV?qA|Qo{%Pa
z7s0$hp>%H>a1LexQ9&dwr>~O5hp!HhPJCj6xC8(a4T~iS?e46?1C;tBzJf(%%lF#{
zM}`g*Gnv?OQbU8m;)ulXdAsZl*%i^f{3Q$juf-k$2V{V*K62Ty-ug2bYb{Qa>eYg`
zq%fCe5yg{lkOzYo+^=#~d_XR4Zn}pT+)u7+#CyYWSQ-uIzoKMtHQQ13hX&q+M{O?z
z=mi7?VPZl;Lwhc~AN60#meH3!&3>eOpslU1(K~N5-koe9#1&5XJ6qFHYczl3ZgKkM
z=l9hMDNIsq2&V0(r}ptq*!yq6`#%eb0R-gK?dBs#u2jR;;%sfQmYK?8?L!K4x;~2u
zA+#;vWx85IQaGLt^TFeCL@t3!7n%h2FnC;RBB#Cr4ow{{0&}`^HEke3Zl5Q{=VOTl
zLsyyZVz$LJltqX4^Su>Lud$cmC;}~}cO3;Fxfz&NO7*StnNh&#IiXagzr-E^fc6Ux
zHbjlGSpR=!M}iN>1{qav`fCD59g9%Ew0KpoF(7$Pw3^UYp8`U7nUSRWb4w5U*_;^o
z4XT^W2G_2D!!Ja^INPN<H&Bd?gB`Q6A}on^cb2ZRzko_bHc#Ei#ieQYJt`<PfEfDz
zbOF`5bk*fDk03DG#r;qO&=D}*aBuKrcLrjs^H(tX@2#Fkpi8zKSLb7i86g393IQ-E
z)ZXAel}bdZGC}7rHe5$Ul|9y7j<q#SyFJzDJN$2;w}(l|k-fI|_5yr-1V~L@0D0Q#
z&^<)7iZ|mdqCr1d)s?I9L%-Xd3gH|1GSe?y++4N~YoN;$Xrd&)*HwnZq-7?*bJUh?
zo&n(KZo*$bf0k9Jdu)5L2G|}<?1P$M$o$6~XRv@uo)3kKh6!lj#8FP-&1e9H7fd}X
zUIjwH8C8Hy`}tRBPzL|U;AvHreG~8ezg+!)4IbahpAlBq^9%oVj)8=`Ac6igSRASR
zmmvVa${HYbuwpAarR7w>%a}k5L(O)(f!$q7Ga`p$DNGu>F(@)T@}X7Q3E#J-r>CnL
z8%3?fNhVT-DAmA#BF|BV2Zj`HI0n>AH1B^P`v(2NaDzfRa5WHydcbAXTu;jzt1K32
z<Ce!2YHA|zcyXKU_eXk6GTx=nFFQX6l61Vxf<?u|z&G44xw$@`c9-FTsXYd$l*_tW
zFFPzQv|`5>wcTN~S{#RaOv;K10h`|}!ly;IfVpR-gD_eO0UQ=~V58mr`nV`-=P=7V
z@r5}D0B||82os%VK9nexPBlB2Fb#~SGJo|hHKYMaq|=FXyFW=`uwJadQLfNTsh5_W
z{9}v(jaT(P`2>54O|W?Ep^*EdDTMxtLx4*kFWCvHGDU@jQ+xatsFW+zBLuv>0EMvR
zd;PS{Jd(lNw`VB1PHim(?)94K!f}m!T(OSVNHev`$*L;)Cb4#?gtD+N>tvDG?7!m3
zWOil>B)6Pg9?sN?`FD*qb0JYE{RxFbvsJ70<<A)J?Y%FTYr*Q==E^k|oA%<VRA5&d
ztOVa*?rUk6;I792IBbAm5NrxVhwV1-L>8~jg$>}3LOvDF*VmU^E*Io&uQ^sz4^vcw
z+0J^SEu=RHF3<ULgIu$T9+TPJ-=^~&waj<#?95|RZ^z=W1IEc$3glNo*V)H=ke%FG
z&M!Z@VNVa-oJJcW8(bT}xe~Z0^p&4tnVR#fS>}U8wWof(+>A2GbR7t15GO4sSgg>B
zdyG(lRG-sA6xe!+NPBX=1S&pt(mn~+Vz}?1)oK35s)DBQIoes3h0O9GT8t=szn%df
z-9p>@$CJYZ9hGopzuvjSZbo58_T=u3XEMP7uD9gX?C|Js%f3B)>>r3ot8!ShzVj}O
zIvxg~^7{lj;2MqAXu>-+a^TH4`)AD#wvM*`fa=`s>)kPvaeI=A^dE$pxkQDj0?-X5
zZR_aRM?;}Mlq4!7<bOR~cxj!<<@$qPkP^1JpljQiNHkApvk1nH-T4FfCb(v2JEr%{
z8PL<w)Y_cpq&G7GO5rgi5ZbsOfJQBB^(MTXZSO9Cyz0TjW9#h9`abgsaM5?8WI(Dd
zjub3bD~9p%F9&e|!3ov<q}(LpAw~7i=R-Wr`0ISdGF1erG#26BKo~2^XuHX?G@Fj@
zYJ<V68A&>f{RKDfVUgpRg3G?Iij~?pVW>1U8+{&!)uNHu5G|J;Z`Xr?b!&|=v{fNr
zLiPbkZ^XQ>!7w^jOVc$Eoou!bhTzVqh3>&vit$+U+q5Viv$gzNW->J(3FUpiLwdP8
z_5+0Mn}R|v9<iaZB$BDdBk>dlkQJFco-5JnlB<q`X*`}B#$(C8jhkLr%;s}dWiyjP
z@N6{12Ky)XsvYLsusH1B2IO7$@BAB)*=ba#R5}j7gW5cvaRwJN9f$$VY22jK`UKuv
z<Y|>23|*$t#Snc-W3?Gr)bWmp#No^Z>{#auwF?*N4bJe-ty*9CI#an@*S9A-#S%q;
ze&`#k%?8(x6h_cV-cK&`#R@C6vaAsNKiz(1s_j5j%H_SCjFAuWLy4Crh@)@vifmn*
zaDX1bzH1~hqt^Q-$LA6PV&)Phfzm`N*;3<q_lr>>*rHMtUua(+RKg#<dTgEt&^%99
zNP9yuUnk)_q~raWe`}p0yfOad`i<XUeb3fly$Y*N$>U-}zfh(|R-1?>TjnCC3D+F?
z4hEZ=Bvnd=%aq0Rk#6+hUT9+jlEFq2I*7T3s(&?e`}JzD4&%3WdC3_4WtT-|hoM+s
z{;+~D13B!4X#Z{g*=(n`&X3eLu#rRxtKbPV!N8P`{kV~nG!`oqdq;6ExGn%hfhC-f
zzgy0#-T(X2+dr)pi^KWE$86SwP?MUh-h4h@;;hK_c!xyJSUuM`;>0Z2_=P1y4GJOF
zl2)w-r1SkDjQ|IL{|oF6M99?|O9^tMTPhlng|vCSw}i~A)*8bAUcCwO;>CKa-vw%e
zagA#_pz5NxTw@eyDC?VZ)01M0s5k&hzhN+y&2Kte7=ghxk<0UbH*31uP_8B-90&=O
zu<GOUMbJ%LpZD|40ZAshgDA5(vcc(8IUI|1BKn87x3?g=#e4|~>g<j&V$Z^|E#ogk
zJHT`fFbVXwEguwQxIxvsEg^;nO|m?V!J!~X8i|(k`@{Q!41Nf_zCUiJt&5AWDM;Io
zo(3)AmNNoO7VDxx0;Ll4_WMp49iKo6A}=r)EN=H?r&RaKeVQD;H_u$<EjZ>}X~>O{
z#4z`<;jjNk(^)XJ*>+pFxEFUQ?oxugJ3$H*Efyd^ad&rz0KtnxX>l)F+@U~=yHhk!
z+`hbL=Hv$?nR%XD_FmUoyLab|nuH{&AI?cW4^<m<yHST($$t0UK|Q?H)z3!eAoF&=
z%Dv^bY*uDgW)=AWdpNGmee{E`#Vj6+upbp7^U#CgpP@*Tr3%UKBqkzIFj$7~4rfZf
z8a02T<M-&=6{v*&hbCL~tV<T~v<c!Z)!EmNCA<_;Mx?~!nW527`=u72=(2kP_Sjn-
zRwBzd7R>vHPSLShBP1m{3JoorxrYbUg8P94c1-^qyqMr1-Ot%Qj!kxMjmH}rX%*LC
zbG<@%eODaQCYL~VFF_#`@S7E;yNQPT@<vm9hF~}uO{SbrtJcZUQsmxZV)~NS?M*D2
ziS5f53Eb4}7J<7{p*n44buFzFR{F;}&&RhDS%MfrD3)gYX2E=jdxNLeqYZm^+l{aN
zi>^K7Lb)hdB%^A@4XiZkJPwO4*!SCn4omIorjknaLC;cArNBIXl>VroB>0rzDFad4
zW?#LL@to#kfbW>D>-WmU6ni>wdBT~vrq?;URfRm1d}~5k`Vg}}Q<8Wbems$1N~<Zk
zMaq=BA50B4+09KPJWx7!(k(db`RCAm@P!SMPCBsIbS>1HIiq9PtQ>vo-;sn35ZeCv
z_J?|$%q)+Q)8TO=_*Gz>3zH-EZM7RCi%EwU+<+wMKMnh*#EIzPUsS9_b#x|wDLtF|
ziSA5STt%0>LT>g~wQ;LIxLZY4gqx~g-z@MWXCWz%-#=SR$D+q~86~JV6j{&8VVr&0
zF!*asXunV+z-`XRkSuYNC4#!vt88oQSt206CdKu2?HQZY?e#fp17P<T=o0m~B&k{K
zHt5Edvt@caO_GqyL+!u!eD!JLi%)}^7#+ShHI1^W$eHyxRA4%39CZDT51c^v)uUk7
z3XzLkQ}TY7nuFAT%=_(dvgL%J@X%_~?(c<#g|;o2<N*zrAp0%D#lot>-RA)^Ic&6l
z$FZWxx;-`yXB+)D5S@x?c(rh!CF(Bt?PCo*bv*#^(d5E`=6Z`u!snv!P<eQ!)AuTR
zAr|OmC3((AkMQG>am*?N9CfCsG)$(Zcz)ntsFTSudGbNz!~Dxqo1s%7;RQ6CnFW@%
z-r+3uS>iILGa*0mQ3cN70KC@&EXhUS$V2STCg=6v=Hmp2UY$9DCEI@uZM@{J?Fa2o
zRh%r0sHI75A`=6zsQ-lCd$9+6XrQ@GBo<qmGBFj-S4bse=-xz0{1G&ql%K`piV44b
zfm#j*$E4!&pGx8vay*ag4Z$W1dwV0E37W8KZSot4lRFGzoh5vP8}Jl-KKxGcRnr^!
z<>C1KFX}i|HWRkPur%!=EY0aJ5{Z{89B4LTm4!E_E4LbH2?vFLK+Jmb691nCa4?lm
zivYhMBxTQh;j>Aiqe*y-;Q&sKWum^q*#+}$8KWj!y6)q3unv1)XUpNcQa33?k4noZ
zHX}ar-<r2A>6j|?)u5*mW2#ze_B6&isC_ilIEk1L?H6cV7?LdcfWFe|ECiEpa$1Zd
z$4nn3o+^Aj4}RryoHGK^)#>Uky;DhqBtI%8kL?!XMw9itrkVwZEpHoL(Q3Eqd0seg
zK{D>PkIxeyr+#T}gq{aGbv8M*I4sTLy-mV7AM6cBqr4SgFYy>|S}mA=-t3wN>^Fuo
zhQp*t?9pn8*T-=v1%CrtP)~c#JyIzzuWdPvS}>iv#otgbH+4qett>M2DtNT|Uq=Sn
zF0}t>dXx~og<F_|&OQ0<d|vs4d!u+qRW%X<?zW;8Nk4CN2QnWud(Dv`zP*&aADPEA
zx!Hun%ZvTvcSj`B8Dkj<pn1434l0*K9f}tJbET5~e9@oHXOk$VTc%39x7RJF90vbQ
z=&(A5Vu?!reB@Yz)Lgcxm`KrZ+L~6m2b*izqQmmZuWo8_arZ+1g(+si@H5?a#o`wz
zG<XJ!ZR&TAq$C9&^cw?KZt___enZX?aCBJhKLiiH@tQJ)w+TNI<!UP~nR8;QpZ)o!
zhtHA3;dTB%>UsP}(3QUF@Gj07CW4V((qYiiQZ4Tq`;n1`>PPej+;){vQcvuYo4)Sa
z6BDBx?rXFle+NHb60bwgG@7VN^*G;V5Q4ke%(^?XXG;B1Vo_bUu!T*W$y|U!@1~%a
z;v#9EB`Dv~=)na9g-A?dpxA2EDBjm!F6Eze+SZdPs)_%7ub9=mWwIY9VWGIwE=L?C
zw?CFL`sB-H^vUMTnS3Zrb|SkgZb2J<Y-3|=1yT98`@wRy7q!WRWEUijYkcQVud)~`
z<^!q4ys9v4Uv|?p2~N^H#g_ISV|8qPeShUZPodqJ*$7G^7>p}juH@tC`Z!*0(Cmd2
z^z8VpzUpo_G0@aKfvlkaINDNkE6WuUdh09O2iW(T=MG2^0L@nw$wikhH?kH<T9td^
z%TAfnJYjg9E+yAX1u!{z3OKeKmhI-b#n;R{)RzxQobOC4jXZ8F8BA%<>M5>Q6#Dr5
z8WXe{!6Wn@!NAQJz%SI0dj?c)gBW6!wQtq`dQl)jbW1%t93rK`uDv<pzPv$CFeRPn
zUS6bZ5~RUJg!U~SC(Fe;iztU*4L6tL?<s}2P?Lw5jC3Q&Z)LWDLOATzy0%A_@AR3~
zbF9*7A9B+szOE{SK$BmdA9HnEslwa(vS=WXwt+O{Fysgl93$bXjDa@C3!xpqJ8hdu
zG$Vs7;>14T{PXU|sWyAVG>k^=7s~{+;VdVSCNCa?rG{E-%AiY%zYDcAl6RX(d}2;r
z+#SJE!CUlSd|%XwF#Zke7j)UAk2L*^uzb4L->~f6CcZnZ*kVp2NbdLf^7`2G+FuKw
zUt)~81}jWkthdM2P}xqC1%=kq0x$>TP}dg}z^492+nu)04Ff@ItsewUq@5#V&V%XB
zT%s?=Z{?V3SOk9njehn@Ukk0@6CpiZ%f3pk&28i1cBz|w4|sJAV`NJ+#qH|Htw;LD
z375*Fh2QwuD$stX`#GVA{jvwNG=ku6D-J@sw{#<p`ltEx7^#4j<HCqWJXOTz?EJcZ
zN8i=TGWaTco-X6>OIJ{j6%DXjZL}I7xc0i{4Y>IB?G%gjG^;tPG&aabU#z(pY2k1y
z|86KI-Hy;9J7Ix~;C~k`Arfu^wIm^(o6BYT-ui?x>)@dv*s?LaEYJ6tx;mK7lqbnd
zJbr89TQ$mzl;=BXi^_==R$st~;@tUId3SoCsdL#|;+h~u^%R#-kAw;jH|=?S;=+u_
z(){xc&a4MM-mJ+<XJNQ5wn~A{!paE7x9G{UFJrGI2asiioM+#hcRe?Z{<acf7%Bol
z)%eT}@qRptJXCBjX}Ak9zlhSSkD?_newCpPxcxKibCih7#9^x8;q*GNmSmOc%-Ei{
zxy=_J3;bQicZE|GxPRPm^B;9*Fdn|1miM7NSJy9wsVyDh7q)a6&kC`PX!R)Cj<l)I
z7D~woypEz_&dRS8v6;-VCbAnMf4b<$z4gcrtz)DE-c}OCzu^Kh`Zt-w<NG*ZeX_+e
zQi1J#2Cp;Lah5|_6z$`wcK{g~P+x*_lgEs9Z#JKiwCzk?+kwj~A7m@pRi-KE7f!O1
zP!kB&zUgcvudG)+oTkc#!K@?qeN|yI+KgE>?i%avsibvw;?nijwti#XcLzn6Ri7eU
z@=g|nY#AZXEp%IV-562xLsR04SFd&@hK>ez=t(Mgc!EvJV(Opkkb=WTX#e4#bA_}o
zYqOGk=B|CcdhL$mf=e*>E=DA>$46c4XDgR>lo-C7gA#AkK~fM5WRg0;n)lCq8^^eB
zG{Jk_cUau=F6hH(0KC6B8emy2p@+-^8bG)70xz%pBVsRSlxza?n1;l$BE@m_k)p1_
zvADn1L?2L98wFALws^ck4Bdq7H4tU{EbxuA+i>`tT~SR?n0c})onu*SY6u}6vCL;{
zaWFbYYDNGyHZ#Bq+87b>-YW^8i=zpYYQ?KKFpGvt&WE19GGm?8JCtxlhYvzT0uSW~
zyB23-RH%`vU`NWFs^{<n<Rjt7ZH~3~5?9>Td78~;eDXSUvHj76a1N6u>5qOv&wJP4
zOxWoNAWE8db9EW<{-`ds{AM5lL#p_X<s>8bFC}zHyg{>DHU-0+m59<!xMoj#+F=A#
z1KeXt!f({+kwAAV!}{Vpg1&<}SOr$e;Io<D8mTYz5yHjka$No4q})||S9-mpK2yJD
z>2pw01aV>J@1Ep6anrE<b?Lt|gp(Us8hPEkVA}7ag0D`hnK2O=f?8x#1DjnhCqyI{
zLC-ggIeaI#mlS8_hhgGGCJ)3yHxbA}h_`dkRv4x~Ax$IKa<#5&=dxH{BxKPbL+pF5
z#0HdcyxIbaE@fNaIrWobXB?a`2W;_HzG+M~oK{`w3pFfLMke={0sUOEG2+S+%skUc
zm@K4MUKMfTWVSt}uwG+cv$35lG^Qq2Zodb3MMDJ%(8u6aICiKNr&a)4h#h{3ncz-Z
zR_RYSaqujtPOO^AW1+Y#f?Ijwo4jmVencrOA2+&Kwe0fnPnTSK{=?~tg#m4iFV#*-
z^bM&0;m^Lv<*NJi+Ne*n_p3~{2u-AORDk)mgo9_&(Ox_;#?P1;%b7L{2{uJa{{pw-
zP`h@Kp^22i1LU1v;USm=p+j@QM`&V!Ff|%3&7f92J=66QDVIs)ftf$e+)G|~%CS(X
zq%!eUUXyv>*ddac@51j`xa5rUH?}!^kj+u0nVlG5{jd>(o-BR+t$vWPy0zr`G=Zq2
zlb(<;fTZJxbzakG-p}H_yahZ$m&I@KeaCUrsa(b_wx3OpLldtg7*LB==JB>YJ_W;j
zr~kP#8%K5jW^sLs_?@Amx#e*CyHaP;K{y;znZ>L4lOCJjriHnimEdZ)Ok0(dhJCpy
zMa$qdd<<YStkUqcu%R0r38yhP1(|1@==cYt!q*8uU*FKkm#-%<p<3J|{Az0=Bgw<R
z;Cki7&zbLC;f!@<;R<Q|oZ+`T;}({!qFkIccx0v$W3w;@8kb2^-Umb`OPpE;2t9-@
z>3uEIQ2DPwPO;HtoZLz58=Gyv3|$Lt1fxs?+_+|uuK|Ww!&%4KGjRf-!!T0)BsA*b
zO_^|5Ov)Sr(igr3V5K(G7A@f$k|mbVSyR(5Uk#tvAEcny(CKg5-hE~bUhs)>D0^e+
z{PDiPDP{MBqmt==Q=R2^RduD7Ehhzkqzt9(XMY=#{d@Q+Ul2>e=@b0I@5KA%Y{u}S
zHsZjPBEfV`R9W<;BTfAn<-8G8^hx;GTl{vL_UZmMp!4#H56XOYKS75QVdh^iCHdeW
zyxj6R>8V>1l<^~S=?c18>l^Q?5l55elNC!V=C8uNne3FmtSU1gdfRlx6ZES&osb%c
zpc(I9AQ^aejQXhVJ&k~bnst0CGn)ptKXEJT{D<nXbuPEjr?jKjp%Y$R!DCLk;5i_9
zi8h!Yjr*>ff((W+7nhU95C8U(a@iblAxDghxK9BMpDZ^!6nyvFPv{xl@~#;Sl|SPP
zbw{X(lOa#I!nbXnEYl3QFH5&hf?HGBHjy^9URSWSnM>zI8=&kUn8}#Fqhh^ev=}W-
zNB_i-%DiM%(3lI%xgj-Dru9ZpvH7BF;l*3tX3eh6OugxRY#*V(exI<(3vS+)?+x5x
zyv>jQPT5T`m~^JwvU><4gu9b6zs^noxEZfYlhE?pYRMrCFZJ`dYp*^gHbRE1(1bit
zw(T<O5%HLBFcAku2u^MbIb`cJU+t`<<0vJ3(9!AUiAk1<8*UEtZSy(8%#z@uv!nm>
zCz$*-nus$mdN^08&puoVn*N+w#$#>Fll|tA71v6{w62o;>V?H@E18^0T>_r~8qL;J
zdT9jiL}L-)Jo;JvIAEQJSA>#prO)I}>*77e;Qmj@{hC)b?q;o%QkT;61sz21#PIi@
z<m$sQpS+nw?#WfGWHVZ7#VxacPn+04L^Qnzj5WgfnQ|ucEZBEoO|pIiU?J3drK4hu
zY~Yg8IIBA~XKecd1|sh8AHGwFTrd*0)ZEKZbpiEd8+F?r)!^%snNJ()bOX2J9b-B)
z^&Zp{DtG5p({<^xONvLAq}oBS<as<2id{7^N&U*UQ+J@SASYO*71bCf%t(A<2I@2P
zfjbM_2(8T7R%|%&haV-GMCyn5?jPW$-+K33Q;#zjp&)j)ih)ryl4vW$H$ykS=t21F
z98Kl`)988Jhk+IC2j6PA@JCp}^wvJpppkn;`3aBJvqYFrLpCSn;tp=TS<(#=xnX=q
zwF7VLgT+Y2hE$)q^rE~#o-S^x>z~ezYq5WF{Rmbzj|zO?7MB36(Hx+|4u4;XEUH{g
zx(E83FUHW!H0#*wyfPHI7W-Qke8~p{^?7*sKloZ!=ZINSnfjdBrE{9<Hss#NSToXm
z)8&lrt(8i)>q!<QBz;EhW-Hy5l~u1;7H#+9Eo<YlurSsLnrY5#TP84y7lQY8G`F#g
zSB@GI!lf_I;u7Z0fir2!bQG=nPuR)p4C=xC^O{j=`Dbm-(oZ{B=@I!lmw{aUEt4_{
zUDQ<1p!l#dqL`RqQijV-qeXq^<T}r^t$Ai^zQ0=?Bz;rs_&1WFZCzf7ZXQoP>0T<g
zYRCwZK1XTJ<JkElHI)rHy=9E=bXmztCkzcx3+KG^`j)iX4S2@!)%RPTJh`Y1Tb=S<
zJL<o5nZPYFbky+jozF4V3ScYIfl*&XJgSHC_pXlCwXi|?WE+!M$5c#LoRBby#>D@y
zU$b}=z!Ynds*Gi5wI#}oU&sq2t-XtY;uy6Vbk5ROXch?WM#Zs3HmhXO3wNzya6S7P
z6i^B|E6IGg8%t;ZQpL@~90SE|7X~0iGPOjVX)q>sqWT11rg5_|#=Ayl-JUF|tuhI1
z)fxu(s)HB4>s2P`Gi3=$gLB!K;9_3cxc-02rS3Pp9(yX2=C6hGRbC4p<4N9Qg)$)&
zcf2bwbTzr(zTx=(4E}_IG^(SWDqc%;9oQ7aMJ4Hmy*Cc!HJp{C6vTup#RETW3V68<
z>9oIQK~pp!AfY0gYLo@k>(8SDA1!v~uZPs8nc9-gdf}b&frGmZj`OTHH)e|UH#*Bx
z`g53L5tN-^72$2n_>;?rgI!&CJ9DQ-a2m)gv+37<`OPZezb$f)Y*3zo`J{)O-S>XQ
z*N5Tn1cLmDDXJJ6i#+P-Pe(c?ucRp#=i7IEEl-gb!I^!P%ASk<`=R#zHhAru_QI%o
zgVlRdz3@QHrTIa;%$T&-?Ut%WcQ4YiSB6F32U^ixL??BBQb2J+o77ADi5S&~hJ+2v
z8WZWeGX;<*{$7oqO~mP^n8VGTeV?RA4tmw)4%h<Ae$nnH7$9wJpi)<2X`c)v9~vv^
zjhD*e-47__rJp__9H08wCKyCgbk!qxa5%rMAiRr%$1!5Dnga{Xav#hu--(V{?4d{<
zZpqncZw;c{lJzUrI14>4(d@4T_BY8(jW<uyUZkYp<uV`s6E<Y8z~;4~`FfFJdm+?#
zV1PO8dA`2Z$w(G51~h1i2ztflujq(jy2!#Lv@|B;pM1dQ?^x0P;?SI#0ZnCeVg4oB
zXrMivZ1~MqrO-MqVu0SGmwrYq4$>DAjN0H%Gqn9Hx_}!eVY&{_Y#vrPj--R^{^a;^
z9PBXtpR_p=y|-*044(etsD0a@Ze;CXsKog^HC6zB{IYwhduN|sRV4HwBJ38NB%=vn
zDi#8Td6~k&_*-hMrkQW@95Gx)k2@Xb*oas=)RF-M2{5?A87ezhzLO|qD%6qw-i*TY
z^G75TSLT;Xld5=)ydm4?JF7$V-_SQlyk(X<Qf(M41h%mj&lCi@@L&kmCYL>ec%I`9
zMtxb?8q2hl-#Qgcu;|kBd|}V>Qg!a|o|n790hhY2jPLXD*GI_vTn14UgY6c9(<`Fy
zEM<bIB{<j3uiG9kd@q-xor?l*ZR&Lq*NpiC`OlauA9A5a8j8Qx;uuApTxHs?>faI6
zx|Wk9`*#PtO>L|C^g|m%`CAfjSpeFSE_nKo?`zb8eB&DBc8<CDV?RW$m%yd^mB^{~
zowHLhtLtEU*QLHZI@`s`eWHO%J6*tvgZznM)CgfmL@6-T{@Sbf0ypHu{G|`7LXCoE
zBaaaqBm6HJ9@3TBQCKg#YAS?r<BPcYGC<kZ<-^z)iGI<l)@2%nCyb&Ou5(}RQu!1?
zCtEbGI1X5*)W-95z$(@_RRkUi$eW19er<fjclANaQ=O0086<IG)iN5<sB>L&<1|8=
z5?JnE=a8b05MMKWK*}qlKKSeCYJc`?(Gp|E<=Zc!Eq`dPBh@8wq})r#`>F}PEZo)#
z569xm6fMAye}{CIg6z6q&8rBpg_{L)=TKto2+4(&U)_fyTu{HnH8b@Sgy)KR9GWBM
zb3K<?M{|n>Ak-(GC`-nmn4}4Z=Ru@ZjYPhtKz^q>7`)u&^39C8+U!U+sk$(y%0mWv
zx2NPFa!jG~JVreRm1_-2jak^*Hd?I1x%H|f+j5Z^3z)Ikv0yJInyA&-ZG$2QyYan(
zDor&A0z}S8Bx^G61?Hh?(M7sF0aKpX9xS}lW>PD3%$*0-pM;k|){-n0UF*D4!Xj(q
z#MTA=kzcU4oGu0qQ^%*FOhB$ru$;FNth7J?*?27K;R5nSWyn`Xu>PeCb~fmTPyJ7#
zKVJ6_9R)5X$^mxuDS;P93vSivsi3fG#`%3l1D)OEqeg(o9q!>wvqaFnBYZfaS-%`J
zJIs14&RNOKb@K0gB@Qz%JNxjO$c%E-P~(`Rx+lPU5dyDjaz<<Kw9g;|7ZT(P5{r7q
z?I&CRDa(n&v=8&d@@C?phvOBys7X=z#u}D`b+^zHnM+7?o5hdchi{BTK*fU_^P?y?
zB3HeY@)E0AcbK2vrDo&Qr?`lTp#cm4HM@G~c;s)zNWI(U&zSXMU)|#45oNJ3?nue$
z=JD;um$>v`DVAcpu<&qA{QAN+4E)QGE`M%1rbwI$d?|LjTTD=?NsK46;*CuLW;Ofj
z&#85c8_+W?8|Fsq?7zXJK+o%lDuG8b){>Ia!1~!b+z<c$5Gjl(X**!vTLim%+*9FK
z+-}}{gV`kn%-etIzGtL`W(I<6lq1uR4DCd=Zhw=oR|e4WsAYH-y26#KpP=xG-2wW@
zyx6i?6>DgX@soxS!7+IiT#X~&@S0B@nd)1_WdAzxF5}adXRo0e7Od{YDj+Emow3yf
zqF~f{xNM&K^QUyl-6aLB3|x+mckS4@mR_KR{Dy5aTjet)?uIj_0Wg;gom8Hqj<+Rs
z+FW|A`lXK%x!!p=?gS2HaTvX&j8nrLMH)q)qvGq1!k{8xpo6~kNC$K?TUo-kJ0$P0
zxeNHc7~RJBDwH-zXIj+E{-yVYJ9gYCRJ@xWt?|lwdBi2)F&UUm_@Q`+s_Z>^!VOQO
z$Y6(!)}HnO7<`X@?Ym;1Y>YR#?k>6p-0XSZ9bRqS8B}R71d(yf6KfLI_F5ryysTw{
zU8Lqa>9W!{?_zcAys(d>3W8sJEm8JMbw3q-jF?tDwn@Df^tfWUPf9K{aQ?nQBZ-)V
z+k<7;igqK?sSb|KokQ)HY^9;Xip9#vC8dhj6T<8;W^lDIqVF|T66%M|1c5BRCR6U1
z;U6pG3NM_6txz)e8&(l{nRcV+OAHvW=S6&tI5$E*$e~t0Q5=|2pEbonDz@)>*T%+e
z&|2Pumf0>LKT5;Ft$Dn~T%+yL)iC{0OIiBu;v)%ReB^tOa@1~X_(0mP&-kL=Sq5S^
zD*ggnjE?K30#kSxnSK&D*|bX`&MJ%NCTt%EBk*GX^aGUMMQ}fnc!{|6hw5d-O}g06
z+XM*FDdqOY2)Aj(XgJBwp`7kpM>rQ?q17Xbpsrt&Pqphy|NTO~W+W~ye)eH?N32hF
z^lCbX8fxy$expmIv%O5X0cd;rPmWUse-ff1z|HBuCn)NXGW#YjxaPfG{?=M74|V_)
zjn&r1Ah{j?EQ=@$8d{9s#RPz>liE~SxM&>SpIw%=OlN!D#5Nw!_qpy0D0)*4`Tr6~
zMqwg<<IF)wp@M~h{=H%@3~CaIvTLg_V!<HE003r1gxvym+-$9Bp-sY&Che^IgfmCo
z-|NAzftIliznxXSI)bM#lhNf6Vvif3eZ{LulzddVt!F%XDR4ru)V#R$H#ZwgJ@Ush
zhUQLYpXxfZrTU;`0WZdEg)$9Qx5zud=~*4VWrDzXtvLcPvp5-!ij=d}yn1iQc=)-!
zp^#W7{^9OhG)Xv_=AXsrOhcw+p!^lh;J!O1K-1nLfk5X6-=0{lyZzP=I0Lg%>fgEt
zQg-X}re*<>eJ_X9{}&t(wPRVZKx6v;)prAbbww>90(}iC9zTl1tcPR-zh9Rrrv%t2
zR#K*I@G>G2>}=_t;0t$&j;E0Ow)g)qGH&c~_w_v-7&x<EP9wvf4kff)AtV$vDF?1x
zt{G8bLK|K%TEoilUCk5m(w}0wz2;AbE!%#lQy@3BYq7WR)4U~cIMZ$af|t-S#8<(4
z`w->V#ec0~M$RW|ft#nQ%=66v#Cc$^cNnaq7}7Wy@P>t17c-kL{|vydq62rL*sc}}
zi;ZH7R?_7~7WZ)Jgf6>f7bEuZe}7T~9yatFdg!g~bNYD^Lr{|VULg#f++QOo6tux_
zs%r@$qC*y?1BkMjJneIU{sgYspF1hKm+t`<H-cc7hri!zvEkX@BFytaU_)oRFbz<>
zYRxatL=rrbyOM@!AL?-fLe2H~rWl@31k0AHH1Pp%PKYdtfa6Rt(`N~{+CH15iYVml
zEBN<)!sP;cpjmchrsEwAB&8b8SL_Bg<{3W<a((FjWS1xDPErsgGD}2o@~ErdD9X-@
zxe<#}>f88mxYk7&hfG4k0as4TQ!hmK0Abp_h<z9wL$3f13KJf3sga(xz60b~ewJU`
zMuXW-nS(AzXQa0e^!px<mJL`L3CYrz1tH|GdA$0#l_SPBnRpH6EcMh*iC^$#32dM|
zHJ5BclqPv+g!X+F&(GaB=iDy;Y8?=FOg?+2l4ZPt-*WEmoPp~%sz#JnKY9lZpMJMi
zSiNfIT$o12FT4(yynO;H+d39fq9TQG?S`*3;!$NFf%!nR->X0U#b~DVrndQ;eG~<K
zYrmH!fO#0C)75TpPk=Y2HKQK!p2kS?P*ryB5sojYw<{Q<{3fvFR;!}_P-u^1)*kSK
zS?R01TX}AcGfCuczhDB&?)0>sI3<Pb-omFQD^bhbi68|j`y`W3C_f*v{`K!)|1=;_
zgK`XUx}X-wr^S8tO6S=A>mdgoc77stPNl*YJ|3UYZ{n%jmRM6)R%Hcd2f%9;9o$f>
zRD_P$RPm%!I1S^X5VIfuzT2gO11Mc*b1unmw>q-F8)hwM<fd0;U^jvnPp*9!Hcac=
z4mKhnHE5aUE;x9PoXZb)n~|u&*fLp3&|hHdfmdtOTJ-_sx>Z`JQZKytN=c{|^ScG|
zBDbopkE2WV(m`gxT#*F>V*|r3`5Eb*y_7sbGNqx#x81?YJa^*T{&63t?oAs=jNbd4
z&OUtp(am+7otQjLBFW)6C^vhW#`f(aujGHg_cobG$56XnndQdGDq~Bb&q+KdHSYIl
zRb&KqB7TLd$f+f2BMbt9DeMpTb<8x-QxzgkJ2#s2kYB~JnQTLDTEdNknOGb}d~ZX!
zg>|)#&YfMSiyQi}4czx90CuL8{5rj5SSs*ZE%EWKjYf8qpcq>Pqr2|Ny|=<=Uidnw
zmsBApz-^ZXAQ{TaUiJzjBx!0#U~*xR&xQ?>d$t?6M&<KeIK*!pvCABz>+l*!xGKTp
zJyLwa2ANS~86)HPUKihvvYEs-%#p!~H_-hz&n)q0XO(I{szHTN%MfR`NL53)Wqra)
zcK|N9VjJYCgMUiJzT?tUA=VrC!e|)zO%=5ok=K3Vc;|M5Wo*M4ozU$n90&(FPZlc~
zFyrkL6sATdp3L03xJ}ySS4}j?tm}<iYYtz5!S1KCo?z%e$o1KCx_B$ZX0nlrfiI{g
z?F}q8cw(SN;&F>>_df<3H-I9MrkVo<X=N+>U?csJQ|iAgHnVOTsMBsZ%df{#jUg0=
z93oz->p5puPA3b^CI=AvcjM3Lj2^{|O$<s&1IEr>)-^2SEeTjw+xSLG)m(G|<uj1p
zTeDAyeKriSLIs*F=;#;^zLf;E5ZHfvF>F}bKMM!Hj`cwI&5_RZ?<U>9<VcVdt^1N@
z21yJeseqgw8eL`ZUEgMcnEJLZt4e@v;MU3WKrCURKYFsIYvpzo7oYhDmF;^MXng1x
zBkh0sM5UYv$I$6lbf!V?XPN08i}Pv26F6KoI=)7pFXpWnj*4^kVLxqF3i76X$6)&n
z!E&_KFL+o)(P>-Kd$)Ou`<oSK9J``%>e_KQcelcdT{W*UJ3xj9?`XIIAu3Gad?}7A
zEdsG<%1O!^<kFM&SquDg4{mu}&{TK;arT1q#t$^PZgMUGx+%)Y(^LIdo4>(}4Z2JR
z;@N;0fL;^p3<{)Pr}G@bAmY|$S;!h<79>JM6L3+Kod}kVB{lOiWCna53w#y=k4N+l
zsZ#<pK8bO0n@ofa(MO%Gc&7{ynB`2^=<;N8nJ|;g{FRk{l}C6b&{o=I9fNYG=5bq(
zIcnyKhfhZ(5^dK{>$cLYo$KiIFaWT0wKeD0)+PtiJTk}j`jz6wzvtR(d#h)hB0fFy
z95#oRbD@^JH|QitE1&E`rZYw=rKL9+A|TKxoWV)Anm4k|ICu=5Mzu%mbF@`y{fM4H
zND+`Un0XX;oMtc6a+VgPDfM4xgJ#~3QGr$koyYzUoTao&mzV-CH-HBg@Fu4dkYY5I
zCF6+L2sZvQzONW3nmjl;fMb&izV|<ovyDyL7+Lf3t*h~{5Q-WG+VI#_NL~-&sLlXu
zrMM>d&H+62A>kM`UK>8V%n_owP3NS%KnzB<1b+=hLd!8cjabbl_{(ngy9yz7xerzd
z8P_x_x2-n^@Ti3j2(A0{w{4`l{Br9z^fB$gj(B;xnx6Ukfnkn`og6qiwusD50VG!=
ztqFR5$X=Qr;1sG1G(B2+H<ua2fXP7IG}AAY_U7zUrI$|<mI@jdnDMGfl<Ag&9^>z-
zA?R5?O2y%Aqdrc`BEXCdqU`(Jte-%sXGZ>A4WhlGSw(v=ZN?MJq%4~wWdFH^<3lGI
zMJ%!VKU}G}NOwU~zkA}mb{Cwkf<WTw>*LrUf}V@Qh;b@bq?cCEwO{9BaDOS8B~u!4
zRf<`t?)l!I?{px;OCBg6!fe|`*@FyZ(Y&yS2sg02qOxjAHfBRreC)61>t?u2?O17~
zBmAsaGc;DI##P`z<vMVUJSQ{e9u6435Ix}g4gJ-ZOQo-4?0DGlPevL;`C+q4=>sw(
z;tdy>?&Tr<Ag#D@?;3`{0H-|mr6oYD-PjQ8m=7L+#P#Q?!7kC_n`YXK5^&!&5=z;A
zWDtgObUc1lyluthu~0~fZbl()HU;^xWnCwZfzkNIim9k9EF#g#-lIU}YmSei@VV1k
zdl8(?x<v_ZaHi7izVPjGMV*7d61a)y`%d`9ZH#m|Wy=1cR1m0X1L<@B5dBV-N*Z=1
zwAnnSJ1=oXQu&#RDjQGu>Z1-zh9R=yLsH6RncY^D!_`qz$2Rx5jByY!MDZiEV7;|k
zMNbmZWBFp4Y%ansn;~MS;zkwB>zSe?aNlmfzjGI(UITnQk3YpogcP*KAij0um1<he
z^RiphQ|*#GA~G_b;52Xd)Zg<wWMSi;Xu69Bohw&JzWE!ZZl5mx`**B%4_j;YmY(Nz
zQnHINbGoI16~?8ePWQ`R=tZ{0cT?<Ynil55yD#R|xpyOi8$v3ce9eDoOrSyk1sO<H
zT`R{)hoS#Ejfo>O$u_VStttMpIrUmyx;;#8J*Clevur=~C-VE(li7@NI2$)q#=+*u
zWKG%r7B&t2g(-_Dvq0UgOeS;*r#e=ae>Yj!MKea!GkG9v`=$(N%}lai!m$EMeNr$-
z<Mp<6144Tv&pv&PwuW_@9-}GC087_^EXDS_<Vs(WMwMfdwQsgKC0OPGE5`3xW=h@E
zv}HNoBK>ZRoA}a-yu+*<d6_DwC6PTwkMT?s{A9sCllH_UuXQx5Y)@!%Z{RB~QZMIP
z-=ZMdrv7fUj))d>S2T9x8=K+v{<K*LWLq&?NOQeSJ!@nBl;2KQ^KS(5<`F_||3{*;
z)>rqP`X83e#y*?BK6GAfX?4Ob-4EA)I70k1g*ua?6Mm*z${a3qaI||mq?JC~e$3{-
z5&M*hdBo^Pt`ZP<xjl?u5Knxl)_FJdc=vHzXLY+RW@Zoyb(9=`yg9XB74UsqWylx&
z62P}*6{ry~1wbmO)}3OcVI+sCPt6E$NV<Unv7cWq{yUE}EPqnQjUn57y(8DJS59<t
zL7mt}Ow;l|!Fts$-emq=Q{&HrJd{9gS%sn7ts^Sv(O*a~g1I}v35q4RDC8r4g7aWx
z`sAo@EB=wGNT{(dOdM&DQoqYxu(`jyoyuYUP0|PgFc8^{k{g3yQku2}3EfEuH$e8E
z4rVPQXpfqtqU`^g-oS$PyHvM)QuBCR3JeFx#2`cjvfnAfUt@zmZ8^~I@NDm|BzKs|
z_82$WTEsAOn4_VgQ9`nR+sD_)nFYWim!fw?I1D;8lv6H9&4QC}(Yn$dE@kqLibte_
zldyIi$;=2hg!yiwrxTl?QQNY^!uXO>+-eER1>M@MV{CSRk&cxE#Qw?2$!3kU!b<Ec
zaa8TGt5$Gvlkp<`S>HL&u#WDAKhCi$9?x7o7w~3G*efemf+koqkX_Sldcz^f;nB%)
zZwbQh8r)4feaKmudZn`=gL}TmkO<R3r#?q1-&Y**rE{i)0)CTEO@3>Q@3{(_4zH5W
zSs<UwBWx7ANa0k7B&jw8x7`uo#8`8YFCAv#ijvsz<<UsfW5A@ghwm+-ihr0@-V3<>
zOO;VG>fCD{8)g>P!|idnCFD=9zSGbr-uP5~FmG3<TlttsWK5Wd(-ZR3`Hz>yR_6lb
z*dgcQwKmtrQgryVTu0-IvHIpzG^G$HR_xvK4EnO(1&^-cVvP;Ihb3c<TE<RYEAl(F
zR+^v+0Y(6w1(;o)U3$KEe0WcEpHo-1v5^s5w}~q>BEXK3h}QZ0`#fqJD#Y*KuK4({
zY+5Qgd7<<*@!G3b#UDd^2VEnxhqI0n-Ww9|LsITc2cUuEZ&Wi|+6EDP)i3<apHS`H
zU7);7eaA8KeY{s$RFKgf00#*MU^;Sxuj6qbt3FNZ2S}vJIp4+HDZs!TcqplIjK&qE
z+qTU>3u{hY9`Eov;;gsTK4yYqQR-LiQic2QytNuQtp94<A^K^%K8LwtGWy3UWbgYP
zHAO;!QebwK)5VWtwzF?=Y*SvbY{Ht`5bfD{$Ca=`4RZqneA)Oqw>HzE<ZzN)#sx`R
zKn;$w2l;a8w=;H=Uxu<t+Zq-uvLNkAQT@X-4LX(+pD6}BVLh%jVx?uI0Qt@)-4f@*
zhRN#N`iM>g-qtnve%k7QoD&$&<2#LC$CiaAuav(83IDeb%B^bVONJK&Q%A$MD`cq;
ztap7q#1zLJ!vDr=nH0=W#Me41J_z5^2&1!mdZ-8opLLG*!mUN5_dy6^1KwYb2>hk~
z$OX{I!5$-2v;*5|ms{*2KYm{rgF`IhFdsFQG6Rf`!9Vact?12s8RxM?6YL(%bjQ8L
z)8&?4J=kjn?;%5PF`U*<IoIcb<J3P!nUtGk0)YhpwFh*M(^)oS{Su9SHTv~(m3N2W
z^jULI_u|)5zMzOooE^e;S%8aYr&R&SMYv~}g^|YN++6e{gueaxqIZh@7@KwfMC3c*
zDhq~TbH9^&l<Z7%yywAYVS+%waU;Z+JD<%JXV`rRg=V>kI~ww<JlL8Pb`Zo5r$+Gf
z>#nlz&7Znc*%=mw(da7!g?BV=sgqdXv&e~!mdK`Iw6YC@+kSA@P1pO={qUyT^~W#b
zo+RUp+~&LbG!=KhnkXP`?&Vpk28vme*7HTYWv}0HGy3?Bmzt4YvH+BD|DZzuL$**2
zhtb(2EDc<!lzCImt?0Hn!bAvf9+-xxuXxe(iW9K0DQ4&G7`Ak&bjffM()7HF+NzLW
zT#DV<pV9kn3=i$XlRh`o2aa^VX_4?nJ;ex`JgDwBI@->chL*)L2I^JnzJ>D)yaIBE
zIVAeZ6v|bEgho0leU3j2XE2p!L9R>9PVv*g^36SoQg~4?VuWu&j_phkOCj)W6ew+>
zVlAx69{VMo^u#Mo$Rw|D;QZdH=GFI#1U=h;rJ48!v>v9QNKQ+4=e}>YFV-jn%zt4C
zda>rTof7xBi>dd4Q}YT96e#qv>Cte$o`IKsmNnx{WEF7vNHbCYUuI;F%`{-8@$L^i
zUV_PU-|GpUptf(pZ!;C}1aOR^3dTVkT*$<XSAC$99z2KlDgmM0Y@U-lSu>My*)ar!
zrc%I)F?jWlG0HxQYDtpy4tnIKBAk^UmoZ-XW^OOOp2Lo9qHtizbLzzgY{7}mMWe#j
zFmfDnFtKe?#X325lP6NzFrCS}Rwq0U<ovl8i-D2IMOS1&!j#0(2u*d6#NPVB13t8%
zmTI2zy_%;6ZB3=oFL2z1o_PaxfUR;y<HB?bVcEnH<dDO#W|z@#z(7%W2nsZP>;TB)
z<ECMC`Y@Tr?a4oscuz6DuK_&)d0w0in~k|IdI?tSHsvq209TInd8C-tpRsga^Xy}W
z;*lOU2N5)Pnlm3tbUKFd8O~GSQL5X=_?EjSZaSRn_k{NI`wuPEITl?hKx6I#LGPHx
zlxcnq`NamGtNg(PL)eecv(@qK+HPE@uI+_Va~Pp_6;gARNQU=3HEwe>gw_Ro(E68K
z%0y!z_b98ju6EIU%92%lM`DRC_4#5^b-l}ljF#Rf3bLnGTNnX90m?~3n+m5v6Il^&
zTJNdtJuSx3Qb`A1vchbQi%PvIGAgR_N+_#YO%*Fw+OP8K9cCWbM)2(ju<3ofvP|VY
z7Kv*6#MQPXdBT6ni7%a0wr{5D;idC@RS(n8y0(e0%rzt6y?V82_mX5uOF`kCD_Yqy
z0ZE0qb`wZODIPVLiw?u0oPz`cGC`aFRwz3`ftN>IR^Vqe40iF_fPYw5_;+j+=|-(2
zu+>zz2c|-I<TAP1>0K0)cgFsW!u^eWQ10%S`(b)h48wZP;rb>el*~X2e>mjs#b7k$
z^PGC|AOF2C%BZ65T%>I=8U;;vS3Q4W+VkO*zg52erqbS1wX(w(Qp50{1xJr{+_R7W
zPXfxe#Bh}x_CHNFLB<PU{G<<!F(%b<u#0RccVM+qbGwf8@Ad-Rhc2h;zghHyWcS#r
zpYwi&zvFAZ-RRPf!}dE+)Sf&20?CR?W0~H3?Iue?fOJF2wAO<kiT|Z%#TNAkOXm*f
zW`8^IuvEpefb}(Q^LpK1e$4(k5$-A#kQjy%Fj)kx{uEu`qp2|YHzQSZ{V}cL6-6`q
zaFJdw=hFJzJ5;qQxM~sZLNERuB0@i(vU@shL$M(Q)Up^Ch$n9p_9ZD8%s4y9EBt_^
z3@S+n%>S3><I2kQ5+7O0-CyV}$y3A7Z2yP9d*v<YY$8_(_oblgIOKbS!(;Fb+>^>h
z`*m|Yql8MtZSPyt@AdMsma5kETl<BT7T)nyy)(FCCM$1s-nC#sC5+a6>~}#+fX>SL
z%l$RTWy*IbaAW%Q-E?zp!F)}B+=3~7x4o?$Ix@<zj*Zw?)`kum9$K<L@)1vuQm;x~
zRsGvGUu1c$^8=6bZzJrNkU0bdOw7d@A#PidMRGaGpa$FxS&Br{u7HdEmGji<AOY~S
zpX{%cI^8h7xxat^7CIM5^Pb1y<!uTF1^X(9SodVCH}4h`7M;v|<hz-Okwh=91=Qt<
zCr!Q)$wbc$ak2f#93t$7L0ENSK7rq^S8K|J7sqJ_n#*oMmb6`+S_`n51GG?{)kK=*
zoV3p9jZ*DQ%3@6+^xp#NhlYhA*rebWVSXofF#4p$fILU?d;9vV8FS`{XKoUmDKv0^
zui2*8B56d)gm_Hf1m~lei<D8rc@W<7XZu`nNjAG9GG5lN)vf3Y_41j`npM5K%>($q
zvhH{L5^mu}`p7&$r&L`_Tt9iEot{5tEp<}dutrzaM@vG<mf8bJ_br%>Itw3a=ak4I
z0$Tmy;r7-(aK~6N2$g6umN(LS7?P5Lh<5308-M?Msw?{9=ye`FqNg#fYZI-+2OM~b
z<m-AgXVA9$(d9S4kiJsDJ*<O=q1RH0B<S^h@D&m^^HB8U{iX8a&j}?zMM0*KC)CXT
z8jGcElwF`yXhe=mAAHKp1Yxm}um6V-%vq!6imcS^48T2hLo(2x4DXA7=j+9T^JtYf
z+y)nx?WP9qqH>V40Iz<Lj9-u>LzYJ^C3Lk>-1(z#S~@A7xEL+kOB!y*3B>+Kw_Tv#
zM5J+ewLgVFQFJ$WIb{r=qA8z*x9)b{jZ_NDl7FFQ%;ynepnQ@<+$og9Mt*cF#gpR=
zxYGi8RI#!BX6=>V*};{frI%x8OjFg+z>hJWxAoE9#R%$6B$G@q6>kpP%@N=aNFb+|
z^P>ai@*47-9?BaGe9NW%X@m-0_Gn=)QfX{`wo(yOTHVTga~n+!U9w1FQt9wMA8K=j
zj|5Xc9`Uw%dAbk7n0F-hxWt9dyN7k087=;fT?q2;D{bIUQNJ=kn$3dWDYyNiaB0@T
zDq?(8;!vvHkRJCfkzx7wgfA1nE?k*qX>>oB_fM};V-dnmz7Ko7oU&Dgf+pYbp-{=@
zhAY?A67r!vNtzG2!VRoh68<eI{m}wUj@DMh&*qb)3!^*J3Gjn9t7uEm$BB&$_eR0e
zj0voL_sN*QEKYFwa#X%GeJE2sw1WdlTY4THHF2WC*v}~&$ZbnPqEqhTHDY=>OPK`-
zUYczFl<e<h8d`{dPXl=8&Stj*OFM{IrQrIr^kv6lfxbSwNnM5QsLx=kfKPpdsgK#L
z2O>41+2f@6{&HdQ-?)&AM;-D08y1p;xY%eCh34&5=34DD{I|4tt3uM|TFb@FpkRXe
zU82nNmWqzBAMd00p3geYbFTeCp=7!BZg?5{nCI5w-Xb0y2JL8w^hj<rL!;~KHANXi
zf8C9ZbE9^QhycnT7CFOS+j!)Df<MM(^3m*U#BFby*WNATxi!DMJoG2lT34fG4q>Fg
zLw1jp16}QzDI&M??v{Mf>jqK<XdRbU#lDl~DTRDockq|A=tGv#o<imzWCjX1k`obf
zQ)y>D%PgzCS+nmU&rM>EI&0QE{ZY`YkYMf4xG<68<|Y+XFZhKLNOma#rfr*QA!6pu
zPT9ko7XOmKw9tRcx6}KfDhpxH5*{gbqX-QVOlrF+e3)DJA{QeE&?Vkpw*2lrP73F&
zyNO;PM<X!@haO<&s=NM-o~zKrLcliI@vI0z;%Xk=^2>I$nBxXtWX)LNpj%E28y@$~
z{1X04lDh+=I8V4PuPJ&<GEpB=)dAQ0KyAVzEz5z-1BP>Tw?97+NE-D*8pbuVh4p@Z
zR97cf7-TYN^kRsMS49TXzVCUX2c@=r7#VoAcYZ+&?FzJgO-VUVl#+b>s}$F1Cy$--
zEhT=p<W~Pf@Q{G`@VwNm`FXFUkKa15^Zr~jLRoKbFN24+JhjB;=<jFC?Yn5o8s{Ud
zY05we>R#;cW%fB@Q_G$=-k*=w`Vxj?@EMG-_ojjvR$IsrzQZ5uJ|Gq0C}(H(*Z1-z
zFr`L}nIASH(3Xd81Q}@4`ELc0NbC(CVP9%Z7T#9MPhbL1(Rd)JI728H+TM62A~v!<
z-Q9qp<IU}8O+++3ve>{zlq}~?f#;=YQ(gqHEN*N4p$VO8w4>%D2Zji~6FZ|MY))+E
zocuGhG~8ykN*ZbQ$)<II{F)=Hy=8uK-^D?&Z-S8p%i3K@!Pfrf(L7D;t@}jiJ7HBa
z%YG5iG9hm^U{BfRM5?d+<M+`Bw~-V&SyGW@yp>fxzP^Qkhog!xTxDSeQ>$v$$9xYf
zi=lA!uy%JcQ}FU`^QE4Jg<>RwTK%gM>iwsei=J2Q7aaYq?z)TPyTB_mmomo0Gbir5
z$N(N;{`2PJ4`i}SJLk<j?mEEWc#?^s7r9~A1bz;xZ^45c<H$Bw$3mbN5@t~-0Y|&2
z7zo2o((6<zA<@|T=hQsKxS3xP5Q{H0C2P6d2}Z%n#B$S;6V1-P7lOq_JU=oxJZwpA
zx0%Z!J}lz>wx_^{E4Iv^mX?=x%`$Jm^u`>SitUmtR|1e4Set8O+1J1{lG70-<P=oT
zi$8#jtCqVc6@sA$U3b@J<*q{6icvY?6!W<-ms$R;N8eWV(zGY6W_?ba+tM^=xZsk`
z5=6)6K0JFkkJ&SD?`udoc+3BeZ%}03<1)ULD>54k>d_b#pPf2%U5^?OYx;I@<<7_W
z@9BuW&lf<+Bdt3A`JOMCr3tl3L;ZVO9f64e7h9{BUC%-Lq~Xv7+UOqeB&mH>7R&}Q
zA+4nzH5}zlr_F`9wrygLeaZob<rt8YkC4-3zf*J~hP_Y5eV~X*4T>Jb+?ixJx84&R
zY++hoi;>&!@)nXrLFg3*-Y;p)tIq8&DoGYuT+)`TP1pM#`1q`8p#q-UdG{`q%xwg`
zA5I=V-SdL+s65BxJ+I4YBplC=f+9@mhVR^YBW_wGPPP8Ui45Rj{{4F62vKY-PxA8}
z?SxSs_TSHcea=ty?VfEmt8XPqkWH}ISmFs1y?1yW$4Y!)&EBw)QW0~r{ExNV4C<q#
zWPC>|y`P-=?xHEo*0;UaHM8ykLVR|xoSQuJIgYm`Q;&oPN}p?Z`X|6qd9SLRYiJ1Y
zg(PXobkh_~{}UNOO*nf5Trt<dhS`@Y^g186O))4Dcbr|cDa8$psN+x1pICBDeU>r!
zJC%TGueL~@BEU?QaTK(;>gJ=s+}>_)aU$HVrLC=<TbvJDtV6YeBdC*~rL(lG4qz?3
z!4n=AtDu$O8znpOn~8xs9TxlTEZab>NOCUdg}1(9+He?#RLhSZoh@4(xTX^N)AoXl
zud#O-4SI2D1kxc$LL4A3_RSNKC8-q>90qN@sg-*BVQhd53qzPpX0Femp*$RePw>?5
z_4zhZFblS+Yy)@j*;ZM9!1IJBCy+RtWW#%_eK$90%<2S)h%P;fNmNTs*<P<x<s<R!
zqDH5xE(>RhqW7qzHvW0yg!DXi>I-JF+L-tH4oW0@=c;8RI%Yvl6a`uaE9t*IE{7K*
z&4M-%37664pD)N2vM%xb{B{@ZX1Po8uDJTgr=@VfpB|8@%C)AtvqU_%P3)Mn0fDdi
z3oa78Mi-|ZO|{eT>5Sb}&81zia`|v|EE}oU=Zg1{(wKQMTJgm`LAQg!F}|uq0rEiB
zOMT<G^Z=m2xHZ`C92;H9%&J-(ZNk>+i+k{0;E;@ta5R1{PJG$p|E)%ws(~*zTF8s*
z`AoY{ea_rBzQ*rz%_69t!`0b^wKe8QON!qz^DL!@$KH#n2wY!Zql~gfpISBId8J$1
zaYl!!mn$0r2QF^mL2Bp=p3E~NIdopq&POPvJ}os*qdUFW{qTqlWIK2JK<o05SXYg{
zk?D;ri%o!)BPL<R=}br+F1p&Z@ob1oL=~DLJf;bSxsvZd+eXSv%Pjb$G)8fpWZFK}
z#j24V%r4AMY%eW5j76Bx*)2Rg75w^WMt2xqT-*7Q<+~>^tg)MbTkG25c_0tr<h0GH
zN|CdLzYg8hpIB-wP30zBc-(v4QxC$5>u8qYDHp3h$?9uK?L7=E`>qn3S*RZVY4}GK
z*N6q0wo`E<1MO;kr}F=HZXEdOG;|}XZc`Sd0QYsKa3<AQ-}GJzNz$VBqgR$XnxpQt
zCa$nW1Of1e^bD@glnh?BK`tb2k^6D^QzDgHzejhF9s8?}2xAM&l`|Ma?(P@3n!1m9
zy!X?i1edQO3Ews#`R;5VX?zeo*O~w(#=5o<N2+Ds6iOm=d$;YJR}>fT&{kJ6OJ#Qu
zmJ8IRZirFw=48X5m<Qjsf2{jRwt7_iblTIww^b8+@qzUxBz*JHtHpZDNL!serWU6C
zA^}_BxVMjvb^l}Q9iuCYy7kYBZKuK$+qP}nww+XL+qNsN*tTukPC9RQ-*Nx9Z;$z9
zpK-qIz1BMW%=OH<eowfvlmYZ=QbciX)uy@h0;?KZ=1ZLfB#LNNw!yJFE5vRp@cQ20
zLTVJoXUQbyW!HTp>UfiI+Lp!a@t50uFw<z6{43+@iRlriKTU?xFSp}O^?u+?xZJGP
z?b|Oe*>wgbfh3#Mqoz!yL%~qerhnEo)%Lx(&bXu)IQB4%7S=j0%2>Gyb3#So2i^ES
zV<7+5n+Z0x^$oDa5?U3!v|+j7q3{mv_aL2MW8}bG;P)@zusa$=p+2*==-DeXnQD7n
z)rDigpuXsudbj43ScO9wZiT*ZGZEo{r(YX=6WL~_=NzqBY)mh8Ikjiy+4d&RYqd8@
z<+>5r1VA9&;TXETMozOnBopNon;Gx%6?y!CjFG$T-ay8foA1r!f|<Z#44A4`=gC;;
z@x6_dt+}m76h1rI^m<Y5QxZIurUTmMxhi!FI)2bMyo7LY&WUtlvRVF#fjXh){OmY%
z)14_#q&c+{13zGc`%Ok?H#V#}+4GW-KhD^VhJyfL8?irFh^+nkqa-K>Zc6Gz^{$ti
z0%KFKLP~-$P&g?Wj>g5!2RH^pd;JJ}s29^3O_QdC+GJ8<8uNXijPK+dR0eZv4UU-l
z@JeebuzS^uj|9^Vg|#g$E&6KHrfhDM6R33ynKCeGGuE1zPNbM<pceD@GJf|kS>Xz-
zgBG3$%hPL2OQtN@Ofu)ERom9blLNM1S(RLsbkB;l#lcu1ig<B$%O8@m!{5u)In>7b
zX7)?$KkOQQdZUBa9#VN}ahB}ktgyIc!qAaJ21D@RCT#1RVXOG`$dcfG(}tnILY-Sr
zv)!k}>%jkF0f36F?Y$0!Lwano(q0_*PEfpi+xtvxFwRe3-fd{KIhwR*NBmx%-lmIt
zDr}Svi0{9e?9R97MA&=IA|o|g<ia<6OBxQ!BG`af!in-TMn&gV!v}v_#6X8xBbuVF
z5pHXF!QskaqrJfY>$Qy(<>_34NMg1Ac71!;!e_Lxe-CNL;{DN^8BKS6w>?gdzZ$<^
zdi${lhC8=>bktm6Xlm|(3knLl@yMm5koxkfk0@V&u^Gtp=0iVC-AoCXp#*Ld?y*V;
z`nH8)?5fTByW~{m<pMxmS7dKz&y=nU7gYij(L6s7Sg}j|w$lAckh{(Qc^PDyvQ`>8
zVUxmUQvC>Xh!;7l4}ZeDkB;&T*G(IENF~wI8!{_nCv7psmP+d89h?;E)bFNDxwAq8
z!sUnGPwe_Lem$}5k;_C$xzVMg-kP6fnbl`O&WxgbUFGJx^OEMJg)5_)f})jhL>NW$
zW#-tuwcj>HTyrl)PiAK{m$%B^j!sZ8wM1|X3&PMoQyP~M+dxabyg$PrW@VyXx-84-
z`h9DD!BdKlKg=)Vv+NAM=ybY*tR3*}icB47+r~*Ci+w}iJX^HZ+&VRIqyntikK=YZ
zYjt8tl~|-!6koQklzBfcwCqhEaWnk@lOo5)E^0fyN1-?ZF~PQOhRUeR--&!4T}OyA
zg+dv#&FDOLS{D}pOMcGZ2KBy3{;U9chdtpeP8Z3ru>J%o)`nLyj2PIV1cQ{NC(xt7
z;RK@}`>zcr!6U0BK^Baoq}*g&x>aRa0Z3LhgqPyJ3__8eKSwpYM&7tw-3?0>B*3XG
zrXRx`>{o6EqujA9Vn%0B!A^Wt?j}%MpVP;J#CZZBZ!GWX(dh_XdByb#8ygSa^V<>U
z*nX_0yf5zatiScfw=*;TZBXs!mwjilbkA;3f{`7bLj^A%{vJ;?n3|{C+x~tg+4BAb
zGn`8T{m6KewpXs?LVLo(64n?)6Uht{HeL9q#o@OL&1${sPsi(7I-icP#kBGqI9V%v
zRM<Y5r1go$LeNr&*c}eZOmIWMOt8T&HfW#}HaDk8_@M75J+ky`kH>p-Yz+BBV+AQw
zkMUzw37QGi8oIxW`GS2^A)2$HE-9`FyBaBRgU-~GLzynqZ!@;<({Nsh_4U*OYM|@;
z)vby|Y;QW8z)D(w3qiYG@wc_&Kzx4J_gy#S*?Q+^YsTK#Y-Nu20|1Y7J2$yj5m@5x
z`jrfQ$+P3>2qMYN7#*FDrBsPfUylL4Wjx;_^G(XdvVXJHacjN309@FZ|KDv9a{HDC
zBsV#DP;QtH)e35`kh$Z<{VwX;Q?7EFzNsD<yxas`dI^%RCp5aX>d=wG&k1&niNBhO
zvfJlQ_flm|j%3kNR?p4H-0xoW^A2|h5(48a)+=>S8(TItpB9^>j*@z@rAL#9jM5D{
z-+aXf2S+f3<{VI4$M{_`T@1ZEce5Pj+m)4Ha7K59uWX0w_709G@zbjtLiHA_Z<BsI
ze%sakPIA+|31&yEZW^I@`{uqlV{6;n-9qP=qsig0;&OPK=sDGmjj$@H2wEZ{8ulN|
zGs`Fd2$s$)rX<n9fkM5!ZyUui<bQmmw0J(A@1HU(TaECyZo_~Z0xZPAMEeUrQbi7A
zG?a4c%iR$rp8C3@;~$FLO0!L7kGli=-VYda%d~q7C6|ksSXjSVy}y5YLUsj5x1|&v
zez(JJo3}TAZE;b>zQEO?&B=V`DrWFC(!l+E8$VI+4>|)OQxLJ!zkfVph0D&~2^7bJ
zS3aTj9`ClT@+H@9s$DDl;f}KOFS&AEeWObG?#>tF6X^_CQ#@%%qm$!gX7^0Sj*qE`
ztnOeIexk5gGtQJ@-R+P+GRB+;cmYv+?`R10sK0c$HJ`^H4pY|96Qv}Yb+<j~5isab
zb&(P{Pj|~JYIm<s9w2#a66!arWnb>+3GTnMorfujSsA3(2ay<4r?)|AzzsEbt7w>r
zLjV#I5qbp<&xf)+sFTG?3~wKw;J85P#x1&V7qM6vu9m;CAfZLkk&!5Wxf}YaxpW&6
zUA6uOyE*6LDf?Ww^&-XmSh&zek#QL{ILET^PeeJZ-7~?1jKd${EF~wNIFQ=qOyeln
zJYB59DJ<flUOhK#8s7P>bQ$3z9k!RPa=yH}EBJ!(t*mPVX7jKxF9f@dd|oJtH4e%y
z<GhAB=?IGsLWf_oW94R7H^p@uZlo#5pM{0|lfb=zxE&9%2_qnL<Qn^&O&v<ZLUNKE
zBkNdiC&Y9~iWHW8XJNt(mnc6_#*bWiF;2t8GMoz%qqmuJmsDJL!aB2Me7stbi3e&U
z;@-SJnCgVM?U(C@qC;46j`A=E)m6W9?(e^WVGm%8|9Tk*ACr9J6!{khL3YZ(%8F{(
zr576|z<;d^d4&-ccb~d*eI8gyCN~emmCG2rmRMwsC`hM67~05x!RH*s`Ulw1_<^G4
z#W{id!++xg*uW^0!-vr%<+4q29-RMrKeeXs4>?}CjOq&Ue|*t^XLUt2nZktuX;v<5
zq(L&#>`d&TuZP|sI1b@WTo>}2N|V*7`|hQ3Pw%@7rYOv$OX&=|+OV58-e}V!bUb)g
zQxd%zD;)Q%WHU}q?F~vbdtW&(qXy=5p&~mEtMY^p!VV4;MFZ~3@5ha{faOK{U15(4
z*lC5_EF5bvp!FRfftx$~OG};3+Rtz~X>`XNdZ9jrnz6C5UUWDX)YGc=TLMnbwE(+F
zS|7(hU@=#VbThFhXxFMBWE*NTgF~9ad6kR=Ed!S{t~x)gEntLL9MN5xendPZil7NE
z!?DZy#n-{{3<ML#Uc>j9q61}PQxljdFeU^rFp&oUCtc7Q3`}g^25gDAti)iE*s$UJ
z6Yh<@lK^}0gOiiP(Btp=w8sJBs?v#yV?nz3QB`2{1kUewe80h>;h_jvajC!sp%^E1
z-KqXwKW%lbyq9xXP)fTX9E;GxdwPep6mec2V@NF@*q9^6A2Q2sES>2k(6__M!j-za
z^{R2)aGQm5@zo1A?yQ;57zl&m$*<km^Gf31o~De4cWbs{X^iay@wx2U9I*~Ymp)?p
zi`nP)F0P>84(Lu>_eI2cRRv8|kX9SnInn(im1Px>(&ASB)c@w4!hL*hAU9Y*`XVc$
z!c2I)xEKW#uWrcV<IwpN7d$J32iz75L=<y9oJ#hy5qNtq$Vwo?gEF3y^{81`7!CG!
zCx_=ZlZ*UWU*6uEjB8AYtYxD}FusgsLbw?4Dujy`@oV`?i_78N)G!jTtsr5ewP}SE
z4C)CuexBZ3$$MbiE%X=4jf{}>mX6Gxjt&|S>6TZ;w4B=Dhwrmg<df4Eiqy2opY7?7
zts}nrFvu{-4Z%DWaN^@Rl?B@^Aek<*GB#K|Kh!be)iEiF(6;$s%EMVf|Gb7P7ZN8v
zciyM&d(M(uJlDax+uAty`4+^u{vO)Y)cMr<(b4%U>=X%7OUM&_aBt|0c$WZg<y;vj
zm<V_4NJtq%nu&RpAqovgMIe!YJC(k*!JA5|zwI3D1#KBusL+=8yuzdg7l{-&*K-^U
z8UcX+XXqgLbA{~6IKT#gFkrV}B#bPZ7R3GS#9jw2;G_UOi-B*^n*-{uQCioPG7~a+
zg~65Anyv^~e6m`q5mR-I(eE0ZItQT4)f;Urdh>HWoer!CgZ_7XK|$8B$f!+#%oTti
z+!Y+22u*}OB$wvjj(YWBCMy+1&g~`|c+4AzAy9q>?q#?ohgV1-Ddo6(1Fs^eLCuQ(
z<P4QjQ7Z^YK>s8Bmk`aDJcLB!vAumaezB|n>`}Di;nN{Cg7Ld2kA<FAu~FlifhCOt
zj1S<kM2;0upFG5Knu@+~vQs&pIxo3f5;?6M`(G<GA{E4*nQ0WEjWX>Hpk5HHJkRi7
zGi@YkXeM`sk2DKVuK66H!w_fy1pi0O*dUpw08vO!BYX1};a6Ke_BQhBfb{Fyj(D0d
z$W!}Q`0)4huw}jXJIuo?*Sy2Fg|aCr21=KmwX<f)F`k2YNp2A3yUy`>f@Ea67LwkH
z_P=u4^xlIOuT9Y(+6Qy_S+SG(A|WCEuQ>6AWVQ+@_MESh^sqlqr>We2#aK|Q{e#Bi
z<z5c@#rH*P&LBxXU`@xmyLN}xSRw~B{C6yh1I{XzFz)a$zflyP&6M(_gEo7)`2H>x
zT`0PMk3v$%9d_<RtUm^R!B{&W#N|F_E8=`m?thH-|1<XfHPQSH*oY#j=j8nsRw5R&
z8zE8)5RX<bx?W7KokJ+oLT4+n6SZ$p6A*gxQeDIm1*i*Rx5)tEa#rF^#gZaXOFr8W
z+{4~7Qc@8aV_s}~<nI6cNd9LytAS=L{=CRV4-;$sS@bA<S>H9(e5fQ;Wt?EgUw(|V
zxG`Lk(6oacJx5OG2UekyhT6NjM4#Dg`DNFT*jMr>NkB5>s6{pxMq6HUn7dRzX#bCi
z`>#oy;XyQ41u?X?370<W9*bQkprZVDDFJP?uOKzGZ$qOJY1aEtFQ(7xgB)e&>xhT6
zDl=4gb=DM8aiCmVU|XD^II`t~B(fYc=V3w~owi`F<M!E?KO=e4ww4H|LYulREw28(
zfE+G760@PhfcXADR}TT;W?D2)13ZYZT))(TvSNsrT<a6*&JAA}@6`y?3s%IYP+YA~
zWb#9ao*@Gz$KUuIK8d6h;B_u5PN9e{_K%SWh04@sMY!@JGdI|Y(()-zOoVNrL|7A<
zN{>$42;x~ORsMc57Vh}pcxpldGPR!%+8qou=+J%xU6-K6X|=dPHP5iy_IKDt7EVy8
z7-Z^?QynhSGLcrh3^(2X8mt)-B=cF&ahC`AqU=?meX(P~>rJ{mGUsl1lXKqSoOmSp
zbf0^Fas~sK!8&GijxHjDThQRW;y#y=VTA4<e!2q~vZQHQ%SH9u+b4J4-XVqd_&4fC
z{5IJ&fx)|OE49~e^*iR^OBKu3l1e`)4nH6^TVWz%V$@h3&X}}VMZ&^Y4^BMKTR;al
z4}<Xye-^h(2xjDY8m|p><ES!C6tj@#n&!>@y@-G>(3a*WUXETfXu_T2Pgxx2KMk%Z
z2OWAp)?H;M=BViCeOp_24D}9a&j;2;CB?fd^(dHZwy@kh8||FZhToh}{+u3gl2k?X
zGyC)iZh94CBxOnpp(!PAn`(e9k7vlbr^ZImf1cp-3byB8YN3jZCW~|Pr8{^woyS-0
z<XaSg8#%GODgp$LLq_%$4FPS(?}t?aUOy{Ha`O+<+J@h#pxFiHbLz&Ber4kUTOAsG
zO;x-6I;187Zq}{KcBjCcDT_V&<|wm9@!mi#cMqA*W`ZWuLm}tUW$-!`f#=8n_&G#Z
zxFe<L{>haqpgUu{O}e;|9R=p@d!K++Y1iXS*ozk{md)Oqn3);P{}>+LJP$Wt`t9xH
z6S4d5p?bc*?yg@i?fd}lSN=XCm|L3f+{70KtfKp|F)|}IlK&(l@@h)`vxURA%P~4}
zqX*)WvI2&N2|XM1PIvp$-90NSkRjl28*uf`CZLv;^=|#s?zhEgKTe&C?C3cg2Awog
zu|sFyg(J>_(JU*?Us@e6W9RBmeN^gSLlBVBqgM6Rni^<^3FlyNUd`VTmu5y<9je?8
zW}tufg>H}9^#uEtDw~pDj3#48(0n(1^)wuS6z?)-<Cc>48szx+ePz5bp(uDYKfwzA
zY9f$2deIH&xdrmOpEywKbwSfndtudfJ(2oux>gKY!hgNdu;ReHufI;L+`C_YlS8Dc
z|Dy&DU8)sw*4*8GBcmD}iYQGn9REs;`{K!7-LAnv2!s5XM>1CcnZv}C^>(8Zk$8>q
zmK8dv^s*?V+R98yqE3@KJv)KSon@#;J(U7OLqnS(I<h{Q>1Fr)0KFrpcCho)<9?VS
z$KiB@H90u$)r|M`dEc4tcI_KmgN85KJXt40JQh!6)aR#XURZQ^)C651H@?gB2g#FJ
zLlzT*NJ@x>dJm`e^(17qUK6}LF&f)PX$No{=Gp=f{~fD+v~t1Pb$&9!s#xY5=>JLh
zur4xAWTw%J0P@kcKU$yw=sq%-PCzQ75*I`{hsdgigmtC-f<S;E7A4Cgph5|j?>$i<
z%2!B?4HOfS&q$2u1AkKj0||`{j(?)4dS^nN=H-H`xZ@7e8T-@t4;uWRX59}zKbsgI
zYmgCDIJam0e0payHKibcJuPg6{vw6AXRey<&4Vj!e;FQh4BF{tK10sn;4D(j6k*_y
z$qbR%hwzRm!T#exB#q0<z&?*_$>*73>hNsu(gbGLx*|j<RE~ds45=_+Q$ayBN~rxw
zP*3M~AwCum?j&bW7JJBoHxzg*Hr#I_+@b-IosC2C*7qk-e0;OJrbL0jAT%Tcj67Rw
z7z7B0BoD5TYMoThe~*119G)+Cg$TDJ6D>QY75*bu@#M5r#RK&zVbNjy$r0e9GjEOd
zE+P5xKyykcBLl1$;S{Skr)-$4Z%6izwE`Lnyn6}ramg9rGAP*Jy}snK5(Dw~%lgI!
z;B0mXE18+im417JxPd*7FoZLNP7ecT+<*B;#0)+0-rz9<s;I<}Xl93!e#wzbQHk@r
zqaL39b3e#OYMere&rL-LkD%y?QnCNo08{sRHTaWDRg);;B4L2X7<q`vVF@bDY~`@G
zqmcCD;PMcbFAl{QSV>WQ!e$MsJ$YuMz<DGwpeYQBM-5C_S<bye`>?TufWs{u6^x(b
zYst$1>=_mrDW>4SFfk#mpg}R1)A`;bJ&H14YIwZ`!tQX!oUX9iJGhEva$vSEcck`H
zP7>?(^#Q9E_x1+1U{BJwvJ#Tc{41~4)QF9DWjjJbDypZ4&vLLqS6w42YnxNITi;yd
zX7`-~<4DG%!NCxoW%6a4Zr4>?A}UHMl_77Dv|iG7W&2@B13vAI4$>(Tmz4uT#N<?@
z%O0`3tsBVcgA#PL5;D%=s9cmYJo*A%cAzsbHR2crTQ~IG`Q>*5e--Su?U@LPW`*<e
z=|-O3x;K%6Z%-%#e>i7Mtc<Nc)T(c(3HA0fe`*N?oXwa_rgD9QHo5zSdhq<AN5|pS
z+dZZ`!pg$ggL3Ug`uWL`LIXQ5Uhjn^+#th3$OhHAAud{9Juec8c@Ac6f=;n-u{<e>
z$Z%*ND8#yLkYwEiMY>(TVt@Tq%Z4P&(&t-kHo_=Dw8oNA177AdK_T1?)-$H#YOViJ
ziMbUh4kL~3k`c}m3O>E$hEXhu+Kgd&-yc(?RjBNS(v%{e*haCbdV|7fSyg>n;AVXW
z#7hd>brC~Y`E7^n7UBh@?{=o|eHua3!6DR?7lSxSp=fs@2h1uR^)_-MMe1t6X=Fo3
zNl(P(>P3aceTLB}1Y1g=Mkr4@yxzE8v?ake?YUpDI{@|i%C5N5qSsIksy}Yo)wwbE
zO<VZ|e)s5mcUZCzhoD39)giT6nnFG~SP9h*NO5BOONk-$lDLyMf82phkx9sB<An%e
z*!IlHqma=N_w_Ghsaa_EGma@BVnm$3W{9``Md15zfqq-FTxoLPX{4d9GrC6?xVgCv
z#m|$?e0uFXS12%vc|pmnKXitz3ZJExiyX?R7Bi3ufwf}G?p?VR%5$~5+ldJgs;d2I
z;9-W-nrdvSi)(L?%48<U-VD#t{0@SOg*H|G@ZKNd4XCJ~BrHgdD)2cyJ0lF9D3J`G
zy}5nr^SbVe26j|7qX8G|I>X<P`*WC_`Yo~sXVdX{B<WAVK#)k;l$kg(pNR7|%Br1B
z8j*~)npHs#(|sD(lLZR?KspIk>VPVxCYo0x8;&W3mf(rLw^LJ1-h2_ddW8*%$A)p;
z@qn1}aZaq-@rra&iG2HFrM1hYjxu%-3uMA#j));Ic0W4R4=05_9cX1k%J>%rQL!i_
zmd^)i=rvya>m^7(pxE86el!PwoLQxb%XCCHcp>q5yEJ%-`X-xxl!&doz|3GjU=C`6
zWpK9Q|Kf8=BAvm75GkrjXfe2622cWYYH<j)Gj`|{*flv~Noxbs=hwpO`M!Z&s4T`I
z;YV^-^LIR4FhMrN@9HZ8z!C7_gwkQaU()v9E7f*vkAgKxa8Z>K)R?TexLd4&&J45b
z9E~8yXj2WRm}(8J<7gOjGNS`ah4dHv06ALRaH3$Rkln~|9=f2a9bLE(vbld+0v%F&
z3Vs%L3V=f~4y~;!McTG<_c!-1yJ~N&)A;;W)Px94&G5rRR9}(QR=2gXWEK~O!_hWO
zE1f7bJzM^!s+a*#<6<$d*6$m!`kV6bFgZCfQAO&a#^KtpWnk?MwLlm+%}Kmy(^Q{U
zPHX}e9V2cAD~7iU(fw=}2zuik{Vd4J!9<kdooJ5Sf;AtUq{B1vWaVCM9T*s0-RN-9
z^0cm7vXxh`xvy%WYd<LI?2em@J+QF9iM!qkcvJYJJ?DBpF}kd4D{W8P#;y`ydWR}A
zKfUZ(GVKFCE#p%#^+uOM{7DAQ2(_*{)k0B$f^XH*rsfmkCvhApK<O(B@&!X@8J67L
z4;=pfGs|R&*kob?ds7K+aCfgeeaj8Z*(_KoDd;l*2p8VkR?$S_3+<MWkbm}3=_sTG
zG!v6M(vK8t7?ddAE)t;Nd>7G=R(t{}%D9Cg4+6!Fk_3hoqyAZu8OkC1Wc~I}4_GId
z=JBV|;D6_k;}OxqAFQD!V>y-Q$ICNns>|aAHH08alEI9D?cwuN`{d<SXb@QhO&($k
zXKp|C0c3rxnq%8mgo(?9B&<-o%XV>X7z`9ey`z#*0+OP5mSGers;d4AR%L{M@Drl?
zMiBos%{G1P)$jV#x}?FK=fHaU2KwM4cHsEiaCQ$;Y;aN4IEnF5Wir@T5k>Pj(ec$v
zwj7@?EN<2nIF17@R}yOY;pgn&`Wz0N>;**6FVMZ3Y%#|!^bLx?`d3YnKL;86PFI*Q
z(=!oM%_D>7@%qei{X(OD*z@(yoWLxNwGD`6H8D%+RleDm?~h)}=bHWFqd_sF!7gN_
zE+c(qlyf2X*j4$DWK}YpL-)Zx;BCsY95zH!%4Vd7<6TDV9<*6Yxi4o|5B}EqKv<XA
zj@bHJLK<b{B$XgrT$Lt@jI}jI?Pqx=wg^%XY!jO7`1fRBP)5{+IOdFSJmlwEJuTM+
z9ZzcjP_G$0{t1!ftY+0UJBchvrh82s4j16j@^aJ{A$-m?Q%oLTKuV1__8Q=Rwy!KK
zw4DgJ0BQ*Yn?(%5tCxFxMLkK9H;=H@sNs1YbbPBHL-Vk0rO}42K*{NLB(2__aY?W#
z1n6Vqa%W#ZLT9}Nz}<u@mC6g^XXX@yw<?q{3#`7@R_N!)CobN3c6=Ti(dT`l{;ofP
zh+)0iiiJgjW`7Sg@aNZZ$Z>L_kE2F1_Q%LXX;eY!sd{_g>=NqPfcnLjQxXoj%FZdq
zpD%yBuy%LY@YS&!$Cd(&LYge<hd3>HSiS9E=3#XG;L=*}0GiScQOkcft_~r&Eo9K+
z%Jd#X!kFc1hWkgjahdE!vjG-$GfKnZonB#o2y^Sca#q@XAPP_ncZIt+#)Nx>vk*kw
zyKo{?c_RgkJ>9-$ba6<!|J>eV7<`23FQ5+^j!y~IJ>!S9DTxu1)#Au0K5!=&MT#e!
z&TJt0@N53A^y1Q^O6&5R$(R=;k(8j^xxR#>L59wTX$uo<V|)Vz@1GKecq@UQQK=4$
z?u3bMXM`qRym59B;Hc)q*J2V^dEB|SkTOb()b$5pC}`mcHi{Jyb)<&H^+sAaU&`oe
z;f-`_f$$f+Tdlvp1*;&jcYQ2ip%!yc!R^@0QlKsu$^FIl*Vw)<9G448RuF*l+br#@
ztaonBU%Co6Ur)>br<0$Efd*`=%SZf#K;%y?f^h=CP}cfpr8S?OU%;{tS&6}S4_28}
z`=`T?io8NWEUuOdDr;?C>sAxV-xKs-0aOZd`b}7$v_nV)B(nfPY8DK~9i~<8RmqLd
zAu-(kh{<Jrdq-yTfY*NnFi&1!Gjc;iYYik>TppPt?rTH!JfUen^IIs9pKq^z?3M4G
z!b)ZdVKRQof}2y%7^pt{Yn<47F8{61AwUAf`gRh^Ne;&$4E0a;-T^%dTB{24|3sb<
z;)ydj@!7uJt}K$eat)r95`65F?luE`qoNe&A-={xWcOn<f;i>cYP4cJ{i7ZajRC;_
zyS<+wL8N!GAEJLli46Pm6Z~@gc_QG%zR!g0)@$%h(N<Td9EaOgfs&FLh}q>dN7R>{
zUpbu1Ihk&_z^?e<cwS{TQQ~f-J6^ybi#hdZwP^nzrK;5qvT@{(a*-*&4W9+gRlU8a
zQ_RJzCZnJT(`BcwgI*@@b#k4lTly0tz~(H?J^uf`iT~Wle?<=;h`*`PtIqEy_K%cK
zAXGJe(*ubTC3L}wyYp__;y65_xTZH0PEk|Q*eTS$U{YGorY|;prN4r&S3YnjB6gc#
zBxqE-zP#>%XEhoRjB}<m7Ols+HOHKZzMH`5GptQ=pEPc;0p2U1ypQ}+D~tA$fF^h=
zzgM{KuV#Uf$yx1CR;7VR?y-cm)`Me=AKMI?XoRMh?0%rHr+3i^Iz5MVmSZ8hotb>1
z7F-m+|EXa8=M%R$Xod;@L+X^!Px?Po8=*2T|1zsJKux}Q+}(x_Qt8Gg^g0n9=l+k7
zix+B&&sO+UtTL|s90OHp0Y4i`PDm8$sa5$HZRYz8)Z;@-R~xzspRM0^K9~IE?Puv?
z>gh=llD0KIzr7HZ?l6CoEbuUafSI}GrJYMjh)wok3Hr2r8a0%)ze=@h>%oZ$XWY=+
zpZ=<I(y9C+ASERg?CTW@jqMp63`d5Nj7vy}=<5T}J2~$5kr2Xnq?eZ?+E8$jg)-*+
zv~DRfDlaM<7#PqVz>CYs^rtr*%ag`KmRRRw&;lqaMa2!>8;o{1nJM^$9VMY<6-8ZQ
z=yJtYXQq{Y9}5ZwyB{v1)_z>HA?72r>;k_2x1iZ7_a8y?klhDRqvS|jh@mFn_xd-a
zW<mUHzE&0Q3zxG{40_mQg*rUU)8}o_&m*4fS0?`gI;tWvHP3Fw(033x97Ttk0jzXb
zp4@NFU;lgy2?|%%&XT}7EG#bH?&{8+BXKOPctMIc1y|l{iS$<`&8IRK8!NotC!@r)
zvS;j?jqS{&MuC!%eq33=5wOFSRXYTr$}Aa-hL{J2hIRoA5G2LLt-d_SZ*OleNA1_M
zEpi05h)%b)Ceyn^(d4KY7%2Bhf}=2xr#bE?C?%;i+DLydz#pFaH^|IjCVp5UYC*!m
z-PPNWN`~HjeSN!ESCSH=d-(zkCqjZ#LeY#ROiGNd6aDsi+{A|eTk&n(hGeb;+Lc|{
zGG_hSA{@KpuoUh$aHXb`mK6a{H%NxdguV24WA#Z$l^3jeUNx@d?G9*bXEKIs`uvo#
zTCU8@oRgs}qo72#U0U<Y;!OtaPV8QGO%<~q4XT2}jy<w8s}9WiEAFJAs5n$v+wn{5
zx0QHh5%9c%WCmc#S~(7UWOf#5oScdCr%^FWQc;)IxF46nn>>ZPMO>fRaBuJCY@AO2
zt_Uj0zv<01(IudS7yv*9fdJi)R;eYV;lULVA;EkfA#(pA0>q`Hz^h3H3_bsJoNsi<
zef<Bdh3@}6ti=hi#AYT{)`Bq7Y=lgnHol=#DJQDm1GwaVLcS2Gh8v}1Ql5;$f&}HX
zCz{KkzbWsvb7w?-zI|v$ee8_{9#x!gBQW+D&7JCG<BUfr<`2&Uz`l>-G;pN045&%;
zNP&Ny84133X>tJ$?ByvZDfQ`+6OD@8^R+-@`t9;#_}X`@=S@RT)7AAjJz*r^NzNV*
zep-v$4tg{=Gb3N-w|v!U{H6k~#H2JAr`|W;AN{uu54DjXg5iL5@V{QK|0OdM0POh?
zTbV!-5>_qoD{@F}z|;BjX(1+{7mQ6os_6-2s5+g<GIZgXySlDk*?<^NDS4Ve9LrHT
z@oHwwhaS$an>KQZ(2vmFEql51>q~Po5QvC~VDVXn^{Jhmox=$2L%VZx^;v%eKO${5
zlRM(HG)$wCUFH*Wt3<3+N{xn7!wDh;m|W7KA$HFTQ0db|DGm!sN%6|r(1{jjXP41X
zVhQ?PLnk`h*vVGYsWe&5IA+E}#-2MhJ!^e}VCVtA|NbNTud8P$|Di^2CK<}YkL5|@
zEhYHKVmTJ49L$*VSm0dW*a#vrd09obPT&2E&MR4nRnT);?6{I=)~+tYGk42xh?Dnd
zQc+<&`eRCbF4(nhzvZKuZkhAzk?plLO?xLNw2B9NH>)zwjTJHMu7(B<3?n0=-NYsw
z?KBN@?TgDx#GhW^ANR%nwK3#EvBme@AiZ|@rI0!lPyIjLH>It1CvgH1wKq>s$Y0p-
zVS4{vg!-?mD<)u?H3|=29+(T2e5P*`b!x4}GOd=0+Lyd(h+3jdNWaTa)844G*nhW=
z!VH-(o0Kiv^Nq9JP69UFx;#u`<|W3PW+R`MQU}~DD4dzDLKXu9T9eOtc3)pz?nZ}J
z?z?(Y`K5Z-2YikL^we5+)Jg4Os~QLa?_J!I*&W!o4)x;Fll~pIe@`2iOv1X5A~W9S
z(Bl$Qf|4#g@o5H$4ogS?RJF>t_s!E63&KUm=tOK}zXw&QZCjn<6Q1aph@P7xjprC6
zP*d&K>r43di0F*QV~FJ46<K(;XS)ws7Rf0p{~0yiojDpG9}%$dqPK}W3h<$_6_E}Q
zwEVcfHPnEwYG9F1jEh=Jq7$QP7vz=k-sSDRE%ELX)#$HJ1(wyQXlqD)R@Mgk%v+wg
z<EHyBEiAm(Ne~ws(`rT&pJ^eBU=xE`Wl9;if+sN9P5KvfP;Z3A6pCN7<(9&+sHmvs
zX=`p{INk|aNZi`krE8XhE)yBe#@IIHFE;Bb>Z%4DEI1pRngX)7q$X+TBy@T2ps%(d
zYRSn!$~oNp{MK`imXg~}%Nz0R^eRVh9Y&}+O(cEJ&)#3Eo5ADb8R8ON?>=ccf+Vc9
z87WckZ+Tq^C`hqQRM?h(HEF0R2{`eF*vNmWLMIy5`JT(7N^<#%7@HjC)g3%FY0**$
zS#pBmTQ-fvXU9iSu3mmhMnsAlc$~lcR4zthIR6ICxC4g6Ra>6<gCx>Ykyr|Iq~hK8
zBh0_alB%-dj=W;(nQ5TP43BC@el<RQ(0W%S3bCY?Jlhut6d%$-9>3tf6PnNYL_n%v
zw&nPcXtQ4axyUx5Ph$f)isb>$l-u&xXII_mN_7wl9iRgMKco3;R=plkeKg;bPX%cU
zs=%c$pdAB9Nx_<gjW5^gfFOj36uQ-v>VTkSdxojTh<AQL1P2F)$w*raNG}lPhIO{S
z7y}5-<t3Jni%@10RORy2Qcww=&P1x#yTIl$uP-T~1LBh7{U53X4`w^l+l0xNM&om`
zf)1ETi^?_?q2^E$<JHt<ODp2D(*873!`H(pOG*Z%I_yreq)Nbs?&0KiBYVP{0Z`;M
zRHM?$)RR<I_t)B#Rvf5Np@c`EMRWtx&e;g^Q_l}!hV2PSk&u@A;)SInf}q+5h?6#m
zRp%3w;!@HhB`N$xiph!KcGZkdv}i;kB9S18JT&l@Y*xZYT3<5htU2~h53{x8<-|uT
zB5wPi`3j3v!Nv|k(eP2i`xFh~PukLFrlzT^Q~sc0cZ~a$et#grq;^G90?r~S_(4-E
zBA}B}QV0ln@i>Bu%P7W&(dNwY)?4t^5@F;ulS0#0gflRj*t@UkaCm*wfu(2Ek#AjY
z@6d=hTcdQu`r;&}Q^fD}hu`!ZzBCybh#sdYs;R}m<S6rJoD_pPJCHqPHL$wAcFj;1
zH;}&%wp#y?;rT(2iY&g|58K)AX_C_7Zbh*1X$EFQ;CR2jwyeC|AFF>K`(_oFd73Of
z8pk&Ng>^v9r|S;FB<)L>6xc^bvB>)Iye3JUA@Z{wY#VnFq;o{B)qfn;+ue9Y+z_A2
zZ__58h)lTi3?Pj>($N%!kETN+YGNFi>(DPaANZ0<Yu`i3kEw)c*8kc|m6sG?L8h3~
z<DAs6@Ze>=Sd(eskKyi9qpjvAo@AqNcx0KA?8~}Ia>kp@O2>|c>-LUK-SWmSozg?T
z8JVUdMp4%+3_{@3V}-&t+@F&dF>P2qWsW`NKoikah-f`gM@^FFC2NB<DIOG;6fZFA
z4mWaSgty)fQ8J+Dqe<v+_I}*-G)6<4&#ESF*wQ4lmxxXRj-KiIAsoY&M`5(#KrvtS
zeV|_z`S@c_z(Ol0Bx?m^O)wiDFHaBebx0fVm0v62Hs${LTxZ>vR0fx6a}*XCf`UCy
z|MVlI?ZliA9~vlShLoi=j~*Uc;IooYMecet0I44K6#b`4Dgqt5IO$Uu0N^hn9h%Qk
z6r3<F?_pO}Qz^)Lf}mwlF==(+0Y4ldTvkzoR!>rLo}vbPp(vK|IYND{b>JQ{k|-yk
z6L8X=^3vzJtTcV9b-Z`Gu-iYdCon=Q$i9mR2ryK~I|_@5&U+WS=*W5jCOZu!(26M`
z`w`BY=f6aNEQ*2%8MdIig0Z|~g3|ffG3L{XW-wD#7x?_J4U#idrW==4)n;<=LVwz*
zXxa<li-S3sWs_|43NU}WBEku~-#$`cJ$gCfULySNdPAaUOO8|clWCYkq-+PoP{%-A
zx6}9T>#9nsc$yQiQ5E8_>x5+Xq|`ZlnnRhCEESik7gXTwNhdPTD|^lhxS(WMf4F$S
z8xxnz_i2g>B0PocM2(fayPX`96x)-@mj~-~+B3()NS4P2s>ynDC1eIYbR<DNG@kn?
z_k+k<Yl9n})+RjSiLDhR%#JMJsb=s?N5u%nv5aK3WaObM^!b-(a0SH{{{0UPxMho%
z#<{&3YeK8FQwgDAAa|7V-K|^H!xV_}!zb_QNUW~JXzp1rP_(ppOY-nJ!=*F%Y;c|j
z!XMg`gMS>6C<HFxFEN;&_aiRlp{D&66CwuuRe+;5Uce`}L77Ih3t!dXgW1tutzL5v
z-w4`ow%mZ=aK;6kcf;32qkQ7jy^COZ?QTz8blrZ1Y`g%&bXG^hzjZf<9xE*laCtVu
zYoUf^<P0G$>hGha<c#z}cfsQ~*fWv3ILLQ|8L=}BW)3vm;6|h}z4_Uh1~vwPNz7Qf
zp1TTJ9G=MWQ1xl|3xJO%Ta+Xh<~UXI;l$XdL42|r!D!tcr<BehU3k&}ilUGVWCqN5
zcBdm?3)M$N<I<4G{L*-bU4l26G;l1WVkC=T`9<fDmIg}Y<m5QHj9)&1<Q?z3xpnm|
z{Pj6Jc#jLiwO&MdTA4u4vvLN#?0(7;Fbe#Q=UdxdBI1I)#ABT5-1iCxB%-^u-Q;l~
zjl28SVGYak^bUQ*X_+U5^juK&65;lE>cNxURRZdkKnvj`K8A1PA0LaF?r`{#X2&vC
z)K+b!P-<#K0>b}Lg*G0Me2I|M;BALqN_G1TBo!@BFT(>GK><Va3~p1Xo>$O>2yqdT
zxCPU>{jVe9<XWD+$ckCK4xntZw!{5Q$0!Vh{?+k>7QD$}t)oJs7I05C?;xzf73>;0
zF~d2Y@Vo1N{`)8Cm^%g$-M*jrw50Lq`GUAGC2%EK0_pZd;3|ZC@KA8WVFJDEu4k0s
z4-UNwb8%^EMT8CN5p|Kf@(oWxqAH+cm9R5@JN6l|l)$8?3d{BhS26%Y*R6-po9=5i
zr6N8d<&$^Ajzm~LJhGvL8$4@1<yKwUAah$)tPA|}XkXnYNIoy*d_uK2y~61FUe^_m
z9coK?Aig=n^MhdmZVr6PjF^*sgP666NI|XASTX+fA#gBa2_C06)IK9c1}E0niSp6g
z_hY?y{&AFqmER-8h9iCb81QqTA1=JOFsU^Czw}DIu*XwuUGGl=%^J^c#>0gtJU<LN
zZu%<eouPXm>0ky12MM$)V&c2t$(I>=-st$mQvTsVD;3mVWXJAWRzwD!U}kaq#5V?`
zsk1x%>iRSwyRd;HXm}drhrw#jmHNX>h1`1;3#lU>(-{%H-rUo>h<-b;L+$NI>HBH&
z8~6ja-sPG@onpC{_5?|tLS*OT5hscK)M&Aiu18%e#_s!_(9)b|IIA1>+kZ=ZB7XlT
z@ie$}^W&ecgT@n3G9Zc_^dGkvk&EaD?T%M}-Xt3#R}QGLBB)|^v5-c&y`uA);VR>!
zwzf7X`+=2FVc65|fjd$q(NAo{XNGy)UTpqG0DI3P=0g#a%k6DCA9Z)!#ku(Sf;i#O
zEQs?)rFnFrw_XDfUu}a2e(f`iZ3A2J`5}Ov@uroOM{J(^2{wfj4aMRG7nMo1db1#Z
z6%GlBN&Qyw7=5`YrI9DK`6G5il=ivOlM-^06FWZc<UqiTef>iqsGCt1v~a}K)q6wh
z07npjLs}a&Nv1Di1|<TKad=ZEca%&<JSho)Tc=B?EybA1;TDFq;D|D(>FjW)EE<$h
z|NM%rV__Rm0i5_;MCDf<1|az?-VXcIN5qr|kR4C=B^R#8Nu3kLguVTZBLC>{p;JEf
z@Pbgn27%ti_xbweyXgttkDmkT^&4d&>4+#P=$Ean@5=M=HM<WDE|EE`VP8MN{EXmD
zYdJYz3qDlS+6XL6pcEhe+WP%%U&T))x*$er7{S0V0KVgb`DT8)yO+zISzG!65!W^S
zPO-gjH4nb2;_&6#?+Zf7$&-#)lELlj*FyPN-DN6WoMyR1U!^y9`-#MbUJHCi3Ap&A
z>d<g*C%lYdn3H*onP{lt{84w9$DBa-?tZu~)o?vgsKcv0Qb=re7z)=@kHZW7isrhY
zoD$Z13-UZhJiLuzL<tWERA$!~9=cD+?(3kFUDx=Phx;J(e|AQe{8vtVSN}L7BS4Ki
z{zBf(RL*?AaFxNI!HhQb&$wG}=q!6ovu!PaQ#1;0-reU2AX1tC1|EA^9#c8Fu84Ww
z^876(NJ~^AjIx!}EUz{@IOE?>Z?u%yH=Zpl7AFsoLo-oHa8^|WNJ4$Q!IjjFPZ!(_
zNz_@VHlbL$D4J7_$BTmckyPKLPjd$n_NEZaME$}EtaDsEx38;jrQoO#DUJX)oyv^N
z`(-AbeGkhW<sz=B0&Qc6okR>K9xfHs91NDy(Fmz3s)(vQqEJ~P6J!q7H$n!rrm8NI
zpS$@}fIl37I;s5KyR?-?oC-ba<pXI+a+X+<kN&s8E-#H4LCKUaSPg^EKQVNA*IXPJ
zr^8!6k4CIX8eRnrEg|YCK|-Da7T3#Y_5MCzu*+PHHrVKB&&1vHj(#D|I3_ZRxLAXD
zHBy>(V9ZU;D5(;BN=&%Fb6dXSb<J@^9Ga$@kgj2jGJ!jRIr)mg&Axxfmxy7%zBeqE
zuCM*NE1ZB#C=0p`==U9&7NZ|_Sj}}^0vd+@zJdiYPu0NHeEf~mZQs{rdt7M&IE0te
zhzhgBU$sBZa~-Ho@Fd7J@^7k1ROJGJjT6iJN($wsQElTX`{fdW_PYyUfQ<-p!<>Yk
zD)9&jrD7w0fx?j8(Y6M9${u(z*RS9rMHj=933Een2a00IO4V-vS=}{pLSTcDvadAG
zPlX6fai+=&#88UW@{oG$pYxt#6IS$dfLcK39I%m>#{N#t=gsru@I&$Vt$iMpYOOm=
ze*G&jZ!#*lj*8H4i==UiXl5kYDFOG#)ASkYaY9on@OxeDm{K!7;oI*s8QQ8%h@E{m
zbi#w$rk8_uzWjoGXgDI9gF}&BSfpJe@rK(66eD@n1R>;Eb^~2Z<XLSBwO*zl&v4o4
zyk$4~kE}_Fa|M}CH6aIApk1=Twofj5)@soC;fLPEB60WER)JvRlQt4YX|K0KStDLl
z6cI_y-y9Zn{3XtsCFt(=gRe6Sb46wgf`X#xj(7L+;t6Ku5$Nc&zIdHp9LrWQtpS`^
z3vkYHdYDqc0m$mFumet!W`!knzjoCMZYC1c>Q8QG<%w(vG1TbzxEW2M@qY8f6`j1I
zRtX0e-J+2bDq()-7v4-L8OdwJg%BBp*V*5Oq^B?%2W%GOC>r`6(Rm|xa%a(7eHpT!
zS?sJ0!kn;pB5!Hb!`7S0?JHU%=Xm*$<YckpB=o^%MbU(W)^aUWR-Ry4<;z4Ru`uL=
znJ5xE62UOJcU4$e5|MI%q||iM`+hk4zwUf3jZx-H7}1PR^MhOPE>2kA?n%+{#}?Rl
zwz}_~;I|{HNJf=HL?R{|SQ^BRjTxdElkgXq1fqRw!pa$1A2xn3#6K}5G%{LdWQ@o~
zks1i`a(8Aa(2!l~nxW6#SX5L9q^2RzE;d4Bct-aWU>VteS%UBGgRzjzamJCVBpx}d
z$}(qpg6W^F3?p7t1g^}Gzqq$69sRM~{le~>TiC#g%*h795VNE9d4e*ejxhg@7{0(z
z+iQ~mVt2kHo|AOGS+b(j594EqRHMtI7L)Yox{zR!Tw~PK04G(=iuyA#Th!N8V{0zb
zHVkgCmUKF!THaSB61khF5YSV9U+1~XtljMxlE7-Ee|AB}Kt2MOXvz(yM`eTxW$H49
zx#IWhNZJQSxA|XPBf`L652d^kKwyYJwuQ7fOuW=Pss;Dr$n|p@21ftZU)7m}19bF6
zQ(dqI4C-D?J0@Wf?ZA>wjFj##%Bly8FDR{F>2l~t`;^6SH#jV~_?^6|%qyw#`4+)u
zy1L=32w*#FO`w<><%NLCy`!w^VmBFZU5|H~DEyNcl+u2K6Pzfu+)SzqJ>ayxZrf1)
z7Bhb4#>SBgOO}WJSV5v!fb%G*^s}k^nk^f)Vd%F%<cP#LA{0!iF8#c6Ax0u;)aj<N
z>3J}LpPF(alngmZdKIcP#R-~HdSslFnM6|#9dTMzG*o{Mj~KzSQz&}AT2^|%rB@BV
znpmzhY7(enS<1qPBIEwdJd^NO(>c4NIez)XJvO8@JPHj_L3c=4qGmd*%Y$vF>?oaF
z{DQKaPG%Sk<6fTCfpd(WtD5Y!fS@l!W3qCdjd@7LhqCpn4N-y8n8B5KEN6l$7$|HI
zk;G#wdEx+7U?UROvYmfI1m~h23`vAKhA7>V&~R+uWIs}jFp-tznSM_kfjPx!PzMXk
z0)3_iaytkX1JgyMzL8Zl$a=Hka1KlvsbMILmBr!$OTH}VQcdTu!>K5(^~7YH!#-yW
z{-zu+p2`hA{V$?-&YfwN-)BC&<P$C?DriX1mh^Mfz5YUC4$%{nRn%~VM57MFDV#W5
z&ftZjF}<zf`wBQNV5&f!qgkf7S>6ykXFvXpWPK`$e`<?;M@NmzYC@#v3W(~Cdwk7L
zPW;{343VLK&Z^_b=tcxow}YfREg&{aju0Oe1$djhY|@fBl`xgo66l`@wYuw*MP+>&
z@JA63!!zgK^0b~~_7%+QTbPQI!Y0Fg;qD$I2+bEGh{obTxd}np#*u_t$`U8&2l>*z
zt+=ov`FCC*$t0K&X{oVmTC$`?;B2;|A)mE_yX2)@kfgC!6Z&D0vO0@%wtH;@Wf7G~
zkxm$76t<m;Q&bV2RwEFLtP*c-2^*Ek1}V|tOvu=0Ni(?OvV)nZ$YcnO$(M4l>zgA=
zv{L1p?1tF+zCgnag9}I=Sq;nL2~e*VR9Yk~wQE9ee+3$pDhJ@c(;_n1N%)zbY;VgE
zT%BL_uh+ytE@WWql3BNk2T#a6U93pLwv#HKz8>~vnMRpUA{F+TCOu*-?e+}89Vq1q
z1A}MW3imMLF)}hFq9j^KV+Zs@m|yg-9Q#w5;;95rv?4pO#H1-PE_3=kaqrzz<q1DK
zxIbh6QxmmXP?gG^fDPZpVTFC`kdP1D@P+$!$Te<kKG8ICk{S@x9mx3{&a<=u0f9#8
zc6tU1%8B&<`wzY<nK`e}ESNCxZFYl-ID`oS0VO`e!;-Gg)>yHV@nW?N%~1ueY>r!=
zlu*#q5vQhfDRV)a-8-u>3;ATugRq19l7lhh7_F!n8ixZ`BGr|DH0Vh{bGT#r0gD+2
zT@(33LfA_}7CR=pOL*EH8G=YDPbA2mX7X#Uk2%B3a)x!8lqQNQ<b}gvyo+dr*hb$B
z6-|d}rDEjv(Cpk{Zv$)&2TdrP-94w<HH2{vFCB$<t0-DW4s-sih#2E(_27l7(M+vp
zW(m@B2py9pFPHuT$5&4lMnu%qiP2zlCMz%8sJuMXsU1P__fL-{k4ggo={*)#2=x{7
zR7+i8Qy%SUiudGvKjI?_Rk`xF(O6LaN|zIaBk^NN{9+?s5VtWihPjqMUhrPGI|C@!
zb1vcJU$jXmY`2_1=KM5^qk4H%6B5IlKB0JlF22TgLD}nU+~2N3kpX9QQ1mXU^soDF
zFx?nI9CDw>BGd0`G}*NUJi6N}I3g6wbvEa<ML-mVZ89`<P<#?@6L2!|?nm6N*F4}n
zC`#l%#le{C!SKrZy8GLql~8;k%LCgWs9AND6HkhC13Y2!*C!3F%o$!Er&DrH8edl~
zP-Ru6Q`b&(E^5HcC}a|fL;r5<HLDu*_HgE3bcqw?6Oj!^1wmIu?wWX=I=^o*`ltNf
zx|uQ|?%)R278g;1PWi|P=-rb8YVoZJiUViP&WAUM=g@jlu&Ge8zKu1gSUC?^(KXAN
z1*Q;x^uu5Uw!yso-XaNykmz6%5p^d2%vk(%PxFNGB+yR?*bpY9d+DAG`3DxO-&ARt
z8ohmES<?TacGlVAh_lh?oo))Oy!w-M1C~$~*Jegg{r!Os1m$7Pe(>N*dr}yT4Pz2=
zf`YneDV=xJXOfJg{{Nt9ZE0r+!%HcG1C|JYsp-jo4IZjEPwRs|0GviM7)MTmd0p;6
zU%WUICOv`A8l`9WW-<%*7ULf>q|I<}^zF>xqzuWWDVSZZ^Y{~d6X+zIQM2CQ04Q`!
zMY`P)Bi=6DTxQ&cM1UOu_89mpFcck@gb0k?-O<E+4JFCuX%TnlGvk#wj$dSSECs1K
zP1SH89Xg0Em*5|vQA6SYRhfy2aDFyRkC8Q>kO0fL5dD`hLSscE2n;Hsv^9=`)E~?q
zQcBH}s<3!+4_zI;=lf$obux>$cUE4FRZtfmF<6a4Z=S&C?D#OqT?ODo-E*%#6rz7+
zB&cX7M_x$@i5C~=f+D;e?lmE)H(pvxu)l!bV2D}$B$0s<sbK4hHUKNj?1min^c+Y?
z$jEX$fjE;{ZS;fMyvM$kYrTJcFrD}b924~-lHZSxoxQEE!S4S8s6bc05PYc4+^l4h
zT*ktC&`699@}SuR*jZEm$pJo)OmTdDJjrCjlID>aj0nfUg9ackHG!Vj8$tcNDNvyn
z`D0Yz68W!`D;Z+O?CJ1yGAHNLO7sgJgkdCkRUvb=8!<6(=rt76#tuXxnHNFRnCf<;
zc|c=kWkK^vmHKajpgsZQLsAG={{ZwO>9f11H^z^7h<plaiQo=IQ{D)E002M$Nkl<Z
z68R$-kS4<0#~n7*Pd%-ArnTKe*v1=FNz-9T>&?Q{l-89AY%HzdU}-{{ylOIA>kq$P
zjE@zZ?QLL5&%tZMhQ?c4Lk%_#HY9;2DK+8X@8bg-T9a1vyr!mR7&tr_x#{sRCe7V|
z03R6XvQ|ZxG^4~ss2iZKlLaw#<rp$J7(Ntrk;m2C%nT+L))*Gl8)=CYbV6MhZcg?@
z1S$z@Nm%p5bXZwikXfP;{Af?nmKTwafjXX;F%bbIsZUEJV$Zo32?qM3kE;!$6B3|f
zZjAsU5Nn8e%Ar6J&VB<iY@i>_BNaGUnUYUaHG=3_ed!uQ4I-@7^kHXi1PePmR2G+z
zMk@e)`}Tsf1FdUI!hpibTbY`WY1TD7JZU;?jJ0XL){)=J6;!C2W7f23=<Q@lCZDPB
z>=O(tT{5b)w8wC2!^P4RW+c4vaWX(G`4U)?DOfdWh^(!xp`+dj94D=#z5~e0B0B}y
z5-FT$zIoD`v847q?9Ir(Cj~mD4x~ZoMLsuW=r>>x?cssYAWerRnJx|L<17Tn&?hFu
z&e{yhv@e>PS<u|H5XRb)){e0$ubW`x#79BT!4Ff$kAS<28RC<&(L10o1`itw3mq8}
zQ}W>G?hP*+Bib$LcTx^UJ~WQjskyK&jmSLH+Qt%Qj4x@hT|IoDRGf)oB~v^xc`PhV
zNP|Ul(ZZbfeG6J!G+$}XX{+<zXoX7JlT7Sf;O^!K3u{}LnQEhgn7u&*2gBdX12(iT
z+tdBl{C=Um)XB*ac6L_uu5zTgZBD5KjNnY>kIqzYHoYG*%hWOM;eoV=mEh36vzYMY
zJQ&LgkyEXOnG{XclGaJ3q>$#gKOPSD!Zi}SIR*~Ku)e;6Nv@~22l>23!rnI+V}}R9
zm>4QvA7W`60!-7WtF?Xq(@-a?fle+;B#G!~abG8Qx%*UKMcK}aDN>6iEq*CU0}3fy
zyA>q^>}rLj%x#IF#Z#p%1bIZnbs_1Y8Ep`)mE;t1U?gdY3lTky%c;|6(VHTE>5~NY
zL6qG5d@>i%Xw&mH*FVOooFscjh6e3g^v%`P(y{FL2=ZM}!Pt@BEnRXSj?1z4&keXh
z642pe9wXajpH6KvFD(+kZP^XzlDdB0Oz4v-#F{U^f^DBiFm6caw%w@@*1?q@+_4$y
z5<M&+rmPdlunz9+t?CNW6R>X6AE?!{#Jt%QSVXV$UxK$<7r#9y1VRsQ$GI4FJilxL
z>FUJNV-TQdeSdA-glh>ocx37{^!IkejgVv5b0HNkke{K(on&)8280LyAWXaE5KW^)
z7DH_<dX!VHvx~@pbRxpZ6s>13sSKAsw+QJJiD)N9XnA(=GYt}PR)aI)UrLe^&Wg^G
za%KjKDT<6SNyZdKMk-D|$I&tjh#(h;TZ*E>W-|siv(@ti<9nIuVRfp4aOdBwj%Pda
za&kD;zZtr>m?Tj;)Fw+Xzy9WX%%4Yo21G2EQXA^(8bVGHZoj^dD%2*hb?YC*C^(UM
zO>dzO+z$ow!q-X2wwZI6{PEXLlJxZ=2RIi&z0QhKZAz%fh1=ymu-Oc^9dbTYXk+jo
zwqq_Ov!eR8Us=8q3m43Vvy&6F$cCCR;LxCkRDWBTC84o<_}3j6kb8JM@m3K55djea
z5djea5rKaI0$r5>=`dkKHHCVUQ8XWIQpt&>9sy-Cc+Absr4Wp96b3bo2yF_v`s2@-
zG;so{YU3$%A2}CZix6xtSz7+u@|P$o#8wJnmyHn={h@}4?&HT!VBmoMxN`NXKzMWZ
z^yi*^2EVP}Kz8yOnDY1}BqS!{GMO|?cyv6Hi0O%pyn$(Cn|mWF8mXxi#U?8YF3wJf
zPe`KtAxkI%MGlS}Jq{x>y$K5Hi_KfNlbYWI_V)JJxBq~^fJ~bBsL+Pv|Fd@$fOVGV
z`pJ>VIdOOQ)Q!5)QlTv^j13tZ8+S0f?Cx&6^9{CfhZTl1pioMcQg14$Bu&$}ySpbR
zIVXBfl9O|v?{CYc3@O&7<^MLC<oEym<bU;ff`f>N^kek1;CbinJ&2BumJB3e5D5nN
zvwS|07kV!Fyad0wVf{MR9*7*~as2)9rzOLoxuqF@`or(Aot_XK3v5lycm2cff6b7t
zyJRp41LJ&3D)#JskD*ote*W`^x!s62i4Zizu%R&E4jwwnVp}Edx%*p8$qA5$m@ENV
z0<r{T3CI$VB_K;cmcVr?fsc*Ompu4Oj6V4DtA4Gr7vCZ*;!N^GU8<0~>N9WL1BIu)
zoyz|Clncnn$daN40aTvPP-z#Qf02JFq7sk&?NRCX-MjY^2fUF?=F^DGQ-k`tdMsyg
z-t5_wD--4Z{@7onC__U-Bl9xDnd9p&9VenIcYW(l9ybd<%>DK8@gc5w9ojoOaQEHc
zWM1$BELyY>w=>tiwzdu@6Hb#kojIyKy;4L)L?6C!$0i(Sv?d}vTtXnKsHl>n5fA>y
z_a(!}!NCEuX3oSnY1n8rS|ZkTONE4Oe7_FfM_l74KmH+FNWvwaT^LuLU0vMvAZ9Yp
zJtHfdh$con`S@e+qqT^h?-0m2P0d8!IfFm`{<m1QY9&sbIK>=gH~i`s|Alq8u9lv+
zmxw<;%%^s86#4r%R?A~idG~-QTxHEF$<|79ikZ>-!8V4B<P;2D+Bl?Z#`#R5PfgI)
z)!B~X(u-L0mmhJ=*oW-0DydNV^0rU=*-}}A(z=TUte<x5m$lzwGB8KG^fNu@Ro#1R
z1o;{1I7b*_fydw4R825}bVBc5lv6)ZVR{EfFgidEjmn0ry5xrsY9g0OX<g@sH-F~8
zM+Ul)av|qSlR;xe0ZJM!B4&Ns!<6P_qWTi|!KWQN(e|5}=if?#wrkOhL_Is7QTYC?
zI$9f0Mkx9Zw)Wam6jZf*aMM-)KB8;K%bWi}DAzAOp04($f+wVgH54Kzyp5^3g8N2*
zaOjDQCAdr)IadTHkbqrI%PD28$JJi>OFV9Tga!BI82W;0j`*H`wQZch59iTy`J?d}
zZe||pL0Z=Tk1cU(Z)+{m^2$H*DyjST;N`supSWJMRF$Bx`pR#@M_%mmMvIYTxkbeF
zlG}|efsZXh@BYri@FvOw9{JT(y?>|R9XNId`13C$Mf<8wy1@?;-hNT{@uMF;$OuFz
zN=nP5h{ZF{KF^|H7u>e~RvbPO&&Wdn(n#zT5fQ;iNi*iNxNh3CsVHY5rZ~^j(-X&z
zog}<vF!J*9N&aX}qb7|-j;>6VvXzVyVSLoo)DjWtn3PH-pdUqQ*YxRABxAwL%Tqc|
z3dJ!hBWh6=ESQI278jp5ksui)W##2kdXz9GM0!?ZV<T=^xkBnCk#2Q?3X`=pVHR1O
zB%&<V{8>d~!=6zYHx>ws=V>OPT|e(DA}19Ul~}Q2IZFB7!l1~{FJwBE9SO)XhzPV)
zN*@zZ8gWe>iL<nfqzD5^q-fRF)$y8c(lxwT<hFLrDk8ERl|D`}(NSOd$hme!=pYZp
zHng+2Z;UBsqPR3WvltO9jLu6>M;nRrY*}2sW9uG_*x3{L#|ZUMEB2fyL_{!)acLNu
z1pYjsDYbSsByVa)c5aablk4I@+;}2E)wdepMbx9BoNV-q72U*ZaNP8C>m(zxi$-Hx
zcQ2tfy9k?FB_WcOCLP3E2Qo2bS}b~-%8^^#0$&zOvtAi3Wf^$l)jbFak062y2?_}>
zDp;zE^NL_1QqJf=7pjSN<Kd)21II^gLl2y_7F;N7K#;Esni}g-R8>#-Ngr5PbeKl|
zkpbg49GL<r_&+RW6VY07aN@u|^xKEPdAu71#kFwecsTvu79<pRBZ|f6<s>R>ChCtU
zUaT(6AqtnC82BD&C}UB2jSg;3R(Rs^XQ6Zngo_;^TF>V}<LVB3J8P0~^+P|*)JC(B
z<a8s%<L@NFTQTwGRRjd6V-c|{TIy?2PAF0jKYtPgw&Ox>0i$<jj!$Q)koU6;!lZY$
z;k}e<7Hm361=`zReS*c88l)aR0HekqhKgjQS5djz`cIfW!{DMC#)YB^coOl(Mx`J*
zWf!uu^U+5rT6aeR2@nmZg8R*cQ+3iPk&&K>)4bM*HV83cUUUSRkV90Y9wSkth_9cO
zp3gC%g#!T&8W<HVDXC)$<QPf|s$kDZ<WO%nNz}^F-e-ZcYJfZgDOj;&7V1m$P)b?)
z`Fl#WBtxueYiexZoUVW~c_h^1!$>BYnMLgcd&MCB`silZ`Ub(9007Bcqm)i=Xv$2$
z$+SAclbYe}&ptN}kR#zd#%&!T#%Oa@DRPKx6A<W+)5qS$h;s;R2{b_TT*8H>!$1gS
z4_A)!Asznl*C%0e3_ysF6Ef4zp}U`mK_37NsH-eS4ad8NNNghVIyj<0Pg^ZYs@qxY
z?T)IFLX=Yx^6+%UkzHGmSlG>C=}?j#=Axe5B)%+a7OA11r}cMK<C%93V)??k(lz=!
zTaipfP80)o6AZ!0(VirsZLkmtu7vz9-9{zHs14WrR&;drlGmk)#&09s>1a_~29TPS
zMdjPbdEo*BYhyBqfaOIH0|tT#!%9-R9mk1OWs6bvYkzAEnn$$oQuiU1<9L+heC;J^
zNJwjhwUrTe1dq^ItBCYFf|eFW$%(wD&^W`9{5_?dJ9T__fsEEcnYGZVlz!|m&K7lJ
z-I}E^v{Vw3w^b@I9_*sSsI>*X`T>a~vzUl##KlD!=ZP!7Q|%2^EGDmp){U@sBv!1i
zt0j@<#W^I5`wSXQYJw{mSqz;)h1QD&_yPn$w7P;M-2bybW9hovac1WW=y8ljNAgxY
zv9A`ZW;)~Xm$suQKL?HE)^OxJ70x_s)#vT4&(p&^XKau>G}Lvx+z1N5{;Fsq(A6+X
zitFL(<qkcaaV5os88@ln>1vO*+A^dQ2H7V-tRdaf_pzG}nk~=23@i5#1bAIU3LKzQ
zDJ8Xts8LSnZ0}{NF=4Hl;gFJW5b>$?m@+v64HZSGBv**9mn%BiPfav-M@LO)<$M&R
zf1Z4%y0T)FRyD(oh2J}Oy@Lp%5xUx`NQ~HzIvRCl0_a1``_gKHM0gSKp%3R0(=cx5
z3Reemca#($H8lx|rHusSn2s#YZG+hsMqLF?6g1+d7++jS${_T*Gn_g020Cl;*rTst
z+KLqja8&XgGPs`byF}BWp|M3Gu61*DL@9Yn5_1}$Ak?%N8@85Va^75kN#(%xOoQ@*
zOljWsHy7dgcaF2@K8X5KIWsdn(b05qy>apOBYLqODf~7FCyoQ1k!og{WU_u}>+xKs
zeaAr8T#lRquCxCB(zoPu_VNvR+C(fu|5yXW{ntOCG4ZRbHYPf<@+AYr>d~t+SZ>H3
z{?JPaW?B&i2_}-yC?W1d+LEY?C?I#jlEsUN{~v(uJ9bM^lIhc@;v0-E2sp$nMhje6
z10qmu{0N6AQn_~Q+Cu|kP&#iZQ80v}uAwqNW5zT|Q4bCZ;G*ayrC<s40<qnb$U(uu
z!P0#NB8r`@9o(2|HH#4mkuud!g;zu;MtILkBF)^f={96$=SWeB7@}MV10a;CTbG+w
ztD&Kkf!RbzDl4y$>P03I=|mVCK14GS^(ZQpT1w{<+cY#NqM{-tG^j}a6vmFYkBE@0
zyLAmjI$3c^iFEl}8R<BD<e0>1aPK{LNhw&RT!4hJwVs?V#~7UvbuFt_tstDN!{tZ%
z%6?u;10>_*F|-nXvoQHEYpVl@=F(1-l@gptE=Nt?Iq1w(Kx&K8t56~NI7#_J!_e6?
zf}Z+Zbg6w%c47}YZTt}7=StYZe12rgaO})^!lDk~jeTb%G@QJ1M{qW)f&3iaIDOy<
zJc!<MYR@)O+X0&&e*tO-el(~+7E$3#D(ixp%GB9{HiY|GlT2(sni?um)yF7UlpmSY
z&tvD|3`XiEVdv|cQPw^}#3_5!S6ATh;nN&<4rCaoB7goMvg>+~cRC3cduOsOcfv|n
zhIa^|+tpBn7NZ7PM|Po&s5wX8-B0qf#qc0v)8V&Y!&yS<b~jhTFK#ZzdRkGCeja&@
zhIG{zqO8pd(_;LHlvITi@d-qb>crjyN2%OKK}opEExQgfl30N{l3it|oFkf82+kka
zf~G;D0P(}-!jF4xei9OL+QCmb4kZ=g+&)6d61LUf#TrGWRIus_u|Ji33?)f05XC29
z|88`VXyw4}gP6SNX1G%6|17PytqSL;7{<+*O~kJN8kfc7rT7t+#<}5O0*y&86aMt*
zV_5U!Ut`lParEQ-m729b!np?66P~%7aH~aRvp#V66qPnNY<}Sl*b-3q%(-$5wB#V2
zAOP)+b>tkFOH>|DWFLJC+m08ID}eKF&>(TZ96oj)zRn|ferr5NIxDd+DT@k3BZ|5#
zC?L6EbvF%zghZkod6SERhMl$_Z&H@E)f{_jf5Zm4BRk;${&^^!_sm1XpcV4Y9!Fu@
z02S7F*i!*|@4W*Eii$?+!Tn^)?&5k<03TNs(#hE{)SQpX9t8@hR8<j@Gxf|_qFE{M
z2FdKq1NCIv9)y$Dgjy<aIhmC-kjs&fQA1@UglJOk80c=s7BYW3g-=Cu&MBNIZbj9(
zlTi9aBZ!JzN!oF|u;Uo(=ls!LaR#sMJ`43oFZL%D5Ry3?J!U%$5PmxA>`9IV3mQu^
zP{*KwgQ*ES&J?0L>pavxT&FH6g`cH=#jz$Tn5jiISiWE`b@DDe_171n)06L@xDuI}
zSqO=V!R{?@qPnUane_B$C1t}}OLDe<yaKhO8%(UBD>{D`)#NOx%sPpj=6<Bd$79q+
zgCmEIFlyF-!n#q+n-R)B)nOmUg4T8n2hUf-+kvpBOxZn1;=ewV1&0ovBI24Wza`al
zY}6vV)r|R*!!X+0gg^i7DOeO%I6?j$yYXJUb36yNd8yKU-gx#!!rGFPrj-$Ng&iUt
zP1w2TDEjqn$ZH)&%Y_qoH?bJQZDeInC*fd8Iui4%&|X`F4l9z5^1btO(vec!19Nj8
z_NH8%Lw(lHIK5{FYKczfrXIpSw;zFyUiH$ZLBfqkP<rDydiVr+jbaJcI!sO;Z;~fA
z!9=HsK$FrcM`6(f!Ow1x3U(Wj`Q{PD&rOK^nzr$gew<<yQ`6swtw*xZQ<;X-1@&mF
zDn^&Z5hwS*g&u}xjvqPAZ@3!UcJ5`=Mvt_D4w&nc@$$}dM3~ax4Es)u<Ei9(8l%iM
zKlK#4h<2D#(uk&vY_9!D7$E#@TWJnTnumyt*oUo0GtgL+fgwV*?|AM-beT1%Og@DC
z1``T12&3&Ag|3!5LUkvhta*%vkq$9a=hFG2LS@cLy!6gl^b^Eme?}GhDhtp#Y=ex%
z-Kg&!!O8e!I%Y<&n_MgczIRAvkCBE_Bo@=~P0WESIYNfUY1F43!G%f^K1PH{gsvTo
zvh6;dj_EU_aq#WEH24XPosdCe+X|09^)eiMLNFyF0Bu}P6FH{`yQ)yssYLC$J*a5w
zBcDeajrC!?y(gaao}n}#N^v-`l<0s_s3@z)Xm>r{isyRMluL3$1+vc^Ms@EPPVU(U
z{~3$m=WL7Q6Yt?1$6L6&0x$15j?u0rq?Na0{!F5NUDMGqv2S)=86c(WzYEV@dx3kt
zAxTwu&g&lf6<jvl575hnN1w*f!kJKCQcy1%ARORaNG!|lgo?+Q6N%-1|6LbxpLvz)
zdwvYpZ~Q<y-%LeUanBtSyT`uZ(+O|CFg`>R%D6Bn4jej+I;K{A_dEAW$XFsx>5qT-
zEhZ6`R7qncCMH@kGD4`pi&_WaJ?mv{jVSj1pI`o5LW>enk9l)%B9tK+X_;~r8WJoO
zd5c;Kv0c=4i1e-yZWqrWq8&nc7Y2zyrgCMZLKr}^W)f<Z#)`PNFjzzpx`55RY0hlv
z*bf>Xd<E%Vixw@Qf#EKtT+N;}Q+gh8FL6y_Q26`%N!whUoe>pD5-=KF0rdVjIM_4I
zDiICz!U$tz>5@fEKO4lh?K^4E%*5nLldza+V&dKc{x&u?hH!s#3Eer1kr9n_%-p$i
zu8Hv>-t}4<AUX7G-|@YNVAbCQ3!ycen>(nCbYjU}4`Q~j1<kzzUfqlbSo_mdLob;V
znaq1F*swGP@$n}}3}}nnHm-n1WkF_64x<@;RPefBH9U^lYrlo{i>Kh!zC*a@=fB6)
zFmI%AUdGLvkH&m@s|e*f>KKThd~Y36k0rpsKB?@m#_cz=?|f!ps`n5M=5%4<^av_;
zeuyO8rz3Itd-`o~*B#4{e!d95{O!-sU75@C^k``=qqjVekrpC|jdo$>o!^7=NCW!D
z2#HIO0Uvs%117J+@^D*JH1)EcNQeJss-3>R1RfF7;O%0EQwNWb@a~rgQ+1<f+#dZ@
zJbOt>*GGkDKH+*}2_qZoU_fSJ1v;uK3D-Iw4pzK(?=bP_`%%=+_l^yeSb)crL-_Xn
z--aDIB(hmY<q;f(5Ms!8F{;y~w?UYf0;L_}m>J`MoQzs3x~^zy>4pOhjLAzjAkIgD
z8opP1n;E|&#BgItF#@N|ga<wRpQZUk#9=NOxc9z8Lui~p3O((Jo-?1uRUI<Rx@kPB
zkX6$Um31H%&y6FrYy*wKCWJ>%A}q2NRa`IYTDwqM-i(N7Dw5<(`RV=Z$WWd`_GLZp
z`ROkZXx)o`l@|>VFLI)28QsXiuu2Qnh=H;(km<NeQs&K$k+^+D1bS?J@vQ|!rD+)8
zhuVPe{P1UR9H~V?i%@;68F}eP9u=86v5aVV1xwMZTC$!y#V*C_S$-&FN=#SF5Ptic
z|3rUHA&f3ja3Qhc-QV7X!~3=(WZE3K)7x#W7(vKQx8c5JA=tks0o48Ro8SKe!&F`}
z@=M9qy&6CH!5W<0cN!{tGoE?oRs3%Avt)&KK`x^J<2HogjR}K>jt5cGcHaZ{Ac-6a
z-+cJL5lIf0^tv(JG$oj+Ij0ab?>5|V^K{e~WFuzD-H5YhM6gJYX*0vg7|m!)a~t~f
z4H$O~z~oSO#4TG3tCB=C>dE>XOGwdB!izH6@_CvtK+;Q?HamX~%!X#{K6#33@l9~{
z2qMwnD9+J&W8)Hv;nsSLxdkDL4g)7f39VfH@sn>ZN7A`;^bfXUkj6o8y9p2e;wNw<
z^L9N`(n{-kv5>qe&Wtq2P@x;7A<@ufOSx@CZU)x`dY^k)FV~^7!SDX-0m+E7VRXZH
z+7fJ-7lhmjJr>T2rWd{wIn_q|^5+ks<WxMPS&c-bb|;CSFQHVO7?rWZuO9vmRD^qW
zidcXEOFeOx7vrJ3<|CewavP-vYrpqH_*&Ya2%3wfG1kxo@S73pfVv)ns_1KIy!N4s
z1aiLKTEs0Db4ZD5diC4dM)0R!KLpJH!CB(-aMPk`<fiZ>QDy)}dRy@HliOgYRWq_x
zf;+yw5og|e4da1RFeN~PJ#@yb+q535S1u=KkP`oV@p-sK&qaXySC7>5!LeW&9mJ{B
zZ1^}a>QDL1i*tlBa5`=dvmsfaf{`Z!e)i*^(y%XN)QC}Iu1ybrpXtr<Ct+u!!t_=5
zVZ(|UsHtnjcOJNlgt6pD)ivSuBN<pUcP5;i199^#2mH_Thw#|re}HPZ13`0F!lSE@
zX^dl-GBt?svy4>f$MKv0{yvI0KURGEAvh1WQ}?n#UrPmaOphKOQb9$7V$!s^a3)z~
zOHB!Eyr*I5RBym#64uUiMs;H^n)-)0ULJ<NIvW?Nn-IHjEgmA#X>N4`ni(av_76ii
zxsjZmlpGr>ESwsPY;uWcy+R0|&6H?od(<`d<971$WE8gGH@|rp70G8YOwb#Th)E=j
zy;%}pQw1Y|5zBEa8R847+F>AJ;m?1~H2>CY=xqa_F?JIkp2qdeC|IezuxZ^iB%aD(
zohqYxaZ5>l>Vu+&Hb#C-OwIlg8R!|Q4~avdJJ&b!09*p6Aym@`gEkgFduR>z()qCL
zmIX4R+6M;jL^MD?wgyDOurP`%-hcd(ked+t`c4f0;s@wF{1)_7Ry8Z{#Yj={`^Si1
z93FiZV^97LO?PjEGC2!ae~+}k@TIp9PH$TO)@RVp^G8WO^L4Bjxp~=ArYZbMLZuRj
zQUCSuPp^>QNBtpEv_#?g_1?Zg4u&89=pp>)PktyxhJ<nS$RB?v)u>$W@AgHWUy%P9
zS(q=#+e8z5PT}DpIP}(YczbU=$?}vEF`bFTa{_zc#>ST17Zq>|n}Kc;5yB%3ZsJSE
z(0XoqBF1U-DMziCvgeL72i~TaJdNoAE_n6z*O1NgM2gdq1Ba2%2#1Q%H4Dk3M(9yJ
zSyToqdy<4H#u?gHBj@ZXbo8s?MYd@Lc^CrxE_xiTXdpIJq$8UieJkSP3pBQj)2GSr
zz=$7VEfge96D-~q^F=bW5qbofSS69{36et*<ulUDreql3OAGwt#b=S2QO;+%sMPyY
zF?;*1x9}bnk^bIx<Rs_NkfsdyhH5ehC!ao!3um)Y)n+2guZC%0V~Aq}v9h`ZCWRxM
z4MR*#^B@+z3$g55ar9&gY~8(??m*9DZmAG4l>w%n<R;-*LNZAnN13t^2~YcOWR){D
zMZ5$JJ`oud@Y!}+H~2aY;qNcMjtgXk7b%0EwN>P%BQ3w0hK+)0c6Kx{7`55)HvaeJ
z{ba3PL^gK|9{A5+A;7v4e|_@+e2Incq;5t*eYJF}^Z8kzodoI_1eLo;9w)#Y%#1X-
zF`ekO*WO~9W*%j9(dQ=!0cVnHAtQ$#T1JU1BnMN_Ko$KUqOl`<FF`yeuE9<u>v%t2
zeQi6=o~gqe@@+Jbn?^~+-=TL9HjbX;5x7))#p{Z3Y9hLxLT!%#mp;6<bsIA4+sQHE
zjh?PX#3yIN$vYff<h}?93m`$}Fe58F@Xy^x5x!_Kqf83C^44}F*LP#?lKH5~KaDqD
z-3FWBQ1W!x(ja{pd!GIW(}IkcGbI|y@fn~k%|^bGo(>$0&q8p3HTG|P9f$ahiHQjm
zJ84)3Am!a%IFZQ64(p{x35;Uz7eY2f^6}VvLPKRPk4*P=G%m&Etd-+9bL=Q1O~EA3
zGt${ZE*uj6ecmRVEj3Jmd3*bAoXu?}S)LD{!-^acB&S<AA14`=n7()kodA?2>ulWo
z0;F`nQOdNBX<|7{!O`&9EM!TKp+f)sv#;UZvl)!w`5}<es4;F22%rKf8YhzI$5GLb
zjI@hryM|-lMcIKTUV05#^<)~iw!#SeO7yirA{0%*2z6IQBA;<9+U-0rYtd{Ntp@3Q
zB5w$lYV+vDLNhTojFg{SKq%7AC*b9O?uCn23`s&sYAVJZ=lmGw%?R`4Mn?n@9iN4K
zET@yJpr+AlWnsD(oo1Kkkge94&(?|;-`;~-8Y<5Iyq6UnbabGJmX?%+=+&F>zz-h8
zj35<C+4oxWAdD6_#Q8a*uCftR=sY=>a8hzy*eRU}ldhD`z1IJTxh4KL!gm($XBI!X
zHVb6Oj<zm5zhxIAcseYbI~$E>lJVBdTbOp_!@g#JFa=KZg|V{*M~-Jpy<$TnJxpQ4
zHBkkBcO?!S$;18Y=i%L5@lcT;=6pgt1|2CU+Y#71kfX?jB(s#cD1kAvKcr{iT5D))
zMM@6G9aHkGtgJCU(2H83$BD-77o>z^MvezQgXlx0#t~k_CcOUocH}b6a>=Y|$RmQ~
zmhC%e2-A3pbj3LP)SYR6E{=9w^UGjl^d&aZ52rHorDq=>7et0bP-|UCwAq0-w{1i9
z7!6+!Mo=i5%S{k2igjPCrwa1XI6ABG67QRo--{4WPsB}|hB_M3P2`WN$xlXcF;hgv
z_u%q+Bc11fP+tZ9&gk#SJQ`HYju1_x@TDgnM?ce3TMCci&3|r%doa`UF5g`KIbkL6
zu`s+vQ&(WJp$A}^hrUY(XV1wP`}2Q7^~i6fmk|TWV#{3JDG^}&9>Tzw^3&hLRB;YA
z^n8m}5j+P`4fL>#x1r$aS1|Q|{z^rPIg?ai<R(jCB1_;}!tk~?HNlbjt%E%@2<X}F
z?<aqV#+rHk8C168F>BMqBpTDggXwr3txXK!doV4gk*QS<cy{x1xPxgsvqM}Zadxq|
zxxBUwF08MJi4H+oF7stA)+D%#LUu+boPERKts(n*S+kUX9l)B5{=reE`jP00-t8v+
zAc@IB5J_??kv<a?ISC^z)hO%GA(o#1Ku^KTGLF*hJm&X%G6h7*{AOPm+M8HAQA0z=
z3xV`%j#{Z;X1ao-n>U78Uog&(iIE>_2a;Twm0Bt?LyU?rQ&`u+6qS6OIGu&x{{0E~
z(<5vi9YRKCE|m5TRB9ubc2!P8Xc&Pc#PenSLvC&b!dW9zSzZYxN!Wt?ohXkA*tiB^
z<|J-s6sXQHz_frlfS~~``Z3rmP4pO>Fmv`yxX?3f88sp+gANkbAp|l)*V9;y&JimT
z@5NFXAL2)x#+1f|?Ar#`wb){aigk8wF-}}4#p6%@g$Cbe8@;{l^~fo#g4V?Y^JY&&
zLroc~8#>@09Eqq<U+BBrnF8Yi56!qF#}R!WhRCiwhfz*jrYZHaC^&~j{90!ZL{Q%S
zM9uRjxm#0952F*ThhQCztsRNKB0}NnWJl)cI%F{&C^&j56~7@AR<P!VwFN;;-|Ays
zh`YTN3slG9N*<LSYWG`S-3;&8C78qds;QHLC89^~z%X=_7NU+tz|&^Tf|s)feY#E<
z6&kWxkHSE`*4+*`FFeBG(TE8NAaNc0YFLHH0C)BwQ#ZVrMn;3OwxJG#CKYa)JA>&~
zOf^Z%gI`1(Vn}XST~>g)jzKJ7?Zgn@)7Hh45wQm5(Hqf5w(XGc$t1$OX#BR3)Ud9b
zq=}OPkj1(=m5VoKPl>tI8620nndoILL_~-O$A$ykT(qo}VSPt?Kc-Hbg3iWD^o?2&
z5*7sy+8ViqWr&+P1NE#M>R@We%$sJ@KonNqXK$j2IybL?HATZDUZg=w!zaJ696=Ez
ziwpH*>Qx>5nNDUqK145n7E@`QFgZ3D!^TngGsUbhCkqBDmO&wr&{|jnl3h$-kVO3C
zPmjQLkn^j$17UGf5zf@AG2<Xwy9P*R=ge9F9p@nxezgXA&ec3R22SVJ<Ilf&km<X2
zBrF}LF*wHP9@D(Ks`2Z;yomL8-j2waX$bIeL_tmlIypzCli<+E8Z&=?Z}c<uY0zRr
zvN}gpFp}1wH)G+#IT&hhhBfE3VyG8GG$6EABTUV);d~y(FxMGFM<a<+hv3i1KLdBL
zqLV?ILmV>>7(yD~DRRnmp3k~2*1JT;O+jdY2iLVmsGYpv%DHLa7%=g81>bRugqZGB
zyqnt%2o4QmKb5f7tA%S0$7x*9XYZshdIs6U8(TZ+NKi8YQp40RqxAk7@FzV-o1fW?
z=?m6kYD6SrB7IR`RDi0Ee#~WN!Pr1MYqnhB&iN^bAzN6>a?`X3$z{;fP>*bqW{Mgg
z2aPRB8vR)V+QbYB7ZkF-Hj-q9ZFI0SF%NXwbgsz+ukj<9rI4Gwn;%A5!)8T;qKWB!
zT*46?77ClOZkViH5FpYQsml$Hs^F+5oc)+en!f{M)^Ji7aUmlg0Z~&K-S$Lf9@Bbh
zO!)?eFw$g?4Audg_{_7WhM|?us3Z$~4I|f+xgL0PZdoXgrp9)zV*#vxX+l;3NlItT
zfhX6oZWbK+u`WimK8`1&#SV_vBj{!-m8VwJlxES1HU+W1cDRt94SQ#<-;D4SX5=z;
z%b)3)O4!kg7^6X}<@#-4nlc8vk&#<Mr_u%d;qkvA*x820*bt-Fg$QOA#b8Gb9aDX*
zqn#&_;C|8WzERGXYJaaM9*_?jAU&<jmG+x~5gHfPmy8S1F_M8H@Oo&ZTr@y_N8P0L
z{gkWA+brf5w3bxBZ_34R+yJ8}c8~pz9=6Z7(BCM7LVk~lD}ififP9n`dRl8(dvP3&
zzTvoY(=B8{|JPt;Zt5A7w_3;|yF&ViebjX(`nG~p)?%a-VAj$VSUP9o=IrL4If1iz
zm6*GH0~XE*p6Fuz%+Gh>%t^AFuY`*fN&C#F%O`xv5~wfCVr@_gf~G9OhLy7<@Q16p
zMe6ZGNG)x`!j&5_cZ&bNoHE8p=jq&9tY+jxZeO+p#^^9Qi#a!Qy}lTT{IYNGwZC3j
zdNPis<YMyNRam(&=DPjvUF(j#Aq<dF8X)Qq7$BuC(THV1=hYY>B0Sm8+{A(J{Tx2=
zM9X3T&pcEMCDU-^CQD$VN#NgYfJ`)b$j>87K$d_k0a*gF1Y`+Ze-fCe2FSm<RX;(t
z<EIke-z%7@LzyN3rw<nFF)x*2p3BYdwH^GGdB@B#7R`YL&^|}sVV<5aK&)UU<gDCe
z3CI$VB_K;cmVhh)Spr`t39yt)Zn6Zv))KfX1H{$W8(kEW%}*b};M=><TT}{N_<X`Z
zvao6I8&?QW?Ku^FM^2#bmyaT%kOiN2ua{D?idhe0eB?<C)HlNP!j_BYe2!wyD{dzL
zAxq#!mVkwf@}dZ@S4Sek>`#5u!qg<>SGIlXaUVWlsJ#stDJ*9Hl3GUMY1DQMNLR}u
zOlnz+;Y+&m#mn?}*5mMrlq;?&w#A=Kzhc|vKl<9qk8?T`-K}LPBqzbYr?FU<g`ywV
zS_+BZU%`604>Vm@D-wzRbG5C4u+_SuE2+B*(l{n+JFfN;A9-9$c^*;=J|s6|500KV
zFBPJG<VoBpif5}?d+}wq0oJx8oXfxBk`2XKNXoi;IbCt`SM`U*h+_wjN_8?<bBnU%
zG_;cQ$9xHhB01q08VQg3Ww!LB1R}cqYkyRwWs)mM_hsHnzLq2b3(lX<Mthg>3VDq5
zx8SKqA4M{GF|ON2{*a@`lO!;Q559wm;39I9C2+M8_*fp0PNI@{nFxIUUk^#w8voe?
z=>O+CaNd0ccCT!My@5rNr*^=WMV+EugyB7n&L7+h*I)e&E@?Tylt>W&Q!+qwEI{@7
z!;`Rn?hUw*C~fG^55sojI_X^bA6WtuO#;^vhPS@F5N8R&X|~alyvh-o=aNv-(1rT^
zb9j3DDTJ}uu(Oi|g)GW+_VhwWT|N4VLQ_h_tGpVbh=h=!iBZP1(<jkOXw@*65ggus
z97duT*^c%i;dCN&Ea3BUwnIM20_&PPF{IbA_>_uAyB=;V?5=OovCf1A&?LIdFKA%l
zY!Hdea&YDXVG;+7M3nND^3W?;(3_N)0hNOrtj&6oOBN%C^V!8OkVTfA{3xBlk;4fH
zn?<xES6ieX+)bESJqbBm;ZC^b7Lsc@Fm1EGvYyC2qY@`hR!#v1NluqRvOEtTKeSX8
zB8m6akUY&YtVeDE$*~C6>fue)AQsyi+RAb4Y&m95_QTmzXHnlxvNmfI-q?ExMgvI}
z9lclpON67NM^MXxWhZqHmDWl`2f2c^&M^0OA~!n=wXHn}3GzjLRw7bz%L#R?yn@v3
z%N&zgr}pD7&mTZ^WH6y%>rhkQ0s{-rQ<5@a?dZ-TQgXi#?W~VnDFs=?yzexTq>o$`
zt$nZ~DPdlIu0#eG&O+}xLd>2`&4GbM<URxsxa>uG`<2(=8a)f<)>7;{l|q!IAQqJ-
z;&fI8i`<709O93<(k%8V3&4ZIq~iAO)_R;ek;H;@f<d{G1EsJKIlTXftrNLjlq`Zi
zg@pKH=+}f|R<tigVj)o`k+n`FAgQPus}@g20?AaH2F)y>S5lVQNXg8DPhb!(96JC_
z@J!f`wPPQ7TGT`nGjz4V%E5(bRxN1mF|lA?i_V5R<P%LXmqyNjz6CiXoeX5baXZ<Q
zkDNFI2NE6%;DF?WQ#gI<B+pZ0&SVc9*>@BJEaWz}<%85MEGjn=%-{mtd;^K5rT#Lz
z@2ajfW)!4ui6|z~p+5;R_wG4@=?fR2JT;LJrDZUUTf>Xo5GM&UUDj&kymn`ScLB->
zd8+Hy6UJPHXP<fuJ%k}`N<D>YLL7JMI@rfvs3<K(CP5@R`UX)}m?yzpJF16qgygJd
z8#j1ef(|avO~ujkIh21e1}N(s7Nnc3G;k+?K*EWA$SH5(y@pxb?2X!zA_863p}Vt(
zaNI5=w5umSO%8N~TlHgwehRr=iffyRTBKn=*`u+t0z(!JtS$YVJ34X|)!@MCRLVP;
z5aONE7&5cQq>IF!J>;Y*D5)f=sW&u4t7;<*>^_b&t(zzLiyC0-<cu1U*=h+Z+*n;n
z!a6<4@d}Z_!t@}H9cv;RO~^(j^dh1KmRHwfu&*B)dq+Z1H^E4hx%%>Q5`Lz_z#?xy
za^i?qLe4Ko7bn!!R*=0tn??03>QC5@Lm6#Yw|*IVYIBj8R>gvHFUp~W#D<N$rXBn0
zBoWkD+)vcM0<PKFX~l3Myt&%cjd*6FjI+4h+|z)g$H*%}o}*#%`W%f<Bglv!95pta
zres1tlYq?98i~uRv8I9qeR(LV??G6Q_+9`y0t)1DzNhAu!jo%-g@}XukDekhhYh+~
zyUCYiN4~ErI1zQLqKY6EBxe=9l9!#1Gn|WOb6YtN{cz^QS?IW~YR9|q^Z)r@3Fu^?
zyB;YQa-b4xF~2Wz`}a#=BEs-~tO25b;zhU%`}UFnVncXD&2N4R%b$Kuc673xKl01>
zIY5MHz2;2k_FqD~{uWsN{NEQD(SP(k$@mcaJr=O&jpvE;tsdpMOBagGa+4)6Ati7v
z4UjIPi*$B2;4r=P8WWYZGv%00=36}xJxZHKFg?tHIR2gFyhz8WQ!vgQcn@WD6^wQ?
zlSMj<Oxh8MB4>hNc~-gmasL+REoz)PdyZTz(L{CX#=iY0$&~AY7hc<jFcP92eP<`M
z!C^=~b(oCgZYZN6Q&*9KhGAQ@=A}x=S%>$&hoI<S?09b<7OhxKo)Mt2p_L54RXCoU
zPtFKJNv5_!OJuv^4l-}YAB44cAX@4wF>U_M<WsOkd1fNo%p|dD&EroyWS=~R7^3aG
z{pLQXthx}NQ~|BA6~_uY;Mmtdk|{O(?8nj4ND{iN8mRiJNQl@0RWBKp4O%L}eq`-#
zWhA2qGpC23uB9Ev55~jMlf;CTRpb=##D0=$^>>ycpC8-O{4`h*ZqztzA>va%8g*kx
zI(PtTezY%~J|w|h?nxj?oRb3k6HBmq*_^MWu(75j3)$5=a%#BYg%{t1e?&CpNk~-k
zk(?m>s;71oZ3Aj5lsn-`1e#O(6NpmggqltRYO{`$<je&X>E~cl+Th50hY8c{gWbDM
zV%gf8Ng#P~3gr^j%iYTnyWb)Mazr5ZGeY6wph7o^HxzpEjZhg$IC&Z&<Q54J36xM~
zdb@QJ^Yx+qhsYPC#g;eYxz82H(#kMcor|Jw6**K|VeK;+Goxrg7Ny|1?H3RiPQDsF
z5zAT%kW;HieNGxW$daCvT7e)(60v1gz{#3M2@TVoZ*CzhW*E+p1F4Phqb)`)73^c^
zB>sO)fE$kQ+KNJE?zUy0MC+IxS}XG~Ocb^K+YS){%LhmIo+YPDD~UAg;p3vjh{74A
zIh2XfN@7#?q!K$$*w^?&@4?PL5^*F+)7tzS3=pF6RFh{UjYiUt${oY{9*J1-y+f%8
zaI??|CTS%394h-%=qSlX8`0<XZ`}?D|8R8Hl|bVah{E&>2#TAHp~gatknz2#_&i26
zPB=uuTK_;_Y<>Fx!oq!V?j)TD4Y`E0RT7~nhA^pG=<4$D;+y*sLBz1bXLD%S(U31{
z!gN7OOQSzOD+NPDjnh@;pouue$9C+1!ZQf{%@r6Yg3>#?c9OU<h$MO`H2hnTR?&_z
zLk)6i2rZl*i<<PaI9b>R=*w`5s6%Oq7Z64Sw|xm0pdKcwRi_f_&TQn?_2cZm-OwrB
zFr5fxN@F{odh&UMM@4XMCZLsy`UpvJpL*nP@a491M;S4#;`0){tS<K)Ia;C-;d=@8
zd7^zCMhy7l-~LX*SR%!xCX**48YO4mL(ixZJKi~f&?tW#K6Vm|moJCfihX|cBw?(9
z9XpRAGSCSJlFAWiH;#q@2gLY|VaMS!uo%hBQrU&`yWfSae=xEV4s(v!AUpXCbcRln
z6Slz7)PahYVWcL#2a{bOeC@{Y>f7(ahMY|2&ljUG^#~#9qA@ut2wFQE6eS%XgmWW2
ztXhzo+l}H3?sxP<YQhPL+|}F325HB)<5WQxto!RxrL#iD(RYb($L(j6P+V3@G%g#G
zqdtf5Fkc)?Ey0pGQwZUlj%VIHi?~Q9a!rk5{`5$+wYK392}u2kz?VuU^jYL0vsg@M
zp+R*lsS+!f&HPH%)QL7UCi?SCL<8iq^Xz@uwU89eAi?K0W9<Gr-`{@u7g0=pxu)at
zwh#Xl`#<g&alYJS3CI$-BmsKVl5_HyelbWQF(Ziv8!&IhIubHYBSdEe=8~8(j(i^d
z<n_3D@p7Elz70dJzHp~vIiw;y;+zEt^LCIfCJN0Ij4IR;s?mvx#m$T6qcS5+f=Sf(
z4xyj$pws3r#QLR#C?qe&hIMNQx#=TevyTwZU1@8Jn^&*G?3qF6>+Qf8y<)enSx*8w
z4~d(^GDf(=%nW)shZwQwhLIlK*~^xbKx+m@seBF%D{=RxwU`qXK%Rgx>6y&dS}a=2
zYfg&62r=imGQh^#TF@B4Y$ojOY?67+osKy(=VSSjNdO7Zk_mU)-DgBs-!O^2R9LWR
z6;{lSrV@HFB}Kfa%36hbdh=@<nixIkA=_|2q9)J8`VDu`d#XoaZ6`_XJbCT;gzi;g
zT-2dih@DS}NsXs3HmqNRKtf%1w06KRWIAqNw}J>zgy;Q=24+xL1Z6*mP_;e?i<*m#
zD;FW3ebYra&B0+EMwp&uqRd<gmAiJ`Y6J&rC9HnI{i3GQxayX5m>cOsqqH3k^f<3s
zcPkmY$p`SEtS$q2GVJ}QV#95Bz{%1<<6{hqSKf*{Z@r1|xZ~KkdMTNW&q@(C@v0U1
zWCQQ$foX&uZNk9@O`eU_tCxTXs%R&9+?rcA;+6%opyId^t*4_6;W2Zt@wRp3aIr!w
znbb8NPM9@+KAee}88vqiZog|i8I5)1H5eyc>;T3TuDEB@29megAuM(ps?Y63VW$n2
zOb?f?X|cA$yd}#?HcIl*C95z!DinGuHTEPy+j#po;bCJ$c}){$-@FzZ*Dj*rHil}3
zvh3Xhuyp=Sgy<Y2fu{rR+qeeh8Og{mt(UHQt^et)FGER586i^pFig@-L5d^bY=a5&
zd)xXIaMvo)M7H@g8}G!XWm8aD+X%17skry{6(oK%Npo^iR3zsup_d(8v0}p-CJ}_e
zI5<o$kua=czeE$#cirm6uxE5?`O0-r8tTctagmcj&%XDIU5JgFNCZ68L|z0dl0x2t
zxWJ3vc%|AJD_7r!^(&^LrnwQW!Lj(>H&?^ndJMzli0}&w<Jen44hJ>KPj!e}atm%<
zzChwx5VD-IWC^-!vT*uB3+Ber$n%|q+iv>?9LL+C8{jx4FN2!ALY(~2QbE7}`*#pl
zR|O*p8rANRxNXC2@U$L4__P?DJg^m0xi0K|{Qx{B&4LFbA~)Z%9`gxZJIXOXQQJ7@
zsdrEazPE7|<wm|Gu4N*cAUKbFBICHGu7(4RK?X#mCiqcIgjSA-oQ3tA^KL37LSn;-
z;xvn#GbHyL(I6^r8fFp#*FIc~g*7lTqT=D~h6PI(z}8xUIpmI6wq!EQTpN4LTHJTn
zTEql7p{d;nv92swI34TnxQi_5=a5lSFWtAZtsS#guEITctVMTA4Wo}<Si5E&rUf{Y
zpX1`9F-{Jl`3rBsiaD{c4C)yj?uJKz9~LZIi3O7b@cRA>m@{oQ4!*ga-=m9;8GUCv
zf+J>N1Lw1VOfSpNMlIKgVN)j@3Ar6JeKO|Gj70v0M8W|NVVE_+6p!3w34Ejk{>2&?
zBSDQV)2jwqyP^8?Z$9!Qd7~@=Spr{@1gO|`G?r1(9)k@@{bFZM$FW0OvE$GwiFhpk
z+)<=6B}%QbLdc96Xm2P$K*Vg!ii(1wsSSE6mDZOCnY3iu&97?4Eb@4?GQ~+@ZG$>p
z8#+iBX2tM@ARV-lvUbg6r&dTGMk_0Y#5}Je=DL#GL=HN?rEsSL5oTq;Q_ntw>be#h
z4Ky7{VAwzfYjn&gMGh3?rx>TQVIDQZ&e08l{&x7w(=Xx_(>yMd^(lE>LDVIT20OBI
zTZUTj#_MlMsU+4al4S`EnnZ{e8V92!ZL^|5)*(EytbMUltD$6+V4Tk{$l8>bpor}>
z_LLbT9J!5XBe#dzN()<hREwB?^7P|R!^u9HOx;s3K(3TQDq)T!O>L<uM(==ya#TT0
zrfCz&3&(lih&W=$H)rFIn_ow-)hPBp^%k-0+rOmF{r}5ov>HZ(&ft7O4XpW27Uodb
z6PkRCGF8)CucBh2WdDdhkO(Om0Z{QjTW%M`h@+zy3Fkb?<gPWfkpJa>83ogUBEkrH
zHMVVe6%|aSiH(V68do)*cxp37G%*NtwZp#MTS@kK9COG`Fh;UG>mf55MJ^g!b7dLR
zp8Ci?!~5}GRwSVm#Bxfd3Ncf|vF){I@Mc09IVPCa!R8kpM{a|Yc>LwJ(P~m4V)881
zH+7H$Vw?(@8cEytVDr<@VdnfexH4i^QQL=5*Iqoac{7?vELgl`G5OT=42cKBmEK#?
z)O_c0^C+Vj)+D#1F~NRu4-6+8cLSb$`e}4IQ7Kw@6AtWs2QR)F&y*q>EQ}Jhv!2JS
zvVkMhgDTkf^}{6n<6|~6wdjqP9wi@$bop!cPcId*s-^*`#x*2KBN1C)CkAYN&{K9E
zPd@ts+33xf861wCPj1HB2UDr2F*?9HFJY=H)hel<RbxHav-2QEIM&BV+-Aij1gT$`
zXeShN;Z-+LfmHB+eFg(Osi;;lQX!gGP!#IQ(((5vo}p1Voy3~fB#HZg5J(KTZObco
zYg+~uPoD<POtM$dP7?D>j6G_K5?LKn0cTI0ioD~y@xs=<)UKt=)1ddl6o*07j%hG=
z_6+El3i&wS#jK4(l)pR9pFAj$=qbthBdsq+K0KS&%^%}bP#-6`WanrgmM>d~rb+`=
zQ?VZEF2kf5<OSiOV;tk-3N^<1oAJ_?H#v7N-glz?tKj+}r0T#mb>H^wNJ(#!);mF!
zDhU2moMV@8cbGyY)?KpQi+v<{v|_&s5<@e|0Np%;;nhEgcHIDMw6+pasJL#-yy-M9
z8fnNW8Koq@3+DuvP?D><B9dv2|NGbHaG|)9+)oNLF`d?;P?F239}do1)OPkt;)<L+
z1?dSp@z+=OAdG~Fc6=Z~1USyQ#{WxA!G4m~R`QqF*gGL0I0lt9%`o$T8FYO0)s^9!
zcg;s>&j_7iVbVF?p|NPGOvDo}yopxwCUsU<kYhwo=M~pges4_Owb;8q9!2cmQS$yc
zIXL3IqX)1zqvqmG<$q-fd_V#pOGNmAW8`1IA_)jGM>Rbtip#6jS9BlwvY%Z7*Aj+z
zpr@0hVg@+7xx>@l1?{a3=pRucG|-m@M>9kCjx;jVaJ1K=ug?HmE&ryr&{kKAZj%|I
zAtL2Og)uVH>X?7+>8L>^(~(q8?ySRLT0=`K?41Mw7L_6z3(|G;25xg=<iNm>Cl!@3
zMo*{;A(XLwTzzwVUd{7%%*M8j#<tBiNn_hiW9yD>t8p5qv2ELTjK+NL=lTBhdH+A>
z?4I4(otbN9uK8~+fFJ8?^YdH-C?%Y~q8n6u{+wt3WGDWdR~8|VAfK?gItk|=g^?5t
zTOG>`!^qee#+oT-0;T^1g>5OIXnN4EY2e}mgRzQ85%8Jz<+j2n+AjoyuN^&t!@ORC
z4mVTR!bb~t?nkpH&H%^{`O#<unrsKwK=5KJXe{Apsh{T(+5x0u=ay&YOp&RaRCdX*
zqhlLH&&-0r3!<ZNju0pf4a@Rh1Ejv=M!kVmEi(y7IuvwEeIs*frI5ZqOZh9Qf`<M~
z*na64-U)1S`H{3cz!CP9Kh%QIP7z5Z0c|a;!L2y0v89rT=taK4^fRLZ9f4{hf#kyP
zC@orMmNrES?WnF`mXmJ*HWcY^!|&g!+SxJaIZPk7I*F6285LE(0)K~p{j-_FVmnjN
zUXhiMN?Se=49v%I1{9l+QNCBw^tV|}V59AV>w19As|cS;=*6-RhySK1!X_lCY5|&T
z#w8UA;?3HSl4BB6b}y{yC(C}dL2Q3fOm5@X^-^GRd74x}V*iFiDpZTJ{V{|d5&|Q?
zd|9Nrvzsr17X#hBqtd|cv6Eq8Fjz|Rj*{Y}X=RLza@Rj51vws(FjiMB!C1P(gLyCf
zJ%$JlDQ@RUqp+zh&@`3a)3d!l(1$AJ@Ou@F6LnTIEZ&MYivS;htOy#-=!1pjc1O$$
zuH+??6CcBk^S%+6<ND#j@NPH&E%xko!w2(-?S0ga+VAF_v%I#vSmQFv&>scY@{L#Z
za!$Y$KiR7%KneSRCuwjx_V30-`&Vt6M6%tT{9Px&8;>Pxk7cQsE2?636}ZWP80voH
z((-0t*p!fjq_m*|0B6Zzg-x3_Gd)_5k7&N|;)ey<rCmJWuaSKjg8`cl5z)C3%NG$l
zJW*{a7+Gp{)Q$sYpR=P1mc;YR6r7L1@~o@jwdaH?iZdxBo8bYTd0Ht|bv;-cbd>fs
z-h_|^D5T>Rv*#?ByYearaTQNWLRo!l;0eVIVFi4f-Wf26ObSrNJ2dzjt6l&~9V^Ys
z%HohJ3D2Ybtc$z%g>>9oqbu}oa;tVc(5A7@Pymoe3b8p}M!g#Yk#0LBk|kJ%_$_4@
zcx1vnVh@e*F=MOlfp!jN-xQ|t<3(zmuxk*JO%u51%MWl>`B|6}C1TGB>bdYw^6M>l
zC8^+<as=Nq_IQS9wGne9KUCO)RwZHOO+A6HXqE0$ID$D)vqs<w{hT8GZjyIR+WS46
zGQA^fV9TZiHSvHsmQd9NFpX~ub;Kh?fq<WnF1CD*wx_&D*@G^6r;wtVYoox%4*q4R
zCahC}tY;=CYO-HAFu-g4;$6yIO=%4m6lIj$;XYSCNC~2}jSoRkEQjS!1NbdgY`74!
zM8(9oewS%M@qzEPhIAZ_%y}M)@23zp6pW8!+S@Bsd`}aH2zAey-vNbbS+4jGaT@Gu
zQ@QxpL5|uuf$N`JGX1oX(2F_W#vIzrptCvCHi9u8a)uHzvLnV){i?J&)r=`}Y2&Od
z?Idu1%t%f>4L6<6`IOoMc2W;bD?a%+exsEs=ZeLR;@fD|?u&^3{b@PWMNDYY8P8dS
z!;m(Wb_)23KbD|28_L<jSU5+|{ZILL4G#PW%*g$mdfqPYG<As#Oc<w&jU<Airxr_>
z+pyi0K|x0DaoOh${m0Y#XQJ48AE_$aWN&ot=C<H%cRC*(vWM?JSEGk(--xA*tpcWN
zJMqs#DZIXJAX7Pov5W$yI!!tb#nkfsog7T`?<9&-I4c1T0l*4S2Ah(rpw=HO$+!r3
z_`U1%%k9@ZWeCtpJ|9r$QT^Y5nF1la!Z*l+7sp|RY`;HyvKt$^37RU5csS;zatb+w
z9)o_W5e_CclZ~j5`ftY?VYGtjg=%pfR~t}*Elo`xJFaSMyQge;T|~V^AyiWnqba1C
z$LtR3y58vpK$cQ=%M5XSZ4Fs*Gbkt4gmgljn2U}raYm%rTzx@gG`s!@pSMB8V<QYi
z+e7OAJFEW`-5Zk285SNBC`e><ek!Qb4DS(e;7be`kM^}`byz>Yz6w1>vnVAiguRaj
zKlbNHMVEyW2xJWVoSkANI<d(q!TLK>!i69u%~DFL_F1`daB^cPZ-s}JS|7b38*eGH
zTm7*$jI}43V{@r9+#-B?f_*H?h+qZP-(OQ`2Gi~(XZ?3j@xLhm0vH1$>A`s+ZlS%+
zDGJc}+uN@v*SS&k_T;>kLOLnG;hY@v6ev8C!~n+EB?rhELt3|2y#UE8JsHpjd!7km
zhZlq7229bnp0Smc?Akh!qf;)!mQ{dyS~ur!95+K4TGvbAVE)4JqcXjJvwyr%|0_cO
zUqlk)JA^aZi{=WuloUHYe%46SPBb<7WJq`@3v?xTmykXPEZN<^kQ^X@E!D=78^L3x
zG{Yx#Zx(@bCO-`+KSQO{@dLzKo}X*sh;e)&w~k1zR3U#oz)6vz)1EI=lyb8ZY;SKb
z@rIfsO<2cC3ebhT9Xz1P^Oc3rATfw%8VAB81eDyvc%eOW{xh*wG%6NV8xd2;11Ung
zTYg`of5aUJyTAvW4BRMS1mQG>AN$~=)9F>ikNpP(cLWsmFK}nGsTm}4+y8vvKimrc
z6TTfmPftgWw-KQ>?;cUU=S&olwn$43^2h+5*xa2i-&Z08S$Y@FKcm0x&S%PGol_4q
zZw%*AS46fL-2IMgp&>lK5Wd+R`cb>Qut86TKlc6>S(xd_FG&);V1aY2Tw8#7G9L`(
zoABz{=eAEH6c02JHP|`X{fHbM<*&_w0{zC?sCHKpW1xRRfQkY;ycns<OcfMo(?$&e
zyw=d@+teDvZ?9|-R1$+TRsq|Awqy<{*r#z{pUu0=PvLSfh>9+i@I{6+49kQ4V@oS9
zsu1?x#X&rXAU*r#IH&<#W|Plq?fNgxP$AE)OFi0Ca}5Zdh+$XCxQHYkU4P!UX#$-d
zoFu<<Vo!EN1_<ZbTYkLe-krHvtb#qi+%>(GVyHm$QzqW!OG|;lk6gSYiO5<(2U5TY
z-}FikOTm*HQ9Rzz$XOX*0fe10fuZ|8BXKldSSly=vuJ=+pPv%8#@!doe^1t|1Q2B2
zmDxmKc$b4A-dCno4V20BjvWNqj1xz)%pgsTU5j-g*;rph{i13sphC*N72AVGOAfe>
zHHc}+lG$+X=%peZ3Jj1<TaRG?pB*OOqiZ=+hag9A<~+N<e|BHqRv!mlddn8K7~|+q
z+_F6ozO~5xrL%E=KFgg@1t~`f30Mm|e-mhyx#;AdT@LJo*I40^1S>5g{aw^M02TCX
zJ*`r*2;_(R83Iq08q#Oy=VNA3+-3|7zPg5eRCa{<EQG`dnE7B55#=1DwL3S&WWtO(
zExM#kTZ(#Ez=lcHQ1%gV$2k+99eDYqsAa>2$9zF4?2z_9eDSsFiUW)eElU842oCW;
z`^|7EE6SDbBLr=MG2Ls);aAhKxU^JI(?yJ<Ha|PcaudF8hVoOxJ$4}dWA~lp-b2+1
zeiUiFW@T}CJFeJeRMR%;^Qm3dknq*LE4X;C9p%rH+28;`kAGWC$!i?;A=2RMr2~`v
zpv|jtQDqa1&LiQ_%py-P6E&ewE5<WY4o<BX-8UAljmR|ppOAd}DMPV@vQP0q;kz=g
zR>-V8Uu?R;n3*OGP}dQ0O?q^Jtr_j;s=cVkX-w3Z!b2+JflKzbD~C^@0dq{~F7cn{
zL!`#L$d7u%w>i$+X_CR4gNZnS`&-{%V=k)SxBX7i$4$;Cl-rz(Hl8)d_SHYw9yoVb
z{RN_>iM{dt-^P<xF#HeK{Mv-2?uk~q++f>ZYu<$1G;oZDFDW<_gbt;QA6wC`Iy(@l
zVmfh9x)wC0^!WAteHmOSNG^|2K7Fws<58YB&}EbX!|GqJf>=-p<k@eYj?xN9+E(gG
zay6QGE1rgRU~6=_rCx82vTBP;U-xj^1l)xlAxCM((DR`V@Z)P8aRC6p&UD;|BEmQ<
z42<E}fh@X7+m>NKM^=`rMK54Wn#%<T2M2inlP5l1g{1&d%C&xTaS`j{f=ETD(&@;S
z{BixSmPI7^X3Q%eAOFqemhaQ7xUdKoVpU(Cn3o<?dxx7N&987v<NBF1BuC1&g4SkB
zq<bV4y6@L!H!j?Q--1MaBPb)J<A{fo>5^nafV4ym)|g>dJZ&K5`QR2G06mGsiHnp@
z-C5Pu`9;TrD7?40&lhC_iJ`#qd@bsCncz*2{|2nyckBleW$B&bHmw~Sqr3huO>>zt
z6jL*chbH3{n81!N5t%Jt&dg!=7nhrj0>WfXHyAfMji9~QzST7yr8j>kyI24}7fy;R
z=S6f}W>TNoo)10F^+DJ&N^%_D<2m1WW=<w9c>3_@hNnr<J$O|un(h0j_(dxwnFfur
z$qI|4cmce+=viWNC~}jD?6}+v$u9DC`c;gC@^KAL`_tO#NQ}<>s{y1k1C?`P?0(!c
z-HPL5=k@Q?p|kZkk=?y$5+O$v*TW3YV-@s`prY&d82Pq3+8M3f;K#YYPSfw(fwj@t
za@E+e8a1?Xg2BJr%yjd<Xm#wmr}IZUvX!CzJ(?C78ttF?@#i|&p<2{^$cy7t+Uct0
zO2SSF;g?SPOab>ktgs(yX}iKXj}P*E+Gdd9D;l9tZd81Q3oDIG@MicFsqGr$hQ~TR
zUJS4Z2BPJMjez4ZH`mU+roQN1p<?<)ZJ+$tZm^Oe==y?78QIbBA%5IP2VE!{2b^>e
zi{@NM8S9Kq0JR~Tecl<QGzqsWSDMjw?>x)hW3R`Unr-%3jOMl{PLT?!`xN?y*I(T&
zMQEHDqG<2xgdCqQ-pa)kJnmeV+Pp|F59R`|2m5dc8A0pVp~hzqkab8AL&1+5$kkPW
zJ_=X9ZCUq%L_A53#N14|k%{{^BTOWHBiCRLSF6E@C3dY|{nU&Ar{1&L3lW_%<m=oZ
zAzudfKflw4ia5J`hpky?k!Dc69&?Cjt#SJVA2&kNRcgOJiXK+_R#TEHf7aG@FjW3x
z;oty(NSx%_<&_=25<icd0L?sIW(%Jq0Dzt;y({BvsR))Ym<Z4i5N~Y`;LY~F(zhZL
z*{q6drD_!;A@-0|al9Fh_;?u^*SFUp>`1|ou*ei#)M04YN#7(|o->X|d$uA(^lk2c
z>0BQzwNRP#2f~1gFFpxQen0{8)hc?3@xLBV8^i;WuKbSI(Oq7TCZ~;(VP^aV9EG=5
z8n8b6x}GjQE<-V5Fxxht&{gJGzE^%_M&fFYg+scR9((?R`K_@hYH0A(?QAyFD`M!B
zm%fCq(L*mjCpWA-i$=6UqhCS1xk)j+OJBMXWpyvr%TI=uHfUdjNJ_V#b=w9E$2yZ4
z-kI=XQt#TXa$q-vaKrmE$Mf+bhc@y$V5k?%W4i@@ean-;ii>)o8Cd7~LcX%`q<7u6
zJ=2#=+Y;c|`Xg?)vtJg9=X&_rWtjB<GitHEpU0CDkD>L_bq(4$u?*?s_UOkGj29Ul
zE0)c^EgH86oq4$%51~KARFPeTP6PToRV0rSAN=H?m6r1vHD;b*K(Vbbt@O{EAp(A<
zVc$<OP?~RncY5Kn(L>0*Ck1AWX!`C(z(h9T3i5JqmAka<H%h)rAJmH$I5Z=0p#7t`
zojOd^Smn%*^@zGvd6rzlxV}HSn7Q&jh4<S-Z!$OnA1mhNn}WPkY6Wv%vofs4c+1?o
z(rJpbam36BDNJwgkSC1|p>c5}F>lDpoF_LV+j{WQ=~@b#QHZP1mf9@PcGY}eka1Nu
zXTq;G+QHV5%L)ZoV5*6Hae?ziP0K<2smlE|?7WUAM=c{B=%u$my^zw1fcrqp6GQ)I
zQlSrlrERybN*Al4jbO3R+zwUUk6eFl#`v%6xbvGkacquWMVTJAq&+q?>lTewjdrtT
z&b{8TRyx|r=9VFw4o`RtDuy9FnEF#U!&!7sVvWLCHaIC@V57f`@FfNG{UjQmgk$^r
z%h1k55z-Xw8Ia2tIuSrXgbYegxR#{Me<G(DKlW>YQYb|;j{X!ra<x|?e)I9YnTwt#
zr=XCJ(t=`TyS?`RNKZh<c{^I}4jDBS)6wZL%@*i*hR4K8E6lzQ08v<R2w4`-sG!Xz
ztpIUL%{UF|i?9%JriLfS(+d9-qL)ya0b+5l--L6+A{vY5Rh;&N{sJlddq58(0-28n
zIk>oFkB^TI_6Cph$N=TSiHV757#N|$9}jS>S4|)aBGSHTu(!8&%KhV{ZH+DOBAFhA
z{onP?FjQEaJc^Qcy82Atp_#D&#gV>0f69a_ny*;eT5-%jRyg&~NxI}WeHU3?jNIxq
zf@hT}hOs8p+c`X<#Ij$T6JJ|ikD1C1e57y^GY9427e4A)nH8kD1z15K&}yCY_O+1~
z1`V+y`V>(cAz|g4z#`{29I9mw$B<ut{VN!;V2ZC{xL?{dv#41RPG@Uff^Sl4bcd9c
zud?0;a?cC8{3=qVM^<~8z{ataf`h!aAm3qdBaTcJEyA@6$hUjJU^0R!cx61#4YyH;
zv_cqOHYYN{t2>d-VB5E9NpMNHF}?+wgXi^U0PUxgxbBiG>usOuSCF`Nc1FMDnuPlm
z!lz*8iodmqJ-nApqbmP>xD8p70*@YfMSI-;4TOE#2od;hvsv%iOE{tP2la2^W}VBU
zXem%n(QhV=Zs+Vm+kBLcb8OGE{)4~u)kNEsLy4F#=vTAQsON7-cSM#@r-zI&?S3K0
zU@U)}zmK>6+SP)81a*yeIc+()Sb*(XXY4Y=OC?=JS|Kh9BJ)v~=lf6iWvo>iT0C*#
zZRU4!HX<quj8!_g(c&GCg%#2@SnV1`qo!=+u5+$c25n#jTvgPmSu>E|>WpJz%U>HD
zYZw;gzM$L}b+p7G%S?Hl5@er5<Oo34?{49;0B5(jV-*FuiztbP*`jI|_s1XGM5(Sn
zD~FL@f<C8~Zy*1PyD1`}SFGTqCChP=5)l#6GSZil6~xcgOPGo)*49@urSRHgoTwRR
zl~0e1OeCTbfXF#ZF8ce$%gf9AQn|N}Xo3-6sYZJAnJ?`wF4QOor>2l$upw}6h{XZU
zD>v=*DQfM*hj$EQ7&jeAg7{)TXlZGazYx+S-bJ|9`kY+ZOGt(6zbaJoI8)}X93Orv
zsg6B%nWL+a+RuIa!ufrFWK@E7;~jOv^;dqmC6s3ydDNJ^xFopLCPlr`J-O{lD>m1@
zIPiiZ+ffA*qEYTHmY#)*3u3BfOX2=HI5>%>R4R1;bEN~}nl`TboqDyk1C2sSCa41&
zK@y>HP%f0-t);v3``Q-${Z_LI?F@D}MCjfnGCVS<(qc0y#q`sKdeux;Rt6$)gfO=*
z50LF;@%VZ)QBUk^FW%Tf6`KbLhrgI&yLr^B*65w<h26gbhT3JhleT{nMSMXJ(TX5x
zcGwIr7wX?JtB{0%C*J(|5LBCWp(tL|=4NVZFJ78Uc71IV9)8sgyBlaXTef}S$UR0S
zOh9#s#|hQd+gn#nE2b;NvYSgrJ31|C6bY02K~~2o+aJlu%>4ypXe072Nm|7mBcPxK
zWnj|-xxXT~!w6>u3-V{PwB<MDKi3-+w3D(T#^wnDW0i`i5bt`sYo$zYo7p>;SRoN$
z4g&^%;B3%cf4ZH8D5>ToB)JmPt6PM?wD(7k|MVh_;QCHzwO?c>LHQOG#A<)%n7#5F
z33j@~Xd{GGr94_yMJ?=LfD*k*VW!(bc_nL9Q|n?|6(gi2IxM`1X~Iqq74KAfu*q_p
z4&6N!JK}VPIJS7zep?7IVV{G#DFk=4)C}CHAj;kpL=){6<%h)y35V&Ko5GtV>YK3v
z%7nDy?yt0YUUx)BiepyNZZz_~FAc}y0etzJDayTt?*Is*`FS+%_QC;MEinO=s$_fY
zQ9%Axf=gtL!kV|*8?@_t#GtqqVXNBVLXTHN5ClQWSV&+WO@49rpap34{t^bjjpRC_
zDqb9u1f_zuZ*xIy_6a29ro<tzC0!8W1tv$AIdPHUZ55ya@wb|9@X`buEujFG;62*5
zvN0h&PR<Yd4Yr|L=iB^7gb!E3GSScKt;e|?wA=wD*WGYP@V)ivxUOeQSg#M4I323O
z?*ihM+){DGvRRQ>VgVzgqgW6SS8P;Nv*H1bbRJv05bXY0il?py^*Wr6BK{2*o1r-!
z4{+)=5fSb@{<(=)cPGJ$SzIR9hDxPofUjL|+8Zs+4MTI?sTAoUF{%qoLD117vx0~T
z2^TZfHkxZ@x-zS|b}T1bH+5}N)H_l^*Qp#?2`SKsYtZ(VBSJNr3P&E<Kb8Sd6PdK3
zje0kY`KODW@`{q$6ZeG^f1j)#3=YnJ4U9yh<U53rOcAYx)cS2lq-}X3k>x5)+E=j-
zE6<WoB!`sbSN^p>knHHSsPn!8PZ+s84Lw*ajRiXfZwn4#=~pD|WuN@oNVn&>9oT<;
z>q=wS;Gs@3<#v;n;f&lpcg2%Nz8ur-z}6np*qhK<c|BkF?)iSfje4*kpy;wxwG9vw
z?0^>@&jjO>BnUXq!^1<<S4Z>egs144-UahF7+aig_tEss$WyYxx6yW4O0jk5`>BjT
z>Xs9erDZ=nwK+02F`(xWW1p;_l%&pb-~YMSq-h|gqGDKdH@iWQ0wF^&L7MS|iLEv#
z6pCj%>Ju%iName#ih%B%b!=abDT2T2JqMMQ3^8&jJDZrHN0uR%nVvBpRC?<%VXh(P
ztk&FA^9ph79Id!7gFvciLX4k;NEfj1{^-RHg?bXQmA?G|)4bV%BaLMx;Oycum}cOk
zeWs+mf6uJLB@y63twpg&AMqw&OV^%BmXY~GC4{@5Ve%{;l2?Yx`ETa$p}o{@SX-g>
zxzPw8$n#&&l&tJ6l-&iXLbq~Bsvnc`*12$X(^)?H+&OJF&&uPa!EQOcR_D-kui4jm
z#X4~TO(|Gx?7w@C2#`1*+wY4ka|o}a85#Iti3-=25d!dJwFu+Irktu-I9IdIBbXql
zbWMr^e1XPva58&TQP|RmM6#2x;2%ttqh*PcbT$<H2uc2o4<R!LW4tv;6{Q|=$2>{7
z%b;Rs#ke+&u>_7@>k^Ew;d=ZK+~ZKuZ6l+T7}Q8?xt13$a|f4+kJ(s~56;zNEiT_b
ze)&KljF?iT9sQWP`L1exi?gY#Yxu*=-WB>cj1cLc9@!$m9z7P7Me^-177OPy#tRca
zFR#j-be2s+rYM0_Nr{<rY;EK>3<ePwTg?>d>PV+0;Q96M3c8m786=q^<<4^vaut3E
zrDUzdPs%1;sHeeU|3qzOh(8otYuP$Fm$pj+Y}^Q6nPkaH(OUhWiou-UamTPJA<)4i
z10+Pq^QW`OT86XM(+Vy>`2S?-W+==5YRg<^T=tF4T5iq!+M)(#)#XCNn+7KX1$rrv
zItVO$LC7@(gF%f==mc_3!o0-Zwob)JG2!V&4GecG>pRBb(V+=Ws56zaIqxuM;DSW0
z=1jJe%o}*J@JP1UxqSOQoD6s|+HpV*j#rt)@%1DMZ!B32f9;l=hC9~;U40T0Z~F;t
z8k;D?c=C0*#m4De1DmP2X<r&!wR|FI7WQNi=QN}hgB0;1Q^&KaXxDbTULxA363*p^
zb8~arMdGDuh5FlE`VrRKH6?vBg~LrQk3zdHbc$42Lmcw|j7$~`72vUz6{RfvfjY&k
z^z;^88%oOh-1}Oyb{0$2u_Mw22Ko9pzkNIqg0r`ObC;xkw|wdx?@9;n;-43eQr4xD
zw$}gvE#3?++&8KU8$hX9mLeiJ@(o>#bLLX^LU>lYL|*b*^$Y3R-Pn>@Y!>k{QyK+b
z0LSe{Zhjqr;X^+Zt^ca0a8UkcAXz#g-V?ri>J14Q85zX)DD8NuOPE@#6**C5yTNTm
zu9Pbf8XBES9@Xcqt)pX*;OntJG?~mPhz%VY>iMx+=J;^#Wt;!yxHm8mm)#r_n^Bjt
zp#+84mB5U-rL^j-EFnIg_FL?F^MweMnIn;)HKqCVGvo<oJrykvY7sBwOCJ@eP5NLk
z%0@W5#kBEN*L$n?1g>gV0$Jju{68E1A1NV4i-&6@hccDQk;=ABkqd>#7~1O_jP|^G
zk7IG#(_-sZGrT1E%W@uDP}lN%Fi29q&U2KFo*UVypr9CnxwmTjh@681x5NDkt1qPQ
zph5Apw0d2JB~n65ix!(%9|lL$!ZjkcFSqlx&aYnz-9FLr)AzA27QO3Y-CqTcPd7CQ
z#U0pl_^{s-D868if5VQ<Pj~im#I){sdWVM|aSr`;T(c^uHtwr{pddaP(P|@TZ%ENB
z58=L9I-bvn79XdI=_M-?2urJOL6OlhO^E*B9c1l<v1+0}I6O8e!pxsgEPmXuJ*c3s
z`GGxSxY{RN|L8h~WyVS6rN!a^ew#&ZsOk9AMY%DP?R@(;&KZ~O&<hc)iWuH$t<=qM
zv*A`0lV*pRqhie9-;bVz=nR-;dK5&2hJuRL2ai1itV8HX5iAHb$himu=m&74K$8g|
zztaJb73%4A_wMBP%v@Y*-0#t23g*k8ou1*cm<aKj+2kUCkI*^Y{CR7=2L&Bn1_##3
zPv%wW5NJpYbh*SN_3L30L{!|B)Sw#Ng2rM@;!Ar=ajuaKdv_(|Kb@V1Rz`sarPC5J
zj;PO5-$f?<X#182U^G%w`z}-7)%YZ&lPc2L8An9V((u?v{Uw8?iF2#2bcp{w;^2`z
zND}#lnBzE;!*)pqlG10CZ}pBy*@P1+Fg77SwfBK-@{V;}l5*C3a`Y3djv@}0w~6&`
zkE~Cj(nZxpXj7EM0ei1KQ`(}3Y%(e=!*-BA$=<~nk%^Q;l(qOnym@yxtk%9`4LQ0s
z4&5Xj@1A0WOzrAN#PCLD_ZhFO@#oWW6WDhZ08vk)SAxCc<23_oX6;!0hraaGLK6Qu
zbEnMViwem`a6Y;ST8OrYS66|+*+zx(;x(BLJJf1DAtcTeAjUo%bbNG(9%yyBw%!-N
zYJfU0Kki-VMbrzvC@AqrZD2u!J<&@MpV~y;A_LA%5p;N*iv*%yKfsH);&!eZDijq_
z24b5J7|?Us34_H2u(;Zv0g|NS@)!EBFCc<vn|yT$*SZNRp0P}aHbBgcO*B}40)MX*
zXVPmP?kcl&KU^&!`E3!8$jbNK9LOh!d?f)lnuYsYKipi+kLF^^eXAgzkdhmrZoy32
zq7{7E>D@o5tC*F@4^H5*vBZE+JrvR@CU28wlI)q{tWlvb%Q^um{K-KegeJ)P$HALW
z5FgR|5$x`iaI3g3euBdeN@0t}dX16G;8IS8bcb<S+@2(4Du*wzCqW#I3o`;>8UFds
zK@@2kb-z-CqMoXUVT7KNoDJ~veTmSzU3WhZk*mPWjJ<H(Xi;`ZC~@l#Q`CB&*4Axx
zL_<;b1EZs>kfE{*1GR&_Y`5XC66G>FuGyrFXiYLi53twkFq~|^PnH;0J6CX`*5vBl
zd(n+O6MCBoUg`b>V@{~+mt90A;YD!wvrvpiwa>i5<VgR!mx#`)i4<erf4^WIgZiPR
zl}!21{bCE1c=E$mzD@sJ_Epz{9BJf3Z{6|kTr!)-4WdoXMgmsL<R){IrDCLaYF~Cl
zktg!^36%_=3mn}uKH*9ivIV+&{p$>#X9kYZP$8#d{syK~xciQ44O$}dm1ujtpa7Gc
z)A@j`?WPOnR*ic)=b19C%!nfwx46$6Z`EoYfgPltC58<&rWF2Lrxi)nq+Go(3H+A_
z>j~XDm<pUJ@*h89&qL%{oNhy^>EVB;*-2XOS(##I?JaCAuK1G?^cL)1yEgPz6m>X0
z`7rafpbR%JjvH+bp)h==qgW_gOc*qxV<V_ntv8@t6s;MrPbI<K{O+%H(NyckuPSn(
zDPm+?#SM<?Gd=H&4;B^Vi#LeD=3lA`7R<nN(B*)w7?fIRv7@p50~zo90#&7VUjLA%
zSm(kq1o$DpyK@;l5)Xy|RO^0U{Og1x`}a!mXv~~b$pE{v-^*k+hY=x@{(Ewo;aTeD
z%$$_5&L`YT3Z>DJflfZ{^x($|B`(&W(d3rrN><Xt=ok^#A)Qv|-!CfJfcG-?cyA-&
zhn#UypL6hoEIzWwbEnp@aHIZ)XBh^Tp#-9Jqf=l6VwrN^)*_D8vAfA)4NpiCw6u=F
zV<|%P&Y7AT0^#Uf|5EAwq3Iqck~DZF!;QIziTb-=LCmBgqRsaBI0nX0++y4>o8_sO
zePp7-Q1=jM^d+?~L~JroR@Z%;e0=EdFZXCc@D|$_8*LOsn%d(TNnHCWX}M)=s(Wj{
zrv+s3li0`%9ryD-Jm{aIhcaWfI=!PlULLO)#9&UxoUk=5ENCz>F-bWnwcrFue4mJ;
zqM|~#W#Wi8X|*-m;N;6KwElDP1+Jm4w0GPxUv$a;Vt`0iFAU0kCTb@ep+}7T%$72g
zuvl@}jK<1JV!bS&iEp%{YJWTd#gEZ7Xh~(AoUs|LZZ0maq@w>KJhfa!Mji}ve;y|R
z*DOfKCZn9P#US!VMnn@Df*QB9$EIpTv*I5-UHb&JejlKiVkP&j$jGl<JM*;t+<g$z
zb0RAu41Vztc`NLcQBs!b`b8+Z>t`u_PdK-?t0<73w;Hjh#@R<+;jc-M9osS_xKAuK
z+^l5WW3(H1-EJ9MlS5u-v8dK=4uwI1K_;QK*19`Y5bSt#N0>~v$YH-1^Pwwih|Al^
zt@PfAZ*@cGhyoCC@JC7=K1c$prl^Jvsc7f|k^yQ8o*%^LPZzKtc9*`5Az}&;E%M+B
zZh5r;tFs~OIX_<Tz=t5Jk%{WEtmTixkuh-UQs6=-cORsnDt?-h3rQH6pe~EWp%W$b
zA?dGIJD{N-ID@jKB{C*Q9?>kfN+jNR+`f-F>rZ5VR4G;)M{+tO_ro`$UjYe`R4`u%
z0fHIub<TG|Wi1R=3O{?-DN!RU{C8*SaqrIY;2BKtq6*A>3-BE{QYzPmYz&%pAt4yG
z>aGhY5YZXGc&x8JXOFYoAh%y`?Z*iM9M@uI;3<7_nh3&7?aWHPPA|7r8S%0w+OESu
z<$FKcdp=~8NqlEEkrtwR8)a?arEaJE*ECkKfD(E_*b$a>lMcw;F2&Zs{6a~nY9!j|
zWD3EqBYhWpEvgiW)gJbgSdoY=e>^!DB{&LXtrNDI7mv@=gi~xo>l9Zo`c>`|!vLVK
zvz@Bd@+XJy<b$S2;5daIG&5DRfq}MKRh0CLMkqF;q$n%L#4a`8+bm>Jjo*g9F|rmj
zZ@KGIO;18GhHWHEs@dpc!<`M9Tbjj=HlGX{nkTu8#U5dz4Q5?rFX7^$35#W{B1~}G
zrNypVgCSre(s?@eDNe?Vp5$U@$0#3pTi!@~D)7jwXrp7Yj{N$<K=a!a<L8wta2pn@
zVz}P&f*BX1J4OD73A!<ux(=-C2{10ND>sa%1n*}eYBBRzFSmgoVp9f=kKI^8rj9}?
zB$djquYZg8tDAiA6M8J93fGt1Y;Ino?nmgE!rLNQeGS0aU2H&wXn;){9FwEd_qzP1
z;Gl^C4~G|6;i>Jwht1b~|88Wh`(xKPjHDnxy*@(;7qh-?^*P%5AI+?jS$z|P_FU%a
z{Kp>WR2V|&%D@f{83SlKpT^Xy)fn6|w2RorErZh-5tZgya<q|=G4w7LzTVod;Deuo
zra{A}TQB8;pzs;vS2Rm>xVi@u!<1%^arAsFYtaWHo-bdOJ)RaAP$E(}rX>%y4WZT1
zNfG3dXHw0KU#i;bkbyB4LnuSF+djx=@x-`ul@!@TwgpU@WGUphnOZA7vuhCa&d4qG
z4Fe>?A2@40y|}aNgh~(1v`kYtK|!37;*I2F5*2EQNG-n_;WE4=>zW(GIIZBv<i1lr
zr5CgUgqRdm#g5%g<uuasD4<Y5A=LDYZ=?(@5ka}F`GD*=m#Ab%ck*i3jQEU9orE}%
zMSW@}n%j}Mff2xPk(6IOIyQD{T|I|%g_@+Vc7AaP$AiY5Y^Eo}?HMhT`n(%E=G=;6
zV~N)D<IjOyf!LlJ@#~fJKpEMHK^lXpzE+%ViEc2OxvApbwIz6DE{y?`F3aV)p;#Xw
zg^*f6WGLrG4&mwhZ>`^c>@Roc8aBl7>K0Rxy7~#2iVY`f(7S38Y?%%e=qh&b-tO4k
z+?-PI8S|^FTI5+<4p@}sTH4yXJx69diNAUvJx~IZenTqAc#e+9Kx1k+QV>Q*BN#~?
zCYzNs@>;>W;bqXwdko9yg>8^$%DZ43B#4{?_oV)AJel(^2fy0Ok>l+sgS($*XkPq$
zu|;t60zn@x_Ih@|+qt&v!!j~4MXa;Hy=oW9NulF4`0t%}kVFGQQN%GAvBk9UI2FL2
zxn)9m)9PNeAle_#kzvu{_C}Vr`r2Zy_n$mY#x<F9`N3T*ggq0!O1V&dw-S=H762f)
z@Vj0up=nUAn3iS_C$!q$oo?ZA-QS&+qaZU$gH=qI2Lg(tN9MK22f7cXJ3&}U$`V_X
zueyC0nDZ)3IhWFb;J;VLKmL*~5;(SiYai_Pa!!jFlZ-fLX;|GF-?Jf5gyg+MB+B?Z
z4gy?kxqLmf-q6Gx@jEQ?pCD<?TGB5C-KApO#<5vrTInb)T;Ps2a$h*fzcSff;-me_
znrp-qu}7F_!1#BV88Nih%xzc$8#4&F*Xctwo*x9a+Z1IT*nF!@u3DyreO?`&$Tk$L
z!);!s&Jt}(2ZYNo-{(7|U37JIohNzie*#)`V~fG&oR2kP6+j+SJRFA*1fL57MeSHu
zBVEk41;#m<03+_OvC@vC2BT#9FJH2Cs8xqW#_uZ0W$`;AS_xkj?J#$aDMI4<he00?
zQ&;~p>~bz<1;eu#P+S@qa0hOM|Ch8?rE*b>k9&fkprD+L&?B2dQ?}g;PKG2tE4=Bv
zB9wGy3_2d0E!r6u<ZX%|isx7G6^z?uO1%|2QQzkIWq?yrVVdqi1!dFLQLi<)@q^mK
zI1gUvbvzi(8poC~{{w8q{|BA;kFz@Sh89n)6~hxwkH9~IH|^`|!{Rw(m;N$K)9ODb
zad);JE{0SSL}7-}>3eNAGB!3;I;rFIaQ@@&YFpWq(V+7u5;?`XdLMO3osMbNyxSQ!
zDJ^ZpUXO>RM2*?}|8a=FNsCl3kv<I2VXCT0n4CbW5#>_*>Wd1IPASD&;$S;V<~CAK
zx*}RAA&|!K^y15{xjgC>)muypW%GI?Z_;A4cXkq$e)2^d7iH}ajH=TzF%8ZZvBDJq
z4kRV1l7VG}!XHo3Bj1D`ABAVh`0zE7F&&Q_TvW8QBChLZqz<&WnEv;_dlpe!)8lDF
z;#6*N63|j=!c^uvhs<Tpiw=kUNRy|f=BlMd{+R|W)*+UqxNJqX%;{FM9gS2hZft8S
z;>hIWV3xz5D3A~aDOP>6#-dijWk{5$Z_{8itBc8(kba|3Rb?ZKopM@ESA7a@7`t5U
zFtC(5=&wbiQTpF$1X{yg!xSVHL2hj7R&FMgO@f2#$kkO8@Fp*CpvDW4#t5L`tF3Oi
zEaUpT-s6x{G^eCsi3JDW^e%iF?-rKevgDJ%<JUZ0?@!kmAJU`su@?IBXA8MtlSbx*
zroF$S>}<6CP5!<P9vPViY*5hO9+{`k`Y&7x<g|99#$Rpwi_Y0HkF?ont5b1;ewRdF
zKR4a=3ThV8hVkbeFqYXgY|v?My$O*bCLC$48A)U~mdNqt4v%MLy?=8vX3R}o;oM2<
zhj!F!K|wDj(YOR0MehNmbG6oC3=idh!3zJ8wV05AutFp3v}7?GQwUi8F*<cvHEfH0
z1!LsGzTh*J_DjjV>7hLZXI7ZI3uG6`cbD~nJYM9c5{4k(&HEhd@$Wotr;Km?YwD%m
z?`B>z2x##jLg^%XYil_nlF}DqMkc1aVO7<H7ZY}R97*Kku!YDFbfV|i=SXpbJg#>6
zTlN2y`atZko*T%l>%T!4j*Xi6dRIp29IY6jmL2(Zbzw!-&`Vzppw8-Yfs4#wr-={;
zX_onPJi%?z$U99Im%&X<NVWso;?euzExQ9H&H%zW#355&Ree=vE|zG7XhUn+i`CWq
z;#N587zF%kI*=k%P0oupPQi9Bm3+!5^yCYFkpAq_J}|ccH`f>l=6tTf7S}1DA_Gl#
ztX5T{G$<n!`kPrvU0ri1=W{DtPMlx}`mqeU$VPld0z4vt_X>WB@yT0g>${P(gePzR
z(ij9)s245mVuSz@!u|^)AJaL*jCB|V=!R!s1l8jIFC4{T2KDWinig*d`N)q@d08tN
zC4t~)`6cqsI*6jM$H6GhbwejUM0a4Y+22&r-ScQx@jjKrBmha*J2JJssOQ-#tkEyV
zp3<hXy2nD8ST1ayE(;9jn2Fgo8}ICB-pJw&XMxYVcP+sy56pNS33@6If7f#&tu9L;
zTZqCLh6!xCj6p|u4WrMn6s<NlVS0tC>uZxFC)W#sf#S-UN*hY!IO5=T2wF9~-X$$0
z^;)>MZ43;fv4+S<7^&2wl`eC#RC_)I7XnRzXP`uSGDH3O^mg4hn;1HjFTLj(e+^G}
z!s$?yL((es)N_eP*O4SdUII2mSj^P_&l1Q$;YS6q0#vg#!BYCr%|dxq66sRQ$`F&E
zSBsmjMV49o0Ue*<n7M`7Mq8?$uO)PjEnLAl-lW8NEC^nsbhN7AQrHi`g%YCnd!i6B
z-}Rz8i5MpoAwW48N^2XhxL&$E9oVg^u6C?9^<1BUDqvzmEM1+;Q=Y*F;dvWb|MIfc
z`@oLJ`HmE3lN~RZ;=ImloD7qW6Uglh@e=-;udoZaF?s15SU`V_?SfpD9aYhI_DaLV
zq@ZCZ;CCO|`6x3GD|f?dCKg1I=cf^PymhIQ<Wed<>Wku_c7NbfNgt)M67S}otk2E9
zIO4HMwD=#@Dg&iW2yuvy)QGS@R-Qf>y3%CD6q_qgBVUFvCTRSHf~{z+&5NM1`C;Ub
zBYx$Qov4N?UFh|4U&KTjhpPcj>CnUkriF3b&PoLoE(4~ci^t$@HGw0!ka}a8B=i{P
zAqyv0sq&mg`a1b96Mn+YCTP~<c?nWN6Lv(;R{s5-*b5#XX2@JS-GTx>rP#|lHuZoa
zUwrlOLVvbV`m#GnJ~lGHiHXVGQ{xz>v!y+spR7Kc=c;1n`e(GPF6MsF0y){U_3BVl
z#9NW#$Len14=or(dJcRGHCU8*B_c&SQE8d!0)vQGkdQnk?s=?Is8$Cv)6*9!9e!w$
z&oH7r8ER9lUv<whuBIu-L)C<eXt*QO%b;3Lu>46q$TAWS3XTkg9NOUha<TRzZd4_r
z^^IdK9JX(G8pDiA&h>JhMBg3ZHiw1WPLlKw9WW*@4rfDPWA|zw9)m5=+0`*HBLTa4
ziWtNuQ^cl!?dU`$jH=!keHjoj)9mQt793ZLqAR!|v%d@2s3L4UTOjfs`2$L|2Y-5g
zE+T^NqnjKLw6KH);I70#oUHVPt9@CC{|w7XLR?eB^Uy?rf_h4f-$xd9K(QQ6wtZ}7
z3Xt{&5UoM3{=l<buI7eGgimjZp$hGXZhttBQzE-;Ma_$v0Wt&E+XQ(#xVK(!L5PBp
zm>(@BSa}m#t<}nQYmKSBxyEuK60?Pb(Wq;mb1*>y78L{&QXrs;QEO&u@*a+qa5RL(
z{r-8jg@f?WQyUFJN<Srdv9z&JbiAOM8cHdLSGwa9UqUUHS+<EH-tC0r<8ht-OWqx6
zJ5<}E{uA})X6GXq#GCJ~teCb9x&BeveCALldF4`yeONqo&deDfK|}Vxfb;)YAw8UE
zjyLfA9amv`zq1)<exm)O{CFNqN=mf4jgn+oT5TS4@x9wY0fmmo^Xi+f%b~TVvxKa)
zGX6z>Y%HvkxHE4S|6X#1*B70)&QlUomGnPhsFlUh(ee`C?aSiD-ems?g#3%ni^z)w
zcqE#|uN?6g*<Mk^_+?JL$s&QW8zs%nv6mb+LeM<EP_YxTN2TaV-TanM$judPPW)h@
z!N`lo*{7qM#w#u@33Tx{H8T@0(I~JJ3D^O!7p||a1tsBIkByJ^R%T;Ag~}9hWfKw&
z#+{rwbPq>=6_AhBXD&oWgtC~<4Q^@SuA(g*991vdy}F`7CvB)pOie`wVJ^G-byoA(
z0{07;2}U`-SO^FR@rj9Ys@wv-Uh=KNh_@yngNMQZ#)S4e{VoUz9#B`GN0o`h#E=cp
zZom5~@VGHw=&!C04No~35#13`SEsTI+529q)@}EvQvzb>@A1Awhuui*x5WQh`b<8E
z^VfQtX+KI`Ir};<dvy{M`9o#6xPz<{;LeO%_{#YZ6?D8*HroRU3#V#C1g223gyUKB
z1&Es_Noz#95iz_r4n?C{AJRm7MY`D`&cNa$OaWcQ+1c6YYT!l;b;W-NOl~C2aLfdS
zLWm5U*tCPkWyuzu_DvQ{W-xvo*5r6<6FA#Ra7oCUO_;a%<h;f`GCn@s>I5nvJz}CI
zje$ZGdQ{Gg$SeDlb9)cDzV3N{+%py<2idcXP^mLBs1uo-u(7eoGKDIBNhE*rpo*hg
zZ6++=K8}5MT_2ABK;ya{%dD)e8GZ%^Q!cFZq!2E;Cx(?d<ZT)D7Mg@xzDd9Xo?sp9
z%tHO%UqA*G6?qarq6AT4VV(Os2vQMIa;4N7ow$s7qDoejhqA|p2!7e9$~6^657Y??
z09CAnp%76*05;HJOv{!-!K9&+M2%_?`v|muAdqrnPFcq!)TDAk<41)J#M(eZhXOY%
z!?nu4YBvC|ufwm$#=pYt?)3Eb(~>du_a~8{u7zRw*NU-PAql<g=&d=ehJyn9DqIVM
zvtfu<d|RFuW<GZ}WpK#IV$;N%q1U~gE*ZAyhFx!O-LBb83e7^#p7dQWFW%?t{vwv7
zTR!{Wf4$<tW7^HMsY?qyjEEOjW6&t|<v?XG>wBSndcFFh`^beX4FSY1=w!Y7*AjYz
z?fXM+9(Nqjvs*uS(Rso)SwkA>ttc(;dWB7?-9Z)~x-sDmeayv$hcN1K!Ue_k%)u+`
zMWq9%=eC4cc;trYuLrk;GW0<6n4TfY$xPqTKN9)OaE`7LA4&gU)w(1QbFc?5*Og6N
zSz>f4^yxb3WHj73pteLI*CtHR!*}jZP7sLr=QL_^h(4T~1|9a0Onb|TR_Z!)Y%9jA
zx83y?S3-3aiJ235T9rK)Z1pPrD1D80t_6cTHcMPfpuD63>!|w4(r;7;S@mHJ1=Q5k
z2p90-VP<<=^o>|zPrL3bjbkwj<F@Er4j*D;J(-jW#j%9EVKD?PaG$74z&bqaEKMfe
zR;{~gY8O_nHe=BPOnBmr)Zf3^nf2tz?WUz>yK-`>Sq}HS>GAn1>tRvwBL?L^#8g%5
zccedP{@1VgCqz8MhuUi7CE{tMD{~}Z?(QDb@LNv;)Nm<zVW^eZ3E*&#nW;)>h6PWW
zf%ySYtOq3pO8l|2m;>WUpifgwLdW<DpHybQ*}ij~K+zXM&p`=QBpoO58Uc5U!mH@i
z>dWFhz_PNwPC;A8<U?oPQWh?K$UFFHo<!#kfl};E*ul%lfmvTq6H@VgI(O6Q&hiTk
z5`KWb>-ps+H58itc-9;12I#e~8R5L3er<v`I3h<s+9(;RW^}itsdcJ)Rg-plE}&r5
zzc{lIxOli=^r0cLcY5DTygpnEWM|Fb7E!EI@bM-3$Ow!)9$;R!d4K8a?}zHHsi|>v
zawqGRj-I!eG6c0HwUig4L_%^~PyfsCH-Lk>y}|DPm?NOW#6*q)hX@h+8>jDl0~!|^
zw&%9X+fdg14eusx2YZI-7yG&)Ho8wgYfK65--Ihc*gu~~NDdtp93vlCElB5I340u%
zmCqf8&RW`v-({q5*e3Bgtp}<Ni)8QUK;uPP#Vh>RjsMS<I1<6t1?8_EgY&-cJHUBA
z2)B}lADn%LI`2NJ#6vdZOO54P9xTHgE@@c)A<S52?cgq_w;{Brs&`r1KRBNXif~<M
zPhlr}wXcB#a`ZMAmvFFk-C)=J0XH==L6Q_im*b>Py(hYAdeT&_`=V%1TxF?V*{0&l
zY6)V<3e^)|Pr3hB`Seta<WD(_D9kz5U4z3CB&Q9ivK(T5OQzX<^ZqI>KQ+(CR!9q(
zn8^*H_GGyQBE@y^f%0Tce;JG$$-PBz?s@_*IdB+E&WMNV2*_1QQvPN#BiTYrEA(2*
znpxwLl~xE_a2I_g@%$3|sQs4rvdgXI<$*2a>}C$c#;N)WgJkxoy~5e3_e1$#XJ`Wf
z>Xb2{xb9lCSXKZk>DMMVavuKOM`c~QPhPIaq~?6W#WoUY5*<&Nh&V69my@^b(3xw&
zo8w4yXGgB$-*;-u+mBZeW^z%xc1~54fi&X@Y_?L5pUZ+?qW{50s3EH0o5@6b{@%y!
z3YZ9G%OKjyMw=fNmZ$)^4Do+pY^3j3R@-e|c*O8Rj|-<p8Q&AtDkbTulSpBfX1TXm
zxBIHs7?^^eUHPAGX!^32yYw$2=p%Mm4<1(htHdnl;1+mOv(HXr0`g!nF*7Q-8nU^F
z%qd{V-Ok}Wm0qC{wqr#uy;KkH3RB>`fmF%!?{CT<!HKVV;l~$`qB%aOrTY321vuWA
zoeR%?N5T6A>DQQq+n-<3KdQ@Xn;KB_pf&7v<)p_b&J3ArIO;#OTCFc7@R?;nK1ELo
zoNfWU5-&BE2~XihXoW$6BEt-nOAnmOoR7`PJW$T^)XCij8nA}D2(M*cHVy6pDu3U8
zEPnXhP9I_T*D1-ThB_?<-^HoYQ)K$qQS<Z4E4}IkBiEoS+$1i^oU&G{R;eZPx){~?
zsms@t-;;Pfg8Y}m+p+O%6phUfnUUSsntkd11nk~1E0PoM8Djom`PpEQY7mR&m9({P
z7(jeFdRaCVO##S4*)u^*%3=_9TS6yS(bdfHq$&OcSwYswhi5Fe61>kkz=)<Xvu{dR
zByn%a9HxobBAUwLnU<WAL-VTVJ@qQ@4q=yfQPVbyhkex2N@PxBl`jv`{(pYz=%#K9
z-X!HNrJ3pfvRP~+RM$7>O8G1DQRAC}?@avoz46*91TpSAch{WeHr74j=%a+yIg<~e
zqW-6X<z9-g63(MDJVvK^-s$OSMArAuin7l@*UkrF%wN`PubD54O{y|)XJ_gphqI6c
zY(09KvBH%);FxGvVQ(pn+U8oi#Q$bo1X72K<UpYcE<Y?|ivvmp%D>t#byR+&8&E3w
zq{hRP_-k4M2S-XZ&Sz>$P#b*mQm#%ds9op;Gc>Jq0i*Dnu0s${1KA-_*;_wM$o~V5
zZ74vU7Qwq)k^mMoEobvIzg5837$<Y~iWb9j87n$YNEJ&-_C#^^en-?;f(nnPPQS8X
zeuHm=r(>~Dbi~554$`|t$k%1n^pO7?omTgUJ)v?20~6|%l@R?lM@oP~5U_#t*U4&t
zRBPgsfSwJ*<#Ib|46k6rFyW{9l24uAYI>gw%}<XYhEUjPy(i(~w`(-ah3=C8)|cj(
z(dogegNEzEZc*6fUzLX*a5uaB`^pECANW6z-Rt}w1Zh!-LC4VFr@;;d)czoLZq<Oy
zq#xp^f3b)WPI0$`r-~?Vm?c!*l?x7`w?`NK0}cGENZ%kiJ*L<9RxPDhtaJ|Or2(C_
z&hPtof5m!_KLbnoGr*G^&E?a1)Gcp=a{S2>-iX_j1}hy1+)*k~x&k}$Xy)MA53u?G
zr;c4TrE4#TuN&bdI+Pn2%mo3C%7tBu##22Lbh_IxX_Z!&>hLtjZvwprbH@(-Z^Ns)
zy8p39E!*MZ$J&%!d&!ck{5P!`vvRWK9tE4mqHkW<hW*kdmjxQJ4iB}Xs){EIUbVvw
z?H<OO)&QaYC!caj>Ofw-0;b~EUnG9B%iIDHk@&@nFK)A`&IFD)cKBP}bi;AozZR#o
z^A~?DHhBySpsVf)G95n8<O+H#WhE!;n&ebf+1hP{Mssd0Z}NCw{%J05{N}OdnfLvT
zTOU@j`|xf;!Ol-Kuo;98iTa01O0G2m6qu+yZC`>@2$$f&{lae8<<6Gthi+evcf9j}
z<AjIDQj0GCudS~Difd`w#@*dr6Wrb1-QArKAh^3j(BLkC;O_1k2yTnJ%i{mu*Y8*V
z*3{OiJzFz#ru+2F^z`#|A~TIa39^&+?GtyQ(y-=5`kY4+{bEg})-6zeRl=V{jaT#Q
z)C{)&1p$U|fV&?Y2~qG}%goJf*GZ>$7v-fNd2ZWXBF;WEs%GcOi2nV0kJd)=BzG62
z+rFHfT$9~S<!fq-<XZS;?0PH%>u`OT{GBgN9xros1v}f_k{hhIkzJqAkGfwEB$xvD
zlc>8EK$4$(Aod;lIrkDlz8!Euqjvv^pToUjO4CMkz=bZ<nm^7}p4vArw}ssan26dM
z@d1ZUF273K4n4{|TQ*->yQ>BMxnbjj0^%ph%}hR5S-ZIt-Zo4Nk%c8}A|@(JuVj1e
zxe3WKtJ2HH8H=76Z>m<t-Hjdi3If~?X9KWHM5FCXjbD)j*d+suWtGmVpjpS60}n!Q
zlh!IQ1&Uz$Y{<?v9Q7-hJ=9T{sK6RsBNE1F_l23kP}d>lV2#X|j#CO=8<@MrKw&xp
zbgWyzI**w8Z1imQ4EAd!YHQMjD8cw<@b}ao)9{GNKhbit&l1?<*w2^i<f*6{8*68+
zMYTVr>ePN=z38|!qN9s-(*2=kVbnu{=s5S|^)j4HMd^0JG>)1x01DVLUfk4B0vD*A
zr@mpJE#RH!zlkl#KkvGW>Y~KYs%;ajctbA(Ys;#GS;h1T=5nWjM(1e(QU&j>ptc_4
zKF+txv_I42lRDRjeuf%_s$rhnF@oA$1Nqy_k6Pm{c&i0ipG#X$qy?vkGUvX>k9T=L
zoB|#7c$0SK)yflg$2T0h?l)|%bTu@8`cylDDC>Bp#@gQC@ssBR<qa1{_laZP>da?0
z1)!I*`{bzYhk$L1_@XoBy*r+({z?7#QS<?3Zph8OvLxKMnotW?DR(WdSh|8r^5f?k
zWS>PqrSO|Dp85-)nvm3G^WhfG8@~D#82U)kf!I<mC|#J(@Ny+(iow_>&Rj4mBL|1^
z^H*ktInoYfVzNx;nbE*ZjBcaKt!U%-{2Q$IY>cjxRYW2|tk}RgfH~de9CFi?-@1A7
z?EaEhvEhxx<}0ZQf3;jPWozPxENBb<3m@Fb_;hhu5k4YQd*)9o(i`wYMG7KvNW4a@
z$&)iW*E5kK%pveG)@#=P9Y*dlOXb&aY8gWyRnp1obx2Kq?hTuUb64c|Jq?>&f5hp}
zR(-Lrywl&GJ1{HO{#aBRT#ZuMkoX{9JlydKH7jyhr6fVmjD8tR)D_3NE~HsAowp|G
zsV{2Ju6w(@S1}N{8kJwdlHdtxMYY7w<1r;7B08({*z$MS?8K(8sK8@=MH#mviTrpI
z$T2$GKl<H<nfBHMQ8%JYMoWv!+;b;Te@y+dmc3<a?iiPD<QvbHw-&{)-(WQ(iOxvm
z;NT$9-)n3=9ZqSfH8VAai9dWv!cJh=@w-s^H=|_l;2>Tdt#Xk<Nuk4KOA7YN6S7$E
zz#>6kT^T4Y2T<9pBhj;B3VLw>MddPzn3nZQIZ{x>F2tKw0OQi%1Nc69SxDv>7;YMK
z1;mV83Hg|Ytr<CCla+U-nVCxNh6Rcfp+j9WrX4+(nzwP~sh{pnxl!I17TZ`uk_2mE
z#iB0H_AH9kI;agZD7P0pgvr4X0M1=Kak#NZx%+<1`4dl`du~W0QT)&Srn@AeL}R$G
z4Pa)Hj<gz}#Fm$)-cFhp)=L!^L-Of~rYu}Ob_tB_DDzDW?Mp#Gv#RK0-cg-W)Z@5$
z@n>x_ODCmm`huwm%+4D;x8mIrnzJYA(dA*y0j`&Iuerl+bJp44K&W?)?z>MUjPS0d
zkTc3s6>Tf~zqt4@2os3Hs<%Sw>Snd3yj+}wF7o+C9!a5&t7=wiu%7~Kjyjq~z;JOS
zyj2KeaRYGea$ZJgO&s0e+B@%|4UT+NU#G2kUJnx{oBxVY$mgUKVJ42(IUSkt@P^@{
z7A>BJh~yOnrc&c3`j2#dG429oY8lb^cA3NC_byAOe^@+Pvs|f86?P^lJ?+~^)mSSW
z(L|>O9@mh}b0UK}i8y9!BhS@8eJAY`N5|h_xJ=^;o--ha#oIc<s2kU7y#<}?AKM(6
zd2kYcYdbh(h(-_%4*5*Z$_iiYRNZ=7t*X=FL>W&i9Q7jWT2Kgm5{7t9C6GOIW$KaN
zSPVK#Y1<0~u6|}VVeT5#`t=M89~Je*%}s*u{HNd5#TxMJ*7nQe7wM7sIlS2#QlZx)
zOkgtE;o;%utCWuokdc;lQtGHE5OqAZH@J1)S#+WEae{-eCM7`i{UBT=jSspmc6p}Z
z3CiVSCM|hD+0oN6(Z6{y5rqD-`j!ujML@)!`ShSs>-J3~VuTcH3Hf8bi%1NoD*SNG
z`N+A#W}q*YiwMJ}4zzCNEtZs~Y+bUs^LGCf{qduQxr~2QPJyNlVsxb)%!!!4H!fbi
z%paCiTf3EwRKnVpR`^8NxQLiIUAj@yO~ayunFOA6=z|-Y#<?4b$!j&umnxnJ_I3tB
z2Ex`tuyS>*QZM_~Gj7du;D*4RnU|m9DDV8LoIoG{Nr<P+5=`7D<^j2GTzSTV%k+8F
z(+!VHgJ|g?kDR&5l$6g8fV~U?tkY-4*-5|SXcBwQ1J^6R@V#y8Y;Q25-<_GWSb>)l
z96!g&D1^D>3GFzo8`}?BuYvo8X@wkiHv22b5rRYe)leLHIqj%C5%cv}HF!R@#p59;
z@9bx~&b$ioBteyU%%(yhON2uiYx4XW?iNS|`5N`WAs9lBHklPITiZGPa)7KYje#%C
zJ)`vT3)`CLxeNzy5SZR{{9ay>_Ezuj&&`FoUK!OTM%QRax$h<2{+9f<&$HbOIZYeQ
z3hOnU=?IA4k!(!~Tc|}#qh}S-5XaeTPKj4@vqu^&n%xFyN2rs}nVXYqK@qaI>LK`P
z;ge;)erqPbyD+z<W6)Fepr!Tlm)BNe6n0<3MvElng?><%%cjm8N?Lp!4u;LrWhHrn
z4ztkGguy%s=@EFfcC<w1XM<!d+u)HOAM3Z$V;B^;olGw5T5<iD?V}QQW`!8$h!Ci`
zsj)Oc6VY2|dsgBVx<^qM5soi(*H3)u=l^nl1lK|R`EG`9;OStC71MZEF$&RH2r(Jc
zg~bmbtU^;l=KNubbx#r(U2Ng<G1!{K(wzc6KVl@-*StX&MFe$NfGOeUzxiX<Dgo9u
z7**&44yz-+7r4~+@$CQOcSF#a+X_L-oS0DMBVOp7S6EYOx5KpuX*3%{IhHTG<_sx4
zr3r5W(PC5{7Tf8uQj9arMn(oKjs(!a`g;lCTGL4y`v=FjwNY?7UT-4o4I6->Bd-7R
zH%lKF9bPY=U^DoB+Z{v|#t+z%P|fKZPL{`WhDJ0#f498w&Z8RqMp~}W+<=rxFL|hD
z%f{nmDOcR4Q?PN8cEVu}ZZrSU$*Jd!8dU|?N&y1MnW!Q!;1{@yuzGcC6@`1Yt*7i&
z&uauEAb*gDkSNNZm_jnhP_D_>SBWf_rK6>{F*(zPq{*06Y4aq}-Pl~2xt`WIC1Oco
z&MCoLD>G7r{bs{KeTY?um8o=z9zLQy>j}^IQLmKS<=M+XPdd;Z6}V#T{JTr#)UfVn
zaCP@ZgEv2?VU5v6X&&q|_~VwV;ub2gu>+f?9KY|D?+c{ZfLLfkL`4NXJjwwa0)j-x
zH`5HEU2Ywnj3SVqG%`WC7ZjX6JG@4yEX)aAQ9Pa3VnTj43VSd{OEQw#bh<6eZ!xEO
zE{O{O0ItXguRu=ODxEetU}`z19TL&9@<{M&3g~TcB7=BcV;qkZoi<G9x8Fd~nIlyP
zw3^Zd+@P!R?rGkauF2Q%aRTAIPQ9|JrbIhdVoW)GZ2#mS>?Pq-s{<!(i|Li5uXZ?V
z3x!v0MIVxy%CYD_arT|{^;*>(rqzgFeI_lsx&|>?4D)!mQ$7^9M7}zqI2_M337*Ge
z*3lL3PDW6e*RfcN#uy|!83LP^??zXfq2k|0`Z>+e!UUEmT-S5H7)}x#PYm*Fr+3i-
z+nbf#IU5TKn_5J<yxct1NEO=VH-m$7BhoAC8HKcqNkr`)ldiH~>))Hd+K_f_7xj1!
z1v!Ok8(`5EkoE+rhx!3`G$Hgo+Vy1mVY@#xjF;VL;0;=~5NocL3-*fAO`iJId75sx
zp%T3DE(AY?p)+L*9?HLEUwJ)C*YrJ|Gn*)T#_;mjL0f4;%r-r{lNTKB73QId`L*AZ
z(sF2Fckj*AIddxfT-_FOYt_zsxqAS>5#7__lK*_}##^ZDWcv8$Ilm$-IOVEp&6Dkh
z8_>ZhyEXydUiU=SSm6bxND2iPLQ=4Db9o1O?_-$F#a@46bUYZEkt?f;#n&E?)P)K0
zdeoVF838DIv^zh#Fg`ajq!nrG^Znjj*gZ&YL8zASQX#+b9GkF4SzCkXJa7#{7}#?H
ze41F{gj$stJ=2meN$sm@q}-3wPj8v59sL%Tzd;^8+ntZugWMk?r;@#C3jTyypl}*I
zIPNdt#$w&1ode@mU@a*GqaUFC8H@)LbRr>Axr5UG9Nk+5^-iIQRCg=RGi=*>Dd?$|
zEB@t;`OC-mpQx6gv{;e1`x{1lWL~bn-}&p0`KsuN1c}4J=~Ee0P2NyQj@|l#co<;+
z^G7E>2+pVW4L2zyzRnT|IR3Q?x|sHo${28Y#_d{glp{uyg|Bcha90V2vIR+s*rj{(
zl-O}Fv!mi5Ab@mWfV0|i51@7&h8l$a!3hKtCY%^gp>P*hCzxi$7KslKB%Z4YfO7Ow
z>!S2ufZ8cJ68+K^z`<bb(_Kn&(=M@HN|sL!zBaxNVP#eNJX^+HV!je3k>5lv!mJbj
zo%-LwJJk|_(20dRW*NrlJg?(yEjwpv2xL-PX&eWHxX>A*-#{L<7-0?(V(7%%fSQP{
zS8A3V?k?4<n*6Jz*RpbQ2J?HRiHQkG3NdTzLxKTuts3?8+s;>CgJLMXY@bj{$3N*j
zbL0`>97M1QpJ{1fm6ViHqv}uAY(CS?Y&pFfZMHgleFpoLid+}k*lTsipuw^2u`-_j
z)322wROVl2Z9$*WwmD_gi}mdY%^b}A#92)_J9%x@8>L$o+cv%rT4YqO7#!Z*(`U%G
zQEmEti2DMs%XDPmVJg*kO5}M0gf5~dpi|0Evz!B7)35qY8oFNSUVI>rM@R$u4t}kv
zlxlF5hjH33njcmRYA-N;`ZT;WSXo!M?$rr_=S6{D4hs==F~{=py5_x*C<&6qzha#_
z(aY1pzWPs#*(|r|y?PZ|?Bcu~CCunT0X?J$QY$(yq3?@n?;fAXp0-s}H2*MP3d2-i
zst)Ty@~wP0*%<kuMyVV9X;FRMMGC=4Y2sohJbvG}dz>hO{Z1-d-L3EscUeun5=m-U
z66}q~xp<nnjwSI3nhgS?<P+Wr^U2Bwm>2bI<Vy-}I@J)xYSJXvQIW+Qo8QYlFI_2g
zdr=W(Zoq}D!)84;D7U;$t0#S_-#sIfL2G22>&Kvn@N4KoqnpD<Gi08>kfMdKvZDL|
zs_=U(Z_fw(VkC>>S~sM+@GH~{D$(GDpiiB}u`$n=E425=dAC<KKbq(m`C7jhrO6V$
zY_G1w;nwH8Cg62I4;j}36eyiH6f-Jn64@CD;UfD5Nb*#S&HH6L(dKj(Kw0>zz2%!w
zGLaVZEuF!zx~0--ei#wNn;~Z69dI&2am#o3Syn+|P}?{l0U8lkyh1~D0~`48LMqGt
z)l2Cl<OjoNbQ^a#6&#1SS(vToBx9W}bZk@s_fz~NqJN*t#z^H)LyR6ven!8Wm1iMf
zMSEf;Od`MruUxwf;VWU=^?+t<PD(^T{iF`ujFGuqf-JrL2)CP412=IoMepoDiGap0
z0y)7Dbm8g0D9aT3q-%=rdqJxxWPxwt2dMq1N!mAoJ+<Bn`Hr)yF#elOr(II5OP)>#
zC`{$|PMR^89rQ!JRY%7a-`sA#syGEzwLt3@d-<^qF}{nR@*W!_@+La}>YVOkO^_YG
zO%<@yy7$XE>Gp}Asu+*F$q|+3@UXz-(0E~Fjpf6onNEG{1FIB)NZ22u%1<SOZq#=S
z>cYusuo6dKhS1c}krL<Lmg-BD&J#BbDuK*WRp{(omyh<~Ad$45cl*)e@81wk8cTnM
zpaZ7$Wgw(kL0i(#VZp6DgCxT7LsTiA-xF2!^r98BTFf?I!$5O(Xh@M@whS5q+!d|j
zq}t996w@>l&~f@wN9oPV(h@P^82ht!fqwe5PWO(p+sQIG2vtTtQo=R|Iu(s@xtc|P
z<+JAsUl+pb@VFcz!3fQ$d4GEjgwE*EeiL`srYJP*pgBCFf+7D%hT{;0`jn@p7{a;;
zmsPX2%8`*|@E`d}g5($ZCzKXk81*kN_CwP7wwD8CG#t*ZFs?Cp{LLiSa`y+{dJCgC
z%?7J}x7K<>mcFi1jxQWaGHt)0zc5o`qS$ADKR-a?j)}O^@tB4cM3s7ca3`-xyGa|J
ztJ9+I7yLg%g0+OgFpfq(FtoVu3fi#+s}0I2fR9gi(y6dNA9Hbief*zD;syOl;mL=@
zOs+|V3C5U{Rd!6EJC<_8FyHEaWh26i;c<a|>*`P^RUWWd8@FRzJa05mj~{(N_uSt<
zG&Qx20tGO_GlsEBN~)>26*oJ+p<`fB`Lg-0Hj1WC=a|^rQ~G{t|4z1jc#7y05_S3V
z+>9;;Ma$3$I(g3K$Bn?GRYr9E6@7!Sys{DuonAYu(%Rm>Q$bq>S3t%~552i*WP^*2
zGTl7=c6Ypi$-Bb4N9GuwL>b-}H~s=_M8Bt8YmshoGK@r(zA>)ALepLA^Cy$KtI?jh
z2u_HdRNz_W&u4F13I{ecHxCyjC=A#4q#<segnwtCMz;}y!zwPNY`Im*$b5<thPn^r
zd#rE1Jj=f>_dMY&7P!jpRyaNxx6XG;Iotxu1SsDRM_d(12HabxRWk(S<~?A>{(M$f
z3HZ67;qtO~DW8Aj%g-kCs@GVCcN^H6D=lyUd(U>X3+a;cVczpAv}|*Bi=Fc}EWRgs
z&}Rg96Pv)4a`lLn)1H!}-{7+UGUi?&k}s?IX1`L+kfpq$%U~y`PIJCNQrdc0Kx~(&
ziXYpYu8{51DpVVRp}`IJc;(ejvxc()iEw%k$$P7i{V4J^qrTdc|Blc1f8i?go!}N^
z!2s!Lm@Vo5aO|5_50>h1xyj0x3a85=-fok<+3IQVn|PEwKy;wY-ha*THk?jSy&0A8
z(-oqd0l6BBwC~aDJUs-bYmj5P9dP#>=Rfw;SZ*J24_}Q(HwILt@`k0&@0Wg(;wgHm
z1dLUAXuq+N!cE2XVLwo|^@@$ZYElt;6`IT$!+4x}*jPD*gF?NxZ<)Kpk)Jvmk*}n=
zcpVQ9RF6y%5gV@`hR1ufL;UD}k0PC(dz5__aY0~Wnl7aL{7vE4S0igjX*D%A9A;gq
z{xHO@(21~8s?Yp{G{ZDvy>%@uxDZ(UKN`aKsG2NggHb0u#Tvx=6`U~yN@sw`#!4?R
zA+V^yMIj-c1zCw9Z8^UfIV^65-o8T7Ru6cx)IqjWUiT8DE`3}&8pI|@=LvQ7PM=SD
zd}55&$>;NXu7VT%yipD9aMObmE)s3b-D)T&w%B>;a=eCWl9_=p(Pw^NWWiYdG%0zd
z-%5n<md9;-cHs?yU7Tvc_dZV)8XyR{+HII9_V}yk9lP4Ciw0aGCE!-w1PE3<^#bm-
zzO}$Cp-*$yqk5+68305^pk0sZpCv1x{zgS&h`~&5eQGw?b4zj0iYVpWNqbT}GDK-}
z0I}zP1EcowjO}%oW%a(y@aLccHW71-z?AW3%M;;8UZ*569^mY|CY;}$W}o;2ph>0x
znXbb$xu60gRAK<;!w@lKb(Ov7kSf{i0^)k?lH?<RYmR5;m{oxf15vu&ULxz$pJR?|
z0=8q_4C!z(`>9JABL6JBo~R(*mHVraH_NbxFnQ7LUT#1#x9QXT`iaA+>tAcdD6(FA
zyj`5vGW{Y>^w!g%a239V5|}Lbz(&lWt$@&*pXv0*_2=`3Hq^k3C{8$SND2Mknw@a9
zeTwBsD6B+XEkD^xjP)2hJ1*=>r3dZi6|ZMp84_WP#ogS%fl%wfgfY>(<;9bxYOf2x
zMZ9fZz$Xsa>1id1^zChtXAT?)ctqSh7+<PJk{o__5h<zCS-!g?B@mq~DBMMvIk1*_
z%wtOdGZ+>T+u(6JX<#mdgQN-_V1G`z8kJZh9*%@@-!O(6GWDzVG9H8!YcA4XVuFK5
zp#YSK8C<RuWpyW%<z+&stE(&gxS^Xox?L&DE8dV{H~l@-A4~caSQ}2s$r<OXPh=u0
zw(z*6g!xX>Ox0PtZSHIx;j()X&MrCpoeJH?7A`c+Ff=pN#fJdzgLryevJAE7=K|KO
zd^~Bno-wM;^m!O%4(<Ipc$Sj5s#1@m`STl3LwFFEDo0itLLa5|4xUaDrnllk__)2_
zMuDLateL*qSq>e!X<zoIqg=}{d#eGnS&=y?L<R?wpOeBMetnwdhm-K42(vjr%aBUI
z1GqpiM7|bv<P_zm$T)>!GbB8N-YVX8Nwad=C&1k}f345H?HtL6HUBZbAnJ~+mkpy1
zq$yVdgnj2tc6av0wjSQXVlHQsM||#eU)~!OC$Y7$T#(Y_tivWu1G)#<W0rA6B|-*9
z?w_$Ap4dQ=RTW<8kY|9!A^)rsz|Q7CBXa83DM0z<ra?^Hh}E>UH|_dQ_l<=+*IwIS
z!5~m*+xIr$zn;cz2b#ZPuRvwA2Qk$8A=F$uj6E0gs)0@}yF>t|@MZiFTsGNK14q!Z
zDDj>1rTIgjl2@w64Ver>XsF=mN@pN*e^4-`exH#hdx?rYVxInEE)6#C9~7=Yg4IZ7
z%_ogs<vF9Y($&D@JR7i=pqA4KtDe_Z=Hf7JADMd{!Lq_u7$)r(>0;hMCvGO%pSV0J
zE1jE5(k^~I)*nb-hf~5=SCJY&Ir-^n-d1sagK3K$sq0ik50x1Ip@82C5E3=jkg<&Z
z{;&oc6oySBF4Sf?&@3o0nq>T4B0r3xcP6k=an1DMjbV9qhi3;POI(wtZ+DB|-bJ%U
zfa%*h9>mf%n5oqG2%lnobR%kedI2N+P2FB7iR9yxrJ0OD?j49=-YU3>n2)W-+g~f!
z-qd9cY=aSxS(h<>O16C4(*u;c$4zGMknotQLhwL1-S;RZ?;Y%=0e8FAyr-LqRAUM5
z)RRk2LE1}2na-0ks!@QUv;3#;d9_WT(n+wW<9VvD$SMv_rwn420s%7fj-hNOL1oR+
ziZVpxQh#cBhR9{G-C$>ZphYAzuBy;C=BCH^D|n8A!#^PF;OhH6=QHPi;ETt6C&^^n
z?=T!+JskB9H=h$HghNx35)MT++H$b}BV!UUM^(b=-p*UAeRlPGUeHt&D8hoO>yeE}
z7>IB}&n$kw7(*wk6n1djvfXKh^F!jv&~?(O<q^{ot2-yD#}pkwri2v3^(NZ;>@p~$
zVcSOUN#3gP?jdTMy6vR0g2FHQu^|BtTw<=7Kbng{VlzwvO4&17PNO&b6^%e?m@c<L
zBtxuC@SY`Fu`>wyR`uc(GU0wrqQRTI3XsFGmdVA%)oOlpK9v?Q)+(_rMgAq;W8Pd@
zbiU>DR~u9PItyfhZ)aVFUX&qWyolHR5)1iIJrqdL2qF5~L??N?OOc6|<?dXTADo5R
zwwEHZG*x_}(xO3hXUAbgEw-@z;Ty`-jic{-A1d4XL0pab+*FQDikRIadO5GoWn%XE
zZ_URWiZ*6Qrzi0>VIY}!#Szm(q5Ww#tj%r7hKgQt>6?NCBy@)sy5fdP74IS6)XlkT
z-B%1$24AraYU^{74mYZo7o7rX29)zCOwYb7Z@qxXq6AUTX}82(ZHlUGpXfo6SRCL`
zz?HIM5ecUh^{Gt7Iej|p*;0R!x8vOVDccNp3=vO}q|2UYS9)8xTvpUlcAWgL%-s+K
zY?o~c+N?eGH49*#p-?+mk|_vflZOvj$P=40h{wXdP#!2D_A+jQS=r+`ZAga9ZvaT0
zaHtpzr+1;&)*EhMtT!H)p>(7nkBXP#84jI|wVd?FqPoVG{z58LKS@@o!A{?GLAtHN
z-qqV=(P%N8F?fqIM%ZX2gr#-!K!5(*MALqf#P%~f3k@j=CW1n*#_`&`+c#D>J=Ug3
zI%^&+O135S*=oKYm`S|G_GFuecJloZCpp$8chjf+sN~HOPt4%Od^oR(mZ6@~Ov#fm
z5I-klT8X3|5M`^}#Cl$#cKOrCd{^CUa=jG-M8{}!v~5K1T2#1fk**Htb<_iq*DBAR
zEAl);5Ym(JwbV+Q1GBqT6~S03gfWS)3ytt<f4i>RN(^w5yWuUA2*PSxQ(-`M50F54
zp%+>#JbrOCb!2)fkVGn0X2d%0Kfc`>>|E`XCbWd{{PEGCi^ai)Ch~Ks3N^rBDMq0q
z5j}=d`HhrsI=gI%Nz++7R~}056U3|j!BoOChVY?VQ!BDi&1}=aDZ_>fGO{aiY5N`H
zs*{yo5J9-%uH~>&glt?`=22Wg%^!PH4$tTGyL`pcAIcTa3iDa&l7hD^hRT>qG3_)I
zW8HWo&BsdUO6Vo6{$ZI$%-67h_en|(TFpzhX(5u#rPAIX6c)$NwY~=~ettr5WSA__
z_r^+&HuT42Ek*3dJ1^%I)pPdDSLE3?9tmq8S=8*^e$BJG`~Ge3`is7vH$Emb+Sa7?
zev^id2E52l>8mou7fAGNSZO0G3{ZHXoR=XAB|T+AHqeXL?QPJ}RbO-Fl#km+%pD8C
zoE*zhhZR-$qZ>n;VY5|K5xqO>f%n_UFk%lm3j4<IsA~)Duk*YUDw&CqP&X|{cL%gf
zM9s5jTB+k55egA>2zlWY?3Ul@)BA-q9Ez{1{oHrnm6Y+IxwL<JZrkpuJUK)=SqcV*
z#uAiI6?Y60yXGm;3Hf6{W6?AJD0Djh>QfE^ccG>@t%g^LkMyFPe$f1e`?w>lPoM3?
zM2*@TWy<cQZ`X$Z;cR)nnV=92RZ4?&BFBexvTAf7-+m|rPZ$(bOR3&8`E$C_kN<QF
zKOBVEfoD#7VggKfV$U!0?r0Cj`>6=?T||2%n+gh5e-2;>{Y3v&hZUeLvWhqJ6O4Yn
zg(|9dN+d9E=GAX^ssP6Cyw!<#i`8Lz3T2%D!K5fAvVxG(S#Iuk%e145AQz;jx_(uh
z#TImGJr?v(iJ5?JkHN2YhS)1;NVWnGB<F?HRGgyLSc<P)Hdipno4w-Hw&C(AW|app
z&8s(AKhwebzZ}sRKI$4rw&uDp8vA2H-iEC{qX~<xs&=YsZ1jY@RQsPx-BYh6!5yJ(
z!@ibo36JCyjR2aKwt7eTY0bvcc6avG=mqc)6(IvipTY(Ji9@6yC~+qn(QF_mdTL(j
zPKL*7n}e*9K(61hyG8nM3Z?X4-9L$+1B2I!PG9wRxLOUXA7>_+uZAwyY(_Srt`Dbo
zB_^q__0gCatCjaZFHlGFx_$p{p9TPSHvwm8xB5a)Zag!Xz11R{Tl3Py1Yn_kY*dv9
zAUd|Y;V627i7?Q9s^6D=FR;P6?om#ZNh)QaXG>kGg;lve3LXD)05c;v@KAW*AJN*q
zVXCCGaG|kD$qGM&=+8>`b^jv0sEmJS@f93-pa1I-2hucpOJ820Q-u^NwtIhRcQnWE
z{^g`^eGP|n<q@bHHZg?=Y8L$l?K@ZfFIO3Njjc-;Iq6>x=AtOt)BV1XdZOUL)+{Hp
zuw5B<6r%H0AcwR!9zNZP7s@h9HvAThNUp49dP;2K(JqUec@JsOh`;`tU*|elgMlOD
zpZQHPEc|Aq=ND2m#DNHqH<>H^u;>WNR!8qQ?i)h!p6tA6;G)vr3g_<a%=H{?-cFXW
zzqkvp;OQ~`bjNo4VaqJptWOXwkb&c>3_k3Q)-TMn)IODZ+2;s~I<@*0`^r@j7%|5y
zJ+Su(VUHUd*U000g(oNL*I~J9MHE$nw(?pHc#NronOLeGd5UEq5+VpesFv>sZz(DN
zeQ?VkxK`<Ft=XLAvIEuBgvwiii=LhxSDay}Q)@?>3h+pzM{#-~Ec;$R#LB81EVn<O
z9N-aUZ3e=82k)*enQUA%>~v8BOFU5QcZM=+QIbpOWk#}B0z^yH2hf=IHIHFh+nMhB
zeq`WT;Ff>V*ErZoFb?P~%kvLU&-*}XvwR+>(zLDfI5%GQ-tMH6cQ8&JA5$(88ni|I
z{hPh=oh?x`swyihVWer;Uc#6IlB!0t)8&$s@CmG+gb4EX`jNv)y52pimlN$T#CN>?
ztB50<OPASKl!g+EWUIT`>SO6))D2-_Z-`qnqFV2{fNNj|_^7imdXIo0QC{Z<Itne8
z$!~UMvm4@^TARfgomzk9c2vAD>0!5_#ss0Exll0$?`zI=e7mTRDI<jraY-ID%!rZA
z?C2`u9>9R{dq1&<U@W(B06O{RHx+CP^I;ETbI7n?-yULyr7wHpqTNui-vobiF5(Fn
zqWS|*dSvJydI>LuP=$9XvOhP3nP1~u=)wXjI$n0yW`xDpj0O_@&&Yh+-A0UsJHzhl
zcs-Bi4!5i6LKk@<l$Fkcn1!AZOy5Sfr-PF2RBE;cTF5iDCi(axF2Wzi?ti$&oZ2@P
z`6w(MzUb(aF4<k$?au!}_vOCwFy)yh+}MnB>fm}G6!dmqbbm+kN*=)o3MUr{4(G!a
z4&cW{6?oz4{=tV0dFg{B+p)N9@fDv&;FaYj!MT692$$7u!O()d6&!Xt&17_0b(E(Q
zJ__H?<}7EHc^#U$>z2{T=fgafc%9hG7MC?(#ZGTsdzSr`oa(|j9=UGiXp57`I6w$l
zoWvJHl__nM?o(KWt3Fap<4C6Ch$k%Kx8Wh+_Jr^?u;?-Qi+O@9rjP0LP7kZgYgZ(H
zEk6905JOQSgh|M`VLI%?-*P6GJ7M!3@4f93KAkKAaENIn;$O-ISCI*zBZ4iKpP@`X
z!WA2VGl4|`RJoou5G$q!yB|j07!@djtmNx>l^&(47p&0F@i|Cq{V_V7-n3!^AcNx*
zG0<9gV>r++OF`}Rtu&uMT(wX`7x!wjB>>QcY8)gPMo{^o7d%kLTzHz;0Z0kJ`Z$vr
z>LuS`eN09LEL%ee`QDT3WG2!J{g|*CA?;f*ZU&szY=&Z=HyHAC!ZxH=4V1L<tltmH
zv&`^?i63I3FUE@%&cC?zXNc<XI3vgY(&X`?0WTWaNKop_b<ccnINZI0?@IrnbF~d7
zMcRFv8JTIfEMg=IjzE7N%uW51sYdh}aHxq=5>Q(jS>ayt;e4sE?b?1L^e%6aFxnkS
zCIKZnO991*p+Tsx|Eo}=!me!IlHr{HvT?JS-7!K`pMJL}_JX)cOP@kj9!H)IUW_7*
zp`O;luYTskz?tJ7iuGP%@X}WZ;C0dVnC;W^=#}dlaM(jc81=^K*7l8JboOnd*00Ag
zPu+Jt5$$spB{(KJo%L=JYrrNUaNmG8{P_hikGd%)kw!m^^w@V}NlJECrU)W9<ivZJ
zg+jVmLjJtvhxp<;c|EG)?Av-IXduHI!o1{<oM0rrt>K4n?Dveh>e(DJ?s*_wD}1J(
zY1d#O#i%e>p)IW7<3NGp+oS0}YJT0<FT5Mr$f;`;zp=3naIlTw%IPSuwpJcIM;Kb%
zC^IqAj7=rq+O{}D5A|&({&nV1yMOD9o@W#v@3^tOa}gBu-ep=dF?`aLM+pG3zMifj
z*Sflft4_I>hF<JsJ^7Fd{JC!V9gacLLLr2;N_g+m@NmN4vTE*hFHr4~<?P|({?bP5
zxK)n)OX-3f5uSqvT;1zvrY$j&`(R4C?uXzc8zx$=O5{Ve)-rB0F(5+O{9qy=w84do
zi&!qi16F>8Buu@Go#orOdby0UB%%*068-~P_}yiR;Bw}Kcts0CWR*OB+|h>5mAC>%
zZh#XG%>8BD%QKA0ow%rsA`i5={O3XGb8!m`4mVCM8HMk%49d(%2t<J{<`Dn`F|3F4
zI1xQ7_$m;cNOBja|4IYy2ue8lvklh6VC0{|b}-uHTMOO5%?uU_xN`#sz)FiS2_i`>
zru1&o_v}!GC{Cet?Wkk+vfbAe&y>hqmbymKyj(w)>jDv;RlUN)S^yqvFRnBy@o+Qq
zVv%^+G;rUBis^XJ?rQ_D_!Fb17RmYAXRY^dj;n==pApZ1^cZztQ5esN0jKlOf_or-
zgM`pFt6A>gu+}lDvsjVYR%woF@>0~Z<!eDH<(x2=qQTUrQt0(nk5CW?fka+sWW&e~
zIyQFHUOz?g@KC=-t554&X3(8g9$^hi?p3m<Mbo1Gd&vYG5uc2*?|M~Ssgdy#e||-t
zeKL=5359MZH@M|5I@}MfCLIg&6av<d=0<lhFH@X)cPBjVJAVefdy*S!zZ0Q`iKC@T
z{_o6kvtJ(lJzW+tSM@Nr7$(J)q?jlah-O>TYckp`4H4G3Jl(`tqWz5$Yz#}dJeQZ9
zhL^94w#`%-k5l4QJn13zyVuA{eK3zc#i^<qM8-U=WIXH`vsh^aoPP~)bC;TM0&g`o
z2rK-PQ1hyRO8Cf=_A4u1(_Xt!zDES<>VB3a@|;M&sOhYQnIDr9=3c6xj8)6QPZ6ix
z0G7E2aCA7Y_gv!aVD1~g3=5wqa!aG4ORt-=>d_DE?FK$)z0sv(J*AaO9dVk#omG9d
zyg4i$tR9D>2`J^nIojdId&gnCx^$ig9G#I>tJAP(?`AG6MFwhJs74F~3|6Z{vtV<7
zO8xpFw-`l`G3LMXg&Lm2!bg(YfFD@6qkyFK_63Ku`w_Rw$X#T$eqnK$rNN~)#E;+P
zIQri6ek$y%1m`;cOTp`%^r+oo0*)8$m+@lM;MEmM`aYRqEf>_!jOo%q0-d#$0j2MD
zc=AYfnHAarX}=sKZI+E(`o2%FVib^_@=Z|bg{937NU`a!=%kOkV-<z9(GwZ2nq=lN
zS1D@5a5G!h2YyvjBC%sCF&)BPo9)OVL>T&Y5+&S8hUt}O^5g-qp*$xTTQ>M?gTPEf
zb|OKJGyTxD8dN0}s)kLft`!#C=ovaf9O3LEn`dzx3+S4vm8sIK>3re7`|1mp_wEY4
z_=~H*Q#-HI`!&v}+=t!$w|OL{$I?SR+2d&bR{O-}(Q-#gJj5Q(WlEPXy-ucZkb?`p
znsBIH>HKVof;IvytuuoWAMOZbxcS^T^3kjzj`ze^FXirV|H3+kW7W6|uRk(Q=Nl^2
z-9=dQhJcBYo9@X--{3ldlqLq0wqCHBA-)_{u1E=~20KR^{;^Kyo_Z(OscfT*Q&bAO
zbRGQ-9?qm6`b!~7!SDis(FK!E)1IG(F;PUpfb?i=0y>I=VZ<!poXVtD%DyJGk~{dh
zap)u5VI-Q7-^A1)&J3P}0ar<iMk<ou6O7#WqBpw?;`+*u#MAs9hvD6!D5Kg^z}_DQ
zO+0{VSqMH64E_j{g)J;Sp=EMfI50-mTGf0v9{d~w{lS$gI5-4-FMw1?7b7_Tx(^Sq
z9}VK;DBS7=b32`bX0#AE{r!iKY1+x=R3&USav+26Hz`SuckgTH;OmN4e_2N?BinJ)
z&A@&J%Q5}k#R?9;*Z|=4kEGRu<2I`W+1Dvo3x>Lo1P^N--j4qHQV9N{c++x*Zw?*2
zFoNyPxan&M=%{I)S92zNO(*b64E$I;kKsl3_<SId9v_F2E}luW`=NUJwD&4hu^4s<
zm{^~I5#rJUy<=k0>h@0Ob=9osR3Sig#l)ur4`jVPT{^xFCgwX(dD9m*=k_sErb%>X
zv)G2-bS_`a;mzzUV-~fj;hsF9`Q*Q8k#0SVeqxp)6;>W3S&!@dqz`p>8{*!ME;ir+
z=yp;!VcenR<wS<0mTc_`v*J(kk3|Ds*;MI%gjqN=_1A7!mw%G!vz(LcHeBtiNnliP
zq@0^sLoQgOa*1j!NA5KK1zfn=p>z2b;B6US)w6Mu7W_uIIgqo_AUhPeu8RfA(CAM+
ztHVlWm$o}PqmSlJ;AuPUN%VSc?~v{py~0kj9g|8*d0~Je;Db6Cd`L^~JD|PXN{Nv3
zDs4JF2v+s(iLaDB6OxW3H6P-8{gmFp;<G6aw4C6hl0h1&DkB{{Q5G5oq<o$9JJ$rZ
zwuDx*NNd#?$1emU=u*uiP?F|B3unm#Uv4;MdJMvFjdNflUL_cG->6@Mr3#YkP)6j=
zu79VHWj|3PeXM$;F?R~!UTE=!GCb3I$L2*FDV69n?qR0LpN;U}T9e&$piTO^QuaUi
zC!nCzU#+>nss)rxX;X<nfC@J)*@gJ`y&^Rle~s7tXk?XSi<7Q81gLbr)Nw<>+R8df
z%b#lC>nF-XfC>>u%YBtGv7eQnnzrpzljKOCnkF?)bW(kb^&V7%&b1#fD<g@Fi>fHq
z$}rN)ziApe>T6;Y)!h_P*B)_U%*5l9-D~f#C=6OEq*+VGB~40<wQ0#_vGq_pR)V=#
zsJ=y;sQg&hmkAsHS+X+|TT)%Gc82&>&@M?(QzO4U6J=p9to438RbKx_X|)>XbBCQQ
zO1E0gL%M4<GmJ!&<&~OxY+?^%h~<)L2CHeHXh%nuxd@#I((|0juE*z~VGOe9gd1s1
z;i?m9L*!3CHO82~p0ARfbP<>=p!z$uH5&=;RgxN57HSJW|11|3v1C*&`BtmCQojK9
zt!XrnQaqjuuiTLnqgR`fUAf6spy{Q;RsDyCg~2QgTPlZyy?%U6N?$S8t?b=FQ^u{Z
zJj?cRI{MAC0_(>7+s`!e4_a^xvY-QL5eack-mkYzeWDB$6tLCNf&5;-JTwAFc`{R7
zWf>%tv<+L4hIX8A$a;k4@S;)HsKGH>$%DrEP>)YUdz#Bv%G*nva#sBvyE2>9N-q4}
zjVs#;evtp1y^Z?5QrvDh=AgFGa>ApTqT6-x(C(-$18JA`>zA`}=c6gyE99#x<x2y|
z+<*#)HH2A9;vNlCb#>;wSc3{j(uAhdf}Mfg9YwMy(~jICp5(9j!s=Tm1}rj<UBg}8
zWD!iN{B>|WpQru-eT7rAy0-RqM@#|jT#Ub}OYA+ToLyKfS4r5zv9cNkrNm2l#u%JK
zifNZJ)Wvt83O{g4A;Th@x)L9grwHPg6lwE*8_xV%rWE@2{U7~VZ-7c6Q?n1m8uK=o
z;zgMoUngRe4CLShUrt0NftC@cb0Nhb0}bInOHfC6Ai}IOHR4GH-o$}k%6<rw&eZ+I
zO}mqf2sEWNPHKU|O*Fr*tdc_%^Q^3q1G{cUzwXziihN6)e^&vHVdDUC0(f<DQ?_$Z
z@+j=#N6T@gWxAcdub4C|Q_5MyvXhhhbaQ*#lstysBcXY7bCdYvN0`PK>)S@SVtwWz
zt7FHrv?C~gLFcm&>ornzbWBZm&5IZ`RKx|6^ba89qP7N3$+Misoqut0>7w?Ze8(4c
zCi4Yr>Ff2<<jAxo`*?%FNKR?AS51cK@zcu&QDj#Ts{g-_XeAp6G1%L}d98p!qwEI*
zPeB6W_l#!LZ;@?p7ZBjIBme>ZYb__ZLc=~{K}Zl(gS0=q$qPGTA9-_R3OIdU$NR#z
z)}HUL&<Z&Z3?Ra06;SZUWEF#>F!h%OV^vjj|5w`x9H9PZF%99NA?nrZ3308R{xA#2
zEwvmaE$}Xizz0bQa$a5#dTKv&xnjzp*q5fE!L`x+Pjgk(nYV0-!D2y7GANr*JuO5R
z(OwhwS4q#k^pqmCAJRH9fl&V~`6%GSfeS&Vk?AiK6s+dM9OWO<@o<BgoTnTyk$URn
z7eLwKQbUO^S2kxzh|(?9hO8zcAC+aM)IpNtW*FoYfb9aj<x#m}ySIob3O%#koo&j>
z(Dsft)3@+AI0PaTANbbQ4sHnA-Ce*^LxhgRH(MqZN+}rI{*l#HJ)AuIu#5p|6^3BT
zvka4Rp-GGLsM=b?<p36`O?|V2*khggGs^2Mj{cOf<g1NgMLGt$!F*9jRvI9n^03S(
zGKQLAD%RB5HuyZMErZk>rV`}82$ScVLO$i@zeWCs=Kn)|6MF@f198wG{h?Ay#G+uS
zk-kOyq{Kmq|B%ybEiZq|;Bsf9pe!;mwGA@z7rcHBZp(!8+HZO?n`+&$Dq$dJp7c_}
zgdDd+=kYC1+gzBN12-b(kKU(%24ye86QBe8i@H-{Oqw(x_*hvPTlAWp@7q=Aj~~2M
z42*!wEmBO<ynJ0yHq1oN!}FF}`$wm8PP$ks6>-Ll-T4)L*B3r-2AqqF3*c!7BB#A#
zW@IsmV7qu*;2&*;J)lKhXeZN-`4goO-uw_gN%Mg=ZcF7Qo)hJNG!bNhxZza-@vgc*
zKhj7`OSiVTNOyd5bP-`ds%Bz>+iD`D3<)>)vB~K4SgazDb5O_z;(>VSIg|30<jX#X
z6oo);zV~onuTNSJD*lA9#ht&t=RTDj&9J-B__K3R%s^;YM+}QjvzgP`_`oRXMz_RZ
z>3)5&AiMh8*2zS(UdOO=u=q^)3)(k|SPfpE2O=TE4kg!PwR=W=J^MAKY@P~utbLh3
zeGP&nB!Y5lJr}~``c-lev-&L@BL`#ta2VWGoxi^_#L1=MCALp2{t=9>k^LY2ywHSj
z>tox_(N2rtotcrKWoCA}yBp+<+Qoa`Mso?N59$i(Iyk{%nKkAQfpeK_&&Vy%l>#LU
zZFKDJHsq|71rb{{K8nM@<~QijgKUJVjz%ObDISu(zP_%-h?qe^>=iKd`e><!nagQ2
zSeleLLIs^C!pw|A`<FC{KU`hiNRv*&?#{F2??PxRg#f*0g%SKHe)zQK+r#9h+m0S(
z(kRZh@-|07&SUskIHK^b7m9-Cp62DKQ_W(iJmC&{BL;d3DT$<%=qtJ;2c2=YuQlnb
zc*Ifz>!*28M#BHnP!6G>)z+Qi%co&Mg0ZOUm80R<OwC^yv`*F4cs$$DS5IBI+IqTi
z8I2H(Z`IMTZQn1eVdB3Bx1{UKM~SxaOUh^|#l!KtDlWfq$EJ`mFwy@pvr|wkzx^N#
z7YK(UkQ4FS;z{95!FdE}O=~(_`PQ)f_d6Lzo#oj=F&HF6&@8Oj8r5XXFH;Ggwf_v|
zln&-n+X4wade7Ss{%=#!TMNZj-(bpU#EZ-rM=rv{3EFndDLfQi*{l=leg+b>Xg^5(
zwPN{aQS>NrgDJS3m;LWgf8Ch1fua*3K<4D9_#gEJ8P$9+fc5_f{V(g3KL@80$U$KD
zP2%rWh=7AK9R1Im4Z0R7LeB@Y<ybh2gsN7~uPO!`F;Aelo`lvIAHQiSvM`nr^ELaq
zsG+C{t(3<XHxGZ9h?f_rE=ynTAi{kuoj}f5boDKJT5v;3*9BaS?JFmYCN`gi#r`@(
z!Y86Q-q6mSaTwC%<nNKYQS`{0dHnDG5a~Dd=!6~!*2&X=(AT^qV-4q`Kl{}6$`qff
zpWZ8GhQ^?tjI@?~@VL3)A^wai%!F3wW@%*Sg>!z^-rXMCqj4z}fXA>o0S!jxGx5$7
zrw>H?I(vp7FP?LE3l{UFeqbcKHi<^SWf(2-a#UWkwEdI@_Myqj&QONnzU^f86D)(_
z6SiE<t$_Y_AC$a+C+G9lS!XWFXN|>6!Bn4j;H^u?^&Wde(l5wp_T;O>gvI<Fy-%r<
zIxj=otp@&%lAK(>jN@*-?uBQKI$FE~n4D0%w=h+Wv0LyXd+SD7I$1y56$n0{4w!Tz
z4O!vuA2hzv6O%F-2KC!_2Vg%{=)AtmVHq}bZA!IXqoC7)a;>CMk~e77e2gNWqUDu}
z+glTM@i~FyI!oId26zAC3*z4e78nFK3t>i9>yY+u?fR$iMe@L)BZV-X$p5Y#ENI{e
zXpljc4gXQEe_lL;#_#U`S2@TU!ZBol)@J{mum9?c2zcR4sQ!PazJD}|BmtVoMNC#0
z{!bT$KyCWbtc(3!`%K6nu*hH_CP1_Q-@1MWvcN~|fd5&({G(||kYci;>2S&ay_S6C
zKnRIE_?CaJ_5SmX7YTa&px%1jzxA68+z-^|vqEdw-&&OE6#+dy5>(pMKUZk~spSYW
rA8gLLpYi_;77T?5`2Tmvdjr7A=lV*o1<#zoKp$C2C5dV=li>dc0-cre

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/deploy/run-on-aws/ssh.png b/docs/python_docs/python/tutorials/deploy/run-on-aws/ssh.png
new file mode 100644
index 0000000000000000000000000000000000000000..381746efa44c55376be1dc96def1f10afcd54dd7
GIT binary patch
literal 376297
zcmZ^}1yCK|vNwvmySuvv*f?yQ-~{*J?(XivT{li}0t9z=x8UyX^723De)pd5-S?(y
zs@8P(uXSpsSJ#SEQjkJMAV2^E14EXP2C9I8K^}pDfrr4s{G}8lAe@1LA^frw7gv%I
z7bjD4vNyA|F$DvYj!a2~Ra3RZ8R~kRC1gg6N}kRl%K@9UKI&M3ga4r<CJo2%1qdVM
zz*H6yfJ_Xf?7$R&#6S@hDk=j03WcvW?^;X7!0;MCeRh`nN6_2Z?t+zP^VR>c^Rd&J
z1#C}P6G9Zg1S<j~tb=!PDjFXfUAh|vhK`1|1pUI)B)vsKKmdo97482k9uh1w&V9V(
z*T?(kr`1h16*?gp9o9B5E+QxJN~_<#9=g{COvzJP-`%vyFf*RwCmj$0*y5R~YWCYY
z6VqIaqk)EF9djEMY-@xml?@u~=1%{P$PJ-TyoA!G=kV$YPVw@z_n@8^?o!0VNK*2E
ztC_+k`AHE#?>{4-Y*nvGuabLiI(VfokU*C6>*ceZ_;Zb8cEH*Dh(h52;k`FOI;+#(
zG6P)YfEFfvoIRei!7uE$aaS3iS+v0JVS3*ULb{2gcJeDxL&)vYffJ?4fo3{B%fewP
zEUfH(Qo4!)hNslHBga%YeHCpMKHm^u`_UF3PO${k_~kzR1meK<Y$hl0_MQsSx9Hu;
zQ~vnH#;z!@QEJf#S*B3a(l(ol)!s6flv<B+_~C~Y!mRkAx6EV8fdn7=taU>|nr)n_
z)<?lDpkS6H5cdiua5x-AED*aEJhdAq1pG%%BZGG<{*-)=6*+5Y)>n5a1tpA}5%Vn8
zA{{NwRY*2p5_8hWS8vWkq{8h(XDuBtLm%<2ejhstNU$nLu;H6S=NDKovPE!*&`}&j
zXEW!cV^$=mQPV&QCFsoI*Y8l{KVHrPFdf7h9$?AIF!}@c1CU$DAmMssYjJF#Li6#?
zz`RBHc;Vs#2pll7VZP>L>%uH`GfzXl^oWyzQ*L47L6Y`RL5cE4Kq!-|OC!#P%ab!k
zfd3ZRP$I)Z84!h6Lg)|gpVqd(R6|M;*`GEl!+C)Qpz4L$iF8c+TEG#6ZipHfV#$YL
z8zC=)=|gcEy7Pj?g{1Y+JK$s^;&!iXakL-^f%EjHZR<D)GX)y-?9v5L6j;#NCgVp1
z-T*`Mca%ry$gKn`;MAik#1=r>N;zfxbnqwKC!7S)C56WpoQ;^PsKlXWpoZzw8J(H^
zBLWx7R!Co1!4ML0{~;x|6Fk)z%CN}Dfg;0i#t7`i=~m-><LdIlW>B~2(F3!!VVd4Q
zNxz_EMd=0qiTV?7+uda3UDvnlNQ6=v#W}#YE9StY`=uJK8Py%a9eydCu*bJI=}Oy!
zu^m;2WNPSeSMGY~8u!}w>F`M=h)6VVOB#;c0j?5$4PFYKIb<yay_ZTk*FxHbMiN6c
zk}%S=-)l#GhjPaoBeF=`i7Yh1qewsn{}}5SU>R|j@l)grxp!h(;=rEmp2^;q8{Zpm
zgivD<M^L|NJ^dQpuVlt#+GIrzHGF6M5+o_JVEIsakbK@ap;pk(&Yx{(PAj@ADl44z
z&87xp=zB<e0{fayr~3?hlVixEv16+H%=_YdoO{iqq-kB`pJL#phWT4n-O6-IyoC-D
zM(I(7Cn|#FHYYSE$|ve45ftBMloHC*D>Q$4sMP5=%FBq3NYZOtrl^F@LQ}&dOK|41
zTOK!_H_kPJui}~_o4W5)?K6+wkNS>cG9aq0t0k&MsS&8XsCSmls&}Z*skv8tDHr-C
zUYSuAT3)5bpYNuAeh)DKt$?_U7=@Fqe<ecvjYk<(!vN$5?2z--6<6)k5YMku+>IKv
z{+d~mWma)uHxZo)(pF=}WzJo+tCG`ZJ(hKG_X2dpY>1N1eOr2%!?7k^yjaYgXP=Lp
z9a*xR_WUU~Z+b*?da-D`L_9Y)i#V%OsZshUl9xX<eRy)Ya5<mH#>N%FzHBr7{i;`K
z;D=?pwWj&gaD?%mRkEp?sml_c^*EaX=Z5{d2}IU%ro&eTdAqFM61Nh8x%ZL|M0P}C
zoEzM4%tTz2S--RP(-$(3Gv>x2CvGRQ#y6Q}b+EcD4httGHkoahXPM))1sc_A@0!P(
zZR&i?O^pYdDr#G;zt%m}Z5aNsd0!lyrx`g2m=M&du>Emvw&u2G@Alv(*5=iw>@D~T
z^(y-+8b(2xT$keb2Yh#HySArbVY>m8W74#1IV7Ajo;{l#$KRo8)52?cJu}cZ37BL$
z?3={EZIufeBgc)bY+@YyHF{s3RZTclwR--$t<JmR75<Tj1cyW{-kAh5H!k;7V69zA
zzf)gA|IY@(hVsV#hN6#)kBZNU&!CU3@6uD#v(X#Go7)RA2Y0%S8ME{F=7xWczZ4iB
z8AZOpmSw(7K3gp{9Lf#qD)t?nrf@pxIu?XjI(eHINjO26S?C8r7&0PqC7d^o9e(GR
zcFrJzI*J{S{vql3;qwGkP)uRJ^7%~tj2y@sL~Gn`ENe_v7j3*Wuo0yiy*t1?w7Ub^
zt=t_-^I%<R`2M~8e73h3attZ0O54rk_v+2vKJK^SYQrktqyU)+8Q$Eh6ztv#<5*e_
zOh(M1<=nH5GYul1NxMmlL}wM5+1u}jdx?9v^po^$Dw4me*Htd8b7^a`AHOWH-W#3(
zwWU|<8q4p}@4}FLB*g|jJ0XN)HxtS;els*^7&6ddN!JrKJbaIZhYVSukZzQp&#sC<
z64EoKyhhzAAWF-Q%2s3qY2<2AcSUtoSpOP1VXn+OeA)YGhv^9GDDka&SdFy~U;JIe
z-%g^P*V5@5d=VCeOzGL_$xRqZIQQiv)jq)>@zY3kyd)JOHmE&lo7Bg@j4_!W3kSu{
zZPn*W>Mp*Ww5?gwex-Te8@=?%tWLtw@_MQ2tcu6K{;mA1wA^-Nt+jd0R=5_jEx60X
zu=I2?6XQ8?J#kx4w2i=h&K>vsqt<*Nm$3LtG3+D8W7MPUD8yLi<MH`g-K8~Ob9+}W
zsIWk#!%1$x-*57x6VeX$1l5X&N$6=G?Q*)KuQQym5~)Hq-Y|ZT$kUDM#o}-(CeONZ
zp(47ni+GnbX+C7hWqx&Okwindvje3Xb161g)2r~XXldlYtKp>&xiM@<bu0O4e#`*F
z51*IVq4gbUSN>h=y&>hW>@bR;U~nPU<m)<(jI*xurRV4EIkN)up&$3>y=JAG^#F7+
z;u<RkD~@h<YqGax``77*7pKwv?Fa00)S7<%-)rcLGrrtgC->GuOVuu|?)t44J{|XA
zyRo-C_&iy5D}Suc>dyg>S?BhLt>>+T_G_DV7mi1k$9n7DbyL4CZ-{I7mo7LiH%?G5
zE{P)uT6kJql$_$7F9+W5UhPTu1k!!dAK`Axj_THTXK7CqEBtsqIz)|Wf3xjz&);+H
z9QPSLTvZAK{e6h(JtRH9z385Lo+DP`R)K^-f~;xnM4r?h+|R97_BZ~OJ|=euXL)x1
zZm|P#vF+HMIbO~89Vf3Qq&7m0@!@g8&*|5WPK8HFC#9KFF;h7P#+?Fry3g2;zs|C*
zn^P~rH%Y&m%72B|6k8mp3lJkxP=ML`!wyC_d?s=(dJsz8aC;LY+Q9jN6!_WSz<D3D
z0<JU%BcetECOSMtDb3{o84%(?he7!V%*K}a-anmgW!36i^n6GRO|h!#Wr8QBIZ}6X
zPkH%BNqM>bf^;1!n8hnEf2m^DIy@MPUSn+?LVKTRu>Y?v$el#kAwB^D$G?3nyo0os
zGZ+{S^*=Ybj0)8i7#IY!rJAOT=2v-uiM=h0(RX`eQx*?fhriNbU;-Y1ze`(F7b7wc
zTN^uPfQKN(za#*E*Z+`NDaigM;$kgGq4`ybOx)hdl#H8&i-nCs2!V`@Ou*^889)Un
z`CstAPl6N{E-nrLR#ta+cNTX}7JDaiR(3u<K2|mkRt^s4zY@&Oo^~!q9?W*ml>b)p
zfAj!NolTr99b7E!?a2PoYh-Ni>LN%%@sFYZzW(i}sfXqNSh92eudx0G$odb5m7Rr+
z_5TazVrlmO1N(>bZ`i-$`nNfOf0O}~EImwZw1AejrgqMMT@&JF=Meapng7N4A4mTK
zsrf%hc1{kq|AhVr>p!6XxB^ggvNZjhrGIiE#4f=4zi9s@FTnawqW&Xs|E|ivsDH~M
zgdo8B-z6487^t7?2LlrUlL3mVd4Qk!LI)5p+<!c}{Q2`o@M6US0v!S#3hqJy5lcHi
zR4k<li~gmLTojXu9G<HGPtyHxYg_vt-=@#Y@wK!wNuJ{5rcdJG&Obby?iYfO8=y@-
zQ0rxnl$R4KvB+ePuY;>|_=45I*0qVM2y~TXXkn1p`u&nSyFZXfH>XjlQq89pK@f+M
zz?!k{BijAyOiBlhWDhZV;Ksz=s`gzJ;cXAgLuoEB>po7{Qy7MyO*&8%0ZGO12ShLy
zUQst0Gz|UK=GVYaPPc7VoV3qYQ44-#7`(4E7F+~(cW+DgpIwc8g0<7A7$o?Cm%^W5
zsiWi(2#5hm3b4PlL#tC6Y~D`9nKm~9o;15eci*~PQeoQ!|F?<%vbrTkm(Ps{Fe(hJ
z{#NO^nIGy$-v3Iz^e|0WICDIs>;v|>-1yR1(%xH`fHpS#du2So6}Si+|NnOQKVx_@
zC<#~`89@hBHQ}zZgl**~eAs6>u+QeKCnTSVLj`AUxz!E8J2q<@au67$vc2Xz{^iH@
zz#{)&DfquM($nXsNLTL(qLK(BLwlv)+H<(|-OUO3U6lu8J;j%H3oU+Mrkt$kG5L=i
z{*MCl{B9)}{syiW&g|#QS6S=R8^igxF8dR|A`(vH8mvmt?qCJ4y<gEG(wEmH6_KjN
zY>3G%pE)=ZI$EW28+!IHu$#|eg+aO-Cd3w}?`xR5Z<~5s;)AqXDWB<u86B@GM}lmb
zkEPpN2LD~a|C3$+3<%W1V3?QBK{#*ZJ7-kM2?(bqgx$hg?WdZqLgvv<ES{uY`NOF{
zFJ0SADYpC}x(wSg5_cbzN}{0R9tyo}&)cqY<5oOymF!D-OK3sCZv(tf?rXykH9VtF
z>I6qektd3!q(T}D)!4|4%X#HSrsKGVR3BFxZ~m(gOW(|;U|YZ9P#25MGUPAztc(P{
z`WSK@A15>;Al(@=GSM)msa=u~E>rr|SAJK+X^^4cD_eLEzsq>7U$aqvO3uOk{0;@!
z@2U5*Dd&T9zsjEN_aDgXt&x~4*Q}8^5W#QYm5&Ij&lY9Tzsd)a)Y@X_8D*IW1;@-X
z)Cc$|XG<LJ51pkAWBoSp_?<}O+C$9e89ZN77pp<^jl(P}So($1M5w{X_>-PLx$cU@
z=wa49`szW|PH8W2TFvs6>MORO*r3noJ+1eb+RdUrNhXE{MZ$-f4I8Hm9nQ=G)t5w?
zym5Z8HO&(lwqNO%k|YZ$SB4xm_)eF$Rz7QwwK}Y)zaXuxg4!?SXxoI$+&NYk%WdDY
zEm0;v`g=G_l|>d|KO?JeAkz*kzkW6t(7mcY&=dX;wC9r${xn}BZnyvEZF;h#chHB_
z`{FBQ!fi#sU)FEEg-ZmX(V^y3y1f0#fUr{?hciFF*gd}+5CG=i0^nbs{A@I75X>2$
zx8llgDuJ>6^R4COsds7&lEA3e33@82vY@jaDOOLih1a`pb1F&xN$NrX8%)(4F)nQ>
zz;x?{m;qMf>D8m>&jP--E2_NOcyX5SizrC0$~7P>3(;#FrqP9<;2l>;GxNl*QruE~
z*HV0j?{`nQiJP-);GosF<lp{N`HH8bpAWe8_VAzZkw~G@Acd9E!r>lc5m#4YK<pys
zWD_Tb7?umc1I}xq>$FpB_O<L<TH$n}0q=>f;W1nHxKz&RjdSCjJ-;bUB$3|w7BE>&
zrvdz%+mk^HGK&WhLd^o8cSu!H-&(|-hnR6^?**0bCl^}3gR=FWdV;WI>65l>=eAhi
zU?`5UL&UWS#|rLt{Pp<U+F4!Z^x|UJ=*i0mEw^1n1l%6M(p!FEuAmvEHl19hb=FJs
z6fq_i4!FCy6u}A2XcZ^ARtS5F%gt5muGIAMA4qd|e<)~=D}IUDoY{lt&*akdC$G%K
z5(2J9m&dlAKdgA*8y8Bqp1#BnvpmF0X6D|QvJ6&=>mbK#@$V5lTnt$aJ)H&edgo>K
z72>K{*ZsdnS>rY$5bmi@mF_zS_T)`2{KqmrFoKJNxvGBtl<=Qhh2K{0ad4XMku_h3
z&vX*VIkzpVDwF&~w?@N2?^!B1dDYt#mt0<4jjG6m$r73=#rz;gzjAR6idwq!cgu^0
zd8!O^4U*5%^`^!?I`NL*Al@byqZQ@4-qLj^)Sl7Q`088w+0O3;Ll)8+dxW*4`#g2n
zKN|4?@*DQzm~o_<G|R#~FFnn@((%-*sY~)MD**pmCFubL)iab+L;p_u_@>B{*+Yx^
z&)2h5Wh)z|)gvzU8+YQ+M6Wz4Eqpxa>`Dmh8;|(uR=&qeY!xz`9AV_hToAp_L>UvK
zn1UTWkuI_2;{z^QsnFZdhYk{|oIAg+7$!S|H-YXQLnF`p_SJ*!kwbC42#pyxeu1M~
zUq4@3Usi8(Lj!vF1Ca=!tFV!uFic*OUk1{|l_F8>u`^}9s{5LSgq#Qkf?n0x?so8U
zT^E>8kMfoKT(5NH_7E3_i<;VvQ`}Z-xL>TNswAl4hV0{^QW!M3f*4mMZh-nO%xRAi
zeaZGfW_RQ=9{W&SXj!x)-G+wYIo&<27Q6oLUCYmd>W4mamA_3J^%)O1@;4*<9C$4I
z<%K7AyTx^ErR#mJ9&w)!VV58{`pm7q+qa}YG`lc_MtTX)8GEIQTBRRuJUJDUb<xxN
zdoJIWFN!DE8~T;8ZR`BP(79sS-naoE1<SR+XZSSnXuOf`VdHxk|5V$HScgL6;+CbT
zYH{BD@jcIq*=5j)$dM#|bF&T)zW_z^`o!78dyYIL6lyi5#^3tTBE6z&xaOoAn`bl5
zx}-SYVa(m**?p=vI~ggb{1uk*<1=bIqTas5W||>kUtfINV7eA>m!Mj!YA|Z_O_%HO
zms9q80@gsYZf$xA)3*PlPumZLME_p|Teul<zcu2-e7?i2A4Pm||3c}%iQ;o}Tx#Kp
z5SG_W_=v$6rAyX;QyQFc!@*&Rb)c6LcvNdh;66#DK|2~p)Z1)O(>)1Vh=_^{%H*g#
zoPQV*S3h8JDHC8jzR^KH*$r51Rk7J<f&B(6)kOK~vlY%|f4~bsW<VOz4?q{6MLRo0
zhV@0&u8^xUeb;7swd1ES-W9E@;8pPQQky=lqNA?>O0V*+`|o=6PkV4RFYI<tPCRWT
zal=JM7Jw_lcJW?VAXyU$u$c{F717YrowxBGTaYd7Y*jNq?F3_6pq3__O6!RU1eL};
z3-l#ebd6`2J3yG}dI=^P_a5JsB=s$aQNp_K-cF@~ey(7XgA46MD&1vFHYxCWg&J)S
zME^)dLssOO1kXvMeuk~xIjwQr@_;n9Twbfzz$`G7bg;oOV;&b{%E{Gl*9UPF*7KsC
zc4|c;zapULprKA^5i=g*Ln%f(ZfI)<Om4~Ry1h~$eT@ukOFDv&P0nb${`1&oR1nAl
z14@~+g?$u}AIpv13(<t~^6s4x4Q(osJ==H_O!ch;*Pmf`pf*;@*8PmjasEL6c+@4A
zM#H?vKz8NjzAwz%LCaCAoa<em3Fxf{kWoQ%mc)vu*C1vx#u+{q><X~*wcs`#`bX6v
z2|54sFXH8g#v{X~aRz|Umt#i1ixiJQ^Gxc8$Xs{crDiF&t`R<dzFa!M|DA7iU`n_a
zw4By6vNH(6Xj{X&**#%w`ZPn!SdOUamq};8uN(?czN@@Q+ELlkDHl0!IKIemXQ(lz
zgr-N^_w+uiZXe3qo0zbtR>p0Gvc?We&r^0C;nC>2vD&qplC<AxBh*s}q&1WBS5#fy
zPUo=!k~`t5u%h)0{Yr%|@9e)L6>2O~2tTk7pDtM8>)|=temPJig?A@TK_!4~(n5n%
zM{Qj_rd$3+a_Rd7esiLSwo?%liJ-fgQ9mcW`m5R3i&VkSq)Q)Av+jI@m4caNe7?6G
zzV(C6MT?=JWN1SS7#Opimn-cF6Pl4KD<@G!a4R*{Ul>^@>kNUch9V>)ffZa38nyo5
zs`+w4ST^@0UPFg&PTOcJ(Dci3@cD<^J}D`O62S`KF7I0Pg=Hx6_x`3Zls;{(z^?`}
zp4FLorC@-hW{hAA)j)rr>eqodbP?sNNgVtiYPgXbEgrDsG(zxk$TYlj154|sp*66S
zdb0M#U20@^UKWNx?k?+EK=;rR>|!ARk6}#tY!}u*v~GCW>++eL^ug5@H23^y_Idg`
zr?D_M!kWExNg6ZFMl7KFi>(nKE8Qj@qvzcD9rgH5<YNDh5&igq#lz!?l8Fgx!tde>
zNSA^aCkyhr{{>Gv3)f*n29N_kSstcuE@l@9>G$wti<d$W70kHN<2>e0!8>ihUgkHv
zKAP_0C6%rn2dbS7xaK^hV3>|qwjz+=0$R%=)EWOl2^pa7&4@D+6<l_cy9E1;?5H{8
z2{l+={Bb8Lnx@(Iw?*o%iWiC+$+&g^q0>j}CcDOd3n%ZBG~7wH@d27oR*1*KA>Ez0
z9^9SIXwkEdx@hUMu=wtn>hkkNPs#vTMo#yiF{sDJiNfGlRO{SUFxqbh53dwuDWW7O
zCLbOK*uE*JrWkL(cMTyU*8k39E@N4X7nJK1u_lVOxOfi)Xe|&WZF>lbnd`-8;joIR
z{NQmZYTIEe7FSOaf}$LX>nn=1zG83<Y)a=HkY3`mIND<#$krkGPFg}X+=u^nms`{^
z)9&}SyzTK}-X-NMkCELfr@SwW5&wP$9ut3C$5*x@mEb^N3kZwHi>6O1H6DrfA3R`4
z7?|uxxNAtf-h-bk0G=?)PP9CaY#JV1sd=;>t#D8d0YT-FgTn+IM3}USz4grogS6K1
zWEA9B)Lb?=f*(5LS-eD@Tze8Ih*Y{&Gvu7sc!P?ggQM{qJ)g|)C<(_IWqd89n10t9
z>m1fdKIJ;ghw|BH_!;8te!W;Wy4Upje%p{O&XZ~`9=gl9KQ!t9`3csP$Fn+)e^S!O
z=ws}C$YFo@mA8D6j3WtW&&V^nhP))(XoeOPujv(U-iU}no0TKd7E>KHh^4^<NygHC
z<=H(q&lM}+h9Cic$$?oBmKg~AbTh8WS?4itTbWedYyWBg>|QNCBln_Px;{lX*^%nK
zFLbCF1U&TX{hezkJoH|CDr)D4Tiy05IX<e;cm361>z8|)*ghdF^S!aq!Ox))_O(Ip
zT=KkM#C+uO1zj+%>Jrm1W=1nr(d?!>Q2n9LDns@84Cct4n!icV+2M6B&E-V-4k;D*
z%dZA7H%IXl-W&%FuyRLoR}UY2oEN)%*IIc^`h>USUWw>@6YseKnqD?cNXIhllkNvO
zZ^R=wyC0_37qvQ51Q##SA@O^(RslmxzC%8pj&~go>v!n0%ik9wPoEx9XH2KqLq5w@
zwK+E%m~+(#!Cw_ync$P?$nO^UW}}(uD!Tj0@i5Bzz`e$ZE9B6)Zp}i%rFt^N6svX^
z*UETD$sbqA<9AFiy2{Tzoz#FH7gWw5kCK9l1&*7qMrL2uUa0xh0Zp6YyW71^CpOXA
zSk{8CQvoE|@chxTB&a?Q1V2yIdY74`2?UXZs;gtZbADzki)jI)D!L_#R0R^gJ%qli
zQRR9xy2`rfy?B%y#VRR}S(nYyU8ZYP9}@@T6}mAXx-16P{8n{c1l4Zg#q70P3F#aj
zNy0f%4SqEMBki5Y^V@1>YoS(wuNPDl?_qK_;uzihLajpkPUFi|YFW|2RotvWL1Ct2
zUiNm9pE#TbEKjMjKTTP&pb1$Vju14s9TXaCs?Ravihoy4CYsFnQRqH(9R*Ip1;-t8
z$IHjZi6B1ER)~UwU5rfVbbliSh5X};FM3O^anR)P1HPmM>Zr^|F74bi<(ns~&*t^*
zjF>fZXkFy#fZs3?{JN@P?X166G@#mqx;fooc{%jmYOPezUGbxSKji^=<-Y4-=w(d*
zQuM)OQ@{d0WO`%xdUzQK{_8MPrmJ``NMh0AGj`DNmT0Lfv=QCo=PDzC;LyVsE2?j7
z2@0ngh^6N=UMK>ImGiN6`jW{Nwro#^R4hAZMpHlPo!?!2^IJ%X0-}$uv_@pa^^YOz
z<0Gql6-x!8(5y>iDq7}H?<r);%TMaJ(=}y154^R<!(QNiQcZ%=fz<D32!HACGDw<9
zXX0*pPVT1m7Ae=6FIU()ef2QkH<up+r!}>3ci<AUogehH=IdE4!0<CLC><oFcuLx9
z)Bg0vOIvr;$GzNCA)d7egq>TMPKJEud=HQnZ+iaD6*HB}GD2?MGu$kQ$M<c!mc!1p
zllo-pKL1Lx$;4o(kV-}mnVR;xpF|^b2kn^cG&Vo7u!iV{_?7ZBx1y_e^UP(ILD}Yl
zRaE^>NMtcZf2wO?(&b2kT<8l1bD)sL<(b+8exODaxObuNhDFUS`Fm4*<>R)j|Mx%{
zUo<UQ0Kj8))LEHW2s?h02Ne`<PSM?`ylns&4R3nkh#~GIxO{)cZXBZRjkI3-g7J`Z
zE57rCbl7?Cw;1KNf2q*YgNs7gxjM*3{33xnfBOwCJ{kJY!a7`)1_ScGM@t33k&6{I
zg<HL>(mLGRkZN{T6+H()u=EGI@$;DdXXP)7*_1doQ_*{&T>;XaC#RBxKQp?~C#?)>
zOY?<wzmW-W?fYqp$ucCbvKj3~+^xBD-y0!DA{BHIo6yGyF0AN8rXnQL`8}!HsSbg=
z`Mm4HPUos_xg^;FRSV@xlZ3Q$W%4obK=Whey>IHEih2a%KiVO*CFD6X`*oZLb29UR
zZMh!Ak>!?005`Z}XZxXFi00&@OW;xuwgZF2;aemrk=K;vJTZBpYksQn{COUvGd%<z
ze6x{DCP3R~cIY5|4TI<}!O;4;isd7b(nJ9CrA<>{pFQb7f}Lc83@QH<|H|5&|88|j
za2e!zD%_+N#U*D!OFkGj_d;2fZD4~Uoyy!CI<_#D#vd<S$_K@r(?piW%6H3%Qnb1f
z8Y^<#efj9h@`LrZXYvdfPah8k99?(Bn3$`s`Ub=gpT=nY*vTdsBKmUU6Uf7#<)P2)
zBr%z^l+-K`oVdDu(&o&$ey3=M<H61}0&a%kQRUJHsSq9t9XLGt{@5IS>q=8_#~Xrt
zWtQhbS@vW*5qn1^ERyMjuM6Uh;GTiSnAii@Wn~o-zP^G;G~92L#t90^VmOB)V|Twc
z1+L7~cQEOA1{^tSKeF9YdZk*H(q9UOwYEz;UM0~`-(9)aI+HI`1P2F!aXGE5p*%A0
z5E7)t)l`7E%vTMMBsS#jv;z_V>`0fwJVyO3i$1Hj)?U#Si<7m4zv>;>4IjUKGBG=u
zJ{3pOdoufRBvGI&Y*#m1|J-&c2z)w$N0SeR`Iz-akx27hdZhCxWGWZ0VOqomdz5LL
z^Ru7a#e!mUB;A1+M~@E{-*J0BA;y9yeOtOi*2O%O*@5N7{XmSArjhcK5<0|S?J?(T
zeEsoZ6a)Q)Q?_g@?S96WRmPiG-vptExlMUfjRI)X$|E?Z<vYAlli48_+C3qOv=q;#
zF`BjgC>F}oojrkv+@p6i^B88=zK?eiSA(^Cjwuxr;9|-Vx*9?mtg2MuWJRe0Xj(K?
z)c#i1;M_@VrUr`|m|^005!s&5VOldp4vz84doCz&uX@Dz$Y5zQ+TgX1e{7a!u0kqq
zVeT!NcbBb7NVv7hK}-N3=T20pJW>(knL$6BI%4H1j;|-4vhcr4@NBC4p`Xp!R#jwz
zCY|BGvQ|`ej^9}Ga>N38_V9_DW{E9HgOF`gt%|zOn1zcksD~v2({lXAX1NOd`BH?Y
ze*bMf9VZ-25uv-@tycs-n6@uXT%yL98AS9|PY^CL%B-l0$3AB)iybYQm^CO+G$0?Y
zw~Ed~LIzvWRneXB27$4q;@*LVYfRqGM8&BetT_M;s)e6XcCK7c@OHEQE=06ek!ulY
zT*CU)P0YuIhz5A<Q1+YnDl5z=aw8^tX_m}2xDxNUUH^+Hs?oAkRNR_Vf2gC<_3nl;
zmZ>EV#~1Lp7&SUCYt|}WgkCUiiz*hNCkR?$vid9lZ%~rOkHtr_Xtz+zBL$8Soo@Wv
zzM}WkeLa$y!q6*z0QCc##~?K}y~suWHx)9g&uUOIR0;N-g`riAxRIw|_K_Ny)Xo71
zsN+vWKm>2^-9i)wS(X?PGDg+qv6ZC0Blg|o_~ij7*D6V4GkPi!(O0h%n<#hIwe4DL
z8tS0gFI1+lv)9I=UoC|QkOil`!pR2HX3*^hIhhGx@f2Ra2k#(Zc$m%2_&%+4D1sLJ
z5&+=wpXccEq*;+IG4L%r^{yTVYfe<qZS<pVCSTc4vf9vrQ?;EPna{Bk(`_wl6fMj)
z12d>~M!BO0OG)rtbA7rCHerm;tD)w1c>L3*vy==hFep|GSfxy!+)K75wtRU;V^_|m
z$voX_(RV_8v=7xE9(f`0->?e9qEg=-oZX9OPXeyUm}SSTw$2WETrCZ;_+y-zy+_4N
zilNkFX`bYA8B+AMwUVRN#9B1ZU2fqcLz+1sahF1(Tw&h&Ft^lGqe&ELCz)x(oD1nB
zN@ZLhLLo+12Yj{EinFUUUl6`s=Mj-B*M;b)c-&V*M>eV-l1d$zx^KT!nv>H^r+J5X
zM@=A&bmD34ahFtI!+qX}3%s9&OUufHl)v^GaL|o+fjWuyxt{LT<mcB-cCtB?Ysb)u
z@F8&t2)-C~>&Y0Id_R$PREM<1r3JKEgm7k>X(Ye(6>iDAETa%I1EnLoFRUsh6hsM6
zDW_#t=Hd>%!BW5l{SKEoOj2F&zI+x226Azw6ckQ&W8w4~>xGm?I+<rRES4Y;Se}O*
zNK!24L<JFb+)_f<5O|utCve;_Fw_9)8{N_)f+AEOg61oKSdYsu&{M=aT-}J$%^(&Q
z$-Wvd6ON!pjapf{z)-9Y#h!-fj4wyMC0g++AEUY{Q`QtEqGIns8YLmePCti`C3a;E
z$4mTJ`j%GkEB0;fB!op$Mq(sUn@T<cIJw;?o7N-nQMN@!ogzIz=VB>J50I(3O4t1H
zt3lZ5$0FBk)Qw!3(kU9jh=9SQxVMB5k)&R%hJ7c2R!nW`;pLEqbv!6~v{v#kECwCK
zej7F5;5@TEVjzq<q$WvstTcm;VxasZ9LEbT`}i#x+`W#hQ2jDA<OnQr<=|>}cLiJ}
zghL8z-NZ8#7;f=wLm<&EnVmc<{5dV%0`Ta?W?p`O=g~B1A!KFMV|LHuEz$c#Tz7si
zhKrUNIGUNO+bNDW{3=7k<z)!R;Rq8d0o=J-RdhH4^(}kl9{|@zS&eqDW;aQOEUJIU
z%&4N@jLaP^7KxdhsKlUkEcQ2YGLm@TaD)Kdj5j-tsZhrDs(vC*cNK%bn-&-!m*0rJ
z*Ae}EZb#u4_9dxwelh<p{PNb8&dGu-(??^daHYd5AN=H@!9<cJfHU(^T_$iR_~En(
zxjbXHo7en#0IkboPoyGcx_e`kw>xG`T)0+Id-HzyqwB>xId8TBzgajek2yEIwn#pP
z2mqs#dKs>v!nU;bj7&Jtu>qn??2CBM>Kxb@6%hD5xbaiO^E)Q?NVjCFLx;y>Hv<T*
z-2r@pW)s>>1f$j%uV#fe!V9K|!FO4PIOEp?@22UH2>CHP4c_r5;o*5(_vHkdTd@e;
ziO-$6{fh-&Px+?|RlMM4r4&SWvNn72&8m6LzZ+sm6gHXdskLrh8%&DGmhoG~*$mw(
zz7sH_EY3wLLTjO`UknrF1UxAzGK^2Mr4SF&)?jb1+4DSJqJFkM)R{ZZtw%;o;srX2
zi(fga;}))`tNKTgyR62R`u60A4&~>z#*+!J4u|S$k&0%&58COh#=0xv1(PHm49`Se
z0BelzXMDKuJ*z4qExWfAy<z1U6#Efasr|{!l~p8VM->+lrxU~PTr##N?hu@%sI9S&
zw)P83I0qn&eg=|-1r;&v&57<D5Q3q`Cd6ScI!yET^eM-tE}}ezO7z7md@rVB@T4RS
z0!fXQFIlD+qEGPE@kfqe|I{0vVl~OrLn9d>fSymu%(8aagIn8E0`Nb}>c%y+H9&@b
zeJi97pHIM7Z>Z92qUH#^EAW}+Y;nl;31yMHp%}&C(1sjB(h#P?fG2+)AB0q-ywd7K
z1Fb}9F9x>?xZ+6`N#=)nyS%UJUCEJz!c*?jL4IZs`;YDd%Nq!eBbW+Zw#jcxq0IZ~
zOaL@piEtg5u40%u)P)Uc$=`B3)N4l*dRT6hG!zR3nSPalNX{e<tq8N}q9X>;sWU%M
zB4;hpP$`yzFxYlVQ!I4~8K1%jrmq!CF|oM^=nfmX)388ImBOTn7b~b8gpfMmfqOf9
zVA+rokLPv;g39nYysS@xD}t*!{M-0gUqj;c*`;k2h!Hcb;|o$f)zD|y*^>7`aZS+;
z{Hg*AIWDOX%5_GvXo@bx33W6jJlM*#Nqq;v9g0Ke*yQkOuh~gMgEDiM=^h<@Ra9o;
zVC4;krhB7Hmpny$b2*8?!p~<IiSD&nbcqwEg=F)@js3kd;Dgk&1ipO<333%C1?38@
zmb35b?;i%H<tG^%t1FseJ11^1NCT4jc0k`zi#^Wy>WC;5fT*gdcTS_QE}gOTT;~Jq
zV|KzD@Gf>R5mZKt{C7^;kwuvk3WR)p$d74_%nIjaYTG&{gi%CMKhR4U!{rG0yte*D
zqOUItRBab$kjheCavv|0M_B3K)lA5X|1l2)BIiN1zcHf;A~@b*S+~?8R_em_xL^ya
z@M&{(wpd&vg?LreJ+@u`*jyf@BS4n7doFQf)%EM^fO9w&{b<5o)}MhyJ@9{YS<LTa
z$GWYllaTM#+6u$t<62tE58#V{eK?bl@r{N!PmE=o7H`*-O-MN~<5;yqB2J75Y203Z
z+W!{w``is`E}w;!7tHrev$r!@u*9Qw9zgnisx4-wz~h55>Cr_sxjM5R#9Ql*yJ)W!
z?WG6hLtJ(gjV&!c7%0!k(+n30D%r_ByB?N~+FedJnb3U)J%`ts4EZH<b1^dMSy1n(
zBs@b?AEBS7R{(_pI*he%eZSHLeR(l5#1AkNWy5|$G57+I2EwN?xCoj*-V|bnrIqr%
z_0Exb+pYP(`CpSbY4@R@%Yw4RfKG8=V}263D-3bF>j&5Msc4Dx%g`%mI2iJDzVGPS
zSQqtmClO9`4!kC&WXVI-1rLUZd#b0q?#JRK;KfxBV*J$V{M+nwA)27bt~Egyx;pgK
z@(RWsC2osf`b&N23*66mQK=47zx<%@mjWL{IO1~`_56i-pzFkW%b};-ax7=V5<5uT
zl^*<aPxD|FeNkN{#FlW$M9PmEG0#$2Jvlyq#@X?`5kAH^1RY22KwoF@F8Z?0=~>n#
zgr+-7KJ)`)HqSaMN>aT8(rl^?C81zkYERk!4em>gZm0r|-b`XD?FfZ;&&5nLzDKE~
zND2(g9xQH9)J&T~EG3QZGuV*5nVlAYSR8(^5-Q_EjX|KWzkZz5#}#kb;0vhLf;P4N
zlSQ^oxBP<7<SB;m2f=pW+qq6%xHM8n7C(LkJomkqeA|<De~4GU%O&9FR1K}K0*(Wg
z`S^%qU7zp4(1x#QqbA@8DT*XehoudLM4NrkrgzHf=TI0oq0&%UTKDb%Xng=K&w7wc
z*ZCacdkCNL;b`&l+)1d`ZerbQ-;(3F3D$wa*RUjz?L<zhoVEWIpOC~4{I#fU0zsH&
z7Ls7{<7`E(TNs?yN~WIe;BbedZc(m??BxAH_H@q<21B~)w(m$`GRZM<@NycsCl(e5
zv-tuo{=!xO%dLCQYOJMtmqrTH2ksWX1E_<i-_Abj%fFv>+V3`4`Uh&kaIs?p8z>wV
zw5N?{DSUS*bO{f`L9cE`hZ}v#hky4H0B%CHkXe>3p{vq_2K+TAxn;`84Cg8*CNl<U
z_KYOQOIHmt@_9lU05Qt>Y5+B<Z>VG%nV2)T1qkCPix(eNpEwprl9s1R7a3$gHY90|
z6748K9XHThiD^V97{ko(R+o>b%#0556r4+puz5a9vTb=d>>3Q=>itcRSs{mFGN55#
zG7)E1G<fRR*b9IATVtoNV|vv}w1qUsLYMyoe)oDyoZdxgwr>*7Th0^yi;rl;uQYyu
zE`{Sl4&qkv>0n$6qjIlRU|jp4>_B@ZEgsLNGyb|dF>i}2G745|H-Rv0{H7Oim;<uy
z=LzxKduW=nba?H1L{2SOypRj4WXyz_U<fys6wP<Zj5^`w)O%ocZ~rwY0ga?AKh8=E
z*K}+(Mf2t>iLnVJ_FZJXd3ja2{P-?2Oo^Ts_h4+H03vFBkg=qyEj`KwHPGkNX#SV`
zEM9d#r{YIk(pKzu$!H1i1lQ0-r=P;LxX!2ar@DX83HIXY6tLu&u2!hzx{%E--;eVO
z^HUv&wYA8&xMS<IHs~G^w0AYPan6{3_D);i7@D$ANZYMbRNJy*=>0H~^4y%y@)tK@
zLn_V^>W*uoD{>ZgL%G{7br}*XUS^#n^(0J1pZx=~RLfgbTsH`n;vWm*%zQW>cnC5k
z@C=FL_oDFpF3_o+8@xrYLBYX6!~pO{vDFSICeD?XyAfmGIns^$mMnm64I4wki9~w2
z0{-OC>8{Zm3jh+B)*6qqTm9t3GMwlU(EY{<-5Fx_`qI)6Zd(*?a{x}nVfPEAI*$2i
zXs(Nv)<x1-mLQLHkR4rO5a(>QjabSm{mV9HfT6!|5!-!fZu>)$@H@P#570)10U6D4
z5pzN`e7<MGfUIi=feOaEZZd$Cd=GF|$9AHYKGg0{TU1m`f5gL%M8yrFF=yRZhL)rb
zQ|>ZG7iC3ma-0m}gPM_=bdM0PYXgw_sgA5=@Ndp(g>%bo7CLUobYjYahGM#x!7j)#
zGcBnsj?x!Sh^JSSqTi_{`x`z{zWX5n5~JEn9ECfDlp<DkrO30bnb?*w{|sdn6bAYk
z!SYW@*DtOIt?%lkbGf4r#*>7G43iP)b2*(bI7GC&@OWz5$`QH}&JNFp_7-9wWw}zb
z<Z{6{qJsnZ>e7v#m2KYbkUQVZC<RPZ3#|E3redNUS9UyHcvAA$n8A0$CFis>y6+Nj
z^D}%{tFyTYsuyW&wC~|J^s~arDAdseFi>?RHq>>3?z5t5w<bgJFi62+OLTFF*W+qB
zpRo8()_<H`f>9zK;rpmKawd2wEPk_jzp++3%{gZK&K6P5X*FcAe%~PBaAwur$!uWw
zN7-OJ%afi%C3t3LBld_+c+`qfpikv$SR6G0tB9X+KrhyHx%G`Rna`U8Oyt9y+&3<R
z&9~<sPuPsgSSwm|ZZ5XM<axQ}!P=*(8VY{z6GMQY+liN5Le>_~To^{~=IS{ddim{~
zR<o@Vqsz|?Xm7aRU;KH;8q}&{Ex_h?Q5<3ZqT@s4YH;J=7__9E8*$MV$Wg10i)`<V
z?c#{(uvCatwkkJM6e;p>M3sILza8zaFJXIx>|y2irTrJondT#({--e^gpXEmgqzz=
zh*m^wK_@P0{1%u!z-L(Dx1Uo6px*Vgd3j+g2#C5!K@pnC5S#9FJ|PW9GhtDg&Bg@`
zE<tCMQ033Gs=bb$QmQnmLaOF>Rt1kcR&iLc)i8Ox;(Ag!$ZFG8_HaOT-9)X;C<k5d
zF{_vq4xYu*-(HF_JWu4ZrWSmk#cuHj<Y)yZ*M$MjOiuRY>}_c3Q(CbPb$*mocPgiI
znlT)*qq8d)$WEYf&WVj3MXWYBGcKM_Nh!8r-Z$X4(D1?7Qny__hXp(Ad8tB~<>6yY
zF0T9uL{Ix8lW`~pJSXvazxVTaJ^jID3r^#NOg#{mNE_G$<dltycOZEn{~Ts744@xk
zvA|v-VM6f+k4(WpKgjrk)2yEoOWt_tb0l(lvc(zYgza5iR+KEDR`}qBH=LvmF=gk!
z=LV!0?TJBwwI6S}#*J_)rmP$dV&CKUL&;gLGvmwMT#TjtaHCe~D@!BU2y)->B}ASF
z&mIwm=_P#qaqj=2QGex(D3}49!NQX0h?JF6=aPIK+8#B8^})rD$2us%wO<R%%mpft
zby#++m#mW$C7J67jVp;IL|GsmOjnW;%1G-HpJdLlofmpaUqOFctj8Pgl!TISH}aPf
zAZfe&GG1i95$JGX);my!LB=KkLrE(yagphMHQeVPPD2jw<@Gelm+=rsr%FenBKD;v
z;r2G33}F4m`C#`7fP81!S6D7jP#%$D>~jNUx*6eMQ!OZF|4Hq!6<A3Jg*Rh#uMgSb
zE*SD%inhq`tX~E%7biH)zz=@n#e<hKT%!;AoAzdyRy1k%ue2#KqF6x_9Zc<v=t~J`
zKGP%3@9`^fxSt=ed-Vn#>{iqjsv6xEzEgI7=C?x%Z3i#}#N(8lYK#eDW%NaBH`}~t
z2m&onPB9}9WV;q7T=7b6TWn%i96vHrL#S&60%MsA;HZG}tDZ<<M;TA=u1kiq5xh9K
z9B4s*r_~hbi7Imm8M$GK2A`a%*jZ?dBpZIPv?_-c9x|x=!~+A(!jaz9>ENVaF&C?f
z(EHyykAPwtlcdu}OBL^^*Bn{Lg6D<GVtMr0edT+c5N6-0eXo50oL<bz62u^JTjEYU
zPD_|UDo+zVRX)je7i^kR7<R=jVzZQ7v=f&i8t9$w;>SYzW%RykFEk$3bQ$SF+Es!q
z&1@7nY86UmXSi7m(Di{eYe8J^W(I`nVw(P1kA)$dq+)iQGXC~iA;OTw-I>;CuVRna
z5j?({ybHr1#J*L5`>)o%rfG4|P6QZv-vw^yR<%mgL^rTTq|DGtOpus_l6FeO?I+`^
zR#jWlhLmHz1k5X?tubzIIZ<XKOGKO@wA(@dpWA7J8FWE(N?QGx70Te+Fr`uT(`K^y
zvG`XhiVmToH75CSgh4ks_EaBlw8+1Q2U^3<;NT@kzVOYAan?etAcjT3Kq)8=jPs?D
z^+(pSBF^a$X)3qh!^PRI1+{0Q5LnjxsagEAk~~0Suo1l|8()j_7x)lpxA%qOj0h?h
zs)d$w?i^?)ed-S7JF0U$Lp*#HLkaxm&UsT3s}m7^p$&{MKr6^$wlK|YirS4+80imz
zUwJxMkh(gJA4?FUT(u~Z_`Qkrw*O(x&cK{FDjJTV1upoueNB%iobbDl^bP#6s}}jV
zE(k&;Ow1t)zLT<gFmEtEB)k>z4+E?RO+RJAJ5KJ^6X9tBnk)FgZi`>~%p{L-3z_ve
zk}f+RPSyg<PlI15c6+E;(H2%EyH@L5<GJYpkSNGM59?xI*?ycxj(*|EayG>9yMEPz
zGn?!>^KFi>pu&J4R#cn3H1H#Ykt6H4!p5sN6u@C33Lk~cs<j^}EklS3K-^wQ0iwzO
zw&B9jWhcIG`-8`>8wzPk6O#dea6XWi@YW*ft!L>SHev^&G}dilQGydWivjU;nY;QH
zVK@zD4&@IM_Bti=+s2ASUdCPg!%gEvl?a##SQzNhG*jAe!#BK0@2hRg;Ef7qR$@5#
ze}PQboB%6j9@DmVTtreCeh_98ZCHU7G<V59Q48Jz4olf9co?T~e;79ga=M;jAqtO}
z%2<J@MuXzLjms_9s!TFOVWsw@!FV`SeQBTJ@8^~+&pm67$7eQBN{;rHHX#Xb<k@^v
z85vp;(f+uKoweZbb$iS)0Y^W9I2n}Jx3twg<0F(?r~Pn}stvW|S>yOT@Sv_wuqA;f
z0pgiRX;}s3id1*^I)y+cbOHwkOk_=hv~L5VayWx!;ygo&U4!siWx77Oq`jy)0ygDX
zdSNKlf^I?eB}pg+IEATVZ-T>!Bdn21W&6Jnmixrab>v6@pc11Q3C!(f<sej2Pk3_0
zuI1OOPQsUd7GXuj?}loREsI<iJ<<;aLGrY}d%E8x%#(i_@-0IT%wh#MMk`D3ag+<d
zzF?ZpXo-brK&~Gn;jB2ms&@7NfK%k+MA{#uup)Pf1DQi__k3L46lVq2uTL9!3nov_
z1ocTFG0_~3lo38sY+A0(_LfnmBMctiN`9R3vwV1_ikDDj4q#Egk1YR@q3`2A1~%4m
zbYMQ#jAt~`CoI^B@%NJ-j-9?g=R_tDLk0@gUqTfZk-wkQk&BkQEGmDIm=@hRDc~h(
z<wq7^RSBArByd+}iXa&D9;U&k-pS_H*+L)Rc$7q%nl;_GE~pC2%-}6zQ8WSTUW)z2
zkFq^x+O|@F2BeJ#wl-n@uI%jbV;S%$p+F0xG6cL(-(5WzkqZ!H5&NtKGf{RVg`yZB
z4k{$@^eM6CC)L!&1)?k>p;_q<lfR&j{hi*R1+@9OGMgz9PIL@XxJ`WhsWT_uFM|~}
zXZgkLcBthP*FR9vgJ>=v!1j5ui6NRWag>1x$+DgX<OR+?jSUm@-o+}l-wwFbnXr$~
z4^Xn@ye_Hs0qB|Mq48qx<6=cbECgIw^c`Fs$tHFkp{*So=Rc7z{o7#shBgDES#x0!
zU4zNz?joY^$SLaKQS8toimOxmw?-L%qSh7nV595FgUD2jAE*(xjLcSHPR)oK*~q%d
z0t<>EP<k5J%|9Son$o(J8j(&>Aj`$25`~LYc#OQzb8aD)?&XI2f2O;b@>$^r+LZH#
z?M!X-`_TviD>EY}k-9tL_2T(B2fMStiL_qLV5%r~Req$TL>{F#UyYx(B_xaN^#y=7
z<};WG`a{cVy~mt<ZbbPunMBNU-69ebT||)%nUYv`tr=KFo|f0H8}bbLzygB{UlE+T
zWd*4LLiiwB`QC^-&Gv_bwqbg^!4^KPbgye0C|*2Gna(VmF?^xx1USP$ljuRcl_3HE
z3k%{^A^M*njZ}LG2$o(G_x13f>0lC*(pJeoMDH4nOlXbIhUMFDZ))o5*ohL<kWV8B
zz7li8V5%BNCSwS$4?^~{r33`#%jg_k(R+R}@4beSwZGC4^p)(CpH}xJE5o~_vmtCO
zqev~L%E%X7WF>?hEqw+l#_OO}T1#+Zl+780oMGV>qLoV_k)rtDPz0+(D#m+I4GfWo
zydCx63Q1XsK_MWHIY7o9XVqlNO2sQXM!BQA7GEQZr~pnC<+CFi_wBbM{h5ME=Yq^9
zvqCORCZDcA>eI8^G6|xz5oAvI$hbnl6$Ab4aD$1wcx7+~pQVK0;vD33p<74YE(d^E
zJMr(K%$Whf^K;UWdGuYj1WSV&Ay>lGMG3MMt-E@}l?fnoEh;kd!f?7f{}6jxTsN5}
zGP6$-9B5U@PnvAjZ1zdF{keD7!z3JL#7@Ei&j~uW_2BGbXm%;D=fz~g;j4U!0y`o|
zbEq`bLeH$Cdu}XAdKxUIWjLso3^n~1a5)rqu46jZJJ%=HVwCE{tli;?R}{P|@(M7r
zrUW$;Su=Hq2ojC~Tx=u;m-8S?tW)nQn-EJDS73X~xq<irIlXOD5Cydu$7Epv$QHCg
zIfwLQ&nT>ttkjP($qIg7X{NJvB4nEN!W}6&^Uvw}D!5N;A05bJ{vN6f3vp6qSJ_+y
zk5^*Q$11au6@rz#pYl=~Ek7P<LOWonLBNETrc{o)d?;{yfdZmT<d1j3Ir{zNSgS?M
zT4PF==PiPgw4vc3zxR=l^`21vxuzSjGb?)ly;vu#AT2%7-y*Yu$8Adz?V7wDnWjM&
zJ9Abq9-i>!QGeaBuNdx;M&9Mo%Vwn=bI7u>&9P^wB(`^$uCszAIWf}_I?_;<O6qVg
zPUdkVlIdX23eM;e12U7(iKK#s===Se`q&rBT#}aTH>|hgG(`6Q2QEO-zdkDp4P`l4
z`S}Xuls6+VIt#PsPD8@zDY)F&j;;4T1zlDW4iyFBSNAQ#w9$-~)g8rZdW$6z<H4$*
z7tNhcgLMoR+;|TT-1rcTK53_rlHN5&w2BHFv5<v*Zkay~p4J+Cx%4$W_41d9PfX;R
zQ8PMlZV$XD8_Iiq6(L08p3tqxSXo@GnebLmDnv&MTj6xE3!Dgpk=&iOx_Vwqh@8?Z
zf%MRjx2h1AeFgK?wb;IT8HGaw1%M;MqUhoE=!7rlxh(J!+;I79B!xO5fA1!&T(K4v
zojRlspMaUu$71Zo3;5cgdOZ64QaUM;iD;uzr7(NKde~WEl>%Mn6BVOe945-TrEsEZ
zhr;|KOuy=O{Cs3GEcM6n)yHpPYaOFVeyot*Yox0ruaAlh(cZmEo6^WReu$#T*s8*;
zO;r4KXdsmK%~w`n{OxzpxsrrfO*w6l=Wwtm6ZhUZ7ZJ>#yf{#gkK1^!Xkp4OlRgWH
zZ4N2<&aCV+t}|s_pVEaBbJ6>KpY3bEQj~Lt&_FcPqx<cbJ#a90z%wcZ*%QX_b$Eoh
z@!5Tg$_XmL-NTj6DuH^LP(hl8NOYa!bD@JlXO2T4Vd5QvXF9|4?9V;hr{vR@RJP*&
zhc8Atg?mlW9(=RvdqUTlE6O)BNZLtkn4f}1;6##^8Wb1T<Ep1-V^~Tg+A4DK%{OaN
zNH{kb{R@kVK>{OG3<N_>_>9%sMuS-R0C@~y(Ic61QJMQaKH72+29-M|%%sp^LCn$9
zW@5~%U!vIL2RC}}?Kyg9>-Wj;NpuOzao>yLZ(C=*+d|eMzmpUjk+m|dHYcQ1Xy$&Q
zg%O>SidLp3eEP)->^stmRC@5IPaV(nflF{pWd(lw+($&Eu^p~O;k?9na3zajCB&8q
zXZswR)UHt4mvuolN)wYhemA_RP^an3vE{4xu_dR9j?)xOn>B-qZU&~#o`+?Ry@c>M
zH>@YzyR^WA@b?~!nKl(!iGe8E`VBS})YB`^++rFSZFS|SWnBqpOF75P2PylDxrKa|
z>?>vZB?{dsd8M*uX=jiRBWWB)j7nqfb3VSN(hI+6wAH9^%dh`N$0XCGI6rqZRbt=%
zGSqhK356umipQeM7UP=BsW1`Qv1#Qx>^@k4Hmf5NhmXT_I$18d<XSY87U9M3n~)jq
zi2a9v`<}c3mtQ;*Jspg`to$Ck7-eiRtKi2x=wZW0FmmM0+;@qtF-k)oB<tID2((X)
z=1LQy8MzLlVJc3x<3tTq@`7Zpc{L-K3Tv_7a>}QDM#S;b&Ap=33*ZeFzUr`(Mvc)#
z7+~lmJp9xGOh^rbv84t%bhs3$0zmG=*38JI;dSTl7a={&0b4%%5-YeT?`6MEJGlQO
zuV3mj*q?jaHTEyaGX&x<(N`lCLOGPB$ypQFHQgk*G{m0~Bh~__tHQn=$9aVd3o81;
zov?xK+6FXq(lJegCwcDOn3J7~rjni5)8LL7<I?GXB|e*na4p$q(jQ|x(~hW1)Rdc$
zJ@*zYT6i&2DjZRry90X;?IMaSgnf|uC8s))rKOXHeQdUx4djQS-<&v2UP*q?)mo3h
zfNVqt3L=gXuc{K{as4IH=_w9FX+sX7l#0>f9&<hUIIFY=(@8=HTNTk2TRBIG4x9n5
z-5xr7tW5pr>gc2lsD<8VH12)$VN4$q18YYU@(ymsK6oODJyA(|%{k}5#=9L@4WC$O
zLbE;LqP5~UbLbOqd;q_`XgpCePMk+8I<CH&k^NP8>81Cfi)0;!9;v$_DTy30KaDh&
zOqxd2)Ocgg)j!AOgttpEyQ7DQfY?#4RNz<A|CG_5{|Bi-G@1oDc?R6}%NuYNbwDlm
z+SWBhukFo4Bb^ey!Qm{LI0~V3?1hAT<6vPSM$Nqk*IqgeVIF4e-Lx933BPZG2S%}o
z-jwX&n7VK=bLAWGx02OdbKD19Y}T$0Y+3pe4r|hI!?knhXo*L`_BD9sxs90fz-`DL
z5=VZXtQhFxSTS?|cm+p5UtfidpTC23dydknmr28jHBg35#^NiO^7GK2VGQRQ?xjx;
z^-%hnsdL%JE^FM%$f&}L*rrK5b&T}y-m#XE-5j(xb|aa*WCHc6vR&)3Zr>5~aF5H|
ze!AQ1am<m0JoQ8y=C<S3U;P4$E|~yTTLspxT8n+T$0(Crkv1wD)5Z?Lyvwdf9n<{Y
z&M_gC>4KtvpX;!#YmSraSzzB9zIHQ#j#(!bYOW}!!Kz$v?l#WVzWto6`E~{d+}vHL
zGZy3f?S(kbdIr_C^>lVRATBwBx-Sj%sdG_ZTaCTXeMnJbD}LBx?#A9P-oS=hf8280
zLK^c4DBiyje|Y>0jK1|&TtugH7LDT|8t7HQ-t@iI@y!AUkza5eReZ&3JNHmfXe`D~
zo6ca?L|nf3Zmj$HGcd7+O{l*&wyj=;y2Fg>Q4oqm6H}tCz7rXfr(;Z(8;l)QINoGI
zaF_`>g`W7uomZ0IB%{0GIKKIE1$O0^!!0-o7tNWDjI`0X?8dus=(?Z75X&}6K;?Cd
zbm(S{9*Vl69e88&9-_+}*dGRmhNfV|lxetl#YP-xZRffOR^AF#b@Tvo59HACU5A>6
zCg}Lqql}SbF=q5|OuJ+ej_>~#?`%I#XZ&b*dq-geb=RE~yuM`7wT!ek>V3&q`26k)
zAYn3sssFaO)S_VRdLV5&rp%avaE~7B+43#XAv;h<`RL*6kJ#jN21`1jqOOGs(nEss
z7jOt7x~;AdZYkH`-kYyr@kv*{*<w4kY~0GCtW9txqAwz8IK~kH<r^N&Na{A^wq)Q>
zkDlB9SjZsY)_b0$og{pSsBDcBst;|%o9}MJ-1~3G==5krs*mBL=l_7(psBd+M(P%c
z!!cd`26mV<2x4GXRKvl;MggUY`}w!EjXluegMwX~P;qTGVxq^fKu#1Rlr1>?(K>|u
z=9XTK>m+IAE+rqQFToW~A}5qRAs>VZb<<qcV?Lozo>VStc5cJte`H=^NDPAMDPHlV
zs5}~^WD>^Y6iHt?^H-SrL=$d6hYNF<lrd5nbUeC>5x>O<iH^gg_q8&z?TTZ|wxh`u
zU}KW9QOUqve8Ny!xEU`#w}x<@ef^*F*-}&+Uc}v3k7YzM9T~bW(PZd`As`JG&zVFh
ztP={ht->>RJchkgcKrSQS)8VpZM=o?Q>Ma9$fWc~1fD$0%`<>P>I!~C_DF&_G4D!k
zYR29TU*P#yp2Z=zWW*oM!N@BgCd8DAT5}b?dF8iwc10l~_{x_xtG_{kL5HVrxtM}%
z2o}vw#EW0&A%&i?%v5?DyPB|m$t(E%+ut!~I2;vgR$~pl^Y7fh5FxQ?xa6vlc>4Q>
z-q?r?Rd5wOth#Q*6%=Z>%$p3?u5v8<;065gt#uqTs|ZtBttn(qnOr`QLts5lLCU<#
z<zk$OP?A^Z=rmnOnlv9*&do-IpEC@E(C6$X%?X{A>&OksJQ>k(pl8;z1sgto7LUHV
zs^3$Gj2ca2(27NDBP!4X)kpT>-J2hz!s<drtR3I2T#ioN>$u^fVaQ_s>a_UJu$lRh
z5hs#i$?_GeK@=o0(pkidpQ0v-Cq+jneI6m=H$3uJTrO36nbQW#NJivi@W$(Z#qvB0
zk_ls!v>gkjzO}XxN4wqWAtaoS27BKv(68ft|6u=RtZFKa2M+APioF}*STqwOG815|
z$ipAr`<mOJ6qjB-4RKU*eG?dA+N$)>w$G%iecO}YxuBFisPy2pv!`Bt+TRM?jSi$P
zMpj?mx=uMi<JN~Ub^@XPG#VAusWLdB8wI=U-{Zw-=^BGD8q0UwFq3EzQNjuBE^2`7
z#=`di06+jqL_t)E!rn?F(@AZ#<piEbG3nPK&I2te8qlMVOmu{XbjsQM9$tKNBcq!M
zXsOwN&p%;EDIf}&fzF5ya^~2X=ctZDdV~*CHM)_r_bdG6<&P1Un84y+wTwz}1-ml0
zH`W``TyIuWjq*rw9_pw8v{&rH;(Hz^Vg2~*I($~E$MRnWBhV`X!zM*w>p>Heyd<-9
zKr?I`j<We}*UNrCeOBZg|H(P!TK>ul#aK`o%CO>-m+;(&I}jP+hIMOKp-1yFu9`O*
zLq?562KV;1JVIA{?^D{awbuL7_B7=Q@}u_<Prd;BoqO7_ann*f^4Dbu3iL!3;eH19
zFx)+FI06D<FqU(0oJ)Vl^7rr+6>{OrF~i3w@XD6r19<(l74|0i>B1TsQP43pT@D#T
zpGVrhl%GmpWi3hM(uxry#vsAVio+Xov3?)t)7KOC{`M}+os<DfYZGc%=*uTG0ka}g
zl=oy;5uM=}J_%DNjfNYedn=c|jK}`*`HA+wSoR_6e)l?Vnx2J87thCw%b!3Cjo9>g
zw-BD2!PnKbV)e(b;a4v%Cy(+W6t9~ouXel@l7Z<dj5_*8k~aMBMCbVnA=Co%tyYEM
z_W3gq;pvFdJsYuQ)oS?1vG{0BE{@g;?;6+<6BkG5Bn#u^<sgR8z`GaBLLeibCJS@v
z869e`F2Ir{Z{g#$dl=0Rpdq6|>yaZEGWR}QJRud0OnX_r<YU{zIF~27D&NSyF|{)n
zvv6RY;Z=U2k?>-e-X1lUb*gi4K+C}##LvDN;|Ljb=x)X4^{cVnL_S32UCp{N!a~i$
z+bv~W8tJ&@@|p0{v|-76PXM!?RdR-9ZHD&pRvqUdzn~EJ{_P#i$%-WJ<Ll07^vJ8h
z@;7~j_g;M&IbFW+<*P=US-4Fi(*rx4NvBX6*PMgi&7eeoRozPN@Ix%H73=Rt2f$`*
zS-A`@k&Hq!ceHQo5)F4^aj-*$O}P2D3y~b^M@Pd!Y+Lyb2f!-XG?r8h0(4Us@xA<Z
zJi&_h673V8hO?_13-^^WUF2Q7zkCgq&}imRx1fmpZ@>rFmJ<znNvF{Gkd}+%;7hch
zywhFf<V9JYh~S`LrT}>1P9l{O7)clSwTd~}Whx&${D(gwJB|Se%1$k1r(R{mt`#5P
zo!4JSYfKC?f@Mwj9r3w)CWWawC<o$aT+0-aA-vy!b<5wugMayeQCx5MyIYta-GX<#
z*J9dmu2DKP`R+fAXI+9|FGn2PvlcJj_iOA`2O>mMg*7X4pnCo>%o`ieD*vMqwPX!C
zDLcja(9%%M9R3xA$`)V}4XRLwcI@Z=+L*f<vB#z{g3FpJp42ILSQXwy<O1cvzRjQE
zkw3moC~^eWeES`$I`nv$=`*pZ8JILH4sU%^$TUsSv(CYbxC)f>LYhDgb-9&HAEe`!
z$DN^Rn8-D2s4d1zufCytXa5n+v;vgoAbQ62$R0loy6!qGd*jcP+Z*kdP(FQt*1s>o
zRbyi@b?()K=|6+IR?1AGWCnCN_#-~jlX@eaxC~^N=|K2b9#WV&AS?>CMO*OF^WW1b
z+|$4L&pt$X+jIEY^%IbgkcKqXN9f?3iBGVjxg1Mh`j`czdyswUMcfP2nGSBn>ci{d
zRxp9QHW{9LN4pE1w;^3cc<;?(?Eii<YpV1=-a2f?ir+0lD1&~plO6GCtCdI|7i|0V
zJ*+roxHE406&A#W)0uD#D?WS=d;IBeuPi|1%o~uE#@8D%2Ythb|A(jkZzY1FB2Y#7
z_-VBn%N~&SQ!)3t33&4D4L}TaHco+teBD&H6TiLlVdPj{X=}9M^CM<_@N^hcLBo)q
z5`q=0jv$t=3f{ptR_$J0hV6Tg_Zz^+OPAu0uP)^th``WMGw{xHJJ5Ld90d6JAahI<
zR<7-UPnbJWvuDH2(uCs5IyBPJI{Z4mK9R2%Zf~r{w+9UvH9H#N0nX^+o26E~`!qh=
zWkO0)1j@hpniWx4zbi3?ywwv?E+QvHhz{@|KNOx)K}5+zmrX}3>sOQ>+KN{mxDVgf
zn&C@(rJ44M8Si0Z_7vzT=e*I5_^YN5!2Za>71KQNA|2O~rw`gOJ9q8CcYD?#^f=Q}
znIhXzdJr#sz7DET73MCSgkZh~)Hl-+EtHobE*!@NApr(B=Ks>mq=@nW4x8a9@R!{E
z-_cOH2@|r$B9aiPYUU1qN<;1!^k$vflx@ijWa02;!W_giXkl?57vESco-+#Xf3+RQ
zq<1c4N{qgBv)~8A77z&F&?|04R+PWu<rjnM^rwiz$6TQ9W*F!V63C$R&na4queRi4
zBB8-krj4bNRY4E^2EG^lYPQik0)9vyrIFsx4ZDkZmmbsSU7|qNBm!ZefgR#3&=dyY
zm?P*CL?xr82@%1wkitAcOG_2@Ze4-BggXt%@<#`acOQDa<h4^dJ9ok(kOjWjst5sD
z#NG6==N@K34uQPzKSvg7@DB+`@~De&FaLh+{dcjmx(9c~hrq*4hbAfnpRXuF9CIP{
z<SMC2YV7!M6Ur7$#E>8_#0(h?e4S5-XsA*Vy}j%pQ2;v;NT{b5=gcoN7W-GOMKKM%
zummqc{zn2IyvQE$=v8K<5XJNdevd2WOoS6*s2{)g0{-^ib{ay2p)ji2#xae$@@8a2
z$UDt#QRp+y0gH%8;il^22k_>{`xQlAuJ?@0z-@ngokE<On2{0jyazGI*J5a79_lJA
z+sD`AF~`TkJTZy2$g5&>6h>vG2NdHxo>g@n>AAJgkYdaUEp?@+Q3W9-mQZ0L4{{k<
zm1t51(FRhCFR=O$a(5p@-Q+C7<w6jZJ_6r<RseO9C@@3;IJ6=6&_2#xIh9N+i?8IO
zlggQvx#1dCGp)+a#pp}!WIozNcrZ$`n`_7^G#ROhV{rX-9Vor=m#Fm4fOq>rw4^P<
zop;`e_Cmh1diicT6&R^GRcTg;ed-Tcf21M}dUY={p_SEHyE_>fVeXVOw~0R3h3kfw
z3E3D)R1W3ibn4sANqe@-&ib==*`N75ZzeNW^UVV3S%Ik7$5;@HmDrl>->}fxGawv!
zJHEu~t!cP)Vg}~ZNi~i|ZFFiAhTr}WoR1#Ih9ygos|iJNuoI1RI?t3fcjl+}`&J<j
ztwU=ItE+N<s~r)Nkd4fsEzCVGP%iah;X^+bmuV%eN)Zs^cCwc7dNHx|@QoOX?2HY}
z8@65V<{wVz8pSZ*m#k*-Jmuh?k^*tE&X4TdLi%|kJuQU$*O7UN#|cAkLr4${?S%yZ
z-<L5hx%a+)oju1NedsN#x6hybZ|Xx{g;y|zsG;;24)56qpSUzSGqngV*@pwiiZHF6
z(O!-{B`}5tI+ZH>z4p)kEuT1T|5^|L;#F^9y^q72*FY7OK^Q$#PPq0ea*JWOXega8
z%m-#J@)kxZQVG=z)f=(*kegCbS;nAq@`TRA3yFlo%O01=u%H3XIzIcul}{TFm$mId
zw2_(-w3dcKn%lrdvnDbJFAH5nYQ6ZoC$Vf#1w4}G;t#*MlW8wpLyUAyACZL^7L@F)
zW$tUYBd)yWMn*V<a}YbgoaoNFMy8A~`rsdr`B8MT6uaZrIOaamnc7&e3txWu6}*WI
z31wZBZmt#oQ06ci8n9(+rQ)bKIZLA0ljpO7{lUZKxc1@aFeNh@U9|=H`tuL4sV*1^
zj`eUKG9UAp0;QKof~==AtZ^BuMBxn$C0M;`6RfUMqlHsP+I4jjqD=l~WduUfY=tlD
zT->4Wl(er<Mgbcbsn5hCSI&T+ss+oxS%!_>EG&d0a`B1z)vH`+Fcbdl9*P@S10YC>
zf>HsJ{>Vs9Udh|op&s&Lt)&%vwr$11;wA+WE&EzahhR^89?A%(e{Lbu)0omC5zW_M
zTSgQLi>2k2QWi^#WKA4XV~r8>?!E@Y6Z~;>^QZXxH;uS^ioh`Ga9|E?I``KODwTn}
zLqQrb0%|ZAICeT)2#uGwRH*2%aB}m2wW*Ep={6+HCPE^Pk#zDSaiG~rdU726jo(W|
zvypkrD_8B{JI(3jRo0+}MQ4FShmYctyZ?Y$BNEZmbR28G{t(|B@u1?%dGA(8#~-50
zs8}aM!|U@f_Ud2a((zf$ORmCqt3Sb#off1dGiu6pN1p^8WE?YT)ZNvM4yL-9)ec~t
zMfQ&?#o3LKZPxARHbCPYiJN9$p+LgzbD%t|_lL4iI0q`~En22ueE#Y>rHZuZtC7rc
zZ=xLk`(Gc!$8-jx)djcy<`-BzYZN1XQ*r;i)%g47Zlw4V$;G~l-`PyKsL)i<A8s_z
zr4Xu$#nYU*kGtE=H2$UY%2WGx<1D{({n1HXNW|7+Les+n2q`|kgX{KFs6&&G=0%V$
zQ3Za%;jHb^!ravcqEAS3$7I&3@K*v3MR_|Ju;6P0)2TD`_@IIimOLUSvqvT%I68{O
zbNMy`_Ek9;*5*v3m`J!bqAScyE3pv0C5J7Ql5`?Jltb2?=%yVFb;#XBCtGML{E2#T
zrEZdQcq@)oPsa!%KEjAJ1o0mh>TqXai#%^TbK(l3mRwoHDl4Q1`wnnz(twghuGKR?
z+MhDQpN=Y;GO|b1be<e0eXghTH{9PD9i<E+sRFV1`kR=SE;<Y`c%)4$qs8487W55b
zjy?4nmj$<BNQgV3?i@Q8b>F_p>?k``^eE~-rG@*kdPA-fShA<p@RHtRpmS-z{>-03
zOJjQ_*6%M-E|@xZAq_?HcG9hrD3KsnI#RhP9eAA!duR8Vsi^0<L=tj;yp1VmoU?90
z0|^JGY+xFo$S|3+9xgjQ^}5~P=PCSs*rbb)!a$Rr=d2cPLLw)Ho{prAs}~V0O{AS;
z59?wi(O*J35TVspasa+l@5F6a&x4=3oyPeac=@Z{NX^Q?;eAXEi3x!pkqPZpyRl*0
zF8I=!<->Qv24ux!|C;Y`{F>>AOlCSl=5Y3I0~Zy8G8~_vDvll8MdQ2&DTMJms2M0b
zT#CkqW~f=<&X4*v;eWIbRH&!3>UbF|R)i*E{<z_M;}ZE6*KKn>t6osw5htp%lPfpk
zE|idmB?kv0ENvL*No7=eGVZwl76cKB|I$m(b58@8Bv5yy^P;Z$2uiugLQGwxGkJ@n
z7luy0oCf;aM6U>cXOX_BcsMeRg=D5vq3?=OPM`CKgrBUGA8?w0%-A5xTG}7GR?|3l
zpuLi!q%-;Yvc42kc8eJ(a`q2UoG3$LgU@S!3?+S{hs^?>Im$jrzql``FUrwEx=EUd
zrPH=15<@6cWXTGzBs~=miE`!O4?6|OZR9xL#+LK^NAD7tD^YNyfW<pz!^KdGBU`_d
z1}HR@j{*KTDO{!9o=!Cq1Jqc*=P+N#lEJ*rA-L(T08GEMmN|h<toWYCNYOIvu60KW
zbApdlRN$H^bFpA}6m1!9gg(OhWCui8Grw5Vi6fPjWE8fDt%ll8&gZL8$^6V=(ZML)
zz7jd#HpBaBat-@ICMkJqwbh-t?4}1XW8x6z0QxDFN^Oc784aUr0`cWOR!C!{Bfvd8
zh~7bh;Fv2?f9xPPE(@wrKvLi0Kf)uq68%GhXpd5X;B6vUM41uD-3Ryo92;rG*^hAx
z@4$VxU5jDa7ZFBZf}ICHVij&TGA$NYVNtjm7T*gb9M41rkT`xq|Bj-jsT0-^=EOPL
zHiU;WtDFnNpaY<d@I7lgJ#AVl5M7*h%OO-XQFw#JOIYEOU+{O%Bln3I=gwYnxN5E&
znGT&Wb<N1%yd5s#Q8ZF)W3DVO!%c)a%^w=UD1{A{b~>BjGbvb{9H`80cf^|?QSlch
zNQKGK!xu4beE%&UD29}Yo3@aJfe!E9g16sW!V1%T7RM?~UD?HC){+-#V4fM{6O4LE
zj-d3I=NVaa5*taM3~%PXs>tL!nUBqvl)}=@{1+>|>(p49yXdKGWwm?ezDgk<CIl)A
zSMs{zgS+s-v%f>T%%>cBPsBvVF;A6-9hJi-_e4ycF`ZR!yRdr6`}oCQmk`G~6|emE
zVGK{t#;s4^j{6^d301uHmRtA^->G9!*j)f?-B++O!l9Ll&i1yI2b}4Lor3)Ay;!1(
zGA9C~G;kde#i*<sCo1z^;gS5pc&3K<-Kh_=w|$1o&vD9jwRC_GXy!SRtt$q{|6}hw
z;Ippo{r}0{1hNT%1TxusPf--yTU)i(>h13BxNYz4ZEKg^R{eL_Yg=1;t5s{ARh-CF
z_L31GKo}tjAp<hW{=dKH_dLlH2m)5Uw&n05&-46#=XZYRe9t-G^WEQGTA>c}h08%&
zM2-I6&%UR+@{bajaJUBILm!Rl*R+eS(M0C?yr~oPG@7MHUpl0r1^D3EDRf4~n7v3+
zY84{2-l^e(W@5fPU*G@LaFth9lYR!%u<dK~*XI^0B%Ua+QJ4g>H&TeByL{btjm;Yb
zp*u%k`);;w#SHgob*1*~+^FZ#pj9YKgZlaa0qER50JasF(+3APBS=6{W9!An=&xqW
z&xb+JsQdm(zUTcvPx)z6=%&&Buus?zRaM7`;NGg30`vWC!4!>vVYtv(RtC#((Fdme
zOiN%lSY^(PzLCIrhu1U_@C_yrrl}VH-(V9iS!KG>iH$c~(fMJva27y0b*(hBc4<XW
z+0v2<4l&@+YXqw9J%KNCcLM$d-U9^=0*9Gj-f=Vu1Qs9&&v2ziHt2=dUUAI%@G&E`
zZPRo5>+_@^%efk&C`7RY?h5BD2<bamSKs_e*LXKmY-fSF;~3M8WyNrarUOZsarQ-I
zLqyp_2O&xf=W)83X*c7v>?XmX1U}7x!8J*&ub5WV=#96w6zQ^0m|yKwMYfdb$;Zi$
z{d^Gu!2*Oj8+F+&b9L2a({X=*P=!#l^>kvfEpBONs!G>u<?kL>d3Lm7@O3O*`n2v^
z_O$Bo%j};wPUp>;s|zojp&LK>S<J}8^|N364Gl_zKK9M86F4pb*Mi5<(k({lIl!GO
z%^WzkV$6Je2ks;tyXrOMl3BX&DgyH)drVNETS|Ao>?wrWI-<6t1Ok0?hZ2ToF$bjn
zX{%6S=~&(M%iA?`OgciF75dfPKi1xaYz;TEfHxYv?SC6)j^WuC>AD5e)p@v3k3V$3
zRX`&T-i1kQLddWeyc*Bh)e3b#Xh@D;{`C*EkoDcz8KE(gW^4ZZ^9d$5U!UoptkCcO
z7~*uNuK4mLx_0JJ(p&iivnRpS&Km^W5PQYAr9AqlXB3r!k2^$R6w=KtTM9MnrvKE(
zud_Vy)q3HXKkME{R}l0MGg$MZ_f3+`OC)P$&sOq}FZh(c_>lz~0N!8n?34P|um7g}
zVZ&JC;9crl)ai&J**d!6RsCecYnTV49m^V}*|W~md<08Zf9#V==ohR1`2GVLini*a
zD?g;6*tZ@>z}VXyI3@;5zmU#y9l_$kQau{O)Jo=(#gE^wzpOlh<}z0u@kRR0_aAj}
za3&2-(okYr=-HnWFm0GKyH4tC)Z=%5T@QC49EeQBWn{J%Tzr8h%)dsd{S$TfC%&xx
z{j*^tOmk@S&Tx#TfgbTTG=e4euy@DZwM|c@48_J1go{f@$_`;cNkBmY>>WoqVS<D<
zXn|maN*U>~2m$shWZ(>Zu!*0I>s=$kwiJW{!_H)^bztQ%*{voEMY~d>A_+7~JMN84
zEU>kAyDS^Vhjae<{f<2tVSSfMh7hX*&ali94eXbL%j&UU=|mWZBaP7rT(58B><x)i
z0<JI^6LT5_ES&BEa;Lj=U8brD8!e@jowY`@S+SH^$B%+n`mzq(dL-y26rRNjiUqHP
z0juET0^RhnTx$FFKaYBN)LaF?(*#`193Q12=U<^~#-usdKK~kYFX{T@qoXjNU$Z0_
zi;UN>a2Zbg4&K+J1dOZ4_3j@gc(xRh^u)c&rC;^UzgH{BS;igOcT87)^h=t5-gtyD
z1gB*0dac`YFv(8n<%3e!YxzR99Kx76ri%sc=oV|&|BCqJegxR-LonOjnnnQIt1p?Y
z9Q=^uNhb_hY&H3hdu1fiT42&$@$E}9@9)G?r=@(e_71vS|9!)i8WPv0RWCfIZ$0=T
zX5R$w1oz;aiV^|W5NO}J`lG4`2X{dKw;@z*>j3T@g(`>kwFvYc?dx9Y{&X^zM=C7d
zysf=fdIDa6=Cyvg(b};FAJo~_q{FRg`qY1ZLDL92odS&)6#*i217&X`T<qWs;TsP~
zn~RQMjWqzxxvdAbY4%8ECd4YDGfVjc26AO18E+EcJK9yU4c>vWrD&VB>^Pu|NjaK(
z$u0Wp=((yQMoLB5L2X?7x}JJ@hXx~vU<Z4Bb5HRHc!fG;A3Zi41>M}k1&{gbzy&E{
z^Z??gM6ge*Ft=ZUwa7p<qx>{-EXc>N^i54?Ujl<*gui5@Iu-vB>El1z(Z+|MU<4D`
zH1c}ZzK^f*XxZ*H!4iV3>?GMsloAGINo&XlfxGiL4Z43KyU5F~9oBuP$2af3t-UOu
z{_@5hnl`dP$vUJZ%XYe&{tejmm|M->FW>&^x@BMdx0ycnb2A5ezP6(U_-#nLuDg1^
z@-xZtIWz@xr(jK-LU4%LGxYqUf6(t2?N-KsbnV42ZAnA1W7<F&*2)RRHk2{Z4XO_7
zrzztGdBi2eoa#2{jWs)w#?@=XwnCTQY$Ax<KL!g#RZ+S}3n0Kgej|}&(K?hD@6@`D
z1P<X~H6Q7u0ePA<0h2O&D}OqVwUGvn6cP^ku_<lPj!3l{Hd^n~h`mrYxc#y!?hczN
zDKiwUe1Q_^2jNPz;)y?M(&U*SZ<9cKo0?s3IFHi^h7=~+4?|;co0^JY#O<)Kad-q}
zC=qcDJ(y23f;o2(+v8qeq-vs)c7g=$u<;i=7VQj*BO-BaagjEb#bL%h7SrypYx^g@
ztr~(q#DhdqQv2hxvPO$*_kgTyGkPui+wnfFFDCJX9^Sp1OBmJBxDgx`+g9kQ7hY8@
za}xn(Zbd`917XRQL+zL!o2ec|nCq6W$JT!?)RL}(ZvlJQyWi1fhXScIfQ3T}rVWKi
z!9Lb?B^=Z1QqnCo;4>J6Pmc@gW}Jp^YTD84WD+<p!VXSK8-auxA=)}w3!O0dNRfD^
zLHl-95RLR{g%Y4Dt9X&_|K%Wk<Le(KLhXn3i^v46FOAUU7fw(|`Bp7_ZIO<}XDA&6
z-|f5Vnt#WC-2BsNTcZ{U-z-o3koSMS<Hp^C{#DgmE8ZJU{T@~ih+X(@=SQ~cclUl<
zzk_VK<G;VI%cc)g;T!AJf6`oy8aG?FMq#2`bGH`nYs1Hi^g8~-xVP5^Tr+dcfw@EU
z@V&oQB<5=uo;O+jQ%UJFAd3S&NK<DJJY~jIJ$T<e+TNOo=O@025VQ+_{W~ll8g$7;
zq<AAx%ut>&$@Apt(=pearbiz5l@=3ibdX0SbivdtG?2r2`_;c#0{Zigz6OFAU>ZCF
zFP+?`;WDde0j*k9O=b8x2movX`UGirNiCXgf_TM4R6tC+($(OJv*q3By6t<J{WguC
z70;v4`sIJt|7n$6mF~|TKf7<*x2|{e_+jrcEaNNo#s2#qwF5g2Ce#?@+LB%D=>Y_L
zB6u1EQ4_e)tQo>s`~B?^v@i{D1-2I=2|!~+i9NG^TM+@7NQp<oUGL$WY12C(M)ubr
zj17uLQ%j0DDs-Cb0L#_~W!Ev+M<(8{(6ftnK1AW#vgNJqy6Bd#>QgsgNLr_3TKMNb
z=|{hR(J^{Wjim623sS}IEn2>`8G^=$BOvJqjceWeW9?Z!`q~;S6w)~BtOtgCLDoi4
z#m2>{Zto)9v+M;OY5S!<b1gA6W}l~ZM8bV8<s-Ua>L^?u%CvUPE`sb`MR21~Rb^D;
zeuISzjfaFq>$-~;sHp?r_@jrkZsk%Prf(+K?5#iEL}0}gR7Z-wY9)|j$ONo-E2?mB
zoW6d~XLaG!Trym2)B`{Gp_Y~r{X7fi(FxdCcW4QX7;8spbKw;qR(79ez54KzdS;t%
z(7e>vSUZ{@;u|X;nD|eg4zmV=c($y9tDpF-UV3i7?tkbDn$CH1@uf2zgMMK91}$wb
z<24q=pnG)&fYi;AOZA9LP#;XJJLp>^fyD~n+KL6#XY|F}FGB;^qD4<Uu5bSQ3FYI*
z>;hH#0AkEvL{yY2wr*AWjGOh1&t9vX)G)1l@h|$;_kKrU)S-4b(EEU5KVWRlN-rAs
zwC1u6`qhtL(SfF)=&Lthpovqb>!!&M>(2*Ew0_l6l_C#!L~t)_!aoNg_~UQgQ>_W3
z0V{87X{yl1Emi722t3EJ6%v!Lh?eEri*RntU{XOv$GfxMSPE;_`Ej$jgF_+^XE*4n
z|NXh1+ET4Y@47<;`D1m}9rFozwTj&ANzQW7)_Ze(4bkBdNZN77{z$gS+BD|IYM(GH
zB4}GwU&|6=byzE`Q^(*Np+QCezG-b^kCU=2Bs$vRS!1w+oQ0CX&a(oG;+<u6O$Y*6
zkSGw$Dyj=4wB_DQv!^N+Q;4p1RWutZ)8_iIOOW9M;@r3lMFt(-=h_Tx*c$MmOl!))
z&c2*jd>!*G6L;6X`+IET25A8wqPX|5^lu2>4njW_?Pszg-5hqm*26_@xGElQBlzH^
zRRq7~^OPGCyx{1l4i#-LhW<i;X)rt6LLb|X*E?Z$2WMY<Tc{G#k`-l#i6JA3;iHOu
zJ2mN+Z|W1^x0Jr&xU3ZsM0uYYIB)FyO-Rep<VnMOeS7y9Jy@Pt$ubQDA;i+0TlE?9
zeMY!+tn7da*T1F8dZS<4h%c1_p7Wy8`?^IltYw(q;FcPKCyv#RzJIgE=f-RI>KFCH
zZ~s>P5#om71K&Zs24OYi<=5sbw5HJo7IVexk?D^Kj#fuqk3;mj7|?MO@-_KtdpCWP
z&kr}>on-zvmY~mk`ZKy|!33;bnuy1;UF#2!9uWM7tcomA<5a-dTxJTM+rELc(gf3s
zgB~0Y-I+fIq2A#`fIVCzMhwP+^Flm2?EtSP3j!Hx(grD|qe8#A_kOkAex0U_!JTDr
zE~z|C;60fjbTc)5;@@=7UB6Q>78}9LiyH!C?F2tnl$U__?K^>nvEGd%eW-tqO-a^P
z*P)CI_BZf>oCnFoAzU#<n}x3SU}c{+-{Hg_Q&6lsUmg8<690kqOw)dk{{SDDTcihT
zJ}3d8^)3Ybb;35z?+|l=?If3-?W1$03j!WD()IIHUwmlD$H;~6TdVuNyAo+D=C?)_
zqJJ{trbjVC*TEXJ$s+NI$AG^>sYYvdBSU_e?_nO%bvM5Fn6|8Wos?~9%E&Iz1WZuI
zjw(?9>@m9Hx~sH)!*_L{BUx*oen2lBtHWf@%)(E2w{?UK8HuE~y8Mu)-|-Dio5;2V
zd$Y|Iio|N!Bll_bQxGd0HR)9E3YKCEgbinTOc%_ZsALG>wJ-fi-~9S7R6)Kj)6$sm
z{JM{SSK}uQb3-<;1fi8ZiZ6);GqVvIp%r9^5A1KfGlI!ZJ~NT`uR(X+THszQQNfRg
z5h%w@p$voc$wV00;5K+1sn&8CWit{)lTrgvHDC^e6_+b^Kt@NU5%nFW)HWDqWL8?y
z+FE_{7dLcn42dUwcDM2CGrvJLysy4prQ0yj_}(7bt;g^Cq@IYrNsoN>Bg!2!TmSx_
zAJ&(C{D|U+W{lOg-uTO}^aAGot!SWoodeplv9{r(*gpv_J)*yZi(PCxUZ*ury`*h6
z9uGnwpF9m8fg=Ex0X?_4wPSr}Ou)qCO1`1n!xU~{7Nz!jwB;sn;0j~MQLn3CEx<Pe
z(>}|wYyb|0xNz!ddz$)YzPD|QC%JS@@+Ui=KzJOw?U*cPC$;MFd%vUSK_u->DayjJ
zHXN6Mx0d`~52WD(_%Zxz&bvnAXv~6@y!hPHdUWwl<rx8pR_@&@bd}=Gqqk;(gu|Mq
z>9^I*cpN<Jn-Ob%62g1Zp7s}mp%e2M04RpI?U`PStK!u=v88}pJnn0i`4~50oHnf6
zppq)0?Gr_L=b!J@*H-M;LqyshnKMQch?nrfs%O=iz&^71I+H?QEdXD^pcXxN&)r(G
za3E%9=>$<6gRlNr4I}m5@Cmc^;g25FzkTgDN*s>=K2D*-^278bsd`o}dX}sQY0T{?
z>J!y}M4oa+Pty%Im+P6&;^vWOI+?%{Fj@zifZu`D&iFge+xB#@W-LCzMA!at2jq#f
z;LW40+Xw=ECD0G7%rT$FJq9h#z|nmaK_22x1X7_Kc2>N(w8PTPuzQ`GW;75s4EulL
zNc_LuFs+f(6<<Yb*PeAmDxbFg{B2ni?%3cwaQFGq0UkDUN!xfq&PJGLT?5t#O97aK
zASjDMh@y@v7lFGoB!To;44OmSnNU-;y)A+?Q$P-(L~X?p0zLr`;#d6j&hKemr|Ccp
zS9F&!teZ$|t4z#u%}t`==rPt3_koeg;Ea%SFyfMR$B1mlP|_c}9vKs<Z7_N>uKt`p
zb^Db{jcV5Gk3X#M{qk|;jU0hy(1<=F3PZpaFMmdNKVL=sI}^qRLZiDsHr9?d^K*_N
zFet79%$?OWMlZz9Prip^$&_}qn2ZQ}RogOIed1yjM{vk=tWx4&;A3INKlH_Kd&|k1
z<}nkxS_;8FX`}SjA5PNolGXab5B^I(yK|XRl5poh^JcC0S_}rb2qa^2o0N`Vq@z*W
zcUI|3Ke|_!o;OByC0q6IPk*3iw{}2S5}N`cW*DZ@k?2H^)>Wtm&1F*hNX^L%Q}Mc&
z^t=1fIAb=sb8~}Yk<=n0;f(R`bu>K?h!&*QrBGuIj5y93Ka5mvMZ_AYRBJ)9`lckh
zu5Nzu5iNVHQcbO=IP<Ax=%=kE)WIe`JJsd*En&`voqwCY@agMGcNRoyt-t6eKl;6f
zj2MBxws+Rp3+!wRhdDon=Bi`F6{PXImEcKn+62zH?`!ud4q<_rGn>ZN)#s1XRIzy7
znuQ^>ISY+R(~yh~9ooEG$F7^DRG8F+A!xQATd%*CzJcW$jNB=fgx78e5%A-67$Upi
z60*l2JT@&^t66}c2xw=q+Wgs>UXP8d2~i?oe)G9{9IL@7_Kqn6%%s{%2iL}q%h$%Y
z$n8uN?fkSP%&QS`bsl5gB)d9Bh^OF_J7z+xZ3k;frF)7AlEqqddzEV@X-AOWh!)Sz
zXSZ+Yh|x6u@z}78rU*rf&_O`mb~0wP8c|_*$l-WzKNv1(1l~FLBuAOqIAgTndBzx>
zX)(ITHx=&n1|`d$&~A%>R~laE8^u`<BX2cL5#jo0V4;d3rVf0+iMQtVcaUjD`RARS
zz4yZKfEDXM`VwI4U2sYcaI?@%){{A=1Kiwrc&8rt=1(wJHe6)Ng6<LyEIUcRmzJ2I
z;79M%^JF1ubMxwZL3dU6%`sI?p_sPEyA+5W%rBZ3hG7Ho{&qdWCF`I4vo-BCzzTwK
ziy=PCu+bA7z757?zVhHcr%euaiV1^T3&H+|=+g5i5gapA8(#Ud?)<O&R0{2DRsqOO
zb;U>T)P%|G9UAVvx85szyJzs-v%gKB`}9<0XQJYJYmt8P@BgXg4YXzg3M+Xx>4j<I
zaqU4gWoyUY!kHfij@iDaOrQJVcQk8!E-{c+>$g9;O9yc48itm<2`xyJ`O6STrG>FN
z+Ltw8tZUaA<u$fulKT*lnfB4Z*c-cwjp^i3b&tfItKh3KBgSgh^wA0@kms`x{Yu~a
z?caDV2)u*-4>yN9?|F#q0jV_Qt<a+c1T3w;8jXJ+jh;1L1sSnAvU`~hgbvq~oS~Yy
z;5e2k1iGu*sclDUIM@0jbYnf$ZP#62`<8~GeNV~A(#TO0pj(JQKV+b0U2us?4;1NV
ze<I%0P|_yiVq!wHkhmmG95%`c*9-;J9VsR6IJS>I4Zp!Ov=5ed`w4n&T7LFjC#et>
z(U*1M?GZbNjF$*yFB|L~OigKKpg4*D;8^wXA7)(vR<}z0KeJ*>)Z$2-D<?z1AbAGG
zTeeow<|iDnY6}t7Taf->ozQzhlV-$Wa1bf8j49&ai{yXDQH;BXrh=3q6gycI4w+(B
zgLb5*rWVOjs$!zh1eBFvLUlmTKJt5wy79}T)43i(yg*|IW@ydUDh*9ZA`JpIiQQd0
zsUIPjF-O9nhnxD?Ul@SBE0k!YHAg{Mm?L$ud%kL&5QU1GPP(cXm)~;BEB~vCwBZ_;
zW&}oy)@`m(%AgDr`T`Gw2*g`tyc$Z%@d;^DKN#wip+p9LW0fMu<#4FAxI?R>wSj1u
z$JCEB34XNTJq@x4rr{j$q4@CxnXeV;b=uio#$K6JE1JGlV}8;MLW$^;2+_uZ#@s&=
zM!mhc0&VX|0#}iWhD~HCWvm<{C`}UwUqoDjVj)_LfQ>X0a8ht7bPR!8kZyy%TAKLH
zH_H=i!?B3bO-+?rxuyXE^8r0RI$NK(ffQu(uG8msY}YTII|xD`A0^spqjgVPcyBnt
zd4wOmzXYS*Paz(_F*8d?(BmRkLnJge*})Syt=-SK>y7~lxMmF+QXxXBTHhk2i%C)A
z71hU(V-@~8g9v=pp$a0C=0v6;lpydC+VN&Hg+fbUZwf~+Qdrba|NgbRG-61aBO*(7
zZO{{UKcL3E0_TU$f`PF(&f(Pr3{692sd)(6F(O}!9{U+7Fe-KRMHAE)Gqjy+m+HQU
zp2Y%$V0sYkeiqq3_Ot&CSigY}+upI`hrMP<(Fh%5FC*w*`0-NK@KCm0EJQY|)YT1y
zo2MBL2LT5&KSrUnm=h)fh)CO;K%oud)kB;3A95`cLfq^T++pYIM<{!&UmWWSpZOM;
z0@H%DL!|6JR85e$Tw?ViYhuj>L(TNfjZ){5J){$<(kOfaO?nGva68CG64*Om^Syjf
z6CqhmZ4*p3e&G;(hl+OS;Epx=<F9J<@gF>>+vXEzV9*#%Vax{+KE)9nsk*8`8M*lG
zLX;dX-lOup+x6&gf39mu+xWR_W+`dlC@sL0eOo(L3D&9J%9(Yrw<o)<Ir%*lz6s52
z&qYBz62sTIT!goQ8;K4Zh4o4Z^I_(YMl5=}0TL5JJcWR1sd-`A3uBjzy0M%9Iys54
zij5_;QtM%Ggvb0?G-P|)n9^9<uYrJ%GEq70MkOR-+U*sC`-$aDhv|fltR2(n+4olV
zhPj4Bv;NGb$0BJvUt1dl-@WsI{`6t3RN-F?<I+Gx+^FGcn0x|s){eRVRMs4)-*Kd#
z8$^FM>EOOys_7h|36mzN;5W}|!<r4eu)pTao}#{_9&6)W&6vtILp}6MPglmQbgkP$
zy1Fcjae!-+5n`;9nkpDRvVugJKTO9C+yY3c25b@uoEg_QM7#0J%}<VkXz>{2CR~py
z8-j^Z7sLF7Xa|hm<cmM9fB)2nG%&V9E1v#eee0)x#BGAe<lqM0<Q-?gA|!(JP$0+N
z;M)`425p{0b9AJfJ<FMvoItd2$Do_vFL~%mCppZtN3Jy(2m*&yl<dLG@eLh{gb`w$
z`vn}WXx93rXDKHujo?Cu$dy~FmKqpE0#>FBf-%E#CBEP3X4vlYHe=3r?I#7!{krPH
zDQYg=p-1n#Q~&$sF><w&-Vj`Cew)V7^+%9hh=BS?)o~5Th*nhzaZ(7@*O)g(DaW@<
z120obzeG|b9Vea#f>2x+f-rk7K3M4lY;l;-8lPuOhuUhHcQk3>3=e+ZF0gDTi4lgg
zPglJ0nWa_<nN>2Te@I{b(npn(9I16LK1%F@UnyqzDCfiJb_wt^Mk^HG)|y=~=UEr&
zt~)=iF<A-Zf`3l;e(49Q%^#+ISVNfN(oEB>z6HswK5~dP73cP)`OsQ1E#x6A5MuKE
zb7r{BMuO!VRtW<L{&%_(f~GMT27T9IOu$)}30On)#f|QG2&S~`FLs+_lG78T5O(4E
zMPRr>uI2)}VqyoRWu&nWjTxo@(~jnHzMy4iFLc8cI58GmYWvG8bor;hsd>ZGnRzs&
z&2@V5{vYeX^`#mNQ)mJRv)ohXUZPA_BM6WZGBb#$6Q{C$%}UK=-ylFU6Zzxp#lwvu
zC=9S)I*4Wsp-oUiw4&qeOkNB_Or{?Cx|1HI#3d^p_k*?yEM(YsPKf9FY=7Rq7z1j>
z`fvWeu`w|kG%rpsY{o~IbgK<8eT1OpdN_0umXy##=31Bljbx0g1q6%3<)gI`D+6fM
zhBD|ug1SbSpv4#luX*<ly320@xUxv?vWuq9T8P8nC6qJ9OvQ~kG58!ORTaDve#V28
zIVu`DGmCuN_;q8BWAN`l(_0NL5;!gj%aM){(i6hioxXVM+GAR6>a0Ju=Dc;s%lU3+
zwOLwPFYUkgj0O0sEGp8(>p!RKXO2W<fRGLgi&anluYUf!CrB@7mLZ%u?MJ{t1m(m0
zx*3{g>#Gl`G-as9r5oMYs`VT9DK%?g*SXCLPFVuM=PJ1i(A~kg$|8+!D5=ki-dL#R
zM<X?AG(j?<?aDT;SN1ggtJ%8_vAAap4|ZxZ@guJI-1l|md6Uoy?$vXD__-F59(ODe
z$XnQ$QJn1_ozs9e_ZZf$(7x=qI5K&dIeP?d6hUMrQpj*<U$pGF+q9ZC|164sOiA&i
z{^K00+O=D2|MrrCa>r;a?z2J7rC3qslcp>Yy33Svwg%~L;~r8^9wRMvqz2EqTJa#B
zJ=<T>>ck9<8#GXtjyghYh%g=5vR@VK`Iz)n@M|4b3!x}32{=ja^WB@*Y0dLb>;HvK
z)z2QfQ*+s?S%XHZU*{4v2jdC^LsGlDNZGeuqnoarr)22bAn1!-Z@i*kedk^^<C+sr
zibWG91>yFhEhVah#v7OttJLAMMD$sO_aEc<6&4nI26wl^6^h5jK^=Ptgnb752Xsd(
zRvM0o@|ND)Ll=PhVfywJ|26NPA%pI*p?-LqlskfhG7Kc(H>dW4uS;ULrIdrEj(N&Z
z_9&RtCzfjysd22_dgmA=CxY}LJe)lh2OYc_L6<T)v}?4%7r;pnIaW%H@Heq_I0vL-
z`pxKz-n_$^1vj%%LC!>mHF|)Sz2YCBePU|w4q+IRz8UGnk=Ulf8U&>9F!{4?)lB?C
zf@#Rwvaa;QziEHTCT%JzSLT!<nmqMF&3R?1mTcJMYn%`Kug<?{90wdGhxXHV$4o-%
zO50V|iC?(87NQB~-=OiMGC^9Mq@FoUIx=Z}QK_!1B?o8vaNTetW<HNWsQSu+kN>*{
z^y}jypzmJ2&|O`(^Pnz+G07${()<gjY1#cR5c%HT+NlZG{}&O{6G>}SqdjXEDQF;b
zba1B)#r-$)uzty;hC?&=_`Sa&E<lDZpD|RI+;j)Q)V`p_bqOj!YT%^fzCN7IharhJ
zPWc9WequxMlk-o4A1Gao`7RisUFYo2iN)pt2`>!!)?KBfhTve#8L#U`MCpkQ8-0~y
ziQYaAAN5dt`}S+ky48vuj4vNA4g-;yd3^rRQJPF7VUr^E4X)F(JXy;@5$+_15O74B
zG{(l|C<czET|16I!2;(UUiT+`?WI3@@4^hXfNM^3?hZ;{7f$=p+t}=(6R-PzLtv<#
zKw9LWZ4J&Qr}rQytKYg|t$Wi2cZ^dec~Uz};A6x?K(T!qh6%>LW9%&$^Wg9V<r5!4
z&Fi!gR}L%bLvrxrVz1%TU%0)f>*-4JWV#+8q^R1kO(i(Tm)PClRQ!j-lr`m2T@+JK
z^v<<9$mQ}n;xMqrt5!8AXwqOztB5_o;e4#F9x}m*SoVKc>!<sI)&uQ)R&J`Q_HEM6
zQiOaIN8Iyy6c+~5N}Dg4_0nQVm>>GUoFQu9P~Tp3*wuG_7ksP2kK2L(Q$Vc0C?VZJ
z_{FyO?w$m3-(~OP#Odg9^KE_@(FZ;e(S2)4E7Z)}(uWYthJCbtqc>+0ufIyUx#`Rq
z5DAzwBV3ILjL*y_W#A0G^6=~00`?~Js;<i|aCyHgNh`Lk7-k5F9z9s@OcE0iWM<Ea
z)r!UI<YqN{jwVh3cTwvk9O_MK?*lk-%!d0AzLIN@^oJ&ehaev?fDRqircM4mCk=tI
z36JXs^1@%0T+yKr1342%BdGZs={dbuef~d2<AWGYyas|1!jNuxYp>epW+*0Mh^z3#
zvNuV2My}m)=j)!QGPQGWg_40o23lm3?nlGi{q5P6;HE3dyPB`rQ|IZKrGL;mqs9Zu
zm?<+A7l~%@P#uwPgS346PF+wjO?d-H=-R8V*PB0mM4P-L$~~2Y|M!&PeO0`tM75>G
znsmwSm^I!+bmBHGf9j9=0>U6Art9#|t*WyH3`{HlXc8vX23<b`5zvKRzCsAuS`F@t
zD_4#hst;<#+HF1Sf$wLXH(GIMx$3bRt2c|3y=!!*1;py5eC#{FRCZ2=nyUBelb`t#
zHSoK~SFtaYVh>-$UY_+XA#{Nt-m{}lpZLcA(^VHuR$E!29{$N^_2AM<1k5Sg3FGV7
zOwQ;uELajVGPLFOtvYad8G%D{F_*5?Ys<<wZ|uMH;s2NbZb(pL-Clx2u|9~(9F>r%
z3$n9Rv~|6<Z!@B<yG*_A6S#H_AoyAv0YEF<4TnmD9GJT+_WBJm7{q=fu*{l$gZ};V
zw-eAST<czVRA2qZeL4oC*mrNC_F-9LOh9aE2HL_{Ql?cYqToV(>8qdB`0NC2hdKYj
z=f6u9ig+cIf;$>G{HY(6l&<~+mTW@wHG2FMqO+H4$(vhx_9Q4(3oe<U_{eaSstu~e
z<Q_=`OT>TB^{js4FWc*h5aw^ZxK-tsoUaVx3!H!XMSAK-tWk$-d1Bff%1t#v$8jAk
zGfYcbHZm>b4kA74syDT*w&$k3<P*1R5GKu?2-1$4Fo<)?j#YzeD{1{=38tIOneS3$
zHlbKSQDQ-zJf<0u07Ahb{erb($4*^TPM&A{-7hC?=_{DoZmQ`XK~&agax^FF;2xM&
z+z@x}Dc88np`5Q1H9P7rTI#2XLMLei7G`m*wW6Y3+P)uww@Ld9`&!3NQyl)Hjxo(<
z#vYxRqG1SQ2xy@7)*q&8-n^Nl(v2m`_YNH>0>{S3sCxZDa8Wbb>psezHckz{UZM5A
zcYXc*ktm4p%O%TAH~KYP2eKvDXln=O1v@)F6^4lRw{PyI!3Gn>JqP%_wt2fQm^VYi
z`;X9qS@X60mrr8;95|R*O_(u`Kz<E6bf6mHA8nseF?!qJ0%if{R#%p)G8myK=bN3c
zcHL5)wIUGNxnnm%&sg%LCver~7&k~hBhlbm0I(R8B5+_X@Moj%J{&r5=LC%Q(Y{y&
z^c|QbE!w1QSRb1qVCIyYG<!-erw%ZoFV=Ri#Xp6C(`et_Zxj6UEN4bsS_T(i*IS!y
z|1gHovHI#fOu?}hsplLwfvs7hHkR+u*y}&9j}o*vw&|drdgw0Qzi_L2^#)`L{*vXJ
zgO$5VbaiDt0@qxZ-Q(4tKdsG&>~wF}sH?xCEY8KY`eV?(OC2xiH;W1EuD{K|&#wC@
z(#%>af(#q|GKpledXsixzF{fquKv^~VZ{3*FlT|VHlw(Hyu6k5cR;Psg<~e0Sv-L_
zmlbMZ@^<~(q-;%^IEKKIP1?S<41p|GaVQbPViL(n6sm12*WqMj#iuzyh*D3$Q-~-u
z+HJ&FH%Cr-#vB`V35||XLZT@!5gM_!4#)TL!0ubnhJ!*-V(IvDt=n;v^2kgudi?pi
zYV7lR+P^pLb-ZuZyx9aOt|L#ps9QqDkkj!W2Z&4d0`Zw9xfZ(M<$m?@hYF{y)5~N3
zDwiB)I30jaybl^+ak7b-@E*!(hdjL6(XA_ys2|20BU^b>uF!YC5~-bsY6*HXLDMIV
z!d#{SbbtiVkWFl}cI=JUw|??(1igvUS^|<CMzdl;kUAq0G#S6UA*qo%grCYvv}Nh>
zxlS9?B@8&lY}tKVu&zh^aozLPwR?utoI=)rjhYVWghKe$uU8RT!0d6E<njEXJ|Bty
z9_CO{{RV2@d?bu$B3dw?afcT#v2-g*bzAk)v#)4)_ASaCJ6E6o-aQ(zVx4NC{4J1C
z`q1f`nHQmFAN!?NuVf>!d5xKffVsc;+yAXOXi3b5Bmx3r_{gC|816%Qo$Xq=_Dvl?
z>$U%}7qxljXw4@N=Y=<YnHXrVsJN<0as7wsk_%@jC5Aks>(}bhSIRYV9C^?eF4TH_
zd9v_{o_W<B`fk(!=H!?n$j5lmh4VBhDO5X&2>r|x+mt_gq;}XDZ^Z|&HiG%I_DNP=
z+75m9p(m9!;L{qHJzlqd`ty48%Xh1xU^qc8jsxRUqG_)^gR6aW)odbsm-&6VC%u}!
z=er*CJs<F4$TA}%PR~ESNEb}Z)o9H1Zv66(l(du-7vRaP;WIcahbgp`+?yNT)MCu6
zh7O15U~Z5%(-J{n(C#!6XvP@EW+LEPH{tKPifI7t7z`{%+_X?PGAUmpK&CdHb{P5=
zJIx$L8Cd)6zuU9kNd4or`hnlQ=WQL^It7LErILMBf@vwiTt?2)SHANV0<e@QK0RO4
zXU@P4q7h3Mr?=(dZfKh+R7BDcwXR&Dt=lft$gIhld+jX*w#io=8i63JJvO}ZqIMA7
zJUlK%*WY$A79|mCB$Gl)9{$@L4kX;x>`#71qk&-rQCh3fn*H&KS2gDPiMr*Q@w)8N
zU0Snhy*g4eoj`Bcr$49PPMnAKJVr&U7i;<Y*Oihr1fl}M;7kfcQb;=ofG*Xyzi@+s
zYfG5RZ4g_Y053W@TNlk7rkJ26ZP-p^caE*G!8O`=XoP<K?c0?|MAkK{HzHkcg4k$R
z7?G{dzhE4}<l41q$7;Ra8Kco0j1E!QyaytT9{mIMr@Ysr;t4;%IT*<%F=smt?I+k{
zyZYx(((QMCO~beDg<;9kwAph=ox*tV$Mx2OH9===EkTsEhb8Nh8}Go3EmOOX9M_-`
z6SZK@I0ZLVYxS~K+OjuPIhlR5cmH-BsBT2_0ZcyhEk$N+Kw!~_ppzFVkHB&*4(7h~
z(7h~r9kRCqeQ@a-5rPlQ$m2WrXw%+9nn^IgoXMB!XWt=h<&j3LOh}7Wen6{UU#7MM
zG-u7PYDd|v8bOwYk+W~oe>bOSUuBC1kDjinBeJj>LWsl8GymbOg&VZ5{sQIYj?{vY
zk$Q3eF5PqAZx!^pTU^xHQDaldd;x*8_%)@B97B*P_8RBSqQ%QK<inS0#^tx_0W=e<
zh`wvu<p>08eK9+qHff|9_r0nQf97tD9y3&%7e20~6GtiSGEx9t@iE<>I7q9v9fT;v
zsswYtNz-R5tsMgDwr}Xzz=`_GE!ScmOd>b@+*_kF^`oEO?F2ynrC{2u!xmfP<z;G*
zPC~P08kEx%B*bCCRXT8Rlz#C2k1HvHajn}->Lp@cV2u?V7DFEADH>HU*hThU4}O1k
zeWUV63{h|zCg>n2?;U8F(3rjjffY;iakoCPN*ERvr}o@|c=K3*tJCzwcRLiqykh${
zop;-pbuoh7K9Kf%%Z@1Xf-mas%W<V}4e?eq+?|{en-~8@FBQisqx~)NCa)&P@L&zf
zour@s;=6ij**5hZWC1p3C^?)w$s1PawcUuJYmaN-r61SlZ=J5vBBFWkhH1jm%uILV
zle08o$|Ma=k5|p1ZF=>kl`vleYitKt002M$Nkl<Z*$?1f@ce0u5u#X39H{80PyYBu
zjm%6`!{NO|SWnb{{p4=oZTr`2#TfajqIG(45xJ`OSLkcMBrt11hFYpi(ENt!rXT*;
zS*bZwW*!Pg6Ipj~yIz0!k6N~8mhSx4XO)%Mss(E{s{APC+~Cz9vP<NRo`e}}E<xDH
z=S?K!XO`9B?nH`I&V@4=0sUvMhYwOv#X_yva6T3h=WG1@TlCYg6m8yj1c7RjW?Xmy
zsU-XAFt}^!6HkI8;x&58mAY|30WJmWwdY`k8tfc{#z<xkCL*97kQlBK0%I<GY!PXv
z1|YnIhH;><;C?2oq&^qyv&~@bG_+oiEl+ghfE;aq{ZTDO$d+-<1)6g4&H7E>{(5tB
zG15);4NT$KX>*ksx<_CC_P0=Cv}ox|%QR#3ATp;+)g5<#S2?S;5R5efSERX`J0XjF
z?FaS7>eVvY*w}!%>6&L=-vi$=5B~Y{x^Cwi7Hu6-8aLxo{q&pRT2oktId?8Ouji1t
zrkx<W8}#Zc#Tq(#gbFvmie<vh2<C=p{Jf9r%gRw1mPmOM&eLSfk}Uw3(KOv?5nR_1
zPTY%DZ7kle`SnvZaMT=q^`}AFwyzS+Y&h;RhqZRm>#8R<(V@i;Yw?sZ%DDOhoqxk8
z^>c8~hFz?A7}9WXQR2X%nm#^Hhqpef+rRQ_jTlzo`QdjB_-($hwk^GAacGWiyZxgG
z)h)_>hqCb}ws;G%2nD|Xy}NMLh@emV_00c1qoZVCun-SM?6+b1?ai&7-)6b!r)Uh1
zpy3ZoLTbI%#h}PTm_F%}&*_Vyud5sv!HD>DT`+&X29YJDnSI&?A>aIXtHcjA`^A6%
zPv5GC5>`gX0c|@_uIX5p7R<O(-~M6*^HGm%AWqXRSfEjZP#l@27u@Y0hjA@BI$rl(
zcAhd3wCzXBT-#@|zVwy3XsOL*hNXwqN<+<It$F!n)ssv8&}$2|j`%b~7EIRk%Rj2S
z@N3^zauk=LOoTS`TswO<Zql=lu2VsFvUZqs-u2NwPcGh7{`zhoI7FoNBlQ}anXij)
z{f1)mHmkNZSp9L`JAckJ#h`#_Vec4a*^1?11Q{TzOLk)kHqK=ZDJ65rk^`)>9m}=<
z(+e~i0h}85Yj0H<Zo?^LY}l;{SAIpG`S2v|W9{zTe~9==1ck<(bkMMgnl&<Atu=cI
zin|o$OqNq3wIMLK_nGCI+sg>PjDR%@GeiBxw|T|pI#dIbTlKfcpVydt(#+;h)*b)#
za}8O#QWbSL{sPa0!4t?(lA;wa{Et>F@1x4cU)JVXV?F-k=2P<@k1wlL$~1%pHZlHJ
zx)<AfdtB)1kMGO5j}sukp6v}_R*S9SyJZ0v!jRru`VuRi+|0l8yiYSHGrt<=vp}8f
zVFUEgbFXUDz;sPR6MXUYw`rc!R<>#N!X;`PJz4pw*vBIQg1hCj5#Nm#OijlVg?AW?
zQzHx$L^LM4k)$j*Sh!Bl{pESpH21?q&m^X&>bL8n?~m6v=MzQW!jJ%4BxHw@+%5kY
zs1EWBvpU9{V7jl4J$j7A+duAVjbQQKoH;@bNG4<Z#E=WMP%D-l)oi4BVJu`@i01#%
zKPOk~7XDceU`Bn@br<8`IzzV&o8UH9I2H$ym`RpxU#3HcOde&ggyAqLV&VYJy*!i6
z#qu?z1YuW)c~Rl|&04YO1^v$xt5kp~b$iS%{pfd(VS;s|rj8w^Yd?Zk5TYi6=<cET
zUvGTlH9h=`dlWu64?pNmf>bo=C%=1CF{D<TGi9_c{m>nnLlC1dQf5VximY(`a`IN*
zt?~g`f`oH@3ZYL1hjueB<P>ZJQN|`>hQ97eJ^WOTQvdBb2<$6#*H=sR?VtQv{V|OS
zGCvmg^lAQ3DFidQFwAajz4SO_Pw}XSR6Jir>sOGIu%Aq6TBOQ|nEnc_d`bWFP^vz8
z%jF!#b97t&IL!1(*AWwgTSl1{z4)vi{W-!0{0S|ZtQ}k4JB&F9gP8X;x)xp8R>zr)
z%kfSi#pC?%Dt7UV3v~_FywUA7_FJeCnG~~RaN}m3Sh1jGq4YB>4C37oAP3$ni(l91
z+#FpzeYj?m#%J<8)<jf@3Rk?TEmZ>ukeCgBXLW7m`}<`9$1tDDsMUi{KdpqgNX?lv
zRF~hF=d@2v$I8^W;YGc+wFHyl@w({Rn>Bf0j7ve~1auI6oqe-(?ahN}64+s0R<!yF
z$DmgfZPNO;qRG>pq50R}gnuMa`Z>_kZ}>3&c@<jw`g8irqYr9j%>W`ApAZ~~9-g{D
zAcytA9`hKm4%hezb2NPHbRq~7k+p;9+RSkWjOzLo%k}IZpCVFf4g?6+14yeInd%9X
zrYM*F*i8Kvf*3`{#44nfT<U9=>zRk2QQBan-tBwXm<S%hcZTzjp;3UHn@{`Xp2LT%
zMZ1YW?QDt#_eq9fTDoMhCZavMdhQraz3^&{nME{cv<7=&z}A<=YSt{wrda}xNJipq
zsh6T+!n9-U2DSGcr0eha6#l2|%|5ZtrQnSh{-Vc86_$)PJ%V&U2a4C~#h2fJh&~Tt
z_FN4aITf?(a2<t6Sh;w;&bwxw8=u>QhJt<guZ?wYLR0^!Y{eM<wA418?Afkj2R}h&
z($V%m^~B2vrxG-C;&7dJ`3*XcN)h<G?cef-iXM4NdlHBdkP%A%Ue&NPa>3Hat8UJM
z!N<H7-^}IDEF~D`T%|))hD0#vw^nM^+CqI~K7z+9KBO1#`h!AhgZ0&qe^@gw#*Z&K
zLOa(yr&Waw%E>R_2x%qQRkoh^#RFumXxC*I&e5cKS8MEyM%F8S#gP^vIs{E-saBT2
z2B0}?AqCK|{J#42T@SF2S~Y*pBu$%toyN_wD7I)lSW7{q2-@)4A!3@7kONc6<RsP$
zQ{E??9LAZ%rvP|<I2l|_2)iq!LVx|!Gio24iueo$J+OjRI<Z)V5Y}{iwJH&Y<&7=S
zB?Akb^_HD0#`GH#eysAK-dz5g9{Hb#5OU+s4DRX~c#?Ai6k)Fm0(TqEuz;nl<Ucom
zWY5_7be-`!$79YE*xV`l5U?)@7!DaXU3v7Sn^A%az)h*WwnE2uJ*P*ORBO!Wp<43D
zy_gp#>$*$M)0pX(C?BhbAhWh3u1n#XMfxq~w4E^9X~Bn_%RvL$zu}YSXv9Pl`h(Z$
zqA|@KFi8h@tk+9VKB5;l?m-hc#1Wn+g0HvVwq-3{l0uTu2ounZB^86a#CZ!mJQz49
zX8OB7wo!{0{sq8gC?$n>4z|qWQ#EbgrEdRsjhP3{ZLZ?2aIJssPvDh0RUWO?$YCQ0
zikIzR(=}(jv%b1i%U3PcpYOk4(K)P_E|;O+dVTUWiyU51kflHV^g&XV#c09I@np8S
zNz*`P!J(0g4(os}TCatV|6U7^<8O~HtrFAe7EGrnqPd+2F#T~#EN+9vec83~4L$eB
z@API{9}Q$rm^GNEWMORV@o<DQra>36eVoO<!*2#rE1Vg}**6F<{_xY^@LantnLACB
zF1T8wr{nU-I*E*CkDz>cYsoT#k&zvReYtJzv-<rb{dDW~7irkEONeJP6U!e=&oMc!
zuiB?qiKhSD!j;0#2KvXN!|dF1#c+h-V&=&^ky#($A^-X|O29Hcgff<+WqY-+Dp+%_
z`iQ2^YrvE~8eG__ZO}zeJp4=5XBDU~_;+Axqn`TPYsyc$Mni{;()Az3lFl{{);~+0
zTdI+l%~wBgky|IeEn~Q@U;ZGicy6&?88ko_&6%KyWD=SJXpEkzLTGd3`M>Cu(0&><
zG)uq#@vqU4gICU*u34Afs7Yk=ajD60^E8*s`XdLl+2JBA+yWP*H|kUWWp9V#(%2F^
zQ<JC7(UklI9k+;s%%ja^Qs!t~Ja~*_u&T>8>6KspS`}o(iQ)V*F}SVwFy`1?z0CL6
zVg(qEXYt#np)W9QX*K%OqtB6k6j~33+D(IosvdW~7~)^lmh9AuC6$_e`FZY)H2B+o
zb@RcT1^F0$_nyEx<DStiW@*(wFH{fx;aLquz&LaAa9w`Wzj2l|DvY%gL@>mqFD=#R
zOXeYHWDa;i5ZO~Yfoosp#4KMkhECNdM$T}(wm-H%Z4N5;ZB^N$f7YU?WR1*j*F6tD
zN*ke?JAI5UzTtMwhV~65$YEq~3;SS&9{Znrbua_V!=Nf#cphw6Yu#zfz^RUIGP=y`
zzaz__L5?8Qd+MoI)bB&{HGJ9v1ai}HCk-LXMwp7$t<nCVBu$-^@9;-`V-PWnf>nsi
zN(HW|1Nw)McYFtC>nyIQecD`Du4%c0u%4<^`93sv8JUW*%u6-(n4%|X)`Dv^os0xc
zC?Y!9_b~{B>MIW*xO`Ddo+;Gef}vO#n-!#wk~NwU7n$&IfQP_4jE(GYVi)bl_C`P-
zlQ>uvo1fLM?vK)KH(#M~dE<3c_9)UqA17`Du_1te_1<+lxQ{b4IZea+>^nXGasNFE
zOF)S0_9SbKpnV|@Xtqwgee35DIjG#42;AFUlX~upgFw(HKl_EworT*}v|}4YNvuyg
zZudM!u^Hcu5tHYrfE=@>yS8ZUT1-(0U=n19sjFqCFU`uEe1S%x!8}m(7KhR<^-U&d
zLB$S*WM8N&=S@c%PRc6+jO;^t`|NL@)c9NP5Y@GP&0=k>Kr#!16-(NWkw~5UV|s07
zII(QV*2Y?u9^6CZ?7wR1ZYeJ-6-gnC&>1j5=6l%Ia8$9`<1`iGA)>WL>y|80X$!&x
zBWBniX8L3O3MZ%av~i;pgNAkK;#CAN3Fh$Ft>}UUx?;{mWs>g2e7%ma$=1LAq;?<A
zA%gU19Ya&Kdi^GR!$=ds0cQR`2e<7|{OGy5d@d5aOj56Lt)aF?<)!<{VY~q|mLvEW
zk{f;4RE^Ke1Yrbf25c*l*P9Kv<k9DKASgjY@%J_@ts%YU>Rs9&Hcq!)I~Ps;K=om>
z9>+|5-!7ttKllj#LPL~JYBXbrOxoYsa7eokXXrz>U!>7_gViq<?<2HNmb&h>zy491
zn)+iVN8mH&vx!`dSreyfOu-Pf)g084#j82&&F|h2dk8@}nsmWU7oth;t4g$5Z@#)5
z?HKvpLCU^!%$7v2#fVI^5X>Dl1w@dh8njTW*R3ZqGtnWD@AdvXl5q}_>Y516ykM%b
z(qdH%fw6XPB{^+j7*06YOmGyAsCaMTE~QPqSXUYRF@pt1V^Um>IocXM{FfJ%mN%3f
z>J}V`HO@bKN6oQF&A_)QGaZvbPKByNd$oG`Dz##s8v<;4?_aM&J^PEcmRbL;1fv^0
zb+&R7VXiTqTf1_j`cFAuQwU_%S-Dd$zp)vM-oBc0!FfuLz;AxjN^LmUtbXmqIue6P
z|HT(*Sa!NXAZ|<d@6;=Qeo%?iZzj?*X1bf+)Y{z@_^AiL4ft8E;f`aww5NTjK6L4H
zd;&QX?Jy@_>*hC>tAw<4A$^hv_%=lu@et&|!dZ<_$BqM=0)xAy->WJu)Y7$kp-lJd
zKvjok%)g4DOr(+-J6Y4v9&LI3d9B=WKrg@Ws-keI=#Oc)`Q>>glpff=9MYgWI2;1Y
z?=a!K+kC?*2C9~Z4rLJ#t^f^CQc{wV`jPerV}d%E|Kg&pdijy3Rn5Vh#(@Q|g(+JL
zf}~LzmYa#MD7XdluP8MBjmL=Sjj8Jrf<_gRmpz++MaCG^;?FgE>O}nTg7g+JFzLPt
zZgA+((fU3*fBqDuvd4C=U82pH*vEoQ&YZ{sV7Mj(Hyu)0$Pit72^xd^feJ^{R1WjJ
z_^ICzpYj?4=%tfxWi^_h0}xiB$|9!4q!H=bx8V)#Y)dC#*CYt3zL@16Q_<#CdiGDx
z!W0&$55$LssWkt-QqnHYx%hg`7+;_`U~#na5W<nCw7qVSE<!L+k4wvHENIL;J2ric
z&YPH{R$^bQd=*U)Img1-vzC4;KCM7ArjJlt)j_RYwL;Y$u@HDR_lyO>L2yesFiO#t
zSItyLk~dFA_#LgF{?Zj{BIS(*g{v>$rNpsU=@OXXjFfnC?GmeC>nc6@r**pU<JaI;
z(x|uAu0Z>@OQDmm)AzpeX^l#1*W%|N)_?r)4}nVpE1>>LA-z)zTIT3jqOr3u!tue|
zPM+9_=Uu58lSh*xEfw6_jwb1-j#O4)PP&(%g`3nI8s%`BakODrTNLg(pgC9Etf@rG
z?Vp;+ekW!@<q?(aE7F=3YgGdQ92_1G(=#1`ZG?}J?8%&4=Zu-I*t-R7-7W~D?f{iN
zI^N3<6ZEu#98>d0Vj4MEiTGzG_9aaaxIG9>@R7r1+O>6!9{t-gG8hcSV4XRJptO@B
zu-N+>h{ByaZI(vm4%G3=eWWK{Lnb_YFV6yl*}2(VS4V&Ft0X{cG>n*2hS+=u6cg&S
z<8QcY<8p1UU>;zSLI}3GV@IXtUwgI2jm%S8LLXAw9aTwjkru!70)GCa;3D_D(<BoE
zq+r}w4H=N4zU<)y7=Q@&l37O_I=D}37eAq8+uAXW&Ojl4wiZbU{y66+o;M52A6(_!
z8Rg-tfFUqvA*w#OLu)r}(XqyGO_)1dgGtS1jE0T&#52a2k=pWoTEBiBKA9oJQNeO*
zXby1$`jJize^+B7a9OCTIIP`Rg8ccZSCu@ZK=GE|)R>LHm!HXZ)@TrdfTH3^UHy>@
z$rp|9Yf>MlnJzo9M{mCJj26AsL}d09%+wnd9F?RI&@lr^LDnxJf%6{W2z#-ywps`F
z?S{epvo=<zsvw7Xf$21aYJEBme1}e%s{%CgEJqah4Vcz0*X}9=hnUHB!@>4E`vxIk
z;f{Tpe({ZHgNJDVf#(QKr=#Q|FWHY@?i*`$1otmvoWc>BZQrp+<1V^h^QMg<iw5bW
zI4^1rmuT}ElmrV`kRodY@rg>+r@$;~a*(MW)S_43RC82}#-NeU!L4M=rqz05eK|Ry
zXJ|Bxe+&5JZ;$;=sW*OG_kQ+bwH(^4pMC)i{mr+Nf)f*T_VEGEgV%6Jd8;#vw613L
zh#-RXU0YV9K~rzge1y+w7Hg=kN`)Jj>Dh$`blFE1D9T(d-+WyM8evq>MB4aG>ljX8
zy~4d6q%6Epxkf7hEAzW=I(9_sp)ISR1I_g_6s1z(&SK4+f4wGSU6f(;Bk-vwZLr~@
zgUrPSEJ8bA7+oxnGg%8($L5Q1)dyoifki|{ax{#;#d_j8|6M=ntKovCW2IWQWQ|%-
ziWoj_CgoWAnAsYeGk};tHjnMvi(m6*%)!HAi0#1HZpFq^TT!I&yo;Q__h4u!6Tnw8
zUmKS^qwTc=H6P2fdT6*em%X5%><dV}I80&3Nd>rQrH-=DN6wh1VTqyOt<_qzszjrw
zP0~1M@J@mcFI}@uP4y+(7dcY5T|5UN390+A`l+osq8;m(Y9SV&mw$9VTIVXQe{+e-
znh6ir6ss9?Cz9@%%nZ=B473MEGXa!YF|1#*R7XQC;y46;Q#qCZDZ1f~1&*dofc9;J
zpC~EbsYOpeqP0iKHZvfO*h0A5b;fJz<jKnJPsWEeZ)z)6DiOpNFtJAU(MZjnH&ugx
z^&W8Dy5d@d1@JC=N;G}Jb(%9_1VNDF=~tslIWv|%`Jf_3BJ3?l)4?sPw0cJ=?gpHH
zp$VEY5sTL35N&<q4ectcCYWFd8EA$fsK{4iS+P4`kA;y93cTMDOj_yjqm_lgGl@v-
z{Rko#%-mPPgA^jfe(d=-l!*&j1TKiq;)6MF2iL|79;-=Y*+NffTa|b_;I~zq)EU6H
z**qIM4});3*<Pp`EGTawTShLftbK6NYN|U%B>02Sp(tChj)?|`8n1LZ{-Z1{Tm3Pu
zKaS<r_$f1#)d%aub?fxjrtKOueZD3m<Z3M4riClEDxNcZ((IW^398qo^;le&H50Gs
zY-lMP#mVJ#G~~%od~mM>eZt3}?>>n^_d0FJyJ9y%X6$DSKFI@+b7B_dFq2osA%>cw
zBAKrlP9@}&?gPJp`8X2U=A#D)K+>S3v~&oxQp_x+th_uJN{ARr!oX}L;iF*-tc~DO
z`$}9Z+zC^^e(?mlA!@7D8$>i)f{9qv!N7N08>cL+K$FC_5nZ;p%$$-CPG#p3EGb}f
z+bU%<ce1eelz6^wgVDU1ro=JCFzNe_;3wTT5u&%6NVU{Y%p@9PqxQ0i&8#dNvnZD<
znD(u?a3oL7#|~*PCN)Hbae>Jy$-8VNIweB`Y<ELoHCGqI$OLFzT@C-AaQruOT;K~!
zL*hri22)Elv14yJ^U;ssJOo22D-&UL@Im&7sa8MJW8;Ux4(lM;5B@JAkXrv#(!f-e
zWA@#tv>|!w7Y@T?5?J6Lf~2h;Q^3-rQ0$cK+}y6i!^RZ`k-wv`$lbGClR22ipq@FS
z`QUK})7o+ZbQuismm87;GkHqp+<T3^H<SZ%&(1xdN?@Coi4Q1DrQPVI*yV7TspI&Z
z6(8v!2n;zqK~iM|!9lM_wC@}S><j4Rd)|9T2k+SH-r-%2Rd(J0&ag8ZUoTxb%S%Qm
z5&3ub9(0N#$Lm0)LO8ULilw-m-A7QR+}vc=O&k7tFu|050|vvSKqMnoW-)2^{=-N`
z`zrz_tD0ripZRSe)k_tswvd!T%8rM*I1z6AHYVLtg%lR;bM5;71p1)w-n-v_`P838
z1kTOrOJ50ONbH2Vx=P|QRB6U-|Dn%aGFC5s`3^m_zmG-@Pl0&GR}NwcnxaQJ6Xa?F
z*<*+Hr~)eyE8&rSoTR+^Xtn$8{GMTWZCP9M=Ev56rl!=l*FrEGjwh&Eb&a<t2NFd!
z7N0y%SYT#pNZ5CK<4qnkM18S9F~2Ry9tbb|`*sz(_w>yeLjGP3MY?gqFx>r9{;@fL
zXbNtFx!GHy(7t3~Xg%!4lQ<|x;dOh6^h}+E0SIf*<gzXrs`ls*`PD*F1}LqqjPJI#
zQ`K5|*ad9L=AaJkX!bE~;5Q?p34*g@*KRu37qdF4^a%K<&p-`HXa;xLe(>!<I^W(@
z2EOt<-3I67)0UUY$_T$U)>iQW4^~+@d0^&1fffBk;1EtAzC!j7zXD3IhUDc!R1nAz
zd}kVzy2CpV9DsZJ_g6W1%Sv{BE_=_zD`{D|<Xa^Z_ko=nc>X8#<&Ryff&tOmjezO3
zrE64nluQl8Bx%AG#ayIJP+Sc&)Z)!F0TRPiy>Gko;|oU+6U#cNhH3RmxCZBBIzg)U
zo8H=5g()l{-KL#!@%Z2d6F{;Cb7d<7hvayX;@Nk*cNe>BZ~G@Yg)@>pV5*-pEv_3x
zu*R&qq|#113OU%52~HQ%-bj??dit42T#Gb<ef7j$US;pE5$7gIt!2-b<Br9|C?_u)
z!rQ)|MKPSfnb>@+LI)1BW=>NQIl~h<!@X2S1Y_cyDBixyo!jw=$q2fMuumHq;3E|B
zX!Q||+9qZOOM$83J1emWLoFRp-QmONf-T5au2MkWCY(MAQMf1G{*P(Dii-BUeI>ig
z{>aWo(2StGg#F_0&#4~_V;v2KX(0wl?GYU)_rZv$<w+2-VF>cds_a@UY1}e7Z){)k
zno~FUHh1hnG*6D<^YFg;mxgxMs-n6@QRzc87{O^X%(e+uEMCZ-y`1&F5)a)H4gRVi
z*pr(BH2HlHWHtL}3V%JLFRG#8yj|{HP0h-tZlKkKzqZj&q&9?@E-x%p6%EHBC=KJ?
z)d=~m3>Zv~b?6C)H&J#(prkkoqfm;EZbE7ru5rM~Xd+Ci3-ZW~&YtcBC$=4hajUSs
z7sT3y{$PKXL4VqMNCw{~qM@ukOjd&$1U8}R8W>%s#7pkf&pvap+Bx%odiBTjDi(-Q
zu>{5quE$jb;VeRwzE~T$G;U0S?FQ3`R)b@Xuuszar>V3IdJ553PCn^lp*5`7dW|+;
zV|&0A53kzUc4$YDM-%&R@tK*M2Lo@o(H_8@9nHYuZQE7F&uH*UB*Ncn+sm%RLI3n;
zhn@wV@IA@yr1kDI%6GZf4?hiOxWP1-O^0`><XG2$T&9eSc!CZF(DZgZ8Ln=rDOT~}
z<BIN!0KDTUfm!J*)>}jRaefpZ=6x~HNP`f_AJ(2y-`NYzXcifjyNa9@PM@?arT4|$
z9`o~q4wsPkop=}q|Dcv*+F8sQ09~4lRqPQ`1{yt`o0q5}&JrsFnFCW`1tB2V<vVxX
zjZ^%|Vh+NX1EX_-Sx;eLAq#YA0fGDXkB3Zx_GJyGVr7~Tfxrd<uW52ESn@t_5vxk^
z0}$jBSkR>)2G*fyfp_`Nwy6GCo5mtu;yepyZyYJy>G+<+<RsM|AqXkYr_gqAH8|Md
zjH^{{0>QEQ8p4|2vuB^%hlzu-ct3O-rM0nK`-qDIC8NQ)gW1~_ci!-0tBQAf^{rkU
zR$QS3ZmYpXPocJNQeD7f$DCUQn>gmUz5a-bee+?L6_JSBTM~!-$+WDki%{a?bYgk9
ztC&4N=A-147=$sEZWW7Q%eiUsZhg?Y<LP?xAIvR#$KyW`9tWXl0v`n1dAX<9wUGim
z`w>n2_>n!@Z!{QmVt!sfy3<0Pebz&Q`erMh`8aD@%01ol&zaDWJ)ijC{yHs#Ze0%v
z!(0MnYkm!_Zb6@X)Hw4f>a-$>HSKK|Lum{mYt1CImdDWAG1D!p&sWZ_?!(I@@@6J)
z#EtoKS>#YdD&d%&*krn3PA5|Ir-Q9;(OP;U7kRr^A2604gjzTY)fSfD|Ja%~EwuI1
z#zk*9Xl&z}f0p?+!YBfh9{!v>;}J?`-r>QcomCgYxWYh6mg>SJp0=5cdA9L#jCq?O
zU@R6mt`%WK_u=5X=^Ky1#LUkvf3)>6+|1SJqm$D2m>wG&FhmO8?wBIBGcb)nvfWPK
z%{;6Nq_pkY;+{nYw?n^Y!Osymnl$$9)<Vx`x_`M14$?G<yv?UWj%Pr&*pThL4xX$R
z3ryEzh~2Mn-yDd5tABl2P($;p^v`2&Pj|lqZl2jN6QC9we;060c*%S8)AEE`1ZTG|
z{Og~-GkKV#gJ2nX7=COP{LGkKPb<TSFnzPVWzTi+-9NvMd13>$zAfB*>!0_S_QZ{u
z^=DcxR;u?-`UUOp&H#7mgg^F}&1tg<hul3gA&*wc=FPg`i{H{m=8e$n_k3LU?d-1+
zF{DI+V(o1X&j`8FU2i`j-eH}#GFN6=8Va7WKDxbXi_Ks7?wLiR34#zY++y_&2bp%s
z?IQz$ufFxnOg;VU#|{mDF-Fq_Ko&ou1!jq1bUo*<b7r3bbLMahj3NiN;S&o;Wc$_n
zWw^p#Z>}M>Z(L-32#QdcgJ$L@$cPZ;w+$x72yp)xYz-TO7S3KaoMRcv48H}zESmzt
z7+H7Sx26pLS(JW@E^qAyu6s+3Y4r^!w9waHXO2FZnWNu4(D1+c!UgUj+ha!fg&;UM
zPJg|%Zf8InYZ}~3%eJNsJsLhMFWRZ7$=B*jAGt!~N9LmuK<id>6nsnS923-rVqrz#
zzo&ouUwY&q?h?@u;Y^l)ueXqp)>fmr)0oSOX^48msRk*-FGegIGiVkhX422n2v|$D
z?`+<CV_0Z_Vq<Q@)X>hqz?pD2PxpsT_}8dudu>g-$l1Wv)|;JO4weBk+*_0G?4j>=
zKG->BjMzIWOqQJ!whwwcpIn=U8x5w0hv<Lz+Vjo{f1lks$HsJik%8w8Pgn{pgQ@eI
zWN(^}xvlkZgeLBMI{835tKrxhv-9dyh|8^0-x+Ru+nsN3gXpZj`C^-*z|IwKZDFDX
z9Gq{jEnez`^Io@}I74iG{oTdZ0kGobzB%otMQlG<zg+3shu~xOb}KM7X2l}Z+xcnu
zqsN&a&@m4mvQJFA$}{dPd7IALEw@joZ#cv7g2B&lfX#Jaya2cN?H+r~)uq2?hHJl_
z(#2MfTXQz|Hcr+qd(mKN5$!Q}a=5|zZi*ADW8JJN-m0X_JO;hBV!M8J?Hwu_L6maL
z`CFV|z~~(4wI1uv!;Xfpg4tK50CBj;y#ri?g2farcK-FglUcV$U)Z{$xHZuWZVZHv
zca}01_NBQ+dgqV{E?pC6{l{SK&M5j|d;Y}Y?^6#N&fp8|BIvubegoufMt}R(Y+xJK
zq@A&L&e{HUv@i4LQucP8A3pr<=t;v-Z4R$nZ|p2)3o~c7-h6aCc#O$#>(~11nLK-R
zscmz&b~qa>8-OXf%#zVtSGF{K=k967+w**)Cq~z^Rt%RozQjlS+BqAjAaHBf?Gcam
zI&tk<-%g}IZJf6LZJ!&QE$f6kw`m(>?r?A$${>4>S3Ek@Pv809VYHi_{pPCW(w^B~
zaK*o$m?!qK`H~ysZu9PNtDS*eb$ySxiL-u~ONe*d&dO8qZSE;s7lv~=BOQ)rGCaHn
z?ZA9_=UI3Ab}pFvU$6X!f!tNKu|mrlox&P$yqsHm9uMTQs~E20+_z}@HqQno|HyrB
zdq*E<L_@x#mf!z}I9X0#+95h{LGg<JAUNSM+W`JL{==dRNnmGczRkvTHbabA>0r}$
zHR)^1yKg9wi891&nr*j9v5odFdY@<W*ZOPkJ?_&?^!hAy3?cVY#~sEzV??SWAVFyU
zavUv=S9&h1?s5CZ!NC|fcJ~bZa!fFr?yQP$JeJ$p-eZzTgOUBVVz+x18=Em(-Sv6E
z9llntM}2-6!*iTS*u57^rPMudJs<A<iNLGYHQVq_{)KXe+tK>y5QlHUoHWG1m+;-{
zcg-8?&K9{5s~)Y|`_m`B!!>Ca-{<0xp6{JDzi!=htxK;zt>%B~uj4*_^*GoJd0L7t
zBd%P*B+5UXrydP=+!`|bmfniZd-pqgtKq-a{SFW_`y&?9&;8pr>XnYeDyk$v8k!(u
zqPpw(pFE3C9yWsB{xjRmx8Vv~yCxv$r5)<qx^DLEHLpH<(yke2;acf_&WM*D>%99G
z@9KH)osT16&cH2Ty6!n+cDwh8jqt>MV{tSXH*@XHn{V$2^wWEaHaQ#}p5U}L0iB;z
zI0<(dq3T~}C#-wR)NxY%9`E7@>!(+k1+-;f-MrCvhqnUYZ^90Lv3kaIq~#CS@m){p
z%b!|;&-yjE0^qiQd2A%afju8aO2Y<Hr8PiMV93DOSXcHA&LV?|X@L!{-X7|0VR&ye
z_!{Hl))LQ{$*E%wz1zOO%)1G>dR#lz4{Px(3y++<MwvAFV(<0J>D#rrHr&@E9>!UA
z#Z{-Bp19}j8|m3IZ;#ky*c>phu5;h!r@L4j0|<D#V0CR>I0D?xYu|kJe4zW$GyE2a
zPv8C#-cH{F&zUo+f6|<xWU!TJ@AdDsw{PbR?>hX<I-rwgW^ZfMgjrT>&bvzBKIFRF
z`%beCV{{sjx_xtI3*5cV-s<4xaFItJIYmk@1Gg@^_W5GOzZGMWdSEW@IRW7bb8mR7
zi_)TwN29<HA)tddf*Hc4$CysqHSKLJ%u-Yry=ME`fz5sT|9E|8u(aplqHg$@g^s^i
zy^{;`OW)n`)8l5(+TJ_7_1%Sw5STQZ)#2Y+-tLS#Wzz;<aPD#6IoMT?>n9ew)I~%2
zVd-0MR=4Xsa20#f*VbAf&tPlN;gT+T*;})A%}<vanh0Dw%>i>@?>q6_wQIeXz_IY%
zw{P4&uyq$e8wC#A;Az6uljqc1JAw1<e}jG6bz4Ix@hS+InoerW!wV<QzODIgn%m#H
zjjo&4u>SjbkZ#-(Sl0&)8<fG-qPz!|z`+Jyx9{BZ1oNJyu32Gud{_5A=z7G}?Vh)j
z_>ZoNHdYiEzTa*Q1gt&#?Z$T!E!)+=xi3J#!<6qiC@fe>FIf9+-fI%J-Xw!>^T>O8
z%NFcADevVt&bIBdt^ZEO`$2ogA`YM3%KM;=;OzSIK^m`_h(!@gq5^-ZU%qmLlwz1K
zhr|3ZA-x<eXE)HU+J-0Gx!ZN+bYDJb`@nF^*{@M!JkRVYZ@j02%KLf$^yh)C4`bTH
z$c`{PrBzS==>a{>wYbDo{OQpUAbAhL)U?GkuH;0|2uG`a0*XHSy>R9*eXpVQL0hBm
zXuM~0hFe`;V&nctEpE=<!A?G_@%mtTMo@a+VCwCgGwst!ZdPXn0DomL1b8zv2G@{!
zTrmz7<KIlA>u6GmalPGe>X~$iO7Bj8{Qc_v^Xzd^@9q3wTz#js@0s@X{WzcAQ+)b9
ztRW9CdN}e-;rdSMoeiAMrtUlG%eiOXHwOJ2sLw&*90dO5AYe2U8iR0rr-mjFBofWW
zyUU#a%R%&CYQQFCH!V&x;+$c_N1!$I%<s)Kumi1wS@>BN49hNZCbam!6!6Xs_8bKM
z$su5x^w56UYJ2M`-E!S8@!N^lV0>Al(O#Y)tNSO1#JS;}gTOfm{4+w}ebS)+GlK5i
zn9f1q90X1a0cR3r=Fc9<^zLcSPy6O`75-@;VCMg3ZfEAbM2$BqV#5D8vpF<nXKS7H
zPXm^7133qQe{~3;y$m9tVlKhK5C@|*KgXK?t1pRj<39(1e-;RYI@1Y5a_^$i=|2l_
z&W+<71kOR=90blm;DZ8z)0@VBP<nc<ALk%&4g&8N1kTydykC(1S9^~Mfz4g|oDle5
zZRS51;|;cmfEM7O1IgpNt=;k#_q*Nd2Lo{LRxkb;VPs=^FN+^(zL)R#AhzvaEAI2}
z>Yx`q=<jOcANgTBbP1gFUa!-CWDuR}`#A`lgTOfm{G&kNoImV8%1r$O^^+*$<~(KD
zhWruN|ABgcuB-n95D0z!^+jrKs8dvQ3{s-r&4VmwX)u0%t{|*5{tKnM`*hBK_S<gy
zuC?QL>fN_z?7#Qhs+=nU5HLmo*gC)gFbBhc+y8K189*=*AVVw?uS+{~LL|v|VL--M
znLr8eR3d1yuHJ<<KhRCEF8pcq{3)U=6`Jj@GYt}U;`q84ePH;01jsq@{r}g$4I?=K
z{-1ZmqE-`ipZ#KUV&<8aPNn@6c=&9)Yq-VsN-*tPK6Aq}mOijY+LX6#)J)2~XFG@t
z`)+6vER|*`I3m~tB)q$Wv%=Ee*?K-@q3@xjXmKgrfRR6~UT^h-Y2S_631DoR4W`6C
zIKzN?y|vl%niqxIqK!KN4;}AHuhp}n@0Zp0^~vVI)wh&6z2in})6Dp7O}w)?>$MZT
zUbJ*op@vJXTMl3MzV^ENV`H|ur$_<X>s!uU{6~X;ow2s|Y)$+to)7<M^L4Jj?*#<F
z+d(ZVJzS^wJ~4`k4JRtO7v((gZFZj7d1Gf&_ZfZ`=MDSJ;;2}Z`VNBXz8m|^@U`Lf
zU?UXI@XYTzm)*WM{B9}TdMotT#%t$>jhQN5JeYT$GVsK=7~Nvg;T?_8b>46`adz7K
zPAc@->U(e`E=@--(dPpm^79ZOjz;k4E~ATFy1{^YfA?3{FMm2lv;OnwyOZYF)__|>
z-E-#XBIY18gS0i}hg(%&-{1gpVy6rsZ7oe|XkgH!TQC;h0m)|1fX658+d6<d^CD+P
z$HpllxLsA{2UJzpp#hmg6itpjXHHMGfA(Uaa5;eijHO|)z*r|0?sc>_=uk<iS|g&A
zk(Qw_Hfe8FPkQ!!xL}(kj9iMAmg9^FdO99Vj+5Gw11fK@q$qtfBsZ0OqVLj(XeP+=
zgV!C?{<6ak0)qx;D}s0S_6mEee-2uigLjQSe;;7)e}(Or5b}w(R`1uYiUxPt+;5Gv
zk4ntYz=Wu?M(99GO?BleDX&#x`T+Hdi7|@X|H}W<Pn$DCp6_L`gRtpXosJ$oN=%_x
z4&MZb(RXQ$o<+wkM`&<oi^__3sOEV0`%^Qsl^h$<mH+iDn(VI1x(R081a~y6@<5R)
z8@t;N>zAS2^ms4j(c5eaI|?n=`tio2s&8ml1nVR^lKjNoO?AKTwBKmW<%kVaOGAwc
zcO43NB2u{pgGE+$Qma`~7I(V1`?jxLF5aM4?JL}_M&DCeIr)kS4+2JBnk3gL&L+#v
z+R|`TMf*#8baUX4eDxs@o6B(?@aD7qYWda4&aERQJ5*8I4Tyw{!Ag&haw+Y)Kx%!b
zzQM5OaIs1&yNQO#)Pc%Qit8yjaC1gZT>|0Nl<m^tV*w0&bbk#UoZ#~0{{2LR%>_Ac
z!JEg*i&RqWPlO}&XYKWk0)_}aZJ&V9UGCqeW0l%>=tuy#f;1$rKv7|q<F@<xfC}fn
zo(2MLZ9`OAPHdObuWR9)=E3*sHuQ)Iw_J|qrbgA1>df+i`*VH2SFb(S?l}m2@E~AJ
zbBA{B4$@bD_#F-F*Qr+@yGM^~4^}}uG`%C}IUg;*`tdpy?>a~z)9#WsC`XBL;l$l}
zJG#@(4;Wn%k<}fm;p_`hbZiVc>QQcWA1&RDp6;9P{ILC7S6!+DWk<W8=-Xez2K0m9
zXeHB0&&KVXcX{HQ>o}JWdd~#)RenLbJBwO53mk(-`<5#mbfx;5avdxWXkWyDD9BWJ
z5b4*f{yQ#~>psHqMSBZ(^?+$6XMSvWu*)~!-8^~XBf=qwn{}wDP_@3Js4_8DN>A>t
z7Vs^Uy!9O|jVjr-PxS%U6aPPZ?*S%dk+luqJ>8RY&N=597#QM6kR%dB1x3taT2a@q
z%C31=%vnJ(D+WN4fMm%zPiAt?Imhn*K2J{~qwe>=*Y|$Z1>HUUJY7%Ksj5>a+~<^=
zA6#rrE;D8WesGzOYK_PW9VnvQ($IIW5p^QUI%;t;2<7>o_i0GC`pP_%RQJmWP&ptZ
z)ETPv>(}cxZQ6vzix(p_G?bQ;vk$@)&x56{0~~DGX-=kI@mX(WmB}=~v!c~|73wQ8
zaik*vPu+Sw0vrsm^QZ5zGp8IjmKXQZ_x0ib@(;?g<KoWB$We?Lww@HYO{k0ySQlcg
z(l!^OD`*xTo-qQ3&E;78!K=u&_J_I9^M9BIVj1gbu0eirH5aEf`~sYLwzhby`-SIo
zn<!qQ()1imdGH|&iws14aVoz5WF_EC1&EfT0o_0Mr&sj1)L)xZgz)RG$HcLtVAxWE
zUstY0RyohwHa9%ayJw)%A|o?LsoS2JUG0s}9)l}-=>OzR3d1!<V@4Wm#!SPkF(ct-
zZ>bp3)Vg-$CjO4szCMHyAL}c#{80dt&7iQ^5Z6tcjfsOjk-BdO)+}2IO;9ils2u&(
zG5Rayl4pxDbANRO9&>XztE!Vc-~2eO%YU`Vyl$#!LcpjvxH&taqrL_?hmWD&!~{&n
z@mH(=1HY%zRwdhm8a`qEu(r0Oa!Em*hR~{lJQS8R!p6~>t4(k4y!fiS@FT^N-^%)1
zb%yW?^`a5NLfM3&>*PjIjI1*iFe5)Vp)ep`@pJ1JN{I2jor(P{tgMNyQlpb+*ou)>
z@fu(Fu^0cA?a}d!*}A%Rm^nDW%gYsJW+t4=GypWzBIk52T4?pRqrpkMc2bl!(2Lwz
z--O0C4g6#LVQFCkZCf)+xUQ?}wXk!xP)-S;#>q{(6SXz1uyk>PtBWJ+H>Fjb!c==R
z%5t($R@nhtCu=eHU%Z`{x<Yz1RW|V)bR9fH0$^)vMI~25uSXkdXq3z=sDrhG6}>LB
zl(X?dr#58_v~UgZg@e6~ehjo7s4FW*c6KE!>@2xibClU8*-txof3r>nk6<6z*jQ0m
z)3BayG**}4Olk>FLokDdiH0=#b8}UGCC|}up4HWLz%|Gp&W<(|x=5$C7E}}#pro`7
zc8+EW{dGLYwywSd_MYx=q#@sw3Y(hucC^%^FeL}Itvc8_aSZ+}O)o|7|Hapoxy||1
z*4Tm;o>#24G=&w%^?!l^D|3Q#Rd_&mdn@W2I$`Ey3mX~<J6qZmPuiba^Z$#-?`qfn
zcT+&*^UlgfSPqKEsG-z}YVxpm#cp&unk%v_xhU6aH)^Y!VPbCw4-XfZbH7pc@Xi)g
z7UZI+xS4yn74@Z_{oovu$*yZa-rnA!fsMTt4RzdPol|!m4A-@r#&)t}+qP{t#?Fpy
z8#}gbHEGl&jcwa@W4p<Fe+S>ubF%)y9BZtx=Da52UrKxX^TTf1>RVKt+P!J81!uG8
zz}N_)^5M#`rqR)1%CP(uiKV=WsRTs_x_d&tknwW&%{_lH6w<{78rrUMmT275uwE(k
zF~o6seTChY(wf4Yu3nDX&LT98G0Uo}NXkFawE+S;nY?j{80D#dLB1meTd1eB7}<9h
zl2BpZu01>yZ?V1_wwTW!jGBw?4FS~<kI&<3(l>E8T8D&UC*ImveF=VcCa<}^Yrb3M
zqoc*jyZFI>OJMs3)`*YY_}x60DUME2?A8jPRq(%0R`&b{+oAk7AZ0}#-`9TOmg>%b
z@Wz*dez@0rKWXj7!ZP@@RGn1qA`{s`r}WQ;(=tyCzJSYRUoq^(C-J&*1}2YoN~Q0a
zp(WaYUdyPk-W{APs#})Vda^9QDnJ$#Hs{M*y0;&Kdt}J%*U)%YU8EINkWlkVD3jhy
z#%@7tDyTKO)_{Nwdk(Y&Rl!l<daZvY*A1R8M%B>U3O-CC-ng?4S22kIL3)^ku(V10
zN@oss;p=_45@3{2Ac*;?rOIi1{9AhIYe8zyCj%$ev8wh$*ltnE&8~*TY^o~)!OAbu
z<|JH2mN{-@+yKZS?VeHI*c?_YEo<<L!(4>{5{emywioqZDxbpgdcC>1*oN*RfKb5q
z$+HC~uY2hWxoVk8>|YLHhG&S_YT_v_XBQ17dkQy=S_gyWB=vM9MAOHm?_ZS8EOR}s
zEK%l4`Y9;CZ5>BI&zm7+WVN&ELF8ZHhIYs^Fr|$umsx#_c6p&|^=xJPth$^94puli
zg~T_X+4K}*8$D^Q?;OG^S^|HxzA8S`-50_+$}HfBd9X&wFo{%6m!u=L*ga8S?q&kq
zI4u6Py?xbCY$wZad`UWUEQvJN;v|0g4}~ooe|}XDrTt4)S5f%$L88NKYDnxAbC-qU
zs^&LGn?sZ(oFp9BK3jsCuU3=V*!a8Jxmo1>Iyg|xNmU+Ns1;iXcha6#@WJmY4_Cb>
z2awcOp!Kl5>Y6|{vij@f-q`z`C<E*x`uU8FaVE}rNfe5M4?6tnb*XwMuy)P(f}9Ay
zBJui{;79GKHe4JvllavoHFSHKmvNUjt9hyji6=jU$Dywu^i`YQBMQ;+)zANhm>_ap
z!M@!XRyyk>3P4%uWDTx9qKEz@DG913@;0KbkN?vBZotxevFXFzB!8u3i5;-xds%bM
zaudN=*J3n77w2YL`Tlz-|6CgZxxNYQROGqa$mR<zOIBX3>NBrS19p37gVLt}KDKOg
z>`eiRN}$ld29?BV!(8NP{#P{TFMOvzc+VfiE~qapfjvK~AP&EzqD^`j+GB4<193nG
zrMQfdKUhmZ!>Sa@(I|@ftauB_@w*W!+x>Viy<;`Fbk0c#EH7N{7Wi-o^Yg6x%MrJC
zi_SMCzsJOL(ftB^eFe&uvclr3yZbFWe(tWM1>bZO^i>}m?3r3ZaA<G<2%^-CRBM}C
z9nO$_p#K%iw{sR7DnJ;adR|>E=3pHLxcdB<REnDuoA9L|n<`$73UZWP&CxkLN?f0g
z_u!6}!+}cyS9x(##;X=y=~3a_nT{u82#m$c#|B}kb+>m&I(J2~iM5@N8~F%*bv2~T
z<2Jm=Zhm+_Dj-!n#9RvTe5%)sV^FZ&WJ6#Rfs_c^b1u;dcnIf+%TE<IqRk`D_kbX<
zaui$As)VqH)C3y5<f^#<8E%v2LXKHKFH{yQxhuVMz{UO0aJk5``mp4_c6(K29?7|5
zFMoLSya;zDh@ye$P1t?T*5^mhsxb&e-~VtJdy62^&jnJoT?&^V2$XCXhx>sqBq^Qm
zzASs=Sqf(V=C0LD%Rnc=l!Y_I4?y&vY1eI2@>UzxHD%%LDSe28967iskR{O~ose`j
zq!?Ei2X1QyTi8^mYqrDsq&|RV#chirVUY3Soh&7qsJ_4j%IUoe!-=3?=XB@Y%w*Cx
zfPR4b9rew>&VJ|g7c`7BmSLC_@tO*rV~xo9DR;8vvET3XM$E{+QbbM5xbg{k@Ixze
zGb%2Io+thkuJvZJN;3UaaNWB+sG?-$tDD085bvjvl_5uLhwHMTNx3|=(ayIY^}xf=
zx%n+D{c6g(?z&PoIajMtqtIsjRx_ycTZug*O~)eq^Iumw8wcCIo%QeFlAIN9gg598
z*T;!!tXBEp{~b`!v@XR1vsl}o5Kbi-JuN>G+Mw60tgeE}N(lTSF$nm8)HP8EFNafK
zq;(8v%Qj<F|3NHg+9<c*&x$z!{O@X$J+9m#c}i;9S|O>JRaP3-yQgQ+EZW+w9U3XX
zOi==N1Z_uSFW<I-!e)H)@HNRAu6bDyGK(V#l+57VeL#aauwBD4=o^W~#mgtx#}jrM
zJNNS7BpRQt_shw?c_y16)DWJp)4kA4-@*>=`mzUoJ~+%87vhsAl3&#JTeutjkyWjv
zo_ZOE2t$I~Gkj246a@4~FjgT=T;9c%T?uwbVpMkv-T+N+#4wfID96obH$m-7XuQ0&
zp*`KWEv!xulKt+%(dgUj1;ltoZ8h>zSR8;O_`kS<50{b{nwv&jmAzF+Q5N>3l{rqU
zJS0di+wDm6coSRY7j5|#5v3Q1>?Z?}s!U`hl@RK3tcn^Z#ShBpZgGlXO>VBm!s4dV
z3YOy=RJRJtq+n>sJ)F{tLM$kYOto3Z+mN!~rbi2`PsUM>Xoj&rax8||x##QmPpwcP
zqd3OS|I~OpAu-0nQ&1@l;mj|sMcee-t(xFJJ|tQa{hiC5XjNFSsf;C<lb71so=&eo
zJMZWnW?&=2=1QygbQ(Mxim89Y^#3T+4eGJ#me-ykF5Gs$GP#h2DOfhn74iqLjIX~7
zZe2vC-z>2U?`%WJa5E1y?4+yxS*EQgw<MsxKGl_?r!QcKiPMV@p01nG-`-Mo)HW1W
z`}RZ_>Y+2wU`dD13jD*>9Q##tGMbdAm4>t$L=K{WXSIQ2v)9(|4@T2i=l(PS=Gj%p
zf^7Zy*yZ25%5ap4ohT-kM6n+8EhjRY(VjNeDUT)ecHlJFO$hFkFNRfm94-P|!UaOC
z?Nc+0MV2YE0B?L1g~l)E^Tmpsi=(Xzz~fVR6<>}aQtm{28+;FdQ#fI*0$b;44ETIL
z0Zausw8~mf^*t)tYu#qqaB0yvCAdte6TS$GARi0wH}%sWyhaB*pN!1U{iT)vmBas&
z$V&=VWwmugL^TSHN6p~eK9=2T_8_ZBSH7B-MvQ*G7xwcuCp_?$B9Zk<k5I1#vIPjS
zxC15R^-6x{pv~ZpI)UDLJ6xqX9haEV30u(p3c*dI)wh&I)*FY?>S4#rY1oW3P!hj5
z^x-JyTh9n$Kylar?{oW;Z!Txkw;BuJ&akw;V<5cg+9%iK?>{bCLN6PisWJOf+TXIv
z!#MteNK@grEsNA)c4@6mOO$YjI~EDR{4lheRo4&tMWAi^g~uh{S!G5mp9^tt8+6yn
zQOR~Yz=SFoY!&DUfF~9az$57XubT!RHH<e)mG1RGzMWRz@KR~?m)&q{6bCDdARG#J
zY(WW41L*-)%P+kcHw|qlRiHXscWKPF7zF|9u*OD7TRR4{CRmF)_OD4@J3n5I_0%Au
z8gncGqqvwZU-pJQIy$y8pa){ymeGVBAMcvm6KSAzEWiFYsax<4XL=fGMJoc*%C4)A
z^`M!6)~jXV`u}GEJe0;0hge<{h)d0)9lb{Bv|PwEWxn<;g~)ThGpY^ZQ!C1=%!KQ^
zeJAcd3V4xMl-A~8k9RH9c8JLG{g!(aAfB%8jsu5J2th-RyTD^FTI@?m>~rVv;*6AS
zdspxM4}ox;O}esz4rJVJVt*bJBuc}{!`UBmn@1lkLhzp@;Y^1Yu->n!gi71>@3qQO
zRViFu;+N6J5`+dU%W$l0jduilI(`3Khn46k6Ro!3@P&slI@NUIW9mu1*smnSH~~a7
zf{P(2%vh1C(G$t7upP{EG=QuudO=;8i-U<x%xtd{0JE6MC@`<^TV@rTNKYoQGxZ<_
zsxUGo(dBVuY<v(C`Y&wv*t|<TNW>GX*c=Qv0iO+XBz!BVvGMVcaSAkw;)wp%4)myz
zg}Esm{eQ7X3?~XaLQ~S|EdUHYsAIk1Bi5(pGQ-`~H|O*7MJW-dAZtDiPcAZ{3P94z
zW>7flVBg$$7)-ie?&g0<@jt{N`l4uztE@!W>py<seefo1Uj9dSIk%Xk!+dPOM+8eK
z=CNa2q~pjFYP#z(Ph<U9xL1Vg;{%7saG&HN!i2HlIt!~5c(Z_^z1dN^ASzJphg1mE
z<pO!8Rmd45*;P|;rnK;AZ4mqalY(Sv4}taUP;)jInIteqlYj<_cipAi?JfYG%|uYs
zmd_<7)@keF9gSpY69G(*Pkfe`7l6OP;}|?1wPOv}=Ex@I<ORsRSldCMppu*8o9cW!
z(LyjN>;0ec0gvFO(QQA_Vs2bCH3<=)|AfH)Lus`;4=8kWa~Vm(4z&WM*SED6KBq4k
z#6c2U++)<Y0Cj07W_BIfhDy<&6EEsBANY2|XQ$jE9aJF1--I8wT9xE;LFyLU@TZWO
z9Hq4>kD%dqus398^+f(#M6zzaw@-G<S7aQ;h*4HZE#XLiY?Dc799(ev{Iy&vGg_Z=
zWDVTCVh5s9^{2oP<k$-rMZx*)<G7q6Kn9f(-c*+Nd3M#R=EDWPo8KtfWd9l&kXevn
z94(eY+}m|uPL{~}?UwOu5K@F30=`5BG^C=?VCP9CZkT0%jcS6*spzMgoSyKKb3H=f
zX^4ue;G<F{d8$9w9ZQg4&0z^#h%VFVq$48BkEc~s87hq+=_}zu5!)N<^16;XQN_fE
zux%pesD}6>;>L2-P6dHI!o8P*x+CtR#c7o5i5uc~0Z(!j0#Ei&PZ%`&;ETqVW`mf<
zC_%5k_8vGAfoLq2k`qmaZF-o<_<9K!a0o3Zc!|F{SMrODNy~76m6!axf{`ztrJLfz
zAmb2BtdR1=tm$@1oyoF$$sT#b3a7)(cj<V_I1FjfEiMT~!!-KmPeQY`>3-MyE6GHA
zf#nU@-KcZwzJ#dE2N0_`UT8rE^YHMbjV5lB331(ykZ)LW+g6Zs4~%B_uYbkP&j-Nh
z2B~<*Q!|`ncSq*IWDX@b5P9@|siJ}J1gO>Z1C2f_?95XGf(E~^c$d<s^}yN<2KIIp
zQs}`b)fEI5H9OV@4mL$NpInR)S~6q7d#k2SqGt}a#t=N$^c6n~S2U0z<<Uhs<qIG`
zPj;c_TNqkZh%*B*h?t;t-i8HMlhqMfaD93@Y!h{H!MNA01Q7Y$grN@c!Fb5hy@>?J
z6xsJ>o=EIZb_4q-D0`&?w1UnDf|Bh7ps%0Lw-@`EaLC8=W?(4_`UE;%{>|q@!%Q9u
zl%tA2ds~Nwm9uYs$`nqXD5vI#MF&4EN}Kie`%bn8VjCLj*-AzV>Ye0#c#>WD$$*I6
zucwfy>)`%!A(AZWl-rjJUo?`ztYKjQN3}Umg8mH=8+Kqq4ir(>?eI^JQWArx_pe9D
z{sF&F9cuG=T?^qU)Q2#A)46>TR`Pz0qAK(xQVKANbmp}S<J+mFR~WcA5wx<I2>t#d
zaQk7;fp#@AnVjod97zyQ7&TXdX(jj$B|^j)6cgH&Z1=079buYHquy&y3GUgwNKH<h
z^}!o%SSbrkU^zY5_)Ib~tp}_5gZE%9!%>Ek<8z{Y4(p_FaM@23E|!p^LQKM$SoyPs
zWGWtqwUd<opfR-12g$2ZQfS{&`9BA&OOqD%!OPhcEmd}ZDhzp62>Z__uHx)5*shWi
zt1v|1KFS^9d~gHoMKg-hhv9e?Xq4HWM#J0)@>5kQ|A*mHcyBAfLi7=I8cyt70#Iok
zrE;}$ev`WpNuDsXQY;lWq8HSxW$1%7*eQf)80do?h)4*kZU)<)vp!wQf##_dguhVn
zslt2ckm6_dj>$SRvwBr^*Qy>wRwq!7@Z@+Kk7%1}jG1JSh|ndL(g8e{-@O%hlxyCw
z%LnR?GCPVJ1QB!6uS426LT5j}U0)Jm7gc{+aZN*;|Gn|foiM}}R0`^3h>xOL`o$%?
z(F0w<L9jP6#SU)bENXr|Y{{dov6b*OMEowDAmJ8<x-mY>fPXaZiqT*{LATnW!vM#E
zCn*M&4Wx>#`c;Z@8C;Ypdr;9#_Ih$<&zr9eD{ut?DFO1?@NV$5ZX`w?W&^=KfTWMb
z7uBl6G56PCS*{$9{?a&EjaXlGZ7z6aU?`VvvT*%Y;xy%|Qew4QY=(3CBWCY_5dRqf
z^<SDu<bY6Jmg4y_9uM<fE1iqn;|xjiMW7@d=7Qq<xkPI@_+}}F%d7X9{w~{R`aQ@N
zL$<@@^bE27Uxa-n6|#Mvz17pDI5sA<oH{9NaR*dcxgwD@+eXoBY7<s18K*sMZTMqh
z5rB*GyR>4xtIq^XX$z|u+R6$E8;<-&n!CmpM9-;JSQu>Ydaf^tZy74j0nPMUa_qvY
zFKx;wlPg%8dtjb}P;HK^-h7_oU%N?1YXE`mc#`)8D}v46$4dKP{eFNX`)yM6XaRFR
zW+5eYDO*S8um^{}rOtvVi*0=oBf-djpxY$*she1WxECD~YF$Pdi9c)xc=~iSNpPt1
z?0Y71KiE-7<U*^kS0EB9xNhS6tf-UKov-rsx{%YBsaf|nZSCzsnk^~8aq*>E-QTMs
zo@NVeLeMp&dt>^G8et=AB(vQYGPkGvm0A-7E1KI0qpAaC!n>-H>nneDB11Tb$`<_B
z&e2!-pt~ofx0r!T`gf2GYtahmC=OLP`2_(A{F@+d32)0G34&MC&kdaA+s)R6zd4pe
z)oQD_-2fldTe=Lz5cT<1y;0Fw`br?$bxNRNlE^LMn~!!EpQyN;i96iEms)6vFD#;r
z04dP0&k!Z<ik0nWGOhLDzZk*8{pcP-QR+Di_s(~_=&2mHrNgW65?LLCNl`4YPlk?W
zj%A@V&=sf^eL-XFR5;D;#RMgmDj@`Ft|4ft?p0V<8vJZ8u1MMDNXr+Z&5&~`g7ziR
zW1YpYsAct+(s<xzC#8w?Wehpp=@eddRGOltMMCLZV=a8`SUCWUB|k!AN+S@Lb{Ya?
z(T4+(n`GdQEh`d$G#1`$;7DCbZ~Gt92?&Ss-$!g0**`B4d*okvJC>y~k$Fh9o}^M{
zkcqK2XGZz{pU06b&WSra;o-K51HZFkH$0wKz1D2D1kHIhLZM{T>q^=%X>k9tdWiR$
z_B3`koo#0WCu-C?3hTuRfVw}zjNb^Dq<^H-%}kEbF=c=zK$%S}Au#@Ghv*$^kipEQ
zg+qFK1JrD?GLhD8nd`-83x&n(I+G0TDa4l3;Qyd(upG-WE6tt!_J~j2^fbKt;>DZk
zQPM0S)iy237hW9v?qr{W?~t#&`8$g?lX!A+LB{DrlJ6p~DuLGa3_*a*e|J|iV`YHF
zgLYq9QbJ2R3<T>z)-nF=sr-$NL`R>+@laCrt#1Fi<L9}W?RAtKl9F4aI@$ZI5g~8>
zfb+f?DR$G8(y0f+Fz@(8_A$oA>#L{aCO52+2Y6IPAj)t<*_Gw@Ugz1Z3yR;ax$wkz
zMyX@8;v7dnu9B6Vql9F?d+K@aoI!G2itjUdT`>`xMmZ^GZ_0F^;Dx0?yMZDef7L)F
z9_HQ&Sz8?KiW?8v!66x;&<_OIj8Dp)eYSMAwA-JwwZW)qV=t%Qtj5ljYc^s{^VLDr
z=YJEXDb}8K919F2@x)eE;gm$Nz{pYI&EVIv_^ZL-Lxj~0r&tzB8MZ2eKl<Oms4<pT
zERb0F>j`;(p=bGinp|Vbt_8>K>v3YqkgV`)@XbXr?o}8#=DuUVFud3(|7Bo&+gWnj
zz!N4%N|)9vJ)#D_&RGEX1^P#G$3QvqN%=qEwR=?0y!56))CMQ{=KcPoJ4WebM1hLT
z71YKw0Fo@fG%t^?7XK^@+Cg>d4o32SgM1lR@^*CLvW<T92IAk8v48>KV}ScVEGvG7
zhUaJh$z|I(F^LE?e1bT<Ka+zkQ!^4Lgz;yGW=P$C*#zCc;%+=h1xcBpqvRCT>NsSq
z><FP>csKo7c#`kJR}E{xG2uXxrq}tYj4AyQC=0#T*4A4%3gm>bF)1><0Bb7X!r*&^
zlr%*m=5D$7Js!smegNN=G&I6wyX`pA#E(jIX$}8CbB(=9=X+Ixtls%yJ@lF;qD~v#
zh}c{t3z0vP3cnG=(1B*N_!el&Vr)#57!wY%k)mp9!s}B^aIRKAa8lAI|KnG_roz`+
zF^NdTC@?b`__KLAF^0e<3|{y;4(u4c;7>OIdHvZ*o(13Wud?|NsM~Q$UH3}Outn_;
zq@{VFk_M_a**Na+QM-ebMH~(l?@TTFks46ATRqmz0B)(EnRvG*yfrpq0Tkh2woLDH
zMZ2D7DGSmkNpmgX<asy_hw~u5^VrB62?7w+ylc#CU+io&t5~a-t}PGY-?p^hkV;uZ
zT}70ruWQSy9HC{+9V8)no3najCB?j~`$}3kgdzmDJZ)9h0MGXY9j6sHhs0spX?YoS
zQv`awW+#{DqJF%egdJv<GT{}?nB~egP3WdZvg<9ZFh=&~35khl2+UgJ-)*$x84@#H
zIII|L6O;VGsxF94E%>Co?6C*-g`}+VaKDdJHHQm>K(k^%d^+1iH9kydbj`v2k=~D#
zsZ(wz(gmP6toYGo=m5G9WAZ8Nu2X<8%88r0Y%!YT(5rkjdK^;~omA`BYXYn1nNy|<
zPCkDpK~z?m(*w#-<Q^M<`_+P6ru}xeAJqhVf52NUu78tL$4$4mp@D!n0OID}P1N2Q
z$Awn1+KLw)OG?=RFU4Y2wf@~TItOkKLbE%zJtrx^j--)c105eTre2gJHpho(ejelY
zfDHj`k;Z!01)s3vNdWV@R>;dj6oE=u;^Y|KXD|Zn3uQg|q&bEELhTkA9ZbyMqGfX<
zH?Tqhhj)}jBRuzER;Z3sP%>WrwMK<6A$5e)_r=u2`3VGSBrwMR`dN~!+wTsyD~I7g
zw#S*6rw(<w?XEHG2U%@<K4*3!{vY?sJeP-(L%EuH81xy+7C)K{Gw-c??~=>z9D<BZ
zlN{FY0;b(3B=Ub2xjoI(D57fGlXW3aR%f3bR?WN-lR(=$fSb6WSbf_(khOgdhU5u8
zJha<<T&xCo3~(73D|%%lhYdC{f;+8tk1=CMNgX+%`AzrUc+gDGi&5cE&5Qk3Si{Qm
zE3jI@NFfss8%6An!}Rk<ac9zD?OrbJhHJbG-VkQWiLlkHJ1xaX7FXBaiR0+5BsxKr
z|B+B8D<OK)k9LoKdW7+Iq=;B2CF{ise7%xmSfC80Ga2uF#?mwTxzXFngcnhI3k+m0
z4{WT3ekbF>UE4GR8n?jhS6X($FcPehD>5o3scS_>fR)2nQoT5jiAOdH{1SFHCY>Dz
zDO+c<!=^VOUra`XFQ%p<?oYj6ocbg<2hDdCQ?Qfpj79~UAP@sLBha*2gexTW;Q9XQ
zc9Aj<tH%xWoNUJ(hXGQLi2FMxw0ASdQ8u~9NLV(!j$qhA#@8MY&NBq7<jF*U>c3j<
zFaZ2<;r|}*P4+7Xz~kzjs?8B-Jf8UG{IrbE+cU_<+-WU(Sm&L$(}()N1Iys8YQ0;p
zJsc?MRyA3ZM$&j)Sm>FLv+YtBo5{>)&#-NGinJbPJL6Bh{Mw%fxx4?^hck#E@t4}L
zb8DIVF`W<FQVtUT958g^@e_Ma0T4BCEi#vy`y;a=P`|oy7(5P>dfGLDv8+fKR+0lT
zty8j6R#wmpPP4rBpE_m%=L!dpIGs07JXX7MXwn85sSS0l02dKi1*H+DgbxB|*IFr>
zBqN&n<h14=EB4>C>qI4ISC_EQz~MdlK@4d^8DSf@3DBOsLAp+u92!oiyK+qcKJow^
z!Z?RoRw7TNj%{zmYK{pf{Y6P$MLa1Zy(o!|mf~@8sDnz@oTMJyQj(GDh!}Jca|~!o
z;;O}ZxES?6d+ohZH~GRL2$10l_>d?!-6eF;PN?yHX!OKC4riuB0>L!G+mnfnIDj@v
z(i`w!PB^pOcdDM<#4<z!>s}_)^3T~yFRv|5)U$ob4;MF%fExFHKm2hJz;$(eFy^X%
zql-{AKTCFd`nKGOnzL#p)5PCXh@R7dw$Q+BAmq!0_V0qjVpQn@O_Cy%;h#2&;?|Rh
z*-=n_BP;`nFTNJm$JDBWlyut#tt<)_z87zuz?p!_=)axuCTN;uqFdz;q)a3ii8E%C
zy+0l3<Ek~v@s}h_(pdW%F$%1<<L*ucY#3_>4B^mHoMD<`wq{YYH?&aDw{Y9zZl(Tj
z#ObvHv3NxwAWzIe`Oksdyp6=_$KJ4pP@oiFiJ$<w$fdbySpz2eU;B{U+UMKNlb&AJ
z2c;LQb;d<B_>6*-g#CGhCa2FAs4#LtdL*2!=1Tnrz}K4<jX6Fp*7AKORRn36FE71}
z%9tRoR7qC@k@6?mCgj$C?F8NTuQr+;zGUx<)`A~*rm@+C3H<g%%EbI(hZhJIzflGv
zfo%0;J=d!rn4eD{xQF*PlvUz4udDh%LfKGtaSr;nKlTdp5+&ow<F<t8v6{<ZyCH2)
zLDT_Og3@KZ-B&f9e(G(4A->6P(`JU)o9B~uhUjuK>8O|<coi3e*#i0fKIcVV?r)O#
z+zI;|jvggbeMG~il?Uo+Wip)vCqHo7H_P{b^P!5a7zCxk{y5+mQF}4@#Dqg^5GQ|5
z)1bjrn>ygJ&oe+D6k014hOAEUFwu&l5N85Vzrv>KON~`ANoBn2biO?Jv!Wh!GBHV>
zRcVYKI~(AjAoU<cfkn&@|0z+u9G<u&Z{pANd?7GOgNr+>kYt`nBrU8t{3JkVB)Bl3
z;@7i)*Y7jk?-U^+GzV*Ao+hj*Sw}QP{mk^c6x0b3EOA5p#B?n-8`oB@cp-A|WR<sD
zVdfhJ5)|$T8u-UBc6=FG-G?cQnCfji+js(q0fL5AyQ4UMH$ygN?F#@|iAirt;E|~;
zlgiAuP})Z+4y>7vOX4PFyr|7+O1~CY*mTcz^29Y<E~IrO=Ego-7ADFb&Vluznf*sS
z6-GQ)%vd~mzs$<PaFJ`7%pBZ%k4IRHryu@pp5}r0tH3>)RHlVHrJLjC-+Nd`(Ha{o
z`$uKxi87M+w(vat){!KQydqas>j)D+)eY`vd)O|fqj(eDzA;U+MLI)1ai}cE=&Pek
z8+b$jP}cPmwNzJEa#zE=y$+{0)Kp%n?XPVFlk|}%ZdA8vq9VR@il)~q7Gq*f>@1=u
z`^6R8<6&+H97H8-A-Xr^QY)6ff9hF?XSD77SHNIv)mb&pAns%!xM{H&#jJ!Jpe!8z
zD-Q>5@5KYXk@>!vn+sb}qJrfNPSEE|Q}T^(t}run((X)_o<E(|b&UDHmqAQ|?;n1I
zT2s#Q;40orDCx;p0t;iz5&%kvp10sb7M7jyGu~5bJL%+5*dZCyn0Ci&X)TyOm+pKf
zn&<B@bSCl12*X#iOLr;hvH^&~2WEZY_Ur%^<e8K|HnQ_5Xp}EP<Z-nJAh4GyqDJKk
zKDa@`OW+~9sA%~Lc`xv}XWajIkI~A+!i*xYoEdJf5Du}`8O8Q-c}R4lLEsG6rMbDB
z_kTH#K(>5dC;e9E|HMs=3g1`%GPl|QqLZJhOm)4HQk|2y!FDb?@q7?*aXl{$p-*>e
z2<Xt$+GAoCG_M7OnT(-c&mx+Gn|6%)6sq6KzjJ-F?T_#;WR}~L9&d4`Xeg(u++FnW
z88*JOUHOnQ-rJ^Y5R?$cr;uIm{K@I1oS$%>c{`Iz^J4#cWhnT^OjM+p`XEQ@OH%TX
zb@S;4tF!5ar@Yc4L|D&;Gq!-+N^Xz=RWKyf{S8~t=X1jA^Qx-;8E%A2jCyr;hyw1Z
zt~WhRA&pxx^PUj9g_JYRY&UeoNOU-;?08>61@^a+JccOZHEHRucYuD+5(644;~SK)
z@FcJDn5MM8=z%yUpM=^<YTWXuT&A%5e|)@?+)huTZLfzN*?mEDU0oZe=z{nh*p1%a
zLCEZM55Ve6N!It#ekKNQ?AGJyk$P3qJ<ZBiPHe~^9qSG^>KhJ*zH=7A>P?y$^S>&N
zy;;QPhsMTzfdT))I63vnW>;`O&omkJ-M@iA-?MTVp`I0;2W4dV(T{$LiMbPzgA+t6
zBe-l3)tz0wSRSI$kwt-z@{-tUr5!di-zXeq`e$i%Y}OmDGaMUsA02^`keV`Du(g2~
z$b#PIUSY#=eL^J}d-sGAVL2JQq1w1-@4YLQJ1^Nkd}&huQ^?4U(blujBwpwha4fEA
zXJaYwR=vQA7gN4E)*8EZIGSa<w?l+*@r%@rUpVmc8ZhaaH}ElF((F&w*ly?iH3$!P
z?<^|y<jm-QMpiR*xh7~(Hz0E@95M&&18_L7KV5o}U_Zt`JRnP$)pPu+8q#@x)zJB{
zB}-U#0cOI*AutZq<$WuGv24ZjRHdyM@U0WB3+x`pyY_#i6C{jHYCg~qeD_cO^gUWa
z`V<qJP5d;q|KKP~A%6Qt?A`o%UbgyK61LA8RhEXznASEpl@5mFG%uv{;kRIgtUy%J
z;%)jA5PaBA{v4?iyuE5vNkRj}tlb~)7iDnw_f^9@VP5s7-VOlmH{(JHx`%d*=Ictf
zNl>~RnD;jY_Kx>|-e8)m(c)IY&C1sl6@`g$W_n%%US?f?B>6$n#9xP{X`{7g+axNv
zC8ZP8(he18<{1t;jjEM-nEl;_u_Q+;Wntp`dsFZaE+0?gnhl*yubFSND>B`RX&^o>
zr#@go+6(HGX&f1e@0Q3FwcJ<$izr-^Ha&3TKJvbRDr;*Z##L>lm`Cu5dId{^(6{76
zITZ^1)4iPgrU+Mk+VawtTg0lBr3tqjEu1X^NG+(*(E&}Pa#7|oU7+E`Q4Cy3^tT~P
znDfAO_U}08+pz4LD8{tydH)VmLZU6M)V_v8cPGe)(AX#y<7@1NO94rE8eT{;HXJT<
zwpPe^#L8Xw2|<(Xol77QsdqXw&Qcbf&UvKbyS}{Ou}tHt$hf%`w!7NVz91`;;A;$f
z*j@;-inLzb>!ZU&7X>y@Khr9cY)qC=LHgXu(<*5gL9vm^tWp-xjpSD3O9QZfmp5|A
zmZDYuK9pvBN18LY7X)p2&kK3^nSLi!Z2d~y=y0A?tOQ)>_7K@FGg`_j(zfeoHWxJk
z9y!ya(a%jmg-bv86q|4sf%O*5*j#Z8N`|#wuWJP8$_cWD?vRTX>6&Jyr-rD&?>UY)
z+s{#)ooi_6GVLu(C3E8hDqWwbgRL5Sr;E2Qq@MZt)*+Jv9reIhcC2>q{wTp0L5P-~
z_Y;4N&s9TLn4fBYOwUT^I9;lo+$IuUM*30Jcy_O35z^s5ZJ>PD_7#)(j^jyUNIh+_
zsIQ0K|A@rz@EyxXWrfxm<tB)Iq60*(@N0y<!2PAMrnvI=>bIi`j$+sx!Fv*9kxh1k
z*ZKiK5zl=&M-eY0Ok@^RIk9n}$nP2vVfE<0++TY21YhbB9&!LA_oEC7Y>s+@U?i2^
z(k+5|D!CZiHDBJDm*NZK=&6VW*@WCg+e7VC^!8Gw{H+)FzvO<(L$cBNe*_9<HoT`Y
ztb|I=INjnqkr!l12=2zWHggKRT(j=<kA^&G)BXNqHH03pdVVqrJ}z@Zc_n$`XwpUx
z^uDftQRtR&S}lkko$Zx}sp5_OMf%U^XQj$t?X>=pM&6iIU7dkZ`#hjcbF<`WJ5x1{
z#KJCVGkp3DUqpJBfd16JDOo-eh|OWu(|p5art0;i`%VUKKXvlDhUt8q)0?cg@7)my
zW-EI$;H9<?*O}>hym6dJnyjx6HosaR0rveWL1Xhmk_HjYZ*1)yJBH1C{rd<qpno%l
zQprq^k_0y`;ex)N+<8q;!|T`0GL2k$q(`KBHw)81H&maWPYFop6!XE6RRsBd5s`n`
z;7IskJQH5vMJzF+#3c-2Qz!{ojT6*l@VEVr$?PT;7q~69#Tk7}a4t6cbt)hOn9mR2
zIK}20wo-c<8~nC{gQqGc*)VtfUjh9gFu?g=p|{uO6{-TZP=rc`tnAB3#!KIW&pha{
zAJFhuRa@Ms<|_%hSJ=j|k)_fex*gY6A{$&K&`wW+e$dAi^xS=0rfKO5jSf_UH`l;-
z7*URuwyhu#L>`N<O6}M$zS&Yf%<wen+p#3&Uc}T|qq*IBN36F)wI-v1a0(3P$9SG1
z8NN}$_sA-tKPwM3(ZPD9Oaa+{+#z?~Z8@y2-a!@V5fSS>Kwsqw0z#z?V$OGr<CQKq
zWm+@bT7IYr|AQ!~5rKmI5(NXnm}2gPDm|aX-=!x@`sgFc$~|r#c?KOfVMEMgaEtEb
z5#Eb1DX^02S~&F(ivM(1!AsT1Oz#|zwVYrFQ)s6j?!Q&w0tM9~G1#rl_P(Kd%HVIP
zfJW*j8d!eAZFANy02L?#gdMyd++zz5ig#~Vy#9?~vbx7yud$h{yxN-gK9ycr4s*WY
z($D}u{AVb@qY3fZ>ZOEh@q)Gl`Shj(x%!f0$${?bd*!>3_Nz#f)X^)I!m!mVH3Ngt
zp<@hy7TSXm@<<*-4YGEB>HYAYo=8$(i8~#ivbJHO(^1*YE^OPtH)5jgPu4=U&x^E5
zP3N_YPhq&^aN5CPLN|WozD#rp-jm?ik`bpHo%sES#1B=-Ywg($qUK8Vw!J>Kl1!|1
zxFH%0=h%2fSQ3#TAft<#{WM;z-VJ#z0pT|a^2NnPSV2K+KU%&}djf6X$gRc=@<RoC
zAXRd=H~{R)Jqw8kK>!x>ob9t#9O>;jj*Iv$MqooAa2kmqjzwf*>eTXn#Zgm+$vZg~
z$?8M=SKAo|XW)1@&e}~rQ{jq-g4frpUw!LoM{{<jsH<0tH@G>h%ZzYK{Ad%7Z7$5$
z9ee-g4NG&I6{f{a>5cWsf2uJh(?f=kJj7sD{&c!!=SLqqDGZsWS-U3KiiJPS_=(z7
zrykbe1C}gUT#Fi8pIo+9mKF^V)?7vw`s8VXd*D$PCOO<6?ZgkFAVRqWxwL$cyA6B)
zw^kz3gt}^%wxVZg<_W&?v?WO|{>|vtpz$*@hUi}8IMEy8l-ey}joYnSzS~K5{7s`n
z|AwW*A4u`NS+CPTk=Vm@#gh{knu2*UOm{qmmaugG4FjHm81jf`o)Gq_>Sl02b5+Vq
zWj)kUcq=lqXaHWem?y?<T4^UFZ^S|RrAgKc(|$KpX%y50E&mHN-xW)d<5a(m!N%4t
z2+Kp_%Tre9BmMn@k!cU}AZ|GBa{P60E}mFcnbDk(M;nq4ygmzMBqqrW2<uNGwri;I
zaK29pFT(AIKk-BmZ>nPx`ltXZTgBuXki$J-wMYy=1zJNBWqPX+lIg`;(aj7FV;hr>
z^<uK^{jfSPO;nA3+TVH!)4l#T8U&5PqPWnzm@V_vR(!Zf$Nn9kK&N4m`SBdL(hrA)
z|3Hv-H4`wek=Ms-jRzT@0>5|Q#3<dQ;7IL{kIKu!wH#2%f5VY{U$Ng#D-#x*i){A>
zbrUUa*nu)ohlfNk;82S8qy6n$LVnEQJ|@cwHWo6dc0s<~`TpWttE!hHtx#N?$S0gY
zk4fvo3usq87+ImkWH?g{un$bgynX1u_y_az22H$l!23{~MwuF)VT5^<7m&5a&<3A-
z>W!O_ZWff$a9VPU-Bfjr^WSnW=RJUG`S8fxF4D<In2#)p;v1b{?kBBNZEatRPDoOm
zM@>rF4qM<RJ9>eT;!7aJQV{EbtWf?34viQgrMCM1y+0Hk>pF%h!gZH;RPmm&i<T;#
znX|M=wJw)ngKTsJIxWk3GQngqrBJo(x=xNkxtyJj{G>XzmJQodN1I|i#z_@_z--EG
z4Cd{`XL9Ee2OOQ)@Q2m6;=6A7ekF@!t5bJb{@>wT>)eu93L9G9=*ROPy#l!><(N@L
zW%YB3u4w0N+_3M)0|6*~s(uj^(A+N@c?~ya+u-br(3kY@;9tf733j+2#U{-D^g<6D
zI4hZRpu=JJ9Tu)rI5sX&<7DZfprZdSwqmR$FTAUp-A+CJhL<MLigqM(_@{I5p8U*^
zgg05hV4d~hKHie%G2(=6<+mKwwz7!9rJP8tVjNnpsIuiq|H-N8-g3wEeaQ$!p@t8#
zw1a+b9>#VMe&?iK{q4!)YBo8mti@|abXlSzR`biSyM<ZsXc0W?5h;U=y-}f<&owj9
zNpdMQJ*^}><WDbYcoi*eB6B}CLGkUl{cp5m2Pxh!WyW_mybtkzi7CpRnAVj3TKyR6
z0#277u&ng`L{b%h_Lm%=L3=mQhi&-zX=jmpo2Uqtkr<IN__g||J)}OtdDz=zY!>St
z#W{fTro#-w8?rZPg-`QK^sp%Ou!~!~`Jrvy-UJ~jMdZVRkLtEs4628khdk~+Z+n}c
zV$v@3($oGh3(_OKm1TrEe`*GPQar+yd|RHAj;f7d7MB2ld9=*S`W^2eTfO^Pb*#@D
zTu-F^+PEU}2U(8-xCV6BqaGTHN7NIkYpD$mHhbgfNO3A?Oh;G>c13HE@10e%=5@*W
zg@Ze<TtJ0n2CWb|<L6k861eYJ>^_YA_zDfCV;Gd_O^H4HpnYTxz!^sH*vd}GBxFZJ
z#{zNwCV>FI%`A`*<vIxMIHo}rBO0IG+1%F<9s|{`uhBkgQUpW3sW<Hv|6vfjPWY8R
zt;*LSp)6t+{}Gy|{^K`Qr6;O%Dw7(N=9QVFovcBeiigfkD?YNImxHftC2W$3JFnDe
zEA5F|!=aSbz*x$J^P$;y<R}U>EF$FuY%SL;5q4uOpTFyQ!@v3S6E@6MRU*mKU~!C=
zW`KBBI9Y$#E%oT3)p~{2xS>M*l~nsS@j5&%#rsETeY(1Kak;<CzU7HCJQL$tey+?o
z_zT_rMC$RY>rs@-8`Jaex5~UAcjP(SyF{u`_v%2;g@e{k;(@1^pF--VE8A*)t`#dq
zRrAYwNUnRyvg-E&+aw0g?i%I?M1F7+vGI%ya9F{8)Fb>52C?yl@mZNUs^ZV?5dWje
zT6y@L?9r@sRB2~bb{mTj*<{3jW6apt!Wb#c2qfS8w?Utp9oqlbnYv~p^BpwaCr_^f
zeG0(qy`J0~gK=}Um$L;-mh)OMerQ%&?*`bfg)HNks+F;#GgrV`t8CK;@|f*U)Uh97
zZd!M`XiTRaH`H-q)5?vDPt@$iZnK8EHnBbZu!T?i(U;*Tip9v4mKr}!dD`zd?5eT}
zVSJ3XuEgK-wqy}DGK^-C7oqQn62?(<_((<5@L7%RWI7tlk@SPULcQkI3$YwC4|C!m
z)pVF6^}hE{P>k0hU(qS_6n}TzxHnI6325!YG}l0?X}8AN$*D1j=fq{Aiv0z89?<lU
zCUw~<kfNQ9eiOAktP9|pf5E4!C@Y+Y=s@gL`iEeuT6NuvXVb8idOt$+`7blCatE>s
zb2f5y_5zag2TaysFb^6uNRsMz13#*Fsbwi3cqmdKU>|@sOkt`_JKi=vj7=7%c~V}A
zt~`B%6t6%sqfP7c7kBJyVOt$u=1UHMbBTz2+dtx2r=5Cl_1{gJW&*s*fsy3#yZw72
zXqo+yp%ou}kg1P%>y}OVakE7XB`GzKiaFdT2+g2<HT?3T`qI%(_I3qab!t*d(w0<k
z;AmFG*OXh75g_8VNA@Y{3FY!8Kk1}<uN0-KQ~h&xnWyM)ZfpS;)r@RzJT0Xw(s%+-
z!q;=df1;wKLBrt;>(p~wqPX~<gWz6sZZ$vI2Wls<gV-jJKgG*bLqm$6a39buH`5MA
zxXC_Z@CLVIh9b$u<-hr6>_BH+z9P4t@~{VhrKLHCOA7-=2{!}&>G00=dfKgUcXoZS
z7K0$2_@mgd|4tp1Uo@z%4ct4HPnY!vW82pIDew&o7V?}4=<)Sttq5ePL|M;7nU<kb
z(qfUtyU=62;y#BC-`NpvwBgvI95V2Qx5Hip;@Y1|vA`vm0-#g|<pmeO@5Zf&0|TA2
zI;pYl41wb1RRrQyDozB~4E)o!99P<MV?(Wf8&=rzhqI^0?d)_%3@l%9;1J8~B)w(h
zLu*Tu*4AQVjjtL6BNX?_pxQB$+%XtL^n>+C0(s|o>tcPc3m=63ij$aqd*q20Tkf62
zxkYlWoa74`vgIxgi?kuAgPY78OAJ|s+SJjAddRCQ8l#K#&dkiI`hm#di~9l_(La(5
zgUJsayE<)CypHwhQ6%$ubiHbkwYz1eF11!B=$pR{p=X6`Ce8VWwm-zK06(hBtYMF`
zp7cOVw-tDWdA;{zn+8)W%x_3Q(!DP_S}CKAsd!iisKS46Qdx8k`op}U*TqvBm2)3S
z*EEV6BPDARBy17&TUW4Nn)=uvN@$yJk5mY9;ojpftgNwjglKnE%t4^{N?=tkk@3vb
za8gvCnKqEDPBWTZ)G5V!kIh}Gf*$buc*e~B;jGz(ycteVMusKM3+&ZF9duj!K!2;R
zFr(A?+aa&9?-Uxyj=Nn_v?3=#eQsx&pz6zYN1H+0AjS@(8@tzdoCLbmR2&r7+Y(ox
z6QF;Lp&x#6E|Q=D;%>G3nBUUJnTfQI-Q>`*>S)lF<oYX<NmQuLtx3hjN|88I7L?8j
zh0i3$ZmY_{`1<OoR@O|;hy$IenDoUa0W$!xWrh@e^XWrhU~Tk?v20}ll#dEj+|b2I
zP~YEQfxR7zb8^9P^KI-<E^;(Dy)f~?rnl#R&hg*|fX2@xf^lJ^=YOkEMiyl;FDlDf
z_dGdZIGEdW*q$v>e#t}xqaxv@1e%|<!Qu4_@#5fcU(}&AH!D)h))mpgc7;Yg0nlVF
zFL{Ng4G_E2IS+Yd72q3Aa;Z5hQ=AO<^1F=C#9TR@KG#*zaG(vz;@qxKae)Ei%MO~U
z9HBx?%m0=1`m`dS)aJU7E`(NHJ_ywxWhG+l&n-u;w;O!Urm${VhzB8w2{4}FGtA9W
z+s<Vp{GqZ;MvtavPtrGlEpT#F%>MjFC<s=!t|{x4lvpfsmn{`1z_;lu4Y)u&0oUXx
z<0ri>BcE$6Ba{8g%lm<#F10hopcnV1&+<4Prxl-`0zZH5ow`9K40zbzU}Gz!>nho5
z>GnE3syjm1sH$jXCl)-LjvdEJfD|2MbtU8@mm~d^MIaiJJ4tz%b6SbUVJotdJqgT8
zu?Q;w^I<B}*O%v$cTS~#7lV=THg)hqx_2a&q0kX(`PYtd{n1IVhjr!xXPtLHZrN(<
z*JK)d5a88E?vBUyEMffwA;|q~-5-KydwL#qk=J9&w)a9`3~eVU&T2EL8YUe({9B<X
zcqF0@ge*jN7VS6b^`I16ZmOHwiM_+L74%J<;kUBO&|`A$gz^s~8EY8EVUeJoEgsx}
zWvuB{Qr%Zvn{=H`Yr`S!>#i7<6-zQs$vc5r6N}a8QcX<3w}J>(XRB}7a1ch)p?zY<
z>ybt5KLe8|)usc*5e15C`+jLu#d05krv4N#{NpK^lR!U|+mIH{LytT?_IYZCY4QKU
z!>|^@g%;+u0Nm3{k^!GWU)HxUuRs22QM#(;`+SIGVxLsg-s0MH2ufPn^m{dM`V3ur
zy=aB8+ErCIhq5JtX-izLct4E<`5h4i8P^-Wc!E(JL``&fg)nj_>^izp#y20#I5WzM
z7+<9QymuI(_<uV4%=mnqOkG>E!X5aW=w<wuq&1mlV-RXWF1Qb9Zb7zq7)~HWPZ*u$
zCAUiNJBUGTc*zio%z3B9xuL~%Y)tY{uG$8w6Z7v%N$mkHy=OJMWV$KjS-!5Q7auYb
zZr?IGOolL-@Vx>q!0M1S0k6t0KJTW7lLYm9m+q{QH|7TI0(SWF^~W&@^%Jq@QlM~J
zVq-o#C*Ja{FC=Ipl8^JUA*Tbs1JlbK*sjsA?Q$eec{y9HO$C$Pkq;DZ`MbH?3oNjy
z+rUKnSqq`ixET6OQ-l3Ca=@{sLQ`-WRT|bfx*;cck@N8WdHYad%BqYfzdC70t0YQ1
zlTAo6p54>zc8pgwigoYNr&h=xE&qbT2j0j59aL79a%23O|4#ONxW4X#^|=joVp7ah
z2?tQCs$Su~Kg20+7qs6izp+hLe_g`vuBS8ANn4NzQ?<N*iihaTb3uRB2fQqh+E2CI
z(<v9}yv*6CEBCCYHunuzFP3w9WcF^4r^|<+OK(48QL`sRe=q2i{cSdsZKc<6@~7bD
zkinRBFad^~oNb!H?tlu>x|*l`@3lG_hvh%fT8-{1NAb<(->e>or1%*jyMvOQZUl+T
zdSEOz7W5Hayr0`U$;yOSQhYYY#AMHM8V|lbvr27>O=v2h2DQ1qK8|qMgppD<3G_bi
zQ!!N9BmJRs`cWYcgub|a`J-zy{!!DU)<C9{xLP3+OHGQKCwo`E8O6kQOD-H&Lm#$I
zC8dEfLZoX&nkYW(ke}J&hv;%3abQ{KT{5ZvB9R>JiEtSe`EZk^vg|+c42LV4AwF|L
z5^%rjla~6Tjjx&Uc-P=AEfL%7oh0cmhT0DE@B6S)<fAhgZ2;bqwQI{&3l3r9!nQ^R
zS1r=GhI0v_HXOJHSdYFP7KFKCQf}6o8FR=da#%59#YP3bqKiqoZAdfhV<%&We$pA$
z$?i*d{LIy>Y#8ceronBZ9<rVZWFWDo#wl%5+miM)5eN?vFLt}Xd1-0wn=1>?>fP#5
zx*q<)f=3XV_PT$TM)-ofDkEt&X=>+OqP%udFpB{bQ>#T*LJ2kMnk0NU2Hnzhw6$=s
zw?6UDQ+K3#uHdX@#I7!;@s|mHoO~83@%TdxM~%T*roc@0?(Old@9UVUu7)pL-$1Q*
z12LVa=fIvZuO`SagSC5@<dUxwPIBk%w>K})&Ujr?$AW>{5C4G(6Ei_%ZViWlh}x@{
zn{Zs!8h4q1GN%036T2|}pY@(cFdt1Kuzf%Xd2*9R!m`UQw>1Bg8kaUUfYa|AEaW`1
zt*N$AE<nOFaaN2DiGmr?Ml|ZqKY%jC-l2$pkY7CViM2k_q=g_DUgEo2S_6r3=WmR7
z=aeke;LAe7Eip$gq5|O5tJ$t%eojG1K}Ki7Bj;Q7Bq2Jk2%&_jL?-;d9jDt@{4bwp
znXp-_>Vn^I!%=J{J=&!WtS>QGVij3QJJi%iCdWIUp?oRe*>6UX*bF^G3GX&^0_|vB
z6aPvoq`L@*W--7?vAOT4|B*0*8|jkp&I5;OY*p%+`ViK&udd4tF3%6bLFWOYLu9s6
zPi~0OL#KP8=;M^rj*PAY$=b$)bNmFn`shB}h<&a7Y>EEUi6_-7CsYR+D*3=wOH6~d
zoeYQVNbak=z#iL1(^!0NCY&8RS{W}|^FU*k#e(gKIuS<GOeuk9LC4`nR0&O;-7hid
zt_VL(zR_IXtnhsG*50wSx6GjL3_!&~J|5W+V=F3qDWL?Ig{0s|)XGoKNB5=EpDZB8
z#@@NGsseY|o(|Qb=O4es)x5d(%QqvS{}`hhd+Jly&`Pm^iX`QJ6c#nDZ|KChJM<3j
znOz&U-j#kb2+dr6iF@Dh1wMW4b|MO09zLt5q{kG7M=31Ux={4&<xq~KknfyUehYRM
zEY}P{;%(5+H1Fz83gH5EK6sAieUw(4Cs(i|behld-#iPYD2%-6SDB?0oLaB*z>Ft6
zC|>j%$W}eqb;~Xlzlhma!-;REr<UV68Ch9Bp0Qy4r0s>%5a~I+J~uZ#F(!FYQppn<
z;Jg|CKLA=lrN6+>GzY<l<DPQrAkLH~&{N`vu)!0s@tgIy>6zhhVbYn}f)uR&DG7eG
zxC?CU$_m+@2!IC8RGLi4ZrFb+D~42n6~4rF=>@HNTFtq+Q_1O{Y3DwnzY_x~b?#k%
z>~CSq6f!tJ>iJ|#<q=Z5(lDheVf3<6)l7+RMCK~Hlp^fY(x?NI@A0BF8F+4O1MO{{
zj4LPKCj&QgWb6))6<oe-1<V=i91-Ay3DbF81%=WkCi}Sl`pG!(`+B_l<qk4VPbP#R
zP38Ijz0N7^zvPSb?c9mP^`>LOFJc#AZf~cIS#xtUc@d#Y96Qd;{wleC>guW(rci)(
z3iGfa|FU7SEfPf_(Z-bPWSj@CP9+gke+`O|W5V!@qNEgfkGq@M{cj+&9oWBq4W54E
zd&c62DJLd~;?dg3xaE=(7!!^q9wu2&=N9RXAq`*UOj4sfmj)dJ8ta;AMcz$MTel$*
zJhkia&1Zx0>Zkw0bwk52ZPsk0?sy5uht9>#+yqsP1z5NAb&7We@Tn<Q9wE?NWq{Z>
z5myUn?TuiJRYf@kPkMXB-f-de2!e`HT-{Y@<boDYuc8>xD<o)v`Tu$!vk7DDqS5Dv
z#h>8kot4Z#;i4d0WPgx^J@ShVh9yj&bt}SM4Y7aaT70*^fDd$HUj-^jBt&bSqHu@N
zI6{$(JP}MfwGYRrNSRw2W8LcI@SFJ<-d!*UQNzY!&b>^|^u=a+#cSw65^smZ$6tV;
z{8siCIwE)^XOp4;N}@v}3Zd2Msn8C(8LvKi3uAyyaD4k3yzs&jc+;z(<~*m^qSPpB
z$dCy?+ZdjduNjYzpScr*gYB8%aSPu1`<qZRJjj$|+9zu8ei^ei3c35Tj&W`=Zn5E4
z{P2Z0{`tY{xREiIlc&u@;;Maw^0%O(q6}*0sgT5+7as$L7I6$}$^XS;D6!Rbtbf~A
zd*DT+jy07R9Tl47gPW0EeItAwXlP^N8d~9K@Q{645GNhXQ?VUnRZM=U!<rS}VcY|+
zVBwup5I<%L?wht7?;mJJn1t^1>^aJO7bZbE%1V$#t$kD`-_Uh519rY#I$Ud9ECQ>g
z&WKjA`VA<8I1{cb2pWxr6?u`=BD_e9t279xm?(&I(2?Kf)--ZHFdZCkp}BcLEu5$P
zi($+PokRf<1A-J8BcYusk!1S6<sM2cT;pQ+5wD}HEqPoml~~EAAcj{twMMyLdF^?T
zDhEDx;I#{WmcHoASwG`A>6kDQj8``!Uy(h(m2lhox+Y~!o%Il19gPW<Xe%ycsDvkK
zX<RNW7Q-fYw+l@QHc-4xMn+70)<}Z0oNYYOw?}#HZ{C0}`#(yKp?3b)7p^@b9+ctf
z`9=_IRaHgsAA2vJV9uEUZzAUixh`g`C8G$G=bF>VXJbPv6U$jN;2$!3lu0A)kiMML
zA5$uM1offNZ#)(+xD|tWS_m~n6c?4Eiv1UdgNCv0)^=ii8PFLSP#W`$2n|okFvmaM
zzL#Nc?nET)#o`a&Mz&Wl<5dN<#mc#d8&knn%RJyDkUgAcS7{k*Bzi?xin1E+>l`CN
zV+<_4vajgY%F}z~uHJq~Ghjl`U6mE?xa)r0HghabcripxX(7sZPP6<-d^*lVESWRU
zNpHzW%tz-af7sf3Dqnep(y>!Tr-If5vD{O!!vStqmdX*osrwJX#@8Q(r;lRg&#Pg0
z<3t2{h2eVtaPGbBXynN%Q>RYC_Vp|A<(gC~KlZdA@u9uslF$b>Lg_JIBz{*yYI=*z
zu}&KKy#iceOyi#<+0)WPDT<v0-|6>F-p{^ExTTu%0}S-{>S-#{AY~xF3OxjEmfKp1
zN+t!>@pK`nrLQe=Pv3+2wKSt=PRN_AWn)5&O{}QY^9W=WlS%)PB72TFa;k}zQuqR&
zCMJ<y)#XBUX^6Hd$vldrBRr1T!VJHzU4;)u-GOBbnH)NnIizm88IQlU4A(NOcl=Oa
zl%^lRHxi~|=7#F*Y#xCes8@-kFXEJ8X(CEf5>1K>p%jimyG#Tr{O;T_&|lE^U-F`$
zy#kpfVR&uHd|Wfg69q~8@X<eCM4H+MfkfYkaYKw93L=c-)ULI`D>sd&1Fi~R|NN!0
zpGK0uX_yZ~)kTPQbG?k3;v8%jgKuXeP969S_3cEknJ`z*4_{!;Gw<V(n-Va3!gSnq
zcmux4>t-$k&W#?Af+)s)iIw?m8E5J8xkXX7%{9zRm5K>>Jc|c!oxt<R>xe%69&dfS
zT|t}4yzQM7GG|1N%Agz-JM|$vc`x&<Sao9O>XmrzgOvyh6FEv`R{hgtT!lted8d$m
zqMpZOTcUQ|8Y~IF0ZU(b81WH9apRoXIQG(~=(G+cKkG3_ojV4UKP11FzVKZd!e<Dg
zM5E&I(M4H`avn~!sR0c>rp<}S%S}c}<9Gydud%dr;%_KNFfWBlO~-Rn1rF~@M2nV5
zyovbx`Rgz6z-ymK(&gS+(DSVS`rCzshXK!&jZ)W4rIRrboD>cDrzyz1fP<!f@a2vz
zj9e+aPlPi4FI8av4PF~i&qdlTmK$wH6Iv{MFoW3%19*1yDaJCbTeT6%1tr{IT6kPn
z6&`!xW88Lw5RHsjsf({Y10alEn$a($^P14DcYH4PY+l!IlKL7ia+~tC;>_*6O{ur{
z4_>AaMJx?H&75fbSa4)D?qBp1yvTUP!_+&3l37tQ|8?`IJ5yn@Othxd!hQ_ksXfi$
z75&8v_oMIe(h2Eq-xn9|##xv7gI?nBd*!FX{Ft<(gMy{dQeot5qQvU;5WKHmq1k1h
z*LU}YKFIS~dfKFn!3E}uWXiN~^+fV9CMG(022s<8U{F*Lo9=?4K@=V(ll!qfNT)y+
z%F0B@yYeFaqBojzM`;F`qIjm2GJuxmjOCYYOx0s-I*W|@n|prr-+dE?CC%ZeT)6O}
zT$eZ>MVV!r6|Xk8SSiZJOq$vdx+`-|CZ++c*o|#WY)43c=5Jr)pIK*c{WVd{zHA2%
zZzg;SW>O?tm|g762~8`;;#E}$52klP2-`!OzW(<yKjm@%KfLhCN)oRYv@|s<NBPxc
z9K?Sfe-lNX0Zdxd+K(O~FES!8(o%Ubr4ohaCbSa;BBwUUoTB`z?@wQ6^v&;`*D|+_
z8S7S&lnh(O9zFNOZ3v<;acuJ{=1};A(8n+()?WCVy}v(BZukzFzZ0s>^&(aFkm>?7
zVLop0-unAdsF#g}3ZIcV<C(Y#@NQ!-SDZf0q*n)%Fgn@?uI?_1hcGrKglHn72Dlb)
zesLvkm9@$u*~$$~XpCV@m4i<_wr$;p#{7d=^zKLa`0Ydxp-7Lo%mwB4b6%vSBn0Wv
zOfRRq7c-eMIahLWCT@T7HO#$XGz|j9SoP`0So+&h#z2QNw~BZwS*flZO|<GallfbD
z4aKw|OU97z!*_501W$Ug3safYjBOQ&t0Z@lBD5D)BFo&=+6mr_XB11k7eh5ptp6QZ
z)0mk$)}4knU*-P&tCr%(T3Xaa-?~WqWsMkcFgqBd=jKNb6$SLhq7>BlOu@(hyc^Me
zHb~mN5$`fzDf@kl002M$Nkl<ZieVrVS5l}~JVvZfXfK|OF2?NT)(pkFUp$22k?zPj
zu?rtR@H`5LmU1^^Vk!oD_tLL-ei+ar>d0i9(kw%7cjP4|VgIfZ7&jykPBdiuP4UIP
zt-J6|@o~1FYJkXF7wLm|-3&?hqdY+%zWXljnbNp+G!WLxj!`(H63#Uan?_XEwo>6B
z!ijUni%;+1nwG6c44D@8ZcH{R577`_TZ7-3plaTvVHm`@<<HE}*svXH9#5k*hLjh+
zuAv}AGO%r;><pl@d`Iu4WLg&ptd5P>vLDhkr5`eN1{8sj3o(TM$oTRTKTCxoJLnZ`
z5!q~rY*0dE+W2gRdUEeMrMGTrm=x&}%ehLbWcrn5tgvO?DvvXzkwML@>+*d0r<6Xz
zv;F6egS4&ZwhO=ME0J+O>puCN#9!7B9YBYLj_a{JaT^x=YYB`Ar!^t6qDSr}38-w#
zp3kbp=uq(WJJ`u~D#rM;D(!87JX6M;ndaM?7)r)iOB%uS=EUB6dft2cc+S^-f3ki#
zWkg7+jIlB}`iDwebVTh`H96s_nG+H0ZH=_Un`zwn6^Z%948Lh%*i$9uKlL{5yUmsT
z@0GjyYQO9U*_TS1nFHeH$yCn$P?vWCtG-`><C)nsd^e$_q!jU!7hus#cPjHy(T}B8
z;oqFtDXBS__srXv${YkuW$E~C$w$~<?8eOhbb>JM*;L^zVifHW(naZH_j<`H{g%?V
zhjT5RT{y1F%>xP9|5;>Hrr}5A#BrE3`5HKxwPM%W<=D)%omN;$MXVZmg|2w_^S6)?
z#iWoj!C0zt8J^fao?(b~m_ejdh`$YTQV&CGAIj*wD5%C(^B5;HCjVnX*V8Sq@KwXc
z%@mt{_<#tnRE!!F#PCvk`1(siJRgL_j)D`>mG!l6GC^`DV`C-6ME@*ltw5N?LoH*h
zpd@5b^bkQtO>M?4kSK}Xgk6e)E{vnUt4h0-WlY+0*C0)M##>p(td92zt)zaDDNK#^
zo2Y@%wC7n$B^P<=eUwZ=MFCar>;3j3KN|Qo+asr4(Dlsx2bDx$88DvRhVkTT9H&g<
z%1b@u@A;K}Y?Zy-g5f|!0F&#%hvAAR_W#bKkeOS^Eu4u8y_EX{qC%OUhWmkM6kgr%
z8~GI1hcbbP(&AXPx2q5{_BQMoISbjx*5Ur=mJxC0t)K`0KlaW7u<P^A|L?dFHv&nB
z5C~c*(o*W~ZmV3ot?gEs_2zfmI=OAPo89iV+uH3`Tiul^v`~sW5#k;~+&%x-=lSIq
z0t5(M*>(T%Y$eI>_m$`QJfG*|Vyg(+XAgj#eK-m(_AE=y@g+JcuQW>c{nuAW6Ud@d
z%X&TV-LGlIz+`HU5XDS8<zPz_=-YuUf=Ecp<Yz9tm`dZp+Pv%${r(jk2ION84bpAP
zo)7rV!RdyhQ@k5xB{Du$uvlj(jgKkUlMk-aRdgxJiilF?yhYOLo${X%iGps1GUL-8
zBdyKbhv>w8_<`r?ZcV{NNbR6#^y$}pN%x>^#|3ri-6tN^_kQ~{DW<bEfbqghRUPap
zB3FSNHT}$6C?0y9(n(MLklg<t{@Vk{nxqB7<1DSq3o0*<4G3j#aje+0(o*6`k$Oiv
zwx3I`K7~EgCM!PKPwmiv2>`*1s|W1C@Vo&Qt<x|v_Eo4Ifyj8t;Bc(%5Ye*E2Sr3c
ztvR1D0QrQ6C<gh@V@Bp&S%e9x5wR1~ku&X~li)p#`oqH?I0KRQHAnB~q0Wgrum}pR
zjW$hJaXe}|i!JfRD;37#On@;=0LbjFS<+1yJJcgbFU0QcA|x(770V&HZmrd2dhgMH
z*K_L#tC<n6Xe>fyzEN0GD6BMmqh~YummD7Z-F!LL{G|iyp}!+ep6iKva`WI7`wl+v
zAOiBKamvVG4y4rtWZqz=A?X2Qtk1j`*#YUVB2N*kI*1-Ud9wQ^gaeQC<rFA;pL-|B
zVYaD#<}^G{@8b^tllzD1q7z)8)nO+57|rDQ1Ez#{AHs{wlkWs0KJ$RW^Q~DQZQZ`C
z*$L7^rL-?;aEk5(D$560*nbnA5v;~TFa(>wjeZMXF={^iG^-fx&wm!*Fgm2p3=(c$
zyu%3lh=tAp*aHLm)ukRp1-UtT_rP;4A^i%|q`@6j>)oa^Po3|)O6OfeE-uPUKVg*b
zy}uPNh8^UQ726aKGr&`fBpNEB>SUX`P0%IGRlT%E!jou1o-frk<vUH2qeFn7;kAb$
zdLVF^$A1i8dr#0oSH%~$-QBg7YBe`0F!sZe4>S?HZD)N{&A&?_$Pr=SU<>nS@+M51
z*EMO*9CCjv_0R*WxrzXg_(YVevvlVdKCMOR8JabFsr2-3)aN(L6%_G+cdsGI?HPp3
z;T6v{`0Qo8?vWqwKZYo#Gz^xI2TZ;$Pme?yikuOkx(-6FLalxQ<lJ!7{^NR!eDJf>
z7>by6wviTsARRF=!9@&3+PMH`K1d9s@!FJ;0M2bQ-5mmFUZQV*{Wi^}E#USQ&+9+G
z`E&VEL%~vg`cVuKyIC`j@5Wo&iYK3Y?bQbxW=OOUW((gpVZnp|4~6pF;lW{A4H*ML
zB62M~U>t*O^{T0+_)9R)=U%U}VqPIExb76%y^ir1oCwLRu5DBgJm!E%gi({T`Pdl^
zL~sZS@zu^9dAjzl@9U;(&QnO&ZoT->Z}rQkS7`RUd90CwC=eL)?wS9#l)TFZa!=DS
z9CYj{)mtw<qOB9Mw9pq1VIrO;YEh`WOAQsffD56f&{+#{ha8TaRKxK>vwX4wesiA>
z*|}u&Qe>^6!*UxB0Z{I6f)C8o+Gcj{$x)5pDf%Ur_65@>QbGO={r1a0P;=Hy#SsZ)
z#tMJ(kz5Wj_gE-)QB{im?RWR;l=+$D!>*)2(>Ik%b@Eig=giA#UPg!4z}2Frj9%_t
zfz6bCB4Wkg_RQwPHAB8g#9d8lY3Nr6Ua8SE(e4;MY!3qI8e(U;f{<1^S;0a`M5%3-
z-?T*D&-&TtaJr8~5pm#)@YLDYp>p0e-K1vo8XCHfJk)RpTE-`CMj+%69spe8Tx*J7
zpBnoEb^T}W(c-jdcVAy;gWkOV*Lt_Xm%?{+|6naxet0j68XmlUo)M84E9!hYL#4rB
zbZrAOdIk7n)N8I)8+>9S0=!><(IOK{$pvN)_wZK=9akoRXM-)H_|wR|5Aut5VZ&%(
zA=!f{1)=Qs<XJ&l-{Z%ep=nGxItV8LxyST#BCW^bNtiD(g8RvN3j&sg!!A;RE%IYN
z%nms1H|(A6EPGyR{4|tzSeFSbLdIfI77RWW<k?b>fjy5(N>F4X=b80}IGpj&6)(>>
zgPa}Ij+PcRGugx9HB$)ST-sdiMkhdnvk(A#j2j-h1@18~<FDRuPd|8ZJ%yIey65{k
z_qcRugi*_0t$qG6J-K|F;wBN#0MF~glNXwLsiqTd-BDMiRZsjxPpzQ-5Bbs27~;yU
zh*}smWw_g@jQ73n<%j*pXrb{?Y8FgMgom_i+p>pr|MTyW%b86p%UF&`Edf!{&eLUa
zGTs*$oXdCS=#-o8)pZx0rr^#xz3}M$dhGo=9XBr>V=Lk4ra)QP#9%Qz90M#0n;+3I
z>LQIWialnpeSEPjQ^al(JlvOkGZ?wy#i@PPaNz2^JUEM%P$_hksol9wFZ}qw^j<&;
zEwQKpW9#!zC7+nOK1O$kM@<tQUFtfugc?qnCts{Mtc=_8URK$RbX|xs`K~$OB3{L&
zZ3WuNeVMGq<mfqb=cv1GoBs6sw|J5-?bhNn|NLuo`)7#+qVCMx8H@GI@+UM%c)D?r
z_M$vxUH?U0efmO$Q(@c(r8|Gsn|kyQkE48L19R5T^ZiKEYNRtwH}i-h#5_DT8wIV7
znpvTw?-*`?Uz|~E7dt@2i~6^q9O8WAE&jo#sBmr|+{Wwr027_4m597%tu&2(dG~F2
z`D6ds3!X#r$gyv>PmQ*6wrH@`xVuzML;=K3j91*ubhWJR!oY0jet3V~<@}EBFq+>O
zEX-&RpxnHjEz~^DjLyayRa8cb(j~wG8a4bZkMqM_!aZheHLB_vuFxw^ju~|ExaySo
zbZCIqvj@2=Uelw0dRl=*b^4O3VM{j|S>*ZW753^I|M71+@8r1@s@sH7`(L!IF-db%
z!qFf4z-#Cc^?!UR3Rgm1lFlCRfOdn`MA<$2<e!z2HCMjvI~0(VMG;|YqBK#bqB@5)
zrLu3BYDpP!QANXZG`J1=!i&3*&m0WFYG3x4-8J4X>V<StPw1wbZ`B-XKp8FTYOK_Y
zzx=MY1g29*jP<erTYF$gunJMmmt6EY-F3&cN|`XI_n&`Mzxc(&nof-Zf1=X{j7EZI
z#vcNZsq0bB6Bl2jfBwp?q<4gB<C{<E*Em~a=n!GiY5l;(&vZtfZ~Nm4@)|Sr4*S?B
zkPmn`Y=9eLjaF~S&YbN*D-m;ZKm9dbPW=%}eKfML<DFMX%X?O#+0=w$4=5Dm@Vc_u
z968Vj$0H3zfERD*BU(6#`A*=h3{ii^@E;27P}`-Qan0Ugcoe2&45w_@frMl4g-8E4
zmpj613M<Q0S>LJYNfaETXw~a$-c?ITI*MB_f(%*3t$V0g3o%HZqNxz~et<WgncFpX
z$bBKM{w7SY2}A@E7l9Fg-0Ym3A_dOB8sdf5$*eubIE`bFOWRS`gooV+Yh$DmDU`Nh
zSF6%OO4y7HWzB*y*=73y7K+V5wjd~Dl32bhG^w#n4T13#sOeI8bPTz49wBjD3$QmB
za=|EelvV4L?zu`#KVFy53f2obHT00Aa1Vl><t(@EsdsjpL<B}sUG|RAzwiC@Aivqg
zhHxGs$6AG1afRB7-5QGSLfH%i9#PJ-!bRY8A7OR%@}p&vjea|AZejEr1quUuAk?^(
zBIDyByMx+QTcN6^Ze^y;)M-mj(egjMuEx|Uyt3EjX4f~+8#D_Guk984!6{J(A$%=_
zild^qP#=4P!M(quQO)E~1(CO8?ffDP`Q3Ny3<Bd2_Zp1|q(ZPL`qStaN1kFgA#a2v
zLwxMO4iJA9J7coeKebM!7o4rctSo)zVk+E{L|n0Qv$p2$QgnPabMb^9xyK*&9~07j
zg+ED!HT6^jr7d0Jbe%RUTL1le9$t%SYAQmpIPnfmiivi;F>CWTu-3^C-2H{iBig*f
z&TQ`mhkvf612=Wh8mFd3$4yRAM#dZ^4ZWzHO;Ji4Dr3G;N<rC(!01q0M(e_=JjKRl
zp}#d^LEepU5w7IaH0R+NY%9^`<&SCmrI%|#Ml=>-kCzT|=LncPH+c4J&+PmKq4ZN(
zkKW<~SRL^KM3EkWmDl#{=nU|QR)R_3eoh4;hRwZ7PM)bn2zzT^*rxEbG{Re{!2m7l
z1Rv>=gz!m}&rpaaV2H7HLXCKHCQm<JSDl}(-&2G!H8lmC?!z-oZ&boblgKxYV11qR
z6j%DW`u2D3(mXs%J7{lp-&cODV&EB3mZwru25iJmp5g>qADDy{UmVXcA#n&LyMvtF
z>SpjCIEM$rF!0Fv_15!<Ax%N(2m#0)T__m{np@v_N&iMw+0Jg}X7xeX6YDHdQC6d`
z{rVwYbNU?B=P%clw|q<1!6Dp_3j8r<`91t_3N8kn9CEg=xZ_`S%k>v3yl1ywdf=z}
z$)oSd55vtaia*-v@(GEcvhZZZWm3#(WwCC!u!%~sF{&XP?YA#%b;ZawY^&FWpPxhF
z#VCv-^wnMV7P3PE0#C0>ca<3BQrH9xVZgv2PeDUo6)lafRzh4XBn>*rUKnGv;!qPN
zjONq_UyFIVdRuGN;D3UC`m=j=Vs?@WElmCM|Dp}h!>F1fRg!XKBPuCX@d&9y<Yg9B
z#Oiy$`4{K;uGzLyzrE+ndZW1?fv8&B%REKOCL|z?Vu&(E-qo;6m3t7+pk<Nt-8JDL
z(6@*DzfNTyH&tN>8y4O-fLC5IG>T(ieW-77xM8U&Q*i-3wV_1UpWUHk3PYWC*;M_<
z3%SZn&!883vDz}P#rr^CaQJNp#&WP5qX2~=^Ev0hE1be!7S<gQ5~e*hRho9`h16<*
zhmsc3M`1$T0)|?U>wYy11Z#5Ubj`{}Q3FZ3Tk5ot(D=rhK`QYY)>r|aKmxqzdtX(B
zmvulBvyRh+crt(W%w}jxsx#8|pcpmcnVV!rD?$;=nk_A@mDHF7Wo6FheebBOb%Nr0
zIZr5RDaq+n+8!d5ut@9S`KeC%JPdSfI`-f2aEb&9Zq&qyN?&+_`W{@NzL`^1NT2aj
zsUwjZ6GGqIQdQ8R&_+u|J<3l%eXb`c<?L|1v}}(eGHB(>*}H!6GzC-atKBpYXqp3%
z{V)Ott;3q(-W>?7Yv$|??%qQwn0A~65sHnERxBxNJ1Ab;0DR)69H-N0k@fI<xk{U2
zo;8F|Xi@tf+EW7e==4nC_@p@p1=vvb$1|)ou7#BPAjmdS$g$B6oE-2NP~0vN-fH2;
zrL;Ko@efr(Jo{~O7jSLp8=|P)RO-cKldEbG5<T$CC^fb<D&7wd955WhkmrlgZE_cX
zIHRY0V>pLBD#<N^241N7$7eZ%p<(epv)<>AgRI7GqLzz4Ox~2a0p4eL>xTf}-bs(_
zu27wE`do@f`K#Hx2b`hq<{b5(b`v!}E>Utohn7G4XZ_?a%Q?H`3KtbS@3Ey5#zHfQ
zsf33&P#bH2>fYHpYi_81|IRLDW@gaUq(C93+=YTpE^B+6YVtRFCOQ({;Q8}#|N29V
zEZp0wWe+(Q8qrc1fwtt;>ZBWPX1{?a#WsfwpMgtFd8^84v$}K%3iin-z;7u^wrsNs
zd&_mP;mPrY??X3A^Pm%q0JO+1QkDt|=qF8^+SDn?t|&S^%>UhI-_{uyUPP<S7`)b@
zKo1n)CH|cCF7`MoDTV&*vs{r;^DuYO-`R{E7EOc+26P}AEGV9|g*@7%wJ4P|rFo0b
zRM2BT)t22;ltK<dFUHe7yX)kil%({qiMT|7X;mYI8Zmy+Md*r*Ks}$|q^T(=Xl1op
za@`{Gf5Vt_nW}S_%O~b669W%DM&rxju06<;EX^i4!B;EFi*#jWtCplp;_pnXUT-3o
zOy@4ns~emdymPzr^PEf?!r8YdmVjX9oL`Nl@N^fQ<`2&Gb(qvj>e>^((fbc03!rp$
z;sv%aYhU<redi4Q_No8W()19me)Tck`@{cLCW?2~)X6R?rUPe3^rTes)n_S+h)xr{
z+Y2_iG>bs?$dp4f+WOLZH-A@??)}NHbn)r))LOJjf5wpf#FkD)#SLiN)>1c*;Fv^B
zj*n9s#=iH~7U>G=Lc~!MX2+IQdMmG2vu1%y(CM$7O!^b8taq2xXzSA`!L#N-=S#J#
z)c9wxB5<nta_#~tzF1dNty6EEsVJNn7OmXZZgPh0GMj=d*c~*R!qqc08(qb4xTQ8k
zQ5YH9FnUEnubr}u)1rRIPR%+0R(;{_8<iF@sFlw?s4xHcPcYR)wdZVA2g^4n%EZaZ
zilRnCds(i+7hR^Wf8!2P;6jK1cuL>E$<YuTr|`m^^ndplhvJi`xacaAbs7ruXvc_r
z89ITqJ7ffKejvYw=gy^L7KIhjU-~iL4|S3-NBWH!8QR$PXk@YMnX?sPwLuUYT5{Gq
zT`QD5fDl98C`8WmHg2KV@9`N*N=(tEQv&tUmIh7FByEFkFJ~r9)uc#tSK4>Bwo~87
zFNr!0CE8VjvQ8LE1fl(A{4`{5YHZS_U%nILCx^``fIR$2hTUU|@7{a&KNS!hca6P)
zF>-)BznFK1uUQ!6U}Ca1zW%yCSh85D7cbQX*WZm+6``l!&7%lN1mSPgNLhM?uDEci
zhW4z{J>U90R@f{&b+!SA9`^3Qz$mpgm#dCGmvbhjYRRRa)wbFv)PYbj^^~i1&*yJZ
zR-6@aGYs$?!x*YIS638jHyf3aI!$L^d#lPHdsWT7bM>`v-mS$b8O9`S4PFnK-8l%u
zP~R$*)VF9p6`SXrd9$uBqOb>wMru0NqYm=qET`PSz|9Uu1`GZk0g+9@U^5Iq3;zpD
zn?linSG08DES*A_=WXBqmBJo-SZfMeQJ7(>ef~P@n!9!3k~HP4eNI35Z>(PEt!|G@
zXg5HNyukB|z1xLg+jnkoSWuVDgH=&qsXgueN~68U<)6Jx9nY<#keTg8uiBbh2!G^p
z?7y#nh?1$w9*k+ww}Lzz4xY#B?x(DYQ{+R-HEZqDNe<n>j5Mu(>2<B4WkT{f3w8FD
zcOVZ;(BrReq1rd?%sPB@{oUWwqKPf~^?&|KwS62kgwWW@X$WXRifE^oHr^9s_Cpv>
z!eU?^Gh+$$B5xDHyUkVRW)J*0D!nUIy5~;9P%-$N{b^lO?4w=eN~PEyz)agI%;zj9
zthmpY;&uJ&h{`F_Y}!#|W=|vUzfPO6vghL&o|!@}tVjO#fN3264&d2}n~|jVA6rSK
z_9aSQkfsa%>0a&W`JL94btvJaOZ2_lFH}Zih-wSA>8(GmRB{%@EWj!3%_DE{o-y0M
zk?kA#_9Ol>YTx%h)AO6*oab!9gh@{KX(=JRb|bApk~Mw7Y5L07%k<}GR;pt#NjH7z
z4xP6&n-)=x+O%ntHkI~k(&TzY&%09he`mgSty!++YqsJc!xHN=MdzM#nNFLNEWd8@
ze=D}*L5b8Xgu@>8bHGBy`!}k1Pm>nSO4H2KuGKAt9ok&crL+tLgN_ch5qj=~+mE>H
z!`)_k7CSRdA3pPn)-746Bto{X{PZ_5K0U0L*OVzbiaO!Y=ajQ=&}HW?ppW{y`sxo}
z(7ZV*dijCZb?TzIIs;GXHFtbpzL5`W*}6)_M*FJCcb2|*!!?@ZvrC`*>MxZv{X~87
zpFgh!QxnxlJC^lZ3U%u3pVN8feKi9Y0xkX<yj0sU3RbyLX>03v*n#!lm+I&Rei!J0
zUw;QKQlfX?d7lC`ZP3ad1pmdl`?4!^$_caZ&U9*hPQEs7=tA*@nfCfqu#PZZB8hAS
z*f~>;GjO@{%$&?b5Kbfw#TXFC$1xwSx3Ifr&e0h+en~f6bD<IfyS3wk4^`Sn+qApx
zLXda9EyoB4yINJS^+T2SAQTn7tc|&6Yv$Bbsr<W8-@5B+{p5GA>!gcr(X~{<jSm?l
zhirr1*lhJm{5AKsd-Q`JW@+cz_q29%9yKt@c|(~!<*dtf(PdOmO^s1|Ly<ltRNRkx
z9#*W&Jl%&I7OLyhPXt3xobLU>mpBLXv#-d}`t>_B^E0<;5ymM`WQWH~)LfoR2Z>VU
z7dPt5Kl=g7c{V+ZOSECl2E|`~tL_AT20J6U=Cy3EDW(|CJLI9Br~m%Rw-wf3tL1Mk
zcm0FS=;|8~qtE>F<vJZfD$KuE8+Yx}niX`pm`SC0!uJlp0$#*A1EYcIipRD6+S8SC
zVz$n?@$0JUen4wV8WnTgxpWA*N*PI!YAD&Jt#3Wg<R<CNOYYQt--*$>74K+sZYf^l
zUhsdA!jCuU!i&$*6#CxOmlf!PSKdWIG^No*ds99UOnb@IW&Xw+do3@Y1@Fz-<ESG%
zB~iutt%o@`6m324glt`U%h!?X9@dNR7b|XJIG*^a`rJ2fP-bwMzK7u1Q`bVj<Sn!n
zJ7058I#)mV0ueedZ&YvaRNe87&+GIBq<lfQ*RA&`O6CPL+%=l!rE`wH@wdU>4dcDT
zvWWYe`C3s?p9u*%>yn%4+uf>nb80mMK7R9+L~d|KwtkqSO|S1z`iv}<tl6T<)~hrL
zCF;Zrzo2au58>VP)j!?)6P-UF{s(;<9SvZx8Ng4c!JW{$dU6f}mAUAAed*@idS`Pr
zf-kfZ!&xgv$%(YAdh4NAbQ<tEcky)H_=R67DDEM>vyH0R@chAEUoE}(GrHvTS=6$4
zS>O5jn-m?INZQ2s&Zcn`UrTqu;CAwbr)eSrX?+tBdSR5JMac|~OxE?+-^iRo)!tMM
z4sR#Q#;5@3!GC-A7@muA+-riG4dBZ^ctzvYfiYt@M)72dh%LVObJ|q)pj!MWz_j=h
z{ot<8&_#k!Y?Hf2HpI(+_G0$_duD$dU$kgA^{-b=Glqp=ip!pUy)M~StGsr)!3<8*
zH-C1QPUD=I%rZ<?$?fgZuH6gt??1Xiv&eDIU;Bn0d1bv$Jnc->4v%NfJe;-%4fV~6
z2cGXf^1d!RZLv;0E>#zO`rE4S|D85gvZhlmr}FjrN+S}mDsQ9S`tvH<ct+8h)Pr-*
zSnAeg*f+93P{812IOxI3dcxeBXjz$v_wt<Uu2S{$ACi6Iqcg6#M|XVcd}tl=2kpMh
zqvu0J(NJT#8Y+v`?sul9r3|R1c$FS0>eiH~HEO(qYW~QIgPpb1)v4pq#j3KnPG?<v
zr@njI6s>#X6|K!Jb2QT@EmPNB{3$KWO3*-Sxr!@u)ajE%o;`3dBPO%%<Lnxp>BF&Q
zg1E^iwliLjzRB<Y6o>U`Sf@ANSgl!Umuu$Y3-p_x_R&`FC8AC-UUatT_zS<K>rYPB
zO2VlBxK(=c$yK`Wq&Yf{eE;jd_AhFC;Lj><=>tbTt<PV3D#dl_sDe><*_$m&I@i4G
z22<YbaLd5y#gk#UdU1kl_ClKLj$jP2wqO+U{<W)>kMcb~bFt>n#vmQA49AeujUCM{
zE6CT9E5529{cwRwx3AGEWSlw-v;zo<@h6?9i>|oXMOgK8){suN4PZrLBnf4=@nGYd
z3&XJK(i{3Y>n*hJ<bTe?YhN;g;3?M5((oL0jdM1<2f)A(+1fjM_P~Qx7PV7~Bk42W
z`A=Oy=OKR_8=F6*72TZcbmvLb=oohTn3uh)z5-=^i`wX5RJiPU?YQb>CCyLQ{1eXA
zr!KD46R+;nHFtkk=Rl7Gdnk~+eYI8^-1B^N+xLG$w2Gfrz5TYf7gghZH#xvhmwoDU
zI{&=Wp&$OL+*zho4{lJ>v}v4c_yOK@OOv$1$i01}N^rJ}FPI_SBn;a}i-PK=%6;r<
zSL*iLZ&FqQ&Vg;KwWYXCw}1XF*Kxw&<AE9Z-TBCxc}4QeJWY3f@pc_IB}R1x8?|9a
zg)Y0}P7H=-tOE;7j$-b0#oP4u`)f7zjJtK;eLujkwps73T(8;|Qo6wJh=r%?qAM@c
z;@O-Vx|VDr<+Z4ONYn6g+Bvm(x;;Ri==En!q>Nd;CC?ssu+8+SSW9nM`L=dluu#XR
z&83~{zpClakExb|hl?-ztZusUG@=rGmA5`eJKkOoLY=P9{qy&g7}BB@Z@#0v$_59U
zA)g64<$_Cf=4mHd1mlsSvxlez;?W5vJSE7g=(yqe+<@ZwllCxJb1|Ue&ET`)P<0oU
zgdiD2rMqA(y%rKU)E1z*<o*2jA08qXGDN3QA@&+7#GZ3aGgdZof$)e}h-n~J#T~2G
zxW^wVtj{w6&%Y)N1%<_^c-LyZ@%B0;Uv;V$op-aQEjSl}gD{qOiej`hs1TwxYXN2V
z@%Hp$?TCTlZYkWPHS6=0dInX1FZr})pM1IeEPn%wW$Cs&%|2xjRenJ_n3+Kl)=u<=
z553%d^}_3`HJ8>K8EMDsD?ga)5GDv^bLac7>Zf1%fjX1OJt34L7&rv<;oX7&IJOF?
zjL-RSnCv^tdmuUpg*Zafqu1*G|9wKCpZk>NPFt)m{p&2<+|<T>ewskR6x$D9h(ggC
z9$*72r|m&-2;|o7fp@QY)9@aI(8|ZcUT|qRb}@iAJ~nF-t>;+#;;o81bq?N^&*<cf
zZ*d{J-EFmc{D=4Ie+zvyxwb&jSKX~|e)dyXyvV17sQ7c(tf*K}<`Vtp*}pIc!hi<b
zwEDICv4*{;<FfqpoBw%)@Twr4Ja4+Lxb<G0b}3di?hT__sd-6jw=bh`Ck0-*cdKRM
z5`FP&U(*Rw!kyq@ZQ}xQ?fS${(--gixue$>ab2?U1zmU7&om8>=OD^^N}Mm=jMbW*
zL`#T_Mf&Q$5k||23JUhsHY!{H_@95QesWX-2sJUqY+`1Fo_u}{#j|EBGChF;9;M3J
z@D|$G4C7KBcplNe<f9HnDJ^J?9(v|A#YbLAPSH|*@q5$p3Sr0~yeuBWLL<EwUw`I7
zy^nyA4PhC!5xKoEjPjhbV15!3%lr2lgN|r2b|?R1|Bcabc0)LrfmjfY$ha|Vq7J~a
z8$x)e<yd#LK%}On=#5ujpiM=RE<0<H&b;~_%|HDb0>Ow|h>hdlc5QfnnO=P61=0iN
zQssL)JS0r%S@XyXT%gNt#2C_samJVaqR~+paC(62Mk<*uQ_R$9z#iYJp&yIOO!C#!
zudUYn)QOs&I!AZ^>(7X)vcfO^Dp>cnetPG()rd7e1Y$p45!Ac9@%<!os6SZKC)eq}
z?thqYy+AFQJxw=%@w>YCre>F4>}V01Y(Rgj)|UZ2A~go68JZEfk)Fg)DEO`$sQy1o
zpZUstx{PQLD~mHBBAQ$#6w1}PZW@VLW2XbxP(ly=DA;q;-Lu^pcV9w}%_43)vt!vG
z^~zfxDw*~h-pc)lT8HhhYwv?0Dk3&X)926B+{I_XOgze{d5vw~2YOnyeeEhe^OyV8
zi-6=$bjSd7n{5~#mbT(C5?Oeqg?AervhR)Vj^K><@<aUeL)gGRt;=UlrMfbaH_*<R
zCtsvlr|$K@o8V<VPx~6m^<3+FTCpNP$IVREAAj;N#?eHbar|^$ee;)e>P4SXP-HBY
zdViN&_vB;Gsy97@!(Yk7g2)q}L6pZdop%*o5KQq0-2;tBF^9GW(p6r0MvuL-P3h1G
zS55m6^Y(saLP0-9)v)+1jKZm|HDJKRsqm*SIaqkFvv*loY{7~rw0YG^q8Mh=ZY`a;
z(u#)2n2T<{3wz*R2s_~m<3-i_*C^ohuC<}X7@a`$*j(B>o#KQWGs^hFBSOft8^SY@
zw|Tvu`^$f+W>S`>uxG<m-aCau{6wfjkjk9YK|8AFG%@~G`h_phm%lSr_01i4#lsax
zLCn_OWqSYhXY^zai$QVoMW{^6J|6lxQ&#{_gDDsWOyR`{DGgP-^u}|K>bb&DWltxB
z6~r*ZlKtO$Ya1PL^s3j|E}GYTG>##(4PqdVFVOG)@ECc#H)}D5(VOo2R}Az`hzq2+
zkv<b1<fEc>C90~!P&zeEPrdXa-lasHfAVp<;HIyVV_ZjvjWET>63RT#q7UDBS+Bgg
z(!s(a5WF&q!K$Bg6=*UE-Wvbmy)atn@Y|ROV-Qb>waDCqSEw&!3Y`Y%D6^-Pbh{YE
zK(8xvHt99eEpp>%KZ$oG)Mt&Bf3OW<?i9^gda1rY<0Q@!&t%>On{qV)V{|AvrcR-;
z^@9RdO+R}|fF4=)p-!BZq2p(y>&AP&qwCC&f?=!{581D;zf(Ji9LP@C1zmX*!`MG*
z;q*DW<Lf`t)r~DgssSJ4`M}t}r$G%nj3QzrA9ae|f)4SIg{%6t7pr#4*}D4rTQnz?
zGy>!;<FR(mGv=SKugpEq@!Oixb=vgwYwF9MK0*Tm0)eH`1`AU~kTox&4cl|eNA}0b
z5?HZPuf4TKsn?#N<IbSh@0`=sf-)M7+%<$zy>Mp<J;dj@NCR7eFElFHXcYV61~BvN
z8FHj|&p0=>uYK?;O!0c`)s>ntIZjh&pQdm8Y?d0q>0nwxg$EI_w=G}kiwO@7B}oyy
z9O&nuMa|<ZQ0bjCi?n&?>UV!Wk2N{8bxM(?G1TO3(64X2TRC_RGXqxXp=W1NDgO#(
z&pK6K`Tk7OZ#pR28%OI?Jb#tBdW}lntD`eCSEW1%8O87nr-hTd?4;Vp8|4HCP4*2T
z3KU-k3l7t}AAYDs3+BUL=jr?#zN{0@y^)Av+U_M#i@LmA<(0k4!U+Pq=2!+Pm6)2M
z-PLME!HWy+Q7zs@qsb*iP32X#p+rrj>%kT}r)*b5GTuj`vit*(A7`JSxw9AQM!dRh
z?VS`>569?CZ4e@5Hm&?nYo4Ly>NKjan>PY_Z_hMOZwUM@B!D$=E`pFl0ORm_>=(o%
z3=)~(@f#06L<Cf%t~_t47M^pxW}a{!(SzNjH%(A@WE9bqJGG(<4{S8_YxfI!>;<Cl
zu0LN{CtjlOBF{E;^bui7+G!}Y6i~ume)3N$;ce;QbP)R;0^H2AZ^O@I7?Y2O&#ft+
z?ixaky=QwgwQ)Bc_8$x(ME_*ahVjL<J9PSq^T3ZqS`_cE9T+F0k>A}uf~Z4u2uVnu
zs%cZRblQd2d6Z@VW<^1Rfl((BS8LyS5kthgO2hGD#V$w7Zf5wkXLS>4>A?rq!hJTp
zx{W(W{O#6idw_CiE>*kE_QsC7t<ie6oBwFmY`Q*xkAwcPI^*h3>#SiOZkHO5wU)^Z
zxvSKa`<CA5nx@Qz9eV7MXOtB88O?<seDQ1P(BFPV#>Ep!(MipS6?)+3PiW@cEQ}@H
zc*`S{MVdqg(N1QJb^HK5A{gDwq|=(B?Rw>@KWn)^?OT03X?jjRa=M0xA2<aaBcH9*
z!@n#Q5E-F<P-jieR7N_5m5C2Xp0z}Gk_PFHjhjFE345%bHhu8qZoRT{3*AjJG!0!P
z82u$a<2c>=MGCUnF|%O|ne|&s-OVrmkDhybEj%A*-z1zr@R=nT?rjZb!1V_%Aru$v
z>8RJ*m9OimM_+UiU`{9n);3SaC*Z@OmYV7hod)kbd*FGYD<F$ZKalgLo_cnkqHn!a
zGnQVYpUydn$jUxNfd3K5EtR>O_4><CDTnSQYM>*Gbpo1yyr#`M(aCA%bhhHU22&r>
zcKoit{`&ihis)gt0SiB2$j)2hR(SA#dg^<ykYrMAu1!Tb6xjNJGFr6GFiW%>{AeIA
zVLH7dJDchKxPHCr@EW_rZQpeS(dLXw>J8P}iWel0aMFmdFhvd4>AA-qQGXD`6TrB<
z3IN>E+@zYyQf*$hN-w|khI$}2rf&|v?cfs~{s!?+2~h!B{@QYN2czr<@oYn0!Xo!n
zm*}l0|DX@BKt_ZbLEl99VHx3;^quaj(#mb66x@h_;bM<RKxu8PCKTmSy}GUzg(FZU
z5Xa5=C?$ApJz+|0UMQi|8<%NKRkv#)(?Ooyp1L|Jn(k3?ehw8MH*1I-DPKZ^lIgoy
zTT!6RgqU;$L}RT%KnKhGgJTe+f>c{ts7+hfQZbR-o~U@W>|CKI-po}*6ctR3kwWDM
z@QyXt*Q=s*7v9dd_1fEOVBR5W#S)VhA5MsPrM9eFkN4KI2L`ytz7UFwkdwcsik6rg
zw2O+&5fETc3P6bB0~++wOY7JR4h3P14GmP*tlOj7nhI^-xIuexKnMCZ5wSK!N#w9}
z(JQ!)aJ${L<l0nJ$a)_))Hi4kpViml)yvzi54Y?luP2%|8fAL+@nssKDz6b(+h;2P
z)Y62Z1Oe!k=U>sTJp-=z${?Y2lL?Iik*Hc+NA>94cwY&VudW52^*o<2p*_1*UYw(i
zIh6|I5P6I;aoWB1HEpQvQ7~aN?IU|or=q-_+OZZhZY*K1W|ah&{DY#jC-*&_aN)Ji
zf|s-Def|7V7(e-K=Iya-j&#_e4T?!n&6+p$MscU&B2j!18Ur9mz2rF;<!;o|5ByI5
z`)VHNAz`105PKcqQ(9&MUQvYetsBW_t#=BC*Lr=V^ReqYQC5-?5)?Sps@!dxwQDQw
zoCx_hIN1B9Wbi%;rQ~nhsJtqwMAL&lF|<`LKeIx;VVoTV3xC#Ng{2y5O7+3pFY6cI
z|8I3poJM#l0qY3$T?j}%mKs5xX17(yK#;X!h7G$bv>s*tfBxfVDh{L9Bq3YoS#zK@
z1`-mlo}9O|0?TPA2fVY*dv5AfvMZ0i%NsPnxv|KNqc$v}hxC`=wqiZ~*z5HCH0w7w
z%2~5?j<%*oB5R7VjK8OsDU3A~iiimF^$(9%chUQL@`XGKYS9i6LBf>f0lYVj^|i{`
zyhbljn9@R7O<;+EUbgP3C6~L_RYb4lr~9vls=)5|8tvLirO^sfL}1S2+jD@4AtZp-
zcRiSWt=*SJ9av7KS%3|m6{Q7Q{q~FckMI6U4Or1*Fra!WwU34<G>Av_)VC9QOb9B<
zVR7CLZCjOxCzdcZ`*Pd^?*N{tDAHJHe5SgpO4V|XcYD{%&zhQA?dG$}vJ!3Gyjdj#
zN`%vrFx;<HFRdsc+@4lU#%v+kmhQ4^+gd&T=lc<&NI9Y(a2PbIxwQ>N7(s<di*^c8
zS{s&L*56F8=Uq9Q_2Q$y*MC0prqX6)+k(f<JKQpn5`4**O@MZ_*V9{>IU5}s*)v-M
z|Lun673OTywxV5B&5y)u2u@J#zZzOqSGV8w?DKBIK8y2r;GNzsYbVuDxVr6AJJCkg
zYRuNq)!w42vSO`!|26&Q8$Z=9zhq4grv;)JRF8xvac2BEU!A${Yi$VzhY1+gIYw5I
zxsMi#W%=9mG6tJpJ^qfergN6nq2?|M?BGr7qzGaMVVQljgX`f;H|(xb?$$N*E&ql7
z{Axa-f0GDF-%BH{b!x)INf;d(R7D7TIrHDTVKdQZC@!395Be~9&YCaAbJcFrQ;%;_
za9o&E^1SD!8Cd7C*H+NAb%>%x7zW~eYAH0fR=(h22#Qnyv<Qon_EZ(?z1N@9kN^Eo
z3QD2(aMvD%klPzUsBbxiv39(_o?f$YN<_x#-BYGbo3|;iq)sWM8wC)`U7EWUMG520
z@#p9aJn=21oAt<(@6cT)0sI2qM0e!7HUCi0<^^a5UY;KIAd*nr4^VCb=zSZ8LWTfE
zv{;k2uX;rf{be<wk<r>+ny*cC3TYpJu2`-z-pHXq>RZ%-#~Z8ZYmUd3T=RN(<sM{&
z@{&T%!D@8`(?Nsh4peQ>laH@g2sFddBIt_I)0RfyTV9Azxm>TjwgOre!LyHyHd=wA
zP9n%mR_ku#d31}YVb3ZnD;=I2PFu%@QgUI}uE`;yJj~G|KSEcdQl=}2dIdGrl~gS*
z*N)8_RaH*~YSPDyM%YYZV<PaHE>j!nP7^Rbcnp8=j@oiU%m1Wzwl~vy6(bC^ZVNm)
zASFYw7&EKLf8MZqBhJT2O$3*Qnya;iRG97LI!{i^#1r06JFl&z7VS|D#@oVn4538X
z+4HRme>?KXM*1>8{KPth@<_bQdD;jM?H<D0JA{%;E3P)qW^Jv>WzZweqRAJ#IoECv
zig#)AhSlnVzK6ubseIKky;t6ik;Y@Zw%+kptE6z}Mm>4|f9s)TCCW~Z;T#Mh`-W*+
zLIi1<HQIvgTiY>+{0N=`kou-T`Y*>2Sx}4IwN`nZfyxSM&<iWJ<A@4F9)fSMhJk)s
z@1pQL^SfWEdMKJ=S1sDHX`S+Rw<#vhUlm=!6qTb(2S&{muf0PHLF8dHi9igMVS#<L
zhFk}aqHr8?Yz#cRtsO%+icBxzwH9Gvp}PaUo!X5je$|`L=~qAgy?Uln=n@*ux1RrX
zHz=6GRgox$^)>8qb(QkAZ$UQ5qq_@pGTF%u+iPF+2S2?<k-Hl9A{2$k>Y13r)L}G?
zGqC)PWqNaYj$+`C!5Gxmy!E0Ann|Z3ea7knm}1@8vPU_aKF~wI{IOo#6{zea47Yx?
zx+9ICv#tz1WvlW_s+5!tjT-Dk{@$YZ*W-PqdrWL>gqn$Z-Lz@DDv@;~Fp_yylHrLS
z_J>6T1W$@l!K$^&KH*ekWTH2H>-6lhorEn@^b7?mECSqVY9{h>h)4?}`#LCg32iif
z+e(k?V)TJ!I9-1HKhG!*gO=53*q>!W1>70&XRnh|CQ^&3Ry((DCHkux*&xgryqx;o
z)u52140z!HMnl_!N+-7!kecgH5j&$dCP4Kzkd{eUd_S<R;S3ut@57&r$AaUfg}K_i
zZJoLXs3aZ{uiEv?wG$aR6vvnuqcKOjE&`iZzpLN<$In$0!@H4v0?BW0!BN$Z+-Llt
z6W(C_$<mPapc8D{@V*}Y^$+y)2kpw9Mq5A}ea4^gkE4KttH|1Y>}@4DR90Mo-nUNe
z=qCZ3Rg)(CfnQJ*5wk>?HPlp%AtRVvw41e6DTi~sKDU|_lmt3&g{m22RSoCZ=+wxX
zog4+v#n76!eUtLb>qs9Wstbp($)#qXa(f29ZEJ2&NnwuOdFe5I_m>Z80!IE|3=AIJ
z0EIw$zk&BtLnU-_rY4X^P-lBkRpIuaD8G!>)kH%Kp9A|5LEWOY@4lox-2pf?Os@d~
zCbzWKD|h2+J^I@p>6!ciO-;a{OyzdU8v!fQ%Ha9dV%zF(b$7O@y1WFPZMl4&fByNQ
zH{N(dzyJO3DVjC|XCWv35W{2n_b0z4-<96COQ%i8LWI!ckr)i%#P$z$D!-`GJt-2R
zn~3$vg#uzZ^7ri}U!u%?7dR<{ycE!ag_xpdH2`u6y|gjs#FJ<N)u#q>4f3n8d@_=z
z0H{DOlXoAD>mTzETjkDOc?eIa!HZ5@WLT~Bn|K!?UK1lh=tfY*Ct6dp;$iv-99S~U
zx|m;7MS-iuN)I2>`yUdTK!{p$GOgPxJNexgixNUC)Y0By!(e2I|NZSXDz37ejeSQZ
z@bWdUJ4Lh#E9@CkDps~2o?%E~xgCRj^(rbQw~sLygbBlAVPP8<GU`JlLH@2%=9;L*
zCr<<M$Vpni)p@FHK53bmj@bFpx7FruBL=v|;d$P>2R%Ha+<h0CoT(IY1CckpE8rLQ
zkleDO{37SMbr%l*6O%?&Ym-H6o_N%Y$vryk@R`KHrB@`@R@-N*ES!^D;BK40WGUfg
z-~_O>6$d6x!6Wan;QHVxDk%2g%Awy8;=H|<S7)m*f@q$A{<dlrR}%ua&mbprdZwKW
zE(IU4#&(wORQdeR>iPRVt%-h3dg?d#=vVLgYgQtW0S1_lIBXvN<RK2Wm8zh!OQ~}f
zC?hW1g&M8hUPO9FnzEyVA$B$w5B}Jz>^_1KRG3%ju`~uIYFZj0*2C-jDATc3Sg=HG
zL807^5&o8iSIQG+OpzkqSIYZbG{0{=-w{q{=fEtWe7BP@DiXqU;>mbC@EjMc`cQ^l
z))LHeS3RMeP$MWqVW9`NL@SsDQ<E_W^l1HtO>Qmew4(}ep`ZIKdF0#-cGat}-13<A
z9aj9uQ;=gXoc6uq<N3?ZucJlaXs#DWPdb_E$z6m}ZKfTSM=(iFpNav_n2=|`{7jMO
znmnUzv;teal(R{@q_yj}xQWH5W;!eU0QrPP=Jh??@QKGlOpSsg+;b~fRJp4N8@Ojo
zn>i2T5P8v5M*nbgj{7hRTJHnR_Uze#6Y<jdO6WZLvgaNYw;=?S(Rg~`o%?>`Xd!TE
zF5IQMah#(_iX2Teqk-`QXsGW1^tTXLO_-v^R6%bkTMIA11DDEmcv`Hlya^VA{T(VU
zsrI})I#u(r#7`jdqOp9lb`p-N_$l<D#p_12%n=XHrG+XN6qb(TR=mPk^{v?2F7{wN
zBYo<06xRWw0t!XyvKtvhqa;!!)xzsnBb3_6%$|YqzTXw=8tsj7UykD+&t4GmMfht$
zd6N}U&`I-CaB?*2!}UaWgiqECs<Im`@<FQT>ugjhVRttCQFiA|ip7)Wqbii<ViKQY
z*n`9{d?)+SE)&XkL1RXb1kOs7@r>d1dB1aWVxO)3Qf__;xD}~IgueQ*?lo&Sxyz!F
zAL57Q2WzhuNVr)JA&v@5Cgp1?#e+Jvef2tchYxLa=(mB;WszH5W%&r|o)=Ebrj7`n
z!Ghva_ssa@47^Q&s>#X2AIb2UsQ94WntSuV>%QC00AJVZdpF&qO;oO1c*1<=rCzgz
zlsmj;$<!{e-oTzcpl}>}mkXa%@@$=!9;xD$t5pG2NX?wA@?rj)Oiq3%IN?!#cpo&z
zJVHJgUw0K772S7CnMQ~yq4owZlO-(2G|$$I>14sl^A$jwx@wfAa_aKfn39o&a^?|s
z-3R0PhwF{c4`6&Ku08A>Q#t=87a4D|T*<~=`5H~7{U)X=GlGy-$1^-M+R0rQAj)$+
zbYuZNll_TwSi1o_<P)n&2~DcrZS*u)Q?oPFM=o<&vxmM;b#fBtsL*qcCZH^)#L(uj
zAYXN6*_A&b<>`39NENAKuUm&S<G4lC0~pi?DEBs|WF(;wSPT!ra2R8PQ3!l2%)Q|7
z^fwKD6JVjNc<}?^;r%UzDy;5P`f&?2C1!$C7}pU25P%FjmE2N02M(UZm*p2$xOavp
z)AJk8nHlS$Xvnw7ISBSP{7}3yNu@O9*5CGbR|%qJicUH{m584rZ6^mka8jE3s!JV@
z;lNRogC5u5xu4<9FujYQ7D^&UBoiOnY6?|D1gOb@=~-FG;mD=PID=eYOg^!VsL6PK
zlM~=gty;Tf2cOUtiSt3cufsRDbB3SbT@POQaw6$QhF=VFLA;Y04&(#C38kb7V@NG{
znLz|yQX~-u<)jLhamuK%G8IRJ8RlFION5G!syz4nS&Nn^fePMD<=Y8urj7v$cpO0#
zMy`FK1qG#^{YpqqqxMCJT5@we@-94b2CXZ5FrXEedFcN{_-rK6xkt#VhL>pP5*WAU
zsXzNR{pLG2E3~CZzrOblt?`8p;e;^wTcOm#qG1`wKat3U6dZh3x2jm13pq;`5lNR1
zt8p|6dLv-;`~qJ=5iBSu_WbD=uj%l6k8;a9hWDTw+^)cZHZpKvX3>+?<Tl#_JC7E<
zm7kA%Hja@BzoDMI+iU+o{MF4xDyivJ_|&<Y9T%+JHR~KrOoIj)wpnpVa|Cv^V7S}u
zkt$-+vWb4Oh)CwY1|t;joy7SvC!cFY>10_Z2JTtm(1N1UeP)(Cl@_C5zFU=BCp48h
zV`Vk)rNa%*K{mYH_PnIrgVTq+B`8VLh`_ZvHHBUt4ld4~lcA^y*4C8to!nC98msIX
zq<f6sv*A7HDu=!d(^~hgFzTRC{L;Gz-uK&jj8?Rk=Bc`q!f=_hl!cMDWp|FY<~J&q
zZbk{<riK4o<Un&(q3VYBHYIDeCS!#48|v0toVX^b9I=9K6LG>DN{x9x%|q1V>d3oe
zZC|@#gxOkfFbCIy2z_*9*x?MaFo)sq_9}*Ui4jAqGC1I@RK2inbN5?OFY}!BVqw^y
z$&WmE9KTq|rX`ZLwotsmk75Ur2tgJCO2L942u455-II%M>oEjFIl-IXjb(~d0(y2v
zLQFlO$kual5bH;eGivO+ew&IDCfF<!Slr(rtyxQ~aFio%JUf_YpalV=y}kN87gIT&
z5FYbXnm5Jrtw!De(p$*sAbEZ*6wyHgR0v^oq4pU{rwI=R?$LYD-Ax;)(R(nwKW5$O
z<-2|Nz|DYvEZh&zg>97m<%q$c+fUE4cpyr85HPjp7@;^|aF6Yq%i-W9?%cYIhW8o4
z*LqD`n0^@j0WCbJpS%zYZT1W!`h-aG0>irlhY>ka3xI343G4e^;EVxip8M>9?J+R2
zxepOWVpg_}z6^aA>()i&-qdb=Lw9`oTZ%x5vGDe>-n!pw4*RDGV-^C~MYZqNPQryK
zdK4K>zAxve4+1#Wu;m&7wR2|HS!-Egd-B%|TZ<K?F`Vv0V6_5+-c51uW54aYe!epz
z;(edtl7-s~Z&Nch2>N~eUDysfki+MFVPs*6;Lwn+0{{R(07*naR0LBsk6S0Llw2AH
z-sm8N$itmsv+f7FbK_a73-z*d<~=um4FHblUc)_Tk<p7LTQecEVIf3z00+*jo#WAG
z%01f7CUYO`>mE6q6ToXT4%!(oyf%f>LLkj!FeXmhnE~rOv}o@h9Ldx@=N6+VXUX8-
zjilqlzDLp@*Kc_4`|jC+PahARKk%*tKe6YSl40$7eEEH>_d42w;MNPR+lLl8cm_Ie
zPb*gMKniG0z$0X^u{Lxgr-ADNIV%>$)QhFO6MAZ`f`W<q2*<N#sT2o0X9v!BtWUsc
z$FmMbhdlJr%Rk(Cu{}O0Vf0*Ybj$dM@kC1xa;-$637$RUa}-V6vjBLG@!7FpH`Z(q
z^_lS{cm8P~*V0C)D*|bBB&l2Q8qb;c-0eAswr2yh=kdT4kSdf4`eArOtzey}DCmK2
zGoz<B<k`GZ#^1)Ka>Mt!{j+W!<V3g7hnRI6KQND?og0@^IPw|~PZ)y#H5>0BR67!H
zl83eo!w5Vxd^gXf<sf@x6sy9Hx6UFhn7cjG&WX_)<5A^1c4)zEKhu4mJwuI!>-5u`
zzo5F=b2Narv&T|&@E#k#-+#@v*LLOyNZ)9pO#TGI8ADJ?%@`C24>!2>@V-&lx_2e4
z+vFW@Z)rOR)_>cgUaVm4$l!OQsW1Y^F9$94!FWFMgBekK9S)6LeFVSQd4z{^HVs$D
zqDS62bu%X~ui!##ARZaeXqElX&hEbZ<e7%ytjS%z@Os<ZL84J2!8a=?Xob0)EW^5b
z9F4;B1UyVe^S;lYQ2IbTUj{>?pGHgU9xEVhbd9OFJXTNzPgN^&fDgRMXs+?5f#E%{
z0$sq=QPyGE)Vp4Tvpr;ZIDTVtlwI%L1HSgbdlZN))YS?P@9K5k5u)&V8-DfM!5H2y
zdx!B#*fvVUsFrv<f3NI|kWP9XnV+89hUu4iwe3s>T5(T2%L67`auxy&pODjf9nG`J
zSQtGrH1y8SmbVp@A9!In@1-jvld|V?y_o~L$*(v1Vg<Lt$%jUYAA^Ux$vP$7_QR#F
zus%DBqh$s!%trsTRH^{>tD~(IuOJ?2QdBLn(F*OFLDf49;_g~xg7y~LYLe(3LYiX)
z^xEX)5nOV(4=uHG3l5r-$sAQq|1i8UxzFTK&YCYcW&3R6<EYc)edV}+8jo%%+oQ$T
z-+<B@KwYP0T3Oml)Pgx0ZRQ>tWTJ83Fm1GAk%1J^jG$oh5c@WomLBTGjwT^2IT_j3
z;>9_;$<Z;-nHbpv&VhOIN6w<>GLM{$j5!*YN59vdWAM5MUN}OF3{K#65ciqqwu3fo
zmRb~yqHX8dE!EA##AWaw+%kIB4gPc*oH@S}h}?1X&cWSav;WvD!`n9&?tcJ1v~{}Y
zBR4u~vi~#qd-$AsWMNN-7}Fz7&Kz}o_uk0$7J2C43cM_>z-X;?F0ppB9h@iIGk>D>
zBEu;z0^J>rpCb=)dth=gdIWPb#OfPk4@R!%7sG3#Kb=-PiEc4LCht?&&J`LS2CB{3
z$wgdf^$)r@mtAHY;A}?#(?GWJBPr;kCvx<D$4Y3z4a35=EXV0%8*H2>)QZp?XW2ab
zlg+Db_~EI1did*)`W1#J7LqcodAh<QV`Ax1pUGeMf|0#opCP=iSjJ!X#BtqiLTZ0&
zfr1x&R=1zGK#2+QL>{CopuJMh-2Wr}?BxoNH_0if<9g({F0$}V%P}&X9#4^~aXs{J
zc##n~3&As?%+g^Fw_grN?cgvzF=yvU;WZjZ$4QGkaQXzSo4Nb4XPzw>KfOCr5FPF7
zz3?0lFBpw;<GKE5&;5v>zt5hTK!eBWh#zwJ8@w=u@*b8J?E}+socqJSee5gt&dKP7
z6^1(a%H6fd2quIXZTV|N1WVhg&Ck`s+y6t~zwJyl6|B=wZn|5=)Vzqs^I{6n;pnx&
z$O_?&hc1r2`f+^9^F6Z5-<DsFV`hiHWMu9~iw;5S3?G=u;c(~hlRfwqBRm}ZbP<UM
zzx0T|@#4SXy{+MB49CCO3p@AYm483VJPc0mEr(r;XWyJ$bQECvQRZ&<_@aH+6ZzGM
zFDE54%SB&|MUA|B=H6p;;wbIgM_tEw@AK+7hnK_IFZ2$tA;DYp&~5g3U-o}|oz30o
zwUf|};+~D1wZpxG`569tVd|BYU6Hs$F~R>kI2uFTYi;{j=V8D)!e<-6K2ZbA@+f(q
zs97EPWIn=P9QjKQyu%LXAh}__<l#)4HAAsDI_gTcYR&Rj^s|@Blr<@0pPcXmpYYK>
zF=6}Yu-%U~pCi5NNDT`{ygy+l=SY`q2knUI#V6)$GWS6|pnvb<NZ|Q*k8^*cJ^LiW
z^oZtUMo#lwehk9|Z!4DCLxfOQH+i~57ub_6ztCibk$|p4yvy*;!V-___z1M<sLtm|
z9)8s4ZVKnor*%h@haU<49|`D3UwgC^|IzQT-^affo_+gxPV)Y<_~?HRLR;d=%TEAr
z`|<jYc%Aoq<lkb?_Iu?~`E&d_4*RvgHPmUkM`c<^<)NnYy7bMX^7OqA9rsydK4Uq%
zaKaxO#~|>J2LTI93rI*;_tsbSi=VD@?crKzhxFm55@lwk1CWp2uHzpMn#Wdr3<Ad>
za0~)}zYrK8#4s`?O}jU~qF?S=qrSE#)u*K>1U_-}RldJpxE-7MF$nzs41od5^LFF!
zOauM@c~ky@)^<!m{|DOaV@ur+0_1x5P$hHCJ6mXLLfiPHDYO%t>Ix+~@5z3bAN%td
z1dc)A7zB<%;QvnuSPtYsbA?``Yf5l*q9(^tp%>4_v2hFn#~^SF0>>b53<7^^2pm(;
z|JD#c_P$RL1Q5RbLo~(uOfxGhW@N>7Y#g(fIxrmj=NJTzLEsn!{xKkcGVT*NK{Kop
zJhf>oto0Zt_{Z4#W2-y{fnyLj27yl+1db`_pR^r0HlM#O1ju9Tw^FLd#xV#SgTOHe
z9D~3=HU!`YeZ5|OaLi2mkBtn+R(}iv#~^SF0{?dqI3fjor0U%v=4%BL#*7N1LY+S$
zWc+Z$74o5=$KC)kPi4T-k9LXtV?J#3RrWiTO=%NgRq$-s9IQI!SV{Np0{e$+N3N&1
z%iej6T=9{9@uOvd6;`*xG6xh=_()Iuzq|>L0@dsNtvUVhz8Z+F_{T^KjM38rh`#KD
zRo{07b3Wz@=%ZitkMz5>xA3DigoFF2b}oN6FaT)`hg|D0a=ONKq>s|#tm3MlRhM<7
z!it@EXvIF~-%A^q=vXwwRRHChgV87N_qK#X7YTs|aaKmEtn)rkL8`IJ(Hux0M}z2K
zbMahb9WkJfqdGp=d-hrL=!YHQ?^gZQ+i}A-*U`R@VUumIJo~iogbv1I+&N*5mNz)o
zzU}*zkMh@Fvj>NN-@(?q|DA`%DPwKJa2o@EN50$tp~HWVJllH)4szZ8A0KQ__Fm`V
z(RS=?e?BhU+xz^nfAg+)?D={3bBv-#V?XY%`qU#ojptpX=S3?X*ZG4EmmfRatdg-w
zPluspj&f5}=}6DC7l+2c!~Frdcb`1(8JDd2G0JWG&*vlm?bhtMdx%0>W8t}5E0@@}
z9+(dA_aWfqg^$tK!_l_Aba0F|pkpk|OTYgrx(OorgQq4Z_QP8aJG{8D|AhxxpO>c|
ziqCm*#(UPifA9N>@ti-;9edw-5S_+d#@-nZA6*gLWPjH2*9`C6y=xi!SzbC}U>`$=
z0mkE%ySzBy-oxAv#>2hy8hbu`dXW7)==I0GI3xs)sEWH|tcSomBREd@9ra4XQ|smI
zM-R-psuI<=b;~CpSg~>Oii-|+J;-}ermYXDc`{8jwY;w(5Z-#$S+7GY=+#TV%l)M_
zJFM0ZF~E0F&Gogas%u6;3{X^boMNIQ=?CfSdLmlEK_`!onwKdHeJ-3C!g7e-fun92
z$FGB`$;%TR)|EIZm41jp9GpV{Y#dMgKf46jS|3@TzE0)t+O^L#LgJE@5*@NH5i%H~
zM7G!OR&_%&dmO8bw3uT#eEUG|$p7#`;b^WbQC&xmLL(<?a$@+$h*R{Z<c#@H<=f7}
zSZd*s&v@TEjLBI~^}>P*zVKISdMZ6PG2r}-kA=O@^%3sxQcZb@nmhYk&(fHfI7QKu
zm+q#}3ep>xFIu(hIq%~H<NYazeeE&68ME>l`Mj&P+l{IAyS|&l#;P$EYki`vuWNe?
z#i$w^)XsbY!eW#%IYFWHAnm57=sxsr6n*r*Ywu5Xj(Q4AXP@Ed<8V*f*2X=ms%_Mu
zZ=hlm6BHjcfiPbB5L;jN1DCkZbH@I|Hqd(U2B^2AS!I<~RKRA3CqyfC(nR{ATHoSf
zc#i#m!+ttom2*eoiP3EPGcY&=nr}Uhz5Q-Y#TZcY?p>;D9@NAsX$q%jsYm!d>}$q<
z!AIDG@ju)QpI*bl!8XKJZ$jC@3>T)%+1cVe)Bkxa{Iwq29+tHKJM7tJyz+c*0$(5h
z+55&b4mKyxz3k6mkE#mG)eerNWK2~s`!{N9$McGhbj=|Bv=J{3kV`xd^PE#UdXKa&
z`$#Vt=S^0K&NO0clXhs_1op09G_r~BfNF}%)XJ?X8JWP%PQk}FKxv*wHxsY%Zq&n#
zcH8!w(dHx32p-;tvf@i`ZX->j9(}+92I;MAP8CN}fzKeY8XHgTR(sOH&%pr?|66{*
z)Vv~}gDC`T9~$NWiBZ_CV0dCiS2(Xm!0jCEGq~9YnbrsRSU=~1L2$zLW8UYg;XjNH
z_N%F;NPAj`lr$w>6RgMb=n3q1n_*LXLn$&_j}nrRSwn&V@1b^TzX#Z#tZAsXRe5E#
z68)YtCMVI4+*WvqVYq>_!Dj5<2A<hGO@0rhxAlO@{OAK-ytDMfe(=M!Zu5RP>hWfj
z7;^XYYS1S@0Resvhpg|s8}`H2g)upV=bOyaV>-nFgKK=~3l7`{k%<g{O$HllczJl6
z@$iOvb#&1OeL}2KCdI@51P}j+{Lf%x{GV390qnI8bJ%}JOjh#;4n|WvXAB){6zbU7
z9iT^ienA2E`X~il2&S?2zPMmd9p>TfO)9Obrgys(5FVq{q(lY#!^7#zJ+cX0J#Z*F
z(Z}9oI6OAI#)9Fo&khX%pJ$$VX6Vf~-_-s0->(@nW(+H>W84Hn+jjQz)7O6AA7kv$
z^x>H@A``?<zPD=vg96o2k*o5y04+J=RK<X~)~vm)u~w@$?vx^ul|3;;y$B}*128Xv
za6HNf<SMV+?U_jEB+W>V)&R}r?MT}&L<<NCQg_2HmG17>yc18=#At+05VpOoQMqe2
zs*&rG(=!!H`xI-FG8%16X<(vU(WCA`e}!QP7~|aAs}4W7(S+S0zhIqo_Su>o?yKBw
z8?<IanWADt9pgFtLq17Y04!ew+_qBH1*Pirg(oO|Qk()%tb8!E>@L}+W$TI+jRC?N
zC18xDqp3yNOHNjH+GKS!l<EEVHpw@H{$&Rs%O`2ApWL|^W9V${)VxzqS4L8__EZ*V
z<;oojjSBtim2^Y)0TkzAQ=9`dB{N-tD2(j<$2N=>^mcYCdUBeMKYpHq2V1pq-A06g
zPK;-V792jdN$?<J8$r;d;+=Ww_DR;MXUtF-@9%@2G&fXf!&Wmu1!&szbWfB5_-f(>
zvb_S)1ox*4{k;ehz{))tMsBpuJIo+w!i7+Tht#d!p&(7*8KW+75jK?th3e#{sdE-8
zB_Y~li0p0G&gH9AH58`onW-8;AoGYYCiEf14PsC+g~Jyv>I4s-Is{j?sqhXz=-IHi
z<LIM5^s%R*0Q%UcS&Nn`Id*~*Oj_%!puM?@n3$%d2<Y(<E9tx!gFt{^zjE`66_t6M
z7G@;zHz?K9uAEgLsIqSYYe-iA{)PrGpbq=fycVpPz3{P(;FbLdUKa9IT3Y7r&d8pn
zK<Lq6e~%i5qIAkhCu&+!sCIs^O54k76clFX<S>HbVdt^;0^5Uu0R$u7Ii5Y(d*^t5
z8&r=8pna^@|3JafDN6`!0|>3=71>{qHxlaypX=`GQ49MWo5I?=;nl$wx#ArdrNK{M
zx8De|-j5A|`+=+<*ZT(}Vu3}#+^z+i9n86cbKp^mY+oGRxBCX!bW`9EB>mVQw^82b
z+D{Zx-+@jwhD^~#Cofc7aG%z{@uo_91~Js_FEEb2@#Fd3omE>YK=4689XYG~J=o7V
zM=vtt+z`mSzp?G(nStjiW;E&Vg%0#~@h%jAP-G9UCO7&;%xj2wnPTDVKd7dFiMr^N
zg^CXy(8jmkQgKJ0qZRuK+M^#Z&fnQfJFA_vlpBPubSg}7;h}<@*>;*9qu!n_wN$ms
z4?gef7pRDs;JxrW%xioAw519R@$7-F4h5JIbbmrR>KTJ}t?dCuQ6G#*h=?enW(dV_
zY{=&wwt)ji1yGy<f_Nqq-G{n*FZ{pwiF(}Ktj-?jHv8A*6QRU#=IA*hoKLB<y+a*!
zP<|BQAY`NP2$WHf(0tBA9D9EZWL^cw_Sla#4zyIOwr5D;kx7b+K%VMuQ)fTd?LF9b
zIORLos|H2ubm(AaVz73sTB+@obqWp*8G{aTJ<5D@f3OxTK1p-aqO@znI&I1+R6s<y
z2MNXjLi6ko^t8!8WueYLVY>R7YPIT>*VK-a%pXJ=y>)vZXY#T+1lUFRs2LxIY&|IF
z-N=nKZP2JV3ic+1s0$_6)@gjejtiG6FwiK}-r>i6oxAfnD+!8@ilb#Nuy1d3^lJdv
z8$LMzn@ro&q_SFEXg)C%1Wp9cJoE584gLzx^u^e)v!I-h0u(zbmiGrKD%1?V-gg@8
z2US*3g!Uj!o4ZKK@zKl+O2Xc3hlkhtL@Im6WXHoz{uuXww)zAHLzCF%))uz9Q{gE5
zL(qNiV~jQg_z$SDqaEHIsyNP8pfBwqnT#DVqtkue-3m{btObh}Xo63Nwrtp>Qq~<3
z>@{4o7z`2qe!a@eD^Xn5e9cQwbcXMK*0AHf4^*QlbiyQ$v4DLx1hpRTeb6$@7m#)j
z%^cwe{ak}#-q$Y#+!_JaxR-wH!UG{-l<v?2p}@DRQgzAa>Z@0we8zeNy1mTIvk^lG
zih&3gJJ!FaU;OrYO`VYjk`1XWCr62=UZw9}cBYP_LUzoAU}xEBY1pIUUAbDb`VBqx
zdXX-;=2N=-+yw|f5M@k3J}A5$ZLMl<YEb^>_x0E-tCf~LQ-K3`O*p9mDE<vRJ7&p+
z`o<T|(eX2<Dj_D46Wph^mIhVh(WxlT)!XmBr|RZDg>ZQ6fcxQXuWs(vSyy~k7cEM2
zL@MvSm-P6{?<qKOvU~55qLv9ueVy&<4NuismtLv)6aDmtf16%f_5qc_XR5agZu9pv
z?BEVGDlhzaed}|d)dfrEC=5(7>spXs9|HJ*9(iGf5@u$PSRwsUc&hg7)(vN#udB{j
zsD}KNTJ@KgG?+b|XPIZ{?+E~(gt<b5QM#&Xt8~xBSLob%Y06*ws-AfE4NXs&slo2v
zk4^$1;1-qQ7dlxt-+rO|sbs%>-D=ehgeeFC=;PWde+1FW%1X^V>k3_c%dLv-t5A1s
zvDR*>h49D296_j~JZvoic%*Ys&d>eS=XCbc#hNkI<0<H)*KuP55g+9hD#+cWSKnEy
zs(L?Ne9hIm`0RP=uH2?a9)D6LErE(MmmiWsX@0$K`_9+2c-lmj?_8~4{Ny1e%t&`a
zwclX7@`fhsb2nV8CE4-n>1fgV7a!C!+v^e3LLA*fTvhi#fX?{TZCW^cy3(dh(Zu)|
z<TZbc87-=~@-nSm`Ii3j<QqzuK2yOMEUT-VwdkCyboB*`6kvt{r)3z^XRr9vpD3d8
zog4Jz&ws0q>DdZEy0xgKCg@}A(hK#?FPyD8(=#+NE)pTGPpwV$D%`P2Z@u}dHdOQ|
zHUz=qNDe4MvCmMCa`Oju(_P=x$&2SHBgMSt7-U#;$(6ZU`@!3K;EDG%Jv$A9#W+G9
zy?6lA_v@`!e#8QO`?`yjH93@Mgh40V4<aO3U+VUjMpYDS*NeaZi^@}`D=Vl;4Fl73
z@>!Sathq6IxsgiTtJiDV%$ZI=0$|>!f25x#G*?x$=%kCT(Us?UcobG@p0)2Dlx|z2
zCw}<{^<-zuA4Bo@&XIXZJCxU)sqcQ}3Jh^kthdhzTAs;z=WYV38DZM=_KPp*nU`~w
zF)i9fYS?xDXjYx&rHWp3f$sXog-V*>r~2w#{pqKFRCi*cg4C|u<_vxJ%fs(LXf<KZ
zy9OVWYs0_gkN%IITwke+Zuz1vJ1L9zaYo%jhS%z(RQz7<W{)Ci27_Go`-ul0lRs<s
z9a%d!n*7jFqxz(Cb-^W9DLtZF9h=_N`}w`fjt)b>JObRX>5gYGHy?z_rj{Um_N#Yl
z_M}j4`|xf3{C6*F+Kg1Uo`$>v&AsvlU4HR73hk=U(~mr^jU}y$jY2*#m;1*w%t+bS
z(W;=h8T$0?Hz+x%L+`%+oF0C0m$I`WNA}NN=Lfy4%gxhCx7?+R&pb)=eAAuJ?$J3H
zU8U)<ed^i%zE*5%RdxdR{S|OCZ)0m|z82j4Rh=_GgLCVnrlM_n>FEblGMK3-YzK8Y
zWtxB4Rl4BoCo9}<NPAF#9{JgW>LtR*@1Tm82Wb8%+7-FGbk1kKpbJi4q@Jp+`qLjD
zSJOZUUO9Nrh^B+0!#Ubr+^t*h`-&E(Mr!+-*Y&d}%Jie}-=;}n0VwDw;={P+#Z@y(
z@)WIm^=Uo()>;g}lQ6U#eO_`XlrR~vv94O@-1=25o$gU|YPPS^)6YGn_As18z_cu<
zKo{QnIh}ICJcao5siI_ye)#VX=&UP0uWK(^BHyMWJ@d@tT2(xt<Oui;1#J7Oa@0QK
ze0}}8OEonvM5WtS>W{yESiTt<`^u`wYykshYVe<_Q_s0fr_PMf3w6bM4M)}V8PGwL
z_k9MmGyr~8*O8#br(UEhmZs>P{${=N?n))b#F%wvT=dIuslBFFsb`+3E3ZCJ-BmkO
z@ZjUBjh&>Jp>F3G8~t8W!rO}TH2eB5>5`?#Ay=Y28y$sIn6lsB)1kVW3awl9hF)4;
zqB*lCYI~0KnR`B^(-vi_K7Y0T>%r&b6BGsAG~OPl%AB1#?)uN`tIQ!Tpj#il{ujMc
zGgWt7bFn5)uyAvu2cGAfF}DexzjM<EdhnUIm60`#=w34<n89K|xjT0t<WAGAU-+ix
zOiNLCz#uSRt{?yJDrHZBHp3@<z{ze5@8{llhZfA51`khFVw{(UH>>>0OZDOVZ|W~k
zzpY8|aK8bArI(J6hQ@xsvwnZyF6C7w>YLxXR<ox>s&LzTy6@kg#Mqq%Y&@w!fyg4c
zIrX~w)3@rp)8}iTtwPWI&##m-5QQ^<bp!-x51#Y16VKBXH{YVkK6~`%mMX1UU#yw4
zlG)mRV2yS**r~iCKmF4^-`3Ja$0;KjuQ@!thln`i;cHgCsXsmS0q@S#Kx>hj(k|7#
zSDvX!G5(GxduDIU!Lv4hqV<}pOZEJ(eyHN;EQMNB%zjHe_UDm7fFK3~@WF=*o$TvU
zbI3$ZpE(x~MTkc!^Kkd!8%=m1bR@7>oqKkf2uc`Yu&Ot2(eYP(L3iGKiLx^$s<XaY
zRaI3um%<dEJYDBxrYl@!dg`_Hic3h--1)~T5ajPOfet~<4xgV{`A%J``LkcupAddJ
zQl~3C$VZL&c}hF~2Hk%9ty+)~uK|RMs+t=04ie%Xlc+_f%+R9a(^Z+bNo%)NYDRXX
z6Qa$--q91HrKc^?@mPoaFbKqBD|+Jf*PH_CLdZrK$id;V|2GB><C*<<Sj^OBRwWxA
z)b(8Lq6_%ILdFhwg&j@=9Y2%+yVueQ_7|G%etX_Xo}lNk`~2&<hW$7afqT>wj7SCs
z`)TL)I^FWkZ|Z_mkC#tlk=|OnP3?p>1Y*^%$jet$e6kBs^4w|y8`P39fXNb{i3@S+
z2DdCmVV?n@%(!D~G#D6xx7;N@af0bVy?NXd0C5Kd&dcd=#QUs+2n+@b=WQB(Uq9hF
z2L+4wS>C;K`(a)ogE!s}aMF8HMqq3qc{YDML2R4bGYi8Sdzdg{ySI05%<}7UcZZ+9
zS8(An=5qi{S*Hzy^ys&k0Av4|x6nSfS`U_LVprLk_l+L0yrckiv{ouK{ajsr{f!Di
zFz73IT^nnnG&79z#|>lVH1WhE8o&^HKRCgRp%3`X9V~4H28pqTr{;kNp3WN$Y(BEU
z%!UPEF)rAds~hkAp{~35Y^B8nsR={U?wSVqIXaZB8FP-)^o(#Vd+>RpcP1$@DP70S
zo3E~jc1;NGR{ick#e_0vK-x3ZsMKlMnm0dPAw899R<9-kYs2L(=+Cr;Cu+&5OEhoN
z1P%6fDzJ5j{`lsDnjDqsoZY<*HS$k8L)ZM%&6+<s8cTP(nwpx_*x0Uc=+^N|Q#E5&
zwvvbj`hV=5cbt{gwfBG1d+#$$Z%m>0j(}7t_HJxRG%?2H-X!;?88xvaCK_83#Tx8N
z5m1ySC{?60Fhd)d0#g{K_ws(%b7laINzA?P=l$o+R%Xte^PK0|W$jgeYwc(M{I2{4
z=Buuu3M5XF1`Gj(0C&BR#B7dy{Ja&=Qt3oGyD*6Sy?a!Dx=@i5uFzd~-G(zEj=nVL
zba^%7^Hf+|rl!Y-Axw8@)s{nw%MHQ%e%?^(qbFM*i{gAST4(g*rb7o$>3jD-rUg?+
z6S3$}X>qYm)HK7_Q#5444CUYuhz#`9L(lJ29=x~Bf$VZa=+tP_V7{*VyLjEXeaR$y
zbk}07L?|OWU&As&i1MLaqGZ|rvk03v&WDcCqcmvH0KNIA`?d2dT*isS*cB*b$P9bs
zVS5u<VZ7$Teb6_)J|1NK_`8k%n1iRGxkeFD$;ha|$S8QTJ)pO_*1$`?jpZ(W!E@bl
zfDw;&?Rn<V_O`?8oOCKzf1EM1M?BmKebfgklkpBni&Puq{W7f5p5Z}SL}yK*HXGHU
zAUf>C`oKNhahQ}ehv=ruuGExqII=NND?&TuQD3X>*mx8_5MvF&%EZY%AU#^`Hm9tG
zH78Ps?CEH%#wiPtcQvt&8SH^UICN}2xzzsg!_-(Cy3L(6qkA7x$vR68;7YO-HzM{9
zf&_zsdm4Wv5QSjmdai}bJ1b<bZ5_<8F?r_Vg}ATaS1u?k-^XAzWSYb4ou_^Ahds-c
zQfKpP=mz@AJi!h5uZz?XPo?GLYY2P0sr&$sY<6nZSf{J`JS4WC@&*r9aATm>5+$xa
z)1m0d0B1~pkN+7@ws|;DI~VYE+nA=eFmB`ZW;Ax0aX^|_3qgmtjQ$!P+oO{Qw=>5Z
zG04NAOZJ`Jj~`wZF=w(eG6rbGutBQZS3(%A)E>q)7G>pH_P3=adDyxz-oH0Du9>p$
z*?s{g%ltTSRX#8}JVQh_DlJ<>iLeDh-|cB(Dm?h9Hm|5xY`%xixW{PV$f=q%ZU{Z<
zRQa(Uy^Qls?y<eb4&CaNyS8?ojL%@m9UUnDK-!xbkym(>vhxSYi!s0Z;;SkL(cwp5
z&TSr}Th*z~BTqx%w?h(qRD0+{dKjjx0Rxl~jDd<CWVH9i1`l#4Wpc+FRW;Tt+@qgO
zkCBwq?D;+z8n)+6Zuil7-#uLDlk;-8@BIsZJJ&Gpwr~6NuD<bJc%AFD@kCEm*EXsj
zdw2wfB!Df|$9ZV;mbF@6+O52(W;J^b)9}$#G!e&_J1HNbzRV{rJPO`EXs|r1-SzQm
zsi~?<-cf;St=O+)aTD~&x4*562PdhaWRKPs6{^HPS{d*=2VDd|baWi(!lTCz!`k+l
zY4x^m8N1W^X2wMiwy!Mb*t63c)T`@y7vFR!toJ*vUp98zV|@WR(}m38+IL6iD#q#(
zO0MS`_cgSH>F*8<8)u+7S*;oEHG@D4?w6wcK|>Tu1h=hsjclFG$g{MLQNsr-JM&FF
zx@axym!y8lnHqw9P_9y<)Mtn?hvW3{QuY4*N}B&Iee3#bHMpOjj_mqadp2&7-^4qW
zJz${HgV{o)53miLc`;fwRNUy1!!;l`O+UW>b){z~u%B9Wsx44A-}4Ji8a+fA$^Agt
zp>KA#D6qANn^ZCn5!|OoEmg(x%bSY~zd?g=sJ6E>s<yTU8Q!Vjh!_o<kjzOC$nekr
zJ@@=F1!d(rJn_OgGdb2*ldEsvIJD*!2%p}f%I53fUBi@0N@K}qYxVr@PUS_jPkY#n
zwS`jN49%D|N2B|PYtQ-u74APHzwAi%pc&uj=?u5aBHBG>Q?M>(oFMyM3uiJ|I&`>B
z_x$F!ns?C%g=4#wAj6B#wkkRa#N))7%E?Mqcz}m~_u3H+i0)8zK&*1|2PicX<T8BA
zWVmr!XVu|--o6A)Lp-(i3`mi14<h&f4$QF(|Ep#?^FL`!zimLv_hXSQLb7@JtJ-k9
zS%GjNmq4_2H>TOGiqfOxJ7lV(VxM}Z-m351v_QG$3EH_qOWuA<drN^?_ysCFF;kPr
z4U>DDOHpgXW7me((@}X)Z!cY`(mD@?goP=0&}fYvmaB^vTqkcf^DiHJUH<XWIvqDi
z-@W`wpp}s-C6cw|y;VARybMv|r|@v{AR+E?Lt^0wW+K2c7^g9t_NLQ{$hkq;!0PL3
zs(Ig2A*s0<85yUw<j4C5dL#J$>7;zcoWTKlnSg!UVF(vo<$=&3oTKKJ7Pt`bCYYqn
zn)S=c(Z|Di;$VTAdcV^T;Tepl%<?Rq8zW#XFQvzX53a+nGQb^!4rbT}M9z#x+o=C)
zvple#jz;D!RY|~h{Jpw}K7646`qlI2Z{-#P^9*(rDc2*&RcUD@_q3n3f4Ed<4{lb=
z*^}}~Oh#}bC@!2%gnB#7+Kuu1p=h+#SE{JA%DH!9YL+wRRwAqC`pyKo8`DPpJI?$t
zdvXtrQ+9l)D@TZX^+Ce=I%tpcLYWni;X?&Qy(I3Tl&mb$9e6%Ugln<-zQIc1sOd0x
zsR<SzgL!tKNV(R-Er{{<3n%Jq3<t4iMlXx)>j!iUIMBhOlg^F8`=u*37~_F8v;FYJ
zW=gpULktJ{W<ugjX`xQn!8m~zW@INiupv{poUxli1q?K)Uy9Z*eoe<W1?lYRqs(y<
zp42Z!=NtwP&6?-bk9lpVKB<$HtRbT?53w&}HUr5ur!Ir2r3qSZ2Wp8U&6oZ-E|OLR
zDX>Db)y#3=+L>P*#lP}b2T;Gzq$Hg>xI?e}xk>&#wK{O(tdf(0dUGr<yc<FVR7__}
zE%RA+?t^H?9~lzhL_#6!znRr@7k<SHci@5TWkech-KM$Ir>I{*r?!6do|b;N1!byE
z9(dtHBcnBZ>{ulRHz~}YHEZdBK2RF?*9!ct6MFSQ*>;T7-c8-w+Ue}e4xD{`%>xbb
zL}QO;C+8_S++7uArxifIBQu6-h}%M)MH%(8HLY(_bRdv4cKW_gHf!zXovI`?q`tOE
zekf`)=UuMxL((<v(kpbhut>`bh`wQ#w=<WJr|OG7*RqwXxL=5ql3+sJ4CcCu5;gZr
zkdG-tZFQ>lAEY~Pz5(MrMztmT^xop-`utES?9c~CK%(-}BXpvQeL(cQ!zQ+`F$H}&
z_V$+X%s%DJ{<eJ5_R2%boc}#d9XnLvAceMmv{X+oT&^=<?4l9}=*Bz0sY!z}HEGH`
z?fz)BHn)c>4yB8Y<v2uZtI9wsG}!H6enCo4Pn0iFx;Eo=4x{FM=HH59bpW&7v+6Z{
zu(v`1eyk@B!2Tm9=%O)0HDL5aAf<O{?+<=Uu7KgSjGx+1H-)pCnrlz1xTKPsc`G9;
z9T*_;%alvo!>p@gOj;lQK{G?I4TTcs&2L+S9@SuAE&E`#>fFD$2DPV8s*N@9Bz?;G
zgE`3BQJCFO-u-}1HdU3VsM0EwxG5#Gzr%~#;6?2UR=?<Cz5dtdv?j>+Ty)g-tEoS&
z7}JBMU!xhr6Ln<YK5bmKSIK$#Kue7qvi?pKsYp}4b-yO%#p8G>lOMGex{QXMJ;R)s
zcL}4wVDxQ`wc5PoC2c&}j<ef^p0IURXOoVcYF7e$d;iC8>&fB`e20<nS<{(%4ZY-Y
zO&OW1y5l?b*H=H3TcAIm8~qVsJ#j?7iHUM!>>fZgJDbj^py)LH^G1%)324-{HoQf%
zv6(X2hVtwO1h=vBh)S#56`z{HKJ--^PN{+-)-Hhbw!~m)v^Uabs|!@s2bgPJ<q4Hk
z_DW2mjVUQfIH%y;7w~t>12q|Xey<vh$Z;H6p1##d=wO~)`S$duwFTPnbZRSrNq1<;
z`fC*6fl*^HPfH`P`Yc{(xfaRoWR1HSj|C+>I$J^G6rpk8xQIwpMjWUW##eZ@Qm4x6
z)Q(IIL<u(qy4_HR(1qpwJBB2w0CKm8wl7flcAY+6jJ3wTp_zlmjkFnm_jaA!zfW~^
z$UBPrM+G5Q(9QVUDDHf-H|Sq`oeB=SQU%j8F$`_+jK-dCInAB?YlhFc`Nt|ZV<1tl
zMQW&xQXIxmc3z@d>KoLCzG0{iTs)4i1#O=>#r*`o_C?RADLJImz>)<|%D~t$wH=dL
zN6Kn&KDH>z-Cqg*-rC;KrARkld7{Xho5ymDdpgca(NXec{+j9<RDNiS6gfaG81p@T
zb^3JG3VpKk1n_QTt?4E<K(6&f`7f~`O>&AOkL_)1MpvyBhjh4>D=}oV644WS4M6+c
z_P(vT@i8yv-T3F1o6&nG^KX0D91oqOLiJz(`+y9vb@*~Ky~`lm*4nwYgY?V}gs59c
ziiY>k(MN}=OLY9S2F#jD4VGy1&N}3-t-0$vR{y2VbPCp?9iGPqkQxhgICQLj^TWF}
zZD68mi$BxTkN!sQZaSkpQiqJLEeG1IqlsuL1|%&Ziam^B<V+IX{6bF|r@IBb&@&Hn
zz0qehhL!@8aJcmHO}=oBh8U9%>)S;N#Hr&&s_PYXLDBtybBDq!$Pu@By`VY!+n&Q+
zEcNIpXb|I2fzioIj`Vl<yIo_Nugy~z`@gQ{q?SDYipn|O5HLS_VYtT5n5|Lpw^=iA
zIv!lFH#S+mI`n7!%vl%M0{f=^$UX(kzC}N}<ysAl_tC*^@9Rf*+(*c-M}vFV(>AXy
z&078HtJ+!FKyJGiMysdt$6u_;qX%mI<hlBf!@Kpn4dv=N?W6zr#hto*-Wd7f>?|)S
zR<&0sWA$UMaiChS>AOu(D~Mc2hW9jT&xTL5ZtHGU*3_!Ljzp1|RLufKJ8p2ArsFIv
zDk#+32O3D@^=Aw=U#90E$Be(7bnQ!dgefyC9>=fM+LCf{sK4;gpOllC%G#&sqWRy@
zhwC0xQ(V4+a2V_>>(b5Nn5o>f2vr~bTyMVcN0pEgkb~34bOh5s1BmLMIlf1)KKrzS
zKwTLRNJ=CifF9_i5}ZDyyvy&=)G<R8=3b{wtCr}Q=U3`1HbzY9VBL7f?V1R}@1m*m
zwfn<+^to4tGRwB=mFKG!;^(Ql>PAht>UxcbN0b(9)}l8zC@3=6(dUbk;}wdnV>I^f
z_P?6}99-wN<lnuMgQ(<F$9v4Q*O%<jlDGDJ<@G@n5KAF?v~O>he)69al%5=+(?>tk
z3-|tqR-T|pRC1K6PnT%h)=m0g^=jn<YcfN^_?}fWh9ju(^Y`@8E{e(VNsrr7-F@FL
zboHFk8aio~=B(eK*ES#3h#40tHxp=6U9mn~@v>fgYrB$Cl3~`Z+WlFDIzL>YkJ7T_
zhw_k~=7N%0K2Jk=yT)EWL=iykPwwBSN~LN{UYtfv$ke+p6evCuru9E+4B%WlKc}WF
znjo)1XjEX>4$e%GujS;OZBQpn-*R!QfudIO+3YKBP&~!gy4vcs@8fs1qoA8Smj3X<
zR!|2%3JwZZ7s^!`g^(hH&1-VL*%P}u>j5%`DmKK+Q6|kh|8>^4?=I#c?!p*xCmn!f
z9mi3DJTsky95^4v;t9lUs}`IL#$26yLqA=u%}!CP7KF>E7(kyoH}q609ypH+1e)<T
z)K%(;P#_BdvlnPm+|bFoaasln19>u=mtsV44sHJk7?n#R*qU{mIi#ZMC|z>Z4NlH%
z^@&4TvU-aGQ6yu4Ahmb6qAjK@;k!k7Lpk8y?J>E!^xB)9NM2jx8Et)MsR}wHl-oa&
z@7Vo&*NJ|)p_F+Bm}k*jwMF}Ny46jyFTa+#B;rcZl_ehl{Yp%u_!I^Uj6Rq(J+c3Q
z&L$1el{en1Fbvz{`_^jp_M=3?a>%>19MAJ~piUx#Fg7!yJ8DnrKvBKM%~+sW3E?oc
z3ax%;rK<emfKj1v;dJP1X;KT0iWY?Z+U0M%mIwtX9fPaqLWP6*?WQk*+?(PH+JCrI
z`4eX8=1WkK+RtdiDjRcxau5)XV*-Rn4@$Wgkwr5qZOtu$)n%S{?!*5WlIHQ|I+W@Q
zM$ktl|NJ*JVMvv><|Z{GlS)3{q<#Fe7MlcWhP>FG3pGlv_dww)IB-}VDI<0D4bz-?
zZf$DP$-}#}apwUACuJy(t@-thbc<?qtBAdF+ps*87k^bBUa#NX|F8}>`Y0)hC}P`j
zo!(rl50-!E2uPpQG^wD*p=+aUbNslQF@O?k4ht)SXez3`Z=d%?IqkJY`XD`}6Y&ng
zsrAXe&5F#Mq*RRIi8Dj>#~paUBYNZ;pQ`qX0zLQJ`*dXYc9ook_&kG^5Fetl&6`v%
zl-cCll@ZoYgLAUAV&gk%W8Izpz#qF&n0CLuTw85pUfBGjl+iED>>!;kJ*DCEZ_>z|
zerl^aro}Hkrl%M0#BrL4Lfnp_uwE;9PEuODGUNRmN87nu;ZQRL)szTSNSt6sAAAaH
zwq~CHvuC<BDHHf#01+>oL2Lf_jP^ChDL*ktd$xU`?u5Y_l-^Gvk&((6k)aQkv?w{(
zOMBTb)<#}5OB4H{lryhQXQ@T9^aCA50m|;5>3WuPm1~a8InYvlTuT>!;JoYnc;m0?
z0OmPw{6M9q4$zd*0ea~WoR(|(;h?T+=zCiB7)_ZTO%80OK3ejgPN9tEM36K95e2Xh
z+b9^wVxGV61+Q$SEd~YRQc<x1+&9>X9GZ;k#wqvN8}DhCF|-R?5Rok+Dh7cx4{ay^
z{D{nYkq@{R+Rg)6aPz!mxwTYlJ5G%QO>sD{1L4)!5+F6!E_utzX}s{3&TF4(ntKbm
zkS%9*xM-`2+@duAv%bZi55ID2J*I}V>AG>=c&YZNHXdxy%-QkI(9ZR+x39Lg7EP@^
zfm8eK!+-Or1PtT=5E+$)8?|F22IB=ScEW7-00upB=Cvj7UhoM9Z&ZT%0a^EApB#nv
z1`imo8*aG-MID3nlQ(s;7$ujrv3a#-in<kZ3ibEY9^~J!DRVVFC4|~Bo3!P?0Ywg)
zq+6yhPzzDSH<s^G2K2@za}2AF##78gorVC-oiZoJar!jYo<UiDUj=8ol?trX8(66|
zqn``+s`Dm4jHHcR9Osj>pLr)F5~C$ZXDW+z=4h3|h<LlPUkoDaiILjE{Feip&$W6B
z&`y1GGlo5Th5S@MqEnRz3UtaRS@W;FQPH8^swm#2_g3#DohMB_huc8nfD9o59TXTO
zlIf6D-6~-0PIHUYz-)MU2$8#xV0Hu1^s`)#lh*oaW9V!+3JTH3k!ex>9;!IHQ_I%x
zQZoJYB^AMflzsE-80Q@r4Pis{{i{ac0<O@q*FR7*&Sx*~WprV>i6`=`_EZJVf?(wj
z8=xf8^A4S?(xmZYlo{5gl9Q#1=$Gs`0g)D@Ai{(BUSKe_bqH`2lvQf%tjjc~A2JoE
z{Hk|WsUa~(S*ae{b)-fuO)c^Y3sxisT1$B&a)$aqhg^n9R7#e@t+M2x7zN-s?C5Av
zRS9GEGZPQq>%kmU9@f%j@8RvB!>%QD%@;o-`=uz1nnp%T)@&^skvXXoZq@C><5UcK
z^1XLIQv~&koWKLu8Tudy=zKG2T@4^90~o)}#JR>bopqI@D90h=87uPDGl;^h1g6R`
z%)*0^;Y@PhH*CJU*$?#?SyAW@hB$?_sN`sw0)oOcgp?}1x?aZ!99@?=NJ;)pDmvB(
zJ%eg7W=(VGlfK(;cj_3qohi~j@8P=lhj-zWOGRhhr{|w~QtxhV*1-HEH8r;(ztKyI
z3RDr3uPbl3MW`t%-@j62J3-8xBLe64&JPO;v|yB-Y)aIEYi?3h5YgwO+q7hT8KH>Y
zJ;IgFzJN#cKsyHx;~)!5*NoX$0DBKYrva5yd_W&;+$p!H6!nk5r0$*jz9$*-z}#9V
zYrS+i&j}~hru@hbt^9bOGDv^1t~qlDAGK?H(36fXS*exqOr`)`K%&1Jo-1uD?b4$^
zzgdZK@yZ@NLHcy7bFHnf{qyw?Q{%CHY8`XEeur^BBqczHKVPSZe{?^5Fjv`C2OVI5
z!|NDP^^q;;i-)*#?-Q1mYvH2*1bG{wtRW-S^ZHYK1l%6Iu94{Yy7epc^WX2)lP~<j
zv1Rxojb79FE_AX0^n~WpJ^ITd=%t^3rZNih$vZ>|alwuZuk;8c;(xPJ!V)z&FI#VI
zTBh!}9M%`@!BRwl(MJuMplc^zLXeYydDTfRTe@0pQK`%+P6TxLsICINyK;l_)2>qH
zppm-ts*!r~?Gk0Dv?^}Ym6|qwi2S=StT(LI^4%>s(?}P>*=CXQcG7K|Sje&y+f=Z7
zo74L!_KK57kCV%T;7|>E(eTV91>!h6TX|F;J@_Xb@J-Z!xIpdNw1S!f$ndlTkh16%
zBa*dxRj1-pDzxOy?auw|N6sZz;;3Z3TWhp<#gg9leU-|6J&`faI-k4x-!J|rGk`?+
z`pse!Mx~y<VQ|A}JUqZV`n+g-WE;*5$+lAka+`F$a?(@e)!w9|pRdI@@6^D7{T<I)
z4^?<`iCCLwvfZ>3+c<rziI+M!G6_Z2Lpd=Ni74K!Csuu;5rc9do_I~cFgF7|Bm^%F
z(7KMgCOndbFp^wyEPa4Ob|{oW5f)W!Zf<cPosJNu-yTHZ&$vZtY;FS4Ctz*|*JxYt
z6&i(?DrNXs>A8=IP)ER(jZOUDX+ALFUHb|(<K`df##y7;RMmR_?Z4{ff=Xor-`v0N
zpeA4SUEM!-vQp!sP}IzLY1i2~bF_8+GClO?_mqcGcAz~`KfC#Qjm!wv&i7x{6Yp+N
zWK2BQb~?&Kd0CYPPr6pO-#ASk7_@IJdP46X12!3gVE<=`0F(=_t_mG=AEuvu`#Oyz
zH_klR!J!c-=$%R#J6}%^n87jumxG-j-~EAp@arYYACRm{B03|#_bXj7ZlD6(+MoxR
z5C(EJkiy6Ac|_frsgBp)ivJy|antoT->EAmrfTQP_cUz5t(rYysG=xxfuCBJOrONu
zyrt)s?o|K&v5v#i3JeAgou}Vke<jY1enj0ZN2X3QF5axY6(K~@(zNHJWqR@1SLK(J
zC!d}sl%7gm_O1Wc+^Hk+fRY<z#XE4^%$qe!>sGy~XI|Ti<AX>vWAnzqFDUHP&3D{F
zly#tvt$$t1V+ZNp8|RY`8o_tT4Q^@B{5cov#b<u44I$U)nQzZlVB0Be*m_)JXHQcq
z9%b`s)K|~c`1Q+m?-Q>9n=&IEPdyB*qP#)_$6u#!-Ey(oinr;nFKyIqPv4=z>G3c~
zgdBalY~D;QefxEU@F|5f7VF~w{JmxkO2NTFlml|~_US?4*rxk_{zt_FpX$L?>PgYH
zgKhnE_qT4=l>Bh5d*dBVx$VcAIxJhEffyAyyJk)queV=Xq}2zi@O%P6&b(CL|K`Pb
zpfNO9P2?vI+<0B_vzIg%$!;kYmW#FZ?N{{d+p83vh0$bQ{jb@~JKF)g+7}^m=hft#
z1`-)H!~_Dlr>jbfUw%a^H<u`xsF+jf+=MiRlWMzzb=S`y*4V*0iiu!8**6x&Xrd6x
zjd<`^z4nCGp7v7=0`1GGA79-y#?fO2Se7CKLJ1V3>@}QGF;VX9fw@GYh|YEdahe2$
zTyTOk6FKt+l6_$_#jl;BGG{B@6#_JmZ9=m&iC$*a$Fq>Uoo4KLOw?fV54#YMW&4+q
zhc{fggF}@vVxIKD%W{j&A;M<&tXIMQ3i(B3DlguZX9?p(H!oLZLxXA?fH+e;C^R%!
zekgqevCcIszU0k&YKc=!2u_?HU&T>z)raVCQwzM5^{ZvM#!eZkFy9^(?AYxX=iad~
zDnC)e%6ce_D2U~w_mGc=56@8?_#!mitb!9sdf>t9)ju_gXS(V5=bQEN!;h<pNRT)4
z#9NLM=g#`Dq!?>NH7dK5IxvFz1tL`-5(jQ2dG?(O^&%4K3mx{<tGdizcl_jjcx|S_
zgF_S)6aeB1$Q}-dE3dj*yEm=UBTv7f<eULKf*W1XJbW;YgCmun5>H><R9UoFn|7CK
z`tTeDlG`7Vnjr18oNDVF3QC8+e(>PanldCyA%R5RI-AMW8>cs4UZiE4_Uc=|ctq0%
zr;~!U6o}sn^@qP+nA2;DojF3<eWP{T&&Z99YS)rCo>OUSuLZ<oeC%1Cv5F3A!fx<m
z4Wiji)J-AUZRjP-qa1O?k9GSc19al^Z7Rh8n>ZF;M}P6aYSy$V`e5-(dU3@`9FpNU
zBs-N#uBh!lSHsmvB(-MuPL2NIJf%eitGZ;Dwk%w)C{iriO&P-&@bjYXOHs4#dE`3f
zM0;w@(+gFacCB-tJ~DO5*1r34cV60YnAYN=BY^LBAai5j!$gZPIt^m(6OyF7NY^!9
zKs|Tw+)aKWf7?yj3mF*K-UE!snc0TO#NlZH=7U7d7JTa`y7c04N{I=>8PKK1xfg5P
z)`RRb2$OlUsiWehrriA}P0bEbRZW8;NGpja{iJHrFfChCq=~a<C_6b?ZD-4sow!K%
zkpr5O9;2qR!wMfVOTYQ?C7?hO6&4%-<>E-eY20wbO%%G?sJCDHi>ka)5_wGD7UkvN
zZPp#zS7Z)Qr{Y^c;)5wPxKclR<Yg4!T=*;qfrAH0sk=qLylJ)~0mv`IN%Z1-`;|TS
zF8$=%N$Md-d)dd-(z%Md39;dh<Gs0drp66lqh}s^mIw5sFfMK*;D}*BU!!B)we5J2
zzW>u-E4yDXXssPu^yDjY19BgTK`8QrPgPWd6=e>Yur6eYfB$Rr)OTj8e`2JQE81Lp
zi6&22qbDAJMh%f!@bdF$HA5M=H&?47gH*zqmnf{ONFP4>ylPX4&^rsjpjk`1uA{a}
zM<_-$97ynZoDYXimukesA#!hMRAFJUMyF&rJJRq1#~R#q^9^@u{+NE+vHD|OmNrnM
z2eQvhj^WL^c=9B@`RYQgJI4L@RbdE&s2~a%n@nEwnu7|@zgiF8MP56T{mDmb^!BPE
zjhN{wu+>_N18PUJ;zqa}2)1DE-b9mWIXEHOjK9&0FWc!ow(jSVgr?uQsl2RQ7u`Hw
z3+Cr)yMMD@eEogJct<-CZI6~>9SWYN2XDDj{bSs<{?$L}<xPd~!brz}Hw5V6$`;MP
z`Ukpp=3t%Py<Yb}^sa9F<@Yr?H$r>Yysc+mUk1`C4d`<x^iJS~eCFgqq!QgeOP)1{
zbR0h1&78KJroe6o=?ZDXbqO0|1rF4KBXFw`oGL15p+-|OYy2hZWL@05PZO=r)xArO
zno9rxKmbWZK~z7vTl2<cQBdxXUVidPy}ItC^7ARa>lo)=ssP=WciHXw)q<JI#@TFJ
zv!!;nChXXOQ>k~KIkN?uf)CfAr$tY_O7~oIsRp8V7z(qkVXh`m+ot-yLVRold<*Iz
zIN%pKQV-lSTN&{&=-R>Z2fbwVKUx~=bk((_JT7}tudS<AeipEA*rGLaSa(v_C3gNT
zdiI*RN{>d4qZl>U%+#1+tMt(CUsN*mZTs!q&ScC!i5`mZ&QctDW7W}vI#y6d5j@g!
za3=X-jEl6gbB(!dK6yr`M_#3ccif;+)ZsX~XQO`qqn~LXg(&;uK&d^}Bh=pBM?e<=
z==A;sW>VwOL8_^pazKq16$jbmi*8^oInzU1KVGH}7wyru1EnZj6C8Wys~Ctm>)TpP
zhPMFH$WmS+?lg1CnhZ~%pdUz(x<+JpfVaYMPB{%;?a_T!1xG`4=T9FYpFUqPoMPpL
z9BpU5=ggde!}J9$-dGEg1p_@R869kqHjEjhE2iWT$)2NipWdef$GddTcP;?|?60zd
z4O+JF9ffBgH&~mFv&ZC?KUd$m{yJqbEC-=tG}UufE8cjGI$MPVkC;!IEm@xo??8sP
z0kulc4tMOLjHEC%fNE_;@eG2nyiA50(#69c=sr3&0iNBv3eXo&03cyIWx(8A`4j@~
zV!vDP%bb7zZvXok_`)%3E(84U-?VRISsocB)8!9z%L`%d5HTy{<fp)Nj#hPRE-;*!
z*bv}gW!k&_n0!N`9HqjQqf5jN1<t&Urii;%x$n7T&!E7xHoB`d7>{RSy0rB(l~l}A
zE`lO5Ez7yE8E<wI+GR*cjQZ!zmp*z$+jrPU9z=%H6^vKg7q9HOoM4z{Pd$eGu<Mk9
z(bm;kucFOcwKi^~Zo6o(!eeu_AkkZE+uSIsfWq-5-r3i?uk+hAPp+TG4<4Xc%1?FI
z^>fwFzXhSRPM_>9Q!1O_B*yvWcm7JZT{%@T6sM`c5ZS%IO8!xa%F4*cQywY*0AKy$
z_iymnT4KyM65-F2@6-u;_W9-N#L3}>GHGF_`i8R_K6Ro7<)x``(?Jz)sh~gw%(FL=
z@DKZK%89eZx*dk$oGEWRj?L{%1FwxK+0l27*Aq40)eHb?e2xe%6_b65CIkhBQ}ikb
z?=wd_5sB@AX@k6RgsE!XP)rh)mYSlT>6dCe)j~U)fchLNS5kV0QgTP@n(MoD`gD;#
zI?<zK?%~;grtbRg9U6lY-EyW>`*t0Z7tq1n(X-W`Ji5^609EfJ;!Zz;pru3m+x0D!
zo@?ifB?{Z4(j$jdK=sOS=qMv+gl@p-ww%1jU)rSH+(hgdfP289!YB}!mY%Mzl99TZ
z!W&jtsIsg~HSNG<qhr;7@IWQUz^J;zFfLPoI3*}1Z5YrzjO4vL<Pn<aoPsdxa+EW2
z`hLQi<>qEN%0w;IIDIiNQZq8tL<HfA{}`sB`KfBIETfoV4OQ8rluALXtgIBZEZ?qR
zc)B-2)#lWVJR-l)Xhnx{WMBPp5E9HK>tNMS5fc!qkK*H#m6kzKvdeBzB#L}J%5T9b
z@m{8~hfLB<ZY?_Tn?Go0c_)YiW2lycX7q_vrx74R*&x@<n7Q*0gBtDDs{8)y4Vu9r
z)WdxONC997K#RQ<MWHIMHh+Z@b!|b(a6KPqACEY6vP9SX=t*4(N+y6@*<*)`bP5Qx
zj~&^SmO)_?Zyita(Ypt_l+ce#)?Zd^zOs|_!A@!DE<_k(L;)uaip$nDtnFVv-s$>=
zf21-|ZoCP3SW*KQf8kEHE&TnWm6#Idz~3!;ctR(cr!O)G<74f>sWa!^q{L7^6!9|c
zd#ymeaV6>)N@Ou1Pb0BrOF88th~hw212vR_mWEzvGev>h8EU1ivRk43J#f<5y0qz3
z<ykf2Q0QBQZj2o_?;s7k<vW^2{;V6$gQESr_38SLR4Agcc<{R%M?sE~Vu<9D;&JL2
zV~riJZ{Ki{5{)0z&;8rCY4zGq<kK$`Cnsq|Y+_gR22_bV$~5$C5gyz9?WYbqJfH`~
z$eQK7hKIUqe_<KQVyB{m!<9WNPAgXK)N$tu>FR5Sf>82T&5_e8_;`;Jv-?pHDNdtD
zjL^W;2vwDp>O@hA8gQmY#w97O|6t}QPJy0n`tK*Up+MSNe6jUlz}kEPL2)Y0y0ORT
zwToV%XM1h^HPYxm{c55aAv$yFB>TWq=|FpPM_i<Dun#t1px5>UDKRx&L0yBH`$Z_!
zu_!TitfwiVR=pe;V@s91!zOCzpj^ebm!NzfRCm%`rw{Jl!5aPDyEG>*K|L)s<o~e-
zYd=zvR{{kbIZ1?0oULymw=_Yi$*CM^7op)8wXMK8KR;Bdgv<d-BQkU&3R-v9<9c~r
zCCXzUip_agpaDhwdMZ`wIYQ%eQ*bbGWKQ80eMB9QJWrt6uq`jpR~1BUM_h5YrjN~~
zfYQhM`O91M-G{%yxR{8(<`9tm%?$CrRRIo8nWi87^g6|H;z;?)quR6m6RrPzKM-Yu
zK^w9LxVJ?&`I_ZjT08HU!s!OAst4z~Mb&K1Ej^`(Xz-=q{;6)gdOj&5-8y-sP=`-7
zvA#K)b4d!fB2fwYbk)ZInSjGRH8VxYO%!d~cSPmx*~-Wtt6P%?!5dE^3!BM1&Cx`V
zM$=zep+W-sCu7Fxj%#n$OrknXwH3h0cdH(m9ZK$JDsq2N>uGHWa92Sy^)Fl`>-7B}
z{cuL-2I#wMFGz<HjUP2vGv-`IwD2H~@Y5QULG(LzjDB&`RT_~WrTy#oXxG-GIA<^}
zKux43rw|wq(oKm&LAGJ!9WF$1?+1D?Q&VRJkWx^lUp~2lx*wHJWGfKITR;e%i_X!Q
z(c`EI5U!)aUfT53A~l8wQrVj$OxtQz(^vzRHyVBla)5n5?dt2`L+vUo*r^t;DCOqo
zYSQeh;IZEN)_*;QQya%E*II-9!o&H2p<__?HiP_<M&bb<GIDTDjcI&9ZV8KT-chW9
zd7ufWjncv$nHrfI0@QuKc5ouVB<|A&-PkTulgV)QY-*|k7I2&t0fp+)BJHSZQ#v(@
z^2g1<X+r;>`JF!4wnMcTAKe~73MH6eEn&cDO&&j%^bVZ)93T4DstxKF6HFgKC^Xk-
zKh|ETL$6%S!Z=Ph>VgCY0D;CCWzA$q2dKgpj<qr`p^cRnu4{SKg=5zoSna*AbLVsV
z?-d*;?I~2Zk00_jOC1!mee%7{iX%#BGPRpp0o|VA8h6oH<=~{L+~1}u_P!xiqSJFV
zwtJl3SiD$gf%gXzD6sohRoCjui_yFCGjwblQ1BY|1iaFVV8y4q%awmge~rI=pjNEP
z*Wg6zGVMLC<4v)SLERVmw{cqk>3}L~qIJ&$Kh&IY*=njT(yLEDr58Wks{uGREiKIu
z0B0z!oI9WN(OYj)E(oZq687(r3dN*nYr?ckm>c*u#+5l50p^pQQlan(m+G!NzDd+L
zT=f+vwCmtW^1#!{HJ*b(j<XuNvAMR?hnlk$)b}9OYUD7|(psE?%O{VV1@Y;t6!f%#
zV<;2`(!{OfLA`o_08Sut&$A3Q=M|QuYj2_$bO2C#QV@dUlaxv!&*_)=f|e@QL$8!7
z$;RsZV~LsD?o&itQ=mokjnI^#y7AV_l@b}I`m((`vS&35o@p1_BvDJHNv9L0>zTW6
z(lCtk;|I3r@$dgw+r1K%9Yt`^3ZOYx^22$769+xf`99<8ZVl9R-@g@pn@HiaY8~CV
ziR%)O%MIGQ`-D7kqzoCHqFn`lD}(xOYroyx|5k>3V7RuFbt`Njj#GHEBg0Q)*y6zT
z=;U;h)@0rL`#Uvr0#0Zu-xq^`I9*RcK%7rGBc>?MWVYKMwPafjr&e*)na6&;wR{ao
zx)dtKPtawPvvr_^{WA=`vZ-31ta?YA8e>4NArG87Kg<tfHfKrz2pdZbjf{x{u?tF#
z6B@b*Ub%Zv_X3+OBACLt$na*?K0G8s+0%lx>Z8wXXimHS<{?f&?`mqz99(-qVWix4
z;-I%vyll_GSo)U4nOoDG+K>kp&^xcOxbF4upa1h2aCq*yue<iZxj*|}IB&xgO+m6V
z%m>Z6Td!XXv&UoUVlpfVIawXG<y!aFV^l&!u6X+q)iGvCUJ+jTF2^_5cSqN6m$LB1
z_s;)i3KvjcpvUSys5&PH6C;9SV1_{;TZ(q;Q1NW_8`w{i7hJD}$+_Bh_=L{ZRA|rU
z<=V5q1Fu*PXem=bF+5?YrA1wuzW^gYBFHIbxAj013eM*`b=$?t4v!#5XpELV$1xK*
zfkf|Ig6@2OzWytlF9!|F?}@kHt2?fkr6{)=yjaia7k^x=L_A&rt%YhCd#kRZx_un(
zzRk;D)Dz^LG?K4VU3Nm3-~E7YyLyf$T{K%a?A@eAtBdvOhubuMaJCW>a&^_r6#a2m
zyK=+HPsLf`mOM|xDSFplb6UH%uF^h5X#gH|f+LO~`G>v7``6(fri`9qz4GLJdefI`
zmIu4^-w*wos>K6!V$))M^8pH7rDZw_tU>-VGon#qIYJAgbKlbEb?@rebzpyizW=*F
z>*}c^h)%X*Vo<=!Cc~JkNi+W0(NPwik0*zG`>NOV-w!<N<lT>^(8u?`^9>+8xf+r?
zLLY2dqT@sgZ@BF;jmV5u9R*UJfAaTwb<JUJ*s5>e`;@Mo!E+GQ=CLzEt~*npp_l!q
z7R(yw<lTI@=s7*})H{TU;#ASttvi4CQ(ZG>f@aN}qXWA>)`seC<wh8@LEyuDnp(-B
z%^9h%rZO#hY@uFWdrWcUbai>BYR1d~C>vOte!$K_W_X5iWL4uq{rRz<>h({_E2Sdl
zckiQ57?~p}uyO7BC3@owUd2F{SK1V(W+JOT@!85p`P%r_Q~JZRs}zxdAyFHwORgBB
z&dNH)x&?5oMyxix@&`PqzB)kl@%tCPsaeBPoj%f8SF?F_7^OvIZ4TVkkGcx$-g!#D
zcp69`J8t&vKh>Q#U8<PO!5V?{;j@!KVNY(*FMhg#<8zKEGXI<U;~#F5KL+Jn58R<e
zCxJW%TRtYj8t80vM#{ewJ-3cob$HL1bafPM*OT|}Rv8hs;n#hewY^=@AVb>C(~NrP
zA_+Y1C`Ae#rtzb5aq2W_{n8ip!v|jmf}Q|fcaS2Nt<lhYM?(<KpIQfUXwUiQjeSqP
zSu)DyCLCcGjUT3rgdBb2M?Y2mM2;M(s8VJ5ajjUs7CPlfFd`a`!-E?;rXIY`Co)^#
z{NZomLd1@GrG6J^fS|}I_5p&Z_f6x2Cu+L&ol%*<_c3^YWPiXoCmt?PRqIH_C&npj
zW~#QYYf@es5H>78tLM?z>^@#ZUW!nxZXP~DDL9=PPVLo!!oBi{OP5<&F%eOU%5Yt5
z&Tw7BdRj3(E7CIc;^~?(b-e!i%A5M6ppi<fxNv+1D-xJ;TVo@I9n<vcOIIm7jHnK2
z4892Osne!v?5Jh>^Xu=ZzB7_4&A?-go=l@b09)?2gKl8t7xvRWxATd8Vl#AT$)EM%
zuyifJ;W6#@hvbv7MQ6y(4`%HrP0Ck$^)U(_Ez<|(@YQT87NZ*%?%u5JD|Tzun%y{<
zpj)1KrnFo){o)CIW8PQ|pFB@9f$tYs@uJ@OHbUtHae!w*#ic;vfihcHZy1<FEfaF#
zsI_nkr57Bo(^q%T0Hwq=Xw~b#)2|nP!j(<B^sf8#?Q7>KE^CM`9TAAayI<e9Xb|Xx
ze!7f#S=;5In6^67PKs<2ltm%6b{J<&P8LOsIEVUFxjy~4L3wvr(MUJN<&D&}{f9Y9
z8h$NJo6Z_c8K;+DdPCa|H{yK7kw>$N!OcYdf;k$gVABWs&Ch?WLk*nMo_jG>dcUtp
z0|&5nGvI@dsJ;uCK*~uU*vaaW)Kc|%^z~O!A)n)bNQ2qF24xjP$7s-;MLqESDzDM{
z>E%RqyDO*+{+9FHKH1~^VLzBKm!V?{J8<;i2#sHG0~`Ve8IFS<9I3NrOw(x6VIKO+
z8f9lubk!X1)@&X6*1~z?f*-w~Sw63Des>R^p(z(lR#I4xcCTBe`+oL_P7;~%9z0!-
z5D^^PKb|v}Ng;u5t(kMG8-!5dj!$&UJr5xBen~;uOWBQOdhV%TYSHEhJ@?Kd$_<Uz
zkSWRf=)I%5_=bVNe3R8)bx6<u@sE0G<sO`NAm-qK3Bx97NIwh#QiVdB+EvkogRZw3
z&HSTgx=kps<|WVFC*ROeefP@AntR2qoQ`mhmhLBN_3*<QmCeZwJJ;(E_dKXV^o@j`
zDpPROjNueV-J6<|TlL_5KgPL?@zRo@`~Q3|d^?$9RpS7yuFxrvSoNSV!bA9+&g2h{
zhlWY+YHY(O4^=j)8i$Eeg^&W)#z_TdNmo!%z7yT`_62p~UaU9%_-j46xR7r*>)LxB
z)@@f^Ok{S7?pd%{k8kWyehi2&h{)F45fApmUL&&|$1D%F&s;0OAM7u8Xzj$SD^+*>
z1Vv_#($Gum6-M;(<i52a5ym)uwfS@AkbP?@cdbD0Ln$b@jf&adyZ<lFH3O&Jr2n}4
zRt?A-rAscFrVqBhtkaElG%k+G2t(vZsp+vO@t|%nC=!!nocE%G10934y1JBO!f@Qs
zXGizz@zbb_uGHJ3FLNBoj;=|6ZEsW`+N_uU@&_Fj#vl%>E-VxK&zePY`{>>I+xY}u
zMw&`o_a2UjEzn4uUeTGuG>Zt~PMBXf4uS?!C;AV$R5|fMswq3J&p)=nh_vwi&FEdZ
zAR}@#d+Y%Hc72s{W6c>_3$MLcBZ+KxHB@WQrgv4DeYZ2WZXoXtzOzovmky!`=QK_C
zA$0*~)}B4f)E4(Wr>Ex(8Cw%(w~OQBrcvE|?AUy2GgWEX3oqy|%QtJlU~=+7I2dxp
zY6?^#m%cS)HnkZ7RlIYZ9{=ql`uHHU7?!PjAN-YOjm%<m)j8`<H3t<_Tj@J<7J%4`
zq`LPube7*~$(C|P)~O%-_RqR}@<8YVZH)ys7l<zr=kB&M+O&GHwyax4t~5uMQSYLi
z)E*xKTKD|;hng~I5XY6y*M_x^tA*TmFX+Njhs+V*K)tDZ|Lf=4SRMcxq`!Xo$WJwG
z5NUQ8nMs`wsGhMJk1`$;7#yu@7Cx`bph-OH)JKU{WEjrfW4l2+zN>dWvycQS3JwAC
z!!t^c{_-|*)Z<lKx?g|#-VgOL26i4OjaGQA8Ohcx(BRlBp8lG6(|0xGTH|&$wlD&R
z;UI5MXdS4nr{DZcnVAVr(X(JuT&%T2-@VlC{QcEGY`rd&;Wh`IILdo;`><h3CZ(={
z+6?;&_i@TVoQjH$>6#zht&1lPQs)_}^)GxvFR$278Uzw5G*dtN;SV$ehsc%l=IFDn
zkEo$3SRo)K_pN(LYbT9V@+G4*>XJM4a9t>Aus$mObb}sQwAneS#A>(D8@Y$2spsTA
z{rT5FQPrVBWiPl<Km5U6N(4D<$5>;$Im0p;?iZJ#{qHQ)M?=#zZ^A&$xZ`p8_1{Vk
zIzdm0$WEG=3o`DAK3=m-tIu#GT@<xauqCYZ?K9V3=YQG~{=5C(odG9;{<UuDY#=rW
zieU&5MS|Ps8#~(#$Wf<nw?cf0Wdb7)2ReciM+6{^ub{1*zx?aJI!_At=WJUO{R|;D
z#Z#Y`mFPW`($H^Sqk+J(hE1BGA>%taVy#A1^Dou0gH}A@DedV>QDzWTM)4jsxTkAC
zQWDTlH@w^wHsQ=Z4;c2LlC#Pk9?O~O!y(`eYV*j42wl*<uiw}6R~ntRwA5+hH-4r&
zubic@?kcT(^H2KKpH_0@MLyo*PVGC=r61fqfsGshlU<?j-usN}1}BT1->*IPb0uWt
z>AKNW4jwv1TK$5mF!~Cwo2it+2^u_guC(e&dBzRqXqGC?`TivJ13FiOH+#!Vn-$A(
zOXfxXmo^Q8(d;KvES=5V`(-OmW6dHc2Bono;1@1GL_zTmV`|pSm@)6N30Y>sCd2+&
z<4foXe4o6RV_Wq4t1qf4JWGRP>a=ymCz^WEMar6(sYH%@i01P0;EOe3NE)8NdVRWm
z9me@Ss_qh9feHO(;VT@$(O+XTOxXd(#Py}8GY~|h6@wkUcg<4$?2m8SQ~99O`Ss+l
zo+MJArD-^B1`nR9)h~ZY6eGj=%sS~*1@>yz(pMkT6K{Q{v@~*!i1xPE?4Ur)ZWPZN
zg^XZajNhV@`?oCB>l;fokczYQI0v5o&1)J4taeB&`M8s2s^_tf<bgNV=E{|?Ozs{L
ztIeyI=)WF)SurW;j<N6Mc2+Mwxr2yOl0q_xnBe_(&QW!WK-rwxd@@QDk1sU4G5>6?
zJyCi~iZ)@KFHr<ejs%Y)z5mK)&73qABt<yI;!uV@+|3svSVNEv2-6;v5^MH-wzWx2
z=YCd+-wNpbZ<_VD4MPt#fw3@Oz5REQly#a9_@f)gMvzZ}A~?s-yyMlU_c@QX2%gD<
z=JF$2b$q`(L1x4OU;BE|bLSS^uU-B`1a~ifSIa3ZxnS-TC1LPRzcd5K4zT(&)mm`b
zl{oZP>CxwwQgJxP^+oz-%Aluj5Eb1arVATgcejYGgX?q6kw-V9^q!!B7*bZ~rTmc1
zGdW=7e;+^9sPW1Dl$JCErRg1NJmm5TJhHd(7zze?Q+TXE8MD5r*;7U-ketRfYgXy4
zPmU^YP`(b7`QZ>Cq7K5Qs{9m)04vx@C0tU0a&j{?U@RyT;B?K8+^@nGKb%Nrbiu!T
zLNw{}D|l`-wFo{_c|DP!lq`)HK2Q_pTt})%qki&-MV!u&$NAgPgq>-PvDQLW+Pb<r
zmWTpnK!=@CT1TCR)0AW4@<@Yu>gV58+b^Hg4YSATs+)kI(kE}rbv#q7#jif7NB_D*
z{qys=UyHK5j_Juq*5EMcz0C!`7XEIzW{ex4grG=e50BKU^{2_jb<M5ONN{X_qViPj
zB}dT1KZF#85gIullek2)4w8$weh<oPrjf209z7}t`uFRFtB6eGQLLs=OBQa?tO?XG
zNe(5SZ>aPVMFLx{P*wo<nvt%>Z=mK+ovvTrHADVq_L1{UG`y)tGdYXim(+-|+EN|l
zzC)<=TYkKd0)XY51Y&F8rVtAI*%}NQKTF>Owbk_SFEL6vV#c+#kbM|i)d%&_if47G
z5kqrGsCMmIt@l4143zO2a`XEudwiyr?rKyHRjkeUw&=OHTeAv@99{#AJTAmrr+4kq
zn%yNzLYcFg5ndPo1x2Uyji3EYqcX!(u=Xvz`i=u3b7;43dYxJ1han#<FS!W^_WoTb
z9dw2{VhjzCimXUY$<w7*ll$CNs|OasXMvtO)s_2h^ml(_<P(8CNZ#l?j!EsG5{VIh
zQ12~$Q^(pOG-ybI_U!sd@2wrG%;YOEGHuWI(vmru*bBAI7G+|;RM)7!u~S|(r8-(m
zUPtFi)q%3;>G392!Wb0Xi#4;n&DPT(7m5rC10Mq=)&a7jeD_8bedZ2?JQSY7S~=fv
z{ph8p3*Pzi%O2IXd4oVp((Au}N?B=%nmcBst{^3=d5^E=P8z80Dhk*xdR(7}C2IiD
z^V;ejDmlOp(0#gzXl+NC{%F6S1`mkV?!9~T{>S??AupN!L}^l-mzI~G)opcc)KLkQ
z5Bp#!P<8(RlzJewndw1PPbaVRgr9=Q%WtXRtm{hn1V|AFxkkFuG3e&W#k-)leE4^@
zUVq|QjRYz?IVVDSV?hxveL^+e-W(%)p1<I!V1{V$(-P*g=138LxMsrw7fyr!dv$)M
z9mi{Racq{pcS$qyph=&tT&I9Z<G7@6@7vY(U0ZW6ojtNuufO&J4pxv0Dc;(><`r!i
z!I6tsOjagP<~Y5qBjt5CRjE4@<}!ZLlR{Moq`tMqgQ#?}61kV5sys398Y(O80lm$o
z?*-ya@(-k+F2UjA%j92opANOD;e?A8r)Gc|mVK?s^6Y3moT6cEf8l<3pz{)AEP~@-
zx9ilr38Y!})7YCw>w|?S6gPk>_8>oR!QmK=;#zw6I5c}!`NIbx=NEzUnoYfq3=N(<
zO<Me@JV?JdOLTF{9e1G@gpl4=ppReLsR56f^O+qCZC4b(OHt%IG$@{;TNu+VAf7fo
zSD@fu8=pNtrzRV_Ss$P9bW#?m5rFLN?(kD4QF)-DI72WlxyFzKXW{=tMo!nT{x}7i
zNV8kN(lO2l4IQMCoqO~q`P{jgDM}B76Y_{g?hv0eMPu?)h~n00`_|PM=SMjaWRTA8
z+N<Xly{Y`1bPb6&XBJLVentV|J6-Uhe!jAdzrNIJ*6G6!CTPrnOF>G4T1oNJCnr0U
z#ly_0-(|;nee|3*9uC!z0TJ4@cZ=43dO%}26Fee3O8Nc0_5L9cbd1LgXio}C#w8`6
zqc9&of1HTaN#UsE(h~MRXKLHp^S|SqkIWdZSU*l$fOft8Lp5~4Mf%`{-EuQIZKQc&
zvp&J|BI6S{^3C;uCpC@yh#Xcl*XtBEL?wZnaONAf%Nm%$jsAM!J-)_kn`a+nQw6m%
za_8OY94+YIepahizpo`*PbkZBva>GM6lg1;6S(=}=k&+7Had5%H4?4Zrk^cbrnKbm
zDP_P2jmk~aisOwGybPk=MYx`OVJQv?Y?q8tnvCAoT5(t_NQV?T?sl5Pg(B5-;dXd^
zHMh;^)w_&tZ2p{m?EFTC^I2}Xryl?BJJfdnGX%RpuYM!<oD6S9cX|8e`}Fu5`>5QG
z!yOskdlS3SzuW(#8Nem?&!6O4Qz+QH_9(WWuXsu8kG3KNQL2qPoy|!_ccNh>C-Bn1
z4NX~cH6xjSrTx9D&%fVY->tahFdbgANDrJY(<QSeu<<jLk`V6{U`fl0cHqisiSc^&
ziAVK$U6^7K_U<F+P`rUiSqDYfb{$d)3>h;}dv+eutf9$Dp%}`X93Q>U{rkhO4C?qV
zHKxKCsVP|rIkWViC_Qn$P5SW77aZd}mmIH7qO?8kR37piNfC))CvxT+k*cTv^cNsp
zF6QnqBX~s+1&y#wlz>BI5N{q1IiruZ6lfd~gnns5H9yT;n~D5Z0}Z@r2**BgrsSdh
zKoxs@lt)C)5Q_h5GYFLrQ9v`gU7Om*<gN%3O#OVRQVWdTDj?gk_cim(`w|JT=CUE}
z!wW93m(KF>g8$vf^(rqprKaQLUFO1AQGzLlPSJIW$na1<;F3-Yj0e(3B(SyyrTZg{
zY0lWi=n5bOKvah<E~(bIj9}{^x90&q{h3o>;lMA$Q-(69&pNq!Mx&NvXceGisQlvQ
z6{56+7%Dwu1Tvn!zFObwD~g;#Z~f9JyaNq%GB!W|K=O8pd=mX>#0as7X*0&n(XDGi
zE@ZRbcq9W-6ado0s;3XeP>YI51H;hBPB5quPhidr7S&mMOrP)H!$eWwjz~#s8%6DW
z{c+sjwKl`NS9!JLKzx7?LI931bLQiMDRus~AGQ$m+dQZxM^7q&w*n}F<L1#1iBs?7
zW_B|$TRYz1AId*9AcDfFz)P_Zr}2GvdmZS@Gf@T@qxJWz9_RYP*Z9erScP~~%dBwJ
zxpv`A>AMG2yz9v;J<WP%A@Ik|{I0&&^}co#mcvhg<{#dp8jl3^CqLYQuk{h-xf}k2
z7q;g3jdDQ5y!_;^bgcLYu)RT?Kb)rcs0amw#b`i65_y80SP&nlhko;CZWhOsqF}-6
zT1pS-b&9vtv|6D!BQtk3o~_g5>u#lbDKp)heAj&H1i7el6%)k~S-^F-uioHv>gZ;U
z$(eIpW709kP@1ws`92zurI0hl&eC~7>#Z0xr*h}(*_%1<I1>2yr>pejGjAw?qKsV#
z#^jV}3U7U)rw=dJ$D5&@b1g`>f(p6nav+B}qcm^ET;2CLf;$1!2ejlvQOWL#V|w@5
z|I*`2Ofk0>tRFn|7y@~iMvR-NOE#@W8E92bIHppUuM*>{Q6Sy`66Xh{bdfG`N^Oap
zFaV!%u|u3e6uLQ3*s8l9cv90x<U(erwBvB0%FZ+^CMipqDRG?b`=2OZujt|DKLMQp
zEq0(P2E}SfPL4*68ms(lAhN*v9Y+GnLO51ecOy{%cld!d+l<VwRZq^Ic&XCI+a6{O
zPy+o>hC16CwRiI;`s<>X*n?0RO1bgg9`^ppqGH@o%rnsxclUTEyN*ieE!-;r_#YvQ
z&W$=u+DmRe1vRsVbLAll)Q!^gizjo`QIxiC-XwFe<fKG6$KzBT+slI^b!`8~dUD^}
z+OTCmpY}H1|JILwtSc5wR$|_0Etoz}zkd2%bq0Am<N+Ch)TmJD+A78MN6`V9m4<Ow
z4&1q_j?|jaSZY{6OAXs`#KG4r-U!cWDm|=(3G*?=vM`!~UvA&)pfu7b;^jsjbJLN7
z3Y&bpE}b%zlTT3CUVTQsgNG|(H#w*_553*9$g3xNyR$7w$>c}A`^HNuTDM-Mef{Tm
z((tSG^B>=+p;-wUGi8CMzPL^YdLr<VoR8>VaQWZ7G)O$aN(K7`1O0W=nc@?wCVj`!
z(X5(vUXq7a7ggz2j^PXCeCIwGwsRx;YU8zLIgf_ia8fx)1rdc)S#mxH?oV`ED$?ni
zN}YvHk`o5%+jrlqg!k8}^lUS-<BWFhKdkr^oFgz?gZW<AzWCRLpIq?TP<rms%8cHx
z2cLM2nlRs1|8bY<M?<<PvR18COW)8dYbuoo6x^!M!wwvy!Q??heZ7iz0O<u$W|6gM
zAnQG(sI;3A0ucnnQfs2PPR;d=@`xm|7(7f#L<<ARiQT??uj2D(a*SVpC6zLVs8HoK
z^%R|hS&)xy#*D$#OE3ULy%5`%JwUO2x(ifX)u482>V$?Q;G=TWH4pz)6H+;^*&H%Z
zK@Yh~!SGHGWogmMmHcgz62}O6y@yFTv#QQ6D75t1rk%R@_L<6y1-5>8qc-ktQsNYs
zW8~Z{nY^Ky3@<%;LUl*2b^--RJ6)CAkK^>LYbH<wqGf!7H__K>2U6?pkDT_uR0-79
zXe=fuLV?7o!#L*7E6<76TWPVf6XmYQ@C+maMtifitzSx&>|)Rj6s5x`Gu~|H#kbT}
ztC(jGx^ABC1fdx3PXyN(lzmR#dMC0zZ|s#?xnco*HRBfk)?BQGk3B<h=6!8HLd^zJ
zd{T!@Q$Wvs>Y_MXx7RqFUGW&Q&76I`QPt32Jcx%x_UrP^2RNC6LZzugb#=a<RwGzy
zIR5J{YFPvkm?|jPtc~!ip~kkDBAZi88twmlpGIFcP5^{;V%G}2harB8UE><5^V3$u
z(qg>){nZHiYU9o_3Ls9@u!(c^lN<LFDc!I9T=M8y(q`b={{G=kCGEyj92ZIfW+mm3
zve~HwoILwVYMj#oGFcO+BRs(i*mf8?L2FHwP9I!{h{F+0k={^{iKS=jb>ie%4JFFk
z*D2d`=FkaA$iSI9lq1QA;F1~$7!BIyoTPwo9L98pff}2(O=|MY%39P`*XiW;6AGtJ
zMi+sAK=hZcGqw+K-m-2!y`D=a*uxi^s4?-*UtZQJoZ+aLIKTasKW2(1kD9FSM1$^n
z;ZZGIRu9cvL#H6Ew!CPEip~aV{FninI`4WN-1(`Nl!j3N)G8Udnxpfv2Hil**T1&N
zsp9U3LI(yH-F$>*P93LfZ;7HtiMM|B^d{w@f7zb?SDUT1$?z6VpQ;*ig&qb0lo3uQ
z1qCY~2fdk+Gmzqm`+@Gyg7(oNLEL1Fy<E=>zJfrVYl)0ow{`n))EW2kjl}sKt+#g{
zmRkfg?*kg>;73}#X1tQGpN{jWLpwIF)vFtiD+9;6AyU84*x%luUt)Rl3wqZVE<%R8
zwX5K`JGEM#(X?T#Rb9EZ7Z&OidRS}<sYXe$nts)P)1R00yO%$sP6{Y4t8MV__WyJS
z2(^68=Q-E0gJ44SB~%Z6#}(=N;<w$%L+Z~p$H<?m6?~d1t`sleH+1ul+qr(6d%^X_
zs9j&QYLWOAA(CcQQliiU?|&G1yRCJf>rcO1uQ+mq5`nG<#U^pQ&rD4inx_mZC0=vw
z2_1O&B^|Bn0$!4?<amw(s5`6*IyG|qRE(-7xdC^n1QM5+oTTIdqtx>@MecH3O5Z>3
z^p`$GVq4_BvnNpb0|%1ZL!J~0XIkDEjSF3-<3QPhi3IdC5h2PP3A_y^3TN{Tj#aw9
z9T#R^PaD6(vYppem39;y3PK9!8+RmV|Jv0$Jb%1~#U*IS?AcoO)KcYM{yn88#8Tz;
zv^K6<M}fTyAc|k=;y?S-3^SJ}@xm(+eh3EVeG`%weq^udU*Fm6fAiUwuXV;ou@Srn
zXBaDLdGGW1W-pPLutGIaI!FOui%y_yb++(1%C{YX<AnKvin^Or=xvtyqOn|0#Ht&k
zJ1{&!=^-$DB#3KHZB~t<I6t+LGq4d3LFD1|^}FvrZB#<<@dDD-;hIC2!Au`v?bv&)
zuIoNl#r{lHJ5fA?*O^D0B3-RS%N)27f{0WM#2B<-Q9XOE4bKXKw4f|kVyu8Tf#IEd
z{+DEf^|{OO=sF|o>)zk_Z6l*V6*<A?ISsJ&Fi4X%n^PX%V=m&}8E|Svuz&iT0nX><
z7+`I!Kj+?eZty?<VfV6ibewo!aT}v+4-}(N-(sy?xmNvxW-2*-h;F`XfUcn$Z%uWj
zijN;8$9k3CeE&E?A_M~mM~90ge8nCA_ASRT)8<Yddw(5xe~~^}{<``P97IJ^U-io!
zs_9dvYEX8vMoyZeTTAxpH!o~I=Md?_sNBA0v38gBE{^G{yc`I0%;vJM8$}h}`es%#
z&^+?}iiu7JW@*awp+x^Hh!&Nrwqvm3$-_-b>ZffL98VdF@rEH{$K-T1l;~LfL_PM%
zH#7pCbA)QNfA~I-XO5wa$4F^I@%1KDR7(-6vJP+M;pDU8NOsn*>I?M36K}zDb8#>x
zDjCE_ItKn8qd9nyC*MDQXpdgPIPX7n7_>@#hn>5$W7keinK)1}9B&jElPPUKi2WJm
z(1@=$)$~961bA%|tjU8K4^b2xq_VKXmoS#RiJXyZJmb3$XwH-|3hOw{8OM+5k(bxv
zSfi*XP~$s)^|Wr7J65yiUq{q`k6tOH;7_x!ZoKK6S}<o4=fhIWlLAz(2(9sW@`c%t
z4xRK(YA+vT^Aeppr5YgThFowekds5XW77t$|M+87`KBn<=M)9pZ0&8YQ<RKr&+u2|
zxqZM|_3nedBQy&O%G5JBR^gE;_C@tePos~G<mpCeXhw_{Z;MePX)K*Y;f`$oRBrK+
z<g^#67H3U9Yhbd-oCQ5+_v+cl-c>q|hv|dj$<a(#QZzZx6wWh3b*END3+RL@`r^fF
zb8ebeTT7acb7r&~`GOQ-19QbZxCU>hD->~#)@7tKq*GU*;S@Q~MW3lNAQdDS^F;x%
z6Xef7@{QR<lUnrQE5Fw}d(9khMbIs`-|p1Ia}SUjp#8NttTRbDJFC5WOBI6SXCQLO
zy5fbCWY>;2_41m5I4w~IBjPo1PNLSXZ&d_^TKmo~=5}A%97$!lzV4BvH$*v!Y@HOa
z?1n#g`x!6cYxa-vy8du__Jw<$<Il{C3!ZEo)2_4pcB{ONkx3CdYoAazW-uouTrznW
z$dOsf8#z@?4H&bh%gIOIp=C=}=x~*X;+)ha=PUpGhw(>a&^QviNzL+%1bMN4u^xUk
zOOJnNp5h`s_1XJN^&|ybv+|Ma6cDtdQmxsVnS$L3zdd`XUEUeyWJOOQJqO|A8isRk
z#&e=V4fIv+5je7ltdslPRaK}XCl3(Go2OV{;IRo5PlU<T;{YzVPT4K^Y8MEa(->bS
zgd7^;*~a0^O3yfks~LngV_rlgy{M_tmqq}Q_R_wg5207CvG+W2;@f%Cu7O*YENOO!
zK2vecR1Ht`P~onFD&YC)I3Jwv*!P@ff7zJ3L6LAaGS^Ub!5OoC+(=HVL6_agR}PK!
z)&cfeD{|V)Jy_E&f(98~P02PL=$@vDT`3wg7la&q!>y}Dr>mG(THh1h*avl0#rj~?
zGMtS#XdBKO2>!7tqKbzzRzvN0c^Q4#^qAAv9DgoE97E<!_Mrur;GdSFVr}cjofNtw
zT@a}6{4s%gZOajjzVUK`A>nGNJE~9KTdPR)L8}Yl7oDixZ@#a>%O-0Kde)Gc^Y!MV
zZzyff?L<BMq0d!n)4DCreQn8%DvYNk@T{FhoCwo6PU!*d+P8U^bB$5AtGQ;@=-*P7
zx;fq9lNB#guVA%)^Ox5&V^oHwkczTz^IiI&31@OZkCU<(h6B{w91cWNJJE%WC)z%<
zf{f0wDURM?PjHPjCOaM^5XK=oKxYdVa$OfXl6_ANe8)l8xO!x+vM!DatO*#PAAkD>
zjT@0eeU^4=p^&CZcdf663NlgX>b>iG&TDA1171*b5)@3fOOI$ZD(&3|rq`ju^?rdd
zGe34bUGef&P8D~1w)64gEqdh7k7@?RT*r-?s)bAb;OJde2dSa#kly|CkMzpXX?o>P
z@G4SGF1zIi+IIJYYKX`qm%R7>_RH3q{?;7jSWF5OT2Zua=1f|oW(?08r;++M`eIFd
zdbtX_W2k-bMTg{t<GbLl=U&=eST+5QI_e7aOwkknL0&vX*owBV(W5_nRGpyJ>@4s~
zj{Hl_WX+JtU2fjUTu{8Mje{cK9=1+K^F25d&fw(s<&+0&_KC@}AncmL9h+5g*%U>B
z3@V{`B&!BLr-qGP_@!n#Jlw~Iey*K+$qSR=XGou#^Zf@kd-5m+w~^xb{G<B)qEDRQ
zSPMa;?>&GMA2jOh`PZxL*e<<R*hQ+5Md#1m@?U!4?tzogmc!PB{+Sc=T%Z1>JN^H4
z@z;mAJM+SnGQnQ{(O}JBt_hrHjG}q2K@oj|p&d_=L7RLiIuwq8^(2??E1NCY-)ScN
z+#ztZBfQF#JV3$efQS#mSYp_0Ru$IMKb~_T$suWO(6OTx@;tI#Yu|fY-+$;iU5k>O
z2plX1qo~|1R>@g-Yk{@PFIHFFc$XH~aEwgwG6zyXrN<|lW3Dn$>^71gY2N#<V;p_4
z^R_A6@f6ma*stZww`$lt&T1d_f7m+@z`BYn@Bh_%@4Z@bFBmY!^j;ELNJuA}unEbA
z^j<b?ck?Bi?2?d#B%44;0t8G5y%$rA!MNZi+p;XnlGUu<yY&6ey!WIhSrRhKvh1z~
ztoPo%bLY;SIdkUBsq6INFMdLw`1Y?9y>tZ|BhSFZnCUWu(C#Pxpg+A_htxfq&DIbX
zm>5{R@a)GVtT`x0DfrRv-M?M0yjrdm?>b2fOIK3qKApa_l+8U-jhnaWkv+9aPc3B)
z%mDAn-97$au3yLh*wiOxnRZvzK-5b_WDoNO17Jrz|A!2AQwu^DOF3p~kX~B<Q$28i
z_*j^oS}toV_PD{;hS1Ye4<kl2*6|&~RUzdCf~6xwchB}{_dHkq|Ju`55LB?)ISI1o
z0)Lr&&;Rx@sTf`u^V!?ktIjIYxFi(9X!~}nNmbwA5BlZnnrp%Dhkf;j-PAYOqpteY
zUQey~27mi?Woiqi=}FJZ!RP*8)mUAnt1n%rbONZwVG^00Rj3>e7K@8>(O~^Vw`?9D
z<^sAFKf%n@_NMzC$j&gt!9JCPNjmf0-9of<=6Ki3FX^83__6)|Yg)S?QTa<x=JAa_
zU@%_<S-}b?Ed*0{Tod4H@gY1iA({5UwBSr~yXT`rM2nlW5Ffp-dD?(Z1t5S3V=V^8
z=HkOjx{`x?l!?i!9nMGl2y#_ltRLR|Nu9AeUo|^l)GxpC4ZVih^g;sf_)HrOcS49{
zo`|*IK%X(OMzsdeq~wBzZEC2l)yV1`#gbz=93NDX-pDj}&RiX9A&zLuLa^CGfU$7e
zZRl=O3)<TZ(qbh?(CAnf2O(&>w}hF+2(d=&J)us_@4JI3EJ6Gl1MxE_Xi-Y^m?~a+
z!5z+vOBW%O3d9<sOgG<j4_fjiT9uWJ$@=*kd*%;X^}#Rdsw*x))74KJmuCp*xmAZ6
z@kSh@Us|ZIe*Gg#HOAT}Sn@0w=j*@siJSGsUvKbr!t)stO<va=Wn->5j6dw;GJ$YY
z@!B2KsoYS$AHVN4M3g7GdJcZVi&9mw_cevZXKNwprgQ)e5*n>R@)5uMVi($}G?@Gt
z_A)d3_nU*5d}uh)vTN!!f<Q4A20ttjZQ)Uax)mnRgkKaFDcsvt6ZTE?((XE{PaO`4
zrWFY;&<Uq3QylJ4JMbsm{>)+JFInQ4uHOBmQoQCWOvbYjQno1l<m+_vS{NWm;t-Yu
z@fo>ry2Au6F4BMe`Zs9K%60eMcWYluI4P%-xMkT9cpn`Y7!1LjJ6^ADtx(t1y||`@
zD>kPD#(f9lji*8D+swr?GP}=y%%RBr_6^QEMw?1EST|tb0BS&$zYo7Qy?|gxbl`HI
zo|arCd%}r@1Cq2fp-VS@`-`gi=qGjB38naJ$0#{9Lxqc0Y5_sgiZT-P)1UoZwF5*t
z_uC>S!`koNd^MbKri6B2VUY)7&D9i;pm#6LaY4|`l_531SSOZ7XhU<aisBJm(5b0y
z+HdQtD-Q8|zxXunboih;ddMq2lA+wv93>GPt!aOQ9(|xp7rm!b%d=9H))9^JffR0-
z%vW1$21_fzmZQI&*QXS_Z}kEd$t&)-`Nw(!936$H3G6}fkadNAe8bg>=iNWVcfK<@
zmCPg|<mG-*%Rc%uXe;hM&<g8bZ$rHvf8Y_d7Gbu$ZLfQeJH$<Sqvt0xVh1k!VFQQP
z8!z52JC*AWwCr&8iSU6Zt<A%V#nORo)cSzs`uJ(XDqf9QZ3vkbF#oP@#jRmdVP3+b
zxDrJa_hMqLOdz9W=^2VDR0HvL{vCgG3pQ*Dj+VMwt$E)!^{LAiYUe8t>-%57TZLy7
zu%-|kV3F0_*{7ncWGs?i(w-xi=&U^AgPe4+v}wK0SXNB%tWZ^NM9^~xO=pR@R3S(;
zS~$F2FTK2v7{lvSQo0J${JXRcWmHaTjE?Tupcl(M@bYvYSb$52HMM2$eqj2Lij#Zw
z?4CMz+Xo&W26+zX>}#zx+P2dU@k6?8{lmCLeGDs}<$Cw^H|T{g|5Ss^R<Or#7g&4X
z?pedWIq<AA<8S=tg8F7mVr0i%_5HCfa?OZ<e{GXzRFqff`hWh0&c`>o=V+BSJomh|
z?%9vK$uMqVojT#{tM%dcpXZ))@3eL(*3PkS=c0R&-K56&%e-G$ZxNVUX9h1;5<GY?
z*;Tgh>d>LedJW^-pNfFEh==SfVWaM>-K%@IcEiB^P4_;2f-*jUFaGMY^zl#b)OT)v
zNK01`pM_VsV(+!J9h*Yjm=0o-U=f92qxs+tQac_Y^UzYo#wDuctQb8{u)E|0-UHh_
z-d%4q(|y>S*&aSj0LX)l%k;0m{RD9ea#XebIo))_w^W{9puz|{&?3-)Zx}IxfmP%H
z{^$4K@?&l9%5Zy$rSWqewhx03_|`@ig#t1=m^G2JE`lC}V~yx`2&!0WL@U02PS?Mw
z*T!K2ogm4engSQPJv=#bMEkZ81^>^_tAs2fA@HcJ6=k~hmcQxblM&?R<f`<9^Q7nR
z#B+Hu>%del$5VJSLWUMx!&=6zn!=P1PiKy@$1{fGdF%g{x+b(bz5+x|^F>qzCk62j
zlwb`RgOQKMrwqw%D<*BpM6g}CESE^Nl?usBa7gFsu~0%lSTk*!C^lUzMB1=ZYzU^m
z0bOY7i!UdSahfx;?5Km_Q!5Qb>BD?3hUkRDL@zM2rep#$1cl`*x^<g&96q9p`c{z&
z0EWZpI){KIOLN(XkaYL-K;+Df)sP3N8m2fs-JM8}5>>pch<w+i&p-kmi@DyMd|rK6
z23$;X8Vu996Dis+Zuyfg4oX$Vhu%RB_jl@BAKj;ax^<gYugoI_NeyO~Xzwxc4GkUC
z=I1vkKPL+b^n^?-B#g-Dz|z*XgB2Q^RCRGkcv&^)o}hx#B3(p!h9V9zkud68Ucnr=
zi$EPYNb1;--jw1S+pLX^FaJrk#ve7pGo|<w_9?gAPeaVQ@;bC^%q^z>;Y6BL4+csY
zHW<SsqU8o42=S3A$6&o$>-_GmesEB?UfobefTeJlA~S!rW)T=+wGc|+K)f%}Op-&`
zq|ScD`FR^&%uqN&IHD4S&tpn10f`RdzfIi{3vd!Xq|gZSw<e$lQs{PSCh)HD&5?SJ
z2s^x+;RSw|r}n}JPw!A{ICCrRLEqDUk9g<B=EBX#WNCw6#W(iVy65{h-h#LD6J1B@
z4yrCBwm^T?v--y`uXk0KtT{`om!GT)FTapn(MZzIx>&b7{8NplFD3w!1x1-#u_0Ot
zreMtaZC^9f*YM~#MPh0joRy`9T^q>dd%ac{kwe`4-6-Sp#Y-%RU1whyW|$n%&Bkwx
zS)KCo)l2hLKpc!w$5{8%HxJWe%z!b4J-wkEe4?~)?Q&`T3*d7Wt}ZP)P#vr9-1KFg
zyQY-1Ij`vVKlqYf4ay}TON3)aEeM=PtBg?8R6z)IOgra!zrDkH3jRSyjgKEe+vDyH
zZdW4~L57p&gJ;FjR4U<Ot|+A0!JOvI9NYo|V&_B!>owXrOhXe>v%Fee4x{+$wUf%y
zX+dmUXz~=8g*Mo@4y=Vtn;1cgHG?e-MPy<w*(wNL){dn}dJ+sT>y~mRLc|m-2P&Ss
zSJ!?1$Gk5I0Sti_M`~1qV}_%%4k`SB%XcO)Gz#r$c)XStXS07y&@~f6njfuYY@b{v
z+L-v}so|Turk4GxZX3d-AXbacey1`+N42}`QFSi8R%<c{!nvXWS`n(Q0|&GPx?T*I
z6>Kg@ynhryv2PB{nt{_7xZx-ej2J%C_zPPxcyjb{2ci_<-3Z-^VvUXU5<dV_&me>!
zW5h=3bq5b)y_o}WroXdV#piueD{*ZY={`y{<c&Irka{VM`ylQBCc=%v?|Tqyol#8K
zR}vlHXpnELwm*-eJr9dc)2fql)qiA6k3|gPpB$wuXr}Gi-lgyGB5(|b7rStm;~o4m
z*Ag~h?-=<yYoX8J6QP*NA&faQb&VknfH}28n^8+spgBLE>Duvidb)dd&3oU$8{{1e
zh$MbUiJrUVM*Z~`?gtQc`<&CY?u@etet4P|oN%g6Kj{_y+Z|6THsJ&>W=t@GhAD>W
z9`Tds`ViKB9Yo^~iiSoo$K^Zs>eB1KtE(<L1^L%O@-mO<<P+EEW1nGtVmZ<iw}iv+
zM4Q&l*DJ0zLFJk`p!W?25$`5n3DE?|17;d6S4(4qLRTTs>SKNo0Clz1dzxh6#={4(
z_h;d=KDx8rjmh{7)8VIQCAfpBN05zAc1JUDO>h;1?+I?HRu%7X2Whl}M-Ehw#*yp-
z@Jt8MaxSuly~Vy}MhxkNTYs#3K<S(Xi{Uls+C&v^O%DV)b|{*?E(JuwjThSLS%Xng
z2nKMeu%LZCJ>bQ_9<@M=lAS*-{N8A*c9nN)>|nXNKC(vX2vxe!?wZMZJ9B_Dmctoe
zzZ|cL2+uUx?tt!jjpw%UiO}4}x^_qFETwH_Rkaqw7aBbqCh%G9XpJK3aU&vF9fT6M
zwJ<Ce<J7*dQhT=T*SeBZwJ<+yY!$fx06+jqL_t(vA^Gc+mshB;kydTpw1@Ut@3c3}
zeqq9nO!n*@m{K1iFl}*Bf!17hj*2kjjtS_{2C^U2R#|#QaLa?Ju79>p3xeBp`_F$s
zSX<aw(!d(t`Q~aIxxHhubrfzcI0T?;c<K*&kjydZSD#77n)CIUkM7Yw-?{~BjtF&D
zH#oX&)`5XWc-`uUA2N85TGM&$xgK~X@92Sgr`Zl*3_<X;R^L{pQ79o~wM;vnpPn5>
z5$EHxo{LdWn_j%@$NKiIFDnuLIUd1J<sqW5pLCgPed-_2+h$l7?xv@P?>SEo=r4N`
z@xhrdu5d^fZV*Gvx9xQiq~MK>NN_l8&sn^O<oH-+X2k2hoByokix%je>xh(3HiP$U
zc~y5e4r&1lWBg5gF)ef#JcF6%TZWm?I8qBD1U2DHWF!_$SW##LN|WSBgmchk@WcaC
zZ%-Yu#5^9_9EjD4S%_GgzcB*oRUApv4}SFpops^@1WzyNKfe1F?My7t0#Zf}K-Z1-
z1>w`yQeDSkeVF(e#uHR&<5P`Ty$-pck#71;P=WbAcfmJOSUN49_x*erXk0z-ce~^J
zZQrLBPn#20$Kk<K0c3O_`mEc-&E06YANmhtZK<aR8CxvYxuNmxZVw+Jg>VuAj&z#p
z2R7!eV;O%Y7f(-|1uz5lS@@XH5K|)c!yjDs58u*)WZX!=y@5_LZT#hS9ST9Az)Vf_
zZE~ygzibz#Oc0FOLE@PP7L)}M>uKRoYr(;6OJ>r!Y3&4aP>w6f*X{?OR>j3<Yr&cY
zDmnQ~JfI%bUd(A0$02ospbz6SR#x7i<YG)L9f7fTyG9Tq(SCa%=2Hh%*EXgPfAduQ
z!!bSTJgk==e-iWH3?$oeFq-Y!zI%^S3()2e7|Ax1kU-4(nDSMm<%r~PJ7lI|^sAuw
zWEGKu%FL0Tz56Hn(Ua{uIRh;g#J7XUmW$u_b$$GOS1FE^5G(P$`P;!B#X0Hi<O7`b
z@|fZQjAzC0-DKUwlXTS{{g3Yx6*pJcoVQwMUw*Cj?cAnEn=ok{-l8M;nGFTxDYL{h
zclSV;Qk0#*f?*)GusiC?bcnPKDY?Zi;GX%5rIw`W)hBnV{2kTGTa~ZN&$|Hg%tW;x
z-lkn;PZ8mx#03GHL$8k+#<UlFW1IFS7g>bC>`a!b%8HroSh0g>6p%22YJuefu&7|W
zclYM<#|cmBkEkAoB0n#Q6l~{6cl}lYktb^ypJhcX&;oo!jd0l?x5x)8RNK~#&ts;t
z7oNuTmlU43S}}1DV+1h)FxE%+m0>D8rkq0HL{}#h2k&6L7pEI~1e=)!=!(GFA@8I!
zHTL{t3O{ive)3hM*IKG<%#i{KcGikCyb&2;2Ac!>&v6u9?iF5zI3Mv?TNSRj!#&@E
zfwlR423iEh$pi4#oburs@U`N(XKOV8Cg1jE+m?sS4~1ZWVPRkn5+TcLJd<hql-8&H
zX+HEJ%)RM9jBxvkZGL9bXl9re=*79e5IAM$Yvl?{E6^%pL9m07|M-yp_#YcIlCy|h
ztmFnJMb0X4stI=jYjZC6ZK%jFhzF3a<{ewKhj}+s<Zy1dz<a~(&Lto<2D4DxcnJb#
z#d_<&>R`i<$)Izz&gwahD4zvH=FcIULT??WU_=&vu9DPu=4*=(r*X^6x`ZGsAGqKY
zjE+`l9qXlr6pz&hhxCn~{!kaLU98sft-9rVpVR&O!c|Z(s^+E^1DZ2e4dXyHhLE6)
zgCg7%TEZO14BMf+`!K@GkTald+2As!eDzISgC27bC_B(d@Td%!8GPCpZwrClG9$*c
z6<3vWV$+Dlfa?h9=UPo0Oy&SqDr>k`dk&W9X9Ryc!KGP4y8iML`pK97Mg91@g_;1s
z4(4bW-GQFXIwTxzabJsuL$b-(fL_0+Qbz_$U3`TgFu#v?ScxX9M+i9EQC|c7jwW?g
z9*oeKwrxGEi*GoAh|n0ya=3tCxcZa+aNtBzfOf-}BbY3IoS#4~_sEo~nmTpk&F*Vx
z(c#0*T0wr}rR&xypu10pUcfgy^irLAN+x;I!QWs^&C4p?Gl3ks_v}9eBNIoUsZ2Kq
zLEvyFk@l5*9;WSRv*~9`Evd9>*uN0=82*`&OcJ?-Gfs=rvoAr*R>bHac(^v2XvOB{
z($$2Z;3$kW4lxYO{H+h;GI0aG<;g^UKYHMZHvN91Qn665j1?T86_%f;_B|W*r{C6*
zn$G-)&C&}^5zjUgi0ss>KcIJ<xe`mASM}Rp;SZ17!jVqq3G+}geBwu2tayYEP(*0S
zsCMoj))ntss5sJN^_g|aKE{+mv~AX>jbU=J3w{@wi%};8>P8SnT0q8@n3Jz0{6|~K
z6I@uDNE*!%oqbL!e#B7-j=MO}d-g`xu3uBL{dU#Zt9zRF=pbpC3KlI>E(b&tHXM2S
z0sZ&|(o8K_s<TVufm;eI1&y#Z2!7aM*5!t_pO}D$GAW(*ox^@4@EheY*@vY`EHM(g
zkCrLzyleG=%g-gaYnL9q=QsM+hA92<-@c%wtIyZBzFe*^{40UAR<5!o@Vdn}JVRkC
zRalMmap=pzEhUgZQVnP*t<(_fp*wgjRuM4C@F?v^_Pgu$#}3$*0x)Y~sr0886pD#+
zz!3iI1mn$zfevLowznSEYv9ik+$DyMLUN!FBvQJ^T+h?d2;3UtVxn-Z!~C3fefx>;
zMMLb7DW!{*!p3Nfv}fgd?)t{zLx?flUor}FNuxzTHRL&|xYk%8#dDxHahNXjqmAF!
z3gb^ZwTyZBLeh-J<9<@VodApw<=&T6)$v|<tvp<^$XKwy34i1!ger+PVaF81Re!V_
zuK@0aM<D3qcCc;e6xcr6utq$@Ap+rS5dF0)cIX=pz(*I}au)$6k93@(oRSiK;KF|8
zW|RN9_EqgVh!#63-{WV^GiDl|Nbd061pV84`5~38%hLsCl9{J8MIA@>lD+0Jb)>O=
z8uw9~;$1Y>jUnD!1nmrf*0G$tynv^lQ{iuqTCh0$B9*r=m%Zc;EhD?g1t%8itV^%f
zu3g*oz^enw!4$p&7s1?Ag6S5nkY3rV$dd|nw49WNXC$$7<7fvr7jHiTyNKI>LM;Q>
zrk9*Su-FF_o^v9(uGv#!mtu*;0ocLI(=Ysf6lH=uED>A4!+_!wh?~J)-QUL8P;{+Y
z$YIW=3993L#6CP<y%%gS{>5FMe9!%2QY@xaNAwUH<pA|HAB6D*-lSQx191<+;T8l1
zPH|%G(a)iQE(OJq>MvoBezX1|l@z3FN%lfr`H}0j@gKgYeoV2QsNMUzF>~HWFg7@3
zzVpP2s2$X@G)uRO;HGUroprz=H4}aW#R>|tG57*b-(HyTPOcj@=_4DJX~ZqI&S)=(
z^O_K{GX0~@TeDD2dpGNM|M+=5Tp7b2j;mo4xX&JL)@Y$9{(76M$&XH^iHua`m7Xs>
zc$ebRRx6sg7Y>L7m)(Pvf~~va1;x-7Q}!KyF}}cf@d#jPfrEX;vcQ-nit$~61d_Dz
zP3+-uj1jYW+rwEOy}0Ea?petm4h`WX(?|T42A*CB|J;Y`;Q&H>E54((nbce9LGd|?
zwTz|QG|-!{d944adXoqQeJbuDIr!9bt+U~f%J2HMwvAyW3E$-eGp>dCKT{##4(L-a
zPW|me6kEjhJt!iMNaD*?KtQhXJmXxqMby{Tu680J<pl22!_RGY1SY419HKw_w*Hk=
zDm$yat>vGtuYLE^3TWG*AO7rsJ1Cg6)Y5_A9jv6qaay<Rd@VkKsD>xOtc3Jw`_|`l
z=cdC-B^60{N})b@?K{-7f14g&|3^{`K|H)=_2r+^<)kx+MdEj$s>-E4xaRalm@E=4
zxvELqZ{4U-B6uD-%!0%8DvapHe|w=)SDf2H3WgM&u%<+}zx*6-b!_u<ISJU1e3XjK
zlu@|x*aoqDai(tk(Vtb6nXWT1QGCyJpVOA>zlR%IfbMvBqt+}hAsyT)y6M}bqWZya
zROK54pKITFovvHM#<2NO-Su>X7G~q|O#V~#ZPjc0_G&d!&J#{qgVn$Q`E~bc!z;Z+
z{v_QU3yKc;we-fmy7^{2M!Sz`%`PJG9fMHqI|5_vEnUtm9k3yxG;*U|Fng2;0&)q9
z2-l`PI}Q2q-7`E0HfN&t=k)xhEATTqTg%p5q%Xa{Oh38fMZj9b;pSQ`$;2hVHZ-al
zIZI0_*Zog!*2<E15r5!Z{qtuk_06B(<KFN4bIAukt97N}dj6^V_0rx!6{LHJnCIQ5
z8Jifa_0qrC?_hA;q>ce$F<O4wm3qemkL#f=+g!D4zy5CWlE$m^=sxXv{$X=$g+Vvs
z?jdGQli|khE*<-adnQF6Y^_v9Gfa%P^ce%ry%7#6R(285*-Az#OgHD53Ank-lm57S
zZVvrM!`DzugnJ1LVwNJMY)-4ryHa=j=yq+d_B<YvmZO3=yp|d3IBrjBd8Qx6N{whg
z_~|Rr&7BBH=qGKpAK60SwqC42>~4$-^U)Y2o~*BaU^yv_p4Od@Z1z2dlhtaiEFe!g
z{<)nUHQEjg7Mh>fM2%U$1*4&~Yz=3=>zg|5GoR4@XCKjRx8AFR<4=X@+AsZBrz}MK
zjf+eZ=I5^1=E%F;KQ2tgecwHwuYTe3A{c!_SRJm_wnq^FW#(%HLD47yZw3%{+`G9_
z?>vR*z;PK`dsV(R{!RMA5C6qQE+4=o{N7t`R$0gjopUzy)Qd0n&c4o8H8velXy*C)
z-gPU~xNi$lcki1J_!OV5?|%A2<bx%EOIJM%+-~weCo4C-n>@|0sPSwLF(s?@q0hZX
zU;FpFRBwYzK35l?dn%^w2nxy$scQE&CFB>ult8r**ci<U;}8>pkG`!c^Y3)yH~I!#
zMmWq{zUYH>O*&<80f&ZD^{s0!&_CVwgtbly&?motr3zAs7tnD;wbi@b)gFAghtUjV
z5rO^mkh}E)e(+XG&iR-=e9=h+9A&)L2a~#cix1BBX)A~cvHxc*R?mb={#AVSC)(m2
zmV>^kk^I4j)xUDJRxHA*s&0qw-`u6-xUCvEYk?MV(8C^4`wus{_l2dOqVIp?qpI7p
zne=oIj`tyK;raT(KYT!|NMSVCRjZxMK?Ua3(NPFlfY~U*rl{0HU2w$>x?{sX>(y6z
zH1sU|_i^RG-C*CAa_xU{uQKx%y1obXmt!7wnN|=*JPhsnk@8pc44R9jq{73(lmd-F
z@HLFv(~A#n#eCS;l>jBAC8({jRV9~P<<`hB+KYRidIFEG6w->O>$*>WgDCL*dg6{h
z=*6;D--EjHV_(w;&R?atPy`ayWqR_tP7W1W2n4(<%?D6}t#@sVKg=n0m^cXwaS6JQ
zsL&TyougAri*@N`?<cU<PqpRMO2&Jpt~zg>lCi<=Aux^a;Na%dpElVPkN-BTgVwrt
z@?QH%V6#PM{=ME?7^NqFf0H&hPEdkXml1RgbK|jo0%3JDs2dB7(19J=yAPA+{FS6S
zJ4GK{cf0;fAT(DNU8?iWI1v|uQSAq2?MFJuH7`{*uhLIHbd{E-hiS(%ck9=`e}?yt
z5yxSvzWn!BY3(KN*C*L)Z+@s&C7Ix=w}0-__1M$AEgEjQISsTrjLWW-lQG5)0g;(;
zYObi(k=>iz16wN^bSYAUqVz}|t?X5EWvwD{S22oj2Zxcs7!{p#fj)lj3;ONTo-tf>
z{r4Sx93TZ>*$a<CM+-Rtnrk6!zDW<vq?G-{OmRa-$$)?>h*`N9O*ga|hply#Vjv#D
zGI4<Cf)S$HZ#ORbZr%44BLyZHNkHKEo@=iq(A4ksY87`^zhCE^yqvU+gW3&&uc~7Y
z1E`I4tqNS0$pM;x)fH6)o@Cy}4r^a!n@-A0CphjHf{RvM%g|^tX8(TK_t^`5LlbJf
zA6E{TaG1kah^e$GW}P#x)wACm*pf>U^~!_$RCO`-J4;G*;pvG=B?Hl}vI^~frAY-#
zuwFrM;|hYskuW0fkL=NF<Pcta0#WPNoT4BCs_tU_KKood7GQbck=3@j_U<42GEKFm
zrGsNY80NF}v32D6=V{;nH^8-ZXW5Fnw(7QfAJc;DYgL@TR9C(CBYN)DA8Ruv+B=CL
zUwCSsPCw@|y>#a$y}Ee|FU!<-uDC?`Nnzla_1)qFNJnZn>eZd^(xMBNYW2E{b@5*x
z)uXRcpYqvHy<4Rj#M&^W8^z7}K$E%<0EBW#E+IR>*pq+oMz@fz`|7{x^krG>7uW~b
z)Yt?3v+omY*IM!vyUpm1xo6jmR-4!)BrZ^y@ET#}3bFihz3=QZarr95617{^hbvW0
zonQnU9+>eu8Xd-ZC?<1}4n6m0-FG4xK(0Pd#Vb$O_5X0CCzB0YUe7$B?FJuWa}nC%
z?l>B;Sf9A;I-Pgn3ig~)RT9tSmFK!tu#ljBgTYE9ep4(5>LUY$w{y!I@gBmi_>59z
z;)2pgjG~SXEW`#A^~E23MdzQg*ku*^D|`5Xm=oB;BfNXK@j+uQEoe8cLY4a;M?iat
z7QW{!ts&^?XKSkUv%5TN-$9CigxB8iP2wiCYyJAakx{~|XAO1~2JB!6?e!>4JZr-9
z*FEk2n6jl3zRSaL5~7A2?1ua14$^h>AdL|a`?Wgi#`3R<J-k^bEyIm)@j89|183`p
zw?FG?EFexd-0)82VO=)Repoft@bKW4`H!a(J0s4lM7vmH#tYNF8O<+DU7+-WTw=j?
zs;Sw0<gqHF*8vDs%~HX)Ms4nFRAvP0Ip%v_IsbQh2sj^fL#5e@lN3Yj%)B8iDpG6T
z{dujr(zI_A&)MR!Xod$jKd+m<`dy8ZCvnrAztsA)7`^W*B7?5GRR8+GN;TqVWPwZ~
zF<H(^iqM|t4!c*IWG5I39+pOq$FJU~kI{BG2fFBJNDMh0w?6lbe*X18D6t@4!*%;H
zi6Lre@gg*<r8;5ldFn#4(`P0n&eT0kN%4{RJ?zpW58V$YEQY8Q5Eu$cb#s*-17VgC
zEN-aJ3@ivxlv1jiT`!Tp_$sYjKosH=&%mVPK~ltJImk?12*<$6z+j9EwJk?yDAFqn
zXkiSl!jqNRy+OZUkDqwzwOV-6h2;Kj(ocSQiw-}1tNyk!Umv=X9JKGaRu8N?Ta7Jf
z7)V1Fjc;9CQo53ZT8Vbf!XW@y2-1Et>$mg>cRjj87oM_OiA3V;KZ-<Z&$H?XEKm-b
z>i*Z6&tv*O<$`ZkEtQH%!%Q>7NGQHLu<>j*12uaUf9|!q`A6?mcC=Yjj3_z*#)z89
zNNDf9=N^q=z7_(bxqZW*G3VXnBzPvdG+derSEq@$d*H!P-xz7m3UvP;@6ieZY@B;i
zsjmF^ceU<nO#FhQ6c-<&2HYsJN!wxs%ovWO)J1yj&fn`Hn9FyazgFj6bAy(gex;h)
zdK|+M156W=GZZsesq$CuQ&%&D-<T|VVnp2f6=Gb3ohB(0qjf*sZxoQugtHfEVNi6w
zzVM5?b@`!sOga;Y1YM|*p)PHD;c;RLkTz*C+AZ2L_94(T$Fy%|`SwQF`ji(0!bo+K
zZmaYoU)1+6Ib8{u-43FWic3jFI~Sz9CFkKYjX5hmae;$P+VH?HbmLt}Eb%F{e`dC9
z@3M}1w7cM=cU(4y49wj$Vi}3)@m|stZQfp?!gH2u9RY71SbMe8hB1@mX}?v!x&0X;
zPZx{}8La8qii<x%`njgyRDI*dTZvwt=*$v>an(p6L~C$JrvC0*cj)~b#6mD7Z6Z?e
zcfRp&>Po*{XPtYoqR%~tv_?(p>_pm)DQ_att@AMJ>}bHu`0+ns`nSNP1M_V_v-QR9
znFQBLFedGbSFO?Vf+C$ofVZAL1amYJM#`p)j5MrMhE<L~`n~r&#X}kV^URe+@Ha-+
z(7~q4H(lfF!kP~~hm<$jXytoVTYty}R$7q~jafabfcFQ*<`JB3iyHp!BIOpQspLfb
z9iJ=L+EWQsN0rW~R9$()Kj~`2JIC=@kB!+JQ~9%Z=+pnQTiK+@TYKhtiam3kF1q?!
z`flz;SaHOl5y6Z+pua;~o_|z#(EofiV3A`{+V#Y}dTiYZdhaEt=<KVm*I!S*M9rkI
zO2&LWH#be)ja7Q~u?O_RVf;H68DGy@uvb$8z#|K)Wcz}dQBD-oUW}=<C|<w5;~_06
z&eti6GIi;7U)8b;-j6E)CVb?z%|XK#&YFLXz2GmmZB!va(;j+pyG~xdkjS&A>+84N
zsSh_aX^a$K`2~3d6zhh54=Bo+_%SgKiP`o!h?*OU(Fz~#PV~k9n^7mjFYh`S*Bmec
zw|Ut;qs!J7j2l5bQ^sm^<meF`hE7*z6oKH-n$^-uZ_N%JXnU6m(R6fF?^k7;U1e%8
zvp@Ch3lw$6I$d(jN4;}^Wq2%efHuZ*l5gJdux|hB1}#8iR=J1qajFa?kWU+taSKW7
z_~2icsJfvGje3rX@-x*|vtLg=@sM^6rE3w|dNaY@i#GecUs)%Mgx}v;L5}pt6qIGA
z{T>SJ>5LuH!<1gQh(LnwijgpdpcS_Rx@Ezzv4)bqZ(#st+v)Mz(S*M*X}m62ldm<W
zoUI-NsHS-j431DrdK$v(U=B~)^}zZ+sWPKLi#QZ=yz&(9JH~0GS#|i`G9g4j>;h@~
z!+QMLlT?^?kxn@GO5OFB<?Q$UO3%&5*PlZGdzfjTh!WzS_Oxe~3Z8Xpnsqx^nFh#&
ze71jj=9FuWep$+>WqQYzC+m!L=iqwLrcNxZ0*KF$nVCf#mLOHW{2J-bo>N-M5=D{2
z=)wCR(TOEF2vC;lW8b|?@5DNVofJ)NGMdID1T*D&;*tCHT1ygrOwhN`l%IJ*zUuaF
z)NSNhugk`w4&T&gZv7XP!hrtWWuzne_!rPf-l)Gkc~r}x5eDlq(@eP@^N0P&f>9a$
zG5j@43%eJPkpu%>iv|iS2(-+d-K~1<rEV=;mZP1I9aPu5dznK7TNXw9wLYa42I&wM
zLt#Xpwp4P1qe=Q8^m6SrM>IrsmttJpOsKML!_)fHLlyXtE@Ho6E_j!@uZ&^c646zq
zS6_KiF=MqFN>7EUcBtZ@YyPd)_aW;a4|!IRb>G&FY0(WOJ#|L;^c0(UGm$137@E$L
zE_|QXFFl=*3&TXv&nLQmvicssLkagkr0*4Hs24$H(dNG@b8I)7ZM1QlH|cuz?;zX=
zQ=hp*nSF~D&OSG^ZL>ZeA48sMc(}nTt$OwzrA3eEoT`I(&_pX~tb_P6erv$n8~+a*
z_dr3C5;onc{<;L^G#u8ybI6RuLTk)Sr~QgW3vY?sqT+{dQOcoWaz%r2W$o(R@{&H8
zlt3IJ4mbYq!<0U-G*K;2{8dQ-bqL4fi6PRVJW~68mf(IVLoI5epR0B}uAJ^2iak=H
z8<OG>Ew@8+%-G%FilqY<c0ur<p&_f-n>{eK09rN=7M<P+Rv6|82hcdue(rwwKlJFz
zWxDqAGf3rlk-qt*I(_9Qf7TOEKCLBr*Wl7|roMCU9jd`{CO(}tfaS^%!H6S?B@x7=
z!ucp47W$EgAJr=6V`b@y`rL2t)D_iDxFe-0i=cO-DAYnQ$u<iF>t|T!Mm<O1&;^&T
z)#?lXPU{z(p{7m(9K(-hXQZgB;i#eu^AO~Qc#sK_?OVIUj=%A=W8mi2B;E(Ka>drQ
zxp2j$=IXj%{Y@V=tY!ZTBpyK=ChS3@ecF#g?r#tLnFdPfTd+sy0^b0u%?puZAVgTa
zs6hAJc$-dOEnJ9V?c6Kguj>8J>pmDlJ5z*_itVCLenaPQkaVv`NE{m<M^I9!p@Lx7
zz^*K<L`!?iwL9;_`rY+p3`rwZDEnb7t`H1N#cQtCoxi`_t=ZbjSM|B;zNy>`-><bq
zk;htIomk#n{kb3N8vi|<ftr<L*;9Yi*M50FaX#|&&>wGCX<@31n*N?oeov=fR*(DT
zAbKmy7aymT)Ktzkn{?k_NJBV6OdDgQ>9q;yOmOY?L)Lqc+YjxI7ns}&gg(>_gz9rY
z`JK)vNnt-Xx8HE3kTNh3XO(Mi_^B@Y#0W4X$m^j^`su&@tA24iDfx<WbaF{H_r9qW
z7k^k?WG#q}OHpoKCff9FZGGi===dut&4ae4U8>*y{pDmG0Y02VBFGYBTtYTN`WtUM
z0c*N2wHz+j??3$y+MZdWM8`WJQ~(x+>&|MDIXC`(f>tub$xG7)Jzu;K0B&X)J`3Sv
z(Qr0^F(yZGQ=?*uN^X&<ZSng{Hyv*YLXP6!V66?Yc$O^A)}L?uu9_R0boJ%ut2hHQ
zr?_PIoM}F*5A4=PQW2RfrKh`tNUZfT8KWVfZQ?y>2`YBHq%E6Y(nEiJQX??a2`sE2
z7Ij<oUOjT(6Zn2CQ)(iCVxlnx22qV6GZUDdFFvnF{(8F}-ig`Ks%sR2DM{mD4zfE@
zVGuBGgmh;f?f(b|OHv$+9wy>ug%;+Ld}}09C+3CfB^GH4YruP6n-OVj>;af&2IC{w
z+H_P6z^20`I5ap6D!!0h%=i6T53#|A!7!(luFy#fved?6TmRD^U=~>eeDEbFif(a|
zrFrs5U<ZiyzH$4D+O)Njv~tmGzCacjD-w>HbU*v+-$_b_H1dGf=$SirkW+UtM9U)y
zow2iXxZ$x_As`3o%^DgS)Qp)?2!1q<GWr%1i*5`>y&WneB4ZGw;NMNx#_+vvsOy2j
z4<<@FE(H)ctSDWO3+sCiTEPb9xwWIyEpBVma(>$??SkQI<b6#Y_^3hnOxTh+R;jOl
z`{(-l7l<gjask9Uk<F`98}7MF9Vut&S~g@e4Yd}`JSb;Dw0`mJZ>p*K6T0l8GnIuV
zI3X2ynk12T^|r#WY<fmJ%iB3f!~;`f1cTJ=`qcoVLBOmCh%5c^4b>lHeISD|EkoT$
z^x%D)weFo4sRTbdJDd)}%)j{L{rc_C{-}JScDvXJ5Th6l)PsFQ8ph9Xkd!06^d$y!
zA^-Jb?=XUEYbS^_8Ie)I6o49EKGN5vde%n}!j8B&w0|)2L6{DQQO_o!qpca3Vj0%m
z<0R%LIUZpxFd3LWa<7r7on1)O!!aXvxP_S?=Ilku{krYu-{AIfHTmZ8r^7A8{7`N5
zmhul>3Y{{@&b2TC+8+iMVYC&+KW8!zB-<P7A=H>r69d53xG@90m|6zMF=hgYY~4{#
z3NDLmoI$!FJ0x5GM^v?IlODPEPCdLa7+*nPjO2c<#b{6gG-vDc&np{TTveLKrb~`w
z;$nEB`$J=zahrMR8Qsmn<Vb80DxMCk;XHWQ*MzofC<Ip-x&ksdEf`)?9l*4)K7v$I
zZAv(K0p`)2s%dS|L83@2wg3wjV%HkcfDs=9&XI%9YWx28D>J%~{XRjb5a`NiU352Q
z{*2Qd8a<0{d}xM!CftFy(oiGjn0pUsNddwo@ZW@g#z0#H+@lA}^vZ?@^sDtxqJ1Jt
zGA=#Dl2&m+w0`y78!>15gw9{HOvxEUnde<5!l^CWqQ|g0x_cwJ*GtL%z-EpZgLMd@
zhlaJ0Dygy2e5yy(+TQMPGQjoEXaKGc!5J!OCMWYv(A4+5mz=o8O3x|4Y}9ZVtpSJs
z=U;qWx8C&^^%Kc97QtvG5%hl>6QU2k>q6zFq^krbz~V}EH}2Klzk6Kk{_aEgM|a_e
z$|{b+aszjR)|R8H=g{7Q`MC#or&*Cq%qS+SaP{Ci+S7)9(meHI%x9W7qb)WjG~_MZ
zYb5hRH|qeJWD&}@RaWaDT1>@aokbd@G|Yt?8(Xwz%Wf?v3V7X~aviNcgo{zGdN70B
zwYNfx^V2+Y0FIl**eDvuhKgO<^vwPG<vq`mnrHz6$sxy>nuUc~aWrmwRXdu)b?zA}
zwFJv4rcd<<#~!);xBAnIRRo=~6gC{%g2^Begw;cA4Auwrs@n649^cnRo@KKXnlg6A
zZT{|)R)bfY-UZ!mtZz_PV;jdP_T(X6INGj#2>%vjM<b|?!mlunwQg%0JnJS!*~^=C
z&);s>qr31;Ci?bxj%=&peYdr1`$0Q+PmA0znrdHQmJ!Y_E-lpkfBp%-M)dwmPs2aD
zKuI9Q2rdxMJ@A;47oDnt<U#x{jcq3mozboOK<1{~{T{)0y7_1WVIJuBa0J(aN!!$<
z#;88EwwXpytg&6%eE`872c*;tf;VO`2F5XhThZpH9@m|J_@iorbBXXx{kR;B);_2o
z|0+n=z4vl0FUp`#xo+)@BFH`jZ}I5ef7HXf+msjGtn_oQR%vdMjuIs4(K~*xr)uI!
zzl7-~OlwJgqHezJF6E}gX-!e8){=Va$tSlXh+{27*9kW+lwO#d0~6Lt7-<8C;X|6Z
z7eIdZZU}iIY3N!GRuhG}9>%#>)m3}73;bLj#47K4N&Bncp-d7s*Vk2YJwTaQ_YtL#
zu><#!PCf9~%{m1QX<<pJqZ<}j?WLz5(=9jNq|gNmISt{X@1VpV(^`e5D6@IHZu-$<
z2zX+FS3D5q7X6Fjv)Apn^_Uem6R3>?jqMHI=WPsjKIq3H(#AL7n{%$)oST1k`KBk-
zk+?{wE-z3@Ci@oVst2}iP|#ogq^rjOhD{+fUI_BtKYph1ZygyCTp>N6?&lvRm47E^
zCb{pNe5FSpbl*?o?`dc?U2z8%&(|6MT0pnvBc6X$I-fDmSghUsyFCBAmDqW7Hr5TU
zKsqKDIuHt8z3}Y#<t)CPfhC_S*51~Q{PT>ngn)&vuy2nS#{R(J9q>7)s)dwfR{DG3
zeSvY%(N@ff!RHuTSEB?873n$C^!wl4iNG#dClrubqG*{eS{AOy9>v{{J@mTwT&TkI
zEG<e%H%39PJ#(MBqe*4CGyzN2Ugj`Nk?|a=_x(lx@7E#v%!e*>G;HBw<^Vdi|K&$i
z)t9NW*WxzQ)#dt=kM+(Ue)(&~1P|$g(=c_;DN-`{Wc$H^O%G_pt`RIJE>>G>I~;-u
zit*JaR*8+FA6&u_3SQWw?TsPg`#A5@4DaJ*rUwkKpe2J?xtd_of!fH?NH4<8GHre7
z1wHcQORSqjE@A@T*5YVdQxkk6LPgtWSZJI@NDUgz9&+!4N=tmV@}Ql|*Icfbwzj(d
zME7%&Kv3p$e=we+@o<@TZGBl!KeK_9cAaF0NzfpX)~!7cL4yP09MRR$qIO(tjhA(P
z*H(o+-KbRnRuL^^j<A%rGw$KkH+E$hg+L&@SNuqwzW>>ORKw@d;Gee2?U`w;Cwr#_
z3~nZ{^yX)tRdZhwdnS7e_v}0qh@fqZ>=G@UzZ$XX=p@h~hX8k<FTlbIt}*-#gSJP(
zJC25%+tV0isX<1qEuL2_OT#i84B~emfR#YXp~v)#U-awUSHH*QS<lEV#3j?(W9$tF
z_3R7Kmb>m&VDVA~_U}~)LPs`K{x{(x%EHceb+@x$178!i82sG%k2<bT{2pVj!r)Wd
zZU2p6?eJVDG~IHRAa-Qvv^ig9gh0Tre)X%dojZ5x*T4R?7IM(=nOIJK!Wi#CHk(=&
zy=QZse1GCs2tG);M6wOQ1j-npV9X|)Dl622mf)<5*FsEK3`7w=RJlzXw^YF><#V_u
zx*-RSn!|OI8IJL0LzGus;7riWM9U6C#+)`)VJ-!J7c5$#C8eeKTVmoi)UNVfuV@qL
zWFp9yos(d@1dLgIojar$qnb$WLr>Z^?+Xkm;hOq8T3rf*h?rD-MIh23+VK?@M17Sk
zTS#Vhj-oIHv5n3S8^#QE!D!caqGe9YCJqSwqWNGXyN!nqstsYln)5FsihL^OwjHXi
zDAzNackudX4w7juSezYXO%oIz*sdM>vvkuR{*ma@3EFhujk@9gJ*lOO(5Amh!QqT|
z>tmjd)VC-pi-VJi^QJB)#x;iN>cPWoh_6%P)AR9rB^V6MU^OOhDQJ=6kn;N4wD<M*
zv?B>_#>^;}pjs?n;BPr*d)ph(c6A}OEmSUAp-!S29~n!~mwx(dec+sYZCw8c{otR8
zkiTRJfdpYv*m$ZB98y1#c+Yz0xhhCAbK6!`;eWny=YBR>eA@9h;|vOsgJwWm1~@=h
zA32ICQ;w3YUp6^@#ZoE-wC_{-vQO#0ue^`L&QaZT%{97ftXS{5aJ3Q$&QgY^;-$S+
zxGXHhJQcG<o;N~ehp7=H@YMuZ2}}UDlE_VL#L2IH&sVOnW=8tjb%?%9D%Q}H2hws1
z@g)VX+K;LZ_+;khyWn0vqG!ato8UY3O}K947GS#NF(34`>-t41vC67?-=*=-l$?Ch
zpMk$q=X|`z%wLSaw}8{FlTHuQ)DKG~-)6j{Emaj&PGfiS8Rx4ghje*p9qP)r>+zR&
zW9^rU**46MnSUFi97nN@WDskI!?ktpsS{2;i@<@|;9e*`k2Tu-;?t^Z8b!-j#CVAa
z&Uh_oN>xn*rgv$|O^%uLN`qN%+fmiRTqUGrLvun@S8+%^FbdhkK8Pe}kMSV>y|ib#
zn`%{$&@P#^ACLcc<AF-Fx3iaUgiAS&P7JCN_(YR+VD*Vhl!}HSjJfG-I;7_we@?B#
z;WDPma<!Xaz~P*o0y%_K9;`yJxmcI3S*lE2cG{b&bpQR&XfT@SxS5F@;647!zMdG)
zbhI?;sPiAiDh1{*fuK^pb?WK?BlC6Z?`T9L%;9Fq8lAMFRPit?-K|IT$_vly(9wSA
zRuQ?*y%wAFt+5t5H<U$S%Ttv_K%Pc?ARl^sqasN+6^4+bZ8%stIcVRRGo#t<&9wxo
z8Jre(ef{-)!p?x}4XP)~c2-^i+J^~Tw=s1xrbfn;P9Ufl)QD!TH-<T|nYnjjZ<o$9
zF{B)`arSpLvJZ3-C?|u|od~TQ?=j?H8JAY96IS7;%U%<P8m5Ch^Upr^f;u>m6ciC$
zhbC-)+6Rrg`tvvGOCMOP>Mc*{v)6x35O`g%IG4b0n5VCQP*n)47of$pm<wK(#yvX#
zEUN3kKm5}3I4~a1K4kblfH1GR?kK^q(v+QsIit-NAnoaDRc%A7R<1b%`j@U4qSTL(
zW4(OmW^LYGLrT3|r6+`gLlDI&#c+6lU~1L%Xx`$|m5Dj|B%ELbX0RF(K)z>iJEY)5
zU3}Uyf~k?O{>gi_wI1yaI6FKTq}+mR=fiLKQ**SFgChrO;y{cJ4RE$PtnP?(e5*Zh
z?{2OmH*devvhxrukZNmywRyN6v;BOXwt5vov+$oKmE7Jfk7~<7w9YvQ;b1_W2`>8+
z+Xrd0>UmE$?q68ZEM0)#{)i4$?9}rw?@?%Cx^kh%0p=>jn(3^oP-FL)lGBN@j>(;6
ziZKFU<2S#}3IsYPHp}C%WLXQ0uGdtt|B)Ly8x6Ucv%6yWg;uKuA<!t90&>W(U~wW0
z7u#W)n@D457D7q530msEAozgx=A*1T_Br;5v}F2ZbcDUE8B340Zqjx}6EmVGPZy!t
z%uEW!J>^w$gqLASSPGmm!=6HSydClMoyL5E7GMADP<6PrQDNX=W-30vzP@`NbmJa{
z7FLs?AS6DEeKc5Qm7)6l55B7pU09-*@4Z!@`oXWX^4!a`8ds#whAKV&&{O&zTEZKm
zOaw4rrh~w5@rV9YWwYXwiBe8nh9QJYhpTE)44}X=A&Su(_n=KEf>M-Y4YgSN3_*`c
z(UlbENqgDe0ImisKoPM<snMgP_ab*a>o^2@J%m6$D<3T^w9eRAJ3#fd9#l<BI{0&j
zvXhBc&$@pCO?fbZ*oIMxWU%K)u}1CXgz{23xNfw4EyDZopaJdO{<2=#S*27IN$s`Z
z1X7vwxF|<kMv0SB#TY`pxMWHbE4FTq;eA+2w#U}f2C}aiOD42BHUblAc>X?k;v?{e
z(ZDR-OkuqcvWG1Y>PT<1Dr<2o;@Fpyk40PHu$pSCF%|dp!|F{f@ubhQ>_?XQ!f3Xo
z`m3xwGQ%0f`*dKU78Db#5y5O@WtBUuPi@!#cVv35vhd%s!?szhj5601@PUfP4Tv;m
zX$Y;mn;W%%$9C<&5;K@RCokDz!uS`XJ;Q1}Ql(ZTY1z41SV>LnZ@!xLOnc2iHO(?+
zSC@crMFsKD90HQEXI7(>D8vP5^>VUepmFa)z_sc54XSP#QEq-AdnrO(r0Idc$ND}A
zqV`h%3s@)=a8@$`&G=<-p}VC<4eaGsLZjk2U!|~HjMs42=l>iFfq)Ayyl~7WYTLGL
zXqRHgr837>eWO=6R3fz*BWOh@nx}dg+fYnPlM@m+z;fte<2Xh;;CmH|+X74(aT$R*
z_xN9o-*lQJI{*)2uGfVIw6z`CRtV;9Fg;NbL|p^{EU=I<5{3$H5%M|Hp~JHYdn813
zY|0|>z4r}%ddFgZxGst=QYS7Mb>Wb1@FD#Z8j3oks6k<vbg`kwAbmDV1zX%^E@=|d
zrZ&s1Jfuth;d}c02j8La_Hz9jDcqjx#>|Q!F#f()Z?LCto5yUP%+%J1QTx%u5E3LG
z-jcHFo%+!~eHrt^Uai07-}S@azpTZJGw^T5#LpypFk5DN*@+ge0j9@@LNbh4RG3BM
zgy%KwrmwL%d`>-}CviZ(`SbW$wv)Do{PRaUM%fT^6paAEOp=aUwljx5{8v<Qu(Gop
zc<J3!|0`beI&IoIH$2Ch1?Ht0X=^``j#Dx+IrNd4!2;~CNgvOA`M=QCy$Q(7_p-mQ
zTkY*F>LO54WIT*8DY3%rFvq*+l(E6TVf*f-Extf_v(Xlh^#ae(7hC$rQ>_0Z;ByaJ
z4$G0;Pwar$By;77b|%6GJL|uHyJk^j0xt7`Y=ic;a43jD(_+j&B>6uDQ|r6aEI_|4
z^;<I*0bQ8AMWPu`W1ab6Y5^}jeL{0=`v8nh8*8!&)7L~SB@z=blQYt0bo#9>yx00>
zcyF}dH+R18zE4lLOsir75SxQeb#*fbXoBOC$)#>{;OLDVl86$_IQRM;&<~T_{$H;D
zs+x+5)K*cUAzTx3(}|kPz>mdi_A1vQ!&xU8c*A%<6`XE=qcuwt*9{(ZA-D=8i%4o3
zL7|{qCRlU(<<tk~{M!I&+A2$Z*Q5HH8ug4uD;q!KXlVE-baDDa<eXpN=836tK`gsF
z+tl3JLEz9>Wgz5nhYR*f^Lg~ltfyUvrwDLt+^rmfyAcGX;WiZQ%z!7>f$<V{UI;Rc
znRk!y@0qP9hXZJ;(QP@t#Q*+rf470yT)Sd4!NaF`vGSPatY1X5!(y%utF|GfXCv4k
z@;pf7@a~ugycaz4J~Q7l({q!qGxktSw95{Kb3p$N2lNHnbpP%8;dg&7;igh|sEV|H
zX-Y?6^5uX|eGC_j`7%8Oeg{4N$;ey<7|>7S6~Pmu@y1tpEJc0v6rol_jas_{l#!e5
zgi|au7iidMn}6}(k6C$o1fMBcSQo~ijrQ&x{&jgN>looSYo-C^Rxdf^vk>apnz8oZ
z&|W#|1vCG!{o2kX9$uLhIdve@JnDfF>(}}QjGRVyntFE0voD%377hGcx$ijgXxe7>
zWHjP!^|fm0B}NHK%|rygmj2L&`d0Gr<9j&POHJ(I-Gf-ypb$yL|J((rodK6-0Ifln
ze?FQvbFKM1bAv!|4V!2eKA+~FYX`yEijUCGR>vl1e9v(e+4MPF+oFiYd^;{V!*kU(
zLq~LclW9`wV_SClE~i~YFzsBoK80fHYnmxCu}!qif!8cDT%nG~oR43VJLj$7`LJ7_
zP1wsMO}aa`ALB5hG2iRmlQvdwe@&9+V+O519{2<xIZsh(62@LU=$Hlf3<pVI7cksK
zG{j}PhIA6?sp(pE>RR%m4{7U`XZ5$TPAw?SV*U`ZzL^EMxha`CmMQNTAM=#!lYWl3
z;hpF4-~P2G?gd~LHr!0E;?j%sxzAj$^!~$o?v<Tt1y{#HlXdnbmuN*c5s^2&sORv#
zO(Gt_C>x(4ksXB0XVc~~4lPBz;f1}A-OX^${CAHnKKz@CzY{g>DQ05gg2{ehL{hQ}
zm56PS&6x?3W-2~7jnDl|6}j%KJ4=-b9GYdG#@m?|-iBwkncLWX<M+IAE`6M>Z{zQs
zt<pHHuz9gD2jSD3pnP(ZT5%@I*1z$EJZob!-gNWg#z%Bl{L!Ogd}fcv^cb#8owEs8
zObZ7eysiJ>gO9dMTi4qw!yOx+2_&uFoZ#a(XZCy$u_@w+up5uhqbuC^@4p{rdT-m<
zcrRv#$!OzA7wy)7F#xl{baLNjUJrKQvV)4HgUl+x)SnL6*rxB330Z6(AP`S#HkzP`
zx=yfQ4^#YMINlFGIJinP{PETIZ^KvBcaIksdG!>}q>T4_{4&=)^~Y;+%QM%(%t3r&
ziV}=YTcJG<JUuuB=6J5VzuxcEu@V{{MZWHZ?l5Hn!byJRu7>?FVHKxCSB_7J=zYjK
zKi&&}SXeh~ToD{-qgV&t?b6Mc&87cn$J_G%GDEu)RSW?<;|jo+G^co>yJbwXZ;!JB
z?s%WZ+nc~K-#q#1_<sB5&2t^0HkM=wCLA$$7>YZ*fM1?5_|JizCoDA{d;(@XeN%X)
z!M1H|tk`BJosN?gTOGS&+s2A*+jcs(oleKL?R4yuz3)Be<$tP&IqI*fS@q31#wbZ|
zGynKTyvpK1wA@FU=DB{5Zj^5@m6l&~)GOeWyMfs0>-GK^J@VuA_X>!5v;Wazdq;1J
z1s5tW|0lV)APe<DjKnpk)nm8QY3OGM%@&A=`-l#725hwlzm5{Y31UHF*Vcs7%`6xS
zqA-F}Ir@7JxZdv7wuke9@w`}UWQA@qL+a-D{8`a)Zg@n#-3Pk}=41kzo%eX$md`_<
ztJ9P<)A5_a9$YkUJ^QNiOl4_}RI!?aYz@K*#@%1vST-ZDtu<nBNOa}BYgB;`n^2e0
zoZGL$g(+ggSS0lmwH(7F;8(m%!h)l;ReIfSFQ+nI_)~m*HN3a1KJp+~zSQ=`cn1J8
zHfoR9R_&<Fu;tXn{ycN{{5HpzBJYjd>mg{%`I@MP3#Igie$MBniMcyegrFhajQH#J
zb0Oc&XHx*LM?(FZ@HN_iP`QNv2=vK}b;KwW0LDDQlrFB!gX1?*JXy5(?z7w=PHOri
zM~waU{p9Lb$8SCHLDO$eXB{oo8mk^%S~ZV3>6dFRrD7T4@x~t){$0@(3|*QyI<8nk
zsr@(Um(w}w9LKR|B<d{PJ}Ck>0v<73uI4anMm5AvCMC>7$}>7?HaYKC1`n_^xK!lc
z5?5hr-+2{DbX_af+@nlU(esL}=jL9{Y<gx>uD!6JcR^Nct5?OuX#q}iz4J90kCmSt
zv0YZ`RW?SF-=PVAxXaxxp>9TlxzAyC>v9nTiR5*#d!ugIT3mH~&uciF%O8&(zQ{N0
zxW}+g?5{r`K!@(#o(9HB&r5cnF5Aq|n@;$ZZeHC#rcUtmO!%umZus{=>$vm;_l=Hn
zdQ?4AdmvPqVEAs`(2ti4x6mvLnX*#Qk@J$*h0G(D)_eM;tgrXWb4&Z&QtofgaXj8C
zjeoxmVGD_{bwId@CrcC(BeyXYTfEQ$G^g&vCLaDrKpl+O)fT+9Z0!lvraC!~Kg@d>
zC0*q%B_9vYdryab_c3nKlLqT^U3Rm_htp=CFLO<t8|-={rE6kuXC3!O{!*JsEPOfg
zwyWwRK@saa8++~(c4sw2V+_$7)?KSH9TSTdi|_kxT@yYIaL9wfHB<)vKlpA%S$MbQ
zqfzZ~n0;MsR^(dIt7`3?()YQUOyDXtJ}ZGyvJ4+imX9SLIo%(1%ghY?GJ%EN59fT;
zwAnI&UQXwg2d`P_&1<02l$Bxo^RDzwt|ry1D~;iOkl`uMSL_R!7q{*?TsiH^TPF|u
z-|ieNv&(yq?z*{pEi5~lBeMN&m%s6qoMye)k1N;2Zq4zs_`<`r(q7tO9^V4ItVs1Q
z!3!1f#>|(CM~KD$g-rQqc^6;m^yu={1&_tRQ<lY8L~Uh!bYj^##M4vEVJ@f@^Y}Uy
zqEu;y;2A-f=BJVD+AS$H$h*B?+BTVCS1`r&;(QM6apFLUmC$#!MRB#piehDROM+n-
z%1&77KB@Iueh}RP(n5RJpdsMSfMNT_CMVZ9IQK0eDU)AK@?B6kh(n#eD7@2aCZxCT
zU*n*l^xs0KWlKzAEFLT2hCAFajB8}_EsSx=QU=a;nusQkY!~@%RzDZ*Tm>Z>7dJ^N
z5Fp@7Z)@Jo3yD3p(Ntmg%PIL09>-Rme>0*<cw_PQd!>i=@<JVw*>?iqe$^QcrBS8j
zGTNEJk#Y_3asXH#9U;Cr34Yi~-$k9|xCzH^J~)Xc-_81sQAFN#D!6BleOJ60bww3g
zXM}<<?1r9VvjejV&}XgE<-EJcMf8awzTo-!rAwnWs~$6f!!hjIqLp`Rlh#Xd@MGK`
z^-PFtU!Z`m9evjo9oY%Aq%5m8oP&3i46<OIxG@s!#+)xalf2p2i<!Dw{rvMed1dag
zQyU--Qjyd3)cZybXAOsTcGU0ocI6*zcXyln->KAs1}}&iPi+x^8c#vOP=$E;uA3kl
z2S4t&g2`}Hp5F9&5pDOGPiTf3{SA&R^<bCk&z`@4b>|@uyP&<GQ--_M@D(SlbogTA
zq<-EOdc=6D2i$*wB$DY<N9Ds)X{HbdNSy#OIJ}6r@P*miC~kWW$*!q|{D`KZw<GAa
z%T)g9pf-GJs%3w6x!e5ycCF*DcugWr_-Vl3A#$yglI`Nd)gu+gzBT@zX=h@9Iz&C5
z3plv&m~oWJM4kU^GW2JM2?xBp(%PboI`Qd!jY0MQiU9`PDe~m(c33C<=+F#u!FQK<
zQMr{b7;wJ=MTVgP`GFz(k?S=NafK#^PdtE<$C}N55yy{^_4;%)_6gqEEb_A5+e=x+
z1pGfhpUHsh;K=tHuMw}c|IXZh;<+{z1{+>h0?3KJ`LGh?+MRtOK=FQyzHWTab>Hn}
zT!ko$ac(KAs*+i-3H%W}^O@WRx*fL2*cG~1(<<o-jivK*md6Iz%Rvv;!A<1&I(Zs)
z&;A$Ze=&eV*vSN<2sQHtiYGDjW~FOQ#b-_Be<?Zq7i0?zM2>dFA-_YX$Ij8JfLmD5
zvjE4|t+v+Fmad&4ig*mgSY5j2!#z?SACjUwF4Dc%vjDQcpd%G5I>To11Xs+2du`3q
z5EDEyqSN!=&m*vSp@7#yz6AtEURe@;0qG57(3g5qVMI8b$41$*-Cu`(d1$Q<Gbb}1
z6wT{g0>nWNYXwNk4bZ2=<f6S+psvq0RWRc?sm-@Vk~@ob!}7!LxeYfyKiHEnQsQ>U
zW-~i=2U}0@vwArGi|s#)J(2Vp2IW{%S}NV4v02lmt0;>c)>O_jLpXnIqz^~tbnq?q
zKdWiG2OPp^z|zig#@xQu(6HdT($`6yX_x2Txv@OfPnh{ev~F1UXu^BI7jk^_Tz!?0
z&}PxxyvVSC;)n^BX(&xDR4i_ei(cJGq|4IU*2z9QC)uZghj(jp4H?2U<n-!9g%cvP
z><KD(h0FqF2qoZKx<>>8g_4U^B{%3*3jWbl*`LdKA6W&DqQG5LKh>2p<@D40sF1sy
zR0;LNvGnqSSV>r=41>GV-}F<9gbXJV<x(IozYbu6vrkX{1XNb+ir)<6#WwD2Z&&|2
z*(q$L1nWF-a{j+s0Lf^cDgqrxWhp<RRJP(w{^V%OFwZTmRAsyz{^2}VXA;_rD2VOi
z_ci(N>F}T9Ck7E>#7&UqLUdZPrD8z(K0mfge&t*IDJjkV@av_TH&m!I$D0StTkE!f
zo(D0+Cm(<bW4PD;fN_kRN3dr#LrWhuFvehb)BR|)g&V)@u9G}}S{Puj(lwA~B7g9(
z`Goi}B4Dv70WO3__bW?`c;~A|*@%8;|G)(&q}A;<1+H_OUUTxUu3hGr@|8$YeF}&s
zD1Ju4dn2;jeeU&r_Lzd<D$|njX|*XDoZ50#On#s(u8bp2jIT<-dd?HYl}~wHMr!5|
zSKtuq)cC&L*l0F7qiL{qehk?e!3Ec&JY1A7Q02a~%4v5CzJo{>L>_IFbsft8{(a}T
zd|}gRXv0^Pkgwu@dA6hDag?mF@f3|cKT))`X4-pZ+^^VPfe<0b<Zaa=;Jlo-$n8`y
z%W`YVX+O%eB+@BJ^Yg*Mom{fu$wJN7h?V`b0Gjk|QLEPdy&gpw3>{Nm{t&0@@gaq5
z?JbLde9Oo3$@PD$)ea2ee2@4uzm1mw6TM<{%(EJJBdX)PV$yy`lzJNBrd_4HWO{1<
z4wvwHqO)4sJ@7^y7{DA)PWUY26?7Y2C}&?g;%xWhJ+o_4`>n|IH|q=u>qUl7)`Jh{
zA|5x(S{EL1`T<LV-S`d?|AWP0^jylyp<4<_`DlEv(%fkn+^|G_rPJ^Q+uDf-F{<R^
z2nnb3!KmFbhkIlbp2<I9fDH9Fk=DT1eeLMt()!WVa>~v7hj(uEZ`i}}8n+m;0G=-A
zcS1WWFK<}^b(qAyP1E3_+%kFJo648|5rL}Wy66hxdVOi)B`iHHyM0!_B)@$B_X_Jj
zv!VPBf!!}dQ^%dN_NuNiv`KZq*751uOjZ&U4L#Gc$J~zdn=My&ODi+e-hUb4?WR7K
z-nq^88=pC`x~=yt6ObIR?>e#(WBeMNUME&B>s!7Gkp=iFJcRv(GPZv|Zv>@x1mPoP
zRPaOV$CE^z!v}U{T#>4%`_ez2kB4OTT<*;XiM{__jTT*DFgZ4wA4VkRaBq^B4)gJw
zvCi-;9gF&|e=yHwF_Zh^KAUHm<&>UnB)y31A*q|?hwoKK4Qz=-9zu|xWtvTz=5<sx
z)J~(Rs!1HzKYGjPeXgduEUc2Nax`-RpJQ!0NxbUVu>nj0u52ndfAGd8M-_yfgDM?m
zWFTMkFP_E*3CBBOHFZcX?zaOUpTF5Wukv0__3A@E5G~qIa%X%03;&5^ITu2cs{~C?
z#)656fx+(?8Q3TK=$%UAJ^AbCep{d%%O`5-@$!$4eWs?%E@ybBaR0oPg973YkJR5D
zOIX|&`v(ZN)hfQ`wAk!9u3MvoxMEB=6k4nyg{;-D1~RjVEJpX!A)!r81+u|k!msO(
zIr;7Hp(krWuTju@h{tvcW`^M(mxx<4sr;`F*-o>LCX`g)s2C(@a((>yKTc|ni;~4K
z20)NoytmA+MH3um-OL1+U6+_&4_u9tF#_E2H;VtC+%`#}0${`BxPZVi1rEr$<8f_>
z(RW}GzVp61<{>Q=ndpWMUxb{D%H=F;@qN#t`KV!n8tQ5{lOm~~KNFOxoP;P&Fu>2a
zwnaL7!y5r9T7j^n3EU_}8C{tU6vLfwjga!%UR{|w!J!b(><|&tYaQ*A0ZR0}#bC_%
z+9CqIZ+BIUqR=!hPercNCLZ3`({C%X>fcdKNo4@<LVMGTs{>Bp5n(Z^*3P&V;xIHV
zB5N^yJ316aE9ok&W)SewJx=3YQdnl62FjSb9G3<zF0T6gyr(GnXz}q8NT_qP*CfKb
zm;PwAN42ISS!m;iH1dq&B#%{_aMx+hv5_(1Mueu@*o4>>JKe-hZui&!?1zTL`w-Ap
z=Et|ntn66d@W90{bT&k1krgPJBEq4>@670`%t&?ybxIAKnTCFOhHy17L%~E@xO+`{
zz4r+?)XIO6IPWA7VxttTt%CxmipH5ja<_x|Tx7ln-+=6<X{K{v`9lyIsu_$p&bLjT
zjjMG@18Nm~P#+#EI$EZR>*2|3tes_|=2VdU+&-4ijMS`&g(ZDiS*9zSn}0+@i_tOj
zuO8?TL-2PF^WhA-kBGm7trC<rHX@0I+|hE7!_AEeE`mLQeFqyh&Z?C-`emIB9Z#bc
zIP+6ZD%2lQu;VYde%Z)QRxwPY%l0RPmIGOo7R9?|>DA(v!g%Z69y#+~b0bIh$5b80
zoC@o5MjSJ)jXbJVDXgr4zI|Cife|JiHj+0U?ue>7Cvb4#;$Z<9L0LN>{WS1Z`Hgjh
z@LFz*5b05b@fRXOjEeAj5|=)Clm{Wvr3u4`)AXc*xnPH3e<0g5N;X)j&SXHjQ?Dj2
zG)kM)p9+SpLNj7Sbp`ceO&CZl5`&T}7%};qZ7ARncaw-QX~|u7w#_LCf`YbJXa7zI
zEN<X}(~v}myCD>BmWo&7-{Xv2yDC_5U3b?eNcmMngko>hDC#DG^1ilD`!jzo1ZYN3
z2haQh&!{H6fA=751Ye)|wfny9;gZ)skvhb)%X1ZZlK50e5v;#iEAFtc)Doo+y>P8J
zdT^kaLL6RBe8BIZpkL77tW(|l{_(!=`t~9Nkd}5Mr{IbE?!$!W#%vD85G6=+#}zYY
zelujKZ~064JBrif1MueTk$s%d{AI08elRTh#)3qxVMxoG*IJ*=dIUNshPnZPWj61!
zGOE<>&cGkyhU>19%*$=QBj;k%cM6N9lDW;ik5$|WkWGbEN@(Eay>kREn`?M$jL4NE
zPe@{5TXrBf^;}_cL(Hj+=Ft=9*iH9{F2*!SvtHWTK62y1ZTZ~s^G)-{8Ib`-f;|ET
zvPM<K&0?n-F2%n;Sr>Gl3v|~W>ys2E_b}dDx|jJ)LE8=&B+ft5xEh|X3@7eTqUvG$
zE16>SA7+F&;%;2_h=L~S?BCDMt(U-@EYW5tC<?<+`?}3f1mE@d1#JH|5aCZ(x95qy
z^y5`x5^xkStmQ81tbv-0rSf1#b$8ct2|taUruTOK6LEUrwZ#!B;H{B;Zeb>0_p2AB
zz0n0Q+*E$n`;dJd#AY#Nd$HE0RhpEOuPnzc_4s+=;UdD=pUz6baOk$FHCKJE9GR4j
zUAnrF+34~JzCc($C#e#$_?t(yQvJJv_zC*SHq58N<n!xb){WAW@LGp=t4TEO=JJOL
z=mRSEP{WK`1`Y++<f4VubQ^{kpMgAz^lUbE(PORdof@!XO2c#-nhI}t6%`ex1m!h9
z3UJ~$*m(GOX8C_Nh3)D9L3=s5n6+Nl%k}cIGTN4W`B%rVU32+r73UJiach@G*(tk~
z$_2CRinnpY>`C!fH&TW3dt26e4r>eoyi;{C)##+G@`6G8<cn|W)~<Ef`=wTmJvwV{
z7pm?`G5->l&4HvrFevEpP1^a5ZET##hP)LI?Ok*{^OexWk;|lCJf|i;8%DEM^YL-N
z3Y2+sCQ7vxjgttZ5C&FDdFB#sp#3l8XqQuS5;aS?oZWu?5Lpp$NMkfZ5hcW?&b(e5
zBtC|PsJ%Q#obO7M!iayvE%46`hLshaRP5jAMo0Gn(qLGt9DnYw1xx8F2wGE%0-PIl
zt(i8LK-|osJ!%4JvPB1i=^5K1KU1q!@ZSkTZmg|<MfHIj5r4+i#VNJWlOf?hX6=t_
zxw)oH4saX*fr`vA@%+$H$R`S7$uK7%Q{DUmdjN;KeJDJk%E1l-@<>SqEgwqaDj$N}
zpSgdX5GWcttIb9=nAzpCRkUfp1=F_BF21b|5FS}!xtxQ|Z;1MiX{xm9lQ~6zg`f~!
zWkEx(QG}vam;r_H+Eyaq4U}EI@2p4rgeWX6Dpj47YZ7qX41>av0cAl@$D~difYd?^
zY)n6qCv(?%tEC)~G0IRY!gnI5a2X>Gj^*O_&~zM*v$sw!4yV}65Lh=*?*{kf5S;)G
z7I<=~P}+csqKS-6GL|KAOwm<&gh7s%_oMITCadUa=gTQR@?cRpkU}k6;-Nf0)1TVZ
z!od~)T1;Zhzs6b~?_9JI`^UKdw_B!@@r-sqArYrs1cZNmH;%|7te3ln(6KHSw~H*r
zEIr|bA5lWYM3>4MCdE8{OC%SEe)=9d2H#`-C>IV^M+nAy*`wJ37$|cm*F!xc4c?F2
z3w>i0wj1<N4IU^<18L2VE`<G)U1A3pgh&?TIe{Q0uPThmbij8uO38asG+R`;rHW*a
zZGb~CD5EKmxpY677VpQ*#EL`pYk2o6(|5ok81>FUbRnH)V}J{;@D|Lxy?zVIebl!=
zc67+gG^uhTmXMA!6`vv<DrpU~AJ_AKbj{}~AcYX@<Y893UZi$2h~dc;eD-FfRxaQu
zVHDM&g^OYxgar5fX>-@l!VC8S@XWwI-NjQOnl?Nup*-jT61XhxN*K09b0@IzV^2>x
zekZ|RdLI21|L#&95UQXa?ZV?`N$Im0Rb}lj#Vnlat>$4uxqFwg(^17@vm?5)2H~b`
zBh5~)CP&~XN%?ZgST`oaSfpD3j23JmB++!K_&0JTXpI#)un{f3Mk~Ooi4`R|xU5!v
zp-xFL(hh)Y-Q$-@6JOd%P0(STC!kivdaXES)S2Oryn+Bz{YPiKp_Azq?n%XB4#mb8
zgGNb!$^$&g?}>$urlak$1Sj5B8)5l5(TW-k&{%AR{fy(wnBB29GIdL`k(6dD?F3N@
zp{%L*gY$fO411iOLS8#0>^wnwzG`Fy7t{S|GDAmFoK9Y&c~Aq>m22(!m2Ll3bM!l-
zo(&f4e1SWIUK?0DcTv4fO*td8#E=wP@4S;Afk+xb$aZP5R}FqXq_o=i!7YM5tuOhu
zz!GW`oNo*;`B5$1Ge|tO!6K1X`K4jjjn*dN{b)d>I~NNYtG=Io0qBO^NR{2)DGly4
z7+XU;eRwFEklSJ6d05i|S{*xZEv(^a7;rJ3WjxN|A8af8O13YhHaUzD(ENClUMsA6
zNNHWo<u`fUq2$R^IjU;9g-`2B$(UvoR#x*58=z!6?;QnWQ0N5%SykQSYFEXX{7l6`
z@U~0KLyua?rG8^oosFd>kt)X%zJqq-{F>OSeiyu3oPQV+1CK+r5(P|?hi|c~&FklR
zmz#UmLK9!7c-P~mM3<XJvq{@17Uw!sS1`7)<TiC2K*`o|06KwyGzcXhe{C6e9h4lY
zZL(6|UbP9j%6P7Rj}cE<uWj3M>hX0Z*214yn*^Qyu&UVz?WLVO()C7=S(&qQ`mEjl
z=yPSxSK81>4yBt+SD3`(G>N?!e2#3Zb-oYG4gB^Z9<60)%__*X))D%S_2;IyIK@gt
z;8fa4vEOBAh!Nn*BCsO9XBN<y3V-?yqnQ66foR#r^qO|i#@SNeB8{IF?`xE|g5C(9
zf)TVHj@E+^7NNHYS}#BDam4{}ERY-cYo+DRoAg$2PA4>L3>)gts+fnMw2{Ke3Zl7M
zTYWAO5ETZvc-J&zC};(v*WCv{8OSxEygl+56L~$d`A_Z%I8!_Et`zktu4{{1F*^LH
z*OKS{7bfd$zIT2m{mhbL5u0;25+^q#wfrCrrQ<#}g3wfwtExT7-dE&O%W^s0zRS2n
zD*j#&N$DcHsjTwCFS~;=Ym}38Pwu6vCf<q%)>XlnaLMYY-u_W*QgR>}EMrich64k>
z$|8ANb{@iM+g77vcd(emK=fHR?rTN`qQa^$G30Oh$@5WzO|7k;KdGpFWEy)#5x+j4
ziio%jnvHNPD=W)gAWrB{1%ycT@6$$S`jUTb5UUpxv0oJ7T!sX1sf=~G!M~jG;O=hy
zJQZ<cF3<g}!nnFh4DZ<mQNyhWyGnMFzaE74x&aKnAXF&<Z}2&qq29SaA+O(qM>`Rh
zJM0*Ls6F)e6%n<GQU7`$^d&V>B829r8M4~aR`z!qdEA?rYVJ?X?7;h@Y|H*bx@xKm
zb8x+6J@oU#g4UDZXB}t*M9YZ~1RYF>wis1jb%cYzM<Xu~dklu7a&SJfrd5x2tnat}
zM*ZDu8*f}*HegjciS5hyw!)r&kG9#X3k6xOPbGo<n9L%=WuE-)KHdCoG1Xv1;;L|g
zHBC3%z*D20ws*33Pe8sOEE*Z)RweRW%UM;Xf6AF#XX9NswX5;PJ{R3yUvM`(r4$W<
z0%S-kMtihnW}e!&)q<JLK2o$;08);H;oBf`JU^J-*DM{J=ftyYj!6d?_|jL2=UqX2
zD_gu{6Cm7l(2~AcIQADu{P^n3FJBhI=X0!qiKJ{Gre#xfzOYAdbsp-em0Kb1N)#F^
zBk=Wcm$HcgN7^>vnXmOWKiB9b2?|OfpRJfiYL)hE-{1{2A&$#aVIU1HtvG9U=V;W8
zgL)1Kc1&FExOp8Z%s;K_+q}Ob*E<urS24R|Ivmc{16zMQR+fyo6Eia4=70I1%sQr;
zn&^mXRF&7?`5q%n!Z`$5oY9VqES1-P<<!fY27-0}`>mtm3URnAFHijs&t>`kPe)l3
z4Hs%O4d0y-(4A^;O1`)^uL%jZ#Wy5N*KV2MWE#h)g6E@<mIAZF^QGEh>*@o+_peYE
z-wV;AaU;ciI7E@81%;Scu~pnylgqx$`s3WuPEmD#s#Qy`$Eo8w5s6%(A1d#9_P6=W
z+TS*1bRAK*rLo($I(XNQchD4ainB*y$!=Sbe}r6t_Zux`+3j%jvn2{?4W%?)M8P=n
z+kfR-@~R~OOqgUFB|8hHG!3%mVNCU*RKZddRy5<@mo;f@)@6a}%4t)Z9Yj1++5yRl
zg}W<DOV7hS(0`_*Pjg5+pVnx$FRkSmL(sI3cNA~%;v+x&mdudoA90o)#16k%Yc!w?
z_~$d3fUu4ZC>;KX{Nm<;&i}|^DovQ(SoD)Vk~3Ip$=y~7MHRLwTd#e$Ro73z=FI0Q
zPY`aj+4Xum{eu^VfEW^)O$Du*P^Fqm7MfF-h&Q4zSDe4}7qcs?^n#!DdVSzF9v1^b
zB8FZlENKqt8g$c65R*hoJB<?}Le$E8900oP$rV*y1m0YTLzVpK`<I@$b73eEoF9!a
z=HPX6qUNj&r)JU7MlCmDC6HU>zN<=&qM~MAQ0p*E)Z+pD4IiF0FF!D+y-G)`?o1fm
zU=-7j;Ge#L1-s8<17(bmr|t^ME;Igl`OxCMmV$J@9BX$Wt@_5@iw8C12x||D#BDL_
zvO_sm>{ip4Jfp4`HS~5p;rVhs))q;XpnEHy=jggMaW}x%gBv@LjqUvHn24=SaR#s_
z56frTSMGjp_6N6uvG;?>N`ZG<Brq0XBH82kCOi1ZhXto@hs;{Oc}9?v4x!$`d`r!%
zqlXYJ|A&gHbHGjX$Yhg*C@v10q_hTc`vU9rxpV`701d_s)=I91pKWo5DK0xunI<N`
zs)^*V#B@FJpQ2@AP^sP<7xhT+z|dyI(L-KE5!lFs?XWtJ92qGkAvOMD-3Wr)_q`0P
zov7P2&O)FAirR#9@l42Gw)p6-bZPCw--$A7qM*b;vQw?lIN*`Nn`HuHM9ld>`E|Lw
z!wq53tbOV1T|xadZ?{elo>?UK&WVLwv97d8KCats9Po;mm(LEr|IOm5iD_SVW}Mjp
zU#ooESS5oz;3%?R0*4BX1(MA*#b~%5mf1{`MflCzyog!YPMw}kI?~}on|e|Y7>r0o
z?0%U3iiW=}>DX!j)w-q#59zgXRr5Nvms5gxWpUVH=X>SN_8Qt76=4%P4278UDlgjR
zbUsI0<US0aIK}5ixK+i2Tmz683Nr@Ihmg;~@xDgV?3nR|ZRFNS>@xbsLI>&btZ5e1
z6r!Z#!QOoEbnF1O1u+PsHM2M;vWzYpQg;3f-J%igBP%bES~8j$y>248htLd+=mv_M
zbq|ZtxvB+mM+Fz^@gV$u7f0H8-`KEsK$LI}!fD)KC@X6V;!b8Q0GfGv{E6KV;D3wx
z7gb1v@PyJgm%4YrV@etTqq}1Cy7DoUI%%-<APbCJ7`iIIR-DXViIVK+meF`-GNq&;
zr}TRlW1AI}M~x<6yH<qf9lB8x4_5A6V)-o*88LmEkIjZFzLQ=A{GCi^Ow1cG;o%L3
zz*?wUf@Wem<NolR+rL(T$|JDhw}12*<{_oX=K3oyRQ$+^24#EW@u8o6+W)LzFmVn}
zBR5w35tH||13C>81BRv`E(CUzNF^)uPA9YW<Ey3uxgcs@`zfKOjLbLA(WlnhnjDtJ
zlW76<Z+QPn(3;;ZAjD0vwN1}nkBQRJFs}q^OX1@N4oM=3F?USGjp<C($QS1YQ*?AW
zmC$=F(<hE!L>>A?_;Rp@*aI6oa01wRgOPPr7tEgQ7@rtbE{cPRqQAn2s;{X8hn!J*
zeeejY-*2D08le4aWWygPH#@p)S`es_i5|S-Kf8{&-0}*t7Fdi5V`9erM+6LM&x}B8
z+vgw8N(i!lsAFKr3he4)dszDNp38Nd=f`YB*XZ>qa<W`+e8GUF(bO-1rq<m4yv1g6
zbagFRa39R_8CaY9DbuP>bn)=*6wZAWzksX<#>_2O+*lKlyx@fEIy}JNlrKbONBduS
z-dx4HqWYKfr*F|@Ms)sPwPTMHmvLQQ9FE4P7T1S?!T6jq1p$Qa=;hZ_9CKu;KIt0X
zFzAK^eKDcp-z6a&I;>?zN&s|x7(D@X-p8XFG|X)wA(d?qXVrBwq@e@JsklT90@<JP
zYheJlC9A!cEK#tt(;W)y?~23>-Px|R27NU0^`_;V9#lxm3Ckw?{=%Mj#`O++x~w*j
zMb*W;$}PC=op3mmNWr)oSpMg2SrrPxj1V>8lPHDFB(HOd>F$#jsr3XdN0ph5H$%c<
zsL|rO$MHmq;9(9skV`*IfldlSqbr&Gy58TF91)#lgjIecSh5L&TMK`Bc^w=}O82cW
z0gZqFHZ;fU{Fw=dxjPz{Nm|5l?7L{Ig@T(kPxHY-?_?@K5<xHfB3x1~J6Ldy>~7yH
z^uCm5$I41v(uGeYhhH+C3(>Su(vA0?rs#(b1kM0)W8-6Qn8H|`0(%!V(X!_?QR6es
zcB*flA-gZ#xkZ_IS-B}ed@zSKdO*1Qb*cKc+vhFU3sN?G{JyD=G&Xz;Ctd%LtO%S;
zT7Z6~EmEWL?)Ixk7($D|IVG7b-+4prP+F5dbFwnxc@1|sbqps(GSQ{aB^Ga^F-){z
zNm)&W_?P$uG58D|60$^XE+_x%w(7olzrO5oeYphCv}D4+q8Rt+<ADQ#FiKw}P*zKa
zVZ@fnbTS<=Fe7r5=ZOoY34_d#oN0^T5l@xg#Cnm<d4Pu(zCq*|{G4tu<KoQVoOm!@
z$Rs`@)SY6GN>3DZb8H(p-ep7_^`+#d#bx5YAKs&Xu#WrSMvn#oL3CMoJ4;lauNWpD
zMzuPK>HBfWTcF)^OoGX{hB+?dSa39qonw&CW_XtuZELCCqfh|hWiHCxpS$%2CF^~6
zDB4cO@&lVg#g9sRAZQOG=x4|v^>LJ#$~BT|(EH5O;<`*>VkDxDKns{u@>yfoaR`HO
zVZrv5t#l1>7yTVrB0b9Hoq4-cdOVkvaPUA?SH`|G=4+U?Im5(AK<S5{MCz1~Unmwn
z*OgW)f9p$-!M70wNk5Wq&rf;a>?JH?A{n%h6TOqTZw%UzoRuuNq1qdg{ALR-8h8#H
z7(0fG;DZx|au8(+88cZ(+!kHun=Tr87a}um4}!196Mxa*i$QHA^a48rQ;Spoyoa>;
zD3X8bT+j7zK7#1l19Q<JHnW2=Gld#y(Zh92Ua87la5Fg8`kRHh0SQnGe_P(p+dn18
zAch|GL4&YsGYeG|8E+0!>cu8#m@4MMpNYFgS5ji{0|tm5%{na;93q}R_|>y=jMgMV
z(vnQ2k)m}QfAem---0xdV8ejmX`@3y4LxXh81<!5>ms^L2nz+$$j>j&1|xQ(e0k~*
zg(0+=%B`zh1Y8CMt$Iq3+dOFBshLsik<cZ9a0fDoR0cc%?DQ%c9WX!Ew)?wl?7ez1
z7j5pqXavG~_?sifxYo+Lh+U#DnCct`_uJ?J#Mpz^A+a}o?GSV)%_@RJl~q^qBp)`C
zVYY5Y;Ol0*0cOOSA<IkxemO_&A*M)Zzt2drBc7hzzuYeNQfqQWliKmJKM=21<%tcn
ztw|g2zN@RV_b;!4OR?fiTj377eSm$gyAu1vP#<4%P!;I2n3j0<CXCPhg$&fUTaPuR
z>zLdWx;7zbTS?pQ`FMbd7WOf>#T6tyx~)pjx8L<j-#Pv3tq?rSfuFMRo^;E1&Tu2}
zd26=+_&Ts*jhjlJS$8+HwL1g4^sAwdXTBGGb-wFcWB?$lg^;l=<9xncdl%t6P=t+w
zG5o=IB2aOEdhXf%J;b_mqVJDFha^kX#YB~1G1h5LDfYaR&55EY#3`iU4vfauz59*o
zDCdy9!}Mj~FgmIJAnu73?pMx1)K4b-^=SF-XP(bJoE<7UVx(D{7+}j>(lR#|b$%f_
z;$5*JRTvw|0)t#mY7w?A^)n|#;fPFlwF?bR9+Lk^wuh{%+5n!7)U$2^=BA&Z15J*?
zLj8v7>AM%0yubq-`Wun?lZ7CASG<3go&ai8@Zx%S$SSeQxmM`L{X3k|J7v|eE5eQW
z@tI8s{az7YIWRImulNpWMm)(#{A#EvY}oGo1I;<|Rl>=efNuse&EwI@{h$93+&@=x
z^ePx+n~hr6e`2e%3gl*Q2KQHwbyS6kMw1W=2hS>Zx`Pd|-ve?hsWb_Gk;K4@u+j&~
zXN0$w!KKZUh`!h&BQvuhAimiZ;X1t6Ojh^J7rT*juz&^1raIh>G8dTenimxsE>G26
zo(J#$DTk}Q3akqzDL@wnE@ezl@##(693Nf4YZdZ$-aQ!Lukp6Y?F(bM(0sg%21-UN
z8?}~VEi6nhHMtBXaYbU$M77*@xEs0sfXHftIbMqzYZ;!87WYSDbLNGM&d2a-jR6M@
zk88>4=fGk}2L&r0l0{Uy`N4=~i2bX0y`IN#0-TFOcX#M~Mzp+X-Q+kS;iv=Pu@i%E
z)gKJpP+ULWakKifTaV_z*7Z=T-B?Pd^a8&68i5}+D68Qw%q|yVqXnhc;{$(Nn?ZRk
z#WCh3avM2qOld470_TMMgBjJ@f61+<l`>IFEvB}Q<iAUVwGgdlp3V%~%fh!I#bigc
zrr|C-8ykKeu`$7DHB8ysMQ#3*md^stKwvO1%|LKkfKyNrpNF#P{8Sq#P*4a%ip^I~
z3vj36{O#4Bc2+SqLy|c@A%rY|<LQo9V<Lx0CEE~$rU;ho)fIGvzxHCV=uG#3gR=ei
z9q+g(RH#$pCNW@URZJ@TAb#P|@Fd_5uI-P$M@(*c9Xc`8E>9sT)>_Ly5Hguv2#l%w
zJIOVO0Bp&y6Nc|u_`?pGtFNTKS1^QnyaaCYw?g*rbnA_F3en+3rE-DN3j?j6#GYnw
zftKI-Vy+&E&1^ikCCI!N0x*TMJsqq^IJ{a8NHrT%!sK=QROt_A3+vPDtA4)iWZU5-
z4!NcL(iqM}25`{Z8IQUaqZB2P5uY9Th6m#pBx{gf&Wg8aBAuwxSI%t<=;jQ`4iO*V
z#OhD9+msbeSF=!A=K@x10qmZhxQr51$}?o)A%NwSK{1rEVLCO-Z*-cS7WOA*xpX7T
zIP>W4e%`_+KQZh1hXn`{(|t`d2)H9+_TKp<rq?BQ`bV+r!t-&>7lh!|H$6Djeq^Iy
zAh`2z*!11V(A`IA-A1O5UEwGKbz}5YwTG`Z`9ybmBN5tv*A(M((xEe&8=r*wvs&3x
zI`Pjt!JQyVi{(84xSQmH_E6D=xubY;^Ja!eh^w)V>dx%H3|jrYmvve}WWicI43p<u
zQPBi$7`m1L*Mlp+x?WI3r_y4Jk=cQ7rz5owrl4B+qUgrEv2cODuurTq>XF}rQ;#=2
zXkA*fW=Y7twWCeqQRzd1X!Z3!nOT7_53ZK2%IsI{d4LUVzXIy!>_U>2#Ah$8XF@{0
zGL=U%o*FnT(yZE2G`y<UlerbP-bM!>=Ygo}U1Pe?TQj--s^Uk|vtQIK=}KYCs*8HV
zSrHKqfFHRV(Qa-er3D6I7Lh4NJKFdy)UpGOd4W%-Dn$92rPnhOE=)ycGoy3}^1NMy
zrZxx5^$!&L_dvh`nUUZ1O2S{HNCjm$jNVrU%$ORo<$<LtIIHd#%m*k-wr1dVxZc+f
zKp$PcBYsG=OrA!=%AHY@!0lYk9j^Op4&Ms<_#-_UOkG)<w;l0p$wthDWJE!f?y@Tt
z;bvds*Q!!1WDf{3V-yY@3k|^sQ8htXg)(XO4y$m|4eR4!+i~z|t?;Se;C!_irwwa@
zvYs<I()asesi=cFj}zR*Kyd}!+fEw~GlN=m^O&_)hbf;N&O+}XKOFs~j+l`oE%y35
zts;zkzWZ_v>I|I$Lf`-jTqJ_vO!~!g>k*|QqBxsv^<e*sG9ZPQsQmQ(SL&L0%{9E&
zlL3~>J_3_d*wCK*CVV0FZ;vy6Aw`lAPBdCNPvT0()M?uC5}GpDLmz%~UN_t`;1nAX
z!?l#$o@P1>)@yezj2cC<?MFmj<+!8g$d}R0P6hRNH!WgrR|D43Pi}x{vGa<~-*Q56
zqt!dH<+m5J!BJM-^jrNnht$9<SmVm+jlheM`0*R|vT?|8d2El(knPKYO}5V0<&8Aa
zKzXS}1M7DmoCpH$@L_wwQ?Hngle%AZ=lm+#Z&WC*==f91n&d-v)zb~;K<&Vvx6e=S
z6qQWh)`^lmXzHx~%4vTuH~Yzr0elWJvZae>R725jwv5v$DWUP@nr+R!o0;qJc!w#{
zqYW7ok1lqr?p?QFPU|=&+sD9iOhCXShyvlS#fTSem+2>&5cl`E5s5RnME?0w|6xoE
z&kHI~BhHLrd|)XGRd8UgxMzXUm2DJcNB(u?RSzrX0vRTo9a$`m)62;XU!Hih`;<Cj
za4{3uTOPYJ1Dn#jB^Q_o;xxSSN1A8a9G&5Wa{I0kl9Z<5Zmh~T=JP0k>l{rv&QEen
z?1_h3l_kxlfm*ZT=M5`YP|<~>*36+GG1YrctChwRRI6NpOYz3ef|6d)JO-#tSEb_W
zM_%bzG@M((cWh2`YNJ6ft46y=LS>;L838Pj=z0D{wSMCKcR?APf*?)oLpXCO`tN$9
zh;}XcT&sT3K@N4g63&RnlVl^l=@rq-mn}A5kE3;O^L2UA!0T}(?cZUGIern%8YQv(
zdfS(YDoWX(zr8PZ&AY;i&|V3LdmqWtXohA14=JWd^hT-)Oq-|Q)hbF}`>bGB+3c@f
z&0OI`hP^QQ>RT{kgmMTo8-Lc*zJ@MbT$0dun2^Z4sqZ<^c^X`n0CT&sc-%<HA_`C!
z+4{$|Itd$gOY40z+CD$<7~X}<PaOL?q30~v*oTXac<OxSblF^$(o3rLMVuANauHXi
zZMZfwT4`d>dz7SX8|lX2tLf+_SxfsOOO3DoioWy_nl6TIFJodBbRrF4A4jV*AMzxa
zCpD}I45jP^cjgjdZ@3AVQo~Th>@nV4FdVbl?-JgPNcZT5oH$20s~l~2)GSd@^dwkm
zk)~`lpj#vkF#Fz9v^qb*XI-@H0QYzmq7HY9IreT>6*kvnd{DsX%}k&4v2eSpqY!oM
z9D)E9WLD5X`Dnb_B=uhrG#^jYY7^NAl^@(H`+5N_oqHFU@rdKc?teA{-p#Pd%%ymv
z1?K>P)#eNBtas+P2f=)4P25?VzGlvgn7i9`NR8KcAt+AQV})#(FKc9v^TgI$;)B9Y
zj7w^qMGX7vH1$`PSdRs7{dQrl^&-g8*{F)*aLz5lH*Jtlp6@90=m+NJrhl&^Nykz)
z+3Z<=4{Am&3m8UiPJ-mKc>XuXn}h!rZZC-Bnfqk_Xc<&M1^n3GmNGBPrKmoxF8lUZ
zO^)t&d7e5g{BQ95I|%n(XD5>H#~yLyR+)ku>}(fnvqs7Zb&=DP0%Vb}VXMXVP+lDq
zh~U`laM==(c0UQDPWs+26PK<Z?@N<Gqp^D<D1|HY;Ednph5nt3)qav^=Xm-%Zv>dd
zW`;*V@l2=e%$_)<v3>*e<>#ld4Iyhq!lLa>kLgcIX(tU}+4bJs;=oavYLf)VSy&`)
z8(Kvruu5FdkZcMzH{kbCr(UmQn(6OsM}*?gkn?{-V;$%j*wOX`S)|3e&fA!I?H?0d
zLq4(}q!Au{I*bhGxNS_WkDp`)De@-cTG4&*(e^YF5V#Qw0;|_I*i}b?o^b?Od=OsQ
z`6aGwGIrlcir$u%j>f88<d+syPMr?Iyb{v5k)uf#<sQnoeQ~ck?+6GhNXYL}$&nrp
zt~lCBN1XthiC0Au7U)$WT6H!Tpr^S|9tk_=U3c!Wa@UUof1XigCRBqfTM0&J@G(ek
z#+~EzrB_lH&LVO!ZApRfkR+xjXefg*FJG_Z&B$Nu$>IccQ6s%8Dt~{C5+<3d(sE|7
z`?By7GAJawLFm1`s1i2(6Am-tT=b3u7vaHqJO8aBIy7V?mB7%PV<B;u6wi_1vqN<E
zPHv?BA~oFI82i@K)Q?z>Id&3VEOw0lGbl3=qu^b(wxteB%E5_Q>mt1Q-!0H7JOJCM
zOQ-9EnXIBHU{h%Kt#<FVX4?Ikw*t3@tNC2u7yLap$spi`I2Yi@ur(bgIuJL%62~-<
zlX2_*exM#NunnVUL-e6&E{-Wei#~b)z&{5kq#tJLl0VUEn3gLH!b+}`@Tf}($yy!9
zSkA5?&#J}AR_R~&p0720JHP0C73IQwcxW(IximVrsM$f{y^HD$j9|W`sj~w`qn}Ps
z)>&MgLaWp8Bpo;#+><Ed@f$>j%p(4JJkPCFmQD)kLCEZRB0<vN+8YXmr;3T{i#Xx<
zmC5O6FCn}!;$AS^JhPBHB$=iT3@ZHu{Rr^NQt;(M%<X=&Ixiy(2~DMmCSw4)?!%#w
z6x4<C;_6maP85_=nj_?ew&8^?`9?hUa6$EK>o*#sC#)L>G}Z8i0Aj)Dlp|}ea<#yk
z?wSWX@$)NR2tRIzdh%zNA^8K@pFmVxkix=%y*_?86B6S^wCTC9uP($D*ud+b|Aq@Y
zSHloo?oTk5RfVTo!Ikwb!d~=|N~jEacMp`%+PSB-qc?b_(B4@@3*<c4xM-==^jdO8
zX{WI^XqX$*`pQZPUz@+0-#Lsi_zYw3a|uJcxY7L)jc_w%enWWP{DVJpTdL7103O>7
zZaFfx^?dYzBY!!REwo-0;{foELtgqD+%I;H?0;S_P#jEORx>?2;$Q~{lLn8yO!`&@
z(y?Sk!s5JX(Ta<@VIDN@^`#zBL=o3Dk3o37uipNW-~G<XXy-Z0?Lf{)1VVK{&Qn~N
ziU`@3N(2ljKvDG1#bR)O3?F4~V~N`BJx@G|kUc|x9E7;*>5c1hV`P4Ske9rgY&WL~
z1QdKxuHJ8JMrj)YdpuY-Cyn=|?SWn6lW>Nu!6)`G;j88*yI3%k2!xhn!<>5W(3vr4
z5$6?sG)EcRSh(Odf4Z;lA3|bN^0k_Lp594J2i7)maZv2~l^MHz4klP*j9?$rYCdk&
z;bhZf8kHcrR}+y-d&|@0DH9r!!Rt}Z`*MA6=}yPBclNBKVj5I|t!|97c*@qolHU4$
z@Q(7t5{snB?tJ{e+&}5?0`7>_jHV{DMSrI;Yln{#@TjxAuf^E&Nv0vjrPiT_YJytj
zpba111$Kb>eQIoVXP69220|<I2PA{HHC}K$jJ~TdKkdh-VKW2Y;F=ypcePR9tn9F^
znh==tH$}5l3`RJut``oGf&fx=@3$?-6VAi(R~nLV0$PfAd1IsPlNfd%$yEb7+PR%w
zL|95SLihmCGGhsw{@fw2lDpkv=C3-fO@XocF^^_;wck0gzUuUtSu4?@zBdx@$j`69
zhpg&hiK{mp^n%@Z7Ax+}gLeHhxv93u;QpOBw-<VAfnQ-|qY5z^%`-dRcjp~sqbngh
zj{9hMft^aiSKN_1$!|<bWiqIawz+)W7$z{A&IHp;Rak}+8+%RK>Xef`+-?Fs@2fOy
za*EHtB}WvM1*{S8$l!}{(9M?j0tdvxe%-;5Q$Sh_Qc6q@Gu}+D@?9gN2$>zWJTc6$
z=yhERC*=toZZtr3Z1TdTDsa*rw}&M9<hP+s=&;S+&{G)wqpt1adG0!2YxkMs43C3h
z38JXMMD4uhT<_9{IS;|d?QTg~TsACnpI-LJ6l>*Jw>SoNJmOlKi4P=RaoLEmZN7IC
zIqXnUanu^%_lkz>;^kdilSQyPCegg@`;Z%V5-H?l$9!=z3C4&hkVgt%-Awzu-X+rT
z(1VlO+_sNLw4Pb!zDdLvIkY0CHZ)I@IU&U-6>?Y5+4H`CS&(D_j!*mq%~&5+l3gx1
z^vrhe!m-oL{ll@J^qP9h0(@A7w08qNE3^RjKr9EAmga>NQnNL9AP8UowOpwxX9?U4
z0i+D?vei%wXWLw&o+7Cj*VboV?w$*DfYS)-9UgftXi;Bw4~}+<W4^{;KOh3Lqd~xu
z#j3r|4bkfYYT-0cDZz$^z693>3b(QI#X3RA)HUfrvs?MnJGKMTgQuly18*b|{~qdB
z_%e|tCe}XA^RrFUXYsOU#mUH{XD7uc-|_CPj5=ClMhX6S2?->kW&9)Byog^%Oer>p
zF8nJw+A#sD35BxvPh@$Fmyy3VKIX3lf)sktFtU8x4Y;mMi7paWUZnl70c661D8pby
zqox<=I>0eKJsQL4#~)r9s<is|C&&A(y;$AXRJgTsR#jib$!ytD$KM9IL-kXmC^CVO
z;K9M?(jIA<grWw+3C8TnOs>cSw~V5ROyQ;&{V+(QyK583x}A0@58Y{5=HXKwxe5lE
z#65F*uU<YBuLYs83^CZIhEg}=<FQH<EY@^Yk6ytkD2l*60MS9@PK;#&O}0pU2m~7+
zTtN`toS&kyEAhy$*kv%S;j`7*z>TPgPTu~`Ql{T)$T{HQz3Gq<yAB%&QC`nR?XvoR
zbF4!($%QPrVOGS?Gs8&DTMqOW5kJGRSPK+fJz2XqoDhT(cXvb#obtYU5?r3o%}AUV
zvFOJ<-m1hfH_7o@el}|~d!XjOaAd)vV;hXQ%eTK_l}jbjG~<rlI!>?38&c41VjH6}
z_Nc_72=7bf2!`ciO<{59DZ48hIGb1Jfrl{9bSg>qkK>WlD7~_n=4_(H<dc!}*%s%D
zXIR{&0=nBO-bYv0V-SZHBm4v9NqOfG^GMGm7@tXb)DP>y!wFu^2jOqA+&{p^#u{o9
zkHe3D(eUdb3Y1c-e2G85T%KU2WZ{wU5aodtJKl6Oh06=n=2s*V_Zo}7v@@Ibz^>q+
zC~_^a5K<;6Xn5{cSHY2~$K$m{p3e`I?T)$NNKs~AOqgG{h9y!~VH$py9-dem4&D)7
z<X2&<7NkAuGBc;#0wInNX~laBnj+y7)(d@j;m2w}$(59i8fX?YyINw}8n2d&3Cb7y
z$K$>F83#w3*f*I8)WfYc1|z<LS>k^`X)`84RsN~=BKVT(G~1g5W&06N%a<#*=)n?5
zude$l(v4G^Miz|`-PA<O9gli64HQlvIy3QG9OL%fFftgL*Ie|`UW>$~;6LQxB)E-G
zpJKvyuyp%1Q@JqgD*ZC#lx8y&=JHTE2q&FYDkYE8IQ;v%h@7zhi}VO@G8QV?WvdJK
zT<UK)$`o)@Et-OhoS@CEqc~YuU?dr~0vnx7MQ)Bnyv@40+Ehl9zl&r_Vee#W?JSKl
ziKAfeh7iOTO#pL)RyMUB@GoQ`%pmKxgngRf)0w``em0d#MS)*YRPg|1>c0?n54^Yy
znPn@)#{3v#vlb`|a9~%_uJ%$H++tEn-kS`n7lq-;O5sjpd^#+y8q;8RgNb!*IPw)Q
zrgVbxuqmrkpWhhN6z?O%IqnhN=FdxGQ9QJ71I?_C&LVKg&b=C@+Hi$Cb2-OFmfFB3
zL>Cy#!O&j`(LP{b<yCh_<YQ}_K~m&Nakhfhf7uKZgfpJuMTKu#5w#5iV`zPrsGdpi
zT8E!x!7JLEDFb1j7wZe6L63Pldy~(7S8c>+F?6Nc&wcG<o&vz>(1LNKVBGnU9HZq)
z3>;QEyw^=cH_suWFA?MI@tZ~$qjN?NNT8Wx#3Y(6NqL+=h||?OM@JO%$N1R<u9r~L
zAFpS{;3YKQB4eF6tx?|_X+6H*#-G=Bb!|iM6d1v?JtcoAg_GX{|H1GrPrdzHs%;=g
z<cY=mxrXXU>g>|r`!}7CR5!_3Fy)>40TtO?t~o62ACsw`do1&*_J~H6#h{z<Fb39|
zI?sdmGp)!astG&(+paq6&JYK^fD`zZb@w1MuGGL!#w81W=?uwX#G`pZU&e^Z8B!~0
zU3Al6`&fGkSMJyCBX722+<}>iXc|t6@d2VANYTgTQv-PaJkt_kM>4EZiMa4IC$B_3
z=IDZ6UrrW(T(}_#6f9+^kG0}5Xj>I^50|K2?|3>S*qpF=Dm)6|PHDB}QZs_^{~pQ*
z778IgD02Cp+%w*gnUs?#8`~}ehLV&Mgw#F`6lg%?-wpA#Il82cO7P__ZzQ8`X<F;z
z*9Fbuq#8-1Xy)|g$yL!I9^y&uZx}xVy~T?%?(i50H?;pD{h@(mm49k~?Txk5;!u(2
ziU5*L-uFHo{9<WBTWb~nSy$^sJp%E_@PK#4uSosBh2s`%z>LT@ZZf^krR6R3sbX*!
zdS1EFQo;U8%iG!}gSg020ese_{6-&moOi(>89A8690g3Ot~OO?W;Fg#&kjlah5rGH
zKy|;w)sg}TVRMqAix_o<zzc+VSu&TVF$D>gjW^Qfi@+k+$=0DO3hbCW@gTQjuTU%W
zpW-K`ZxT4>4;2l)Q>KoA(4N~xZ`xwS1u-4SWX7Cs+D7=GG3F;uCdAATr5S`JZ_9*t
zuoo&jy%?uU&jPRQXFMZRLbO6iI4#>0flx;jL0jh_nVF5b5sWtDjjJ;HE<aq~Yi~kf
zow4$+rajnyG!qeI1^1$|`s~Xgc;K0Kv^F)8AnpPV9^8WymxmDQX)^v6*L~!bwjF=6
z=mO$>BfN{y+Fr8tf>!;3-X2|_@nlnO&QdHHpKKzSR|{99%wDQ|cXgi`(e$omEUp*H
zbF>9atw$mC8fi+oa{LeR8XDTT15D+TOQmN@<b1?X*bWM`LS}J2R;-#qHeD0ckeU75
z+N1CZ^FzgDKeE13nI-9%kljILtH9R>HS_|}%hpJbXiM7wJZYtun0_hOZJIGvxp6Cv
zIq9m8qNrGVS|Pi<2~}j$4Uce!8$DStaUlye6*odNZC!&gmtLM)+hBN+6}YXOsFVx2
z^tiaf%5OIQ{<~%LP<m1jWnDRT^aeB|3g;T--B>fJobm$lVF;~leN;gSE}%z5fgs93
zxmHe-Fb27R!k4g83oEpl-nu2i2qFC1eqKiitSE>7n=(YNmVt&fAv{xmCoTH~j4K}`
z@uJTAO+q?3$2gjOW~y*p^fR?02NO9d7<RzrLA*?DgAI%i>_>n%4Ms|z^^Jy&{9qk9
zhBdu)fX@^X?TMFb5G_@gW#AGK#4r>YyHu7$f06h@GZGJq7tO|;Fk*oRbNmmISZ{EU
zbrPdOAIGk?9@WiMx<tp2vBoPzkW;2NU3jg8!%`;X`4TJ7Pr3zA`K6UGi7|a3MghHt
zMvmzEfKYQPzCr>wzUcdIkOlyrd$mR~{$xIv?<7VYzGq|gFigXEPr}%RAX2HT+>Xzn
z&4yZ?a{ASGQ2xHU7F+<?XI>naG#K*59sQGVmmi_#%1w2*kZWW_3Y)ntjR39;CCR4J
z?#k!<P_^0V>@y-s?82Ew&5{c~WASS$Sw|}+d`Q=4Y+o8LRK{jR7%)QspJrvLBRm|W
zA~UG%;t3+8vn~5f2*U-cn>wj<v0gN6h-bA!8JSYcA+#|rT#PDqGH)1HE5Lpgl_Gf$
zrO%WX(gCt1e|QI_{uUa-xU2DeX;aokj5KmS$Hg$&zmew2^i*6!iDC#&n1i5r86&!L
zl=FCs7^sD(!PQm#mejpy^sr#8cwZ}xMb#Xqp56>K>QjuBr2gmAnDUCo3Gp<_uo9G$
zZk}J+1%=29Oh-33=sbq-^~!VQIns8LIij0mR9;OtAmqbUg$Jd|!(_s5uBRb_O3;9`
z`>?DsJivcSJ!pi5ibg^1;j*gs5#p_BCBKR}*BZVbk<ie#Gle1dd!-5Fq90p$Eu&}9
zMK7;@4)Beyc?I46K{;Y%|CoBS{bPMP_a`S*(ePl-HAcg<dA?CIL0SghSJ!q*?~CD?
z<FlV(e9knsInof*-j{>)j0*^GNks_z`Q^XG;~A#yYOb$CLEc&H*|!fREf(;m2VI~3
zRK*Y|4MUxf@(@xY)KoZ|aeDtcG}@}+!tvL~hSix{_+t7#;QHBA*G1zV$29vu$Pbkc
zkm~Uq2~pw1gq4LY#}K(m7(O$|Yy^XYGzxJZ4E2jl(8+nwHM(~3i))Pe(laU=n8m0t
zK)I)gW6g$g7l*)@BFLR#)be?=iRSWfG)L`)quBV#*Ki<5k&4{tKdR7hV?cffl;k5j
zD-$ylR+8Hw6wwK8*nh4RvmbvP{>~O?sw%?S@Akrz2%c(ik7RplNp#{Cnu68AlwHQo
zGgd^fagwgifyOomHv^QB+v3pi^UOjs8|ujE_|;z`@RPO<v^LhGsNg*IAJ~iYm4*m(
zVQOEIg?`k_Ok6b40uwDT(E|Uw7BJEa{x`f0l?YXT1wBA3=*6O?o&tcqdlcUP;A1i)
zQvjjVV=ZjOjIXlwh{Ajd)mg(Trd!i(!g7BRUV6D{cM$dk7l}jWrY#TTLaJ5ZRTQEa
zGiONQq_y-S4rH9gfFW~^Qoy{jm&+DO;jM?Mh8pbQX2rNqk&JJlh^W<11vh}++Z|`}
zkT!*BID!%|C(s6;U+~AIgdiA_ZMmp`R#`YAh8~<s;<?4UAd9^LVUQFzbyjdb)0p>Z
zq2Q$CyrodD7qXR&QtHE(yLwTV4Nq6DJ~Hz45B!pRvvz(#IQZ|knftg2%NEZhQ?~<b
z>4EWJN)Ul<PbL%Wx{v;e4aHQ_>FJUBC`IZsX4BGrsS`Ly&KUPgbOXf`MnSZ_GLPnS
zS49}o)ts#<9(EQJl2i$kbT>E51L?=mrLv;;i}%$(mlcrhO-l}fVtOsz9CS?X;~sXy
zKPVs!k$->+-R3j7m^)(%$pK@KLNce!%VlKw4}s}$FG>o};HdBd81|upF_xmt%D9re
zuY54?uB){e3R)<Xif2SBcx^6vo+r|!)LnOpQnS%lQU1EMA0j{-TGHo`S3%{d8p9-I
z^JENZ=mHlwx|-wC`FygR+hEdt{@88G6g(uvyWCcfbA|M9Cq&|r#~vZ!m?z0>Dv`|;
zQAIT^d?m64_nBet;ssEXRa!~CHLg0k{uJeKO!cpvaZUe^IZrOQ?nPHFzwJ-gJnyOp
z$e)5qRk6<~^hyOJhc!XEiUKa}E}0`5sBraC(W9etL?GvS#ACNoWXx-+NRhH%$#YHY
z1_w69dqS<R?pJ9~owwRdG2SROqOfj2<C~5|Q&&f+VJGvaixEq-&GAc+?r-UXYsyuX
zR{p%|G6V&8002M$Nkl<ZyIX!S-u&uIuX*0+7vk4fX5r{J`VZskz}L~wTM=mW4K)5a
zY)>&V_;B8vnp065Lh)%bnwPdI9{q8932!D0;oY^xXft(z4JCZ>YW`r6K4ZKzn8MM7
zh8yX3LI<cvU&}k7^qB$|q7jRva?(Q@?mr4-TQNRSvtY5{{|@$<D0I?qV&E8C<PnPg
z!IvF<U-?qX=oh*_^$o+3t9Wj&93HuVU&w2^(sOFa`VTYv!kPWsQQT!h6vlY;kkVdU
zOB;*Jl{%V{9bLSo@=)r6)QlC?lOObz;rP17aKppnithSxUtQ-9IcsduKVj^5V?Prc
zp}g3Y>v#qK>880{Z46`XnbHT+@A5&$lN?vJ3k5BA<?6BbzMap~X4YKG%V_wNY%wP6
zcRS{6>*2T*kM@;v8~caG4l#_quA%}beWoQFdDfxr$Zgh=$dMj@{lwtS^<%t+N~DVE
zyMkyi{rd0!U^b3=ESxhL?nJRUFnq^b9Yn5{L^7dA<CC}lhSMDmaN+(Uc2YAsIWE=G
zL;rL#QG+(9y?7KKf18iMU~;=~PF=s1*!LV1T&9%55hAtG|G|cq&t{4YC4C^qOj)~>
z_;TqFc`m<~wM)j3e*YNzEEV@|VttehJ0|q-cQ7MEH81qw|B)y0=txo8L@X~vDRIte
zr~q3KdM=Yf-$wdVZ|C>m5Ydk0X8G>iCEPK0Cc{#~krW(71-*`p>tV1YKT2`YS?sDe
zL7Yu9s!7afD8@o^XXKFxH?yYyn(P2deK-(1Dwqn7>G=W!EwJs~H)%9#Adkm%crgUT
z&e<KFOh+0OpFp%<EWUj24eYAYz{iYcI9cl_%0vrHw7^6Q{2#Y~s3bQn0%dB+B{|2F
z5oG1HQ#?bKj$Vj#mTf>+#A7IVGVKTtN_-#&d)BnVEkf+vBU=b}0Xb)wmS>d$#T(gW
zWRVxI7&pg`Mv|y-5gjIc>BvVP<8-fVCt@YOQcTS#6w+(u;NYa>E54m|Q%h*6SwH}x
z`I}L&JPlrv?nqvkhR>aYxgl7ijf~}IH|&5V^L7sn)iEtx8~P|9$>yvX7O#p3i(+gb
znQ$+23R3{HvS5BuTAPhWJm@M4U^;<>?ZGSLRh4~AwnhqGx)%HKi@n6Kg_1BSYu_5|
z{^otf*fB447KMOBBu$DX1emeatCr*IpS=zQa;M}btL*lakTmps2|QCaD+6(9@J00X
zxu!<CN-LYV^qCMUjr64Mw=@DvTV1{4)zJ>LA#=-T*j-9A0b}Jwtkf413i2|BY#o^k
zoCck-!jAr&8~xVOn_%V{h_*xPkWb>Iz$qa}ni)e}AxXD9m>#OF3TL-`2bJ27MiP3=
zhgy|Blj=*}S3x}}f()zQ$3QwCdY^18MR7OaD~YPbQ^b&=ydL|SQn%7)d}RYkKFrS4
zy{U5QZT#R^=d+>7A&Fl#;jD?={5_CjN<ui3PHaryk6HKK$FxJU5Uyg5Vvesi=D)6_
z7_^eYL1dUW*=e)z!5@EyjagJy*s69mWR~v^XFUH5_*1AG8CYZLBrl<R72TJ*zjdFl
z|MN<}%eYk<fhX#Ou^+zr`uDu%+mVL|ibm0Bq;J?k(l1&G+Zb+PpV1>EPo}3wJlN9a
z^89{|S#L{g*jtN%te<@*%heTcA^*5(5h2vjifzTX?|yp2!O#!`5o8(9E!{58V1YP`
zHen<R9=)T`k|CMts0Kwt5|1h?W}*xVp390!4c%=;5SCbNA~X26tPc7j^_Ks)T;%a1
zMd<=wEbSv-UvnSjT-^m@F4OhcjjkEF&)D<z&l~;C*B)KFWUb&G4HQG0m_+%8qw|3M
zMu8Q%btFq8gx$c33ivsu+9LZmZ#H(`OJ`d#7qQ0D0j`APN*wb|>!7cU-nf-tKk^LD
zVd*0S3gT=KML$*UC4ELRImHl6WD}K%0rr^@y>P<<p{C(b8E34nC><V50Vk=)q|fa5
zldHwJa&=qZxURZ0uQ`A80wJ0N>I-UUU}Wfk$slU7*WvxG&59B}Zbzfx!IJ6fES<=P
z&i%Bxtd7VD5@ga~rFi7U*r1qKctiHDpnimNKvJQ~d>K~<{qsi#k^awdFFE>$^u?Gn
zcyIlmW4;|dTX*cbN0jSSii@1#G>r|Mo$M+i?zD_&klIThP&Ze`(1=<7%6;_@9rMi(
zeU$r|+A(~H<a1qY2M1d__@%jH$I*Uxxl@_aqk?#&Ti-v_498%Lp04JaBePk9roxSQ
z|LZEIE9HJau2Lr4_qi{SRo|IVYO$n5gjr+%SO36<kEx`pLosJ+DyDExj!$IfkeDgB
zlO%<^fBzBOBEy)Lkr4fwd{|6gf%s^W)DAV_#KD8id2a?c4sZcvT)&L#ZJu)V-_po}
z5+Tw}x>H%Z*jC)Tm3hE>i@DmJ^T0@%2Qo^P?o)ot8fYkIkhIi|h9V{eMO$?l-<@-c
z_q)1UitMFgdO#v0n;RRsH%V9uXT9EnFml)1{iS6C7&`|s{Oo(C++DzIHAzTd2qG@H
zBFxJLLp@D6yK^(G2%hXS&g=`-M48a2%k`nX{4hTF;55^u(#$(<xC*j>3jt%xU6?M@
zfQv|2m@`gp`50TjdYf5m)L4)*L+P^w%27UK$Cp0a^!yu8F}0%ty}8{NnK*x<1twZx
zq6Pk!EudSaZ+H_SmFY(<bBx~S>qSBsV>B0@#D-m`=&7KmN!cT`N(!Bs%kM{`+JR}F
zrg49Jn{j*0U(H2Qzm168po0yLT{%TekSg>tzP1J3l!hI>eBqbkhttPYm=tA4Xc8^T
z6o$1!WR5Q?K)(r<b1Hgb)zshnhSeW;Ks?>1WN$CVp40hA31{qh&@BAk(Hac#MRD;d
zY_1(dgd=121_)wrD<=6JH)|7H*f{!dC)-Kq8&mRd<03c>sWu+4XFS~?nTxyoL<AHg
zuE6dD0&dFO#e^@?J7`JkKGj3mdb(3cWh|z=zO;p0a9f1}599R<a*9Y26Nng}04m*#
z+s!(Ry_>$m;uqe+i>s%@&NYP4X%z}daz{#i8W)BrY@5K=f$6T89><a~x4qST1IBk*
zh{1z|Mr>fd-`IZ9SB#3Abn;b)L~IvtMISx+UF;thHxgnIevYyxB#_~{zfad^w8$`C
zGY@S{8)IwA$jjT_yfs8An8Vk25Zg}WVMbgK^S!3vw@ZoIU~FJLA%tr#^)Q7J`&Pu?
z!Lwv9=Q|rYkgLF#^=3$gIdi8|>8PUXBGk%TiIE)a>Y|ZF2g4g+pNUt^gz2{IgGjU{
zeMYIQi?QQ&ejX$>Vn0gT+-8Rj6Gak0;rmEg1MXr97l#1C^DW#kSX+ne1MA^8_g=!?
zqmX39G%k#Vt|cq9K=SP^967#$@}fNC7WOmkOA^z*v8}5!(H1j_jPO)1sK_c_Dt$No
zh~FZ#3E`L0y%gs9nf^;f1BSWzkiugiV_89lC<6cOK7fs-li(DC2OF4kgRo`Z-@=0N
zvozKz%=zNY9=?F4E;39rPS@JWn-A(>x}QMKGcCF<lZn?{h^QC`+S@`_@)ov{6Z1}A
zH6U+PJw8`!Y)=ikgmamyk*Om*nZgHs2QX~HG-r%^7tf%9wx665c`)}1W_+@i%*<`*
zqLgcG?+O=V3V!s$`;(keMMENtV&Oq}s|c{Y9W6TLT*k-EswBs*bDY&9Ok3Xv!q&{R
zbVQ|Cun&wVL<$Uf)O*8zFs7%-oT;fK9#j-HT_G_aQ)SuOXn7sstQ2s0+uJJ~Z~C=M
znFk(B=}6;=CF{Zb08GIqYZsxt@}9Ds4V9pyr<=|*6S#*a@{64ai%VeaE)_>AEMf@k
z>u5k;aW%7B&=XE2S{(2C8sD^Xe1_JDAoRfkv@!%hW9$u25~D%03H=7X6tqdU#b<Ek
z<=T=D4P(t+c*C~N7DiBLs9ZS1k?ApsSA`h~GYy1;z`ze4_O`5thV>%bxOB!<{c+Ww
zH>u(eoXNw~TYyrgcO+6Hz=0VyqUQr!*CJw468lt##1cbMTYU+`!^l^`ej6CJps}Nd
z?9l^oa&m-s%uMnvkZdm@j)Y&r9l`aJQ0UykI%e!plj}ogtk5B|*yijP`8-5Jg=E^W
zv>^Wn6<8S${ldm7^O(maPqrCh{#t5X!_0}U?-L0p;RNOQq5{V>iz9^SH))3}hlEH@
z%v9Yy9fZwm;Yet{vztARW%V)rrxVKahmnwC2Wu)v8X8iSoY&*bT37D%L!V^bGBI;O
zTU92?Tka*tfi0W^IH$Mopl<2Uu{n11-)#RdzH0yAj@Da&cE&LqF=K{T)FM{pLxd(J
zDWPo=lPy!Ov$;~23e25GI{vL8+&`zNN?~mGCJ)1Ke?M~4H)8A8ALGf_zQAMiqX<9?
zMI5hhBy`?h)k}&44IX%88p3_-P<?R^jvY&9YP>*psF*iyRBkfvc!*{Y<-)d3S-Ze}
zf(8UrrZ?mt=I-1xWS=Lmg9knQvUc@#a6M*`Of!mMjs36fDFFf&!_|%iyfjjlTw+`{
z$I`V==Gw%n`up19(y5(ztKmEbnPsCv<p~>lxOvMTUx*azA2davjR7{E%E9d9XgE?y
ze{ppk9Wxea&N+xpN9*7pWY4Swl+y-F(B8w)Ib%n7Fgp*eEF@m!c%X4#S&KL?dfIAH
zP};&&hZc&jLCesi+}sLysDqi+LqZq(QG9kUc5V0^cmLr7{9+mRS;|qVT=R;F5U}A|
zqqNbFf2)ZrCt6^l1twbHwp-w)3cA3ghC7M?diuP4ykSb1VoBCneEP-OYaV18JQFc~
z%uDMYfY}uAF7~QOWHllODI19X61_;E`&;n}`&dx{JCE55W?@EX5ay*VN9O)FaQe)c
zm7a0xcPzh~YFGnKo+_rGPbk`L?gXN&TKc);*p@@Myf_)besP%RN1>*x3Fr3ip-0-A
zv0akhgc7T!02eM*Fvm8}3s1(qNnc^znRB{IGu|vYDF!5XM}AQu3eqpZfvlxXMU{kw
zQDG&aRruUM>{xpajdDf6Y@|&k!!UPv1>Qft33T_o=}!TjGGF<SEf%a;j?T(UIC3Uy
z+!Z!dd<-b@50S;Vks`V|Jy&3wy0%UVsHTMCB`^qb!}~bbQlVV;s~3{s<Un&nE8)|M
za5nCCBbzdX<uP5LKSSbi-PJBgKa+|1h4Zm6!4Ij^mLPNAUzr-LQh%EAd(P6;a4>Dd
zsZ;qdCVcQ3$#~q^SO3BGqIcQc-xpcy_M&+CG^PX!#~qI3ALwtz`9nvkfKvfuCFv`r
zQrCxb7fX>EK_d@C7@nAIi;oXv>TYTnj#Q>)a-gB0BD)0n2Tqg3$P*P8$`nD`mqJO{
z%tWmJ@EFd>6^1cbI3q%d7w_ZyDu#;dh$>gtSpT<%!m$NOiZ1UdL+jlw3?~goJUu^^
z7tSNStOs!gmnpP{k;sZ60_`=(OJ~1mP1wtf7o-AW<L(JJM?(@0DYc=1?SS9^W(K^e
zC=L>uCmaFe%XK<RPLK{%4vjx<$T2?5u#O(avD(uM<wI|d_RG^48R1N*?2TY%15k1c
zi*SECMKn(%OA1L1=w>@ePGHZ76w?2%!C8V+bj1oGjK6f{AE%g(ZgyM<1*SMWx6BW3
zZ64_}Dh&6}o60=yCTL{1K<<H~u=Ee1WBa-wa{Z0R{N{WZHiL^M7YEK3ATCS||In%U
z-6J#c-=7~Ia~`m0<vlPPZp6{D0&M3PT3*{mc%>S17tF(^lON%{PM*cghyREGcNa2M
zw<7<<L8z=L*i!q{x32#8xSK0Subd;t<UGY2O5<J+l?pdId-g#H`qyTT_JN_R4+2QI
zDfS2zb7bcgaV;Jx^_{&O^HfwBn_9*YkTUjjnH*CwB@tJ3eLb!>f8{j&u#n$o_D=K|
zRw}IW9*$1Tn;uRE{d1(8VsT4lin37VW!9}-ifx2x&32MOIVn06?*5bU+XtuPwXf3U
z?9pNzFctSakc#?>JmeR*)1XEJ;Vl_fs8lg6qL;Q2dl-&1DV90s11I6HKU;#|zqf7l
z{_<hY(!1bDM9R_TVjRvX!-~o#a#46Ab;c~}YyZIc^SZStcIAH{#K%J{iYPdBLeSWX
z0ZG?J^7DGeUVRJ7lcaDCjD9uxL3e26r|w%S!Q#SeNk{s*yAbA0v`_SG+?%o$>yD4i
z<&pEFQy1g@G-Kps=g`>dNa*evW_GBAe_A-RjNF0MnFq0fv9hCu<b3x(G@a@9a*<!!
z4jU_uJsQd}T*)y+!z4pQ+=3!F?{*?HvvKq?e-aWJP+^e}lGglO-JYZzEy4i%L}qt?
zxAb-w&W-GGYFDl=qtpJz?<=EPJHkqO=;9^JtD1=<U#20Pe-F}+zl~ETFLR(L;rW@#
zM4lL-yNT^Hf=C(jfYG}C&<EM`P03BrTu^|E7cL{&F9tpyK?tP6-z8ilTtBa<=p*}w
zTK129SN0Dl*+1IG>>osp5b<O^bRGxJ6d)>K7Snppz+>}v;NN@4_gP@lJlxN05=BfU
zR#V%<{Nf&1wkiqL+52%OYkcs85IGvqXf?<^xrX(YSnj^g4j8GKa&c-L%m$i}c`gIT
z%LW;1Etztz-_A;#+~AP@YbE4{C+^8E#ZnUbdIYGEHfJ{0A9;`SK-a1%_y3M0gswF7
zbRa+DFzqp(96P$!G9r|}k=ZV0JnRP-cL$O!W;4xgF%8{z9QRjRRCgyakPV}$-vBda
z%|cqD51Ki4w{6Zw6G=}0cZD8*j%5>O3n<>Q8%3+qknA6Tg{dA~pF45#*lA__GNhkq
z6H~ZyZQp$|7x7_!@ClxVUo78`x4$3XXKqomaNmP2s3^XK(uz)|z;nWqm5FG+d=|&E
z3ddE~%A8>u4C5Rc>O?*J%uGS+j5}rGkBJtTXn~0q_&;fZn=0s%d%LcJi}vaO_^X{6
zXVio|=AkuIxgaveodhsLL=qaJ=5iU?iVM&=HyUpAv<Ib3!hvtA7+)pnrgUCdB{ujv
zuOum+RMy_G%-M%syP}}>T#fj7ci|N`KV%nGD-csN3wnqM1B?#$M{D6uZ2ImXS=Ynp
zT^+p69dG=V#7>L1X|U`FPG;VN5L&m1RwEqo1ooaUA)Gdl5K7sM3~6n(!rt%qVtN9Z
zbOWMs->=?Aa8?dFhK!*OV{BQh4|-ZJBmL+BTpIR7BzNCSWP2~HZiRP*CuXgD88!jg
z=+>Acgq}|?GYXzG5E#-EB682wTi%Up%_H<s(B6-t`WU>*v^WliU09r3gbsRwd+F)3
zbo9og$x*PRb^cuDVTCC_hEzI+6gaZVYmgM>PXfPXc;|1fC~xe6dtej=Rzk!muxce%
z?dsYn-U4B3C)9FONV`%)$ukRcM>v+A!p?2G5ajzHB4;dNu5-fg^UIlHiyIsJ%*MeT
zk<1x9SbZ29Htt0bW9+#5D$2o?yZyi?=?{AI42RAj{d5T;?o5QGwJEs@PT**6K0O7F
zyr;lg4d70X+2${H(dr(AsbL{l`OG`;PR?TNxdFU`;xIKKkSVUJm`&gyGCCc}5^YH~
z`s2v0x)=U5GE80k3%u$!P04{8$UgI>1!h1IY&nia5~AXK%Y}8)n7Pp_vja74_aGwF
z5008joML)4Df#6^m^CXAYRe&%l$K&=y_TeRJ;*F5M0H~-)1xKfCx7@mCKZ-3Jsmv?
zk?{x*@S-uPlaOoaGgfFsIum%hK*(jT=><0ZLHS<z1g=2XIrb=&TiQ4yzbp&6C5?P{
zrk`5%YeFzf=s6{9n10i&gS)8I7Qu?1M}55FEx$X*lZMK*!O&Tp&8tGJUlL-{?#Fu$
z(deLVZ9@2K=>?7*68+dxY2N?YHrz2aj5&t=vG}pq;SzNg4c#QqQb!>*DI5j^4K(l^
z#J*ZH67G@2POzG{*cC#EWgp^;T_3E)<S0MPPmaXmhhBh3#4M(@>tuut;g2rlp@{HC
zQN|i<+H3=x(>ri9BZ*lY(hxuQZhUC%#d+NVV_Q#5W;!n~aw+7T%)qysvJo2Oj6A|(
zbm{n{o%Jn0rnRJhXi(9ufw~@&KCmGn;6-w7<drr;9qWs^tA9%me<?jfWOFBfLgxM*
zIFemS7&JXrwtjf#r6=L<XiDgJKZ&%}47#B)k@4M+zxLng<M=nGSG2Y;18;o1g?%n_
zSG<1uhLYd%U!E@>*O6mV4;r~@O3tF7`YvXN@W6~^FT&FI0wKhv2#R5xtbkJli6dBL
z8Y8TO!*OcuXE+oWh-GO>So)Kf=<%Jaw5P;^TQDra%`Y4g%&)#{!<#sCv=&a`4(KJ?
z=2nV^>$)}9`V(KR!QmKxETQ3S?%mJAna0wRnl{Fp8!^qLHxi?Q&~WKHeDkd_oN^D~
zNcv=is}~}A#!|fgwi9v)88@(ULt^428dc0toOKLaww+?XdZL&lW~1|lZKUfJeOs}G
z^z%mMlpN%b(etkve7a-$F(8gmFCTa8`g$XcLSC2_o`|2k_%UKg0@*^ML=$?cEv=c-
zEi@YLh86fn<!NlGwPe2Z8f@RVg{kFS5F0)PkN^4u#LUT|SC{)NW1U_7V-Vv$g!P~0
z;_!)KIB_{0)X)G$RM?5!GE9vLLqO6(eEOaT>e~mHYRw1@WqH{7`KRdeV0?2!84@4-
zIZ~PZLJT{@^yqpKt}{%UdjH5d_^&_5C<u<Bjw&45@c~K+I#J$9#*%cLu8~H%P`9Qi
zdj!{X71Iv(pWKT5`{Urx)S8Lh!{2mO6Cuz8Pjv_a)UN1hCtzBXa|KCtrL9M9cx%77
zA842zftj$8m3a!4X|V_jB|+^J!rTjc5$;UH(^xOM>>osq84R7ivVRbo-lFUudE@pE
zHyUiedwVS=MfzYivu8Z;+*=5naS;t9-xTzU1;b^0gQKCgYR1R!72x#Q3Z|o+kB1*y
z$#~|aC?uCaXID4*ZVbrN6M)Idflv|olYeFp>Q!FgZh-QNddz+H*CcwiL4Da-9NoAL
zRzz26>BYTKQO2FV50sOx%wyd#_640MDsOV>ockW>Cnh1-cP`>)--VB@Jdj`62qUJ$
zOXk|;Lu6mxnUmP`O(xVq!8()_cM~I$M>epIcl*=mX3Ind>1UC7z8X#;;p{HD<i{4y
z7ej^vkv0WtW`~$LJsQ<!JxKp<9WGN=8C`P5UUOTYb?6QUF|3{}LX58~+1+i?Nfg<s
zf^4#&sud+#y!dt@Y8?3FOH3ny@f}m6ao5xT1^?vp%yr()j0Pr3pLsHyK!|fczWMBL
zI7Y)FLQ?SBLl44YXa(}~FH?@{rp&_NDraV!VVt`u)2|hsJwOzqGa`vDlkH}rOtip6
z3rw`Y|8)x}3c6UOWuYHmD8Ok4(5kYB+BXa?j6G~GD??fJMGQMx>sY-htPN?52^H-`
zd37#Io4OG1?ur1WC34iBAaI>4J?||7zL1sZVM15!^Axy;;2R#y*!6!?aOlIzI~O7<
zW-8PX1B6m(DVzvXItfhb%8+w0kIErQ%M?X=e2stbFR2TKUnaG}h0__RVQiDH%@A@<
zT|{w%27cj82S?aA--`KTJ<!^G2yeb;jb|QRi5Mz)i~K?vn`)?dRa>g_uw%oQ_-@ll
z1cirlqZ@)7z3!X7+X-*0I}xZ3$2`VQ8_<B!LUIyGKeyoFWr~a=uvKy5+;Wk3HKrwd
zupTw_9q>;KL7G>P!r(hZ?}aIahk?#|r0?F3^`CBoUjWnCQHT!;aL1S5Y(}`NBN<VH
zF?rTPB#C8UxE=e}ZG+dW`OFc{G&&+tjtZ$f&UO+{zZffq`T2TfkmP`_?6VV_*D}<7
z0Qan1irCO3rjLqK$n%VhN&Lsr)zMgm3)#6avUa4fCmZke?soI<qy}zOmM<JTf(oW6
z3v)F_{&{+lF15gcG5Hz_H!`{mnFrou_%#0huQhn?iMx;#9)UY3I1We~aSU}dmSErJ
z^;rAaJ_NGQ21sn<?b?cs-?GhY?m{Fj^|KfHP@pqJS3?PQZ90ls53f?<XBDV1r^xkN
z<9hYyC>kb#5f5D~Z9;17AbN>b$lHGqPTn@mse2jA<~75|-wO3bB(I^6WX-x>Wd7rC
zJlycWUF@3#=4*{eP~hLDsxBPayam>=(<rdm@ZOwFly${&AoEL77YT#&m07I&B=bb(
z-jLFdy6+SN3sc~^GxBlcnhmhlE<sEPQ4q5OI1H#rvNi*kGqGmZF$y-0N+`vxzqhU}
zL^1ZKz`KF>_n>hqg&`10scEdAK(bZH9B#!n=BiftsuAFsk5}IQ0z*&TgQ;<3w_of-
zA)CMtdZ2n*E3sqeclhG%&Gc%85h6@M_L{ZodhI{*9p7K0H6sgr6aM_CchLEZdod>^
z0aMc!B0;$qa}1LyzqR5LE=qbN@>{6gHTe94FK7q>?p%<<9K&}Yisu>_i_wc-#`N7-
z^Y*Lg@ed@Tr&aOBDqIyxABbOjY+?V5%%^Mmz|<P9lybjavk}&s#fS>>!*mMSim^pQ
z<6FTgtlN7L!5$=}v`a?9v{^{-FlXDaH~3ydBu3JjyC5}f4%<YyFosZQsq>tQ$|-YL
z!!aR6r-Di^vUa2Ok+H$yPS~@XSt&Mc+CiR%<uu-fVy=fj6=AaIGaP2m_c@sN$O`2?
zG7tKgl_4U?6o3EITj*<l2=iu4!IZQ+5Kkz!gkG3%u1QEzRo)pC5mm%sU3#SStG{xa
zANaxX%9K{Y{(X4;H}9bPrTej9MlzCSEI>3_>E#)cX0Wri5Er=)z*-2N46%s{y*b05
zRxO>0sFb;cx>B?z)4MVEluIWMVD0<=Kov7ixR`Kou?@xRsGBz<ea3dt+u+2QEA1zJ
zrVKZw*>vY$yZ49^&to#z_<M++P*W@B9&fpbxBmVwbUbnorX@sR?xH}3|45oWBO10D
zMnDus-sxP_Gz`FvUSV^3G;6YU<E?*a@$iEykQ^C;1zhL*$dYcvJ~E<GRFb(L1qJnR
z@U~Wzt6{=*9fJL^YwczP*_dKZY62pYQW4Ecn=mC~>3I_zd7JC7pBb8(nHnxSk%qxo
zro7XU_6gFbk49c~SOV8Xjt(+n4>jjfLHUdemyooMtSz$gjhlBeuH}8%Mm(k^$44Ly
zU>n~5l;co?Rf}gJJaIZA*;l=771;UxPK2k-L4Xs9Wx3ysyVVbjhN}+?Ih<>V8&2&w
zi|qOHv1n2#lEb30BV#u-u6&8B;PW!-#d|+?|0q!Qk5=v<9;5q*fw2`0^Og9=YyU*o
z<M(4GGrgoO@MVm%pbxlCn2?FRw*$qQd8nvthqIR(x+`l@-`vN&FB-mq%wW#FNW-u_
zS-ZNMDzN9kPJI7)1_G$)4%5)sW;6+lQX*kSh4{?bL)cem!o20fjOAt}$1fbCuPw$r
z4HZSU@<<=Z8_CHdeK0akwRGTzM*3mR$6qq`99Xt674fMH5W!Fx1I`0O!tu`^+l#N?
zc@=Fwez4)*A@`Q?*wau96(Qo0|Bt=v0FbJ>+TYCX&TQ|!_g+{!EWH;ARS`u*G-_gt
zA!;-x`X{JKEHP0NO*9s;VFxv+NC)X1w)ft9o88&@zw>67oo%oSnrN~&k)4_M-hKC;
zbMNWr+{740f8^odk#A^J@Ithioa%95C?+3_@7J@okB%}^3kkNIJV6kBD@}NrgO4kY
zZQqXUStF6?Z-&fsX*jbtk8)ZJVQEyGI~a8q;$M%)H~blmKV6NPNux1k&O+`p;S+Rh
zkQQ|??IY*RDH_7d;6lelWp)XQ%NsDB1}&ceIuJMqG4N|y<glft2s^faiFaTAf(DE*
zhTHc$`EO;8!<`I6U>E|!5V#lwRBycThG7Sbl<+UY!)Z|^!|rM#)G;j_u2eKbn8(z>
zf>&8NdCUcrYOTT~q>1FhkRXQ7`2|KOMM$a(vXE2Ef-xkJ8Y@@k2nR?a5VA7T(4f~)
z&`?;7@$t@o4U~+u6x`EzhN@50cv_zms9^fB;D?@hUzI2*EI>g~J~Fe4pk;oYFsGq5
z5qyORm!+OX#QfWF|N5KZ(v*U~JiP($>}W!CIECL%f=sbYVKyaM8Bm9g!-6SedB(OE
z1)C037UtmikptL%=sZHHG^m6rvv1|BRGxk98nXy9Cnmt1-ifN>TpZrD1!+y*n8^I>
zwwe;oAsa1ZEN1<{VZb?2qA`#OTDyD0&x^-)G4I>U!-M(rYSdK}A&r*hz1#Ld6~yy@
zSr|ryMX6@o(U5^^n?x*_o<zZ0LUSvTa`GUyZ99sw3m3pi+krePh^6#cTF|>8f&h3v
zXf!=o=2S+q(^GiV6$MH`bFaZs7A4Nikxx&yb?`_`nvlpf6v(rS87x^{iK4<n1!ox<
z`7o!~=(2<|!c{{{cw<Et>?4=rk>A`6KUD$#`N9);>BC|c=;4ujof3qMPv%floQX=;
z1T3F10g-_`I)~$_DapsF;|KBio)h$3MN`odK}tLUDkT!aDr64kQn;03QnfTH9NYH=
zjumM!B`E<a=6+^oqz|}udx6E|D{)5egG7kLhI%oyuo|hSQhD|^&)8-PNN9KnJeX!s
zoOTWcJnBijj~Z1gi@#T4^u%e5(ujtai#_WzR3h!n3GDiGC!*#of+v&G3o}#bnWuur
zz33AV0$-kKUX+!Nf{Hq&vOZS;1xqsog@^JTObp3QW%LBkU8bO^2zCmrD2tWRgW!~i
z8T24qC<?!*(0Y{SW}~F0ksfB=FNnU3CSGHGQ!%pYJuq`ZB+ucNzGzpV;#*T%i2Q;w
z=7^IX5NAzQxyUyU#qzmHhz|CLot6f{vLc*4#n8#^2N}K?1r1@@Tm2o(uta$hPkBdk
z8S_WmFm=IPM&q!a0b#3atVLm77Shwwke*cmJK|r2H47fqm6FwnNegB%lsk%fnN%`)
zZ+6;Q?A)>yRSp5H)Xng7Mp(4cmmLro%HxJK#7jDlVWVfBMamQ$w3;C}A`I@1mMG0m
zMIMzTS)&3`<9Y>;Ps&8A`zTC_^kLXLFK|t&=#?zV$wHZ|3;t%%@TjJMU^rVDt4b?|
z6!xPMC5c+L&?||Asjn{LxJ%&Z8G?vFH?HkcWTa*D)G>Myc~=|hn*)n{hK6{fxv~(M
zIr%(}Nz47sXRET{7`XtmlX%Jn=T=>sizB;P&AQf=rzTLrsONExnVD#!QPx87rq>}Y
zs~F>_U4?iGyMD|&Hn139Wl0J0iVKmK!!^YD+Sm|IR0M|BI3pI{RJGUg9i^BsdodDY
z!{O!b#MF!i6y;|#Bs>i%sl~8y;t_gcgj2P!uv96=&Y8!(6H9#9^WJ(MznF}@Uwn!j
zy&L>!Ea(uWg2!cg`-Q;Y(^`RZen}ON+LN4Y**9wD6bHlG%^sC`>BuRqA$>Q4i&rp0
zd>v6$l#A@#5*|Rn!l8s|D{B;Z_yr=+*9Emj*~l!Qk%VIx<)EpIIIr@?+(nakENK|)
zDOjPEdBUYdMaU<NvNN*L#CjDPD(nIy4e4ohMk*?tqjA-=v4{u@fISn=>nqEUo0HBX
zCs`YXUU(}OPPqV6;XV2q=Gm%|G(C~;khBOX&c-VGH5JIs%7vZ^T1Q(e?0q<A7JC(5
zCEHq}A{b1;R2DJjh%thzh{tQ?aP5`1+9Eb07!EuxEjuLzl|1iTId7f(Ue|6-WiXWW
zG7QMiApdBfTFQ;BF>hx|Lp{dNU<8sE&lG39jH<jt__S~|HvQ?R2y<%2>kr+5zoxVz
zQc|pxO7%lq8FIH7BMNHHpGD}Z`|$8jS3+BU7!R+%2iXQHo*IeV?p$wO{Da0>`zRy-
z@F)Lxr9b|``I~d!>T2_mU82Q|`7;nhBZ&tqg|{`<p}eS=I4eMQRu1Y23rkC8bJNJ{
z;Oz%rPgi(((M#^;3N4G|RhHx-<@`x(-^KIb!y@27%{BYnIeO0@$Gz9jgR0^bo>*}U
zj{AkenQN}EA>8N{*O@soN+$^RT1HwWCo9MM2@XVoZPVKzia4<0DIkJ_GDh+UeYY?)
zvi?IYCN7wRvGLLHbRz9ySbJ{zIr5M#D7EsVoW^{4k@W~4DV~^|l8V*8d>(hN9FNr9
zAL7BEJpl_U!WLA7DFXIsifqVr>*^DXAa6(V=OSe03N6v#en$A+Lj0Jvm)Xx#9PgkS
z>7NDpLVI&L8m!0R$)_G*{g_(3`^uAe^7ZqK%3}zAXEaVXZ7(Nnufyco3lYcE8*dg;
z6*^H~Oq>-JA~!o574<sS>Ch5qjL`A)CC(^Ec~Oqy(Wh1vaB9j5keYG|yT3XJs}R;B
zp`od384YUO{oM2mv&JifML&HG|1=DNVF>(hKtT2S>#r*c`ld~r5Jrokse&#bAg!dA
zgu+A_<J}zGc@CumLtuL=*uqR$Wx9||o!$dGGAa+Af6GTHz&0Z@yMP?k4uOH56a;%e
z+YM+D7&KI%o2yHZQ>0`-c3<QtEeoH>kr+}8XQ)laA|9va#uAiNwBr|ldK=eG3&*)#
zALDoTZa`~P479v%%8ZopP{?YnD?t`R*G-$1s}GMkb5IJ5h;S@T{tEM~rT4X@x;y;d
z&La?>b_S#~1dg$p2=;dA2ZpA-_y1X<99rph$t$ktdjkumgogRE=nHe0X~`D6i3Fyc
zxypiQnRzAM&#)L-C_ND6>8Yrtf!K%2g%lk!f|U=lXjVpM9`Dy6fcf82<Y^#?D=?y<
z+R8lsEQas(ZfaIe4W!`SZP4j`$Hl*Q5GO6HOk2dG#%_J=pZM|o5y(Bd9gp4i0Lmg`
zV9VTGPWM8S@rYooXHI2$UfG4;<%5}{Cqlg`4k=y^QA7o2v`0{=X{^deK{*R7bv6w{
zXaij>kd~G;sI}YmT%W%bfGF`HBbTr-vqwmXCyTr>W1eT#R+MC;vW{o!Qt@)6yH-3>
zQtU^(5LNl<G@6)9j2ZSJ5q>-$IThs%JhIL|m`W@)T|QGsqflwJfhQOSQkXVYua+kz
z>ZH0f2jxVv3$HnMUh5j8z)A#CLo1b_?4qupmA{_;LF}IqCB{Y3<Jw@q$g^r1?##KK
zazzm_nU1}?58PQTSd4pOoRe{Y<4El_4j!(|a7|{aQxO(C3(hX5DPy6ME(NgiQZjl~
zW@b93s~5aj{LL87FE66UjNYT7G#(RXN)-5V|Ii4Yy=zo<6-Ai_jhgaol+_v)HG2x~
z@;;fa;GnWN3zhURy7~seiPj=hn0DcyqlL#j<`nmsnir!BY`GW|6wq*1P9c-8xM<Tl
zlP<YZp<uP+?1CyTIX7khsl>Za=t;<O8}r_C@>$1#^fcI&^psSOm{Mq0T}2LxE4#yO
zZQKLk?VzG3Jck6B(Nun}^bd-+Sa3^afNQmchSw@79o?&Wu;8b?6$_Fn6X<nuFkktc
z<V{;p;VGgrUPFDtv<W}(;}M_KRQWUEO(mL3mT*c}_>k$nKEYvB+!!q+aj}GfihQWC
zIu}Lc1+G5+EY2u3O^k*Pl5o^k6ri-K1y0@paJ8Yv$NpRDOOVgAsFk(LBe|&6@>d%9
zcz;t}1qw=PV8dfXy&SA~JIBNSBsxLG;~>-1vwQFs<Jhd=AH+hGRFJx0B%rdOp+HX@
zrL)jn@8;!C1E{R89-Dy=$$I74)MW*ks4$@$=60?K@MO_1j#X-nwAL0YsR+IM?fKmX
z0UVd5SsU`HjIwqie1k%0=%iYvpjp;YJBzkvGfIvX^2tAJxE~BON_5ncSC+CMS+5qq
z>KAzYCoAFDoQkLKzaL+hYvFB21y@2iE~5#}u(Z_UT(TYyKKT&F20LT##wYR2u0lk*
ziQ;eUcj){>jCnMk(+i))Iw9RVp=*##3kam+fRS+$fg$9*siur+Np)Q}bpN$=L%5$C
z@z3ZkRV~wrf|+`87beAf;MkT|aQ7eHLj(;V5>e296S+!<0vcGPD!E^9C~=}JE#59`
zn6!*IkTLquFwgpl%9zABq~V?^p;5W2SJ!@_;feuWIU-is2Q>0A3L~r14!?csVMJOr
zVC$=o;nkxmMDw__Rz{i(L~}*PXs#<pK`9NnjvnNf_Pp1CD^p5IjmXnm36nxbCtUl`
z3%F<51lAn*27g$67g8CC;H+oyOl2J!p(cArjN5WHdqHwKlOK9F6}<feVI%yk6K5^;
zmE6lcWjf_4jU&Q>y_J(y<Q)Uez4sdi`hI1w7^v6b--jVE41xaz2&i6r?KQ)$UAwSx
z<Hmj!bOC}eb_vNdkZ87DBvetnMoQr$<`9}%u;p11qD1*Op{oPJS2<2biV~5@%KeHE
zOf$cP=8DDLC?pK{c%KVGWF`Dcge@;tk`5m;1q-j50f&|%Z2tQOJoR=FkG8UAm{#9#
zCV9UZ1yMPlRze=-yOQ5YVQwH;h__A@QbmZR`%A_x@`8mZLlm?JdalpF`}sjU9+Jl{
zKu`oq4oEzeGDoF?vkb6TlM21!O_A{nEJZkNr=TkM67NYn@kPF6(rCe^k}F0R5omi{
zGP&!*N8TV|a^^j7Mj=j_JMqqSb2&H@A<LG9vizvX`NxgNkCx13QL!3)y77;A<nQMY
z7e((fan_rO%6PkuB<hVb3AOESKH@2+tfk;gfhXrKNA{Q#4ntYH7hrPXg902X-J)o5
z{dU4r#vn&~Df@+$mM`oV<vCGU={=M+BmxC-Cg}lf+&`l5%35rv7qAOw@|y*PXThUs
zKLT^<$BcN8wPAYg!tnS(_P061nN8tnToe64%Jj)We&+b)NGw@vO5<M%tyRLFMat`J
z$~d@Am5_b@W~_l=T&v&GNQ9UWRwBt7_osX}u2b&OK5L^B4|1<4frO(dxKeDgH!RJF
z19Re_jp4|$@4K222S%I?4F|?~clSj_ko%Qe?4nJ^CiKOqJk#4QG*difCfHR`k+l%T
zpKGG6Q<3TFv=8m#n$?qbO3Dh6rr@l5cJfruwJP6@H4jLSNUO!Wf90*ecW}4Le!3LS
zWbX*=?gwW^81Y1qe)f&f138XLZ*omM`Wl9ri09Gh`Rzmo0U~r+_F4yF+NB&C=Pmo!
zcunYtf=^j<LWjA(g*IOBgm(do{Ylk!dB5xhSwmesP~JV@X&CHRjEyp1rSXpxdF;kN
z$0D>sED9HE3U4!3<QJYLw5MIxb&qF^IFVNg{OHm~9ShLKto<35^Ek}B6T7g5-s9ZN
z42+n6Gj6%*8pH=W<J6}wv$n=dO31gG5iurPrAP0B`Jxz=Sa&72-kNnTTxm{jL~cnP
zX{~X7J^9B3@{c)#;vdSpi60}KFvXeCs4EJ!>Hb&b1EE`lTNf^cmI?n<WFF#$c@NMt
zAAEE|ETh(%aeUt)RO-lI6u!`NNA;Y$z}bicp)Eb{?7n8C*QQSz@gvR*g?3$JA8?WJ
z<P$p@a%@n=qw?}fbS!jh$+3&PVA^Nj&vMqf6qqxmC0C-9%6iqZ-cTS<Kky1o#NrhT
z;b+x~o$vh_>z~<8@45R0?|Glm$oeNdl{qU5%Y+t@PMGc+ql_syqZ}oBjGY<fsBf3w
zTnQ}t%y0OIVF(OE;C~7NlvF9_^dkGcAs`B-<m+ClDZ)(en}pdjM`<Wcgejx0Z1i-9
zVsqI|7%lVd(zDwz_onNydPaoNLf%$`lLx-W#<vc!N_%wY`KNu~BK%3lLlB6Rq=>nf
z3d>UjapiUf3zOk=HOWuA(k2W{sjS`EkuXq$$vgV%Uxak|PdxCZuv7%A{vP0Z7!uAn
zhHjiGV7el~i#?Fh(rcHIiB&7t<JS4{N=~2T(49NH3!7g0n)%3)N^WL<@`L=Q7?XyC
zGYVaNPJxjLJck5_eqpHqhk1J4>p*UkF?Ow!zK@BJV2r@%jkErq>0lUXf7?^mh9c1S
zeL;_3WPi8y8efkauJnrtsx&C{X)-{Qri?>28h@87?N_qzP5SNekpfv;57>6s#^`aM
z@}(CZL^<kvlk$t6I51f@7w$6RfY&bk(*K7`<ACEN3@_D>^scNK!m)eJ^@20uIuJeY
zT3^zKDeY<-kal&$Nbn{bzdtziKkLE%I+S;G-z#$vqryPr5t=p7zA>d|U85A^DoYV`
zHWQf2YXsi?b;gw}idz>QG4AJ!tvPltU^Lm2#%BhBdBVP(27<n=Ypfj01>V~CuHL^W
z2890ht#V^GoaL(-6;e^)%~)$BFmfbD#>QaYwZeDANT{J<gXwFBcjB$L--Q>CL*rq2
zJtCi!E{FJ!q~hoeJciS*9R>N>Jo=SKWYWbf-gPlpUM~Moh9NlXocpD4rYP*a(PO&k
zh!^H~!Mw2;A8$IuI`cq4NNA_gwx`P77X$+5erTt0ke9gD%|kA>52%^4h+Z9%)5@6w
zXf<f%T!tViak@bkf9yNAp1+84U6_%9$yeQpdzX&kk&3K>&tit@C-&j3H@5I(mN3$^
zULgUdUC8?HP1}3prStyY`&)L%rQ|5nR}Ft2hQKfch9U4>LqJi`zw4J=e*fYnzWk?^
zC)t7IhimLrs4UH64!s5Qu<MX|at~g3;{%>gEv0a!Fx(&-`x?}Jon3W>Ee@jR%f4kO
z@cDO)M<z0qG4(%j;*5ZHbZ}x`81n&h?c^LFWSsp5uf6#e^g-cp6pwKK5BL1_dx*21
z1HAaRe;3X!K8k<y)gj{Saz{55*be31%N@`6>AB0A@0D--OM_qt@3^cvTzUjobjRh4
z>2jXAqCsB9!+PeoI|TV7b?-L3uWG`mIObN{*(lLFhPDP)4&Q};KD!aR;1H%gFbd&Y
zimafDNIsy3jjg1o&>)~N=-&Lpmi(jX^7sc~@vXpLXc`R&AyGV)LWyAO8EJDFARqK2
zgBm}hJnZesXK9!a+=y-K-`ym-#D+(E)-$~%JJ*FZM_Q4Saujd9vk7HZ!Hj+ogUsMY
zHK;k8bT!CsO`aJ3I1GVd2n<8uyM@4@LfpUGA$<3}3-Oh(;2IWuE9**LQaK_d)SbCz
zQlQ1?(fsa5GW>?YLcoYK7C9<r(Jkih8k;qOLw%KkR^LW`*kH#pd;owzf4|=_1co6n
z41r+?Tmk~3)iIPiFFU_i9d&t5i=ePT7QrOH6}P$)BXf!VuHf=8|F{C+UE!lw64otY
z=DMm3<W_VgRG1+!IGA-B=pE-z{{g$f6+z=lU~xq+9)5Tj0>cpa!9&0p;!ew@5)Sx-
zpHBZ15TY)*VUF~cyD?u%T)awdo`kLsE9d<K?LYjh5oaB+_KHAE0GFxYj2;rn6CO6G
z{D+Y<{Q6-C3`1ZT0=*$X8ZDvR!93YS>Rxoq6rKE5$paSE`MX!MhWSTt<oqzcOFp>9
zISer##^6qgijX=J5^(X|ubLkQjD|-!41r+?{BJ<Oe5j#7KV+~Nb9MX6(fc9K>c4yJ
zO5SJ7Me@%6yQe=qyI}|nLtq#JKO6{9NtZm}?+e8q<{v+trPDQ1QOaBUt>@5n$MD}_
z2n<7D7y|$O5HOl4|NG}T;0(L0{QpaaiUEN!eBt{60fveiE0{BIfFlymfoX-{@5dl7
za}>(ljD@qr;dbd}ti&t(^Sj!k5>_b@8sA?3#xcv>lnTn1p5*Wy!w?vTz%T@cAutSq
zVF(OEU>E|!5Ktg6C<R@Pq>#cUQq}jOLN$eN^Ufo{E>-FL`v8AY&gF<DQG+@dH$5nQ
z;rlS~L63p+BR;xL-7$SkIm)W@coEazhd+O}5Rf%@Nkx!&X=TwR(<8P7j}|-%?FZjT
zK;(!bf?-fiDoM*RUxU4WP@VR=$?&bmPtuFXitlm^lyc-)ulN1CzA8s^aow2n2q>c=
zlCb2sF6XGq{>Ci*%aM;lL%WXllB&+_ZBndjaMaXDpST#L(*G3~4l~m8t})4)5yO@o
zZD@MLQt!D4Y~)xq3Ck8<AZaxH@-N;ehBIUTa>9u60_CW*zDM-+e#_v#OP>N0<sH($
zvi>`dlj{ej%KgeQnt}(ppGG03Zb?{he>gC%3C=-|0ptDhgrpA1dhI$QOzGr`{~=6e
z9;8`vG@%F%QtiCY+LQSb?#B7bIyK@<IemibVQ{PHiU<2&+kvc)OY)n4>+FsAmbEG}
z$i?>Mzjau{FB^uycMSodbyvzil;`D$P9xpEQkp$f{?P>@<>+Ri?VbC0ApUWIp9#O=
z{1y7%$!|=Ls_g3ba{h{g#dy4EPyR8GJR$chyorBhOv0OF{EB|tX8@h_iZWDp+Sa!#
zM|HhYk*7os5qXV23Vj@ef^I-fX)bEpE#c|r%%ees;CFRt@|P3spi)tsUjUuCGrT<<
z6=ay)@%{P8@vC^Cu&%C-XMUD)oKEl$c46@`0_FQN$}1cRH-n)awH0NkscYkWc@8=!
zXKrBibDmx5;^qch8%q|+GYX{tJ@dXouziP*E5dOnLF-R=Hhs07y}d$2#DA`wVXtdO
zLo<)t5rXmqZf#<C;L$lyX?gULhSRzD%4=(HRRnve%wb_+Nd|ZE`-cV^IZ8}N;%q!$
z*uuh^M0jXD^u3#gJ`z^7if0$AS*f|59bW42^1hv1@h_s}$<a#Xx#_5CHi4O?3&I23
zl;W61CG_&vldNR}&#^Ae&qZ~MN&hrT1crvflFVC_EYmB!CnbtmOI<mN%j#g|-~v}?
zJ3f86O_Xdk+M4T;mRr=L6Ib6rxY=1L<1yWL!p9T^k^$KT$;oKwe9F?<2Z0`T1Yb`j
zSD3l3xfVJ3B|Z9A*?S?(+kr<yiNYagOI#jIMd;zdh^Q_~MOmFG<m?d|?nNPlHP{H#
zuKq>Pko{0snTz6zW|J{lAUG_Dg|&FbJLhnrCyrl2zAH<zQE{R0rtvZV;4oNg%rA*E
zlb7`WkuOtmpu$%gDzZ^fdEs0-z}gj|K2Ch@!iW?|HP=-jzqqnTfA;SF@OH6Lj15;R
zg!bt8f9VVHEqm;H5q-t6n44P=-wa=GtVeZC6Et>qJW5S_nY-nG36aBd{cnK4#rQ`!
z`3D7~i|`L2tW<~%I#d=Hqo&aeE*`G1=l;1|16&vXs3-p@>W6=5$UpiTp1F@%$E&TW
z7HMh47trDt6bf5Qa~@4;lH9~SPlkh9p2SpA)pFrp`QRPE(>$y#$YYIlz<OL9^cN$~
zZ|4ae$!YmLo^tRa|8TTXWJgo}A##y{GG%sJ4oo-`EL^-1;^x5NO1+D29xg`gww8M2
z<`pT&h+cS!)*WGi&QzeqvCw`0h3iB7U`E=A_GV-!r}Y@u#dFXJ+zwV>9W!ewoXlF0
znQ{gNb$SY4J(YA>c<rstu=NPU=%nc|*Vp3siF5P<(gHx?v|ojOh!glWZ#UPPBkz0)
z=H7BA9-clC9aZUg?fHKqzr_sJTGn+JP4)-fL^*Fmi-iM5PaXp&doCk6AD7e8Q_)d#
z0TyMP!tSFf&^UR(-<?8}T$c?s+`bbC2xwaxDiuN0+T9o44pziR&vmA@(4s#19HM{x
z09MbQ2xoJiF-z~UT5X`AB@KW3zh_Zu=>SXaDN{w`I~nk|+9`>;T91s%b}YN@cC45Z
zhLrtZ<F#l14ucew>gSk4F-nw|7Gu=XyK&RJ5hy-)1h4+?Nz_L~LEEnDNh~j8tSTC*
zI_lEUk#IA9wQ2?|8jA4tBM;!LGZ_bk$jc!C<{As+oKMDl(v`*Yro*DX3SYeZ7krXc
z4Np7kf19^RtlCu>si^4i!jD$2LtI1<9H%p}3jhE>07*naRBXB4+8dE|<_KPVYb#_8
z`Z?S3Z1th;dqy)5cCD<mTyGJA$xClSWUxP+?QKcx+fbU9g3sUGgma}<2#fTEN-=))
zr7IjF8XB4r7&8@9=h#D6UygHUQqjm_Q0Ck$J$J?>5G<REhL+TnT=>UM#w~YVhX5}(
zSdo@DRTtyXt}pP}{<Ewi9?rgH+etbDXz(#ZV|4-Y%iVDGEq5R`#0TouI-EbgA8&8j
zi-6EDXgN@^^i$12MOqrX#>~J?i&nwklLk&JE#X(g`}g6E_jkh8FA&ZYm_+CpDlHX7
zs;(#vB@MQiw|or}qeI}xHKR7LDthusyz$!msCD#2gr}o|GZh7m=Bh#zR5W7h!fUZQ
zAp$NAcAQ@uinGq+qc=ApxycFPVIDAWf2kM+*H%}EidJV#p1TIoVF7UE{TAjO6!<dm
z)yErgG^HIOk)b>j`{IU`0kJ`#s%dm<Zzw~0K_!wFU57QJqIuk!h5}tZ($5^j2k#w#
zbFeGd3L#2CsW>YakyEe2f@`ATLkEC0g}j!=a-96;YrMbhD13rLVM9Stin<OJrUQ=i
zf8c`1WNj^V$jC2+nUg01UF@g`4{29@t58x=mMkM}5MyD`*lCzH!G-I$jK|&<u;{CJ
z*S^))4R<ySf$tFnBvPs43it>2E#-DqdjlHGJuz{15*)3`j|y^dCan;bRvLw;4Q0Zj
zoSX8G>-*s!Db3^`p+^2;+@LD*%nl?crz2p*Ox(3<9lRW^QD2^i9iM%OL&?pEi1vlP
zRUCpe_BK_bs8o-n`Ae}fG6Wt@_H@dCvTPpqZu=0sPG%w~B7*v&i4l3QlMsSIV{>Xs
zHe5r-V(slK5bWz=<R6qJPVC!(557JE|Bx`s%j6$4euy)uAT<>&ZZTN7?oNaUdMgIP
z_U3AwJG>X0w(f>kKrrkq#c-fkBFt(s)8G(03D?hCfj}P*8Wk+bi^$7PAHvIT{R?)U
zeuM!H$wQW-x+f}5f|`sJl)6Uax?6unImDZ%o#;_tRciF0i@+iT-L$D_&DYZ#(Q4&~
zg{!VdysH6UeE0@__viN!6B~in77BKq&779^^z;H;yW}RUSuq#t;$z52K90<KbGX{c
znWz6b#?UoJ)4u|cCjQ{;6M_+=#{flYyvKuKniwTuYbjgl`!!zEu@CAq1!(i;EaW?n
z!-LDNM{IyC+B!r@5*97Qg0i}<g$y$l+dg~;AAfcVo>6|3aYe8k)EkCx8Z-n5cODUw
zlG==`*WHLmrpBY-&==VB-iOdT1aSjW7{0LFL=hlv6bl}WWBN7<ypHhm^MJEe15^z)
z&|7v!rd)Vt&;$9;-ALM^oc&wX1~<<DL`6rUIrogBWDA{<CD&s*a=$2<HT7_E^Fw4*
zBrF>W=#?W)?!5m>CnN9eXfnXe&I94$5mcr%ux(SJ$)JWKiDTCxKSOzsnv6i~#J04?
z28VIX^+h;rXNKCQHX39^(Hlx{mv^riHp+6UvG~3xurMhR?hdxlkUq<<7i)0R#7USr
zcMd*$^{@Ega0P<>ot2RE!R~uAMlsaUh!k`CZ*c2XQ{m;HVMw+yJY4$kXrF``Gv{IV
zr<?KIKR$vtai^kH-4wrO78a;aO@ZH{+i>^o%i&E+dL50V&p-MEj+Ax4)m}rMF*sm}
zqN=a2gqi<nY`E`t7|mlCZOKG0g!NM~m@;)5<{sLCfBg1Y)cA$MiUux~g*j@oQeiu3
zHD0=92_m@1B!WuXwCOW2bLL*Wc>iN4^bUuUc?&Wd&2i_SHe!B)KeU9Wq;d(*RMB`g
zDQPO^&0B=cFFk|p+3oOgrRAOsey~mWQAKJVX5I4}ESWYAUQTw<YQ@`V#F<`jHfJ`r
zzV!<J`RRE?#Ra13%vp@Qc|A7Fp9X(d2O1+J8pU{j2k)OTYZkux=uQ0j%`Xudmw<*d
zr{R0U&#__oJOns15=BDk<bJLfgFXq<r_aGRUwwiX{=5lx5s^?c9NiRWm!1nb3zhS>
zvQ)HrPs3vm-Gs4G0W@N8T=Fo9+|+3^F=z6Z`1O-7G17s1%`+Ac{Nou+3ZueK4}#!K
z_C0qwrc9oW`ST9pjX!U|`DQ;FJ}B@Fa!p)%tpDA2h*P96zZMf0F2#czCZPJ<0lfR>
zD=4>Ol!WR^JXV(vx2gsm!lo?4RrBX#WTZbVHD;*GIf%!fd>Xk8F5vO!iXrUs-!S~-
zFa*Ab5Rj1cj#lKhSm2I+`3Ek^KPaWZN~6cw^m5$&_(NDYF^>DV8E1}e$LBkqL}Y9<
z^ewtU(`!lRswMv&xo#l-k<^2Kgp-Hs$;0^=l2hujdi`TqF=rfHX!I9yt)lWP{ATAT
zZ{qP6KPG>QM@!}zSj}FCzg&McB0XtnHY0uK%_1Wh7}+~*`fM!RvkiZL>_zBkIH#$5
z&`uFWcBApJ%`p_e{`12a9~DT&*^0*I?vVA#lc!?NynXoRAOC=S+hEw6>rth%#qGa;
z0n=!VaAZWChJ05P9<o79nluHo=gq;!=YEG1b)N8a&>}rM4>xak8B4~5Qt`J?B3dO~
zizvmUiBm9l-c|VM)#vg4kt!Mo9ET)F8CR-A0GOdDB?Xh#+>2F<XCaUVcx7B<zT(Zl
zXo$NR1+R*hQZ%+%z@CDhvx9`EiC0}1sWJaRyv=5`+C$|G7Ye&>j&`WbZbmzWQwz&5
zGF@KkCoFZ~{3GL_Rb=Rr6Cjotg=cr}Jz?Jxf}nUcI0j)xiZG;v`^nYrCOL22%1`I-
z0$q;ZmH$>3mmobk6)J|}HR#P486aWcCSnQiHRl8j9EXUD#vH}Ye)van_xgI<IetZW
z>%LFa54lfZW(=X|D|~jrQ=S?aHl}?J{P}{hrNbOHHny<0wM9umF3Ot;Pemxxz}bz8
zN#Z1U`}x4d@o7BsRvw}wZJ^T`Jr%NWdO}kf7{fXGj$IbH@qS~t{6IJp^;6EH@7av7
z5sO~e6(fwg@X*zF&!>#vjBCUcWwLujc$PlI3o5t}_;<CQeRusTYm{T7fY`hB#j(ly
zBw><}nXYjw&r%^Ug^>^wOD(T3Jjur|3?mW}VPBpMEyKK<%{8=?as3jeverB3>9V5N
zE`P^+czpj>)TX2&^p;=a;Wdk(rH8PU_7fRl_iVbx%{dw*Z*~COaEt<W*$)Dfp|>I8
zKzV`5GlCNtxE0eYILFr37U(3@oj;i?vynmZe(rT4mhxGIb;Vew>v?|<SSz^hi?e}n
zY`Vvk5O5prcY1hrtsDytR_&xyLJ%(tMDkqcgKac)wX#pOu9b!{&XQ|;pgxWDJ!H<N
z?P6&s0w+-pP472;HX2%6QQhu_@e@ZQ$jc68g$2kcD?w>#4J;kK5D^)H(D;eC@eWH`
zx*x>moDM|U)7v&kBdX932?cj@5~Yw{--==smz1Hhx)uhF9Rg^v4)Ke?qE)v*)l!2e
z-b_VQxE<QM6js@19Zj@}j=;~C%tJ84%0QzCTCLeD=S@KMGqt{x?fgaPb5T(RCeDh+
z*fFuNW0Xo^K_-ex+iM&2@C=GTM6e$w&07vjMsaNT&2wlEkA=1|8O34q@chjyF(%pv
zmAR?dcjy=ljG!1dGLDf)^KsiF^?3B5XVDTG3=540PLB4BK59X6ZYj#j%h<mTwr<`C
z3k^ZksHwQ=PBXOp@qQerw}H16qsj&aQ^6;_*s#L531blE?}p04d}IsGN~&OH>xS^i
zDELQ>p>>^>`qoFWIkO#&od1~dqY)kIgSx^zr00~Oq_`X%8V7_%Mk6RV4$E%54bau#
z*}Zk}<^7J14zRJZKtoL_;Zu%^sw(Kstl`V>_Aq)5<}SYxX1Y2&{NjE@MY=Lvaqvb4
z@$i`IE0JzL0l&ZZPRtk)OaoXMl24pKL0LW5o(qBlY22{2frb%m4V7iE47P?N<%$kn
z1M+jrP+n1vdh#e|?*N2{1Y+cr1;kkgp4qSgC7LkUnsI$x9syHUOaFU~tB!lLcP_Nh
z2PJpDbDbIg^x`+%3(8*XagVV-F@zh94ZKLg(4F7NXN0%PZ~RYc0m!=VYJF)}_kQo2
zGGcPAleVkLXEcl)sc&wFyH_w0$ZyJZC9t<pqr{B-hVoK7VR*5=y2hgX?J@QqSBCUu
z{LC2`ee=&5O<@5YojVp<OBfg-qM}JtP}(0o8_Ej?!macn(H9rwx`A*;zHKm=;7n){
zpX2nEX39H^zjXiG7iXLgX})o7^yN=-&764Z6F$tbDK(tByH-9)ib8MWl*knqWM9(M
z3u~?OS*b(OW2+1>Z{zL8xeBWi1xoUH2bL$g2Pb1N<9ZVLSk{!#?f&--FMLbuNZzi%
zmN4wwWL@|4#ttOc^~PEM>!(X@?|xTbd9d#&On(u6X&^52<~IHF4_ES!FySB52IL=N
zU@ty@9wV3DgIVJe;AE{)aAczhmP6e;#4jz;C0y5o<{zJxA~x6#6=%<4*1BKgdMc70
z^x&r+{|2Wr%NYeXT8a2tc+FZgk#9ZwO%;OZ0d-)Srk$;pX>9bQi;PfNiIxrxyaGcJ
z5#)pES6vGWeKXeoeiM0CC?f<0XB3uHwwk<R1SKrQIKsoVtqz67*(fO~L1j%V-2B53
z85)4GGZw*$&atQe@Fa@$<b8he7(XEqUYd5w3=F$3EkSu@BOJW~5fL7Ogb8z)=B37S
zzyA%=TZ2iH%oI7tjLzHAf&!G4GF`a78J3Jl3JVKGXxs$caJz;nJNMz^f)0e+4PK6t
zHKG_wic^rd@@H7LdIiE<H7F~}#<8=R)JN4!Ven*1au)?MT~+)nzgy7L(OTMwlENyq
zML5DnV-G)WLmdf%zOj~h5UsGbcY=!<8M)dWR%E^^ZjiFV64V#!q4sJ;Nlrd0=|Sq*
z%;Dwl4+ksy54pLy#5ynYFLNyNGIG(Zc0zEF2ij}Pm?vJ<{kYniijF78qDE6~2?|PU
zDPV*^(_DkBg7WVBw9W|eccJlsn^p*qyn^HIXl+1ddKy~z`NO~eLHSP}3Z!CWqcLts
z`7Rr)odRY?ei>YR{NY3;D?KZZcAl<g34bm!D~_YRoy19*f-qk-bMFdJSkaZy$WTfr
zANWxY&B`f+v!@T46opX}xQlnOwz?Fh<uyv@4pg39DNIRN*yS{Zr^tAOFbFfI@^R{u
zC-J~P4`1kR$~AZ5r)!rZ!Y2q*=3I?KZ>>jGM<nbi1PSe{t1LocdEJHk<%6@AKRt2`
z)$Os7Xi-8NigGhiPa!*y9xO^<$SE|z1vK#X_l2gt0_jE7uxIXBU44CL-wp^4bwzbf
zI;wR>Zs_h83`a|PI0Z(uw$+rCqP(h3QRqwuWaI1sZ&xSU7g>C$lerjl^+?GrhCSiz
zVPk>Z^c-|&XPUz&z@LgApXkJyyh1jLO5cK_%p5RXzq_fdh+|)6t?4MN3b9ww8d{W@
zhX$<^LcCm1MZvz@B*)j;lQ43$q;T7z<TE!C7Da^>loAYRqLotw!8WFYoU7n~D>C^m
zirN}01QI?L6bj^jB4p|HDrMZ16BXvG7*>?4Cc`6+bqoj1B}^SSUspTsKPu|o`$?W<
zXjMrbDr<Fc_YH)jmB6*TkHLI0FkPailuAi;9Y@f!*}8bb%f*p~1Fkh*lo$=!XJX8%
zFJo>~RX6kjhFJ;>yZ&QW#{OjAm@DDP`P|Pv8v9YKmc|XHToM?m)U;>{;mplOwF&%1
z<@XH?WSA1K((0=)NFv!>ynBt+$m*3p?9Y*lP-29yaXiXa?lFj=eUbjU#yn78BF<<S
zV%=O@fgCEBrp?;L3%>M<su;p`fp#fyl|q43kgLm(Z%Py8VKXcE`g)SVsFYl=ob$&}
zwm%!M{NeA&?S!)2Hu>7SaqlfF5fT!Eq?s$QdBcn7aEamiZ#B6~?(DybfyK$o6U8TX
z;FVLK;N#Et^t{n_6rO$QQA~;T!RYA=5V>&|TFjhaLB1gCLrVqm{FyYY`qg@j401rm
zsnc+dOn?>lc{}km5WIT6r2Cr)-&SfX>Y=4Lb>M4c?EDBXeRQDv27X%q$A4hijM0c0
zHx*OHZo^l_^~lOI!;SaPL}G+58Vb(it>^B>t6v#u*2;Sx#~s(tL;Qp(n7?o@Uj96b
zp?f|!_}T0DDq}BppERXyz?3!X@r#?ThJQ2#fJuw6?WOnO9TQEM4$iBg2;Z!OQjmHQ
zU!E(+JHPu2P77h`Y_aoi!msXHi)acS6K5{PM_)b3jU9kehO+M}KaJ1+_9S-a(|Xt0
ztVjI_&;9C7j0y?B_?e60|K7tuND!*e@5dXtyYcasuY2NJ?T^R*{tV`fW<=T4xfuK6
zrznO8O$JnKx;nksUk+7cr6yj>u=ba~#Kia@=&RGQ`Sn-ur;Xd7CLQi*Bw^)yCeH|h
zfvHzoXAQLVrTF^27xDVxtvH(7J$`xBHR~V8ZH&eX8#xve$Bn~tpC%(R(t~ug=Y1C&
zmhqZU3o5M?N}FmbkXzhyuSqQoZ`n7N8g71xonsg$X;EuK6|xJ;dUS8)=m8%OXBwDD
z$4NH~Z7nFw&PS8l9=08IsOub>C;6A9p$0hxM!Ch@$*ViWocyCOBaikd7x=l^p(rg!
zF}4_Aq-Jg=(@}(Wala^ZlC-yiLR)2%8vcQP(2@>ztqe*i6dZF<qtkHjdx3!{XrUZb
zP{?s}{WMTM6MoZ9LtzEsTWrWe3C(L_DEABD7`fSu9LETs2Ff7$CDlFVY|m5}FQ&qD
zDSSiSpIx1eXstrswpUS-&yaSN9lV2mU|*4e+qudbv<;jW$0D|h%WUmp$fcaA6G6bF
zS<v%kO;4Z5fx`DhrfR8cMstUnbfW{!t;YLp9Bg1g171yKXRa(GKOAkk^W!CLrIiM`
zmfpv|(U5FMnMk~M7i4M9MLe2y^{t2|PX<#wOj}hM^LOj385t*`tb!^Ij7l+ML{M3j
zh4MPqnlWjf{!Dja1c}t2ko76*VoROiss=tmA<$Am$to!Ae!_yXNuaADe<%f3`GA4Z
zc-7@pNEnsYwSKxDup__mbg`$>$KtfjP2`b<P&<1w(uVRxpSz7u$=YmX{ScAO9bJ9l
z?rg<7)zC4@FSoF?&r|j;Zgd1W5D#5%9r|AZlQ|=w7=FWjmy3E+S&iK}x-!y`X**=U
z3QX0E%2AWA73bujwnv>5?w24&_lWFH1}j5qD9@s-VqmJ48dY_j*~zw^2=%mNu60i5
zU@2P!1%jv;v>{mx0Uc_-kBZ{u@DEx%XuPgKr8)t>UO6B3O~p7{W`Wpn4?6w2X<=`i
zb`H3oKVrn-b1K&X`G*zx$MYuq!;k#KU>1rJi;4L4obgQ8GeheBEx6~--$J2~W>NUl
zE01GZqCckoXbC?0@Ie@YgV36D0`I+?ii011j-FLvfCrv^4NIpaAdV@~a~wC}80B#%
z(l#+#UaEBvXXXyf7jLY>*~7a~eCSIhQq1(Z+aLK0RxO-_h*3#M8odjD{q!3|2Q<^D
zZ~(_s4&nDNy=QtmuD$m$tXnY$A#r0cY4SYmd2K6=22nWi<=Z&?+DU9bnAPL{*rm7P
zk)N!gq8^3uQ<ma`r(Q)z?BL}n**|28C}|vl2bau2sH+9C&+Nuizjy%q%SrqBHi3jV
zjQVc`l$w@a)HkA{w2H(n9QMpJbSDrCoAmJT@PQYZniTwKBC%lXDwTBycE}<IBm$MK
z2IjaqAv7u$32`G365s_JhV3=imLuiVQS95d3#oOs2%v?fgG5si?Wvp_RKB=+?K~md
zIR5!ND0d%+JJ-%dbdVPeRG3pv9>LDt2T`H3LFCv4SQg}jto^(2Swk@HeDH3>1$k3z
zsm7_pJMh<yhp6N;k)KMZc>OzC>tW>+jioad!irvh@o<<iDWZeS<M1~p(Mqd@;zZ>f
z#jEBJGyy+Z;)}Y>6ukMb^SJh>_hV8Va~jk-Ci64=?yb*It8;_{J#oSpL@B9ANrlgh
zrC2w63d0rb(O6!9y$l`PQ{jRi&s|7QO(ssAJP&HAN@$3ZUtDX3F-ddb=k2WEqPD1j
zIe3TB!bNCC27Box+dm2grpo_Gv9xx+fK3)+2}d)AW$xMhJiO^$x?|ZS7H9H6^c*Mb
zKdh(Ky&b2{XCY?dTwHzoG(-h>l8NY)yv7}$e}L_0s_Bt(AaO7XGo+Si(YZ6%bvEoR
zbvU#AZDd%-;I{R*Aui0H`NhoFIC}zL{A(L*6X)WpB~HlC$%04BB#e%5L*AKhu;I_I
zW8qCd$D*V-3U-<JWb;NGEH=Q4l(2&qH;?c*Or!7{8yN<7=CQEg0f}x34j<Tuy=ODv
z;N;Cb#SY@E85-|IhTblNjzZ1mk9J_rEkC0-CknRO4&<jF$Ge-iqNLiI`7kVWL8OV-
zwymk0!k9Vct-KkN$Btr%sTJ!rFt_yZ9&Gva6XZ}B4x&J*Yi&e>br2TcI3IS*L)rQ2
z+Xz~^20x!Pno7A9BQlsW@Yz;uKh}uo5KB~58xS2g6){mBsIP57h&RL22{&t3f84%q
z9kmr>D6X!q8Yg${Mky6_W)sn&XvPp%4XC2g^URIhPZP%3LL)%%n1xvCXHTIo4abk2
zq7{WvE~3@32@RkefTVe=$ZWMJ%sPhMrz;rg!ZkkxEnThF!Xq*sGolj^LrbR{LvU$j
zKv7mQ4jtHwedn{`<m|;gMXNnuD#WFjxjtcYufeS|C&Ay%7WE}r*!t1?Xpl4tlP2aJ
zvAUNt%*TE5LOgia3>K1dMoU!@c7C=IwG12Rx&BF)n8MM{J)V-Q!;B@XF?Hft__;El
zn)^HL!~uNv>H9ce=Ywc>E$d+DVaDA4hWupc{6^#U`+q{E%pa=OTAbbgG5*3LRz!(1
zX5kGo_A6}$!dBobf>+P68+%ribJC=ov=lf;Ou&y<{S+g@1E^dZP{nn-pY-Cx-315^
zb|e19z$gQw5KWquoSco&kx97y`Wc9i2vj_bwUvdWcZYD`cm`>y0p30>c#(>dnEQgl
zqYzCpZXjOF+MBTVlTR>b{%rU=vkrp07slJ0A&c^e6DKYT^rf13bF_B$$UAu$XWK0h
z9u-ZRZxr**HP)y&bqqUCWn&}_b=KC*Ve>Y}SL{DP082bH<(`M@l+{=^eH2;?58?A;
zWw<eZv=Q#4yGFKjsZn%a7h%IO#1BQMcHq7(Z!${5n<*60idWQ3OD=NiB)&R!8WYDP
zz~9md(a{0e^vzdLMNdWqL-(vn%a0%3hYzWoM<*mu$*f0Epf5dK#FC{uM#o3v<<IC@
z*L&c|m)oIsa%UfIMxmPPP@}8B-oxKu!Hk*kjdSJRaDpd?*G2<d|2wyP?8Z;TF9pr;
zFmr7B&BKbo;};r<XeyMli|P2pnSZ^CQ;QcO#4{8guAUg<WsW0u4*25v4ajD(H}}AB
z#1P4{PBdCAP9NKXqgl%k89;%RO69m{E#7ajhBoaqk{db{!9&9NWqq1yt<iYu1in0x
z&ijI3MHq!dX>o)`uShC<vNtZ(R19A<)aL+U^Drtl5_all?EKfqigF$n5sHS|X4nNd
z)0@$TY|?9u4Z{&FEg5B3i9MgE!^R_+Fy$)Z+ESU5Cf1$Bdk2qW_N0kO^s<MiJNcr4
z5gG=8>`<DlOYR#p<|-y<!7pwsZdrLN5~G3@A-k@sm|>!au<uA3jYC2NxJS4WYe~l&
zJjdhq^|vD-G61wGp}Hgo`@YzQPj?=Hdw4XgTB>01jKuQUi(%fBkK%T3%$=SHLrpHW
z{PSrXcbbdG*3XBxjSfe4eoO)JEF!#^uc-;ZlC|?;(^iX}Z+wEex892>i7{}nAZ*SZ
z#@3JD#)&FVM3LXLlg8FtxM2!YNn9*DaPFIfD5ip_oaRA;R*TLavzJ^42i7XdI=vUi
zip&rjF&>K+`=Yt28NQ)z3ct}hxMSwcYmvkvn6wEpvY`>@_U%Oug<_4YVZtYw@Ch9`
zjYj(!hzs#$-liVqd1=_a?IV1~RG6?3XDUA8WgZIryYIClkC6X-x_+V@CoR~V{Cr?3
zwCnCm|J7JBx9HNhDR*$q57?~8+xlywV_)R4K94XJi1z4y+X#B`Zu1Go$-R3})S_lc
zE7Mu1WT)g*VFDw4?qZsYFN-W2+E_E>>~Z{S^J_R)?FoNTK4~0k(3vA-)I2ODPI3<J
z#g<AZ{PdT1Ab~~?a9y1_vJ=n0zMpiAg^y(qld!AJEa4SB0<&Vs!?`D29SJNtZVGAS
zJGg%@4rUZG^~WCmT-&o3Ek{R1I`*G9i`EYAN76!5w2N|~qb%-~I0K8wqbkcYv18YH
z*jY8A#U>Qj-?oNyGnDcmBZ}%-h&d+>M-LxCZfQ07kT4_D-ueYmpC9E3i9BhcQ!cks
zi}_2ro++z&(|9CByz<k}V9)L&$gi#^4Iy!(0iW(%q?T(iXVJ}=Dd}F!mu_pRMe@;u
z`0VX>QRo~9Z`QnMrja*x=4vcwSZy*r!lR~4N2r@QPVM;@o`35Y)~)|7CdK-pBI_tN
zZG3~ZT|8k+zAS8cFinXxAl?pru^r~lL*pM7G)SCJE5i*B{{~}%Y;kDw>!^ubjuE1p
z^}xztMq<3DUDtuIa94c#>;@E&&R>duOo+F{=2PiNxPBS@JlxP)Q-VWVUqP8q4C2i5
zP#-iJE|kmlG<rF?2IH#P4tU|L29}Iu+kUbcZX$zlj|;z1lLqOtPvX-fDVRJW8jf^y
z1;$%rTc(~N^Ab&Z>3N8;(8k>ZwK+%d%ey}$-F9OdBn{%E;cC)>mh-2u?Z`2Pp^ss7
zpaa5#sS}bL8ya%)>wAB}&o+wOLRS^VDyGwwpT#Hp_hZ`BB)Iz8BZ!7G;WQ$ro!a>|
z^foSvOrfk>%H|qI0G>L$8>i;XM=)i17e{A=^WGMUwu+%~5bHvsj2m+EF@7cM!T7nL
zzBC8#Klcds>--QN#0a@|M(nX@?nOzXfy5hz3N$htt%W(v_VzY#CD%Td<%RY4-Hb6*
zJaVXDJ$2Wwp&l6zM+)u+S_LX=t5C%W-nsTp%$pQPC9RdrgyF5^_U4*sjGsIMvuDr1
zyRSTr57TuBvY}eSP2XCd51rR6tXOpe?CZ0jt^+2lxfRhKmJAtfCvvq&N*asM06X0C
ze_!F|pD)Fd30~OCLT5{&M<dFI#fFGr8(V7{1!5`Cyo299{Tf^;@K}%t)}*H)aN#Xj
zxncp_wY0YKUJW-uLscpYj~vFS1`9aT+ArR(jJ!N7U2#8dSUDP*N54V*!gZK9B9Pwb
zHdr!bd_+PVA{nyx^kYw;+$Nk%lEQU*D#qP>KW<#UfE(NzO^pp`501cy5wVz&-h{Z3
zVaPoA1ya7)fktOnT0V6o&W$Lo^1-xuOOV9=MMcOx`3*8Zq4MwI4qI059>kjEbKQ;P
zsc#bjq?uKvm9Tsjd_1+NsAy7hOKb>#8@Di^CL7t@K-b*%Yh1f*Hhi79*f|F&EEgR=
z3S;TXh<xv5Jij@GbEiVY&{S1>5%LY=u;iNS;iay?`<3-rv+53v^s}QvOUaXA+!K?=
zF@(|z2inHtnpshbBGuAn4yjZ=f=Y&8#6+XllLjD5Z452;HmLZ`lQ@;ufQdi46~DOq
z8hG1N*`m>;O{{+uqY_4qWnSidO#19iyzuf{=<th0LuNYS=G~3!ub+all;a4Uv;vdI
zMJh#-v<&r6h>xe@9E9hdcm$~(A#kD9yS<Ixer-G+d1ei!kB_A%uN94~mTqntg%R}L
zj2$-;&;R3ZI9+KC|F&Xege}FgtFM6rKReSHFqy^NtZ9@|k?C6}#Iqn*1ib$GG`8h8
zp}fottCMD6>D&=y3_2RTEVzE?$&Ma}RpFx*@+yVJ>gnOG-}64s7y=PMtG;*=#Q-9z
zsbXF4gH0;#B+&U8*%-6%HY{B|6Rp_?@qE<*Y{_><w1g}h7!}Z(hT_n5ShMDO=J_^Y
z%ioXULt2SFnGfAM2<{@*>Z0Tnj9PXNetzc)c-qn+*M$Qz+7W_-x%07S>l=9f)em6d
z9R>|O_|>UtnEexmLeVl9KtZ#ip%I3V2qYwqKw6%7g+&-PPD(<{8qc4_sMYu2#+8d%
z1j`o9jg4pv4o3p_&G~djPAFP}sPb%lui8+KjJ9Ar@Yov6nL3KhvJFkm3@ImZPDmWf
zu&2@Z2Xm~yNYx|8)e_BR$#5FI1h@Y9CX8b>><(QM8g(kfkw%P4&V?EGhT@r!aJ@dT
z9{dlAe#zS<B+b&wlKWdr1&gC5th@H4eA19s<WpD~x8x4oym~RheVu3|YJiS3HZo=e
z#*Q6__z|1%=qo#E!KYwMVZefOJYU*|CF_2PRot6F?)F?$jZAwWO^u{CeEK}*S#QDr
zJ@yAQQb_V7!}6hE)yCo_Bw5rwq<U#PEPWVm-sv@xC7@^2iVSfm6Bn<5*4`N&GBu_M
z=xJb)s1I~B(DMqo*i#Xpe57X@M@_LF_L01iDMS|fI(m-0@W{#~G|V>R-9Np6nxNGb
z0FAGgVGO;=!}L;E*pi9Tf=#c2j(bd62MrqL6spDWpr<iQlFXH*#A}Hj?g${<4HRgz
z3W`yya)E8iY0SRqQOp<_%K9txG-|X|>JkuyO-c%Yleahdn_i(o5}GXOI<<_*FnB~M
zp?qrAE@*DmDTWc2UKxDj!VwRh0}CL!aSa-A#+4!qia6A|5@*7!*dz>B5n?z`OP@%@
z@cwq<xK)s>G&Q*WNqae8(h*tDdMaY#9k#HcvP*b~fFn2)6M~9G=(2g&D0=!dv<9@$
zV_TcTD2Y(+1$p>VjrT~@SXo*#rd}HeUm6A4YBF%(;J=h7?Pv({_w7K=fxS3IVK5>l
zR#6O;kXQ=QZWM-;5Lr%2If965y;!{ILDot*zKX`-rY7-flAaB5fpBFj%4cR-6XvY`
zIc``n524;p^lmm$n4;m1g5!i~b1?7tHazkB-=S0;PJ>G-yr<oQpR8Jpcos;~QJJOa
zfv6a!J&YTV(FyP1H5z$qifRxt?RqS_nl%)tnA9*3%F4=yN=hvJe1p))l)oTG@ToPn
z2=%A8OZNg^{9q597c9oI70cmBxhi_>e8eY4FcqW$YOM`o;}em<$cvZ%@n>u=r30cP
z3+b*?u;glbYMk4!DfcA4IaQ7*cNg|og8b%@_~XhO;BVc6&t5u&9gOUVzHSobn~7Z4
z#zIXh${3D8aait0qZJV<kxH$snvay7*qg0JoU;L^a~rYnhF@UKwF^jB?C5l9ApcM!
zDmGE!`7s~8hCjY>6cPS3%yX{|W?5Wn+59hGj~bB(&7|4TpZpvL>m3kl(Tohe4Q_d0
zJyy(>++ND0^-YSrn~*4S*BE@jS`Aw=Tj6Wffub5~TsM6&R!;T9enzk@3?GSDhG=%l
zz9Zsd5bEWQ4S#$EE_9HUojprMU_I{n$uhWG6aT`)8C@pc*%711DLj1lmW}w$bDNQP
z)iqeW>T1|j97I~?DV$*FuCJp+-AR-g<q{H8bGrT0)5~zvBiCSFtS8QX{Tklc{3U`G
zti!L?E<;R^yHYf|mBu8g5i&Aq8s^WQjd%WZGyZm#3aSH1fDq*&G@-Zp7DncJBw@ot
zD=}$Q6!Wmj_gfhGMcxxXdLnaz=i-A`|A1$=Rw3T67OB=@xbKm5STH4?1~~&78(L@>
zv_j&jF-(UViFg10M;s{EGJ?exlNKz)><D{CoiOEsv5fXKhD6ce5uJ1mA|m|xoDJfl
zLa85^<Cb6k8Ii1`)6vV>aPjG=ApAoj97Tq`z(4ZHKRo;6AEaL9TIh<C;WA|%mQEdk
zroyv$`I&cc!^Tw#ZNK>YE^?KG)Ih#%=^ud@MFt*#e^5XWEOlXOEa{;m>%%k@l;HTD
zI)wR|;Y@}L9=U%JBHXP|Th|18XVSefi-4~-!aOdT;pEn2Jrdzc8A1L>oSm!hz%=S;
zTBhQ3P)DdMXUYqC^Pu>RGA-fxO#4t<5)Uzy2g&nE=cMr*7pTbq^_-(#QnchRSs}R|
z|EH!RA4BayuiP&jQ$?eKL_AuvCPo*J6&ayx-u4VLmlP<8UJ;xLk7}yU0m@_HV5jA8
zjL4z9LGv+FusAMJPX^J<DD)JPA6~=!b~{Fr7p0!Zn@7n+7_sEe3ioOqjn@ZG8WnUQ
z+=?*JqhoGHE_@rlxVC}0VQmK3aM5aQDTs{@rnlD{t}ZMv<Qsx`FAdIXY&c-%HL`MF
zO+Cdl^D#<cm&$^4q^G1a?27pTBsz{P`ZRjtL<*u4v1)BS&fWAN>RBYfl7N&kQ2rs^
zpmd{k!R+f-!$DJzLp%4Pkhub8iY3DrdJ1C_8Z5w);sfz42+&%YgD<yyf!yj2Bu+>o
zBPH{lH5>B}9l<+?YN?pn!_wUrHJRsdWWP16^i8m1PVnf&xX!#R!D6=rAPBOc_mVUT
z{&Dn>R+ZrBj;%PERf&+;k(j`I!I*KAv1s`z{O+Z1U>D2<lC%alEMJ6hcXMQ(Ie_iE
zj-rXj8cm)t2Z@Oc@wPNB23d%ls&SJkfB9|^_^7Oz@uHf<M8vV7$auW{LX)xgXk9<h
zil=zKx!?**S~1R*#T%&T=@{3oBaUGx#hm4j;?~s*SQx2Esib@2ObTNeY!NeN0u2;V
zSb5#8sLsC+?^1{hqeYUAA}aozk0>{^z)re$9h@B6aBS~(<TCeA#iC}BQU1`G*-&Aj
zP-MxxGu9{A_13%eV$H(BnWHdztR2qo`5dntsK>Rptbl*yXiSgy!|}pXaHdDtthE|v
zj%6b^w~$pK%awed$oLT$IeHXkEnbDHvSK{NTvu<p0WAne$?x+FkB385IlZKx<48&g
zeCdTxnlynSljCvK)mLNhlkdYTA%GTBKWupJHY}JBPN6FcJNF-;XSN)+UO^Z)aWdk@
z&BaeyHT==LHlW3q1;n@})a>8Z%@<RX>```}9^@1B3OV~>^7N?)4UNP66-%)3jwj&h
zAC8pdqxjOif+1<G@C%E>n8Z+&)02NRHD3vRGADs-sbk2pnIr2Wkm$(bxMY*{VU9q1
z15TbkjI7E@NDPVOo|=j+hyMkGQxrX~W;}XAk9F(f;pyqXD3CMw<elRPWI?nx3h0Ar
z+>NN?Ds-Y(P)(1~sgx|_<rSj1v_jc~(G)00jvlFaaVkoR@$BX-1bdgjnMQ-_m(53z
z11o+W-;J+!pJa&&KTMl76Y+68j*9)tG{pwo(Nv0(aaZH<l}i!hWr?hF2l2&@BWTjF
z+BElXYyu02@J8_t%6`z&TXa6z4i7y=!^HG>G*{$f@4o#wpIr=%i!a7Zn2f{`({Srr
zJxcC)5X~;Z$Z8mYKdxDWBvu%$$vcZLx6^x6)q#;yXJ8x^20O-jme-MdOU&mLGzqsB
zr%4Tu6-haJ8s-fZs9@Zy82!2;7A(jJ%6NiA^rD~Qp4+cQu!|YapE!u4M^B=xNrlja
zF-RJdh=t46vN~!Ro__lj;$ppV=5!i<bo0ab>5W&x)mo2K8u5<NGhEWx4i_&!#3hVk
z_^mrUe4KE!fEH#)hAUnWf(UHVN<BU39UWotbGM;V+>GM#A{=SyKqPa?%{pjI;Mgy|
z`UoEi7&<D0qU0GUe04A+GQFVC2sM>AuDRt`SVW_k*3irdfuq<%MjFY`QOV~@Kc9~|
zKYkR`NBblD=oUQvSteGl?^}UZ#vmyPgE;|&R7X*0Wqq_Ul((zN`l2p^G!_C}>f*%E
zN2WNi&|nISnM$*>_g45*J3~w=>6L+jx$A-Ruwn6JXq&mF&X!~1v?OS(S;K?Vu4adV
zUoj*%WT&H;{7{;0*ev|!)@zXv<jT~hRIbhAXi+m=g^WW*Z?>$ci^0^$^F+WPJn2gB
zj-VCcNR;R%dMrW^K*dR;Zbca*OLmmClgALBEiDxGlwm0U$PO{5m%0va2xjz_rA0gP
zIky98t>jr0T@+!Ko&>@vJvXoOK`Sh}?&r9AN*tq*+K`rU0$-B}2Uu|Yj9!uxBQ8#o
zNOKt~TFKsAc#N7Od0bUS7HVy$<M)4ENJYVgdzRtWYu7L$X(wKO{!OS!t8CRY60l>5
zfDjl|G2%x0kQO#}Iqtu9J`L>lsLE!Z<M}g8;qevAg7Jny{;T0$OFy5E>DS$dyKi2~
zf`(KqP9MRsBPV$pO)FeHDS*e1L6EB}tM~fj*seUd$1cP}Yp%hVP$%Ru;$Y|Y-6&`<
zR}_HBN%TNcS#F~7=65fBgb)%WHI0~-tWQv|>tpQBw!qcbU4`Jdktok(<^4asgMgXK
z>7)q7C`O6+eXtkh+y}xx?A`q^%9<Y1W83ld!PBsG3B@!PZ;Ti@9c$M#A@hz$(H0_B
zF0Nx5F)b`aFru75Bc3!xUTY+dXbF>R21fshi#I2W5hGtWp}mRoV}Zko6Jk-9os7e$
zQ_;$BekE_2(L)&xW(cxpSmn7?%p`p7xMdk5*4i0Sum{KJIjwHfa4h4g2(gv{Bg^Va
z3h?5tQhIxwO{64cD1H8slLv;|zz|T8Flh{QWYshg=FT{int^NXdK}j+o(2b1EspHj
zjsr)MVNTvPZN_94WuAa*Z@UM1>mNdP3uRv_6y_rP(t#E`X)1J-eYbD@3fYzINEkm6
zV@AYaoXEaMkKx_Jm2@B2E4j32s=!I+x8~&+DLmYuwL?t8NGhx&FlXs?D9b&D=Z_TN
zh9#}=a1Wpn)gAllOUSz&Y1FA^T7Wr^=A{n6S`*OH_)fa2rt+14az7WO1LiDQj1eL3
zjI2Ay2-$-?9isu(j&6+Di2)sx(Ad*2*+e<nK{)^5oA3_MxSLs<fM*}Q1ye^dU57cx
zdl*r2E~}U+G~)SBz*t6P`iBPb9qHh5$DNO^#nRbhpl&Y3zHK{k?0g;^e4{XP)?`GE
zpM~qFET-K0Fv<g1$dvS2!{`ZpehNO^ydB{S=-i=0ZIpE-_HTU`N2~p?_Qtsgj-80B
zz5j--H1G#<D~j!NuuY8FVzB7JKSIerZd<AFkNS!fBmXdC1Q$D18yb*W6MzjjEQF6u
z3$|?9g!7~wT?XyJzPI1|L;)cW@8&ncr+V=Z=4uQ7ICh%*Ssem5Th^UnWK9jzUo#vv
zC``*^1knGHp9doQ_}8d#9)XGB4zREyve`tVQqs4$MzYh>6`e+YpZ25Mv3AZ_@(VT6
znc{XZxdncalGbU$%;d&PG~xY{zH^}w7ozy)Q??sVuePhBoU+0?W7(8XXn<D=71LRK
z;Vp!v)KI9U6Tv^ug=rpAsj?2)g@urWktiCA+i0CtPE08-KmqkvrFkaN@UseI=<I2(
zp{$p345cbBIMcY@WhTEcrhY$&4#B?Eq42V^g&EUp3(lOv!doB23L0XZB`t>f=?#VX
z#5EBm{~2v1J$n;Nf(tDh1$8y3WL}IbbBE;!oCy&wux6NSDRU>SSUoRrx*s|+ELjwQ
zp6+_)rdMPT+ur#4v-j{$&Mq80O;@;Sv%41Wy>|!vf=6KH{9rtLxD_$(H0`rl5MXVz
z@L4ltUZ0nY51!kAziv(Ge%dD<v%(2TwNEEPEuht9;vX;Eg?IPVD?<Eh+jD7Yx)+H)
z{#49^f!$jv;77s2(FfIMcHyxvKUDf#e9t3@8xco_Lxq9smH+S23=~Gi@?)#2#`b^x
zgG~8L8Z2lUpmqG=zcR0J0zIC7k%%ySij+(P?z(*j1tS|2XMBUl*4>K}B#aW8_r^C_
zc;oSV5lbqi2zl}r+1T<<c`=(5`H)Wr!k6e%HmEG{%W0<b==$8w;y<EE*EcjV7q<Wj
z%xnD7xNuh4<$^Bf5l5e7Lp5Oq<}fthS=)-8n_pq>=Em;Vzx)F3d+sf)7#~LEVI|(Y
z`zfZe#4<FCZ=hl$!m-xd6|T*h`0VAU@Z{Tvx}SB9#%RALj9isK1B4DanWyoGH{L_U
z0t<{BpNPPY0){2OgAaFPB7SZXd}4gy>Ep=f9SF<A`1@aXW6NiIy8G+=boYi=apR%{
z#8at>esd>MxmV+fvo;DV&K8Y0u;(p2@YH*>jv4vFJx{-h8)il^UoR4gB+hbN#QfWy
zzyvDIb;W7;=dW+ZCWfbXwHHQRh1WJbgqRWI@S`;|@XQ-u!#AEpfwB@KqmYrY3#;$`
zJzXcnb2CHKTAsqMuAdH9uShK7(RK%z6nOHB*YWAQM#1vtUp$L3V<J$We;&Vm?1iq^
zD1ZG!X{3^M*yGoMzsNBVwtj&)yYCRro=8T_RWx*PEeF_bV^K#lIGPoaBTmQ2;2>C2
zSvq!L7m^JYh$6Z=b&G*J@4V7Z@2tPSKk`xz;HBrcW6S6J`aHA#_iy03`AlOOF$Ur9
z?nPRm74G}xbcUR37+tdqzr5`M<k8qBc~oDXFTyjw{4t|TxPOH>vbXe%BK-KK*$n@;
z=Mh~;@YLF$;edhHX@J|fD+RAU{R<=n@Oj=ZYq>i2Jd$qs6()~~fvzeKZ$G&XfBUlP
z!nj`CxC1Y~{0Bz&jKb1e7U9w78AeKPeq4kf+8BoW_VbV9<?Si(;JSbJtyl2)E1NOv
z|Ji%*z$)wW@Bf{>2qc6A5<<uzKp0_zAR9r!i5pj~tyZnJwpu&2t>3n`+S*#JTDR5(
ziZVn%1VoV`BMBrS2_b_dgpj>=^1QD5<Uj<h_<erQf4`5CoSbu?`~J*pU+-%Y0ZQEe
zY&7uXjnCml2Nv}~!-pw#(;9vJ+N*e(z0{8o<FG&&1;o_6*idv;Mo-dxmtUx%;eOh`
zd8NL4|6h9D{YBmQn}6tz%jQ5EmuSuET_}D<3YxS;7hO185!`eCrj>g5XOF9xZ}klV
zRUNCp229ko3t~tKz|a$b;$!ado@R{K!gLR2SK{@!N=eaS7%C2G@7A{@_9_oh1_q9E
zZLtZ%Kr$zyrTCBnX5FBt|9p+&LkN4sTGuxeMDBLz<-2RO{OvaslQ;rvJ<HdUt+tp;
z93j+)FyQqoUvwVlL)h%8<|2w_z&Uy-lO1qwt$!pX!^rb=@+}>L$A}3r?W#T`jhv_X
zbEa#6Zx^B8JGGlqZkDg_iEG^lTI<kb@`tVc<hE-yDk@Mr-=s`j5ar)s_HC@2tNI0B
z!)Rb&Js1A`F<mobG-R(GBSj!aL=*r*K)t`o!+W)E#hco6poH+jL24%LApdynxeWlt
zDj{*Ovj^Q@=*EK?0Fg;gFVa=_-%IM&AT=I8qJ3N8T>#m6ljh^{8MwZ|_9bHmMG<e=
z-0%HblST|BU8s!I8B%(D;DZ<$+X?6Es?1i`=u7n1-~C;q!U61JZTbM<=Fe<zU;Q>{
z->)bfgSiJhI3GouopsJF5IX3G5?S5ZU$@-(J&mw^1`to1Ctl>yG2k8~XmC`7e*61>
zsAI@*@=-YxtU)0Lk6ZuvtQIH7U=$$g0mUJNGL%R6Z_x*Dy{>&#UhI3H9=w`!>yCvG
zcA{K#jGC(@OXd;^>!*V#(U0B-Dm;_b7Y|Myyii_aboJGVDsM3Pqd^NUS)`FM15{a%
zq5u5yF0IIICFjyZE8beI`~UicmMy@@I(@cgJil615K`DSx$VVz=THCC769%>%_pUY
zvyhecxn6jGg_iV<)wHRUz$2u10M`~lw;`+Sy$*~ATXoC5zvmDS0`z5tcIP+ij}Kn2
z*rf5g<l1q1VnwPn6~mBui`a*@#x5bn{esVU-}#K+5E=|$>$C|pWowt*@_S96IgSV-
z<e@)2rlY;M%V2URgIkMLpEO^WT)aSJ5@_d|cXan}d-0`L{;j+I{Gx7JFj0#xyIe1>
z`Mv7F)+s7;<HtCUIparV7)oSR_!;m2|8KZvz!Twn`^HsTnG;CrW|@wSy-+i!PgH;Z
zPVHT{T6g~DN$5FyqGGvTPCKr*fAt;G3dd^674!7qzgCe>8qT_7l!2f7w-@Nu=YOU*
zJ~`pYjIXXeqUZkgfTn_1kQASwj!miH0*$~B^M#%(*r2r=cAT*cKmY5Sx^7{TCKA!1
zwJTJ1;%W?l12sBffV#IqFOXvcNFxj*6~dwmTLJs>n43tBegLIoi}mHITq$<3l1L}*
zZYkHN@BL1HU0w+ARvbzk0L(lwiU`ahMCg#|;zT2z(TV@^hK07594ghdKYx%EnmA;&
zJgxZ0_w?L`8f;Q*+$I<Op0)T2_4lk+IQS1;^KJp`6B>;ov~}gHy8j=`)sHe+wI%Op
z)6oa@<PVo}&L(T=HDmSYdzH@F@*n~%ZQl+(zjB2x8BFBs7)nl-W$TGQzo24LZ|7V*
zL!)sp4IUGumJ}j-`*WaA8o~d|0D3<1f@hY&*Urj6a^WA#jei`2PKHBQaBgH5>ax53
zsF^sDj_!V6FTRHS1&)W~P(AH&^rYA6KJCME`15T1qr>4JA1a@{ph|yqGb*<5luir{
z?WGuLVlLJ)<P_h!qx#^De{1?rpLaetBfis+cRYxCs0mBb!_WOmQ-<`x5KW3F(RV=@
zkN0g{sTJ^>CS=rLjNiRPmUHOw>6btgy{Q<|SfYk-45=5)Rus<U!`t_07wN)LL^4}d
zB%_?JhO|cnmv^T{{q@zYmo8Eq^z&HiF75gJWA%$A^47fOtXVZ{HWfPkI5;-Jl#=c8
z^e6m0M2*GiTD9^Wt@%7%3B#i>7Qjr;siEs3lgk_6Q&L$?mJf`buOCjvXn|3t3}<v8
zxMh>$hapQ53T0@O4szpZH@93${{b+YiaK&=o8=wT2SG2I5UYXeM_ARDTQW6mHeq>j
zGZd9YSP0hhhT?jJ=z8@T+NredAL=O9jF5z}K;g#&sAlD902C`C#J0Iv9)yd8hN6ho
zpO6Q8z_rDgI4ov92n?xvHap7sxJeTUIc#w(8Le2ab5X3+FNy0>W-%NW9ZJ#8?F~xA
zv)#|9PJ0g)tF*XG;{n$g7|H$Z&bBgGatsxB+{B?+d{O|+!kg=SdiEdo9SaGoJesGy
z+qNla*aSd7TD2#&Qia(CYMT~M?nSV|6TFpOJPSo4f-ufTfR8>?N_Vg(P8f=JrxWkj
zTD-W+G-@#3LpZu4k~$AIWzU<=H7TExKfxY#^oM$(c_tWPDKjtC*}Hab?;`v-A<`U_
z_!-yhA4y9QHW^c3h+^a8H3$!QBg}j6t_|9T5OA%9Mhyv3)A2MNI9#Opi!WC{KQr26
z-D)_l^vptCG=b3YXhQaRFS7&hszVv9m*i%6wrKys&mHA_%*2T(Wr%mClyETirWI(y
zjYJSIzHU4!PD7|@XdwJW_3E;YS>t}rrx<E*zpH_=J!`fXI3JxyIN~63j?9QtjiBHK
zAc-jjhGy9(nm!gWUo4u1X4jS#YyYPoNwMQJDwMr=ppbm7JT=cr!owe|L1X=tl98bK
zlVCKIusDq8D8pldmR=R%gvNLyXr93PY2^dQ&cd1;lcX?VCB%ba45O`~4)6R2xZ;Kb
z#zd}beLnfv*{ZwZd`iZE;yksFD%09ECTaxdLrO_&A!HLv0sZsx>qFED0AXQc5RCC8
z5rbpr@Y}65-{}t0pzeI_%sQmGGsi1>6iRwxthQIT<E5|C+#AQy??L2qlS{bkpn}MM
z`Zh|a?SCuxRDCQ%d%wU6Nt-_JLh|<k)L~hx${U^1Z<cS{hn&(rk*s0$9E~th4BY?#
zKmbWZK~yED#<LCVfwXNpN`BD9QG+nL4bZ`@A1dXl%asrw%QE-m=mI=4J|=>BXwkv+
z9Vq8Mc)$@XI3x!TY}D?|8#Ee0-_DKs6D#WC^g|Tt0pu%{>`hZ@?MPj5C1FSqQ%~C$
zHGl*XoiHV4q=pPN8nIeqqla+4r%K4tUyE`+oUlKC2%}f(er?%jxwi?9C6xdZOHca0
zY8)eg&`5_EM(mNZpgn>$ceOE89xKyifW*d)zyMiu3?os7&I3$sa3~&L!mYeoE0vcE
zux53mBG^0AM+ND1ydam(OV)4<70t(Uw0ZrzD)xy3IhAw(Ld1r^n7Ud{XvOjj&h-!n
z5X<#!+io6&+xzH9dZwma`+%;TGf{zU<y!yAD!uVVlafY&`_XrpZkTl;VSt27(`P%<
zXEqEZq`GwBg-J;{C{me4HVEERe+7|~-X{oaGa_Oij8Y8<@W)GzDz~sz1CpTS2zKeD
z8~p5V*C=VAulB9opjWpvD1wLdmTxSV*^iv?cF!QqzG@jMHUW5u?YU?6#;AmuczozY
z!Qo707FLn-PA;@5Djq%nKsD$<NtAy2=*^l+E%3^`y?PG?x->Xm13U6y1%$jYNfzGL
z)?6arMR#i<N_Xv%J$n8hA8Pc^XAw5eTH+NRH3H?10`2{XG%$rVATXFTs$ljkX)C0*
zn1{I0!2WWx)$Bo}P+Oz=CQj2W7vV|A3U6aP+YoGfc;N|asV&u>eHlasS!p$gM4)W@
zaE3D#chQ|%ws5*a2%`IR+lL6Hc^Wl3$(26TGcC5(mV4e@o~fE?*X!0x=4((uoA!RZ
zQGdozIy@=Kp)cmS!^rPoGB|670Mo{v>jM~8TXnh0N<sWTT&q4KBGiVFY{AtxD9QjY
zzhMphJm86&w>uRJEPOMHG>x{#<68CHAGB(BAq*ryGr8BYB}BK(xJb7x-K9s@7HCv-
zKjwxIK@{CUykLEpQ&Z4=pr1a3w%1mcDK9GxeldhS@NFm;V}ukPEvnJn%g3RpM5whK
zkK3mAl?k5xgz=$jCYm6cbECFey|;Y5hGD>yM~IS%vhnw7)8U+bj&hznaXfA^01gje
z(Atrr3uX|uIdq6dEbOZ_Bpo{I>Pci6;jh}Ct!j8iheb9tS39fYhw<8sW#$C5^)@Ng
z!98iL)1e(}Aqw$;B?f_&5v){d31=-^r1>NIYe>>GE>3j@V$V2v2ArGNja<$7%wF0B
z$Qu3;Q5uhTJ~j1(6M|_US!u-hVXVE+Xd>fW*gJHDwXWB~YsPC7b?IBnbCr>uquC3I
z&>_Uy($~=JDXUbd+G%`m@F*<?$mnP?rSb^fw{vz{SL{(v^8!3<eSJBh@-<W<sOI^u
z!DBN4u(Va<=FTCUKTa(ta<zTS7vxj}7>EJUO|#thIoIE|H>&W~S{nd>a9==pjn;O;
zQ+)#lfzF(apQKbN8vv0EkEi_PG=+HA>dUQPz~9T1y!bLo(T3<m?jC*e$sxs%sy1=P
zG<nj$-Sn>pgPunNHK<#NkOB`OEDWqA6Ff<2X=x;6dhQei_crD1Sg*YW?M{d~%S<0E
zVv>LDO#tByAR=eB2KNtCTJ<sg>B$vDwIr*ONUK>(V*rc`1L(2{Wnw$l|7azHN8}w7
zqW$2cWaTeZBGC|u6DL4(R*{CnKv`}ZV@Y<Zc7I;3_|Yi%zV+IRf>%n~O&1DD#6ZJ@
zf<_~+R23i4myQwETj$STK(!?o53eX|I>o~SVSe3!GFMlt-D3z|%|yQa3Dr%a^dmq$
zM+epF-8Y}pzn5og>8MDDZZs7ZY7hG^gvB#`!2w+yzJTw&tZbBXYq7(U6wp)+P6PB5
zCH>p2Gd$6=QT5&vH8VM0L0+vokoKvgoF`A109FuDVf57pOqcA>*Q&dJEYhU3gi?M3
z2L`LLEKQp~*`g3|I-;R-@!;X4yuVSoSE9h-XdD4>sNQ{wew%EH(SxWPYxPYX;5c;1
zkA4Tlu+}BySTj7wp9Y_F3GvRQ=lsKDp0DK}09YFTXzKPudG%5w#$~Uum+G2JXQBWX
zfZy}J8lu4nIp$J+=?Z<e)6e<l_qlm)oy*Vo;pQJrXXPKeNVglV0~J;kz(s>Sk=0Ld
zm!I5wExMq$_I|Kh%fBelq`Lvn#8cfrm^23Z)P!@>7f?*JT=->QDoO>)mq`rj<B!Ty
zq4Kg4bfshR9X_0i?NRE_=-s)zVf~_Fl}{Uy6OFp#;X44SjZuAZhSt3Qj#8T<Ns|Vn
zs@Ei$>=5KfdQW<xuKw{~v<R89qw<J0VxZcbM`Q`2V=HN5mcC_Tp+AvyeN4_Fimlr_
z09}@-%laxE%{vT8rytmCM%r1Go;CUAUbCmT-a_XMBzg>Kna2<B)T7V6Ky;J00tSrK
z_20b#JeImTwZKpU0zhH32lQ%o;QabPjMFMrTU8?u!o)*D0E@%A=MS(}hUNLro{W+m
zt&yS44)Dq`K;@3*ki!<=hypTI_uR2W<3|ls)F4pa9YXC|WIqPn*#yvvaW4AmqV_11
zJky9;S97f@D+(C!5Op_mFDDiQi#2%}!TrKGFj(LD4-rr0pp6?7?1H(sLX;iJ5yo$$
z@#qN&<E8!OYy`_uG@F|V2mh_vbQlV@<yKT=w<|OWf^4gd^~1v~KF;}|&xD#O93=yx
zzZ1_xO+gNjlA$1~vY58<yinH4$x`5kPH39@q5R!OZszd#7A=4FQ9XrFZY`jHxT1z7
zqU>X>abNo_exF=L2)Jzm109P@OjeZjmkngUT40`82pwOd#Qyz_k9i+t{NNBQjtCv0
z@w)kkzxm1&ZQpePv)UJLU@KP3q<O*Gg3vtxfILU4XU)3FiLFKu65$@60ID^@(5(5Q
zyal-L=4@c=0SCZB7GbXZVC`JiCkq9({OZubL+JaLs;lnQ!fWr)qFECGZ-_>@4svZ5
z*3Qx!ye#b0ZebJU^wEqq-Q)x|scc^}4nhP#B<*%AWmenQJ)a(c7|MrA0)!_T)r}gv
zNRK_b&|!MEL#>CV+!(-^2cdz{5uu8Oi0`AzHm$87_#Vr`v)O;JgU;UC(ySl^Hn-T~
zVbq+dF%MmvS=QJy&H?7WY;r=z=vN;?o;dyt`G3_c_cS~yN;{VCP{CrnM8k(@-hv5Q
z`M0$^dzh{pIYyCqbFw$3=)nHN>K~f~9W-)tu8q0Gl6#`GUKcI9Qx^cVVrvjWj<H!%
zZ9m%@xN9bN*D;Xz{)Bwu?ZTi|ix&z+I_Df%bq3I(Aj&Rlx9vk`*aPt1p<Hxvu4)SM
zP>=zLLwL5vt#z!_m4klr+#sy?Bah6&M8k&9A`;<z`e=qITYlHdd77oy%MW8tKgIE`
z<(*lcd2LA<U4WTk5b=QK1rGEi$BufMD7n72sHTXV>(u<0>w42sXK(ggYxi#E8GS&T
zh5D)6e*p7(pB7o)oiP}Fuycme)R#WD_MybqPzeP+7%yia%%U14>O}D|(sPEZ86&3|
zGHf4skV2A_)Q8t*q;fsbej94~ag_6rdAI5QTQ31<s6#tGTBTn<^}a@qpQt*F6p44<
ztXpon45f~}im}D|aC);bd7(hI*5>H(UuNnf@FC)`@HSIp`I$c+qSNpY0AkLkp6DES
z!u9w34CCkk{r0i<)gO^*<_!;O!PJqe&EKt;{^u11#Kr4GelEIC&xzp>w4$^e$AA$G
z4#`h${DEf1kq?XTcXI8&(ka`2fGK)6Xv2FiYtx@z1CVhP$~LI(ejPC0Qr-H?$3SmS
zR%cbNmcRaj*6bs~W~{%CkXLDgbj>+nTV2um-A$LkWUKVndv7`();Z8Q6Z%@gD=4o9
zh>nPhcEaNZC&cT^70)YsJ<($HZRlul80O8=#3_sPz#pUZ@)J+!t*t4#{-&Sldl#SY
zSll~c6lb4PNDw@+s$`$C-`}7zA`=V?*$Yp2-O)^yjlNc|+;OFb5A@dlEo=0fM_*Iy
zh*4PTPn}^~7ay$rZME6T_g<)f-g|>4#P?Os?hSh4k*72e><2raMqRt{qI;ROu;74R
z`t!q_8?ZL|4W=aDY|Wbkj>zplCUR+@e)aIv@;m=xU3$|GG<|5FZ&(9DyfKQe#9+U-
zhWtPH08ma!{dfh@DQi)f;_;58Q}4D#NyCPcx)h~xa}eO){sYkmA><%-;W3KWrkDS&
zot7U9KTHC7cLC`I$qTR1ucD*$PXLnA8bh4?Pj|T~1FEuAIM-Ii2;$}gJ$uAx0se6X
zhlUfn?}fp(RAmK6<P|$iElq@g8zVq*BCiGP0&@PkQK)^!D?E@I@f|Gy+ZD4%00V@s
zx3bTSJ~W`PRs$U!!P)c=9E<_o_7V57z0^ZjyP=mR#9Jhs)9h^XA{eDEI39J$yzUqh
zTUwh{n3koW7#IJroKUZDkhbf|f%OVkNS^^jJ9!ZG)N>!#-M;asc~%|l7b5rEX*Ki)
zdQ6%D7RVs_X>bgF>^s{<!65_KbL@`b!MYOsoTWVPtF#E7PwFCWe##03DRRm6x_vfz
z^T=9m3GDPv89!qSEBD}L{qW{rXvCONkO!0<Y7wUs>~v4k1q!>2`s{~rz-%EEuC2fN
zBL?=4`f={b_$}|#J(b2wPx8>7hZ=@S!?A1)ntF{cTQoyKaj{NBixc$%&9{A0j&e|U
zWS`z%^?}xeB`a>>4T|$G){4EET7gi0=Y+)yogSkDo9p$<<$JU+Vw{4P+^l}bck8{3
zJzBG)20Q;WU3uvR8Z<INp%LH*L5tmVzK*c(YK*NPXK&Tx8U84;oB*R+Fff0Q2BqhQ
z>$gw-PF~Z8X-~?>`orJ<DgT5~3L*mM2;-O<G{7<bwbfJsI*ezM^V|XUP+O<TCe=D#
zQ^&a=tQ`d*n%$I(%)}}&woX+b`yz5uj)`z(NA|UT^~k=grLh8wR4K(Ua`BCKYyOP!
ziZvci9q5xh+&qsaYjS-o_uYG>oHV5_g$*6%%yQrONu)LmB$6Zy{HGDTx1IEpaHkqh
zc216(S-ZxHbY&bSdg%O7l)PM~|HMqvvGQsMIsV}Oty;IO052m7{~2j2$QYbs<KfoK
z@Z(_AIDhej_3Or<EdL4c{Xq6n69BZk*{?(hp%)kii&ZmdCz3NXdIXVO25#t&kt9Ue
znQ)wxtBQ0QPtIKj=emQm9d)Lu4A6)U4;*Uj4Qn<}c2eDY&5;!8lBG0t^jJh}y)8;9
zbjTQK{SkQ%BvOPud9KYsAHRuzy!7~^+LT=nFn6FHd~lgY4)D^>Rjc&+mMmz0tO~l0
zI`X2;RyT%2YmJ0qGt{0j@Pa{n<=immr)YtF<5WAvKeAMC8vpnk{3C*W>Om<qGrU+c
z+Ry>e-moj~(xUT{RGs^UUZhN#224_*A2=acfSb?*@|mk4yzUOfU<NIH>xuu-yQC*}
zlkz+Hyjk$f8CrDXof;G!sYibDsJfD%DJHx0bj_j--6ff=k0&FKdp_^|%arXvq6QBf
zJff>e6<M};iUZua@|{=o#?})6kVXP(2(@$XG4^>tpcmI4(wv)qq3>NehgjW8eYol^
zJ+*Qd2Jlh%7CBU0Zxyh@;7=KAUe_PDt#oqNqY@`-{=C_mI%$@E^m|g}o_w52FNLJ?
zhMw}GpS}iXp<Q-=({~Mrstyq4)z3bpRl9t^#_=cmG)?z6Qdwb0^blo-W~-{IN)Zv}
z)9BTVzyV&O4vKT&G3=o9N;oBUf(J*asqCQg4(-*k6SpCghblVJ5XalGJk{th6WZV%
ztKZ*wn=YI+29UT073LmRZWUHeh>IPPq_JZaYG!Vp(YqPXGs~b&Xk&R10z1H348k?H
zHXsf#|8bUj8qAr`&1!}W)$_K!=(&KtoZj4Db^kv7^HYBt88i<F<v93lc7&77eShBQ
zDx=d2!eL7OJ)sNCSz{S}KrLl6u*&4QL&a#PcgOnBi2yesDvlB>cFauRrkqF5265i_
zmOoC8i@9<vxDemWZELhMua>Y$%Zmh8puA9<-d>|)SiF)!?>%s+U4i|v+Cq@(Dvm3=
z@R*Y$%~G6d%!8Ge>8z#fSvzI-$VV||Y|G)S8H#8Fd95JhK)2j$W=o7R>b^^_32+l-
zXRUhX({5*8EkCp7B4Q<7{1g4?h6^<q{DA7>d>zTmR}1$xfh;6E21{40`_3nS+xU!;
z+cTZ+JNM?!{B{iogMhgJ$Ar6{gK$(<08RpF9Db}*?;T^HP`2{bM~c)L$_;z=s}T*e
zusJHBb5rJ~_hdN(adoNp6!!GU-80t(|I42a;D~2`^=;p!?HTzRpBSmQN%J(S`vX;x
zzA%dXZLfwC+E3VPF37K=$tyKxey+`Gkrx%`>ilKD*1b1gNIA7YEb;lu%{YQ_g?-8z
z^e0zslqr$4PV#*{hDCA?Y>z?I4WSoLk_R45XW2oq@v$@C)3^m8$b&Er63N26dKjV?
zIcjbJk!|bbz`EzpS(l^%@<$*t16f7U5fELQ@DRApjr+dNn&ItjWKHYhrlIT-xndnK
zjo;abE&+!qVFxJCwuf_X_YM9(_-*^+Y>g>U@F2p#P1&tGuH2klx`a31JxKc!-!<0T
z4R};>jlF@#HoSjNjyS>$6X&XYVV}_$fCFCUOsMNEd%iD5*Xo=M06fX@zwdgD9PF=s
zc+MYr=ot+iPTn06E}nrX3*{wBKai>t!p^>4nKZ?M8Xe8YRL#8gr>s^NbgLT+d0A;8
z!aC7W$8+`BhWB*nr+?G^*U#1TIg51N_LX`qkI>8|AW@^pt=+Ie)fkSZ4GMK|MFI&M
zx1n2}VL0b`JmJtygc=^nN>@O&AKqK`g>!yg<2v=zrJ(!xv?z}<pxz#MI52)-i_yV@
zxw`I0f7ExD&Xjjcsos6%1wFrdKe?PET>10%Jd|%ct`?RyeAf3gBQZn=KYv$Gyt~tR
zgejW#c_-fRd|W?)!x$@bY65=?hUK~%3xD&~k=LKr2XF7!W6wXRMH7>B@g)~%BMN$D
zNg<vduukx8bixD99D?<qBf{RTsmfC;>uW|NFX(e~`5_gDT&QP%ev2j~hLXmzUVnMu
zuj-3e%<k95_iWCrKW0GeCVJvX8GvEW-%dE>U=<Kv{p5YW;~w#f_BA8JDO&9MW(PH0
z5%dsjeE9vuC!oIH{)m42-NoR;%-78e-_SFKwaU*<mv1GJ9pA79zO2FFVk)_Z+c-_2
zCD-*}9(*7Q10f~{PjFt2rRngAN=@scR9K%-;S*;hE_!U=X#j_#suFl6qU_#$?lHZ&
z`ASLz-bJYXL~@`n(I<a;opO9hjM_H22~N$bHSjnl!VEQm<n1|tjLzygZ#u7n2Z3bG
z<O8{7q6d4{PL?x!{IcsTdZ~j73D7IAU>EKTZ+l?i!}1PYz(K+H{0S8+1d+O~kzPaA
zIxr~nSgvvXvtQPFM&x<~fA@S$0R!^CIWyLM<4>kUwSuPX+}ys$GdsbcaKa>={bKP#
zJvXr5c4y}e*K6ZD*4)o^{y6#L<b|$xdM<LmYkjomGb$Hd@dU}5ywaQ_g(xi;H{BQ7
zx4F)Ees^QFz^GJnA=N@s!T&L1_t))f=z<R(rs9I5NQ^|?ph%pppS>UB>S*tu^bx*X
zizC5$+RZ)vf`Z^JOuBnK(DTkZQfH>zrk~t-wMM~vni$8C+(V@L@J!a)mk5|-(4rk0
z(z_EZs;#TjnAorMg+J_U`QUKz^TlZrIz#u~^?lN+hGS>1aq?+TP?ZHM1!o)Y*LXn4
zpck$=HJO(4hJxYe-9^;lasA-8ztBZ9NTVxC(aZn-llJ;0Xf%qXMW)!gdiw=93KkYn
z<S2L<G>Of~Z9Fh;QW2%5nLTQIr3*RN)oJ%nGc;M@Jd3t=jlq7pURghaS%*Audluew
zukN{ip$7K%S7lMY4rk;NJ;Ofc1_L5vl{7q>%ZX&+tJ`-RQRC7X0HqDlMT}!0Q6rtK
zLs6ZF660gIJrQXk5%QxqIXNYupo1zu$V;z0`x4PZ*XTS#%STQ)Pnc!c&uv<;Z~<vX
z8};``{w+TgG0$F#n0ZbcaT2zf;Lo6Z3~aY2(vX2T@@tosJ)3L}Fyw2)QDWcXvEy?7
zwu|^_zb*8-%ZvLE3F8f2w9<N(>S7LW7q*XGg+YLyWi>61jA~=H=000Jf4c7Iy19MD
zX%4-`*k`yTEK<*&;``7qb3&O>$mYBqrOrU`++TNn+x7FC_{SxeU#!jdKdvb^{8IBK
zk!xRckd&3x8b4z;srwk$PMC~t6a?M@jY`0=7EYLJ1!2*yLGV=0W*uqC&h}zD%o*yB
z@@#$U-JJ29<R6pH;2$MD{KGMv@m-VgtqM_xN2u;vI)S}euGg0TM=3;@3?wqb3)$HN
zLs1iQ3)B?43-7U+<eXGi6g#qouXm}Q`D~-s<=(G<{pf0qojhAN+^|W1dLMj?SYpy_
zL(|%u>s8YNt+h#nCIw+oa%oV`$0vW-88K!1Aj<Z|xBW_YEL(u{qe^SvdQE?OV~a);
z72N9Z3D;`a6!??#nt71O#VhV1t?d%ZF}G0eb2%W1E5UIZ?W9<{9_)IItyvqm9mT~J
z98uE9_C2o;SA3?2pZ>j;o}Z}2mn_lVosXytL!;r<7{50>8CwJM)19JSz1F~%g7a*;
zj+Ke7B3*Oi&Uz&gy@T~u0V4^qKmNJA`;esW2lKFYrXXZW2NF+Lrm7mM)kJP<bleci
zqWG)y04Q@`9@IQ)ZcZd;B6cX>$E(=XT<PSTTs-?c(3Hoht0qrxzwm-qZ`rM*1{#5v
zrlEbZURm)!3L`wih-mMf>>L<Y#XLdHpvU7*VPClx&FQ4|)BU2??^9xb&zm*@*4!u0
z^%s-k{O|mA@{*p<+=tbDsr|NVoITR>+TJ^L2iHqaJaK{$w_+71vK=7+Lj)m;Cl3UQ
zx}nxu`)YTLNx;k?<e3@i&gbqQ@gqiJ$%i9;^$YHg&+WH+fb7buBRfHd{k+%r{2lO)
zQAqop7Tb$3QeO=-<d8KSJEVXA`1d-1a1!JNiofv-Ybf3Z1lY}??u+FSdEfDXnU-#Q
z#~5*QWf2UmFXM7Q-1XH{KToaRsVh#sa(~+Cc+~r9$%Qki3GJ(t%`5c6KVH&dz;9X!
zpRT98&`tL|%7Kb!eQX|2-NXILeXmo`cE6&(#);A^q4O5y09ekBSM=cXyRj?>0dfej
z>uKH?sDh~-?m^g2f0nfOKC>~qpXFTMs}Ihm>G|+0*Vs4so-5?>tDm@^>Hcl~KY0az
zcsda%?oVC6J2@u<<Ab&Qo$VlfPN1}30{MddwJBh#Mn?8g+2M3$e!f{Aaf#}}@N=%s
zh*DQavl@NI=;DiJlEX<T)24Uy!jrEk7v|W}(EtJJ)^~sM7)Y9N+!g|ENSsG2ohTDP
z<PosYE<t#6tqlWU(7mnW4#6cfxfyRZ>Lk}Q?^qMvYiZ+ehatG<!2VWN6%2rL1}II*
z{#Cl~iFHo+R3ipNck=={G?*OIzLYqsEKN`|;feS&pqwyOX8~R7X0lKd459b3IqT-W
z(f@<rRGv{SiV8M;C+u?F6F=ALd~$C6s$fX&k%D`{KJ-KZ>>t{XUXTh>UE4$K&6<qW
z^(#^|m$0YI;N^foG9Zf@XgY=$b})+GufD6~xKL$${*j)1;4h$XS$Q#+V&WGCXkp4H
z`q#eooMq<4-G#G`opaCuy&^R!n92{<Oc3=aB-xsmcMK&$N|dtofGRJYt|7s}iikC%
zFG2(Wc77;}3%~m-ExOIRYtM4g4G%mUXrKDY<4=<!k&O`h7d`RoM>Jv>sUvvAoppDf
zaM}-B%y#fP2t6M<Y#`of4-5_2y5f$9blb9p3hb!VIy~opdux-DlE;9^jX}bPR4lG*
zBtN+ZBk*9Ng%-^RnYJWdE1rB>5u-sph8724K(TU^<`FgQKw_qxG>C2lNn_H1CfM*^
z93`?+DVv#pxKJ(QV;E-`<*odcilHKZD<vo);U_onY2A1JM9VWA5+BN@StLnYO@Ruk
z&(l9%zEe|44LP!RtDd?0cRC)Ms7Tm!8=fF%KA1Zvx~IJgV``Wl`PY4#Hzh%(hj#15
z-#n-<f`@1n1gq7;uzM!ZjJ3A?&3Ng@n%UbPp1~TE<gL#>NLBe&b2NhR;K&hEiEK^L
zhc7>_x0_MKzRG^TIttdnm$}Fz)bH}|PT@S6ua!J&fa6T~wh%u1I#PtHYK?B7Xi+I7
zj!xyGboIkvXS~syFl=l!OqHi5V(8ePnnH;BagC1~se#dhoM*cCi+NN0`VCMNDR*{r
z4?JXb<i%PDvI#s+>lt(-k$)zfo2Q}+iPhIJ5cb;STxaD`Aw1j*4;!)umvg=GUXzPl
z^WBc|F%s{3Ah-ZdWEDKa^(21wJks^xv;VX?cSHcLbw8bGbBq_RFWWOs@9@Sz;aC!!
zS>rvk+}T*@m-XKk&@~TSM{~aIx`p%isW*9L<FGgQz8w{2!TH$v?GVCN=B-*CjK77*
zx8=WnP@mR^ay}}At=iQvbXv%`ElwXir~xG}D&+&#6<YDN%|1eDGkVk~F}BCkM+a;i
zXH1qwL6{Kl>f6bCvUfZK`Y4V_I^z=_oW<taDpCkYb;NsZLovJTq0%lr@N@QU!a$WA
z+^T>7^=YM?s3xki76rId$@A~l6Uj+VUo4ohcRM+P(;jWt*k`t0b!BZ@ylgh<iX+r=
z99)8Dp4G-ZndIRcZZkLZyFl-(0?P*)!~*dx_!!^zfsYktWayF~Jftfxnhvcm)_bqK
zq&0i}G<K91JdB6}c%u!Z6Jg#2c@JdaE(7eavtxH*zKpAdQ9Z@W_AQZBu2ajKJ<X=n
zb+!AQUW4JoW{h)P!6*@nW|GqAq224>0;usd9f5~;QE{oZwp({R_yi><hqCQZ7);YY
zxLe2SuTePI8cDN*6_YRkJA0P)9`(~gQXDT_JX!sSeCw*i7*bwJB<VoM@zYhgU%&l%
zuIA4lr2*v0$0brF1qEqnOf0Drmr^aMNWcBp9*r4Abg4ze+PNg!Eh-kHzO5;(-6Cfz
zF*!Kr)GmP{nCA^Sp$!kTd9=-NLs*P^z$bs3QPo*EOx`x9wtiM2f>}N3jkSx79)&We
zj>2iF9N(f@f81rEt^eGFz22hC^uwp_(l45*C%pY9je+)&MG7zuI^}T&|HwGSKZr~q
zp+d2V)M+PTATWBge)jBo7w@s>*`0W1TQiYI;L^PH#YOD<b-MZok1F1b?$E7n<e#CD
zMEJ1&c5Ye#LaoBc<32Zb4;#mS;~zWVA4v}XXh*>}9q^E9nrhVm{T@7eo`$k_Q#Zb)
zXE1yxCM6Kb0ZsvHh6KVhdl&}KH`;3R<;z$cEfab&VVsXrKHRUen-*y>-r}hEc$Cs?
z#u#OmPp&r*&1ek?Lf5cc5q(-!R^?Lmt@Az2rXzr~h4cgITCnV=y7SgcaRM}H)0?m9
zk$=Ch5mf1ECz8`ecWlh2oP%w0@Sw&n#He@oHHyLTx?|M}{rN91D*@%a8yVR(%k~h~
z%;+;;?8X=vXkb>n7cw3hA@wlM!aOzN2n)lw9v0tU8Aa^`PUFBtCif(bH8wID^6grK
z<~nE>Mm-N_U&CXFs#bfNYEi~J;Q#!gQho;Wd~-e;Y4HR^&)fcRl`-Pev$QU9)F96)
zt8Z`s7zZU-Vtb?ZR~>V%E<aJMwh7d29gby%e2`kaC^m@r@F<0%1m$nvp?8;WR6B=t
zOcYG5@u1TB0EmUvkhoSZWrLrZk&`_z;^gV%dYt9MlUJR3ar#xK|Lt7KJ-rdo{2-n-
zCYwBKMo_)T1F#%nKP<G4(ZKn{Mslk4etqg)zHP<tN>3o15JCvR%>WAns{aTfC;^(y
z7zsBECDhl(WC!C^V*v>J$Sn-ickX%s3sMBn>jq6TQ|sS+Q%wT_2{23jsW1HRUeP_@
zAOz+9gclA8cQ$|#4-2`m5;B%&EJ|0kVT3{cQ)ndNOjF4jJ~}{&5rZI%cIa3qzSBZj
zt>ve=%Obev#Ob50nGfq~)<Z`CI9t!p_MZDLy@qFuDsX|~!h-=HE!E!D&uV8$e<hEO
zL^+{$@qs$^Bh<{uwCj$i?%{rQwkJ7H-~$hYbhQ_wOd_=VQZg!W0w3*>0bsQgZf$$Q
zp}icKCV)|V2yLR$73Ak@@7k;LCp+c5=jx9MA^hVV%Z)SU5s0_UkGVX14!UJTAs5jJ
z`<Cs=MigTIC|`t3?t$>p4x_RZ3U8lCDf?aR&bm?~X2ojQq-F99Nm3B?h|?*Rvi?XL
zmPEqxo4BESa-5!h{SRBej`lJIM$A?$YtVS2M0-AXPG43;P=DJ{z+1r^Xi?vATLT7T
za|*Fj_6e-VgwArt`V5S9O85CANE=U;e+88wOjNhlGmphwk73X1WA<UMsV&<v7+Odc
zJ5GJ^z<uT1w3ncCFc1`|h=h?`u}*REaW<*UsjV6F#+hqwYRBrz9g53P%o>OWA#9wG
z_j-ij0a(HPz$s`$I2lYCHp3KfsJD?K>()8W5m$b@#(hn(ZYAHh6}lVNKSTo&{8Z3H
z?dic7i%glahm-n(5yG-*0_|*q$I(<tB>+0tci;fP0SBpef2-mW2(!i|Z)dZMIc#Vo
z0)#zf2<VN+QO><)>hFKJN0Wy~DwncXPu=r-oftMsVJOPxy|EB7S9B8NXT43}bsqn<
zqhfNPb>^99f!23punQy<-14Zc&z7&%*a$EhX%#1%H|a=KCzPLa5XS~A+Ri}FL%L9Q
zeS`WbDiYe%Uat~LS9K6oP>(VMdL?)CcIffRd!6wD#%V?~o^OcV=?5~k<mLzUy_+w?
zqg1brZ~j}4ytoFd^f)#@d(5Jk?ABp^DyTR{2xgOp#)qm4W&N{tZ)<(!U`>l?)Cr&S
zsb`KsfiqWu;+e+0C!(m=Q>Mok!-pSAlxvIF*)%~DKY=p`yu=8Fc?4kt#hdqh$WQ;q
zIpqQ;qN0{Emu18C=jZOn6CS7H)UA5%;a@5}2IPGNQbT$-fv1yxnP;e*aM+xDFa7EX
zj2<(_093a}uRiuut*wtz@?b*KN!_qDG5TVB#rBj{e+WX+bSb5rF$^s2O&G>7bl9LQ
zG`pLK7o&6@tgoTmdK(}9y2jjiG53S{ynsDgS>FK$2XxJG-<V!UG!6TVU7<)Y1$-@v
zhqcZ^@}hX0Gp8mr`I_<3n$4es0&eqWy>`84zpNQ1%!$K?b@9(1*JV>k1H;sX)kr(v
zeN8XCvqb|(#=&##J^Ib{jpY@fPxCBthx_71Xx-E#zp)sUdd^KZVKnumLz$yOZRGG{
zQL$<yzCnab5nk-=7emBKw<-w{j}M5Tgdi0eAZDFilqlsHTx8ukr{>HeXU8H>{MbiB
zCPt}gD`XLx-a)vsH{SU`3}2q?rv}1E4b0BQW>Ts}nD|2ryqQmPsC44Zr-me6T4X8&
zSmReGn|%-JX_Zy%BpOma<dMn+{zM8`1db#1@(Fta!Lg0;8801pUJOWaJ{pz~314K7
zIpf3AZ>cmW<i_*6y38<PwbM^N?W`}lS!X+!ZoS(HSv3WPy>+su+~4YRTbhR#5iK2f
z{;YXIzZw8$8u8r+bTbC}5O9k;xF+kf^?Lq^7v+(ZL{uhu|A2wXXQmPdf-K)suk=m-
z(YmywM9PdL|9Xh>)6x`~Fc?oB6%3rY{AySK+sfHzW;Cd83MSfzs8KxL>ASb+g%7uD
z=*Y1eJ7}mnY7QtfHUVFZ8Fz5X*lhDfFJ#Q}^b8GN@_pTM^<sq+=Kk@@*Y&SeJ1GN7
z-n0Sg!XK^S&mlUs0>uZuL?oFnX&(SuaBlDpLpy9nqQN;be~*=DwU`e3zjt%`+WU=@
zubntHjO(M|;X!IRUZgMIe_nfQBbA(BUi)UE>)<J2Va%mtwy|TwgAOQz(5*>CIYrO;
zfg)I=CTPpnqC`!fG($7SfI~DO5TMaxDhDXkpEQ;Z(g-~Q!-!(=P{x;^tD5Q{K_0KG
zx^0~P`225m(ZnH&8#aZo=a1AxXt^(Rsiibafzy}i#_O*H+?MCFm&-_-edQN_P$>?o
z0Qk7+CLRIEH#tS5OhUu`Llu@d3M=<c`NrXGgvPmmmAfcwY&;41f_+hoGT8)Jun*;R
zeLG3XGg^$n+Y>LeJOQf=HIPu|7a7=Tr_JffDLm^7n<P%0(c;zyd56U6$G^TEP%^M!
z2<y(@vq`IW<zm?C!_n<gR#BYI*z8YK+nM~syzwrcX$A;W1FfRap-gg~jeqbQXZ!Nl
z4rJ^81EU<Td3`0}`&ER)pQs`AxG(yWkD9ZxHRSqx^j&aB!a!m7K+ZX`i+%g((`w_~
z_#n?(k1QJQocIR{Whc4h1NhG2W0la#E=2|VsU+)5t$h1M#UxFXZ}S0lCmVJJ1&3=Z
z0d`K|0WqX0H$um-rND2zjrZD|LB~2Qbg^f=yvh0*rDmt|o+motiFQ9NUG@W=4{tWQ
z*2ekx@SlIs+A>c?Qj*MM7Ml-W=wSxR`K-(D*4^K~hEl>E+V<Y7`k#Nir4h*hQX|_t
zC1Bx~7NFt7^~X@ohn&Am58U@1C5HQJ?}oSa*zccJ2zU_w$gLgL2Mi2ON&q@YBQ)7~
zzR_M6-QshHs2R`a9bP*|Bcp53X-{v;`ex-Q;g+B1JBw#4l&C~ogPbou)(ek4r%u+u
z+m!HbTjd7jSJXR3d>^6^hVTRr4Aq$F88^CCCG%zz^xvB8uR#c98Aa7<B>XfsAprri
zn2_L1=WDs8$5hoaMMI;o>Y%jOmhiqIzQL4j96m5#gJR3gEtmJe8ggMebjR<eD+&OM
zE`U4ODPRBiZ*^$CdK=ed_I{6d;8J-ocaEsj8aUi^mK{Rr006`aY}IM~BV*4RPT_ob
zmLFX<(6((#&A(M+<0C1>Fi-P>-qY5URM*vGuBF7&AcP_w#ig#J$VYY<eE@Cg2qK5^
ze9f6mE*wj9WWT?*J^LTk4)&ogE6nvQ&-}J;Io3S939S)*9j}J_`!-L;eB+4C*u&i9
zA8*ju$Ry32FkK&0S2?vD&?CU#OcAg)C^jBqKtuq!a-6j@8=JIeC!hJ5^Uj$o^EDUp
zwri$Lu=WpSt9IoR1{tp6W95KWU#s&F(7f27=FxB%%h%uNj88b@?1wacnRQf?lBnN1
z>^xmM;uWn;O(TcZ7iG!4OM(G&q3i|ww39T&Zy4Me&oQE82iTYfK1;04fZBxu?nMMZ
z(ZQbm!Gy&Q8-}pSweCBxQ7rhk9;M3&Qh*<+2nil~_fUrG3c{P>;)gn6v37>chq?dr
zJz5NK;mGj|HN0Oa;N0LuY<Wilk+j>iy>B!1_7v9A3*bllnh|vWP^5&`LIC<gRm2($
znSKL#I3S%G!Q>hnrdraJd%Q+TLxw6Yc^+%_srDD4cvl_=_45)!RtGw3WriwetZg8_
zAOD^vj>KX*W+oL8Rw^|MuO(n<o!(P514Mrx6a*ufW*qVD%GR!|0>bD<YEb-C-EdwX
zy|pV1rbj3)0*o;qb4A6CiUng3gIo6z<(xQ<rBWI=oZO*!f2~YANa;B*9mg7V)A)Fz
zL3~tG4skVC>HouKOd^cB=IMEdbcjfU`J@JnA3IB0`3$9v`e3kW#j|2G$&6$}0J*^z
zNiIPg3UAucT;+iUFflq&3kV(m;Io&MnucQI4Vt$T78a~YGm_L)+d$}ixmx<3uRlHf
zQ%z4Aq{Cl)q{r`mNc%7b1s7+l6tAc?->^u8KgxWtZpJ{*Y4q%gc07yWq?|Ot2M!$~
zXY#c6-5*RKXPa;}CcU|)9L`><r|!K?mI-uvTYT5U`q}rclvh=;9$of5z%{!uJPg+e
z+{Ly(%xLgU%}PZc&p4z-H~d7m-?|imxK<lpdrA*IyBcC1p&SHtHw_Js83xE9p6S#A
zctMe-Ck{{xDBK@^@~M;lusfwgQ<pAQ;;_L8#I;J_o5kY^1;>yO3YvFe-Vx>1xbN1}
zv(a<G3W%e05?;PKJa^<bdWV>jj*{bS@^2V{5igLPi)$10$Wy=2+zD|iB5(7b-@RX3
z;Z4DP3U#oM4NUVQv@~V_SRr_g59RylXOI0}OJ*gL%CJW-{Q7RapVLZSaitC%C~&Sf
zOo!NkltaQ(6-GVvwuWO$&&qdw*6ZihKb?%HE(m8(Em2U#9A+~pn<3VvNc6h&%$Mw|
z{>)?A=EEwv{J4@P4AeBLEWEI82YG(f;qHQP`p(mE3^4|Z*-&x90T~(;*biW<4rOI#
zQ^xhHw~d~EJ)XBH2V<nE#v#1)$>_YNqwMz$AE@Y<7{CC{>)593etmc!#C3#O+sEdF
z7&;K|N>Pq-OKLSeK3;QXEz(;XpXS^k1i^O=Lja4DCQcuRk%FL1Xhn9x3C(~O#l%n0
zh47q>sfE;z#;eDbBPi_|2Bt%GR*~|y6sqsip1C=fHFggVxyXqCAOdaJc}oGP`kV5+
zBgjEK0Rrdul@y7=2=LQmc?VqI*juUwa^}7KLZPQI>e#qeM~O75F7F^Tbc{kng7IWl
zL3{0>^h}~dhyl-HUTlPcV1}^#I+{ULLQg-24o}iB!!`hz>M-+j9_M08@=PuN_&?g8
zMo6r03tmnaod_Qi3ou%sI*_(aSul{+b7>BZ=S(nS)mI*+G$Dpn=y<CcP<Yoc5l%oq
zN;(0IB9bEt2Zb3hO^7${8%RZn!9$2jVUJ*`R%K>?hrYY=YZ)2$Hl|~OvKfK=K!>(D
zC_hn#P4I*|ypna%Ji^bhCRCfEcN#4@If|1nPK}B?@(1^aE;^o8{`Ltpw+_zSqH^{G
zdl;Va(|f03%<H4Ba_F3;P(h14nvd)Fgv<52TdvlyzMcB=qyOj^Pp@>oo0fv|($j{5
zCow1j+Fo*;vS)RQ9y(N02yuOL{p-;C!SbppAqDIj#YN!_#Q<BJo}x1Mm%qht5A*KR
zJRzq3M8c`@sH!W2Ra8=;#5v#5<cY|gFelr_MvAOu9V$}m#0U+aJd0s<Ye!m+cI?1O
zaKSW9o;D4`Y=;Ult{){ea?mK0J)905MBL>ZA(WZ#_W|3$8=9t$R22XwYkGv`nwwf;
zY``%p*D__CgGkbBWAW~rg6_eXZJ9ceX`H@5|2n(-5S_bQ`>6PmJbbW*C(qK=<KNPI
zsRsb-LRRa+<cOmtc0Z~mMDzUvg=GM(HY+}2xaLhArEM>7#z7IuJ}gy#qAFr=EOa*2
z>uBnBI)i-D)6TLL+#j9s+l)?a)yEV#bh>6P0LZU@0LHsk?Q6)^^B;UhC5j}+z+fI^
z17?m6X;R@4_(!ds%3iHE|ER4&PXZff)eGA6LJ!^Rb*a7l<7=z5I6RW`yj9<S;2#L(
zLp2<FXnk+zeEIk5uPDw<Bvk<ro>YGXBRS2ms9X&rJNzRn-y8n%$T#qhP&~nd6cO*O
zk2W6yuyhuYaa1!wj`;Xct0D&<QASAzC1b}?sluN~U7Treq^Li8I{-`(KkpMtJyg*1
z+*7|6eD7BHFc>6MmufWGkhBEHdC6SWVZaO<6h#Fg9F}a|=2~#a{4ti{D2%emS9Yp7
zfU2^yH2u=s^~1ZCX-Ht3K3z`bsmEW(K@g$518Ht*sQ!av6%KAlBYECYv#!-ofBJom
zjPTK(b#Lj1zj;y}$kzUu2f(6n^{?-s7)6j;4MEZ1er<r7=PAFu$B^T?1Zy+kn#qaq
zGh}p}*R9nRM29EX#&(|TPnkDcgMogw241SkCRNJ5Yx|m477Y~#0Q;t$S-NIsGLd(&
zT6%Gmo?Mp-)&e|^&J0?7KWD*xBHa%@?YCKge8WL<FDAsmLIf!zRtr*c-j4km5bdLn
z50vVLMU6nz_g4)TlcMw*=C4Hs$4mJepn&A_b?g1N>a(;$HjlTi1~l$sLUDZvYcy}I
zi*&dj>N*~7!tM;{EGNeO{>kecCcr)Tz=;-cUZMc-zO(4|w}pQA_tX5#uc0IlN%;U+
z#10^t4C{&s^b@YVN^w|<Fq8l&Q=oOF1@<lYiQR*jUujISy>E(%{e~}wM1^VfyPxR%
z(d6@tn5?`0{ud3|{F&;yqo})miH6`ku+WFE&bNKv28fj!4^K0mL0h~Q3<nMovjP1t
z8h7paR!{%wr)v-{wsZO|&EpVAP52DL$Ui%8vW8wcUkk6hOTHmd+J=B)9!77%a{9-N
z(Zbmi)mXS$-@pGUz!%0i%Dqtr*L=`x_X3>u5N8>gD<f7X`)6}!|4#O|=RUmWX#XuK
zD#F4PJ2Fz|UG@uI-?3g5q*;tRKUP<=Uh(~WVfMX<ojt^!;jBxq4)jci^Q@j{cCq<F
z<74&KbE`CC+%QcaIbL`E?kNpkw;rpOS-7b^>)S^or_9rg5dqrp{=fCq0rIt=alQNO
zjLUhH>($9;*n6%)JJzxRWI-M(E>;y5$RWwIb>G936H6~5<OVM&`CW$(98^74I3uCf
zUCXEHJA9yauh^x+i_S;Uir4kO{F8#W?4$ZY5Xw)54(!;aI{!W>$mrnF!Q`Ru1CZtd
z%^5k^+3!d5()H%nS`8l+h4R{Ko~&=*(3nMt;6T*_z;m>`UNIvEYsM81A|QOMN?U{3
z3A*Z<rHT#q>Jhk{sh4+9i9Xu2N9PR(>3G~sJ@L>zTE9IVW&3<xdgIMXpahRa5SZ9z
zJ&KJB(3|UaY4+%0iW@OW_y6TVeem&a^4fxQ!L>Jm;7)aTERHTQ4;?>wgw{U$zGh7t
zsd?i^>edJUrSP?sIV^3&i^`gTuo*uApt9rv+PU!+{b}C;t@?0>7EPVZ*&L=DDYNAD
z?ngufb?fTeFV}3Mf2?GcD^K{;YJS7?{I5Q?a66yi0cy<LrcXA_P*UtwnzHCRJxe&$
z`t1jq54_lTWBP=}YVLe;&ah11_rUKI44QlPXREbs+7!iIdx551NNF=@*@n+Zi$Q_v
zB%fg7+{-j+V6C2h>Mz=oQK4Tx^#h$Z29M#10u>ZjYu?X)p(O~pj%AqMo57`ya(~-C
z+or1S5R7mZTJ}xD!cayCSq`8__x|%<!uYzhiwLQLGO8&!w9Qwer_9ofX(;Ia?K-$8
zLm#gx1Yvj-wR3}=JW2NxEWa}kV^}E2>*gh~@Wk+m!xe?%VKmrsBhRIo*Py8=OVJlx
zr)4)>p&@;}RGgWkI=?vm`k{wWkjX{0ahV{Gg}fkTi?-&}YOu#)Y7OtCuK;{NgTEt{
zFnFx)dEhsCbJeHv35|8=bzB&AUei*vJ#{~GjCV6)x_)`jg{mu|rZkA_m5qdmvX28P
z-Fg0k1sXd%nsB;O?cAB7LlwRV0F1$sJkOz-u#uI0RQLYnA!_`NAT8{e@(W5e;b#wO
zCb@&|2sc}^CL+{!?A)%M2MhI`U;Rys0B-bYte_-XfregspB};kYmo`o%*$>@^Zh$k
z=##Wq{m<j~X)@vWUvB?GM~+otxrcGV^Ag9;(9H8EQ|A_23PR7uw+}*dM`Dz98@#@0
z5O^N^Fv>T6uHC5%HGcG^N}hMM{?g*5&8c~OIat#dU#juYkVZnOcW?N}x#7^!OLW(=
zDJsm|rxfZl*Al5@-s#ZD5t==Jjz-4zN14ynwk>-B$O3p2nq>Ua1bEByG=rQM`?ecJ
zwB0oFUd>U02Mi@eV6C=nou*;YOEhu*)q3tPeY9@teyWDB?=UQknR$t($F%9CfB#(v
z`TnL=+q3|q<A}uZy7><eEBGVM2eMiGgoO^^pg)Gz&kpR<X1rlj>4?K?&fs6xC--6M
z;c>=ytbe9VvM1ZS0jHaEfgbxsz1Dp}c;}RhwDh{mG;{zVn5nz<=_{Wr0cGt-+GZU&
z0Tw}0v_{RmR`(vKdP+%yV#m+XlJk=>FE9@Xw5P;4d@smZgLzcWu|k#Awt-MTMazEp
zpdxo?%NvJDCpn@=z$Iy*&UV?JHQM^=R1Lf7Vmwjb(Lef-9<@8iQA~Z9qrQVi0O)j{
zyef9-rXT)U!|`l(<LP#f&W7~$4MhNTtsI2<Sxasu&4vi3dLn<~DBp$5;oUb<H$CtZ
zf;WhkX{*roO`oc$1|T~EZHy0^chkzZI=hf{w+aEa$`+$y#^Wx}emF)xQ{M60HG#b~
zX8sNO!3iJ7fYwD6#NsQj)1;wc+z0;AOD_2ekG9UUHjbRvGb&p;M^528ef;3frDddL
zdMa+p0{!OhO6@+3@=qkecNSc(3jw|~P(dQe;b{C9(5?e{0DL`u8GRa{^1Kub%9XnA
z=I?-!WI5|biil1FNGpdljs{TWD|daWy?f?rOza#@U39g6PpZ@BR3h>Snx#b-U#W2k
zVSs}g8u<>VOkoMa`^hoT+0(g}`!gGtKf|ji%T;kjqoNar==`NWQ@Ph>Rd;x4!kppo
zaA+nKPE3xn`_MfwUP4rplZmV~1xMN-Jn#FpuNVVFQML-|uhdM8k$xRj$Y!8$^Nqxo
z5_QL1tv}v1U*$QV%BN%!ZOb0V0M<Wlk}jSSkAa~U9I7vM1k9r`$l`7AEYlZF&S|qX
zZnMZ4i|le4l^o0%`eMznMG|8B>ZMmd)HHb7#JF*~?Uzr&_tz`0q6OPcmjVV)&=vE?
zL$lx4gFE&}AAYK|xziQ5V7%sD^?f}63|Pw18u^AU(G^QCCTBiC#dzyCyt<njaPba}
zb>_|WNM}zt@_o;qu(w_FYI9_><eGJ(u`mK6PKZWrwMaI0t9{(ljAl1h<R~=mhx+A@
zzREv3xi&B)8s+{p{!!*l>Ly<D0jCTH_B?OA=^5$u7_n@B5}`trg|)GO)iq;0vvXnk
zR0nhD%qjEY&>H60&W)9lwPyS#FEdlOoD=^j(e7>Q0CFVKFY6uc%9yLz=<_rh+V#+V
z_1e6<Kuf?jSTvo=IPEpswQrj?+hcMXb?ZY9=)!4(l(ORsWgjgA%nrGa0}w-byV-MQ
zD4G!Z;;dY4`p>6;ViUOv&v(4iwhwGiFhBOUfqI*+=#F@{Ic=*fP~hZ?bn~s(Y8W)L
z>@X=>9sTvo-#o-T*!iFjeAg2?QJlVAAMZ#}OGvVQbjQsa8xyJ;qLfQ&JazxC9wMW}
zb^dLRIhyR@ecG^jqsn4$)t`QKg*vM9wQD!`$0=$1)uNH7&s#thop=~gmG<mO)vnxj
z4I70_3-5K-fWA9p;LLk;6Q$O(2IhdZ=NKrqZ~s{3uYX@DSIz`QK3*5y@`#%ItyLbl
zO8vr<bm=u0ezOPNLTkOrzt!G+LPiMD@TWXf({XLgZB;D7M8)1z^3#{%X^W&I5dOn$
zN+c(B?b^*sPK?*$c@wqdhPxrqgfHU84<+n0oylq-)H1$5d6tBBInfPFrsbM<VLkN&
z48o6c1LoP`4TfWbI@O%N$%U}+Ie$VrEj-RPpe=~|S|0>me}Ee-e2O?VqScP5ef+h$
z@va|fd?ZDku*O)9ltYvTj?!H}8_D<aln}>!X#1O5w|V!e`}c%28ja^GG?)v_%kJ;X
zW;bOoC=?|qjXdesBXsA@OEhfC1-ftYJnNLIO0%_dcco^{vRo*JWVdh{&v#rbSfv@W
zfn}KxV&N1v^fO6s&)2@;myOD~Ehb}|OMg6xhK%l-=(Bwpt9F~GMkNjck?{qP+C8=K
z{3*KVy4yAD;(9y{5PCol(so1u06+jqL_t*BtY4jkn|yf?(<efgdoGA0al-EWd_nOg
zRIS$lorl?VHfB>kY|bn!&uLhvo&I50weecBFeJ;x^kvP|m8TC@Z`R<x7i-w$MY<0`
zqaMMiFFEdwWkpKab4*hgOmmcJBcApUPYempGgvd%9J<$x1I}~(cz&B{09@PY*;fhP
zU+7=|M!3A~T1`YjyyL!Pa;uxT*vBagVSBKy_yDCg2?M3IyXVxxKkaX4Ycjz9vUl5w
zf`|aXZOi&EG;Ks8Xy{`p|8q0{Y`oqu-yA*jgB$gsXPk!Mb#w3v%yR4%s;JJbdSgwp
zg8eQ6cxArs89z_n#JiQj2%mU&jf#hdX+UQyXv|US%=lbsDHj7QwLk%QE3y#eYdk|0
z3CBPb|5nWmb6(y-3T@r5_g8OG^esy?eClHTWYl@i8uVig>PriivhSFt&7111fguG)
zQCIu$C$H*V#=GpI^EB_OTQy;BEdWVa!P{z7kat|8@dQ~aiCNlxgGZ=i>vMW*%n;p5
zP0gtnU8fOK7cnv9EO|6ha;!unDajMSm~E=ty8{VL%G8tpdR1O`;=w0x=+=9ZSTn-d
zS(_ljtPI?)>3Fsp&GQzJsKnZj_1dZg1qWWCF%#$OzQjqKe=j_fo+`*Ws(|=7r(}%i
zf0xTi@~!6T+%G1i6iIH>$asD9>dP7s>aV32&DFGvu18q9lvE0cKQtha5aDh@=dyR#
zgZn`}Y|0sqCi395CtjkAnYWhAovN7^-=v9isK9{|=ug=%OOGnpzg{&U0n2lkVulVx
z(1lfnMQhR3H#&63VOrK%URZt4V|&*tZOba<d6Lgd=wDA%@_!i)E8uBor%5ebJScvw
zu8IW;h`nNff#y*#k83-nY0`IpspT&{rjqDk8e+_fJ?JoB*W>YKfpx)a6L9RjdUNhQ
z5G<OQpT1x|oNKcX>`II+Bcmdz6aqL9o`L8w({w}f40}?~ZLHteq5*vSqAg2rY{=2L
zaig>wFU6|37%jU3rE=z_8a8gOx~MeJ2Od|6VEx|m_q6?3fD)raK=tPQlfE==#v+ZK
zIv*v-6$WQC#gxR_ibCz!v_>o5_{ag^Gv)o<nh6Ciju0D5C@t&T?HQptmtF68?p%_F
z4aL^XUlkdjQ6lsM=hm@tkzi&(hgw1~+AP&{lP=tg%x@Fm8Vb@>m9c{B@fagChYcC2
z%dQ{pC?FjslpD{3hZ!<&b_!vOFF&och@nbEfUxs<uFa6cM~#ZryZ?GaLk5Rqgol4z
z`+dz=To0Y~gYWwTOj@MvAFj~yJ*6l<cr>U(?iCcK=_oqW=2~8}%a~$mXs9r%z)Q1b
z%{yATX)g%INse&j1--O>8Nak}QI`juzcD_2wHZ(IphRA*Ry^}E*eSlcbis5Kplg*p
zb1}y74hL*9fHQLJz$PNIsudYGT#bj<>h%?ab=!3pDrv@LiYLOQ30b4B0Zk%+?%uLN
z@4fLK^|gm#^f7+o*LFJpLN0)}S@VLAIPwhp+vy>Kij^H}0%-L}akJ)JeY>VDsv}h+
z5a<@p<lzGV5WS=wAwx781t(#Ur{3GRUCBffBqt=JB*#1aI3U17X<NUP=eUWAF}w<+
z@N6SE5ejLC`VEiK{;h;1j~T1r1yeNjf~$4jLY|EmwYj!fum0+GJ(ueTKy190Km8)l
z_SMB8E6+hmpLPKtPwW*d7a4%;&;>BsfqdinjDZK7^S*!{cy|(p+cs1)=3SyorVeIJ
zERBb*LJ#^683zC$p{Vq!wLVWr_wCe<oH~sGcf(GEm&psZ#)Ov>kL>Um-lo|o4MW3A
z3DMqq`Mr&Vxrb<6(j<NVr^8iS-z@(=lo)HP)}HPAHTHs8c=J$Z>`2llXU(90Zh_yC
zOW04o&?0N50N6FYJip&SU%mVMCQTm^qq!&{OK<zJ7Su2o81MTAbSdTYeTtwOMwGJ#
z%#5XNdPOP{;ADT+JS=8{e)RLnJ+hJ=PtGx3%_gvVUVZRmJzqLVV-x)J{5$VyAgN>*
zoIjrG3HNGtZ7r#j!IU#5Vgvbc?OU(vlWbCQVuDpt0hl7V4<`GXeCwhf)^iuVb7Ux^
zsnA$E79RB(`e4l#Mcs6flBQomgy#&^V+05x$Go;EPpSLi;d3W3Ud|SCG=K=nqKd;h
zR?|g_qo4AR<f|mTfZKOb_Vl=BjV0F^dU&GnOSJjnC=od0>e}&|M``S3qcr=%CPITj
zNoT+G?aN7l@24P(Y<g|I1|^Pg3~O}U0$1S!HrK3i0IG|5_%YntIk8AXck6~0dHF|a
zV8L2F^L&`@_|8&|A2wOH|9C8G4PYU%Vju8MI;snmX^{+sS0~4p>9PN;RDaIY^a)8?
zcIVHu7#YC_Pd1qzD#=aN2d_V`1GZ0iprcD5clz|4TUWnKw{Z2-`eLOlnQ|05JKLsA
zauzTe`S@5}Z}a$@Jm04B$~r!Gfic^Pm_poaxJKWGf1sf91ZSsn3mOkYsK=G(>Z`Lq
zT{OmwK2GEL&bfgn`8#vUUu*zNVYS(`Q~+x>M0TlS$^AbU{!yN~T-);dYs`o!J@e>G
z05*pZ<uyWA-f*WDT+yZg@Kd}=TROM{EStZ+OVx%1WoM9L0LDvjM652nDozXGmz_=u
zi#Z{fQ{>b9%)MIs#`D^&2#sV7Y#feZhTp!X4>1NvEV2;m0*`d%YTNk)RP+SV5z_`@
zumU_68NgChuNyg;QMo6Bmwee1<H>&SV|r!vXBq~7jTu5(5Z@1_4DS_jqn*9h>l^TL
zoY9{&>%-+6)Er706Trnuljjl@HWU8T!}FmJHir#FPkgp*gO<Otf+|@_&=(AW<p10K
z9NJ}T;NP<bZXMFSqMRkeUx*1S(96%iqtN?rp+xc|-FaUU6{wg~fc*kVfA(5-{q?`g
z&CS)`y?a5?4R?es?*El$d3Bv2DF%iixRey2812yRqDH(V<gyXw*x5G-kMuERP~`oy
z>~e@B;6NS}uiuuz9FgaNch~^bDp0ESeesE2e&k6t5&}?BoTn7RRLZfOSQJh-b+65b
z7~t1RZQCrR0=&=&*of7V?LCQRC+Y^BGLS*nL+C7Qs0>f5A>f+Ts}aesAK|z)SojX$
z0Xb1m4lxAP>iGI#(I`8bNBzsg&cB?T9DWl9#~kGHUT#jdG5`tsGOdX6aiO4Bmg`V@
zs<LwrBkTurF3mzh9x#lnt))tbKtDcITnaJr!|D-At><+*T#i+{wO+@L7AiOUpjN&1
zj0*gd@zxDh^-(-G8$SiLmvAnqq2<jQ$Y4kaAzai2Je<w*)6)n)$yFELQMc9WD+}_C
ze=uJlAHoV0%pxBzIs%YUs>&KcHH8Q}%Iq2U-~bW28bP_dweP;8swS+ecqz=w(pXom
zlEOTY4^#E|w#{0zZkzl=`Z3o=4p9Cf0>OcuD$2`IDj-cYl)SLAG$+>#rsR*|!@_)8
zt4fubc0h-*vId5Pu-={ItYom(`2eqY0=NLvpoVz{{mrjzRcXpbaKV^!ykA!Opn*L3
z+(QSn{+(y_2|$Hj7=<$MbY&G)5Qf^9gF~5|)=Fg)vXTj!YnQDVhrt)msvVl{_Bv%T
zzv+2}%sm1?zi{&1w&|S@)6I@UXgk($#$8=O4PN#_TFP#1T)#ov_aD`OFa#kizd<OD
z1xF4kEj`y^LY9;J-}bIOzZ0Oq2nfZhm3W6CvQ@U$xwh{PlWUYg*y&;92R{U$le6Z`
zZ$KE~I-h>1J$Q%>LAEe2mm0k}?9nvBUbEEY6T~XJg!ksM<BA+JNt4Epm1j+sUVCY|
zsyzcy)G+(F1@|-7{p;W0C^>>@+P1aIuZP|te6*4W)<FHo!<ngC_tw9(Ewh%jsbjtN
zE4#EDCL5#>_QJ=jH>!^P<OLV*pj6dyLbSJi_!cE=@+qx^0>}0FM~`u)0@W|9uePmQ
z>Es?+E?fuUd=<w~cs8!oJ0DXA89=&h&R`aE)#XqAdjPpfrMsy6jex+|Y|UC>XtjV!
z7BHs=Qub)mh7H=XBOB}o1Qop2N4Kq4N(FSlm>h~=UDa`=ZQrEV-(E-lUpsp@Q)ze)
z+abs<E9ckG%m2+Mb}Bl+^%w-u=H|64@jjE^Nq;P*qM0&dWyMEzDC3}ZeetPQul-mp
zgnoJ(4JFxnU|5?zTKSGna5meWF}JZ6wJOR#to`KutzNYORC{vmQS!V2v#KaL>d-c$
zb=lArqjj!-_WCe&=2MkffTzZjGt+C|ef{|G&awZ4z3V_0t|9-eQpZb65lAQx#NU;q
z5**G-*B4tq(lfvPyD~he?@L&CJDj&iHaK}}qd|TcNxg{%X(wMWH#42{l;`C2I>0O^
zul(;X>^=@ngSL0F&yKQ34j;BNl1&|6_jp~uv$NnSc<syZ)ROf|3PGs0tX-#i3?MDc
zXG^P{%OV{_QF#BIH}uXY`ASOYk1|e<a7`(Eq|O;*X&L3Vs1;mWT}7y0fexnZ)%umM
z=^wAGRTzq-546gJt8;C3G`-r%Kczlwb*a@Nr@yROPVY6d*Swd0@PH2DZLg{Y*}tDD
zy~i<%+h@6FTyN{1nSM|y24;hBR9Rc4N?S9c+sZ3gGsaLw#f~G{ncDf;CcXIZpS86v
zgwj54<o0tNfr*dMIOZulv{4_vw+oQCPBkHvS_#(%qJ6RnuU`M|>-yX4>!?;j=`}q4
zHRWZhVE>j=0<H8!xoYT74gD)AgkSC5sSp11j9&cU3p_3eKb#TAV9i_w7%vIvrxwm|
zu8ln>3qGHQU}I4~r}&%GCpgT&@NR7c2&e=M4=bBvWjC8RYsK)i)IHm^X4QIlc&md!
z6HF-Nj!mm|xC-Hj{BJ8^*wR?9BIx~RAAhJPAAMF014oi)kEf5)hF-SksFaXO1V9VU
zgg}JVhB8tT;Bg1CD*z3m);q?w{LF)(`lsr6*kmmuB_W`*Qg1){XBCi2U_7viIm$@c
zr4L?vQg41%qme_%(6<PIzz8+$-=xDe(0F8*HuAwMO^({WQ7=9Es@lW)sjQIm4^Uxs
z58pNA!IYyxfpz+5^(QFEfaq0}I=sgAMAo50q(J02rMs-c2Cx=Hh^<g*E#;~lp3nR=
z)T;Do0kTh;c6`1?Yd+kJF(t%N(9eBW7%#(t0d28}(V7!Si4?K*GMS70-E%&TpJgF)
z?MHTCkd9-<)02E+Ut|oT%Z#Syu+B!W3IQedL}@p<$Q(LeeuOILZPjOacoqp|Y~>8M
z5V~AckgcsBzO6Uj--jVa$P8c~<?g3H{S-jui4tUklr-wrL%V{)!ktPM8HB~|&n#oj
z$Q>rCst`FNg%JGiHWb`_h0G{Mffn<WHdDExz$sDt;v=uCE26JTDXp85c1R7a7$q=X
z`Ug?@1)12!)_bh(v1etmXQ*`0PI{pyJbXZBq27OYpE?QQH4GJtGOGoE?NHhtz5k#8
z!`^iOMpb3)Z!$BP^xk{#^xmZ?MGy;$h+@OGmeo~P*In!Cx~{$Vu7DycVnINpNEZ<Z
z>4Eg#lbMuBCjWQd%Y+0HNDy89H&;m}^WMAf-gECgy}XGxSMOrdwNS-ths+d}7PQPV
zEa(7v-y(W2Y3(JU7E24V5`YWCYwE~n3GbW7#Ef2yPu{z6HS&xUScsbh&l&3~naO=0
z)_nK^wq+XWLe6j=-k-tnG#wLqweXCz()v(eo`daMb}$6ImR_RX#D7NUE%itz9@_oG
z4w#)7<4vB+!u?y**gzg%=m8T-GaS}Pe3O%rjIG~&iB}$e0R{fiaHj%Q6g&!dC<b7L
z>WK$iVGi+^;4Ai<dL-jG-dDVW9O%YfzGFRB>^j0Eo~#T++#-0plJ87A^fSI+|1H*h
zwFmA23^`y$2B-3!_-F&y&A{_Oc#1`{nsNC1dFEHV`T|z|P=m;DR(YTiQWSo5T+^TT
z9Y9vvVH(^#$w$h$<#=++m3U_d#$K1_W?}!&2a#FJgkYqxg@xr9PG$M@p`nTbsXXgP
zyz}NusB>bHH--jCH6tx$4=eqsAdT!wKT1eH4v>DVCH;8%g!F@FiT~?pjkRYUpK9_)
z2lwsA;j{|aaa=+}<cE!pqPH!LXUxfvm(6p-vm+H+4(E~1Gekqqjqrk6?h_qDU+NY5
z(KEeK=m&X`{VCbZQ^C9w<e{BWfUh>v<44cRR!qRw#BA&N*=g9hc^zJT=0((cg;21h
z+(GyH(uxYwDOP_dE@C(cOLYs+!0;xi_r7~O4NuQLgf)9Dh>vjP`fyh$B$HX$i34n@
z+^Q+gRrr+jv=nA(&tTdR3ip&Nl<|uZL&R(Hqy@qgbMN#-^M!_r>{DD>&+$|7OF_Sw
zcwar28RUyN_YOQ{{k@-_EjhJya3r5vPWn{IkeMQeAX#aCX;B_!j-Rmpi{<$D%OAlp
zB97&$CD9`%AZwtdK*u#`H`OtuL0~Iuu%DicMf9*y*MO^~c)A&!Jy4gv1-mjTDOw6Y
zXXM#4v9xgxe%iGSn$=%?(Z2SZZ}9TVFSCwrJhhUdX6)YhP6}b|NKeaFFKE0F7vrJ0
zu!u&xG%cO+U()R80u@38Bi0lO!xThPGjj-dcf``&CznD9hmPRrXyg;1(i{VsutAU#
zk&C$gXqapxlW`2v{K8ptod;1gD_z+gA=Ek&tAb3HgOD3t{SfWP%;9{ma*eJa+3DE|
zBA|aLV(6ByFih0iWRW2~X`>rXe5^O>OVd$MEj!-^k+Ctf@Tji2TE@gSuzqF{HJKuy
zMns3HvA}$Lc~&-RdU(N2Pb|Zg(;{);`_FOD?T^ACiRI@cX%81w$2q2_AMW;4L@diu
zP&;=VXWQ+r-u@7U1_?#G3F(XpQko|-EHgQh)y1{vCk4=A)z%?5IYqH>1w_Zdfk|u1
zvvNDun6Z5k(TZD$yhUJRqhah(rurU_kSKUj(CxNn+(2R}t0U7~s=U~NSwp!`h|sO;
z5gZmp21!ezm`(wfjd=`WS-MX{RaGq!Krt<AArVZP*H(kWxfR@MfpiC~W*ii)-OO?j
z86CtKQ!`kXMWIL51&~bmT2X><zepC;riL8k6^ZT;jQNh`{*+QNq!H9c+t4bQmXX6N
zPKcsAVQYOQ4-~br(wuw|<u5i9YBcH6l9|Kzvjs9%)`9zwx-c%&$@Z=Y@q{hQfMn3E
zM~GC{78Di3?48n6_okdZBCA#=kjgGnAk%q6SPbVt?Xo1<(6JO^TCN-Z@yug5e^M0o
zfBio0VSJ4tCV_F=-QBJ_JLtm)dB1pxRHdgXF~p&f(X=F0Qn;=nT!IneQrZDidLz&S
zLvbnQN{Ev}N@@l{#(KW251E#d$+_v^?@a{B*gc6kilPfYrBjZdT3%|P^HbAUxW$24
z;e%1cxKz<Y2L{+Pd6>*w!!sD$aR#|&^pwytSzv0a;3OMF#K$Pi+>yKzbyhYWh>Y}7
z$HOxwRs~Z@8uF?w%D!l&+gTpVI{60rqLlT~rG-$tZ(w2&!6CnPlXW&wp(w6aMYI6B
zghs-T3`-3y8fB6sjm@9mIWeQ}QH7dV?8p@GYzmGE;95A6;mOQAqTKHi5XO=r)RvPW
z&dA78zB}2EaL!q3==OQe9GWDbYGSNlewmy-HW!``D%SKAXi~G(u|&ovDuQZH!nr&m
za}vv$u`C;ftTQf65;Tg%NA_Hw8*dgIAy)g&^LnC($w0(-Fv*tU1}@eGdQot%$xH%?
z*EDs9=?*F`?TqWA>zpK7Q!OJF(yYS@{gUo|145YzSmNv%k6g;9Sr<EjjD|4qeTHSn
zIAy*<Q?k;jfa>_}M886bcNhwdqBOelpXkPpYk{PrPD~8gSYN@jO0N=W_FjnecO!G%
zP8zM!^{_a`4RSBED3<k8IRnb(AS2Yqv#<6U8Xm=r_w-83$mPcIK?2JpnkgWtc{$9;
z%Fa=SBR_(I!>Q=CMSWog3L2<*@rytdX;#-7wz4_8(-QS@9g~7ZkZ~2)_G4+LtcQUf
zIOR+{R?f@59Fq$f%*+gx=Gys&AjFllk$XbDmv!iwC!&BJH6bx^<U#1&o06u`zbNi&
zZ7W?JiF4Z<%9T(78G-Cw0~ym&GK32Krv*ITpNYAuil}&&RU(b@ZbI&m8}Z3sFNUX0
zIbOd0Vl2yQf}4LBSa3t-mAu1{z{Hg;E#$+f)sk}}-X%3u#>zH}jZZ)=^N<LZ3yF!M
zazH)jZoyITA^tc@cU2xh@Ng0F@Uc9Jz`&LBk_`705fy_&8MBU8a0$J64&1aX{d439
zDmw^O5btMxrv9Bxj!}Bo5ievAN1g0g2ZuA^8qJwX$P4*}7V=oR)&a0DXgv@W<*m>R
z>l!QYq2j%@hV-$7HH!loFV9Lr8Cm&!z!8yA?ku~e@+Kj)av8{UXLittps)xy($G>(
z#a)^7Pb+PFxC`@0P_U#TKw5BEBn?Jn>?N_`;X<ApTKj0lP9uLQEi5()IqB*ec#`*F
zWDNHwdF77l%1yoe@F$#|c(3qeQZwCP(~LusA3{g|ljTP(ENz#5gi34=R%GA?k#ivD
zMtI+1o>}GWlQ)SDWc;qsQGqqrD3{|AJ{u0?i-TO0eJ$Z%6{N4i(S(t1prsr+`P>z5
zRHCvbe11s>O^)*6qKOfz9R=iN1uw##{rI?Yv+#VF%bC<z-A#d!u{2gl*n#Q=WIa#v
zNKi-w1udSjBRTRXBI6RtyGh1s!biSPkRDA7jdjRowT7-ucweo;7j(x#>RBLjNsD}X
zHsL}z#W44Uv9bV#R2)c)Bu^yS@a10drqs7F*&<`t8IOxZKUc;I(=ETS29Cjz2xXyk
z@hTF>qg*OD70%Gv4{<^6%6&&l1j1TGMKea9p1v6w>Y8g9Iupx<mbFNy>~rT!%P@Zg
zc?UC-nSJmI9(?69#51hROeKf4NvW}4Og{)aNt~TQI-xdK#K!qjxD`(*!b;9E&rNG>
zA(M^LO4-?*8Rinud2R$Y;>A(IvG<97M8vR~lPF*XraTL6RM60~ktzRq9zCceRrh~5
z`E6S<$Z(&CmAjQnf=+%+<BISwFVZa=;;c+Xwg`+OZlzq=LItALyH18IUOGqECoy1W
zQ*gEw|HwS)#n9H$Kv{rmd$J}npg(hUFx!3>OG%!zQT~gH^5mG>P{uV^Wm|3`zYrz~
zWr&ce0$Bx>oWD2M*@yFNQe+GcyK@c9^ybK+p|-Pe{!uZ0ooitI-ip84j8ve?c_d#P
zOK-`RdM1o5q>KOR)vMdr(%N()0bM#HOcWOnJ&BN#{ksFG)<Yv%kwx<-42_CB<s`CZ
zAc;#GEn<yKeq>9xHaohMiFQt0EQJ}9*lP(lR3@j4noJY5BN96x+R&o}$2ukTH+!ej
zfEi86*o!quN@}R@RmUm}xmZ?|YYH>Q2e^sFbwac-GVYE&t-M?eNa6ybSh&ob2j?dT
zT#&QGiZpi+OnJM&LYPYf-Ek!5MB*>S4aY<U#k5(o;6-8S%a1-rR;|izowexCxc=IU
z5u~lgr?20Ke}7m;cM;Xi-rAqI*jO{!c88<facWHvwz$`E$p~}8h`>Aw=id92`6+c<
ziBwE<Lu?}=QrxB}sEJ>aFb^WwTATDLZmMF1YvE??M8eh^<TpFD5w|Q7iq8E*%qZ(2
z0SaQ(5JIJ+Tc)gOD>eB_5+AOKxS;9Sw~5JeL>P4tcQwLWlG_<bU@c~9wM7HRrlAY2
zxb#abW3wPgS!6bulen(QH7U~}@s&rR!HbGwg3s~2jU<E`A|i2Vl5C@j_Eq+muo%jH
z+@Cy>qQO1dhrFdPdsh_}#SL4UASQ`N5$}NReJPhaf5_NHP*$FjXWG>Hsg^o=Nn|(S
zyqmAZB@5@nyCn~AJ@;?C{CN)R#FN-?FaMGzuoEUx7y}cNDRr&^t#VQ~TVUGOxdt8Z
z=AN+S`Do|aH!&%h)UcIU9|5DdTuCNdHE!8@zv^ZtR(d1jdu>?;%0Rb9iA9!v<eYUq
zgUVWnB0$!Y?>03uP?SuL+$;M>Vud6Ijzd?P6%mRx+Gu312WH!Jpp+w;TM37ei1?<H
ztidmVVNcI0aOJxt30@Oj0%=fI;H<j&3E`q8rRzasC-RJ(MVW_WtrZ2IxCjdF5SU7M
zgxn)_S*7N@%v;WY_1p{FF8|BEQ}CLikhNazX_P&D$LA&CknBfpMB)~%B^OrqqCK_r
z;_=ih%w!LbcKeEBlXE8XmGj)xmuI?l(D8@#LxL@HR=im{<|lJiFuGjld<8HH9wdyc
zGnO$5?iC!=D2c-8c4kldqu_PU=_unsJVdw)OjUY73zIM+q!CI;0?(r`8UpK6X&jn@
zbA`St)<NlnU8xj_7dz&p;5F_G*%p$~TWFkezw%ynzVfI_-vm!k;8y4;nP@69gdW={
zIGpE+dtWG&9ku$lRJzE0$80z0s^3jUA(_UNnz(cilRfIdeIYRJeujw)d7rqLO1<P}
z?nQyA&;SPt)RMKjD_!p#2!SQEM0gH$+UmMWhU3nK?Yvq2puo4|+%y{LwZdDKjQs3F
z=#jkF+T>mp-^m%Ht1kbRvUCQXQ{i3sCmVm#2qE*dp;ANku2^;y`X%@AEJ{qJ>Mkwx
zf;kAwl97@0CwzYA#qh8x!%Md=#+KFyxLR1wk_u=E&*A1GUhKN>lx^EV1DRltW7it!
zdQK&}2mtav!3E;k)Dzv6wGjNK7a+6AewMIBp*KfotiD6VEA%2$<}CYNTzaYSBQGYn
z=v4QHz)M`R1xG3PNEyk|AJ#Fc__yPG!cuU9B>%K}_lQS<)T}@Dj7guOuoXNi8RrQ;
z;vss>kzPp!8gW+_{386Mq9hldgUolMz<`qyUR0DTvJV9IQrSRoiH+bSnK$ndgOeC8
zgrAfWVFp_9g+57Whfe8F<$Yue%KowPbJlZcovron<JUm2l^91<9>m14anYFpo8AI)
zhP%vL;HlGb%nTFjiT9PgD`%rC@2jo>mD-&&T>7Tvn-+Dc$|E>fp~+$t>NrHQw}k)c
z_73tt_TuN=0Z$RmRg8C(uaSO)zmjt=xJ97{o#)013Hi-YiHy*J1LVCzx4ZIfUGG*t
z%f28lBw6sy^kk5HO;&ee!8gL2%3ReZ{de5U{7#L$CdL>CdK*YGx6YVn-mK15#esrz
z<Q)=fNhHKQz&<$_g{JZt37%{v%ybmp(h3ISo#l667|VR-?A?gRZn*>L-f{4zvPI;Y
zqwVz24`pw0T(-x^VV&>kfRDgbXraLLSYAu+m3??D{Setl?zdi-b7`d?DxaxbQDG`_
z5qV`vBq?EewhC_SF0YBvO2#3)vUo2Dy%97k{EC5w2T^ACgtO!=%GgA3VLSe;C|`3N
zVz7{TTIIQpvCBMU>4i9Oo=PmZLd!)F*;W4Qe!t)q`KIeR5ZOa$vX$<tdqYaDi8fT-
zZ^z;zd8eXq<C#)%SI=wEF$-mk%Kem$#Dhs>Wx;2T4y2)d70|7n9bH1}Hz)tCSQiyo
z3G*mD{Qqjg9NMrpce>dPPd@!DCI;%5A+tnrTeZ+7)Gss~OoECZ*R5iG->1+@PX#Rn
zj^95dL^Im4iMek7-L*S97HiU^Ut<?3dsTQDE{?d+{XVxV2S>uAx6~rl9ERJkyOK%P
z{Ft!WhoxHDSu_3<JaqTd%mACfWQ^Szv0nzW{;z?+Q#91V<n(3#2vU`dyuuCk*)5;X
zDEm}dvy;8YItSHq@yp<G`kq#5xJq~2aDAJ%bsqA_$>x0e#@SO(R{X9G|Mz-kdK!6`
z?_>}0pB#MIt0!!o_sU&;xc`Km{dX?@CTrFOPA9{q%DGqIFCgvf88{W#_2=BE_sX!u
zeYkJuvWGEoJ{k&C8ipNZ4893#?!E+G8Vcw)F2r&ZYbG=PR#B`mWbA}HPk|?qDe2NF
zkEMs<9s5=PanZ3o{P+I0j{Ri&?Nmo-JvYB5&Q)oqDs)H~;R*LdzbA5Z|NmNiRM)fo
zYpsJ!TUiT*S35Zl7Vlia@3L;EW8Pv2LPG}1Vy?im*Uv+MV<R?v@ecm;#y%v`DAGtJ
zjuo99w?xW0R{82bD*fn$zu?9`@E_JaA+r|TCC~h!TDLV5ezu$T<+M%3ieIezf=&bd
zYu0VmpQnD!L)xEl{95-a^J9F5Kg&w&*t`ku<A)(Qzz@E(GKs8FU(RG_OgixCs%5Ab
zw+@Lh`n3jgTKhdY;ybOcJ7H(P%~+JR{M|2(dJ2^UgH*Ij9bqXiQdg0Ob^Eqp*~-sR
z$E^I`jI|Rtp;I}tJugN7*FfMYUZ;I+3bGe7M1PtP-9A6l|2gz$D5rXF{}KN0XCD7A
z@Vo4xzA_E{x%X5z#IO9^Z?b0nfKy+Xywd}c{+yfs+<Su2DV#cS8od;l4UOfW%4n4?
zXDC-YoM`2;CLBCL59%cap8Y_kC+)E38~TrndhX}H@$IiY_TS>%^p|G#56-7)PoLnW
zztve*)}oKcK@=T*y!8Y#J9<exX4~yp3bkrK-g~DI8pazJWoOcj*<G<Ji$L0I>jz%!
zwU6Jyh5np{-)c|%YGC<|*6diA_S=H~s{!EuMaOiBt1gCF@5-#ubm{A`9=0~sF-b!y
zf;wV3dUNaee*uDlN&L1D=wR9;YD~+Fm51|-;N>3*Pe&>3K+DYu=)`Y3(1EuNKwtm@
z0}%M%h5#`#-Jhwr7q@Zo#U5w_5Ey{K9~1&)t{B%`Sz6BIer~MF;>ui9{mAF?2c7l6
zuzx!U^xJ~|+YRlH_$CcAf(EHEij0M_*3#55^?oef+9hV8d%XQ0agGCH{#`+U61dcs
zlsKC<#;iq0F^L;>#RJ0m?`o9>1~UMG0SF90fR^(%X3!>YKrAh}I|lTp1Dj$10)JEp
z&?|<yY<xq)I01UXFwR*Na06`s0;dZC9a&aRS9b&bNUWNYSb&K~IvcH=QZ_@{!13*b
z%D{)y1OZ8wD9QE|HzK-TDIOXF8|^efcc7aA2n;}A00O580+M9Q!I6A`>e_yqdL8Iy
z00ILL_-{afyuH*yA7}#*_%$FPMMHm$eowj2o?<sd@c84jUq8m@j{}RIM=ERl$6C{#
z=ka^__ScPB`@PI^U>v^$0)j!clFX7xby}(EA7}#*7=XZk0|LSy44l9J2IL3cNh&}E
zkyeNRJvol85HbLt0SF90;P(ZAKDoF{*$GKVrPc{O!2~FHDTiOQ!(GajoZyM>mz026
zG5!6K1oG}v_+R@=O81C0pr^fYG#e?i&UR!*mW=mWX3i5P#roa<8qE4LUpWbqnN~R!
zeaI>=@YS&_gp!e3JU1k);#AIJPlM!OY}mJsYc5SnaP+-z$sWsja3YehrOyeudKyQ^
zcgkEj7Hg#eWju<T*$E!%<E755uO}O8C(i3z$D&N>zVD@spsoXNs@Xwh&AQ*yx4wJ3
zOZ1yMmO(Mqmm|NZiX~yg;LZ|ry(`INOnT*<N}qC8#eGk$nM@w9V?V0bdd_BQy{9wj
zi4{(=Cws_%YK6BXdFp*!M`vigUrMlapcd{esTd&2zhhy1^toO?b?j{|_mlMbzof*l
z!jgdNacz2qSsyQ0*G1@hPjsS>-5htDlrU4vRSKTt{ybfHuIrn)Z`BGBB3KJH?xpdP
zz){Y!f~!0BP;XBhkGo{gN&IThO<<@Myl$lh?BC4$Wk~YAfp%&TQ1%evs=$!@zm;Xa
zPA0gj`%ZXb?r7qgHm<|Tp6So%PML;VaV%dZI9u?3*CzcMOS4eK^1t4}QSe};sDXS)
zT_H5^c>n^xF9`HWKyPVkKr5^MNli)-#(HZ?EpXN&wy;K^J!=j2y$O|Jt+}RJl;)Sh
z$hQbGks70RItO_8x-w#Gpjjaxh*#>c>!sc^%e5)>U#%B^)8DMWYHEPFU8~g5l#KOV
zTN`UgnoKPe>ePDma=Edl00m`6xcd6T+r^m_POJ&XJlM69-{`ZzRD=>?_*x0m(@Aho
zbqY!?PSzon+VU+-ULb2OOhHdgVxOeOaXYgFr)K1J-r^Au3O_e{#;B_`k9&E%^ZwpG
zNdE?VTQt{JA|t;<xy?H;0zU3`tY^tuvAsRW@8wx3!`sqgWPQ|jwcc|_9rzPG*7GGU
zlu`$kH6~k3HOS1!CvNI!uD*y5@{&T93cPyq#<AXdvfr(&cPq7LrN*t)zV5YAFfcLS
zgw#px9W(N20-nW1SW}aAHTA4dPR?3tvf64|QJ9sAYBd6eH`*gKG8oQ;wOYUW7oHB;
z*Q^;^nwpB?7cIr@Q%9lVz_)mD#U^-pdn>|d=iCIg%wu4yZAa;mR8%x}%!o-Zqay=g
zC-s(Dceagi5e16~4SLoy?J0x4)U%a)#A+}1wX?pnk+^_^fgOdIV_R#p2`s6q)J@fk
zkbUe8oqzKRYZAA!d|-WJivmMCgVa}MiVxN?HnC={)KHb$rG0Knmm1WU6r#AcfsZ*O
zBHE9Mym}u+pZoqD-zc+Fl%;LWXfT;!(@{V9bPie8Uc*`v#s-#~;JPVw*iRJ+DFfrE
z$ty@rYGYLyGKwmcZ~a4};OR{KB(<*Pu>ImDb&IK_V$vs*v6-)=aNG+XkZqzTK7!eV
ztdtC;a)#c`A5noGXeTW(Ge?Mmw|V1H+*ONg;%S};wK@49KEzu#F>#l*4tmF2S?jm0
zxd8>48ECK~JDnS1q61(c{gnE`zXaD=hw#7iy92IVUsFvHG776Zrydj$3s*bRT-NdK
zwvS|yg(u}RjWwlwM%{OUorA-p;B2QQZTqF`zyyNaN0!Dq((wGwF?j}t!Pk{^mi==S
zy)+ufSmV6WWFqe(^`52Xx%C`yFYDTnmSKY%?|B6A-Zt3$-qZNJ+zF9(tkHZT#ZxbD
z8~Azv0s|2E^&xOPr*zCa#hT&l5eS2&2{pCVu-Mx5$he3m(MIb4A3tCIwxFhp_tF~D
z7s6qq)uXvI9X?Yo!<Dy=hKDT++fy4Z_{CJ8kI&y*h2j<pfn;n?hvn^<DgL4U6a*~>
z%$lNfl-2VuuOLLb+mk`<9d-XY6O{Mq;Kujc+q9sjt{yFv(yT(d><7KU6`r0P8<~q*
zlMz*oHW+>8)wpG75DxF%fp6D*0~=cNY-ln3tw-M#Cdy1GloX_;QaJ=T1%$J%rS8AB
z*Az<(h4I$<A|&V6KocB}ICn=hH8gc0y4D$R^ki6VX(3HbIRpeG;oOCb;o(F=-(o^u
z`hINOorcheXc#!h{?lT$e}mSHl;lhV4H=IMCr^igY-oPk9&Fy5g0R>G;s+{<<W}pj
zI)2Nx)a%<(S!srcmk;rS4o!7+sHdQ()}21)ffN17&V<1=*A*hOgo0GyV9YyzJUm<+
zsQ|E`vLF+muiFOrMIgbQIOP->CJ~<P+&tiIZ$Kl*YHDuj8IQ?$^tSMFbK}Nu1I0u|
zP!yp=VdfP}uBy5a0bXO#lKD2mXl#X^Om{;;Dl+PHm_Ba-LVes6A-2Az9EWy)i{$J!
z_=iTq*}#;4RtEn>u<7j@ajUke05u^KF>l^^m=LMM@{N_KuCc(^Pna|HLBSt-DkID_
zhjFN=5mU~-5Rv|_ioj4?UW9MHTn`g$5gi*2JyjxFu7ibD1FEVlaC4<{mFsM6@=>ob
zC|&_A?PPQ*L}>NS@bhHsZA$~H8XCL7Q}&31E6Y+EG%(iJ!pQ8_J?{<P$i1#<u0^Ar
z7lM2oDX5!JQE61HoVLzxtX^QxN>+6+Hc_bRWKgXm>-o2Mg;eL1V9W&zFd`um#<Faz
zUbzkyCr5?Wb(vbv9sCi$<)pMM`y~Rcxzz<hVNPf>8c}OB5yr=!;!~U;`)Q{LT2X0>
zh$!*}txc$}7lrQec%n~lkd4B9r*AjmP;x$E#!SJ&>A}!3lk(yHTe0I%DWc*NU_(U>
zMcm_XQJ>zyr$iB|)3hNoB}EaSV+JR}LhVsIEv3iqDG}lfJfnHMx4|(6^DbIQ0Z)tC
z;=@?8ekYuRq7mdoflu}{$KKjdLZxCYaa0oKo_7&EU7V;CwxGHw8=tP-3iyO0!P7w8
zWmb$7XsN@YOe#ce!ZB;^1yqQ-Q28XKTMO{<7wdU&0+A5rLS-IB{nNE>eFM}V^e*lp
zDvES0a(C!Z1|mmI$E7nuX$T@Nq+on-$7k4^(u!y%^|hgLg<PnZ$rZ(=O<Mz>DL~kW
zDOfZu3=XzDKMgfFxN8IUr?w*|Hc;^p=|5oPKxmb9O+7>!9y|o+Eu5^-a8X?5ru~FX
zKc*opG8(p2l6J+l@-EqfI&B+DxJ|+$$*c3Tv9=D4<e^nOD*fBR!IFYXvlE7n9tkgP
zEmq$2FK7}IS^BSuy_}{q-~Y)EJTU-)0SNR0fnEi45fa+Oa&8xi%Wk<IF|Mun{;QAi
z?AzNAAMdAV^Q?V}rnxXB6;rOf85hl)0bS)GJo)_VD5wP7DCnHb8mTbdHSMtX2}EpC
z66=SX$^P52wzD%Dt5UG>{T0YEYhX`H3H>asul=9=ts{aitFU0=oGUOiAs7X$d;Q&>
zbl5tGHTpz$%+m)h8!|A);sW>%n}(~dxeS4}b@*!KGQ6>JAL8PC6h0v}B@I{I^&n<V
z7>c^QpYZ%$|3s!a9I=DPVb-h!>@ip2s}-M9vr7SjRs$h2|D7h*-8K|g>T%A}8xa-Y
zip=e6u_>*BR!qZhuJ{lh%S=WglU8cCk!Rta8|EN3)CcyOW~A-@3imzxJ>p}%$W&Y7
z80m7ZF(Wng2rm2cy*OjkV1xwtz|o!p6d8=#stTO5Z!_Lrz6w<&&iuN6iU$pu8B0A<
zN<47g-H%|(Xr50`HwtNO?E5^-{&_n-eCP#KM#jOJ<LD}+3qNg3SL*EKRLs8SPMm+{
zIJoLske#{{5B~ib_$9=l{e)J5?mI&8L~DH|JO|Ihl3OPsDkK0t-kxxxw~h@3^oHsR
zoPG8jtX=sQR_(4vT!ec+U6pBh&^03^(-nWc?@COdA}Rmpb-4AhWe5)nAmAiRtJ;Lw
zHkG9#VCEgTY5o{mP{hqv<&j0$RqOGR+3&EriO{d7WjFW0F1&N!zp;;Jb<Et$@aMB<
zAR!_M?o={>3H}<5tdo4vCG;dp#pj=Xj)VC|xH}Qr$8u%eLDS1$^s3NVkdjf4bFQC>
z;RylA-MI>1uG|9usCX&_I_-Cg)wzNS9=Gvx@Xx=VhhcFM@NiYfWTMc#V8H_XxaAwX
zzbOw(Z@vjb0vu@7GhzSQ_wd-)X^5kjleoaQn(ATW7Ky8FS&B$E4Sra^8qd7`DHh#w
zH_n+h4(75$c=2D)qYw^ow54EA#ZE<~J#M@GPZ%2Lh@ZB8g$JK_4{?d{Xr&UTTjO4|
z)zl)TB@U0>c?Dv8bl9=_WjwyF9PO2Exbe<QaOU_Jl%;OQzn*)S&IsPnb6?0lJW<or
z8e2}GKW@}SoHJ`YYBIOt>*wEvDLRHqm%h2&pQ!)e<24&fc}*qRa7dVozg%}ALYxh#
ztIEQgkH3hlMjf0eWS-Ea`(%@%l1hZ2w8I{_|LL1Bh@LH$=2CpQd>KAilZKc$p3Q!h
z8RH^S*`za9BDFCLw?1$S#tn^wj|aW$s3@wbn2-H?HsjsL-b7POJnY*kq*E!@|JH`e
z!&#`Hr_Z@p{{=I~Bq`^hwITz{m%W3HSr&x5X*;~uc)qCgOi3xioXc;+d1sDAh(B>P
zy-FJEs&Mw)gZSdDC$OQ&9Z_EPw7gcpHE}xrar0!vga&rQUB;S9%pvYtx9VMdytSM-
z-WSbv6|fB%hC8mh9f=Xayx$8hP7b73EazHRO<Xq{o4))QZ?E}@@Qfh(u$tHVKkNUM
zCp5$%FxMhI&lk7cdo#uji$|c32iK9Fb5v-T&Ygq(yS~G7&wc>+m{`(2!jwEpTWtYK
zb+NeppSNQqm3V&M9u(^76-ME<^4xQ=Z_66|Yxxg|5<J-7Pzws@I;w(FQ;Tu=U+%}0
z5rYsM;0;H<z{2V*HA2IG+JrZkeTs%Q=WdF2QT*D`3VtZ13b*|8K8%kKhi!Wuept5}
zPrddv;u69*FS_89I0Z%eVa1d0Vfw^G3`!V}Kc5$aXTLBZDTZDFW<?e8zp@Pm<~aa?
zKLiAH64TVP^_8(#WJhbv4Y2p1yH<o9vI6}n1lCX!OlGySsoJ}G3e-UX(TIzQfHCC=
zwT~5OweeHME=nDWAl6}CJvLTUTodiXLs7D4J>ILzgsrI(`j}~0a{f#>(N(gE!pKo~
zoz52@^PxlY-N_KEafCfv+&LaHC~CaC@ZF51<vY6ZaU8Q;mv~uDSaF$@`^@4_CR%(x
zuOX9dW_I?TcxN?^i1ki|M-axf53x<hOz5~jg}B%mT50{#VbI8lm^*C{k}W0p`kT$L
zi||w!6`8Lp9CUwN#-kS(Y4O&T@o*kJ#e#N!s^`DNieRQIsLsI!fuZ4ald4e#4A~p9
z<{civu08<>kBCH#r3mdNLYIl{C7ZpO>usiRK-O0olY!b@8MDClxb@<@l(<p?`M7<m
zT;t35?9t~`p~xDC+Bt8ogR*{|f~`6?4Ylb8TQllvT`_vXG)zeF#kQ(stk|3b&lo3l
zUv%rA+kyK+T2FAcUXy*(Z9l26JK;~Uyjsjnu=5{`zuoi?j2jXUKQGqS6asEBq9LD_
z+V)xseclw%r7Ll32N}eCEWPI`Tr_7gd>yI4DJno_PAznfw2TFXV8pNle75>SWRy4}
z)U{`q<empcfmPdrlq?OdzWW+1K7SIm@QoC(Q&6XIL0oJ!W`zX7nbwC#?|mLE3GvX=
zHPEUi(lTVgTziDy&&9miGckyJR7;C$b4@nt_`(2D0`~mk9>3L7L6@0Pfw{MxiP<y9
z!?vj&)wNZ~%gKjD>x6)i5R9H2gh1jchv)u|j}EsX!l6Gde6n{qdcxbwFAx!t5oj;<
zP=rp|^9sM;v8b|EG^kK9=1NPnALw1%!L-w@oA|+&;W-lW(=3)PHW7C0sH|u#a)XKI
zZ{ormal;i05%22=V|5v_QnS(2riZ7uAA+J1h;QAHymK?Y+g$>$cyF|k$?9(tC2Om(
z1_&C2Nt4FFwWSQ-?B9zF;_4(S6c{<7G_e>q<fS5F)|I&K+DkAr%#*@FDbf#SqM3)*
z*FO+3!^XkK(1HU8UWN}X`?29JJdbT~)SQi|Wt(9RaHl1cXOwQfZS<rHjEF&;j|JI3
zbI-E5c=^$FKN78mYUt@w-D=}NJVEzoo|Bq-EqsGQ5gFl(qBIZXe(O0>{*WJpnU3zC
z1&2y7`=%Q)del(pc}~+0WFeGZIysf?@DzN*H8a#5B2&t9VkgQ%t{b22y$OO=1vwKv
zRO)N%X~nNcGapgE?eZ+wTwKDfITQd?K&!t*Nu#>bQ#nK7=(v0gf}i+L#(oq(Sx3Ta
zLaVyqmd;!+f;+mrw(|+XOZw|6zSMe8-+rq~$-EkS{OzI(Fm`Y}nR*NAoEnvoi<UMC
zOOV<3Fo-_9K;Uhli)~7B1{U6S3#N`41{a27wYM1H=3-A+h{uB`d+NvOC+#z6XgSaI
zz^xBmLtHl+dWOVhGIXQK;6=V85p#))*gO99*junope4NvE;{+JWvwXqHfk{bl3Or$
z#w5gq2g1dUxf=NH7S~2NJ21q9UTZcUgbnw)<oc-0N#|L<1J_+XAE6%hD9lVnN|hO5
z6z;~K5d?2HCm6546FL5g$StnG4L4869O7v^dX-hzR3Sg7KsgJ6#9gB%1|cXg0FE~I
zV|9Kz8VkxW|KiD*J8vdj+ZySmR*j;(Y&2VJ;YYkaWXu@|4krH9KY{1JFF>r1GxuOW
zVA>TZTyF_S5rv}l%qV<O7hzMrSNO#)d$wyAT|X;cM9TK=x|jJ5@lLSvXxwwBBX@v$
zZsaH(A9pQd5<Da4MhPn-eoxKP;%`shhB*_5z_Gm^8L0=6TT)NQt5_Nsh9e}{pUQa~
z-1+ZSNJt3f*`~t0DFzQcdM9R#jD;QdVg8YP<S=Z*(cK4O;W0pM6xv>11si|&ehKJ`
z2L?UNQc}ur-F=T^0qL5Lg9Rmpg~&Kk3%!#of&)WI!{hPsss<F+I>FD0I6!bM&zauN
z4yA{ZG5(@EFmv*F_&XWs^;3%sPkKLr9(RPbP!7RaI)*Btbq_XePsOkWBQbpP0{DOS
zFPLnSs32(Sq}u|sfi?hv0SNr}A)pf%H;D^7w#ng_a$$AU`W6*bqd6oHjyh*}^TsB|
znA<AC9+`cSPFou(wFJ|`Z6|@IC??UVD=@;rPfuQ#NQ8`P5t({AcB#!Z0C54TOQ*!Y
zX!tG-V-wq|OYy^Zxu~I=XN>z$T*9t3bi?c5l&nu5_ghO1Cr5&^!)d6b=38wJh=>WH
zw$VcOGSyvPz89uS+}8@zQ^8yrYSVimHk!<wFj++Myh|iW)G_wVj+SXlT>&zR$&J$@
z6CdFNi>ZoMsbb~X@Tge29Z?y;#}uB1Oq>YsW@9zuh}3)JM*pxFCT7wqiCs9|emCjc
zOySK)K@II>8W?A!WMw9iD<wCnp?jj1*V`!+*^`kauhG#|WrU@<j$<ygehKf82zV2j
zx6+l)3Wm;$efS{mk*)NPm6+5P3UMmjWFHCP5av#{0tFQA2Q!ruU2S1P=!JkwMuWn<
zLljU{AtpF7mdpm9>ncQ3Dbm<Pms+l|P6C-b=Osj^jY39~vUl{dPoyzsu%V9ej<i-f
z)7`bTt{f=^<?1z8x&sEds7e)nBMezfGvkL_8Bf;E{mAhM1EOIcpoQPq;EW~;SUSmt
zU`us5-%T=4k1)cyW1TzRs`js3V~tyJSK~T}f~)V1b#W;G9X#xaf8Kf-=1w2Xc<&0N
z@81VIpHPJR(CtwX_6TF?mj6Ux5v%9qMYm$^nG@h`+kjmg*5dV-K1LnKENkTI8%yhp
z9U3$~@S-B3&C-3uCmNrQOm%r$21d+Tin(Wvfs>^HTi3jgr=NHOId(x<a>Wg};-WJ#
zddhU1zvyQ?w;~mZ@f7Y9VUqif=PV_^34ea#BE-5`aO6lK0)jlr@Eo<=p6LDEuW{T;
zz5*K3+UUBUx*OZR_!L`t##-slY^tk7^yG!O>XP#i8<m7v7hQvoZh8ooqy%UfBj4AR
zkUb#pX`v!X+)O3Dv!^C=a0m%U+0K>tXVyApttDY%)!{shTqMejne-&e!1Is4f?RqH
z+tXSsil&y@B4h>+#TBz>Bi_#*nR_<it+zi!N}(vX4D@`kML^<2%sC?(CNr&&JS(SW
zJ?H-B`>HC+Flzc@45c?gdBFjsZ`ue=c$8w9k$H&%%R=jMq3;a*<En)i7Ve4C%-wkJ
z?YFUZTMpcPT<A%q#nj7|Vsc<B931tE)u^SJ$``VA-hqj@`0|N(_`M?-oalmPo*$tJ
z&4hVNGjT7^uQXA<i5qS!;YJmwK$&osRZ;h&f=7reT8VencFc;2^|ZMVbzb8zedJ&Y
ze2v(;aSc`<EXB~Fv3#Us&6IVcJ2Jaf@bNL-badYNOOz-LHBAT%2*hSuFH<*tMsmir
zhzO_hfC=b2@8ob9_g>d%hTc)E|9Ui(A4Wz6aSfYiKm_ANnKyv%x6VoNh~Qc15qEKq
zHJJo=N!|<Y4dR^o+FG=0?cgM?^@Nf*i>c7zp65D=g|C`Q$}p@z002M$Nkl<Z1UpBk
z4t#Wy$tfc{=7-RAGWF$T>L)LywJs?L6-TmR@C}BRYeQ102s6h#)%QQyzYW9SS_@Nv
z35zj*`Utd@<)XlzAq;j@aLaz6z}uq+;skGzybVWE@-S`DZ8+<UVX$qi#D-OG;>o9$
zq1-zfH(dKCTrz(OCZ0J9^OJwZJ71R|A<kKGEk8lGotGp5TRpAh?s3;CiZg##=4~j*
z!QNsMVg@IWD3OMV@JpPiAQR&Mc805zS;pXj3#K82VP+Y-*5Sp+@57FA2aK3{32wjc
zVnip6!h*kEftw$EM|qcyf_8q|kJ!29Q+jC?cH*vx2@7!5;tS|alYp5QU4^yR--jwb
zVM~Q(>5+q2|Ltn5`<cQybNrZSo%b0$14}PojA2ocm~s9k`0lgYVDhE=J2#B5WPNVD
zSMZjQ*4oNK6qeU_KI#?_27fmPZeulZn&f6_;u+N&9H_Xp?##~HyL_MkDd7x_t)yuN
zdgqA3M|I(@E=of&y(O*9J17jEPIjaTr{+aR>_~cFi#CSTI5|7f<E8t#5#ilpZbW6h
z3HGjD&@q%UVZmSMeGw1GwkmA;;$8gZ(f8m<PmIbM5B%-v2Qh!rP>dKo9SaAo#8+lA
zBpC&``o6ny#&CMn81wPPrz`Q{$6KKF5GA8M!lL5f=W3*4IH-$fn{|dK{w*=*Wprzw
zw&V_+J#8$!boJQr?bmqy<xkMyV9#@}fxCYk;zJp-rT2#mW634PT$&AeW}AjWRCrCq
z{pU@Gv#FFy0~dsN(bz$FDy<XL<jc5cg@4c3w+p$mM<X&e9%sym#PY31?mOajIs5}{
z00ILL_}xH2w|@P4)RHl9ad9DI)%}1<Y#yqbQC3pL!yOK1XJ`15>uKP9`Dtm8bUM&@
z(nW@Xxw97>7>lWAqKaz9BiGhj;2Nq!UG6~?Hn?K`W!ED<*oz3X8F`0x<Nei}DI`Y2
zi|#osRw12)Jut)*9#jf=Q*%w+q!Pnkz$MJIp4PkCVj}(8cFb9L6|HpsN@ADF;%uyY
zcNJ1OzStOA!6eBR2~kUJ7BWgXo=dJ_?3ORBlV%iU9mHEJzJ_yf1OgoB&O{3@96gZ`
z>W0$8JE4s|3m07xOV_spc<ZCDp&4`rZo2(SI9p2b)#vZyh>2D^Cz1vVKRTOM#sX)+
zXXqKYcK&7XA=B1eUx6Rjzl-k=H6uDE<~Zi7m+2k-ns8{Ni@yuwG`+pq9~nhE#y^P?
z#YXRd;e!Ul$&=PevXgC$^)4-~LMz?uG*qOB`*sGc4BD89xc<+Jn3#<T!<uUF^QLdG
zc26qfhvQ&Nrm6eNAANOipF|ri46q_>bD`vS=VHl6Hd#N>Tr2meUv>QC*l9sOLQdzL
zc}p>fYu;R4gstn|!>&wPL!u}YkQoxIlg7>&L4FQsqE)M|p0TTB)PzA&U4|Xt?MIg~
z3THJ;KqXFE?bH%SCAdM;P>)T!_QG$>S-9!KVQ{r?M=o8fKlp45!lPrAI9fCF8hFrR
z>ERZL`Zgx);(!IFTE@=D3>t+%FDH2s!vLAQu#z@k!qiOVh?k!ipEskjj0`pRpV~+O
zX?gOZwb7PtyR}u7Xwr~?D%@PR$@lt4yTZIWqOGwWnFn`c%hxOL!ip-q`r#vp(%N^v
zNBJPu4oe-%v<bLo<VXa2=#jDYOZ@ZB$57-IfFN<rBRs0gH)7XLTZA#5*pB0u`Sq=F
z-PlKYyAE?ljzxfr0i`Lsu$qEfu5TjTbCZ!*-Jl4tZccs}k}w2e?OUlp@}c5|@sm8u
z)hWqLOm;iwjEhG8p0ALijYCL;pA7318kH$j)I?B;v+@1s@J@aXEw2or@$iBZ1ttTP
zgKIx{7Lh}TBG7j@W8K4XPNV^!Q8<pFJkUShL#%1)KXqUCJX~Qq+vx4HpCKkKWSU&P
zTu@e6f@mf$Qw|5uUPbn9?8!AN44~OUd`MT;VKc6zb<P{DHF?;$emU0eI7l27Nd~C}
zRn^tVT=go}f5PNLVd1n0(<0MVVb$|cdi++#UT$p1CDTU1&9;rP<(c>_qZ#poDQ{5E
zC+i@>V<D9s7u<O_5<`7qVwT^PuRV<A8>%>_Knm6rw9R#R=h3^c#fyS><P=O5i<Gt%
z1zDxadJdU17xC}iO|J;zAS%5S;aG5jf)~`GDL6oIL6`5k4qCwz%C(+1hn|v0+Mz0(
zwPZZ$O%OwD4r1e`oiKPt@QkW^UEa~TZtVP|JDq3S#?}#DWVGAqCf&@?lDxt?daJa<
z%)Yp7I-4jR^v+D=73su$DaF`!Fcl+bU5JH)qlpWQIJkQ~zTA|H*w`Riw^ZB(TPFn3
zL!zmn0d;kzZn#TFyzNEYWvQ<st!$tb*awM2;;1aCL~inrXm*dLrH+bUB4$xc*40<T
zfpZ;6*YL*55(-k)lH7rlJn1Yu=O7RfMYzUz1m$7V@$Y$)q37psw&!5p!YF0Sp4!>$
z@(Ow+=`ALt)VSl`OXnlZr3qht{2d0Jx1`f@s@&c4GI!~y%SQ?p8s-`>>BBK-*a&(F
z>4;Bu;-hC)pqa`zWF?dSGsz&8K=!Ud7&T}RKK<$^It^1fM**%+5F#AKCEmb12sI@+
z$l9HZSMGWg!3*!jJ;Mj7=UIl-(V67UY0S;UU<%Wb{$6OR$itS;{)5ejLy#DdgB>~L
zOjyWdqdG6f1rNncTHQB$492z<FX6-dBd8)dadRi`qRYC$V2}07UqA$LSFrymc>9K7
zO1vFDYK+9ewaf6```IWgvylFKk_U5C>ZosD^B#hdh;xId!`&+clO{Oh)tolOIjYvx
zF3{`sqih{5_f~yLhQdFMpLrhU#fHJn$)5Ou-U<7*V9l0;@Cl2C3(2mtyD!3Bond5R
zwetFAo;6ia?D_&(R+>3r>+FxDNPAS!OQ62WNdASf_xZJ$dd{U-93KpW5|UJf)StIu
z-OdyQQ^+M}qR{ZJ{q_EtxTKwDR_lhiL2=w4HONceg(jCM=ESjdo|}e-D(Kz9F_JuB
zi?I&-v)jpUFbsj-OtsaO*#6cEXab`U;BFwVdkF8W{t=UgkS}$o<$CB4G`znP2%C=y
zLt^1%W5njKmf?ZtRv;uS6zvT(if9_Jb7LA>^zH}{Jkp=@Ncy2`t3`!&FeZ%{jv$6k
zq;6h^f86~zj6Ol|;og-)S-F1$cI>c4IPrx+5{xQ)ki4&iSL9aX%KH~%c!VRief$}m
zCM-mR@a@*~W&JJdLXDp{^A+sLJc9I$QbZ1qz@WI%_;|x|v})w6^j(qN`??HV8Gyh5
z1Wp$ObZghHRfHj5Uti^SXU8h`CPAu2Lq#PSP+H?$9N<e$TXLog?tb_g42||ie(D~)
z^1!ncT8YG6U8ugLfJo*+Y?e(Wd5Aq@DehZzHlhPPY0V}=C&Fl*aTcaepN7}od;z=4
znIV`iJ4z-=zEet#NFLUcWtNoTiQzvpJqyFn<~uH$iI^ZC3L%owS4v-)G5>;v*t7l<
zJn_yt1k!D(u__Jv_>1uDBj;jJv?S9|35=!nOiY_L4WGXLC^l7i!Ehu8qi?$oOQsE@
zn_Lww#GYjOU5Sv#ATnw&JfcPrvC-l{?RHW`AfA5iMHIGp!QHNrR%%;ZbJt^-J!J&E
zob411s8IDYr_aC{8$V?{-bd72Qb?zODhh^^H(FNp%}74%jK42kf~gdCnkWF+J20sa
ztutZc=i%Yuvy^Uh20g0DG`xGmE!Y(v3xAs?CYvh7g@3vq7tR_Be@_<*jIsn4%$PA9
z)4u--Z$0$}8hNjQ!WenbzPCOMUAjBAOJSnLLTS-^;PIC~!0`A`h3JSNJ9Y8|e7)*z
zy!crL5=l_AvU4zd=>xcS-Z13;xCJl#;}JB(B{Ih{=apu}Wj9=oMdyx1@t&{o@@p?(
zUs)R_Ty!69Uo-&~hjt-q>^Yb-YXZC+nTeYg@Rq5QFmmu<JpSMda2Onpj2wSFdFQnl
z66Z%td@CHOfD-L=((Ehn^mIuxL>`!~ng#oJ<F(uWfr3yw)J0r^`>&l3ciJ~TxbIqQ
zXbFZtW5pU9=&I;FAGh9lF<hIA@!4y4;Oz`Q#Pdi<7T=RMdLvL<9}?P(^<RE~7q@L!
z&JgTolL<JU5fb;<`rHD<opAwT62hS|m0`yZTaZsUdNY1hLMo~Xi<wNzABj;OFf&$8
z7|=c}n=({cr#32rY#hjN#s!iYXhB|nF7_6-B56=H4o04XCFe|q7hM6HsXPjeizS2U
zjXkBbM!M2^Z)Qep{Y2cm;4C<|l;EA`o<QV1@2d)nzJQ}gfQbOshWaKO3O6zE4^kBl
za^AH%JNSw|v95`8r<G7^3ny<X+X|?*5wp^9JF&+Xbbi^lA@T%M&<YF<<8x$~s8p<_
zfNmgzq{5jY2%h2aC$jPs*ANP}rL9%8LVGD;0TyP?ZZ#J1`v|xacQ(>GW^eDPFz%u(
zl~~4pF?^k)m*I$ljv5U8cnk_<_zSH_6?q5wy)Sgcu`L{+C>U%kwW!yR!Q>&~aANpO
z`jKS3wV?n>WKPW{6TPSmaB=gc5D<u#N(vUVii-d)%z5s<9jLcI8$)B0F!SOGcxFX5
z;=*Y?JrVGFxu$&0A3a?bD}OtcR;^Akm@sYv{2a{Kww4O4J-G-<97KUwtbSd^>0UbO
z0v|0sY%1w}GVZoVuw?pZINM0FH1@*1Qdyjge_#DK7^9*nd{c;Frv21nGnQPq1ecu|
zgMDki#@LH*W_)r2W3_E5Kx;6a?xjOxKf%9V{fq)bEb{5<Jo(Cpal`zPDBQmtFW&ti
zYKXgB6x>yZMb})4OBYN;*-z{6+Uw6?N2W8bxc(vv2LZ_5^DXZB>r?Px!ZSu_AfE}y
zhFy3IZoGUB+|0%J`0ZD)c^?%m(M;9Yk2r3s9%091kI6Yk-1P7z6eJD!{?+%8<8+2%
zLG20lT|m~Sk7DW0VTjv|D;~hK;h{LPbp_TjpT|gAo@Fne$~#1<(@bH=mr1fDbGZ$Z
z#2r4IgA|^TNNpjCMqi4Hi057DDr}~bG=h0-B7$vEPMq$<_3VoWiYSB~T|y{m(-Y`F
z-(km3>71_tiBwFggYG_ZI^rRZRSwP>5<&%`4wZCC-t}!Q5{LTX(EbqIch>^M`n&N=
zo2j%7K>X}6_;%T0G{!o!Dor$e13TcsIcW8E@aH;L)i!ZGJ8+jLD{>s6_ZU3_?I?7{
zdv@F}N+lhww%l`76p~w*nbpol4<~m|w5Ji*IUY|ddR{o<D=PUj(kgMm(z|f}*%O&y
z(T<L1+|R^Q;+@tq_Z%$9*oT!LzJxEgGxx;3kK^1)Lr{6}3tWEt8;FUakV;xEyqGlc
zifgAgWqqSFZoB6WjP+~BhA-d6qc44h@v|4<wg=Bae3(DIi5P0f`-MieE?9_54(`Nz
zFFu3=mPmMVfA)!7@_8Nm$>iR;c<Bw8F)AGS`@hEnxBeTAaq;xT(@>#&2;p<D!)@2h
zhqtB@Uo2-10X`?g<(49^N^5(a1^!gJ8OS$!hdH3U)=2y%oSFQmnl0wfqte&klO8k4
z`22;Hd{Ddt8Fty;gnWiAcu>F(cca3FUPB6hqug=wAL6x9ORvVHGbbTBE*$N~Qf%Au
zJtRN850&Wxp9<3OX2$>|hP%_SLK?1&i-s9i#%ah;LCDnWaNg7*sLkGmk5>GE8>U~%
z2~x1{sd2B_koF)o50yoQFpmhN!HC{@%<3-t%$_i@u4PY;4}3QOfdL5owh+*HczB?J
z8vf%ML9v9;9h`|?>S{|^-lH9k)Sfy}%Llt?Bqa{UASSDF)3qXz4Ac?2<+-{t0*qyz
zY8e|UC}QNyt8vqkc?j`#Kv8x&eoV_oi@_ZUgNI`9xLLTJF4p(odJnUoGCL(1@BRpg
zdJ3b(OiVZZvODpo%g#oSw-YM!4r5Ph2IJ;6@C}Yc9Lp7r9LZ!uudQTeUJ|mn^YPa|
zU5Sy=USz_KVEc~;(V%t5u;GJ|FlIUv+L5_@_9=XEgf6L^kB5&R+^E+*uyY4?76oJ6
z$Y{(s=N!~9TlA)Nn-M>DB4S5P!Stcq@%YNkDA7gY_DBAL3#Jcew&HqZAKHs7mhkYP
zr$ADC49>pbauSAi-22pW#3v*$(OzF%eW}@J_GhL-r|?mU;!?7B#ugHJCi0<cSR)h3
zlvLA9NYO_KR$Ub}_skdPOhzdyIU5Ua{THq}ZyJ1@EXYXSk3$(na1V}FVw$F&y9AER
zKzrkZuOc>{T4-{p-Pw>|wi$&6c5ftl{8GFbV#Z9P7QF>I8M~04Z$k92QDiuWW8o4e
zE2`%jF!r{Uv8s+v?(kz}fCf)aimhF&cS^?;W}MvML*~a!&kZ^*(DK|42Y%<}?heNx
zW8oJ=3lzf>e%Mz`D?kk5nIB=|^jRn`&cq|HZKq$V7d8A9gsFz^rfu+}kmtZS=LUva
zl(C+TkZ4IDRY9v~GnYV1&kCc?5k5Y?kT66$S|8}8sxl_D8kyVe;Ymf8M^hV<v5`^Y
zA?Pc+q|A+{&7d*T6}%Fz6ncUTHQ1Lf7NFx-MPN|F`9?T<GZU~KOy#M_&M(LLOK-!3
z!7LR+gL_p;KKAd}j1Ry5fo^yV#pur&69K)=2uF|6EYoJk%-Qv*sw_aQ#sdcr7UH(Y
zA0Y$EI8(add&P}}yQeqYo&8|Uu7bhG4*U04<Er~E#*k1K9Q^iUEI-_YhpALJ9SndP
z!zeimR7OPl)EQVBjDPwN76#jPlsDEQZI=mwakO$%ApO-E8D`nfvP2rLxi~y`jW-rg
z5%O&m{<vUdV0FB&f%vqxxs}PM{4jL<Wk~wwDeT^(uCY%LT|QmuI!R{gmoyQ+Ek=ge
zjF?R&3T;!g4i}{z;-0662038m2#Mg^Kn205ne_Cb*9nE-;*32Ur$5@88Wl!ayp9BC
z$-y<)JHbIVp8QD(rOjA|ZQJ%^$l}QuH*N~P{&qX68~v&1`XwVj{#gk-sioC!=;WJd
z9SuT5$zlAseGO{tBM?Z-x8;QHw#WA;--ri}i1aPY!d%blBxdGTuw$;3KqhC>Hsy2f
zLjMFxgq6&n$`ltDcX)YvAmNPJ7{)|hRR!sEfo?!Vd?KPk<1lOfJmlo0;)C5LIP-Z&
zM|b%9Guc;xvjT5%F%=6xwGDJrcO~xfhsB+6Cv&WKD#Q8>doVO<F~&}wjlbNOf%{+D
zg`}uf)FsTs;zjcj!z6mUzy1d6zSx6^xR`$LYYDxo;P{5j{}X0SPC`}2HoWl0_n7?G
znaVu*;}<o_=V56ogehV=E;(-s^tHM8{LPp7_oeE7WADno>*rgjYN7zqz&t0R3?pD}
z2cGSU>OwRV7o}uaaNkSwm|LS22Y)`mbPL1b;N=TXdmj`wQi!4gg@@^+Zxkh;_)Ij_
z<)OOO7DI;(!q59M)DXVop5{7I(E^)L1i3TZfL?eF75UiHPOpgMG@Nzi-!XYa7>eo2
z-r$vp1aCK1!U^KKZ==VXJ2VXAklZ!WgzwTaA4D;?nBXou;;w4WFMV4B0%EyCsic$h
z*Y$n_aa|7iu?bA<XHO5rrrJ7W{8T`E5}{BE9@#!L$uGi^xEd$syNINRl{>3o<l`^}
z!t&}m*s)|~LQ*^u$DPSId{#KwaSyVp+TcVtar@{bL=)2q_hKtJjh-AD3WXA`-ontZ
zW`}X~G-0X8wlXy3?x3px^W|Ky2$O~dpsF+<KkYq)iUwAH;5tRd#4?A6H<SJ8QBlwi
zA7`OE1jb35@I^X=`BdPWwYxB!c@#!Ypts!ZS-9)TZ!jp{85JRuaQ|iJQz_tq{p&a3
z+Z8_|B1DUAKV@RcnG+E~kAPu|CgQWVHi1}BX-k(f*9Hwy<rHG~ZQQ4P=TF?|L^xM4
zPsgF4k+|vn*@z9Jfk)GXq9d8u_5GLlW=|>eAkZs{ik`kUS=S2AHJ09|o>T-GY0%0p
zsKbTV-i~o%@FLtRi}UgG_V2NL?G8kchD+F3TRW?^(3+oN48>zq+WF8ee${8MV{a{$
zuPS=(WeCjGqakBpIfP>7J!#+;^b8JSbro+Msb|QRD0lh<`(C;q_<8^W0}wc^5YUOG
z=6K;;eTRisb02;zW4+;OV;eKQIxBIlF@u>Unq{17s~X_Q811OJv@Vy~!;Kc>779eQ
z<wVMMvv9#VXCc(r0sFuE1ozzjA0k1qM-VFvJ_~O?bT{2%$KwiGBky=}Gh<hMI$~G*
zFh6RHHAV{JiD%<{W{nT^aYE|$ukiFe4`FwWYWWltKly@dFfpPXVL{$VJ#5DH(`RBx
zlrJie?89ph-iVKX7QvQfwWeHwm+!osnJq_P+SF+j4i78ta^gB!LZtlgpZ|u;L1*Iq
zC-21|_ZDoWRsP{u*5EIXy$-+W!w?bfM_sEKlNMi$SyP6Q(JjSiul@`FS+-4;&`7w>
zzxe^&u=rd|KI1Gb`0;yulH800A1S+ZGUkD<y{$HgiL&71*B-^oHR_n}c=!!04E999
z?loBY=rUy<>JJ-)Gq%W~fyr|c&cm4#M#0xbgMI5h!IgJC1&u4+xET(x=<cU+!}*gj
zm_+T0Q6J*nl2*h~g=+4PmZZyD`~FeHO&wev8nKBngm*r%Tz%AIDxQ7zc1#)+kJ&S(
z;JY19qt@)GCU&Ent;AFbq=Z+tl5f#4i(9Z{G*|YpaDQZe8k@{y=IpWWr_XWqU!PF>
zw~5BXZ#;oH)EY;{&?Ow}U<feb#RqRkol^J54^Pkv??)y*ecj8r;|=a%YqNJjWOM>-
zt5OsKEN;JMGG(G^7lJ4w66*mu3yw!+z7!^dUs=a$?drt81vWNRyohVP#P5r^*Hcrq
z`}C6J`SXSgdC;n|MvPr>8>UPi2_1#8=5|}Sj2eS+V@Fdl5sZgF+(65=2bsO27LT5K
zwtgpXXs7#~4Fzup=9`e@b<Ir5g!U>Xq`4JmjY~vL_C~DPU5smnjG;iM#+N#iX{<kz
zg2?HNTR&qYS_*!~@(<o;x2grm+W#;4-3?y}gKO&mbMYbAjbDT@^d_)vZ9q=)cI;$0
zLIUSY`RCVfT_le#ANgMSOBIO4#n9D1h{}td_&N1#gp7~Fm>Kio7aGr`enqILC`9s)
z+wkqSWOz`)=tJ*>lX(*LHZcvAAx*^=44TZ41D14aYGS#bgGCfF!#WiRV(l>#7x;SH
zE6P2I4>wjasT_p_5hmNIWIUWpON;UnQ8kH$E?Sw7%*$Xf<LwO_aMtvZh#oo`XN(z&
z*S`4)Q7j>K`Wn3^m`9|zsvTF(n?RUYkj&7Pw>RV?c2FW(XrZJ|t>4W;?-1vh7^Hpi
z8kWBCxS|}s=AX~va(^#HF(|^v(RGwhqUaH23xx<OslQwGI^$J8R^L1HT)h6kpD{8d
z5{c16;PUN<XmT^qKu1pnDq%Zu7w=ZLqU7)BxJIRD8~gMR@W+lXU&8ZVp19|lb1{AH
zrF7Zdf;YDq@Q-J%#IR6zWbOJ2kNy2QG(<9cITaKADpy8@ji$90B`w2o=fZiU>y=os
z<|Ag)r=6Ja<1y-NT|Rbxcd`#kHfaiu$r*LH<v&Z1;HSrrU#!8()Fw<#;$G^Q`N{g}
z@jAgT-aL9|PdIR2w-KhzO~ii0MYERPiz&k*QS$TWwA9<+`Vpg)m}#@{HKY$l=8-X<
zl*W=w)Ae8SjM(TLh)?P4%iKJCYu8P1bZ9&`8s@=(HiEebSV4l;=4Mtepy~~^p)+yO
zIWypB%E$X_zQWX{gGjSfk3NMNAfP+(mx8<O(N;>u*{B5=n-~c@DtdDc?Zj4wr^Pcw
z#@x*vMMBybPFTzPL#MLJL;_=y%{WL!z?wocV&X;7Y~&s#4|H_%9vyd=Pwm_fj;!~s
zWsLdZ)V+8w<4e5!_UbNo@Z*@pSa#oah)EiYvo9WrN8bGzm2)Y;+WTYTV0*m7C5W+U
zWUieI<+-Ss1XkHFA$Vpi#Ew&5TEZ}`8jL<KiC$O%Fjr>b-6!tCdmD1$=j(wIhT<V?
z2+kcDj%tfP{5cm16YOh~BzE@nP&&BrEj;Pti3e|3h{>}S;l`A`c=4OVxZ^*MVRV!a
z^7d}P(|10Of)ILn(Oa|Z`#12(kYFr2n>h<Ez75aC&OkNkmxFr%hA?EMCU-x+T>UZE
zz>`bnL|(;(VOy=JDA(fthyIT7^eAg&GD!{T&gik@FlOXXgnjZhUjF(B5&|9iz|+YL
zRT|FhO>lCcM-^G)a?<e0i|@dc3B#Z@)lmUr3+GW|NyA4`_+O3(KKve$?#v#~Tt>+U
zGjRTGk7I0{KXP|{LIV+L?ud|1+};}uB?pZkFXmO#GnR99r=rk>Lc1syn8J@&PUZCX
z_O^lV2Ouy2fnNdvF(UNZL{@X8`%6ki4HLIDvV?>SUF4iGoK^-GXT~)Cl#PTzzVM5s
zw!Fv@E)KM}GdWzz#(K=XY64=JP^~U!58i(4DHJglJS><sv1z5uJ+Kb%Z=@D-@dQK+
znuN&q&tcL8P<&Te!CnWXT#(GwjJ?Td!=jtVDzTgm#c5di`oFQeDFB0#)Wk5td}eNV
z16!E&GAh&=o?+p1+l*ia;RfvAzDW_z6NV0>Yqt^mHot=(CXYfiYbqxWOCnQG#+81Y
zqTy`i^=u+7U!4h+wT!OP)i7pIO*+=XSYrtf)iQC1w7JuV!o$#p16#gSg!912NLojj
zB&aqIpT4*p!-kH;+##&doG=O4vJw`5#{H1#I~ht8k|oL#5AP5Jg_yBFnHp3w6Vh~c
zF3hgX@B=27(b8p0VkO1>E;qdubFYtQ7H>}&OEd7*`>(L}Y8Vn3C#&(#!SbiS!t~LJ
z7!mA^QFAW9I}a{{mJEWKmc7&2jwQFkoJgX++QSF&<(gH{2E=0sEwMlC{T^F)Poq~x
zAOaaAJ2W5|MU2svb?p3i%)mOo=RMXY{cGu#+gOo~Eo)z+D?}_t#p$qT-y!VTlZL5-
zLl|@Ff#`64$0Q0tWOl>!CLBm(?gEM3BAL=V_!6*gNQ@02BTa!;m_4gSv)gzm#9Ci0
zpE!)8W8#IQ7d!9j%LgSFgz}yf+-;pQ<t9;7xJ3=Y6g{(MZ~F>c_oh)mcS6G0@t8bj
zFs7fgSV^Gx-1kL@3v^TFbb_9bUg8+qDLg1T8^vvbVvCkZ!OoirFHHsBefLquqhChr
z4&5{jZU|$doplHI;;=CS|5`j3Ar3~Y{$MpWWyoG7V`+s)$Jnh?IQqi=exl$@S9aUB
zQY2R;;K9qzrB&YrMF%$F!~ZNpU^K0a;<x;JX|ys~C;$YrEaA!*-egSU6&O1x65)x%
z5jAK8J!2Yh)~vH}VRkZBfA|8{WEv3X$*SXGn(Aj0g^`iWeIk{Y;swIg@wC)2fm;_8
zD@o+)m@6dSm!*4{oQ|ayD5!ItM43VXNFSVptIxfJ@$1BcR2(t;1ya*;6zj9BrNnPz
z--q}q<$Od;8H8!GrYZt@KZn}w_HKKEXIaCu+*+Ip-?MJO=-6Q95Glnjy3N8r8d?f!
z;^%T|jeDDCTJPe^`3K|Bfn<gfF$1!4?!;z$Y*RO{R3oX(Jb*Qye+FIXAPjXg;l~4q
zkd~TDA$l+Z0zBdCW`j&-AQjhawZG$gQ}6_@%Wr8WO4~ycqwxJZ|73Vi7%rVN0`r#K
zjkci{rF5LBa39`(`%z>EMk7(r1an-+jz^Cj=y87&jLfR8V^;M-EV<_{jE(j{`qoeJ
z#)@6az1>~FMR4e6WB+<8fl3Z0W6XuOV%E4g)aL$(51;-}@lrUx>{P$spun=7FzUob
z624(O1q<d*g;Qez-g)6kR86^q!V$fw>{!;!#~xcVo4FPuFsW<T`>Z%;;(4LsiZI<q
zQRQS_84B>FVu~oU_?&o5vE8urVhj$@WA~?D;NA5Fn0Ptq2hY5>iwDCSn9qcQx6EEq
zSP1U2R3N!L2KU~30pf$)P<&_$mOuX<{Kz|4x`i)r4)kcWvuVJ=JZId@5QX7U{-{2(
z7oWcOI_v_%6|Wn~DdEC=915J25%usx_BfS=LT4hqS{YaU21+VbJn7}<&k!97hK>y^
z>-ilkWXwg3FLUz*hEV_TDN2o(GbfBQqQ{Lu`>R{wOa<fPe|r=jb*Xq~`3ffOY{!yB
z#+Vc3lvn3rFO?IsDadmBv2qOE*QxxFR#ILCXYW7?<worJVJCCVI5EVAPUR<Cs$-^O
zrZOTaAsTyDJdRZ{;kf#|37E6^Ml^)gV)o=jw3f0W%lpq@zgGkjnH{~6u{|M#+egpb
z4+s0Fuw>p8OgS@<=c$c&(jLuKxmdmC4ZO5+EAt)1<6sGs$nrl?ltxWFi)+U?^i}U+
ze{K~t^wO9xV;Y9XjKKW!Ey&q%19rDY(Vd=NRsER<@qj4tJY$AqGI4FjmM`e_m&q_B
zXC#fAh)JW8=(%|*lXo7%EBhM}Y084nm<yQkeJpv)!&vpf2Rwj%_qw8a3yo9|io)1N
z&pa7kcGy(b0AGf54733V3_#%b0|BDV-kUHm24}`b=F$z28aJB|2RJ(of~%)L%b!(Z
z-+`YHIP4<2w?<%up_%bsKxJJWexi%-0>(r+YH7W44#c0H{{Vj?!)PVTiUwK7xF0e^
zZZ2M|D`|(1nan7T$ou51D48C{elxQN-GV%n7_)pPdCMqmVaX8}#bTi{989Xl?3bRw
zOt4d#!!kdi@S}CFjTzX>s<O#lhLFKCp~=QpK}%J&<)|kEV8>)@K6I%R!YfTmbTDd%
zZf~@75FQlONJLaFS6CTga-BRFXl#21V<Qd99Q|4AblLJV7`JN`;KUuv1}=1m)973f
zP6bC0`)np7AmOpXK=q{w+|+RilsX>bcWoTEdQHlW5mz&2QgT2A&|>OLqGu%0IoxIs
zKL+i$(h{nXGI`BAQCeqYEVwIUb^Uoai6`f(G7o(j&B^bP5gt1=u_fA&fRz@Op+2J(
z{;^ud=~)oU@AK%YS7&NvnGq(|p;b?sb<Ze&^!nrITP)<3v~)kMsi{Np79)J3XgyRc
z(cJ1r6B&A1y*W>b8x_G)fG_Ti<kpY&E(=12O<-;HFzMSkj&^a}#JgNNtsgm58K<MO
zJr2{t_RuX4Dheg#9V^oC{F{Hl#3WigX3fEKUp&J7OBYLCR3XM4++||Qxjc4o9iWxl
zT1@4b`R8kCm2gM$#*gsAh8oPi)?V#T?&BG6qO08bf4%{elUQ^6=Wp@xTWb+FVi+>_
zrXF2P;b_^cC18|4I(}FM7WGeAo32;8e&z*XSanS}m}-lg9=ZuLM@6Bb@E|SruVW|U
zN#kjGJyrR<3s}0o^cR0_BO~ZQe9=(86Zift53}b@#i$_(hz<>5jt?iec?MvlACvs~
z_`vz)|6s)dW|8)G?7LE>bA-ZH5=4R!)c>uE-g$@hgR<_*%^lw`)TjB#Vfam1jG6Nn
zAjp9hLNarbP}B9ZRZ0>xS#+_Qdh?N2zTM5RifDvT*}8PXYj~qvPWh=d8OG6OLmI)D
z=_)7ub@nJ`lr|u5`wqPPeHmioJQb!^aPTi`vcWnxS@*Yh?^oa{_@325N~+M1(@I66
z4Rf>bF2<r-SPeox>LnyVF+_F2C*3<b{x42JT!fq3Y*{759WOlaE~0|mF(&B@Tpi1|
znhLP`<K<Yrn=#P|OtZ?$8C}oq@f~*ghEviK@0-$6;6CL_oI7;{ETn&H-gyE4$KG`S
z##LO~vqh`kd$W3V!^VZ)3>ZTI^8rIlNgyGR@Y9o@kowaLX+RQ6LWhJ7#$b#KZW#Ao
z)O(j!y=&V)cb?Xg9NCs32IYHRf-LR6H*e<5%$?h>@AO%SI<V)6l0YO)GtXpSy2Aep
zsv^{lZuSUfvJF{Pgs$QCkn`}P#S0PZ(}|bgcnMpA(vjg?tAuOysKBehF%%)j0v&Gj
zIPH&mt?>vI=L3E1xaGbtV`h>cw!iT_o;}!)B@3l42w%dsh8pW|)$KpUmzb^EpXVW$
z@3@zIY@=efwRRVN$Z)w{D)pq~+;J~?Xc!FjE8piCqB;4pFJjR-d1x=+iD!TPJR;7z
zk_rmmA^+!pBdzie(k_4ImN{_PjeCCdWh|PXjedFoz4-j!vB{l?JTeBOE&tqow0Hyi
z4^&~v*MEcSFFpe%3TMk+ej0BSg<@*56D96&{OCTq*E8qHm`W(#!APG~cbJR7+K=}c
zdUW5fo?;G-DVRTNCN06UF^!=Uk@P;3^H558g+ww{nR3S`b4QzQVQmNIB(o-ahIq+r
zLVEgahW^cfUvCJq*T2V4%8<vTuNHcMwU959tG2NUjcxrb*O-eXw|<Xyf^Dd6>0&5Z
zBR+U<6WSwEkRKrHn(8)Ud2$z(RSZeUqi5vfzkCAOQ=)LejC1h$6e^9KO<3{zi}>fd
zHsnuDW&19=^$)OOMi9RLr>C%B7E7bGRbchry{K&JMm&uiXNU*iE#GIk%NOwALr=3S
zSxS*d*9un0@nGAV&*B@u9`RUws<0cc{oppFrDkBk&DZ0d-@b|XsZ-!Q8AAsbhtOa-
z!%Md;$M?VfGkTj2UoVv4uTOrB_08jO{*_nbukZXCB@C^&?>jdlm&UUVFT919cJ|Z4
z9>?uFyhl05+-UrdNooa8S$s#~s`yCtId7v9{;02|0exu%Gy<P60>>55$@#<V7fN$1
zg(+sTcAHWW#ago=jH?@BiJ8jsU9@uEj2ODzOv!TcFk%8H#`HCFP{C0VwCGav=%VF?
z41#h29z9sbRM%8jF(a-WmT+2ZIw|zhh;zb=l*BW{_^+T?1WV>}zC+4GSZE<~vj+5N
zW=oasU>0f*OPg33M`R?0lLJ7AmJx3)Aw$vpyP~y|?r?OOV*BFeEq580SdU2Vgo3FL
zMW_)Dj0Y|WoLET$Sl$C9%6`<qdgx9kF5JHK8<Y|mqp!i<HdK_ed>4aK14vv4<>e=J
z36rR(@0h1JO^U!9-%-fs*g%g2Uz<OLb>c!|O)=PrtCS~|8yRbA+F7%hhw!kxq}u$~
z*2lc+r2df6D+G=-D)Yn7=yu%Q)5o|wH(eIReUUNS31)0BvoqsyAI!mYW8^)@zeZy_
z`gdtxG~O<Ba%@)qF6R&>Zuvs>M-D^s!)i`NI9CGtn3u@|3RH#bn6F0TKlZh3{-_Ve
zK6vtfseT{hvElEe455@@au2m*$ChnmkNg=wnXT64o?Yv4sAdUfPfbD)^C_fr^s<&!
zoZ9g7N;s2lk6r}cR}vc&a}=oB&vY^M1fPxDFP}|DvJfvm{a1BY5aSPRv>v&b^|ylA
zfHCbR+_-2Cg@AIr^!s0+NbuUVTixpo=WxsZ<#n*41z9mvZjHj!n5*LrT%MHgEsVeP
zFdn`FcYOB`xQPOsyQ>l}JoX5lU(-NACQdPYa`%V1PSk@B)22KlOT<a9nxLp81UkC$
zCOuo;{0HOa-2;f4Iv0x<FTtYo=dl+0Ok8ur4ftT=uP_uLv4=u$CjM55v6ci`)hwIj
zV2LQ^9O8?m0X5mGAz{*j!@2M3JDGTLknZbTA2K)e#IYtYuR&8a4)584MwV8KBSW4N
zP9~l0s5QeDWZ%dE(*5pV@&2bSz|Ci;;oNKQ#NY2*Zwyqu<>Y>FyB@hju<W5=nYr*b
z%wi4?OK&6Aty-nZU5eB6i73R8hK~D-P)*~d@^j?3aUN1%N-{?$+WEWCR)S#MNad7V
zf;(X`#xGy1PmLcVzsnr(@_wNH<+;Y38}Ls?iq9r|?U^(1)_0d6ndKF?FJFWEpId{R
znP;$kr?^rNKd%}&!4J{~i>nKjo|*Xh(kqb|;=t<1{)(6Oiwf6>vF&694AmxSX7iy6
z)No8`=}eF(w3&TVZ{xoTC$)S=zn_uYRNNHQIPtmfEy0;N%u%uWY5e*h8&t>2__MdU
zmoV`ewmV?2;=fI3>j^@zB*Nkg6xkeZUw-$N=~N92{}5coN-fG&7uTpy+%>rV!hE!q
z?ZNX;Jgx2uAT1K5xPxW#3M#0uXIoMx(_~^cb)I?Sk!P7cHwW5JFBRUd`pA!tf60w%
z-i0>uyd`dZgTDC1rxzl^(uo&deiGZb2xhYspN+Y4Bm;d5Ynanvi!s|=KZ<sNw5uGq
zed~U@TF+<5UJYJ){4qTJ4&9__fgYj~#(N1K{(%<k-(P?$Klej?{)USX$fRe>{`oW>
z_~%B(icf>1tC<#I!PA9)CIprI9OIuyoJGbkdu$B77=FQ=1xxXn8y7MhF@(ze7UsI?
zQ@LIQ2ju}W3XsG5Yv4F1or=U%^>lJx7U^yW`rLt-nVrZ1UcgYQVDdq|%mK0!0m)NP
zw1+PE%T^+2G0V~A%*Abab67E<7wxTfEWY*%CPjT6FRiLYUT!F96?e6oh~dti@E>E}
zxL5@tD||P;^Z07K{^MJbkr;$MtC!>9zrBS_<~(vS1VPHB)*qs4_&0xp1!v8Ge@_LT
zeB}Fh?5!e%l82LO3X8w^bA08qSK|E3uSIRePCUTc<C61493;9sYO(so*Abq^Y9ztJ
ztkRot;|nNhxtg)?Q3#1m;%9wubI-|1I|{brjDqV@&T#USuAx?Jdw&bPO~Y6^GIjV2
zU&Au7OGy6`5F8_h6yS<GK1=UMNs9L#?!W&z^;uz2I}dhNv?1*g_?$ibn+la0<|m5v
zw~p*Hekw_rL+NGVL&cPv`J(EWY%oaNE+^Py-l#8)fJWeBKtKuTLdd;L*VuPbxRdfu
zuEVUq+Q~!A2Z`xqEE9s!LSbsfs`qiujcv${jiT!{S!UM0?CrO!CY+4n@{mw99bAXs
z{OJ!EikwYLHA`W6sTSWU35Z;b=k4odvJl1=ib2y5eGO7E>ejLS6K04^g}6)!HS&m%
z>AiXE0FE3|!dw{l$c^%!aSF(xO(!%}r&tbKIkn`Qk>-s1#^PgqYkb24Ufx+x>nuNZ
zJNmHw<)7hyH}qjn6q7TJa!(a&yja`^87JlrNrMjsY4xl;e^QrsEPQA@u0yqD^wIm%
zD}Q<WEVtO0NKT2YN?=zH9W{32v+;ha=ZwbrwYSyb4@Q0N{dKJFh4&K+nQQr8%>lKz
zeiQ_xCadw8w@u^ig1?%-BOmjwV@$gDot#bMI`YiO2lCF5$K{4m?JG>Zt$3gDHsx)M
z_Y2c_<ZHf7G$sG8Yy8&_x6XI-&Ek+Iap6p?tuov9NirnXgO%FuF2-NS@cY`<UNUSl
z*TKkPDZriNe<Gw6j?vNAi7qNcoJ>M!rq@9TOP2W(e}#p-fe92{-OY5r<z5R2#RXU0
z%w#$JSo7lZSXmasc4{cEN#ZNI%JHx_(QDz7tL8GPTqu0VBwqKEXRt)Le6}Tupx_85
z(IEqtkaGcEe`OBZDmLTie|Q|8fH+z<xDUyAcXzfbIwQW=fuT(H7eEXB$V5rcd0FU^
zZ6bqPgr#5m1#Y|k0$BSR@G1q5hu_?Ssf<k?G_v;@w||JABlG$p9zMY%LI+g#V@n|N
z^72%k1qqY&8EC{`e*SHAe(z7X`?~X)RXCjqi+oW>?;R;KHfq71;0?T$Yf+S52MU{*
zlVK3ybTduLiAK+E2h8Ea%ouUCFim)WJLxhRdMUXS$b>;m2@G38D!LE9kMF<pBH_-(
z@91)$5>D4yu6bcUy_YC_l9I!);!P$SV+c{^w0XFF;ZEgNJX%2T(ogwp<Y{R|7W&9%
z-tV}gNFV!9<P5_-^Up>`To7GI*I>=&y>uhbVLwQyk8_P|pON2=bG!OV-fg_!+l%p@
zU|%@~p)F%u9{bmk2IW3}CEt#;Dj)E_kq?a4m|Mk6VY-UtwZ3;HOG7d+b!d>n@=OXf
z3#Q@Ob*&7$37~>t;;IQV^G6I+)FEZz0;Hx+QHibcuJ|fmzT#`@EOPUFvN4a0jS4t~
zq~hnVz6NJ|DPH`?-|)&F2l8SVct(XpKNFO8@fM+RHh(`>YzgB!AdTQ!b~CA&DK(R^
z%J~Q+9f{7r6feGbu__NI9!QdLI+9^vPE#oU>r21IHJ@q5`>USE@1HJVY;qvSL)Y)#
zc62gcTzNoo9fr~a&&R9aCR9?TVTini(4uhPXnJ;Bk3hztzwy#j_`t<VAXH7tx|ByG
zr)pScG<I?AcI{oMl;%R2$H2|My%#wVw%yHYCTUFKC<=D(oib}jU&h>dECD1~)$C6{
z>8}MD^A=+&>D8h2FW`@_?4lcZ6!$fiBhnN*C8hG@l6CFJlAi|-6k^G}Kf!00Tnyi#
zW|d6up;tHl3+)o&+%JHYbM_y`rAxnodp~;>B7AzV>iMVf^S>=a-gFA*{R1p7%IxsW
z8S>WC4`P!DWQIOky>sjjIXgtLU_o2;UWCuT3Rhoq1u|lNuxsNxSif!~4pcO-&x6==
zz!$%NL}L7t397N?nu-sheefdY8;ZoI(wQ(ZH;Rc~cf%Q&g7lfRs-Im?FUue}x(l%L
zU^_!06Tt!yc;(Swpt7t8m$33dIFq_2B&8vfiPq*Or!a|d2($G62Fpra$P44SBwC@9
zxJ=wPQtI%KJr3Xa#vGPSr3VnbIH%;F!5l?%@wc~Xk)IdNQTL(KoQ*k|QxHaevyAn?
zML5r&D&7DLW2ATG8-IHfi>_c?`IKm+=AR3^{HU5^r!Y{=576!(4L^E`4alL(ZkkM8
z3=fbq)CU1J=40a8=_fxW{szY%g>i<H^H%v3q`3epX?wYU<b3m<;o`k1XE;eX4PC)F
zi@a~n?3oNZ;v!GT#~)sJiDv_MqB<ij%)Mh}8D0<0y6Ovf{lX;-Q&@)k{;*EX(~!`3
zdX<R*?efS_o~?Y37^XNbtCdQX6ZDvm=}RM^5%?Gpph8-h#bc2Y3h1;z%VqEFBB3%N
zK85wrg9cIDCfl4Y?TvI{W1@<@Y+5au`M0apaLyTE-BvdbreL~tO9JBtmR<|DF$suh
z%;kR+1g!ke&7@wEu*7>7OPdCXGMF~8|2kPuT1ppr`zke-;ci4<JL`RuSqcmffrrUI
zSQe53p5dOZm?&xmC8o?m;xj~dsvhBjN!UylBi2URv+~6}j&KRf90ieJxd(&+J&8*?
z%fpVD=g4Rb(4COXODx@us<)$$@!^t)Lyd$ONPHOIpyHY2-5=^wQ^gkL;OFncVEHn`
zsI*6flm^vf;_={VU^yx#3*%v7ktAQvAP$`ZIiE92Drr*;k|O*`u#I+3wlBh{%R7yJ
zi0eNuQy}M&M~-S#{+15XV$jiqB3WbVejWo{Q&C~Ttodq1CHZWB2CQ}4Jv^*gtDAm&
z9vA1;t27v%48c&s*ZMhM>I@^ZXQdW+MDHtr7?<}qzVVpXnD5m3_hQNwCrTARW-e!c
zWsFDmhYV6-C(V-SlI29kOT0tWkh#`<6d;7(+lgcL;CNpcb7o;IAUk2KA9*8xXRw~3
z*y6jRh($|BBU<~ky<>Q0(b6W|QO7o)*tTt}V>=z&wv8w5I33%zZ9D1MNk@}&X6Bvq
z&Uel=zrLUQTD58w7WUq&>aLA&Gw+CqQSd=F!7*hiY}(qW5XhBMDhk_sV#~9XMBc3L
zZR)NI==<l|-TmP5a1T2K2{@(gTfXSy&FCb&+&WYM+6j$q6{mPOVE{Ane!68^YTe}c
z^>dOHqp=wQ9rq}lQiK@NWPA3bI4cJi@&vez@;Fc5axt@pmjo5kI5(PU#-u=c(GJ$-
z>{O}j76V&|E<OZQmJbQP(`rb#5dowloCW1dggU@GuqeV&6DQhPm~F_VhuJYqBkXT?
z*EPw8?>ssbo^|i7Il<7Co<@{q{eae-U4@5M($GYGGYiE-wJfO&qC=pm{BXmtQ0qv`
z??aYe_7HlhzH$Pybc8sVps>C;Qj_{bcOaGf!QaLFx>jsm<plp-4k9?EGMu})h)&5d
zQXuFE_Laalp7Ek0M;mg5uXWiDjX_R~dRHGNl`30-8*|Y<J99&2LdHU#oPxHa`Mo(t
z8g;2AJwu4bM3{TO9wKa)kNGz&ah`+&Jf80rzICS=h%Im2uDsK<if!$l)WPQWvAGei
zB7OOKh7g7-t5tmyQ!vvaN4oa1g#?`ObF?Sq2k;5jEJiPoDI)?1n)=DBl}G+aZJYG3
zkc(ufsNuHoTEx?u6!XxTClYBxm+tecgh-;g67fG=US2dL=#FIX`yPjbeg|iM9`#SD
zO#1PPWv<UEXvt>MR`>Dh`zj4q&qPGn#H=sOVFMpQDJ`ek-GhCwW`=pT4D6|#_NRd?
zH>W(8qDrHmdDfJ;Oeow#uCepk3AXkQ%Ic7Q%%7XtHWnqYUZT#?J(cvD?LV`K({FX6
zO)L`D=fpE)g_#C!>aEps^=YfRA7n=24&Bbp1&%EaN&E&3aVtHohbQlN!q1w!OJY_R
zC!ykJSiWL;(t;hKU+sQK3(^<|h=3l#754~6^Q>vxuiEJMwrXZ+oK}}(YU}wBlt*2l
zae{LCsc!v+C}?cNdvH#XmyZ!`s04Uzdtv&yyFi*vPYqtC;)w<`{vu<tv$hqS7o^F0
z|DbGGX3mXfLL_;|ev$K}DoKlr6QG;<uAg_pwTvJ}zIT8hmQ_nyxTiGW;r!cr#k91}
zEq{9SE;@xzYfQ5G;{{8%<<5|R(llbwc}|F0vkhlPJWeD%v5b<9iN~9$s`bw6`FyTi
zl41S&(J5|kdAe&H#l~E5l}WhWWTOi%lZMkC3X`@g?dF1mc*+mzOwR+O+ixd#B%3Q~
zkxc_8sbO@|$Mgt~T|MPjV%3Ht!Oe{WJvQRH=~P4pH4nyQcI1695^I`u|9JNs^p+*G
zDEa2#d!_TLxgQ^1eTAQ$%Htzw^7<>20C9YTpq&KX4LzMNW)W4hp^`KPKS$D*eFgn|
zYd75B0i+1AJ3<0TmK5gSrhdEqW-C1<AL(ge2-mTGEH1Eyk;RGoXo4GYoV{DnP5kIk
ziOgd7^e-o1bmFhFARV7hu+`^lNz2b(@0N?O>q`*oD28l>w7=7N4fnV!bGTE9B^#_}
zIXn|!gwVR1s*i-6vNvDVxy;#LiRdeFFB&M@ks=NJipIp@(Wm)37A4Ru?(34BG-u)1
zMRZ}W_K)wMardyb<qMf3A~35R+Pn`)I9_|v<IHX)Y~b#q4D!09(oOm4%v+MxA98jv
zZt=mayCahR6o!R&(q-vo5XsVy%ENWb)*vCGC^<sZ?TK3C!N+U}padFM7+;#tfuyUH
zJ;$heHRadgz{j|fuNwoqP$~K1oOM3)k!PU1X`wVOTzL>FP_PJ+_jL=6)B9rd=LF?&
zWhP0Fj_UR>_gl`S7cLejU7VjXLno+A)Lzdms_9g6)!7y8PaKFG$5ba|;rjz(Cc{fI
zr{ZB?$ZAh3gv3}{Y03G=i^L=vinr<&@iY2Ht~<?ONPBG_N?isBvja;sRwwxbpl%!T
z^NXM>C+Gp0Z1~5MQ$zjZh$Lx;eWYg68yzxHQM*J=z`_2`rt+MODS`tr2_iDCI8iIQ
zwv7Z{Lf~}_^!eVtGNlEhhm;Ncm?XG4e(%0$p<36m<%x+@;}F7x<hZEg<Er(iO@oIK
z(vid&$?LhHAC9z4>2c8@6<_9q4RAFj&NI#r`y!Yg0tMTO0Q9yd1Ud+(O!b<q3d5x)
z0cyTcm$iEF(bX9_cO)HdC<bDu_DKsVV>+0UF3kUcEZjT-aR1b+*V`?p<ej5wOtApn
zm4=rSWSr=jrCj&@e`5iN^0w^^qnKPbBss_$%+Whk*UiLG$G^cXLnLuYH7Q>ih_%E?
z8+yb+!Jd7(lF$o$TjVvRH(E`BYP)^-Q8Q!{)X;nRRe)`35~^TbM>G)PwQVE)CkmM&
zZ(_YNrOqwmf!|hR0YS!29P+v8h0I&#Y;1@_HI~IbHbIdiPB@|AFGVXuY!n4K$PlnL
zxVs&hEP{a8<-hUFBzUHa2((>PQL+U6crBL#vVPIFDo{qTK~qcg%$hoxmvD7)flLsv
z)c)Z6>@ZXaTKXg{)sGm9NwYtD7giHSq*66Yh+_ghYl`skv(g$Y2nH$;H>4o6k%94)
z2wFq)0|y3DtMm3pSvp#aq+7m7mF>#~A(72crtc1lJw)aL8X@_C+&c02)8M*4WY3?N
zf67TgT^!+oo3J?9b8nVpPw?u5{-GaI{{o-B+-_86;`^%IR~Sofy}1>s&lvU5x2mGp
zdZFw6ewj;TIpw^UE*_GA%{OT~L|JSwblxh7Ksrk#M_tV3z$LLDCzcrl+U8!mqG9!R
z-P_;tck0z15_L9&8FOe|;T>7P?@c#&F1xO)BH1cF|C>x;NC-HMkirbl>bSdjFoa=3
zXu3m7$Qbz2FVl7AR%#$xXB2V2@CacdmGb?c0XyXC+`Jn1LRlu`%}_c<2@1lHLvpFb
zQr=TxqNiJBawfRqxyjktC^M}<>juZ%H!PDy#)wQO$j2WmOkUC$PaCRLxy!AtMy=>#
z9z^+4jD1<{wPxm2X6AB2%)rBT2KeCuE9rO+I(RUAcuK@E6W)q@!N;`~rr(AtEfMzU
zhIg-kopYd!!_b?D1sN)hW@_uo0vG}el4EsY25i;OK38(HaU$c3lPb~bY14+tTR37q
z2L$M%>qB3bUsYw0GM75x9h?FSHEOq^CCkLz=G3PV`Q*kt%zqe^cva^LM)6i;Wl-z0
zVL=|Juv5Tx@2{^*yc*qmILipxX^PVeP_^>ckk!&;^m!Xhdh(N#&Pr=>0SKRX@7ct(
z>~mG}bM)lbaPY*BYUgHqEtYCGjCV!lc4IY;v+gsw`^{-r{ctcw9QqFYpno%OE~60l
z(-<5=b6%*X#`!Saw(rEe54{k3*=)FFdS_FXWE6*;P0j(f<V1}_h5edckbbYaTuB|w
zOKJBSp=3qUGEL_Li`y^gmmN3Eo;g>8)xhiWuRl@5JtpMLbcEBVABvZE*ycu;$%2RI
z%ZnNT*%j)WQXnM3FhYoDj>GYN-H#*HHIdRPWsXDyEEwd>7ZsJ_8=A|zxcZ%7QW!iR
zH`lp0W510C$e&4q9!kj02e;N@lQ)3r{cWchIZ;zWZJW04sC1d*E#0L%>S`a~R%9|1
z)#m)`=SXJ1lDX3Ko_QKYfG3p{42d4tG&Un1jKOn0gOtC<-;>`6Lwjmz*H2opVdMVF
zB%AFy5ObNC{K0d6y)dOxQ6{JYQ4+e@`-^W94xu$+(OA=f1JO>UDr~*38=^3o8Ec=~
zxmIh6zQxbzziO_Pe(wyWKCgu*J!p9sAdcs2iHK4>Eqar~r{VnCeABvKEf!n?Mw9D-
zB;avR2R;u`TicNg<FY4ySTsYggtef=nfqGSeqH3nFV}WrV!KLlUAfIWC|a#zQ1#oO
zjsfO~_p09_<?#@Bj*Hb7&ttrbgZ~9XI%C4}o;y%?Ro$oGM*7NwnharHJX7~a_^?a7
zkf9BujOwP4C=^9W>!*cLi>MgtR5l<K`#84*4V)~K-PnA2P=nR<J3Ti`(hwTtSk&*?
z-kN?3>g`u;7)70lbBYMd1~tgrO4Xx<%K{lc6364*un7xl53s&u^Ryr<X`9@eHlKU*
zEDb<grP~65_+H<%;b{py5nVyw{vyRq0Y$lk-}zOo!C&u<mzV1N>&&NqCZ628mC5Ei
z?_J>DI7(5#W`*KGxELkG`j+d^*eipuBPTqjGx(Jmxl3y*;CJt2gHZ{@u)$?Cp|!@=
z2-0tp&`h-aX;-GXeD|sAiA!_$k{M1jlbv~rE>aI8_n(C0h)La*wIpfYqV&cmMclpq
zECE}T_gcT&y|h@-^$LY5?~N)A)^YWpSspuv4uFLU%6lqktnxU<ZQk`a`?)@_EJhm|
zPislft5;h>9~GRJ^_)k$$T<l@rJHiBrP&cJRHN61pa+R{KUJIR&uw_888vVJ&Oup#
zlf#2970IOKcXbgkbiXkxD=WL#B)Z46C6}9IVe_QpndG^BYbv#0$t+jubYocz>v<K6
zX{oXaj}Sk3l$X&KF(f0do4bBwx!TLBKW21L!Qg)n$?Y}u`Sz)!o%gfI&GpgvGN0%A
zf#x{B-Vx0AHY{aiJc5K|Q1&-3obcRRt&J7ac@$54a)x|VpSsd#`!FNc$XW((&#R@6
z^k__fWXbYQpwrRL9M@^fCCj$i(s=jI4TqajlC7||8~EklTzx~&V0=XWH@Ex62BYDn
zp!75!Dytg($SdDRLf`iVPqw+ir#Gy$3FEAidN41yD<Qgw9UYid@jyl}I4)K+FpAXB
zT`>I*47%ntE<cCW?-P?ULVxa~u1BW3Bey?~h|rwpA0kE#{+@)ot*Zym-kRdCo8SC-
z-JDqa!$Xc7eN(*ujuxub0f|shRghh!$}gGi!*to<DUhSl6$D9BS>CWFF<Zn)&w+Er
z*A2g{=S_fOF5jEdycIPT3NTpw1$O_L8Swf3!8RfdCFZs1Ywd1IMm#@HzJbZ5(KlyQ
z>t;t335(6+YWqDYUN`Ct=4t=oLJZFzRo!nF(qyUL!0df3v5$J_QDr4nQ0pbNPwgA(
z(8gPco#CgtUj(05nTwt?CPt1Qd7y02O1m*4jI_2!_bMiQV~Rx)rm&6{JN@5ktD4JS
z%YrVTsAt~j=7%$;TIrCB)P7h=120HIdm|<N(QK=MyDvC*_{c6Q<p?lTE(^&#m8T<I
zZcJ4lJA^Xf=<|stK>}9Yp<&AYP?Fzap7H`}e_}^4rMK?T%W6NWq)z@Ge6zY7lEYLe
zIqa1K0Y>L6iymIA2Y;{y-w9cfnq{TPLWH5pW$&8&h6kD9pu{=WZ9%$W3K%oeRZ$pJ
zd=bZ^Zb{T&=sTCe*gtE8f8M{dT3E;M*&XJzG9#A-<D*<_N&)rqpIeEFrZP$jl1A@U
zSX3)|)R9IllQA52kqgnlgUH(CY07)dKOKRR>KRPajlx{|00|od)~46`I-T&bjEM4h
zA)XtY<MDlA11ZVC)-;etSIb7qO*~%yY+_h4tCM(ZPLNd=M64A1Lx3D^k&@52yW@AB
zm?49lbD(Z3#x|@5NOh&9?)kc1$8X+zqk6RY*1l9VkjhMv>iu^<OC)t~zthOae%7b^
zcLh|XNGc*lA!0RpxgBR!%bPU0dm(HdCSVblIkCZFK{aKr-yr@g;d$_y%qV9Dg5@*m
zM`<eAC%W5ySLn~+qMQirZdX{_&R_8j23&fYol$BMuy@{^4wJ`SVP_1${#z+qJZfrj
z(_y51%~&NgZwfEyC8m4YwK`YaDlUpJ0{GJw-%tU)vunaQ6VZwq6eF~xgy81S7jk+5
zmQ3hWW&3`0f?%E<hUfK6?L`_D^8p$KxLU%m02U6*yy&=WW#c51s0h_A+q|gDL(JTu
z@uL!=)Kqe%zwSiZqcAX0Q#sSLud6LDY5L-;K8Sf0X--?20d}8e(7M(lvx_<t?msRs
zMMKr#*!!@Zxab9F(nfiC91M=zSH`S~<}2YM@^hr=G7G*by=F2rtO(aUOOv0#!0mB!
z9Q9}jWwAV#1xO!^kuMROW0y}AYqbEOe$Ycs^CPcqZgfw2R~0nHekIt7KE`AiEhZ+^
z3!5#rh<}o&Q)+PT5~<d(E+zNNucdI3p<>c^A(VgIqafVYws(C>WMyK?^d>b#!Kat?
z{P`^qEk7>yJgcmQX4$fS`*`-eD9}U{A-qE}-5vUDw-o?P(}}Y4?m`oIedQU+&QK70
ziJ<W>)q~N%%gjFRFey+2rP`+1l2#RQ)Mpkd{nLW0t2%=A5X{`(ED$zvWB+D*M2e)f
z>p0gQk*Rc{!Y=!;$!Np2<c^v0?wYgv({ZKC#I?H0N)#MD>Q;|q6LMg@qE{~<{$#_j
zJm(M5y7>m{69b#RwZ8O-gM(vbnamPyilN~p=(!p19og}bLj>vm*kEq&?_AzKUd-O-
zqvh)1=zGecc0ga%dpo0rT)v)IGK=vGc0uorUu0nFDGpLj)-<V)PMm5DsTl5RE#E{-
zPO>i%hWNAcE?4;PTWaTE3Zvz68YRaIE?zrhO|m0i9Y!ZG_DgPm8M{|<8KtAXqZ47v
zUe^j^gs12upOLng@#l#-s|3Np_br#JM87}%F@?J1D14d%!tEQhNt!B}@@vZe_cXTs
zn{ISs;=$;H>uQD<O)wkwcO}>`Ope`Qqqb+`u!_qhUHtco$a`La<GUtJZdE5${4fDv
zsvBkpUufg>y=~pqef<2m99QDElFN!3MUdY_5KSI3fympNYv5i#xICvi!yBhWX4INt
zX_{OMs{L@etFz_Eq%hdDUC~&eD{zG@>}>n7F*k#%b9ugwabIYLimgiFG|#e%w(EHr
zMi#z|K@u&+Wt~uV70zjjQ~<Bg_Tq9NdW!$lqp&#U)F4<hgBja*z`5of^~duw4k^p<
zbZ)LMa>RBg?#r=$$mfS6SJ&rEc|+8ES!wj~Lxm3MQC3K<-?9ALIfp`C)(b}{9?SJ6
zfL=(;8tc0jjgnGBOcW7B><_y<`x`My5<0D#dn-!ozYtq%^4zBKDy#QmOj~k@5hA!-
zME1amgaI+ugs9^@Dtj1v3%coRF<BT7>?0jdzAl~h@Gw1{YOR*PG6I7gmZ#T@;05;M
zkQ)F)tUo)e;tWi`y6Vg(RSOXlDTb5XFHL_55zH}D!|}>`^CFc!0%xdTU#gG(=uK!#
z7NCm=dg37N8}ypFgU*+&<ky-K`z$?Ke1*^ea6y;qr}=Te@J>v#b=rDo>ezGqZQ;o@
z*L7k77+SFe#9}PEq1rn~a)Gh^XHhdA+6<d&QWkvg6B`?G{$*$=B8WR>=K7*0Cl{O~
z<^)}D``tUmUh7hv7VgG;4?THE%)dldR)vU1f*sUgh@>^;%zE~7L;d^h3|`u}!rL#6
zV1#9^XFAF?#zrwO*an~1`P>X?_U-Bd-FC~&^~`t|=r~E<;Wi#Fw)g%_HzeMe(4EFJ
zcJV;Tg{kG`7o)D0Nok0ix#OHU(5I(GSqXf0r~CZ?`#uR09v4F)a@g}xX?3$Swg}@5
zyJ_oldUS4(kI8j~*8sk1-6actT>h7uLlZbK689vzq`j-EttJqL3C#MlG?=zGF`l+J
z>ycK_*ctZzC^B=MPKPhHB3#bV|10>I=XAU``l3||gZE)eK5IC-fW@Omy7Ef9OI!xG
zGKvlz^7=Ykf9O|*r6m4ZIA+?ksP}@ANmi}Di`SjKxi+S^$mtdVs(h3M{8F_$^0<{q
zd?ra?ef$ly4zI>W6O`!uUftd%+lUcWbJj-vHGFE*UvC8l1yF&X_nL`ucsjF>PdB$T
z)rvjxB4Q8m)B)tR3T!EKk~tpOIo{^{8S9Y9liWQCh99LXogj8|(pJ)AVilFB+L4b&
zPrcMyB+Rwf#nn5}GO2xI?iM#a)>KpyBfxo$?o<Yp?@6dde^$q`u33<UO+~-6D2#(2
zs~*W5sRr}+2L*&-i*U?&<aKwsQXH9()>pmbc^`iqU$Z%KE9aCNU6H??cKd}lS+XGQ
zuSL1=z%bHrefQ8w>egqE`UPmXmI$4dtHI$j4A5D3en`WdUzEkljL;*0oL-O@H=Sy1
zIlCN8M-hWfRmCvKB}<rSfVCZBnBLoFZAryn^Pk>)@sTZx`dc5@8IX0Zs>95InftMa
zXnzC$R5Ovm&?%g6NmVFsIR3rkb}Um;a$kr+HXt~lMuopF2ZF{_sN{@GAZ;9}Ld@Mb
zdm$R`T=xM@ZDMSAZV;C7DG|5EXw{=)O^i%Y81&7u_SxW_n)huD!{qR|%-qB;j@G7a
zbJXPK_HgUQ_}J%tg-Y+Ep$4AHQs-#YfYrNIeTB?bUK(0j2n(WMW9r>Zm~Z)Hu9YPo
z2Y>*Jy=sZc_;{Cpwy1|PB&wAty8!ku=)ODsv{xJCFoI|tT`v0wPH*TA74O-MNT)K&
zLO+OjOV5>W&$QJWN_y##M_@OXaTC>z-*?}^NY2ixNBE02u$+_&6~v*TBznLDQIQe>
zs))x+ntD@pdhob=@+Zpu7~?z&{+J@J14T7o2UvAi7v9tg8cYNV21~#cz$J?Eci(AB
zo%t~LIkjFe%@_EZ+v6c^TmxLO<vp=cB|S2yTt#VutuFwPH~ZVTdD>pZoA<_zK=)8%
z?pbhDV8lT3cct_*+cIJNO`?(sguJ~-+do+(gA22fe;6eQ1Ej0mn$HI--c(5$Nos)~
zoO_N`>aSxZH<uM<;Tp~eW=ZV)zDL63OOA<V5JXvHh?o!|HfLKy^SHk?w=6IaE~xrq
zw!1(r9e!C<u7)1^X^NCP#op?1X>oEqrq7s9K`%R7KYBbbJxv|0?Y6)80a;E%TF%z+
zD&4Ophi^8wyF*)-w?hCM@}a!RUs>%YH7}5TxuUD7%!t)hnv<`qIVG6w%lskLpSPxS
zXum#vomI?ALvd0(gBswJ2u9aZud+HD@RS3Z%dHtg2Bk@}lVTt^wxUikx4>EYdEQ#S
zXP{qXOg*+>QYJWmpYFBuyZtdS9VhW}$>#nh5l2kRhnOH_cJB3R=T60fs^9hCsN*!g
zNv-zU+YFm9uEVJO-BB1*=ngjT!4Zphm71Lhq2oX&seWp}=M)o5+^Qwv9he*_Lb6Z)
z8nB=0#e0Bk0=OGc8Djy{ScHRc^qWHw%D`N2o)ZR4>icksuOj-Y;;lc^Qie;Nfb^fm
z>8SOlK1nJoL|OWq(^`hMjKg!3II7QRo0Dv|n@@}wMo^{3cigIVxC|9x?fs33iVCT$
zMz&J#4l-ZH6GX5~aLNx%ybnBXM@;D_Q}xul3t6wd-=j4e-wSj!V^oJVx^q~^yh;KV
zXWFym_#H4jj|*tWqtruIceH`tJZ(f*oMP@!bWa)ybAA=nMn%3nKGN~^KPK=Z07g&K
zZ?+?<C%G1pF$d6Fs&BQb?}r^TkYAwulCCLcOKz}=@B338=L@@z=xz_eQutM9tH-<)
zv&>v4_-aCnF?2}^D5b7WqQ>t4@NLZpsGw3+$oXr8s|y=>;;~zAhQhk#ged67$0_(c
zzNWhkX46hf4q6MtWmSx*sGFO6Y*6;Qc#Z(1?sY1DRtbg8V`4X)V4?&3TU_dg2w-qV
zQ02cWpZy?8Qo*PV{t3!083q=J%J`H8k?wc2+OmH(;WZ~m5OCO;pRe)B(<5?tV{5JZ
zK|svnWI31}egV7_6ANA4jFUDe@clc}Y|oD#)kv%KbYKS%_Jc`k3DK&x+jMg0=fU0X
z*r(R1J&<=+CVJb!J-hBxsW$j&#!R1+9daiH_Tlisc5-Qse{XQhI{OQb&2I2<vYd+a
zN|b)uyX2FI_hCa@QaaoOd!^nU0P%M^G7MLD0(Bo~mDdfwzFgg-udYs%b|#<bD1V+`
zlzr@_XGWUy_KG`F3^^}$58nAOE-+NY@yU@?{;E_q#*#I*7KpgCf0(957(tM+K^U2P
z-z7qO!roTe4#e$CV;zY*n<94(NmHf7ijThCNK2fkNy*jyIx~L&_jj5^4&JR2dP#%i
zZ$JO%PX|m;46T{Z=9U&L9=A(UhsfflH+?Rl+P4+$&=dM3=_k{r;f*JdkJd6Y2c`sz
zs_m$Qw^Jw+l=R<5Lk5+rA%x62sd<vlX|}_WE@3Ym`-TIiQP8=f>4_`3myhIHV(j~s
z&0AvCWWn@eefhX)8WtqR#5I->n;SX-P>97xOI(tJ->ecimW`f{8f#dLO${_ixg(o)
zD&dPJP6&#b9sWXor`(+oi}9ngvZ*Fa(w0#u5=4#%E*z#{BFtvf24c(_FlP&yu=>nQ
zueI?=8UUWBu4a(4V|V0v0_)8Psa7E6euzCAESf!wi<6M>p|ZENUaWS)LQ3NRks@5C
z-|0#jw!)sCeh?Dzfn-wZsIBTkML|g@D*EHpW5wb-OIcUZ4TiE^8EvM5rkWMB6CxfY
zbsb<2{r+A<JMl_pLmiRI$QBg+gku6{uC0RqXzPnuSs{au2M7R-wbEz77Wd>2W>m3c
z43VgdxA;vzkU1^TE6R5@C+874R_M#@gG^(A#FEC2Sur$)ld7L@ushw_n4pxx{BtuJ
zuqhIAYwY(-_Xms_S(xJtLz)`MYhV<(xl*?WfBw^iIE~b7pF%$Iq0#+`ocC@guJ#uV
z0krd(3-BFz<3~6jx^9K;lzlK^+kUN2a{*-ga!0V68?!YrKU!nRYkIsH!}n4wsJLlE
z@gKN2zawjnCttA+ocIT<{c&DP51fZOA!k!+ka(VKg#@t0^dEohJyitEo@{htKx&-?
zP6Wy8_(NhLa`p_E%CVuZyl=gpm}~!vKJ@W>hn(>0A+=Qf79nV9iu*veelwxJB`7Hl
zqpN*Z<Mry9eE*#=x(#eh3bhUG?)+!L3mvX`G1FjXl9Y{xXH{h*I!ff+>S|C<4#E9H
zyu)wmUN5U=#(EmMi%aQ_wyxw<HL15fM8CFG867`;b6D3Z?SQbWZa^auHsN%>PD2Vr
z<!0=&r8)C%JTCr3v6VU8%kCzRqVl4Y75%Ys%RCANCO&o`n@Qe;iGi>%Xlrrna7!sq
zD`s<7GhX+{q47OyQDgWR8yRHME9S3}XN9GfFl?{v5mQIp;uhf_yFwMKHDJ2~D#4PA
z0P1&ZXiZ5&okv1c<<)g9B~3@Hb`G1c%qJk1M4yRs!%70b3ptD6o!j1A|MQ<5+qNec
zw|UUb5ZjNf{(-No@W+dd@I->SIOyqs;062aCM_l#a#RJ)?18SJ%PPJTWg9oZOLOMk
z%;Hk8$g8W7xdTH!RxydOn`4eqT`@5`Z#>3|qxEJTWVE|tD@>tv0-~in0Cm@eaX_$7
zRP+OBS)|NaXdSyPnBG=1YWVX_rQ#<-p*9)zwS&cNFb3M|{yxAzb*vvMdSal0s~-9E
zbYLn4cShb0jw*<1p4NFp7@|CB3+r>`$0W1NFXu@z8<V&4l#F5_Gr=nQ&A7DOcNT)p
zxxlYa$jGr?*;Ow$mho3Z<}jt`q7rpk9Y)tmJJBW8DqTzw@{B*4`rZOf7dC<)_oN-K
zLyq?~6~>g7@eCqZ)^7@Z-?e^9*B&(<U+?Ja38PG-SX~jZuG06VUACmA>aa9*+~DP`
z=>>t4C*dH%DmgbbWLW%=H~#u&Ij+cdTH@&-FqwJ=EQ@iZgz-hY(Q~&zYConDv9kcU
z%BXaj6E~7SYn7#7I|bHwPS++@wtSf@MVy@~=>@_cw9DfY1W=WP=!e=b`}L;EfwgZK
z)D&7H4`xd4(Rqxz4i<>IYEMqpq{J1?XzkMLlI`icKp0r#j?R^*t#WrS8GKaJkYBM<
z&HNg*=+LlLJs#(NcDm4NNXO0Rz9Tz*7v{=?=}-dJ8@d5dIYNeoDYj~0RGZ!C@^kL6
z%V(n134E64a+=;?8wxjYxZ%+7dBlZi0Ph;yO;vj@bec$nmC_y}d47@2pZe@eY6jD(
zPZl?Drh4%SPMNTl3tb%iZpugxalk;Th}wxAy)a!K*MhvZq~q%9JvPxCx7!Wr(cBot
z`XDFsV?5TKomiXF6FfpX3``g7g5Y4K9mM*b?B(VjhDh6P9SIOs?v)ntbzGWSheNC9
z-rO7!Pxi_?v?_{NxJ+{aQ7&`y77;ypf<2MbS>cSi#nsr<R1E4h9`uU~BD#}H3v!#)
znD>g2!~qo3Fpdhr{%<LByKvcr7w`tMw}NiCFs?QO#)y33n^PJCvjzE;tFaAl*sR|*
z#AoS)itq2StQx}3nqpp$dUEJE6<0$RWT`EM-+=4G@>XsD&mx6FF}_=d&!oG&f=1x0
zJ*?~MD8~&SDz}|U)kzA{rLDnBmt)l|)UAT5B6QqPeL+bhjve0%<L~U~!6vR#&L(so
znsp=*G1sD?A|ooD`Ux=hu1*+uR^f0fPlInF5FR@zSKBd&OO>JH3^=<7aepB#8FyQ$
zq&`J=cc-~fi^8%L64NgeAbq2_yPbm*!c>ok!7&=H3q>XmCG+}j-hivBKUIwm1ptFI
zVY(xRlCl7ipuK$1o8Bd1H8i6WJP%34By2T2e$lugkP5x-oX|XbYA8-U>eRQxi4r$C
zSdRv_@y`GN-!)wE%rj9$+$0Fo0sK~ffEhVuG?du4Y42TAn~C<BHN}9&LR~-aYY$JQ
z0~Q8&^c=KwGI8BQ*F52~me0g#d+k8vJ<8#;OqvLWX?w2jMQmF`d4$_ytL;Aht53d+
z{5qt8#!I?)Zcl-B(@qtol!TPAI_OHy5yeOwuQN%Oz!)F#knece&Pllu)jn3x#G5u6
z4|{-`DqcM3-Fq$IOg^w@;6$4h6eqwB7qr-PJk>+3N+%)=;HzPZ<|?_dsg#x#r#94s
zhMt1GtynWUN-iSd)RH2#{0AoL2Q>0lNClqO>A;V897CzOeIvEIxmz5^$TE!@dv0%C
zlM!kvUUb#6racvABvCW%?B0Sc8g@iO1Rk6*EC!y(((J?(F#Vs8WZ0o8YIq#jVTre%
zZ6v1qw@yw5hdhx_6{fPBba}O9lF)`I)F|?^tQkPYg78#%fm9|f!DS)<%1#}AxqaSf
z2x}9~qodRstEAib&$hs^loXO9Q^<XnvBhmBsnhVn>n$ziXtP@G`3uTSdXc@ad(=x?
zDk0r+TuY}Z5&jeq5tET94sU;IqQGLPB?K~Kj*MERO8K`i12VQk{P&A{1cR(wC3R`n
zSd>(7K&|6Lb86o2!5B?Uoydh!q)>H^o$Lo~i@s>GAZa({!!~<W64YmvnFQ+^kxe$|
z+^ZmcZ~m#w1eke6$fb%`K_*Em;3`_+U?ngW8HaeSe0+wu7?oL_us^!=v?bA0+f#e2
z#&BFnw8V@k@Jxu8%LINT%j0{7lGbJ{7p7jofFQ?Xl|~G;FK(KkVSA3Rk8{!ZNd7}y
zH@r()e&QXyI`{Epz77b`IY#R^ActF(h5bjB>SI*7aHq(JTDtPdx%pfp&XOqKUupAv
zW|n3D3U~kucB)aE<($<{{yA1d-NLllP}$&&OhId%8Xq<N%U&@p9Hd7#w4NhN5*9+E
zqN1(9F3V~w*U!sf%>-rsLDLO$*>k7SA{FfZiT5@;c$ji*gv8Nz;z+0~^8Ie|p&b`=
z%wLuZ+MuY)@mQp_{R7ih&`FvH>86*|k<wV=C;?9NK#8XjJ!_qIDLkS0(Fe2QJjaS!
zoRthExilLQ1win57C6jTfEgbgKLjGr*qP|?RVQWPK_TUn!f6~MDTWs1MTnU5B;O5s
zW=4!6HDT;x^^I117fdA$S*7unygNjE7b(k|P@;Y$u`tT+!zxY6dOL4cMz7<K0r@rj
z)&U=_jocW-Y2Gv8fjaiK2&N}5bvse~Cuz_p5L2f{JMo-Xr0$4og{bY_Hd!;14-onZ
z_`Y(#4>sS2bF-r}NpekGhincfIrrdH<})|RHD|4&IjUZleD7^?G)3SME*k`up>rc0
z+X-Q#;YeihnZVO;CV@f@)t_h>Kk!aV>4+1Ul0h28QPi2girW{#HGWHBJElH!S9gM7
zzoN|(qfH#m&!=kfvOhtpzw>)gZ#ixfvtPFDpYC=6xITmtrAi48+`8SHPGHX{Dvxy#
zOZf3jZ5xh`#2Ei<v(>bFbg<?8L*fK_TOVXlO=~FsZ3?>_*Tih-S{8{&af*vvsglaL
zPW*))1~_3$$249y%<KF_SNU`B92SEZn7|6DRuQOyw~s0^tGGOhIg0|6%Q`*MR<pV%
z3qF<+CcLY&_oFPrf67g1?0}65b1<Jurq$ZyPY!fjm|5^OI`+zx#@;fncx;%U&Dkpm
zw!P#*MYkg}C2^vbBTe#UE)lrmof?clHXIyHzl{9z-No2inOYc_qGPnsNh%JG#S;Tw
z@Z>y@>`We&Y#*s)7r(ul^K65Lp5`z~x*I5?8RQk-H{=OD3`MFdrM!|mk@FNQxJ8Np
zpwp3{$nR2$D4R$#itd+I$h%x4UPw{^dOi+rknkT+E#-xSIvH_4F`)-1LJC=^<66=S
z@b98e23sXW!zQ_Z{*CoJ9F4Ulq@)6i=+Lhvk?`}V=;ksQl`zUD)0&D+pI~9+;D`ux
z;#;y=gP)n7s;*znTTK`pDbAP7zdx*?Dr1~#C7d$nMMrTQdRS|XE&L!fr;H&07(vC~
zDWe3W%9y$9aj_iFzD7vF;BT(fyMp1oXiAHx9$X%#Vr!!QIokh-pGxAuIyd^jv<xA_
zy(yC{+LQQ=Q0hbCm83Zi8+Tp#>*w}wlky3={3X^D*<(7+%p7WdRp|wTi{ege4meHC
ziFqdPV(6PDWgzE*W>{pXEqF<Gz};Pv>R?E-y6pi06nPw_AIm{mRp{l%kRG?~Q>XqM
zDpLv(A3L=emW=<N<K9oA>JCCgA=y+d!2%(NY}m2`IWlh{jS&ZiRav+nRCML4`mc$X
z#p00Eh`7DN9egqffT2nOUvRg2VbK@J>Yb(PZ3V;t@5^au2=wSzt@mHzf+Y5TkkaG*
z>8sP~XTvgebfyctS{7E(e$(c{-YB!7Q^CPUc*Q*(96C9ElSJX>Kz$AWzS=8WE7VX7
z2`T+TnyOYHypt{(#_+Z0ZdoGf%n>s4BS<-tFaWwpZDm@uZ}BT9E1k#hi`aSaK<;Z(
zvBsI!gb1*VM(vsDn_F8UL>k(LW}FCT!~>or^;ftzacy|r$#&!2WYyYoHL)cq{AdD_
zsw((-o%y0OAC}xx=`}~vr8HA~mmqBlTs14qdiR8S9(Am*O!oFVjPb7YS%z)M_yc1w
zZ*Q)VE7=~tHxSHn_kKKw&89@H`6B=R!1qBO(nCW<7y`I4@<;NY$PS<p>y7=X!J9OZ
z(l6}DY%!DWE~3?SQo(&o|G_G2w805Ujl5h6GXAzk$?G=%cah&9zeECUlZ<L0>9jUd
zBD7-IP+h@=K>(M}ZmYP^w{zhObcIT6VFc%Pt6)f&T+wKxa~tPu(;k)OvVgG9tpy8n
zNfOpd5j+WmnWmPKn^s3#8AEpI1pJxt%1YwhijkmByofY6Yxq{>5x8UeCf6IoWQIWQ
zdn4b$h+j9&tIFBP9Ob#47ohTOb6jHSp>(;1jAfI=bbD+K*=%!^A6M#AeEax4JFMeA
zLRQe+iw4MU7F3)Tj+kPxH%IZ#y;XDq&Xj~@)~kXt;_=jQlLZ%l*Y7nzOkMDyzVX6@
zo0!hC>h}9B!)+HgW(59^ro&;HuTMIcqR*iQ1a7aXwLn})cs0sjQ__I@D)iG_C^KdR
z<hfwA=&%@}Wl2Oy9?w%`qMUC8q3fdJ%*Wq6*c__)JOR<3cLRQh<7ArZ5<(DlRb2$I
zQ#m13fe+8azwryDWKl+$qPyU<q<3q&!%`>M)h=N3mZ2)SH#nF9`Qh;{gshrNQ46|X
zwFnCEjqOSkQ}9~PgGd^yxJrJ)D;X|N2TNpcMuQ>di)E+s*c!Ryh8QY`=<c>WIpy|-
z)En3MJuvg$-JZ!h4{@L9-0d4uUHxz>UK$iVk{u%QAa=L4&mlz&to>Ptf8`B$36_Q%
zjUgPv{H2S=X*$(8R<!Fj?j7mXp2cA-V}&qZg}}qY#zOIJI%BqBoWx700aNF-&QDBG
zRch(uP!Ub%)KthcP8xwNHovdulhlu(Q4g|JZ@7IRM87rrZv18{{U(R&7MRs~-VVM5
z2d#@|j<T48p*0XDdXsl@%=uKt&ZQrSE_ZceVHFf~EJuo(6S+g*w^~cUGnc#nMm#M|
znLJVdl*mSOe*#rz_-Uda$G>c16HcLRhdDm@FdGz0KMbPE7@ql#oH6DaE7N|!SBDYe
z3ZEO~NKmE-@IBsjlQ@ZPc6wWmS`Jf^mSp*JP8fq_WOJ*QW6mBLFE+DBcOS*4X5OAu
zbu95r8PZ*gVVcw7Ed7w;C737@3xs6*%wpJE+Ljp?=!6*X1PBqtAeDJZ7<2u!p!jf6
z)gRi%{63k0G&Z|Eo*sh1v`@<28rqirjmsZ6O@ftAezrg~)?9#*7(E^4oxNjXeyc^*
zh$#}sK(9UJ#K4^peJ0>S&r<m6GzBbm?-<1)NT{G<DR<_iysy^oMsSuFgzA?FadYMw
zrKx}CS5pcvPW#z|wRP_vA3YOZa9R~IHajLwVcY&RuWFQDd+|U~+sg87me<N;^W-1d
zDe2x&vKoS)rMh4Ef@Cwn=qSxgH#(=EomWW9hsG)dN_beZg;^r`K;FkrK$AYPAPtcW
zg1XkV!XS@)vE5jZpgjEJaE)T_#8=poIwQJOcmsp%hA3f*3(?lMJ`+ic{#u6q40`$z
zquuwy+GJmtAO_p9Ex4ZH%h%zjm^Je(FrXPQX#XMragm0dRu8o<PN8XFCCqdC^Da#E
zS^ZU<T-IWrN@Kkau~IeC#mzadE1_t7ZvJ%37OHMI&@GrhCxajVS4?4)@y61s=4b-p
z6(Da<*bzTCVrY+Z;<9##(StwNyTR(#;11c7SvD%#hotX-!e^BC4i0V23+C~rj3C=>
zdR&wC{qt#G%KN~YSTJ8F8|^skiFZFs&Ur&G%VZPV8x0=qk{S(xe#k0rJ4iNk6JE)~
zS9Y*Uws3}vyw!;@ZW?g;$)r~(V*G)obqaJdJb?S;n9~zeuaxyw55rMoiIklZMRV%R
zN**38)AMih`N}OdfCD}7=FgFVHb9lsyH)aZV!lH9H8%Pwe`pFNS#k?cvD~CAnzcbf
z;qnxQtZF({s5%nCz*T{@WWR?i(8QxEcRad46svOq!mM6$jxrQ^msjnF2x=sSjCrHN
zX)LX|Pr8qZHpa|vxEtycb;Y;k^80k3$&v)y?7>#+NLKAA83~aL89jD;b06Lm#hc^B
zVwv>YVS$_B+$=HhSfxEpfZJl&c31VvWA!T@Pl2kI=ka>z^GKxPm)~H-t}d+#uI2CE
zqe)yPAqBy-+Y{Wg8^+XNrh`O1Ot4DyQqu_$ok9-TKWN#zkyXr86#a>g`4+b&PlfKH
zZxUyG!?{ZDW#T)Z@SuQ9|Buh_OngXR77--5284clVIkuU&#cf)Z<N~fbC7xX5eZ=!
z`cU*0X#N$K#sHcxd~Y1sMg8_sBEWlUOI~71G@#B4Bj4B~8xoKOrc4z9>5nm`;Q9L=
z3@0EC9fU5=ufqN;?H>RSH~|U>AS5}DNnW3#+q5}^6pXhD4OBk4U*qN>a}Gi&_Lyll
zc&F0V&&Bu=JFDsJkO8K8?Daz9#0<!lvNy(7Re0svJYstMkU+HrKM^x%H-G$7=)8Z5
z_7DD}A^y)~@KTmTql<K#xL=Js0%#y+_j76g9O54ed{Ke?w@$gN|6o>VF4(RACB?rb
z5fy|z@x=Z&egDbI0EI9Q;$Dgox()XKV*Ou_jOAZ#kpFGi|7!cki2O?6g#-f>Yas8A
z|Cz`cu1yc@zjgZ;UDDB@PO$E!nBdz`|2>I)8_AcH^F9zKH&*{&BYgGgDHQxtGsy_Y
znc`pg{);_!oF%>pN?u`R_Lu*iA#nnv0{k&%C<a+i<o~^`dQM~gtAc#ns=x91{(C+D
zp$7N=O9<^5g(vj9;p%_ytRzqn8=+jZ`8=Lq|DK?P2b5cC<FpI({+aFno(%uAoq_T{
z9s2*XE&Vk&6x^WxlMG)gag?No_A=L-!Wr@3`{V2F_{*od8~+bu(B&yaTFzwuizENB
z+WyyLz&!rf0?oao5sdo}v!5ISR2HSAen2h~_@9~JfRIPbYwpRPq31#VXSZDJ@lzEG
zxe5QD;s13d%DaJAl<<7>eEal(N}q_H$U6?x_VOzoWdTM@qQ|XQmXrGmkH74vgVnD(
zs*0jK`JV}xPc3#GzO}T<|F6yIzwE@w5Bk{(%58jHiNRcfw~R&ML)Owd3Lg97{5E^0
z5EtYRZN@Uo7ZO+l3~ZX%8l6g6KRi%5&z;K6JDNVk3+z7(h>LqDWa3)oV!ZtPBsBpV
z)M^pZ%u=2G1U2b5Le2ugyrMY&vl_<?IlLN7qF-zL&X;|H>-d|CeRPfEm5{Lq5#w}%
zv|DzgHnvx&rPa<fTfT`P_nbfd!2ay=pF83I)chI{{f(<=bci||OU_VUH`W&|Wg!-M
z%I7Tz83oNhZwCqd`ESoDmU7@mg!Y0PUX1_nexE@%DtUQIWV{M8^ltA=&o|zN4=sv`
z8Wo)Uy+NbfUpiNTLD@}j$8_5Tzdj`WqWn)V(L&P&Y1-D>7*CvgSq%eh+z~8*jmnaI
zOvxR&!l{%J<oxCUOZSpD!}?V)FI^_B_Ra9@!~jM8B-&aB2IMU|HB|2NMT?&oBBDk(
z`JlA+Glb^=yjZM{t<|*<qUF(v-j-^d*I!|3j#2PZKo2PR>mn2(Zi{@((Qi0sI5Vrq
z!KNs7;AC0-fi+(N%M<hs>+PI5sMSrigjs#v;m3rQ$mAV}@`n1(^Z&AQWj#V^P?#`h
zh<;@*^RsgL$3J^-I6c5$v$XCqDw*W5mCtYa-`+~ArlIHM%_C|8p&94fBwal~`-Vwz
z4hm0TLKp=y>+)#mM!GX|F${;(F2>1FW}(r<)G`r&BVro#;y>*joniDgm!K%{K0`Gn
zyAYH2<t|OkPuMJ(1jMz26$M#7KkDlD6qyeg?MEr>?^5Uso9OZ9C98bz7=H6ebRs<K
z>GOy$+5z@t;oo&89l$!YURo3Mh%NhoSSIk7ayDRFAvO@PuRvBg6gt;7G%q_FeJpYi
zQ82;D6|LWFEp?V6W`(@kzVgy%5PaP|3Is^x_~0{ncwBSh{XFwd-p!~~Wo5_g<=g*B
z`_hNW$TtQ9nZjt)%;BMRGvuVOn}F#`$iNV*a`{GC&CNDFH)eN9CvN%`oiT!0EfJ^>
z(0e{~tL+#J4E~1ekc0QSMBNa6rkx!|ktbI2opST{e>)Dr0^&vk=|Qm|V3=F>a(D^u
zcxIuI$y-|szhmFo_PTt!kb&AXyd})4iHmd%aV>;p)QM=k{}H$uTe}FC9t~Y&E7YNp
z=oSI@IV2xL93(c?C=%ahn!55n3fE@KeK}#8kzP<@;O4IKBxb7I@+t(0(^w7e#XH9F
zh#Kkb-zRAPbQUNIsrfw-3tQcd-b~+$l^wP{%o4}%PrXeWoF2#G)_*b9e!#8fHvuPu
z^{25v%bw}un1+L}rkTKw_YthaMnOD-A%$)qN_;MdcX`<FUBoycBO$0qp5k~b_)!mY
zFaJb|h<SW=IV_98uHrm+d2sgXo%18LHUhCX4}BMTR#G-vOd}l7_Hs!XF?;N%v$!9M
z2$c%9Ne3m_j<?<N#Qp4|y1yvl7rdBFm2TwJ>#=~BZlM`m02b54auan$E0$b_dc}J~
z!mExPu_4$-ABFvSV)cb7u*GqXyiRa@sic4yrdFz8qL1_Hv;hAZs$Vu!|J-ZL;D7Pv
z<?vc5^L2MIB!ECK61ENreg3*~I3Ta#ZFovAJPwqpy-!__$KdtnAXtt3o1@pp_)JJl
z{5=AC77G4R4AKjW=4P(}17L+P1ZT9s&F48e%Fp~7(lfga=A{Dvhif~C9$+dL2hZ3W
z2ZK73cv)m>ynd+PZva?*nkzt^>P=VUp9B2q{6f+IMaIPqd-kXI<lvgwubcV9V6~9G
z4L!RJeIKky@GGcbm{$)gCge4*y%O4irMlN|+l5UwX3+g~HrGhT4`<K&!v!F_^YD43
z9lHiQoY|~oB}wknaT9x)CA2Sod1vH8+s5}-^S%9F=3NO!KwNL2yjgl*Ykf6-kqaGE
zq>PYvO^oaU<MA`m5Cy`rEewR2zQBr3HYF{s*l|2I2uMJ9bgal!#a%*ra^y*P*!4)J
zHWF?wT++PNB(o8r?wgl|WLDvz?(%m4jF-Gt-q2Y}%l+Tycw|?e7q}3gEfX`m-;-tq
z=uuTmWBFuqv$~nG04{@}`dHH&i7t|TitQV{D8?I;3fcQ_SU7oSH4Rc}j=PMy^pM|I
z`2@6FdA+Mg1VgXKx6<L8ol1<6ME3N@o2=ROH3l<#sMJ{Z<zLi;nGA!(sEBPRP-u8a
z9V(oG4~?4G-x-<Bke_`@uTS>PiFO?%6{ZCx%(()+C+%;u??p=vgp1{kTwcm`h&w#c
z84&nOPz04Y?+yLDIq6>}ysGbqrK9q|z-s=A6S1-36K$%A&KYsd^ga5NnkZhh4^a!O
zFj$gu(SqAFrmQr&(7e5l8D3!vTYps<8DsV|eoKDxIJUA^?8D>K_pW9L!N#o2Aknym
z-D8O6=BY~NRBPMf4h^TVyS>@P;yfa`vtso-C?UO2cKG`yinSiM8OhM1n4HW6UptL3
zC4(eTi}WP)alx(`R{@|H?*Da*LKcnqee=EMWfaeZFmJM-t0up@A2bsYKhdK)%xXbV
z&|#{Dp3fGtZ4x=Xk1I-o$4HoJr;Te`^~C*||0&@Me7DL5M!ycj#Ak#+=}wS)hbC3J
z>Nw#;GB<jBjL(cX*0K7W37Oc0z^By5$+3IPTteUY$p0!cTr4#?h`A9JQhW3Hvgj@z
z2|fIlj^}3N&(DLI+Y7>+0*2P@T&p{SPFS~XK0ujNf(73-f}XDft|POt(!wxb<>O-B
z+B%W<O5cy=X|8Mg?GXZ3C;c%3)}tN^pWrI{>ZB?tsRj+3EHIj<vRe6+BPvFmuF0b3
z@z0GQgj1~{5BHsA0TEed2hvo3>xtT(XdqaX-3_Di6=yFV7GGwM0)pr)ch|DmU$=#I
z^vVQ~2<yuPmt*qm1FSIb@2y5YNJmy=<ih*UX)`dhARecso}IM}5DXCy056?rZ;FPI
zcKQyh-oZu-BK!v^c(NEw&(q;}inIw3T;`?#p?O`^lfRHdglu#{TSPuk=B1M6-RPtJ
zuY$8}yS<}YsrCTPGl}Z@gsa$sfvj&8j!_-*C#2h~9hfbkAs>!#!s8*up4ACnF??p{
zxUp%{B$`h7)++ml3N9Geh+Iy1_rfI7yc`D|$hQ<4tx_a5s>L^N+_zaza)0&z<)xjU
z9z3|7hb)b8nYffW99smxF`WNSrR6l%dIhm^xQ^}+3Kl_8HVulCBGX3;3&BG?9obwT
zHC5ojrlX-uB8KGd4flF*-F5)t&avitNZY<8?;8!x-vne+l6j%xXthchUv>jnAzIyn
z!$0Mfg3UB&np_c#?IVJT1+3{fwSJl-6PYuj%^t7u(6dal(&1P*=i(B}-)M<uC8OX~
zp4z|jC~9oNfrk?E5h0ktkoOTpEN*fwgv|XG_r;93f2K^w>nts+_@QQaD)Noa{v$^S
zuH_x+WIn{K9YP8kq|_Q;P*#=ptui#84z6Q$H70OROGAVh`BY*m74C;=2!0oG#7H<i
zI~pQdqAUSq#_!S&tQtZ-5^OezLA026q+BcrM7rfeI&u>;ifacT1&;CSx{`9=$K9XH
zOZC3cU{uyLu9-b5L0GsUMkYQ~5Mo?M9g)sT%Ch=$^FZPLU74?HyX{SnuD%$N=UMJB
zkZ4uGjm{7I*S}C)7$(2)65M9c+uMDSO8JA|hd9FJrG?VFDaltDm<Mxu3D8m(Xw15g
zQYdOdKS$9ec9~hj>3bxC6@U!XG%I0CU*&L~{t~j&>oDa_1!y(6w#8?pzzWohX!@k2
z0PhqBntb{T643q!SLfg!2h?u+*qAsI+vdc!+1QP3+fLIa6I+cNG&URCPJ_mtu(#))
zbDsNr_m9|nzk9vEwbp(__;-Ks8+9rnUg%>vlc&CAY7TDsW=vG$HzlwIR(R+7+1x4>
zpF<x$c6{lo-n78$o15`GRu<?{364v_leY3_=CYRC-b{<?Ceu16tw`Qkj0wVd_UKxS
zRN0^hV6G(6KP#e+2qjjh<cK26V|UcoW?EmO|N8wSPZ8vrZ43RGg!9iCL-UvRW1&Hu
zF96`l4DYCjK%bZPLU<(vPA?#PcJ)G%5YGm>oU(3#Z>Y?QYI7!$k{;d{z6uG`x>?6{
z8bLTVHaYqmDE_vxwn+;ij;2l^L`tpXBZJ(!r>3bEL&e+j>ecO?)-@#G7K25*>mHXx
z@{f*>g}1ZdlO*f5VJjL^@~0WHGJ}!^Ie5(=N|@<({5xyasDct;j&7}NJ0l!-;3^}G
z8N+&_00kGwh|U8bUZ&5_G$EAsB@MxpDa6RWmdJBB)3Z!}4iB}sn7XRrfb6_Z646+O
z>xT!VhT$$U9H5Q5;(nw()QndoBr44)o9KfQ`U8;IGH$AEn)~j=P}M>$7{*iEM5fYB
z7CAcP_Jrw<v7VNiUABJ_lj|6AsNwfElqtM>a?n#%a_v+krz9xj3`{pF-aVBww_~Me
zNZlzL*xBeYs3`q3?cMyzeY)6k&xUu<L@RE47j!u6QNZ|n4C&&}uD`spk{r~xglNr7
zhbYg=jxM-&ZQY@YQtDVm84V~;p3IW=!6favr{y2m&1u`CuIMj?IC#Ie<foJ^(YlaI
za$}0fNLV<Ry$ZWCQSg~cE5%P3iZ#p502pW~bbPevl!~zrW`)&cyh&R~Nl9{^@*L*;
zC!LWCPu{#mb&arLoC>j+X&%GkQW9Ky<OuJ#zkLP&plIwU3<EifnIhs>MVozQ4F`$h
z@0$thv|wE{%pTU6H5TU8K$c5P*PsB<wh2w?Z;Hhco6iyp2{l#4C^TVzzBEyX?|9v7
zN)QRgMvqANheno^wICN^A(L=d#7SMNZbilvx-9-b9b=c8+Eo$GlQE1IpQ351V4xPS
zcB&i9>BPp?b5FA9uXrs33#IzMSpZKf{FGc4M3al_m>MA0FHyHesR9}qvRf5k5fnNr
zqrn4@urabpl{>l9@=w=Jh7|cKisHdW_-vgJDwdsRlFh~iv$7m#2|Hr8_77E-E;G^j
zFU4j>fDI$9=qU$Xi?v@WNNO|GQsbiIN|i>}yy&~?60Fk(ZG;74PbA7yiV{%3v;1W1
zuOy<e7ebH4KZ>#>-N<3)XGVXzj1Px)<DZ!Zp^IoB7ha%scfaG(9q*4A+6>HTN2^?Q
zp$TmzTy>n@A-VDtnllg)<Z+Xh=L>ed&U1rojU)ed+fnYr<gGq^puAl~{oF%JsPM|m
zNXaMq>O;F%-q{n~bUZ1ksm)<&9H61!hIyvTIvUUFG&YkI>Cu3LzU(<tyB&E*L9i_L
zDy-@9j*VX+MsR~AadK8UxSUjdbL}CNnnNmk<O1c#%|1x#^SR1#V$aR4A@vkJHancS
z_sQ5uDxp&^#mKW|>tUIARj#^-96o(EQh2k00(A|zCa<I*%@D;S7eX+I7T*vsis&0K
zVInO@r%^SFY<L2MOb7YQvSxZXC54MU+b4t|9c|sBmkQeM9qlO3MeRTFn+^6;L1N_k
zWJXrlEk!STZd4;*WB)BcYb5u!=Hef&>ZF230kxL<NK`nom<T9NEm2YOoe=-cpws8E
z3FTq{lxORr*=RYs-pxAKs-*{jHqjvjaWn3C*YM|bXjv_Oy~tlO=f5K<u6vV4k?KUv
zD|qHh@jq`~vWX6VtKUetz+=VN;Pd~<QEm&IQvoZ&$8@z<7vbla_#r!K#3iDOtrtVm
zo=zd?iF^5WwdYqdNAL-5#}5gqSmekn;=~x2o3Wz>MzGu~acc{he`D7mm#hWU%i680
zm5`#o+mQ1hL^GqMZ;$*s-T#=hCC7<w0>{Mu@+ScH7+vX`(wH;0CSOiuB#)#w&OE~!
z6tY<#53x}n-=&VTd%B~7?p3!E_g%<w573S#bG628?8vym`l$QWS>Sm4RaMOTN_-MB
zxsuAKOajnFNd9}+$~x39p(ka%pBB=a;>)Xkc8MwG4qBwAzKV{i3gTnRMYh!M@gZn+
zWnX2`L;lr<u;a&p?nJvu{&@?s$;!+@nHHWMjM9K{+4pCuzOYS%1Q<q_?cjk9__rSP
zip^<<d71`C8*xp27?Hq=Y*p6QiQlAAgWOL5>yFJNl+96}d9e_A(PfQ(zByZ!%Q8Wl
zSF|n8&gzRrSRScBg-|gTjo1MUA3M!<^0C*F!;js*e<N^W>7}UG*BhfCd}s&0)ud0#
z1xb?-@kNq%sEziFa2)P2i26x+JVnGYnOkblwQk_!2(5fP+K8(uYVzeqxJ~{R9Bh=0
zwz{Ueq!P>mAS&iJxb=|lZ6aKMOf~*@`+lK%B{pJfZ_2UBhBUOCaP2?xA`gZ^|G*oC
zjqQkv=B%mrCvlO$%6?~olEhscHAr0~mVL9CWDPkI2l#X#vvIS2R_)rsjl%dLi-6yS
zMwb^(p}x9GE{bK^{vfJ{xY6kKaAtdrwyQth$mLtH#14xYlJ~?Qyw&Li`)kY=rGnlP
z$Dxoi0PZs6aEH^y9Umwm^k+it`rJQ(O@Vi8f9-RLcwIks!nda#<!T`Hql;dQ<&^Pt
zao^Z3p|p|@(&cj|G1i`x#8|RVL&bM;*m4e*jtA2;;<KnAm#*aOGqETa*T1M3(f&EE
zzBBl5ds(bVNlz8K6_}KB3vMexenx6i9Ir#J=OiVjNJnCCR}8MR@e*y$DEKK(>!DjV
z4rcs*luj?T5Km{c@5!VCty3H3Nc4B-gmhXCqs;>RyC0iyJfu5ODzC}?mwPil6J@;%
zRu{1xtAkd9HwR^7c}-|O(>YV!;*>O)4cm3PU-JZ`y8|$b?j32PpPj$lwY4OMm%Noh
z)(MVED1?BbxT}w?{JXQ4q<VjkCK!HJP%VoGMwN1Tk5*tgJ4^U=5*$tEqmdgjxAxqj
zEH6gxXYTZB44Z?z%y9ApKI%`JZZ}JljU!$B=O_!M#A8A+ph;l_;p*7n>Oc`}Xv-9^
zu=-W+j$(c!OE>yRN4Z0ZhCCTGKF<l!Z8Yb}S`*krnPIjLLV{IJWg290Uwe&>0^`0j
zOEb4@+g`}HL}DSgkAs@akZhc3wD9u67UOWzto@O_?I{dmR3P`gLF7K{1v2PJEj3B3
zXdDEwjCb>?pVUJqhirwL{^+0~Xa&g74J#kL`6eJg1^*6ca~g&jih>kYbi*6JOCcdm
zNjdMaOftSfiG7S)LasfDk4%NonVpRiFXN>^FO#UYNIwTwK(Si9OW?L^iJG=&+52zj
zL*vJviY$Zik661gkb7Wl&k9?K$dFVjt}Lxc{G&y%NW?E_dD|yk(CO~xlM6ncq1?F=
z86FDaMtj^ak7mXq5P4}?o$@#kqAbk)F;H$j01xvEzK?nSxYE4E6E4t4tX&vc<`LJA
z3gV)1Txz1+(iYqTyy(Eiz$mS?y9oZrIr5e0GDpk3{dVm<4*yOtoz~bs&bLTE^BXHP
zc~Q-D(BeXT>y|@9wFMNfP3O(CP;dCmW+OlsyF1T#>!if*hX4o{*q+W2Fim3B`^b~;
zZ%GihL<Uz%Z+STog>+qPCj#Ww2m}U(tGAi`>q6GC>=GtYwR}J{OalN_ralu(5_~q>
z`|&&WsntAnogEVs6U{rb$Z<yXoE{d8uXKiFZ%MnQZL<NK?E0JId%a96pLAoh+lN1k
zO{SlIL_r@y<-UM(b50%-fl!a!(EM${K^Hos(aG=*7-=&-+RSJd=|evm3@xuxvP(&c
z(ep^YH=OQm^)heKXI)rm1;$4RW&rb%uS`$TESQOJRY@@fzsyu(RP@DW^WJKchqyf!
zaf(XaGZrKjYzqU^{sm=X0#9=CC2`KRht$(w2P;elphodTDdF$%St7}|@P{;q1{jVI
zxahP91l`2xeo03!8U>)Rry<9c{vJMmzN<oiEFF%J(x*>Nm)Oo79&>wiDZ=+N^%2=N
z?ZldDzM7uOPyRh&T}Vq$>@mfYfO#iv=zthl?D0=mOS&%wy**t8N8ngBTXcnuybzVL
z`9fiaJj(8Kr3TcX8;ihRKVZr#PSZ?MF7*5vN4yzQ#i8Ta7Vk|`ZuNuO+Uf^`ETQpd
zln>f1#2*^pL2u`Gl_{mK2vJzagJDArCzWDJi;Vo{ihFS2fIt79dDYy;XNsIjj@tQA
zT!AKpfQ>z%0AiDkN;rQ@Z_eYzNb)aptK+%Pr8%fs9O>K&laWCS_`4N8cvPE~;NWFh
zIQTO4(A4-7D0bnOp#nRRREaPR^5i9qgGcr^#LM>2$QDvvvrF?sK`!FfdoYJbcofvs
z&^wSRCNA@$T;>_jFk@QW!SOJd&TXagv(?l1ztX1Lv+`m_Aym|pUjPCNT8xfe;&e3<
zWpM)wl8-YVwW1m^G;^lQKCqqXbxazJ#y{7VBhH=c7CL`_XFU;Ky=a1EvKfu0>zcw|
zsoH#!Ue7I^Z%dx+zY2=R-?(zhpkKSBRxHQCr*6t)3gV`QIF8L$*%{e!=ZV_Vk?&Y)
zwN+9QdLn|tHWnYe{1jpt*J6#tXDQkg+^lK%sw%d2GsEF!E7a7HY~o%UHzUbG;s$)>
zGy381IDw3dNK&_l<4BDdMVUR95>@M>vA0isA)@bm@E-5-IT^{%!U;n^hNgr1T)o82
z;L6#1boIUCWa7i{RUWxzRvR411q0I?!rR(^M;npspE+#&X=#w5?~8G%TjmpTr<)mO
zWiBtY3c?ii^}&g@jClT--Na`@n&jkfE4SOynOlq?zWj?I>b0SXvKH=(-@>K%7wXNo
z+!|kbaPu_aW~bK|w|ZhA)taFQZ;ar2)|ff90KY$IuwzRKO<gaxW;5{cbH%}MMM$)9
zg+Cz21KrxV2~D6Vklp7|gTb;iRTzCPO+#<-vn1TgZi5^}oj%rdD#DiU{di`Y2xa<f
zm^I4(cb7B4)iFdkm+qK~O@(*=$lTwH5sTQJcJI2LbZIQdJ|Pg3p`86XQwrXu+dUKe
zV525SG@CG-Ik1_zl;)$xN(i|x!zU<FMbUp}r;Ayv(7bcQeITtp@lr2Q?7dev#XM(X
z9o+BijqB8%q`%orGA8D?cz^Aj`;6Rw9;MXa`_Ib1^TAlo`n}&}TQIDvOP)l)DEu2{
zFlIr!2M~s}o8iS3z4eMk(rJZ43n41~G$$!eL-+Hq1Zp0(41q6=KQ)-yn*d_k1O<FG
zZ_subYUmB2X0f8x#ppO<<J4y+LuWB0kbUYWY9UU%7JBbJ8`D61{ih)4pT?%Kxhz~6
zdNCyx^n0(p9po!h<C2EIk-Ivbsgreh`gV!qtL`$)<`#uF{a)WI#cuFOY_U$%OXQLV
z<(*XbzM|O+tn7B@jdD67|MPI^ni;54SsQt<qT~k?wA_q?ygZprJhja$s|nJ^quTaf
zfG5H&h-Z9P;+bLVVmQF4Ju(#5u@i+*J}D&Os&U}}3-)9(mhvnsZX&}Cvl|TA{={}L
zw*wRYP9VWFhio5er)|e(AYpwC5mhuHX5W`k!z&{g7yC6)y-uRvL86XIClGUO5@rcs
zM`7N+?uHRLH#8Z&r;faVC=)mu8_S!4=R^^TY!K0OLV1GB8#AIHvzdGs@zYKR+UXn_
zsMUpgx$9lj{*`qsk1GXj!bsWBn|pnnQ26DrT0W^Hg?oe0BEGC|3~rzeUuZj+%rIq<
zf`$)$rLK)kUtvwpbt}ntFM@t|Y$`ge>X^i8tZnRsi?XeJH~cT$aWVffZk=<Hx+EdF
ze+u?dlr6(rgiY&tkwO!Y8=rl+3z)9Xkr|$yv~m3_7=`&y!u9R<V3gT5HsN$PRJYwZ
zeU+mZ6>SWNJ*W^l;5m*vHl--e-==ZC^<@FE(**?cb32Gsl90yvHCu;CE1X~@G7ZgY
zs>;Hbj;>!%M|OhnCWcf}S`tOjVP^M&U}p$peW2oSYTyTPoz(Ju8ARJsq~|qaXHXX5
zN7V*_X^%PMd`Y0XK{MV}-CsaUt;lsL(3vZ19QV5-VvYHxDQPafrNmedHi>I5D#q9A
zSWtsj4&$rpn<u;Wp$Q)FyP9zmlAVSL$gml9!*nhD`ko~llyWd##`?0upXby!z%CMw
zd;`scBN}?qJaApy{;yDs!F<}zp+3(EK}|WE6ZlZ!y_Z6MDdkZB&)wd8q{`k`>X*=P
z-;LQ=+g@-<VB<C=Vmb=@WRk+6gz4>Klwur!+0+BA;HTB-ITtu-?2p(Z(?1;Y<_yba
zr$I5<Tir04fZxsz0vs_>G2-PdmH8e#^EaOP!p@@}0T3rR%$K<WzZYTbJl}8+^}`Vk
znSK=4+Vg)ai)V4?L?$qGJl3aY$M!QZ%5d~lN$qBoH0T6VHTKw+I^FX>-D8eMepMxT
z-~HUx{u`j&<JNXGlEcZ&Pr&T_f~)ux2&8xVN_3m6?FIm)nD6V?WNBmKlFX|~wYZct
zB#$z|s$Lh|%iQ_ulfy5dsQZNL!Wcf4IQ79cF(Bn}^Ae;Ju#9Z-8aC7|6RUg+xS40p
z<3OK3rKf=9-|0b$F4uI_`-$Ks75s}Ua-S%-yjl_z<w<ITOrFI(dXU|edxi3sWCvi*
zImsD|3R_({kqM`cVH_XjETH2_o8>w2>E`)WSF;iU_i8Kl!tklMtBm-gpHhB2FAi9x
z8#z=8wSig8<Ifo!GODPoh$%CCBjOwI(8=IHEN!vuS)<oZ=;u*C%-T)5dVRv)&Ti!h
z!vd@Lvp4E^yRI)HB{*!x-6>Bbd?)1W^*TR*T-4*q$jVA}z;lqfve1<})TaL#&e(qT
zh5WzfXiuaQ!0f-lde<_n5AX9Vtzg3%KNReR@k#MlQ6Ueeo{tjG$$%&Lb9HH!)5<;Q
z$=+AfD!($>ul-paIjSY!)?JS7-Ln}@Uxa6r)CJkUC|d+zTQLC?<}l8QR0nJPw6HF1
zik~lag>RBZ-RKxFI7xFF?W6>R^HBzV*eTR-qh2{}j|Yz6N8%YpIAlypBeev+uJk6k
z1m7X^43ei|<yX4c!hPIg98J#1sO5xx65xNV?H~sZLfTUsUAJ*A${^*K>UWUKKPwF-
zT?7f2?ZB+Xj?9A97#}s#K7h(ey}l7rh?rVgQI6Z+2e58xa(=Yt+v#h;V8UyFtf24R
zx8s{ZvHIcL8f6v(KZ0e^M|ujAbD*@6ePPHIhVb>3Sa}8mk!}63p$N3^>rQdyo>JeU
zelX-fpZe`r!u5U3RTW=-w-DJxl?>4z=fNYJzwy*{VRv<q!q-$cg;+%Iz-xroUY%sv
zVP0y>H1=v!!-xX0cY2mwaBQ4kmaqAQn?13^_1nTmv;W?c#W1PTF7KjR?Sccu0r?If
zq~xj$W3Gw{Gj|vOv8en&%YWa}_42_|X*^~v#m_TBk^mK};*J#ls3U(8<H3we`>13V
z2|X73t5V}JYs3A6)R3>PKXjD*x({88v(Z(sZpsh+EP0J1t`5)Jz{^+jpu^$G0WxyH
zPRYav<K?g;TOk%D?=xw=8D_;o2OVVAH+Hglz7Qv+f%418++gTBhY?E}(`PB+-i2#E
zzOW2Q?c>^T)!0db;pO@=Em-)~it4t+A~g>mbsfgCvI#rnqXa>zG&mYWaVJgp`!IXu
znkftL2{}o{s*lCge&Nr}%JHLpX1DI{i%=bHlGwi$#;1elt?a+uoA(KcQjQ$&?#NJU
z0R!q;2-!D#9mh8imlY~T0er)s!SV<0Vu3dw4r#_5SWVWlyBGU((j&@p_RZwyJmWim
zKiF@?>a{6tBoVTooCh}Qd(osEQQX5><T1>r-P}=&>+ut;PRFBQFTG8E2Olq?+JR~H
z%C<&)k)c=2=O$Ue{)TQ=Gl;8aNRn(N-cL!YrbIU8>KrK42CNH>jwWnf?XvNYE+a1n
zu%k9oV$U9!RprZ2DI5kE=zJH02Rjga44)SYBxAh*cfyclHF0k}G<c^Q&yv-|KIA*s
z3ol$;e{{>&oRHy9!I0+W;%`1Xk_t1H5IT5DZhTgW?K_7#*^%Xhvsd?c>}Vnujt72=
zh0c(HnUA(G3G$Lgr!#ys!uRWEHRFifxO9%DS%NcLm;$`Qta(#S^67{AKew-tw4^uh
zYWn`a=fY|=&C{SD3)G(LT6*(D<;d<}?q#w+qe^N)K;WcQui0-dN8c|uP;^W)uO2Z6
zf-y!;DW2tdy>$C#h35PpcS<|5$h9SJq)vMOPzyqWl>0(G1D~PYykA@$lzIIH!M*w|
z1sv}QdTv6EBC`?H>~#a{Z01%i*s7FTgj;-sm3@9PXREb_CgiUUZ)(R81aAOlbfKx2
z&XQZVt%o}R&UswA(8~U?=?i9lr?r6&d`V9awC^c9nA1hnaj`N$!?)q2RYpDqQ%cSW
zXcF?~;QJxZ{sm{S;rac+mJ2ewWsk+>L4UBVunlpj*CU*>FYiU(Xuasmhm`@Fco5%W
zOR4)DQJAc2MFtkI7=;{=Kz#jXmnIU6p@p{M+scd^dUnz7iT~dfC?3=IEwT^Kde&+2
z*Ruw*MsmHFD}7z}-9%d+-HukKSgesO<4^TFEZ%pMK<-!xXIp~~zXvAa2UL4ki*(z^
zKXQMEnftaucC?iHQFR=tW|kyWFO~F^w3J^(!K@&^K=SESUuJnEnskED?1)$3^$FUM
zvVsK9lm<>Z&UJsk`y_K4teqPVrwh|yUF}uj8aOeoefy*=E`Wpg1i?@&J|US3lncEr
zocC^wD@3vcGDuGDzm6@IEq!AUw95xc*XBAkNVV+C>_}z7!a|C{k*6!%I9QV0XCt?Y
zs|%m@O;KRK_BH@~gMzwGiV-$4h~vg8dFFp2kd%(sz+n*6!RktyTIm(1l9Nxno*<BH
zYbPhqJW=G9Pngq~kiK^8reP<(g}ai786k!kkjis%F&O(2X{iC50E!FFbJ*zITt95<
z33G|2mgv<rwj{-F0&BnuO`2lt*x}v?R~FK4FIqnYyH16^w-J`mySv{pe65UT{Pk(B
zae(JpeZz@RQL3jt!`ca)&GFlYxv$v!*X~|KSKMMesc@vY3j&pvx*L50eBEL`a4fc5
zde*3ge8~pGj8sP-9_OI)JVC;0OXEe1TxNln2RbE5Lh=y}#w)>IVKTq-w8PbL)PRB#
zQzOjk?F2<#YjRhIe<~F76)jJm`+|rV<Y<2ZruwD05_N#MCzEc7thlUU8umz09#&GW
z1;_iTR^G;5V@^1ur?G5lkjI6V+wM~OZqA^YuLXOD6A2lU0&&pO$l4_;V*8N^GVJdF
zD%73l&j%tapVG{q77y?_p^R>($YN%9aEZbk>r0S(a#kFdTR6h36O4`)QG`m8=;L75
znx;-skz>nr)^-q$wmOA#(x&vk5kgqn@a0ZX)VH|EfoYBMi8HX?&O;5RDJX<;r)2YV
zTXFZFLc;bfE3^%<8pqLeZNy#JEe#SmsuHbKNC<0TL4Gwz8DaiW5q$P%LLe1Pdz6`e
z=zc2;tF!oh>&=@J?9#~RqIO4u?Vy*SA!}=m4dG-Vo1mslc=X{Vz1t{6AOi>57W!zz
z$MXtSc6QBgl1~Q47+PRi{qJ8@zY-7>H7_KeZ)zJ*dlp{k@fzf8n8(J8GejsTjweq^
z=t=^jPknag9R`gHK1d>{?AZ`yi6g@1T1sPdS&D?4$`7CS+MA@tQa8{4oGQIiOMR(9
zuf_W|)E{%LJav#?9=+X*A*YyJSnkE9S!|hxD+UC)5+ap{bj&xH7`zhH^M0h3$l+j7
zg+ayms(xEggc<QYjC!>I@emIWSF3CKV6rk4V-54B#s1Xj=Phq>cQ*Wfb(1Mz4!aT)
zaRvTC{i-49s9YG88p8;X8U1d%y@NpwdlMy#jktw&IWGU!?&oJ)FP6}zE}&(lpR-r+
z8@gV%!`wGcceB5n4eM~&zkjLdyWh>J>5(yK#nqXURl{6Vb0JDhZ3#Z2PtQbQmInA=
zbt{=-GR-$C9PR!4y%Hp=z&O#zv`l<ta!i9rby&7ZKUE1)OOI8^G*O?Mf$L%|Smuci
zfisT__Aw|>^1>Kp?p-E#vW?`nXRp(-%`{*K;*k{BFe5Ik@dYM;oggjyXW$>1|5Zc(
zYl>?#0JPou%_`(26qu`;uswFY5!W2_cJ{nT1^N$5V#ne`{ABirrhTq|%LPlmW2fe)
zqvEtw8c5p^V0qdth`Bh?vlc0HDwHDkFRy%#!Q|D0_VjP%1{<1&pbA}EQnUoW6fRh$
z_7a^OZ>g|G6jac+H1U*tB^vNe_dB&O+VRrCLtDJQUkFKC&dhG7IFsR^>I8`XayJ$6
z3KNTu6MV|=IHyWfAYcr!;5@LxqB8t=M&xD^j`MfHba^pb#{%=<{k?sb{Gw_}NfAlL
zXbSiIOvN@A9sx@ezZNzn8aqOSUAe8?B7L;d$1q`4g6r{JkkEpjW-PM*3$CTqqF~bD
z+;1pH$BLY^nfEme<8B?SboW;?g7riNUx-*woZYh>5-N(e=r3#<^^bJhHE;7x*(24F
z__Qytrwb0c1B6LS_}qGkOKA~kguI13r3U2drqgL?4Z`$#3-iQzLM{<`8b!4@fb(&N
zr$mL>QKK`CNG1|YE{4n~Ws?9??vij!DyCTID-a)JHZ3eQPr5|)j#@c`?!3m@oKiIg
zUaE2jwlLalN@%87Gy|m2fp57NV!Eh&)4_yKQ&urslX*uPLtqod{Ff63;h8HjBZF*s
z2;D;j9s5&mUUB^r5}wbfKa3KB^?Au@bqs3OP-dP(pM*tFYWAT9iEVo3*g6JElE3W;
zb3>{{1r%)e<ZK^ctM0}6r<X%%YI#!yLCY{~>@RGHy}`I@>)ZxE5^T$b`Qj{U_3WG%
z+$%-~m6IJM-Ly%ZX^&<s-Am0+zU95uv^?K9XB6lng+#$={U0Ivyi$DB#<(PUcbn<H
zVP{&*`SPl2WgFq8+9c4)-U&Sm<vrXdDaP?KXsWWD#NN(^5#5QYoM8>bqxm>1Xal^f
zvDOnjM%=swYuM+j-44{-)#SzF0#PPBKHgW7$ZWpL%^Xy1EI0~&HpRqYeTIwgKT=x8
z(v=SZSgHx=DD_=~jb_6YjpD?6W2RMS60GG#I}vvZ5hbxONPm@VX7QF+qtga@r=9nA
z4*`<!i0{qvdnV_*t1|z3Tdw8W*lFo85R_=RD^n-9rRV>&x1K+1#wVfOOPY>^nGks}
zBv5kw5k9L{)foVc>%s8!f4VNpXsv6E2oOq^6m#iNCuBe)+KCOYDWTtyXPy33hr+$J
zxyYKVIuw@c6|Ua`X(_ADC|X>Y{(AYSy6ll3RYUn&m|`;rh+z1Ba_nB-5xTeiFDC(F
zyce?Bl2!ntg`#AXl2G+L_WUPMm+*ILBm+dfVQFJKEF<2coGteFwLJ5RXgYKTVEMH~
z9m=klZ|CbwQ*stjoA)bNLCR6U5jQ+xB5BL{b61=oPYWgmHTW!FLQ4+A85pURT8{r6
zsoOV0Db0&s*R?0C2Pr<G?^?B^e@Ig>Fu7StXXWpIfBF7rmqLyMS67K(oTL%!X^2V9
zp4pfEH3iYR9WQ|}cT&07&o&l1<m8GtPXJcz@7m9sk(}zr;A0I0GevRXyAFBhJ1nLn
zY`V$yV|;o%D;f<NJ1_!GH&9DHipG}<g6E=H;>d&SA-g}jTNaBwN6qKS?TW$36@qOS
zF%Z_|8v{2x^bHMZLz8En@K~}{hDt4vLI$=v$#7K@f7=~HDp+8`PwuQk;L)S>`=bol
zy#B;h?^KOlw7G>o!L{v26yp@(lu;$qr#)Yq3k_2#Ake1$^054QN!BBTH&Ex(WfFG#
zF~;NOTuRlTC=1WC@HSqk+{XpwX0S@wVT=XMvn7Lhwt&#y_KF(!?zmQ3Rv#ej($X_Y
z$7_$z`}pDRzGka^7yUS501en3XPH#U2adBuG^V#bXC3~<aOl-7LPCHwauZ7%8h|y4
z&Rv!s0KA*14bexH<dLKKO%Z0GFVS(BdG)#ZL&H2WK#6?ei4&H_e0qn=(y7wY`S#E1
zj^?P7q9uZF&zb#u=Zd=tC`8Cz2dsHXyOgs0lOeYg*lG+G6*K|puKT82O5WV#^jg<_
z?`*VPw(7v;_-yD2!)l7c5rhxRsmea&67tBsIA^n+Gnp}|<qsf{F8fUp_gsXB!Qgvx
zAtHuRgDXzTd^za!*M*NaanH7XGiu2}tZ;-c4IkffC0u!7!1kdch1cRn<jF!30XBO{
zBcSIqB8$Y$d4SP-+5>AN(xw{)t3ujbGX9lWK6A73sf$2D_bX=M1SR85u5#CRy6QA#
z8kIUFpfVdsJDe%r+M^jg>uNY58un(!rjpA}MW;dns{;;^$_xP42AJ5O+HLY6<zLK~
z`RRzGC<Q}Jg>@q3?PHbfI1(gyD?Iu;IKayB@*y`xfB}5+=cHITH5|H~4vi{h-}^;>
z!jGxuM`EVfV$g$)k&ye$#igytF+cJP9R_zvN>Z>;#Vy!>x)X)&cCyVxHaLG{8(wax
zRhq%bSU*J~j6H%sT2`+AQyy_0!l}~E??Q1<s)I@DIWEfBgv^xWAG-sr{?w=eO$BqC
za)3g#{jR}2^km3-tjc2O%G2CbqAq;{_x*Q4NE`$eZ-pawhWJ^O+kg;J{9jN+-k&Ig
zx%#vJtEc?W;rfIG^XaPu0c$u1lWu|U=2KZGr^|Mr)q-?a(N{Y=MMq^+-BA1N%O%7^
z?oh&#>mxjiy;j!MjvxXLZvMV~>h}7r%BYEKnyGdfCF?@(e&H;Q3aKogwP=j;H>wM%
zB^7oRx96z8=Zc*S(jv&8<|VgihHk-~x@Bj7(ri13^g4(PD(|T{r~M^Qh=z5y72;6t
zD4|PpIe%$)B76YC6@J^=WJ(6u4;P=1JU?q#(<_^Xd`9>0?3CA%D=e|#Ckr_L+&XfT
zXo*jQDG+WupyI?6m}gZ&Gz!T#?v+ERLSK4V@yeklAZ&+&<_h92@&1`#)n5w#WU+it
z@^ZVp*HTe1q-F{c7r8jLG5hL)7z{ZhNc*OYpl^0!k{g=J250g$D-qYr=`eF6GwmkP
zBwdM1>UpLQ%GlGr;z%zX>sIjYV}PQV;G#dw8F69CZ*ax%S~V$SLfEfaAgbif^FUXs
z=Lf&;kA|!-mU30SM67YAS;s5e$-3=7c18+SUpf8l?sJw;Av;_+Hk8=7C5K&9D>JoI
z33Z6R3*vvR8Qo7eU)J%=c?o;MSX!2|9Xp}Q)wuPC_#|MMG#Lh7f4FE#c3i)XhD5e7
zrlA4A;;6Zj%9aSxH8r>hy@vAK*Y3i%5MnR~Wf=6y&^Z`n=Gt*EEVnjbtMF?qGK+5i
zG8`NBsj9mPK|VjJD)EwZlHm`r_dMoL3&K-<T(mEZ<XVw9<$YB<^zaMTlhQn_YLAZV
zF_=EO6l}~f{nN4sw26MnVpiqrj-y*{ovXSV48n|P-K?uO1311-i}GZd8pm5?SRM}C
zLGmzC)WDRmgMa>x9C>aS<4<!uKM(3VE}6EoKpY(2(R@%swdilMuHrsu6@`lHZrA60
z&r#-r+#C(@f_)v?_Ua}kn24-eLtL`FgBL6|d3=OiOFXCi3m&qmR1Fzt1}-HBsTv93
zK6&JSywhC2Y#3DiuPo&cE}qR4%sBndSZ6(>3)BfVYn<r_T_cOqhr51BvV~s^k%xgm
zR8AYb<Cx7%J3H~EX~A}i<2<9~qdiA8(vvP#u-#MciU3_z#gZ2v3%R3_dvyR8>PUE;
zmR%aP!&Y*}AC!@aLYZ`ImPYi)&qdYp4k|eCG0k0OBJczP6Q2v@wYcQDuJE~b?61~@
z80Eb?7RL1D&^xL_u#Se*!Ne!bi9+UOkKa9Ny4)M7oGRNVvM#*YOwWPu`Tva&{I~S!
z`k7K(@p;~4GjqNcVDqKUp28iauJhFT$kb^l#-%sZRP4i$=Kbuz>PUW!LuA>f=MK*K
z?VMWAZwuR~=@tR4Ie%omwjwp1l-B|!fbqTkkGvuW=K_w$qm<k)tm(I(<BW@*b5Ae#
zJ?|hDZ2IKjpo>Kk+J9c6(UgyjWY|Q${Mz}m=Efp<*~CWz599LoQyny6JpZXA<shpP
zeRI$NP-XqmpSO!ncK+Toc8k*t@6PrU?*mo5bqrlOArbyasAp0X5xQ9Um799PnT)lr
zw)FO%`63Z;mLFp~6Cvm&fdA_(K_fBWG%ppep^6AKeM-;B;(n7FG8E6^PE>TXuCGUO
zMnZOM2Zc|O8g^JI-RDC-?63%)0}PZqy18jwdkYLjy0(0}oo8q_q&wb{XFbM8Zpjw%
zlE3AC=|gAL;u^0sa0rOp+0B3^w)wfW0stg3L60Yv&#N$ng(S;H?IkFC^#d?96%*z9
zDGIK02{zvfFaR-h|BLPSHX0ekqlJljA4afHw|3zvN+uYoo2|AnC2@@rYtdpoA{}e#
z%8p1h1}^oJnUl*eAAq%Bm5@lbmkBZ&AxCj#83buq^haQZU~=E4fBoy>@u1N4(qvjF
zDwy}YM{9kn!@vgk$B;)N5s)CwP=W|c*_-uUcNItgHYkY27~aLI$hIf+tPfEVK&u{?
z2y%}#tvPE85x1n->j5t*RFs^p*iiA1A?1gGR)5&y(_^83dvv#>1C<f;$~ax-`ygI8
z&OB3cB0Hk|FTl}Q2p>;@ND}&R&P|2s#*NG0ckD}WPFQ7|uobb1B!3n<(VcrQ7GhaG
zWMHKyg;9Bp)(whihHUU(40)XO5&aY3Xc(Cwtvavjyl?;Y$XwGJmz#p5Z<^HQ3$wB%
znM2)`+cgflm0S=n-Q+8k^$~6H&pouq3%w+72QCXe!v&q>fD_GNL34q{wM5YlU+L&*
zN^5sE#Fg0bbl|eu<g|=|3}N%nMQ)vZf^hJ<IN0M;n$sSou6XuhqCPvw-@ZgdM0!G{
zJab0TNx^{;gj5=!d{3ixZlN19PMJ?w7$*bul%9h0Jvv)sXKDObKa;h%&$Ir}P5{nj
z$7diFm@XNW)W+LoKx^lnv7_n7UjLnTG?lP>f#4Yph;l1qpOD056@?uId;M2dcG}S#
znFZg}uj0~7wX6)VSAd>K8aN+r^mX_;8Le16bM_FIijca05`-OeA0x_~2MBSWCPXKx
z#vE1jaAs%YGbFZTQjU$J-{9o1C6_yZNqQl9b(pNZ`<R8R9~k;2TBt9yl?&5`*#!z|
zs_}?htbZw_2)D%E%ctLy^a(~Y>~5Q`e71>3y$Yq6qK)=y;RNsnVm5#KYB3Ox=(h9a
z^t$9|qwi^;=*eU-MC6&JDNi|6LuRJJ6g1P2|Ht2xGeJRJ9zaDmX$Ci4d!0MUle~{D
z>ATLz^S$mu8ZJU_KzFjKE``ki-<MM)cfN2zWFn{V%mM)?oik0+9$}s2Pw|q05T>dD
zm$7>q(6*`VQ0_kdKSW5ph|qGT9?2=>FiYfE>+2G3z4w)WlA(xR%SWiiJzY8w;FJ_m
z?pNryV>t7UefCH?Qc*Foe?HCyCmmyCoeH9q(qFX>2>`}TVZ7*-oBewG)^?Ep4NdZQ
zQu6orRr!lRbv>T%+5h6|%gOvW?Z(ElqJaqIq=K&M4%8U}dBArgo=Y25k_zj47R@gf
zjZdDO@e|puDkj25Glt1QXJk?uSS6<&@jeI{?9&oKUc7?xQmq+FUn=RO$gvae&tMxb
zlVB^;JMIZ`xzpEmG{YoI=P!bqK^1!)9G4ZchhOwiW*T;r-sv07WJL$CQE#q%R7Yn_
zw=evaUwLTFKZ+%>vubTwC<Cxhx`I?o*1g(mPUCVP7G2CkPaRat?NTFukRM}{SoS{_
z!&Z9gDivmEHXC8Q0290#=!5j7uxn4&O(Ur#+kWhIlAH9s&0v)_jbkBsUFxEt2_F@n
zNr;hSlyguD(?Ta_Viu3-j}l?UL_*0v*lZwhk<uq;CJS$kokL+~<=gW%=L;taW<#y$
z+_dr{h~DRk`Tsy@0<sEGdU0Lmo+M=^<buk@{P%be8cXaO_O#(uuXbb-e9_74M%ba~
zR-7If2N0UFI#lh?)4y<Y#<e%oP8sml;%#3Pg~muSu_vpzDOl#e5e6p~6|r;qdpVp3
zQMa=oS|XtV5TS!bk(81Kt{qB1*YSyRHDYcQ-iNt~VH!me(H#ZK>BBi;@LC?bW-r~a
zb$0MFl+22jFw(^Ajfih0`)OjwvR!Wpy}Nz-wfjNsTB(5Zut#p4N``|M^pvBQ>(AOx
zwu>ZNVe|M@TebJ`S7NWtLI~ez(a;b4R^njm21>uOD6>t{L|lrO5x|=tRv=UbJ(emZ
z>FPv^TMYoeI?jaykvJbLUiX50E!8lojSLyBh#k;ajVIh|oqTqCK6_+oZATv<CR|T;
z*jpVQsdKooX;nrG;dlL+A0syhJ*$b9;^qm550*W|X5o$+ov%=w{Vp)O5?#5R|8uC4
z6Voh?@F8S8Xs6!5sFXirNT|`3*fadj^P8VTO2*!k#KrL+mR3)esHE~K+hA|Ui6;#s
zR#mz`_w44SUUyGCYWWdXARUXGVbYS8>DLptH%(NQqalS06*WtEi#q!y7qiynGxwKX
zVLEihJwJ!rCy9+PrtDvs6v7lxz$Vn$_ZVYphdoqOAbSK%iwO$9aY|KFio^KWBMvG7
z<$NQS4C2x$(%|`wg`_gaxyVJOa1Sc0EXqbaGcYG~lKmL`r2x8SJvu_B#U81kI|O?`
z^4<K*0v88sT5ykvf2-$vN1a&RS)H0!9tW>hxS8djG3fk>Fz8?;l}Cxu_2RUqF)=!1
zH;H{V5B$8OB-{qBbSWdAeeN1PAN2|6RKKnF#6RSt)C$w^553F6^fNm06CASl^vV&0
zR_U5Z5KSnGmF_rg-A`9CE(IL9H4Fp&Uj%8UaVL_>+N|&J9fe)xTgpB-kd9hb)T9vM
zM`7AwN2?p#I1sUJo*}nhMUbh^60fVjlaA65|Duxee?3y({a*u_S5o+1IXQMQpLUL`
zIM;hWRQ^MCWCFAmqjjW*N4EE(SLeU7W|hm!?&MFNY#@Fu$VF@s))wVpd;!oM63imr
zQG8sVBPM>&i<BpS(MP%Sk6X|!MMw6Du%WOvdSaNzCj#K($3-4$OT%%(X)ge4^jnA=
z9{HN;r3o<z4hGRTIH>vY$8nB#i%kCl1Q1mV3linF7oWl3$Orq(${?<z7>nyvX=iFw
zH`g(%*Cv!&Yn#!Nz=@kFQ+SKBZ3`|&gfdcv>4JnftS!i(R?3064+n{koci)=+(|QN
zviF&jkovL6V-@GQ3Ex~YRws)lzMujyNG+ZA8flE%^~yUSg~aS&3;1R77qxX}as(Z<
ze0Kz~H)-~MilORK{U55RMlM};WNDy_^yu`4)6<?;qt(U3GrzDCSp$y1<SsP8R$0W<
z$<<wkhG&FtyDJ(^3w~o!;i3g-&|^4lB|?8s{q<c@V`(+%O*;8B?23}6o-i+w8;~Q4
zb!V+&j>6l4m#Xja<?(ySoSvUw%>LrqAVZZs?TyOajA0MPOdIt*aoGgaU18?=MjUK~
z{jS8tUMerZM-+lbqY1k)F}6&pFn%9GPdTZraVPq&bExrp*}of7yELqKXTrMN^#d!R
zf-62~9&YnyYzC{xiQNDZjmiG?OS}G}9`D?h4`ORBy10RI4zu$LlB2Bn)r&jXwL%|r
z*cnudX^_?G7}e}~c6NY?R^4Cf+Mr3(k;Ez9UALf)<*yr`v9O<KzdlR<)~GAsqdB6n
zxN+KvR<q!Iw;EQl0?<D>%W2q=3I}zyf7lK<n#aDxJApLZ#JDOdERG&b2cE(!N^;u2
zur}{AP%jvI)`X+QdeW5tT9h3iXv*A-CfAo#H=0wVRK>_(%~L&UM-+Oz9A178Qhp!a
zJnv7qj*zR9Q@c<B`M>dWjPq!v#TKqQBG@XKzf^nYigv*3L^B+w-r(c=;Ud1HAY$i8
zgeN1XGoI-&4<R0?pe>S(MjG}ekK65?n8`+Oo&AMD7*JnVidOMh{)U+^MyIxFsM^;B
zxdZ3pCG=m)p08b_l;l=$foUULld7&BEAOhSk3FvB??{Zr8Nw_3P5)jZbcAds2&3Tk
zzQ&lHGcZYr(HfFjnFU1@Yk<7Cx&$$HJurh%^RZ5wd$4wbvc(XG(*3rQrDE!2%txpu
zO@dK__sC=LQRpUdEl9ST$y^HX3C9GaNC@9JUDom-Mc>wd-vt2~xd{%Ls!?8TsIO1w
z{YQiE^7#~^mx||Q;b{u^QRb-T{YlKwi0wv;S!EOx-8K!gFjad}Ul(^Gnr&sXO<w>#
z=2Jy7aH*V8x+ut$xU=dTmpt$Idh%yXbVvdV;AT6>vkTeaNFF^QCws2IPQ8Q$7;~Cz
zH35Cb=^!gL=7-d)Ygc)`GX|r4K`V?{@M2Edwh++6jH_HF8wxe+k9n&Y-|>6<EP3zS
zR^t>Sb!^&00&~g%T7>QNjQrq_PI5WdBji<Oi}{@tOQE0pdO`dajETZJI?;kCa++W6
z+(rp{V<HDDPEg3AsyvRUe+^J%VsLu-m<CZ9%)!xG7?-UrjRBb@+3_(xCFw0k4HKJp
z)8B08oPp^-M6C{IMl*Y#m=DicB<UCOvZnB*KTq~eYNP7*Lg2I{5iAZo&Kd%j;U*{4
z`p_{yV73(uHAZpWisIn)T3xXNr;H)jV{Bcze4))OkGy2U2O$n6L~9}?rn&t0nEyf3
zd>7yS#dX4ERRkjs`uuqTEoUfKx(F}7E5cs&hQP^6kqvcf9MN}6e%}#h3NHpcf9ekQ
zpmZ6^-Inf^Zu$gQK4tiwN0|?MHm`OH)M3Nk>ZBK8qZ<-BdCr1%k48a74HWv>6Lt)y
ze%1QP)*q?ALYbHN0#IQH%9^gJ?SXUn^bT}bzLc}K=A%Bqpb}ulBQ|{@mnYgZ{B)Z&
zj+&awAJOO$tsozuMXj3j+Vi9?wa>;UkQ)}9yqniznCiOHB0rs{D%l*`@6B@JT+_Gw
zAjEl<{M*X&L`{)ON3%qMiHRFXV!m2N`1|K%f^<GE=NA72Z}57S?l114u!6sDK2~1>
z8eFX@GqwZSkGM2j^Q^wd5Jc?W7&`psT-3H(=V@f;Z4}|=<v&d4#mlnS7yLRl;I1aP
zuTnIi??^)<qTpGu+C=WKZG>4*3{I{xWBY+3!(c8O@`)57VP{lT424JavfrV4g);Om
zPdrJ@|3le+SmpYOb00FD$8eYUc{2=`|I0Te$yhA}3Y3yh=Q;H~5gZd<V^$3<h73l4
zy{Ng>6!dp5UylXnQtmz@Ia@mXx-L>~oVR9eBO_D3xj!$%7Q;?H<GaY*h`*kClWjg;
z$b#5?c7M)99~Au~x2k!GpMGow%f4l3+rfHzthRli|0`y|VH9{K6x@(pwa3a}1X4)n
zQcww#{OU4{NP|1onswV(=%al{5w6j#o-Z^!VP~2Vs+NzcR$t8-Xsp4hqsI_W`yeR*
zgOoHJ5)ZyUlyCjs9Gdy|{5!(X{${z_0W5lDU`2DZ$A_Z(6)KM9I$%r3*I`1`%9T8U
zyf(stgKj>NU$;AgVTL$$;$)wHTj6_wYXisatF971-@;TsVU0d5n31Q7T__pf5RkF$
zO@73MR6@@ihmgH_xzT`MU*nJUfp%@(xKvnGlmxmJ-@ca_%i)U2;J6?4*4w?H237xa
zj$K(!k+;`#aoNtRaGS4DQiB?1;ln``hpZMqX0X~_DbCZDCUGN-`yryNO5t6hmYv@F
z!^T@<)12zWb<s0o29!c-cfTod`B!h2<jSeOU}|6NB#7127S{XIu?8~mf-2-)tVEYY
zOG5rt0XRQ22dI*G-c#o?uD14kz#LE#x{W4Hq>6lQ6ihfYYScusE<5rMwZ4{1)}5ys
zj5$RSpyy>oP$%ZX`XuUWK1Dp4y_?`*b8I>H%dp*rP`{@hU5dVyQy~ih7S&DN&>xL7
z8erse{0^SEx1e%`uKna)A3$8+^}@wU`QrqI2MY12)RWR-Ym$3H5+RRRsr@TR;vzRq
z4+e}yupPhT(w)-0o5Nr`Hpt3(YM-<UW;9>O$>DfDI@^;Yci0n=vFDO5D*twhe=%9u
z=%B_+H=`fVu9B@rxACjnPWtl=S=YxgeWMT;R#u{Hem-ub&5l9LW*fs~Iq~?s;pH$4
z3J1%P#JRr>P6z$8xHTN_#*ckGM<biOTuS?)E;(=g3)mGAGVee6{^I&E;2=r!8li}v
zrltvn8yOVeU_ym{R8%$*w5oP3d7?P@;8OcY5@B3^0Eqd2Q#!7B8qgXg)G*#<kq*Rv
zFOjhVU+8rO(%q;fb$Q^RG_frZfDOjXa@y6<5_2e=1*a*SqWJ<9IEk|Jlc~>4`>z;k
zdNQx#-)c_V_${%iFEI!9`w1D1c6f)$i*FraQ^d*_c*us*LjvK;5nW`=RiT}G*-5)6
z#rI_XdLS;uUF!8>uzx%PrQzl2Fge>-wrQzE(~DhMSER8Asoanb^UTtAJ^qAu)cpNV
zyAKx}noIXXhUHd=7i8-ycr;X5pGI;Ra*0CF1EFou?1kuMpK?!r{Y2h^ghlyL*F$Ls
z=KJ>1BMC@{r2eH@)iMmC<*Rvz%1bohp^ONJ!seHpLcO_=kbMA6oubxvqoY>*rsm7-
z$vn(3{4-r_yAtE;aR+Tr#GnTV*U6{6JLtzq=BD>}X)e<f_Mr0Mm+MdPG_f&6wXf0;
zXuhIg%IVlu@R0o1Vavm-LmkpD0v2OM?7zP|cdU}aS`fIXek?h%N7)i(ZY*do2pFWd
zJQ^~BtQ$5uj0W>?{BV0|mKiIu3-&sdaNIVH!+l}ESDp-OX(H-;BroR6?^%8H&Sj|{
z{X%>tBS9AAF&JS+<0xccpuVG*63<<s9(c=Q%*RN`5oP|k2=fxR;p%yek^4EGvs#+J
zWX*TRjN*um!RkAieKdyS;J7t$*Qp$PVrcj`H^8V86Nlek5_oKLe_$uB;Pu*!Kly4c
zipikvb$;>H*N@bitsTpQUQJxv7V6ICsj%Y>x3%j9=B;8ge)CWrdndpxHMo@w&7XD|
zN;21g!N{&s7+q;3kmcWFH%B81uXY~RUW&PA^*P<5ExI;%ERQGZXc_H@Ho}rr9~PTO
z;cg0VDmU*rQK|8v4+!Dh_gsq8x@I>D&mmBea#xN-@m-oTiaCgR+#lxWqhg~r%o4}c
z7<929JMv3kuw9x<3^HsjTG&Som#F2O>9d;5{w8I`ze~~bG9|UG>_#JHhSI1?oi(l?
zuYz;{&YF&&bjI3Z^cQ&_Z%AN!KVT!O>2NX`V=nxL=#gC@8VEJo#>aBM#7h)Cr`nrP
z<xx(NCdp_Xijl#)@M$dymTMW;aq(+GbjUOnOL_Tx`q596p70228Ib-`4VZ~wI<oP^
zmO)18P<fM6BtZZyBgQb9LaIZbWz*9Yc8TyAeAiBYy)`o}wY4_|S@Zdb9e>)1K<j7-
zbvWR;%cu`{)@hcpqF5BHf%6!IrK34M=K5v^S8E1l7iNeJBXY8J_%3xi9^rfZkTiLa
zH;#r{()0dBvZiDqGH!v6QXp9wpK`mu`%|+e|4SFlSGA6~p5?946w0^9`h7&xE}Wud
zqPGuX%!6klYA!-?hroMp>Lw~Q-P};i#~{e~$_M-@be`|t4f*aTV#Tzqv9Cc)-&=)D
z;1Q)xe!QCP4@qX{1QZ?aiW&fUI`;bFPdzFqL<b0LQqD*Eo1Bc!@VWNsI_7hw1N&iE
z^0i`1O9>%d^MR&vh%>3Oi_6;kV?~3_>J+fFXn8e3jJN-Xtao6}EbO*+J9v^Owr$(C
z?Q}Y}opfxQ9ox2T+qOEklds=>zJ2ySb$-FByVk0@=Nfa2YmDW341al8CV|!7{Q-pK
z5enV*4Rldbt;f<q#uyxFe8KE|JkUa8vhSY=cz`Tibp@{5YP~-EMuCL9xzf{>(6ELO
zKAjL43>_-j8pcG=uKQ{H#EeDbmrCZ5F`X?w_eE0s{x%?Z@2V015Umqkf@QSJPMgI7
z6(0qd+n5V8dT-h7s+!XDc_-E!iy~NW0fXbE!R4<@<)K6H36b}@d7?km?m<>*K-nGm
zEoAhFb)of*ye)9<FFO`3&naVkm$$59KS(z?KGYD`&->8a4WGmmIJDvG0TcYZ9cvB|
zAn>yhj|>j=H1(6oV&0fE!GeKmeWGcjzez^GWeqYbJC(~0+R!{$#lZ~&ej%YZvRHe)
zy?EE}{MmU7YfwQaqOA?;gU$?ghGgW2Gy-#ols9B$eF=n?($Y!f(aM$}CR4$W6>||e
ztmc|e0_R}5!Km<Xb85b*&yi_LqCy~aNZK?br8hKrF~yCJ*rpPSo>MtyXl%-WsDE-R
zY->dBvb2E%-AeFD5LaVTdPtIJHH_|gv7Wn*93UbReos{lr4ws$Q8MqR#wFFF_+)S3
z#EGIkiPdOO_LFInV@}s=N{fCWOqhU*B0STT^xmOx&@azg-)j(6)n#Ifb;HAv(A>uh
zCF@O3?73U1RL&;EuSVu|-`yXUJa~Vzc9XW+L&|*r{_piD0SkCFeBc#7)ZUa-*oR#N
zqxnGKC8fZIvn|UqVIi4B{Fvbl)B2dSP8HCI5}O%CYjkj#MPE%&63Dg+W^PepLq5!k
zFZ|yufWQcoAqb|WKf{?F*vnz$2oLNjd$dqO;`)ICO)^<{T5)x?t)k!wSgy0W^FsOZ
zm^0%lWU{}H{Ouu3EUgU)X=ls}hnZ~_&Meuj#Feq9Odz@K+m(5t{0Ea%k%PsZ|0L(@
zPHJO4a!?Jzd~RKqmfFr{OG)Jyi@YILRe_jE4#M5UDA*%5gu5tC<Rf_$_ezomGj*^+
z+LHSJbSzeG7gRNb_8J3^Y-27HMLravq^gSh1vK(`CPaW%3d4ZY!mcDG_M!+}P-}@n
zj#n1orb6Q<-5#=DlAf|r8KYutkQUVc!JRswu_QIE-`k6PoKmt(!;2p-YslAcTpQ54
zBrI;yS`u$@qqWFVEwJB#yGQfLg>UIOF)==t{uylu6!EakMPAk%3xaRV6#qFu$O{^{
zJ5tBq(pU#VP?$)C2oiNDprLi-kG?N-F2F>Wb$p-jR|*pyiP|N<Qg|1Ev@k5KZrE+2
zm9#-uWeE$yavwqM6uX!Al-#8Iua?vFFM@?o&Y;xRX`J!#S)r<|fQ+1%D;7n}6SLyx
z>bFI#QR&ApNx=Y_(LLN)7)X(=M@|uy-_OXHiM_l>Mh(=Z?kHyP?X^j&oK>uu2<|~c
zgj}?K{DUJ*&v_I#Su>L&2jMzy7hk?ZApMIY6n=fx+_Q4K<i%!83TzAXj+6C#ViRl1
zN+IgUxql3c*;gW!6RLF`T{k5x8jYElQ!!gYoV+d1C1@$IU$i@3$|?*^wr@!91hwf7
z>G>wXIMw%~?F7DDn0eADx9Xww-ro{$h#DAP3r3jnkw0QVOA@_b_6uV7vonhGeZD<k
z=8ObHqsh?xCnE?0dT+({{IoXP7ff;`SV0S*IGDew=UY%9(FXl5)M`paO_BB2X+V|r
zsj!c_n=Mrr$FcgHh6eh6=<$(^6ttd)2}=Hrp>p(>ZBO=m;oNX#xV6ACr{xPK(|bk3
z@3LdJjI|wjCU66nc!{d9`+>_hX*M%5a^TlxKeqr;(t>BQ6kQ<a5u^wd+lwd-mY7yn
z`(^QKqg){`9_yd_C^EY^>1_-Y)$(AJR!A;m-%nT&R6yh3iJY&;cA?{9Loz9$dD$BD
zGuJgYieT#*H6Db}Vx<8*k(dR8|HQc{SG$3Ng%Z(_Nvubd$|oCIQz=>Ghf_04F`d@)
zZr3itf{pmcfdHF`-t~ihNlRdM33(BxAWnZ~-OI*~38)E%eb<*@8@B{R7Lc*hhx@-_
z%BW#lSpJdYRZ$pQea(idW7Km;Fyb66B<$548NJZ1`HGi}j=N8J<btewz_zX#UK79c
zCZhW#To8{byYuD1nCuy#1ye;C(%wpOq|NHdBp!r?XS3e=L2`g7bgZ47++>C>Bw|M*
zZ4L*1Ri`K&yAcLy7%7C&@akj4i?qyQ|D8#{vJI#UN2Q-_&ufV><C!sg86hMYPZ7fP
zblV=|fet&sVhA{$`~CkPKtaK)iT~xzk;)v9xk;hEp2dLn$XHjEar0quTu5+}?&cOt
zB@Z@v#Zg4(No|$p<4bnw2M7^R6*x$Lu?rF944*8>@A*WX^ei?=$s>)+Z5G#K#c@+8
z3i--&O4A5PT}X~w#GCm<uY@NxUVpsDvnsOoBzuw;K@9#@2P%uVHY-PRO|pk#rt8Qe
zRxN^-H&a?;2IIW%Hb7XUkoUk_HV?5Xv2N~{P`5c2RU_JugJ+EA3PCtKhK`N$fhy7>
zZ>HtMyWdB?@TZSOx9`%ze#PCjEL%zmfC?}jqoU}~{S9ktd8vaIvf67HnD6#HsfK@{
zI_^P_@kYHsM)I1C#zH1ulvZ(G0dC^EzFU@9eC2{$7$SM4f$L?q!04IA#Gfknm|STY
zh(Yrj<4F-RhllZb#IjM^BUOFBUbQU;O)5l0{HUr_(6*_VAb2P6?>Ss-R(>JobTuP3
ziK>@3&icBxXLQBh_b|w-2mvx_i!E37R=gcbO(H>jezlo%)}aWzPHN1hryw}lQB0*#
zX(v4PwjV(G!U-VHM!%<6X+mL!iQN9Hx7b{Y-#Br1=>5z^M;S596>nQ1LB2Pe5f&~5
zgJieq3tv}vb6cp{mUAjgKh%cZY~VxPm(5|z>E}j3UBLY!WXr<$_s3>AT5fsQ2nMTW
zK&pp}xEZmj(u0M>Mv-=Mq+c}nd2c;xLTwl4J^Xn%E!qBD7M%tv)Gs+%u4Z7CaNmA7
zC`C&V_!X@%B|H^h<@Vw99a7y1S}fwc=lAJQ6*J<WiP>NH>&VNQor<1eym9AWh|yuR
zv&ffg2ZGWj-rG|7#gI@a+r^PTTqa=AXn^-y`-GmPFtP$+rr!Xc7rpMUefBR~NdNUw
zaW3D(k9{{d;-p=Kp9thDK7ZI|;Ei|2HAMxf-(L(l<D4@JXf)vDqm?%Or@12Cme;_;
z%WqVsZDMq?-cfr;GjW6Bv-%Dyc_pj|zvDPtI-qmZ4vIGdDinTxJBr7qRaEc`j8wzy
zhaW6ytI3W7#<uQ6{z6BQex|Q$lvj6Vh=%c{j|sjdSYwhyW2tq*F0<TiA`(?jicQNS
zX^!(q@2O-bpS4HJe3$<Zw!s;wjR%|sqy$~Kht<&R@HL6w4fiOEh4uSH6Ln;g2k$CQ
zf=eL~NglQkHy6gk^a|0#<Vj;I_euM}I7p?;-J>$2lDdHxOT)+*&Z*h>bvJ1lfRXk>
zLXrkF-b0^nq=mNb{QQ}XL2mefr6Kp~T-4;469`U({c9C8{3mjh(Z2-N!8H`_Fh*PP
z)Vmj*c~j9ekk@1~bLGdvTA6T&M0SrR(y*)G56`~-T<QK@y_>H#4o=wq1Xv!s@Un8o
z;{b*#oaeN=_f(fdpIC$m?Kd4xY)^$ZP7HRV^Gu$48ecJwMu)A3EJ#6S@gZ1kTtLv&
zPJiF3Lu820Y_L(wFH_|A{&Y!cN+feA7W(~2eCBkJe#VJ(g2m-wH_0-G^5)iJE_;BU
zf$`=#!S`b)XI!N6YRbSNz^)kC^D^yQ8T+Di(D-Fu^%eJ6d+FqL@P|7;tXk`9je-Fi
z4}7?sC}Tkq&tT)$QnjVm1wk-aOw%>5{htx~Ah5I&i1#@svB1kA)5~R!6HXsrn834s
zf5u8|c-T`i_>V6Lcur{Bq`H%Yzn7p+Deg2vxYrY&qEDHO6Uv)2?3d%KuEF8zZpEa6
zLFVOA*CS9lLK&5iP2uj%=CyUPJS&I19gLYH`+~!K-J?xCT?m>oRR$}s^8N$>a4iG&
zco=Kp@K?rN?o}PU*qn-UZh@Jf7(wwsU2Uh|73F^;>_wWBc_|!zE@C?p^{b^teIV{|
zRFhG=;PIlozbZt0ppPBzj=Gnq2cBDes{DVpPZ5ZWGN_g*J8hB&-|i8nrXFqgU8<Nm
zmR%E<zSf_y?4flw+GTn<g>_NWQa3jWO_dTQK1)4N2j>`q8qv*KNeeAqRqY{r$E8F?
z>e|F3dh2J7WVb`jrA-Qxlk=<*`E1>#af-vE)`$-q(dvI_m2)nx#8S&>0EaX#x?#h7
zsqlxFRoL*@R+dzb`ufbwZYeQ=6Pd{ubrW8q<$7<KHHYahKrp@;k`(@@dtNWB6#5c^
zvWgs+6cFvM^8axmy#xvO-Yn#NE!Ksexi(v`_5YBA9VGE__`fmvUI{-x^ZgJkq4v)9
z<f{WANdrVaurv`Jf@dVZS0n-@&Qlw4cen#+Hvmbn(1SCsm#F^V*t7pVXaD1+FoREq
zhlf+r(TQ1E(ew`t)HptS{Qi&c;6pFK&-_+amuPepsDiEBVV;{zGWK2c)2IF<-+mWQ
z95)CB64s;iU^g+E(cAfdI~D$+SU7;G-l>0wL;_OkC<RzsGTt=)9~oSm9elKqrdYA1
zrOSF&rw$U;h@OdvKjQWk4JUXz?tf1HpXlko+?9!NFD?0HddLxT&SJ%X43sxxem|zh
z$wrqb!m-{-qmL|!#?YJ{8wHS5KRz)JVGaag`bPlw@m?bmCndh`CxL#=uJoPF{61ll
zy}fk?L|@(S7Y8|Y+kyZ6q>l!nUcR0sf$7axSM|9QF2kuHCtjqB&Kl4K?k4wd&V~hv
za*^G7Lc)u-Nc~Sw<mnW)Qw2p6DY#L>!>D~ZgQJ&%L*9cVR;Pq@*$RO-KOhg8Vzwr6
z_`cS>bbCoYkAjMCiVQRVPX^1w_K?qi;Ah`O`RIf+|HzT)(TC>yqvK9!l*F*7d{sqs
z-*xeWFEpeg-T#i>{yYCS955A)>=l6>V~l5`z%dj|UEvts%!6V?QdzG^M%350Wn99F
zooS^a*}eMl4l2;^@W=$;QwnVeRk6T&n!-9Y=X7sWt0k|b0OXNQ2N)~wS3_~%tjE0I
z(#EMT?=j{nXDxLc`VGU0V(M9116HLI_zw&c)B<VtJ1_h;#J{cH25igx>eN5fj_{+?
zEl62#AJZ6`=Bput7wz84w4DXaDUb)qU=SieB^ENjvlX=-q7E)LOxU{-T9CpC^6xXX
zcK~9h9&~>lN;%i3huV?u8-Aqq$k*d)f3(rKrr=0ze~NtSY?|qo|3rCC3AaGJ+_d9P
z^cf<o;wb#@%fZ(SnEE~asptBfaIo|jBk0tPfH9nh``5<Y{33j0O=w{$*?`0t|2?CG
zD0$z=KqclQIz+FNB)(3U7c(ItZe$h0Kl?`c?O;*iz(Q~5Qcl#|r%_bE`I`0h(Ab_m
zuiH*wY8qOEx7E&I#yR@{2fxt7mUmqm5I#i00Z{?hO&b+5zl30L3<Y~SKXHE7s@P>*
zfOekKo+Rz^3jKxRh=gMm3~y?Qvf?b{#68V<8RNLK15LN?=jimkgs%jgw3UbdI<hFO
zFXC@K9)i7Wc4&xT6G)Ga>HVUeZ>%Cb*JC|1pSvEK+EkJaeGxT`TI-_}`>jUN%=I>`
zitJYy*|apqgxl9pv7@2oHlJ%~K|$JiT=Qyze(#-6BJx3q{D&L(kJ}b2#6&bO9DowR
zZ>4~`Kgg+0cD<i1+fk^+PPp~AS$$&Xn6|?mHP!v?cxquWk42_>7@XdlFS8pf|CgQQ
zABzF15i3L?Vvy&axg`$qMuJ>U9to)>k2`P?N-&UhPtm_)DX6ie)cJAod@C-;>lo-~
zG=$drbrotqlO?lj2bzu(b1)YAjY7A_WGrYt>>>2HIs#+f<pKw~FTg1aS4hN(@cnu(
zvAeY4hoJjT7tx-NXjmr#OZ4Cfg-D1icM9^>`H#vNYq5|&>){NU?)kneQN`GH4a2Ox
zPs6vk0<ans{n(Y!3v8-DzwUWMjd>U%9`(Y<tz}sCm@|;>Yw`Uc1JA?RQ2z$`mGOaO
z>+)f=KPfr2AxI@Ju@iC9?TC=i=W%oO{qch!+nI1<B1{PR+bE(B6yX<KjQiAZ4Z#~N
z^T5>0&5_>zCrXwiR-yjv1ddQ%pZ37w4OS;2I#R@*E6q0h+8m2{Xiv*&*>_B21}K)n
ztS3%%m`rx>V*;Fr2-PJ6Wl8RCUoOk3(QMVXec+Fkm^t%;WT-eDP867ifn5&m8I@cd
zau|SuA=SS6=fO+)<QgO%COI4;CUVs54=@O8@~bwWTLHc&$4hQSNoY02m-fQJ)71zQ
zVVB(h>=AF6gm?cko_h6dj3p%}EfZ6+BMIrEOLsGTC(}m(EDYRU0E!~`!Gg@E@j?)V
z$CH4k%MdC~R96|+d5S6hs~t>cM)p)27klT&J|y|8OmYu#W}Nw$I|B?l;t?XB0`{X7
zc3as>EB3j=<mVs<RwWiY;(L-}pM76$TK<v~-u~v-%+w94{D1>BpF7f*Av;@bmtZZd
z*l^MMmnLZ)I(q^rGhK{^A2>NUza$wQw3*TWl~N0Z>224}Q${@z2DK;+kB`Wq#)QH3
zF+w?{MW*hiuXY1vLqccBY8#oP-h(hC=!{l|+19(G#<mDF9zx0q*lhj(VY&xS!@?Vr
zBR1euKT#N6-eg}~vWLzPU{_}igp!hvuW&crc63)Z0blf~x}`A{R{}#N{!y*F-u6Gh
zz`Xx~IEK<h(ljcHynS#r!vg)MUELtaksup(w_LF)IMQ!~VgX3dklGa_U#o*@X;s`F
zbH>qPdy8pdqBxe#0)Z`-a$)yHy579g;!kiJd+V%J_bSySJ}u#X^2=xLh@|~EHaaqh
zlxr~6U1!p0u2&+KS>ql?b5FM~U5-~Q^KDC(!?(yd_-^|A^$Q1cK9(^2UhkyH^DO2~
zJiJdt%Pnr{R3GR4Q+ipEn^0$ri`OGpTM~$SC=gEi(CN?BBX$!{V01+{nO_#XUOQF5
zCb;|@C$yX`r$|OVUXp)ESFT8KcJeFtU<~%+ER6N-q=pICY(84=2;Z*t!~hgFhbyTj
z-8Uvi-qIk5`W_t<TKK~Y?BH`iV7Q}?Bl_5jwwsDie`ZIApz2lIafR1CfSy-lajl<A
zQtxVL^pn1Ns9slmnuMRkX2NT0!`0We@M?#%V>731^ST_t<!e0CB+JpJqAe;bG`*(M
z3M*1pe3^B|ndiiLx{cocFcUydJg9z+15jx*UYS0-6-@Bw$ywNzn-9LPG$k++DsN(V
z`O~mTc+CPZ(v5#=<Crl!%3B5pXl3L2sOW%Men8AEZsrRfbCw`rkeYZ;q`P=ZaGlUO
zVd14TBe1@hpG)Yo`h2dsH)9PX8ZZb1eZQ)a@j__LVep~peSMt7V=#|4(R!+bCu&qj
zZNWI=NAF+azKQJ#dY0Us1MCT}ZQK!@d~l0<#qF6U>v(V-rz6t%S9pA5z3p_{xX$SQ
zdtmAnfB@%bhtOeC7LK~HF(J4JG*iX4&0<5qTcU92te1NYo{+IE+%D>kd`z}gOqE4W
z$@=80#rcd&%nl|U@nCInC!yyda8J5vX=&^?ek5e!jv{KF`dv&M6B7EOErXYS=ESWG
z+a0D56_ldJ`jP&jgOo19S6XHp_*;_`luFf{#}hi*TYkPDJEU=tu-iH~Rhc!xa$0_8
z^XiIyaqeQfOLT~=e`W&=f{1=C2pfS0^Htduc3!ou(6P`%(V4j48`j*`I&f_Q!-xN&
z<zQ2dsLqTDYj$NRBqDt{-fR~wB54(IyTF$bl5|p0;TPRHBp;G6d3k-i&=Opvj5uYJ
zdx?DJ>;vqwJsSd6%5oqgG`VG)r+s^2goma^9U(s#(soch<AZWgE9QlTh=>(IzY$Pi
zgit=G6w`fJfERzRFT1jaNe+J7j$AuE-9!HdPthKw-#$0;Qf;Y#sg}Sd$WGH3yt6b7
zE3M)0L`?HC`@<tby&lPjaDb)+rUZrdV%XQ%*XUv}_Mrd~IT}l^jF~+Cu%DAVq&PVt
z^Xvc|B$&A+D(*v`fTbW4S3fPRbDU(QwV;t8!8(K>ZqUH5<zgery~>U>TjFi+2nYLF
zRG+wY>P6Rrx*h*N*#<$#X8g$K{x9~hkNM5TaJoc!Hxx#{hMI=g%iThTiHQbNU_r24
zVs@GkL}!g^5%QX<apmxAIoaL_>FIItqga@_M;G5cJ7%}%s+um(A$|4URKKg(44V7r
zTtQR4P)tvX2Y43)Y^?N#48RF9!0&RKJ0&_;=3+ZLKqb>cxf1UX^{@Ivo>%ri=i!Y_
z#qMdb1N^^E*pk^w%Pd7rK|m-uk&2^?oHM4(Gd8_Nq6+4=HH(%%M&YaokqrlS$6ni~
ziH8c6dw*`M`v2%(o3Ru%8iQ(VGe+`>my9hIV0=!%q_ALI3NjJd%pp@--`4QPL}4{m
z8a}B!!_I+$q+>hcCD74$O<Kcnz)ggl0X!44p@NAI7c~@NH%(#VtK!ull8QmbAjya2
zI^~wYBsV=?O%XB@hhQt|EiYlJv{5}lY}r(uVlomlGlPp<!j)RvXO-bgN=o^qtTD#K
zJ7no}Q69K+d-P|xxst_lbqvGq*86b26LwdOQ;RP*u_7Uh!Jz8RFjcJ`c`s~7g|Kt1
zDn_9vB96Erp{mAqB7`;V`LHhDb{#3e)#Y%s<H#2jZ@GiwfW{?Y3$@Uz9}LI4kcmMV
zv`PH3lrI>AGs|Ow0^Z&mTBEP<lQjRV=_*6&f<O=r0vNS6K#?_J7tq-ZQ4}GIH&*Yt
zUs~#Q!#*|EUv_`bY<u?pRMW9JnCbd$diAjFc@1Y8lJ^^2C}Q;oOhA`JV4W4KCGtHs
z+mpV8m>>Y0D&T?G#^bu{*`s9pyJ0#jge?8}{`<3gaBX}u&1q)M?7C^Ad?p~Ris{Ss
z+QlsbpD3m}8wd@rd|b=Rqg7~wP2H?E>ri<=ULoGIe@6GoUgBu&tYnX8uV7bRCP+hf
zNHVmE-)#%eXc8(BtakOW$?=!EVoq5%LVkkr4x@e%_T2h#?R!A45RI7j98z*6!i&{8
zs(E5@8x8*{V1tqMN2kWE;NJF<V)j6n{evzAHpvAS0jyCk#p=`YvE`}iW=Ry6Z+S-Y
zACDKvx$`o}wKezg9ZOFIaPD-Pb6;n|q?%#hC>8xo&;wM=l3u$Y(C;4LsiSjQ>8r+l
zE;@M=y#N8xgxyHFSa>P-2m2G=j{oi~VO-C?kWFp+9cp5U&z}p&52|%jXnQ=DCi7F)
zPz71mkAlq`8xOQ&+HQpHb$(QgKYc`m*yzTns6u{G!}`7m<5Y^1I&N@=1A)8dGD7};
zM(#RQy*f@{j9a+-cKXEYI$?l+%2a@VWSxY5i$S^K^F_N?*bZ;5GBSsBQBHD(uL!Kl
z6qEbO1~CppK$O-u!Oc0aIG|y<BT(>|uhzWXd1Z1pLXJ3D?Oh(gsc1ck;VvWgR%bv*
zT8?0D-u1!(?;S)D5N;B8E_$z!St?UmklO!nqaG(4&PMg~eJ{jaPEJz^{exe%{=T$+
z;rhh_QG?<xHBxuE&~@sB;8#Kx>|~C_ZGoiSh$l()w;$~MIyfT@e&n}ppN83b-Bb4n
z(urN{e&c}K*_JmD`nKe+k0R2|#JwZqE>^VF*&EK;2?UB7Lpk*P?c<-=__>pCMU5xF
zsGRB@%qt?O)IHnqaTqiER>|1&D=)-45d1<bl4-o1$g1}UB;3Q%chAw$R>L=1J<N)B
z5GM?ugftR3ZIY9HW?vXhrEZmY-6eN-N(;-P029q0C%9)E68%Z$n#yBDPQ~4&K(Zz{
zMXPT-#m1a0-S3|qUUqGzZ|!H!(3M@E2BNR6hKd@oEE3PVkzhwaD97BmZ4m?rgvr#)
z0rxdA1_-lip%@T=`tz!LD{ay^VS-8$n;KUerG-T?=omo7Og8^!E0Vs8Vzj5hd>4}y
zArddjK!gDusrWBUq@t$AOpSS26T)E8!se{b&w!ek%%P<gKEEJE=#X~u!pzM;xsDm7
zUTA-+EmHtctFkPx3yzXj?I0ofoRN+mmfaLPAg!sOkr8GDfk0TLhmAJ63?2u$Tg-1(
zDeV9U6YyKZvso&(ntVAv4Kk~ZYEgiyu!8aefv~~x2S;Ugl%SpG;t_8D7uZhU7KLCP
ztu(?2I?C|6*^!I>*jrf`v}-~B6>m&7C!i{^hgmOKV{W2%eI9rCh<e)S_CX&`U=Bj*
z>+#V!Puwm7K(kCly-4t+jvi!UF5%#c4Da=b79qQ}XJjNK{anzZfIu??q|G@C`_r#|
z!Ic$6#<3ZW74MJwSh^|09}%4qJ0Q@T0h(#RfE;N|0@5{!g1-CAF&&ap09dDX!s7Tq
z;`Sjm)m&$tRf+9qMxkxLBbUv>6h4Z;Zhvx3LN(rB-5W3urlV~o6@wytuXWpfaA7M*
zl@7OvtQl?}L)o*ok?yMm0M5+nyeF7a!}nFzP9f(O=YhX^%q#WW={~wdp-O}#M9>lx
z2!^i)q-vkx0<xG+K$FX}0@Z;sQNa~$_*B1s_gX(O^Los%wrXML6*B^OTD$`$h3taX
zvF8qiXzMLg6}f(@#Rbx*04v+ii9{h=ow}d$+8Mz-TWp0b19_h5S&qKy4&G&aPo$Ne
zM*b3m7OlPPrf2wuLS?RK&36`c<C?|d4rV(&0`E%dgTR%iGvM)xW%Z)Y^9Obf?cmUY
zyZ;i;-)Gd=cPoPlXi!`QYD21WR#<y6<>1r36?0##`T`v$st4%8nY3X%R1dR8GOoSh
zX)t`LBjJSGfgxa7Y@-#b26Jj0M&e$Sm9kPQ)TRnoYtc0`#(g}LNhi^o=I^sb4uT&S
z;F=X4_Ir^!=Gpa#2-P|8zae277Y_|jo@^w`8Vp}TZh<iYlsTDT<b=c7phg7A<%VZR
z<g?3)(fRcd`)pB<Ax0(c#}$vL>`u~qczl50x=tvde7LK-_=`!V2eVgpaSP1A-#muH
zH&5GF@T}NBjrx{2NQhInT~uWsT$=B^_+meKy2Z<~FlJb&`C3mVGD$>0V60$Lxty+$
z5EtUegkFzR;17B_XBdZJvkLml5~L?(YJP@Nq4XORm<TFa;W1BV_5R^IyMfO=iE0e{
z#5*k&Hb_R7QQwNN(B}9vSZi)qSvY@9bHT%)%VjY8^gAy_I`k!#sur_UNIsBjd7BUA
z*xO~b=Zt_;3n!+7pbbD&=)Hc%x7tdFMnps(Q8L<Nee;s?UpE6MV3_JgPs(3UM-XFJ
zRr@Hy2^uv3ASxG>#>if5TAOdx-u4F+eNA3P%g~911ycgTt-ZZ}F<lm}YNbcih_>3)
z!vNfaCtht4h|r@s8Fi-u{_t}_m@3togrou2sh?a}A-Xw4>m}hsnzr-U^uuLKxfPg;
z+Xfbxkscv@U`?yr4f@_<GkC67L^QeqC;|Y$RoW3+^w47SSw@hW-0+TN;YnD;o;6ui
zWYbV%OM3}FE^kxj2=GDFJlQ-h&GwcAcES)1B!eteF`c287K)ALOeP$Td)Yq@mSShG
zaiEQ|FZo>BYIB;Q5q4@JP{1<RzIC;(RZ|l<ks21T5zz?3j|1-A&YmEZkg!2`ym6TC
zqU;aAW)ALTO6JCGWrwSCcmt5g6|!Tn7D^5-5!m;Q9pQLONj2+D`S!Q`is1uP!(HN^
zCBQw5Jks!?9EdJLx%SP48d*4o6Kn89WRpnd>#Z2C%in8VmkeJz))il_)qXsY*L|H;
z`c53T+}_@rit;=Y_G}@o=(yt1XB<Y6Y;@HHz^;J{NZcuk&m!mADQ|)Yx!Lqb5)cGu
z=?G*Br|56V(df(VS?Mc^4QCD590bU$Y{do_CIg|cnL8->*TMwsHbGk$hsVY<PisWN
zZ;RZXLe6)OjEiO;H>VYDTT<BjwagV#w)A4f-VwA;M6%G^=d7cJoCG@CEg%iODQg|&
z%`M8LGe?<jK7b51JYs{kcwXf618*6B<~&M{F-r}^Zzz(pc<4LMKxoy61yQa36@JZa
zxZQou>~UN0+qT|Xa;~K-OM#|3%&JBS=Qv$%rBWrNSK81=-1D#>9QD|VMh0RhGPHrU
zRLhIn=t5j~K%(?hW;!Xv@#!lI6B`n@?8VmjLQ`<~LUTk#+S>UqdvfdgIKq(!xy9e2
zVd$iX{``z`hjDnlo46f1e?GXBl?nP8Dbp2!Zi;sod0~Hlt#0sC_@f9<jf=|dzV|wg
z02}_CA1_&7fOg+-eL)rKe74PcLR9nYm;K9&MP?12pd|PX?}Z2BLn$TZ`&k)S1EXvP
zJ6g=}7N^G-?-S_~At!Ud&YTu~a3e~K!R<0uoQnV}`}l7X0dO`wG5bO2%FeUjtqzZ%
zs|y2yZ8|>oxMq`Sp=6tpuHJXt+kCp=iWoG94zV2uQ)sOwQk?Vfc!Nnx&jucqJ%(A!
z%#lM$L7bwq!XGIo+aZ9JD5zxZskXMTo;@FJ-y>x3c1>i1?5hiu1FZSDuuN){Ar#>>
z4(p6o@#k5R5n6lruUppS6rt?RJUH8V!~D!TeI)f-xeeVenCoL6lF0z?qt<7YGG!aA
zSb^?N=mm-(%Ki=_?GVqunE=ghLoR;zcfbYpg@i;h-q*uCN+&~@CqXh$80G1w_RR^M
zRzQ&q!a*0uSAa11KwS^!>+;0y3)?x;)qJc9(gGqs8wh+pZ$3w$(!TMaldkW>m(^;#
zK$s#8wJ7Gfo}zNB1<h4|JDUvBsp&*lWbg07GNpZ16T8i)HE}QdTIjqh)Oi!1PaH9D
z4isubm@_P9m)9*#&&ML*U2Tb2N<Z7PM=~|iJ%dgN8inhA@XEGC!!+;<MPoP3vnC{#
z53TRw{Kj3M)tj7N$Ac^8>J3=a*@JN<)OSkH5VTX7;V`lO(|k-J#CKnLG6x0HHhEH2
zIGmRWgbDoK^XSY>$qHX~h$S|4Tbh+eu8##lH!|&3NVPDc<AHF11DVNX9aeUy0q(l9
z8wyNlj?s2h(BzctWn;Z!ue2sG@)xVAHao)f;ICP8xw6uX!3IKPg0BOyiwZ2i6zl2C
z1OR|c^R+jpj&wbd6VRnv>TsF%K=Rn}+4_#aK+MXF3)bj|Q``YDYzeCHCu48;AHDJ}
z7&bJoUp%QTUsFXPD`sdyegE_++RgHuE0y)}V4UZAxJhHuA1>KWIdoQ%cAw>Bdc<TR
z#pu0Q<My^q96T<hzPm8YOiBRl(tFd_?Y&05kos@b?6NuxOtu=s_apQmgeTQ*n)jXU
zbkR>3bSJvZ-i&WI__Hisdu6rUJ{#_AZ(}%9Ss<Y=VII&U)tn20m%;2}JK~w$8y*i$
z><>~y-Cal%u<=S<B?Y3HU6tGN5%_dJ6yZuUIP{|OY7lX%DB!`+6{Ep^Ld3h64&6?$
zG3L``8>_)8@9zNpUq2$>2+F$}XtT*lV$K;XPg1=E*K0p)Y`Aq_CN`op0p#=4`GYxs
zhrm|?8yjJ@T@N5=Bb>zE!fS4xmc4>PG^t>R8Hy5_f<Nv<8yIaYS@TUve{A3(9$5!p
z{b}Ho<5&xB%$Av(tq<@))7I8DsK667aiAZcpi$CU0VbExA*C6;TmdV!olw%xzXPUD
zc+kYPCI;pg2f_LO0UK1bSr!yXs!Bn+_vXT)V{+LH@vF6S{Cm1{#!{c*H5b$thO{D&
z)0vadQ}XNE;kmn*K1BMF&``qLCe#<$x<C|qyy2DhOh=cd2P>><Mh+D*8fO!HJ_fm1
zVP}<jT_B3(A4AfLpt#5UWEdJ7wTuhdcmIrJ55P?n_ZjEnr8R1q9jAyGorgf(h65F}
z{X9oc7kBO`$IE(W=lk_-%mQQ5omRUj2pnKAh<O>|o8_B0@265qn<9qmgBxQ8XZoBk
zdcz<1wH+_3TJ1ieTTy~21o*Hl&bZ9YfP#aAQejXL1MQE$^Q!(Tk*-9XlL2^{e;*&C
zr*irt3AJE5?7av9sJOZ5{jcvvK8j@G5)wjmFwzaKRl*meST&)Y%Id|fg-MN|HWcIH
zV@$OUhGvWBU~^OdXwv*ratzA>p=(6NoU8JO+A%V)kfN`Sm90o`kaQy-z`S4CF^&I9
zl)=MXBsAS7#(ZYqzo-K%{d>4`3kAGK7z{X)iAzcf@c!x1>Z(o{Y@{zF7N%=7;*J7R
zaD{$oVwxXqM0h)o&73#{2WM|c86>5^Heb)IuOO9|q6^D(t5mD(lE=w{^@u8LJQ-DL
zLtR=O5TA(hW^OXi+$d^g4c$}fR*e&5H;<HA&_Bszh<p^KIjLI@;cIR+kZ9-SPs#jZ
zqUO?SH$W{L2`}jBO*Ng#6I-9o-oD6<^n(G`_BgP%jsn#97Y7=52%Sv$$<YPkUjOEJ
zf;!Lm>@o4Wll-V`9DzW8=R$96Lb)rgCNqW-2z)6GZ{T&zW&WR8KjSZNbxk=F6;Bnw
zKEsw@E2BoE`pMpAyaE;&BCHWKt;l{`r-Q7L7US{toL*VF;YQ`|v9N|qflm_JVL^ax
zFB>coW4*Jesx@tZd0ppm$S)<md)}CoJ2-8I;?2m((oNWOUPx*knmYzu33V}?fC36a
zZenniV}{<NHVe1=32E?Fjy~5fE$ScY#EWA<GOjUBSMI=rgGeeBP6(92^FjUiLMKLx
zmDq%1{*SM6D@L~QoaIGBZBAd1qb5AY1}9O%X9f)wK5N`eKbX(i)zsV+1Ky%^sF!z<
z79;UWhV{QR@m}OuZ6>(Bza(CQhY9<G_gMD6PC32&zIKpq>>GuAJ^7%aBb9W21}<0Y
za(cdzu-g(Fn$iwWecEV#hqmmZ4#HPNt(sNcO=CG_&gv$HdhcY3l&m8qnoS8SPRdsP
z;_!>;`0_)4w{KKl<9SSfzwNEk=$%j(QHW42EH=rLv-7VFheB;6*S~D%nNz<9!vA^d
z%evzJj)}AFZzvA>Nnxt@PI#r$lZmcxq}9|ApPuf&UJb(8_WrNv6S-Tc+uo5?SV#+n
zysfn_KEYW>3~5|?Lg}YUtPhD3bwEB6K7PQEMwOVS6QAyydLMhF8RSUnNwkF|?QzFK
zeWou6hGY5eK0!ol_#`W%JNZlo&CO`!J0i+K%%ZTu$5~!REhSS}wt6zB`vo|(r3)}D
z({Bt)DfRbJ^rnc5+hH=vL(qO^VN1d!5e|<Xt@dHq_Auy$%*4U#UQE!S6f8pI$kB?9
zKf~20K`C*6>5tP;Fp+S}$ohDz#gJ`$yv}~ZQ!;}ASM24yAiYs3LYNpi33XSDE028y
z%_d{!#y8vfJ=)}Mu~T)cy*+}ERDA_-32@{J0}PKPfBO?$rV_Cp(i$L|vrdhT@k>S%
zOg_b+<0XauV{}Vi@rOmG?P(~ElAf>Eq>o1zmE3!<z+hEZS26hVoW^e5`wH>=4#;DX
zSZiEn|7R^8yL@%#49f2HS)aqxz#bl)IU{uMJ;F|q4D!cWpGwTZh<tu&HHHwYEGfCD
zexKTT>!Qc)OT`AF)R~^lHU2Li7*<rgfs~y_!>f6~O@ZM?TQ#ag_Y3UbUMx*k63pA%
z%!3?!k(6~1)e<aJt)5zfgO=$3SK9LB2f&tG)GQ(dlEsEf^+0uk4j}UJ!T0f&K6O4r
z7P{VJQry>En||b-$nt*Aa+A*MY-%m}B@~KS3vpCYv44C3{=jj?iY>^mY-ShO6?}A~
z0qvvv9?viJEe!ngy?1gK+}?n(+as&&zkZt~&B@(-w#4in(7n@N)s2JAbUjk5AOz2r
zv%^7lyW^-e(l?MS>Q?AaEK2QMYzIkvbx+;kVnv$mxFgAsw-4-aK<mB*I=2{o=vwjf
ze>;=Fdp`iidnoWWUrv9u9+Zo=_ssO8Ug~B};|`)BY)cUJp=CX~uf9Ccy<C8*uuHAE
zP;L9}`H23Sqf4uPQue@}-|IPqr%h)GH=nOsz5jxFIT*G}x=teKbGp6y<_*?_7OJq#
z2`=frP}Ej}^h|efzLxL&nA6=nzKs20C&8*#xc7CUmls!ui9#;XyEYEbAFq`;D>qU<
zah&+NxO<VU;ILuzjaik`^Le(#P!|>vlDwiFUW4xicLSX-9Q@}OHBseawA=F(oSUz2
z+Fsh&%Kia`T}g-Wk9_m34VNcW51fSC!9X6^onO_%)ioprgP3bz47~nT$puISrnb&j
z&aSxt*-o&*sVp#g5Fx{DII+!gz{5CU;3xORn&Q&81;It9TT*G7?}nCIOp9I{7y$wM
zAiLpl^y<ed9ncwGRMwB+$)q^1<1a4$&P`EX3O&c}`3#A{+;*6^uNUqHRUsUP%x}9^
zcL=b{Gq>aTyhp=!%wNAl29%flRj&C0qooQQny4k(5qNlnrZ6?a8LkV^#`+r}PhxBa
zx)i<>vB{S=@6oSb7sRzYSpD~)7nr_BZ8Hg38w*Z&wbr(B&#e0QY<!1uws~Om^bnF#
zRH@Z(M$a1dL;*o1U^)X>HN*vWsnCt%W4j1)FlfoXsFyw-ka;HZ3VC@NA|U2IxRnq*
zqh2|~*_HUBqDp%UWS)zRfI?krny5F$q1luTDtpdUSVFc>SbqLB{~Jo9?HZM*Mka-i
z$(ljy+>Z7{nyP&ZS(W4tu#6r~uUi)J+s1PKKrGa!Vsi5Rjca}|T3)FIern0Z+J(Qh
zPUkpou5On&3PUmkeBo<(s9qn$yWDQr!Asbi&+js=6zK)L=bYK?Up|F@O@25_YP%5N
zcYIp9QKKu*$7*<naqRtN1JvbJD=tiZ?wZZ)Gh`?yGf^I3j>h2f8Npw?H{CQ|D{p2-
zhHKbg9w-p{gL+%3(eryg%6E39+)i3bTcAqj)(@38(af!^LebzrYVxqMM4dXR8ea|1
z6)s1<;kJGdb>8lx!K$Q(u%WQ;TWQ%F5E;;Ee}B|;5W##m?7`zwC;w5z<o9OT_OQhB
z5+>IlYHGNB85R*71g+Hcfa=6_buidL-~71QVelAH`Q9A<<i<_Wb3yC&+C_5<3az6q
zyFQ7$5PFM2c9x*;Ri<|3#hZ+C@S^zgxXy{2c9MJ|5|fP94cj2+$&;{yfuvv=W-!}b
zcxJxYqu}wqNq}SUiduphU}Ewi?{EGBm|WCTN_!alC^G$VIHcKpT-+d;v2inDQJ;b)
zQkQ1USIa3VCnreM;EM<X3cxn67(QqC4(he&_;qDL%T0M~zMlN9bv3{xm(}qj<N3X7
zOp&bZTy#y=8&DRH)o#h4KP4Bd*hk8uk2t~J8MLFv2Q#g!q$ykzqQd)}*;ac|mbCV-
zSn$QIl+1U#5ru!cSQ@im^1v-;T@pseggqc-CmhhQA6~f~JfPPZ4(EIDf#(srDPS^2
zEy{UTn(J6xAJ7{qHKORcr*cI@AzqQVBWrqa8A~A6=?iNrD>c~dd*z=&_gp}L*Rbcb
zw@>|5)zivRn)cREzXL$1(iV<I#ZWcP>gVzG`N1<kn!y+(=J&U^%jokBY7k<VcSXZ!
zxT%GRe-rYvYH9WLuaTPrWOACSEk{$i{WZ+>emkHpEQ1{ZM)w`L#Di;xQ_q9j@^-2C
z_sw3Nw&sFWUt%7!iM!d&;Y5uJ(yC061tYGt&EBO+5DV<V?ICzU;;<W5N$-E8D@J+$
z9YjFu<F|*HVn=jU7UOI}sK)mT?J?c27#Y(cgUf*o#bv>BjGh)Kz3?eJa3socV6cV6
zE&IXbJha6lRN-SZ_PcM*>{bly=53>6ko?o|63pe<_Rq+Lk?CINd0BN6&Q^Ew{y%k*
zM{6VYg^NEb(WN45tAfk&A7|QTh4Y%Qy%S-X?A4tY#0o&7SHc91>52fH?s4kuJD8-a
z&wJXABzMdaOajX|F;PE%I~Blu`aw-*)Zgq$K!7X>@S@wot2?JHAl6oASN}D>c|)db
zOda4#nsoF)EDmyGI&LBO`LhIVkC&W1{iFrkqRi8Q_ftB^ooPB+_ea05nmX6Me+Sme
zm43uqBgtz00V%m$&d?X~)}QNA+Nr_tLr_tM=n#cj)Z>&5@mJvsBfe9$erUI{rr2C9
z=K?#HS;9$#h>YA+f)46;Cf_yipf{ep25Pt+{LN6m)Ulh~CBrzXVh>As<|j576%oGO
zzXBi)KcA7HVe^xah{}l1VY~C~@ll!LuOg;6efe|zel`J}o`N6`yomnQeU?T<MHKVc
z09dcst#KIU&^E}!gb>O5!VE4t2?G!msqzSM8*BrMW+Ldb=o*F57KE*)f5Xr2ua=H_
zIZx=*_3RO=X#ZOhchLskhxNNllIpZXr`vxy<CjzaH8&xvjgJL`?7*Wo;xK`2*BaMH
z(=Uqh2NE{NYfB~Pg`xME8^gBZFC7D8!}iy!L~aTHNd&8)pe9FGl9Fb2gY$PKQCPuL
zAe{-s=W+us3rZQGETQaR(|cj}tU@;8)bg3UF+UeLCA_`NO|pUl_3%TZf1~P%l=<Wd
zS|1Hcg*Q;<=}Xe%f%N&GR#1!E5*R69>Cw1MwBuv$mJ^=p+sJX<-E$$liiwB;#1yq2
zhVF&i6t3{-8=FzOy6I>TSe99?HYPx-*dDZAFIX@EAq%sV!bdk<cT#zQ>gQLLvO$%t
z2q+(0b0q5Hm)AmKLrJ>YdnP_#876+`8`BYl(NgaYxzgZEL^a@svgT>^R~K{g9=nT%
zg#B3XQ9>)w&g{YLXR$~Q9Dnbb9UgiORw3v5&C3Sb7O_!}k_<gcS+p~4s1(bT7TV%o
zkm*(38i{C*D3uMO!%;qGH<tWaesznEgF>gN(n}Kr%jUl*Bg_W|8_~SSHpw8?6vIdR
zV}S;K&Z;PdefW@QrYdRTSr6ro(N1P!it3Nb7iMzh0#D}twUUX5U_3l<BUVPu@Vg<P
zKdCmiDXxWJd)1vKG4nTX3jzPVxQl4ZD;P2hGTHbgxhN2p06FelvWiTnkjwpN<R=y9
zJ;Y!q!Vp~<a0NkX7GNPNtsjC@+xswieN||!>gGpa=A2Ge@W9Shpq(nQ7toSjP{zyd
zQp(Fa;*4vc(;}MvLZ=NjQNIy98P4m&ECPuMSU=HT_wdFU#Ee?jQT^k+=L2|lBWfK*
z=M4V7py+pQ#TVQ1y$00#7UWz_E2w**<>%F^TEs`jgQSQl2?|tAX3eiF%-qvKs;y%z
zF^fm4ml2(lGSFL=^JbbJZ+D0m4d4lWjKVc0Jt^?C1YX$K?BW%OX+^+u489MD8{Vlb
z+z<udBYNni&JJx&vx4dUYczLz0=~3&kXU~4dUZK4T;#W9QlUQoW>Nin==xCVjf7zn
zSskNoC}zBWEQ4ZTXA8)ARe#}5L$~q-1LlB<i1Sj$nwr7RPWb}$3@!}!GMP+0`a0F=
z*h$6jxl|TSvWGK#H-tx$%vUW^J>o!%kwzLW4$(bY)(}&Iz+_Ei-p+JNOOqS=&{6h<
z+lJ*@;UOFz%rVl>v2!uBkZHuPlG$#qYg%)#HuGu#4S5CO1~hpZ#QftKBWPWd@Ry1~
z_nKPwp6wkbDuL}aSl2dQ1l@f^<B5qBZ!E$2*lJQ`FBHM|irZF`Otsb1y<b};j0{Y$
zy7<{)x_IG%CVZRBhpZ=9)<d5tZRX}@WHDe3_KIV~Lzd>>CZw=CBfbKgqleA6%>Sbx
z9ihIc0i5%aGbyl#li5{^_l8Q~4?b(pTHlY)nKEt{kHNL-pksyAe4ur`U-_*0lfbKr
zP>g16s7SC=hxDZF_9_l@27_`0e2^>5Li#eaUhkYb;xp#~8x|Mon~bWY#r0iH7%YEu
zBi9m*kZ>^t<2b?g8#D={n;KJDM%6EzBF4h%jPF|0t9z%WF3lycgC$CJ4Teu4dOgX+
zklxFI5{?9b<YvntIh}mXwt>LFy<_W=Mmxni`+1jmGpCfnOK9dlNPUT?*))%==tpw`
zEBIX^bzZKf)Jf{>R74=q!c`AK2QZNBZa7Tci{m`-zMj4?I~HLZi4EGP)x{=*qob(P
zvK$;4lc|Q*n3g%JOuc4B3b5bm5rh6RlR0T^=;}<SNkc$Tu{{mD%V->e51!?OTVyQJ
zY$WpRxoB{r`TUiBcqEoe`@_aTU1^ige+Ct0@M9rrSP$?cotlDBZX<-(uG+nj{#K#k
z{=e<N!ZP`&C*czCJ@Dv)j3-4`aef(F=Qamz{7B|!;FXa94Tg$|PM=J0*C*7Q;#{ah
z`1s>PfbVAF{HCO{22=`$on5!Y47|!?Ley0l7KBIy<vhu(u7E2xCQa_=Vx$x%9LbIS
zVOe05+v)e5IE?z#p5(yB%Pgrc?2MBayWG9^9&PTP`0o}lt_V3+SIs;P`t^@$tqN<A
zmX@2QFGYvfb>q|!3Q1QHRb8}C3Fcn(2rRC+S_G^dxRg+~=W*mmU-hJo?d*$6qX?JE
zVV@QOWYX+Z4<D=$PX+vRa=L>rGfyMpq`qBT`~+knDK9ALus(@g<&7Hiq)`Ij&4wuR
z=%u7~40!+rVfOi}D%<$KD5p{MD`DJk`i-Ihu61leeR~IP*Rqhps|0;MFFUA8MWtn$
z=C&(_eaRe#9>TCfLm&EPa%9OD{x}eMCdVN{PsuD>wHR<!f@#mua5m<2m!T%BgwFdq
z16`7aRYg4s+eiVNQy0j`;MadIFLDiUaCc89Z;!LZW+0$YYtV=Mh;v~&bF!-sk11)S
z#rQ`EDfxe}pjQuB(KAt&*%o^G^35}N1**7b;ey!}LGv*Y?Q(^lG0UC(-43{$40JX?
z=pzK_@o4=+wd`*x5)?cqmu^JlFo9h>G!G%)pDtrIM<#&Uoox#(nOWcc8dMa`1rfGR
zFP&!4C;FoooVSX!0~0%>`N94gF^(~frKw71@z<==$U2&j*LDDC?1c_B4T84kH;g7r
z<8Q2C$tU`N7l$02(`J!HZ7bX{Y{Dt1sF6?cCrvV)qD@*Wp&JocggXi}<xLI{P)aQx
zO2J^cqoLB$QsP}aoe8SeF=k{1isa?+>T~%<<;bHPW?dPS{bN1<*9TG<IOrgl<Th0%
z2X9zq#1&x?u>?>LN|+Fl5se1CsKe7!(Z@P@&d^y;KQm#DlYn#-RV^ZXfaN?&x};&5
zW}H?RZh>;S0Ih+-MKZpR1QMP&pNZ)HwSP`1*5s-!)nry*LmEDcXl{+ZDL@6m%o#~U
z=(t-gGNyq1T=L0^YFuixhtq~Lq>FEpgVU}kT7a@J$I|cJUkMX!PgVLJwv_zF5^115
z?7liP?Jo6#<WF@T(CG+&pllQ?f;<Zjy+$jV3N{Ps?*8bW7v(hpP8+iLc+z6JOB!|R
zdnDLNO6rIl&Y-fJdD!TU_(YRo=jilQSdbGX0D~Ep<OKD^9UO29f92pMyUdGpGZ5~X
zKUdp>9pVFIBt4to79~SLGf2;4nysKctyQizj=XhEWU?>dkDrl=Z-$+!1(^oyd4*&U
zLo>e)1dQWaA<%K(aXvlZDzmE%EF1`o{AZm0k6ORWA^2Yl;dCi@mAZUM2S-<Oedq<=
zaD6c%b=YiB>BG@RZRH11UbG+RC}RXHUeZ+mGX4k#*oQf&M$DhVVpNh-HGwLvYzOf7
zs4W8kk)<(jWQq)(jX}55Uk)oxk$VTGfluww#aw0T#wK;k>#H4oA*Zzk(Q(7T4&hxq
z{CvC*Txg3Em)D5oJKtafgB}m=^IRyMqSn~qAosa+<=FZ=OK3J;KPdzh7M|h24XW6)
zj?p=>Rz}y2gs?TQ*a%sv7TeNyW7N~-Vzr>Gyx*hmVc1r~s{(A-*XFk3d<PC}(9sg%
z#S|nzfBg!FALe;V<Ds%ik&uY-ZT^{F#j5QC-?Wozes&zwXMBh!NkhDf%-7+6`|5Gr
zo%|JxI-i%92k|+zbeh!a6YP)-Ev7#&l0fLd`G1)D#^B1Lb=!{Jv2AmA$F}Y4*tTuk
zwmP<LqhlK#+sVs&_r5yk|EgN3QEPs44u!z_D(DdX!ciI-T_$Dwsr!;H_ZvAdd`(JI
z3fW124E;Tm0n=F*81MkQ6<CENlK(P`MdhpGboUiw)#Egpq$UCEti+9Pfv2O2PB%L>
zfdoUQ8_TI^%R5kI)b!@_`9jtFRl^S?3%++x)rSoZuwfaFmcr2QEB|;t+vf8(RcAtn
z5t3%eO*9pmWk#REj07bRDW1K4S7rG5!Bo<#nVlZF$_9*v=3~5Psx+FJ&Nd40Zlm<p
z%`)u*qRI#d#kN^>CEN%sYR1?i;^kL#H|#N!^w0Ou@k!DSpQ23CRVap{TiT4!t1;%K
zMspZ;hht53iT^|MpX&<_DCK}qDGt5G$j^-;e<GPzt~dYCV`SN&&|9sIz9tJJsH45#
zXbOAazavZ~@n=3Hxa0{c$D($SS!xT_M&y0jQBv0@rwD;ESPLjl8~?o|LaaMXbULVm
zsg=k5|FZzNzs51Zg%$M5iC~|kUCBkWJ{LExIBFvlg@1q-i=J{P+p|pHH#3nV=~5V6
zmv$`cf2)=n1GVp;sfC<Np*KLW#5zGkR>LqM#O#aKLo^}F!gku`3LACG)kIrE$H~(7
zNMtQuT7prZ7$SJI@$ol0%-lcXyDGZ7A;GGe1{dTP)RWu~5WGl}e{j;|!oLw$_2Iqc
zTZb_2@SNlJA)aXLi0eoBD%O>w1gHEUgvZ>X%*QVfid%53iXO7dwg<2x&5*9dsy+={
zBmugBh$Cu2LXMVV!vVCk&aIHfQNcd9-jsaCoA8(*9k|p5g9|Hyv>4iLlX4a&Nyo+|
zSb(Lrxbit1Eq3gU@QGe;E!fgn#h|^-s7$sW^%4-19WrCuvRu^T7w^vZ7vsiY^zbe4
z+Du;1aRuESAlKmq1%q~?2q(X5OOz|{YzJcRlpd`Wbj`wlCk(kaX0-mWtVx42rRmL}
znNU#CCI|S{9r;Ceq!3vY#JmVdg;=~F5_;TXapKRYoAddGl?Ip?#jXC<xg@rU25b0f
z#^HkpO4I$UlVe~1D0=ie{I8I}`ea(83ZMdlhf;JTFZ!vS_D+#sG13@b(>=H1hr{?k
z0-?@)fP#YE#7ayQqgdQ_dYD)=xqFezG|xz#XCLF$-E;PT)G4`AAP&lgKcjVv2@@hh
zZfZXTgmHUd8`4<D(rol0hD7{o2$gWM1IOt>m$x2~kTV#Eau~aN+X6T>Rnrq+Fq@4J
zT2q8#mQ4Cb4W3F|<D}Tu`ukBhqwC=b{kYN4P1c5E#ry8Uq^m{dck!Pc))|bH7iJ%-
z1DE1hH{J#-TBk=u+--_{%0s?_1pEKS=n#HHr=owBwH5tj`_d;yR_Bq=_D0d-eTJtV
zIxI7cIpQn<%k&(A<bJ7T9GhAj^rf%xm!{Yh7<)!Q8WodoK?sh3&tUQ)^1^6Q3Sncy
zLO*uhTRLU~h^~aD$OP&!hPJCG%fJZ!RnrYxifdE<t-L?u9rn}LkHd{?ZPY4}!<<%P
zc*2z@kPM2@4F|vzg%tMy2c4HPc}A433->!2cB%hoeJ$5Keee|_#R^lC+a|xj3m%O_
z^aFLvcmpP?>vJ`8u7Uk5F=eUOLB?7iK1hO`)-T>3yR9hhcioprBnLD2q)(Xmk(8@1
z9uwX~AJ~(%*$nKrh3l*FzmUAT?y-{)*!;%tp|<TOiFd&dW0kB-L+=Oj>6x=Bj7Jao
zp&WPmHu~X@SvA*hp|Ey~3GD1#YXjLfvvc~yc8Gb!vvW8f(v%OSz9vK@lNs>!(3ofi
zryjs$DWrk;r$0M#Gw5T<mA@RGIfI2|7}Je4@lDAFQLQ9{;l$+jDh@E7gIg1^OO<~&
z+kGc5r%h-dhE1eDj?XAWz8hlN-HX!<{66`~sCX@hmY>Md5K7Tfzm;r>hx@3ttO}Ew
zCrqcq4VB~4Ti&Hr>p1e=4BG^LVM*?0%tr8)`?PZ^i-{%4hB@bYf~dhzPv*;Bup#PJ
zR~+M~qpW-q{0ufoO2o8UmJo%pbVc1PETAuOsH^o4v-NAvD+E@?FEJ2ofW!zsV0TQ}
zr5b42g}?ZG_eLIet5R577cGdk-G}+}+sMSo%jKhBncO9dhF_fCr0z4LoyN)m`I-W1
z_2~wP(f#fv`-4$n&z7g+i_N>;=3h?+!DD>BVw1AFwqQ^|fLt;tDE;vBI9`9#aoaGL
zEA+AYvEHfkrsF$KqYbfBx;=@GIKs4lc8k0}CB)Mj$+@oJS4u%it%=MPnR)jQ#m5IM
ztFiIaagp>I9_hE`@=h(eQ<+Ym1r#5q4e>*hB@ulU*{lpvsf{KB5W$c0INwiPtId0e
zc^JqQQN}=vqaGc{k%rbcRYqg@9x+y9J>Ob#pDzj!?Ze-Pw^+M!!V<{W_+@b{h9N&R
zyaJ|(j9bfDY=cY&d?(<<j-h&za7Fn<ZSlSfum@)4ZUW@7wLr-6i?Xv6tiZ^v`bFW@
zejoV++HI&vExfK2tBc3Ns!izNZMGqFxDX1n3OOvm&rsJi4m#F!LAH$f1EB_RXVNU5
zEA*Yk3s=Mr;h(oWusyj(hUPP@(LCg-$p{b&z_EBai!exIOiiV@W^^7yF$XKe=Z6#(
z3AGXhBqn)+c|l;S#76irvhrsF*ssKc>-FK;)yitI=HjSav3=gnA|-rpDv{1!PdW+j
zW}j>R^Fz{=0}+bJ)h*9N*``RL2Co_fi`@#;16@ioq0~Rq{q$Q4mfN}UBXU9~?5}$A
z@*DYdz`Sdg9n?mI(q9g(*-MY6JmEYNRcnt#9LTlI$kIQ^os)ONGL3bO-!%ae5-R?j
z$`2iS-aABz=2x=>#Yx^<DP=TRcBk#0Az-xtC@Z(gYgrp3F}+M8`I6xEj$S$V#R3Wc
zxIPQjNt+#$ZcHG*R$~;M`@j)i$(2e`Y42pw&kBQV1_*{acu5Ti^Iz2zpOIi|8A7eJ
zwS@~$ySWX!CZw~F?CT9+i4L*!y(K?A0-|x#xg1h!pYD})tgS`hxE-l+E72TMf2GY>
zssx=VeB#*bos>Eq6d0VOM}SJWS~lz{=*d_neF$4(shR6rN~OB6jQ0P40RTo_A`T_2
zjA275KjLXZcp3h@mVU;)oh}hs6+**-^+V4Ym<cOe)=45jJF>C(_3J$rByzOcw%<9h
ztA~jcBa%-hdWP7i5BEELfP+0L)e!bf@9trhQ30tNLhf<(;!cJPiOS<esf(Gev(#9i
z?#I(k^Xhl9N^Yi^OzLeA5UK8h;LFKJ#z_~z%e`^fnX(hz!q(;uhlRnRG<CGFYUobv
z4PKg^5Jku~n_9fz{cjbaOvTbx<a)2#Vkg60C~pCddS{4CHcS3@A#gzicnAnMllnP(
zNQ9bAFP9i3#JL;3NZBckB@TvIHr>b&JsBG`WON$CY3uy=EFsSa<)q5c9#rOAXb?%a
zsso6~^<>9&V*LivQqWMEwDzCp`e-NT$P#(2yOS1%1lTVsTNZ=A%VxuI!n*~=ocNzS
zVEyNFAPWRS2SWy8%V%&R|Jq-7snynu21h_Wriw2gE)LEy;lg$iR~dW38@I9zC=+!1
z+>~bgP!a!gm|+b3<-ap|&T|6Tghxe`7!z+_JxfCnpmQ*UB|JTT)>3-WlL|guk|Cth
z<+LMYa?K8cBIt%|FiS?<#bW<SAevjuIJZID=KJ9A#uAL+KT(^xD_I@0kUYPbnVstU
z0dJ`AH)1>gR<tOUvNBD8)N$LV^LaPbj)J9)_NhzYNWs?G(b%hg66SPYKK6y!QCVn~
zL(R6@ewY_-oVA$LKA`j|9;OGEs7w>8-p$bM?Rv`?>J7_+H$plrrRwm>@R4Equ8&w>
zcZ<w@!*T+y_Pm?Vkl?K}VZi9SfQ+pt)cQDL_uQ!B+_nB#aNDnXSd`QF@9O5<&k}Aa
z&am}e33<+sS+3_~L9vswHvbvpb{Wr;42zphOy9m^u{UIl%dv$tjIwHCmk5N-O|ejA
z0(|<;PbJ+knTXEK)Cg4%Q6#0@fa0<uj;pZB>H_C%>`}MsHd<Bl0CKx7hYgJos*3l|
z{Rdqyl^3aL)cMUY4BK+OqMzwIbhp_#_zIDJTe>l_Ce}#gHS|0OYcFA;C=IeTKelLz
z;p&%El>3v|5DcodpuMst6;gd6-?qFEptQKtA(IGP2f3+h9tw`}XZT|-W?{-Y<<`h-
zQ`+<^D*-Q&pe-z}CpHWhyAzPDtD{|@mrE&$(JNX6{fB=I!m#AtcuEz9`)blkt{l@s
zL*|^bRV}t;purnc7x_n6*ZyWsN5InXfFQ__VEYJk8$H7-qp>?UZezy?AfgVAVud|z
z{&*5no%VT}pXiU#x0;@G<1g{MK!1&vkA3<)BXRw_?f7!m-rtV-5BE+00srSgw|uj|
z)l&KbOG-QTeA4Os?HwQ3^{L~PQ)&prL}bYMgJanKfKasC24zd?@M_}a$_ruK)YKTH
z^|zm5<as8w4l4DXfjf0+Efmxh3)uqw@uNWUAa7}QN@s5<8B7;+`Sg}!c0D-hUT}qZ
zO3tZ~PXTD2p6GdSsxP}!kl}NVxK(9GSeXl-+uDDM-7;Oqd!&3R>TQhix=5VHL8@q;
z*@{bpG4x5+-oBKdc`~k}|Ced`PbT%$zghL15EIfP=U6m06&M(#P~nt5uObXJ#j^af
zTwT2CFyjo?VH20emcpjubUh6fD!t47jd{fqlR2Cu5Wcw$LY)*e4(}B$^{DaTZ)X~y
z3i0phy~<%;c@^A+byZnSg$T^q%*2cs4=&kcisFIe$ui;61g+r66{bU)#W1yGPt=L6
zrIo<VA1cHq74p$3T7lBRe+Y|=DVpeakE)Na0KR+4N}Yy`uj)Q{5|)+)<-ZPww~V@9
zA8N5*JklWgp2qM&b(DeTvY~7JpbM%Pj!_(;!_fF4l%r-SwNfj_(NWla<@d%KW&3G|
z2WSP^{2Bv>!^ZsnzCW66F0UhV!L@y8s<xzCmRQ9*!{yyp(~;9VGRhZjPlI>+oR`No
zcG5@nI4Mlo5ZyfAP`DL>|7C%RljN!LrE43a0@xj7?`a~%WaMIe+OIPV{!Io8IU4+C
z=rCeEx5&shbO}`mq@|>U2oRNRPIolv8}UHmz#e0-WrOq4e?%)2uCud8mU1<hp~PU&
z2;&zsED@aOWy{Sv;B+nxmQQ;j{8mZYH4BP0njoY6!m;#dQ7!!Q3$a)vxUAfkrS;u$
zdR(W-3U4oGrmL=}1$FGS7&>!i!7X2BV*%ch|1zP#>*tq#>pdx7GUV(^W++R+z9n5a
zI6g<HGkQUF2bA$D1TYpH6duEm0fSZDh3k+H7I`&2eRPt6Im$nlalZ;mA*UOYnp{SC
zBm9hQ#Lx8!AcN;L*670OuuVZAvSB5hk1^&RePU)Uz!U%8fV~e~U6r5(1dClZfg({z
zq6L<TuE|sVOK~h{C3FQ2;~ht};V1FZUanc;LdnxD;<W#J8<r^!&*E6t;)3B$^S3cM
zfp&E@_#=Vu58{|yjmFH-S(y*VT1}jW#2GK}4}8ZU>veQ1d8CV$k+7-K(d`Na!c6MB
z$G7VvoEAxxE`TX0D5!UJ8iig)CgL}cwHAa%gS&ikyoyG|-4OG<ONiO=pVhs}alxPX
z2#VopHMJB+`~!Zmit+_1>mU>cvSScR2=iX<t*%WflQl{8VT1W1c}RC?9(KrW!sV;4
zs(ZWp&`J7X;e5aQD+;?@qMDj(W&sZmFehm!P36@eH83}JbtEaAf5}ykvQ-IG`^!Zu
zUgEchJJY5x{GIYj!IH_z?^$RMoapf$&QV%?Q~s>cBqc2~Cru=2AWOz6s77OCN9@?a
zw3=7*IHY}6M<0tR8G=PPq*v4PAqF0yxxG~WkbpH$`Zo2rI?~^xj<RqBnj@sj2+HSi
z1?x@2<R8mp+vI1w2~jpQK+ojOtM}^ct=uPDNkK$Nj$=jIsslg;Y4ll}ZFIo>;7DV8
zv7T$?-C9vn1^%HO!bMU=(5D^M7O>`0lWOVz+wp%MY<n-jq8_v|O9V7Ghum?7>I)yB
zMv;9Ijviy(!($DI%wA<j$EExgmW#wlT4?T%#J0+!pGqq(&RK<%6|xp#Ow?6hdH*lg
zqDLS0{SU)sLAhGyU(f}eh-1P|En-VIE4&OPhS{z}O*f9X<U9F5vy!v%uUqGh1`$Lt
zHQOLJ6gzQb*v|*EQ4sniZVvgOahmsbpi*zxi3?SK$UFUHsRJP&xnRwoHnb*dPT>xB
z!+*D0@$2<q_~0s$nLRiyTTb|(=P)g#@C9h)ev@O@cM%WrF9y${yIcz5VV=3I?ggQf
zIDH2s@9$w59RyN$U*RbDh(;9}ZbzqDaidrc_7#SXX7+Of^M47htU036nfoRca0A!B
zj@{L*S3};9)HmEG5W<Kv+7p(7RY5r_!3qLu_p7v>=As8HPvkf5uW4&v&y}(mElBI@
z)e)kQIgN=iWNw6x>Yi>kTu6p*C4xl<N_li`evDvu_clZ0k2T_?$D8vtqnA$0_>UAT
z)f!E8;Yo>|b83iNqEm}gEOM|TPcTRb+b+Uve4J$ZWybak!;#YeQaoWG!};SA#5-?Z
zj5RE5c_bbT>Nv;d+&{4l1z_Cvo3GYBDWbK(sxqKAqFE2Os$gorElcAbMSGalB>o}h
z0sG{gMGL#ms@SrN&{Li(1dh4(r3W7Zoj)mOobxl*pALa%B#EPUEK2p4>%hQY#D*HT
zALw1WO3(URW&zeszZMF+GMD6sFjc|Hhc7Tn@{97pK%~Q2jZX*Em+m=mAe*2IA*!0n
zk+V4mUKPAZsn9Cufc9e%O=Z;=L#OYM((UIal^VRLKc%(YczIB%LnC$%k72VvxH})V
ztQJgi2U^5t<jqAaZDS$4P#H#0NK`D%7poYgL?DwjqWn}HVsS&X=Tn${(i{bw(ZdnY
zSB;Gb#;>xtKC<0sWpzWbnRzEMwF8S1x#aU73x~Qx4W{D+ZNhXGo{{1r1$D0v)7Y5(
zI7YPMJYFp!>bMeYKl?rgW^R<0D#(Mk8}X^BOxdGti`c5$->C0xHe(423@u;V<6S#3
z9_Y~r<4y#aHlIzk?MWq@@#K?W|E6vRW!iRv=BQdcT2AVa(Q05DYVd_%N)m>ODZj{e
zr!pEVl(E?DnBkzjV-mCIv*vUbVy*uy>HXyf`Dyo61?|$TXy9)=MB{X35mdZP?)Dab
zS|<7RGgLOK6n%*a5=2Q&Um=Tdt`DYmgn3l|2lB<CUDKa_H7-Oz54)qF*yJE-4NtU8
z?j^r7+gTBt?0G+Kx&aLy$F{YI$2?mZv7RQI;o=A7)BwI3#*UdRV7V5;#@!7}RXS4M
zZ^#L+b8aq<V7?(R0;FaZ+wF8t>-tq}+o0En<??G&Qy8_lJ9<mA)a5G3QJyH_@v?tx
z5d4|WnBY{!RZwK8FUHWF)HBskk2{4vE26Jm>AMVM85sv>{1u@%_O3K_>xakCLtCg0
zA_;HAvvFQ6Ri>5TM_jutqO43d_!YSlljkZYw-mAU*_P426q&g`pKZIB2XUj%TQ+gH
zBsBSa{@7|F%t88YOveefeE(j3okYd~nU-p?qK;&i;c+)fwPcXw56vX5kf-tDx}n;X
z^4h}}{UN(bIbNO^kwnt;cp|h40w26hLMu^@silURPB6BbqLx68zo0oYz1>~p&@fDb
z3{__lo}X#2p~rEZmd{r5YeiJzka<n^0#R~A$e-bS{r9fJ+-HN4k}@+5{GebSA?~x5
z0qIcRr9H@dQH)zFfu}dSX~@`NDz0Dn!?L+MZYDfbr3C;doazn>Y^x3>J1)maHo$KU
zAu&R)G(zvK#pObeH|8Tzw!RJ8tHIZ^)Bp|8wqvCqgi_$<ltQ%9*`4xkEO0}qsYEFR
zc&U}Zz#{^Bnzxg+89)(950VTw>96ystgmsbyZ4h~&x{Cmq%K$!(6MT#6w)cc<jQ;w
zHh9d=PD=ywQ3KlC-;~{{V}BW~ZeqW3>;Yy@X5T3=b!6LE@AYjF7n<4!#~6_KHBzts
zx}!0%xwl<ani5E+s|-)O{fP2fwlksc9yXWkOTRCN2de1Mx4|dGt4-3>kjJy#I&6)T
z0k$aL|C)(;AgETO@xXdyO^bP1hIWyWD`O|Y(+TIAo}8T(B4v$^WbG|~ljunyT7e1e
zibuLK>Kna{aliK_Tz_DMi{>tqX(yCpYj?<)hm$B#E*~JNrEzVMO1BT9XJoKACxwUa
zDQxN3VZ8nHf{LXzA+_nl0hh~)Wqk$ZoP7W;14B0|6@}#@{8AV!eihvfUKR@aGaGru
z4r)t%M6mGhV!S3_Nl#5CT?Og0H2)b)?T96w9s2pne0M#oYcM;%h2V(mDM-ZZ!^r_f
zthAr}TGZ#EB-d(eJMfmtV}JZ0g%oE8LlBuQ5uz*<5;{6Y?H(3^kS{ned(7Jf5nwgF
zU**ahyn0cAjEfc8K>(wMbOd#3U@DMgs5I>VkBcULT@KIzBVh8}g1wm#eQH7_lAl?Q
z-IpC?te<~fLmoY8R;;MRq+fA;(?Qs`G#<19g4}wW0u}yiQ!<VM)~Qn0On+h9KgohP
z4o!3M0Kd~lP>*D&MDVo&E!S7`@U%%!6em4wJy%h9mMGEGB0T2Ss>Fc{Y&#i%%yO#u
zPW_28pIX;Lga6a(8i&B!8!n*#REO>oS#m1M*L1y@*FxCrhBCWoIyd{*c;pwCZy0s-
zk}+yb6AL7sseK=MMpP7tQZUREIQuxiST!On5d{Wo8RJZUe&BYWCj@<h62pLFcq)w{
ze;NI`?~^h2=c7xI9XupeXW1{!m9*pC%#TteVgEI*h?mkY)<95*W6bz%IV(@ZnJ;(J
z@39s6Qx~{>MngMmdldMVL-B`IyMz7$OVPkQYHtCs(sT!=>=x7yHB@%V5|XLLghDC(
zZJ)55709dM?Siqgy3jLOh<v|yWbb@{D+#_c)dJtll5EZAFiG=0>J<Ft0D^497^)&t
zkDfj|pOVbx=XqsZN&D?1R8*`zjl=^N3qb`7_(1VMz--H{e%3hsX#Jd9orD#@LI2ie
zU*+hXNRL8uK3!k%Js)fkFgf^H&LCT@kEnZON+DzQ{D>W#^bdKHcN8IDEg>9ADS~@`
zyyj@7%Z@OgSR%DpQ*3<tvn!nXk2S#P@Yt0kqWW1g#*}b5=e!U!#B?PR-csV#%#7=a
zz?koVbH6>M_-Y?F>Z*Gys!QNfos|}6!p7r=5`(4UFmNJC%{h50YSC03k^V`sBja*H
zo71q>GgwdYT$m2H%F6=T4Bou($1^T7BZ~Cf?o7etm(R+nNd^|*@<i%&ZW#MJbw+IT
zfZUD6gj16$DPp22xrtu6N<{oi^*pt=)H9ZtrWZ4u8F`SxJ!U-H1ZSzroP$kN$AJNI
zpnq3f*&(`2H|awyDpPpk%DPmg5h1)tY4SXI79pcI*t4>_7-QtgCq~A!16#5vI4~Dx
z%WWU5<bY)ek0e01`|~NSr9Rl^Ign|8%Xs>?p74-Bd+XOVy@7}t8qT{^y3i4jG)w72
z`IgGBcsvKd%>@m=WEvp9E!+9J7Fyf1WFRXDo}A`txVC4~E%Yd6Su#3bK;g~D*@5LK
zf>^HYDc8H~L)>3NH$o^>n%9(QE_zXZ^3O&@$AXunR7UxzlFsn7AtQ&-WMls|<fu-s
z9^Td#wi~+Vuj6-yFrV2u{LDP#_1Gi-M!N-a00raFK5@7iIdoxi6=|gmltPj{Bp0s_
z%v85R?VRx#9K0vh6Wq;CTzT<*++B0(FXrS1ef~~-x8y7HDXEbKF`fPksnP+>ffcW2
zCBff=N^~A;hbxVi)Uh6qO@-jOT~DT+@8*R48w@*B(YbvG%_?a~Xu+9;Zm_iJQ~rQ7
z@sCM{-VdIa^{W+qChb#fJWiZY%d{=)NAwnb(2<r%0uWhQRx=t}F?|WPej%7aiVJKl
z@mgMaf72Wph%peij<q}Oo7THJn2l2Qz_qiRRlw8kVk3jF!@0<!@u2r?lA#qt^{eP)
zQZGPalvhx%8eGFtzpwlGUp8X8{}AlNk(|I@|6*8{2ld|{EN~D4+ErCmbK3U)styKR
zt>As-MqI2{(7jX3z<^nE+H2tz<;H(yvvrOIn6p1%=Vc9#kD_7y{=k*fA6HSETmCvg
z*Bh*pe}-;9l0p^=NpW1PRO4oH^Pj@c0UJly=lg#O*8id01wf|$EmnM~%gcXf-5t9#
z&09#x+}0?7lZn{Rlr(Ew8p^mHI9OyQo(yY%qW<}de#Wp(ZXX{Z{nZ6{E?vvJ)c>?J
z<s+EsqGB8um0ZJi!q&KBDx}Cjl=%IpTXa~|Be5Kd-&g@-+9U-Ddlg0OiG_D@C98Tr
z;v(6mm2-g!(*Em+&Hj)02)yvesEm{##s2KV5vvhZmVWuAsE;fs%QTsO9B7DUTDmO4
zc~&|}M4K*Ly<)s%9u0ttPdR%ndgCX;B(@=~EmjeFU0XNG_QiI<R0lxs*ev9>6Q0bN
zB7Uwh2>%X@5`Qfk^mSWt+0Uh2aw$=E#ML)oQ$)4Z$bw1Rf5P5lg!dP6<5(7RPd3Cz
zt-C2Foz;~2ke=;JO{|_{wDWH&hSC1;0jN6HZ8ms8r;tSYbQs=TFJkm`CoPwf;)R6M
z8A%64`vYlKymNg0E3*NlbptX=a=`FXR-Rq-^LwI<bj#4Q0m!KS2WSL2#^UX~;L#Fq
zL=E&&p~<8+Vmc=qlvc^HMP4!;%%QP!qaYJTEWEN%A}lYs_&>uabCYp*v;}8H4ZQ-U
zA(91-^|Za`Zu~N%KMc{Wf0mX}xW7q48V<Px1%=iAM&Pgf(StnxYQ^+{iN&P*O5!+G
z>UuXh34QXY%CrfsT{~Zk;deG&wfto1mQqqf<Bqd<(Y~69(-&#9c6)f7#p{O_F*z~P
zzscERK7lc(Eg>U*1juMHF_){sYXsBF%!cv<%%jp0qQ=%x>53lycJRU{&H44z59|1o
zuG0Bgy%sVE&SmA*qB1h%4)gOTn(N{MHJR=in0hW*d>OFQ`-SwOCk!-}!A!o^GNbKw
zpt;>QWG<=eq@yXISoHN4CgfOoL{(=NrFH}NUdo)%*XrLT1qrhFgIqP-p^jZ!*9!^Z
z1Q;~zmE(AKb%lbFI~K;p@HeA^n*TV+5%MaPl9|y}5jf1E+Aw-L9C-1f)~~1DZ1>hB
ze=8{~AX8@JC@G`GDPK56KenQ|R%wXtTKo`evFbOKNVY4SCE8KoQzIK*3b<@=4|O3f
z_BWeZAZq@#WUr%^-^h+AaZjPB(EH{x14F}Dwo@3OD9KlKmOQk&^3cZ@Hzy8(mQ2QC
z0^uyfWI#iy#qzG{jq3nVjLS~?nQRoKN2t|04dj97>%?t49UIN#s+Y8c;&SG3VPOCo
z%Iqcubc8JJK9*=^@mzB}W>xu>m*B3Nk2yWf?CYEc_=}b}9}p~TuG-&VDW~rBkAbZ`
zKOLuIZ(`a^E^d{oKVun6K@5j`ug5Db${d3HW+~`9VAxn)5^Oo)#^bt2aQU`maJ9!}
zs^s=|XJM=$WouSZsk!NTlGab-_Ye2@fEvn~<{*=c%gTyLsnK#y3f6f&l7WUj079Q|
zN3VVt288t!4bc6WxV5)?6LZ_m<=Vx>5#B@AQ#OQz3W&7cerNWGvO*A~K=%pk>`A^{
zMBnIO@N$LkaJdlc38z2RXJ`DSJh;~W!Y^_jH-5{Kl#EX6sX?zWEyB!2H`qXO;_kv;
zcJ>@IGixZg;Es$Jx-~cWLCd%g>T<CXeLk+^#xnD^3bwAmrw0NEN1!x@gz{+ZN!>e%
zL1c$qtSu=O)~A?>?KGgVa?ec$oXfn)Yo-M!-I-#Y*^b<aE-2t=o{F?VBpn%(a7axf
z(yk~l@qA3l9{jFATkusd8`W8n9eiGB5WCreliTHQ0)U>U{fz&mXfOBF%q%G6jGQoC
zT$nii6ZP`yo@>99e6u^fCo3OxY^>aDRsn{07Fy|vXyqEux`@`(S%#j5c3eRc{FCN#
zv5DK!!mX+#;Matno3J-#GUc?{3~#{9<Iy*S7P9B<=gU$}m9?EY6u&b!9<6l^{k)yo
z%tvy(!K2AlhiZ|wmj8y|1~^%5KcmE-w6Wc{0HEI6BBv3T%GBp%tkNkMu9%ulfAz&s
zZ*pp4K`N*ON8FMW-Ev!$RCHA`CdYi(JqEw%AqpXn!yiK(``jQ-BVIZdL97xTwSB=i
zX``i-Lmv^3y3fi{g^DH}{N*y9A;9C&hNavaOyg2VeKLT8T@P#17sW#t7&vaXvMduE
zPbYOdekrxDUqt^*P}*7vk)#6b?OfyW$i)c<EVREu_6O;3x&xO>!^-z|&S5QSJa8f$
z?Rm*@>CtAtOD!ju*>(Q2=ClZbEKX;0J-j`B`y_0;%MYql6!+i%eH?=brLx#7EXhmR
zau@M@w&)HhNs9ahvhDw*<wT%Nag~S0Rs#t_mi03}$>DA1OF;?~mE^aJGrG%v)Qv7U
z2M&q2fEpUkt@|A`l4!zh>{tkx6iT%Ea)Co~Qb&oHYV*=)!C%2Z!>IHc9q`}~q+L8X
z=_kFpyS+g{P>;?Tke^Xa<VR)_<VW!}EIsbhAsW=JJil_}6}8G{faUfgvFum@uv2YF
zs@2A+-@6=!zAJ<Vl3~h9hAz?{aA(m7K$M0X|Dy4iL}R>c7!+5bQik#`%FrrFQeYvE
z7vyUh6Gc(m9ik`Gs96C!cT8-<r|0pKTqOvtkx<ShAt3g#BFB?F&$-`str%|J!1YP8
z>+7*-$53+O#Er!f1)k^SxrOBrS<w4`nVrCD$LW$u`bgT^Ksz*%?9gLt%#WtMgpsjo
zP*2L>oeyIv4LL6`$qI7RowukR=GV)7;wV8|p`Vj6mse^q6V94z4rML8Bp0%2u#<){
zt-R)|Z_Y9LaP`1sM?-rgWzMt_1v|l!Z2CCCTk)K(S$!57&T8h2B&luVf{AU}&b$p}
z8;u5$x<XSWRr@n9B5b3oHpxPh_9|iTr<rcukPzsy!9A>&-W)_fkwjOYqV5^yUH%yf
zt|Vc91|LXw@MRoPbC9oC_!upXGoV^(B!EJ$(MNJu#%a1OaFxeO{LO^EfW?Tbug;iZ
zC?xl0pN%uO2$ZWqsq#xOZrXF5;f`GVK8DpEC$Lg~Vt4ZIn{^Pvf*?r>9YXbAdaN|4
z^xkpbaJcX~fsNf?9U%~tOa(xQu&j7}F;dU7!W^Gk(u*oIp`3Ewrcpl80$>#km?oyj
zj-BBK=NwC03Smz*&?hM`Av&5JenoOcgu?}RS&v?${-|mE;oP`@LV(~;yhSocdo~$%
zR-1FVAR~#xVhIk}rr?o36&Hbv>P%Sg(AL8dtS1@wJ3=<wu!ueBeS50@NVyrhfL!}1
zGQy_sW#BH%QxA=qLz+6xn7o+j5c7#H#=rcZ!GWcY39ubTDa^j6>LS#eA3Ok9No9sf
z+#>$SNqX7!3C>{nv8ULl416(0!24nXx96jh?)LL&UZ)*JJQTWexke0K*CSw|Q{Blf
zwGpdKh0V<U0@K-W266d}hjwKZT5~;4%KA$Tu8nK-h-iG3>YeL`K8&_d4{&rtfZivt
z9b;unDJm@6`Ud)RS7iB)7&rk4c;&=|5?$6jA@vbK_w|8HAj_7}NW^V$XCQ{GJ@ojv
z0T#Fv*|z?$>p?bPQpFIH@Oe&2X#NmeiOE)Pk_9!6ID+|`WRAYOFG>ImPZUBOqVLI2
z97Ac3zT|8zfzHp>RwudB*{mpgm{Hw7%t~r&7C(<fWf9(H!_2L5(K^Tv-@s?H!^j?q
zn$hrvPLA`oLIm}uyxB2o-v_h)>!vtJ7PrL87VcG*qM<fIC~H-9D<JO4(|nnpV+&*6
zL}@pUsxAPwN5lR8a;|fE2<-Tc4x&67S(@Tei^}27t))C2mU%A6)T>HXYC@ab`UcS#
zpwEm_WYqs;bc}Y*>q$liUQp45mfqGR-}c&@H4dNd`5b>I#1MHaOnUdH1>1dyr9mtb
zY-Ck{30S6#n%8>Catkl5%c=Nz_(JS-xT0{QGg8aZQw9V61Q3*&gGgGk_ln9wI+$q)
zz0*9(i4q9Ss@f15*rVu+CpJZZFpZmv`+?{IqiK&169XypMnwgfUD+H#T)OABB=O7c
zM=t}0^XNxjK2jthjp;;WHL$4l@F9r3GJQ_`uPMzQ_I+7#=PSWa_BDPC(<BTSYY_dW
ztE7wt3tL{ojYdu`hp2sc7}r@g%45XP*oZNYCbnQa^rET17Imsva@5r9(yoIdJ+-!$
zMVVJrncu;7c1}4!S$CpX+KQfWv-V>j=y{^aMN)R1wH!B)@(OjxHJoxM6?}?14CEf?
zg4tT)$}6cTEjHLpO*5yj67yhLlGOGRTtnwD=FIwi!G80=V6lq;ALP2>z<o2gnRZe=
zR5zpB_u>1NLN!w@8C5ke2m3q3^=wvlhl~ds2>^N}u0{T{IrTyzjj%lzHxU^H=Rs;C
zZ#G($xGwDMWSqJ`9!C2zimH<8>G}auQn8m}pMK9nOp10BhWb3rGq1c=jm0OHVaC8K
zX$j}=gDUu|jz9xGav1`A?X0G~cd0>sDscEoRN^#(NqzXh4Lt~W2#YP5Eo*+o)OA?T
zZ9V=J=~?`~IP&xm?VfvkVtYL@WXRtxA>LcdKgAe*D*N(P_k{k#MMZ7-?6kF`V|Y_L
zJQOKFD<48rvIhs<j5<jW!Yp197VQa1P*9u*<_2koEOAoWgeAe=)%&%^d%H6pDVX2v
z%aaITyT$XA<@gleVSLdQ4Vv5y?zlr%a}GxujA&X*f9w_3U!LS&PZ93tGhy_abTw?)
z@=5p)3L&64ryMQaXD-yg!V1V|5PYGH81q1}M2T(c300`1^o+DpOIwvhaVeo;TZ(Dp
zXq?TdO+_~1fVpX$bbPGtw1k0%D(pKFL5_uY9MMHs)4#@}aeWIXpLXVRA>mEaEy>kc
zYw!XaMR*cBNss$&N6pmBdyts`?7<72EDxUgLIYLwj%7K+uPUji@#UsXQ|`h!S~In<
zq@$O7Z1WLjP%}m8T<^0@$7z&Wkn;YHrH@=4c%gjV^E%Rbicbq2uO4m;k-Udr>*cG|
zw+(?KivDyZA7ES;tsF>Lo8S1WdWDz94u!wzxYNgB$1#Ejtswj(0l*KM)WmG#ABjsJ
z$sPNvQC150Lu^+ma^7!dap?Wg8h=nWY#lyV0+<DEBJ0VP8-1*7%23afs>uHZ5+cv<
zdsnx;zsOUHB>C(CMtrZ+&8y6N4NL^I&Coc^>rUg_`<a_tQ0xVm9%4^3m}+x0I*)T4
z);dJywuB!S7cp^p+ULY#JkAw>&nBA2H!KPsget|8(uZXxW!2D2gN<Nqdu_YXP|=M~
z25~)k`(8_=b<ZbnhqH7fUSdvjVBo+xX-)Huya@&T=5&dLR{}QxJDnTXv%<GpiFQ01
zb1~wP?{->JPErM}SsHhr$@BlI;&(?aK`Fp?{bC$VBS?&!>RMHaK7?{v>G5i-8FiTB
z^IdC|*jY%%q$B0nfikJIQ9JlobPK)fO#>Wg;-Hr?bA-_@HoLqYscPbWOe0CrjXUk^
zGA1wlqId8%p1uq`s*Svg%i-<5$HRR5w3}DiI?M*9+DH!_%jT~Q^U#iqn4Yph$z*}$
z^u=)4olE2wXT@g;A8Hu$ghl2OxsgE^z=a$926V$7%hJyDyVj?OhRR;Txrb~y&aW1}
zY8CrQf+T~|KzuG!=e?Ls;k<=Bob~9#y}Cw48QEk9<n&%MdL!@VqzJImsT#WbKhd7+
z##M1p-d?|@^7WsCBliErUMNWp2p5Tj)~rA&1eJHQgUk6i^d5broQyN5-xt~5Oz3Q-
zuB2gd?S4<zv(^1qrdy-C8@qLkRC0X(KBk*5@R0JQ4|_sghJI{h_-|8g&wsrnkJA#9
zpzB2ktrQ`Makq=)J`0h8L6USNgF@=<cd~o^1^D=eyjoO-2f&NOAROfsOpA8_7)CU|
z1{^zCsInu!C7ahWAn-yp4Jve0kfx59fXkc04)GMwr9K9KQveOp?!~@QIp)t1$`PZ>
zJa$O_S1xvD;)@1m8YR#l^&L!h=uZB57utaa_p0UlityRYfO|T&+bDg<{W0%tN;1%5
z=W?Tge}$|;QpzWl%lxoFs{P!}@!>pMdbIrfOwWC#hNXR_=};NoK+k8|byt;L!xU8G
zJ~1@=VDQgpO253N0!^MePm#a4_v;q~gLho(7o-CWKw9)FleRxByU_cQ@nu@*q=%w=
zJv@gLDUk(wtdT+Dq_p~o=3MiMfY;+n<ot<fLvg38bOIWUOxTQ9jZOFKW%>?Jw+E~#
z_=_5L<@8R%D6B2oXR?vgxse(qV~`LSE@2Nc<3YXLSU^-rzQslq)17@!THNc~zWs74
zgc6zD;fkgBZuYNm)|ofB|MW@se48ZBtHCs0$c=l%{Jxmj_nT);mEUSfY*<djHq0-M
z=b#BM$}7&sG8I6cJr+=K(P6LZ4sJLTJiZh><H-4>{d~vBd7_Ov7hIkXUV4KSdd_G8
z!fP{@TrT6Kcy#;^?37{O<&<{T16#l35^pN_fMQyxUI8S?gJ!p){a6rv)~9BJ`4?|`
z1-iOuVD2zUT69Vl3e?xyrGDxt&eCdVs7HtZQvB59WhTE=BY|g=^N?*c9;22I*XZZW
zg29$;1|e`1#)E*`F!^UD2B3%pwPu>lM0n-%qgc^n+4uMVU+^o#mZ3dAQt^AHYBEql
zZ18x`_J`HHMD49DUi>P(gEn&7b2@@-c7mE$48EJ-5VP3Elm)p-Zer=VQ5BVNL4^H2
zYY#;^IPcebwpmdAzJv|Y#iI}xE3JNqDsb&LK%B_Yr(^bsKRa=PjnZO<2IBwT?|%W%
zA_k(u|Mh6*J)A?f65El=xR>7lrxXdrT;GF;yg12%<BB-)b+^Y46xe}N2!--D0bRTe
zoKiVb(m%4;5BjvbFa8=a+3x|9Fz?FXP3i3a1M7cZteX~19^46?*BiUEaGmd_-}`&d
zuD)2U0t<wOz`_-%$0!pi?p@CJQ>nKEM%}$HpGtSB{r5+ar@qF|_tOy*|Hqd2_P*{V
zDEil!hB~~ARv?vZCrWtYf{^%m9WQn*Rnp%!7BnG4%z<1hoj_ju4pYuGoQR$uorBHI
zE2{-)^#S-zRh~~)l?9z|;SvN;IO)0I61`S+h-a2pZ>g9s_5#&ltREqNdSt_Iy;h9T
zvX=Vq$7pqfJrNWXB$yl>l@ExcKJ{Vfj-GG{h2Br7P$m&$Q%%jUZG~@YYMQL849yHZ
zfG|DKW%*?=@%FVDsfERjN#4SNJqi~u$gp1d2Va*Rg6xRTk!~41zpMy{6Or*~p$n@9
zeQfBWKgriB7<qc8N6N#|c)h32NdfcoKDDT%$l0(mw3-1xO>N#V?W`#8VH#u~E@^A<
z)28CW>tTjl@TSDZmQJLv9F}6>h-1k6MH@zf@)ahquh$nSmOyel8Jp*`9t*k=3S^b^
zv3eyBC-SC_B-)U+UL0kl6Tx&G^K5OSe*1iXdr*wo+gn2+Pl<P*HRj?dc+9ob)dsFx
zSpVN2^#5Syx<?ko-Eys~^SizRqonBV#n!(g(vG2DL`>$V?BWWO!#sS@n`gWP3n11W
zzux6i;CI`^Xe)Rt;I#EX@AE5J1ZhH6%n?D71{Y+Ti44-+en|!T*7xbCWGJSt`m8c1
zjmd`ua_E`pEmE63E*r6s?KpLAVMar^pw?y2NC2k7962!#y6_0dks*SuC%QF8QoK6)
zOx9{1uF7Fbx&07{HvO${W5z~4Ka}i?p#HfoGn*wMj@e_@RfXR~LI2LZQFc9Fp!5%p
zp0R8+OJmM#$?)W43@aHg=w0Zo?zk8E<O3^EP$2;*FSM;rPJi6U%|=*tPRv9D;!~Zl
z{EmBe+0(7>UU8BpU0YaziqS<0+68}d!sN&x(FmuFahw-GH`FYPS^y<Cyg)oxHu<eq
zl+n^h{OO-EetfmJFzUnP`YXSzF$1>j$DkM#Kjlf1BX_BX#D7EWq}b2ra-*#l2`4i<
z8^P7pmBny-)-s9F8X`cJ;{J@}hjAFB#j$05Jrbuo^X1p7G`vKlSZyv_@J~31dk<xx
zTFiXEmDlU3Nz$*h-OGZvuXzVL^Yt~&sW5zYY`B_m5knt}M?wyein+x8e)pi(E$0HC
z*J=g7FzCRhpU(L4SUfMVba?nNa#}QXJ`=LSuSzQb=LBNjh9DyN#zljIAs*P4ttRM@
z7)~RvI)r2lxmL4jGN%Uo4s7j=9~*WfK(~Cbk?v=_VC!nTzmZa7uF>q6Oaz+GPG%!8
zjx3+ge=U*st%ok1zlI&PIO8z-7ebak5y=~!=3I#@sL*=ccPNU)+;&Efmamr)<w&A)
zr!Z0AW4-^anmTtug+ymY9|BNnQLUf7J}BJ6dl$+3W~aGLF|UPnJ0enGGfGrRO|)I%
zlCv48*jw?%Cb)KO7JMDAf@^sGO|1G=!f@5grrAAMvl)Cg>X%Kt;(hsCSUa@}+cA1?
zx@LC*E~seB`zJF?4OZ)s+3P+~&oy3<*9HoA3@)ChVY(ceom`xV*-fyH7^DO|S0(Vc
z?Vv9_Y^%Lh#Cx(TR4)EA%6L-cbsc3`#${zi)z`C}JYa&wcD05!D%shX+X$o{p1}os
z6oMcqQ;|*e!0>&X^0Yck3x9_i=QkICA0{`=w!avjv{I`#y8Ton=~U317bSG|$B)R(
z8`!>=Dx@^LIkb$qYV4#Ck$S~4Z*tZDGG(oQD3|-xsc>9k%R%L&E%4)?jTUX5dW<%;
z*2NB4P#yk<mFmEDA5={9gpxrspb|vUZ$C7yraBi6LRzx3c2+&oDM7lsrV0}|4Gor`
zW;#Q^u?cnhuzmEpQ#AkeZVT%b_fa&crN>XNc}wSI7EY`0WPhPi`XM62^5FrScd_`*
zyoVJ})b!m3JI0@{2cQ6!<NT3qR!P0XC?A}^>wIxgYwb=QD%IPOg!;J@*2;_hZVz8S
zmP~uRdtIhG?L9mk&%xTOoUXub{_?6J`|}T1yq|5A9{;HU?jOxM)~FmO8*J6nGP&<z
z4F-7>+`F;9Q>FQ6>=Hd_&!suS8mdkXCY7Vcqg_P)DL^^tiuy@h?uqApdW0A17!|Wy
z2YD>*6@F1rNk$`(1$WO)3uHGHz1#;RjIMrS=gtWPex|-=A!h|{-3^sJcR%T(I%I#~
zPL}<F>5Zbr*l$OlU}mOAGj7VpOZ<$wUxeO#bN%OK)a?3b$&<`*1ok!;$(88vISx3P
z_Ho^AS3aPzJefe;XqYzk?x@YFlu?kUz2vX|Y{i^b`IQ9#XruoEOJqKbXv1Ru4yf0h
zca+phc3f_Y3BqvTo#+@4cZKA6noM7ku+{CFp}B<oHR9NHO{h(q^gwbqW{ZFJ38a6}
z0PBjrGvNSxw%Jn;%GsN?8~#1w`E3Av#_zHN!d3;bdd=KhE4@tK^_B@qnB(uwj~EO!
zEgOzahC|mcBggkWO<~W#ijitgm*#tN-+rPRf38N>=XIQMt*QLGZIk8ecx|5RfHHbF
zT{ZQ#mFN4<itaxl4B{;>AJ?DcRrmq-*U(NNt%p$h!rH8~SUhd1M)80=a@s>#<mU*F
zRI0vu6kkN_W$!rRGI2*4sA@Fs`@0uXHY;qRXvjc`y*`^!2s-4HXrmt!KIk$}Rc%19
zbORF^&+;gev-3P4c`~e{0#366RspzPy@8dO28q~(pe+q@;DvDm#p+zg$bx%mQ+;Ap
z#&6@^rO3z40MQ*3nK2o}_xPCSh2g)wpc7lUAz92YI!RVUL4$#nC;_<vD7ZXRP3xda
zyYU1|_u#3bL#J#I$omnAnV?9bH#;wvf=GJ;J-b&v7YBn5@dF!RN$aiGCCy|ejdn81
zt>`W=aB4pE6Yyb`Tr;#LE^vCUkmVBidfXxdl8z@EVfYWv_xYwGri3Pordm-HH8n|+
zwxv?3+S(Z39?=e=na#DoszQrexKC_k{Q_V5!n8Io5v7po43CcGDR>p!QVrD#Z4{wC
z<T+Poz<F^EqJfLv1mpEvTQZ#E{BH%y3fyLc?V_o8w8}zp7&Sc(*0wPFjep5_8=aiD
zqJTZln}7TK5(Nu6kcd!jWl~#@?c*|zW{=DEqFHBRm5Ax#E(5*kzK%6Dzt556C<f<;
zt2J)3twwZHn<~6G<>cl;UYR5OtbAJCQX!BHi4O|^4|1uBx77(!b3w)LV1Y9Sz2WBI
zbKMM}?mTy5lLuNkkyzE;b2r}?2D_mxt6}Dg0_C683?%XH{Iptq-5=S{P)MwHJF{-i
z_rSCcNM@>a>O$UfO@p#TY^tkko7fVnjpw=E2lkPV&IEfrQE`o+Hr`S6;wl2vMBEB@
zhTU1k4Xx?9p*HZ(LLDowu#wDXcrQ9Xqy^ifFIF|wV9H##VB!_ByTr4Q@%O=#vPT7#
zT6~si*<ls>wrbN`O~PI5eo9G!Bve2{tZHyS;E{*Pf9Mfn21rVGk=A9jO<xE{x8*mz
zl1ZLTLPv*-`K98vXH?j8w}fO8ftQE-jKAl=tP&mD$Qn~(069<t4L;-(8c@&pyr43|
zluxXRC}(7l-|6w`sit{~<<&$FkkP_V^mJFk4Ng--<hK%mq>whlu)LOFxhltF_!mcq
zNO^6*b3r={4JB=5$dx8hq#Zj^<%~*nz?ph^8Tzg|fld;*W=vJ}ALwL+Y)P$IHzHyV
z$ZF1J>Z{C45*`hARdr~0fE_~HtvfB8gZ%PP$S+>;T~AR%z|(AQ1+>X`waKu(B5~hu
zqXTTc<KeQF^qm1;lVNK+8mc5LmMlink)<W&$PLAjbn?y6VKS25zrmI@726GCJRY|w
zI8t2o4GziiIAEmlSmzLt^%&v}jOMrpJY()=7oN?nB*f%7HaxdE2dia`MW6q(W%z%i
z{Wb#Z2|nmXF+r4&5My85xshVVC|&XPqtphwAv=vrSNL{kE5ew)z87`Gu$KHOC<L0x
z!@G!-KwVuP4rz(5|2^636dT6wU+0?L=tU?bTudqb0!YdjRlSMhTzw&7yw1%Bap`Ln
zaFuW$TCa#}B$Su6u>}~%D)4&b{GiEZrlx*(&9SsM@VI<)OQGSLeJoCI5R=6CIhO^4
ztCD1PMck-+cH7X}zTrt$eD048eX>Hku!qFq>A@<v1Q-#k;koQ9eb*SSc}2moLH1xL
zSaMF<dgw(&JSOaxjqWfzMNr#Jn%dK;#nI(Z;@@MTQBY@GP_#z%xVuMEaFDtAO?0Sj
zi>#A69`pudMUBg;djn5={;O=T_U~G9!{BBVfs`0nVn&`-0z&PrebG_4Fp#%Z2NusW
z8b%DyGgE&P1u^*&(tkVK#k?+kPr$yuVpP3d!uc^exmY<Y1PwKMpr_RyR$or!^=tic
z7?->1rCGv(9dMmsRIS(@%ivGJ=X}x6DjgcsQHtzENjAF&mX*rWaU5(Y1^-BViWE~V
z$4p9<cMgB5!Ja@?6T_h^jsH3Fs9STs-s7Ehb=hNj>hqIuyy+spna#wpGdp`*$=x6E
zM%o!AAU@MX<5$Ko#-9mRn|A~HPI$SsnawROMJTmlKYMN4yNOYfM71~j2y%{)n1QId
zBJzF<-Suawfemkt7+{;)G58fDomX;#G&A{2<M3XuV?V)#XF)w6%i42N7V0ZmqO_Wk
zgrEldfNFkKT0MEb*6brP#z9$x+-LV_m2?<6-riN8c!lbtha2+=)gO=PVGw9`lI~ZH
zb$9VNmqMF{)V!Xq^s<n{Bs{&dh3fWM+BnU&TxYArjyc*^%i}LMw}sB}5!mTqMNxeS
zu-a8r?9E`gm*WDGnh_*^ef@Re&TCVNq{WK->83CgV>fNDeaQl2|A(n_jIIM(w|3(l
zJK3?V9Vd-#+qP}nW*fV4n#Q&ot8HvG+3}8V?>ENz?)ks|t~K8E&N-i%C=I_*2Ch7T
zlQWS+W|#uhIQ%$!nhBDfb>Pv@xz{XQXVAUfeKG6-r%>V~;C2i15^3R7HtKo{nTOM7
zei2ah<_MzkDf|x$0Oq{S>XTTr>~-1ah_%t*8~S+>i<PR(`fgN|&^`3TZB&l~+JV9J
z$k#Qe^BVMcR)}}bi2}vLVN`iQZV$vG8VHroHmjphAo1XE<^F@;zj`Ih-5==Ql)~)F
zc{ZIt^(3!NzpIK?E?OTQV1lWKEVt8w*40g=pUI_Tip|%Yzl~q_=);Q>ghuHYC76!M
zE`XWqnI}fZ4bVi1h!ZR+dA|B>{m@KY|1*`Q^*fS(+F3L(miPBJWbp4PRNKGCkgu3&
z-*KngQ)<)RPw#CVEq3_IdYrlwE*NIRe*C~3O9lA4!nL)j0qKZE1QHy=toFW2<&t2+
z>hxh4oKH31MtDuo0fh46&D4kif2EEsb^?ZSkj@?!iHRGz&5rgQQh}E#$<Z3V_XS?y
z(i^Iwf;p!B{d`W}Sgd&Qj#a-lS%t5(e%rA_%6{9fPS?F`^1a#d*wwaeRKX*b%tfCO
z`UE2+OWbb2))y0kaTAd^zN&6J>p6}&;vpVlJdCf4&3i|GcCX;a6ek8`ipWIi_iZ8c
zQ?kO}dO9L01IG#cLhv3#*FF`pw6jL05aB|j9<JX!5FT_#Q3<Wyq993ZHQ@c<SJyR<
z-u0CIuO-@rHn#H7!Qk0Z4Aio^!u7v%o{rG1{CC*A+{2^XXg|55__VeXQ^7#rfOLL}
zjEbJ0b7ClF68RG-<pD8+F^X{&HK<NWexW{u;V^~^3wF^dDgDr7f6GJ3U)y?$RhD%j
z;{8NADoSFr$w|J~*T<$RM4|y&0?9>9yAJ_<F@8(%rM7GKf*)fB^!ATKt%r|$$#4;B
zp0;e(C*dHw84O532cvcbKWC-ca7^P))XEbOL=Kx&F^<Xef)Du>-yC1$qg{`eQI$x6
zeNqYXx&Llsdfw#&9&r#z%a;}l#gpu9gVB4^-gsS#VRyL!MM7TY{fBHgymsRGJ-|=j
za;{&`EM%2zf=ba)aOymimn{b44cwM~WT=JQarX4GB7Hm_6Q$wdN{R$<e7h4eZZX9x
z3^~G;Of*hrI!3Fc`3b9oPR5aF9f^`39%D>Uq1)$aX)9pQ)O40cW>LmMNO4dWl{0Hi
zmeA0Py2e6MJFp{gy7V!*l(J(Xy@~9_`OPkPwpqA;MPVqSCAKu-OHA6R5jZ48FoJ+e
zeMd=JM<QY6s~vXJ&B|I6YZ+GX<zIk&ACk!1pkq`?J7&$UREhdIS=eEJFlqurYTP`H
z+SFsu2Vo&C%(=j;zu;>|6adWrFFGedj%$pP7R~P?k}_%DYPAMUA>0y-Yy7%)Zi{{|
z{ktvzT*f3*d;<|vJOz)G$5CM%oK{LI6bU%M^ECRA%=HAo+SQcA57<==tiXo69t`%I
zy{2lyvS&xOpxbu3T-lB%MfvG|9PO{G9wmcxrbt_d2vBiOJcO<jwzuNlhRyvLZ4vcE
zlGm<YNR%teha$wK+nZO!$}fwc52X!IBOFnOBhxJC`Er{qemXl&H;7s4V?>E8Xx}s7
zjBpW~^wI*b$Cbo08{uVmhzDlpQmDKK$ZZ8`Q(fZ5=Po-CGZtZ*-g?)r8Q~m1GNH<y
zIg^V}#;)FdiK1zrsrL8rua{;1Fc>W`GK%AzIxhI{500A*<Wj_e-MLjK=i`1Y(OMiD
zu<b+){{;e1qrbM`F?XJl8VU%Z()F*0<A2<6F$;EY*ii`w4<#C0cA+BMpo&cv{_x$s
zUrXKI3qzc~94$zQDZAKFrdH8SjM7mQk0itO_D9usurXau<%aBw=vd4bV^|i(j&Hwv
zQ>pL0F32HnunoktE?KOnlUW?%;*3}&*PJj3HxaSD?XoXRaRP0okX|pN^<4LF`|n?h
z_KIbQp?$r7aOn&QIAaFCmr1$W2}iAbJPB!QOHyXVlilXgmZR6agk<u!vGd52RvN!a
z2=aw+>`{g9iV^;*J~^><7+YDxHNTZ<v^Xr$AA##Ai>EKG&THS%nYN~);YrjJq-R!#
zT5NF@w*LEy(<u0k^*L_GmO&uQsy`6kgWu#ni3N<9EX`+BepZzzx;W;T_(dq;5n))8
zT3%ig6#TLw{Z3L&PeW{RU|$B>%q&Q-YZn^%lmul<FOuxU)(^8*?&(@qoG{d^azXw2
zlyi$oh6g|#Ia3bPhlcXT$ZC4c4-9<RRq*w2U|Fm_$l`W#o`_;_<ah^9e*y6bWiR}W
zgFA5?zaYqPE0@ol2o!PWklb_|qC!mBZUwsaX*&i!A@&4TV=Zj(mQJu^Y^HA(52EsZ
zKu=dzmD#cty=~9!%HEp3Lc+#D{-%_H{G{lGnCXLJ&$?}aaA;SCF(M+jXbUzlW{x6p
z8CxFU<L*Z3bQgT>{ysEbR{i)i#9hglPS=7q)8CQozW*|k#q;K(Zem2)8m4{TleVl5
zoGh!Fmy6GrIUtlr;roUbd^l|vS|c98TYBO0KUrbdsM5@xMEzd`NK5*J!FM;IOj%Ij
zvUUdExY`HABi)VfxG`T!F-{rS=veG)E>!g0iAX!#g%{1keU&j(*IF<*tuLhI{@L=-
zQOD3O2X(3i0g5UytIQLaFwP%`Wx4#p0Pzll#0?nh%kNKt;|<5petur0bEN~9p5F~G
z{w?(XR|@%mV{J|zbdIK|PTjxRq3LOSw~~w{tk;*!SWiL$J0W{0T-0iGikaqi0#h=@
zD3r{2a-ua7-dXUs;yMk%@~w30(m=-5d@|4Tk2K7iYecEvJ67`{4`det#av0%9n?tl
zk?#vtN$_+xT&>@@^D#wj&D71I^_59Af$(rxOjj2AVXxuCm_?a@=}B@PW%$~futdfz
zB(((`<yTO6x&sj|(hEl|W*QeDE@Eug-{d##%*Q~8_X<V)o9`1GRlIgb_0FgAc}6Q|
zhT*~9Q9!Jmg#4l_b9dns;t+C??=1ix9c64`MttLbN2JMomEN~IH#Oh_81L{IQ%To-
zF|`?hSU&F*NUYLl5`U-Z!*P$5S_^gA7Zw|r5Mf|M*wE{P7CnMc($@o9#mE9i0*EQ<
z6RKwnt10;#*gjxJhG$KoxA??MO^>n9D_4R7%pY6R%@*RHG$e)ed1RS=A+Tx5Zmw0}
zmDLk?l_CExKM5n<fc8(xs-m=-t88mgpsP5IZ1TwJxaTQzoC0WyuamOGf?<jNL`Vu`
z<L_8R;rEeSjM^&tdYzObeHG+P9kg{^4!;=aC(N6$o*9&;ZDfM<<?Tqc>tfBta-Ox_
z&P*RYt5TocB@{F`1K%VtNHC*RyIN67`MbZD0ADz#G1EbzcwK58`sC-?Ro8Rq%I4rt
zA;dqK2JiddakGDtpz1Kr+<17X8gD}5vx!LOsFY4ibKKBZ^N{_ZWR#y1V$rrg)k6r;
z2UzUwGrTVSmg)EBH*##vk9)0U_I*Dy7+Ew>hMgUWTC;!*$Wp4dH2ciadUlPg71O#G
zyI2L|l`9$C%8ev-BkoI&3~TDdO9b%lWzu57Jk59uge}wRL`IKOvNHhqp$e<_b)h?4
zJk15F9OPUjQLwNiZ0~Qy${;1M>L<q#OO;CHo^YEi(yUQEK5Zwly#WeY0&Wr?NJq)v
zNjlfo%=ut=zdQ6qQFV3b&&niT7Z%A1<9=10*nMK|-ZW5AgY&8bmyXVR#F3Odj7Y}O
zHL|VO%&okb+^+G1-B=j-oS$xU<~fWsLPHupBSPfvp!FFTHsYd;lTGyqc-mN?i#Wz{
z;_Go<558}X0Ix2gViRnl6VYH;S_xZ!{Q#6U*_=8?kDVmg8sFMBeGh41X!x@A50Q)s
zVGcpE-=*fnGW3`m?(jS1f_+gBWt|*Pv#@FcZeU=3NraM!88i&QXsq8BrLknk!rzD^
z+wLJgov~Fl(hq$@NB~L-Vnx7Ho3CcI4;0CliikW2p8atI0Cov0M!CD{k@5)WT&MEm
zmow8k*v&uHgL@ubv7=LnW3RvCne7VHHvJ(E6)nuT38vHz2Y=9No<;BAYrqeGai%~(
zcN+c8NCXc%WxQP6Q%X3gu?Vt;kW+rsSDOX0J#pU)I&5O(<R*q$R4yJ)!#p&|fbrJ6
z{6pcY5{JR#0at^W6mCsi*}D_(hPr|h?5R+~R2ehnExHvou1CL{uYN||qqSubMqDq+
z>=zNeqq5*KdGdtS{6Gs9vjd9wF<a1_q_)?>$=G+`^-_ZT*Fojdei_0Qe0rYg#*BEd
zQWL*PBEcX=QO^R%y~YbWU*QBo89wE383s1mO!ujEBz$La2MC`;a*4W%ntgU!F>p>Z
z#vtT@d8q(?kx@B^v5@6vaYY_1o4h1Vlj*MQ8iXNST*uHy_+;G7$j-dD2ybyvjMKWR
zS^BCeY+nO%yGA-FR2k~XQv=-1?=_|f#S;b~U0CtQ76wpV_WY$qNxnM}NV(0_Kq;D#
zL@8+fBoX&;P1(YnLZ2Y5P8k`-@|{3qSLYvwCJJSziY~wOWW+6V*^4qU`)MR>-<8P-
zwe`2n_2M_|z;SqQgT=WP`jtkl|B@#q!xWf`GZ?trmj}7cnM+)CD7LJG;Ze6kbw0q<
z@}Ey?S$rQ@#ou)s`ft~CK`Ghs7|7ba>dELlLGiLfxb-tKfen<3PNIsS2TL+Z??Gru
zw4|YBskObRbx-};cr_E9I4TpzfkBI(vC4`xRHD*hib*)vYUia#8<K-7gImboX`(<{
z#^>b~dVzgW&Ez?96r~Axg4p$B(!_t7(a>MFi&SR>Hufv`%=D8g(TPI!IIZ^wX2(;d
zf@$-zBhaLqP{H0(a0G24|1u1G<cVplB<RBT0nE9n;S5VhI|JEH6Ln6KGl>GAfFV@~
z@N@Iuh@9&T8Ix#}VtCZbN#6^yNArhRSjGGRT<@L_WKD9C;7BM9OI2oBr@O<)c#_2O
zWc#|HYZc#v(Z8~ZkK$4iAZ<gDZ1E_>qP{2Ii_N`+(jNDX>+f{ey+oCzXD8aPXZR|`
zTwL&j589<zh?w39ks>t2P$0e`{4`vgbJ9Hbgjj+|h0m~@C0Y7?8aTz}bWI-8(vn<1
zr;B=TeO`B;QnDy!yAFjnJkB}9MPf{;;VOHj6pOs<1%hv<mFU@6rSF>lTsKx-h6?N!
zUJ2d7^kk95o)oisx@{T7b^w&h=hvFh`};%C>zrt+PkXSKbhZ;fsP(2jSHGM{Ew>mj
z+(ih4dUn(Dk%S_6f5#T51?``o#(xzmVwZouN}~y`g(?G|SoDuwEY#!*2cbQL^dVG`
z7=uwrqYy0?cWRtQkI;=r=009&Da(fFZQBNEIB+vg=$xHyB2#>@aRbpN>MSzU?Z|~s
zG({(cv%SvflZ`gtWQt5Ef9Wn*-VZN#Vq;v`50=afx0)A<-wcGcQpc~gIKFR{QrTB@
z3`GY7^Pr|}ea5+%8+obaNF8{l75kw|P^>$2%H&Ym-w4@>PZx!!z7%r*IpSbsbhm~-
z1L{|QE+)6Ak|&>Ei`;bReEhhJHxxZvCIZ(MZA4SZQ8jzc;y4E0iEx?3Xkbo!PjKR!
zW1ao{k<rYdEjh;>2Yp2!Zf<XZ)G<IVQfNfBzxzU3R<WJD1LbioP-ujmZV~rNfZYE0
zNWoZ#r}A-6p|3IR`ppqo7<BjuEI3I@)JoZ1Ya|x+w1k}-Lj!IK?qsK<Svc|hwmal<
z6Z|av<&JT{`qTge$Wt28<y&~>S2*RzGIv3&^9Y`OQW*Y}y;_M4Qtrn6-|$g&3K{}$
zf0WJpvyWxl(}2NJDEwm>WS2!@ckU^sm(Z_rp~LRkQ(x;8IoOlv-R*;|sV19K%q$QN
zQ+~TYKTnu;ZnURB06bGgFy-gc?nQA0cHZ!wi_mpFZtT!$h-RC*Uc61&S*a%!eB4?B
zViCs=T39rD*CJAsv=ZO?p|m>ckTDHrerc4@59Cvm^;>VU=iUUF^Qqv7KehHjiOm=p
z$JH10B#;mZ4_&L5c0w*s&5=}tHn=uIKIYh+z!-}8T^N7b1e0#GQLW#=q=}>$3N)&u
zTfRpwkhT1>a%@aBkzk+?OA(?=pUEh$f_7*7AxL=hO9ZA|8bFRP=`<fdO=&cg%+6?g
zJ#={iJC*n>IlpwDufvOm{Rqb!Mv>3km8h+!pUh#6EPPeA3Rp<?Lu&=H^)|P&I=asu
zI3+$4{MW*A$tg@A{^~p0Y>h~`;MDaI_Tz4p3Zv|m^oIL~Mu*k4q+~_X7_RZ0IomEo
zrmZ(2sHi_Iy_ADem~>?B$|$iiTmteZg>(Slcp;XnwOPbpspDlRVb!w(<6bpA0)2ZU
zX633uyzFPe`JcqGe0vlFPrBgiVWKoXcO+}&XXucS0M4#(^CojNzj#}+Op@)e5Wxow
z9&03#l2Mg^S|9Tlo=A3!-#A6?--guG(_`S)M8t6x0Ji!HO*bMR1KV2=;z2PKu<gxo
zP=7a>jxtf)=<PAp{($ifeHggxVgFScn&-c_C}31|Q8Rottrs4S3D)=BlW(je<5%@V
z3wwBqxgn^e0WW^c3W${h4p9`gK+=GBr;VU<by(YP*OO=5>t3OK*6e0D#SDjqRqJ-0
zFeSHQ+)}#tHwNoDT6GXPv;q*!+NLkIbLU^Q*N26W-Rqc;WRQWdyZ(e~S~Dh|8e$W5
zs@BuL3d((67C}qq(J88ImI_ag%+Ep*3=4~Qzq*dq>XL@QW)bto9k@iof1=O41xht)
z;>d(Q;RR<)!#&$Nm8xuyt+^HRsMcKB{~U@Ta~74ez#rP*PvdbTCS+vpFh5cq^4mPk
z@FjK;eqanlOi2XDMd;k}^xVyAYjWjd;1Z?8ttCq8DVI^rr*QBQRAoaYGp~1FdI{bP
z5suITU^S*cX<PXoo`#tJ+06hbCYqX(walWNl&O`jdjwhrZ^pIWQE6`eEU^25ve*B|
zfOraJD9_Hx*4bWMLP9A2%NN{jeJVT8e`1u&_p>UOc<8Cm6;gS~?eVfWn6nal2&w}f
z!(;6a#b8Zb&sN(px+wYv8bd&2I}zqiNr}1i2qP<c15GcJ4wTu2v-2I3kCi-iaFrqt
zoGN+*Cg~k#1+AR@t;lLs3LlaBYlhJ#Ko0+;V;}91hXjT{?<}F&YIu5rBZ}$elNS!k
zs*|`>0$kdpoGbw^4(=1(=@cnih)<<hxq}Q$6gC4icRTPzd8oJba6hGq>PrJCvXc>H
z(#e_~93Ne9r{m=4c<|Xtl*mE~z|YkZi`xZG5tf2>&9lG7<bHuql&hGR+y3)LWDG5y
zep>s}R8y!*{80&Iz^bY^7L(U}Xn=41&$-*l_eZr;0%V=Ad4V~kckiz(Eo=oQ@<da%
zIHToQf$9xjDGI?1*Ide0JN0YZ#h;`S4w^g<9D`s49!RWixT*GHk_YG+;ph>WJgA)n
z{`j<B0IO~Tj=2vFv{U(2XB&RZQEuZ!>_()k1_i=AkdkNSYu{tQ14xLgB`jj3^{|~;
zg#$4b4IlXJO3ErU!<FOFyS{XHkN_bpMA@@XHB~0#?Kc(b{(U*#<qQ?UkE6Hm=87;8
zxPFJ)8~iTB3$n)5Qv#7T!B4Qs9V1lil%JqVB%wN%_kQ&YM#h+IB?0x@V&-IJ)a`NJ
z8fu%p?&TdnThV%c%|4?R+6p>@Ar?}TpLYB-HuH5^NZM@$RMx2heG8>^k80~o6JFX!
zJj#NSZ6gq8&6ohEWQtMUqv66l0jHl->lMN7wd@+{@8G(ZKmq}M#~g%ytK5@XtuE?k
zljr2!8H_0!IC^`kuA`SwG9pov#VPh^H$AV6II(0-n=dvU86%vxC5g@(&e*ZKpiH;N
zkGc(#CHp!;?zN!%whY7P+7gdMX05oRD|L)133#ns2~hjBJj=(Bmg~y-s7{(iFUh>z
z_KV$p&lJ)DstVGB+S>dRovBI&t9uJRpb8mMaB-3JvLIosb->v*Ec~b#=LL83`1-?>
z`(@(Ge<OxEpvI0X-o8O^7)CyCNh>6c%5)_$*6Sr(`B863^e$U~8JF+RSlc1IaZ1H1
zB?B9&*qwbvMukcBxzBS0$TTEXd?gti+pXx{ifgdi6DhKAPhQ&!b$Do1UE#`VIPlK>
zQQ99!5qG6TEeq;e*SFvE9l9+uy{a3#bRxpp4FrQrc6fd7Be)Ys#n*cP?}na6ufL9_
zbcs1-26UF9g^0FBOc=gS=YF&2J64K<+-`5vq%6jQGbw;Prm)`<96IQ5?+G1hr2fwW
zWGIZp*Kww>B0^vx0QU)h=wmB*SFY#nop!_Zui;GoXHx7KSVr-tKe@jEPPOC8&%r@$
z!6ZCntfIP(&;;1^SUob3&Rg7sNqHuJa%JZL1az$|GvPNTgen?}>}*mO6(4bY4NM~Z
zl=WCp4;Y0&pt&3b%DnLwHrW?iBT-ekp{r(W6#sSPA6cRPWZFVe2z<F&C0ypp+qoi=
z|B!({5U1t7;_^<@bF8kDS$Mz(t0<SPOovxvz_*=$6SoRNFfWC{B%r?!qZh|A`az4M
zV{OsDNdiN#nGX+yjmps0@~gHx7l+;T4=%YTei4?wAercn94$ttCKzov(^#KgRt2ks
zRwa<qa*SZD2=V`1Gt;8R5iNCAusrEY%f7c{G-4>1gYY5d9^m+w8zI*_x;2-yGP4)C
zY`RIS9DUUnP~9k&;d={#gRjlL!hn+m9#-zol$00aU=rseM39Rqgl()2nYt827ET3D
ze@^Yp5LM7z^u?;Ig;iJYD>aWxQnj-HEfTU+W6yrC<FX0r9m`0N8iA2nu>C~}=NXq^
zO7|>Y%H*?M4B#F*Ue#JDh1_DnE}i`zC3la5(r-Am*W~o~)RhI5!VL50N7y<}%19Xr
zq(_LeyImSV81~%g8`Ev`d2kMhO+!KO?npc>s6&CcP`R=A35D54oID{dSa|`!!~AeH
z8wDYI3(8&T^JZOw7u-uYs-nMqbK%>}#1dvEDd9!|;H!EiSt5+VHYM?~t)XXDRvziw
zw!KTTM5>kn>=l`|{Uoit#Fj!NY&G~yOOkt%$g|$Zo}x%?${h|yZ#9PBwRM_JY9(f?
zl<8&k<t-(h5nxv~__)NI0a9t;6>l47+jf66Ap7&Ig_9EuqfZ+JGfgrVW=Vn=U5zWY
zpN6^>0U`yD+TM~HVC8%?sWRn89Yckcw2n?*SbU|xW23*8Gr973I-wFZia&q|eNpW$
z?~#hDADae8mlODCTFe`9#GV{o2L*!*Un6*Y&?6*W6iy*L2%ui9`Th+S4mC^CftrJ!
zZ1gj4VL^6Kbvf2}c~T@V#UJ%dvt|-R4*H6SDGNkN+6juG9*9clOF0GZ7w51Ig}?k4
zIRu>apL8s>OgGYLfWnci=vs8mL37^PiJ@s>3lMwt_zkAM^)N~ABmyDmGMPkC&uWn<
z4QHwb5+9Wi%09h3n1w0JQwaj9AvhlWI;NDX3S;VQD22{3z;Tsj!q->*8PcJZL3XBY
z-8o+x+}dW|6gzdgQA%g9BhWF0!#$Ar*pWw?Hd;N^Nm^_SZ`Oz!hJW!#)H}o<>2^38
zW$0wV0%*w%e@Q8OoA*}}526;^#w|ySjlQ)s&^#Fs{r+3i7}u&GbT;LRCAjJl5%z`%
zI53Y=A7hCKri3Dh&73kp5PIo%@nj{0fb+vVDt5mkA&JhD1@7PK<u+S_Z4(JN;fE2>
zEyv(}jNLl)VWXq4(nJr89)&&J(5*vx;p=K^q^!1-;kOlRtPv@_uwc~GmlEUS;ZabL
zF{YRr7`uvnON}9l7o67XhJqhMhSuzup1^?PElxdD!141MVU4Bf4AlBVi_N=s!hamp
z)i@n9I32CfT{j?TffX}wD#gFzfhVFlEAiudV2RzJq8C+@TQLL-)$?-qop0G(g2OEM
zb67>1j+*ZkGI{JWrv?~RSs|wLFVHy99Rb=R9Yd_h^{Po%v4|TzLk3M;@XbZ1jxWlh
zKPVqXl}mt6XTQqm0NKzv^<=)l(%JrLkrym*J&D6wTN9xeIV7mMUgF9um5bI4lJo!m
znPZc+PaL^zxYB`OMdwUnzd`bQ6m9-Y8uD(_-Z?|i4@R)u6LU?eyd6z-gH7fRd60-g
z!D0#rSEZwa3muGx6m9aV$HOU3E8P7lbMjRrlt=KeTe?8bvSzaFM(_4CT}>6;{+6~*
zI0?TDeFo-TgGZQ179!<v&0)7JR34DY><|yck0ZzzxA$0s^Nabb#$?&W`X{ugL}+9m
zR-{hWbIn!QwRR5@enRqXMilOP+xXMjrwRZ^9h>lZ)mn~q6x1apN>@VxGhfhZR?u(>
z7yU~paw;pev!M+RoUK>w;O`$qn3rB~dwbD4(E<p2jPNlI)%9X-YG~fho1wgGUrovB
zLc}v@jJ{eF$cKk7ZYUDFm~S9oQtPO&4ed<s2oz28Yrn6mGc2TKL`C|wTll&Yw;SN@
zj@O=`lS)A~e9;a%bD+=6XLR2YxRg&M%!JqR5b_REUi;)rndM$vykccy;!2_9AI~Ao
z&%|tNnK~^mUHFKLkDWZsA{(&@=XGL#RjewptHwscUzaxfGPmKhjaFW{&x}R!t?;&7
zXAgdYa7QpbbTN9Sb5oHbk)s%^z=WR<Qm`>w3OJDD=3kB62*`GS4*eRUATc`^<g8WK
zvbnAB3S*NmSb>xU%_BaHDH1O>Ll1GgAgOP){gZe3lkftF%_L9iN7TrVlj5)Mo@W*6
zpv<rE7pIA!V@A2(<`;a+nDR=i`(H2tz3T%`^4wD$8Z2yMET|d~DJh#OoKCe76Hm4N
zsG(c2mobs-FD;+Tmjwk=^Zj$zBZwzBf3#;iBKFfh-_t;P!k>+m@Q2Wajw^&Uvx_Eb
zU|m7${A~|j{;i160)?iZs><-2#;{vbvLyyTuP}ZZ6>V}_JmAaEEN<T<b~Uc4@O@BM
z3|shTC>p+wa}ZvVk~m6P4IANMGWr9)`7^q+m~JSq{&|YUxk5K)cJ7Fqo@?nMw$D2l
zj~V9?O+~t=s<cvrYiKGK`1k7+fM%%ojw14uHhF~7*3KFQdNqri!h)KD<jUdQscf#Z
zhd9&0J=ryh|8z>Eqiwyr<(<#wm02n?0#{M*v+a=jM({qClxSO9dvS%4<6%#X$i_z8
zQ7U@N?mwu<Q1z<~20vqFUEqg1`B#_Zh<%t3Z5$GLrJ>~~fw*|y3gL6#CwAN7M#T_b
zs4V|LJO!d}wLc{Zu0ibS?^BShgfsO264rmw4;UKdbhzlxd#!2{3#G>}^@ot*{mhT-
zz+e;CaagEB7WL3#509A{#J)-z>0@i#OA--|Dhp$mwUQ_2G`V}*Dmn({i%QevH_mcL
zHg&63LOIB2X(696ChwyYY46LV2&SALCreUmoWp)!P}c|hlWFNv%ecK(7E>gO_V2xZ
z>F+lAP3Z~fr<NK!b)3^%a*J}xph~;qvg|6j5BRH|te^f~(avX(&V!4=K!8{R*Pj#J
z^>RVL)t~O*lvHoa;2f1A!}o$ydHO1qe?b7z`^L0vCKd4|B5pa2;f!Qg&sn|y{efZL
zMI92e|8ePd*GW&76(=%{aVUJ~|B<!P?p?G>d@d82`&~Qdch7`Sg{Is4FV4iRRa>SC
z8X-Wywm&p(6;g8`6xC;J(u;Ey<N#_I(@`37?!uvjH@6;91y_Gi8YLx$Cw^EtVXlMG
zGrMQE8(-u7yY*)YB?NbtunwPB`ccAQ`!)Zb4?kh^vON2lnwgW%uvKsPaRFloi_qR{
z*|-}SsUvB}jUU)h3y2#jL+?e}&-0~^Ww(P*NjJ@p@fp51)X7z+4bviq-6`-D-rELU
z-I<MD4!0Svhp(00;zcGL^}Sw)8ZK&;J;$XIwpYDVFS9?apEX|>7fx~9pMAy!AaL~^
zD^YZar<cy5mpE=}C9V07vo#h2?$I}$kp6$x)zxNMy4SViuBE--HnC`V27M<>O55k+
zVm59)$mL21R&eU9QsMFW`be03*Vc+a;yLDM-1Yu*DoxvcAWGV`yl=!So%ZpN1}@}U
zXblu({10n)(_}TRO*}OvfsOl*qNJ4Du>-&1`O|aNdcTy5+<o#F`VXvRw5*~<zjhF_
z#YrEf7Yxj1YK~8++WWD2pe9aw%ugIgK3deon8q)7ohBEf)(tD3mnoGq*ua2Wwr1Z!
zPgnx|CYNbicrBqwN_dObU-}C*WZ^Gl9jH)=a+KL26;w7h;s(dZm+vH*X(Esjlde^b
z$!oC&pC#44d5mmp2PP?xbpUh!10&<it1IRHr-#dpD5@tbO=5%80QH1_*lylRkVJ&W
zx2v;qa04@sloJKZz1`z<1V|&##>ON&n^C$e1o(yV6_b)4lw8r?7T8h}^-FP&Y8a<5
z+~i-L=Kp7k47HI?Ui$d@FJ&Y3c(}EOY3#AyuXi+nQ<==(eZ;)|5BS9Qg`ooY%2J2#
zk6=SIg2OM_=0h8}KpuB4VZSRa!KWjN@=Eymsf`?d{@d6{x7J03)(o}spdKMxH}d6V
zJVdfGrjq<bZ^IzKxct<q!6Znr_5FEq8&4^aNyDMBXJN8Dj<;Tys`X}QP5znUTF$fQ
zsv;XCb(52%R!w810t)i11zg`_@Ugx93_9Vg<xCHZv}p4|Xv0nb+(Q&`B|c_lg*bo>
zt2qmmqpqYo(zeYI4)xT_R5r&bTPn|a%9$Kb8j$BZ-f|gGK|Coe@<pw7Zle8n@KDdP
z7fzJR9%oRTn78)H)8@2f99%?T<q$UG*A)WiwzL<IYtw<I4(i{RmWoEz_0`1`RqX|Z
zl`H5uDbPW=Tes+YC|TY74tmf?`?VwXeBWWqn6a%sWQ`tZKLC$ju3c|`@T9VJ*^H9l
zE76uIi_XUkRvBPMpVVCEk@q6!553{BG6Y5`vq7E!mep?Xe4~gaDUH>L<g;SRSP#>&
znW-n^m{MT`{^~0#kMyrqkZ;)r9sl^71ObInb3S&iVy+Xn**(&kgx%=1#0?8?))7VU
zo_iut7{eb4p_|@uEpG#JrdX9L{()(aXlv5c_)<=TJ@^&Sv2^Ua-?z!9Yb*9FTIe=|
zxvgPIj-_qzYxp%HXT+SzGwt@1{!zqnbcTm?U4^__Z~~;pCY4V`($^ttIgOOqMqu~%
zaOoRRBtafwvZDS%)Z7E7q~NccKWyW0BvSHsg?*`!q<<F9Y1OU(*Sp(_BK3X4(S`8E
zXipjiu?0N7hu)cP4o^}O!muS-so<JJzv?P(z4&ZA8}ax|cE*eYNWKkm&B6jK@}^tE
z%i$x&v@bkQ@S3(pVp8KP!eOwQYYB)UJFs4WWtDC!)6;T9WB6xGwr6*(xfkvnbOm^x
z>jYJB+>&)hGKFg~_FEWHlu>QLaUH#k9yM2ZDeMh)wQ$@Cml%`I-0kp!qz}#Y<XUMc
z6wKAzFVDz;bGLZt%W1Mg(WwEb)Xc(itWzrOfT7{dBU4X(>YzP`=C^v0z1?;WOk&(&
zpG;K0CmJBvt-egR_|hk=8(02K=5_e(5)=zc5UubR^0tm9L0hG(?nhs}d6+`a`l&KX
z!53RnX}ZLrPzb&0yx@qWH0P)u%Y*Y==9nBWwmdNAU`%WhY;*azq2%E9FZO#Z$etcl
zEMOv6rMj1~CrU<Nb`}f{p1T4%Zx^+Y%Fg$hc!g+dr+WC&_aE9~KFeYX>8sf>1wmpc
zS$HtKqVjwn7e0okzA8(Of;?iI;b<zvBIgBuXBSXb)U;w58B!SwiWmaxN&GaUL4Qk1
zzdV?qw>)$u1TucsleQ#X%DWM&Uuxe2CnHfy|2Y-j<ASx_9fBaWPTq<zKF@yJ@o4+@
zuKj<mh;1}DoVJ@quhAUpAJ-%}0{51)2`SEFWS8B@=)E0i1qUEFX<N&KRVuRn*gp+#
z6vig~DO<tjZWw5Ki||J4vdo#Ln*=ko%WH7sDI|1dPFn8l$R3ua27ZAhF|vg^9JAiy
z$V8d=LR84@7)TCl!#S$HY{H#+?=JZb_8jYlDDFg;O?#;@TG3)q5-3EJAcW``O2;}j
zd-p$pZm#0kzi@uh<gxEgM@RP0Hpor6Gvt3-)rGFGfuR%4leW{f$9$W)KI_sDbH1gC
z&NQ=H2|pOKQ)b-zJIpqM9kT3j%zRc&PS*^9dXoA>ZdXL2SW_3?T!QNQKFXX8B^*1Z
znG=Fo&*;WXG=KXH3W{OI6)wV|WCqw1#NDjW&;Ceoz3mA_ta+->9O%Gq*hdivIqCDT
z1mthfqq!$WUXS-|E#(Nmfli2tHqxE)1{_LRh!y4pw_+5vgJGcX&lZ`JeDi}oT>O}K
zRaLt}+<Rz-?0}O7u?o}ze_TLQ9ZfUg=R7aWJ;{>?WJK|iby=<>ZjbGb;$$eIAfX|Q
zy?yywZ5DaEuuQM|GEon%eZY<W1_8YjY&04vije}tR`4NWorobc_M3%Jk2rfGYP-7W
zZXA>jJ||LwQ8VN>*$z6oP+|<(JIrP0{%2U+LUFn*uU?zD<+hUYU&(SARaD%|xKh7l
zngdq6$E*^lMhtqIU-09uRCV+d>=J=OxePUo3CgCA`9ECa$YjlmQ@Gf;EKTUE63~KT
zO5DUun*{XNzlOQ~n&lAQj+KSn2&%p&MS<e$2Dfk0hRj_RVI#oo&Tv`yq?*Q=P2!Hl
zB0U5$#bqTxy%~I##m=ur+Wg%oW#{`-<htc?(foxp)dqk+s^(HnCZ|Jc#NtV}ja%D6
zeOI??1i665R!b%)fPSnq?_OI&9U@ZVXUrBmk~Q^RJ>%(tG|kE`upm0gX;_yvYJ_%I
zY3pL-LCLjzA~H>P=pyh`Qtp@XVg05E%J7)dU~7#Aa598&EQvHKw2QSB@kX9NfZ}Qo
z#)&^PJ(|{Y0hiKx0yrCl+`>o9dxW*70VVI^c_b^6jO|1Sk7yA(Qt8jdK~nG~gc?g?
z=tMznY9OW%q(8++U;%ilESBd@s=T3$!U^QOOFu{~^6ibut`n7t?PW*K^+cvEAIDNx
zg35)0`O|CB5&YA!zPm-Ru|AIQ*QbtK#~&Qyz7kT1Tyet;C@<>UgZGt6X7L?3H1M&M
zfm0D*<W*JL;gf0+h8D-vYgHxJV9*N}mStNBQVTI=gruLR^E0k*+NJix$PL(&aV$&H
z1DN7%n+>W^L?v_4^W!~I)qH`MW@3@S9!?wsHAFClXNbry4U84@R|zGGqXZZS5$W<o
z*{sA+WGa<G8Th0TR1Zux6j}RmBqA>>blk0pA>`%r^MqTgGaeE0)ciV`kEm;SG9zB2
zpCU8M1@abK<3R88VPzP^JIEf!BorUR1mxV|Zm`s~@exXPF8>}KZ;oLTAl9Na`-Bva
z1*Yp?TrSX0Ggymxh<eR|Do9-lCP<57>dfHD|Iq@pR#UFWZ`k=R9uAYYNe&PT+e&md
z2@NmR&^*AP@vf9P{V+W<`A=xISbfK!M3l|@63de=)kBB?FK8ce!{et`$92PtTk9_b
z%N%;X3^nbo(7<2mPzrJ1_f610V3F~RVzdm9Qzu%5jc{7$bVdkQ)zst<LnxhJR$w51
zB1Fbb=aenkdo}(sACoOD&0n=d#`Y#9@*;A?(vfb|M*6rP7&UMEXp!d4<m_^rJTW5B
zNkqpcc{bjc&T|`RW5wlt@+l9cSpEUMA(qeKdmx`v^j-Vu@%J|ZOAZ^e-5EpxMVWqP
zM#4sKo~ge9o=i}L3b1mf8!qCc+28boLi03c?@Wpl^RJDlpBY86%O+~SE6i%3e{9&%
z=ZSLkp`ZP|P~7}7X=43v`Cz$@`?DIr{bE^9`6rW}0F2$5zE<F8SWmM>;*VJw7X7=)
zogw3dE^X<~N*YdPFW?a~@l3IZj4QK(t#|lLIzXsyb&1#r0f5T2IJq`4U?wd_+a&IS
zRaF>&7BejebsWiXF1EqdCkV7EP_0hi7K$QEf>-I1%`9mcMo~G!B4dAHEWQA8aC}eA
zD2Pbc{x&PQPyMK7yL9$SAXp0ER@^+?8$Q|QbT;R+M}m@nb39la*C8KJw=DW|qVji-
zEQuiDQj{wy_U#3|Y!U>eaI9Q$uNITulQHqDP;8<D&H4%x@;3r~r^^j<f-ew*KqiHf
zsXaEo*$u@TS6r+TTPbOjZ>HH-j@9a6*Rpan{!F~&r|f8U8no<(*NlH4JQK@KY+eFE
zB#{Gyt`+es@rz<)cNZwBIHD-W%m*J)eQW}ZqRtbtNZx@-k?~X1peXw!ZKMwI?UV4{
zl(}WO2U3QX7x#(j$Qi`UqRF(0DVc0523sl2cc-h<^E&NL&D>iX4UeA|r%Rsr6IrEv
z&t=1(dG*XF7=+|c*e)ZjY7skPQJa$yF{u<&LPwxRMmDjGUt@+h48VS0zNwVki>dl?
z7Lk5LsyqsbIF}MZD5B$!sn+Y5*X+g3?>9BauP5L4{SfXsTQ$EtmgX;j2W|hz?c-4-
zG0bozTdM<S$KnjXp+itUqr)rx$r)wF``c;>Z+en=4wU00LikswGn7f>y0RP=ze6Lo
z)@8-?Fnz0ull$+gJVv4@jG0lP(%fg)5s#}9&@Yuss7Io)HEt&Tv)mz!ql~MN_M_KS
z@J|TRo^vAqdXj#H2v^?d3r4P*2|P4TOFBEVx6Tj=TWw`^P$s?a5|o>0!#KZABB7Lg
zv~<?vA+$MJsQkk~c%HnA6Qo!a0e<*qGu>%O+P-hfg{>u%%>qY@ixh40+R?A-O0W3Y
zm+%I^2q0fA*uDWZ8<1EewGo5V=~%+v4k(EE0%7wyvmgsFkEVi~EjXWL$u^3bNUd5Q
z1xvr7$P#kE`52{zm&Ks%o?Js4-VbjxyoheVlz+2eWhnMgilTlp9Y15nDPk;B_xW_B
zocl>cLZF4iPETYt8Pw;&tob^WAAwit_gxi|O#B%Q8{jcF!IwCUzzK5SO>upzGXo#I
zES7fU@xz+?QPZJjPP%zs1TJ1W1kI6%rJe#S2IJuwck;U&0f{QZBT*WvIny>aqDx=D
z61O6?B-VT8f0-YHAO|m813An9j46L@grA1#L5brg{q2$Qc$Z(C3q=QPo?`k23SsC>
zIiAh#mMb~}CPBno{bX5VHIS#D|B9~%{)xW7tV_*Z?fyilw3CWQtdK;E&y2tM%nUaV
z$8<y|Ycz+Tj3?*pPIZSQjK5F(qoNzxn)jf@lr|q@uAu+dOWC3-3i_SXYGJ3Opr9zO
z&XwnHKSKaW@}}m%wal#9%(h+f6tzgK<}0tLxDgndmb~nlcT1gKnm0Eyf16*Y9;u+V
zI9Wf1u-}IJ%=fFvOly4#(hur2RLcl~br5-mrIj90y0=pPjXe%(H)W))q}}^QCK&IQ
zP-|v+M&cqp>BPVkrQ$AF*29sNQA&p~mN-LCb-TLOPUSz(71-qeN&fWsl8i8}BUg2~
zV9GkQn47OMK_apwbxvG5<Eu4Q(D0ju^v?AUraYYv(!wya*pviO126VAXTyZQSx`W(
z39>wc?ZN0tyN>MB$T3!kur_M$AP&DZ1Wtyk@HA52UfKK>{wTX5i@hMshd9u*9XwOa
zgEb)QOKbLfR(pgapRi^)z=U}4?%D#x$e)FBZqf`%R)#yA*AGB)5eZ~#`(iPWsIa*~
zMi%L*eUyUdLGpn(?Omeb+16aWZDNn)y4Sfj67FP~=`BZB&1}SqL}LuC6JzL6QYzIr
zz<c-&){6>yRN&LE&X~+hgKN$h)7RHOP0p_zjGok48gy;Fw^)Ti7F3k#*f?eeE~b$#
zOQaVYUy@p-Eb=s-?dwc<6VgfuYD4XsA3|P9-`{4w+~HyVp?<0OC&7Qmi=<5%?_cYQ
z*}IX3sA6J}5k*(F1UkMuqCT{9T4{XJU^+cA9Xj~cVgAg;+}(-C!tzs|5Y%4`l8k;S
zDG|hfS?x!Pq+7CQUX`!<BsNOK`<4E$ZIi}o%|*=CU(ry$`g*v|AG+7)PqKh(QLSAy
zN8e!1`XsxV0A-xejd_h&W;Zvnm21P9uJe~WA#_QPpX`CpQ!xN#c~xkQLgVcj+Z#7l
zLdVIOF`UXI1=?juq0H9MO_#MVLd-(Ag9O!*UQ{#(Wv9pbMw2^h3zKHH0Vr^lTbJ9F
z!#=+rkdkEmO-dw_bTf|IK{#ARmoogXm0&lAo76jsA_+!HMY~Ak!fh>p>CxsDkx}2*
z`>BFjRcvtI5Dn&?EnsauNP1#_@+%){wyCi`XO@FG_dz=v2^^eN0w$(3vhZeJTw0`p
z40>T$NFAAC4sllx##!*qfVSY{pZ+1P)Oh42v_Q9347=SjkmK(kkevcITPl|Dq1^ty
zE<BR3<U2<WHZ)!SH8=q*hw2vNPS(E{+oJ28#S2y_vo)#Ymi|T5x$XUtS!6Q%V^-hU
zT^H3~-^$YH8)@xBF$a79S(h)b;_Lp>^eo{s6cRvE8D6o<@@O6BEnn{%T`c_ImaG@F
z=vZmv^~8TS`z*l+-VrRwGJj_D{>qiwZ$&~N%NFOz2EdeW-F%7PcKh12M&*5z1r*Ml
zLQp{UcG-+lj}NQyyiKj6oTa>$;fg$TIOnxE->Q(^F(&XDb%KQAZT!aL3?y)6&CI?V
zYmx7u85xR-#huy&#3}r9@`QxS!I-mqSQ2l3DjdnJouaq%UrL`j`HS=5wWXLa`7Dpg
zkU51`Yk)KVN|21pJhqb=C#&>m^E{BT@drVzHcpiOdfRBm?q8A1;O~N%+)8FdL>Sur
zFr950mYNQ8`(9w%wiaC;%tl<eYgz11C<YlgOb&5u#um@;RLo<i3P4a-qnp~fRKRpj
z#!QhVR9yW1;8=tVD1$-O1|?xI-5@xkW;$5>5dq%VlCWn*MI_$q-gsTQ|5i_i&SI-W
zlKFXy3MkwSWU;nJj*AaR_)}L~Cq-Y@GTnE|L6k;ZJN)8OuWovc4eYq?3it^B)efSd
zP(w&}D{1XOOG}e90ZY+1qsn<M{Jwj2NYo|^kIIO)82s5^OnC#(#pR=ZeSxOJ<^nH5
zGgN#DYOAwq;=|ThxPJXCbW(9M@!u<PFpGNV*KuAgVr?$$3T?7VQ~@tr0qr)N68^?d
zm}(1&fO7zZQrq6_l&w{k)hp!$xQuVJzmLu{pW=k^W=PQciwz^#{My3cXcnHhvhsER
z(RffsO7ZGo@{T(a;%C`7Oym+Zw4eVJTlR^V;85ADhXjGQFXkc}WGDx*wKBrixJ_NL
zO{ed&1fTY6XLXasFec^lTLIJaW@Wk<CV!`Rxx6B~T(DMsB98t|e2tG2HV_(JDzWa_
zf%g4KS<*aRB+m-f{=}x;@30nfJhdrX8D5;>dcBN8RthMz<l3ZVE@Y3MaYC`JI-pty
z3=4&NUG>DTi3rP9GR4)T=GkO_jhEg}^41?_nwpx|HBecD64;njEmZQwgAM&jg_&06
zy;~Dv@3w#QB%6Y)m8%g(6o1vr0W*aY%N-NaoRYjMeRRoErBa3W_h*)E#ZoPPRL}Q&
zkCNM})ow|g9m#br%T-isX@N#*y*%9eNU<gODSm3j+;9vD93K-4|L@k}@|%FA!{6y4
z=5h)sZTp8I!ZPg%hq;23Sx*x+cbKCnS++x|2{x5WXAZP5g_*#Z$D$Clu4MliLSES5
zt%k&FQM0e-;+cF?Smy1?K^CGx52P2Le?{pT{*jwhB$0(vl`@7G4tkO5f#~t5r<a+Y
zBZVDlqCJqz4uM;NzR#*=W4(37I`p9jHWx!(=vyPtLC&&C)<@9I;AT1*QcwLe+fG)t
zN9_%bZv^*7pH8q`6`Lb(2)vAbfiSN*f_ynfYj`X<LT-dBF9-*0%6+%`Qw#kS;Oy_I
zKn@3@;*%`v-7g-F)vX+{G*KyRWxEW9UV}8O1W&bv!vc}V#sVQ&;cqaQ61w#@8GYwh
zy>M@#dbyHjHQ&<kY}mrT*UZ}<bTBcYuHRq#lbdnTq9;YT#L~mEO#Euk^9%<3$$!QC
zJLwVi_>3PI&AWvXv*v}ICORr6nMq_PT6=lS<W^4mNoT>S>RVsh;B*b>+Fb@gV^+@i
z)V{JDOHV<;_B+<@*QOJm&avCFWN|(G<LNA$*b4`ueScr|YW~;N5U>g*0^S^PjO$RS
z6fMq&VXrNj9<$(a={^<sdHTcbObV4YNmks{76*D==4Z{Y?{OAe(~b=9P{NRI8SHU6
zQ<P{5_Z`V9`&y8b7tPd(#%zp~kr#K@NxyR{zlOfgD@F-#cn#-vz?s={X14igmWQnt
zx~O8{r?vL48hAJuQcc0b_~89V|00aa%|H@#AMc&nFSbu~NqZT5c7`lQNs0Ueej(Rv
zYy+w3C|9M1Tq{}`@Sl%1oz*|L{%FTC(?UZvO6^i??Z;Z&Qq_oZSHV65WMp#$<G}6M
zx-hEnaF%{pL&j+1CBGS-zYRX^8Otdq4o<6lWUwoAyxWnW0aC)qwxh6SZr4|{*0XE|
zZiqHAxqN3<#Gbn-SAt>t{hv)BZ-3V;UU&FL#^`S13+y(%O?|TAJdV*MoPr8CiU{GR
zl)QLPGcmr6+61JSc?QxSPm@^bji>eQe+tLSh(p32ZNMFiwZsW|<tVh#Z#~bM73VVB
zax)H&H!ttZ^?2B`R(u)!iF0zkFh9tBAoj@EN96F0QFXSJ>}P>>2WNfhR}w>ZLuQgG
z?iBCZ^qMd>ikMQ}OnBCkSSO({n<78b`Zbm@aHj3(p_qEpFGye74_kLs@zNg{Dk6{2
zW*2J~lKNaj7Fakmtv|k#e%T7buUwbQ6{anGiN2$gmp;CPnLCPrOiIk{43ZfoVAd?m
z{JA{Ec-}UQ7*Nxp;EARq$NyeOBY?|~=~fS!o(qne3b-8WIaO=~;wa}}>s@_CrsO}9
zPu|~YQ=+e1rr45^LGBSO(*G|8+F?cLQ5m<x1<`8bxW}B3Oaf26ybXBr^|u7|l8aSn
zarz>ougnPLfRXynoz*yN3j)&pje9)<GpvyUCSguI#px;FrtFh=|IF9KzkI%>P+KMJ
zkqw>n+Ws-zh7k|XftlXaX1hIU;58o#j~9Jn#&dC5Nzk{cZ)Si>bc;|riUR(QvyZ~D
zxkj>>J(w@9R|Wmzw@o#=93c4BY<oweUQ(?Y<S)Pr1qkntw<mtt-dz>Q{-aIlj}!7>
zpUq9aOhH1O3_tGNFV~}I+!Hp%g_20)gqZob&AH*IK|{AUNQi?<<9pY`yI0nt7JkCQ
zn@uG3cil?K#U=$ah%c#>;R&6!1X81S7LU}}^3z|b%|7FEM=fWL)&Qy{^k`s!1=^+9
z8RTwF1y1xqo;?4kWnER)TzOLXX;1!rY9fL7^yKDl8ctAox?@tYiDq}!2-tshue`_D
zAR!k)a9<5qHXk_KE-#&bU07!%VbCaJ5r=Eg8{58!Pb+NJ{4sE`vrAi4FIR<TfCa#s
z!|rwI&2@Fp&RotszFrmRZC}94hF<xwTG||Kd87!AzXwlMca{+_92>3Y`q8~z08gas
z__@6-8g_K`H34GE))PT;{_e~4O!dpTu#Z7ccY8f@w46GAd$6<p$2pLr`soEvbGknF
zX?(TJZrHWMih=pH=z=*XX_|WNMTXY0%;X>Vk4Up&=^Fap-|_23{l%Gsyb#oeIp?<p
zRmJ=FY)7IK7@h!}Zk2dqdNLlk1RD6Ye@SL!XO?5rpfFI25SRsY%m2dy_|Zp~Trl#}
z<Q?v^#~bw`>&n$Qn6zSn!8DOgskjpN?AfA(#0m4KD_hV0myWCCB=9L;v`OIomvzHK
zNqL}B9s_ai3056TYikWFgOte>w4<O4{0vvc_WkR0_09|uGJ8gIsg{x<M}$;7&#KsC
zxW?T+sU+xsczesJN}?rAv^coCyF=mb9NeA4rEqt*gS%6>6k50!P`JChI~4BjFx>8Y
zdtOhkyWWpkYkp?t&dA&mu_Iz<MtrerQQx4$vfBnf^h>OuJP#pLe>ouc(L~IF0mT~n
zVP$?%KeX)Lqy_S4m=dx?x;e!&2vbkDm8{uKxnRP1FL9_umJ9K=A-Ams6H1HR$YcKx
z9kC;@=B}|IKhlv6Ko*aN%5C=+-(`I_kEzXEseIM|LC(RW`%qk5-_N}ik^kFkOwX0p
z>F{IK=a~Dw6$A|2HNu%HPqEH|j3||k&O6Z7`DJu^Gr$UGwT?TxPRpa_b4cBz9t<St
zRV!p~bvJsCL>!Wf{8j3OuW-5<SYWCi++Re}Cs|SFLMmdeifg3<UE|WbM_`<XA=t3J
zl%49Bn@=olqPbTh*X+e(ZnXl1iXy|{oJJhxO04j+>{qkEAWF!Wd`!D?8ew!}@W&_5
zZfajd-{(3eQs78HD$$2P$J5IJGaFat;I=ZwFQ)NxtfL$oI>j_eWk<+-wtXXMU#zz1
zH~y3}1_D}JVH1RhOzo;_;RLC@wr#mz(`XDNG;_42HfB0ZMI;;$3@)IhHe2FJ@#1Fj
z)MUZAgm8HHzx;FG*65?o@;*9(K-%j40>X@{Uw(uq*3S!gn<D2re!(l3Jvt!@ZNQ-D
z5B9qG8=UBIR{6F@M?R&qDOHOZhkc-ZF@7Iq)fo!v$MFxBRVYN}d(dMLb=1HYf_z{|
z_ngM~b~(qn$o8gc!t>TnSW>aijqlt)rS0zo05R3MvhIMuudym66tq9F+T}fNo)|3h
zxEbjCdOT-E!TiX`ny@1LrH#=4aHX*Ae}M$f)fcNcd>d>+7jTh&x)R;&ettD*yH4HQ
z;X@V}FSz)9#}QNuJ<`;Hz-+KlCaXclxD@6hgdT}>(VGCb5O-ywa`=Zt-ffqoXymw9
zM2IvOMG1xmVuBVBDxb;8(`d8o<c8~FiI(mH%e+rvnD-O$Js?xs7M;s{S^12UE`5@-
z-pyskMG$5hl@#F*6-E$59iIH7rTL{(uU%QSkpqunAKRBKCJsIk82CcUK=4v?Fi_n(
z!ZYo2#rxm$>RcWHLc&7vnisZ5%+cK6kx#QGNrvt^vhYK>AMps!+RuqZU6XcA(EW1u
zl{6y7JTR~<+jE>h;wchYB4JQ;hXjy+2-#HR<<N2{fF)f^hAPoX0c82Ay$IAP;`{!_
zyUy1e?PkYylA1<B$yxlZrSduN;w{*8O5Seaam<*uFZoW42tXrHepX{YC>X#hCzoq@
zo#i;#J)!p9&1WEfIbYg5=PIO^*I~pfK@AyhOK5_ej|Q)mGoG51?2T~0Ow{T0&$wlG
zNyW~a<om2cGt|C(nBL8B)>?qtU*4RJQHRnyEo^py2m(PqU(=hgG8N3WTC=SQo=prr
z38{jj>d+wxVIQC08jTzkXzp!&(K<o<YQa+>eOuQ-L?aPrzo#x*a~JS)S|99eG=GGn
zuX1ir#=Q~-86q5tGwi@<^-o#%em4=w4~;iBTvS)<8Td0G9@>+5hNrI+%HsSG<&rga
zqpw}@O@qtI8JK0R85R1CkI%R1%ec+D6rznYh$EvCE8|15JMJ9*IsL(S5ghdX*-RKa
zRH4Ba##pX)bI85nii3)&JB{;HfRLf2!d{$4&rbxLmIMbcET4y%MM~E;q3O$Kl9t4M
z`pZU%BB2I&2%~`R?#Xe6;T61ym<LXB*-l>J1+|y&c|AX8d9pgC{2BA5&+X%(RMnNP
zu_b6vpK1Lu&Za=WdYgAt6C1uDb|6to?@8R}J+!c9A*Zj?_eSp&M@pC=ZU-M{vu%52
zG|lK^wSpf|UxoC>zsMgN-HhDO{Ttb;I@YptOmcalg)c){ZLzFbHH3Ah6NdTMIr#~n
zZu}Uy7B~bX>VZQHF&pr%o>$NaZ&Lw2Fcj<gK~mgWSaq+>@qrtc=V2YhG5vhu%gNf_
zsxZTu2;;bvQfq8rqK_7zfa3V-o*N2)S8TBrbFxonTV6j}vrhHNIK<nT^iQm8=%=Yh
z7j=8f@V+8^JpR~-pa|9SdE^52f9F&q=c5z4G32WGGQAOXA_iSahPUFiMUCYy{C-&k
z3$&Ih51V!g<okYC`X6f#0{|Bg2LKzHo1E?+cV$JKdnpf?XoFKGQw9Yc9T1A-s5J+Z
z5Dmh#!c7Bily*_DG9w){R=}pmu4WGL&oW=)GEd2`dUSR+vxSJyh0)l4=S7ts49Uv%
zOX@>GRYocd|3aoc5tG=^n;816cnRpvK`^;4u++!vm4WFd?@pqkeh*Ijp(~LJ{JO~k
z{ACuDQP;_AV@#y*IIA}hj66RchYAo4zJqX`2^;<kqKyuu33c7^<qjH_NI@C)A}O5?
zvM?dMrDnyy)%^?8*TfFm7`js0Ddv5iIY^9_W$OIW_oOO1B)cCVT7PPs80X=6%a4-~
zADy{=6mvVJVB@)c;j#OqKgZ!lMv-RSr@61o6#af{_N$+}#`<J-=_3^LE{2d9iYjqB
zCz5B8b!veCTt0bMweHt_MQgp<0#>?n!AqB(ePxW#`4M{bZ3Rk<^p7vz=(X^_-FTYZ
zLI!^M-$SqPa&7%?UEIH=$z9uv=@Kg|)vGcum}$)!E{1@|#}S~S$gDkrG?wtVfrAmn
z0*4-1a##eNdf+9$Ecr+X5<KWFc&<D3WICeH01Eed#Omm)5`N5IN)afWV55cLuA!Ug
zj`)&*2}$aEVM2tVK3=xI9(4R|-8vym69YSSdp$njn;KT-Xz?;a*#))SEhmFt)i^s@
z?gDAmT4`Nz&)F7yOrGROZ<Kd<J>q(j0>22(G7~0lsG-#*pcatlH5fJFo>RQeT_yMg
zTG*nFAu64);c5gHSR3Qqm4yz6(@Xfe3g9Pi-0518*_v`8?(to#J-7?RMEhx{no1ou
z)!s-POL^m*UoGSV(wN$CKb&7&FYOq+s)8_l-5<KpP{Pzsn1rlqV8f2CYu-XY;iF^w
zO?kfGNz5pr9u<Re0uuPu0$7kJRZ%^po(cy1TaAuY|LHa`0K}w#gIHmpY)sO)+NJ?D
znI$#jx9}g`uw!b2c5_H*^E<$E!E2519<Yqe*(N*P0Voyjp!3@~m`gu-a899}FvbW~
zeLFIvk>Dx_;}hQXhu|-0f#$r6FHFX~&E@RGM1bNW)pX)nnnSr4uiv+<Y#y5aMLg)^
zOH=$LK@^-{6Tf@m|4l1Cnlu?cW4EOiKK~WxVQk9s49x&z5)FHnfq-JDBvsR>p32g~
z9^q*f#BmaMq<~Wz6NNhk@AUDrB$oW*C>rqn=X1vX3MH=mf~2Z|v&N3gMe8igJDCrG
z&fRPW-#818E>!2$&xjG+<bk&!OK8(|fir*nh|c$&Cf?R#TCl%fI#$y{&TjTldpFm4
z{mJylyNzJ(h!P+Lekz4UAbO*#?wdriQ_HIab&?N6PVJ)DlcLMy+s09L^X$;U+z}Q2
zM=yxNGc5>LF*lQ%-UXQiv`@B{)fu=zeF`xVu`yxR_V7&gdUCay$3ps3#E}ltrJz;n
zdMa3eoUXWo4&u{*fQ9&rY#E1lQD+MS@zWx;d_;5!zQ)=O)+uV_SN?_(FmjWO;NQF4
zjl>vFvA03cj(XU7GS@M*E|pMqVfL|5;gT2@JlAs-CB6KB2Z-E;4GiX28Bo8!*$8+Y
zk4zH{*X7{)*wBN!PprsMtq%ere3LN3Az>|VX&J3<EJWlR&5E!SZ)v}$kpb@>(c``h
z`VY^h)~e$8N2_3r-x<7eUPjpO4>p4-XtUx3dHxW20^@%l<RmEi@Z(<ZqGwM?#lgDc
zuidLwv^8XZl?(Qsb_(dhO%?7MY}P8X`;W=}BTz|032;6L5;VLb-pn*YGC!da7cVIJ
z#PP{MmEDvXpK|Dtk@b?a7m%^Oy_BTIO=r%gdJ9f3$%G3~<OZ-JL$#D25|5NzA>1Zq
zB>1d&-oV;dm34eZKn_T#D9zz&fs^yGu!80=JC@Xr4kKC@#_R_NHC|;jpPc>AD9MKg
zh6eGOf;kemO;U;1i40dd9Zx7TX6(sQ>>90NXbV3kYYl>-r!DzlA)E1yqQ7kQ^U3T0
zR{OE{h#}ZSPve6&<(n<^^}Mp{PlJyNFq+)yS0(vvE;02cMqqI(t7Bc^WUy{g=l0<I
z|M3ipXn;szpuGLCR_hyx@q7C$^MMyu(%GzEL+9k)Be&`EEJg(pEyQPSM%HrM69m^c
zHN8((aUZ~h%ck1`h@YMHQPpz)cbta?cz%*Ir*%%Q{I7HWRG)u^f*62VQ6VJaY<-zz
zng3em|0RQe1dIQ0SaqRYHRy|4bRwn||8ipg(82#r<nxZMZ!k1aps&Gm&j06?e+|q6
zhrkH(MG{gM{|~DFgMz<^!Pn0bf|w%5&HtVDzbJf$u3-OQ5Hv|S+5ecxKi`@e6Toj~
zTSoEkX<z`%@xh4d-2Ox<J^j~u`fqdj$MZhvjP(7#m=6M4aem)V@GLdd|C8_k;t)3j
z;6Ddpk=`l&J1HG}0W;nCzvu1<^c#VHMdUvv|F1Xrufu;xnfSI8MP(B6Kkd-};F|*y
ziW1fN!50&?;D0B?A`zkVCdH6=ud08eO##mS{;ZDwe`1!XTSh2Q)@o6NO#1hf>LUDW
z+uI32Adoh$Lh+4FO5wHoe`@vrnA@lRp+O*&mX_ja*fHZ!N$1=Bd+l)egUTEl7G~_=
zaA}iTaINugNp7*j;9Rsn=+l4iAShP&8TeRGF8l992p8ZS=g&ey^2OKJ7Y7H&pVb-o
zK$t7@$?|_${r}Ew5(BIO-Sj3um!NI~X4c4eix0ozUa-MIW`mr$c>{-@ZKZAqU<>Na
z70oik*oy-|7O$R%#1q8*1agS}1-hQ>ZZ4^+i}M#&KzqWu`&hQW`lzNH(oF#wHGveU
zAL`<Ev%6*Ro)qtO8VV2}QlE?dDJ^0FfEhA!TwIPNAD4XMFhbq%FWXy@dcH$yrodq2
zD91Mk+~|kRgdlLv+hu4869fNcBg0joujD2V0b02bPAclW+V{z?fE8}cfxU_pr_glg
zFF0!=h26BrRF>&)N4WOX2Y;A*%*|~g42Js}ASQ$nEcT-9suL#VY~ucOG~vLKt}NF(
z_S8+=`?7ZmGFmS>oS3$|RnPa?0;0oa6<*~R9_r>l&{H3~&wXE?w}KkXdF}B{6*)86
zi`0W@$=ZJKuyLz|_wy`lE>3z+<bgysZfblG)(@7Tw2%kjhKhsw#Vo~!Ow4rTtJ-z$
zWo_jRaf!?Cyz@s|&yRMsAqtD7e|_5E|FAz@T9RG5pIAOTR^y|iCk2aGu)_2Qzk37j
zpA$>$iECv&AV@h#38++Fh$ETxal+!#u275mduQ>xNd<XX$fF@R=H3L>504v9f~T~I
z0kd>M;fSTx-9q_eqyUKiLPg|e<R3CP>Q){UQE{;SeWY;S_Q)A!_}k3TWMrf`L_IID
zX=UU(^aWz%&uIofC<qi?s<%~RBDg%_wTRMtp<ftHS1Scds;a;MWT@!-)vyR_&DSg~
z87E4MGhro5IFB!07Ge#|hpY?GP}~dS#C5grESjxrkFct?!Gz;692S-*qMF|j0s%hF
zQi_|`TQ>I}4g$cx_Qd6akMXOVRD7ro!vu$x_Clt5t0l!O5yO{v%qPwo**Nkru!wUw
zp6M4<td&SQ#4D{{rldG`=aNsN>g0ymF)IYc{;|;wnqlpoQGl;A3<^5PaFJgnO;NED
z{ZW8Swt{V&Ym%lO>VAhWG4ykZ)eGhgfUo~@D-NV2d_`<jR8Soq9le>Sl3(Ib4!#}q
zZm7lmKIW`g@qkwU$%PK4FQMtptK@V_lI?pB6cS@4Ya1sPN}QnLvn^pP7wQZHq8S%#
z8S*c#qH?*glB(?$KHdiT^*}DOF!T1$`{SgYad-IPcieA@4rTFx2dTeEPT`YBva66!
zX$1C^j9x{pGKyy9AZGgBpxcx-lJyK)*Wa7;Ss$N3iwc?3w>p;nMiSg+2}in}Pg_Dx
zIf3?Gl@n>(?~|tQd}3?z>HV6<J9^b88n@#SgemN6Vl&8W%8nxGY)iQ!l{rX7*%K!A
zE=8usZoRNT_Q?27(BX3SgkvKZ(&=|aM<YERoYOne2wbAXS$Q5}r!q5?kU&FNzgu$O
zf~YW|6*p!qYIIYmu6TtEhrbsBiNh)otOs3Ray*`&Es{|$fLFht#d(<7ljf84um`>e
z%^wDJFj%J&HtZEoSzn8a_+aoi$42+v2BkQdU7U=0wz8nf-pru>wHLMqQO2I}AvqnX
zvK~R*e+cQIj=`S~XjJQfiF#wcU(GWWkWdl6>mma&giq5sEbvN!NH2rzWlwn&c|r>o
z0n^h_JwIf|v^;qjwd-J%!8S0w?(#~D2>3QVFfSGmNhrxs&IaR1Z|<44XHQbe$9j?Y
z9@wsQ4{aE_b2>lhvYejd)2VykmEGt=5V6dGa4oM6FO}|F`H-;Om&a86&j-|79OU>C
zt!D@aiJEP=(=NYJ7#wdL4%G1De-eQ0<`UY*%zM}mW_oy<qpHm880<vePmj;fBEx9W
zL7#zW_|y<(mAH34VdWBf2mf>w+sAm7{fE61e1b_^tkek`8If7C;nbVGQ?4KG&w9xE
z8o=g=O7yyJtI0}7=&Qdr!;Y>^okFh@A<Ni`Gx>4W=<)1;zXKR1h-E2El9ynVN9Pje
z>w~bJS^@K(@_F4-+zjk&fYo__2eoYwgmEicjSA98G7G2YbHr=Qf1@rK7J<qmz6YZt
zc;kEw?k8s%0cXJM)>g>47vA_u^S$|CL`Oxgs5|R)-g4c%A8{@mr~k9FaPY6c>=e-V
zWJ8Mqe|(|yeVR)Z=?aF?<>$>4J9*xyy>QL_r!AjtbW4eAkn3AwW*8H<3OFoW#i3L5
zTTnM2r;X|fS3`=kJ#b<+jxJezT%x1W0^L#lyN1NZvVJ00W5IKJeyIcn!N6{5I!qEz
zH{~HyukKv^gzgOQ69RV=c;VxBn+tXEV>M+2=&?J4>nV6VOOi1#5W4yC`D8(9dIJ5M
zMw)UgHlk00r0q2M8E6<82d+;p`eIV6Lkg~F%Hjxmu8818N@(4Gz|xnp?sgFC#e~ob
zFE!etzrPGH%Af;Ka^eG{G2ri^J@X1G(OHyHP3Zd~O@8zU2Co+57Y8QyZbaB^@;shj
zbOz*htGC_v#a%jqDA3w0%3e^J1tg-NpW$WVy-s49e0QzHREMUu-7r3EmV?Z#g5vki
zf<ZsLYQ2Zs(wUcbgB!m?s4Rbks#E*Wj2#t!p^W!v37dThQm+s2ah_Yt;4PlL?}lCj
z-nJD3izwgHWBfESffa9ELW%d?5*Y0L+c%rd#P}%(<ViOY73B4v2AZ)A){8si*dKux
zK=okub7;5LWOeGd8YCyhf_WVgU4+2))}hCl({(Y~TH2N^IhG8vR>wF~q6D}vywTy|
zGGb<CUyqR?RV|=B>iil=+0HA6?ZWrh!gm5ibRBL$P4B;5rJ`W-)Hg7TL#+EVT=5;A
zvKOzWCVeCJYY;q?yEuoF(kkK|A0{!#1>XL?<fQ#=<Y*vq?H2dgYPB|~<_$d{dclPI
zSqj6d?C5~C*yb3c%Jr`xTeQn(l%dh!W`Eq{{plbfEv-b{8MwHFGe+d#k{!t6OnLsX
z>0QgshXqbJztsAMGGfh#ib51JcPmFRXlC?Yfx&-$OvemGjmUD?;<*CT6BZm3{}2RD
zQ4)q{>>mO_Y4e728QT+mN&xXNzvB=O-GbjQ8B5!Y9>MfCZ2N$5Mc8qckg7(Lx>=a)
z`|Cz<dPHQ@;6j*Rbmg2%PFcyodZ!0kW5{p4IilmmJ~SJ{3|GXh@C6q8%JTv@zjdx}
zk;w-By`_{7+vY(*1?e3LPf<#}e`)WC{Ku_Ak{@BG8GOCb7bn#2W<fQ66dnG$qK{HJ
zS2_5^4<{l@>4y)81Scm$5Z7`(WDSoHc4w7q<lT9Y?}onGC?wb#luy>a0Tn>m876lx
z(!a3r3stB`KTnyiiV3`9`i7EGUo^@luh@jWwz2wv%CEWoDk8c(><|sVe`TMN-+#8L
zP2}8Ne*=e!lM}I<pl5%W@5<56;7x=cA4bGu<K(Ua4lX#n+E+3~v?lJ!%@rls4?SWs
zLE++O0BHxa4rL3{O4{>?LIxGP>gpf}--2N74T+;I>LLQEkyVkW&;xJBY)nG%l<wTV
zK-n@}Ue;LL$Q_fE;62Xr7=+D_cv_M>PG)ZO4Ny!z>EWZ#xhVeS6a3wQ?wfi=k-7}O
zA?TY+jC|u_S^GD=TxUGHBLgx4n*pLe3S?)lSE26fr0e+%Iqj4h+#Y0{auH!Y_T|O%
zZ{bUA;;ngSv>q_jXRX2fS%=lPPhM<+h6Nt0V*SE$*JX~kueP^gRJdsH95sTzN^cuH
zh8xR9*#3;dI@Q|FVppTQ93CDE$X;w>gDkBk@yE<-lfjHS5@v^EIU+n3E-<8)-ADy3
zp5L1%>*6fb(hTF2V352z;Upgd{pON9`_vErCC87X)YKHkhu-Hr&8EWYCWW@4Ht!+C
z+h?>3S6idL&BSN~2XEcnd<ELP!!Nu>p6h4I^N>f1EdK0kQuAI%vB1<j%q+F@c(VQg
ze&^^lm9ayQtI~qS4y9&O<kU!-%^4o!iOc2^7ZtujQopK{2NR<f2R@WH7u@|tU&IXW
zy$%f8%<p*Alei}CD!8}Rt)Gb@EsZ&Y$Zj3M`@fw`)?U^4JzkP83pkg*T4%eQieayo
zTtFK$XBcdpfCfAzP9>^%epJ3JH?izae1G=e9T4Nfli_V98YaL7Yt|c#*=oMZEXgpw
zh|u!B83yQjJv2Iwa$u8^c12`d15Q`J`x6yY=kKD$bFks#E_)7*Uk>bI3c2NmL%Rrf
zJ#y#{MESF)xJ~07^bAoFB<TF!mP6Ap_F#;btG=i$xHKd}3oGd?fDa(O8X6uB881h+
zf6AC%w-yeD4q-fP9Udst|4xWI?B2(jyZO!UpS!><5UD{5`9fe|;FP4Q64&-+hm+SP
zQvxZ={jR1S1yR!l6n!=>wDlY8>+baC_T*#-cmZ{UAKD0W(e^pCoxi;?S6dLE*%0Q&
zmc-ar%w3kD43AH8zKfg&O*)~QIlk6KiB1xe0}g(a1AzB)ea&TvBGKU7^3U|bD+Jur
zAF1iyU3ApLencPsJ@&63!@h^Eta+VvxO*JqyFWrCW+Z3*mUNd*w%xZUJhUtT^dM#}
zAWVWeS>AcQTV7L}SM>b~I&7&lJ9*JUq*$lT29(T6rnSuU%$b;v3|t#~k#;rOGXI=f
za(>;S+|4e;W4tn2;9%bu*I3_uzcMqIcT(P)uL#w1>Wg+qcMHy06W@OTp{)Ksxyjn{
zWbi^>O>4k_G=tt;d?3OCIyf;jxJa4F;0y!6Y!Wvv0O^&T;QeZdmr>!O6yuD08@UbW
z)O07Gg>O+`ZoYgi!w-dL_8@1W%tj!2E3srG6+<``p!r%A;BG-d_BuvWU4pBis7}gA
zi~C+r{zuwt|Kc26(vKQ0V%j1w_NkbP*)KyOaW?7JNtzGptetz<NlsW<Di%RVN1ha~
zRpJmv9!$z48WN#?_mOrX9oOA8(U8=Xam+{+@?}&jn%AmmFH%uHD0NQqh2xGG*BEp*
zUSekxLd>x>4VEvQeQFEdaxAN~$RgEwDh4s-1In$&FYFhevjcp*z1TPSsaW~Q;05}8
zwsQ2D8DM-%ptGQ|JTsS(W#zCk0YD>UA@2#vvPEi(iORTNmRN{Eh8=@PJX-7~zjYG{
zyKYN=n=R`Dh14PCy=QCdCypb!uPEnK?eo+(f0mgW4+@XnqVlh~F-&1DCJM7ctWa8N
zQX7&Cqjj&W2k#DMg#J#s!ouE>%}Ztcl61A!&rwp<j6EG8eOZ9g^I0=KyOLQpy|fY(
z{%BIgK=#Wqo}@}tO_bUwQFY7ER$Q4O!0|hjA4Rc5f-q(P+KF`H+D^VIeOw@44r8UR
zR;!mzgbBxDl@;g=j$9Pw@7XX<kKx>=!im4cW0*Jz8F%=DE?bIi-RQHjF7yR+ivq-x
zm8}c5z?mW(CL$<VXpEOaa<Iq7eqg#6aY!#dV6F*Q%BdreNRohF*Zh|Wt`*wr%*t}G
zYD2+dzsrJ9W)v$@nmY4^C4;DGBB3&DUt$W1Hk4?xaZ9)Up8)GzuJt6N{N^z3!-=Vw
zj12ADmKH}Ep3Yz~N)oau9rIJo$fW5-{n4kXkfo$=nk_mGWmT?^n5rZy7G-jfv@iY*
zc&&al7sYG9`nyKBaXr=@$?s;HC<}a9PpSr^&oZq%hxK2Ke<JRcSmP6YTpnZylWIJb
z@<KR%z`*C$M7)(&ikrdL9*T-PLOzFIU7G%F$<A*MU*?9MwAf%quPcTwi^JX?dGQNu
z7#+@f;M#5Q69~3{ORzJmkK2ZsjcoZFs)J%^<KNGOp%OcNDlM@Fe%lh7D!v~-X8Sbw
z)!IJ#kvu3cP>9Vvqv+V^T(tm_CU06A23wXV*1L*v<=O7cN{r=gtJi49VtB7|cgt&G
zvHz~J3bK0|7Uu3IDHgpn6l<}t^as^pJGqs?uYRb5#y{tmAi@t+*UX*rfZfgMQQpZ;
zR1Lk~co8NO@b`Az8W&GQUzb$ipKN*p9^vf<Y~k~#y4weP`|iRPAm@v}P8(l}s2K4d
zK!-qSYXRaB_rx)4v;pxb_`@vn-3Tmbc`4~Bsxis*_9pm``kHcNJ>YHi0^dgmLP`^{
z;CBWV1+)bRD4@isDQ4#VcSFTjZ1rfin$UO*t-W*pY$8mJbp|ac2&~!1Iya;oVI*b&
zzN03|zORVaq8^AzD<^aM?y1Y6?V>W{Y&qZw6h4>#^P7x$tiM(i>sS0`Ntg7dT=x_#
za_h60w8TuLQG?G!_2>M7tb_eMLw{|ZWs?{R5u)eiAedN15g+ab$BN&@!vW}!{Zg5&
zXgRGQOta5kp1<Rt%4vJgE19;JxkeK*@T7N=Abv=Gwd>qv_3(}-VHKavidpvZAa@=w
zc3f168mNbzc02A8B%rCJ=yo&<h=mZ0;o;&@R#So5OyYUGJ+SL<Tcd~Y%A8tV4$Lor
zt}%W<t-nKr9z#9L2*C+o(<MivI)h#=u|!kM8Fw<Ks%qc$rT>{KPZlzBac)Mi(hgqD
z{{lEBS4E{2K|})(rodD**>I5g{N!%2<|^~5z_5s4qC}scCH6<ew-*mzB$2uA4B4%u
zdQ5D(urCuT*Wed)-Q=nXw}aA$!dD>N2)#J}<A|O*u2o(5zSyp%sC_hzJd~B$RO(*y
zcICXVJ$t|;Vdg{;fx*gcJ5D3$iSY73C;|=+2fIyShfR7E<v3|tYf(u*`0kF}CB2QH
z6PBF~Mo~208<I*{P`<-`Ps}7SnTslTb#v8Br#dBSWcK2%_IS#5F7E5aFSp=Eu=;|P
z?fDML{`hN(e^V?(j|)n~*<r9>17PdwvD@6<bm!L@bMsquJ4PRaa3us=-vcUy&`}E6
z#7kvn2Kvu}NY?MMK16rDxPE7IlK7dJjw&jGwFW{a;0qaE{w(`=uRn^$ufV=*5*m8w
z2VE~rC`;VmZ;smK70np#mHfogbNt$T_>O6KgADcrVm|k1y1r_FXYUu3-dgLOwOcaP
z&i8Y^+8%l_qI3`|nPj7_C~gFk;yY1(x<rA%t!((;^A!Ct%@!yPs{Y(|Bj6vR694Mr
z18$?p=5Cq1g^D|_-B1ff3@qaQ*hOSgyK{hzt~bxC+$Y2nm-5L@eZzM9`b&v8weV*r
ziHgJXsJ{8dYDo{21J@j6TnMzAEbH1j`yN6Tw{d7S_JEiW>Y58F(yXKq>2xRDlIgR(
zhr65(X#8t)G!1Y(1wvEew&3<X^<R?ST>~s0&z`Jvr+G{w;Z{snsvWU}4J*b}Gc$<o
z{Vos-E2|-tscV6B3k<>#pbDKbxT}5Zk8WAMmH2d2vFx0_ieBit$xbx<0+4c>A5cMs
z#^frmD_l;_I}8&3s1+Z3J1WDt)v=^3Q5~=$#do2i`x7hhdfk3^Z-Qpcd$m6;nV`3H
zS(x~|@v?RB&+H(7k~SN@e2}Wc=viuas`GrTlt<lUj7h+Z`dT#KK_<s`MulE-81jyg
zBM(bM7tj(+&{-PLa{RT{e$gA}7mE76{P*ggBEkF>ttRBpG!(oJe{ou3-d6|+qDEn8
z20)k$_M;JY^lhAoERG2^jh{^>$RWeWM}@5<_!|zhm*x#b%bEloHqz*`4{2!qDa~=u
zTW!G?k2PhiR+<@Yd&miIUoiDY=Iee^*=Kd0bLwJR^$df*I0i;Hg#;Yjc7ev?@U+s<
z9Zt-vgTpGpB_G-%XBhq9GRT>M?nF-J`=ku%0N#P28rO!r`YmR+3orPK89cg(jw`d8
z$qJ$&eIK4GX;=x^x8TP6!7X@q{qVJ47H~qVjwTnJpU|LsCNupeo`G+o&j!EW3Zh@a
z;vn9Cb=ggjRg7juq-!08!{qsYCYjG^U>I(;w$|=;k$ON-bZw3aM2MK6L+3uSQdhS^
z<!IF<0rtP#axNMiEMV8Rv*0}Fx<Iby$T)1Z_g1;auZ(X<Bx$^C<)2*i%s@Vc8+J{D
z!7(bHZ^op4ybu%EM~k@yQ&+g@b#YgbQ@04uy1_HB^9rOG_7hBqw)p*AYgIFzm~}Sq
z4f2k%2pC7G`Eye`I5ohMo=#K(8yk$<=<piB(yHkhlW8Nm=UAUG%%M!@_d6dqYT<hn
zA{`bP3vU`0M|%l9f8=7eACxzqcRa>`=T=SLs+o|{2A=M376J}kU@QScFiTg?t{2MB
zp9&@i41<OE1=A{R#NLYPs!bP?+aZXUg{GSISDY0<Kd_L%jl&YgzB;D1Nd4{Hy?n6~
zxq*&0BPwWpC3gQ+`|qKB;a}<(2nRcT@5#T3nH#v_5c=2z$r(#bcKB_1SM$VYN#`XO
z!vEZmZrUlj>nq0otoM^-tAu9g8|!8{V;A3E(OK;E&PAtgf=YULQr=Rqjud8{F4WWX
zn&!}Kwqa2TPk=G~3XimXr;$AvYs6<p@uW=Bb&sM-zPOhn>_^}7IIH=`gK>u^HVfnW
z59h)T&SH3PUu7mib8>)1Z^4b@m8qpm&XJ^$7+{R-@d|OkGpCg>eGO7TJX@+UB$3aO
zXL<vX>Aq4IoM4bhFrsO1B5HeFOkttZ=ED11J$vu0^=n6d`>*$Z{tD@;N6m#K!gi7Q
z0nCWX<d&Y0$wU_9aOOno9l~nJ>dM=dEH#q-1&cR6ITV3E{0ren@Yuxg^w^Bp%89T<
zTT>PMiG34s<C+7DN<N_E=r}lLB{sT3EA5lfX8VEx7lO6$S6Ehvb&P$K8oGH;*^$F3
z(1kBhvM?!nZiFcot#xV3Jhk36;P&Sp(5TC79Eqbxc56MHnvcH5I&{~JuLyRXS7=aC
z5&75U(CwTOCS1j2ephh1HOGXn?;jlz8fCo++e|8MJml&t?s(dvlN6e9-P*D5e-R(A
zitYQZn@cI~D@?xG-fmMU#{-dXZp^nMfbTei2)OJ44C2O9Xu2-_h(ob1JKvKfYn(OE
z&BA%eBC1zX_;tPJ?<jK5me!t#^!>(T2}A{+3l6r*b3TYG(te5<!loZ>fSH(?DzmAN
za4O|9xsnR!?UGfDyD)VR1_Yu(+dbxZJMNm&p$aqD*}EgN7&x@mBhYI%BUvNa^nZXZ
ztZA-?#XixXu=HLq*L>~(Nz-u`eftcDFj2o;$<hy2h;%*Ht=4?roaRp7k<*U|X-e?A
zRuCU{GOSbbW5h0hmje=#2^7F2klS9(9r(uv#>UuRH;F%l@>wzU&nPdoJfJ!C8;BI3
zN;2y9@&1m5hqbq6Lb!by)FE>E&3ZK$<{Lw<XBbXeXIZ>p!u+Q4db)p7OAib&{)H?6
za`TvlG`?T+Oyi<X1zoCUnY;%;P1E2itLR3Ih2-=!aOp>6@2+m$%Jdg<*@&@&!d|2)
zG6W6`u6B4R27E)bFP@r?kN2W|#?nycvXuG*x_+fM(KE^#<Mj7*OPoXp6zE*t(^9jR
zYGseS>ZBd5q!{UPyW?_J@gbj4zg|6Hcf{RB%f)J=^2WnZ7*eHB1<jSXxDV`J<J<MU
zqR?>yUEwlq*V7UOkTrDuAdT9Oj=Q?Qicngc<Ko2;_Nb#xU!g|F+S!Y(3kfdhxxjul
zBcV0euAmgAh8tS8t`=f!=<d1o^3ZbFi5cg99670xjJ%ggGuHNXAgFko7S;6iMN;c|
zt9g7T=i-81wxoT$Vd{)(<m*9|eZK6yZzKW^-Z?Vdu)3a^hp*B<a&s$eODE3j{@t4p
zb9(K4JlmJ~EACb>lo7snQq=?bgu28!6=3UqPfE*|TH0F56W4Vd#W`b3P0;$G7AMcA
zZ<0H@++_IF-{j^AaD$fI$&cn4Dk^mVRx-Z6zA@8L<CiLLKVI2qcL~){k2&Ht{k<MV
zrBa7jS)r`3VTOcWG)gGHMl*fUn}~AXScymxVl&fwhFo*$MVlksh{(s*m(bJ15U~I$
ze_M~dVbTl8wATvSf75X|-qI;C(^ocw+bUux-qV)u*853{bFxc&n$KKx?|{1@pdGj;
zzn+Gf8hhK+yyW)CjXO*;{&i4aavyZ(+H|v!$;`z8xbI-RT}ot5v>gX(^^>HVAT%YV
z(Iy=o;3YVnpy{^r;)k&4dt8QfN__7^nDB<PrX{id$~X}D@kX{7(tk*^9=4O3BWByq
zRrPd#)^wcTe@UXcpqQ7YiIz@pnbvB7t6r@YBCq(0gnVJw6-`2Y+#2M#8Y%0z_XFn?
zA-Zdi?SPDKBo*YJCnyzx&>#1H$v*Z@1<`LV@BOjV|5gxpQS|qFQ1$e&>`vkBtjPFC
zcyPUP&sdvJg=Q;~tiC6l-{XyOjYK@=&ZB+d-q6gX4<Xn0S|VCQ{loM81~DVN10hcJ
zq<b=R1w0wI&Pd}A5Mev&H1mUcevdV0$BQ4A;a<M3hw|g{`l@1Ftjk!205SajWOokH
z6Zr<cYt1+YhSRB>c|T{ulMU~5xM|KiMu-?j2eXuQceXpO4;dB_0)<&JVp?ieJBsuG
zTVx6?r~DUYe*3;ZV(0`4>LeizpUDhfUFeO1m*BdshNzV$7V7KM<HB1ZSz6A__V4^{
zk2J;;1U6!F{?t}q37cN4q8vw`ZB9Qf*tj2`R~<q_6PM4#lnY=Rv;0V_bW_6;b(-Sr
z{jjrPwGPX^4!VASr>$Cc5G12@zBr`l&B=;gTXt635OQ{|RZ6&%w4P@FQ1Umdp9k--
zrhSbmFk23lfkXRF-$+WGYI|5`wc?37<|S@Ms`E!zWdF2O+;#x7>AJ-lHPY<1?S>%s
zzMm`^OG-eXux7AgV4iC&IQLzfU+WFdjR0Om-+~KiF>J?q)8k8Og-*1RB6Ul14y**5
z`FQ^;r1MoZ^+#Z*eW|2dKC&h3!26+8*J9^8u5L~630A|sz<T@d5%Ql!_jC*T!9khC
z1C72{8gmbj1`}n?O-8%gaIhxtEdE3xbtxqjHch<xCqVEAs84WkrbU9m^6GDnz``$4
z@prD@$Vl#3bsg`LGa7nG(jOOFPcE;$q2q@Qyo1&n=L-M15xgJ<bJo$pFP!|DK|I65
zB~Ml$InD4eXz<AFRFY<}xHv_Sw4&IlB;zV=!P6WNNu|O#-j|r6rYK`$t$<;?t@gd^
z;Jgj)>K)07f1~JP(UAQvJzmfpYyxP{$k=j?<-)PBo2s~AC}9Ju?`KT+YT?Tt&Rmf%
zF}Ao7+0OST@6Tpb!Wym5zL~m;%EWA16Kn*}5w0#48V5r$Ttdw&lF!xoNj%nB(Dg2K
z&1XI?O~DiB3+`xr)>9hcnWS^=H-vLr|AhU*Z_x5eksBM^(6{L1s19uuu*kkos+z6L
z=<D)@9?qjD-1^h8X}8M=3w0Qtbc|$X?T=&NKuHfM`B$lU>0BN*4t52*E{7%vmcZ7M
zijrZB?-txolWP&in7$rc#f`?}ZE{O^QJ2^F_S;I*@MiS<#G}~z<XPGWjA-1?=`+SB
z+`6j~9k`Z}mgRgAIA}*F3zZTToiTV<>mKeG!u2}i{Mob_9bU~ahi7Ir{8r_b`V8u5
zMke%vdlGO!DZZ27BnkP5BYIdmE1YNobcH`^jBJDPK5^2S0b2`Rr_0v}ZiJ;`M)#hY
z&1O9N^qm;)fBtM)1K;t<3V2c=;IJ_8B+&&wIIW7fwkts)F!EU;^Ct^r*4vOx4Xxnc
zUq+Y=#xjM?_~3!B12LMPID5P&QC9;gl{6*iLb?(i3(!yHjCpq=$mg%e)hJ^7qIjeW
zs45!N5f08M={Ix-J!|Tri^xQxbPQl^0CJW@gsd<>6u5`80lMnD1X%Oit4CaJKP@6&
zStO$eVLW^la&uHfUT{Jz*XiKo<eZUIUFJM%k{@Yq2N7Pn4QsF;L+tdVX2nDCFOGJu
z4RhUANJ2F7=7bdd(1}c9J~d30f*H!S?qL#X?n#8b*^RGh9ms+#w3<x@MDjH})TVY;
zyAj$#5Trfjc=q;w=pg~oXH&Z&rq+{^(Vggn?(Lyu+9*+w{^>onD~XcKLR=8CeuytA
zRS2AX0NP;9)Ahi>pG><ycjsYbhx)G7Mwi{49k<mMjc&Mz0~5wR3;L@9NGda*dFv8J
zUb#alQv9dMQJfDBa%=zU@joosx$uhA{0+R*6e7MHt?od?9K09^P|sL)ieW`nOqWTb
zvfBMto@|{Rv00Ag+;x=U_d|bxaqxbOQg(V%Gcq-2Gx~Z#cucDv<6!T==)G)Sv(+6)
z<X0Ii_)59-ysr+ytPmRGs@dR63xHa!^*hKcIXBe>m|5?%sB7y2E}A^JkIQHrPy-5*
zWIvRDY^)?DJS&Zn@J9%vk3|+D<_v!k2$Gcxg7#kFE<uY=N=LFcXHwiKG45;+3(XvA
z>Z2sfsTHa5#2y3@;F`5?YGQ47;M;cvMoY0L6ZX(mp9`slq2H)i_LJS00u@gzEVk)k
z&5X;Cw#Bv`>*+_u(LzW|OJSGv=fO9bGZgVFEx3w|p0Y5i_S`U%E_zQI@|BVE`$M4h
zBqNnv+E6%L5iwHSdQt&_JW*KWabv^DTpI(kLLEW*f+@l@kEzi~u+6fmdDXQLnK`Rr
zBW~1?rK2_e8M~0>i$;hc42m6UA-RV;mKVgnS63w^rM(0#Sh<a`&~PKT#=oFk5#?*}
z+Y`ivx7%*844(QImCCikhy_}RaU=AYnK}`<^c_i6EGHNNgNuLK!}KSW=)bpudDhL~
zWCn8Vf3WV+_vO`)fdL=i^qL$Tp;oMAYF%FxFut*Ohh<EK$TlzRRri?F^w~BYtRBLR
z6rptSR{DP@t2Spc@~i>A9&H#Ey3S4qQpEzRxC#bf7fpDe0#Z5aB?S1^kz0yt8K`45
zer&&IQ&k}Dzt`EnsN47*z%1g!wQ*nEIA7OZ-A@)g>ue7AvOE=gr*cb4pF)9;cTz?|
zy7Opk?{xIwig%N?k39_=epB|psd{3qYPP`4FKNeuFJ5#f2vfAdQ?Jw$HLL(O3WGGJ
z_Xm&4_^)xnobKzvc~~xQidw<eJ&&W1>g+vcz;~I4C{MzsRE_dzg2tub(HQA;qfO&}
zq=B7EzPqK;JtD5q%#n!F161?85d@J47!SJzHlN|_+FV!=1Q3_rRz6Shi!a71#h%=J
zRwG7d3c6xvzyZU+W9dCGFpcKTFS;Vd5q2!g_q5xhK$82Lr*Swf7p5vg>x$h4am2e3
z5YPox*UW#~COjn#0~H-u)hn}+y+J%1p6*NxK_;CEybs$RAU-4V!1{X&fSx7!A`%+7
z?gPx9SvcxN!8hJr%*DIx=YM4qasl?Kvpqzjc8C=7MF0^$Ph?+3QH_KhO1QY_(Y^#x
z#2(2}TX*P!^Y<<(9JK`%={QL=^#|dJ%G~m)V(;$AE|4(}1wZI}Lb-$97N6m-A9CV$
z2JGKbsIp~it$IT|?uQBMi|7aJ{r;#a?s{`zRcZl<t(lMn@2L5eOJ43qL~}|!`k+Pz
zD(x$t;Z<KL!4#5FGH^^^vPT)G>cdR=zn*bD_`%vYB3lW*J1w-qkTJH{@Zj>%8I{0c
zRQ6tC2}t9v{f(IuaZAM@zc~IVq+-KGdhY9ZUd-7UoIKqt!jD`z$R3D@CEmg3`tBdS
z7SiPEN<g%s1OC-fn&J@8%XZX76<k{x%|q(F7D>hLd3d6I2Wf0{G~d%0oKxTh$M<$<
zG(6(ZRNtee%iCdGlifO*Rg07uf|~2&>Qtq4M_Q=>)3L);A1|z9!klM3%*XhL52j>E
zyR2f7rGr-Ty%}Qz6_gIY2+cQ(eZtCjZF~s}KiwEqfD}x6o`JAU4i-*CbbOOc+ugqE
z<F(>PM0grA|3}Wj^wJCb+FR-TP+{}^N(hs|Y&O=IsWnu3b}WFM5QLiZwsNtJO&D~3
z@E-KrjJo4?c{DyfGcIbOcqf4YQqyI2k(rMzK^Ou5@>+CraPm8M<kF+T-0O=a>o~Aq
z!%jD*qD{Q%?6q?{!6+kgR!)PFbRrlQkqUg?!wB2=W3+mwQ__<6q$S^cO#EwlVv+I7
zWx<2~%(=eL%|ngN6TvNq#`aatyY`K(OL3gEH25AR9_nES*K#pR)COz-&5{d(t&Lxx
zA~$Zqms4IlQL2WV1ZkejUs|So<2l~=1Vha<$!3l9&-T@W=MP@-KV-QAcR-K*e^MLB
z7B-$t$IO79iElrqyDW!gJG4+t?0R}T-wO3HZvd>oD3nnlgS}Pa0jBSE(-))PI~`1@
zrrv^mUyb6n9cKD_Z2AM*CwDiXi3_?W;M>pMELebYi~gSBkrQ*laFlN3f!w-!*J<0Y
z3OBZMHRdg~ozz}x878Ed>$-k8z>YKDwgZIV;yOe{0o4e}oEBow+R~=2A9S@xFCP#;
zjzT7CX?Ej@cdy->_H(fy6zH<72^O)tM`yJ|I|XmWUr#ELtN_g}?V7Q`w<lT6cmhdV
zHMH7wWNMDW&X*_7_6)&PW70)NzYJVr&-2gjWH9Tdx~b-3{2bNnqooUjA^KGdJaVKt
z*NR_rC%qrq(vgUKEDzCABPKo(ij%YhPUjq!cofQ{=6aX?f4A^HXBU$NbR!n*AsQ_y
zDaFA9KeV((`aWyfFce>}S04xYIKuP)yf&M^N96EdGgV9!D>6Ic{Q~zCvHHS!VQ^X5
zAfGns*g?aESWU1BE@}dI8q*Kcyj3R^n=c?FiGHt~XcaD7^i+en+SuXGmBSND`0_RE
zxD4d!!SsIrw~?7n5MLH+Z|kmj1A?0DMQ*_ZK18hlarff9*SWQoBlLn;r~EQ?b1km<
z$}uhB2ZRk@ps;q->%F=C;gdqGUmpvRgsu<$G40aRD6!jCr;gk4Ma-l3V&i*YYgL9~
z%nAS4y%%Pf0?%d0hgQTd82dSl_MnT+r!TRNw!w9nA6~@-ac{R^V><*X&Gciss#nq#
zFJs6x4sLksIFVP4_Uoa3=f!68u5YF{M6Y*?o5ib3KDth;ct&kGB3&?W4@E1x!*bUy
z;LWe?uQXjrZ$ZKv7PVB`nji$rxXlM*)JO!C)yU|ZT9;w|U}XhRc))kyM{-Ut(U|h=
zJ^y=iF;txI2jf`LUJoRGit)`P&m>@`;Z9+HT5JTOnffK&EK1mS!%CqyBUu&&Q|jaL
z^9%JolP@w9RF*5JKoq!|?c)T9i=`&>T;nX8a@H~_`s1A{qjK^lfI37Zh`lqF8$apT
z*~^iGeAfQtB=7QvXI<rvu|4S<*&Eg4k#&e23oHM_-^>k0Tm49s+%}Zu)Qupu(QCu~
z2)4<OJ?O__pgW@4)o16YFk-=Xw1x4sfT-#>4E?8x0(#&VE~jir6+5N&8$_R@)y^Uv
zk`S$Xw+Uq#Da7fymf2@A5>(%$F%GPYzDe4o6zhd!rma_w14IA7WevYq{L?KTm+#vV
zv@k|CL|o_BQbuQ%!kP`P;^<LLKad+o-a4NDnI#>-Cfa3F46(MlnmH|3*<yA(w(%iU
z+W&1uc$JA9l=Jr1&M~8mgBx*5FPQw3eX(^*Lc&!sfh`=mgfjn0Qx!A&c^|pf0DL@Q
zR*K+{T3$&70v(rb)l+yRQ_l2C=t`t^z3VNh7|aU+Q`^*@G=mrsNXB|v5ooqdAEI;_
zXD-C5^YywZPrA=+8s_kTN6{}46+2*K0*a#FR!kqN;X)5_&d#<-x(axGK#`w%79P9D
zQ^GF1)?&%l&;WZ`pp}V5=uN-_#8R>9DS@QRcL9Oi_~Wqml_kWfFMHTt;zz>E_19=b
z%{;bZBsT`+*)OsPaRbM0)VrSsHpRb9j{V9*Zxla|B$^~yan)zT;oxVSgSXv#xjGNv
zLj4gR<M4iOD-f=+ArPCqvpdAkkr$bA(D!7O9`m$oR?Pv3>`!kKb$Bmc*cw8wPUNB9
zHh_~b)5dtt)L|Av7qPc{f2QQfDuEqSw(lz0hXo_G!~Yq>!QJ*VSkeb+)g+J@-0=)p
z51o91O-@IJC0%+We8(F>i8X%PZhOc5CrHfT5y3=Eb4t|Up|cUOP@}3Ra0!OOjO8sw
zWo*=pr0UBvSq^uoU#A1YQFGl^w_G6WxX0e`S<`k;$Pgl;=s*vAz}ZJ=0#&Svd}(Sl
z7kH3md;cTMhk%vj`x*~BsG-&e+-vwU)fI1BvTkfypNfEYtjmMlGG)}{O2i3IYu@kp
zUswP^Xww%0Y1*PJme^+BFE|*6k3L|?dpsZxSH&v#2&G`JBzsOxyYQJvt+p$hPO36d
zXB-hapL&TMexo$;vSno0O>~B;U^o#?{PN42=Yy%cHdglDB3T#nVOP<^*C~*}vaJlE
zrkHBW#H$tUzs*a9m4|2(o$<60hB1?5&P6?`hV(_-CI~AT13wC?O8I)fd^6XzPsW#(
zuz9qj<kW=GesVzKAAm{TU)>xb*)n|PYu=L=SGLRUYXR!V47XO8-xlzg6i&R-a-3tQ
z-nr~fb-cw`mq0To;dMn?7s>8pneej}nAYYM^wT<^2)Hj5*w5W%AiXVzM>gI(SNJtC
zCRrni{dRq#q)6;WJsF4`hwy7F{4w5kY@6Fz6MyP>IQ&}Mjew5jR{Ts#m}rs$eb!y8
zwNFupk<gV^^@Zm=ZqD?#`7T;qGpW`emsRG>z;c|}>695N?a#=O#LCw_yJVnnlqNh<
zs#pBx?UIbaE(5`FD9ZR!lJ##g|Ngla>jB&0R}$e}gfOJ7@gUQB8L4T&FP=l$u|u=2
z1Z>OCe|=7~*YWMa&KDOt9HUVHRta}TIY}Duj)tH70mU0fA~IeGxVc~$#J>3Rh!~nv
zXvU&~4cEG2WA%~w!eDQ|#pAbQgjgqCgt7I*KM^EiJFT9t&sS%IT&!%s8Rq^HRSJvB
zlgGW9@W;^vj*d!?H{Ny&5m(9=EdeRC+d}Lp1zhN!n05_@8a(cx`huY>u$cAE1TORF
zU<HJ>dJSyer<3aEE5z(Xa3G@uXaue~({F_SPR15E@Zw3vwFiPiazqsPU$TEr&RH~?
z8dJ(|1T`rne*){N3Lb1{SoiW&H!1#>uhdgdN?9PuNfCzC9VE}HR)xUrb-&96X%|wx
z_BwWNxg~jBrm231`nqd~KtQ8HRooxu6wxoo7nhLbCk#-Gg(Vl&i~@PP_u7T$qfinQ
z{WT!qzeIo=;WRLFB2qEmBMShjS>h9|7MH{5bP0u5*c=jQPkiP@%tj`L{i&6|N<+s6
z=+s~%ovlO|r|Je%v=oN>U&=A2^SzA$X~S8B>%WAmTeZ_p|4Lb#)<TT48PHg+!D5Mi
zBfGK{cs%@DW&8NBEB?86mV#Fq9A->UmC%Nn3MDExGry8^dLG9<7Z;AAW?c0^<Ui!?
z2TV&!w1f+<s85Fqvief(-?X<Quv3dzQy-CB#Ea?HFvC`#(H6*J?<NORPx1Cbk0aXF
zSD-dhohNS?rX@=ns#rqi_lk+pXnB*+Pok5y<Ay&FOzB3Rx-`!~MEfvWM~B&SL$z2t
z1mEx|;SF&EljRXMun8s(Dsui8QSZQ>NwlmDXJXs7ZQI67Y}>YN+nksa+j(N!ww=5=
z-`?kZ|Dac2-L<OfR{MV$tUDk?`rU`AS~pMYcKZnu_(JGDG1JxPI_b2`-VX!KcewVe
ziA~jsexh1ilEUOvBoK{eL-AC_cBnydO{y>Ze^v=Cn6G5!7Ee(kVC-5Ea9WARhL5@j
z^OoXf<>0z}M*&je+ei4YcPH^*-RKXmuy%y-nGHM^%vSgDlM9pL?M@k6W!5bqsyO|I
zMLP=pRcXE_j|-g2&ExudO+fk%;7i-`5iZiInc||SfLG(!ICIEbra5of@F+$CzxH2>
zjj{&4RF?ck;TbyYcR#qCE}6MLsw%{^J^HrJrK4F<0Cx-Rv&hymtJJTX!U&dX%-M|<
za4i--SPOKU%OVDQ(n+M#dz+H{K0!V4S<5(gFY95HFVp)3bd$l{z@*l?cCdSu2K&XB
zypfBcw8E6N*3eAH3u331vIUfk3fOKJanX5uo8s*u-0qx&x5{V6zpm&RdV8c)+!at~
zh11<ctnvN0nR$1G8$qCSMdHs-g@$Svq~qPiZZlz1M;!jKSH7M=p!3u63IzT8?YvTi
z5rU=J!BU<AJ~~{gIj(k~9?4FCr#>k?RfM;?B)S;@ee)WW%fuIfeqFD)_)VxA@hxeo
zp;+G)+if|60Jsq{iWJT}<`ldfN#&o!YxtSJ#aWrydhI^4+iL1wS$@I#N#<j{^>e1Q
z3J8mUk$|-L_-qlkXvA>|W+oDHXsr{zyc#f~hN7oIUuCNW&Ir^XANtvYp})J%rjNoG
zYY{mH@!iD>lx<<TxJbk1j&%_XRh7SfQ(L4NGk{t!d^(@IJrVMz{1}$^mUpzp9W!=d
zLT79j<`WOx80fv7U@`55|3lmjs&aDi$T4NNDiIWg^)^BM&B3$;#%B6M6<I*WagaS@
z6Dx710Z}9VC8kx&nU>Yjy9*9ek4#|V7`XP4ND5w0KBr4mN*aIpX+zp%ayci1q6TeN
zix4Nahv-<XW5{2rLxLglITiMxHtxz8PZ+OI{jV&ch9iZd?z9-qw+}p9KYnmLh6XVS
zGq7{<{AM|iS3<~uKd!AvF)Y|i!Ulw7bplhrC(=Zs6^X(MlXF_yd(3-Wc|ky5l)PFh
z*Q*TC>UdhzeGgYV>m=fGleH7RV2M9rwlK!W-EG?I#L&iY^n}MkddqMs-)s7iH<AT}
zDFMmY*TjEprK~!z3_&iOi$kZ7tgV(sDw=TWE4hedvXE-&P4G=<+1~OOD(k@mf8A5F
ze4F2BF8R9}$a0&@@>Yz?#IdqFj!a)J{Yv7aN)%x7N#>iIjM?osD2Tm>VSG?QQDLy@
zR=`H3IXkU?`p3-LMn$v8-IC`vavV@8#;WB4;3weKP0eymw)$@(MI$Q{P)2~|)JdH_
zDMXf{d?|DAzyR5R0|nl_ROjeNz!I?!>e_ln9KfRD__vX_ta#GL!5iRX?Ui+e0a(aU
z7`%F-y`0E&2jK~6Ia~-@^`*0*3k3H1S7V+4XOkiomwY3#vflOrqIja2WN)sovnpc^
z9MjdYuPLJ3zeWERbS%CEfQlyPr%YXmc|`Y7A(xqMgT=U<WjaJAHWDUwjCE!%1xrIm
z%>8PTL<$@uV~+T4xU<ci)k{W;Zh9i-NxTqTMZ$B$<Up6+`0P^2Y;6`L(2Zc7AEENG
zyTNsUZl_xlq|DJFF~1X3_sb!rw&tI(*cSHRb8;5<G7R}+Bv4cVRY@&1EH}lV@kKak
zS|?OAyM!asI2PFAggYCUI)#v{eBQtY)$38U8!`cZ&m(0e#qBY{h;f(Cl>ehR3_u9=
z=VE33J)bYbK7gHzkb3I8y%Yh73{uT29@!TnT1Yde0Z|bsWV->i8{i&xbl%Jp139G=
z0F!o<(5eqaSPBD)P~aLo^3=q!b1-IT34tjqg;`S2Ka?DeW2Z+!jU}}@pLfuV>Yj<`
z>+Xd+ZtSVo+P7}ejfaXMF9$!lRt!5P+p6q4`_FF+k$j|iUGDeNP1mlfBp7R8zvOV1
z>-XB%l<Rw9f>RkXoUejkMk(8@Y`4Z#c@&m{Xsy+?Oo4D6X3#E9zj@mo@#i>cSNiC9
z`V8paf@e5a*1Kcc<rm*#nW%0HXi338eFXf^C<5zc9buSo(1Xc1vV<{`L`l*r-9}`)
z?gv!g*A;s3xJU;RuJ+>D9&-9W6qH#0F>xW7$ui!^JZ}$039NUP;fizXq$DG2Vs|;e
z9uJrm;TBhGSpe89C7Xv?3(2Fi8sNxcrpz{bsYHiT1WnH>15%SINlIN1Yz1Kt-M(Wp
zD3@w_ZUk?bR0KlxvSl%-bP4Ia6D5;Emt@abZ|`f3yS3N*I6l)|r|Z%&O)hvOED%FP
z3C%K-at1(NnHlqzL~*4T?qhlVFn(`^Ed5U%6gy~gjhZ%Fu*>@1ljP;GR#nl`JZB@L
zZVwQe1c=}^-K}%b-EVA;wsGFifSx#f5fLy4-1c-W^7xK^T$kH$T8PQB672pHkNB0E
z8jGUCIDi21&PUm2t;x7(&BgGNrE|trnf5W~A2P=-M#8tsyXBm*mwyiwBv&1Y?c12+
z9<q6D*Bt^92Rq$tT?)_{jD)^NI7}p&*gDYBAA$R~&34`p-Ml?BOIAMs*X7-fkntI?
zc%UzSNJcd8H31OHxpBVh)L8kqqf`#W3tzk{*HxFq6;w-VxhN|0oa^gjr2{^l`jMUw
zQ|{N0le))eilkwam)5z|Fcl>!M(UvZlMd$wUI@h1H*Sr35(2`oR5>-3umQ-b;L?yO
z2T{<Hc)gG|XsCSx_UKT112-zg*zAhPs1;rfUoOA$6@PqV0nS+bq0apu@2^t0aJXkN
zD;F+k4@)5e7@Xoobwzh#(^==A3k#wexQ;nG8W+nJTs|m+efxm}+(>?OO{69Too;Z`
z#iH`PN-<My{w>N&_9su3dhKnjJcfTy8uS-&{0SvZ*=ehQLxH04_|a%NdPfX?O<K;0
zQLqHUl2B?{LB&bUD3(twt@=?6Bv}{RWGCnVYBe1M2uuPyCgu!c`Q-`OJ>_{J$l9r4
zVhByv{89+Q*fI${QDl02N@{zS1@TCyan8Id@>9?d9ROQ{t(#+!4E8U=ej0AYsW1Up
zw>W&Z5EuH~8ib56v8OcrZU_Zt<-jt=4}3KAVSMGb@m)-I^=1V!*}QRb*!Bc`385x<
zdwrC9f!6CW;APW%Ncq=dcZt@ArLh0P2;#W7IpMu$0HfaGcI7Fq%snz3B~7~(c#Nq+
zJw3j3_Rmsxin_@5m!H5BN2hxy9u$$|MSe8-^XMY<@;B^eA_K{|69v**?}f<ayY-~B
z^pZHu<lE!EWSFjNnQGpDmGSnWklsxrRZ#V$Pwu=8M)4!k5o)D=?u@{R?p3NtaUjr-
z`ZF~cH<mISOMX-cIFi=kUxH7~E#gceN8*4+YmV=|^JwOAAk|^^XkO{XEYN7tx+fS|
z@jwE(@et%Bt3luUUGsx-pIrxGJh<v)X%<Ju^%5|>L`WKXaXFf_N3<lp)t!=4qgn%W
z;|FjGqLOmc0`e^}eWk)J1x*!>ZdCC#C<gEtsDlKnz}SFpyEQV|@|GS)lx22r10P99
z$OO8)M9DLuLa-@$=ay!xU^o=3MRw-+INQug){RjYaU17N(x_AIyHrRD!y*vHhhY}?
zn*#&Dc;wIM6LL7y<SZ`_UYO{FB{(TRf27RoR!*?B(9~8TtaC#su-OYm?s-3)TFVxZ
z9v;vtikRm7JC?|QWOJ<ESNJC85(o(%%2U(RBjobYWQRH_{kmS(ZF^<4r+qJ=^oX`M
zCUoau+F$63pHX^;Z_JDm%#X1?Cv*2}=@<Sl(%|y=iX@$?rZ0hQ60B*V>zV;$yX>oO
z{k;*VAg%GZ{O)H%@fn2vJ+fmWmaVvByq=hv9bV0u1Hw;W{GMSF@M@b8`}_tB10sVp
z%=mV5H0(E;o=2-elIQ9Ih5kaFwR9&R0#D993^g~om%*4vPy_`pt8(}Ypvg%=+vxHr
z$X~3c;!PhU^c9W&7<e+!;qD?C6WBAgMJ3*n3gWp2lc0)KxTZzlY4)XoQO_QefwfUN
z^^Pi0bRG5Z;nd7t)`J;t>Uly`OV7i+qiKa}5#}vrGZ!{<viRvG(oTKZAIm^H&Z*5u
zBBzf?f+g~x<`a@9_v;%nig;97mitH$2Ko)_twRK$-IUG&Nl4&u34;=VfX8vkoR|gv
zCkt$jE}}O*16n>T?JGuC{Hos011`zY?1_bGA~us$bhl#bcPUgn&InL&Cu@EO$fz21
z3BRM&2a4Jur?^kFZ}&Ts1?9=>X`n(p9j2GZqMo7(#^#biBUHs2@($1O$Ecr_^RNJ%
zPr#$q!B;^SF$bln?WLUQ!X;Ta+<osZN8Zr2-;GtWBi=uzD=CJyNvkW*_}PuqC!wn7
zCgNVD%&DY4r@5E0)8f`%uVBW2qel`2`k5IXX_+52p3|IlkL9(B`%c_CR$X+$b_EfB
zyKL@4uVc<FlrX0ezk8BOPNq=W9o$cd0KTQ7N$0zJC?U@<f|h~{5_i}219R*8=og*r
z9w?U8d5SEqZYEUg7xH)F>G8JE>hmiV`Rv}Yd<2i*4OQdC!6&2uLp$4y-e#W%<<NNY
z{1KLusfXuO=ked93tP=g^&i@c`X(U!zMW8xI3X%jcge!db2G)}grZ0G-Vh&rHS0v4
zNpJ$4Bt(hmVK($pCwFvr;IRw)+ufVuJky_mfqaqpJm@K}Ddt*=%tTjNhE7GO78y+K
z>P=?iAfO13FZ$p*pRl0X7~$%BI4^@@^Bxy3vEL;FYc#I(F&YCA9dUHu2yayW-_B{6
z$K7Z*UeiXn=x<@_YQ)pnq?Ev>PO{doBlwH1>-RzOlwaf}%r(?)j>$Z0txM2Y^ka|e
z@A1vELT=S0VOpWKT7}5wY&#`ZGZdK|B2<eQ|Mf#jhyInLnMJ**;UzCTbrPXS;iCf+
zmtR9c;Gk478ZHCu+6`v}uofk~b;mpB&s~-a6kG2vW@hcWGB41eXkIcP3eQXva_|yh
zHk&QhPD-3WA7JtGGS{UqEypf8-EwZ-z2UF`x=L6uSCO&2+4soHO|D65#n1P+I+b-V
zd8kjbelvY5bosMTkOnHGXiq5Y;hcD%lXR0Qhho5Ng8Zt^O5_NL8k&z<vp0YiKQ?w`
znhG+GKfEip7XoO*Au~-o;9e3Jkl$YlIh_NE<FF6UF#G?kWd#Z3PMr|twz;8hSVmdo
znq}^YhzK=77?!3bF@7neUY-=1Qbj4<W1%hY80!<o2-y|AvdPK47BU1KWME49<WB%G
zbkA?^iE>WQHP7hHjXl`fr0I@6d_#loIpHOzi(ZN`!1qj0PA`ZGe69?HYk08yn7!kT
z?qcn+U^W379=A`*o&;HLjs2pI*3z=NV@~<Rz71~*T!|FtU(SPL<juof*M;QD1>!0r
zbB<&1CZknl+<=r&LF3zQ(EHPV9cX%rnu%x1$=DG(T}HjI`I>Xt<{yHuZ`g$rPze)&
z|6=C!WV;~qDXT=_EkPKa#<I|z*d6p2+&nqW9&0#J?c>L^e+-~UPT+Wyfn+Uu;EQ0v
z8euLS(QStf3`9q^V3r?>cJvQ+1d1R~&@@k9!JMS9zlbV({ZUbJ=#WNY6<0R3;84|f
zOKP}60dwj7jqUL#u{ez`%nPnGeH@rr4m<)WDL<(vW>yZQyrtx{d^YBdG?jU#S%yiv
zuLh~Oxj)D_1J@K<VY_!}k-^!>Hq%7ms&zl&4aEE%VKhoeK{b=?4xH%=%(@YS9hdYz
zR-DHjA1&28I?DV>xs!NH!Ra2pBi8X&kz(jqceQ!RR^x>$Qq>b-r6Nr1^UM{;yW%=2
z9Y|$(l16*KY<pcWCIFE-+zV4_5J&+g7#!4Hk-fjfo!J%Y2!AW<Of*XN43$ar?_rh(
z>L8Oa!8Pmo)K&N$eR*w&EvlY}_Y}LJR;Q3-fq@SQ+XMd%X*6TuGMj_7*F*@PmyIwy
z5KI?f>)ck8M)A4BYYYo2bUKga0J+z8Q;inp-l^Gqq(EP5fv!l)^4w_>9tSV+k-gZf
z^&Jqyza#G&D6nkhxx$hPd<>USyQNBjeDY2m-J?$*9kAtzPdc&_IO2LK&S!);I6S!j
z=7ROGsBo;LSIw#9j4Z)}7ro5rFCuDeq0ZZnwOSWz7Tj}tB4aL@8=?Kh&J4NThmUV?
za$c6P(xKk!K$nep8(}-%T8b6|n22~n;JV*5s5L=shm(Xpp7p4X;axf-y10%I(?tgx
z7UzLy>=w0mMxD-HLfpqRU|3+kTj3fmy1A3UeCBojg2=Vd07O~gVKv0t=N3#oDuB{!
z=B-+uK*%}S)CeSCc>v6Llvx^^Jjhm|RX7EmZt<rKGCKB#i{zxJ93y2wD<UGuJDVC2
z(`s#noRtwx=>cJ%LtzG`70t7x`?AA3Knjm1#ftMN{Mu?RM{3N+xr06U+Ld1fUV6U>
z&0Ly40kKsb9oMFGEe_9!$ijhOZjtjhL}G8v-eQq0CF(G!G^i1Xtis_Pm*g65ZqIn1
zdG}ND4Y$Dr=H|!3rlw|BUWXe_*L=QJMx`=}6h@n%%v#W3%*2Q&yN+&VAlx1`9(NZm
z3%ABhjN5$2wfpLa;4qI*iiQc!PWTTuM7^)XKWw;eM}j&6h%E!|t^LTBj2a#6rG&rz
z6Giz6nO2on*;@9rb=_$4Lbksb8eI@jw8`)TQ2CwY-vzJ!EOWlW(23|y0v8Xm5EA<w
zhE;Bf28qe=J`CO*fI7E_&D;qEZ%9)}tBt?BhJZYWv&=siB)un;-oCN(W%rwl6|`d9
z{L1jJ$ksfShi;*~*vr8ZQDaG>Fv@4-D^mgu*ib;9LroNqQ3mc8Ej29dNKL3PH9|Wa
zn%c9@EV3KvsQrYxzw$(usm+miE-gq$#PdI#{0uit_N8jjCFVrwTvE|DCDl1N9)A~~
zk}pU?C2+17TQunb*Hq&KlO4Ra%YsyxLr>hg?0wcV8VF!+o!1n33hi1U(hu{;19pY{
zu!meJUm)1(|2|F<M+OnP{aR_TB{-VO3@LX|N^jUPyf8Xgm&?dF$afEzIp8ap{yUU7
zp(l&PfqqC_+li%uQWVc`4ZWrD2>xWoIL8wN2E{^7#+mur8-xkho+4X*39XtC!KR2a
zX{9|8HJd};nTiLIP{1pMPfQxP1}>q?I9|<tb9uTQ%;?|%CFnb8V4T8EOw0#sG(0tY
z^ZaiQ$f@;i3n!erTKElAT&m>SK|%|oxotegv-p<(&0S0K!o3}wICFn;W%{-8MG+W(
z{5fc-IeptCSV0oHMjbP%$2&u;bTY}}ZjI@+UCX@xG+=9qIvM{3p85?%i`gpU>~EHv
z8xV<uME}Pk{=uW0a;w4MpSY5~u)cmk&V$XtA$>{kZ2$S1yM+XtLDP%F%EOrH@37YS
z&)(?uFqUn)phZlrtT9e}9^1p+Mt7ui%hAyc$nVZV=|d<tJ><V1zg7N--P<Gk1{mW>
z_CgixNLKrq>5#BlQ@@426K4llJ(-yniH`mY#*)NA7a2D5zd?nLk|75zh>-u*6(W+v
zp8h!U(+R*XZK<?lGcwqa6lKy9RI}lyrfy96N@*?_tAJq}ia?=<Sn3L4PWlOr)?_+s
zz`<gi!uzBvc@UdYJ~MNGJCZlnY?=A3Ex-U-Tf+~~x4l}Jb1^)Ub0ZG!kI6l9OvZbO
z6pGv%Ya?(GQ^c-=pMSb7zE*9tJ*p`Q)qy3@PLEYM-s^4B#iAr|C#EHjT|+h*r?A<7
zVE~hGp$8-@&V-C7QmY#hrxRpqN*PhWduxS@?8Dgq8UHOSsZcOxkH8FL*$ZnA?PNtn
zqL`^gS#u|CL6KB>0ndDe$68xm!B$urD9{E@lAFmMqDX<yvI#Vz;HPzE5VwPv?+U^^
z+9<8$#dN73{@n{Y<y)(f$KRobv>-|9Uphtqz|xysS;TQ4gHvziH8l$^zR{~MZ)^Z%
z6~x&oO#}~BBTd~9Ur>|~9OsaJG}c5zxXgrRVereoc&4=G1&A3+EqS>p>8pV}Ofq-`
ztSCpWiGcdB)Nc3y$3k=R%@pKQM@;(WTgCjYYWDtuSoZMZj_`w43m%WFgEufeAkZr_
zl;EViMuK5^It$j}z#ZdaB_V(riQK)E<x@3V_?xB*&;}bJ8&Xmk?{#e`3JS&~Xd>`r
z^9|hNbvl7!`q*PvBRrUHFB(G5QgL`Z`nPh#`Rx<@C&`;kN?tv~1+tr;niVC<mH;2b
zZ5YCW-08d*$X{7h@@!H+N&^jx^Y=nHSVbQu*Q+O~a@~TaO0@8iy#yte#w`|xb#e@V
zgAOFOJv2UmvRc#!Xt`WBj^;Wq91~ohW!5H0dlYDjYBT#i%F*w^5l-IM@5lM2e>=ek
z79bKx(EbwP;lgxw-l;V5g1<FDKQLGi#rsSNnJ6@o{HPVL<OmWv3{FCB!hHlvKhO@c
zbWcE1J6ue9l%y6Mp=vrl>x{|cIL=5-hfY+#Z$C8Zx5J|4vtISPTM{2>m~Ttr84o%F
zOx#SRd{VWILb<XB7Yj_54T}Z-mc(p#YUurD__!r&ZcTBG5`1ZY?N2JZBIOX419!vs
zka|y7l*Q(#q$S>GJi5(bm@HZaY7UQKKW~DDO4Q#>_F)q!Bh7B4-n*PpfXN3mxCp3|
z^+>41Or)>^OkNNrsY+zGtuwnaqXLl&V$j$t1gHLp&>qRY3#bA?oV%v6#;A6rnt!pQ
zs04iE+4|`)is0le?arrJf)W~#j0y-cxlUYxgCQO%jeV*;7cdXf0T1EY^sk8}g296Y
z(O~A%aW({<n6TV$NVXQ3JYnQ6+#TtHLjO;7_~n8q2N~gX8zNU(?Dns2n4zYt^OGOT
znT(3al%$FvryVNVu`Aa@bc3}8CddfPSTU8k+cFa`4~WsyW~N^5Qaic)cb<LMrYCAZ
zBMH_ye*4#-#g>NLSXjQ>I1>uG4_`4322=HYe#LI?J7TxG3?XZ<+8oLUr`!2L2Q_eP
z2un~ghqy%Dgl3l={Im0_FbQE;I^)s=UB|tg^^mPVzL({-9OvyDRa%%>R@?9YUJNTl
z_EU&2|5#3lmDnvmkv8ff^lIPXnRaF3v$?Tk^t{lZwteGNgW{R6kEY>>0_5(c<(0?=
z6{g_5dwf~%M|1=0F|;FkxV-i{blQ?ZRodl4dpGl<zkZi0pRdGeUA+jrNKo8iFjJAD
zk5eNQwzlL1D}t8N`@(kFuFuMi{S*DD1{{~@E|xPULN`006QZH;r4-@trf<7la^S=x
zJCW^4Ob^>_&Fp18^2|ulb#H_fh?p75XYzD^J=D58h5VutNjFLb6l?DWETH49bR)nF
zQxF@RsPn!&*wxk6{i45;FE4AM)Y|m7A*hy^N=`^;qDH^Bb;lK-W>6Xw)Vw0HXtkdG
z4a!70xoiE_<D(N$oFIa=VgmzxZYRi{aZiN8FckEUn=s4QX_pFx$!*S_lj-g+GN|P6
z^>N%K%|D~J;IZ(G+-R7zjd1>EgvhgYMBgD)+H@}TDb~&6td^8)A{3{mWO_O5i~%l+
zTBzmiXB=%_He{?Uj-v8Z@^IPrA`Ytrb_4V|tj?-`Uxbd4PlY^;NcF?Q$*O3Dr9n$b
z(YE~GYY+tyD-v|vP9W2IfRC964>Ay5M&j-X38q^Lc_!+QWSG>QU4G`rA-Qz#U3=|%
zbLLMSxD~0s>8Ti#a6%$GVm1jK6p*eqLZ3OYmw``mYrfxLWmr(H4|l7qjn2KhpBgKz
zcZBn0L%SVr%e%svbMiPy5w9EqP)XBj0KH9w=^KOAi@mXjZFV9S77&`{7D9fqbZisw
z_5HeFEM)+5NFbjLQjalPqeNxA+MiNLS!87K{j&}Nv6nAl@y(L>;#w3iEQlFFUnLph
z8(BK)&3e74P|-XtgvmuRa}_cY-kIRD51*H&c7kpQx58moI1!Ql^!;|+kQma$KfbVv
z3kq(~Zh+1QYEVz^!j2HGZ4n<TOr)GBme8pV?7l8jP4gLuo)$=mJIivoBK8<2l>F`J
zjk9&BYzm`;i>zCLH~V_%l3}%w&^tp^)aCd@Q<iLhL|<s5X#tyqhCADL-7`uQu6K;0
zEy>VT9KG6&2$+!=6ZSB3+U{d1^CFI-nBd09w#G6*2jZ-AM>&N<+u`{&6544bb4a<V
zHYl|12eHK7ZO<2eo_8DGXLLTl#g-T}%A#sc3>}RcvA!g8JC<B$T@5(iut7L3Kb`{r
zOh8Vw=@m+DC>I7GkYRo#f~K08z=No$<b#8eW8K?^Wq(=KU2-viAtYf&d16JZK+wPj
zXD>9=aIQaW_OuBsXG1`<l&Ok-bhm8@TF<hWhWbuv$Unpa@XBj8HoypRQAkh=L9Ww^
zS+QUYts6<zSnZ>=UFL=1v>ysgQmR7`pf4VOeQhJC>PoWtn>d4eHmQLUJg(52ZeO2=
z`c7t6fEn087K6jK3P!c71?yfMZltx?UHiS4#a{anFVp-tQ#5~jqWEOrj6!PA{;m*}
zQ6kHq{UZ<=S9FqB0%3`9*l28l4ATwr15>m;J)VyApJdoyhopcstV5R+^NSf0!%;}u
z?oVC!t8s&FGd6T3_YVCh(pJcOp>H&kf3UJ^p-y)--emUwKFMiV>-gEzN3n6+1kQf0
zuiuBqg;lA*gJ-nKi!9s%Alb2PCqx_OnggHX&awEr&ifj{m~R8mEV1Fyl7=<zA#b};
z3OxgZ$&|&4;kT7$x}t-nHYatUMq6YLM#oz$$OJ>c62>D)7EtQ1<Iu9$NO4pa6|IN$
z%ZQzxbkf*fZQ$6<L$=`rOjsz$n>U+(mv775VA7wn0**nErVCF_@#8ViW7WQI(Nrcw
z^l3px>DvyG+{0>$mRfVXv<Fxiz#1t^3$A9UH$FQ+B;^cZZE}y1-52SYK(QuV^A7^Y
z(z#iY)3(2HH#nG02sfu-F9DU^W;oLDHX~O#ER4Lq^y0Ew;l~7Q22Q3_v`yR@+{cE+
z6yS_F66oyK8$#Kp_%DBnHNTS!5)udEUlP<Er<Gq=I>JL4bK3NYh&NBxe~8Ys1ur2*
z95Ck`7e<0}U`;3KvB&r}Bc-2WsK6n6dzCe!<vcdh2VXlnrL^x{xQwQ6$vlp-9(r9N
zE=DL0Ll;Wv-7lt{7x>Ya8D!F$>T$j5gN$GPp};l{UPjKy(uHI39J)jAwAdu3Tai4N
zJO$bG3L?XkeSiX`1yvTwT*88gC8XKgmB&@)3rxP`RM?{Wm!**%{3kT#$SkzJiqK;Z
zN#b`ryr4v%35Rb2{sRwgXg~K|SJIcPy32RE^6QHdEwJho$!9>eK*SO?_wQ?g=xVTL
zNN6cS6?%xI%s@T})IS&n4HXbY#LD)wkKZuM=|OcT?q>G1Lt!i)xs3W%t>F&zg*N{X
z!I4)Q(}XmJ3;~(!OP;PdV3Dhp|2H0Gg9Cs2g69OG2+%Ve-2t>-!F5f0^Vb=A5G%&^
zZ)tS<g%!<n2Ah_n*-L%KS9(ymbZm|HKyD6st|=`>&~+H$jE?C0!l^b-c1;dWE`+~L
zjwmiIB%Web9~kYj7XM6D6<6H}!*#zJ^7U&U5pp}w+A3g8E6q6nt{!>5?umrZeFJi!
zqGA<Mty!4&zdlg_XI?9ap6NC@gt)nN9ZnQ$jAwME-wRa`#wZ#*Hy=LkZxWyj;lU#7
z0=p?n&Be$;g<9CdLk$Vy5g2J$Oi*Vwqa_FQAfre|Sd0NKbRUsit2e_>(j7aW`lMS=
z=p{TIxoul{kaA$h4-Uw?%{&kARzjz@yzwt#q4$UR_eB<8oF`3TqhN<dL6Oz}(UB=h
znnuF=U~OLqXILZ}cIr^^#`l`C8o^EtK+vc${)vPkXMUNGdnQu^u`5p9Li!^+FlP;K
z#OhRu)uk8#Vmzrtn0=L`xP=P&kd$!`BR6QmL5}cZ^fXJ5bqiunyt=9!8&lac@D+0y
zE+rpOL<G#W+EMRt4;nm)6E(|%L6ngSg>4zgV>upQ&jN#Tte_e$HnbPu9HYKuVeYgg
zDDVWI#8cDiLv_T2<B!t3A#X|I=xRF{Y2qiLU`_44)<A`u{}L80;Uxn0R}+`<dX0yv
zIcEt@n}MUhg4BpY)#rm6TG-rE!9Xebxh8<E6?Gsix^MECv_J(=YH@c}d?o__`Ni*Z
zFZLio9)Fr&nn4~&ER2{WF3}DnDev_I*{Yp~q2!Y%Z15qI#J5=>up`1>ISmu~yctI4
z3N>0~Ha)fc+5H@G-H8!~OO$MtS!-ayVE!U!>tD6d)nRkWoqyO~j~*A%h-`CGQDJJJ
z8pXsziEJ4MU-*4(6xohRUFJB8<U~Z@b!r<Okq@8oB7!eu&Z-GHxI5h8H|e2BIsB>7
z`p#0-{>ep#6FlRSKvbjN=7@(Jt}w>B+;t^?Ma5rMBy}CIIC0}=ybvvfsyn-(A$zbG
z90S>4%npn#_R#Mi9=q|49<VOhQwD^S$63RuldOaiz0>~q@={6kP?DoiumZY)FCc9Y
z%gS9UWoZ7QnHf-{af`IH@NS@PkH_Phzght!XY+{Kl1%Koz{|_;$o(dZ?e0W>*<A8s
z`|9=5syL#3*mM>C)NgkFPv2P?*tB?Lx4+VMfVB_mtrp{QGwx$ce^?#T3{fUl;fF!1
zB(n@8k*lP+H!{1JL~zJ)o;}2>_b@Qt8L@&aIkDU*&(%S!%^*m7d|O$}G|qwzoy+d~
zz`^@)AyCdFYkD@ur`}^hKun)G>g4bXd9F-IMCJWzn7TB*aQZ3mG;%%a0^{@%Fcfe!
zz-MmwR*l435o>&R*3t<L{Q;e?=D`F5(L9c7olYw|Yg{1;l~F<d$tpUh?PWek>)rJ(
z)7e9fPcIJ(pTQ6EJX|&}7GX;|Wr}*3(oaQ%UPIs1ZeFIE5sKC?iIcuXKRgs&jhd-D
z!;59H3-H;}lsQkn)*!;wWDYcZ*vUeS&^hNYkefr)uGc3n)po|4D{H#@ZS<G2R1~o7
z1Vq=JAiH{0wgEC8JO*k(IqFh45t90@XP|{9J_!wnbQkFrR~1klzFJevi|g$-NE*wt
z`}J}u^Pb)-5;v_{CDyt;6i&d7=QbR(qcQ0tM^kq7=6JEL5pe57P6=p=fNif-V+6d?
z+&n*jI2=D}*CNf^KaWz4@3E>L>#x^%S}Lm~cTv0tvJ3Ebu-q&!Z-dx&AQ!2GIx>{b
z9AqbaVj<0ks1hNwHh0}Ia%)-43wbwga<sf=KjnCh6vBz9KXSN#ap|A>EE~aM-Avd;
zlpye_G*Ek`z9;rwL}GOWpYnmjgSANS<|Mqf4&b@%1pTqv*=5ah5GUVNt3aW8St=-a
zLHxR_MT3-!9+a3!BcYrDxi5<wu9~nNXypRv7Py2^=tOCrL*HCd5a?g#I=+mM3F2Tx
zR(H7SYkP9j=kQ0Tx~ko3Uj&dCSuL<IeDp<|%4X-v8=%eSx@sU|B>D4MeMKB5d>QAD
z=ue?>7+qY*FS&*3HrOY#GP&TZNGBfE)%5Iau3kYfvAFfdt8;YT-Q6z`Zik6w4<tv`
z^sQv@Su1Q*IKxWJ#3WvHaoTg@h}*rHcl_RuV$;f)TY!KvuCd$u><cH=uJpao=)i%8
z-8-6iwLit&QH?6NMd$tA(<=6pP3QK+MA!4fFr6nJl{_ajtErEDS(S*Y2AIyF%I}f)
zs`Hc6snjtG8r_+<KPWcJ8AKITLTIy+FQDS}pP_9R2KDp%;Y%7;GpOoL{Wd?q<qme)
zwhi3wACtspfvDI&rhHC~pps7OxzYM?cs0nq>>DKEmzaRe6sIz4EIwtkJdq|;g`pK#
z0F(Dv`7->^zHo&oUr^9z@sApx((m8FKJQO`+uM1tQ%a7v$AJlm`N(xcER2XLSv^Cc
z4dR?L6isoWdKmM{e^Tc_Ih+KRyg#XQJ)O8ZrQ#!&h~hKzuX{8D=Gtwguduwsx=l78
zYHR3e#FqMCt+(5e^pt5z9ZgAp&$1@+#Cr`Taiuu@^A7S^)JZ<#C@g0r@R0pZk&s*I
ziw-hBU!dVnIToH7T{I@%#S<m?XJgePC@;nXhR}6lI+L4w1^+t*jl^ji-ur&h^39r^
zcqYu$KmwBnx#R)lnXLZpGi_pKrA!<G>a<fk^Y++Lc41U?j&*L92B^ye=SE5#Zyr|Z
z9ze+d+39~y6It(d{_K7|Bw%Qz#YoJ@*7R}Ee2oeu@&s<oF=L#7tD<LzfNeEiZ#IG5
zc@NM6KwJm3Hvqqh9_6Q$+R0x9Fs51;y;89~7b8aRrLE6~SMh1xj)f5v1o}EYrhqNP
zA1d9;SNq#rnrVPm^Nwx&Yho>GYldoGTr{^lC9Gc#I(nlvHoGF)y;11=n6}%It1iy0
zW0{3mQCN(v_F}2PA|e?8Qpu4uPy^&EZhL=#vmYcfCY$dED;V(k^jetWvKyk8mW>ov
zQp4DNoZ=m^xE1-?3S?T*a9+E-+&p5`b#NrEp&upK*aWkiG9Sed2#YrXf4iTFu`;@(
zik8}1RSgB?WZj&uE9_=L>yf@HYAM82P4NNcgtdW(;q25f-|(2Cotz5Wco4bTX~enZ
z`lMcl5odpe>`_o2<icQ)FjcdiuO}$XZ_z^Yq<c#+X{2CNpdWFSiAmIHVW|4Gp~vun
zH!Z0#%ZuROMWApkP~(qNx6}k!=Qh#?PHQqhOy(4io6~Em<VMlqxc<2A!P6TndrECQ
zyRi``omRq|q$rJw&D&-EwOR=S@!_(PiHgq}VPe$q#NR&D^X>MYI(Fk4<@d%!gWH6j
zCpr#a8iVN)!>Nm|n{)nGSXCT9jZ*fyQ2+>{EX$Xkbmq%1RasmP4BQwPt)UH+q5-9j
zkVf|H|ImUB)_O9)rKctN<-^^IhX6@Lno&qeh-i}4-FIb~S>V*ulu&SDZYN@*K?HBF
z;43x!^vPC2+VVMSJcp4v**c@v+Cq$=*ZKYn+MJzu`^g?&Y_Q8tzzd?*y9LX^VU`uB
z@JQ_(Y&w*0MQ7n}wAQq~UNu}ZXR8^^*W=oJIlD(;R}C%}R)(y)kSQR7>Y6gb+^%ZJ
zBPoEp)Jr+ZA%V{@U!kUvCP<j=H&qYYEiJOCBq>f}NdM*}+z?Oq&=mA=ls^8!9`X5V
zicBYBdJE%SbL4Ejm{m{E`HEGs2K8-QWi?odM_Aoa<G(sjKRh;<oF61KLu+h$!%QNG
z0Uu$Y$dBe|fR~4FOwiep-^<Nf!c~c`dAvb)(73R0klWsIzA&a=&c}_xa$;N&z)}?j
zke!@zS=pYEsDx#<f1t6osT%&8ti1iAu$mp}k%5U=Mq7GR(^i8-gi{evU1G6?s20aQ
z8;|!O1kqZy2@Wa!h@{Rq@7IYST$ZoW=!!~Jl*JMAd)Cc$kqh4a1%1H(asNH5uD+q#
z3eE~*H#;vkE+tohd>_G)Ol5CteyId1+5nCVjisHr?KF*7*epk~8QH}82ct4<{~E!^
zYXIt5f@E@RldtPXLCyvrkpa^j<pUeIHr7$)Z%e)Hd*_x}yL_*GCdj@_7{|3t6&e~=
zivZnHEo4h~-N?UZ21U}tBJqBF-4#i_oRBVDnX?ore4*U|#mD;X=I|6);0?k5ZY@xv
zYYH7$F?o=)D!f5M40yg?|A{SEHCq3IegR`_987pWI?2p^OS-_@g5noq{Cz!Wrgs=m
zP=v*{+30zK{zvUkEbmo_jaG~Prkj_0&5Vz@y?#Dq4W}nWzU#WBb%nVR{hewuq8gY&
zBRqkqDqEwJ{dTZ9_U&P0^$9v1?KKYHZE22~o&1q))!VX{`}ut}ETW{7R~y0t{mo*U
z{(T>Eh*YvV&E;gwC{Ghc<<>h3{KDK>N#n0DbDiI_Q|eT(TSq0Q{h5EXW=Ohsj75ja
zIywnq&Q-hPnz7heKWhj2Kp?=McDSIK!o5smsb=aCDh0lifvIjMkFXi#rDR0lmI-mZ
z+-!!t?7orot}lw~qyuF<3v8b)liltBMI~wqY82BM$kyLiPMx1`*qk#Bh0&>u8LLAZ
z9mX42D^nQFFCX&?<`E;PHG}6{zEfHCaMCa^tpraqiS~G^emx2fW)5m@OfhB<n{#K1
zA{c>BvG{N0ZiO|Skne7GLk-Vg!3u8d#LU^Y9H{nGS*f++4mdZAM)R4|N?WFekrUWQ
z>i=@@+qhOa-5IkY;h~!cuXD^AGtHWKEBCmR`yCFDx8Y5cF-0*cg{y^H8U2x;ICwxW
z5bH#G;r}N_<SdGzyRJ&35!|lx7Cf<hh@Fz(=Xj<+PW{i6GaZ}7aEl`<Q+$e+J*Y6|
zNm+H`YYCZjG<W!sAO8ad+3diC&>0UmPhmAL1RH6g&H!=B1F_8^n)x5mJ=tbtF+Cj`
zA1~u#X@Ldh)Ab2O<%uTtpeQ@aJs%x3J$5k{wwHg6FSIu2<sPRN01^;kVlu|)QfE!+
z8kv+{j+^2<`XWJ?UryHV!e0qCBt^d!4EknQ$}bjX32v46QKd0c`~^;`#eBIywh;Lf
zU~!lJsDxWi2?T3OD}e9Jsbtx~O_8h_;6<GBE-;pB%kr{ap^As=X^0B*QbV8gjO=0I
zM+sW84PC1Q`f7NdDXPx`3s@>{%Cb>bo(54ARsU1DrzWH1hSx?1`x?hEdo_GSC=)M;
zE}fJ%&~FI`I?5<S4E6Bc#SA7rs$ol%RQad_OJCr%Du<aMaRRsOL*9s?q}=Ds7Oohw
zlJG#gG{%~uW1>Qid0oYHn7DV}OFcEG+X^eI>B8}nlntKCR$Si}O`4YnomZQYkpT|?
zt?#DCpm)L_(Yx+~{M#bj*>nP4MSj17fh)!NRG6M=d>!r%I;(w$na+HYSkeIvW}K)k
zV!m~L6+R`Mx%aG&h%Eq$zd#=4(Nrk4gGy?WRq!vi1}&RJ##ozJ1Nv4@0MCi;g-~eZ
z_h8tE(QSauM;)6YxQJ;-8upU33GEM&{idYt@69V;^$_5e?xF&H_LgZO-7%Wg?{@+e
z9W9;OS>BV_VNCE#kuX2eN{TjMq4ywdk>I_29K``ji1das`>a~jA;pD2oQzahra3lo
zI(INX;Wa^qMl;2X#1JJJ_aTz%p}soji9w7}NJY*^3+b6>`X^(2=yO3yD-^x-bq;id
zurDegGr7sba!-rkE{ZytU8IOaQHMH@4>4;eY%dU*pTM6;s{6qLUC$dh_TE_^k=;m*
zO*(y^C%B%W>7p{*0pX))Z&pmmpPA<y@`<Fogq#%>ou(~cs6gF2JguKsQtJTZ)Kq1V
z5{TAzZ=T^-+5uzZUZgb?+%&xrg@O35eWszTN33*NE-mA2MgL@q{YO}dLWUyEiOkAC
zM6?144K6m@YztX$+DW_V;HV|t<Xfg&SGEz`%M8W{1mz99*u4g{k>3jkXsH?wuwMq@
zX8wY=BUeYcmDVQut*x9Vr!u-Dd??8H!PwBy#iEX%F4}%tUUxMq9;06bltdc>$O5^1
zaHv;kdle*;A5M3XFFDXm^-AV{Da9Z>;tBWUoE!q!M8trm5uq!L;Vil0<Shc$&fSMI
z6+sclvDFnb^<YEING^oq5l_VXeyl4ARpHAz1cKWZnd|h`{^0+0XNL%rU|Qyr`>qV1
z;O!^`ZdVc?PVuvcBZd^|vxLEpj7-4<pL^VK+?FBO4?Sh#Y}$3=z1wNDmyrtr$q+)D
zb|YUc+=)5|JVr;y1y?kJ{fRCpK;;<r)XzqfkM+}Z7=0U9?V|Qle`9nb`RD&Gco@W)
zJNt>cp~|kvGVS=+9rLa#^f{uU16wR*sl7!v#ijHHAj|7gYRVDSy#yT9Iw+Z0>Xu(#
zs469vfqNzt0Hr1jPbuW9xxmS2D=s9(rqkhW$JI1T7UQ4<T!?7>S5S^+nqgQwBJLn}
zFH>XCF`~u_TDROiZn!PKt}gr1gJ`BQr(SC1D4(zV!3gI-qwC(Ik5Af5+UYLY2M3P;
z0nMjDW}FiuweCdJopWw5=eePYp+8p>nri)&IJs9p$`L_@JUCr6^Y8Hl_xNjWZ+<Ck
z>Dax}+{_qXd`?c;2Rb&+ggWXbCBX{Io+T;@M$Lsto7G_d-@kpHx!hjh_PI4Dd%EU(
zihKk6NYcOZbDIWlol?5U18{iKi4{*G`i7#K1c5>{mt^Vh%0aL`R8d)uVyjzkwvsy(
z$Aak3O&DAu|GVH5zyv-<3!-m~m(h{cJuE!APFOJPJQzciLUp|LKo+;KuSc#jq?Rrr
zWP@}L`kk&Uv4ru%%h_y^F}ZQN(@^d?ku+buLiWJ;(M7!Bz23cqP3_sy;Pr9M1Xtx@
zJ4aa41wupw(p8wi&V5luYDXFBQkB*!8_R-2lMV*!z6QlqN)|(UKO~nvH`2jnAec=p
zOA1WwhuM-PrZ;8<yKn@}RAA|>MZjhRthre^>&;?5Dg{m*l?;$vR{lsC%2(YA5JeqP
zCfaYl)Q&_Mzw7#=bR=wu4sWE5)w>{rgC&cwSjZ4n`*$&gmBGb<C9#|sj%6;>(tlEj
zIhftE)1k7s?2?88Ok>+HJsMMl?C3Xwj%#`_Vno9F160vGGa$eyN`GNiK<$5NGM_Yt
z2<ZXISP^|WKgx|p6<F{=4f<vxwzo3wbv5V)zY==xL&iGB#8&;<J(Fdzo(0AWI3$FW
z(!{3dpB1ha>xrqyd3dkjhn^(`^eTB1Vkz`D<^_cMu3$rkj$t!~Bvg?t>#{c9RcE@B
zy#qw&y)`gS*EKo8_iEE)$2SyIi%WM}*rNLD*+`o+<N?bN{^mm#S0FeEqs<AD!=UU&
z$l+QM)p|l6HGIo*_C<M|JTL!@ytoGRaQ`vRHrR=Jtt(hTB8jgaRbGq*+)5E}=W<p3
zeW0=h3pCo(qq$<SMCzSMIpQqNSKxUj^I`Gwu+=b+rKhLc>n3UIaUtDF@evQyA}rrk
z$8}*fIQEV^%@T-IoQ%BY<&iIV<h@@lsiM?@U`2IQ43a5!b~R{nj+pK(64j?h>VG%+
zh6{Yg%I7q{07U2WDhPiE_jkA1a_>d&tR#5QI2lzTh@W2VYE;jSwhi4PAp~cjk>9;M
zrj^E3LExZ>YxqfTVDOso7{1XTXEk*5o*Tm%U%q}M)Ys{}>R?vxPkReFV8y*S;y`vJ
zRR;j;9OAY>|1-`G<PTa;bTy`9TY!I-22#A@G0UY2JeHWIzhcX+MrW>c9_-ry!|03i
zIO@^;Lb}7Q;_?1^MA&K3qL+Xia-R79;>`UnhO3C+ewf`9%`eK1%l;Qw)DIYMefbAi
z9)0B7^dZ^jH%f@GN0`X5+<RS;*+}c?jW7$(e2!E{LaJ8KKm}LP{;e(&oNBnp?4FbI
zNLGWm(iB((6!YAWF2{o?Pr;nXtjAon!SmVq4U4MK%G{NmUAb^hZIt{;2du1mAh2qO
z>WzY5=x|-s_76p;*m#3H_m*Nks4RQb!Z-eOBBQ{3sBKBroyn5V0w^F3*~Mo^J*nMs
zgl8ruKdXjOQ<IuYny>%Qh*!LNO|L{18#w+hit2_d;n%Y=BMC4SSJELb$_COoy@i@C
zDM?`VKVthin9E+b=3&<*C?AndEY9<Tgg&vg_(rJ>{^ahG+!>{DKBiW3fqQzlJxo#~
zfAdAK=V>;ODF?c>@1~yy@Ynxzf|&lj9=k#O?rf-&wc0x$6rc>J9EX93;(NwoR|sTz
ze<6cI1XHCuY32X;+2aUPKHP3Y9ipZ!Lp{phhVarK86o#dhNH|OkAsnuznf7zQc3^i
z;P<tzkBPWW+!7D09P`e3QAoEWaxY%_yNa*cv}6q!BnZ}T=^xfqQ`nnVE!Hc+wa`1^
z{b|)B<PSM?AHKIub{f)Ljje^tCAqj$Nl28s&FIe4rR*yQkL6D6LLwT!He`C_sj!iY
z3P}@D8L^2JM|me!Iw)6eObNLAe{=*??3fOe#ir@!*}<)mkIkSGI8ZtIeI&}%Bg<;|
zs=3G5u>G?rOTDTNGabd$D8rMQv3dLXkt06F3KSz@`)4_yd2|1I1N(x&`AD~=cZ%>c
z=d~twn3U6rqsQ~#%HGU_E_b%8fkTpZVyl@mhWVkmYLfdtp)Iv^SmdA9dqK>ehr5k^
zeLl)|a{3W7Eu?wtAgqlHtbm<BBD~kV<pyzPF`?#-O50i;jbZ=R!utDZIRt9jBcA~q
zAr(@F1U!LXn`IPaYk-t{p%T4zv08oF+?=UrVSRWWHOhD*3F|$@FtS>!1xtX$d?0Ek
zOT@)~I`13NndA|K8I5naqt~NV1C!8@5MO4-X2;A8)E|y|X4l9ZBK4_g&>+>>_v4D>
z*}xfJ1?8$85y2MNBtTS`c{k<u1>tkLN<!mWzc_Cw&~DVH{jV2*#L|3ADmgUT(3w!=
zrEJ1<OQQ^^x}_;tIa@2C%^t2S{IcO5Ad2z=dA9-lmw{~cq50re^LgE#9t4t%%s<~P
zq^15o$6Uv4Dt9_F-Y7_P8!4!&$1ee06JlwCB_G0D;+GKOlZJq6=jw@W&h{P~HWRQa
zF%7+324>#~4x&TI8tLs3RIDgSM@)FtVC|1fO4-v;M`t03+(b#LrA9rZC#L@HUh#B2
zs-ORCNpI@%k2qDJ^J?!n-$c)0RUMvHP#KX6SWj_H@0@3z76fTMuJz$chL(d?ZhbBT
zFnGVqO&>AB9nqmR=7~B|EOL)EXJHecJ7dcf_*RJm4xh`00>xLwMQzbOUwz;w&i_qL
z1keHZu|fpANKoS}_HpoEZ^?Pn&0kbFZ%BSGR3pyvHc3K!DP(;WrSB>uB#1T_6|4;u
zIP<4RG?$!A$i510FlVPhtFr*eTWiq|ouTKszfs(4e;3qPfb{(AXs}Oe%Sr`289?HA
z5>FUuzGIj8Hp}K?PMxP${obF2cC>Hh#i_B03jrFaSB>)(DAR=@1RdkAVMPLT(Y~W^
zUn`6PEnV}|Y8nJFEP-RicIz*EyyUThqICq`qV|%p6^->k6U)9)DKx1+izrrG<EvPK
z>EfO|qb;)@gxHYY)eo1NeSzio(=y!mgxA}6&U3FP?Xxv*&JnYzo-Vtr@XyWvRE|24
z9cM(&YuOD24)u3CSIBHAVBuh>pvgMZNcK;4y1@tqf@%dh7&bVwzkY@=Pj>&EItf64
z(SP86Lq<l4p+POVN;vEn#VmkezMO0vRuUw}n@S!a<Y?q%b1U?JsGk2ns7ZjxS<7~E
z3=w`&^g<@oYgs4(p5yC)T=FU_c9u0n5eIl(T@X<5dfLAH&vg64PDk7LAl{XpH@WR<
zvGn$cWpWxs1Ybi*G9B`H31hSM8~@*P{C}m;mk6<r<)kM*qygccmWZ%#C4E@>B4K)b
zK@m!r{KpyBwA^g9qbu`24|Nknq+j3_JV@uTCvZEKN*vt|>Xl^p??$Ot->?3EOY6@I
z62J!jc7bh2<l8$pW%#Z@<Wv)X*FKlACcxRX9x3|%O^d85nfm&{PXmf@$@!8O5)?hb
zgn0TbV5KXtWo><Yy@Niz?DID|<ssB34PKY--R%E9K^CDM+Gzjjg+Yd6w}`=ud{r6?
zAz!YduTybqr0GTAxW?ZTkRMf#({}-RwkO}isi&LrPVNx>S3OwJ-@{-pvmL61=1v5#
zqdF_yRrN35@LyLF*H2*_l9qZ;HK`TJvBe^9yVak>dA!Sw=V@G0Ru67;K|?v0SU&vI
z_akirq~tsbTfc}pkkzkvbDD(hU^mc?PZ2K%8vgh2oC-wttP$rP+`R-=>cZa7E=z?C
zH7NMMgl?W6^rw`yY8F@?i7f)sM=};+k%gXN_v~La=E9_=?8kzvWlkMSb$z?Hxr9q~
z8!$a*={?k4rT}K+-ha!oG5qqWT0<<1xOp<*CC&ladu5g1_mc-^ms0VgD^ZDs)MQ|4
zqSm~)e~uGeMU?~oA5-VR9a^*{>)5t^V%xTJV%xTD=ft*c+qP}nPG`Ix@7^B!C+vke
zSIw&LH>l7w)Qyup_H72LNLR&_BzNi^&xw)d-*Wf|zXK8DiHD&whlVFLL`;-Fb`O^=
zv(vbWQeI6LXws8RP!pCy-JIlSkmAjb?s9ylPh)dK1w3xom;A$}1Mk(E^ZWAjimu3m
z#o|kTX{{utfw(ZU)>%98`jf8C+rMl0{y~FFF;DA18c|rsjHY;w$IL6~OSg{?N`I}h
z>B=qC`Gn+{#>+GE9_|HFd+;B!b7;EmCN%Xw!v1#!|6b2NS`fFxKYw&yPk=yB=^U8S
z+hs6#9!#ilQN!zjJMN}RCj3r8*y(Sl&j|Ug)5rZbGX;wldQ08hEQWgC4h`dKmf%bd
zPmam2JVHnMcfvVR<rIDnt3DHV90-h>mA$R$TSEz07lYl0xa4<MVXF?2`0_6N-``~)
zxjq=b<S=levP!K_3t7K<MgfHMmUqIIb0yqvhduZF;k;hH`QmKt11mI^UxY`q0LF7}
zp@Va8Y{g{tc|7L`DQTfJOfpp(W%%@adQOWab}QaB^dBLFv|5oBubuOn^6-wP?^>U#
zGZ4^ohhped^gJbSxi_`bc(07cs(f@H*v%HrYRZMiE8AMlFU4~!pmuz^v0%H<*ezy=
z1^VTH&^RK=6|Zdrw^=ae@~Y{_)|LhvB2DLdT*1HHkkKxpx1U{JM@shVGj9A+%8@mD
ze(*5co^Bsqia)P*Vna>&Ht<ir5qi87Jy{m*6YMr{;fcow0Kp^%B<0njXkNc7=;~-1
z;C#MmB{ilR@ONT$6_;x|8c_8OY5THX$nuJ)(RF{WtRDwS1sz2_4o2`}yF~u8F?I1k
zz8^TKCF_IrZK{`uN!^)EX0n47gpl>-62_Lz%&A>D>tlRMXA6|^{W|bGP<lK+1)`o;
z3S{I!A0@6b%;QTL^O~nn*|t6ivfcI>CZp_yEpx)3<lcK{Vi`#6oRd{hIAI37UwHgN
zeZA31N7xH0*C3`OFl?Zi?VL^p^&LrUI=m_HJa1Q0nV2DpShOqUtPgx86z2B%mo`F3
zb1<aS8G#o{WQ9u=xz3|Y`h#=e>EVQjyR-4!M52ZZeBF@Xx13Qwr=Ct5GP^$dXV}&s
z3dx|fn2SkK+GB=*e;(-ZIzL>ZB4=|;i}Ap;5#~o^`YO{D|6!a5x36;!u^fA0w277o
zc)N1-Cy4C_1fw7HuO%Stnu%)Ggr97>I}Cp+`NcOtb9Y3QwZ3eF-;U0AFwpej8pC>2
zJU^D4znI#r_>*4YJ{b)Q?b9Fh2dlCBk|OncE9}fpx_{SCZ!T=%uREqjrbcRVcIdgh
z2pV|-g^&yr6Y@fy8ItPtzd8lIy63h%eIj5%2kIzpgzc{GrE*xJkw#&GnIcpCYEn=)
zrlMq?uK&4PdiWteQ$WIz`;%ZerhJ~>eNLm9xlY(P8B;mM$i5By$3{_DN6bkVjdqug
zU6|7a8Qj;cgxenT_)es}v~2`(E3iD)t2N1oTFm8e$&XwYAM+cg;19^;M&vC@oR2nY
zKlvenh%@!+{}ynR@-7LGe3lIrJTDF29Jv$r8a0v@>;>Jy4AWKkW+V}yKWsG2U)N<X
zON8cEic-Jlq+zfy!X~!^vv<-V1AZ%r9W%7zR~5iHud$>$EI4B@vEu4<1(T1`1Xt8l
z<O{GZWWGx;14i_ahocSY-lo36U!R?t8rv*NPWVcGn4b26MH=dRHg+xe*1uy@z4q1N
zYFhOuC=UFX$w=da6T<`u!FkgoWLIOsYJ-^BP*@U}#~DS(S|5h>v{&BUiG*>ei>UK$
zI0Dfjo~FuJMc!aN=N-G|(=yin!eG@m>f3b}QI?3!B`Hm2vcSFPYz}R|QP#XU2hCs>
zna5Van&b>rQ$330Vm$2-WjdqA9MCQ}U)vVE@uF6c{;dmRWWk`#hb$^XxB`hRFSzx4
zsf&3HK+*_6S0$5vA+*jG?if6+E!o+KUfUm(c}ami3)rYYS9@5Y&4>QUYw<#dv*8O$
zwPQ=HrLmV`z@XkdNbEJ+O!8q4irg*mHyyIqEBI;eO(ezyF~ydS`6RXUqHc8uv75|n
zn8Fe{w%Tvc!Qoxz*&7Y<>^RMyUx$;?<a$r_ya0h^+(6B&y9)2K)MK>wiLbdp5%IbQ
zJ*oWc76~V?;he%te2RO&jE^KW#T=otW7n@=hKn`NaRz}mo*vnMvJW)mft{(YuLPrR
zwFMI`DiPRnf>^`kxGj3Cb)S6&*|spAfys;w5YrmzbB?jjsx|P=@cV1GGs=Ruc*wAn
z#4|BQ`H|3r6HIe5?LLZ0&?~>oN8e+Jh4FfD+U9(TNm+4OPAIfeqPn~)v=y_x&D(tD
zC7CLjKvo%;DK@fY-fS<vGmb?S>2?1xqoTTfBwfEBR6n}Ra=<OuVd8aN_#&o*nP~=T
z2jqibG9T)&e5bx<>7XKxZ?YYF-fNaB1;4k@q8n0Dgzl|#*Lt6R5w}0&nRQ{zll$f}
z@34<Gq3-eNawwg|r9Qomd}qANw!Z-RpfW7$;s3%>L)$v4ZWpwD8u&Q4<u?Cst>-^I
z=Wk);2JyM_noEUzGDHCr_PKBuS)I&@GN+{!p=!r0*ol+$*)WdI9C>qgp8tmP#l7MF
z2G4EE$_yM0cj~a1U$Fy1ri#vZ1VXI-y`61?q0~h3w?NzrhMtoYNhOQr^c<l519p~U
z!SwHEkc3kywWw3eOP{o_Geh2$HMJP1%v2FOm@v_B%CQHDbkIy~eEPT?BMQ>g*XhN@
z2|5Y)YIZ?ELI0_yW#+!JOwVgJ<LN+&{`;s>E2opez&l-Svs!E+NmKtJ&I3tPl!Z5O
zy7mISp%dq)Hq2^+3+{JQm7h;dote4ypMe`FvA-lG#5~fEMOIc<M0*7x86{+(#DS)c
z(y^a`K{a*oG*eq7E;M#8&qQ8wrl<N(QdSpdp|TesWTsc)K|6QaFy9$I{#`s>g2u=p
z^|XP$i8~&8dB+3clYm=OvAg~Va~7nsH)a-tdr131D6x7ZA#VQ|6TF604jlZYeRb)6
zRMa<Wmm9n51&-MEBV<p+`(?B1dS^GH@ig38-P;HLxhW@SK*z{a{Huesn~Qg2qhI0b
zGpoax9cl&8zHI1`>n8r?9t_j&+j^pNk}I7varF)@r7h#Cv}T-*tQD|P3^w#9t{zY3
zfbMuaf|-%G&TDWq`e)C4$t-JzT~!}TDzQ4@niJ3yf$>&r>}Q0HRf?pt(%j`2l%Xg`
z2dpe3Vdj)hs?JMOApF_eTv&o^SP$O8Hk-G7nv0f@4nVHbb~x1_8loZr>$}E6x+d*E
zWcNDYwL6^#-KYCyoCxJIHR%teAFa;s2JYSQfRsN)AplI^=vfWy30W@#_Tk!x>o-#6
z<%Yac&`j_Z<jJWcXC+h~<bJ=-_3prE(N!fNuju7sc0(GLm~btDww+=O(t#uM;td)m
zK!7JMgJ<u#&#f&3@9gSb>bBsZeylVc;Axzi?Hx@vVNXW=gS^>Y+vmsEb~R1EX<5$A
zt=2Xdzrqa}Bw&j<V+RXvB^Ee&)UFz>s?CnP_wyCT`E1n@-4CI!5-TQnqL9D5u@Dw0
zdL4jAVdEPNeno;G(}S&^u`Tol-m-Lnc9UI?SO#WL=o@5XJTKXgXai^IQa**u4m&fN
z=3(Ypef&~=<t%{}iVr{~45$ntBBy`Jqi^9y>|bw(h>Z!1j3Fih<}m|XX0fZyt)hf)
z%4&|@4N4s}8%{ycpUe&ZN7g3x(aQI<nKNxu9krDw;_Apkd0th!<N;1hI;*%gfNNCi
z9K(H+<wgpdozffBYLkyp)=&j)f$qwVOe?w9HM=m@3>2V{)C5cSO^eToTGdP*6&`(;
zZzpfJznV`!Gg$OpZBiZB+Nu^QCJ!Rmc?5?^EssQ)%<?Txt8YXV1Be(_JiC-`xfQXp
zyi@r!^-f!@UR1@olXWJFEPFw@zqQ*2TTWQ5MApUEd`xU3&bSg#Y$jR9IOBl7d|%lh
z_}9ly^cNX<gPt8;AL}m4POl?8z1`(BX5lyBrm8jGZTv0vZ+?A$S5?HX|D&^-1fE8V
z&C>n=qd#x&%{K}m(D3w(kzN?OiiO+_*9%`wt)-+QltImDmcq5WQDiuymExl{(-d9~
z9_<1SF%pfcOCOrt!{~&(>GHqt@V^}|6wnVS?`dE$uN@m#+$i1t5U0zc)ux*_OxhDI
z&UOP{P7R%~pg|B4%#@LxsIWAG7Vf~_%0Zd<P_UirJyt~H9c54}6t+<wg#j@ZCzp5_
zCowZOlFs$x*VD5~D(oDBnYK_IrwgMXHwBVjPuCPpsWoXGopka#u)TSm-Q~^ji>8ag
z>ZX+aP^nUD*?U}zEiSKmg8yi%F#A7wW+;Ubyx?cH^QqfMSQ6lyu^aJDC)fP$cK_nG
zMe6m_Sb>FRZ<IEE6#+7|O-{fu`@YlC_1GtDmwqPl5FxH{>a}yrQ9qTFMg7lx5plUl
zQC%U#X6v0XfgmD!@<eHWWXy7?MQ8aAdn6OtVUt5Q?M`9}gj?b~GeJWU2CQ}rGg*CN
zYaU$hlIlpP33+LpbGiNzKpEpdr8l|e#1}c@+qK|~rIarOFmk!7ZgNx>;~C;8Ie!wS
zpXe>L5b%(o!VvPRLD7)zaGLuMxk@t;@Q~p`yJZ{W;?Nxa9v7m(6>~|!|I9JjT~!fd
zruaoUJ;QZ&{HALYA@i%p3cjA1Ctu(rL7+!t!m`Mq?5eHEb24D#ARu4?nlh4J!8V=d
zOSJbD2I~rA#BVZ}PHe_Rfw5`%ENqEGy@>gmvUGpE3*nSLiu_H5ejn32YwQk-xDzpx
z2MF-WZ2dMOH;cK!<pDWR5->7)Wy7o(F4t(JvaAd!2<7W9>$9-65cOnBYXt-hOT#i$
zmD3G!@5rkuLz{xhB!&5DL(AvE{4*Ua5FccZ&2;jY8Z!;XRSXp~9?Zz*4ef$SQrwG-
z9Hd_4GXnN8hmN3nssm{yst_R{(U;8r;^SpUNHmmdA6Y1OUJ<#-Q9v3b=c3}5z~j34
zar<O<UNOQ}(;0}P%v{~^AV30Q7T5MgRk=DFR0u-HINe`}p9LB%7^kf_c=RH}FK7gy
zLDg(Hm3{>g5kayr+ux^oF-{yp%UFJnvUztA4DmdhSH=xJ!K)UI)`KzVF$bS4UDvk{
z0KcR6+G;+hwGw7i9jQhR(Vsqw;PwVOFrXZIJ#5iFf$+eA(F8$^b$<qj95uAE>3^6C
zP&yXp+K9m0v23U)%4>iWG8=GGsuvIn8Sp98hHQo^Kow)d7-3Oewuo^lNUY)5`br$^
z$kxJYIB*S&!FXW6Z6|~Uyp5@l*@TPl(~Zi2;!`^21Oa_c5P<t<Srkxk-gI|*!bUKz
zg!sKf(@t-Iv2sl{QY~9#zn_@9BI3cZ_K|?C1?tX_cF<cYq;De$=-;)CQqu8$25Q9|
zI#RUVex0(~5C_+(dJl}x*aF1N1;XjkU}|JnX%%(<%Y;haU}OL={z-xcyqUX>33}sx
z05%x`4Ixa{Qrc>*IeO+-eruAMxnSmk1$F6INv%*;l%-7<JcE-QLGJ8d-N3DkWJBF<
zFnh>8d4*KtV|7~g^_kv0W0tE_pEKB2LJF*DEadcZsN1QH9KsbU93dbM(Tv@|(26|W
z$hcejR(G>m6-|F{aK|`aOGMfZhG2~a9~9G@kr7>+!P>fnxZSe=8z{Q*iS=FaNGi+%
z6x10Oo*8~QK3yoFczuFE-L=KC-Kb#C6*ltO@kFGZkB{~wCZ!;OepYxv!y^(T>u!*0
zlw%H`ORG)CA@bJ_uKE6a&^Per{Rdb~i0E+BOc;wX4%7SNQWE4%-1vx#8!v!4e>@np
z_2^G;?Ax<-Z-{6~zmAv(r6>RhVsPSn2k1Qn4!D->29P`==Ie_Cb(N5*9cr;|LIB7<
zya>@P&W?f_vJSrH@vl6{g4sS09zY@w<9{;DDMxw#(D)*&JAljP1faIrmQf70xF+IC
z?!}1g)vw_>HoSlWcdKkdwx`^8w=n@O=c<$B?yJ;LF}SGY<qL*IRJS7ZA@fi`(q`qe
zLs6|drukqXd86q1x=PgC&bTo)dIL>W(Xp^Ma`&?+{gQc#=GqC!X$46>%q%W;+ry3`
z;og@86ItF5A~df>{s!T{Yn0dSAGIjRt*}~hyuE_?H1@3hfBX5b%Mb_zOneTx`v?9r
zAcX|d@Z5B4we<Fu$yV=pm4p!}zI+sLfi!kxHt5Lh`l$wiV-PpET){1%yOYqTn&h@@
z?9Uu9IBv1_4+Lr0aa3ZwSs+}^7mzLY57xam-Jq>a7vArT%V~+p*3uNNi3*nYn6|j4
z_!OzFJHrdrb{k^N&$BsmFvI?H*oWOqm1CX-%|Iz;${ckz=^+V(Ff_CEI0_U(_h5pr
z2XZ;R#oK7ud_tCgE(|&K@?Ks>^-yJaxE2{>U6H$6==a_lWcV#}4!1?88NT!u3d}3~
zP>}t>?#*hb4413n!=6V_Ng@5}yh2q`{VSt@h1>dW1chQT;o?@nWEOa>cb6GC#b)cN
z{UknWWJK45-A2KW;Cvj!(Bn!7d%iof6RXfsPQ=D(OE%K3=<&brH}{>o?+OU$a3bl-
zpeF7D`ha7f#>Up&JTc#B1rn!4BUa6cmV+^L9RbB2I?T?#bu7YK<<SYM0pgl(zy@UR
zxVAGx?pr6?zX=0#u1GQo)>ikl8t~r4O^S*)Bl8)@<d(O~)~<vJAm#YsYa#1MgE1Yb
z@S80Va8De+Q=%icA}n|OyZt{RR7sz+H}(Tj61R?|d7|Sh?ADZ|F__UQx}K1*$t16s
z4hp}sR(RbP7L1Of)Si2gUWq#a<oI+*Lyg;~89^i0U6A_IU1|^4$gdM24g{H#b5CrJ
z-xgk964W-S9OwfO&{;7Wx=2k4!!iB!zMa7}^%rwH(!=}lRCX({3uB7$mPB=>#1DC}
zGJ~+O+%;x&#<<k)HkBV6k#43JHjNAb>sKqA0TU&|d%Z1GcjHpNfvjoED5rz^`%Az-
zp2W1NnPHzd-{B=X9|+Zi;YNdCmb+j=cY`x3$C(1v?G|S>e2{WHUZA3X#QL%{g!kly
zsl4_?BJGWjO*+4hbrtmCO4=-d?;C|~PaS_k%?ptCiWgDoGw8kFVXU;?0Jpyl_`Gm5
z_ZY7|v^=+x!ko^89dnQafW0YpPsJ<*Va7c_>5aXha45pD5=2D@&QLpB>OjZ#8ly00
zgCuJygt*eKPa!e*EKoXGwADJC!JCyI2^eI+9m!5eN{Cj}!(3((rEX_7ND2RHagNUR
zvEwETM-PGDbVt}yX2l%~?q&1h#p*h8nH0Y5efV|<tB&=8lNQat=fSEM^|>AUb1`tv
z!F#H928?WPNDcp9HETl{j!Ro^$NTDGP|wPhlEM>|SlhuV+Tj&0l|wr^Z^&3Y9Prsp
zxE$(X?RS8i?ql2_oDmW=A`B&0SLA&ca$RAQq{v>I=0|-$7L<_;-@_ZVkN0z>n=Png
zOuw`@rujVyDg^tHStg%;@#?n+xgNllLy}TdKvBB;$jn=+{i)yu(Y}?be`JxpfT<kb
zq{TKG!D2t3c=^QcXk}xCin&YY2oWQ?ZaB!HWPhdTp1>!Url9SI;w|gmyGrifzja95
z&qkfF<+pc(ihf>9CowXhY#CMOyA8Hwk5BIZtmrM2)a<q`y%|ODjG=6#V#E2Hru@pw
zXt9KkY}sJ|Cq7miUEmonszgq^m-F|V)Ke7$`&{?~Lyn}sNeNPQDhS0=b~bnM74~*p
zrfzSxH-gBFLFu|ozk-uKOj<a4b}JI`=p$ETu4FdD%yFiv-cX6{Ue3;nfp}^LQR3{}
zVrMBW<jc=BTw#z!-Ek8c!`nC;gRK^k7Njc205$sL&|~<Rn;XsTD-ZfWTM1SUuP;2W
z1*8~JFfj_Bpk=9G-UkS`;|L7~_KiPHPG?9;@)ds^^2|wI%6_q?t!O+vXe-oo1hf*e
zO6LdMw$8sXM^^qiuec(_3lyd*u88*hyIsYezQwsQl=#{B2uFm?EUBL-s_MzXk(*D<
ziwZ5o3-jT<Oi}92H$<&AJFI@Mt*QW1S<mCBv{VL1;P3lk-PzLEfSedwD65jVfXWSg
zZ!|};Xulk?m)_p=v3BiomW{S^YwoW*lfj^1a=T=E3K1~-_z%N{cdNm|;(qZ8BK>Jx
zOgolP!R)e>t>_EdtY9?_ozqsh2&yP^Ry3XZAQjD=-7?o@VoqxQHT(!JjaW>N7=h^w
zMN|n$i&M$~hIfO`$3bHZf#^2kU}DL-$-^~9T-~l8Rj<som{tOOX4De``>~t5+4yiX
zI*zcFYj=z!lw7yD079-7H0bJI3-Km9cGla*-7}RO@jxa-PqUSoA>APKj8-f|xY74f
zST2G7dZPVm#z+*Mfgq9i{}~keZBT$gz9TW57}ydf7Q$)~7ZAiqE9;n{xhy*nQsO0t
zIpG}9ro||l`0y(!gv1@d%P%a-XmZiYbcL*wss5abT8vW8uG=pCSU6KM({CXj^Z>|a
zmFA%>N^A-F3P2MX8t$GJi)sq}!D=u2F+R~K1}?WW5At7UQsyh5<!bAj7(mYUA3#8m
zNLYL)s^m+BXG4lRI}>7X-$wdO7M}j${-BMIPe_B4WOh`E7#y2I=Jol8>NB!(Y(VNL
znR9_+SlQGHwQr~q@Q{SyEO)GP{Wy6%h%5-N0UW72!J(5O^(>Xia6%r2k0oJJ3yiYX
z&0F+@=+}IZpED?gY{nNfP{f$)1Q(qvt@G7al1Pes{rgYCG6cWLCY)J!=_$d!kbs1Q
z7Ichf@ie~PVJ}rFqAWMHIik4^)!N38frL_+&~3^{P;fsU;rKM>-i2q%$DBwSND2e+
zj;<Ik-XFxI)(4|=(=VGcou<MIb^kib)6bAav7>CRt#4;0cF_j9wUZ>bjkRzOX3K-p
zQfn8P9|OD7+*qK;<oT@sdulO`b3V~|d6NKxbG+7ElNG14;2*CC$b1Ri;NNt`@AEzM
zJ$$N-2}oXEpUZ-rvs^PgsSEI-!*pSMPUps;+k!W{97bJwYZPVHK}fn+uvUZheW~5z
zE0Jk`Z8N400_#`#AhP0vddu`$*RurjyylXU7EDwYmRWhJYWsUp?m&~Y(z}|fYSA8b
zX6~TmI&2!JP&-E#5p0>At*tIZj9qX!IR>Z}`3&PNY$KW7{_ZazVEGeK8V_`qbC3`<
zW3{^LD0$`Oo*IdKnwg=Ao^C+m1i`r_wa7~z^-{CzfKXqEee><~#CX5dRlUn~VVR^q
z06!o7Td91!h{fBaPyx+(nF7!UiXntF9H5alt)Y{n6>isG&KLs74)7YKARh_{$xZMX
ztSAPXi*@3dGZ=_dO{1%=Xy>C0Mp&T+^RH=Y4^NS&swILlEs*&b8-SGM8VncdXBey`
zaX`7!m)ms_0Tpf*wIb*=N#wG~VE?*L5{|X`nYO?#H=1FqOl+etxWMiTiVsw-ztVTO
z1Mm{q5LJQc<nTd2<iie&3rc|@{_2h6LGrN`tKY*z_<>JN6;+or!B-v7_3aNuG7v4p
zDjSQeW+jEn27>tr5Qas@ntI#r`(ufKdB~c%`>-eIU+jLH<wG`Tz&Bwx)i5C16_6@u
z5kDH7Q~lKK&}P$ASL5C0afOq5BN4T}v|mKl<rM<v*$c&GB1DcgoaBdm91X$WGf*!9
zgEz3C*zg2RnEdiZhzrp1wgdaeP27S<^vxt0k9EAN=4S?Gx<eQ9gcNk-{f1)sMD?f1
z88fR!Io$w`mNtd1d7XSEay|XQ0|L4IL*UFlw`MbwVkusDV?min`xO_WH!Fe3DGhIV
zOy$Df+=F^Lns{Yil?4{P;3@O}pqY3aF6tUOS&;LDMAXJwE}!8=LZnMvu*A?WANJqm
zbJe-^acju4=Qh&kNGl^6inJ2YGvnCL{eysc3_zZt=t>P-K^IJMV#wC=AQl}2m$%dj
z=?ZO_T`Uo>I;Zb9L$T?Ot_IAS*^d;$4SRk$d?URm3ki*de~E#kc(@TXv%m`WOd74W
z2$Hx6A<uU9L98>|_+;h0?!kfV1P_@?4TjfU2a~DiV?(;}3!s(Ojt|R&Q90MAHt3aU
z=TF;e^M&+)BH~OxpEb)pR=pbaB*C*mQR1XHUiKJr5a2I>ph;CPSWNXZ{jp;+iIOPf
zZv-6*{Z(_@!;^J<r2ocCQYEpIl!zWo@FYc`P9^pD0JZ4+YA&*9i6;1hHuirObqK|=
zSz!!|X$Fa7u()3@O7tbLW40>;F`N%>$Zu0a_)~liPjCbU>`^B<U4t0D^pr~R0|qmD
zIs!jJ&}eZ|t}@xrz62_gEkex-9i}`)GG@9Z9USUzlSk}7me2`CHz1#iKv!p?D6UAs
zhpqQk4F?#!4iQdu)kP~p`H$f6gRn|I$xW_8o;4o9riW~Ax<Qc>0%tXJV{kngQ}d^i
ztl`8qXCYgkX-yd3E*4(dC;a&FR%B(bH%GW1Ha5Ypm9{MhG1sN+J8e4Qf$}O|ijQI;
z%smxyF{Al*?6FBpLJCUif&vPJPUqhVycg{zQ-zt_RT4X<u+)ze2b%tyyP_A`%Yc9Y
zx8G9;@g@lyD<USyAJ~-B9kbJY-{yQxfxb6Yay?>(gga($zKDSr)KLTyOWQT^-f;lq
z)wthc=Xy01<BHd>%c`)zAm8XCAgQzlA_e+=JNoycg*n{&VBloTt!;O=eB^GB(c;o7
z7{toUi;=lWdO(y$3_tt?AbncJ<Z5uBg*yN#n3{fXSnNAOlQu&-M31r>=L?g!jDdJ9
znhBvPL=bF_J5IV<5aAyH0#?5$&e|`qX}YZ<+U)YaZwo!9#AVPA*vN)=eLbqMk>J#m
z1mIsIm;5S%xef|Ss@)H;qS<<d048L{O3&m#T;715gL7}W3`grzfKzL0ke*Huad&Q;
z3p?iJreInHGE(R)N%scO&!F1SWIJ~aghmZN$OnkQq?zhx*K`1gHQzlHWumMh@@ay&
z;&W>nGkghaWnDW=mIpe?ASeONIQGtb3*%~j(J;4e89rVH$jUXC-h9zwr3EUG&rQA1
zZFqPQLI?ndA)%0vb!eqO2*v^QQ<Fh#uV8BgWW8D5<EM^Lh!!N&AU^IW3}oMtv}j$C
z7yP%|hxh6-Z7q|*9Io76Huf>0`=Ib<o!K89O=X~{fBdFMcGA??5Ng4z2OUk0Mc%;P
z-AR5XN{ou_fL&@#rJZA=jk{GRHY2|QB@^J%op<)!J{Lr<qQwSUf&dW}OQn;aP_ES;
zQg7aal|^o6X&9#$Qn#_1HzQ~auK48sMggCrwaP3~yMNNqM@UEu#3CmB4gVC--6GJ$
zp%R1HU5a-tco8s_=l$3Ubdce-IuSNCA}&f^jqv@nqu|@*(;Mqei)TlIpO7(P2Y?r3
zp&BeM_52aSogsgVem$Pz+duQQG06n`Hj*5;(fy#I#mn)tUpUsk1iw&_v91A$J-;&R
zFJ~K~%mtC_ZoxtS8>W=j11;tIA|~DTxFRmzj_Ci*+*8!{vBwMMe5jFu3GR0ca;h+7
zaGYs>Ym(c83;v>aw2gNSj60QbMn=0}$f9mrwTy`6&9LoxO>41VWe*mAvK>x_--)Im
zB&t7l#B?`mdEqCH_zAg5LZ7wTwi@wBM*pYNI2x3WN12?4O`qWeANwdWCL+u2x!SX?
zY8t}V!W-KhHBS;JP-M*K)B(yL{Y!t18fV~OuJglkv*8}M=nmACFQjPiF|SD8{}~nv
zSWlZGJ-#6%c;{t)a5=?vVtR?n?giPm0Zx(!W~S?d`4|TJh$AtEe!_$~?kvad=^&NK
z7^Fz+>GktQ?)NSHs!;|Aee)fnN;+AuKT*Ba<Oj{Gu_p;f42v`>Ebq*TuDQ|!St>_6
zW_K3gdIKy59K*Ly@Kv!D14ps~B;p54DLXY8PYjl>sr7>Q`0bhWGZ1)IVIvm;Jai^A
z)=V3kh^W(C$D3NG{H$1EV?Rt2P1<N&5*hAhGxUXZQD0mGDmW~PXU{-Xwga<efrW^A
z71vpPXFvV|?|{>zcOprJpPB5XMBdneJTL%EY%VJw_Dvh{b;=msP|W~goQZAk=mT$X
zxVhl#`}--D!Pg3lw#^8TcjFaE+DC`^rMbGYI~GWy3VrIRK%vzUGnw}@J`5@eiQb6u
zI0{-Qi~?Nq8BPCmXaEtB4NmCp&lQEBIgOwzW;?_mvSNbSsepZ%v?#JT|J_s)i%QI%
zTu$(rO-JN40Rop}W4CKx{Cr<dmYYW2`fL1bTxui;hMGz+DLfC11Tok=fF_unp96}|
zvxKjEsvmn)W@5XSF2%DR*}S+m93Dm6;URSCkd_3%jFg_>ANT8=N-{^r1-lc<&fC#0
zaXb#pmMw13884;k%)piCh|nDL25c+Mkct~`$m?$>WeaOT_zbyB2f)9Zo(RaOd1lSw
zSIhl`*^l3^MDUPhT1;S_j&V>~Z>Lf{Ad$y%V&q$naMn8ErZ>K591gN@iDQwr!>{B_
zL<0eCc$(VEF^>g{|Jp}<h<zhxB@Jk`JioZ33O%K{K<97x9LctKcz5@*TOn+@oxw>-
z3iZ}2-&X6+jG5(WNnZS)tc*H?{gbT+5T#&_*tWrm1}*8R`vbsjMs5e`)0@!uwodT+
zI<Tb@6^5pJP-o_&iKi3Lq!%BhI5??}<US(*&x~J55P)Ic=gaMB|7^sXrsA2vukl&c
zE(R}RZs5Rgllz#4u7q?Gv#*agEZ3!Sr_^}25v6dpFpki4Of}qP9B{gu5M~!yuIevR
z+ALw%vkRFw@jnxmM_sw$*+%-mWRH2s%D9lAWU(aWz5V}0X%-Qd<#IVHBx$YYQTjo|
z3*8J=kr|t*g~BOe&WQh}C9GlR_}qg|QE*{&(qh;fngA{t!^je+bRiInHny<jr)$iY
zQ(By2W0#>Cv;<<*7f{KHDisL%x+5CUwZj$c3yDiF>aC>Q%sTP@OZPgL2WL8Av|uDH
z_F(K!uo-MEBro+yuRYl~qJI<pn<0JtFA5lrUS7_XPivDKfr|yxJ9rHO>+WZr3!c5B
zg7J&bvE+p}VJuO4%xK;dD36pMf=EPZS+K6Aqj||$+j+%#>8U1PO+|a6dQ~1lAc7F+
z_X`esWKBz#AZ1E8>stAF`PynrkO<ThU_SAR=W6*J=a`}Bd1yY-j`q9(exFn2i*oMN
zY~IOYVp7x>?N^F4E@{x!cY{5o)2*&McNjXPp51!ctjY2Ia7<5TE-bJvXm^t>Hl;bQ
z+Np?dmOV!*;@(Vxd?l%771Dj(o_U(U_mP>n7gYMom5+UFO36v7^>sDU^iP|(9i3IB
zMvv<7sbc5eY!qy|Q6^2D`0LBZ0oZJ^TOca0?pqeuW6yPcGc9)r`UK%3vr6peW`(lM
zTp2{=t{>F$Q@fWP0s+*1=ngxxGuzdhbwdYzb?T~HYX>5s{MqXEmvQufX1;eRZ0)X>
zbAGX5=NhVH=|UR7?j}lVL7TDdwz;7gpWo^E?v}&MCanPKX#bK^mY+|QNV2%LLvD$2
zRqyduaq8QY5qr=JpPl4;KXY=oJ?urc{IDifvnT>#E6^pg{(fl7y|+p2#AGTRI&0Z%
z@_0SBrTftw5u1S#un&m7=Vst!IsSYu263{0PEB&`l|ApoAIsA$@}w;^K0dWHpHWg?
z?YZiBg&R23MvOB9E-8dP4i@(9v0>3QqfhPwHaXDWDZ@SEVTqj&@sL}SF9~hir5qXl
zc5P8PEb^iQdNgf-2P6H+gN#hx8*1l&?v2f}=XKrrrcrAa6Or@Y{hWxu@@qe|-8<?h
z9Up{#yj!z-@HJIo|6%V<pQ4yUzzW$LGK`3;rIw@Fg**wzTu@?OFSpiwDXn7@KG^<-
zv8X&%fK2bu>n;%viwwJW;@M_7WAnXHH~tiwZ07`Ftf+n`_IQ>6saxdGXf|Ut>7l&Z
zP=w>PBPJo+A414Sh7E4NI~S3R!BBpSl=J9!2mDf^?~)f;Q}DPSfj7VCvluFrGlcCB
z7N{CQl>dAC-pe)6X>68}giM-gs691cSzP9p(u`}<XF0$L0E%&aJ4e24RRt5)CqtS^
zoOpaVBX4`;bFa1{uoz}COv$HXb%n*PQ(bwnQ(-&^58u4vX_-=tCWvvY+YuEtEeWGf
zz&_yFzrUz<&b)jEd1AiyCTYW0KD7bo`EtRnQ}Cztjm|VGrU`FhA@*|=dD?V}GJxGG
zroQh1`l7iXo<6sXDf#qiEiW!IOFGG1az4y1N5c~S{94XOh)DNsQ=O^niKqD|Y?-HC
zfT!`}Hj-;pj;?ZwL&Hfsxjgn@$lop}il5C=Cr=Q3L;0_Iu~5_!$)R#=FI=B9mdYE<
zdO}*SBN@F-C)&MW!zBkZXTXH-Dyl|P0DV<1FD?m5f6&9)WR^M;bSbnBr8~`gj;)W>
z%w_rW@JLogiiL(pBNm+PQVPN}vZHCKokunsT`Zqt&vKW1d2XpN_c*QUaxcR>%mq)f
zXU-i;6rW~$x->hy2``!?%g}mb#W-^F7xAp_Sc->FQ0^yo(RZF(a3T)=TozSI1p&AS
z>dO1lOHbZ@A@@`UR|bnE>CBFLxz{n-`#IZ<VOC_p7TJgh033W=0)D3}E19U6o9r7@
zZ6bOu?e)rDmgeu0d?s4{ThChhs9?R>D3>+B-Qu1+(@vtxbH%<@5CmEI_N_4WXJlv9
z8Mw#29gEvhWqMjsi`~-s4m#V87k?(p{o$m{VJo)rY1TeAr@tBKSvtLS+x*EH9~TG?
zFw{?|m(Y$Ah_I@v3J-JTjPLWxoAo2UtwLaVX|^1%Qq8;pVQ>w{+zP<ev<KRX{4U1i
zl=vT%r@=H~aR#@t5BsIO0Y$WcDM-)rzXj?hv=mbnRolQbNFf*Q46)JX$@7z<oju9h
zO}8N%>*RHt`wo>ZJ9&myq38YM%Yi8lqH@%}K=!Ulw<0nV%5a%s03#({>ZP|f!}qWC
zk4A6cLcK*tkmS>tO!J6gXZqT=ryc)~2IFW5%-OzdC-39+BFRWt<)h)Lc2i4qY=Y4U
zvx!3k43W6>3%vsPiwOtglA-g#YSX8Rrck?-!Dyrh`&xM2$vth#3TrX5z9u$byp+{~
z{Qh7ok8o4l`?A{>%QsS4rg|in2&CM$OEyY4_38?f68le_n4d0(do<$FDHSK*&InfC
z3R9)(w8M`kxAWz3(<Wj@sT$yu#ZRJ|h_SRtXNHv-h>7LAEjn>*IR5P9fF@MN4oSX`
z8x(560u$@})sS<eJi3|Y9xitPwJdenpXt99YsGE&4i)J_9eWc}K{7o#KBO{=8VToi
z2Y3|H0o3#&j;%k0GY5Uup6^MdTU56R$}1=U+hC63BUBe*HN#7&7gh*C&RA!|f{|S)
zv_bMgcF60M3dzgmvE6)QC^DMXt8=O*&Wx;H$n0e38L%~-sc{@<)Ym+3l+TLzc!Y|`
z#pIAh60U|t<#2qlg$$t#eoJ%b`YN@x!c)UrY^y61AgNW-2AV=67y~k>77&v1ln3qr
zB1#~Izy(`N7Nna5#1x`+62VxOcO7}y5^3sVm8nbXiQ1DOH%twifn1cVl5?1O<e?#V
zKz$xq0nWb6sy74C*tCqOw!>JREvl!}g9ye39wnY98u-`~PBL!_Z~X<sT~Gvp2XP0F
zb&#!db7dO626cd>gJZQ(x<`q8O0hsXf%@aY)HR*WqB6KblShBvN8o*A9&|UM@042B
z<*_P1zEp-xQFkTwb8?UmFbKz_9*%qFDOPem`_2to9`XRpZ2rg`KJyw+!l>}I&I4i@
zUe-$@(gIDe`l71%K{M4&WF|43Q1aYfP<6^9DW0nN#?GS81?{I!_}Yj<Wzz{z;%*iZ
zw>Hj9WtSCE*{*Eqw{f}^>Uh(~rJ(cGY%K+*bNogGk7+SLVYk$htMX&CTA5Uv=sHDq
zKO8At(Ny^hS1B$XaorjD9m0T;25wm#qW)+wmj-5qzb`gTaL>0JX0)7!&a~v={OKG#
z1{H&g4VlQ?mx>h%3ggdYc!1h+UeR7}$L?>v#?$JUX!Lo|8J&*^6G)Ch0HV)eNDsT#
z3&PX(9yw{ezk0T!hySC=CZD-$o&-~;d&(bwOp{jboFSTsYwc<{%|Moyf6_jr=2_4P
zFeD0Z*@>!)A?y>k5S^#AZg0fb2^a0L)rqq_oD|`{uhSG@*w-n=0!>ukj2x=m`~Vpq
zSIhvS`g2&%?vst`LiUq;SIu>2fIFduhize`Q`Ij;6+auQg3EK8m<Vx(?}h9=Om5-m
z&tizC1+3q<#pzF<21h4}Ve_t_czo&!&&ytYiluAYsw(b=GgB;Hgc#4{sZnYXlT?AT
z@+E~f_4hmf;%T4*x$$8Z(fP%xBBt4TQ~e4N6H6?(Wza1tDCj=wWP}NMCRTsl<Y8fi
z3H_Mk53&t%F!t+Q1aP?-cEE*yEjH=F>)*Up7(vG3ePyS0M++eMo9HH22L?b~7Byx=
z(wI82QBf%=P&XsWAm`GYTN)t6#ksXyI7Zz<1V&{1tU<P<1UiTDb(e8?uUa#3G_P02
z{<MWa3remaAdcu&kNxRiDoH%Y-=+M26J|Bh6$^B`yj&nS|9jZ({y~6nKl~?_n3;Kr
zYXEe-<8+rD{~hX$eIsNYh`$OF4Nae&G`DfmfcfCV^))wc+EANSW!LHIvulsTKKb!I
ziF|45=@d&K<Gj0%m27hIfD7+r<jC$KZBhH#nb$n@g!f4*<OXD*%0;n0Y|Fm|%;EL+
zmWTwTuQJkSFx7R()4GqktD;u1!*^GBl$Y;q`rM-a=qbUG=zcb((XJOm1fJgb))rm<
z2g;K)Sy0LqbyAn=2)vGjR>(ar^}u^NJG<gL8I3jdP9x&a=SqjExrG)AcC~C$rR`Fm
z0yUKC49PX;3YX7%rr_rK#u5u!-dnJ`(<oF>lN+5$yO&&AHeAJWPq$B*fuaKoyab``
z7!D|M!5Zr>@ijMgZZTz`n$%_fVo`)85`|5D+KTTki#xYtw{^`7`2oWEO9@2Iw!dk%
zzVxUQyx^W67Ny3RDhx0^J=Vde-50^-aIK$^mx&H!^BA|fEV9jvz4yoZ^?2#6R^kV@
z|5gYQ+ubmEkAt=xMoWYnzU&S_j4$s-Yes28mM5I_>(S*<P-r-`Uy8P&gTS7Mn$TYz
zFcPg|HGU%#V5<&w>=l0lNh<7h#K9G0`WQj-#f0dQ&gl!I%!BE1$UOzZa-!<yqUb4Z
zsl~9+zE_?rj|23kxBaD|fzX?@6;^l}Py;2cb|t}7AcpCx52>cd8{W#d1w@<;%SN9o
z;(M7f5d?gEdSp~)BdbrCvZ8PppeVNABOG43w}Ny1@D70<5wWl|>_>O^GUZz;y_kLY
zt9c}Q?gnFkt)@-s4VqSQuU~3WE`9jdWVq!v!29*Q(z6`$rG02jCc2Cg<Zl9Q2lL6Z
zvV~E?3N@+S8Q4RJY?<YYvvvg2J!is8>?bN*+dr$D{IRv1lHmQD(-h#NtC~Pb;B9qt
zrflYsIzN*CRPlj7msrYHUwwFy&bC>s?6%_M>QuTn7`@(Q9oYTO7pwmd%(8MO*!}E%
zj}A|TwQghL1ii1uzJgYprxcG-kC}DRY^pl?y>F{W<?vB@2Fk|yfg2zqlI5Y@)ohNM
z9f$zW%j9bzn;<C6f~ZtA1o5;OO=jWXrDj1|eE2s4%5gfvlI3N;0<z)kucKn1j_()T
z)=vYt(gLpRF>q#jeR1vgg0G$6Y8SO~iZ45Mt(x0j7`uX}v>}hRkw@)GL7sS>A;&C=
zZr5!rhN&?B-TBv~0sZK1OxVB!l;}&n&``U>w?!g<aC)|!i$5{0$HNF(!jZmG5joto
zXCAhL;OOKqs8>fwQg7F1j;uEe^gF_}ggX_Jy{}k!Fp<r#L;&qLok)Qke8(BoKd>Ul
z(=Zz4=g>P+Cj@c39FpKY$z1qV0F2A|cddYyF&bp(JyB965dY^MQJkj0iNf;c-qL3I
zURmf7GnIAuto<wFDdFZ*@(bb-k=)wK`j#QZbK1>^oA2yRpvL}&0mS%^w9r|yuo}!k
zqP^HL_fsDD{W|Y91l`oBaNzVqw6<?hE^uNAV&lZMsbi87?l#ln@&)_3XV9Xvc2Hbl
zYWLc5^1?U?iQHH4MYM}O_qKz=+-;Bik3xS<l+fV0&B@b%5V8gE1nU2*4tEjkAU**h
zf->3df!NNlj*gG-{dgV-pTM;qSbQRU4Gm&1#$x4q^mufh8o1NC46i)p!XT_*XSNm-
zMjYo!@{SXkfKYb@@PyM(b1A-ct7rw}5i({^YX0Pm$Tccj)yyrq1UOlnAQph^vGRIT
zs)R4R$F_$=J)g3pcOT_PCS_oiMMTq`m(lzgpH3Ybo<;#kfX=wfB}{6HJ#R);?bXT5
zJ@{C*JUBk#aTQnF><pBN>#x&|E*v(;4jAI*l~|!w7wB{gkbJgQR63?cr6$d8VjVSf
zy&j2vY#SB`PQ(jx|G*j=A-#Zo+iUG9DKB=2^{5LwnI4I1Bf<e<!bs>CZ+yW(iyP*#
zIPXy4P#Du-#2vomK=_JFFE8d1eORlVQLDr0uO(rfbLNC$h%ZXMpO5lSKKk2&b1W3c
zi0SFYj$H<!<s*0I49o;j`PF|Sf&aG8%*}CZ)CYwUoXXE~MPmDj5P)Dq+vU}KvD!WR
zGyP4pD-#vi8?C#Jx#QcN#pQ!)J79VelOkbzl`)3j<w)j>$UaiB@H(e;V+4H#F3Y~b
z@r;X(qp!FX+R}SVo(dLCjzq*b0nYtK(wa7^7Rm;-(tM6JmviLBYwWXj)djWAQwIgU
ze)QG!ec!TTz-vf|)@TJuE&B;S3Et5=*5Of-rDA_G{<#bJ)CwCKCHNjTadB09X-XP$
zHoKttm_hS2p4G=qUz6H^I@}if;khiXBXihP`-Ks<D@~!-B3${P>hkGjS<-hz_x+s3
zSZnu!Hz?xfBG7Z)_OYX^@a}?f@{k1ZZ?kdpjItM(lb8zI(mr+l<{PVSUO{pQ>%<)&
zgmz*l;iZ=DE9Y|Q`jqbb2FB#$+G=FuML?mGb#F^g5X{l!r9gMibeYv$!G!vWyKOoR
zz*2CNAYfOIy)Y_H;Q7*>AOAfCs_TR%g^Gzq;+IY)K}9XU*J(UHMx?wdp27Pw5IFgO
z0lSa+Z|%|Z>jIq0dBju2a(xly52n6epVJX4Vo6q#fX5h~_opQ`bB1%D+PM&fAv<ew
zp>L-dXxmVMQqvKI^=iFtnwqlMRD*dw0ZMF;rHN$M^>?gX*<qFK!sgi`Vjlxvk9-?P
z@c8%`8fO3FTFtk(w;SP80Xe2|?ej{?#fk2>DbEI<Vw;^KjLf70H}frhElVzZY~b18
z^lRD?<_y2dwxTxKxjdz)+ZLAs>Nfp)n{4Vmb757HaeMXIUv}tYGaHqd=jCH_b$u{G
zM(y&=!1}%vV2(dtfZOh5H4YZOS)be6YY+qBV8-E7Oz&ZVVpBk|v+m7E!nn?z=yZsk
za_onv%ivFSw%<UEJ)kc8!e27CE%c}dlNcBPe487{?=Pnf_!Wzq+wNV(3nh?t)oMA$
zayb>3RtD!nU{C%tJbqH%&8jI6?mlkLk9G1+FugT;%t=bX*2P~>$-xU)wh=*0%kVKz
zIeS^4$IH(Wj<L;Z1D#T9Kzmh3tSMih5n!7yG=@++b&Vi<S!m?`8{psXbp16hS$9ZA
zYAw(DYhogy=&-bIpZh(cxo8P+@R`Tood2n)IuRhw1hKc*)+8?Rt2NLsk&g-iyud7;
zrCB49d||Q`HX04q9#|{0H+v7*{W5Tkf?<P)%GUz7sA&R9#|$<;KX_b#4`$pq4m5eU
z?J5ZrRfWr%rc0v4sc}~0=2!iK<Iy0e^`GFL%R?s6+~X%OXhQ_j>kc)l{6$upL3nVZ
zF)XELqlsA5^*r^i9|l3#mMMSYHO1Fh#RRfg*1NrtJ)794$Tl*Q0RZUwB!mT&{1rZ<
zCM}u)Zvur)*D1B_&xk}|dP}=38+5o?D>)oFe6;ZOoMk2M%i7zJOl<yY^&N5@<X0s2
zlNRmI8lFdI<w?bxo_jlLngO@v#U_VWRmO`;D2CJ9VSW#RR{RK@DSf-WEv%<}qpgmW
zT-=~Pl2aysZni4mu}xg`-Y75M&R9VVFk4UIj>8ZycRqqzU+Wa6K>%5z9g}C{G;2GL
zIOn%1)&0^yx7=#$q%l`IrEE2v>-Gso9td}puht87e|A%_2BKM0Ve3>|LH*`*n+2G3
zLHT~8vE$5w8ZfkK-t0R&Iu$1``k!z2^byB1SY&uK^-4}w30c4DAa{Y#mY<GYV#sLF
z_k(AenM}CEFDZe@ogYE>2GaxmCZ;ao=oo;yK4g9BCbD!cRa|s$RFq~D0p}Vw(4{Zy
zwRhlZe+pUZSFO0FMV$+~)>j*Wz3w0e2Jh~d;ht>S-XfJNAJoE+yN(Llecr4LIQQC1
zv_rGiOG5E{WEBo0UOGq(St9&2d)o2PouybX%0?+5h>wJzvO@d<|EykHmhAvQsCttW
z=OH=QOW^A#Ts)wV)6X=RH=8f8p_Dh?Disk`I^_C3On30tS#}vg;_!FcX=*neYde-C
zQwz6Ra+OzUvhJOyM?B$yJ&x69YsWoQ#s=2Km>BQ9OL}n^_;)|XuTlKA9L^-`n0vzw
zX=$=1pE@p?ZGD~(TR{PO^epgzwmRzq&pRNk5HwoMx?-$bHPM!^*2Bn{(ABMbXj|{Y
z7l|y6b;&b2l~wRp@>pKL;z8d&_rnuoANKix=S8mf(24$Ud^&_TevUmLgr67WQ9rF#
z-~-liasFgdQbi$u5qm#qO8ilWX|35ncj+ULEHj>ry{@_c5C!c#Y}tB<6&=Z}G)}&G
ze0<z29zE2vxQ<bZpDqvN&#Vh=KZhj>(um_OcKv+d-n_P6Y9)q<ym$2YmZ<0|6{bm5
zFB<TaqH&WG{pv1Nu;UsYRVvh>!95kSboDOEjUwjLi%cuk3?NGiVH;?)(u>eN?{#=B
zT!Vd}{}{9_5mtBHzKreEwJK2;me0hsxw3hU7IJxXFoZoS*9jSLCJiU`6<YQoD<13i
zt~rdsNdsRh@O?ATOWMYwe#Tz~>>|^$ihwU%PFJHXm1@bQCJ2u=7_X4}S@su~Josle
z8-&z@gLOEY_-)s`VRfsy8Hzh2(|gBSSY0Qf@Pw0tldo8UXmVdk{^(3Ds?Wb$1Q#L2
z0CMEEpwZJ>X*XD*V3ql2!qU*s1hp<2WfK}Do(f6mr$Jl2&W*TOJDK~$BAx4{!3Y{M
zHtB7L;l1Cz@WcksMp>bf%B|4cv?;FW*6}l;X%{OC@F_zAxpY=_c_Wl&KfU!i@P2>R
zVuJ-A)G=s;q6=n-wyH`?c8b1^Yn<J+xNYs$EMR0~E`ujTE~S;wUp3wU_t7A3w>3XU
zgB=X|JhG=DMiB$zL=oi}I1Vm9njG)WuYzc82r(~5_7l_ltAAcb2vn{tgQEo;A9Tsn
zgNli|zdWAXMpQ+3VCoa_<pMNc6hIulA}019@8b!+%hV8ad%dmwxA4m(1HzNgGBP5`
zkog%t<evGs;GWFxJfS@KjFw4<RQ#Og1tQFcRw;+W$oW`WSU72m%l43fBO;w~5q+M7
zAEq<}F%2ASeR+JW#cbI5WMcw;gUF~l4FWl$)h((k7s5l-x~Su95}8ubk%VDry(%XY
zL4q9ONokwCMO@0hJ&RbfT?FTS6!5+c#Gl2r^$sj1KzTw)u@dynT`fLvyNIjqG-0t?
z*5}mt{li;55||<!2iFh&#9zMF18aik1sOJN3XXNT|I^x6hQ-w^-4Y}rIKdg*LvVt-
zySoPo?h+h=+Ymw$+=Ij5GS~pYeQ<XO1Q}chyPW%-^B#H5cYodfH&6Gj?yjoYyJf8^
z9_^I9W=gS2>B(nmn-VD#ukOz`TG}QHikTMql5Vu7o`YQOW1mWICb9(ejg{JeV^kO;
zl#-!E<~QXSJr_UMJh}!=>9!q{Ac4a_Va!Xl220Um<m3wDNlJ0ij8UoO!1-y0K3Y2;
z#Ua(|UubhcrOLjn%$o@jp^Nb^`AwCwe<nYoo7+)nIq8V>$VqWX*A+^R9~_fuN$QH)
z4JO+zO!gME8I+OSHm~+MV3emZy4Q81f3eK}^m8=M1qh6#F5s5MEG+PqL~ybPNOUsw
z>{INI#((aep8s9}?Q+W*%k;0&Hj~(4Ia&ykHhDPzkrN5n6BlxBFBG(}IGWJqEkbHK
zko~sFOcA&8x(~NzufVWnb4aqcuh$Q-K~=|;bu8<wN%;!iql$S(J);?lD>xe80Qfk7
za2t77Z3_r%E36OO77rxPs^`!yZ3>L?2rybJ5oG=nBk>bMa~Xx&I3q4-oj19*ggj-m
zzKw=ezfD>RbBbQcwPQ@3kF%=Cwc1jpB%Xe(A@7!HD8pH%IFZI~L9w)z>>Ded;XTGF
zW)!y6$(%SE$aZnL#B!wgSB-1oN9as!?hEoi;kyVS2R;tqZyS8_G6;K4&YDucRoe(b
zeN_e3zluZ{4duLUhjf_bukxf9%j1DlESsrvz{J}N3$1x%jh$}ofkxCHy4dmPr1qL}
z>Mdx45DE#ArTyXh-e>S1c>(_6Ro@=IMI5e%y#GtP#XLb7GYQo_&w)dni#C+-HOHwn
zQ=^{{|0@R$wl&(Z44!iFFarOJx+uP~vi1xws>u4#Z7e#wj8c8ak;U|>{@{ay?|Cl+
zy6W;u!{#knAhXBXPr~S-K(FO>tR%d2PBO9x#*1D>QU?tuu~jOdLkGh9pDkxxNjloO
zU&Y0(9B*=4ggieJA8g86^iwZLo_^$95#kjCF<#N~ze%AaOv|-HO<wU44gW@Zbn**n
z(K}Zeg_{at>jon{{h>8yGEgW@2w2528-ekK@7UZ@xytTvV#s0cE0Kug3_;YzGUORa
z#)K`Gt;NI405bU0pHClE?|m$RFUu_)T9cNxXCJ!o)CxV+6)}JFtH43IUfcVJpQbap
z%aMks{TFNeN(7`IZ$R8T^dFokYP3B*ek@3*RUwmHG1x6yxyk$}ErsfJ+U<WK`LV%I
zHZ-ZuC*HZ2Rge&x0pBEF9GH)>W=&N;aj!5B*ErYw^_DzNL>xIbEnqNe6&i5VUnn-P
zeAKF>tw7rP3=<d)EfhsAx>7_+u#6&}BX3Gi`9Yye6bn9?$Y(BhP$$|T-MfwR#Ow(t
zxcsV&n3==j4xG*)V1CE0xAoOm^E4;mn!fi{gUg!vZ+y7I651OJoq!XA6_h+<e{4kI
zz+_$?>8$CQuKclw`_yF{)ajlZxdfil+C0;W?e{sFP2zg2!*dh07M|Wk@4Kf8I*jO}
z;&fyQpW~V-A0eL)k<|WD@`%r&9cpXsQ#}E~bQm+)u13b!IcMIVwqdDJ2_fu>_mgU?
z-I<eyPef=(r=i5k`Oaw=A!*QXZvQxv&3>h<bH1HkOC{NV;2Ou6mDRU+iZQ5HGH1p_
z<g|)GKKuHJC-g+j>q>@93ww|3A;uq4k)mW^#?nwSI`h4})EjWU`Mf9R|Jw>UBt^QD
zh={1TRIAuRg`9W|2-!Xjo)KG=9pGL4&L{oInjHMyO1WV*dzhmAaA3Z@R!bUh%|pBa
zwk^?1Q|B`(=jDcVRNJ&`zyceLug&VZ6+N^GgC6(*urH(eM@@VI$*a%IW9g}}<Hp+)
zwBR<CSIQHX=C2KdW-3~?$`%Bxk8{&C*=B53J!qS~X?46oimen21qFMhs51=8K1o~c
zUxQEA<h_B&hrkLNX?{{rBAW9g&oCM%W`jK?#;E^*UPhO-@BEVaO}RzYP7#sdVnO5h
zu;)YU0;)5Fd9~RTii4j@@|?*}!C%I?lIo1;yOh$XSN}9oa@m6K+n!R2iHD}zxuYe8
z*b-X<cvf!v%lQ@nf#u;f4AWk;&zcMvdsZx2>49;^usOxo(HZ$1e}4r7YH#NDQzAZA
zua+JEjGQ<0tN3p`Y!U(8a@LQICVY;%NTG_l6D((_;PSE>QvxcS_z(7>OiOmWB~5BS
z5vsK<x(obCEvEM6pkIG>*h$Gx<MT+2dH*aY`V=(TVR9|1qL*|<$vicB{NN1<@_jMa
z75cLMP*3EKNC=9#1M$J}vXaZV801uw;8^TA^^;Q0&7l)>I%yRlZ$K-BSvkHXW%)u$
zdgG^rOaHOoF4Ox9)Pm6;xS#fgE{+b^EW!q?@x2X>TOKQA#tX$DPs>yA4CYtxxppw;
zZXgyJlyk4in73iJEHQP6PJgiKvDKR?4^NVjPA0B~w|tV=H;G3#za`S}ms*iBOFiGJ
zTaqF3)xp3x*+c)`8WuI)(SYMoKf4n_?gx@xFVuJC%^ik|9PD0Xb0Q!0PXxF)v#O&R
zFP4i$_UTl8pD7G`?{b=?Dt^=NKPLw*E33b`Qu`C-9cYNo_26v)b{0a^PAJ+QofGP~
zdGUMB2<t#|wEc>_<s37GiKOZqZ4&)rb*U1HhIR8*Yws(L8sFdMh`gpww_E169`U{$
zXcEtea<J{pH^g{P9((8&qbEO;(d!-(T4=Tbs7?cCoOLMOzxfFMXUP*oVprXuYW3<g
z!NE==`K4fby??hBT33W&w~)@%@&NF4i1L9u<iJb6FYekdcbz$K12*@L@hyHxm}sL=
zBE#XyhG_JlXrW8+!P6d%X7J23g_gfRw_D4k(OQ$DjKOyDQHg|)K<su}1WLkVT@d)x
zv2q08W952L9!XaHF+Pu_K8PC~ywX3*=7JIL{gu-0`L*AGA$4MYtHC1h=P3AwgxqOQ
z>*{dWeEzB6tnUkTr&kFPin*3j)9U>M`+oM*Lu6$tUKoyE#g~#o_z<|gR4w?15Oc$1
zLcA}c5w6$^HOdE=kA1nqENjT?(fUvTS6^<}J~%gfXc2uX4OaI-r{H0Q?K?`Irq&pP
z&48|y0!3KDX0tWfg_{zbyxPTyBL`lr$zEDMFdNuno@e2PChWEKW6_B{g?u;j^MCbD
z^b#d-s}fp5^;2f%5_6vpqeO&Bg;F1L;iF=~Gl|c$x>8pF1x2v!BP4sRMQ;VQ<(-R9
zl;}e{K<5j`ypu2jYOT2z`z+DXV|)9#nRf}Kl=S^WXzR+AqlfEH!SAcUaXqawsmlS&
z)){$tTS2H&z5S7=JUsn^rV@Te?{hG;P0b(aoiNIZXH&rr5tY0lZ}=r&Q~&HeJbCZk
z4!;InZ=+jHiGv$Y>;tcrD0Ot@nn?xy2N{E;Pg5XrYY#_Yd5P5R!rIoS)k|j4#oT$2
zFT8WN;~6z`qyCvUZ+=f(MW;9!a+`A*_Vm#H_~;eE%qmtiiMIy_vj^#z!J!Fn6FsAO
zR9LO-_+QTSI|lB%Yh|L8mzNqZ*$+}XKLY|TpT89Hi<~_H16gU6{lT>ocZvyb>8SNw
z{H{CpATIi<eo)JvX{i7`yh7~Y9p}K^ZWpThJz`5gV_e$<WxvRMUY)WlA56to&x-cj
z(H6G_j0hP<ri_yq`7XnN0@0PTWoIvpm!-B71n_M+XAP+Z3G&d)6R%@<y{UJvaK!hk
zqt157Vo<K!t*cEY6T1#l>D^}0&Y#*0XDvmC7_8*E{juO?rFXj@Yo~9toVn=JG79_x
zoO28XW#2o^%D{su+mpP0>PIOhc-+r5WO~s=?_wn+xD_tgQRzpu{PS~CM}_n4^ChpK
zpkeDL7)`O)5AZGjuv_qNvxTt4R9WLeS{enH@R`o15{y3$Fuv3&YDF~Kqp@1VTNK(Q
z{0L@FkhYqb5?aPZc@p>!X&`J>3HM744z)2%|N3+LS?}>%`zx}^N2<=Rjr2kj#r=GT
z<6VdEKSK5Mo^Hkh;qS38s!?;WT!J*so_bQI`I62{rBQ2kOkk}0*M$d^qE9C7cHs5e
z)M$Vc7v~qb%R@Zu{j^W|Ud8KNDiFzj2MXoDZd4_4jQ$nDX*@+0wPS)x04Z*0Z<!MK
zN$512qlS-S0n{IKA)N8kfXTAD^ClsaUlc>{g7y`)7JNNQ{ye#?G7@5VUDx2q5mOMp
zJ&32yIL4X0g-Q=>$n)v6fAQVT+9ze-oCoZF;uFJ6ddI`ft)t@AsuOY7%+8=Bpu$+4
ztXuBHVJvf+@7RO2j~d$lVM>&|3`Vo&TLh<4Ewf``VAX!v_Q7!*@ZstR^JN0l%n`Gm
zmkF)V99~Ze*zOb<a4S!u7raR&luL#}%b)PAanO%7^+I_A7kB&JE}kyXm1Sdg2SHZ$
zhJcJr>VEfpu|Dj+&5p=nVu<gc)q%bLj3AOe^)87_V{P+x1fA@cQzzBPMVJr%>^Uxo
z874B}H!K#6`_guE&0KtJNO6iv@>dZf<)q6HtwQk2Fi{Gk1Mu<O=AOV%ntz1oLq*gj
z_44(Wg4;|FLlY0htQCl-2(>ymHJ>C5s5KuV#VR^Urc}z$jPm4i(R>Xcq;uJ|k*oGq
zLuiHg&)YhSMKg8=m;`OLUd}gw-2{-e&!)t#ve@^Gpt=sKrlGU7)FwJDl6n@nep7dk
zCVK6AQn5g_aJqH?@4Y2+I#ibEpmc5LV@aCY3(KTiyfLyEDn|q{qn$)OhpM1^<vjKp
ze8S&0f7&Tml~nhADw?fADw{7U8+>LmW){$Wb+ti608r7xB8NP`j9~~Id@q|&nNum`
z+-9sayIgwA+C+*zUGheY%hd&Y;<R?9pt5BS@53$^PInhADglQ>@l3p*uMcO{sMKc*
z!r%jcr3C`}BAv+2R_A6}gKfh~+TLl}>m&6SLwLh{#~q>G>DQZ=+=OeW9yXX1lH-8s
zkJG^`Lq;=7#%|VE_z@V_`!dUS!H?20XuBhG`ekYzOU-&ItXWkuzULQ5!4M<9j*dCn
z&V47_tI34lDoWk4BP*z!#4;L*;?|LIRr-1KaqLr3eu5tWHdc8TiVBK!hj~V=V8@v+
zm#=;dUq)qMmALd_!odjv;^^EoG4vFti)6ivAy2KMcfr^P+|dYm`oix%34^=?+cG~b
z@xQ=pQwPOAy15)j8hGd*oTQ^+%fUiFQ%1PHzqwb#nJnHP<Exku{=vLv+=k}f?rf1o
zE9~&oH}yI7lBxLX$iZxU*NN>HW+ltRY!eN9Yn5yRt1tiD-4tlk<4#WLo0PF8JRoAe
zqNWGo?NbNpuR3;NRE{Ujwa1JQ_>fca_pZl$|Atel%CcxQ_dv+tKFP&(g+*pRXC&He
z*u3XvUl}CP@$joIU01tj(aWzStT)yO>GVV%I+s*#ZMPt!XQ>c@0J4$as8LW<3FXZ1
zFklxx&-_H`j8LmRIubVl4Z84X;@|YVto3bj4A#_Z-+)j6{E-6)CYrwXe>Q<V3i32w
zZV@^o3L*{e?CyDte-KgPL~&O4d9jChG!wF)@dMl@j}uv^Dw=QdyA?kHC;H&4_vkt<
z7sf@zL0!(1iwXBTa%ytwOgAzDyrEcb9dO__y5I0C4d&We@DH(K=;K@pfm~Ug$@P<Q
z$Dqn=MrEt=ux^LSQ=MnSqM!ytK2^M$a>RC>uFjGM*4>%T`wL*u*|4!p$FA<EVYOyX
zyF<uo)aD0ddR=8lmIFf&O;pBAzyQL>E9`J)?qd0OJ0^undaLV~2FD1iTBmOnRe-v|
zMir($XA0!qk|HU)D!uJ~*_4#RcjsH40ViAlIz9!{>A05NVr*u%eO}g)V$qx?B|b<U
z3O$DNrdy06FkXE$yMG!eDQt%p3;02peTGr>d1Pb7?_zKFM^4huwDYR*;`SO06oCU>
z?>Gwh5Tnj&YQd-)XhyP&)w#_*QMUuOWmvrr+wb+uN#>O~Wg3q47A{^BJ$u*7!JFUZ
z=T&VDZiKPvt?HnA4qhHlLsK9WD1P|aCe&r`%GK}BB39c-Em!F*lEm)UA%5->A*-G<
z78T7~Iq$t^*6ir`NskNoCEvGf#}#6u>eRjQxyr<fxI6r72Q-C?%D{KM+ciLGq^U_$
zSCrwk?ovUF^vq8k8l<Ll-mYe@c7`oA>a}mvK?Nhs#ZhFY@F{sBzqZKvGh=<vV+dib
zS_{QR51D!_o-4N=FSg}vkO~X*foxu(NmV!N90i{u*o8dYj*1l7Xgba~J!HiWmXn5k
z<p|{g1Fnx(SDoc26dSy!urPab$x{7doSVY&wP89fPpkdZdc)=(0F*gs^nL=xY>Qct
z+1lgfCxX$T9zSP*)|ff>{IVnl97M*8Pg}jmyMI%I24fJd%h2Y`L(R5qYKgi?Dtqe|
zFY4Kk$%G->@H;POzkOviJ^c;Lz*=?Bk~cIj%&QDw9d>cV=b-v2{8OdB`kIlN{m%<e
z5Wgdh+PlVuXteI$K$a}B9k=~3g1ZH|9Cg5{w-X0OaS#2vs^We=`O?5%n~7y&sUyOj
zmnV~~z`E${Qi+P1a7B7D+p@0|E|WZC3WCG+qsStBWQw$QOMzQwMa?*95<kJ!9soFm
zY_G(pe^U%_XVI;3<yGljgkR9whf=XE&Ud}A1=YzUPFZ(SQ7V9J^3z=tD*4;Y?)J{9
zj$ps@ibZB4k&(CKm#*GstJom+8?|vfR+P;{B(7JV2@KBBd-lu?9&?0eE!_y@S)~ix
zrN<>Apu3>v5YgdKI&29_f$HW;O*Jvkg!cQbH9I(qu^TQzx0YsuOLsnL{8+9_jGaxT
z0gH>&jOfEzN3f#bA2MiJtM@MYc8pvnrrPwqomTV$PeK$-aB&*jx#{=htI>+Yf`;K~
zTbEX>qtzm!3^*|qb=}cWPMx_R{PNFpidVZ4Wc{o8z(5gTD}~oj1i`ig8J1e|*xoL3
z(*_;-c*_DJMRlu!UKRMX(oo)R(77c{6t{qmIP=}r@$_SvC0aZXg?=`C$GVn(H0+f4
z+7Z;NGKz_Vc%-|~juw&cnEU1t*Fx6;xwEIl`Qb{hV^ZAb+onbS!ZZyIjWKY2blg33
zd09;)4-=a5R)AC2%`R{8WL~fVZUKQ#peATI+DG{dY1ev}2c5;UACxMBrWBYu)i1U;
zELPVvD~P}_4H*?ANfx`Q0TdI1HY4aU%hZoUVHE|bUph)1PA0`eB^eiqLRGX$`W?Kh
zPr-iMod}k(2Sz4pZ%5eSMI>|EC>O1M2bU*M3Qe2$l}|kbvQ*Vx`Ja6^N?%=ynKjE%
z<BUXLeJA8(P<cMD;t2kU%ljH2&cRExF;<kXkJ^!7L9L+C9z-x@bLZgCgEAHxG<g<b
zWY5jryYbfI2AQ^AKfOUkO|--%@RB02TX6$~LW@*j1kCuErXNHrFE`h_I5jg-*$4OQ
zlNLHq#6Hl#o;`ZzP_s?IchkTq=kIy4afoJXO#om!riDqOFP-D$;l+|)CKcKOGpE&C
zua4E(VJcLCi?fUr)DHlbb6z%e#Wb3MK6}aoW0B?ozD>@P!&#|VnWop3E{x^cESCM>
z<M-VA$RtP9U5c5p9OnYX;6*_Y>|r*3?D$_|<MUr;xRZUmY&S`^-YPjck^Sl0Z_?|y
zUGTo_W^{P(VaVKdZ_d+eT!Gz=l*daiZ$#OyC$^?hUK(x%MDAOV?${s+y;f6y<@-yc
z%vbT%m)91UzyiupSq%n`;5o5-?!fa`2~2^qxVTC<-xF0?s0*YLR<Awcr3+3E!=xvF
z5Z3<^CTKaaWfE)BdX9SPZQr*d*2T~iA3qyi2mR$C%%_4v2J9+i=hxDT*0;43S%lc-
zH;RJw5=-qsJQp1m2f#3mMNXs$Wg3C5ct8=LP|iatjMPWm3y<O>wI<{qd|T)A3<{^y
zP|SW)c8Qvb8E6jr=$3s#tlD89FC*ts(de?A+u#6Z;$oFGKmF?N9C`5_CtP2R6Wf&O
z`A+H;u@ozln%VL_i|%ban!=E}t4F!yH-n`<1=28;y)btR;I0`gQOfVIo$}8T_Mz4U
zMw>9aT!*ho{n#IRD<THEYa_l6fA5v-INJ@Sxo7C#dQ_v#B}4zHLP}@-sJ};15olFG
zdbe@>d78CajMF(K`a+xM(5%X7CY&!(lQY_@6+Pv=an|oS@w?6plW<X=*WSdd#<xsw
zDFW0bKZHjYu-~maEuavt-KMaY3o_^_;1+pe>3pk_GP<JFv5>6YTpvf3+2d0rhK7uG
z%!Dy*bGd)hR#Sv#`GZR{e6zB+avM5UzHlSc#f&`Io?uvyQLKP9u*I6qYEGF@%CQ{!
zJ=kqiZPYj{&o6XrESOsP{tmKU$03%Z`m>YaLcxs=Uu<v?(Yxl>#CpIK|A;MJ*VsTi
zz)R{u1eo08mhT8J&wXuY-Rmr9n5TOTwo=Ja4anK;{xtdC!Yx0m2atnWz>B{A=0}O7
zg?E2bGNZ^j#9NG)IM8WE@C*IzwB(l)0S_Kye|3rSN1e61Vm>BNtD;%Q-lt`uuo`XS
z*EI{DXP+K@<vof@Yz+|+MT<PXkGyTfelKONVuzxU%c#T#fU0|RNLApd8k?NvX+qBN
z%;x;p{Ni7h@$QL<oxM(quwur2;m|d@Dn!mj#w!{Xr{sC=|0qRyd&jpK+M>30bH97x
zx|eFL^15Fo!1zYcS7?>wi~eqPXfkE_b&$dkt2)=LJtbP=tYwn@nf|1OwtDKI>>6n4
zD-Py$DQLgRJHTh>{cq`vuUti@(WUm$c#YqLvKl|^)itJ$Yw8;O4rE{6Mql6{MeY8e
z>(zDGLcE6-i;trA8Ta)<!^qkD=}#kg&JT=dr1)w%w4N&ajK_Q|7SxVjO4ZhCY5p<E
zR{eYYheLM<azE1el5RfbgSRM9m3;KJOeV_C^7TjFmg>)>M<<z8Z!%9paD(r9xb4k_
zN2sn`fhz6stN?U|AJ4_3i>$&Ry*zu-!fDkk0)@fIX(otev#$6ZId&Wsa`j}a{V}{I
zt+{9K4N^i}xeQ@cI7WH>bb^elY&Mz&PBRFyI?=`<%W^uGc*bn1#>#2nn~v6;_k7L+
z`tw1q@XMsrJ6#hM4vCcPfKTYVj7$PWQmiJ^f|>dUb_H!xoSm$~<(|i<Tr+Qs1T@}o
zYe$b;0brds7eQM%VlkVkWwv|@llAvQ<%97Qph(N!MJ?m*7jH+nk^RFd9E^zB;FnRu
z)3Q*rcDQ|?U3GfG;nb`Yk761xc`pKM-G+ptD@p*mZms({K|Ix3q&sd0+P<((-I?W#
zjc=Yq#vwqlMD%3AW&ev2n^ynE+JP4MA<ocMDIpwVXgNxal~C}5b`awom9HYmaKH73
zh<C(}YMj<gHYRf^K;uO=lPs^T4p)FAj>Y1y;ymwI-AdDf=Q~5<{!^wG`8eTzRbraa
z-m21_8=1e_t;x%O)hee!PH6PUN5k?Ipl#K0E?vaszCn5Mf_|KjQV^h!tADd3<rqsk
zVw4og?c&3EX|64U<Xfu`KzyTqzeP7p=lZpPA$GdJv{98HZkI6AS<pV*v~bj<GGgi*
z4{~y0Mx#Gnf`#KQ5iuvd$PPDlD(&MU-R?e@F<1dm*@6V!NAO_8#fp9}PkaA%0BAMV
zVQoJAyRp>JZBQ6gM13*=n^tXN>6$Eh%Z|Gdx#b9Jd9d`}djOjiZ&(^#n&sToUtKZ2
z!%W%N+N^!>zCHne@&Z$6m~dMr*4i$7)c3WVo&$DS@=*u?!6e+@(v%+8D)2(}^TVi>
zSuBC1q{F%ux?HtRcQN)poQjZx9-D1<v-@Pn%&v=vT90udhqJg#%6H1>t72OZdFp*Z
z6j0(gc76eWk;kvKXR(8fIp=}DE=VqrJigE2%r4KCafQ!wjyC^s5AP#xn4^@6E?zMf
zbMd3LB>h;&r@DtXS*oL(^>xx_f1myqEmWiZYWx$q^U2-F_O=?M%Fm53(<9r$r{-v`
z54ZHgNvM?4uWr}C1a;2zR(7&}^?XH$(1c*WCjh~0fG89@c<SDxziqa~V3%ZV;<jbq
z{j7PbP_r%K!V$#B3G&i1IAAHTbgxN}h4W5(Na{2cg^C$`^C*rRcvA%(soQq7EBqlT
z*!jB-f6!5QNLbVraqgmv=7x?1BO3PuR6qOGulyC}3Xs;u<U1Z(dgVejCv_CQ;SmK5
zr-ooyj*sjq1_tf<p!trZJj1FadT}juzTD}R+IrfX0@EKCx`-YpKfKPYyjFz%LO5oI
zcA`+{8pXjlNyx}zBf?cE5UUdZAX6l-hEe48E-%S_05mTDy0<M>VQw6(&)k;H+<sbo
zG)yOU!6UtKpCs9_cObp#RJ)oxa$>2Hil;|wdGq2SpYL6Lx#F7Nu~0o<jEbIrzMy>f
zkL1usa{7>7^&C@BJ9YL<8aaM1Z`PwH&vGa7SdD4YMbUouaf?aHn9Af)F~tOX@LfHP
zX7NUvqhgFggksG{EtzsP@Y<tug+XG4<|cQr?{OfhBD*Frr)Q(Di2sJ-;pPZZcla4K
zOCO<&%^V*!M<!;6f$}T!$t<R2=(5YGzdtGnNmsd>83Vn%_k&^PZ-tugaJN7Y?5pa=
z_~)TOtP>klDpN#lH($2Bpu(Gk`I+jSS}1-Q3>3X}?H=i(cv&;g>viGh+)~rzh=qGj
z8Jh$J&B+>WZ&>#m`b+fU6D8BOltVDTy}Jt5camB-F=9E{ws3k~*un9-yrvlwykzy9
z8_zTg7>yxd{^rvsPy-S57j<O3PNd$|bpMLC8IdfRT~|LsD3O9Io*-^RG7t;7uaU9s
zX)FnTmfu?%!Mq!1Bze<T`mv+2Q8;e|Ik6co+sy4fkTVgl$r@{dTU<qh<~kuxncuPu
z$SW#(aufWLTZx4c`+x)cKWbFOJV0;V7f*ctY|b}XwUJW20Z1%t=rO+#|Ijv3^y0`x
zvXSU0gk)S;hb4P0D#L-8aPMp4C)mN*ct_h^g&`NMuF$M#u9@%y&2yk-o?>X2!e!%Q
zt!N{sz2zLfPv8o<efNxjD65PH$0_!PePFH4y`^Xm{7vI6VH>7wmqkrczwfm~>n|R4
zj`3F^rxaGOPJj*2@4_PQ=m^%EB`h1GFM-JpDF0sNB0?VfF1XV|q_hRkrNYdy9aG&V
zr{X!i-SiHgC0{k_0x?&kv&R$V<<5?s<j)ENjP@7C<4cRJ1V9IW77Coi>}#aci3@{V
zQ$j+kRiZwtm58Wu1m}*L0%IVxaUYyzT(>-TWzMM1_H*KjmpSaXUq=j$Z>OmQwTg{5
zo3+7E!TYD^$(WF5uVkBAc93$1&y5@MP3SXxCnk9b^Ink-&^o(AQMraBU}1}&tR*QQ
zz;U$XgFYR&scrOxXj$JZ0}&?}JSE(e1p9M7eJ4oY<$1-(M&V<5kQgk2A&kT#c57f=
z#g_~jPv&M83}R*b;9=9bMf=)V?8LF__H#o?K43{u0J>!>hjYGnQDcQh=|79PWj=*Q
z)Tyse$9B|oxus&cHL-6EyMO~Zowm$V8(IL${v_9lH8IsWOJt3=F3h3kI*$_21n~!n
zP{FiNTl|B7cgc4e76V&&1C~c3RY>Pg+o#{xvd%JjKG$gaAFT99@OpO%4+nF5;0>wL
zZ!B2Z%>s+9&RYItb&-;)X56(bPET@!=-Sk1G(s`B#UZ=D_Ml8hb_aI@;@D_QSg6{g
zaI9o9N((2%sln%K55uGQXKvl!(9`_LV<F<>Dts&N38mUr)@o56$ef<y0%6@KKB$QF
zYeQ-@xg8RDD{>cWF0E34DYsz;$X5e2VT&rU`bfu0;O|-EDWSzhDyF#Z2y6*FYPfOl
z@@{`MohOYvX5^+l@UG(Z%C6{^u^#;3VU;(A;hxml#!wGB!v>l~eM^W1yIfF0*eP`0
z)Q`&-p3=ECo_q2Lav{{AHMMA)uwMH5<Qu!vuU7jTw{7ClgRn2JLlqS@m`-(;q7H;r
zCvoH`hZEu^7$bZcJW#@$w>E!m_Q?@>KcRO9<j#)$cB?<N+X+F#T&odsFR&|ptac@p
z3)#_hd~j{KbU%x~Fa0%=23Uyx)Umu=)6nNoS0Mmw9Yn0=Sv6`^C-iD;v?F$o+8#(T
zZDpA2^%n~Yw9Mce2CpIg2Ii~YSp+OEq(}8wZ(THuV;Ae3eH48;`}5oZ?Axp_Hs5sa
zDk!BX;@hfqcQ|r+pmuqG()Y$8La#W5#GBE<JLnj3?itzP3pzDb6zcmrI+#NG6w>N%
zDz$#wUuhI_(A@D4?&Agxj)m2GQ{xmE_7fVIE;z}=CPV$?sK1OG$kSu|vETKyK_$j+
ziBtN)t(Wa%*9!nyoo$-T&TrmP-j^)>{_hJEUz535hE%bYSagLNoSGV(qU-E>q&tO<
zqMa|kEkYHXS$x_p%`n^WG@Y{^kN27FJr{)2W7i5;cagL@^m1a4#6Gy6uNxCxpzrL$
zy(X`#njRT2HqFlrNeSQYwk-|U@K%l{ZU)3v@a$p~);0`>Tf$j)gy$-`kA^Qr^7et0
zru%Vv4s31O<d5@frUtEMF;utT0%aSXw4vB8EDve31A2ZiJ;6u}HOp1+-oHB}8DcOC
zBDG5g47ONr_q|21^+($47fDXGCbxAo9I+pclm>K(txUuoCSFJ2^qUZfp$w61mON=o
za!$rHr)7M#Aj~^|yW<Qq$Z`w|5M^w#Btxw7LS`5Z6Vv7ouHZX$sR{~d7-l&<^1Yaw
z?0DRbU-L`&((_MP89(x)II`rM!8;4KLfw1S*x>b~WxL&k&oEP<9pt=(qRr*qZp1qm
z_<IYrr~1$u10K@}*_beBf70<iVE>CJ>iutApWgFzcC+8;?JpliJv3L%ro>$XujZzW
zDl7kP*%L!qDyw<g=lH;RdXVqRK3MS+hYNM1J+MSv5`m<5e{IYDj->cI71o?aToa)8
zXn{(Virpm*V^`&Win|PJea=Som*)O@(g%|YXQ20jv?R7X^uN{rO){G?a^~ltjVp*l
z^M7yB7K!7E2zMyqHrDw&N9;d(TJMtSeEo;Wd95}1FB;juHR*IfeZiR95h>WM^H1~s
zHOT*egD9p-3BK!s1<kqtwQBmm7?l6f+&Yd=1=TvspQ@y6*UNv?>HoX}{{~h;Cw-CA
z*T-gyV7&Dg`S`Ee{}FRyDBfu>ZiWt7-~W%AfBYm8Mid=F!45_GHzoXy$NY~L!FI?2
ztgNi=l5n#Y!1BX??`mCAu2aw081(fk4t5l0v(E*3pm@{O|Ds{DmoLnurF(j^!nQRl
zRX%U5{@cX=Wwz@&(w$)22^vgHOh_!)6YTy!Fr9y>=wDlUA_qVq5HvLR<nyTzx19ez
z%1-JNdzy72pOf4;iC`XF@9Y0fV5b4j3CgQiY5_2damHL%m;dfd&*I8x)&T&I5m*)T
z=8^TkdC9*#!jF#FUKg<2{=fD1S!!KJ_Sq||xYmz8{jWj*h^pdZ0Oq5AuCo6_ZEZuS
z{4XAKUM{bU{bjuWkY*$G+>MP?Z{Y4f4*dT^mmzv2fAjvK&#g-W|KJ4t(-r=&v~|b7
zb>mplbo?(<{1;O^YlWG1J-=<}*<c?3FM~abb)xA+;5{9oAUqcZ8CB^@$q%9b2iFpW
A3;+NC

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/deploy/run-on-aws/use_ec2.rst b/docs/python_docs/python/tutorials/deploy/run-on-aws/use_ec2.rst
new file mode 100644
index 000000000000..dcbd67841596
--- /dev/null
+++ b/docs/python_docs/python/tutorials/deploy/run-on-aws/use_ec2.rst
@@ -0,0 +1,74 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Run on an EC2 Instance
+======================
+
+This chapter shows, how to allocate a CPU/GPU instance in AWS and how to
+setup the Deep Learning environment.
+
+We first need ``an AWS account <https://aws.amazon.com/>``\ \_\_, and
+then go the EC2 console after login in.
+
+Then click "launch instance" to select the operation system and instance
+type.
+
+AWS offers
+``Deep Learning AMIs <https://docs.aws.amazon.com/dlami/latest/devguide/options.html>``\ \_\_
+that come with the latest versions of Deep Learning frameworks. The Deep
+Learning AMIs provide all necessary packages and drivers and allow you
+to directly start implementing and training your models. Deep Learning
+AMIs use use binaries that are optimized to run on AWS instances to
+accelerate model training and inference. In this tutorial we use Deep
+Learning AMI (Ubuntu) Version 19.0:
+
+We choose "p2.xlarge", which contains a single Nvidia K80 GPU. Note that
+there is a large number of instance, refer to
+``ec2instances.info <http://www.ec2instances.info/>``\ \_\_ for detailed
+configurations and fees.
+
+Note that we need to check the instance limits to guarantee that we can
+request the resource. If running out of limits, we can request more
+capacity by clicking the right link, which often takes about a single
+workday to process.
+
+On the next step we increased the disk from 8 GB to 40 GB so we have
+enough space store a reasonable size dataset. For large-scale datasets,
+we can "add new volume". Also you selected a very powerful GPU instance
+such as "p3.8xlarge", make sure you selected "Provisioned IOPS" in the
+volume type for better I/O performance.
+
+Then we launched with other options as the default values. The last step
+before launching is choosing the ssh key, you may need to generate and
+store a key if you don't have one before.
+
+After clicked "launch instances", we can check the status by clicking
+the instance ID link.
+
+Once the status is green, we can right-click and select "connect" to get
+the access instruction.
+
+With the given address, we can log into our instance:
+
+The login screen will show a long list of available conda environments
+for the different Deep Learning frameworks, CUDA driver and Python
+versions. With ``conda activate`` you can easily switch into the
+different environments. In the following example we switch to the MXNet
+Python 3.6 environment:
+
+Now you are ready to start developing and training MXNet models. Once
+you start training, you can check the GPU status with ``nividia-smi``.
diff --git a/docs/python_docs/python/tutorials/deploy/run-on-aws/use_sagemaker.rst b/docs/python_docs/python/tutorials/deploy/run-on-aws/use_sagemaker.rst
new file mode 100644
index 000000000000..d627befbd8fd
--- /dev/null
+++ b/docs/python_docs/python/tutorials/deploy/run-on-aws/use_sagemaker.rst
@@ -0,0 +1,91 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Run on Amazon SageMaker
+-----------------------
+
+This chapter will give a high level overview about Amazon SageMaker,
+in-depth tutorials can be found on the `Sagemaker
+website <https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html>`__.
+
+SageMaker offers Jupyter notebooks and supports MXNet out-of-the box.
+You can run your notebooks on CPU instances and as such profit from the
+free tier. However, more powerful CPU instances or GPU instances are
+charged by time. Within this notebook you can `fetch, explore and
+prepare training
+data <https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-notebooks-instances.html>`__.
+
+::
+
+    import mxnet as mx
+    import sagemaker
+    mx.test_utils.get_cifar10() # Downloads Cifar-10 dataset to ./data
+    sagemaker_session = sagemaker.Session()
+    inputs = sagemaker_session.upload_data(path='data/cifar',
+                                           key_prefix='data/cifar10')
+
+Once the data is ready, you can easily launch training via the SageMaker
+SDK. So there is no need to manually configure and log into EC2
+instances. You can either bring your own model or use SageMaker's
+`built-in
+algorithms <https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html>`__
+that are tailored to specific use cases such as computer vision, NLP
+etc. SageMaker encapsulates the process of training into the class
+``Estimator`` and we can now start the training on the local notebook
+instance:
+
+::
+
+    from sagemaker.mxnet import MXNet as MXNetEstimator
+    estimator = MXNetEstimator(entry_point='train.py', 
+                               role=sagemaker.get_execution_role(),
+                               train_instance_count=1, 
+                               train_instance_type='local',
+                               hyperparameters={'batch_size': 1024, 
+                                                'epochs': 30})
+    estimator.fit(inputs)
+
+If you require a more powerful platform for training, then you only need
+to change the ``train_instance_type``. Once you call fit, SageMaker will
+automatically create the required EC2 instances, train your model within
+a Docker container and then immediately shutdown these instances.
+``Fit()`` requires an entry point (here ``train.py``) that describes the
+model and training loop. This script needs to provides certain
+functions, that will be automatically called by SageMaker once you train
+and deploy the model. More information about the entry point script can
+be found
+`here <https://docs.aws.amazon.com/sagemaker/latest/dg/mxnet-training-inference-code-template.html>`__.
+When the model is ready for deployment you can use `SageMaker's hosting
+services <https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-hosting.html>`__
+that create an HTTPS endpoint where model inference is provided.
+
+::
+
+    predictor = estimator.deploy(initial_instance_count=1,
+                                 instance_type='ml.m4.xlarge')
+
+The following links show more advanced uses cases in SageMaker: -
+`Distributed training on multiple
+machines <https://medium.com/apache-mxnet/94-accuracy-on-cifar-10-in-10-minutes-with-amazon-sagemaker-754e441d01d7>`__
+- `Hyperparameter Tuning
+Jobs <https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-ex.html>`__
+- `Optimize a model with SageMaker
+Neo <https://docs.aws.amazon.com/sagemaker/latest/dg/neo.html>`__ -
+`Build Groundtruth
+Datasets <https://docs.aws.amazon.com/sagemaker/latest/dg/sms-getting-started.html>`__
+- `Getting started with
+SageMaker <https://medium.com/apache-mxnet/getting-started-with-sagemaker-ebe1277484c9>`__
diff --git a/docs/python_docs/python/tutorials/extend/custom_layer.md b/docs/python_docs/python/tutorials/extend/custom_layer.md
new file mode 100644
index 000000000000..402752d067dc
--- /dev/null
+++ b/docs/python_docs/python/tutorials/extend/custom_layer.md
@@ -0,0 +1,266 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+
+# How to write a custom layer in Apache MxNet Gluon API
+
+While Gluon API for Apache MxNet comes with [a decent number of pre-defined layers](https://mxnet.incubator.apache.org/api/python/gluon/nn.html), at some point one may find that a new layer is needed. Adding a new layer in Gluon API is straightforward, yet there are a few things that one needs to keep in mind.
+
+In this article, I will cover how to create a new layer from scratch, how to use it, what are possible pitfalls and how to avoid them.
+
+## The simplest custom layer
+
+To create a new layer in Gluon API, one must create a class that inherits from [Block](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/block.py#L123) class. This class provides the most basic functionality, and all pre-defined layers inherit from it directly or via other subclasses. Because each layer in Apache MxNet inherits from `Block`, words "layer" and "block" are used interchangeable inside of the Apache MxNet community.
+
+The only instance method needed to be implemented is [forward(self, x)](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/block.py#L415), which defines what exactly your layer is going to do during forward propagation. Notice, that it doesn't require to provide what the block should do during back propogation. Back propogation pass for blocks is done by Apache MxNet for you. 
+
+In the example below, we define a new layer and implement `forward()` method to normalize input data by fitting it into a range of [0, 1].
+
+
+```python
+# Do some initial imports used throughout this tutorial 
+from __future__ import print_function
+import mxnet as mx
+from mxnet import nd, gluon, autograd
+from mxnet.gluon.nn import Dense
+mx.random.seed(1)                      # Set seed for reproducable results
+```
+
+
+```python
+class NormalizationLayer(gluon.Block):
+    def __init__(self):
+        super(NormalizationLayer, self).__init__()
+
+    def forward(self, x):
+        return (x - nd.min(x)) / (nd.max(x) - nd.min(x))
+```
+
+The rest of methods of the `Block` class are already implemented, and majority of them are used to work with parameters of a block. There is one very special method named [hybridize()](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/block.py#L384), though, which I am going to cover before moving to a more complex example of a custom layer.
+
+## Hybridization and the difference between Block and HybridBlock
+
+Looking into implementation of [existing layers](https://mxnet.incubator.apache.org/api/python/gluon/nn.html), one may find that more often a block inherits from a [HybridBlock](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/block.py#L428), instead of directly inheriting from `Block`.
+
+The reason for that is that `HybridBlock` allows to write custom layers that can be used in imperative programming as well as in symbolic programming. It is convinient to support both ways, because the imperative programming eases the debugging of the code and the symbolic one provides faster execution speed. You can learn more about the difference between symbolic vs. imperative programming from [this article](https://mxnet.incubator.apache.org/architecture/program_model.html).
+
+Hybridization is a process that Apache MxNet uses to create a symbolic graph of a forward computation. This allows to increase computation performance by optimizing the computational symbolic graph. Once the symbolic graph is created, Apache MxNet caches and reuses it for subsequent computations.
+
+To simplify support of both imperative and symbolic programming, Apache MxNet introduce the `HybridBlock` class. Compare to the `Block` class, `HybridBlock` already has its [forward()](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.HybridBlock.forward) method implemented, but it defines a [hybrid_forward()](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.HybridBlock.hybrid_forward) method that needs to be implemented.
+
+The main difference between `forward()` and `hybrid_forward()` is an `F` argument. This argument sometimes is refered as a `backend` in the Apache MxNet community. Depending on if hybridization has been done or not, `F` can refer either to [mxnet.ndarray API](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html) or [mxnet.symbol API](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html). The former is used for imperative programming, and the latter for symbolic programming. 
+
+To support hybridization, it is important to use only methods avaible directly from `F` parameter. Usually, there are equivalent methods in both APIs, but sometimes there are mismatches or small variations. For example, by default, subtraction and division of NDArrays support broadcasting, while in Symbol API broadcasting is supported in a separate operators. 
+
+Knowing this, we can can rewrite our example layer, using HybridBlock:
+
+
+```python
+class NormalizationHybridLayer(gluon.HybridBlock):
+    def __init__(self):
+        super(NormalizationHybridLayer, self).__init__()
+
+    def hybrid_forward(self, F, x):
+        return F.broadcast_div(F.broadcast_sub(x, F.min(x)), (F.broadcast_sub(F.max(x), F.min(x))))
+```
+
+Thanks to inheriting from HybridBlock, one can easily do forward pass on a given ndarray, either on CPU or GPU:
+
+
+```python
+layer = NormalizationHybridLayer()
+layer(nd.array([1, 2, 3], ctx=mx.cpu()))
+```
+
+
+
+
+```text
+    [0.  0.5 1. ]
+    <NDArray 3 @cpu(0)>
+```
+
+
+As a rule of thumb, one should always implement custom layers by inheriting from `HybridBlock`. This allows to have more flexibility, and doesn't affect execution speed once hybridization is done. 
+
+Unfortunately, at the moment of writing this tutorial, NLP related layers such as [RNN](https://mxnet.incubator.apache.org/api/python/gluon/rnn.html#mxnet.gluon.rnn.RNN), [GRU](https://mxnet.incubator.apache.org/api/python/gluon/rnn.html#mxnet.gluon.rnn.GRU) and [LSTM](https://mxnet.incubator.apache.org/api/python/gluon/rnn.html#mxnet.gluon.rnn.LSTM) are directly inhereting from the `Block` class via common `_RNNLayer` class. That means that networks with such layers cannot be hybridized. But this might change in the future, so stay tuned.
+
+It is important to notice that hybridization has nothing to do with computation on GPU. One can train both hybridized and non-hybridized networks on both CPU and GPU, though hybridized networks would work faster. Though, it is hard to say in advance how much faster it is going to be.
+
+## Adding a custom layer to a network
+
+While it is possible, custom layers are rarely used separately. Most often they are used with predefined layers to create a neural network. Output of one layer is used as an input of another layer.
+
+Depending on which class you used as a base one, you can use either [Sequential](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.nn.Sequential) or [HybridSequential](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.nn.HybridSequential) container to form a sequential neural network. By adding layers one by one, one adds dependencies of one layer's input from another layer's output. It is worth noting, that both `Sequential` and `HybridSequential` containers inherit from `Block` and `HybridBlock` respectively. 
+
+Below is an example of how to create a simple neural network with a custom layer. In this example, `NormalizationHybridLayer` gets as an input the output from `Dense(5)` layer and pass its output as an input to `Dense(1)` layer.
+
+
+```python
+net = gluon.nn.HybridSequential()                         # Define a Neural Network as a sequence of hybrid blocks
+with net.name_scope():                                    # Used to disambiguate saving and loading net parameters
+    net.add(Dense(5))                                     # Add Dense layer with 5 neurons
+    net.add(NormalizationHybridLayer())                   # Add our custom layer
+    net.add(Dense(1))                                     # Add Dense layer with 1 neurons
+
+
+net.initialize(mx.init.Xavier(magnitude=2.24))            # Initialize parameters of all layers
+net.hybridize()                                           # Create, optimize and cache computational graph
+input = nd.random_uniform(low=-10, high=10, shape=(5, 2)) # Create 5 random examples with 2 feature each in range [-10, 10]
+net(input)
+```
+
+
+
+
+```text
+    [[-0.13601446]
+     [ 0.26103732]
+     [-0.05046433]
+     [-1.2375476 ]
+     [-0.15506986]]
+    <NDArray 5x1 @cpu(0)>
+```
+
+
+## Parameters of a custom layer
+
+Usually, a layer has a set of associated parameters, sometimes also referred as weights. This is an internal state of a layer. Most often, these parameters are the ones, that we want to learn during backpropogation step, but sometimes these parameters might be just constants we want to use during forward pass.
+
+All parameters of a block are stored and accessed via [ParameterDict](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/parameter.py#L508) class. This class helps with initialization, updating, saving and loading of the parameters. Each layer can have multiple set of parameters, and all of them can be stored in a single instance of the `ParameterDict` class. On a block level, the instance of the `ParameterDict` class is accessible via `self.params` field, and outside of a block one can access all parameters of the network via [collect_params()](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.Block.collect_params) method called on a `container`. `ParameterDict` uses [Parameter](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.Parameter) class to represent parameters inside of Apache MxNet neural network. If parameter doesn't exist, trying to get a parameter via `self.params` will create it automatically.
+
+
+```python
+class NormalizationHybridLayer(gluon.HybridBlock):
+    def __init__(self, hidden_units, scales):
+        super(NormalizationHybridLayer, self).__init__()
+
+        with self.name_scope():
+            self.weights = self.params.get('weights',
+                                           shape=(hidden_units, 0),
+                                           allow_deferred_init=True)
+
+            self.scales = self.params.get('scales',
+                                      shape=scales.shape,
+                                      init=mx.init.Constant(scales.asnumpy().tolist()), # Convert to regular list to make this object serializable
+                                      differentiable=False)
+            
+    def hybrid_forward(self, F, x, weights, scales):
+        normalized_data = F.broadcast_div(F.broadcast_sub(x, F.min(x)), (F.broadcast_sub(F.max(x), F.min(x))))
+        weighted_data = F.FullyConnected(normalized_data, weights, num_hidden=self.weights.shape[0], no_bias=True)
+        scaled_data = F.broadcast_mul(scales, weighted_data)
+        return scaled_data
+```
+
+In the example above 2 set of parameters are defined:
+1. Parameter `weights` is trainable. Its shape is unknown during construction phase and will be infered on the first run of forward propogation; 
+1. Parameter `scale` is a constant that doesn't change. Its shape is defined during construction.
+
+Notice a few aspects of this code:
+* `name_scope()` method is used to add a prefix to parameter names during saving and loading
+* Shape is not provided when creating `weights`. Instead it is going to be infered from the shape of the input
+* `Scales` parameter is initialized and marked as `differentiable=False`.
+* `F` backend is used for all calculations
+* The calculation of dot product is done using `F.FullyConnected()` method instead of `F.dot()` method. The one was chosen over another because the former supports automatic infering shapes of inputs while the latter doesn't. This is extremely important to know, if one doesn't want to hard code all the shapes. The best way to learn what operators supports automatic inference of input shapes at the moment is browsing C++ implementation of operators to see if one uses a method `SHAPE_ASSIGN_CHECK(*in_shape, fullc::kWeight, Shape2(param.num_hidden, num_input));`
+* `hybrid_forward()` method signature has changed. It accepts two new arguments: `weights` and `scales`.
+
+The last peculiarity is due to support of imperative and symbolic programming by `HybridBlock`. During training phase, parameters are passed to the layer by Apache MxNet framework as additional arguments to the method, because they might need to be converted to a `Symbol` depending on if the layer was hybridized. One shouldn't use `self.weights` and `self.scales` or `self.params.get` in `hybrid_forward` except to get shapes of parameters. 
+
+Running forward pass on this network is very similar to the previous example, so instead of just doing one forward pass, let's run whole training for a few epochs to show that `scales` parameter doesn't change during the training while `weights` parameter is changing.
+
+
+```python
+def print_params(title, net):
+    """
+    Helper function to print out the state of parameters of NormalizationHybridLayer
+    """
+    print(title)
+    hybridlayer_params = {k: v for k, v in net.collect_params().items() if 'normalizationhybridlayer' in k }
+    
+    for key, value in hybridlayer_params.items():
+        print('{} = {}\n'.format(key, value.data()))
+
+net = gluon.nn.HybridSequential()                             # Define a Neural Network as a sequence of hybrid blocks
+with net.name_scope():                                        # Used to disambiguate saving and loading net parameters
+    net.add(Dense(5))                                         # Add Dense layer with 5 neurons
+    net.add(NormalizationHybridLayer(hidden_units=5, 
+                                     scales = nd.array([2]))) # Add our custom layer
+    net.add(Dense(1))                                         # Add Dense layer with 1 neurons
+
+
+net.initialize(mx.init.Xavier(magnitude=2.24))                # Initialize parameters of all layers
+net.hybridize()                                               # Create, optimize and cache computational graph
+
+input = nd.random_uniform(low=-10, high=10, shape=(5, 2))     # Create 5 random examples with 2 feature each in range [-10, 10]
+label = nd.random_uniform(low=-1, high=1, shape=(5, 1))
+
+mse_loss = gluon.loss.L2Loss()                                # Mean squared error between output and label
+trainer = gluon.Trainer(net.collect_params(),                 # Init trainer with Stochastic Gradient Descent (sgd) optimization method and parameters for it
+                        'sgd', 
+                        {'learning_rate': 0.1, 'momentum': 0.9 })
+                        
+with autograd.record():                                       # Autograd records computations done on NDArrays inside "with" block 
+    output = net(input)                                       # Run forward propogation
+    
+    print_params("=========== Parameters after forward pass ===========\n", net)    
+    loss = mse_loss(output, label)                            # Calculate MSE
+    
+loss.backward()                                               # Backward computes gradients and stores them as a separate array within each NDArray in .grad field
+trainer.step(input.shape[0])                                  # Trainer updates parameters of every block, using .grad field using oprimization method (sgd in this example)
+                                                              # We provide batch size that is used as a divider in cost function formula
+print_params("=========== Parameters after backward pass ===========\n", net)
+
+```
+
+```text
+    =========== Parameters after forward pass ===========
+    
+    hybridsequential94_normalizationhybridlayer0_weights = 
+    [[-0.3983642  -0.505708   -0.02425683 -0.3133553  -0.35161012]
+     [ 0.6467543   0.3918715  -0.6154656  -0.20702496 -0.4243446 ]
+     [ 0.6077331   0.03922009  0.13425875  0.5729856  -0.14446527]
+     [-0.3572498   0.18545026 -0.09098256  0.5106366  -0.35151464]
+     [-0.39846328  0.22245121  0.13075739  0.33387476 -0.10088372]]
+    <NDArray 5x5 @cpu(0)>
+    
+    hybridsequential94_normalizationhybridlayer0_scales = 
+    [2.]
+    <NDArray 1 @cpu(0)>
+    
+    =========== Parameters after backward pass ===========
+    
+    hybridsequential94_normalizationhybridlayer0_weights = 
+    [[-0.29839832 -0.47213346  0.08348035 -0.2324698  -0.27368504]
+     [ 0.76268613  0.43080837 -0.49052125 -0.11322092 -0.3339738 ]
+     [ 0.48665082 -0.00144657  0.00376363  0.47501418 -0.23885089]
+     [-0.22626656  0.22944227  0.05018325  0.6166192  -0.24941102]
+     [-0.44946212  0.20532274  0.07579394  0.29261002 -0.14063817]]
+    <NDArray 5x5 @cpu(0)>
+    
+    hybridsequential94_normalizationhybridlayer0_scales = 
+    [2.]
+    <NDArray 1 @cpu(0)>
+``` 
+
+
+As it is seen from the output above, `weights` parameter has been changed by the training and `scales` not.
+
+## Conclusion
+
+One important quality of a Deep learning framework is extensibility. Empowered by flexible abstractions, like `Block` and `HybridBlock`, one can easily extend Apache MxNet functionality to match its needs.
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
\ No newline at end of file
diff --git a/docs/python_docs/python/tutorials/extend/customop.md b/docs/python_docs/python/tutorials/extend/customop.md
new file mode 100644
index 000000000000..96551ecf4fb3
--- /dev/null
+++ b/docs/python_docs/python/tutorials/extend/customop.md
@@ -0,0 +1,236 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+
+# Creating custom operators with numpy
+
+In this tutorial, we will learn how to build custom operators with numpy in python. We will go through two examples:
+- Custom operator without any `Parameter`s
+- Custom operator with `Parameter`s
+
+Custom operator in python is easy to develop and good for prototyping, but may hurt performance. If you find it to be a bottleneck, please consider moving to a C++ based implementation in the backend.
+
+
+
+```python
+import numpy as np
+import mxnet as mx
+from mxnet import gluon, autograd
+import os
+```
+
+## Parameter-less operators
+
+This operator implements the standard sigmoid activation function. This is only for illustration purposes, in real life you would use the built-in operator `mx.nd.relu`.
+
+### Forward & backward implementation
+
+First we implement the forward and backward computation by sub-classing `mx.operator.CustomOp`:
+
+
+```python
+class Sigmoid(mx.operator.CustomOp):
+    def forward(self, is_train, req, in_data, out_data, aux):
+        """Implements forward computation.
+
+        is_train : bool, whether forwarding for training or testing.
+        req : list of {'null', 'write', 'inplace', 'add'}, how to assign to out_data. 'null' means skip assignment, etc.
+        in_data : list of NDArray, input data.
+        out_data : list of NDArray, pre-allocated output buffers.
+        aux : list of NDArray, mutable auxiliary states. Usually not used.
+        """
+        x = in_data[0].asnumpy()
+        y = 1.0 / (1.0 + np.exp(-x))
+        self.assign(out_data[0], req[0], mx.nd.array(y))
+
+    def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
+        """Implements backward computation
+
+        req : list of {'null', 'write', 'inplace', 'add'}, how to assign to in_grad
+        out_grad : list of NDArray, gradient w.r.t. output data.
+        in_grad : list of NDArray, gradient w.r.t. input data. This is the output buffer.
+        """
+        y = out_data[0].asnumpy()
+        dy = out_grad[0].asnumpy()
+        dx = dy*(1.0 - y)*y
+        self.assign(in_grad[0], req[0], mx.nd.array(dx))
+```
+
+### Register custom operator
+
+Then we need to register the custom op and describe it's properties like input and output shapes so that mxnet can recognize it. This is done by sub-classing `mx.operator.CustomOpProp`:
+
+
+```python
+@mx.operator.register("sigmoid")  # register with name "sigmoid"
+class SigmoidProp(mx.operator.CustomOpProp):
+    def __init__(self):
+        super(SigmoidProp, self).__init__(True)
+
+    def list_arguments(self):
+        #  this can be omitted if you only have 1 input.
+        return ['data']
+
+    def list_outputs(self):
+        #  this can be omitted if you only have 1 output.
+        return ['output']
+
+    def infer_shape(self, in_shapes):
+        """Calculate output shapes from input shapes. This can be
+        omited if all your inputs and outputs have the same shape.
+
+        in_shapes : list of shape. Shape is described by a tuple of int.
+        """
+        data_shape = in_shapes[0]
+        output_shape = data_shape
+        # return 3 lists representing inputs shapes, outputs shapes, and aux data shapes.
+        return (data_shape,), (output_shape,), ()
+
+    def create_operator(self, ctx, in_shapes, in_dtypes):
+        #  create and return the CustomOp class.
+        return Sigmoid()
+```
+
+### Example Usage
+
+We can now use this operator by calling `mx.nd.Custom`:
+
+
+```python
+x = mx.nd.array([0, 1, 2, 3])
+# attach gradient buffer to x for autograd
+x.attach_grad()
+# forward in a record() section to save computation graph for backward
+# see autograd tutorial to learn more.
+with autograd.record():
+    y = mx.nd.Custom(x, op_type='sigmoid')
+print(y)
+```
+
+```python
+# call backward computation
+y.backward()
+# gradient is now saved to the grad buffer we attached previously
+print(x.grad)
+```
+
+## Parametrized Operator
+
+In the second use case we implement an operator with learnable weights. We implement the dense (or fully connected) layer that has one input, one output, and two learnable parameters: weight and bias.
+
+The dense operator performs a dot product between data and weight, then add bias to it.
+
+### Forward & backward implementation
+
+
+```python
+class Dense(mx.operator.CustomOp):
+    def __init__(self, bias):
+        self._bias = bias
+
+    def forward(self, is_train, req, in_data, out_data, aux):
+        x = in_data[0].asnumpy()
+        weight = in_data[1].asnumpy()
+        y = x.dot(weight.T) + self._bias
+        self.assign(out_data[0], req[0], mx.nd.array(y))
+
+    def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
+        x = in_data[0].asnumpy()
+        dy = out_grad[0].asnumpy()
+        dx = dy.T.dot(x)
+        self.assign(in_grad[0], req[0], mx.nd.array(dx))
+```
+
+### Registration
+
+
+```python
+@mx.operator.register("dense")  # register with name "sigmoid"
+class DenseProp(mx.operator.CustomOpProp):
+    def __init__(self, bias):
+        super(DenseProp, self).__init__(True)
+        # we use constant bias here to illustrate how to pass arguments
+        # to operators. All arguments are in string format so you need
+        # to convert them back to the type you want.
+        self._bias = float(bias)
+
+    def list_arguments(self):
+        return ['data', 'weight']
+
+    def list_outputs(self):
+        #  this can be omitted if you only have 1 output.
+        return ['output']
+
+    def infer_shape(self, in_shapes):
+        data_shape = in_shapes[0]
+        weight_shape = in_shapes[1]
+        output_shape = (data_shape[0], weight_shape[0])
+        # return 3 lists representing inputs shapes, outputs shapes, and aux data shapes.
+        return (data_shape, weight_shape), (output_shape,), ()
+
+    def create_operator(self, ctx, in_shapes, in_dtypes):
+        #  create and return the CustomOp class.
+        return Dense(self._bias)
+```
+
+### Use CustomOp together with Block
+
+Parameterized CustomOp are usually used together with Blocks, which holds the parameter.
+
+
+```python
+class DenseBlock(mx.gluon.Block):
+    def __init__(self, in_channels, channels, bias, **kwargs):
+        super(DenseBlock, self).__init__(**kwargs)
+        self._bias = bias
+        self.weight = self.params.get('weight', shape=(channels, in_channels))
+
+    def forward(self, x):
+        ctx = x.context
+        return mx.nd.Custom(x, self.weight.data(ctx), bias=self._bias, op_type='dense')
+```
+
+### Example usage
+
+
+```python
+dense = DenseBlock(3, 5, 0.1)
+dense.initialize()
+x = mx.nd.uniform(shape=(4, 3))
+y = dense(x)
+print(y)
+```
+
+## Using custom operators with fork
+In Linux systems, the default method in multiprocessing to create process is by using fork. If there are unfinished async custom operations when forking, the program will be blocked because of python GIL. Always use sync calls like `wait_to_read` or `waitall` before calling fork.
+
+```python
+x = mx.nd.array([0, 1, 2, 3])
+y = mx.nd.Custom(x, op_type='sigmoid')
+# unfinished async sigmoid operation will cause blocking
+os.fork()
+```
+
+Correctly handling this will make mxnet depend upon libpython, so the workaround now is to ensure that all custom operations are executed before forking process.
+
+```python
+x = mx.nd.array([0, 1, 2, 3])
+y = mx.nd.Custom(x, op_type='sigmoid')
+# force execution by reading y
+print(y.asnumpy())
+os.fork()
+```
\ No newline at end of file
diff --git a/docs/python_docs/python/tutorials/extend/index.rst b/docs/python_docs/python/tutorials/extend/index.rst
new file mode 100644
index 000000000000..430cd3fdc2fb
--- /dev/null
+++ b/docs/python_docs/python/tutorials/extend/index.rst
@@ -0,0 +1,55 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Customization
+=============
+
+The following tutorials will help you learn how to customize MXNet.
+
+.. container:: cards
+
+   .. card::
+      :title: Custom Layers for Gluon
+      :link: custom_layer.html
+
+      How to add new layer functionality to MXNet's imperative interface.
+
+   .. card::
+      :title: Custom Operators Using Numpy
+      :link: customop.html
+
+      How to use Numpy to create custom MXNet operators.
+
+   .. card::
+      :title: New Layer Creation
+      :link: https://mxnet.incubator.apache.org/api/faq/new_op
+
+      How to create new MXNet operators using CustomOp (Python) or NNVM (C++).
+
+   .. card::
+      :title: A Beginner’s Guide to Implementing Operators in MXNet Backend
+      :link: https://mxnet.incubator.apache.org/api/faq/add_op_in_backend
+
+      How to create new MXNet operators in MXNet's backend using C++.
+      An example custom quadratic function op.
+
+
+.. toctree::
+   :hidden:
+   :glob:
+
+   *
diff --git a/docs/python_docs/python/tutorials/getting-started/crash-course/1-ndarray.md b/docs/python_docs/python/tutorials/getting-started/crash-course/1-ndarray.md
new file mode 100644
index 000000000000..3378cc85829a
--- /dev/null
+++ b/docs/python_docs/python/tutorials/getting-started/crash-course/1-ndarray.md
@@ -0,0 +1,127 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Manipulate data with `ndarray`
+
+We'll start by introducing the `NDArray`, MXNet’s primary tool for storing and transforming data. If you’ve worked with `NumPy` before, you’ll notice that a NDArray is, by design, similar to NumPy’s multi-dimensional array.
+
+## Get started
+
+To get started, let's import the `ndarray` package (`nd` is shortform) from MXNet.
+
+```{.python .input  n=1}
+# If you hasn't install MXNet yet, you can uncomment the following line to
+# install the latest stable release
+# !pip install -U mxnet
+
+from mxnet import nd
+```
+
+Next, let's see how to create a 2D array (also called a matrix) with values from two sets of numbers: 1, 2, 3 and 4, 5, 6. This might also be referred to as a tuple of a tuple of integers.
+
+```{.python .input  n=2}
+nd.array(((1,2,3),(5,6,7)))
+```
+
+We can also create a very simple matrix with the same shape (2 rows by 3 columns), but fill it with 1s.
+
+```{.python .input  n=3}
+x = nd.ones((2,3))
+x
+```
+
+Often we’ll want to create arrays whose values are sampled randomly. For example, sampling values uniformly between -1 and 1. Here we create the same shape, but with random sampling.
+
+```{.python .input  n=15}
+y = nd.random.uniform(-1,1,(2,3))
+y
+```
+
+You can also fill an array of a given shape with a given value, such as `2.0`.
+<!-- added to improve multiplication example -->
+
+```{.python .input  n=16}
+x = nd.full((2,3), 2.0)
+x
+```
+
+As with NumPy, the dimensions of each NDArray are accessible by accessing the `.shape` attribute. We can also query its `size`, which is equal to the product of the components of the shape. In addition, `.dtype` tells the data type of the stored values.
+
+```{.python .input  n=17}
+(x.shape, x.size, x.dtype)
+```
+
+## Operations
+
+NDArray supports a large number of standard mathematical operations. Such as element-wise multiplication:
+
+```{.python .input  n=18}
+x * y
+```
+
+Exponentiation:
+
+```{.python .input  n=23}
+y.exp()
+```
+
+And grab a matrix’s transpose to compute a proper matrix-matrix product:
+
+```{.python .input  n=24}
+nd.dot(x, y.T)
+```
+
+## Indexing
+
+MXNet NDArrays support slicing in all the ridiculous ways you might imagine accessing your data. Here’s an example of reading a particular element, which returns a 1D array with shape `(1,)`.
+
+```{.python .input  n=25}
+y[1,2]
+```
+
+Read the second and third columns from `y`.
+
+```{.python .input  n=26}
+y[:,1:3]
+```
+
+and writing to a specific element
+
+```{.python .input  n=27}
+y[:,1:3] = 2
+y
+```
+
+Multi-dimensional slicing is also supported.
+
+```{.python .input  n=28}
+y[1:2,0:2] = 4
+y
+```
+
+## Converting between MXNet NDArray and NumPy
+
+Converting MXNet NDArrays to and from NumPy is easy. The converted arrays do not share memory.
+
+```{.python .input  n=29}
+a = x.asnumpy()
+(type(a), a)
+```
+
+```{.python .input  n=30}
+nd.array(a)
+```
diff --git a/docs/python_docs/python/tutorials/getting-started/crash-course/2-nn.md b/docs/python_docs/python/tutorials/getting-started/crash-course/2-nn.md
new file mode 100644
index 000000000000..88bb030ea502
--- /dev/null
+++ b/docs/python_docs/python/tutorials/getting-started/crash-course/2-nn.md
@@ -0,0 +1,142 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Create a neural network
+
+Now let's look how to create neural networks in Gluon. In addition the NDArray package (`nd`) that we just covered, we now will also import the neural network `nn` package from `gluon`.
+
+```{.python .input  n=2}
+from mxnet import nd
+from mxnet.gluon import nn
+```
+
+## Create your neural network's first layer
+
+Let's start with a dense layer with 2 output units.
+<!-- mention what the none and the linear parts mean? -->
+
+```{.python .input  n=31}
+layer = nn.Dense(2)
+layer
+```
+
+Then initialize its weights with the default initialization method, which draws random values uniformly from $[-0.7, 0.7]$.
+
+```{.python .input  n=32}
+layer.initialize()
+```
+
+Then we do a forward pass with random data. We create a $(3,4)$ shape random input `x` and feed into the layer to compute the output.
+
+```{.python .input  n=34}
+x = nd.random.uniform(-1,1,(3,4))
+layer(x)
+```
+
+As can be seen, the layer's input limit of 2 produced a $(3,2)$ shape output from our $(3,4)$ input. Note that we didn't specify the input size of `layer` before (though we can specify it with the argument `in_units=4` here), the system will automatically infer it during the first time we feed in data, create and initialize the weights. So we can access the weight after the first forward pass:
+
+```{.python .input  n=35}
+layer.weight.data()
+```
+
+## Chain layers into a neural network
+
+Let's first consider a simple case that a neural network is a chain of layers. During the forward pass, we run layers sequentially one-by-one. The following code implements a famous network called [LeNet](http://yann.lecun.com/exdb/lenet/) through `nn.Sequential`.
+
+```{.python .input}
+net = nn.Sequential()
+# Add a sequence of layers.
+net.add(# Similar to Dense, it is not necessary to specify the input channels
+        # by the argument `in_channels`, which will be  automatically inferred
+        # in the first forward pass. Also, we apply a relu activation on the
+        # output. In addition, we can use a tuple to specify a  non-square
+        # kernel size, such as `kernel_size=(2,4)`
+        nn.Conv2D(channels=6, kernel_size=5, activation='relu'),
+        # One can also use a tuple to specify non-symmetric pool and stride sizes
+        nn.MaxPool2D(pool_size=2, strides=2),
+        nn.Conv2D(channels=16, kernel_size=3, activation='relu'),
+        nn.MaxPool2D(pool_size=2, strides=2),
+        # The dense layer will automatically reshape the 4-D output of last
+        # max pooling layer into the 2-D shape: (x.shape[0], x.size/x.shape[0])
+        nn.Dense(120, activation="relu"),
+        nn.Dense(84, activation="relu"),
+        nn.Dense(10))
+net
+```
+
+<!--Mention the tuple option for kernel and stride as an exercise for the reader? Or leave it out as too much info for now?-->
+
+The usage of `nn.Sequential` is similar to `nn.Dense`. In fact, both of them are subclasses of `nn.Block`. The following codes show how to initialize the weights and run the forward pass.
+
+```{.python .input}
+net.initialize()
+# Input shape is (batch_size, color_channels, height, width)
+x = nd.random.uniform(shape=(4,1,28,28))
+y = net(x)
+y.shape
+```
+
+We can use `[]` to index a particular layer. For example, the following
+accesses the 1st layer's weight and 6th layer's bias.
+
+```{.python .input}
+(net[0].weight.data().shape, net[5].bias.data().shape)
+```
+
+## Create a neural network flexibly
+
+In `nn.Sequential`, MXNet will automatically construct the forward function that sequentially executes added layers.
+Now let's introduce another way to construct a network with a flexible forward function.
+
+To do it, we create a subclass of `nn.Block` and implement two methods:
+
+- `__init__` create the layers
+- `forward` define the forward function.
+
+```{.python .input  n=6}
+class MixMLP(nn.Block):
+    def __init__(self, **kwargs):
+        # Run `nn.Block`'s init method
+        super(MixMLP, self).__init__(**kwargs)
+        self.blk = nn.Sequential()
+        self.blk.add(nn.Dense(3, activation='relu'),
+                     nn.Dense(4, activation='relu'))
+        self.dense = nn.Dense(5)
+    def forward(self, x):
+        y = nd.relu(self.blk(x))
+        print(y)
+        return self.dense(y)
+
+net = MixMLP()
+net
+```
+
+In the sequential chaining approach, we can only add instances with `nn.Block` as the base class and then run them in a forward pass. In this example, we used `print` to get the intermediate results and `nd.relu` to apply relu activation. So this approach provides a more flexible way to define the forward function.
+
+The usage of `net` is similar as before.
+
+```{.python .input}
+net.initialize()
+x = nd.random.uniform(shape=(2,2))
+net(x)
+```
+
+Finally, let's access a particular layer's weight
+
+```{.python .input  n=8}
+net.blk[1].weight.data()
+```
diff --git a/docs/python_docs/python/tutorials/getting-started/crash-course/3-autograd.md b/docs/python_docs/python/tutorials/getting-started/crash-course/3-autograd.md
new file mode 100644
index 000000000000..b7cb9f4dee8b
--- /dev/null
+++ b/docs/python_docs/python/tutorials/getting-started/crash-course/3-autograd.md
@@ -0,0 +1,99 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Automatic differentiation with `autograd`
+
+We train models to get better and better as a function of experience. Usually, getting better means minimizing a loss function. To achieve this goal, we often iteratively compute the gradient of the loss with respect to weights and then update the weights accordingly. While the gradient calculations are straightforward through a chain rule, for complex models, working it out by hand can be a pain.
+
+Before diving deep into the model training, let's go through how MXNet’s `autograd` package expedites this work by automatically calculating derivatives.
+
+## Basic usage
+
+Let's first import the `autograd` package.
+
+```{.python .input}
+from mxnet import nd
+from mxnet import autograd
+```
+
+As a toy example, let’s say that we are interested in differentiating a function $f(x) = 2 x^2$ with respect to parameter $x$. We can start by assigning an initial value of $x$.
+
+```{.python .input  n=3}
+x = nd.array([[1, 2], [3, 4]])
+x
+```
+
+Once we compute the gradient of $f(x)$ with respect to $x$, we’ll need a place to store it. In MXNet, we can tell an NDArray that we plan to store a gradient by invoking its `attach_grad` method.
+
+```{.python .input  n=6}
+x.attach_grad()
+```
+
+Now we’re going to define the function $y=f(x)$. To let MXNet store $y$, so that we can compute gradients later, we need to put the definition inside a `autograd.record()` scope.
+
+```{.python .input  n=7}
+with autograd.record():
+    y = 2 * x * x
+```
+
+Let’s invoke back propagation (backprop) by calling `y.backward()`. When $y$ has more than one entry, `y.backward()` is equivalent to `y.sum().backward()`.
+<!-- I'm not sure what this second part really means. I don't have enough context. TMI?-->
+
+```{.python .input  n=8}
+y.backward()
+```
+
+Now, let’s see if this is the expected output. Note that $y=2x^2$ and $\frac{dy}{dx} = 4x$, which should be `[[4, 8],[12, 16]]`. Let's check the automatically computed results:
+
+```{.python .input  n=9}
+x.grad
+```
+
+## Using Python control flows
+
+Sometimes we want to write dynamic programs where the execution depends on some real-time values. MXNet will record the execution trace and compute the gradient as well.
+
+Consider the following function `f`: it doubles the inputs until it's `norm` reaches 1000. Then it selects one element depending on the sum of its elements.
+<!-- I wonder if there could be another less "mathy" demo of this -->
+
+```{.python .input}
+def f(a):
+    b = a * 2
+    while b.norm().asscalar() < 1000:
+        b = b * 2
+    if b.sum().asscalar() >= 0:
+        c = b[0]
+    else:
+        c = b[1]
+    return c
+```
+
+We record the trace and feed in a random value:
+
+```{.python .input}
+a = nd.random.uniform(shape=2)
+a.attach_grad()
+with autograd.record():
+    c = f(a)
+c.backward()
+```
+
+We know that `b` is a linear function of `a`, and `c` is chosen from `b`. Then the gradient with respect to `a` be will be either `[c/a[0], 0]` or `[0, c/a[1]]`, depending on which element from `b` we picked. Let's find the results:
+
+```{.python .input}
+[a.grad, c/a]
+```
diff --git a/docs/python_docs/python/tutorials/getting-started/crash-course/4-train.md b/docs/python_docs/python/tutorials/getting-started/crash-course/4-train.md
new file mode 100644
index 000000000000..0513dae9f190
--- /dev/null
+++ b/docs/python_docs/python/tutorials/getting-started/crash-course/4-train.md
@@ -0,0 +1,176 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Train the neural network
+
+In this section, we will discuss how to train the previously defined network with data. We first import the libraries. The new ones are `mxnet.init` for more weight initialization methods, the `datasets` and `transforms` to load and transform computer vision datasets, `matplotlib` for drawing, and `time` for benchmarking.
+
+```{.python .input  n=1}
+# Uncomment the following line if matplotlib is not installed.
+# !pip install matplotlib
+
+from mxnet import nd, gluon, init, autograd
+from mxnet.gluon import nn
+from mxnet.gluon.data.vision import datasets, transforms
+from IPython import display
+import matplotlib.pyplot as plt
+import time
+```
+
+## Get data
+
+The handwritten digit MNIST dataset is one of the most commonly used datasets in deep learning. But it is too simple to get a 99% accuracy. Here we use a similar but slightly more complicated dataset called FashionMNIST. The goal is no longer to classify numbers, but clothing types instead.
+
+The dataset can be automatically downloaded through Gluon's `data.vision.datasets` module. The following code downloads the training dataset and shows the first example.
+
+```{.python .input  n=2}
+mnist_train = datasets.FashionMNIST(train=True)
+X, y = mnist_train[0]
+('X shape: ', X.shape, 'X dtype', X.dtype, 'y:', y)
+```
+
+Each example in this dataset is a $28\times 28$ size grey image, which is presented as NDArray with the shape format of `(height, width, channel)`.  The label is a `numpy` scalar.
+
+Next, we visualize the first six examples.
+
+```{.python .input  n=3}
+text_labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat',
+               'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']
+X, y = mnist_train[0:10]
+# plot images
+display.set_matplotlib_formats('svg')
+_, figs = plt.subplots(1, X.shape[0], figsize=(15, 15))
+for f,x,yi in zip(figs, X,y):
+    # 3D->2D by removing the last channel dim
+    f.imshow(x.reshape((28,28)).asnumpy())
+    ax = f.axes
+    ax.set_title(text_labels[int(yi)])
+    ax.title.set_fontsize(14)
+    ax.get_xaxis().set_visible(False)
+    ax.get_yaxis().set_visible(False)
+plt.show()
+```
+
+In order to feed data into a Gluon model, we need to convert the images to the `(channel, height, width)` format with a floating point data type. It can be done by `transforms.ToTensor`. In addition, we normalize all pixel values with `transforms.Normalize` with the real mean 0.13 and standard deviation 0.31. We chain these two transforms together and apply it to the first element of the data pair, namely the images.
+
+```{.python .input  n=4}
+transformer = transforms.Compose([
+    transforms.ToTensor(),
+    transforms.Normalize(0.13, 0.31)])
+mnist_train = mnist_train.transform_first(transformer)
+```
+
+`FashionMNIST` is a subclass of `gluon.data.Dataset`, which defines how to get the `i`-th example. In order to use it in training, we need to get a (randomized) batch of examples. It can be easily done by `gluon.data.DataLoader`. Here we use four works to process data in parallel, which is often necessary especially for complex data transforms.
+
+```{.python .input  n=5}
+batch_size = 256
+train_data = gluon.data.DataLoader(
+    mnist_train, batch_size=batch_size, shuffle=True, num_workers=4)
+```
+
+The returned `train_data` is an iterable object that yields batches of images and labels pairs.
+
+```{.python .input  n=6}
+for data, label in train_data:
+    print(data.shape, label.shape)
+    break
+```
+
+Finally, we create a validation dataset and data loader.
+
+```{.python .input  n=7}
+mnist_valid = gluon.data.vision.FashionMNIST(train=False)
+valid_data = gluon.data.DataLoader(
+    mnist_valid.transform_first(transformer),
+	batch_size=batch_size, num_workers=4)
+```
+
+## Define the model
+
+We reimplement the same LeNet introduced before. One difference here is that we changed the weight initialization method to `Xavier`, which is a popular choice for deep convolutional neural networks.
+
+```{.python .input  n=8}
+net = nn.Sequential()
+net.add(nn.Conv2D(channels=6, kernel_size=5, activation='relu'),
+        nn.MaxPool2D(pool_size=2, strides=2),
+        nn.Conv2D(channels=16, kernel_size=3, activation='relu'),
+        nn.MaxPool2D(pool_size=2, strides=2),
+        nn.Flatten(),
+        nn.Dense(120, activation="relu"),
+        nn.Dense(84, activation="relu"),
+        nn.Dense(10))
+net.initialize(init=init.Xavier())
+```
+
+Besides the neural network, we need to define the loss function and optimization method for training. We will use standard softmax cross entropy loss for classification problems. It first performs softmax on the output to obtain the predicted probability, and then compares the label with the cross entropy.
+
+```{.python .input  n=9}
+softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()
+```
+
+The optimization method we pick is the standard stochastic gradient descent with constant learning rate of 0.1.
+
+```{.python .input  n=10}
+trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.1})
+```
+
+The `trainer` is created with all parameters (both weights and gradients) in `net`. Later on, we only need to call the `step` method to update its weights.
+
+## Train
+
+We create an auxiliary function to calculate the model accuracy.
+
+```{.python .input  n=11}
+def acc(output, label):
+    # output: (batch, num_output) float32 ndarray
+    # label: (batch, ) int32 ndarray
+    return (output.argmax(axis=1) ==
+            label.astype('float32')).mean().asscalar()
+```
+
+Now we can implement the complete training loop.
+
+```{.python .input  n=12}
+for epoch in range(10):
+    train_loss, train_acc, valid_acc = 0., 0., 0.
+    tic = time.time()
+    for data, label in train_data:
+        # forward + backward
+        with autograd.record():
+            output = net(data)
+            loss = softmax_cross_entropy(output, label)
+        loss.backward()
+        # update parameters
+        trainer.step(batch_size)
+        # calculate training metrics
+        train_loss += loss.mean().asscalar()
+        train_acc += acc(output, label)
+    # calculate validation accuracy
+    for data, label in valid_data:
+        valid_acc += acc(net(data), label)
+    print("Epoch %d: loss %.3f, train acc %.3f, test acc %.3f, in %.1f sec" % (
+            epoch, train_loss/len(train_data), train_acc/len(train_data),
+            valid_acc/len(valid_data), time.time()-tic))
+```
+
+## Save the model
+
+Finally, we save the trained parameters onto disk, so that we can use them later.
+
+```{.python .input  n=13}
+net.save_parameters('net.params')
+```
diff --git a/docs/python_docs/python/tutorials/getting-started/crash-course/5-predict.md b/docs/python_docs/python/tutorials/getting-started/crash-course/5-predict.md
new file mode 100644
index 000000000000..1804a6082494
--- /dev/null
+++ b/docs/python_docs/python/tutorials/getting-started/crash-course/5-predict.md
@@ -0,0 +1,161 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Predict with a pre-trained model
+
+A saved model can be used in multiple places, such as to continue training, to fine tune the model, and for prediction. In this tutorial we will discuss how to predict new examples using a pretrained model.
+
+## Prerequisites
+
+Please run the [previous tutorial](train.md) to train the network and save its parameters to file. You will need this file to run the following steps.
+
+```{.python .input  n=1}
+from mxnet import nd
+from mxnet import gluon
+from mxnet.gluon import nn
+from mxnet.gluon.data.vision import datasets, transforms
+from IPython import display
+import matplotlib.pyplot as plt
+```
+
+To start, we will copy a simple model's definition.
+
+```{.python .input  n=2}
+net = nn.Sequential()
+net.add(nn.Conv2D(channels=6, kernel_size=5, activation='relu'),
+        nn.MaxPool2D(pool_size=2, strides=2),
+        nn.Conv2D(channels=16, kernel_size=3, activation='relu'),
+        nn.MaxPool2D(pool_size=2, strides=2),
+        nn.Flatten(),
+        nn.Dense(120, activation="relu"),
+        nn.Dense(84, activation="relu"),
+        nn.Dense(10))
+```
+
+In the last section, we saved all parameters into a file, now let's load it back.
+
+```{.python .input  n=3}
+net.load_parameters('net.params')
+```
+
+## Predict
+
+Remember the data transformation we did for training? Now we need the same transformation for predicting.
+
+```{.python .input  n=4}
+transformer = transforms.Compose([
+    transforms.ToTensor(),
+    transforms.Normalize(0.13, 0.31)])
+```
+
+Now let's try to predict the first six images in the validation dataset and store the predictions into `preds`.
+
+```{.python .input  n=5}
+mnist_valid = datasets.FashionMNIST(train=False)
+X, y = mnist_valid[:10]
+preds = []
+for x in X:
+    x = transformer(x).expand_dims(axis=0)
+    pred = net(x).argmax(axis=1)
+    preds.append(pred.astype('int32').asscalar())
+```
+
+Finally, we visualize the images and compare the prediction with the ground truth.
+
+```{.python .input  n=15}
+_, figs = plt.subplots(1, 10, figsize=(15, 15))
+text_labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat',
+               'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']
+display.set_matplotlib_formats('svg')
+for f,x,yi,pyi in zip(figs, X, y, preds):
+    f.imshow(x.reshape((28,28)).asnumpy())
+    ax = f.axes
+    ax.set_title(text_labels[yi]+'\n'+text_labels[pyi])
+    ax.title.set_fontsize(14)
+    ax.get_xaxis().set_visible(False)
+    ax.get_yaxis().set_visible(False)
+plt.show()
+```
+
+## Predict with models from Gluon model zoo
+
+
+The LeNet trained on FashionMNIST is a good example to start with, but too simple to predict real-life pictures. Instead of training large-scale model from scratch, [Gluon model zoo](https://mxnet.incubator.apache.org/api/python/gluon/model_zoo.html) provides multiple pre-trained powerful models. For example, we can download and load a pre-trained ResNet-50 V2 model that was trained on the ImageNet dataset.
+
+```{.python .input  n=7}
+from mxnet.gluon.model_zoo import vision as models
+from mxnet.gluon.utils import download
+from mxnet import image
+
+net = models.resnet50_v2(pretrained=True)
+```
+
+We also download and load the text labels for each class.
+
+```{.python .input  n=8}
+url = 'http://data.mxnet.io/models/imagenet/synset.txt'
+fname = download(url)
+with open(fname, 'r') as f:
+    text_labels = [' '.join(l.split()[1:]) for l in f]
+```
+
+We randomly pick a dog image from Wikipedia as a test image, download and read it.
+
+```{.python .input  n=9}
+url = 'https://upload.wikimedia.org/wikipedia/commons/thumb/b/b5/\
+Golden_Retriever_medium-to-light-coat.jpg/\
+365px-Golden_Retriever_medium-to-light-coat.jpg'
+fname = download(url)
+x = image.imread(fname)
+```
+
+Following the conventional way of preprocessing ImageNet data:
+
+1. Resize the short edge into 256 pixes,
+2. And then perform a center crop to obtain a 224-by-224 image.
+
+```{.python .input  n=10}
+x = image.resize_short(x, 256)
+x, _ = image.center_crop(x, (224,224))
+plt.imshow(x.asnumpy())
+plt.show()
+```
+
+Now you may know it is a golden retriever (You can also infer it from the image URL).
+
+The futher data transformation is similar to FashionMNIST except that we subtract the RGB means and divide by the corresponding variances to normalize each color channel.
+
+```{.python .input  n=11}
+def transform(data):
+    data = data.transpose((2,0,1)).expand_dims(axis=0)
+    rgb_mean = nd.array([0.485, 0.456, 0.406]).reshape((1,3,1,1))
+    rgb_std = nd.array([0.229, 0.224, 0.225]).reshape((1,3,1,1))
+    return (data.astype('float32') / 255 - rgb_mean) / rgb_std
+```
+
+Now we can recognize the object in the image now. We perform an additional softmax on the output to obtain probability scores. And then print the top-5 recognized objects.
+
+```{.python .input  n=12}
+prob = net(transform(x)).softmax()
+idx = prob.topk(k=5)[0]
+for i in idx:
+    i = int(i.asscalar())
+    print('With prob = %.5f, it contains %s' % (
+        prob[0,i].asscalar(), text_labels[i]))
+```
+
+As can be seen, the model is fairly confident the image contains a golden retriever.
diff --git a/docs/python_docs/python/tutorials/getting-started/crash-course/6-use_gpus.md b/docs/python_docs/python/tutorials/getting-started/crash-course/6-use_gpus.md
new file mode 100644
index 000000000000..b78c38ab7077
--- /dev/null
+++ b/docs/python_docs/python/tutorials/getting-started/crash-course/6-use_gpus.md
@@ -0,0 +1,146 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Use GPUs
+
+We often use GPUs to train and deploy neural networks, because it offers significant more computation power compared to CPUs. In this tutorial we will introduce how to use GPUs with MXNet.
+
+First, make sure you have at least one Nvidia GPU in your machine and CUDA
+properly installed. Other GPUs such as AMD and Intel GPUs are not supported
+yet. Then be sure you have installed the GPU-enabled version of MXNet.
+
+```{.python .input  n=15}
+# If you pip installed the plain `mxnet` before, uncomment the
+# following two lines to install the GPU version. You may need to
+# replace `cu92` according to your CUDA version.
+# !pip uninstall mxnet
+# !pip install mxnet-cu92
+
+from mxnet import nd, gpu, gluon, autograd
+from mxnet.gluon import nn
+from mxnet.gluon.data.vision import datasets, transforms
+import time
+```
+
+## Allocate data to a GPU
+
+You may notice that MXNet's NDArray is very similar to Numpy. One major difference is NDArray has a `context` attribute that specifies which device this array is on. By default, it is `cpu()`. Now we will change it to the first GPU. You can use `gpu()` or `gpu(0)` to indicate the first GPU.
+
+```{.python .input  n=10}
+x = nd.ones((3,4), ctx=gpu())
+x
+```
+
+For a CPU, MXNet will allocate data on main memory, and try to use all CPU cores as possible, even if there is more than one CPU socket. While if there are multiple GPUs, MXNet needs to specify which GPUs the NDArray will be allocated.
+
+Let's assume there is a least one more GPU. We can create another NDArray and assign it there. (If you only have one GPU, then you will see an error). Here we copy `x` to the second GPU, `gpu(1)`:
+
+```{.python .input  n=11}
+x.copyto(gpu(1))
+```
+
+MXNet needs users to explicitly move data between devices. But several operators such as `print`, `asnumpy` and `asscalar`, will implicitly move data to main memory.
+
+## Run an operation on a GPU
+
+To perform an operation on a particular GPU, we only need to guarantee that the inputs of this operation are already on that GPU. The output will be allocated on the same GPU as well. Almost all operators in the `nd` module support running on a GPU.
+
+```{.python .input  n=21}
+y = nd.random.uniform(shape=(3,4), ctx=gpu())
+x + y
+```
+
+Remember that if the inputs are not on the same GPU, you will see an error.
+
+## Run a neural network on a GPU
+
+Similarly, to run a neural network on a GPU, we only need to copy/move the input data and parameters to the GPU. Let's reuse the previously defined LeNet.
+
+```{.python .input  n=16}
+net = nn.Sequential()
+net.add(nn.Conv2D(channels=6, kernel_size=5, activation='relu'),
+        nn.MaxPool2D(pool_size=2, strides=2),
+        nn.Conv2D(channels=16, kernel_size=3, activation='relu'),
+        nn.MaxPool2D(pool_size=2, strides=2),
+        nn.Flatten(),
+        nn.Dense(120, activation="relu"),
+        nn.Dense(84, activation="relu"),
+        nn.Dense(10))
+```
+
+And then load the saved parameters into GPU 0 directly, or use `net.collect_params().reset_ctx` to change the device.
+
+```{.python .input  n=20}
+net.load_parameters('net.params', ctx=gpu(0))
+```
+
+Now create input data on GPU 0. The forward function will then run on GPU 0.
+
+```{.python .input  n=22}
+x = nd.random.uniform(shape=(1,1,28,28), ctx=gpu(0))
+net(x)
+```
+
+## [Advanced] Multi-GPU training
+
+Finally, we show how to use multiple GPUs to jointly train a neural network through data parallelism. Let's assume there are *n* GPUs. We split each data batch into *n* parts, and then each GPU will run the forward and backward passes using one part of the data.
+
+Let's first copy the data definitions and the transform function from the [previous tutorial](predict.md).
+
+```{.python .input}
+batch_size = 256
+transformer = transforms.Compose([
+    transforms.ToTensor(),
+    transforms.Normalize(0.13, 0.31)])
+train_data = gluon.data.DataLoader(
+    datasets.FashionMNIST(train=True).transform_first(transformer),
+    batch_size, shuffle=True, num_workers=4)
+valid_data = gluon.data.DataLoader(
+    datasets.FashionMNIST(train=False).transform_first(transformer),
+    batch_size, shuffle=False, num_workers=4)
+```
+
+The training loop is quite similar to what we introduced before. The major differences are highlighted in the following code.
+
+```{.python .input}
+# Diff 1: Use two GPUs for training.
+devices = [gpu(0), gpu(1)]
+# Diff 2: reinitialize the parameters and place them on multiple GPUs
+net.collect_params().initialize(force_reinit=True, ctx=devices)
+# Loss and trainer are the same as before
+softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()
+trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.1})
+for epoch in range(10):
+    train_loss = 0.
+    tic = time.time()
+    for data, label in train_data:
+        # Diff 3: split batch and load into corresponding devices
+        data_list = gluon.utils.split_and_load(data, devices)
+        label_list = gluon.utils.split_and_load(label, devices)
+        # Diff 4: run forward and backward on each devices.
+        # MXNet will automatically run them in parallel
+        with autograd.record():
+            losses = [softmax_cross_entropy(net(X), y)
+                      for X, y in zip(data_list, label_list)]
+        for l in losses:
+            l.backward()
+        trainer.step(batch_size)
+        # Diff 5: sum losses over all devices
+        train_loss += sum([l.sum().asscalar() for l in losses])
+    print("Epoch %d: loss %.3f, in %.1f sec" % (
+        epoch, train_loss/len(train_data)/batch_size, time.time()-tic))
+```
diff --git a/docs/python_docs/python/tutorials/getting-started/crash-course/index.rst b/docs/python_docs/python/tutorials/getting-started/crash-course/index.rst
new file mode 100644
index 000000000000..124c94ae0814
--- /dev/null
+++ b/docs/python_docs/python/tutorials/getting-started/crash-course/index.rst
@@ -0,0 +1,52 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Crash Course
+============
+
+This crash course will give you a quick overview of the core concept of NDArray
+(manipulating multiple dimensional arrays) and Gluon (create and train neural
+networks). This is a good place to start if you are already familiar with
+machine learning or other deep learning frameworks.
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Contents
+
+   1-ndarray
+   2-nn
+   3-autograd
+   4-train
+   5-predict
+   6-use_gpus
+
+
+..
+   # add back the videos until apis are updated.
+   You can also watch the video tutorials for this crash course. Note that two APIs
+   described in vidoes have changes:
+
+   - ``with name_scope`` is not necessary any more.
+   - use ``save_parameters/load_parameters`` instead of ``save_params/load_params``
+
+   .. raw:: html
+
+      <style> iframe {width: 448px; height: 252px; margin: 1em 0;} </style>
+
+      <iframe src="https://www.youtube.com/embed/r4-Ynxw0X5w" frameborder="0"
+      allow="accelerometer; autoplay; encrypted-media; gyroscope;
+      picture-in-picture" allowfullscreen></iframe>
diff --git a/docs/python_docs/python/tutorials/getting-started/index.rst b/docs/python_docs/python/tutorials/getting-started/index.rst
new file mode 100644
index 000000000000..9f783922647c
--- /dev/null
+++ b/docs/python_docs/python/tutorials/getting-started/index.rst
@@ -0,0 +1,48 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Getting Started
+===============
+
+The following tutorials teach how to use MXNet.
+
+.. container:: cards
+
+   .. card::
+      :title: A 60-minute Gluon crash course
+      :link: crash-course/index.html
+
+      A quick overview of the core concepts of MXNet using the Gluon API.
+
+   .. card::
+      :title: Linear Regression
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/python/linear-regression.html
+
+      A tutorial on implementing linear regression using MXNet APIs.
+
+   .. card::
+      :title: Moving from other frameworks
+      :link: to-mxnet/index.html
+
+      Guides that ease your transition to MXNet from other framework.
+
+.. toctree::
+   :hidden:
+   :maxdepth: 2
+
+   crash-course/index
+   to-mxnet/index
diff --git a/docs/python_docs/python/tutorials/getting-started/to-mxnet/index.rst b/docs/python_docs/python/tutorials/getting-started/to-mxnet/index.rst
new file mode 100644
index 000000000000..a2c370912f85
--- /dev/null
+++ b/docs/python_docs/python/tutorials/getting-started/to-mxnet/index.rst
@@ -0,0 +1,44 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Moving to MXNet from Other Frameworks
+=====================================
+
+Comparison Guides
+-----------------
+
+.. container:: cards
+
+   .. card::
+      :title: PyTorch to MXNet (MNIST)
+      :link: pytorch.html
+
+      This guide compares PyTorch and MXNet when implementing MNIST.
+
+   .. card::
+      :title: Caffe to MXNet
+      :link: https://mxnet.incubator.apache.org/versions/master/faq/caffe.html
+
+      How to convert Caffe models to MXNet and how to call Caffe operators from MXNet.
+
+.. toctree::
+   :hidden:
+   :maxdepth: 1
+
+   pytorch
+
+.. disqus::
diff --git a/docs/python_docs/python/tutorials/getting-started/to-mxnet/pytorch.md b/docs/python_docs/python/tutorials/getting-started/to-mxnet/pytorch.md
new file mode 100644
index 000000000000..d66e172328e2
--- /dev/null
+++ b/docs/python_docs/python/tutorials/getting-started/to-mxnet/pytorch.md
@@ -0,0 +1,463 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# PyTorch vs Apache MXNet
+
+[PyTorch](https://pytorch.org/) is a popular deep learning framework due to its easy-to-understand API and its completely imperative approach. Apache MXNet includes the Gluon API which gives you the simplicity and flexibility of PyTorch and allows you to hybridize your network to leverage performance optimizations of the symbolic graph. As of April 2019, [NVidia performance benchmarks](https://developer.nvidia.com/deep-learning-performance-training-inference) show that Apache MXNet outperforms PyTorch by ~77% on training ResNet-50: 10,925 images per second vs. 6,175.
+
+In the next 10 minutes, we'll do a quick comparison between the two frameworks and show how small the learning curve can be when switching from PyTorch to Apache MXNet. 
+
+## Installation
+
+PyTorch uses conda for installation by default, for example:
+
+```{.python .input}
+# !conda install pytorch-cpu -c pytorch
+```
+
+For MXNet we use pip:
+
+```{.python .input}
+# !pip install mxnet
+```
+
+To install Apache MXNet with GPU support, you need to specify CUDA version. For example, the snippet below will install Apache MXNet with CUDA 9.2 support:
+
+```{.python .input}
+# !pip install mxnet-cuda92
+```
+
+## Data manipulation
+
+Both PyTorch and Apache MXNet relies on multidimensional matrices as a data sources. While PyTorch follows Torch's naming convention and refers to multidimensional matrices as "tensors", Apache MXNet follows NumPy's conventions and refers to them as "NDArrays".
+
+In the code snippets below, we create a two-dimensional matrix where each element is initialized to 1. We show how to add 1 to each element of matrices and print the results.
+
+**PyTorch:**
+
+```{.python .input}
+import torch
+
+x = torch.ones(5,3)
+y = x + 1
+y
+```
+
+**MXNet:**
+
+```{.python .input}
+from mxnet import nd
+
+x = nd.ones((5,3))
+y = x + 1
+y
+```
+
+The main difference apart from the package name is that the MXNet's shape input parameter needs to be passed as a tuple enclosed in parentheses as in NumPy.
+
+Both frameworks support multiple functions to create and manipulate tensors / NDArrays. You can find more of them in the documentation.
+
+## Model training
+
+After covering the basics of data creation and manipulation, let's dive deep and compare how model training is done in both frameworks. In order to do so, we are going to solve image classification task on MNIST data set using Multilayer Perceptron (MLP) in both frameworks. We divide the task in 4 steps.
+
+### 1. Read data
+
+The first step is to obtain the data. We download the MNIST data set from the web and load it into memory so that we can read batches one by one.
+
+**PyTorch:**
+
+```{.python .input}
+from torchvision import datasets, transforms
+
+trans = transforms.Compose([transforms.ToTensor(),
+                            transforms.Normalize((0.13,), (0.31,))])
+pt_train_data = torch.utils.data.DataLoader(datasets.MNIST(
+    root='.', train=True, download=True, transform=trans),
+    batch_size=128, shuffle=True, num_workers=4)
+```
+
+**MXNet:**
+
+```{.python .input}
+from mxnet import gluon
+from mxnet.gluon.data.vision import datasets, transforms
+
+trans = transforms.Compose([transforms.ToTensor(),
+                            transforms.Normalize(0.13, 0.31)])
+mx_train_data = gluon.data.DataLoader(
+    datasets.MNIST(train=True).transform_first(trans),
+    batch_size=128, shuffle=True, num_workers=4)
+```
+
+Both frameworks allows you to download MNIST data set from their sources and specify that only training part of the data set is required.
+
+The main difference between the code snippets is that MXNet uses [transform_first](http://beta.mxnet.io/api/gluon/_autogen/mxnet.gluon.data.Dataset.html) method to indicate that the data transformation is done on the first element of the data batch, the MNIST picture, rather than the second element, the label.
+
+### 2. Creating the model
+
+Below we define a Multilayer Perceptron (MLP) with a single hidden layer
+and 10 units in the output layer.
+
+**PyTorch:**
+
+```{.python .input}
+import torch.nn as pt_nn
+
+pt_net = pt_nn.Sequential(
+    pt_nn.Linear(28*28, 256),
+    pt_nn.ReLU(),
+    pt_nn.Linear(256, 10))
+```
+
+**MXNet:**
+
+```{.python .input}
+import mxnet.gluon.nn as mx_nn
+
+mx_net = mx_nn.Sequential()
+mx_net.add(mx_nn.Dense(256, activation='relu'),
+           mx_nn.Dense(10))
+mx_net.initialize()
+```
+
+We used the Sequential container to stack layers one after the other in order to construct the neural network. Apache MXNet differs from PyTorch in the following ways:
+
+* In PyTorch you have to specify the input size as the first argument of the `Linear` object. Apache MXNet provides an extra flexibility to network structure by automatically inferring the input size after the first forward pass.
+
+* In Apache MXNet you can specify activation functions directly in fully connected and convolutional layers.
+
+* After the model structure is defined, Apache MXNet requires you to explicitly call the model initialization function.
+
+With a Sequential block, layers are executed one after the other. To have a different execution model, with PyTorch you can inherit from `nn.Module` and then customize how the `.forward()` function is executed. Similarly, in Apache MXNet you can inherit from [nn.Block](http://beta.mxnet.io/api/gluon/mxnet.gluon.nn.Block.html) to achieve similar results.
+
+### 3. Loss function and optimization algorithm
+
+The next step is to define the loss function and pick an optimization algorithm. Both PyTorch and Apache MXNet provide multiple options to chose from, and for our particular case we are going to use the cross-entropy loss function and the Stochastic Gradient Descent (SGD) optimization algorithm.
+
+**PyTorch:**
+
+```{.python .input}
+pt_loss_fn = pt_nn.CrossEntropyLoss()
+pt_trainer = torch.optim.SGD(pt_net.parameters(), lr=0.1)
+```
+
+**MXNet:**
+
+```{.python .input}
+mx_loss_fn = gluon.loss.SoftmaxCrossEntropyLoss()
+mx_trainer = gluon.Trainer(mx_net.collect_params(),
+                           'sgd', {'learning_rate': 0.1})
+```
+
+The code difference between frameworks is small. The main difference is that in Apache MXNet we use [Trainer](http://beta.mxnet.io/api/gluon/mxnet.gluon.Trainer.html) class, which accepts optimization algorithm as an argument. We also use [.collect_params()](/api/python/docs/api/gluon/_autogen/mxnet.gluon.nn.Block.collect_params.html) method to get parameters of the network.
+
+### 4. Training
+
+Finally, we implement the training algorithm. Note that the results for each run
+may vary because the weights will get different initialization values and the
+data will be read in a different order due to shuffling.
+
+**PyTorch:**
+
+```{.python .input}
+import time
+
+for epoch in range(5):
+    total_loss = .0
+    tic = time.time()
+    for X, y in pt_train_data:
+        pt_trainer.zero_grad()
+        loss = pt_loss_fn(pt_net(X.view(-1, 28*28)), y)
+        loss.backward()
+        pt_trainer.step()
+        total_loss += loss.mean()
+    print('epoch %d, avg loss %.4f, time %.2f' % (
+        epoch, total_loss/len(pt_train_data), time.time()-tic))
+```
+
+**MXNet:**
+
+```{.python .input}
+from mxnet import autograd
+
+for epoch in range(5):
+    total_loss = .0
+    tic = time.time()
+    for X, y in mx_train_data:
+        with autograd.record():
+            loss = mx_loss_fn(mx_net(X), y)
+        loss.backward()
+        mx_trainer.step(batch_size=128)
+        total_loss += loss.mean().asscalar()
+    print('epoch %d, avg loss %.4f, time %.2f' % (
+        epoch, total_loss/len(mx_train_data), time.time()-tic))
+```
+
+Some of the differences in Apache MXNet when compared to PyTorch are as follows:
+
+* In Apache MXNet, you don't need to flatten the 4-D input into 2-D when feeding the data into forward pass.
+
+* In Apache MXNet, you need to perform the calculation within the [autograd.record()](/api/python/docs/api/gluon-related/_autogen/mxnet.autograd.record.html) scope so that it can be automatically differentiated in the backward pass.
+
+* It is not necessary to clear the gradient every time as with PyTorch's `trainer.zero_grad()` because by default the new gradient is written in, not accumulated.
+
+* You need to specify the update step size (usually batch size) when performing [step()](/api/python/docs/api/gluon/_autogen/mxnet.gluon.Trainer.step.html) on the trainer.
+
+* You need to call [.asscalar()](/api/python/docs/api/ndarray/_autogen/mxnet.ndarray.NDArray.asscalar.html) to turn a multidimensional array into a scalar.
+
+* In this sample, Apache MXNet is twice as fast as PyTorch. Though you need to be cautious with such toy comparisons.
+
+## Conclusion
+
+As we saw above, Apache MXNet Gluon API and PyTorch have many similarities. The main difference lies in terminology (Tensor vs. NDArray) and behavior of accumulating gradients: gradients are accumulated in PyTorch and overwritten in Apache MXNet. The rest of the code is very similar, and it is quite straightforward to move code from one framework to the other.
+
+## Recommended Next Steps
+
+While Apache MXNet Gluon API is very similar to PyTorch, there are some extra functionality that can make your code even faster.
+
+* Check out [Hybridize tutorial](/api/python/docs/guide/packages/gluon/hybridize.html) to learn how to write imperative code which can be converted to symbolic one.
+
+* Also, check out how to extend Apache MXNet with your own [custom layers](/api/python/docs/guide/extend/custom_layer.html).
+
+## Appendix
+
+Below you can find a detailed comparison of various PyTorch functions and their equivalent in Gluon API of Apache MXNet.
+
+### Tensor operation
+
+Here is the list of function names in PyTorch Tensor that are different from Apache MXNet NDArray.
+
+| Function                      | PyTorch                                   | MXNet Gluon                                               |
+|-------------------------------|-------------------------------------------|-----------------------------------------------------------|
+| Element-wise inverse cosine   | `x.acos()` or `torch.acos(x)`             | `nd.arccos(x)`                                            |
+| Batch Matrix product and accumulation| `torch.addbmm(M, batch1, batch2)`  | `nd.linalg_gemm(M, batch1, batch2)` Leading n-2 dim are reduced |
+| Element-wise division of t1, t2, multiply v, and add t | `torch.addcdiv(t, v, t1, t2)` | `t + v*(t1/t2)`                              |
+| Matrix product and accumulation| `torch.addmm(M, mat1, mat2)`             | `nd.linalg_gemm(M, mat1, mat2)`                           |
+| Outer-product of two vector add a matrix | `m.addr(vec1, vec2)`           | Not available                                             |
+| Element-wise applies function | `x.apply_(calllable)`                     | Not available, but there is `nd.custom(x, 'op')`          |
+| Element-wise inverse sine     | `x.asin()` or `torch.asin(x)`             | `nd.arcsin(x)`                                            |
+| Element-wise inverse tangent  | `x.atan()` or `torch.atan(x)`             | `nd.arctan(x)`                                            |
+| Tangent of two tensor         | `x.atan2(y)` or `torch.atan2(x, y)`       | Not available                                             |
+| batch matrix product          | `x.bmm(y)` or `torch.bmm(x, x)`           | `nd.linalg_gemm2(x, y)`                                   |
+| Draws a sample from bernoulli distribution | `x.bernoulli()`              | Not available                                             |
+| Fills a tensor with number drawn from Cauchy distribution | `x.cauchy_()` | Not available                                             |
+| Splits a tensor in a given dim| `x.chunk(num_of_chunk)`                   | `nd.split(x, num_outputs=num_of_chunk)`                   |
+| Limits the values of a tensor to between min and max | `x.clamp(min, max)`| `nd.clip(x, min, max)`                                    |
+| Returns a copy of the tensor  | `x.clone()`                               | `x.copy()`                                                |
+| Cross product                 | `x.cross(y)`                              | Not available                                             |
+| Cumulative product along an axis| `x.cumprod(1)`                          | Not available                                             |
+| Cumulative sum along an axis  | `x.cumsum(1)`                             | Not available                                             |
+| Address of the first element  | `x.data_ptr()`                            | Not available                                             |
+| Creates a diagonal tensor     | `x.diag()`                                | Not available                                             |
+| Computes norm of a tensor     | `x.dist()`                                | `nd.norm(x)` Only calculate L2 norm                       |
+| Computes Gauss error function | `x.erf()`                                 | Not available                                             |
+| Broadcasts/Expands tensor to new shape | `x.expand(3,4)`                  | `x.broadcast_to([3, 4])`                                  |
+| Fills a tensor with samples drawn from exponential distribution | `x.exponential_()` | `nd.random_exponential()`                      |
+| Element-wise mod              | `x.fmod(3)`                               | `nd.module(x, 3)`                                         |
+| Fractional portion of a tensor| `x.frac()`                                | `x - nd.trunc(x)`                                         |
+| Gathers values along an axis specified by dim | `torch.gather(x, 1,  torch.LongTensor([[0,0],[1,0]]))` | `nd.gather_nd(x, nd.array([[[0,0],[1,1]],[[0,0],[1,0]]]))`  |
+| Solves least square & least norm | `B.gels(A)`                            | Not available                                             |
+| Draws from geometirc distribution | `x.geometric_(p)`                     | Not available                                             |
+| Device context of a tensor    | `print(x)` will print which device x is on| `x.context`                                               |
+| Repeats tensor                | `x.repeat(4,2)`                           | `x.tile(4,2)`                                             |
+| Data type of a tensor         | `x.type()`                                | `x.dtype`                                                 |
+| Scatter                       | `torch.zeros(2, 4).scatter_(1, torch.LongTensor([[2], [3]]), 1.23)` | `nd.scatter_nd(nd.array([1.23,1.23]), nd.array([[0,1],[2,3]]), (2,4))` |
+| Returns the shape of a tensor | `x.size()`                                | `x.shape`                                                 |
+| Number of elements in a tensor| `x.numel()`                               | `x.size`                                                  |
+| Returns this tensor as a NumPy ndarray | `x.numpy()`                      | `x.asnumpy()`                                             |
+| Eigendecomposition for symmetric matrix | `e, v = a.symeig()`             | `v, e = nd.linalg.syevd(a)`                               |
+| Transpose                     | `x.t()`                                   | `x.T`                                                     |
+| Sample uniformly              | `torch.uniform_()`                        | `nd.sample_uniform()`                                     |
+| Inserts a new dimesion        | `x.unsqueeze()`                           | `nd.expand_dims(x)`                                       |
+| Reshape                       | `x.view(16)`                              | `x.reshape((16,))`                                          |
+| Veiw as a specified tensor    | `x.view_as(y)`                            | `x.reshape_like(y)`                                       |
+| Returns a copy of the tensor after casting to a specified type | `x.type(type)` | `x.astype(dtype)`                                   |
+| Copies the value of one tensor to another | `dst.copy_(src)`              | `src.copyto(dst)`                                         |
+| Returns a zero tensor with specified shape | `x = torch.zeros(2,3)`       | `x = nd.zeros((2,3))`                                     |
+| Returns a one tensor with specified shape | `x = torch.ones(2,3)`         | `x = nd.ones((2,3)`                                       |
+| Returns a Tensor filled with the scalar value 1, with the same size as input | `y = torch.ones_like(x)` | `y = nd.ones_like(x)`       |
+
+### Functional
+
+### GPU
+
+Just like Tensor, MXNet NDArray can be copied to and operated on GPU. This is done by specifying context.
+
+| Function               | PyTorch                           | MXNet Gluon                                                                |
+|------------------------|-----------------------------------|----------------------------------------------------------------------------|
+| Copy to GPU            | `y = torch.FloatTensor(1).cuda()` | `y = mx.nd.ones((1,), ctx=mx.gpu(0))`                                      |
+| Convert to numpy array | `x = y.cpu().numpy()`             | `x = y.asnumpy()`                                                          |
+| Context scope          | `with torch.cuda.device(1):`<br/>&nbsp;&nbsp;&nbsp;&nbsp;`y= torch.cuda.FloatTensor(1)`                    | `with mx.gpu(1):`<br/>&nbsp;&nbsp;&nbsp;&nbsp;`y = mx.nd.ones((3,5))`      |
+
+### Cross-device
+
+Just like Tensor, MXNet NDArray can be copied across multiple GPUs.
+
+| Function               | PyTorch                           | MXNet Gluon                                                                |
+|------------------------|-----------------------------------|----------------------------------------------------------------------------|
+| Copy from GPU 0 to GPU 1           | `x = torch.cuda.FloatTensor(1)`<br/>`y=x.cuda(1)`| `x = mx.nd.ones((1,), ctx=mx.gpu(0))`<br/>`y=x.as_in_context(mx.gpu(1))`                                      |
+| Copy Tensor/NDArray on different GPUs | `y.copy_(x)`             | `x.copyto(y)`                                                          |
+
+## Autograd
+
+### Variable wrapper vs autograd scope
+
+Autograd package of PyTorch/MXNet enables automatic differentiation of Tensor/NDArray.
+
+| Function               | PyTorch                           | MXNet Gluon                                                                |
+|------------------------|-----------------------------------|----------------------------------------------------------------------------|
+| Recording computation       | `x = Variable(torch.FloatTensor(1), requires_grad=True)`<br/>`y = x * 2`<br/>`y.backward()`  | `x = mx.nd.ones((1,))`<br/>`x.attach_grad()`<br/>`with mx.autograd.record():`<br/>&nbsp;&nbsp;&nbsp;&nbsp;`y = x * 2`<br/>`y.backward()`                                   |
+
+### Scope override (pause, train_mode, predict_mode)
+
+Some operators (Dropout, BatchNorm, etc) behave differently in training and making predictions. This can be controlled with `train_mode` and `predict_mode` scope in MXNet.
+Pause scope is for code that does not need gradients to be calculated.
+
+| Function               | PyTorch                           | MXNet Gluon                                                                |
+|------------------------|-----------------------------------|----------------------------------------------------------------------------|
+| Scope override   | Not available | `x = mx.nd.ones((1,))`<br/>`with autograd.train_mode():`<br/>&nbsp;&nbsp;&nbsp;&nbsp;`y = mx.nd.Dropout(x)`<br/>&nbsp;&nbsp;&nbsp;&nbsp;`with autograd.predict_mode():`<br/>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`z = mx.nd.Dropout(y)`<br/><br/>`w = mx.nd.ones((1,))`<br/>`w.attach_grad()`<br/>`with autograd.record():`<br/>&nbsp;&nbsp;&nbsp;&nbsp;`y = x * w`<br/>&nbsp;&nbsp;&nbsp;&nbsp;`y.backward()`<br/>&nbsp;&nbsp;&nbsp;&nbsp;`with autograd.pause():`<br/>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`w += w.grad`   |
+
+### Batch-end synchronization is needed
+
+Apache MXNet uses lazy evaluation to achieve superior performance. The Python thread just pushes the operations into the backend engine and then returns. In training phase batch-end synchronization is needed, e.g, `asnumpy()`, `wait_to_read()`, `metric.update(...)`.
+
+| Function               | PyTorch                           | MXNet Gluon                                                                |
+|------------------------|-----------------------------------|----------------------------------------------------------------------------|
+| Batch-end synchronization    |  Not available  | `for (data, label) in train_data:`<br/>&nbsp;&nbsp;&nbsp;&nbsp;`with autograd.record():`<br/>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`output = net(data)`<br/>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`L = loss(output, label)`<br/>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`L.backward()`<br/>&nbsp;&nbsp;&nbsp;&nbsp;`trainer.step(data.shape[0])`<br/>&nbsp;&nbsp;&nbsp;&nbsp;`metric.update([label], [output])` |
+
+## PyTorch module and Gluon blocks
+
+### For new block definition, gluon needs name_scope
+
+`name_scope` coerces Gluon to give each parameter an appropriate name, indicating which model it belongs to.
+
+| Function               | PyTorch                           | MXNet Gluon                                                                |
+|------------------------|-----------------------------------|----------------------------------------------------------------------------|
+| New block definition   | `class Net(torch.nn.Module):`<br/>&nbsp;&nbsp;&nbsp;&nbsp;`def __init__(self, D_in, D_out):`<br/>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`super(Net, self).__init__()`<br/>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`self.linear = torch.nn.Linear(D_in, D_out)`<br/>&nbsp;&nbsp;&nbsp;&nbsp;`def forward(self, x):`<br/>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`return self.linear(x)`       |    `class Net(mx.gluon.Block):`<br/>&nbsp;&nbsp;&nbsp;&nbsp;`def __init__(self, D_in, D_out):`<br/>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`super(Net, self).__init__()`<br/>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`with self.name_scope():`<br/>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`self.dense=mx.gluon.nn.Dense(D_out, in_units=D_in)`<br/>&nbsp;&nbsp;&nbsp;&nbsp;`def forward(self, x):`<br/>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`return self.dense(x)`      |
+
+### Parameter and Initializer
+
+When creating new layers in PyTorch, you do not need to specify its parameter initializer, and different layers have different default initializer. When you create new layers in Gluon API, you can specify its initializer or just leave it none. The parameters will finish initializing after calling `net.initialize(<init method>)` and all parameters will be initialized in `init method` except those layers whose initializer specified.
+
+| Function       | PyTorch           | MXNet Gluon        |
+|----------------|-------------------|--------------------|
+| Get all parameters |  `net.parameters()` | `net.collect_params()` |
+| Initialize network |  Not Available | `net.initialize(mx.init.Xavier())` |
+| Specify layer initializer | `layer = torch.nn.Linear(20, 10)`<br/> `torch.nn.init.normal(layer.weight, 0, 0.01)` | `layer = mx.gluon.nn.Dense(10, weight_initializer=mx.init.Normal(0.01))` |
+
+### Usage of existing blocks look alike
+
+| Function               | PyTorch                           | MXNet Gluon                                                                |
+|------------------------|-----------------------------------|----------------------------------------------------------------------------|
+| Usage of existing blocks    |  `y=net(x)`  |  `y=net(x)`   |
+
+### HybridBlock can be hybridized, and allows partial-shape info
+
+HybridBlock supports forwarding with both Symbol and NDArray. After hybridized, HybridBlock will create a symbolic graph representing the forward computation and cache it. Most of the built-in blocks (Dense, Conv2D, MaxPool2D, BatchNorm, etc.) are HybridBlocks.
+
+Instead of explicitly declaring the number of inputs to a layer, we can simply state the number of outputs. The shape will be inferred on the fly once the network is provided with some input.
+
+| Function               | PyTorch                           | MXNet Gluon                                                                |
+|------------------------|-----------------------------------|----------------------------------------------------------------------------|
+| partial-shape  <br/> hybridized    |  Not Available   |  `net = mx.gluon.nn.HybridSequential()`<br/>`with net.name_scope():`<br/>&nbsp;&nbsp;&nbsp;&nbsp;`net.add(mx.gluon.nn.Dense(10))`<br/>`net.hybridize()`   |
+
+### SymbolBlock
+
+SymbolBlock can construct block from symbol. This is useful for using pre-trained models as feature extractors.
+
+| Function               | PyTorch                           | MXNet Gluon                                                                |
+|------------------------|-----------------------------------|----------------------------------------------------------------------------|
+|  SymbolBlock    |  Not Available   |  `alexnet = mx.gluon.model_zoo.vision.alexnet(pretrained=True, prefix='model_')`<br/>`out = alexnet(inputs)`<br/>`internals = out.get_internals()`<br/>`outputs = [internals['model_dense0_relu_fwd_output']]`<br/>`feat_model = gluon.SymbolBlock(outputs, inputs, params=alexnet.collect_params())`   |
+
+## PyTorch optimizer vs Gluon Trainer
+### For Gluon API calling zero_grad is not necessary most of the time
+`zero_grad` in optimizer (PyTorch) or Trainer (Gluon API) clears the gradients of all parameters. In Gluon API, there is no need to clear the gradients every batch if `grad_req = 'write'`(default).
+
+| Function               | PyTorch                           | MXNet Gluon                              |
+|------------------------|-----------------------------------|------------------------------------------|
+| clear the gradients |   `optm = torch.optim.SGD(model.parameters(), lr=0.1)`<br/>`optm.zero_grad()`<br/>`loss_fn(model(input), target).backward()`<br/>`optm.step()`    | `trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.1})`<br/>`with autograd.record():`<br/>&nbsp;&nbsp;&nbsp;&nbsp;`loss = loss_fn(net(data), label)`<br/>`loss.backward()`<br/>`trainer.step(batch_size)`      |
+
+### Multi-GPU training
+
+| Function               | PyTorch                           | MXNet Gluon                              |
+|------------------------|-----------------------------------|------------------------------------------|
+| data parallelism |   `net = torch.nn.DataParallel(model, device_ids=[0, 1, 2])`<br/>`output = net(data)`    | `ctx = [mx.gpu(i) for i in range(3)]`<br/>`data = gluon.utils.split_and_load(data, ctx)`<br/>`label = gluon.utils.split_and_load(label, ctx)`<br/>`with autograd.record():`<br/>&nbsp;&nbsp;&nbsp;&nbsp;`losses = [loss(net(X), Y) for X, Y in zip(data, label)]`<br/>`for l in losses:`<br/>&nbsp;&nbsp;&nbsp;&nbsp;`l.backward()`      |
+
+### Distributed training
+
+| Function               | Pytorch                           | MXNet Gluon                              |
+|------------------------|-----------------------------------|------------------------------------------|
+| distributed data parallelism |   `torch.distributed.init_process_group(...)`<br/>`model = torch.nn.parallel.distributedDataParallel(model, ...)`    | `store = kv.create('dist')`<br/>`trainer = gluon.Trainer(net.collect_params(), ..., kvstore=store)`  |
+
+## Monitoring
+
+### Apache MXNet has pre-defined metrics
+
+Gluon provide several predefined metrics which can online evaluate the performance of a learned model.
+
+| Function               | PyTorch                           | MXNet Gluon                              |
+|------------------------|-----------------------------------|------------------------------------------|
+| metric |  Not available   | `metric = mx.metric.Accuracy()`<br/>`with autograd.record():`<br/>&nbsp;&nbsp;&nbsp;&nbsp;`output = net(data)`<br/>&nbsp;&nbsp;&nbsp;&nbsp;`L = loss(ouput, label)`<br/>&nbsp;&nbsp;&nbsp;&nbsp;`loss(ouput, label).backward()`<br/>`trainer.step(batch_size)`<br/>`metric.update(label, output)`  |
+
+### Data visualization
+
+TensorboardX (PyTorch) and [MXBoard](https://github.com/awslabs/mxboard) (MXNet) can be used to visualize your network and plot quantitative metrics about the execution of your graph.
+
+| PyTorch                                        | MXNet                                          |
+| ---------------------------------------------- | ---------------------------------------------- |
+| `sw = tensorboardX.SummaryWriter()`            | `sw = mxboard.SummaryWriter()`                 |
+| `...`                                          | `...`                                          |
+| `for name, param in model.named_parameters():` | `for name, param in net.collect_params():`     |
+| `    grad = param.clone().cpu().data.numpy()`  | `    grad = param.grad.asnumpy().flatten()`    |
+| `    sw.add_histogram(name, grad, n_iter)`     | `    sw.add_histogram(tag=str(param),`         |
+| `...`                                          | `       values=grad,`                          |
+| `sw.close()`                                   | `       bins=200,`                             |
+|                                                | `       global_step=i)`                        |
+|                                                | `...`                                          |
+|                                                | `sw.close()`                                   |
+
+## I/O and deploy
+
+### Data loading
+
+`Dataset` and `DataLoader` are the basic components for loading data.
+
+| Class               | PyTorch                           | MXNet Gluon                              |
+|------------------------|-----------------------------------|------------------------------------------|
+| Dataset holding arrays | `torch.utils.data.TensorDataset(data_tensor, label_tensor)`| `gluon.data.ArrayDataset(data_array, label_array)`                        |
+| Data loader | `torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False, sampler=None, batch_sampler=None, num_workers=0, collate_fn=<function default_collate>, drop_last=False)` | `gluon.data.DataLoader(dataset, batch_size=None, shuffle=False, sampler=None, last_batch='keep', batch_sampler=None, batchify_fn=None, num_workers=0)`|
+| Sequentially applied sampler | `torch.utils.data.sampler.SequentialSampler(data_source)` | `gluon.data.SequentialSampler(length)` |
+| Random order sampler | `torch.utils.data.sampler.RandomSampler(data_source)` | `gluon.data.RandomSampler(length)`|
+
+Some commonly used datasets for computer vision are provided in `mx.gluon.data.vision` package.
+
+| Class               | PyTorch                           | MXNet Gluon                              |
+|------------------------|-----------------------------------|------------------------------------------|
+| MNIST handwritten digits dataset. | `torchvision.datasets.MNIST`| `mx.gluon.data.vision.MNIST` |
+| CIFAR10 Dataset. | `torchvision.datasets.CIFAR10` | `mx.gluon.data.vision.CIFAR10`|
+| CIFAR100 Dataset. | `torchvision.datasets.CIFAR100` | `mx.gluon.data.vision.CIFAR100` |
+| A generic data loader where the images are arranged in folders. | `torchvision.datasets.ImageFolder(root, transform=None, target_transform=None, loader=<function default_loader>)` | `mx.gluon.data.vision.ImageFolderDataset(root, flag, transform=None)`|
+
+### Serialization
+
+Serialization and deserialization are achieved by calling `save_parameters` and `load_parameters`.
+
+| Class               | PyTorch                           | MXNet Gluon                              |
+|------------------------|-----------------------------------|------------------------------------------|
+| Save model parameters | `torch.save(the_model.state_dict(), filename)`| `model.save_parameters(filename)`|
+| Load parameters | `the_model.load_state_dict(torch.load(PATH))` | `model.load_parameters(filename, ctx, allow_missing=False, ignore_extra=False)` |
diff --git a/docs/python_docs/python/tutorials/index.rst b/docs/python_docs/python/tutorials/index.rst
new file mode 100644
index 000000000000..af906deba22b
--- /dev/null
+++ b/docs/python_docs/python/tutorials/index.rst
@@ -0,0 +1,175 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Python Tutorials
+=====
+
+Getting started
+---------------
+
+.. container:: cards
+
+   .. card::
+      :title: A 60-minute Gluon crash course
+      :link: getting-started/crash-course/index.html
+
+      A quick overview of the core concepts of MXNet using the Gluon API.
+
+   .. card::
+      :title: Moving from other frameworks
+      :link: getting-started/to-mxnet/index.html
+
+      Guides that ease your transition to MXNet from other framework.
+
+
+Packages & Modules
+------------------
+
+.. container:: cards
+
+   .. card::
+      :title: Gluon
+      :link: packages/gluon/index.html
+
+      MXNet's imperative interface for Python. If you're new to MXNet, start here!
+
+   .. card::
+      :title: NDArray API
+      :link: packages/ndarray/index.html
+
+      How to use the NDArray API to manipulate data.
+      A useful set of tutorials for beginners.
+
+   .. card::
+      :title: Symbol API
+      :link: packages/symbol/index.html
+
+      How to use MXNet's Symbol API.
+
+   .. card::
+      :title: Autograd API
+      :link: packages/autograd/autograd.html
+
+      How to use Automatic Differentiation with the Autograd API.
+
+
+
+Performance
+-----------
+.. container:: cards
+
+   .. card::
+      :title: Improving Performance
+      :link: performance/index.html
+
+      How to get the best performance from MXNet.
+
+   .. card::
+      :title: Profiler
+      :link: performance/backend/profiler.html
+
+      How to profile MXNet models.
+
+   .. card::
+      :title: Compression: int8
+      :link: performance/int8.html
+
+      How to use int8 in your model to boost training speed.
+
+   .. card::
+      :title: MKL-DNN
+      :link: performance/backend/mkl-dnn.html
+
+      How to get the most from your CPU by using Intel's MKL-DNN.
+
+   .. card::
+      :title: TVM
+      :link: performance/backend/tvm.html
+
+      How to use TVM to boost performance.
+
+
+Deployment
+----------
+.. container:: cards
+
+   .. card::
+      :title: MXNet on EC2
+      :link: deploy/run-on-aws/use_ec2.html
+
+      How to deploy MXNet on an Amazon EC2 instance.
+
+   .. card::
+      :title: MXNet on SageMaker
+      :link: deploy/run-on-aws/use_sagemaker.html
+
+      How to run MXNet using Amazon SageMaker.
+
+      ..
+         PLACEHOLDER
+         .. card::
+            :title: Export
+            :link: deploy/export/index.html
+
+            How to export MXNet models.
+
+         .. card::
+            :title: C++
+            :link: deploy/inference/cpp.html
+
+            How to use MXNet models in a C++ environment.
+
+         .. card::
+            :title: Scala and Java
+            :link: deploy/inference/scala.html
+
+            How to use MXNet models in a Scala or Java environment.
+
+         PLACEHOLDER
+      ..
+
+
+Customization
+-------------
+.. container:: cards
+
+Coming Soon (CustomOps and Custom Operators)
+
+Next steps
+----------
+
+- To learn more about using MXNet to implement various deep learning algorithms
+  from scratch, we recommend the `Dive into Deep Learning
+  <https://d2l.ai>`_ book.
+
+- Check out the `API Reference docs <../api/index.html>`_.
+
+.. raw:: html
+
+   <style> h1 {display: none;} </style>
+   <style>.localtoc { display: none; }</style>
+
+
+.. toctree::
+   :hidden:
+   :maxdepth: 3
+
+   getting-started/index
+   packages/index
+   performance/index
+   deploy/index
+   extend/index
\ No newline at end of file
diff --git a/docs/python_docs/python/tutorials/packages/autograd/autograd.md b/docs/python_docs/python/tutorials/packages/autograd/autograd.md
new file mode 100644
index 000000000000..2558ee04bdec
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/autograd/autograd.md
@@ -0,0 +1,272 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Automatic Differentiation
+
+## Why do we need to calculate gradients?
+
+### Short Answer:
+
+Gradients are fundamental to the process of training neural networks, and tell us how to change the parameters of the network to improve its performance.
+
+![autograd-gradient](/_static/autograd/autograd_gradient.png)
+
+### Long Answer:
+
+Under the hood, neural networks are composed of operators (e.g. sums, products, convolutions, etc) some of which use parameters (e.g. the weights in convolution kernels) for their computation, and it's our job to find the optimal values for these parameters. Gradients lead us to the solution!
+
+Gradients tell us how much a given variable increases or decreases when we change a variable it depends on. What we're interested in is the effect of changing a each parameter on the performance of the network. We usually define performance using a loss metric that we try to minimize, i.e. a metric that tells us how bad the predictions of a network are given ground truth. As an example, for regression we might try to minimize the [L2 loss](http://beta.mxnet.io/api/gluon/_autogen/mxnet.gluon.loss.L2Loss.html?highlight=l2#mxnet.gluon.loss.L2Loss) (also known as the Euclidean distance) between our predictions and true values, and for classification we minimize the [cross entropy loss](http://beta.mxnet.io/api/gluon/_autogen/mxnet.gluon.loss.SoftmaxCrossEntropyLoss.html).
+
+Assuming we've calculated the gradient of each parameter with respect to the loss (details in next section), we can then use an optimizer such as [stochastic gradient descent](https://en.wikipedia.org/wiki/Stochastic_gradient_descent) to shift the parameters slightly in the *opposite direction* of the gradient. See [Optimizers](http://beta.mxnet.io/api/gluon-related/mxnet.optimizer.html) for more information on these methods. We repeat the process of calculating gradients and updating parameters over and over again, until the parameters of the network start to stabilize and converge to a good solution.
+
+## How do we calculate gradients?
+
+### Short Answer:
+
+We differentiate. [MXNet Gluon](http://beta.mxnet.io/api/gluon/index.html) uses Reverse Mode Automatic Differentiation (`autograd`) to backprogate gradients from the loss metric to the network parameters.
+
+![forward-backward](/_static/autograd/autograd_forward_backward.png)
+
+### Long Answer:
+
+One option would be to get out our calculus books and work out the gradients by hand. Who wants to do this though? It's time consuming and error prone for starters. Another option is [symbolic differentiation](https://www.cs.utexas.edu/users/novak/asg-symdif.html), which calculates the formulas for each gradient, but this quickly leads to incredibly long formulas as networks get deeper and operators get more complex. We could use finite differencing, and try slight differences on each parameter and see how the loss metric responds, but this is computationally expensive and can have poor [numerical precision](https://en.wikipedia.org/wiki/Finite_difference_coefficient).
+
+What's the solution? Use automatic differentiation to backpropagate the gradients from the loss metric back to each of the parameters. With [backpropagation](https://en.wikipedia.org/wiki/Backpropagation), a dynamic programming approach is taken to efficently calculate gradients. Sometimes this is called reverse mode automatic differentiation, and it's very efficient in 'fan-in' situations where many parameters effect a single loss metric. Although forward mode automatic differentiation methods exist, they're suited to 'fan-out' situations where few parameters effect many metrics, which isn't the case for training neural networks.
+
+## How does Automatic Differentiation (`autograd`) work?
+
+### Short Answer:
+
+Stage 1. Create a record of the operators used by the network to make predictions and calculate the loss metric. Called the 'forward pass' of training.
+Stage 2. Work backwards through this record and evaluate the partial derivatives of each operator, all the way back to the network parameters. Called the 'backward pass' of training.
+
+<p style="text-align:center">
+    <video width="600" controls playsinline autoplay muted loop>
+        <source src="/_static/autograd/autograd_graph.mp4" type="video/mp4">
+    </video>
+</p>
+
+### Long Answer:
+
+All operators in MXNet have two methods defined: a `forward` method for executing the operator as expected, and a `backward` method that returns the partial derivative (the derivative of the output with respect to the input). On the vary rare occasion you need to implement your own custom operator, you'll define the same two methods.
+
+Automatic differentiation creates a record of the operators used (i.e. the `forward` method calls) by the network to make predictions and calculate the loss metric. A graph structure is used to record this, capturing the inputs (including their value) and outputs for each operator and how the operators are related. We call this the 'forward pass' of training.
+
+Automatic differentiation then works backwards through each operator of the graph, calling the `backward` method on each operator to calculate the partial derivative and calculate the gradient of the loss metric with respect to the operator's input (which could be parameters). Usually we work backwards from the loss metric, and hence calculate the gradients of the loss metric, but this can be done from any output. We call this the 'backward pass' of training.
+
+## What are the advantages of Automatic Differentiation (`autograd`)?
+
+### Short Answer:
+
+It's flexible, automatic and efficient. You can use native Python control flow operators such as `if` conditions and `while` loops and `autograd` will still be able to backpropogate the gradients correctly.
+
+### Long Answer:
+
+A huge benefit of using `autograd` is the flexibility it gives you when defining your network. You can change the operations on every iteration, and `autograd` will still be able to backpropogate the gradients correctly. You'll sometimes hear these called 'dynamic graphs', and are much more complex to implement in frameworks that require static graphs, such as TensorFlow.
+
+As suggested by the name, `autograd` is automatic and so the complexities of the backpropogation procedure are taken care of for you. All you have to do is tell `autograd` when you're interested in recording gradients, and specify what gradients you're interested in calculating: this will nearly always just be the gradient of the loss metric. And these gradient calculations will be performed efficiently too.
+
+## How do I use `autograd` in MXNet Gluon?
+
+Step one is to import the `autograd` package.
+
+```
+from mxnet import autograd
+```
+
+As a simple example, we'll implement the regression model shown in the diagrams above, and later use `autograd` to automatically calculate the gradient of the loss with respect to each of the weight parameters.
+
+```
+import mxnet as mx
+from mxnet.gluon.nn import HybridSequential, Dense
+from mxnet.gluon.loss import L2Loss
+
+
+# Define network
+net = HybridSequential()
+net.add(Dense(units=3))
+net.add(Dense(units=1))
+net.initialize()
+
+# Define loss
+loss_fn = L2Loss()
+
+# Create dummy data
+x = mx.nd.array([[0.3, 0.5]])
+y = mx.nd.array([[1.5]])
+```
+
+We're ready for our first forward pass through the network, and we want `autograd` to record the computational graph so we can calculate gradients. One of the simplest ways to do this is by running the network (and loss) code in the scope of an `autograd.record` context.
+
+```
+with autograd.record():
+    y_hat = net(x)
+    loss = loss_fn(y_hat, y)
+```
+
+Only operations that we want recorded are in the scope of the `autograd.record` context (since there is a computational overhead), and `autograd` should now have constructed a graph of these operations ready for the backward pass. We start the backward pass by calling the `backward` method on the quantity of interest, which in this case is `loss` since were trying to calculate the gradient of the loss with respect to the parameters.
+
+Remember: if `loss` isn't a single scalar value (e.g. could be a loss for each sample, rather than for whole batch) a `sum` operation will be applied implicitly before starting the backward propagation, and the gradients calculated will be of this `sum` with respect to the parameters.
+
+```
+loss.backward()
+```
+
+And that's it! All the `autograd` magic is complete. We should now have gradients for each parameter of the network, which will be used by the optimizer to update the parameter values for improved performance. Check out the gradients of the first layer for example:
+
+```
+net[0].weight.grad()
+```
+
+## Advanced: Switching between training vs inference modes
+
+Some neural network layers behave differently depending on whether you're training the network or running it for inference. One example is `Dropout`, where activations are set to 0 at random during training, but remain unchanged during inference. Another is `BatchNorm`, where local batch statistics are used to normalize while training, but global statistics are used during inference.
+
+With MXNet Gluon, `autograd` is critical for switching between training and inference modes. As the default, networks will run in inference mode. While `autograd` is recording though, networks will run in training mode. Operations under the `autograd.record()` context scope are an example of this.
+
+Creating a network of a single `Dropout` block will demonstrate this.
+
+```
+dropout = mx.gluon.nn.Dropout(rate=0.5)
+data = mx.nd.ones(shape=(3,3))
+
+output = dropout(data)
+is_training = autograd.is_training()
+print('is_training:', is_training, output)
+```
+
+We called `dropout` when `autograd` wasn't recording, so our network was in inference mode and thus we didn't see any dropout of the input (i.e. it's still ones). We can confirm the current mode by calling `autograd.is_training()`.
+
+```
+with autograd.record():
+    output = dropout(data)
+print('is_training:', is_training, output)
+```
+
+We called `dropout` while `autograd` was recording this time, so our network was in training mode and we see dropout of the input this time. Since the probability of dropout was 50%, the output is automatically scaled by 1/0.5=2 to preserve the average activation.
+
+We can force some operators to behave as they would during training, even in inference mode. One example is setting `mode='always'` on the [Dropout](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html?highlight=dropout#mxnet.ndarray.Dropout) operator, but this usage is uncommon.
+
+## Advanced: Skipping the calculation of parameter gradients
+
+When creating neural networks with MXNet Gluon it is assumed that you're interested in the gradients of the loss with respect to each of the network's parameters. We're usually training the whole network, so this is exactly what we want. When we call `net.initialize()`, the network parameters get (lazily) initalized and memory is also allocated for the gradients, esentially doubling the space required for each parameter. After performing a forward and backward pass through the network, we will have gradients for all of the parameters.
+
+Sometimes we don't need the gradients for all of the parameters though. One example would be 'freezing' the values of the parameters in certain layers. Since we don't need to update the values, we don't need the gradients. Using the `grad_req` property of a parameter and setting it to `'null'`, we can indicate this to `autograd`, saving us computation time and memory.
+
+```
+net[0].weight.grad_req = 'null'
+```
+
+<p style="text-align:center">
+    <video width="600" controls playsinline autoplay muted loop>
+        <source src="/_static/autograd/autograd_grad_req.mp4" type="video/mp4">
+    </video>
+</p>
+
+## Advanced: Calculating non-parameter gradients
+
+Although it's most common to deal with network parameters (with `Parameter` being an MXNet Gluon abstraction), there are cases when you need to calculate the gradient with respect to thing that are not parameters (i.e. standard `ndarray`s). One example would be finding the gradient of the loss with respect to the input data to generate adversarial examples.
+
+With `autograd` it's simple, but there's one key difference compared to parameters: parameters are assumed to require gradients by default, non-parameters are not. We need to explicitly state that we require the gradient, and we do that by calling `.attach_grad()` on the `ndarray`. We can then access the gradient using `.grad` after the `backward` pass.
+
+As a simple example, let's take the case where $y=2x^2$ and use `autograd` to calculate gradient of $y$ with respect to $x$ at three different values of $x$. We could obviously work out the gradient by hand in this case as $dy/dx=4x$, but let's use this knowledge to check `autograd`. Given $x$ is an `ndarray` and not a `Parameter`, we need to call `x.attach_grad()`.
+
+```
+x = mx.nd.array([1, 2, 3])
+x.attach_grad()
+with autograd.record():
+    y = 2 * x ** 2
+y.backward()
+print(x.grad)
+```
+
+## Advanced: Using Python control flow
+
+As mentioned before, one of the main advantages of `autograd` is the ability to automatically calculate gradients of dynamic graphs (i.e. graphs where the operators could be different on every forward pass). One example of this would be applying a tree structured recurrent network to parse a sentence using its parse tree. And we can use Python control flow operators to create a dynamic flow that depends on the data, rather than using [MXNet's control flow operators](https://mxnet.incubator.apache.org/versions/master/tutorials/control_flow/ControlFlowTutorial.html).
+
+We'll write a function as a toy example of a dynamic network. We'll add an `if` condition and a loop with a variable number of iterations, both of which will depend on the input data. Although these can now be used in static graphs (with conditional operators) it's still much more natural to use native control flow.
+
+```
+import math
+
+
+def f(x):
+    y = x  # going to change y but still want to use x
+    if x < 0.75:  # variable num_loops because it depends on x
+        num_loops = math.floor(1/(1-x.asscalar()))
+        for i in range(num_loops):
+            y = y * x  # increase polynomial degree
+    else:  # otherwise flatline
+        y = y * 0
+    return y
+```
+
+We can plot the resultant function for $x$ between 0 and 1, and we should recognise certain functions in segments of $x$. Starting with a quadratic curve from 0 to 1/2, we have a cubic curve from 1/2 to 2/3, a quartic from 2/3 to 3/4 and finally a flatline.
+
+![control-flow](/_static/autograd/autograd_control_flow.png)
+
+Using `autograd`, let's now find the gradient of this arbritrary function. We don't have a vectorized function in this case, because of the control flow, so let's also create a function to calculate the gradient using `autograd`.
+
+```
+def get_grad(f, x):
+    x.attach_grad()
+    with autograd.record():
+        y = f(x)
+    y.backward()
+    return x.grad
+
+xs = mx.nd.arange(0.0, 1.0, step=0.1)
+grads = [get_grad(f, x).asscalar() for x in xs]
+print(grads)
+```
+
+![flow-grad](/_static/autograd/autograd_control_flow_grad.png)
+
+We can calculate the gradients by hand in this situation (since it's a toy example), and for the four segments discussed before we'd expect $2x$, $3x^2$, $4x^3$ and 0. As a spot check, for $x=0.6$ the hand calculated gradient would be $3x^2=1.08$, which equals `1.08` as computed by `autograd`.
+
+
+## Advanced: Custom head gradients
+
+Most of the time `autograd` will be aware of the complete computational graph, and be able to calculate the gradients automatically. On a few rare occasions, you might have external post processing components (outside of MXNet Gluon) but still want to compute gradients with respect to MXNet Gluon network parameters.
+
+`autograd` enables this functionality by letting you pass in custom head gradients to `.backward()`. When nothing is specified (for the majority of cases), `autograd` will just used ones by default. Say we're interested in calculating $dz/dx$ but only calculate an intermediate variable $y$ using MXNet Gluon. We need to first calculate the head gradient $dz/dy$ (manually or otherwise), and then pass this to `.backward()`. `autograd` will then use this to calculate $dz/dx$, applying the chain rule.
+
+<p style="text-align:center">
+    <video width="600" controls playsinline autoplay muted loop>
+        <source src="/_static/autograd/autograd_head_grad.mp4" type="video/mp4">
+    </video>
+</p>
+
+As an example, let's take $y=x^3$ (calculated with `mxnet`) and $z=y^2$. (calculated with `numpy`). We can manually calculate $dz/dy=2y$ (once again with `numpy`), and use this as the head gradient for `autograd` to automatically calculate $dz/dx$. Applying the chain rule by hand we could calculate $dz/dx=6x^5$, so for $x=2$ we expect $dz/dx=192$. Let's check to see whether `autograd` calculates the same.
+
+```
+x = mx.nd.array([2,])
+x.attach_grad()
+# compute y inside of mxnet (with `autograd`)
+with autograd.record():
+    y = x**3
+# compute dz/dy outside of mxnet
+y_np = y.asnumpy()
+z_np = y_np**2
+dzdy_np = 2*y_np
+# compute dz/dx inside of mxnet (given dz/dy)
+dzdy = mx.nd.array(dzdy_np)
+y.backward(dzdy)
+print(x.grad)
+```
+
+And as expected, we get a gradient of 192 for `x`.
diff --git a/docs/python_docs/python/tutorials/packages/gluon/activations/activations.md b/docs/python_docs/python/tutorials/packages/gluon/activations/activations.md
new file mode 100644
index 000000000000..e33e94182156
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/activations/activations.md
@@ -0,0 +1,276 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Activation Blocks
+
+Deep neural networks are a way to express a nonlinear function with lots of parameters from input data to outputs. The nonlinearities that allow neural networks to capture complex patterns in data are referred to as activation functions. Over the course of the development of neural networks, several nonlinear activation functions have been introduced to make gradient-based deep learning tractable. 
+
+If you are looking to answer the question, 'which activation function should I use for my neural network model?', you should probably go with *ReLU*. Unless you're trying to implement something like a gating mechanism, like in LSTMs or GRU cells, then you should opt for sigmoid and/or tanh in those cells. However, if you have a working model architecture and you're trying to improve its performance by swapping out activation functions or treating the activation function as a hyperparameter, then you may want to try hand-designed activations like SELU or a function discovered by reinforcement learning and exhaustive search like Swish. This guide describes these activation functions and others implemented in MXNet in detail.
+
+## Visualizing Activations
+In order to compare the various activation functions and to understand the nuances of their differences we have a snippet of code to plot the activation functions (used in the forward pass) and their gradients (used in the backward pass).
+
+
+```python
+import numpy as np
+import mxnet as mx
+from matplotlib import pyplot as plt
+%matplotlib inline
+
+def visualize_activation(activation_fn):
+    data = np.linspace(-10, 10, 501)
+    x = mx.nd.array(data)
+    x.attach_grad()
+    with mx.autograd.record():
+        y = activation_fn(x)
+    y.backward()
+    
+    plt.figure()
+    plt.plot(data, y.asnumpy())
+    plt.plot(data, x.grad.asnumpy())
+    activation = activation_fn.name[:-1]
+    plt.legend(["{} activation".format(activation), "{} gradient".format(activation)])
+    
+```
+
+## Sigmoids
+
+### Sigmoid
+
+The sigmoid activation function, also known as the logistic function or logit function, is perhaps the most widely known activation owing to its [long history](https://web.stanford.edu/class/psych209a/ReadingsByDate/02_06/PDPVolIChapter8.pdf) in neural network training and appearance in logistic regression and kernel methods for classification. 
+
+The sigmoid activation is a non-linear function that transforms any real valued input to a value between 0 and 1, giving it a natural probabilistic interpretation. The sigmoid takes the form of the function below.
+
+$$ \sigma(x) = \dfrac{1}{1 + e^x} $$ or alternatively
+
+$$ \sigma(x) = \dfrac{e^x}{e^x + 1} $$
+
+Warning: the term sigmoid is overloaded and can be used to refer to the class of 's' shaped functions or particularly to the logistic function that we've just described. In MxNet the sigmoid activation specifically refers to logistic function sigmoid.
+
+
+```python
+visualize_activation(mx.gluon.nn.Activation('sigmoid'))
+```
+
+
+![sigmoid activation and gradient](images/sigmoid.png)
+
+
+The sigmoid activation has since fallen out of use as the preferred activation function in designing neural networks due to some of its properties, shown in the plot above, like not being zero-centered and inducing vanishing gradients, that leads to poor performance during neural network training. Vanishing gradients here refers to the tendency of the gradient of the sigmoid function to be nearly zero for most input values. 
+
+### tanh
+The tanh, or hyperbolic tangent, activation function is also an s shaped curve albeit one whose output values range from -1 to 1. It is defined by the mathematical equation:
+
+$$ tanh(x) = \dfrac{e^x - e^{-x}}{e^x + e^{-x}}$$ 
+
+tanh addresses the issues of not being zero centered associated with the sigmoid activation function but still retains the vanishing gradient problems due to the gradient being asymptotically zero for values outside a narrow range of inputs. 
+
+In fact, the tanh can be rewritten as,
+
+$$tanh(x) = \dfrac{e^{2x} - 1}{e^{2x} + 1}$$
+
+which shows its direct relation to sigmoid by the following equation:
+
+
+
+$$ tanh(x) = 2\sigma(2x) - 1$$
+
+
+```python
+visualize_activation(mx.gluon.nn.Activation('tanh'))
+```
+
+
+![tanh activation and gradient](images/tanh.png)
+
+
+The use of tanh as activation functions in place of the logistic function was popularized by the success of the [LeNet architecture](http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf) and the [methods paper](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf) by LeCun et al.
+
+### SoftSign
+
+The SoftSign activation is an alternative to tanh that is also centered at zero but converges asymptotically to -1 and 1 polynomially instead of exponentially. This means that the SoftSign activation does not saturate as quickly as tanh. As such, there are a greater range of input values for which the softsign assigns an output of strictly between -1 and 1.
+
+$$ softsign(x) = \dfrac{x}{abs(x) + 1} $$
+
+
+```python
+visualize_activation(mx.gluon.nn.Activation('softsign'))
+```
+
+
+![softsign activation and gradient](images/softsign.png)
+
+
+The softsign function is not a commonly used activation with most neural networks and still suffers from the vanishing gradient problem as seen in the graph above.
+
+## Rectifiers
+
+### ReLU
+ReLU, or Rectified Linear Unit is the most common activation function in convolutional neural networks and introduces a simple nonlinearity. When the value of the input into ReLU is positive, then it retains the same value. When the value is negative then it becomes zero. In equation form, the ReLU function is given as:
+
+$$ ReLU(x) = \mathtt{max}(0, x) $$
+
+ReLU was introduced to neural networks in the [paper by Hahnloser et al](https://papers.nips.cc/paper/1793-permitted-and-forbidden-sets-in-symmetric-threshold-linear-networks.pdf) and gained widespread popularity after it was shown in the [paper](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf) by Alex Krizhevsky et al to perform much better than sigmoid and tanh. This paper also introduced the AlexNet CNN that won the ILSVRC challenge in 2012.
+
+ReLU is the most widely used activation due to its simplicity and performance across multiple datasets and although there have been efforts to introduce activation functions, many of them described in this tutorial, that improve on ReLU, they have not gained as much widespread adoption.
+
+
+```python
+visualize_activation(mx.gluon.nn.Activation('relu'))
+```
+
+
+![relu activation and gradient](images/relu.png)
+
+
+As shown above, the ReLU activation mitigates the vanishing gradient problem associated with the sigmoid family of activations, by having a larger (infinite) range of values where its gradient is non-zero. However, one drawback of ReLU as an activation function is a phenomenon referred to as the 'Dying ReLU', where gradient-based parameter updates can happen in such a way that the gradient flowing through a ReLU unit is always zero and the connection is never activated. This can largely be addressed by ensuring that the tuning the learning rate to ensure that it's not set too large when training ReLU networks.
+
+### SoftReLU
+
+SoftReLU also known as SmoothReLU or SoftPlus is a nonlinear activation function that takes the form
+
+$$ SoftReLU(x) = log(1 + e^x)$$
+
+The SoftReLU can be seen as a smooth version of the ReLU by observing that its derivative is the sigmoid, seen below, which is a smooth version of the gradient of the ReLU shown above.
+
+
+```python
+visualize_activation(mx.gluon.nn.Activation('softrelu'))
+```
+
+
+![softrelu activation and gradient](images/softrelu.png)
+
+
+### Leaky ReLU
+
+Leaky ReLUs are a variant of ReLU that multiply the input by a small positive parameter $\alpha$ when the value is negative. Unlike the ReLU which sets the activation and gradient for negative values to zero, the LeakyReLU allows a small gradient. The equation for the LeakyReLU is:
+
+$$ LeakyReLU(\alpha, x) = \begin{cases}
+    x,& \text{if } x\geq 0\\
+    \alpha x,              & \text{otherwise}
+\end{cases}$$ 
+
+where $\alpha > 0$ is small positive number. In MXNet, by default the $\alpha$ parameter is set to 0.01.
+
+Here is a visualization for the LeakyReLU with $\alpha = 0.05$
+
+
+```python
+visualize_activation(mx.gluon.nn.LeakyReLU(0.05))
+```
+
+
+![leakyrelu activation and gradient](images/leakyrelu.png)
+
+
+As shown in the graph, the LeakyReLU's gradient is non-zero everywhere, in an attempt to address the ReLU's gradient being zero for all negative values.
+
+### PReLU
+The PReLU activation function, or Parametric Leaky ReLU introduced by [He et al](https://arxiv.org/pdf/1502.01852.pdf), is a version of LeakyReLU that learns the parameter $\alpha$ during training. An initialization parameter is passed into the PreLU activation layer and this is treated as a learnable parameter that is updated via gradient descent during training. This is in contrast to LeakyReLU where $\alpha$ is a hyperparameter.
+
+
+```python
+prelu = mx.gluon.nn.PReLU(mx.init.Normal(0.05))
+prelu.initialize()
+visualize_activation(prelu)
+```
+
+
+![prelu activation and gradient](images/prelu.png)
+
+
+The activation function and activation gradient of PReLU have the same shape as LeakyRELU.
+
+### ELU
+
+The ELU or exponential linear unit introduced by [Clevert et al](https://arxiv.org/abs/1511.07289) also addresses the vanishing gradient problem like ReLU and its variants but unlike the ReLU family, ELU allows negative values which may allow them to push mean unit activations closer to zero like batch normalization. 
+
+The ELU function has the form
+
+$$ ELU(\alpha, x) = \begin{cases}
+    x,& \text{if } x\geq 0\\
+    \alpha (e^x - 1),              & \text{otherwise}
+\end{cases}$$
+
+
+```python
+visualize_activation(mx.gluon.nn.ELU())
+```
+
+
+![elu activation and gradient](images/elu.png)
+
+
+### SELU
+SELU stands for Scaled Exponential Linear Unit and was introduced by [Klambuer et al](https://arxiv.org/abs/1706.02515) and is a modification of the ELU that improves the normalization of its outputs towards a zero mean and unit variance.
+
+The SELU function has the form
+
+$$ SELU(\alpha, x) = \lambda \cdot\begin{cases}
+    x,& \text{if } x\geq 0\\
+    \alpha (e^x - 1),              & \text{otherwise}
+\end{cases}$$
+
+In SELU, unlike ELU, the parameters $\alpha$ and $\lambda$ are fixed parameters calculated from the data. For standard scaled inputs, these values are $$\alpha=1.6732, \lambda=1.0507$$ as calculated in the paper.
+
+
+```python
+visualize_activation(mx.gluon.nn.SELU())
+```
+
+
+![selu activation and gradient](images/selu.png)
+
+
+### Swish
+Swish is an activation function that attempts to address the shortcomings of ReLU by combining ideas from ReLU and sigmoid. Swish was discovered by searching the space of activation functions using a combination of exhaustive and reinforcement learning-based search and was introduced in the paper by [Ramchandran et al](https://arxiv.org/pdf/1710.05941.pdf).
+
+The swish function is given as 
+
+$$ swish(x) = x\cdot\sigma(\beta x)$$
+
+where $\sigma$ is the sigmoid activation function $\sigma(x) = \frac{1}{1 + e^{-x}}$ described above and $\beta$ is a hyperparameter set to 1 by default in MXNet.
+
+
+```python
+visualize_activation(mx.gluon.nn.Swish())
+```
+
+
+![swish activation and gradient](images/swish.png)
+
+
+
+## Summary
+
+* Activation functions introduce non-linearities to deep neural network that allow the models to capture complex interactions between features of the data.
+* ReLU is the activation function that is commonly used in many neural network architectures because of its simplicity and performance.
+* Sigmoids like the logistic (sigmoid) function and tanh where the first kinds of activation functions used in neural networks. They have since fallen out of use because of their tendency to saturate and have vanishing gradients.
+* Rectifiers like ReLU do not saturate like the Sigmoids and so address the vanishing gradient problem making them the de facto activation functions. ReLU however is still plagued by the dying ReLU problem.
+* LeakyReLU and PReLU are two similar approaches to improve ReLU and address the dying ReLU by introducing a parameter $\alpha$ (learned in PReLU) that leaks to the gradient of negative inputs
+* MXNet also implements custom state-of-the-art activations like ELU, SELU and Swish.
+
+
+
+## Next Steps
+
+Activations are just one component of neural network architectures. Here are a few MXNet resources to learn more about activation functions and how they they combine with other components of neural nets.
+* Learn how to create a Neural Network with these activation layers and other neural network layers in the [gluon crash course](http://beta.mxnet.io/guide/getting-started/crash-course/2-nn.html).
+* Check out the guide to MXNet [gluon layers and blocks](http://beta.mxnet.io/guide/packages/gluon/nn.html) to learn about the other neural network layers in implemented in MXNet and how to create custom neural networks with these layers.
+* Also check out the [guide to normalization layers](http://beta.mxnet.io/guide/packages/gluon/normalization/normalization.html) to learn about neural network layers that normalize their inputs.
+* Finally take a look at the [Custom Layer guide](http://beta.mxnet.io/guide/packages/gluon/custom_layer_beginners.html) to learn how to implement your own custom activation layer.
diff --git a/docs/python_docs/python/tutorials/packages/gluon/activations/images/elu.png b/docs/python_docs/python/tutorials/packages/gluon/activations/images/elu.png
new file mode 100644
index 0000000000000000000000000000000000000000..4164793ffca515923834f98506ed15beb42dfdd5
GIT binary patch
literal 10707
zcma)icQ~Bi*0wG~5F~m@bcvEdbRj}?61|s<8r|rf5S@q~EfR!?7Dn&A6N%mlL-abz
z;M;!ZyUu&w^UwFWE-tob@4cS2*Sha@uh|hVRpp2XXb3PcFo+c7Wz;b+FkQj#=v#Q;
zrxnTs3}E2Er4=-9fiK@%=HcLPh?Be?90P-p?B*MDbj}|Q21Q(DbzL<aEnPj#Tr4o`
z&0L-A99`{fUf*}OaDm%6I^5^u;p5@wx^M03>Lkj``#%rxIJ#KzGT)WK#lX0ap&;{2
z(=%gx&dXPG?5ul#xK~>GK9^Zo*~1^ZW~8#O?vM)o#bd$>X3{sDo=zU8ut8`Upt6e-
zv3`V0VNzCV;)c{QLoS-|ZiU<-yQS14ibQqawz&miNlP2}uzPXbXKT45ftt74nH7g4
z2hxx<w%kliWT(=;^nu~wp6|%&!sXw;l%^zwkSVS+F+G!p7|F26bL2#Y<3LzlN#pP5
zgzzwtu_TVj$^^^k$~t^^n5(OjjiR_$t3VnbnDYL;)2zP1uFLjAft+gVpTVc6r;)R{
z;qoo~U345OVs5qJ|9hQ>l_ZVAwY9Y@tgP`#NvoSLbHjUj<X^*J5E2rU8wZm*!Be$$
z1qB5eIk{h-WeKC9O~i7B-x0ZadGU#fGX2Tie)O^7iES43hS5;|7-)Ai)PO)G3fjdb
z)V1Y4H_qdTp{J+!yHG=}cXpTxR-o}U_Nhn&&im1$oM9|H$}~ZUu1&E$(RWt~9wAov
zb?$^LOPQfyj)846)@YP+6M5=(og^*Q-=!KEnPBPZG`p+QUcnzOhmB3e2eB?Tro}3F
zPIKeDg*i~m1}NJ+6?yi@kNQJ{XD^~CxP|@EH5234NXhH7;BLQ*6YdFbGIDaS>q`_N
zLw9%g;hzMxx_jy2HN206viITSsnu&3J{rTGem=&GRo2Csmc=m@<6UD#9#bYI+7D&P
z&pj6WbV`ozs;oOZJ2xDU8#r{bG3}I(AEc8ACO9qPl+H~g5y#ki`=zg{taEmyzQ8D%
zfn^aEE;pj)eUAQ)MU@!UAFO|g`;Z;5B=@DGLq^nnOLy-yNypf%FOie77@45<G{<9i
z-gR?4CI}n<!-o%8EouB#p6fYb>%R-hGsQeCdq1!$e-wy-BZqz>Y;_ns7cnuu>68+;
zl$o{bR@v#bdUr=tw|`(&l2=yFHEH&D-JQ?enX9L4z`U7N+I;o##)R|D(hBe6HxBjN
zf*7N}eo>3Ma)7n-)v}-3O;<t!IW_Vh1H;ZyyJ|(+#YcGem(8wGs|-&K15&N<j5>=m
zX|;L;`^D^>IUmz__PSk^vj?BGvl~pg|4C%m*81ePLL=_s6Gnz~TxiM+C8Rvqu3jKx
zU>I77lN@UBI;48?Ml-G2Z5uIh&`u(08f|udw1Hmb4Okk}F<CuBW`DID{Af4dKpjZV
zuIhxE^Ef=2ajY<Fe-|~*?|bS*!DHC-{=xH}-d?lSzNCi3Au(NBy(V9g`1ts(`Ns5%
z-RA7mQ%_^>4OU~X)q77bk2El}OO1`nN|0GTu^>(Mf66iLucnyrz4=`bY`-&E{#L6<
z+xh&+X1da*9Tq2%4@ELD?DcjrH&ppjdbl~ZsX5mCXshy>3UP=|BRRtVO9T55h;(8*
z2%v^_Vd4k*G*agZ6&u!m2noUQKG}k_)WVSkCe0nCI6R!3oDsn8_>2r?RaMp2)9rhY
zw98CegM-}>wkF<*Nbc&{HgMny?(TxwekaruMjrIQ7FK5Fw-My*Kg`<kq@O>ZLfB4N
zz|Pmw9d&=0LokHqJ(+E6Y|PEgF`PDwtI0@7pUKGJ9B)prv$BRxPENW=c#t~ld+gyf
z>sH1abHDc7FHyZGs=xIEGN<no0I9(96i!ykQt*jFB>(&(ceotIn>@+J#dSNZ{ncJg
zZuyj&_&yHEmEqczSTWs3Z?|XF(TFN#-Z$pvESDE3<l(TS)lysNY(VoNtLNqMgw4n&
zOj;p_jwPo%)YN+4kUGE5beWi#2=YVUz59@oqVtzZ_Rnhtc0OuJ_N;eLn9|NoNtoKC
zAGuy6H6`~4Vq)tyctX2lMVj&C)zpwJL4=RA1g%E`VcoIu$;mI=-QAx}ym&=TO)X$E
ziW4|hVMVFTC}4fZvH8-ybp-?m#vKL*RR;%$@=cnjzQVgr7d$2bmrsNieBrI<v`^m1
z$jjdXX~Mm}85bM7cC_)UvjkbUSpu7H@CvMWln-LzcfqTbQ88JWzAaa6o)yc^giNIA
zT548@W|8_5RP#J!z6k(<=MpX+5seITD6@7WKV4o`wds?Jr>AfNyIMZT)LHi#`%3$n
zs!2DMG`>)u-NqFE%kx*&4s*d9%@=dK!p_S$^kVM0N+~?w^$`St`zzhQK!61E$`aCo
zS26KBzK4xZoU3>b8MJ@8636dzVh6IJ9pq?%b}<bGNJ?-z(L6?Vdmv>^?rQk@ir;|1
zXs!Zf!_V+=t3Rt^p0BUGPja)CGV#5gEH3{(jcC7%ik~%DUS78QNoZ&XQqX;ST1)q~
zx}dk843A^qXz{=~PrT%HE;+Ne(BbKM3=}dxUDJ;!I<f*(vy*!0$5hdz^k>P|z?(HK
zCG>`hM2hFVqB)hS`O1v^{P{aj+2%=rdHT$B`fRd110?Ke#uo>d=57(~`$5HS=!s9%
zpDn$-Vq6`e$ie@j`xSQ^G9?)Uy@*B0#1zEx57aJ;fJXu(yr0C_Y8|`D<K9r2Vy}LG
zDTU+fxz+?_`l-yJa)dAaDDLl77sxzMwvR}K<>2e(jtC6pOwl9}_*$Z^glrKsQvR%=
z)<sB3h+#?R?abpESm)i@hlH6qI%R0Iuk`c&EBGRWoGsBO9Z7y3PRiWZNF3j{vqBW+
z1?zO!*<t91O_(fgIz{I3yNc^P1?Sla;v_6f!iUf6;7_niO_5IN+B;eY(~fXb*T26Z
zfjQ5^M!(3Bh`O$~RNDL^7~kKi-*p+&(s2gKa0p<ILOGkyf}phFK(W;wkAy)vjzMC5
zqLfk0eG4PQ!advfK;iskTM2;bP2@W{dl`{(riWl}pk4Oayb<3|gAFW}Osv+g({SX3
zeB3jM#9Fy|lZ0myR^i#+e>t<j_fGS*f4*v_cWVP<06L!Apz5xsX@&@#4GM*3h<U^U
z%Uu9uCL|=Z!-pm0q@}T**pB}JZ}7>W+JVyE3VphN<G%4?1K>>>h5)p46Z$|2<3mEi
z8Gx9{au~kHtTR4p7CtPy>L#0eM4V^E=dX$tWBcMDE?%O5Y^@pDdX+Td*8)sxzP|E#
zdcMw#@gkb4m48@V<8i4@sWEOZ-C}X|{4#)coW+rx=Qp7IMJ{f%IUr!jWXfi;%#1Rr
zM*_Zfd%+JykD9+|j_jP8n!-he5z{Mzgx#L0=B;UY7oIp=_}#E;NMFgJ=`A(PTYUx}
zx4N&bne3c(D7Uc_?9RLD`+(cYz4g0pdGK!Ybyh(E2jDA{vY~#zM7S{B?{8W~lZ3FA
zP*&$hKUCx7%X<4g9`9csZ%O(mi@<!GWhEAF0_FO0>*X_Xy{FZH`fw=|IDDT)edgoi
zD=?}TeD&(p7GMWP|5MDs`9_}`z8oDL?HL@j`1x6ulH1_R3nG0$xQ~bR`@3RjdC%y0
zwTyLSB2nFH)%#;5n#NSq@l878jc6?YPowOvYRSWXD=|Wzfc4hLiYR+~dw+k|L(7`s
zgCH?BW&laOhHAc+@H;=k{PgO}3uOQ%MWsz2K@l+8=!1Hlk}c%Oz;8M52>^-f`bbcm
zWYf_-jDL3I5!{@)95}neSShvb+%s<CuF;}+n7#QdEiFy6SpOdEHX#L<=@t4cMWyPB
zC)JoSepDB+LbYO9Y9UpR2lA|)_f2JDm2XMFaSnbBATc)8jBrG<KH?1&zNTyv#=>M#
zwzAHdmD-%;9`j~zZLh>XexP3(GWYiUc++F*%8qnjt?+9Fwa}xiH>5cORC5Uh4}($|
zE<ckPeSxAAj42DbK&|Fwt}4D^1y6nYjh)EW_zG+5ss{;?;N$yHSt-CuA_*&I<>rp*
z>1h!!lU3|rDig1;W~NP0Jvc8bagF5mfBgLZ-kDY8<fJaBh~!mNj@>xaWdJ3WmzOsY
zKkq1-*Da4y6Jq7xNUplQGdVJ`RMQDo7Z*ndo4kw+*GwdtVBa54@u}hENtP?MUEOq=
zT|4BVBqcQoS5rn#L<#-+s&$+9_VEHpznj83X{fbQxocSElfw2l<>NyzN=i2@Et8wb
zH?+#<exiRspPU9l@=;$ip+i!r0c4KqkRLXn4I7|<4fw*M-^qlBFZ2!Ggqs_kyjcJ5
zcnNF0>giQVg8Qqg>s_gAEeTQ?h$`kg1h-L|rc6v(=EW8Om3<p8gaghM*h`u|$Cf@n
z5ca4yYzzF>mHPNTf$*}wJa`VZQynp20DljeFlqI3Rslt~PNEnnlS!~kG4O-T@3-q;
z$o_QiO5N{D^@J%mbbX8p=k={+4yC+mj7b+0Skw`!-ggr=UQta^vR_pF#5zJ%U>QxV
zSDA@$|A-JA(x>&fQWR%mx@X~Yz<Gn~+q4sl$7V(lNZAc8siyQxnKmN?$Mn+)eKC%X
zl>yW5h`B$Ea;_LBqo-ylyGhW@5dS;tu;>WBA<{GK!0jxEl1zsL(j^18TGf1w&(D>P
z=nc;GeQ}TTkqL~bAh#CFiMo6*4j$5Y7q~DNs9Azk!uUPmo;sp}klC9}$B`tq+wc}R
zL!_)s2eYw3Q<Oz|+`-HPd+Og0%5J>x&-sw)nT3KhnPcg6`(BocSFXt-B}i;QLl7Ft
z9W|>_{PcAnS^6x-X($H)m;m?;C$wY5%FES3Tm|;o6B)!15~kgBq<xlE9*&b4t!&lG
zO*lX|m+<k`QWJ46J0K>OPpfNUp6E*)fQC0PXiaJk-j%6FZYoOtWyLt(*-Ei*J$sDK
z&bIWfcB_t45tr*b9wtoNE}@IG>y>8=y8*bUqmg4rK$<M~xfhb#vXkwt<iEZX=jZUe
zd50U5GK%H+Jm0i>i#{prEkfaWv=;vW`Y%4vWlNKSWH|P#<(N}`T;!KGNiR*p{s<lA
z6@=qN!$r=-#o4o>;k8^@BPr)epBA<CfJT&;auVi}G^hz04C6mVzICp2G;E)Ea7GVg
zpS;xRCZ*V!-R7$-`q9{+ZQ4^&y5?LE^8Svm&=2PI8dFFc)1f=l$xX!Fj+{Vlnp~Kf
z&@^8<@lP1*rAW^H%o)A+FX!$XT*PHtwD~{;q9T?ZNnA@4flH))<YdsJy#sVzgZfdJ
zma!WOgWr-WfK}FsQQ@bV8RGbHjn%Qa<j7Q%yyM4-!iJkUK-!`s5y=Yym7%++y!~lE
zqxC7Ku89Y@w{LW+$r*CuLYFEaJVMB@*Jw@)I2W8qhof)PF$eFJ6@In*n~q_}y1t1e
z96E63`-BF>R(nUmM6R*Lokb|OSFOb^H#$07>|=aq&6I6Kp>}9M?B8eRH6Rwkfa6a!
zEmavvhV}Z>zKiF_#eVUx1pi2R0d^i1rc3@xX=geUuc6FHFIWHKVa+OWCh*B{1Nw}(
zX)?KO*TH@`#$L;*-^hX06{gG$8+Z*HAchUN!v@B(2$W@lXW9_>XWaoP=npl1hPwRF
z(sl^Jj(cqb#MiR8v71T*H~c_KnThOA(~M7smE^2+^SE1@OFe;umtrk>9W)=$iT*KO
zi+(!xA5S(eQi%42`taVQ&n5s9ngE|qVrIY~=+0KJw$#JD5~yV()Upox0+&)ISB9M*
zP9-FkHFBpt`lm!8X<@KI)*n@A66`wcU0vIHOsgOTSz4f8Xkp7S(EY-~LI*ol87w{3
zthaJVHmB1Lk$j2Cx0Y_dpKsdiupF`6@71sGI`)JP;RImXZVTAjXCMSij2pfGnU#b{
z29^(eyaW<1YpA1eG5KGZ6o(ZS4YfB+z{8jkCrb*)Nh-Xa>;4?Vl0`T2F=y`Msc6Qh
z)g6u$_CT)^Ql5*zuvroXnc!Y4gAZP374)<7c~D8($!Tz?PpZl45boG_kU6?CEIoFr
zeTy5RwCm&FDFPb5R=i{PPU|9!2-jh;%7D#oA$}K9v{vG;XgU>P6(mQ6d(KX6iFXY~
zvh%ZNv&J3g8@q;-KL2+2COH|BcFV*Orr2V!S<Bm`bJo8ePftA1q5;j8<yX7`Rd4Uj
zXn5|N5Fa^eEKDqEKD-%+68ekxRF<AV|NdQz-kE6w<Dxg?qzgMDL%UC>+m3a{U<dZw
ze)Ot&H!0;X1K+wG*O($z<UMxUOgg9O)Gt>#A=|+Xc#ulShg+*n8Vq#{*u9eJ;}+x1
zhQP_?hk9OLG5cB5d<iya%H?+@<)pObZoV`nlw`|4obVC-^~KBHBusMyWTt#A+V9vn
z`Mc8cMo@8WMG=9+88(Q2&I*h4gtJYgSZ#$ZEQ{t-u))<$g7ulN<RqZ&mhij<N(cKr
ziWZY4UjcMc7jrqFBN>kPYV3-0?O7HE7NL%DA&plTNzd%Anc6pB{lmYzH2=G}1g!G{
zti$tY!OY9$%G*6J!x9K3!!aq`YIJ;^q0FBjR}FSzk38sLecRi$R}YP7CryBrJ*<NU
z@IdN?Ei2^(3UN<2LX$A&luy7ptml>}XSRTbB--iUD<}}iD3H~;x<0X)Yvo=LL%mW_
z$GL@=kvl|cM;^e*F7ZbdBGH-m)MRuy_`5nti-Qd?pWWE=J~L(b3w%XEB&kzU?)ZLH
z6@ddT0g=Joc&u@KHtwl`=??M+iO;li9}5??@BgH(3%`3h^G(9H=IU{C&BjBA&DqI`
zC3$LK=`D<G2hp}iCR@wwa~~h?cRd2^n)ns~MA_9M#DBjursGNJppu7&dTTL8UI>qR
zFKcGcpLh|7Kmlp5B*WG-nN+az!)lPWY`>{0=BW+~*|EstBjKWtQo^jd6M&fmQ0LzO
zjsn*r_>aB@ZZqh{g=>B~#I1H`6l-5#Tpvn0)^d6@Qkygq11)bUZgCwBVP?9w8XDjT
z8ynKRa+1S8M`*Ven=bB9pVPLKVuazpCXFZHLxo{CT-Y<gN^N?3o(=+|J>_q(V@+@|
z6(P99xBWj!zB-o??%5OIk;iATp^aBrJX}#-b?7F%{A?#9#{_%UELs7F0z-%fHvee4
zM_+7EpVq24rbtJPaZ|?E1_(X2Ap^P1^D!?C-0EOeum$2oXnb1N)}GK*crOp(p4-ht
zP8z2umM}P}XB%;g#G)%TOEd1}YMkj8=XIteDQ$<RD@eBY-lFLnUskNui@udUC*$4g
zF{nhiKPj6g-y|a){xN>A$(k-N>=fKAIg(kTVtXdavQGvO7pJybduM5dNbYoX(4K0v
zFut^%lviDv67u>+_8s4=uPO2&2P_Nlm6TTljH_=FXDz15(r%0~y`$U(N7g3=F=Dwz
zuQ+Uu&hVrei#Gp8;GjiAQ_VtVK14a&*9$K*^;LWQO>5sxUi}$}#9(>jrneEhrB;rA
zsV(ESJClx!E<{-hr9db?`*Q{g`~{=J22UOKlG-07rS0(q`A=Qr??W%@Wh?^%0>et}
zdoLS5o{7@!W3qzrGH)dGYOhxtugbQ?@m;~9wgm_vvPE|WiNOxk#IGtdqItS`E!^sd
zQ1CQHviG!})1z-dICpitQURk+8qb+9`@mRMWyKVF^JkHbCOCtKKxoHfm=~|=L<yB&
zEh^;4ZmA-5@6D0{(!|LX(%!P%=we2{aiYcZ&}`*)zVX&$Mn~a+^hX|W$2eVvdSl$`
zKLOxXvE<u=T>WdU$r}y2_P%BpEG8Su17}GI6FRVt)M&G4$enyrOuh13l@?`^!D{*6
zs>}elI+u!Om1!Aya6pfCofkb}l<d!NZmjv7Mc|;Y50*!#B9bM2f5en}Fd|p$#eSj)
z?#+JqP}ncq5jP0h5VewYi$6E@q2Ip}IP~U$JUlJb5{unOf1RQF-Gtf9Fgp(N69X{#
z{_`8CjyT)2nJB<>yuGU!4mRKf)y`=e>}v(pf2xo3Z%T@Ca*^STy?ujL=nS0E;k7dB
zwlRV4)Ek@cSpsk7K*HJ&qGV~OQe8h3GW(2_E2sdWgZaV;6hg}}_v%kk`Q~gy4*i4l
zH0Ppp5UGF4_WZ6?t*+GVuGHeL)UV>ysqz*FOXn=}=Po3u&Q89oI&IJt$4V44;_+(X
z{f^j-hW?ZahFaDl+|$EelNa6{2N1s5nO<9`MQ=>?E!aTafA#6}>Txy$+lktpY`iLL
z74dsuO{d4)34n#~7}P)9jot#dyG#*BC()HkAthuQ!#>WgPHmpiznY^bxm=cfql+IY
z;#PM^IIx5I!_5@3SBW^$gdTSPo7s09R9QIwtcj(bUWpRtTk*M>ljx5ZuK_>Ys<}a$
zCE<iKaocVDPDJ^*Drj}<*{=K@D?$#f$pi-@l0nPo;CN>Ry}_Qj{_7i1B_HlD4RKIn
z4`5Uo*qR&~$}_dvul=_s!6Ee+8Y#tOz;O`hvU9p5+J9-t^vq`U<gW24N)XS(x~J5^
zVP(^ar6<v6@-yOuR%)e4M$u-5)T*^OHpceg{4ogP;a^mo@dQr))V{__F+HI6jr~{c
z%koSfVJOqEWvKczZXg0FW62Z6W~lO(=3x!%JkV~-vO~cJw3DvFZ*54rKr!N(1jwmj
zeQZ@suj-7WxiODk2;sPRvpa}2&)($_maD2ayeQP0zV=IfaMp!5AZ8mDyFod#_EzPn
z@iTEiL&X;mYN0nVSoYt#;qQX-!*{U)q$GVk0~T9b2wl^)^{n5~1So(b5g^b!<w5(R
zdyUnJLn0v{dg~b>2!4WmvnVD8(_)K*Zi7<?N1D?4nb3J){?ku@@JUkl2mRNsJ;Sr-
zop3!+;m2;m>I%Tr8k@7KyH}NVV$VgN{Gk*AZ+03B-mHo{boUx>{MDK)Km4%J=SbkX
zkV1NMz*^e!jpygY#VqQVMQJ)hz%NNNS@+63zaKIdM_=1boe>K%K8=7BDu%HkZjJ59
z)Uxb0Pd&;}T1`47;_}4!TOIl{TY5}C&scz;J<ja<G|2DFN>?C<SBqw1@AW69yf0wE
zUpa`qtH%XnbPVVM<=)w)GPLi~iz>Qa)AIu1=|^c%*&|jmg}&~7nDCvzH^-*GyI&SN
zq)}Y|hy1|Y)^t)rgCbm$XLGF$z4WsBN%RHQ8-a(vmaiivJH(1me4jn|yNrrJ5}5p6
zWlDPPK;e635T1>xGkXg)7!<zFSG*kNXCr>GWO<G#>1W(o2ldXfN8$+UY2J2dC&{ye
z7wZ=+w^MKDSQM52xIWcmYe<ng6dm*Qk?{aQb|ZXRA4!rl1<-0Y-!db8?EAEmUVUvW
zV_?8+_B(>g5V!i<H{fV?R#%AX#TB86IH8=-^O+gbo<|lty!bu6@SAE}Uer0(EAA*?
zFO!28=g~5?;yRfjF?-A36f+d^Wd-M~7~wQYsLPRuf$iC)oWZHT2~8=<rt~lcG%HT0
z<1MgtPlT=vrF42iB#)+Hdehb-DYXl^!=Se_rh4&)k#L1%zavKHAi)7d$dwwz2x}q&
zyUZEWE)}1hM9LLLb%f>0NEE+wIf*<hmh;*Gwexf>sU)puQWzG+J`b>*ZoI90y9`df
zg(jhk^-y*6qMtn#vzk!Uy;m(%A;S3<NSsefcHG8|)r1co&}4B0fw~Hvf_PtrPwb12
z-yGKA_+aG|+<0YYh-H4`X~EnqEM0j>7*vP<S4%}Q^NIh4ZBpzDf=V4~`FM}*4uNv@
zc+;Qwg4}~%<uLM#2Ntxsc0peZ?pyGiTL>fN#zNP0o;PQ=YrXA%@_N-}$Px*4&=yY<
zIpgyGPI>&9S-gpe)bXK*v`|+FRsgmG)`vhuS8Cq)w0#zVYH%Rpy&u64-CKT6r}Ki%
zt5?Eaw?<JS<Y#9cM0bpdXez^qJ)W(UjC?OW{CaMTwy<n!KlmL?v?c5v3D9!1DhH(U
zqBHV#BV}2)emzMGRRu;3`Acb(4l(|uuhX9(<^{KFw8?zv;Q{=?F5MrC=Ovp}HQlv(
z@8Ml(?<>?q94l0fPK70r%evA<QZa0Iei6K0`Q)w?ZUZ@<7Tpcg+~S3roDaKe+o_q|
z6v8<wmjrWF;a_VKM$$3D2qaXoyz41dA<37cQ(<oi)wqsYrlY=63T+2(?C_X;tjaT-
z|KhBNO!A<MjY>EQ6&r!KeYyV`PiEXTmM0=IXquZqqJ;1#QI5A?!A@PV{!Ee8tOfMC
z`z`u#^D?@wP6Sr+s+AjJ2t?cR6)kTc6=(&ucm`Sy_IaJ<71RweM%<mVQ-I`{dMntg
z^#1Cbt7_=icvqD-r+q6V<e7ZI;c&>+ZaL&~;plzY2gWe%pfY)+@J^X@fGlH}tndte
z!Mz#vQxL>8SKqvqn~EDjx#b)s29bbR>#sX=Iptb219@mlBV}l4c&18vab0H^6=B#X
zR3CSEda=1aQiJChxBddu$qIpR<Sk>uO2Mv5fi6wVci2i2<jLV4#ZxI=%<yF!t!kQz
z?h__ab=+!}y`+oVbNcC4(P_7QW8ss_%C_6uak6a7tl9P=eHw)0<bWsP-N*Ubi5HSD
z$oBCaVgRS|U?+QVP>`0UU-&T%bI&QJ<Rem{b?nfw!~s^%^_0)*oEiPSRseo=6W@2V
zxF4T2U({<L6c_!|9;t&S0>rysL-nu&eZ2K}c%l?sGGMv63!p4Am>^`_tASn0q<O5z
za*H!ZX#^CGkB^&uye|4BG~;f=#-BIdxTc9%slyf|_B!}rvXMVYl6Pik2WC~%s07QO
zozD^-(@K{OPA99wA+qJ($5-9!iZr_E9#Ob`PJR0DUp*Otcmjagn7j*SdbJXvKsoo<
zy=A{?T_7DChp`*eHCUA;DL(W)2XHWcwPK~0;S^3XPR}O79F-5%I$O!4uc?;Ph%t><
zQ~*!BYd4#6VF8L(UAO#_YUYC)>1&RHb{CBcyz{dr%{Pkc%hjNvnJK4MAqdT6B>$yX
z!JD~a^#cOEGa>%YDy(uPwj{>p<rg$%Fa?ASJPjY$S68!w-<L?B2XnT2IIGyRUn771
z%IF`&|KvO<4{BoK2}DUZk^SZ-WfoH>AkNZ04_(mz1AH!6X!S$H{b9db`ruLmQaCla
zxAkZsxP#~=Ghg&cjP2tF9SzU{Qz%V_c8td!ELGfr+*VwFHl!cbE=q3O)c(Pd`|PY_
z=Ji*R9uFx%t>0&Te;i(Uq-Un6X_vg+0)HLQnIlDe`uf@V#o{#dyfQ>n`ZvCdt6J1L
z46nMdvG2Coy41+Xh5RhgW*L0_&DnmlPN0-rwjz~7Uno2uSRb8jh?Zt~k0T=;aMC{b
zeJVSaJCQoF-t#B!#DO!S(Mf+vcB}JOq=I;-eMQaj-7I6YAg_bRk|=LQ=5m$~WYbxf
zM}BhtF2kDw3ep#@=2TCZ+9P6bf6-IE`!jy&cL5C;6L$NimE-S<1E8*ekzS#3{Z@=L
z>3(pa{5tq7EKlXFHS#SP&k6i0dZ*2XwYuK)9CuTIlFQ&gbWrvePeZ^ydU+gPYoF|z
z?_DS?eLLh`$Q_#Hx+m|-UOo?phUP!BF0eHYf57r5pgO1PB`a4*c-bAM_UB3s#bgwp
zKXNenv)m^;2cKz=PCN(?Kb0VwzW4ug$s`0QVfpuut7;bNeQaS}xmfspwVp<cHTdoO
zcRRHNBa7_r>P+F-8V(jCQX1R5LfKqBs9?**2<N8QFC71VK=Y&q@c6$kuH0umvS}jz
zg!8Z293?*re>>wUGuDNN8|vS`>9qB*o@xOan6-_bdK2vo7K|LYh=E$hK`p;R*Vf3A
z9c`lZV9`_d(R$X=`=?1EMw@es=SraL<j67l47IE%GHUp`!eQy75e*F^>=eumaer%c
z;RL3u<8~bD{L`yrgAIgrrRuX*7QMf8t3^6SM>sWh`51Pq6FPRPTc@kUK#QWGGe+Il
z8l9CJj?ujwN9U?1?uI4X#s_XtRma+s2{xEAiXgmDCFioNl?vWRb?6!|c<)E^f8B)f
zSj}1ON}Xoq5}O{gtcF@v%3=K&KObZqA-2Z;j<-yp;z(kL!xWg@Vtl_cb@dwew#QsH
zf%w|0rwJ}w%41&A0)wCn2ZFr;i|LTM6Qf6;B5kv)P%egV10PRZrUa$<A(|Br(Lvoo
zg(Vm5L815drk`Nd)$hn**GEPaN$%Om(yQh983jIQ32-ttFCr<+4_+a}ja{buYP57g
zSVO!{Y{x0E&D+8<ajDQre<*dkY}n0U#;v)ke*big8Z_YQ5$;ydiX9vZUE7Lc#6^&V
zRB0Pg^r61J5LT2?f+hCz#(PD!J}At?Mg$S>I__5a_)f+68U~@$jzMYpxW8HPAY1~C
z8GgKW!t+I7_*ylin^-t553{RZ=X=$EYM_Q%6_PsgR9L1z#v5uFjn?7`K88;*V)e51
zv%z<S3Fd|@8|Bk?iha+8u=|S<8{^aDEiR^Iz)CIY<`A})9O<}(n$y5i><q3%@krYh
zh0DOZw%a>oYX*yiFl8Xe8Yovd>^^V8b_d9wt|eJYdjHbRu!46MhF@xZM4F6rxp7%6
zidQ`hDT?|^Tb7?Xr9%r7q=8+*qBmYXdtkKVwtozLlJP<9n{RnD292(5LhG3&(Qn@T
z7@ZS?P!kQ%`N6W*9r$ilj9^Tu)P??vBp&8eNmu8zh(^ITDh_SGBjlo?$3Y+x=u12f
zUFtOrTf|xpg|iP2h3nsTQ5eZW`|BhI3I#6usSdf`;}1MCd&00_(iy5hAW%v#XJHXt
zNX&(qRW+XVkk5yH$RU)!WLNGfmcEXZZi<hQZ*B883=fQEPRpv&6CE2bi)JlP>FRM}
zuI|?gQop3*U_s2X({$Zu&b$L|mRg3iaUIst5bPZ+Z^PI0a)#C5))wP3x@K29_~CXF
ze;1nkv@%sMZz_%<uKzf6*uDj0^RE<_CH;*(9D|3F3v9i|q7#3X<9;((MMJvN29SET
zk{qJVPQieH!Sp#QjS2nDu{zc~PooAG<)l&W7ExP!#h=to(<VGOSEI(K&2><}%3n{j
zENvkrc`1+{VGfW)o|L>6r-@r3$-)aZ0bH{!NgUdks2a8yJVntJj|nN$Foa-XO<Gs#
zo37OLLzJ^Wv?i~nDoZ_g;eegh&XId$T38NStM$y;tuSSU<~Ew9rd>Z>PdYtv+9c2G
zO39f)Y*9S9iEBfpN5jzXh(B<9e0oCB`q!SXNfR`ma_D+afVW%g<Aeq0<Gv@;c_i<h
zo9c705>Gj&C@+fMw{^c%@LGqPzK7;l75TWnxBckTaLMhYcjd(PndLsMJmyngfnB`}
z=DY4#)ptKO-L~GUTOhFdynxMx#nf;Y$z>`XZxa(_5xP9Nem)afvDrFv;lNWyJ^4rU
z6V1Jt{$tNuL$B`oJmM&k5WXM~s=3Oih$&)1={OHFo-z*!(FMv+lvi}SaUs41`nhrs
z#m%G>Y=3>w5^~s0{rD%sQE(aUQtOD@p|!QC&ccI@9z;kAX;`~{Oo2tUrG2uc_^1t1
z9Z9Rm_kOd_XvN7N`Gah7yDr8V5rdsisqg-MQ+Nhj{7;1Ek<u%0!H|>#G((tNfSmb%
gTtEC5Gn@v*O*VXO5ukhkE?{CP$g0YeNSg-#AMw`&fdBvi

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/packages/gluon/activations/images/leakyrelu.png b/docs/python_docs/python/tutorials/packages/gluon/activations/images/leakyrelu.png
new file mode 100644
index 0000000000000000000000000000000000000000..5ace7256d24e8627655ee3021a45ea6ae52556bb
GIT binary patch
literal 10769
zcma)iby$?$*X~G2iAZ;dC`yNP3Bn5qg3^tWN;3@IAkrWq42^<>q#!Z00z-$yAUSl0
zNDR%{^ZvebzVrQYuIqS>T(I{ud+ojAUiVthj(nk^OiIi^41qvMRaF$VAdnj{@cRYf
zP4LqeV-5x&cy0=+I)vb#AE8wQ_?yT@#n24`A)&zjzcDf!Z~+D-U`hrsZD$*p=WD1n
z#PK!E#laco@YaIO!y4-L*4c?oh);-5n1}5R4CW%q&;Q>K@Hs<m`R`FF;zJ;85LHEa
z9k0xtS#K{Y-MQoay<ohb6t$Imx!o*mL{;@cRS|J)#5lMiCnq5f`_}tI7h7TvatG_<
z7~^i?ynI2W)Jgu5&RtQE=rzF&Ya)9H5s{&k#2)Kgp}QPbTQGOe-68jt&E3t-uTmd-
zD>QSy%37D!L_|bfmn^~{<mCIesA1#@kWV3e>=bM|!%B)c?}!^BRDxc}(~=Y2;aX>B
zzoCF{s>t!AC^tF+FM`dvy!3VZ`&!58+BuUi5fSL<*jO2-GDn>mKQrESFna&N17t=C
z75Tm|67GLLP1BU`f384Sar}w)0XKK_yLWgaqoY}I?6Mpj9Pd(ijVYLzwqaD{tqWfA
znkp)6j~^!kDHbuVl5r;xdtOeBOt!msgFSYpB$$;uoT;W)EMn4}V<c>1CKH{Fa4q{&
zk#N)9=`(B6582ro1_tmb#Ihjr4h>C#rX%|ookZC~{I*J*(N&1Kp}iEu9Ko8J7KNb7
z_gBb2jN)BKO*sc8;Xgn_ns!>QMFmFM5sHd9KO>WGWuI6vzD=8?ZYi(IUK!~y@}+C@
zpRO;Wj}_F86%<6m;T;_vLgnQa<K?E)2@{gvOoFrvHTE}4DpqA=Ve5DDl;inEs}`4*
z3=vT4xg~0DJps?%8K^B45uV|s#0)*N@?re>$<av~vAKCzPVJkrEZef!D%3wBb~@W)
z`rM?ceJj5kNN5gM2QsQ1CaZ*aOJ2#d3R>W~ttOWcXmGreKHU&SUHc>!Du<1J@y-IP
z7?Pa5v{V;{V0HwDl%S~UAz8J(xn?3VvbA5ztOFUc&}9|&d-pb`YPb(p2c-7gyW&|3
z&07Qfjwfx^GsP3)<*vl{5JZ#@DlIyRX?P6U2E@?)e-_&5C1LCM6P~E48hgam+13He
z5L_@6pM)k;44vWJq6$Q8{K_N9`zMxNM1(pB92>e6D>jm+Oy+aA{sV&!9Lkh%`(15I
zCgXc-b-X>fJ=>V-G}~}vxKNX|)@e4qZYCI)fP#^+@9b!61Kldu>~r)v@cL?d29ea?
zug>DVOrFAL`fMm$#;^hQh=hjc6*7h%vOQfFFCTi7oP}jDS3dN2YoHt?iBr=CRcd&+
zht4_PnNB?&Q0z$My?iT2GO=UXOT9xkSrVp?GDMBi=tJso+-B=o&ApBjy)Tl=9Hd}V
zl+nFFiOej_b2W+w<wiA8ri~uo0xo?@YhvgGL!4Vq-*Z<qC7vFvjpoYZj*N_~&o+u-
zPt3z}tD86d-aj$(LxESqPv77A1iV!G`r=^5rJa=H{{8(+4Eo@B+PT_m-vU);+*R;A
z<IQmH5Bv#zBe7W+rFMb(jrmXEEQ@7za~7-pY5nOU(Km2!R{EVntE#J2RaIZ_&LDvC
z!%j}TAjiA28FiViOUV5>-@W~lrFgfKKd)~yODbAfaTIFitO8@)efTh9yhQ(?4VUcC
z4+7Rd9h91PC@I^~*H@lFZgUPc{qP_8s!8=nDC0D_K-p@WfyKOdS)6%bWIxQT=l1V!
zq2%}eNk;T3chGk3T*rpXP4!YUGeFUm8fM1Msv5?Av<3vQ$4Cx3ODE2{4`hPKZJhV@
ztmn<TC3@fN#<_fsHkA|=@uWPqUrm&oLYfXo)DG8wg|_&gL+9Iqn>Gv$4R0|#j<C)4
zet-Ardx*fRHi*aG+`u=>E-xUSUc`<{ULLWRg;gUkZfAML_gaU^pVay$@|fM($hf#c
zd+Wf<qlp>swJeBEnbF4v_boFQ2}@+m0#Gx-d^2Wl325Oa+oMO3KpAPk%Up+KFqxxK
z{Jr2&I=KL82qmY+a^qf0pRdz$cY;o_Rv4dYgA3T?$n{Tl+0FG0SOIU;m$$Qb%&z?T
zL%#a#k(_qc4wu!m@-G(4vkYo2rofcGp`h277`HBF&rD(eovE5qv*vX7E!4|uo98!g
zGr>LMU@u<WNk~ZW+wY{kMJK?vCjx|WNJ^&PS4$BHyt-)Gr~$h2@IS@J$4^K~O2X6^
zxm9y&X7?+z`YXn>$QV?<x?w+2&O5#rpchTUOGrXuIa=_X$Fw0?Y|aZeD4aC_UQ*r=
zwbGYb?YU<LL>g>bugOt=1KRh-l+{@GaxL1AR97J^U-CSn=cAdO3-=Kz@pivt=)0d|
zUygVWyf`T_5vT1(D1n5>HXZP?u?q8?bP@Y1%dXo2Ckx?C6Lgkv;2*$_;%nP+JxQDg
zKS}r@!!`B`&N@nE{H>ynQ<aX>qC!GKD$kxRmQ=KG!3=wPdLG-OVoFPSKIi2f6p#Ts
zPnh|V25oOUXynK~fAi)cctXi39P7XR!b2aDlNEtcgJ)=bqG=G9L8pzneBVp2F3+p2
zdMF@Z3B<+UzG#!eh}LS<-#^O65pph3YskZV%h4AVwlCy|Y}bcB#jwg{2V9>0YWDT~
zC!Xaw6e`sIp0s@hibaKXGC6g0i~o?f>G)S&!zwExdsJzUlrQl92e93lv}vctvazkc
z3gEag^uq7|{Be1p4u4W%)=X@QO45{7&5`w|6S5-AlDw=N+U=w@w;D(nf$hy@b$6=(
z%z9>EK$mLjK|kBzZV3==jn^J{5RYNiJ3&k2^7BT_oG;?~>Kw6M)z>|EwRiem-_)IY
zy4JBz>R~!sjla1=?)p^oXM4Nh_c!GHEhiQU(+VBv=F3a7x%lU=P`S5nZK1{zZXq*R
zJ@Y<)cKAVJ;(%_+8qAXN<qfUNFDMuR=UrS}jM}M7`H8>TayNMGT&64S1m#n{`5W#{
zd9PTjvl<N(MRpk3=S5{`@*Wab`W)Fwx^H&$rwQx!E8)xpCl#y5$K{!}8%fL6hId7K
zzZI9AvS4G7_Hvn%m|&-<*Ag~WH>HZ^?h<mg;P`ZR@HVCtkOZxpD5G7|+DK+)jf0_{
z=C|mg#rIneAGsqUB9edDU(dA{X54n$6Un0c{jGbUwCNe(4*AKbZE<9^`L>l;{Hi+R
z@Z5&DH<oEUTS)Dovf-V4vR2^kWusHC89Q4MqOrm#TgSoTyDfNiHqnanr~6(!*!iXq
zZ{hr#81^e337P0eB$kBbMq%Db?8Pt3zK$jgehU43+ZAErz0`PeCYj^47%5aCEGwH0
zhtrie?luVVKkcAhUN+b@WEh&GEaquxCga_jfJu&ck16oCnTHQuZWQJal96H4V1KrZ
zl(YCu$d)>Q-Qiq$MMcHoW?zgORQ7008@ByB0mMtS+uC5$2FNsnjEkb8qA38Y@6glt
zh)kHYFSLgT&%E~7YuuCNYd+NCLo8FYTpmv=gilcK>kn02rHxO=?{^=Wu6w?OkGc}C
z81o(ut+gc#WSiQI?z%Yk+zdTjTX1u3+z|x{U-|m#a;ULQ@yflygfE5PTq8@;T|D4I
z-2eR8x+8+TqqFlH2oq6<-$8vV?Ck6z-QC@8DM=qcb^;5Zg3R!CtcYAKm2U;)fPO~5
z{uL-Tm+6(8^en~84Q`H?3^AJNcd+`moC@#<HfIRF<V_o^h#hr}S~kX?eSI2UjjsZ9
z03h7Nd*ve*#d*wInAn_8W-2Qyn-<*6v7^6e2yW4}9d3+bF@!bX<aOlxA>R6y5pnlT
zDiG-)4YLOAFLfON1+U8F1ZJU(SiI(Mw4AOoe$^@diNzkD!-wdAb6x;y_eAVh17es2
zEjqlnCJ}C&#<dPTj1R=6q%v#<(pO{xN1uz`0DATwq}yV@a_CAJz1X;nJ+w28=T!DM
zC=SiVKh=}xf34Q=`DE)4qBFKDDKP8TuU{Mw9spXQ=Hem%xaokgjjPnxnv`<W#ww5(
z6msOGIGg}9FgJH(M-*m08?fsANoS`|ws>UBUd*y=OdwC9#p)w_CF$pMd3HoZN;=Tz
z?$JH<6Xf(?^{$p7p`nJ=Hl*jrJGk?`+!a=*e^>Iqd`Za6?B~q!gU;Cl=v1+=urQM!
z>h0w^R-o-99p_#wF`1O;_p-%RP7?C6wBUHY`A(d<*q~)x!Sna<r>B!G{!%~`E>x4*
zq{&MVK-tpKggK|QbY>j06pe)6F38fw+J(10XWjMZ{vhrt$MGDUF^FC5)eZh7%bEW4
zG@Lzr$bYM>4lZO(3^I%bNV!V4iA7)k#CuQ1s-dGp1qfI_qn#M=7m#sBs%<r=zQ1h;
z+0}K<zC^bKS!LY^7qO=S`Jmo}Z@1-qmmOWn=IJ$ya9hJZyxV#mfQ(}{UT$J<vD#ge
zI&s3dX45lkRG^Ns;<VWrWa~n_d{1VQlarg=Ce{YC`ancn23+hvnHB56_|v7fv7)ED
zm#-YX9o|*zYvGdSY(3<BKXkcWJ+wYq%?Y4%Y1+AU1(2b?y*^4f0|oE2`%hhj{ins|
zf1pqGLlsQ<sM{hJ+`8hJOLlK1K%c1(&c`Wzd;HZjOCPCxXS<U`+BZZqe_Sc#3)>Fm
zqGMV))2<0qpL20qe+HGR!Omu9Yj>Bp+3-^Z1##EKcUQsp<7-ktV!lJMcy3FrGVisW
zRMxS59&4+N$+dU8ZW@6vney=(*K&Z$qoYHyFfW3Ph=}Nubzh1`*N129XI=hH2TK73
zr1cRCGU-yxh5bzY>py<{_-;QjxIxFRrF!`t!x10Q^f8#3QSdxHPNVA)ZC9g-qGBd=
zU5CMwFYb$OXnK=)Ijt^pQ(HBs_?!YQxvPSYo~S2J)O{K5@cY{LijCVRv4;XRnT{kt
z-fI`(46+CNtN9Q4oR~IY9Puwwqrr@`KZd(cjA=RkDc@_A)CU?}`*0{V%(7nw2`@v!
zd696>SV74lbw$6ax<-oAzTTZ%TT{vO`lj13rg$zlc`@X7h|P_cs41r>y00<|Ln0%*
zrdlX%;roL(B_H2jaklh%(K$pGLZdUsm&%s=Uhn~75fKwtAO|`5kW|uw#pX`~$m;W`
ze7&PKsdomeAx>p7gcsDm$(U{?me!O=Q>!a>XhWeqfj-Xn8sqLC^qvq-y&LDcQVJ-N
zHsw1CW(@Lp;+C5txSFD{nqe0cDTwS?pU!1peAikrInY|mvJku_>E<2&UZbm6U}XbJ
zf9t}o?D=<;%?(zZOOipW|E`(FikM0$$saZI*D<dk_ybpwX1w*6f7`C~e7uRz0&*(^
z3krIg=~mcYHQHK4MkJ$mEqi9FA7}6KZvG7vbH{7|MJsrp4F}?-+;DBP!Bnk!KTM5K
zL2!Jr7PNlNbN7cXHe&DoXz>rW)qFVd+RT0@uPu+Imz{l)d{QDsj%TtBB-JM2FZ=^r
z<1Uhk`gn8RQtVnAm+s>ls9p8tp?i(4ZyRp{<1qLinJeP-ax#+bPff{OT#cY+M(P0e
z{+ljwkf^7s{bscZMWRREev0~qOI}&P{CMvuAJMCu(y28wjC&oW5PrqiZTVXu3j>ul
zC?(YcIpaE#Z>2onQeGPFmC$&#;!8rA5OgqRJAJy4n$+K1EVaP9CBfV!2uk|JiMeQr
zwvasx^n7Kf{?TWsxq%7D(-(R(S7@&{r!;)6^>;Dm(>)vFaoV5_&<Z<*NsiKVj^sPJ
z|A!KP&3sD&YX2S=Yhg!MEWw=1q*A&x8qbVdri7(LN1RaG^O6OA4<kL-+@E?!C@E65
z1mnp0MAHy)@-b}dc;8GNN4TN|Zr>>8CK;#s5-a1I(-2Yi35+pRN^CXzN2)k;*BZ9t
zV6Jk)`*CBh<CkdZgQ55xdKX%+V?5BY*!KoiIaV7f+~M9yp9x8cJR}n}N913U@4p3M
za`}pKIzH3ps;n7N^wP2L-YHMbuu?~1^Zo$8U)BDdYpL(|^xf@%_vaJ<P9tl-GiWJ&
zS;2h4*F^70+SI)vE~^O?;x|&!H6(B_OA50y3b$1bC%C}RmXXI!Xh3~vv}urcbUFn9
zIT9(^a4}bX!yOr++0*lFrW$syl|`=o^E5v;zBQEpGTMELj?na8C^0Ra3rG$Rm`J;#
z6@f{{nPX3$vtPj*?!8fB8WZQx1E4W0Zpjm{qYln(>BN2jmn!jkM<)l}PLhO1H?g*>
z*Dg*|H>7)7wkhAAL8q$Xq6?09AE?ANnOiD6ZO>_5MN6^*O`)13`SAc}C_A^>M6u(g
z=CwTmdRKCAG_QejvY=(u^&;H}EC!DkWm+C^4z$*46^+@Mo!Sr&z#g3Go5bD7bJ^dD
zIUb~5Cue*bT||T{T<b`<4-#&Tgfk=IJ4kpyHt_@YU~WN)(Bf!|HwKN86&ew22EaZM
ze~&-45?=HfNlnpdlMd@QC@zNxnM%x1Gh>mMCj*sJIW?XYM<vi}G8`-D9>g8o13^ux
zvyBRWQ{;FdbJe34Fm=wzAGf7iN8_FqNBN@nK%!9Q1L4IeUG)#T12Ri>Ua}R61jylF
z{CVV+tDL9FWHzHut&5~a+apbRG931xiPD<pAmO&m5Mg@D0tQQq7(v0v$jB9gtca6p
zcr&Lrtdpj^DW4p3duth%8KjdPk`3W7un&aEnf>DwEEitmkzFI&Ys5HS^fC`xs-bu>
zzL5X8fHy8rshOck8o;%C@EY;Ue{l|nNdO6hX)@qB9H?-S6VYuLPO2MIsTmbS1FtMH
z)~59;f4CykXZ_&Lj%@o1?R@QDWr~C+ugg|~S28ByA3B;M6HG=p#x_)Ll$qeq8ZnY7
zdOrkxHu&lFWM8-|0m9dX;Jt_ACfH?R^{G<6!i6oy^{NUg^|bNvYt%S%W}NmTY?yTI
z$R+e1dM?%%z2wbioKE7BB;2INddo{&ArrvjpK8Qe&viVs)1SyKpZm<q%g$n1W_?^0
zd*Syu4ZqIx+4TFf@=s#LVSMZ?2fgXDr89o-Bqlr-d({q`DV?zx5PxEK{_^U0WhiEQ
zGV_uCcTY);FZm#JR%*`;hyx8MG$&Tw;Zj^Cv|b(&0IZVLw2Wx9X^}5viin-nV@B|^
zne#(GvuoJ^oNP>Wcy-;Q^ZDAG)o83_0ha&lIWLU-QFUwCR8M+Vq_kuciT_l%1asjC
z5S$=%3LCv^X4)9;+HCBqh8ET6A_vitf2)>Ra$p-HM*gdTB_2JjOCax$?OP`I_6YwQ
z9a#+WO#x~g<DsxSWBTj4MG?tx5FH!8TsaD?{>(97zA@qK*icD~2SK1^p;<OF*kG>l
zCzY{rv|3lDa7pC`(47{0p9ST~THrBs+S-wxd2(b!T#FFI*(1G+U~TD2KjBM$;-w#3
zl2XjdQMo`Bw6JZMB*@&KT;I4~$;yW0e&u3pc}Ae5l&?Zx?f~3p1(qI7wzsJniUmpz
zTI_eis!)H|yNn~J1ET<e8Acc~7v579Je*WjB(6CL_vbm?fQ(i&M4-d4K$#o_PMEpY
z1$KJEIbGkNZF%)LKX24E{~gfCG82L37gv24Q(3#+-=E^9^OHHiF;ud@_d68u#oQhJ
z*q#9#l9FN@#_<TBnTnd64a2tX&+1??y)fG3Zcjxk@^uGH-3K^{7vzn;_!$)&elq$&
z4Tzxx3ZVy!5nf+OBotoPgl;v6S)b|Bf`j*};G5<rPWxWm5=KWZB|i8hE!7Z?6vbEq
z{e3ie_8wcS6=`+;0er<ycjC23px$nXQ%td4%;c+>(=p0m#SSSz3@|*gYpF}NUe<ni
zWej65$;9L&PNVfeNUiTH3HC)L{(zcjY)Z${fXPnMthjqDFUr9(Z;38$pM={kMns7D
zeLEt=KK7EF_cW((N|ISwI=_kckT%FSIP<fO08(89*+FM%K|x8`hma(HRFM<^;GpZv
z{^1+L)Iz82g3#O>`Th8_bZ#;({YH$wpH53_4oYhPA1eAMmf@qjJNx1d)nY@CZl1>4
z$v4RgCVqHjIxq1dxiBn%Uf?HPHLU@TiDQi6c7hK3qAPH5`aPuU9~}nZ`MHL(3>wN5
zWZZ7@b*MS#GRL=NjwCU-Ew!k*#cx(1K8G&;1I(0SuzOX{ae=DwhrNOF+Pd}$yq7Y}
z>%TbL2L!&g$4=~b*AbT1*az2S8>B>dp;AIsXBr(SQ)?aD?oD{QOUCK20qf!X-cP=T
zHF>F?h*|Uf5Igy^R5Gix><X2j-rm85@^x{I%pR-B?^lmYO=mY$w0;2R!5O2>aj?9s
z;Pig&pxIDYx&>p}$ML_SAbO&P+Oz#db`FOsN&Rzdw*bZ|s9^~Z%EmnovcRS=Zpp{X
zEw8gO<L;ROG@y7A$MFR)0;YjY)cgt>!(R0Ds*{`Y=u<Ra#9^t!t^rkh+AZ2>=e?)R
z>DUl31Id>HQd%Qj??=K&ndo(yq9Q3u>Mofm{v@YM5vNV=x;m9?kpeDKgxlUA0uCsy
zh;Q04bO|sp+X#_$*EHP)lY1K=0zq+Z^vO3|PVKZS4W=Ci)FMJqrbtW4GDF`6IAQ>o
zh%g3cNJ1h-j8lo60UH9Y@Y_*QCwJ|6tI_C=A?2hrIfNMa9#k^%1Ojw;F<fGg`7^oN
zJ|k61i7O@Q0p3AiLtpOTC@${;(7AsO-wZPA*Ls5SJJ|6mPQfs_CPvLv8+R`soqE1M
zJZs<u%06Y`Xf?b_;Qf3qSCgP%BEvW=*j6GHL@{eOmM3szUvg@ByDQ-R+mA>*VZ$!<
zXC_tdU_8!JS_t6aG{8Y+zhv-@cw@w=K%jZ`zlHj5ELrRb$DX=!CtUNO5F<KL;x3cT
zH2IuBekBB0)Njn$Rw%9E;rrnaY-X+Z6F2203AtzsoWZHt^gV8mf@poCbyIC`T`fuS
zE^tsefJTd<AhQsl_auG@=e2K)HvpE<p;^?Y)Y0X4C!_7+`l1)m6MD+=-VO0ZWvmPp
z0E9EG00^hT_viPpKzI>@-Kuy>=HU1LD$5=y-YF%KR(n)TX4*kuRbPd0b7d+V5jve8
zyn0;p1<Y)GL=e4-d21bGXc;r<6f<cTa~jGS8mU7Angzz+KK}M^-QQkkfjOE@Y^W?2
z_Kqre-UzHhA?Ym@f(^i1AJmj1OYZ}%M?8QC?3K&4W#RH$fHo6w3OT)1nueO=${Nu@
ze>wW!x4-|<5lHBLh4pg3PQ9BR4jtFGch-SNyRk@YTEx61Im8J#?D(t575cPrq@G(c
z1x0H2cd_0~XB<EKOJ=Vr;3_r2WI>H(HH9SJ0bd0lAn7QR;BbSv@kqRPz`YCjqV9sC
z4cnG7eoE_=@|Rm56jpdi#{GF^HLNtE@&7li_*<&M!De8ecJSjtEsNv;iKQ>pb?F5F
z|DcCMhI}`@NPat&`474!DB?`ZwOoFTUnJY_`jIM7uZ9I<$RkaXOQBcnE;WJ=<}Tf`
z!l99P`OX0v%h$5#!J650p{31efRZ-~`U`oD>|1eRRLc49j<dwu`#r^mKe;{(Y(@Z(
zri~x<j8<x5yWifK93}|CR>)<WXNk35*~ASh_<cR!E;gFIJXqBhKJJr$Cn{NiaS*)#
z(L}wyQsH^fiOQ39F}3B}8y?;er)UG-z%~4}OxY{oXlUkl34WdV+#ndA$BuWG=lxq|
z{Yh~@ZOv%dVk^=S2N**9Avql@c&te8<Aif02M3^zDN%(_ci34{vMkeTtZ+d>+aFAB
zd0e624sc2pWFW8K!sgraXgSWmW3jXAv$eq>`BA(C6)=H6Q{V@&t4orl^Y1naIe;Z3
zJo^V=#nW0Q^~Dx4(O->@{w$1h;~<3-fvE9n0~iV5NbNFx+{;~oV?5-r;{IHrEGz*E
zF!HqzjHydo$ScrDjv-9XQ!-IU3!ED=Y4{<*q#*Tb=U`|1;s`P{`#bi}O^FXaBkc<u
zZ12XxxTt;4z7jVOwq@wICp@?I*<gC5LHGx8uADD08W{3Vs?ItWPzOZgirBFtXs})F
zA`!RsOVzxb?^|ZQB;&L1zeMC$e%k3?h|6N7l|O#t{^$^qr&!G@lQmeB+M4I{hD=W#
zk@>aetFFtFgvE^)O)9k{P?I}UTAwFJ|4*wQTZ#EoyX}*MnxlRM>a6wxyt$CR_(iiw
zmbq9%L=H-s!jSgJ<V?VP%424x=NG$7ywA^@n^-g2`b+p%r*;Rp#NjF%K-*SXj-Sj=
zF822@Olv(uwZipomb>&O-)y-flEXllbIV{vi`Hq*{;QGfXz)Cn70W#Hl>9yrH*`;i
zu$XwU`q&jjgYv+2#Q?jpH!791SlIx4t*B=e>QK<Vs^{C{(Dc)~;{yM@DYM(0T2So9
zqjsML?x1~7V56^X%tsSSBt?#)0tq9aUXqwr;CD&sOK0x#I*!gVVV#SSA~0Eg@}J2b
zsfTB1*+X**Z8-3staQ{u+OLI%w(1jnfUFY;-oxd?84_s%{j#5ccaN18Jbk{%`n@n}
zS&B@CvkN*j;&q&@V!glP|7JY5pn6jAnLIBn!Ef!8T<e*wp0gaXy>&~yHTq}fz<sS}
z|CU3qk?<@ed=?3p$R@tOsJk1|vbm<+baCY51Zugm%W26B)%;t%GvHNqF>w>lts4|U
zIWdCVr8U#i)O<rd=6!x5^a&Q1wPZnZd>aM11>ll_1@<lgT6IY8$O~#E#*9bZs*^2y
zs1`hnnlgvP2&x4!LWXD$hxWouOQc&25~{U|RAV>C|I?SC_DvH=h|<aqnZptPu;+?1
zT<GT-<+9=sQD37#!+Z5eSSox>o%U^-M7|9|rLX@1uW-g#se_hqm1<wFhuk&#yckSb
zH>eEsKj4;H%Uod=eC~Z&tCBwUXcE58-clru-&{GAf83N7lSZtL{|0pHCQ)OXBUQ~B
zeu*_H-%A}d3K1$ALyIsUn!-_&-Dw_;!bak(l+4v^v00^pK8R9Xe&|=aO%!2)M(nH2
zcX;Fs4C;WVj}Z|Sg!vj}G5mP<epzE{SUef^8aUB~u{W^~&M+5xDW&aGh_Ipk+KmIq
z=g*%9xla}YOKUQF`aNikJQv*2ni`&2QZmi+hcm|dgJGUwoASDZq^Hj0<;M5mdPny4
zB;OnFta0}v4)9i59&o@v%yVrLq&_Q@#=qpu4HYb4cDX}G5Q9<SDt-YN!g9>dD9}DW
zX+pWRY;=xU+TrOBa3kIA@F?|47#!Q}vV_ax8egr^EabXH)tr4(dR75=qtTWSvLDjI
zhN<53v)|E+zn{d8sB%;3aiLyM>^Aozd3(PQLE`B{0~#d`g7^rJqg=O!`{^j61wfeB
zsOK)iP8~koCM9C8|Jt(1w&=KWR!~&qaldlpsUcUax_(^ak+HhIQVV-#;8a@wHVGdZ
zUX6d+>fa5xGEY?rdj(zN-b6Wz;s@fY2=p)g>pnIa?nam?_35grhG#;G`$C9Gr;FJ)
z$LY#dZ&T{9+C+Oi?M9;y<wf_jh;$o3vktTx3)M}jZ40d%pnUal)wTx9{I$=w-e-<l
z+~ysAp3o|X(5v|>naj!kWz`eEd7hqT&0Tjn$4rqMQ7_B``cM)T=JnQ>3}I9YezUZv
z#MOh9cgQ(Ul;i_lPHkFd-K8r8Qjc#%-&D$c{n8+?3OgIPLW|$*-OQj^TO9bKJgT}=
z{4odIP97`cW4t-l;Rd!fiAPSJQ&>>?|GbQXWn};J=E}ED(wLo|kMv1|+uTcij5u8~
zbY0rXC9wZ|a76SO?H%(LZT{NH^Mfuz3@Gw9nD8IzZ#?|>qjPMs2_^sEw_bkc5T8jG
z;!?>Yo+<zPDb!l~n*sW5<OqR4Y(u9T|3n8Q_!$Q*XXz0{DpGsx23YnxS@vsK_KPSr
zwPXpdDqHsRS@vsLs@D*A*N@Rr?p;mV$3*g5v@|2(1G}dd(ph;Qj$~|NB;fqh?n?b)
z{mrDFjFzijcPFa<ZL>T_!dF1oMX7)7<Bsmko^wp`?iJ&n$DW!aH<wzAd#w<%R1oRl
zxpy7-&?-9^3D5TnL@RSirusJ1UNpi@_z`sz4=mN+(F(cu*D)`~3Kqr+deXwXxTpK*
zgwb?v{dCp$(cf<(;rz4i>uQ$jm+WS~CBE6FNVu^KMYqwGu-p^d+<TQY5xru~B$ebv
zx4+{aLv%^JFMZp@F_#m;QrzNu=pf}NFDL%t;ppjI!TG@Gw8F2x(=`NN5p{1<i3dff
z2%+de2Cu2&9o<AUYdwkAtbZU=<rqm|qs^0d=K&A@xZOqE#L4lo59HcF<JaFlVG?*^
zKCjHbRzl2l7bERh;mkk(j1A{^qVtq+8x!VIB5dd23GO^C#?c>hdA2gJq|FPn-|+w)
zJcaCr8wM6-1C^*N;_aJ!19G7~&0GiLZ9<s*y5I!rkK@fPf^Ch|HF3l)k1D5)&ZBEY
zDGd~MYs&;sQ@SXl)bj<hCw);S=7P`uo}-`aw%{VpN7%G~=wH;LSZYy7#2zQ^bTADy
zLW2y*dE<5o852f6K3F^>xZzt`V}1G40%2g^dP9Q-o*Br>_4T$XuHV}-nYU$^q$d<~
zdUMjUy2Jdr_%3YdCA>lOLVJ+*X_WHo1FsS4wwLxh<?PvYZCsY>rGhqg_^aK{@6Rpw
zF4$#PKUzuY<zL^e6Va!0cBn|axbi-z$9&;{Vt6ZDJof=%<yqbcK*D2@@S#{i(`KsT
z`qt>t^D*%#Cmo4z&5(2hd$Omy86v~=AH2D&8oe*mKO^DePev88eTI$BU0?EZTN(Wr
zb$ceH8JR4ww1;rnx)8G8xofFzIj?5fe;d>2oo4!w_7B4!4n*rg(y|A^rREPMs=rgv
zoK>B~9qSnHJ1#eVDe)kerb{Y|!qiJDFN?m%{5MOMW>$8pQ;0f0=~jM1z~8~OySKP}
z{PkuGWS4*WZDFmxs&2Q(fe$E*Z}YYHQ3UCovee<-riQPHBUmvZGrT1~SNB!^u~fg~
zLV3qK*qRB7W)Vj6aAKAM5X3sFbs)T+&x50I^>`e5@nLS{Yz51sW`hm7g```WiC-q7
zueYU%c3a&{2d`CjT#@is&}i@PHOK8CLu>qhP*Wd~aQ#?8(y|(ANhejJD?aKf&5y%1
zPcmzc-~8aroHBD#jujjztqGr<)$Afp;|!h&xT-cv^xdvBx%kp&jW00he^WsUihet>
zmh;2(AqtW=o8*KaWw)O?@#<B0jp(OJPEaqz2!68NOY(i?qW1d0=UH$-A7iuuTJs4(
zlCS6s5vW{wdC|N{d(e3hIcjQ*eRUso%+<5k8ZzTg0L6T%i1wlUOXbOWY1{_f+fAu|
z^w7b)MQ~LMdhMyH<5He@VX6m7nz3y>#7`Xb5c85x$VtrlZFahh4v+f!)Ex3OR`6(c
zHt*@|Xm8=?c%0PL@EL_<h!sRRnE$T~h7Do+a2v0DK+OOC@Qi<}3?eK*?2qh=<`5_!
zq<|WZVlqhM@A?Nbqz7$e;qsa0#Sz8Ln32+{4nfSsSKPz!M9G--)@01I{Pe;MZQ;9l
zyZKTdpW|wNIDe0OXK~KcqY?Av*zET|sY&OqzS=E2FlK?B?SPy8F3uUT%Pc0ldtoMF
zSEj_vLT%i;JBTzt+d^HJ+Vo|b^@h^|Z-3u}^Y>7?Zs<h$bdSvHU+T&}|LHavXR1H(
zeifIjn1EWw`T<Js!{;vn{x9%juNZ3m|6XIf2z<?apE<U%@iVx-2~kzjP%Kw?74p9T
D_fJ;H

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/packages/gluon/activations/images/prelu.png b/docs/python_docs/python/tutorials/packages/gluon/activations/images/prelu.png
new file mode 100644
index 0000000000000000000000000000000000000000..b06a9778fd1b1b7e713a3e82c668a20dea7a652e
GIT binary patch
literal 10202
zcmaiabzD?mw>K#QDgx31GL(`I-AIFkN=S!{fH33?U4lxtC=AjeAt{J-C^&S(AdNKA
zIW+I)_dNG~?tTAwFP{VK+54Qm_TFoK*ZQtCC+w+~%1sh_5*!?yn`)|x+Bi74uAqH(
z{TlddgEa>oI0Q&}HQnpr$M^cnQ1F@9S=A7UgF{Mj^@}?=<$ne`#a)#QTy>nRT|HhP
ztZ*D&xH{WAx!T)W-gCD?AZ?u-?+Ne;@CowVd*$ltEWywJpBwm`5H|d*cN7V6aPHx#
zDaz@3rff`k`RXoaqIYK0spWrC{Di1C6DS4Z7+#Nf6P^`%r;>}SJy$nl7Gva4iTP~g
zREqI74Zr_t#DajFU?5zba6CMmWBn9jY@iTO_U=&v_5!LvFOR2YEns84>nqDFleE~K
zV$*x$NpHPP*jEe$0tqaQ9>P^rY?ahqfk5QsgZLm6Z2xOh-cP(DS2KCMx?0S<S6h*7
z*2>y?vkD_3IqE9IyAOH=1O$A=)yd-mY$C^=C#?!CK>p{nz7oM^X>Z=(m6w;d_w{*5
zgzM@kDiVDC`t@B<kT|1K&U0jae?@uutNsjJ7Z(>=SV}aj-pgWkPR@{`BA&VV`P67u
z?ZQ-K`!Cu(4_LPhtow_0)h}9DqX638_0ryC3g`IvSjcuz(TXV}5_;8`DN{UqWQFdZ
z!M*mOW`led4tMaGg<}uwbdrcf8eT&#{-gvs7N3hcXLR?7;%(LPH<6xAcGV~<zLD=8
zpp-iAppQ=H`Lg4s=fR)2S5#StJ1Lv2FO&(jE`oBj(8kB5rkeJQ9arV0d56=Od1j*1
zUiYSm7a3OME}T$4k@_KfdE93wJ*v*%plD`h)^QMsPlQ<MXNeIEZ%-l#52s6Se7FB=
z;&=I_K~!HU+Qax8M(aB!*9fkYEG)SkSLk1upTG3F?s<-VrtJCtkcr>H$cT4IdC<j}
zO*%Ov<A#eex%d#XtYHtC@vZH%*%DW~kuvnM{*?R-JkpkU_M)cGC(akXhADPk=?{FC
ztvpZ(*S|)_WTNc}PFD)dxW76SLMD3_+mkFRXwi&Q>$ZlrN;E}-|Gl1#jee&IDbp60
z7v~>5Ha}&n#0-|2%W7+DuMB=BWqy6;(&1-oYwNl({;{=HQ8|+KN7amf*JKUS>*!C2
z+T+KTdrLi&UdyT7DdO>7drK}84pkqe-2eOxGI@P|fD_3qn>IYGOF=>5x!b{s>$&(*
z|92&vM&Hbf9sF8#z$cqt%i{1i9zjwiJ#vxDz4iNp>g-*4u(s$(I>F#~?+8w?TYWq8
zZPKRe5IKIo9g*o%f64ihS8Y*h#eM>Pe>cC2(a;i}m7XrQ*`(JaE{;0vFzXWQQF&Yg
z1$qi;^)i2ohKkIa(yFGtBc>aC1iklPnfYu8GkLGDG*`wPh|c)pKWg$n_dGuswVi2{
zj-cVU{`ro$J6*P^GYP;4CG8_}lR@MqIGp{zBM}Ua)q%or%=UUkU-_#6$R`npPN3@0
zu&_VD42~B48FDw6q>jEV*DQU4(~3IYZHs1P<>Cq_lQ{~QaB5-<uu1nzK%r>E!^2lb
zOPRT!7tLOrZY}?!F@O2v4WW?3`2M%$&hGAojj9>zow;8I@YS&jPJI&>!YBq&>v?pP
zMzToM1D#xFaKunJh9{Ve*=%u?SKIikY!T;)fBmOM8qxWxQTOdJU+#*DIiXp}SxcBk
z0(6O{I{b_t)~!5Ck39$&$<<`=#%|th-u}~~PICADgLa_tT_6&%R$TS|!-v*tm-*if
zJ|2}zHpPqBrb}tlI`_rDg{9WZy{YR{b&1D&%YvTUv+v8J!otMY_TB{t3jm9fIbNV7
zAtgO-lfC#6B|3e*8E8*cO^rodJZYp*KV9UdPMTBWad5I@ZCHN({lBB7eLbQx-LEh9
zm~jByff?at7*{#kPt{f$l^W-Wv`e7cj<5o@6KwAjL)a*_+dO;%Q;|2Pj3mFksXOr!
z?-C!)fj=Ib+I(yt6CRG2QC*E%S$UPfYwV6$Cb@a@b9FUs#!tVBp`oFU-d+wt!T7SW
zhjVjtz!$X{884dRI5nNSb7-ZgYG~Y;s`JQ`dwb1(tUL%HI}jLr96E{txt!{ehLm-I
zI|N}zijAul5UQ%7!6u4a&Sx*lVM7Aqh6ddq;tGVmEKg+Tx$)6hoE~oYVy8TCu5={h
zcaj3tYze*@78A4FLMBUKLRjs4yepU+5fQ;EC`cKQBxsW$*)%z6@=4T5%fNt^l$yt4
zLS!@vdU~+d22QTMqXT!g8FzF#>n6R>Y<nE*qKy7bqn}~<OJbc|jR(gfE=Suhva+&X
z4d;{lU^lI%>%G{A^!H5?&HcrZOWiE|{4oGPP@hA4zPj~t#PS1(O*v+FyL+){3W?GW
z=0M6-oXGTL$P-DpueaB2HL8!6n2I~k;>@?j@O#wm@4HTWuklh)QmUz{iaEfR9*{}z
z-;U?doEuc<=N<cPfGK;?(%#;FPgwXPxL3l}IqEAr8{778Sd7arD*ad#s{JjQ%=PA2
z7KNiO^9y+dLLgbxNoJ!)US6J7=+$lDFkk>GIBXmo?^9AV<mK^z1@h5p;n7OE^NgB$
zn06=+=m7syz1J(bI*?5y?Q^)WS+~U@E?)Cq{i&jvadpxY{q-ded<kc(5EAS{$+IPU
z^U4NZVd0U4b9@^3Xvt@=dzr4M7)-G-9S~UfyjTL;s?Cx&qe9Mkj_ff*YxK+pw?40T
z^?5%UlO}?G2|RJ1a4M!>Nl~iIc81*l;D-Q(k?=x|ajk?mSEP~o3h<*wk~DF49X;H$
zn;wFp*GXUOhU!9o%HQqLStB)7%#k382(R%Nr*LPI=v`~kc|~Q~x2oEkPG+jec3T>I
zEOAlcBHK4L7&Kg6!7Mn(ZKOa$UXW#&WT<i6N3Cp%kg4);T60aI;BqC8<1+X@e><kv
ztb={y({_klYDzSco~o8+cz+eio$c>8DtDvKeM4eCg<tR4p?xG|*ZlO>vY?~gwH}6H
ztGTi}KjqH~<<GgJn2~e)H6uwjVE0^7@|6N0>MwTj-?v;J)`(u9*<H%lwaogN8tobk
zwx^XMQw1<YOvw=&8{3>A_m)=7*+PvtF?IRF1D%!W2Fb9nut`K0FZ<Yc`>~(wi6-*c
z%_*<5owg@F(!g<FGikcb<bU*{)?-Txn3C6glqk-qtvkR(*X)nCtNoey01Pcg#+$2d
zO08}Nm&{8=mSfQh^`6EMMx)`gnx4Xzrb9#$_WHWv>eRCR0AY#bX~#W3zmv%rnUhce
zzjDOl<HL;!72r_mq`h4`&Oa|F#mBdxpfH@H7Uqj)W2c5+e%Prtd@`x#qn&v=aReJB
zrxuTgj8nq_brikuTpRQ2h1M|2{mrT5i}O?bGS^k@=cQ(v;A~c=>O>=`c_r4s+UzQ-
zXdD37p(fjh_XYIwrbg>z8Z`yqYLxZc1d*bpxN7=ws)XCCfvjJ2@RqzK;xfAtE&wrs
zTGv&1Z*Pf0{nBd|16fKp82?I;FNKn`cBb|LOsW|f(W|Mc?XQo;r=;8!ah$yVD27Qo
zG%D(|R;mOWJNxQ~Y6#Vl^}K)DxjpYFM@k$1`M4zGa?u(5=J{RtpzyEJygupCW29CO
z<!rCy)=NM=0Csjkv&{L=PZDIJXu7_+-$!_9!{N6H8a@+G^aB@CQqsN?6G-L2_Y~OV
z-US7p{quAhC-tmovOWhhMk9qbY+m2SP##%@bx#)1e1i`*?fIY;qd4Ln@S@sJpFZ;T
z_U_mK-PR}jt2lX6X5*EkW?eaI@%QiF$FTtFyesK`D68x9UFRTtm_y~s9RUtre3(UN
zyKdIslhG3=FWr@+i~X9aNdzgNeXg66NiOr~feM=^^ul(!o8S7R_HJC^`V+k5fp`yJ
za~FS8H_*>2FtwVRnnjsI0Qg*;JguCZoDbb=Ch$3`R5Se?kq3O=&raT2+4stz;T}2t
z-_>%4;<%LZ`rKCbYgA&GKKq}aEG#VaXRDA=^BO4vwfcVgk(}bstW>}Hi+^;l^Gs99
z&5v_)opH-G<#D>q=w*`vK>_goE`Y3QrAc+n$H+4ETk%n7Tzs_@E$yCZbtw26Su51L
zDyFBsMmkk6w4CNJX`-0&)q3;=(}}bA1BYy{rR7vc=_)U`qhGYO{2_j=r~4Ob58K7P
z-#~5Yguc+A*H~+xi4Kn;ll4R9gP6$4<+QY-AG16%gm-jwus|R`SMNhco<G&?ez(uQ
zF6;fKgSBp;Ue3Zljgc7<78S+L%^k76Zl9N}Dy*cac$<M?&Gkl{ozF?UFwZn1Akpzj
z7To^Zkjs~z9eFZRQdXJi<KlLRwq!$-r=(8P47$2&VrOpwqt&((I<LS7foL7Otl_4m
z)P-~N*`b|?T*a{XXDKA6iguq<L!$S(3X;Cr-tFL4h$r6JYdPUo0O>+y?@qi@q-NLb
z%NNjimE6I6Gt^LA{m&h!D>=FOzoW{SAF3(lZ*Ne~nj~WfT*^p66n!>MTLG?4TEQM#
zO4R8|sA@5E)javw^_Qc+j{{N3djI6An3>*&yeRtHwM`=J;>y&(+ZLf!Z^A-7Cr07e
z-9&y2Qo)kj-%Cj{Z{9OC54Mu|NE)Vb|2FaZuGIU`P^`na$2+22e%4Gt^Xfjsn;9|Y
zbR&GO@C~h5LJP?<hW1K~|7x)X1Ty_TS-%<eO#|}t??W#|u7jWWTV)rW+b`QcL!DV~
zlYPU>iiEawB{}|!>St!lDYcG<hx1}J%^zcaNO9cUQZ8Ka)>8QbO}kNJa?}1R#@ZN8
z8W0np-lg@jIMu#bvl5dl0>^Z}9SaN9okFIU&d)HEd!3+rU1K6#B($P-bNZ|B8zP}`
zXkpqBGJ~~L0|+E7xj`tR<MJXbE-|IApUDAunt3TY1@jx-1PM6YQ+>i?>L59hD)((;
z@fU@&H;n(<KFZJnY9x-i#Z!)vA(mSG%maa#ecf`Ff#q5E*UOojF<_#~V#i&c)<@r=
zipxM`m4&)&Ss{)*S(r7eKcMoR^H`4~fvVpGS0>1IoBbNx2tf()1V!^QNHBKf*|lMB
zlNV4;AG>oiTU)!lZaOp>{rq{z#a9d1fZU6L-1D<lX87vNs+9joX~U4q-As9!xPU6&
zWm?L?L}$5*`)2jhVG?rAj(dNfW=5z!lJw4s1puJM*HO;ZZgSk15;E{HuL&inpR??G
z%gh5q9zq9|v9o6!&$Lt!A3)?~vuM~qP;NA-NNl_2o`Vh2OpTrwqpA%h$noO#*y|i+
zsHrsO@i>S^N<^DqNoY2#JAFvjYoLXgcy-~bvz#FXFqjYK$+#=#{{G+CV`!;+CMqO$
z<?_bwb3t&kGxAZyt4ZsUh&N<yrt*8iCxYnDwl_@`@z8?lC4WxSZ*5QVnyT7UZn)lU
z%D&p+!;O%Q9KWiluZdOjxeEq)^GEDpQAZ8TRZO#CG5RjYN<BwY0k?o=j6v+)f!Ez)
zv^n!hY88oI7-8vnH#~R+BL*d<RwqVT!`9ygbPF_h1pVwq>6L=T89Pb~`juMw4dKUA
zMN~!8ZYWWeT*QmLYg#J1KJ*B3@}~WhCV$J#T~n#yOUn~CslHV)Tsfc*f18Wi2C2(m
z5+zUUf4ys@5o6@E09$V@wIBERK$Odyt<x1O^;%sYK!WgURSY$4q_oKia#Ljz8ca!#
z8=S!a)zyLcXXgJ#Tz>n!E{1A5g?SH1)TRx^;Y9;0Is3T>4B4Ea{5U<iwqKeL4MeK}
z4>DIRuI{mmZhCF0Sh`n}ZDX7_Prw6CM9fYJb7iW8((^FSxf5Wj#IJy8m+nSjb(&o{
zw&%6=B0NE5xAoq03I+2)qQ#yWwDRX!zxSyRFr4TAtae_jr9$G#bS@!;R%C!#)59=y
zFbqA6Z|C_#a$+-#Q5ve^6Z-_&&d=g7kpA|ss>=Ns65dY?>?^K(8wLIID2GNMOf}XA
zJ5lT#`}OT<No{Ppo5QMWO2)G*A0tG-N3=;$hDjsP^5#qkB`8!%KHTe;#UM2E_Kprl
zXv7&g^F0L?25+@rn-)Ry2&3z5Bg1yJ`N)aoCy=|<%jfJS{XF59b->0Ys%rGTw1GDw
zdDvl;eB&-Y(*w!4|5hm2__40Y7RxC8tR403Mp4iMR-FQK{nMA8$(%uHgf9)@G<BtY
zt$N#eU;k4BfG=G=^OTCl*k2Gv$AD#gF~|5IWk%6_2`*nrI%QNx^!r6*&2K*)S&Jxr
z?J(7+UdL-uzF)ohW6p~x{l-PzB((Z>_a*!M9@YAgC8h_|NyV^1ek|<i)m`0qW0SgT
zW!eR3#Eyxow$hz0_Al@q6fZFw;@xir6XJ(CKFOZfSM)lVwYa7fhYpA0w`n1;b#y|Z
z*R6I#9TnfLPA&;+CMPMiu7O%c4W}RRX>^JOx?26!^uwR4s+Dg5dRx~n_E+f}PalNH
z&I||Thzw}86$k^yIj1-D`gd>LjKln<%+BN>FJ@J&G8~v!JqK(Jm+8`zDzVb3lXcY1
zSwgEA;ElZWx7&(XldltTarW9yRpx_te<zn@@}jaJb6?NiK+u{!Y)u)Vt1zFz`vToS
z^m{Oslx6XvN5%6pJAFPK<(z-P=S$^&s;Z*Ym7UJbTh)2yU;Z+gT9wM1AGQNVS@4~3
z?Qmyud5w0slAZFzY}Pf!=m~(;NV)jwee4tS$8FR%z5Ja4e71<d9<;5l;Irp=?GK9^
zN`UKQyR$>|9+5YVV$Nd_Cdf3*I}h=_>Sc3jz?#Jw)h4eVVefnWH9Bam!s{@%cRw`7
zMI!n`77L*K-(nIs7NTCCw{Dv`&|&s`^X66Qu2%Gc$}r?;qnirc`zK6}SgS1#0RX*_
za>&?WGdZmB2~*mY^Wwp~kS`J-Jp}_rT6d_S7Y*!pJGm{Ej?`aiH<b*<<=|aS6<e9W
zxm3h~oRH&~j+2)!RMt{?U(t`{ATM}2?lEz64@QJF^J0-*8y{R1L)-#SJQ%G?#_sLs
z!K-UwNT&KF2v`*RmGm2uw-#KUoKTDlN6lRVrlG~ih+)1Cwukrx<0PSrPmp`|)e0~^
zdc~E=r9gS$<#H5VzAlOiCRDuGBA>1)By!QrPKjpI%Mb=qb~?|Zd>g&VnwWpbb@AIR
zx!x^+0LOu|ef&kbe!nkj@;yZt|4FMPqso^nA6GPiL=oBWuoh?v^-T{OE{l*o<#LTP
z+MN9wAF0-r;Xn0snsy*6wCr{stD3NFcD^d_6^WAG;(xLAhtl`qcbaA4NN0)np3U>Z
zt}u;IsC5B!NymwRf_%p7kQsjLj=c4{v4FJ0fdL4VT8LsvBnqfB^JF65$)uCroK1DA
z+Ku=xi=86`xPfv|AJJ{+dd!7}^1pPHY=$A9BMeV-rMyjv^5#PmJ!^w6D7K9=s<VhA
zp|B_@9U2u0cpI4wCnRk=)hES8C$XQamZ|iXCWnyXsWL%?T~j<7CD$wicUT(Bt@w{G
z7z!kTXmx@-&EANk?Fsen5@6J<EveCLW(ysqOViCa(Wv5P*I6!Dw~v_`JH!lBY2pmI
z4Jx@46NA}?F6u$>px`fb&oqWJS7Ojc@c96iW?%S*b16F|dFiLKt#nj{Jxx>15~O6+
zHEqZx=h~}njT~x&YJcX-(_;yL%x8A2iB~2V2=-iThQK%EEiy&Z)D`RD$iruw$=^Rj
z^V07o8rS_TySSGt>>78^U92epMB8^k!!LH*9TkJuX16VJ<!Okum3}3FP-Nw!#S|a>
z`Syg^;Hs*2%l?`Xn`*=MqBnI>-_jG=EsZ{RO;G%m0TvQ>Z-1veRl}${Nz(hfm1pT5
zC<4o5ftdujD-g&3U?bvB!5Zb&?Kz+uGNz*S(B1Bu=Gy3D<sX2JzfGPFSx_*K3!pwb
zfciY#Vyi%#KOQ=91IH(GwJ1CgN6tKj=_YRNR!P2Bdo|L&syc_`$4p6#cg2?XnLV8Q
zKGHnQhjRm+DgaBz-K^^oKii2HHktY=4o=My^uN98w{HuS;>fX~m=XF{RB&@Oc<pc{
z;%S@KRW9^4%d+dLKooi%De+!R17HL1DZoZ1S>k_TW9J`i_&<V!*;brLMW9Y2O#ZTE
ztcm71O#~c#z}Rv2e*KlMKN`8_sO8x6FKC>Fl9;ju6wM&t5y=`TTiUZ;EHCkah?Jrn
z#7A-O7L#Phl&vg<p6CZr<s1rf>N!`o9YGN2DRBN>o;X}evpA_X`}XS2A26hFC1iWu
z25$K6K?sebCfjER7}#L?U~EKadUD*?w^!+~>z3y=j|ex3=p!hAD|6SRdn}-jO6jD@
zyQhJSKpXymBF^O<E-dYJfs!2^<Byq5Rbb?ori<$!($9tLl7`bt3cszV49c0AieG65
zL{tq2P`D#HIB$&qxA#<rH5yC<Q4ziHSywjV+b6d;=WM3W`lzk>r#hTUYC<oFd4UvM
zMIc4xqLL+9w$He$nUA*``-K#k2rk(99H)P$OlQLu|DUWq`fb1vB#O+xXn=mATgx;L
zfilWsB{b-g9>1&>aZ(%6tLu+~Iz&QABcbppXkiqT%u^7h>hMh0{&-~H%D$^(zVgL`
zZ((7s67LD_KY5N%5;$W3Po#y><0PR`rA3wS`VX2p&yUs;*OHnXnfuP_1sleNfrlq3
zvnvI#^Du@dR`WCCV(^L^s5Ej!P<6gErM#2ffaTLoR}>Gqur+&G-I$`KBx1vlXxQ<E
zRQ~vqT65;Ugs5#3UWyTAd&QdX**y<P%P^5OZc{miiEsJhD;jkJrlIWa{yndPl9=Sm
z^hJ2zqS~FW(i6}8!zZ`<y!Iampx^!B+5;(&%AzLhKdDJ>liz(rdD#{7z1uF?u>j1n
z*nD(Vm4fX{!?WS)M&?GsqDoBTN`oroN0(7oJW_ve9`m+dLMW-Xm;pqzH=&&Hm4-m_
zW<4~@YV$RI!v}S?`wiH0k=*mLRAN8=*^}1iAY$Fq*$@V@MFYOih3XD^BqNXJIpNh`
z6p;QymjE}aw+9nw$(et<+>7y<Hvf673GuR3rC@RU#>Y0DCr?xG!i$siDyF=626c-5
ztJM<v5ZFoVATg_=9SCV5GA6yj4Jw|eB}}cNQ3#+KXmMu5a7I(lOsfqyc6-H*v1Sgk
z@ts>(J%~?Rq0yuvq{+WBF!{YP5^J>sR%i4G^y7=HOaCnWH*$QDG>jO28IZ{Alq+~~
zUdeuW6;<LMD;k7?Xqo_Q)bklw4z{uB!&Jzwm!GGokx)1YD#MWRE~VCpi{%P8-wIpV
zKC;cnReM)~#EuRu%CjScPV(q9i+6HFGI~OLLJah?0O-*0jQPj5S(j#k<i^w9LIoTN
z?`I&qKW)+%Fq<5u?CTdILP7nv&#NeGUfnPyxW|oOKB{;8f>7yKw?TE%1?O%}=@TD%
zr{9M7f7tH3(h?kG!Ck3A1P@qknO#ZalI6Kk&1^E`M&l_SPmuVV35=wRxzK7W|5zl`
zJ7KwEVugF6E?BFL>Ov2b9;^7-{tT<AKJcZWCGVZPAtya7wGN+|c1v%Lo%zf0jk<6C
z%wN-fl-EKfn^7zZQR2iU&HN~N{Z-cmY!VA9N)n~G{F9$l89&w>5mVKw{%{%XEAw7X
z*;n<)g$)W^FMy#01U}~1p1kM2P)Jvf`{_H{hb+pZ0VG%0kIwjzdA`AXKSxs|hwNvV
zZW`c_Ts6TLqotU=gIYf+FF#Jcnv5{AH(|O_B&InO@Rf9)nP07h$#mn{H(9+?x3VoD
zdVnyB>#a}~WtNU7b+rg4#(Vq2E<6=%QYTE#62Ma#bHe-QDo_3gQZBkeF9(&M#mQr7
zy_4i+K^gxYHX#<d{!gT+xaP)qom1NjxJ4cK%8NXj6P@&;rI2DGPcK!9RAW)ZJ(TLm
z%4xQ02mQ(Gw*Hw@e}*mo>E5G88fTa`JmlYt?WRA$$}VmXPmY+3WQvm=H2wbZuf;y7
z3{G=<7(=|O{_5(BjbJwNtd<k@08iZ&Gzu4uIzXc|(I}OAs>iK)lkgQUkw~a*(w#e0
zdw>GaE58%JtC3^(#T49o+XCXu*iTGr7rD9s3LG-?W(u!gZYpE8#JdX>;%!HZ8e1|I
zzx2j8n0VtP7gfsQ(!z4_Me*SE<m*D|vLuq!KIgVdIAZk?mVY05=!wLbjmCkmjyxpd
zR4YBTSBe7}KbP3xN=rxR1a6-^Ep>^KswAkggg@Z<euvr+WB?C%_;NGyhYuiK9a$o0
zc@IA(zlYKJcuD55M(Bbh$7~t{@j)x1BMH%X-yDo<WSN|U3z-V1=h<G}p{7ELHO0R5
z;uQa&|7a5!%v&$Qg{9Pm{vAB*LHt5OtuI?s+0SL6YxE<ethHvesglBTLCJM1W&Xsk
z((ZUu_va2D7+C0!g%wrC2X^2T6cpIpX&mNTq}uxdBfKkdF=Hu%(r?bRqei;E6oWqd
z^9@d~1X=@emkrVqgXa}yv;%Q3gd(pGG3Ui*%&BEwUjHVFZpD`jWNBsuY|6j1EdRG&
z_2!_oJ~W-n*@*@@-|kGW`0fizX*;98xH1Th@(JV*xIMdt86PoI=a6~!9Yt0Xfz^dF
zcT2jNk3`hCycCQ28}!k*J1W}*TYyrkZ_7s37{F!mW%jhGcOI>W!{~5uh`6ub1<(s0
zbMrX}KYn9<Fr`#vNxdT~A)c5nZVXL)eLVTgWO?{$a2+anm~8py64lj09%ik93Gt#x
z8iG^Ix;J+Z5KvKGo$jb91$r*)xN)^0|65d4$knO&-up}7FvOoVSUQ@N5Ad(=uW?zE
zi-#ELtmK6sTNL=Ax+a{Co7hb&Eu*7#p%I}KcqRQ^+)KNM#xfg-G>`C_eVK0X#EvNL
zvz3@0vyLW_m-=<6CsfWgzwXj$y{!df#J*sxIBRV{nos!`4+KFCKK?@+$rTmP(k?9H
zLRDjd_Y5Tfj^Tf4!iu*py7QnM48NcEgoqLz`aq+r?(2{I>mq=c>=jq`^a{s4A)TTG
zF%a(XWk|eJyX4EWf3AX!n*a9h0VI~huY3rXE5k%D`=3jv5J}igTN@ghVtNla=FoP-
zBfU%pn4>VdNCbU{0jAwGiOq*v$3v|fpiAQ<Ow3M^hW3$TuOg3|X(`R{>0}?Gd61VA
zc9Dk9f4CY)LAj@ftUM)E#P@tUX)7vpzXwujh&DIe5T%1L?Z}lh(O%Uop3}mt8DLr5
zs9w&lHIvdATAk5zCMU$}ggHAd^#ZT&P&Cg&bdlTZ3+DSapBmAqN$ssIO5xNl>6wZ~
ze)J+0cGu`dq}q;LZiG5-h!q;uf<~EtOhR*Zb^Sl@LLjjH`e+oIO3M9%&!;Ff>X{kw
z%i@FN*N;?YEiG7Ljs6~LH6t?kZj;$xlkfC8yI~P}y1UkVMBjU=KO07}IYA<H@Tc5|
z#B=5D(7WnHQ^|Xi4H}Xo&LtO*@=te>%}6cY($0E+3%u>x)7{&%_{{Ylyn%PHi`SZi
z5r_jqnS+t#g7PiWhqqBgv>7yIzuS)G1Ei}DhYpW-@%V_e1N1&S`Y?I6`W?sZ%IrP5
zu^oSE1H&$&)0TD$uULW+eB%VMSxb&`ES7#zJcVbE=lvnAnHCmM7?8A?JWc3treip@
zS9cTjTxTyIS2{owGoD^l$r-p>o!}7l72|JPEPW7S(L`hkNBA#4O&Wd8TGDr`T1NeR
za?1^jZ+^${xtPn~6%iVZE=V=3DBtU;^(6y^C8HDPi}QdU%=llXvK4apMpGcZl_TFd
z-Ix5362eA6w#qO}1dVvtJU_LSlAQAx;Y&U??VIZ+^QD}9TCjWHiQcL9#&3A>l);a4
z>Z3v0c-u%i<G13E>qgHjv0GHWJ%!zfBDHLn;+~IfG+l)PrA;sCu{IBAMs2z{Cl#bR
zn&%Lkjjgm5leB_;wDGgqs>|NyDHj?yGv_{XaVF3-dtt_>W>2hc3h~=~qBnGuxlGPN
zqh90jcGj#x;TvsK(nTo~ottSoNw!>{-_wO44lDPjG2^Ca)KW7r2Di<br4je<Ce3WK
zbS9<8bS5ow*<CV9;xPxFqIsP>{IG7=BbHdRA48sgULR>HQzq=zk-C7?oiy{z>SM-R
ztTxTwzzzQ7`<%Al&1fLAOkMMHL3jn>i{iv2Aggq`3d`+Y)*ROZPpGdy|HAN5vi2w}
z(9|k-EM{%ZEZ?W+OTBIV`vF=?lF1vcl3mDk#O0(-WKz`HTB8b~-k23=<M|r(ntn8l
zHtSoQo)x^lVl#ywx+oPgny4?%wRiGFGk(!Ooxx>E->y{f|8iNYEq>E&%y?fUw3H6^
z$p{YLuV?@0X>PY5sHZlHt6o&;(>48Pco#7NUWTzPp4wpYAJ^{{GOR`)KMDI)xzW!W
zD6k+vLpwK(&%!k7G|v3^$t+(uVY3BRGdmz0b0qC1T-IxptT&yCM%{;Lywyo`aiBHP
zoBlSfZ9Tc0OR|Jz?VqkbyhhS?;O6FdbD19J>{j&;qh$RbLXAddAHB|zE`ILK2Zo=_
z2l!c>Tw(^A{$bMwKZJO-q81ro-m@o7$vQ1HTzie(gvzy8_f^7$G<wJFK$0eQ$*=W~
zAN*r{jW{O^F#5U>mD4(>Jq=AK!jeu|6QvJ?DL99C<{xUepe8=|i-=0N-A-*Dabp+V
ztHb>%w^^Q61tS{q&&s<UiUax{FB)uKRMC(2D`@i96*ND#2s*_dDYxgt9t2h&X8m5z
zHc57lqT)kd`ZU<{ne&zIn#pbpm6(g+kZ<<&a$s!=QppmYF`JlI>3=ZN!Vff0J4wOC
z)+Y8KOc)aR59CS^Gl>E<Hy#c&Na<_aZGEHfr4B31VE9<uv)n~G%Ku^NQD9x2e;_=$
zYqVJz+eeH2R?wXMnMrc|TzrCHaxqFuLR(jnO_iV%B$uHnZ$agbO%+s`6yLo2|1{5J
ZJ7bnh!pDL_z<YQ&YD!v)CGr+;{tpNEB<}zK

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/packages/gluon/activations/images/relu.png b/docs/python_docs/python/tutorials/packages/gluon/activations/images/relu.png
new file mode 100644
index 0000000000000000000000000000000000000000..252228ca21b4d434cdf1b1b52355f8cab5361af6
GIT binary patch
literal 9581
zcmaia1z6Kx`0qd|B?R;bk~%;{1RO|7s<edCHM&PLI#rYbLsX=}ffCXnQX@qi$mo_9
z94Vti@}B+vapQUJy*v**U(U|Xd*1rI@8_Iv^kZ#Rx(jR<AP@+hx|)(M1VV-c&&Sl1
z;IHGpB^ZF5_j#bMPYr%TsO?bTe;Q9U6CViVA`|JCY+x$v7z|1ym5q^l9u7!<Yj1mq
zn>EtY)dT73Y;(=e-rL97!~L45u&A)u?Q2d*q~|>mk^ebC*u&dVgy*sn1q5;pqONpb
zKOkdm%0EEge)?>4-;F%d;^BJC&f81xs)dyqzki1-e}4ftkwJwjms^|cM<Gm<?{g!K
z_Dmiy-|we5e>r)$Dz}e0=%RQw?-#|+lOx-gvM^q|w*rn1TY+#EqZ?w5J%1Oveu?c*
z`Yg!cOk+`~$fidb!iq{tArGGlLz!+|UV=icAN>Cwp`x$Z5q()t-KMa<*CYj?Oj}IM
z%wok={kcLceTp7aSaBK}nx~UFD76kT+@C!=X{|aSiG~7;QcHQ96^n|Dj_Ao|;pgX%
z`ts%O=gP`VVPy@*D_4}a7rG1d3*HXqqSRm<3_IXMmyu%i)sYeyP740&U5hFH4UekY
zqemaL(j~N$d7$5K`gVS2-S)TbvbODd`CxQd3|D*y=jwY_Y&J~})-AAXZfce7xN7?e
zJXoklpNHuz^f{70j5U{JJQy&Ypg$Nm39b|9dib1+|IMtSOO_eJ=$vY-c__~Y!#*J#
z(!A#d2ln{Y@uRZg;OC8=4E**{b!TwWBOb&W8DzirC7+NJx^9#;!3wRerlTXWxbW;-
z1lhUc!>^wtXF?JN^L1E`!%lWbeU1+{MavcsHs>`l@{P0mCrA5YHf_|Hp_|b6czliQ
zUCGn~ZokT-C4pv--t~MTQ-q^2V!pV_tG_=t%zOCBi0Rh$Hs?fGzoO-5I~w1WA%X3n
z7|Ni*JS~%QtEW0Hml=B->%1?{xXZLYlJv`dIByy@V-a}n<TWghCgNz(dM5F*alPN#
z2ix{nXm-h0V`Ij9#3}q10lz$$&rnfOG3kvL%8(7*dUiDY>B-00t!ES$S;ZW>DDcMz
z^YJ@t6UiwltcQDR_=WCtvJO0cK0f@I2VU=&YgXglaI!a{nRzdfq>|^XA|*Wsb*(9%
zot@pr?*yIG+gX_K(?E;x6JPE3vOi|OK97!z%SDWqh8!;ELo_p_-mFd341QE)cporF
z<F_`hoGc87jHdU6AAI9p8m|(B!C=LvRTm;s#T>l{wIpH4rN`q{i1tVt=8($g%C|ll
z6hs9c6}e5ajb@kyS5bFJ;Dg8KY-Z`LVvZK`n{F<==#}{F9G)a%Xpp5hXsUT?uox>`
z8My5rf4uQ9PY0u|qCy#dIEXQ>^X3~aGFnVDviKDzIXzpq_+D_kS_Zk)vNBv0jYiuM
zIum7ghx9^@H`}{XV9zsU{NH9|XxiEFs>XBWS~LbVY~t9#Mq?=H*p6N0cvN5gexu4|
zJz47o>B1;r?D0t;xW9i;j{Rg%^v~6+S9ge0>A${3iP?8vmT(<wNfSpvMiWK?fjiGx
zogD7w<mHLlbzG`;{Yg&xOif!mPb2Zx7mu0uRNNu2{}CehxfT<d(5R}j-O(V9bkU7v
z@%Y13zLvzcgiDcbfn+^}-JnZB3wA8B+Y-t5AT<Y-YT=0C!Yhrz`;@as>&=cp(9Y^8
zLxj&_Pk+7+#=AsPOe{f$So^%hti}d?ou8j7B2m!D`|M<&`Relc$?nQ<{Zf#Pjg3^u
z!JVW1b$&UyOswzfz-RkM-rn8;D+T$+jX~nc=H4_%du#K3*$-LZqagghJ!g=ou6^%m
zTIFJJY3Vr?Lmu!j1i3O4Jyq{7*r(jN<m%4F(4`HXF`ee*e59X?ig2qRQmh%7wrJIQ
z9%}xmfX6w!09f~{Jwj;I+=tF*p{s5w^a7^UXrS(Aul&*Nb5!(p{U4P7j90~youj<$
z`ms14@`@D(qW2%qjoJGuuQ+HFLqcSimzQ~O+|Vu1<uk2uhpz<Ajf^vS6zS$_d?lbz
z)ZRWmc>Ry4mRO9N4czpGSlDQS;ciiK7!|j!NHR2NpV(b96NayLn}A4#9wNU|SC^~A
zvAucw_CZ*feC$6$xBTF#si}7nBeb5~PcV|eZ_~l+jn7(SgNA$!ox0P+rM>2qDpuK~
zy?KGT?Y};wpl~d<b8r|aw|;obps=mbplGZy*uP-4Zl$mY6(3J!g-;Ut^RqH)Z*9U7
zi9w+p|KZDLJp0B)JhC@?iFD`KSQG4vMZJ3Eu+Wt{8FsXWTpfv<ZVXOHOAGqyt(T|y
z>m|FpRbSTqyUqhSW4>A`cb=*z+~_Z}RM^@&UFR68UvElJ5;7YoF_XmlulE2+WVQR#
z!F5FJ>V@gcLd9T{8B$))9J<r4xOxnIdcw=gdx?cb6_}Cp9ukrjc5(<pCteti-DqRh
z0l_|xi{lXyi7$oMdr!F4z<`f1ab3zeA-W6>Fy~)ErLKDM{4b@%)I?c=X^03fFR$vQ
zz)1lC0py=Cn3Xune<vW?d`ZcV{@cvoqy$HwzoQRNoQ=gg^H(KtyrzhG4YQuj1#>7g
zyUP?GNwGTSvvap{y=h?4+Tx!yZ$`$=)|sZCKL+Ti0==DrOI^)o`b|j{jPR~}^`>4@
zp10eg%H(!zwRZAHMHWVZcyxkepp_p}_o^Xb*bGahEU8N>NmmI3Gp@9RuFXMdP43|Y
z(<fgyg)D;f^p$eZ)5aC4XYLBEMLgL4f`YSj_dpGv9nlT{qsf5~fa{Z<-{>H>EgDh;
z^zz;z<do}SF{s5RP3g8T1`}6*7E#o+Q5Yd;DT1zRf9VxkmxyBE?rDoA9DTS+fmQqa
zM=mH$b+IJdXV6quVKUW_k45j-xhc$=&*0!--9j1y6&FWnIwF2FC^7_adbz&4!nU0W
zxMHy<GgUUw`|Ca6MwKXf&ZU`Vg^Mi0w`;>dM4-79nyh3%_K7tH?S3#Svj{m_tBEM;
zCT99i{{W@QX`)(4FJGHN*tJX$nv3uGVe1+Kg{;3?x!RX!_WnYt^iD4Gui2oP(n>F*
zTH>4@^QjBwsP&@A9jCr)H?-2)KHK9{V74k16Xyf|5|Rsy%D4gp18KQrQ+!uPyugT0
zuiXB|u#u(LPy1vq5D-`DGo!e}QdGG^-<_TuRXX-^l{W5~>gwu3)U>qhhYJlz!3IKI
z%<s?G#qre`wPlL~S7!D-`^~TG^G+tsX@i8zg0<vR*Gc>6mYlhKm&kSxc@szz@=Kpx
z@shX2(Si(?_MC;JOSnN56fhEQ<9(%Y8Hh?e*GVP+i*3VF^B;Ly=@-I!J3DVV^@zKU
z-6_;Bc#wF@z-g@9x?s&$`S*j1OoJ)g^ISOk+IK}V;@;Q{CJLjhWYM>KEa6qXjmDUa
z)``hFAO5n&JvN1t9cCci9@GJf)t%pyBge-fz)LcPh9yB?-CUvt=Q7`&Gm&j{k_#Ol
zS=>Yj`(ra=3pae0l>dGXbw4{jNmU4!hv1V%7RM@VzY8ZPCMpAbEr8cc09V>959CIy
zUcBi?ocsZdLZ%V&_Nu5I^?Jj$_Sf}Xtz7fs#!}QVruYvBu16!sZ&*XKQSN02YW7vv
zY-tZ!lPg9haLX=9#8TVU_4cD=;argUL&YZB&j=vZwKX+qLE&2Hky*c;LX3`yvB9CQ
z(y~jSfnNpz3fa_%`L5_QF)=NTl(1*s^POwlpRQk;38>h^Lg#Es>|0e=$AbUR4yt@-
zf{PtYXcmQ*a%ai~CjkhB^6>m6)~~b5__=m8GhXe9Pgb*DFPr{~f214WV5~~?tJR4e
zU!;607rf`WT2k{CgV6yMH4xO7Oks5;*^T+0kD4Y)oxX53AEPtS*v-Qzu-sLM(O18J
z+vVdgy%CiPfu?U(CODatv4vml7rL3|9(e2KLf<**H6B8KyLv0!-9nzBTm6QhOq9QB
zyRMiTn9#YY7#|Ze*YSquLhLoi5(0QoQkLF#yA3_vJB$$uX`a6+a)uS;WTVm<6w;BF
zCk!+M`V0&VfIiM2ds#qPU;lD<x}-mrma*BmV)@c~SYzfynbVJMxrf_Q{<3|728BxL
z64~2%&ry*WIaTfLv14(hvtjIy=9S+_X~M$6JJ4Jw^hi!&+fuUJumpj2RRB!dL1CLJ
z62#uQb=x_oCn8e+0t%9`pnQS<k3CT*IM8Y~mv-e+gI0}ft|H5u3(oVWn(t5S&V`DU
z^xBSo@yBvtK>w`vA|dcbl-H*EP{D!=_KKJKQb@p(&D|(r=#`kNBz}2Qe3PwXioc}?
z+Yt3BNb4<^NOyGL5Nl;WYvor~v3AyvI+-Z7$LSS8RCq~Fn}J=<<)4M`2MGuIb?2s7
z2WbPSQI#WpWYCB=O}8|a+D601yds-JDpIZC>7I_wBfvpE(>>X$<OXrDH;Up`&HH@O
zsDtTW#jdzs^*n<{7tOjlgqrx&7HK)tdWHbHv2z>{(UK~Bs6h$}RrY&<AQq-A%3tXd
z=#T7ujR3~+$TMFdmn3(K$W!#S?OL))s{fCs`f>9=j9#rpc*$)LBgcR9gui%%{Hl5R
z;v)D$DW#t&Vz8u2y5&KZNB@iBDnLNIM#gKGmYhT+_@TOfcuB6`;irwypBPpDmV{;h
zMB??P&mVGS)xh%7ytGv4D0M^hK=|(aaZ7)n=Pt=L+a8x?X$7VybaNasw2)t;F<!SA
zrR5&xgU%)X$&e*t65IH2ch+hu^rLB~NOoO$t~@gSqs5MU7z&*e>ka$-6y$!0RGv|X
z<kZ$X`9l$?wd1r-@WD-392FHD*FsO1Da>yLDio8U#2@_cpE{BQXP+r$y>|)P$;^*0
zAQ@@c7t`H`%PdlBRQ9OTfe2k6VMdyZfBz=6FP7P#ZHjf(moe;q`?Q>tfA;~)oAM2X
zBL8wx!X+JsRkdD2-%R0lM$#&QGj5<}S>}iqn=#{d+}4oNubzOgQIp9??0)X^)_E}N
zUfJZN&}~xq`GDPg_TJjMsvwi9=jf9f+M77^Nw<Qh#O7=l$~0hcVk4$Nyw_>Q6YLrI
zaf1}efXu0>nO6V0CT6%k>)&YRWgG7R_J5hAm$#oo#$0s#$Uvhq=li<skAsBsbfCsY
zz7t#AVwDkmgQEWSWd^5@0;&ygfN@%cP;bcRr`=K78^2O4?v|OsK<aAn03ELSgpcFf
z)tzOfK5?EKH_9LxgQUY)xk$o~SjnweKe~VbPnmo?jRr~OpwxVy+HgZCU7^uJ$s5>}
zQj&>4M%))tnArlk$<f*Aa2gu=$*A%3ARmQLT%R)Ur^ci8wUE@H6vvmo&^-I^u0S`i
z*YhQ3?!B|Sg!I1#`+3JrI)Xq%5J`jvl~`q<leVbaN5maMUg;I=e~D-xMhZp`E^eg+
zd)yut3;=NAEE28O7ijN%i>5vvw!6nFjQB*-7+}KrM7~vqjjxh2bsu#EU;2BjAf>M>
z1HOIVR`pQ*#%*KO_<wu@f{girP-&9T3;DeSQ9nbbL?{V*b)|JT^$pV=vM{!rAOaow
zDXTf;zbc%1QgpBA`(krm=j1CgdQJY}-Y>rs$j!ju-fobrDg)Q<0G3029LeQeeDOW~
zfI#S%y{o2G(~u3CqIM32&{q|eZ&9TtFC;xyzw)_Y30JzaV^KU?+d}a@X5W4>{|rHf
zTdou?Cyj!bV^q~V3t}&l$lso%%UtUJ@M@3{Nl`{V#j4lNTG_&y;DT0%;dV9+tf5Rz
zO;br<*#)aTZR7LM#F9L-rn$9uc(WE!#2U4&roE!&20xU=w`qAKw_p4q9LUup&?9X%
zg=TMA(MDA5ad`Gf+DEHNb^gFFKK-d~xA=RLxb$AXsJLb`<VU(da<$^UjcnMBpRC(Y
zVR=FQFVL`kHd`h%j0W`k7OvV#52nt{-fGMdZ5u>CuqTTb|3XSzx2G${pSOAHUs45Y
z5wNoPUs1y;)Jieu!(qr&*y-t-HprT8x~U-~p1Xl&=kB#`)1&7{`xLe07D~Szt2yO=
zBwQR}ocK(y1E1+%_HH#K0bTH=F$CIqy~*WH`44`BXtgn^P~l{+&1n6C%h`EmVGC<A
zObq0kL@{$eNcct*FCz`hi0OON+V>?<I$O>D7WWgzfeCahD6mz2be9%Cnkw;jbsbtW
zV2XeC&X1b+h>S1G-3=c^a5HYkRt3jz4>+c?j>)G{QCch@rLR0Ahbx5;#|nJVvHs#y
zhec`b6zHB67Dh~GZ;kA|aJpswp3hdU+$;KegC&an)L`;TroqHPkk7r|lS1aHw4!Ch
zYXW`BZH}jv;+f5*ldT>$X|R>fB<*CqUpK)x{~ou(w{Je9eWliP7N=S);#1xcKxBiT
z@aQmd)!e-)&0Sh87!2$!@(J*+OZV(NsW4{)KG!A*Yxo?n7Upp3&*Hy___hmAmx61*
z$OHAwd&2MFJhOs@D!{H(WGGcWzF-5Oh^}yc&w}?^F>j$^jMcb@Eij;zSK30Ka!yxq
zl_oG5=Q97_MYz#Gt>3#rJ{q>{ST*V6?W3vN7WJ6+s4w|5_H-G-s*|LZZu|&-wK=eu
z&N`WW-8MY1V34489i-LfSijz4z%}`3m8uT`^M>M67bHkY75^JBfz+Cp=!C_C09Q*F
zqoUtPb=^pysO;9Bk(o&&U#7xYo*+DZ)DO!c@G{R_DM$Zo`fZPyr8^;S%waN2`C>l)
z4HGcf?+<&NB!eY5&wRQ8`V5joKv#o$M<TuRa|s>Ym73gF9u0RgYA5ruWJ@C%1^V%4
z8rEym^9_>slBz*j-ytEqO(@oZ`|N1Sok*N{nGGL83SK6yPHx7KX*Jirx;%^(*0IRX
zGs`BO*(vyJxeD+1YUBhZQcph_EP4Hbw60Z&J(xL`>J?++Z>j##6fCW=Gb9M5INh9q
zhcpo(;SJStp&|x@C67rg3G#N2W1he#U_MoGecv({EUgBX=8z6dqMA!^GM9-N^#Dsh
z&Xq>Kxj+mVEaA!Tsd%ha^B_%S33_FcWC;Z`u>h|CN9<h?YmXOvv)FB+XxI;2N^j_s
z*QoP7h22r9hXg~F9oHVWMxfBpUz^C&JFn-S@1oQoxtucHAGb&Pvc0q{uobas?k~tl
z;4;X;N*OYn?ybao3A9w5_{s)JY(Dsq#zS?<gPb`cgnI}ICEEgyHcn3ZQB;G>_kF$s
zP6*|7iHefANeD;v$c?1Wla08P^R}7~f`WnqCHbN9Tk2c#4x!Ca!DTM^KH<1bVbze1
zbjBbBt6G=?%N-V0J9Mf6@;YLT7U7mrB_Vw8-gs<xG^(bW<KEYt7prw2eC2MKPfRa+
zx3zb2E{%+<kBl#jjE}Ua%?cvcoG=DApz^!nS~bwX#TuAW;j-aaKUJ*KEeJwLN7%TX
z$de!4e5An!#*Mes!NFrQWz*hwtWw*){IMU)nQ3@L^r&x^|HL8CBJ9JcIn-L%YyZh<
z1MdsOJ@L7wqVaq1R|jv%;k;=33|aiS%^!D*%o0c4mrEHKUPuG{jx1;h!Grjd_B-Ub
z*uI<VX8?lS{{r&Oo!!wZW(R%~jHli{lJ_Km+hY{<grU|;MMDo8DqYxXaN>&~Aqww+
z^s#nW?;n)C=OL7tHZ=F0y&fFGo_|e}X}4IL=1wrf*MGE)F3ia@yAlnuv$Z1@<6(2s
zr0H;6%3rbqAVv8jBUUZNIOQGg5BHwujL3v&PGy?Aff8vOuY>Ny=KoW&*eS6aV1ojq
z`e5{uKU5|PRHIK<4Wes?kwp&%Ik0Nfl_R3oU+?X4$aQA~2Mw0zfys73w^(Sb!;c*J
zA4zij{*k>__d!c8AI}&CK<t5jYbhWUX`NM>>|VVhL&!%!mFog0Z6;hPnM&JOgFpkV
znl`Pc!b~>a+gqfH-mbHFCsbd@MY&)(sdh>0$(u{tI6x6<=BdkGEve+^RRK78h5`Y8
z-Hwp=5EMWep5Ut4Bq{N!w-~*w*htiW5de47*u^M+01h+{NZP#4OlYtD84YQWDFo4~
zUI7{sCUQV<dHy$otM+<;8qV7xq^4=x6CZq4W>&Y9IMlK1&5HucJTvZmdi%Y_m-q5+
ztJ??S$uhtN(%|^ub!TUf^`BxT7o(E4p^0JSBaM8Jk=N}0o2dD$uW2E>Zvl9@uBh`D
zdVlHp*&n-~xaXSK(sPZbMaxM6D!>AEVUvOH1n|0brH^chD>FDCH4*^Kvkp-9W$(yD
zC+!w)CNqLU_^49}bc*+(k|G!?M}8mRrtoQB3UMH~6W{|AuZ3CD3vNFy!mb&1FuMXM
z`_s8onU}hF42jx5{V(d24t5{;$(srb%X<NHNQB+^PU0ln<suiE3vQCyaNF(CzFZTb
z8)t<H0I8|B9Xk0~0wzt<?a&1@yll3H#t6y3pCsOtCj-NdnW&rFpwrx=M?2*n)QoVI
zkKCve!9a8)8~|&{fu<ZUt4MHm%$3#n<}1sIPcf|@*wF(dVMCFH-CU>+j)T$QQX+SD
zke066ytlh_#_$c1jk0$(bSZrw4+?F*AARbME+7k`m9;79z7<qo<@d5mqQ{h-g_Cn_
zZ9%EE6?c*<d-Q|y5N?QRFLho=N;BPPxB%K0K67g>j}rJ&`dg(|vxQU++;{#bHKz8{
zY*!z)@f~iwK2WKfxh!59Uw%TTRx#4_s~*ft{y$h7X#<=dDoZC3Gi1-vFvR3p>HZTq
z)Y=)h!g+fJiBcc+&v6t4I?deLr=BuBqRSeMfpQ&&wKG}(9$j9lRa<F^0F~%Yj!sO>
z`Idi~nRcG<$qHa*pb|w}eoBr-VF>@h!$7TN3g1t3!SaOx{NJoJY98x~mx<pRoxlz@
zd1;ZB$rE5<R5-MmzklkuORoa|`F5raP>_RUa(J7LpMAz${PyTTNub+%y+I)w2&i)0
zpjm@S$VWdChG`zT-8O)NFgex%@e{W3ho4<d`t|oH+aQo@=g}ai<R#Yg<wD-2;2u5~
zwzZ0S2>|a33alLUpA_lePXAy958(M0D_A@}!vky39ArP;P!a{=ti_og-bO`gR(mbK
zoz9bZFE_xRBq>F}BDe!QcKr<VI2M+zNgTHn1Qi7!)|&(2HI5tOEa_KAN@#2kt3kpR
zwA>{?$zMw`@9`!6b<g+NlB)f6O%Gw-S~bjOE1$l+J;19;8*;%#VsDjnNd^(bL`_yc
z&P{6fuTb1u_SxyJ87k6G?QmN?HhdrXG>qXWlo|9Ho-YE+>a993vQ2o*SylA@S!DuX
zcC$FH?qlN&lq0|c8=>D05AEKy`<qn%f~=o8@;9$eX;=&22iI|LF&8Dcp>i_Ao{l(y
z_Xkd#Q)WZIQrYhv)=4q;0%-8Age%CgW@Dt2K`ulqDY53ANT_Jl@P2Zlhx6_iuR01m
zbuS^L>PVFT?W2+B7>r%;1%7xI3v5oovlFXR*LC77!p)z%*_Y)<>r|F!#uNfHTU1?Q
z<fA%#8JenT@y7+b+c)b+2J*=je3Xmsi^s^fpHwnAKW-HDh(kXC{x$`TQ(Z^;2NFkJ
zC?h;Jl-OQheb~5?c+NZnwFr6`fdIXoR>s5nv*{iqa9`tK%x_}~$i#U`fMPT}J03Om
z8;4un$A!UqVKe!O=Uzm=izA2Uq%a<S?Q#XjvYHc1Y?^fif;gvX^Jf@PW@6BNKL66y
zXff%)E_TjGySENKpxO6T3)S8*9=^*h;Qb?y8=A1KEw<L8y2vcR4*2Tj3p*s*Rpa63
zt%@<EU7sHMiB;<CC<4s^tvJqA0_Q4*)7&w50wp8R@4!<7HI2((aS5$#?38QMv+(Kq
z{Yy;F-=V9eUphbOj_yArr;#H{@2LKh^H`wuj+_4NM4sI_g9q`Fs%uwTBoJ%#h&5>|
zVb6~qolG<wtMof@%Z`BvJ6aM;tA)PV+Fa1;cW9Y@(40?Z6lA2m85VFv&&K&^r@c`5
zQvya^3&E=JiN^@e1&BM8u%MO2;%`&hXm#C|NI|4<`uW+uPU4+q(N)!6E}_v-WeuOp
zQhGH!48W$5QXb@TbWTr}_7_@CKU_3HI6}lAb8~aT)FC=Mb@RQ1x^$G<i#c$-p`$2c
ztH};nm=$mN7@%1wHIu~PNcWO%aU7be2?C91HbY1r7K<`63NV++3%HcP$!~8y5dy~?
z6q&Aq|JRFRbQR&SSm&>VgXX>R`V4;$h}}UshvgHv{YPx4if^J;?Z|MashNvqOavuX
z5>T~~ajE>(n;O1}aVxjjhYcjIGz$MOSH;x1ikM$WGQP+E{rwlEf_5MTlzz`WaE=s)
zLRlE~0ZiBZTSgK*(9we46TJJ$Pdp@W=4P!Y6Rfsj;5t|w*X2{QyXzab{V$aIU9}x~
zS3W6h+ZD9$P198RF=b881O3zJQ}ya2W8o~zKW^y2VG@xwaYUPd!lT#fP4?3=_v8eZ
za*Nm=kbY$BvQ}n#!L9N?OIYZ!f_azdIW#Qw1=q~tF+4fAo+Z>Ajo05lHFZQs(<y4L
zU0jP2$EDo1?cz~RqUksl#T`$v`i!wwX7~B-qG5uQ(qCFx^=cxIrhi#D6wN%)cn4lO
zXd=A}09&BPT(&ErOAA+2&63*?zhty@sNG_kb)k=Pj9%%o&)MG=wSB^VL`7KXzVB=u
zlluh4UFzAUV6FpO0lZS}KHMzpb1=ql%Lo220!%cDFv=LWe1Ck>A~h?wPk7VP0Fm|6
ziZI?Mq_rg3tgt~sxIWGQ7;LAg<uZ<0W$y|)r*OWuiF)>0>Gqd%P0bNS5yn;zvsX{a
zHIbI@jVECYL+AWz!$*8AOIO|G?u&yeoChj2i_c(Oi#cM=1hJ-V#a=2>AZ&$(wOq4$
zdQ?cdQa36gu1#I&+8mWQzBUW2T>=MT=X-W2SL#vi9m5}f7FYX<)06Yj^Qri%8Dj*4
zF(P3ySqCCe+f*red6N3L%=a(XCtI{LQ_C6yWwjXsfAmzj%ot%bfB4V0)(hMZ`3QYg
zEf)}LPY`P`D~dZq6G6{z^@)6_70D3}Y8j<^#d0BjE@?B@5)xHhW%j6ciX0yYizcoc
z&_!+%lN-ZDe%Zh0KkCO2+r-lZwXf|Z^M{9j6(!mVFppMTxkMCr&pI1r<4&|in;_68
zF6d>BthXA$<{9#ND!N-)ozW*0eI2I35ObXo#cwuAoA|Kpm5aii-&iY|oXoqlbj%P6
zLd)*o&5d?;IO3bWj5BkM1jw*(XYN;y5G8SU&l|GZ>Q4185d12z5&ln;Jx{ttex=c4
zE0=D?)Rm3qO9y^#M#FT@)hUM16m9-Jy;VA7Q@TCjrEiE!VQXn`;Tah(hD!4PN;8yN
z4h#RyeC^VV3w(Tj?)PaSEiKiJD?XKaM|i!kpB`ikNW|JOp*Ef0QZ4i8D#0Qr*v{Zj
zvf<GO6<bYSPv*XD=<3+|X&?wv>QQ`X8F}Yms3SU%`=B8uUG~t%!*Y}m_HB{Us@zxS
z+CP=_nDsK7fUSLu<&EwWsnLbj<RN)SbU~zG*L+FU#n@>Jso9dMM`+k!9E`5GDs0Vc
z?(6(qvTf(XQ8GZ99x8iyx`fwpisJ58jBt7CC~yipq~0~(K3)`W-<{4jdSKsa54$rE
zO8J1@y!VU3KNc;Vmy048iA6++2HR%vs>`)u({#hhI9$pKE+uVIWIo8&^Pw01P~%M?
ze&$_yV?=mG4b0)@2Jf9o(_n0*J$Y5L?B64n`FnmXzPCdS1`=ME)h;fbQRWyUW^P)i
z;uaUT;$TLzht0uly}p54wYMq5*5xO1C0_@IVXs3pLK6S9o!-jXfa&Yfhmz64a6`U2
z@$_Dni=5%j7tNExYoFbule)-3-gFZ=F<9I4c#PH@WG$bf-aeta_E{MF2IJ%JI=(&U
zi6Y)`JuCI>dn4F0w>I(J+4azU9V@x=o=`<`0n_B8{Z+3_<qA1PU$<}KMzy$;^~Mjr
z6yD)i_V>?;%muZQp}e_eCMMf;Cm?@5p4M8pmkEBslu{8D!ZXO`r;Z$iN+pwzD6>j!
zxRas2SFO^5!%Oq~emaMhmavsZl){&=5$hu6Iyb2zIQ;#lUaZO`i##nMVs<C-I5O_b
z*!4`}yes<#mF3V!&_aS2b{?mK>()2AfEy^ak`6?8HUA%vZD&+a>ot*)nE_|;Y7|6W
MSzD>}fz`AB0TcK282|tP

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/packages/gluon/activations/images/selu.png b/docs/python_docs/python/tutorials/packages/gluon/activations/images/selu.png
new file mode 100644
index 0000000000000000000000000000000000000000..1717501b8d2697953e3902169277e60250b04d56
GIT binary patch
literal 11342
zcma)iby!qU_bwocii&`A3DOb{h%_h;UD6#gl0(PPt<ut6Dj?E1bV&>;NDWALcc<Jv
z-}k%E{r!1)9vEhyv-jEi-D|z;U2C6^cS_O^?^E8#!oqqeD+5)<!oo&?|F7=e0e@jQ
zk#^t%*GWQF{Vw?Ny89s*e7|QeqvM2yg-?X}#U7gWxdM%X&XU^BYIbJMuJ0X9v25Nu
z+gsZ?TU(kuMVLA|S=!k?<>BJt;^la1;p}WL#LfNxZs4+WH0OTy1bQ0_>nWBj^o_b(
z+V-@&oBHT==l*H8664nUSy%dd*fCh40doPNRJw#J3EI)RgH__3x?#EnMcR6L`eBr$
zWYAwlJ6}~385sRbOUgg<;og<M`$4c-+^pp>kqi%iGE?T*VIME=X@=mbu&Zs8lhaC?
zQv{NfknkS$o0&(iq0qFPb}j}Y<G682C{8$0eXxw5iU}Fvy%%y@3=G&(AqG&!7S^(`
zU|e!01YtB5@j^tzE31*NDw|0(RVpdrzTW!!`eh+<aFu;EzI~?3MsH|nsG-b~kg(Y_
z{I70?qg5z))c+pLEhnP!<@x#f2Q#yn#KhI@ciGeo3=9^3%bC7?`!@4P5-PWs+@6t~
zEUT{m#AdRpgM4w@eu<+H`N7;g7PQtQwi{@^1jo(sDni`d-CtUdzmuINAct9umwx+K
zPqeQh7CVm3<RbokK(5{OTphC%^-iTH2jkKOUERM+4s6)Uw1$!(uq0k{rT9^C?+a(d
z&XoJcIk)e*yk?1^L0;XzgT&^vldAxHu07UQgn3B;tqjb4x~<`#^t8*bU&q@ihfGVy
zD{PfCU9eBrxfG}yFcDwg{5utoG$D>Ys6Zcd5@_=H{x+m-=K3sO_p<oFhP|wwM${v&
zprk2<R@CD$F>%}0-wJFQWo7dgKkTEO>D1@NH30_oi1hsl0BgD3tQZymkzBxjj>5Fc
z`fsZwnE|xKuyJj)V9c`ncs)0h`PHigFw)*!AOVkgKQ-oNK}W{@{e5a-M7aMgyqJUp
zlstE1`>yZJrCLe4n70ryvjW!K#b!ya>+X#6?o4`~S}qQzyW`e4WMOfU0(8eh!pH9D
z<4xTn5P7Yj@Sv=$EO+KG;5I&($5Z}kXJRlx)1|IxZMf)k2Ls<w#Uc6-v4LQIu+g>c
zJ*nRN%KJw>%)$3!fL(J}WXU3ZI%RBr5MGy9`|of-JlFkj#p|G(1#4+(DJnB_;Nv~w
zv%NNgkS|}%PPYDvT<x`GXJ@OsT}Dvxo6R)1L!r>p_icBi!pYz0>QZKUpHR?w?O}(J
zax(Dn#9UvV4u5@fE5rAndxrZ81rZUEQo-tvRNL=H%|0U6N5fdTCO9&(vfthW-oe1p
z-VU{G*q6%i*kHqS;o;%w4_<FPoq6``S)gfeLK#4th?uzcY#|I=yUv-dK(pvOn5@3r
z{3kFgf}`Ep=GI-kY-Z2v&LV|#k7sSfy8HFLLRymuZ=N#Gx7OAir?bA|#4Ji5*8Y6c
zcOH~1)US<+f3D^D*EA9Powl}2h1qX*4PPH0vC}Ch0tyNWTs*uApKA}Sm4VEG@iJIQ
zcsTA?Fjey+79pXOlkLgF#Ypj?JT(?{kzVH~GQ+zqacpXCH`k}w<4xYeRIjYRzHj?%
ziax$Ro#i!YzZ(!3XgQR9@7A3MPx<)bg<LkG;^Lsa37pu$k&))$6F4!+-g%FK$icka
z9ao#aSpIf(xN^B83j(Ha|HYGWcu|A{Hyo?)&IFF0UuqGO_J+s4igz=AsMg7RXn6Si
zXjs{O?ORaI)j@CV{Aa4QnMPrJGA@x#_*)4HR(5tA)(X4RiUBeCSY}1-Dr*W!NlCDG
zSj8(*p+0kMK?JMmt~1}@W5rYUZ4b#|+NH+WhpPkT^R1ttQc^tjb1hCMyyAL?a&B&C
zd7Zz0{d)4`i8`xPbc*%<%JtMG;?922#{N}N<K273Gn%`h);2;fvfIn?{7VXv>3$_s
z1J^_S>B0cOrK(BW#ebK(=G`$&&g(;;3JVL>LPW*IGc^h{|LapR85xgSrfQw!i^N%2
zg3NxW5I%X*zFE?&^!`0PR-%!QAR8Oo(#i@>sqc*s*7xt<1-#De^IRTMy$UA~JM+`2
zFoSrV?WrA)l^A7qb;+QM4SFNR|GDLRSy-^1pPM7f>VWwqi+aBL^y$+FQ`7zJ>e-mM
zIQ8OdCWain>dP0+Y0+}M7f2b3beW>kIjhKTQz6s3mz<@|?F*ZJ6g!=~ew#F*1rtTu
zWdYgQjKCk=fv=n|(%r~tYm+;z^u1qb4>?*J3_LqKQ}v7!xme4Za-H?YSQ{>0;y?Ns
z@6+kNBuHSB_hmUSt$yKM5v<P-C|Z$YhVl{;IDi*KKnU<F5MDmpUsS$!)78JeI9^8=
zQR!9NipB>AJ)n?y^9I|&!9gMAWe6A~gI(NqN?>7OLFwAfW9T9O)N6fxYEh3P<3C^D
zU_9)l%|zvauuAslN%uYSEHzSMLdw3rCB}_RvXyuN1xXKKa@Yh89T_}S(4weF%laA{
zZgb6JDEx>V?@yh}=8Rr-hM3V+3LjkH=IZb{WwTqf7;khYO`qs5MQ-j7%1X4m;k%vZ
zzc&P$9>Nsrkgw+Tm-CC}J9ULebyJOt6obErt;VNAp;uCdKJ)-tRMnRLk1ujRW3mki
zGnh|AQAuW4JC}_aL5vIw;`&)~i7`#vSObqDbB%(^J-=&^DHC5xw^*Dm4z>i*qFERg
z_zRH=7xpBFn!EFZ-==GXiXErFrEBiA+lD9K=D@8Zhw(%}T(Oj~6DeS?uo7!C!-7Zd
zsx?h?6?RP4cpBrO^w)&Pm|0!L5%BF`9?gdcRuEg8A@KcI8R^X#RG!Es|L1jX!fC@6
z#pHz1_fx7p2nd>+1N;rXyKkVhGwa3ti9DG!jw>5i%T0SpO-xL%#@b=0GoE%io9u#@
zS;29f-kUx<?(lnMoErxVU4|bu4}T6F>@@~k)x<tiea=8n@6V?8L){}4xcYL1!|wZ-
z+}yziLrzr`BN4?)>M)3R^_bynYip;x4u7|IbO2knzc^Zd3#fH2Md~zKn)aJ=rWl8Q
zO;jhHZyMlBFbQ&g+o?Q313&@mgIRdMTowTr322;R;(?I!THa}NTAG5134^HT$x<7E
z0Zsf5<;;G-+2lMQ?xqX52=N`;Sq@)>b|oWLmmnL+j*p*kA-sYipOI|?*9_eIze`dF
zXg^LrPb;dh<j~ha<fi9mRGoH^8D;xiUvL;Tr2z&dqNDS*njXxO4hadF-|wWu`wGle
z<e=*r)|AiXw%65ur@qr~zHl1hgw+hsFW9&4?6uq?h^tFy$@Gc=gc=<a)A}nAPgYKD
zVMw0)_ESLjIr51_w6sczTn0%m-nZ)H9+MCzroqm`m2~>PHQ>z9jwNb7WN4%r(pe9m
z<^&=3{BV`mVFB87xg)%RF6srgtE4zdMMpP4tSs`z%*-rj;|8!!?Q()1R&YoNJr~!`
zNZs6=F$gy(wSngI6)FtMAVL`VUh{#t!T_iz5RZVzxCN^veoy{zInn4~L`|6xv(C?*
zK<Z$>aN9EmPm+g1+w*G8pi5f^2l%y4W|XNXH19XI8$&Er#wO|0W9>4^GMD@6^3}n%
zny1FHFfc5PEWHO}XbKQPc23S<zQ%J}&n?Ko!9kB`V+k^$)@gMpr+0n2gp^Y^JI_s$
z{by;{L25Oxc7wb4K*U=2Z5k?f;cUl2ih4Z_<;lrrU*8$`RT?ZHhkl2W<nZ&QUzu2p
z{3MkALM=FY_`|T#W28tgaku$~){lSIt72r#$f1Ko1jM)8CYb~-1CRbwLv&>m179*S
zp;Jt^W3>Qr*!9O&e>khQ`Yk&@6QX(}sfz#k^RSv40cPb41wbw?n;GZa28|xaHHZDe
zArTSFK&r4nAp0YAmcu{xH;rymfL*8n3{|UnEr%En4i7`Z!k#fRf0m79PCsl+<SQzX
zQ9Nnor*a2jdW!F>X!o_Zxw@shy2XTHE+Zr3c&w;|()}YgV|;x4?tuY-SU{%FMF_AD
zOe}6~ZQwPkAjY}ojZN81AKAY0*>U`QZ2gP3dF{=+D7y!**>@`&oBpC`{T!W~YL{Z<
zy9Ne2b$fxrS{jozV0`C|G$SBrYz_z$mgYVGA&@D%LGrELP!Yrt)^nrK;Ijm#$wzye
z)d_zZ-DwOD232yF=haj^vvg$Gr8DDkQzASgqeMjGnOzuUC}O+Z=l^-mZ1RSPf;nz&
zSu;HlH!fll7ETQgzH1l6un;U7bU>b7o)q=84NxxS`5uN0l1s7^IQ3$HI@Z)osD2ym
z7#tk@6^NUeM{nC<K?i^Y305-ZwjHi5hWAA5f|ekevRgoGSHf3~3__vp%{1JsAEx?V
zzS<FOtt<;^uR6NM%#jyp|7;-=5*;6YPYQ9{i2?DTPA8RI3ornArX`GwFpmqZOMBzS
zmsvR8(WaF?a4f3lCd??!Ru(1`ad_a%54rZ$J2SmOOu0LF+665NfCM5>WhL93=ll?<
zKba-yTf_cBTIo6F_Wj>=jS6Y?Y5!=b&PUX-mDEhgqABTNmv44_VE0z}!xok&J&8$)
zYSy{!XOn}2Cx38^{Ty2RY5yZa_InjwBs(X8G*LZdr5cr_fl7);CF!8ZAId%<%q%^%
zr_E9&h51o*%R;5kPcdi-lu^NSu}*RBp(n_nlGk($dq_e>Tm-l?A#8bD;vNBUkdR9F
z(oL3JZ(H7vq{m+qAk4VIxCkNjl3<w}Y5>(0)q3HqQxci<$#>}jkC{h#6K-kaMMz;k
zb3dCFuBV;4mpQq|p~32!D$&dTToVu5P}UhuY+w8hC_RO<t(Ku`3f^-<UuphO3k;Au
z>9H-NC}2b35X+P`d1!tpGJ!=*r_DEEd+FEndY(e9->HTDAwQDj{ePn;IN&y7d$jg%
zx+TyPdFTmTxQ&Ff^ITu|Q--GH_}y302W@63!|{ZA9D88<0@35u*xnJ4XcYOa2Y1i{
z@4)mwH8j7BZT6~5C|FE^tZ&%XsH*;6gi>8H7(Xh-)t)bQ%Ky*A+p9M#+~%g2kKnUW
zzEjjS7$~9*FVGXx{?h3`<)PW%6U=EbFgRNgbu>#KGm9{ePtv=`OJ#%x8%>t)vcht6
zRK<D1DPY8T#Dp8$Q4LDKmaa6>^NZmbRY?r+WOr$zh)s(>!fW^C7#4EBP;)<Z_h9!V
zpjj;vzWhU9S5^J?fC2=x1y`jfPxcG=hjbb@AD4u0C=Q^H8);&cRn)?R&*Hx`^zN>5
zEY?4~JlibQzT;xEupQ;~1*EWN@!gpv%YotPBKDGGwn`Su+xA{DnDz**=E?Tv=IQn|
z9~(^Y23A=C65n=iucWd-4DK3bitbA(JA4QLASUY_xb$fg+wpa!@SfnCqvP)+)(@19
zPYA+{RT(erShIQjQV2R`w(%2}K>~gPtm_>}I5Ux)!ikV!)jTo^)KQu2NKp=Or*A%B
zIk6`+g8e_^B%eGX*+)3irJzSammvx;T3S{oehtiny`Erse_{<P^y<q2LdHiDz~&|A
z^PbSPx%MN8u%nv~l-r|v@aIsdDh7cEa8BY?uGMC+TUOc}iHPB!Lp^^mqlM=-Yj=9e
zBlbY5;v}yM2kWfHtdm9I@;xs%=UGU!w=VDdchaDJ4AZ_TX4n+lDQ@_Cc{*k9%mqTJ
zu|W^KbwB^+x-xS1x1p3o>V*Qupjwt3%~wz@Fs$%crQb<OR+^NGZV+Nu0nOhr&1K%c
zT(BRUKM>GhxVmMwRJ@SnG>v5T#5VA%!C_LThOfbbi<cO6Vi)w}oimDyVU+#!<ECzo
z@FVI)<lsGLtM4i~N=}TxK{qI1E95XVB@9gtyAV_<3C2yhYGv@bULnGmUQciaW2Md9
zb`(yGnj+)|N30q>^CA4+Ee4u=Ehx@MWP*4@b$}h(bbsj~M1Qa`ulntMZCXsuLo;V$
z{J;-+f@|Lj+@7)Y>bm#1AvL4hJ5U%>u1sYYMEobsBGi)L1NLw@L_PviLJ8}4`f5mc
z?=df#?fkjjD;|bClIGgV*y^@>Zc2<NkH3=b#UQI#?4P)mexQf-3BVLmAk3W4pZEIz
z4h<GRbMD}xy|y4p%$WTs4j@6=GnAR*B%>!JcUGP3nJ$L4;kmkS;zH!T!NI{t)|ybS
z>B!Xb^WT#cM^*-zt5QddjM_B&`GK9De|77r#%;sb7YtvLYj0aNGAzuZcf{J$d50~g
zU&M?_vAlb(kR?|Trq*b}jAd8n*=~)4!Z6m^HyXT1h>$5H`u(py8Kv)rACjuy=BrT4
zZlNb0JpC0?hoOG~j{ubtPKHw`{qC1tY5WNGB1DSnYbdqT7L?w#Psa4pAJ(U!^c_Ad
z1jtTmh?@e$q5oDzdWN>~&d2uLE~@O-wxg^#Fh-MvOpv83{_rnFRK&-|wK&{n1zdTF
zxiT)kYG;gI*q(BdmA2g-Lr>0+xM93JnxD*O*~rLdZslXb)$DS5Bh43#nbDq^jM_4L
zm&W`upgK&XGU3Y~kp@mFlap=xcc8H%VJJ&OG@}`rC>4M4*qew!_5J24^~#s^yvm<O
zVE_>;^}I+>%8I`Kcb!&)667f1WwI{?l(Xc#BY^+L=_%QYf#k)BvBYm37Hcv3dTeB0
zlG2@>VL2wzsEgbDM>(Hx;(B+8GrU+Or%4WYVt-y~m*Z|>PPUC`i_<d?$2UPzGmm8Z
zB@`hHj+!je@jkU41d)%nQGaSyFzs(JT{db<_z%$EZLX{^sls^w<h+a^W(~<Rh7A$E
zeR>0>hMZ^DBnJK0IQ#bi7OMH~1*dyKr&=$*I=zgw3m^WeDvt3jbD7z?OwSoz3G1-B
z27z+2Z@@*hjx0mEEJ4)nqv0L0(B#KNt~9dZey4w!vYpc3^w`d|ICY7DfTZN9ARxT~
zbE*e`rDH`oab!Y<Nn^psI*izI`dyaXbv(Eb1r}bDY<U&u@-fkFQ&AzT4y;4uD~dOo
zvKT1FdRHFpN_TNJsiSOPmI(G^S0NZ#-mgOB)w*~AuykAIhubKm=*#?<j|CFOBOt#}
zNja#b$n<<QXg)d&-^XO7wP?!4!E<M=L?y?+wVU_k(vR;ENWnDL(kr*Gw!>Ve{#4v1
z4st$52rN9s#Pc6a_I!K!#K$2rqZc0Aw6q>}x5-?r+Lx#n@7^E!IrMsAgyK@%fA*H-
zqfs!*hxMvkQ-C1q_zK$OtB;vEpyJ5el#${pu)cT3CCFFT)c-fq>gecX*lI%A=W`36
zF)AzP$kG5cF+r(88I^XH-}JZ!O<r-^jf(t3xO>(Idib_F+$I3|ie5-)0ywpd<d$Gu
z=g%=xV(Je`$!SpMIH~wM?2*Lv$P;?FjXc~&Z;o^h4&Sz+m1bu6A{duAkYt%h-6i^~
z5+Yk^>6TIbHikVL#TSj`+VC~1-t0%k`+@g{O-1}=oP7B&{|HXBm=zgdaoCqD5#}{u
z0^E!as#Wo?h;RcQnX4wRh2mFUL^PIZ(ei$z)?xR7!PR_zF@P-x3~L4Kgy`$P>uu&A
zH+J~0M|Pd*WXMy5fNBJwc$_~|M+zAi)|_KU7X3i1mGn-GVyd#(X21TcyzqrzEhQUS
zs9P##gJE7L*ob#c+i`u9A7;hOZl3-ZFolNKAqI@Et!G^0me%uL7Sf=AT7*h|^bls;
zCt?EA`X9CR-aon9+k%>8emkl?3_Sa4YQDh>r6g}UJ5Q`Y6)RIOMX8}X3}oV28S!n&
zaNlR?F7*V5LpF_TgZTg-BD$}6)6gsR|Ei~bpq~Ey3!{CoH3T$9NF*k{WnT2=!p|)J
zDltD=?}=#S%UAdL(ayCHWO$AsF7atjhLBa7USUPB$N%Rc$pi^IbKS~g_AdIov=kGH
zS#rw{z{-7=CXaQdZgzM8ceRMy?tD|pQDp}#C0@qB8)VcNt<;G^CJV8@$&$-az}#*O
zKT$y`87VY&y)a{BQIQ72aDc^mO*K91q=+w1YGvr{6OOS^#a!4Li8N>rq7gN08Mn;C
z*o&U)NzPkm8@nJzAAn)IbUL+0oyQpckC~ZN!=g=303b0al<&WhluQV5N<5tY?0w~;
z%@H_Y2P92<Bp{5luiZw4%hx%=ZgZhrQ)z#CCg+1Q0lV!(O1ztseQ#Wevo4VHJN`7F
zIDUqS-E;?o<>^=Fi=+A!!*S9j>wv^m*}yu)7JR8r_kDLiM2!?<gR%&VHdtVch|(4+
zNfVW17Y>1kLkhwnr-Fn@GK%Gn(vKDmm2<tbCK`Cy{e#~DOnaQZ*Tb;)e9;q3s3g{>
z)8xDZy7d$Ob&@zUC)c&%$q91l!H+QrRVPTrhnkCs!K%?a<!CMkaj^%5*t%}IAXSUn
zrqxI>(r(89rmK}D*WvV~3IzQL98rJCoI)z|a9LhdwyCM9V66^ev-6P~cK5K_Ur=mf
zgAuKb3-nCV%lg@KfDTr-ocw3C;RNyec17t{MT(_pW~j8ZFcS2N1H+fh>b8c`2#WZ^
zfoie@a)HrLfG5Wf!u&)_)puC;GK1J96qg;&NziZbE5Jnu19_qgU|fd;;zpAPdz7Up
z-AjUjw?z9fQ`b?Df*$_EYHl>dRhGHAv6A;$jIP%CN%o4vceWLYIfQW(gt^VTzQ^A~
zGJi~-tQBu@{!c&qWcv}_tHXtW%o?OJe1~SLtf%T3Uuvf!1U<2f9g+R`rpl9v8o_eZ
z@b+D9?PBWLhjXrfNBdS>yXcSNMr2uF&rDw)sJH7Tor0V*k`CcTjPKjCQeDBjHeDwM
zh<N0uYMv_C)lIR$%%%X_vHA=;@hr7OY4ny*J7DrkOY-i5EZ`sc8ASCX*5~ef9d92`
zlU&@Okz4JAo%sVIQN3$%9V3WH_98Nh742yT(?OEBY_rRMIs&D)rM&M(?=&qv;_INs
zC<L~!?q2Q>{Uuyq-5Yhkj68Sb5`rbBz)$a=?N9_aQcMthd~tdVrIFbJ$1O1;&*EwD
zzU)sVL527S!6e1xwp4jZR@(5I19TtG^>i1T&hIRFPF4KC9Is%?TfJ3u|B-c7qu_2s
ztI+6_^-UIi^R2eov)X&i0#78Y+>aLXkO@JvM%`NQ(}VWo|0uh+*kN&*{o^?+IyX#L
zd&+B~r2Wk+M=tY7QLE7N&W7VYrOE!rKw$^H*;ll8*i{8FQ(kV-bi3iQAiDMTpU29@
z9;-mg?I46k&4%FmWb*Z~$4<Cizuo0Xu{w?Ft&x0!ZyLg)6#zYI_n^6=d1L%}GTx_^
zafa+j%9e>pgX;Tr_iEU1v^SeV;>?!x*PJcmZ6!hstUd=2@9mIx&0tmk{v>7EH|+Sk
z`6(|_Lc?ytHO^Ap`Uik;YOtB+{V%KexC)CYWDozhb}s*tQmhr`dZ|Ri%d`SGOeY0Z
zW4m05IHmX5-<O~q3wz&xhJROB`Bqf>rLz^if6>oBqd%Hf#9w^iIig(F9u<B>3saC|
z{~x8-6jOJ0H@!@Lu1EZilkd6}ck_|*AJ+z$1|6uhf=#CuRcWaQYP{Sh8|D(`1_Tby
z@St^)OISgpSJwyWSPxKM1>z5;hv4GfmBEO=FQc3bxJ-z9BEktZKCGhUc(xh$ri)z)
ztw8k5;rNU2BeUI>oIQC+Q=r$*W(7-ZdCz%u#?yePEwJ(KWBJ1D0_Ths)9<+A^K{!v
z+0iGN>?^C2Ov~m)+<Wj>{Fm=eAm{9x)#vjUWWE{fE%Y}RR<|lh?H0!posExf+t3v0
zwadzFEzBxWfBMAT;_4eTXHR|sHvA5C)7F$;Sk?Z(In|luZo-tWJDKz*sY41;{aJ3n
zV$?n}N74gn#M|mkeeoEyLzDYbuSWg(BHnZI5hTQyhO_`4A36Z)4L6QkSlNn!k~avZ
zBTetQ6^cJ~cJbC+HoZRJ^iNK|*8|Xqvc$`L^(#Lxa?^K)@e9!peF39%68}^zd_xLo
z8rz<BrWz<sl&-!2xMfK;>Sl+_GV&<+axKs~XNTIc3p~>E^*c6dnNWUeF8p!Kf1HZm
zlKN#t(a`VR9zhh?5=@o!nPA8rN)2|O_GnOnrog$vzkA0Ji56RiHmU|bS!pXaEw!Py
zHn^L5vsw4kS>_|$Hc_4~uvjZm>{(WuqvIh3s13Eb-|lNFpev%ZP|rIf1JVS-OzwtV
z^Pme?H3CAX1K0A@t;YQc!+Q!(-@Gs7sab1Vul4#DWbES3HvI{+R&@u0?5*}O6T0C_
zVka#bmEc{gpsbXw=M=$Ca<kY@;Yn0Fs9S%ph5Yc9JxB))iPO7WBF;q0$Gn^I9&P(w
zr_Q&S0rog>^x>jYAxmkmt7EO&*{qXvUv+f+B$;qIV<@+Qz82yC&O+vFu@o-=6<LOw
z*kN09pNJ5zExvACV1)l7t(E#fN>XE6_2;-@PT<MXwO6nB`B}{-N8MZvT%zbof2@Q3
zz)^qfw&<}y=dpmRcpB?+x#OE}@0uTu*x_@=>YqLouKUqhH2l5C=c@3hR?}oVS;5KP
z8s9WI<=7O6o$4vXA&r&Ia-LH3FckJ8mTtAJK`rO?8BgKm(b0xYh+_K5RD!nV#HL&V
z+irO%T4SMB4KmK)`km`a&~nlwGK|~lqkpi_W5Y3TBfO2uk0=-Y#H;-2c9$@#gTuwH
z&ySyM{W&Q>0d3$upBnLCV<M@Htm+UUz(DJONdb#OMeXiwap-jE3X>$=kBW-2ms;S?
z*h`@TZx&=?-UUef?lt!JS)4#GFH-_ZJ^#v4Nrq!%aau3Co|dKg&-e&>R`LLe7TkKe
zL@!!ZL~?AeMC;RR(vKhOebOG><Gyyc;JjP#@Qwdi;^q3lm?OyJd~5PIN^GC0j;6_M
z<-KNsDF_?m+2h+6+&a!!54cyH8XU+|MYoh*sem$=%J>qIg*jyJ1hN1<fdlIkZ6T|v
z6aJwM*K(&$mf>S%<uDn-du~Zh(*N;|sXm@Y`+&euBskJy+sh+%2~p4W^{o%Vlq=@Z
zhX2ur)P&NwhOj&t!J=vJVBdbDfl-IwH9|^G1@d$6OcE*CxSOM98`9sT2fw3juBxwY
zBXo#c35}*)FN@SHL!7?<5viE{e%szI=-C1*V6=!s+X=*s(scjyE#ViX@-R$0C@$uy
zklOUD5bT!B1w49qK5dFgBW2<-<1Yf8KXoi6R743q80wm-v-ph5LGxGjne|`SI6b5D
zXi!Y8goKPlsE<kA)4$kP$C&|VN>U96&(1$3=m6!W*ml@J6C)F^YLe(z1xg>_h{chk
zISO-0UQLnWslT!!gs8O1AhYB54LGr#T)f!$PN5203pc<~Qi(R*a7{g%B->mYsd+Vw
zVje2A9W_!_Zr5ThlYiYU=9qTl=}Li*Hg(XjR%4ze&lNPzMyY@Y85b=#G6}ma#Et@{
z?#e8QoUalqf`~b~Fh_3yiSA=}-2YbOeBH`tJV~(xeeAY@5&)q#GF#ONDC^mYhFw;0
zO4Wi#A^ek=uqU%*v%gEs2Bb|obtBe*Mzso&*><8WBX;}{yWg~}0fFS=UvDkqIe)d~
zMCOEH7%rqS=<tjc#^N-@t5bjZ;+WxW?P|=IshN_85m^ikG#SClKz?Q-**5PQl}z_)
z!B5KFdVTJPabON@L2!PsIU3OQf~JWfXVB>L87`btufj=ox;(Rz6HmKRFqQLfEtaz-
zQAf!g=edgv&b9HHs>Ej```$n%Yy%qm8{-}4;`yioG4>u)=14IJ5{ODAcs;HAjj=LM
z+3^OUKa>Lj+vy=_LU;r6o-bwS7?8~twsjg*KP$d$42rB+Y=Knmi1&b<e6&oG=Al^h
z$5@CzVj*-#F~mkEvAycy8o7a<s2)`0YdfuRXeh@gw5s+}laBoRZ#iGPR6k6gN)}(n
zn%)l$6)=1U1+lObKU5&YsC_>`;>#*owCll4<4)H;&ZFgJCxH^bw%i?GAN_Y927=i*
z1cDQe;e+{a7)A9)b6o;ubos-xonl#a`GXm?W%b)teB#G*y^#Kq3ES-xnXy5f`(NF3
z1$B0gOj&yW4re5K>0cdpYzh>4g$T(_KFYDlr9ReL5+zlhV44k#4S0}7Mk@jQ<U4~z
zaOmA{mg#qe_#V;sG!_E?_@31oBIZs<q67qZ1Qto26fF}1);H@1_akmmZqbHszhS|7
zuDJNqzAlYgMVmu8&C89){fRO?%VGC=uUwpP5tmcKSi0d#rD=}3X89whgzdN!vC!wW
z+Y6Es0S|t4*r6U%Z#B7c<KN@Kt=$a_47mHK)n%89n3fU>mD#gAou_{T{dgy{j^G}T
z+#d#?7HtBh{cuR)Eh9ZRemEo+yAVk{a9sECBW`L!g68^mXz&OWmE;V!X|3&Xe@O=t
zXnFW{w^6GPuC9k_W^k}ue>c7j<E>0VZSY0CRd5p26b}7=<P>_0>t24V?`wOFy|<#X
zL4g;3%L2TJlM_Hf80{o%Zc>dl^r$*UB@MQ`GcFaC4{Sst*k6$7+ia19t3jbX-NS~E
zw|*flSa6$SUiy&WU=|S@m@jQG&I$#LCmg~p{5TdoyCsVk0k@IP#UUglG#B{$TnY;H
z@{_(5$KI#{EB7mk_i}$`oEbPTAnBkHzK~Dep#f>4By%Ixqk`KU)!uh!D)Ve5kJZeG
z)*4m(-*>1hTmP>YqEK|im+E$S@9u!^p#>&Sv^vhL^xr)!J2#-J4-QUC7ta7C=q{bJ
z4<`Jmt5))g9&)u0;j*k{JD}1i1?v-n^^ur1G^In#q9JC15VI->bK!lHv~60O)c4_k
zO~Sibv!+O4efM2!*)u=RkRK*N%%sc4i`lcL9-oR$S%kZnQZ8JiLd=RFX0?zNZ<s<O
zPmf0LKcTTpe$>Z$@x_Q8Se}hWhpKgIQ8;84RAXx*Z!YE7c>j>cs%;&5XXh_E+MO0=
zhB+iJ%oz1XKpOMue{gl2y!{MWiGr-~GxexX{^LPqlfzCZVVb0R=oXMFriTHTHxl<J
z26uR<rVj1gQM#K>{BvGk@k_B}SgRVSN*~<Co0zY&QnP0(@x`m@xIQzT)6$^co%NUQ
z(XPI2p&oI$1DX5h6n77?TYvI8$ukpCuu#K=CovVtax1nG+1zvZ&%fS3aXQit+f1v?
zeC)y6vJds`LT;vF|CY{C7m@f=C+y-@KAURn3!Jcg!6sv4{$igQ!@ou?9k02KP0LJ`
zZxPhdiEB?d5WK{CRlB=cPd9eTNSY}E^4Q`sbn$c3QJ0oU^u!{1;&+SG(>yoq#X7{~
zQ{yk5gAe8|@<T@(8&mytG>{SOQMlHXXxnmh2G$G;X;%<_TohIww|Z?9v^kDFR`zmS
zecuJU2w7i<l%I+uxCQqk)u>g(Gr;Ss<Pyg@4u|~Cceo7n&n!g7)10kA;M>(L|A@lE
z=%h`Tzd!eLM^9X%CvLTf7zeYI0Rs^aBW*b`8Q>K>Y_Ypb&5;0=d%l<*<|m4tcxYN!
z-I53#?KJIZbe{IKxsA>AYEvuYT06cUp^~sMZnW%vliO1hnM1ALsfo1bzdphxjW4gu
zgqS5mR(N3wr&jmZ^)th&BlGlFFPD0#E;qB8(Gv!97YlbN6ID_B-m8O*#u+b`Yy6}w
zxu2WrblZw9v#D_{hdbb!i|aN2(XAgQA0{8;NP<t#Zp9<2YWx!Y#1}mVd{Ltcar)(<
zs)m~mr#}z2xSe*w$_1L5<?xYlrWy!pjX21US1-uUB9JHVLPq87`S__FZhQ`~|Kw4&
z3XQEU{Jti+qmM75-eYCAT@q^VHS5k<HtD3_lJy|@b%Du%9*(el+~1(h*_Owr$(oj?
z)FFf9#5fObd1U5%Ne3^8pps4<_-?ty{p_jV4TqFq(IuAWi(C%w*C+^b^{A3u7CZsC
zu=IYY6JM=MJGZM^42OKHiMdEcPn^mXb7!m-Au}mqhA&Ys%h8(a4Xn#&`uR&q?77eV
zna~rWOj>SmUy_PW^5H(+`rhf6$m%$r<QgA*fjp+-S+eK9XH!hXaBLk;wJm}+Q(YbQ
zadtlLKeUha9JKgac!MS4!d8mbP1Tu@gY~(il77ZUI$ZnJlvkE3u?-fxzonRzzb!<e
zwfpUb{te1Uy~82BfwdP|6&_ZuzW9%Q;PU3~SlO*&*!pI9Q+V2fZJX~yZCKH@G=*tO
zhV>>tigEses=s)lrsuHM3pd>H^vgQ_Qkr$Z&pW37#<n7^H6->Ie-`Q=-L|iV{Aj9Q
zKXN=R?<x3V6!zSsEk26Jf3byjF3e<;7B!mnp2ps^Y&jF>_jKgsqTCskzpMWqd#IDp
zi>CJSm6qI&1?`olqSjGHmC?%`7vtNdQ{M0Ro>L@Vo@>;27+k3<I@q+2eo2~dYSiZ`
z-k37VY4n|Q&^HpnBHV$QXI*Mr89luGDPHXRE0JtcSqpnnnby2TbjQ2m!C2%UGWECm
zIX+xW3p_3OJJ+=VCXIjd1*&B9L+WhHOo21kH0dL-&v&j|@QP<mn{Zy$>{8YAGGmpH
zdfJp<YERw`KYmFXAwmLPP5p;{@{ECD6?5S5|M3=V(hXkbts(4g>Iw*W_ZLf6QVCit
IVI1&(07cBG!2kdN

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/packages/gluon/activations/images/sigmoid.png b/docs/python_docs/python/tutorials/packages/gluon/activations/images/sigmoid.png
new file mode 100644
index 0000000000000000000000000000000000000000..e5dd4cba599d93fff1f39b7ee977e040e0598fc0
GIT binary patch
literal 12763
zcma)jby!s4*X<zEB_bisP!a-zbR(^V#L(SHcc%y=DGkyhphHS`J9J2QNY~I^_wc*-
zyWjWMeJ;;4!<;kcytUU}d+mMR`SL+o4iB3g8w3L3DacE!fj}spz~@^`bl^RzZA}&U
z_tf>Rf(9n==Z9$y1CFtr<aJy@Ae`rqFO<=RfLq|Cpqq@go4TW=o2QA31<2mS&B@Nu
z&CbS@*2BWZ)yC0*mYai{;|)8lwVRuh5GUt<U%=t$V#P`SLK+POq6H~POKNy!>@Rrx
zsawoG91SOxCe{?cem_H;=wHMhBcUoSjiyRC;!I`X*0sJVK=3n<ODHC9Rpk1zPBe<*
zRmgHgM95Fto>^?zi?1aTxi4Pjk2EVFmuQ$@=l<Y%tuR(Fb#u1E)+^|#T$=S`DQkW+
z;|2yn&reiq|3k9%BvI|%RaRJ|+B-BlJkR_@?6>5|cPzxAJo8Cvte7@+Z220`MxC_Y
z{i6M`QktkHDc@<pk{|NHI>)fi`(&d)DjYx9$2&g}o}i|t7DI0KkJXm6-05H>7qz#y
z_y2q&ZC=K|U!^At-1qf=c7(F)dhz4OkI>UoH*6dnRD%e(NnBi9mCaaKd3m`+%6E+q
zKcOpGSy{@8invrlZW7W@3}WDP2(pTb3V9WkLbL9eU=F!oIUj6lqhL+1!QnD%Q`j4k
zuqJk}u*@1M;kMR-DbTLXWN{k0uP8m3$97V&^*779)Z%G<D^8S9|1DeaP-%8=+wP{?
zS9IUI9|w(UEl{8_$c7x@wo@{bS?~;^!ElM3(1smc5(RlF23KVv{*j?96}O;Z^5qk_
zG2wYQQRA2~XCrT-K;|?RdgGk^m(FjST<B)Fh@kdmw~#S1TusmHu^>pPP3g9Bp>0Ev
z(zair?aTEtk(~gR(b2|@sw;;J16Sp256jN}hMFADImBk&`OpxlKN71gptgIv)W#~b
z5vGWkZ74Ko^bqsf{^mSo;CXb|NibHT$KTqF*!r7ywfu>|dj9g1-wzS>Qd&;#q=!B>
zW9<0hC^qnL+AKCnwB_<&q)zomXi`c-axwxAEvnltNC(L)D_frL%{1K}_it)s!+oP5
zl*61WhrM9@Z1y#;04zZ?#*9~P3nbHms@ff2LY})s?!9=Shhq3)FJ}b#%2z)-F+egp
zHoBN-1-X4w)m`L3VP^T{4a##OqTPX{`X#>;rPfx&cbUlBhTG#6!%Slgwt4Z(<xdq(
zDvF8>5XfI<Uij>v6&^|v!?qT)s~;BYzd6~mn{}=zg;aau5@aX^b)<iHx(bAcPs8k=
z$=ZGTU`pQ!i^0s->BJ-t4S)9XW1gfJCSJ!|7-ppdeW#stXxKAyaOh{uTt13iK02N*
zOQlMkT8lBex7skg^q@f|Ok<*UrVtqM_xy|g0=nt0ohR(guQMH82RZ#6Se*aNZI9{K
zIuI>LmAkp*G?M!hBDW><YP;=&K8a~NMECdiuOcWM8AL^?!aLiER7BcCv54f~z5Dl>
z)Q}beiK|`oiQLTcjhQUb8>@G<0)xR`>q%O5cjt39BRME@HTGr)i_J|ZL&5{!cq1o@
zwB9%^ODy_d7=Sj0GW*5vj|ie5`tB1@k&AiPwqGk`-}oJe-VBR$?#)!%%+<Vhc6L5J
zSbB)kZN2=5bANx)s;;lESZO(!`o;?XwQdzHKYz;2L2LHfnz?l7_E=$Mz?}~^Hulz7
zA;ChUr`1q~Ab^U5^z?pU%ys)Ub7Z1EJfPa80O2WpSG>EMi_K&`2Rcqpf*>tJ@6VB(
zEvYtR1;5>QHLp*%JMC*1WS3g}y;nYyULN;z3VQ63c`kZ0H+mj0v9d;_y|EfC(&Drn
zNNsQNznO0K<-a-KU(F4t{uhiv{WFap>YZQS)t>xG&`!`Rq~s;Dyr@@*eYkQ~jRh6;
z$2^0vSGv3rKh*W5{+WTl#D1X_;|sY=mH)<vOV7t0HH~f}9rpR@VXf+Nx|0#6B4M0&
zn*?n)>D_+djVaDA43!L5XJkWljKwwY0$awzApg=xgS&0Z^$r`z#l=NAJ@9Z@GArQP
zCgA?c%GA`f?sWA1Hn1Esw|jebEf4p;BpmuZyi@v}D&lut>5eVEGNcAwh-iv0v9VS=
z<G%o68u_6>BpXB7YnAPfsGc&^tBFcXe6cZ@-W`TVQ9ieSx;r&&R%hhW%UG;aiB7lz
z3}EeQGkbIC;hrC8lP8*-*Ysp#(1O<>@b0YXxR1?lzV0Y$T`@x-jzIkO>)$*%z={K&
zOa9%vWvwxgFvr%r<enaRkG<*k9|{S8cWz`q+*;|RqO$AN6x->H6{-|EF17fb&sh0A
z++EE3?3P%R)!WbU1zfIRC})ZEqLJ#iHD4?ibys10_`T5EAlj{6z58l+Eao?hXt=4?
zSN<>9d3w9r4ll>EPFC}5mWt{2(`Ej0^F2>j^7NY`P6}&;4WPvbZGhwP6)`$MQ4mK^
zWYFI>Wm=lLPNP59dc#?w-fMAksY33oT1Ni7Qen7)_V*3$yJUQJWJ>9LXvVjfN4Mv5
zjut0n^&8~Up;@B7qu*t4e9rf<BrcE(3h&=P`TzqXBO$33-j>J7Apgl~taug)l1;PN
zc)reAT1Ezi%W{A$wz{CO@Qq2wQ*LhVR~#H1!eV~gqxl2r{AI-k2~0{u*TP2L>q#Pq
zZKyAZiI0F)=W^TnK+a<=p{$IrUZ7~Qiii#k4b3?B)ihu4OY*xt>IUiiZ0DO>SR~zj
zYTAJp!C)|2FgWD$(u@1e8vyD``TZI#3F;p@M#mLf=8}73Td5dpdz61FZDozX=Fn?5
zzvC-y&lC{8Hls&f%+DFh<d^GLgqydZAWw8;IIQM6&QmxH8otKIcK~eXb^2HCrJxIg
zirAI;jGuY{zmFgxAt7kmuBPH9KX%_kI5&6EU-fu{VE6v!U~?s$2JjsSVBx5jZ!8lh
zw3EaK01U*($7?rvL1+Rl=@OW994`Q$l#e8NJzZr(q+FQL)6?@VW!>g3aq?e_*xR>n
z0W2C${e%xAohnSbRmH9aGac36u+#_x9<}^!v7~OVg9KNs>?I!k$BFu=J~=nLm;4w6
z)C_?80#LmWIqspqy*(K&9Hikg2KA?KG7Ae+G%gs{B<++0qheI}UAe?ii$-*=7$fKF
zaVhxrs`!AQzdoiSo}ZHwl)$WV{D;)YOJ5UMeJm_25WsImcGo>|^ni`D`lzj}tWckk
zkjN@1eCf7O0-z6&M>sK?rqdr{t;E-@&?3!gJHAEV15rwR3=jkY$q?}#U3Ce=r|QcP
zbOjYP762ln3xUvVFibqiWM^L~N%EEVF?s3P)R+Yoj&&Ie^6>LmI?z7ZvJmk#;FNW<
z&26XdI?Z&i^ggk?KTc^iuAITd!C5_<QW2+NqoWJ%ruK~jW;F^h1pr;yJlW_b!+;{q
z5--55UW)k%Ajo86sRyTjo2l52l^KaMDW*zfi}_nGwcNclkjj7Lxyz$f1$lYnksL{Y
zoH2r0@6XES0Vh7)`upkY*JlMP*+cGA2Hk7D3ELAzq<7b6Y&gqm^l~w4lX{L8fT=^n
z!%+_l#qYiVPM?K_L&D{E=|s+Fhbd8J*o=vXm+yJ7I3IAcf4V<+QS}X3w~Fs`wj-mV
zF>dqVrZFf0MB>1J`N2l<eCLXup&)!EZ$CFJj@{Xi!w(C|MJhikO3?!q6cu^aIVT?p
zyuo9i{Hsq>QhNG(H#Z)S#!Tl`g!5*mC%40VokR-pi=B=eBR%3|0k6P8L&G~0=9w!-
z{fSEAgm91Jo8RN`jW6;Mkb=?GSoi+sh{w5)=D%lQXE$H#i7VX4g|a@=+!yLw&A#u;
z%JatEp3nG|$MHih<YMU+)M?bI>^rHU7u^WsD>Uu99Toq$zK}$>S^h}V$xKwT$^6mx
zws4=fod&;ztXJ<>MYudsw`Z>A)7_{E3@3F|DAfx8I%&c2AqXy8D0$$+N4D3d^l1;>
z?F=5)<XaUQuK_KpL8bc7iQ8<2WTSYY8&-OB>+45DF0-xE()R|?u{%LcVBtVN&d*5z
zN`}i%F3Z?BMsh*pI@`fzhIPB8jq*xLXS=ph)S};3yJNE<{(yAitsuHs@LaO!OJo5x
z9U&+NvP2to`&p=X)vlD4C9!H-nqwv!E4}W+3>6!X-{N^dSr%L_-WpIHPpvpTd#-kt
zX$M4am1?8P+pldq^X8kxuKjbiGXYx+WsBn}s-n7R4Gd7Zt>nh^rY4cnh8<R)3#3vk
zwdf@v=1>4B@%=Qk=ut*Ia!F!h7cdfonrh#h{aV+9<_m5BCPMBzUB~PF^?-`lo~>pB
z0=Vyt?Lek*$|KX}$q^JNXX^4b1I|e)>hmJVD$6?zhodftkY2Xj|HdP>8aZ1XDftQA
z`L7J=(OQDab1Eu4?ZP_(8nKLYhlXEv%N4d|&B^z7H)GAdo|Z!yIyF&`z6y+n%Wj%0
z;Cj2TVjH<c;;}#b=cj7k)>5lD5CKd8Wn<BLfJz|J2C#r)p>pQsR$eS9mCv3WVD%i8
zY_TRldrtT<zmN#{ohiI%FzlFCKhVUe`pE#Qu)pB);6qM1yEkW1vxlX1U)eF^Zqu`b
zLF4!J@uiT!1Q@|FgU@lH!Tt7nx6FcGrUejGJodARAt6sV8n(ctt@l0=k&$U){z5D)
zEIc2_VJn^C*?fr-zj_mxPk@_804+9IYS5p3f6DwA^FcsN(XMx4d=zAWGtN78;#uKh
z6~hask!x8Ofg35Ot4;Se_qeTGVk3;dgk)>y7f$zP67t`taJ%nlXclWbBkCYXsr#_c
zpCJLamtz~g<i}H%l}UJ4jRAMp6?B18Qd0e4x9*!_YmRnycAus0OAVVF_Rjh}gK^2Y
za@eO)3<It#0K*UFNPe1b4HOsl*fZXrt+uEJcmarcp>GW70Wk;ABGA0oqyD0KxRm4O
zR>qqM+^?Q8t(@7KETKqCOEWAsS;sTO9TU6%OcN;FQ8{(C7J+<?Y7h_q^zk71MyICy
zE}sMM%9k(ylV9+JQl;Crnr4}yl7_|$1qFo{BqTm(LO>(|<c@Ds4@QPqlw%I!6}GHn
z>2xPE1^(gTx<RUw>O%cwIo!ale?iHB{MZv9Jd3h}0Oy!vKwVjn{=iaIRkfR|iRQJP
zOq{5&o8bo1vR5oDChZ|;RwDd-e919X!pVbg5amFGt-O!1qIj+iby$zd6S+Cll)bR)
zy-UfW*>cgKDp)~U0x}6$o**&*>%)OJ!-rD<sa9P9%N#=n`IixPyT8!L4=}0l?$_2S
zqd<RPRa*ctcY6`|z>kWKOG{6m*C8YuP5ybop-`*LP~@nK2$!7aXK^wRQn|bijb)?A
zBwGXSM8pCCCERp2sdtK8NJlO-o&ahs`^?jg=DD`#PfaBs4>9$Pd&SL5vU_(a$7b?)
zA!ea9N8*WOy1<i@lSdg3?r8EpNfq{V$Zyx1oA`XOWJIal*_Bo_lk6PNNmCbkK1i;4
z&j97z{@NO#rWbarq@Z8|kO`TY^?V%!kar#uJ0Dt(t}8N&Rr)vbvHW?alT@thv{db1
z9((qDrAs;wENU<T=&-5I4a4lFYGbwq3&?QMzUWSEmdY87tDK$rb5;mnp4>)PMMbau
z`Yj41#Mt0!)_Hn*`rf`icx~X;SrR0yxR@PGPmgNwF51_BhQouZcQwsf)OuV(&P$_3
zy{N%rw#r7z$45vmh7!w-1s*0ND?3`ClrE8?n&&|+)@v)iXR3HG$zYIUmBYFOhpteQ
zka*0dCA~vqGsBJbKW4-0kn;Ms<M24LT-}M+c^dw|oyH_5^Ql(<vT-tSFnvE}eg%~Q
zst6P1A*L9^{>{<USik2}r};+Kf70dy#Es|c`o{r(<Hd0obm8<nMV}*3Yk*6pF=KoC
zUvbE~JNC9I4?fP!^s$Yclw@R1*UH-8zoh65FNoJ3ucf<*U9Y6|7EeI08lyu`T=FJH
zg1O&G*F^Rx6Q*sL1PBveNlNp<dN{${vlscS(AwtP1$;Zcm6jEu#Iobocbx{(Z-cag
z*@7L~7zkMcjgdydSBTV>HtA(vBT~4ALCvN$b+(;A+W|&Rp^V*~Fp<`Th-1w>21qy>
z(ijc-(<WkUSI>tIb6;?LN_lGV<=uooXJGB8@@FeqKa_(xWG09fGjhLy_B?$v@NwCv
zEYZuQrI8rqPw4&BGSwuF9W|-59Yxw`IJV{eq8zG=zO4)<Hx{Bg_s8z>VtGxGjiHm_
zH8F#&*!85DBIPS!47-;Qc+2H*#6sw--8`>!?JRWgh-rQd<zj4W*2%KApC;%Egh-u`
zU>Iq-4T*xhiH3YZq;}QS)$KrwHK<&8YGG)-+flpeIsW@_S#KibZtxIzR8_j;w?`@m
zFgzNkmq9SMKiHlNJX&Xq#Za3U!WoLx1$z5%ODH)KK_<+Frj2X&M~Kxj5+=e3Hq#%x
z$DMx-P7kVY+n2?#&^psMjLV!Uy5O$R!x@T<#CH>fXB~a)F)|wf8>`mas5C67papEs
zjV8k9NEi;73v)un@r5}@@_+is4}XC65o9QL?oIe^&y_;%W_huIyQzVKQy+mQQlt-=
z=RMC?CzbPF^(+x69R)nffx$d_vV}_Mf1MMn0m=pkwKN+ADE3$c6XFnJvg=;;zV1c6
z1X&m+Kb{@UmYRlW0Z_am0F;jQ*Z3jg1-;RVxU8xlPvn4N7L9KFx89@M=S=t`wt4Wj
z$F9RS%9d2#uA<)}o8HJSeSpgXWw3SnN6@)!syZmz6_K`Rq7(ux#<I&V#$wKYon{)z
ze+580ky9We42@+QcbifUxF2v47C?*mt#nh#_4Kfq|C4E;hfd%Fu)b=$J-e})<;oy5
z(0khOA|C`?HZ*f|YntpZxHc<><s%D+^PVRB92eBBx_ZWl-|2q${E0z?ngsxVA-QaO
zfi`bvyzNCHKA>NyEFhy?zh-3)H&mGcRt|zu&y{{8cx<tKlcL1-Pwal40XN*^QBi%1
zg80>}fWGXfkJQ*`<n*iV@BpKMs((aac~A~n*cmN+)T)jJR(j&}3h^m)HniyM<9j#>
zuqbeHz)T&{7zz!vF-&-!s0J}=RwIw6)y^$kx1{N&P93PW)Dn+@`rxmRmpduA9a8rC
z1)X?5bpdz##p)u2XY}w*QY}T`JN?_oo2h{{h03tpRP>6dc0rrn<lyk;N8bkT$E&&r
z10#Tbm}PvU*l4ct8dzx!;#>V$#=P#DB3<Rj<}NFW-!q8;lXuqy#_!$R{GN%4n|)**
zI5zy;2#b0iw-2sRmo9*gY{(KS5XZd+9|t@3Yp@<xy_cO}OhOVqk9)QZU3Q_R(&hx?
zL_>Z#*bAY?K52b}Fpq=<SV1MN{%%NxhFhmZ!6}+%TxdXhJcvC$#0%7upx^7W^LR}k
zqk7S~5R~WkY46aIAz_Ta!pTPK>pgmZ3bm&@sesNKMU_33T&K{sz+mw&J>-Nh*KH)h
z+&a?nG7MKhax_t-{R=ex%{l&b?+M320Whxy;JypDeJ*vR^Q)1U@=ush93$jc-(;B&
zD4HpLhcH1nI7;Ewti)XJ)d=W9vg&^9;5v4QQd#ew*Wo#hNTm-)Ll_aMqHP|<<yx$-
z*k|Gu9z2v_kzX*~Sl)U60SYJtZX|Ir;OF>?Pf3z(PhN6SN=Sb*b06C~x5#~m)^`t7
z;4CwY%{>7CxXb0QI7IzMRS){prfJ*)`M+*Yf~o_K$G!#K{vk_PFZ-SZS6C{reB$I?
zn~YK_&2r$)D-Z^;BrN|qU&XwDq*ykYidB<bpBq<fsDc%korD3*$Wpfb68<hz#={V@
zI~oH~1E`EDZ>4lx$FFst3%~I1y$5vDx{l4A1ehgInuyQBihhJW^g93nH0%f}_J@7U
z6i?e=SmBsx;{OIrTVJLe3%m12z*Gs30BWoK6%PA?Yhm1N33T|2bqsK5)bO!#w_OvC
zX0EKm4|i`=-D3kjCLPmSIP!HLN1Q#Ahgv;)czgWo$?5%0ju8~3#=>KUAqX6%-6C%-
zrRBWbpVz8Y#{jrow;LAe1qzlz$x-kzphMQpxz}m1HkgBtpeWSy5!FNS<BHcc_KJRr
z<^V~-XMu}cTo1ia(h1hMDjETdG$;YQ!=G<f8#`{B4;KOu&B6~%*g3$DC-dKTpb)OM
zDGi)U$+&$!xhDn?G3zT}QPz(;&qsgHRks>U_yeG?XIBuFj-1By*`D5K#RR@bdjZf}
z%^fCvLrt6I*zY;$0^m3=4#$S_)<}f`+@s+Eln}(I$Nl9CriBwTRignK2A(JMK*y}s
zid(<iK}%9vycfRsekNT>(-5KsU>_Syj)X7jHLX8e$rbWw#O44F4J#22;$4iJ{8K;=
z9{^4{o1KhG*b~>xvC4<%0Jhwz{-cL)LPVCkQW_E!)(D?U{$UwE-NFLU_M<7q0Giw>
zx$8cj`Q{uFx8Po6fJRf+VIVF0rN@JCh6Ow}G>Swggn;9>8lNW}#{zAkaH*9g!9zae
zuW*3r+KSqeB>0WvVQwcQ9Ozyk?cH5)(s4L2tNL;a-2HDdXaJk~4W+-nKxtDp8*ngF
zgm4j%?dC3wwVg=h66~t?amH+r#5G-{0xlGi%)gTLpD>O5rYS3Z{lq>RVsu^3mw&s_
zSdu1yH@j5&cnQ+xR$ZM>y$ihw^R|8z?TaEwoH?O_n9CaM0?N8>_P)k^v_Jv1p*jK&
z@U^SYK%4kg?!NQr0$b+-=vyf4L~%UE&&o%}l%Zz-*O`@7&NPGL^vtdP4CO`bO+F`l
zGzYU|hrj5Ej<isOyZ{%Wwzkk1S%O)VjYHJ7anv@b7zIIQ7JsMqTWxa-7~YGy%qZlq
zyM0!#{}H6oHYm($U_I(!Zp0EA>$<JceY|A<wET6o?ej-+rYMtr$BkfrLXwnzZjNT3
zFx1SRf4gdKg4GY?5QC_5+v}$qAI9EjK%0;6YC3@)fNH>FpzSN;nTIxi2ck#|!1-sg
zHYo!?sb5zEm=XmL<e2EutH1Cbpz1@w+0GDJ08{S}0$*@uu(O+_0f6@b;5>n2A=mpz
zsLYRT9sVi6)$NiUF#_%qeT0f3KncJo22vMGfOaP+$dRmD{?&q?o`;**Wry}run{u=
z_l?~6t)d8Z%Z)Wx;~y0506~s;{G6uW%3qgn>Rf4L#UB_Y(AexI1p$bwz;hR*^<85S
zh+dNYWSxZ+_)e<fOjY)9(ss?W$goCiT|N9P<Ec22()}V~HRv4x-jGQv3jAjzV4<cm
zcnZ+p2i6+lE4NORN9I7Nt~@?(A_u%GWb|T=ySI<cvO~t6&9V4DPsk)~7O(FBU2bl@
z3Dj1;Ye-nv73CzvskZ^;#FemndrZ8)v32n5(}c2+A-Udd0lfLtpTL6NKaTFtHjiIw
zQ+DLAt!$&Ek6k0Y^|5cAX5mO6J}kt+cbZ2eNOKZ72lZOBW$`-dzWesW#p|~iDCbZa
z(VkJGDG9xmt~!X+fTKTl>U=3pXXH4voZP<u1sCTCsqk6wD>qJ!Ki2kcyMW3d5k(ae
zr<qx@4C~piym#;hsZIm5XoAW8P9)E~P@U9z^uA2_v5fW|>x<0tw|+hJBj1IkyR^r+
z)mLgAjsMagE~s<avEruU=K825!tWjj`l?1at<r-=fwb%IZ|}}ZezC7lvKMjWYNe(i
zT&INA6E_1M3*GJcv3mO{cSOk|_!NubM+XvqS`WA2cULgv2dLyq`s;83mkQV%>)c0`
zG&IN9kbfd|S7xb)S)0b5DI+uvhM<HE$$QmQK}x>C6P^8cSDyg;VOMJ9|83EE(b=Hk
zBnyX>uVG#OL)mh|ny-8!6#H{FQs0SjzVi7jLq}m?==!i;KlDFJ#(i<)lrsLp(zQS5
zJc~1E->pN7pc9_5^1X3+BbF9!b7sh1z<7I4w8cvp;Dz3j1xG3S$hhyS<-SNEjs!Dt
zkDvVBS7!DLps~9Kaa;vD&A16n_3wWU>#XCag}*HJCbr9U`txS9ML*xKr|7jO;Y&vF
zoQ!!PO!0*4!Q@el7)h@Q6WScys3i%FEf$NkEM~MHU{o_Raqpd`A+&`h`Tao(vF)&z
zEjY%6Y$F(pW@xvgFI#j<)x^}clN({7pt2_^sM~EmggVwy3huTwDDpgrwaA|4wv)D}
zfiH>^8y||Nm3|H)s7O5pTsQhiO$Keb4h_l9jamYo3CYe>oD$nqI3?Y1$ZnaJT4U5E
zmIsq>K*og-%UVbQSt>@9yQ6W`RXbV2flnQ}_39iqFwDR6U=oHxSA*84|IFxBvae3;
zE|HESt|IzJ1P|^&^xGoM+2t+2gvc9gWlyQXBbu--o*K8#t-4KBMckrX&W_;{rm=&2
z!SQ};J%k5E8hxKU1K;IMudE?+GnKP{TcP<KJ<IT9iSoy@3lMW?v;4GsZ$>3^(z{R|
z*c@o2!oulw^6R3iclGEHM0h3o74x=#%<q4YDd+bbIHzz~s%OA#=N5H|0OyeXZOR@`
zY9JerFb!fDUSFxuNv_@9-dz<^PekkMIM`IGMtu066sq&tEC{2Y|9Ko?+iDw`nPQw{
zpL$>A!;Lfl6(c@1`QNMYuJ8J(zC>ggir@53%-VVE9H=a}%R9sQrJMuShgJJjfVrMY
zSP^c&0<QoWWL1m|;Wo}Kjb=H`xqsTb%*X6Yr)ZRHtdTcUuS=UKQ~7d_TA+g1!3*PC
zCTZ%wZ`lM>tytj!aWiViAP^z@<6eMj{PuCPWoVGbe`jC+gV-nbj3ju<C8YTxU`VK1
zzzL$l;KdA;h*#R2=1uHy=OpLyapB5s{}U}riIg}lQz5&nE};n7U!cPs2B!Z`$;$qD
z&%c8Xx38i#)mn0{Uf1NR+Zg%oywyH0=H)q9zImc&{A)?{X*gf!Xscu-^~lw+gw~Ip
zms9}~6D-4lm3To9p7pjxqOLf8KC!%YQxlvMS(VZ<#sh346&QY}7y=D%Mu>m;0IA8u
zONtAc&B|zudRuw9S)MN;P3)<{z2t%Z9h8`n#eQ_WEF#!U0U)|uF30CDvM}9b;M9Ps
za1Vim6}Bqod%HcoV9IpNByL#d6Y$1l8Va7)4CeS-iP$fe;;!`2>24FI3NDqOAOfU`
zvUc4zEsJlZx4AhfkY%i-%hB1R>7R&C_DWUjbkjMlyG}wgTYOnZCVu+7mHVtwZKxc8
zr4m@51gK21>}ittrLePy;K+k;v4kfES)S<gc;V_Lc~<`#B^{}J6s}Sn>Wg#_vX|@_
zg<Dgt%61icTA<}yoKE+cWJk3UDVn@mVgyuSZ5=msqJlsdv_PErSnci`1Myr4xOY;J
z^O2?szp9l;h;h2pGseMk^M(>Vw>)d@t_t<$@BH#c`Bbyd3SiR`=!ZSyS?1OeKvPgR
z{6HJXw9cFrx1r{-rrbJ)7n;1O+m?DY^x2z#`x)9}=>%GGHt=1;gQj=&%9fL1XKZeH
z%h73-efPyf1A9vmuXKBRw9nJ%V+tT1v%OXtT)X4FNfr5lO8Ou~b$*L!X7u?a45NW(
zsc!k*F6K9TqmJ)#Zn)&@u0B;!aCiLdS8J_6i4j?1c2!n>RUROwfBp0{i)?Gu&vu!n
zB;X?3FeG)7YmXB!0or54F9MJ_e-eO=_6L7)Kl$xeUkdG!GTHH_M9KyYy`_W$)9s-r
zEGGQE7OesNpMYM%ZDztMejq&5(Jl<r6Qts3A}_tw+JE^5F(qw0@bDICa(PUL>4+)n
zi0K9eWNvmNoOjWvGNpd^{7AP}!^b`qKeMbHVM%~>Z2$EuSLz{k6@}ft@!i=f0N0Qp
zKz|Y^+X;3hWIyz+Vo}t)T)de)Q*q2Na82pOsX7dE8uPOj#uRIzsPQA;iqN0}Z1%)q
zAIV-Yu+V0vl;Z3;c_QDPTkV=C7Z?c(ODC0>Lz(1ir&uHaV2Tfj9h7PlBR<bT-C-it
zY$vTn#Vwy`Vy6|=V2;;Z?@(J-KkRb*v)z}3E5JVjm`17e@FekUs*ksldgy%p=Op*w
z_2l+dE+KGrIoPzA<6G8hi7G>D7Mxk=7c0)OC=_0!dp}1=?d!;L5Z)|J(9K77O-gp}
zbx2LD4$WQRa$u6K4VoyEn8ddg$^2OmT&g%h2)F@a&@dChY!uLLwZi##J{e=G8CA3t
z$4#d?b`V`l1l5_eO)D@gt+Uvlu*3(gfg6fpT^j|5$ar9=_%*~uab~aQm2dMzev!T}
zm;Thuedq95xaD_)<sv!O?tR|fAzTgULUvCqzF)M=4*{h>ntYrVv0Er@_c(aBfPJQ!
ze$VAC=vLQ79=Xx%EM#6T#I~QT8nEt0zEQI9C%DXL4LbLvqf=@bK2?5JuW&O}#40o-
z6wkCyrF|>V#D~e@`}}%I^#W0&6;P;$>~r)m&suYQxCuPHxz@cm^`+74&JA6xMZaDw
zZZ|XlPihNa6o)1^>-!%mlsnqzu&Gx^In=Xd_lv0Z+<rD1xv5RLO5Z0v&eJtT4AC8T
z6!DDv=g)XM&5=%~6-Uy9*UIUx9x^C>N)|;m$b_Q-iP|vb5Q}jyfA%QrnG!PP#c>CD
zoM*2axuFYZC@-d)ce`BI?8^o3AMSN)J6a`2D~&P1OQPmyV?{pi6@$(jn3<Tqt(iB=
zJ8{V`q_-FRsoaVai_NMLT?^|nJrGYrq=;e0{SM|RyMNUaHc{68Z7TPYfq_BS4B<(o
z{t)JZX$p=vjZ!GC$^>v5lP6eTY;@0<+sa|=^!MHO{IB$c`@aqRXa>=j)B?i^e&S)P
zv=w-lBOhpOnWejQQI>2)+{nHQ8|@0<d9JG?$j<kchD)oB2<vw#e29zl;?@#4@#GzO
zBPKhM3=RFy_SMUjGL)w)DFf07^jgbnHYgBMoBb=dgp-Uo6g*gNN$?1s`3XU1p^QDH
zImzn{8ajK%5*I$FmKhw0n6CImKTM@-gZI1lTDzJ)1|QTwbFDRR1EwxMcu_Ju)x8L=
zK`n`UHHRNMJsDi80qrORlDE8|2b8x#{LzpHc7E|Esd%Rt6+u{t)T+wY>YdFVT*}OH
zG3HJT_H2R}HUE01-wqs=RIx3}s0(6Apb-9$Is5U-<eB}ozVb=w^+b!Z>-35Q_TDYY
zd1(!D4yV`~Qj#mf4?Dm<TA;gsYduOx&Vz3^2#=~=G2H&k4SxQl30oik2kNejc`k%$
zqGX09$j)rw3d~LDg`fJNc{Hg^V2_bf+#_@~oZxGElCsG;N`~M|c?YK4tF$f>>L2-c
zv`6tOO}m0Jp>foc!BrayKdSc>ib_A(c(2elOr}Iyj$_c}jyU9;p)S7)mP$8y5E7_E
z!7uE{B3^zabe7SB82KT}vki)#eXIE1_CsZu^Q65SPEllcLE+Fi$s#4TAZXeqY6g!M
z3>27xeCuM-EbEYgPn*DC(<c*~>iuMeM2*c)!<$3n<4rTppYNSN&%hTx2R`Eq<I5?`
zxUH72mZvPgV(BnUjLmyRW97W9f>Q7U#L|o5i=w6mI)Bm;q4LZ`T=Iu#O4yM8cO{O$
zY7%d9bn1PSM}t;t3f82nEPcloN2-YMbUkvt8*!}vwy<s5e4XN?Gx<6)e9f|6E_F^V
z&_o<_@Oh7?XAvju*WsLLIcZisCD!jSwir8eDn{bJJ^KO06aJ*LZ9*=acC~c5#zo7_
z3<KAb+I+7SA96L?{;M#Cfv!0Yq_U)Ol*NjIJCWdpl83cG*n>M0C`3~KK|FNyfgbS<
z*+4+MF}^zLfvhde^4&T5wGe_h;c?yhtx>K|5%!ES6h?=OiR$Mqp__}9_W}G09O9?P
z^Enri5oM>~q0N2{lTGbGgz{J`dgYPuSSL%^=?j0WvVO1FscjlmBUUVfmiG07_2SGm
z%P`q1Asd5n;RCKU%LXJ(a0lhCbtdq(dAX=Q_U^dN=0fWC6I0ha*qqsv{frrFs#Z&8
z-Ro|5y>m5}>s?L0=I`DL>bc!#o0q=XnLx2%`!M`iZ*8$kQkn@h0cEz@cJt5sY!-H=
znBt!&$}%Ok>**Ez|1=afs`wG#9EZH5`jk8aW9l3UHAa-@lZ}vqvTut^qAE(Fxw=R~
zLNjJ}%0GuDi`IM&rK8K0hw(0ukSkkDOLc(rFk_>@#gaUPad=-~ALY%>rNB~e{jP`K
zvXsHJ^;YA4kKEqEMW;+^uP@$Xi!RMZ76*cq8z&6b+%kOj+<f<5IV-tpk42AN7n)@R
zIX4dLY^155^dVPG3|VS#43)(Do8%u3Iv+K%pg(;X-iH8zhEcw4@N;aQCoxGzUm=!E
zTtvlG@ClZ>;dTD@b$;mPuXn#BaIijqB`dm-y-MQGg#OoKhTt!49WM4HPipItOCZ0p
zyM17PsX&rLedF~=At>@U{%5~AvPv20tS9l&5S-xJpbKep=1Gzp=|GZ_M75p0CUSuR
zvPw*C4TsqThYOtGqn6i7EcqJl+Yssh*GAP>#!~kYJ%q-NyxEJC;<)z9sJdDc)&@{5
zJ=?Jc_=f(@N5`lyMa8OZ9e4UL`3in1!%3c!qK{Rj@S>7x8w031k-JT8)PHY_b?2S8
zLT?Dd2AarohZ@+IDDA?@Tc@DRH^5HWcbNtT3ocskSlmgf`Q~NJn_zZ?goQAM<n)W5
zu)P;RHQGf6ZTR37*jC0o9Ok!0UOGL+8}JHDVIXb;uJ|3|0ld#l3>|2pk;c6QyR?<m
z{3WiUSl0~~mND1G8$aVDoM8gD!g>n9|BfQic&l4jZ|-CmI?kvM-29V;E{jNNr|j6k
z;;<ggsm%vT0{eJrhVK14|Ix9E+YfU(OQgS>_H>O}Ve;ZC{YTD9q;)0X(O{Ri($uU|
z1&GO%|Fe5mtJX$IW=&6v#GehU#Oj5#FXa=KUgJ}-hj)>wvjql~wxP9g)Bd&h;Ws9F
zhT`_r1BH${OPnTktKk9NsVsZ<j{ndc?PN4)gSQT835wMiW$LO%3Zbl&y3Y;{yGK8L
zYjBZ`nuRldeY|mjasRYwr9qtVvK)P}QBy*E>W*xgpTM0F#b|PTvv=+SjSSB(+FhqY
zvTc^Ul=G=Ts5_;X#3U)#i!F@X9lm{U<8Qn7JSy3~IV|OK?sHx4t-bZ*YPPlW(Gb08
z$eic^InKZv#EX|^=OE$V9RCbtrJvTl*fm`MRR-DKL5p_J9G-4m@ABT}>~(A+QcZJC
zJ2nH%qI687SejuQ<i$lkXc7Fa58B1U_}ba|{rU&hOJru;Q4sM)WcJe<Q-i!Z4l<kp
zzGPkrGeR5ar?SA8<^5#p3kmeYX~5>;p(gCio6Vo!*&Ci*PLjC}kR{;x0sD<pbU)hH
zWq&Tj@kn_%<VTHZMEg?6*TVC(2Y9sb)3^mC873dGD^$U260uu!G)L@*cWNnmJ(j7h
z=bbg)^|)r@`yB@o+n<+gLyIVWa+Xha?zMv}6r@{7;*h)=Y8D6YbHvq&etDs?J=-nP
zz%f%q*M4XU_9C$NYe6<OXomyvQOn5>*NLSt6D!-Yp)P>#hAb=|Rv;8I{EO~95viZR
z&k6k4z;pT!D@FTu?kHubDrR>>DrW947jyy$sk3~C!j-)%Tcp>V+wcc|8AHU3jI!RH
zaSb|V=fR)HSudL{)0qu?aj6VM?D+J!@2gzlZt0_1K+jvAy7+%aH4Bmx9l#&IhGS=!
zH3GsMVkym=wK0g+7;Ea!i0f|e?EP^yf&5Pc?WMcKLnZra&hB|R%r9bs(Mj24eZPSy
z?g3=vle#|WbLwqSN+qL(n(|W1DlgpP($C*}xe}-K<{K>31nq{>Y=C)vvoX9GVi^Z(
zVge6FLjtIz{zP8<;ml^?G{CyGi|S<;r~k0quT%i5;zpR)HT=T&wN8kscFfVBD87rH
z&?IfNAbCP$Ajj?2bxqJT)Evyl6VTS6&96;*P`Z`gR!@#%_XhF9_ZtfsPllo3RBTfD
zkCABgS(3|L#wx3qs3V6Ta<@SiMLigTNWI0L9S7!|L>l0Ew%v+m$BVf2M6=gTKEI3X
z0Ts8dvWrix58hNftDA5iAE2cztP=i?u5r092$+!aVpvs1r;3b>1+1jM@RQzP3|_``
zPOG(v!In5045lU0-s3xPE~cldLDgzdGJ0C&v2YMdd@US|CJ~yw<40OoIA1l{2Cfz2
zs}HC#c2~LdU1-LzLl1EFFV{^f>p%12nfy!E`0;0HruJO~Ktzs-WkpO=DTPZkUf8?Y
z1QI`XD=Wv&bapgQcekDwm#wzel@$JM6V1<lV;o_-VAnAV@pr}>UqA9^(Mkc8HB4?=
z&d0c%xZ0~~n=JI$>jK_7DV~a()0Jct*fy(jLRH@BJ~uwWN9Z-SU+>u<W-A;lJXB30
xDg}M~J%Pbqvg$h)C>HRD;{Wp}2qO;|t}-;wJBzZTfgfUk6l9d8OWzuY{4W4+J%0cI

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/packages/gluon/activations/images/softrelu.png b/docs/python_docs/python/tutorials/packages/gluon/activations/images/softrelu.png
new file mode 100644
index 0000000000000000000000000000000000000000..5551d0c873b2a6bf1b45377514177bbdbf51bc19
GIT binary patch
literal 11210
zcmZ{K1z1#3v@Ri1N(cgyB1(sJcS%c$lG5Enr!a^Jf^>)AkPbbxfP^#%;?Uh)()IRu
z@BQxm?t71q81^}Powe8g*T3TIh?nY$_wgw4(9qECE4`4@LPJA`fX`PrcfoJ#Qxh<N
zcE?>tNe2gf`Quo80RQ8<ym;e|hKBzD^@Bb#6L<v%g(32<A==JX5HC|VOEgDQh>L?W
z#KG3=v8Sb*yREa+V;)W(PG0uMHV}x52p89X-@xhYX3fPwDu;!J_83h`PFlx1V`s+8
zpVZ@~>tIYU6PiZkt0=PZr4s|s3`+w`PWlJI$3m~%oUG%uo+7WT0&HtpQW+8~VQDN<
zb)nceqQ?m@zhar;plbyu%v|5xjPt)Qr%&Iuvi3Uik)ZW)NS#PM9aNVPz06GAV}|p~
z;LtZ^XAh%8iDc^DlS1gB`@~wm!id@wUr3Y7;Ly=y8R5|TL;rsl<bp-5k=3@^Ghbw}
zy#}>_EMsNmUM0APh3-;4BFAg}*E<e_nmYF+8JvaV1g&tIM;d8{(ElynVCvTn3kSzD
zUf%fJ+$VW?ID&Y%xL?%L1@7FvOGzV-lWpoPJ=EX-RXX%8Wc?R9EMeFcN@WA@85lrK
ziKY>yq*27l*0hX|)Ur#`+JtJcexG(<P%hF{E()lbV&uR7Qb$KlP7X?XBqGNm4?bM6
zlC*VF+sbC&H#AwOFKRv2-{NdNt?A-t>}=YN9WuljE2+$}hX>tJ*Y&<2o7bMY)_5mb
zCzMPia|WGmGc!IP;v7s)u@5lj7w?7DBSr9AJ*U1^5+`aAvcPJXVKvLk%cCxyS>`hh
z9)CCgM2?TY_S~BtX!1F-<IB7_-f9$@*c;3cZjXAz+e=F)8JI!LpfGBqZ)<D&?R7=&
z_Ccj(Uy4LP!|z97h4}qs1It(jO0(W6?FEC#iz~b9a(VbMw*HnJ+{4VmBGJ0kw`qCV
zBFlG^Wn^TedvLI{IwxI~QIP4<s54}?SeK2A%h)i_R+oLfJ)F>bQ>Q?G99qbGQ{l^X
zJC)s6Qu{aGFY-M?q9u*&uLDd+1#M~vcTC=`tjc=G?YEjBF$sz6-t6}`buRR1b3wP_
zuCA_f^75{80jHXVhW(0k0Uf#Wk=`qbdKA1?_p}StEy0RAe*Qc>nR4d!J8@8mrtT>>
zZKn`{Frzs)pECwr{G}HWNh3G$Kgc~VGippqcW$oVKKQB>pCayeBEJ-Hwh;bz<9B#U
z3WZb4wdY`_D7KWRr$FtjFB>;EcSlFZ{%;LQZjVhJ0%{>C2M2cTeAR_tUu6I8&QJvh
zUSCwWuMJji5K!`cNJt<RaGaFW(s}?ie){ZLY;3Hwk`gXg(>?}1Ip<RWfut~eavq=|
zqgvXX)`j-)M`GU3hlhvn(Tb%oC`O|_54`eb)W}i=0y0HBiVu<_h-MuIvPhaP+XEGz
z)tKsJ*EpqGs}UL)-i^Y{pK@w(lTS&$t%^75Ix^xe;t@CCU+y&h&PsE$Q#&^THX(`2
zgh0yJm{wk1K8j8<Yp>-ttJJ8`ZAkp|x%;YWmiHnqvdm;?Kya@A12MzvMo$js@5f{&
zLD&3fU?Z&B-`{tbYtG!?cX?H6*wY+vIbLl`qHq76kZv$W@}^r?&q#OY)OE_S_Fz7g
z5M%%9Xz1vmi|%OLJcirn(Bduub$8q|mFMXLq-10~0T+;w-@lbqR50g(<t%?c68Ghc
zB;#0INj70*V~bqsii!XHSt>A4Lh|N}0UaIvoALKF>+FCeuI954J!79R;LiKAetU`E
zyXI8W_#*sv>%(1_esbF**@3<KM_?-G-MzhL*B8gz6IJ2Buw!*Sia5@YzoWuguTvC;
zlJ5JgB+WXdmobEM3}OqQ%_L~0Di>W=kR945YF!4~JH~w1($OI;(JNa`wazjHaN@HY
z{SXy(PsDTk#aO8kI)$JUHBc>2_IKmSq&+SnA@<vq-o$T)^>KA|LW@gF6IIrPmnS<V
znuCA;LQ0zsm2-1*U#P2dPW9d_gwv%6xjsR=xw!#C*W-Ra;<qEs4!RLW0|qc#?v4#U
zj&uNj0G!g9vi%Q3;Ag!K<K^!fcN_478~2*ikGIB>?_ajl-2`W6(*eLf6%}o~aDI)$
zrK@AN8oR8+uFvc4AV&Z0#wpz^(O_MFOO)*q-+1R6&iZFY1`VD&%F4=kY_Gn(ovgOK
zM<bGevW~Z}si~>uDFUgQF7PRwF0gLo022eF{588}44bHc-__XxyHP^|@CU1*tbs{{
zy?sKN#}*QhS$V*vCmE+<_vKy-VQcp7m33+Jg<TT+o3<~q;S_u}FA)VsH!nNp=jSUg
z^|vT3AGT3_KRo92r0!b^Z6vXwBYt&^Z&)~Zy5e?q<|-pA`$*K2T|@ksw5X^EvNxM4
z8%|IWbQ_4a+Mn9q`VN!J*D>_&eP(9nLdfKpkrz#s)j)1}IZ?2ASG4P5M_41(WUccM
z05mfvCnFbE41o9SZZ#erp3Ow%GZ+l^l!+->T+)00t<_)#Zt#Qyqoia@uVJ2I%<3n>
z*~ef)VIgyCL)dJChy8q;RicU-8w*PW7CspoGP6_9&{ZCGM4HQa#={@)LlyJf^sV*#
z6T5|z^Re@gX7etBi<2D{Km}#rPbh~ZZ^h{8>HQCTn4+kK<ABtwtE-gfrfu?NpQ57~
zA3qKThMWz&+=+Zo2<#8`H4+{3F44n>oxtljS)(1NYAi<!HRTi)d9C^>78e({SN)Iw
z=xvOZ(Ty4Vam+M&arxb3dd->+r11k9jQ#w%6;M>fhY$0IeOv`zd$X#kJS(}jpD4IP
zfcDSrMhh#~vH~w`^VQOS0{@DhxVk*3)H4a-Z}i+@onq0SmApOIkT_o^$<514<+Uc*
zYdTVYrt$`7u6AwAKg*~x^d_^-$IeDimWSEvhFV=!b@U;ZONHO5lkea4g_?V9?%md0
z{_@hAEbVibbk>n-{^I9&Y5X{@8-EkESoF6#ihuGpxpRV$Zl!OZW!#j>P%y}S`{Ra_
zYxR%jS~@BYKcXv>>=xW;IqW~4S)byb{>6?8{<-f3=tMfV=C&6aj0b%7d({=XVnaBg
zMEtSZFZ=U)imDy8Vgwa`ddm^!j=Trse=K+mQpAGT&gEhLz9uG=@4bH})_%>wr<dcu
z4)|j%a23~Qz+k>UTzMio<CefzuM;l_$=>>qZ0s8Ws2x9;+}QiwpAx+>8<B^8=A><7
z@Y%$HC<Jz0m9v4u(`s&!L9DMidhtwN*#^ITnh$%~?mgo9s!)~~!nqm8P``Gzl*{K^
z+YBiPQYbpYnlWe?nrZUkMUgqh^I#V3`~;711J|suurL(4pPhLFLO9zjnG<dE-$9J6
zBjHrC@9)twFyw8YA1rkxd$^{@#kB#<E4_Fz->_B6z{ZAO?pbx&&pY(1*4YGjac_gi
z*0yIE8?=m-m7kYe7%Mo{{K53(MpB+w$^Gl}qWvv`G4(2W(&?8z)}Lob0(QTj&h}x`
z`AcbOjmVRyFiUF0_jCY4wiz#{XVS>}8gzSek4_?EbX13o{dFr6Zor+Y+R@boXxQZk
zL|H|}XS<>cC|m|u&|;x2Ozd>py+4f~zx)OR6$SvG%mZv$9dG_2_dqG-YdfLtNjtQ*
z<Qh?ZpO9Z9+Q=gie}<}INZft(9u^iBh=%A19&hZOvj8XpPTm0QJ>s#z>58Ud5EV@a
z<l?#ouYEvCsSKjd&IxlWh@8H@A}IV<R8(Be-ConBrlwl9J2&nKpgnl-fOFm42*>2~
zJSmTT+&@9FY`1@&cHmRAHp|eYOiM=+#PQ($u6xO*bHSBZe0+RQPY?aGXJM_aQX-J`
zmjVOD(eZ!uf0yWS2{??u^*VDlTD-Wtr1Fgrr=PYRJMhmkNmJKa!@2Qx6wF&P)XJQb
zIR(<v`D|qX9i5%8Wux~td_yn+2C*7QBc`C}PUE*1@;`fyrf1~wP$h|d;p+T|l$3NI
z@Z(zUnCBMqQ$|KcWr(r9zWz!WXR-cW8SkpATrFB9^)IeAk8hiJ34$b-G-w-0f%1Z_
zAhAs0vBV1u3<P;7kNavre!~!dswzlO3VL_lyJF}x;u+OADI~y3Q0ZMTIwsoImyaL^
zfxW4+vRq2olc-fm=1la=zC=Z15Sky5k^Rh(4rTq$Gn~S0P9fkx5fKpq(xoI11Qi|K
z5RKQgG+?rwsoJ$(wz8&mgtW9YyKz%WPdrn6a&mZ&#bN|gR*qg-@9C;Z!0{i|Y>7;e
zS)#}V%+%bdlkp~j^J{QSDy(iCv3nOvs%>HGT|Ws`I;}SbY+b%Rehf5^PfT1nzK-d$
z<`SF0mZY8MVmsNtQWN)Xoe$LidTB^k<$vyG9CYoW%18nfsr9qTBWmLpxQXjCX&IRR
zSx7b=B(IpaB&3ShkX*MEE&5eh7DrGh)}-U>xq&SmZLf)Q%*hA!TIv;IQTdf8Z5}cH
zt8FBy{bY;-bIPW%5vk@RHuLnEPQtzWA+!%3$T&HbUWJa)>)X4zyW`?=@Ah)aj32G`
z-V>k^>ojrNQmZAq9~G_b_+GHS+V=PR{(e<AH@B$hC)3PNpF#y3#Y)T>G;870=WKho
zr{q8X+IPWSXX~9PjelWcVs4BN`+16h;$>)Iy6S4<$WgA=wgbQ2my)L7=RIdPVi_EU
zhly#a&jlY!!p_Ew4(?!k8y6Tv^Z4U~iYMyt8f;^Guy-x~AV93<H-};mSp!k-pnlys
zPIiIC;W&S?tH-I8ZcA&s5-5{Kl{UH#&yMs&>lZ8Y#kjwg47(c2;Grh;h??{=^c2ZK
zu?_~;U&ri0`ZQ6DS*XoUHjqu`xOA3!#P08nMr`~G&_~9kjGnT1QNm<Vg0i0SfSz(s
z>Ju59n3J2BmU3S&fuWhvQ0R$Tao{`hB3EfWWyD=Z%n1Fh*<i`%le#|ung0nCafuUb
znQB)X=6IAn`eR*)bIpOu7wx46%R@9MMptIcQz%hsa;8bg^0~prIA8yk$c&rLC;SHF
z1;sv@%bjkF!)hd((L+@ndPCT#gXu_<&LCNOs<6PjiyzJPm%maOlNnn%q!e==66GgQ
z+FLh;CukXf@2@Rn^ibm2Gf%>F!c41+9ZiViZUAbJ!36qNX}Cu;vVrvM!HZ&{IOGK;
z+#?q$!p*}o5;1{GFLxY}p4Az?Mc_6Rd@}uDYI0o&(VH3)cxSyV0Z$kCv+zf&(^$s?
z8Jsm5F`p!sY-gr?NAs-4o>MNbbycbhg}2<F@wUs=wlK+8z)b}iT->~_PI%}yA0L@F
zHG*vuMp#SPUq9K;jjY6>hpO1XeVjH3cq;Kz0-R0s1xu|VPisGG--Z!MSl{I5`KS6u
z^^8GG-;Om@?Wu$PYb)69?q7mgiq==EXfqP-nn9>*<3J$Xe`GzF_?%lfEdMyv+ZvKF
z1Zh}do)c>@#?f4R&7LO7IjP_oNE}H^E9L$A^Kk66dJPDw5~iy`g!}DTBv<FFSsjQY
zIPj8Zpt@c-zNx)^8f1|AV?X89^i0e5Vb^;MsPR96167_4e^@Fd%I3&c;(<YxCxIN}
zl2M0+VV|N5F8(SP2QgC#ROHz_X(X5NMvUog)35B?&VJ{O?n^s&0&tFFB&Ub!={Ji<
zzPT0cKKn{=FVfZ$LLKuM^#l)sWzxgHi;f)#`~E7yi$K)<sO7P)DXXexq|3PENb>&d
zAR&Ehxh;r-?LH+48P{&um+GX469ECX6NOHwm7m*@$asIMr-~`(+Y!<r*yv3(+Eo89
zS$I>?Sqpz|x6NMGzF;&8NF8<+i$ir2x<mtNpo($Y5e`KCQ+Z|wCFcLNK-vMbVX=DV
zPZ=R*64}p#89mDX)BoD?zSmkw+Vi@3o4(dCn-mZbiB^n9>HAu8cVJ=Xk6%51DZlvz
zf>$KE>)-f`uAz*zh%j4-?W^oDryZjLRQmDn;Vj&5yq^juE^6`eb}<rBK5e|DrdLtI
zS#UKrXpXGTvrge=%o>b6{uI5$ZvpsVL9<y|^k)GT!BOi&ntJYH*syEU$A5yx(~Vk3
zxjcUr$dY8$e`y5c(Wr4nW_AH?QztyrALvb9dSdTxw6cm-P`mg3xBgN8;tutoxZ)w^
zwax7Fj`bJDO3fJHO8Rk&Ec;w+XTny)aEmq+h)P0<`tmOqWAo&#wsxm&E{+G|X{d2i
zH@uHNe8PzWYMTehLJDD@ZY9(nJ%{!<EL5BqEt4l6Zh$?acwhs0TE+yn+w?;Ve6nw-
zC1cU~Uo!S(*wsHoTa8n>=%;c~r*e_Fa?y-(5qc>eBXr-0&FiMOAEaO<CZbLnHs%oO
z?1gQz3(?kE{VihUqQ{R%u~MN#65GFe{)1W(P96Ayr+gyO8vm$?|4u2!PfQ!QO7H`e
z_nze*J+or$SNoyCVigs^^z_%<`U{Cpzz*tC1p5(Mb|qSy3^1M7ov{i3qk$sCElLj+
z1m?OLfdG#^>so(3`M!_PSI<2^2OI66RTb_*?LwBK@A~w=+aX^KVFn)>?0&4~`NyX9
zn2$jgHk}5dqL<<`Sk_ae@{zuII|pXpu#9o&Im;P%W5|<tmU}tXeEeO=^OnxOQIbR8
z$7QXWFde}u?a+5doJ!CBhpU@PH}Q&q{6N^Jutvx)q0d~Zat9~bB-jr{tNRbhB3FCD
zY%SKv56+=a+uB2-qL+VXu!0(p@A0tw8(^zd6Z?}YMuu=b=M8y>sh<j62R<Xokwm$#
zzD*^!&*|;05LUn`rcmy`d52|P1}9lGa8)dR%^9Xrb#b*)!@L3MQ$yv{hQ%Eg3!vCk
z9!LYx6z6^%XWq1YQZ-Q<?7y%p{GZs>F6ahPtIhJiusfefQ%{e=?k4rW2mDX$Ha7$8
zZazhg7d#|Q<S|wMAK2B8dhCe00O)fya)(;NBYGM8Vz|ll!*J{-`#I%98KIS>W9&B-
zOL|$&rBv6V@)R@$>L?E^tQZJa=e|xCzqd1y5nj4!0m%^2Km|sH{|mgBFQ~xyq^cEu
z%z(zTmcj&$y)BjY&r*2|xTsaz&BGJ@&d-yzuN*DziR%jhsFlY8Zj(d!*B<HJT+d-n
z|0$!XWdhYgG_xX#yG_H@vsQ}ar#?9S{y^@uYYU;)e24NIS<txyQ6DQPsIz7F<9~oL
z7sFPTTt-L9e&9&ucTYFxJP7+XRJ3-Gr>5Z!U-0RRtKS}JHoq8HuCQ--UTDCZxAZ|7
z)gLt+IqBzZs&in{^R(>+g*63@qG1Slh#X8klf!gzTIb0Y_R-+F(AD+wGQ9>P%7~4i
zp|^xz69+1(#RyCsLVO;PxI)w%sF8EoWM4x7Q)-|A>98Xu6^){h6slQD{szI8eu~^)
zi|(k2+MNR8+N4oc8o7}7A$yQvih|CTLX*D=7f{OXGcY={dud09+*FS&@6YZY*;xe?
z?F1h$f3Y>$eaz4pJaKxG7$4sB8MQ97GlzCl0FObsaERsGu@^uDB}&BOm3<cDYhUuv
zM!}`w5-vcfh4cU#qpnPNA5pxC1e|j!YIYD01ffW3vs|kxOzEg5&Z{{A6hk&T8l_^(
z0~ws_$}B;!(O0n1u~M9;P?v1M=9%{V&;sx48fM>`I5t8bQ9Xw*k_6kHKOj&dr)qyc
zGP+XHZgI>>cl!)OpKfv?-Cv1Xj=|}{F@_|HI12=q8l=xH4+It``B%@=mwRQK+a@^v
z2BB$Sl95{w%hXAOQkZZ+iJq@J`;qw~F5YJ&+veP#*<ZCbt>&Rf5!lBAS}kQqO=U+=
zh7@gVLy0(Z5UU%VQ^Fa3(O!E|1(tcf3W^Unm;3dOR$5(Pqx5glT|Q~qId#TXBk645
zBnV_gY^+;l2$8~t%M6!){0-ZX;oK+mOopgv2E%VZv5f4+)$C^!>}Sv^?qB5~XtydY
zfFECMv?}EXRfSXe*3SQ66MaOZIErB_Mv_a=-77j|epEA3VN2m09VN{ID4e|ht2-zB
zSpO}YE&mzR1#T9f3SuM+EC12W%ykhRd%)p2>$yePzAG2UR40<*U!3Yo<9`k|NPcAb
z36wCs_5kNdkj8W|XK<S0&&aw^ZW72)+Z18c5(ANaBdA0si*j-RVCX(@tA$YYWpZwi
zF>z4+S(*`D+E1n>-9oX?S;~X>Fw~Qoc@A!7aQO)C1DLvyOt7#*V|<Rcj2wjh(}2bD
z6je5)D>!30AoTv-xdV!+M@Ob0Mv=6e>4Rm~r2=U9%S{gRinZ`%vT@q?CTv+S*%&<q
z!HDiNZ~&+<3!gnjf1}q=;(4yyNsrj-FZc^9$^&GynaRDWN6-AZxg!`&D@m$LQE5rC
zpX0d`sxT1EGvD}A91C6oyj%84B{JTQOi;DDg?zv+UGL_ofPac+xW@<{!G!=6Z=JmT
zxXAsEw)46Zgn9uTwdOE&GBREeo7J}Z)L$Nm0?%*jOGsf?O^KJ;oiHQQc=04RuzWZc
z8jkV=@?rfWPzs>dd%F8E&&z%Smo<>7<u`QyN%jroLzqGIzhc0>Is=!L6SXj|ioiJ~
zW25GDdq07-9(p67jo4>kho;sq@zy>NW7bTDcJIsR*H>Rf4Nb$r1u;VuYW-`lVDou?
z7v22Q@-T}5qPEafj<hcFude&cZ&s)0l0AvP0BF*xf=qJ%tuwrTK3ts1+xE3aoQNPO
z@%LQ-A?&|2fzg<+PX1#;^UGR6Fphy5mtqOm;I%yPyzsoY1~dKsQrR=)V=)R9?bPBP
z(cuzPGJOj4SAGr{(&&fItkewlABkw60tUXca?S^30P;)%sxr5lOS#NUPA?``G|>iH
zK-3@|AKKkhS?j(4dKoJK#-)|A2N3hDbedC|r6A@|to}fWVra3}t}Q|y=Otpsd)xhm
z9W@GF#Ky|t_PYjv*2Z3KuU;5)cwaO)p~gW~Npx3PGN}s`C=qruFD`6|)l2@>I=aem
zP34_e$~!vB;=8?XWzHJ%3I<*Gwg>od?=R>1Jt!hyc7{A%Sjm`RvBS9eicr^8=10>?
z&?>1$HX0_V;bePUIN&ZxOvt$wrTUP>+aOViR~z=IH@=a5CJdpDnBhY6#B`f*r7wO7
znz=U`g7H2VVsxLjWRq7x3HsC@3evYkEt+7Vw|SYS|NWY$&dgKBo1;qpX#5K9AnyC#
zYxe`pxyuDaExJVrDAN95H=||!(qGe~E=Al(*=H6a)T5%JAfBjMTNSGt-zF0WRN8%A
zWpz|gbCz_#94s@A)gJvVE+Y~poH;)89rV>32XUmop%!-D5)R#Rza}T%8O1mpanrc<
z1b4wNPLOtWV2`JczZ5}j&uRCG*I+#v(+hKq88+e&B-p^5OeC>XQf-d%LHh>5rBB4e
zZr|z)`S~RrH0{6Be&*NypmG-IB@m=v{i&IzEfr+GQbEGW8l$vt8li>Hs0fyMxP=pq
z;gpBNb#gKoD;@NjjlhjG@oQ+r+cC49@&mk?tM+zOLQ%0gLPQE|A!kw`dyJoz<CCU9
zB6qrVMUwg#VJ;+qR_b0}REYibua3R=f!^<zBRQ~=5V)WKFzOO}*y;a6_S*Je(ADe$
z!L<!*BxZ&C#GGZd?KSF-k(xYfipv9qwh)Yz+Z<ANQ&bWov`~P_RKL}pZA)Y}!{ba&
z-)Tsp$oYUn?miqCHt$GlgNV2{<MOhH;H9Y$DuT5p7p1DcY08|1Q*rzG(+TrwdZYMG
zClr|$gQQtTJZx0jr2c_@`czBu_01=!^=E!f9Bk0PFgC1-U;d<6$6bsSME8)b;0QnF
z3GjAoQjjaz&c3>76b#436g~RuG4pjyZow60pfJ#z^tKr!JIQ2TKAy@2-AvIu2H~G(
z=-RQzLxyIJCjJZXdeUkvcE+jf^ELycn~L+(gX%}sclm4_Y9~eDbGxGck(p~8Erg9<
zS%12A@=>QnWB#W2%g$%BE0vW7@~e(#-v)$!Ea8v*(&x4Jr@QOvv1CcA*6DKcG(*(3
zap3y8rmpoQnebWI3N5*m>legp1Lc|IKb%;~y0<lp?z)5d#jb|j_p$S}|M0~zVxo*J
z8Y$0fz7u#;W5+d<COCin?^VI-WNL;e7SrjEREE!9q=!88w<*U0d(5OO6G54Wq4Rf%
zrhj=(p~+Cn=G7oCrH|>K-@%Z1{J8GW;P&l4;hh$={UhuZ=SFA0ZN1$J*4uG`(iyYu
zwYRRM2x)+ohs%JC0XYSZyN4`r!&3tNNWVy{UzLC-cGb2;7`eEWAwFbJ+s}+}mKl@S
zjNYZJ4fVv#33SJa3@(3x<A`UfM$gx#-*mm4A?3;qwld*p?z`}n?XbY%Q1anoc~+Fh
zW^nN!nYAmrh0}ZUqR)wUyK1FpW9P!d1;X-;zFZC<r}B+*l(22aDMPjzudY8d$OE;c
zG>cLNTl(ffUb`{bB;gLIE+*-IN#MfyZ<l3jp=w%!+dFTjzKPyLPlG@OaDS}@+C0SP
z$ff6N@|^on`1@;4q=<9-J3~|xKa^>R$4SaBe`Y-IaH$XRKI2~ZYXZMltretFp+Q8|
z>QJ^U=!w!V5%f)d&#g-$&^Y?NxF`%x7Eqzi0u)vq4kjKZKWuzcx5B*BCY$OPBw4yX
z@^_J#!%C=u_=G(?>Y>Xf`PLD9@$E02b(b(i3bwKdC(38!GtEI?zU7?x_BA4zcTL5|
z`z=!z#dM;t^h!;y%3#|&ek4ZoslgV3YKzpm_rrVMqzyAT_YEGMGD6E`@fr$wtC8P&
zgnpR3+BUt7kh$>0Sqn22-<WBL^?<ffk6(U5u&gBWBhBkWsieogATa_bh;UqUjErzX
zf%i&i*biJ>Gx?l`Upt6+e<UFO(iG%{A?qigxUw65oc+4CBgP4%`q+E-%=Oo`f=y8U
z@oPu=reIRYLKQpN7TI~D<TlgJ`Xhb5dke7iM&A5mT#VD9V7gaQyAoU79J%nFg&8L(
znI`B4?kfY}4hg%LLOWSf`>)TRhr108!?^tg=I<mM2rRXjKFb)W$T}Ebz+1m0f;6I2
z*7A$g5TVKw{}50r7hu-=CwgX-)zUGIT(O-B%(Rz#ihk&yiJH_mb0loprd;<Vfz-bu
zU?Y_PWyQY!Ge&#^g6ze{zb5B%C*lzCmQU~AHKtP5gm*9Iy+pg?QQ;Kk^__O|IQhh7
z0l)yi%x$~h2Wgq{_HuA1fp%7v(EYC0#onT&$DI%<>_3W#Mrsjp45PBp4}%JdWNnUM
zMhtkHXth`7V_~uVWG&h8j)il9yV70RD}v|xE1?+fFP}FUNv`t|h!DsjJ|@ClG_8eE
zS@Ia>9@<T-qW;zfC|frZ+RRA0Dg#$6RwV|hrxaZWxkme@6#EuK4fCUk;UCe;bGn_!
ziT|wn{<XZ;gp=$$&#K_n5ee}B%h=~lS`ku=<!@LJs`lUebQhpryy+YngF{OYb+WMf
zE*6o1jJPHT=6;3D0s8%E+_VIXVVUflbP<(Wx3Ym#=YCMwyH;+XsnVF)OiNI<Sc%(E
zk93!xGC&L5t?<8dm7CaK;|FWmp}XKe#+s3}iNuQRjOx+(*lE#bA@D_wQFT>|-HODj
z3b)TWC?9;}C5yz>w#9{Yu5l-4bQ${7x^MWfgMZQoWtE60b?(4gX0qB_z6MKa(@W=L
z$n=MeR%JZX$<Aw*P}H{kslLa&teqVa?1D#C{j^H@$(scl9LL$%?wT1KM`XnNFALqm
zsWLh-yjb7)U8<*-RCBW3UAL#sc7^maYL4x!^BhifpN(B;TP{ud3}BfXi_tW;@2Gao
zUTxnmaX8yMsz|Q*c(A>!YjagTAA=Rtf5!Gn5_wWS`7-yK<&|`X?y>M5xk5z{#aakM
z5B@szSs{AT{QEmlfhXnCyfT!vkJ)qiKcyoWt6kkG@4kQWgRHF+v?@;2VU2}pEHGLO
zS`3@9QY-Q?1fPSN(}C^Oz21RW*T%hpgm0bCup+)K89m=#+(-z5*POoE>A$R9V;y?y
zEOJsRmdBa7@Zd<{Jddj`BjtF+RQWu#<ygzC_zIpa*~^8tVjb}9>gs?H=jmf+(%T#&
zjF5Jmdj#~)g`!$N``!tM5|e7h<jlVe!N9=5pd9zXmccm%T^DPN6^$}`OzN_KecXA>
zU{Gc%a{dU2MPItnicbdJ+|0%PFkC@QqXaFQL@nu7o42X<X1Mgw!ct1-FZOUX<szk4
zuh;3e*0yl32KH1UnGbfZn?j)Jg7-G;UxU^qgjbCdoJbP;021T>_erFY#0(J=HePL3
zY$3z;WI-(ThUd{GFG@FK4gWCI7kBBoBgXU<U-8Eo;5YDs-gGrGLSe1&g_~~7axra>
zuDFMIGG9Gt1;?P0dm@Rpbi4~}1?)oTe_V|#@!f=QmZ6#fIIgy{)nVYC#Rp==IczWm
zR+uL%j9s~?1Ft=w1$M#)<4MpO(>=V1RxbJk&KW5eh1EC(7-hPC(PAAq@p{3M_^QVn
zG{{9AmK<F@2t<$klvq5z8P-s-J=*h~;+ZZ>NZWjG=*$W`0q2F-U`fm{N<E$|gCsrO
zJrkCP4wq4<Q%D#AA`vz%HpL1<GQ&KlkhfhsQM^F1-tJMrmm2qpRllAg`8U_UYnp(B
z#hK4V2K6hS^Amc(TLev0>RNU#*eE+}6c?uBTff$ps8!<NVUws;tn{pm#+u4HTgxs(
z1;Z9DzDl@`4#7lXM66`(;W}W3yo+u{Z=D<5B1g}2JiWcI`fa5t2-CRGVw^swh$?wT
zrBz!uYRTEaGc)+{6wl6^zWVnV=rxx?ST}Z~hi=Q)m@#O#H8XVg*&*%LV-G&m&N!oO
zBjW9e_7ROpCPhi|d4+kZZ^=WOgbomQiJ*DCZ(X*H0Y2z@=m%0j%~`bk$z2UZ?I7kt
zD;GGsRdcD9F|eeGI!rO6zjaSE)Crx;1|Eq(V!yk7&820ezyb@l5ALifkifcmY4FDJ
zhzhH}4DK@;Qi<<p5BiUDoZCkB;H?G%$vD-XU12=Y%N*TwYfxS)`qn7<?v%uqejZ|Q
zM89BimmICJ!nkUTY8t~oA0Bv@k`1Qc;Cd7K4pV<CEvtN;6n-4jT7;*OY!c2|B0Gi-
z3X1~p{Z6;_cyRTgFeDK7g!6@wYgi20U;-QM$xl}p+dBvNakKJ!`?oMH)=p0&U9S|2
z%L&nvxNM(j_O3|?9TG0=H(k+0?Q?6nG>(LcecMVh7*F$F4F2SKm++;sqqcHDUc%-|
z{tMjzPK7e<ydwX+-VZBSzYOg&v5;K$IjNbhc9!+R9`7R4UMj=y>W@GAn}NJ2={2@m
ztN$3jCIlI_Qxr-$Q0M&a5m=ABsM9KbRIbc|=sIyUHc#%w>c3;$d68w(IY|vM=~No`
zf&UH{7Orec&>BK#d|V&bQ|Iq95oxzpuWt}jtiqy8cZT6DW1nea>cWb&3X_?-iG@j#
z3w9K5x^uhvV4=6av-P}7g=nJIW$PQ*LA64@-sN&%4jE;txS7T;cYHJP-3GzN!wSO)
zo(hJls4ms*oIthgaFN-U>JOO}lQXq8W#{chL#Z3eYfIcGJkO@@-9y&gl@tB#a(-CZ
z9(#H_C6rn-R8LHP`_)qzrt=tSmh}yBQ@OZ*h-Ga5U515UmgSF*y-~?^lF)wrXs^gh
z4pp!Y+{3dIU4eru9d))1?vVsP-+*Si@>J9u-R9p%28630Brzsiw%SY)<Iq$D{&r%O
z41vG=hORK4MTxCb)4BJJzLgg4k^lXja?x{`PUzgO`3!f8le3{-(Jv<-`)5b)mto&g
zv3Cfq9f4e9ebi;t*B5)*a&M~v6X~%L-rEc{>_jTua+e#}U!N-fv^_8|Xx1v`Lf+0y
z>kqDUuT|cw>+-Bt33|L&yLwl!@ULDaabHQs;nqt_k9E<vg<8cCa34tFj_}&(iShjA
zl_`f@sucI<`~G@P<L<9xUUs87F`r)H<s|L|GJOjRl-w64GE@<^#w1}C8^e38$=M|^
zHK+bfjS%sDdlgMIc&FxJv1O-WO_!jV_{YWIbhn+Z$yX^>mzQ3hhP#HwC$p0Kct|el
zV4|sps<|3Y54U!n28^wtot>4W-=A8cb4cE|agGxX?G5h2J@@Ok134eMvCNy)tkj3P
zDHi;3t9khit3k7VZMwOsJn)MJI;>{WLlPOlIfr@aP`gm$O7bqI_xp*&ly41P-Pl8)
yWmQcS$IqhrRz$gYAdCCo=R*H~L7?LnGu>r_7mFvM37oq{Q<7JgE0uX0@_zs^HPN^L

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/packages/gluon/activations/images/softsign.png b/docs/python_docs/python/tutorials/packages/gluon/activations/images/softsign.png
new file mode 100644
index 0000000000000000000000000000000000000000..739e105f4e0e4444635cfbc7d388b98ddb0906ab
GIT binary patch
literal 15627
zcma*Oby!qU+dfJooyyQKbTf2^fOL1q5Yk=JqJ$100)jLmAYIZeFd!+yfHX)-cgNZD
zzTbEK&UMZo=WxNy-ox7KS@FdE-0PWm9W7-7Tq;}?6chqg6-7N16x0~t`tAu9@an6D
zegV8;_&!%Pcmn){KCzDj?s2?SOnp&M@JNw=sJ|D(?|=`*{gh1n^gSK@0&RU9P~2_(
zyxctf+??$g0vvpNojpAm1b78_1-Ths`uTZD@bUeh2E3j=PJB${ikK)U3@ECK@&-ZK
z`-{P$1{<?S$IEG9%PP;&Y;o$UQPEyY^2U97hsWuGnSOF*<{oBNX<}_of4c(>-h)y+
zRVS&OE^D6p^5qFT?z0c<w#z^0&{YRJoj$fK%Z@Q?{IJhaWgR}h58d=_yv_C<wfCuN
z84nu^>jOpWsc{$%49ugl7p3l)2|<827dbNE(^9PepEq~yv~nSRaCl}<G<Jw2KG2O(
z=l#X}Bs`<}(~$pgAAiu1&F|m8+i7F;)cm=?J{7_nX9getUjkYksH3?=vJyr{G&s4q
zI%wbR*vreyGqbR)9v&X<>!j|Gywb2692sGjlF9}oF@cu|mHkPf_`$)!_KuDpv$Oa1
zy?{niKAftGpWdq^#_hS-wZskPDKaZ42E+|AmpGg%t>>=^cXdDvjEsJlXwz%rcEI9(
z0T-1VoE!4FF3_N-8HkaS@<`jnndde*k#T(}U8rN2#b{k5$=erL1*!#szq{J>ayOFu
zY+%>8gPz5MG%Lc;S$xHK6|fge4IGr>3FAPS{uTOKw``Yl=*@jzkR)!=!nCO|%Q?1|
zn5L7&rm*lMH@Hqm@wXx~kEm!<FWNpgwTPR|NCxEV*RM5!hgJ{ANfF<h$AY(VW^j^o
z_7TR`EL>dW;WxP<2e#w6())AMliIS2Hgo2Or3&p*WJ}rD7iONyUsBB_xFF4CyIGE#
zdD4>^Meqy*sD_J$#rr_Nz+lhzV=N-+Gva^${@FV@HE}}jZkHbq-VBaSi$S=}8(|ZL
z%1y5331rbtOZu_w4@*==<Uyi@MGI4=@r-1k6`>pqdtt$@c@8eFHEh`{R5Df#o4r4^
z?Z76ZRG2p<#*?uLcr1t=wC?hjm6e$^x>0f&RjmAE%Uf@p_kd1(QF8lJ6P21u4opEj
z;BaYls#IT9Rn_+H>X=3<h&ZyQh96}wK=N`ikRA$$*<S3;T7;b=+HViqA`zB@p5dp|
zteTm9&E9K&nmm_HYw4t4rQ-3me^5<fg*gBIiiN$^|DHk+kk6`-(fvi~eGt%*s=E3b
zAe)JW<?Xw7xV$avq<{jN)Y;>7-EHvg&H&vT2d_k7qcmU4xqg-k7C_4V_qzy%+w6NB
z@PMA4Uh{tAJXJdz6rV`;jvqRZ=&;c2eV(^`bH3ZM{!u+cGLV-^IT1O(UQl`$ZEIrP
z<Wiy>N0Z<5Mj2Pst<pMAMa(7`I-*KqN+7jAwIW3pG_{y>KNH(Fn_{?k;7A2p<a@n*
z=cSP{EPH>Z4|F>)Kp%lGr>jenmX^kCQWNRFI}HiF@(MpIq_4IANlF)bBroEzFt|5c
z{}~XzJ5#+i>y)=qm?VP+OoVR5N%oeqX)%Brfj~@6i%1C{KinSro^9&}94x46X%QB_
zNcdaTc2H(bM@>B(xEwa{p09JTeq3g_&S99Uf}Wm!JX6^92`=v6m7eGYpJAS_MMcNJ
z1|Icb)7Ll*KYRZC`QZ3C8!KzfR$fG|Q(oAosi`TfdE`hYOSGMi|MsIGwL$9Q4-|#l
zx-&Z?1H{S6nIYhSE4tu?J}z~NKQ8+y1*o4>SomSrqYQB6w%nNmJS*rhM9$l~4Mu5n
zpNAhTw*8x_rUzumSV!CoQ&@P(mp^z|=~l(d`+MfLTAXXzknulP6i{0CAZamHJn$zs
z#iYR!i0neUL<K=lqL$IjX{*_2Pg`@m*Wk4Q`(|{(>m(v>f8^}#nQt!-*~G;^ulB~G
z^G7^{cHZoiKWh>WyYb5nzi|$~Y+Lr;`vZ9$9Zk)>-0aOHx)j7DBO}Ai%*+qy2G($O
z*TX9mR_)voz*NTPI!7JX24o)~Yw(0MIJIYmC!a}M08ijjFdHKyBT8RipAfZ^g$0d5
z48HB@=IHvyMs37n_?YZtD9XcGi7dqS2S%}G7922}9am}1SwLLVf)8G2XXn}ed^)f(
z8I|TuX#D;=1|L6uJUTvRWMS#w2-(s54$p5DPnrV3AWh$E7EolJ?Ph&!TG;-6n_S?#
zO%wo>1#r9OMwMp$fL08Rd=_lk%ygY=s5JNNNb?<+PEV43$l$7O|L8FM$@^bX`ZS;C
zKLa%z8wM0WGO(T54R<YDxrLs~oxs-6wk>Mu=;+)B%)k1#H7dG1^6n`YNZf~c#xjr=
z`~y%|Q((ebBJN;j<~QoREnoX@@iRlT{SQl8E~koKCdm8y&KZA<wRdnJAtn8>-?TiY
zKqTFV)VslCCh=Ei?39$0DD4k7yB49x{Z5tCbaYyPN4P%&c85~H{)z1UKW*gT|I|X2
zqfY=eKt1fZqzh=v+1qoQuC`t-HhKT<or;D=-@riZjCDlEg8>4Vu4!ke_{PQt%KFw;
z8*F^`e8zeln?AHV%d_(*FawLO`|a(QfFc84gg#5uvx*N0D@mh{lCF{zmVP9jUH2jl
z4Grs?n<s$Fh4}AGiFEK)mzBy0T7BB;5KBFYq?=@kY1&_CNr|No2LTpycYkjRcoZN;
zwVw=eEd;G40E@^EsPjR`<x>4Lt!%hdczC!*27eSL-ZLFj)1S+a4}ri#swyfgGhGk<
z(I-gT*qyF~0=^WVUM3s4M}vcdMgy;w!!Esh2}F;^Bo6`g!+)-zfSMuf`V6p|uf@fr
z#Khge5J!P9F!~x3ulaH*1c#8&ak5yGpz$d!?dbMI;cUH=_I*_7bcJdA*>80iy=*QC
z(+ht0x$ie8coe+$faBH36p9CQyfFO1f5vhE(8dgYTQu;Gzy0rl;d#vlEwLDi<)9*{
zqIWjG4qLCTK6h~`bH2;Z?FhLPaG7G~Q<}O~Q&-2q!(-y(ODfebIRREX<->=S{hD_8
zZp5R$<SL7D^i{_0D%`l8;{A>{3}P_1M{Yc~O-2=&=XtAj5jWTb&zSl&<eX5Dl_?z}
z^7idp17qXUtF@%dov*)GQa7_HXQ-lm3Vs`@+qNWp!KLm`6M45AD@<<uhrP7SS!`Ul
z`0H<~CWM*nU)jh0e*Ad6-Cric5I=o)cVive3SLNG!f%lnx-pP~sXq^<1A;u&SYOAZ
zu%<opu1;_bbEENQ<L!nB?oc^LwZ}u^l}&^-`1So0q7ydoU{b~}6s$`e7Vw{>IEDz<
z@EYN+LT5XjAu_f4O55cLFTcV-sP4X=k)eWz>eCYSN@L)yr3m3PDrsq^R5ZG@9srKM
zk0(54lMssRB$0=yJYkbyjs!aW^879ym++hHA7u=4RR*cSJd~~~U!D)FSVd9=+!S{T
z3@*f}k9d0lNB<-qw>v&VVqW`Dv%TjOGc-^&(q$=m$0=rN@Uyhl*t=-h`V34=Mk;3!
z#87J+g-=EP9JmVmffkQLzyQeFCXV<*gZ(gQ6A>EL8sXWM2}(VA^V{vo``BM%H8XM!
zjBkV@qVrGiq4+IDhS`IBTS4LCY$se|xz<{a^5TF<aZ>|Tf6ZR&t4^8c&lQPrlI3AL
z6g{xy<8$l?SCCxx+RE}$%+i4dfD?0dGSLf;iK$#Q)3==Mju_e&KNuSH6f6i3ZlwCO
z%iyH?e#yV9(3cEjLZc+6{c-h`-7%q-RL;&G=(UIW1r`bQvp-DXvw`HA_*Qkcy;y+t
zSOHcNd^##R+vrZ+wDiigem5KNt(JA7zYCR<1VS#{0`}*W%*?2&J1(dqZYJ{pA6JqW
zo@sr*V@#J=LzcQJm*-nSrLgF?$A1W)A~2kD5zjV<7cA%v469+pYNv~cUBzPFKp0rx
zFnSkQNJ%9|g*^uWV~fln{`e&t=j8JIZ`<~R5F|*|vS}Xa)sfMb{_#rreSATE%5!&j
zp3YA9TWobrP3iLbj>hfIct&b0&Dgl`X}_PUhww>)^-l)1TlSEU9IIHL;cpG*=??GY
z^!)g@`rbnBvt{rTh}N>3`D6Ot{$>-B%UhU<0z|2pno^1``jLJ9{CR46`UJ28Utk65
zJr*VCUTqiLNam$;oBr6X?ivFeu}Q}(Vb__ejb8-{dy8%90J7Q~&l^WN{L?YX(yA)B
zN7Lz2$RWh-5C4xJKiGMA`aPO_AMY<uH`1*EoD5s^*aEPC-N8Z&KAlv?_375>YAn57
z{cO-yP9Wgq?W0YGhlfpTY*246_MubXbZG#@MPU^bH8o`b^mlbV9x=9qD8Ifxj2H((
z^%&p}Rg{!4frsfLu7|CONswU(2?haYOLgYAv!L$=OpZwE2t_jR0B~S2Y`H<N#eFxA
zcFL>kd^XgEhKI%dwuzCU<vj(T$1k4C3<#8JfpW13F;^j(*L(B4c`ck&6Q6)Q)i5R3
zROROQ`EPCVI?3{k$s)t^VKSy@P!6bPWC91UG^dxLpr5q`H^a;7uN>{J?34Z3%Gg+7
z(dD_$KHh;l0k{1W*31$TnY5AtHLf$f0MOe>2LKl&V9z8s^h6cqMG~FmGRpvM;6jfw
zeOMn+#FfH9`>EE$-IlEHaX-cBQ7=*3oks4f9S+`(GrdoI)>^i<jQS;78z;kj*bLIr
zIqvg~Bp}dW90^kpfXg!XKeefI`AX^XbJL+QRg|Z^3-Pq8nJ2i_N~<}4Df5r5LeXp8
zkJ1K#7Bdj*dweAJ;_|(Xft^?(DLC%njq9o5?)gaH3MLX~6vWc~O;)D+4?GT9eGA0s
z`wEXH>!4Lk02=l<{ujys;}Q@QYyko*rAWnU%TA+T+dTjT^BV3Gs6|ie^X~B@YoP-I
z;dfUz4nWjj@aiQvpR^7=#sn6=xV=65?k*G#qwH0puvE0QwGE4n3|#QW&I|Rqs85u=
z-4isbviJ#rMm#wO2bO^(dhC_DVczn?W#_Z)2^z@&7%Dnec0+<k!NF$Zub|yQirhct
zF8Bd$wh;k1Swvg4|8(fg6W8?(Z2eO~6m%*>k?bBCnqUVx^0kyNPw=LCwTx#3AD@rj
z)g7`gmd@{QP7~qcmU$X~tEgD-yggvz;Yk1l9QBcD1Hp85=b$A4(hkHv7zpyE=Z?p`
z(XXrOdzOAW=*Nv&uhj?8iCVcymu&j@<<`+5Y?3_HJl;l5gWovu-|%<Fu#8L^g~x9;
z?s(fVua+@eX}?PPw}tg!Qq<V><DviwNbguqm3A9KT7gkS<P(rO+ziCNP*NhHr2Gj_
zn5OO2Y{0Y#-blLxETKwUalX;r71CJTx^eJ(=cAlSyD+}?ws(9FsP~`({Lp^DFw=1*
zT&i8UL2>BjG`t$>2!n0SQnS?@CsVpzZ4`0mN=EInu_Tkzeo-yRI#EfGS9C;;zzB_U
zfM+;S@k9`QlUyc2>!XH#p&53GYGs=AhHBh~JBFAB?goTB++8E1J2C+azY9v=RaR0$
z$o_LbTItdFEKn3a3&1_S<*U$*G~?EV@wFt`v1lTh!K>pn2pmYtS8f7Qi-iz8f$2tf
z(hN>jD(_U@gq3uIcBk}29bgEfqvOPs(C<v|3xlV9C0Lo2PU?qay#Eab!7twTDIa2Q
zdoLUWm@9@xp?5!(W1VJ_@PxqkoWI<mcPm0iEEE6fK{c{kai=_*7Iy>M8XKQwjlR&)
zA&w=a{R?Q#T6YvW28Q1<mR$A2z3deZjTv^Amz1R4Bwo#p@zF5T;t6ZhAs$zdC{Ie-
zeAkwa2c-<!Ru0+l-f_HzNEx5^Y7~#BqQ7d+?EZxTLodkpPwiS$q<G|TAIbvS9UT33
zAF=Q+pbgMyN<l}P`dNN>js4G$9y#RgRofVfXR?Bp5kGfIJyJkru6q@WS)=CW+BK{B
z{%MG*9tgLX?B{YgD=~HP&wSBiMZ0aHY_H5Qj4^*h9YrkEpWRzp_0fvV1m93|A@I<Z
zsEk)qfvt;#L_{Ut3F<8cS0USy7bAW_i<%y$MxN!CCR(a2)t(Y?YV*VtYa>k{>=9W=
zz^K{Sfbi2dzskV}NbvH1U0Zspe;OH>02!m$Bocz##}5*(_>#;z!^HijwG^MB;h|y?
z(ro*z?FVOc>^!vJkUxj!Z-;<nc1PM<sT3$W(`_G1Nv)QGGKd--Z0qM+-0ZDA@<cx1
z#r8YXKObTO(s-{<6lt76moIx#D{NzeFo<d5sx65rkp{+?8a_Ous9UdQ4n)NVm>d{D
zbTVN#FMYO^U=Sd8At5JU&q3kD%`g74TKY9EJW@`#IMB`oo?Bt}5IBlDeK5*8xEz~R
zg1<6$iXC`G)?;w_H@>gtDTo#Pv-{En`>|f)o+Oz>PN?Rmvz3sJ2dtDd*<>Zu#yjM-
zikcc&P%vd-Vd0{ps<k#hp8?2t+Urlj1Ey0dC4DOtFBgY(f)*qpEgzEgfB3o*o*a**
zC}Lb6=cN;EDNkbmqa7GxX(Ee;6u(eBz&3(fR<$mG>dh5%F@D1!T(nA_s7nF_yZ42W
zU2$==ySeY2GFvlCSsor9nAzBx9uwFRbV|$Sy@M~}IbIQy)9u~`QE_lrZ(iont|agb
zJh`~sC`l*WIKYb~rrbN+|7Ae2nS2XmU*(a837hw=6K7<+)3>7hV`LEaji>#IeZ&*&
z5JiGF(4}2)$FxU~>#ldMWk((WT>~|!`^I_cmpI1pj25lO_ed2=$MjNhWFDMydv(}c
zi1$JVc7hR2z7aX}gS@K+fsOS{^>Mx_(5q^3DCjfsL@DIkfhGX84%o$95QJ2+%i=B!
zptdwIJBclFr^>~ev+`c4rcD%E`S3e$E8H24?Gg&;H<8J{{8Y&kB88C;^;^o~yrsNS
z(;V@P<u$Ldmlohn1GvpDZfIQ{0ToNB#dFBs_Z{%p>7$}1$E}`84^jpENK>_@9o*<?
znk=oVb>)!{egojtxpFC@GIfiYC*YgtE5Da^{P3d^f66=CMN}Yu)vsTuzLI@?@<bC~
zt-<)g)^!`mV5__Qi}W6n31|HcHF=WS+oFkn*Ef6EMdY@6a5JLb99Bpvbg2T(edAFX
z0@&AS&#pY)bGmVd@+9ymp9H_$^a>Y%e#@26qbrJ+FTEI&b<5#>=d&0{5f;-E?<sX-
z5%apv<-nx#?R|;bRqrT=g=B5SFc$~n#SMmN(Hz%fjeqN4b$oe*U97x+=|Jx0Cu6hz
z%Hc*YhPKqAz^^WPI<zm4Q%{R%zE;<T#+vp{VlONidKV?kTR+6~wiI#S>T(LcfVXvM
z7G-`xj({J_=Tc!|uz2{aR2~1ILC|2%<@$K}_uwGMYPGD|#+OxZp6aiu)ZY_;w(JtN
zIjrwISs{vN=#M&s!!IvAT=rwIm(`qxlS3cG_>@v!?~t28b;_iDF3ftH9diYF>?}X8
z!%M$zP1CGQE|y$%$%6W`$=&sJaukrm)J+FvmP1hb5^J=?8+vyZOM)KW<8NwGc1u%;
zc&k?E(ISc4{p7asS0HUKztfGI#M;(P-f_>#O+g>E>;8B98Y=JB<6Ys+B;WMhOOVqU
zoShHt_04%xJIx<rTrO&9s6UME>B01CJeH+N8?fu99C=QJ43Iy#aD`O*zhdwFeDPKS
zXRUIzbeb(jwn}<%DWxBXV7cMCJ^T22r5C3vcEri1;5utX0Y&@d_ZxVM%*KkpX{$rB
zKaEl(M<>pNYWEcy#7cw_yKpiTJSC#X2Xc}tqZv@A^rvxkYr4P1Q_Q^7h?!BjG#C0N
zKI5k()+?ARxC~@}IKf<&Qf=4n(2@Iu=>*hp^si`GSJSl~QElEF2wSaU^WJbWb7L7>
z?(Df)yIM7Ko-UKxoW4ODx;*K*1e7$NBsLd({V06sh%Hn@_~;pbN!qRzP6Y(MF8Q&C
z%H|WconG0Ry*v6NaUAVZ>j}<U1$@WMyuRPcA|kFZcmp}dqx`Wjns0hzQpunjbhhkb
zLE2NQHhCns+y)lNPuS%|_}dqmyJO2#x{+&XM9sMjE-l(8LxUaK&o&st#R}-Rh!W0q
z^3@uW*hr(xJ(x5L{}Dy?z%!1!%~m|!O=dXR{|_&`*u%N;EQBX}4)tEotV`=RxobvC
z!%whwcr$hc{c3Hw`9yxD0rsh)-5>^*m?j&l=@=#q5)9Aa99XxG`dMi?<4mod04W7h
z>}Ik3XL7+m|DoG(d4}Dh$=Q>>VUcsp9PUR#G7oFQOKS<(n?VL5%rb-9U@*iDGbUc8
zE)E0bG%{@rQ;~VX`Gl(5d!D^K>NA*>2RzZOF(6gcTD+E3X=}-oFRaMGfpACwu+CrY
z%L!5N2gz~zz0aUDpOkrH7D}j#;(B-mdMjVe|II5$<-HpemLvE;#O+m)#Ru-{SK~#|
zrh?7|y#J<`^Y4mv_hhm-cWyfMD@V;C(=Ormhm4z9*UQbL#pCA>w>RFen6h>Rq<*)R
zK^&jflufi-OD4$TO3n4P4o7X|#wnB%2D*{qXHQ?S)gci4T5eG!#;8W_s}VCa4B{fG
zK-diy(<z$MJ`6P~Yj`J6uq}o!QN6!=k%u2H?`5xWU%^MTUUz8^dEENwdvuD3P(t``
zH|ds6OEmtZBFtX8@V}3?>2C2_FTXM63k!~W6UR`6IQVWzQ3%-5v_w1I>n~<APh+@#
zQpjIuu@5udD>4GB?0bp%6XWV%SHX_i8IFfqLkz_0B`to-*K^TE;ZLE-!cwhK>4ErQ
zslg=kG1~3YCut|3`_eB4uIqbIA5-|xPA)tPmldDD@_m+_LR)v4ec9MZo_*^YHJJI2
z)Ik#`g7KE`_wz^Ru?6Y2%a<I&4Gjy$4VVZefV3zRCaj3LnjrpXrBEC8hx@r(@W^i7
zz^^MWBFvIhCu9Sbq86{aWqzN=`K-CWqVZ--+}rg{8$Z;7<8xOG%2;k?(4essGtC{P
zfdT1jd`zEHAKxPK7pJK=Qg<+rzKbQq__?3J8SU=0Bd2>q#ZuTiviSNV@s6%pkLI)!
zRv;D#moQTkM&LlJ{BLLOw{Csd%d1$9+s)-dZD-o(aP>IGfA{4Z_y$y==;M)j_=o6o
zP1eT|;Moslta^>%|GB37vrqTre_H9oqRt{rXjl3hP9V9O?4{q2uODa}leGJ0lo>We
z%9o22cBRNG^m36aQ*7_i;Nhn3-PKqV9>I$c4sc8gl*+VUcPqa=s2pBcYShauMmBT1
z#>05g-JG?sGzAPU4u`<zZ}&^r7;6-bT+c;%U9XY-eXb2vzy<mgt&{Ivqp&WaM%j9}
zmrZ+I*+meh!{X|-m=K@ayIp>1O%5b~t5O1daMj)I_;d%J{-Jk>{(K^Zj)})wU2+&T
zZ=A;KWZB5?#`#}<?ccL+aL}pZg9X$TFW#%f#Bux7-fe;Fl#vcaf`oGYdxa6wbEYNC
z7?w?@+4oMT+un`3N4zP)%R3zI8CP!h3fh~+<GW!oc-phc;&G_Vq3taRiHqYqLi2jc
zPt~lX_(GJkkw8Tl4#Qr;d?v|L&JX`8W_7S^R+ie<`G7uU)T=}kV($y|W;?V30>8c+
zx5^M(a*12I0~?SB*+;0g43i1Rl*+|GHftcoR6%$dYZl$0)3=b~1xO3!FYJY~s#5kz
zi7i=Mx-;>C{m~rGGW+FHQXs!^ZT-daKEDjq`H(Vyt3O2cfAg4`KB7nQlYSrKzQzwa
zlC(^tAG0IE%Oe~J_`>^`vgUo5i6T$OyuGPPwSI{04wYz*D=aZ4?HO|f27w{XJ8a#}
z-0VJ*A8_xICikL%Gp-e*6(aE86Q|5LZa?M^-xn4v0-;7bZj`$=<6SF1Vi{iP3~!j*
zp00-eh`s}*fhIzGK6hbuF_@g!_(OB_tk&cEKH%VcWon_-QBja44@V6|y22oa{-+85
z!N)ez{1XT*803FlX|rS5hFXKN|7U-lL~-n~$I=0jwhb>jee40Db`3<@5k|%_*RIy~
z;$eu}qgq2O8)>hRO-3B^dleLZqL*I&$#EJO53ke6eX=)|->c+9rJjk=|5=O5NOj55
zrhj|ad(<l%X`Qbu@>$>F=jx<Gwj^`0KbOWe<V^c*(gPW;_IC@bQo_Fh0P31TxkN9I
z>gdLWj(Cs+>nZM33Ea>#;0KjVIFuGf3X#kbhN4PAVH17%5;M6GJ%JI?cyw>p-%B;l
zjw+v^iix&p?;0JQM^iuGUYG?%FiuSeaX%}X`@uxC1A$kd83|`zx|VpSYwL)@@2Ejr
z@j&=b-;+M-(av9H&N$9c7VQ9LZ5tI1`#1Ca_H}&*<5Bc-uk0iGv=m|e!+arx13?Ag
zFGNV%X1A<IOr%cp_2J6yUm0{!H}Q|!J1Z~mLg!5&dHh6mzbiwiZ}8u81cnIl7MXpq
zwEMPHDn<Vgj-I;lqta8}-q+Zq$r3<guxvfn8UM@~BV!rN-l{i-L_NIjFryrGvgn;K
zPTZAqSH{3RL;dZoljmYhdn4RvHa{Z2UAT7-P;BzXqE4=KgxFkS)4{Y#Hvx~=p}y)J
zbgecxiznKJ+G`;a(ZX~82Enm(^>y&VlbIji!`xPyeS1-IHLL3tyF^hYfRoPY21I)5
zh6~Qf7anBr^JZ<^)MRy4eB9V|8Bq`1+4*S>f*wbli?r#YGF`?oN)5<_Am4JwJz}eK
z-x^2Q2n#KDV14A<t68LkssPWag=jMgKO@FHU5a$R?D>>#bDgH+H6=K2G=!;%U*?3q
zjSfaZp|L>L0t_z_MPw1=JwAh5;Nf+>X(pj<9nSv9>7!l-npsYmR7tqLGE1*oI%`Gb
z{Z^+iOTuiJmu?~|qCv_pEyI|;fA%SxXtr%7b89swo$(vNCHeQss6A4Y7pyTM;cJD}
zQ6XKg!#R$Q`BpZ#R&Ecay66$*|H6Pll=3!(t9phKa-LMi?vI4x{*b7Ss6?MFLcxE3
z7}<wHEDZ|(P8!e&J;%D8q2%A8+QsU)N~W#XMwGv_(~(wC6$!m9OCulhS;-!*KzI1P
zu0_PcRPqMkrR{nb+=4t3pTN3sV#Z%<@|jo$9NrVhSUWc;F<aZG>6Xy~V?QXL#)|EF
zv+rqkPdn?*f9BJCM8Ur;RsGf<!0oojt13=38PQ-Hcp?A!z|pAQZt~Y^OY~zX!-f$k
zkm=2(d$6RMIH3Ej>1Nb4%lK`NI3Y5TCsI#fZ#XVZs{a&N5sZU+d-g~HOlG}xASN`u
z(|G$j&%7k#_orT`B-48yg3qXhj^+*T<Fpdh{nax;1~ZN@1nos~H3PKLP~jUdYL^WN
zf(IXKaKU+yUrHpesZ!yrs;~`OCUmiW86QEc(c1fJgD#TRvu7-@va0=BTO}Jsp*pf>
zt2ffOQr$MqMG;V3GfrH_5Yf;)tbwNPcpjbbIkRWtC~$Goo)793lF@A$(dPu?mJE%}
zt6sC!tG@>-hXXFFU!&)l5u8%zV$#d1XaKodg@-(;e#Mey*qL4bk``I?Nn)8EyBJz9
zr4d-HVVLR}t*kz);}iTW9Q5(GQIm`TLFt}4m2C$88BH$uRdFW9T>VLJ@FF}cheGV|
zXzr;J!`vG*rz8%F37<s2IsU*uebau6-w;D`-+Jd%Eu{<$8f3_#6H5B7EB0fG&RIv=
zlM^EI>fd{U4*e8%u|1I-A6~IB!|Jty-}Hy@Jp@Snp)_W0;o@H?^C$2>BQw-@_h%3C
zx&3T>Bb(oPHX`v*@N0`^*Yr;^I!!fmoDVGaY@%->8c?80eRJr!npy75w*TDDk5;L1
zi9Zy1Z~k(Z)8^M6g(sSIKb@{Qcq;j2_&Z7Su8osb@;XO2>Ibo<SKhJ0;~;i%%n~BG
zC3&$NTf~76ksMs2CQWo9#*^XAdBXt(PC_6RT;XN*Id0<Xe3d8f>OSfR-oRc_CNX1?
ze8#*ZIl}xz3C(r>E=3$fkb%FBDoPDal=vi#yFf67CMyiDxKPySu&N|*dzxr0K4oC0
zV2=5Rv&YEszO0%vYRK*zr;OmD&4RNy{JazG2Lm(wW-E!>Bj+(`J8<)9?n{FEXvt&r
z?NB&~Flg=F*F*+1lK5%Y!G3~tO6a``kS|JSwX&1F6D$y#X^s8LPFB}hpX+a%xm~9a
zTFFd8<G}K+^(zU;HY^YAd5)FsC@eP8`UAot?gRvoAaPe{d?>~!pSz7nOlP-eM+4C@
z;TZ*B0ir8fulhI`)+Q=6GQ8W2gc4w2{6%?_>gIc_(2J+3=3FgC${VZ*hJ-SW&E08h
zT+7E{GX}z$hwbgHwx-6rvqQAluLk71cv1h%U$xsj67L#Y*fgJyQ-VBbx1+(8$G|Ga
zzx^dM?Ht`|?4^-(-EdfCG4NE?^2=%qQ@n{R#&f$0{cz3N`O3C&b>9kRT~my-x6_zd
zz>D8vkWvAH<&`0aLAtmWRQOn-?<u`3m;4o`nQ_H6ri7*_<cz2>cW!IVlOSina<rWO
z5`7t^q%QGat2fY2H@+IWFyP$o(dn691quEF=YKlL52wK*PDXNpzIGYtQmdc{iXf|s
z)(e^y9;n7sFGWQGn)&(lsOGVYW4;QkCM@hTR>GsP)Ak<GqmlGc(eAQ?TP&gME$WxF
z75CBO?RPtHg$ne5#b)ad&|Ll5Rt#d$!SR6E#S4E++n{|tL=}8sptTDu)VRM(1-<`O
zs!ob<s2XtJm`?hdsa9fJbPoL)Yz5$j*7{;x7tV@;%HKEM4o$&Zj}{gz`1G!=yuo5@
zA6u`1P>``sNH9tkUm)xC!&!n%tf^E0o<T*zfp#Od>FM#k$|<jQo144GW!>-L!>+)q
zXy;|4ROkIyS`ijqawXBP<O;n@j42+43oLd&8Z-MFgQuOpL=xwd-Lwe6%i`zV6ASbS
zmaTk?fukYQefNu2s`mHCt@IhijT5HPt@PO!FJxsb(@Gn{ze_yYRGJ@9{8^<3;7fEl
zlgXNdjlP3feN_W$^4sp`kx}T_jVzslyy4AZyBr8yY+tqbogHIljv2)YGx3`y^l`to
zoc}>onVd}Ea5?4=%R~OC{BUkz_*W{Z-r8S@>Jp72vt642oZhzir>bf#EC0(Df=MG0
z8SQntpVPWS2d*que4#M!kvukV6!;@PK6>Y|oQ9e|Clnu$NjcXPD=JlP)w8d!nkW)|
z8-*}QFXt77?>$RqixPs4sI54x_$#mZ$QYe;>v~`sDfkl{rSJwSeblS`V6La6`!-=!
z`u-g=f-7iU1`9gEX=7Lz+!a7ojp5;Dw^$NcB1ICbx8%dGT&<!f9eeSqC>49kGWl!C
zM+5ErczF&Yu@CN+$>bE%$LXY;hzX!fZUgm|(#IWD<P~e%V$Goe4ii~JnEW|DGHuXz
zl*cSG6o#q6jsn=#{==g!eBZJsiDbn>Kml(zq#2_1X@!aZIiI1SG?Hao_zt9?(NF~(
z!e35k-*V-Fgu-Jkq|QW}sgw?xU3jpwm$TOAzlPi+<2;fX^7xZ$`a}*mQQ-qB!M2cn
z63lCpGor2CD8G^<FOou%Tpj@S=>FBr|0DWr;#=9)FCgiXq=W(wel1BhNJ3^z0TRj9
z^5Le!Yr>FkbpPSSQUkc@Bd6pitP&Ofo2E&I`7-ClG01kTnVySzuwaqIlOzj+eSSft
zc1u#?zQzr*lFageB^B2lOB|}-4U&+k2Da-h#4DGMosCX}7UL)O3@EqF8A-oy6OK{p
zmVl1%+vGr7YkgLf;Te!_ZX}ow)w=*Fns;ky2aZx|{_hq(auRNRg0P6g=L^_3yboAo
z#JiVi)*{fW+<|Pw7x?`J$;Dhp@%A_{=BS=~HRWQRQ3gOZIiO`HJR=1q(Kgigt&%+_
zF$#@O=6&iK`J^xS%ea!k;*Xf<5*)m<N$Ponr1cp+R1#A>=4O*K6Y~IOaD4Y}cPJFL
z2)Wf=AvdZs!IeUjn(e#6i+YQKj!oz`T$Ogh4#h_jr0u>pD~IJXp5Jmy`hl{*miAHy
z@JAkSJRmv|Jwl6Y100zY_2>$l-TS_uk~qOh5{w!2?X%b?I~F26eSJ-JZ!Ux`;l`)q
zFk8&aQLzDFk_g6y`<8mrj6+Z!(OtPWDhUqC15UdmEVyK%PH{k|W{xeOg)&Kx_MjZS
z1;s*0BWuWDeBHw?2&?(IA|aZwSTRYm0g34*yDym+NlkaZL#b!npq33AG2Wf5=$dX>
zZ&)xxI&kofABS@c=%at!Gi?^(0Z<CI#im_2l~)q<?y!3~%j8in@S@tlsr);lZbOCc
zwD_fD`cBLD4UJNfKYr{<YFRn&mA9Rl5xz(cfxu{AN;T*M{Z`K8e<olH7y?4A(M&RL
z|L5jA-7uW9)AIl^HGdG28UEcxw4rNYySC#UzeK21N{A$Ojm#R>n1O`Cf}eXDxdH{q
z4sd}E$}hA3p6DK9T<-$1>XEXZ$}FcV03{*xTH9O5s!%T^Ir;Xfxi&F9-C%$3xqu<!
z6xj}Qwm&VjRyLNJm2I^oXS38wt_#TBRyqy^YN?w!{3Dwbbj$PsODUrSPAL1Q$uwwb
z6nxyiHTC}Q$GaCBUP9H`lEp+P$Yr8JG8xd>nBKD_+|9Dus$9b&gYmDx*je{(I^zJc
z+SF^w#@)}T<6ZcCn#&JsQ5sgE?)0n@m%{oTOQd6X{z40f-edglg>!V=laov2dT;`L
zwj2%kQ)1lSp3qb5`sNrOt{DLgDu~3C35-qKyUjX;kdU7CEt<TuCz<_g^5{NrT99^9
zhGe^6DnG*;qu0>*7R~}Z<&j`ZQZoh;#5dW>2q^jiO8FaPxhC`&*zKEmju+Xs2vg>0
z;(73+w?zdGe}6CnE6-8wB?=iqeb@v&QhT|(8k3Wt@?tKzkKd^Izd_Pq|K$c#zY$mR
zd|Udt*!OD&<V)HOfq8zh_@C^(RPyQ)6*mDQocK>8zbgyK<UN2b>$-nQ6n*oiX63F2
zsL<u8K6WjQjH|%3SX?|cp}r9O1xjH7D@2Yf*|X{FeaUuaP5j@mVmqVOFvev~&&X5)
zkn<YT%7JB8mH$th0)Kq6E>xSqJ#bVLSAornc8IDA{=4-}m>Jb{w6B)j1ouN`=A1p6
zZkcpqF7Fz^`>mYlhPBxFS$oD7rW&_|a5}T_FGFPLuZ-8wkc4Jb;xY0tDhUEdu}1OD
zGPf>GbcESX+)5r@9<M%HN<J&URxe!|UhF%<_j(&ggHh$B!2~V^*Nx~pl7C<Y&)1Un
z2*NX@6CILJWGRyID@0<`fwe9b2?<^!5uqR3GL{7?J-7b=sMXM}nN->o4OD~9>+}0-
z=Wgl5;|TP%Op`}`enbusCI&9Z-rgNdhK<lHrU&bP-^DAE18B+)6V;*w;Kb&Ig$cv^
zt;8996*ci@Lx6W{tD7ev>Z*@Mo|1;F3UxTibFcw?T*vRn(8fINz`VZ`<u5);h{5Mq
z*%3v6o_CB%I<uM7JrFnLN`7eLQ@KDYpg54*RG(aTx`m&Q=UDIyeR^zlgf()pZ@dO#
zldGFlP-e*E11%F7IOzC7^=emtHq}S<QP7~e1~|)?4d+Q<tSx8{m@x>uO4J(#0?Mm)
z;gGt|k3*toNrao{ZR0FvdA0cl^+1%Ob61+fIk>-Vf~h@m>W|Q1<CXd~&&Yw;KxUiD
zWewEfm;Yowh5KjV#lTN~)286yO;xw)RTeyQlnrojHXV1H&tL7x@gh&s*6NkXR!Y*K
zMP|tKUl%9^IjL`IOg1#>iLjPUG%wz%mjq5LfJ|EXTWQ1T-y4NH??ri1@arJgRUuZQ
z-;$YWE=0h8=Or^Ne|)(NJlU^B+x&XA_EQ`hk3T)Eo94oY{BPNJ#L3+j<=sZP!W)dM
z<Jc_y=729UA{HEhfOA<)gv4kE=KTGved*0zoxRI;@t=Km%dmP^mdXY!_To^}z)A4V
zT1RML`v==&+q?S%-xeE_il$z@GG%WrNSq4(kqAFX40pjE#_`g?5$h;>vLd-#3Oa%f
zKH>C&=&YMGc7sw5v^2_`=EA>xkd4Ugnd_MCi^V<}IbX|=6vvU>KUCpJ@@<0GJTY;C
z1;0>yCSFYM%?s3}qp3vQD1Jl@-85LYg^sj&zzbe6K=GGper{d!3H<Ro5=!oQxVw9|
zy5MUoz=&!#Ygks;e7h`+-P{Lx5Ves`AXQ-UjnaleHh_S7Zj;t%CNt;#aYlsJLD`X{
z))tk!)rGl9oBC@Y9~S~=;ht~T6(){j0~6-}AM>Q<Vc&Tc25ZL;R!8}FgP|H}<xiFD
z8Ec4bR^&RIOW7oHjmKm=#mDB&lYnA8C{U}@l_MC_l7dZ4v$DH?l)58qrhyNxd#2)5
zyItG3r=mz2<=yR4GMj`}5{Y8NVdTE1YP?Gg{Uk92Sh>qwohQQoY1;f%(3a}bBX7!g
zDCX6Z5t^Ltq%rEgGsdtpc?q>(`S(C2hHtR`up+a$A}MXdTSbPF&zg%EcatEf@7#sz
zz{|9_b~LG}p=Lu-BQwL^^jqKSAg5=pm+Lx~u1+Sv2(yJ>@3LO7K?MHHGjbxz|La2N
zgW!-mV?hVco-)QMUo}oN3%oraK{|xe22lFt;VA+HK1V3r87MF4i^24o{~l!_3yH72
zXZjwHZ$Pn%4Av%$;6U_&C~8zi=BLeoYfXZ_;^#-Xs++j+j8maukdyGL&N}|a`@_i9
z={Ekk2#wSz95xB46AF}pLn1@6n80pxyYnkyV$Y0nN|c^u0%-DHYo?^QIP?2=A<x)?
z>_7W43N1yUIkqgXM`KSpD$C?C;Fn4E@WCL*L@l7ksCgPc^@Nn<dK#26rzhTVq1~Xa
z<j*51fTt>H)D0XDUmXOYzTGf7oyeDAv`G1#gcl1J0E*WX-)<KQrR`_|HvMeYW#4{1
zwQlzo9N_e{eqWKXt&8sq+yfyp9*?&%G=ZZ*M?QMyd&3V@It-I=6)hCR%M0rOYYb2?
z7unp7Gnf5)W1MyXWehA63SeadjS<Xvd{jTy<tH?#K$?Evrk|uDLG_uQ(o+!uAGM#r
zA*)RQ2R8DGm$Cd?U8jb9jV$y=sb;0U2lkJm=g&hj{sCDT0bK1IfCaB={;E+AoDPX+
z8pCVbeG<3Qe7U<|OeMpEyfVJg!TEVa3TwSUB~Wio=PFQ>2lcbVP@U+R)2T4v`x;+B
z%~f8uYOjmAOkw0xSMwThvtpl&$7=78{pjj=xa!D=a7RW#7_()CJ^_>z{vkEI=F00v
z8QYadj>Ax!Oo754R`9(n2EeaAYGpv>ro53Yi|N~^+1zz8z#F;ow2;ZTa=x_zKM*YW
zUERRZw{Q5rX+J@SKBxS{rYM=Ouh#I6jzdD%KOv{Rj??E|`5;dzxM>Q)EiRa&ucrzW
zou-N0n(ivcUZsF2HGuL${S>GQ48PdgfcXB)YgHKj7f(gvhnrBeqSP!Q$oG`WX)ORY
zy*_a{87|`h5{Rz$%pX4H(f@!0blf3#pteq5&v4DhT$k~o?=2=N1>NA+sENK<^EIH>
z2KXQu^i<K#{)0@k90!8q)qZmEfuE+g13~{&kzGSkE9b)IFK~#Pkfn2B&B6=SqTOgI
zxtEcA06p6G28)=MO3uga<-K186Y3Q0-R&Dp;_VVkHLjx8M5{CqGV-{IWvF{A5wN6*
z?D3poky5RwE7r)8$Ui7z+$Ix~^c{7sDq`J|(UQt(e3P9h0Q$t3qQ5G9eur~1gWjQ6
zcDrL3Ul3uyUm9%(AS+j|?xq-mlHTaMv)~_UrB<cJEHQZ_pq+i<RK2p{$-F)_jv^``
zSOIsa0RH+vH%+9`aSQC=Hl@CUplm1o4afMwaFU<I$x2$MDN6S5<7mi}o0Jr9J&n{j
z_>hhlubJ+utP~CW7q1<~0uzSiL6SU?*x!YoD+tF&$Newn0@i*3Szpru&$#Cy&%(6_
zK?$j$Wl;b@VRyb2%5hXxxUd}Qx)`s_QIvWsQJC=G{C^TsO{DV@R_ZLY%tB(y4t55A
z{FIe7Z`;FJWZ*gTm%!tHoFoJ~(q&dlWK4SxeD9~t9%hQm?cbNm1s*V~^)$ANn%uW&
zVsf@{FZ6CmfFnetg{$N%Ii~D@0|bkM7sNJ*|2aE0@#IxgyrXG|X}#tVfcG_sf=E;a
z;X<=I2Ru~9^DqDNyrySTxu6z{J7$b{l^ib0CMs6~1nUYdW0zf&{-q{?B1e${w$G&x
zVpoxwEQ2tV8(dC%dK*8;iK6Vj5^f`0Lt$bIo8AA@$J9Np#Ef1(y6O=4S67W=L#9b$
zLJ)pOc2)BJ+O!*?H3?ML@D9<!TU|;8dG^=u8$JO8|L-U!%hyPEVTr)^)f^^xA*p(5
z!oQfv6nriH;|2>8tHdwf52k5WEU#Mni#28)1kpZCK4TpMUUL$CoO2U<`yX<NlMi$3
z+FIj%c-zGPbvHjKD_+D85|Uh(#3>5vR_V)Be_HB_!;pA@Qzjn%UP*k{@0^FOxQ<6M
zyL<KUIHL4fUGeMSzr)+z#Khs+WyeQeyX%y-(>X?PiUeaZiB_IMYaIFe!RUk`zo9J_
z@URjyaFi!L`W{G-6W4xGk;Hr0EnX)IU@O^oDx(R&&zdRtaY3jR*m^meNmz~OQE~!a
zPmcU+A8>7Qp?54RkA7b5UD)A^eRYL$ZfxNJ+9*T~I`Em}Sprlvn!JD8go&=KD_fvP
z+9OHNFssVo$L_Fh^~B@g?)jXfCUp!}ad>YOI`7|BjH_?;o!XW6Z8i<6;jjH8CWUO&
z*#@@oveB@D*G|!R(O6O2vNw6Ko{7ULT5iVBA>FnE{BsbxeGe(=I=D^dK^c_r=o6%P
z2;_?~n6JAQ=oE-p{j;puokiP_M7UCym6Etn0NE7u-Q>L<HM#5vQg>Z?q^US0G+8bh
zris`kgu=v~Vb^02tJnTdZBjaL{2;!OGcX6iX=$zaoaV>jNCkMt#gf?WGw6u1TzKK^
z*m6N}qJl9Ra1eJ5&p@*w;2&NLdS!3-@`e7Qub8X8F)f`J;|F(`_@6!>^iWpt^1Hzw
zgs|S(`^25Q6V|fo%4-_Q-<Bp_yq5o9<K@DKU7fJCHiI|ki#z0r`o?5YJiO1eDU$cW
zC-HHxe_ubC-LwVWN(v_xM`R<{V;c!)jDBPPwM_l@VZ3r)=G1g<#!%U{b@e46dDPhf
z*7PgXeKi!S@hPqk-Br;(Z*4}YO_+kB2IGonI^=<8+PSKY%JWBmtB3W{bmq9aA|1L*
zq-f?J*nljbaY1XwjO}t^BWW`Kiu8&+RKvTlf7v#Nm&5-%eHaS<?wcCqa86HgQL0es
z{A>T1=NY^D4{lAnlSMmQ8`Em~kFQoNTKaz%enE2FyVDrzBRD_kgYT=jo@Y`5S&;bE
za8XZ|+=WKpVltIEKC$L4PBra_o=!P{V?;53bhWkd+;u6!^0UUkviQe(?WaN6aaT~5
zx1un}p{UhCHak$-rns&|5-$w*d)XyddDZ4O7+988h1Mwuw->cqsT?0>1Ta(rgpIZY
z;bW2dv(E*)H02GVap%)g05Y!{_wH8_Fxq^Cw?wW*PMTW!TM+I6kG7hzgA2f<R-%ar
zl(u0*Q*CEItIJl_i!DHR04;ttopUk+f(~V|PnrVLU3ygeukws%sX6BUec2mC`NtJ@
zz05kqJk<c0x<Lh0&I)?~k=Zq@wxXO^o>Gje<X@sIA)nF^;2CPAmEEPFNgHc~E~vyj
z=RFr#49yLZXl+=e=Bq$AW7Wa5M=#I$Bpu_g^fP>gv*fxwGCc8^(FBJHJqY{BUcQ($
z=7GCVHGRzxN8*0sj7Nn&C++=g2jLz@fLLcne8QTKhIN8JVz@x-9T)npg-h$&x!zi{
z#ObNe`9l<2Eh&ZEhqbnp)hk|te{aUO(JCgNNM_5Flt+c^b~>3XPVNPSzQ~F?m0;ve
zk4S&vjWdnjK`7oCQA$gj4((zz!M;%`3C?*4wq+?!i5&IbYo#vNbfNY71%Y{|YGQs8
zuMA0F3;Be)@Gm~-Y|N=BRgd7TQC}5^U}i{Gvd91(ph5tz`2TtHNKZ)X?84}5-T-`Y
P8AVk|OR?&?&FlXQPAqRk

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/packages/gluon/activations/images/swish.png b/docs/python_docs/python/tutorials/packages/gluon/activations/images/swish.png
new file mode 100644
index 0000000000000000000000000000000000000000..f9a80390f7330a4354057976ff5554f596358efc
GIT binary patch
literal 11634
zcmZ{K1yqz#*DfJa3P^W|f<uea2nYz$Fm#uI($XE0k|F~rDIg^dJs^!B(hWm*cX!=0
ze)ryg{dfJBwW#+!b7JpjKhHk<oR6x?viI?*@zBuF?#sQFhN7XNyMVv1aPESiR;R|`
z0oomBNjVK1@a2Q^CLH{a>-bvN84V4e81;=lIPG@@9tyd<(s5CDuyk=Zb+SOSGj(ya
zb#SqLXU6De;pF_z!Jd(qi<j#e2jg287e`@k?*D4Q<=|w+%}gqdg@(q6CMW$;!y|Qj
z+S5a0<h*Hrx8sfRSWCm*Prmr3n&kKoKVTqiBeW(kvO@GYBl5++>)9Au8?wKWhX2H4
zV~Y$3mw3R0D}o#PSmJd6(d%}8ITmL>|8645*Nh#CE4_*{eTUu81WwbvcSYRoXM~(r
zW}IP2YNlYK^uD$=v|u7h!LUazOu>7^pZi0IniXD4Qb^)(GvONIFx3VBe_s@{yK5C2
zy_hbuP|)6i;xLICDJphj!ClXKI;)>&R@sh!UR!(npEv2yjW?d1a`BNwk{-BnYmzwg
zj~`{?GX*C(zN7JahlhvvT3)`{mY;?$!co=4gi$GlzqPNgZ%s8gKk=T*nw*?ma7f4i
z7zYNG31eoHvr*F1qXthatVcT696=-diKQ%<-glUAF-%y;)`%`=SSCl~!*#Wr1tw2Q
z&oEqH`*U@ey1ZJIz2SN8<KV!~q0pPBJavES@&b1eNV~w@ZGY|LN6x#9yV*p4Yu-tY
z>N#L>C4PB9efCG}8kgs9$9r6+VC~mkor@t31o0`xI?I<owlul!S0Ev1C#x40WE%2H
z>hiDHbF$Rc)!C~>PM^8&81UOoXsjG`#Iu$=Ez9TaICmv-ZjF^@ukD^6t{%?b-c&&+
zFU}TY<&>4VF1$No5Lz*xv3bf6B5hqfc(*}iSgUx((w~qmwprtZ+b2H|Mra&p;SmuB
z$qx#QJ?62Jc2OM$hlUO|`un5hs$>|g+xcGYx4G<0ef=9vgIVG@FPZn}N${_Yk=>Y*
zMIGwm*iD(XzkEM@{1f1r8S3(U7$xEv?nbYR64KIo+T)nlk#L$c5qHbh2(qmSM5Om_
zJw?Dsp*9IAskEP;m}U9b@Nk^D3F@;U6>+7u7`pWd1bew<58K2E60W~K+bI6v!2=5<
zvMq{AsxSS;`fvdWJ~_ve<VL@1uZPV4I+qH)$Mp1R9BTE&{@0N^)3xPxll&_^DQ%m@
zjmicFwDsPnTzUeaNs(bKMkQgoQN7ndwX;>OQc5T?Ie}f9n2xUJyIlO*Oua~Tb@e@Z
z(O>!cp%z_<133y5!tOiWTjS-Cl{D1Uo#lOE3RU*gYm-&ntG#K?fAtxIwJ+TKdGRah
zSF5h|cq*;bleWss_>PJL!vg%rGPFgQzcl(ehqrVjrIi*uq@x*~JNFh&sP?(OIDT$F
z^@Yy{iS{Wj?&a&(INm2)=q8hub|^DX*SedR>Z}dr_3>>Q*LlQoH(q@{JM(}+zvtyq
za4N{jnO>f3|7mM`W<C7mZw#IH^bG~4o^+b9YkN;mj&`xp)rvslZ)*gv>xM?9{j_|c
z7W|jT{zKP|VbhDFb>LZY-?u)u*QaPMYyE*&S3WTe;>yy}ci6Rlnp~ajqji6I8dUAP
zim^8rM0l`WHOuR^r8DDuYG~wt?V6R9RrHZnNa*W$xfKZsiPZK4f{Ndk(%AQy%=~W@
zlZZ%4gYV_RpaS>)u)50rZ)}FWDd#@TwQHg*VL>PU_&lwhJgdkFQGv4Tt`q(AF7hOF
zarA8%R8GD?+(co?{YegSfXJ4=_jfMCnv!=zES7!gJ(YHo`MTvS4Gj$y?}kEu{StKB
zo@gae5&aF|F<xm$gL(JfFUxCcK5HUM%61q;{cItU8;5{^OOM~Oo6^U}XKk$1{3~L-
zYSynd|F8GO(K}*d;<0LHR!nRHj+*pZ<uqXdU%JJ%*j3<MOcMZ7#V<VQ0Sta;fnvTF
z{Qv>{4l~`EQepPL(tux{dF+`qx3ugZA6pE5m$RL&Aw%=`_upx_v`6zg*^)CeTTogg
z&Z_BX=bW2Y$;QRqTb_-7xPR(8eg9klo@oQ$Wj7xnVr_G3Np{h!UQW~x|M<}q7_s~7
zvoO<^(5E1}@7|+qs&U=iJ3KUB>(5k77ya7YED3CXY=b-nqmC9CqI}0;+>q)%>zmY*
zB7jLmM5L;ryt(NwHdbbVpTcjqkeA{2P*Cv8?;vb*{*(bd?K9#gToM)@`zgVl`coqq
zw8CzZynMVnJ625I#pQWVGN1WOou@#(a$SXBF&LOVqU#N>X{inyX*SVJul0y%sTsFi
zl9R3&ocq4@of?<hkp}LhrKL9?f`UBf0<i&%$xBO1(eBX-eiE4ViTAs{ctB6D45)N}
zf8S-pWjjn+ON*R}iYkio=?7E;5RZx8v*5vSkzvZ*oXNuC;yCaK8i<O;HU|HtKOac~
z;JVn192`;I-QCctks4PUMHUk7hBGs@?JN0%BJE;&U0vPLip3-Z9C&GOF;?7Zz8MP-
zA78i9mMZA(eYR5@;w)irt3$PhSn<9oZ(-t*gO%BzX+3-Tx6y8MP3Y-92XX)$V6ltW
zr?c3$AGfEfoxuW#0su!q0l92i_kR8QHH!X4O}^zdpq0X@OL{RM!Nb+QJe$#?3Y*aw
zfOHHZdivfESoptAXS_@G;KRkn8PPNXPJJ&<c|gzr5$h4zt#h6}VNv|j6t5ztd-PJZ
zP>VBHIZZx^%kb@Z+2eYzV>UJs&x1FBXO&e|iNvpu)CaC)qN(=}y11P$x2v)dmVd$@
zpr$cZEEm3E?R+IPb9F<p6K&fZ<f~6f98@!)ZEF=75i$4}`&W2mWVTco0axTp4Ngz-
zSFSJ*rWEg)Y2`!V-#@*+iwF9#<k=fT$g&YJHM-T-vr7+yiDKG2s`mEso__HmJ!Dv0
zKYC;I043E~pDU1}2q$se)n}fo4fM|oNqi0yetbencK&z5s*JhGYlx_)mG|9>`gO`)
zO=aR5B1w(r7;_2_PeW06SNry(mk(sZFj0CRj;Em9XR$K)qT(k_x6NWrwbJOozacQX
zu|h=41Vcq56R;YiA3ksLExJhzHj<NZ$<U|g)URA>4#7uzRnMy>)3M_kLd1XAAoi!>
z`@I{X1{!#aR5ZaV_0HkIf~mo{Z&9n+@6oCS2hIJ4A@(0{PHQA_bS@4diMwJwQInI7
zR(YW^Va{pR#!><`ms1qRK5-}8lg@2)?vAs*r)0-usF+UoT%o#qpZbr!LwzfNs8oiS
z@7h2%Hn1k=#uhs#XEcb;foxeqdcU)In+r4C`}KO>9@~h~wUI&!!&<lH^VJM&+vzPg
zz^Dc4d4mP&tfBX5WbEzj3#K>m)TUe0YyKP>GrXDOoEP<L<CF<gXFL6oS?o>SOYgYD
zPvaI?+qrXZuzkllJ3Ct!VNC|q;I~;R5fzmj(7edRyu7?afN>1R^})w(J5wK_5h3^#
z2OEXO2YvoG@r^gf#ZzvRcHL<rNkCByhVm}RX!vaznV15>)G`s4!})4}sS<!9#((<M
zRA$k22B@7<+)orJ!$Kf7{gZNUL#7|u!`-6vhO`PT$$@n*N<~;0PR|}xJUW$MySp}+
zgAXLx>||?v%J+2o)%0Q_(%0V9bpC#AADxiX8@1Ci$HN`xDu>x#U^HAjJdC+?@11JP
z^91Eew9lVEf8jC3fIuKitE&{Q@LJbRt%kD&d^DLDS~);{#gl!&RAVKkm@mAJ&}``<
z9zS~2U+3wphp>Kix-*TwPOKe1NJ^@AzBs5e=zMs-5T{>2w&mdR+QO+g*e?k1=+m;x
zsg&bv5@KTL^B2x5is>E;xFCdD6FK$22sjuGl%*K*<{&J4Q-wsY_F6`}RTLHLSY+Zm
z9`&$G!uAd(JlxWLwWpBO)V`q<lGV86cVVvWtKY7iJX=X|09oD}3kx#7OBf3?a}bMC
zGTL4TYX)f#gP<d$LLvvc$#jjYL5<6Ljq7gRp)xs;VL)|gR=uh7SLZab)E?PIULpBh
zTDvb=vToDn+^Z_zD8{k?G^+1tN(ltSO|PD8j6|ws%M8Hv1@tQ3zSPhl1uo=3Y}I)l
zQVQ79DkVS9beL^;Ww<1Edv!PkC|_DehWBkhW3qsQ-*in&=zV4`t{A|$=#>l#6x>Ep
z9U5+|l8<Xg>jya=NxMEsa{4-6crsLLL6=T7PxT^P{<QjTS7+zL=g(oOskC<qXxjmO
zg-1m_7yc<<xU-wLN3bq*9_4lFzod(IZn<JNOz6DbF7-H}>T(JdKNCQM1%1wVK-LyU
z@710iUrjXgt^bFo?YR+@*GYHZQM@JI^VMrffw93Hg=m78IBMPcDSq>9#!%1o(X@}Y
ze%G4jJKdaqq^=g_vH8LWVt6W5^ZmLrGd{3=HKppt)8j_jYLONaFOaOid-v}1J<(KT
zaz({+49pj+n*~IzbP7ESANx)Q5bu<XD5uBmyi|YUw4@j$^V`Wop~M9R1u|g|X-O$5
zUk~LfMH5Jc<8^hG^IQ*6UitcZ%+8rE(VBWlo&tfZ*;K<}GU(xtni7l2u`E3!^nDBM
za^@c3tLu1$!xVLt0dLdwp5t)d*^s#9KaL`3V?t5Z2=XKtzph3sJ<gEksYSqQ6)B3~
zG!*i<S9qLtIqWvW_BR}Ayq03WJ(feg5Lx*kNv}&B)cWJ=t%-`{)LVcrkG4W0$#ADZ
zy~C{}Y$1;oUIsDJSA||o0>&Luf*MlV@-K>D!eGOZ57WzqeUAJTLS%2Z-y8okLTRCt
z$&P9;GjL#DP&0?52KobckCjp0W}1ozog|Iy3)pMvcXvcy9jCP1duN={7Fxk4Oq=Ii
zaetZu2P%J#@jKui33srg!YX%)=bQH&O4iu>e}i|<ey#I3#pJtyok<3|5w<cqL?RUF
z6;zHvB8g+wQKjSQBFFL75+9#FD$DXy#sHHhu57Y_HD-38mV`N@G~`$UvYZRa$x46@
zIC(b`HREu@g%poE6zYU4*|(`fspymw-hc0~c;YB&llB9_h1P&}k;L&GVe}0VGpHvU
z5*8*>A_64km*!JBpMxQ}T!Oc)FQ%z!z{i{(WoBSpu;qM6*p9ejl1O!KCYHo$qZ5t)
z{mhvDn}al+C82|Zi=bw29RE>9V1fjPV^aehOF2RThl$n+B9aj%9-XNiF1|a%@Ugh#
zayQ#$?e9bQ@nO@Uas=>jYF7V`^Pz(m-z``6&yW0u@$d5pKT7%}@+ySLXf--ob}Z2`
z(0^(&#J<OxiY}(h<JO&sNg8o5ompOY+VYg;#QLu^A+1dBKdm%c)UhV_KeuI=PDZ|&
z$yb&MTXaI9#fWKAAvDhU-#)jk{62G4hIxT*owqR|wf7#d`KMs6;5`rgsx*_HKvJ-n
z-Tjt!AxR*l&Dq<6Zh=XotxvNQq;l^^@d(pOq2T24bLq&yl51K@+T%%a%=QY||7p*>
ztc&OQMvlXioE>PmTz=9$=2aXa3`v~W=ewINTOw(_SJ+;IxqkTr^S`hP!1h?Q*Q!>f
z+;$W~Pc@Z1pGHGX7c=>9I!`X+2R-eNU)tNK$Lh9BfTiolp{7YmQGSR5&3q<$ov$&W
zqLE-!D)aaIhB)&N@ZibfoRd=4TJ-FBr`gKkjRQ>8bpHlv-lT~sxn9G%;jejSn4*<I
zPs;vpdE0RDFU^<wG4G9cSE6=Dy4M`15>bhTzKGY%OpsZ*UN_Ci0C<>;dN|qHSq}1(
zfjbvpJ^Z1M&>KdIv0EcPF#t!|%h~kAXY4Ni39%(tSy#9723&v|cxvzih+g+S4wc!F
z^c#4g17QRis^l>5cH@48QT#L3>P?c2BAseI>I0~nL6sBBi=?kzC;h^rFzq%s_{obW
zTK~K@$wNVUu(m*Xz^Sz&J{C{3{<2hZ=`AkP)Ze;d!cuNX(5>iLJaT?8$n2FI${6H{
zyTYvp(|PBM+wki0_@i$+R45>XPPz-HRTmOI{bu(f{g)KrEDqGLQaKjO`|S&q%`F%n
z-dRWA)^OfQQAkq$g}${|DSKhi-n$wpd!erHrRowsjOxYHuH}Int-@l?)3o)e@3{l>
z9-{xc`6=?L+22pJ_q$jR6~*HhPlSV{a19}v3J?SoLRJimP?tY;)-sXA*>yegVJR1z
zl|8jYeDRJ219HSvW;SB|9My}}lt0J(tsE|gGvCVFgQa-+Je1L&WLKOK^rrrgYwq$8
zeYy7y(TjbMMv_R>O!E%zX}#>HoV#4|MxKFs1V&Gq))B{&gu_U7_8Nkag&<TRWYDrt
zQ0rtUn;wKLEjf9kX$O>rc7p6q=t;w>@*JcMvs?CsV;;E}GZK3k+e~A&-%aPzeQi@O
zVMCXLh`}QybT@IA#8Ig{^q?;ODc&5;b^j~xw*;{4$Fjs;#Gk$+ZpLp;<!y0|aF~kT
z@05L|QaJ6;vgZMz!2%o@C(r5vKh~YAfNpg9)<^}kD1Vg4ndig9gI~Ru;}AM0zd4jp
zk<7tvWm{Z}OT*<THJ*2(Jxg0;!ey%Km>vE5!1sXIVRkgA@_sX*|3V>HKLY6;_=L6f
zVelUB?3Thx_U&WdYJUY8R~uUwK_itNQZh-6kHOAgyNwVT>73Sw-Qffv&ErHVgrK2T
zddPaDInIf|{>*jU*`@rC_gLqypjxF-nG$vq_a(7qLHsDwv_~Ra>IeUlxjJ~kU@!iA
z0@KQKqu1@ohEssB`ZFpVhPH&?#{p`1R;T_SYDl$~MNtN%*O>pXEvNK(F-dpfVohg{
z)ZI_~pnVHiZ?{fO7)N<XtN)r*w1hYqArVy~qdn=Y@o;-=v;_Y+{ccp%o}M@2c{Sk9
zGE09ve30O0`f*ZD*MA(C*RVlhIKd5oM+H75`{qzjR0JwR&x;CA>6|yKx?dUu6D7n+
zc9lM-giQrjfWFxjZF;seFNM;RTNlLJ+og6S#h@WXWG4f+#OH4Qc;!2`vj0?7cI=-_
zzIIu7b<@BfBO=T{7xxk#O(09k4B}PIzTm5;Xw9R<Ttv{>st1*4`}Av?8lo%%KsjxR
zyG+bg0V;m8`u6lrQm7d&AFSjWkG<!J9{uZ<Bb7}J3VQNPN?elRDHrqYo-^bBG7KsT
z6yHcwp)6p(rs%~LTRf(48ERU`Q&T=LA5Mc}!6)hst(^=>f6O4mb+-~K7}tt2VUO%3
zCcqdLUY>@u-YR*AyK-?AKPb#x1Q%Gohk}ocBws^(jvA%?+-($OT;9EL5zL}RjZx$*
z$`^@Bz_L9#SEfdOgRzBBYc^mOX#6Cb^k=~eTIb#9QnM_FV$=80fF+!k3|JVHc3#En
z4X!7$#>E;jNyqhNsT_JL5<jzdXqj_61_shVZN=`Oy@w>7Hx?5NHo6+#h#rw=w>(H-
z1eSZ5TO9y8hdyhMtztQ8T4GMK_k0M*B`dj>SEscgQivmEB5AJ#EnA8>Vaf5nA;bbI
zzpW<kyBQS_OjLBdA0v&V7wn36ugrEY6b5#ror_{gY6}o#wUx!0H!IEK+CN3Szqj2V
z@DB4~6oIT@duDJE%o;E14!JNZEmLse_tm9m&g=Bl!#GPBi#7Ai+bi^a1JoBOP3i$7
zxTPTRlD6sj*>~zJHq`BXDhqw4bGLBsZi4cfd_isCq2A_~6j(|wEJ92ERbFncmKz`e
z)(gi=T>jQH^Ik)`HRR94fq5L(_uoVP*m>k32{;_semO8bgR)OZuRx@*ybT>0J^c=f
zZF);iU4&7DuhVw-Y6qDJmXex3h-+x`Le^$l!)BWJ3yv*PPkgSTJ(x&2DNesBDyMXq
z5Pu0#%G+LE6}Q<RlF#yQAIxC&tlLMdKBFSVn<?)R;1zO{$Y+(0gY&5~wq7sQO09ah
zNn?9GFSY}I$7_e*N@`$Yf95^AobU*(DDH!y!-17F9ssuXsP0<9xcT)wu_p7TPE2{C
zr~WGlnEBQUWcT?3n$@7Q?o&87Lh34-Ku&`Rh}nE=;+0PX-=+FlSBZYOHRQU&Lj=`L
zfm8=o-QKq8-D%?9qd-*1{0abws8a<*19(tXWGq(TuYx$|NC>KX3$TdGWpw3Si*j3Q
z<Bd@H+m(?JpecL2>~0yB$1`u}8NBd)A8vp=z>x{neH@C%E?0=E5Cv`7RaH-gY3I#A
zjO`JFgw~R4QYyTW+aYR~W$}ZS)F6O=(pg)L$3maw!N8KyaPfn;E`rTyP$`OLd|P3k
zm{|8m2bcQ#@4YWCfc@`11o$L$=WpqbXFnY%Rl9FFJJJhE?SUX#=6|CiTYIb3z+^e?
z(wk_`hFAEJDI)-Gb5PIiKBYLF%HqhZOOC4t$YgMWP?AeX=Dk7eV0#r(Tgz`|prH;T
zz+M6nBK=xfbpRlVOtl^fqYGCGu!%$)1oe)#(&-8RPfIS(GG0u@Ccor`rIy4|gXWeN
zio_^V-_NvLAD0aMOri~<v^j`YrDx&1m~cQKiy-!1qW)zo0yRyh1CC1l%WJTuaF!&j
zMi?8&0&dtNDu#<UXD)Sdc$Bc-x9NmmFL<GBfN3k_lpZpvvNs6`6<Lr8W-jm;*g(J?
zpdSRmK+#LezNj@5V6$k{Ifi|=^~lw7x$m1~PL8yRBnfda08GhxP4ZVMsJocxH)E~L
z48`eF2ZQ9y(HS_!Q5}`E9oKq<x?8tj*Qe0X#q6P>o=fR^lHXL=5*|xpp0znFA^p_E
zP|l+9@;hcyHtaSXA8r`NI}rfBWnwb~8xKBp`Ri|}bb$^k|6--PDLB8VVZu37vsAA8
zN>l$rdKl~{=3mklWa<YrzJ;47Kusm0pw`*Qi;{RVlB@}#gv1tSW4cE_UfEgR$lGsI
zkg2az*C2&o+0fZ!^xjzrVCltE4ByE8*5BE^?7gS9xAJG_EP08QOYN!hJ1{)rju)D^
zh`G0<hAhgVzIj9Er{7;EBMJDyuz|Ei7*BE}12vm65XdA2T`?sH85|a&tfb`X;x6ft
zvyDsf<#zGu&gOLL14C+Hnmtv5$1XWAhRQb$(l*zx-hmLnuRyBlZRYRoIm)`R25Z-R
zN@!cm*am2N;=H?q!fcRcO~<QouBdCG@FBg%`MOWR`C>n{P)rpExWV6lHigk2ctazd
zneRwH5IfKql5;Y_z?@2&L^RSYD?&W(^eACf#_ijG#7ER1$7W4$b30(FfkpZ;YU3h~
zY<*fGg-O3P*$fa*@o6(Fm`9@W8hKO0gkHyASvWPpohoU6P0@^oDs_FVuuW86SJ);3
zLkfn0Xmu<d@$TFEj|@mPBAsj+9qB=^{^~ENpiRF#V2tar*&q8<34*LH3UGnJcVOv^
z#9eeO2c<2hsuTRYc#shoEFFVW8$gS@GqXXKt^gJDKG0W0%~<?&Vr~AF^j6S}m8oe&
zDS#<AfN40J+l3i{|G5uC8ujShEeb>-AOzGxKuT=M5#0)~>J5Dq=m#c~FAUHnO?GiP
zA$aoGg6B5ywchZ36mu*Cg_I`Kb!QCu-&F9luH5A#`;T<6rhE+6p)yn^StC%`I^>RM
zYzRPp^2ROWmhdAdUHb5N^u(6)&N8MvdME$3ImRs79{$}~i9P(X7e2+J*dJnt`+lJ4
zP*B(2k13WTrnOCWe^>4PzIY8i*A)f@F(W5B?`OtMQ62o$+Qlqs1~cyy!b)GXGpxx~
zY-900@udmZ&Sa~DD)Fl=hUd+6sF45YvZC8yaKJRgZELPGCWj%e*ige$fj(@9{~(Cz
z5v}CrWIASZ#7HIP@3v8`piwP}$y|7d$y18Lj+9KsHZkrt{>HU9bMK|ejGpyRU!k@}
zT%>O%tBi5Q{Iuh13(hKDtY4myr|^%{ce)&>gdRYi+SR!E4U|EV)dz5_2;ZRU19bI?
zqv%ej>jN75Dp*+#WD~P^^A1jtdOEYgJ5*R}tO$)YVi5R#l#f~VV`P7l+N4$WvRthH
z5ni)HU{kV;tXBe3^$X$o_^2Na=~ALtG0*Hx326#fiEr00Uc(~;+lkNEHT@^^rkoWj
zn`T2~X~o5LXqOcY7hduyZmjSykgzs$>cT&jpl4D19#Hy*-|=%Y;a2BwJjHwLK1KS?
z8M_Jj@rfm0%&3^}8|%EEZ(SJ(TJyTpD~Hn(>Yseuz~%v4M(J0iUrcYE&emV(*LvtM
z|2kIrUU9|e&iO(%s3y?oX@PiWTD+%gw#Il5qmc~Y>t(gKf%OV=ZQ64ORBy+~b_{XS
z{(iDkyV|*?(}5>rHEzqj4!F|CHE;jQ`X~DB{jkOlJ^n!z*_9`I1H0pXV;Xo0nF&-1
z*>ZvztyvxQODjyKPXI1pkB@PFp5s5V585N(c@L?eQ9C*|wCSmHAlPq-(*U|)1@zP2
zu_>!29!y#8T&U!jZ)`tiJ9(>iHn@Fuf-8gFa=wy;W5zQg3|oe$Aokt?M-(fFiiMb;
zVXi8Y7mzhK@u_``U?by0BY^M5;z)Dpq|k19qt3K%l9VVRLR-%>y&fXE<qWP0>#)gO
zhUbt^r#l7$Y08l;*Q<NlAR;C<vWSN_fAc6aasyt1=5|9<dDU`)xz_L5e;PZtX>7g!
zC7$Bz#q8UM%~CqgtCL*9*ry^Mxv2T3UEv}{(N_s4{kG~h$?`|)x);JeKl^euU{8&_
zx76srrUaiGjBg;4i3|RgC#;KxcCQ6>7l7+W#?Ql&eb2EfQfvO8k6quc!)c0~7$HH}
z6xWJ60o;*!N11*)-y%PdWnH1>l-+MutLc9@YwHV}IuX3*GwAlX>%KIr(ULpEcCO4P
z28FznOgT6s1r~>NE5Oc3%RYl?OS$7oz;^DpTBCB;FRmoFB3^-z4C$eKOGZ@hrH`EF
z{{j}-O)O*J?+G%Wm5R>g*)d-e%r)@!{uv>+u+Du#qQ(tV$vaB4B%eP)HFY^*w7sRE
zHNXu-n2gPS8`aSFUlcG!SkFuy;X62p$=@PfJ^i67)^{(f8Q(I|E~-ju%iqVI!BA|H
zgddBi5+@KPWb&nsS=Y84uF32XA`tPj7gjhwG>eo#vU-@*ce3w$<@r~ZZ`r}TW^R}L
z1uI^k>N?13dC_;#-O=Ql>~n@k^)_32@sz`zs#DEs%`E!NetFP-*!*3b9%Q7>nD16C
zKmu}+XGuHrKDl2#PhFNWZ#dOPzE5h-(*eyqy&Gc<kHvhFlZcs(p2vFhtZk9U%8K;b
zd0Zyopfr{-N3}I99x7J36I)DPQJl9m{qWM@Sq1E%x=iWPt>`K`)|dX(HH4j1Pwn|_
zXC=({y0DaVHb!#XobM|s3j>3XlGqc5#=;g`x!G}WzGLUpYm9t(MJ-s5-`~HWMfsdY
zIHvOcx|TA?-)6?n@bRnY^3-}5wUl>Nsw|fa2k_^WY30_G0)|>|lrb&@$(7f+d~R54
zmUn(cDAi&WYLT)r{5(?)$7R>f^(|o=8aeO8w#&#%z=iwtcT#MdH*YM)j<gYOgP%*W
z{O3WCrjsq2fLI4KmRkv*Ys~#a!X|D;9PwKTj1Q9ggaj?zj7uA6%{NS~cS#8#hedKp
z{S*B3me!)Fu|eu0cClNBmCMmJzvqPd&jnPAm9PZh=VCje{`$^e{#4NJ><*<0xg%IP
zR42MZtU#48>BR7;W<LoA9SV4d){~OtOED9DXX=L3n``{;<jBeVcb9>gpKtHuMa!sH
zG>Iv~kQ0@P=El2s&54Y3u?;n}$lzA`SrI3`7T1NB!;eo4c?sa~lYx@0#m9P_mG6n+
zA#ZJ%<R||sX&}w|#yT$ybhT{H6W)_+24Wc-1{Cuz79Q>oVO55e&8eWHkELIi&SkTS
zA<Z&*)!yR$-Jl6R!IwD?o1>xp0O28BrZBLrIR93d-F10)LN<}G?&6fMo@_#ep|Djf
z)S_~G+=r^H_vMOiyD&Zl2^06?hBaS`)aGQSr}*1vOQG&=Gp|8`x9(*6fg63}YiSQ0
z1rSXBDP9Q^i9|cialz?#p0l60t20)=VS*oHn4n8&XlUff*xSus#&rJFg1>{8>hPVD
zvjYt99^iNMvNKNFvaqsJINKAfOwk^6kCQH0nm)Bs|B@8an))hGC`Hnl9C>Bi-u3ql
zABn*ySt8Fn?3d5RgU<{eb>{_`WMF(~XAKa&a-nZP`d~lQCV~<+Q%{7t+Yn<Ni%58g
z=;!HX9<$dk`$Q)6?601gOyZg;8)bo#EO*S#pFcA_`_JUcllFXK9vw|f=;vs#&)wsB
z@M{*Lh8qUS*?R`LQWOima|W$?E7vNU-d9fgIbFS}b<2lqc_}_Ip|7LMve#+_RDH&8
z+81p@zfOIS^eG{h5PP{q@>(of^0|^Eljv~9o~r*w$Q>g36_sfs|BVqGaPT^Dh8=2X
zxA0VG*ILi{B~vX4ciDWuP{VXacTOq>NH)!8UeHm^7E=Abaau0gqfZb^K_JO<hVRXZ
z*d}pu4eBvC>Ah*TyK|QOR<%Vsh+3c_RNqDkY`UD2dQyAZnT42>%Bd{DQHWdzkMpBx
z#m=t=Tq&GQ39<x^fnJ&D&wo&P9ILUni`jYGt1|C}CK2l4!M|IqI)n06u(R%;9D^0r
z0}C@1!-sulkDErrP!*Axc9wg#G%>})?I9FUJ?$T4-=W|ZhKq|!Mb`=Il)6^>a<X*B
zXZw|{WrNQ{U=lp>sc{Oq<4)6Fd>FpCd(R%sZ3Z<K6KWN*?8?VO?a`5Dc}@YvgrGYA
zV7!h$$R0OOMVhW48b3A%7BQeFJN+{?b-sC764U3Mx;TvgSgMP|T;W2S#7{o-tAfz^
zlI~A%GSpG7vwy7is-s@HMEuz14pf{YAbgKV4|zXM?^nJX;OnY43Kymo1q_xhPb59=
z6*S|3D_N-83Xu`21pNQp)%qV^7<g?qLfOa!bvs5j(J4|XW6EC}kTJj4jQXSbbcR|R
zA9WcF4_pQlk%W*LrZ?O(P+Isw`LWRKQBg#}&ins%A50XI$;~4i!24evj0khZqDrke
z@6FRS{{6<ZPv}p8F2bN;FsN3JhQ>z=XBbpA0V)fFx+j+UvF8l;$rs$o8OF<j`IG(m
zB?&={-n`b3-{#CV$bxPJ)Egua4t<9u=fQ;4IVvAmz-a8nL&3*YY8i`0VbFc~f@}E#
z?gS{J^U3PxMc4AtVi;aYW1H;uDzxiIM9!F43M9u*Rlaq+@3u`I@=OPkptg0Lra$t2
z{M5VU^woHsow(6u0yK^*yuh$@u|yl<stY+@CAKhds)IozVbIkCD0XB|VK!{yZJ!hk
z7^h3;l*#)mn6Q*G?rWQEe!_2<{RCqe(qt*K55}<Xpuq#o0}5cr;+QNGd8?SQ8t?0F
z@2n)2ay-aiZ8jGm{NftV%W_#7iFUQ!R$)%o#&>TIq0)#^XEwag*0+gGkF_$H$BMyj
zus6ep@o;;{w23?C4<F?jQgc<^bC$y7P6>gkgz=meMVl2yuUW0~<Z>H6C5|)(n4>qD
z$As9m%ez%kRct|f|027LqV2b$_L@?odTsCEv9PkY&r1w!((({u=(Jc~6x`on(5B*m
zgcIEiH1i*9T9-F}F}r?PGiZJjaMZBz1uY3V+T3glqrj0B@}d(hQzS4~v$`?mN#7<z
z^!60`WcBkuH%8i2aMuW=Fj6cH_Yb<#52p;}=hSD#mE*X>P<cM5`I|7bwhClIz3Bjx
z-X^@MmAE`IL&{<q^cAhQ8L79a&?IB>D|`Q!retFvOVhfUKkNHzTw{vY+=-&{?2C$+
z0nU1x!Muq!CP7d(83-R7Z}EwJ=(Wvm7H`8!!m6NF&a8Q1Pr_fawnEY3#{8&jgU%*5
z`|9BOk9pX8&XCAUu#1G`AR#%z)la^s)N{*ZsFOfyp%#DkG2VVdD5Fn?@u@b#cr~4-
z48(Htq1Gh*C9qe0)^P14gdXHj-Y8u?op%}9OS!xF7jdYMKz7I%Tr`1OignjRb%XY#
z6nkQO4=YEn9#*b=n@yUMhHMJ^d{y_1NrG<ZHeql;%%3TX8|8%Pb=I+o#Ingl$nbt*
z-26l|7?{21sI&~>xZGtnMz=F0LiWs1Fvli|^Wc3^?=;Jf(e~8rc<<~9&J79Sf2`#s
z%FX-*us$omsspw+(~IjZHT9_S{^Y|Buy(M#W=v>0PF4>=43zp#yFYoEa5@Ib5kqU>
zv{3Vm$sSrPyPb0u;E|rBAyOC*9g5Y4bRJ$5!{o{u`}nGF*bzgJoU4+@hmZ9(x#hXI
z)8`OKn*`{smi)+&p7y)O$IBkZKbBJ2@@fNg;K$XU@uP%|+*V=GXmE$}WF_w~2PW5y
z?Dc=8ZreyF{6{_6py4jdM6tx%knMOk?R|_B>3vKdq}V3Yx1ZFF*`XNwdYj(|$=Pxw
zVNg@;+|p|vkCKYAUTD_z!KlTSgFwI5c+uU1wiDOmO|}>tK^_{)97Hc9JE{;E#OQW(
zte37NO(CW0g2;*7o8~%9A{BWN&D;6BZZNSJwh_JUF945NWb3;;;M>!`9O3c}jAHM(
z&!xIj^@ce{`)O-mX4yf%dlOHNhMo7}8qcL@o5pc*6Pc%pg0!3jy(7U{(O8*PsbQw$
zonM7xR|pC7OP4#9(*C4pv^e=$g5q~`lfH~n>gaRC_@1;e*(wog`fiC~5ujaevub32
zem{OoAGpmHVa^q?`J}S4x&EZ>>ht^R1CNB0i$IDJ-#N596MbpfC(e*&thP0NTjQ!z
z3K3=IfOYNOXC|T7V_PjJi%$L&e>68Pp%1yR>a4`mv7flP>XEgPM{&L!<>2oFaXyJH
zkEd$VXXxK_lG1f50j|o%)Z3Dll#JChR%KYp2F?mG(^so&pzi<Xq0SqEfBm;>yZ?g}
i05$o);<0dx?yURmEd3yiAKZ^dlY6BsT_kDp;eP=y%eLhJ

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/packages/gluon/activations/images/tanh.png b/docs/python_docs/python/tutorials/packages/gluon/activations/images/tanh.png
new file mode 100644
index 0000000000000000000000000000000000000000..442be1ea2273ff4d21b6416ab6f2c1aa5550f599
GIT binary patch
literal 13530
zcma)jby$;O*zW)dN$Jrz36&h(AR!$BlafY|?yeyUo0Novlo+5RC8S{p3<O~!0@5K!
zGrHrv^L%lx@B8O)T`=Bf&z`*R-~GE|NFxJHDsonG5C}x2t)*@Z0ujUk|DThQ0Dt`r
zhz^05YyPU*rewfR1ep^8c)sqVW#JD3QPAOk34TsTodb=s0UG83Cf?2g!S;TSL7w&j
zJ|5ly9<C1DL680XUA?`y#e~I##Ra*a1O)iViHQ7n2VrkN7m-^G>ck)rH%MFko@q$l
z=5%O;=|SW14mLeMQWt*BiKe=e!1R$(S2=^*bd}}j(VwI4li`uBBku0ytut9)4pE!f
zFAjBUt)^2I2u2YvbF&QiMuIlCmKOX58P83p$f)Q%`aDYU@xR~ovK4S+?l$7fdAO4T
z7X!Y%@3OPF6V3;)L?|Yk#CM1q4R+K!b3k?gFSjzmK&!<6=R?|!P0$2HQW6$V$wId&
znh5=yQ60275sP{~`l!)F3`)r&_H1Kg<4L@IBD92oc6>U`FX-;K|20jC6Pp^hq@*MR
z1B1``@y?TY2g{qhyw78ZNYk>j!J9^j(28trN@8N-fc4R&n`&{~kRj@xS~$ws*jV-6
zJp#3O>ZBkU3^<FwBu#zMl~$G;k{c_?OnVZGSbWiYpEhTq#-j+a*pTwvGim;K=g<D?
za8KITJL>8y!zC#kODw~KAtRV=N*`_dei34^=^n@&76kdpcFZVBludoEqaIc#Nv2C)
z!g-d<GJF~0cUK#xd?WRyK0^+TLdjsHx><sjA+0v4Gl6&M+D#B`C>wu=0#4%*Mm_Pa
zM>t0^1NwJ=QFvJGEjK7^DMVlK!>bYY(uzMJ`W&#jgF%0NMOfWnfOV^}S>;F7e7VYX
zLj|?S)`D!DZS)`4!ZH5t1?f$bg~i7MW$f>m3M8K?S*Le4C27?`DM5$Fddv-bPY0Bd
z2*Izi6Lvq|+?z*=UHkCi!}$d+dO6d|%hjKw#(7|2KAwwDK%l>-Aydc-)0=vWMckPZ
zbTkt#vRn`@%xn{4U%DQ)Kcs0<`-C=MKA2>#x?lJYF8WGdG5l~}aK<F<u3$5<)cJJI
z<+lCt%+<Y#-WAM}+(YaB!^p>roVQ(Y^E%|zX2#~0+S(e7A9~zwmzNbaeNmdQ4|{uy
z9X^;V!R<_^_C`5N3VOAegGz|$23c5GY;Kj@b9KEFeL7sIszW(n<uNYycQ8L30-cJ!
zy6~)6AJXJnI$f_+P6a<KGoRN^ENP*_%4^T(P3FIQzWldSK)T~?wCbaXu1;BVo9>=Q
z(v#cE-0tmi-z#Dn`J?m_RNB4nP_B$1?LS$sj?1QU=F_i^-$8Qb)2*hl8~UlTe?#uP
z=jlq%18eH2DrjH#<>|V<<E#28?*{)3k-_y4+68q)LZ`)+!P)NB*UbC<JS$>Xft$Y^
zpM6B})7X(!$@W(B!`&l`gF&n4Iy8MpA91)|z8vhrbGqr^eQ0XHfaKT{9i0~jc`^^Y
z+b@EI1O$dDgd0@&4GYTtV3LL(_h)`WtyNez2mJc@AZ%|ydOaKJ7%-S88@BNq3WF))
zTK4vrm9H8Na;5ae#l;U|X_RDK<vZ43u*k!4mq0*A<m|s*YrQ;OKlqcPJZjS#w)*m>
zTEIdV@^x-*DFw6euPmD|ZL>1-A~Wm25oDlQx#b7{<v|#1gzI8OQ!a4LltbJ(aV8ws
zib9?Kc1&?~aCn={CT-v{@cIJ^6-!Dpy0G8?gIxe(RO#50Jo4GGXYE#s!q1KH1IEpE
zYb`gFDVLJ<!MAZ#VBlbje&xqYRP);D@-3$Oj|0ownkrN(uh!i-!p4*Xw)3xkTt6!R
zg;TD%&lMUTUorLQ{RwG*9jaK^0h)72+B?b=QK-3wu$T2IMF@7gK2ds$(tuRc9UB&s
zEGNL5)uw+}+RlC*{raxx;c#zu;@0_Eu4All^mYg7cuT1G`dB6Dn=fB7&1@rIZA~}(
zZ_nKul=(yXcOaKk#lV0Xt9+$ksC2>uye(PTvP(W@8-0O1P`Wzz2ZXAlGWx=OCUiOf
z{-n0H_H}dIXt`DBTny>)@t+iJef^~$MQZz7)48F0f1$x!k1ASr`|cln&k>L3y4;R8
zH8uV5K^IV+dE$!3zpNn6kU4^|qb+@FYmOQhtTS-O=ldnfOYN8I?dElEjGsPzN?NmQ
z4ibt!o18t~sKFY+oWEyx0CQ_r+O)oosD#>|DZeQrYdaxD_@DVDwzAJe-%yIC;}a%Z
z|0__s9<ntYQE=|VOxj-x{!v3;|DCl%!|@C%^^SDM_mE<;wtydbPrabWRm*@el`ya1
zJ`#jFWws;sVR=cxPSmz6EYAaJCm3yjO;>uU?SC?Wog7791uGrT5iKk%{B8~wIXl9U
z+O7O3(t9m&9sbFrB(1P8+M(4OSS`V_v>6VNlb%;*HlYi;&B3@CeN5KNmuTLsM_<<~
z+XsR6Z*1Nz9YAL*kWIvn29t=5o~NgfhK7d6bknQr^nBOK*WaZmE$sG-l;~y&^Urj~
zb4B&Ggzn+Dee=w9MV9RT_sg&XGN(wIOPuiS3D7EiXZ31*{nqkWrFYSs5AU*5@$S1l
zKHtApN?jIWqBnO@W<To&1kL7bp9+SvhD;>Luu2^CD;Z}BTB!Q^LfywIc%jf#z)s|?
zR2Jr)2X;P)h){U->J@)6TV{7pkBODl<U$y%Ze-cE61Y3g=yPA->IQ0EhgU|+*_wkk
z@p$Px@LF<zZ6pEM+UN0g%39<Mw|?uG$xKoH%d4A@w)@j3fy;phZ^Z^;JEWs<tIjE7
zzjoHg>bK9A!@=W~0@$lL%0c#-o?*}I<N=J%u2#L*^lbqFd1&NfFYW2!rmlknr1W9A
z!_gK~QqW|X-N9giVlK07L~2?Z9f+DkjzLt|7JzPt>83!1lf`s?gIgSmVHv{KO+Vgg
z(j4MuvAN!DEc=R5GDlPX7GFGAdXhQby?y%>uLu5vvOVcXlNFxPouLIj2jBYT4xWCO
zHDIA>e!!Fk^X)EFo<G4JZU3?84E<BGY;G)CP}4P+suX!D>i<^{lf>4;5j@oqIA*OG
z6eI-zwV*}yvj^|)hgRp?9VBzeXNGSz#_tWvry%M1{U$x@399Tm2&0agXQ83HPP@Ci
ztRswET-d2b|4;zda^!=BLG=6vPP>cW5`oX=dNm<)#9az*w?7$z2)FJV4=P`s-#^gH
z5ql0Cbn5DGiMS(%(yQ&nna{9b<J+0%SWwIL3yge88lmh3o57jdP?(`qeSK|OY2k53
zemA2+ooIr*2#bb8%LN4+PTQn}V4}3FOz@!n0rrdMBv>&lAOj*+B*S|Z)I;5Yi<7rD
z6=~r2$$3E+5^^msoHXwWchhsrNJBE~YSoeYmDyfBPXCs>*084*<{AXz4yzld_^`1Y
z2lY(dlX{|<H`AphYJG}!bwMZps5ttFD*qFIm9IaP`bWWkI6#-2Z$l6Y9%ee>lG!9%
z9mo@7iz3*VTx7Se9g6I)ujacFZEtsjn2^?o-iA6_ey58ChP{GP%B`=vGe2cnllr<G
z{i}GOr9M&_8-2;BtPl*I&C4DtmP>RM(xt@Ijt;5Hv{1x-_h|FLFlcRT1h{*C=wuw3
zI_cxPe}KbyGS{W}X6g0Z5SLzq@;U4(2NjSOjzo+<;{gYdY`8@_9B=k*vR8ckJC4)m
zm%X{`D4dhrpA1fPEuZGP=NG)fb?&x(KQ51`d-BbKU4ryyM_U$=vhmJfEc<r=bUi75
zWq<h$NJ-ofObzNjDtO*Je_wU1!kXZ=prC@Q-iOf;fIr9vZDgC-*V#5J`z8}w%K5Fi
zisvmq&Ajrw^6!>v;^W`08PD+~`;q;_++iERjO@@23=Ax{;lFh&Hp@1uabR-e2D0cJ
z#C}dkQvW8h=pM%DWyo#w;RuVl;TF2flgCiy{CsWfp!UDT;6=81t0|$SMS)NMbr&GA
z_d-Ku@z~p!aVL>WxnRsDoC<z^9&tZ~bATpln+TK4(XYX5{p-p38^AUq@uKFpsHJqi
zy}0-D;NW1&uU`l+p^g5_e&dg`@{h&1#GJ^EW}}r|3L~XNTKDPjc4W9%-w`mfk9@ls
z`P*uPHz&GtTnfGc$Z@<R5*_+>3z!d3AW|9RQnLJ(-9)XcUbbiqe?bT#92lw4qQ=>G
z>6Mw7+ppUd&0BJ&uawW{X&9K8{4P(H3j-bp=#B3+?kTK%&#X-U#d4Yw`xI+sdM<MH
z{Z^g3*jOR7fr@~YzUj9|MAV*e39cdQG$me_i?T_^xYO$jcXcUpa&qD^QHvxZr4|j_
zf8y1&T1?+>Vr+c8a;wDM2{3TV#~n1F<8G0t4iiJgZo{Zu`l!R2!ks@{SJ`Y{4KY1j
z(F${Cp=+N?R)*e@x{sC>p-?XXW%V1<U?$k^X11+*+|MK?CYCMh&&LH|W_qVzPsUe~
zJlm^_o_ep=nBjc3(_dqfq2z97ZST(a`W?L5pYYs4j?Q_!?Mw!LeQzJK0T=_H`RB}G
zQX7YwT#;QcGi!}UkJvBHj+9r&LObn3K)KT1fAjnY?E!*=haDlSFQDz69xngQx<Pou
z?8R0v5$0;t%6n(Z9}9K;$phHXd%(tIWo1Eng-ZE=zd*;#-1A;L-NF0zW-&uWY@1`=
zJNPJAh6>d`kN(1!pm@?DEXUtHIrJ2X`mNybWFaf^{WJI9n}0E1+#2`^(Mi%Xz15HV
zvt@iCScN@GT>I4}^6>!GP&w*+Cnb7uAwk4T;_BjPR{74QiZ6x-wv4d<H8olpJ|VH(
zXqV(~c?Z5weK*tlcYmNbiPhv&<XyvLu=*k$?f6YdBgD@PzLbLK4LjQTfXFx%EZwvC
zGL;bW_9k$Dg#fJs_=Ui)v9X%ytIPYghnIbQeOiWwCB?;Yyt<ir*~$PmX9`=B|9H|?
znIO`Zt$cNY{q^e?XnQHk*7(7LAtw)gsvQn`>SeVhd$8z-gY`pJ^87Q2aq-m=5%Q0p
zOxnVWIfSxT$WIXW)E8oUVq{eas_v_2madJ)iJ?xtm6oCjkEumXKB3Z8lWt8!#80>F
z|I}>+aQ@`4bi4M$hfL{w`nw*#Hw7vho(9iGX8HcXKmq;1-#`kdB4OsmfOk}9B8xa!
zAw={~Pm1!+hYNsL?&RYl;(Iv56M>IG=9;8uBg6qI1>l|-#w#S01khkf7OT#xsnylh
zGRwNVwbB!uj1~R`;u9~y>UlwN+*ChMeY)esdMv-v=UuJU$bN!pY8Np-^cCUaF|U+{
zgNcHrA6(MgC1V3Z-Roro*M6mY`9<o?8nwLwj?WZ!FxL?)=kZ%q=<eMYe(FWCk1vA+
za1UCQVRgkQ&-L~zs}Wm(8Vf4A4xLs~ug}R}73n_2QA4#HpAuEq5zl_Msr?Tx3nWg+
z$dKBBHkwl`9KQoeqxwZigK`^I)};bi^@rNpjI}klsFQx%pY73CPvU<;H!i-eCT0A<
z>|GH}5iNZ^5Z!3GpI~Fz9G}r8^VdL+2AD#1S;K+s*9(@i=K%!U)u>aONJ%Xig@zM&
zy8z)~Jfd*LsNqCgMFu!^+`-@1xL>=OiP{_6;--+E3s1j{4f&9xe5~Jf8JU=2*V$e)
zgm^?ZmGH!YKSGQ4-tIJlWCmQqxQ)Q?HwfoKXIW5|D@wR)GbO%fvEGf{D1hTzxVpL$
z2#lF*dj)Vz-`SHE_~zS``{e_Gy2~>LxAtcOlboZ!?A_43>)zne0tn7wM?zN>NiRcc
z5hU%|-cqzN4nix-u|gcNCSI9^;AyTjiOE4fyb82}0DgN~TAImn0_F`WBO~J_z-Km%
zUYi|1-^-(2Xe}evlf45io2pI$PGWF?@;tUDTZ`hvGz~TSqPHBa!_66D39Co+UdT{!
zP-!n%+I^C`{L4wDa1(MMqrG<TV0GXZD*$ls-@JhX)b&d{3%&21J9jAAWke_JCA@KR
zS{sC}CGigpF$}-YUx@!T9&7tGCgANk-4l9icqPNhOfL7ZBb{Q(G?2LJtM^Q6)bJ6p
zIgb9#D@TfJnc3OB0OfD}*q5Eo7)7v3Z>99sO_Zou4jVq!v6@v?^d{p%v$P?kERb7t
zGoA`*ujjw!W;cuZVR(z+y&(uG@2lU{Cv>Kbpm$DLV-B0fgmeKmen7U0$}OdS^My;a
zwl%k>#D+Bm8nsE9tyC+mC}-u(GaO~^AX+SIBdH(_7G*iC_=8T>3EB|(GnG2J;xWMt
zSbm8TACC9?@R`n*Pa=-wrkoDb7%EVnA|x*Uj&upnzYg0JN5UTZDO!^bUPSwsn}t7`
zb?F>`ZWas{M}F?I(=%~P?z|M5$-hsyBF&U!$k(<)F~~X--yoAhmki}Z4xB;3e_mZ!
zy=jxG6)3pq=J#vCg2j#^wH=D{?~84wZ~&|(gI>)uJfsV>p(mH`Tb+>885U|;3AL%h
zkA7GYEA0BCbF#EXxs+9p`ICd4bxg?t>mOqL+CFSkEK8)BQDJ2q)mtB=&FeLciGzm=
zfKBsdqx@tVI`@XPPI!CFgUe_}u3H<j8ey{!(ZIQNFqD&)AN_sd!sWh8Q`GffGWhG5
zCm2(Uk564Mo|iD}L<e(P<VI(2-MJpXDn@na88>m)a9x_u6yL!aXSpH^ZVH+FAQ66a
zvpR17K8eBeQ&xUi@Nn|Ksh`$~iuMoRrGc5In?36I)zQj2yEdF1guk(vjLq10#50BW
zCnCY{vX0g~;VSE&Wdswo@&!C3w2kWOK?to1#1y05NfN|Z5VAhqGoaMZ^g=87%zQ;d
z0rPA_{p@MICMDZp1|1i_9{Bh0Dq$|PI~lCkOuzdQ456b@p#Dxk4wr;fE1-qsY>h^&
zE=D)0&dGZT8O+x!!exFnBwqhEQ^&nCm=vf`>x-imI}5rvl`0@ARvEN1f{}ciR+!F&
zdM<U-b_Q^p!Gck4l2r`ZvtI*6NM;_6sCkKnur+kWW+z_PzbCcNMuE&%C^d$Sn;P{!
zi&1PVkhQz}#;9SYW)n<*SA?z;WS4>sh>^$6GD4~!sM|254qG->KvPX@agTO3d^`O+
z=5?sKN_`D(Nmh|O8Kw?R1rsJQzKI&HBvLaCB;2On_7E5iB!3}Dm+C?LO;}ji%fAXE
zQOd)NN)DUZZgIuY!ZDhZ)~71id!r!F2}Th*H3Fa4*nO2ZKkDJA%#UuJFa<+G6RV~Q
zLyiQPq%N%H=g*CY2)@KaXL&5Aw1T&wjH*M6Pgr#OmuHE0GJ!K(&~Pv(a(dBgM5|56
z1nQ5ZKxj2#7ZvPs&%$_pQW5)fsWxbhEd|H7C3aLifw8~z3`?hL>oXo)G^gj?m<{QG
zfAU!r_$afodYI^1`jS2tHA+~&5K-h!WMeua1o3M}eD_bdFjfD8=p~seK__iB#lvUk
zc(NqX7x+P4j!Jwc0uTw_Z5|Y#?y^xNi_RQL7{|ta&ffUgQA=Mi)u9hF0|}h}onGHN
z^_tt_URR=0*wkPLq~iC@+cPA8Dz|GVrg?jA%iHunBOsttJ5^W76Ry4`vK!EhseNvJ
z;*nUXSSX8)9o(&*DA&*=0O!(jLwSPzn=o4zo2ybut^$Bk9iOh5AawU5K5lMmXd>x*
zH^wA=Vme$p3a~{LnBh&<VJ1KacIwVd`0s>-*t4NW--^Y32;}93YBhJy<rw*YZ;bky
zeY*zG-PqSU%L$=BSPINqBWEAnKM6KDkPIo_%ezWlo|G!C!}~LKILqZrGR}Gtc10UC
zKpgIu1k>1jbZ32EK;5i-7%&Meo0r6qb>s8~4>idEusxBZR|FD~T_8~>zKA;3H8;iS
zHfZ@{WJflMycxjhSAO*myVW-$i@x0=@iE|sWp%bu6?n~Nk5+ftQKEO=4I)sT&Y?C3
zhi~5n7;1C3YU&IV$LDP-;7)(+0qzPFuz&dQN@<|zMg+EWrmZsZ!Rj>abqVM9DP9*v
zIP>1{vH$Ak&wA%`jW|PG{Mz~}j<Y`S^rJJ?SlXI=;TqUd0mfd>9e@H7yXCV%4d?6M
z3LaYdG^FjKEbmK>wf(T`dTxOI7$KkU^tK1U1Wod}xh#9xq*~8HafbUfifOErazu2^
zDqHdeJq~)h77n7$J-LH>2nxl@u<!>g5YcK|3VJnFF-k>dzw-PJ^%UTUg3164zquKJ
zE1ikXxYsH_%-hIF#FCOt6(4+0yI^?-JWK}&cu&wdVpuWYm-L%P8#T|DHSKxd+k_M>
zpNQ@{EQe067xXyl=>p5<uo|2+aKwG|4zUT;w^lsiWlUUQb5b9%3#0b)I%+(S#5#%<
z^D1alLi~`3jT@<tXd4C4yOZipr3ep()L;##Dr~hJtp!dg^Q_Wc&)hSbsOfzN+7*z4
z=0&h%ppLUUJrO!P)Tn`4r+rsJ*zq;JpTmvRkU8HuV>?Qpazx^ez#;L=4=8$gR?IBG
zWlcx4w;VY!>N*3yB}dp^U4_;pm)I$y!8)>XB?l0A^HRX0ZdQkHw?)Pwn|cwoW&kTE
z$t2v1r1-dY7pC~vpn`-XtjgL^b#E!xD3AdY8qS6#X(Y{qrp49sLibsyFM1|ddFUB!
zc;X1qoQ11|!8W3^q%-?D2DAY1SO`I8$-kr_1pWFSGcz;CGku1F*fLe?DNDkifPnxt
z|2s``YpdZ~-^Q{}jJ9Rj2ahCx9sR-cuUBeBObm^jy@XNA;^8k*&IGdroQ$dSF{7}N
z|KdqS7!SFoU5AqS(61wa3|xEZ|0a)Cs5R>5%j*=N7?I2nrgW&#yoSSVkilLKCmF*j
zGhHf0OgKCgWjP{h3!|7`&PSi2ooF1yid}PU{743J8@L5W#>NCfyO$ER+&f+0lPDVc
zSGbzDUt3#mp%~0|4oL-%f^r~Yrs`>MVwHDH;<P!G3Nh&n)W_d(ypV5O>!x;mRU>$e
zHyAmo)-NQB)Qo>jAFeAPYhz;Q+<t?O2%W(;w_?*9j?G0Uy2ktm&Jha4c%l<fO&aQ3
z%&BoU_uGk@2t`f;bvdY<5_(DO7`NY`EFDDWUtQ5|%u-rNKlh}+d7!l`xof<VNA6yD
zUGcV~<;SFQhonzy<aW<zTf!r4NZFs=hba?a!f9inLSIe|e>BqQHSl@l_WFkNJP){g
zo%`l>K3melb6V=nr!#7bh{5dQ@mJVl$2;HrNe23xuG7gs_Y`j=?bF_TtzIdtU1TQp
zr{_l291p1@1Rw#c<`1vFouuUl`I}3p`@d1xAJ(F3D(UQUpHH}o*(DmtlIBy#tIUHY
z(osdy^ol^QcPxA;or)|Zf!$wh9yDJma;jM_>H1e-D4ENXmwORg*v_ULqQ|fdj!Msq
zk0||B@u^1Dvzm69>x0>%007Cwy`?f>9&weHA0vI1>Y|u-K5+SJ?|iVbrF;ym49vAr
zS{cu%9B<G%-y+}H)|hu;kn&zC2{n;XP|{(c{CuDiZC2V^J_+W+_dVMXsdAD0ej*vL
z#&S5Y#FRBG#2ZuV^=-y1>abn)h+uo7+$gf_WEWE#!%IRxdFUMzyyY*<kQ-pFE;asR
zThPAY7Q43foK<vO8hgk(^URnxB`~a@d=!JtrPBL=_|Ry|{&IPuZvqu|As?B)NIw1@
z>hrh!<pqO>1ykE*Hg!K;EK)yEa{H&M%rS37I5Z&XJE8@k^tXje$-Nyw*^wfYdDNAc
zHx45>XFPML0iZ)F2G$MA9=9CL<)(byrht4|&F=<w2>4!Kn~R<~H5`VnWKv^A;E}nV
z&5FZC01$otP)ujjLLeploFZyI(SY@ye1ZU6YNoEduIc^PgG!YMF$I5?jZ2C1NePap
zz%J!=cIQ@g?Rv-?_Je)4IB02ZRyO24ld(6#4W^FU70z#8y!ydQRgg<lsJu!k;H5_<
z2+cOJwJzU#e%uv0=qbu97{ikAmQKgUS2p%6fDfwl^vY6^v_s!pw*I!Yue{&|HTw)l
z<7RHQBeEl}X>HwZ-M#8qVVS}g?Q}e(DZ0k?fZ|SN!&CctuMq0@x?IPD2|hzaReO;k
z5ppe&A{m<r5rTg+mbC$}(^Yqh$L9~N2{QQ;dMUPcvFc6;Y<2VWiM;xvgUA9DTERmz
z6HRt0GvaFau>xLN7|WI!AK8#l{A+ZirkUrvH7u8onbc*PyxzHUC3H}9Iwd(i%lj$E
z)$;x?v0`G?wg)MQE}|47f1j<Z7Qh!jn?`P)q3vNDHz5@8c(}W|6XjsJN)I6mS{@>1
z4BI5B$2_p5i4X2*OR74f|9wEf;7xihE^jknl3ko@Vc{Leop2xvXHqhcOZ}C{Ni_F@
z-+t7(>J(db^4JBOw8$yyRP5h($B}%Wjp!RE^}dTk9CNTw?msjJ(OQsyFWG6$T_!qe
zth6P$ER@N5K1iC0a$~I0Rri}dEeqEuD=m*hz4JLoVBaw$Gkr}ZJo-%fqWu|bNeK-y
z-~LCA=t&qjTy`G}o<5r6$8so$Korv~-(r1^F`Yg;(W*1e2Jg-*X!4dxTI1w`pRfDA
z-diak$PZW3F$5?<C{J+gGlpeER{$9v<KZ|NG%-5S?y+gzL-k}(<kU&&#VE(zJ-wL7
z?JDs;K9G{4^y{^5-=EcX>nE=$z6iP<oG?@PM+A-xW<}@EzO%Q9q%V=AYnEvbYo_r@
zQ@Qf}fsNz|@zJam{pQ{fce5^^CywO0w$JH@L883(R<%QoxPwa}qf^F>$!m<=`eXEk
z*RJR1x!<NUnQ}@cqg&>oLIx*Kbsg176r{|KEc06@an7yn|Lv0q)(F@A;(NDo|0aJ-
zd@3{Y<`0|0tDx%-i+R`O7!iVmOazpy5+5aZA~#oTWU~KQxo+RceZ=JS_7jbr)fdLk
zr;F>4V=f_{NV|_gWgrlP0KOJLMCsd!>I*}^)A1gDIosPiTP<A%gOxusI0s)c-ciic
zJfd`odKOo?Z4BtZC+qapAzA1|kwkfq$})7V8nfqqHy^Xs*Eu+3wPgQMFfe(;tu$|`
z4rXL^au5(H^rUr|R%JcHW=uXsuKgrQ;@}Siy$i3gw9pg`SSl|kKjUlBq9lfUC(Nq6
z5!K2eq}2w6f#NM2gh8k#usP++SFc9Cv~NvmPSn1J3Vp5tG-=4omQ2Hef4P=U?Hvaa
zDJEjTT0tPD;q(!~GcUm>awJg0ZobmiMi*iT8_91+Z`MgL^j`CrWA@kS6A(_ZN#Su+
zVA!RNopkmQsG>uV7CglBaOIcfsQ#D|_*D>F7R@{l&6J<Wf`sW9;gy48+V@W@K*&Bm
zWkkJQGY!hQy<kd*Y9chBm&Vx?j`hTpJ}o-~ZNr&;!Sq7>tOb+0zznK(7udud6*aRF
zTA==*3j~nJ5WD3L@-(`I-j(q?6GdQpF?<_0PDQrUxcLTG@LFJvB&HCcZ3=dZX?lWq
zHyVL*TR&d=0bNgZa)<-oQLP2I(t#WB_6FV$MzpnIYXF2}16VWtTZJPfteVNDX&RCW
z?~p#2iShdx{{9E=c>20V5>gJB>{)LZPmVbNQ`t8@-VN<$9KH3eivi#MrlITwz(euV
zyueTM(jAzB60lBsWUOI4`9Gyspcp)%RDBuzCT~!CXCGGpgjf=H692XNze)VwW}RxX
z%{hX(MsExN*$ia&3sC)lZ6(ae;eiys=w+fEzYQ5X77V|y9)twqk<dFCA&JP^yo)8L
z!*wR0jAO_2qb6lA0S@KHw_PE>i39t@qLPV)@zkh5sUK&ACwq~I9WXsqGkL`4P1y@Q
z{9^S!BJGv7%g#<M=BzG>O%HTo1HQJ*grxA=8};sbzam55P{LD#y6o^BiwaRp1P$28
z+`EF1;TRrntji(M)}3NabspI+U@h5zVKp219)<6%TNB%i=#^Kw0wrv@4#cVRa2$Kv
z6`@9(Yj7lo^E?UN=zR3Wy;0k1f`nj+Cr0j4o_gjHjqQoR$vEv-O@_Yy{nNw2XoMnG
zh(7fKl2(Dz*kZnl;a+MY@&>gVjf^M-GB=8psxNw@H}$(dgZV=N8UHZQ?Wxxwxv%x2
z1{eTLzxL_<5?b}>Ky^;?iV%`^t%Y2n1SN7Jq#ImFToUQv|L-jD7l<;V?~FyW(|#lP
z+c1pK%0!%x2>DC;2>Up~y*>dJg}&;#7V+gR{2s~hXJ(;A)LZIyT~r=@?lI?lbZ`Z_
zqIbHWP#tfB4gsRBCs-p;nB+=?@0tT#?*tkzGtPj_NL6ua^JM~#M=@2IW*YJ_AY`ui
zd=UHXQiu0-BH3WP$MtaHVE_YsXm$D>I}$o?|F6Xj+0i8fh*|B=FK@+uHu0uUj9?-p
zK7Rweobp<{6L?S`=R)6%cm2pE0}mEAL|hVqFc1`<uqv+t6n?EK%-vQLI)H!1>xQNs
ze%#*=12Wzp%K&~RGUztuSrV{VOZ&+-vHSa*IzV=AJ^T0LLN7;4E!ena$TKKQ5bL)t
zDf}vo=2QQ$3syRRMRN{OcV}0T-+>~uylF$N)cvNGZSM7roJ_TPHe%0<AiFuumlRu`
zDoL(cQj8gdqV{&!+nNq~A<x6Qe3-W@P|k$7rr{V<)Edtb6zg)ShQ76R8Xtp;2d6;o
zYd+7BUH5fhuFuJ4c5}$4QCM$_Vmf}PYsNrJAPlnc>OaM(PZYOm%CtsH8`J%H$MFJc
z&*<i`Qi9R|ZTZh~FBz-zSgAiu(CW;J=6YiabDv%%v{R{~LiR%HlQ)1!<c&_L;y}|^
zNyM6EksSeK*9RDHqz1z;37DpbUu;qukEqhJkhJhaa^dd?`dq8XO#`7-U~T~cfhV$#
zU+u9)I?1M^qET?*-hPk$4`GF*QL~d7YZjfgn5fT?@)1>n6g7A{6y3mN3}5CI!Sq3F
zM^X|BleDqO$f5!rOKQOYTajxvdm`MBR&}27$b9ne*S?@ogr#tST+7+)LpzfW+O&l~
zVAuLR2@aKy$oYGA$%sXe|CFmbkFNUVS|-asXCO}<a0a{rv!<_4@j)78JgE(z<Ii!Y
z+w4Q);EI9qQ|nVl2iuyGwAXJ@yyy+kUQJBy^jIP2Pi$E`eKw~K0Ap4#PmXm8os%t=
zSp0d?+ABvVx?=veYJRFH=Xdu(uS+K(W$|K*nV60TWy=Avw^cA+>=HN5J1Mt@bK0hj
z-~Rg`AYXhR@45$bIimnc<Ujzj^}1@^j+mEm#EEK)1He8c7?352o8d?Hpkj-&r>iRX
zah{EY^6qb?b#2#H;6)38$M74dKFvl_S@#ftF!4;x0|<skXUMXOENLG~hF@F~ZwemZ
z+ZH<WVnKQNIZR7hiAX_U_-i}9TleCMgX~qX2LHyxBQj{D?x1YO;_LXW4T-eX5Ippw
znQb}#ng?>YSOE)x|7)4FI!b5Y>aplNQ<rCOgbV_z6J`49mDQ2&C_*VfS341?UQ^El
zwsr0yaMuwK2VqXOm50liuC|wSv50-9)Orn`o=7FvDG;r!t@`MhLUKZH{nTSDqF1=^
zak?t92P^`F)M;oNd;;2m;yFFjXaBEGB;kGR{P(DII0f7hZKb5K^~AgawXT-fll`(B
zh{OXiPObG!cKk8E0N^WP&f@5x23`O9PBia!F^ZGIw;A)S8z`&tm^E`QN!5#I`ftT>
z?h^qO9OWLt@lgc;yjKh*Xf1dk<B&9KAhBop3pljC3BrYIe+_yO04%8M*8p`YGqkA#
z1e|+TH8+8(ISi3Nr1LCQB4IH>Ym9tsc_r#U<A5Fr!auD|VN0(9iSyEP?q6{R)k_Wg
z@=BPgz(gp&=&2tjXf>gj8g0)>Uw>S4n}fm1cRn@uMq2PfqKUk`?WZsyTj?#a3>>n9
zinHK<LV_|f<P{=iYN})=tdx9kXnAgMtMS`4TH3rr<iJggqh4-nhPBF3#<le%C%KmY
z1>|)_FA<ak21J*u^_M_K@0tgpam*DNl+$tI{ri>|SCiE#C+Q?F)4*JtK8Z+_vmvjf
z222+|PVm=-Ncv1~nTUumqnSHnlkLwd-oi+YKu<;KJXsmMxCI6+)vRs<<N~Pjna-0I
zOZpx){gxvc`oIO*5fCEc%<NOu@*)?@!V27wD>L8^QJ15>BrBKqJ??u`5QHOkd<1xf
z@n4({%<!9SA7DWa%MOA8Vi8~-e-{<HpD!ZIFFumYl(+*}fB<uv*2er7YjRup@|gc@
z)53YqVkySMk2_>L%lOlvQTR!geKaJ4+p3X3=yb>1U+f*QqD=!W<^~pj)Td9S%eI$o
z$v+)O6wX>#dH8?B1~JD#Kv9kowv^Q`wWn=!oyJx&70NRGjw21anU=33#_<%jTPX15
zbHD(nmtyoaGXoCr=F3Fa`VB-!SIg_Bu&724aow?vc}z{5&tzWe5cRk0Ai%~TU7vq=
zHFbIvSaycWD10RPbaF#g%JDT|-MZd4g+2b}(tI-A#{ndHqFaK*X7irZuS*p30?W@f
zwlc#iOEK*nqaOKDXZRl?Or!S7eJS-f={A2P%07OU0VHPc&MDbRh)%cCsJ-#a0;Le_
zplxAw^KnXQF&|`(!fi++%wVNz$Zr7(2M2wBzupo&d;4;UjT~QaxO&mFQ?g#{v)XVO
zwAP#bko*8)FTU+LhqNPB=iv-tU#9jZMzhjdRG_S4;^CU6fu9iP#K%h8%QqawpcVV(
zl2!@H)pgsqZ8z>LrBB7OD?L;*j$mg_T<eEf5XoNcw{>+}sEQT~skX*WO1){cbsCO&
z1e;#Xv?l+~oazbqM!{2l*ah}Ta|eX)!GTVob?e^8Ku*A3n~sc{1hvGydsPR-YdeSf
zSOLpo^yp#t%+EgznGcOa-lzR^l;mI2!KHpZd3Wk(;Cy$va3-rDKmG0TU?P$#^@$@G
zw6T*T3~~lV!i{0&2RYF{H<eohu%)@j9%DDrt7BWbv3e51BO$HnStk`G9a$%pV26v#
zgA;>-mh#Msv9B_gpSjkjo*9|Vc2SeParYx63JY>jsWi}InMxlH3v><$3&uDF@YlKf
zm9B|lBr3<dxFI;u1(9HrCd}5oLyC~Y;0gU+Z1-m}^J}6{e!OAwICNrBl=p4I!Cj+h
zj}1;aiBCw^!r=kC)pP4ZPZSIN^fB}8g4<#IT(zN(+M_r1*?Dzd9<gwd;>$X_d(yMH
zfxnHzzC2Bp&A~eF!*!mX<!RMtTr;Ng{7vo%`83}32E`oLkVH5qQMekTa?|I=${on{
ztp{qI=XSn=Buqq3j~~}<@t3UI4ss1;jwC{h)sS5;!R~N9T9&&IZ8V`>quet0Ljtp=
zB_={4SwbiVOn?6-FOVyGq)rYbs-@MPKFBMpsXqiYXcE=`XArKte8FH&iuPN#tAEH{
zfPX_V&kWAlXbg#glB?JG9NTHv9TTDV5WSYL0k?|0qzX4+URuBmfgM=^L-s!(QcbGO
z(hv+p&65pbcL%!zH<P+Y<e1FX%^#K1m-<pY9I5?Fo}Y%i$pA^4PTR6fb@r`G{=Yo}
z_8T@#(||(o`+%u(OmuyJi!ZKwh?<)$p)^piV)S!^Wr<qP$o#~=vhea-oc}th>hS^F
z(*e>XTCE7D6QO2+u4w?_l1J=L7kH+ISVRC36Q6mTp5v#8vS6+gNVSqyV+8H^VkX5|
zZw$n*5V3K9+^pta681f^$7j=G8lw0ilNwI%5G{8him@39y_arV7YSO`XVQa#%DQS_
z856KlhV-d0NcF(Q`IVqbjs?W8YZ({r-SEe+esXbEgFV{kXbdR!ri;loI2xx}*I4|B
zI4`x$L2QW7hFYpqBYK?+Lt8Z#J&(dyJZV=Mou;3A<*?IQtZ1vCSp8}2Bz)wym0m81
zM^jc2XpzJbhN?us4{Wb7HB?>^p38jSx#Vn+?yZjCmVA1)r8IZw+}G87<^_JaxHSy5
zPhN*`cSK&?*fy#?G}E0?_MzBjJ~|+rdro$~`7`Fu*18uui(uh3@%b&|!jjBOYj{6g
z+SiLIgk;;|*`EZ`!}<Nal;canLE>UiZsgw!riT8=NV7Lsg&%CcSCx+ZY>&t#3$hge
zFUAwqbPN6V1Py4W@rbpGu;_F>T;`~(1r#!#e;~MB?$f>Ig>#!b@$tMxSxnT3YBGV9
z0$_F~<zadtba8q)SL(ZU%q%1ndbQ`PBtI9C>8xvVq;>#DF@0K-I|^hOyfFM4#uZ32
zKw4nZ<CfPfU##hKRuiKD^IU&kbMwIn4O}g%CQ@0gqb*+eU%C+$1_tttJikI5#Gn%P
zpsF61CV2Xl?<wemlC5e?U&t!ET|4UUHH!+g6{LE!6&J`ewZ=K9%(1v#$9o3N^ZI%c
z$fCuAKeQ{P*p|yG*`SL6WXi-WJqAlCsAQrph3L(Nh)Jp5lspPX9=f;@bVTQAI7Sof
zn~Xqw(v*HktK?K|)H~x&rfiV^;Uqp7v8yMe+D{m0w|c1CSF1(g*pgYC!sLu{D?;oO
z&oHHKZdE2ox!)C@!=k-?X7;>&4E(1TZ8hM5YFjg-X&1bZBTyYWje8`dt)1{WtDVhn
zCCzePh;@t^JQ_CgE-GPcK2P{g@MKCRK@KfhX=A&t#E}`DC{2Srdf~}tWhQxll>B2L
zvuN`>ZiE~@98pY4_&n+{^#VJFuc3XP@YN2_eITmRMy2QZHd*!P1deEV;m>;Oy+~8$
zmOO1J-LWQILufpupl3;{Y=GFgH2kTg3P%C15@fl1$=1iv5WUQq7lA_iL(&lG44EI-
zu1-ac^chf1s_K_KkWVPqQQsKL?cnSEE~w|i#G{|qqOORCCTuJI!}WEoBO>Tyn5&JI
z03(LKu%;yk-F20%LT^IF82I<1x2(_?UoD6qtqxj}U6UugKJpwL1QQ~wHBxUvXw}D!
z$xv1061_TjF&jGFAM4>w_41AZuAC>Yb9)I;0lK{KXIPUF$9sM1Zqap%7a~0;8}L1g
zFcEXHy%&>lI}BC&uqy$roJ%TC4u}}XviuvK@4j`5xN7!>j?Mf45})_;5<1teDY~U#
zE_dh8SjZ0*Yv~%a)WaG}B1Qa`pYu(XZw`gbMHDfDVCX2j57_E(pRkM#knllHJ7KSn
zElsC3xDl<Z;+MS;L2{fr+d^sg+>o7Zs)s4h<nH`T@D-H@p;Qys_kgR0snvd;u+~M5
z+ZB)U{67IHeB1LsFBUJ!_b+44eCe1~UYw1815l?EQk61@kxi-^a#6M>IsZ8CPcpT(
zl}ja)bnK$RT>+0~Zg032GBsV8yS=Ip{`*_*PsGsCb`JB-#q;#+7jV|2=JhifPh%XR
z?3TZGy$92_Sz(~JpR!9M`ltobQxPGB0S{|w%D-{~;G6|;I{yE^c(Z#&z{2v#&eOyQ
S4tx&@q^)6~UZHCD^#1@tW?A3>

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/packages/gluon/blocks.svg b/docs/python_docs/python/tutorials/packages/gluon/blocks.svg
new file mode 100644
index 000000000000..2ff614810092
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/blocks.svg
@@ -0,0 +1,514 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="572pt" height="220pt" viewBox="0 0 572 220" version="1.1">
+<g id="surface1">
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 350.269531 182.550781 C 353.636719 185.921875 353.636719 191.382812 350.269531 194.753906 C 346.902344 198.121094 341.4375 198.121094 338.070312 194.753906 C 334.699219 191.382812 334.699219 185.921875 338.070312 182.550781 C 341.4375 179.183594 346.902344 179.183594 350.269531 182.550781 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 386.046875 182.550781 C 389.414062 185.921875 389.414062 191.382812 386.046875 194.753906 C 382.675781 198.121094 377.214844 198.121094 373.84375 194.753906 C 370.476562 191.382812 370.476562 185.921875 373.84375 182.550781 C 377.214844 179.183594 382.675781 179.183594 386.046875 182.550781 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(39.99939%,74.902344%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 372.597656 141.277344 C 375.96875 144.648438 375.96875 150.109375 372.597656 153.480469 C 369.230469 156.847656 363.765625 156.847656 360.398438 153.480469 C 357.027344 150.109375 357.027344 144.648438 360.398438 141.277344 C 363.765625 137.910156 369.230469 137.910156 372.597656 141.277344 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 421.824219 182.550781 C 425.191406 185.921875 425.191406 191.382812 421.824219 194.753906 C 418.453125 198.121094 412.992188 198.121094 409.621094 194.753906 C 406.253906 191.382812 406.253906 185.921875 409.621094 182.550781 C 412.992188 179.183594 418.453125 179.183594 421.824219 182.550781 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 457.597656 182.046875 C 460.96875 185.414062 460.96875 190.875 457.597656 194.246094 C 454.230469 197.613281 448.765625 197.613281 445.398438 194.246094 C 442.03125 190.875 442.03125 185.414062 445.398438 182.046875 C 448.765625 178.675781 454.230469 178.675781 457.597656 182.046875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(39.99939%,74.902344%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 434.339844 141.277344 C 437.707031 144.648438 437.707031 150.109375 434.339844 153.480469 C 430.972656 156.847656 425.507812 156.847656 422.140625 153.480469 C 418.769531 150.109375 418.769531 144.648438 422.140625 141.277344 C 425.507812 137.910156 430.972656 137.910156 434.339844 141.277344 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(39.99939%,74.902344%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 403.46875 141.277344 C 406.839844 144.648438 406.839844 150.109375 403.46875 153.480469 C 400.101562 156.847656 394.636719 156.847656 391.269531 153.480469 C 387.898438 150.109375 387.898438 144.648438 391.269531 141.277344 C 394.636719 137.910156 400.101562 137.910156 403.46875 141.277344 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(69.804382%,85.098267%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 367.691406 100.003906 C 371.0625 103.375 371.0625 108.835938 367.691406 112.203125 C 364.324219 115.574219 358.859375 115.574219 355.492188 112.203125 C 352.125 108.835938 352.125 103.375 355.492188 100.003906 C 358.859375 96.636719 364.324219 96.636719 367.691406 100.003906 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(69.804382%,85.098267%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 403.46875 100.003906 C 406.839844 103.375 406.839844 108.835938 403.46875 112.203125 C 400.101562 115.574219 394.636719 115.574219 391.269531 112.203125 C 387.898438 108.835938 387.898438 103.375 391.269531 100.003906 C 394.636719 96.636719 400.101562 96.636719 403.46875 100.003906 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(69.804382%,85.098267%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 439.246094 99.496094 C 442.613281 102.867188 442.613281 108.328125 439.246094 111.699219 C 435.875 115.066406 430.414062 115.066406 427.046875 111.699219 C 423.675781 108.328125 423.675781 102.867188 427.046875 99.496094 C 430.414062 96.128906 435.875 96.128906 439.246094 99.496094 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(39.99939%,74.902344%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 341.726562 141.277344 C 345.097656 144.648438 345.097656 150.109375 341.726562 153.480469 C 338.359375 156.847656 332.894531 156.847656 329.527344 153.480469 C 326.15625 150.109375 326.15625 144.648438 329.527344 141.277344 C 332.894531 137.910156 338.359375 137.910156 341.726562 141.277344 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(39.99939%,74.902344%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 465.210938 141.277344 C 468.578125 144.648438 468.578125 150.109375 465.210938 153.480469 C 461.839844 156.847656 456.378906 156.847656 453.011719 153.480469 C 449.640625 150.109375 449.640625 144.648438 453.011719 141.277344 C 456.378906 137.910156 461.839844 137.910156 465.210938 141.277344 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 340.222656 140.074219 L 353.855469 118.402344 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 355.984375 115.015625 L 353.855469 118.402344 M 352.585938 117.605469 L 355.984375 115.015625 L 355.125 119.203125 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 342.800781 142.582031 L 385.289062 114.179688 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 388.617188 111.957031 L 385.289062 114.179688 M 384.457031 112.933594 L 388.617188 111.957031 L 386.125 115.425781 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 343.558594 143.980469 L 419.789062 111.320312 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 423.46875 109.742188 L 419.789062 111.320312 M 419.199219 109.941406 L 423.46875 109.742188 L 420.378906 112.699219 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 365.480469 138.8125 L 363.308594 120.53125 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 362.835938 116.558594 L 363.308594 120.53125 M 361.816406 120.707031 L 362.835938 116.558594 L 364.796875 120.351562 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 371.664062 140.46875 L 388.667969 117.738281 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 391.0625 114.535156 L 388.667969 117.738281 M 387.464844 116.839844 L 391.0625 114.535156 L 389.867188 118.636719 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 373.808594 142.796875 L 420.835938 113.3125 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 424.222656 111.191406 L 420.835938 113.3125 M 420.039062 112.042969 L 424.222656 111.191406 L 421.632812 114.585938 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 391.71875 140.859375 L 371.109375 117.082031 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 368.488281 114.058594 L 371.109375 117.082031 M 369.972656 118.066406 L 368.488281 114.058594 L 372.242188 116.101562 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 397.367188 138.75 L 397.367188 120.632812 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 397.367188 116.632812 L 397.367188 120.632812 M 395.867188 120.632812 L 397.367188 116.632812 L 398.867188 120.632812 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 402.980469 140.824219 L 423.695312 116.632812 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 426.296875 113.59375 L 423.695312 116.632812 M 422.558594 115.65625 L 426.296875 113.59375 L 424.835938 117.609375 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 420.902344 142.835938 L 373.945312 113.753906 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 370.542969 111.648438 L 373.945312 113.753906 M 373.15625 115.03125 L 370.542969 111.648438 L 374.734375 112.480469 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 423.070312 140.46875 L 406.070312 117.738281 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 403.675781 114.535156 L 406.070312 117.738281 M 404.867188 118.636719 L 403.675781 114.535156 L 407.273438 116.839844 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 429.246094 138.808594 L 431.449219 120.027344 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 431.917969 116.054688 L 431.449219 120.027344 M 429.960938 119.851562 L 431.917969 116.054688 L 432.941406 120.199219 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 451.164062 144.015625 L 374.972656 111.769531 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 371.289062 110.207031 L 374.972656 111.769531 M 374.386719 113.148438 L 371.289062 110.207031 L 375.558594 110.386719 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 451.9375 142.582031 L 409.445312 114.179688 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 406.121094 111.957031 L 409.445312 114.179688 M 408.613281 115.425781 L 406.121094 111.957031 L 410.28125 112.933594 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 454.554688 140.050781 L 440.8125 117.9375 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 438.703125 114.539062 L 440.8125 117.9375 M 439.539062 118.730469 L 438.703125 114.539062 L 442.089844 117.144531 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 342.421875 180.203125 L 338.570312 161.605469 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 337.761719 157.6875 L 338.570312 161.605469 M 337.101562 161.910156 L 337.761719 157.6875 L 340.039062 161.300781 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 348.273438 181.0625 L 359.585938 160.15625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 361.488281 156.640625 L 359.585938 160.15625 M 358.265625 159.445312 L 361.488281 156.640625 L 360.902344 160.871094 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 350.984375 183.363281 L 385.890625 156.285156 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 389.050781 153.832031 L 385.890625 156.285156 M 384.972656 155.097656 L 389.050781 153.832031 L 386.808594 157.46875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 351.914062 184.851562 L 415.199219 153.78125 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 418.789062 152.019531 L 415.199219 153.78125 M 414.535156 152.433594 L 418.789062 152.019531 L 415.859375 155.128906 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 352.289062 185.738281 L 445.4375 152.289062 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 449.199219 150.9375 L 445.4375 152.289062 M 444.929688 150.878906 L 449.199219 150.9375 L 445.945312 153.699219 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 373.632812 182.773438 L 346.257812 157.277344 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 343.332031 154.554688 L 346.257812 157.277344 M 345.234375 158.375 L 343.332031 154.554688 L 347.28125 156.179688 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 377.273438 180.449219 L 371 161.191406 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 369.757812 157.390625 L 371 161.191406 M 369.574219 161.65625 L 369.757812 157.390625 L 372.425781 160.726562 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 383.300781 180.703125 L 391.71875 160.765625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 393.273438 157.078125 L 391.71875 160.765625 M 390.335938 160.179688 L 393.273438 157.078125 L 393.101562 161.347656 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 386.503906 183.046875 L 417.195312 156.816406 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 420.238281 154.21875 L 417.195312 156.816406 M 416.222656 155.675781 L 420.238281 154.21875 L 418.171875 157.957031 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 387.597656 184.664062 L 446.226562 154.09375 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 449.773438 152.246094 L 446.226562 154.09375 M 445.535156 152.765625 L 449.773438 152.246094 L 446.921875 155.425781 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 408.050781 184.699219 L 348.542969 154.035156 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 344.988281 152.203125 L 348.542969 154.035156 M 347.855469 155.367188 L 344.988281 152.203125 L 349.230469 152.699219 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 409.109375 183.109375 L 377.628906 156.710938 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 374.566406 154.144531 L 377.628906 156.710938 M 376.667969 157.863281 L 374.566406 154.144531 L 378.59375 155.5625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 412.214844 180.769531 L 403.273438 160.65625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 401.648438 157 L 403.273438 160.65625 M 401.902344 161.265625 L 401.648438 157 L 404.644531 160.046875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 418.226562 180.394531 L 424.023438 161.28125 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 425.183594 157.453125 L 424.023438 161.28125 M 422.585938 160.847656 L 425.183594 157.453125 L 425.457031 161.71875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 421.972656 182.707031 L 448.585938 157.390625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 451.484375 154.632812 L 448.585938 157.390625 M 447.550781 156.304688 L 451.484375 154.632812 L 449.617188 158.476562 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 443.359375 185.28125 L 349.332031 152.199219 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 345.558594 150.875 L 349.332031 152.199219 M 348.835938 153.617188 L 345.558594 150.875 L 349.832031 150.785156 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 443.71875 184.414062 L 379.597656 153.660156 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 375.992188 151.933594 L 379.597656 153.660156 M 378.949219 155.015625 L 375.992188 151.933594 L 380.246094 152.308594 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 444.605469 182.953125 L 408.972656 156.117188 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 405.777344 153.710938 L 408.972656 156.117188 M 408.070312 157.316406 L 405.777344 153.710938 L 409.875 154.921875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 447.222656 180.648438 L 435.441406 160 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 433.457031 156.523438 L 435.441406 160 M 434.136719 160.742188 L 433.457031 156.523438 L 436.742188 159.253906 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 453.082031 179.664062 L 456.445312 161.660156 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 457.179688 157.726562 L 456.445312 161.660156 M 454.96875 161.382812 L 457.179688 157.726562 L 457.917969 161.933594 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 540.894531 182.550781 C 544.261719 185.921875 544.261719 191.382812 540.894531 194.753906 C 537.527344 198.121094 532.0625 198.121094 528.695312 194.753906 C 525.324219 191.382812 525.324219 185.921875 528.695312 182.550781 C 532.0625 179.183594 537.527344 179.183594 540.894531 182.550781 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 576.671875 182.550781 C 580.039062 185.921875 580.039062 191.382812 576.671875 194.753906 C 573.300781 198.121094 567.839844 198.121094 564.46875 194.753906 C 561.101562 191.382812 561.101562 185.921875 564.46875 182.550781 C 567.839844 179.183594 573.300781 179.183594 576.671875 182.550781 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(39.99939%,74.902344%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 563.222656 141.277344 C 566.59375 144.648438 566.59375 150.109375 563.222656 153.480469 C 559.855469 156.847656 554.390625 156.847656 551.023438 153.480469 C 547.652344 150.109375 547.652344 144.648438 551.023438 141.277344 C 554.390625 137.910156 559.855469 137.910156 563.222656 141.277344 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 612.449219 182.550781 C 615.816406 185.921875 615.816406 191.382812 612.449219 194.753906 C 609.078125 198.121094 603.617188 198.121094 600.246094 194.753906 C 596.878906 191.382812 596.878906 185.921875 600.246094 182.550781 C 603.617188 179.183594 609.078125 179.183594 612.449219 182.550781 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 648.222656 182.046875 C 651.59375 185.414062 651.59375 190.875 648.222656 194.246094 C 644.855469 197.613281 639.390625 197.613281 636.023438 194.246094 C 632.65625 190.875 632.65625 185.414062 636.023438 182.046875 C 639.390625 178.675781 644.855469 178.675781 648.222656 182.046875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(39.99939%,74.902344%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 624.964844 141.277344 C 628.332031 144.648438 628.332031 150.109375 624.964844 153.480469 C 621.597656 156.847656 616.132812 156.847656 612.765625 153.480469 C 609.394531 150.109375 609.394531 144.648438 612.765625 141.277344 C 616.132812 137.910156 621.597656 137.910156 624.964844 141.277344 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(39.99939%,74.902344%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 594.09375 141.277344 C 597.464844 144.648438 597.464844 150.109375 594.09375 153.480469 C 590.726562 156.847656 585.261719 156.847656 581.894531 153.480469 C 578.523438 150.109375 578.523438 144.648438 581.894531 141.277344 C 585.261719 137.910156 590.726562 137.910156 594.09375 141.277344 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(69.804382%,85.098267%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 558.316406 100.003906 C 561.6875 103.375 561.6875 108.835938 558.316406 112.203125 C 554.949219 115.574219 549.484375 115.574219 546.117188 112.203125 C 542.75 108.835938 542.75 103.375 546.117188 100.003906 C 549.484375 96.636719 554.949219 96.636719 558.316406 100.003906 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(69.804382%,85.098267%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 594.09375 100.003906 C 597.464844 103.375 597.464844 108.835938 594.09375 112.203125 C 590.726562 115.574219 585.261719 115.574219 581.894531 112.203125 C 578.523438 108.835938 578.523438 103.375 581.894531 100.003906 C 585.261719 96.636719 590.726562 96.636719 594.09375 100.003906 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(69.804382%,85.098267%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 629.871094 99.496094 C 633.238281 102.867188 633.238281 108.328125 629.871094 111.699219 C 626.5 115.066406 621.039062 115.066406 617.671875 111.699219 C 614.300781 108.328125 614.300781 102.867188 617.671875 99.496094 C 621.039062 96.128906 626.5 96.128906 629.871094 99.496094 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(39.99939%,74.902344%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 532.351562 141.277344 C 535.722656 144.648438 535.722656 150.109375 532.351562 153.480469 C 528.984375 156.847656 523.519531 156.847656 520.152344 153.480469 C 516.78125 150.109375 516.78125 144.648438 520.152344 141.277344 C 523.519531 137.910156 528.984375 137.910156 532.351562 141.277344 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(39.99939%,74.902344%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 655.835938 141.277344 C 659.203125 144.648438 659.203125 150.109375 655.835938 153.480469 C 652.464844 156.847656 647.003906 156.847656 643.636719 153.480469 C 640.265625 150.109375 640.265625 144.648438 643.636719 141.277344 C 647.003906 137.910156 652.464844 137.910156 655.835938 141.277344 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 530.847656 140.074219 L 544.480469 118.402344 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 546.609375 115.015625 L 544.480469 118.402344 M 543.210938 117.605469 L 546.609375 115.015625 L 545.75 119.203125 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 533.425781 142.582031 L 575.914062 114.179688 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 579.242188 111.957031 L 575.914062 114.179688 M 575.082031 112.933594 L 579.242188 111.957031 L 576.75 115.425781 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 534.183594 143.980469 L 610.414062 111.320312 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 614.09375 109.742188 L 610.414062 111.320312 M 609.824219 109.941406 L 614.09375 109.742188 L 611.003906 112.699219 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 556.105469 138.8125 L 553.933594 120.53125 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 553.460938 116.558594 L 553.933594 120.53125 M 552.441406 120.707031 L 553.460938 116.558594 L 555.421875 120.351562 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 562.289062 140.46875 L 579.292969 117.738281 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 581.6875 114.535156 L 579.292969 117.738281 M 578.089844 116.839844 L 581.6875 114.535156 L 580.492188 118.636719 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 564.433594 142.796875 L 611.460938 113.3125 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 614.847656 111.191406 L 611.460938 113.3125 M 610.664062 112.042969 L 614.847656 111.191406 L 612.257812 114.585938 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 582.34375 140.859375 L 561.734375 117.082031 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 559.113281 114.058594 L 561.734375 117.082031 M 560.597656 118.066406 L 559.113281 114.058594 L 562.867188 116.101562 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 587.992188 138.75 L 587.992188 120.632812 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 587.992188 116.632812 L 587.992188 120.632812 M 586.492188 120.632812 L 587.992188 116.632812 L 589.492188 120.632812 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 593.605469 140.824219 L 614.320312 116.632812 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 616.921875 113.59375 L 614.320312 116.632812 M 613.183594 115.65625 L 616.921875 113.59375 L 615.460938 117.609375 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 611.527344 142.835938 L 564.570312 113.753906 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 561.167969 111.648438 L 564.570312 113.753906 M 563.78125 115.03125 L 561.167969 111.648438 L 565.359375 112.480469 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 613.695312 140.46875 L 596.695312 117.738281 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 594.300781 114.535156 L 596.695312 117.738281 M 595.492188 118.636719 L 594.300781 114.535156 L 597.898438 116.839844 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 619.871094 138.808594 L 622.074219 120.027344 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 622.542969 116.054688 L 622.074219 120.027344 M 620.585938 119.851562 L 622.542969 116.054688 L 623.566406 120.199219 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 641.789062 144.015625 L 565.597656 111.769531 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 561.914062 110.207031 L 565.597656 111.769531 M 565.011719 113.148438 L 561.914062 110.207031 L 566.183594 110.386719 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 642.5625 142.582031 L 600.070312 114.179688 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 596.746094 111.957031 L 600.070312 114.179688 M 599.238281 115.425781 L 596.746094 111.957031 L 600.90625 112.933594 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 645.179688 140.050781 L 631.4375 117.9375 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 629.328125 114.539062 L 631.4375 117.9375 M 630.164062 118.730469 L 629.328125 114.539062 L 632.714844 117.144531 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 533.046875 180.203125 L 529.195312 161.605469 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 528.386719 157.6875 L 529.195312 161.605469 M 527.726562 161.910156 L 528.386719 157.6875 L 530.664062 161.300781 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 538.898438 181.0625 L 550.210938 160.15625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 552.113281 156.640625 L 550.210938 160.15625 M 548.890625 159.445312 L 552.113281 156.640625 L 551.527344 160.871094 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 541.609375 183.363281 L 576.515625 156.285156 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 579.675781 153.832031 L 576.515625 156.285156 M 575.597656 155.097656 L 579.675781 153.832031 L 577.433594 157.46875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 542.539062 184.851562 L 605.824219 153.78125 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 609.414062 152.019531 L 605.824219 153.78125 M 605.160156 152.433594 L 609.414062 152.019531 L 606.484375 155.128906 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 542.914062 185.738281 L 636.0625 152.289062 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 639.824219 150.9375 L 636.0625 152.289062 M 635.554688 150.878906 L 639.824219 150.9375 L 636.570312 153.699219 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 564.257812 182.773438 L 536.882812 157.277344 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 533.957031 154.554688 L 536.882812 157.277344 M 535.859375 158.375 L 533.957031 154.554688 L 537.90625 156.179688 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 567.898438 180.449219 L 561.625 161.191406 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 560.382812 157.390625 L 561.625 161.191406 M 560.199219 161.65625 L 560.382812 157.390625 L 563.050781 160.726562 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 573.925781 180.703125 L 582.34375 160.765625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 583.898438 157.078125 L 582.34375 160.765625 M 580.960938 160.179688 L 583.898438 157.078125 L 583.726562 161.347656 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 577.128906 183.046875 L 607.820312 156.816406 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 610.863281 154.21875 L 607.820312 156.816406 M 606.847656 155.675781 L 610.863281 154.21875 L 608.796875 157.957031 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 578.222656 184.664062 L 636.851562 154.09375 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 640.398438 152.246094 L 636.851562 154.09375 M 636.160156 152.765625 L 640.398438 152.246094 L 637.546875 155.425781 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 598.675781 184.699219 L 539.167969 154.035156 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 535.613281 152.203125 L 539.167969 154.035156 M 538.480469 155.367188 L 535.613281 152.203125 L 539.855469 152.699219 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 599.734375 183.109375 L 568.253906 156.710938 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 565.191406 154.144531 L 568.253906 156.710938 M 567.292969 157.863281 L 565.191406 154.144531 L 569.21875 155.5625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 602.839844 180.769531 L 593.898438 160.65625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 592.273438 157 L 593.898438 160.65625 M 592.527344 161.265625 L 592.273438 157 L 595.269531 160.046875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 608.851562 180.394531 L 614.648438 161.28125 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 615.808594 157.453125 L 614.648438 161.28125 M 613.210938 160.847656 L 615.808594 157.453125 L 616.082031 161.71875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 612.597656 182.707031 L 639.210938 157.390625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 642.109375 154.632812 L 639.210938 157.390625 M 638.175781 156.304688 L 642.109375 154.632812 L 640.242188 158.476562 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 633.984375 185.28125 L 539.957031 152.199219 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 536.183594 150.875 L 539.957031 152.199219 M 539.460938 153.617188 L 536.183594 150.875 L 540.457031 150.785156 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 634.34375 184.414062 L 570.222656 153.660156 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 566.617188 151.933594 L 570.222656 153.660156 M 569.574219 155.015625 L 566.617188 151.933594 L 570.871094 152.308594 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 635.230469 182.953125 L 599.597656 156.117188 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 596.402344 153.710938 L 599.597656 156.117188 M 598.695312 157.316406 L 596.402344 153.710938 L 600.5 154.921875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 637.847656 180.648438 L 626.066406 160 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 624.082031 156.523438 L 626.066406 160 M 624.761719 160.742188 L 624.082031 156.523438 L 627.367188 159.253906 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 643.707031 179.664062 L 647.070312 161.660156 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 647.804688 157.726562 L 647.070312 161.660156 M 645.59375 161.382812 L 647.804688 157.726562 L 648.542969 161.933594 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 509.886719 89.78125 L 666.101562 89.78125 L 666.101562 204.46875 L 509.886719 204.46875 Z M 509.886719 89.78125 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 777.9375 244.621094 C 779.699219 246.378906 779.699219 249.230469 777.9375 250.988281 C 776.179688 252.75 773.328125 252.75 771.570312 250.988281 C 769.808594 249.230469 769.808594 246.378906 771.570312 244.621094 C 773.328125 242.863281 776.179688 242.863281 777.9375 244.621094 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 796.617188 244.621094 C 798.375 246.378906 798.375 249.230469 796.617188 250.988281 C 794.859375 252.75 792.003906 252.75 790.246094 250.988281 C 788.488281 249.230469 788.488281 246.378906 790.246094 244.621094 C 792.003906 242.863281 794.859375 242.863281 796.617188 244.621094 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(39.99939%,74.902344%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 789.59375 223.074219 C 791.355469 224.832031 791.355469 227.683594 789.59375 229.441406 C 787.835938 231.199219 784.984375 231.199219 783.226562 229.441406 C 781.46875 227.683594 781.46875 224.832031 783.226562 223.074219 C 784.984375 221.3125 787.835938 221.3125 789.59375 223.074219 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 815.292969 244.621094 C 817.054688 246.378906 817.054688 249.230469 815.292969 250.988281 C 813.535156 252.75 810.683594 252.75 808.925781 250.988281 C 807.164062 249.230469 807.164062 246.378906 808.925781 244.621094 C 810.683594 242.863281 813.535156 242.863281 815.292969 244.621094 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 833.972656 244.355469 C 835.730469 246.113281 835.730469 248.964844 833.972656 250.726562 C 832.214844 252.484375 829.363281 252.484375 827.601562 250.726562 C 825.84375 248.964844 825.84375 246.113281 827.601562 244.355469 C 829.363281 242.597656 832.214844 242.597656 833.972656 244.355469 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(39.99939%,74.902344%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 821.828125 223.074219 C 823.589844 224.832031 823.589844 227.683594 821.828125 229.441406 C 820.070312 231.199219 817.21875 231.199219 815.460938 229.441406 C 813.699219 227.683594 813.699219 224.832031 815.460938 223.074219 C 817.21875 221.3125 820.070312 221.3125 821.828125 223.074219 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(39.99939%,74.902344%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 805.710938 223.074219 C 807.472656 224.832031 807.472656 227.683594 805.710938 229.441406 C 803.953125 231.199219 801.101562 231.199219 799.34375 229.441406 C 797.585938 227.683594 797.585938 224.832031 799.34375 223.074219 C 801.101562 221.3125 803.953125 221.3125 805.710938 223.074219 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(69.804382%,85.098267%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 787.035156 201.523438 C 788.792969 203.285156 788.792969 206.136719 787.035156 207.894531 C 785.277344 209.652344 782.425781 209.652344 780.664062 207.894531 C 778.90625 206.136719 778.90625 203.285156 780.664062 201.523438 C 782.425781 199.765625 785.277344 199.765625 787.035156 201.523438 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(69.804382%,85.098267%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 805.710938 201.523438 C 807.472656 203.285156 807.472656 206.136719 805.710938 207.894531 C 803.953125 209.652344 801.101562 209.652344 799.34375 207.894531 C 797.585938 206.136719 797.585938 203.285156 799.34375 201.523438 C 801.101562 199.765625 803.953125 199.765625 805.710938 201.523438 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(69.804382%,85.098267%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 824.390625 201.261719 C 826.148438 203.019531 826.148438 205.871094 824.390625 207.628906 C 822.632812 209.386719 819.78125 209.386719 818.019531 207.628906 C 816.261719 205.871094 816.261719 203.019531 818.019531 201.261719 C 819.78125 199.5 822.632812 199.5 824.390625 201.261719 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(39.99939%,74.902344%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 773.480469 223.074219 C 775.238281 224.832031 775.238281 227.683594 773.480469 229.441406 C 771.71875 231.199219 768.867188 231.199219 767.109375 229.441406 C 765.351562 227.683594 765.351562 224.832031 767.109375 223.074219 C 768.867188 221.3125 771.71875 221.3125 773.480469 223.074219 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(39.99939%,74.902344%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 837.945312 223.074219 C 839.703125 224.832031 839.703125 227.683594 837.945312 229.441406 C 836.1875 231.199219 833.335938 231.199219 831.578125 229.441406 C 829.816406 227.683594 829.816406 224.832031 831.578125 223.074219 C 833.335938 221.3125 836.1875 221.3125 837.945312 223.074219 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 772.691406 222.445312 L 778.308594 213.515625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 780.4375 210.128906 L 778.308594 213.515625 M 777.039062 212.71875 L 780.4375 210.128906 L 779.578125 214.316406 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 774.039062 223.753906 L 793.878906 210.492188 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 797.203125 208.269531 L 793.878906 210.492188 M 793.042969 209.246094 L 797.203125 208.269531 L 794.710938 211.738281 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 774.433594 224.484375 L 811.640625 208.542969 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 815.316406 206.96875 L 811.640625 208.542969 M 811.050781 207.164062 L 815.316406 206.96875 L 812.230469 209.921875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 785.878906 221.785156 L 785.078125 215.039062 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 784.605469 211.070312 L 785.078125 215.039062 M 783.589844 215.21875 L 784.605469 211.070312 L 786.566406 214.863281 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 789.109375 222.652344 L 796.296875 213.042969 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 798.691406 209.839844 L 796.296875 213.042969 M 795.09375 212.144531 L 798.691406 209.839844 L 797.496094 213.941406 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 790.226562 223.863281 L 812.390625 209.972656 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 815.777344 207.847656 L 812.390625 209.972656 M 811.59375 208.699219 L 815.777344 207.847656 L 813.1875 211.242188 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 799.578125 222.855469 L 790.664062 212.570312 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 788.042969 209.546875 L 790.664062 212.570312 M 789.53125 213.554688 L 788.042969 209.546875 L 791.796875 211.589844 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 802.527344 221.753906 L 802.527344 215.113281 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 802.527344 211.113281 L 802.527344 215.113281 M 801.027344 215.113281 L 802.527344 211.113281 L 804.027344 215.113281 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 805.457031 222.835938 L 814.4375 212.347656 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 817.039062 209.308594 L 814.4375 212.347656 M 813.300781 211.371094 L 817.039062 209.308594 L 815.578125 213.324219 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 814.816406 223.886719 L 792.695312 210.1875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 789.296875 208.082031 L 792.695312 210.1875 M 791.90625 211.464844 L 789.296875 208.082031 L 793.484375 208.914062 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 815.945312 222.652344 L 808.757812 213.042969 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 806.363281 209.839844 L 808.757812 213.042969 M 807.558594 213.941406 L 806.363281 209.839844 L 809.960938 212.144531 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 819.167969 221.785156 L 819.992188 214.777344 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 820.457031 210.804688 L 819.992188 214.777344 M 818.503906 214.601562 L 820.457031 210.804688 L 821.480469 214.953125 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 830.613281 224.5 L 793.433594 208.765625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 789.75 207.207031 L 793.433594 208.765625 M 792.847656 210.148438 L 789.75 207.207031 L 794.015625 207.382812 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 831.015625 223.753906 L 811.175781 210.492188 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 807.851562 208.269531 L 811.175781 210.492188 M 810.34375 211.738281 L 807.851562 208.269531 L 812.011719 209.246094 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 832.382812 222.429688 L 826.699219 213.28125 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 824.585938 209.882812 L 826.699219 213.28125 M 825.421875 214.074219 L 824.585938 209.882812 L 827.972656 212.488281 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 773.839844 243.394531 L 772.402344 236.445312 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 771.59375 232.527344 L 772.402344 236.445312 M 770.933594 236.75 L 771.59375 232.527344 L 773.871094 236.140625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 776.898438 243.84375 L 781.460938 235.410156 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 783.363281 231.890625 L 781.460938 235.410156 M 780.140625 234.695312 L 783.363281 231.890625 L 782.78125 236.121094 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 778.3125 245.042969 L 794.308594 232.636719 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 797.46875 230.183594 L 794.308594 232.636719 M 793.386719 231.449219 L 797.46875 230.183594 L 795.226562 233.820312 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 778.796875 245.820312 L 809.304688 230.84375 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 812.894531 229.082031 L 809.304688 230.84375 M 808.644531 229.496094 L 812.894531 229.082031 L 809.964844 232.191406 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 778.992188 246.28125 L 824.96875 229.773438 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 828.734375 228.421875 L 824.96875 229.773438 M 824.460938 228.363281 L 828.734375 228.421875 L 825.476562 231.1875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 790.136719 244.734375 L 777.90625 233.347656 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 774.980469 230.621094 L 777.90625 233.347656 M 776.886719 234.445312 L 774.980469 230.621094 L 778.929688 232.25 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 792.035156 243.523438 L 789.632812 236.152344 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 788.394531 232.347656 L 789.632812 236.152344 M 788.207031 236.613281 L 788.394531 232.347656 L 791.058594 235.6875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 795.183594 243.65625 L 798.480469 235.84375 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 800.035156 232.160156 L 798.480469 235.84375 M 797.097656 235.261719 L 800.035156 232.160156 L 799.863281 236.425781 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 796.855469 244.878906 L 810.734375 233.015625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 813.777344 230.417969 L 810.734375 233.015625 M 809.761719 231.875 L 813.777344 230.417969 L 811.710938 234.15625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 797.425781 245.722656 L 825.535156 231.066406 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 829.082031 229.21875 L 825.535156 231.066406 M 824.839844 229.738281 L 829.082031 229.21875 L 826.226562 232.398438 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 808.105469 245.742188 L 779.542969 231.023438 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 775.988281 229.191406 L 779.542969 231.023438 M 778.855469 232.355469 L 775.988281 229.191406 L 780.230469 229.691406 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 808.65625 244.910156 L 794.382812 232.941406 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 791.316406 230.371094 L 794.382812 232.941406 M 793.417969 234.089844 L 791.316406 230.371094 L 795.347656 231.792969 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 810.277344 243.6875 L 806.753906 235.765625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 805.128906 232.109375 L 806.753906 235.765625 M 805.386719 236.375 L 805.128906 232.109375 L 808.125 235.15625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 813.417969 243.496094 L 815.625 236.214844 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 816.785156 232.386719 L 815.625 236.214844 M 814.1875 235.777344 L 816.785156 232.386719 L 817.058594 236.648438 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 815.371094 244.703125 L 827.222656 233.429688 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 830.121094 230.671875 L 827.222656 233.429688 M 826.1875 232.339844 L 830.121094 230.671875 L 828.257812 234.515625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 826.539062 246.046875 L 780.109375 229.710938 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 776.335938 228.382812 L 780.109375 229.710938 M 779.613281 231.125 L 776.335938 228.382812 L 780.605469 228.296875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 826.726562 245.59375 L 795.792969 230.757812 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 792.1875 229.027344 L 795.792969 230.757812 M 795.144531 232.109375 L 792.1875 229.027344 L 796.441406 229.40625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 827.1875 244.832031 L 810.839844 232.515625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 807.644531 230.109375 L 810.839844 232.515625 M 809.9375 233.714844 L 807.644531 230.109375 L 811.742188 231.320312 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 828.554688 243.628906 L 823.800781 235.296875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 821.820312 231.820312 L 823.800781 235.296875 M 822.496094 236.039062 L 821.820312 231.820312 L 825.105469 234.550781 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 831.613281 243.113281 L 832.851562 236.484375 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 833.585938 232.554688 L 832.851562 236.484375 M 831.378906 236.210938 L 833.585938 232.554688 L 834.324219 236.761719 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 761.75 196.1875 L 843.304688 196.1875 L 843.304688 256.0625 L 761.75 256.0625 Z M 761.75 196.1875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 777.9375 86.621094 C 779.699219 88.378906 779.699219 91.230469 777.9375 92.988281 C 776.179688 94.75 773.328125 94.75 771.570312 92.988281 C 769.808594 91.230469 769.808594 88.378906 771.570312 86.621094 C 773.328125 84.863281 776.179688 84.863281 777.9375 86.621094 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 796.617188 86.621094 C 798.375 88.378906 798.375 91.230469 796.617188 92.988281 C 794.859375 94.75 792.003906 94.75 790.246094 92.988281 C 788.488281 91.230469 788.488281 88.378906 790.246094 86.621094 C 792.003906 84.863281 794.859375 84.863281 796.617188 86.621094 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(39.99939%,74.902344%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 789.59375 65.074219 C 791.355469 66.832031 791.355469 69.683594 789.59375 71.441406 C 787.835938 73.199219 784.984375 73.199219 783.226562 71.441406 C 781.46875 69.683594 781.46875 66.832031 783.226562 65.074219 C 784.984375 63.3125 787.835938 63.3125 789.59375 65.074219 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 815.292969 86.621094 C 817.054688 88.378906 817.054688 91.230469 815.292969 92.988281 C 813.535156 94.75 810.683594 94.75 808.925781 92.988281 C 807.164062 91.230469 807.164062 88.378906 808.925781 86.621094 C 810.683594 84.863281 813.535156 84.863281 815.292969 86.621094 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 833.972656 86.355469 C 835.730469 88.113281 835.730469 90.964844 833.972656 92.726562 C 832.214844 94.484375 829.363281 94.484375 827.601562 92.726562 C 825.84375 90.964844 825.84375 88.113281 827.601562 86.355469 C 829.363281 84.597656 832.214844 84.597656 833.972656 86.355469 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(39.99939%,74.902344%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 821.828125 65.074219 C 823.589844 66.832031 823.589844 69.683594 821.828125 71.441406 C 820.070312 73.199219 817.21875 73.199219 815.460938 71.441406 C 813.699219 69.683594 813.699219 66.832031 815.460938 65.074219 C 817.21875 63.3125 820.070312 63.3125 821.828125 65.074219 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(39.99939%,74.902344%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 805.710938 65.074219 C 807.472656 66.832031 807.472656 69.683594 805.710938 71.441406 C 803.953125 73.199219 801.101562 73.199219 799.34375 71.441406 C 797.585938 69.683594 797.585938 66.832031 799.34375 65.074219 C 801.101562 63.3125 803.953125 63.3125 805.710938 65.074219 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(69.804382%,85.098267%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 787.035156 43.523438 C 788.792969 45.285156 788.792969 48.136719 787.035156 49.894531 C 785.277344 51.652344 782.425781 51.652344 780.664062 49.894531 C 778.90625 48.136719 778.90625 45.285156 780.664062 43.523438 C 782.425781 41.765625 785.277344 41.765625 787.035156 43.523438 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(69.804382%,85.098267%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 805.710938 43.523438 C 807.472656 45.285156 807.472656 48.136719 805.710938 49.894531 C 803.953125 51.652344 801.101562 51.652344 799.34375 49.894531 C 797.585938 48.136719 797.585938 45.285156 799.34375 43.523438 C 801.101562 41.765625 803.953125 41.765625 805.710938 43.523438 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(69.804382%,85.098267%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 824.390625 43.261719 C 826.148438 45.019531 826.148438 47.871094 824.390625 49.628906 C 822.632812 51.386719 819.78125 51.386719 818.019531 49.628906 C 816.261719 47.871094 816.261719 45.019531 818.019531 43.261719 C 819.78125 41.5 822.632812 41.5 824.390625 43.261719 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(39.99939%,74.902344%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 773.480469 65.074219 C 775.238281 66.832031 775.238281 69.683594 773.480469 71.441406 C 771.71875 73.199219 768.867188 73.199219 767.109375 71.441406 C 765.351562 69.683594 765.351562 66.832031 767.109375 65.074219 C 768.867188 63.3125 771.71875 63.3125 773.480469 65.074219 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(39.99939%,74.902344%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 837.945312 65.074219 C 839.703125 66.832031 839.703125 69.683594 837.945312 71.441406 C 836.1875 73.199219 833.335938 73.199219 831.578125 71.441406 C 829.816406 69.683594 829.816406 66.832031 831.578125 65.074219 C 833.335938 63.3125 836.1875 63.3125 837.945312 65.074219 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 772.691406 64.445312 L 778.308594 55.515625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 780.4375 52.128906 L 778.308594 55.515625 M 777.039062 54.71875 L 780.4375 52.128906 L 779.578125 56.316406 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 774.039062 65.753906 L 793.878906 52.492188 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 797.203125 50.269531 L 793.878906 52.492188 M 793.042969 51.246094 L 797.203125 50.269531 L 794.710938 53.738281 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 774.433594 66.484375 L 811.640625 50.542969 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 815.316406 48.96875 L 811.640625 50.542969 M 811.050781 49.164062 L 815.316406 48.96875 L 812.230469 51.921875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 785.878906 63.785156 L 785.078125 57.039062 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 784.605469 53.070312 L 785.078125 57.039062 M 783.589844 57.21875 L 784.605469 53.070312 L 786.566406 56.863281 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 789.109375 64.648438 L 796.296875 55.042969 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 798.691406 51.839844 L 796.296875 55.042969 M 795.09375 54.144531 L 798.691406 51.839844 L 797.496094 55.941406 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 790.226562 65.863281 L 812.390625 51.972656 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 815.777344 49.847656 L 812.390625 51.972656 M 811.59375 50.699219 L 815.777344 49.847656 L 813.1875 53.242188 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 799.578125 64.855469 L 790.664062 54.570312 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 788.042969 51.546875 L 790.664062 54.570312 M 789.53125 55.554688 L 788.042969 51.546875 L 791.796875 53.589844 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 802.527344 63.753906 L 802.527344 57.113281 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 802.527344 53.113281 L 802.527344 57.113281 M 801.027344 57.113281 L 802.527344 53.113281 L 804.027344 57.113281 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 805.457031 64.835938 L 814.4375 54.347656 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 817.039062 51.308594 L 814.4375 54.347656 M 813.300781 53.371094 L 817.039062 51.308594 L 815.578125 55.324219 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 814.816406 65.886719 L 792.695312 52.1875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 789.296875 50.082031 L 792.695312 52.1875 M 791.90625 53.464844 L 789.296875 50.082031 L 793.484375 50.914062 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 815.945312 64.648438 L 808.757812 55.042969 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 806.363281 51.839844 L 808.757812 55.042969 M 807.558594 55.941406 L 806.363281 51.839844 L 809.960938 54.144531 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 819.167969 63.785156 L 819.992188 56.777344 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 820.457031 52.804688 L 819.992188 56.777344 M 818.503906 56.601562 L 820.457031 52.804688 L 821.480469 56.953125 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 830.613281 66.5 L 793.433594 50.765625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 789.75 49.207031 L 793.433594 50.765625 M 792.847656 52.144531 L 789.75 49.207031 L 794.015625 49.382812 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 831.015625 65.753906 L 811.175781 52.492188 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 807.851562 50.269531 L 811.175781 52.492188 M 810.34375 53.738281 L 807.851562 50.269531 L 812.011719 51.246094 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 832.382812 64.429688 L 826.699219 55.28125 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 824.585938 51.882812 L 826.699219 55.28125 M 825.421875 56.074219 L 824.585938 51.882812 L 827.972656 54.488281 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 773.839844 85.394531 L 772.402344 78.445312 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 771.59375 74.527344 L 772.402344 78.445312 M 770.933594 78.75 L 771.59375 74.527344 L 773.871094 78.140625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 776.898438 85.84375 L 781.460938 77.410156 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 783.363281 73.890625 L 781.460938 77.410156 M 780.140625 76.695312 L 783.363281 73.890625 L 782.78125 78.121094 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 778.3125 87.042969 L 794.308594 74.636719 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 797.46875 72.183594 L 794.308594 74.636719 M 793.386719 73.449219 L 797.46875 72.183594 L 795.226562 75.820312 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 778.796875 87.820312 L 809.304688 72.84375 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 812.894531 71.082031 L 809.304688 72.84375 M 808.644531 71.496094 L 812.894531 71.082031 L 809.964844 74.191406 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 778.992188 88.28125 L 824.96875 71.773438 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 828.734375 70.421875 L 824.96875 71.773438 M 824.460938 70.363281 L 828.734375 70.421875 L 825.476562 73.1875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 790.136719 86.734375 L 777.90625 75.347656 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 774.980469 72.621094 L 777.90625 75.347656 M 776.886719 76.445312 L 774.980469 72.621094 L 778.929688 74.25 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 792.035156 85.523438 L 789.632812 78.152344 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 788.394531 74.347656 L 789.632812 78.152344 M 788.207031 78.613281 L 788.394531 74.347656 L 791.058594 77.6875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 795.183594 85.65625 L 798.480469 77.84375 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 800.035156 74.160156 L 798.480469 77.84375 M 797.097656 77.261719 L 800.035156 74.160156 L 799.863281 78.425781 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 796.855469 86.878906 L 810.734375 75.015625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 813.777344 72.417969 L 810.734375 75.015625 M 809.761719 73.875 L 813.777344 72.417969 L 811.710938 76.15625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 797.425781 87.722656 L 825.535156 73.066406 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 829.082031 71.21875 L 825.535156 73.066406 M 824.839844 71.738281 L 829.082031 71.21875 L 826.226562 74.398438 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 808.105469 87.742188 L 779.542969 73.023438 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 775.988281 71.191406 L 779.542969 73.023438 M 778.855469 74.355469 L 775.988281 71.191406 L 780.230469 71.691406 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 808.65625 86.910156 L 794.382812 74.941406 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 791.316406 72.371094 L 794.382812 74.941406 M 793.417969 76.089844 L 791.316406 72.371094 L 795.347656 73.792969 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 810.277344 85.6875 L 806.753906 77.765625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 805.128906 74.109375 L 806.753906 77.765625 M 805.386719 78.375 L 805.128906 74.109375 L 808.125 77.15625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 813.417969 85.496094 L 815.625 78.214844 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 816.785156 74.386719 L 815.625 78.214844 M 814.1875 77.777344 L 816.785156 74.386719 L 817.058594 78.648438 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 815.371094 86.699219 L 827.222656 75.429688 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 830.121094 72.671875 L 827.222656 75.429688 M 826.1875 74.339844 L 830.121094 72.671875 L 828.257812 76.515625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 826.539062 88.046875 L 780.109375 71.710938 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 776.335938 70.382812 L 780.109375 71.710938 M 779.613281 73.125 L 776.335938 70.382812 L 780.605469 70.296875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 826.726562 87.59375 L 795.792969 72.757812 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 792.1875 71.027344 L 795.792969 72.757812 M 795.144531 74.109375 L 792.1875 71.027344 L 796.441406 71.40625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 827.1875 86.832031 L 810.839844 74.515625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 807.644531 72.109375 L 810.839844 74.515625 M 809.9375 75.714844 L 807.644531 72.109375 L 811.742188 73.320312 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 828.554688 85.628906 L 823.800781 77.296875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 821.820312 73.820312 L 823.800781 77.296875 M 822.496094 78.039062 L 821.820312 73.820312 L 825.105469 76.550781 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 831.613281 85.113281 L 832.851562 78.484375 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 833.585938 74.554688 L 832.851562 78.484375 M 831.378906 78.210938 L 833.585938 74.554688 L 834.324219 78.761719 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 761.75 38.1875 L 843.304688 38.1875 L 843.304688 98.0625 L 761.75 98.0625 Z M 761.75 38.1875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 724.4375 165.621094 C 726.199219 167.378906 726.199219 170.230469 724.4375 171.988281 C 722.679688 173.75 719.828125 173.75 718.070312 171.988281 C 716.308594 170.230469 716.308594 167.378906 718.070312 165.621094 C 719.828125 163.863281 722.679688 163.863281 724.4375 165.621094 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 743.117188 165.621094 C 744.875 167.378906 744.875 170.230469 743.117188 171.988281 C 741.359375 173.75 738.503906 173.75 736.746094 171.988281 C 734.988281 170.230469 734.988281 167.378906 736.746094 165.621094 C 738.503906 163.863281 741.359375 163.863281 743.117188 165.621094 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(39.99939%,74.902344%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 736.09375 144.074219 C 737.855469 145.832031 737.855469 148.683594 736.09375 150.441406 C 734.335938 152.199219 731.484375 152.199219 729.726562 150.441406 C 727.96875 148.683594 727.96875 145.832031 729.726562 144.074219 C 731.484375 142.3125 734.335938 142.3125 736.09375 144.074219 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 761.792969 165.621094 C 763.554688 167.378906 763.554688 170.230469 761.792969 171.988281 C 760.035156 173.75 757.183594 173.75 755.425781 171.988281 C 753.664062 170.230469 753.664062 167.378906 755.425781 165.621094 C 757.183594 163.863281 760.035156 163.863281 761.792969 165.621094 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 780.472656 165.355469 C 782.230469 167.113281 782.230469 169.964844 780.472656 171.726562 C 778.714844 173.484375 775.863281 173.484375 774.101562 171.726562 C 772.34375 169.964844 772.34375 167.113281 774.101562 165.355469 C 775.863281 163.597656 778.714844 163.597656 780.472656 165.355469 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(39.99939%,74.902344%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 768.328125 144.074219 C 770.089844 145.832031 770.089844 148.683594 768.328125 150.441406 C 766.570312 152.199219 763.71875 152.199219 761.960938 150.441406 C 760.199219 148.683594 760.199219 145.832031 761.960938 144.074219 C 763.71875 142.3125 766.570312 142.3125 768.328125 144.074219 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(39.99939%,74.902344%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 752.210938 144.074219 C 753.972656 145.832031 753.972656 148.683594 752.210938 150.441406 C 750.453125 152.199219 747.601562 152.199219 745.84375 150.441406 C 744.085938 148.683594 744.085938 145.832031 745.84375 144.074219 C 747.601562 142.3125 750.453125 142.3125 752.210938 144.074219 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(69.804382%,85.098267%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 733.535156 122.523438 C 735.292969 124.285156 735.292969 127.136719 733.535156 128.894531 C 731.777344 130.652344 728.925781 130.652344 727.164062 128.894531 C 725.40625 127.136719 725.40625 124.285156 727.164062 122.523438 C 728.925781 120.765625 731.777344 120.765625 733.535156 122.523438 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(69.804382%,85.098267%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 752.210938 122.523438 C 753.972656 124.285156 753.972656 127.136719 752.210938 128.894531 C 750.453125 130.652344 747.601562 130.652344 745.84375 128.894531 C 744.085938 127.136719 744.085938 124.285156 745.84375 122.523438 C 747.601562 120.765625 750.453125 120.765625 752.210938 122.523438 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(69.804382%,85.098267%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 770.890625 122.261719 C 772.648438 124.019531 772.648438 126.871094 770.890625 128.628906 C 769.132812 130.386719 766.28125 130.386719 764.519531 128.628906 C 762.761719 126.871094 762.761719 124.019531 764.519531 122.261719 C 766.28125 120.5 769.132812 120.5 770.890625 122.261719 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(39.99939%,74.902344%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 719.980469 144.074219 C 721.738281 145.832031 721.738281 148.683594 719.980469 150.441406 C 718.21875 152.199219 715.367188 152.199219 713.609375 150.441406 C 711.851562 148.683594 711.851562 145.832031 713.609375 144.074219 C 715.367188 142.3125 718.21875 142.3125 719.980469 144.074219 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(39.99939%,74.902344%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 784.445312 144.074219 C 786.203125 145.832031 786.203125 148.683594 784.445312 150.441406 C 782.6875 152.199219 779.835938 152.199219 778.078125 150.441406 C 776.316406 148.683594 776.316406 145.832031 778.078125 144.074219 C 779.835938 142.3125 782.6875 142.3125 784.445312 144.074219 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 719.191406 143.445312 L 724.808594 134.515625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 726.9375 131.128906 L 724.808594 134.515625 M 723.539062 133.71875 L 726.9375 131.128906 L 726.078125 135.316406 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 720.539062 144.753906 L 740.378906 131.492188 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 743.703125 129.269531 L 740.378906 131.492188 M 739.542969 130.246094 L 743.703125 129.269531 L 741.210938 132.738281 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 720.933594 145.484375 L 758.140625 129.542969 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 761.816406 127.96875 L 758.140625 129.542969 M 757.550781 128.164062 L 761.816406 127.96875 L 758.730469 130.921875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 732.378906 142.785156 L 731.578125 136.039062 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 731.105469 132.070312 L 731.578125 136.039062 M 730.089844 136.21875 L 731.105469 132.070312 L 733.066406 135.863281 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 735.609375 143.652344 L 742.796875 134.042969 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 745.191406 130.839844 L 742.796875 134.042969 M 741.59375 133.144531 L 745.191406 130.839844 L 743.996094 134.941406 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 736.726562 144.863281 L 758.890625 130.972656 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 762.277344 128.847656 L 758.890625 130.972656 M 758.09375 129.699219 L 762.277344 128.847656 L 759.6875 132.242188 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 746.078125 143.855469 L 737.164062 133.570312 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 734.542969 130.546875 L 737.164062 133.570312 M 736.03125 134.554688 L 734.542969 130.546875 L 738.296875 132.589844 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 749.027344 142.753906 L 749.027344 136.113281 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 749.027344 132.113281 L 749.027344 136.113281 M 747.527344 136.113281 L 749.027344 132.113281 L 750.527344 136.113281 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 751.957031 143.835938 L 760.9375 133.347656 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 763.539062 130.308594 L 760.9375 133.347656 M 759.800781 132.371094 L 763.539062 130.308594 L 762.078125 134.324219 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 761.316406 144.886719 L 739.195312 131.1875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 735.796875 129.082031 L 739.195312 131.1875 M 738.40625 132.464844 L 735.796875 129.082031 L 739.984375 129.914062 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 762.445312 143.652344 L 755.257812 134.042969 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 752.863281 130.839844 L 755.257812 134.042969 M 754.058594 134.941406 L 752.863281 130.839844 L 756.460938 133.144531 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 765.667969 142.785156 L 766.492188 135.777344 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 766.957031 131.804688 L 766.492188 135.777344 M 765.003906 135.601562 L 766.957031 131.804688 L 767.980469 135.953125 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 777.113281 145.5 L 739.933594 129.765625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 736.25 128.207031 L 739.933594 129.765625 M 739.347656 131.148438 L 736.25 128.207031 L 740.515625 128.382812 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 777.515625 144.753906 L 757.675781 131.492188 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 754.351562 129.269531 L 757.675781 131.492188 M 756.84375 132.738281 L 754.351562 129.269531 L 758.511719 130.246094 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 778.882812 143.429688 L 773.199219 134.28125 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 771.085938 130.882812 L 773.199219 134.28125 M 771.921875 135.074219 L 771.085938 130.882812 L 774.472656 133.488281 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 720.339844 164.394531 L 718.902344 157.445312 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 718.09375 153.527344 L 718.902344 157.445312 M 717.433594 157.75 L 718.09375 153.527344 L 720.371094 157.140625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 723.398438 164.84375 L 727.960938 156.410156 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 729.863281 152.890625 L 727.960938 156.410156 M 726.640625 155.695312 L 729.863281 152.890625 L 729.28125 157.121094 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 724.8125 166.042969 L 740.808594 153.636719 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 743.96875 151.183594 L 740.808594 153.636719 M 739.886719 152.449219 L 743.96875 151.183594 L 741.726562 154.820312 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 725.296875 166.820312 L 755.804688 151.84375 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 759.394531 150.082031 L 755.804688 151.84375 M 755.144531 150.496094 L 759.394531 150.082031 L 756.464844 153.191406 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 725.492188 167.28125 L 771.46875 150.773438 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 775.234375 149.421875 L 771.46875 150.773438 M 770.960938 149.363281 L 775.234375 149.421875 L 771.976562 152.1875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 736.636719 165.734375 L 724.40625 154.347656 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 721.480469 151.621094 L 724.40625 154.347656 M 723.386719 155.445312 L 721.480469 151.621094 L 725.429688 153.25 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 738.535156 164.523438 L 736.132812 157.152344 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 734.894531 153.347656 L 736.132812 157.152344 M 734.707031 157.613281 L 734.894531 153.347656 L 737.558594 156.6875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 741.683594 164.65625 L 744.980469 156.84375 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 746.535156 153.160156 L 744.980469 156.84375 M 743.597656 156.261719 L 746.535156 153.160156 L 746.363281 157.425781 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 743.355469 165.878906 L 757.234375 154.015625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 760.277344 151.417969 L 757.234375 154.015625 M 756.261719 152.875 L 760.277344 151.417969 L 758.210938 155.15625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 743.925781 166.722656 L 772.035156 152.066406 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 775.582031 150.21875 L 772.035156 152.066406 M 771.339844 150.738281 L 775.582031 150.21875 L 772.726562 153.398438 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 754.605469 166.742188 L 726.042969 152.023438 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 722.488281 150.191406 L 726.042969 152.023438 M 725.355469 153.355469 L 722.488281 150.191406 L 726.730469 150.691406 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 755.15625 165.910156 L 740.882812 153.941406 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 737.816406 151.371094 L 740.882812 153.941406 M 739.917969 155.089844 L 737.816406 151.371094 L 741.847656 152.792969 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 756.777344 164.6875 L 753.253906 156.765625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 751.628906 153.109375 L 753.253906 156.765625 M 751.886719 157.375 L 751.628906 153.109375 L 754.625 156.15625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 759.917969 164.496094 L 762.125 157.214844 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 763.285156 153.386719 L 762.125 157.214844 M 760.6875 156.777344 L 763.285156 153.386719 L 763.558594 157.648438 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 761.871094 165.703125 L 773.722656 154.429688 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 776.621094 151.671875 L 773.722656 154.429688 M 772.6875 153.339844 L 776.621094 151.671875 L 774.757812 155.515625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 773.039062 167.046875 L 726.609375 150.710938 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 722.835938 149.382812 L 726.609375 150.710938 M 726.113281 152.125 L 722.835938 149.382812 L 727.105469 149.296875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 773.226562 166.59375 L 742.292969 151.757812 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 738.6875 150.027344 L 742.292969 151.757812 M 741.644531 153.109375 L 738.6875 150.027344 L 742.941406 150.40625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 773.6875 165.832031 L 757.339844 153.515625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 754.144531 151.109375 L 757.339844 153.515625 M 756.4375 154.714844 L 754.144531 151.109375 L 758.242188 152.320312 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 775.054688 164.628906 L 770.300781 156.296875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 768.320312 152.820312 L 770.300781 156.296875 M 768.996094 157.039062 L 768.320312 152.820312 L 771.605469 155.550781 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 778.113281 164.113281 L 779.351562 157.484375 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 780.085938 153.554688 L 779.351562 157.484375 M 777.878906 157.210938 L 780.085938 153.554688 L 780.824219 157.761719 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 708.25 117.1875 L 789.804688 117.1875 L 789.804688 177.0625 L 708.25 177.0625 Z M 708.25 117.1875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 831.4375 165.621094 C 833.199219 167.378906 833.199219 170.230469 831.4375 171.988281 C 829.679688 173.75 826.828125 173.75 825.070312 171.988281 C 823.308594 170.230469 823.308594 167.378906 825.070312 165.621094 C 826.828125 163.863281 829.679688 163.863281 831.4375 165.621094 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 850.117188 165.621094 C 851.875 167.378906 851.875 170.230469 850.117188 171.988281 C 848.359375 173.75 845.503906 173.75 843.746094 171.988281 C 841.988281 170.230469 841.988281 167.378906 843.746094 165.621094 C 845.503906 163.863281 848.359375 163.863281 850.117188 165.621094 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(39.99939%,74.902344%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 843.09375 144.074219 C 844.855469 145.832031 844.855469 148.683594 843.09375 150.441406 C 841.335938 152.199219 838.484375 152.199219 836.726562 150.441406 C 834.96875 148.683594 834.96875 145.832031 836.726562 144.074219 C 838.484375 142.3125 841.335938 142.3125 843.09375 144.074219 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 868.792969 165.621094 C 870.554688 167.378906 870.554688 170.230469 868.792969 171.988281 C 867.035156 173.75 864.183594 173.75 862.425781 171.988281 C 860.664062 170.230469 860.664062 167.378906 862.425781 165.621094 C 864.183594 163.863281 867.035156 163.863281 868.792969 165.621094 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 887.472656 165.355469 C 889.230469 167.113281 889.230469 169.964844 887.472656 171.726562 C 885.714844 173.484375 882.863281 173.484375 881.101562 171.726562 C 879.34375 169.964844 879.34375 167.113281 881.101562 165.355469 C 882.863281 163.597656 885.714844 163.597656 887.472656 165.355469 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(39.99939%,74.902344%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 875.328125 144.074219 C 877.089844 145.832031 877.089844 148.683594 875.328125 150.441406 C 873.570312 152.199219 870.71875 152.199219 868.960938 150.441406 C 867.199219 148.683594 867.199219 145.832031 868.960938 144.074219 C 870.71875 142.3125 873.570312 142.3125 875.328125 144.074219 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(39.99939%,74.902344%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 859.210938 144.074219 C 860.972656 145.832031 860.972656 148.683594 859.210938 150.441406 C 857.453125 152.199219 854.601562 152.199219 852.84375 150.441406 C 851.085938 148.683594 851.085938 145.832031 852.84375 144.074219 C 854.601562 142.3125 857.453125 142.3125 859.210938 144.074219 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(69.804382%,85.098267%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 840.535156 122.523438 C 842.292969 124.285156 842.292969 127.136719 840.535156 128.894531 C 838.777344 130.652344 835.925781 130.652344 834.164062 128.894531 C 832.40625 127.136719 832.40625 124.285156 834.164062 122.523438 C 835.925781 120.765625 838.777344 120.765625 840.535156 122.523438 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(69.804382%,85.098267%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 859.210938 122.523438 C 860.972656 124.285156 860.972656 127.136719 859.210938 128.894531 C 857.453125 130.652344 854.601562 130.652344 852.84375 128.894531 C 851.085938 127.136719 851.085938 124.285156 852.84375 122.523438 C 854.601562 120.765625 857.453125 120.765625 859.210938 122.523438 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(69.804382%,85.098267%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 877.890625 122.261719 C 879.648438 124.019531 879.648438 126.871094 877.890625 128.628906 C 876.132812 130.386719 873.28125 130.386719 871.519531 128.628906 C 869.761719 126.871094 869.761719 124.019531 871.519531 122.261719 C 873.28125 120.5 876.132812 120.5 877.890625 122.261719 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(39.99939%,74.902344%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 826.980469 144.074219 C 828.738281 145.832031 828.738281 148.683594 826.980469 150.441406 C 825.21875 152.199219 822.367188 152.199219 820.609375 150.441406 C 818.851562 148.683594 818.851562 145.832031 820.609375 144.074219 C 822.367188 142.3125 825.21875 142.3125 826.980469 144.074219 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(39.99939%,74.902344%,100%);fill-opacity:1;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 891.445312 144.074219 C 893.203125 145.832031 893.203125 148.683594 891.445312 150.441406 C 889.6875 152.199219 886.835938 152.199219 885.078125 150.441406 C 883.316406 148.683594 883.316406 145.832031 885.078125 144.074219 C 886.835938 142.3125 889.6875 142.3125 891.445312 144.074219 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 826.191406 143.445312 L 831.808594 134.515625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 833.9375 131.128906 L 831.808594 134.515625 M 830.539062 133.71875 L 833.9375 131.128906 L 833.078125 135.316406 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 827.539062 144.753906 L 847.378906 131.492188 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 850.703125 129.269531 L 847.378906 131.492188 M 846.542969 130.246094 L 850.703125 129.269531 L 848.210938 132.738281 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 827.933594 145.484375 L 865.140625 129.542969 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 868.816406 127.96875 L 865.140625 129.542969 M 864.550781 128.164062 L 868.816406 127.96875 L 865.730469 130.921875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 839.378906 142.785156 L 838.578125 136.039062 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 838.105469 132.070312 L 838.578125 136.039062 M 837.089844 136.21875 L 838.105469 132.070312 L 840.066406 135.863281 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 842.609375 143.652344 L 849.796875 134.042969 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 852.191406 130.839844 L 849.796875 134.042969 M 848.59375 133.144531 L 852.191406 130.839844 L 850.996094 134.941406 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 843.726562 144.863281 L 865.890625 130.972656 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 869.277344 128.847656 L 865.890625 130.972656 M 865.09375 129.699219 L 869.277344 128.847656 L 866.6875 132.242188 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 853.078125 143.855469 L 844.164062 133.570312 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 841.542969 130.546875 L 844.164062 133.570312 M 843.03125 134.554688 L 841.542969 130.546875 L 845.296875 132.589844 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 856.027344 142.753906 L 856.027344 136.113281 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 856.027344 132.113281 L 856.027344 136.113281 M 854.527344 136.113281 L 856.027344 132.113281 L 857.527344 136.113281 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 858.957031 143.835938 L 867.9375 133.347656 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 870.539062 130.308594 L 867.9375 133.347656 M 866.800781 132.371094 L 870.539062 130.308594 L 869.078125 134.324219 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 868.316406 144.886719 L 846.195312 131.1875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 842.796875 129.082031 L 846.195312 131.1875 M 845.40625 132.464844 L 842.796875 129.082031 L 846.984375 129.914062 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 869.445312 143.652344 L 862.257812 134.042969 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 859.863281 130.839844 L 862.257812 134.042969 M 861.058594 134.941406 L 859.863281 130.839844 L 863.460938 133.144531 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 872.667969 142.785156 L 873.492188 135.777344 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 873.957031 131.804688 L 873.492188 135.777344 M 872.003906 135.601562 L 873.957031 131.804688 L 874.980469 135.953125 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 884.113281 145.5 L 846.933594 129.765625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 843.25 128.207031 L 846.933594 129.765625 M 846.347656 131.148438 L 843.25 128.207031 L 847.515625 128.382812 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 884.515625 144.753906 L 864.675781 131.492188 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 861.351562 129.269531 L 864.675781 131.492188 M 863.84375 132.738281 L 861.351562 129.269531 L 865.511719 130.246094 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 885.882812 143.429688 L 880.199219 134.28125 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 878.085938 130.882812 L 880.199219 134.28125 M 878.921875 135.074219 L 878.085938 130.882812 L 881.472656 133.488281 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 827.339844 164.394531 L 825.902344 157.445312 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 825.09375 153.527344 L 825.902344 157.445312 M 824.433594 157.75 L 825.09375 153.527344 L 827.371094 157.140625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 830.398438 164.84375 L 834.960938 156.410156 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 836.863281 152.890625 L 834.960938 156.410156 M 833.640625 155.695312 L 836.863281 152.890625 L 836.28125 157.121094 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 831.8125 166.042969 L 847.808594 153.636719 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 850.96875 151.183594 L 847.808594 153.636719 M 846.886719 152.449219 L 850.96875 151.183594 L 848.726562 154.820312 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 832.296875 166.820312 L 862.804688 151.84375 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 866.394531 150.082031 L 862.804688 151.84375 M 862.144531 150.496094 L 866.394531 150.082031 L 863.464844 153.191406 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 832.492188 167.28125 L 878.46875 150.773438 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 882.234375 149.421875 L 878.46875 150.773438 M 877.960938 149.363281 L 882.234375 149.421875 L 878.976562 152.1875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 843.636719 165.734375 L 831.40625 154.347656 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 828.480469 151.621094 L 831.40625 154.347656 M 830.386719 155.445312 L 828.480469 151.621094 L 832.429688 153.25 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 845.535156 164.523438 L 843.132812 157.152344 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 841.894531 153.347656 L 843.132812 157.152344 M 841.707031 157.613281 L 841.894531 153.347656 L 844.558594 156.6875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 848.683594 164.65625 L 851.980469 156.84375 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 853.535156 153.160156 L 851.980469 156.84375 M 850.597656 156.261719 L 853.535156 153.160156 L 853.363281 157.425781 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 850.355469 165.878906 L 864.234375 154.015625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 867.277344 151.417969 L 864.234375 154.015625 M 863.261719 152.875 L 867.277344 151.417969 L 865.210938 155.15625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 850.925781 166.722656 L 879.035156 152.066406 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 882.582031 150.21875 L 879.035156 152.066406 M 878.339844 150.738281 L 882.582031 150.21875 L 879.726562 153.398438 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 861.605469 166.742188 L 833.042969 152.023438 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 829.488281 150.191406 L 833.042969 152.023438 M 832.355469 153.355469 L 829.488281 150.191406 L 833.730469 150.691406 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 862.15625 165.910156 L 847.882812 153.941406 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 844.816406 151.371094 L 847.882812 153.941406 M 846.917969 155.089844 L 844.816406 151.371094 L 848.847656 152.792969 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 863.777344 164.6875 L 860.253906 156.765625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 858.628906 153.109375 L 860.253906 156.765625 M 858.886719 157.375 L 858.628906 153.109375 L 861.625 156.15625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 866.917969 164.496094 L 869.125 157.214844 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 870.285156 153.386719 L 869.125 157.214844 M 867.6875 156.777344 L 870.285156 153.386719 L 870.558594 157.648438 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 868.871094 165.703125 L 880.722656 154.429688 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 883.621094 151.671875 L 880.722656 154.429688 M 879.6875 153.339844 L 883.621094 151.671875 L 881.757812 155.515625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 880.039062 167.046875 L 833.609375 150.710938 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 829.835938 149.382812 L 833.609375 150.710938 M 833.113281 152.125 L 829.835938 149.382812 L 834.105469 149.296875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 880.226562 166.59375 L 849.292969 151.757812 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 845.6875 150.027344 L 849.292969 151.757812 M 848.644531 153.109375 L 845.6875 150.027344 L 849.941406 150.40625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 880.6875 165.832031 L 864.339844 153.515625 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 861.144531 151.109375 L 864.339844 153.515625 M 863.4375 154.714844 L 861.144531 151.109375 L 865.242188 152.320312 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 882.054688 164.628906 L 877.300781 156.296875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 875.320312 152.820312 L 877.300781 156.296875 M 875.996094 157.039062 L 875.320312 152.820312 L 878.605469 155.550781 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 885.113281 164.113281 L 886.351562 157.484375 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 887.085938 153.554688 L 886.351562 157.484375 M 884.878906 157.210938 L 887.085938 153.554688 L 887.824219 157.761719 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 815.25 117.1875 L 896.804688 117.1875 L 896.804688 177.0625 L 815.25 177.0625 Z M 815.25 117.1875 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 802.527344 186.625 L 829.277344 187.074219 L 856.027344 186.625 L 856.027344 182.960938 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(0%,0%,0%);fill-opacity:1;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 856.027344 178.960938 L 854.527344 182.960938 L 857.527344 182.960938 Z M 856.027344 178.960938 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 802.527344 196.1875 L 802.527344 186.625 L 775.777344 187.074219 L 749.027344 186.625 L 749.027344 182.960938 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(0%,0%,0%);fill-opacity:1;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 749.027344 178.960938 L 747.527344 182.960938 L 750.527344 182.960938 Z M 749.027344 178.960938 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 749.027344 117.1875 L 749.027344 107.625 L 775.777344 108.074219 L 802.527344 107.625 L 802.527344 103.960938 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(0%,0%,0%);fill-opacity:1;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 802.527344 99.960938 L 801.027344 103.960938 L 804.027344 103.960938 Z M 802.527344 99.960938 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 856.027344 117.1875 L 856.027344 107.625 L 829.277344 108.074219 L 802.527344 107.625 L 802.527344 103.960938 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(0%,0%,0%);fill-opacity:1;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 802.527344 99.960938 L 801.027344 103.960938 L 804.027344 103.960938 Z M 802.527344 99.960938 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 470.847656 146.625 L 500.875 146.695312 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(0%,0%,0%);fill-opacity:1;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 504.875 146.703125 L 500.878906 145.195312 L 500.871094 148.195312 Z M 504.875 146.703125 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill:none;stroke-width:1;stroke-linecap:round;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 669.210938 146.625 L 699.238281 146.695312 " transform="matrix(1,0,0,1,-326,-37)"/>
+<path style="fill-rule:nonzero;fill:rgb(0%,0%,0%);fill-opacity:1;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 703.238281 146.703125 L 699.242188 145.195312 L 699.234375 148.195312 Z M 703.238281 146.703125 " transform="matrix(1,0,0,1,-326,-37)"/>
+</g>
+</svg>
diff --git a/docs/python_docs/python/tutorials/packages/gluon/custom-layer.md b/docs/python_docs/python/tutorials/packages/gluon/custom-layer.md
new file mode 100644
index 000000000000..9297d9894c70
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/custom-layer.md
@@ -0,0 +1,127 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Custom Layers
+
+<!-- adapted from diveintodeeplearning -->
+
+One of the reasons for the success of deep learning can be found in the wide range of re-usable layers that can be used in a deep network. This allows for a tremendous degree of customization and adaptation. Sooner or later you will encounter a layer that doesn't exist yet in Gluon or one that you want to create. This is when it's time to build a custom layer. This section shows you how.
+
+Defining a layer is as easy as subclassing [`nn.Block`](/api/gluon/mxnet.gluon.nn.Block.html#mxnet.gluon.nn.Block) or [`nn.HybridBlock`](/api/gluon/mxnet.gluon.nn.HybridBlock.html#mxnet.gluon.nn.HybridBlock) and implementing `forward` or `hybrid_forward`, respectively. To take advantage of the performance gains with `nn.HybridBlock` see the section on [Hybridization](hybridize.html). 
+
+Note that we've gone through rationale for defining layers, but `nn.Block`'s work even for non-sequential network. In fact, you can use a `Block` to encapsualte any re-usable architecture you want.
+
+We will discuss making custom layers using `nn.Block` below. 
+
+## Layers without Parameters
+
+Since this is slightly intricate, we start with a custom layer that doesn't have any inherent parameters. Our first step is very similar to when we [introduced blocks](nn.md) previously. The following `CenteredLayer` class constructs a layer that subtracts the mean from the input. We build it by inheriting from the `Block` class and overriding the `forward`  and `__init__` methods.
+
+```{.python .input  n=1}
+from mxnet import gluon, nd
+from mxnet.gluon import nn
+
+
+class CenteredLayer(nn.Block):
+    def __init__(self, **kwargs):
+        super(CenteredLayer, self).__init__(**kwargs)
+
+    def forward(self, x):
+        return x - x.mean()
+```
+
+To see how it works let's feed some data into the layer.
+
+```{.python .input  n=2}
+layer = CenteredLayer()
+layer(nd.array([1, 2, 3, 4, 5]))
+print(layer)
+```
+
+We can also use it to construct more complex models.
+
+```{.python .input  n=3}
+net = nn.Sequential()
+net.add(nn.Dense(128), 
+        CenteredLayer())
+net.initialize()
+print(net)
+```
+
+Let's see whether the centering layer did its job. For that we send random data through the network and check whether the mean is $0$. Note that since we're dealing with floating point numbers, we're going to see a very small albeit typically nonzero number.
+
+```{.python .input  n=4}
+y = net(nd.random.uniform(shape=(4, 8)))
+y.mean().asscalar()
+```
+
+## Layers with Parameters
+
+Now that we know how to define layers in principle, let's define layers with parameters. These can be adjusted through training. In order to simplify things for an avid deep learning researcher, the [`Parameter`](/api/gluon/mxnet.gluon.parameter.html) class and the `ParameterDict` dictionary provide some basic housekeeping functionality. In particular, they govern access, initialization, sharing, saving and loading model parameters. For instance, this way we don't need to write custom serialization routines for each new custom layer.
+
+We can access the parameters via the `params` variable of the `ParameterDict` in `Block`. The parameter dictionary is just that - a dictionary that maps string type parameter names to model parameters in the `Parameter` type.  We can create a `Parameter` instance from `ParameterDict` via the `get` function which attempts to retrieve a parameter, or create it if not found.
+
+```{.python .input  n=7}
+params = gluon.ParameterDict()
+params.get('param2', shape=(2, 3))
+print(params)
+```
+
+Let's use this to implement our own version of the dense layer. It has two parameters - bias and weight. To make it a bit nonstandard, we bake in the ReLU activation as default. Next, we implement a fully connected layer with both weight and bias parameters.  It uses ReLU as an activation function, where `in_units` and `units` are the number of inputs and the number of outputs, respectively.
+
+```{.python .input  n=19}
+class MyDense(nn.Block):
+
+    def __init__(self, units, in_units, **kwargs):
+        # units: the number of outputs in this layer
+        # in_units: the number of inputs in this layer
+
+        super(MyDense, self).__init__(**kwargs)
+        self.weight = self.params.get('weight', shape=(in_units, units))
+        self.bias = self.params.get('bias', shape=(units,))
+
+    def forward(self, x):
+        linear = nd.dot(x, self.weight.data()) + self.bias.data()
+        return nd.relu(linear)
+```
+
+Naming the parameters allows us to access them by name through dictionary lookup later. It's a good idea to give them instructive names. Next, we instantiate the `MyDense` class and access its model parameters.
+
+```{.python .input}
+dense = MyDense(units=3, in_units=5)
+dense.params
+```
+
+We can directly carry out forward calculations using custom layers.
+
+```{.python .input  n=20}
+dense.initialize()
+dense(nd.random.uniform(shape=(2, 5)))
+print(dense)
+```
+
+We can also construct models using custom layers. Once we have that we can use it just like the built-in dense layer. The only exception is that in our case, shape inference is not automatic as we have explicitly defined the shape of the weight matrix during initialization.
+
+```{.python .input  n=19}
+net = nn.Sequential()
+net.add(MyDense(8, in_units=64),
+        MyDense(1, in_units=8))
+net.initialize()
+net(nd.random.uniform(shape=(2, 64)))
+print(net)
+```
+
diff --git a/docs/python_docs/python/tutorials/packages/gluon/custom-loss/custom-loss.md b/docs/python_docs/python/tutorials/packages/gluon/custom-loss/custom-loss.md
new file mode 100644
index 000000000000..1d8bfc7c2f6a
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/custom-loss/custom-loss.md
@@ -0,0 +1,232 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Custom Loss Blocks
+
+All neural networks need a loss function for training. A loss function is a quantative measure of how bad the predictions of the network are when compared to ground truth labels. Given this score, a network can improve by iteratively updating its weights to minimise this loss. Some tasks use a combination of multiple loss functions, but often you'll just use one. MXNet Gluon provides a number of the most commonly used loss functions, and you'll choose certain functions depending on your network and task. Some common task and loss function pairs include:
+
+- Regression: [L1Loss](/api/python/docs/api/gluon/_autogen/mxnet.gluon.loss.L1Loss.html), [L2Loss](/api/python/docs/api/gluon/_autogen/mxnet.gluon.loss.L2Loss.html) 
+- Classification: [SigmoidBinaryCrossEntropyLoss](/api/python/docs/api/gluon/_autogen/mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss.html), [SoftmaxBinaryCrossEntropyLoss](/api/python/docs/api/gluon/_autogen/mxnet.gluon.loss.SoftmaxBinaryCrossEntropyLoss.html) 
+- Embeddings: [HingeLoss](/api/python/docs/api/gluon/_autogen/mxnet.gluon.loss.HingeLoss.html)
+
+However, we may sometimes want to solve problems that require customized loss functions; this tutorial shows how we can do that in Gluon. We will implement contrastive loss which is typically used in Siamese networks.
+
+```python
+import matplotlib.pyplot as plt
+import mxnet as mx
+from mxnet import autograd, gluon, nd
+from mxnet.gluon.loss import Loss
+import random
+```
+
+### What is Contrastive Loss
+
+[Contrastive loss](http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf) is a distance-based loss function. During training, pairs of images are fed into a model. If the images are similar, the loss function will return 0, otherwise 1. 
+
+<img src="images/contrastive_loss.jpeg" width="400">
+
+*Y* is a binary label indicating similarity between training images. Contrastive loss uses the Euclidean distance *D* between images and is the sum of 2 terms: 
+ - the loss for a pair of similar points
+ - the loss for a pair of dissimilar points
+
+The loss function uses a margin *m* which is has the effect that dissimlar pairs only contribute if their loss is within a certain margin. 
+
+In order to implement such a customized loss function in Gluon, we only need to define a new class that is inheriting from the [`Loss`](https://mxnet.incubator.apache.org/api/python/gluon/loss.html#mxnet.gluon.loss.Loss) base class. We then define the contrastive loss logic in the [`hybrid_forward`](http://mxnet.apache.org/_modules/mxnet/gluon/block.html#HybridBlock.hybrid_forward) method. This method takes the images `image1`, `image2` and the label which defines whether  `image1` and `image2` are similar (=0) or  dissimilar (=1). The input F is an `mxnet.ndarry` or an `mxnet.symbol` if we hybridize the network. Gluon's `Loss` base class is in fact a [`HybridBlock`](http://mxnet.apache.org/api/python/gluon/gluon.html#mxnet.gluon.HybridBlock). This means we can either run  imperatively or symbolically. When we hybridize our custom loss function, we can get performance speedups.
+
+
+```python
+class ContrastiveLoss(Loss):
+    def __init__(self, margin=6., weight=None, batch_axis=0, **kwargs):
+        super(ContrastiveLoss, self).__init__(weight, batch_axis, **kwargs)
+        self.margin = margin
+
+    def hybrid_forward(self, F, image1, image2, label):
+        distances = image1 - image2
+        distances_squared = F.sum(F.square(distances), 1, keepdims=True)
+        euclidean_distances = F.sqrt(distances_squared + 0.0001)
+        d = F.clip(self.margin - euclidean_distances, 0, self.margin)
+        loss = (1 - label) * distances_squared + label * F.square(d)
+        loss = 0.5*loss
+        return loss
+loss = ContrastiveLoss(margin=6.0)
+```
+
+### Define the Siamese network
+A [Siamese network](https://papers.nips.cc/paper/769-signature-verification-using-a-siamese-time-delay-neural-network.pdf) consists of 2 identical networks, that share the same weights. They are trained on pairs of images and each network processes one image. The label defines whether the pair of images is similar or not. The Siamese network learns to differentiate between two input images. 
+
+Our network consists of 2 convolutional and max pooling layers that downsample the input image. The output is then fed through a fully connected layer with 256 hidden units and another fully connected layer with 2 hidden units.
+
+
+```python
+class Siamese(gluon.HybridBlock):
+    def __init__(self, **kwargs):
+        super(Siamese, self).__init__(**kwargs)
+        with self.name_scope():
+            self.cnn = gluon.nn.HybridSequential()
+            with self.cnn.name_scope():
+                self.cnn.add(gluon.nn.Conv2D(64, 5, activation='relu'))
+                self.cnn.add(gluon.nn.MaxPool2D(2, 2))
+                self.cnn.add(gluon.nn.Conv2D(64, 5, activation='relu'))
+                self.cnn.add(gluon.nn.MaxPool2D(2, 2))
+                self.cnn.add(gluon.nn.Dense(256, activation='relu'))
+                self.cnn.add(gluon.nn.Dense(2, activation='softrelu'))
+                
+    def hybrid_forward(self, F, input0, input1):
+        out0 = self.cnn(input0)
+        out1 = self.cnn(input1)
+        return out0, out1
+
+```
+
+### Prepare the training data
+
+We train our network on the [Ominglot](http://www.omniglot.com/) dataset which is a collection of 1623 hand drawn characters from 50 alphabets. You can download it from [here](https://github.com/brendenlake/omniglot/tree/master/python). We need to create a dataset that contains a random set of similar and dissimilar images. We use Gluon's `ImageFolderDataset` where we overwrite `__getitem__` and randomly return similar and dissimilar pairs of images.
+
+
+```python
+class GetImagePairs(mx.gluon.data.vision.ImageFolderDataset):
+    def __init__(self, root):
+        super(GetImagePairs, self).__init__(root, flag=0)
+        self.root = root
+
+    def __getitem__(self, index):
+        items_with_index = list(enumerate(self.items))
+        image0_index, image0_tuple = random.choice(items_with_index)
+        should_get_same_class = random.randint(0, 1)
+        if should_get_same_class:
+            while True:
+                image1_index, image1_tuple = random.choice(items_with_index)
+                if image0_tuple[1] == image1_tuple[1]:
+                    break
+        else:
+            image1_index, image1_tuple = random.choice(items_with_index)
+        image0 = super().__getitem__(image0_index)
+        image1 = super().__getitem__(image1_index)
+        label = mx.nd.array([int(image1_tuple[1] != image0_tuple[1])])
+        return image0[0], image1[0], label
+
+    def __len__(self):
+        return super().__len__()
+
+```
+
+We train the network on a subset of the data, the  [*Tifinagh*](https://www.omniglot.com/writing/tifinagh.htm) alphabet. Once the model is trained we test it on the [*Inuktitut*](https://www.omniglot.com/writing/inuktitut.htm) alphabet.
+
+
+```python
+def transform(img0, img1, label):
+    normalized_img0 = nd.transpose(img0.astype('float32'), (2, 0, 1))/255.0
+    normalized_img1 = nd.transpose(img1.astype('float32'), (2, 0, 1))/255.0
+    return normalized_img0, normalized_img1, label
+
+training_dir = "images_background/Tifinagh"
+testing_dir = "images_background/Inuktitut_(Canadian_Aboriginal_Syllabics)"
+train = GetImagePairs(training_dir)
+test = GetImagePairs(testing_dir)
+train_dataloader = gluon.data.DataLoader(train.transform(transform),
+                                        shuffle=True, batch_size=16)
+test_dataloader = gluon.data.DataLoader(test.transform(transform),
+                                        shuffle=False, batch_size=1)
+```
+
+Following code plots some examples from the test dataset. 
+
+
+```python
+img1, img2, label = test[0]
+print("Same: {}".format(int(label.asscalar()) == 0))
+fig, (ax0, ax1) = plt.subplots(ncols=2, figsize=(10, 5)) 
+ax0.imshow(img1.asnumpy()[:,:,0], cmap='gray')
+ax0.axis('off')
+ax1.imshow(img2.asnumpy()[:,:,0], cmap='gray')
+ax1.axis("off")
+plt.show()
+
+```
+
+![example1](images/inuktitut_1.png)
+
+
+### Train the Siamese network
+
+Before we can start training, we need to instantiate the custom constrastive loss function and initialize the model.
+
+
+```python
+model = Siamese()
+model.initialize(init=mx.init.Xavier())
+trainer = gluon.Trainer(model.collect_params(), 'adam', {'learning_rate': 0.001})
+loss = ContrastiveLoss(margin=6.0)
+```
+
+Start the training loop:
+
+
+```python
+for epoch in range(10):
+    for i, data in enumerate(train_dataloader):
+        image1, image2, label = data
+        with autograd.record():
+            output1, output2 = model(image1, image2)
+            loss_contrastive = loss(output1, output2, label)
+        loss_contrastive.backward()
+        trainer.step(image1.shape[0])
+        loss_mean = loss_contrastive.mean().asscalar()
+        print("Epoch number {}\n Current loss {}\n".format(epoch, loss_mean))
+
+```
+
+### Test the trained Siamese network
+During inference we compute the Euclidean distance between the output vectors of the Siamese network. High distance indicates dissimilarity, low values indicate similarity.  
+
+
+```python
+for i, data in enumerate(test_dataloader):
+    img1, img2, label = data
+    output1, output2 = model(img1, img2)
+    dist_sq = mx.ndarray.sum(mx.ndarray.square(output1 - output2))
+    dist = mx.ndarray.sqrt(dist_sq).asscalar()
+    print("Euclidean Distance:", dist, "Test label", label[0].asscalar())
+    fig, (ax0, ax1) = plt.subplots(ncols=2, figsize=(10, 5))
+    ax0.imshow(img1.asnumpy()[0, 0, :, :], cmap='gray')
+    ax0.axis('off')
+    ax1.imshow(img2.asnumpy()[0, 0, :, :], cmap='gray')
+    ax1.axis("off")
+    plt.show()
+
+```
+
+![example2](images/inuktitut_2.png)
+
+
+### Common pitfalls with custom loss functions
+
+When customizing loss functions, we may encounter certain pitfalls. If the loss is not decreasing as expected or if forward/backward pass is crashing, then one should check the following:
+
+#### Activation function in the last layer
+Verify whether the last network layer uses the correct activation function: for instance in binary classification tasks we need to apply a sigmoid on the output data. If we use this activation in the last layer and define a loss function like Gluon's SigmoidBinaryCrossEntropy, we would basically apply sigmoid twice and the loss would not converge as expected. If we don't define any activation function, Gluon will per default apply a linear activation.
+
+####  Intermediate loss values
+In our example, we computed the square root of squared distances between 2 images: `F.sqrt(distances_squared)`. If images are very similar we take the sqare root of a value close to 0, which can lead to *NaN* values. Adding a small epsilon to `distances_squared` avoids this problem.
+
+#### Shape of intermediate loss vectors
+In most cases having the wrong tensor shape will lead to an error, as soon as we compare data with labels. But in some cases, we may be able to normally run the training, but it does not converge. For instance, if we don't set `keepdims=True` in our customized loss function, the shape of the tensor changes. The example still runs fine but does not converge. 
+
+If you encounter a similar problem, then it is useful to check the tensor shape after each computation step in the loss function.
+
+#### Differentiable
+Backprogration requires the loss function to be differentiable. If the customized loss function cannot be differentiated the backward pass will crash.
diff --git a/docs/python_docs/python/tutorials/packages/gluon/custom-loss/images/contrastive_loss.jpeg b/docs/python_docs/python/tutorials/packages/gluon/custom-loss/images/contrastive_loss.jpeg
new file mode 100644
index 0000000000000000000000000000000000000000..a24532d984ab6be6051a8d56e986689f4fac0f5a
GIT binary patch
literal 32367
zcmeFZc{r4R+dqCW+0)o}CRq!K?AwrRsYs-VNwS9uA<W3WD~h6w5DHCYYmu=pNw#cb
zpOGcoj2Oc#zE}6>{@%y^eDCKue$Vgz<M;gj_|7qJhq+vHy|?pxo#**F&$s=t{Rtor
zfG{)vd4sP5;G2b=h2_8j7EU%cR`!FO2M=;_a&d7V;^W~y#CwQ~i${oumtR0oQ1BpB
zSVTxbgik<FfC&QLe}IL9g@r?an~PiE|MhFX3E*W1D6Ga1rXv6|FB629X}^uB2mqK^
znEv(x{{CWO2FJ(B#?HaX#RD)iK_JWrAb&;+-Wdh{4LHEd!Y8d}!peWajqS*70rmS2
zU$D!Zde<yyH$ap<dHqfd2d9v*h^UyHyn^D<V;Y)T+B&*=rl-x!EiA3hT(oz%<aqgt
zllu)1PcLsD-=N@-yP;v>_hRGX6B3h>Q?jxjJ<iE}@-(lgxa4JNS^2Bi@2hKS>*_yz
z{M6Fg*51+C)%|sF=*RHL=-4=J4o{f>xv;pj{EM{tduyA#L)qP90w8})2mJZhg#Kwh
zyx@G84;+9TVEZ#4CgxDEL3j_aNUO2(nOtCVyUl+@{XV<EsfRD#HFL<Ev?B^$zcauo
zB&#teNBT3Rzs=~sH=&q+YexT=&_Cw0j{^=un7|E#@B(mvez7P;5%|CD|E|IRm4W{+
zWWeu)gY3im#)pa;)OW}|6d!1375|)IK_w2G8glZ;!!pGjE{oG%;~LSkYMf!Boso_o
z`<cuHZ2xDPyq|D~)X4z@NTJILQhL~5!(V>*V7ANjSDAER2mUo$YY8Jqe~$7&voVII
zrA9)qteW$AcXP`d^JVKF?7F%it6($9-h9xMHcNG^`X8FZ|J2(3*Zs1=NP(s#M6wag
z7Z)0%Nik5xCl8qAR0RbnWK6}zw7sB?+qRN0Pn^k0l+PrVgKz<q_YBXkJgdIvFDahG
z8f{M_D)fG5wlwyZcBzR+aRW$JWJ}%+Jyn{3YKv3U#~_x{dQ_n3!+JG`ln_zF?`%$V
zGN##Ptb$=@GPgk$sa2&j<FWUa{zp*1#E@G>fN2&6V+hf1k~Wf8<tc$A&!IVTOiqka
zh2x;zx{v&1=eJ94O*ngZT%+hd5R-uH#?`k5j!zRh80wS?d}gwgg3Eav4!$OOTI+;T
z_Ggbz^N`M~48gtbntP6IXrkjjuzPX^P30a(X%!w)Vz9s12V@}~Sh5<LE}w!u?f3ih
zzg+YFA6bq6KVoiaH>0XC;rY32y7M2|;zG6~HnX5m)L8o~)RM3ubrcbUe&<LliEW)B
ztN9}KSlW@>|1L(wN9*(gDgsl4kw+5!%mZcuz=eOaIkutx!@gtcmGE$i(mp_Yy4^=l
zSjFbsvI*`32W<C&EG7biD$>WuHbWF??oR#dMU8)Z6#wq%|NZYt>lCsS<$t(0@WB>R
zUX%hT{wM9IHq<F$b5oqlCm=vBgDdPox9t134rUP9tT5ov_UsHR+{X`#W5c`F!eX{G
z(R0WYsgXb-7kveC{F2Ok&DqF-^6!gA#R5zlbRQq#^%N&SvE*eTbBf}B(T<)p<}#D_
z{<E_ej9CPp7Ha@CPX#<1{K%P<9z6WMm}@S60>K624?8|`NlHd506FE()G?7%+#zeW
z7}llJEV%?3FRz3$Q&vt<9?uzSw>66YC>*YFF_+e?o%-zIVzVjpg6)Ahci>-g_m`-8
zP%7H>9AgahQEygq`<L^~;>;r|dSa92(n36q9?v$){DBfi-#ymcpb&+&DS2Y2s7Fkj
zmBD+7s9}eT-G$Jve}~9@;2#*dvO7$F!Z^7EJA}Gv1C2aAT9Px=N?LIgcH~p`z$upU
z9mf}EXP)3s+rr?!z{wOkdi!*8A*HNv;Vg@lB;jVkm<MaLAtNQ@=%pKucA}SA4}=QJ
z0Rk7y0EIIr^GFyRocy$ZAMjd}v8ng7YOo-v1YGcY5q7<4VS#b|7JSOsvZtV6;fr{a
zw$b4RA~O3n{<F0qyzS0J@r=`D%NsGGmffe_dzgkanEL-FLRNWymR{f~C==NlHUA>U
zb^%f~+NQ2dq(t(g1`339(ju|rlbCa)>PXz(I)+N^-P>i;&lu}#YMbVh{<k}?_Ota1
z*bC*}cAaQ9;FIk?bDxl<HTkCG29JfUnT;`bP>pA!9SPcDlZ>blZu3VqqgdWlMrzJs
z4qX+O4C?$8|Ka04a0Z6Nedpi&tV+_q1)W2`+8~5iE@UWd{tWhTWGu_XBJvSLn|)y1
zov_n{!RfaNBbCEz_5tAWr{6zjD(m(heIL)TBpkHV-A&~=q^{r3R!hN<?I|R;QtNVx
zDLIEEIGR-Kvg}ti=A3^tpZeJWX&iOSuNUh9rzoP6-!M*`BO(ZOj{}RsM)ZrzCVstD
zcq}mrDY;W-jG(w6FLlk{a9p|=tVxOA2Uv_Y-RTW$;;qm85T1kIuwK-Cz;9|Zn4!|>
zxNHooFI>oZy<F<<5lFtDOsoA3pKj_~xm5mN8jcbLf!ev5n!q)~GMYfPC2=~tv>QK@
z#E?cQoF=yC<E@^M7QW7GewSPC>?gcvV*3#CvJ%VHSfP-pF*<s^F4oB}tatV9Sto&f
z?`fWTIg{@kRR(`RQr?@%`;V5F^oka;lpGLKTAWfMKS#IBNUk>u)2s%M5WlRyRO+5{
z@jPlhw!-6;wDUmgyq<ue&H3WPFp~kxL&n>GwD%~KIa=s%T7lgMy3tqnfw6#|-P$NM
zl0G?(5<+zS)iKd2r8;DA(r{BFXJDqw_Umio>pF%Ug1aPet&HjK8~JHhC>+q320?rU
zir-ILtWsE=OJrWHRlqG-dA0CH7638yhI|6`P7&1&kjm(3Ui*Mm5oSP)mI7Y1Lylk%
zBH3w%B(M0$Q2gac&8X1VRrJdG+qwz%YR7oL*hib3r>r1K_JM=@fKOloOl13GO(sj&
z=oymK%Y&>7*3P2VdTs1>^E^5|Oq71MvcF)|G(1!=y(X9c>jM+M+^j1i$BOP}Ss4NX
zOh<K!50ADRX@Pw|@%~hw>D+{7p^3htxsr^pCs?y?D%-}!L=JKs)30OR2Wnm0iPmih
zSc55E?gNNIaCVAwYair9-H_(d08EyvmQrM+tKMQBu;Zjy6wUeHY&_5GM*GmBC|_aX
zV%u|>Ij`0=c~#L!>mQswhWTgghOBD4RBneJX7lcpwou5tfG(7t`YBHf?9^G#1EK`%
zJfnqD&JO%h@qfpg{tFgt|1Lr$y()i0qCaT#zniqq%gPzvx{^FWs&{wb@i%pw;u@c?
zzpa@#-!y+mSf^XEq7y_b6z)zRL2w;2XBc6PVxyfU{tkEQO9((KH)b2pY>o)cCP)<<
znjDt;2@aE)j|EtGZ*U3M5*Bg6NXlAQvl$YV9Kz~RuTJ>+n=__4XcVHk3C}?J!f-OP
zlF#z6ZP1ucUdaY(tWJv#MA!1)8ti)2W=Gb6LwAg&@X<#7MGZFO06L~o68R%fx!(2A
zu!!%aF0tR{jfi9GV^Tlwm>x$ZTM4dRo3~{iO-BwQYB(rz#Y<!{tp^N6(oAvywweYn
zJWW0KZLCMXp4b+f6EKq=p@IxWHJ^oh+NA9RB1VcdI0@0x<@K5IwI)J+B(%p*^HHz-
zhPGVpPZ{7r(B@Vl)IE`WmqH?}NS9MO3FP8Ae>=AZA0zIeYbk^M1$P5)xt<N~HW=+g
zh%gM01N*>Rwp5J>wAf0{jBumquZ!Vozl4P<0)x|7&)!@zx}uh)9dN06OLGrKB-o79
zpaII=p0?1`^01f*oBCAS&#u{5m3?JV?e{f*E6@aef;;G`H2V<l0`Jf7wIV+4GVT!q
z8$mxN!5gxKjztJZ8j^<QzNZsxxF+dkmOtjE?|%q;CSeeuum1LMrtezCZvun8X0I6e
zeOgi2)r)+M1fi#*Y_`>Q^P{RWJ2m&9pwa$gxc{k1FI!)=>BvmJ1ZQg$3M=l&ONMz2
z1n&lX<!8Kmj65y1I$=RDhDjlDC_znZ#y%jb0UHmRTQ4LB`NjtrxKub7_$xO}_s@qO
z?YXyZw(hqEPlQ8*ZQ=_!2WU!jJ&LOu{>P{M*Jjh)qjlYXwasTwW+OQEfo8b|uJW+D
zR=jYh4L`b;lG%=XmP4GfRG+r1+dZ=fk>=V;b6iRi5dipW9Fr*o@(@LixUdh{z8z)A
zc-P%akE>$o&HCz5per!<posYn`?BjutBl~nBH5pEnIZF;bN~%C&@`$K)xSE4=q)vs
zec#ooy!x$7wjjtD24RdKh?lnC_vc$sOpAXB?PzyJVgy!%5=2@cF(afCyM6w>O<S5f
z^{YepwTqoIfRPj>)A<9{74zErZf<DM8rQ-ZQcUmtrtWS$Ruw*5oAY)&(uESj(x5ic
zEERjC<btBZg~b9uf|IU9Pu~aThmiuPv3ievpeP=Qko)K5|L5PsP;0zY#U*S_$U<h&
zYYL{_RU%4VS0>rO@RhiI)I8t!5Bf<>&0CmTMbU6}Q~){w&5LrMNzu9|pG#70$DtEK
z{A_BMM{}K?^(b)HS)%=bGtce-U2g3WxvSh%r7^}ZmSepEfk&qn=w;i*+Gc(AKY4z5
zTCN@^_^4X#di&%1Ys&}=aBrN9*!O{!Ui#AolFD=J(+Y~-yzjBE>K)^6voZb57|@1e
z(y_A{D6xHDj+3%j5ynZ5$_q_d)1Ii%W?l!b++Odxie!$4do;j{&E}@KYz7N+d|te-
z&%o&(msPj?l_DVQ-lGP|o*aokQa9&NYj}Rt;_ZhojUO@wzfWuB_$*v|C8pSOlD{7~
znFs79l6NUeFTz&lJYNtb{W=>D6ZI*M=RX!qN6AxK4`lIhCd-ISTOTWLW?R5Fvd|yw
z1MY}Sc#Ug%Al@YuZ;>3yKl`-ngpDS>E!VMPMZlwpi!mV~)igSab9fF$5dzxyGrpsw
zK0m5KnA47yRt6`k<zj?rXCC3pC*G!wUBcRmwWYGZTYY_kj>?>g#BK8z4gv=OU>4A~
zWlKT!qTj!HOw~S)k;_bNxPbp$b>aGzpCqBe070#Q=JZ?4*&h~{lyK{7MGdui9C)k3
z$7if0Qbb)Wn-548rsSmnv<>1qR+YxTNUqLo8V%B;@li5$KMfu%*}Z4@TTAT3y#tf>
zyz~*%vBfQ2@vyFCgaWGhT;st`k{{WT#QlTU>k6?>v*)NlWRp4kUh#@(V7plWxP^v|
zhE||4CwmartIBN_S8$ht)6b3lEVvxaCB1V)amLOYvg`|*HbuWrLTG%{k(D#UmHyi*
zTFVC=g>o+>A9)OQ@G#(A^-D-32T&XUCL@V32ji`Q*?j=7G^khz)dxZ8F2yBtR{<#u
zc6S;^Gc#3K9VHgoanZDiuMW?{R+Dt#JMH%z1b{9Auc8B!gE=4>Zv;}(Z<GkI_9*^Y
zbgK)hqu@w>_uy8(=hG&*M%91};*zJd#mMJVx0HVCGdTST=S&F}oi_2<>WtW1U-KTX
z3V(W=wtc+KT%6w3mgPPbr3e{BUBf2s&2*snf9(S`qispFeSjw{zF?;H5+Aes;MVr_
z!Vc)EvBOfc{4|H1a~@%itvOOW#D%9XmT$?`<(E!a`1o@kJdiGMEVGMqxRCjyO$q|4
zf>a9QawNW7FMj2tmG{imJ~z*-x&C8X0NebnLweV!{4Op6OuOL3Ss?UBHRy5YIVm(A
z%}qN`n#9#kzmkl*<dpGt(BjCWyFr_4hog%}tL~tP=dt4vm*|x>SOH3GJ2=P&_p+rt
zd(ybGMXB$585=d1b!)W%!81fQlH&#N$?qu-$@_p5g{$f9xhGo_M?<WiG%w5czB{>f
zd4jQrd5@ZXg5^fdxga~Ip*}}8*SCj`R~wmg{C3_!?Q+{i>uyj&I%96{19D1uM}`2~
zK9Kars~t@|D|zv3Z3H?IA=wa8=)0tSTGP;EUD{KUN%2DBDEej8nx(lnWHYq|QE$_M
z8SBHhp`dW@2v3q$!Z%L8+vVC^Ogj8+zksKP4$*FXx_GRefeJqTWJiJ2P)M)Pri!W6
zkHgma&gITWm2aw7DiNR*B}|JxRE+UWol@C^o{$n~NWqJzMNXf7bL8>KrBIouOARXw
z_AjKm-))TtNQhSdGjnD8KpgTkcBrAF+c`WLDnfPyUDydzW7tdkfAVPlcs7E)k>-?$
zqDXt<rFU}(-YYK|>93t@zqB3K=WSIjhQvUxvjo>9J~W20G1^gr+t~Y{4`5(IJx(*h
zW^Jxms@$HFbef%=zWngZ_3mG%?lk7RpyC{-ArBGHLHm%&f}k8%O@v7{sQ3EdVVu4X
zap!IeA87mLdZe>o?>$|%J*u4;gg4A=-$;-WCKWuh7}xw_q||O3^zJG0#$H7LEFQS~
z=*dfrCS`*()4C5Z8A#wdsu!kbu6C&ee-Y~qo%-ddvLhYOCYzmB5Pip+8NH@JH4Aem
zdr^GK!!T{3oGt>LZe_(#y}jAYELoUq+1qYR+&*v}HV$b=iz3w~k%KT%71t5JiJ{j%
zVg|oVt)+D{ye@w2hz)2rcn0YY+G1<Lj61thJ`*Ma)8I9nX0O7Mkx^XD(HlcsBGr!%
zev!?8AxlEru^OVuxs*}R9j6mtqxdJ52Y%IekcZB{ADT85DY&w5xn-W&oJrw6F51vL
z_+`0&YK`v=rmN+XOkHeW&i%umI2g*Vev9o@8MuB&KuoZ2FnL7@B<qp{N3zSWF4vzc
zzue|k^SCJ6=lLD&qik$~ThOM@)GEhWZ1hH;d_;alI%mff&xh~7FC36dUbiYd^y{B^
z2&e}AMWe1ay1K9BTtuhX3Tv!QDvvX3tIJhw-nPconFrl#2oRH+;f;KLNSSjN!9XVq
zIYroz@N^b<95xh;M}<ll)B9W5GUq0K`L&}0Re4;HLx{T2R@L#9^UpKl-9iM{Yu*)z
znIqJ8P#>9}MRfUX^__iNKe)Bxr(|h2J%ZCr5GfY1HxqGyyakwy`Flii6wW@LWdHh%
zcMB8ScmnKP!<bl>R2vAPU83Cg!^b7~eYRB9%*o++3A{~@<}@dCm=FwCHqM;VF|cL(
z9XJb3VrY}RKn0`twITQoY>IqFagVgLbJJg)Ul?{WPMfgK{EQyN-a>hy##&F&3mUAo
zlawR<2v7mU<du92mk9fl%{A}exk&+2k3|gc8`V<iAa00Jl#%SnVORNt@BD!!ei@p*
z2IWm$YVu~V^{*09YKV8B&$`j^kVGJR5d)LupPYMb=Bq)sC_oNDyXN<S2IhJiXSCt`
zLC%{!pX9GTe3+I|gm#sr+A#)CM6wZkALnFWk2E83wW-rnTMMKT7qWiM5+$SO&7bzb
zTB6}vD1V#reV`3>2x%1N+_Vp{F%E}Kv`rr+9kUJ(De9@Pk{SQWa6JC<)C-or-u~l3
zy-`qNK7%6ypZZ*k3!MTNH*gDm^(i4r-{i+n%NOt|^^ey<udk>vgZ_9Q@O*{jyYkER
zI4K=Z$;NjkPps*YKPr5)EikKrPbKS!NZwuoV8;09lQeiy=iC(3W^gvgXAX2c#^pXf
z=vI2IXzP-x+;+$&$n^@<p3x`fwI)r>zd)So;WG4uZS{)VpO<~#aNa#6ppd`0^LkXp
z_)#^`0Xazq=%w4LX$8E)4ZMWNiAWpb>9X^~_}s*gW^zK2_kNzK|G-A6SUE#$pjTl^
z=%AN8`$CF^QePHOPuxf&2;nBJTC8H;owvEUre?Km=eNa}zVYF{Tykv3FVQPD2cf4F
zna`MZy)25>RTYU;AO^N6<@=!p!kjHg-0iO)#kidRs-KW-)y;WX0&;)w-4Hp5;`9uf
z=qd#1_u)a21ea=T*`8~!t`KVzpsg4#7&XoW+<{BzxW+>iSd&vt9y)<0UF5C3dD<=F
zxn7H+><wAImyb<k4ogHhdi)h43X;5Ng7tWzs#%+HJ^X~#$E<<cwM*Q`7cS@fg&%xx
zxq>#|Y(Zc5ThpWPGq}LV%YP|Ejr9fY10_2GOxiSF!dyY-+|2kJgmA0B=%|6otE+PD
z0~Z=hxo%oEGuBPD44oRc+9N|iN;ZEV$W|rJ(Bo9GTemUq{%e-y|De#BxFdI|H<3St
zu<t=t`&<<uXpIs0b@rx)lF(xBN4K4>^>QQkm8K1ZNgnOfs3t$v3)O5}8GIBvVZcrN
zW>CtyTa;xf5_nxta_AAFB$#R2YgL7+Hx(&P2~kZpxFX|=;y1X~>M**kgwFHu+RZsw
zU~C*NsqNM8IAkAvOQ!_&*;vYjYK|N*;-^1lNK8@c+j9>dzBrG&ViIdO-{#?-CPlje
z!nofeI&QCTO^$lm`vK*v4c}NKAJO~;bxPId-H;vC?qeK%Ts9o(aNrI*#PD{S7XdTj
z*qMzSh7oPyTr{<erJlKTamTv;9KpZ`U4utIqcHY+a>%bfby|BK>0h3gD6v#E_z9>T
zZYff1DVK_7GKNV++9Gi7WcPsp1H?<pr%r-be9iXBUrt)DUcStEApyw^D~X$XtA5@>
zWXW#=)FC)7q6J>7-MU0OHy7?CTI6=<#mh<F#qo1#YuEH*#}f}A58tEpmyitZ9O)K+
zb?K5Q=&6q4H$Yt$t6vQT5k{2+i(T{7n&ute?i_gIpWJp`bj+~Yot_X3><MibLUAU8
zIS+yb{*|Tte<H&j`w%s-Rrk4l04LmjD=gNYl$#dJ^+e^;uz!t;{lvs9U+7JoIpK5m
z40B^VNN23%FvR;c4V~6Nl2d5V<;*;hSKu9;1wa|1YkoG?6BEe^B%5f1qlA4xU`6k4
z?!6pao6uK13pMe=cO)yBq{lEOgV^&1+@Mr`>Yr$FvJT6{C${NskJhDch>bd_ot8Vs
zb?lX$@qq{GR(Pxy+>={~K+dLrr0E7lCd`E(c)Q=YCd`NX$Xq>PG^2><z`i(fLhpLC
zk@5$Sh;pK6S*Em;^zm2K@1oXL3B;qWh9pPwIePIv;6c}0X`f+3o>*aM4BZsy4J!Qg
z-2?sA_56=J-N%7>plUgfu37qiU8Eg|yh!ChHJ`4T!QFA6g9?r}gR7Htmy~<iP1gAr
zd+~yI^UtdxidKXN_Xukt`U!qSFY*d;e&&7O+A!)(m=4%iJx-;;>iSz+g?RUG%V#K#
zkO%Ii;(}BZ@pH$>3}2%J#jpKtm=JE{Im@1+?!?03>u_dEn&O|FgW`W+KrtDpz?s-j
z!x>LYX<?K*9i4dX=zU=D#tBtgTre?6S)FO8CGDBiL}IB?N4aX1TVr(|4zHQR@t{*S
zKwZ?bnQbiASjvy89T_%&^4^GZ5>DT;ld&EnyEp~(I89>s+K`Da*4YhLe)w%t$+DxR
z^c-veA{hY$%7+lAtApgG)3RWXWw{pGGk0G2m;LBa+6E+20H>5NKD<`O=_@j~93Cc^
zLVoa0+JksFw@?xA2PkNQh8my|`s}lJg``p`KgeHe)Eg$_Bi*@U@yuAVF7ph%1p*PI
z#8Mshfv=ac867VC`+(Vm;~E_hcvQ>X`|VPHx9s<Kr&F%X2gTd6S}D9OWX9w5y$h_a
zxTgzLf0kf@yiyp%DxrMQ{FHnKC!F;8Jmnm*Tofrqab5W~Ogm#jm3~I6l3!2b4Lzp#
zGx@sCLSqbRF^P7C8oV;v$WB3(Pg1;QzdY-hK#-n6Zhjg%+KL;OB9VRcV7Ht1V&_y_
zU>r0k(E}adAX(g8ye!nya$0?}PB+Wq*Lp;@u^@vrQmh}z6nUbb#yj_6Ja|f7vhgN*
zMd87-1`K0JO_t$O(TI_yu#i64r02qU<TB5v^jQ-(l8*<A>p%IaN^WL%*?f3E^J!93
z-<j=C|B_}>nhm5VquZ_+s8F?ijKpFtN=(ZQ59$IsDhH3<aNC;tIXa`cYXzc0-Iu(@
zHW+(w;moJt#@s-v*{m(<IS@5U<)etf6H)K$TMZ?TY?d(E6V}}_-hVS#R3HVp&Rs6x
z8Uh*goJ0?i3CY?to^tZ#AJCu9!GAIwOq7*B&js^(y+kUb$!;H5`i&@=d55LtVOlb&
zLaW#<TTCySqEb)G2~3<JGw1FD7I}2}|B7<4;|Im%C<HQ{^LJLaM3xi0z{K|QFTT_>
z4Bbk0RVFUHD)P0h>OhxISoz%MKbVmIqXOEM$GB}^TDpRLAG&rjD3CWS1jXvtJR8*f
zQ09R;thf};idoi38zuD7gd%3qG#M<v!Aa8b4(_@<`o|&6Qwv%{i+WlVPMwWjT?ieq
zVGbKQ4~FOWw`>G_vXV8XT)!$UNl0nwv5M|_u`MHHB2SZqUQt76=RQ+B=ETQW83!r%
z<`q&KEPUHhwTl<gtJF}*i!zIDoy+JPIOrgyV)?P&s?mFhRnqLwj|2Fc5069oL>E)J
zB`0nr9A^XxJ!C6l$Rc0Sj#947&BP)hgAFos5eJ60qV%LCyp6WfX?D5{1@~@+kA>XW
zgJFJWiFf1Oio8coEPpx|#}(-36L8;7@y9hq!HU1KxJ+m~x7bXZo>1}2sUzL~QT}o@
z<*m{Wf4n*n#ewO8Q)HpZ$0NB(X#7UY=Z~Dq;h!?(4dv>dv??q}qBipA&IYlF2k`2%
zSYkYA%Uv71N>AN2je6y$wXSzVy=G>QF9o2Zin3yw&8?cij8_r$D(p87PF=b{E{8AH
zpmzETIlRjohuLW4Ovau-)NAPO8jQZB$n11XI)vjehi!I0tDp*?x#u|Fx0)Dc1P430
zXe1qmpN}dXGNVS0RED#c_xK!w&mPF5UmzjN=&5t(ylhv<)<@sKH=c}RUm`DqUlCY4
z|B3{~uYXjV(sy=}-w2m`Jh6Sj7*-j5JqN_b(t43HM;TIR2QgBUg1}=1C*N8I`>W%(
zvL{PCqG^^8QX#ZOb;71?6kfx&)`_ohG3@O|JEgXcNS_Z&&zW@p)tpH{G#YUdhwEbv
zU`5_SmNGSit1sRs+h1czXNLYn#KDQ@p^!cjiu{B4Wo<M6(NGz>O*mtcHW2gGhF*IE
zGjl`42?!Ce69aa=7@W7OOpB52Xr9QylXr7Q%At4to{%!T7!JE&Sa854D}?^i;LO9t
z?m+Hy3rJxe_iaoONL|AChAb(jvl|D@jvueBI*0k02HSOK<~g|??#S5H>;#vA4=HLR
z0OO&dZu2^aH>8UVbWz)blt0k$0!(`a4Jl3}1Wp)_ZNtXjGDFQ`61WIcQe0L6wOscr
zEqvq}zu&FAS%Uq_L0Co{jReEOm~u)FVIz5>wE&=q-hSh!))F9>N)3^*EIz=z+A9Lw
zxIg*@K&B2{#k8IaY@3!QIwZWn?*oTA=MX&KL?)NjTvc$*3DT&0g{)lwX-EgdM=yi>
zz?0F{n=4`qXySvgil%!GMOP<R9<JPO20KKZ5LAg*z!=pqZG90WLYonkqpEE-I3s^^
zSR$OsYb4_t|JubKJo}3TzHuCv!L=K+q%}lHnCI_{KQy?tRxZDFQL@jQ);(Tn=cYdK
z)m|t~2ROkdb2umk_)*jtPJguzc&jF%0cF|I1~~CuL*Sf}lKkf%wK}!-_t$Qnw|Hb<
z4HT-mt*p5QQVoz3Xt3mGcdLR9nKc|n$GtgzNjx3-+sSh7^i*P4tNFov^RTP)X`azW
z-;44-c53_rh?x`UrXCWMe4g^O<VoB6(uZ=Zhtu$uYU%_An4fTE0k`%xC553x&(nk+
zvQd4iolqNoE#0d7vgd_knWY4|s4^1U*>iqPjWR>?;>Dkb0{q7!49=GAj{XFVBQO7z
zFav9n*J<U*YL>@?m`@GmHop`h+joAJSju{5e|vQF*wYR|25W@ld|*f4xI1~6(oIT0
z!20}Iv&)^UJ6~Fqg~hPvo{yeCkP3ZZ`D@B*1@UIfmW_~0Qx9C**au9{*X{%HDJT+d
zlHx|?VGQSObl3nhkq$osH8a_l#ir_t@8&XOt=rPC-<pVXb?YWb9Q=)VqhZU;l;ZTK
z3gM_Wa1Tt4S|0N8h7fEhN`bVep-J#n1&||JA|cEeeW*<nh9qk2iyIh<EF<(#&CaO#
zo(3>NaWe$f-XdjBi5wxr5U^+k<6gV%L`L>Y;2yVt9e+j*P-DjoJ>vu%6Mr=_!j@+~
zA>r$iSqPCYeFGz8)J=<1P7<mvolI8VQW}4h@$IcHtO`K-fcl^%hsuD}Bid(Dgj?5O
zUKtCQWdrVtE{==9EQ{R`mDjFgkAneV;@)LFg?4|3k6G6F;!)Ghp6;}EWYOQs8rT-m
zG$HHRT$((LB^Z&UrA6I_a=C<SpRdT#3VKjx)N|N$A7IwRV~J*c>F9CS6QsZv>O}fm
z-v{Z4^X7rO{!&5?h&%DljPX4BIZE-Y5)`B?4J_s)&q2ogF7aQne)#;wkLcD(36Pjr
z44T8H)MCcr<R?QR`cF#ES-0p-m_!6T6XliYREO?l--3aNhQ-!EJF!q?AN<gIgY_Hz
z$*V#_dv|jKdplkPPg)$b{*iTW?_l0$-+17xLXsy*Ww8UBno^Gv&|D(<U7OKxm3nXR
zveI}f680=3=;2C1C^?GafrsG~@P_rBGbx`U#wOaz7(AKmlbfGh-Yth5+dBCH%##p1
z$F5zo1z7d$2L6gdBcY(Ze@HwAn*HkIPw|3DnjQD6S{R<XmrF0De*}FE#;L{_dSauD
z70H_HKnZ-)r<#8^xTgBU&H1N?<%JX`cf)CZV0;;qz06UGY)A1u?m+hWv2^XdY&-4v
z6x=!~&CX%?jcm_V8}i*Rq0CS-HwknvF4}0JDCI`Bg^>2A(t80qg=_;C{$Wz6_J3I(
z+XFTB2;W#ngvCA(#cA}vO%IcbKwF-!(MkoVpgvoGV-uR_1l`qKgD^TGP;`Y4n8t%C
zOmfkHmpt|;YPJ#u)96dn%Z+1jQaC2soz@!5$e-tj*q#YRCFm5oeC(mEp4YowKmqm;
z3=VBD$ab5<QrQuxB?7|TF@e#JW>H0XZH!}^r@4p0T({pu(~kDsAqR!W-!h_iCO#ZT
zjbrYBu-JSdSqJb^sEtIw0x2%RMmcP9txZfL>p;j1TiCrTRT<TIu3eSy7z5qkcD=mk
z?Ro6=c{r9`<EcK8#y^naj9ypmA6p)ew?F4!DD}Yu)~TsEJTFAnxfY2p2ZEpLlU?aq
zLO))c1<t__8e~fq+~{WVQkW{~TQrvFT+O834znf~63ySe>Pnq+2{LxUeb0~3TdO|{
zn^Ah^X(V!EHb!?XuYzhvS;P;|lW0&1ND6RhC2~ChlS!&1x$&Wzk543EhyB&heq_j9
zjq>}=#o(wQ^4o*_-w8j`G~y~QOlW#ajwIK>Cm@!h@d96S9UlK&Tl+olG&Z=V_gJ6#
zP}QokN>TOu`t<7w(+4;tyjhvcB>*;GfRc+xuho;B$qu76@|0oXhDCOZQ$wl1Xrwes
zwuMdN@r}nOvWISW80#pAU`oNr%Pfqm1;$y4lt%asXeVro$mHAPOp3}i9zQ8@tHa3a
z;U|BH?n;{2t}uhkv<i0x`CUqENER2U8fnJg0-ffPP~VVY>|LOY1k(5>W)mv#eF?6D
zlw`tkzWw_Fy9iZQpHPU}<u(t`ycM6>uJ31*?%ETofQa6*_CA~{UfJRTF|(x#+Hy~c
zHqzNCR?!)uch~&@=d^2-$h@OCv7W)n5+U4~NYNzawy1@1bz~lE_6hY0A*XVc-G0(^
zoynFUZgV(0HWuNf9(@v#GDEcIz6vHu>gy9%w@#)zbxfS8gYxb+_In&hCmnjkM7?y#
z$5-68MF6@(PM^8z2qP8aJ5q`8xg0D3#dF4eNF&wr`G67aA8DHC6OC}jAQD2)H;~0`
zq=&@4Kg3@9HWeBYMxb2Je6?f1G?_vQgfRLMHI5y<DhAfG*f<<Cp+QOK_f6-HdzJ58
zraQ;uZcC4`FF9wP9c}+ZO(tmeQ4~Yh0xKh}DG$zppBpqTEeCh*Rt=}Zs+|;<-(IUm
zMFhg{Vcn0!A4%u6KT_W0AOhS2X%LmNwZ*v&NZ7^p!N|)4>kVuK7^kxNKF~VX-s)0W
zcGGVBL(@|UQh^qjl&W8yqMCx-^mB?IG~GZ|aIH3W(#n^-LyF7M<rif;5Zlalcn9-+
zhN3d>BzteCRXEup(oC)v_pN8R%2d8-#zHl-qY(!JO=P4doC|v+FcEk2UASXz9RgQ>
zI1Q#g`!sw{Nf8X9mp&2q0g!qBX2e0dDRqG8mH38nm}c=IwU5y{RB{>rK4~j&{qC-t
z=>%MDRS83(k_;k6DTeqy(NwQ5xt4FkXKT}~MN8qze)oT^7BA=F;qLoDY$)ttqey!N
zB`JR<$w<kqDlF6P5t3~E)O=3pUR=7R@fTKT6%ZcU4#j~yI2~=E0g}cW39cf;k)n_?
zauTrvqnw+=TMdZDB?&gkGFwQ^2Dv_b&A=5f!`UKr37A7)`ck||8Zkqr{Tkb>4Dv>?
z$y@qh;>No<3&RdaGG(;NK$|K>yZE7j(>>ojYe4XG_fg(5hCvfUkeEy3jhMSY5>7I>
zNSM0#?PD#GB6jzoKuOIl8=WbGBq&pztgq$j52mfob}a5QA+!m~QI_6-nw??tF@im8
zDNSiSl1n{D`QB#a+ARwycU4;I8($<~5<xc$^d#=tWJGE&TDV7il&$*!SO=<082D$X
zLvJ@95ZfJMaEem7!-~nDKN6!B6C$nLOL~eb`+K}z?G{*9#@jr3$+Vj=c`d_S7juiA
z1y;NG!N)^q-(=7F`H_bMq_eCmdhq!NhPv+jbbOx%uoV^_n!wMb^Usa8!i2(#%sKOn
z9tTBb52>|%gWD?o?qsl6&n0X6p}>MCp1>cysPY-p>&IbPoeO!cQMl8Z+m18=gbNDF
z05g+bX^+EK`TBX6Wd~l+MMkqx%@P_f-_x_-ylHBkro^mulkJn)HZ_=bZ1FD|KM3Un
zeZQUb@Y&*+XIW*S=t<Q$%@%Cf<?j}wJ-z@<l%5`P#lRmll$=E5yWj<@zy|9e<hSr1
zUZv3uq6lWOmBE421|3cA-}``xXePrX2m68(Fh#_`h<4%g#J)8Go=Uy6xfl~=L=w4s
zJCF;qn?fgIn))cmlG(`Pq_>iVZ(+Mf1p(wn*u_g=ia&lU>a1i7`MdaosL;Z|6{p(v
zNM6st7zUFkwX=0E9FC~AaH;xRp^+fSO-X<o#eK2aRiz&Zqd;3pl+Sphk6crxZnxnN
z{oH*Mxr0IuA9(E*4fIH{M1(IRjv>2pMq#{0Cn5tVwdKu+smKqJn#DC=O{}@3->&tU
zg>%3!7)y*Mzyo?wO{f|bOmkm`l=zT=>Kk8cexJPQ<gnkDZ5HOu#lJD-1~C`zrAdTf
zTq(6w+cAnvyU%PU`;u5<TVPU_xvU;*n!QSqRF=#68CgygPS-FVc^TDgrCDWx-&p$%
z;*u)0*WE@8ndoUtpev$J>aHb%zJw%Zq;H%sYyO<5+I;kyyuWi>UTjpg%jmk1U-}u+
z$8gC^oo;EzLXkq~VNzK;8duhg6vC3O&Kxx0jVQZ(di3N{ozvvz%<dWW3FjX6Z>(XW
z%N>5zf8_5bmK`}F#ojo)^)gEM(%@UobMl+UuA_$^%<XA-v2k~rK2X9Lc)Y?b8S#v4
zE45Vkc)1(4wfBh&X?ga|aw7#mf5xCul7c26I_MECd0MZ6io;AVz4s)Yt}ChNzE-WY
z%Q@3><EA|7Q=c*H^$uA0W|Lwhvyz)<15&xnFbUlFnf|MkYWvUBLn&F%hIdvmzh{YO
zuVvo*otXx&Q|-WvJ5m*{P^yW9*1g<V)peGdyt25Eqb_SV4nL@TU*3I~PY)8?g?Zm_
zaxPu9d5=3Ix%je4sm~=fWucLrE<T+Ba{zGc1dH~ge0$E)Gci>PxByM=>tDG%3_}yt
z`QWpPGax_YK^(#x#gYP>aN4PnC+~eqNPaz$ni}5J+<B@}|HR=KeICCuOfqWrK{68k
zZu?=w@oWCRTR2t&_V<N^iLk6j+@BzUqy4X1^XGrhMgGO~{DasL{4Goqh{apblx)V?
zI&;-}e!BX;mCp<?X;#r^xrv&9pI``XE!;-rB8bkYWFzxL;_s#h*1xaz8NCY<fwx4P
z3893uHw&S9AmS@UYBR<t9|>@=UZJI$b85=h9%LM-Zai;DHlNuUMz$Y%Ok$()3D4Ej
zq_OXd{Ibp7ue9ww+nGq3Z;La9%~9*%#ADQ$XwFl&t<OG+KV{VfmP8X*7+f5c?XMW4
z=ln!jDm9dJWIK0~!*yvAj=x|u{&lOD^fZQ`D1$g?jMzDyA-vU&R6vdv=;jhK*W3wJ
zj%TOt)$a|w9U9hEm%U-agLpHrJGy~sfgb23=FfV?H>i3A9LH!F@`*ddhM6dL^L$-j
z(l!d;DTJC7VT~HK!TLA@6jf_Q4EdBKKI=BH9wOotVak@|{#4HFri6Njxk;xK{!0Zp
zoL<~`JTl;Ozvem9*>^<D{cUe#|EH<%p9Qj%&A7~0So^<;tZmIML>uK7f%U;szMs~U
zXX$p-R;Oc>RM!|ja^A(ss`r!K598FePVML#Jtk_m!zst=Q;}?8;|<b;%ou(j<PqLZ
z#oAO?E7J;kOXWKrlfxNMUw|%J)BvBgXh)^Oc()x<;*l~SEJeLAEJFu|YSjw<=#y4T
z!pYe>#+<h~8!`HJWhZn*OzK5Y=@oA43kg&6b=5wnPlYpQy=owS)Dbm2X)L^i>6`hZ
zKvqTyVZ!*Rvev3Va`+FnG|Yt?wL>>+A3yt^{y<Xn`e6*4g2A<gMl3~+0F$81v<&%5
z84~p<To-?TZ7oeVQ@v7zW50U?INB`}SPR^flokb(cyM_z{448KZO)u9onvcP65Mnl
z)aUpJr?2_n{>>akcmREgd~<oqBEpNM?X1+%hd0b6f276kjV>T)Li>QF1rgP<b`jQ)
z!Pp#wd1ioq{y0n2Z>qjuCO$A4eQ5iNNJD)A?A_Du^^OC5D|x_{o??liB7_7XSMCFk
zsubvHpA;CWb(l#`S~w`LFCxWA7A**zmjz|z<(L&}Zlxks>8V$`wMy;J#wNbA!H3?x
zf&K`=3~k&;NJYv~O(@XwB#U@WOVz_4lAR)K3EyjSVH$T791hP%Yzr_g3z9H2fs-K6
zud$J0O{$5GE!JF@X{r%S{=Sr77o2T$CyndH$9Sc5b*ST=XJg5h*KC}e0Gc~k4DSw|
z$Zgd-_a?Vw<sO(sz#UHEvbGVjkhq|@BzXD>b5$Qz200z7_T(qYyr#<>EJ2HFw_pro
z#>EsNi<mLf*$gZZLcTKqo~kOhgYk>!S4^4Ng`jude{J(Lwsfyi)y2H<{?~$Co-IuG
z21P|}vr#ru;&CIsIdl}~9fYWq|5LeFUm*CnMm4@s&P-m(FTaP)ZNR!R;N#w||E?|D
za$^eqoK$<FX`racJ`n7H!y`Gv(hU&A*k-*+|1TljLf;p4`&2mj_eT9e+yP5?;gZ<d
zx|z)@jWE<0CxR~Jx0O!@!?<h*$_21$V=9-CBDh=qhth_V!vA)U4P9z@&zdTZ>V8dA
z+y~wT^nl3+6(B-I;3-(hod@<hR9*;8XapsyS+~KB1nJxZvp;Jn?@W=U5#00n+zZRy
z3+m2FYDYAc^wKMyK_A^>34#+>5E7_?-mpFf1!Tmt{I-sRWoBFZK%x_cz{v3HK<%_)
zY1tMuz0pqCR*>t-eV|beEK<y)bAiHZ2%j5i1k*;vc_cKQqzay|`jLYrTK9=hTx~x}
zFOBqzyESv<E0pb>HruY_lA!>TwZ;1=ZR?tvEWC4ljBN571>G#W*Q^UWI^BE2xW6AH
z!75{C>*2hD%0?0tp`)z3vL8kQc<uu1MSY8^KM}9uW*tG!TwtOzJJOL}O*1XK^6^8e
z1=T3lH{}jjIZM;dZ`s=vTec-Ejn8H-B}|5V{@sX2v_%v?Vs~5k&WT>W`o}zdr92NA
zBus5Z8s+4yH&PVLqi4p=+P*6GxioT`Deb-ai%k4DJpJz9G7En@KauzX)436Un_kc0
z(!^*)$&75vgY)(rZpMu94`M;)@LCdE8<ui3upK7cVX$H?Km4XRcj(&(UTK;`UNqdr
zPrEJ8n8cR2t)ko~_x4L&)>yCbOy<4ElZ^yj!6H4oezwIfPyx-BI~jgnnQZhr1gV%I
zDO`pcxJg>1GY}+9zpo<X_u_xJ_MhB=N5st#&-TFL;e3($cnU+A(&?0K@!9!>Thy^9
zUNUf{w5^54aY+{N^xaq5rTBFtVM>eQ?**gGu!_u!ko$<IGepx}dtaE4o6c`$pS6vV
z+lbdrkMn%RqIxTzj2rm#dzFgtpq?YGK_NRbAIOaS0T(vly>rl!dGPe+#G4hrqBtbT
zFw<}UNeE)n;2@2`mw6v`Bg*k?O5pL|ZT0;=-c0kIXe|Wot^um4Vs&+N9!?W##aoaQ
zGb16h-X7oX`KNt)*#a4~LSOe<dZ$g_HkK5o@xtaBJt(P!?&77x169v55z{@Hw0Okp
z?af3QKmS}OO$jpw4r#%%p`!OzPi5c3n8*?OG-@sHWt2-E;B+HP+56k7pmDeCEYq2F
zUM@ZWBAY#k)j`b`!s{DCbLtVSM%=v$$p-F~*<~}o#LPXZdi7uXPdV!U3^0+27v1QG
z;AbX6@fl^*>x?n=Wh0)Hb50{1L#;Ro9$Jh48`Cbsr#)JzPfTlwYIHTUX$3ky1k&3L
z+#YpS$|^h4SIawb$I%NpHtorfG0xoz2FEDK#WFI<VME!SMmjM{=%n0!yb@9}_{ozy
z(Oc?o=bi*h9NMLp-%@0LQ|0+7<7tR@lVE{DQtZ-Ki)~DMAVp4&5T;L_84ebfW9fKa
zK6cX(rz_aGEQ*<sUq&Qhng94N6eWW?*e~}$A>X@U%kfV=ad6U40VZnbVB<wl){<yv
z!IEZ$*OBo4n~qMT(6rU#RaL7&jMDEqX}ON7OXVrX@aZFsIV6kZtqr!Y(2Jv*spYG|
zReDqFZvs4YE)&d@+ZfD^e)Iwwv^>%+vjZVULwL#Rq0?4x4ABhBu3~yim!7!s&C*Bn
zjohOyMH?A{y-HJJdq{|ON$z}l$m8d~Fw`u|G}Pvi(G|~ItbU}63=YVv`UI>WnrG>b
z)=)^1%fy3-nv3S(-gY`~Vnz{ZGlCkKF*DpD7!g0^bEVG@&EJQQT>XZ;JZCcXyvx>b
z3DN=gRq@>+K+`k=$EmILP=3FM>89=V{ZcnIJOc9!$l+t*?i05$MGcyBU$k4C4xx*E
zgl_xnxx^g}^!H<4vm{)I2clTQ7FUajm`lXWgl)JerS~+Nx4}<mIHb%V-$7<a$ZzRE
zsj$@N^oTEi3uU~*?zi2OFI-En@Ryd|^Vi(f2RHXZbPvfLU)v>OHdHkVDeKjLxDOPL
zF86f?@^;Rt#xV3pJWwji!TRGn_jn<M?RF#*lwV#0Wm4EhJSw>)E&kJ&nztD?2o=dm
z)VvvO39_~MPbF^54He)qx|6RW?@|Kc@iiDR-$ux%EWB<<MG2prbkLKDgC(!}blkcG
ze}Vb!MbJ1jt2Ep~Ei5#6?E{gZ@^))r|5M}-r`Mz0GWls76sA{{dV9(^-bNhfJnhsH
z9A$Nbr_0s&MTV!_<6k<Mixd}dlEWl)3PYUYd?l8gO1hiN`r_q;%gaX`$9qD;9>@8m
z-fvk1i;%t)GdL$eg3<*KrE)Cq1A7LJ1E}g5+5=P~f~<NOwXA2$)`9*%hU4SN;0Pqd
zU7H~exoXTV#{XrUj->#tM2o})B06@<MTl_m(@5FS+L7Aq3tw~#em*?)6bvZC-(_!C
zeFbev!)J0yj5T_^6(4-T>suiA%>B&0+3jDNv4ko1u;ON^!vjxl`09nUm%>*{&SFQR
z;d<EFq?1P}Q6xR-^p)36YTM1pO$>AF*_F*~276hBRw_X?wUO`Rw@}NRdRDEKCnY)B
zv{dMK?4>2J^x+H;!`L)58jN^4X>mZUCZn^cTvTBbv-o+gHVs{Cu{KlIK-wiUWJ&Hb
z-=B0*)xQqDHJWz1j`hL@^d-y8Ay$h?!cJC^`5%Kvht@7jz_ezQa-|62@OY#zxOt+D
z;+cHNe&k`&je&sJ);v??^A=nNtGp=5R22Z(-_ChDrThi2xu9_qWU!><V$+|KU4NNV
zXaRiog}O50U1Iz7vF5>;w7sXr%njga==PffjBif!W%?c@Sqe=$&anv9Y#({1K#HE)
zfDktt*HlW?7wOzdJzr@--%Q`NhY`=9Ikgku?lzpgvdF7>wZjXAC>;06BWUV^W+4=_
z(u4&^I&L&WepyXitL;py?PE)qvMO;LxOx=*js3*#)_CWS(Z&H|7#dDo`l8QB^~!{A
zZ0-Z;u>bfG84D&E{u}p5!KliNZx4{)q=ZK_tWiQMMyf96L|5_WKCM=~?84Ux^`e--
zznwGRoLN($N>H*14STiUACoFNjQE_}IP(k}dg#xd*q*qcWpqgWPUkC3aw8vwfuP6-
zwtt4R(HtmVFRnWMf@v7R2W3J(=kblHvp#qhZSyUKClh&ckBTS&`FnrXuLN=^v9v<=
z*ysrIh^1RTOugj68JD1?Gpaw0VNpwB>tMw^U#sKb8$5O>u+Tcy?}_*wYw?2>kR_8U
z&RrOT!>w}45B?4n+c<jitiM7-P1471-paLFWULP6jUuzIKjGPRGsmm338JEsN`JCi
zu(nK!G4@8f<Jknn@s+mvkv%8i)|r6cH$aSZ@1$`yepg_|9zo64VtMl&DDksUrU}cd
zK4d%Ji(p>G=^It1YkNW*HRHO)w8cgPv8ar81Y7O;5+7bP9)kCGvT{h8Z)V@jCQdCd
zLUz7{qgFeKB%}bNkHJJNw>f@J-=f|sL5;d#yve^0Ks+($-9d*j3`F++uss_-nh(+9
zzKY1#;O9D%4ab6>uHR@$ySJ8(+IUSrLt!EAsm7yOB6;U@hP=q;4+C!!7<mU+`ta>w
zZadQsPSZru1Wj%sM_;NYztzW#D_d+Hfzj?^Z~bsH156`AxzkH__`BYjaEi*_)!{6>
zkxk*Gou)r4%q2H~{pa@JKg#%QJ*mx4kJ`DzhnvJt!rD$LZACCRtw=F}vDo*CsmE`Y
zc-I=<Jf7_P>y~}rgH^+}>t}X180^i&tKM*K+C|baGIzbxTG!A`mqfT{+|MpDnD}3N
z4j{7+9-@sd2-AeH_%9vkISg~8LG9Dx%nLa#uG<2$dKPg>unQlb(d`{aPefiM!O~DS
z#|?N3XX1&0aScWR4X0j-z0G0FMc2o@6SXUUNn~H?jpX>_+!N{ZaDhh2m9(^*$2Gz$
zhp8MgPpY9`%`}*{ZD@R^b6H`+WV`B-`Baac6xD?vH?Hfd9o;N~ot6H>47g;lgu_?S
zCAcM2wMEN{zCqM;1MTZQ^&h<*GJl;5Ezl+9WD|E<#OjkZC&q&53TNvh@>7$a9D7~f
zhq%~(Xdc`+4?1>R7i8hk6h+VHv(DNlO}v82C;=xL;+2~h;P)R_GuDv9K3YP1Mo)d*
zZpJA2UCv^E5bR(Jz#a%vUV;{2X-J*|9+gHR!Uz{Gs^hFscwMgFbr#p(L7Tq9!RifN
zFcqUfNi?N=Bsw=q@nePVWhQIZfBMmt4%X>=OqjdgGKEC9y`B$R@`lWK#4DW2I`XOD
zb?f1w$9K2De=AY~_XSU2^|<oZk2d&(tE)_wa_Nif(0WpHA+ks24>JA80=gC-I)YWt
zI9Lm|X0-J~gZrHDN3q+_*F0i2;m5t@&kC6X4+kF2^PTX9%-c|u7uzrti$<of&=y|n
z)J$IA7Asq+vqCEk3~?O%>lZkK!-qJ-5Zqb4KNpb<o|K@OXzaC>r18C(0Lk=DUK;{}
z8N_2JBhck<>8Do&V_24dC%X~XO2m9P@38z>l>`}Hu@lJ0R0ZNjOV0EGA~exJ*Ykx+
z@N*+B#g?tM+wqJ?O#SnRwjg=CzsVgS;|T>Qrl*LV_m5vvO=74IyA1~0^4{>af9tc(
z`M`XP@ZCy8RB$6lty{3fN~0k7bqYpa?Mw7LVLIB#8I=0MR#`9>w8?B}pkdwbb{2k0
z)$B$!A6~eEa_^D5>S^e^>n!!rl$M0?rPtA98QSzmMvBX7(pjDZ@*mQT=W8xMa%a#-
zzoCg{Xui@HR0B$d`4n|ZyA3LRb?b^-TI_By(w=Eg&_UDhLe$Ht3Eqbqs-jXK?ci0;
zrPwn7BMR%b;z-1<t<36kG+U`Om>A~AHXjtS?_5Om+v;Lk`)~>^>c}3DPX2PTO`A7A
ziuHHfrAFyGw8+Ikjl$koMvth)tP+{QVdtH5`r6X0;I%vKt7d>T0<0D!Ui4&)&_B>_
zA3WUkbJf2k<7Hzn5BT85`RGHIFejSUfWZ-zA6TNnsJuD+W=jq4qxz=I=IN#H1JU23
z#CsfI8(?Kn1(7u?7|KKmJS)T6bFQvV=6|*Kol#A--L^qMnh-#G3kZrzQ)$u?e32$#
z5ET%FARrwPsY*yF(wl&Qf)qhO5r}jJAyiS2BE1uW1*wTb*pTAgzUO_<J?A^$x#NyI
z#=U>;_akG6hm4G!{jB{wbImo^-1PTZ*iyd{o~XAdN)*^YD#G}f;W-R!IcjJ&N2vI@
zc;DJRT+1Bwg;zp3^h0k0B+Nqa1MLh@vV==^<Q{!{bxG1%(#4?7>c*Qm*$21#`tDk@
zML~}=_d!m^(8n92%N!O=Yq{(_7Kqjmh~POl#8Y()sQkdU$zeOI=oxJ?UNr-s7aVLQ
z&rC;l{8VO7O1oXzODckX#80VQ%%h}$QyRY*O58a_q2z=x<Q}|wZ)ID_O!7Po+nn6!
zqtDPlJ~m1YtwmLd#mE4w9<L@tPI(G#8k63&xjv#fAWqWti=dA{QmNz@gm0+J?>BO8
z0!d5<uLpmTm@GcncFn@hHn1!ZbsQup2Y`TOyo802+;|Xs{d<Yjn)bX|nDs*_z<m?j
z;fpy+&+GaHfxcyCdF}l&p&{vc2SY&W`nCO>OvWDW71qfwc9HZF;fjyTJ)eG8Gen5l
z>J5w2UJgrNz&1|__``}UFecazFZc<N#10*8FXZ-dJ=1VsrDDE2PAl8xOXuqy&T1?n
zXn$7sgWsWy_rdQHul*KQfC2-a>FXE}H<GNbHLL+M%LZ{*Gp!%Pvdk2$SlGi8Y~yI`
z!)$9dAWnaa7Nr;HvM$Ld8wa?QcHfFU%}AI2d31=zJ%tfmbMWrK`{0tAhdK+Rs|yZ4
zb|DYN`&Dftpv4s}^a^*bw<lO6lioDDL^{qEp9}IXvF!@?r?gBF&KE3upfvQ0RGn_0
zxq}shw3sXgj)3q*1v9o-^DQ3lQ7Gh@JD5(6?k<2tjf}1jlzk6N=GP!DxCx!#OT8UX
zAP|TX{6``M^)wK~<K_Q_PWdlH%H+!Lt}zfT^E8lq=@OG*NAgwzcJEj;xaR)n5&R!3
zMNn0G0i53p#)1)<0xn-B$G%ujYVmY^u)6=|<44h2Ue_AV)`O-v^S^9CjJ~6yzzQ!6
zW(Ico_f2lk8maaa8~Z(-E&iEv>CJBn<pyB0*&Dyp;>R6|y*$1pakMn1KIii36+YfN
z*n2Ss7VE~%|8MeUO@B<von~D8=ZUS50BU{kYp3uwO9V&(Oaj~l_BJ837zuU1*s^e1
z%%b6C<ia_T1+35<R&2|&q9tI<49v~<CMjh4Q)zlWSY<=<t(~4@-`TiuY-BtBXRIdw
z3-R`C!?|COh#j0QfFp->Vp(Xg5Tc*Rn)QrE=B;O2`HZ{v5^~<-Q<O`YsPwjD(L@AI
zFo_bK7=CQ<8yL7;+nT2gLvBCoa_7);*DD~JLmvi;NFMZHKFr{_OjVhqIRoRcZf>t}
z@Q&brbe%2hPLmDYSs&>Tw;Q+8)<8ucR7e0cNzq*>W{i}l6c4HNLG6gz3I!MPct}Mt
z4#-cFoAlF%<seAcVbHDNHo>(UcT>Oae{7=mACCM5NzJ8dQux7Sf9WXw0Yh*>Xko3`
zz-NFlb}gCl0tE-R5_eradO7usNA4r@#>Yevk*4Ag0?)Ay4d9RNI1b7kHgHp2UA%Rh
zzHka<IC=X(@NI-V2)C81W$U9%OVgbb)s=r}byk4p1|ZW9s=tjeM;Fj`!0ydus;|yE
zT%@H@qX5<_%Z>H(RxP<6E@nZ+P9MNq|AKho1iY}kJ9IVz;{ImAtS#9}ypR_)TbO75
z=?c;PJr#F?K0>!^1Ml0Yezex8u3!;O*nz)Khg;!dL(7QLQv3}oHQtA)cK4q}(6Pn1
zdwoDsJ6wF+@{3CStoMU(gYGK2S8uZJwbD@W&Y7lo^_KdYk-&cU=H}NA2yz~0kl!6$
zsA51IgC8}GTyiY755vb*$+~Q&-<SPq_j4<G6tST~?m_}u&hL<{p}tQi(7w9vpY!XQ
zCojrjL(@+8?q1>9W(uR&kgo2g2F-gfPbMpJ;}dl4l7tf6wQ2j-$<Oxq4$&tcV9389
z3kTXBGYh<zHuK0ho@J$GS&?{3fCmERN`K}_&;dU{X)X~iK-0LWDS2xtZus0U$Rw`r
z7sNHst)Bh}F4#Ri2O_JxyD`WeTqMZb-up17j=J9_*rc;rsu|*Fx%H+v1#w2i=6hUM
zAryC?0Rz0xpZ_s=an!f!z`jp{4XhLr7#LToBm)|VC@|(>jj%|(TOn(TY`2)2)S1bk
z_qO(-x17pOxU9GqN=HhCu9(WE-cQQ_KQkpx6z<Wxfl;`=`i+7eZ3w)!zKvkrtGTWw
zpXCOX;frFImiPr}R_Mk0_F^Tl9T#tWYOvPS;9K2Se)0-`Qu=1(<I^kVX?v3!>zAIL
zuJnI?USIG^AS6(L=>tuLr1!0Bi2d41$b$Sh4ds7p@M2Z9w%*6041MRs66PXe^Zs9)
z?0;k){(j#E#!%EF^wCV>(0Uf}d6mr11|{~gCZCEky&r}iu}x@T``TVq>`wT531!+}
zH>$J$m?Dt=`hAcr3*XAQB4{SKN9Di{%K}MM8NhM6oPQAr`tqq-Soe8KlVKs%?ZckU
zq~KmYRcU633T4b<6adYaT5%n4ZNx3ompQzmo4#oR)Atrnk2LCqf`)ykR#u2CeP%C=
z=nl42dF8<*)`Br?faj8=Str?|2{{&|gzC@sprk#1pvk7SJkwV4tQ|CPc#oK|rzp~^
zKuNxYJy6)QkBF5nn3%G&o3ZsWNjKnio)VSIFyl8>-&|x-!_C?g(z;s3J8A%h4!A71
zM6otJ$+0U+4T=vRSFB`qy3M1SNI%#E)^}9_HXPcV4831n4L|u?B8`u?-pCNQIJSPh
zo8;?&YmsNExsi6mG0!yF>Yv+YUAmra`=~hfHpf+YNWr+$F;#t-5U6$I`vvh0`x$mH
zja1lS=bRlBwHkiY?c^IBdnu<6@1U|ITpWl@qkJ%<WVCToZ-};0%!{?P3gsG^H{;GX
zbNy9vLz0uD=8f*zY*e#?=b7CG1$HSf#%On+*`f_V{kVl}7r!bO-3B?{^?`pwP9Rx_
z-^|Hnu`1lcG}7$fE2fdT5CR;5HUc{iI<R6uGtn?1F#lv&e7}U7!+j%sQWh))4T|4b
z+yEQ@GMXB>ushsHm4f46Juoq2EKz^zb4&Q4RAbtKMg*a}O?w=3)C<l=e}WcdWD!xq
zjy*xoE8UMQjEkSZOCCHIP`H$~(Dn(?Aq|;<D{+5U0oSL@TZP5by<W*ihweik54D_D
z#MCoQ|5gSH=HNmLO-T(U4r|(_*_j^NL*cg{l+(AnIe(yLdMU|Clpr8Tke*!-G$ib;
zX<7<kjV?Cjq;*E82CG~Kcy>M)Fw-NeFh}k>s?eYAgbgLYRo@=CruIg;KuN`72yT8_
zMs)Ux>rU@QFni8UAmf*I6K?gCWi4ZQRyEIW@Y;JlXvtg=(69)8zVyzBU5|YWB|-x!
zx2UT$QSxrwEt^g|gHYyflb9-X?lYcZGRMyH8+|-?edist6^)?68M81jsdg?*4#6I3
zJK-?DwjeZ$!*qoEhREc4^F86H`h4qxQ|wHr!sx*WgwOE`J2=i0=NSwdTzn653E@Pt
zke^hhzhbq8{lTst9KjcBh`AJ+`kv8`BH8F^UVW5=fQAw+h?hJ0f@3vdF`dysBHa-O
zYElT#5q0^yJ&VzlHdWG?^1^A7ZuzCgA2w&q{5rCP-zYR($G#5tVBR<taPGUKCRGl7
zw~k)i#tgW><9#1zna$+;qP_glt!43>RywXJUDGjPN)U7@*`27lVP6)ye65xQ?JVS4
zT@X!lXvUO=R~GkcJE1p3(=R;vmPn%k%T#v81il{DoqzcqLy=VWSh*^^=7Rm|#0!B-
z#TzD{&|NW*d;7U`To3rQ@`N-I#&jDS36rq2hK$1SggKn-6F>gQyQa)ajh#n#;p~Bi
zTbL7!*$|FeXtX0|J!tXJe=Vp!-G;=UNOjBvohIPmt~y;vX`nw7UPl~mJMIB88*k^X
zdrApfd&-&rsI<9-6_%Zv1Ubr(bqi82<wiY!sJp3VGqr4bhi=9{|6x^NnGaVRxs|*7
zmeUTRe*!Edip?vKg((l%mT)w|U~Z(;bw6Ut$zKq?Gqv#{n9>-`{3LObCg|6tT~)r(
zy42FjIlUQ_Vq-D-r8ZV=&h@ovff^)4C(DC*Ybdpe|97YM_E40zPx1o)Cv)w|)LYI7
zg?HjXjrr*dM&fhWAmgfVQ(s@n{N{6<ik9!5^6}ods^1O?KJYe)f&!HMNdZfJWes=K
z5n^jO+%JK&%eU;lQXFmFsn1U(>IpBj#t*X*q3aolC3|eg<(ngjaT)T#nzEP`&f1}R
zOcJWt2eDLv_dG3v@BmPxwyl7ISF6pGvr@^~eb}u=3T^<Oy1-6;f+Agk@_HhmKiLwu
z)OX&zdZKK-I2hwl3h%4_h-|?0qXhtqQZzr{TO4X&GA}2@%h>;l+{w9IGp{%pR`*5`
z*$N4fcKSCtugN{ID?ymS`IvkL5m#6%oZu^TLvlC%OPq1j^&cIxfv<l-8bL`{KIkr{
z3FkK>B~i>tDOGa^xslgwCaSYyMMO!KuxHNNdtH>QhCIqLm^G@9GR>9z38=WeR<huj
z@#)8=myfuYg_^{*AWAV=cC&`=zK3{?Y7F?<Kk+)9jErD=CBUTqA01ZydJhx(uQG8J
zN*Guxg@RIyo;4_4{=!$hx8L*%W(>x%gMxs04%Vr4j<U;vzVS0k&5EtVJUKc_d$I(8
zcGdvm9rvJd&X48{pG}=}&OFuOFX5FJzmX5N(yW3G8-E%|{h?}R2*FsQ6>C6AqaC<-
z)O+W0_wGx@EE}Aym%o3rSt?LJ{Ma9bc65h}Pi#67q$~J{-12y|<kD7h0dE3+Ntwjj
zIdA&8+R;pZY1jY5kGX^U!ng{Adtvg7d+$*6f(W@S`+MD$2Acu_pFLsb1ZVgvA*~Fw
z$58ly_3%54zD631D>Ef!cI?|E?OeOXxnNkam~<sd>Ft@%DcuF9b9XAtE!n<8HxMK&
z<s-ckt>n2>71oInAZ<pnwzq`oa9`E5I1(k$BJ2~Hp)c~a7JpM^dW+ITPgB~Vf(Zpz
zryaeqPR?qMYu@e@;ho5FYo6Dz(}ZYLMQF)^2;Fali*Lx{-Ea<jd?F~`LMssi0*=t5
z&E8VY9ZlZ7Q{)guXo~8w-qSnA`nPqJ<@|h)i=R^W55-gGOFyR%3JM&`KsvEPFg_rN
zcGbP%U+uYg2rlrQZ9}ZFr9yn7F)jOS_o*no1skxO?Y)JMUSMhhu90w^7`{f%BcB4_
zE7rD(rbFCl_VQ$01lHY`({C0<StlcUx!#MuWhT5Z$}bXS`{1wUROzGVpYw2E_yS$|
zQsi$(uDGW~tknkHc;UBvg#I*HM{;mn3Gx?2*RMh3DVF->k=S15vT=`!1ZEg3?#E!I
z9h*RP?gyvvTP&-HwzrhlL3e$9>2f#5b|kiEyq}Y{Xop=0%9&+*+U3!+J-PRzRaa*=
z)<@^f=cisdF~Qp{*@g=w$cu4k7ml2Bll22$r^oB5ufH*`)#uK$$*S*JP{G@#1M!Uh
zq+Yx*3D2|>j_OJ(DXjO>zS=Om8KwABO(nm_21u{#)mj!@!;>r)f#clYHrP?1Xd}%v
ziE%hdAwa#XIO53a?dRf6D(6n_oF$~*2C1~InG*t^LFj@yfbH-=e4ea_Qbjj@LADEs
zZB$$&mQc59PjMKI7iI3A*!u;+3kxvKU^_r*!smXPCfIMmwMCckUiSjNF*wxD(UW!m
z9Qy_Na}eT9A4O#qg0R-bFR@S(K`35EjnTAD()7&ML<j5S;(ZxC2V{<G4N^|_H%Z!$
z=F+dM8L^c@)On>rXLb=taAl!L%6Z_jt(dlAZcW{zYbA}tqdq!Jda>&P_G*s?AebB2
z4rPQ3DjsnNqZC>~6z)!gBA51v^%?Iv6(%k#j`R`wk8@rWIJvC1OGcF!IXNdgXGg#>
z^d_+6KaKT5M(h{f_famXos=oiGceB$h)*VeW(gP+%z7L!tA%5yKYnXQZX)2@9iU6c
z3Q0QFQfEfqQe7t+xLQKDuP!Py5B_+W?WFm+;)T4_&Tbp4h}1)-&xN3+PY)o!BtEsZ
z1Kn5@@oq}10HW=0&P?X`?Y`1El9pxjN@^#GdV|q#XT0G+%Eodn3^rqMzVb8M`6ks{
zb=R3vjRT!-cac{W=KT{vF9N`uau0|kH&MI*-*TD;8Q+n{3-tMNSQO+`huRSrhzqN8
zhrh%vtXe)_mJ4K-w(%D3z{MjPgzzpEH1E+3ngW3ve#swn1sYtruJ^fQ^TTs<yaMa$
zk?YAjxXM#{$A1IpkN9YOT}U@#3P#BxBjW3!2Ub!1ZIa#-wX^n!xCPm}Ze@lU>KR(1
zm`%<M9`=qQul+EBO&3x$v}^g;8#0b)ouR8&F=v|s>nafQ%=T1!$vvC@<!0aBhz18|
zsDxWvcKdb-^GF07jGd@Qj9w{u`g63_lEdYWAHDvSK+soc>A>-%4a@$zknDjU2~6iw
zrr7kn<q1kdX&4T)WG!%mR7U_S*j0f!cX#k2^L9Nhk{=&I17v$;MIr5-N46~$Hfe6j
z&<haScL1bYSQ-j@U>w_323_~oLl#ASa5u*rxbJ{*YvD|^h90T;=48#>zplkrZCoGf
zP00iLbWh(bSw++}C43=&^jAD=(v)5E9eG^j487>RiNU0}snpI+Fk9*GDJH;J;0J(M
zsH6k>D)92Toi~E7nxK&#oDIp7<Q#sI7ZyHOr%8UVFaLdVh0`32xRYMZK4OE9Lv92{
znR!2V<giSRHL|Ki9P$1!%HVkTlg3QP!BuF&<4@X0{ld;;SW<jtjvG^l`&(8NcI<wO
z8;W7jAY>#=LYIkZ8EXBh%t3im9Km;vm->m`lU_5u?3P?;`H*Q}yOmYR8PM!$My1k}
zo&j-&Z_EZ1t~8`>&ID>f#h*K2?PiLg`3PU+)Yf@=HC)QLd_db1eqxl%?3q`OCi^4$
z%4g<GaVPiJ(tIoEID-3zC|RE3PwL5u!06K8WTjq;HkTPp1PK1HqhtSC`2J7b%2!Sh
z(RU{A^woDQj-A$r@uS`7FVW%Tmt7$R+yJO??9tS2d?DJ9(_{Db$^OlPLh(bYFY-K9
zqHf<lJ?2Vw7+P1Q3Vg@bqN1yxy?v8(jXd~Du)*RI(^SzNnIzqHwhW}=UA>p9ikA{0
zYzOdXL4^AZbTW}o11lZ)1a0F~UFM?&C1<9N<%g6xUXK^mP!-J3@Ou~~@!3^M=PTaW
zEA~z0wa_r*)^T<5`}L#iPs5Uu5_p8pj+?0-UV$8ofJ=eF=zu;;MzrGrd(eu(BE&BQ
z?90~<|3sCDSxU9fbJ$EY5pBQ1mh37<?QHX;$FFbcnl9nRXjWrRVRsoaK$NfJh?YRC
zgB~PyRC>So7TDiAIWh*Z=>`eUt7$O^KFn#_Vba%V0{6k?Zk?dg*JDb)qUiP{@k)1=
z<m#{AMiZ&dnUG<&NK6-uSq1N&Hr>>@z(2l}${nhh)0|<{5jA(=OX8*k|I3B|#z5H4
zvEPl$m}`uAL@lf*4RoBH9xksVt&aeQW~M=m;mdJ&okCI&deV5&;AY{MUR`IrWqrR$
zU@Ls4Z<-~T@=5V@(Mm-T1R;HLkQH<qI1_3`<T&!+R;uC2t}(u7)MGsJZ%arRqXS!C
zo}#{iSfb#_3LRoQ&$9P@7vt=N-ZUM&nQnC@J;zgI26-_~&k&Pm<#f-IZ2-Xo7XpTe
zi#-DfVsDSHG7New;c(9qicN9t_RQS}&So!jP!9*YAgu2%Evj$VCLg+#2s#h{6Mtic
z2S7swfIkmlUpfNN^YvumM53XFw(rjSrdT&crnzrr0-3XCdxExq?*Jia@7MI#@Y87c
zgsv>C3&jQ8>9T3^wr@#Nau&`nz7c!XEsrbx!9~?5?{@sn#i>~eXfc2R*Pu@CVw%Qq
z!^^Fjepi_>^}O2^)i#Qyt}L(|*dN{$cp?O7VF)d5y!-_@Z`|<<0$74+aId$@WioPO
zlK4d<8-9o+9L4-Z{QFi+zw{172W(AL-&HyWVihdl&fv(Zq9Mtk&=X9rj;+FY8`$69
z98q*AuDeJLk>G86|6&LCMpu*&&bG7|=d%st1`WrQ)7<i8>gwW+#h^v2;ijJ_zWxP^
z&>*48Y7DlNP<}zy?U(Ny4x4H%U8VZG)<=GR*A@&$uno=oQ=1T@7r`G?g<0+&eXCyC
z+Asa(^O<u}?}x$Wpc%H8HZ#~pR2)cO%G0wLCq0)!Q~}VrQDigzI(9{UF9}>P%K+JG
zn&YH8M^Lao32!h)7_au3#)dp!^)KNH^xIOrk(9pH1ga}cz#2hp?qJq4Zxp|-AJ1Yu
zM*DFP7ciSyJ`>4EaczrIla-}*PY%LJc7;$np1>Bp<MhTC(S6`dxR9+J+m0b_cn89S
zlEik{qTxZfss8TFn10Vmip}F~soMsi2Jtv^6dlyqcw7N)fTy)YO0Du^8b_N^lJc1i
zMsgmj_$_=r$D)6ayS>oxGU^Ir0C5mT80reG+)+q>5!!S$e+R|8n$qvE*#FZcfIhw?
z5X`BtgaqWuJGeu7;G(0tA;fp?q^7uAx8h>dz6-P8Dr<k;(tGE?`sJzYZQPh4=ssBw
zx{L`V2PcnA7SQei9i?baz?6s*U)?O8HEr;8iEljglP{i#3U&r(JAih(7SRCucm{Mb
z1e<ym5(piBl#h238-GE*A7<W3&qh>XDvfu&2}8iqruGe+i=^FzHVxn%7=rifdUsU<
z*mCh|!6`D~bK5^9zR}0+s1tu?4+vg_7k=-;2zIK!7`xKqMaSbn7JTOUCOF?!$G}o=
zV%~WP?4*ttz5U-cB7bW|{_zMsK(9gE?1#>{EUjxyOfnmh%aT-P-y*8Y3})u4*{zIq
zL?R3SV3J52qG#Ye<_(CxF}qQ^;jRl(K+{qXuebJ<F29*8x0`PNg51+wOMk$zeWWe<
zq<EDJk}z;^t(&qSn`2U!iE518cse#CNIma=n)Lx(wVvRwFE0JrE&IVCFuVR|clv)f
zrqGA?z^CoYum2>7Hq2kp91AjwqbjEt&!Q)_vu1Cn8<k`ZU7oV3n9z7ZnCG&eRBLs&
z98uf~@>Ri3`MJk9+WQjNeoQ4--&Re#0NKl=Xwe_<Eb@5o@4mw(-cSa$l&>Gl-OukY
znQ5ADOdH<d<k5x*Pa>{Guw8(F)WmO(Im`tRZ98M6ftMW!$u_}7kER^4OC$BuxIpUr
zh&^RHhwA?Hl#Z*2)2OT92hU(VRKys=cqW`WQligqM^EKSI9$)NTUIkY{!@oj(SbhO
z7t+C!Yb*GK#e=#~a-mt=MKV5Sd+YGEc82Q-EPKG=kdyN^@OO=97@>*{gzRw}YAW2{
zg>t3RJX)M;YvK(BTwb(GmA68CrLur5a6QN*4HTqNEj{n$*D3~h&9EH4&bl1%EW=3T
z7o`2F@e-;rx%~h~4~Ff}Is#&Ynd<CCag?wbFCI0AOO7r#ihul3!n<^O@=Qx42>!-~
zfMdwSEnR!6Xef{UH&3@(`xHHg=10W*aro=&(9X7UoHSVT<oQ4nrraZ;qN+9Mcw{Z~
z-sSQcR~Nb0`>N@H4Q`mpJo{+2hPCBt7ptu+-*;qj1pMHn?$H-muw_Mk)lqv`(Y+1e
zUgpa3<Mo`VxDH@eo^WMd`UO#}y%^5adlT2ZZE>rs`<_MFi_-a?@N$Rly4kt*rR8i5
zQERKS(uukIowJwgb0;?0H^IP_c=a*z(^#^gQ>gU*RTNLC-vv@^f@jG}Zg^W(shxa|
zpS!0>>hT<EMM=bQ<_mvw-G3k7{_I|4(&bx*C4$W~NtGT6;Y2*Now6VFIYVRWYJ%2u
zercpUOL8*VG0qSoE%N=`cQBQ|JCP4s35dDBQ!*nai^;jX+m9)>9eVLLxwp>}!uf2&
z>W>H$QEBH^-axP+;5P%n`-F3^pVCM#ZcvG{7II_;!rT_5fj7h~5498xXx_R<sP~5!
zInS}f9kRn>`)bJ(dQH)R3dq9_K%(TaL8W_}0!^>SvcVgwrhMKA?fQJ^0)z}wUd)v1
zqrWYk;*c(i%Qj>TPJXt5?z<P(lKnXXsUwiVR&A=AyPNyJp#1;74B(Mn#a9Mj51RJK
z6)d=({vZ&2a@B}kdWS^}P`L1E3j|zGP$IZ5Cgm+)W%lMhxeQm(d|*kWEg%9lB1CW>
z=2;tB%rdk2<fD78Y~FJw|A|;k%!o*N!<%GNiRYmw`$DCMS(?Mw#Zn$>TGt)zsedn*
z=<(=GbNYk!)S}cK=NBH$N@;O{d3q}5^@&{<>k^!r8Y2Jb74SM$Q4|O{<XruqIQ0W!
zKtH7hNFyTCq)z04j?G9_$qVEwp<UhSt{uhGhku^8#rE4s_zvFopIcsEeFyHD(Sa%)
zc|VK43*ECv&9#1+wfmzcFX73TXLr~>cJHb&4blVvou#cv7>^o?6>uE0_q6k|*W~*-
zB=hA<!2F{h$Zf&-5uOtskTUa+4_c(>mQ0z)M!_=ex4e$R27C`LvQXmf<>#Rxwy(jN
zq8X|WzI<lE`97z1@8h96Cs@8^l(6v4;EM*NHqGc7BdfN#9LHkhJn|~|i?R^r#JBrv
z6j$JK7?~9vfQSe@fwryXz&D5un3tU!F_^+r&$}<QsvQ?#N?YdU8SO3*m<UW-$@bQv
zYE_k`#2UyxwT7ju(^eTD;nFQY5Y;C1z)xgWwmp)6cJ`*$qPY1H|E(-HSPEtbGma2m
zZ(~C<kqeWmzuK18zsgWhmp^Fs_~831VVh2vh)Lov2<@;p!MdE>LU~GJJewFg7Ppl5
zaH2t>7%%PW`^mU)OD)2GMTAlWT@3twv8+huZn-6s<58+JW{3|nx_BT6&yw`8X6)IC
zka7nTxb3Ekmc`=wlPvgv<E+UQO|TUtm!6357;vHRjWr;n@sfcS2A%tIlPN)EYyA2{
zEN3*BQ~KCF@|dEP!Fu~!%#%<UaWaY_*O?J&({m3IZ*Yv}Ia%|u{a%)p3C+;gdqL<E
z?Wz++dm`)R%W1?yQ(Kd13F4}g;T<?Py`W8$rb|9ShQ{i0J@pWqPAR4a7dk)qP}BC~
zi+=mrBIqB#CGGz^uS%p?D8dwo1e4UR+@0im*(on(TN}nJOB9Zr>VP>CC}*phk25d9
zB4!5bsGMkHWVDplo9wlvTTQ`R1iauNHpEx<NuKCzF+WmtSw0mC3zq3)elmef)RlY+
z%o^vIk`HFOSANfUFytA|*)Ps}hlhO?Dm_u}QFEj?qM`)C(-|QNdanQZuqa0zssUPf
z60S4rff3f;Utut35C({5w#-~r7X&-ZZu)l`_38{+L6~3QoQoq)dV)JtQrXPZHTzyq
zcW?eK7Cz;%6oDI8c-kSX_WuNP|93m_KN}yK+}1(zuT1V=e9Hd%Ra;ZW1o4P}o2FIe
z8>yn)uWfv2DN=d+5Q|-?Sx4>#Pcg}ts?TJmr}I;<%}-@~3^(1bo=Ai!WCQu6NqqoM
zaIo)`I$o<C8&hF)q`B#u*qJ_&Q(Y%mc=ffUvc^8?ixhB?+)_x?&Ssl9H7@r00deyt
zdv7ya<9-RRQ*3W`<TLJA`2OW2VGsl=3*R;g^FQMS*~4rj&gbAm3y}J(XknrS$l)_M
zb;z-OGe-+1DR)V}<P7_|hf+t|45DOrV(yB!a%>vGH^;Y=l(6P4qYdJ=I3H!AhkWB>
z1G$=4T?))&ye4HU%~JVGt;&<;Bb8O#8`=}61%w)F;)w}thF2w+^!cZ3?Z0PRQ?g$f
z`}*EW60q!kYOQuD#*$4R(sK>G<WZXxF0Q-E$~R4<6U;s+CJSCNwlXDf?kz5tZ;(l0
zgo2nL-8O|9^0CI}hK&hN=aj~~N3fjv_wS3Y+9uoJ*Jcjp6Z6G(O~==tjs^Bl<#Oej
zJ%mcsyy~#NJSIlem8ELZ6i9+I^Pyo&Td~_orCQfE>Zh^|_ci7cA+(v_{**V<;<5FG
z8|o?>9Ar(CysmwD%Hs*}TuV-WxO++~@)GojTqmu!didZS@14rPXID6eo%8?Q=k-7N
z$Nv+0{r~*`Huo9hiwy@0soJ4N^Gb@d-BQ{UpQ*68w>myt7I$xl9=dWz*2)M;&{AXi
zf+t<_o8?rAz%co-0gi4hKo&97q9LXgH*oX`^2g)%5}R5wY*up=oGJ*D?_fLhjsljx
znB%ky_58rd5_(?q!joH!0qzM?JvV6(DcuR%-cFk~qnv0RMmr$hA(M9%h`6NO7Wy-}
zsjzUE?EU0G<&xx7fkP@Us7H*=LJN$Yo>$lw>K|t!IA_1c-A4#*LsJ&m8_DjmjzkBs
zubxk1?H`$&e?1|lA%pboyB#*^{r}(s{s&|KzbD2pKzsK5$on-34=&X=?UvdZUZA`7
zlFE!Gu^i0xy!~K{G!{TL)TdtO@)uzAUHa!PZ>if8HK(Y9cUhP-M<Yar*#@x<xNel-
zZvY7g`80OMVWV9~F4Fdw>K!YSh76yLu*V|Uel^i6lC~Mfc615)Bdi;ai_woiR7pFr
zG)4P9cx+5vOMt_#`t(neh3Hp~U1aqDN+?(z7N@F(axQjBL0|EfF)yG#GQDoik@z4R
zLXl+CSMVmry@Nt&GL6G-WR@IG^;Ww@!x}RWbKKF7-S<T#+1TV8ARz))C*J>0Y5YH?
zJO4lZ4V9&rVLf47jE|^8TW@WMrd=jXAdoUT;JucNt|{8yMCzaM{)r;E(*!^cJ7Kxi
zjEL(|fbxfy6SIr$&bmhR9jpE1dnb5y=HNrVUYmA#`LS=C_?bM)AyUYOCox49boz?d
zJ?AuY^F>3{q<Mk*{R2Ho8-ae0J|74dut@-&HnQPR?C|Js1i=o{>dR_OuGkLuHqNxY
z2%rUgXaE5m8}FsrfcN7Ew9fcjCL*^^Cq|qsGwG-3)>xNIIqRHwLXz`k4gcxR%mOwq
zFw~a%VQlynPcoLlV*d+5&IMBhmTQ1EyL^w7itOly5DjE;#UT6kl;fmy5_0_osmm;I
z`qU?xHxKy+_u_J=cl#%stTE9g=ax3ay386_eSLhF8{?&(^8JBZVfFa8{q*mB_5a1u
zBvhb&S{Uu~M&W^x@$a4pEXi_ccFhFv#}sm+RjaPsdF>|4l6jg+j|{-Gd=~GWJ?C_2
zy=~b8UY?2|^wI<-Dc%4F!Cr+k)VR}8v38Uo{w%AKpz3e|vt5hN&k5Eu-RDtgn*g8A
z0OLd(R4#huFqFy3yE%2O-6MKqhpx>h@telUXI{bo<CTSghVpe_HW{(3Pxglwx<rG;
zA{%CJs_RN&6Jo=Kq|?a|`lPO>^9PAi<&Sf0EaO!hF=Q4YRSZPD)<arXPckHGNMVfF
zLSZmbFa#ewB#OI22b+`#PY^s{h5}7cSVr-z?r)Y>9DUa4DOIj$Ay)Zlu3gS6ATqUe
n021!ldSdL+Un%$RwEJJLe_exr#lXK};9oKDe<ud8zo!2O$h^+V

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/packages/gluon/custom-loss/images/inuktitut_1.png b/docs/python_docs/python/tutorials/packages/gluon/custom-loss/images/inuktitut_1.png
new file mode 100644
index 0000000000000000000000000000000000000000..30a3579ba514d1e1e55214a4e4cdd1d78cf76a8d
GIT binary patch
literal 2031
zcmeAS@N?(olHy`uVBq!ia0y~yU<zYkV3g-z1Bx_Db*KR;mSQK*5Dp-y;YjHK@;M7U
zB8wRqxP?HN@zUM8KR`jt64!{5;QX|b^2DN4hTO!Gf}H%4oXjMJvecsD%=|nBV?6^s
zBOQf|l9B=|eSL%iz5JqdeTxR`wG0gGKAtX)Ar*7p-o2Z*+D*bCF!<@;_?h?Sn7o`g
z*<!;_>1(^zY4s_)&CWkoG3)tHMur1b|7B_!85kA|0NpH@$jTrfh?OyM`(phE{P~)#
zzi0OCYv}mO{qFhcucwc2F(@dlVPH@?G{PC5%D%7`@Le|f`{VwZ&wCC}W%zJv)*jL9
zzgB9n(A+3`;<R1=vfeJnYnDa3-&<e%Rq6u@IkhmIPybvVXR<vgO`jW7do=2QHL@P9
z^T%$v9b>qD``hn6=C|vOH!?AF%!ruyIfePoO^?Z|(q%IK&+8OraBzuWWN^u0{ZwHy
zufN1yKApK&`C6BCzVp@p-<H<fAGeza)%kJhcl8OT{)_o64@LNF+8<V9=Dud?(Wvw6
z!rBZC9i5`~TIb?t#vI%_GtH*_s^(Q=wQydr%m$#nH@H&5_s!#ep03R?4QkxRj&Q>R
z6+WxJrM{J(jO30@r=ACHyFa_>_06o``CqClUp_Z`_54xBafme=l#I`RezkmR<}8^7
ztPeJ|z}znrryeesQ7#~K?(|>FWl6mIjiIiZ9re4dY<*HgaiP54<Ko@?5GN&x#~o!j
zAj<)AImmq$d(N$^=@oC7I1>~YK5Hz_Tnaa=U}Zi~_C{?@auO_UM>opkooy`JugURy
zlPLdvs81Lf_}02ZV`NTOxWR$a9Mgvd{O?!)u>Li>)&LalV2jo|_+`GFrch8Dw|J9n
z_UkgJ7M|mm_4eeQ<*wTPEH3ZIq=Wmw;-GAiq&*?{vvt!3`FJTw@1Nyc`u3dzWt*g=
zqaXH6m92YzhW%FMImWM_k*&~MY!UI=!TOJ1<@y_8IX`61pM`39D#D&Eo6-Jx_v5{l
zPfT9*&HpY2bwJFl;_w5abAHB5*}6Uz$*(*IXD?!`OS_#9NmjzzZJWP1>~?;0z++A7
zab!E&ww8H+`23&w0oR(y<Iq4kXfBh%e(sP}2`@NJ0yT&4vAMESJAwE0vDz7+P=>@^
zq4sB+^>QcuFF*Ot!C2u1l8}jeJ`Jcjfg33`R|xY(=bx6o#aLkja<vgksqyH;uLm+w
z?B^c+JuPd$)_Whzp{dY#RCvHC9rwI0F$L;~h!er)4E10qLZjs0@o!=bI_Yq4<1WYW
z6mi(4q-QUCY3O_T;`D=j@N|lm@#m7w`Tojz{rh<QJP;uR4WcJ+UvM)ttS)MpyC3W+
ztR_G*@t)&Z1^0l-?YAL3&4Cg(Je7|?#va*P#(E$3c!mQ`*YqFQ!&4OYstS8N;;7f~
dD4tl)sD9dh@+|K?(}9&UgQu&X%Q~loCIB~3jUfO4

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/packages/gluon/custom-loss/images/inuktitut_2.png b/docs/python_docs/python/tutorials/packages/gluon/custom-loss/images/inuktitut_2.png
new file mode 100644
index 0000000000000000000000000000000000000000..b3cc71eb23f7bec3077776827f8f85967fbe30fc
GIT binary patch
literal 2217
zcmeAS@N?(olHy`uVBq!ia0y~yU<zYkV3g-z1Bx_Db*KR;mSQK*5Dp-y;YjHK@;M7U
zB8wRqxP?HN@zUM8KR`jt64!{5;QX|b^2DN4hTO!Gf}H%4oXjMJvecsD%=|nBV?6^s
zBOQf|l9B=|eSL%iz5JqdeTxR`wG0d#Y@RNTAr*7p-t{iJ?Z)65XuR}qe&oy_i$gNH
z81(G!TBOhFS`%Sd`%@~**3RC(o`GS*UHkNUCI*Jh0zg*_CbBXJ2x4VS?B8tPIR8!%
z*Z0pBzlA$0{x<#o`TF(rqg)IMN^2Mxln#wd#;3w>>I@8vD{bU%9{=^{&lx+h=bIxK
z8C+UK85~?-j3b9%Gkj?K)L8l~bt4l)N5mS22d#UjuUapE|0?%m9LkT}YQI)*+wiWc
zt$GvOoWKXZIWw2sUCngo=*{(0_t({~-~AXYs}m-C^R?s2bouMMH5q=`cLQxdl4ROw
zyl?6CVuk}g+v3)KxNBeh^-wL)Zb)1`(kKWBp9wTWu8);*hc#43$7-FB^%V<bB~IR)
z-nj44*OC}9ZH9)94M100aP8T!x9|VGxzpS&>R2MO`mI;r`&&N!->rR)88%>5M_)Ca
zT6)4-hryx`o-F#9)I;SCuC*2V^Xt8Z5R2Q^<F8-#%fFW2{GqBU7phBXT@ACrffws4
zYJ2hpW=#g^P1<O6Q}n*;-`;fQhKm-w4>FLPA#>%X@YiYD5)3~MCyNzt1O<*xnEH;-
zho|iGm9RK;>w4sR?;LPIbAa5w_yF_S3YHyD7kV@N%YL2vCj9U2M}9h>G~O+`{39Q?
z#@Q0B2hTbBPA37={>BK|2YUCvy}lIifG4hahY%=Oz|knf8h0VXy@5el>fzRF=LKc@
zA+EgD*{*s2`R(S$rD?mO67%Dd`;jbmD5|Sd*f4$f9qu1JyP1wBK;m96sjWPi;mvtb
zp6h8K5vU_)v~w{=y<3x{0u7Xud%lbtuHDj>xN~fuJS28l50t#yc4&vq7b~#yL3y=M
zgyVp!<u$9e+c%;i8nrfjiJe-`%J4gCcUkYhUvHjs%sUVA88}zFH!>W^&r7|34B}VD
z0?qWe%^%)emn+?>nV0<C;dc?l5H5x|iQ1#LiddOXN5T>eFqbDRJpTA$MDKD?;s&K@
zp4^wsPo7>fxs^IAdwqh$+x?$?kmBi1!pkXHc7N~5@i+gfZGUb3$?-$_dd~7{f!fn|
z!}5~r&q5qwxc6edFWa~0&tC<C;}UFm-~+Dm>Jx=-@9TJde--#v|Gl~AF~3gP_E2~%
zol}js5vW=gKL7jN{p$+Y7w@l=J=y%b9-O|#wD~^1iaoR9J)7-v*4w?mp4YpH?P^0x
zllN1k?w*fxPu-WsaQdfZ(`|^Z)pPIcyte+3|AzLT6}h(4PCHMB=glL556op`&pw^&
zdF#4N!&XT034N2t|MKjM-RmD(cTfAzpHOq{wSCON4|dSl;@)|TF=6NKn1kSmheWdj
z#~GXK!&Be<RroS3`S0#Md~5dh{#`v&K4Uv188h%azq|FxUDqS#3v{+W<!YM=5B^6Q
z0u7f%_?`A|`Yw32hd+M(#_!h)p^1g7;d1Xcg)K|F_9KU&`I)^8$3H22*>Ught+Rj1
zS3^=P(}PL#)=H(Xi!ffVv(@^49M}S2thIg2;=GxCcGu@$haianlIK|xxcc7gV1nlZ
zlyVJTz>P9s<pfIjJbC=a|H1O(H|H}P@QT~ta37v+u~)HpYG0D1Ce||+uG_LJ`;F*B
QV6%e3)78&qol`;+02%iRBme*a

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/packages/gluon/custom_layer_beginners.md b/docs/python_docs/python/tutorials/packages/gluon/custom_layer_beginners.md
new file mode 100644
index 000000000000..958736b99090
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/custom_layer_beginners.md
@@ -0,0 +1,247 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# How to write a custom layer in Apache MxNet Gluon API
+
+While Gluon API for Apache MxNet comes with [a decent number of pre-defined layers](https://mxnet.incubator.apache.org/api/python/gluon/nn.html), at some point one may find that a new layer is needed. Adding a new layer in Gluon API is straightforward, yet there are a few things that one needs to keep in mind.
+
+In this article, I will cover how to create a new layer from scratch, how to use it, what are possible pitfalls and how to avoid them.
+
+## The simplest custom layer
+
+To create a new layer in Gluon API, one must create a class that inherits from [Block](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/block.py#L123) class. This class provides the most basic functionality, and all pre-defined layers inherit from it directly or via other subclasses. Because each layer in Apache MxNet inherits from `Block`  words “layer” and “block” are used interchangeable inside of the Apache MxNet community.
+
+The only instance method needed to be implemented is [forward(self, x)](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/block.py#L415) which defines what exactly your layer is going to do during forward propagation. Notice, that it doesn’t require to provide what the block should do during back propogation. Back propogation pass for blocks is done by Apache MxNet for you.
+
+In the example below, we define a new layer and implement `forward()`  method to normalize input data by fitting it into a range of [0, 1].
+
+```python
+# Do some initial imports used throughout this tutorial 
+from __future__ import print_function
+import mxnet as mx
+from mxnet import nd, gluon, autograd
+from mxnet.gluon.nn import Dense
+mx.random.seed(1)                      # Set seed for reproducable results
+```
+
+```python
+class NormalizationLayer(gluon.Block):
+    def __init__(self):
+        super(NormalizationLayer, self).__init__()
+
+    def forward(self, x):
+        return (x - nd.min(x)) / (nd.max(x) - nd.min(x))
+```
+
+The rest of methods of the `Block` class are already implemented, and majority of them are used to work with parameters of a block. There is one very special method named [hybridize()](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/block.py#L384), though, which I am going to cover before moving to a more complex example of a custom layer.
+
+## Hybridization and the difference between Block and HybridBlock
+
+Looking into implementation of [existing layers](https://mxnet.incubator.apache.org/api/python/gluon/nn.html), one may find that more often a block inherits from a [HybridBlock](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/block.py#L428), instead of directly inheriting from `Block`.
+
+The reason for that is that `HybridBlock` allows to write custom layers that can be used in imperative programming as well as in symbolic programming. It is convenient to support both ways, because the imperative programming eases the debugging of the code and the symbolic one provides faster execution speed. You can learn more about the difference between symbolic vs. imperative programming from [this article](https://mxnet.incubator.apache.org/versions/master/architecture/program_model.html).
+
+Hybridization is a process that Apache MxNet uses to create a symbolic graph of a forward computation. This allows to increase computation performance by optimizing the computational symbolic graph. Once the symbolic graph is created, Apache MxNet caches and reuses it for subsequent computations.
+
+To simplify support of both imperative and symbolic programming, Apache MxNet introduce the `HybridBlock` class. Compare to the `Block` class, `HybridBlock` already has its [forward()](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.HybridBlock.forward) method implemented, but it defines a [hybrid_forward()](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.HybridBlock.hybrid_forward) method that needs to be implemented.
+
+The main difference between `forward()` and  `hybrid_forward()` is an `F` argument. This argument sometimes is refered as a `backend` in the Apache MxNet community. Depending on if hybridization has been done or not, `F` can refer either to [mxnet.ndarray API](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html) or [mxnet.symbol API](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html). The former is used for imperative programming, and the latter for symbolic programming.
+
+To support hybridization, it is important to use only methods available directly from `F` parameter.Usually, there are equivalent methods in both APIs, but sometimes there are mismatches or small variations. For example, by default, subtraction and division of NDArrays support broadcasting, while in Symbol API broadcasting is supported in a separate operators.
+
+Knowing this, we can can rewrite our example layer, using HybridBlock:
+
+```python
+class NormalizationHybridLayer(gluon.HybridBlock):
+    def __init__(self):
+        super(NormalizationHybridLayer, self).__init__()
+
+    def hybrid_forward(self, F, x):
+        return F.broadcast_div(F.broadcast_sub(x, F.min(x)), (F.broadcast_sub(F.max(x), F.min(x))))
+```
+
+Thanks to inheriting from HybridBlock, one can easily do forward pass on a given ndarray, either on CPU or GPU:
+
+```python
+layer = NormalizationHybridLayer()
+layer(nd.array([1, 2, 3], ctx=mx.cpu()))
+```
+
+```python
+[0.  0.5 1. ]
+<NDArray 3 @cpu(0)>
+```
+
+As a rule of thumb, one should always implement custom layers by inheriting from `HybridBlock`. This allows to have more flexibility, and doesn’t affect execution speed once hybridization is done.
+
+Unfortunately, at the moment of writing this tutorial, NLP related layers such as [RNN](https://mxnet.incubator.apache.org/api/python/gluon/rnn.html#mxnet.gluon.rnn.RNN), [GRU](https://mxnet.incubator.apache.org/api/python/gluon/rnn.html#mxnet.gluon.rnn.GRU), and [LSTM](https://mxnet.incubator.apache.org/api/python/gluon/rnn.html#mxnet.gluon.rnn.LSTM) are directly inhereting from the `Block` class via common `_RNNLayer` class. That means that networks with such layers cannot be hybridized. But this might change in the future, so stay tuned.
+
+It is important to notice that hybridization has nothing to do with computation on GPU. One can train both hybridized and non-hybridized networks on both CPU and GPU, though hybridized networks would work faster. Though, it is hard to say in advance how much faster it is going to be.
+
+## Adding a custom layer to a network
+
+While it is possible, custom layers are rarely used separately. Most often they are used with predefined layers to create a neural network. Output of one layer is used as an input of another layer.
+
+Depending on which class you used as a base one, you can use either [Sequential](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.nn.Sequential) or [HybridSequential](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.nn.HybridSequential) container to form a sequential neural network. By adding layers one by one, one adds dependencies of one layer’s input from another layer’s output. It is worth noting, that both `Sequential` and `HybridSequential` containers inherit from `Block` and `HybridBlock` respectively.
+
+Below is an example of how to create a simple neural network with a custom layer. In this example, `NormalizationHybridLayer` gets as an input the output from `Dense(5)` layer and pass its output as an input to `Dense(1)` layer.
+
+```python
+net = gluon.nn.HybridSequential()                         # Define a Neural Network as a sequence of hybrid blocks
+with net.name_scope():                                    # Used to disambiguate saving and loading net parameters
+    net.add(Dense(5))                                     # Add Dense layer with 5 neurons
+    net.add(NormalizationHybridLayer())                   # Add our custom layer
+    net.add(Dense(1))                                     # Add Dense layer with 1 neurons
+
+
+net.initialize(mx.init.Xavier(magnitude=2.24))            # Initialize parameters of all layers
+net.hybridize()                                           # Create, optimize and cache computational graph
+input = nd.random_uniform(low=-10, high=10, shape=(5, 2)) # Create 5 random examples with 2 feature each in range [-10, 10]
+net(input)
+```
+
+```python
+[[-0.13601446]
+ [ 0.26103732]
+ [-0.05046433]
+ [-1.2375476 ]
+ [-0.15506986]]
+<NDArray 5x1 @cpu(0)>
+```
+
+## Parameters of a custom layer
+
+Usually, a layer has a set of associated parameters, sometimes also referred as weights. This is an internal state of a layer. Most often, these parameters are the ones, that we want to learn during backpropogation step, but sometimes these parameters might be just constants we want to use during forward pass.
+
+All parameters of a block are stored and accessed via [ParameterDict](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/parameter.py#L508) class. This class helps with initialization, updating, saving and loading of the parameters. Each layer can have multiple set of parameters, and all of them can be stored in a single instance of the `ParameterDict` class. On a block level, the instance of the `ParameterDict` class is accessible via `self.params` field, and outside of a block one can access all parameters of the network via [collect_params()](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.Block.collect_params) method called on a `container`. `ParamterDict` uses [Parameter](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.Parameter) class to represent parameters inside of Apache MxNet neural network. If parameter doesn’t exist, trying to get a parameter via `self.params` will create it automatically.
+
+```python
+class NormalizationHybridLayer(gluon.HybridBlock):
+    def __init__(self, hidden_units, scales):
+        super(NormalizationHybridLayer, self).__init__()
+
+        with self.name_scope():
+            self.weights = self.params.get('weights',
+                                           shape=(hidden_units, 0),
+                                           allow_deferred_init=True)
+
+            self.scales = self.params.get('scales',
+                                      shape=scales.shape,
+                                      init=mx.init.Constant(scales.asnumpy().tolist()), # Convert to regular list to make this object serializable
+                                      differentiable=False)
+            
+    def hybrid_forward(self, F, x, weights, scales):
+        normalized_data = F.broadcast_div(F.broadcast_sub(x, F.min(x)), (F.broadcast_sub(F.max(x), F.min(x))))
+        weighted_data = F.FullyConnected(normalized_data, weights, num_hidden=self.weights.shape[0], no_bias=True)
+        scaled_data = F.broadcast_mul(scales, weighted_data)
+        return scaled_data
+```
+
+In the example above 2 set of parameters are defined:
+1. Parameter `weights` is trainable. Its shape is unknown during construction phase and will be infered on the first run of forward propogation.
+2. Parameter `scale` is a constant that doesn’t change. Its shape is defined during construction.
+
+Notice a few aspects of this code:
+
++ `name_scope()` method is used to add a prefix to parameter names during saving and loading
++ Shape is not provided when creating `weights`. Instead it is going to be infered from the shape of the input
++ `Scales` parameter is initialized and marked as `differentiable=False`.
++ `F` backend is used for all calculations
++ The calculation of dot product is done using `F.FullyConnected()` method instead of `F.dot()` method. The one was chosen over another because the former supports automatic infering shapes of inputs while the latter doesn’t. This is extremely important to know, if one doesn’t want to hard code all the shapes. The best way to learn what operators supports automatic inference of input shapes at the moment is browsing C++ implementation of operators to see if one uses a method `SHAPE_ASSIGN_CHECK(*in_shape, fullc::kWeight, Shape2(param.num_hidden, num_input));`
++ `hybrid_forward()` method signature has changed. It accepts two new arguments: `weights` and `scales`.
+
+The last peculiarity is due to support of imperative and symbolic programming by `HybridBlock`. During training phase, parameters are passed to the layer by Apache MxNet framework as additional arguments to the method, because they might need to be converted to a `Symbol` depending on if the layer was hybridized. One shouldn’t use `self.weights` and `self.scales` or `self.params.get` in `hybrid_forward` except to get shapes of parameters.
+
+Running forward pass on this network is very similar to the previous example, so instead of just doing one forward pass, let’s run whole training for a few epochs to show that `scales` parameter doesn’t change during the training while `weights` parameter is changing.
+
+```python
+def print_params(title, net):
+    """
+    Helper function to print out the state of parameters of NormalizationHybridLayer
+    """
+    print(title)
+    hybridlayer_params = {k: v for k, v in net.collect_params().items() if 'normalizationhybridlayer' in k }
+    
+    for key, value in hybridlayer_params.items():
+        print('{} = {}\n'.format(key, value.data()))
+
+net = gluon.nn.HybridSequential()                             # Define a Neural Network as a sequence of hybrid blocks
+with net.name_scope():                                        # Used to disambiguate saving and loading net parameters
+    net.add(Dense(5))                                         # Add Dense layer with 5 neurons
+    net.add(NormalizationHybridLayer(hidden_units=5, 
+                                     scales = nd.array([2]))) # Add our custom layer
+    net.add(Dense(1))                                         # Add Dense layer with 1 neurons
+
+
+net.initialize(mx.init.Xavier(magnitude=2.24))                # Initialize parameters of all layers
+net.hybridize()                                               # Create, optimize and cache computational graph
+
+input = nd.random_uniform(low=-10, high=10, shape=(5, 2))     # Create 5 random examples with 2 feature each in range [-10, 10]
+label = nd.random_uniform(low=-1, high=1, shape=(5, 1))
+
+mse_loss = gluon.loss.L2Loss()                                # Mean squared error between output and label
+trainer = gluon.Trainer(net.collect_params(),                 # Init trainer with Stochastic Gradient Descent (sgd) optimization method and parameters for it
+                        'sgd', 
+                        {'learning_rate': 0.1, 'momentum': 0.9 })
+                        
+with autograd.record():                                       # Autograd records computations done on NDArrays inside "with" block 
+    output = net(input)                                       # Run forward propogation
+    
+    print_params("=========== Parameters after forward pass ===========\n", net)    
+    loss = mse_loss(output, label)                            # Calculate MSE
+    
+loss.backward()                                               # Backward computes gradients and stores them as a separate array within each NDArray in .grad field
+trainer.step(input.shape[0])                                  # Trainer updates parameters of every block, using .grad field using oprimization method (sgd in this example)
+                                                              # We provide batch size that is used as a divider in cost function formula
+print_params("=========== Parameters after backward pass ===========\n", net)
+```
+
+```python
+=========== Parameters after forward pass ===========
+
+hybridsequential94_normalizationhybridlayer0_weights = 
+[[-0.3983642  -0.505708   -0.02425683 -0.3133553  -0.35161012]
+ [ 0.6467543   0.3918715  -0.6154656  -0.20702496 -0.4243446 ]
+ [ 0.6077331   0.03922009  0.13425875  0.5729856  -0.14446527]
+ [-0.3572498   0.18545026 -0.09098256  0.5106366  -0.35151464]
+ [-0.39846328  0.22245121  0.13075739  0.33387476 -0.10088372]]
+<NDArray 5x5 @cpu(0)>
+
+hybridsequential94_normalizationhybridlayer0_scales = 
+[2.]
+<NDArray 1 @cpu(0)>
+
+=========== Parameters after backward pass ===========
+
+hybridsequential94_normalizationhybridlayer0_weights = 
+[[-0.29839832 -0.47213346  0.08348035 -0.2324698  -0.27368504]
+ [ 0.76268613  0.43080837 -0.49052125 -0.11322092 -0.3339738 ]
+ [ 0.48665082 -0.00144657  0.00376363  0.47501418 -0.23885089]
+ [-0.22626656  0.22944227  0.05018325  0.6166192  -0.24941102]
+ [-0.44946212  0.20532274  0.07579394  0.29261002 -0.14063817]]
+<NDArray 5x5 @cpu(0)>
+
+hybridsequential94_normalizationhybridlayer0_scales = 
+[2.]
+<NDArray 1 @cpu(0)>
+```
+
+As it is seen from the output above, `weights` parameter has been changed by the training and `scales` not.
+
+## Conclusion
+
+One important quality of a Deep learning framework is extensibility. Empowered by flexible abstractions, like `Block` and `HybridBlock`, one can easily extend Apache MxNet functionality to match its needs.
diff --git a/docs/python_docs/python/tutorials/packages/gluon/customop.md b/docs/python_docs/python/tutorials/packages/gluon/customop.md
new file mode 100644
index 000000000000..194056f35ab4
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/customop.md
@@ -0,0 +1,227 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Creating custom operators with numpy
+
+In this tutorial, we will learn how to build custom operators with numpy in python. We will go through two examples:
+
++ Custom operator without any `Parameter`s
++ Custom operator with `Parameter`s
+
+Custom operator in python is easy to develop and good for prototyping, but may hurt performance. If you find it to be a bottleneck, please consider moving to a C++ based implementation in the backend.
+
+```python
+import numpy as np
+import mxnet as mx
+from mxnet import gluon, autograd
+import os
+```
+## Parameter-less operators
+
+This operator implements the standard sigmoid activation function. This is only for illustration purposes, in real life you would use the built-in operator `mx.nd.relu`.
+
+### Forward & backward implementation
+
+First we implement the forward and backward computation by sub-classing `mx.operator.CustomOp`:
+
+```python
+class Sigmoid(mx.operator.CustomOp):
+    def forward(self, is_train, req, in_data, out_data, aux):
+        """Implements forward computation.
+
+        is_train : bool, whether forwarding for training or testing.
+        req : list of {'null', 'write', 'inplace', 'add'}, how to assign to out_data. 'null' means skip assignment, etc.
+        in_data : list of NDArray, input data.
+        out_data : list of NDArray, pre-allocated output buffers.
+        aux : list of NDArray, mutable auxiliary states. Usually not used.
+        """
+        x = in_data[0].asnumpy()
+        y = 1.0 / (1.0 + np.exp(-x))
+        self.assign(out_data[0], req[0], mx.nd.array(y))
+
+    def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
+        """Implements backward computation
+
+        req : list of {'null', 'write', 'inplace', 'add'}, how to assign to in_grad
+        out_grad : list of NDArray, gradient w.r.t. output data.
+        in_grad : list of NDArray, gradient w.r.t. input data. This is the output buffer.
+        """
+        y = out_data[0].asnumpy()
+        dy = out_grad[0].asnumpy()
+        dx = dy*(1.0 - y)*y
+        self.assign(in_grad[0], req[0], mx.nd.array(dx))
+```
+
+### Register custom operator
+
+Then we need to register the custom op and describe it’s properties like input and output shapes so that mxnet can recognize it. This is done by sub-classing `mx.operator.CustomOpProp`:
+
+```python
+@mx.operator.register("sigmoid")  # register with name "sigmoid"
+class SigmoidProp(mx.operator.CustomOpProp):
+    def __init__(self):
+        super(SigmoidProp, self).__init__(True)
+
+    def list_arguments(self):
+        #  this can be omitted if you only have 1 input.
+        return ['data']
+
+    def list_outputs(self):
+        #  this can be omitted if you only have 1 output.
+        return ['output']
+
+    def infer_shape(self, in_shapes):
+        """Calculate output shapes from input shapes. This can be
+        omited if all your inputs and outputs have the same shape.
+
+        in_shapes : list of shape. Shape is described by a tuple of int.
+        """
+        data_shape = in_shapes[0]
+        output_shape = data_shape
+        # return 3 lists representing inputs shapes, outputs shapes, and aux data shapes.
+        return (data_shape,), (output_shape,), ()
+
+    def create_operator(self, ctx, in_shapes, in_dtypes):
+        #  create and return the CustomOp class.
+        return Sigmoid()
+```
+
+### Example Usage
+
+We can now use this operator by calling `mx.nd.Custom`:
+
+```python
+x = mx.nd.array([0, 1, 2, 3])
+# attach gradient buffer to x for autograd
+x.attach_grad()
+# forward in a record() section to save computation graph for backward
+# see autograd tutorial to learn more.
+with autograd.record():
+    y = mx.nd.Custom(x, op_type='sigmoid')
+print(y)
+```
+
+```python
+# call backward computation
+y.backward()
+# gradient is now saved to the grad buffer we attached previously
+print(x.grad)
+```
+
+## Parametrized Operator
+
+In the second use case we implement an operator with learnable weights. We implement the dense (or fully connected) layer that has one input, one output, and two learnable parameters: weight and bias.
+
+The dense operator performs a dot product between data and weight, then add bias to it.
+
+### Forward & backward implementation
+
+```python
+class Dense(mx.operator.CustomOp):
+    def __init__(self, bias):
+        self._bias = bias
+
+    def forward(self, is_train, req, in_data, out_data, aux):
+        x = in_data[0].asnumpy()
+        weight = in_data[1].asnumpy()
+        y = x.dot(weight.T) + self._bias
+        self.assign(out_data[0], req[0], mx.nd.array(y))
+
+    def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
+        x = in_data[0].asnumpy()
+        dy = out_grad[0].asnumpy()
+        dx = dy.T.dot(x)
+        self.assign(in_grad[0], req[0], mx.nd.array(dx))
+```
+
+### Registration
+
+```python
+@mx.operator.register("dense")  # register with name "sigmoid"
+class DenseProp(mx.operator.CustomOpProp):
+    def __init__(self, bias):
+        super(DenseProp, self).__init__(True)
+        # we use constant bias here to illustrate how to pass arguments
+        # to operators. All arguments are in string format so you need
+        # to convert them back to the type you want.
+        self._bias = float(bias)
+
+    def list_arguments(self):
+        return ['data', 'weight']
+
+    def list_outputs(self):
+        #  this can be omitted if you only have 1 output.
+        return ['output']
+
+    def infer_shape(self, in_shapes):
+        data_shape = in_shapes[0]
+        weight_shape = in_shapes[1]
+        output_shape = (data_shape[0], weight_shape[0])
+        # return 3 lists representing inputs shapes, outputs shapes, and aux data shapes.
+        return (data_shape, weight_shape), (output_shape,), ()
+
+    def create_operator(self, ctx, in_shapes, in_dtypes):
+        #  create and return the CustomOp class.
+        return Dense(self._bias)
+```
+
+### Use CustomOp together with Block
+
+Parameterized CustomOp are usually used together with Blocks, which holds the parameter.
+
+```python
+class DenseBlock(mx.gluon.Block):
+    def __init__(self, in_channels, channels, bias, **kwargs):
+        super(DenseBlock, self).__init__(**kwargs)
+        self._bias = bias
+        self.weight = self.params.get('weight', shape=(channels, in_channels))
+
+    def forward(self, x):
+        ctx = x.context
+        return mx.nd.Custom(x, self.weight.data(ctx), bias=self._bias, op_type='dense')
+```
+
+### Example usage
+
+```python
+dense = DenseBlock(3, 5, 0.1)
+dense.initialize()
+x = mx.nd.uniform(shape=(4, 3))
+y = dense(x)
+print(y)
+```
+
+## Using custom operators with fork
+
+In Linux systems, the default method in multiprocessing to create process is by using fork. If there are unfinished async custom operations when forking, the program will be blocked because of python GIL. Always use sync calls like `wait_to_read` or `waitall` before calling fork.
+
+```python
+x = mx.nd.array([0, 1, 2, 3])
+y = mx.nd.Custom(x, op_type='sigmoid')
+# unfinished async sigmoid operation will cause blocking
+os.fork()
+```
+
+Correctly handling this will make mxnet depend upon libpython, so the workaround now is to ensure that all custom operations are executed before forking process.
+
+```python
+x = mx.nd.array([0, 1, 2, 3])
+y = mx.nd.Custom(x, op_type='sigmoid')
+# force execution by reading y
+print(y.asnumpy())
+os.fork()
+```
diff --git a/docs/python_docs/python/tutorials/packages/gluon/data.md b/docs/python_docs/python/tutorials/packages/gluon/data.md
new file mode 100644
index 000000000000..fb9b6e6af602
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/data.md
@@ -0,0 +1,141 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Data
+
+One of the most critical steps for model training and inference is loading the data: without data you can’t do deep learning! In this tutorial we use the `data` module to:
+
+1) Define a [`Dataset`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.data.Dataset.html)
+2) Use `transform`s to augment the dataset
+3) Use a [`DataLoader`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.data.DataLoader.html) to iterate through the dataset in mini-batches
+
+## Getting started with [`Dataset`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.data.Dataset.html)s
+
+Our very first step is to define the [`Dataset`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.data.Dataset.html). A [`Dataset`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.data.Dataset.html) is MXNet Gluon's interface to the data that's stored on disk (or elsewhere). Out of the box, MXNet Gluon comes with a number of common benchmarking datasets such as CIFAR-10 and MNIST. We'll use an MNIST variant, called FashionMNIST, to understand the role of the [`Dataset`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.data.Dataset.html). When we instantiate this benchmarking dataset, the data will be downloaded to disk and be ready to use.
+
+```
+import mxnet as mx
+
+dataset = mx.gluon.data.vision.FashionMNIST()
+```
+
+So how do we get data out of a [`Dataset`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.data.Dataset.html)? We index.
+
+```
+sample = dataset[42]
+```
+
+Choosing a random index of 42 above, we get back a single sample from the dataset. A single sample usually contains multiple elements.
+
+```
+len(sample)
+```
+
+Our sample has 2 elements in this case. We have the image as the first element and the label as the second. Although this is a common pattern, you should check the documentation (or implementation) of the dataset you're using.
+
+We can unpack the sample into `data` and `label` and visualise our single sample.
+
+```
+data_sample, label_sample = sample
+print('Clothing Category #{}'.format(label_sample))
+```
+
+```
+import matplotlib.pyplot as plt
+
+plot = lambda s: plt.imshow(s[:,:,0].asnumpy(), cmap='gray')
+plot(data_sample)
+```
+
+We can access all of our samples in the dataset using the indexing method above, but usually we leave this to the [`DataLoader`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.data.DataLoader.html). We'll see an example of this shortly, but first we'll see how to change our dataset with `transform`s.
+
+## Getting started with `transform`s
+
+Similar to `dataset`s which return individual samples, `transform`s give us a way to change individual samples of our `dataset`.
+
+#### Why would you want to do this?
+
+Most of the time, samples returned by the dataset aren't in quite the right format to be passed into a neural network. One common example is to have samples with a data type of `int8` when your network expects `float32`. Another common example with images is when you have an image with a dimension order of (height, width, channel) when you need (channel, height, width) for the network.
+
+Augmenting samples is also common practice: that is, randomly permuting existing samples to make new samples to reduce issues of network overfitting. Using image samples as an example, you could crop to random regions, flip from left to right or even jitter the brightness of the image.
+
+#### And how do you do it?
+
+We first define our transform and then apply it to our dataset.
+
+MXNet Gluon has a number of in-build transforms available at `mxnet.gluon.data.vision.transforms`, so let's use [`RandomResizedCrop`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.data.vision.transforms.RandomResizedCrop.html) as an example. We're specifying that we want a random crop that contains 60% to 80% of the original image, and then we want to scale this cropped region to 20px by 20px. You should notice we're instantiating a class that can be called like a function.
+
+```
+from mxnet.gluon.data.vision.transforms import RandomResizedCrop
+
+transform_fn = RandomResizedCrop(size=(20, 20), scale=(0.6, 0.8))
+```
+
+After defining `transform_fn`, we now need to apply it to the dataset. We call the `transform_first` method of our dataset to apply `transform_fn` to the first element of all samples. We had 2 elements per sample in our example, so we only apply `transform_fn` to the image and not the label.
+
+Advanced: `transform`, instead of `transform_first` can be used to transform all elements.
+
+```
+dataset = dataset.transform_first(transform_fn)
+```
+
+When we retrieve the same sample as before from the dataset, we now see an augmented version of the image (with the same label). We'd see a different augmented image every time we retrieve this sample because the transform is applied lazily by default.
+
+```
+data_sample, label_sample = dataset[42]
+print('Clothing Category #{}'.format(label_sample))
+plot(data_sample)
+```
+
+## Getting started with [`DataLoader`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.data.DataLoader.html)s
+
+Our `dataset` gives us individual samples, but we usually train neural networks on batches of samples. A [`DataLoader`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.data.DataLoader.html) retrieves samples from the `dataset` and stacks them into batches. At a minimum, all we need to specify is the number of samples we want in each batch, called the `batch_size`, but [`DataLoader`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.data.DataLoader.html)s have many other useful features. Shuffling data samples for training is as simple as setting `shuffle`. 
+
+Advanced: A larger `batch_size` often speeds up training, but can lead to out-of-memory and convergence issues. 12345 is just for demonstration purposes: you'll more commonly use a smaller batch size (e.g. 128) but it depends on the model.
+
+```
+dataloader = mx.gluon.data.DataLoader(dataset,
+                                      batch_size=12345,
+                                      shuffle=True)
+```
+
+We iterate through the `dataloader` to get all the batches in our `dataset`, and we usually place the code for network training inside this loop. You'll notice that `data_batch` has an extra dimension at the start (compared with `data_sample`): this is often called the batch dimension.
+
+```
+for batch_idx, (data_batch, label_batch) in enumerate(dataloader):
+    print('Batch {} has shape {}'.format(batch_idx, data_batch.shape))
+```
+
+You might have noticed that the last batch contains fewer samples than the others. We had a remainder at the end and by default a [`DataLoader`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.data.DataLoader.html) keeps the incomplete batch (see `last_batch` argument).
+
+We can plot a few (augmented) images from the last batch.
+
+```
+plt.figure(1)
+plt.subplot(131); plot(data_batch[123])
+plt.subplot(132); plot(data_batch[234])
+plt.subplot(133); plot(data_batch[345])
+plt.show()
+```
+
+## Conclusion
+
+We've now seen all of the core components of the MXNet Gluon data pipeline. You should now understand the difference between [`Dataset`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.data.Dataset.html)s, that return individual samples, and [`DataLoader`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.data.DataLoader.html)s, that return batches of samples. You should also be able to transform your `dataset` for pre-processing or augmentation purposes.
+
+## Recommended Next Steps
+
+We used presets and a lot of default values in this tutorial, but there are many other options and possibilities. You're likely to want to use your own dataset for training. You can learn more about this in the dedicated `Dataset` tutorial. We also have a tutorial on `transform`s with examples on how to compose transform operations. And last but not least, we have a very useful tutorial on `DataLoader`s. You'll often find that loading data can be the bottleneck to training, and this tutorial contains some useful tricks to speed up the data pipeline.
diff --git a/docs/python_docs/python/tutorials/packages/gluon/fit_api_tutorial.md b/docs/python_docs/python/tutorials/packages/gluon/fit_api_tutorial.md
new file mode 100644
index 000000000000..cca4f3fa395e
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/fit_api_tutorial.md
@@ -0,0 +1,269 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# MXNet Gluon Fit API
+
+In this tutorial, you will learn how to use the [Gluon Fit API](https://cwiki.apache.org/confluence/display/MXNET/Gluon+Fit+API+-+Tech+Design) which is the easiest way to train deep learning models using the [Gluon API](http://mxnet.incubator.apache.org/versions/master/gluon/index.html) in Apache MXNet. 
+
+With the Fit API, you can train a deep learning model with a minimal amount of code. Just specify the network, loss function and the data you want to train on. You don't need to worry about the boiler plate code to loop through the dataset in batches (often called as 'training loop'). Advanced users can train with bespoke training loops, and many of these use cases will be covered by the Fit API.
+
+To demonstrate the Fit API, you will train an image classification model using the [ResNet-18](https://arxiv.org/abs/1512.03385) neural network architecture. The model will be trained using the [Fashion-MNIST dataset](https://research.zalando.com/welcome/mission/research-projects/fashion-mnist/). 
+
+## Prerequisites
+
+To complete this tutorial, you will need:
+
+- [MXNet](https://mxnet.incubator.apache.org/install/#overview) (The version of MXNet will be >= 1.5.0, you can use `pip install mxnet` to get 1.5.0 release pip package or build from source with master, refer to [MXNet installation](http://mxnet.incubator.apache.org/versions/master/install/index.html?platform=Linux&language=Python&processor=CPU)
+- [Jupyter Notebook](https://jupyter.org/index.html) (For interactively running the provided .ipynb file)
+
+
+```python
+import mxnet as mx
+from mxnet import gluon
+from mxnet.gluon.model_zoo import vision
+from mxnet.gluon.contrib.estimator import estimator
+from mxnet.gluon.contrib.estimator.event_handler import TrainBegin, TrainEnd, EpochEnd, CheckpointHandler
+
+gpu_count = mx.context.num_gpus()
+ctx = [mx.gpu(i) for i in range(gpu_count)] if gpu_count > 0 else mx.cpu()
+```
+
+## Dataset
+
+[Fashion-MNIST](https://research.zalando.com/welcome/mission/research-projects/fashion-mnist/) dataset consists of fashion items divided into ten categories: t-shirt/top, trouser, pullover, dress, coat, sandal, shirt, sneaker, bag and ankle boot. 
+
+- It has 60,000 grayscale images of size 28 * 28 for training.  
+- It has 10,000 grayscale images of size 28 * 28 for testing/validation. 
+
+We will use the ```gluon.data.vision``` package to directly import the Fashion-MNIST dataset and perform pre-processing on it.
+
+
+```python
+# Get the training data 
+fashion_mnist_train = gluon.data.vision.FashionMNIST(train=True)
+
+# Get the validation data
+fashion_mnist_val = gluon.data.vision.FashionMNIST(train=False)
+```
+
+
+```python
+transforms = [gluon.data.vision.transforms.Resize(224), # We pick 224 as the model we use takes an input of size 224.
+                gluon.data.vision.transforms.ToTensor()]
+
+# Now we will stack all these together.
+transforms = gluon.data.vision.transforms.Compose(transforms)
+```
+
+
+```python
+# Apply the transformations
+fashion_mnist_train = fashion_mnist_train.transform_first(transforms)
+fashion_mnist_val = fashion_mnist_val.transform_first(transforms)
+```
+
+
+```python
+batch_size = 256 # Batch size of the images
+num_workers = 4 # The number of parallel workers for loading the data using Data Loaders.
+
+train_data_loader = gluon.data.DataLoader(fashion_mnist_train, batch_size=batch_size, 
+                                          shuffle=True, num_workers=num_workers)
+val_data_loader = gluon.data.DataLoader(fashion_mnist_val, batch_size=batch_size, 
+                                        shuffle=False, num_workers=num_workers)
+```
+
+## Model and Optimizers
+
+Let's load the resnet-18 model architecture from [Gluon Model Zoo](http://mxnet.apache.org/api/python/gluon/model_zoo.html) and initialize its parameters. The Gluon Model Zoo contains a repository of pre-trained models as well the model architecture definitions. We are using the model architecture from the model zoo in order to train it from scratch.
+
+
+```python
+resnet_18_v1 = vision.resnet18_v1(pretrained=False, classes = 10)
+resnet_18_v1.initialize(init = mx.init.Xavier(), ctx=ctx)
+```
+
+We will be using `SoftmaxCrossEntropyLoss` as the loss function since this is a multi-class classification problem. We will be using `sgd` (Stochastic Gradient Descent) as the optimizer. 
+You can experiment with a [different loss](http://mxnet.incubator.apache.org/versions/master/api/python/gluon/loss.html) or [optimizer](http://mxnet.incubator.apache.org/versions/master/api/python/optimization/optimization.html) as well. 
+
+
+```python
+loss_fn = gluon.loss.SoftmaxCrossEntropyLoss()
+```
+
+Let's define the trainer object for training the model.
+
+
+```python
+learning_rate = 0.04 # You can experiment with your own learning rate here
+num_epochs = 2 # You can run training for more epochs
+trainer = gluon.Trainer(resnet_18_v1.collect_params(), 
+                        'sgd', {'learning_rate': learning_rate})
+```
+
+## Train using Fit API
+
+As stated earlier, the Fit API greatly simplifies the boiler plate code and complexity for training using MXNet Gluon.
+
+In the basic usage example, with just 2 lines of code, we will set up our model for training.
+
+### Basic Usage
+
+
+```python
+train_acc = mx.metric.Accuracy() # Metric to monitor
+
+# Define the estimator, by passing to it the model, loss function, metrics, trainer object and context
+est = estimator.Estimator(net=resnet_18_v1, 
+                          loss=loss_fn, 
+                          metrics=train_acc, 
+                          trainer=trainer, 
+                          context=ctx)
+
+# ignore warnings for nightly test on CI only
+import warnings
+with warnings.catch_warnings():
+    warnings.simplefilter("ignore")
+    # Magic line
+    est.fit(train_data=train_data_loader,
+        epochs=num_epochs)
+```
+
+```text
+    Training begin: using optimizer SGD with current learning rate 0.0400 <!--notebook-skip-line-->
+    Train for 2 epochs. <!--notebook-skip-line-->
+    
+    [Epoch 0] finished in 25.110s: train_accuracy : 0.7877 train_softmaxcrossentropyloss0 : 0.5905 <!--notebook-skip-line-->
+    
+    [Epoch 1] finished in 23.595s: train_accuracy : 0.8823 train_softmaxcrossentropyloss0 : 0.3197 <!--notebook-skip-line-->
+    Train finished using total 48s at epoch 1. train_accuracy : 0.8823 train_softmaxcrossentropyloss0 : 0.3197 <!--notebook-skip-line-->
+```
+
+### Advanced Usage
+
+The Fit API is also customizable with several `Event Handlers` which give a fine grained control over the steps in training and exposes callback methods that provide control over the stages involved in training. Available callback methods are: `train_begin`, `train_end`, `batch_begin`, `batch_end`, `epoch_begin` and `epoch_end`.
+
+You can use built-in event handlers such as `LoggingHandler`, `CheckpointHandler` or `EarlyStoppingHandler` to log and save the model at certain time-steps during training. You can also stop the training when the model's performance plateaus. 
+There are also some default utility handlers that will be added to your estimator by default. For example, `StoppingHandler` is used to control when the training ends, based on number of epochs or number of batches trained. 
+`MetricHandler` is used to calculate training metrics at end of each batch and epoch. 
+`ValidationHandler` is used to validate your model on test data at each epoch's end and then calculate validation metrics.
+You can create these utility handlers with different configurations and pass to estimator. This will override the default handler configuration.
+You can create a custom handler by inheriting one or multiple 
+[base event handlers](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/contrib/estimator/event_handler.py#L32)
+ including: `TrainBegin`, `TrainEnd`, `EpochBegin`, `EpochEnd`, `BatchBegin`, `BatchEnd`.
+
+
+### Custom Event Handler
+
+Here we will showcase an example custom event handler the inherits features from a few base handler classes. 
+Our custom event handler is a simple one: record the loss values at the end of every epoch in our training phase.
+
+Note: For each of the method, the `Estimator` object is passed along, so you can access training metrics.
+
+```python
+class LossRecordHandler(TrainBegin, TrainEnd, EpochEnd):
+    def __init__(self):
+        super(LossRecordHandler, self).__init__()
+        self.loss_history = {}
+
+    def train_begin(self, estimator, *args, **kwargs):
+        print("Training begin")
+
+    def train_end(self, estimator, *args, **kwargs):
+        # Print all the losses at the end of training
+        print("Training ended")
+        for loss_name in self.loss_history:
+            for i, loss_val in enumerate(self.loss_history[loss_name]):
+                print("Epoch: {}, Loss name: {}, Loss value: {}".format(i, loss_name, loss_val))
+
+    def epoch_end(self, estimator, *args, **kwargs):
+        for metric in estimator.train_metrics:
+            # look for train Loss in training metrics
+            # we wrapped loss value as a metric to record it
+            if isinstance(metric, mx.metric.Loss):
+                loss_name, loss_val = metric.get()
+                # append loss value for this epoch
+                self.loss_history.setdefault(loss_name, []).append(loss_val)
+```
+
+
+```python
+# Let's reset the model, trainer and accuracy objects from above
+
+resnet_18_v1.initialize(force_reinit=True, init = mx.init.Xavier(), ctx=ctx)
+trainer = gluon.Trainer(resnet_18_v1.collect_params(), 
+                        'sgd', {'learning_rate': learning_rate})
+train_acc = mx.metric.Accuracy()
+```
+
+
+```python
+# Define the estimator, by passing to it the model, loss function, metrics, trainer object and context
+est = estimator.Estimator(net=resnet_18_v1,
+                          loss=loss_fn,
+                          metrics=train_acc,
+                          trainer=trainer, 
+                          context=ctx)
+
+# Define the handlers, let's say in built Checkpointhandler
+checkpoint_handler = CheckpointHandler(model_dir='./',
+                                       model_prefix='my_model',
+                                       monitor=train_acc,  # Monitors a metric
+                                       save_best=True)  # Save the best model in terms of
+# Let's instantiate another handler which we defined above 
+loss_record_handler = LossRecordHandler()
+# ignore warnings for nightly test on CI only
+import warnings
+with warnings.catch_warnings():
+    warnings.simplefilter("ignore")
+    # Magic line
+    est.fit(train_data=train_data_loader,
+            val_data=val_data_loader,
+            epochs=num_epochs,
+            event_handlers=[checkpoint_handler, loss_record_handler]) # Add the event handlers
+```
+
+```text
+    Training begin: using optimizer SGD with current learning rate 0.0400 <!--notebook-skip-line-->
+    Train for 2 epochs. <!--notebook-skip-line-->
+    
+    [Epoch 0] finished in 25.236s: train_accuracy : 0.7917 train_softmaxcrossentropyloss0 : 0.5741 val_accuracy : 0.6612 val_softmaxcrossentropyloss0 : 0.8627 <!--notebook-skip-line-->
+    
+    [Epoch 1] finished in 24.892s: train_accuracy : 0.8826 train_softmaxcrossentropyloss0 : 0.3229 val_accuracy : 0.8474 val_softmaxcrossentropyloss0 : 0.4262 <!--notebook-skip-line-->
+    
+    Train finished using total 50s at epoch 1. train_accuracy : 0.8826 train_softmaxcrossentropyloss0 : 0.3229 val_accuracy : 0.8474 val_softmaxcrossentropyloss0 : 0.4262 <!--notebook-skip-line-->
+
+    Training begin <!--notebook-skip-line-->
+    Epoch 1, loss 0.5741 <!--notebook-skip-line-->
+    Epoch 2, loss 0.3229 <!--notebook-skip-line-->
+```
+
+You can load the saved model, by using the `load_parameters` API in Gluon. For more details refer to the [Loading model parameters from file tutorial](save_load_params.html#saving-model-parameters-to-file)
+
+
+```python
+resnet_18_v1 = vision.resnet18_v1(pretrained=False, classes=10)
+resnet_18_v1.load_parameters('./my_model-best.params', ctx=ctx)
+```
+
+## Summary
+
+- To learn more about deep learning with MXNeT, see [Dive Into Deep Learning](http://gluon.io)
+
+## Next Steps 
+
+- For more hands on learning about deep learning, check out [Dive into Deep Learning](https://d2l.ai)
\ No newline at end of file
diff --git a/docs/python_docs/python/tutorials/packages/gluon/hybridize.md b/docs/python_docs/python/tutorials/packages/gluon/hybridize.md
new file mode 100644
index 000000000000..cd4498937e53
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/hybridize.md
@@ -0,0 +1,306 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Hybridize
+
+<!-- adapted from diveintodeeplearning -->
+## A Hybrid of Imperative and Symbolic Programming
+Imperative programming makes use of
+programming statements to change a program’s state. Consider the following
+example of simple imperative programming code.
+
+```{.python .input}
+def add(a, b):
+    return a + b
+
+def fancy_func(a, b, c, d):
+    e = add(a, b)
+    f = add(c, d)
+    g = add(e, f)
+    return g
+
+fancy_func(1, 2, 3, 4)
+```
+
+As expected, Python will perform an addition when running the statement `e = add(a, b)`, and will store the result as the variable `e`, thereby changing the program’s state. The next two statements `f = add(c, d)` and `g = add(e, f)` will similarly perform additions and store the results as variables.
+
+Although imperative programming is convenient, it may be inefficient. On the one hand, even if the `add` function is repeatedly called throughout the `fancy_func` function, Python will execute the three function calling statements individually, one after the other. On the other hand, we need to save the variable values of `e` and `f` until all the statements in `fancy_func` have been executed. This is because we do not know whether the variables `e` and `f` will be used by other parts of the program after the statements `e = add(a, b)` and `f = add(c, d)` have been executed.
+
+Contrary to imperative programming, symbolic programming is usually performed after the computational process has been fully defined. Symbolic programming is used by multiple deep learning frameworks, including Theano and TensorFlow. The process of symbolic programming generally requires the following three steps:
+
+1. Define the computation process.
+2. Compile the computation process into an executable program.
+3. Provide the required inputs and call on the compiled program for execution.
+
+In the example below, we utilize symbolic programming to re-implement the imperative programming code provided at the beginning of this section.
+
+```{.python .input}
+def add_str():
+    return '''
+def add(a, b):
+    return a + b
+'''
+
+def fancy_func_str():
+    return '''
+def fancy_func(a, b, c, d):
+    e = add(a, b)
+    f = add(c, d)
+    g = add(e, f)
+    return g
+'''
+
+def evoke_str():
+    return add_str() + fancy_func_str() + '''
+print(fancy_func(1, 2, 3, 4))
+'''
+
+prog = evoke_str()
+print(prog)
+y = compile(prog, '', 'exec')
+exec(y)
+```
+
+The three functions defined above will only return the results of the computation process as a string. Finally, the complete computation process is compiled and run using the `compile` function. This leaves more room to optimize computation, since the system is able to view the entire program during its compilation. For example, during compilation, the program can be rewritten as `print((1 + 2) + (3 + 4))` or even directly rewritten as `print(10)`. Apart from reducing the amount of function calls, this process also saves memory.
+
+A comparison of these two programming methods shows that
+
+* imperative programming is easier. When imperative programming is used in Python, the majority of the code is straightforward and easy to write. At the same time, it is easier to debug imperative programming code. This is because it is easier to obtain and print all relevant intermediate variable values, or make use of Python’s built-in debugging tools.
+
+* Symbolic programming is more efficient and easier to port. Symbolic programming makes it easier to better optimize the system during compilation, while also having the ability to port the program into a format independent of Python. This allows the program to be run in a non-Python environment, thus avoiding any potential performance issues related to the Python interpreter.
+
+
+## Hybrid programming provides the best of both worlds.
+
+Most deep learning frameworks choose either imperative or symbolic programming. For example, both Theano and TensorFlow (inspired by the latter) make use of symbolic programming, while Chainer and its predecessor PyTorch utilize imperative programming. When designing Gluon, developers considered whether it was possible to harness the benefits of both imperative and symbolic programming. The developers believed that users should be able to develop and debug using pure imperative programming, while having the ability to convert most programs into symbolic programming to be run when product-level computing performance and deployment are required This was achieved by Gluon through the introduction of hybrid programming.
+
+In hybrid programming, we can build models using either the HybridBlock or the HybridSequential classes. By default, they are executed in the same way Block or Sequential classes are executed in imperative programming. When the `hybridize` function is called, Gluon will convert the program’s execution into the style used in symbolic programming. In fact, most models can make use of hybrid programming’s execution style.
+
+Through the use of experiments, this section will demonstrate the benefits of hybrid programming.
+
+## Constructing Models Using the HybridSequential Class
+
+Previously, we learned how to use the Sequential class to concatenate multiple layers. Next, we will replace the Sequential class with the HybridSequential class in order to make use of hybrid programming.
+
+```{.python .input}
+from mxnet import nd, sym
+from mxnet.gluon import nn
+import time
+
+def get_net():
+    net = nn.HybridSequential()  # Here we use the class HybridSequential.
+    net.add(nn.Dense(256, activation='relu'),
+            nn.Dense(128, activation='relu'),
+            nn.Dense(2))
+    net.initialize()
+    return net
+
+x = nd.random.normal(shape=(1, 512))
+net = get_net()
+net(x)
+```
+
+By calling the `hybridize` function, we are able to compile and optimize the computation of the concatenation layer in the HybridSequential instance. The model’s computation result remains unchanged.
+
+```{.python .input}
+net.hybridize()
+net(x)
+```
+
+It should be noted that only the layers inheriting the HybridBlock class will be optimized during computation. For example, the HybridSequential and `Dense` classes provided by Gluon are all subclasses of HybridBlock class, meaning they will both be optimized during computation. A layer will not be optimized if it inherits from the Block class rather than the HybridBlock class.
+
+
+### Computing Performance
+
+To demonstrate the performance improvement gained by the use of symbolic programming, we will compare the computation time before and after calling the `hybridize` function. Here we time 1000 `net` model computations. The model computations are based on imperative and symbolic programming, respectively, before and after `net` has called the `hybridize` function.
+
+```{.python .input}
+def benchmark(net, x):
+    start = time.time()
+    for i in range(1000):
+        _ = net(x)
+    nd.waitall()  # To facilitate timing, we wait for all computations to be completed.
+    return time.time() - start
+
+net = get_net()
+print('before hybridizing: %.4f sec' % (benchmark(net, x)))
+net.hybridize()
+print('after hybridizing: %.4f sec' % (benchmark(net, x)))
+```
+
+As is observed in the above results, after a HybridSequential instance calls the `hybridize` function, computing performance is improved through the use of symbolic programming.
+
+
+### Achieving Symbolic Programming
+
+We can save the symbolic program and model parameters to the hard disk through the use of the `export` function after the `net` model has finished computing the output based on the input, such as in the case of `net(x)` in the `benchmark` function.
+
+```{.python .input}
+net.export('my_mlp')
+```
+
+The .json and .params files generated during this process are a symbolic program and a model parameter, respectively. They can be read by other front-end languages supported by Python or MXNet, such as C++, R, Scala, and Perl. This allows us to deploy trained models to other devices and easily use other front-end programming languages. At the same time, because symbolic programming was used during deployment, the computing performance is often superior to that based on imperative programming.
+
+In MXNet, a symbolic program refers to a program that makes use of the Symbol type. We know that, when the NDArray input `x` is provided to `net`, `net(x)` will directly calculate the model output and return a result based on `x`. For models that have called the `hybridize` function, we can also provide a Symbol-type input variable, and `net(x)` will return Symbol type results.
+
+```{.python .input}
+x = sym.var('data')
+net(x)
+```
+
+## Constructing Models Using the HybridBlock Class
+
+Similar to the correlation between the Sequential Block classes, the HybridSequential class is a HybridBlock subclass. Contrary to the Block instance, which needs to use the `forward` function, for a HybridBlock instance we need to use the `hybrid_forward` function.
+
+Earlier, we demonstrated that, after calling the `hybridize` function, the model is able to achieve superior computing performance and portability. In addition, model flexibility can be affected after calling the `hybridize` function. We will demonstrate this by constructing a model using the HybridBlock class.
+
+```{.python .input}
+class HybridNet(nn.HybridBlock):
+    def __init__(self, **kwargs):
+        super(HybridNet, self).__init__(**kwargs)
+        self.hidden = nn.Dense(10)
+        self.output = nn.Dense(2)
+
+    def hybrid_forward(self, F, x):
+        print('F: ', F)
+        print('x: ', x)
+        x = F.relu(self.hidden(x))
+        print('hidden: ', x)
+        return self.output(x)
+```
+
+We need to add the additional input `F` to the `hybrid_forward` function when inheriting the HybridBlock class. We already know that MXNet uses both an NDArray class and a Symbol class, which are based on imperative programming and symbolic programming, respectively. Since these two classes perform very similar functions, MXNet will determine whether `F` will call NDArray or Symbol based on the input provided.
+
+The following creates a HybridBlock instance. As we can see, by default, `F` uses NDArray. We also printed out the `x` input as well as the hidden layer’s output using the ReLU activation function.
+
+```{.python .input}
+net = HybridNet()
+net.initialize()
+x = nd.random.normal(shape=(1, 4))
+net(x)
+```
+
+Repeating the forward computation will achieve the same results.
+
+```{.python .input}
+net(x)
+```
+
+Next, we will see what happens after we call the `hybridize` function.
+
+```{.python .input}
+net.hybridize()
+net(x)
+```
+
+We can see that `F` turns into a Symbol. Moreover, even though the input data is still NDArray, the same input and intermediate output will all be converted to Symbol type in the `hybrid_forward` function.
+
+Now, we repeat the forward computation.
+
+```{.python .input}
+net(x)
+```
+
+We can see that the three lines of print statements defined in the `hybrid_forward` function will not print anything. This is because a symbolic program has been produced since the last time `net(x)` was run by calling the `hybridize` function. Afterwards, when we run `net(x)` again, MXNet will no longer need to access Python code, but can directly perform symbolic programming at the C++ backend. This is another reason why model computing performance will be improve after the `hybridize` function is called. However, there is always the potential that any programs we write will suffer a loss in flexibility. If we want to use the three lines of print statements to debug the code in the above example, they will be skipped over and we would not be able to print when the symbolic program is executed. Additionally, in the case of a few functions not supported by Symbol (like `asnumpy`), and operations in-place like `a += b` and `a[:] = a + b` (must be rewritten as `a = a + b`). Therefore, we will not be able to use the `hybrid_forward` function or perform forward computation after the `hybridize` function has been called.
+
+## Key differences and limitations of hybridization
+
+The difference between a purely imperative `Block` and hybridizable `HybridBlock` can superficially appear to be simply the injection of the `F` function space (resolving to [`mx.nd`](http://beta.mxnet.io/api/ndarray/index.html) or [`mx.sym`](http://beta.mxnet.io/api/symbol/index.html)) in the forward function that is renamed from `forward` to `hybrid_forward`. However there are some limitations that apply when using hybrid blocks. In the following section we will review the main differences, giving example of code snippets that generate errors when such blocks get hybridized.
+
+### Indexing
+
+When trying to access specific elements in a tensor like this:
+
+```python
+def hybrid_forward(self, F, x):
+    return x[0,0]
+```
+
+Would generate the following error:
+
+`TypeError: Symbol only support integer index to fetch i-th output`
+
+There are however several operators that can help you with array manipulations like: [`F.split`](http://beta.mxnet.io/api/ndarray/_autogen/mxnet.ndarray.split.html#mxnet.ndarray.split), [`F.slice`](http://beta.mxnet.io/api/ndarray/_autogen/mxnet.ndarray.slice.html#mxnet.ndarray.slice), [`F.take`](http://beta.mxnet.io/api/ndarray/_autogen/mxnet.ndarray.take.html),[`F.pick`](http://beta.mxnet.io/api/ndarray/_autogen/mxnet.ndarray.pick.html), [`F.where`](http://beta.mxnet.io/api/ndarray/_autogen/mxnet.ndarray.where.html), [`F.reshape`](http://beta.mxnet.io/api/ndarray/_autogen/mxnet.ndarray.reshape.html) or [`F.reshape_like`](http://beta.mxnet.io/api/ndarray/_autogen/mxnet.ndarray.reshape_like.html).
+
+### Data Type
+
+Sometimes one can be tempted to use conditional logic on the type of the input tensors however the following block:
+
+```python
+def hybrid_forward(self, F, x):
+    if x.dtype =='float16':
+        return x
+    return x*2
+```
+
+Would generate a `AttributeError: 'Symbol' object has no attribute 'dtype'`
+
+You cannot use the `dtype` of the symbol at runtime. Symbols only describe operations and not the underlying data they operate on. One workaround is to pass the type as a constructor argument of your network and hence build the appropriate compute graph for each situation.
+
+### Compute Context
+
+Similarly you cannot use the compute context of symbol for the same reason that symbols only describe the operations on the data and not the data (or context). You cannot do this:
+
+```python
+def hybrid_forward(self, F, x):
+    if x.context == mx.cpu():
+        return x
+    return x*2
+```
+
+Without getting a `AttributeError: 'Symbol' object has no attribute 'context'`
+
+Accessing the current compute context is not possible with symbols. Consider passing this information in the constructor if you require it to create the appropriate compute graph.
+
+### Shape
+
+Accessing shape information of tensors is very often used for example when trying to flatten a tensor and then reshape it back to its original shape.
+
+```python
+def hybrid_forward(self, F, x):
+    return x*x.shape[0]
+```
+
+Trying to access the shape of a tensor in a hybridized block would result in this error: `AttributeError: 'Symbol' object has no attribute 'shape'`.
+
+Again, you cannot use the shape of the symbol at runtime as symbols only describe operations and not the underlying data they operate on. 
+Note: This will change in the future as Apache MXNet will support [dynamic shape inference](https://cwiki.apache.org/confluence/display/MXNET/Dynamic+shape), and the shapes of symbols will be symbols themselves 
+
+There are also a lot of operators that support special indices to help with most of the use-cases where you would want to access the shape information. For example, `F.reshape(x, (0,0,-1))` will keep the first two dimensions unchanged and collapse all further dimensions into the third dimension. See the documentation of the [`F.reshape`](http://beta.mxnet.io/api/ndarray/_autogen/mxnet.ndarray.reshape.html) for more details.
+
+### Item assignment
+
+Last but not least, you cannot directly assign values in tensor in a symbolic graph, the resulting tensors always needs to be the results of operations performed on the inputs of the computational graph. The following code:
+
+```python
+def hybrid_forward(self, F, x):
+    x[0] = 2
+    return x
+```
+
+Would get you this error `TypeError: 'Symbol' object does not support item assignment`.
+
+Direct item assignment is not possible in symbolic graph since it needs to be part of a computational graph. One way is to use add more inputs to your graph and use masking or the [`F.where`](http://beta.mxnet.io/api/ndarray/_autogen/mxnet.ndarray.where.html) operator.
+
+e.g to set the first element to 2 you can do:
+
+```python
+x = mx.nd.array([1,2,3])
+value = mx.nd.ones_like(x)*2
+condition = mx.nd.array([0,1,1])
+mx.nd.where(condition=condition, x=x, y=value)
+```
diff --git a/docs/python_docs/python/tutorials/packages/gluon/image-augmentation.md b/docs/python_docs/python/tutorials/packages/gluon/image-augmentation.md
new file mode 100644
index 000000000000..a2cdbb7cc97a
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/image-augmentation.md
@@ -0,0 +1,150 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Image Augmentation
+
+Image augmentation technology expands the scale of
+training data sets by making a series of random changes to the training images
+to produce similar, but different, training examples. Given its popularity in
+computer vision, the `mxnet.gluon.data.vision.transforms` model provides
+multiple pre-defined image augmentation methods. In this section we will briefly
+go through this module.  
+
+First, import the module required for this section.
+
+```{.python .input  n=1}
+from matplotlib import pyplot as plt
+from mxnet import image
+from mxnet.gluon import data as gdata, utils 
+```
+
+Then read the sample $400\times 500$ image.
+
+```{.python .input  n=2}
+utils.download('https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/cat.jpg')
+img = image.imread('cat.jpg')
+plt.imshow(img.asnumpy())
+plt.show()
+```
+
+In addition, we define a function to draw a list of images.
+
+```{.python .input  n=3}
+def show_images(imgs, num_rows, num_cols, scale=2):
+    figsize = (num_cols * scale, num_rows * scale)
+    _, axes = plt.subplots(num_rows, num_cols, figsize=figsize)
+    for i in range(num_rows):
+        for j in range(num_cols):
+            axes[i][j].imshow(imgs[i * num_cols + j].asnumpy())
+            axes[i][j].axes.get_xaxis().set_visible(False)
+            axes[i][j].axes.get_yaxis().set_visible(False)
+    return axes
+```
+
+Most image augmentation methods have a certain degree of randomness. To make it
+easier for us to observe the effect of image augmentation, we next define the
+auxiliary function `apply`. This function runs the image augmentation method
+`aug` multiple times on the input image `img` and shows all results.
+
+```{.python .input  n=4}
+def apply(img, aug, num_rows=2, num_cols=4, scale=3):
+    Y = [aug(img) for _ in range(num_rows * num_cols)]
+    show_images(Y, num_rows, num_cols, scale)
+```
+
+## Flip and Crop
+
+Flipping the image left and right usually does not change the
+category of the object. This is one of the earliest and most widely used methods
+of image augmentation. Next, we use the `transforms` module to create the
+`RandomFlipLeftRight` instance, which introduces a 50% chance that the image is
+flipped left and right.
+
+```{.python .input  n=5}
+apply(img, gdata.vision.transforms.RandomFlipLeftRight())
+```
+
+Flipping up and down is not as commonly used as flipping left and right.
+However, at least for this example image, flipping up and down does not hinder
+recognition. Next, we create a `RandomFlipTopBottom` instance for a 50% chance
+of flipping the image up and down.
+
+```{.python .input  n=6}
+apply(img, gdata.vision.transforms.RandomFlipTopBottom())
+```
+
+In the example image we used, the cat is in the middle of the image, but this
+may not be the case for all images. In the [“Pooling
+Layer”](../chapter_convolutional-neural-networks/pooling.md) section, we
+explained that the pooling layer can reduce the sensitivity of the convolutional
+layer to the target location. In addition, we can make objects appear at
+different positions in the image in different proportions by randomly cropping
+the image. This can also reduce the sensitivity of the model to the target
+position.
+
+In the following code, we randomly crop a region with an area of 10%
+to 100% of the original area, and the ratio of width to height of the region is
+randomly selected from between 0.5 and 2. Then, the width and height of the
+region are both scaled to 200 pixels. Unless otherwise stated, the random number
+between $a$ and $b$ in this section refers to a continuous value obtained by
+uniform sampling in the interval $[a,b]$.
+
+```{.python .input  n=7}
+shape_aug = gdata.vision.transforms.RandomResizedCrop(
+    (200, 200), scale=(0.1, 1), ratio=(0.5, 2))
+apply(img, shape_aug)
+```
+
+## Change Color
+
+Another augmentation method is changing colors. We can change
+four aspects of the image color: brightness, contrast, saturation, and hue. In
+the example below, we randomly change the brightness of the image to a value
+between 50% ($1-0.5$) and 150% ($1+0.5$) of the original image.
+
+```{.python .input  n=8}
+apply(img, gdata.vision.transforms.RandomBrightness(0.5))
+```
+
+Similarly, we can randomly change the hue of the image.
+
+```{.python .input  n=9}
+apply(img, gdata.vision.transforms.RandomHue(0.5))
+```
+
+We can also create a `RandomColorJitter` instance and set how to randomly change
+the `brightness`, `contrast`, `saturation`, and `hue` of the image at the same
+time.
+
+```{.python .input  n=10}
+color_aug = gdata.vision.transforms.RandomColorJitter(
+    brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5)
+apply(img, color_aug)
+```
+
+## Overlying Multiple Image Augmentation Methods
+
+In practice, we will overlay
+multiple image augmentation methods. We can overlay the different image
+augmentation methods defined above and apply them to each image by using a
+`Compose` instance.
+
+```{.python .input  n=11}
+augs = gdata.vision.transforms.Compose([
+    gdata.vision.transforms.RandomFlipLeftRight(), color_aug, shape_aug])
+apply(img, augs)
+```
diff --git a/docs/python_docs/python/tutorials/packages/gluon/image/index.rst b/docs/python_docs/python/tutorials/packages/gluon/image/index.rst
new file mode 100644
index 000000000000..d15d9e8623de
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/image/index.rst
@@ -0,0 +1,105 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Image Tutorials
+===============
+
+These tutorials will help you learn how to create and use models that work with
+images and other computer vision tasks.
+Most of these tutorials use the `MXNet GluonCV toolkit <https://gluon-cv.mxnet.io/>`__.
+
+Basic Image Tutorials
+---------------------
+
+.. container:: cards
+
+   .. card::
+      :title: MNIST
+      :link: mnist.html
+
+      How to create a convolutional neural network for handwritten digit recognition.
+
+   .. card::
+      :title: Image Recognition with Pretrained Models
+      :link: pre-trained_models.html
+
+      How to use pretrained models to recognize what is in an image.
+
+Advanced Image Tutorials
+---------------------
+
+.. container:: cards
+
+   .. card::
+      :title: Image similarity search with InfoGAN
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/info_gan.html
+
+      Implementing an InfoGAN based on the MXNet Gluon.
+
+GluonCV Toolkit Tutorials
+-------------------------
+
+These tutorials link to the MXNet GluonCV Toolkit website.
+
+.. container:: cards
+
+   .. card::
+      :title: Prepare Datasets
+      :link: https://gluon-cv.mxnet.io/build/examples_datasets/index.html
+
+      How to use built-in MXNet GluonCV features for loading and preparing both common & custom datasets.
+
+   .. card::
+      :title: Image Classification
+      :link: https://gluon-cv.mxnet.io/build/examples_classification/index.html
+
+      Pretrained models for inference, fine-tune models, train your own model
+      on ImageNet, and more.
+
+   .. card::
+      :title: Object Detection
+      :link: https://gluon-cv.mxnet.io/build/examples_detection/index.html
+
+      Learn how to use Single shot detector (SSD), RCNN, and YOLO models.
+
+   .. card::
+      :title: Semantic Segmentation
+      :link: https://gluon-cv.mxnet.io/build/examples_segmentation/index.html
+
+      Learn how to use and train models that can identify and segment objects in an image.
+
+   .. card::
+      :title: Instance Segmentation
+      :link: https://gluon-cv.mxnet.io/build/examples_instance/index.html
+
+      Learn how to use and train models the perform a variation of semantic
+      segmentation that also classifies similar objects into discrete entities.
+
+   .. card::
+      :title: Pose Estimation
+      :link: https://gluon-cv.mxnet.io/build/examples_pose/index.html
+
+      Learn how to use a simple Pose network that predicts the heatmap for each
+      joint then map it to the coordinates on the original image.
+
+
+.. toctree::
+   :hidden:
+   :maxdepth: 1
+
+   mnist
+   pretrained_models
diff --git a/docs/python_docs/python/tutorials/packages/gluon/image/mnist.md b/docs/python_docs/python/tutorials/packages/gluon/image/mnist.md
new file mode 100644
index 000000000000..9621e16ac69e
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/image/mnist.md
@@ -0,0 +1,350 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Handwritten Digit Recognition
+
+In this tutorial, we'll give you a step by step walk-through of how to build a hand-written digit classifier using the [MNIST](https://en.wikipedia.org/wiki/MNIST_database) dataset.
+
+MNIST is a widely used dataset for the hand-written digit classification task. It consists of 70,000 labeled 28x28 pixel grayscale images of hand-written digits. The dataset is split into 60,000 training images and 10,000 test images. There are 10 classes (one for each of the 10 digits). The task at hand is to train a model using the 60,000 training images and subsequently test its classification accuracy on the 10,000 test images.
+
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/mnist.png)
+
+**Figure 1:** Sample images from the MNIST dataset.
+
+This tutorial uses MXNet's new high-level interface, gluon package to implement MLP using
+imperative fashion.
+
+This is based on the Mnist tutorial with symbolic approach. You can find it [here](http://mxnet.io/tutorials/python/mnist.html).
+
+## Prerequisites
+To complete this tutorial, we need:
+
+- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/install/index.html).
+
+- [Python Requests](http://docs.python-requests.org/en/master/) and [Jupyter Notebook](http://jupyter.org/index.html).
+
+```
+$ pip install requests jupyter
+```
+
+## Loading Data
+
+Before we define the model, let's first fetch the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset.
+
+The following source code downloads and loads the images and the corresponding labels into memory.
+
+```python
+import mxnet as mx
+
+# Fixing the random seed
+mx.random.seed(42)
+
+mnist = mx.test_utils.get_mnist()
+```
+
+After running the above source code, the entire MNIST dataset should be fully loaded into memory. Note that for large datasets it is not feasible to pre-load the entire dataset first like we did here. What is needed is a mechanism by which we can quickly and efficiently stream data directly from the source. MXNet Data iterators come to the rescue here by providing exactly that. Data iterator is the mechanism by which we feed input data into an MXNet training algorithm and they are very simple to initialize and use and are optimized for speed. During training, we typically process training samples in small batches and over the entire training lifetime will end up processing each training example multiple times. In this tutorial, we'll configure the data iterator to feed examples in batches of 100. Keep in mind that each example is a 28x28 grayscale image and the corresponding label.
+
+Image batches are commonly represented by a 4-D array with shape `(batch_size, num_channels, width, height)`. For the MNIST dataset, since the images are grayscale, there is only one color channel. Also, the images are 28x28 pixels, and so each image has width and height equal to 28. Therefore, the shape of input is `(batch_size, 1, 28, 28)`. Another important consideration is the order of input samples. When feeding training examples, it is critical that we don't feed samples with the same label in succession. Doing so can slow down training.
+Data iterators take care of this by randomly shuffling the inputs. Note that we only need to shuffle the training data. The order does not matter for test data.
+
+The following source code initializes the data iterators for the MNIST dataset. Note that we initialize two iterators: one for train data and one for test data.
+
+```python
+batch_size = 100
+train_data = mx.io.NDArrayIter(mnist['train_data'], mnist['train_label'], batch_size, shuffle=True)
+val_data = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], batch_size)
+```
+
+## Approaches
+
+We will cover a couple of approaches for performing the hand written digit recognition task. The first approach makes use of a traditional deep neural network architecture called Multilayer Perceptron (MLP). We'll discuss its drawbacks and use that as a motivation to introduce a second more advanced approach called Convolution Neural Network (CNN) that has proven to work very well for image classification tasks.
+
+Now, let's import required nn modules
+
+```python
+from __future__ import print_function
+import mxnet as mx
+from mxnet import gluon
+from mxnet.gluon import nn
+from mxnet import autograd as ag
+```
+
+### Define a network: Multilayer Perceptron
+
+The first approach makes use of a [Multilayer Perceptron](https://en.wikipedia.org/wiki/Multilayer_perceptron) to solve this problem. We'll define the MLP using MXNet's imperative approach.
+
+MLPs consist of several fully connected layers. A fully connected layer or FC layer for short, is one where each neuron in the layer is connected to every neuron in its preceding layer. From a linear algebra perspective, an FC layer applies an [affine transform](https://en.wikipedia.org/wiki/Affine_transformation) to the *n x m* input matrix *X* and outputs a matrix *Y* of size *n x k*, where *k* is the number of neurons in the FC layer. *k* is also referred to as the hidden size. The output *Y* is computed according to the equation *Y = W X + b*. The FC layer has two learnable parameters, the *m x k* weight matrix *W* and the *m x 1* bias vector *b*.
+
+In an MLP, the outputs of most FC layers are fed into an activation function, which applies an element-wise non-linearity. This step is critical and it gives neural networks the ability to classify inputs that are not linearly separable. Common choices for activation functions are sigmoid, tanh, and [rectified linear unit](https://en.wikipedia.org/wiki/Rectifier_%28neural_networks%29) (ReLU). In this example, we'll use the ReLU activation function which has several desirable properties and is typically considered a default choice.
+
+The following code declares three fully connected layers with 128, 64 and 10 neurons each.
+The last fully connected layer often has its hidden size equal to the number of output classes in the dataset. Furthermore, these FC layers uses ReLU activation for performing an element-wise ReLU transformation on the FC layer output.
+
+To do this, we will use [Sequential layer](http://mxnet.io/api/python/gluon/gluon.html#mxnet.gluon.nn.Sequential) type. This is simply a linear stack of neural network layers. `nn.Dense` layers are nothing but the fully connected layers we discussed above.
+
+```python
+# define network
+net = nn.Sequential()
+with net.name_scope():
+    net.add(nn.Dense(128, activation='relu'))
+    net.add(nn.Dense(64, activation='relu'))
+    net.add(nn.Dense(10))
+```
+
+#### Initialize parameters and optimizer
+
+The following source code initializes all parameters received from parameter dict using [Xavier](http://mxnet.io/api/python/optimization/optimization.html#mxnet.initializer.Xavier) initializer
+to train the MLP network we defined above.
+
+For our training, we will make use of the stochastic gradient descent (SGD) optimizer. In particular, we'll be using mini-batch SGD. Standard SGD processes train data one example at a time. In practice, this is very slow and one can speed up the process by processing examples in small batches. In this case, our batch size will be 100, which is a reasonable choice. Another parameter we select here is the learning rate, which controls the step size the optimizer takes in search of a solution. We'll pick a learning rate of 0.02, again a reasonable choice. Settings such as batch size and learning rate are what are usually referred to as hyper-parameters. What values we give them can have a great impact on training performance.
+
+We will use [Trainer](http://mxnet.io/api/python/gluon/gluon.html#trainer) class to apply the
+[SGD optimizer](http://mxnet.io/api/python/optimization/optimization.html#mxnet.optimizer.SGD) on the
+initialized parameters.
+
+```python
+gpus = mx.test_utils.list_gpus()
+ctx =  [mx.gpu()] if gpus else [mx.cpu(0), mx.cpu(1)]
+net.initialize(mx.init.Xavier(magnitude=2.24), ctx=ctx)
+trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.02})
+```
+
+#### Train the network
+
+Typically, one runs the training until convergence, which means that we have learned a good set of model parameters (weights + biases) from the train data. For the purpose of this tutorial, we'll run training for 10 epochs and stop. An epoch is one full pass over the entire train data.
+
+We will take following steps for training:
+
+- Define [Accuracy evaluation metric](http://mxnet.io/api/python/metric/metric.html#mxnet.metric.Accuracy) over training data.
+- Loop over inputs for every epoch.
+- Forward input through network to get output.
+- Compute loss with output and label inside record scope.
+- Backprop gradient inside record scope.
+- Update evaluation metric and parameters with gradient descent.
+
+Loss function takes (output, label) pairs and computes a scalar loss for each sample in the mini-batch. The scalars measure how far each output is from the label.
+There are many predefined loss functions in gluon.loss. Here we use
+[softmax_cross_entropy_loss](http://mxnet.io/api/python/gluon/gluon.html#mxnet.gluon.loss.softmax_cross_entropy_loss) for digit classification. We will compute loss and do backward propagation inside
+training scope which is defined by `autograd.record()`.
+
+```python
+%%time
+epoch = 10
+# Use Accuracy as the evaluation metric.
+metric = mx.metric.Accuracy()
+softmax_cross_entropy_loss = gluon.loss.SoftmaxCrossEntropyLoss()
+for i in range(epoch):
+    # Reset the train data iterator.
+    train_data.reset()
+    # Loop over the train data iterator.
+    for batch in train_data:
+        # Splits train data into multiple slices along batch_axis
+        # and copy each slice into a context.
+        data = gluon.utils.split_and_load(batch.data[0], ctx_list=ctx, batch_axis=0)
+        # Splits train labels into multiple slices along batch_axis
+        # and copy each slice into a context.
+        label = gluon.utils.split_and_load(batch.label[0], ctx_list=ctx, batch_axis=0)
+        outputs = []
+        # Inside training scope
+        with ag.record():
+            for x, y in zip(data, label):
+                z = net(x)
+                # Computes softmax cross entropy loss.
+                loss = softmax_cross_entropy_loss(z, y)
+                # Backpropagate the error for one iteration.
+                loss.backward()
+                outputs.append(z)
+        # Updates internal evaluation
+        metric.update(label, outputs)
+        # Make one step of parameter update. Trainer needs to know the
+        # batch size of data to normalize the gradient by 1/batch_size.
+        trainer.step(batch.data[0].shape[0])
+    # Gets the evaluation result.
+    name, acc = metric.get()
+    # Reset evaluation result to initial state.
+    metric.reset()
+    print('training acc at epoch %d: %s=%f'%(i, name, acc))
+```
+
+#### Prediction
+
+After the above training completes, we can evaluate the trained model by running predictions on validation dataset. Since the dataset also has labels for all test images, we can compute the accuracy metric over validation data as follows:
+
+```python
+# Use Accuracy as the evaluation metric.
+metric = mx.metric.Accuracy()
+# Reset the validation data iterator.
+val_data.reset()
+# Loop over the validation data iterator.
+for batch in val_data:
+    # Splits validation data into multiple slices along batch_axis
+    # and copy each slice into a context.
+    data = gluon.utils.split_and_load(batch.data[0], ctx_list=ctx, batch_axis=0)
+    # Splits validation label into multiple slices along batch_axis
+    # and copy each slice into a context.
+    label = gluon.utils.split_and_load(batch.label[0], ctx_list=ctx, batch_axis=0)
+    outputs = []
+    for x in data:
+        outputs.append(net(x))
+    # Updates internal evaluation
+    metric.update(label, outputs)
+print('validation acc: %s=%f'%metric.get())
+assert metric.get()[1] > 0.94
+```
+
+If everything went well, we should see an accuracy value that is around 0.96, which means that we are able to accurately predict the digit in 96% of test images. This is a pretty good result. But as we will see in the next part of this tutorial, we can do a lot better than that.
+
+### Convolutional Neural Network
+
+Earlier, we briefly touched on a drawback of MLP when we said we need to discard the input image's original shape and flatten it as a vector before we can feed it as input to the MLP's first fully connected layer. Turns out this is an important issue because we don't take advantage of the fact that pixels in the image have natural spatial correlation along the horizontal and vertical axes. A convolutional neural network (CNN) aims to address this problem by using a more structured weight representation. Instead of flattening the image and doing a simple matrix-matrix multiplication, it employs one or more convolutional layers that each performs a 2-D convolution on the input image.
+
+A single convolution layer consists of one or more filters that each play the role of a feature detector. During training, a CNN learns appropriate representations (parameters) for these filters. Similar to MLP, the output from the convolutional layer is transformed by applying a non-linearity. Besides the convolutional layer, another key aspect of a CNN is the pooling layer. A pooling layer serves to make the CNN translation invariant: a digit remains the same even when it is shifted left/right/up/down by a few pixels. A pooling layer reduces a *n x m* patch into a single value to make the network less sensitive to the spatial location. Pooling layer is always included after each conv (+ activation) layer in the CNN.
+
+The following source code defines a convolutional neural network architecture called LeNet. LeNet is a popular network known to work well on digit classification tasks. We will use a slightly different version from the original LeNet implementation, replacing the sigmoid activations with tanh activations for the neurons.
+
+A typical way to write your network is creating a new class inherited from `gluon.Block`
+class. We can define the network by composing and inheriting Block class as follows:
+
+```python
+import mxnet.ndarray as F
+
+class Net(gluon.Block):
+    def __init__(self, **kwargs):
+        super(Net, self).__init__(**kwargs)
+        with self.name_scope():
+            # layers created in name_scope will inherit name space
+            # from parent layer.
+            self.conv1 = nn.Conv2D(20, kernel_size=(5,5))
+            self.pool1 = nn.MaxPool2D(pool_size=(2,2), strides = (2,2))
+            self.conv2 = nn.Conv2D(50, kernel_size=(5,5))
+            self.pool2 = nn.MaxPool2D(pool_size=(2,2), strides = (2,2))
+            self.fc1 = nn.Dense(500)
+            self.fc2 = nn.Dense(10)
+
+    def forward(self, x):
+        x = self.pool1(F.tanh(self.conv1(x)))
+        x = self.pool2(F.tanh(self.conv2(x)))
+        # 0 means copy over size from corresponding dimension.
+        # -1 means infer size from the rest of dimensions.
+        x = x.reshape((0, -1))
+        x = F.tanh(self.fc1(x))
+        x = F.tanh(self.fc2(x))
+        return x
+```
+
+We just defined the forward function here, and the backward function to compute gradients
+is automatically defined for you using autograd.
+We also imported `mxnet.ndarray` package to use activation functions from `ndarray` API.
+
+Now, We will create the network as follows:
+
+```python
+net = Net()
+```
+
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/conv_mnist.png){ width=500px }
+
+**Figure 3:** First conv + pooling layer in LeNet.
+
+Now we train LeNet with similar hyper-parameters as before. Note that, if a GPU is available, we recommend using it. This greatly speeds up computation given that LeNet is more complex and compute-intensive than the previous multilayer perceptron. To do so, we only need to change `mx.cpu()` to `mx.gpu()` and MXNet takes care of the rest. Just like before, we'll stop training after 10 epochs.
+
+Training and prediction can be done in the similar way as we did for MLP.
+
+#### Initialize parameters and optimizer
+
+We will initialize the network parameters as follows:
+
+```python
+# set the context on GPU is available otherwise CPU
+ctx = [mx.gpu() if mx.test_utils.list_gpus() else mx.cpu()]
+net.initialize(mx.init.Xavier(magnitude=2.24), ctx=ctx)
+trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.03})
+```
+
+#### Training
+
+```python
+# Use Accuracy as the evaluation metric.
+metric = mx.metric.Accuracy()
+softmax_cross_entropy_loss = gluon.loss.SoftmaxCrossEntropyLoss()
+
+for i in range(epoch):
+    # Reset the train data iterator.
+    train_data.reset()
+    # Loop over the train data iterator.
+    for batch in train_data:
+        # Splits train data into multiple slices along batch_axis
+        # and copy each slice into a context.
+        data = gluon.utils.split_and_load(batch.data[0], ctx_list=ctx, batch_axis=0)
+        # Splits train labels into multiple slices along batch_axis
+        # and copy each slice into a context.
+        label = gluon.utils.split_and_load(batch.label[0], ctx_list=ctx, batch_axis=0)
+        outputs = []
+        # Inside training scope
+        with ag.record():
+            for x, y in zip(data, label):
+                z = net(x)
+                # Computes softmax cross entropy loss.
+                loss = softmax_cross_entropy_loss(z, y)
+                # Backpropogate the error for one iteration.
+                loss.backward()
+                outputs.append(z)
+        # Updates internal evaluation
+        metric.update(label, outputs)
+        # Make one step of parameter update. Trainer needs to know the
+        # batch size of data to normalize the gradient by 1/batch_size.
+        trainer.step(batch.data[0].shape[0])
+    # Gets the evaluation result.
+    name, acc = metric.get()
+    # Reset evaluation result to initial state.
+    metric.reset()
+    print('training acc at epoch %d: %s=%f'%(i, name, acc))
+```
+
+#### Prediction
+
+Finally, we'll use the trained LeNet model to generate predictions for the test data.
+
+```python
+# Use Accuracy as the evaluation metric.
+metric = mx.metric.Accuracy()
+# Reset the validation data iterator.
+val_data.reset()
+# Loop over the validation data iterator.
+for batch in val_data:
+    # Splits validation data into multiple slices along batch_axis
+    # and copy each slice into a context.
+    data = gluon.utils.split_and_load(batch.data[0], ctx_list=ctx, batch_axis=0)
+    # Splits validation label into multiple slices along batch_axis
+    # and copy each slice into a context.
+    label = gluon.utils.split_and_load(batch.label[0], ctx_list=ctx, batch_axis=0)
+    outputs = []
+    for x in data:
+        outputs.append(net(x))
+    # Updates internal evaluation
+    metric.update(label, outputs)
+print('validation acc: %s=%f'%metric.get())
+assert metric.get()[1] > 0.98
+```
+
+If all went well, we should see a higher accuracy metric for predictions made using LeNet. With CNN we should be able to correctly predict around 98% of all test images.
+
+## Summary
+
+In this tutorial, we have learned how to use MXNet to solve a standard computer vision problem: classifying images of hand written digits. You have seen how to quickly and easily build, train and evaluate models such as MLP and CNN with MXNet Gluon package.
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/python_docs/python/tutorials/packages/gluon/image/pretrained_models.md b/docs/python_docs/python/tutorials/packages/gluon/image/pretrained_models.md
new file mode 100644
index 000000000000..c2f72d876418
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/image/pretrained_models.md
@@ -0,0 +1,245 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Using pre-trained models in MXNet
+
+
+In this tutorial we will see how to use multiple pre-trained models with Apache MXNet. First, let's download three image classification models from the Apache MXNet [Gluon model zoo](https://mxnet.incubator.apache.org/api/python/gluon/model_zoo.html).
+* **DenseNet-121** ([research paper](https://arxiv.org/abs/1608.06993)), improved state of the art on [ImageNet dataset](http://image-net.org/challenges/LSVRC) in 2016.
+* **MobileNet** ([research paper](https://arxiv.org/abs/1704.04861)), MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks, suitable for mobile applications.
+* **ResNet-18** ([research paper](https://arxiv.org/abs/1512.03385v1)), the -152 version is the 2015 winner in multiple categories.
+
+Why would you want to try multiple models? Why not just pick the one with the best accuracy? As we will see later in the tutorial, even though these models have been trained on the same dataset and optimized for maximum accuracy, they do behave slightly differently on specific images. In addition, prediction speed and memory footprints can vary, and that is an important factor for many applications. By trying a few pretrained models, you have an opportunity to find a model that can be a good fit for solving your business problem.
+
+
+```python
+import json
+
+import matplotlib.pyplot as plt
+import mxnet as mx
+from mxnet import gluon, nd
+from mxnet.gluon.model_zoo import vision
+import numpy as np
+%matplotlib inline
+```
+
+## Loading the model
+
+The [Gluon Model Zoo](https://mxnet.incubator.apache.org/api/python/gluon/model_zoo.html) provides a collection of off-the-shelf models. You can get the ImageNet pre-trained model by using `pretrained=True`. 
+If you want to train on your own classification problem from scratch, you can get an untrained network with a specific number of classes using the `classes` parameter: for example `net = vision.resnet18_v1(classes=10)`. However note that you cannot use the `pretrained` and `classes` parameter at the same time. If you want to use pre-trained weights as initialization of your network except for the last layer, have a look at the last section of this tutorial.
+
+We can specify the *context* where we want to run the model: the default behavior is to use a CPU context. There are two reasons for this:
+* First, this will allow you to test the notebook even if your machine is not equipped with a GPU :)
+* Second, we're going to predict a single image and we don't have any specific performance requirements. For production applications where you'd want to predict large batches of images with the best possible throughput, a GPU could definitely be the way to go.
+* If you want to use a GPU, make sure you have pip installed the right version of mxnet, or you will get an error when using the `mx.gpu()` context. Refer to the [install instructions](http://mxnet.incubator.apache.org/install/index.html)
+
+
+```python
+# We set the context to CPU, you can switch to GPU if you have one and installed a compatible version of MXNet 
+ctx = mx.cpu() 
+```
+
+
+```python
+# We can load three the three models
+densenet121 = vision.densenet121(pretrained=True, ctx=ctx)
+mobileNet = vision.mobilenet0_5(pretrained=True, ctx=ctx)
+resnet18 = vision.resnet18_v1(pretrained=True, ctx=ctx)
+```
+
+We can look at the description of the MobileNet network for example, which has a relatively simple yet deep architecture
+
+
+```python
+print(mobileNet)
+```
+
+
+Let's have a closer look at the first convolution layer:
+
+
+```python
+print(mobileNet.features[0].params)
+```
+
+The first layer applies **`16`** different convolutional masks, of size **`InputChannels x 3 x 3`**. For the first convolution, there are **`3`** input channels, the `R`, `G`, `B` channels of the input image. That gives us the weight matrix of shape **`16 x 3 x 3 x 3`**. There is no bias applied in this convolution.
+
+Let's have a look at the output layer now:
+
+
+```python
+print(mobileNet.output)
+```
+
+
+Did you notice the shape of layer? The weight matrix is **1000 x 512**. This layer contains 1,000 neurons: each of them will store an activation representative of the probability of the image belonging to a specific category. Each neuron is also fully connected to all 512 neurons in the previous layer.
+
+OK, enough exploring! Now let's use these models to classify our own images.
+
+## Loading the data
+All three models have been pre-trained on the ImageNet data set which includes over 1.2 million pictures of objects and animals sorted in 1,000 categories.
+We get the imageNet list of labels. That way we have the mapping so when the model predicts for example category index `4`, we know it is predicting `hammerhead, hammerhead shark`
+
+
+```python
+mx.test_utils.download('https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/onnx/image_net_labels.json')
+categories = np.array(json.load(open('image_net_labels.json', 'r')))
+print(categories[4])
+```
+
+Get a test image
+
+
+```python
+filename = mx.test_utils.download('https://github.com/dmlc/web-data/blob/master/mxnet/doc/tutorials/onnx/images/dog.jpg?raw=true', fname='dog.jpg')
+```
+
+If you want to use your own image for the test, copy the image to the same folder that contains the notebook and change the following line:
+
+
+```python
+filename = 'dog.jpg'
+```
+
+Load the image as a NDArray
+
+
+```python
+image = mx.image.imread(filename)
+plt.imshow(image.asnumpy())
+```
+
+Neural network expects input in a specific format. Usually images comes in the `Width x Height x Channels` format. Where channels are the RGB channels.
+This network accepts images in the `BatchSize x 3 x 224 x 224`. `224 x 224` is the image resolution, that's how the model was trained. `3` is the number of channels : Red, Green and Blue (in this order). In this case we use a `BatchSize` of `1` since we are predicting one image at a time.
+Here are the transformation steps:
+* Read the image: this will return a NDArray shaped as (image height, image width, 3), with the three channels in RGB order.
+* Resize the shorter edge of the image 224.
+* Crop, using a size of 224x224 from the center of the image.
+* Shift the mean and standard deviation of our color channels to match the ones of the dataset the network has been trained on.
+* Transpose the array from (Height, Width, 3) to (3, Height, Width).
+* Add a fourth dimension, the batch dimension.
+
+
+
+```python
+def transform(image):
+    resized = mx.image.resize_short(image, 224) #minimum 224x224 images
+    cropped, crop_info = mx.image.center_crop(resized, (224, 224))
+    normalized = mx.image.color_normalize(cropped.astype(np.float32)/255,
+                                      mean=mx.nd.array([0.485, 0.456, 0.406]),
+                                      std=mx.nd.array([0.229, 0.224, 0.225])) 
+    # the network expect batches of the form (N,3,224,224)
+    transposed = normalized.transpose((2,0,1))  # Transposing from (224, 224, 3) to (3, 224, 224)
+    batchified = transposed.expand_dims(axis=0) # change the shape from (3, 224, 224) to (1, 3, 224, 224)
+    return batchified
+```
+
+## Testing the different networks
+We run the image through each pre-trained network. The models output a *NDArray* holding 1,000 activation values, which we convert to probabilities using the `softmax()` function, corresponding to the 1,000 categories it has been trained on. The output prediction NDArray has only one row since batch size is equal to 1
+
+
+```python
+predictions = resnet18(transform(image)).softmax()
+print(predictions.shape)
+```
+
+
+We then take the top `k` predictions for our image, here the top `3`.
+
+
+```python
+top_pred = predictions.topk(k=3)[0].asnumpy()
+```
+
+And we print the categories predicted with their corresponding probabilities:
+
+
+```python
+for index in top_pred:
+    probability = predictions[0][int(index)]
+    category = categories[int(index)]
+    print("{}: {:.2f}%".format(category, probability.asscalar()*100))
+```
+
+
+Let's turn this into a function. Our parameters are an image, a model, a list of categories and the number of top categories we'd like to print. 
+
+
+```python
+def predict(model, image, categories, k):
+    predictions = model(transform(image)).softmax()
+    top_pred = predictions.topk(k=k)[0].asnumpy()
+    for index in top_pred:
+        probability = predictions[0][int(index)]
+        category = categories[int(index)]
+        print("{}: {:.2f}%".format(category, probability.asscalar()*100))
+    print('')
+```
+
+### DenseNet121
+
+
+```python
+%%time
+predict(densenet121, image, categories, 3)
+```
+
+
+### MobileNet
+
+
+```python
+%%time
+predict(mobileNet, image, categories, 3)
+```
+
+### Resnet-18
+
+
+```python
+%%time
+predict(resnet18, image, categories, 3)
+```
+
+As you can see, pre-trained networks produce slightly different predictions, and have different run-time. In this case, MobileNet is almost **5 times faster** than DenseNet!
+
+## Fine-tuning pre-trained models
+
+You can replace the output layer of your pre-trained model to fit the right number of classes for your own image classification task like this, for example for 10 classes:
+
+
+```python
+NUM_CLASSES = 10
+with resnet18.name_scope():
+    resnet18.output = gluon.nn.Dense(NUM_CLASSES)
+```
+
+
+```python
+print(resnet18.output)
+```
+
+
+Now you can train your model on your new data using the pre-trained weights as initialization. This is called transfer learning and it has proved to be very useful especially in the cases where you only have access to a small dataset. Your network will have already learned how to perform general pattern detection and feature extraction on the larger dataset.
+You can learn more about transfer learning and fine-tuning with MXNet in these tutorials:
+- [Transferring knowledge through fine-tuning](http://gluon.mxnet.io/chapter08_computer-vision/fine-tuning.html)
+- [Fine Tuning an ONNX Model](https://mxnet.incubator.apache.org/tutorials/onnx/fine_tuning_gluon.html)
+
+
+That's it! Explore the model zoo, have fun with pre-trained models!
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/python_docs/python/tutorials/packages/gluon/index.rst b/docs/python_docs/python/tutorials/packages/gluon/index.rst
new file mode 100644
index 000000000000..85bb04faad42
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/index.rst
@@ -0,0 +1,276 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Gluon
+=====
+
+Getting started
+---------------
+
+.. container:: cards
+
+   .. card::
+      :title: A 60-minute Gluon crash course
+      :link: ../../getting-started/crash-course/index.html
+
+      Six 10-minute tutorials covering the core concepts of MXNet using the Gluon API.
+
+   .. card::
+      :title: Gluon - Neural network building blocks
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/gluon.html
+
+      An introduction to defining and training neural networks with Gluon.
+
+   .. card::
+      :title: Gluon: from experiment to deployment
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/gluon_from_experiment_to_deployment.html
+
+      An end to end tutorial on working with the MXNet Gluon API.
+
+   .. card::
+      :title: Custom Layers for Beginners
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/custom_layer.html
+
+      A guide to implementing custom layers for beginners.
+
+   .. card::
+      :title: Logistic regression using Gluon API explained
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/logistic_regression_explained.html
+
+      Implementing logistic regression using the Gluon API.
+
+   .. card::
+      :title: Saving and Loading Gluon Models
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/save_load_params.html
+
+      Saving and loading trained models.
+
+   .. card::
+      :title: Using pre-trained models in MXNet
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/pretrained_models.html
+
+      Using pre-trained models with Apache MXNet.
+
+Data
+----
+
+.. container:: cards
+
+   .. card::
+      :title: Data Loading
+      :link: data.html
+
+      How to load data for training.
+
+   .. card::
+      :title: Image Augmentation
+      :link: image-augmentation.html
+
+      Boost your training dataset with image augmentation.
+
+   .. card::
+      :title: Data Augmentation
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/data_augmentation.html
+
+      A guide to data augmentation.
+
+   .. card::
+      :title: Gluon Datasets and DataLoader
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/datasets.html
+
+      A guide to loading data using the Gluon API.
+
+   .. card::
+      :title: NDArray - Scientific computing on CPU and GPU
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/ndarray.html
+
+      A guide to the NDArray data structure.
+
+Training
+--------
+
+.. container:: cards
+
+   .. card::
+      :title: Neural Networks
+      :link: nn.html
+
+      How to use Layers and Blocks.
+
+   .. card::
+      :title: Normalization Blocks
+      :link: normalization/normalization.html
+
+      Understand usage of normalization layers (such as BatchNorm).
+
+   .. card::
+      :title: Activation Blocks
+      :link: activations/activations.html
+
+      Understand usage of activation layers (such as ReLU).
+
+   .. card::
+      :title: Loss Functions
+      :link: loss/loss.html
+
+      How to use loss functions for predicting outputs.
+
+   .. card::
+      :title: Initializing Parameters
+      :link: init.html
+
+      How to use the init function.
+
+   .. card::
+      :title: Parameter Management
+      :link: parameters.html
+
+      How to manage parameters.
+
+   .. card::
+      :title: Fit API Tutorial
+      :link: fit_api_tutorial.html
+
+      How to use the fit API
+
+   .. card::
+      :title: Learning Rate Finder
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/learning_rate_finder.html
+
+      How to use the Learning Rate Finder to find a good learning rate.
+
+   .. card::
+      :title: Learning Rate Schedules
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/learning_rate_schedules.html
+
+      How to schedule Learning Rate change over time.
+
+   ..
+      .. card::
+         :title: Optimizer
+         :link: optimizer.html
+
+         How to use optimizer.
+   ..
+
+   .. card::
+      :title: Trainer
+      :link: trainer.html
+
+      How to update neural network parameters using an optimization method.
+
+   .. card::
+      :title: Autograd API
+      :link: ../autograd/autograd.html
+
+      How to use Automatic Differentiation with the Autograd API.
+
+Advanced Topics
+---------------
+
+.. container:: cards
+
+   .. card::
+      :title: Naming
+      :link: naming.html
+
+      Best practices for the naming of things.
+
+   .. card::
+      :title: Custom Layers
+      :link: custom-layer.html
+
+      A guide to implementing custom layers.
+
+   .. card::
+      :title: Custom Operators
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/customop.html
+
+      Building custom operators with numpy.
+
+   .. card::
+      :title: Custom Loss
+      :link: custom-loss/custom-loss.html
+
+      A guide to implementing custom losses.
+
+   .. card::
+      :title: Gotchas using NumPy in Apache MXNet
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/gotchas_numpy_in_mxnet.html
+
+      Common misconceptions when using NumPy in Apache MXNet.
+
+   .. card::
+      :title: Hybrid- Faster training and easy deployment
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/hybrid.html
+
+      Combines declarative and imperative programming using HybridBlock.
+
+   .. card::
+      :title: Hybridize
+      :link: hybridize.html
+
+      Speed up training with hybrid networks.
+
+   .. card::
+      :title: Advanced Learning Rate Schedules
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/learning_rate_schedules_advanced.html
+
+      Advanced exploration of Learning Rate shapes.
+
+
+Applications Topics
+-------------------
+
+.. container:: cards
+
+   .. card::
+      :title: Image Tutorials
+      :link: image/index.html
+
+      How to create deep learning models for images.
+
+   .. card::
+      :title: Text Tutorials
+      :link: text/index.html
+
+      How to create deep learning models for text.
+
+
+.. toctree::
+   :hidden:
+   :maxdepth: 2
+
+   ../../getting-started/crash-course
+   custom_layer_beginners
+   data
+   image-augmentation
+   data_augmentation
+   nn
+   normalization/normalization
+   activations/activations
+   loss
+   custom-loss/custom-loss
+   init
+   parameters
+   trainer
+   naming
+   custom-layer
+   customop
+   hybridize
+   image/index
+   text/index
diff --git a/docs/python_docs/python/tutorials/packages/gluon/init.md b/docs/python_docs/python/tutorials/packages/gluon/init.md
new file mode 100644
index 000000000000..30b347cd824d
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/init.md
@@ -0,0 +1,322 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Initialization
+
+<!-- adapted from diveintodeeplearning -->
+
+In the [Neural Networks](nn.html) section we played fast and loose with setting
+up our networks. In particular we did the following things that *shouldn't*
+work:
+
+* We defined the network architecture with no regard to the input
+  dimensionality.
+* We added layers without regard to the output dimension of the previous layer.
+* We even 'initialized' these parameters without knowing how many parameters
+  were were to initialize.
+
+All of those things sound impossible and indeed, they are. After all, there's
+no way MXNet (or any other framework for that matter) could predict what the
+input dimensionality of a network would be. Later on, when working with
+convolutional networks and images this problem will become even more pertinent,
+since the input dimensionality (i.e. the resolution of an image) will affect
+the dimensionality of subsequent layers. The ability to
+determine parameter dimensionality during run-time rather than at coding time
+greatly simplifies the process of doing deep learning. 
+
+## Instantiating a Network
+
+Let's see what happens when we instantiate a network. We start by defining a multi-layer perceptron.
+
+```{.python .input}
+from mxnet import init, nd
+from mxnet.gluon import nn
+
+
+def getnet():
+    net = nn.Sequential()
+    net.add(nn.Dense(256, activation='relu'))
+    net.add(nn.Dense(10))
+    return net
+
+net = getnet()
+```
+
+At this point the network doesn't really know yet what the dimensionalities of
+the various parameters should be. All one could tell at this point is that each
+layer needs weights and bias, albeit of unspecified dimensionality. If we try
+accessing the parameters, that's exactly what happens.
+
+```{.python .input}
+print(net.collect_params())
+```
+
+You'll notice `None` here in each `Dense` layer. This absense of value is how
+MXNet keeps track of unspecified dimensionality. In particular, trying to access
+`net[0].weight.data()` at this point would trigger a runtime error stating that
+the network needs initializing before it can do anything. 
+
+Note that if we did want to specify dimensionality, we could have done so by
+using the kwarg `in_units`, e.g. `Dense(256, activiation='relu', in_units=20)`.
+
+Let's see whether anything changes after we initialize the parameters:
+
+
+```{.python .input}
+net.initialize()
+net.collect_params()
+```
+
+As we can see, nothing really changed. Only once we provide the network with
+some data do we see a difference. Let's try it out.
+
+```{.python .input}
+x = nd.random.uniform(shape=(2, 20))
+net(x)  # Forward computation
+print(net.collect_params())
+```
+
+We see all the dimensions have been determined and the parameters initialized.
+This is because shape inference and parameter initialization have been
+performed in a lazy manner, so they are performed only when needed. In the
+above case, they are performed as a prerequisite to the forward computation.
+
+Dimensional inference works like this: as soon as we knew the input
+dimensionality, $\mathbf{x} \in \mathbb{R}^{20}$ it was possible to define the
+weight matrix for the first layer, i.e. $\mathbf{W}_1 \in \mathbb{R}^{256 \times
+20}$. With that out of the way, we can progress to the second layer, define its
+dimensionality to be $10 \times 256$ and so on through the computational graph
+and resolve all the dimensions as they become available. Once this is known, we
+can proceed by initializing parameters. This is the solution to the three
+problems outlined above.
+
+
+## Deferred Initialization in Practice
+
+Now that we know how it works in theory, let's see when the initialization is
+actually triggered. In order to do so, we mock up an initializer which does
+nothing but report a debug message stating when it was invoked and with which
+parameters.
+
+```{.python .input  n=22}
+class PrintInit(init.Initializer):
+    def _init_weight(self, name, data):
+        print('Init', name, data.shape)
+        # The actual initialization logic is omitted here.
+
+net = getnet()
+net.initialize(init=PrintInit())
+```
+
+Note that, although `MyInit` will print information about the model parameters
+when it is called, the above `initialize` function does not print any
+information after it has been executed.  Therefore there is no actual
+initialization when calling the `initialize` function - this
++initialization is deferred until forward is called for the first time. Next,
+we define the input and perform a forward calculation.
+
+```{.python .input  n=25}
+x = nd.random.uniform(shape=(2, 20))
+y = net(x)
+```
+
+At this time, information on the model parameters is printed. When performing a
+forward calculation based on the input `x`, the system can automatically infer
+the shape of the weight parameters of all layers based on the shape of the
+input. Once the system has created these parameters, it calls the `MyInit`
+instance to initialize them before proceeding to the forward calculation.
+
+Of course, this initialization will only be called when completing the initial
+forward calculation. After that, we will not re-initialize when we run the
+forward calculation `net(x)`, so the output of the `MyInit` instance will not be
+generated again.
+
+```{.python .input}
+y = net(x)
+```
+
+As mentioned at the beginning of this section, deferred initialization can also
+cause confusion. Before the first forward calculation, we were unable to
+directly manipulate the model parameters, for example, we could not use the
+`data` and `set_data` functions to get and modify the parameters. Therefore, we
+often force initialization by sending a sample observation through the network.
+
+## Forced Initialization
+
+Deferred initialization does not occur if the system knows the shape of all
+parameters when calling the `initialize` function. This can occur in two cases:
+
+* We've already seen some data and we just want to reset the parameters.
+* We specified all input and output dimensions of the network or layer when
+  defining it.
+
+The first case works just fine, as illustrated below.
+
+```{.python .input}
+net.initialize(init=MyInit(), force_reinit=True)
+```
+
+The second case requires us to specify the remaining set of parameters when
+creating the layer. For instance, for dense layers we also need to specify the
+`in_units` so that initialization can occur immediately once `initialize` is
+called.
+
+```{.python .input}
+net = nn.Sequential()
+net.add(nn.Dense(256, in_units=20, activation='relu'))
+net.add(nn.Dense(10, in_units=256))
+
+net.initialize(init=MyInit())
+```
+
+## Parameter Initialization
+
+By default, MXNet initializes the weight matrices uniformly by drawing random
+values with uniform-distribution between $-0.07$ and $0.07$ ($U[-0.07, 0.07]$)
+and updates the bias parameters by setting them all to $0$.  However, we often
+need to use other methods to initialize the weights.  MXNet's `init` module
+provides a variety of preset initialization methods, but if we want something
+out of the ordinary, we need a bit of extra work.
+
+### Built-in Initialization
+
+Let's begin with the built-in initializers. The code below initializes all
+parameters with Gaussian random variables.
+
+```{.python .input  n=9}
+# force_reinit ensures that the variables are initialized again, regardless of
+# whether they were already initialized previously.
+net.initialize(init=init.Normal(sigma=0.01), force_reinit=True)
+print(net[0].weight.data()[0])
+```
+
+If we wanted to initialize all parameters to $1$, we could do this simply by
+changing the initializer to `Constant(1)`.
+
+```{.python .input  n=10}
+net.initialize(init=init.Constant(1), force_reinit=True)
+net[0].weight.data()[0]
+```
+
+If we want to initialize only a specific parameter in a different manner, we
+can simply set the initializer only for the appropriate subblock (or
+parameter). For instance, below we initialize the second layer to a constant
+value of $42$ and we use the `Xavier` initializer for the weights of the
+first layer.
+
+```{.python .input  n=11}
+net[0].weight.initialize(init=init.Xavier(), force_reinit=True)
+net[1].initialize(init=init.Constant(42), force_reinit=True)
+
+# First layer
+print(net[0].weight.data()[0])
+print(net[0].bias.data()[0])  # initialized to 0
+
+# Second layer
+print(net[1].weight.data()[0,0])
+print(net[1].bias.data()[0])  # initialized to 0
+```
+
+### Custom Initialization
+
+Sometimes, the initialization methods we need are not provided in the `init`
+module. At this point, we can implement a subclass of the `Initializer` class
+so that we can use it like any other initialization method. Usually, we only
+need to implement the `_init_weight` function to suit our needs. In the example
+below, we pick a decidedly bizarre and nontrivial distribution, just to prove
+the point. We draw the coefficients from the following distribution:
+
+$$
+\begin{aligned}
+    w \sim \begin{cases}
+        U[5, 10] & \text{ with probability } \frac{1}{4} \\
+            0    & \text{ with probability } \frac{1}{2} \\
+        U[-10, -5] & \text{ with probability } \frac{1}{4}
+    \end{cases}
+\end{aligned}
+$$
+
+```{.python .input  n=12}
+class MyInit(init.Initializer):
+    def _init_weight(self, name, data):
+        print('Init', name, data.shape)
+        data[:] = nd.random.uniform(low=-10, high=10, shape=data.shape)
+        data *= data.abs() >= 5
+
+net.initialize(MyInit(), force_reinit=True)
+net[0].weight.data()[0]
+```
+
+If this functionality is insufficient, we can even set parameters directly.
+Since `data()` returns an `NDArray` we can access it just like any other matrix.
+A note for advanced users - if you want to adjust parameters within an
+`autograd` scope you need to use `set_data` to avoid confusing the automatic
+differentiation mechanics.
+
+```{.python .input  n=13}
+net[0].weight.data()[:] += 1
+net[0].weight.data()[0,0] = 42
+net[0].weight.data()[0]
+```
+
+## Tied Parameters
+
+In some cases, we want to share model parameters across multiple layers. For
+instance when we want to find good word embeddings we may decide to use the
+same parameters both for encoding and decoding of words. Let's see how to do
+this a bit more elegantly. In the following we construct a dense layer and then
+use its parameters specifically to set those of another layer.
+
+```{.python .input  n=14}
+net = nn.Sequential()
+# We need to give the shared layer a name such that we can reference its
+# parameters.
+shared = nn.Dense(8, activation='relu')
+net.add(nn.Dense(8, activation='relu'),
+        shared,
+        nn.Dense(8, activation='relu', params=shared.params),
+        nn.Dense(10))
+net.initialize()
+
+x = nd.random.uniform(shape=(2, 20))
+net(x)
+
+# Check whether the parameters are the same.
+print(net[1].weight.data()[0] == net[2].weight.data()[0])
+net[1].weight.data()[0,0] = 100
+# And make sure that they're actually the same object rather than just having
+# the same value.
+print(net[1].weight.data()[0] == net[2].weight.data()[0])
+```
+
+The above example shows that the parameters of the second and third layer are
+tied. As Python objects, they are identical rather than just being equal.
+That is, by changing one of the parameters the other one changes too. What
+happens to the gradients is quite ingenious. Since the model parameters contain
+gradients, the gradients of the second hidden layer and the third hidden layer
+are accumulated in `shared.params.grad` during backpropagation.
+
+# Conclusion
+
+In this tutorial you learnt how to initialize a neural network, and should now
+understand the difference between deferred and forced initialization. Some more advanced
+cases you should now be aware of include custom initialization and tied parameters.
+
+# Recommended Next Steps
+
+* Check out the [API Docs](http://mxnet.incubator.apache.org/versions/master/api/python/optimization/optimization.html) on initialization for a list of available initialization methods.
+* See [this tutorial](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/naming.html) for more information on Gluon Parameters.
diff --git a/docs/python_docs/python/tutorials/packages/gluon/loss/ctc_loss.png b/docs/python_docs/python/tutorials/packages/gluon/loss/ctc_loss.png
new file mode 100644
index 0000000000000000000000000000000000000000..73623979048d57e4000580bbe9029e6fd667e2b3
GIT binary patch
literal 14513
zcmeIYWl&sQ&@DWWfuO<NJ-7w8!QCB#TX1)W;O-6qf;#~c+#$gU?iO5w2fc^K-nZ)e
z|JD6*sZ-R<nJwLW_wHW3dPgWJNFpQPBY;34WN9fe6%Yto5cpgV2MhccS?i|;fe>mf
zMMagQMMX)Kob1glZOlL*sfd)+*J`Sk*n^!n`d;}|AIYgMsoR5-Cq&cUaEge)Q}sbm
zQ3=?%gG_|Q!C2z2X%S*!;+4f1=3!y5YTtbS^r<=+hN1N6Rch@QZ?~Hxq1~Owu~FZf
zmDAz-u|KaM`@3)<Wwl5k%OE-B@UH~ybiAPvB+#f35Yz-zZto~+eNgZtRC)8E0mo#N
zMq!IJ|H|Q-|8q^^cE5!KR6vk&?3S6sl#*bK1cBz;Auvez)rYDfOa*7WAr~}W(xJg(
z9>>(B5+1wM)6waVA>Ru5p+SK?8{z0sr2deL5e7T*#V`@c1-Y&*M5SxULPl2KM*jPN
z^PYx=OH0APdcS3%E9nQ>ac*hFH#EvUU#v6Gx`Xq`<hFl3<SqT~Oi887^$w-RPo_*z
zg!3}{<gJmEctT?&{N8Ya@WPY*iane18a~g&F4TL3@PeDz&zV61-eQzh@|{1kP!bX6
zV8JD-w7;;`r_Rs*{JK5A@pAK>v%7AL#~tldw0;p<{HB23<))yTw(l6wOT>u~rDUfG
zZ=<x98&1!Q8P4$6Fjq+*e?U@B^z`^moSWE2`d)>3#3ktxvlw=KJnw+<hlOc~%*m*j
zK`oEM&MoZt4Sebm${8rPi{z0-n7bCN(M72B%4H_w_rlcaI|oIao$%WT=?pn+FRRTA
z8>l%`vZ<btW#WK+cJ6ze&eJ>|UDRNfJXy)Pom((S%SaHX<P~HJ9b}5I<IL*=DxCv;
zVBUxn*Z#sYa*2Z-AMnnrDJ1}Z0{X5CR|`b24)=aC!dDm-?$x9r&w3DLJgk@@*7sKg
z5VE(hOWk%#Z|J&>lwQRKCw;=peudMm?trljb<<730t)S>XMv^eq67<jgd<FnQb`g0
z42&j4{Qkx%l%LH1EjTjNIA2T(6p_#WfkF)_1+qOsR*HQG!;7f*v08Lx;?)OK{E$^)
z14D3emx~tGY>2!OUpmZvx6v6JH<~{DZV$|v#y6zuptx@NGx1w$9}?mKgYGRj$ag?c
zNHiK>9}&EWaXeV;W2Jaxyvk<;ONdT_!Y?MafHnz169-)U&<NH6Zet&o4O}L3Dsx72
z#9~6#V9TEO1IztL#se&Cb!wFevv7w2W+sKT5%cQvA~v*5_};MIFy175(K%fk4oKO=
zW7xaFQ3F(48e2{4*IW3G$KULhQSAuh2I)6q&J=D*`CsQnF!!DIlk^&N-K>ZG&Vw@z
z;_VJv7Y`?cgZuMQMT96nW<u0MIGu6`)iwN0A1QfUy6Cb5zlaUhD)k!-8tHmUfkX`2
z=w!J?2pt7Se04l=(rVHVQ~7!UPu2`YeMKgUJW>M+ltdxbUvEP(-G@{n)9X{5l7A|A
zLOd0VR9wrdr*W;QUFjQBy&~?C@1!HC&B*&xH>InR%HypP4-&j6<dbI;%%z#r#!{G4
z1gU&=?;4SmB{T>0LMgsz6pQRs%H@rGW00SH&nd+z?-pf~bL6^!#?zKvon1nxSPeO;
zU8ZU#!xxz<XrB7k5Ys@>K;K}*8(S@DF_pC6f4OzZbeG<i-In6>(Wl8L<iYa~6l~9g
zSuI{!v~6@!wEh4Nj5<uJNL!D22y6&(NL(Z$rXJQ7Rum=y)+xQ8vN4?}UD4ZPB`XcD
zf@oz<HT}vqnLh#NSle@mdx(TM4mg;M+|BFx2$jaA=mqsU+qFZCDURwK>h|jS2l_Lz
z<ux<7R@GKNtXiiRrzK`A55^9BuJjIYrVZG(GZ@V2zC?Rw@D%44Y!s5sa(|vIVl68x
z!K==wSt$J@7WLVzB;`}1Y=x+`oUR@pgio<m|33eX@}L5Zv_+gk$(W>`z>3t0e9LGH
zg;%7PP5b+HulB(^HD8>^lsomi8zcu<H&|NOd*l<`p3vD)=}@fDuQ7%c1QZ>UeuXC?
z^C8!WFo+e{*LbSY+5_4HR|CP+NErS}K1t?DT*ZsUti^nCeDa-H+F3=JM6BMXOcp2B
zx%R78Ph&k3LDtGfj^l||`)18!_5J5nDGBOrGf)+N)|svWSJa1Z4(&OcILWhMvy^ph
zm$a6An|$3|S`Awn-R6$!uI;W5kH#1L-D(}byKwKHl@!*QM4IGmeJ5`h{~Z;}fbaCv
zWs{r83E#n-caWFFh1g-zVdI-xGr#kU({{5)Ylk6TZKC#|mi_X`vY4I4vS16}w;D(L
z8wh`!KqP+)|2BWGz_37`m!%h#*Nk_Jr>=L+wcSnk-R+;Fvwf3_`sO2@>-3tcQ~b4I
zKg>?0PNz<br_d+z=UEVU7nh+$*A^rPB5df43TOQK7|9F05~q#rcCP<he|Ho;wh#&{
z$`8bSq@s{&xGKaPIA_Eq?CZDhh29gcqLC5Ry({3{H2ExPD*X$RY&o4-nl(8z`LLIY
z>mw&7=Q?tWK2Ft|hQ!>cuk)?JJ!7^0$ZRf+o>@`P&oAT>VJuR$Zhrpze9L@IHmL%Z
z1%E8jS7y8D!?BfK-+t%q<IR`>zW%hnPALbOx6-)ME;7p*+?l@;)iN(LPsf;dJLRvX
ztL2{3(UYCtnbC2uHL}LUV`sT(kkUw@y;snb9alKT#)n4?c|}j8aiY&deU71S0l^z)
z(_z~$U1+A7#d#(r9&s8g|9;m%XFOA8vtT29T}9ZNJ<7xE)b8Z3AEQ00w~|nj@RiKo
z0oi&qzj0#mTO-k7!z@-Mf&OWGhxsc@(V@__^U-tJb0M4P<$0Y?I<*~!d#_JP4iJkF
z#6x_u2)Wds!v7>Zo6MOHk9u?YGjC$8yyc{4Vti@|Y26-B+!0E7eAmcfWIe~3W&65m
zs|m5`=EPw^S(RKjQJ1ZO-Oyr+X{WkCkHoEJ-nV_ayuk^@GR|WhlHspCtbg3TbmPaU
zNY`nwp)vVQ(QIwUm*nQ+`i9U!U_z-(NBjMsZPI7MXR=dKdG!|cqA<s>FK>i|&>cTt
zShVI@SEN)#Rdf<z5fYWcmo>k)*45Q5QTbX$T#YjqN3830H0Bt$#fUtB9EOh<(@)a{
z|2rbBv+wDr_s>()LE2VT<PukfQ$A5X)oC!t_PZDk8V-~Ov9B(9R{~3u2lHM#nn?z6
zxl`Plj`~jzO`Iya!b{U3%ZOXYJ=b$M7|d^6EB8EHXb*n24@WV(>-}zZIxe4^k<F<b
zL!8U5&S^bycsxRGB^c$hwc}i=?{MAWI$RrbS#aT4X=-0_8hlc)z9~3qB2LSVau+z!
zZy)S%zwldsJXt+ked;Lkef8M(l<>L?WmRy*i~G-qN0Xzi0va|&nY?oTs=Jl-n*pe-
zz>8qw;IFwMg5K}?h`g>O2S^OU5OY=Y(mIs<mHCWWo$RKqFUN+dn8OU<4dgqR9<q+s
zuG=U5o$nOqgzRI)V~{(s+H<_VZnyuuFC?iHbcuc)CzK<VmHwc0DZQ^cYSB8LV?f^7
zmj3<<>$&kLtNZKhsoxrjVv)Q&JP-L;G!|qc00SN5;f{F)KM9&R0YMToufDSfnfpE>
z1wQwE5%iMa1gSL^p1{PAzN6EFnh>9wz5;;@33m_-Wo4xwMDgHLvA?u`A1&pe&B^qN
zYoTm|0<GN@7gJ}I6nCf>_6mymj`US><)tDWJfqBJG%ww)9q)sl%R65wgu*U9_jW&l
zKu|fBYFaK@@^ZW;_O?t$ruN2WOdhrl0Ja5z_&s=mkG5tmMx-9LHg?Xu9s=ZlwcrIl
zzr1E9C;h95i?sl`mb?<FsJ)XJDF@R#CKhr*1X5B`ekW6NUKKHkf4T$T1js+QxH#}K
zGrPOHGr6-f**jS<v-0rpFte~Rv#~J(Ef}3W?OcpJ810-X{vPDtam37=O`I$pTrBPF
zNMFV^GPZYh5g;djndra2zvVRZu>7By?418m3s8{x<rijFCKl%Z#s<3bzr5vDvh*;s
z(Gs(?HM4UD<`CrIddL4)`~S}`|1;zN>Z$!dJz2T9|98*-^~*m!`I%o7_+JYBy;^_0
z1(r(?fuH%mr58jX!m#`d0x@7niwUcFKpp42p2q6O8Vp<YW(~-fPAP^`1OMq#G6Bg#
zpeV%1DT8*UK$GE6n94eGm~zRKK@K0frBdFa27-b}!!bkPf*K|&)DnE2IS}u!8y8j<
ze0860vTuCt-7QKUPTFox#+I9twyc`q5a8oM|2$#=kTJN11bIp*bkcvG7!Wdv8~ER^
zf!|R-ZD8Q3f61qWK>z)`^O=7w6#)MGAr}n@RWwx_4Nd%a$cZx}l7GiS4@ZK+7fev*
zAdvZ|GiCe|LeTzHiLUfm!+#@EuG0lp>$U0(<xvQxI<T_-D<32te&P(i{#wiZUvZ#h
z!5?n5diwkR>nluc_+xNzS*hZ}YUkebND%6a0k-RopmPaTx@+h|DOZWwN@t#zb6y<*
zRr=MycqT#e%#P)3#>_I$rb!C!RMNj<gHu7sd0pkPSTz4GMXgLgXq>vSoZ`QtqiR9F
z^MS75)yV!wd8sZsKs}8JR)PN{Hhht|@<7Y+KZz?|B&Hg;{7>RQDo8I)ZMjLm^uLQ(
ziT-Kh|1V*jblb^f_B$W1bsc|wM|0epAalGu+em-rcHiiMQ7)FlQ*N?ZNS}-0T765W
zUQX!*G@^QMQ!xmxe&1YhH7@3hvD@)yhd{Z-Wk0<mqC+QN1V*{hO8r_f?y7$iu!#p@
zVAX_CSWIWD_2^TX4XWE`E43-ca`-Hy$eyu<ShTgZ3&f+a7oW52I~LM}-lZ|=p(Elj
zVYa#*C)&S<sO*ens4lzC=@lp!N<fu49$1NJ9?n)x-R(qT(f5_BSM)7>u~1!0{$*9B
zQY3b<Gg@;s>2-Age^&5f(~x<1vDLF~)r&+b*}myA)&6WAXPf8vbUuXKZ_iIRoz~3n
zd*LQ}QG^)$en%OEXW6y<$mTa0?7Uw&?JHF-WYg$ag4r4*ln|LJmS15SntAZPI-pwh
z+$C~6n5L!LCggLIYr8*bT|~qKG}CuEUDZ3xYqXxzP=RG9dodd!^zb8!)$QT5{<WuT
zF69ah#>r|uzR|O~`%8wxc>(Y1a2x|4Wwjc8J(r_-WuMDQNw%-enXpJtmnA~YIuga~
z&nK;?eI(DfgL0a^j(_$l%A<3|Qg|HJ18~{Rqr1MFE;ZTqhmrWDzr`C*c+qzqUTo~F
z+3Tu2oyWQO8YZq}kGb>Vrz%y!!=H=YD8AD+RhPX9;iKi|sj2gr5Zk3D`D{VoMGv~<
zQ&uzS1e&w%*CgSsryaTtRx>i6;*?&_G^rez-0aq7zE(Kv++#04jGxN`-8B;i5j*_u
z>d=}(F`~NF*Y0$+-416X>VY*gkXF8Pc(zKnKhFQbWKGR^|7X$Wtgiix#E4<7>R;m_
zfys?-yDU8?J_&C&E!kc4V<y#Yb$Q}bC{jp7v*cd?dW^uJ;isjU3hCP8ao&+Du1IL`
zk>hzoBKV%2?dht<pGrP00@0z=@B9Z%<G3RAkVVGJS^AU-*+b4dUixYcJ3e1$;`=y_
zLbEkfp;<9&KlH$)-<E#vmCn?2dsTR}+OGS~dR7jJfK#pubY2lnz@^Z3v+C_vMSLj!
za<+ehMQm>k8)uKdt}$`iuQDK0s=~9Tuvq@2RE&yK$tDwO!g_NOL%@Yi;{Q-r;`LQ`
zUe~_e>_%f%@}+kN8S3Kpkq2-4X~#V{^dOi#d~&l0^Q}_X@Fts1>e}MnssFQXgZ-L5
zB3rsL3pT<{i@#1Oe$tD-40so)6nks4{7u>AWSK`v0_EL~E2=QKE-L8PXqF}>tx6x4
z{7Rik81_$4LP{{@{q-^8vIU)#kG3XYYxZy-$IgV;ySz`^=iK_t>}zS5U}T5e3G>fY
zN&EZ6rnANQ9@%C>wD@y)UD0V2bJeD}QJe#lJk$s`1^R{xfUV)DMy2b0?QLyTb~k6>
zf6OZ3+Mgrjudior=@cX!;M?EBG@sdx0xeWcEZ`N++;Q83GE=Ain)$w@3gMjk#lK03
zlBx*`jtf1vRiIfIy)|@}#6_InLwjZw3Fc;fjY1s#CyvI1uTHTonk!g29!K9pc&V6F
zUI_lkOm0`;@p7u8^qJ)8>@}<V$@{qjm1ZY1<tm*<YhMgs%!G<bp;2E<U5(pz{}$Lt
zJitwhWkNn6kuboJ@Hl;TGSeti5nt+syT>*1M%4cEa53IrQPWW_9?`@muzn?x035aH
zRMlR8(UfK9ae<~1e6%k6@9|_Z<<P0f1e};52v{nKc}a4$^=BCtxsHkAJ>Et>?uWDT
zhlOyVwDjS)Pm9(y5tdWMmV9l)t7-I_Obego{@!g$RD|BwtUAQLm&!$sUNUJ+j1qCg
zRr2!ltP-`86xZD%4@tDD#c=$`!*Y(83|bQxu|dkvZE6uoB$7u~YX#zn>gvD61#$aD
zpVjcV%Czc?7gy~&?`CQ~60PF4i{<sfHnN+Kpw_gVN6t4`s_N+b=@O+i^~n7de5s-H
z&wh8OaY191qaWpm9KWhfY9T5d)Iz~!^S!&AJP(Fat1}kg+D^a0&FDg)l=%iHm(C(w
zY0#;!UC2V$WHyXF`PJS)S0b*C{LlIJRJ3>H$G(%(G9*K}nAEq*Jsx~(FX0ff4hIID
zC?>Lmxf)z_cA9~o_DB|&qTw&$BGOa9`DiU&I(7v>k-MMVc5@yrE5PGG-Tow{`+^X-
z@%!k@ID!p%`64ZmCqPO*W(r>c>reHr%cx<BMKfP|M;MwiUK%=(%cph_?Yh;8jzx@x
zoZaKhutdA4jiiuwUhoyv(TL)l-{L1WGYSvoOppXc)vX8BFmC9N!<x?O<NX~MOg3#i
z?l?4326V`~{TA250ZOyy=<e=U=%j%G=x_}i!Dfd^G>RHQ#7}P=(63}pzHPm+rAL+v
zRvXignJQJLn2TYmSsZZcpmDg|Qy$e)?tV1@_u$!<2#bs#t|<6N>;dpNSRB@~n63xY
zXrf_nuF5@9MPCkjHClL{y<^qeD?F2#<j5GJx<;4>s<u?X(?$)F_`iR+o9n!m4-lxq
z``FyZwdx_dkcyeUd9K4!1yf>k+hWugA@VYc5h?0C8cAima$OmX#-QOeRZ<U0iri24
z5}Us93~tAQ`Vsa8NjB{!wt$iF7nt#+NLm|;!bujgRjL{or_}7Uoo(KGFHIWS^%4+s
z1-y;Ne*cXlNH4(9*0+ZHRjR_6=5id$QV<R(tVdPXoOF`E;7I^#;5(mM8XO6qYy|TC
zogYbqmYi*Q;7vGEO6ZWk5KWgq7~1moB`n3J!BMcWZyqSU3<AbMI+tiJ_hb=zIrL{3
zfe?JbC>&<D5maw`BXz70uo{Naq>%bt#GjX#1cLWbB#jRpuQwYeJgjlv9gDp%gr$rR
z4~Trqcx#YL@fW;uP#|>(9WRni>jQR>u(8eK_a-zHm=<vAU4RSkKbWadw-|nS2_FGq
zOf<Onpx)6eF2tjO6R}KA+XD}0Nt*48)id<@M(Z?4pE&}207OG0^{vY1@V7b*rCm1)
z0ICqnU^9aY1L9dQ7~1025XZ9Q=Vxh*674fTbSbIzDMcZ9k6fBUY0&R%mkdGQmc6-}
zJZ`@SZujQ7{4*hV(*VeNaZ9c7K-oK+`3XyGV4={_0WC4iN0K_I1Ef~!od8Jiw^vw)
z4(!4cT>5J79^Kt`*sz6^0R{h%2k0xE-c8YED-cw|KFi(mB1KnNPXs2@do8t6rEY2L
zwlr`ey2GN7^A6IoBpWTF{$H?J2pw+uD-g51i5x4{%Fx|2-NfR#O32q6{GHamskwN@
zysSw!23+z+aP#kr-ElrE4oPpqjjd5yxUTW7vyEO_mCv1O9X@|9BhrKZ-d4^)2%aFF
zo5`+<%93vgS1}o=m+<~O58AaN&7}wP%a(nnL4Di1d$~WgubWH_vuUX$)rAawlxL@8
zmJ8S{<)>80CnS=RsrQh<$L47_(jB)PKt1hGkibK{bE3bk%LzJ=%_m!dB0wKNipip;
zp_CwKx;q+8*E;4Gvj}JfLz~@}hNt9{qqwX0EEmcJJhBfhS}Xx5xQ!H;{WSrE7mFTE
zBPX^0UK#+_*NPQ=>8s4(SVA6h^U+Kyz)+~^G^;B^kORr={Wf#}2+|Lnz5Wb#iw6~7
z*(+|x&5yIQc>t7Jyr$UU-25gS46DPJ#-P3F!BJl{v>-<~z)2;FR@&Y&k&zuag{+X1
zI)2|P3lV<?tk2j3s?IE*0@T?Az|M$`-Z0s(w&S@1#+!W+i?W@TmwS^1{?AY3?sopq
zl-*i~^L2&#$H~t^_ebo3IEx6h`fcB|Y7GOMv~7n71UhgBIEzGaOVRS$JTHo^6&DYM
zeS4&qSg5~q2l9KKb5{pPrWnQ2cOyxWB11)Ypb&B^y#$KH$s~rQl3XvD^$T{-^IxXY
zY+l5L+8(tD9A4<^*S~%w6=;7kC#R>UpEMgE52=%{M}1{>yDqw;7E3H}@D6cR5gHyI
zlTPjJh9^DEf>70j@uD1RS2A4o*<cJoN!u;pDXdh*hS=TWHvkkZwoq?I^b6Jd5O5-3
zTyvG4d1`uoU_8Upi84ZNhpzuIcobs(bQb$uN+=;z0A+=})vo_sq27I|6oF3lk>p|J
z%sWsSe<sMVBqNcf=?C2~U}(UpZd&a4cA0Ka93MacX4%|&_h!7q_kOt)|AG=~5Nvok
zU8z0gD9DCqs8gy|rotwzCyz0}7z@ZykwdB!iV}pv>$}*63Tz@R<vJ`>Nmg`uILt4J
z)^v*o_^$m``a($><SmQ&TBE!C-RC+HMjP}2&V@;3z(U{E8uv%KE#f(k1Blcl))N9<
z4yRVGCatb95(f4W{Uo2o*>o5*{&B_9uo8QCT4qqt_fD!2?^yS%T~1fz2EAr=_;4C?
z)Yf2Jl;_1x#Lj4z?DmFw0ifBNR^W(pt+*uRxD70SG3pIXHzAG>paU$tq`ZZN1YJoO
zBuM7)y^;KOG7KtU#-P>xbT-@5@x`?07d}`Tc<oaS-*C4>4PUZuCM%pY?-Ez}#^7_H
zzr^9}jyg&bK^Y=m7gS*OIqql@Awg<-EJQM6wq$NNlPt>292V(yq&%HQD=M4tlqBof
z%DTjCOKC_Za0qGja4`}CUV~9>lr#+Cy5_s%I(Vp2Vs#s>iu5FTwd>F+q&UZ2_TLck
zCNo92`Hp4tSWe}y_}*X7WH%b2ZmJi{Wi;HBDWC#NX!h&$gEG91dX0WN*rF;&r`f5f
zksv)2ObI_-rK`zCm6Q<h3~!iexzQ7%g4Lgl0)6=d?x%Y7d+qN`oC|H9UzC2YFspKX
zRz_nVdyl;d>LwjY{#YmPrVVS?w<{@hm$1^L--;p|`dhdlKEUc_<qmjRh0tqd4F{s}
zetxxE>(Jw6ME868v-@-3!26hEzd@gR#D$-jYY~5dGlK_a?5s?qDswAEFW?NwHF`&a
z1}WFmL7}x38dV3}B<2ukKhm@NwP*cu{5XN@^qnp8l-uVB)H`9fp~GMl!P>;etp*@0
zBo4g_L=LW(;f}UVR8a{M3uO0Uc@IFXDX@H<8$$l%BP!{zPE`gk99ZpgoU|dy6^RS#
z_JB0~Q}o02*Ul7a>@*3tX5QjHC6bz7p#*mEOb7!giyJpem_{~94T*B_uPkmqJu&Ju
zuyFL=ZD<<!Qmy*l=pAyMzX8zlC$r&HOO-P=_}jdIk#gz=XxW*K8WYLN+hdRBoTwSd
zXNRr<eUterqCn!ux4?LMV?e~#`D$nTwVm}Q|BKT!4J<qgCaThm5SChqc6@*>wAK>?
zMSmn#l5{Eya%#ZvK*!xqmOc8;#_SL2Bx>z3{MdE4%kYc{R+9l!23Y0#$gn#3AB&j>
zKBUMbmDztbQ2f)&^;G88*ZCbc#mXAw#GvcJc5S~Ulso-=7k&YVw}X<DkNoLN-s6b!
zV-BuDEP{gb1w|ZxBDMIz@Vp~&D>5lzKqXoRujH;~?>sf18UP=<GEb(<l5(yGRxU=^
z$KmXd@WMi92+{WdpV>?3!3PfxhNRG`2PrE@HTIoGSH`K9SN?X;t2GoJ1<9n0MOg!S
z6WIZJPfP%m(4wXNvVd00T!kjHBGK+hO2CvpW-4mt648!{`lG7FN2S{g^h?E$>m~6J
za_HT$99b8gyX;EGgoqD=XTmC2H^{K4NO5wu-TCyC(Y8OAsPBbpLlu*#<;8%c$aE=#
zZ3W;W`ohsDREiz9$RDDB!$58|S6y{1_RxdK{7o6@VgwWj5Ea@#4n*4zf?<(=u6bRP
zz>Xg+eCcQWx(x6kYY#hIxhbPT1)>l)66P9T9~Wf9l%1!?`zz7GXV%A*eJn#jA*_Dz
z>{;OUa;LvPy{&wP+ulU}W<QRBT!zH9_$|%~KzBepSHnGokl^)=eEQNTGn6qZYxeB3
zoub*d-W@DJknu3?|3tC=d8OHT*Shx+gIew^rbfh^WB6wbm?dUQOwES>__I%jH{d>M
ze6MR)J|bAo*XBps5N@LlaQ=XXuiOZReR|jx!b!1ZDtz}UXoJ=CJ1Uf9yUAc|1~WaM
z#Hu3y?~jm=YXJ5d{1vrbXyE_oI>JK?PU`~hC6v4ydD@#S>{s+V$N0VW{4~e$wnQNZ
z-C?ND;r3^dtjUltr~$|h$#)1*qznP#Bq1<>k>*S{a8$!gvK+m;`!d%cwH+|Lh;)K+
zn8ma1Rf_VGuoqG#&@%Gts+a8k9wKMB_uYO;V`;~H8P|dm4N+Ch3j6%2^UesZ+9|^$
zpu6f}9<HzQ{>`gkLg9H-SKb-6_gpd6hSq#OjcG&?j`-@@vx9?*6`i#UbPsGB_nd}Z
zAl)I!25TW{+@<7b`%lOu@5;SzPGo#(<9C7R-d`9eNQcj1MQJ{kZMlR8ltDPRdR?H5
z)uZt_WB`T)Uz{?9-C~TgeE_kWA|U<&w@8`xw&xgth)t$X08N1HmLqG#y&;L1NA~cY
ztXiFyX2<1G(~K`e(;WW0!XRmR{R*uY{hGUigw`^-c7Yf?tJ{%Q)uTp9w??Jb#=>-C
z$~CYcJJ-9Md2oxYKEA6STgZVZWmfio0Gc5(jMz&GNdCp%*{{A|1~y<Z4tcX-UHzMb
z5i*XSN5{jttnD6p2O4fqA7a*-wq8|H3$pN(1@2+gB%T~mj)1p@<wcY261CUm9vQL!
zgX3hSHhYzS51RIA9zeX!47ECJ1kDP?_+p$ocFtJpT`LyRUAmgzLM3C#O)fd|#jmiV
zp~T)+-&B0EyGuqZ;2J*p5NFBW*+dVVpnJu2U@5^Bt-1bBo}+(M?bC;zJ?MlbIo-|0
zh{V&KDmfvqHOSoZvelAD`IpH8NWk3H6!94ZK~FbN_ovp(CQs=I1Au9(&5Xxf{qpul
zudVOz{+>Y54;(VfW-=iE`aCTFB=5;GyJ2Fu+v7t+=(v;8G#+PN0|S0(wT-lAs(M~w
zc-#)3K1>zI`0f4h=YQO!)!&YTLZX$qg)<Z+QO(sy$WBnd8DDM|{ayL12p?JxTcX7b
z%xYCBL8ISjl@h;#!XOoj;?a$ooIvWE+cRA}fh*`h2{?rE%MNC?95xI~Hnh+57{}4Z
z8UBBU4I0wrh3)w6OPr5kA~parfo8>!Aubi`8_3X0L*q!U@W)OL!wSGRf}`2_$gRO%
z$F)Q+a73|`c#XxB+hUAxbYY>ow}dF1?o_$%b;i&g3$rVD-I*OAJsg*8ze)@3m(%OX
zD;(vG@sOLsK0uD70BqLCLhLbdA+kRAmL9h~Fc8{*P>llb%)d_}w>}7lT7Nk3)$0as
z4X<Xcj#zcY`txCfCl$^zi+un>wVsKX-GZRA`7B1#$xb?-pFYbD9QQcg3yzcxpQs(V
z>7{q_fy*^(m}>u+0E7}uc5E;ukW`{`SWUyJr1)xU-uivN^{1@h*U0h0`auATooXFN
zz{RBPYCL8Vf>V6R+K71b3U`Qm0Pg!@qjh7OX<E%8p0|K_w9_a-19>`)DUf9q$fhxo
zXFOwQoUa1x6g1&a_>RMNHbdn!mL^)FEz`MnPa7$X7TMhmy|@`@VSE)>1RI+O5*(Rt
z@C_d(MP9hMSm;k?L8!SNbv+4*+R*7gZ&Z%@WP-<0BwSHv@Ip}}_>B0qIJ{UEw@TC%
z&p1S~YB!NXS1Pr?^Z^+GjD}rLKAtTHbC7f~eNFV7>Gt6+5S-d=e>{cR^-0>ljV2M3
z@B^sNS7^4_21srlhL_jp{<UxoHVY~~55ET&r?yJ+{0{DiO#Jf!@?+uVDp~{vO>arI
zdk?nhQaGE28HR!!SMPVBp^l;(CWH3l{pEhrk&49FOK^pRIssGIJl<XP)8zRSAlKY^
zQf>n{VL}4Lf-Dm825QfHG?N4R&1WsQ6V2`^MxP{>7`Y$(fk1ZSr4KK5b?t@kH&?Bf
z`&E^B!j?oC%qO?~x@b;x`x(HXOnFKKf&oEB(qgk8p-lUg$C;GqZ;aq@?qiH@aay-k
z3M4^wpHPDl?up2TzO4NUd`&!YS2CAR{xCqM=c^XlAf-8*%BcI3%5nSdWTmwcR9$Uq
z_Z<^06K#A%_8CX?_u`V<@nVJ|E#JT{Av?OhEbfwNn$aFI36UHS`5NSL>gLciW=vHK
z&${v~R$5%4b>A2k!BQ=Pt!B!LZ0+l97T62|0)z&JQt11E0NE%t(NUTlN54TMgvrI)
z4RLplD+40B)#M5L=oet3YD2MmR3sLR7x2D~i`Ogpdb-J0*VYy4`fbpc;xv+4l_Hr4
zAdp(?!WbSv+?Wa5PlW%heRDKfQg?N#ITrDCp&+DWrwXV232<7rm+*Ngu_8m(J;SGH
z3}NG+NkzV>)0k!4k&go`-VaJw0p#NR>(jNG215Ql^LpPGa0Meib~Y)z5rJKY$Krz+
z=Y#aBCs4IftwKYdlz|Ugk}MXwiK%PKHywtaHP>Sko{V4uNeF*_7{RN{H`xOk7k{v!
z$|`UTHaZ9Js_;i`f_@JkipA_|Unyk%AQSU*Z;kfvVoiCoXk^Px8iP%Nv!<i}eM&Lt
zl(_-z>dTUZPsjRruutv2YcW-{ZGG>%hKd$bBSTFqoAYoSV|hapmA)*kPQUgpiC8}O
z6S-Csj-ej5fuV)&*7e#*vi>FePCak3=Td2aZ~u$>Y)5rAUF+~&oQ0nkgkt8-Gzr#Z
zh#9crbeQ7uiNU8|xZWJb(EV%(lV0afuC+W_C^=>RYUdVx_MWEL`xM~Fl#66i8gAGE
zeWCU5p5|+c9{G$a4G&<C#e_b7c{<-7ZcJ$a0_e=<EvfsX0P!75c|-@!M}cxB_r7Yj
zuV{jWw=s~`TFqZT-qm=9snrvg@RM3TGaN`Yy-vEK?}nssmnf&~1mu4K<^+B+S=h;O
zUoC5o-mTk3<D~0H6k5KGGZK9aBX~w*f15PsF>rUcFYu7I*ZWz5!r|jAvXx0}8nZ#{
zVcuTUz!Q9L;^UN5%&9thS1BVBUbLKfkE5oUZsIo90B0z01}`o66?+GzD1LY|aD@85
z(Z@tTBY=wLOl8nk@%~d~SBdgMSOD^x`uaQ+!_5L5(cc7GmoGF={jPb<5H`E-oy)(B
z?+gC{kbf8fNo`mg;c-|xa<VTkULMpBF9pFWL5<x1hCp~J2!3E?{EHKLAyYrWYn}gw
zcahchd?C+Xp1|)I>*0#OT0u}RC99V<E^rfP5wX<^D*rSI=YOf1o<f8Fo(2LvNd}Y*
zd0Wh7|0x-go&!Ziu?|GkzXt%w@i3rzxwn_eNcG<wK-u;W69n|Hs6>FM1%=j4$^VE-
zt_T!v|DO=R){`rey8nXL0y#WAcsgDmX(#Zx+bOqsI3`d?zCm<nFPj4cNi&7p=_(_T
zM1RK8`vidkNFwByT4_y1&p8rUA)A}b?PQr&A(x-_EQ1bUNQ%<{V0^)DwvL(n6^B_L
zbItcAM`Ok3&v`^P&maEdrW2P{jiG$~>M3KC_A&B(K9_pCm6nJg7{o|`HMKIBRtGqX
z%v>BcNs!Rz-caNzKS&fdBi+GV4Kt7pR<&nxSQqc3z#gWvny_j9fJ;T9XmCGW&D1yC
zduO+-H6=Y5OB7O#<HPzwEde*^ID$R~&6(oX-ZE^D=Na7F_1%~N6kQ(>E`#RMK{L2{
zXbw$xybkM-0G)k1by54<by>{NC?Z6>J<TeeBG+1=T;Ey&ze6n9ReoYPvOeq%6pS4`
zGZgu+rd1cyICC{<&%u=kV4hyo5Hw!L&AtX00L3a0um(WMD$91oo~X>M0~JAe4^Mzm
zgCD;z<%i<q%@FW*^N~QeVA5-$OrXP;1}bobJWgWw=rfF4Kayx<t+9l{fl~3}ROzL>
zTxoQG`pSkS>_D{opKcUC!>iQq#b3)Y0)q5Sk(Ak|0q>Nh_<J4-imydhosbyLa{V)T
z@>8Z>4ko<@gYOQ!%EVdo8>(qRLBNG{to}T54?`h|<xy~B@qh9OB=eBjft#Pn(Kop@
zY=697dMQqm+po550*P0FL8qTFz;<|{9}7Lmol&g<G(q|We>ly$8qN1Q>$z&<qxrgm
z_svd=;RF)K`4|_w$X?oj&;Br7s_Y}~F~A0e)iQ<H{}!g0hIS0D^yd$N5d5hDrYHsS
zEX6D)I(R_qd1Z2m5U=YaJZE}8F@Qs6=3c=H?$>-hP?9k&crP~%WWL3IDfF7yh}aCM
zK;k!nM7q@vgl}twi2-Myn$Uo1oLikL?Jp8t-OF#!4)-VWgKrNiYCbO5p8$34&l@Hm
zx07ym(D?VT^LQd}z+J?7B#|7@`MC%6p_}*9T8(DOKrBGy{=M-!Y106j#pY}~3Jv^a
z(xlQAh$f9#p1>rh#87+N%gxTlKyv2@6pz$C_kM_1HY`<n5C<t7Nc6rIY6HykT$74I
zElagTLA<o_AQNAv_qAwUJdj}a$Nx;@*lKx-l#cfeV78P3YW9scFR66wzD=2t!($xA
zO80sKJS&TFUwFD(mu7eMg}0mpnSet91}SUYIb|;Z3zpJlFUcahjSjoYPDVm9OTGa(
zhWh;14-<ufEljZ5A*Hx-Tk=Q99#S5aZ0d(Mqqk6fQ{l?B#^7Dnw(8Ea&z8vmkob$%
z18VcK{ze3+yWaQU?1M4CA0k7BPKo|Xr&lU8JCn1Vwu@k_EarnM9rv?lHU&$8>v@v`
zu?X~@ql(R_f%^vZa@Wl%^E=?$1G`U?{hA#-Z!R5hmtx8KhLoF3a0K%do_i)|1-}=P
zibq?Y<sW}~p+X}XiWDI{^GP@upbWJrKIyoJx0@yd1=kgi?>Fdx@L~m0)Y1@Amr%tZ
z&SC)=lQ48SXg8M|fXEqmhdp+P=?XsD0}G$lCJw)a!<YG;3SnExc;yH9B-JRxQ`_SL
zZ5_6pfY6nq<4H=(53bifCZ&{1np;-a^;Z=OQ|g*ugt8s3o3Dr5a@)M_E-jCFf3Bw-
zPf^SOD{^Bn{Mu6k@HGd5)QL3fsVC7K-yAkXqKWfmFEM0{q4oDDy`hA&70$GaxG6r#
zxZlRU^NTYVdfTTq@~{HutAdW9l%I5jc7W?^s>q|?zbo#KjLQPKSLi(@pkfW2Kz218
z5?PBX16Z$xyt%_5Ek^N3%t`&Z@$(!5IFJ0+G<`4H>SiGEisT!_Si?{%N;+cpVKo~{
zw$|Dh51xSxe3QZT>@7tpCN7cBa${s%n|~4Fc(F06tZN{K0N{5?6(K1K(Nx7z;jxp=
zfGfeZk9fGBz!Q{+$;>&q#L^vj=X0tEZMQephKa)%7EH~l@%b~&x)==sr%iZNoEa0E
z#Q10FB(XSRfsr(iqxb;5mE-w3(MqklO`ZLPLWG_+(qD_>9d%+|)e4nNM*cv~&{%pM
zo&wVwMJ`r~OhT6qjs~Om-~74Ad$y75xnV+!U%^1`7DbaL@ai6tl`)40m~QrB=<ra5
z9}G}^<OD$Jx>T#Kpj5Ra7PxK_szGLh@&bj^Q{27e>%w<ab?qiME%$c|9`ZsJlN(9$
zn|gVs2yx7RZ}vYpUX`L49>Vtx^r*3e3+*Suv}wMD=6ko*KmM*R8uX0(3Lfz$Fn-6B
zQdsr1G0M&|oUvpuO;(I-sY0AVuSKoFZiRP;O$5zmwT;ItU@o0N#0P*BeM4qb9W(*A
z6g$Xrto<|P>h$F2;h<s+G=gD<+r-s+vnX@S3{6)fN%!|99*Q6emz^qaZkYXc;-lp7
zeFmSe8{JTOWQB&_Q7f(P1d8J40)vSZW!HP}@OwLVZYdV%&p&!fd%J|+RutVP4q#F?
zg#<o_YkyOZ&IsokMBzV!$O$jde>IMcdA;*}6u4pXPJibf39W07PGUdWct+^Ehkg30
zb03Y|qN%Pi+)H=>>Skm+gl~W)!*Y)Wu6%;%n@0$sOd`<*kugu<NIz9LHWJC`1H5(D
zZkfYIk0X}n^W%-l7#S*+#_Yk;1ONGV3YYR2vR^BKo-K%l<yuB~uFpL9f~!};slUje
zZsU^~bvdiWDvzS>!UW$$#wh?yy#D3k+^J5??ME-<5DWY3<0TwTYz^2S2!8Kl_P^BE
zi<wQ$Lh3zPbJ(F<6mwW{s*livRP_dkyKnMILC@feWlsE19{UAkc2OwufS9gGro!|`
zzv#JJL0m8b79D1EWN2-w2k0H*;|_UKc8>G-P8`G`*331`=nlwa2RV=k#;LvTB4oc~
z`~;i2lIIw?iGcAq6x_Q>q6%^$bSp*~4Ok};8aw+pCVfnkm+5!dFhYZBymd>weJ894
zK_8v^_>4p2nlW(sz-#we0nsOClqPNsVioJ@KVmFq4mBlcSj4Ir0y!0&^9Porq}o^G
zP3TrO!X}4?NEk?2?tYrtYbQ80H}Wwe_*Iv=7?-%-3fchoxkjqmy{;m8Vh@Br7q<XM
z(L#58heXYfY)QiMPy%lyg+ZH*>GbH)N(?A(Y>i!fbcWt{DIh3d;YAao%%NONzFe@#
zlXoYmSt5;BaObT5#rOvP`yMzC&1Nr4;cP*)OXSPh#ic-k5+4_E_hPKlJusHt^*{|u
zDE{SE6L5<u0w}=f$%efN{zfh#jFbqlqWFrIldNcCE4T^R`<HJsStC3RGXT(Vb+W=|
z=WdVLVm^u=Xj$z7T?o(0^!5k5)=We$#)C`kGd6f^c%BPxKnXxZV%NoW&`a9sd2(0Y
z&4cj@Jq!M=EzTNrrwZa-<qEa<h*Mo2-JU)Gd8<=N^(%?XUY~IXs~CHCGz$kO82$=!
z#1`x0Uz0$%Aat1@i#iK<{EuX9F+@MZL6b7x=3%2>>2DLvcI#!CuLbE_lB5Z;ydNfk
z$j<#t6i2lajb!n7@sZgIm&*;N(Dg2~*X?56cMe`_a^<S^XXlohrkBJI6|KpLq;U)%
zZ&doIbAiAKMOUNOdoBh3$`nK$HndNqhT`t;ua9EJD)+!0<Yzav6dnPeCQZWLpL<vC
zF?lkz6(j~=<;=Uv3`n8G<Z4xgteyq>zs?>o{7sGS!%DD)-(Wq*q|CPO{GNw0hR#-m
zzQvhMrbM%E9_7;cfkd_|Qo(`d8dKzm(q}Bu7xoIh79Fnl(gSs)v`j&K#lNB@iE-iR
zIsL6)s*S#~tc5zwt&L%vu<rpk7BeotM}T4Sorg?*-T@mY-V--~P^T2gauErWk98&o
zeEzKZLQm)1M^~j9O44X;hs=}X=C``+C+=hgjFSmPY3%dK+Dp2Z$@HPH>3evrzY?QU
z`B9@i1Mkr*`KFM^Vmx<-O=2$qAPnGdvs4l>!jm_W2w&CwlU@PYQ6OZmq^01KSX08k
zi5espxaP{EAote=u)lISXdpon`6R{}_<u!{=U2h>zIOxQ$h()@+S1|*V$~vsLH`HN
C(AJs&

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/packages/gluon/loss/kl_divergence.md b/docs/python_docs/python/tutorials/packages/gluon/loss/kl_divergence.md
new file mode 100644
index 000000000000..65646a839d66
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/loss/kl_divergence.md
@@ -0,0 +1,138 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Kullback-Leibler (KL) Divergence
+
+Kullback-Leibler (KL) Divergence is a measure of how one probability distribution is different from a second, reference probability distribution. Smaller KL Divergence values indicate more similar distributions and, since this loss function is differentiable, we can use gradient descent to minimize the KL divergence between network outputs and some target distribution. As an example, this can be used in Variational Autoencoders (VAEs), and reinforcement learning policy networks such as [Trust Region Policy Optimization (TRPO)](https://arxiv.org/abs/1502.05477).
+
+In MXNet Gluon, we can use [`KLDivLoss`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.loss.KLDivLoss.html) to compare categorical distributions. One important thing to note is that the KL Divergence is an asymmetric measure (i.e. `KL(P,Q) != KL(Q,P)`): order matters and we should compare our predicted distribution with our target distribution in that order. Another thing to note is that there are two ways to use [`KLDivLoss`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.loss.KLDivLoss.html) that depend on how we set `from_logits` (which has a default value of true). 
+
+As an example, let's compare a few categorical distributions (`dist_1`, `dist_2` and `dist_3`), each with 4 categories.
+
+```
+from matplotlib import pyplot as plt
+import mxnet as mx
+import numpy as np
+
+idx = np.array([1, 2, 3, 4])
+dist_1 = np.array([0.2, 0.5, 0.2, 0.1])
+dist_2 = np.array([0.3, 0.4, 0.1, 0.2])
+dist_3 = np.array([0.1, 0.1, 0.1, 0.7])
+
+plt.figure(figsize=(10,5))
+plt.subplot(1,2,1)
+plt.ylim(top=1)
+plt.bar(idx, dist_1, alpha=0.5, color='black')
+plt.bar(idx, dist_2, alpha=0.5, color='aqua')
+plt.title('Distributions 1 & 2')
+plt.subplot(1,2,2)
+plt.ylim(top=1)
+plt.bar(idx, dist_1, alpha=0.5, color='black')
+plt.bar(idx, dist_3, alpha=0.5, color='aqua')
+plt.title('Distributions 1 & 3')
+```
+
+We can see visually that distributions 1 and 2 are more similar than distributions 1 and 3. We'll confirm this result using [`KLDivLoss`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.loss.KLDivLoss.html). When using [`KLDivLoss`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.loss.KLDivLoss.html) with the default `from_logits=True` we need:
+
+1. our predictions to be parameters of a logged probability distribution.
+2. our targets to be parameters of a probability distribution (i.e. not logged).
+
+We often apply a [`softmax`](/api/python/docs/api/ndarray/_autogen/mxnet.ndarray.softmax.html) operation to the output of our network to get a distribution, but this can have a numerically unstable gradient calculation. As as stable alternative, we use [`log_softmax`](/api/python/docs/api/ndarray/_autogen/mxnet.ndarray.log_softmax.html) and so this is what is expected by [`KLDivLoss`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.loss.KLDivLoss.html) when `from_logits=True`. We also usually work with batches of predictions, so the predictions and targets need to have a batch dimension (the first axis by default).
+
+Since we're already working with distributions in this example, we don't need to apply the softmax and only need to apply [`log`](/api/python/docs/api/ndarray/_autogen/mxnet.ndarray.log.html). And we'll create batch dimensions even though we're working with single distributions.
+
+```
+def kl_divergence(dist_a, dist_b):
+    # add batch dimension
+    pred_batch = mx.nd.array(dist_a).expand_dims(0)
+    target_batch = mx.nd.array(dist_b).expand_dims(0)
+    # log the distribution
+    pred_batch = pred_batch.log()
+    # create loss (assuming we have a logged prediction distribution)
+    loss_fn = mx.gluon.loss.KLDivLoss(from_logits=True)
+    divergence = loss_fn(pred_batch, target_batch)
+    return divergence.asscalar()
+```
+
+```
+print("Distribution 1 compared with Distribution 2: {}".format(
+        kl_divergence(dist_1, dist_2)))
+print("Distribution 1 compared with Distribution 3: {}".format(
+        kl_divergence(dist_1, dist_3)))
+print("Distribution 1 compared with Distribution 1: {}".format(
+        kl_divergence(dist_1, dist_1)))
+```
+
+As expected we see a smaller KL Divergence for distributions 1 & 2 than 1 & 3. And we also see the KL Divergence of a distribution with itself is 0.
+
+#### `from_logits=False`
+
+Alternatively, instead of manually applying the [`log_softmax`](/api/python/docs/api/ndarray/_autogen/mxnet.ndarray.log_softmax.html) to our network outputs, we can leave that to the loss function. When setting `from_logits=False` on [`KLDivLoss`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.loss.KLDivLoss.html), the [`log_softmax`](/api/python/docs/api/ndarray/_autogen/mxnet.ndarray.log_softmax.html) is applied to the first argument passed to `loss_fn`. As an example, let's assume our network outputs us the values below (favorably chosen so that when we [`softmax`](/api/python/docs/api/ndarray/_autogen/mxnet.ndarray.softmax.html) these values we get the same distribution parameters as `dist_1`).
+
+```
+output = mx.nd.array([0.39056206, 1.3068528, 0.39056206, -0.30258512])
+```
+
+We can pass this to our [`KLDivLoss`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.loss.KLDivLoss.html) loss function (with `from_logits=False`) and get the same KL Divergence between `dist_1` and `dist_2` as before, because the [`log_softmax`](/api/python/docs/api/ndarray/_autogen/mxnet.ndarray.log_softmax.html) is applied within the loss function.
+
+```
+def kl_divergence_not_from_logits(dist_a, dist_b):
+    # add batch dimension
+    pred_batch = mx.nd.array(dist_a).expand_dims(0)
+    target_batch = mx.nd.array(dist_b).expand_dims(0)
+    # create loss (assuming we have a logged prediction distribution)
+    loss_fn = mx.gluon.loss.KLDivLoss(from_logits=False)
+    divergence = loss_fn(pred_batch, target_batch)
+    return divergence.asscalar()
+```
+
+```
+print("Distribution 1 compared with Distribution 2: {}".format(
+        kl_divergence_not_from_logits(output, dist_2)))
+```
+
+### Advanced: Common Support
+
+Occasionally, you might have issues with [`KLDivLoss`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.loss.KLDivLoss.html). One common issue arises when the support of the distributions being compared are not the same. 'Support' here is referring to the values of the distribution which have a non-zero probability. Conveniently, all our examples above had the same support, but we might have a case where some categories have a probability of 0.
+
+
+```
+dist_4 = np.array([0, 0.9, 0, 0.1])
+```
+
+```
+print("Distribution 4 compared with Distribution 1: {}".format(
+        kl_divergence(dist_4, dist_1)))
+```
+
+We can see that the result is `nan`, which will obviously cause issues when calculating the gradient. One option is to add a small value `epsilon` to all of the probabilities, and this is already done for the target distribution (using the value of 1e-12).
+
+### Advanced: Aggregation
+
+One minor difference between the true definition of KL Divergence and the result from [`KLDivLoss`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.loss.KLDivLoss.html) is how the aggregation of category contributions is performed. Although the true definition sums up these contributions, the default behaviour in MXNet Gluon is to average terms along the batch dimension. As a result, the [`KLDivLoss`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.loss.KLDivLoss.html) output will be smaller than the true definition by a factor of the number of categories.
+
+```
+true_divergence = (dist_2*(np.log(dist_2)-np.log(dist_1))).sum()
+print('true_divergence: {}'.format(true_divergence))
+```
+
+```
+num_categories = dist_1.shape[0]
+divergence = kl_divergence(dist_1, dist_2)
+print('divergence: {}'.format(divergence))
+print('divergence * num_categories: {}'.format(divergence * num_categories))
+``` 
diff --git a/docs/python_docs/python/tutorials/packages/gluon/loss/loss.md b/docs/python_docs/python/tutorials/packages/gluon/loss/loss.md
new file mode 100644
index 000000000000..e3031fff2b86
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/loss/loss.md
@@ -0,0 +1,308 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Loss functions
+
+Loss functions are used to train neural networks and to compute the difference between output and target variable. A critical component of training neural networks is the loss function. A loss function is a quantative measure of how bad the predictions of the network are when compared to ground truth labels. Given this score, a network can improve by iteratively updating its weights to minimise this loss. Some tasks use a combination of multiple loss functions, but often you'll just use one. MXNet Gluon provides a number of the most commonly used loss functions, and you'll choose certain loss functions depending on your network and task. Some common task and loss function pairs include:
+
+- regression: [L1Loss](/api/python/docs/api/gluon/_autogen/mxnet.gluon.loss.L1Loss.html), [L2Loss](/api/python/docs/api/gluon/_autogen/mxnet.gluon.loss.L2Loss.html) 
+- classification: [SigmoidBinaryCrossEntropyLoss](/api/python/docs/api/gluon/_autogen/mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss.html), [SoftmaxBinaryCrossEntropyLoss](/api/python/docs/api/gluon/_autogen/mxnet.gluon.loss.SoftmaxBinaryCrossEntropyLoss.html) 
+- embeddings: [HingeLoss](/api/python/docs/api/gluon/_autogen/mxnet.gluon.loss.HingeLoss.html)
+
+We'll first import the modules, where the `mxnet.gluon.loss` module is imported as `gloss` to avoid the commonly used name `loss`.
+
+```{.python .input}
+from IPython import display
+from matplotlib import pyplot as plt
+import mxnet as mx
+from mxnet import nd, autograd
+from mxnet.gluon import nn, loss as gloss  
+```
+
+## Basic Usages
+
+Now let's create an instance of the $\ell_2$ loss, which is commonly used in regression tasks.
+
+```{.python .input}
+loss = gloss.L2Loss()
+```
+
+And then feed two inputs to compute the elementwise loss values.
+
+```{.python .input}
+x = nd.ones((2,))
+y = nd.ones((2,)) * 2
+loss(x, y)
+```
+
+These values should be equal to the math definition: $0.5\|x-y\|^2$.
+
+```{.python .input}
+.5 * (x - y)**2
+```
+
+Next we show how to use a loss function to compute gradients.
+
+```{.python .input}
+X = nd.random.uniform(shape=(2, 4)) 
+net = nn.Dense(1)
+net.initialize()
+with autograd.record():
+    l =  loss(net(X), y)
+print(l)
+```
+
+We can compute the gradients w.r.t. the loss function.
+
+```{.python .input}
+l.backward()
+print(net.weight.grad())
+```
+
+## Loss functions
+
+Most commonly used loss functions can be divided into 2 categories: regression and classification.
+
+Let's first visualize several regression losses. We visualize the loss values versus the predicted values with label values fixed to be 0.
+
+```{.python .input}
+def plot(x, y):
+    display.set_matplotlib_formats('svg')
+    plt.plot(x.asnumpy(), y.asnumpy())
+    plt.xlabel('x')
+    plt.ylabel('loss')
+    plt.show()
+    
+def show_regression_loss(loss):
+    x = nd.arange(-5, 5, .1)
+    y = loss(x, nd.zeros_like(x))
+    plot(x, y)  
+
+```
+
+Then plot the classification losses with label values fixed to be 1.
+
+```{.python .input}
+def show_classification_loss(loss):
+    x = nd.arange(-5, 5, .1)
+    y = loss(x, nd.ones_like(x))
+    plot(x, y) 
+```
+
+#### [L1 Loss](https://mxnet.incubator.apache.org/api/python/gluon/loss.html#mxnet.gluon.loss.L1Loss)
+
+L1 Loss, also called Mean Absolute Error, computes the sum of absolute distance between target values and the output of the neural network. It is defined as:
+
+$$ L = \sum_i \vert {label}_i - {pred}_i \vert. $$
+
+It is a non-smooth function that can lead to non-convergence. It creates the same gradient for small and large loss values, which can be problematic for the learning process.
+
+```{.python .input}
+show_regression_loss(gloss.L1Loss())
+```
+
+#### [L2 Loss](https://mxnet.incubator.apache.org/api/python/gluon/loss.html#mxnet.gluon.loss.L2Loss)
+
+L2Loss, also called Mean Squared Error, is a regression loss function that computes the squared distances between the target values and the output of the neural network. It is defined as:
+
+$$ L = \frac{1}{2} \sum_i \vert {label}_i - {pred}_i \vert^2. $$
+
+Compared to L1, L2 loss it is a smooth function and it creates larger gradients for large loss values. However due to the squaring it puts high weight on outliers. 
+
+```{.python .input}
+show_regression_loss(gloss.L2Loss())
+```
+
+#### [Huber Loss](https://mxnet.incubator.apache.org/api/python/gluon/loss.html#mxnet.gluon.loss.HuberLosss)
+
+HuberLoss  combines advantages of L1 and L2 loss. It calculates a smoothed L1 loss that is equal to L1 if the absolute error exceeds a threshold $$\rho$$, otherwise it is equal to L2. It is defined as:
+$$ 
+\begin{split}L = \sum_i \begin{cases} \frac{1}{2 {rho}} ({label}_i - {pred}_i)^2 &
+                   \text{ if } |{label}_i - {pred}_i| < {rho} \\
+                   |{label}_i - {pred}_i| - \frac{{rho}}{2} &
+                   \text{ otherwise }
+    \end{cases}\end{split}
+$$
+
+```{.python .input}
+show_regression_loss(gloss.HuberLoss(rho=1))
+```
+
+An example of where Huber Loss is used can be found in [Deep Q Network](https://openai.com/blog/openai-baselines-dqn/).
+
+#### [Cross Entropy Loss with Sigmoid](https://mxnet.incubator.apache.org/api/python/gluon/loss.html#mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss)
+
+Binary Cross Entropy is a loss function used for binary classification problems e.g. classifying images into 2 classes. Cross entropy measures the difference between two probability distributions and it is defined as:
+$$\sum_i -{(y\log(p) + (1 - y)\log(1 - p))} $$
+Before the loss is computed a sigmoid activation is applied per default. If your network has `sigmoid` activation as last layer, then you need set ```from_sigmoid``` to False, to avoid applying the sigmoid function twice.
+
+```{.python .input}
+show_classification_loss(gloss.SigmoidBinaryCrossEntropyLoss())
+```
+
+#### [Cross Entropy Loss with Softmax](https://mxnet.incubator.apache.org/api/python/gluon/loss.html#mxnet.gluon.loss.SoftmaxCrossEntropyLoss)
+
+In classification, we often apply the
+softmax operator to the predicted outputs to obtain prediction probabilities,
+and then apply the cross entropy loss against the true labels: 
+
+$$ \begin{align}\begin{aligned}p = \softmax({pred})\\L = -\sum_i \sum_j {label}_j \log p_{ij}\end{aligned}\end{align}
+$$
+
+Running these two steps one-by-one, however, may lead to numerical instabilities. The `loss` module provides a single operators with softmax and cross entropy fused to avoid such problem.
+
+```{.python .input}
+loss = gloss.SoftmaxCrossEntropyLoss()
+x = nd.array([[1, 10], [8, 2]])
+y = nd.array([0, 1])
+loss(x, y)
+```
+
+#### [Hinge Loss](https://mxnet.incubator.apache.org/api/python/gluon/loss.html#mxnet.gluon.loss.HingeLoss)
+
+Commonly used in Support Vector Machines (SVMs), Hinge Loss is used to additionally penalize predictions that are correct but fall within a margin between classes (the region around a decision boundary). Unlike `SoftmaxCrossEntropyLoss`, it's rarely used for neural network training. It is defined as:
+
+$$
+L = \sum_i max(0, {margin} - {pred}_i \cdot {label}_i)
+$$
+
+```{.python .input}
+show_classification_loss(gloss.HingeLoss())
+```
+
+#### [Logistic Loss](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/loss.html#mxnet.gluon.loss.LogisticLoss)
+
+The Logistic Loss function computes the performance of binary classification models. 
+$$
+L = \sum_i \log(1 + \exp(- {pred}_i \cdot {label}_i))
+$$
+The log loss decreases the closer the prediction is to the actual label. It is sensitive to outliers, because incorrectly classified points are penalized more.
+
+```{.python .input}
+show_classification_loss(gloss.LogisticLoss())
+```
+
+#### [Kullback-Leibler Divergence Loss](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/loss.html#mxnet.gluon.loss.KLDivLoss)
+
+The Kullback-Leibler divergence loss measures the divergence between two probability distributions by calculating the difference between cross entropy and entropy. It takes as input the probability of predicted label and the probability of true label.
+
+$$
+L = \sum_i {label}_i * \big[\log({label}_i) - {pred}_i\big]
+$$
+
+The loss is large, if the predicted probability distribution is far from the ground truth probability distribution. KL divergence is an asymmetric measure. KL divergence loss can be used in Variational Autoencoders (VAEs), and reinforcement learning policy networks such as Trust Region Policy Optimization (TRPO)
+
+
+For instance, in the following example we get a KL divergence of 0.02. We set ```from_logits=False```, so the loss functions will apply ```log_softmax``` on the network output, before computing the KL divergence.
+
+```python
+output = mx.nd.array([[0.39056206, 1.3068528, 0.39056206, -0.30258512]])
+print('output.softmax(): {}'.format(output.softmax().asnumpy().tolist()))
+target_dist = mx.nd.array([[0.3, 0.4, 0.1, 0.2]])
+loss_fn = gloss.KLDivLoss(from_logits=False)
+loss = loss_fn(output, target_dist)
+print('loss (kl divergence): {}'.format(loss.asnumpy().tolist()))
+```
+
+#### [Triplet Loss](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/loss.html#mxnet.gluon.loss.TripletLoss)
+
+Triplet loss takes three input arrays and measures the relative similarity. It takes a positive and negative input and the anchor. 
+
+$$
+L = \sum_i \max(\Vert {pos_i}_i - {pred} \Vert_2^2 -
+    \Vert {neg_i}_i - {pred} \Vert_2^2 + {margin}, 0)
+$$
+
+The loss function minimizes the distance between similar inputs and maximizes the distance  between dissimilar ones.  
+In the case of learning embeddings for images of characters, the network may get as input the following 3 images: 
+
+![triplet_loss](triplet_loss.png)
+
+The network would learn to minimize the distance between the two `A`'s and maximize the distance between `A` and `Z`.
+
+#### [CTC Loss](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/loss.html#mxnet.gluon.loss.CTCLoss)
+
+CTC Loss is the [connectionist temporal classification loss](https://distill.pub/2017/ctc/) . It is used to train recurrent neural networks with variable time dimension. It learns the alignment and labelling of input sequences. It takes a sequence as input and gives probabilities for each timestep. For instance, in the following image the word is not well aligned with the 5 timesteps because of the different sizes of characters. CTC Loss finds for each timestep the highest probability e.g. `t1` presents with high probability a `C`. It combines the highest probapilities and returns the best path decoding. For an in-depth tutorial on how to use CTC-Loss in MXNet, check out this [example](https://github.com/apache/incubator-mxnet/tree/master/example/ctc).
+
+![ctc_loss](ctc_loss.png)
+
+#### [Cosine Embedding Loss](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/loss.html#mxnet.gluon.loss.CosineEmbeddingLoss)
+The cosine embedding loss computes the cosine distance between two input vectors. 
+
+$$
+\begin{split}L = \sum_i \begin{cases} 1 - {cos\_sim({input1}_i, {input2}_i)} & \text{ if } {label}_i = 1\\
+                 {cos\_sim({input1}_i, {input2}_i)} & \text{ if } {label}_i = -1 \end{cases}\\
+cos\_sim(input1, input2) = \frac{{input1}_i.{input2}_i}{||{input1}_i||.||{input2}_i||}\end{split}
+$$
+
+Cosine distance measures the similarity between two arrays given a label and is typically used for learning nonlinear embeddings.  
+For instance, in the following code example we measure the similarity between the input vectors `x` and `y`. Since they are the same the label equals `1`. The loss function returns $$ \sum_i 1 - {cos\_sim({input1}_i, {input2}_i)} $$ which is equal `0`.
+
+```{.python .input}
+x = mx.nd.array([1,0,1,0,1,0])
+y = mx.nd.array([1,0,1,0,1,0])
+label = mx.nd.array(1)
+loss = gloss.CosineEmbeddingLoss()
+print(loss(x,y,label))
+```
+
+Now let's make `y` the opposite of `x`, so we set the label `-1` and the function will return  $$ \sum_i cos\_sim(input1, input2) $$
+
+```{.python .input}
+x = mx.nd.array([1,0,1,0,1,0])
+y = mx.nd.array([0,1,0,1,0,1])
+label = mx.nd.array(-1)
+loss = gloss.CosineEmbeddingLoss()
+print(loss(x,y,label))
+```
+
+#### [PoissonNLLLoss](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/loss.html#mxnet.gluon.loss.PoissonNLLLoss)
+Poisson distribution is widely used for modelling count data. It is defined as:
+
+$$
+f(x) = \frac{\mu ^ {\kern 0.08 em x} e ^ {-\mu}} {x!} \qquad \qquad x = 0,1,2 , \ldots \,.
+$$
+
+
+For instance, the count of cars in road traffic approximately follows a Poisson distribution. Using an ordinary least squares model for Poisson distributed data would not work well because of two reasons: 
+ - count data cannot be negative 
+ - variance may not be constant
+
+Instead we can use a Poisson regression model, also known as log-linear model. Thereby the Poisson incident rate $$\mu$$ is 
+modelled by a linear combination of unknown parameters.
+We can then use the PoissonNLLLoss which calculates the negative log likelihood for a target that follows a Poisson distribution. 
+
+$$ L = \text{pred} - \text{target} * \log(\text{pred}) +\log(\text{target!}) $$
+
+## Advanced: Weighted Loss
+
+Some examples in a batch may be more important than others. We can apply weights to individual examples during the forward pass of the loss function using the `sample_weight` argument. All examples are weighted equally by default.
+
+```{.python .input}
+x = nd.ones((2,))
+y = nd.ones((2,)) * 2
+loss = gloss.L2Loss()
+loss(x, y, nd.array([1, 2]))
+```
+
+## Conclusion
+
+In this tutorial we saw an example of how to evaluate model performance using loss functions (during the forward pass). Crucially, we then saw how calculate parameter gradients (using `backward`) which would minimise this loss. You should now have a better understanding of when to apply different loss functions, especially for regression vs classification tasks.
+
+## Recommended Next Steps
+
+In addition to loss functions, which are used for explicit optimization, you might want to look at metrics that give useful evaluation feedback even if they're not explicitly optimized for in the same way as the loss. You might also want to learn more about the mechanics of the backpropagation stage in the autograd tutorial.
diff --git a/docs/python_docs/python/tutorials/packages/gluon/loss/triplet_loss.png b/docs/python_docs/python/tutorials/packages/gluon/loss/triplet_loss.png
new file mode 100644
index 0000000000000000000000000000000000000000..140ae4a9c135de0593e33248d0ffa4674524c6e9
GIT binary patch
literal 13670
zcmd_QWmg?t(=CjJ;4TU7F2Nl(0fM_b1b2524#9QffndSiJ-E9=aEIW*Pjg+l@8=og
z9pn80=kS5CXLqmFYgKhu&8peq3UcDe2zUq(5D>_c5~4~F5Kx%F?{+v?;D1belmG+-
zLZyX>h=Qbu2#JEDovDSj2?T^hcvA9P6=e&Y{*GHc&u^4LWRzD_ZGnjsA}J`GA3wrV
z{sf_+;j?moF%lMo!4`W<ixB-LR#B8;0rm}cCCWffPI=%PhN5HW<jNW^*V|*Ez1^p=
zQJ>q@v*CxaJ800s9$av7B@%?i7a7H{27ER;-jHx&C^QfR<OF0^&nQ~;m%t~;A5BO4
z9Fvi1dCgY*t4Hg8FBS1Sy=L~1{$CWMw@mD(6$GO`;j7aO!9WN@o0kn?$vM3raz^JR
z8R{?KaY$Y+<graY8=VOXZp`C{f(Yo|2*ZFR@dI6sFxZkUg?<!Yl<C|;RJZ{xg42I)
zeADs2=&r53vJecY_FWOWmVA^R=a!U5p;qj!u>zxa1!j}V>})?~FQ0cLCDUbjg;3!o
zQpCx_d79*SsU^gpQX2~E7)%gcdazxyWl-F}XB*juc#RNTaufMFG04H2jk1Wd`!Ne8
z5OVhCUZF|)30vlLeD~wm?mmB?_01`x^Y-(&gRPRrHi6kWIn*9EIpvI9d!KGRZny{q
z8+BMKg_X>3YIf8x*iX$=DRul2Nip8T{hTN(zLi8riFw31;R>q&c6>bhka5t=I9Td*
zR8+r`M{f5I?D#Ew@-gafh^$WHrw_v1l`v|Z1RBuJv*7c^=`(hFdEDKwKjD&K85~c`
zO|UiOyfNu?_s9y7{{b8K18&DzHjg%1;D>B!@%i09Fc2Dsg1CjyplJ*UV}xBNUT=t^
zc?fgnjR-N#8lI6WT%1^ccF+1Gf4m8(`_6Y75ct2~bSA@ngwf!jCk=RheW8ej6*a&f
zfW`-r(!efv*(#vWbr~u^M+YY4yw8Bf?NYVJT!FmpBL4sp(nbFPma3BiM%X<JVUmPW
zf^aI}D+%HNiem^rsUHnYM9AlFq7o3{-}ueRRgjWEI}@ZuIQMUO5p{#gMOG)E&C&3J
z*M#*AU<x{&HL&M`WexdK-#l~~{$}My*Mr~de)C(c5vlx3Oqc9$u|HJa#6<r3U0ZOV
z0brq^ujqU~3E@9}j)f5oDiw=}Rr-!#0n&<-+h$_*Z~Y`_Wd9B?B%Gy>`?L3ljdx6@
zl%|ZPhy?`7ffn65eJj04pZh+nSE-aD%)uS`n;7L)hA*fte6*&m$Mb^qdgDdR_cgO~
z!yYMvXbfjBFtU$wOKq$E*Uc85!%3s<3YslJOh5fb)Ni>zB>Zo)!<m1c^%D2!ci#RA
zJ<o<S{=(b!<(F6(DIDBgkkUuOZ&4E>?!u`QLuf8xC_hQaVp2s`KJkCFrd*>!!K9X~
zrVxn7r2U#Gvjn0e=ZGzjB}!OJ7&Mlx7VuyJ%j?NAk!O?WlcUB9DR0w+V7Uz`MWj|I
zIVMiYd4N3R^OanR%V*wMQn}FAC3}Y7C*Dg&P??bRCT~iXCH#oBj6aO?B$rK`i!+sE
zP8myLN)n{>(Y~)kQv9UeryD|EqgL>7w^Sy3q>({(PKQ&1Q`R-oI`i0N5uK+sqdcRK
zK)xJwTDd~mM2h!uI=5-Mu{Nrfyq3P!kT<$q+-y4Gp!aI)is?SJHKR4jJIK4<JNVJ#
z4idI|!ld$j@mHI#319X4aNnrDN&eW{Z5j+4OcWdwfrzDxy@ef#g^ztk@2mKk&Vw$W
z=0w3#%`^9_BBzR8X{*$o{{{BWJmNkg0j@nR79)4luWtyYpNlYZtF?A2hZvI_R5?`b
zRKFeS%}W2Mn0;qiZaHY#GP5-EY1ZO!?9lsK_YikRpLGY!U`ki>)dS2^@GW;Ek93av
z`(!>#adF}Ma&X0B(Vb}Icay@ToI2?e5i1#OT|N+>e2d=0Hx$KwIciC>7`ehRab1B`
ziB;L=(Pnbb2v6%aoi@+5{(BW4+^3{_)%#l{dstUkTG$8V(|6q=b0Lx;*dYy32ITnU
z?UTNFr@;%sH;8W#OK@)9D}UAO)9kzM3#3BA^h@wgFiqepSSnyC;FIB#?MT;5&rc&{
z@iJyIJGIKPTeEx~>z?>xrD*6d9&dSI(ll1xdr_7Yr`kFTS>kJz=Hh=%b%b(c$63!w
zmJXY)sBN>XvFuat<LcaE(8B0Ce_VBAdvkO=zUb##=`i5Teek<5ugWOGD06FotWE4Z
zGMWL;amsm<o6r%@-juhWm)M!ee$sxUQKgCBY1VP4Nv);b;C*GhX1|8r%E*eSt=WoT
zGhbtcgWWBNzf~ZDznOoBzeiwLAluWzlhSk6E6PLLtK!D?w(I`S-SO`Oqmt^TW38Li
zin24j^<iJE4uuZK4zuTwXR?<$2<}cU1GCO8P$o#&zzGfR^V<_7PmEIBR@OiBy^XzH
zk@Pr1s4S?1hzCgd!R2seh?#Ish|4%PG&({$1Z(J|gjMXhoSR19#f>GmL5UVKX+`Oi
zLz9pD$?v>nL}gq?{$Pw#wxl32*Xe0B*1Cb$s*g?PQ|OuHb$xw<uMox}l&cmN1{RtZ
zDl$mqu+8|R2^*MgzaEXP_W1NV?VM~z_3`zl{Opjhm!gq;C+RG;0_INJj#o*$N;?~4
z-s_OPkt~;aPQ^%cWH+JXV69_`ip5EHRU@I6K-ZB|mmZfp!@+|`42GsBR6Et<p}N3S
zH3PjLX4PUnC|YcyoWp$~AsTTU`!R4|OZR!U*m}`g_@<1YCu5X{*|E*hPcKSyRChJ5
zFs^~r&K}un^jqD;Qez$AQSBUdDZbuWTe~T=g~(9I`o-vl^o5Z1*OdjW9IeWBgZ;N>
z#D|Fa2x7rL=>%M=&tZ3QFGlmG!=qkYe$1QLt2CVSOpMRX!7V#|^1DJwPwaIZhF0^O
z={9fcx9Sn=Z%^$P6_v@f<F#39*$m94nRd%_b%|Xo7JS-fe$+amTEw`IgTQ{8!+Ixe
z%eTIa@^l^g8)}n{@+RxMKE$_|H@5`#0uu_wTADg{J0vd#FQjK8vZ~Fh`JoP>H7G(t
z7!Kbr&04apN|H(<OF9U#2?&eei<@+;w6(Phl^V*3%5mpoh_pSA#~fm|7?JysL-F26
z^-_1jpNFS({CuABnmRM?r)^P2E_9JQ;}hXio`K=mVUOaV=0L3#ZE()M7FeD<T=3jg
zPtcFan&wt_(0hKY=Ty=bUY-eFLEJj&zM03xWJYl*-FJ7UJ)CMAj%0SzJ#TS5`7u8$
zomo1DIG<6T*>Y<Cbd21BKgwle%eh+J?y}2uv_9s%=*+QN-?r-5|14*9n|oSMl#&(c
zCUC0P*5B@S>HF*HbnW-rb9=rI^i%6|+}mQ*HNg>2?mP1*qvNeyYF2rv>>vDP_p85d
z`ykT;E(3`I8?u50z1V*edR~k75$lH{W+`W<v@7^2@_lA;w4JfK8XKl$4%LU(mu+Wy
zOg~<~X`A$Ox|g39vWpUnLhitB%k*s6X`6b;BQ6zm{`xjXC{rRm^-<$W@<4gitYth?
zpRA)bRp%P}rS3Srt6}cUcb!;1Use{LhivRCHfX{h69dBC4eJ_y5@O;M0u-NiJ;3(G
z)aMB);N@qHpyww}2$j0L(>GBh>~y-26Jpad*AOsy1iJ_Z($bQTBJbgoacbHIMvFLT
zGt)d{nkiZ#A=d8;3aHWx3))rldIUv%Mt+uZWhWyYzM#&5o0jj_PYxhnesn;~g}^So
z^mIK#KtN_%sAxEA$ja~<+1W4|8ryv~VRE;z2e>T+1iw2k@YBY`*^tED#@g12*Ij_@
zpDTEQ->-+6$w>aW#Mw%KOhZ<IM8wX~goJ~Mo#_LaAOZ;q3BRMUDX)^~r@zAiBtT~F
z>}=1=%<Sgo#^lDvWantc%)-ON!~B7jnU$3hxPsBi!`9i*ozd2b{NG9bGmof=laZr^
zy|aa#Ey?S=hM(<RoCV0pUT^f@zkk<h;%@Ojcd~W*yDeaY%&%vdS(rXB|95U+DF5qG
zUIhzx6Kf4o3mX$#C*U4}9BeH7|6Kq7IrBev{9hwA|7Ro%&xij#@_(KAJCdLIbp!uv
zL;qUqpQC`e1QGa||7&_d1bX@r6$l8L0ZCC|6?e#!O!z<8s(1aUUl2*E-{qpElSm6}
zD!r2;`<V;dWK42O6VR6n7bXT)h=-Syhm45Gmxw_ClUYegzM)B@i4CWLf*;7WMu1~_
z^jp7k^F>a1XmQ(J@!7o;9B*TD+qJ&rG23mw%yL>&J}>~kgMmWxhkzl0fP~}5;O=H2
zntc5Uf&h;Ci$j=v3Hc8KeS%S-V(k_;{`VvVfXt!)Gt^iV!i0?6TQua~nLxlm&j0%c
z=16_2dcnV|&AK%ue?K+g-+A%Ahc!%gMhD~9^8n?lfrXPF%$6t(Tq8y&>xNXc`+uzC
z4paRZ>`(cRkwFmjIvdNNZ>9E{>Qo3Wq`?*4k1786TrVSRqj3-9%3r#+fLYAMB;bst
z{B5IU;4y%r@6|zQPEeW+GCwn@Q&9i$j|og=_fExotUzp(VbSNq^o+lLi=*&oMIW@>
z$^DD($o*O4gyyzk{`G1Y1!Q8lgc?2Wn}6{DF@PVdsHp#SD~JL<M@F<-tBK|>eum4D
z*^c4h5q^ClAYjNO0Y}Hr4vGE67r-+@=2toh|96p+P&AlvrD{wF|KeC70N)wO%KdAK
z00|5bILx@tQ4x*C7OJA-xp}eG!^r_RehQc_0Jt;ECovElhy)+z`FE&Ls?!)xf33xI
z-$(`z8@^;bEDVW)0Wja2a)|0skPe*p^WC25&oK1e!;%Wlhno|&<$4>o6dlL#gY}M%
z#Tqktoe%bzXkioq3(UgpPJ41}UN_b?p4XN&rlWW@ZfAOz2Zh<5+2eu_BLogV(F>%K
z=ytL#XMceEqVUtHWYbbn2zgW0%Cy9)4eb@t2B;uc&D8Zg^1)noYlee@R}&(p8{NUC
zOSKler8Q%`$NX<eAObe2(X329_k<v?^&CCjuDksx24)4rV^XCCK)>bfrN)5S1R2c!
zP~F{(WWK?=i73B4X;|kxS#IDGdODCFjq$xAm-nckVd&432I2g;KAIQ5fr=4&c2};@
zYm?B^FHH(>Ic*+pq{Fa8ev3+UMdUrN;}pen+PFow2x_E(GyZ5Wmcg_8qoka*#q~rg
z{VPL&5>+y@zU}j$exU+ndH;R%Kg;XSrp<0=L~V!<XV}PuyfZ6aXKit<?F-T1SXhP{
z%YutZapLn|!&qeJ<l>-+8-d#uBc|s4Tu?+1G3au4^hszj20eg~&-LfNWu8lMl`56#
zXj)R8u>R{hYqj2}LkA$inXkl^q6<B4ppN3|d!zH_)0a1rITMhH2Kx8SiE3KT6rnE;
zN7`_Sb7K4B>bZBNv-xd?Q-7-eW^afF_wiTT?HXH(o@cSg^{jdzj1a?!diw>o(~?CY
zp=$>8IZ3RQ$;ZHh7~aDo5kb5M9F>`N-F)FdSkr-6((=hjK|O{fc<|Kjcy@N(&}3r6
z!E|AIx?MYul|?)nj8s%-u2|&AZTqc2jma?1UanQ&TMT_Du&Wb+(+Ep}SR~FGLrXHF
zcCwv3?*T<#SByBZPc7SuY&s`cSzch)dcA#>7I{?bhiXZ(@`_RzmbA&Hyx)BSkNq!@
z+L3rNN@92^8D$j)S<c&?9N|FL=8CJ3g%-CnjAqM-G#e<UWOz-yF`*alyi=B_$zr`W
z&-%!n{P_xfAv3XDyY=?nZbar0$)*U(New#8#pBIN>?Y>}rOVTnv$O<x!9}9ahyeqb
zvGU2<c5SmVY3#IHR!K48fb~r>P~a1p0zM>%H@h6A5kLNVYuf1#$&+n+_qzu*y~$~h
zH^645DDJ(GZ!56s2&@*!)HDRTw>*nDQF^b2ug+vR5Y91&_x|1Wwm`eVR#TC}IS#3n
zVd=-1cGnjTD)v*ug-XM<;pP3wJg0JrfwSNO=jO0d8|4C-@Zcl=1zJot(~%E&Td|0P
z!M)_TI~1}&@G|I)z%H;^uGdr^p;s@r_Pjn!vZxBv--MZ>#jLZMzv*jvI#lmq?I-fG
z9;;Ae4(D324_s{XYEahWT651kSgI=l)*6Y$XF`_Z_vht)zGGx4iC(%4SwRmjh>Rpu
zNvp_ewb|v{W<J77u`+d@u%kpP{P)9M^M%T9lO*e7nS6W5Q$NEoNA+Ls)e9ffIBhI}
z_j(Vj0gJp>3aruMS~j^5l0)XCr7@UajZx1K6bj;mA218zR~h<(=4{B+CUA4S$Y#T`
zzbO$(g9AxdEn}{g-(1My!mUq+dv=kuNv1U|xy5i{DBwV=&$Rhd3Otbt3oiQ-p~j6w
zZ+HopF1SIe00(t(<+*5*0?vMU+zUASc7J_@-y}YFm{Tk|eck@BdRBnY-SiGEwmNO(
z#1jY$qc!7#>GNGDhX`lWg)t4k^AJ7D<|(YSGbq%CMBkMNXY#tVHhd63>ygkn5>Ks#
zM-2FgHeuO(IOo0#HiI%-FObE*oRVhtxID~P_>Lh9j=Xq$P?;ZVdO@FP3^FEC&hO0f
z*ag2JriKcXWmg#wViaa<qdWu8murOfP~7J{nbz|@{BM11I*x;sS%H{sQNc+cq@)*W
z;ZXy;F*GU-0vf8z`=H+>Au=^~K5h}ed^p_l-HsEV(qi4;gp=Z2+K6{?FkcaFf4VIy
zE8!a#n`t|LSbq^TYiH6KO03|nQl#pB?ppTMYe7~fy$Au9yeBst64n61)-SG(yQn>L
z>aYDmQF`;uTv}r4c3|Z~Iay7ZGS!JZ@t8zI?)RurgRuHTPgil`L|`t6i*(0TmwE8h
z!!6&A$1r0<s-5p;x}1ZwfD%Juc%>l8Os}ChQiI+4TF}YiLaRs3q$eR9j8vRI(f2fz
z!naMyZ&1lR$?^GIOTqje)m6|b90+<&SIXfG{(v8(v4fmgNMp^xv1Y?1745tg>O!U?
z?`o_USqu4h+O^NO=&BT`^gMR3^L*EH9M5}kQ>=PCC7O{?5Ut?hwK%(lNm5|8XUdHF
zqqB-R%?0sDZnqQU)0S;pPGV_TZwoM+xu?)0#_vQ;KAtEryWU|i%H2llKX%jd-AO8@
zWSs^#O1<vE?>Jl+KfEK59jEMN!;%g$UeJ&v)&`2X<FR@#ObVJha0s3{<JV}<Je0og
zByrEQywHh|^FsRysoTNSPURLs7<Ir;+vv+NK4v=-!MknD3NMjW0w%JUjFY80r*J$l
zcbq!aj9m!}lCW5s{U)@WkAWfK;l3fS-CAqTA_fZ78_M4cMltUNTDfdj-Zc`GX*b-M
zT2pw1Sw>v7`#dNWP`;tae%hc#>>2!mDN!r4?L_N|+&DwxgBy2-*mND;BB}?TsAXp)
z#8-LLCE$#1=1rY7w5W{2fJDY367+6tx>>S{S{wTku{*)6*ScU{>%;mjlKee(bUqni
z`GNV|aLfL3L<bA+*k-QUD~}b*iy_ehU4r_6MrW6iEP>^LMO3JO&lEYKZnvkaQM)st
z=uxiOLVoR&yzqmfcJ7C)l9XDKEaiD?$`0X|JhXLMmtxa#*DGV8tfcbg^ZI^mm&Yy3
zb{(rncwe2`7Ohavn3wt(0#1NLJp#mf?8fh&+)J7S;c?dLp|C}7lG?m()4*(IyFQ|h
z6Bp=A?J#TJm$Kya89K0UvCI5voKEF^FEI0-A}GRCbKTC?Xs3&4Y@)?eK%;3Kqs4_;
zY4eDQEyo{j{X6U(M|KZmy0hg}ZpdxG?x#(AGO0WlX$1ub5`#gSgo8!)rC9I-p9>sN
z42g0bJ<1%g-BujLr6@tr3k9<2$vB8L+*4#^plJBV9cDk41S(m_Au%JLnKs4Amtf^w
zN+Q~+A<+e<=%45Z;Vws0v*(YO<LRh^e@IwpeMLylV7BeVY<*4Q;A*~C!%|AKbR7OD
znM(FJU1>5MPG+R-vG}zM(4s6>Bx=%FX(-DVpNAXSqs+Qo)<=LsaGGT}d=e`(^5Cx?
z3Tl2=%Ppgzh%3Iy5dE`pFUzZ(=lmxoCTihh85alR;#W{kER3_z0Nw}3?Sbz{#40MB
zWo*9z-jfFKDiy5l*ey?OX=)A1DfCV6a`+X`qcRJ^AF3*_Wth1Sa6E}1<0SZ|tEWF(
zF{o`zSRGgO$z#Slyd@&j+h}lD5uJX|m&A6Jc?8nI=pKGaWRpNv2Tj)7u3GMnfD<?w
zhD5bQVFpMc>n7KqPTk~q_JuwouqmgZ_Ty@rkXhr3PcVJW*?26f+n)?U#(xZ52nFfE
z6`j=CEYGz9Pvy7E=_LaF`>7PD{%MVHXF0zy-XjKbtG7_E;r3m#@6$bxna^IP+pbS+
zk(1#i0ZaT)P)4@-p_T&tRIlOmRB3djE}>%v_D$KuZa)rGISi5eFUX1`IUHzQMeUap
zJ3stJeVzdZlu`^P4&e-I^mpv$vG8o<<JlF77wF`l{-9Q!k)uhPkd@LOk?0a6gS{(j
z)I#&S>fgZ+@P07pgm9Xc<$5n0<BH8wHANSurNp1P#Z~5)U<-C!<!kf2c4R~WND_L$
zdp1+|XL=osIAuB)y9KdhovAR-3<)|+!+x6Y>Ai6aa5~MrwP7QNvn7}bn_Mu}QU(Bl
zF0h&}-+g{K8`o-18~ny|tu#d+Pof6jbcE6ZWhZv$KmcV3XC4xVWvza<IPPn@&52)}
z3ffMD=fGq1Ay5J6^NxUbV2C<*iY8$Mt}8+4CJ7px)UWG#{o6kAfne|YogP%7E1e-L
zUfo=Eb<Gc7Xns387`LcNFw+F!+qF)5L_9B=W!r?H5aO|yEQCa6uznM3t6c*rz)u5~
z{a(@&Z=lTpW86LaE)-zLd$kgHZz}Gt({>ESN&>0MOAN*r%RY6rl-e0>)dc&~2NWw8
zdS->`Zdyq?Xs%w3h$~@+Ww4@eBM-*>%pd-EuDjWGJ*REDSZ(rrych~pxa^JUr)5BZ
zb<A^t?GEE_KV7ODc8Vh`V0H&y<E-^?#k^18*Mh-op<wtHU~8rJj4{p*JgAWBP-$X?
z4npOuK%SVcHj$p+uZqB>HTeusl+ad|{1V9&CY1DoW?sP9W`3}1Q?uyrupy_>VsSg9
zncHUe1E|lE4UGW;yVlInPw1A>(Eblkr7g_B;D)C~*DB0&*y{VFr#B6+OK<?^Ah-<U
zEMKI}<X~}`bGQFKJDd!IJ-a9<GLe<EL^L(@hcs?znfrV>iY)r<G6htC3|uRa1wQ#S
z8Me043Ds%K-acMe_}Od+jSg|OpCgZ3;<%#tHfs_|y#Vo^WBqQ-&05WxG!_>s4X>=L
zhe@f;(`9^fw?kZKDqK0^ML?F!e%7#syL)U?LUbDlFl%0C@7vyxq1jWCNc(39Jpm~n
zB`Js~HZ6|D{r9i$=U0U<zXDOy%(6WCX3Dg=EZ(IADlL_$mh56AaiiIj{eI=QKyDnR
zEhl@rNa<h)Ozjj++#eRFNOfhP+wSf0i&q6#yuo%ADpVdv;Rt(THts7@zoQrk=S`H5
z1lKPX7C#;>uZ>{b5iwldMEAqW_4`Zxs?WhG<A&6PC=<OK5K%N^FvfRn&-X_njD^3(
zO95tK`l~bGS>*UF3EF$$MN|EVI_qrZ@O^7<Y4%%i$5a!cTng9TmKbQ7`QhqdOI8t)
zO<OXNT}ZEndAp0bNaAh+4;GV3<^$g3DV<|~X7SW%8rS)zB*^yH%kymqpRRWl9!IMS
zAD!xtZ7iX&xF4cBn>{GjI@QME2Mbk3eJLFtm%9@d=8_k05OIu8Mwy)<!xWTY*qMa&
z)Ki#r@tRjWhn40-7#s}6P*s|Uf#6)z@YpzfPjo0{toXfh8~#k{wTk>S9N03{=&&UN
zBmtegeqKF_MEItQS^wG(xxWp!f+U4>HE4xc)ydH$#6So^Sm7klgqYK>X?IivRUonI
zO}Wd_+-`n~-i4{pz9GIT1dA>~cwP6I4*CRA-cfnW*zt11I4z>)#Qp8r8CHr!pJ;A6
zIS@QqR9a!1nmF%2(+HUi2m}Bn(oC(@JX0+hY2+6*t{E+$oZ49|QQ^f)q&lkx3{f4b
zY>Urnz0Wfx^*$^CZ2%*{>$IuA_3>g@u6VXob6N(>+B?y<5i}mDaxoON7BA%>Cf7uf
zCE!)>HHywQ1p|cW-KaP?uekf^lzu^;#G&3m#Um@2bNX+N!*MvuN8NKI8@h1jzuV@<
z?UT;8`tfNy!uSH>)v4GgOt<=?PGJz<u^n^AK;a~nUdn*zT{z7rNL0%4a9Qlu_{V4P
z={^s13j1hJ^rB-hbY;Sso9MXMHI~Od0C9SG#l8nw&I9wn4@OE4Ow-GR66psxV{C2F
z8qYdU5=8HJXhUDbjOCH5)9PMzH`maf<V2#CO`ch6IXjdAQ0;>+4n$DzF=MIcKG;G&
zzMm`CUA`>a6zXfo4~SQzVsBHLW{_6BIa{}@mB4@sFouZ+YUsQ-tur>?XsxgZ=R2NV
z?EH-dlO^UDin!LjYjSfFiTmjkqrYSlU`#;lR2lX2?&Vl~H)Ql51B>y0mZZl#Y?+-J
z$Y*eNNpw8fDLlSl{<hOQ&a;=%!<yKU7>tOEcNaVm%K!MgC!ksw3>Op*dXF{%;kt^S
z*1JLGIOaOaG2bXUN)yd6rG;;{JrK9n%!?<4`-axSgbcy&Cmvyk$#61FEoC`rD2$xo
zo$)w&3@li)1KBZ>kr=}<Artiu$k(G+2h-E0l+u!^Vm(t-5)g0{q$GYA7xWus)%UVl
z{EL*9eGcq~Ae^|Cv-V}}bFLAUqpxh^hES9xG7|KeJWi%4liSo59&AV#{$uhM1No`&
z16zlLZYvvQCyp;qw_b>@2O907sKT^Z2QVd~ydN{h6a8V=j!+j>spK+Jxvca)zt{J<
zp1lz9WSJOhQ;dyw(aN_-G8`50OgNg#mmWK`0r+p$9i267b)`z}AEkv_IAR|tt!?RS
zZ|T^8lr)4NwBO)hfbfD)Hn(Q)lh<)vJuGv!*5;(zns_kuP~|tG2-IPYtigOHq>j$j
z#!2Cox&&!XLZ5%aT>^}ZN1P^VzHx{2!?B902W}$S6wtxSNQw#~jfIC4fZ+JUnmK^_
z26|~qanqX$CIpq(wwA6a(wt-E;k0F((o${H{IFghKAsgFn|9DPTDQg3<MN<K<;&W(
zW3faLgC~8$9KIR~5x=>VM}*bF5vBJ5y1_4#gIP^1>Bl{x7oNQ0j6)*+jUY^f5UR<p
z+&)`TF)1bRgtJsx?z>Mq&)YrU?RstwXNONimO|=kgQPM#(FXke3k-yA7K|*EV{b>j
zSIsOCMf?ZQ7n+<3?|@)tcXhEtcDO`i{IF7K7#PR8jhhHB6a{tM@_24p_1${_NY^LE
zkeW@7`5s$QTo;O>7ycj`D4@s23Um@=@%I4w$)(7;T+n6!wVl+-*s${XD)g8vjkioV
zB^YazNHU`@f>p5-Z&E~dnzm;E;GVW=99B3~5l$LF0yEjqrQpF$3`2vHAQ|YCrtD>i
z_6Nb^r1|4~%HVae+;`{v@;rce*WU&(?(eH?ic0O$<>?|skeGFR%La5yyUVq+HD==x
z5%Cv|iSX)}dP6}x@Q~tAcryHc&yTfLJ4YW;YSG|~(>v>JmPcf{R<|=s36uwgRY}F5
zFI@=3l_cI!S22K}nRraPcTNP>hl2RAn~`p2;7sNzo!M;Qeujcrp|oIi+ZCq5iRSJc
zvy#dgGGcH7AfaKo`EI61a%CPBjRU{8&JPKzvI>JGK2f6f&sFFXn|r2p&;a!m=l!-a
zJ2J;Ulx+Bpqhd0~^e=#h34_#Fo4Kon<JooK_dQYv@1+6uo-_GbFBpUIHs=MZtS3=M
zfht~cu!lvq1b!Jg)~+O|_t(-asa>k@8jOt~kh$rhv@VT+G-{DB4_yVg!J-Ep0^n`d
zOSOYYc<eMXi6Y&WnhuQ2dNgrPWkdp=-&;f9u_@*(IXLj?(wKa#;l<;$ewQ}$aMp2F
z)`iAPhE5V|0wr9A(cI6@h!p@dO0Le<*Z*mTNeX!N#}J=kapA|g9xoWOw@9MEE5j#;
zt2jFXytx`EDG-yIP3_I8#7QX<%A<LlcCm5?w&4RDq0**bW|ZZVUgYX}7@}32x%WPT
z<fe4813ho#fP63m<ORy3uUiA@=S_QA_(jr0CS*n{8<-lc?hgA*(cX8-Mf;XIExb64
zS}Kb11pn~&3a!Y4d`x8a(}`~)%?25vAOk2od@kqxiOvm7=%HN2B3rIeZ%csV8e|JQ
zJ5kK(5&05x+mom;UEEFpjRFOSYQjd~g)2}kQTvvV)M5_<6-<FP<5Q1wJ=GTS+LUIe
zk~1Q7JzG0T)B!T?*8;;`?W>XYyWMoz*9sI?7Z!cgmv=YS=nD*E_y@{Mtj1qo5L+V<
zKB~6Q?>sjEp;P$F9>Z6ThC1^}GJ7ICkJ4c4e~Oulo#7}y@V3fMP9!-t;aD)`2aBoi
z6LRo2M4oGw8S+31J^q$qoBwAZVg=|GP8TVXvl(^=iQt?|rFbMZb32z&)>sye2y3}|
z1NL4s8_yE>q}R*7^C__7`^JIOlq5A6XguMLR+qmvnoHEmShYOW$_+TW)vX8z&Xeo4
z#@hkjuD9rdX`T@pO^QZ?6_5kXoKN9*Vx>>7!ZOJ98D`D*!N%#~q=8R=wle@)woooJ
zW9w~rqwOlM{9+ugsSkVdxT*q`&Y%e`(A2#CDwt0(m0EXICI|-;k=gmHD{vGDylwJ7
zsy`=5h=n=<6$Gz$wxCZd?#~bB)*4Z(?*%-agP#ek8-JGJe4UBR61-42SiiqL;X#%e
zOU{uoj-)sM6e%&u9Dp`u-QAS8oU$7AeJ!5w5f~CxRMVHlIRrQ;GW)}~+{lF4hMDJ#
zJoZ0I*GjBLRCJc21fdWx)$%z&Bi?kTNHJ?X!8mfe*eh?sD8AZn_PYiRl#<pKRer(a
z_Y&g7aOWUy)PQ%Gv6pr8dS0XWhG+f$s@?K|Pcra9nPn*!zP)I~Cb{n{16YCec#!7=
zbfwYJQLA}ESke_01d#!n8DBsVEhZ&fdQaO>7(67LaiD)j=b&2zj=ZBLHW$7LNt8K8
zSe{)b(90D6!Kz3_rkbgP1mh?Tnl9%^hI)OqD(j^=EpVt9`(&^}_?kZcm0d&tnT1t#
zF!&!~4gv-Nz_}k*2C@IjF7IDumtrZVzha)kI{*i7$d&(<T?Wtq*+oXxk@PR_1~3hh
zn`7|5avdPs4T&26h(`?g7h}C*!^Q;qe-$edRzT|`Q%=%>`xobY05C#NrVi=95?>rC
zpg4hlY=8W#I0XY(@!jb*!e7M+2~d~@R48$w{>3z}xZ9pu{jb7g3@A*Z1$0elf3YEe
z|9`ihBUN7`P`1~$C>MVP(08a6;=fz}jSlE;fn7}hE)xQ31KxO*6}=tq=RX2xKoNQ;
z0&X3itdD@EB31JEyJTM6v5zEuFpWZ=b?$U{VLpEZ1h-Tb_7uQxuXY~54v=~~Oraj0
z@ZT9Hsr^mJ?p;Q(|N6yI3h+yzyClJ1KllK4pR6z{NBb*(DFc|NS9<fW-S;s8Kll<l
z1^>kvfE+eJtA_zRp|7?R%?9j#b(BgU^e^5{1hCwBJojHe+yi#c)$i9K{fi3$>==>q
z^RFL(#j&D4@$Mr0#XOXN@~7NNf%4Z6f`Hxk=ULR@0GwkCU#inI1*iyv-dB_2p*lU!
zPY<I&ErG(5JWkB#di)`YPL*|utS?7q0^uLO0{Q?DO1pskF+cz`q(T$Vfl8j8{L|MD
z`rhZgr2S6UTeZ`2Y(V0tllPr3*Ij!Ca_Ue_ug6=H&5pW?q=or1?UaU)_j{lE>qG$S
z!b#--vdWHdp?tPpd-gctlTfKfRd=aY-4FidSM^O^=xL_bd@}b@Bg0Kf4jfx-1-!kx
zKqK8u?a1co{<^?ox?nfcZEeu9nF2Pz8hs)V*az{o;L`XUz9O2FP3(uoX1S>V|Ljgc
z3y>|-s%stJNzu=`2$sJMZ6-Q4>-zF$M{@9s!%mWVa&`i%0w9<<S_PicKGi#5tL4`F
z{X~CxQKyv-8!K5ByIlu*&K8_DOWUgnyzPKebMaVPVdN>9^qnppH)p0}h`l^M9?-Zp
z4c<GQC5(QtTWOq>b^>VTqD*ovNh~&0w%@adM@z$r{<2|Dh)G8KQtK4Z@M>tSt+!kE
ztvufFQP8FjH#~NzM;qV>U>*M1Rb3t(SkZbBF)w%4dMU>&{V+1uM(S}fNZoe1+Ttdp
zVJgO5<Zd~SK&^;(UuKU<Eq8Tu;^F0Y@UnBamn~GpZmp3y?NhKWrF>Vy!0ES&fAj1P
zH0oPhuS_l{d=ks8cjBbn`xBKGZGuabt`*;yN?Z3-A~S0r#G?QNB<Hwn$m$a4USG#Q
z0FCP*;$(nBjB-b^I@x&O-RMcQc03vmLDxVB5MWLay8|Uwn-0LU^iT9p^d8i}6Ya=m
zk>8D)&5oapu+cN;dGc*~3yl1h8=u#e&`K%Rs1cYcS0Zjs>@rJ=tU0}l?WBLCZ}sBp
zGn8N&4b^_M<8$1Wu(Rqg{$#0S+I*3oP+B|`q6uUb(gKFy>)}7=TW##dS@DCzLSGt7
zt7TuFpN2;b5VpAT79VGk^PB`cuYOaH3c^%>`n8`I!^vhgb~UwJV;0jOJ1=NbZlmPt
zS7dW&^pK4?T8E6s{(FjV>nmHUJ?qwePlnr?$0y<r8pix$R*A>koal!$Oh5h`QnZlH
z?qI~WUx9B?IaPnC(8gH4HT==iJm}ZLJabCXEL5w1V`iJ>dv9@;>Ql~Ty)f}m#vJ80
zag0`HgIs?VX*2J2dwNYQXxt%V!EzX{cpEY`{|t@Fudv3bthk6G`90AuQJC(1l_?$e
z!m0FD?dMS`lj{v|uJt=`Dg`UoL53i&sjTLdL|JmN6R+*4mRgO9w?#5<g%i++DlK$5
zY>=_U@Ox%WG3kLhp&U^^hF{>Ke@Ty9Yn*v5eM74IP-`a@iNkoYHs|aoVZE%*dw5uR
zP=tIWYYT9e(4eZ@kZA2Hiz7DzndY13#oa9bb=Gey$4=V12$nCNzDIdJnbS3zHWR)x
z?UXk!ppD~xt?Qx0pbI154dgSQu1^Mey)3Rvjr*<dH$3@O-O-_HDtt+lJ(t#%Do7Ve
z${nggRbKWhD8qg*vwiS&(Q&v^NL&c5z>CISX12x;Sw|}Pl>f&PY3}>3klnDctO=*z
zbrb2U*#7WAS$i(Ked*g$fndDz$uzN0l}`PMcK%O6tP>H9jVD8Ey?Q?lso)_)KP!EN
zaDC^I7m}N7BVwpxI0`z<`nF=(gS@>OoAn#bjMS_r5q12T;cUSOBQYC&i@O*3N1M5V
zGrOK=ukHf!{H5gK<lerz4x5D{8|i}do-}75794`3fI37?&8#o6jnYH=Wgh6oDLvPe
zLXE~#O1G|07sy`L14NNgsG=OHqX@r83itrwq}&W@8r82MTxQe+Rn}+^f4MW0&#k@%
zVC1T=GJYf1k@?mB<R_cuG+mS2B$XLKlU+vT=%`Jrbyo)p`V7K2U!W^xTqp~44coRv
zLXghuflk$RJ@D{H#7J_{zDL;&1OaRi#p|?0HidR!?Mhalha>Rw`DU-4Tky?O4U6-7
zq9$$Y!+@(3vCE92-ek7>CcIsN!Dp_l6RJsTl)JPdbIxzg9HlF6>pp9?&DD#)DLWXk
zPbBPkO)3+7jY(rQhW%?5=%-+@)pN^MZ&CiV$6J7(uZ}%BW<@6R!<X2fHhd&&5Cgu#
zFErq5Qq)$&=zpn|4^EjKX%{$Hi(P2+<deSPTVz?DyG|~;u{(o&fK44*IX3CWg@MBO
zE!1O!RmZj*{WR`nC42LtUKF+DTI~A63#5x)zue=h{IEqQpTE%{TI4lRP_h+%8#QEF
zHjA(ad_H|Q-EdH3tr!=-zgua_cs)A=QzDgEtcv_GDYxvW;IGG|eY>!F)+cQ9+XN0I
zKhK-iXwAue*Bv|Jq37S6NZPM7OqJ>pmCLVLc&SjRwGG#~Vxcj7C@5N663OUH@&VMK
z>s6m|lT~@X_>n{(W(M}!`i6eRa@kYuVY&8XZ2BD{eZ<zwOJ)Dlvh&`PA?9sD5+jRD
z@iSAnZS-IQbxIxd7PxALQL)3<XMAP}`1ItgQAS?a_|NYRy~<BfcpiDf*V;^4!*t9x
z&O5Ym+UnrGZRU1>%&t|vHtDt7Nfc<H4wlRoMMjR+CQ1?NRslI>jQ)pAWjL8QKgKFC
z>%o#OsvkEvZFudX75#{Jt;$IJ%-LF|d2RMW^((Mmj_IED`V#euQ|#*!{ih$3)_gvP
zGsRnkuN{v&6PoA~3|gVo%7jzzkH1BHspywUA*JrbY0ZUMWe<>Pz<<^&OHDc};tr7q
zUy_Z*50YoWg~dx;Z9ln~cTA>h(u&wMAE->XmbqB{-2I*hhelZ7f+%x1^Evk#zET#%
zmHQkDR-UM7FPo&4o>=7+B9~kacJD{Y86HEJ`FR}sSWHjMX+b`105VHETv1y|+2M;Q
zv1apA;Q6I$bkVM|B(9okUca34qVFgN5=7h1!B+Mj0hOTPs{&QLWR~h`M~qy48@`!B
zyIJ74Gc<Oq#l#cG2}2Sq00n%0QXLwb_?LN}Qz>8=QElpq=?MHwk#ZV=kBYF|o)mch
zlFLbefzjO4<@*0+V4G9`S6#UlIruw+m^?tN_e;W{{t~>1bbx|6%%kQ9{-wqO0Ex=2
zAVYX1Q2>hpK4YT6?9~-YMERHTDO>@ZETTPvhUEX@1w5nv*|thPT+<c>zA%N56q6Gz
J|7h^#{{W)PFK_?=

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/packages/gluon/naming.md b/docs/python_docs/python/tutorials/packages/gluon/naming.md
new file mode 100644
index 000000000000..ac53f041dc0a
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/naming.md
@@ -0,0 +1,209 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Naming of Gluon Parameter and Blocks
+
+In gluon, each Parameter or Block has a name (and prefix). Parameter names are specified by users and Block names can be either specified by users or automatically created.
+
+In this tutorial we talk about the best practices on naming. First, let's import MXNet and Gluon:
+
+
+```python
+from __future__ import print_function
+import mxnet as mx
+from mxnet import gluon
+```
+
+## Naming Blocks
+
+When creating a block, you can assign a prefix to it:
+
+
+```python
+mydense = gluon.nn.Dense(100, prefix='mydense_')
+print(mydense.prefix)
+```
+
+When no prefix is given, Gluon will automatically generate one:
+
+
+```python
+dense0 = gluon.nn.Dense(100)
+print(dense0.prefix)
+```
+
+When you create more Blocks of the same kind, they will be named with incrementing suffixes to avoid collision:
+
+
+```python
+dense1 = gluon.nn.Dense(100)
+print(dense1.prefix)
+```
+
+## Naming Parameters
+
+Parameters within a Block will be named by prepending the prefix of the Block to the name of the Parameter:
+
+
+```python
+print(dense0.collect_params())
+```
+
+## Name scopes
+
+To manage the names of nested Blocks, each Block has a `name_scope` attached to it. All Blocks created within a name scope will have its parent Block's prefix prepended to its name.
+
+Let's demonstrate this by first defining a simple neural net:
+
+
+```python
+class Model(gluon.Block):
+    def __init__(self, **kwargs):
+        super(Model, self).__init__(**kwargs)
+        with self.name_scope():
+            self.dense0 = gluon.nn.Dense(20)
+            self.dense1 = gluon.nn.Dense(20)
+            self.mydense = gluon.nn.Dense(20, prefix='mydense_')
+
+    def forward(self, x):
+        x = mx.nd.relu(self.dense0(x))
+        x = mx.nd.relu(self.dense1(x))
+        return mx.nd.relu(self.mydense(x))
+```
+
+Now let's instantiate our neural net.
+
+- Note that `model0.dense0` is named as `model0_dense0_` instead of `dense0_`.
+
+- Also note that although we specified `mydense_` as prefix for `model.mydense`, its parent's prefix is automatically prepended to generate the prefix `model0_mydense_`.
+
+
+```python
+model0 = Model()
+model0.initialize()
+model0(mx.nd.zeros((1, 20)))
+print(model0.prefix)
+print(model0.dense0.prefix)
+print(model0.dense1.prefix)
+print(model0.mydense.prefix)
+```
+
+
+If we instantiate `Model` again, it will be given a different name like shown before for `Dense`.
+
+- Note that `model1.dense0` is still named as `dense0_` instead of `dense2_`, following dense layers in previously created `model0`. This is because each instance of model's name scope is independent of each other.
+
+
+```python
+model1 = Model()
+print(model1.prefix)
+print(model1.dense0.prefix)
+print(model1.dense1.prefix)
+print(model1.mydense.prefix)
+```
+
+**It is recommended that you manually specify a prefix for the top level Block, i.e. `model = Model(prefix='mymodel_')`, to avoid potential confusions in naming.**
+
+The same principle also applies to container blocks like Sequential. `name_scope` can be used inside `__init__` as well as out side of `__init__`:
+
+
+```python
+net = gluon.nn.Sequential()
+with net.name_scope():
+    net.add(gluon.nn.Dense(20))
+    net.add(gluon.nn.Dense(20))
+print(net.prefix)
+print(net[0].prefix)
+print(net[1].prefix)
+```
+
+`gluon.model_zoo` also behaves similarly:
+
+
+```python
+net = gluon.nn.Sequential()
+with net.name_scope():
+    net.add(gluon.model_zoo.vision.alexnet(pretrained=True))
+    net.add(gluon.model_zoo.vision.alexnet(pretrained=True))
+print(net.prefix, net[0].prefix, net[1].prefix)
+```
+
+
+## Saving and loading
+
+Because model0 and model1 have different prefixes, their parameters also have different names:
+
+
+```python
+print(model0.collect_params(), '\n')
+print(model1.collect_params())
+```
+
+
+As a result, if you try to save parameters from model0 and load it with model1, you'll get an error due to unmatching names:
+
+
+```python
+model0.collect_params().save('model.params')
+try:
+    model1.collect_params().load('model.params', mx.cpu())
+except Exception as e:
+    print(e)
+```
+
+
+
+To solve this problem, we use `save_parameters`/`load_parameters` instead of `collect_params` and `save`/`load`. `save_parameters` uses model structure, instead of parameter name, to match parameters.
+
+
+```python
+model0.save_parameters('model.params')
+model1.load_parameters('model.params')
+print(mx.nd.load('model.params').keys())
+```
+
+
+
+## Replacing Blocks from networks and fine-tuning
+
+Sometimes you may want to load a pretrained model, and replace certain Blocks in it for fine-tuning.
+
+For example, the alexnet in model zoo has 1000 output dimensions, but maybe you only have 100 classes in your application.
+
+To see how to do this, we first load a pretrained AlexNet.
+
+- In Gluon model zoo, all image classification models follow the format where the feature extraction layers are named `features` while the output layer is named `output`.
+- Note that the output layer is a dense block with 1000 dimension outputs.
+
+
+```python
+alexnet = gluon.model_zoo.vision.alexnet(pretrained=True)
+print(alexnet.output)
+print(alexnet.output.prefix)
+```
+
+
+To change the output to 100 dimension, we replace it with a new block.
+
+
+```python
+with alexnet.name_scope():
+    alexnet.output = gluon.nn.Dense(100)
+alexnet.output.initialize()
+print(alexnet.output)
+print(alexnet.output.prefix)
+```
diff --git a/docs/python_docs/python/tutorials/packages/gluon/nn.md b/docs/python_docs/python/tutorials/packages/gluon/nn.md
new file mode 100644
index 000000000000..74ad76e415b2
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/nn.md
@@ -0,0 +1,313 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Layers and Blocks
+
+<!-- adapted from diveintodeeplearning -->
+
+As network complexity increases, we move from designing single to entire layers
+of neurons. 
+
+Neural network designs like
+[ResNet-152](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/He_Deep_Residual_Learning_CVPR_2016_paper.pdf)
+have a fair degree of regularity. They consist of *blocks* of repeated (or at
+least similarly designed) layers; these blocks then form the basis of more
+complex network designs.
+
+In this section, we'll talk about how to write code that makes such blocks on
+demand, just like a Lego factory generates blocks which can be combined to
+produce terrific artifacts.
+
+We start with a very simple block, namely the block for a multilayer
+perceptron. A common strategy would be to construct a two-layer network as
+follows:
+
+```{.python .input  n=1}
+from mxnet import nd
+from mxnet.gluon import nn
+
+
+x = nd.random.uniform(shape=(2, 20))
+
+net = nn.Sequential()
+net.add(nn.Dense(256, activation='relu'))
+net.add(nn.Dense(10))
+net.initialize()
+net(x)
+```
+
+This generates a network with a hidden layer of $256$ units, followed by a ReLU
+activation and another $10$ units governing the output. In particular, we used
+the [`nn.Sequential`](/api/gluon/_autogen/mxnet.gluon.nn.Sequential.html#mxnet.gluon.nn.Sequential)
+constructor to generate an empty network into which we then inserted both
+layers. What exactly happens inside `nn.Sequential`
+has remained rather mysterious so far. In the following we will see that this
+really just constructs a block that is a container for other blocks. These
+blocks can be combined into larger artifacts, often recursively. The diagram
+below shows how:
+
+![Blocks can be used recursively to form larger artifacts](blocks.svg)
+
+In the following we will explain the various steps needed to go from defining
+layers to defining blocks (of one or more layers):
+
+1. Blocks take data as input.
+1. Blocks store state in the form of parameters that are inherent to the block.
+   For instance, the block above contains two hidden layers, and we need a
+   place to store parameters for it.
+1. Blocks produce meaningful output. This is typically encoded in what
+   we will call the `forward` function. It allows us to invoke a block via
+   `net(X)` to obtain the desired output. What happens behind the scenes is
+   that it invokes `forward` to perform forward propagation (also called
+   forward computation).
+1. Blocks initialize the parameters in a lazy fashion as part of the first
+   `forward` call.
+1. Blocks calculate a gradient with regard to their input when invoking
+   `backward`. Typically this is automatic.
+
+## A Sequential Block
+
+The [`Block`](/api/gluon/nn.html#blocks) class is a generic component
+describing data flow. When the data flows through a sequence of blocks, each
+block applied to the output of the one before with the first block being
+applied on the input data itself, we have a special kind of block, namely the
+`Sequential` block. 
+
+`Sequential` has helper methods to manage the sequence, with `add` being the
+main one of interest allowing you to append blocks in sequence. Once the
+operations have been added, the forward computation of the model applies the
+blocks on the input data in the order they were added.  Below, we implement a
+`MySequential` class that has the same functionality as the `Sequential` class.
+This may help you understand more clearly how the `Sequential` class works.
+
+```{.python .input  n=3}
+class MySequential(nn.Block):
+    def __init__(self, **kwargs):
+        super(MySequential, self).__init__(**kwargs)
+
+    def add(self, block):
+        # Here, block is an instance of a Block subclass, and we assume it has a unique name. We save it in the
+        # member variable _children of the Block class, and its type is OrderedDict. When the MySequential instance
+        # calls the initialize function, the system automatically initializes all members of _children.
+        self._children[block.name] = block
+
+    def forward(self, x):
+        # OrderedDict guarantees that members will be traversed in the order they were added.
+        for block in self._children.values():
+            x = block(x)
+        return x
+```
+
+At its core is the `add` method. It adds any block to the ordered dictionary of
+children. These are then executed in sequence when forward propagation is
+invoked. Let's see what the MLP looks like now.
+
+```{.python .input  n=4}
+net = MySequential()
+net.add(nn.Dense(256, activation='relu'))
+net.add(nn.Dense(10))
+net.initialize()
+net(x)
+```
+
+Indeed, it is no different than It can observed here that the use of the
+`MySequential` class is no different from the use of the Sequential class.
+
+
+## A Custom Block
+
+It is easy to go beyond simple concatenation with `Sequential`. The
+`Block` class provides the functionality required to make such customizations.
+`Block` has a model constructor provided in the `nn` module, which we can
+inherit to define the model we want. The following inherits the `Block` class to
+construct the multilayer perceptron mentioned at the beginning of this section.
+The `MLP` class defined here overrides the `__init__` and `forward` functions
+of the Block class. They are used to create model parameters and define forward
+computations, respectively. Forward computation is also forward propagation.
+
+```{.python .input  n=1}
+class MLP(nn.Block):
+    # Declare a layer with model parameters. Here, we declare two fully
+    # connected layers.
+
+    def __init__(self, **kwargs):
+        # Call the constructor of the MLP parent class Block to perform the
+        # necessary initialization. In this way, other function parameters can
+        # also be specified when constructing an instance, such as the model
+        # parameter, params, described in the following sections.
+        super(MLP, self).__init__(**kwargs)
+        self.hidden = nn.Dense(256, activation='relu')  # Hidden layer
+        self.output = nn.Dense(10)  # Output layer
+
+    # Define the forward computation of the model, that is, how to return the
+    # required model output based on the input x.
+
+    def forward(self, x):
+        hidden_out = self.hidden(x)
+        return self.output(hidden_out)
+```
+
+Let's look at it a bit more closely. The `forward` method invokes a network
+simply by evaluating the hidden layer `self.hidden(x)` and subsequently by
+evaluating the output layer `self.output( ... )`. This is what we expect in the
+forward pass of this block.
+
+In order for the block to know what it needs to evaluate, we first need to
+define the layers. This is what the `__init__` method does. It first
+initializes all of the Block-related parameters and then constructs the
+requisite layers. This attaches the coresponding layers and the required
+parameters to the class. Note that there is no need to define a backpropagation
+method in the class. The system automatically generates the `backward` method
+needed for back propagation by automatically finding the gradient (see the guide on [autograd](guide/packages/autograd.html)). The same
+applies to the [`initialize`](/api/gluon/_autogen/mxnet.gluon.nn.Block.initialize.html) method, which is generated automatically. Let's try
+this out:
+
+```{.python .input  n=2}
+net = MLP()
+net.initialize()
+net(x)
+```
+
+As explained above, the `Block` class can be quite versatile in terms of what it
+does. For instance, its subclass can be a layer (such as the `Dense` class
+provided by Gluon), it can be a model (such as the `MLP` class we just derived),
+or it can be a part of a model (this is what typically happens when designing
+very deep networks). Throughout this chapter we will see how to use this with
+great flexibility.
+
+
+## Coding with `Blocks`
+
+### Blocks
+The [`Sequential`](/api/gluon/_autogen/mxnet.gluon.nn.Sequential.html) class
+can make model construction easier and does not require you to define the
+`forward` method; however, directly inheriting from
+its parent class, [`Block`](/api/gluon/mxnet.gluon.nn.Block.html), can greatly
+expand the flexibility of model construction. For example, implementing the
+`forward` method means you can introduce control flow in the network. 
+
+### Constant parameters
+Now we'd like to introduce the notation of a *constant* parameter. These are
+parameters that are not used when invoking backpropagation. This sounds very
+abstract but here's what's really going on.
+Assume that we have some function
+
+$$f(\mathbf{x},\mathbf{w}) = 3 \cdot \mathbf{w}^\top \mathbf{x}.$$
+
+In this case $3$ is a constant parameter. We could change $3$ to something else,
+say $c$ via
+
+$$f(\mathbf{x},\mathbf{w}) = c \cdot \mathbf{w}^\top \mathbf{x}.$$
+
+Nothing has really changed, except that we can adjust the value of $c$. It is
+still a constant as far as $\mathbf{w}$ and $\mathbf{x}$ are concerned. However,
+Gluon doesn't know about this unless we create it with `get_constant`
+(this makes the code go faster, too, since we're not sending the Gluon engine
+on a wild goose chase after a parameter that doesn't change).
+
+```{.python .input  n=5}
+class FancyMLP(nn.Block):
+    def __init__(self, **kwargs):
+        super(FancyMLP, self).__init__(**kwargs)
+
+        # Random weight parameters created with the get_constant are not
+        # iterated during training (i.e. constant parameters).
+        self.rand_weight = self.params.get_constant(
+            'rand_weight', nd.random.uniform(shape=(20, 20)))
+        self.dense = nn.Dense(20, activation='relu')
+
+    def forward(self, x):
+        x = self.dense(x)
+        # Use the constant parameters created, as well as the ReLU and dot
+        # functions of NDArray.
+
+        x = nd.relu(nd.dot(x, self.rand_weight.data()) + 1)
+        # Re-use the fully connected layer. This is equivalent to sharing
+        # parameters with two fully connected layers.
+        x = self.dense(x)
+        # Here in the control flow, we need to call `asscalar` to return the
+        # scalar for comparison.
+
+        while x.norm().asscalar() > 1:
+            x /= 2
+        if x.norm().asscalar() < 0.8:
+            x *= 10
+        return x.sum()
+```
+
+In this `FancyMLP` model, we used constant weight `rand_weight` (note that it is
+not a model parameter), performed a matrix multiplication operation (`nd.dot`),
+and reused the *same* `Dense` layer. Note that this is very different from using
+two dense layers with different sets of parameters. Instead, we used the same
+network twice. Quite often in deep networks one also says that the parameters
+are *tied* when one wants to express that multiple parts of a network share the
+same parameters. Let's see what happens if we construct it and feed data through
+it.
+
+```{.python .input  n=6}
+net = FancyMLP()
+net.initialize()
+net(x)
+```
+
+There's no reason why we couldn't mix and match these ways of building a
+network. Obviously the example below resembles a [Rube Goldberg
+Machine](https://en.wikipedia.org/wiki/Rube_Goldberg_machine). That said, it
+combines examples for building a block from individual blocks,
+which in turn, may be blocks themselves. Furthermore, we can even combine
+multiple strategies inside the same forward function. To demonstrate this,
+here's the network.
+
+```{.python .input  n=7}
+class NestMLP(nn.Block):
+    def __init__(self, **kwargs):
+        super(NestMLP, self).__init__(**kwargs)
+        self.net = nn.Sequential()
+        self.net.add(nn.Dense(64, activation='relu'),
+                     nn.Dense(32, activation='relu'))
+        self.dense = nn.Dense(16, activation='relu')
+
+    def forward(self, x):
+        return self.dense(self.net(x))
+
+chimera = nn.Sequential()
+chimera.add(NestMLP(), nn.Dense(20), FancyMLP())
+
+chimera.initialize()
+chimera(x)
+```
+
+## Hybridization
+
+The reader may be starting to think about the efficiency of this Python code.
+After all, we have lots of dictionary lookups, code execution, and lots of
+other Pythonic things going on in what is supposed to be a high performance
+deep learning library. The problems of Python's [Global Interpreter
+Lock](https://wiki.python.org/moin/GlobalInterpreterLock) are well
+known. 
+
+In the context of deep learning, we often have highly performant GPUs that
+depend on CPUs running Python to tell them what to do. This mismatch can
+manifest in the form of GPU starvation when the CPUs can not provide
+instruction fast enough. We can improve this situation by deferring to a more
+performant language instead of Python when possible.
+
+Gluon does this by allowing for [Hybridization](./hybridize.md). In it, the
+Python interpreter executes the block the first time it's invoked. The Gluon
+runtime records what is happening and the next time around it short circuits
+any calls to Python. This can accelerate things considerably in some cases but
+care needs to be taken with [control flow](../../crash-course/3-autograd.md).
diff --git a/docs/python_docs/python/tutorials/packages/gluon/normalization/imgs/NCHW_BN.png b/docs/python_docs/python/tutorials/packages/gluon/normalization/imgs/NCHW_BN.png
new file mode 100644
index 0000000000000000000000000000000000000000..8bc33bdd37caee07b84019e73a5e68f8d674e1ea
GIT binary patch
literal 45584
zcmeFZXIN8h)&>dzOb9JVQ-lDDh=9T?O{t*>DkwqeNQo$5s7ec+Kmbt?tTgG0k={E5
zgMtcyD7_O<IwZ8v;cRBUnQy+C^Yi>V*ZiR6BF}!FwO6^<eXpGu<Sl&;s34Sqfq~=t
zHQn0`49wgN44`PpG2oScUNRT(gVF1@zBWTqr_dbm2dn2bOD_fnf#XMCj7}N9fVUtR
zuIp-<U>FxiS(C))Y{s`k`+|KojC(h)bj#-?zc>+xlM%;?FQ0<D$C65)3DC-H;T=OE
zzWwymds`Tbz(kwj6Q|hTw(#4t-FNt6byx3LX&6k9mE+9K_*}(N^<cAco6Q%yhgbht
z3-i4(<ugq_d9qE}v1|?h?Xm`U@=2=f*d~YkF=iNof${%*6OBYH(U*p)i*s|^d%RPZ
zpdg577{kAQGqK1tV$?1)3p9`9u<-dmZ$4V<ni%=jJs~}<e_I#$GZuzT`_ETzv%*fl
zzjsgkKNb?zwHNvK-7#QIKsd;!q!X<Fch|tO5bgo+f1c(IgP69Igr4?)TvwZ!{vS)g
zuAN0ZNq8Fb;y>02gJFaJV+rQ}ZvDSj|F6URH%$MVVg8#O|67v&qk#Xnn!^6S!7vEB
zywblR9?2JoM%(HSJM++u&7K<-js^dU&eHfYCA6J_lT|>F-^dBgafQ^a(K_J+`!m<S
zQ7)BT`xhAZ+c+!*;y)rK!vkXBt|_;u?%)618ouRRJeYIF(XNExc-V5j{O8!?cT4fb
z)Kq%PezcQv7m7gbGGE+R?l@>oPT8NBdt4Bz<=Z|YGyTCJ(G`vUp{J*}7;`TD8|iVS
zmljb4o#ND$Ua1~PS4i7iTr3ElzmxIwfWBDXr6e3JuqN%fG^AMn_|H=gcJ_g8Bh9pN
zmJ72lSFT9SMmdyn<4c;S^_%(hP`%s;(X_D1h`{#z>c)LXFxbq)?`?;MciSR0qj)&=
zpv2vLM)?@W+%(Ozz(@^0Ja<@kZ+s)>O|-1zo@7%K!K-~y-CW78I6104Xp>*rLD(^o
z7g?VC=g$Lc%k$J*us-93?H|&`uC+yi`pQw0tj;3KeRS2(g9i1@5iccmbsM#s*9ILk
zl4p5y#KI8n0NQz6e%=Dc{PY4%y0~~ye|(5%w3E>2NZl?-5ne%3di~1@^zS6L;fMV@
zzupbHah+S<y&&5%9`eb(x>4yE69|$->gr1Ew2&7i-@?|`*4~IT3VJ?NyD4{p88-VG
zz^WhbCtn2d@t;_kX0r<}aU2iv=A-!S7wk{oaEV-!hU@))h`B$oIGsIsNT2KLlQvrT
zQYKnrjs5)j^K4<>YLPaK3AA+F;_kZa&{OuH=Tc=^mN%Jih$1o|X9u1XfI$9yTWVAu
zN=C!&Y8F|i@lC4^uwdz*k;kJU!9R9+YmBQkL+yRtK6l*<h@<k@a&Et5Eho(FLku|!
z=x1o3t$lWL>K#RDYOO2|YvM&be$nFL#e=1vqk}m|Hrp@mtvz!TX*91EaO!xzr7*7M
zPUm~27`V_BJ@u8O5bsBrZ=bAuAd88MzN6zf9+(Y$jTD%<9HfWt>5`iMIQZ-iwojw3
zk7_tkcUG#N@)!B9JrA9|seZ7YK6u5hwWTT6C_ZK1G-F1Ut01G(Yjc6_;EtaQJ^Y?x
z-Lo_!+rN}8JHzE~$0n#5yO#Ti8Zr5PFmV4qP1dGx_}s{i3)jac_m%hXs%h||LAEiO
z-x+oVyMZ0H=&hj5(v<z?Po#cD_pei(th2ETh1S)JV~v4TB_;L-1_ph@!!9~HI=)*p
z)Nr+joq`wNFT3E4-r1E>hqi3Oxx^dY$WZox!@2H{{ri^1<4wWCJx18PPcAz=JUmKQ
zuGG4R_Nyrr^PIb-+g<x;DoPzgYu?$}S?oxbU&#H`1dED}Ug}I!^`#J50{2$h`uBtD
z&nbJ1#rD~>zelg7^bHIo39APvr1<UaKYS~#@q8?BW4;%!(hzHk?B4ld()+E_){i=7
zvvxvQxboFGBPE|WS#W$~uBEHU+}vCYI@?*~r*BWDn`ArkGN<EEYhGpcK5{qd;G*AX
zg<FM9h0&~sm@c`Yg8Gn*843~n72=-#Vn1W_q8EmsVZHMX-Lw<inrNL-y_G;O=O1gC
zk-X*^&j9il5Y_#*%YSbhCRx&49C$60;^P1pUDuQm3LRkUIwY-eJsXs&kiLfb9<ORw
zcb^_@i#%BBQd(GRmpynduzdK+4|yVLL1S#UDB8*Ku>H)m7;%1$Yj;<{hOeZBb~0mH
z1artPOX^X^LDC-#^apu=)xZoJ25Z9CLxg!9@%D$xb_K)m&=pxl^c~z><Zl12O7J*q
zc?RvzzLrPPN?<6;$G>G<x!&A<#il8?TJT(d#bWW!YWn7%phJ_<gPpm${cnTOZ@!o?
z=_~|3a<82G5+5>6c}@89ZGK9wQ)J}RFro3D@rg^5uTIdXEJ^xU(iOWy<@o+3b#sz<
z@pk8S9l9yB-{sEY=F(bP8~j$P{vaFPh>@>Z0guQ9y@DbGZYulS#fxgC4Sx!*2|vcd
z06qS)Wn-Z~C%iNqD<>(3+5VH5)6e((Q)bbei&Y(|qoaUL$i(88ixdPMAVq#i-b>|>
zl9^Yp`{Y>@dU)WNQy6$N@@l1j|CV6ISY70U+mcxuePvN%q{oquh&vB8_V@RVowGqj
zDrI-XkW=4{*Ivg!s{@ukB-%bzf%Pn~r?N3SZI2N`HCOLwS#?zs&cAz6uHwM{Hei3G
z*6XKI-)lb6S;fd>b)Q5FH|-i`A9}iy*HbwjYARD2>n$A{m1SOOf85IKiqvKazSR?x
z7nK&$H`zVq#oIse7f^0Y!(d{?)=_`7GwngPjLmNaZ}OWkO&o57FUSU9O(WMt)HdW0
zWb}E3EhnSC>~gA6AnLBUN7ClzL0N{@VZLn7Qcg(07T9)oLfH1$c4>dlB309o9<O<)
zI&_NVch|<N(!<G5(cO2JSUA`Y4yfB}y$lpfs;I5gm>qR9SR=2qrYit@<qBS|P?Vyn
zZe3oz?Y*mBu~n0?edt(>^8DHYO(Pu|=!<kpZyylWoJnk3K7nH{a*~EyBKxifW-SF>
zh6^9V_Y;k_0)x~g8d@V$0`^=qOZN!rcM6%xWk>ABk!L7IYMAYs`n_sf+n|>8l;}(O
z6vLnp=)I<q_E^oex)k**^DL(|VTUJ+&#&}rn#PB7$L0HnjEc!CUh#iW<vu>KkVn>C
z*h#ZJgUt#Afw|zZoke-g1>tqjN~uhILZNnL=ZKUpc>xN(XxAjfXZuo2TN2`Ob5czE
z@>J2BCVjx+r<#czG}19mv3E<QTu{U$=?+D*C*a6atQ_NVKy_pC8X8Sm{93EQRuU`_
zDf{#*UXE`26=P5}-g|h*78l#Kl;74l{;t(`q1+}sI@qY`tY!owHXK*d6wN98R2yn3
zDf4sH<>1ozBAVwEf9|9#K1^1BsRim%!Ik&$8Qsz{O74c?(D?oQ5=v!pu$w}YZGT{e
z&3ed^Lvi5fLUz9`hQhKx2oGNWS*aZW5nm4&>WXWP<5C42$cQnTZhj#Z{SDuWduv+B
z&HMUGOXX-tpMr>n{%v~%1Ga$;cKUZPTWJu4S9hW?wBYo1D`nIuu=4QC0-JE2*`W=W
zk&K>);5ulpTKLm&y~dJhe4|`JI6)hy6X(b=)tKKcLg$Hy-`aiEViYL0_dqkhNS;Pr
zy9GDB%oP#-)G-1SWfwZNBOlj=Vca)3XP$Z<|FFfZ1)kA-cz_%93%OV6PgKTG_O{3^
z+Y;lu?sj@U3bkDX|3eYk*=PehsfMi(kEl#XR;caRA4R4EgKW!)x2r~Dx69>k_NjO1
zFRXQ?2i0gahLq&z@*l3%_6qWkd)-x(hF{F6YBArcORp~(ialc(y}Jw%y6kD+606f2
z$X92AWYvLiYonBHY5jwNd{6lJas57yCXp%}QYO)$c&KjR=5kzTc3xEk)QqCO;-9AC
zBPFF4*{@V@u5~-|0^c#d$Fl|Vjygff&xNj(6-|ft?Y_{5m(FB_YUD*`$q!j|Ziz-L
zy)|G_A0=jPn89-X7$|KdMCN{iX+?m*QQjc5Rc5%)iTnA%V%`-et4&IBw01v2DJXKF
zw~{9N5SUj6r!{=5_-u4qkozQc^!ufSwfBxN!3x_IpI((O{b!o&sO#nYZ{xd+6DHt{
z%&Sr~nzMYd!MDc%S`uUKp*GJ1X4zF&?^^Ev)axc6eotlDi&ef()*A4Q;GDD@z8uwa
z_PF1rz0yr=rk2y>6eICgE_Z9Nqibbm;Sk?2hn*kZ(Qfhq;i26a;g2F~m7vBii`L>R
zHCDT)uPj%?ZPTb;wmIrO^N=Y_XUT&wSgXO9^gCk`7YFI>a;cu=3r8FCk3@nc8@}X&
zR^%%=Jo)&}HKghM7A!p<A#^N~Ln`4@=391Fh39AvXENryR-|&UQa8Jx5lbM4N`A3C
z8A!nv^D;$I7%k1%;#@(LC1199befF$USrC#V?>ZPR*vM?=h#iz=#`((;)<ww0EytW
zI$y%Ni>N211iM$IM$<nzwik3=wdmZM$s1g^+AfHkTe$)~K5`}uVatM#N$9`O@>mQh
zhZPCUb1YtwT1O$*aN{dwr0PS<(U9i+?uCLaKaA;~+{ni51;VOCvB*X_^-jpW`I-#-
zUwm9us=Fd%84ZWtIR%d&udQmWUP#q2#|EEk(Oi)b$XL_Q4_4lDqV`X;H}bSO>R7b$
zr1qFW+KVLki?^=Nl;@LNQg7zD6@QR9Rpl09=<LGS%S=uV;?p&G5SSlc29?kA$(&}G
z-$Sx+@p&t7s6Wrh;-G|uKQRiL1hT{FcPnw`JmYe=e+YOhS+?g+gV4nXzh4<C1v@o3
zUX`plr{A+9jexXg?5TD=JD2H}=O~-^DLVz4w^NKTRAkc50IAyK18(K3;ua?|eps<*
z&4C&*({u@XUUhrg10qUxW7NjEdoWJNBd)V}AKR1O)16Jc*|XH2+Y!>MYPt7ZFLifq
zjQ^1B@JryhZDB7u-XG&RERru0=@=~VUaXD~pe>JH$Z*{AeV#MT7rGETI35T<F(3;z
zbh|hxoN^RA_cB`P6O+=3z~GOHf2EM(`=6f83}N=B+eSdwR2+)Xn^`-4kKIfH5iEjf
zKh4E%bm`nVw!sMN#Low3@kbCN3!&(h`QP8Smws=rOts}aFuutg$x6ySWhZgLyj%<?
zh#GO6qFJpl_Xr^rkN4;zg2nOlSa;}gUOC)Sx+BSVr}nx$>Gx%^``rtvOZgOal$~3f
zQ(TuZ^3PEyNdUxR^#$UpDDo1vYqM&fFzqxG+OKM7{>Dt$;!}<$3uK;QB{6;FR^Git
z=RGTeRe0;@oM&qU#AcN8((Y?B*P8DR{cT(S+2g$#Sd}+>##H9AK^k5*N9C`EOXEQ&
zecnXM;mwZe_%NGPsxm{+`i1lRStZ$oqeg8gEQ_!Z8#-%mET&za%O2q1Y*4g`kEpxF
z1(h>Vos`emHZ(!HDs`0@?${zZo?>RX-^x~e;F^l+ua#{IBg)u7O@=LYo80q~Gik2%
z5=^KizqMLcAb$i09p1rLo4VoT+*LJzTkTu+=7azva~bcGw9Oxnt2)>ine$H5R70-o
zaG;AaBc%9F9Lz4y4~0@=HW<TgdwYed1XBdMNN^XE{lf&iPGj@4-vUn1QUneYm0id*
zBiRC9_T;-!ZKvAt1~-&W9k@(w<k(vK8a9ewGSNdsig8H2`@)1<V$ik&<zZoHac9Br
z`A}SVTqoYW*XXnF+VJ=BN>?jRlDzi~fzVJ3iN@R%-tS^f1=br52|*%V&(3D<n_}<G
z79e*Ymy3-RYj5qk0|06NA+>tge5#vus&#XGYus-qsq5#huO~!+a{>MTH|=D9QKS}j
zcWmZ!U~%t~vU^d{p1INv=1u}k3Ley&s`w=uXkD#ml|>Kg;3BSf-6%%;x1+z`0&i>{
zb{4B;GH9DSlY2Q{PTl902^tHrK@k(X%on<p=}b=JQ(4yuvQuma)fuau5>qYDx_x#N
z6V2>)U1ALGwYE(?y27Hp;g<G-t2e7MH=sb8$5jc>kc*~BmCP0pq8!C=oz8hN5ywmq
zIn%yel~p+S*`Sc-%SoAMC55wHxN~``KwAGxsvEu;FUW5N-$;bLZA@!#t*ARwGN{47
za~wJI7Db-XCY%&Z@hPeu?z)*2G8DL!pB2x&l!$hGEu{VJXH1u1M$UVsE^b8TFi62^
zmd<Z~0C}y8NW3C6Z+U?^FypDQSU{=qt&mrKdj~B}BI%ZgHwY@93AK}nXV&HE0@G%T
zv3?48PJX`2?5@Y%LW=BGv|8M#yAv^7y-OQ`^S-3wW_qZGae4J({vglTrzx&?dBgFI
z_voR!-l2m`u}(okq>Wd$&YH7Mo1s(XA{TWON;&t|xZwH8D&{?pCBPeLG@XwNV<Vrl
zC4{N<%L^3qoSH?_iHhaT#y$EvesDAGTGOkUtNcjHOzlkius5Igc^$>8NYr%}e?=Rl
zzL%6qej}#AbJX?p5ugi?@>J5~?RYhn7hDNqO>6A^z%vyrg-5<mO}Xt#Klr`=OU;}E
zROv+s=pEisIN5Vx<CLyQ6|lvKSxNAnu8bAKkS*sipUb}A$9zJ7prsWgpADR6vc8+g
z(t@D&lAwB@llJ~Y=kdNbLOpl&KV!qsD!zF5$17)!dY?R_-;Nj595U|}(h*Bf&woWO
zQA=^GKgy@RWgq?)WdC$VM#eOoc&_M1a*+l@)6%zj)05slQF8dq;9!)Z0#XxHx0QKO
zXREw8f3Qu>a7$ASa*Y!TJs7hr5A_b&Ky%iuM&xSl(cZrSS|~WxDa$={=0ly$dj6@q
z==HxcpP$kkcVWH%mtjC$jL6EL9=`J_9HF~E;ekQ#-9x)o?M>Dq$v7>8&C!sJr=eRI
zQp9W5x0QpdD^^a-N~*hgwR7ybP*$hTccp9s7j;?YJ#+6kX_}JVeXmBAykC(MRpShO
zHlCld0dPjbq%+=*0*Cb%Kh~Ije75q)Z`Go=$A=}xGxy4YE#I+9!@8#tAWoa))9g~2
z-^B+N&g`Y_-h(;^O7S8+Gp$cl$l_^*!Iw282UJ22f=qa{KEDKgAUAw?({(cl{t)gG
z+S4Sc4U3GZJKsZg(r*`ux)s#7CB$O03~*9hAjQL<2PmU}@i)<tsFN^6rWFX?nQ5Ss
zzM*}va`R$f60dPNb6Rc2`cx(5f(nv+R=ihXvcf1(+YRzqsbDK0sMY05-WffXi$(z)
zxWc}3PS3g~y{{oqIyLKAoxh^yhlWYFob*hae+`lqOb=UvOxm@2i%n-^1j4IQ?amz(
zJ-Cg@4lJyPy0QNOPLqqg?zCdMt2b<gZ8qDjr?ZWwEC~bzD`%vR&ZD<m%3Nx7UOjW{
zW@~@j>vVH%%B#oKfF=lnRNM)JWu>JU0$CRjJh8DX-b(AQGWPEC9t-Mo3hsN$QW`4|
zHz@XZ^%ud)qVzEME>ZEpo4>20lF4D6DtWB9V;c`jdXq<97OLb&{Etp-b3D@sck#2l
z_1`IBU+GNW!TElKDbcjj>Lzy&RtzA*{w^w$9|BeW7MpzV-~V$+CX<Y)g!N=#i++2~
zlCx$Xr8>3z{)ZZi<p8wbQ`azzf8TEqo6wcVU%VausS%g)1D7rMnk@2<)&HSev+#-P
zO8$*m{OAAvea&xHK;!E2Xgd1;2;8D!2rU)}H?)po;rjP|{bL6}O6Lbsy4Abw|FOzH
z--kp32MKq-%zx_NRAp=duv8hpQ^>#YuYWr`uL)rN%u~<5>iwIrEb0O*_1|Os)3pD+
z8~<-kwNsWZwx)CBI`}F;6ew0=Z~=HZh-iscrdhrlCQbl`5X}Jf=zg2=MSTMN7ke8D
z6Mg--yzDmMh=_L3o<@b?H)hy3Lm&d=(T=$PHTU0sLzDt;`?PW2BH-U6;1UlI0kJ73
z|04oi0k^&9q4^d-um2k_M_29uc=Jm+j(YzQ0oFhS3=GGA(Es-ckO!9Pz$bJ6dmw)W
zqEC`ecbMb;_dx#t%P}%#%dh-(2DqsO&1+qn9-GUf<!vINBNFG%Df?hHdd-)Jk!-%R
zWHHr1`eN?X3iw}5J^P$Nk-KiH^Da03br=Yjs_Zeex3~9pL0wh#RNUso=ZFh!Q}O?L
z6Uf4h=+oY9X|<<;OsRStGxs5Ju{G|L@8lO&RsYrd-J2m>Eh1GPKAb%TaLZ2V{<(9P
zGj@7DF7_2#)4!dGA9!&_KjnGLvwvCARRVDnN4k#@27KV#3q)vWsENVUk2lgc6K_<i
z7unSF$9b(xUbVh^*HtC`AlFLl%$YMc4?F+Q3cv_X$s5klBiDX>8n*MYCV021cVUCW
z;DZ%ZkiIy`vmcB`qgyV^%gZY%D?3F*L?{8Y7lqpJk8BTYr`Z>%6vM15et>yI!60<D
zoSK?YuUt}k`dEo>f3aP>ZQx|gx$Iq<nnpHS)cG=oX216Ue&&ByNU#di;J$t5uhT%4
zl**La%zH~1L_|ML{a(;iL?jg1W@&J=wzl@^nQ1}-+yKh|=bH-w0Rh<uJbil3YUIm;
z5N<)yk^ha5MT;;Ps_lLQP`P}iX>&%QL*5PpX<;b}I2fhp(;XKc<8BtkN8}HG1qKGX
z-Me=h%FR3UCG`R>1(Nmf%Mdf>e>WC}xB<k*Yh-K`1E`P<PZQLPNPjn(rs`*?95oHf
zqKTk+ai!X!kgO=|@<xigm@d)|GS8#c)q4)~&!P*gR?jV}I}j&G)ndTfu+IL%wZ)5!
zuQ%4kiir_()s*DqV37=YC^=0{0{=1mA(JAHvDYyUF+=hKpy|cgvQ@%}<Jkh+;QtMD
ztT2>ZI?4I@#L<nP%|(<ch3~vbN=s90w9W6$zS_+v7!?(j9di6_BEWh>xa(?rla&7v
z+(<ZZqE%OeK4wN{DbWOW?w2=Dg0N^Id<Ju`6**G^HZMzQ#xkLJ0p#`1L@`jxAVz%t
z9RnQ7<od*nZ1KSG0cL}~Y#?1#@@urx{9{bsBhOv>lCu`^gU~AyFVQQm8jy4HN;5a-
ze{irvDo&S_mEFsuWCxC(fZ@xUH(1JNmQ#a!JQ8O}Zx>Aa>_>r~MGCYYgM4#=6mUAM
z`8hK$eDBNIasw|%zeKa~oinnj`>zQH=FgLZXG-CxX0`p*dL)pSVHcP1(`+p*Een0J
zJr-mP;NU;UwM$Ceb<;%?v=Nn7#0#-SGlJR)2n65#n&R*<B2(lsuk@S?is2FFdz+Ja
z1m7>aMe1tyef2}c0;o{I8Q_3Ga?84R>Rs<u1}uriq<vyWO?+#vovS5c>-yP9)=hZQ
z*A^TBQ_<EMpq$}&?jSo15iE4K&ilN|nGqQw<hj^+=NGUn9_vh%ZuZYw5cZTp|E*If
zZFyl^>C|RLF0LYRVnoUFyhypJYD|*c9E!+*(iWWr()f+j<j6JWvo9PJ0fegnf^zkk
zP|`xnj<Ya#1tLA$m?AJ?Ff+5_^M+e%dP%gafRgtJ^%Y(~w>zP?FdK}2KwE9MRU}3`
zifLnP5`7yU|GClgbJ8raKUU(4CPaZ*`wq)!bb`I=JJ@C1rn|C6sD9?Pql?_9>=j%c
z-^`+GmHRv)G9TI)C()b^(%C`?<VEm@!Bl?^z{w=TfMf?Pd10Z;{PZHNuewRGS4s^a
z$#bpETCzocH&S<=?$_r#W~?&lDB4^bPFKXSLb*alQq}jS3;A|)bsX(NYufZw?#gQ^
zvIBCQtD~dD{FHrp=W#A!mHRWDY0p}_|0te>4croCW_%bC0ib8<2gkqJ>}6Qfj7I<_
zGnY;NATTSJP4(n@;H3p+#Yf03wLz+KaWdJ6giDjVbn!@s2dZoIsmQC^4A@R+2j=r(
zaKD)vV!$7haQBk1xa~Sk%gw>=-Cu#l(Z}unLeV+k60`QDJP|5pCg)=Mi)@}P_Zn#f
zToT8;Dyn=4N*Zf(>E&_O$0V4XF}(Ja)*uO(D_4;6dGR7K;&(tEKQTNSQhqQjU+Jv0
zY&Qtr-^}X9dOf~^c@EU{3WH2vpqz-%nSS|Ix`Z1ZMenzK1cYmBfr5qZrOjTq7Jewa
z8qw0$DBDt;w&<FaU_Mj$R+?9)S}>aM;2aFicE8kyH{@X^Q|n@ttk+O7LQM@xir5zJ
zQ@f^Gt#K&H9vCqh93mAjhibIiO1C(a>H@N_#}Nh@=12JhF0=$v$yvvK;wwH&uBC~}
zRG=mB-4{$l)n~N67ViY{Ni;ZwlG258I9)s^o9aQ9QDD?f9VGYk%_P|U8fsPZ(@>A`
zbwN<pp!P?=VbkZ0C{6u0kQcxk1f8S*&}YDALtv+Q9~hLr18|T7V<ea`N#YOjL08Hb
z-0D#qaLsy&BFft44+|_74=amE7!GvA_N&UmptLN+`1Td|cRERa7W_n9oL<ArxG-YG
zDL8zDHV);RRTYMd_e-6W!23s_%a;Dks$Ku;1sE@x!S2qLVNn@Qz_=GZ$GaByeva1d
zj|rEL*Spal5nhHDHIw7SaI7lVOxvuK_v!RtA3zOH5ngkNkrz#fL`c@S*sio$b_ACD
z<NiQ4O&rDypY{)5$jo5czcs6BsmQoKu!buF@-DJ1Zij>%XPpYc-O6CK85Zw2!N^>7
zl){fa(FCaEN6fZq`NJY(HXIxwRNuyYiq`F$;289n=UDx`SxJ^CWohgSrP9IC==rY>
zvAM5nbO;U<aCu=o{Zo~n6lzdAj+uy~?_8>k21#X(-*KLZTvx+^xe{GBerC&NsMwm+
zWDP;k(<N;lIi%grn@wpmK#4^-0wp%tSRTMCv#-Na&hLf#X_F!Xyxo@Fd({Tqg>{l>
zWp=k$H-nNhR$!E+UD8+;;25f^2kEw%5Q^4gHwe$m(`p`xfCHLb?!TEhy|EhL9*s%L
zglVC|wONR`5%){{?ARZ`bg+s0t8yabwf;n}9)`ASyD+iYI2R6`mU=IMB5M_*C4z=L
z2viht#{UV;KTeKv9xcI(;+cMYk`Iovz9=B~RXb8nOYu1I!q?#wEI<!AKZP*=bN&Vp
zf{c-@)q;vVyyEzn2qNw;w1oJlqfj{sNKyCMI~M_u^}!0{F3`bn!M(x#$w=_`O7r0n
zmoYoy?%oe>F+)tZEf=qv31%Ojaz(rmGj>;$8Ju-Yd#&{82HN5yOq|@2WiecwYLHJu
zkobAwhJjBKHdDaEmgl|ude0Ew2x%_?hF|)<r3!+0-vDy_oiam!4IR1&BSs+C5Ma}*
zpZrUN^#Bg0(0a@c2<?moMIH!G)vJ8^xQ<r6Az;O4Q9j~^*M88{&cnF4ct!C!#Bh^a
z21PfzXIxO>hxF04Gg~VMy}6t?)|<5$ZboJmNyHPf)?JA>2G9~WIXOVlW{dQxZl7pc
zZr(5$o^e;|Po|s}m_XfZza6q=J6g0G(ADqa`}o@;-frg^BO@!0ENZe}9DW1BGaFV4
zwGMPzz@rbJ9sK!3y=B9yFMRIpd9`ck*-h%(;sy24k_iHvG+ct+pC_78s`%{RY%6>m
zKiU|$8Ld7VY95{2bss3{SQ|D;HOzxfww`{oMa?mbl{TOz<@vleS<|8D1WVIz>IW<>
zaBdEKqTGvS&7##Wq-9Tfu!opJxn8E;J(l+dk(BWI)0kHzI4?LeglT!}RWB{miopHU
zz*~<-wkJub8=hFSY~=It$Q-<FTrJdYqX?j06SrsOO%Ps${ROp}rhC+t6mY+~p~VHY
zxe4UV{Qejl_Xl`G=KT%6{QiK&p^lgPTVJhA*rtY`rv#5?O-8eR81ogjsD;62H4bWS
zfJab`U-R3bw<i|>sE)naEbb)^+>Z6#Oh-1ZeE621)A||`JN~3q$#S);<pI}7F{yts
zq2KWGY;|@t>y@EV3tncJ^JxG%OGq{Vjo2u6%_KNNz;sO%4i^{rQ1tX&NZwhMdorjb
zGK8z6g~{4GAw;wCeOoCdtc@UMc$q>Hdd}5)khB)P5^moqpeXQ6put-Neonx!II)&N
zxK9>$EVWQtk>_Qg-gdgI0vbL-PXW6C-lMo7EEshuigjh9+ks_125BSUA&E{Z8Jtmd
zy?sP;i2xzKJ-{i$0(!%b#CU$*y|Tpxhc|3m<xGF}J#Cx>>r`-4dor}VRJ&Wxd8~0-
z%Ny$UJw7272mb!REQxkAn$)!Nq~B2TT2hIn*5z7v(@^+3U}GLm*(*Q18Q^_R^)%7B
zY`H8$&hH!flWQ>e$oe?6*W}JkTp>6(qqS91Cn<CMiQMd?S2WSRD<jf|$U^4PDCn;a
zY?u2$z{r^d4*)p^d5FB_#NKAZ@6X%F${rHm@a-lR7x_M^oZ_=(<ADl_>w=@=n8sdH
zw0>Rd=RBkuB>{jfeO7QXaPQk+kM2orzb#TXvKCz)0!`buUV>JqUA#rR(1P8%)e=$j
zrcZNZvUsh=0#ql6Eo#o`JTANIrgdfEc~q;P3&zNnaH3^Zv3~q2CgGi7@u6Ct!&r;0
zOX0Z4=GCklj|+m-WLx~qHN-8U%qr4A+*#V?0@>vzGjXFnwDb*4^omW|*Z9uPt+}_S
zg(R!Q)UF-J$x&qCOED?~f?cR27_|2LgPB_2@zH4Tb;88mFHEau#EAD1>38I_l5aHG
zxu~mME3#v3!71Uu*CS8kSg-DN-cVT+!b$;E&;%u2{v7gKR}=@<^z;83GWpUj{fDe^
z@0b?&X`O#2vK_^$Vl~)$>Egocl<|N@L9Ohr2(z2K5<#1Pl~YyH336h*?$$PKml_Fa
zfZ@(!1Sri7qrh+Ba$@IS5@y8IkfGqj{ne^rW$S@C6<$%pSp<MeZYOyTxJU@C42R7u
z`{yMian!u`=eoG@*}!BQU$x3YEFub|0{>N6z<0v?BCvbp6vFtX@M9hcg|`nazsG1U
zn?}c^72Zi<=jrLl`dD6W=>CMGODD^>MMP>VT*kz+^B!c`UkyR^#w4u2(VE5mwXK})
zr=Ph7_;RD*cpK#pXQ)8*psRm|7H~&GFBy{8E*|RYk`i9Fe?KbN8NfCUs#pzhi1W<a
z7`Z>e@V&)+X?k~}s_~k=YMvU7wUpK@VwHiuRzMgc`nvp_9|y{q+gJw$UmasAAMGB=
znk6?pufn&z4}g>FA15-R<-`_fq7Su_5_rEK_(Qmg0!R`0!a&*{Rrv$hL>sUPD=J*Y
zUWlIY(LIXQf6M*G4o!%WSqTa|?W#cG1L9swR0P;QOC7g_!t|$h1$tgjQIlX92iHqz
zU_)|?pOB^7G`SGC9rs?fn?=r8ZF(-yccPZaaZGe~z%K%cxcAp)GTK_06s^f?%bcSS
zp2;=duslZZYIs;k7O%L_N=dCXN?W<<Rb4&xFF)p|=Mhhk9ZRjZ&m^ca3WG@bgW>(1
zA)8-&HMo|m8UZITuYW}Dn{(v6aXXvV%=JB>XGY79i~SWkFPUcE(1oL38Fb(|z#vcR
zKYq`t&K23uc~c|tRj-OuuPf>!11j+Tqlxrtz$8~QSg*gJYuc?8>m4RPK?~pg0Yu5&
zwpI^aOzoZV(VaMCxzgxEcv^~)MZECXu8}scgwUEC3aRN}^+ouWLv@bRicQ7vgQ;MM
zC=Ec-lq{Bb7{Xovq;l*waObd}3G}0)x+(w{H!pZZ>stoORuo&v{Q?QLE9`*npgc|6
zC4hW0TiUHNh-1wnr`&yeNu$dPL+-F{AC6(#Y5<JKc4WMTE**^LR|4FQ^x%IsxcA4G
zcYWg&Hw#3AA`}H)>FsrhWZg--yiDNAGD^D@&6>8wd80_+NY(NI5>OvUG6?jB7wPB8
zR!W=rSodp2D}UV$%A<`TQ}wND=f3^bNJ<hpACj-^1wm;OGW_RFJVs3cc8wn{zWLfW
zlJzPZ*Go>R4l<*|?FUX>&wwNKcZHr&P*~1xt7iq4fxT(=u_AO|U2(oXOpizBEs-P*
zMRlEr9UZhG-bXBQPJ^Kkc9m7~2#%XicZ5)WJmr&DyXKy#o0O^f@ya)84B&?l6tN#(
zFqpd8myIr1;^g@ubeb2)Aj@1R>!tO~rdc4ku|RN_?E{sxPHjq&dOY`pB3G5&$I5B&
zigERBnj{7TvolpUTQ4ncK@o2eJYKecJBlhjpvO7oG#!qhuuM`u6Y47N5xjdBv4Yia
z(brB&=u)sBCHKde-l!OiEF;S7^>mtUP)9uI`{&GCnAOcaMwd=#f36Vj{?V@R12Bk9
zi_-A&M3d`3cC!KmRgWv>DHt+Jm3uUGsp{ayl;3HMtlu8ZRv7Zuq~U@3!5B!R&+60E
zSDvvB6tUQBq*H^ZYB&A7sKB#W#CaAqqx(m+L6rbmi=r9);A_aijgu`AB?lqS?{jie
zLKXYel`L0YzweJvm~7cA=-=4vKejjf={`6Lr8pDRtLfRj7*V}IzY3nG>wI*f3p6-_
zS^}_T(O{HYi&k*CoL2FiStO{c&&%Jx_V|Ke)&8wzz7zO9Eq^rJwTd1$9)ow#wVS)2
z7$JSYBDnwDx#4k$2fENHND*lJPHjwko@-=kz24Pxq#>yQ<+gXQui!kW{`l5Zz0Yo`
z`MhnW4B)dkJw^6_eV{e@`n5L&e3tWX4nC&&fNotqPyY&+2+H>(ZuvxyzMqVMzEWAR
ztrUYxd_(B)0bQZ5grSA(C8`Ju3m9F8>`VbK^lwtiVaK<ev<N&qhLQ@9>gY3XcYanl
zEoGP_P3(phO?xKEW%UfFzR?kdcq*I_uhDO2@%F5ljr=+|XqlRxUbVYPH6Qy^E!#D)
zD?3sSX0v@#G$}_V@q1f6B)h;17HslWPOKgHV-7PKT{XA$WzC#ER9kdMMn>L<f}A$S
zt~8jpZoit-*exH_sIN8YlrOY)J(x8DCT)LGbs%@DP7yjnGuD1jtgdHYFqgA<R^<YW
zuyIKRU0V|Su|CpZQ=@J(ZIkAs!+=c!D1dt>)CzLLI6-`Z9VAjR6x`U@__MG$K3=~<
zAAjR;k*WNOW-pog@uXg>lG%#vR1r^MamMYFg8ntWz;uErYLMv-=q_=E5>8$H84i|I
zmfa-{m&o8xM*F>PhOt1h_6be0dE|)0#t(HFZbgHQZ{XGsO~%c9roFt>>3JDgjbDV@
zG4cseu666Iz$j7jWgfzBRb7*a_JwNmEJWeBUZImW8!82OjC1GR>xwHn1FOayv1bp}
zdp^Yd`DsoOeZUb3hM4Gt-M+0@CGRq*i--(MPEN+RYK4XpI5;>4n}T<xj76P^rTYeX
zMW4cdXN$<ZRy0KVm~8lGa`H@3KC1|8%Ej(nmwc3zNwfda5@8+E?GTj<f{L0ZnY=kD
zi((a1tEvCBa5V>seCpMcUG-D8YjfZ%7tbyB*Ybv<YEL}YF2EQ3vKLX>-8516<HU&L
z(X5v&=%N>JK;oMLhC5BPfedpzHTjJpbk^|KXuY4CrzcrLLL$}*d_C(zdRq%lL)JCl
zF?{)%8jfb)>|*6A2;}4v94Z|B?rz{$RRWDu4#M^FR8aTK19l#FnI1QEsbeaTeV?an
zTNk*ZKtT0V{TO81Dkp{mtJ`*;?uBvj?6qWN%_{QTFPVKvc{!dp6!gHyU{x0M1{gcW
z?_ETyhXI3eS9^_37Z75gmWwRe5xSZN!Q)&KI}L=e7e6Nzy^xqpVi_9F&PiQm=;Gq-
zt<ci3F@0zHrr%Ebm)ydV#m9@uqm2}Bbl%&c%M%xVsr<ILd<E=S64wdwD#3i>r{tbv
zOcV%o?MuCYd4w_t+QTjk3D<tm>sgSg$kU_TxwofA;`glte;G4bm(wC}TSb#y#j4PA
zBJ-P>D`|Oi4<&~@a3CfYFcda_Kq6bj^}eCl0lfa-p-@0%zhxy?5B2l!-{K_xp;D1q
zgf`gX8PHj(%f<?vH6&l!4Y41K{cs@>%?`$Gw4Th%#<9weJ;Lv(iK;obeMGqlUS;P=
zi3ZoRwwR5VPkxBKau_0Mm4v{QRGq5c)cQ%pIa0K?(#PWrF&+?ZVlXCHxw~UhJ>+^f
zo=cP%AybhI21OnR)Rdm-dnjve@*DmhFp<&{%?h)^y3)sdP~n^B$w0fQ=QY4MRYz-f
z$*S08xM(O#MTHY176jC`YUe6B8}7)U;R?g~)**#Jb*bF0v$4C;r#5gClkgi2m%uZ%
z=3L2V;IHCQ<a#+=!ql|}mk@7>prwsnd@9!nJB{0=1=LS>&wKO2@rP%8P8Yc8ks{2l
z`fGwd01}hrsKkTF6xP`ZBqK3PCmI4vVXR!Jlr*G;&cns=j@q_F9A5aN2>Vq<E)^JD
ze4ofjsguk4`ONR}h)f0#{Qjp#rDEhY)9zz7-?ggUl>vwF{F-{9qu-}8Qm*2R8l)S#
zxzK_$=Wj@k2<43jz#3#Yk|ye!&rV(Am+)3myY{PSUvG>0LF2SPD-Ynm)=vS}qa+4k
zoh9TN?!p;`CG$>3>>!X_^Xw-f3T!+*PTGCKa#CvK4+Gw{w|r7gaE@FOed5SHUCl4C
zwW^!A|6}WtMfYaI^k?a@&`7_eYov&Rs)MNQ*?Q+eu3tV6k^pcy2rvx2b{stGy(JTz
zqwGhQ9nO8$^$Mlkmw45LVxt^vUR-bDpeK=1JYB3M@}jaWKO=E=L+0S;8~*GRaEK8p
zLgvfSjRnF)a!=wuOBD~m!N(OfA<jJ=zV0(YRZ)7%`AZudJBFor9z$vOu)fY%Ho;^L
zfmJRqPM7clFkA=xP*A1uZl1sA)Fir`MOgfoSYKXX;^C4WkY6T%#t%znjT4&+zmZg!
z_;$TFQ;w3|Z&<U;=8gB*_?4h8d{?|Szl&+qyO%}X>01nfS7M7!_Itof-woaxTl{ib
zk6;kD{ZJ$AUB7R;$h0tBKloT5eBnA6jD~x|60e^@x&wgfv4>q>Crj?fk8VLhLeJcx
zx?oqd!yGC+0Am&hjMz0wO;oRlUa#adxni9-@k0lbpun||S7zXyi3dPnnOl(<@Y3t9
zKWhM)G|iEBzIWM?bNh%a$l|wQyw|$>8=2%u(rk)Wg-PIOh_v}Ui6cHr_4l9JP=WdC
zd)LQf4p;_b<ivI^pbhQTAmiWtMIr5g?!Tp7sO~;Recz{;=RZOCy6&G4eWB};DDoPx
zWo>sQew9K5UPVoABGN2snN1h$St^&+U)}!giHOWj@a3=}(rrDbK|c7Fxs^fU<PfRD
zZv|fH;+<vcw)KzC0`MHT{3H&(yIL(Mp-bYwn50f~WCLi+uTgWI)+Tt4Y5*#l_oD8(
z1IS{gPEev_M_&{!A+%Z?ImZ9v77jdIJF`KJk<-d5X_B|zj(ZXk_!!8<$~qQZvwois
z@m<YBQ~VmR6eC_ZCOXEz_Kp!_)nV6>va|>wIFvHF;iDWVuWE?u^nN(lw^)wB--bLJ
z*4cs!$VqN-Z($aO&HVrcqb0}?*lJzp_di3PnX?NpVaeswh8ApG?utaFjAP*F>nkIg
zTw3AFey1cP)oSJ`pTqr<@bJrK(fn+1)TNUnvVk!VFBsAucZyNNR%o0p%o|s@Q!XIa
zL?hz*N-gmT3)K$@bGwg~QQGU)-55eHcaxuLRfpR<Pm4a_6d&{BSB9p9LvmTUhH6kr
zydcOm;K}^`%pQ<R*y5dfOjHoyESMNM6lQ>sTs6mR++RS4FW=<A``CXH6Y?z7knSX)
zYWq1k??kZ*9)7kd|6~v{4ggF6W*rKPb`ng*-mtp^<$x(^(bnti$(H-<w_-3aFZLh_
zMm*}T9Ijk?t<_weQ}yQ-5$8PYvb#Hj7%!y+Ry`3y4YnK<#M6hS+%#rz;Hx8(GV-8E
zSf;n>6Vey$Witf$Wsmkip=}hko6)#uE~DQNGhuomP+ixVA8<lZXp|t>h{7P<@^$H3
z0WqLJSjhdj#4Obq<5|iL$3S#2<QF$zM#s#n8Y<h6!~i1t_^lpS)Op)A`&8|#a5FTx
zJ%OD^LNHi@eXH#ZJM>wv@o9<{?EFGw$ho~LAphJ-XvhW(Iyt2~{_(&PGM@iHly#wu
zeu^SpNx!^OYmNb^x)1YfVUBDM26hUVK($92|EL5C3&WQnE;E?OM}tFP3KN?VV@}~W
z4Ce0}RC`{H@I;s@6_wboM6`VS>T<H<X6k6eq!KXYxK~iGR#0NAbga**5plV^NKPvQ
zn`*0+WVKvc`v$(o&#oa3<z5)K-EtBGG6(j%`k|!ItWD{DW7A>ausaJx%=P@<(=yO`
z=Ah_)zic87K7LZ$?$S^FOl~0@^(XHg=@CiPXkW;gAP%Af%MXM3wdPRG90Nr?JI+Y!
ztNZ{nzA6jM&K|mbIer?cj>VG~KfL!(yScKf!Nqk9=%|~ONJ2lv`8av`GCMHkk=Agy
z;jTJGE4ga*dEeFEwVcj-`%v-3=>Rv|$lQ{QMduq|h5fT^*NSluQd6yIZw=X$eyeMe
zU9)P2%PY@rQuTs0=MC#Mz|f@LFNIPWF(v6cjV755_@i`;ArHBKtp|v-e|`!Dwg`fg
zL;Y;QofQ^WL`$sC{Yog^>9Db^;p{STdhJLp?o&%LBQe?e0r~Jm<B3=1S^gS%zm_s$
zQmE(RhFHH^?C`(6y-NsqvpF2DX)8!$_`G&CDvu2#rm|B=4qjY?K?fiFv1H@&{-oEd
zh73dYy?N01HxUL0@gF`d!v%u4@&ctgTtxCba(1)$qN3OA#x9`Tw)*j{@ugi)2}EF!
z9fgUM+tJ~#GW<<HpI2cUc0Q9%xqbgq{;Z74R0<v1zaHEi)_e@x>U0`9a6?D}26r7;
zJrdssk7#p`P9biDeKqL2oEUmq?srzWvX^AQ5j35C9u4kqI2L{s?Dp05*DJ?9hQBI)
zWD;3>{|EwB`u?dZbUST`jKg{Qf2$(pu<@K$n+2(q>jIi5Fh8R7iUNGp$hTqygr~vH
z+xtN{U+*oNXjZ?hq+Nwnfl9dwRajzdlY!D?muI{Wm)W*7KgR4U4rAlVR+>CeQ`kN~
z>7?CT-do<Vym1nRux|s!$!VoMM+*SWdPZb+Fg$x))s8WkzvtaKblt>4>rq<tHU@z2
zv#kz6MrJEs@}N0ijSuoeyC&T~L2g1z+k<fjKxti=3~>3FH<p(9RTx{X$x9=CwQ}-0
z*hYr3_RVUFQzOqsvh3W2!PQ3IsiAE|rEq~01qV9WqFJISZM;8h2PCHE9BgL~1?JtA
zfHG2Ld`S$@Q&Pf+%#je|k1lXgNnIof7t(9!%$r$P<r#A_nubVx*g#dbsd|wDW))Ab
zx=6mvcFIE}y<ZO4#0H7PTO}|6lfav4pFs{Bj1I<e6C)&%ckt87jek4TZy;fc>7R<Y
zss-Gny2ar??TJgjJ{SPcy1K2cttnl;?Cjv+;JdT_Q1$WeC;b!aOu1!TD&*TqC3yIV
z4^=E2eQrGvPhQgC$)-)D4>EaFWip^zT|{SNrI|p&eAjTSQVMgIFnN=sOH}4GRGh$T
z=PL3~b!?0>b03j8_}JcBR+knWI32|WD@bA7l?F6YU<ycVd^q}pj=eP&fc1m=8mliP
z)yUs~#wGNy$<83SGdDNa_1-<=*xt0VwTFjkQS>x8%D9kO6Si=N1DKnjrKYId7nYFh
zNup1PqN_3kxsIE7-b0Aq!RjT!_%a0}a{+9H11G!tJ66RYY=NtT=Pk?$Y?Sh>2Y;I$
zd@KVbjH$SmY_XEG<_Iv?qK?Q;f=H;0ekDhG(CT+Q9Xob3JOBu?<z6$LgQQgDoJ=h(
z?aC;NB(f?M{1D0`xDQ}PLkI<nhT{i`M42OpZhBYbmI$EK=3L_KDRvhV`_&nCh_`j7
zH7b0%etB&|3vm1D&12KR*w%Sq*y^_SUxFZi9{ld-R(;K#pOT1ydcWevl{k-ejUIT_
zp$M&f`J7RDELtpx79mox&|j=yc^nuETC0*eDPuHcf78i7pciNV#YIvQNh@ylHJ|I#
zqGW^lyKSc?=)JwuY$>upUO)aMl678MY|QVPswR(d*0&kHyob_vr`8hk)v=KeA<lM%
zyMC4Q!`Y8LRMnr0=ThQRE^aks3wj--@&?};IDVx2Jp!WfX?dZ=ZRS4KkvgCD<*~+7
zipJNDK~}i+fk(iBaTeg#^xnlgJ|V+?Vy(PmWMn6#5hsLfm*lgAkGhA*D_k6V$K(8p
zdEj6CwBa)r2$rD8yS8{^$Y7D2m=rHJ{5Phh+&PuidAxVipH~9UYM)AEzNw6^Bje+8
z#<OR=$_B$}z*Fv`33fot?75$M%Z|Ok((@uaswJ9hcy@O7?Ucs;dN+Njzsa}#k<^C1
zaLrXzxX70c%*SUs7jUdAv<do~UO|Wo@;)LH36i2!iy1zF-Y6v=SLA|L3Y3)m#kkbI
z1mVf<!W~LDTTxzq(Rg_DNlBUC0D|V~Sb$962bP{k*~e_KsQRqq=8glLS%c#~q93Kj
zJH(01z?{5`;IGBPpNy1-O)r?DHPEo3zdU2%rEz_=O3LIs;gDA9-rFGths%5_`F%5>
zW=NZ{TJu?hmOVfwopIkxCOg?j((EJ7%L2xx0FD7Auv%jCptF~rAB{iVF%72|Zr&m?
zr`>!?1me%LyCuSs%M0&MZgTtl`tfN%zY}36%t>(?%SY>FNUwzRP_%AU2|WAx2<wB=
z&Y6qXI4$@xBZdrqf`&`#LZ%9zlL$w*^<S=pceu(yY0Betv+;P5=SZvo^nv7-5@nvb
z&HO8P2EL)Pye^;xY@~<V`DB%H4Arq4vSaVEZbjb%fL+^)22aJioxAvlFcSac%3g~k
zPN?qRu6r?6XkVk?0IpA9g>vwl@I=!xBdoF40r@%+IAmP|W0E?kp6`T^P(^a`$vaLl
zX+p;u_ta+FuCqy3qFMN%Jl_7TFo}*DFVhw#&32^;r<~|rL8olh+7>?hAvD~ik#-9e
z4!sp}C+oVCt*JDyJVT(XlYsFkXI|Nj*NQ&AW>-?{LM7HuBHNxpdG>C%|9s=0CkTI{
zrOI?PM5G;dhIqr((X1>Mlyy>P_7p1L$D}*@nSiWbR*Tt`;_mRwqT=pqOiG&2rBl7v
z6ET$}M-Rzi7eEC5<6oTRS%15dtD+Db5a{~&ezOL3HYs?;n4ln`vwjjkG9%hFs<XtT
zxDXQ_xh0anQq#{C7aaU9`e&aRIcT6bN%i+;uZ*q|5w|_pi_9`UpCy>VY-hm5xubSf
zaEV%ZJ?+ua1Clo&G&YMEQpb(A#t8EVHJ+64(OR!Iqc9o$#DEc3b)U1*z+-=7t!-^f
z!c<I~V~%3*{`b#UckA^(y3O0i@2`%ccAIZfq0OoOohF5j+IP|(H{n5#35myA!MjVz
z`w{f3Ir6@BA)^xB96raWvgCZ)E-V~3-+KeUd@GZN7!JOkrhiEnv~T-Ft&eaodjH8|
zbZuMi-X_ZU&wL(VZhn=fq#CV@nAwk1C7W0OWnBS3j0fi3^27kWA#SO|>Mhd#@#9~>
z)9!A*zI_@8EAhjp#%NKwF10l^&NE#Z@dv*1J)d%?-upi)^Z452ac`*U5w%5*QY9jz
zA!>N7#r(-5wC&B{Z=rm8;GLy%HMOltZZ59Hy8b4~FOyD931VuZhTq<4terd#cQq=q
zf<*88-TP&v$0OlNHSPIJO&M@^{97vs*VDR-GQjBBqAk$#Z2C6vV2hx$ne@4j8CCH?
zzt7O;^NWXOXYuMgvpEZS15);w<=UBSNA^12-!VMrtZb)Y5<!j8tOQ?=frI^xl+m^B
zTWeCzw+CDYtEG8pIYbRjIiXX+RerZP2U2%L2F^gaP5>VgCh`8B)bDFM=}Rl^jZ(1>
zuAKwWdeH?8iGwPK&4~66sTFPyJrl5BE|-@AZfsXtROC7VWShDpI(U>`;LpnZ9IlQw
zQr~#pNF%b*C!fg<)q2e=0yA;O<&kp_J;LP_`Ai$l&c%9!tfls|uoo5`h-pr(kAicT
ztT%fX6PIHZh&VnX&J0x^bZCW<J01zP8wu^Er<%*62Hr5Bh(!aBw{tO=%;|gVssaE!
z)GAK)Y)QQWlUN*)6QCk769m5bHG4(13FXrQ!;z7Q%ywXFHe(DOaI6_iXS({S$YZjE
zy|U4oM=kDS4JStxY=j~CzC?$3bw?(qu{Be0)X(&tIyp*Xf8gTs<Rz3gxL{c6x~8a!
z>a=>G-qI3yCytisR`wGI;Tc*;{?h~D8Y(W7R>mhYp|p41RIgk;Qjp-W1-$_IHTzz<
zTQAf3C=(CglaU4FR8F{>bKR%%;@a9P))p2|b_`Ak3R*4f6@DKc+PMwkX>uU+8`3yW
zjC)lk%4HQ2-I6S1)V6*_FwwuN_q$$8Ty&Uy0t~$ATc}<#CPq9jDN1!#Gel;vnm?Q_
z={zTEq>U-5CbZEvWvmE4W1MZPINGs@bf5;VRw!EVA0flDZ9SV>%Fzc_qfNo%vB3)f
zGv)3c?XxDKX3_RygNF#r@^^O`QM77?KqUTO^_7=$iOh=8s=xI!`x7BNR;8{pfj~R^
z>3aoCk^l&l+tq)$JVBT<AI&P=U8q!LQ}Fuc?Vv!6SXudN_eaD&y;D$;>(Yi%hYXH2
zz>=kZYwZhOZK(tp#+s!SB<=BoR^%vk{e~6={iI#{``?hFP%bq?)d{zeR+k>NZwcv^
zVo2vB#==8`ACBE)10?6+&E)-I)g1aO^8mL1?EDK$*V-4Q>hwzeouzhkV&e@d{b|dj
zk7@c4SdH3!t5SOG#~~}hp7sBSz3+}{GW*(<q9h;$3{6S^(SXz_APAu=sDvgU2na-_
zh!m9$(t}tiI!bRrl#U=>IzdrEASf*$V3b}%3ni3$GQaPBcjn%|?^^edJ8R9X<p}S4
z-gEZZXYXe}`#I0VH0kfUl9QqvlK8H{;Cl|m0q?}($~LuUf0o|jP(Y7r{)<CPN!m0V
zv5$*}J_`{wCRJMuurG^{6pZ}Kv`=Npb0Tk7+<?H!?|b;j;h-g~xj~v_ZB>bOz?$m<
z&p1~@mInc{SU4wgx4vGSx+ER!Yd~khSPvkMkKY~luk2|mGwO;=Jfn~aR?@iz3RC?v
zJX`Pxh3%kWK>U1oSfw(`mfZSg5IwZ41PdHKdl4CNh|vPHh2+mAIyZ8C32&)c$awEb
zx~rE=9Bzk@Ta)_jPsQAcEuUXTVqAjZ`A;72pov-)Q3LAr=c6t!hu!}kZw`f9kFerc
zO14)#m^p_4v!<*UX9=>cTjBo66t6LcKUE)iR5M0A1pT$|EZc#jy0!-bt8SAT8;8Pu
zo4NpD7*msw0E#pjYm+;|IB5L{T0Piqtwjy<W~y~**Pjn$YjWVJAJ{_yPdDapQ^>=C
zrU&sP+A@0@0XM&fg9@Gt2X>)ORbmNR6!z`kxpSmEJRc+fu?<J5#mo=J`GE8y`sSJ3
zGrjdUY0+IvGPJj1e0R^)eB*PdkmYklEL0qBJ{Duoo=dva00_m9N>M;lU_^KVqA8Vi
z<_bs1_m98}7YFyNU<s<D7yMDb*sW<Zmu?E1rexs`iP?5#f16MB9qqfFU0UkY+1c5L
zzVz%GWyM_dnr;FUsK#Y!iejvqwO^7M_7KIJ<m2EC#vrK*PQ(EH#^IC1X)M9&1ZFFt
zz)nrG;`bnD=|D<es%1|mzet6AmTgIZmOn8!qZt443CJCoUF{;CYOwY1pf99#X~M@m
zTGM1@0{qKtiLzgB)||jA%s1{EqcOEr<`uLiHC#(c!YtonV`HonTacg<3^@Mk!`JPY
zpGtt^sKLGcn7GMt`ZD#~DUh4d{N-8AW0nB(!?D<_U$3Yk`G}+Qzj<VNT%4CK`qSS~
zYm$J<{v7!dA%8xwi^&*Pyks$QyigXn@mxz+U-$Q)m-P8|PlvYf0@hFOa~2R!0Dd3X
z%e)=mm_YV;%q00&RpO#_`}6`^JK_<PZ)&c(*7jWlt}q8Nq~MBUkzG97AK(RCG&n-b
zZj%fi8BEIaUvr*zA5R5FZ0tFz7nJBeJn|{w9EOr$VTSNY2KWMwWpN}v`4}VQ4S;(u
zd45U;d&Du^ZkfrUuk)A~X6vJEbEC6s%l=j-j|K+7-#5pTgD3YLgK|dkQ`$+JRfMjl
z7%ZWV0#Ki*5sCV;mb+M;c;zR8w$&_l#HPgz)R=YUZ{?`9g!#?!Cr?5u?%SM1&k*)M
z$#7}?$_6;NE*k>0`D-)lBvabS7lU@UUW4}Dusxd19yJdHSgRfv1-KDHqiU}9)t;m=
zq5@CUNha@Pt2c(prH--D(Bb%}L(!K2>nv5yWLwmpX2p<*w0+_5tqsRjnt~!*ka7W7
z<6iphYHf%Q@NH}3PncpUTEW`HX>A5y9bT^RzX0Cw>LF7_Amr6`ElV$zUtMoB{oGpZ
zmct2N@L=j#8WZ(Bi<6hZHV1s?$dQFR&DTh2dP!(>EeQfs8L^()Q~Kc}eKWaC4sCMi
zuL6Dr>Flt=ouU@?Ikg|emIr;zcoa55o)+epT-y`hXR#JW!Q<2<i*Q=u^q9P1#mbwR
z(~8F=xvaU=BTKE$p@&13^!6=+mSCexO{-bsL$1S)4x7=U7oO=kFRqN87W^LmnhQ}T
z?{^6g!A4{`#>zRBTC;TGScHe+QtH?HF+>JW2%H0GB^121E6sS`mI{rf{0ckBjpag^
z2R1uSa($@)CY4#k(FwMy+A;pe98K5Ueg?T~2Kx^;wHBS8I0aUj@+O+a{_MHC@c|W4
zTt2*aw%^IV7k{YNUDINn{7d8*dw|OnCrE!$y#^0@&z6j>$TMaf1H<W_6It9$g@$ma
zgEMlfAGs40v|FBlW62oa|C^Esb`4R(AQzC+&T|nNe%Ee(-}CPIZk<dCuh*rD<b88e
z@R&lWpl^C+@RuobbPwICIxV<#{e8&Xo$1@g1;1_rp#AM>cJlUIh1>oOJ_K09b2Hyg
z$>AIAEq9+pLzNqkjKqGjo(cw_G5_rR`#nSm%ydn(DaAhqypi5=x6+R71u>BlI0G8m
z+i*{q6sXlN5VTmDF4g9^q8C5EP_?oUe_74ZpA}|%Ft?v-n*zBcox6gs{A!HB3E04$
z;FyfpfGIZfERPyh-J4m%2amiwy?BL=pf6a;o@-8RyV~WkUGhU1HLzQOnjp8@!C-o>
z1_;-_E={qc^bAQ19Qv&9Q>-ol(#}OKz`5II_>FDOIG*}mezZaWxl3J49mKeJhB8O$
zic=T%_x1cy?ks`41p&m4_CJln+>kw(w+bCA7pUTRO$lSpjucG1ylkR{CEyPfeklzd
z*?Y=CYfJ^?W}LNgJbQpuHpOA6jcH?67vRzC?K5C|)9EJfJQ;ZbpP~F1G29wZLkm|B
zw3a<>T1!c0VjDm<JXhHKLyZoE)fU{Xp|JAd!f$y#;T%y=K>|}m<UkFN*~}ZQ3KY5f
zL6=8$c1k26@(fJET-v=Ql8F#$<Z(F{FheuIgwIr>egH!CPbi2F@&Q%xOyy1cDJ8hC
zAo62dWX_&XTE6VgsZWwJlO8!0fK8Pe1q~eG*mT|qaIh1yfhxg#xAwUWIwT?9SHTP)
zEpUoh{$7(5<jX_<bupj(dF30vf6@CmCCh<Lzo<cU0%n{nbVh5%W&Ek|aRVH*rqPM;
zd!S3`(Ifpucd(svo7BcSmC4!$f)UEo81PV+-rbkSCJ&9^7*;4+p7VNnv2=vP{TR{n
zf_J|NjO$3F*-uX7&(&0D=g7i1E3BP%_uMrbs=@X#MCF~AsN(=l1l2qpqjSv9!U5+J
ze(4d^Udis6s>805k{1B7#L)f*<L+t`g94gbU6D)fDpNlJ9*%T5w}09+@9|fVW+Rkr
zAWAUBxpPs{v4k+KUgqD}2zzyZkX)SwP*7{_rXPOOWDW0PlR60wR7DW7-g(6jX!lZL
z_|@mGNTChS_OY?U;0+7=KL8!#OJr(43(MkHu8P5<-6LjgYg84o_9-g#+F6LC2~pxg
z<hZm6MWLjZGwR4r&?{7NQ+qSymANPmTBX-~1O}*Cv=G2nbsGv*)7#zP@?^}WQJ$PW
zeYhy<5D=6Dkq)KZxu5HjI0}Vwh_nl{v$iguoGdBY%1f~`Z4YpA>CHTTmbRcSO>L)*
z@%m!FB1G&~C=PWGf)VQ*8>CG2pasxDc9HZ@_(r!~X?1gX`ESRmLY~yNokACG1WmUl
zf0+1(;i(4S(T<m{nGzcpvYto<!~~k4=FSgS4qoB6c{lR=%IKrr?Z?XY`8WOX%b)f`
zN;5x$FfoDi7hf5jU~s`<lM!^z0L`>JbSx4;l5A{j2}j{}4SV<pEMLXYMCzcso15FF
z+SMmx1x${IU0IJwfjX0J{yl$uu$D3pzDVPX7VnM>uIFL`Mkg8d(q-owPb4S8MMTu9
zsN<TmIu|9^T9h7|+CXRUTFOz-x~}f-YiBV-9r<n`Awr0V)I53eL=sJh-U{?s+X};2
z4bNUR#F@AhIwhNAsGsOV1B{Q==qT*)1l+@d9gc8)bzqtwd`ttjqtQoo25I^noqXwR
zQ;`d)Jc(xlFs|z`Cjm$2bUe~YNIhSPOI}l!mK<shJ+KZd^BmV8M9Nkl#}ar|PHn$z
z+BDZ<jXFh$EKzoY9P>6#E~~QOIw)Lj0BrCqHbUeHkBf=N!>Qm6m7iY}h=K?PAiXUG
zBYU(>cMlJ_Ifd*%_m7?LNj@ejii)>c*+JSUa2U$^l>a-}#@bQH4Pl)UoVP~+LOT79
zh1lgR_ECj5Am57+(sdUO5&2G=ypM4=+q;Gv4^jX7Q&=fQR-^7csp7=H!Z?t9dLg%O
ze>4{lM`fSte%EmRI6_F(`!V4vn~e@jziLZdR(SudN53J!)hofw=buvWRqlDq{G;3_
zIgyo4q!K?k!rLHo`@}y;yFb}x4v8a>Xn%_6oU*p_?elA|db;u=1N#+CenbH*Pmrt~
zRP#~;4!Dt(gH<oh#6kA=PA<hDnRuN{HNG*9W$oq8KP}YnMMvjE!L%?#0c{z7ib2>x
z5(D(`VpZ)<VqBh7h<k$K188xJS2dhyDHHGOz2I161CqQlz1o5EYZ*o<6o<T<C9%fI
z#8i9U<pIA$0#pA-(=dDw<nKt18-!O+>8wrbV?+iPtd0TBJ+y)Y(*Mqe9#;-`*Do^v
z!Rnw*hN!VI#4`*aiR{fyMPYTMaxwa>BcQxz=IiuG3ri0h>SwL)F|W!JWiTczG0&bo
zOMlYk@*|4!3*_?juFD^dtd&PcxxkWe4E%BbSYM&opDs<-ZmJHFtZmnX_4R%A6}%&e
zOJ%7WbjH!$=a3PAG1SEN|Ms3>jjP~&AIU&Xu-8${M=vq*Q=*G<a0P2o;iJML+4S>X
z?I2ga<X={BjT%aH)?t}f^@mw{5vRkmD;QUE+dg&+Ec?!_pv2bm+LqfqlzueIKZF$|
z3V^IN<qR()1Pvy_-=&q_@lh5Qf%8u;D=Xv7croelUY!Dh(fi+ArBojew$@V*wo-<Q
zr|c89IT75>25_Wzhz%FQdo209sL5e)f=RhK4seC11DnKx9H_)8g4L9s{FnezaTZHF
zdiAj(1_O#jey?r>SgPb(GCW0PtEXw@lD%5r3XI%Joix`;#tW!sS93JOO5DRK>N#LO
zhW~DFBt8WmT{^G<bWwn{N)RIS3+X7Z5OC{nvY&I)OSle_@3d6DSTac2`AaPcI5tn5
zeU-&}xo|SuN&M(19-?1*wd3xZR&rUz;_)U+2+Vh?eF(5Vstz^3v!PKe!I7Z_PM)8Q
zL4$n^;#k5Ih1_B-d@55TSQX$SYwx!WdYN^!Rr)Xx=bO)}5!dBPzpZ~TDPbC2PtHa&
zlGWl-VteQMolmSYfL57xkC-Qi7-vCH@%=n~c5jOU-jm08lOanCbj_sJ+@CUZE1onl
zar@@c%tS*`^Z9GfIaSl8M^u-5gLOn!v!}udOzKXOr6A+2b1$Me`1`A{a(!=%d{1w=
z&H!yQ3*Vl-2en5g1_Ey53yx)8y>JWDzv^Sgp7G(+sUHDUDf^yQ<8tfLpqRMg0W{GW
zmkkJrP{Eoi4W^wqQS^{Yn=}YHLcoGi)6QN5LC;GqG$#M10*f&BT+Cmt2sFUR`p_;v
zLM$e}&R}R|<3|0K3#$v|va+S-QW%qK_=xY?Fc`9Z$Gr@Iv0rFjMV$y3Tqp01#k>Nj
zRrNPM-V_gOp^K{e=ClWa4QvM7*|%@sE?9a=24&Gv`UO%&Z$IzZ!_H10cuiz+@HTbd
zZ=OlcmJ3e-jxhFY?~3~1v(fK_Pi>KZKn`_1ait5O#AYpToHQ+F%)mQiKwlp29|a33
zdDPjnH|wHM*pLtnRV(189r?XY@_k1^qo@KQ@~m5L_d8t(oLka1)8@UUa|a)&&HtJi
zdM+%oSYs!J@b=A<+UnCiR3xD0Sds!NrId_UpY-u3!NGot^t|{8qXh`12?T<UxdJe3
zRaM%_$P?Eb*~#H@z5Y|*j{i6%2>J=1w7eH9P5D(*jsqnrHPTK@8aD*AAR%$ms@y9z
zVoi2pqP=eg`oG1}xA#mguxI6s6aZL))1>p7u&TFY-P1PE?z_+$$<j;8OGKuwq~%aY
z5s3V$@Er{YEj$mHMB7y-j%+6#=^yj$K|u{yc2?8@V<qFVl%a!`LlVGjvcGf*)eaNn
z{0@zQR6-aO`)BxN;d+O{i<$eFe^JP7rV59mpsq`0wYACp3w}x>8HTDMyR|@u^cBUy
zGNR#*G=tK8NSBbDQRz9aA=h2>E?^5vG?EziPEBE-y>dQIxW0R0BZV_DC6#581iKDr
z?l=k3?%a~Eb)qKs>4%nVF#!i^eUozZKnW<Pf@8h-w@pm{a-~*&3f_q$fE|#ZR2%tF
zd!n4IQ>qoEsnEK~k`!Gly&Hw5+5btf^%<HnR%<P+kiaxAYb`oRAdv&E$O$pPYIM2t
za&?LqFgos8U?comRF;z-El>e}X=3Yn!4v}%Banm(L;U@-5X&S{^p`+m2cXYUCJ1bO
zG=BvF!crWtb$a;`V0vn6sn9}q3TSvwRyBMxr7h;0c2$0YYx$vR4WRVnSl*6(Vy$^F
z$^i#FwosA7T{~Sj>4f8_0825!m3%yVB23GGQ57{(@8@a$*s_FK$%mMdXT-sJ)F?yM
z@pGK;Gr5s`ri(|IS5Mp<io``ms{3}`McQWARHwYHlTY=?5Zu|J$<&$uxs<gTpauFP
zUOIKubD4n77*?g|&eu*lfWLCoc+EoL$lo<8Njfr0x+){_x5+Rm3ZI-?ee&neAJb51
z1_TZYNVh;0(UuLSQqlL_=}(vNF_Zgm2M^BA0q&Yfvb9xXs;&gRK>GRfqXidnH2%j&
z4tI0pNV(Ql#?0+x{GB4>BZSCAWj;7?GIF4a!J8}VIw;{#8OfrifIlgb?-T)o9BTz_
za%)b!hO2&@&=UeHA#$Nc*6Zg7P`u{|D8YRVKlcc#fsOLLOAeP{>H9?5rjmuhsn-l&
zJPKEoJ<L*fXQ%EAJxseu7jBtLy!GKfr+UjQ9yrM0yC4sN1KvSp<#B&6BYN&(Hr7Y(
z{@u|XSh@*i-?~2V=T-=*AM05SdQx7>e!ID#2cl%wkKBsOxhYKKR*#7QK!ljM0qV}n
zHjgI(kvNW(&fxooy?_6H=XT-ckE9CcMPjxtAYWa5s>yp<^f>b8TGKJPo}E)>jUU<)
zlL*hESoG28d@cm{or;R$)6Co_7fyog8{d7b|IX_W=l_V(besSdkSO~#5G0QVVB3j}
zeFNMb5Fd5%_MJ)O;6BbNnFgkWx-sbXlg${AVdk-fE9wIbIeamXK#k+eOc)p9mAkw{
za4OV|4}7np*&iifnrXl%kg6r;f!dBN6sMra%H|z)_=dn$CHT6nFcL5jQ%)U*q+fS^
zlB&$71wK58=DHs+Q}H38?#4<g)Xd9Akbj}(R!-!(^AnB3L0wEfeP6<~ESR~Gk|+p>
z2E6vpQD`O!>R}rf@Wa5E`p5Q*)d3QD0{>h~x&MkuGmCyaA~vuOi5_B(NivZAbv)cY
zu~*GtJruElBCkKT|EqiVyElDXEEWG8U}XJEHyPljB_&&+*8j=?pntX_FRK8ac@99&
zpc&~X#QcU<0(-;Nhz-+4{9HZbya*hkG;~vbg_E8hRvogmmD-qcocx!h2>2TpdNU7A
zCI#SMf6M9o_kZ4kTu1W%{|1PN|645K|Bbnt+o<bzRTBNjxacDsrbEt0E+AjnhXbLJ
z`T6<IWybQ`|5{V6a60l*`QEB81DelE`2G9!#^&boLTn7cD*pL{M0zjGX*T&pZ3is2
z6@-?>H8nLyi`rEG+cy6G=^Fqpo!LP;FsPnFfgCd@CnqBn{o~?)&lY`DQgoR3gP~n4
zPi9t@@<ue!53!BqagzOq0tIk@N112t%P~!y?f!jBgq936oQHaI^cR7!ciOw({Y93G
zyW4AY)$pM8UVUZZ(u`26>(}3_`7I3rQh)7PT{Ub92>brm*vvsmGJCbYW$BRlC6FFA
zjRJB7hmeW`aNdRzia_cFsDf9%fB!x=)N}J0D<@q|rOWf)y|U4l&Vm1U3~)AY54&`m
z%yy0Mbj-6t3KQ^zpc)q0w!S`dZWj<z<&8XR+x_>4d<Ws;E032>^*VK{t$^ac_#<oI
zyIR5Mn_;l1q!y^By;k^yV>kZyFZqIBsx&Bn{UczmAn&zO=*lO)MWC9VBAvf>L8*jj
zXef2=?zI=&5odJ&X^UET96et8nScG=Upu-cpP#x8P=0##IDRkI25zw+XXlp5Vq{a3
zh8oahF=rF&^l`TP_s8`!&se>8wP|3QFB*o>wOm|JG^~F&pnilKSNh0VzO#LjJ2*b>
z36iT`9=;<-j=Uy19PIDehG&-yV~zsH@xQjkkS(|dA#H?`^ontgkGxSD8mveKh1J&@
z$F4Rd0JQO67Qsr7VDPaC(-$+6ldbL!5SNOSgwzB3ywlRtW0rtu1b4p<aZ=oZ={JbX
zQWSH(RA|z1+JB7wHmHO@k9u%%D%*)2$by(w9O=I_(?|UA1Ky>-cSf?oWcsPH$4^ug
zD;;h8=Wk}v1GIGa$y_1N0elme_v~jQ*qr%&y6lXhW%Nut&k5DWI1JF6{Kt^LJOl30
zuKL2|L|r8&M8L$08CUj2kAX6t-e12Q`hJ1{I8ziWExG^iA^y+$kGmWNNV&dKx~l@D
z@z!3bQVLVgOMV3MIrQEiKL-xXv46cRzZ$%(swPx_z^+RmGnh(p8Lp_P5Kj5*1h;sO
zkK=Q8a>7&{12ye`&FJudfy&%)le}ox0b;DS*rT1xCRs-}0viRNwV?lPZ9u;21dy+q
zy7jiN80+m_Db1Fnajb9l=75q|U3vL8?tcyF%O(7j^WD(eYekLHIooFhna{!@88Qg8
zKdK_&EXZGntiy)2|C7;a;X4@ZkOixA6-y9qJzx+~By*V=qr<>*+#DT00l^x-oons?
zn?e<6)_zQ6j!Nnf!z<P|H;?o3@pZn}DL(wq<p#wV5KhW@9&#GL&am;Utg^C?l!5{z
z#(&HL{sh<yJS3vU4a0stJiD(&jmG@HoZ2Ed%v@?DfBY@<_0L~?L*GQBWySyUAat|&
z|G)8nZLTN>HGZXM65njz_<Rt&oS8{mp#O7QKCX&-<Y?1l{?B|7>__y=bxdyR+&pp2
z<bqZN-&ettcK5hEPjIa<p136RHJtOP*dvHZq9p4V$l;fU_nzl`WHTBVd*-ZOukfR0
zHfU|vXfrf-DrHV1sZQNuqokko!{SEXT!4nLMNdG;bb%^F)P%m*NUz;s|1x-R7{$?}
z@k(sL|6vi1Y~K<awX5kGhu)VR90wPS{9RN9_ituqoB_ni^y!0txSMrDa0YqG8U+Iq
z?@0z2EQH!0O#7MyvtNyjPupv$x)XFe4uw5=;o4k!OKWTRwr&Dw2;|5XD6pOuNM((c
zDQj-lRFPO`%wXH+4`3>O4BlA@x_7gqV)b5t7+xTps9B!SNP&pM&}d?hM(|vblO|jz
z$}RaGG6s{J3!A)v_A}RN4OcZ-ndt1he-+<?)3RqSCx;J`n9jkDt$(BI)cuE;TXH1>
zGz80lL3%7D2TvvJadHbCfd&Yg=09hLH*L;U-ngM6lmf&dB-+c*u?g`ZKKgtQ1o~Q$
z##ku)rtnn#$>-qzZHQwYUrYv-j(6ZR^Denq05XFZ8I+Mfr)OUq&N%u7H0$94<t)IE
zte$LFv-(S1>iL7gLI1eC_ef*bgTj9AYz2J$g0p%Q07BAwurwn!&sPDgF~m5>>poi^
zg;J&EHT(MaFf|AKt`)Rl7l-f9b~+ub&eN#6<V<Mj#!lr8*3j#G-YIRhg13U11$a&J
zKFga*Ugd_tYn^qtF50C<w45Ys+j@Wz;QqIld|?C%HAjUHvRDDeV3Vwfe(YEH0;<Wi
zl!AW&9S{Dr5S^7=Ex`vqS7Pc1x1==s@!jcgW~`1(+rv3VM;yz2VenU6Q($3AfID$_
zKi>hIk2u?m@4XT1M0hkymIgVKMci{enTH4YaEGU&=Mk6=NYZ(r62<rE)VBxmCAa#l
ze!kMO0OJz@<7K!7p6tqK@y{o_)1Q3zOvf|&lL4*Qkxkw`b#M1B4g0EPMHwR$kR$%C
zmY15w4%Y|?=-40naN0%`!*BY$zmPj040s;D_kgM0Dr%dXc@6^m9e51+@x(WfaN2;7
zaQOSxS8xVqJ>D;mSPm8@_fS$Tn2a4H5(W=hs$!xMqnKzf=A*HMn-I7Ye+u!!%k{u4
z!zh+Bs$d2P56u`2!8mWIV~7vr_MZ6AZGf9jSaIX&07eFfE<8#@xJ5CO?_wvke}E4R
z?=pEeJ&vMz)*z0;r*xm=4TO_9^RJ2mA%}P3e?RdC<7pgAfRDuoMK0vTY#9hPt^$po
zOK(p}vM@rpdAGU}m}pOuICS(IKfGXRlOkgYq3l?Kz8qpmf?&wsyvBN(fKm?8OwlR?
zuM?!N_jV!kRd!%w|MRP_fXO^vb;J>*PfwWl2Q!#x`eNv<e&w#d%bsm-xZnq~xg`1M
zUa02&_ols}mpG`qOHK8w_p-uohhCz@c(F|~;nkUN9Mn;m!3`9mI=FxRDLt!10xqLZ
z3cw>lp+VysR2!B^jsP1*oXmH-TIN3o#VLD}<7a5q@(Ux)&AoqdpR|F780l#VbBxxS
zWUQPsMj-uJ3cGx=7rLM`Dsvs@dlLc`{S?cbLGnh|m&F)Xg920)BtNHwqb8bV$Gmyb
z>>OrG_u$s}{-+}0eDe{(w-+auU0EVg*%tx0esP1MH60bQ^u4d&tI@#y!VS)uNU*I$
ztW#7^5V^o^UoU9XXj}$e@yf9TT<If0-Tl~cC9sQqlH(8We9soeJhsW)Mu*Eqv)m7+
zJy`m@;RKY-z60^&OF6=l;Dk;WrI1RON3sI2<NG_yDZyaP=3M-Oukvij!ZoR{Nwj?l
zad_R>*q?zOwf-BlS3b#+gEqpFNM>8SG2IVkLE*L$;Op|u!UUxU3c8y9_CirC2V18q
zN^#xmQ%mm_M`B3kTSgx<F|5bV`ED*6)Ko9(4^A(d*(=d(#&1)<e*PL7^Z<f3H(Uk`
z7F2)ETI-p<J69VW@y@|K2s6#Y-bZhXT%DOe|3Y14$A#FV5N0h-Oh0t6dJgFC0rmwy
zkha=c-}7{xXbqgXv`oT=;WuEuZ8<PqE`mgfjhK3G+Qf&$^>=?ze?KC{hmaOVo4dc@
zZgFIE%TbL5_u&3M$JQwv=FZZY1<7+u!l#x`-mz2uYkl1D+Vp1*w*1llDU$E<+WBnJ
zt{!NLH+ia1SmR-V0AmKH!$}>9)E8GoiR=uc7>Y!*ugdB#4+sb1F{@7W9h5Mx7m^B)
z%(yB;9~rh$7|%HAOj?>kn!Ufv#HZ2cvVd+=P`Wf?A4FIvMfaYG)Mi@Kgx*%W9B^Xn
zzDeGdmp#)ZBLj|SO&W~^?&Ejnvp05o2X?M5{W*+AtAxbTd#!q)8Oo{U3~iS^b~(WL
z$FCpemkC@kP+BuT0XV8z@8->XMFJ>VVb4d42smv#9woN>5MN2AbY1nW8rUnQI0Vg?
z4FSzG<0kJmpqaN93iE8)`XTSvg7=BNb|d`ro?@?B_gKoicegj(NldTqt7?yZT^}5<
zw5FQB4IA#)Pmie+>3*VHaQ`4NX08*YT|~MBO-PMql|J4y+|3_vJlKCm;~e_2L7+T_
zm~{q}#qYDJeJ4>IJVIRjIThR<NzIQ4m1A}InyXH{^Rt|O29zsxK7VBjyHAMR@(0Fy
zarnIEWq;1WS6;tO-pq_A4kZAmKT~rkNnyQyuRxtK7IIY1#-Fs1K-_MGzq=yYw;>X9
z1=4S_gO^4e(bh>$QLS6ZejyN7_Tyg87ws;k-&#p>+zm3i*odYs@-jFReqe8ZhN*i{
zlL+km(@&o2yTnXcuEcp=KV|y)@=H~0>cbG!%bA1Q_y?KZBtq>)%e@Us+-*Uj_L5tC
zgO+Kt{*qp+)t;TeG<`eK5<jaJyh^@R-?RaQrzWsXcib;Pxn^R*qFLlcqFBV^RrxP!
zW#no{pGJ?<&Nn~}Widl+<nV~NS<19!t9C0z)Ihvv1iR)(7<AabLBPov{-qft(W<6o
z!ClTSoF_$y9Qs}|Nsjjyo=W30Q45f$Dg6E>#OQW>|Jk<4Av0R<sS~UD4>VUoQh@jN
z00ltr9N~h)j9*#ZqW4#O9vQcXPFYl3Aw;@$@gQ6Ta8UAf;q}%YC;$UtR9)rJYr0Hv
zjLZpD5tp$Mdj7>1umt;BRrfr&2;P)I|3TWiQAFl^>eU66elEnvCXZ$9i29H7XY5PE
zhTqNsr~jg+F)La_q||coA$6#@!mM=f7_zUwV}T`ni9V7%*#aozWvIVDWfl-4FN(Ab
zS`c0la2f8S2sN}$OSS=@V!$@Xv=l8=2)WxQO`|edwmEVPLf}%mn%K6n%M_zdXwfyQ
z^s;2Y@rO<2!c%UwPbi{tk7}akmgy5x1SX_U81b=c)eB$Xbs|V+;5gzVhZJz3#RN1X
zL<nS8_tKAcf<W#>xE|0l`Z#tS<{5XyEJGZQMkn`Yk#e|>*=5$I^Yo0f!F8Um!46s$
z6iT^}=9vAKsm7PTGG4KSkcb$#a2_`yGI?Y~ud_egE6HDds*~q=5dA<D_YBfB4%V<M
zEe_D6R%Ri=J2a&D{_FK0!AbQVvIY@Fy=c+fdPL|M9jfVR^q>a$!Q^iY6}iu}K0gsO
zy=Mn{E;k~xbQug9BwF#@2*fv>DNB?4_`&m=*Y@rzMeE^;qpNo$fYxS58jb2KpAtNn
za3|>G!!yig9~kL#boqmgyz^aB++Q6j8S>6BvQ%b{AkuZj!mZhJQl_ZM&@I|a=V1Nk
zLG+nPFP+JCl-T5hbN7WRjDhp8O=hE)cTpdJwKfCEHTme$I}d$2Wm3S}Ci+e7siz--
zyy5|jB$Ip39l+kr4_-Iqa4j0Yu1tFi2D73Mf#1x#JUv1*?I=p~qgaF-ocC-rO{zMD
z{xa}+q+89<QRQ~c_+ZwkMUQ)G!(OL~#P2#<wb>@6+AP3K;LM9U!AV{GJY&p-nLsu(
zO=hWMwa0h9^+b0&O9QjVe*?!MS^FDku&&}f*?<^c{ls}FAeqw2W=5_4p`YaNQU2z>
z6+>u+upe(+u=eX_N_XsF+qAx{hagx7kFmtUdd=TH$s%&U;zDHDLocS@-8zpC-0I0m
zRD4IPEfw+6v?$FB-1-6p=nAVy8jQ1mO!sD7?-ghG8@kZx&005dl1J)ZNUfXLEC^O6
z;@IFv>|0eP?jE%kLVT3DpG+zB6dJ0rS1DU=0*$jalU?&&Rhp|I;{%Rkq!pv*JtCG_
zav;7MZMb3K7y5A?bx;=e(;MyWzUrm5v6dBIl#>P9r(x%5fd!gHO9<pWqc>yLuVCSG
zAg(K{aO?zvc%p)c8MC{=ff);qY=1g5xsgZTc@aPT+-x>&ts!MoT8bumKrI&HPD|kA
z+#4>*L6P5cB4rfLwr1>-)RF_2Rwpt<O7nV(x;=hnJXu(-yZt>Wkk?(|yw4f*;~Tpw
zmce_@_CPmDQ{rL3VCH#=x`EeyEa5BQ{q1Wv-I)U=C>>MbdpkhQ(N2N3Thitn%%m$J
z0fPf+pH0<A*^&8Kn}ksa%ch!%vnslBsd1zJ?R$|z+Eh2$kbfB`c&6#SmxD;H164K;
zXcFcZ?6Fs7(i97BaSxKzO!t2}#{Zccn^cXzs_Zwb8}@vfyfL`Ei>6FBY}8lElrG&U
z6!8H;-a~M>JmgCR^0O~mrahO2;tyIg8!OeFN6w9kBxzsbl)FRx5_#t^OLj&63#bCQ
z$4MV7{(V2)1f{tN>AUsR?b4Fr&vL>;<Dr(dOt*USMn|6);|k<6m_0rEl}E5&<TkTt
z6d5M=>495VY_{KL?Vs(d#PivIa+arYGCTgwhobZY^Y>LHk^%LO26ptQ=PlRAT4Lej
z_M~z?&qwbU6}+6h$X%_rTXI?la|9h5quo#S<Q#vI02B{_`7pH3lHkDF!!j!T<X)*G
zRmVBtRzG#XgY>MZW+5tpNX0>5-pPH~8Qss9Ga?rz!Xq^2{ethA=wB9g3MFE{Yn9Lj
zztFac(YM5w(<xpTn&+QsQ`1cv&r9q|f~tcDFj?>V-~wUBPlx*gh<Ph&*=CJJ)5I6)
z7Rj7CQL|#oA~H>zfi?KL!awOHgKqhG#;ouoKTM-tHEn<6wcg$phaVxgJ|zt=QXqq6
zZn(SGsX8=!$e<#MUDa?boYEcPms!{TXJb9kQB9AOwYRxMUb_*{?fZ&b)FpGaDQtLD
zL{)D0;TJW%rRKs6%~&rZ?~R8hjlaEgI{TI|AT)r{LxW0%stb7LaN_-eyfuLibr9B%
zf+{!e^F#mJYNMwX3aEx3$>HzpKneppH0aQ9=+K#r%NMlGc`mhTTjf)<0(2}vIqu@T
zm`bgnPNao;w5WDC<B#`@V=3$O@qw*x5R$!fJMX1xWI(f5{KC26Ew7)Zt5lC$H|8WO
zzz}b+!IA0`br0lbwoGq$ACHev42`=nr_wzHauP+*C{27QXu`;BjeHJ;J5}pQm|=B5
zssyxHIAGpi3EoZ<t;EFHOUb{;;d%&MC$F2DZ4*#VtK6uN_MiT)cGR66{+mPFPV0Gz
z->z3fQ_#oxw;IO&CdE;GOZzTC$tDx&l(p6x^4cfw`OvD4<?L`4h+W=}K^p=P6X;YB
zZtL)~qQoool5kFeG1-{_IjqK`(P)Y1+%JBk(O^#wU-HBoa4E`M-_%gKKhautM3oi(
zu&T8Yt|R)QE^^pYf?0bW`?0m8RK%_cPraRLrIlg4EIC6GfqRza^0f;ymbSgAm*3gB
zylJ!U^T0%K3D;S(Zu0rbod*m7JG!l@Fh9?>74kGL^I7I+JNxn69%DJ19_hwKZ^YJl
zvQK^<^TJ_}#(^^Ar7A~BK1O#glW5zd^9xaN`HZ-zw?r|RIQ;5SxcN00%=`MIrFoP{
zhxNe3CWgY<nq|fYD#C7_kBFiV{iZVpp|B3ugPg?~!|E~bqMTpi$DaD?T#@_T*!4*`
zqFhb6NqzBiXj9D^F2rOTjUE~sL+qxTtXSJ~Yf9Ws4@~|wjT5OrJbcv)3E|k%c-R!<
zp7en|!PtE=2grW<r*!^=jeu?mD!_m$YZfKXGv0yXl!c?QIuLH}%Plu`un`^C9Q`?K
zE)-&Q&Y^2JhRG0|Hj&FRZ2K(pdahbOO54$lr9G=?>Dv}HIMi_~@jbhzID0P=EvdUc
z;a=<a6g(jJYkV&VyR1$HY0UW1<DSiixg~OVH%kT|u=X&mOc4KNvUY-W=mA3`>_^6~
z@@tefxTllW-4$pylfzKRAArKlhNHxa(f%)oTO2u9Vbz3Sjw4C3oQR`$tII=|g(ZBr
z*T2LWNe0=sRZk0_RKHtTp7Y9*7i3B2R|g7irPM&l+QP%I<+eRD2z=L;a;v->L_Qjq
zzD|qq7WqDzGx9enIb)-bzYBcTjk~6Eky=uWhL4~miq@cF&m&DAN4sj&yA>vEL_};^
z`14ajd!9c_Vw?6GDWbO36g>!R=^Y|UD#tTIsS{2V^a-ta7k4EC5D1>H7Z-HLUCqS%
zB{tXw%$vW*2%l1izzT`Np=qw6u>&h8?axt~?c>A#r4+B$_*P`klZ9=(_13Pxl0c|?
zzj}~HzASK|hr&(4>7r+PQ<#}E*|wz(w}Co~McPi4RSGR#<XqClrwO>SdI}h%9T9$$
zV^kN9PG(aM!Z@y~-u3<%BAGYUS=>BmHZmA2<=qoG*vzXJh4Z|jGKb!gLMu!E%D4`L
z7cRB<M;FZP#S|5G|CLnI5%<zBad+yTcVfGHf$N|pZR@S3LS3(Wav>T~8N6slyEL0l
zQMC*<+M@F0y1TpICq-~T0GomzRB5p?a@PPMM2E5No3_1pGCRCD2BqDq?Y|J!T`@bt
zD}V?<9lgVe5K6c{{s{kq)e#3BPSg{jFJ$=b5PP3w{9@jK>C=^>1(kw|lT1-8Ki5PP
zjXmua-^P{1K|xkT{gV^(-IW&Tl6E?Z9wl^36(O_Y2L=7cYNn?ni&N;cbs5YS()5oK
z06j4+ZOo%#E9P6SUp{;)YrITg_W9Um(0_jb#ww9Jyz0C-GX`OWnMd!LL|<-WhQY4s
zYOU>nc$$rzb~bM8;UigvDlJhCc;6k#@AqAF0e0|ga8OMZ7_o9kL$$U)>Zy6^eEuGw
z1UlQtzy2IqUEux(ggGT-AGtuIt$C0<OfZPwzj+5hWe&FC)eR$v8oOf9x9!e3q$%(~
zv+;~m@^dJxk0GmHVq0JAg5cbq)^Q#r?l3E7I~2}+Y^MD1VMzqS4cEz+Y%Ti>2tvYM
zK0G$k4g|N#P5`4%?9*QD<eg~kvt#fyuKIHl8(~+fKSGGC{RCXMxgWr(-b;c|+Js);
z%oi;Mxz2A!5(;}d2E+f8d4*CpL|Kxr2=+;DTj=)X_u&jF)xX;oiK#lG8OM*DG0Yxc
zbJ$2EDcf;ug8sS3N+TMj$_M+G7bHOz3f|GxKCAMzvyJIS^{<RP|Fmy4W-TMig2;Si
zNX*Bo?>m8&>HvkC+}QXtGW}<xVSh)E@FmhJ8YHEXx?ng*FIEHg^dv+QV+9lbT~9WC
z>CxgZk1=_`sY*g1W}KeDaBPzc8nGopaAf{Huo0CpeU5CH3|<rGUvE&^&&b+8@nfbP
z^C}<WV<51bAC|g`8hLny`H(Z4f!?y{$2P0AUW9pLf6ixTz<fJ`Bmb3b2Ni@PwP8yz
z6a?<wgX07YVB(;nuPXX6DUQJW<8ch?WRP-9lNGyt@79h%4Ek|FAgZ)m`y9q3p?=Kt
zX`0f={lB7B|Jr$Fp<9)*a|%!Gc?1n{Ti;86{&i|<swLN6pScPUw(9^IILGT83Q~l+
zmS1P1?^S4xhM6Xv#}w(Re`V*_A9p-^_mT3~$n^`G=-M?W5Glu?f0akwPR%Qb_z1j4
zW~!9&C~c!#oUA>%dS|I8o+X1NaBEOE_xiBwl~YVh0Vc7sHsC#fz<c6q$jTsve%jL5
zDKro-=asxZB6PKY2%UV~#&l2}1-B!V?nrP(L*48C?4B%{+~_~W7K2W{P&QK9yOJGk
zKlN*qYxY5jg*_ost_J0!D@wmSv2rB(J800k_G`?MLmwL<=b5arqcA`XIte;GX&PHK
zviEu--R9T4;==si?U@a|i-Ou|#-fRnLKw(VxXE+kBv6!a7~*PK2KX=sc%JGj5{MaI
zNdx<B;=Pak>UIY0U7#!2Tp%Wh_}O&Fcg?Y)tLj1HkoEN8hKkVyRO>v@t=ZPCDQ9(R
zV?GG{(&2lp?GY5_s^!E7rWEl0317_V6@sr&p#@>z+L~oV6qJmNiKHP<T7Lh-rdT_z
z4S|8v0VgPNR?I}JMQz-j1Fra6OF8=1gmySnc+wdB%Oj|J!?)I+J08jYrI)t>W$qXq
z=)-eKG6KYvdkBUlj8bOz227Ljy&yhziaBj8HJ?_RGP<uPsYcfBFzjzH5BQv8hy_v%
zg7KBqSLX$hmDt;i;;>;aH`A-kJ9N41&)4$0A>J0iUw6e)e4ewB=f#Zk_>gX%sf{bH
z{<uTh;*tOeHo>#gpAyA#C@tg?_*rwNbA-s~)R@CyS@GaxjEL2F0n8TP(z=gj33isw
zKmm$W7bl|OIASLS8ZK}cgb@&!EBeq-zb<1z2Aoyd+h|5ZG$;t`Eer=~>hm)25JIdj
zy$c4fbq<rvK0X0Z{=j!$-yM0C?atcNU-UQ0Q=@h8T+04)!T<S)qKw-m{H&J_IlQUP
z9Jcp&<)l&@Y+3e_3&+8dCWiqqf--w>g+B!q<ej41kBx|C@gu0iyw@%unU5X&;+wv^
zbB}%b-GkNTO3TNf5aY^7^3qXuDBhXB3WZ%&W0a)VO2oAuqvCky0m-fY@Vd{_dNGXE
zC<nmY`BONXy0RApQ69LolXICYtT10)m-iynuQ;vOjB_e?W-3(`L#B7i+n5QF7X-6z
zPO{!^!vTRwY1%{LcCJ^%3ziSNGC=$#k<NC1lP!q^rgsvrvFC7Gr*`}ImD=}Y3<REN
zP|J`*U<x+;(;+CPG9-qUEch^lQD-E@nH|SQcp4A-DXii%&)t@vWSs&1bj`(rbcMEU
zyu0hj-yusH*m}c117Cq#OOSm003Qlv5xZms&4DB7{6nNClU+)BnBRW?{nUxGETKRp
z=z*a@IFg%kEATsgbLjZnf%^Gn;FL~|qPYPgDaH4N4Gu~<WkJZ|Qrrs`@}nn3;NKW@
zenZNTb|d)iZAXqA9CWPr1UkPaW5_0$F(f8l0J)_sJ9E9%Q3uq=>rMibdpqz(j*d)d
z2_c32nDoGTHg+_JpTPdRcDi+9s^M%vI2IcbvOMVLMbcUAy3Am}$<GT2Vz0u!Apwsz
z0BqKYw8xBKO-dLeVKIaQZ?lj6_dJ<mFIlmQ!>b9WV9NFy4b$OG?qt!2@ya4{{lkh2
z#plD_l7A(s=2yH)YrFF?BBTeWmBsuU)!OW?J$=}|e<)8pyzT_i7(6_rTCGaY;dZ!R
zrV?#n?FaRfQbpG&A~?EA^&%_ldjIo!@`#XKz!`M3Np>{2oMN{@-4FX!!++!3m(PYt
zmVJ~EAJLtm`_w>MUP-Vb@%^H5*23z24Wm(RnT-uAlSUs6HxCc0DjH;>EWq^F>F12d
zB1V=g+3Huk%;Jz{Z8cVY*=R(*>4Qu2F%Je}CrX=v5CY)|RP5@K9~zOD8IDd`9=37z
z7_K_5;eKr`NZ(pmB1>eKm~-$!JH8N|Ul`3Yr+9Xhk8&ux2diVRFXDd>&vluevx$Ju
z+pknox(HCSZK81<s@~JtnY#E@dY*cN&w1xK67y!To9{H*-v$EP@0+cscKuLZ?CT5f
z8gA+m^gP!8dJMZdtq~1fL=!&CE0J4WoJd}TP{d+FaX*q_uj|KS8AB2!?3|~W^wn$3
zPeIL*d+kp@WL?hziGw<~N%Zy@E|0OnGn*@14#>HrQH>i@L^7|vm8xUt?)QeqyV$m-
zfVo^*!{ren<q-bi-}~(;D6FK#{54T@a`D&lY1{#tl<049g>gqP&r2squAHoW#qo^{
zh#lgA`%v#*I2_&$0UIq30TqYK;W~vpWI=C5ZIn8CiyEay9a<Mo%Q}HhesjLD;zBa&
ziJf&R*i(7x^atB^Cz7sYFoMw=1kAm1wuig{n&!_vMoi8!|HTU=^*{kvMXAC$bmjM>
z#^t`sy`=t&w0;{i;7!}cUV1d-5M*UC%QXYB-D7IaH^&ugocQwm_ybII+5()Ppt702
zr7FK*hc)cE%IQe>C8<m7P8(gTyp{0%6q_on&*>qX!DoS_IZ+dOF<DepULU{ASjPvh
zGNk#QLT6h4EL&DlbMGmY8(Q-;f+J4`-UBxZEWbTZ8~8Bd>qrHyo3DLWyZ`dj3vPNn
z`kutd7(ixNv^cb$oK$yu`r{d`Co<Rk@zE?SV;)bq<8Ojoh7lwXP({XzT6i`_1kx6i
z>EO?3)LsP~w4mxPNT5{x9FlT<WgcF~x_(-)4cKK@5zbpry+#g4`Xst!?F>hIz1En&
zPkqG>Lu>I*Zxn;-=N_m9&KTR6nK8R^5tG1O9ph7!p;<{xEsq{@;a-ZaY%J&E?fZ8~
zV}5rA#uS1^Ag~kZ0?v(p-rOi`OU)>K&%YoA0Xoim)*8b(`?_si(0g+=JxebrqD8Ge
zz*m6I2ZH34Vn7PL#r6?P@Jr7S_^CkFoTHbn->XXmfO76~Mx$eVcgl_16Rjs>5*qX}
zBvg$Oh?%v$Drkz)!V~%5NeOKJYWIxHjrLkNATY!)ha(Kk?chM=XL@D<BHxD$F!;Mi
z&F2&pMyNC2D)**#iW|$$IgXI;zUDX7Y?>{z{6^n?-QR{P*leiOx3WRf<W^L<UdXa=
zo}TYmMMh!8o3CAaADoYVwNxIFiy0_*_n~|`Qt!|0(V`J)8<1|u(Tnf}N(F3;PuLo6
z-TyrH-i5lPK%>6vzeD@22Z3K}s{yJMuf9L<VtYolt>yb<GhMT|*cl{-wwlgldHbdO
z1t`%wf9=d_eAt?_lAasp=LUs$+!fIjxjp}G$#%?c2+&2>>4zY!1S8CK38WP`dG{q!
zeWEjmK9UoO`FRE0Z{V&GgH};@@8Ku6eoSsC+hCj%nthQ0^a%tOcSvd<#%A3uXMae_
zlC=dEwN7)+`~`MalJ(0vQR3Nf&+9*x)W_f+ge{$!-Tn5RnBX%w=-s-5n_F*ivj7FU
zY;BcX<D8VYxX7j!$p!H|Z>32-yvs_*vJiAEYjlfo<$ShQyKc%#O}jKb38KSpo3dM=
zUtZvtzntw9C0)+TDzoB1SoKr$doDeRVov15xzDEllypMmFY3Z9Q&4BCz-Ho?PdBk5
z%rlZ3*$K<zJrP{cwh=WA3H~PvKV!gUP7Y|`=2jZ4z-5(E+_ikj^vnuABQ7LbOBghe
z!g7=|K~GlL<CGBSN8wW%j*I#<n#3#)3ajyZXauy(+&&%y(Z4>JMIrraprde#6)pH^
zTC?7PYWVC~wTk*JdppfC!4dM2m>~6rk7BmN-~zh4;9jkF8L97KH`1&95I=8DXg~RE
zlXmXn=h?EVhKe_L8#k#`{@0_qk+pBir7*<%_^**sf1Mlc==!2F9hvFp=Vx=Bg%z+9
z7QrrZF}4A*831VaCk=xR%1NR2_LcTFDevW@OlhTV8z(U;tgv^Wb7N;UB6uXK09-2p
z8rMg!QyiiiE8fIK9PIuQ7|k?m1b?7}bziL+39?$;6(XL={yH7q_|Bktb4|M^9d^U@
z`dow-odA8O2KE+k#6^E$32c6R3!3AZjUf74nn#)ni$5hq!t7Ma{Dc~`{fY0L<gA64
zWj5Zfy$K9-enP>|^!{iZq;2C<L59`sS6Usdv<jY$W4Yn(I2WZ=2cA7!&3;N+yCu3V
zvF!A@7hx^+N!1t_48A{pqoQ;)qdWol1)x&PiIDl$HJq@0I%v1AfeUf+UP~1oGIsr{
zzRt^@8vKknkLd>RZmL&o|EM3cDYXO;`{&q$pFEtJJs;e%rDd<EbGnoQBMlmf`zu})
zrJ!)j#>Pgx_^XurF$#(-J>o^}^qX2>D-y#DDcX>ro&s&5C8=l0tqRUli%ia&3)l4!
zm4#3<$zlM1yd<xke&znx*?dG`egzPOcp_T50$%sx;O8|S%~Br?m+vk@07B5hUj`P2
z+#vZ)GJ1J=RaaCf_%o3YAeK#Lz9wlc3TtXq@g%<m<IIeTP})>OFgcEtMh>|;LX-9X
z!9O<CZEQ>`_;5UE_2=v0^<6}gY@hFhx1avl_0?uSjqzLo>0OI$pqbL=I-A(v!ys(7
z?^X9Uxc0V`vN#Oki-iJYlYZYuv1U-Im6oU8M*K3BS}iMs8`)Fvx%$f;L?`>*{lc`-
zXMh~y8TA6{>6Yex5~RxPWLpTVTdtd=CMPo#l<24HPhnpQAbu6E_;4k_`SRy9WR~Z<
z3vHX}?660rT>_98iC@!NHUtpCt_&wtftcD3d~{$V$%cs$h+$MWH5ol7h6Rt-ucOFQ
zlXqx-eu)d)_cRISpfv4|f&(SAHp;KIPC@EK>pYIdN93gXh<){+H-)r^SB~OXPFLw|
zcK)8$Dx}Bdu24Cfnf;YZ>|8EIU?pvA;vU4y07~KQyLazK28p8tu(zwPm2Q1mXxgK^
z^`WgzFGWLZ2w!kjNA!>wL&K4qTL$%LP3KUR81&W`up{`%+C{K3US5rn`&U<jo4yu;
z?t+4zrahrcr!W))-oCRq7NIPVbku>D&B<QaW@VdUC(uQQsEZ@M+pCkUlk8E-9Y?Ii
z0M{zh-ln`Ks_4iVWRp}Zj-gO~{rn!$cmTo`FONMG+9gUTYN1dIe3<Ia!;Fnn=zO!Z
zV0JhKR4qXY6pq-L0uqJTl20Jb6y*W)XKo$wz+4K?1<5nYD5u|HDISm3d2UkR8z^X*
zObMPIT<ponDDNprFI2=lZj01B<JZ|eQnGF$X<B-?Tv$ST>!F3D#s!<6hk3yk9d0#`
z!?KusfV46t%<J`~&J}Aw)1nKSLw%^E$1N!e&POgE#0n}efIBLgp|O=Z6>92A!2nli
z0!MvTV{k_ZdE-ufMTN7?&b2SE$Bz~WA|C|}Wt{iw(BSl-Y`yrPya1BE3ij@?Xx7_m
zM%JY1=%#xyJc_GY!pLAV4me{0+tzs#3!uyWe1}VG8+cxB@QJ3ctnw5X>x#8Kk1C$f
z*7S<Ubt^7f@mvRa6U@%G#@G(r6H&|Xr+H`R+XR--NO2(TuTKWhzO1v`C^oeP6(7M)
zw$s0rr+xYvw~QbHg{gmhveaFtrH7%>nK-fbGVxOzQwAL&Fn=)!AOI=gX$mTtC-V*I
zEp_KKH8pGXBXn>-@{C6oWb$;}*`E{RrgAwJXg!`o65WQJAeA`T_ZRnD8HQy^o>>*g
zWgx_2a;>v0BbWIppbxPaRteDna3OVg=DbBgXZ1@SD_dJ3W~iW`V5BLOm8TK|+l%b!
zlMh$HY;6lF_y*CEJB8v1krmFyBcJ7ZNYT0c0mRarw<1*=aW7rf=E|1b{E4trt|SQt
zO$j~rp@%2q^n_8Xcw>E5pNn|<B}98J&aNrq_%f{=PC&?ATpF#f{_x?Bw6t_?=+*|M
z7b@_fr4I(8lcs|PQu7C}IB+>HaI8t9lQYl4U>fL+)Gpqp7do1@1&;NH%QGYnNO#|G
zB<$O{zRsDDLi=5B=?VWs0I;Dqj8pHo_x2^c2V|XoIqmqO@nU4y7-S|*aGjXObkL_|
zUo+v!8#b5KvY9?owtU{1>Q$4pnALpl77MvOU8EW9E|L-0KLvs9_-UE^2?~X6D4U5D
zvy_1CBoxd|*<=A1{5>RsBOn+pkgveFM`w21r<whMGp`?U@bK`Us9>w?_O9e8^<5^u
z6$x&ruWJ%mrGbqs-}96IVZDB-z(Ia_-M8uCs=K}$bNw3E4zg9}S0yso#-8}QM8(OQ
zH1d>F&!dTXU96dnOr#a0=KH|2CM?!jb(<>y>)qUbNZB;nsfhsL#15dN6XShz6CoHG
ze46o`^qGw#D`0y^8ffhBBQZyZe)9$T(`n!wa(j2~TU?Q?Q+*ydlRo2>Y$D}qa4xrd
z_51hlzcN*2Pd}Law9fH{(ck9{po;iRsyVa7vigtAr&C^Bj^%xv0oZzU5@3~Sg*fxD
zEZ*qHTZ5zp&>De*E_!rizm-7qUs1FY1Bm0FH^$Q`b*HubQP%Yxmth24Z^Lk1qJ8%T
zf8qe|o1YMFx~#d!7hcYp1OR|o-MCR@Q{%C*dNx|_T%FvFfSuXA#ns8q7&L!QI27LA
z;z}ySvAk5`#}aOP6f|EUhli#m>@S@G`ymvh{W~MnJ|Tc_2Qp=wLU*XF!9|C$I%3^=
z_6wlq6oglPjCl!!+b~m!zl$KSjyph`=Yde#7%N%(hiuC}*CilhB-VH?`vRo$ik7Dw
z=(twiny|)3ET!SIi!rlo>CkbN`+GY+LAxuT4L6&9y?py%I0lt1^ch+G8<hK&2RFtr
zlaMbK(@{&}1a)}FnUY)5t_pxPyUa9wL3nO~DcqNid*ozDo&xy7Ol;KHNxRy6SWd~m
zEB1<MhTwGWK?8v`>JWYuR3h$35OA)xMMF|#ZJt7y$B%;yQH($F)Ti@RFCv)K`#ivL
zQhm%(2Y(%SAE!N%8c=;93bHqDKbH3b;(9qLP+01nXt!j%@y!i~E4m<G+V`cPw5dBa
z;V+;YHLdr9PNG$e9!)|-IRV)59|k5iyaA=1be_`xJnD|baM+T~N@7jJCq(eNsU8Az
z(yIFz8bhp6$^Ua9*(7fMq>UJWD@%3<^MxVs4nJYPXFDTrJSThA@2@Y{Am~_3%Vl6A
z60?;5(cZQHL!It%O~V*9nVJcqVIt{*X&Tgo9YVXYObn;ixYbcq*p0R^rDohK#HJK+
zW^zwim&_JwT+X&mN>iv@YD$hKjHXbL+WmZ;myXL{aL&v8Fh6}?pYQj1p3mp=JkR_6
zxon1`r6KGp;Zn5tE}tUA_cxlohoZM8vS&-VIgPC~JZbJ-=p3V`t4s4OC8aU43~>*7
zvoS&D7SPgE4#%*>%|{&I+6D|zphF*z(T)1@uQ+<;n2}PXd9IX~+U!1>RM4c6&Fw3y
zvsQ<&yMnW>=k6Rft#YV51$Bfm<5Wy@{H4pIzou8%1bOWB7<3L&D~xO{Ya0d1s;x3L
zacTbBH(L(fKCp~m0+SJbGqy+%g~iJ5Rg<*O3X)6eV<5$6p=p?4h*cvbKD2^Lq|r?p
zoUk2T70D)?+TNU>VFo-_Ss6dBNWS`-t?yjun30NUyI$@w1#YCRgDGfz+ZF(w`_5RN
zjK@G;%d(^8CcZGYgYExZ3%45g^gKJ7HQb=H_doSo;cV=BQ6aE$5<%>0Jd9lz3t+~1
z9=$q)My+c=E30*OcCrm@ay^Dsyh2=UMsp)L0?U)ma)_P9_9=bBX)P<-P~&ZFC7}ej
z2W1z1M(x`QCx=JLU_m$Dt^rO63XxH|5|Bo)yTGq2nsikOq`1dA`(iP!u}4U<P7NA6
zQ}NutKxFoIQ7PRu2D|FF{4d93z&LDdFOrMz0SlM;i|D40N7_FW0yPg2<h~=yctr(i
zV3`A#>_C>?`)ml!viQg{*CBnhR@V-u{Op53U_Un8_|!9YMEIPc{&~Rj!{g@#rfP**
zZQprj5+Dh&9$Hm$DG=S>2A4<H-llVKQ?&yqOR)KJ=z#-IIyy3d+8P<8Vs;ucBcSW^
zK<ZXfnR|f?&gM{7%DMDPR_*4-XAylvIITl2@Yb%JynEZq7N|n=fW5#|$!cUTbB7rI
z8K<3x#F~-U1kF>h6hIB}%)PecDEdM;??r<-(1*#$0cdQbKU(1z6m&x27qz=^zuERz
zwR(LUhgU;tvaI6)+a)rbEfao<`}_S92SwTMJyq2ohUjgJ|4$**uBuiXKUr0VyL9}{
zCD(3qD)hrN4U-hOdS?vhD~4aUIC3O;o+e^blA&x<=TCjzIIYj)ADlQe!0XmMe-)Li
zo<*oy9rXB1q=HT$T}J~rAW4H`ocp^0D2s4gL(v=)cL1(f325(L0aGXs)fmZFAen0N
z4)bcx+e_=;`3*GWq?^eO0_hAOxtY5B#M&$zVSEY3`Y_qAMNH7ndL{J~R#C~2Zt^|2
zDVng^1`fX|3{jZSl9id684b8}NT+!j&^;fl6pJ-ud7Pi#psR>Rvhha|S4)}S^w-7I
zUPpPWK_r?YEK1?mmBSwa2}lR|a))*jgd-aXEZxS^+(Ju-;X|!yiRL3vM&9ss+=;Q<
z%CQ(tQ?^#3O(E{|@<VRG40^bH(@%IJI;vJ&3bDiVZLm%4u@e;Xn&&TfI%Xr2s$>y@
z&$9*xHrWau#d+o=PJt9<*LcqYU*A48MwFU`?eO*0>)JrBq|`M);})*$3#KZZkL6R9
z#|cMqFW$SjUq&GQ3nuqP$#8zPyHD2~uh|&#*(kkVN>gtc4vo7rfwJcs0x;&bpQ(Yk
zGJ&NRx6r-h56G%BIZLtqH~q-_E8bP%z?}x7qa3$W$jm&RO=MeH`Yt#mG6R|_<S!xS
zn-E@d`9KWjk4jw#;L~3T^OE*MbU6T1t5IBoe7r5h@HtBY7F8|Y{nUU!B+A^B)$xGa
z*GIc$Tu4eJr?`PIiep3Bz5rAaG@!MN?BQcxtkIbu5|D~Zcdl+}2UAXgeH6slP=(3J
zn1TJ-lI{gXJNCKwu%j4fkkMjsjYiuv1J^i1go<{gK*_dgg2}c(wkP3PV*}t>mPj(~
zpUSN_Q(-$`Ej9|&(+)=BMf7FvMn-72CE#J&`x}ZsYFlL;Zzp$LRA0RuoA$eMpBg~y
z?28ad9f;I5%k(;34tWzhP~4$>sDq4ZE!yejrIY9)`E3(XZdJ59*J)Mw>-w{6|2FVo
ztKRaX<uuQEJD2e@lxw!+>=i#??>t=c*ut8@yq@p+!v*}e-T>?jY}!67#r#?!x;n)8
z^NUPMzPAC<*kIyw-(d@pcEAL^&3&04>L6LEqMgb#M1}}8zID22sg`lE8S$Ey`hwk_
zr#Y;8!>m>H(JI<0-semOVma5CJ;V$CWyn^!<wl+Yd;<!dWiXRZP{YcSx^n%ap5gbg
zq8&-dq^kH_L?*mZMJ<Pzs_-MOd=!+)NTt;II&mZ-&w?62@u1T;G=3BV!`r-|Cl(8~
z1Xt%FSRxXMUBh{h&FRLrRM35#A(ID2`0>n>8u}7aPf_WW{V0-ZB9&^}Kh`+kx?k6S
z29>}BN(!Ci0)mQ*i+ka#RQt4%n3b3I&$KkUdw(~<OV*d{mQFmgrD;TRP<+d)e?1ur
zj;RyENwQ;f?xTQ3biM8-jj9ma&!9PC)c7f>R8-Kbs{dmYZ!ldDKbVg6Td%vhsKi$s
zl5XY;p+ne^@ynUu+JRDV-DMt>U}5Pzu;++-@e7pNLgKpqqfo?vlGLOTS6!w8Sv?5M
zuOt7~1f;K+m$VI3(Ud}#9ELB@IwbjEz#psTopxsOYLOJ6VF%uVh))k6wnF{~pk)93
z<43{fr#vrW)3j7PSoTN+esywkhse*V&(A}vLCg#h-2-PEcxuWYyaF#YGx^?nS{@?2
z$rwmMCO#$IVEXg=5=}7ETi0kK_EWwG`7!~*|JgEpph(XY`C!bx5a2L2H;BpU`u|&r
zoCN^BB;BI!UxDb#up{rd9=D~Hb!|SXVtL_wj|oK#OjQacdbRE#E}hik1hm!(Jf8xb
z!8CPILXCoMkD9KVo~n+3#7I*Il)uKLfUgSv4lX+Hymy?OB`Fm&5?#M;<_lDN;6&R~
zT{=CR77@1sbXzrM=S<_lH^QB)@v#5uS(5DGTNkTV%oqVcN=S2-H}!6_Bqe|`tibP?
zX>AOBUl^P;HgA?BJe>FJ_3uth?Nr-kAc+XK7C6n81pUqu80TkC-`6nP8S}(2WF*Wj
z#N<lNEySdwn%F_+EMls)%(<qi{%OuNO*#$4WajpWuN(5*W<AwufXV!CZPvAaV(OAg
Ula&KIDwW{(or8;giJf26Ulw}XcK`qY

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/packages/gluon/normalization/imgs/NCHW_IN.png b/docs/python_docs/python/tutorials/packages/gluon/normalization/imgs/NCHW_IN.png
new file mode 100644
index 0000000000000000000000000000000000000000..02a725003a6cd567e8b0aba6f84afc6c0897422c
GIT binary patch
literal 46218
zcmeFZXEfaH`UZ;V5;JNVgA_!G-Unk4M2{MxOoZqpQATg0Bw88<!4MLi=+TXYB#7S2
z7^1h)%P8k(zvsX9yWjo!d^l_E4-qV$a@Xs=ulw;*Pe<(%?KN5w5|T?A>M9RNNXQvT
zNXR0oDS__{Gxvai|43gvP=k{c|7KePzMyeefB1rg<TB&=KhmceeZa54NHkQG43VU(
zlRh7AEm`0Wt%d@21H8LE_CKUtowSL)rFn10H^ljZ5UEphaC3)ml0=<=WjT6ytj)f~
z?lLi+VwK`p)Y@5~nBq^ROpFXUmNPT@jrcW(^@7hE1+}&7`JE>LAGh-2Uj52{)%hzX
zW|m28)F_$d>gG4O8)CDE^V?8F8;XRCnlt!6K4gP)Cu^R~PF}ch!Ro_fOFJbJQgSft
zZy!Qv=`P-Z<qkS-l$4UD#{I`HpRaX?obo|utH3|k<qQsHxZw`|$8Ma#Fg{W^%22EG
zzmI~VX1nlTtGvD+4CZ2C665;MQv=I#k+uKVS*WuaIaSoZf7AM}bzo7=BLB4nSspnx
z!vhf!rvH)(_=fw%{~i5*Qvau6{Lf&>{^zFui!lESj{i$Z|I4QTpJA9Q_a!d>EBD@J
zv=_g&Vs20{I25#S`R}-F#4;F+@Z&grMM8F)8J(V9wUF-jo0$`OwD|G%1YDBl-ykkz
ziW+=lr!67ph8|5wLIR85%u60gmzCz`;Mlmhv8e{XwZ-0#lG4&O4(T@(z__WOKQ;WO
zL#V1IVlG=ETbPa66a%5PE;2WRc#E39zF1gY9xRX)5kY_y4y^A~i#dOkTyG?<m&^O@
zRZRRoJoNEf&ikCz)Uc%O<|qRK&6`YH)lZai1Y{z-<C6paxa^?T_R^2qyNA{mEG^8J
zg&H&MHe`Eh!zT>NFA>Xh#hrW3j{E9HK$BA-0T3y9A02`I{nRTN1{fy1d3ia1=@Z7<
z-k!I1>2z(pdhPgdd+%#^!0*JQBy(taL*)GY{OV>qNA*HRP-6p+m(O}>L$&K<nbUBw
z%hw&>2FTZMbvn*6VnMt)!FvsHm)YrT)3B0`a#p@eIWE-_^<KXm_2<{jwv-Qs@<&~o
zY~@e9GYZC&LBnf8u9Hg<iJn(|8ydqumBgG4f4){_=+;o=Yq~x0Xvy=XsR95LN*hSH
zuzL!}ETrTQXeWZsPWEbt8}8ONY{t69G$yb?AaP`<%U1p(a}TQ0wUtz+__&T(jf<Gj
zU|6&BvuC|`Eo#Xr$ShcpUcXl(n!1WBicP#dEqqKz#2OnE_Z~p*!><CkJfw(%nxU!+
zFGS7RlNnqqwu)=Q(X-1k3nJ|7+PsLd$9;VdB6F6y`UfnONwEbblH-^?I|%`n5IFVX
zE@lZEPN$F7i-H^cfKz|45>OGTd+@`rZJ2et+sbi-Zkt6zyY@yq91P=hB5hOoVfJB{
zOOK0mH@*?M`IwnAmnEcD&i=vnda9J$J*nLUWBv>BlA@xIiF<3&ZD2iqO6_GyVL7?$
zu|gQ)@(_5V&wbd!TeZ6%C+;ABBx$%UC?J4N{J2TS?5L%!UDWmdz+|Q7`P{2K0a|`T
z`q(-5KR@2s6TW|2?VYUKXdF)u*c;95uEV}9N}OE&I%es)8<q6T-ydq0(e@@vH$^RC
zEW)NePIXdcAQp~0a;&H5;>b{&YkW3)Z(tBU{*iF6+sMbLx8J5kt>bk!zBTfr?TegO
zHU4`|E69ex6JL+c6=_5D99OuFjZN=quSlT=Q4nRqqnjRcnl4Zj=MdYDS;&xiVb_|m
zTNB5?PUk!6a3~w$$@VRtv`BfS&q=GJ*GXma{f?}H-`;A3?Q&(YjcLhBl2xG3_$FKu
z`o%}By`sv(w$I75qt9vi+e@b2+SzUmgW?0IZ?UDaFUOlNmQ-Vw4e2{aJzfsSZ>Je7
zg`OkEET^delZ)QAQ+yuO)fTXLJvNQ-#-Y3$CuvY}T216CXquGE^z;5?K2<o)<39zm
zl=QQ0S*<WooOcUw8#O6!@|kcRUFmM}?<Jo2sUAHSQ#{2lT=%j@7Ck;O&e#a5^>azq
zB}$W?ZRA9p;62Mt<lSp#wm0_UQ>eJRPLuj2h)$7G#dZCnk$q>IyAda^N;c@qr;3{W
zmgSe{0y{glyDZn`m9G!)(k)!yTS>aUC$o><ccljTCO59BbTyu26Dw*&doFVb`xSL>
zr<!y%4%bN>x1~znb^C0JH@PdhYI3qCBQzdUJ%t=e+NH-);evF#xFuf0ooVUMhA&8@
zZUyX@r$+{PjKxl_e}+6H#)))p>jrQ*PAabB+cJo!Mouq@+@1SIU8hf_EcXIg&*tk#
zHy*i+Fm<^EIA@H;AC+-@n5daDWjFxbkX8T|r8k|X*30mtE|`xw#gL1dG<1FCiRA@Q
zghqmwk)0vptN-D1Lw%pc%*d>F0%_BcbM}Eog@&ZG@A$4F`ku20e=`@fPtHGBd|H-O
zLi?>>SJB1sPNoKnqx>fyE1$W9+$}~k1C%E!<C=gji&uh4!ly`%z?wM?1X}Ufa873&
zQmbU2##b_ww`M5pzC4g8?6)K3QZlbUcOSPCap%2uP(O4`$EeNl>H{b6WCg_NwV_Yn
zUgR^jiYe=2+N8aBzBgJYOg(6_vB;E)jS5|6=_Qd#arewn@&-$fLZ_yJtO8ChnWdV6
z!1xV=HMa(Zof<x2Ukk@WvqV?gk24`*aYpi=X76?Mi{xvpxJlf}?R(+Tzlod<9**)t
z@A&qwaMgcmeRlm<_Asi--1d?|%JXm^?4E->9zDFGZ1H9%rN{sIfUj-VFdAY2JNR)k
zUL`(qck4xYb7cL@X-!kW(Byvnv5U_be=*fb=ks3TLHFTWb%W?}=Lae}x7}Gg>mt#w
zi!8rO0_qVQO_f#kRP~~=3oNT3q&<POgrsSlie+HCX=5qY$WBmE>rzQy1Q_pEUv~1@
zi9<r)_8^xdT^~Iie=^z?aniTab$awhj%8HmSK;pRl_Nh(ZlkK!Y~RCjNY-(FB<;q8
zRr#>Q3&&Uof{U(r)*8pA4`j1l=%{0Xsj(}A@P%Dr&Q5W&CrvbbZfQZ$uhfpCwzm}L
zJr43E*{oDE@4g)L8?be`bmx&Eg5JIU>`1cOfX?C1<3aT^E$!DkEtv{>sHnm(DjvQv
zQz(=-3DH4gp(cq#*bCuuJQF51(*8rZd<CL&Yx?d<0m*_YD6HbQzEE+S%fW@9RX6`n
z2oAqg#i5CP#MfgLl8_lX@D1i5jxq}BKlflf4^upz7rzS%D9_bknLWt9AH~O?e<hbO
z+fBx(MPGGBl|xa9<+4%A&f5G^|L2cxa3ddM3R9Mp@~7R!LU#@Acl?d+8FJ}SSg~9?
z8OZ(>e>&y(AlWm4qDV7=y2zY`+DM_IQBU7(_mR<*p`Fov!=HUpspHywVO6>;ew8&n
zUb$JhqKk+5Ba7D!5^j&);4<7Yyffgn<bxn$WdXhHr~9qHbgq09uu}M1nQ}~_$RI%u
zI1IYVh^R^Av&3KQ`190@%<(YGM!kfNeR#}jOii}9@@z0vg)0;?-1xwWb*=hI6qpao
z=i{Fd;Wkm}AB)KHT)aY)V8_m%VukzG&O_lSI|MJFDry?co^yb(xBJG$AsVWxQw_Nr
zB0Qk!yEbCZ8CB*Xk(TkYfmYio!_H<}DbJTAGWS#5J6BGW+UqUL5q>O`ziyq%4jkDQ
z58vqjO_o0$lvI83fWEst&PQ;os#^TusgUGqyQL*@=*YnGjZp!)cGJdeFCT1VC9;iY
z%CdYm-}5MYh(_1<`%aC?1GVF5g6^5EVEbwshZXVBVe`s{Vc%|-5BhX3FC}1qsnU&L
zL^`yY$9genoPY3W=-3KLAp^OATBtOzlV8C31s0GXMxu!)Po4Gz)2%Ytb`wEU*YJ1F
z=Fje)$!ndBEf@rE`GP=!qa{`D`)De|Q!}DGZqIG!)oyo&6wysra<$7(swr`O%jc{`
z-tMf2e-XEl`8|naVgk!m$a8!wW0g)u{`GjLRf3p*6QWYvx^3BV<^>W{SSzMAKi&SA
zI{dkGKGyO#M}0MG<=vjW+dQ6k^cMk3@8X8a_;_X_aMR<NoJ!+cE{!;rW!`K(%;vj=
z&eJzZRxRIn{5k4hEI>uR-e12N4Es%Sg^6oHE^3BLk2!=V!@o~qH&0hp>ZF)9j!!JV
z2LfGus`N#p>!q^oZ9`GRmp%SRReJCEs!}|xw%Zr_Pu%HPt{G8#%#_UWGV6_CaGnMf
zTn|P1$^diJF&w4o(6`lKd8VE3?ktt3MawVl;OCuU#lmgmxC8ylOWl+0S+I5G!+<De
zaA$0|Q8FK#SC)7G&Bd8_iA>n5^2J12C2ZA_PpuG>wr}8w@u<kI35;l!V851B+P&jP
zU50gn=Ul|Ya#!0li%E1@W)7Z}kZ3A>;AX$c<=^k?adB}s>rTb5m+RtZy|n|P>P3Mb
z6L~S_+L`d;*+(TW*FGVWnZ+GPH`j)oRNj%PR;H`#IQ+U@cOX)-Mn5;aB7EpeQ*1V@
zQ?l^HC*S|fuU><A78u_YFL5E{j@!_gcgp0YKU-U4<31`Ld52X;zTHpZ!5GEW-G%ze
zrmo=^gO2$0&6Kppev;d?mv_*vYCF4uFOH6EoGe&>AI#zpdN**Z+G`RH)@{ZHa<;}V
zPWBb6S?&Cjn&!>|a6<-`qr*pWz7HFJnCq^(4K)t-!IN4Cc4jR%je{``JqrQx-bKjq
zvG(Wy>7lY4DO`KqxUU^q#+NQnE@wcSstQLkq>l5AL5h8kaQo?bqr=D!3JU6!f!IF_
zRts}GxU)`kyW=FM)TCV(NW}4`jRbM3T*9wJvD#>Qt7wX1*8KPriPF2OlY*2YxxK^o
z5nZTw>C`X8kNvb-Clqa)=9d5t`Q#ul48u%@m#};li?|7V9z>R(y<%Pmn1oS4bndTm
zJ_$K(SuTU`YW>E7R8?jXeAj>sO`EV?G(WktsTUz`Yy4e}3-znO?Ot{2PS+=HqsMSw
z#tcP6*4;Iy=aXd`5tN$?uDf-6%DKW-DLevVUmK0Lw@ib`v71+dw0R%VCE$Bzc;4Tg
zh&PF1k>nlde>TjgM<-}y7Oo*12%i%uv?<6)(JhJ;&-09pn=9fr>Qk;xcVBH^FyhbA
z8zPLoJZngM{yn35(v)h8Mv1%RWsMoJDT4sR_n2|l<A2d^$kbN}7&)G8mV4F}OK!=d
zE$b4aRc#ouEsl+CjST`SrF(W1O0?Xhmd3p;LiH0?sw?ar2)0zi=}nW(FY}|$Mx2J`
zi6*B-r=OozN%o<chf{5dZL_JGUKvz0*WBR!JPj1;&t+FaG^yF&292*b)@Ir}8kGe=
z$n(r!ei}3uD?E7_1QbTiWKLFLEXWJIvSieMlsQjh)9+`~#;0f$Xq1JL8{M5rxMpMz
zzpacj%ZFoVT|VPZ!+&*qvCLOIaNyTM_~eTRtiQ;Ju619$-cMKyJn%dCYH)SdcDR1r
z$O|j#O)Q^unOr+Up6-!3(qLT0$IiG&#vD18cC(GCmgzAqAc+jVIkSjBkf3=*oBQgL
z=ixUdU1vU&Wqb<!pxPlq5~5c?nV?-1CjL9jOr#=ZMvx!=U0x=`l8F`!KYSOo5ZFjz
z7GU7n*BZ`IGfeep$E|yC5^t5}bRaeDWW!dE=-y-5DXQ9QRfq_tWbvQkANbhVSFT?j
zQ8Yhwx@l}1csDV8lKr|EX7H^crS`Q2d>6;)M66Cy!9aEhZ*{6TGP@RP-;8Y{hDV&V
zn45JMiCh)Wo8Xa@)QN@+D|-7ROGsj0Of|HO=N2hWby$@>)D)@?w!InL>;*JB$Cn6F
z<lse9SOP<UK|XCIpKNV7ScxT6$RAI)f`jux^={u|Askc}trga%CSbgV3Ve(LC3cOs
zT`0Jhy?PwS@?tiBJ^oBzG}K}99jvFxO5v2UbEF@Y-AJSzHPd#K$K0<9m7<`k<){_S
z4cDqy)sas3h`HU~7bPU;;S{Af^mF4Ho1v|~CV4nbQQ~zWgX59-5+T$z#MA-qprM8g
zx1hbyge@UCR-RIYT_BwFsYb(tHxwpIgOIviZHy+2Bd(ffEn=R;q^^6`*G6=4ubrGJ
zJSi53viaHVAE;VzefN5Yy`Y_<Yav;cGKII0@=BQsvg$g02YPj&!4b8&P@j}asidV-
zyBx+Hdo%McQ_-g{cF9jux2sYj+s}(pFMvN8>wRoV4z@RkO<p%l)+->F02$jVvA6`-
z<o}AfXBi)KfuqR`F2`@6YKd3*EO>S7p-F*{#HWyyH!I_VbYJ*+zbzQ$=-E!>Ke!U~
zXuQ>soz%0PhBVY{H`ga9*L1~ASAKVZ`{Id3%Lgn!yB{rsnWt?JQ|o5-!i3*OLu%sS
z8Wpp3uIIOCp~fo~ab<(D%E>#8nEf#1BEv5x%WlLJL0U0dh;Yx%lduv<(@Gbu0L}mf
z=GqYzPs^+*dzFLteKL;3`!k5QX_SKrX9outaDTcEBOCaGKgAy<Ty2~&m~_w#s*;fm
z8)JDyz}x7!^k2hkZM)@HpVbXZIBoFhQT3B@JjJU)gozyfuYa7iw%<FT;J|UpO!f<;
z7Ngx;>mn9qernhbWp{{*p0ik!;E~BXbE*5yqCiL-Afp9|-{IAxxW^J992i1*6ir2)
zB4qes$4}0{{;0%u|D#ZOqsXOz7KvAZ1@pt1H<-2Fj$fv%H0WP9w&A|Dun_1&4rIBK
zNMvwnKWoGna^`_>RvNQ=Tlr-85d$amTHBowhk&(X3tI!3ir>az;Sr(^$@r0YAw|m}
z!cp8=8m@y2^Nc$1;FOd|oRU@Qce8M@*mT=nR10`Sp<T<@T75(k!UgNG=@L+CTKBD$
zGCn+=b)i%lc!jO6?Zh)xn<vyZ%=o_pfe%%u$dr=0k4c+ml%CfPE2F^IE3OsloBX1-
z;et-j-W2}p?wl%%5jQ#j)6pi&Cs{P_)kDzFlJnl8R#G}h6oEcWNJT0x5o!V>-0lo2
z)*7i}VDNW*8~LTT7Br^f?bf#x^M&&`YKL;{7Jq8o9CjM7jJ@V8s<8BhpuMA$^eRR%
z9=H!UWo_m9A+kT<WNP^rIii5&%3@{CaBAc3@BkmGXGP6|O+0TLHT3R7#a+SAsqaE1
z*td&9=@RaEJbSQ`hSX$hh#0`a=k#+~e@%6jeJMPp?nLHP4Kh<+i?M3dPYSs>Nu^MA
zNX9<3hH%j^sb7KD=QS3a66DNL3`Ii?{Wg)u<0o#=D#@hcM8cD3cUY49K(-HVB?&&T
zvwvLns)#b~5uumTQ=_Az|7<NxAU`DJ?jZVuhf&nNPjJ@KnSYDWN}b2kufFUjrAa0s
zZ6X|tgq74ev(!`|){}(%rda&l^Oe0Ds3Oq8lzJCgxa14(d*s$Qp*(lr(7$WD-!uIT
zf88(OX5Re>K20ub>g|XOe5#QVpTc%>w~7Uq;ga5M)sv!K?VP(z-kUSu?Y~}g*7-Q*
z<ii)uccZ9Sf+G=El;MaiTC+F0+V7Pg1ISx5@>lL%c3J~063q94TBX*AJF(x2l)@@r
z@$?`uyeH@4Ad6CaKHt`IEoL|>&Mn-1^Ot8q_#(1*6DYkvEZcwTeJ?pGRQmS7Fq<MQ
zJJ))(E$&qL20r(3SYPqX)6**HFz)~Pj_2dLj@kZ#;@M%YmSgxw(THiKk~?Pe6@C2k
zHn@F{n~pAoAKA&p823)0HwPjciG;}0OHGYDXY(V2pq&(Cdt*evkF&k_WAg*6rh%x?
zuvts4Q>iJVY0Z=6mfEq|dZ{^+;qVgKRTGNM`#T#%X*&Fax04V04#=Cl8xRZiPR*Br
zA~k;9<zVi*{QGTwDL@bP+uJ2Yqe+L-pp-!M?V~wn#pRavj~u(MERjR=@(bI6W9?GN
zUsR6AG$6`8uW_V%nO)GnR_p0ubnlGR$@Yc)q19@6ai6}9%_PO4QX$0=f~K8s61~-#
z)xpu9Bi#+FI{*1kJlp3ic25Wi7o5r-N<m64Nq5T}oZDA?zq~QoB<B`{KKB8bPdyV<
z-KcNW<Y2^RW+ctb43vkoHI1r9bN4&qsV*PY@y#v#PK)N7&&&y%r{nrI|JHk-!KM0F
z=4;Gg_WgH!#=uVmWX^o{ne*76^mZHhWZW_RWrn7LHUVqQ>)bE$Y;SdRv4)z(_r#)v
zgI%nsI2WAG0lI+Qz`Jc`__9N{e%$Pp-8rHuX%LRbHm*@xT->fC8id912|EpbS1Y-k
zpkeY%uQ}6QM9WwAwT5xYWT|D~p7Hb~mE+!L1<Mw-`p^RN8ue*FPJuu8YpurW$shBE
zH-?{W1ZV}v`*llS63*4+Q5ZSCXVJACbo8du?Kp$rwlo+3vk?sptG?Dl5?gZiBh78D
zbxZcd$Fa~Z1DQH{#v0CP7qo6WtS!iM`qm1rZvvx!cQ^H5!A*h)aeIjgxfvw$CbPi>
zw-<IdV(wc|ATBxOBqfr!7WL%0?7RhBVcC|r-t9+sF^DFBvr55p_aHB_K@(Lc_OC$2
zn0pwx2RizBLaeKVC58by%4X5afwKe{xf&wW#qzK@ByjD_SyK4c*Zhh*BRypjZd2SY
zkwC}K@Nj?(QMdNf{P2#h8#(q_K~D7xtwnQx3U}I>KJgRK3QfuF1|7r9jfHH{JFs3|
z7r(K6RA6GVr#CldU(q6kP#+`~G@d%Pa{UBA<)~g4N>TLQ{8{Z*NSmIzPWhBPtcX;+
zVJNBp_aOUF`ruTPr_fonWng76^2c!Xpb<lbp{F@=%pCG)hxMY`v5n3JA&A?TOKS<%
zD_<)WEYcpg6%C9j<EhSP9(r7WSq#_cGU#8-f+(||H;sj;5n^D)>dY6LdvWa{%U=Yz
z5wo@RcfNJcs;lR`0s!idh7>%~^u4c`mA`*nUB>X&xWykc&L9{67hS6OzXnJan}Ul=
zG0!dy+*a3a9b^e9@ZAY&(j)t8vwTjVHy7T}DS6|ZNP2%CmKkEkl}i^^{0Gw!Isa@m
z{y}EB$p1UW0j2@wF*yI4$Iufdwcs<9;{HD}<lmj@e-Ki7dcfsE*QH;@{$or8HUvPs
zhtcH<|F^{d4ZG0t0Y~a#SNj44+JEyWz-K?qfFTs8XG4+yTuBLN#?#i$@=E?CrhiYJ
zJOnyCPXvD-{l_E?uo7=B;O9!-Y~Szw=MLu$cQ~r>`$LX@u0+AYslvM$_i^Oki~sf6
z6G2$i>q_Yr<$tV%!2s8zn0&^3{Xgyi<c>n%v}LSM1OB-Zvtcke6C)S*_~t)E__`7}
z?a)jiK*apVMM6Spfd-aGyT0>(exH=QjFs~*+Tb56{ZASH6yg8Rlp$=`^W4Ch{61HY
zz4-kv?9Sxpb_(04aI7@jzx<2wFOM0?ArWNoqWRI!uxJ=el4&55e(o`b%E8?)9ao=L
zF`hd>3Sd&bdspUE|7o_mfY}CwKCS#O>(mCU)7~#;>tBK5Z$nu<Jb=q6w3M6u(+FR2
z1Abf~f6wQie!Tb&*gRwUq2NEAOqB_6vN6cV`hRY|qXBHLODJUhr~0yhki_f~uMd<y
z{~0WfZo#5@c#y`?|BMyAK&|wus-0KppPL6TURmz<JwKG8`t(npjEbcthjRvYm4I(G
zcReg9X>=57Ixb_E_Z?UVh%v#1S<v5)*~0;d;k2gQn4Isn`^u1DUEoQrprD{j-6S0y
zU9UeA6Vv$bc8<S`0;33s`sCTuqH`6RlXWW)3oX7r{|@lDu5~NLb!H0tvm9qNMTLbU
z0|W0HPIpV(WV+9pkKtmAa=-Pj?QtB6Q_mp~m49o{njfGGNA5?o1KWhK(Zs~Ws1%9c
zeN@v^X6U%~>~L$&KHbF9=leNN`}=cKp3#p#f1Y$SZFBxj6ytzEoj<qY0C9$VphkVU
z<vLM=h`ncHkT-B&sXtxbFRRI_!Pm1N`r^flPj_#A{P?lgKh<vf?|Sh9bE=GxMk$_$
zF&}V`G{fxdtUg`X=;)}nDlJziWyt{ZdizsDI&y8U*HLG&xWCCn{bn<o+TlH#ij=(e
z28MfIS=qd4ZK5%tj(_FO3f%AzG|3>ou7nn+4<!pdA?~Z0R9(4p#s2CIdjWE7wKz5!
z-8r7W-Nl8*IY1IO^ca1fE=L|te_coyn?%bpQFDdXihocD@D1Rir5gCIgm0dn#<0tJ
z@X|8a+1W{Ym}72ZWv@2<+r~Y30b}A~(^V!3*`qL|nR10S0mf11vu#JDEB+w{mqLLl
zQC&m9uTtQ27fuYlDdmer7{wg~$YWoYdT&hGYoQjEPX9L1iO~jZMY09WPD(C&xo3_>
zHy!$Qj%?t52pFLW>1VmdOqOf@V`gT?_X?)j8i|x%{*8GWS*jF|^Tq}Ak|`6wp%F#H
zSgXI8{D@g_2ut<9_~>BRIyoGMxqB%^Jnq}^u|G50fMeyNNTtG+>(`^(G)yflVj*<1
zT&M*#&=%<L(gKo61x?%Pxiu<Mz^TDztf&IvTjp058{0JW3MjNwtY&YsL6M~y-=9wY
z+wv(c13r5|pM3z}81oQKO}ikGa<95l`=Q0PWv@rIaFR-CYC(o7I))12uIh>k=1S9^
z7oKyJ+~i(2^-2KH_-MxfKoR)@1pdn_d$%)k$K@1^t26w+x>gTg3o=SZxu}tZ&{7L?
z!zsK(Q=>@8*64Vl(i@pAS;KFPQ+~EY)7f1<-ekII*RIwzBnzr&TAhR{7tzqONrPkA
z8Sbv-cF5-w;r_IOtm17dS2{&U&A1d4n{Jk-1)Ei(%+b3065*U7!F)dm<+MN#An@qJ
z==KY{etsv??f828JvX6&wQ*f^z<3^M>b1g9@^Rn*M$DWlxigkeQ-PjePH?`;WqIkE
zMI|LDlv=Fh=;Di0K?A6k^l}K+y3HY!jLcJ4+I#)cPON)@JLkoU+(c@?8G5-ZQiPnZ
zD>B&M28V`$uQ*G@LwL08b6Ra|nGV9YJ&9kpy|H0P^AF|_?#=^22)F;qCaK(f2GjXG
zEi4%bV81G)Mp=QDm^vql%Fz}>fFWAE`Xhu}l&LE3_h>veMufsqpECl-ERp!|T(o)k
z1>fW3r|Dm!FALohWEprILK7lR#swa^3st|%(|J8zvjde4-j5c*5T-xZ&$Y<7z4=ti
z34wg8*5u&W6vjV2@xi%wuDbbjkYiCb%uN6t@dm&!uj==C<;W-)ki>CZdVY2-8=~eG
zBA;jVId|Hs#qRWU8E*bK20(vu2F%<CaLZjz{88B`1Bfy1V(O)x8e8n?3DL#g{s%BE
zk|jc4bxlA7MV+FXH2ef^%@ZOn2<j71-Uf58-ujN)zFWGQLDS@!<JyGmUA?nWMJS`9
zmJ9Dhq{7j{D@DTt&+)phLu*d-sR5H;NaeITP|e?T{{!R!_4;z4m-l!66I!q=4d|_t
z@d0MM9#?!I3i5f4dG`$u38%Scy4jPd?6uUFv-r79!(Jl1RLO=1CBkUd)ow%WaTBV}
z*C)z#2YZr5%?mx4o~u2{kEH7EtiBAnRjr>HMmc9>dJ;p$aJK0<IaM{h>c&<us%#*C
z^je)Ylz6VTBnrp={(i+pp6rc{t>+Kf$f#F2!8)bh=vYN>eL#)*iv?JmYe!gx>ebC{
z?6Z<#j`e5g*HzHY$Yx`*%t{#Rh8k^?cyAY?prQ9@b9(5g1UjZ2f<zMDM^Y`<zOI^6
z8hf5QKCW;^yJ5+ZKd(uTM7vM4qBO9mioi}F{~v-ex8VwV7jl5W4xtqwN+~V7_N)*%
zow09A+<p=}fBT6I6*O9g)5tn}u%EQ%gvMr2KjD6Div&yZk+UEsQ8Y>s87Hhg!C#w}
zCRqozFeZ4Vk$d5cN6^}(42Et$y-hW#YkS|RuAyj1qcW&D^{9au2Vdo;eMv%=28c{A
ziNfg5W#5r4>w>cHt*DL*cEl5QlUZ&sxAX0`*#=f>mIJ-ajol+NLB&G3_u;~s35@Z!
zT!2M}B`xncC3U5*>34(`RVfS3GILK5#$R*^<MBTu0{WkoH$87w6b8n3fRqQo{XfVB
zJ#oM_v|zX$dk<%eI`iuBz^p~kuFC^->8h=1uP%`ILS1iu;MsG@Fm(BzIa9nAcQ&OP
zsTW6h2b^i*`k+~LZCQYSEk2ArCEy@pl*e_q*l%2??8x~68q=MhR8K@vKKA!5Mr@3@
zdAt~*i9dG;9N?8l_qtyJL1COsR>TmAc~=TWQEjgEzszbwm=Y2h_{^ypxf`M{2r|t)
zIG)lD(NhB9j=W!tPMR6>n%lpDp?d@$Cca}vZ|+Yi?h~eGA=msz>KJy!pz4X*_-ab4
z<4bNVn><q2_f#P_{NKbU6IeM=o0t5qh;c1_4*)J)0)VQZlZSF-)E-LKKlvB((UGaO
zcXRvF+wY)gw!4ItfUGC8x_cb~hwPh*bQq!toOd+QubJ_I)#Pv_CXp&=2H*13ZNFr$
zMQ1#_I|_*=pd+binND9t(xPbOmNvbC3hewD0KdZ!P>5SxrWORf0z)dskd<0Jf>WX)
zq*u%}Hcib)!>RCzdon($LMR%?H(*yE{+n}mBAN^6F8MtlT5a-060GE*2NjW2v}w`8
z;llXplKf*QL8htEi%e7M9jF_t@Ly4|JRrW@Z}1zHCjWH@(^6`<Nk7TDHL=Cn1&N{z
zcHVnw6jj>pb|mwCkgbm(5>JPk=B$A6UbsK<34E)kEAyhCM5>D(lJI7!k}zE^_MH~=
z7Ea5w<FgM0RFo^dn}G>clYDKGE4_x$Ikzea;IL8Y^7DWi3Zn+WJ1$%@%w;Mp)8f*q
z!SlpqMqfa{AMUxjRoyhK=wOE2%E3x9a^JG8$zVo$_O9MMEef#r!D#X>zsR}>#Qq*z
zuCoDU@=7gEE#B5C+v{N7bU-tkdslDY!#pSJ&0%by$-h@>Y^7fb<=xIEttvjOa(w;6
zw&~WBR=1CVw<8%EJ@~d=-JHhZ_TZG$>fJw~zuB<ll-2@^^s9(3o>#2`Bza*6gVC3W
zf3%*}3RZMv2`ibf9d!?PD$IweSVA)kP6t+ZYDQ_!3a#M)HY@|@YU}ErVLlT#x*yvX
zULYl3r$*gxPE$J&yaQy8S;(X9cdZ4d5+`Ozy~-4i)`)<2@yJkj<cM=;oiv#HR{Z<r
z3DCxMBS46LCX|i|dI`cIu53SVPX7)FrxGICG6ZehpFAb_PPT@n2>OKAni;gx(+{k<
zrL$F($Vgg$Ztv8IHP#F+QY8slJ+FMFp2kwEkY$RJwA%sU%l+pCa*6GI9o+S&JNH%c
zJ|FBYd*h}uLsHmx-BPHT?l&x$yFNlT^>iO>80A|>tkJ{9_T2kzY%6(JyuwdLes=E?
zrX42(m$F7Rr(6(~3{F|B0SboGV({@t+jfo}r%EzVX9ML6dtL2aji=HGI8QrEw-QOn
z4xsX-A~aJ-G3_ngsJdE0=-onF<m-t<<eXU$zd0I=YqSHtY~xatqDKIJv)E>+6GBkP
z%)syeNnG9(H4$&!%iC3jimS`Mue60H@4GzbSog5X+|r!dnpPfuj91#y$T#JGG4xdV
z{mu6RT0IN!8_=jw6dFhrmk?MB$K+{z`O}_%CG=q+bkyGa849-7wJxAl^3%N2K0ym&
zt&jiQXi>{(23f6ekbUz?R~5uGGyW0xk!gCZ`-qvFZSyX@T8a%2Kfi&Z9-l#`ICjL2
z8~a%u8bytyTlZE0JGEfue*ZI5Yy9nmiNi)DVY>S6@Y>~uxfY|d4}|y_7jB~p&+Ui5
z7`au(%fHMii9*#wd9@DQ;+7<>_q71U2h_v^p_@*)^znihl!=tw>kfvLT;h2Z<u}=l
zkB(}StbKRIP?WM?`m!_dUn<pJZMyLW`CqSRd~f%5lA^k972lz;alvn*m7IbuZ2T$6
zfwc=7z)anuG)CZLHcIFsd=NE+wpO87DG_qsM^{1Yzotw3_Yw~`uEE^LaJay=2a|zE
z=SMZB?(#lnmmJ5De<U<73^6S?Vp4{4>4;Y|V+k&1UNH<oA2~Fn7DJ8$PwPkBkOy1r
zJirLt0BnHW)lKP+*T~W_fd56Af%F$KQb-$81Cv&YQ#4M%d9y&A!<kEBF5^{1ET_Q1
zt7U-LnYnWd!YFMFG0lc-qzyc%aaT?DDXUkeFXJLf>O6b4qo+hOqloU+M3+xjA;-Ss
z_!eU)*9e+evT&N-rz&Dl)2;Uw_>eZmk?$45O|lmeST48kp8$uUuOpeuqc$`J>R7B?
zhW17YO#0`B+nq8CKWfljNpr|zp!etbmyb;s@AA(fL?%zJ3fXLvf9aSrL}N4q*tcOJ
z!kKk`C6_hnT9kyQn6RSJdJJn`fQ6c<USfJxy76`>Z3={M5wb9K^1-;$O`f3b8_(ut
zw5ahRlK$V#AqT-P>n?%b5|t5HfJ%ySOm@dA@=!Cqg--zRIt?&DFUFY->K%2?4-@<L
zn_tZeQzrR^7cGDdCV*2RYdR8kN=NnrBSa6o?YB|_<2zK*JyVchzOD^axicTWGf%l~
znR2apB46($DAifqdfV}1D-g2(u)6we=vA^S7qn+Yx*;uPquqy=Nt7D&_LCn}3uz^w
zH8Ly6v`C+1I0FpsRse6t>~pX-)K36hv@B+7gfq?r!V}x0twi*49quHNO3uY>TScUE
zX!gFJX;)iD=EbfIKIOJK>T?H;K{}b%$~aFbKg4wamh5Vd#QjwME)D=32mSZLTp%K!
z!*CIimj2Vp-p23YwT0g$mkP&=Y(JapYzKVJK)|U%#`1atBbVgueM;Y{QZsot{2;b<
z?fKNe(53IL1*u`ifQT_Tb><wvL4p10&k@~IZnUomY-E=ELyDt2n~_6D=j5rW)j_g=
z<-L8+2guGa@=ML?=+_Ls&Jw;|k>;!T#^Y;KK}Y&ZYu?D$<lTPgp7?<G;>+^^gUKIV
z>AC>u{ky0VzBn&CW@U_Q`u)5UH7{!tkTy?>#ncKOq<V0C)^K}C@KozMP4s@jskJy8
zXFW*G3<C^`MDFE{L<7zb-;x|wmIHON`!nf)C}h-Ri`%m1>6+ItM;Dp!ILm4d3CePG
zHqI%H)E5OtB+@L?6^JlhF?Hh4TZa}8`x;NOjtM?Pa{S85N~s3KmM|>BR5l`|5p6lK
z@2z6K1&VSg5jw*bcoM%P4V!W8Jhn&^DnGp1yOKzA<Zrsmtg0A!!~wb007%Yl$CV!E
zGaNiAZ}+K#sy|dP?^^(QKF}!DTRZ6<+Slj&P{03$+QPgNd%@FO580F9O1I|v>hjQ;
zRYolf_aaKK`a=ZO%J}%PDgw)(7&OSj-gbQJjWE}piaMeXJ;=-@`PX<ft<zL%ShngE
z7G=5m?vLdDR^ybTGLj%xGN3I88VKjYy_@Q2Kyhj5A?h||GP@&fH^x8PUh>HZv9NT>
z9FCx6n)-@cty?c1HQyi1>ZYlc*)dC-yQz1PG>FRPJa5V}@meCmRsFitS+4@6^#?|?
zbkovr6-QE;xo%fgeS36vow9C#(@w2ETA7+DOZ$iC&uxpEtZ*tNF+<}z7H$*)qaJqU
z*Nuv6Z`68!LH2ARfy)+^NW9W(r@(sy^{ho2HY}z<;#1&(BZU_o)=Ha3y~<*8IY&=l
zj7t%{OOj7EV<Q@furOgR^T449STT@0pEvmNvXLD!>mf_0wI2xi&6Qsf1XVqEPUl_b
z=SkaKRuR&2(AlEB20>|GRP^*M<bR41#+PQyI=*+U)FPZEKcHon08G{r04X@^$sd37
zNg+qT#UX3mMjItM=-2N@PaCIHFP~`7<+nizu5{l4XBd=}z>!jU)UKMdf|z#Rrz!9N
z>)(>t@hK6;$3wLaX6(=e)=Dk+bVRo*0Kzf8=YBH2;4DJMPKP2)*L$1mg&QPvKiA5v
zTIG4krA8eBFL?Kj7fJx;j#LM6=3NFbG?<&0t#5JSbG?uN0{dI8OaQO+TvHi|S?gzB
z=UJfJ?_h+W)@T%Im_onpDSEyKmbLP$4$qL!h$LmI7JA_qw7Y)<=FUwGGvjvNmC0;R
zIu3h2f>Wm(0PH{hyD8fJ4B6U9tQa-nVCU2tp^0m{sRsu-WxRMD+jp<Hz&f4L`euqU
zJMXA2CFXWWB=SH<?kUR3>@c-F$uJ6FVuA2$AWIWM8?DDSY`7=ZY`7O0t+lRG{JI8l
zp7<rVkM%PK1Zq>e^yOgIJKd-eSCRJ6>jm$tg1f^9LRQ7&y6ODp$kIWP)eO)zHvJa(
zak=%gmUcNkgW_+q=7OJ^<DL9bYTR9o8!@AP@x8Y)o9o_Y-R!9XK;!rmC}{|Q)|j@|
z1&X{ilQ&;vn?R+}mc{DG*Vz{lp@GJ?pz4eVK3VJpVSFxbU@&v}on>7n&YwjP?v8OO
zc>pMP_+dDW4~T*J?%6j@Ph#`;d=%Bso0(U+1&EP<N}Du{Iic#bfRzN*cdQ2R&@jDK
zm{7ySkk$8f)R>HZilgv&Ucbsiw!OVADJSP7CML!%R3M;RkH2YSVP(46A>nI);?wE%
zD(DEEx_k2EOQ=Dsxz(d4dAG}U^L#bCsZu@;4tZr<p=P%lgx&UDj&c!Kk6&Au>whzB
zaIm$#edN!3|H?#%{0_FW>||iUZKQ}|gUaZ;h9F4Xw=;MDEPt_Lc|kiPU@7!uF)}?j
zmn1b7wo7i0F>46;&B{aW!I;})jX<;*%3NV%i*Z%1J!b4TYj~)Auwv<__fC8N;n}^$
zPKC^IY_QwLQ8w*6Dw>rR_)mVlv4xIPiP={s^jRm}PVmT3GcMHWnQzmNG6&*OCVEq-
z+W6X3TAp3?@&#iz`f8bl$aXQh3w!w-koL$GuR^jSLuSZgIb1P0h0Is+(bf}(hg}B^
z8~xwjpA|N@lrD}#{|JKa0qvm4s~)=)0rv`h3s8I&>ZY4(9~zfG3N$%OZdlc>USF^c
zAt7U7n2ssEA(zrMA%MBxJTdWTV8(hRl`}3jb`<E2@auDp6uy~fFiJm}Pk7lEK{KY1
z1}+$Ei6C&U&$cLeU`IZfu;jawUCk)^(ZtglcEe2*)KN_GAVEVyO2`Qf#)d5&VN7Dp
z!tEps(1hce;IwUFeDRy-5i}^OJC^FcXNIrcJHpyUBWdWSZfJ(xZfgy@1U;3#y5o+J
z*WyJGib>}7y5#UJ5o-(dKv!G|NHw&6$u1MOFm7)@eg5p?;V}Tbd3jPHJEBt2bs-;0
zEYd>2^9h_9jMy;7aGF4wx?Dy~3zqqoa1JA{4cgr{4`GtzR0W`f?7U>f91jE;;OB~;
zYdAnmlnrI_Uq7=6G6BI*@(r0%`L|Z`AYFEXyKGFsyXQ1tQ?>CeyL5KL$Df9H0MM-W
z5NIy^T0dHLJt#7-&YI74^u&Ne@(e<d=xH7`$F_$ROxZ{zrp$XFi*s+SF!6KV$GJlJ
zN};oR_>dsKT<)5RxZh0w_qPBXU-nMKG``Pi=GTs(QDyTWr0|d~BvCm})d_m|vap8J
z2>EuPwx>k#Pn68L?VUe-|E^jqxH`X^yWeUQWR<*m0nw9stm`Wkxp@6(Qb2rm!SQ-x
zy=S-?B*tsZ5T|$(-cc1%+mdSwUwv_NR(sjrv-Z*4B@P(_QfyZBH|h*ecEK1Pu3*^J
z^S)l`<<W=%GRFYSGY~z8f>w3-fTLX3BG1EGyS@A6*I^7;@-&jAPfTCqTEozq(p(pL
zjPXyAP)VoaK%?j|Tg4V0=;E^LoyX`NS?FSRR{7qCS{{A6Q$~I>Gz{4@n1nUsyxM>}
zsvfGY7qxsZtYk}%52+QjAL}CeAYDJow8bfieJpl|ST6XaWbB^I+d#gxh7k@E7oHnP
z0>$xHAS89ae5hne?vX<g8{)n?B0`2rUu8DxWZ^#0X4!_)H(yEXj3B^B;XDc1E(Gir
zR>Sz<Dg-y5mwbAM7Id6?`_ii%JE~JfgMEruVD3j|&LeXPPddU}>+hOH(p(GPRQY`^
zIQ&GVBW%5iawsWxAH8@Yg`x=r|9M7oFF^zX@gO$H36%6+1Z`d8=7rYy{-)BdpUGf?
zi1#<RoaoI1PxDLx3TawB7Zt2S&%((j@B5yFD_XvodvisQ>A7&Op~u!TPo!)JUA3ib
zVFs_H(}E8T6Yy;2>u~iWuiWf>zjNL@H4vByAWVmPo15<Z>P+hu^40$oi@<7XPIa}E
z7rc|9X2J$?L4Od`AAy*jOOn`lgwueGtJ#IWMC;&pON5n#7>cY6n(D}b_c#+Dr;ab0
z^Ru6wlV&SGW?W%Deqc<R#+!dVtPR#fy{vVWc1KR@hb&aRuQ-EuPew}*iE$g=d6!7_
zv*G<{K*QytqXVW|QLQE{Y84xHYsCzWF~Po-+Y!?O;`Vx~!bJQO<4HyW5-q$WIO4w=
zLTF4@{gOp!ZgSY?7k3G$42Q6)jOTaf<vN9(MFpA+H^+1v%^z>Y)N0MHU2oI{u3ppr
zP@F7>0chfP6+cm~Vnk<T#1}U<bG|aCu_YjZ&h5*$I_R>+c?^@tWJRNUhqYMsqwrst
zJF-eKCl}+@f>v7;E1-*{%j=g%<As&*Eje}_7gRNz47O`7dW#VsM*H^gY9YUyb3fW6
zV`n$36aNDPMc?v{^}(bT*J6lT0jyD8A!i?7t;9&_>qiOWO%xYB(U^de7P5~7Sc_`8
zpOT_9b^#_0F-lzAzlcR0j@y~kkB7F&>+MT=mQ36B#(xa<yrCxz!~^V_DD6aX^Xh8h
z+GJhz<Y$qfVT$8U6`p&Wn#%WZjUHcyz-h>(i$9W*BLR4OMe*T)T}g(xS1)V@$fCa%
zwgRDAdi#`>@0Y!#BK^aueopE|7n6uAXn$hiezYZwHw9r)V2Hh*MI|{GAp1zRiD5s4
zVaRSGP46I^FHkqSHSu*Q2Oi1wEf6?=LeBhqaK2Z$bLUzHO@NLwF&O9qzdj#ekYEvG
z)#nsvhxGm=GteRT1q%4qFfiwzCr;+9$~@64q@mqkPKkG0QMu!xL3N)B1bbXEZwEj)
z409wW0CfClAo%512cSqfGD8b*?A8Ho-T_<{mG|abPUy%_`N$yPRH9l}_BHwG4`DHx
zZznar%;H1LICjc`zTO;Y{Ojr0p6`h?w4ma8px;+nS(*$KR%@3|fxjqX4!lrxCT(q<
z1M8I!A`}sed)0n(Jxlw>9$Z>cI?@3$y&6fwlGvXnO;UngN6HU}b(v2zR+_3GzPJHZ
zznM<-fxpINq(uO0-}Ob7{<%=KbmO^`L2A1J(u^ap*q6En!i{A08lbX<4%wkGB0TVF
z3Ny$Kg2&4yq1Uf2Wyr4sH4cfP_l@e&Ku=E^7p`{zE#LY`DvfIG42C*tz)|1Up4Y1u
z08_Qf`1<pLIgIDEvaFBwuP?A0T331o*}SO9cZwcJx7<JN+)S(zFylS*4W52cbk?Xn
zr@MWy>C#aFwO=xurcy?ODXq2oyAUssrCnbPNK$#V2+GGF+O*WK0>B+M$+}a=mP~sW
z6L4knEdon#!2o3Z+M5?n4;2>^HlWqe5r@kzM=|y{wa^WfUU~O`9!+*9z+(cuo}-CN
zsEFSea*rDt{HUze^7)@L`DX(f1_i=gC@OsR{Z!Vo2Hw=M{0|5@_;)qW=3Sf(3tXNx
zoGS1^5i7NaxqZ&Vl=dCSr_|#4*B6;Qb}MtovG?oelvYjeguq3$qzBl<5Z_kx5f4lY
zLl|j5q3Se2_mn~vZo<*iT-aRi7gzM9A`e=CZmxpWm1e2a>a23-qnHtHAg+KdT<}L@
z3lx*AuNwF7D3W~zBIS?CyO}U*Lkk$st3U(vG|T73`KEgaCFz%p`p5gCZ^GBiKw$q_
z?1p1v-D=WJ17x!XB6;s9Zk)04p$QsuzhC0a>vVZJSa5)<(@*%ou$aI}!-z$7K+qFg
zLTC{E&{{L53F7}6N<q!#tf3H3yv<|^Vj3XR+=ztm#T*T?iQf_KS+TKSlPKG>ra7D3
zxr#42`SIQA3h?rB_&0I-Jp@u{HB^nFbY>)@-mw7i$3|T+89=5uk(OM|>ti$H5a-v;
z0s0!$n@rz%q4w$GOh9**?}b&@_Q#gT4as|7!<EbpUBPkhUhxV@I965EJJQi<PMJFV
zZ1ev^RV!%tWT9P3EMMI3ZPlNU<uV*In>|6bQis=ICG0?wCkRv$5FYhnY72&MSrx}C
z^}PgKxQ7XdH0h;fdO$u*56<9PLWwvgW4?eN#3c7LB<#CsjLq*RQU#Vx5L||T-Y}S>
zFm$ktsXamdFy5ZMLGEmu)VrdTw6Hz?_N-BKhBUN^UD(K!6GhXJo?EndKu9#ZY2#E~
z7D+R<=hHNXt)x>p_!C=cOzj){uesf;fP+2>?&Jf?tm|a?h6TWzE>cB=fYW($j}6Nq
z>mUp(8HPMCunTwU`ZQr%DE+-vyi%*rON<MAIm!|Mc?b=q(6dK|Qju>XXnZdTOmIV^
zHi7~Hm+AznOHrPu;lT50InojsN?55&rnR~>(_vwqb*ky<$}LkH_Qli+`_2nSiAN@E
zLywqA71cdHs0bR}XXOS%nCCb<^<_@J>+SFBdlJodjrnPT4vJcADyGFZ8hQr`Pz82w
zeOs6x#J6QP^~eAbGOnsfl7=|a0t)O!)11~}$uHeCsQ?m42%jb`JvP?OtiVTo!?7T>
zgesioK8s`Rk_6tM*OJLsTdjnmgnaq)x)@ha9Be?e1!wZny~yX{B_f)pRqoNXvNX>p
z5+zl(&wkelAl=<NsI&}&F`C4G&3p#{)a~L;g@St|A(OPZGuf#r9tC{^&dGYfedl8N
z)W47@#fxy2@{=k?44E$@=j3RIX&Hg*tvYsZ|6wKb)iv6Wu&*aCK??D!^QxmU!>u!g
zk(3JdH}Fc)X&uUdb?5?D3tTRD1+3$3a0c)4lio8cpllW_gooD$&|Sxd^(@1Rz|^2x
z54KXY99h?}ia3eQ?^bebm|R^f&iB@IN#rF&Xtg>J%kCYsnyv2Gcew~jqNv2CW?)H<
z0|}SyeR?Es>H=KX6c7Y=hLUn_zJ3{em*|DZQpkC)f0%3x7*9-0bOxaLr%#_&JCnt{
z0F+ju=@(v0YcSwHC}sF@NKFZ@g9UcvV+)X(Xj1{MfuiK@Qg%vvA_Xx@M}rshU;<hM
zP1M+{YMm>F=;=&&Q@d7Af?A^gMJfviC5y|h#JOum3g@GLoRI*gk(s!|Oar_@aGk8R
z%{czU<Kp_8sCC4Q4NFeG+LU+{YSG|3cwQ&O%kQBt(X;wn=XqIYCDNP<p829N#xz~9
z%(S^xFH55s@t!0q`ISlFj-}>C>9{JGm{yeK<*s3P)A@0$_9429Rl!Y=tRd^u#zJAl
zpfXCSDbE%@gb2lk0qz2PT;#zkRqso70D~Iq02k<idw=`J>*QcF_T$lvljLDtlaE7`
zAjRWSZ5^F^D<)2;jwGRRv2LghFj5m88d2T2Ta=t2D8+9EVlsTT#nhe~txSo)`XVu>
zHG$D)>IX;3;Kc%11{SsGxfo)$*v1q~1@8!JgwPgFP%t7wi{r%J>jUBpuH{oHqKCrJ
z+nh=!@Bc-@(4LQ32B)bo0Gxjh;oVj{M-Y@2ytg*~G+yB-H$HPdVRl}8rb}$2cIB=d
zl7I;~XyvHlHz`&rVg%{{HoLr`ZX_BkvYV^nU0+ra__fVZzr0cv92HLUNpJ19pPETK
z0}xnPB*Go@#RdItYYQ@<!9NlJ0qPZqR9+NTw3M*JVlueB>-aHdIZNeAMf9Ia4lpl=
zuWWOgw3CGcjEFd($@9sZ5H{LJO@Wg@efRM$ty|!BsuVf(=8TS()(G(Ib*(Q)vlmH|
zaEXEJ6`w?Y=i)bE$EvX$VWo~sEICFJ@w=V}%39Qa%wg?Ce7lwo52#0rghL3NxVp5y
zO4c5~Dzginhc4D5&d&aVT0Kuy1+v{v{;)%gkfp0OkVQwj<yzqYPeH8_yu*3R9{l4Y
zP;fx@X>H-0;lY#-2C|iylrKjVrGIjzj|XO<jf0BkXD4ZyX7Eb(K6={vRkb~2768rX
z8|7L7<=w&S?PA@inT`#8oU^utxgd~<nwp`2kRfR!MeIl*Ijs<&kpqh#l!ivI3p|c}
zELXfz_+HpXq;Z;^H1rE|otG;)J>o`!AT*e_9tL!Hq;Gs_F|BqQVh4e;G?xx@w;YpO
zQNScbmT(Jhe9#wWaeW2>7jKL+D{~Q95>qlUM{jpKV#5SqBd?H>zd9dk9gtW1$}fcB
z7qY+4bi)wBe+f1wj8H~Sv)m4+)}nr|14D#5>IL>2v1Pf^cRgd8IWLfx_Mq1*!h_)<
zyTVG7Y;DJtcrJ6%^S=JwUSzBJMLreGTnlS1G=_vs^?XEiIsM}V5QnaZXd~dx&0h)C
zw&7F&KJTKp&O2m`iljO9^Y0{QF*ITCg?U^=qe~|5Ir#E`!)YKFFCt%W*wBxb0)^NI
z!;)~Y_#=e{d`P?>%<03_8dV~&K>)D9BEKTP8s<evZ?Ar8YO0v0lk~G0l?A#nd~$En
z>r>V2<?e(OxHZNtdB??Ipro=Euk@`{yJM1%{rWN_yWpdt6TqPxO9dLd#$152aAU(9
z*(n99ym~<48bQ$?3QSf#_4f%{MVBb&hp?!x56k&WjK!b?8|xn2W$uW_nSRkW;gLZ0
zgOb&cb(9N@zI{crI$F*%HZxv*BC0Q^6&)SD_lv(oC9{>j-1TVt|6%XF0-EZ&zd^xZ
z5?X-JL=2$PTNIQQkfNdx1f>dsL_~TK2)&2|upy|FAkswXO_UmHP(Zp$M@mF`i}cRy
zyzlqT|9QT-nv1!cyKqAG*=Mb_SN*MZP|<vd9haUlX-+XCS4JH9U8<l-@p|ao(?%=Z
z0flq+&?`3k6OulHzXq+tVR0zCGoRJW-WEB_EjBc0oZ$N%z*{4+v?3`Ky!zx#@?Y+Q
zfOmcST)Sy75DTWR8Z^lEn|<hm0y)X$+SQI+#A}Nz)@1phGG;qf#Opvn|B_(~BDFG_
zu@J|t=)aa-v$xXby*j0L_$u&xRnLTp;B;4NIjxhI6H`5Jxjf`p79XwOGw1F7*|qK9
z@}IakI#OYL+s@4r$2(ujdc>E*7!eVe^KaJN;#rydT-xJZvOUIX%SY?h?+Xh-;Jr?9
z-9sN3qa`ri!FRrozr?<*r=(W}O{d=q7z=$9MC~I4QJ)T;cl(SdIZ7jQy)au&*4=Ia
znA>Wf+}dkB4s&bLAAA+z8?6ZgGixM-fUrIpX}=PGV;rC#FQ}=xf(+osU{`S8=vt;i
z>96+5N!x(GUwJa>l9fC&Cov8v{tcbUZ<MN&e|@6ZMDl%Oigk!F5k=_L?9y-6r!Znz
zikyr+3;{!)i&$=)gCV@Hr1IJilrN-~7L5dmVk?$#WKb*X^u3(e7#7BU7&xGE!8t4Q
zN?+sAsJd1#qp%kghqC()?mOw8Nqap6hC2+<`kw~@D06N?MQ@;HbCi_+q<}txrdQ{h
z(()AFD&rmd>4mV03(KhfYFl!g?V(9$lCV4LN2j4$nffl4jQcse5m4u@`rN31wxgL|
z^aS|q+oy^Nqju2h$}+MUxFUr0b>kFyJ}hg+UpzZ$Y|#@55#XF<TfJb~vr-=!%zL6#
zfuHx&CbnZpb#&$7eq#B_%YJMv*FlV@;?ze_?`M!t`>lmgy?1Ae%(FnlU)iMw^|7Kb
zym*W5v}dM8R_jZN_@9E$fngkoc!(<xKmJ!|8t#?|0=mmGNRfs3Pi|e&kx=Ndc$WeD
z%#qnnQ1vmA3F&$U5I-_aJ+<}k%duO1`<@$7yN(-@KN*q7;K(7ng=M)nl!mRD$2EVu
zD{Aa+QW`Fk!w#0X`d+BZfE1I*#}i@@P}cngu!Lo9V`>2h+tZ7!agyGPL!~pB!gH6*
z3Xn^6hruUMsHH&RVT|EvPo#+R(b%6CFH=GV1a>1FulRP4Bwr$STXsn$fX_jT6h55h
zrg1>^rNf8R=wnH5-WK;SohaMH`ubWEpsFTze_@;eH<E5ZdPY9gZLU6XdM2}>mYbW~
z*}*}aPZsCwEcNNeA1$JL`<QWIKI^~-;VPxiqZa!?@&qWf=1yzq(5ph%&YshBWCE&T
z!}=pwQ(aH$9Dv19`zrP=0KGh)xU#}C4@RTHq04HRyLZ$AJ6H#9hO~VdVpkVT0EVsd
zmoz71XeY$$+u5j~?yhS`S5K~fuZrIKPzyuuREl0GOrlz6Xvc5DkR?5?ZkCJ)axeEx
zS$B$MmZv0kD<a4<SZ=p>7EUHy7b0zA4j*>=YQF|7r=}$pOEHD609P#5XRO2)wr2bg
zonEvzF9~X~LKn4u>9>X96N2|R$Ph3BqJL{YI5w+w{&^(S*n4KPthteJr%K-56iBD$
zi0;=T&j%OY-1jZE>v|vb^(A}2biJ`T>fY%tb6|de9i;dOop`8T%b-b2b7f5kJkqrK
z?0lYrqE`FeB9^W4{Ky~(wo3Y5`pBrV=EM3)p<&PA#b1sEAFf%}5m!#LA&|jS<QU64
zEBlSjm%4nqxBkTLmrk7CNC|xwd>M6&ZBa>4Qa$YwAeHb?qBA4CACG|oluQ_17AA*{
zp73#EA;3po{R&nhK!<n#Ocs4ZQzFW-u%egMu^*Wyi36Yd59ZDapkGqcm`Sf))YD|a
zLP0pY4=^JO5ZqMB{|TQu^1PROwzChx%reC8=2tuhCEu<8vP8UJ8f$Rlovo(NS2&_-
zJ`wcK6R85OdIMcsN1i8CM{D^;-7}6jSEx;C=;s$GE6PvaY}@|Plkt3)=%Oho3B2w6
z_qPCw)tN(aP8#`Qhs1^>i@YgA!6*$HSH8QpNk_{?KIOn^P6udv^R22QN4S-J+8gD@
zNIspTr2gk<oKHX))!HW*1G+RW0FTl60)-to9%h0lk(nPcy7*e5YoC~o)l7yqVL-wy
z!#Lj$h8!BZ1E#cQTGUvHV(e38`@612z!Pe0>22m0s@p_1mOd5zRU>@H3=BS10;Wn8
z>~W)-2-f!(bN4NFjcn>DEMOe~XpVR|(%ey-5_-o4ms*aY=6RfrqurM<rdwTk(uI$r
zq3s+V_``HDv0QdDkN5EV;@REsHmwaZKTb36<ndArl{&7wsOdG^Bvd8y`R?zt0OK5f
zM2IXy^_2h^FjI9?k8Upl8pm?#uNwvv!$qe_pZ&#0>mgf}Qmn6`q1&4(DQ1>lXFNVG
z9-7npRCU6j0B3m6v}w92GmrqlUqWPpib@aZFBZG_#2j^r5{h`<SON9%-C_aYY)Jr!
zw$zW005~u<s;7`)^c2WL*;d|X%Hvv?8s~1bx}`ke><7Zq@T2+l&mNN@HO!6&j8A0Z
z%p3X%KDVgBOaQI(wehHouIYGV<-|M0j>7!wVBRl{gfWtmy#UF!tlE6pw=z8NU5NG4
zFsZqArzNeXO=bv9(Gc=E9eyRv9!S{bVVhW<P)4{j3|ZajWjMm0%ON7)7^c;DKfq-W
zhLG?Y&l>Ex^68z?Dxa1ynLrnU>}G1PK>7Mxv$%cyV&np<1yoy**Oy~z+<0cHy)f}}
z%4R-xZ|f=ndiuaby*%8p4mIEpDFIPq%grP_FzMPfi!x*CIUvA<<lg5$E5EvA-{2R>
zLD4{M>IAnLh12}T>WI%(z$ay}u==w()28<gn2=qem4PoMHIzcz-F6CYx4!qr?ia5N
zFPvtF1!ZAGu9T)r1QjBM7&Ovuf(MHm8gZ7Q|H@6>DP`HYD1;>z^e_BadDyYV^Ew=X
z3?yCIcVvp(v-Eo!5B;iUuzO?YTm<|C`Dm5&+C;I$kTUcQj%?ACun^@VJDjD8-3qff
zy`j?Yc;ODUP?izFx|m#2q<s66&w%Y|jM&ikYkurv2|&SAZirfhsS85@ruO{!m|9ff
z(1K%!OcC>d%i+5c$|0BIO4RYc)ba5c1n(3jRFmUSGYSq=>Tld5E2bN!k)7PV<#u05
z(%JKx4OTybJZ-+t)B6h;&l3~^h*auKNl>W*ZZ~S)W+OnMo!jJTZwJT~hpN8bNYOeU
z5U}`0ZwvZ`SN7?8PzP<KngBR7|LLUwh`fhY+Ii#}pKj<N-|f<2wXJb3w?y>(`its2
zTGdnGsl0r}s#f0QFd2Sksk<5K?2QB#N+^9yRMHn!$PgFrjjQv^AETM`2NG>QZQ!B(
zWWv}c8mBgmBR@oB2izTdUlUMQ5Dp!Ub8EMM`E%)P{T|JG2DcxrC@}(>zyRp4U7(?3
z%duw8Jqx*!aD{4HO#nJzL-A8JC{0Nt-1dGkl!lw7##};JYU?Y9ab&4-H<uX<k?S41
z5g5>WP6Ou&Lyr3R)(1~CRqThDuJV8!<GAEz(NGe!@Uo368)|Kj4aWfPn+e#EWZe!@
zvD;h#)p8{pb>dcO<LEgdS)ArMO!u{d!!80qE6(FISF_u+6e2+t9p-}f>j1)&WRp&I
zg5$_2x5|y=$QoO6=pF&Cb^5fovtv6zcj}tG=w3-9YfC1*05$~1zETq+b`od_!8F<P
z>r)qwGa+5Kh;Y-I!dLh<$hzIgG|y5BpV@~=4e0KxEU#MtpX~qAo^i8($#!))g8J|*
zqC~?^`T^a^{TrqwW<+XgX7fV7EMnkx(^0ep#`jNsMbO}%>XCVA1Pt*z;3-Ir3Kjvm
z_8XClWdcAlX*MzejzC64nz$4x7*~9Jqo)83hZ<^~zcZa1)oT(||0M#k_LZQ19Rf-j
zc{guGz=7_b&kMMC`seyB&$}9DB_#j5LM^4fx~8!RxlNMG*dX;o{e#a(rc>BSZezq6
z8;=GnIj=@mzvE2~eg=a!FIlwzM{CeQhPVwBB5%}1fycJ7%DFi1VjjRH;>?WTyYPoX
zXM_4&)8?xwLXX>{3wf}14r_)$yeVLPM|}tpdTrURa}zE$u6ov}e8Lb<a>{qTQ<>$1
zBe%>{jC`^i7*+acjbI)tZv4Kb2qG*;ZQ5MxKUaWPs6cx32qZ6y7}g4YX740kXe5M<
z-W+ez=_uTFDIekIL@lhJ<=yBr!JdAEM6LO|mFz9S*Tk>`oG3PaFSpc>@2y}na;J`q
z>`@kP?CO<b%|<OR-=Q=lAzlm&Yr78qO&=yeYX(B409NlDExG&r4HwW^R*=3%2$<gh
zS`tDn3Gi=J>j4Xt{WccHsh?&{Oe?Vev{G~yu_lon84q<)tMhe_t++=IQ2(ESbNN*D
z{I0B=&B<^$IQeVT{FD4<Wfbo_hQh11p1`(k0iJ#xi*%*~k|YL#$b;y@)1$6Lbhoo#
zan>NsKWa>_|LGwH+z%s6$jeJg^{hB`45s_%&RP46zBYn-0%Yy@gLaY<|LUj7*BPtx
zIP&r+Z?T1AVsVG=T`y<yXt4djsJvGjNSJ-rGGqQX9qS;kaAb`TyB;FEKVHGd6GqVh
zm7x@HM7DtvOOsyBxIH0qrV(;-Z?25Z9A$4pJ}Qom!vYgq-E1MUSL@@sRWKixL_5mW
zE?b$&ysiTWe9{OVZ?R$}nBX{gHvVHY<i4T$x)4jfA|K1CWm7LI-_J$=j{7G24MPw0
zwp6AFJ|W#MZ(W)A72-Gy($Y*Hi`1a@rV1Xs6K7;u+Z4l@1w0R77t2wHAtR0@EIcl+
z-R@b43G&FiFPV|CQC~5rm7%-Q{pFSF45cBaP?Jm>#~y0{oMnPaoStS{68+9p%@kL}
zm9G*oLlh@A3LvuY8%nZR<OH~n<1-)B-L}cr!T(h1YWr~@u=aYAy)H%R^=0mL{~C{?
zC->zb7a?nmj~R}+-YC7c{7g&ht)@fWd<`xz@E0vdh{yYcA-HE1{TBzG&Tb@Rnk;_*
zra`Lye0TFjzbb~9ciR^vIKO-$fR4A%S4lA?rykbJK(-*i7*P6`(-`TMm3UDb%YRfa
zb;I9D=o`NQcBA}e^6QD>5P;m-FG`YN!gAjt^lRESLTq7N0kYj61;}C4H{DL}=U$Uz
z8zT|JWb&hq&d#CVzwZh+60(ZM3V%)8z!0Ag@xyf?A-#84oiEbIfx={Vd*OX8_8F|8
z(V2DUWOViqP6>l#>6dnsmVyhb1~+aBhqnGmt`T8M{1R5`7y<XewjNlV!w4FXJ~i8B
z#KUNVCA+q_w{^f*ms$^#Mh`#NY5BU3*N57VC4{sMYC_mxfY%z2K+{5T#>4B}jZtWv
zb|JZgO`=&@!|0*9Z}GkivlRh8tirWjM1an2rdT0z&0g#b9AV_*6lw41xGc??+tVRb
zWA=Bd$Bs<vk)v9V)dpShHK-1{`h<NZ9;o&5Gq+gjxj4F?lwRk>GZk>;UbwG0=<Sm&
zl|!F~ygXv3Bg#gGFgQs{O5SifX@!_wSW!;caPZa3zMJ0<F;u1-{D!s3Cb$|fa;^=H
z<6m~7-8^SY)4Gl^z?!24%E1KA?<<0CDo<7fV?G{9eR@3XlQN|B6j=HEY_FaMJx=rN
zrgOH~EaZmh-cS)X8QyY}E6Z2%<bJ0Se<&Mjrj(7o^mnY<9pT=2oTFo_h$t8X!BXxv
z_QG70P2`W$MHf@PpxSv=Jxb`KqYN-d+yBVve~0te17I4`TaUO+|G8^z(Q&IRhME>Y
zQjZfIfg!8xhUSIYQES)D%D$mB!q76ZvOmwI`z(;mOVk8<-ds8=jKcWFP_0ehV2+1+
z(4r{Mx)Nj9+GP+$$u77QfB-OkQo}<3{!|&nJRI)WsrP!Rj5k18j-sLOryzwviP+~8
z5)%_aPM%C)!3NbJ5s2$=71=FIUKV14MuhYELw|u)|9JNspdn8s)rm1hK|B&V%E!-O
zT7t)bv*|leSkNW&g{k}Mb-M^XzPD+(G4~rd$ON>|bJ16p*USvry6wuU#O;eOk;Br+
zps8p8g!ed=lLx@_C2;^S8P`~0D!+sd0ZPhTXr%{gr~h&w|I0$Dx8d(OU`23pn9*(3
zRB@EZmu9TB)XS8g+L9o3S6D(DLYX~JFO4YqXeSNhq?NX20%BH98yo>nX??lg-~!q>
z-s>Fim1|x13-X1d)CUy&EeZWc<1*zvNJ-kG;C^xoy`Ls<<c=f-mj@5dG7in`{5<i1
z;muB9HxX*r`SwYb^vrq#O`p66X}hbEUju=7JB8jOsvlA<U%Q&CXQy$XoZo#LIXYyT
z+cSq(ZzGH@qCm7a=pFD4M0?yL6_{|Sf7cCP_nkEda#$4R7s#q#02zd#`nx8ezVYG+
zPSb~fLKn_;GaRaX?ViL7by)CP706L^2ETU5MnA}D0^wa{ESre^kt?7@q?dnfD9u*W
zE<mW7n-ld`HK6YWzY<4{nz9977Y%RO4r5nj1V+UTWMW8J0E1V9vx*0nqo+p53*Vx<
z{nUW<_*{_i?W)*=k;?;476}jT-X(jT#tsbGlj_(||7}{eRsctt^nIcdcvD<}lxc2B
zMxnn?WGy=2J$RciAXmC!a(UM&8x|Q42Td)@8i`<lp$W8V)+ciX25d!CVp=~#Dzaen
z0SqB;{f_nv4JT0)=w0XpUXyb9L?T$&7A&m(HRrtRR+)Li#3_uui&3U_BN+b!mC!q}
z>0IDvdd1NXv{ux~#hD5o7wPW6P1LgD-jKs~m+LO_ty)j;15$z@>NE(VPHM+L0znif
zj7zjsM-XckCXAW+O=nVnIj8rDtG}L<R1vY(u87B7rONo8o3C})(pjaCeJkokh|D?I
zXGUbtqk>#hrSE@>y7ZzzxMh&sPiOHIv~PlDq@1oQc6DnDuvdd#S5{>uqi08tVSjBH
z<g^Q{zv0~+p1m^C*%i=IHpi9K>RgjpLLV-!jln3&pLfnUN`P_=?I&G0j}igRx5M}C
z?E0bB`I@GY!0E;KqCpHHpjQ3uF_hMGWknlntr=%~>Y=(RNKUW`I2SI6J|;jdRS7z!
z=u*uJr@;E_JBxdPBQn0k;(RyUi4Q~VEGK9ul^x|=OnGrROjg3c%-sBG;Zz!0qhLTj
z?(e5Z)Q1@;I>-)y40kW9*6#rSvGn1wXE(>D0a$J5$}uc9=&F%_6TpocDApO2n@+d>
z$EW_%Lri|dU#7u_GTI@IIZUi?yRXgM_*wr9jGsPNdfh<s0t4I@hSaR!uafRwvCcaE
zh}ODQ1Z3J@xXlC9@9jW**L^L$)`d~@iK+$e7!zxTL*DQsO^66&h+R=>N*cLm5)UdG
zX~{KR)z|1)N^&GQ7Dh-4RhU0(Yim38?=Le01RDV0gYhn@w2tD+Zve0W+%c|k?0G|1
z6QDIlIANkDb4$Q~b(OjJ%r@MmyO$e=C}ES`RMsTAcaoF{@$k#Tzv&R)uY-=aqXO8Q
z4)jR+lZ*u2U<qsmXgTgt#`*bGHhx&2c3>oO@!8j3VmSSuJ`xmoj+J5_v7?~1P)G1H
zR_vxiq{02<KOCTbxk;;E{ztV{pCk2kq<?j;^cN#QeFDlEnsnQH3&jI*obq|63SEy}
zh9lQ1)4In&UY!69zeqoL1kx%B(!sSst=vKew?Ylhc6owzuYerp6KN!U4JS+*35;J6
ztyUo$eI))Q56F*)9)-R$fh)NFDy}Z2)heo_vv<K$00fLS2(2(nT(q1ni34T{@jAy1
zkk=#uDM~}3x48c32jD5mNMNaSygd6qV)j!wQNL<@+Iv<W!b7DP)`Ig*;B!-HbiAN5
zRLsnzLc=#^@@0J$&>Dtd-RqOSDBvPm0S^z<@fjFzMZ$NcbVVZ@8`VlM(g=@`_Sl+-
z%|X=5BRj`IRX28DOtNzW^df>fCFs`o2i#rT28vl+#dGSpo}L~TH2M{g>JQ*BN!08P
z`a*JrXadS%^rTAy+g(LYvof!91=X(0<3uQ2`wDd7RXMhzqS(<>l1287YfVx_M5I)e
z(ZrXSXutaTbe|tmm6mkCh{dJp1llL2byp_5CWj$KuhZ&k8SA;s|KZ^cz{95{Sz@4#
zeZgGt84xw=e$o?$dxB{adufNBWT>@KnmgWM(UG+Vh6Gyl%?hKT9LBo)3$r&m4-9`x
zMfe|H_x7CeCZ?rn9u*l4+8imf>p~g&s<-^wSo#eI+RF}5N^SXw325#aWLi6g?f#Zk
zdyDV*@h8_|O5*LobtO$3;2gX6SP1i@X73WmnM~$W*QxcdkJj3`gd9?zFEG5KHC%Yv
zv_rz7j(GQ#Ki{(BK(=aQH~u^z*Cu#^D!Xs508gyxfe!#fFqZukchwCw!I6!Z=iEIj
zM@JP;)uyov98UD~WS=~->B>ZaO4s<I55eIZ08U3CfaXL%ciR6`IDIMuWN%IN>Ne12
z{q)HMxH3DxfI##yY|kp*Ml)@O9;71UqvPCzgL_^5eIi|;!XOFUtcuBIM{({Or0wa2
zQV&oFr0Zz)ahCdu5hqPct|d?{$ew@H)C>lq#b_Q0JqJb}#5%Z@NCN{Y8OkO?(A6rd
zdZPtH^tfmsfmpWqd0Yn+DGsT)A5deV2}5S8VzU+Q9!2~6UQj#d+$^g7oGO^5^7=0R
zGSK#4NJ_x50E+TraA1)7SRv=baM~aNB~ZX<`QDi(2F`#$=$~GGuRCKRXQwoRH`s+|
zYk<&iu=EB(wuLOR6&4-=IjZU|e)%1zqNL;)*}T^(3P31F&z}=A@CN%6lY*Igf_DwP
zsKU(Li~@t})L3nRFkiLaWy_|a#!ELZS^(w_!+<7n3<B;e3ulN#%|km#@U1HXQr{0N
z`x^;z$S@N*w|9EJ20KAr$I7^23HO+35h(MnFNpOEYJ55AAg#iH^o9H1`aleXfRGjR
zRn6p|T}m^ccXS?);&6U^9~wU7##!bA*k;`xHhTQiJ&-R+Jz{#hbxbY&(*H~d`0uAb
zH$eoPKnmCVKVKmga}fyS-Xc<m=BxbA%mr{$8F2DEDlNn~|9u@~M`+RQ|9_KzljZ*t
za`jvey^?_|H7|Ikd+)}-xq}DX$t5jIew+kB%LRFPqbDx5r~fg_gXSB6jQW~6adJ}+
zfL_4V5f@+I3O8Hpum4`?e-AW>XQ9{C)xCg5TZe^(oj}nr=l>E1JK)+SSN_eXhqZ&K
z@z0-obUHfh|Gwc};EZtET)feGfq|QcM{h#td31F7HugTv2LE$fhR#Fw>9SXpzb*`F
z`?LDPzka<<R(wG2Oi?9GR`bYOeWtx~<Hbpp`<tTy%kiuDRm8gg2v8voyPM0?t;{0?
z+gtzKnt>mX;8p?pWV-BEYI1Tm{Y7`m!=MP-OFb-P>_LM<p(W|Qp`oGlAt<DC3Er;q
z5;q^4nXxbD>00^6t^mPwn7WhGe~>~pPZUWCW*a|}Sl&-DipVo9x)Rz1;>sOM{yuQB
z|8ehF5RRmKk>rP2e#9XFW@8b|IBE0#`lQr>8NxsdeVH?f#T1!;{zR}_8f@3~nu(VZ
zfWo+cp47j%|9PYeFX_p^l9FA>Rh6Ur)nr8CoGh>8KSs*{0_uP%C<f%@H<Ka`7zj&#
zwkr!5J?JYk!UyzZn6e}07#K0a$5(P%`^BXKwvu1oec}Jws*xTTQIc2I98g^2808Lz
z(PRsgKXs`&(cWNU+V4Rjhuix#Ha3=3SMyswz}>rdhLHtK@u=tGt;+2n|C>w&4E9fo
zOIbp$iKjhaAKhwFxZ-(nwO#HzFI+se)%hRfp~(*e(7a;ZovofX5GR#Cj~1j4o6<lF
zgjw$`DrIK+dxmy~39Kscy}3H@byZ%wa{6s#{d)~{ZXkKL#7ys<oa;lMJ<E=FGI&O?
z(gQ`H@BZ?rFftQMq3>iXmtl7O|DnRVRRE<&2TfeR{dHyGJE4&fys-XWgKKknOkl(d
z|2X)z(7XQOmX&|Ya5a$OKeqa@@A(~%yLYK>UNUu>@h*>8e0)V)J7b9zKpY_OVU53F
zBJMvX9$E9y;-316oB{@v|4jP2D~oJ3RdYVkpPF5zB=&`voLmaxWK@3W<%7e;ktgxL
zni562|J3Tl-jj}39j&^PHs(HccKWT-Pr09Ew%=Zf>-_q5N@?rW`fEY6d&a^ZbZk7@
z1}67~<16Sm<!YHBFg8K;hezn>X&<`FfCw(`j9r`cyww=8*Bp}HzrXc&BH4LDOHwZ;
zCX`12<d1ZUAjtpz`~Ui#v%dP{VpUzIIrzQq#@l`FkHv+qG+k>UG8b9ZD1VU|5c%hS
zbBhaJGP_rLJ;R_fpVC)g0Ub<y{xU{`XLAJkTQI!z#((zlzZPD&Hm<qpvKUh5P9Yx-
z{M>Mks2_zm#(XWR-Enjq#&kujy;6*h-{cSkXx-2yIrAh2Q%BnGiTMvxV6O}KR4$l|
zgLmuXC4kl~t7_k~-p28w|M#<k524r9ACIHW^3`i=Dm^A!=eAd;pGjvtV*ek4s9UGf
zYZ8JShx<ku&dHF!fB!yO?iBV<nwh-=NrWe*xmY*Ij}r|8N8J?n{&kBR;P*EIxkdhY
zk&b>524e3VCt=}#U(SFKU>RFd*#FIs|9(5f4yO4S_Uq{U`|=eyjQz^rTg3mxyZ^P)
z=hL9|Hmfi$_TQJEbFiIy{`(X9pR9nf*=vKK?7F3-_`fgn3JYH1{c4B%Coh0-ya2+H
zTxD|e-<Q!k>W|S+yU70}3Sh`dIw4IJe8KyFU$%|~)Z$DHhyO$9)E}y=03vzMnP2<&
z<$5tXA+nen6Y?MY08H}+L|W`AeFfP54}ygLza+FwwD}Ni&t?_%&rI`rZz$MRdErW{
zeszfa=keG=0j8s1dM^;@vh}yAdbIy60ek;en&%9GIZGK(8aBI3@|II-tvbq0P4nCe
z3rtK2@Dn6+_fh&@>sFggFrdd#zJsK*xF11}m%)S_1g<aK+C*Gdu&fRA!>`X7Pk)Wb
z<UXx#tp*-`yC+UnSlt>yKZ}n!l1>iWyZv*l3QZ9hvAc}ZeB$eTS&$_YNO7&{FMsK=
zdJzLOyIEEJHHt>uHn2T$w}=L(P;fT0T(H5M^Le)+u(!H`l!jH;1B#;9OeA!lYjQo9
zNX7T7P#pWcJrcF<eCyI>-!kw;OWhgW{QdPAz6gRN8#J#9!;E}mte~?&)Q>p=BDX6&
zZ1J-{gjK!Oj!pSMVC~?zph*&+8UX<e$MY!-?n(P-N|MagtpYUe>YwfB47m-l&8t{4
zVb$JmnIAHDTTvI>A{vbo`>jMruM5Oj>VrsJ*pX0Ac=f{271=7`gYWB@tpJ6P?CAyv
z$qtX>a+Ju4HvU>wlpN(99j|`I>Moq|eS=Yg*z>^}M&$EE^p)==k(}Aow2M;>nY)HK
zO@HBgg`>)q%q^FIWGFGh*r&{#HWH77Mlo=p{*;6hpvT9oe^RY0j_#+x?^xa{2<{Ju
z_mkTAK|xKtD@&}nfNE`4#&`z&dAuu2qPPS6*#zN8^z_p@X6=>OXwc$G51Be8c!{X1
zH>gh!xdLC1!0;{^kqOMLTQ&+Iy_UZXaGJI5E6G0%+E%rgXy3-?ks(LXthSicr}Z+8
zVYuUR!7UTb(cy3{-Yz&BF$ys0g0ZbOoNeF|VA5+>gY6LSs2!ILjH1gOkDN;vv0Eif
zoNAS8Rd$ci6nWLUvC{flau`mj#1gD(jJf!}UJb0Oq|b<4K_w_Lf53>UTRQ?7%IST)
zFQjfQNzaA4SN&*>@oL5an_)N{es;+#<UvS?CDHw{e{n}YeIx*%9gqp>kEeIJv#Hjv
zLEmjn5tqmrOa;IV?Qp*wj6matfAncTtS<v6ZTQt^6g`AhrW>zpCXDeNTE{4Q`J%tf
zcss1Ges^4NJBlNBH?58^VTHky8xhf*e*^pJ4Z$sJJ8D(^R_>idfpUp*3Q`a)3{G>b
zV`D9wAX;Z!qyOrmQ~&CC0>Gq4&iT2U(}9c#;7zK}QFQP~M`cgLC13{)S-?76BFvXD
zW8vyeQ$)?Ku%E4c2;{jVHuq^ZDDw>9%uSa89(u?Hj*1X|)USDRnEa1v^oRa0Bd#HG
zV{?z9FM9cSvWc{(PfdM{c98lYbgujwsBZ8jH6L_>EjvyH`tGdFDsQogFyjFw$728`
zwdRH|(9vf=Brsc*PLI2aD%Z+p5F}UR;p$5?rQmVu1umi!5bPqFU~h}iYin!gwO1DW
zek+P2zjf-WMVe&-9w)Bn8bP?FsfG$yaWWRZJ?Cqu`BBW0Ib?#9zDeFiZ}6NM(D|5_
zRRl~5Y=m2%M!P*JdN`}cpyn~D+dqtebc^0=ha@OT<#@-1*H;5>tmeuF=%zV@G9rEq
zSgpi{3LTpE%TekGaNWYGf>@UvN&}nrf7Uq@N;u|VEh~*t%*hweLQ~EhFJc!gMgSJ5
z#kznKc!R!D_4lS_S6HOw#Lm}ORj9Y`UV+YGQ2O+-BvMsWk=MUF&i6YQiK^SXaEN?1
znXr>|lQF%z97cdDT`_;dapryhWq+U{enj;+ol<{Tj{=(uP%nc6Mj)^HQa20f_A8tu
zYw{5hBSD`n-rQV@)Gh)FjXdY3FOz?egiil37gChZg)-90`MTR8in&&46VnV-X6^K!
z@Cy(Q)Y?%3KU2X;gc8ETqDAq{CG-)S6V0_ZITSxXWTSMSx88-oszs^SebIX^k~q!Q
zM5Xj=3O*f~c;c%Rc*y)CE*zO!Z?>^Yv1Q^yedZJaYOQ3mE!8b`zSKWxP&@30v9Cdb
z^!rhuQma|xf1DQTlC`Y*Y@hvhcQ!o6Ba26~^>)z*aQ<-LdyCwG^b1~6^yL}=5~njB
zSvajxwNw;<>Up+45TtId&vmNDrp9VHY1gJTPH8f?(lpb{I$A#*UN`T`vhAXf6fcfu
zMnrtbzq0zOk-&&h+hFl*O;tjjM-*UE>(qg~#&}}i%70jr#AZLlIp8!I81|{w_BWPu
z&_b%5b8cU$HQ@B9TO$GQL>%M63D2X4BUTpun~o@MXW-mC9n9=>v`B|$MRmdW;6r`C
zb6Nc7L*8>f&>>PoP(I!xzU+s3L@Im1_Ws;wpx)j&3cmjvlaqoNQ^=aOgCim)uWid^
z?VU%AO>FN$t}`Jbgmp$lF)bfsCd54XMm$q)#^Y$m&ae%Jy#9J6fF9B*iC(VDo|Z1y
z1gBa&)H58~sH(4}Vz}8bm)z{Xzn=H=Fl6JR=LTuYa{*5#5M=*jB@Eo-wv^aJ*ZNi?
zppBMARKci7?0Vo<t@y>UGeC8SPIleaAoHJ$uyy>q?bjHVexvSSnoq=tjruVK+US8u
z?N|Pfk_Kwi2)LU$Zzo}F17_AigPNO*etdN!#L$UMMwUL7zplp3QCaw~z+Z2CNNfHV
z4YxYTVU$i|BK5Eeb@Fh=-#ah>U%KPMBf_sO-B=!=b~f}QrJ-1{*Z%s1kfEwTt(#6)
zIXjBE?e&V?l7dL&^4~yzpizzXqedEo_oo%xKCl|rz1G-}i#j+<Y3P<txn&plY4mv#
zI<k(X(Zl!p!+Ht>0Tx+wG(bJ=cxXWcRI8K#Z)VBFw{3THA)T%iZO$uVvX9`*;@P{9
zt^74+1qVQn5Pk=97aY5Z@;PsOG`;LbmrrSpULWZhFkAw*BW_q$z#9rf*+~?$uF-nx
z-}9M(FupM{8ON~v@iy(N$~<?IHN?W<HuBQCX_mn8BH@evKsHXrOnu+9EDBpDxiy#n
zSRJ<s7+H>s*%ZE*eaw7+$);_2#b+=YyzXhn+qrpn_1$n!hQ@!Lgdbnd#aX`6$B(QF
z)$(oZ4s<ah9Fx`q1%?m0FKhtXlv{3q`TCC<-32THt;SxM?k_Z^xk8oV&_XOb?IfW4
zGi{}@ysb21+yZ*R?#2o*A)V3&6pgArQ2@R3b~8NR4W_J(#E<P&Pn&r43$N>08|)fK
z2ORMe#m<o5-u_Jmg<HhRvfDeg&(nVm49~xD5}4ao+ni<tyb3ht0p_bcI91>j5FBbZ
zPV-S%RrOsSRUpeJ)rvgRfpxy>_jsRoDPbA{!&(5ZWpU@D59Ge6dpFfO+c;|7@$=#*
zJI!!tDPpkvpsO)x<nJn;*lm0rG!1qE)ZvSUfV?8aY_I$B{+#ti6JLW}qb{FHEkhW%
zL0;9jc?axLBd!8bpc?JRm!G(M5a~VOLo}G|nuIu12<!~xRkhe?g2_<n(N~8>(JtWJ
zex7e0WhX|}w-tu&Osw|KWfe|0WGuC9uBj2*jiNS#2yi4gcX$IFd5k1TaW%bDS6N~n
zG`NnfD4AyEYuq>gy?PM2k`rGv=Kk$qBh}3*RT9%u<@52NZ^P4Ka%9urBXH7UQ$EEL
z`)8Xoi$B9*!0zw|v9oLV?ac!$m$}|z_0>wJ!WGl*k9yGgQ|i{E^uzmYif&8+`VZ@m
z0qNcw2!}>zaRaIU)P}}^UecITLi$tBlc<K9i5t2&a*X}N^g10T+Vf3rXzG4pYV*S3
zZE{%f@w}y6TsceR!H&hAQl=+>Nx9~Bej3}ppcdUrEZlTnAyNhBK_g+bVanX?7Gcqx
zItcuD5pHf*)z)zLiuE;MYK40<OV|c7>Q@VcV%<vYfN)us9T#br!tmb2%+LKDyuov1
z9e@`{>X;Q|S;l6!9q8B7RiZspcA~OlGg4AZv7SJ;+U8YZ2c1y0&8u(To&A2~h&<1=
zOBzPeOkdou196eZ|H|UG0WApOP_0Nzmy#t;Q_92XDvo^Zrq${H32H3Z!6Xh{?v=&$
z*EyRQ#ey&3Cy9|ztvA4P?pk6h>8c7jSvw~Kd<o_cqaju-ofsL1IfZvO3;hq$&&db@
zNi3Kvdqnuj9MM+3g}Dz${FsrdQ&KTX&XSsjBX1taf0na5scY{w%^$7hfB!vZHnDiI
zO?4_Tf53-c8iaD@xh}TCXw5VM)E|p^d1W3smPP`;p^859`8r8rfed=QOjHXS35CI)
z4y`qXk&n>n_eMkVfzC+4te?0cZl~JSWgFA)*g3D8WG+5U^ndpK6*(;Z^2tr{>1Hyp
zJ1Z%SY!UNsR)MzQ5W9C>pNZzI^wc5;ut1$ACVha(pN^TI3R&cPjUYu#%8pEgEj{?C
zml<qL&BXcgi&S6dtl~B*n%iAouBj#nL|5I-DqJNV5l>M3H7E0+TaJQX?1Ro?%H%%?
zotN@FUpV*R8%jW8_f=b<`QY7GP4{Y(uZNwxDC?IyftkF1)nHP;63PE((@%BvM^<cQ
z=aap+R&eRqfZ6rjKu9O>w-ukg+GMjg8&4=?*zOlCGSRofM@9OWcQa|{JQNgoyxX9W
z(zDEcJ!a~G{~1j14sb+&X7uumj6Vyh9N91WD?V0sKey?UB)_=ZdEOmc*AAg$4rset
z??rRZX7JXPRnN}IJ0bh{5ntyVr+^o`?eOc4PcJty**mmu$BgnI1k`Q|Foo<*2v|te
zmT=gcfGF9yS5h(b6CC6B*JFLlkMD;A#%C-)Utr9^IMwB{XFz{mVf%c&po*H5TYvR+
zCKYbolqy7I9*<>MWs-o;YFI{LwI*R5I_aqX$D*8@?|UOv(6-ZQo%l12Au@3IO~l;}
zIJ<eO#HFhV8}exuvqLFVK~=;#V+7$XGBMAT{3ALlR*uq86R_Fm`RKe5`G=F`HmsVP
z?UW)1jBTqzov+Qopy)&g9LaG;N*;SPqf*8~EG~AvMtpfbbA6Z8{2?4J-v7Y2I5;p7
z%+~5J3#?l!r7$0wvV<@ApMRggC-OdUKSzLbEu?TWR$KK>>jWCp;`!4eR6bj7`ot^*
z4mafxF*?1ws4{(M2qtAY-0Xa_lWxAB+~OpfELqylFM#C#kF(Fex3}6gpCXpC3tnj3
z`B9M;im?C_a3bv%LH!fGgF*EiUo4LVcBVG5P#xDPHXm`5Pq!w8K{Ntt+!CvDvH3J&
z`;J0~D)wsQjIBS)3;!0(SPSd_uzHAu<et41HT{7A7XYe~S@e8NtIgJEr792VZBbKh
zWD`dtAvzoy^x-W6t}k(k_n`1F(Dh<gumBix%!*hVt2-3I5G|AzWa;Q9fPJ1|DF{^$
zpm!q^<TFXfy!g62nUN8c24!Bwoau(hT*b-G<@*3z25{`-X}d97Rny-dpjXo01?-;;
z1v{9ABSz^IgU>N@PVtGr5Z7}@KGlJ!WJPa(eZJprSdIc>V*|TuI88vRt}qDPujA4~
zbrR5a-t$)9;LmwxBH{C8KIT0RW<?f#6a960=Z(5T^DnwguDhm+g7DI;^)3FSLiPLu
zR@O=*;TqN#DK){dCz&NTZ+HFN%8&3uIjQyQuu`Xh<KmvsX1%r3liDnVCq~#z-JWn6
zx-H1k!8>)<a%7$(QtA14>VD^gw-Z9U!*$LS%&B#Sv=;@+3Uhxk^Fx~fHZiM$HLSrq
zvst|(B%l6-{Dpvv&e2CgXMsM-G!@-=T)zkTVSimqK5FXOaeK!RiIDw3oew!pK^x*1
zi#C0*zG$)AeF-^bhSBRg^JA!k!@;68HM8gdFuuS${QXA*mYAyQbUu#Z(Y}Fo7Zvbi
z`sZUyl6U;JQp7Pu$E^6UvrkhaG8|OsKSnt%jmMA_u%I00sSsT$g|i5=o7!5wGi5-z
z6r1Jk`hGz_Rn2mLrA>BTmj`q%DDmWn*S`XirJJi~E97n;Dt|qTSL9xuV{6wiE5dPP
z-7H<hr5rWC?mt#5@JRqTa9v?R%UW8)x)V8kXAGH;xi{`alU%>_>^KD&DlzB2SkbGb
zT9=n{%m)+QCz_(x1)n>Z>8IOiCg*XX7UoQ%lz7fHpY{yNU44Hzq!zLi2|e7KzSu9M
zX(b2Jh1OyqVfK{f@A!rWq5*ua`|x3W>d(i^RUejIa=X(uGQduP7O$2iX>hFKz{oGz
zb%o)a2*>lNoltU;!s2Y?g6?_il5ar@qq#4Qc0Xp_tt(H=1qI6hd6f{M4LI`LG)Jgb
zBzzb6R>kX^f<^smJppr=oM}>UyI)KSbXNKlW}vie9cdN;BG2RN*_@C}@tCH0fctkY
z&)lnCrlyryMsxA?1Q%?I*I{b+(nre|o1Lfie5q^r$Hj{-W@CvAKYUwnj}SMHi`*kc
z0;77h_iFAw-$Afxmrd#|-(pG1YJm^0$Qh#+VZ<)1JeQKvycRKF+!gle@x9$d59g`3
z*(?2i9`Sf*U}z9aU~0z>B~qa8s7O;IEj9BKuqq3_tx7w7Rfx;W5%8MPBfa&gy0~&1
z_Is9tQ|Tb5k(ir5SiV%ZemGZ3324ULDLNe3^CKV4u#cwv`dZRU)U!#SvUJYsON_Xd
z>G-y1J~DU#dV=JP{;+qp=t^G1k94Plkww#*GwKyVyBk#+sWy0?=1a*;gZImPTBzfe
z;r-Kg8$~br7sg~V-!dsdIZ%81%OQ8}l*RO8L4Hp$uio`QIT=4cs!f~W@6fUqAo8ay
zra3AJ9~NR0<yuoTe079xr7=a;LUB2`C@gFuXjErA+DC9+!iLh|=QJs}JccL6tY-;b
zD5a)FTe5t0{MEa95O86?lF!Bg4ZuQ%U-zXG+?swCk6b1iM6L2eZ-uz3D18-g4(aF}
z7+dRPYh3Bw@knY~uH6NZEo9~Gkx)4%7<(snG76;J^wRjM92T-pOgG`k548659(5PU
z&cFE@8`SZ_6|}dBVtjv>gx~5pB&jGdBXimRE|EG9oLOlO5Ezbm8q0=a#!s(T3yA!x
z!aD^;67&K@)*8d)o3i*&%ud+{$KL<y-5Qg}W=|qfdy8%%7STZl(}V6LTI@M41$5UZ
zF+T(yy$Z(+U-x|B#xx7kpQ5ojQN`0JBf~UsBWKKVF@olZW4T%|JgDkpiJOO#*sDiR
z^<Oj9`knvbjSWbc3==_wkm2>SaOv+~bcd-RPSaJ6m~s@XHD}hMJOwZiMiy|63veU>
zD(c`LbtKdUq{++D?N35r&p8#Hi0=HT_mpZ@ma1c0U)TG$PnTZ||JubXQjJ7m?iBPT
zNb|y+7|&O91YJEU7M1!b0<H@j_F&+A<u;flNdKvHP@)$NhbyW5C|=Uf*{n|46ve&?
z@a>kh2v&0<#?(R20%iT&FNP$eC>rsGma)15B8MwoC4ly~#!urk%VHxm$YF2FtJfX!
zmyY5z7jH9Lc~KfZxz>OA<=61pJ~WVeXhtml0)p&x&{WbYdZn_@Qp+K`JbyZ8dM+`>
zLhF9#-y5F6sWyVDJm+NbI|jsV1Y)iv`~#mD4;_6Zm)X*tfySyKWm@j(7*4a#xIqJz
zM10XmARJ4y!jF`A&PyjqS8v!a9QK-Bx5;sZCZtA=eUV8Np6uX!*6x6w9Nm}=vUb!J
z#5I67DwswKT(lag_VWxv{n=_s<~COrmB2pMo`ND!`3LiL2TmtHf5u-DlcuBJ1&+jm
z#jg!;1|sVPu~$#O<Lhl^dFq+(Y~@zqaew^lQe!&R+N!raujXU~ytjC?cO$cGH1^_1
z4?z0oB{nYT^L5VVrdmNk?|p#Wi>n)%;hq_tktCnx!h@aux<YAILu8o_81(_fsL%1X
zAH4mhHLr;Cd|wRm3vUFptBMY3S%z+Ss=OcroZb&1yXKP}!c6|45p^Kxci(){7I=qz
z{&H>1D10BKV)$$N>@LU{WZ2!Jhm;9{_ftf?!?Ot%i-zKeF)8roq?CXzn!8I>F@YgA
zys#Ysa=jIu<%JIN($d)Ml@HSzd{0H_1&~)|<026Pg|eP4zHnV8<Xm9O!zcczz=f++
z6?)Lt0M;!nUPlTR9}Tr4#(b}o6U7p3VgSEhyjU(vn{Hrzk!V{~!pEZv#0x_XvlS>O
zNmw?uWut{A&ivVksWjBeRI-{fm~N~CZ~HJlhHdv{qg598Z>m3bzZx_wqHg_OJ%Rsy
zLvlbvQ6oWDj+vwC+!>5}dHBobM_*4HL`5k@!e<}ExX${c6f>I{&tO|V#-itT*5{WA
zaGU;`qDjLDklaR}ow!H@g*!9@h%z}H8`Qu@8g0E)kuqMZu~`IxuGAV<wlxgVL$AYQ
zY>F4SEBF1fJMLzAs{Ognx_=h=Lkrhx*cmAhviD$p#AghS$h>`HBT793u<DAW)knCL
zVH%IfpC1uRe1E@%!)MR`-E-hUdC_SGUtk|duLsktDGjWMBYDBvX#yf=F3}Y4PFc)y
z^=!Sq1g6E7snRy%38+LFa<ZMmel%GCN!!y5RcIj_7JmGy5$(q<%B5Spq2dt$UVHYu
z<KS6(B>|5oT1y*eK{lc0%sFd-zs7c$#~%p|0zS!xu!{<yp}I9dK`WSV&H4ExHT`;^
z(G_--|LBE59-<~u5Hr*NI29q@4U)vv-=HPCWHSX0M;!c(4#}@QJlI{I<c?e`b^)At
zYYaGHBEAeNFirGaB3=oId-;T*+rZ(_IHs4cf^IkKE|k>;PX%m1qK~TM{_1pB|JZ@Z
zB{Vznm3n97nodQ)=F*M~>n`YozM}Q$D5W9x1!Na=Y~BJ6rd`~M8a*Ulh?>j9(U+if
z20PGj<T>Cc9rDf@%uHGDB##KaA=lN3*~>h>wva7d%ZhkG+%WDD)jGG-tuBH_h3cu2
zM{|h=<q`a~K0l|APYe23Y|%_=nCkr`nkcpF0rgbQ_o)u(Ck;VWWK|pUiz+`0Rb(G{
z0xWIB7*vhNH9aN&?j2yv-bbpB@Od~B4d&bM)BdQ1JZ=}e6ZA^na+?7n&NfGy2++!(
zWEy;cRf6MJB2A=Bfk@qU>I?>F;AAMii@=_s1VfY=BKpq3Lml@-FAe-T7in(gi+(cq
z8F?BKfg@)gMQ=2QL6EnKXe{^SVLR1cb8PV<9sMx7q8l-WIzIT9cEp^sSo3Yq(L2oa
z*s|Kubp7ibWt9@!P{%b>aBz_xiH*%N``hdNclv$IE5IvPs!X*C?Al0nc$$rV4AQ@X
z3m0@A*56~e&P4lhfwD?jQ0PxGVNb0;CM$HZBz$q#V!{g|0m(+EU%an*5t~gF43h``
z$z-M>M}Q`9M*aff{u|K;($*XdeK^hj#n1D7HML1vg?EBn+rxxSO3x+B00El287Uw0
zBTXlof#t(bmKvMm^!WTusC7FLUOBAk+Dp_pzDjxv_y~bX0a+^Ud1EX?Z^2P9vLCng
zXC=J1acQ-?ZK`mx{m7G%w<IM@z_0yZQzP6mx2ub*{I-A3b(ZQ{DCbVqyzyGr*Luf?
zT$SnA-l%O&J|Z-M`fShkvFi2L?X~#rC4$cE@6;=vx-8Qma(^$ZxYc#I*LA-!>@5O7
zVa+G|h+qdpY@kGZqf6(SwN2<S&z-^pkHxQZvw2K;sVR4E@Bd<9=)O<vwt5JvmeUDV
zg|7K2MTIVboYGDWju<1JDee<LY>V^XsMY{ap~7pT!1zUY;HnJPx2)E9&LVO0(1@Zj
zcL0W1c0@dFD{>G%L#+i|2tpQtnlN%kd9|p}>yY$|1?9Ux_pCQz<h6Gr0oDE}gg*kB
zP_@(bz`Y?zvcJ?ST|>2uUqot4-Z#sh9Ok1q8gMiHW*d$iAjT9dq^e(dr>>Uu1t{9N
zS-ajEys00{U|RH!UGX)z6BauP;-`pXEdtLiYS5JFjAxc<-FLePwkQNomFtdA;qBWa
zwOe;bR?|pNarg&`mq#3P2ylzZw>_-{=yV<{j=WtxwXsBiD!7f_-o$(>kd*v?#Da$O
zK(CrsrNBq-yi9!|14rHu<TUc74t~w(?d`p>*#La@Yj{TFg&Rpz=g46X*1?RjX@$}c
zMV6$pss|aQ>;jUruup1BYzmad4KBb9i%p&D*0Jh9XU-k0Px9pHlZpyLg1^rZ;Td9x
zGIw&=@JL!>s<$el@Q&}tsz6Fy3e+MSWDU955TBn0gYrZUzr88<$6GjZb>7(q4SxYJ
z*{&<BCyQ!5ig#RwJL>|GlfE`D)Sa!KAe3K-4;n49T4=(~@MYZbiu#cnEt9|G?HO<s
z-8mr$3Nj7~WnZ&+s#2hq42%eGLlF3mR4m;s5DkYKhmZ!WoaQm&*sC@@N8)~BI5<%6
zH#J->w!ydvyt^QVc;9kRl0Fgu2>5kIz2Q>WaXn&pRHAzUs8aHXU<)H6DGhO;C^Faa
z*&@{Od7r}YdSJOl>Bc7*Xw8VFqx5v$`qEUBB_MK7Z#&+Ci0<@C(%ozfTf?;YQI>lz
zgcm%k_W;pBTaK=<5E>o|60!TsbHX_C8HxA9Po6caBGi<Y`~)$I*p9V}&$AkaLV}5^
zjy@nPTdT7)7T8rmwJMMNkpt~-->d4BXWYcYgfl^QRXZ9ugxQg`FH+%bir0XufAzE<
z#>Oj+W=7V|91PuSCxoc_BsSXrk}FIDGZk!syYZIYt=UcYbgROc$`v)-dLuQB?~`bC
zuWkUF*dZfQ=FXC<$gkuRd>;eqmoMC)Xn>b^25dav4IXd_uRjGI;92&Sa6z<XC?m2E
zUlSqgH8Ne~m7s$0^~2#0!`<%s7(4M)X~?*FpSBuZkdgcr*l5c{dWR|<bySZse-hqx
zCPrP_FtL9kk$WREnaL>Vz1`K|>p{Zz<nEJQXNth-&`IMSBS8J~l?OVw3*wH1egFnB
z-Xx<{%bHAIPCB-@n3pF5CO)_d)R?PcSdq;270EhnM5qF(c*@1M+j6=l+1P?9^cJI$
zuYt-~U0}M^s^ZDeHT9O*W3F=&(PYA6ul_|$i#bvd6SU(MaH1Fp@*7aHmpANX)Uj3u
zCK7`sQs2!dcCYJP?E-j#`}nmB7~jgO-gWhgTF`$Y;*cW@d;k4b!c!wZ0#x{Hv^I|H
zJ@3GaI->|$I{F8OV4p<*Hl?5`E}@<PamA}5UGX)~1-!>nM6p+6sKvXda(BOb9Sa4#
zC|7PgktY*(mfhY6U|y@AE=R(}E&WB_57!vXIOCHWIOIAnDKR2Cr=~1{A*WU6Mp{%r
zJ?JAiZmC`@6sYCn7_<<0rq>iC>V0#=PD%KZ46gzZe-ZiuMrIK9oU<IO$<IjVYk;#m
zR)dF$ocHBLVf<G9G`ek^02<v{5IBHGI!-}g3s-;-@QX*JI#LSb8~ufeg$afj$_vDi
zo9A!)QXdG^0o+CaFer<+%z<}f!Qrcl9(Tmo>^9Zxx~*3*ai-uC6sw)r&PB?{7kVWG
z-JVA;-L5R&xrhHO?2wn3r2c|otYrGSY*DjnOZg86vvY~VFEOqmlGqB552P{u{?#*v
zsQKv!PN`!*X479Pp%O#DkdJLHG77{elE6ae=eBin*z1mWen`ku84+f!tFEZnsdQ4f
zI7cRAv4q39P`8(5EVtG?$YIkiCy(gtrl_BPWzGV!U7;37YJDEduaj4&iw;FHDR0GX
zgnNA9_(G}U@5x~Rs94ynMzg=Kp3xvL*sH+XNN+8c1O%4?n1?2vt^V)fUnAlB)bZ<^
zraNURP*DM1mO68m_xzHHbK5ts5umySgNw0wAjg)wdlY_uQ5T~?%iOr;haU;;06tRE
z@GXyH^u4|KV<bnOE<x%wg~!%YjK~x(h0`t7v2?t0Qw_a^dcFnowA0^6N0D~pyQ<5A
z+@T%4p7QqGl=^G*mF+^F=S${GBCCDiI8FLjfrZ~!$KMZa@`0!Z_?!!d!(|&XF$j*O
z@M@opWVd26p;=ZIXhv1=?t+Q8w0f*7e<W049W$_f_VZ^JqoWSuG$+hKdnUN;Cjyyk
zr^#S|FI@16ZgMFAnR(9+@ZP|4uHo?j&sBdFkpHUsGkpue{%e^@Hfr)ck@F7AA8S;i
z>!Y3(nbg&Rv~)HT@?d)pPo!3#kfZp%Em+N;6I|BQU(+2qEVQhx*!jI8nIO&oo;$wu
zyQ5(57|CBP>t^Q5*!})h>+7W#I)Vz&Ld}M1h(FtJ$%HlKmTmX_o%MBFzGK+8QxEPJ
z!9Ih;ll{lO6B3>We>*OcyHZpwCi}EAZ5jW#vWC=dT)_HvuVb$g(Y+)kfbaNaS>>L`
zg!qy7yNnK*;Af30HsV6Hi(>&q2>SlhECb`8z?SKUl?3?quUDf`IKsmUhT!%!kl+YS
z1Zjx-R-xX9UZED{t_3KyK(R}T$&MS#Al{)PM{Dz#`jA3NS)8<#LKjIz|27Y*{eYO{
zPdCK|B96>pPHDTtOn#9w<1M!-8o}O!WRDlB!a~LJfPz09jr>ND6%Qtw<{HAF^B>vQ
zuJ}gVy_CH@TRVF=5|@xyR8{BBCBlzyAg3R*UQj-nFLQDe!-kStiAf4f&!j~(A|V4(
z-cWFNP!t8afeFDKFd)twdKoB4ZsTM|&sXXP8nvF&of4b+2XlEveF+M0*VKxn^=St?
z=)r^apP%|wksJSkzy!|T^~nln${Xfc$C!<>U#R2I69TD|_T`FOb0~px*cQA0tGz1^
zhk9+pHAYMw4JJ!Cj1&nqikQw=B2tE<j>HTh5h=T(25Cl4rJBAhBV%w%iAdS9WEq8H
zFWHiqVq^(fvZn7D-_>`T{{Q~?t}B1d^<J*u^1jdSS?>FO-uI?`KK+plXf(DK%tDeH
zw?7l~R_Z(wC42QD=1swu0vv8bLPk>PyvKBZoa1V7OE=O;1`1PJxac?Cp9<uk9*y_W
z<XqXCQJ0Y=OS=T#@bj<A)>PH&>v1pkIPFI){DKje>m$Ij%>8Q_J;TGBaIMLo1Eb4%
zC5?Q{9Bi9p8#39TbAfi?)};NDV(PmD94D!pfigIKCO~({)bWiCrfk9fi9Avb0%+MR
zCD7H=lXb7)DXX+xA54j0R;hZXR#S}e7&J;Wq)@!yCD+o9AZP$7itLNl8<7RAgFdKh
zX6aGwQmXVinF)08*^Pb2T{fyezV*8X2%x-+H8G`wONgb8Y2qxW^9c@s`fWv9(MgU$
z`9(oP@<9UO9;lHGJmV_NV_hn@hZ4caYmHsCPomm!4f6=0L6P`6Nx#31R-N0)gHRj6
zhvM5=Aw6cja5AO?`bhLB6(#1?Q~Fe1B=~lWoqHRia0DBNr?fh7v-d%v#|WJ6ngd!O
z+*%~*Y$%&-Fnchpo_ZoEWV|7CH^f8<<zrGiK5&BBv|W)jAxxH2vFZ8pD!9wlL3Q2Z
z;S^aq8hKq_k2JHtrYTs%zo`4xYjLPkAV>CwK#?>U+}u|2(#&Ilvs=da%YecLcoBuO
z*V|F`hp++)Zs|D1aHGa6oI=P1ytXAatt+&+%@Xp@3|z1{UmgkRfWp5%&A$^Am%Jlo
zw3HIIIQwaS{#n3|dLd4I;bH%~Q}(z#MR22A{c9Mgo-+S<<5A9+6Co!dWpZ8f@S)GC
zN>kM?i8Rxfz#<%}>!~BIBA%R)_PE~%Z{_riDJ!AeXAj>}MU%nTlG3LE<~wTvU->4Z
zG{Dlsrdqmj{`#VQR3GlV(|Gvvk`kZrhah|_9bGb7YK}nl=zV;5AuDC7KPE;KEqP?@
z@{DtMD1$DVf5C=R5!tZlR*2BHazCrw_ohZ_Ena2x?Nc>^tyiAf`jv=kgb^|l5zaD1
zL{N(9ol_)i&-Tw{r#u=qxhUT`&c40(Ve#X9o26(Skt&MSPSL^P*Vj#CL|fCmJ~GnE
zgfzgaiy6K6uWq^e{gl}T@6a51T((S`M!a<s4Zf{y3M8gDi!$AMI0H_uZ_<!-j7MpK
zXxRPnYl&Osor38r<1ZS^h^*h7*;>`o*&6$)F@3?lo$SySci^#a>YpZD<{FJi)cbV<
zg#j|=g(i+Njd$A-cq#D>6bPKdSjFyH@-G+?K_0|iGhtt6TBRH*FE)g4$Na<cO`@!~
zU8T5-Dl?nByv8KunVD7d&PI)wY|0`C32h}kn+Up3mnP1<2gN)2%y82zwEDL+aObZf
z>rhu|U;?`rESyRJhc8Ld7IojxLl6==v{+L_+bTu6sjt2q*N?S$a#zB69OtB^KP7Iw
zcQFsxBjBjY<dqDYXp_HL`d(UG7+>i58ajQ?;ERVIZhGC!3w_XUDS*EWWkSCB4k@nO
zCvbWi&UUIVxC~+dhEUe0w<U1&VM3S1sePP^S($wdA2`5X2LfGhGS0bVSg{SFD6Qy}
zI0Ho9&abCk#)RvQunJFF&cEE@EZI8krt-&e)Y>fsQEW-d`e`c8d0o$LDIo8k`ZCd7
z_b=;m8wCqf{T#UT=1#4U@K<)Lh>Mj9OWE^%H-B%Vj#!30{HO>q7wXUv<IbJJT39;z
zGMg4`9UxOU=cY=nOns{r`%m4$raGjRIviiB>Z7D-Ldj3n#Jrs+e!NE$;<2uW%NDxF
z+7$iI#>}2MdLr*wn6MmBus*h7WR+~)a7weNGBJk~AryH(4lMTVu6T<UQP|kEKpuES
zM=msSd;dx~=?M&LSn)Eh|6TTvB}!X7$&=`#I(eBNcE}C6ovZ!YKQoO@d-GheyD$PZ
z@UCkpAwr1UPnfBl(An&7K!gm4MZU`-`~9}2?_fmOLO~eWv4~y=+jDgJ=1C&V0XkJL
zJRNhj9EsdQtcm=r#e~GWZ2$f7<WY}pgpQ;y)%np`758s~TJytN6$MP!+p%{n(}RL1
zy2v@&r#3)2G9QQ6RoN+~*Rvgj7@rHuLRzTf6wh%l1d_z7BT19B7QHJFTm3uGXW0TS
z@*~w}>uto9?`S4e9dzI(bF@sSD(kB71ACTMJ@3}ifb!#ITINdWu(^IIJ<Mrq3*Sa)
zX@pQNJ?XT!*~Z+d0z9DL;jF*J7=IZ;ZeXk<qN0b%KWxsXezY6)v}g^gbxFkF;lMvQ
zD3w!jT5cvUc6HFu_Q)y7BgRKMcMLp}+@i>-NCN%~PJcLphg~s~NrK^~kkDPVDLIM(
zg&xlNoHkH^tp1cX0KQlh^%le02LlzK?Pm(_0>I;0qeY28y`PE@dcUX+-f{t+XvU%h
zkTZEf8Y;o%UU>9yaf@xb=?AW7PTx9Yy8LiS+DhqZqVChvM`QgJ2lg~xCFnGe4H>ws
zC2{uR;O}8CC!OSOr$VK-8|(yeISy)pGDGcK>E;D>a{Bf{9@zd}Z8CC}E`yae{$DIw
zJ%)DW;5e1q@fY111;O22<t`;S!`4Qe%4yfNaDuOId?hL-VZ3)+*j(LEf_JH1lI*99
zGF6q5ZgU<8RREyGn`4p?wBitK8hRK|uX(F4oHGjz;>aOtSOls{ESQ`D1e4scHnX$C
zkNc8_#*VCVED0QL15sXn{$#<1I1R{@XcIi}Xv7F|hc$<WHyS$SLj5w=%pmuseu}P^
zyhjlw8iSI7J8;4_-~Gjj_>9IszT#E<^mBKuaH(~KM!~%q)g~GJqmih&uGtSSZnuKW
zxcz$Om)~xbiJ%Vo6+}R?>KDjkGv{6mJxX~4_X5Fxw!(2Ts0fY2DYg#^^MH}LQ*=^s
zRV)1s{pHa$qBWXU{@1R@vS~~KHdRpW8Cl)v+NIUDEjtwBbO=G?PQPyMdt*DH^$Qw$
zs0!EKinDif1q5{$EaBj*2t7r^UL}OY#y;Oc$fA3dgf2K{3T`p0dH=GKc4D)#VOp8M
zS^-2zxSSMTe<+rP!uaitBy80nn%Q9()=dJO-CxbdcYqfGtYO6b^=Vke%ZAz;CKzJF
z!wDUk4;IVS{tg_Q&Lq{wt@`Uyc{MUtPPtF6W)4f3?GWod6B5ou<Bv=fXm!u~Uoz%K
zCDJSQ4G{V%;2gtX4_=$Hxdv|=HWlA~H$(*&2Cj5**a{`ArAkLz$4@Z4k4~=i&1z?_
zM&BaP3P>L|0abKwA@0+w!qP)HHFB=uwfOl0#j78q*3;|c07$<hCn5ieAvEag%+tRr
zg84_jDga>Fbj_Vl(P%r5Kigf`4yNFyRTmP+l@=1wv|u8hh^!K}X3zus20+#^;A8nJ
zm^rEF--yP91MYoy-v3$#5(PPY!lLWi&M_p`k@D$z(ZKF$ed+hkKz0cK7xOXfbwada
zAEK`W##gR8r~In)v+Vv(Z%p{9)y#mcL~UYCsSQJ!(7`?6<gkhEq8_KyH^CsdJ)qH0
zz@jJGGw2}}Je_~M!NN$+^yHQ+d*R|l_;hd)q--?Y$0S+igPooJpQ$gRH8FQZ;Rc6-
z=%#-F*WHn7c<qs#=NG3_TX=O+oVItj3JPAS&h<n+4Ny1y?>UYV^2?z%gm7Q(h!V_A
z3U6+*R(63-T?H|hZ9Az5*6^%VCzDN0v@VEYZLq55tmAP3sxkfTze&~1(N~?DCf4*`
zf$VHoO}E^Hw^0?rEQlA*V#b{YTD=9}5#edG1@~mYH(0c6aB%LM_UrV4-C1Lep2`U5
z5qLi#%CkYbws)5bYO0cEZ$!sBJ31S&sep0^=)K)7C|sCBTSlrOXw|30jwk`|CJ1GK
zwUR{;uBi5bQzRmgsSF9MXK?99)qx|4Xb;`>INvUIn-1*wYTRuZ&<>2}g*O@-1U9*W
zmt|8QX}bp_ke_+vQNfdsVptz5aK{rVkE+Z&Y%1@M=F`zM4=q<&fc_<GG)Yg74N=U}
zvahNRXRS~a$wdnPXPoS8v~+RcO$|1xDak+~B=Y71wK%JdS~b5)g~I8~KqJ=*QA3mr
zdv7C_OulZu?kJAUwhpHx*D){tq0QPUf_co+qEB#kC=e`=TxBcH>+Hj9<pJ8blUJU1
zSR?{}v4z|VeOJSEvb>DLmq$UCe)-(h+U^*pVI_Ov9Ecc3Abrir*!~;weECj6xMF(B
zMnZ?%DcklGK}duQtaR%_Tk$ur7CiDlxapOkA5q1uhFPp@&F~2b2pDn*oA5RtU2&sA
z9O%J^9t+S(5DX?TATb$=KuW6mwBb@@_Oq$5SLNzJ9+c2{CDc3d=J_F}s66w|`@B?P
zSS=n&3C`grPr2q77t!-|X$FKJ_O&_Dw+->aqR<&+v@d8Qv<|6^rz~GiAab8M0zc|s
ze;85%RQ%*wZV70p-k*2hoELB`m)r(%M+#yX)T`BDjj;c&Uv&h{m*;-f4}c{-BOQX@
zrfcBNlrHO@$1*18v@#{?t&-U^=uTsy;{E=<P$%hT8-F@j4N-W-m}}4V8GHNmkP#2Y
zWC+d8ESG7Uph}LlFRq0;!<`q0ZMTcLJ0!z~iEBv);?ZSDKobzly>le7=BD(RTQ-((
z|L;1Xa6k^?#GBvo4H_m(z{{#>XEOPRo$f#yw=wxSf6tVm8hB!x6C^zU@HedDhgK>Z
zSm?}Gxqd&iQr&=S+&C}E*DJRYkqKlx>Apul%=-7eWlFHLN-H%i`38|BUeDUWmfC&%
z!=})oV~QX#zrO%dI2Qze?x}ZUZt@RD0e};Q%+-Zb^t)MdHvu)?ehW1f{$Xh-5u{P>
zzI^|SZN7Y7H09?+f9sU{-%oVo-zfVhKF)p%$0B)a28qE^BE!<51H)UFAT^)4kJm#%
zfZT)7$Br&54!%8_sI*eD3R_yQ?gv}E2qr3WDF3e?hBd)NHP6QW&_5{xIEhU9Ns}Lj
zUEmrnQ|jjYwkQKxhL&~b<saHAtH8{wKG?<b+oBd=A?|69C1Uu8qrl9gUgW6r+oC&Q
zrOj5ZYFqe+rJ*(RKeC1I%o*EE2NO-HwXx>6+<(L;-#G}wWJT}=#xa>`-+vJ>%~9YB
zqFAo{AHAV5Owz?I<OBS}<<gM){G8}-#_)5Z`K<J(MSnAl<uLctb^O;s!;hb?gU|2&
gbRGX!uH&o1VvqHG*|$~974Wyu#BxuTk<*2L0}v!7#{d8T

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/packages/gluon/normalization/imgs/NCHW_LN.png b/docs/python_docs/python/tutorials/packages/gluon/normalization/imgs/NCHW_LN.png
new file mode 100644
index 0000000000000000000000000000000000000000..24ef2d7d3b5dae64abd7bcf5bb2b7d2670eec6a1
GIT binary patch
literal 45487
zcmeFZc|4SF+dpp27|YP0h(Ux#6Iqgdr?QP0GKwbIw-U1NTM|l^6b8jG_UyYsin3<U
zG9+Wm*mu5{&;302{kfm}-`{_~+w0YMRdZhFc^>DnypQ*BoY!-tzBUt>3rs;l!F21U
z#vKX@8deGlsz}fo;9rK>dLh6s%BOd<)hLR(xfg)H&_BLu_LPF++}YDVl(rfDzz`LR
zTN<jyUX;rd^eI=C?oaJh*;;lh*)|-&Cp7C=SrQ&_bC(en9>6Ye$3K#76QYbsCda3o
z$I#sbd7l+%ujDg$bh+(wtg{n!+*jy@1nuXgVexL^ZHa#r=O>?ftd1tjcD)}>?mjUc
zIFXh1sI|?n!{Z(Ih8p~Ba&l{v3<iH33>8oMh|-*)VNs!={I?IjaFyAiLi0eoM~{A)
z--+oG0r3S<Q2qS_X%&2p7W7hUR@l+0IbY!a{BMv7IQSLYtT_1J#{Bgq77*(Q{eMoF
zNWrfL7t&JuKMSFtRHNDc_qCmV<K`umsQBpTum0mXK`g=k|M3W#JM=6UKRkRW_-}hW
z{TiRf1K$6;cZy(RsxVMq%6a<#m={<&-G5BU_ur-eSL**&nE#sTe?829o#TH)(*KRV
z|Ba^qUtyS-k6bTK5l=9fUh3WVv<iDEF8QeqroTh1WrRvpNfo3?6ZjJNXU$XL`t31C
zRZ7Ca<W3{Fh#w#JFA&%KfCj{RX13Fq6&K$8`LovUdUDfYb8T!)OxV}2UmuftGas-2
z`YJ9bx8Hoj+E1Xd@zbYI#8KOfm1?f}o(u?BR6j$Br$H|Eix^y#44tne2w#mLH>&d=
zF8boVcd*6T*Jq~uO$Gcm`nD0i7G_Qnu16C#=PN7&SKi_64M%xKO_mo2@x=AHF3iJ+
z7wgFc29(8MX2Ezk!#Tps3o=>W%14_6#Oan$=VU8yZ`u0wPSL*A$&K`#a-K*TRzM00
zG$LO&kTx)o@!bbcpGuNGVjnm;UGZ6%_ucsuu9JK<eCp(A3bM4c<j-&gfe25w@VQ)n
zyuajcfX|@c-JfjmmHAok{b}|?Wo6~X<lRCjkM{3#iEMXA!Nfl{pcmWui_=TEtwV-l
zsn)TfBlGZ?rGhHy?v<Cjb$*r{l*;J~yU9CP6_bwR_V)IA+qCgRV|}mD^@?JD#Fx5(
z=~<6`#nn?(><wbM_$%|90t+s<S+Yj~I=L46Or!W&)4_zQsnO?-j%*6zOPNWMxr(Yr
zAKakU=fe;^BZ!ZMF#3_@cz-xvh0i=VF)@)elCfg^LcU^fa1b}A{Uka5k~&~#Y5;r{
z10?Z{)?L2!m4G(k=g)gmE8*ecy{qhd6%`fVl0Rk8FM2Z3g3gJdeLc?5I9w(F+PvP}
z<6p?xEmr0F68ZF^Nt1T)6?ECwjisN;Ic}PvE<A=Q&8I+}4Hcxs8eMCB_bLsX4|^b2
ze6Qv#EyzI=JI*78%lo}DUX`8vXU+JM;p#%bcxxm?+aH8u4?Qz+9cxptXsHtF;utm6
zPyQkhr;+g@!2ZNesW#Abb^)A}lYPv<=D>a9akKXL7HXq9;K--9Or)UK4JQ@}71q~C
z6Z^wev(=g%r^MBrCVy_CBfsRsK>4`8$r9dNai;5rq9V_CgQJ^mbT0+e3tRJya<j%Y
z+%JT~Qx;uSXFEFj+@@+hL~94tma@rO8H4NjA^AH$wFlyR)@D1h?KeI|SNk1LH8~ue
zISj>rCyULiSKG})h7mII4+AB7x+BQ*FvqZy>0Qr2>S8hJR!e{X1C1l$bce89Ti9~s
zv}LzUYwlI)R{n#-j`VT=hUw(xqd@mU&VUmOP}h*$k@NK<NV<I(M3K#NWtG?4S+u^C
zn-I9;B*0zbqIYcC)ur?_mos)u>STzaE_V;1t?t#Av$5o9zGGQ7&J{4HSeI<ybeFhl
zX&-UYYSHA17&=$*q-W!Lp|Y1?7_V;`tIgmmHK&`O?kRN}C=Y;AcY4gL*UaR@a20I>
zlKh8X2VRoZZE9V1%nny{_zyq7wApp*nmvkC-!(IzuloKc-PMV8xAGE}1=YdI6Uh!+
z8p$#rb${Q7oz3F)Me{yy+<k)Xfcsl?2=U}@_OBUtAe6hkqp;rIoHHHjdb%BZzz?pV
zcg>tw<Wl@DH8nccQEs~<(js{@H*;NWW57?kc-Kw%;KWdw4z2Btiy4rxKXU)({Hz}J
zHm^)&ZuXsPV~uT-e51lzvBW@`eSpMJc{x+}d)W<@DUBy|h^kH%XL(NLwKizqquE&!
zQ=%q3uHw&_4f<T7&X~o1eW$&pX^q{uV;U!m3f6~eTzY@8@SG#y(r8(bnm0q?5*B*{
zJ^5iIB~xDa{vvP^JYl%#{tLK7kK)cf<F9wx+j6tlQ1(V2ztN#${d(Qm#oNpgcB%R2
z7aZ3$EZH+mE&Zi&AH&Y1A(s?i9t@egY9?Mqy&hwdl$12=#m=FuSNG;KT+J`*2AHrT
z3+sl|Lx1PZ4JuTmgU`SMc8O_m3(wG&{-}>gQ=A{!dgDoXdcH>f*<5ggMQR+}Gk??m
zXv>!=wfLRy+&y;l2qOJqRGQxjent5$>BG@44N={aoqz*R$-{Md^<9X8R7lZ&AhE~;
zS^bc*Raf$Gm_tx7(z3pn@#LF6N!h6-2(nl_9cnol5J*s353zPIy;87y|J~5)q;rJW
zNJfixMxAouRGIkfC0_&Kjlc~=h4@H%zZFk6{qaGSG{JSkAtDfyanQkW$8Y}Y!N9up
z&47s#>-_;SnO5bR%A<E?45FL+Jmx|T0b4$jheyxC$=f6J{;6~8Pl}42Bk7is1m9mX
z*dmfIF)%c`7jbr*ozHIm&Ght#g0S<GLDyE-G2g($omAqp#+Jzoqk%lHmyB~WJQR$g
z@@8{lv*olF!i<%dc4s<Z9-ncuug)|CZZD6fDKWcC<nOpU_3H@t@7Kq+thK}qtnF+z
z?6dTi8ifANEYoTUogC1q8fp&>vq&-<*tWj&mW|hadFXjvY%%<tl%2mZhy4*d2afTF
zL|#n4j!m5Mv6r8O{M1sLc^57@_DDMptya~8wKl4mvCif_@ehET|H96ZTX@QU!aK@g
zT*k12>Dk>fM<okS-1|O+Kipg@y_5gD=@R}(W%aUMoU3ofRZCVC>>)r8%uPiUP*R1n
zB5Bn_;q3Y4xQ8EAg+H&UCbFTh#+iL6FiH-!aEF$}w4|)ceg$=G{MK)|v?B|DPtMzf
z)`%U}bUVx5CtN$j^wMp1PSh`B)(vnPTzlWYPPMBb_lliG{-NDu{w}Wn!`|L=pSm8U
z&0h6B3B1I|+bKm~mZj}D%LC*}O<yn+M#AgSXZ$VLCGIcul?j>)dbJw~@1w>1yN7$`
z^Q?#5GyK-SelZysOcW32mgwELPtSjoUzWR5UNmMgW)cRvD*L{8>g2J&A=zr&bkifN
zXk|;fPI~%aMbO-|PuWIz9n8Afh-Nyh7=B0OwP517xNI>gf8jg1$J{?4fSh?C+;Z}w
zLJ(O()@Ohmrz_qf_Fcv=k1J(8X&Tp0Rkk<%rF@8#tl#N9>apuo-gl6!A6MFVg&Pc|
zMb-s2NN#95`2_BLWeNDH-y8T%X+t|U{PE+0)-Qzy{)FFo6siawrDHz<zXN)C*77YQ
z#}^Q@=`~Gx8RP2pz{b^<g%4&$I-V9E9{iFwh!D3za9u5uYRxF|K*eLXj<B-F)7dXY
zJ|MAcGGZyYh$gCpDd`Q@v6Dk*+oPq!m-^&}tt5R3MY3@h4Uet+y}?rRQ2nbO3Ik70
z+Rs_W=-=LIRLZu>Q!4hq=f6y9DE6BZ-S_ZX%**wR^_C0R!*W>3ZRaZ(dSiPW(%}#(
zUWB@W2D!)M)3^PYPHI|EY9qlhkqK?W)8oOoRd#cBALWfm*k|ZpbywJiI`SlY)DI5s
z|H3{&RpJzo@mXamCV*?m=rpKNIQ4$}wV?#YJv_J9!T8j1^CKxp;-%EwPAmVbvN$Kh
zw=33>oo0zVV-}59$BxlcUfww?SZA!nOG-+be1H~17Os39;}e$(PPPoFS*iC>^Ic9!
z(`!3(-@9K_3AdiT=8n3@&VlMQUPE5e3pLG>vRMDL-vF1$FCp!KX=f0;#-;W$xUx3|
z#(5GSp-=P-*grO$K_n2ssD5|f5cu7-=tt|H@=5j+o3b^#a`v*u^d_2skCC_;0ZL7R
z|6CcGn9p8lD|T71MY*GH8Mo^vu6T)L+1uZ@M#x7H!G6PebfT+q1~P;|C(fU?E7wDw
z4zW!|uF69R2If}j!yaXhg`mc(o--#)o|(p`la}Qh3Cg`bfrI+-f!l|kUv@S;?-Wv~
zw&GR<PA1n_8i|hwTu>n5z|fcZhDg7w<Ce;H8~x9#_5bW#IR2!qwmT=vUn@PGd*#=z
znPXb7J+dn*!yYkU=~K^ooPD<|Ja?Jnr9opWEN<^@P6Yu(JgFUJoVs4+i?&Z%I#|B`
za<w8E0vY!a#qIVt*xYcE#>Rbi**gEpgpRk2Wlp$zH1W|v5ZcEtAuzt!7fjk^cV*A9
zdZ!Qfpep7MxH5PR85-La;5Xc}A4A8qZ`L6k4pJhGjQ!b7zG8X!Np5I?#-yXlQ(dO3
zU^X|li>usX&0|omZCD*ss^-ch2lIW4oh^;6LTiWr0$&>o2kpQAo!P4<yZ|>h?sn&z
z*)V#*>srGnmMHCW`}Oa<Bvy39E>>I}(~wj=z}Mw9n(1*zJbP}yQzuD2HAjtwY0Y`c
ze!o7Bf@%(0%{!OmI%@WihD9Vk2(B)-u#>F+Tbi^Osh^IbA9qDw`fRW(U%^e1VnW4B
z;X>FTEOIZKh7TGU?UT&ducwh6y%WB>CTt+XO+w+8Fut~qwcDwLuwV7o14UFkCw_IS
z6+HE4$N%goegAbh_Oi>jmDTIX0hP;-z+kZ!dq>}s)v7<`dvrs|Dt^1{OQx#o_3R-<
z1xstua!+FEn2&2*6)XLz`ihL&gs*(L3FWgx%dws;xlW}IiH*p%ojd0#_O8dka&U^^
zx3o|^)lgS@M=EvS$16~@cej3GES|g8tZ3IZW5VSLY%7r^&~l2mG6`DyQlr&oUULc-
z9Js^Q`=leTeQowdM%!XaAylua!m;Q17FkC*_L)(VJK83^pz-}d#YlfOSlK<s_1sx6
zm3q_1_}K>iWocYuYG;^!sD8iyXg%ZeIQ$T8Be%tw_P}C~Nl}%kxC<u%#f{A6e_#b|
zRsM$?ey76bC|RVT29mVirl2AXuaA00M(uifG9HN*sv+>pYn{n(GYi@N#LK8h?rP&p
zD2C#Ad$}X#2@{<0=s+TDg3$NpcAQvGK-YOo&zE(km%itYt`55lS4{1#?KRLUIs1cg
z391$1DdpUMo|7sgwlnrhCXUHVL;TalBP$fj8%~lAC~;RkmVhoa6xS2F`@AM67xVS%
za^sGrvX^5|;mTDx!scXQPL*(3Pm^U}HR}XNCNsmyq3JuKOjFSjU3W#&ttYc@CzujE
zmB>+rP5WA2-(6fwE+}|T^8S+9`(_>Qh+MW`x{;f+QA;RF0)yYO7vx*3taB8WJ7(ec
zYu$=xbLeu-VB?1)g>%n&bH@=Gad-)Gm*w#~b+*?M7&69tK65JqeFgW;7=L5y02>f8
z#<Z5i5ipa^ZO~Of(UOeV^9|4fvYlFXJ`Ivd%o?i-Ut$paX8!rb`~>}?t#uYv_P50W
zTX=a>12z~ChoNMbBChsxg3j$%j^7qE4zJ_A@$h!AuKT!Z9j3`T#?;5x!%oI>QZ%yJ
zQmKypc;3X)ZINVP`pvTP%ym<_pQw#O&*a{0&(}vu6+=VLpumxUW9hoYmRb!^;J1B>
z8yrjZD#YM3w9wU`v)jJ_Wd6>Gl;Ky4>FQ_9sEXxO#uPRW%38VgCh$w?jz}z2Mtiot
zIeX>;hxx3_Nn4Zkx!$!q)}bz}$dQ8a>diax*dR<3ba{6lPn$wz&$R!ZU@HA-XIyAz
zc6+~pw{B~1_et%<CLp}EZLsD{l}q`%*hwSdI=KAcSyhZ$T~FKmu>I)IfsPd^<w<Qo
zO8sD<)RFj5frf>2OQlK%clJYcR;qOV>ujjAbdxcyj-pma@p4;6Qtx+0kBh~Rvxbux
zC%)k)s`C%?&lcd-0+gNH6i_Y|F=_CswVetj#w}K41|5v=kZCD!+PA>_q<Vr2OO)Jx
z6ybV62z6fac+nDB&=itU7C^gc_eI&MO7JlIdX<D7SDg6>R(1Z@$+$e)_IICr=-xbz
z_B^1Dpuo>^;Xh_Y0vC875i~xXVP_Pt5tAygy|%Ag=S)m_e69$nSJ1d0nx06@*@)8g
z>L8%*?>C#ho1}*jtS~;D^!wC}gXiS|JDsA%%QDZ6o8oxm8s_fhLiVfC%pPY(d$8lG
zo4em+CcOl}_r~;k=5yCkbNN$hXETbEUnsd8K0CEZ(B?()-Bb-RopxIwm5`tUL!?AR
zkRNZe^Q<<h@Eo;M)4ZH#SMi_vKCO2my@L4}nkT9l0KvlvE2%GiXuI`ZFXal=aQzUh
zxSv7)b#1Rkjw++Hn_8Nb$G9lfM@`c(+<A6$g}Rk)d4UT4C2;U+o5fvwEyP)=m#()I
zP}6xsx2j>7UF#)d@i?Ndz6_zs8o}~sr0{HDd%9OVcX}C1ixqXnhnvhh1-HU?!pQ|&
zam`3Q%0x;z!|iS`yIj?UF`nUuD}7Qklz;;4Y^3wnNi!+yKTAyq{1JoimUZvK#@C$+
z3-GM8;_A<9O0hi#s}<&zyKY{xoF@uXr>sN+z)EapeFk>_z>*Sm&)s^`Af-VP5l#bR
z+Z|-hNP*v5JCHx}_us;c))s%dZ+#~i$cyUnS&7`jdrzg@KA+&Sa?=UYV$!*K%!^OV
zmx?@o_&2YquI{l*S2#StB{v)6#X^=mm(=PP8q*(;FAS}!<`QE(N^rTNBHX(z#ZuY1
zVA6A%udyui%kYzDBM4@$<19r9Lw{kxNcN>$<f1>}Umovf6eMU$i}z-8CA6|0w9{P?
z?;}s49iuNe?#vf9WuAOSi`zBmo5=`I4WhSKO82V1QNb^aF~RCTQyYhWval#$V(Wdk
zumtw2&ueTPUU_211P?sC%u?%III%X-b^hg&!Oh-6j>!d#pINzI8u!fX-p=u6m}h3#
z?|FpG5AfB=EWNG|bA$D4`Uh@%?t9UvdIN7>VQlDJlcix9xrAkAYf#Iz#J!$Ngl{nx
z`pWwwE$J6&x{61ZZb!ql^EbW2`#QFY<y42UoxQ6^ldGClYPhlwor!;-gX|oNlZ%~+
zqJ8dL-<&))aSQfl#=*Chy8Cj|=1lJ!4)~Pg`5Y80V)97~Nz91s)J?eV+A&}GH(1o8
zntQsAUR1TI$0NDC%insFNl>xtgeK=BPMl<$^##4EtA2H~)Wgdk_AS3fKCV#D03Gi^
zlz)IuzJ;9ez~Pp*rpD#2?MxjEH`yGq9X|Cn&VtS1w1or4?@2Bi5kt1}$eNBc8_^yV
zCx-G1rb`h85mn!>6EXtpIRpN@+%`HCHE<R?=-Qrs9XR_el^DnE7@=+=*(Y%vSgDK7
z$Oy<`Wgantp3mpULLDov_m=95@w^+%YNnpwg`ROg99p|?I<h55M>_zw-Q<*B8BSo9
ziq-G*SPJRnNMuMgIea9?Ae)FuEs7ns-0L*WvZ_L>kZi-78x>NMKM!8Jzw}Z6S;c#$
zqE3MZ_k-|^!__K3_!Qe!yq}{Cj+g$eLoNFHcLPk!@70#j&Kxh50T;RYNZ{B$0HNci
zXuB7PuOwHcj5ZkVEoVP8cmefhh8P5wwCFSXhzy0_%@4~X3>0O3i2eY#k)s?=<U#$G
zI`Vp)IkoiRUZNgqd9xq8rVti2d45_M?TUz03-Jtv`{uJO?&P2Ec$d+6tA{$S*r?8)
zY*9nQ#!Fg@PCFwFMm{}?Ybu$zJ>BIwXi>!-Gv>XKQKEHO`S_7Q{f6aK)9sA+-A$6`
z2m(XlpTh@P!dc_=E0l>t0<6jhTl|Mn6&kyG2cHgnpKrW!y;1)x_r=$}oTEK$Ue~Zd
zJAY}?wS=$4Hx(ZMcNa!(9g3cjafu*5d)W11T;h0W!&9fNZ+j`>J%@6#x=P1Go*nlP
zd@>|pc~hHj|3Ge3De%~D)15LABFDOIv$wzB1~t&=FjAHso;UtOx;y@70i-nH(v%<L
zLkvHcrM<Tpb{`=CkV0tHnxrYZL|HD=4(~*iMfcvSbYo|}%bAHknbV`@PHoC~Z^r<i
z=uiSf_@<IwjrAK-PSVDlC>6-q9E+iQ7k78=SQy`sTEGJ^jD{+S{AnaS;bXLZs%qf{
zHWeKGyoK<%13mVE`oK`N=7n-t0Ig0}uBe-Dp+R3##vEr%bJoNiGO7+<Ol%)z$$2CA
z;rwy$d}WsCPDU9sW7Ed3<BUFMhGKMAs3?cR`x0eEk9w~$eNv>X-P=v6Xj;dW<B-CG
zRvtn;=BmI(x0XtWvhp#WJvjCROtUR_W-+D6w62U0w}Y-#i2WFFI8^3XGt2LN=S%YU
zE=1z3wPt?VfF@<f0I=~UiesgIKaDQqH01uPD?p^Qd4BmT^VI%cj$;3{Fw>?yOq?#a
z%X#F(+Z;Uq(@k&()LZ%xEU2Xlj0A#D6xFlk#iO{!T7P_N;KPYn7CyEoqNTe3Xtglb
z!FoCS;pe)>{?UFBU(+1dP|JSox^#1`VB$5F4*tH>{Os|DwtaBCtQ1t^uSZe@(*a9@
z@e!H+(e4n9z#cQWn8(F*tZ+mqI~d3ueTtI`fz5vTs~zL?gApCXg6Spb^8U5N;27HD
z5XChWv48f3@VQd%YkV<(zsLX2A^%tp@(!h1*lU*?92fpit3+@;Er@lU!#3dWEhDFc
z-xvq6WPZ31+kT4Z{@zLf%%K)m-~N{O@8yisSuO##E3yz_RSIza|2E`{c@PV~>yzK7
z2L6xb{9|=0w}82C*AnkK{o4e<;B^kZ|8C>&!us!N{HIbqxU$MAq5MzP33j4M{cdv|
z`VbCcG{g4UF?!RdD4s?X>?Jky<<F;On7`B*ET|HM#J+6F+C(%T|CHf}s_@AG8vPUZ
zmi6xvKULH@Kn+E9wyhTaV{$z}qh%ay{#K>?OSe^OXi0ZVe~!m}6sw`wIZ5=RF57)Y
zk%lQ1_@~})u>$MyuatH~i&IjCg*dZt<l0hh&jD2`zW<#bDF>{l-&um={J)tIP(LC9
zXppfZt>)rC4CtTs^#6wuF-*3V-`FW>76r-cU1E{)&;EqjX82#{Rt~72&9|)gx>mpU
z`yKJu*9781ZpNP*u%^Fzp?^sZDH>e&Y&8NXkdy%R#Cf0Mf&z`%@87;H%fg!e$1VRQ
z^?!{3WPP9hz5KJQ_nHC}74}Kl#8=ud`-9OQ|36PLR#pN)g^aI$BW=}5z8hcM$mKFw
z&zTO8xsUfZ7b<7TLjQZJz+=*ANYKm##=pvqv}aRNQlilI_Zt0bR_^3gbW}JFXFZwp
zSt=}PY@GB|IMOdvv$3@uKTbcbq5iKTjOYLXK#V8g)RnSnG#|XK_E<8pyZ#PxB@unu
z?uHln!REmQ85afd?*9Hey$FGXgoHjXNKEsEzd7Dz0ig5@VQD&b?9qJKfQvb~x!D|N
zUc7h#x$-&^v{-*9^n{z&5Wynl#m9QW^H&=3Kf~j-fKp?>$Temv(4&i3p&cu$G>H7p
zpNXzVN~p9b{o^A~nl0by(0x^;%Hv;OUXi|X2nYx`tJ7?SV~K6gp~r@ef79IFM3wn5
zKhp%vBfu~p1O^XV_##N{z3+k|LA4)=r5&*Gw^0Pji5cwU2k!1t-@bp><R<_>MdKWH
z&r8%l%lvCgia;v83F_zoqO>n%8qMlp=2V*eGXhZgtjl<;r$nsc_c}j~FJxwy_F|y+
zA6#MdvGf<TLt26sp~T)yb;Ar$-@C)me_B(JN-|J5xF2#&4#*>}VmqLxq&|-@)&v{|
z;_(hi@5QpHRD~B575xdL1T&tNhQ~QK?I9O7j|U8tdGvZi=^$KkJ>gUTw0VG1G7E}j
zmmeoz0Je&KOa)^hC7oef)P4xm%fZZCFSRz)(Y(5PdYVUqPA)E5tKNY?E<n;d{s;8<
z>-4cYfK#~Ia{d&ZoX-i0q?jyg&LG4)VXzs41|evKUGMIq7;&W3s`Z}V@0b7F3(}3!
z#a3kR%p-t}c~C-SI;>V*UaJ^40Ofz5&WJhiIe9QYZodeo{4vvPesP)oBIy=0m%$Md
z*B%l=t5_3E8Eiodm`T>LG#k=h*;rg!);^Gp5%j}&8jX*!8{5w)lTa(}!*HCHo?tGx
zP6he}aBK0!GEdbhf?tEYyh>`>2@wvF*Xv8q60Gs!>6{N!unCpf+HqR`+Pm32Th-bV
zjV!$N?IHUq<nAj8h3qr48;VrE^aaJmbPa5<U7cA`<6eX$IQR9zw8g;eGtis+&8?w{
zcRAOjMFooPk_t-Rif1mHr_5<n1UCR!YP+BK48(^NrL?Bar?4o+^1)y?2stlH9^Geb
zYYj_KuU`BRq*5h(ulg~+T(ILgW-sj*4$sAbLUo~9-J0LTZnTO#B`wO)(b4cRwwX^>
z1c{8oPAx3(hSA^R4=73vqWaxsqTkj^ZP|f~DRH|96XLFtco>pIuis`$!-9(fI%74j
z3*v);w4K4r3u|%)3^8W?={UkJ#7N9fPzyUPT)tNhR6>8#Ck9O_`))f`yUpEpPrQrZ
zIL~rZ0Gk`=bpwli&=f3=QRWY@^bGba9FQwCQqV<CKh7HTc`2Tma2{|TDx<(wJ1g7o
zTF~4UA-|m3eVLbpjuY6_KAW?zk|D70gA_qa=a5wUh!{*2=5nT3#G?H6%;CPL=31K<
zNPxO=(H*c&@$1(spUzbo**=F|B!Np|#b1M(1E?H4utRhKU23Tm{7QNd=qBob$6u(;
zW4F<X3NGOCxf4{OM6yA0mO6=5Jg)nMiJPieS4BWS)7{f@{}dDR0#Liniz-pQFSs&P
zDT41XIGKI?Q=7IaszSP?aI>hMK-ZA8;xUmUD>EIPC0G9L-8%-;VvCoJiyDO!)0j|w
za6?>}=j<AcYK@GYaAueQ?@4W<5L_T=E&HQG3|K^g9Awt9<<c7ZtE_mGX84N6-O6#I
z%iYnZUzkT7(!>Xjd(#eYD4)_M;ZQrEsb*flg#YL(0l~=-rqWJZ+dwdV5_`|2r<EGW
zWgd<=cZHTS<O$&iOk@iNP{Onq80VnmaSnHdb?Av|LHIUgD_JFjesu9p?4xD-gbgy^
zi=di;{+D?%C3JJ#r8?eW^mU0MI-b5{D3#_Z$S|y8><Xu#dP`q0j^dke5hszG&)x7I
zyoDiP<j33wzlQJ1IR)1fs4WfBmVI~`+?z>oKSVU3Q}0Z)Hhmn;tCli_kFVUt(}a&!
z6^2t`)T%;a01B)X03qkMIUXE?_{7C2!|7`CKm5*ih?T=7(WRO2!G!M3<{JqMP4*b_
zt&;g_8~{~i4S?mSSe&Ykm4q&`w?Oz<#NFmJhD2X!#^ir7kF*BNGo9wg+rj&Fwb`)+
zo3*y4e#?cKOkn7cGp8}exFi#tWdY^5BF+~d8MNO{KBoV1@xe%FB>jH=@%bNNW@+L5
zZ@fp$b6^`)y0TZwJLxKDdUsl{2mp#Y!sGGTE-)*yAO^dIR@~MeIvXqjc;`1y@TK1X
zH)x`QU}A$^LDLq1h^l`4DAkv8q%xo$avgHkW~CF#-d@c%WqWy=*s{OBuX3P3@_5Vc
zg!2g7zm3GIr1atiGjs%|DL=Vf5>fMtzxNxr^}gc$hJ;#`EO&WwC;e#}guMy4Zp|^S
zD-3`t3YORqgW>m&`v>1+EKp;PmlSmGx`fl`bLVG$LFG35*u7sx1!i);WAOu!oYG&C
z>%P<6N-ZW3F(K#N<nbbs9?WX2CE_KdJ7N$E1VR-HD$kn_Pes{zc^Uz!e|Yy78Jkvu
z79xF|+exC|zugt)iLOu9YF4L)u=e}q+?@4F!E(?a+ud||D$>sZA1=ei+;ynX9e7en
zBC6go#e~Sw4wd<i6iCcaAfT^}4`V2(WEp@yB*yM#U@w7`#ccb|4Au;g*HmkG{d~T~
zQxSbQtGs#%xeYAYWinc=%8Eru&|}wIIoPYYz%9#(sOom~<F%SW99dY_Yl(&s_JIws
z2w>r1An#j}ya3y54(!m?8{3U@_f<iifk0iJ4CcY#xnq|yp(|b)bo(_9>VVoz(a+A#
zx;NjgU`p%T2V7nv)EbhKa77*yLJNLYcqZ@*jJJRT49M&<!1)KO0*huurz`+7O;Pn_
zpDi@YPxV-ooy<RP;!Or@{rSDB4u9ztX?p{=6gy+@LbECtnnQX9pR!;VO5FZ5CPa;9
zVi9OX%PwCi+V?c53jjYDUg=S3Y4H#h`0?c7*2Rhgu1X)w<j_`;|0kQd)}(NHqwRSW
z&L>k*s{nW?0d^O<WxWV!SY!}^`o3AMeInt4iOx@E$l_C>!sxZ|L7Qrar9gs1WJjLJ
zcBu?%HX8=JA||FyRGol5$2C8)x4#T#&B@6DLGw$^cj`IhwDMz+&3@wnBfuk!v~bds
zmw<><81dN6tu6*RU2yKSP$Yg6h{>vp63(X_J`ih2G-&qrgXF}`P$p05vVQdf`~7_r
z$$Mx+(HN~8_NHkdD7;~Ayy$%--Qq#ByxwLb)n>3<7(Lo@^|2u)B!q2ZY`RLuOEkPP
zsNU>yFc3Zg6GlmCglifr{K$<NDOp#uZW|n2k(klIZ*gJNK6?6^oC#I~u1v5wJS`Ci
z{V>Nmdrb?%xOTr*1a9s4*OvuuZT0(*d<!$ACx%M=2kj4%m?6;WMYcZUZC(h(eLQ4e
znXD>2;NuXSx@W4W+q-|6k6gT#EB!@7>?i<e_S?UmGVOb7tI>z^34Wr`*@A;q%}EQE
z3+Ti?#1LTCVJ=Urom%Qb*b`a9=m!rjJb1|>5)A}F!%CX2V3n7&#@FtZj`c9%&o2fH
z;pS9@<GeHtXT8EZr&c#>vYJ+xh)W-~?8E84YQ{y<3omof!J}_wuAlt848#hv#8w&z
z(<Tb@TrEUD6fIN$z;yl)w2+8hFIFWu^N<RV_E2TokbRnoD4YSUs+LFyyV@BAnA^H)
z@nx5%r1B2365s;(fM`U`#S26uCz>C@Sp6uE!C$W@25M}*;@^LaJY!MXZyG2j>+&ja
zdu~`_U+#m#+N=BSgI@{BkwWS>oY~v2b-}bmJ&A>1-ia+)ZzR#yTJuk?KWN$mCQ7hR
zb6?V^EeK^SSCT-^KlcT8;_+j@xTr|W6MVLQFeIk~sYEFzz;@NLY#6<$q~YwV{q868
z>+O@HB>GD~A^Rc%Y(n?0RV|rly)RuSzDb16YGK_%fbIYmk~ToT@G2?+0acOStGy(b
z%qOd@EQ{3a5;Z@zaQk`jXt^ujx_<}#`wd|4(!chSCLnk|Elfyi=^aB|IQzl81rQ|B
z3!QZq){IaE1tbVdR^buemJ_+zr&ARy62_-XLb=_@*pSFN0Q^Y3=e?KuDJZ3p)x&D@
zR*9LAL<l&I9{TzKbm}_90OM-Ft#O_zd9!Fltwa{o;^_S-3kuwYG;~5(mCsO-6cuic
zwYSm$2beZ<RFGi9BsaQJIoA^4Xp|$)-$mwI$g&rG!>4<*lWoIROhAG@<yqmw=L8yv
zT>0UCV_cB;x!@x{O9)ioY@}ew5$gGl8t{g9PQBp)pxw*DZL2^xk1M5go#~u4AF-wA
zqo|u^AI0*?kn%RFOD^T0VwPQ#5O!bwCMKZwYoUuN@PTGxbP#b%nI;DBOCWWE1JJPL
z`EwBHjDh>~^~j@N>w8DjgwS8>=c;4QKk$k-j;{*w+Y)1iK;1>(AN8E3*UJEuEfHe{
zGIy0?pzider)AJY@;2(;I%mDmBgr2M4Q|)p2o)4s_lDS+l{3mBv?X+Votf`>blO6k
zWX`31S)BX6OvecmoVWZS&JSp7Sb&d@Tun9gf!T)1<Lo2=Q&L;$D`FrJHgU^Rw=8_X
zlRG^hC!%QK0`-3V4;K#@&0*!{8@s+=#Z`I4_022wQdmr9d}&zhgaMV-58naegIF*T
z(7AS)jb<r3m)A$}TIZG|TadbfZj_}*7QLg=h1OQ;K<-SnFbq>GBQye7OapML7x#|2
zArOhCib6c<vI-|F!u5^#&QCFYQ~XbxM-V96FOi!!oc)(rgvx_w5J35al}}?Na~jB8
zNTv_E7FGMQKNehc>$AvDVYB*DlZBh+14Eovo;rD#WK@{44ThnrWZ&Cw{ZN#6?_|}~
zjyPKN$jVnG2LI{W$#_xn@T+)>JdC@oY|p1lW#j87Kt+~jF(GJSU48vO3WFiz+}jCD
zD?1iiq(`Wo-@~;rBZG3z;#HL%b&2j)iJg%bw(q21V||33`;4`ay6*U|y8vAc*vi;=
za)mnIIZ71cAkfBx0uLTMeFBg8I=GV>ufM+XtT3Ven~Ru;9dOXS+iTqw`XdPxD8nC;
zZGbrLG#o7pf7fJ$ymtJB2}v^3JN(25{n^qQs=DnRf3MggV90GzGo|;%<YdMgS;bnT
zl<M~{w`U>(QfQ8<Om9x&fGA1z*?eETNjh(tWO(F?>Sa1@;IY08$^uARAU?>s`Va90
zE75jBVS=XR>YAm)x3f$c8wOR22X0;c9TaGi!lJ0I;0fSFwQ?@%^k?kCtHR&qIENFR
zPih#K@<mq{1^8Zww+z=^0Zjb>@H^V-iywoN=x=>=S56iNT%|!lOmFc%Q`)mm(7U8$
z=vOlivTZnh)pfP%r($8PVf3^=)vlbSYJ~zYGF|bh7Pg!!|9sKiI&H-W{0IWFEf9-h
zn@!a4zoN%dVs!0?JG%JD#;kd&qgXDFe(8ztlflbJ5$g%RB_im)R>VZoJKQ27*k8|9
z9E|dGa=`?LZ>h#x04U(?QQ&rvncnP+3BWsgl+M;hkY2E#ok)VpTZiQsPM%^k5#4HM
z41twEv%(5wkQTtHZP!syfIgff1HK7!zp67+UkQH==awsNk2;&}iZU<Hyr05?Vk|^|
z)Rs!BepuZQvU9Q}u1aJvP5V>lzTa(-ie~{<0EA8#=UzD<?fC5Mq4b*X)66J($ZPz~
zlz6s<!%vZP?SQ6l$hjZuottaZ_v!^Rk0lDE*&2^b;fyx{+Y(cHSS9;y6!B#9EpkNf
za|l0xvem+Z`y?<xmsW|x{I}Yy4;|7bfTUdniUr3G@@ce^)3V%&$5G~$+lIjNSpJ;k
zNKEaagrzZx582za3wy#s-*7;jEW_#Ko)oQPffWazLg&XVbhM0ay}p{O8_mfz!^L04
zgUz`;br!cp5?SY5yi0u3#!i0Cg%v(F))(vo(SF<h3a0&bi!L!%r!uwpYPh)LXcmxv
zJ+K0Jz<toqV)`GpX=X-%RjNH`NfmFjW^a{WLNP<ef;3EH3xr6RkVX>)g`02@-IS`8
zfG!JdgzgP{F?ck;!;Hli;Pb_Rgt)M<P~-*@iw$7Qhrt99AgJeyWtwjeX*r*|#u&lS
zHyk3mCE3=-TIG{3CX4CXvgQ^oQ7I3$XgvDy9Uw`-F>cW=-{As~P<0Tzy*9tHzMh-o
z+__c)A><^#($cliIbtu73D3RwrYu6e9KeE$kInF0>)oGS&`U4&e7lJ2A%kq3ghWwO
z@XnXd_19k%Nt~F@t9cEt@_?rTwjb|q?QK`x_*J3Mn%V<!Ij!?bJ2h$MiV+punklQ%
zm*|o?hFf#Obt<^Cwzeg*lBf)$%Bst_ChIHi8c~>B2;QgfJ)0RY{$5%i1nknTq^#`h
zeT?zI{e~GmPbhERaGad02{Zup#@$16M^jt)2fHnh{b|hz->N(g7`(ND!Ut5`8uy#%
z=8{=HYgK-9S)zPc)b4#oMqCkrSHVks(W`h-Z(P~eSJZEL#%}jpzwuq>@j$4h89gy_
z>O2!~T^Z-`kF`Ovzb!mKw`8ymAnLp}zRP46!{s+0+TDLXh2+Fw949)XaN(()XP5v&
zgRRW<u2F@wsIak3>=S1<U*%~_ntU;pmnHk-DbmY)SfLsn9Ua3L)UL06fdn2Wv(C($
zw>O?IGUnK7fntJ|2jsKm-KiE;h;sFTK+v-$*K}`2(5(&5AMgZwr97T)4i=XM=o_v8
ziBd^)Uen%k;dgSWjup`o@jaq~JWWr?Svj#Z<77CQ3-2TM;mwIXY3RI1DOmC}aPI-+
z5Zqi6sp5XeY)!m{`uBaJ>dz0%)?u_~-gy>hN1o0CgeF`#0R9lyB}Lz|A@QLAL#vT^
zTvH>9<`E3uJ~R{V+0MU{5)W6nnPps(Wta9V+@r3(pffXsCc>Bs40V5<Cte1}5Q^_j
zf|?|hVaHP+sWA<DUh!e}=#<A#{C^O_-rf$U52O2vNL!zWN6^Dy?u;o3*ViNnow`ic
z5Kik77sNol$JjP{O^gck0|_{ohzkvW@O}V6NgAT9kn2rUkFcPf0Z>xOn2xDUr&L>O
zXsl`Cwt41AqOG2R`Y1fw(Cfi>Lm41@fu9r(j#zLW>xq_>ma^W@Gf5Gbl!WqFYH4+D
z$*eh=$`HgX-hqpYy2Gr;A<J(v@v3#*?OU(NrefV;meoUyf!jaab{hxo>pdh1EOF3=
z)1A9|FRC&ZU3hI%1i3BFZ>F``hLy<jKq4GDGn*)B>A)<hp2pZ1lD)ySI>`7Iu=v~N
z2_B=SDus|ApSFQJ5jD;j%ffPjyUuEf(P;-59Tp)Cc6D=?wB;RcSeu`M6d9ZafPSlN
zjz#l|8_19GWWI#&Gdmr4>yTJ!YcO<%*phYpLEzP0l_a#?fuzp140#_d;May0IwTCb
zg#ps6-x@OS+cxr=9+%o$lziwLToz7m$cYi|Hr(q4Qys{rAJ{fHk&BN5d@G1e_#m@6
zYGMnyidH8`r3K)lz?G_Z-?z|{ZZRxU6My6x(SWEi+u|*7o_sI%(x-*Ehon<B%0mD3
zcJs!9H*H_j{(-Nb)Uc%{F<4Hw9Ps_;s(Q{py&nee`DUQd#UgY#@nVUXJfEALM0W&$
z97OF=RmH<V>NRm<t8h|lMu3RypT~hFcd?DkAc)%tvnB+ZC2^4h_Q-#Ed%=5j+Vbd#
z`?_krS+5*|9V)>8Kau!|tFSJ8p@`zNOh5zjcG}58v1LaSDOy86<e7pRLP)@{U%TE%
zx?b_g?ot_^KxD~GO;3+ar$|^L2(IRt0&ffW$GZS*a>&!?ihCknTo|uHIL+VWjCANz
zcos>o>|rnD_tWL+Zg4^?fTduh%NJ^$RjX?H*7-^k7}q=0l31w$sF7sBfphB3xQXcm
z)q$|W>%cN=+IKlAY0d&*o1P`0AUz1a?tm^(V%a2VMR3zb+&46CzmosK4562Jy|{W#
zb9!Uruk+qOGl$bT7BWNaz2lSjyZr#|MhlZ(m4VMjunGbC{c+`2bJEiTjX>LAy7lkd
z3MS6!NtR)l2*8f0N1Ge;q|CJJEWd5+R^wxO3%$G#5p6iBrZl&f$*Dc1`8j}Zv#ikb
zjAbgdo-+s76+R)AD3;dgaR)GtpmhDZmT+|I&w4}6K8H*B;&V^lwH8HZewSR0#`KTP
zud-{LEUpT7^eoyIpLZKqGs*JT>u3$rbA;DdSD*JF%SlVKZ}wkvhz})%8cdWY(Jc)S
zyG;Z}>_eDjGjPiV4+H?b@CZXVd2B6rnD*{Fx#8)y-OZ(-Cs`f`d)+wBfb3YY!S!k2
zW}0D1!OCe)NI~^g;$bgF?Nuj8jLx%@b{r-@1=)3dGtL1n+4uwky)k-FQjz4ATJnP(
zinKlgh<|o-yhKrKnl$bqTcFgVA@+9MjP(Y2!Dp^O<X)o!T7Qs<4rM5)AkxX<TE`8B
zZuZj`=s#k2*Vokz8yy|}24tv^tK|ghBM(L~g^}->9cq)KPGNQLzKiJYUSfbMi3f=T
zFtRIIjBe|tx>t#>JlnkRxV(ohw>xL1H|g$jaTY~e7ad}txeFvWi0I6*m1Pmh20SQd
z6<61LbhGG-e5@p!<^u|LO%ynLJ2~H@0v@SpHsRTo+4->i+xQFL6JJCiFCf2)p`(JI
zmA!P20(epJ0GX1`oq$Rqf2}HLZ8QIr83iy+I{_&r6A~@0VXI2)1Sj5SSXP+e$FB9K
zSuy8J)38&}03O#D0GgBO;%lX$`u*YhY2FtHsG=fat<()fI<``@DI|uw$#T>Q>Ab~>
zXdR*4IVsviBdkG?8S|l?uU7~(gwSfI%T1<TG-;dgUE>t&Y!X0=Im(<K@AsYv(F+5Z
z78i45eWm}cPw(;=ZSNtL(!R4gzAfPq@>|rBJp5WSNDdzOUPPer_~5%TRsncyG>p;K
z+(vTPgcE~6WwB%?i}PD(ZC=oL^y$t*?rRUuOlD|J1)@RO>S9oHI<Q#Zb#hl315Qne
z<omMAMe1<Q6~)#-@0c2$^~Y)(Y_h&YHpSIlBG0W}*X`Y1J212N?oIPm?a+^)pOnBf
zi38pY+hc-I-z-)171rI|!y6HkSFT4&VF;a_w@+`svGI}Y6(&2aqVR+QNmjc=)`>9s
zzBOatJEAGuvra-?2^$)Iqjd;@@|1FgFWy$y<Y^XxQX4YE`F@u)>e6cykoD;1Wq33U
z=JoM5p5vtP3VzH*)?aZOFY+ghPQf{o#|kxRl!9e32JWYfbI)K1sn+%x^(Fq}grg+7
zIVQ;KasAw&XNA^;kP2P0I57@p_9=<poM^nvEWPEM%?usf2ijeK{^~$V2S4J>%)Km_
zj>E1LnYt^ilv$hi)${{AD+Ha(?(bOU2YPdgzc=5-v#Veos7j+}qEIYbWmQ?XGflI~
zJJ(k68q-U2!m4t@o2ylujIJILA3Gk8?zmxa<v~CSkX1bXX;Uz=?|B~CQRV_MV=|4o
zxKMY@h*@jJo?#FG-xrMQY6xMP<8y)D0T=y-()IRMVQ9$Ibzd<lSIamm8i+X?x1{lX
zJ_{`z3XPwQAsJxVxp$wGN3%7$SX^C(3w*OuuR`o*b6pn9uVyIG2eGLF%LSPSTLa+t
zI+e*@->pwoO30M>`o$l=jS{^@EevA_)E+6g=^!#zV!ZkV`&!OkZGU5V_1WW&jYz`B
zYre*_H~piZjBm8EGw2qHU5y-`s^+#~wqTBnY!|tgIj4F~-pE=D#hID#$aldjZyoc2
z{i3lA&(7Ufu>i`Uhlww*JqZBVebA-=GGk0z`L-ViB~66L19lBml)Lis&82)JW6Y1a
zWH1Chp9VNH8*DTtd^j1Ua|tVsX+7q+dIkOB&F8cSieeGs(ollkGam7rRI@%U^nEaK
zXse{=vEn^53vgA>l?cg!XpZxXhzE39JYw0&Ji<$7Z>+)v?9xnDn<$51!H1{)Ix@Dg
z0M(fgq@VA>;ap5|Fgg==v}iAT<}mX<yE-A1(lPnjy5T2(hO?=j>sa%9p9>R$@+0WM
z5Elz(4ABOJUu4h-JXe_TdEG@dN#SYmkP@p$^If7i#*|alD}K+Vjpnp<8}LMbz2_x(
z26R`mua&0aCax1D3}o2DEfG83DqW#7%TH-7UIRITf4h01@nzkpNIKE3%2+u}SrhaO
z%{JT3FnUATK5*L3;k}l?TV6;qQ`-zbQVhthe4Jf}IDp&HxGXz7bnESdDG^;V(4}UN
zXn2UDXt8`Rl4#GsVP)n~*Oe2JJLKrsj}{)m#~ekGytf&uDlK1Zdkj+191BI4W+XaX
zMq@%cX%9Y)?3t4rX*$cUj_qVOxrTom0t+wRH4W$ei>OrqCMAwa+5+j4ITm%x34O_s
z9pU;G86mFPh3~``@j+pm4Z`BVNkQAf0ISClc}<=(qhy@>bMBuE6?V?f*3h~Qei4Pc
zpUlsJAkp>houy8Fx2lisOI^7Yr6%ps?`W+BNC;7NoW6BYL8PPc^PP)G0boupXD1W(
z;6<=k$^%DSw5hS`6+2DCuKCR1NRUq)W$+bR_nA@~0A<8+elmU4N)t}I<1+q~n;ug3
zlQzXfO{K$3al%Ge7a89R7e1TKyrqrEJr0<iqp@y7%Fs&!MNc|F8(>mI9-7<2CH8U0
zb$)wp)sKm4)z4Nf?wXr30OfyTb1*e=t%DbmtDjc3;eZz(#E^5(FB`KloE5^IJzvb0
z|N3To3A697$HUh+g<?h#%+PH?Iz=wGkq(-QlxkZ9GvM|<_J%e+C;k!hNU7T{712dT
zJ(%iHpT`t6bzYrFKPM)<>wEq;2WArJ+Ki;&C%|qTf`cVMKY@$ee#aXb5TAhhGY|*|
z2ghHI^vSGN3Q+i44Lmt$CE+}-qLMMWStPx?k?w=H64g9FwyFlkvVsb@F4*0It)KMJ
zWsXQDhO-fz0GgrR^2d^cD1n|e&(0|RNP5FSy3%!3ek!8sq%ZXbP}I5#h@+Oq%AF=!
z5Nkr$bTr=NJfHi{&@psABT(sEcemCH+z<%3Ysz5Tlg=dLIvEoZQGBvrEY?3W|LIC=
z{;3~cWYkGiV}Rn$|Niv-t&T0_N^+kwbE!DyXFgT-kQ^o+0WDt@Rk-U^A;+gcs(-nw
z*GSb07m%9r!c$ZI_N$UVEqylvczAziwiU!^fVF3jKfTZnW?|-4;s!4F?~GD|3l;(#
zQ%ZcXyxzZFJzgFuB`+8R%AIzXI3zwk+<n6P(ZX_;y*=ty)iV<KO{2{Xjg3;MG>Z8d
zY&eCu`Ie2!4740ZKV+#kAZJm|*pdn#ol^h26Ls+Xb(K;|(^<m08i!-E6w4PAzEuge
zOaNa0(AoH_{&!mt2uE2j+p{jRoJ+~bfIR#2^SO(m*Q)PS-RYee86d-f`7W0}-dY^Q
zHHD2ufMREarNIy_cVBIQTh75lKao0Lr=@7>-SN-y)5Kz_<X)Lsl$%fu)et`r!WNte
zG$-;K<mumYEGB>0*@D~E_=Khp{_u510l}OhB|H8tnJ=fi3MkM^0H*QUS)Ah><vFBw
zG}jONtSQ2DlTN^(GqYGdS#5pA=j1&~^BNE-yFz`s%C@c9?9K_{tP?^i0qTIXs%mQD
z(G@1A13<Ic$z};1zAg#EY1Piot63=mXhb{*v~8WXJS>S~W1-R{dd35$3U7(l0G5@2
zDM!s~0GZ2~l=`6(4J&`Aw91bZ0@7n~C5(X^vWrIXJ0=5&y~LLLOIOyC(6GA<bZ2Nf
zulDA}^!lu@muOmtRp=Xa)LLk<z~1<~KG*|zY2Yefb5Rh`%((fBmdPI2Skc4A7ZZ6|
zDFuRB?CKCm!Be?#5F^M~PX#b^&AXrc{Rrv5#W7WKF=$*oBh)?IV*(8;7+;GbV;y`c
zm!RiHP+B6!pOPb~*4%(?t)A274~aXM0R!rfz}`+<($+p(^#Sc16jXKa=tapEc(l^)
z>JU5ZZhJgsa6W^GATkB(Z*g=<1bKcSfqQvXO*M3~>1nHrc?mPrlq6odSVz5NOJyjF
zC663mua~^jkgICR&L!d}pfGZsXbGh89{|^XNk&u@MHdXbVWOnGoXy?5^{u4snaWOq
zLb~%_JoXPH;bURh(OIZ79^tok!tw2@FIz*SRQ-_>BUk&PEPT{%JD>63s&%LmE<l(?
z^@7iG{8q2VR3Qq6id!`l=_AM6Dt7{SIE?k321z{_-|x=h+2W%n7MPG^Ru3jf8#XN%
zD1$igv(&Yjyx6k7#EOe)ZV9E!_NaO0dICONTy*eSDy+)t%`^`R<&cJb8vek8Fg7dz
zng~Mlu?`~u4r-|$WP}*RL}Vv8ylnw`9XJ%(KPCyk-uLY-WT+~Nc)aGcLPg`eVJMIJ
zVdNUiEvpv)Ad)(fDR)^v`SJm}C*g}p%Cxn{1c6Od;B$eSxV#cqtdSN{<s$L|CHmqI
z8>3HIDCiCE^+%_aPAfo8tx$cjF)m#xk4?UMA1u~%=!PVdzfT>0Pt$TdIM%fiRk&8e
zK2-S7-QP;^+tRk}L1Tbv+St1Bwq7`$Vf$kzL2jN2un|TvW^keN0A}tvu}{%E^>cT(
zy28R`EJ3j<tGL49ZAqj7d*EE~D?;e==g+a)%kXCFcjjO0I-(;#R}n7b4JSvq>17Hs
z%o17OdC0#lnu%>1mIJyKLLT;aMp;>Xf85*|^=fw|(APg{ia$tY;k5a-iQX)@77X;z
zDd-v)M9$64kw1$0U#_25T(#H9gr=Wf&e@-sjZ>cVd@tqdqq!EwBdwr2g3U^aC^DYK
zReZ%2!P^O;cM4Tv8SqUArZ9Thta%0eBd^CBzrVjv#wvU=5)Cu5GAC9`-@T|S(&M@0
zc}wv@`uPT4n7cgFXCQ#B1BK%^ffvfoNV6jo#!lU|ACbWlcnyX{OzN=G)(<09y+VIv
zGr3<!Otb15=`u4!`LN_9f4=y%_h$5Gc@q8b@C8h3>ZQkhdA9v(en|FKL&to7FF!$U
z?oq}8Pt<e%GzyHGT&~2+xg#D@b>6XUk!6!bPCM^nnNVvO-ti5q`UIcZ_j<tQ9w=SE
z&H0L5bU+nbK(*6zy|ebr6&WgN4rFBX>6;057fqYEy3-XeoWAWMmPejrq7tir&h=+C
zt8|6QrQIVSq7?;YT2|8Ik?%`|3WQfg+<=Z9%kE5nB^ZpG#|$zXoU6ZCdkqBjNfhm#
zuOHAQgdSHt2TGp>)&!5b@4@1kfdiSk09(w?wz%;?UZLawbsnhlYy+uH#S|@tNL}-3
zf7xjqu?RfcB00QG9rsBvuP{L_h1sTi3puzcMtPt#Ux{N_%0km@*kB{aPrM3PL6EWv
zA>A)Sl5u(MKm_&1QY7(Tpq%)B<bC&BQ`^?A#9#;@0V7y|&@>c5A}UfsN3cYSbdVAi
z6r@QF9VCDX2r4CX*ogF^H0cslkgoI&B1LK_p_e;z&Ueq*`@4U_^#>oHy;tU%Wz13D
z@s6?hGlsYrkRMlCpNOC^9SbE`UTjlK@;0y5F`%0zY|qEA1^7U_6`q-Ka&qD=EzjrW
z=U?AH4>)R5y<VR};B)svb2WBti}(%(8v-A=nK%FVc6P+#xT6Uxnwj-l`ppYitvVQJ
zM57(@f(ScPUlxZ0<d77&!Kb#g@Sl{iRf=MH85;)S=T!uyH85dK%Q6X`Qci`@QA1L#
zJ~eRtq9ye3_Af@((AfUP*E5<c7^StIF0S>TipvLVDocleUp1LwZ2QNWiFunLD@@8-
z47sN-MJ9R=ZzeEJ)cY#$q3EE$z}7uIUJK-`70Q5=NMCrOj6$^<wFC;3U}sxCjv>52
z8dsm**Aw>+L4;qt)L%%11yUE?<^aqTUy3vA6VUmSQEu|B_S>d}a()-f)!9>-;yC#*
z#c*KZZAv~E=|O|}fYJeZa2sxnnX#SW`PatxQA78?Xy`|nAV|Yyks)q79zxj87T`bM
z>de2v9LOHInWhr**U+izF;ac|>+DSS=OOS|lXB^I`=XQ7i>VU7NYQMKq84Qb;-G!T
zk$`TnhV1OqY;@2acC8s>B79$D>Cl55xF7x!6C47slqnF*ntenjE|iCbz~Wc)P0Bx}
z(CM_|{4so&o8<&m2}Obkz-$dT3C0nQJWWpr{m4$iC`APS&e3rz6L^q5K{VBqIWdd5
zB->N3d9z#s5F6E!Qcs{C&H(T=$#PU@6*Hlh8)i6(3c*Rz(K(~Ta8NoZ4zv;+0YsQ!
z^f>LP4kQCYV2z?!3!y@A-6V%Jl)`}DVaE?5;cqU6I@0#}eXmUJL}SwG#1sjgDLg5F
zH1Z$km9@!;Mr118#aGNl*&huvZUJ(tNQi9D6@9>lUN6g7zcCaS7nh|6^Fgg!8Fp*Z
z{-fnd4A0Ed+M0<KVUVQM0lZjEl<y1B$A^)NXN1wYi*MLgPQ^P3ooBb5PWdbn9Fjj3
zASV`{_7}jKdwXp;O;X@UBvKpn-MJQkFaNx-VB4KZw1=ln)!QfJ>|c3g`9=GJfIV2Z
z12OuJ%pbe*@S3Rrd%Lc#Q$m7+!hoa(LP3y;jX<S<9!@y=^>eV}f(*zqz7vPv8jc!i
zU&0;Hqo~uvJ^!pkqR?Rlc6yF?s?5&^!JHe>fW@k8Xl(p4u`-1*Md+>Kx|FiX_yf0u
z1~#w6BY5&sek?OW&FwHgg?9cxa@i!(A1X8OMn?M^oSejysg-7~AKzKri<qz}rCnPa
z@e6$}04~t)a(nsXJ8L`#+BQG-$@PzhgV_VD=uGm@!8VM7Uj&7hsq4)h6}q{seIx=o
zeeEp1{_NbWLx#Ny)<S<9#AoGl{?zNde&GJ^wZ#bl>ikK-T<l)Vd8-FyN418BI11&#
z$bnI^cfP8JlA%{s`;ABJU*65c;CB=mkQ~XPVnj!G=_`nWplQOs#_0>Sq3)%g-!b^7
zxoiam5yYV5Ovr`09K;I6U*fMhlLV?tsv!``uY)L2&>5?Qje_9=X_w80g2C^)Qw1_?
zsFH!Bgn2r;B%VJ#MHr<9QqsMlE(VbS37{ZLu?$S*DuzZMsroO;{-1s55J;9+k_`(@
zR)!@pCeg={wwBbby8sXHeae7XT84Q8zVtR=68wJDmIySeCS9SBC#lG}C!6t(q1_ZZ
zUvN=cR)i;YN-4$bR=E7qDU)j#PNP{--H+AVt@LWbq@;c_!_e0n<Ya%;S|(xIhfEbr
zEbH3p^<VW`4=f1cz@6FwKIiOMq%cT|wL{Fbu@>xKt#gGg+Dnp4!(TfVhJp83qD8S%
zl2*}XObC}0@y+7tY85@an=GC@xG@-Z$JYGASc$^RkH>w7$Td}bc^8K~h0co(-72bX
zIH@&%H5w@*U<K^Nu%W;(h)eAUQdItsp(d|CWSJ)}nhXgE1Xnz|<N#%L1Z`Mj$3YcW
zl!juPlq=ci#vMcT@<JKY@|VB<N^(9~^H+aSP|6ujxQ*{^f>xzz;U}gA1Cwhe+!tRN
zlUVb%{muYZ27+UW6?M`baiQmp$C*n{guA=HSh+AMT9cs{#=x!n5MR0Tl@BY_Q_x7}
z*YW`J((G_wpEY2PiEw?zF&93cLp3w+e)(4jOnvcEOjkZ2Y<p<!`&`+)x{j$&87)Gh
z4axc?EjyQC`AKC#+(cN#GTJpR%^00Up2w4g=ZiMJeanhQ(laNTOwi?b&3$zLm<-Tc
zU?dWU!ZU#4kc5C6#ZJl5k-&E9o!?EN3#ezrvZAy4%e{_DGJ(X%M1U(p1!imx^LmKm
zSk+~WE8unP$z8S=LoRfr2?4y|`N|JAd%FkMz}3AAYAT}cU;YpY5S4In!S-|BdVZ+2
z^g%NNV&#K}=eSTjAH1Be0kE)5?cuO3y&IXR&jU|aVMe~02uK#G_g8!cVRXz-AkK0C
zF|;>6^K>M6=j~>`<BGq>%OMV@VI~s$KXipM69_jR7??FSN;dFS{Y+1;T${ajd!n8$
zHv{#NVv+N~I=aW|v{(rTlL1qJDgIgeU&dZjsV3;zh^IEa?ZXRYLFeb!EbP7xzDA<D
z@26a#-1^o)L{6IY*1dwvsFBM1QX7cD`*(0gby6XVy=8IVf`iURvC)s}8D0VXc)@DV
zDQ*F4k|UTp#D!kGiuXa|25#<a>z#z1L@%%);<dj<$|G{hHKTpQ|H9PUKk5JZ0I?A9
zKnqXSAXr>W6Bi>E*+={p24Nb}0E@o_J&9JeB)YoDT2Fn^X7Y!zqqD+DA0=)~)RzRo
zCW)}G{cuA@fV<oR=Xc?v8aO@)CRO(9vgw<c3BzE3Z8IYV?2sXOcct)`f9jnl2TCl9
zT{YTJ3W6UW+Re0DK%}vw0D0_8GOPC6wsxDcQy>%^E>mY1YXw9e!e=ZV<e`*y?LJq5
zxRM3PqLZy)%n3TE#L0eu9Y}`w7RsD&II72d)#n)W*7;6hT&EbU?V`P`76D^7QU6>|
zJZ9N$tW1%hm17zZG_thbYl6mQTRt11ruROnA;MQE7R}8th}8xL#6{0QT*O|+KM&$C
zIe1CcTvQo+{zmRpeZ79mQF36iBpr`Pydt0h)hN~*BW?eDhkFs>-zkdo*9lrB`7Nt1
z!l^apVl8M<RT9_0ARIXjpj@l<@}C~hP#B&fd7!FfWLE&sMko`!N4lKzHlXpJVLaDB
zeBkzQ0g%!f0=Mj#cCbew(I+UF`go_1;2;<yTul_<L#c}OA#Ac$Kf(XciGiJEK$LNB
zmEVN`H^wl}Qdt5=p#ZGa@rg->8@>Lq{~B-B$8aEv*6d$`myAk>xR>m%l9gTfub!}4
zcrN|mjtMn>x{Q7!!I;6YF;sgjlmj)Jd+;Nl(Np#Jm-UUytCGEoh;Ps6$$`cTY_8@v
zX&E?jbAXq=d{)?F<4(zZ3G!%YGrakE<+4bg0wkKFsDYaX#&sGg8jMnfa;D|)T$9yI
zORST&P^zc%Iy)jK>n>CDgPF58bic%Yv+Qj~Whu6EgQs+KR}jri<OBFka7IQ3$j55)
zgo8+c<K(P0lbh4YQVl?1P=n81`C!%1-K{I-wqWRDn>Uc7P*f=+pOx63Gaw-QC_ju;
zDuMr@JaaUmt;pU_KxbnOcf5sR@uY~rx5oU^6JTQ9%0NOn65^s&Q{CzGSoAJwJqMVY
zRjvsDKZzOPhRCH+>D$zs2t>&~_3ruCe5!&GfY)kf_R6f35qT0Vx#)mz_*r^31m^7Q
zJh8Phxp$|ZPN@;j&7&*X{UMBk!CTk(S`wR&f&ma=;ZKc~&b&}Gg>F45Y>BawW1H3f
zta*<5v~Ezh@#Xyp?~FZ?gEIo5d#T%tc?e?jI<2p@71zzL^si)up?SI0K6CU*t;fIX
z1B`h=0g`{+i~bn>f$_r1VECn1&3?J7pT#l2MNXl0V3P-CmO9XzCk*jnBAnMY2MpLY
zqfhXx`&pkA#S2fLK)H<KfP1a<%>F6h1up1)8v{^V5CbFPv<#D$noC(W*+CZ|F-OHO
zFd%Y@ERA5$N}9DB4b$Y+@}ko_s~gwZVsgG4@LqBjp@Z*pPHJdqsF`P<em_P=p=aku
zs@&b55i3V_-kRlYJrGe{^F^S1)_=@$#QYL>eHx?Hh!7VP5_nl`x?^<0PLrO&w>mv+
zR{d=Uzm_k!&Z`b8y+;Dl7`E8qdLp_v-~A;O)z-O*(_>qwC<<x}7BJVqLFfvr=@yv)
zog#nqfo_U}RDqufW|!nM>geoLN@WOJ6CrTK&akKmkC4EQD%WZw91&bi2Z{sfX%(xp
z!T1lXLT`_-f0eMyU$pFz)FGti-Xg*V>=l63cmmig5WI3(b)4p%`nUrQESB*5wL1An
zy87i5%_PAaXS8ecm+6X|VbRDEtk9U`!xwC)0enK{b&ZpRTt%k-Ww>{IX(9k*zNRAa
zju0D`LIW(W^DUPR^-4(n%7&aJ0@1DXo(5&jCb*2jwbT}FUs-3vVkr{OdaYH)&WVCr
zhLd2Hzpe0TtU~2rmiO%x^dN898}5tYOJmU}bk^GS8hrzU^dYX<+vg7L0IzqQq&8AU
ztH0Vi_3oSVyD*{AZ<}_?1g&bjh7){Rs$f(3?b&1C0WTQ{8AN$xaIq5DkjYW&0uWQv
zh%J}#PoKWVq#=8cjP{KM0;r#WIWmqy-#qi<sPQ+RACeT`r|Wutf}r~)tz?AAunS)S
z<d0bsUNdc79edCGW!g%%di0Y%&H3sF|6-U`)!!WM0O+fxvsMd{N_90xGdm*xoPH;d
z015Ik`jOhtT3oc06^Bg8&oc`fFJjqb&}=B?-h^xct*c<VId<B;umA{?mNwVkw-*M?
zaKy3Ykq)jBAD6qiF*0-h+7Th=aI@;`^&Q_D0vl0YVM83Kb0XccQEY)@Tq_dKC>E5_
zRsfBGLIhpcmXBr(Ck_2y-rwL<XIIf<=vh@XKhrFbS_d0>TS$wEcgE~@Od_)nI8Od#
zMXfu;Z=S3U)5^W-P@~pDJSbDfVtFl=I^Jl43@HPVVSygdz^?#*a_&~1$jdAEMPD6O
zl#7FYx8|1yC^gn`racMN8Gf^45xz!BIrm5Mbyucl$Xh|ppR2qXrrPQ{Aw@D1=W(+>
zOxt7h<z-a+CP6mEaV4;3*c}3l4(h}Kf<wlWy7=EYU89`!DP<t-Qj^rYWwI~FCF3GQ
zD?Oa^2NC~repVfQ+#l_5gxtE;dI`NxhZZ*-nYdJ*0c-Ufgoq(3Hz$z*o-YTYIV!&g
za#v0#!N{Y;tBnJ(_w{}CZYF(sI;1QtY1F}#jT8iepFl&k(&dq7P2@B`-sR~Yd-m!K
zqlre63_I=eW)(Nz%0G9Z__jX=e@eX(s5qb~eoveNK}rBHUdNu}AhbA>7f0xPOBq*s
zTUnis%=}mHQS@+?yLjGcHTQe9v-q!`MTiZ*#bZnew3?*^_vp&r1MT^vkW$Y@dLkTQ
z@6i1G-`j8ua9%M|Chq|u*oP4i*D6P+Q(vZOM1HbM>qyFm@POKj4lb$FzTVE~z4wOi
zf2#OooI=aJtH2s~{8oNY7MHPXsN}S>Isv9tGkRd*A_D6<L-~$GnB20wH!E=8SyMqD
zn1~2|e?@uVTy|$Wi;)ApH}WOgEq@yG?KNHQnt&z8GxGTPoZovE1iuy|itEpL+yVs?
z&!4J)ogUKQF1P8pg$LftQrS^ct01^%czF2rr1f4t9543t?8UEN0AR}l&eg|%>nf<-
zROj{W`M!(G8q^akWawH=mD)Acjs+q^&&Z=tcS3%rxyB|4{(&;yu<VK5>y(`)r>(sH
z%bq1B_Xq@4D?%u+X#oj$$iib$2!w(1T%Gpi1)&s>n`LA@iPi<hX1(7XOgek69D`P}
zZ3PHsJ~OK#L#c6E-!x9c5tRe$VMCy$f=<mzG=Zt?Bdi@2$%-i-7_SBi&I%?iowlUJ
zGhiq5A!gZkX~r+_eNvx`H+T12$q6O1xt=6I*H`efm$r#>^zkTlNfK{I$#D5n=98Hf
z_I`bEB-}5l<P5#a`AHFrqe%P}hKFdHoZc!RGXqof-y+7Bf~hb6N)B8!NL(+SWxsB2
zCD)KZNhR#+4#eIlm)f#pQP^Bn7E&Q$ytZzfMD3kgj%U*8g+Y!&jNk-AllSsS?>Int
z=`s840%$3;VMVXTaexj9dY?60L%kCM#nVNCZItT$Kl9R8TLOSai{$vgBj1s3XyJAG
z1nlrLGJs+c=>_SE@lPSmH`fBLf?mnzf!No*)(geJlY9t9<agkXIZg-lJVCi-_?i}x
zu9paJYpDVG*jp?#B2l8fCVtiQq@`emv0I^I6y2>K?(LQ(F}Py^>ktv1>M$W?QmTY<
ziwYsqms0}N0W)g?kb<z$3TQsZL|GBY?(JU~k(V}h<89j39+>gt?Y}qKw<wc3N`?R8
z=j_^})ta;|`tcAt=U1EwMgX(50@^lCTY?W9c8)(8s&~@$M2Z0^)g!y^Xw4PBn$w{C
z;`64w%$eHxv=@}Rx{9;%+dFrqyxgza8k_SU<+MFia9eo1b>ObR;YyN{R!e~$Ew&=d
z!GD{o0bfBvd5YdrRWl(R!RP-u5}*#j&<<OW2=)uW(%2n)<rkb6%q;G|V_V<x^%DIJ
zLnSaD{$4fS>c0>=5CYGqjKjuu30s#tc$nTkI4*N%!Pj}^MIv-nM4(#`c<36O(F4F<
zD1vd1k(*UP-lPc<m?b2F?X+X^yhVIpc<LzR`YAwb@Iu8gk3F^GFUF+dH{E&UY&1<C
z2f=hE!!g#&mjFB&pcKtkqH&?2_t6<Jxp1Iu&Yu4U>N{>RKGX(eovhyl+EvkK%k|-&
zUmJ*id%d@|)=Q^~%09#ms!Y^pPd1VfHJcwOY31C<iLmTHr$6{Z0NY$Y#4Kf6alQm%
zZ^!|@Cb9#vEVBuba1hN8&{zMJ)oeB>kGxXg`9r^0*T)R>+i1QB&hLOkjibbE$5F1e
ziv=CdG&>oVw%?RJzCsz-G$LSbwghVER-4}qu$Q$sXL0dQuoY;G@wcqH3<H5w)Tbx-
zuR5G#<UpB)GgK3G1hRJOi|W?52a^C8a4<K4-=61`<rQ!X+KB6FY=D`23$daWiYRx2
z=r@><3<#0x6{D+v=jbbr*ZPaR%whIyf$rz+8vR)tuVow4Gq>nI+uv&Z==tIFFo7^L
zp)wKvb%63>?e;`U@)&a_j|}@km%H4_+R5(?6XRbDrKB`cU_E03hAO8X8>nS@ciz@C
z<F#-T2G!e&>>zEB^GVN05X#!5`q>061oT@IG3rH>uEo!v#J3yq38(+9i}xx9FlXC|
z4IHS}6R0Sc+>ibHj9O~z7n)|k+2;X4wj6N+@H)e9bm~ifgJ`EoD>Zr!;2%8TI5+zh
zL|8+RD7FPSxQf^4Z0KBve&qpD=ie%-X))M;%D(?qA^o4fvJ!w$jFd^u)9O+G{*(Vp
zX}SccZ2x(h_OCg>Ytt$hYv1`>c{+Fjh*fBOtO!SMW}sjH{*!=8fF^a@g%;QT;{`N+
zh!6k;1-N+r({JK`ewS(wmi+(mG6-$G<pZ|CYU$d=rB<$gYiR%=&U#u~i*M1{9I6(e
zpF&u8IQv6uL*{?%c<M0NzNt}P(R~!eCJ3}hK!LjZ&C{Xg|Etr@|7{?Zfu!*tK=%Q>
zi%W@1OFM*?JpJ!kU8nifzx45O5^RR|?lFTzMI0{l#ouKGJWT|NT{Eql0ic(A>H#b0
zl5nBIH}B-%MgguLGo5E5XRs&z^!8VA-%GUi^_L&D69tnAe@&h>t#CR|EB8*6aTURW
zR2Fw*%jzi;6O)OJ#i5C&*8<o$zT1ZHX{4h6ytotuR$2QOJ30L|ex3q^iM>5<T0AS|
zGOINN&(F&vCR}p}J_=(g+4t#n5K;W(Nwv9B2XeG|f*tGs6i{O(M_HBpYkr3p3I%zy
z^qy&qhR8iWV-K=b^k-oR6xYTtjNd<Z*a_@#^w7KQg2-fVn%_zpd{Q_iJNad}8}<xB
z%+3~;(ZTgo051HW+0r0sa6k0lTuo|U>rmjy-=;OdSQ@QKsCp)&*`3es`6uS3M!DYa
zT8mcTWc_V|S*hT6^w(^XbKf1Xc7OQG{t4kvMyyjszEZhZmX12L5tN}nc<_MMG(!6V
z5!;vK@y~O1MW#OZT4}dJmse;kaIeZu%;tsT(r^W(th`37s{-`Ykd7bS+9DX5n@1#n
z!d$w<7IaKK0yPC^j<dP^&)c6`d_;^*W{8py1(`cFF|zFIer02z_))t2iMzg0KmIml
z+V%e*0)^i1>q!X-Llyaa^O;u@rJd2Uu6tW6v-98I3a0BbczVAxR-}2ku|q;H7dk%v
zbLRj4%)q_39-s#($Z5=$9V@+Yx*H=QaiUFq|B_I(*38SkLURNoJ4pTgLr4AF2Ob){
zBkgMvxTDsY&I6On()lW&{z_F`j{E*;&F81rWh8w5zRQZKLf}lV51e2(Vku=NtbP|v
z&%FB3$E3kbmDU(!ktH`b7Yg@MT9N(duMoibk*91u_6nFu_OhQT3tpS)DCe<0vOFgV
z3px!Nh2X)TBhD6Y{w@msiZ#GA%+>6}K5kUzPslBMXkJ+a8RdZ1^e$EQb6JyL0eF+D
zPy1-XKd#~c(4-F^eUG{inOoN?zsmeT>v@{*iEcra;sLI2{20xDPVEg%NQd*j8VZl}
zVq|4yqk^<~;BzjQmSb2|jEt;kt^fWz{O@y}MY*Ls;PV>_ab0_)<B$qCK`>NMBeBUY
zd8GBZ{)v&tm+XJg=Wz+|@B>byu7<zUJEw$_jSH$qcXv&X_`hKQ6(r|4Uw)<eJ@@%-
z?d*cu+S-;BS_S{t*`UDp$q4?$YdQR>SG>Hvv7mp2fn~HI;vaLj$O3a$nR+xG$e~ro
z0Me>~$;Z2BZOQ&Sm<=DWL!oW=#-eqfK$p(sFK;|*`@5>-H327MD;Ib6@2jM(HyR)r
zSqtwizy5cufB&mVA9N}*(R>eRF@M__;MZ7!!xE{oHu~pn0J8y6n`%?<+w6aeEwIBz
z;N8vk#`^z{EwlwgeWX6L`Ns>u`*gv((>^A|u>Wsc`2X=TEJTOCUjiPRVyZDDp^a5?
zAp>8+MkWE*J*!x;c!1f{`tg;sKXThqd9wdLFjr8wmo>!k$cN@U`iH;_dTAY8_FrEH
z9bDp>@rf9|Xg&cw_0KASKs0G~QBqR!FA7aufgNQ->ps#cMw>DM(UoJ(5xxhC#{lv#
z3Z!f*zoimH6n(UoA4lpNj`}P&&@&+~nq(=G#`(ST1N|Y;pktsDijp8ses&+2z}fwk
zm6g;}V#s?Tg?gs|*R!@@C!w?Dkc9zxFSroM$A6F-TF9hDD6%ii&8Kx>`68IL39^<q
zpCn=MH7$+_D)(M2w_F?-RI%}h1f>x3@1I_MWi0fsE(R4gkVR0HE3-3(z$@PkP63{x
z2)HzQ18?$Rz_mITDT%$IfkmMcKk`mo!7IOco^o4gTu{NrBQ;i;SaK9kYxH#?T`?Zi
zx0xMms*)n3CIIDAs0tXHfpXp!&`m=QZfg_@NkW;H#lQ~zyxhsZ%U)+9;wv4@?OJ>`
z4<5W)K71;9|G|St@4YG)%Z5JY?V45R?R-C}n~}q>#0tkUXp0uz;9a{}^xQk{$U%8k
zlH}@mUeJuI^ds_SV~F(L(pDlomivSkN79()4c??A9Gu1JxpJF>&KE|cgv`XZaL)N3
zaHCggBSVip+tTRE05ECEfnLAfisbFA1lNViq3a8QUOWfNcS6L@VVky(mmNp!To~bs
zqqLRw%x#*I9E)XJl}Mej5(?)77@2|@5E?lKkQc*5xZodxZ8JoL2;1L*BX{_x;jEAK
zfG=v(B7BVwa_U6C0ijb|l}t>GS_xU&xa0m@B?vBUR9+AF5nBs6kwxL1xzzMLAb!li
zLj>2<!{yZS@FBUjM9lqEfDagQ72sn38uAVp(l}IDoHpbQ0;O6uZBL;}V?R;=(JrFO
z65Y>?tX<l<vsu6uj$=h9X3T8vp2#Y)$fKm1M?g6$z{Dh`5n>oMLT6z*CQbZ)@{s_2
zD3Kw*B+BUouOnXoBf_cnSs0_#;h%|%CGnS2?%|b{!#b+!KKXqT#z`UDkjG9*xqu%>
z(tdoP1-|2n*m2Fr@I{!{dJhOC$wzuXplCr}Rnon;LacO<Ax4)#7JWFv%tZjf!i`#=
z?_{^8j#F)@ln<CRhX=lXkc0^(7Gct0$r<$T)ks&4-Rx)`k7+?n5M}&aPt=9Cn2B@T
z5p28|b~-+?uCcN-18cw0_|v|dhfn%6J-6l@!LoBygWwA8cyjCr{PePWIoF&8e-R>p
zs|@oMEV{1^!xthWWv*=Aw5J8p0cS}?{`<Hw=fRLs#wtc&$Xnw-o)Rd|r%+rzA;mrS
z)qJ$o?(B6(Un`e2rL1Hw*|3m_+6flD7j8R7zVdkT7L!)W@g%c4p9u5O#w(kD!O}r=
zSXudTY3xw}M*{MpB3MGF>z6KJ2UpLZNm3Jsz9P8CrTg!7m-HI~Yw_UNzwpI|T#-l<
ze|Ug}xE2J+<jr6U<K~=+a2>ldQ~X6ZFf)lE(|q_9G0KBY`X?+VghAI@6BfiyILHEq
z&*b)`R3S0G^Oz9%*V{VK=@Y2++}MUoPGDu0t$w#GG$xellF7u{=io3{_;^*Nh?TA<
zc#EPHgJ6GbJ3N|S@ExYuKO`SAAg700W&6aqGj_iD&IH}@rc1ypx5q`kZI5GEr=R8m
z=Ou!h4J8RGTF|CM8tlF9|5+bWf&Jw^(iA9YI<wQ37>r6xdySbmTqG0gz#%M$<D^uc
zNY`no3n$G%8MZxc>dfu$Q0IGkat*kri{1q*q!-*~MC_dWpXq%A`xcgF%ME3)Js(LK
z_p4%AFD|&eUnT>-OJ!G@r*9tRso=3+05oI%!N%Utxd@Q`O>mlVatMYwj8+>>#o!Na
zCnPKQ)uLXEV?r`Y_Zq1q%m@)JlV97HPn>&qkX?0K#kV?%Km^VE=!pys3C8?%5LrfV
zfGo$BMq&7<tzv06-My+ROIUOyBqkh8i}-P~4yV_WG-r6zS$<j<yt*sHSdp<-1fHF!
z2c@#T^*6cOwFHo>1QzF}8tVgy2Tymtjmd;kjk-;(uR`N)_2C7nterD)S|^s^to@k)
za!RR;x#Ryi2wJ<WhI%-A;Q3qWF#0Nlk9!{HY97HOH+XQEZD8LBdhYn9>yM;{A;`or
z_t@Y)Q}@eln!HJAzTmn|ogfn#5gJvuR1%$@t8h<)JdwlswKz=^1HtVQV?6?91~c)F
zlDe#|vCXNC-k$+S=Kr}2qD-`#s(u9mJm?A3?;&d9QQoBAni}k9-=!yIFU_x{s}Joe
zP@5K`z(q-8L>|;VN-}k?eXhWW+)-_7PZ7k<8rZ+WlZ_^n#(3A@CnO{U1Wq`O63?TS
zp1!#~ACUwe==&hk?#zg62mF0*BHV{<4a&d_B<{5G4~c)>E9D6#zvJv@b)Rc(QVECK
zI!SdFU^!}JB2hd6PcAo29UrFpWdG^NEPi|Y>U)8E+$o_ko-BB><V5{@oduhR%1PIh
zNJYZk?H$%Fb5IA9IcXw%2;93uC))ja0MwTqP+vn?9>>A4!t)R&v$b-XGv%_6#_e^2
zj{OOnNer#%(2d|}yR&@gDy`F-?u=S69tCTy;{34^VgImkxu8(L4hXMhQm~U}n|JaF
zJwl{I=bE#9m4;X_yeb$b829x`Ql?C|63Ow+&vg_hKUj25DJJcJ<K3`_0^+J33A64L
zB9b!2%G((XEJ(Q9-CK}i*3(z8v!Um%WVQE2H4xh<sSu*CsT)aCXl<v>hH6eGa-VGU
zu$K8GJ)`6{m)n3@k2hgJCd+x1_1%{IR>tBCXnZw6$E&Joy8ncR91f%xc9(uXlH2Ai
zS*EV<w;_2k2yi8_gW6&YIDOu+Y~Q|cnXQ6dH}A;Tu#_C@aCy>wQl!LG*~Q6~*+9)6
z%f_*qKl;=Xkh@=26TEln3SNC+#Ju(ls=NGIl{mHQHtlf=ufS2zKCxn{$-DAc)uG#E
zoFl1WDOu$rJ`706Xm$|%K@iLb_CG3bhK-d!QT@9!1M8<Epjplsr%-s427kO44uX%A
zw=N^}vYdMM8F)dX4O7xv6|P!#bQtg9ipbBWcl;wA8j2@<pIbBH$^AgMDrD}!9f9;D
zJ)P;|ctCHR*|PxZ2$`K^*Pr`)nsZfhWtP5egtL-jz-2OCOuGV&KPB~;(Ak&U?&;e9
z4cnSV4ir&oF57ml%kxS$4}qIb{!WCSIm3_Zzr6MBSC@AF#JY!+?0iV_4qmz7p;l71
zPn4Kz`%Qu3<PU17M7@Z)D;dayhFeb7H)e2Bx9vz{jR&1Tg`!LM`*w?`cRM(3<mbQJ
z*(7a5eaEw?lzyCIB^83H)Y|1*LDkp^L)1`TLaFh7e>j#f=(C;t1NJs9XKi$mD?igG
z7>;B@?AQUdedwk{)-?6npB}WKx?Yo*h;QK9(Ut8Gl)B*`+5WRz2B%lyV)64sN7O47
zauA=a=AXzfys`IiFrV2e139;f!uEU0ucEHvmE#sZ-YeWUOwDsv=&^R*)s((A6C$)`
zHB!8g7OS9orZ_CBm`oIob<Wx`W&utDl9m5nYfCYlbr!Bmps1WeVH1oAofn>zm%FXs
zSp3X#293jFXLZi~{w_9iXX^tKvKF{QFUpb~_^_bd2!`ekcA;E8&W3*I!HznoNV-qZ
zI_7Gg^#0y?{;4~&_=n1W7TkD-fUEZIsd>%zK~_q+n9^LG^kQXoo8{u#j7@n|`GMMk
zvk%aKikv_nNe<o=hT}BG^~x2Z96lxh@4-_g7#<G9>Se5{Y_0bQy&@-`JToHLuy|qr
zQM|%!-U+4Pua7dlas&_>xhk%Y?{3Tq>2li~l=RulC-;?eplo9l6f#-hckdo{gEk?j
zWDdPW=iLqr?kvY8EJy5YS7+e%uVseT`mU9R>{X1O5<a|sfadzx+6z?j0Z<*g#ib7+
z#V2nNB2_q67pZI65m>2QdN(-oECR7}1?VV+D)D!9v+~@ve`vRIJwra(K^mxl_cBs{
zFx?$j-)-w~hhXcdlvD@pBs91h(+k`EdJL``#X}xyHG-03Jekq*)WVq==>&z8jd%`J
z;pX)@opO%m{4k&@F9229+x3&7Ro~(=wyC`Lk4wANTnK#rr7Js*d){4dAT9`|mtRSf
z>D}qhsFW%;Ml$iu_LeWr#JlxO+zbJ-PlU%g5tQ%z5&Fs(QR>vM9KBz?*<(G(jNI_y
zZ(fVBv*`%!;R2q=+VpO^Z^4W%0kg^CHYih`^Jpv$I}9|5CQXwJ>E$ypqa!D${r<?j
z)vP>dXjnRU>aLKvH-0p$?Q`mov`qY~24cEu8WX#u*@x}D@Q@kG<b1)P_584)Vly9$
zdu46<Yk01!pfLB$aLxm{5J`n>mIUs=Inj4-b(6kAVN^oUaY3<1ED;&)J)_$m1!><U
z?bsu5w;5P9R<n0z+^NagyKj>fG8;8=b`$3^FG3j-MS<@!@HHLEz{P}J@Qn2HIk2bh
zjbs#X)(iwDOKa0Z=U>ATTo;+=+!GKTlo=N$3I2(JQ1{#rakIkOIgQ#aza_UrG4CDl
zJ-R-~ZP`C&%FkR*<O*;TTgBV>sD9wHopXRmkXyGmCC(GpYWT}UAgKvz!;TjF&g(k5
zpgJ>C5n?S^>TtmFJ7#C>)6>a!nS`(-0da5!Hmly$92JYl0W%Jx1<5sYo^Sq0na{Dr
zn2(8roNHf?blUgIoc$fQk@@wa85H{W(AR4tl($P8c~x@0Bhn0P`gMD&EtTn!NmCh#
zE`>7i9;If-1DDf3U*xo_)W)Ukt}hG}_ket@0sM{AZdb~|In~*+K<I@5j*mW~=W+ea
zh>s(gqgQ*p(x+!UQ#_~l%>{}j#8pX8p5&eiKmb37$CD%g`V&RAJIQp<YdDjMQ26EJ
z$V%?i$(IK?k(ItzraVSUe0|;PK7j8^R#Ok}7EV$ud=4cWi(DdHxz)RVoM&}r50-KE
zbBixDuNz%y?GQ|AXk*cqxpj3lRp!pN&->vSeWixPvgmsom(oCK2%?uynGEeJg!F7w
zbhurb!SJ~bC(ijqHcI-ndwcwzk-n4NXGMr)<%?)rsZq#u(>VoEVQh7Xcyxoig<o-8
zC13$tGcOYr3;fMmk8H(>hDT*r%SVM<UriK_oy(thwGq3!yeEOGXz?5A_9S)sM7l%6
zjvTp!y{W3Cn_l9X@ET^yDX5x78P9S%Scbm~Od8^9Af8P&<9<JPa)Y6_xaVn;^EC(R
z*3ClesSj-wUOwgNJDE(jWrxFLqNlj-;%<PFT=H()*pzF+T0+N@#hLE&erv0nX-m5o
z^LuW308dT>w4+K&Fkmw*Q<Q0&$M7ZnS(<T4Hep8IjY#L?P+zQIIi_*e!_n9|b9CDj
z>lAxfnO%yR@F8^4!$#UCel)$EtmNuxI!s}e62oP^W%2!$@pe~F2KHyqpmB!Z^@h4|
zN5_+E4*5ZFVK4GCt}<Z8$$_B`?-LeN7Ws!1mrNFLnHLkfuFq+8<Xd*{O@qXr^i*Ld
z@GEk3n5GQWAkg4b0L6E(c>#?Ph)l`_Bx|4L^{lQBW}A7L^F<Xpr2Pih58|{?Ax6eM
zqQ_~zmOg|l=Z!Y3&>S`5_o;5UFl$?rL6@W@I{@MnmBX&5B+nT>mA98O9z>?NR}k@Y
z#(3ozJ=elrO;*&N!5O_bM}SAXIp$aA3BcF-mTo7yk8(X?O3L)N#B7;*Znpb0SKW@A
z2On3BPw^bmADVJ78Re|d<9=`EHYK2FQ3s5{{*JLsfF;AbuEbZ5W!hq$ol0E`_a6J+
zf(Splv+ZFzY6_@r{VP5(vDv*ko7Alz4v0g)=4g${oocW*X`JWylx0%>JRhs}aQ3Ms
zW(ULnaJ^H}*{lNYVWa4^mfM*EEt*A<)-SoU+9=~y%e5xvK56DWYeP)kl95{0b9iOf
zH_j<`rOzT3pB&C#crsMz+PzoULHedCY(6NmJhUf=MB&m+qn>?^0(L^j=C~zaXQiS{
zrVcprg7{Tu8R|&+eXoR%d$`ik%EHD7U)h60Vf7%`)m4kVyP{U%f<f><x|upov+XJ}
zFk$r(#2M5&r<7}w4Joo+TjLE$g#-9@F`T~gNnN(iV9=ZgDZ94CD#0+H@1BQ!GEZs`
zt3S->B!h^2wSVvUWj0>L@pC{?-xNWC?YkD>qMyl%e#donYR2W)Ch+4b=W3M$Mbt<7
zCaKjvV~=X2Z{9CoJ2)r`@+s_E9b>W$hM##I`2$SJ{I@qKYAc&Wu%G=c&mGL=bgB1B
z1_s!X6yF_dpMEl6$TZ$BO4i^V8r3Be=bC1G$AGa}xq!`*==MJJ*iG)(34-nFmw7|h
zPO$Q~=F0U1e=dPma=!(J$jAX4+yW)F*K`v&{T02I&EgU2qTOr3wH=<LAu#b*%EYuj
zyvGa&=niF4JJDjv=gfHlC-sof36s$!MZUVrT?WcJ%bW4jU1$)CmW5X?R;l(p?eOWX
z=y(l#)N_8mL)S8AyV$IJez~}o2&1|S+u`{^tm6TWXhwUL3j%dR&bsN!Gb1Hg&S$Y~
z*gui-bnVsA@ooRu$mO9mFTYqj7ymIo)brlAuH?GK$@wYwmiEVy25<!P&5;-<p2C(_
zp0(TU@ICvH%2L$vNJYM`&ZLkz-i)=x*N6wNuu?4-UGF@<JLYc{^-cc$oKOv6<vtY@
zXvyGz$w#{1vl<jy4z)!-NRmw-<qTm)?ATHdGzTPlz`j+r_pKsPzkObNA?X0Z#Luhx
z>3Pfy2+I=`-NGah&P|ql5$-X)+z0uwqhgznQ*up|eMyEOSn89(NnKpyr7D;y#prz_
z2iETVp6>GS1;$H8>&Ej)MSK<}P3FcQ-gi1NDnjpNGLW%DH1GLiD<dLt%_7I-bK%sj
zgbWoEyz)}W!i`7U)KSu10wwqJ4^6$9sH-|{Jo|3Dhfh4<>`7}DUh0MC6fH_m@b@z^
zu%0&Ol~tQ}Xsl}YZSSvjB{>eW`K9o>w6uJTC*AEmx&DczQ8v^hNZ7+JuGVvO;Ov{h
zFKEYmO$+^xbd_bQ*wGKae3s<Sn&Y+f*pBgQ1~L>L%!s@n=}_8rv$+XS63jh!)~p*C
zkv3KR83IXDJzfuJ4_W543wynfUshPo+xGMUyoaQx{bZy2zMB4@jeyQ5ChmctPma+=
zR?Q>`-pIW?=RcFMPc7f|5}h06sIeqDR_h33`%N0cYR4C54M>q``=HF$h{xA4nYK+H
zK4uBp$MZFMtgC06h^M>L_{aPX_ve5GyV%n|r{;KX$)?Mesnqn|JAtIWKCko)O6r)c
z9<T;FZJQ~5DTOn@o0kC4UO#~LigHu?fcFH1uYov8;g!8R-<dk^Z*Jz7q@Z|)rRKSF
zFuYOrwfaPT?xZv7w1?wtFQ?P~z)DB??7)h`1zZ-(PcCqpc-ONXv~2-^WANTu%Hh3z
zN&{lv!DP4P@>qdl>53I8axk$su6=F3=7$rZ)8Rvo!<<|4PM<U)3_E)}9$+&DKr0kH
zGXops4@m(vWibm<Jz0TP>Zru8uD3{9;-U2k7qNtaB+MlPGfbO4FLzKEnV91ejGie9
zicv@Dnrc)nhrqKPWOP<;3jP6K!!(+*VIl{1L1)$ogIMlg;ORa2a$6o}fZK=>j7eJ5
zQ>gW?05QdsfGFs(b_>u)M*_@9k>}gxv9rb8A@C8F6;IF2joEv!c8|`G_<e7gW9;a6
zM}0siC|C9DQV@d=YJuyGbqWgg+neN>UqfxZcQqI(GfUb0VnxzQnyUzb?d#`w8W33D
zhTU5AoxL3g*7+E$v-7VQb=o>>FnmhNt~qOKiJ3W%+IJ&c9N>uQ;lY_46B$P7FB71U
z(JVq9l;Mxc;F>~nFF!4yzq<2oPRR0i{n+7w`-D>ZqSV6=7jR7*DJG^UrzTgNj}g_q
z9*)c$z1ysAVFm=vqcn4nm6WZhfIB_+GZOiG4ydoHTrnwI{Ilb(pZN7M`2<<=Uc*&P
z&#pR_%J{TpI|LUhcyBrSWCY!NT7NA+(@SjS+JKsk?Dyx<M7T4ecW&9AV)4SHS_Uaq
z?aw+vu}g&l7uO_}2Mv(kl~5C&IIRh#CFie<6ZIA4FEivn$gshYPLOY0)0j*kx<PQn
z!A5yqeqmW8re1Yi2Qr{Gmk4)eM$Y+&W4F5X$K9a}3noA+9q=t+vk(wYyz*wgj{C8E
z0Q7$4#b3q}e7sBP1rQrWZLMCfrCaeA@qn2*Iy7fkIs``XKKmfwi)Wq<wV=`?0R&th
z43(eQ2e4PVfQz^rA+lo{1r5Xt>Vg)Dir?GKi3Kch#67!-y@$yjIf_AWkTf-qz%}_i
zX{fxsI$n3UTh}+^i&-7-e!mgw%m}QH*ee_evcLx7e@Sx=cLDWs9gqqci=$b2i_8Oj
zNuA-iPu(N$vFG;))Kwux%&JARSkJ=&C+f3%8<-I3-_S5IP~M7vNDd6ky3zPu)#64#
z8zwF2HCzqFJg^f3kjYMPYH8QSB8~(cWwZs)mZZs6^!_Fj!{Q0lznBn}N9tEbV8i+`
zALhbvBu`Rg=eXa&j2{u!(ti$N*20LKvtfase18Rwc;9`tS=s-yH5jk!Ay&g*u>oRA
zigb`LcQ4N-!pVWb<(V6ywP(=X@Y4t%5@`4VdKerU^OSYg7M(3YYlXmZ=W#bay>8%0
z#C#|f1Ky*k4%p0AH-je-CN`2HiGmIhbBVQXWj_^$HEeRbHe%B&jg%~$#<-Dqv81Jq
z4`1GJ%g}bKA;PJp0}X*Pl<@MZK2D2{dVM5-n;!VcZk<jA#7ivBAZ;Lc$HjZ|)#A*Q
z3eCosJB*o)RC^T5Ui)(p><YSfHHd(Su-(UkA9FR}Lj!c8zPs$m6rbWaKUiGoV~>k;
zkVU5I`Da8bo&}Dq9O@LbXnh`-h*`Nq4zxEl^~LjJ^a7Yu4yIzw^*fT~OwQv#81yX_
zRP<V%@u2k89k@p4UIdpn$K;t)^k*v)t#?6vq5bN6CKB@384s;r00e!k4RRLubA>O{
zkk^L~mEB48^GlN5O+;5hBogg5q4WlHGJ9qBd+GthyM`#?q<EKAXHo+zUYYzXObWz0
zHl`1{TVxm(L?RcjE{CQ8fEpxAWSb;vPPy4}7YZtl(+*yx+@Ff%bu<j1HW9f&q>kb$
z+t2d>MN!=}&nMb(zBhkqHf2dfa3&F8p+!M;`-=|qY%FnwmMP9`$!ZpNAp^db?>`#C
zC=oE()4Hc2&||>6VTM{?f-+pW==g&M|F%pb(Puq636mFm;kqFTcx8L(Rz4-N5x7UG
z(2)+k@xSoOrq+C$)Y|yma8EYgA}r8YtAqE#;jCLxpJ|Vc>v;NJLk7&P|2FAvjNqw5
z{Of`Xn(4^gP&n9{oC99!<<(TJ;SS^RJlag=rwybb&{y=nBS=MM|08seI>x*P;u2xy
zcI>Ny>B!p-HBRk;<^g*p&;Ul{&r6;(rvL|fCGG4$!=(8p6%JM+dcfx%^x>QmXX{lh
zgdzJFz9B!27}Dm;P4e9`2=!diJupY7A+t7k;_Y$!O@m*fJ-2fOL5w<vN+B-Z6Hom5
z*6G=orI=jlYVY3t>egJbdzIeSuDf$nJu}zZ{cQI{;}4^5{r(*&Eic18GxsoMwcAK8
z@s+lHwtK;+$TgJcRqQ9P%_W%|-`y;3^?F)sF9)?r7Gfs0*JeUl5Kc=`p@|BXU7a03
z#zETXaba5&>2nsuUA)e8KZSF|C}Ygjcz<hdbxz4$#NRR)-onHxg56qe9_q>O$?XZ(
zF0cMmAUbyC{aPY^|Bs?52(4sh65+QKmPV?|n{R~O^WdKeHN5+LJ-h5;$oUZXZEhc}
zdZh!Fcm`I(SHO5l+vUH9mYrzYwC$@rtX%pzH%+~kTUZr06%03Flid8vm*AN3<j@v2
z;vU}CW649W5h7d;;Fg%P=!FKioR&O*m|~LUeJWXAy*KVviMdU3ys^L1n^ZKrI@L1I
zZXXQmIhc(80^TMS1ysJ)YVYmTO95enZ9HMJ0Oktpi>*?`zYsv4eNyerv3a*#XKTB(
zCATVeEKeQfqJAmwl^e37!aqnZ-;ERmU$fitB@}f#B!~}^siTIgO~lanQp#S*BQ;bv
z{*G9b(HWq%Bfz2*3Sw?qltBRS+E@7+K4Siw$K0F^eP#F;JIed%Hg#@Cqz~YL_ufh8
zHk`wKdOqlqlJM$oyGLXLhs|%m%B|YS*$l?uW$Uu5Z7yT8>YR0i6P#Fi9Zv&6!4Kz0
zE3QF+Z<n>ZpX;EX;%aW9Kq&5ZMwbjE!Vybuf^B<woF0yAciqDdGu+is3!PCt_?#r$
z5a|Vf5C7Qr=`MiTM4b?>`1R$dX4JKZ$ZjvdF@8695F4$+k<%9AwiLTRZ+tYM41gGR
z<taarQlAuK`SR>t9*h>6FSW;Yx~kR9$z&kR%#HWEM`GL|-vX~SRxpCpQSs6+M~dm3
z8p`Fk5LW8cP(_L^HyTf__|CHP#U;vqtae`>+vF{SOK9}uDe=E->``;==cP8?waYob
zM04J3D%K!=stsCmwB?+D9%BbLzu6iOjk!RP$IZ6KIi@q6tUZaw**};Kfq_khsaLJO
zy)Y-rs4F`P{1}m#_Uwt35QkqNRtwvlY>XIlk0-)*9TGep7?2wdKCiFZ2E#mfbY)E+
zo=Lv#|5*YAD=m*7m7c@If0n_`9#r{WCt=R+oVn2a3a^G5mPL8+Vx^ASj+^(rA_T#$
zBcAjXz>&w?<d=U*HUtW4Cg+*QumD<r^|J=K!ck5#aROIv^oC-=d9{$Q$QGQ^$074Y
z_`)6FS$OyCmwuN<9#EU{EM6+hVxP!?Z4Dg2apVh^GU0w7rrabp<}cf|O*qav4={(#
z>p?K~g)jrgCo(rvX(PXN+1MmeEd0u1a288I%T|;Z?XXD!xF3G4Ag>a6rN+c`p2eN8
zxAAlCN5bvkTT9t?7hMG_sz;JmR^8kUZ`zl0l@$s8S>ymO^Il6z+QR)Aw_3H^ipO^z
z-cP(B_WJ<j1?Rmc>Qlc2isg-R8d*Ss+P3TU?<*}IE`7NcfPTbD4?t{i9`|%x*MNK*
zkK6a9)p+^z1Tp0HjPxz*YrblL3R(=uuLqu*PK1?L)y@`9)N{^Yw-g^6Umkc05E5k~
zh|V+hewE!SK7-O#7dE(uDdp4RHo=n}7xmICZCgqcLrg-?19c4rdw0Uvh#QW>rILw)
zaO9|q0J2gWekP6=*I$t4dmJ%Zu;e1ZYF|Kv4c$a7>HSY)Yir_G?`XWUbz7n{f6}=L
zC9lOk@u#j%xm`ntHQUAXMA#!vk7Iww;!kUhYk(Ah#<lp}o12RsG<Z&P)J3Io?D(fQ
zXT)vRih^X5Ci3-Sn@RB;Hy%f}-(6f6#N7}SNy2w!(gPVFO<Db}&urUs8-;9}yfL{$
zt<Cb-go1?If1NfGIHS$VSCq+%svy?(!jr#$fg>b-S@`rTyR(Oxj5HnX@`ip)uRDW&
zhvdac)%ZBq2A-{8%S~J!pK6KCto!T`f3j(pUuW=>ul+}Ufs-<76+P#OqHypU=JNox
zzV*|ImW#Kzj3b=GZ8hl-C~V*Jcv#Wil^#gc!*wMPkz%Iay{L$r7Sp18hMAaj<3M@a
z{$hhbHv~XK^N8FG5c&pw{_%p&AU9#;+R{i}Pw_=9!hjE)>XsHF{jiZ@?B3rP&N8|7
zBvyY@ztFvt`m+}**+Ar)H%~u#H)2k!R6o<SIYArdp?Oa6MkEDP{848EO=JF)F2WxU
zb+|=ZzgQ0R4gJa=qBJ$yqrM{pqPEk<z~wB-ngUKO>GO=iXo37P%TvPEG%zoH4!(my
zaB+^4=!cI$E5f#=&Y+G0G?pSZZ^3C0$aG6xxM96-=<8FGRzp;5=ZhQ)Z@p3wOfyB^
zuIN0whfu)a(pqwp?wX2NUC`9KC2}B#@vSrP*4yBHg&Vq@Aj6P_;hVX&)i-~;7=Z*w
z2sjl#-NUh@DYqKC43L)s6?!ilxMa22QNy2}J^=C^hGj+BR#98mToZ<T4t(v~JBq)l
z&jhogT1#DfLO-tq1=-~waDG^cm#3)Ixu)TMwF^IFv+^y&v_77^wnU^>v`qmgV5XkB
z#8k=&&U>O>Rk9L(DxFL`d1<V*LyQI^->nNl@DwCtJtQVLv`d5;bp0`n0J?2Hhb56?
z-ZMjE#CTN=-F7=_HwNH7E5E)j%vY2!>XRr7iy1wdBTd9a;)f|3bYue3*LDJkn6jJ8
z_r@yE)^mZ7aYRE<*cF5m)z+eJbRb6z7JVg4T%DuJC|4@rI|Bn7y6Yt4M?^p(QS{M#
zd%LO+!}0^U7?0BdCb47r2afwAbJnNOduu(~Bub9#Bl1f0XBM3-ro5}>BBp;1s-99;
z=W=tGxyl^rWHsfCXi=1l^1*>#$lPy(JCi?l+^D_zCb?Xdl=1qb0*LoNuH1?Jt88Ci
z9sO>YI_gON^8VWOAVJIpaO3mhbu4!v)Np;bykZ@j_)NuuX^P*-)>Rx~I2?C_gz3oz
z?a)3Aa#uQLG9e^#eib0m?amEYi+mB>jmUV7Zjj%d<JBWjKy#S<!cUHh^0~=rice^<
zRWN6o2pj1&Fv)7+*&>2W;FCy?_U&R=CmyCq%K<bMLgX<sH{0(vZMC?CCtDFHmhMw8
z63BtlcD`@(ZmZq5jK5FPny3ebV0|HC3<z`uysbb>2Z0p9GE_HkSe0*Qvjz-+jkM~D
z5^;)GtQ~0{sCKr8Dv_;kt$FSlZ{9gvF03*2>Dmv<#Z0)K66KnMhK-~K_C!z8z-14+
zC;8o0xA4je*e&45_b{eAwcoI?!7JB@D~n=DKZ4(jK&?aomB+dvr?(Jtld5nCPNPHN
zuzF`Y!J)U2<6y@`p^c^iw*ItD^+bqAsDZ8kXs}nFQ%>#~LAQY;0|b1ntoPOkNHgjR
zW~-qX5XJ*G)28hzA#e}#-(?=I(H^0>+;k9UKm%z_WAB;31%xR!y_%0uIQX=)Ghav#
zfAq3#9u7y=)_@bGM)%A$vXMbLeLS?K(7?c81jB#~fWyORqYI~#ol5B^>WPxj>mK4D
zl+s!bCBigFTeWBQ6#W$ifb~*4d+U<>sehpYe-00Jmkq{zck_Ai?mwqvI!%>Ud`Ogz
zr9`-bi-8MaB+2J#)<s|76}|NUx;8e}Jik(B@am&?pIc7!KO#kHTU3nHc)4?<&V9Mc
zMymW=`>)Wd{0M-y$Fbm5YeF9FwooTS6*rsW%Lyq-0(i+gUTH6Zsx)<PCpuKs`6~e1
zQtMY?LOWrDk;o8n@tS+4wOZELxX%;x!OY0jYm(d<iQSR{2qG-R!TkKEPawRCH0ay=
z8tbzDlE%{la$TmkE(3wS2Vf%U)@`B2$B-1CjRDU_$(b7f8FctwU`EwHoll1@`HRaf
zvVn`1!t6)mSW&pFOYD}cAb#d!YSo$_mPqQMDKtNw%lv`{CK$HIrm1DSwqtQN2i4RQ
zs6>P2H3rtlHx)awGkWj#)<bC&0hSXqxA!1MbS!4-!|-Z=gK^B~>kWXT;@cID-6A&y
zTH1*+W!nY8;YUw=v`-eR9eZ&CCHgSkkZ|=$m4X8)a#LeNP-5<LQ(%w-Hb>JU80^^@
z$sCa+O<ta*?Qfzg_9=i2;d!vPJx2qi`~vl5+l}t3T_nZuY2lRt^+p<5y9*dUTx0w8
zH9ml-hi6hByDiqBCKf9U!o%{tJFWR@=yw9qCa9LNAA52Dl#d%~`f1~%4_>W0ihu)G
zsystF8}R7XIDqU``SdJtK?c~`qdA>g+Le5sfYjWekyPV-tfduXv#Nn>$=#2yZW)lA
z>SljEvu~u#Ke;f2B^k-)wr@iLVQ;J@Y`A&syLj!^{rz8n-=FBfmweqq4N~d{UiUt%
z|9b~H@XXbZIlzz30g-_2**e3&6y1&#m{yh5ihVc`?2#VJdHBdWJM9(-Z(qYTQ);m<
z9<fDCnUuP4B|Trys#qSYt2^l32evts>Fiqh_}9==^FwbM0uxD_3I~%4y#kjwR@BFl
z(XBI9+^cuF=QXiDZgn{_;@goygYM0IX#hot0mK|bHQC(r0r~VfN8`9N7S2s^ejl_R
z^h`7BCn}cuxG^Kqaj&3G75&2~InR=Q3_g2ES<QTJ7r;4l<#qe{YcpI9(4GL?5p#iU
zZ#B_L?z?t=^Sxq%S!QZ)!LXa)5I0Zyf<%Q5q_jBFA(04Ad1G_vP7Yiv1+=#a9tE=y
z_;#?_%pY&Je7Oq~^)&pYpdqlgS>DQHZCfjaZ99?Tds@e@+I8wO|Dq`NbjNT!0}fCg
zF{K>ZC<tiH$BdK^`9JMlX;f3!7A`RwLM)I_nL#L{3>JJvM#B&Y0wJha2SQ~KjS~_H
zQmBH2paDuD2!tSNu!>Bgh!9L$5eR5h3M!B)KE((Pl|gu<0)k23j<qhHKi>cM7WsA8
zy;<j;bMD?}@BMvypVONAqTOH&(Q$7NGsjKjoDDe~D_2`-cLsB5sn@og1@!lFIb;b5
zg5_T!q*YDqDXeY?G;y{OG_}a94e6dO+w%|<sEgT9v)3K8Kq3*co+f{JcFr=<{VucF
zW`Y4b$W6(kFerP{pzVy_gQO+KIuS_@d5N1bKwCm(UT=dP0<%Jt8}0T3(XydY<-#vG
z!_7%1Ub+|M@sA&ck6W1fkn!AmxAn#Aa>nt+C&xX_G!)|lirClVO#^DZ%i5>)98IK|
z-`%D95--A7J?C?Du>;krq;Tlyzm{$CK-8+n47cS*8`Kt^X$8;RcJ+m)W4h{34(_rc
z>530W1l?=8B<Kg5!M;rrs?Fx;is`%{Er*HFU+dAG1Zd=4Ukk8x=UqxJ&Q_YVG4s|>
z&$YlICtam%%IcDRZkN2$l{Dkz^U}EH?X119p;c2r3eh|v(TOEL5)tz$u#W%_F?L~8
zv7D!heIb!?h{p5WGdPIkzJC*rAsY<Y_3LrMjGoYM+%H&$wU+6Zu;XqVQ@j~|Em2R7
z<K9}KX`C}EuWGbd4_;y5p3?(p?(IW-Z3JD(8dW*<9dAJblm#739o!61m-d=+dlE|v
z+$08B-Rh12hqLs66B^~;k4XagIZnfJHWqaZwlf8jvaJB4rQQQ$5?-g+GF-s|O7CZn
zc$HxpW7fMXIz$=rwOk{id`|u9A4oMc?iHwxx@PK|--?x3NCukle43L#BaBOxmG(s_
zri%OET*yY_{K2~8#t(gWrx0^`G?PPyVzSU&*$z#IyupL^NU1rju%Y{_F^px%`fJht
zhde*E@OYFr+RQmM<I``~lyJ~<e(YM2LzK(-bv2C>GQ<4=c(ZA%@qa|W3!RimVtM2B
zIUF}L&8S17-<B((aaduS`W>3owBn~azzNeYsq<qF8HmGwaCC-s{gcz8prgd)ziQ&m
zPF!m+2Mh>eMm`eqIc*~S4;@kD{9b8Vb*S@~UOxEwd!Oh-gyIrWD!noJWI&Pl`nIRy
z(BX+^H(qC2vFuNGAH3T=rFS)D6t*R4k_94=c<}0G8}bHlCJrZ|KQclEvt;n)*8t}3
zu3ZP&L$HeZaB0NB->P*YV5hqgjbpPyo2|{Fc|uv@vM}b&XK!;v7MtkF>x%=|2~3Zp
ztbR%=g#1eAp>TT`P6e?<^}s7@H<dwFS*e5(x6QMmWHj89B)X$R_hTiLaqt!%qDtm~
z{M9$3LEKw+=c5%AKfGBrP~R*0O_R?F8gxLiL1XJYDM6NAktij!wF-0%alAZrw9rVn
zyst~dd~0aXQ;)3fDU9o<XNi<SaY)<sc1Y+9p|J@%IP;jzxijtc&oq=aNYG6sq>$}G
zq8k<AlX_R%Ia{d+79>U)c0D_V)^uTcL5SE#ezB=7V{XeTiTCn^!mg{WowKX@_6AnC
z7Vi#1xkdR9t$v1LuUk%@)%{9;tQCNg>EM)eYT{uboM$XP^Ag!5u#dtyd~vR*FTdqW
zrFVWnr~GrTGOG>l*#h1XKz4t3YJRMNoQrCM9XYlNP6@+>85t<o0}_RNesrTQ(fDYc
zE7w)Kat0=2$~mEH7!38JG3Xz#ERy^7k#eMJ_<HcRM9v}fIq|At50HSWihPt#|Cib`
zLy*Hp$bVo%$0+^moF7HMV<xgXVx>BlX*a%@W6<$mgz;=5yQqGt)2VxI_!aTIegc_H
zd_um1whJ?H&n$`OovGs7JPbzq+fd^RQ^^`bVhydS<~_Bt!j|VD$L4zGvVjxwUYqB)
zI))tO^X$oCKVucy=g!n3O9xl*qK8iTG9VK$(<||_YoSHCNk-E9J{47ow9r=xZ{s6c
zMSdd_0_0jyc+qrG><CnNjZ!dHktC~^P_h;#mpb1}UTKmPa)5uC*MzOS*(kWuow=o}
zreKDiLhKbB;yX_20}Q?#1Xnk#cM8aZugel~JrYr^I-UC>H@e8lJwvd|YNP+&KZ18p
z+jd4d3Vvg!RfO=!fUmGnu6AY<T3QU#r6V%a8E~Md1Ym0rQqv{`B%efDL-#kF<Pg$Y
zT9;Nc&!&@N{jc8T3wBnXPnd+(mWq;*@xYKkmN7m{84FZyMCmaAsFzD!ved{<B+;-p
zBp>SKCo75Fi`KAGMAuluwL6Bi%iq!c#2d@16BndAlIrNPu$H9|zh@Y}04ARGE+m5E
zCNxOMTiV=*RNwDCD!YOGc|>8p#+uMJLbv=nJb0flE+W+%&u^1>r_>al@s)UomUh`;
z0KR~&2E{BFb5arnW+UJj9JvxFF2SOdD*G)G!jFleEi`ooA(aTrYd2a&=Bj@$R8^Ck
z1M$e3;BzLRWTBCljQk|6c*=u%7EWnfKR-vb95Vyz0Z>vSzQg8qcC3~Sh@GiKRgDw-
zobCZ{umQw|aEAhZ6AD{t)R8a^=@-}Vt>O(^>7v}I=HNh-{eYcbGL3D?e~5u}681b4
z{?+E)A+zg70|IPGL1~a^r<lF%dAS)<pfH7<p=d*5B#(#Po(N+9hCLH54F)7*A!mTO
zAmmZfGrm^3m+@=gjv1a(ZLnm^J{#g}gvbCAlEby9ekeWxV60VqXC)BJ`zh&DoEA0@
zyTZ7anvo_bd<xYa;C7ws<AfdrU?2M^5xxsUZY1<pi!nv2Th~GLfhNn1>O-_Vy05(p
ztSUiAG0F10)I9s*yCvfGYV>lmx^b$nc*m6$#>;!OIlvAjz;f5K7h?f)k_O(+H)0Wx
zD=G$es{O%+Dfhyu0f7Gsgx2+Vm7TEq&6}Q&8&M{%JUAuFCThD(P<^wDhNX4Kl|hP2
zJYhS8LFd7g5u^Ax1TqZk66+7ATLoflhS><TYW!;Dc|Mnz8HQ4Jjcd$j<lL8CgEJEY
z5h?TIO~8Pm<F*&9sO-)}?sNTTMY<CYTM)ZuDe$ry@VVBNg_$d9p5EMOqGgP~<C&_X
zHHEnaSuGEd^-qVVA5(d?-6d>-LF%9L16LGphdnV6$U9a}gq5W?D5$=8TbwmlRnx?Q
z3IchWvh}GF_W^tiscMM`(t&XeyV07N<#Fhd2n60Nux*_RA_zx|EwO0i|A;g3QnD8C
zL6sK_BbYPy=gF%++<AAg?IcKKKyB^i_kVl;G!g~xQ%~9vxS$1i!~s*hZPWf5+y89j
zkvABOC1;C2dZPv8>T!DeM?PvK5#r6UwBhoP-WUM0wb<>26Z^k5+b)GI<w3>jjr&F|
z9ImI0C(|}z%<G&PHAg7*OSS$M#<8!wZtuVHvo={NSyrmLSu{j8>n{=gFm^^BnDCXQ
zP&o48*1HQAkhA~x_ZDkJ?oOlQj{}LCb@q8_2Uh$o`&p_kS6}$*`#5q|mB||4k-;CN
zuU1Vr>wTfvhkJ0LyB!?)HP`zf^=p9+Ft$L?YWb(*KaA%u<;sWKW=sk!BMaMDP=mJc
z_F~gel(#q`$|+cskVTbwuL+FBHDqy7eebI+TABZEv#U6kiR+Fo4AH&$3H$>5$iA0-
HBGdm35u6F#

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/packages/gluon/normalization/imgs/NTC_BN.png b/docs/python_docs/python/tutorials/packages/gluon/normalization/imgs/NTC_BN.png
new file mode 100644
index 0000000000000000000000000000000000000000..07d7d799a2e632ac693da7be1108a8d461626c95
GIT binary patch
literal 37519
zcmeFZc|4SD`#;VY8C1sJP4<$=R+h4kB~pm&Buk01XBlBI_Q=v=Pe#hVWnae@O4jVl
zSSpfX$iDlXSNC&2&;8t=zkmOHU(X-aE3P@Ob2*OV{XX8uan2JRZS@m0Y&2wKWG6JP
zsoo+ZgVU3d!NL&7z)yymdg;JF<ZidrmB|XbPyYn}p?0}u;zmZs$^iW#w@v#3K7u0C
zP*u|RB3~p>$DSF*Ol|pZ5ahSYWqp>d$z7`9EZk&~=dQ8QztIS>(|amrBj`24Ko@f1
zl7Puex=;MsvF~svUzFLsh!;p=IR8aIJh1t>orzUH+$Q$2z}GX~XIW%rYcI(f^=S5J
zPMP~PEc#u#a>amPV(_>wVL9b}`Jh2+yH8re74u~y(Mw&4NE8`4{J;LfGQjF*XJ?&X
zB`5D@GOS&sg(0{D$o~G<nG0eWWa-1idTA?)-01)M74+m;c;@xamJ9#<6n8)XJ#R7N
zKgQw?K%FO74!)(?8T@|+3Pvna{GU;Dt_L8~nHVqe{P&!|uspE6|1$*QB?GsrK<fw1
z|1%CMyiwu*3;}xwN6=pv7iawYa(~Yl$y-eKzp4Lk*Z*l4|1%ia|J?L{5$1ov@qdu?
zKQ#USjl(3Kmt_5oy&MelTYD9QQw6<?`b8>|8G?V~H3LG8*Mhw<CbBRDTwtZBC^>bv
z3opNaSbv76@#3vBe`9&dOqOSq!wZy?km0J+yZ5IiSI6l*KhSBV)LVU8oKoy7J=)hO
zRp}>eS14Lcp*b$?{cz@b-E!EQg<iBUm)fJfxwWYNdO_`gZ&Wzw{M^pkkEM<{p^CDy
zd(M7qDM9Cr;wXQ449KKmHcez7egD`vSX5Xz_CsN-dJ*mHzc)_R-I6jxQ+K#a^qgs?
zOi4*8{gLLcFcGL`dNDfV8hf-gUu<#X%FRaV3>^<RQs5kJdwOn@^lc=6G^v`<z9C#n
zcUyWdPjq9+oNIBsb%QD>^pRPW^l+Bb#!}AI*!$(Zf$n-~_QUfQQ&Uy<H$St64imdy
zfA;oibu<p^Vy1K->)hO|SXy*PpIh<Jm$&fSnZ*!uAFo?R+(hlUm@RF6xgk2*uCOUy
zi`nz^bvb*Z(^a1#VibwuJ_U%XWpZF3H?p!((Z{SuVOLUb^xHf1)?&icSZS$<zLy7K
zvB%HT%eK~`pr<HFxYotGRbDyi@Jl4d%riqAknKhX42G~CCCLagpvf;NILoZydfK3R
zt5^h%%)SALt%%7`B&r#9gR~x^=$&ahb?{N7*}2AfNgB;hbg*!-QoYuHDe}INbl2gY
zVq*Rsp1~(2++u}|`}@8X*`alLj30tg2C$SA64#h2e{_Z_Tk=ERUUKrnC%ss;w={@g
z!IceCGQyuYi+8N@+l*ju^?f8-aeJIJUS#1q%v3c|d2{Nb52ghZ`BTkTk+jR>zx{GF
zriO@k^?JNMx^b^)V@XB2^ROtdHg)4>)y`U$#Qui##$w-&&+utu*Glv4B%@9R?^5Tq
zhP$3a1#5I1l6yn_@_WhOw&p&kJYZKm$`y)a7xw9n^74Arv`EFZJW8i<e~5VE&YUy8
z+IDK^7yh7SxHIG|W-s^dR!^lx*-DLU{kNiOJ$b+7%u&|ce{gx)_#VcNrg@I<x4qaZ
z`f*nXv!`L3y>XOu&b#}KbHldj)E(E^{WX#)ZSqvlT44r>jp=6Mf!>zQO3|Ig@vK*I
z-Dh?lq7OpTT(oZrZ!TUUmBpq#ezzPi?&I3?<EFw`DVN_PFY`6S{o82)c-MN*rDQ_g
z<9;bADF=J|&zV_Sj#gGpoPOI@vVQws6|Np%EHE2`i$B!4FcN#1_xh$`h3&Z6H1CC+
zJ->pjgoFgG;q$#>9tB4OIeMPE>p%B4(T*zP-uu7WOE+%geMg-LH8z(nUD{mlzd2Q@
zZBA?D>e^Rm(ctu>JGE50{-@T~!6A;-sOV1DLgB>+BXvU_+bdQ+-=C?MIQ~cw)hpbW
zGu`~!8ae9X^+#St=5B|6%SVGEc4?=Hjmz%yvGTt@FjAPW%}(1&f4NW@qp783!>3yo
zlg%NSbHe7QZ2P39W4^A?7+rbH&#gzpDvlYReG2JQJ4mJRFZe8twVVTqIC-a%B*Iaf
zaH++e_V{Q$N27K-{DjQps@~|`F3}d(?`OT#$gsVX2zq5_o{lio2Ur718K&iYWf2xU
z>XZ5{rmB0*e|K-IW^|!U=$cm_hUELJZj{Pgedp+L*67pPgRfH_seAr2B0BR~H=OWW
z=Bq`Whbgw5A&A-{#jo-G-h)k>{RtB?{^RCpGp(`*RLbxKn~_S4d;4UAL#DmrYu8~9
zoy7F$gBU-&v-(7ezqPYOh((&eUrJs-oxIa#X<?n>SMA~L%A-lA#|O({@<PSi3Px@A
zJ{E;Ji$yi7`0pJ%MTP52J@SK<T>hJr+~QG<Iv5L+ae0rg=kV^%T~m)ew`>)AoGq5e
z74eoPr%JvXzMog<c_>TcfS+DC3X9%cN)S!fxHE3<<}v7bBrBc#HP}9F8M8iFvy=0f
z+*}XEA*Qtuo73|4<jcu^6;=`bSX|`z&wUrl6EtK@XPB~XB#78>+@+-OyJfl3D(WZh
zV=<E`vtZgV^sYTs7EPi(Jz(<1X94jLv(WqC%O;i9!i9%_$PZkRbN%`OH%juknR+#K
zt#C&&XVQE7;`x_4^#+EDapx1#+J|qBC#9x#DKmPwc<{)2z$DX7mun?uyX9@&@%`1h
z2n+O1dr%dCj$iKRxIUK^U3SObU+*%Xt@+bFvxNgu-x*pHcKa?G<oau63i*L&L#st~
zU%yOGyxpKn?%<U68+Gl-x4|Sa$;_VPV5MPpe>KmWo2({3v`YMTxx;?t5)1?yR5<eC
zXB`nZFQW?unS=fEOUfv+ZAJ6;4SzI;p+~p>_fJ0Lfw}K`r<di1KjSg}lOAmzTcIA6
zi$&GWy+`Y|yPLXOCci2P^FhuonAo)alGg`=(#Z#1AnYgHrrIG+B)mWRoG@jUrY34P
z=JNTdF$+x*Tv<!|fta#YS^eV=={&rvg$Kvn*SFnefx-U2HeX{3%$D<Mg%xJ-J4Z1D
zL=de=esATD|JP?e<ANeb2~)jp9Q*hKJbM#PY^K3wYHnG&pe8EEZ(+p$^ZClBdF|(=
zzba>W@5^tsuKMp$M#+fz^gKDF;A8L`rW8$c5jjdCiu%nydrfQO*^;!s$@IZDl6dK`
z+01)$f@hfL5!VO*rl^<OZfQi@rtrBzsfGidOR0}mS07H{TLul%pDgUwJE_9-d|<ZS
zuRh_;4Z=(~JUku_D(RjxOMQrO%R?$H3!U~_DjzQCz9Fu<!eKRdhKqOGd7}JK8viS5
zRBAwcHT6EmK6huM>!IH*TkTXn#Ra8tE{UAZWOWXAVYl`9J}NbOrUbntChqK@9Vp@y
zq%9OQs_=xWN%@W@U`$;59N4MiZtw)ESqt>`N->=h;MR$qChqmB(An^WTwsdN-XFtv
zh7laUVw=w#hRSR|8c@hDR@=jAJN79E-QZGtqOq6az%1q4YUF!Ws$p{Y;zL&9BFv(&
z^Wz(vleP+Le;kZWu7pZYEWO=BDK96;J4rbEvAydw)g}+yG>X&WyC-3-Y%^#Fr#!pk
z;y7_vYkcPNa~M!!f<=HadDH4Dz<KGnlj-tq98RyV_0(D&*)A`;+Zi8h5%#Rb&Fm&U
zrW=S3Q<HmCPV?`H$Me1wA!pL-``Mzkmka&-Fyh-!&G45GD2Azz-s~BgPc=65n19zz
zbyGZ_eM;iI<@N@8;z;WoZDG)1R~GuFUT@BcJuUSKn!~n~F8r2WZPq#8Fybr43K!?v
zRvZ7xI9=9cInv?m!PIT_sj1ytGp%W^Sv>P)a7xi-4?c>+spbbK1ZS81JbGSp>Yy7q
z27fHep^FsyNiBui<=aIK#j?#`sW|&TM)=I?$@=Ey5m~#JUboE*x46l|&fR?NYuVyA
zUoT_6kiGpW41q6AS>7g&`*?&ivutl|bY;tUBoE|la&UTtM{~%=V<L0=PA(Yl*sP9w
zuYNjL+Wo@cd8Ab4`GP~vrR-Rhsoaw(t2Qz|n~NAeR{dq$*N@x$j<)<2*+bHu%M7~r
z&jbZ^yo-L#>36H1`9rpEtDgM5bPqw*-nWg|oL2$b!aD6^H%kWb)1qZbmHNdQ8hcqX
zGk#w#?n+JF9HU~ou&v>kwH47X(5~e-s@swOO(RRm?pFP(hXKdx#>eI13{HEz?`icr
zfwpiY!eDwbt4rs`wQ4}7Xvodc(<!&JEMm~L&Kq<~scgFzHT^KZrqt&+Ix8xzUE~q7
zPa2O$<OkhMx>|c~yD!qecF3$=dTc+oxLh~hH#8p+l<DlBZR<a>ao|+DdU#d(xn;x8
zsinAI<K2tVc!BP{cCJYRseK~cQ!%%t=i8%D3<X@#;+M(HQ#C(3HvJlDQV4_1LQO5p
z;Z#|Gj9K!)m0vGpgb&%M*{-Li?b~~VAG|GA;<)HB5W$0;>&qtBN-nybvzB?cwDaxG
z!2991&Knyi){8YbZeSww&QJAitr7K4SQv%~v_5$GXf|QcJ4|c!7f18Oqx&b!=$4oL
zGDt;Mv6bsqvC2Y}Qhr?7V-CHpZoN`vH%|zbM$=k!@4HIcVP2fJ_rv^qy8@me(kqV7
zDsx}N9_@Vg*|c#&)Tg<7H#=A1yU_k6&Wekn{Y0~0%dRTF4%}NdOplDv9Yl3P<+9NP
zt6ogCXrg<2P!RrL>oaTGk!4ZQ>W7s>CD&)^MT1scHoIn|myt!gy`*yX!}8j~rpl_w
z%O{h4|KQ@fXxfxuo@HFOh<Eq%+*%(~%FA7>e~xMCsA!MaWbM61hIvsG9UkhiJgjSI
ztCn%nT_!-pFFQI+M#ePEuiIaR*g{T8pSC-Y#VHVc)F!Q{xsOe{j!$=fVIY+%f?FF*
z^C4AZZ_KG1Zb$pLstsC@2a5Xt+Nz7biE_;Czaw$$Dj&1Nx&<~-?bx<G%~R7n-RKnE
z;ewsDy|j*osqa5h*Nwkj9=n+|#S?%U2jY`sCE2(-E(5lEY~PH)nts%jgFpBzbpI%<
z_T$&B#Z<SW=d!Fv@(sj7e7$V$;N*OBkj++UVTRcJ7^O|Pp=U#HXZMEaCI7g!`Spl~
z?Xppe9qXyXK=#MKqMRh!a6h{zSwGRLv;}ninB?*}(}r96b>}v$COQCLKm4}44lh1j
z?4!6}Ys?<SsS~C)HJ``$Zun6Dm^nqGe@5=?zQC~%+C7hf)s|0@<Lt8=cVpz^<F4}2
zGw~lcb;fH74&y$BvH49Zt*zYgg~@QB_!!(H0t%nu`Yw43T+?Zs9<SHzQj>S;$S=Jd
zLpQp9Sd1AI{kAnI7%dsiSJVLkEkB5jrja_Fa2fs;Ez-oH|4)4Ckrw)w$B74nR6^$S
zH^~D<0=Dx+t0MJho%*)9YHH8gP3mT4nIqF{WnZDu;x1fkYUaLQcRV6h&3jO+ar@M0
z`j1Ne?6?2$pl_ii27S)11wNi}G4Tt1Z?6*<_q^Q06U$qv%m*a5w7m(6Q@i!}Ut6rk
z`r%^p`p1Old(IADtU;L*Ucin=MQ?vez`yihqH^p{&^QvYgI`rxUzuI|@n!Gv!Jb^Z
zC{FA~CCq@`eri~SkCEv$hkHjr(ZS{RC=1V*KlTr^betRd5Pp-;Vl#A2pFE~V;WV%l
zTX(da;vK=+3&I*^oE~IrWC_iy#K|1zs(Ej=$K|_!NT#&B;B<0NE2#Fzedh`G-6L|e
ztU4hM_wzw_RNeM@v}sW0(a#A|2v4MqSO1+qJZ^ka>UUnMKMDt#iLsH!z3XbIR35E`
zSKldugUI&F;S#W>m}|W<5oqQW4t7jN&6Eet$+1a+2BT5^laBosFGU*aL|;tyUyJzq
zQk3X7EG{6@POO5LH3+(_vI-60<?K2vjGB)8Ccd?aT5Rs%Fp>?Niq3vVT}OTcX;-4v
znD6?*k#@=;_oKF{z@h?SLZoD>qxB8^K}ue2+!3G8u$Nx!G!>`sr_`a?GzE`5=ffKE
zovn?vr21On{nxdFR=?f^?H+8^%F+o0^m4Gdql3Q6X~AhN{FTv(F0*cfyX!&2*itT+
z^iTOh-)twA5An4l_xyJEm+%J@JA|!6hne`qot^FK6qw(31^(GqOS*yHLB1^G@st7b
z{$dvo<Mo1uOeLOReBZ$NlgSeM;}&N`eC{s$kHrt^&3<2gEhbQ<le910UE`*DSa!nW
zlw`B=Y~Kch?`YlycH%>I%MpdSqO-V2vt5n$ki+|SZ0@dIt}*Nlhb28@MhB8=Wflt(
z8DGDvM9V<u{6?w>9J3?2wyj+)<%j}|gTB}CBx|OpY!fLt!sMK{TT$A+2zq9V%V%>^
zn&|(5kUE_pQZ-Ir<Gk2Yur$(7QVyzVd;<%s=INUou9j7>GxZ!+@LHqV-K^ll_z(!#
zC4XPH9{jFX&*Y-W!gkBe&82e|QhN_7CoSB_+i0CQ9XCn~`~BK5wMWjAkBg3vc0Amk
zI$CLa{qt*tpsnkda>m1bJq)@g$R^9ZZuxm$SS@?2JxSu&Af|O=>b2s%O_dndgzp}m
zu!Vi!#k*YWrj&#Zoh;t$(znrd+UC6>a|1a;J70IYH*j&UuDJ2jvh7#y;P2jfW%R>H
zD7Lq<vc|*uFqvk7>1Vp$`A}ht+ZIW~Y)ZJb3DG<5KX#8Nx*yi!9}iV1t|j%{v$=Kd
zmd$!o5dK{^`T|vcXeBR$PWU^iVFKg6Bj@1c*s$bIpX9DddvIY^I>rqBsQ0G~%}J&U
zKbev=qS*`lUuv|+bY#;jjv0B#geuF`4_#HpJTB|bjrr|u5s<f01HFU)<>)aOHDZ2z
zV}HGvSVm+coAU77Dm-2z8<aNChcA22&NeRttJ%Rhsch3teJPEp6`OIc<;Gn;FR(4t
zlYFp|;JG}9lU1}kh}^=Lm2mlcc9`3yJNxgn;_JVXoLu~t*(hpUzDZZ>!n8EA2;on-
z(6a*^=Z|Um|2(0$nfVTXkX=ibp>45}C6?{(|AkQhwWvPlN8ro*9WvRzbw)9b#m*Mx
zl9-?z?dJZQijE+2{wy<an4*o@%Zc8^$qH^ipKBpIDm^kkG-=6C^FQ2iU_Z3>+?gVJ
zM(mlpiz_Y*DB361kho_W9(0T5b>lkT)?OBv?oB<v|Mq0EW<=Kg?WwiXSpj2u=>dTh
z^80TwN~z9EEjt=bLpMb0Uq-3w6u(7%N}Ig+$@RsQT2flM;zNrKp73TRd8UW+Bf2{W
z`t9%AT)%rf28l8f?oEo>jH!ORRnNl|dJ$AUO<^*rN|o-uLmsPK?2pD<-h^x4=#}2e
zBK#<C#fac=X4e)5!m}o7ZvH$S*qF&-6eC(q|Goy90ZaHXfe1cWU^M=9URw)B!@gfY
zw;^1>Rfy@GTL<*ecvq^dv)8|YBkED_ioJ*TYAP<M!l-@OfIsA%w;^Hh!QF+-fWS*3
z*LPVgGMop7XyBXUQ{@)(;&u(ad*4n=e_P1i$1e(2D$ZK>?)r`<%M6vtM`tJrH~ApA
zb>5NjD90t|)BY|SV?!y|9p`No_eL)q_ad52)i|r?&%Akm<zHgsJgzaeG)C4~FZa*t
zE~*{AUXY=U?R{(i$KsZjQ+uwq$G?itxU-N<#UPh*^>;zq@-}&DLIIJ@?pjr-AyFT0
z-WW^$SD`xF3`9#OtD%_R<z7@PjOK&{jneR?-qh2a-t*~;KO&@C7XGSfW2rz%my>(u
z#Gg}YX1VOMng6rt-_>Rb;YLaNWENYnwEq^cRiPaAL5r@#--OfOgC?_ZtJ;~qTKZ=N
z8kqxj=Isgf_w@g$soR-?`95&12>VBQ8ub>?muFI^Th;zigU3Apo|oA=%K6V~<1-p0
zuj-f!6Z>CV@vk>wGl2;6zeW7DUH^}qM5E^0+02nY?eeK`@A6jrop{PWjVoIZH%uI?
z_8y9A`~Z8ZnwS{N{M&p#oIwR3_3U*GfzfiylOJlo8=iYVn26-5V9BWar-hfDL52Uv
z<NjlV+)}{&Mogcs^8U9+aQ8k37Jg&Jf#sj(Hv)Xj+FraP@}Cxd16a6lQ>n>6eaM=T
zTQ$6(RY3HwBm3)3!qiCKGI-DZfBI0kR>1!Yi?HOcfa%=<yJ4Bw*p(u^G*Rv5IorW(
zW^UefUlxeAsWKiNuR-0!%-Hx;g}^f3U)utX4h6w6!?n&pGME+Z`W|L);)jB*D1O`8
zus0{?yqT$KNmSGcy<%MZXVC_SWv_PG#l_K5E2Gdae~VMdVsL<sQ7U~%6z)~yImMk%
z4)Es1#eDgr{k6@JyD>?D@DSSJB8$qkA3e4_&C3Uq%cDk^K_eSJ#*Ts{khcHLNJK&0
zTxHvTN(SqrhKe~R0l2`1m1f$_4~#eOkq6${@|Kr%{Tf`qQQ%yjmF73*>FK#xns$)y
z*ZS|+3z_U)1d{hDwv89Tjf`$&ZP;$j7rQCH@lt;EBcBTGW~OxgCy851d`#}i6#vzf
z^Hikq=kbh`_i#|`;w`SIxDUzX*Vj5P^(>m@?(96?n{z&#b8v6QUs}EKcjG%H2#RFs
z68kW6xH4;%2hq?_HMIg)D|TAqfdc=3J!lbtwDOgWS0%&7Qw|Lemol7I@J*yYCR|CG
zqW<q~xTXTkGRiYE9^rlqg|_JZo+fLeAsF%>H$b6)2aro1yQ)Nn?Sr$hu#`|bP2p1s
zkFCnyCH+?zS7YW@J!SB8haM_4rMBTTK$>{ww8T%(Sfj$*@0XUAg6_|5{oRGdfU8@P
z(Kv%#=roM&iOb^1`~2eJLi6#;URhb$&0jJ86AIf+bYt&sBy6T8Sp40Y8!dq?HHp*#
ze|?6K+xh;&L__i5=go0ol40??vk4Xzz?S+8IJx|`XVTYx(f-%Q069)<B6JE^BpgOV
zAI&MBocd<vS&Z)oLqkK7P`S2;*KC}TCQnf2kJmoknVDldSvRwEfuWxh%6MH_Q9%+C
z5=CB9nl3NLW9HSS{C?EPN*V1{yik6Z!v~`zkBaV}g3DK3b|DW-05(yFT5gMCSDYiI
z9!^f*xK0k@0+t7smzTGhPKWsj3Mg@1>HVj>UtM{D5v%n(*d=UDZMR9^=V^`|9N65P
zo6G#^`7%gBK>Oy+@rj97U7ei+6&OR`hDMxcmH){*gFLvEX=b$1P}gFnV0~>$qNk#~
z!c=8Di_egD9lBCrKLE7^qoE5uV1FAhj^Qt=KkmSNl6*7d^4R&oTMWE!$5sc}vC-7?
z`XLE-ygzlP$|iV>tPS>iFiOCbnv`Gk`^_l5KTX5vo*VKyX@U9FCOLkxAhUMUYDP)-
zLl||8$ht$apZ>G3;b*jqtei}FYKb~b8f%g~%E?QdHwviftsk84ctQr6(gu_UDi7IG
zadg&UCpjMOn;q&l2U)*EFh=kWHt?%vPrCQAeGa8+wPEI$TTtVd(@xZRJL3@y%Lt}w
zU7+V@y86XiD?HZFr`^!3t*33s`Xwtle3QAC%RIFkr{3RxW(F&o*Wd)R3S^zRB84>~
zJfQtS^{PUrg0qI4C`6R^=_&{kOV4Lp3*i^j@R}yR@v!67T;Y}D3qT0~>rO9&&w^92
zqs&FnMa1=lQJ0pMW>PW2cxVOWE?3^Ka2$LUiAqF1ve&Dyf*F7*c`$-0c}0=I9BDzY
zC%_!I$l-68v-#u%the*fM5Ytwm#|{#0w0JKl({GAA~tw51tL~tRIxH((eju?96g#X
zwGj#;WoH3^#mkLfh2GA@IiG&gPwz$dHw!bf&lR6P24@b8cKg9<=9=<IWfTO*^e5vI
z3wHev7>b<Nm!4fg=TZ!7jubuKHdldZfm!9wFJli6U6NIFLx&OEy)Qwe)cfK<1*XPo
z2d4IB=NYvit(CHF*svC2f{SzBFKX}=UMF)AQz@LlGg7^QH|N$AP-&Bsq%NZeXZcl~
zAIZ;T11-}7EOS7(a~F8FH@U}%2d%~tRydf3o<F#k=0RL~*9uNRUO>~!3vcW8bVcOW
zuNrGC5-c@te~Q03_|0)BBNhSvT650?z@@4v(%MT$^(?d}H4m||;6Xx!RkMmr7ud6p
zABZ$?<>Pb_gS^o1*~sAwp#L%ZM*IeE0ICco9!z!hHW(67bdJ7P@}x+WJ!hTgRx(xB
z>k6@lqnN~%T-mOf=dxfVD}ALoE`Fqnm(B%Qu;yT}BRb_^M+V>I(?N$7E)EXs_c6*x
zG}f;)Zi*zH^7PWlXaD{1H<}NpIGKpofjQ4e!Vvo4m~`UK5J3pEqpU%4GW`OFXn&=%
ztIO5?BQLUOnbAv3&W>dLWQ+Oj8`fZIW+ncrWD#&A_xb*{3Vsk96ZK<f!48^YMS!KZ
z^@gbv*q#F`X|bnv7nT#K5d8h-Ah`Qy)uU7EdxdrCiM?_0Z#fZVqQFS}*mYn)W-;at
zS;1X{=B2UcG29YlO=N&fQJ9?T^!$}~PsDiM4P_Q02KXAgVi#O(3PAk9HHzXZ?k?|7
zN7t`3o!|SEu;*zn+o(Kbd<NrE=l=6K<||`bwyRLS%suiDs-nV_gW42~&*Lr!LjC)@
zu4y%55oi{EhozA`X(LBRq4)k|YS@MCy4}hnx5_TRtxO;M-A@O?I%`gjj-#Ly$MV*_
zt~v*>{sC@)QfxXVXMw_Z7KEs*?xa+7YNo-)o7-7Gvn(vi{N2l)r+glE9S&q*WaT&v
zlkzS^H3!)b7mh;R7;{}I6~)CmvkN9haM<Wa3Rcd#InETCll!bi9d!GZ#r4r))Z>Le
zs(zAHNSnu@Ct3(}&(EKS7Eseb@!8Bt+NC|<WY9*zYl2J6)vfZe3^!TNM-}5|Xy_my
zMDmo=pCUM!JPbpX0{8FoE|mmZt25oSGCkQgyeD5<HM%6G#Klr%(cmX7r;|7xwmkV_
zI!lB$JHy?TkCk(~W4CeIDPY>qb?ZlT2zB*J<880ljnnlMJsODPG<2Rq_}U`p>Qw92
zB{tDQt<mSQcWO@Y1LrJ^$%rH|aGrTTuBQQi2rMEyE+G!YAa>YZ2vzH<ySQd=SgVjx
z;fQ@v{ryTO!bL7%gWW_DjeuO$G~RvqZvR34GXzDJNEmf66-A2NJL#}6s;eOcu_liq
zSyuk-6;uz@CJY)Vf)NH9C7fVU=9Vv4aK<Ad!Qd0EhFZ%W3UVVZGuXYNiGg=w*cIIE
z6tq|2>WmV7x~vrp#Cpfaz}*c%lBS>YsRnQ?W~@bh)m=sO<YFWVW+FY2Agt2hA*?GC
zLS0nbf7*|<8hB7Ti^mM-c<j9{#mF+PNHVTl=?=?Pxy&egre8Q=G&oO@=?-V&tRsb5
zS-A7j4H0_0<%jf-GnT|v#XZ0rqVE<|VM9DO3m+Gk|Lh=c9=V=i6A(<vR<|6LJTTbm
zm82`N_kUWS<YUz=Tq%Tc#dI8)&ySCe;!_d($<(_(jgoY2PF^g4<U9wfKAWC+f*jt=
z*!wZaf5lXCMhWk^;4?Atx`cW7+|JKo)<vTU%2O)G84+}=1@Ut|aUoP!@xGSRMRB-0
zkHf8Cd~^{72^01qR5f<lYTWaQ*&@nIHnsbivd5epwiY+Fdrnfa@gq6URF3Oe!A-#d
z+Qp?s@dY$q1`z8v6eZV7U~$sEylUC7L`TDjU)m03Pb$7kSLtPiZVoyy6^@!3WZ^vi
zbq%X*2R6G2H*|fW-q)+RJi&F40_l{9^3u5(Sqc)L5s10@bNK;sLr(p@g#z$9Z9hgm
z1<h?Aqq(Itu<`f<)ivn67z;QyrPxl;MKGoRKH+!`l84)j(lbw&M3?)S&ROgy<EiN)
z99!&xDck}%yoO^1pmuku%a}QtX!FEo;z78qV81wi4|=)7ll419-X})6RdY(r?v5eA
zWDLsg3=r)^<pfFyT>KM{vaZ~A&(eRDO30Dd<OU&&5{m36KxCI180Ck7R&~T#IRTc6
z95E%<_8Rw}w><kk#Kpxe9!}mCdq2WP@GDqBvEHa#gux*@Zqn3(UeP4+*o>0;oSXp7
zVkos4Nk5Gl?NI2XImiMwnrR6#M@L|eWjQ?+^e9UlDjOJS@j}m__{F9$DjFndqWT5l
z0Wjq3sionq5Gs<OtJBJjs{UdzrL&t2>{;u<RNt~a9wxc81o5ts2&Wei;OrxY>VB(P
zpzXtsvI9`~B-8~kY;`UTW5f*BX>g>$N4)l{;bX#8Y&JkAitR@3hMwKDgXM_N$Gx?@
z#IBvq+4>6hwd<BSv~Ojz03w3V0I}tky$%bUfoa5Ls^oB?)dy{6w;K4wz%7QIAGAu1
z&VEiP)nk=d4Oqb~#8dlYbb<3u0_#(OAH6y<cdd3rmk(&6NujUzNL<G_bTl=n=WDo%
zs@rqfz4ev}EDQHJ6v3`%*E$+20!ZQkkQBm81ia-LU=I6y=)AUuqvX*%l3|p17*%Tr
z9W5h%=X-R$%l70%FxA$DTL(FMz~<D})3J2)`=$OCpSM>mKbt3Me<Gi0;{K%F@S`P2
z)@McuEoi0yUNf(qr^g7th2&PfmL3OqEo6v6Ya()0qtgX!Dfb0JsgJ%Vk4m2&EnAlJ
z{LE=$^IfTbiT=x`42W6pF?L{4^2=468ETy16w9AKCJ<I|Bf&u}!C@1G)7sjON!m7g
z;f+7-uS(wm0Wcf5E>tJHZiwRIm6DcTzVpO_bpW*9U9*)Nsz5KcEBX<fx;aV?*BxvZ
z$}O&vLaFVEoA_aovGw)5P4xt)M`O)?b{t29WcIv++`Tc6T50*wR;s&hw-Osa291~&
zRIK73*?x9f)?DJGP_WB&wCBn964e}WMZ3AQH|?f+q@;~=%OTPq2vEQfPr*rwF|c0Z
z(}@XCra0j^tTLt6+1*_eJ?FleZ5z2%U|@loG&Yyq8jKuWuN(csvo-Nc%xWombkL~4
zpIBr6ZQS!m@ZQja?h{Rx*9~#YZHvyes4j2sD(wM%8054idwazZ&egFKCEpr73J=74
zXT_BK$qrnnUlT?sh&_V$?<<4!>?`0s;d%7}j$v?#uc-8<rSZyAvu)uE7e?OSZ7%`0
zS7zySAGRz8y=3pG`_>%H9764OKT<?9(Y41sd9#_K*0t54CAIdM`GszbrT#$@a^=U7
z?cUX}7WmYIuVs{*J6M>{5x-Nd?ZFb!I#CBfM>;$)!jUMZ6Cw>|D6vpaloLnCkO>wf
zQnjw%)C?6<bHx-Dmlb5sZa?_ylsGLjVn%vCK|^1I<d;KoRy77s;7Z%qzBB8c>nM0<
z4+XgIwQ9HQdtIkF(Jk=Qxngv#O8&`~<{)FDN;UKjd2(gACncPXsdqletkNmT=^J`|
zUf1mQZ3h=3x$||dvRxlm6TSAkLm||(H-3QuX4DLC*M3(sy6w3o;FcxkP@C74=cEb3
z4tU4?bw0z@sg8J4h1jGD!uxz3oxO(8Jh$WUFJkYRRNApV0-A|m5UKTg7;Bm_gTuw<
zbf0!5PtEqPH?Kf*2?FaqDP?^UE=aEvP+V8{Dz(OCuJezS<VRD*VgykfovDoCkpFhX
zS>PH~4IGap(;M%c%UxDGmvf2XMq(^LjPXFHYGp668STce*9Rsj5rppV&2aRE4@eNl
zil+d3<poCmL_%WF!3ag(+?7&m^i2`7>z<q?^7IS*Jrun&$^_S<FHFnaTzDDG2|o37
zqU3^DM1bruWjYYF)E?-}fi&SHP#fHF^f}7gu;C(N?!LX}=AsEKST|W{0N}ahOT&AU
zPbgt0zz6u9Obo>$Pytqf%@nDDS&^alvx*+Ty9GR~A^C<|8-(r4<59QN&)|q$^A%BW
z1fzthfK_u%ZFf^i?IiiITp~`~#N8K*YKAul{Yr^FD}+RFYgVyy&L>kHuBX!Ewh00`
z1$zxBXS@#tBL2u~tk^5AFDbFk(oVI8D>G|dzMz@-=;-C(<38aKDkIUVmFe8kn%Wm|
zR?Yl3k|ymVFkvYE->p1FJH*qg3`J6Pl@e_@GF%5p63uEEAAcd{wU5_Ov~(U1GksSO
zPscd1bkWgih|o0?Tf1%8YNf0oXY7W)GtwumO$riXgef5;n83cl`^K286H(%t*)v12
z!Bj0Pk3o9Iz>KqfVb`L!v`gtZQP5>*WTb_*w#?N)Sp{+uN%4=^Nv{TSp_L}HJslk4
z(@{ypX}=&ne`6;3s4~PmIjY?7LM$z#H)%PL6fn@=LKwB0c(P)7`%1hD88#ZENA7jl
zoI5@+B9RE<mu-JPiE~(UgGQej%x7R+JHP%umZllb#py8Dv*Ag!QRZgD)_=1cmj}>*
z<W%h+V`3tQkI@4{(Vkks&y7P`Sy}O5laJ}Qy0(Bh1+YDPqbVY$mbilQnpWe9<<OjD
z%1^(F6^ACwG9IY~VyGrCFY$5~o2cuSZf^bY@$qj2-E3*hq@Y-?`9klR66`GWw318R
z@OdnQRiNy_3hAfpgk!p1!A%>$^f-Y%c;7Y=f@R8Ha#3oHM+aM3HMiB&8hj(xqRplQ
zp9o7cq!d|L0y^ByY}Edf^nmuMTy_J;h;l8Aq8aWse504(Suvq9O!n#Uewo?z3g}+l
z2X26hDnhz#0+hQXcKqZSR`-aKAuJ2v!?(RZU1(ad>@c^}%(xqU`U-=cif+C)<s+*=
zSnvsS?H51q8KoW$sxVt`DOt%T%B=-QfuTEjC=t!x?4ZT?dXkrmbC$%<Wh^H|c>x0p
zMtsYDe;jreIP!I6Ny!vCMnmUiKjOfEr`cpQ9i98Lp7BcOwWjy8n|A(dKB)qjwd7s^
zX?OL;rKX7Enh)#Sg2!rZ8+e?ampHa{&2DuDt9E1Ica`NS9{_}Cw$G5*M~|3qdDEa0
zUf7pD9_>^;z5xm`sEaTs)sZ*skBh$t2EF@<7p@W7B)+~PuN-{OlUy)Y_6{jprdL4Y
zK)Cl3qyUN)lJPWGJ-PrDQ{aNrEfn&^6n>2H06{qsdF}c%aAH|ny5|>hC7);k_lt@i
zn4*9Me@D}jArp-OFSJ%Rgf!J)o>)4%eQA*QTa}%7XQu;5g_465b1vD8Qns*4HF7=_
zB)igM&rCr+&<qP|4l0*K_sQ|YZ!vJoUCSxYAtK3}gLuU10KbZZ=zhni4#=D5;0U_p
z()%}NXzao(04MhX$@QxWq?5gFNe%eb!gHH?u!3t~9<embkS<G33mk6ZGE;##Igk_r
zpM>4tm2wWIikc}+w*(&ud_Y4Jh`I$%M?+FN4#clSN(7y?lYGaWIFz`YvHOftQEpr*
zCuof0mJ5HIF2E1t0JM7EuH}|69628iR)ZdUp8Ql4<t2vaxZ$Y|mMQC~p@j@L0Wo53
zC}R-_9S^WSe?9<R_@7D3OU{EIDH&x6-}RjW$Yz?zm?p1lquQ~YeqRlves^%$0l72%
zghvGsr4WmJVDmAS15{TDh0j(jxy1YiHNd85O9{oX>Ecdt&M%Wg=ha&Qc5+;}aq~B3
zVXXmSFSlW5XW!<IeF^4wt4qr!41wf4j-7D?9b%vlf+zew4us0slE%)?r(NCM_f-re
z0qNtOt>+=s<n@=8q4U%Tj#yg$%v#a6x80h9UYe!V@F8QsYvdfSY5+=o7p5S$GRP~p
z!XTG*227HW>-uDc`(0*M;T15!)5jD6oUDL7ZIY&^1es4EwSb)Q?N?p7@93hzK8$AA
zZ@g7NES^Jo6{nqSxN#M{J1#!+HNqWg&^KGsa6}?3P^<~BlRb}!DYe#?+p`5F!TqGP
zi9c95I~}U^SSzUMa?7F|%$)}C!qqL79<s6wyg~=}T9>q{`&lx5D(2NVUB;U4#s3mr
zItf3}a>;T%D_#Aq@qowWXz7sSd*G8#OXT{eu-U*D-#97I#DF?seGY*xYIh}DES<>3
z&73?>ahHX+b9AUXo&a>!FKrnk28hlY!pzG(T3>>EQ8l;Kr4WUedBl9#^&b=OT_(s+
zi?e1LU_Z&oa8GXq!%xClPv3iSc(Nu9JuyWA$=?4d`5<cp6!@H+Nq&10v-r3k7#zdc
z-y{aIg-dQu>rHi|ZONlc?DuJX?Ve(!UzhE(Jz42pj81j&4@s!ZT&$sGkYVSqsv(6^
z>G{kw@y|59mDRD0pV~uDq4W1RPBQv(WZm|{@mY=^aI6e2Y5Lrvha<0D1&987hlBZJ
z=@>K36N@vXWq>f#5;Vv>ta9(Wl3lGOVzw_lxI2`(&?%UTXVHfKWmAmW8po1I2sK`N
zXN5bEKRp}@WPadS_kOO(g6k9(sL`f)_9UFfFm3o15XMIw)ejX*P+3qIe>R>*xe2d<
z;I7yVME@|b)iM)R`X{jSaBB$t-;56`3%d+hDYI~{J4~9dO&fc5QD~bXJQQj1x5Xg=
zp=tRJ6OtUx&kB^OiC9SxCkA1^d5A%!9C4iS6cc}bZ7D{|Q}jD$ydIC-<%d3ek<Ef<
z-#yXg&IDA0yG{P-MUeX@f(hSket!yvkl;o>1SsB%`c_H}vV~ywaXk|6Q_?Ei@7#3p
ztrm6gX)s@ja@JxF;3*)8@Zj2T0wA8Ka=(Pg%QcE$o&*>Tqjt~Yhz7c{*WMADr-!sO
z5h!`Lz|dm(ab6IXLs<##Sq?8OmEKNBb7iI}zgFv8S*<xA+HAg4gxT!s#O$##TCfY&
z<K{JHlTt0gd(9N;bxWk44oG!91A5M4jf3>e;W8kK0>R=@R;HQCBr9hX*-W=bWx;qV
z_ge;6S_vrE|4?7*UcIgr3MOJc`68Pg<Oc!(4tP(Mf?Y?!6f_e(D?`g@{zU1dH;d<V
z>;W~_D%JDca-zL0=GqWHjV_=W_yY`8Hdf|v8mJRY+^UFDa{*>B6mM}oT5?+h`n2vq
zk~BB=dJBB`LGm~)U35*PIEbtRy-YU#<XrrT><ZrwOYw(UpF%n_v;kL)5C=)%nKNJ^
zI(PL9;4-3Okbuy;W6l>xqs_$sy*cQvh}`Ao0ye!-M;YUrtZetHn@&O52h|o8)XQK1
zIE%h;$1XsA47LRN#h&9;>WB0n!>B)#oK6mJ)=Hk4Q4)4+ysm--rf~d^IFuJsIY{>y
z;Ag7f<erDVSylHF{Oh{_62U2kHUcGzpxZRj%8K4Bd_!=%TTbwoYOs|0!6^iG;<!1>
z6BNp-`8?YGBO_@j*64Pk%i~hp6j{vpBs*zovSKvpyzEaAp+`@jQPGzPTs?i0n~T3H
zi1#!7W-~nX7p{R?L3P@R_8WaE6IeM=WMlQ+^h^M;8<`v_sB-Gb7PGSY<j{7ombGFj
zSx_qs?u(4Bm(@gTo~H)d^v4kDEFjvdSl>?uku)`+Mh2^#IDIHzhTk*mMf<ZN=tfi^
z0Q>^X`CU#TJ*1kiST!#c_+&7=DblZ|0ogLRIs=MDrcOL_3$Fs`7`rvM93}gnQOU6E
zXSkiv47pCT7(!hmzfvJPe6C5u>QPS&{#x;&{vW`{K(RQnGv*zHAUMj^nWm@gnD=#{
zKh<Siwt#Q`x!z@XF7rwgcJrv9KzQ7h<C@y6zJ2FdGwaF>Hkup?Z=jn*IoNg9_#`M?
zBaBfT{2|BI*49Q#n?dlq#Uv0)b)HSf7U1m&6*$oA;8{UQ6IKYkN&*1)84C=}ovd=r
zm}PkX7^0bS5X}rd3r^I~M~4kw!wUrF-%>MoY#D&GFI_ih-C)5~SC0E>8UpYb&CL3W
zkboXSTw+%Ox#<EJBD+1VybVAM(jrCo>jFUaTxpvq1osLKu$v(Mx**_ZZ~)s#Uq;==
zP`LYr>~C*rTV3B*h4zkWx`4r(B&jKtas6PsQ=RSz0y1RI2$-sA<gGMUN+60=S0@o3
zSWRpcpN5G8it%B>`y~g~3q-lEj<VOILC-+Lnry?RFEX6|`>sA;pPW-O4Nb~}rq%YW
z`~&n}fA(8JpTM;ErSF{p#)${h+V;Hg1hozW6~<D(28$X_kf;dCi(b+e!PN@Qr~hX9
zpX5zcCcx}mE4kjK-A4J-xu2d-(nIra{d17z$2~rr*)3s;`&MQqChsd74wej$pBPB8
z$+c(<qjGa|Gx5UV!ptOxmb+Pi$IC$;Zvbs-mr3ba=u#jm2KS25ji*-w+uGVnweXH^
zF?j&XvA##eG9A?ckC<s{YYR1R@T)J`DCLD)1J)LvQ!Zf70Y>o1hO(q=sN<66>JS!e
zv?e!*jpUq>p4xyPxoKSwQ3G#0MgMdZa0ypn3~irAxq&eh3*qP@k|h@IK6voy)Z=1e
z^HD#Oju%cZ)BW_~;^NyUsDc-)y_Ad|7v>B3ehbNe=`zY>*lwEd>YS%#+&>Nt4LLl1
zTwS{H&Kgh*Kth?g#du=hpn$MEIjQ|DKnBD`RdA~Gk}OxR;x55zEgDne8pKtlbN$fY
zo9a?8yPlz?a}PTX*)5==?+0_-biu3znt?_ct^^`WsNtKZ2zL#TBoEZlA|-A=cCNZV
z+!B02&V@NG^-_vO$;PBGpWOTxZTUOq61+Aq&GMrw<5uE3(8;GBBk$}<nDL@{re5!v
zi8U95rU+YTnSpYw`3J9D+wpOBQq#wv<hLfmpg;jarw-$VJU~f#U^K@oh6z;aTN4`)
zPgo;~oLZy3Pt3b7<n+C1B7QWTN1-nDX#gGa7B%DQ40&qiJ<l}?!5UWXb$27~t@d=H
zRUjSRdfn{dNr4+_@&Vx%P}c)FVAb#u0VZnf8ThJIa~XS5togM9K0Y8ZHZ7&xx37F*
zXDE231~m)?|Gup!-{U{3WmcP_B{hLJf07hq1`=f<=p}J4xSB%;dk<pR$Rj>Ri7r)0
z8d$90JL0;5`>nDV*D8(C5>7Bm=y%EQ6O9c3>N#HN0KA?GNCl_KK0q3+64n@c%`x<I
zlC$6@z*^kip52mUr?$Ndb?H&1WCir2P4nOrfCbco!&}-d1vghuok8A6oK~FPwTI{)
zZs>cxIHffNt8ET;EbJmkGYFFFgm+HB5bn^In&@?D*dXT<QP~ijw0o8n=P`PNN06I`
z{ZGDxK=XFDmd;=RmAE_L={jaNKx55dzuAG~61l@Lbuc9Q7Mn?lAWb1nrN0)<{)NB-
zeZ>n_703(>5A1TEpiHpnhCPCft)m~l0!pbbT&NzuR1>NAXiRy{v4>@uri=rOGT}($
zXLkd)1RZGZk<^TmSuHtKX59qijFsAc1|gvkrJ&hMS?T$(M)ID5T%v_oZM8#%xBbMe
z($gR(l7L3hAM?%6r@>}In<r=*<pH*b3Jf6A8?H}|y+`SrD7K_VWgG&nx1?78DRYr5
z;A!EmMc48>>4K?i(8Zk0AVXtTDq#cJOavhKMyAibq0C@&*J;W2cH*=;zvk+W>#yP@
zQkBa$K|Z=$lSRCFJ_?#TeFsF>JS8u&!qcD2`KMdp#A+gw=1*>yt4Y%_ShFatC5--D
zS;$aw+zH@sUf{@P%Bjx+KXs+t{}}Ar68w&Dt2szk=v#c?EYi*Zgrs^0)vkGHqIq5I
zZ9?=eO-d}>Pa#^O|Ai(WP7bdT0`Z+t@$Nq2t&kYR<6=qpFEhz85D9Ykvb|REkPjk3
zpGfER0f|uqh<2(g66ko`{MaCfktUkHD2YDrq666k|Aok_XNp<*?`60hdo?Haj-D2%
z(#e+{t%k8jkZ<e(%Uld<DFRr<2Wu6ZS<x;1IZA-6Oj(^R>EY=!I#7w`B*V9@RK}Iz
zYXFB|(lXtjP@myoy$EJgSe=6e9()dXaGJ0^bb}M_o9IH!-0Q94F#Y*ZwCCksqc4yW
zWgmSo-@~3aArGPUc5rsKjTzu}Kiydso$)$}ff)=MU7Oc4WcVDRpsHE2st;<-{Gc)N
zuTC^8T2N2pVk^2+{}3d5R;-C%cyxjQm;!mH^e#jN+%-Ymdk(?tX@P6^Aj!532so$I
zqkx)1hDE?Z9W3LduNPA3*?C@A94$x&I;0zE=)+L}Q3KW9gd<cz2tw-K)zt_1j(ZxQ
zaABd0QtR{Q&o(Sp;5Gz+*4}FVcgmo>i1W|(A{Pi+v8tU=`u~9k_?5b>ZihF3xP=Xy
zC0Fp+hd%9>3|M0TlK+YTh(&7PPeR<5qah3)O{)G~6q?9uAlh=b8fJAPB%r!--4!!c
zFm*u)I)k6Zsfqx>{3CnpS0i4a$5HbMfs*%s3TsFNluN#h$DMWrd<cbRpNZyktbmU@
z>46BYg{g=_)c{NUSs^c}PKNym=m@X9ScF+5pcKOS`4SgF?)s7K=~v!faeztDk^+NZ
za1xlIdy!U7-#kDz&<>@#1wK|YoH>BNo`M$QAlU&Lv@{T%Jw4-JzC7R9!+<6kZw8Od
z=Ljai6B;QQBQMrzYk+C}u6zfBH6i`jb(KL@rU|TGre_?Ki5P&g^4B9RVEoWeD+-|b
z;yhS?rouSXy!r^d{qJ<=KY2FbDu~})g$z3YK6=aEbwT!8z=|c%Ki=^JFENeOvw(jE
zoIbpiAOH$M?kLnuQ@TnRxF?_5&mz<NTHi_zNFy$JZNK)7bJ@Xr2zKfZvEcScFe0Z+
zVguywmY_cRO75`)EnR#<LMiRJV|tf?XrclhJ)u;ZgaaoW=)_3_D3S_@J)w;&QV@Lg
zx)D=fOB0t9mmG%5o}r9ne=NxRf0W8}VxLjMISyF9_pZP5oSb1sDM;32B_;VHvATgW
z$NaLPKUG#HA>%g~zUT10p^Ji&XRvFr!_(k~8%R2({}?erbDbu*30gHb^RRMWNOa6k
zB0S=wo2$rg_yFoqQ=`q3feqi(1fgg$vUgYZ-8e1MTSQZA%h?*B85c;S`YI|Wrj(7A
zMm@ie3PPIK6JWY$yf1)?<~Qo>qkMH|QBbLv>Ronsxd*V5l%GhrP1p-q6jJ2mxZ!aV
zM}&sn+8ph$fBWn~at#}lN)yIb{h_&<0^_kui4Nog-wQC6MyeH4(lcrfrfLqVr~6o2
z{{0AXoN3+^ibBOnkKGipwN`=N1k#Mfa;4_S20l4NwTA#m;s9d`hZqw;psOI`lyFr4
z3s%#vac_e9Wki1vNFmDD-`2Z$ng;E);H-|sq4@V;xnY143y09i?p|&<N&@yC`f=Z)
zwK%gxg%}Sw>GZ0^&9Z>cY!EymSGrZ9Ud8`Va(zAks=&9jd`janl)t6@O-BVklMvMJ
z=*(QuDFYVC3l<s97WsxZ_SK(o`l!|#!F*B}zzf0(z{~OU5NZ>zX@%=V6SSEn6nq#!
z@VWLjJ%=BrOFMCbQOYw<{8A-aIxJMN?7Uo}8~V9%y<^2Ag8BMq!uWWC@Iy-)t{13r
zt3Z*&Y%(i1w-<Zk?PcHGbMm)Bs0*z}iN}QI)j926$dZ<k{#0xqZ1q4ZNgx0)L;>m#
zu%1@|N4To`YT1TnxOu%#?VMOsj9isRzr)=w#}}ZhAV}OO6{ZzNy|ZGeUU|<(8C-}t
zlI8*TAcUU!n;TV=Xz8|PbyzPE0U)mcFK>3{yB<ykyGnkf)E7qGFQQq@20_XjxgrXt
zYzR^j#eL_$9Ka5pn+ak{4TUaVK-@c+tOI!W9en)x6eSso+nW5S$<@~Rp0WXCJRfAQ
z)?6);(d<1>OM`SD1Eu{hgrYaO)ki5VTSKve2XN>i{jQr<fE`@{n?095$^hKz?6l%#
zZ3yxP29@(Gm9QEu!63*Ku(3N_mNG~UI7<1QX6u-n6@#p7{w>&$b18Axd1A8zj;1gF
zM$P$?{Ywx3L`@(6YbBnC&dQ^ZT=Iz!d>zOoAj~2D?D`BA0q^336rAA6=P!$3f5IqD
ztjX08u+TtKYc`5UwuJ|s95m^cMY`92l2!9~t@wEkp#4Isy0VZC$i=hO8~t4_gi=R_
zb={v(^apZ2CwhqOEnqe}<kWP54*WC*c>%O1OBO9fHFaFXmJV6wMHGXn$~#IQf%(US
z`B#p;7z4T!H`Y3_*wc1P<*s$*!-1=-(Oim7J#uTE+-E0ms6s`Onn|UIVYwA_O7A7T
zT$qnlpp!w(Hz)d6>@Q;@!j(USgiuP0wNMKFR^!9LBKZQv!S}eRUI5(Xs6g+7!5{DD
z7392je`YPM|Lm8N&N8#U$P*h6;1iT8N@}%{yl)2&RqRuY1AyLnz`>0=Rb#XQ+BPqP
z9?=;xZa=_h5g!C`bdl}R9HqWqsP^+iLvJU((ExB1YJX(T07x?&VgAs3ArQ3&-rki`
zdKH3ILvoC<oX5gT`HcZR&#xbzID99cQ7ZVJF9*_#ATst4UP%#vvzM>1t8zo#$!C(2
zu7i+31KU}d{{HEOl&oyZiE-_8(f~a<$SJ4KdTE;gq?sMcA|VDq%+zTRH-kEgK`)OS
z)XSp|KohB<qT6W-V)nyXn^}-^0hZEO2thWuj^PDe$5^}$>h+yfW)&hLB9EFrT(>bX
z0ks<{sCILe+Yd})dpwp65KU{?ZgIAmqylJb9wo5M{rCZz2N>?+YM#(;Kcm`N35aYx
zi`P42j5&~B@uyh7)Q$?2B{xvRQGyXzdPmPdQ5WdvO_v{D=$8blHSSv=^}i7;5@ifF
z$*P>{9hA;<BDv+f0)nL(poFZrFK3kk%!e*^<ru(7U~0WEnG}d4bRa;(n@yI`D+Nq)
zKTAh|_|0=*nKGmK`VUpXZqhd;a{S(kf3-D{!7>1an(kBQ>MF$G%p^fE1$7+^e7;=D
zg99L^m}ck}a6T<CU#X2zfN9*ZjNlY&jN=thz%-r%)^NQ*%RCGagySz}FhKW!vcq6Q
z+;Q;aUor*67Dkwn#TPEmIngdsgCJD{Y<gZ;bq5@NDQL?9jkR}U<W{eFLLoqAd_W)c
zYBmR9T(%7^!k)@q%go6UfTaRGabES(EozW~Y5?>Gmt0?kP(g+UtXkVFJcs<e{aG;c
zII)uFAHkA&oly}ns5f8-pGe6Ks^NsO)jwZjdP>z7hr0{LQo9N&BKkT!ZGX%yg|usr
zR^n%$%5QC3H3u1*@;r^&^p@8<=WT0y?kP;tm}bz7mBzq}pQ>=LkK4~O#oNwmOr+3h
zd`?@j`1#=NSFcAq*Po`EB$u{?WtQGWwL{+~z>SOqOS~X_^$Pubc;mi)&tcw=ljB_e
zk3RWrOr)M^4&G@!>(j%|S+OPO+KE5x<J;Vxxgsmk4(cK$H)0IH1xE?qt31s^Lxv1c
z1+}-wTkM_iN;oaOcDL4O+y+N&Sv)GSn3Yq`{UZU7wrZQR%>=5N)^CRUpIZ;lXy@0y
zytvW7x&5_FzP^l|zixRy_n_l5da+xrQVsN?O)EZLJMTkGQt@oV*vYARA$K@$oBukf
zTv^aWLuz)mQ`*n5<lBChZlsvz{eiO7*UK(Y9|8-I*S#-6rKR^EgZ?n~=p{S$BPGb7
z*(Y4Omwm1K4nGMIK;!UC6C=qi>oJ-QH0lPy0Yw02^3ZXT9m*Zzr6n#}wGUPBDH6g~
zEOp%0Ku4lG2qftgQP-G3?Fgul-y*>`A5lvy^UOiPc~1WQK?xcv?%VlWM3lo7G>u*O
z7~PXw%75q0#%Ludq5iYBC=TeufG7xS<`&npWcSB!5I3<x#0q@5`BB@s5rC)L@nJEe
zwP}~FEVe$l?u~`TRQ0o5p7McxdCO{$h>PCt?9}v3ZUJB6w6Qm1;iWxmnB-AFWqv_d
z@e+?%bYuSB54WMSoqF<UdhIRWyDjok=r>hl|6hA=9#7@g#*MRWY=mu=3>ziNSc#0A
z3?);h%tM1Q)5);Ow9P3|sYKXiDk91hLL?26%=3_WCS=y{x}E2FPv`gd`}w^8Jm-&2
z`<#2Pd#!7&b&cQewMSM~5_nS5ibs}@&-;D2{p{FYex?DvPB&Ocl_`huQW?tCYf9p^
z@wIPK^6*pYLIQfa>lTULALF$2NhBAcbxp@ZP2dYNU>H?ho)Esg9@V92g`QNZ;UXMD
zvuLCa{k|gYJeUPeD=6=I9e)5(Oe9(rDJF3BkasoqTj1Ek(3G^Udg0WrrFgks#*^9}
z>ErhO&ywq|9f!N*;G2<!!3}|H)I|?241uU$8~$d4aw{h4HpJ$O*R|JZCpCp8Pmre!
z3~3%JVuCk%x&MyI)OZ(8bA6bodqH+y*GhJFzP!$fu5ea8frT8M12^-T>K!ER7K9>Z
zIHOg6L9YcQkEK(O<u4BL#BrCp6MlxjUA~iUxlFp#VJ}#RW<9t!^gb7+0e5p!vE&mh
zFLz2>$?18Y*gc^+!@Yf8EW4^sqCjm(s_@skzpZks4$Ry{oVS3vk#gG~b{0b>u{>R2
z<>w=^5J$vqR>JB<J+xK%Win`zTZLrCIehu1A&t%gk*sz|!&At#=0o~>cqPDxBWe#v
z;M03QFHT7RLLzBK(y_n17sa8?ou`vnVMYu{Bw>d?WX(_NtAHceeD(AzfS*QdfO1R9
zdP)MdtvzIQ*j_vCrSe<b--GSNS1;F6i@)sU>F4?@yp`L2Vq%WRA+A?O(YvK;{Kmas
z-i%`13WAK0h08&<5KP<kS)4xh6IT$KDVqri3XisalclZ~^P|nLXOaUgdbyTVGsG1V
zJ;{3t-|hw6Q$n^H5G)}%{X3CaNtg`WViQa2uO^nL*%`f9Bq5Vg;G1En$tScfFPDIv
zhL&#4a_d$<-$$Xx=Y!1>3m!4DBpg||C(6R>71&7%g7nTEP*CdeFQ}l{!6b~FtngO*
zgbaVEV@#U)dUpNAfty?7o5eHn8o7Vts>6aQ@F1%CB%S8ho?PwwNFn1-yhCkwf;$sV
z;1KwCq3!~-;2w_9?xSNl$<gOT=WrYHQDp_99QQ*s4z5QrvKWC*f0lHoRu|`ClNb+B
zAwb;Cty~TA`EXmTvCD3)(TR=)_geU$x<Pjqnkuljv#q4NDGU+X)+dL#IY?4iE)S_3
z9hk}wVPxKRVo*|RR%dwxTn`eb&<oeEy`l1}!;^g{I;+pxQIl@NLLZT(aUV&)td8kp
zX)X=iBsH;uN5j&{5MeB-6^0Lk@(^egY>E!$A<!4CjL7*TLkZF?zMS}YSu~{P9dtTN
z1}EeC;;lv`kGsc!+xz3cxq&tU8hUp@*4s&3E9TE9{*NxYLg}R#UR*2<ge&0+%<>;x
zDR|X+IG$?XwONk|5m#4NXZ<mco3Mttd*nJW=#Zy?KZFHqBG|qNOX(d&0R^e}Tk!6(
zS=9E?>Yqnl-Q3(9PbvMp6L)zHAKY0n&_3;i=8EuG9@mlgUzu!v_48l68a&Q~Tm#YA
zGzk`+Z2IvY8$YjXwPh<6yT?K&vDbdy7s5|w0|=vcW#vTnv7&Cw)ql~4oFS$3EcOB0
z1(C%bmzUiUbR0RBSH@;qob~}nhfmphquh-DPZ9kSj{p-yW-Cc)<C9*J#^G>T5quf6
zJ%dEEz=0dsi~B_on+dY`&mWRNa;?n>6}73Td34-i78;wGg?KbO(HC>r*HC#KrlQ1T
z>!l5>mD5wOj{yeZPr}#{a8;fq9qq5JnsaskQZ>vtRYYZ_=6U5xy<1<|oe1u}SpNQ<
zdr3%!>pw4xM{<5g#CZG^A3k_6yD^dW&6x=r>u?2n0A~*PJeSd`qg(9t|Io4E#Qy~S
zG?th6`U){S)&Iy$!Av=ZmN_ZaYa$97YF!3c3Xc2KU+;BO6+TI0liV=>CgtWK>_d%j
zkqu0Q;Rudbx)t+nB*O#KiC#&eD2mu)&CwK;RORiXz^HoF8O6xgRC_BTB0}!uH#sOx
z{r5n?9Be7=L&f$}p0*7MQO;8lWb(SBZsp+_6ckkRc%<5U=|}w*yNM}DUTw08&d_kw
zZEbiF=xG1Q>Mk=$Gr{PZZ~Ci^R<7wXi@NUD4s*j%d`%h$gk=*SJ^G{}<ajW2zLxZz
zhAXcoyyh7yDB%kj#Hn{dCvtGhzGv}U$_@C3<_YDu1ZsAbW7wC$DR%^#C?-!`SWkq!
zKW}(vA-KBT)BN<y&f|(Frt!X(i`in6tkjekz|DWw^=y`A+FsCNOL}ydt+^`Q$;IX9
zbzyw-uC$EIL&X_~xog^>=SU-v8{XJa`hAU-p-oDMyn0~*ptz3&b(oyN*h|q$GV^~h
zNpCDED|58j9kWS2JK09%{dqdI?R)DKD;$z0@tkKaha4}fvh?OxxV@<6B@aAf6m@o(
zDE|3PRaKC0@Y0inP?4PhYWmfL@M3}Hs*qnxKQP?Ew?^fS<{m;{ZvdDMeuHX@gYuLE
z&w*{8zZpqLg!rTsJ9gm6ke*93B{q5fqYFB;79DHUoX6|kfDuAhhA3gD>qx(J_;^Q^
zB{w%#UTm-)oP4sefqm3DJRy#8OhXtZ{u`GxmKo$tV!kYL5lzvb=LeJGB>5khWIo1Z
zh(W%`XG_!OdP)b$g}q41Y5j?=lWfp-nV5tG%fX`W7@})yus&#X)|>f^x5H`F*{mBx
z^@S9KJHGLqhL8KL8-yk2ceN9-ms||((Jg>gJ(K%Z{(?nH$Kv+#w^DP7ex4tb-_yHY
zaqWJWwe)I&^|=A!?MzS0QWPYFzR1K0pE|zs8U8&MPQQIOcUAHje<XYMElkGYW&oUi
zh<oF1G?;AN{#**xPV3lJo_~+j%I$UTOx8TzBt<H3c@|OE>H9Zhy`G0BdNdeggT9A3
z9V5#1IK5)_zBDB#za2E_@O$AyRZF}1dNqyr&VlqDn@)#|TBk#T7g`)AOkKL0WEV|~
zg=HWcJ^A$JCN^+q(_7wqx=rcY&)hD`ZR&ctItup;_a`g*XP#7?!U_@gr=Dhdc3ff6
zd!>PUk7;(0ygImpjd#7=d1v!FK5<AL=_XhL{!h<chl&(d=wYbu!>^dq+cd{l(fNKi
z>>>-$=Ja|y`gy>AJyz*<j&uXa88X5904j2Y&>tBx$hX=znlZ;DbV8)51Oga98tf5}
zSTK`edHHc3GaX{8;WXT*E!u+h+oTy+95*R04V>;3(*AgJ@med&6*wMpo0s-TMKeO^
z?cL7tK~%e8?_q7nr}fYU12U!<rWk=r4O>-mE5{`u*_b&PmWEu8xd8jKk6%IfNY2&0
zz3vpjssa}(O<98mnP~p$ngw@%1}6hQ42!BW)8|n98VpoE_@S-9jyy22{rNt*Od|G)
z-FD2$31#6(U{2*edj)GUCRk<SqXAhTw3`TYug{C+v>>smQ=@0cpuYGf0upikkhPlB
zwJ^9Bg7zX;=+7gHh(i)cDiR8UxvzkpWlT`a4^d`c;cgxMqLT==E5gF0TDhLUjTH*7
zusMGWU5*5(5hfW7?`Ry+p>j{PJu&NyF;bD#>UQlBl24x1&b&hHnnvff8ofw+NevXM
z$j&PunbwGr5XmRnH;*#{3IqBiNak=Z3+k)l*E~D0wpj(p_5*)Q-KqU+6b)_h$|6tB
zkIG7o8a}#0-=r9^shP30Kt*uZwQCk6tP~gm5{Kd<?5HY{ehvw+BH{G+3V;)-p>Ah-
z$CcV2tUn8kw+tgIZ;LbS_hC|XF2SN+Fr}+zHhP1^AA^%m^?P3K?7s7i{}qIvgP^z)
z%!c35amWpSq&(R-6mRSsvo>BX-+tMpZrnfc+}QQAfwCrvqy_+(iHVDA3?#7z8xT3g
z6J?!vZhqNndODDnnkv4V)Omwuedoyx?0{L|Ted8x(SwLBl*}Rf{nxD{AGRTsObMwC
zJJ4$%ymp`G`ozL)SFe)7rQngVRI4sYi)+<4XIeZ*mS#?;YY19a622=f-O#&L%KTtD
zJfs$Y@w(D#T|GUoz@jIoK5AiYu8!*Yy(3tURS9MUBCTj~dG?zILl<-o2lVP)&3XZl
z3KY|#AY;(d_U)avqxKIi9fz{hDk>eTz1n9h^zwSr{Jk1<%Y*4BtX}jK-=WzIdDhg@
zo_k?#dt<pf>O*Gcp>H+UR8jTKHH=ehDVX2oHxK5dhq725R!cdoQ#D}qLc*I%lyq{9
zQ8{XGVP|*9W^2%Q%FG+!lRVVnj#kcQ|4I!#GqbH;#8ppOxUs5Us6%^mwwsw-TUQ<6
z9i3_VHidzF^Tn@$+0SpL2)92+sDR&Q=II=j7vt=T=Qce_&9<(tV)DF^`;j^VPTvGV
z%NMNRdjTr%3Vc%?fLRa${~Y^*GzhK$l6GNvrLNi#X_54?Ia8MvHpII%U@nY8@nK52
z4>y)K!PRdZfW+rPcW8V^@Yxsx+dj|@j}no}<3*ROE&n#S5^UEF#{iEa$*GDL^ru#R
z5PX%@A_3tx{?pWii_O$GTkr)mPouJ0w!Itr9!W83x3u(|`?LU0hRJYm41cGpd;taF
z&-KXbSuNs`F{0qq@#MUKxXYRCV=V;-Zo9I=_Q$u>>fgX(aU<v_derXZo?6JSl5PLV
z#t9g~t5ap2#@+<|Hq0BAOx<)j9ZTW-E@!8=9YLfKQj<vJNQet>+Z*ZvH#GJm;EhJ5
z*8vO@%KkBmTG_j)zohVqe9>54`0vT}q`=?ly}#ZE?2*u#D;(1!a^)3-O@uQ3mQ_nY
zzZyY5fqqFAVI46e6>T^0#mXbh00gBi+H3+W&$8O^-~iWm1HS#UancCRjMYN0zf%V|
zHd80%&x2;{IJ4{xwUP63;UiG3>N<jOfU-c~Nq6n#;ZnT93&XYy`Entp$Cyp0DwFI_
z%BoC`uUOOxSgqVri`zhHeTr{wAiDMEw)%R_7$CSDk{HaRWVBlxh1ti3kp}itS(Bv7
z@H=RJI=k9wQwoGe&)g3JK>{xWJxCM!*>cEKo|O2%9-{>UDs5XJBK`V*vz&&Mh^Dn+
zTeNf5M*D>Tq6|G&A4TRK%RG#*S+Cwdf()VFf5N5#S!32TvKrw=Ku`RWA3G>YTDTz7
zH%0C;|D;0fgx|aOIo%EPECEs9;}jtCQhw!!S=u1S3ir&3vxl`-A#+w(65u@syM~t@
zcWQwFapN7?vluVnp*`7h)uv~{7~tp_`$SS?`X=7n1W3;>k}wK@?!Q(V@wlm<tHqb|
z!i5WS(8*T=c1e4p7{>0q@06gMFs(xXU=d$6Zx7=|M#h+8zExxgX@5ekRTY836&ee(
zKyxO?m3QANTk>F6uCCRFR|QVoklwlky}GS57rtg1vH>yrofF-frNP$Olq_+eO8%06
z#*(>+->OFR&Lrs8tDnMdQ$$e9F_hU}JbxR`GWIE)<-G>slLaC9*B6gZl=JYiCNDJU
zehV6&e!BCyxa#fJ$E?8LOUb|1d0=A=onKb)^0M~6?z&;%0A~4FLdOg%T)ms)aTM)?
zeAFOv5rnGr5xg`0Qidz1Nnt3LUin`md~cn7CZoF>Eva2(E49kSI)Cp>yZ99OHdT#$
z%o{%bGr5TlJWrXp*4EZ>dA5|7E7p{O**ES`KQCHe^mn^@wX3bo=ohEbg|(pc#hSPp
zeuYWvEd#+nm{<HXlL-rX-mjDAr4-XN5(}nLVVyQ7D~f0H1*YapJYwv6`RDGs-d9%%
z<(*c`#0stJf}3G+-ICC|n&L$1BuC{X>gD%yb*v~ty!4p&X&l1WJJIvVTmYL|6KEHD
z;7-A)hEQX<{*!a}7#ZCxPxcDQP8zhDm_5u#(3i&ZV*Qp67c93VRlZqqJsFwNBs72B
zd1wxhACPcNo^A>g<}424^1hE~-<U96_eq4wfo<Vs*D)(cvIlCfXDAKz2fIx|hV-rh
zj&*UBUjbv!k&Xj}^G}}O2(~Lm0m%y59=2sGL|!~wcaQeja-Iw2dTSu7@ZU<ToVq{(
z${+uyovHzAG*S^e<0+B%wy6+dlN2v7e{#hka2DJfs{~rX_!ltCS2}w2P<O|zApbcu
zMR-?WQ&MH{eI#M&RBhP$T4A#bb`Phr$-9>}b_N0EsGIQt$xZ@*C{U{lJn;Gh1!aKd
zAq1v6R3mgLGKNx>j>U+#KgYOqVaz0!P3B}S=x-?1GX1AiyG8>rl)fq_pAEL-uuC?t
z7oVCDOAu{t-<zDLDBSD=125VpFEXGo8Hd0fH@R+TfiCBV{DntXfBQSQRrIU*t_B2;
zr9-dTe5r4wr1YGui(%qaCr!a5Y!f={287T_44|x%9F0pLdmsg|{`+RF1hBJIbeHXF
zrC&P%((<%4LM;mG_mdGOmLsnxB_-GH!|qkpI76B&_Hya`stDu{Nhg=M(VN-9%$o1;
z2<AB(@d&M7d&?#u$&Ur-1B+K6DwwI|tQ-AV0`_#SA*7A|fYYyHX3He)u5`z0dt}RS
z%N5D1+9kAdLuJ=WZURM<Qc^I*yW{`)lR49~^EFX4hK7b_n$B&AdXJ5*%Q?WJA5W4`
zo|J9Alz`~7Li>xS;S&GTkIFU#-!b9)yVv@fN{#;pA^<WW_j>X8YU$C&#zta_aBWzZ
z#p9nG2>N9Y&3YcOXd!>J36Y}POI}@cyYkuEpvL#siq0$v#vvU%_*(raz*b1{D%o>T
zrFTqD4264M2lt#l>^1ZObJVvrO)MDKIAVF~n-d-VNy=nr8$hN`26)o1nPlH^;Vl|G
znLyeP^)pi55w+f~u8+Ebj#;c%DIp?`1`W!;doqIxgY$?7Mw0CBh=VWvNXwyd+^6m;
zG6;;U^&>!(>1k)*qi^)SZlC(;Z?9zt@`2XXXFPv$q-qz=w;=%1e?Z!zkW}jG)}4NO
z19JCKAI8S6RQcV$5}f(?J_5-QlsvJidb-Q|zt|Mk)JfC5^6>v3o3gYxTxX^U!KzRo
zMK+|+pCc}h(X@^t6Ig2Azb*AYO$*3~H3*(1Tg<T>sDQt;J>sPntHYu8!t8uCN6rY~
z5W@7;kT;+Kdn#0O={#6Q+(F36#hZt{f*uzHq{jt4g22e=Ac<nU^uVrMch!o8r=;Mi
z_gs&s84$$c$J*o@Q0_U{F=WI+d;nvahdR7QwlM>$FZXC)1#qAPvd?my8}+$w<#>Kt
z@^*+aX#5|t{-OXkO^0&9-(GD9{tG-WCPS9Ys9NKj#LDe)e*>2ku3Cf}ilYF+)FM>J
zXuuyi*tc#3B`aWBD(o>W6;V~K;Ag~1xWt~WK~%nH5l5yP$pwyrmCN~Qct;9?QMJ5h
z@I(qG0{pLi3hYz2U=mOUYt1l16NNzmJiU677V-xo|Ankf{kqkcBNf4(eLL*$FU2z~
zWD4xf#K`=yzW^5DmvqgYo&ss85Z}CcBMSSf9zp%qT7r(5IfL|>hHiAB1bFVu15C^>
z%WEli=y7W20yIgR7&HLcv8G}A+>pS%faBxstMoe_*45p8mLojrun1r?+dMC-;?#)&
zg!qd)&yR!Zguox`v!yQwU%=zD#YDx$lMssT!R8?&Pg2Oj38yw-1LFk-wDBMSZVH+c
zCymvhvJPN`194B`TJ!-krK_t81Xdv;vV%r>CBb%oZ^C$Rdu53{#{f_V#QMF_h#3q#
zvp&INd)>~EPynPrxhpChdY4skhsfo0{;mMlc>Y}h#9fBr<1M0I@i(gzQ*+x^9-9rO
z%v9g&+Gu*cY)^Ny<h#Wog~=aPiWgH1-r5SKWHI?BFnr6ugg1=gKhmHQ<$E<J5|4sC
zqB(I4CM!%HI9&DG>48C_Wc6i0pO+N6`CPBxTt4ujeLSq^sD;R_1=&nyp=KxND>8Kc
z>2g#nXGFAzWHJ5^Gd#c~AuRcWtgpe9k3eyizm$AZ$|2NPc88{%PNfqmYuT=rbmtwY
zNSAn3er;Mc;=18V?lc&WY>`c?qF=P5apbOz)%k&S^ll*|2q6pD_Qi10+H@PK0L1+a
zl;|cf*xK$(4ucB2E_0$g@6c~eTadi0_m#`2lq}-c#^fyw4D)r|X5saj#p)ljn5rAX
zIEYe#@GVKjbrTnY^b7nqVx`9SF_1ncPw2F5i_W5dHiY1}XA(VR87?}DeTJ;@KQ}`^
z4gUfGJ_K&5)w=xR$u_M;%jwW8ypT3l4BqF+vg@sroO`*L8>w_MgF|Tv+G~;ODFJg(
zWDm3wKRVy-l^NI|0$fvF!`AC+{=HxLD01Eg^V~%@sGP}DF$0hIAJ6=j#2z6;GAQ}g
zXcww#gjEX!<usdh`JEIcOwwf7ChkqS7W;@Sf;@wDU4~7(Bh6W_jysFgjv`LmUt6~K
zg|kn|a<7liBEM)swwMjsIX`OKW#2jq9*U%GsqY74Z&T>><(HoIE&*vjfdd%c!kj=s
zj%HvlJ0NuRjbDE~r-S|K)c3%c))}tsh(o1$l&_1~UgvoSI`{Rv#U@{F$8aAUlGlZH
z1RIMrz5@(L1gkYky2NcM(%gEdw_9rzwS2kQ-cRbqKZCpalwPlxDcYP=y*8|q;unG7
zRhqB0+tmgRzoQyfX_MFSrCX5K^^TwktEH&^I-g_kIuSAs>%U8VB^3X<?6g0m&Rb$I
zF?BxD%|G9ZE>@%NjOeh9<V7h_tm+xe6P1g^_sl^QM<c(At{Yp1YrN1%a`Ud}FZtqQ
z6)n-*a8LJ$m6@@pz_&3P5v!)0MwSVMK^pmyys`|Bql#&N?aLijx$UjpZR5&>!{f4H
z|FZK-Xs{oT#@B`~->5?ms|=JoDc;2QJb!dV8_RoH<etWH+6-5miPV@6b}mzp@j)lk
zU?~=dCux%NtHv42*rW1jwAq9(jONMvt!^Kl8}0KttVL)ZD{^vcHoQtr6i{$<bLPa7
zLnRDUKfVo3j*7taoq(}f4e<Sm21DiY(CSHKwb(eOMyDifvSN{xKs!xMjygmOM9mj5
zN=!Hpd7=)??A_esw-vO+T%V`lbtarY>@I@seOK-x^nT6P$A{eoAic~zy)@WKRY>@#
zKcc^j&qmGEhL;(SG_8Ld{NY9?p4c@tJL%T-#ZGmfHKDnmbuy?Xw-=A5F64O+ogw=5
z7|GBW%hUL14|-9FLwJ$_v0ZYw{*!F{5(8sOe~y%r<lx)uzJ$dgmCT5?IT7^5C=p)I
z;4Sa!M=2K-GfS9yRTNsf&dp^9jj^1uQ$<rRo%yz$S6KO=)koj@+e$PUkqaG;kv2NZ
zM}tk&usx-1+V|Zq$^M-Cp$O`w1EtUtzf$RH=}ORyO%O5FC$&Y_F#PaR0Tx3{Eg^Eg
zoWrq-qA`6YFgpvCg~Rv|{1X%t7dIcD1Vd-+BJKf15B2HI;xp{TPn0IUNiDjUHsv;G
zrO%M?M6XQ)+V73OQ8zQrp^q>FtH(&x>JUDpMhLZJCk5z*F-X4Oe96RXidi{WAg(a^
zsm7EhV-dH@7;Q)hryhq5GlZ?n(r=?8N1<u4uyOimtFCge=`)9#i72#5UD)JZvL_+6
zlPIDFEHRd~MG}LZyZ~0+eJ*xe22~q2Xu57u4gU^uaUVsGvtk7rpSxP2G1x?XfO+s|
zW#zB|%-BMXC+$C<G1xK#Sm~iV9LPx%1xSwBYIE`&2E$k~;+A)X2E+I{Cu#sYsSaCx
zE;IHX8F8BOV)oR~kgey<{D%@Y;^KG5LT|J`Y$wYX!E!{M<A|15R`yL@lisPUq$jTb
z#y#kwx_CXhUYe18-!2YsOGVCKyYHikN(dRT7b6-)?UX$=Iy!n%$6tatjeY>@3z(Jc
zcFM`WB^Pjx$O>c=J-ej2p$E?fbVF*2DZ??HLd#SrG^SCoptMwGc40w6Ntl*#T&ncJ
zPjoo-Si4%>I0u%u(cLW;jd{U=6?jp)!~&0BRmVk8JB=+_2x;;jq_?@wgymf^a2g3n
z4p|&>l?&1csVM<*_TDYEhKFBZUPMwmI5^bZ<d8729upy!P(qo#vvcw!3l>=8qsB0v
zCO*1=$JYhfTo*9YF0{c@rnY|BBAifFnDF;i;Fsgs{OL24M4h6GsuR*Df+-6Xm=Yj|
z4k~H{tgpoxV9qEwzHZW3^&ywnabH_%RYG5-4s7cdrYRBt?s?|j4=N#eX%rJLH{U|_
zynA1klv}qWfAc-OoGX89_Z{l9wVkQ(4MpQ#Q<@?{_(t{v4OJ2Yu`Fe^X@0#yy>F=e
zwvt}^m;R-+zPC~eUGh8&WgaCMhHz@bVdrdiVtYzf*>ZHI9?`2TC@#H}XT{X}l2G$)
zm}iPP7Y$Jk{bAOYQ{XJ;L+je`w_#GUoWI}jd|Tpe4I5rrxk`ZFAL^vqJ=lCBBqd9i
zHt?(6FZP374Ed*2Dj$;(1z?-hAE;+zPKbL1ZLX5}TJNwF$@4zK%6H}MXdH{6mdck>
z;A{<Bk{WOw9>`siv9_-h#G-tX8pFI%U$P5|c!jzcI=-R?593K1p!#BsTOCk&a9C(O
z!&Q&k)&8bq{4m2)DeIK!{6WQ55af42%J2J8q8G8dqZZvu6wwXA-$ViQD%V*X?F<^C
zwtijD;HzEeBB|t2cJ+Sr?1?M*xo4kD;9(WbdmZ{TUc!+2E|`qeCYch=5zSm^sMRpe
zQqX!L&709BM_RjpXGKyz_6`|2f9NSt2!1FL?Xu$opKLv)YA1gXqE)xwW*idV_vX0u
z3{E_$%vW~bH(q(yCmEY3S{`<ZoRv4qWDwcpTzeDu(({V}9q~E(;(SA8Oy=kuZdIxz
z%(SVgiQQ%gxf1LkIx&;>L`;!@hn)2}+=DW)AAV?z=g~4$ZFuLfN}u<zF~I{g?D->K
zR!~k2B0dH!gV{D;zM9`sqG5EipRh`%%kWF{X=`2nYVmRHbKBuz6{SmJEI9pC*xAaY
zNhWesE-EverZ%koGeIdG-{X0N9}GmJDIwQite}$qy-e$Jr$pkCL~Ypq!n_zV;t#kN
z_4n0)R&9E;AWxD%f_iJ@Jaxs$cWTd`Q0O7`;1lCd+6<m-S7gk};`U8@)R$g-rJ&;A
zY=7zX`-?+9K?6ESUoC)^O*^upH<?WtSboUSOgz%PMi5lsqzKQpxi#b8^04?j`_Xux
z$Kx0lND%6V<;#~;(xcV<Qz_I5@u!WG(U`U)P?RqIYf~qrMlwUv&bQKD)$*uaj-(2~
z*2nlDeDyD63Nb>e4OUhIRH6KeX`yNa$%v;XF<3_(&|c>1u~GOql*3E$8bU98t9fMB
z$-dF4!6^p$&{#C-jB`2=tup8%4`3K+?|l!*h%R6l^J&J%&=^)xk5+h0-{ZXZTsg`~
zb6N<or8Cag1D~U0`r%e(niy4Mu)=4dOk^Ov5<y0ELZ76ErL+=UGS3iaX4Rlck?0d$
z9bCbb4^APG%z_1t@q&9hnbk(6MLJFNMviCQr{;zD!4ej5vVE;rPX6@nRk=zr#UupE
z=<Rn-2`~!zVmP(3laFMuIQ^6w=R^;W6Z_;xl!JK&T<zT5-J_#2m5F01&p3DH=jSg@
zOs%XG$=@1F`7lp4xDoQOG%O`0g`aonEpJ^|7X#QhY}t(vy}L5!0h5KEW`%q5iIDtZ
zxhkla^_@z9sgkTL=X`&~1&5s#e`aEr-885-pT`$13Rc9hy)_#I%{1#eie8n$<-tjs
zKdt3E)R$qE?eUH8>6$kgn<#zFIx*Vf@wXMC7jZsZBa=6~ZWLiB&)WLQN4w%wS?OR=
zZlLkXZX~TC7RZALpuQ7bgM&5!v5PgTBpFyw7Ax<Hy=^7B<vjJ0F~L>|i<681TokRk
zEAkC2VkQ8lqyRP3_|z#X;s#iPWit!IHz@GH-YB*oM}Zaq$KOP12hrDuP@aiCXpE+&
z=FOX9r+yAG9~_)k#g#;iwL&k9A{Ay+rpb-k=SNvnnQ`$dkPRX?zK7V)b6`Io7Of_T
zL)P@I>ZVK-SY?JQGdMgx+Li&Chbq|42=&%6QLw(EFSzowAzNkk&g&t549@;%x@TKx
z%*r8%S#t+8Dy+dAiL;gteAMd!b5&$>N${ZlQ@Ek;b0%3pASSSaqqtO`qwsji3RTrX
zoPufX;sq4i9!y%Ju3H35s7o7XWj8>{BD4H6=f4IjpWUN0A~&`&l#J*H_jXy<0uk>?
z_A{V4vZNsQ<>@I^)?51_z`83DN$rGkHGxf~{@bP=qr)&Aqi{(jBS$6Ef(4YYuH>v*
z|8Y}iaNZl8#0+e4W8JVfM6j4tjsvP8HKMRAv&6h`czy;3nQ`6DPzDhMOUwL_fzR&#
z1Pw$}s)qk`eyRzFT_9dbcpeP4u7$^_%Har^#&eVpBv^d`$uQ*-a1ZhXQ@_L4xqNvF
z5n&pE-hY1oy95fI#1_pMU09b;8#eJR4U}rP^MNj7IAZUNU#?z5iP3^P{31t+$<YF?
zxKCj0ZTutDi0;b8{SusA?aU)pua9$c&(kyV&eGS0<>$B;9EIIs!|GLlDfhbY7llys
zEfD)711=0v3oW4B)y)@qQ`T~0c1E|9SLoKechH(+Kz6<^eR!kZP?Oi_nAJ6(<36|_
zO$XkJ)rJimsS2trq<`Pi3s>4y`S5l)85quRjCb2v2{6joEo}#h4=K}2GfgVJcB-_D
z3=C1jLynxOY5VF%mfYy5N8aSG^@=>d)2EG_lU-#7&))5%o_WxPs}xI<hd?VADI~|n
z;%z{LRnx9T=+8``I(-^aX6qx`tb_WidxB3@tk3l%-sR?%xUImM8gOdgVZASfo%f!i
z%^6t)6r$zDD+4y1x5Tw<9JH{e15{6yr~GIFlXDnVi=H1WJ`G)<VQA(Y5+mB5J-TLS
zWup^y{eBe9!$@j9|NQ(yo6|6@;=eA}{zdTc`8C~^=|Uhl1taSHmXhL2Gbl@6TU|*Z
zBYMLq-p8u+LTa{A#gU+cHzu^|T(sqN_dY}(p3pvRF+g?k{?vwcZrWw(+OV^n(yEA8
zts1UMmkya$_Gidyy%cq>F)Gc;wj+Z0>s$10j}3flWy<dpb8Xn&+;n#|M*9$u!n6$J
z;cpW9&WWH$t2)dr5woF%=vtR^6Vxm;qV5#mFZ(N<U%q!jP$4n=ydWB51PbO^hJN{3
zk_*wG7VLXxt4D8`p1kg!C3J;%byS1xn`~WnJ^Q|dvsXnlJA1^+7g3RuYH+U3KB2#d
zXF>JSvOp5p%ePW-gs{|5-_H)c^lqFo9>%rYZY@EEjkuRpC@$enAyu>zkZ%Wm>D>}g
zty*kYfdupYI|6vyw>5tydUz@g@E&W;<9}EeR+nzK{&U%m@T+|@XoH5hpAts01WRE1
z;fHy<#-1hRR~(%DDnq#QpKFJ|2VxWY;;fIBV*-smPw=Xdv;#>@UPcv5H5QE|933Fn
zh$16n=02;YVuTzbDLr%E`kyDUBii4xxn3{Y9yvRQ#WlykvA?gyMNLelY)TDQ-0Vd*
zF4f`u>Ln11VFY}b&Q|lKIMa*Y7Z5WC0Y*RTF<a0j5K&ykj9mel;{LXXDxTh+hQuO4
z@75II;dfar))!vYxRy{1t9-iOE(43l!s7KQ1079pO7GuCF@0%7B_pqTM0d&i8m*--
zM6i+$-Y;Cli8EJ$IP@mxq9T}jaoDf7p_?`@@S9>JtliB+<{Uz51P=!X&b{cetR7{y
z%8j}(2Ud0bS*Z6_iy_zEci={0<4pT<JbUlI3Id%EZ*J2+6!^jbr<B+bbtfz=?8w%j
zQTBzp@R6;$0-4?A&f$Lc2f?i!MzQqcT8P~w6fO>F9g~1GGgzk~Tlekiq3vx;LbGii
z{S7i=07yQd#C+b;HaOTOTEB-I<lovb@|C+uYsRW$7ySBbKa&Ty#}}6x_#CVre)z&3
z4MRRoNfG0{vgWfnpicP9%d4o%?{Ss4Scd?%7yoToCH!zhLxb*{7guVh4-;!acSNwM
z`Svi$DkyNLDqcU?_g0?wm^*)gI&s~#w_>46J^+4{t!`_fy1G8qRGK)q7d^WABz6Cq
zw*A`67CnbkdMK;)SA}<h4<x%VOwKP!yBtTcd`5hV{zdCLHrv^*{F0^k?X0O3-vK7x
zMtb)^bqMidjzl$GJ&WV<gU%)f<S!oJFTqkj&^pVGVcxq3kLbUHka-^?pOR?yCI6O6
ze1PdKL53ei5wd~rR3SMrtOCi2abpOBa-t{TYVyM+RXu+I-GS+=4h~+2p9kcKV)zg|
z%8oh(cHxwFvDHm5J`c2}9CYEkx>l|%INO&WxN-64aY~I!P>ERw+)~uq!1Zr_W&884
zyO|T3udT)^!aVPWzf`2dF#6|L6|yAiK@u*lSlastmP68icY8zNO!RWARda6Zjd;7R
z+OU1kl3wWIYQto`MVnTCEx34(wGQ6^yUL>C97#ru1yMuPz(nk&2^cvrX!^KgG1q3t
zd3`91F`)S^kHgvmJFk~ACD|=hEk$ccO+lHl6YX~bSSDL>2w*K+I$DeI+diVY4$-Y#
zN?Wt9>+9!#)ZS!AbwXrmsrkjl0>bPfbF&fnaW6r}{PeM6kPEnOiRMPvK`v_X*zDs0
ztL+!+UIy>X)bkwpQA*8@x&SN8Xa2d&3$s)->gIe2HrMjdD2TKX5$Wk}IZP#UG>%`q
zh(I97D)_HBG6`o)G>u8?m{8uneLFHvY146iv5pa16tOidyTK^^2|f5h0qn4f<t3-!
z;NM~|e+ur!&M;a-w>hSgAIm{@v-N<kt}eSu?L(WOQ|}orDg2-a#Shcx&!=WA4pj{9
z%wtPwim{XDK?PiDkM%Q#PDV$5oPwkgWGb|tg(`W8U{zEA%NmCy15gpNau6VTF&%tQ
zJxO@~6Dk|xvHk~&7D=Ts2Nte??fqgD@KOf0-JMOw8s$xcVVcf)IB@a^R+I0&pss6K
zS(#}#EfRZgY;64SORqcm;sWe|A+e?tV&*6pd9E+FCSeNAE@QFDcq=%i<<ps^Jec=U
z6SDp9TB_h&?Qk6!Zbx~Js)SST_E+?LKH7t9g@uKnU^C)j)#q@5how%ZMp)cgs8$Zx
z>K(M^4*cPhrvC8U#4#Gtw32uywxmP)m3XK*n5WP1v)LTsIf%Cf$G#wZ_$3-M$|2kL
z_V{*P+`#^z-88+(1_|p?8#@&jS<bpJ<=p}5I=$bEbpWvJ;}KBs=<-paOe#Ejeyz(d
zy7j>i)~wVA?Ta-hn+;b7xm{*u8>ET;y;Q0ygvD{o#am>=a>RLPKj%(kB;iqZ&0)P^
zuv&im>jDq`?@i^yR^c=aYx9iC8^fiMX#v|!i<;Hfd}Vw3_xos4pQ}a{j&RHC7YZ_Q
z7|tOP-HQBRXRN}wn!?51HN>EHe5FF-X(8~dblO`kpy)BTwscQ($1d(R&Cf^<b5sW?
zZGQ<4kX8s9=(s~8qOe}U_xso4Tk5U4cZAV*sf#3{nL<C-ABoOdzdkgvm_}YwBzHab
zCO!?eL21UV4P7E$z%8`BbGYGUoVFvswkbX1?GJ-RpBPG%#$x92H<1WuQslzri3fGz
zvZ{yY^^LX!6c9BJp2HOw1CB-<8L=B}kcibh+{O!&wog9!HUC)z5}(bkJa)AeLn6HH
zteb31xB^PJhwROHcgcufC<V?ulY;43TI&tIeS?pM7Y&WC{%ZO{>AVl-`>?acmj>&2
z4h*@8Vr(QB@?vBhp)8B<VMs9g<C<r*uhWHBHVlkUtJzV8bZuC8cUFG+F9_(&AfWrp
zdnFb>fyzYeR@32e_DDYYiNbr#@`iivgP3-B(*?7O5Q}+8a`W<v&zA_O%>;yUDG_hO
zcJi?G>eI9x(br1!SbJADtQWLcaT*#_(g}6QrBBcK(jY<^*l5-Ib`AqBn=Jh9Vfb~R
zC)q#n#NrB`LzH7E@=_3QJA--{L8JWp<>9*Q!{4HY1#H#?6xMI@1v{*F^Hs`JUN4e|
zKFVe;e#eP>f%sSF5#wc=tAZeu#tSws0(g=oZ2X(z?+^=hKzfU&dVOq}-;Ms1cVYoI
zZ>6t7B(;1+m|;~XYkg^{fTSik6kS?Qc`IBfNZ~%7XNh~xG5oIWX^$YDWDf&<ZEy8O
zrO;)-&vPj&aqa%R9#v<AEM)nH@$llca2ikgNLfpHpH%LAtKp2vf-fT4vdYRfmf>D|
zq4u!avC9z2f+t~lc9as0Nf7raxaJVu;EH5krtWuC=c2PPES6Vxk6clfu(Abhz!cOk
z1WQS<zOn<dNQSfGI&C3Cj=D#?m)_Pkr;266dE7$+bo{WFodGc!+2FJ1NKz;?7kFPM
z%uVYAnqT8-S$3zlH0#oWykxhxek<|p&Q+%|q@`36Mo_mJ*7)`X|4MhK58kO;i)38j
z*;nmdaD29GQstXr_M1Y@#CV1!YpCLT_0;a}?qcPw*dZPNzVh}4PjBs&PVF;KEMQyb
zNX7eY75>om7*@eW+Xc{$X@P&auP-n31@Z_fTwnKCpJG&k^sb=@&nFSA{Fy>XZb{fw
zS@tD~`y_#lTrIYW!PB-0;NW~RFfv9F-mgK5!RVYaoJgyorgY7?U8n7+)wFX9&vv1@
zFjRty)02qQln{@+`TAkqPMD%5Zy$S6%bJbl<w`zBN+GB1rFLCIO3K!HQIwN>^asP{
z2Upa&4tbm=U;Y{CF>vx`h#-@_T?sSdubt5p+E-^s)Bl|sWx;fYPrJ1<&#P98=BioE
z_TXRSFH*^fIh2pLO}#g~kWdj^WE?(Z?=Ovq!LEGkN&4r%ApZ+mnE{65OZM^~Pa<|y
z3GQz|M~B+q-~I0(IqZ-%w^B-hwELffFN2{Ne_bN<=j$K^QVa@x`sb9wzovoxuV(;)
z`C|~6HMC-Xs5e3L-*@fJvj*|%HeTf2`15t(-GZ@xRvsXy_s0nEmT|}<G&;EI{Ch1V
zXSfsXW37Jwo}OeHTwf&@xPAW`#2Eat9PzX!mVZwVvKAP~S|H*79|C-44*KW+nW%;F
zL-0E!lq;$nf6HnNwVPJnSMU3Usq1L=QGeCFb&WGElDqOcxbXkI%pMkxgR0l1r3L=A
z4qUuCq*5Y`)Bju?#QBOKx&J-<pFRJdH2%Dr|C7f5q=EgP-uRco{J)|_iN7gq?ws_Y
T+f-&JgMZGd>#F6e+J^prEzQ~g

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/packages/gluon/normalization/imgs/NTC_IN.png b/docs/python_docs/python/tutorials/packages/gluon/normalization/imgs/NTC_IN.png
new file mode 100644
index 0000000000000000000000000000000000000000..60c854803e372cc52f6409cd97b61ec1a8a1783f
GIT binary patch
literal 38694
zcmeFZXIPWl);0<ukN{#rSCFoB1eK!H&_omvq)G>+H<4aLD1s=UE~<clbg9ydbPJ+L
z?<Ewag(@XLC}+m~z2Cdm-v7@zKlYEsx`Zcljxpw#<Gx3E@>E+(jfRSaiiCuOM*Wt`
zJrWWqEeQ!E5>5erGs4h|0RJQPxTmH>Qu^b<GWZAOqg(eqNJyCJi2sq=W%PrOph(nJ
z6!m;bSEmA!FOCFF;|Dx4#-{EI?@?JY8<9ZOLztr&F5Mp7%@Y2kzO(<H{aYfB$>X`s
z9}oDVF9=n<=}i1|>-_~2w8|^``?ue8`dbUVO6TvIzwT{~qr065rzKH;Nur3lNV-ZB
z(DHqHVfvs+!h@+=LQZZ-PTy>C@L)LXvxbkMVfyxse4oP)W?o%4n4m}$3F$w6WQL;_
z$E%&N;*yek-Is=K82|BC;={L$ER{%=@&^u#+9~wd{`qO}_uKI22}Y6n|FJL$sah0@
z@87p0!Q6v{!*i33DF0(2IPDS{oHK+3^7kLww?mLw4D>==|MvS35-v#4zo|kLh7!pg
zZ*6_)Ka}89$wd77+94<bQl;>F8pYxN+!JgS4&V6?`H=jVP*K{^PZRz_)es~`{y*e{
z<U--Jx39dX`-eWn$B^8;u>V*iGoFM?Nr2Zt=|7ZGf*$^deDHWWP8I%7A8-HNR)0i5
zMWHzUYxMsb{hwa<Urz^@@xS5azv1QI<L-ZB`hS7+|3^To5pVpA$S*KVl%9=K0oBW%
z()i#-=D*XKB~esVFE-MS7L^$Rk#e1UWO3M^lm-b$1aF3k6m#kSjX-3w1AgOgIwlBF
zhZmQYHYAz*JKQn*^7-@R&Q4WoMuv}C>;?Dv`FWG!r(2l7ApGjs{2Tw3LYJbgLtL3m
z1ml-jwr{QzrJeL8jMqIDo;L;_RHUY+dTazP%9z~0YoVsU{*^Dold!jjyWi93v(aE^
z5hM|?F4R0H<X_qmHcNFBrfY`Bn{PDGU;7a>*O5kP;&tbEO|-PpLbmb!(CO|m8$Jf+
z&%uRXSt}(ZGB>zRz6suDb%=a7fm!1ULHUuMkPEtfdJX67Rkn0->0Zvuw6wP7Zd^Vo
zF{z!b_gI|#Cfc%UU|`_?;c<E7`egmQ{B}xDohbU?TUb;1Ty;<oGZWKff{<a&)ZyWw
z|H3N;FYAc}zqbhx$ADet-H4jY&9UzmQ{9@!YD-U=1`M7_jyR1qY_`Als9lv@`K1u-
zHzl_xgIgb<cFTAoxpH-|spXTV#rad_GViJ1{u>P|RwW)m)$4TcgTVP@wgCQy=6It@
zf_Vkm@ckk)8Gt2BukNi@_Sj-?YiViOlD4zDeb9L<G*Onv$z7!w)f_R@M=1>FoSE$J
zf7SSC7Qz766WshH^TVtXZEF}1S0prL2xlem6m8sXIj^M7PX-{pyorRXeNR$0j0B=g
zwdDB(#_rq7ChIFU(cYaZHRam!(!$)Fy+VBVgGRm+Kk9mb_x8C#fA0<h+9)N|228}Q
zY1e>>S4GF3>U>0NeJ+3WeRVF5jH%J@cbmq%%CMRjB??tyV>m-v3&@k{tp?{-5twkO
zXYkI!_F^T8q8jazCrm{81L1HNH>SCG8^=#3Cn+KEYi|Ra(an{aeXeS$3NumbX17hf
z)U4&ynbH5~Xn$+f%U|o+UbdTZ_4M4_+W~KJG)5_=DFE+Uthe8?=f2#3DZhfhSo?-}
zQpapZWaU?8>LNRzyJ^>FoA*D7+Ae+&5*~A%XfiK4T+xdQJa1=bS9$Ef{`Bar*TVwo
z3v&MAGMXy+I!h)N-L;v!+qi<sky5j7TGiS?4AV>1f@Ku`{@)4qtiNw~E@{`el<K`q
z;yB(|DKjiH?>_u+n586`&MSWH+F#XihviJwsfaT6;QiUv18aw&d<l*)^V87>aZ~sz
zc0&#wi(Z1(gdvB4LMNVdO_|UipfS-Gppq}J92EHb=T|~aA->_Te0n5bD}6=AqBBTz
z@<X<KNkfr_Kf=kx|E}Cluku*)cA0=~>5;U5$+YsJkIH9%j)G=?h2`Tce}@ygjajVd
zW2<G+%?B37K5mm<>wMTg_e#m#m<6r<k2H(s^}o`7tempoD*V@<-k1sqxL@8u6+AqA
zy!-4&y1D1NPWFi)A%8=eAhWJ;ePJ>>CIh|uAsz2Vw)`yhVC!yRJ?^ZA>zU$~@nOQ<
zyUxzr5~+MM=*C{J7O$cDN~Y_k6}uee3KDnM+}9P*Rr4oJ%J_|d(W``h^pWGpvF-$x
z*>%E!+&ga?Rx)W}^XtG>2;cmafZvVl@tP_#TWv_{o-a**vZ*CBfR`V1N6%VqFT2}h
zy2;N>1~{jq-N))Vss>n2;>zmK0WzPcPIm%Fj;}Q~IomWksJY3yOdLj4w;>d!-2=p5
zD!Fc*Jzdjfs~s6%N7u+6)w9|<d!KHm#B7{9mPUUW*Cw2tr<OWt9XC|FBYR`9xI}tg
z;o3Z-OwSxgy2rYO*?L2gB<@h3kWL7wS@T)1N4L~-Zwf)+D#EBJr2D*%Csc{KH$>?f
z1yml@8x)j0@-Zy9FVigSJdM$1^AE{1deg`2K6V@xJET}QHJp=WF6j#k@y^X>HANv@
z=)usMLQGsR6YP)$^<i(^NlWdveP4)ioWs;W{$0B;X0&(&HuALa>u+&OLq7?7|HI|{
zfMEU|93g{cvT*%G9KNReYe-)IE;(78?C&TGxd5%*A!!!V<y7;Q{fgxqCE5OtVtH*Z
zVk!DG34J8$VSGc_p83pUe-E0vL3tLH3UR*78{~sej0e3P>B+VHHKa663pf=wn0LLZ
z8z~y7=%Sw;=8nr@^Bmop6n5$;#QZHB3FC>bRN22VVTbS7bzA!O2Gy$VPn(#?_5EX9
zSK?r2N2BZ`RX3!XIF16w@g^3$O?-N82}7bbUB5=Qb_aq3Ge!u?`P|ELS-U?Aj=NMg
zuE^zuY^K<~`O1zb{8}`?U~4c0yVZ;yukGX0)r?=Pl8=IV<?mk}yK&;Px+m%)cBJMb
zY_#u)lX}1!_Vp6$qu?)g4eJLHF>7)*VSO@^ttU3rG{IvOt=NZuQLXGM?2I2P+qz&y
z%~<;nk+U%8^5Xzjv|~lvCN1vG{bb9y<CEP|LIVB<euE=8fKx^zrP^XQtHsr(-*Q5f
zB?;Z96pyyeY9Ep7b>Eo!UMP{@hHoD1UQw{Y$v%k7sIBdxHdthKc8$E7GS<^fSi5?#
zw!DZ%+kH9St1czbR33X#b2iW4+SvVFcE9@g_*0M1)Tu+(^F%o&s-->ki451%qgUh{
z8$Ro1^`uQc+MSyo`CdOeK07fk?SCrsaPZ@i)vnuqao~71vS&;|KZ9UB64W<tNcw}7
ziNYacz`(S7Vl#DbG~>j1B<Od?-l**aVeL0PDRhpRQzfc}rjQiU247N6WD2@I5{V9~
zEKntzKYFVeMIpp;&K{ZLc0;1I4>d=Vs-Q^5O!InYZ6P~O{&f)jyS7S-R+ip0S-;=Y
z3r8V0%SJ3!$RISE5}AE>MOcoO4$Pl*6&DHHTd=6uV_8u4n_0&P82_>n$IZN}PTFr3
z6Pw3YYYWc2%b?hko>SWGMd{e|;v~xZr1AvsY(+s8ItMbfcl&XH$4Bz|wl4UsFC0D2
z)n-As;up#k?=toZ+tMjC{WZMw0}4xilWDbR2FGfl)S3$r%RyPeC42W+3oVL{y`WE(
zpmL=({e8lYwboHHuy#SeX3b-%_3`Efu|D}hwWG}YL8>@h>i*GNlx9#j$8P2^-ndGx
zBs;ixqyT|ynsU=g*<rtjmURdk7`eM^Fq4JPKHO-W-YpeL70|uh#JA%Xv$1`MR}3=W
zxNl(*fw}s%1yPMo!8LVOREALOqlaS8>_1DiW2@HrDI(K9v(eJ$r4ZyeZ-To!hB8pQ
zWPd=D8fQS-{=qATxjAn>)?v`$S*+In2O5pIF;1_pX837G*^qY{p<z+#r1a=FVVXS$
zm+H|Hh~;6!AIG@{hLw?)ALhqe?#r|4;Y_yCmSjHzqh>6mZl&zLMq3}0K*H69))5)2
zbqaO|yB!1GKf~B!Hs1wpqbGy$-%cOm1W<k%5wI3VLzC-Q9*`z9jhw^`9XC2#9^Y9^
zBgC<<Hz2;d1&OlNdZB;1FP`Ml^rA<Vo+<3j4D(z#3O?$ws9F<1z4+FnFy)R79u2+a
zv0p*6c^VpV9&KMrWpa{%aGlC%e{ENirm&~NC)0r16}l{)tCID_=YqTIk5_+HJ~PAH
z6|+;(=_PGcdm9#Hzfi(_?n!rFce{VUsPVzlWz$P$@#!bC#~h?k2(zVDcq?nv92fFg
z>!QM`AHMKzkYMAo56vU-<i3!-HrSf3wrk()$Kdig*m>Y{azQ4*lTAb#$^bquH@6&+
z#!x^HXUf+28MH?;v0eitS=Yt-Yj%xkw8TfZCi*PZ$=pR)9_^Uu@kTHj>tWM}M?%!Y
zSemphQ$ebL2g~~Nv7PUA^6Lqh{);9`d~`7L7R7<;#MbO{_K~0n&SqEt^!0Bl&&)S)
zf-;G=<bx+HDn(|2smneb4-fF+x0gFe6-K+0OiABRd^`v)Be|oP#<WYz(d?kGLgwFM
zzoqu;p^xNge$a9!C);jzaQ;=-+I`ac!}@9Z<HW!e264xU1Am;{TUE^4*f;!_ZMrTs
z24)yG1s&|l3pZPQDB7zYAk-VZiQhO4Ve3D!kaInab)kR+td*a%RQNSuOtoB&Jl@cn
zjUL(U1||0FlPKQ(KH=2-xw(79$!oBEzLE{UGy%(eI2+17uvXqYjWs<P?2FN5Vz2xX
zblSIpb%`8z>ihV-DN(VjxMNpgFE>VPzHD6K0Nt^27)7&J-NIv#=vJQ1$3A)?2zQIx
z?5ys$p3znl($GYA6MWo6AtT51{`OojpU*?p8l`nK`d8hzSjX#b2B+OgQZ)$yhs_pV
zgB^EW*Ee7sxH<(Zwafgd-nRp{f|r{Nv<S!ewa(}0ARAmw^GN%PIJEwk<C(9qgunL2
z%`x9HYIVg<(xw<V?RBPFg0`1pkN50W?XYs*8~l-!PBQ7Gv#@h+3*+(uS5B2lFb42_
zyswa;@+H8WZ;;aT@0O&u9AMrxs}9cG4||=?6vR2us`kxW?W}Vx4UKq}J?bZHaiXMg
zV7~c`O;<4{P6?i1h<(0iveTW%L}MMu|H=Z3VA@@Ca>rH(JfS!kam6=|w;|32l+4%O
zpR_3R@+&@1isjhepp0+|7F&(m-^$&_Z<AR^&`|9TZupZso^&ReTfI7r<48LQa>nc8
zmY29hq#JGq`8BGBd7_QuHV6k^Uh%I)i_jZCGdOZg4@P!VaVUBD&YyBG4Ox%yk;2;7
z>t0e~eQY8Wg6$ueune9M6P+UIo6S#t$+6ZONir*rb-Nkbvb`(gIa?JNN0UboIovZ#
zvK*W_RF;bs-%o@|nPc7NRA@e;56xI{$Du(*r;$>%QvGQRhEm`5%)ZnluBubhiDSbP
znb+Vi<{tM$m^q{wGH^Z?`C0bM`W44a(B&#R5vL}DQQzoOo)FYW2&+Yyswru7ED6L4
z{<Eb|_o?_ypH;u~#ruk(6wC*TF%;s#FS6W3BshCRyhq(mk7n|*A@#+2W2Ap!-%x0Z
zwj4e7riyI7vWs0OEFSlycO{E94zEdpOFFsMqljymINmxufk@M#^7{&ogGj2(SeK7;
zvd?--hwv&4`m@A4u^vuwjJ=gRY|h93x+B_Dm5cOOaNL^3CNfg^qSPb`oTa62ZObn4
zKQX^^(n+Q8NVGH`9}a<9oXl-cB1|$zr1TLcb(`6DN5h}(t<`J!>_=QPBTxiBhz-MU
z;1p2U(wdeB9&r16ZK4w){^Q>MS7l198mkEk(?#Dcx@@fY9(z#ijKAv2RNa;hoS`?l
zn?3MLu-cxxEBG02+@JvBb@^slP!=wDTe)duIPHwF-g+cp&msY)r!uZK!;^4}iUHQu
zbJYQHBM0B?_wL!zj7mb_JM^3?OZgoha89wR5DK2dJUuc;8c)w{u22f7#eO^UdJuKt
zCMxV4P3tzc=4{2bAWSiA)e!gYNP5sD(oIOL&jHqy2u#T#d=}U1)FR6i84~+Yr#w$}
zgCpXWw2uXsNtvn|!v00cjAk2VouGT(GhE{&>H;=n@JqCZ!_V5ayH1@7lHWI8U(mDZ
zdkLqa_ji%9$P2{wZ1hC31Wz4#9I=<RBF3_|v9^XJC+;$;DEiIgF2AG*66~q{$Z_`<
z8P~i=moUK+$1fF(eNK-T6fIwm-o?IfawSiv(jIy%#Ae@zGUHNhQJuvwmE`M6)|kj!
z=ew>}1+Id--r{`kBeM(lXN#)JNpVVR_db1&DtmUy^s9YNDPzLhYu#P}B^?hySE7{@
zG*QCr-(QBn&j@D@u!<-5>QG9{8ikS5RPDwTQe1oOCwEE{c;ZMIwyHtq7eU{_{#C5A
z*}tUd294*r`ew5rAs_OHqXrg+ch4tisJ7#Z<b{{vOP&`vIF}08*`_yO!~W|-h89zy
zS&NL{1wP!v>PGeA^eCEo@qA4SPx;=AR+PNs&<@GU=9%`Lk6C>j-?X><EeFlKv!N)z
zv-xXl!>*+iotCjhE$gJlcJ$h;eY50JYE%#KTq}ZtN7v;rd#+ae=#5>QM1}G@bcZCf
z=<);DNio|G1&4fyz2A@0@0%_e?-q;>|4Q|^wp15d>wK83c6cFXb9BLjrS?>GW9mXv
z5lUGY=KLliBe1K!#rZFllMzX&c7>V)n&@xysWt<Q^rd5kT>hHwIw{pl70*E}AOyt-
zX(Wu4MAMbiK#}&<A?;)$R8-<2eIac<7MV{^ev^g{Z<FyI%XiKpjz?-zuP5EdCLemV
zNjwWK>nJ-$b*3`l7Wt|NvZk6AH26z<j`u*E4WC9FkXYrj9M(e_1`l}Xs$%B!$pf(i
zePYK80;3M-Fxj$-!VBYeWnYzoQmQnu`0a_Fq@k9U?Vjxb8K^j-?e2#M-;Zfzq!quN
z2G!$RTSl^b#y(IPd+I+?aH9v^DaAM!TgpSPk#F?%ZTK$)ji>@P5G_+Wl3aHMcP&31
zR7xuTlp3XlHv}%kjWZ&mztJ;s?~i7VJdA0$_auXQRv?uFLk{|F1xldVN2OYWA%U1c
z#YuW=-v+m?E>ddWnhArPie#hTQ%D_b=N!);F1O?k9`rRSLCEQh#eGLYa_h9Vf@>as
zIvog6U(9l{kUrB?XTRV-Wac&a@UH{axC6JhJ`H*gPrQz%&yu1?o10(ua5R)*1I$ud
zM~pF;X%qjJWfQbWlM=iM?HL`$lrEWd%)q>MXQJ>ixS8@;p*ro9BLOXGe?IiZ*lA_y
zcBNlB1q<KIZN^3ou0f<+v?rXl=xG*2u?62yhXXMA`dtW{_jt$W#LHua)CbDDUY|nI
z)tcw}oUk%--o4FJr={7fPRovz85^``m*`@RtJX8B?r=FC;JojDGN)I&JnkIUkYh9U
ztEEz%$twhNdC17rQ$K3sJ&Ec!Y|zwYT;tK6_pbaQrBE3pCR?&^He+0P<n#CuU}wm4
z<{<;;pVZDLz&T&g_d=tCIddW<qV~vqy30#Fa*p5D(~0|~oqjgQ-g11A)%FT;?x^7;
z|I}7LEV?InuX3yq$>M4M;_Y_Hu4e9KdY_Y{1Lwxq&RUh@P62|?D7c$SB{pg;hVHVp
zm-z3nJTH7Bm`$M}ovUm!D8NRgdlZNitVF216QrMTFhI49*nxVS3w73=^DBDLU45FL
zUMr!~JS$kL+23I}b5<R+&gm9*fq?OIH*11bzlrtlRmnWK!MMZTr<s4x8Gksp*EKmp
zxR;eYoz#>V$q{^AW=fq9{42q*;_5R0g@isB!vC)OLoLy!>l}ovk%qB8ik?+>Eko_=
zj!CzUa?%qwP4;M7n)y!GCx;L_Z}vM+N(n9j>uzJUn=Rg1spPOP&ZwifRXt4H_)wTv
z^_NC({uV8>{XJ!GeI%>O>67lt8PQAk6J9Ow?MoRpu0Ky_*`xspA#AbH``{+^uhlLZ
zi*o9Xqy7Ntbra_|FcF1PueHa~J@+@a+7?%<qQh8mZ{B;I_AW^I4)Ho3-KVDLdQ-FY
zLyiqU!j5am@aLI0N<2MYqG!X4iijV4NSJq081q7}ww0P)9ne}XKSq!5@r|@>$HsiK
zbM?FOr+KXmTE}NFpYI@1mPM#7ID}?g!hTq4n<f5d2PqBJ5y{t#juJOLujwj^vrQBi
zK-_@$lbB-PIzGx>TMVO?D^om3xn@^^saNP%#2p~5oxT<Ysq7luV4&ME7Ps$jz1`!~
zy9f(5Hq_$NN~<f=YN}~_utm8;l@KVxP*ZppB$!um%)zL##oZH~jAbuX^3%h>b2Dgs
z&oA^!(B}7RJr4=JgfA`lBWWtap=QI<2<uKhFPpyQFphzv$wH6U?2{sCWV>q15*C{B
ze5DD8|JLJA-n@+Za{Q*-aFddCW@SN?ZvO>2nSv|XRGh0hq%TPc+kSc}m43!g=9(7$
znU(&h8<R&j%_0|1kFK#|_gfxf`*Ay@-Cy_ZRtrcqd-t2~emE}qfSoQ^;7D1nW0qf?
zM5jb>{y4@Ym`SnS*h=uGR5c@Q6K!yw;|Q$wP_R_vwx8>?5P4|NF^xs{tG1*c^>FwX
z&!6~E>v&RroqIf}Md|mWYwx&hl=j>n)8Vhl6VG>F6avS!o{ISpXDKoTF|o&eCszoM
zPT#+i?DQs9?kgZHdiI%RZ7~#QY?x^37*I>vOMxNUfh80!RFYLLC9LCb{R`Fwv%B(n
zkcUHjRljxL_(KC*hK6Cw^oL9qq-mjbS0hgd$NTtK(|jzwQuPHIRN6bbUZMmyrN3-!
zu^p64SOv4!n`uGU(BIBhk4PPERPfN}5i}k?>NtBkl7JA8RC2F($%if3lZb4*Upw>I
zD>BBAX7AKX=#<qUn2mJHbpq>csM>4bHQw&z{j<$aCUmy^IA!4DvoHRGR^@cgEj4@j
zMY@yXLTqbUn_+(fvoQ$Fjyg4we+Cg+;pCnT;g?h1iLC@}1aMdl!q>(a|J836pb~p;
z9bKZ8PD`9YFy_j1TRSeiCe8LF`@V5su^!~lPz5HFyj|wAl>KF@f31hwhBUN?E=cqW
zC`yZ)=5s&Nk6W0Cp!?Uj#*0u;SEnk`VEfmKmoj@%YFtYU6lPx)lMpoS>v0BIy668-
zf25Px<c0rhBtnHZ^G?Ld=g|Kdop^2>a)b7!$d40nM*kS?h|&u|a`ivS9{u-G3h)W!
zpBnf-9tS6UiyThN?Q%Uj=6}}yKZ7NpY7YPZZVOB9{>Qq`006WzC)UpHQyHic^(@fM
z99SN#_pAAruqYA8g{RuoLaeHs!laP!^6{{KqVRMxSldHV;t`^r1KM__hQ;RQKdpqy
zBn7=Bir%qH{~RnJfn1?Na(^6kj6eVHW}*P6!Gf|&`ggm2<Oh}52SqxzfA4A$4}}HE
zsbWu(ZpQs@voy$T-~!J}Zl?Tkfxn%fJqoxbsi*hTe>;f(D)BcX0f+tXN&MXh|6g$@
zjgLn5UC)7E?$8+-89mCso8g@#YT@m(G2LyD0(z)01iR;5`GcI)l$7P&tL(~uyKSI<
z7!JRbWkf>?ZD(BaZX{+oUc@GEg;H!x%#8@xw{PFp7rIl&)&>i8*U32cLu9;Hjp`V)
z9{x>B=w)!pe93fVp~%tuGr39TkEtkm`S_*=?kcRY`~9TN=Tw|+-^GngjaAxBffnep
z{PFVLMg)4lW4#T=G3l3G3`qF@u4e)T{Xl9;z(P<O5a!FKoe*fk!SM#VMaF$jg^6=1
zIDfz$cJ{)f4FCvJ58~t&hDS!Gn434r<v#u22uut#|I1)FFJMM2&~if0l|qB_1!Z>!
zb;n*(Q89VGCZqn_zVI`bTbIv-rEXC{k#H4DclSQC`p1`qy1)L#M@V$^_a6_!)do9x
zdn#cW{pkK4fNB_LZiTxz_yWXHVy+7!cpIp&nU&l1ykUD*v}^SD>tX2&0^97GF9>Yn
z!kIj@Rs1MUPW!qmqjLXO4ip_Y5q0rv2`Ya(CgN_Ec<_ftsfW~{Mg1aV;RYy`v3UL>
z|HqhRfguP|<`y-e2qQtYI@KsO;a@w^9U7kQo%>DEk^6IXb#=PIFTcMB!zC`z;wOuU
zM4~v)Vp3Ah*P>5PipxRIa?Qk^t#EU!CwN#6Kk2dFpL-{0<d^zCJ}Lq}IuW5pi{hli
z(DP|Myc7fo^JIH_drfZecDK~j@81;z0!YyKAM=)b^LHr3tO4|@shtI28iktaQ{(X3
z-rgP)pc4JC8QSQ#y%3Z;TzHn~$5iL5KH$tY9zS@{eKe3?u@nVQ*UHLh=m|wRLKc}C
z*AcFsQ+%Zp0o$cbeD}yQo4-;oVS~-gZlTLObz8fM?(p$qZ@v5c=eV)lK6xhHECJA-
zJHy+%*S0d%=rhHqaG>sk8!kC>Gylc~`M~R}ITkI!$J6t~+Y*3p#LAsBCwC%jW{%`}
zI^0I%c4gzZdYMQgVEIXDCSNNme>6G9Ot^i?Z(M&l7VE@2-DntpGkErQRZO{5h3>J4
zk+Jb(At50-d>PNLyu3Vr`7dF>%Tc$$IdWA~*eGW5VrnAc)&r3hz&oeXrM(0RFB0<y
zKTt|T6x-VQlY(az-(QfwWr@;>pABAxOhmvw`5%bQ&Zfk&qGcZNmku`&J}jDy<d_In
z>H2C1l3FS0gcIPL$kzbE>_wFPkOhjI5y-a61B8>bo*u^!;>-mvFYnO+&%upbzS_db
zKOcBc6;FeR-qlUsEN4r3B>PefgUlKw8~4DT%8jpghNU~+PA>lo*#l~Px{&PShf{hY
zW1<lFMV?H=Pw8~US{>0wN<`rOYMLoH{Rg<h1N!Su`66k{?&C^h{!5VJ{3Pl1G#8#+
zv8-YQE<&_?VzYHbJ70e3X7Y^cKETo}kY<GU)5VjKLf<kLaLe)=yb;UdZ$IuOXJAh=
z_H^ym_qYtPQd$zxI*5ko0}HIt@iM5^#gRZXsgc~`Q;=1Vh=Nb@$nsn7iz&w8-qBq+
zG-a3z9)Rda!af}$iBEI9MwtOyzLD4H{rOB<`cM9EPaO6Y@Y{Pv0_mgL$20}UKJK&O
zy3uVFKet+x5wai)^k7?Zq|UC>$Zm(G^SWEr<Jc=GS9McE-dmV#nqGs*%Nm)Pu`OQ;
z@KV3kU70C{DY_v(u(!&H83fLKZ-aCYNOqfAoRR$}_0l&~@(aURybjeQ2Rr|*qJ^D^
zt6t?}N@+Im^}%~1O{&S-Po8@@BJBlbI}JZ2DPn-i=1;J+C`$(5EZN$&9N<{1L*$E>
z`9oeXwTHhj=835gKhMq(5fDMCmM`P#SX{@8r!CNGMNFy%{ZwpW*}qE5yQ4dI1F*9H
z<<lj!@GG+XbAK#oQH{C<Y~uP!Y&J^EgHwb5syjLf5}e6?&sV$nl_(GJjVHjFv?GBt
zvAuH!ox`_GQCzb85PG6TTUbfie6|z2{T{7Pr@^x2T&#L42x7Cv=?jngh^w8`d$Aip
zxsUPd1ba+2e-EcrJ--fIC;_OW?^q!M?!7VR=gmyGR<bt3><K5tEfVJH(f+Z#Bg`5&
zQZ)A@$IVv6#|KovGk3gzbX|{tHQxc!R-p9K1ze&*hzlGMO*7L0mHUy)^-OwFl**X~
zaZ~EP^?PCcNj{ANr7KM?`xu|^VE>`O^uT1X8#lh@hCaJBsYZgqfCHl7ohJv8lnEpG
z1lBbE`7V`ua_=3!wJ+mL><lJ<{qaF_h?A4k*d_W226hIVI#E!02z>P>VCqu-ADDna
z7;asX<(E7QOm%UJ;DSIzi`*eT)eL1|Ut;x-fE5adQx;Z<@2X^)lU{zJS0-u#HV=Vb
z7M8)!g?jW%tbKnK6f(FxO(}A?Syxdp=Q`Dppj}evXpWj~yN@G?UiO?yefC3Sj0|^q
zU6=Xy?!+=)Si{K7$Bucu;nNTZ<%<W2RDoTGr}(B1>(hA3#gpGcKY|Wz?+A_O8;_J(
z_=q;|TQlYC+>glzD1><f_;~MM5il^#r3s-z{Fr1FE3{?vvGx0IX5_xU^s^zL^D?#6
z^qhNqJ6X^`O;uI=@t^~Zumj~oHFhK4M?YMJsLlq4<ORDO^O#7B1k)FU?v+%U==j%J
z>zmi6Hf`O1MW^ix+SHBfQf1OU9cSf@s!LCYf5#KjQ+3zb2AtLvZq)odnF?Fu1rhm)
zOweIfqxYIIK=h42I4o8G=8ZC~i^1LQgM-7IzTBYjp}yNn>58mx<K9tXbVzK?4L7dw
zrp;EvNf<>iN#BAP7c={HYQ%*Hyjbd22V+9AqXmIqmVM)$_HO#k{-_K16>|emy*P26
zcd0W>H(%>U1Y9~MY?Cu({jMr)fhq_9Oa_wCAe2z;P$Qy$IX+0vL_|uvD5|k9b#UMD
z2R53n5S{Wed5`tFsMM3&Z3UTC?BTt;+_u&c_Ew4STcYb2m$VBteV}h<#%t!t&CJm~
zqI+3^WaNp?yac$jIjx+)0fDd518*&PJ$)SLTkbroxGXv~!N7}T&l%aGmblEp&R}kz
z->@t<@I(v)7O5dFA_#o+-QYtL;G<EkpSnA$nb&Xz>GKV#pCTz2oqvRXleBB6O}#d2
zKjXo>zlo}R?vcFWYOF9lA}FgJ<vkrMYgB>tLKozo<9*2bKE@&7q&`bhHk7%<?T25v
zqnoLg$vIMA-^Trsq>-tXy46Xyu{2oH>hU9=3FSxLf`kdH_WU*RNRAtFxRbBB5_k@F
zb8jrmej25;+3fd}Yg$~P3+-Emzs3qb%Yx&ApW#MDiA6EtxI<9d5Ek*}M{9!ZVb-69
zS6K@iB_H*tQ8JU!{INjd^l?{v7`K_c*yc7bzdHPi#!P3JHSUs+#cbQD?{K|BRgtjp
zX!z5PNSJCv?7%0EU8$Lsu5iN9TwMTf?;q=U6Rjf+tg|v`GXVnBGf=nIWXS(Eu(k)#
zipuB={aG#N3n^klGvsIC2ye&gJp~IB-`;0GU{qNVl;N-@W1+$?zh~R@S(ltn;`|dI
zbwJz0{EgE}fbeKQ&ralJ0Va@AKEDdUXd;r*tz^{U?B38|7DYRF(7Gtc2c0vk_);qV
zk2F`mPH~v|YUix6@dhr2eOs4_Wf&>J%g-t{H9Ld%R}KM4?citc14fC;{JNQG6cj<p
z(iwAAgHx8jM#&#9p+5IZa6z^pw6u`8T=eiWf7b2@m}(87BfMvd&8NlFVQN9c=$(W{
zFBa16^=N(a#sUaAlz0FQIUttu{IazJ&7r72v3xQcPA|@tczLLRm7bT$gfoUfg%H30
zmiOtv232|J{hN%y<DdgSH5o6aZD#V9)AE|W&mJf(iESL|`7ulfyl7^1tWryjQ_RZx
zJsS~Xvza(m&Q)J0fF3FvxzbrgbVM1C#^x-hw5HXo>DipaWQ(Y5dA-7F1KI55i3m!;
z_h{GTetPyZ*~9Nf!dclFQe?E4Lkpyd%1V2H_^m|CsRGZv#1DhOA0`#B4uwC}_b{Xd
zQt3?zZn=oQ+sy35k%a}&i;YvA_?((<Ni?m~v+vwfUwQ8fEMKWd4<2POs|S*0fp>4h
zhq>cnn2%6T&%IBy<}O*T(^kY2YB0HUw5G<!&fvF=!60aldX79kjg*XCnHBP0e-iAy
z7BKZfYl=THflv)UI*jnbZ+jPh7RB~mV#IwIR}%Tu<KkHdj)GIIvkctdDu8~y-O95_
z`?8SFFzFG3NIFg~^o~TVafM;>Bg_8!rg^ud@XZ;QXL@KSec#?9+93NGW$5XELg~o<
zrsdGhnA^Y!{ncv@uW+z`@Sdp04bz0m2iavN@-qWv&V+Y$fnC50MoJ_xC|cw#bxsa;
z;bqf}M8vt@R_&^U&|S<{sf_IVg}f2N3m)t_gWDTrPPmRRLJ?ta5-zXO+vCM5xY9}1
z9`@GYDt-dkJhj{>d2<TJJXNb{;iHjkv@-`lE1a02`BUpO|Ag+uglEK9VD)e{ZrJAG
z>%|kJvoR(`t}f2sDVV(PJp9@oUhU=M=P<VL+T=0p%gCzdRw5t&)2cPGAyd<}hDx8|
zN#iePHNT>T6uy79_K#WVdI`HyZB$}&T-YO8XPB5jIsVa;dn2q{tnJ=MrxDk>%Ca}!
zU*gPvI#O5fU6llhi5FmegIQgYwCA)@S#5K3*4_iTsl;?~f8W=e^^tB{S*g|b>t@X0
zjh-J$;akvl=xIz5%^L=)63ybq^>d_3o9We`A>M1r#}d>DrfO0Fi07A(z?Orf*>jUS
z?Q^e88XwSeJ~xRHlfjuzbU+Ds>{P@0<gaV4$9ob!*^LW{h|wECfBIh!!3}yTaqg2#
zTu5_-!J|iDy4czB#<gqn#3@s9csfIK%J=4oS}{d30XwXgv3-974>8tvRHyKl%DGK<
zIa({es>msJtGW(cyrB3?m8<fhOeV(5r;lR9+wohInAJNoG~)3rrgF2j#Fb*X{>EeD
zks3WXs8$kFr<H}=$5~WMru(c?t`9#A6jBWg40x+l(&XrxR*1F9-x{Bt8nc@EgB|x)
zzcv<B?gkOcyZ{KbT|QK?79GN3Ru?*;)U<hPo#6uM3&<@8H;^Mdl@J{QgTZ^?hE$bl
z7V5M@qz`9?OH9^d*_v-CD7dM)jYnQg)WmdS;wjk2W~XT1EM#t?)Yz8tu$3X<6gM-A
zQSut)SVrN`zOOr8Ps6;U_Q21>#Icq3P}F&h@U30~D(SE)xXL=KeJ3q_!7{1lsep!d
zOH05&y^ve|YG>G`qR128a$*WD0xrD0y7Il4O>zcK(?)L*8L!#R*$!6?dg=-W=<hIo
zw6718h^kp^^^G#5O$g6Qq@uzDO8SaTl%YvEr<jatN7s+gRx!x^(^{nC*kOP46z|%+
z;~An6#7erIK-LQ07Yv89#&Cb(%OtU~vTAEKy6+KrmhCovFrr^GpESNC5L-V-`UFJ&
zT$6alNLnC%Z7VV6Td^dK#M4GyB8I1d*TrDWSp>Z3X0P+$Ucw{tQj3;1ns0qqN3TG_
zF|P%P&|{ft@bK3Z6_f=y+iH!KU(kJwb_cWxwmv$<RBAR?6;a{x)gQn$6%fckoF@T(
zAGOV~R1pJ(H#+2^h@ojpoV#qQ8pTPJzSQIu%^3mY?c|w`Fd=41rEnXW7&SBpv0Q*r
z0R)21!T#OY^AIhII=}5a8D9{PIQ9CW6XBE%Di6~QYkMO*7<r?Mawx}ckwa*RxW-t;
zmRg{a`3FZ{Ej-`>afLW0e&lkC2Ex>vaL$wb$y3T{$014G%No7)1+R0irWRDp5m9H{
z_JI>4^>cor6W2|Rn%Y2`+QZcwg>zgRD%AC<XBG8dIm3X@a<_KoU7}H!Xs0)-ZCeBR
zb9(8=)_X}IG;gc;Zxs+RZS>Kv)0gl;B(MFWJUnP6b`CLdFT*IAx0}V<N>{|JlJwGE
zj|C;8d|y1uf4c%~br*23jI%vl<<ywm+)Gkm!29zTp(DwM%)cD)cZ8?_XBeN-G`47@
zYKL;LKg{2FRQP$=C#6PTjGDd}ds&F2GNXfdvM#vugFr$-fL8_Q;=8$K$Qii5e;<|N
z66RqzM-=Rw73=$=L-HqZ`kHW<Bzz)JX2`;PF9bCN8TK0q2&a^m&U%pF;!=xbcjoJu
z2U&V+5Qq(U_eU_y3KUHJPfkvnda*vdLk5pO1B9Xwvl-fl-VO&_Z%GJUVn<~nd4<Ou
z&JHkLWaxmh`BnN3+$gOP3lS*-q@oL~Bv32wRi=+Jw6#?FWc>7wb6~2N)fD?k{j`(!
zdhx=&UQ{$WW|+$8^vBulnx|^Jx`tQ#RB~1bcr6pakA@``7_X7y@X}YaoQ_RT*9l^Z
zYL_M(7AC8o?Txy~&;UH@HzWGZRj`vyyb%n;1#Q@~YSLY_fkpD`Hi(X$p*(^<7us{t
zdpnC+Sh9Q*tB0km7gIb&k4?i^^jw<d?&&NWg;e;wo#*``tYM{ohm(3>aIxf6NTbfH
zqn?$ClV5K*fIG{k=Z)Oh2hBh&^@6@aK?ozL#_L6W%|r52ZGNfe@81dH;LVYG^t%mH
z%F_O-q184ybHI<z7*JlcbGN2XgDZFS&c&>n>G$vCHH_*TLEPF8-75zEpDN|n@avb2
z%yohH4DaPM)isg!piz>g6YLG}0zN1tme(=tqT`#XeTU=RL9bES4HJY$>)~-Ro1d6n
zTic}XNVoTJdTGjojVG;Cx80EicTx0mLK@~n`a;fI3b+X#lba+MNx;Oa>b(kCLG40l
zrh^P^Of#!kVa7ET?M8_huGr)>Lpr{Bi@GA)n~?4KKoJz*ym?Sj<*ah%fDOW-yx=1i
z!tG?DZ9Na6aKtMIN5O_BlG(O@y&usChH6KfxJ>?Z7MS^;z`h6d`u?RIqC<^je{P(1
zOwNz;MOtQSUxXTihD2}s&0O#@?Tc8FSw(3VP0avm`%*-=pICs~4>B#2C6Q0rE=-)V
z_8z!<QqXoV0R%78<fjD%!t!jeVXR7V?_*L9cKh~^a`a|WYjA||^iRFANU#=yfnA9N
zGY+V+BcCmdSZeZugu{mJSL9wNkDhHSj)0|KFH$O`J!c%UX=yKQ9z_CS1YoQe(#cEo
zo(6I_Vui_;kOhPepH_-@1GNX(ziJQVe}U9YOX&~S7YDlZa>_TJRSMbUjIzx{(ok#M
zz1#ToDP?U<O=3d$9SO-v&Q*5IM<55?28kZveeDdKvhYeX{-Gscx|??0Z`v7wjh;Pw
zHp`Uw3cP$n^xLoE5{v-0&Ov|AD%MVTHN5A1@e1g4yF}BhmL5bFttVeVoe>IRE7BNq
z73kJaOf(jl{~Sp?bca28;)7l`V5Z?u1*+w}c7g2Xp&1|uR)8SbS)x1!I;cb7mnE*|
zf`<26BVi4C^A~0n)g=s^Qo%kG#_@-oy{=$+?^@AnKzK>W#qC*G=R3nlQlD_gXakFd
zkFE+Dk+z3fralP;LUVv;<*&;|07gT(PwHvCNWjHMtmgT<efMcigL&<~FRyh{TWhR$
zUtAwo!98uFHvIyrs^5xDt>!^oci~zMHV#<+{5g%ac4GW<%hT!&(Rj9{jn<#>9`y5j
ze6v~xg1j1Ma@~nsIUb-WfA&XdknEI%Tn%^r>A8{QSj#a!tfy&rI&p@U;X-j)S){@3
zu4_ul#-^VC^IZTKb=+Cln&_q%q26Ax{Wg_)=#W?c=V!W1+_IJ=gvoc)jKTucp2f=7
z8@+ZmvUZ=iemgbOi{z-3YF?4M;@}pgUR&oY@rxZb1?+wgD0_=F)|v-Yp<3obD2B`w
zp4-pz3l_aT7Nth4Drd`c;#jZ`M2ejT*&gpijJ$-a@s3Q95`+R>yuO>&y7rBWwXHPJ
zvOjlu0uusWwei?$-Q8vAM6+sQf8mQx23M5`F~i|t*BGOPvlVTCT-Pn&$Ua$9xsd3Z
z!N5U|J$=uTLRT2=oj{iU93LxNzG*piGYYq-m_m*T4T-&F?*f2D+umi}RVb5F@y<kh
z_*#2d%=m9Ks9AFw0|o3;V?%j*xiK}wxGl00pIvghhzQGl-ftw=N<@e}B-Q_tmm&#<
z9qcEk?F)z+3tf~1zz_R^jw-iWok+_2zP;`Pl8bF8&cwTFZ-HRbiLoZ;wl@bAM`=J=
zpGfRHsmsBpqk_mgK%(;MHQyXMjJZ|&t8~%k)t>WZ4@DVWa**ub8xx`>zuUfI{zE}-
zH5WlbKvg>&URZs7SCx~ONG{sLlKpiCZMq{VrG`H{)7?xBxk5}kIait)^=Fld+rC}7
z$pA`G??9L+`0+{v_@x;ni>RMhwb;azBFd^g>!}w$vwI=9dYQ;!B8TUe$DvA;fX9m2
zx^=3XxwHkZI8y(i!`xu=r{dy6Dw&KBO(LZzS(0}t2-lSAH3fR~KDj&?D=p5*%g{k)
z7P0t4Nk*fP97H8CkiE2%7!`v$3I_hO0~!>CduKX!`c5QMUToT@@TN>+pxnp5`in{=
zO{gs_bF4y}5WFst!R;ywvhWu4McwwbcBtK{mf-=PBAWX1Do>@tZxC2gF5grIb*fr$
zdEJ=V{{)5=A|0@{59+dqR6n6ikBxxEZic*JYHm#wYdfLc;8z{B0b(M9Cv=s-IGYLp
z5L$0<AXNgD9wVS^dgH>*&YqBjCzMR=8o&*=CC`(ou|F?m8ymPx9+;fTLm@r<bR?1p
z@7`m`4;M(G;~;D5-DAoNhUZ)qa|Ua_xF_o1W#+@k`?Edln2CLL{$5PUx==~!<l*?u
z-DoSNJ*l@nerJK*)|vExum0^&_#rmL)6_P8{#Z}=1p>_C4P7YRYZSjYtN7{A`SLl8
zb#i74jTt&x`Laei9wmt-^#G~5Cj%4&Pe5wmjY0fu#M<MGd9r0>wc{4P@+;jMS<YD>
zw#wAA7@PKwG(0C*LDC3H*joP!*y_^yHzr<F<K?=9-BOcp(_}x!I$R%Hf0w(yJQ*A-
zYp*|{o00}telPT?nan-K^P#7#fSWm_rA_;`t+CcK{{~GG5Mf2ZoiP`beL@0RqwAHr
zYrKQe$+*CHVJVd_0#=mTV<VKa)e^h%<5he}dpHjh?*)*tR_LRx(>6H^%~3XHOuR-R
znLKBYbYLEYwT;$S9#11+5B7Xsw2*(`1MMs$u)5VRU!c}UK2XLDdhwm@D~pBd6QTF=
zVk=?u`*qD|TXmh*NXp1Bjq9#&K}}9>#!X<4bj2A0=mU5SZMZt;7Vu&j8C+g|J|*2N
z#~{IHM8tyw#3Ozw2j?vSlS_*$EAP^WURs9IJ^ZF!phLAk177bG3E(K65YT`+W;>Ja
z4dnpTuk+u1*XV^~LL*)~Gwj8HdFF69trWW|aj@Z#lA$9k829@E#D$1kE+u@sN|{L^
z2*kp|!sOs!{PwIO82UZ~nsod^;9ENkqIZcg>lPHGigPO_hD^|V0RL{iA1bUQQUk+M
z)2+K(pq`E#tp+!zlyVI43r^${07B>1Py(+RL9`jpXp#~FSFTtgK#h|T7)sChwLhSe
zP5`2fC#v+ENdOL!mK#+Q0lviPo?tkt7YM8<g@_<_cWamdU`{`=>!<CVQK!G?*snch
z1ZoT5TICMsmBPaTu|aB?Xwv~Bfv*CYRRmuJ;#d##0Yo>gw5l}}bsH=zP%Y`D$|(yr
z-y9F8;}W}yS$q@Vn)yv8{knTQn?i8l!(R{7@PI2!ZnQOt1Ht;8eH@#(P`;?UKzk%e
zVU+C2yFyUPkcL6s+6j>+d>9OoXr9Da1mK()t3Ck@k!}+@zo)1RPH~blGO1?oUKWY0
zPREM?{=EtG>6qBM4SmbJzoe$na7o4Y<X~}%`08n8k323qtL-E7^x^Z0zT?Q7<Z`C(
z+~a?*3S9(dd0`w@@!Yuelug?g)cltjPAZ9+s3x@-w|xWor~-tB>ihTa&SQ=8<@PDB
zUw1WcjaEPqgO_I%#8+h9KuVRK$_5n#lG%4?7r%t>w~&RycxG++K%mP6#}#5({D+fX
z0?*U24_^B2{Q$`+8<my7|G*4#vH^@~*6z&w_f`c7q!HPh25V4Z19>~r&tV6`BD)-4
z2>5Pk4Km2-9!8OT#|Kpl-cq$ygP1o5`t@p-gOUxW*i~TqmpYX^;FM;9fno%?x}-dm
zysUP}-%lu04j5|9;f^C{cD{r=pLs2P!4Ih(p!3}yaIF>h7{?+up%T|$qI3-UyN65$
zYV4zZQlQQOh)uJ$ENH-S0TLfRDi}|RsiF3C{{4Bi|JBo=$AdWu26(U9&*4v9Hs&v_
z<hyag>OVR@)x7yWT5C!tq`acyReA$bHA~>39OQxSkC^+><;{*0wJk7#BlU886DzBa
zt8E-k>uy~{MCI;ig+9^KZUTK++{eTraA%%gGpsLnf|%kg#?`x;yw;W@@yqI86+urP
zs++XJ;DzCo2b&>x67#0;8$-d}S{8jRPv@C<`4#Owl3!3GoartsGOgZ=d1kPtj2GW0
z4vj^Dt_;`fytkR~D(%cQt55`DcsPyCzqg$t=e9n)N{*P?zD=qMWTm37s=A`F#K`VE
zn0U$q9HS5DuQK7uZ~?pq4@d~Od^(F4%=E{Z0AxHb-Kes?iwDVY(JL(vqxTsv-z<Pz
z`Px-ol{gj&+`)jJ9ngh5s5Jt>#_N?ECF5?ZL>^Jp@Zeg)eO+J6Z@%HgZ2#uO7GMPS
z;KC~vr6zT8Aq!E?#OHF@L{_c85cdk7%6Kbmra}B=yxDUlo{-i@#2Zk*B?!i>B2Y`j
zRrviCe-q$DWo|V;8%7`>15DCJ6Dnb#ysQNbP{*5v0%?LOXB83Z@6d0djv;?)-n+Fo
z0{G$K47~iZ=W65nK*e))MW$gKLL7g15e0<erqPt3<rmuAwK(0iF=RXT*NmiWsHrV=
z_%XzE6T7#%^#(OSl%OeZCMgg4`V9T=S<DI==dVk(3Y;BjuL02ZioZ7)LQ$sv*%9u%
z;F{Xh_~Q~a;<K=9`Pb&Udk@n4odZZPfI)@B$i&YU#on}R6jKZ+JM00g+K<OK0lXph
zI(5Y9BUFg3Dz8XNd<4u%?pbRXAXVBvKvlr2Do@$S0ewr{^B$HmubCjKsTOPi;>`|-
zH~mf?j?Ym9=)1G@pVz7)Vu?f|Gl(rOU7{<J@?BPog0l-H;z`21R}AI8nALar>6IE+
ztI$(|m`2u4t)=U<xkLW@sfRzptl)lt$yMnBozmBCmmqKHwxW&nNW{K7oITjgyyN@A
zmM^+xSYCE%iB-?!=(s`xG@pe&`L|K=%l2a6RCK(O9pSs(JoH@*uQxNF)8T+>?Lak3
zDS3Uh%xXv&v0_oOMkQeQJreeZ7Q_eKxq~x5LoY7i+7gnynVHLmaRf{bq$_a!XBBeb
zbk!{Lfv%isHE%CgQ?3$QM}s$eo+#690@C4jGWeO%8-VX%mB)aGfw7heJohUxZwLom
z6zNUSmL^WSXP)C-8lOHaFW9!*=D1ii$~=MM)CRPJ80nOCiVvZl7egyk5%dffzH@lG
z+I(G*tuw#|k6i^G(o2j)i4y!nz}4l6;5+cpBxu-aOPAg|xSS$Rc$tb{qt~UILa!mW
z=6Ouan8R;+Ert7ZjGI>N{cMX&r5B%yDJX2%+S;ZmObMt%1;KcrGRVfyXnvsvQ+60%
zC>RK?XlPg&hNV~vzY)B54|+%iisv>qpF~7O?a25c)y)rO$CLn?n>KiU6%iIbBfI5D
ztzH1Wc<Bt7UJ-+`h!XR4Yue68*!%dD$K2Yv0MQE`PdHO!jG&;Fy8#G7Is#!WAoUCQ
zmKaD(L5#i)GB^0@ZIIX*27ag`2Z$$Pukx<kiMY)NU-yv-YcfEBLO^D}Nq0mX#xdpu
zE*N?<hpLedz$+t%sOwV@?(eX?a^ysU)}>M=jq-aU@GFAAOVMXNB}s*3uX?XvIFJd}
z4vT&dg8CVNykgH;%824ZXhBk(!^MB|0r+!FU*&uV_#DLXOmrv;Li85#tNK7#P{w5^
zmT~(m!Ms6l0uv}raH;|@U@%FS=IQ+aw%v9?Q4+W92g~FZ8okdYYT^P~MlV3M0A*SX
z?j$3yw%(bp1>$4UL|D+aNxcFV{y1nHUwsy%l|R3*xLA2d;&F9^bodxA$T%56#tB@S
zSO|+E-@&*C2Fgz0R#sM`@ITg(aAlA_gTa=x8syEtw4-UMm)zQ#g9t<_)cE*U*Pmy=
zPT)OMxQV7Ops=8v13rPn;bJ#4`|>OoX@F(Kqk2likRT9)?;hdoy2%CjnhS8Vs)>wo
zFeiWlN{~uWs5)qXD+~h5g(zTZP8!84Anuxg$Nn{Kg*+DnbhJ+!yE6)&x2i?t7FmGj
zgCFdHz&=DQZOnHX5S0cEe+04NFT$`nOPb{9Y&Cx`_a4z+f4&<I0?NMdf-C$~5O5x1
zlEd!w^z*Sd5dHRUj$<ZZy;|zHJRVRZH#F(Io7|ZOTdq=&Q024*VkDcyRq!B-P<G$z
zogWu`<c+G>Z1obBedMH(e9j45Qa;Io%DHX{u&12<Q*+^#z~bjM>?aAcAl|GNK!v3q
zd1nwYGF7U_rX+c;h5WsbMOWPatrx0g3Sia`KflqXqzBAfs&QOt&&uz+ys(>T{rf^D
z%NA1jzM`@q5im&7FIRCFi%zsW1C=@rZIHBxSzVJYcfw2iY`^xn!*r4THoq)o$*B%I
zz!Ku5@xO{cjZF3d#PV~sgk2>2>g#7TRk{E>!Fn<TmrK?C`}d_HD2>NihzXyan#Gd6
z?B$WVzIEc8A8$dr8CCj~UkoC`U~fVU&Fxyo=!8F@If%70u7sW19D(*n|GT__3kIwC
z=&SAHuFUKoP4nTY7;bRByTCVwQY%z}JO-S5#4tXZxaf5QGujdEE~956Ra*1HNiSH0
zZ)Q8xD*{&YT#Z<Ccdp_%1-;*4fY?<v4u$6+wA3Go(5W&x)-K}jY^4Myas~ush2dv}
zkWN4-G3RI7AofK8aN??<$gYE5yoB@zzki|U<NU57iwH%UyU(F}ti&|^1<Ecb8lWM#
z@10JYG9Y>iAdSKAk+upBeuYZZ0N;(LXO-TW7g&XsR#+xEelKm#E)v<Nq)*xA%$Zic
z4;T&wa0wRev5TN)d9l|s`2_;uDRA|)ttM}!;y%=$Qk;#~zFmbJ4z5n@0F!?;GH!@>
z=CpgrhO62hnW5QQKsZsC)7gG|{A0D-du?5Kv{b6BMy%`VpsA`PvCICyZv!F$57g+5
ztZ5MaOO+_$El~P83oyA2eD%!Sp<as)>8abv;ld<qHWmFkHNw^P3G4$i%Nq46$ZA=L
zJ(=+IbdXM5BBE?KEJ2)}wa_qg9UMdE^xgwpBXabzhIZY|sr;(-Hs1WU@1wX-3vi7{
zK})F@0WNAV>8aKGYqnm6Go2u*-6yDoN`NkJuK+a_(sLZ5#(r@$%lh5pD;m8mvp+5)
z>WMJTnP-l=51Q8D;NW>a+8TmdR%NRzF(-b_8r2=AWLZl*KFc3rI#uTg1sWjjbMlT>
z{Nnnjyq_RJ$qFi)-o%5iG%I9;I8dJ<Iz-Ntg|RU~k+@2tclgDkT%P{oxj*o#zQ;s_
znJ^;ExPBG_GBaV)iM1Xh<5!Q@mHwcqC3D>e@kbb?<lgs1g3rfFK)VZ6-*hWe4NWZD
zKen<a%mEB00rHZHLsUT!c}*T|6)Ladz52@3N%DXx$+7r+Mt3AP$maEUiK+dQ$AhBf
z=XtN2y&gGP!vi>e?h$|(#bRt0*FmO$J-bQ@N(x|5qBk)yhaJ$g$`Ay7#kYY2M1o99
zv8I!ThDKe=dBV*1SH&Ui&?#}OVzdYQev#%9-yLg+^d~Nv9;<5_M(;Oge#FE?9$_O@
zZtbm2ENZ=xO^JB=R4>I5oM!|OmeIOG0QkHrXA{Z))83c=L)m`uXN=4cGxm@%))1va
zLX52x$r=%%1u0AR-7uCGMX6*<WnYt>>{}bjQuejQ5{B%;_uM_t=lML}*Y`j8zCQg>
z8FOFvb)W0H&hkF*bMCru`Uejl^xoMhC}RJnkt_k0?L%wp<vG};S(k(c?jpwi(z6>B
z_ua4FC@aZ({EUr{i+dHa@bk?sgjH%qf-ln4nIdgCBaq-R?ep^-$lel=?CnLiSm^0=
zPi3o2@y1szPQrx0Y7pLhpMfR19!c`2ONn8@bI^MdbXUIJo{@oNwYm7vSvmG-P~#er
zn)(iG)X}<6FHR(?utrjo<s9Yi{2K&j2ODKzcX_Jk3}|0S52Xs~?ZU}KOCav5-b1i?
ze<h^wb^yyJtNMBHQD9hnD@lL{!OhEPfKGc8w7+miyAj0Med3^>+B{OvegjV7L{VaI
z?>SK&$&bx(QV@vRoeMGGgFXW==htL00^;pxlS0xaC+*u@W<nb-p2VyQ;TA?|e)#PD
zv}kl_?@P})OV6>!o7d_!s<_;m{?Koe5!kpQv=@om^q#>~LSwH<oFdzJ)s_$G%L#=J
zrGk~(Pvd>`kH3Mp27hBW$n+oMRR)%5w1i27H!|pMk-ir+GIF`Aw43RuFZf&6!LKbL
zk6I%Els%2VRaXN2R+=h%xWkb_iJyU8aODe5+rVd7Aa5WvT1b4p`@TI=6xwcfbAcH3
z2xMh8bNV_UxF}eCyEa-ra`Fa+o<(MlW{wdE&p=m>^BFixvGMWm)xKe})uI7(yA0b%
z%0Vv+GNSWfsT=z}VqPnN&oL$0YXHr^=v_*9Q;!ox;Gtv8;wxa^MZt%va*Uxo1@NE|
zb-fl4y3p6jV(H+}{`&zOVGxj0z%Kap!6MenIfgI=T??dM1oqWmtN=J@%BYKSEp=U8
zDV-2DD<$sKU&p`wFD5Y~k{r0Q0$cw#<>m+PcI}%jaJn^MGI;aQLjvtK<b&D$pL$(9
z?Og_QJu9X1CO>DnH!C>a3#G@+K5=vHq|lp(c6e)q&@(;%^5sis=Hivp#!eP4m#$n9
zW&FsVQjj7}wb;8-alvP^q|(o`+nsIQx4mGcHe}<+pl9`T?8eB(vSNLR%!W$w@qhUM
z&w?Uf48Cw3pN(L8eqevD2#e?h?Krtte5H1g((kmCM3^gsMU8m5a@OQhD5gRqUX9Y-
z-7Q>C0g!0e{o+aUkpssL{@6+kzcQer;`(LtU8Z6#4nSI}#Tk{a)#Ep5XIFpU=~rnv
zF<&%H*F_-9)2%+<*XHq}3!@<=ef?y8Q6&GBpS)V3*vF4P4>mOwf987vmR+dufd@L+
zf8**>5`YvHb7#+a(P6jT9?2nU-*`FB<N9>ZhuF@UPrb)x&+K_x#RZfgA+|oZ6tja$
zUT)iWeC;0H8=0AE?b=B$La%a3<}Rm|V9+AYfw9t8PKXu>=>VS9x&HZ~I4*d~SAZie
z7x5mnsw98DVsD@fE7jPY5FKz&ciex1541I9a1H@6cR^1%>DPSu;N0FqyBVo*id{M(
z+Q<uEFzOOu9n>*L)U<}8GhO$xb^W+2Z~D>SHBL?3$nV>^d(e=JL&6%jUwZQB4pypl
z@bsO_>52oUlNG%Crt8j>Jv<#NZ~u$$gI!w)_K1qCb-Gkaw?|ir0$`&&5odICF1d>2
z<mS?C6ZB?`9Uq=^JvJf3wE2f(5I%E{?0ER%;aTd3vW-E;+p{bC5A^R*AfKtVn<M-C
zejhk-m*08VYfN<|`owZ&zLJdME}B1{d@$$80lz;iAuU6zq2nXJ*v1{VvqCZN`50O2
z5j?r$=hGJ%?pqmXI)=Ezb;HLCwpEv-aX9upzyNyQQn86ndh*n{!vE_?%EsoteWOTx
zxEUAy1CBhR(WR~#Wl>1xXf*`y@9Su&&p)B8Llp?#?sc2Q;^G|1Zp-{vA=S#sJlj8B
zz3^;vk9U-=XG$7|Xa!fh(L(rWNsN(J<=?%c)5pRJUc28@dvh9pxCHIy6M|!1bnRjs
zt;=ZYeaeLq0e)BAmqhuI>e1DZ*?sqB#*65NwIKc>3xg)QZ+AuFCP@+P6N+V&vsD{>
zjOwIs_Luuet`G4y9Ea0*cb5IS6uYntLh91!ziehgO{=Hacu}KId>{11eJE;s%z$)P
zRs>Jtr*Pvhz&#Nh+SIh*DBGhW-*v!?fEccqa?^ty_4w+pJVjyiWPKzP@z$eYWZ%#}
z9k0petc``-%cUhlxjDbVb<uw%0O7?=&~twRv8d;@;C31as39~N)mv5KLkeFwMD5g0
zTsQw?)x9=OFo=8%#~wJ4Q!!js_BCLxV<8XmAJ~0x?(mS-@Q{B~H)Cp*E8`lJEn2M~
zTL+8k3;?ZNN&0ZPOXl&rN|>xfah*Vjo~3`L3LITXt~vZMY#-GlR{C4O_M2N9rH?rW
z=gynma2hzzJ@E#OXr6f23%0-b{C>T_Rpfhn)g+ck^Eoz&bA!c@wgKJ65>luLQTiFH
z9>fW&ma~5Uf(!O`jm)K9#5rEP-b9#Y@w(*kVRLf4vo}?sW{aw`U&pjq+Ir)y78W;U
zk1K&p#|*gTb{FCegwXf7#E=suJZx-+aHKc8jQ5;1F|jDETvBqW4>{`@lCsZa7hAxW
zyE?0-9$5|3rL#D;W>L`r^0C9Pd)%C!Ex^P87^)|l<v{*hAA5~U)NMwuyUHUAl`f^x
zoxtv)MZ*_8;skDJ8U`1J{&x8jfGVR7INb^-C8fl~v1IAd)=I<K#!>o#FDgFm{!P*T
zu=6b58(6`5{ms4aze=_oUM{YvC!6H$7A<>nE`FmHNFxdK<kCG&*#@=r*ew})enGnq
z4ZzUx@{Z|8{eH}IugKD;zk<<0w_q#P#=j`b%<29d^^a2HPreHVIC96f%~>SwD?39P
zUY-n5&^86k!r4(NCs>~ceOwI0Q6Iwcyf8Z%?H4;Dsb_@*<M`d@gY?@*uDnJ8(-Gl&
zeEs_M?sNeP)(5uh-+$pD3$CV<t9kAPi>alhs76Fuv6$F@eu;eTpRhTGz1<H3`EAE5
zVGY9C_X~Lb{Vfgf0r4l*^*?x_A3b{XY7H-+D3B!gBIVz=($fvP$?^VZ!C6`q<O^K#
ztCr2=h}*UF$|F!nQX<v#>!Zq33))R}{&l?<=`0gsl+aMRBPuF2*Ux!F6%yjB|NU2l
zagY#;&GmBhyIgNNc3}iSsaovN_IL1`=_|fwVZrh2?Kkiv9sT?g47ua3p_l)v-TeER
zi{YGh9e*E|%`Wdbo8UX0Tbea+>05t?{VoO;g4YQ7U1{si|Eo~)AKbs)gf#rqVQt`t
z@((vRwTRm&O14RU1uZ@Er4xb_)8p^{?>6W^&q7dI8vMH2gWM2~tAs=Wbg<M-m+i8Q
zB`5-SiJiDd!7XF%HPbQ6ck2I^QxM4rbez7dNkypta<6Tn)2g~cuIdA^q1WD>gJmv4
zj7Q6O)GIb0#34}c-xo&Rf@PGD;`2WC1M)YAKPo-w7NkHe)neVZ_m2nPy<<2*)EFui
zL%g|vE{XvMQ4HFHGBGMG_gw4^viKz^rnP(N`92ls4Z4>R=Z3CF5Red@@|!)F7mk?a
zzp9P@zGozeQReu^yWuQe+Z)4AnflyQcP94fwLI@+XWu;`M$6}tTuXW!_u6aT#v^cU
zsOnto!NZ3QTV@d`=D%|w;&W;>UW&Z1!Ytyc>9P}A=+mz<uw3cs>ApEff2;ra__SuO
ziGk5A6l39SeeKS`jPjn@e_b6!GC<vlxPNul!$>aBOr6if0rB`1eHQIJipaqsH*enj
z`3B8*GdWv&Vgk*VTN_;a5;di=_{@6A%fcM(#b1;vlG!4Sltkzr;akh#MVxUs5hnx@
z6lp0bDDN|M{$Aifw~LuN6zEk<X3$!=)IBXpR#{-cBZUp15{|O`c3oj;K7d(dp@xX^
z6IX@IyM`e%rAgX;w7x>Bp8FAarYYDbZEx}j>GpR|GYgB>fG@U@nVFeCJ-0mtDjT*D
zFo#4Z&nnO!_RMsX?uxA~Qe~K!n_|fOoB!KZe&1l>dP82PI5VF@%;~I*a#puX^<v9a
zmG^&5rP0Y)Eey-A)jfFkwTApo<+IzlUKQYOWYkRiN6pQx4~8?WwM<LU_G>1K(=0hH
z1%}ecrS#HcB<_8;s(_-t<<1)ZIElR*nsBz>fwvl?Z<9!*`)$v(KCU=Letnmu7n|&a
zbIUy8FJ`5hEQaKh)U9hYKmMvKc#8knB$-@ou?wnM=B-h4A<uWMT;!q#(z^*?go2#U
zr=N<ji%#sF3w_QE#BQQ67z8AXh4ixFk+D@}yUkqHEzX2fq6R_11dpm3TO@u$8S-BK
z7|BZM2nhh{3NSHP+%5M}e<(i*$bm!@=?Rd{Pt;SMV~`PLV8nJa%EB&T7%|EF`xCOI
z;nKSbg~s2%{XDp}_&DnB=LK!I%R^OImkr(+#<+%RpwKrdEGUcJa#rD_+4i&oT>8HQ
zvXk2)d5;8qsMg;!6{CB;2h%8#+`uk-CRo?CT|_|LZ1GuGsxpnrNajVH!dTPrRF85$
z5`c!%yKkD057VyqmZV0yUp41_n0ap-7_)%^)gg!Sr7G`=Ug)xq+1k`wcF#dhkdajT
z2bU4!FTpbc_@E$Bi>~mA=`Z<+e%PrecuTJo=4|>Spa`cT-T5_Xm;6-MmHq}y7dK&b
zVRePfuQabrJ@oq;k_*f@tVDJ1Qg-`g;!2&P-&RsVfM<O;mUm9YZ3+t@#hTZ~g!!7^
zeB<x$|GK3<p5c&D7spvo(SKi9V~KhJ)f*ckIv|T0*c+OscV~6(o>o|}s;sboTEQZE
zZEquR2K5lK!eIS16>%E>eaUMqu<12{>-EUVroHQSwDhyb4EG{W5h*J)*7X@?@tf>)
zaj+jQ3m~Hcq;?!$_#iD|zx?1%kf_;tQ`52)Db+(9PA;12yJeQfM06%n>o2-vDXYpv
zos8__UPpam3M`1+-z)r|5H#QhN`Xf;{m?|rPQI&eSo28{A!(Lz6l|4|SB?aE_Yge=
zYg{bXFU~;OLtuY@>hGGSF63nsk0tw0n~@HSO<!e_bYW<ewwn^shLWhzmM}Q1H#YCs
z&wpEHYn$B5h-$zzA{9|5{=V7>{BgZvv3~Vhv>Q5jE26Mb1n=ObXC(SZUO7ShloqI|
zn*K4Z8mkvGx}}Rc2!(kdwZF>vjA0RsMBaMf-(c;Q{ZyjfAN!C`W+;Nfs)cps406o6
zFe9oTyQ=E>6AEbpsxALilKr6bt8oI_%G#^0d0%ir9`89nn$CQ=<R-}s%z=IJkz&5+
z9pUYFF+Ptg?O`sLKG9$Dmxl?xa~{UM+9YP>3RWjku-C7Tj!u2ssM`N&!l~i9_+6+)
zX#Aub!wM`k42zAO$U9)jJ;Y4>oR!_Uuv}MAIcu=tXt^Y+laaEo*hVOmVz<`3U>!{0
z<>&vX(lm#KAe^*K*bh02?fs%gn?K#OyM1kJ>Ra2|uF$xiT1w9Y(8rhDrK~<@c?VM5
zBsVB9Iy)2igEP_BrLLTdQ(?Afv$|+@pdv{JCT#2P{Mvo`te|XKTon|Q*->MzMkG5W
z0Bb+d|JT6#@Rq)PBkICS+t3XA3L7z<Q}7LGsRmdKzVl674;c0O`YSWJQ7@3v<WD|@
zN5I2*7PLUa=k09$w*0V%`CGc3Kh8D}(#3BqOni+~l)L=Ex=(oaqEzA=3w|E=-P0W%
z2Fv8=HJ0N&SlT}0gEG`~i_Ac~`4w7;8<#cbY4=vqFIu*bO<VNp_UN^vs&QY{X_vhD
zqM!d*+-g5P+*ax`@BTVqvjQpd>O6BS?T@vP&LAmnr|vN#F9FD4j9S+!vha$^pNJc@
zcZeNP*#lGwe!|!3wz)|R@GWdw{k=x$*|q{)s&>z1>#l#6<tH?n;@&B}4w|EdG+deN
zl6Rnkd2I4}wl1_`c7<lhHy9Zi(ezEswa_Dbw<&lOR);LXf<OCH;QGzU5aEqgO!j{c
z;E%h#bHM3otuAb-W=N=Se;S+P`f_`;dJ6Dfb?t7G<HPhj*F4+lvFqHvaEBiaAwbdZ
z3U7S`y7VaM(yws|3_|V7WNWAc)A#_Wp!C#nFTHEsi?cW$D9VJGek-ME_&^@lnE%5k
zRAS>J?$b~Fo*MJ-7FH5_wEw%b>P}FuoZrC3_~-EKAV*_+UPtscMpS)Q>Nzc7!|(_7
z2hw<vm!f55KQ%c6D5xsZzgBKfhdN)lEUbiiCK4weGWSBkUwfM~>wr-~BfIDu090<B
zwdDnE1R3#Xe<dIFAa&&RUQ}DjBv!iMU9cmUXis!r-sI8UOORHm50NleYP)U>1v4Ce
zAn~h+#6@SbbpnQPo0m>%*RGr#rvJT}#i7Q`W@K9*l4YI%{nBf7;hZUA=anEF7ea9E
zV!G2w^0!3zMfW6_dg4xd4&Di-K*1CMR*_UIb}9a4<teBiDqa~S^JlDWG%X!>xp0|E
zd!jx>KO(#sH%#x(PhFYbe_6{qcuL1h&+~R6tYHu2w5=7n34r-qq2r(wbO!_5{=B>8
z@0UY=JYxyRuGePp!U`I9RH{rtLDNqVk24~kZkG!ii^$jtl!l?SIoE33>c)%x?tn*6
zvH>6}BpwF$kY>H=0h9@UIO*#nRCZwQ?Zck}t)*8Eby5?wFC1Zo&T36p&(W`QzZ(Pc
zuV`S+-35F8fDtf~;m$q%i~b*0XaZ{{4K}2$uU(Vm?fJqjn$gy`)tzlJ2(6Fyn3&sD
zEP;n%4;XaH;1*yuL^?I7v^I4QLuT$2FVj$_EN>#KZ(6_%4G2k_wZm#snK+!0uGCfl
z$G2{|&Dv;cGQ+2`lF^NrIohp&h+@4cy%Vv?3LDgs#mP=bYiohVDML|sX%dp)DA<`^
zfuTs^wY~IYat`UV(;o-&(**5i+x?`Vb#hIUfiFKHW5De9ruDmgD2x??`iWqdn&uS&
zWOa96H8zHU*$9p8V=*jbVDqpbY@SG{<|_!J*K8c5z?9*+*-y_^BZHYCgArh`Lrb3=
zek1d|V_Nxn-oF_nqS9=}%nOnLh_6Kc@^PXRoR^}$_&z~=Ol?KvL78)--jyBoV)-R{
zN9u{l(E)ILP;k`g0tle~!1Z$0bt-A8V{C54r}*<cvWk{!GXWn~SX5SS*9?c5J!bl|
z4dsgD-AP0y7+a?AiJ2fiA!uixtvT44=wW8IirEN@*3qcbLsbV>^7C*wnK_ztDfXp~
z^86B~&g9IV8_M7fEUs+&Ffeg)aTVh0RGL2)q5F12<?qW2vB@i8qB?%izu<{<0j34^
zA;y5X;v)BEL0=3)*C!-Sf@H}wlp4m_4Z07kb-Q?47f%y*&sBtrejZp{zaeKYI9jLc
zGvi-91Do?3oGBo;O(R?D)O0n<XwdJe*0bd`X;qp&;4)zW=k}V4AialeX!beTh@RX|
z=f=<wUwSrym%ns8zD4bTW9qkQvv2uQQspkCUzyFLYQa@Qq5U@$9Or0N%5*iMp7r6Q
zq4FfI1D0-3K-4Rhx<Ve>MxsU_cs#y3Q2zAgPRM5Mg`m?iUQZC+cc8s^oaO9H8eSk&
zx~|&AeV@l_Q(so$FV2Mc0r?ZJH<?Z|ZD8$)&*H0}=m_WlmK;ze(E5WtWw6|}<+QGD
z1u&IL0Du~;%QQ?cGrAaOkz^*+&I2p)Sw@6Im8eFBVrxJLd#tjIZv&@n=FRQax8KZ+
zRU`~=lAs=|KIBxz$?PM~rAOgaV8E>b?_Tn}gLWzwu&w)D&<6mL+QE-U_QHwWn=EbJ
z>Nw3QSR{ZDbMg*ozWz~yz(FjY&0)8aaBK(x@52Y3E(GjR=es!wSo91ired~2K)uAI
z@V=11)2qll=i&>QMY!`!|0COXSYRuhr%~t@XhyJ>^NNL>KtTlJ9>PVtf$rOf|C7)h
zs_wCBwL#K;JU=C+2HpSAb+i=)y8+Xdf_s>2c|jl$%<_khgGtLSDkp*?@^dLJNT2Os
zSP{UTT|6_A+6Zq4@~M2qaM^#GUSR&B=n!L)qK%GL$Q@1J`STBH_oRCbQG(YDwqR|l
z_G9F=6J>pB_+u0`$irvA7CA1`8UTrz6%<0x(HMVs-y-QgFy^Wc_SU~dKBLe#l<djR
zK9xKv4apzdwgi__$HA;DGo*ayUmPi-^BJIn^jHrqFa4}a-?yzfTDS1rLRD>4^_#hE
z2xgav^b~?RWn3B4lx4Vs)Nf`K97Kp)8sI46ja~Ik(SWW%jxMoPTm3A8?GCiGCQ8#y
zF@w13uX&zYDgjh4HYuqsZz6e3J<AM(%esF_5ppY>uuV#egqKLf%j7I%MPhsni`?@e
zd02bR-d;RDT)>Q-At@$kJ;xE+*SHrJ7FHr--k(x70+#bmsQ=%+<F+waVQ1K7QT~jw
zBf!ZyigO!6rSC@%jj-nfEtAcSgWjJv`{r)?bAJ&r@N{m)6nIxeWm8j=q>f&)^qXib
z18)C?>V(s=8AowN$NuzCA(w*I8!8zASIG`)iYdmGlQQ9lkmWNsOY-@OUU(dq5F6Wb
z5Tx;Dbl#y0#n4fd)BDhk9knXN<^ioyL+Jy|^+WW$kW>wV>D^N8-2>)P_@yC|M%KHi
zA5(#r!5EN|g;)VU1%~$9!of5;za8-VWFbx{I13n#rr#<){e`@VpcVfR^MFhUv{S5t
zO!tMX=uXb8;wuhy3_!Hz>&qo`08P06JbMn1V=xn1A;00lKD+hRhca@#GURt#ynCUv
zey|Yofuv-F82g7Qx&zhE0Q9HHyxjZW6X<!Aruyw3xgi8-_1A{yWlShFsixaq(>Ysn
z4U~N*d6xu&3EEOhJ*7@hp?y&Ayi9u62ICS~<RO&b{hu@SFJMuxk{3~EmAWKlrSxU%
z@@|VK?;dhe>&SO&q10|z0e7g^a+0sWh8?x;+5VB2Rg=6=A_c(j<cW-3TLdHY`z!x4
zV34u`h9fk{=;9<ZK?W^|5hc#>;p4|jNXVV=in5tDv_)qD7Lx%aQKWTOB;%?f=VIhO
z<ZF-*?wRT;2Y`>Zb<_FR_tSTwTZIdRR$;*J*nKOK3qhW(;cJxM{V`YFAP4oL@XDD{
z<b!^>m|bK!2<nTTab&`~PlOKuA;6DIivU^Awf#GSfGg9^1P6~Dg!orQOc3cb3?N(g
zs@D!JGZ&6RCss5}Dt?mTrOP!1AIwHi4&qmT*iq{XRNw;;@oA73jGa--JiZrkmypEa
ze^|kXQLW%2$PDj{K~Os}DNtE)w>Zp!gWBE^mTE=|H{aFR^bTCQ0thghlCJ@^9G{^M
zcVRXz;eG()BADbfe!@+`ak$#QH<hQnPmQl0Y1#1(GUhQ4ZGb^WvT8(a2l{%}^EJ8T
z(Uh_D?cm;P`O=A1v8Cna_V&|?n{y|n<7v3bZX3K}yTe%eDzY8C*ArAb?9#HQ)~+UR
zq(1Jky2F=lnlPQneZjQ}0|Bvk=v_(SC}6^GZjQp+R2;j@^!Q}7gKFt%1nTJ-hgpcE
zWDFU)xFjWk>y{CrD=F|Ypp@>5lIe3)-+43QmXy4L0?+dDa#+hfe%$=8-rnAh!472_
z6ndvX@JUk84ze1WLLbVmc7&6|6o=bGF0>Pusg9~Gp%T?DGs4$SvB1L_U^n4D<}^?=
zlzN0JONPoCWk#1S&|XZ<rGqfd3h`nxYlbC+sQy?c7}Si8LSKAVw<eukYm_`<b;@`;
z>m*3ruz2wyu$J+HvGiyR{*C0zLRW{L!K!0r-&L=8wQ`6+jcC>$g~A}9i_y59W+h7B
z1mBeP_&x#B$SG8*N7($7-nh9!C8^6RWBKLbF#3>&;(VD4JpL{}NsC_5GNY<5*w%R(
zZZYm`wk7IXPfix%zigI)iaWuj<JKDDphI*LAXyy*$=NLOh_)Bh_Es`#EU}8-;k~1-
zG*)k#p5M~4JLz?m>&2D_we6j{h6k*JskI5i>=A~hf~20o!5KjvXy<Zg1)5c5I|N~H
za<-vQvCsero?HFlVl*iml^c!~SK58#e)y4#UhTbPu3X*z%$9DSSfWy_PjgXWb?nBx
z)|Uo5Ssfsp9kz|6>>!t-)X;~?JILSY%oy1y^zT1rGWfQe!37!4toysZ$*h<oA2Xg7
zYXx~!f;>EInD9hrdvad`dbBQ8dnWV;V+voE*%?<nA>o2~&Dv{H+(vOYwm#(JmtH5B
z-36FkAX8)z$rn`|3Lh_jx1td`rD#-4fgez!r-dxETx&|CRlVBZpk;QU(EHi(g6Flq
z(xEYuqI$^8O|M;D>~ijVCV|X)QHJ-|UD?*n4!FDOkoPlL6#5YtUQqG^?J**0M3CH3
z53|zb__e`}?qD_+2LW1z37UU9OO{#DW@H;Xj4!=<Cx5o&WwBe5FezD|MnLUFsH`M3
z2a)6x1#)k~*<vP%89gZu_~B~&!I{M)t(&P06m~+REfpG@Id=E7u^(oINs1FS%7kMc
zvk>*7YQ3~yc5kw_FqL(`TK8_=()4n6@JndU<!Nadm}!+~&vraPoYDKn!1lQz#I94#
zN*w0m8jhXkF7sWPz1nlY^0le|v17+9ZmCwnt~Awwg2dfbVOV@wHHm^5f3<#SE84>@
zZK(VL4hR0W)2GdIaiqRyar<<7I0-3o+J)^;tI-`w=W%RmBdxoab<16A2!z*uYd1m@
z`hIE;*kSQR>RD2Rl_(x`ORWw_Bzf%UC#Nr+%-u%F55wg~{ck!4&L}RsJZq?`s^Ue~
zuJ20l7ArAIn-meHZKjCEd=Qh?M>bKx@w18A@H7@jU@g98#dxG~YPqW1Rf}kGN47)P
zLm)t+&DcQ4p4A^0XfLjnMb_y{VOn=xI$zfNFc|u*ty<vtDc9{R<mNTyHjQY^`7@*l
z=VM3a1WCzuWNqY2&$BaJJ29}Yej)2A)ArdQ3AUBEPDDB3UaN!X&$&4qJw1Ik4V+wW
z_bwq6`W@Ue#)R#dAj#?w*|naM*ng36y!+d@Bjf&9`6neD52d0!G6Z!ZG`;jZsump;
zU=7COum`sdW1YRc<Oga310#Nf(sz2Bv(cbt7%?n;`2{SjMC!5}Gym-OD95+5OM`nP
zqI7vba=00D*|J9PWqCDjH>Z&VW-hgMUDD5v{PlG_y4lj`huakoa_qFYj@QyuH*7YY
zr{TT42QtX8)J!huu#c*$nx8mO99RsCgv6sfR-&^Z+4F>`Mn+V1)uhBe??4KDL8EmQ
zC+cK!3zb;^z{_-<GoqY7kUvdPVexr%eF$KfOf1hAp_t%+teh_DD4K#f8dIzaIxiG0
z*AUWURrIM$@1RU&1W93m7)o!ym7agw?bHkOA;vv?e7TOkPwGR4>K}X<hyz|JU<Yfg
zyhIw*F@|f5I3G9Z?*VjejJS1<g9`*Y11~DXpQAwjnV5ZZo8aL10eu65VYwmZ5VC^?
z^#ru`lVn-L957Hl9TTR<$*oi(-^~XFjB&7t)^MyEnq*~A=yeu2vW5;qgZqI^J3H1}
z4cGa1_uFA5MTi=vr!0{jA4>ujAI)=PBPmci6@CgT(V%|?wX#9$8lcoD+6TQ^z!1D+
z#S>zTaaC^*fNSQI*R%j5ee2e^hgl>-`}}9`nze#RhfxLN?AjHt{MP$E>|mnQ(xCt(
z@M;{mXTG?$>->)IUrsaZArP2ZM4u)k<S5hTdADDB!gPyN0C%y`esK}zvPuM7x2W?|
z=^SQJ_$7hRSSF-|Coam+-jPKccttsW+U<EhB0Ll%aFh-tUtFvSlHZ3(NlaTR#o#{6
z`VEJW*7u~;f>~GZj&w1E%0TfkThLLU)Fd<WucOeIYb;udr>ARJ2{{g{)%4`WqaY!@
z>lc;Iki49e?)<>wO$|T;q*Rx{!F?$j<q_9mxYZ(ia<%UPj0|l?vMLH389?`I{?-X1
zorJ01&w6$Mjd2r4zB&?0f3p5d61u}(g+hPKc+m6+Si*S008vu11}w@=<4=20=n6cZ
z;HG~i<Tc6am{P<?3VsG9UE^o~bG~#A#8C)iUeR{2LStei@QSAt6-$RaKk9b+G7P&3
z6E&tYdcu72YU}FiURG8rHcIhabSVvW&HNp_gB%Q-k&Z*%1hXg<<&pexo%_Zew_Ug)
zWt#yfr!$Y42WJ`U?R+_VcHz#;6b<(xjX1j7T%BBr%Cs9B8~ma4EiEmZx@e)jq+|t{
ztGfskBS>N0SVdE)BNmf_@)Orzl&79^ko>}iC!GJ7yX4t4DS7aS&#JG!SNr-kIc3x)
z?9cVg;%HVeEg~P8x?dwA@n^T;<XUir&x5>c6#DVA3-WEA_u_ND_+pKOVY6kJKB?ph
zr$RSCp9mpVGiEk8K^Ps#jeEbczq^Bc5|;SE4Kr>UlnI*`7f*sLr>Gw*A)%V_WM0~M
zH~K0ku;k8Hz4kxzh1O0A(T9mA%!Ek9rvTJEEq;W-%bO%egI#lW4BdRr56+89agt9;
zE@JjFZNrwpbi~04EV?y;Bj?dcFoBh|_-GaqxR0nL&6dWRfmk~oSz1VukUL-^sU}+6
zu*lT?I=eeUxyBuQ@`zE!E6plr1?t<R6YiEy+;Sk8VsymtilV8*O+xFjZT9zE2c*zx
zcrEMtp`oFTk0d|NQ@@oOuk(x_#`{Nh9!gRPRHh@l<H5Dk0(nPY{v?h<pGDb+D|vKu
zy0e{vHVV)L(IeqQpEuZ2!<&dLO^P$0Zb9wcyEl8UK`h?0R9pn{NCsXVtPk0HCdqHW
zTLtk5RbjXgO<7@7D>jA|F{|-rvP>B%APc?D5BBN#BB59of`%P_RCNy-LVnvMZY&Lo
z3$*TjxdjHq=QEXnhmSc#yLge^^bq_vHdq8B$t{`_PH#OA?Tc8o7Rhh~&w*IZmCGIv
zsue~}TdSQvV(rcy%$=u6J?SN#^h%m=+(AGunAC>cR;_8t+K^}qCakAx<uGVGxS3C0
zbpcz3g;gH%g^dprS=+9N>B&noC=Ri}vDx5VS}AUWlz3*~#u}>5!M4<d=elhyD~hVZ
z>L4D1PyEr-k)H41`u47_i7V1zpBy+E+qO@G(NaCq*$kZhvalfhD39HcS`g_O>I<^S
zp`1pgc|y();3{W6mpSp%L`Np>d#%y}>ra&Ve=ME*TMH1W;MP&=>OIkzB0=X~<Q}J*
zA}`)1^_{zAMf9)vb~!tpLjU8fSM?P#TpXbLe%)5^dmx&5(vfkVhf7DtpqdptB17;t
z(?yd6NXZ;z*9OYLV-Y*72b}jf&fMACUSVakPvpqmGjtto9f$))&3tUz$7YISgS5A`
z+CbdFyGlL%{pS9W(Tuph>>3spqI4BFfIfx};5x~`U16m2sTjU6`hw2gIPE}W5$-5^
zq}l9$9dInnE>TLEK9S@1p!1Oi3KBld4pyq4*M3$?B`R;TJU7LF7s&^+bMJZ$TU>$8
zAl6xd(4o-4C!l@&WS#0YndasfsTar?wz24~BPDxpvJ-l|r!uFW-jxl-f~IJI7v!n<
zt2TU0p%PIsUMYdRJcL)zV2U9y#hpgk!X$VbJIbE+;EYUDv+Fwlq``UyKl(c-L>b@(
zp182O=R>aJrU>?082vqd6#^WFSyw%S=*3WyCg{Igw^Fmg9F*liBr@`9Zb<=^ivb&H
z#vSj1FS|oxz%es3pZyY`HClIhXj&G&DUY}?f7CayL%}H4Kc8IqimDHJdouUsAux_3
zz=M4lN%F+v#m<34e8YV)5_Yf&K7ucOsgpC0diH=w`yG<s&ar4HIKN`GQ*DG5b#P^T
zIJnH}()nK_5PCiKdi)s9>c~}?UbeTlEZD?bY<R~U9laepI15i|s2UO2O<4KNz?#0?
zQ6YL8EHAqu1D`;9C}^46hj@i)P*Pww%yP00S{Q?^F+P~@1VK@|fox<b=@<;^b2T*`
zp2QT3!Req;4O20pJ>*-8QV`k+3ba3jXUe|8D@h&$CExT=BOI3Rr3Kt-u&K}=n<9im
z?E0YHFx=q-8jdgoSuuKa{zOtxtu0vUr*-q$j_~hbOdqKfld=~0qm7SCXJBj`RL%ke
zhGoIW0g=aJPA%R@cq3=_zSaxtk(OG?0$?AVzpAQh%Eo9jrmXRg)`2Rc_B+*BtXElT
zf$c4a>A1c%I{?zi6OWSP8CRO?ae0NG&?ovcg9SW09NYPuixA5Q#!L*@9R(8W8bPGT
zDEk*+G%l|(ttb95sya6bbRF=$MN|KaqI&m0U`b|^;xwp8hLp(T5Z2Mj<!ECEs{MT<
zrUNm!{Y~FABe5xg^ia~n@!1(8BLR%8qL*QsL8P@mBkKj^E;=idm#9|NXLFvAo}ea&
zLH$2j`m_&)E&<I`Qt9;wp7s}Jz{fXmV{p(s+qb{+d!5pU&t}yi=ifZ7gAMWv!UUK3
zo*~XrRVR)kmZ<CCGWKPqe*#co<a3*}qwHP(8M_9TUAVB_E35K+LuFPre23jXTP|1p
z5>|?JH#06?7#tilj^#OVQ8TJtJr<O$-%e&6Zt(iEtlq3bG$sco0uIZWAQFLG*jYNW
zhzCP!1-ha1QTS-9S7~VCL8bD<aI<~i7*hDsED@Df?&^}6lnA~Ps=LhH+K&Jh6Ji?7
z8NxB2BKArTa9Er#!ikQ==EBN2FcuL+HJtJHjyQ$6Xs`9@4E2<dKtq6sOUt2nhZEJ0
z?ijE;s1p$-{eAn>r%zKD%5cEObzbrh*jjoIVGXiwOiGfE{}q=;>UMdDcbmn6PxksI
zu~lsZMSg^Y26Ro!4k&aMC*+qcloR*1$C98hl$u<CE&t$r6^8`0AL1XG<sJniQY6C`
z8M=e~3w8{s%_InU(O3V3ysZzz%<~WfE~CWF#bb6FF?7#PK0k*-a{~GxS)Zp50iVd9
zShUQDyK3SJDiw*5;=8|89^e#1hCY8@B_HhGF|ZH5va*7x8EgzE*oVIOiyU_{ia@pM
z)uq_y-2P+t8f}UDV|R1uy45u{i^h*abaoD8X6ya?{tohU^dn|AkLHa-vv*dmxs`@K
zPt$ZtJf{G6Sd<%o$2)i5y57eNe+U%Tew@}lkdt&FXuB&`*?UP=JIPDZ*7Cf~ymvmf
zzv+`O0kfRt@>(-dQ8uq`)#D_20C}vpR~>xg|Cq#=xu_%|QnCq5Isf4iN0b!i19UfJ
zCzNSgFdC*c9fY4iStQ6_kF-Cg6A_)5l$1x9EOdGBi2>*M{6`g7C1lqZziQS@-b>Cz
zqP#PO=}!vM_|keNPYw@Xas~lSc+at-kf5!yveNejh~cFMUGd2vcxQkb#Y`ggDMWsf
z&YkeNb3uU#`vG8>Pa_t1Ogkrp3-g*g-<%Dxi$jmVF6OHJKXy@+D<bk?53%49XW3OS
z>~GE!TfgD86ty9FM4>;L^*k~G6>qKfJK@pdnwAP9?mi_S(dee2rwgJ@@MVukea>8h
z=Wjd39ZBRB)p&W>@{YcjbYx*MJA{glI)+ciCqe=7t<`c_5Ujv=Uj{%ASnh>{gp5+D
z&a&4$&Fym6o7IDA_rSv(HOhTZD~9Kl$iGlSSYBE2mXv}b5e+Z=iHMxp66$DR+N0Sk
zdCPR><tgv4+JZcju()?w_l7<owImwO)O3|idk7efJpr8vr1$V>u4F!k*hePV8M5uK
zLu)Ui0Yjr;9+Tg##j%Sf++DFxSj&q#8t&d(V)NqQ_si@2rSGdhPWQ1!NDC0)eb>34
zER^CfXTZ{N4HYhmM}6OV|M)=oiF*Y1PzZ1^X9A$i-Z-|0BdG2V$`z=(*Sd%m^^~D;
zG`fI%V`X;mOyDFp>It2oQkm<IHVKV414Z94>;iD@YGv;ydCeG&y`1<m=ps)27`WxS
zy0tOKck^XXjVQKA5OziC{bv0H(zOg_?}e|M9M5l0Co{_)fKXz-CbvH1yY}^<r(m4?
zcDCeKutW&q0@l7g9<2+ZuMcs|a!f{lhe-Z|twU=fV`F((!LQ}tYrh2U2Qre+qb7?&
zV*{Cc+fwrqJHVEt8fyeT!h`#{$J<H(?2HjCM5b_Vcr7RS#fulCHQVrB=9ry|uI52X
zvY8N48tCrJHUbpTe$>s^fM&EVCE+Z%j^LkJ(>C5ir?E#w_9Crx-AB`$s@Iym%M8cN
zzWBoTkibWhs5DWw8n$Ig@}efyA3*>SqDCysmn<xWbl!9xqGVG#Y~a?fr*zn~rb~$3
z7t77CA!Bf3U5RChj((z&XdiYs#is5TMPDYsEsy$2c?#pV%UbN2YPQy=r*6HGb5oSq
z+NI7x_z-mUVK*4ztbI?IKN10Sq3M<YM^X<qu0S*wzO0<|bFpc1my60&IF6tBg8$OP
zY@y+V;!0V-LHCu@IDu@hsm>dD!YpP_Io6_YvEl{UjhPee&k;R#jJt&m2X6bX!m<@s
zTvXo^>>%I_@%D(IK+T*g616^zoOeapD~|DRueMBOG=Hr6JcsC+beOZQ#kA;40X**x
zmuF4LIY3}YlECJknnJuOS42Oh*?Z{Vg@M6~7R@WiVE#T?B?XRb;3C0m{|+g6QLXy5
zl}><R$bogr8Q>A3o+Wzh8ea^9_r)7W{Q(=`U@d{i>FV;bZSN@toQ-ZHb4)1d9y|yk
z+wsFR{Ois%%tq9^55^(o>V&ols=x)Gez<Y`>(@&hVvV1$$ng`@ieQ>3qV?Y1+RVG=
z!<Pe%-QV%9RKNzXXjnN0{)tac#X+_VfVT8ZsuoN1$rz?FmUOv<#Kc^+&?8PmyMSw%
z{gjul#JUOs%~%F>n6VvCX`{Q$qu%yxu+A{r5%M}1k)>{(d%U27BIF0_IuN_P$-_I^
zgmU?+N}=Bxs_c2UuMd{I=$y$5!S+`$<8M<uyfi3H1`RJ~ud-5bzJHRMcBX`qoKVy0
zQj2mP<05#}yl3D**8GpBfrRRfS;cM6SWYy?NC;9PX+{dCl9|YfF1u9*`uVn&#@xJI
zJ^GrH_aD7we$k8iZFGw|Vi#CeS#vRD{=TDBwWJ4O>@KfMpFu)>SLZ8k0_KxX)>g~K
z&GqSgL=7<ITUJd?x#CrC3@ogrKMn8!-i<3Kpc7qielt=H)=wt0kQ3)s1_R4neOKR)
zwo&uPJNE;uHg6Z=G)veuJk-lKdvCO^t&DcEC*-*3qHH*eVw=lsAR6m(u$zEbX4$>k
z4NkYGcklu3G;7eM9t+AteqjXh-RR!dhlK4dd&Xi1+PK}>g(;GhiDD6Myz9omw&m|9
z_j~UBk%strwfj<1;!3OipN5>he0PC6C-u<tegXI?7@X)6iPL$PmORH+S8S#cga3BY
zK@VL4Zh)IbK*-*gIV2d#<Hu*}W5w}k?13}!n7?9sNwo6bb{nu3tLK|G9WAU5>v+wN
zHBHHc!zRo^AnY8I-W9NI0Cd=8#x<)Y8<34-rI$CIE=K9O!etq5D9)}KPQ)2fWy^Y%
zmm)nnROOqk=+VUkvyzDSs({?cLx@XFM1So>p}+qX#($6%TDNeDMCLiXrm*zJ0kKeq
zAJNlbfQ`TaXFmLN0L91Hg~ZAVNhwdlstKZvO<d!GcTxvitpTIiGQ;SI=xM_QVzb6R
ztN+V_DzJ+oK!KY)Hy7c?Po1X3JvQN0PzU1mlA1D+v1ZUfS>4rW!5Q#-{)DIbgq<>v
z1P!Wzs3FaW!HZcYBVW|}ZDd~^B7(pY-_y?(&`Kg9ecc(AO&PzXZWG6D)1P-f9Yh`l
z)Ut>J7=E4oFQs(gWvdQv9z1bk;vTJAIT7A)VQz-WR}pZYqiZ8MQMDZCM;LfU6S1hr
zZ4xhykck)PoYf8bS8(;3qh}Xez%hao)0)jX%)_er;r=Kgc$OGt#`T%u66w*E;XwW|
z7X|AwNu>$TyWB@<Q2ij;ehVe-AQBz;6|2)QhdG>QSSGR_bWLBH?xDX!yvVaFJUqN%
zYin!DdFCF^A}s(@(5wM@`KZ^z*SC=k$l_^!=79L=Z_NluqXNq1*y`Z?y?Dd{nV#@H
za}s4p0h9vL^R}Rp*a$I3=l#p|h@senfe`$K{c%(nGpj(KwZWtk&+KT}+kU?mLBUnE
z3u{f)MigMVYX|I45*;hcxJ8u$xBT2aAN*<-SCT)|{0lshRoMH~g7Bvpyd4RK-9DT&
zMxCi^qXxD(g#}hA@mV@02=Rhl^rdgO(99@0d+*2bpUrD5oG2!&Ml4?O_`Lkw=O{3V
z@(V<d*OF*6)`+8ty6@M<ZXXDPmufIWT47a8N9%;tZ+NT9g~97}w$D<ztt7#634rBN
z?^b|FRBdEA7TY+U@9Bse<{Jv&7fl!%;v)<UauL||MG|ApiNO@p&kYoyQ>l7Mm%d(l
zEErW^1XRR4R4AMmsD1^zC;yw;fNMbfcXooRnYeK$4x&FehcV~*{DnwXtO!gX&%15w
zF;H;hYn|}xvj#VzXC!`m<7#;3{jtgK!r-LU!>ci>%<`uLb?O~N0Lp~ZHvur?sbo(w
zyXdYE^%CEwjK}V&gkmQhK5{k>rNiMKo7jV-2QlX@Y9B3jx4uR|X%H&Jn}7b}2KkwZ
zS3h^2{_Ds8e4e)-(l!-OJpc3aUl)+~!wOnF8cg<ObDnkm6K$cd;;EjlOvW82kWqWs
zovoWrv#78^i-YE?e-cLj+>4uT&&?<nbHjf=`17CSD{!n^yE{GqJt;gfN*@v`|NHXa
zYxO^A{CC6tZ*TnXq<~iXpWgVlYW|-z{=b#R$Tp4cz5H9H+oRb#;Lk}-U5z|7tKk0w
Dg`>xZ

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/packages/gluon/normalization/imgs/NTC_LN.png b/docs/python_docs/python/tutorials/packages/gluon/normalization/imgs/NTC_LN.png
new file mode 100644
index 0000000000000000000000000000000000000000..c37f7af3da37d5347d0a552e87ef4e16dd881e0d
GIT binary patch
literal 38531
zcmeFZWn7fq_67_CI0Fa_l8TflqS7fnfQm>83L-7tDBTPps30iP0)uoT-Kn7DNOy?z
z5Hc_{ynB59C!TZOkMHmObUv8Oz4uyc@3pRL#XYYy)D$VmE|C!s5Kt)HzoSV&Kuk?Q
z011Vi1OGEZi>3m95ISor$`cgzUS0wJAa%O0>r6nvZ~^~|&@#0jJc1xlx+C|<gAg<2
z8Fh8cWM)riXvsx=Z?ddx?-><FDS$jkJ}l$@YaNZ!0(ei<{iF+wALuIWbU&Gq1W`-C
zo?NhwK5n~WrjTLXc9kxcOEZH`Siwc(ld&edEolHlN%J!Xa*RM(nRB1Z-p-D}!d};g
zSah<%<K)LN9o~FR7bhmP9_uD-`Wm_|bQzg93cn;JB!(jh{`T89c66;Lc2TTyV%@P*
zfUk@#fB*vf_umjfB%E|?<xdMEMMnejIR5L$lt{O?p7A7qTlCK!{J}6g;=e70UpY!S
z0M5=pBgFon3lXqGCjMh<@Xuct*zSD$`bFix)<J|di~iRVkQ!nb^$P(3n!l?6)`4Fs
zq4;;L{_KR{C6o`=SLqJ^uYG~&ME|wr|3qi|pVj|&VgBc)|BEpH3y%MTr2j<U|DkEX
z|3@5#)4As>p79{=K&Bk_9PFPK4o~@Gss5e2F>)iq_(wx^s1a=P$jr=Z*abfSF)qY@
zGgJm{LXmsn-w4nGAU_}GJ2k1Tf}3%;O3#CZII6*lDTkzlgo|a>)mFVJ(zcwOoRr-(
zKWSDDd;9v5vQxbB97U!*<|)!keoodne^;Nm=*j*R$?Df&D)alT0P7RsMIEDX?5doO
zqR3BKU!yLcwpY`Zxcb_0k(up;qJ$E@rd^8XPw&*RPrNp0^ckx^+NiVe@*0=gn{*b?
zsfevLsCH`6uXE$k-J?;LGNGrZ_lh;Ox9_PxT+RP(YjiSXH08{aatAQgJh-vCVTj-w
zg6$bHIhi?~m4qZZI{JGg%S@y1gnqrp51&(Ps+gXho+&vHwBxvi^%!~3snaNkD{a>e
z$lu)FF0Ju9@tV2oDv|u<n(xY;nFLSv2gu#G0$q~r1>>ntuInfcVIAwoMUs-e<H$1C
z3bIA|3fFhajwNXc_dK)un)0rblpbP?3=9grhGGiUk4UQ=J=Y4W!t0Mz9UA?Lu~NX)
z8X*Mi@^qaAM96b6se@n9iy;h#_x+~NPWPC6lLSRXVu)dmjR%XP5V9*iYf>u{9LdSe
zfONL|th^OC+v2$j=h8Oz^_4!r@VVul^4go4a)@KwW-e$7f+KHclqar)5W~6j5phD7
zW&I{UO1R$C#dUVRzPQJ9(9_e?+9lo#ajkg?33i|Uyp%7*j&?%sT^=LZe^phx7*R28
z$sznPY&DWg<4b}aM^Wu|m37l6rMS0TD#r4_z&>=wD#S*5FJG|3kpV?(^)4%#7uc4$
zAqE>LrEHt&TGv|#KSWYiuda$4IC^77KbxH=O#X^eilbN-){fu<e-X*xv-Vg{3w^(L
zjc@$STDsOx2fM<=&7(Naz%_W=YbPhke#WtIesw7Sp83NkX)mm}Lxn7RuLkjLBzbPO
zv{A|08Ty~>{*urRxpopewbqbV%)5I(?)SHM9N!Ox-`?@+mlAx$yivE=_)C!5;9eSK
zR=Xbhc*XP4Mv|B#XWY{q88XKm!x$arN773^<>wDR%J@p?in{0B@6tKj9Klo*Ho4T;
zo~~1vPJiy8(y@1Nn>_t4CF0m^<eg!D`fFwC_qoOw*|w)g#Tb^Hv#og^?3c-w=djcD
zh@yj<HwI-^GiQ-0V`siCvY-3TwquG8j_pl{F=H#66<udrCp=hRs(9atBgMu^0mq_?
zSXr4NLB;hF&a=9nF2kp*i8O8rXZtlLUB_88?NW|$SX*4_Mw2z4?S4%0v{6>uvk-s3
zTwA{%r#{mupBta4Z+H&#uGvO1VHGy56Fez0q7t`1boF34)GNb>PohO9_nD8VKb2ie
zi5FUwl^hW~D-@Y39nn2zWr#S~wKc$Qr*w>57Fz7EOP-Nz@^8q!vehR|7?N{%`NvX8
ztEq40vZknSuT}rjrz6amxAJzEui9GKgUu1kXqoJt^gb<LhnhZdhAUPE4y-?As4Dn<
zduOVjbByUMWA)~Wc#D&glT~F#cE(au?(vlAy~Hs*O&!j6&Fo5MK2w~(obz@YEu&_x
zd9sr*v4+(?QduW2_xCwkuY$g<o9~Q1<q?WeU5ox4n<-j-c6vk=uKK}Eqo#uD(Zu)t
z&mAnv^hqg->Q+V>C!be~M70dcjWoSiPR7;iYCG@R7fXO-5#a-=lRn&Ey&C~Mf{4&H
zSU8tsUc%q2KrO7u*)^JSe5gg!a=SC;mXhLPf@O-5gzjp5^@5e3>-gXe=V%E<hyE9I
z{26^u(Skz){hJtMql8Rx11|AL!i7W^MB$R~<^gsoS}jgOsUfSTrsk@ZhUd_jH*U<*
z+l{Pj^D*?1tB$m=b@I*P;asnqj2>Gp(9K5G;@rl=LbbHUv=`C0l9Cj6A6?f>zU{ia
zo4%W4nyr?)T0A0R-OxMf+B@{wJWqWVifP$vnmLJ9q8#s2+YBMa)tqf=j~tg33ogpa
z3<w?$pY6r26NIdZ8pp3CHkk$mV|JPhmlOTXsg|2&&Jj8EdyugDdF~xpAH^=&Wx0KS
zNtgP$>#+O9$Z^Vv(Eg~<UhJ+Clf^O8R}G3~l`R%J?-sXnKh-pl)!(tpXWL@rnCYnT
ztm`H1j3;ioRR3aWQ<Z(f=}(5*MI*y8U-RjgbcY8Yc%uFMc5OzEv3nP>E~^1D>NS&6
zI%4i^K|D!UCmp3lUbZdSJ=v-msateqYUe8P9FVX{7OJeiR5d5I?(B~%Gr;CjjV+Cb
zj<D-xb4m{{CCIDzI;=WBN_{B7He~ACSl!1$(Vo-Cq3U(2Y;@Q^B|z!IpSQF#mchz=
zf9>S(yqfxkv&6~A(?S8_ow01CHFV>>mY|e@NZvwaC~5i!-7YutewiE6D`k4njYFRy
zKe_1Dbl-QY#U+R%M2ylA*U2}`4@Fj0zQv6u*V#hBRmIek>uRIH8&OK;1AbrEnWmPS
z^ipv=FEqp_e_ej$u-@^AN_CvI?t9OCXVGr8ZzxWvHbec0z9;)6YB>MUMiT8zHa=j#
z8QbMZ42Kzk%Ehs0#yOY=2a56~r|Zo@>xUf=qA9}%q9tyuB61bd9d8V-JjzyPMv1NO
zldc-)VzYaHaYRbJ_vy+EvUKGintML;-YTJ}EqyPaCi88$#EaFi8p>n|MX!dD48b*C
z@~3eUvQMidWLAZ(eK!ydBi^hYv&f9@Rh3+tEJw3*syBmHvx&TupQm}{wB-vfpR7i$
zB(cO?^S&3j=CO(}+U-BmKbe$xY{9c9z3Hj`EPr7&i9h7BpvFnLn(Kq1u>t42{BdRb
zyJ53e*A7nNSnLv-4!>=P9ZG2EcsH2yQS2+@*z{&TopE@x6~pEF)(VhKdm!X7#6?h0
z<Ko<{>ugVVr{hyu{br}-w)#eeNNKr!VkoHmTBeoe=5!~<swTn~PaM3_$7MLCgH3;w
zt)YJX9t+O_)MzbnZ(`5qs`y&VIgKNu#=7lL%=YQ9clf~sj^HE|<K4KMAwVAOYccIT
zavdW<$F{5zn(9?)emZ#3o6Tx1yNsu??;MN2RU`#n(XlM+?dp$G3$<2}Z)n4PmUK-Q
zF&8N|3*38B%UmBvp$`2<B~D{P^_1?XA?D{93bJ{cAklqKu*H(c`|A!TNyk*ovvp-v
zE>SBG_2dLp-$mb|)iS+kO5R<7_9siJ{xafRLs+j!8RcC+`}s?zihT{+owvNkq)zwe
z-DR;m2Me6Z0$F{A>+0pQXzvQOb*gff7<T3>)q50L77dC=L?p1MjjgupOpmmEQ`UqA
z*WU;iA88w!_j|~Bsit;pG(S!4I;<b6+8tBE+RS`9K?c&UlbeZH^&gg>edsccl#U%7
z5$EcTYQhnz%bx5YzgjVm$5|zhv#$A$DIL+TmrXS)9O<r04jLBm+V~HNHOOj7=1`QY
zo5~X)t3mner;WNz5W*f}wBO3$Rip<kzpg%>{%SD8F=Zz@W$@sK&!M(HGreo);BSrV
zs?lqAtQ#@jyR{O6t0l{>A&?az*1^v!BDrNB)*s~xWfDE-QD@GSi|))Qkxv#jNI_9*
z*K#EI7E5^LB~4#v(77I?kZkO_<HFAuWEX}q&uyE^-*AwAs&+WRBdx8dlYGY&GdQLe
zdgW>C>P_1*fuUPl27AnydRZ?3zRpQ4CvO|VXI;H-HYS}OpR^x`Nhd7bk{GS9r1s%=
zojtR~z0vf=p4Lmt97N($W=f#nWO3~d-}a($sHcR1S)_FlJ{{JkGA;$Se2@7TtJald
z_;_n8FsG*_262>MT%G(!cZ_7sx@GIk-YXNGA}PfEx>J(Tj{OPLvb8!{O$Sz8$18&g
z<dGA~!hSdsZ}0B7S{(OzQ@?qy5uc;nldev!B3VbifqlcH`7Ee?1KlH^4Y!fYWzwj~
zOy1r2a!Y2PREmdw`>sc-rM^MEJNpuQ_xz}6(Mvjq1^m91D=1CqR$_l@=HdClj6U<d
zgQ5<vNb*ZNPg2#%s3$Y}h7D8w$4=gO7t5@<5ajn&84mE94=-iQ#EIk@uGHpHu6S{A
zYcCfnrFJ-^qWVmIr}N&DeW!c2Lq@$LQ>c&@LfK#8_`@%2=7V7><Y*^>e7nE#p1V@N
zgsVkxXH*KU1y_O2!J%rY%BJL_>m))qQ^$GgW_}3L1zJ6*@@-nc)7IeYPJEpd#h4F_
zgv3%8G%W8!Pc{^x*u<UnP|B1cQb{|_>+Q^2dy>wm8Norh{tQY@3B<D48EVPk;`Rq!
zW>My?{Q9v0$FenzQ0-_5BVNS)zIh32GMdu0Sk+=mm&22W(?U0=<Wa-oI&+JW5nIEQ
ze8t&=DL$v@-{ktFy<^UsXJ4jcM4QYyF5Fjdde-q6{XvFYZl+)j_gcb94NAG;JM-ej
z9>J#(R9NH~8;5!T%6tWJ%qr7RSCCrS0r#&iZL)LlJuLi9?|_*)*{$14E%?x#^_iv7
zf)BlK;BBqob}37LWlyHUyWLYzTmI1V<S1U+Sw8Na)%c3h&eX;IGX$aUMs?rMouxOD
zVMCUcl)_V)21E>^t#3A*ed2wUngnl|xh_ay!g+e~w-i!;H^@U6;8bJ|yPm~F-l`JH
zsz<?o(UkoLW0NvhrHgxcofe#qP&;dRSQG)fwBzASP`iZ2ltIaRhohaN12JH59^*P1
zd}7zU4>{y)bsaAkcAa!t&1%fli9)G8sKsWg#jagmNxI=`=7Nx1t)eed$XW=Oq?kz4
zv{awjS-$YraAv1^`?7Ys%b3U}N-a@uvAQX=aIJc6{v?RRZ{qC@l-xPjmo4Evb1cfd
zw#z3en^keL$BE4FEgj4Pf$ZTUG4cG_m(nD2(KtIuglCit|H!uR)Mq11<31~;;kdK2
zdl;p&*_Q4=9l7|gGyh*tZq;zh6VadU^11am`jzZ48P42_{@H%Cmm2S~M?p1qXlobD
zH_^p}Az^V(<-@WL6=Zf_+HbJ)?Ohr4^ld8=qN5eO^<*f4a7?*;d-%@G>ROVwuS4x@
z`L{mNy|KdW4B2e&n7*ET-`Os{CLd#ZcefDoajNc;z2M#_Xc;Zbuhx^$2%16L2dUO`
zl#^xH+-@?s{#a=-7Tj1kqjmt?<cYwdA^}n#vdWfgn&X)4;#A1Wa3zNW`<2h{_eRxq
zGOEGP{e=7cGnX>S-&dK&`l&xW(>`$Z%9r>`rs6UtSG-apm?hbeF0u2JuP=mstEzEO
z;0!zc<i(|B^_h78mK0;>+`h*mGtY`Ic#X1{rB+$mt_cRqI<TJ2;55%kUJX1@UuPt^
zt`)?<C56r~{+WMHKjpZh<BfRm=`szEUTo2Ufh7O@b&T%prH=xVE4}c<Ec*l(-Mhus
z+N43r+Ix#rzF*E17|=_ZIEvak2$M42`Tod2Vv_Tr%*NSk#X`-r_ig)+Xd73Bz1_#|
zcuhY%TbHTZJ>6MyDPE}QbLbJ{qE#}g{b4Kar51#-naLHRrsdr6Qup#b>0FeWsy`X-
zz~bu&hM><;uh|_+0O25A-ah9VIQVeTI%4(LGD0ugC@(0JI(=XUld&1g-%aMqR_yd_
zQ0U@IU7cdjp@YZQpUjZeteF7YJLe1H)+V!3b$sI!IZy&r`T<)_qrahz9VyeYfIOgg
z-Zu!fRh><@IIp?BYS|n6O?HZ8)<IOgJf6qB4>sC(ej_OMcMb)U@JjsglF_K7pnA%U
zCI9Po0l(ALFJg_x<=9;%JN>s?TcQx|3Fr(-&dG91jb-Vvm8pg_2^YP4S=Gcl2gSc_
zcM=NW2w4dsxzuQ0Cz*ow+N-T3FbO8LY-}BobpviSznG;mOusl=HgYP#`DA)|@v*ot
z<FDJ%ywD%(LNh+to~uqP6;f3)>qZquu5nQrBWt)f4%0if$4vN3br*G<@`Wq&r!<TW
z9ud$=q)J9Mn5(JS5n+Tvp7s+4E;=(KPQ2s4Cm$A@k36TyYFv3fQj*6pxmNyg1nJB<
zcDV9EUsd8-#YjBq*n#Ke$Yy1?Ere?At)O$NE`)(86I~Z}`iKUMO57RHZ;y4>89p-%
z*L}Z8`m|lleB<WU{<K`RgUNm=^P`g2E8e{o-K=GnY}{kY*G`KX=H=YHCSSsMMbQbg
zV=I-?nXM5aTOXKNkvG%`*NI$Em~*!tEl=96?$&JJu06d6V)gFn^AzXQsi!ZGK5kS=
zi52%6G787aHSJlZ>NL={+j=@Ub?itv_<9xTjP--i_5H6X#m4`dfW4K=rXEU2Oi8EF
zad)P>j>tkRJ{Ckj+G<C|$=1QS2Szh1DI<=1>#vK}FhdEC*wU1so2yplOW$SGEO__*
zSC_3boC=k*2Q02fR2SCfzpbUbExjVX7m?U&V{8-KjG2Byth1g;zM{3~3Jo%<nrGQb
z+NdJpUfm!ggPwNYn)+1)spXJ!WBa0o(>s+l<$I3anHkgLw7~>p${iFh=nq%GNz~4w
znav(vXI$2f7+b0Ko#PvAzB6!U+?Q2d>FGFi$aHvwJAZCyO-8c&yZd^Kue-x3YiR(E
zqJfj4K8xs4NSTBG4_gg{dtzfFQav@V*ne%i`V;56|D*w%%h=Q6KKH7~AN3oQq_u;s
z6khSXZ#qZ%*rijCs_MMui#6oeoGl%exvO_B+TV=s%y+%<(QV;H*8&LgZ2WlyaYErY
zJC5K8<J5q28ZOKd4JZ)UA6c7O8BDBjM#P}xN+!i{hn#nlIb09*Lx1$F`K=KcL`x{7
zo?tAamDvY%HJ2WYyjo?VpPF9n&s6^Y!nMziVkc)ImwOdq?zprZYw*j_H$BU1XO%54
z<z~8sS8axZ`QVKPGObAu_{}o%JFmGJy7JT!Gg-3R6Hus-%eBKVSRQPTn-men%W4bD
zflPA$&T~XM7`ff18k6n?8B;)C{m+Pau*G(06d~QN@5$Z<b9~*%)Mk@q$Bq3Foc62Q
zm~@Ba{q^kH@9|^|n0PXOEA>FnAxeTX0dI718Pn^;X~`Ku|C1j@2d~7u`F?N8H0t5_
zMw#M{f2h0mRHa}N?@kCkyy!m@tF(LY?z-M$PJ=B(>oP>5WO(e{VZlJ8Op@z8B>Bey
zcSiJ5mCVd8Mcu|Pl=0J}iw%m|xHUCsq9Sym)<q65V21L7ofa4B7vrJUHHY<n3o$zH
z`y7%9(>lnI2A<`-J^6huL&`xLc^@4N*CTh*yZ9zs&P)~tPF3ehagGg~5ghDVJUZqB
zhqUchC%hi$#v9D&ou`z2_>QzE35<ICfdDH6cAoZIiX2KUg)(t>JwoEaiW4IF;>ZuJ
zY>jA%C#%7^Z3Fq~8E-QrEF=g<#3Wa{tq)da<%`!)`pK%Un4NSbYz_R1=cg}cxzjg8
z<pT|)CA?SRtFEr$1KFvaN_#}=Be^4j9=zOyU!k=P&zh93lPtTOq<T5*aU<i_t8lfy
zj9Xd6&cwYnJ7@gVyL^WHtM_<(-Na|yI^#nz@JkG$Di`iaNlhFD=vCc0PL_SvO*JY+
z?Kf~bb|Sy%@k^oO993<+f#Y<C>^>m5<dWJ!4nhgZ8)cT7^*ijQr+LzEevT9~Vg0i@
zGr9y%f0yCjhtV{wMH|pZw!zVspJn!30`ghKYWmdN$?xpdQ&!G+%t!p%fQ-v7BSw>B
zi)SLGaZk2AS1EgUW!BwybSKs}U=B5Hr^7O<HA^YV4h#82ZpjzZSbYs|ap@dPW%#~b
z99pW!J$8^?G8{P`*P#$vc|k?ew-Fw(<6YrUsP6t;!y_$fTJ>gCc-}a)c6S>@;-B<Z
zV^m3zqrE4J^F^E$3Iv3#B!d_GLE%P!b@^9<i(3!`mfR}7Y^-Mq;TlDkiWECWug*2L
zXddiDOrj3=wf#D^-ZtkoWX!(7>3OR5^)U(MUuEyuiAdeJIx~6pk-2$GvXAAI#h`L4
zQ5rk@o`R)!R92dz-lLZVJtCf~Qp@!9we-Y^xZWQ0D45+f_MIyO2dYnY@1VF024Dzb
zjJ8C{4D1fr8qLXUKCZnfqdc`#)mii+TVcB(>Q>T(qjl;B%FCtL$fGA~TBuJmrOOK<
z8xkI}AVB=9iH>N7q*l7S?Z?inkLyWX!j6wrV0dWOD2O0g8O#4Se-Nb*fTVL~6CnSq
zq%T9pJ?6OSzg01%%zR2NiGdLYMi$;l_-m{qOu4zq*=K$`Q}nNgX?eF4ie0<UY@wRR
z?;Ebli9M>GF?_!@Bh`Ho7NSsw_`i<AZ6K0UK=Ady7TMB6%ri`uatNkW#_=Ct$8BVt
z4Jq)XPuv##=gfvVIcO>HbZ=At8T8<KGV3@h$+8^!&+(F+rvU=gdGwaP|EI%0TSde-
z5n*tvwug5A-pjx*l1I;WC+F$M-G6TJe^0`5KxG|8q4@c)t^PH<0=9ss**qTxoaS$v
zAW)EC7*h1|*L#1Tb3veN013+bstbJk?}H#nGWZqZaVJ_l{Qqlx1OYM+1cU!~CHOB|
z)!YyTLE;8_-7VAyTE?OA5s^Q1)4_!MpEf|CL<xKE<V2*<acCqH5X!p_7vxqEOc*;d
zq}=_7X!0~{cVLBIx%mE5CJ|Ejl|kY@oBz}rrV46M<@D+G|DgkH@G2rqgGMU<pG*Ir
zDr~|)YxL9b?JNI0gTJ)bhz5pL-728}Pp#3Qh9GY%(D~*+4XX+aE7DT-^zT+8B>tZg
zf3wv8PU3G)^#8|oG8Qa{Xc>ZgiArq-4L@=J)A|~>^+a7gJ!Oleq$HtiwPZncso%i~
z$;s<`+9`_va6jN9$jjhXi$~$-*N*9z-Rc&Hbc{rdPPgNt11l%&)3=PBod*gE3a-jJ
zHl0*@{{G6)Q{%jJTy#DY01~kcJ=*dn4a5}&rsE{EyCkR)#`l4{vA4uCgXL&EK&LfA
zJ}@`;1{PD+mk4F`L37>LP~0Cb)LTq;pB8Xz*o{3nnK|>trcwER`zYnv-}FG^3Qz+y
zI3UDSm(p79qH&5*=vj(@JSazVJgh~Sj^r)@(hw5IXQT!pZooC}HS|dxZ?!1}pgLB=
zKHh%bTEEpwz6eU-BFSnUKEK}>4J!X_bdEm|P<xnFJziaE0P14zTe+?>y5zQMsY}Ov
z%taZ?#fkI(dkk*gg6Q}1*I(QOhpJ9IJTlU=yEYQtFNGU5B~lg<5;AREpJ<F2xC*`b
z`^vusu^^J$G$tif#a99XIw1M^`ARD;ZY{^h$7$CVB)Sw{#S{K3>L8Ke1c*F6s6j81
z9-RP7q-BG{_a`+!Q2g^VZ1ljYPoBQ_`g`C8eb-VD#J)Fkvk($@(x7ic6J2zJHtO-M
z)qlmu1rXjptabHMBT#pc>57q@$|Js+Q~R(W?d%CT8@Bf3<Yc+B-<SUF3D^!b+nr^i
zwgDL1Y*RA^Qy}h!d7_DC5_34uUDEq#A7d$NYv{Fd&3Siq2#MYPcfDad0LPZsTgyQ9
z7}gA$n;$>y#ON3bbai$4DxVDNIVSY<sHQ8kfu8$>yNu!AJmnPF;l$np91R5S@#~#p
z_s^ftUc73xl)+vsD=jT89;_XlJ3hePV-L#8PW2lvpV;-;$>o;dkdM<^u3`agUtwgb
zpY4Ry!KPccLQ{pQ-*K0oMS9ze+(0Gas_n)GdAY2yQ8PV~0Fp*p9vd4g^UN^<_XAC)
z<_U%fnh+S~4;2+6*j#nr&dEk!Nx2{9l$O08l_V^x9HwfIj`4l^6wk$kV<(cHmDV7`
zHv@$?rqqi^OugPtIJFNZwvF7KYf-PC64a^OnI4`Z;Z|eBj<Adi$Zn-52@N#v9fNwC
zVkYGCCg07kn9rqhgryJ%nd|G>K~sEyR|L!e<^8ItG8Is8B&Q2mRb^#2lViQfwBM0^
z+2_xn`&8#Sm(LTEkUv%nY7Mdz*N=ibFWp$IHJ^y?*dsm+f$lAip2U&UoNlK_TReXs
z6jq@AI5MfEDW_AgAw_OZ&P(Q`Zp8$0^cq9oVhUW7?ao%QDi<{*gj_gRE2sA6ohL>{
z>Ww||si|UMydWfr^64!9oSd7uKq#rp`LaUhz4`h%Ic<}cl!UD~)=_C8BtLt;*6BRw
zI$!6=WP_c_qk*iKq?lWIqk_6n$UFe=@THDAYM8w7Af*aC2AdN#9}FF{`NGJF7U-^g
z@i7t<vSSZMLvYA0`j>?~CaopUzIOMWeq!t}>6N-|7okc{GpcjB^6r)dWS4wUsP@Up
zZWex&7GVNeb*XE)@aAa_`PJ8$F#5-$u|Wtv!Vu^s?%$jB0wzSn25<SNuaVx8Y(M^T
zK#IjQZ6-FIW%88O_Yj0POnUb65&Bv%NI!XYv1>;;q93ei1q`27!MFg3gClV#jH)!o
z*+l+FazDn)BfL2Wva)6DX<0vu(zytu@ygx$=m>dydAZJYSqwj7tC5YUU?3aO)f$cf
z9!Gl>MdZDwg}&m<bC0=$k<a<tV^521HH0b4a%o7uo_(2Ni$FwcaAd=P=-JMLj^nS+
zZj-@^6BbB9#)b*rL~S--UR!^Sg^b-BPaMY0DIZ@d;|2AYf6loOAnPE!w{vRSJgNwA
ztVQR8Nv-Kl4KkY>wovA6M9flj{y4d&LpztJi_G5G7*^RCk<{ut@#wx+OI-{(98%wg
zJColb;-g~b1R$^vJb*1#@^^vK;Vg;E?1qGG<D-}SEpf3{YT~QssY*K=T7x%zOYB+V
z3whDsVqbOiz_-|?zCEZrp)rxye%S$3j|GuOx0IWKkT{TGOF-(|rwg2BcRlpW1Jvi_
zDjin%=Pak!<2S`^`J6q1`PtXL%>O3w0V`KrQTg_I3zRQA!C*6&a_qvD07MCdJs7I1
z-x^#1i|n8tEs48vyrajA9bnLN>keAw(_^f}kkRnkU&8)=V}K=h>n3guHX%u9N=3lH
zR+wN?U1}DN0K^Y)kaypdN`XeDHrtmiwl}KrQFL>^`60Yg`?+u0^(O>lB0c$4biI4(
zl;Y8ELYD5qVL)!}Xnyuvt)^hU<N+YmTuQM)1dtZ8FK`yK-v;rhr5ZKS{I>am=9Vos
z%x4l9O({?Jd_6<}+~(Rp3WpOw=0SiBzxC}1M3E;*#<n2C2svnJ99f?dTjdtpMBX3y
z_AWT3<@<F=edY_Ram{6d41s4Jt@VuKu@0F9w6~Fn(tWom^Hy5wTO~KJad5{4s|OxY
z_VzfEKk7A?0QK5l3xNEI_?FWcWxbopR6O&M`GBuh`=qRW_nlEod#WM&RencD$E3nS
zp0bh>{z2~qaY|$a7_pfMd3&%QQdCp>qr#MB^!n4D6|$=)t^M}c-(tM+n@>ExNI9MT
zxP2-1>G^9;-W~Dom_}pIiTHFdv|+qQayXTtbQBm75n*P8wh^&X9L>0wRRsIMh5neL
z@G6Y^5}r9Y%^@jK7=JgL1o9+%=P}C5?lX_BAM&?^Kv((m=5qut#3x>J;*;WE%%d0>
zUYGlDN~zMtB7QB~>6S{^F_(?~&|7-XmiWwk3uo^(m&#4Y;d==C2y!Zi@hyZ&DX-z^
zs6)cFBj+!E+%o)A0@v-EFJphT5}gb;BJt@4<6Te)rxlq7CnpEHe4c89uFjSUC2D_m
zW09YoiDfch`po8a^K0oRhX-#Yu*Q5+qpCmbj7#=<(j~*J#yE1Gv;{x%Ft+nB#;o5;
zNW6x=B{fPndLmg8I$-hj)V{uPTGUz8?2UkmhE+*pql2598;mNk;y_D}_yNF4u8^1z
zaEFs2__$p2>^u!E%kq1YWKR?&hZdHYmgTrxlG%GDu<o%bYfyX}achuF#PrAPqfn@B
z&=h|Qi=iR|XZ04M37^0sKY`Ta^?NgM;9hmjApTaC2n3UYtEO!~J@4Y-G-Ozm-o^Mr
z^`teph$3wq=}HHqdLiQ3Ewn&IRZ8~z&qpg4fP2&4fIsO&eakE3CWeRIoqa8Rvhzt=
z*>j48-#*eatCXCEcU*v`Xe_9t%&j~4NQyP!M+g+~#E*HGy6#DvNxPtTu(<A^RPPZ=
zM$0pLTF&sDY$rAq^lef%rDTHE;9bdfegBTPo=!dSs**M-kf?X?mtQR19Elyq`!H)b
zMN<~Lu@kxJ^XtYW8zH1s>++4IX#y^_i<6H=K5qGR6Nf+@j+xe(;_)gA0+l;8%oBjh
zuL63dJgQJzY<jcIk`}Fr>;Bf=!M9Rg{;`1V^n7b;5O*>;%}zb#o%eDK>yE5#!4(s3
z&7tJ&lZ{HiRXScuX?vj9bp5F=@ju_$Vj~}Z@FIujNUWrMKrlZ!HepX#g$;NYb0lCw
zDu7T4y6S=y_QMs_7J~U+a-Q@?0yLD4pNaEh;-s{YNo_mRWjM>_VVmDghEF}vZ+c0{
z8be5VrJht;ve-?e-|v-L9`br98%If1&R234BYeQb31|5*_=_7Pl;?nVpZ_&?ml(dK
zgz5weIWln8*O~J<mt=RQ6tVvi;{ldM=MsyVr!OfD03Duz>W-lhsP6c*No&IQB5`{@
z@B9~6q;fQqT*2%QX!QIQqqfvgy&$rs@ylg*X<q~zn(3u^M(&Djb;Xe}Vo|W#+E#GG
zt~}*iHXmeQF&o2$0`r?w#$mqmFJTxZ%tP_Etegc`WRM*oK&Zsp9endhCImVL(V^ya
zkx2ff9Z_q*v4`ueL<q60InG+H2fS+X7-Kx~{zXXBHJf)62+x(i8~8L!c1|wVUQA`Q
zGVZWo)XB5wZJL&LJi;TYVhO+e4X}K+%-THyWC}zk6f7S{jlbvadgvxsPKZ_+asM3h
z9F@WD!9AjeR^kYCV@xvhq;0w|>s$+$&BKO{nJu=idT+5%LA*i*BH+xLp@j0ptWS}G
z0JN{bHNk<GUitGw8!QWyj>RmggXEmja;Bcbc&;rJeo+(%dVe|bxXB6l<t;h}ys_*(
zLAaIy>|}rN(gzacIkII*+%39k`!u_s5AjQt>U}+1fHH$q!H51jg<w*5ZA{^%z6o1V
z2dAo#0&4{pqk+ce(}FP?-Oh#y`7u;F-lFDJ6nG@Pg7;nxvx}etULMdIq>K|@cef9H
zUKXe)LTS(}FoV9Oa$CT>`$vQ6O`eo3W0ha9X_m(x=zM8?6hD`W(_G88tEn3%fqXLW
zbDY^=U(MERub~HA-5iVG<9E4)h#ZO(fPku%`D$2zBA%JFy@b{d$aQN;s8m0TjC`x^
z(v$F=S!iHPFx(UF+(lKSw*z9xA;5A;_wX9G7Dix6ZBv#GCSa@*_jsq_>7F?=?+H6Q
zJH`-LxPP?kJUF?1P@$T&?b0Y}C##+~uom|7)hbL42??>1k`NX4z~U;$tgF^hwzzC|
ztqYtOm+p!PRmtJWm*OIy6+%C=+t1`DIu_U^q#pmw(@p+ZW_c`TrBlFf;(9P`PX5so
zZHu+)c3#@FOB#BMBXO&9lG7Tjm?5XZymk8OaKz*;+TFdwc@&s4@v)N~o{b3d;(ad4
zR`FT^QMIK92&&(X{g=VP4+-({_x*n6%r!h+{t}e@_GMyKHap{l=SM3<JnGP4%vQ`U
z`(YZoH=|)*UUFNDe?vt4Y?ZylzA9)UAD6U`s}&e1YwzwJYYO@1{$0NkZK`aA(>jza
zkqn+9NV6~AeJ!nmMx~SW;&4#hB+)?fGb1#n31Sn8h=;t}d(vu0`(O&pfIdgf8oRoR
zh?jg!P8R8S%d89u)~0EEr*zM$3OcfixOB80PNDiqQV2lcgkD`5sqa^d<L66-nSsLg
zXKSJ=qv5ylVW94=u#3!5OZ;X1iJ9@euZ)~#tw9-$HtSO~G?&IV9)XgaJV>ZuHM?Bi
zC6HQrKWpn7H=1}K$0sCw_RW5Y<9yHG;>IFETXp^!41|%ulZk@N+0XchSz)I(S0v2D
ziAm0+-){VD<N<f!c=!V3)@%T?j1>?ldBS90rz4!ba|b0?C76PKds$p_Zrs->Mk1;*
zTw+Me^jh$3i@;W*WLFgHi6rTLANOpZY1mbhz!Q$q5=+ie<&WJvI~(SdhZdq4*6Wo5
zV!UH|BI9*Db_Kty;F=Sj4~HMcqlYso2@MxfeWzDdDyvw>=TwCGL~oIS3@ijhNZQWt
zt?*?&6v5nLPtx~Ha*%C(?)Zj7B;4RY^``qifNC9*Uc(!yoP$b{0o@YnhiM6I2nuZo
zQM_#u*c!CPVDI5%y0?8ku>6M@(X=%Q?v#F~&c08R93NqgyFjJjB^dn{&PdC(Xn>Hx
zW8>l?HmaQtcUBe+YoqEe+GYPZUGl|;th&1i&z~S0h;m%wjiLG?Wo`uRm6mlIb{?p*
zSMhK#D7@*|vc!+vLX`|#$broEWq_shJAO3RHAoxTB$yDsc;`15z9xn#%z0h{apwHc
z>u#yHh)8}=OxTBm{|Fx_sdu*`q-q1uN7my)$`r?Qe(F;_C<3(7fzz#8=kjEOTslKk
z*<VF4X17S;A)(#+&a`A~fMh@^^cE8i_0?ceLlMZzudTej_?%>NCh){y#GKN-jVQv?
z_>3i5d8@sUVH023X$1!i%P!=y4uI79n3$V?1@qFch2Uh?O)@5d8_|y!Vbf$(mAG@?
z`h9zbkY@5IdO~-C#|mI=4dpjw{eF-Q$ClF~e4Jk%VU&s_fKY&J(dtQ%)|3s2%k^_p
zNRt=uWh<U7*aP#$`5dJOc~NL4oPVV2gkQm~>~lGUWRma^i3wy*4!Pd`={19sKRM3*
z0_O)O+xx9SKa(P^0lI{STplBy@}Zr!M81`-Hqg@(s-+>)(uF*@fXQG>`vz=nJQ8yT
zWt*i$kx|mPpvx;u-SgWU>Uj8#pAk$bM88=+rN?Xz8&(%c9)~-}UYe7O7UNodO8_AP
zLHOM4)&<Bs48*n3iF);<SFf5ke#@w3+IknS3)@^01DcJx{yHNht0u}3Xd`A|(f1#T
zpAd6*9J=oTpT(hnU72VK`8Dewjz975OmJ5a+y7A|Lp~&?z0%#=`*vG<TBx)+3~1T%
z?PvWDF(ivgpaX@c*>uXPv~gT21(oQq9hYSX#6%t>1=M(OG1f~81ZheIgvg6_h(5T2
z9iG%YJRF;lRJP}0nJ33)iMOA^L`kO&v8IXq$Xgai*+`XQybOtp#ylcqzhE@)%SP?2
zDto5rZS1(TLCQ%8pp1C%39Se>;Zy>WkZ#SPOvXgMLnhUQejOPj5h)S%OrGrlVV%9L
zw-|1gi<xtII7(uo*`TsH0s62~CguV8Xj9NH6{4ewhy;}65oYwHxe`YrdS_NU!kYgS
zpVRJu`?;*#Mh*+Vj;=_K8NN*<5mDte-Jp(uK6HCw%4t_9CMtzyWDXzrTqiEPaW;Nx
z5MN?)c8<5{Gt*UJOle4N0>>iJ_3WPSy0wUiBc}T|Ka$Z1Plw6}pL+?l_g3UM`BQC6
zhm}B???&vgMQH)5bN!KdSq;D3%<adOnta;70*R(Xf^+8}O7RwF=Ump>&#8ipE`8wE
z;=2{eMY9PODM67Z2oRT~>_#m=kyE);X88S~mJh*Ixc6`#3^D{D*dQB8oWS`iTwaEQ
zpIW_c{_?Bb)8~1=lu(0tO45vgdZk!5b(v{aV(eAX%$8hBb;2q>^imTJih+6w{=N!c
z#H9r<;*x%~!>@K9vO>u*CwHGmQTNS-Q3jUFQ<ZZq$ycSgY!0}gNI3B*LooWm2WsRN
zkf7C8=mB6Iz>-eP_r!=SS#Fd1iQNs@Lfzm|m4=d5`yz3K#NUH5JV3ec9&lYoycs~R
zB89Y?GW{04DeHfJo^Y;(?V6zi0rEL$&$QG1I>Ad60V!%*evR;$y}Ca|i~OEzjh+5_
z1N&t#%kym)ePTamP>5p|M-mLZ$jAb`?*^a|nI)wf_=>SU;fRTgExVM{g=2@;0pwIE
z3Df+2Ja!3+6s9b~nd`Yr(!dgIQq`8NP2kf$fH!YP=n~<JAMpWe{Iw(({!)mIiABn~
zEl8=YP&ujpbkJF+d7T_aHF^a+LkXUd7}UKEp5f2HKT}2hUq<++Ql&ttPI`+2pj0y>
zUMYIHzmy_u4azDP-o1PkCCR<qp$et|+wf)1fALr_tU=8tb(=UWE}el*Dy;EUt}!Lj
z&I1wkktjkJh~Wan=zpb42*ijoVq<36Qq}SkTMSnW*kVhm%-~EX2XWe=LlPth|E46r
z2HS-0RVl`AU*EhV<C5~gjehX7vDtGVKOfI%t)Nwigp8dKTC$w~^%Ic&93@ae;PlYO
zcaF>?-vmSLXjEwduNx4R^6-1!RiE88MW0yBEZoow9-5OIsDiCV{;TjufP|3@>e^LZ
zWV_XxmH9vcX4V#Ty*59q2k~41CTM#e5)2h6zv7BOd4kjHv30fy4}d~82x?6@lx@w{
ze<|)xGwUY(sE{7LY|GX9Y3fL(2`yM$bg690dU&Q~aHrh!a5>o1%c85^xKD~rD#wIq
z$0FuXr98y?nMVfO*ap?pLbd)&A3bM#?kULunh*-=JCs)~<Oq;~B=6PIV?Fm+Ns61x
zzUC(8tr>=)W&<eGE$tsYf35~*m#(Y3biZ?(sIRc--Ytw>x#Pz+loHRMZ5dv3_ATSU
zG`o^2Zt5T31;E)2?cou+=$U+Lx&UHmu+47WH4}hPJt9;sbSqU~cByn{#rorU3L7}D
zfJ<*aT>)cAl1>_yc}&lj1acEkj4rT@QkstV&6k&%V#FtY2w$VSs5<-QPKVFlD-rDa
z8+$BMm(XIga-mgQu=iX`x1onqkBaD=@$+)kbd$}})R1x3YNEBAJ*fAQ1k>elvI$=#
z1r;vE=1IDi0Z5k-AOHp4%OC?~7PxJEl*{?`Gkp*A=N7^lQIfiZSlz8ZNxWdy$8WL8
z(+oK_0w5`5QwoK?6JZ?!###g+S-7|-R{(NKnjsm*?#@C=bv#iCx$#iNMBcmKu#&&9
zi~cs2Evlz4^;y;)$<4>;qo9U?^DwI8u|_UZkh^n&%!%<DIhbbtOw!N%ElMIX7-}je
zO+HEo6IapjaI&a1XNgY|HFDQ9WpNsVOzeh1R(z54)Ptu1TgG3sm+S#Si2{d*h`9TZ
z+A17TOSIy=^ll3!kYqMQw@$}dU*^!gpn7)BSS1uXV+>U}PTUyEt9fEnfL~@6%uE4?
z819MkVh?DR14j_VDFx~(jWD2$j4M#aXH5PRpQ41yzI&Brx8vkEbHI;K{xnOE=J>Q9
zj?op5_#5~%2<yjL;VgrtswJAhD;@){(Du^?;9Nrt6q1kx3e_aONqEhm5x<okx#B>{
z0}{Qlr}*N?&xyEei?sJQ$#-I(?m>g0;(8Hd;4a7mL10WVH`(EZKp$Ompby))>do@k
zJ(aZ;A%O0t+4E4xiGi&}ZcVXfv=P-gOjX9=YeQD?z!YtlKCWJYhcq`*W~`&HWoyJa
zvFswZnx{wV23`e5JqYH}FbCiZ$5mc%UD;bFw^QvpPlKDv`%&~}i)~f)O(!sgF_54|
zrv5d^AIULm2zo|9>-HyCKk!`Bcp7hfb`HEi7_BF=>Ok%eRpDmOSAHDoe}zA>xzQJC
zaOWMKC0N<F)&WiMP57LoxK4I79O=vaO#sgfUfS{heq2_<Z^9>aThg>U$h*o$Wup=Z
zG;@!}G-9%`d6Ysob!i@7-w|+N9|UTV;ftrR_g~opKny4%N^m)~qY~USr(Ct;OjdK1
zn!vp$;nmwhiQH&~K|uP4wQaBW9xY^F&ngz8()ZYkxIVG?b)%x8DUF(}RS%p{v)}&p
z)U!jCDw2zwfF9*kEIbhh<GP?m0KX^s@Bt{X-D7i%NA+HtYvC{LW(q@q^Z(UZaNs(1
zX<IE4a1^#E`po!r(j79I_bzj@KVejqiCgDEq(v)&!>2m04u}si#J!E!+YQONcbk;b
zPEpg6xqQ8k(vk%O`F%D@Ov`!v>Z=I0kemn07Z(|CR6i~_GE!%8*PqC3A~n@2q3liB
zqNJL7eDS*BVt3G}D8Xvk^(aajOl9gXm^a7<%+|^E!<qwL*@@thBMpSe&<g?pril5_
zQ+4Tq60cXKZ{F4jeK^<RhTW({zerw~Wr`*HMHgRc(wZvm)^VKhb&7v3j(4I7N#h)G
zo@yMonc9S`*Hg2@nzs_Klh;<>D)1$qlLNg8UjQa|z%gEUt}6_Id2r6Q2ET16-kXlo
zNQo&K{JGq$1l6FSp~)-?4LQ06ez^H`IetW<=MuI#1UfJ<VBz4<tD>rUFro3Gzzo^I
zFIR<m96#BtbVS%%eoj>C@jVni@&&T#;_Ww^4<LER+uM7>aOg)f@z;^|H2q9+fD8)@
z=FTUFu4NS!j}L1Bs5O#;>dFy>6GTqqN8tH~3m*7EkJ%c8sMJ<{H7ylVo&>asLW+Vz
z*x_LWb8>7HcLk_ncR;j@dig0H&mjy68Ca}NnTxPJUF|;aU~rZ@{ZWt7;Q)B$8FgU*
z(g2$gWRt?bl`2oiCIzcl;Bu!1sC1XBH2euMjOv-S(ke0_6`0<yk~|atZsiMrslT$G
zwZlkYR9EaC<q4M(0J`i_4_cis;ehHu-ss{7JW|2KJ~kAcniW7<3k!?)bv)cWJlBXg
zwGAkTLj$P59x~++a|B8pk9^j)Hoy!2A)o10fbmY)MOEje-sE?&Uz6Z40}#M==rfLC
z!Jr5Qx2St)Lrg~Pv6310vUX|)|Gp)Q*K(TtaF6^;z!Mm!!`;7d0AEwr(~SNN<ZKr+
zBLzgI7W9di-RfyTf5c(3THsNb)9PUE?AqmL;3M*OBAxmlfDIqM=f3WOCyJZv>+9%z
zg#*w6zz2!|rpQkm{6)=T{#Y?q7`4pFih5E^3?23qy#HpV5cFM1MzCFkb@4>PqhGyh
zVnI8ity#d47O449m?MS@0mvc29r{06veO+6uIuCqY~0APu`wbN?GA&1ot<*=7r(wC
zM?63IfD?0(U~aU(nVKZx!3pS}Zji>uabQlHbjz<WHm5M41v{c1&p#hs1L;MK@Y?T(
z0b9)-+G#4T;DiU$dhtzrMj*3Ec|7Rlz`5i=F_hx{*97Rb+0y%)B;_Zz{R!&i<m8Ls
z1=dOXmLu)-0CQ3+BNW2k);7Q3afv?u#@T@dK9lN#ZP@q|!5LdQ)Zd~6!gcrbfRM4!
z71y)6YI8XzogPdKMNv|L68cLYu`>L3>HMn^LV*0Gm-YQSy(NpVkDe3s$EDk4O40dZ
zAhUa#T#KUt>FD(~Fw8w$M!+7}SEw)s4||L<hdQcIeoS=TG8-T=wVRT(2qmO3ovL&m
zR)+R#X=!~o5OcZsCTkR*<Xx8b8nn_Fu^lOTz9`qber<YeC&8-f>?Miyy|^taf3(9`
zUx4aHBhBVHwH|mFzT!HbmH7<j!Hu4hw(2Kz<!BAgD6?A23;N3J?YLP<q0TAg!1`Ld
zHUJU?TH4;+)c*0Hae%frOMsWKzOFkWSSJrZ(GQmfg*~L7BOn=WAzF4&yq|f1TRc~B
z@!CB$7UUn%mJhXE857{&U|%ldtCW|LLQhEgj{+Ing6%xeMu9r`IyoC~zCbHZvR8;-
zAo(ymA14m{v$2KVs39H?AwR1k+qr*lG$PzFC9)gOa|i$=R$g8J<yQspASK~Xa{$B|
z&900J4c#E4Y>l|q`#9t>z9L&?{V^dysa<NeG~{;GK)>xms~K}}C?4KEG`=bU?_KBU
zfDmrzKMe4@fmog#lqS3?uJlE)!!_fhD5#1I8zk=ac;15CK%()2JevQl4p@{fu;1cv
zrUNXPch(z^t%vlBz6ZXP;OY7qpPx5SoA~;G)Wjtbz?t-cL2BaT0j}B5ZSmmo$JN>D
zXQwY2_;g}5iI=A3Ol$B}sgkPkACDQ%r>uRyCoceEFd4XXH$Q-WGa;$Z$%ubLQ)Z#0
ziq%B#r-tIHhFme`d;V4LbCk95c{O}Di%xrCm6Ar^gH7cBg&6=m%lYYMtmI+5Y1&w3
z-WJ3~oO@>$@)%!9LppK`Ff^^=L2j||RW0BGa3HUOvA`{S@YisQFX59av1H|EpG`m{
z0IiUg5vtD*$uGlbYTXP^Xoaa+Xo<f+1JM7^Ar7<|@>5$dsloheFJR(U+gWrErUpz}
zfnzuPwCFJWBz<TtL<Xm4U;vE5u;#@z+H;*_E`$u6R#Aah9Ow!@Q;vHZnDgpc9S+|;
z0TJgKA}p$PHWkpm>gH+_c?qfI3oOKqBEqHnWHh3pqT%uZ2?+^lcpu;j1DSoqWbl>T
za%pmQQrdgxfrYP(QRbWjeGssWMl3hJYIaEck^=l-)%Mn~{_Exv{7K#DX_mKTU=+k3
zPQzC&;OPHO4(`sD0P`5rKAxtDYBs@t9ucARume6yP9t22{2Z4z)qpW^d{?`s)@9h9
z^{R{Bjd>qG2+9x0wg~|xA82u0g^cl=$g}!Z4&>DFIE~3)jZhIeM{3qa91-V)85L+V
zCl4C*;E@0^rJ6s@o+w1APIh@T3Y<ypVwOBeMpFRI^Q`2)2!?2AoJ;~G-c<IDezER%
zi@)x2?wl3}iGDnK#ihHyUz3AkadGjz90whWU*u6Ucnf*6`7L7nKJhwqob2JBx&bxu
z{thEyL=`>@Pa_5IwL~@|r^ibzjrrNp{9x1o0FCnLIbMTWGf^=yCQgJaHNR4r6!?;Z
z^R=Cg0Xz_p$J5QK`~!jE0Re$Ts`HZ@d;vC^cZd(ks)eWkqL|RKfaUHGx-TL@eFGBv
zr;iE9lF4Q1X?*_ZaT7#y>o$#oP7uv+rd!-)0a)rWd7G93830mbh=bH?;E*sg07s=)
zq#Qudl^b~a@ep?b#L3K7XJC;Jz#;&kz)KA991!zS_k6V1LkQ-O3_S>T8+ve#HH1&3
zArzqGV0B+XOBFb+768s(w@mE=lEtN^=!wUMb#4&?RNG|&mSzB(-idGx$u&EJCQKhw
z*iZb7R09wV%tYu%6YG089c-JI)z*$@<Z0;;Z;-)RCNpPQffQ*#ijdI)GJKm>2P7!^
za?`A}K-guT`v-5}pqC{7&KwZ79N<#voxf%96$yF3ILJQh#>9b54MoXA@}2<cpE+rR
zNmps0x>xBID95mAG=YIr!Bu{n@#*QJCd3B<<RxNZ<DZ{RJGzJRb$Y<}CD_(ZIZ$L8
z$G`?}0q851wFPy&6R^*bw8x*y0q<;UkXp8ahlaPUMnElSNVQ}t$AN2a0~^-M2O<_8
z7hEPJJ_IG?9ps!fUNRdX8JM6=jl?sON;31jr$Dm5W^miU$IhpJ#m}W8K$ix34ORMW
zmUuEAs#Bz|<S_KjoV-PZorL#a*q(_WAyxzLbAjMK4IXGP@Tk-UvkK^I<n9}jbEP2`
zBC-`z0QQWvz$c<W6qmQ*=>*K@A*_6YM@U@Ugb~a3dNJby2QYRxtYhL6IQp<W@!a!}
z2>h9<XlXqJZ#7y~M4Z(ApwBT$FPPYBE(c9+H%9&+={=v{ep=@Ku(D!ZR#p~$JoxZs
z?Qrgvv585`aw$o#R8Bsfk8bf4L%D?a5s^{nBRO$QQFHlvvO7k`#L}|tc`6|ttc(n>
z1rX=c%#7(ll~|g%7Wi)N4HNkZw-39fo0Z0HpmjDODmB*NJYPg~=J+w7Xm4hGTu>Ds
z)-P2hEahw&2B3VA@(ZJmGb^n`OjpLQj&0D`Rb;>5|1<18n*fmBl0yDD-0A}YV-iSC
zg{kUM3jO>rLfM>(Mk5Lx41&&V2@i5a)yoKOibo@>KOGJy01VAcQ9n80As}14*k(=%
zn3Y}Z<HuKq!4s%F9<EQjlT!@KITNSV9ckJimmG{upZptLq9d!OG|j<-?4#<>U#jw+
zv<8FGxl1Sfi*h~_R^}ovOiI;`s08rg&`!(bF1U`7;G#=!6wrb0FKIA6fY0+bs)kIO
zY=ihA2r#cG3Z?)j^PYcmv&6AL((pN}^rd&%02b~6X&LomB-r6^0{);Pw_al0E)3uK
z*m{9|m9No=@BclwcN{#2e;<kM&g~Cj7r|w8Bb>nl?zq@1PP*n1ykbgvY1dtMc~tJV
z5>3PmJ5JgEnU1gmBv4-AQlbsWfDu!8d_WgoT4(+q56^kf9LFz5#JRdOUSVrtZy(V`
zpEpkMq)!^@YZB-y;pwxv(0{hjD}{b7fgyjeXF&qkS28#gcU5^L95Jhbbcxsay50<u
zJ^Kq5IclgPes*S-g5u&NHAS%+OL^2i5UsBg@sGc#?l7mbHu5lboKlX`j}-<OL)ZsA
zS;~>^HE?Po;!Zp-I5&&coPs>YTgJ6Ycuf`rXDG-R8`7IOd|HDPk^=Bb>jMAnv2nC{
zgZK!!`^PR0*WV5274XZM{vn%jJWh*p8BFSSL*opbqa0^f6&X2zA;av+(`3LQ;p-xD
zQQW|gQgB9mBVY?feWNu8ayNvOj0$wXo)sB2rG$Gno0F$k!Z!0&jVJMTt;{k7WE%bw
zJj}R$=LViIBTewt3KN%v5ohE>@K>Tfrr4hpeEfERZ1C3$fN4?WJ<#G3E*;Ai1~K+F
zH`vSS+e>t}%_1IJ6Ki~h^M-j<&<%_t4)QobsC4!TGK-Rkb3=X$U;IKffxY~h3;{WM
zLR<<8N6)G_YD^{HOy&v1YG<+d5_^S^g75r!xrL%)R9ECS9tgUM0ANddS!J}S1V<eV
zrLK~E3`)eGfR$g<n|p$TD-Iy1d9OE6)0@%sl_%W{O!-;3xmAYWEmP?u={E^%7q50+
zQsd~5LVHlXdGiKjVeMG@bdLp6jPI$f5qfNzoW4^pDN;EF^j>i!Jno9*tmH}wV0=jr
z0D@!E%RqqPo{%1LxV{I!h=_UL_>`o5m6Y-S)83o^L%F~4<BX9RV#ZzyL)poc$lj2&
zSc~jyD$1TUgt0|ZPLd^xtl9UqvZjS(C;L*eF9~Jqd)>}CpL5RlKlpqe@AE^)aNqa5
zmg{vb&+B<z_v5)j_}+La3jpSydd&!>4CrZUX?YKxXURQA{?Zckp+|4?u8C*T{4<K=
zwaFw3K;0l_v+KOz1gkLW1p2liBKD92*=&rGM$5Terc_Q4j``06(l(6}2#yPGW(}(g
zFBqdb%+x(9(=wn>O5g&nOVJuZUQvl0D=KUN53zW1Kj6@4DadZ`$5Ug7${hpS6|!=9
z;V1lm+*aqt@s|*`EM0p^MT!0nn)yrlWlb0X%&wjancbzsU_uy=XD@@subrJtcEYF}
zf`Mx2Tx0}{={bBAvvHzq1~vE=Er<yMwy3j5txDdyCf)KPKSWx`lcQf8LhXQ+_}R(!
z2o2`&omf&KgKM~KFot8rKQMjJwPhc|fH5#Ifb&a@<)Bibxu}jgevUNoa)5nmWR5Pr
zYom)5oHGpLOtJMx1m<W1yKyRNIpiI>XpGDy;c0UvLfs8ig#2GB;vbX(aR>g4S9M`I
zw2klIWEX-NrSdle&UgW&L^XN>DOMr9N6Cs^iV>;cK4L7mKjDoxTs3;NSUdABI2yux
ztlhwA(*`evfD=a+y_*gqh~CPXy=a9=+1{A}vSiO+ws!p#EYt({Bo+*z0Cvf;hUNjX
z?0?6mTv+gCy7sZ8Nx^XXrwoC3@C6WocjQJ<WE62Qv8yvhbJ}RvJJdt=W6N*3Pi&Oe
zqxY!qO<NVTz5stdeXi&r7$C?OC99i0Msihq!JXLW-=G%32$*}6mSWssIs$uYXr0!9
z>xK62gi)Y|VQ)Q0R9Nto=*!GD14kLsGcq1}duLC)^A9U5C9b|d1mg80w(#WlcSp6<
z;kKit{BBDB01y7`D0}0(oA=dYvu8Owr`=x(>Rbx8DK)+}96}@TEu&>{@0il11;xBG
zGz_vf!+m{3IXO8+w!)HQFNQO;o=H8B|9+M!m67X_>il-Vr&i5aHCwrB*_}DJ$=CNw
z$Q-@i+M@9!WrbPw84cQI*f9?o=0(tWe~$etUEhSw%DcfJ8C%@*e7m{hyy`Zk1Cxz^
zTzB!g)^V_S^=X_<9M)S3rQ<^FI?!5d{V5(IQ<vGET-6}#^b#pX6cU#=N4o}3l2a$w
z2k>KGTn9`BreZ~A=`qY}^?}7Jos&Q2Qsd(6o}8O!fwN6Pt_5L3*%I%DllGz{$~uo*
zX`PEL9eFLNUQyLczBk_jF-3yHv8$g*Hcy0%7thrP?volToK14%%*e=ys7G(#mjS)E
z&)X%;opQ~pU0q-^5bM&v!1A1)`Pf)p4n0oS5do|S2w;WC<RLt{b_d-1PwIW!Qo=tF
z@?Hzn^-YPZ7Suh~4&FuHnWSRRvd7UgJ2>1hZ#z0Gmsnt{?umEbyx@h#Pl57iiwR)|
z1u9IL7#)eGeIJJ%LxQ+V_D7d<1=2yA6JF03XK_>#Ez4ec$2CM~BYHebYJU%a-2W6E
zjV6KCQqN8_Stuy!mQ*w5>|FJsXu49{C?q#be$})Z%eijh>;-7PQ-QC1V67zzR5{5u
z9w`r)naTPLUQXiuk&zFrEiJKc-kf$>7_9uV9>y%NZ)LP#&$rd5C><$-p0DQi)qotO
zX>3}pQ}gVtl|I@o6)vIkexG!>yI(=lhuy^JACOK^=}FS1A@4)8%`^TS{}LoqW`8Ou
zb!BQdu5IX<wB}^tMZk>;N(?JM&=3Un%hi=5miVBYdka!jp{0ACHK8_vGf3~`+jJJ*
zlKGva340_c9-KT)24l7tR{!XdCxo+)L`H4l584jZ#Xr1J(?lh5fSiO9)zKbK@Mi{X
z1$Hjw`IlP;C`{O$CqGlzRQDJeYYDtrPW2c<k$<VS4a`p_ew&uSUqap?=oqmd*M*)Q
zQ6pK_L;b^R31-*0Q=CYMXqek_6UV&7uii(8W8pYh9&_Yi;!{8&8aN*GLR2dZqsokr
z`ar;!lk$m{pUnh}*M<(VaEbOg#v}i>@>+2<zDoRMQe7616Azvc)wA+@*P?UFh&;?(
zqW#7!p9Tgbfmu(1(m?nNM&UZ<XkPr;JJ@Dp4p&=`mLLehT|FcB#@c*RhNzg)j>M>&
z`~eY=0DAroVL-C=<_%<RPmgdsTq1Och2DU`|DPx&({$7!mY9Z5u0R}wwe&O!8R`#v
znXg$}f2^-pzFAJaN4@1Up+zqokOl3~(KQ+?#91jV5fB02=QrrvEyqLWLy>OUqJ7me
zqEikGRv}mp;?eyCTlX95dJze?oI>TrH+ca*uB?<v3G}0{_1s*W|HFa)g#_+C_sOS0
zG_;Hr0|DZ>>M{%v@YX+nl{;Q2H1t0Cs>5ev>U>AsEpOceT<ZPKXI=Ni@1i>mUv5EF
zPCTTS+Dh0mUSm{5i8l}_SXON>$SO6;z0wkNp&pi`0LVa^3DWIEg{%YNwf8vFNAE$-
zjla*?1<0JH#;2jf83wmey9C(xzf?N`MSh9wDmp@g)r&T@0qtS?E;>5;sWnK>kHcTy
zW6%sCSSjrStErs%EA{9DuQfFLirJ-C2myK-wBVWj-9Qj9AjT<ne%E=-`kDDO+P9T<
z$&cMF)4H*O>TQNtTBgCP47;cX=vlY<+V~mPK6d`YF*0)@*jb=>#`V8iX}z%dq?v8T
zUI0O?#=zhPb?QAjP9QsY)J{L7>^}7P`LzbLg!kdf{B=ZxIvTqs@MJT~0A=(MV`=pL
zm=vaW@UQ`iYdvNg1vZXDauSyLS9e<l;@=@qOL$luexGJi=kz{$*QPHqpeXLeEoECg
z+C#pJHi=CtX5L3E<aO1XNJRd!Q-_3_KnSFG_7p7j9y9}cA7}@_ChvTDcMwb+^4CBA
zA(24SQ%bU(rauEkS5bJLgvsZrfBzA=>`!h4OEFKRK52qyiff*u@g4#j{8K~|3JGYQ
z^q$lsW*D0;mxw#HU-z0FrwRV&0#NBF>U{=<Bz3k+Ikiy9^wBo|)#HPaH9yD0p+9BS
zBJD0#JwI>1Qit;`=n3>l3@&*2GU1-*aCyPYe+?GKgBGrhgF;gi=DWGh)GUWhUU_xD
z-1^>3mF=@K`SFU`5NmSlbvL(eb#-+op;L(wwN?MF<xDyb$GgME(CH5pySUl$#n+n-
ze}s$r7*^d=;;CPrcuQw4MTaCB{+XN)xCcwgtbz^Dv!*zI+U411Li=Z|q9!dKK~q3r
z|40ZMwc0QA-`3~EJV3xJ0$$d*lA-BhtWJg|A7#B|jN~J;ST2#a#C<FT=$Gg<T0yTc
z{6Xj6i|tR+hV4+2)OVrV1la!MuaJ?9W{%^b<Is1~(8%b#GQ&bs+u(du{J$E)x3R#a
z(o0-NR}-M<?vuj0$wfNuBerdyX?pWaCBR=#Ame|QonHR;LJ$y7-&bTZ$8Z#B+{Vx+
zc_bhpC`(}I*H~+!+&RxA2|RQMZFwIhu%9Y<CdA@D6~)?5;AZ<DIi4)!^;e6x-z!xw
zbS0@B_z>$F7l5KlyqYAzzyjSNq7V#%i(A3#I938QRI`CX^MBX&C2Q=DvMVfm$y}&~
zqCJUWNlI*zqvI_t>JMbj_#q7#HivTxNFJeWiR}IMH_LBJ%l}8DRIyGfU73^jQ;MdW
zIX+YvH$aJC%kA}{m;`$uN#4A9Oh!V<ns@5RURgOMYY`$3G0VS<yDa&_s;BuBO14w&
zBsfX$wWd~+w=nm3Yke*Lr1HPUIPnLFiz&&3FtrVF_|BI5Ha~JUJO!Z7M!O+R)kHwG
zol>G1HKyEgMDvJqTP$XwG@15OO9ZP^qAEL~Lc+M@)lnXNwZGyi$?GNuOpl}s-PS*l
zxcnxFG>2MP8JWlFn)iMSeYMbqqePE00$}IaUvxndQY)dipzmRn>}<!L+bNgUQmGV8
zHJyPpct!g(bgqzK&NA@$>}K`rD{W?R=DZ5cg*>Vkk~7Y0l{eMuI~X&~z+EqMp{9Xo
z--8Mrt@7!5t<iSR$k~m>l#Pc)s^|}_ZEN#8Tpa^dJM(LdUmyFYAp-OU)D+9ayNfsQ
zPBD@dt=7?}+xBB<nSY4IJFI3p_Qc}f(SDiV2L&fzJr4%@%NcZhOQ{b;`oF06_4N_d
zLz9<ZG$>N8mpd8QBzQb4bR65=pc`;xGAR=;u?ZxB=w4IejM6;W#Yg}40r!a$j>8B0
zl=4!1a*7qn7i0r&$;GX9-<3QRsa0#=e5*cijtLmhYHy__c%b^F4LvwgZwXXlvy{10
zMa>9(oS>Z!q8C>sTJ1_{+Ost1@+tcDQzenMAsz&_9w22=wRD+~d1ZZ5+-09+Yks`V
z=roYm$}c_F64>zJ<*QeTCX=LCr&yN!O~KI}wh($;k(<!vXAe#Jf#^*EOll)FH8t+S
zkxbu`(BQ(o=y=hjSuaV=fFmwT_WndSmDmr=PMl50dXgl}2DSD#=iiLWqS5g|_3x~v
zNZ>xuUUX~v{Nsn~u~D&s3zY--&G7cyLnF$DA^mGZ{x4HvI(~b6Ke6C<;DR!GMh17#
z1I^3QmK46xGlmqVcD>MH?1>b<O5Jrq_eqRQrRIyC<kmxp*~dx^*N7;G0Z|Egr?2gC
z*bN?aC*G~KbG6%IQyy_qkMo;-;(_FN{e2O*RPvw*mbW}lpf=F}r7k`VXrAX@Z&z6*
zaaI;VfslnmVi5L~t7o1trAMvqY0t=9q^v6Fts8Ac4fS^3&5jKWa~6xWDd>E_dysl5
zGW3I-_is8=aeK3jM`a&XM`!sA;q^@GWpUd2ATwck{W5<!i!7wdD*`EJnh*wtsPfPz
zRqiul@`uOBlSaXnPe9i&3Z5lVi5(Fa7wFwH%ND6VM)#A6>k1#18BpXSr)oKgzaRrO
z8^gf-<5u>vO~n_1?8b@bu>Z<Ku>XpG*IIoB-FTI*c&iDUNtIoUKb|Gcqulp#(bhBa
z5+73ERe^zteX)Xxaqmv-G%~RfAmVfGei5XlieNY&9$u}CU~3Z`uQI06iA5zt8I<X6
z5!e~VHgaeg-_Z5soNBRq7xUy7dDP#fG|JziR>tCrR^h{L3E5qsMhhA}`pRQL?Yc+V
zCee|j^)QX*j8Cv4BY)6v!0MxuUzP|Ws(+P%C5O;_mi@bC>ooY)EPU^8;?H7je)xD0
zV!Y!0yo8g=?n0H@_bU6y0rJ(Wo(XKBLh@&O)qe9MU+5W05`{Im4{q@$RTKSt+GVI>
z3xWOFr+4oc<2#;O23eYiG{ucj)N#F&^DMQAI_4~45-PyzcULWg`Z7JP!>y~vRKYZo
zWX>nj2sLN#AYh5qzxxYfAWuru%mS<58TTJeyQ_CHWL$D|pkdp(Chtx?NDewu*^v~c
z5zoS(Ib0Rq@W-Judy|c#jDA~~f>;-6N&mcA@~Og0L}k>G(0?ucGUgF*__c+_>3|N*
zjbQV)8e-|zGCJ+!Hs^*;e9{<L7BOlnnb%_GcRT1tk@oD(n>YNrakA;?L2@AQC;9Iy
zr=<^`Zt6BKAARMy{QTC&bjC-p$vVng#X=|tW2c%+i~P4m^1Vc~2m1N|4bsT+)+uwI
z2_yX_7Cw-1&;UZ9-E`Qi>G7VR%9mH|VhfmW$0jkPFdXc*>{2>3B43%Cw7+JE{E!Z3
zl3U6-C9p=U>mCV4ET!5G!&9H{=g!nEhF~h}m1>5*FeDAREEIYxq>p5!03G7VZn6Yu
zMV7iU!vG)-)9$6kbp#I=;x5xOn^=E#+sDd9lvJy=r~14^$OabI=nN2Yi&nomZ^7`}
z7JZ}WaD(y-&NaVId6~I&yhd72W80L4<*tHreiCuc?by!;L$5H*7q~6_zL2_8;#H5)
zvruv}|C77go;1N}Blk}34HJLG1~b<e`(K{lTm}Msza^S@jt)<7eY;(wwekWmXT?&?
zRV4x!=mO9Fhw+3q1AqzxMGf9r2F!{wL+MVb%!45*qMc_XE3Kru)2aXzC)uoURl}6e
zAdh-H#`U1;rSM3`F=j4Zq1GzA4U*(q=}+J|gpVM-?Mb!T40Wh&{1Hntp?fTqSm*W_
z3#{?wf|1!WvU5XVj<TR-z)Zd~r=FhPLrUhMSenlpOVYkzC6Y!?1BKS9+4VuVk?$YP
z7xrUucUi6)0$WE5(3EF;KOOS-FNaiwXYcU!vFF=D!cZkyVIFI-c=zhe>*T;c2cL@!
zUK=7P)*q7U+S{yLK-q|+V?PpWT<+lf%A;(V<9nYy-BrEbU>c(s{`HC$w@K5o4=SFK
z`r#n%Nf=laUKT@0JnHfSBfvGgDRKO@9f!e=Ypm0aBCCaG)A5GXSjiyljOfgrB@2tF
z)7#Gn_IzN#6@|VI!e;r&%2(W4x%OS!*o^C~_-I+Mu<cAeRjzTAx5?H0gH^hF>Zvm7
zpl5C7MVQff`yOOBv0YFlM3+q*``|ucQtZ(rYw8S?rsuwpLCft(c<XQ=Q&#>RZ7(Z-
z<_=Uz_1H{Wm5X&5db4w7ipCF42!BX*W8%`~`6vsce+C<Iaz8XuAoo-IfJ(v(>t8~W
zjvJsp%9LHWvbWuqa_mVbU#kP2owu`q<W!cf(q+Cr=wO`b`*FK>s-7xZBg%X5H)l%C
zbGIo83oENYH4jzmFNt7Up^d&WWvD1567RAuNrFx<YC(2)HMjV+^SEAw;Wi?KwdJ7O
z%=JLFwO|h%hheZkO<Z8GIWSn>4>?zvxLA1JAA`XP!csp!Y{-DZ+!Mn?O=+&pABY52
z>-4PBABRe;L8(!15bfya^=N%RC=qf(WRNE{zIustwf76xgNrm`fsovlLFz02X?z3l
zzji~Q-J2#X9Jx3+XZ^?$V#Htp+A=KzvY|XoujJN)p)&l<kEWhURW>&ttaP1QgatbE
zMjj>^1CuoUo&%f{^|x!e#|_5#yfcD@7t6GzwuyfpkViwpJ4Z(Bsgj{qa_l8Mvc{p^
zb7$+<m&Xwa<fv}DCYW-gt}#s60j6x`)+G;Fv@4zrND3HYh1}DEIDp>U$ZK+cKz&<!
zHAIyk4S{LcsnaBys_5pHl)6MZdGPqv)%fK;dn#F{P&Zjo^|m+~q%B3ef7k*^M2~K!
z+nCS<1$;V?ceWo?a9^300Bw=D?067U10`v;WL=M**_#)oA$zJqz~Xu)WC7wypN@T8
zS)yQh#L?s9a#&ua-_5rzu8<{h3IDaq52Cf!9D)V^5X@a_h=PMMto=op#KJ~<QYHM3
z4~m)n7s0^3F2{d8ML0%jTEEm|znMGR7^IM2%xO5kM?b?>#IJKW-nQN@Rzm8m#f?1w
zR>exM)vP!u-`CMuV~O6Q0Sy;|Xy|V&DqZRxA=Aa|`N%$CON(>u<*X0vGkUy1Xgdf}
zF<BxhdAgRHoTtETIj|$BVxt^uQFHy6@!F5@M0=}zW8$?N>-`3P96=9t#gYO;OG`_?
z5H3U3fD!Tq2w^0v{CG|8H-_}p`K_QKc0ujXTQ^tv<U=c?p~CqiIG;AEayn}0Efzps
z?7gok+i{*{huTRA@j0bK%qT-L|DVOJ-<<VoIZdjGpy{vw{(S3{&)m!zPyFTC3)cYA
zgNVCLE%+OVu@Pl3R&FiBswVg@Qs0y5z8Pj=e-r`t80}(N_3Jq8L`Y6CGC{y<*?_d&
zOP)emz=uuY{JUP(10TDRS{VnamzyUfCDzzNdDPoX*^urEKnAQ1yCrqUe~01tXwn`6
zJ(ARW*Z!_0fC8liXfaZy!bLtyy~}*z9m3CM>xClKM<+&nI-w&2Kl&~ngK$;^pLZkL
zEQkPU0cDX3s_1y(UFJ($msoL;S4Kk+<XT;ROkGGBQPe<8gKEOnWd*n|)TKH_4^%IJ
z<jFPk`M~HDM?s-!5Y215{SiWU7%oc9%`3U7$(tj|z9u{0Vco8BqS9Bu7|-uWx&5L>
z;@!Uj&*Q5qggxY{2K3N#XWNG1r@*6YERYE{py5XD#g`{kd@%7qAfMhf6?<T9d4dRx
zee|RO7QI*`J@eez;Wb~2uhHkn>L#mR1qRgze%EVw$oNiVx9}*k+$}a=n4GnXml{1@
zg@OJbA*CSi00L?=Kg3Zqh2UW|3i+Nq+Na^GMo;VLgi@k*3s0$*Zh_(E08(DtyK5O8
z7!`hPS7bDw_b~an-DO}dc?lgx4wv8f;y}0Tc(SmoEf~x3%;NH8-b#flw)cv=fMNLg
zSSE~?1?gT;BG?QGt_?KguI5Sowg*G1TX+;E$$7@39l;y@4<#QQP736rf1O!OGe~g{
z@RrJ$wDs9n-#$G%0ii{D=kym}aS7hhDE|Poea*bPH!|+pjK`^vei%)GJkf((WkA5e
zkq^cWteuIG`LbJ+wK6$mhMl4~dvd{nftC>o%#(=8`kqPOV(}P5(!dgEuE}o>bUfwK
zSHU)9fsdQ@@R|Z>yH|hGVpi?zIYgM-DF5(vy%umpV~Ul3YIQD-#_K+o9E&c|z|k_h
zHr21KNtNHY_oDp;bUgc)CnpYxzyQs5cUYhzYVr6`<wpWZRD#>74M=7>exTfOvDyyS
z{-u<~dtay>Ku4dq=9+Q@tFmQt=sb`KEfFZJUGotWm=!!AhE4INMiP+kv#^Sa^7+AD
zC=*<E4~*}8zvRlsY`y@ErUo4jf|rkTb91%La>};GALJ4M(qbV@WrRf?@GJ^*%V-5;
z>otYK`(=7V6aYd3Z7(U5I8`Gq=IOXUac|o<IB9s33~^*<^^@b3OBPUy2Jm!7A<YAg
z8!j_m+K}I+)rw!+@1z}Q4NHwA0sdmG;{8do@|dIw7DjSCb<r3lkI`78D3JnEMHC!i
zyuAO=+aXqbiCRF)?Q4Tb%#fmr@0;n8ffN*Uet;;I1u1e4;%!FI3VA3FSxV&*RJDh!
zPHB7b<p+@YlfSX|ZU89=lr5u_@Fhrv0eznfq3<vI!k2Ax$=^taw3);}qR}C3$Q~+p
zkLLhdfEUyT-Unc4tf~GE`Y(X33gHwL|FKD>$d6}}-?Bi;gE_MNkbf-#A)RtQDgj4y
z^bSyOxaUbiGt7{k$~7);nVAvx;CzuUWZ)73Ba$31zJca|W*=>%O&K$gZGH-0>}jtW
z77pn=!JeqJV93P%NoKta^p4Opz87#3RK(zyZYvinp+1cUx;XK9!LX;E#M1KOgJ8J$
zk19M+&k}~p)oadd$P6=<lyas^Y6YT*g&e8&@MeH}wS5_MnrUriI1JzyDHq0D`n?z-
zQ6L!*oNOY)Se+8r)9a!B)dTe%9S@<S)I{wIo_S5lbvnVbJ&H<7kjL>ozy-<Q+i>sd
z=VH*=(Df5?sfr9-$91912KWFjqg`$DNz>h$cW5-qlXhS+z<&zxvsVrSwQdO`r?+*=
z8qgudh_$q|>`S~w@@$I=6*xeE|NE<^-^HjPI#ub=MQUj6fpC;s+*7*V&?4MLI10g+
z?G25j;UQxstbE|VTVWXcc1bmNsds*_Ed*8DPnb{<58JLj%)Op)p!{2^#^!IoZhIH4
z4&leFj_o&>+qsONpH?H#ps!8V$=p+CI@lsVLgY0g*Dfq*mY1(5xwHm~H4F^qpo3_N
zh3~YgA127hVur6_@qK4Wp#_Z3*qI3hk84{UdQDAN#UqSQsrlW^aVW>(s^|pN?K0=e
z=_T(g%ScNb_>9z)Mx|kK4o?{E71<U84f5D(aQ(;dTqTzrQ~0!>fc`JL%Yh>^q4SQ6
ztQJKRlWRov*~@os&<%aP<u}_9<)QB&nryTo9YC5p{wA&8h?Y5s=8$@7{gZp^N`t$f
zg0+l>1V|P<1QjA91|O@Lv_|9!rg7F-3|Ex5nX3(-=GWVAtSP5mdIwt{cx-aw(W`sw
z%A%6UCn4)tduBqUF~p<By54BaUiM5ebs(`kqoS{Q-a}OKY+Fh-bD5G1fomlN0%I5n
zL|aOp`kdwz7c>X6paNWbT*`8Vq5Ls%48Y3p6@`j^<>6oL7j40ZuMd2DEvJylMhnE-
z>|;c1?H2WUIvD-RGac&Hf-Yo`0s;a<%`xKa0uiF2rux=`+LvQtoySx<_)zE~It*h`
z)Qw0AvK`6=2lstGpPQ^jeICpFxFN8@^+hmAlgv~P<owpVw&4`yO1ON|jA#20Dd{B7
zSWDg{L7pg5E=cT!SroiEXQ4w!mVs$dMMY7N*{FjsVtPZd*hj)dH~2<Dw@Kah#Y$wz
z!s$Bgfw8Az@ncG&?9Qv4bkLKGgBds-dQJ^|+QGHsH!ZgL>fGIj>A!7%ty4W<97H3x
zJ%8*-ci%|U@URV4yAwT1Cm8Eo8oa%|y#VA+Z{X{Hv!#=4NiTX!JRKWZPqk7rSDCVo
z#@O)_RQg&zWn=NNDx*V^Z>rxsU~CAQ9jl98vUqrLVCp>CJ{a5E5D2K9=+5_<{Ctt-
z@h~j9jFDgT4$bu^%zJzJv3LTp5+dU7Oj8t;s9CyuEc_T;k9e5AgQ>+mqGiU(58v8T
z&T!wC$8*`e%8{xFsa@sVjBV&$%x@nY=c^BN`}*=e1vws;LsGxD4|Dubq!6;D5>_`3
z+T2Gr2x<_9!r8@T$CM%@(jtzgQCJQkQqd%T^gIgP1M8CWZSoQt({j<`BmAD&d}F4|
z3pE>rZ62$0`293AU-V~xy_e)t)-TLndSvTgBtC^9n}ZDb*rr&cF*#yRpO6hID`0Om
zAB9roC5s)mt_FG3vTx`wE;U-Vi_OcPXzRjMX8t4jQ9Y|sFimuoW~c=lo}gV=dzWZ=
z;>gEtzu0@(V$znE->jY}!B>c>OP@TsI35%>ZA*7wo~UnxKU+tlWxhQ8_79rnilwCB
z{TBDo@QPL7dpzu7phQWq89<{U@sZWiIyK2Ann~A+g?n`Awudk#IOfZDrh!)&Uh}$a
ztu%OIVq#%w$%!%PUTw~M6#8N;elf5jZIjwn!I=FJ>D(FfE4I2oygq&ieBku1J?H&N
zzfXK#$83N4@k!O-qEqBff6u!D`V}@Z3x5d*mx||2clSvjTzN|b8}|({(%CU3+r{5(
z-^z|M@6&<ToTcZLA=_J6d($)0oglFA_#<;)JW2NZsF&yH?E*>P1My*`ts|lN&Z$dD
zrAt@%ueQ;nx-sB;2&Iwn1kz|qt^HWtTkGv`(l#o)K5*lVLl9b#HWkR8flNFBOQR_f
zevE{I*C0K&<?;`c{A=eBT_+O8U~fqBEV%0Q<VhNQH9nDr&{Cu;dphYXB@eJRJFlbA
zm_Zgu4F7P-SAcp(Z^;wmT2N#9EFSok<SmqP&{nz;^0*#&oN-E7avZI^&rc^f5I~3X
zm9~6dQ_)&tP$d};w9w;5P-X5E?vEd1W5F>JNEQpI`oK#4v%j0zU^PsxkIfI1hch<;
zz@gW>GYHRf$qwDY+nfT0qM8`-kb-<3-Qo7Cv-RSJ;rpe%fwhUKJ9q934G){K3*3bw
zE?+P1!S~mKKcG7n11jts6NpDa0e`q_Cyl-)N>GV)c6N;baXLr1ZK1wC5ttH8QbWOU
z+vfqVEgOD$J*?IM>pJbBgx)iKgRY*6Igo;E4?3ny%mEZ7z9NI<LsIv%JTK13br^fn
zm=QGk#p@GW3I0o?F0xM#kt_yaDeS`sxIkbR@UUK#U#9tC3L1|-6Bhsc3`@z+i04(W
zcSwOJ{?xUQkq<qk%bC9MBI?Wx8`No-nx|Ahg9rL*_VAJ{IN;PAUno28PeI1PI^kG$
z+6!vi+b^(N$3I}^YZ_0Wq1OrtxpPF`rs8LaCe#xF>+Ma)B@}vw37&OHE5%R?HumWg
z{cb)8q_Ur?o&q)@kMcaLlp+d!O#nYux3EyHcJuT%AA3u8zK&!uOlrQEiHV758JoV5
zQ)#eR?LZ^g!}o|i#1ovfU^oiNIja7khfAn#M;EvMDrfEeZZ>#%aIw{6G=XMCe852A
z-l_GJom^9AD;asu?TuA~#{R@g(j2LL<9F>>klWw57M~k4utpvrxeFBEk5i(oXpb}T
zr!I{uTn(4IZ`JyoY2ZF*sKL0k2q=f7#yDBuXU23)K{OMegp5IAd|(tmo611m1W?nM
zpmiSQIc+hn^AG&K2OQ%zpv7&RtbF6aK0Nf{;2&nzOrZoJhKUE9cYhvn{RodEbf&&2
zi1Xx5J;Zneu`sNf7N>aI3cx0hjUSLDPg4)y*`psFjC~eF6SH{g;q;qT3p3QN;k@;+
zv~oQiv5>^|xC}o~WN_#W&V01R;NOXAp97onPF}qKdR{uBEn8o5TBVjE*5&B6J5r$T
zLE<fsM7QBkXfLvI1A2`7`N_GYFK}t(btao@JlZ$lej*u*w^0H~SB{SOgXD}_YzSJ|
z?mftMveQa}qvg5xwjUc$J-8ohF<Id*Tm~3BK_><EzLAmNPCG$1Qu8ME6=8Pb513Ci
z-Qs-y%vnxG=6z3D2&U2J0q2Urq||!8zQF3yVY<RpwHPz=w>6+?v|&`qkI!?^kU6M3
z7;gFatAEotdqzZEW_X}Juz5wu`^rExZKGx<#2oB$7lhQ$#soa^x+4sp1{yY|11lY6
znO6DKy{mIu##MavoG0<ljVUTh)BxyNJGFjy4Bo)p;s~PPw>iANs-j#FN9I6uNB9Xm
zob`TZt@RT&H|Jy06~Tb<|4>GuY1sg*H<33LXiKQoMfSe(g;X!4)_$;Xld>ZHv-*Jk
z-uJ1hp${w37^{;iv4;a%NefPZ(f(~XEv2=qM4r}|0%`g>%s1Yjhvlbgikv>1=d~+a
zTqfHi4ow&o4U14iOCk5U&h2RNzR|D}`mwq_p=O}CCE@ViIDegh=3v<;f|x|<xDN-v
z$0EaMk!io2uLF1e=-fWY_CjM!i9y5u-^=%y`ercV{8(RTxB9f7U&Rw&9vDa^pf2jA
zSJL}yc1&q*%e>~K?kP{AZVT}GN)Cg$c+uZ<KFOWz2Ew2;FOd?rY|q4Rg2Sz?tyPWq
zZszVkL6xgTpvIg+{(jq~E)>K}9CAdCS*KCxbhZjd#BHj&Sa$MZZ6qR-7a5Ps(UCWi
zSsFf46TsjFRUjrho9qhCk0e?z1bf>HPpRxzVOw52@JzHF%iP<x`VfWI3&s{UKtqEh
z|EgFGi%VeNf?dNSOifI#45UL-6@qp)h)B-|4t7cuH}&I&pdm%+P?qGnrO}N$UmuWm
zE>+jwjCJz$9p>cb_7L;6=c#924D7ewnprl*F^^vfa}IxNv==vi+kyv<t1PUrTi44F
zXa+`oG1lqx@3iYdBFe4%`EDEB!?hGLU%jqF2*$>2r=9=Q+8VD6GLk?%<}gg75&b)=
zuF0Ecm?k%bW?7EM)OnowAPzBj44?`)Nl*Dn2%iyA<Y#;=H2uOvZkcRoj=n}Uk0}KS
zh?~Ej8@Jg@M}CYRUss~VUEVnpdig)Y0e^tKLQFpPR*c&Nk{<cem{Ly!)KgQUgrq~C
z)d!+z)5FoUco0qP!={LtyA8H)J-Wy&7gVy5h^@&fSeU@%{BlLm`+8UmquzVZ1h6u}
z*i_uT_Ib9NZ^j0dt|y8~J~eW~REW6(4ekLXjGz6=lOJvFlNeD#>MNy*ydTn{(09SX
zV$naw2~(=ofMy9Axb=R}<w)qT{SxfLisDl~65#>RKOO*@geMfZ!9_jq?VVInLh_+0
z4C8BCzg*66wPY$*6`8`H++W#8kY-`wd#wGJnF-optdc4oPCKcRzb=h_`I5T1y*?u0
zprnKT0OstGj)(%Eb`<P*`V9_ngD*j6hpZ#{bwfPR7lP_trv?@RZ{hMfrr1PKXYPFM
zJpb|x#K1E!UX|+aMu7$0a;?Yblpdp?V8fqNQPOSf)_ltIFU?IsE=6DdCgh;6ub)>|
z@HmM*S`=LJO0SKlIuO$9K*v2`iGs7H4ODm(`WUz{qu;-Gm_IpIbt7vpe9wNjj^<_9
zg^2?LC~$&d&3jx{gkdmO*dePa&~2dvTN7|MEjxu4gH!$5Elr8qj}F4@$4e@KDb)a*
z{AB8u{WL6@w4&ky0}dyrZ!Gxg`9@71xG1JF`Tw=$!>|nM!H<z$h8LaI41NA=*nGHY
zI1`q#z5aj{^i2rDP_t|GVgWC($S_V2b#`>jfM^mm8&lX^IDuD7kS<O;?b&v*G-A~S
z2KH&%6I^f^BziI7ynRSJ8-mI?nbj0qn{PsE>{f%@^F`H8^^Hc~))d~d%HcLQH^Z|X
zRAO1w=0O>aK8;FA?lz+DfM{Cd<|#hy7qDF&^&D(uLpqaJ1J{k_bbGnLm?qWeTKP>W
z^_nKE-{Ji+_CC;1|Liwaw$R#9@UTqj5Uu>>y{^;BvSO(5uALoypEV199xVIdsM8$^
zaxQHb7r%-VzC1iU@8l1Q>=G%x5Yk6W<^H2SfNpxYx^`^?eAIeNyQyT*OMK!sUM*f?
zdZ$P3*!Ui9dC!7<Bb6i6hlFk8!H0tVDdV%31Cz<N8*Jv}m0CNnW`aSH3wXeNatF`#
z(sOyd$Jslw){V@<l`8XS!Q4Ef#E%YlK09`YiQvQy=lu4goKya_?&$M~*&c`!V0BFU
zLgbnMsc`H273UXC(%*s4xO|1v*#=U%F2Y0H&QYJAupz=gfb$I&sU>q?m9l@WH@O2M
zNb-v(hDoV#`&8P}XpFh<`<&!TJ;X3f8dROM#*Nh_WT!a(J7R!RDK0QW-+N8MPoO6c
z@~3jjjnzdwtds~Kfl*3OTR+p+)vrN|>q%ch3>gcYVuRzR{9woik$}}j$7zQ06EfkO
z^?@&}PkRqQ_tf~n=^b6B_6LA>I@dS8$g-$LW7Hw0k5wK5m%%{GVuom0{=1-Jz`VG<
zo2@7NnZCU!@oH=&bd#ky5Oaa*0OHzzeDCVlaBXq>=2+dU7ddDBNojD{&(ql&z?%jt
zxcFVS)9QxJx$H`giq{8L=uU8NKYOA&a5n@yXl(HWHuC~A*I>20-rz%I8V5Dj-}b*;
zvJf?#XCi3FgJ6~1ofbzle-i_<Ll6x+cloS2`Wi3s8VsT(h(<!F%pr#b^mKgj^v(zN
zy*|pGM10E(8lwxAD)V*Yp0i1m<Uz!OlF#Y%;$1?(*07WVEk*oD_tXtKw~x{BXIvEI
zLuGuncVs8nxN=e^CBZrpz&a*>XgZt3O4dNEpNK?Szq7-xb!1>*s51B4E;SOM$~Fh<
z2yrLGIp6LuG7$>mKqpC@%;#S+B>%)SbE}FPv+d)6Ju5RIJUL)&aTW~bzG{yCXBT&W
zj4irYrr}RI2dnikD=q&tw($-lPkkewF_`ay&S#MqJc-U^J6}8UB}-*mhH+~j`?^19
ztn`&Pb?AJ6jWaJkaG>14B+Gf`{AbYneBjHT&wQxJCWP<3e;;>fd2H|)gmDO_aFC}6
z5E5?z^?RIW)QnNhmdaZ-LYlFCmp$<-C+CS}l63c^(0O;TM|VwsbF<nQc^c7DlrS<%
z13f;XiiMw_c_<NWcVIT1I961nY2`;ywpE*xfAdmaR`!PdRMVyz*v-ODS1+&rM{2aV
z@ps|eep`i`<Kg1*95r>rETQYlyQc8%+qbW8-f}UjIOAWd07`2;sEC7{yc<Df>*rV9
z!ErO(ezA(Z!XCOrP$V{K(E)721&rhEc}Kt5rwZPam_jxI|D)pQal!XLO~wV!F$e5w
zQw(TRG4dyjO6U^w?Oqrke;#h$&xRUc3$1yXUL%FYmE}5myD)|x!a+RuBE(mnFVdGC
zgG<lypJ&Qc12;uqA>x`UYT2~;XY=A>&F_VR&-=U6g7Y94Y1|6O-Dg9IfgU%?sX8hS
zs`&BgK>-rsj=xrn3`?n!MKH-HG{QFbZE~Rdq#ogx^b-IM52S^FFUf`YlArj&F;{&7
zmdXJFufc$E9_UUoZoAic&krb#Sr~AvaYLn;6!cdjHm+_=GX)g1DxG8B2`~~TqzA!j
z9R}rdwjiD_5~dx5ATt3$`87L6k&zZ#g9Z>@9ZlXNj1SEg-TKj~HPo4*l_;7Q;Kfnz
z1rJ&sghnFdRWLoXmA6>QN+|Q&tKH+a^e2CBxvYcbrDNJNZFma_zD37<!@(T=9J|Av
z@UrX;)}n=ua*P}XUH}Wofl!xyu?)FyIy_&Zv6lm01peC<-tVQsE?`!I-^UAb{ru{X
z5!l00y`G8?3>3WB7{*YL2jR}a=lzEYlnL$>6SI4*t*wW|Yqp9iE}u$J7*QTQ0{(%i
z@>548z+WNcSpMrX0E-9UPJ6+DJ>+#%p~OA`BYAoGxu+-nUSn7~wmn;wN2^E&bD4U7
z-2cVIeNYT-1lT}{ft!^XTj&U-^Zhh{!M<j|9swXg?IS!STS?C}#U>ZByc*J*Z!R7X
z1idj9rlxG1!*Li?aGCWv@X0hASrtY9^&mW#Xn2K|f9*k7I+x4<#p6PFB_*Fk9tXdz
z<+r@rp()Jp`gV4|>!FEnOQ`m9r;DwEV9ewfG7gKuHV(Yw`1>=HpdrZD&D9^TILOi)
z1c&AB5Xi`$&UA8iZZ|bEJF=iJp-okJiR9@d5>9h~`RDg$@oGO<;BYm`bc)iWs0O}*
zpEL3KVzC&2yi5V3S*M*gcR@J^Df{4tFV=tyu)}d+YzT3_nn#+v>y?vEQ=Zvf4SwxB
zP@seT(=y-+(l*}jXm8&Egld^x|68qKNo3WlKS&$>%CtB%=76)8b25sN3$`{9_o59o
zBSlt)(;2}wT!2;Q!~0op&8ECsFc%lE4$p3WQFZjayT``TXmd*7LiJmkp@u-yvAV;T
zV&AG--5Go0_<DLgHOran(c{NomumIdjk0z=7B+1XTCptV6Snq@TqSzW6MWh+Ftj``
z4tnxuy16y|qOIBGz=b<0Lks5l;!iNl?wcv}uWx(yZU6Gj-+Ye4xo_s@R3v?y%b3aJ
zyvG@O653pt&S}F78<Y{9>UN&>vnTqjbGQpdDHG`1y9rsc8veBpK^<coO;PA3_D}+k
zd#X<C(qgav(C^n(Q%5$K&-IjmQz!mly*h>FwiAF8FJ2yaz?b+tbq#`<f@R$ougPqP
z;au(Pqre{L1B;L~xJM0QaUu1e&C8nW4bFa7>a&}L>w~^75m3x@<V&bhB=)()y8cs`
zJZq`!^HCsATX%z3Uhq}G21t@Ui1dK2ysCe#9yOb#a?P8XA4<ya5jQBiGrcOJn~A@%
zn{5Z&JCEt0utF;v9->N82ohMOj`#49fX2+De4Vw^9K}la2r(+cPaiSw%>_ieKG1J#
zf*U#a?B+v^T<XMI!UI>r5Sv$Y&PEy>Dq(_3Mf96MFzrIx=aHc8=HsrK`LDz=c;5oN
zHhkoohvXfI5KYqC(>P|IJsIG##3G{_D6U@vX#XOJT-s|u?7^y4A`|@d_+wNYgilMW
zz2<%u@eK!&Rhm?`UC>0pZW<bzp|LSb_L%H@>si4NgouG*GRiDE$|PvA8;U0_h3R^(
zThZ|C^vmqx;6A*U+O}nau#8*>IXT6Kqo5?4Kbi+nNJx4RLz_P!<90HJz1!)C^t<P?
zXAy&Y?dQ{_;9W8@zxcXZ{_7)#vZ1m+!}a-FC*HPeF~agpN;Cfm`!{-KCt7fje`~9%
z=J=Q9Eo6QZ7q45WAaHkmAY*vuv?zpcK~Rr)#G#UXOv&NGJR`jG0)+I-%(NW237KyW
z+`4|I+B6R~rR`^<u-#9qAZ+`jzWMFa3SBD``#LN$-4m7w_Y+76*?#@ow{M2X(xgPa
zFTmn|<^bYYEdTT{DG5!Mj_x-5{cP8@z;^e%y@Zmf4}$*PpuV|x{gUiP1x0;wY;oaA
z4Fc=L+PbYmwm}9~a#EZ6Hi1M4udRY95a?=DukpgPz<GTRXv?p;G=R?~gq}KFVKoo{
z1mTJpxe0^M)CC1}-I6|>)Qkx4vmFrzKgGfMoyFDD0Lg5R<*Er-+*=y0K67UXw~b}S
zZ7izJy;(mbiWX)(Ygx~3?$Zt)ZmOJu!u(@#`u($wGB{t*feTRgZoIYH^9N0|Iz_`z
z0^UXTNZmejkBa#O?DVULd1a#tOXSv*5Ft821V5(7h69f-ur^SUIdq_Sf@Y{Q1;_$H
z%{1Oa9yqYdk}RM1EwF@gW*G2_hLU*dgWw%lt+zmxMt!1<#;9lmEP%m)lLFDJ>0pOT
z^xj?~JR11jK+Q9YU?vc0$X|)c)n09P0Dq4?W&gv{8$6wQC`~(ft3bimI6e%)X6|c-
z@^KYI;O<|$jkq7>Mf{)}=~9Wwv+i`<VGGO#=HSS9dn=-H@~5KH=r9cD^OJVq>1qC2
z9Xc}uQV<Nn*OL4}u;B+_xQ1$L7rI9O`SI`Hs|$e(eQ^YV(*M2^ejq7<0zg7SbPwy&
z&?iWP9EtIliB-D@Vae`=<m=Q4m#!Q684DChQp!04jtxG;-{00vE6tSL-@&o(zaw}j
z0*K7)3(0>jMDEbe1;PE_#eeO}|5oE~A^Lw?Hb{jiDDQZ)X8N|7o%s7hellYTPeE@J
z5t2549hy-^l>3*3lq;NYTuMYn&jz{MI)3>-M{M^8i%8HY_?YPb-`TB(v-`h`|I#v~
o|E<Qq_4@x-<Nu=7sQpc~U&2;y^y;=g1^jpJw5Cd)vPHoE2Y{W+X8-^I

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/packages/gluon/normalization/imgs/data_normalization.jpeg b/docs/python_docs/python/tutorials/packages/gluon/normalization/imgs/data_normalization.jpeg
new file mode 100644
index 0000000000000000000000000000000000000000..8d18629535e88b0a115098bc5ac19da83343c337
GIT binary patch
literal 58978
zcmd432V7Ilwl^H4gGiBHEi^$u={+{OfFK<qDk4pqbO=O2dXxSD0@9^My0k>PfPi%A
zB=izUsDXs=M$bL>p8GuK-21)v_r2fuZZezs?LC=UYt77>HUG6H=ReOE0SwyeTIv82
z5&(dN_y;&g0G_M)I=ln`baeqD004jrKu*F0ASLF0zbD}YkpIpD0NNy6|DHD_5&n-d
z7cT!to>=zRMXdQh()kSFK7jPXg<l`yMMnIPQ<0OCk&)9-Qc_S|q`7#JmWGy=?h+FN
z-6h6Lw6qMY42+kVSy)&u(zCI%GP5%=voQavgoKp%9Wruia&l^BI$ApB|LyDiD}a%T
z#D<KLl;j%V0wW12BguIufR9KkImz$qH}St{zd)psf|81whL%{Mjsb9igp~9G8R;*o
ziEjgm?*U|t<V;s@-=(;0@Qm`BJG125gj}lY_o~`h42RMDQqNxpQ`4}rv2$<=2nq?`
z5RsOVm6KOcy#GK=T|-k#`>~O+iK&^nh0P1wmv;6Jjvk&~-afv5{vq!|!@?sXqY{&n
zQ&Q9ZNYBX2FDNW3{!~(0T~k|E-|(feslB7KtGnl0Z{Nu1*!aZc&#7qya%p*Gb!~lP
z6SKE}aCmf#Jvsfw7YTs$Z)_3of8*>=d@&OFx<E!oN=EsMFOmyBzZhpEBfol^g6XaS
z<umuo*CgLkG2ctbt!ksbE@g;jdH#BshLv9$A%OYC+HcPOpD`BvKjQ4)8T${urU4g8
zNr;_C$_M}g&K~DQ2?PF`_VlOamJawx@G^zH&ILM1eP*YIvZD1Se)_7)O0MzGZuz2#
zBrC~2-v$dcN8+X7W$UmbPsHa#cP|_DsFobO()fC!Mg5UR#V|+!n|AaTyNgzAAL;_J
zDAOaHhDckgdLTmYc7)_IESkybgcH(Vv|RyzxZI6Mu;i<?L+K;+LkY52heBVWyH%K2
zcnr#gbAtX49p$b^7ALP=jm`Qs3;Rj;Li!HNtp%yQT2W3;-jyjWr#>oX%U?=UuG6?k
z8X7v3h=aVWEw(ePP{Z2+Y4NJUSQ|vzkr1B0Dd))bXd;(4<ryLVCd})5`V`;Jc>NmX
z>MQ!kR>okjC3tiWGhPbe8lD7xjAjc~Z!N*@jOql>RSYaYvTS-`bsx97w?_AcVu&5r
zGMR^WIs4dRkc-zDQDp;5pbz6C@BxALUR>#Q4(2zdXPzu-$+ojE$Gr)bY}Fw#tfIC)
z8_}M|i<lgaH%$3U7A6~k6N+C>C%s;MQXgI8vkr6Fb?N!R(r3TG+#-Rb=W`;^d>$hV
zBb{l4i^g6B4T+pki-g7OiZE8Q^QFW|4T`Sz!X0*FcxkYs=zT;(T+3BNKkX0$Xgkwv
zWZtiQ2Ngjoi~At``udAofwPhyE=etbm9X)GP@Fl~3SELedi%0TJm;C@7Hg`y%DsK?
z?fRL>2EN;--po&*k{EM8csUw3nHadsz7K4M4$%1NS}?DP)CVCm+!GxK9oYir91lza
z<Z3j2Fq?J(DbE3p67lDNTE4g4g^LhUth&OsKq}pRqY2w=QKiWHOrK{SRHuE5={BFL
z-+6xP_7D_La#jRFLOS?ZwqSxk7CNSnLv~zV)y+wcr62WPaIs<F5R!k&#=oN3yO=1S
z@!E7o&)K+k2X=T4xOme<C%WC^#?+VoltTV#m!l>tRPoZwfL0HJ3T7u1-P3Ln9@(NB
zc$S&Fu?h9?TJDud<p`!vm3ax2BKfM~w2Go#qDP5glsbUubYG>f*)$_yIX6yAI?+y3
zIkA%2PHMmk$92P>i?Tq*V87ky1@64FT;O6hK?d_^)Ka6B<R-M7c9&s@XZC7vJ;hye
zvls3kya#M7S^BjLKDMs+WDKal5vZW1EHiLiL6x+_M50@YX8V(B-LBs0p#UWzyV54r
z?%}!seFg_s#lm;6z}RhS4_EP#<|VI-6~QDNWZVpNPbz_nyZA&P8RX}so^ycK$QR(W
z=+T?8Am#-us6CtM2U{12LD^WUqj00Tf2D)-Bl&82)Ao~tZ{IGymd!Z0Eg8q_ta@ZM
zU#}5)pZf7-&`$%4t!6F$I_HKwEzx>^`c8=`Rd%eYU7?k4{$oOah|$=!k?n=;y@A!)
zZ|nuWv1Y`Hrw8Z}Y-+;qot5HkF`nmu_Rr|GF20NFtn)rG(m0%oNBijaiI_)CGA)tf
zRxi%hpLzt7+Q`csz`Ju|=VgY2uG^mjE~#?*>P=c<tj>}gr8X_b64i=+-eLbF{&>}!
zKXCj?4D<=6i(4B!0IhpbQQ3PA$VsW&SIL;VxkRHnsMT1a1+(o-wt_;2Dh}!nRHi?h
z_E?Z*OS@7PmAMK<8h+oTFUogGRSN8b*^nIHy$KkkMt$HsIc_J8L;5Zd8Q2*O?^uW?
zaG}TCu_Z4#^%@XbMSIFWCq1qgbXINV1I6Vetld{&+~<JMhI0Tt4F)u&A58o*yb6H>
zeGtdzY{#pL!K@W<CIgK_JNwx`^S5pcW<)h5Ih>fwxmv+YpXu-?8Cy9N_UmVhNspyT
z#+^YuPZ#d*iioZOIq<I7LL@Zm^!TkRldq0?5`l3!Vl(I(#xtqs#f$eZ)R@%5ZDR0W
z6H;6NfEofN;Rmn$M3BT5ga#&zb=4kjv9v9YwU{`*n?B#Sx8R>EQfZ#N5+>=_ae+c%
zggrt}TeJdI6gN4u6IA~LnQ9yn%4YlpSEALE?jhW4Ubk=Uc$&YSA)MEw&n}!71^Dk%
zOGEbpy6GH%;3wYR-6t?eU^UP=c7z$6>vT=6%fbpC9^id@h9yNid%$|&^zx0@Wz%aV
z1|S76J$4u)f#mC=b9yjd(IVu7sJJNYb2=Ep^DVNtr=MG26yM3cbK)MVyeB1|g@GdN
zx(4nWH(@OiyY%t24#)J484Z!Pe+0rF;O4a-+%*^_uU6m=d9RQkQ!LzVpt~<PqGKs0
zv2ikIwq_O~J(qoS7-XFl!1utw`aXJSDP=OYMFlw%_*jFJGFJI!Wky`u^^=5fs_$_z
zs(Z4d3z#dx;Qhtjo}m~%_UC9qS+TF&gXj*5MN7xi<Z9^`@6Q1(0iIHJj=7}oPhOpo
zmmA;Ez7^NftHP#HxL`YvBdcPF(zkM{aCWKiayiX;Gz?Ol0=r?8Fs^13TnN+f+RKOv
zaYV(X8l;x(On}H4f~NB>X+rSpTk4vFly+#HC99!Mr#YVv*tKCef*VuxW-}w~1OEb-
zXyDXymrV%wi`TE|n5&GoWY`ysy}X<%xAqSvpSQLsG=fn<X(*5J^*k8ZH5Sf55X3^Q
zM`0}XoXi=r;cgQ?iVKl~$@kqA&z2*vUBPU9>!$8jv(g?e7_*uQWP;7Wy8>5}GKwls
z*8?fleC^)B9b84f5+ujKUU|KUIoGy5Dla8@YTwnDNGV$0<{<!+`+JX&jE-<*aZdo8
zj-I1q5kwb%NgDLZif*TfFdhJQ`S$80AZkCh@f={MY@*MhCjMf^M7K6Oa(lrA<}W4T
z%H||E152IM<;~XD|8}Yxg^rJa*M1y@*kuAEVV4^k8ZFa5>ep;C*7yY!Oo`kw0i|m`
z`>+(VCz^@T?~#ZhaG={T%LMWt&(MbNvJN&}3KR>xx7B%m%69QHXwqn}CA8)F_8HA4
zLw9yio|RB<9HebqD6+V)a;LH!T2ywR8iD~vAGlJOsrglG-g2nl*GnCi9{8Tv*q-;c
z|MmfCl?q8k)Ct=W3_}^AY`VA`mN5}}mUIrFn#eEtX*C_)Cfvm4QaS80vv;DTKM<d(
zB08q*he_)sE;x8OL@eK<5$Rn<y#C}$K!BB$y5IHcn_TI%TKi<iq92xol;2QOKTozb
zlrmXKjM{e=WApJjHh*7r`zTQ#VPFWaI0y7v4zAT1fRsUC2W&bz({RK^^#&HwaSlL7
zZ<<ouPea32|2PZQz`<Vk=^-Ws+$iYz7rB63K`PjWuY4@h^psdo$5GJLFB1oAsT0(4
zH^bHf$OVo=8AA;Rx_xmSpIq7WZtKn;*Hu)Y0xSodXGgaf43Z1IoUFI<z4b@V0VTU*
zrf(e20X$$2G_H;FfvOm~E_A3f%jZ!5kI=ml!A{9fGm%#PuyL-&ljiv5UKSZpEFUwB
zNvGH`=W>f2Vhx=&yWy&G4hSpyqqkV<!;O>|LT*EpuDmB-We!VyYT#-C_L+4_fxM+>
z(294MTiPg<%7ZB%h|gTffw4!n5H=KX4xrd<V$CcEoy8qLX~YTUuq9nT*~9Fu7=_|G
zQm=SSw44KwYuy7j9rI#8@QjOAoC}?*x5p83PDj1k78aH<gd@XSn({6rJW7cq7+~bQ
z5C*l@4G+l>ia!vOY$q&O=|l(WJV>7(L~-O_EQRii-XK*_-r@FNQ#M?Sl^0uM-$s=u
zrsQ}+SG@KF>jkBk1nV5Y^-b`~9DmiJyeKrB1Mh%|tHa2i1HM8oN~=g1jMe^;Xk}_L
zDUk3A6@9Thz~*W+gU0=>9<sQox_s^4&G}wO9D(xTYDrIq#-||+ZO75PVm<m*8V;=G
zVegt(z4t;Xmn;}}w9Q?zSns=G#RCpsgfsbOcJkH7C9y>xXnLvy=%be>Y6J?3bOPQs
z&b^Gt_Pzyvx*SZUJo#}RZ-LEP8e;bi(Z;$WDwq~@epKpabypyZvkX_C9jEfgQ<6@;
zSEuLb@_5|U{m9UvezrU@Kyb3ZaCcJwdLny@kg9EMqJ<N>m=I*5(K?~}7J0_iCAI19
zz!}3Cve6(oxczk|^~M{rToq~#j&3(!<q&IKG;~QW2J95L<VvySFP@f%zmLX~AZPgF
ziwCW=A_uA6(uFn261l37Eo(>0Uodq=(4~{zq43F$DyQS2GIsK|Cfrj%Lqbkk-=OO8
zz`d?+<16{-KFO3bPS9G=V3+be7aE%5l$_M~(*xCpJecQjkSN{}oBzxMioM<*#5vL;
zFOaCDiPyh*4tUi})#*aZVf^sDI#=$F|6~A~bcwvDn~w%G=cxE-*lR%KR9DWR+H^iz
z(ndf(cSz6e?($(p$@8lwPfDbxo0{q-GN7$~n+}}l<YZH;91WY%PkB+BiWj1UKmO|^
z+68>Y7dt()y^jVOU@Ai#Yyv)6%uTmn9kcPfc7wk(zFm=f@dJ1+&<RcwT#^2K<LT$E
zg%SalKpB^Bx_e#y)vi0HDwR=KcsJ3K7(%fvo&(4mx^BSOeXZ%khD)t_^M?X&OkS^f
zF57>dV(-U$qZFzxk8g`hW2(1<CQe<68X<I^5<?&EsT0qP4etq_Q<nOeACbMX`oQgl
zi+5=x1Gil&=^GvTk2y4}+D5rr>czE5QOh4*B<DeedyDO-9A~`i=C;${IgO?^j7-9H
z#EXd*dJjS1EFY!<zEJlWqtVSr>T9_5RceS^?l_3<%NM8CE1GS_Mds$Icc@917juwg
zZ+sw*64T9{3luF*=mu>xD0~}iOBs8u*-Q-fOr~?@I&1I^<*o<nen!o=T+`%xEWWs9
z+gP-UFmnr*-q`DtiVit)Urk(XP13Pca>*V<pca!}kMx~x6rv#esIk+yC~Pwlh%&%*
zEK0P;Qg*_bz$}Pn-RH+$KO7_-nl#eg*ER-7HHTQmxn6apB>3Fs;I5?l1iKP60p|`P
z>HX+igUsoGUa|lyPe`y1S4fNY3XRtudEJZ_mGXP7%JRh+kaGR?vRzRQ#Y^Lpz?hQ$
ztloR$*LT0{<U(7{q&EU;BYh*pMyGL}lc29SizsYQ52Oy*0}9E%hk?e!czm56Vr@$n
zI-FDrv$kuTI32Zv)YBXMUVVM>z9c5Lpwh#-470m56pa^Hf?NcvVx17eVErYPAZiKL
zT_d0Hho2iBu4-ken`Kq(1=Ew3h|n`*LAPQy?uj!QGiM}zc*1tA<)kn`+-TJQ(4=)(
zyG152F~B9Wx*?#I-mmiB0&*x+oFHrkR*E)WSmLr$cC2xL+a){K-z??dm-VwtHK>*K
z+W@I-EY!Mo14AL~;LD4<vApc)gpn58*RQ1KJlaNQ{oH0T?-GOr;>_Pe=;yzQ)kcMM
zEbJxiGM@vcfYIjw?W>i2PoxwbYpV?PL}E(YA0;N+_6zVFPR+^W>jrL2n0T8wr9ouV
zPVhtdK|FX<G_)()`Q=i3Q}I!`qH4Z)5_rKbl}~U}*)M^BG4+<CPk&vkeA&twT!<z`
zYQz%w!BW@?xi>9Ji+q=Sp*ont)y$WPKZl%(pOibZp6tB4Ik+z=W!o)zZ`nqs3-5tF
z2jKJwL-Ty2hkOhYZT+}bJ<Lt%IWEm`)9h2i%PY=dl6{vXsrlc2<1xQGj4phoXQNEP
z>K-%JXe1=_{cLQvz|hI+Kzk7PBxp)qT;CSbR(HzhHVl%->z);~SfZ1NYfs0VgaNv)
zI6_s%w^OVY8dlr*&?M50nIk<bJ*ta$W$62Bk9~6TaEa2OVK@hY)Uk=>m^g!Je02y6
zf!2FU)G`YcvrjG;^b5_(QQsCw3rT%K64EPgvcK*IdlnI#B}cKm@~v@c;BiXroJ>Hk
zm2}Ru<8bp2eMYj%-MaZq3Dus7@o&B}#LiXa-U?zHQNCYFw4^q91zS12ljx^b(ok|7
z1~tMlmv<$^jfhJj^eE&kAFQ?r<;B_|aymelEU;xrV8j)tF<o^Vp41%sf)M2uO@1cm
z8!B9JqIMa09MjPaXWoU|L#8G>AS~e9c^es74bfLyY9_!k?)({n2hRN3>tzyyaqq<M
z^WG16yD-u#uUG#}#Ot=9!8?RP;Sh^K_w6J@10Qh9aQ&ns%Dyscd|bY7rVt`fIDY1d
z!w(Hd2}9F^t`mmg4B-2iVk4|6+O$Jau&QF+1T(wUv>REJBE5C_%EkAnh;X|rap9Hf
z3n>vNPnvIbzDyCIVbvDRFibLtd0t*QGh>ptIin?-sprVJns(?hBcXcJ*RTT_7~j!*
z2$JY#n+|YHJ$0R0Q9^}CrtM$wvq^py{1r3Ow27tt$}5Q}jyeY{=4W#@VBaFjT<LuC
zwa|fGF&o8$FKb$?_P(Vt++MSxz8#WC^I18VvV1d#S5+FUj}1o-kS^|qfEAqbeXY?O
zlm6#`xEGC@P;s{1a>n~%UkR9RS3<h)K+C?%>lN`9ig?;Fw<bI48Gi6%lm1{_Q0uVK
zmofvN8@PBcDQjXtU;&|j7-RyH+SFddi!JS5!cK2lHvI4kaJH~wMQ@yjPg(lU%#vRm
zW?eU<-~fE!4hg1$QewGq5AgS~(dA%mtOpW8w;sfetwAUmr;;*{=oDH>e|pHoK%HRI
z8}s?1S=rH*x62^3er9}RYPKl0PW96~lRk|a@=O<h$2oMkl`u?08<++Zn%a+XJ;7x3
zpBa~E(8iWUm9P&67a^0A3?Gk6kM9(+?M!XX7b<Ri`Bc^*ZaPQYl`n!T1Gm(`k%)!3
zUrOXma=#0Bv4wgOMcGcP{Q0NGlEY3|ZctT~;bsDh+1_3Dpm%(KyiM8!Ru`kleQ<ZM
z*G-xyJg*|4%=Vn+)8bV{1+W+Hv+5)QT)1NITOPqwy^A?gJRQE<<kU!!FTj^088J&Y
z?zSc;`a4z>ZXbBtmi~CH<u$Ate_EqbN*q$Dax}tuZ~n`4!gn;%6yFA6hXJrS6GIrw
zuva6hj<$#+ZoG)m@3q`VE}j}fSD?Fjwg9?2VM*-LuKJ|sj1@?`SvP5F{sDi~-N_Wy
za5CP`3!*eXlE<FNMeBMGFMuA8_E#CSAz8GeFpdV^^CQvG4H<gtXo;B+q+OUNd@8<k
zVz*=p^aL9<{OTanUeKI(Pm9O9_UYv>!Y9@8i~do2Tjv0}d0)#V*KLnH$ExHpX%xlk
zJW_7Cpyc`6@Wl?k(15kKa2t3CgbuI4+xpR$se8sG6927ITrgv+7rZR(B-NCEsq4=5
zI-j5`SSv(4v+ra_w)oh=P%SpRqy1Ttbzq(m4w%vW;)EqwR)g=BJSuASv}MIarqF3$
zZKh9oA)qDaL4eV|vLI{xxV?w`bY%tqZ1E+vJZJu<&6!EP@Hav8!2Jj3fbEYC3pkaR
z4o!v74p*#jaTGsB;JZa&LyWSFOOwoaKUth<OlJj^O*itnM||;XsRJTz%I0f>bp4H+
z+8w1GiRbc`JtHLCwATIZHzl+)<zpkX@+#f{q(Ycbl}6WRF2zdS#rC34ymH=6$_9Bg
zL^-3@5_MH^-kfs2aoDj>lsAH3%Rjv&^}&iJdwOfB2{z$*Vl`L0Ww+jxY5OGt>7YT%
zE{ALEHmN(z$Z*%Kd|3|V;WDj?#_9R)-<^OvL8f*)yxAs>n_%f>y6%45m$+xjgKtG)
z*DY<hdEH2MMC}lK_?&nmT!tfefSl^H_<`CND1Awh;V4@4G$h#iQszUf&tgN<n3q0d
zzmv3<pZhi8D1k)Cyk?()ug!aoFMt<k&;qb18Wy@V#Na#FqqC>1165<@OgWwwa8<ef
z=#wm~@D~~iV|DjIhabfXJ=4I|%oBOxGCwQu2>fyWZRmXxt*0X?b1Ym+RzPQz%1WSk
z3aZRh>(DM3(k7E&di1pF3c%7B#vix#z2|_X9nXwG4Yi}#j_NSBDH#iM>jYtVbJ*N|
z-Q2@J=Hk3VdV8KaZ9&4|w0J3WNIM4W({5pRq!-H#Rh;qL8wmGERVsMtqThNSq5saB
z5&dvpI7wqTzd==~IT2@OZky=YoW9>u{p~wIDbDzgwZF+|zCpVPQ_Z6lcvc>CBX@0V
zz-|0CdsB6YdzpcjkZY)JZ*gUs-t?0bP#f&>WRL)!vkIG!kvfXNP-NzTrp{o<(GIpp
z8zvJ`cLisgE?9-nUMGqCw!8<1AZn|0&H-d?*t{QUMEz1<Ao4=t6$gr{_pn=ZJB#f|
z2z!;d0y9;%+M$p2ID>mvKV_R2y|q;M_I56h9TmD%=NXBU2P;OmSqq_~e0}V~m3925
z!%ChuG+Y(RN;m(&Cv|%{&n;AgM>NCNA~E(sdE>{WX8#7ZfI6Rs{=rlKA48v$3z-`~
z7+ZK?uQDfdwHQX%Z6Cv06B^Jy1TDT`;5MbrIiO$zq=*k36N7ORW^yQ%osrau{q4v8
zw)0Uk)Pp~rcQn&nj+DiB6)5zd_I#8%^o}jC@P%krjci}ryZP#)j+1W#>lCN3@smWY
zuYebcPEtL*B<S5v_@!BURpDWj*T6L}TV1nm{})eN731)iWQ;#2i=*@^^qus*M9c8w
zIlXXoa3v+jMH#=oJeSKcd8pQc3a^nZh)R4K(DfX^>l=uIvH03bpens)a@ZY?G1FaF
z?Bd977Tu$~^Dv4;#jo4Vakq;v!delVjx*9hJ9W&<5H|}B`9B?KOLRZob>?np%B&w<
zX@7zHNJ-!g(h$1-c7qeRZH~6=IC?bWKp?MgFoWif5m)%vFE4j$#herwuH5C{p8K3#
z>Lyx%t->jQ<*-XK1q4}iMSY!zJ?x2`>Z|&F5j!R2Z&~b>T1r~i`7r}-iY!?(PUco+
zb;V*80U-6H8B5-|n?2%rB_rCp_awFTZaZrm>T2EX?V7SOZ<X-e0lO~o(XP*5IIAI0
z*;j!zF?^kT3|)cB8JSL}ADdZ~#;EdRjqkjx=T6=_*4h-E^rTZCC61=x_r71P=kD9P
zfVo`W`|XkKTMmSW4n1yL^yfUBlQ=TQ8sv5$ejY4Pm~Skg;}__3^ixO7^HP-(Vl?+e
z!_s!%M$%pYu$L_`5X~%!a3BlVxX?GPi<cwZV6wI~#}=*=`7uFO^?<B%tA%2j;v0u*
z?FM2^Kgn5eWiMGF=ny+ocra4$9$K-}pD~zJG?yQ+zhC@fq(R4XziweDgDpz+O3)Y|
z7fjICdjV-R*#V-%D&jYW9XR<JX>p&+Hz(<&@O=eE>XNs<KD811Mi@G>pok`J1xdya
zS}D8zd*Vj=+pA~hoD^5{Bvu0wZoXO<09+`giMjOd)+Sw`5{B<c8>@wJ?F{ea6+v57
z=9hmq5SaD3Jfr*tKq{2H&VL0UIVBJ9tbo!JhH~hYg^*cXofbl&D9=Dkjp{(fK1<3(
zG9B-m=A8Ub;}f@a6qAxZtQMtm7TD=Mo|kKGcAk@&bUp$FrMmca(>*HFPE0mJ^yTF<
z=N*jqsyfUSmuZz9s$3#2&JVF~{^}(BtK$%wO#tF`$6M5p>_oJiW?2KNt1DIJ7_}gt
zWbghZm51l;=^J$gTbTm^Ke;Hlt$rv>7DIQ`T;U5sHB}sBJ9qUQg^mTx0PGz5nB6Gv
zh=#N2RoH*JHw$+}l~qn^?N0XVHJD8pb8{AKp9A#j7p$lK4&=Z*=&k!`$_^0rK3*Er
z#W}6T)Z4-#nDRa><=bZGKI(@XIk*ld4i+0}0WCUZ$*v(yWAQhTJiME}GIX>$lWHGK
zmK9DWuDyE`@ZoH6{J<^*V!IFqq;Ju(gRb*(;3z6ynzgwf;Yws(42xI%w6odcH2fQv
zpi><PcxQ!G9f$#IGQDr`%C#}d+<E??8|@!iJ|EmuhqTv9!b%MD%8^hPORMS0CWpe!
z3MaFb6l(2TPfA`}_w5YGRMwB$&h7z;*j4bA@t~XYzNXd~*C<!iHfPVH39qd`DxE9M
z)VshX#`hJh3SMAhY=Y^$a;i`zjb~|KAeot#U>xQ?q}DF^WvgAsJPG>v9sbrl&v0#K
zpq%;(`{{`(ZHkMxIum-ID^@8SmngF=R<I$~!cfxm^ivSK4cW|=Oo+_NOk=d%_ieMi
z&b@$%Wb}uIn0mLBQN0RyYEkm1z7M%7;vX_k_GTuX^{LMy@M^^la9Z(}>jBCps@Hya
z(SF+9wsT8_4(jdED+Sn%JF&G<3w7yFTOucF5GrOrH@0$%Vp&@qvmM2zsK_F}T{7yP
zqgT?9zcN{k-9PfedSIkX2xAz&&<bcBH(=7BU#un3=50%7Kb$l^Q`yuC0i?9gCL7Kh
z0RszX95_AgM9Isa-BtN{{8FX2S2{PmOu~+HqL!r=2HOffwNyWHwJx~#4#A>jnWD5%
zd91U$1xZ*YP+)m5mpfENK93zjVy4p~4z|2)WYc6bR|*zO0Id6&?EJjpg&urW))jV$
zT{_=}wv3cclWhJGum0kuy7cyM-@M-nQ<mb3bDnLAZba=5Mex<Ebu2_#Bs%g6qPyyI
zgFZ@3Cu&_ud9%)uXDb#rQxgS$CL;AXw=!0GW)pclzK*QLN2l_rj}$lgjKc=}W;{9|
z_Pddc;8$2RjD$9NAhv5REwmD4;jG6>PyW-CPB@;&`)Q*8GmrUl`-Mx&To5#jkDcgw
z+r>t*2G>7#(8SzBN~HY}bpn<LiVrp-VM*wQ-~$P=b;k7!))eO=2ZLbpa_UfpP0QxX
zvmcV?TaK2Fbg&v86+FiH%P+*EPcoUzQ!kzaCK-^*0nR;juZPGD8g;Z31iW|3C(^WB
zT54wkHM2OA63awoD=Vt&cIp=Nb;0pDu&WNL+@ru}-)7pQE5$>7O1nG&7={Qd?xA3a
zw=7CPi??ye|5V~A5)d>vFU`JaKjMB4AYqxn_8@w~@PP<-#DIs6P6gw`SM~0iXpwD&
zI~QLSki7jWUqR34Yx59e7w+mQW-^1aFy0oN4}jcG0>5|~+jpP-)0K(3TZt*02YHjQ
za@eDl$<vp~pPF7rqqx93GbfJVOpO1F`m;0``H*P2Ti2)cA^teG(!9B9t!<Gtlt!`H
z9NrWAtkN;)>X;`ztyP<9&KW$<nu>SAHIqy?jE2jX>>S`JmkZ7JihoDAjG=rh*23{=
zec0O9Wm*^!)~CgN;>6zW95~Yku<_nLT+4_WT3idc*+R+}oOY)nvmLWDRcs|SSgOOy
zFO~Rp=lR`1b>A+Q&oyNVIyxRV#7>EO?%GY*QqE@0iW>|a11h(48dq)RQvlmzO&9s7
z))l?rQ9(jr9n1s8y6=ys?SDAEpYMI$iLb0u1Q>CH=L>8SMJq5~)iXg;aN1n1pK1tP
z3dIvvgdm6N;W~lpEQ7!aR-8yO{h@rr*`Y+^yN{HvSjU6F#{=F!11Bk(GS30w*?ePo
z*CH%zO@Cimt@zH-L_@Y`NPldDi`CnD?j09Rp^Q>9^Lqv<iN}>Au-OVR|7Q3#7*vSE
zJ_+19KVs;AGNVSY8wAx6;%V^aSmSahPFM+I_Y#NrO>vnl!KLkebInDTXW~>3kL7Wt
z;~GmE?Lz>pTs!M7GA9bc)S_imGQe$e`hfF5F5Xw?Lg6(}ny=j4XSaI+XU0hViw>%{
zhbyJCT=g2prDxtoidE)cid}c}XYeC?O|Okm=E6zhZ=)|Y^VZ~IwWntcBX*+viZT{E
z7v}GZetx_Ika_o6tk0m@JsIi~P&U0e0~GRtTe4Wv-xy2LoZ9EFgQc3lGB-vs$`vna
zupk`BzGh3RH9MsoYp5cn*D}UodR$fgQqF~0VQQ#3-r#Z2h_(2LM36k5<%jc5ZsT}G
z-Evd5{0FJKT#_PTJ-R*o^hZwfcn|b`2ZX8CrqDM>qkiJ1kE_`lnY8oh>swdfFU6El
zG>TCky(A(wk<yLQaGEwd*>vBAqZ)zEZnZxGC+x&^-cCJ9;l}h<#y>OHzCD^^^mq#r
zpH<O3KX{5NLT1rKNfql&ByNp9fa|BFZyfGxw9%>0w1H~Vx^^#ZJ?^n9b&`9(X#vTe
z2^y!q<o50iNg79)@6UN^{RJM8LxE?*JQwxESif~HlYuin{EpEM`NE{$_cg$drPad2
zBi6#Zsi|f&Co3y!WMkROIOKz=_(l<I6)rf$k<^u;5eav37U2=u<eD4>E2vbAeZFOD
z?jyzgZHM&Y>E*b*Y8dY(?Dkoqj0CHrPY&ZQm@#idF(%bu-QrpgU8eyQUz=*Pe{sK;
z%+`{<TXeSN23qKA5XFS&ndM#dHm7y&vsXv&b;+tPs`rR}VeP0A+Px(<nk`C%+iNI^
z9(xXKl$DPuI<{i4MCv4QfP3^_b%VF3hVk=&#7hB_Ozjieat=`cO_XS#slM)ZWjXFc
zc6ZrYQhl}Fq>~y8m-XZv$XWmLSt>;WB}x<{yYOHV+M@f6C5Ex6ti&p?G$7UR?a%(V
zEiT{4+~3^pav}_7ON6wma$^^KVOA5l#lGQ>;a19;WYzEPeI?%teiXA*1@%J{RWFDN
z`?wQ@q~m%JqBG9f>$!IngXvkmK_-+;pVmg2)p%Sg3%Y=(Ee797>z;t|g)MZBq+d)+
z7!WB-Gpu}Q*-~wGIjKQNVM~u)dkmp>JF&$^egSW|sw*KX3hkOP%;xo+PHgBIaQ1c_
z@Q3z%Lqo$AA(5)mcqM&4Y{!y#@ka+!3Uh(a=<Q(#j9FeI7pJwJo7Dcr#W;NnIF4e-
z&K)tt2g`9+8ISgjG6@bS61$m@%w^uYT*c0RI$0pGor6yCS}KRMUw-a-G5ci)f6*`X
zPe-s;sYf=0sKN5+wYL(7WdoGDoSxI2$khW4>6k=KseQ7SB3b%#fS`MoVVrKIrM&|7
zb<NK8OBD;n{X+#k^ak2YNxEHg&8?nJ&Z#&^YO5HtfpzXiD6_#G@_&`U`9$+qkp%uC
z(3kh_*iGO;uG8k`imDL!CK}$YPc?*yXLrb%KIPuixS0yKPo?`F!*L0lZuazzugvZ;
z`>DB!b7;?mZhP{QVCs>soKr1y|3R<EJp-%9Ab<NsppyU}x);c#PC*$~c?*b20p}tE
z@9_Z~_&y(Zin$(JGmdzEd*!<OBxrL)YkQHgIs2>g%b{l+P&nRvagMJ?m1(TycE!|L
z!~L;}jKR&x0bl&LKb}@DUk&GJqnxjSAdIkT1ny}pO(4a{aXagTy935SuGHlkgL<xw
z;ftGEkIKUM>+dKG$s8yMF+QtqxoL0wuFyZ}bzNGcVbU*~HQlPNuw_sSF{ddkS1bxT
z)rDVgBO-AhbC_CWiV6L^{G*o&9UvvX8N#m)0=WDhRL^UUUZvx#)FB7dYjBf{y#cRv
zTXQy}p+r#nXU@Ix$DCp5g|pMMxofYgets<nQh-KaZ?FM4b}%c}9BGEXi+}=tnr|@z
z)9J=LN9X?F!wp9kxO4DCz0q*oRn|r4x5I8@ibL_1Wg8GD3}u+b%Mn-AVUfT;`f`6^
zO{#ZD*XZJYR?Z4B*rr>tKm8n4uu*8$sC_%;!{)GOUb0a^vQ55gdV@DA?_Qa9p}m%N
zzm7zvZbNb6kCPb!&9&SEi4c*mk~t9>W>*fbGmQb!j|=J0677p`?qY&IP^l2ioz~9*
z5jix<q8MvBRz4O7$aKQ`bc{t^)LEVxIP=qfwf<c7PWRGYz0ulx2jVE<N9T0w(oAY#
z!cxwlMbq;dkc&WvRJxj;toSA&L-#LeJ%wdcUgJ*rCl~j6jbd`f3l+3`9ScUEG?k$$
zwvDwVbLtwV!$l5{;q-ods*pClZpe!>eL{OZ->JMYUo7Y{*l!6q?b-n%Qx;n?BXBs>
ztY0^6oJgHe*p>L1dSRYk>Wy1t-y(YL{THAXULR|WaE*aoPU}#;Mf9%MOKaK2`PAVa
ze760ubH7Q@WDz&P*EKZNgMjxq1+arX5SCV+KV$1hYZKd)b><#aNy6rFJ~q>}eNx-a
zwC_3)?w+$*a^?%EQU+evb?<32IZC;^`NWjh=_;YTN(Nof-p7Wxf*IUs-LWbg7O_~t
zhiE|Qph?vrQ9(Db(Z~*DfZrh%_-<*%hyx})mx@W?(N%FDZm+8zqbxfF-g7{5j&Cuc
z7Y{l$p!2WK#PQ;JhCKth9*(xq*%r@ia3(8ueMn6jV&3v$1vgsTzSk0*{XqGWcYJyV
zFNiKI?xKZ8Bsf?q+jtenia6__n6f6?Qr-nwp95~bF7+WF;&B@9#=N9uylW6&n`9(n
zxa$7SWle_v#70!jVzcjP6gi$QBH5B1g>SW-HYLLDRxLxxK%1dRf68|HcOYJ6OQh3y
z)$qXr-{wf)nfc&VT26YTH@`4XFH0j%8hhOoJC30{2iOCnv{(waF@LmhAsT0$M{eqQ
z58gcoq*jx@cOQ<1Fuglc(K*t@>X-XUnWM87LX~&vw>bNqjwc|~a!zZYTWy)X$&whn
z$UU4XJux(G?ko$qo^=jbaWyyxEJro9+)Y68N>*oC7?;(4NRihYJ27-OExTkYm?JfT
zsWnbY=^gomVzJ|@pO|#ft&CTLLyKT{abDP<fpdVvP_XJ%3}uuIl+ssnhpXb6{$;E`
zZaZn?n9}|68tNH);3j**F{|EJA{coUEsC&v1HM{l>ASWB>V|MmZymq4_=$Zz(|L3n
z(=hm*MzM|muDQ|T&Y@^bA_?xYK9;z_)5DrlcDz8>W%UGKF)^!+9y!*`%qrlQE+1+R
zsG@7Wl1=rs(gxA6?Sv-6ppiBh%Fq^S+h^sSI`{olI*(nc2?s%*ao3d-QrcF_$h)u)
z#rf#9jLP`RO4LPe2OmXe7=|Q&$SjobA6YrdwBP+?l&{b`R^5_l*~)2hQs(&s<7~0l
zPYg+9*6El|i*$*Ni$uDTRYn(VDu#sW*LI0qrUlY)zvDAW(ZJBSqSBUj={Nam!?))U
zmO5Kb13{Br?-!ojzxZ~3n$<VOXK80w;R<iF)CTA=_#sx8=pl~@;!)&_RR-E2>7*ck
zR9W4ayS&7e?{zidv*v68H<RdFG<@3zZPON;ry9NJWTooNWuR-(hg51#lAJA`^?Coz
z;!X>KyE-OYL8dJj$z5X9u~ZdRTwkbdRKdc=5l|gt>%%{uTo*6eY`VIxV2}}m_-$s*
z8Jx^7NJtUUX#J@U(`u|U&W`ZI`Z9)R%)+|{X*r|oq=9?<if!U&N<>6Xi|K&D+o8U5
zz??izyk!fyON<cMT54@Aw^l+|WWy#J8T7W%Lk!9(=Fe^23icbkRmxUgKk1=+7bZj<
zYj|Y5m^W+qI@&M>IpQo-{%U-DbWSR-{0TDIM^|RA37@Iz)ry&TyTpgRF-hF0>&yh+
zbLMJ0mNnhCl=sp){zfpk9#h)1?0r+?krNw2uN6ao^bJE9e`vW7-4x#eyyOcp&m@M$
z61d`oC7y*T2c0_1h!-|;T#4<&&cv9G%u}4D5!k@e7}kVJVgxB`lXzLdRPQqfcPHtN
zSB6X=|FoIwOnyEW=P2CBpZl&IiC~2=*Sq$y`JJjH)vXgdd;{4?(dcNebX;vh*R}kr
z9dj*}ebCD<(E`zriaB}`lW(-=7wZ+|E!`l3fQBniHY$u7bE})i_i!Q=gL?A^Np<sm
zBC@T`WhXyR8jh~(UKlzW!4BIY;4GU#G9!y470+~i%;7k%f#rzj=2k^tnMedz$Syv;
z=pMBJd;#LMz+S#{R_xl77ErpODmiheHe&KfD9a}13yD7>=9N@j&j>6F-S8Cz!18s&
z(W$-x9VShJ4QGEe0n?xE-KU>o3Aq_`?{jo;{_0e7TwT^PoFw@62<+wnMzX*Cy#;pK
zMpUchzPn^uZG@q^{rwZQbn|NM7m51Tj_!E<R3cc4GaFke(Tg!2b6L@z)*dm5L6xVL
zo~nf^GQCT##Wq%~^uL5x;N$-YqT2|PJS$XXEbh{e;H#A-Vj-7M#i`Ds?U&mM+6uc9
zB~M;IVX%~c6y)O8k-F&#UraMqbY2K0i1p{sTRC!CV%z=1#GwTe?$UxXd&>#&0#j~U
z%89xG3}^3P7vh<7h-hYe-N@ia+x-}4{?$ou4<c|pZ<iGB{KpYg6D>qEDX74b6Utg8
zSQ`ziYrCDMqJAZQkNbuPZVhtSyu86T4|F%;mHJ`)a#lYT`5OO{mZ~Vll#L3f=!wu(
zH(fdApRFCu+C-ImIcs-}t-dSRLGH}WH_6oZ2i70dx27-~fRcD44Ahtbe@~BfNX`Kh
z%*BQ7AhvTr6$k;1q-ebwt%c({*%3&U1UBzKxF4VCI;XJuMoD)Gc;@H@Hb8H6TEs`V
zf+_tQG72Xn=J_u6<W)JnNi-`}Z)$UMD~Pe8l%)ImZnqv5GNvlKdA%!x5b_bPgkg)a
zyqCe{)Zfq5DogdC+Oo@yMXhVr#VtutZ}!D|>MnqPA<GH`-JooH4tQYMwSbix2A-(K
zV=c2K&jER2$Xz_gdJaD44sVH?9|$it!~q#*#rO~|9_SPF^9ihLG4S<h-)3T%n!nIF
zK<Ec$se!fWIe^xq8+4%7Vs#D(QMr5$NFv%Tp^Zxtr`P7i&jD@Ok;K@(1Cq^T(-dOY
z%wp2c7+u*Qi$g@0+wv(8;o{p*h-iq0qwVq_dx5_QT)vHW#XM~KaO#ie#JD#Hv#%ur
zQDSWGUT=m2U3Z>C`YPz7VYh+%e``_qcZ;Os!cftF(k$f94FV3eMTr{fPtEfEStM^%
z45Rkof0m2JpG5MJ-YBmH`WLxO826xPp)<Zk{T+#5UX1_dajM5AL)%$?9^zw|rcFY0
zAe@xWouO@cM}FO&blUz;lo$OL*#9q-5C4<S{3Y5e&DbSm09U_~Ie`vaC)Dy%lMSc;
z;3O*7`y7x)P&F1L#%~fO9S_9V{vHPZ7k0|X29OI{h{+B`%mpBhBfz4vwJxlKn}jXB
zY*ydrl1(`^bK3oTC2gWH$IK*~o`$3eI=^>nR1RT}E-B_zC{IR%T`+n%2w((+wm7LP
z=VwEMf9C{bs>bQU^!$-vRnseF^7ek*%9{&!J6K4`8v#epgl(b%!814|?9t}4PxBzB
z;|$b0RkqQ?K<6C^&h4L0bZeP2wq!Z{rFoG27_y}mj<s^4sqCP6SRpxTP=)ovT-fa1
zVgAyPCCxyV&=h9EktTm7WB?GL!a|IC`0*GrEQt*z`hf(_0ZU_$j=Q?VN!^cRBT%KC
z1L7Z1Rt{UM5uNYka%Yz%B0$({AcBepUYdE~;34cPWHAzgJp}lp{--tFJ^NTRgiiwg
zTg7*Ho7H^h(onoIv}QZbFfRPv3~CDF?qI-mOlmPPBsMsc5Si8~foGH;s^gFJR(UPa
z1Wp>)Q$Y{|9Q^=}_m9Wg-+-^DgYOYF2GcnJvk5qy7X1cCd@Ld=CR+?G(RoUAdG&ny
zpVt+SB9{d&|3eG8`GnC4*wu#YKsm&5JoIYts*{41G{c1f=2-5M@A)ZSw(?8YgY8LY
zgrV2|+-&}Trv8nJchsK5pIHF@g?iFS`~P8G^r(OA$KT?f<*RjT%s|TjlD41NtcFM|
z6*$KD2`O%2rzxlV{}QSHr3cGOiLMRBexz;@RG>1mbc0P%_xQ*Q6<6bM<bX)~7UsUU
z9DLD-@z#}d0IOoi9iA-a=zp7tnxq-78Gj5Sq6Fkw_6jFs$H9-0d>6Gaf;)V~-AcQo
znCqb{>Wf-;y36#fCbJ3G6B~}^M{ndx?)Y^90xj5ilk1QKcI+Bocn}L%X{-h0TxR7P
z-|20Dsu__k*%cQ|e<rCpZ=~6!KKvySuv!a5<V(k&GOJ#JAUKG_4pRy^j1ukp%hSI+
z037=NA1@XLnYO%Ncbgh~V+s4c@_LM1hzwtPoEzm%6WadQ7$dIEVKF1X^+(vZ#G@)N
ziMUdC3FNQ*{GTWEpGv1k1~eVtVZo9!UB^d<-RYYdh~txWrP#a*?&UXI0v)QD&lz1>
z3AOwy6Q4*YgrUO!<oQ2&L^{6y|6S2iE-0}_s_HY4OYPafuz6-lt?O7h1DYrbEnw3+
z;J3#65Hy^jaYn<(071u$L9pWzc<Gl3=YV_Ro%HzYGJFJ?JG@XeKcb5M&rkpI;Q#$%
zzB?5-17FuWBTyL?{f;1|&!ew$12W4R@jRc(-8!Sp=i*b^OM|rzShf&tJu3Dj2ZjcB
zNLK%_p&Sbo(H6b{6pgDmnM?r-fUr#vVmt-#**T!diSsaIq@@@kgiq0r<vZg0d=40u
zi3Rw#{t5kLI7_|ydOu?9;pV3in^jw~_tXdEnS758g}W60^P#N!){iSb@zox`qde0(
z9U`P{7;he~C@$c$=-)xM^(P<)&DtO7jk#$o{&Va)m0)d@-}96ujnr!oUHTK>#W1Nz
zQnQFu(*3D=(Qm3Z#8qX%+{dlskb@Zk|1&?fF5rn+4kd8w*qBJXXEMo_<E<NGA%$W9
zW&<?<i?tK2QzXkuLzJn0)8N~P)5|8pA1{HB0xfP}6^wprG%rdPmFc*|-+m%AUkxMg
z%t068$@if6=u;opQ?I!CAbz4@tbzfa1@FKQbSCt_!cVRdE)cf}5WRE2h}kF7FME@?
z{Js2s&`x|`mKs3rcP3s8(gYOoVg@Yy&M4Or*6}4{k4VA4GZ!`eO9@7&!WW8i3N0K=
z+=+)mdR(0u+SNly)IH7|hf?8ld?`fEU4LhIEr{;DAfruT64>8zeokk@CyzgUq&WGN
zF<5~fYpm$yy}6buvzN2NL+`i;otl<W<;1)+f_z&;@-7@QWw%;`4)qUYRv<M*&aB0V
z<PUWMPhV%hQ8E3m=p|D#y8IPiA{$TSEv4F`B^-hO?AwD3Yef#Q^<3UO6^^l}k6|*>
zG%H12QJ{Ie{L<9;B)Z}I*NZydpEK*?pE8yZ7*8HOe~iDrS9~eC?!L&3Lu!4}lm3g0
zJp$i@&C@j`<&E#?!^R8_E|mWZuDE`)r}1Bl#CR1#{R7e-h{<`|6HVmqpsQfA*fbq4
zcn(OwEoq#^rk(>%pO4s+j(qJBV}3-U#{7uX%IBhIxf&W6KJGGR?{9C}PWP47;Q4bd
zH;Fy`f}HYNwv%@h0CNtAViYDGOm=sZjczrKaX$xK*$|zWAnTb0^|j<6)9|T7A@IW!
zVi5F3llEcTor`;qanUCR%f-iRdnLjjsqo1JB*Dn<##tA>^w%U50Dgg|@M=TcAtH%U
zfcReh9f=iV2~QmrwTfxvoPg}USf^$9ocnvK(-#D<peR7hoe(VBUjFZL#QDs4@~fs#
zzZ)@1enb}ZyC&sdjTj3slkg?x5b28l-H4VNz)uLJ#6%xT#3oPZ)&t?25_Z<*S0Cmi
zKt%S2rUM9C_lX$1EAckkn&SVNIGp}>>el$pV(D~Tf>W8<lPRk^H5Rj-Kl(3U;$dg_
zdUp3(<?tOwu@=XVWKT%ql41XuVU6Ep7UaFT{$EOq^gG1i)BB)-1(v}sVn^zmpolRu
zuO|x%d;yam2&0fa=LHsGMCjvj_(7b-K_M=G;|?G%^>6y<{$I74f7OQdV{{ThySO_Z
zNB<MDk<tA|#rnTIL_<_kEyctYE-4ose-`;mpE($Bj{ilUnfzmDQNe&rJWHaoaeh3b
zc*t3a!&&t++?~%-^zuDts8;to+pmVTGAzh`>88IYqIFL^(6C$&CZ6Dh&Jnljx+A8Q
zlL_F*=YYg1v<Ct11v-<{y2JZL(QxEaa_!$S3z7l67O_MC5`rV2gRG~O=e&jNso}}b
z0nZ6UJh-6Bq@gqCL<mMSGd^2Q5+eP3u|#NtAM_P;B(bq@s>1^CG5QB8{!Yz5{+Imc
zTBFtQpFjV*I0rD7nr#tTtT>Y~JJt!-N45KXca;p(wZ3i{wQwz|ND+@ebHY;i6D_od
zcm*SRt|{I&F?sy$O7Ly%GxES!uRix~nZdZwFQXUNEAxjvo1JCEi=@8SV^H&J#ABP6
zLOz!RqjFdd7T%p2$K`&Swwl-QaT7~%zOc+w7}?K2^l#*+$^$N2)q3zp6uh{Q8>TLw
zuioW3CA^`dNhc;5993NAcx1tvBX*SQ!ISmDY(csr^vQb){js;$@jg@2swdS3hV}8M
zXOD>3Q18EobMFJ+E$jio_C(cLy>JeAXrb$G>zC8+dcp^jhWwl&Mm`pa3BFO0{D<*G
ze+^rB4iKnW+&v3&grB-3wz`=n;0=HGst*A!^Q%{_zX+4q|2GGl_aJ|ntk4^0#qi~1
z;91xK@JwmSw2TJe^`|BK&kjsp2OM$h+1mgn4zMbH6XN2f;53r96JO{DMai851m`dy
zLYE40uxqYBwLwj)$$;8_8y#2wGH+VV4xl%%#L*}4n5bRDw}A%(Hopv>zp2(=>h+%{
zqHRE=;0IQN(0OKe4$!GBJ#^g5awX`6w*!xawt%Nrg^wu9{tn3*@+Oa9<qI6{Y~}R5
z;ha*DC!Qg3YjTZO74i-5G|j%){Nu1f@U8<3>U;I856_lnAH9J+RqHl&o4OV|cjxHB
zUCPS98;@3+R>P9SFE1N5fHo45sZqjL{_zO8=r`C6B1NyT3g-Y&6O@oy0JVZ%!xDF8
z#9muF3)=kEYj0HCJPQkWZrK0*XGTafu}Q><bliCkIHGMj2MkM?1Bn*Fax|8f&~>In
z{GEg66|RxMJ$<nLIpAqC^o;2+2`xd;guj7rgGS*8sUm<K;t9V0@-X`g_-4+!6A=Tm
z1D&w85PQWYCjxS?qT*lhFJAo3W@FiPe%9KMI*;VfrZy@_(9BI4i@QttNi}5^#BeC)
zfhf!gzl!#b&Y$*ueIsJb!h@%^wLmGY@daZE#XZoff-#rgcD%uR)G}HrM|(+C*jJT4
zG8-@<ZMI^PVXtE`#N77KR7CC`B242F^B&dFrvUp0DHGweIL#-%UwkcQ4AAh13TZ1Q
z`R`4ktK$jnX}U3gn7kx&F<41?I#?jbG6B9m`4LDh9jO0ka<GuuO>=a!;pE+$YWb(D
z_Y-Qk<ZknC_X;UMqg5I4R9GC^mWTj~V3gXBw4%P}fSQGSUz+3sDyXRLcSm*Qvy&Ss
z%pBM6sRbHiEbn7XBZHXj6@lT(5_S$edv|1bL_OWPSZFFgeJK5;1t3pml3f<{EYBC5
zNiwlh7F_Y`HP7y~{bac*`+0U+pJ%+bk5?hrZGF?9<yjl$ba_a|UVIoqbX3}v-5M-4
za-f6L(WRL5^qHh@eX(Mel#uY{n|8fh7YN`CH8F^q;?Xez6X8H|9_$}r1@w$2I<{a{
zX~27H@WXWmwHAKaODZpJFP(K*Z{d`{ntZIPbiOv7`VyG|GM~cGYdN(Vdz$*=$qmDy
z;7npA0IUEqOUH0rRdC+1$-_55KWd_VRNv(N>@`+E(jID6i>K+}`bg`-krJ_B&f9FJ
zanDu{<IzJEsYj`zIO%E!+I!o7yuW43b0<D`vv>mL68iA|fa49dJ#&e~)|`QbU7*K+
zo4K?+Ev1MI5<~Mk&9YHTBb&>Wy~&5DT+>uT@}fjH;ydpBU6Hj6A>{D!3&b76s|KQ}
zz?x*8Zf%FoBxvjsHw?1Gk+~7A^^yMJKP-Wy#90L)PAcL|0A>81R8LrzA<S^%3dciS
z;Rc8b=+_EI1F-mS&dVt09Ay)=<s;%e<N86g>UM3d%SHPj4!b7;L@eQ1EAggxWW3q_
zhUZ^toPT>xnGy8Q%mr(wf1wfq#5fQlu4u>!px55Zd6%<itV&e!K^;WBKOwUzcI$E2
z)u5u58pKr~x)vsdnY8}<zG>jUQNrYYdPE@#-AC}@E+6KciI0?xm-TtUPZas6e#JwS
z6Q${$a)-C#7u}i;4s?JAKj(&)p76D|-tSS2tt<yS7g=nK3QXMWs#o>J43T7tT)I%z
z_H10ur?OFD;D2%V-f>N>Y1?Q3=}ka-2?~ldkuDt@MFbS2mncO91f;h>5Cx<+0i`2d
zO6VXh^xk{#y(ZKEA)e))+3)Ok@0oe$_nq^d^Zm|0EK*jol05fwx9hrYcc6j*2!PC^
zKP>j;vBiBi+H|GMu~ROgFm?{r3z6sGh0azKtx!x}OOB7%w5(UOv4NYwLYVr4L<ESK
z5yS@ihFjK>XTGRttH=I)&7R+)BT`rWaZEcI3Zt8*dW^3RycH!X<lC!7Yb6^Hw+zI!
z_fWOZ;gksPLb!~e50R9+ipXV^!D;QfT9oWpvW|H9FLt9hb=}x3<!e@nj9BIE4`W!Z
zXR##9O>)F9t42lKp(5<!;^KE_O5<MDed%e$n%|MNDd_0qz1^!kD?`y$CKJP%QNXz~
zipWdO;<gS&TcaK-qjOWc^ENu|S<4&~TnsX_Y#O!RpXt5FH&Q38ryyc?-e@gtwMyz^
zeB5}@sTck&h&$@{h~|G|oCWv;x<C2@@SpraD#hOK_Vh1~1S;5EtUth)bOu6Q>sG{x
z1Udj%-kRgz|HT6Xgn7e;Oa7aNu+~r^8OUP$C!4j=m4Va?4wXA;4E!tKOceNVuH=$D
z1l{LRBa$BS;0>(dO<?00w85w@_NqUriPhz|<Ikv!)dib=D~V8&`2-J6MdvNdPZFw)
zm#Gsf=b)pt;!lNT(@7FsNOV*9ZIB9PUpeNw2>UW@U*y3E>m{~17F*XpU~f;&&FXCl
zb!J$`x%ipXMX^JFRC0T@aJ!vcC5@O?GAS??xOmi`J-ubjiB?%s<-OogL+-R)D2}iX
zw^T|-cMvJ9f0lgoeVZ?;5SLE@A0Oi5&huhW`Ar{5x!35uJ<q~odlc_UKHAH?V1s_j
zxiF&iMsNDm)30B6ZTU>T(3+^=<7z*@_||aOK3pDcfr7Oe)0ZLR{G<&Qc()C5*~rDD
zI+9hZv~(xy-?cr+UgM9^HZ0;|kPdI0_ph9?XlA8v2NUjc4cXNyad;-kSgmqt`MhJ*
z8=+S)Q|N{+34fd(s{cNE94tq%NwcM&IU}a6J-DCj&TPfL8I4Ys8XZ=>7#|r+G|xyF
z86lCeY$`3Yc|8mnE+}V}-Q(?M{!IN)n*Rr@`UCPeATR6GEm@DtIv`k>tOXJ;h#2hr
zS<F5YA;za{{`@5TC+I#c03)bGU=qYX!M4}>0g5wq-Lm{oPy`*|8;pU@&&G|~E!cj!
zTr;>&aoPlUp)WwQbbJfQZy)1c0_e3!)cxN#!@q3Pa22=+8VCY8PJs-Fng6#ud4MqM
z_kasH;sB}0U-sz(xgf}afh+~^K5KbEoV&?8j{o-^@?YcR*wio7VV$>O7vvkTbJr5M
zHtnih*(oLpSky&xz@KbSp5s^Y6n}6VzlT?UJmU4rSt8EF_JaTyz`ZqpuE_79aXEMd
z7rg{J4lC33KsRwBI}f(WQogBjsyO6B_shB>t2Kt65q|U@(l1O~%nPU7?u7Hl24^+9
zi(*45KN%?3j4+>)gConC`^`viQ&Fddq_6ey%r|*?h@zWO4kLP2S7x?7pux9GVLW%8
ztl3ymAMKvGGqRO$pHA`nXNuO2yJ(MVOjn?d-)K#VEyr-Oun}?$)s8RT?r&8Oy>bAn
z+yTPLs$XTaZ(^P%85tz_f2&3Pz7PHRNc*RR60QyB9s@q!29yKH<-ALOf-<MHKpy3Q
zZy}BsR<)(n_g!-?3(Sgr9Cmx4rY4ccM@U)rGf_<7tg#NV`y>z<*O~;=8Ys)Etr*Gw
zI=WaJVDTjt_el|_8G**Li0&#gvT@T4s5}x|vzQ0Js%uqbv`Lr8q3+DKRQze&apfu{
zX}{V>wxxPG^gL=FGI#_CIE*)7uN4iKy%wD4TzX~l3c;t(QB+cY*<<v6$}jRk)r=ZF
zV}A_Sz|p%Z3uBG!uKjOD)ZVZaJ(T*6I5K;oEvXRw71-CVPS4KJU+~#?TfGkH*b;00
zaV=30GDp{SM{Si+bZ?FX?UjvQU$~W&HfJpvvzl~H+FBhecLzwhJlAeqiRcjHW|&%S
z#EZHQkMt(dbAy}&j1Mb~iiXq7pi`ce)APcFT1DaAd{H<&rHqDPzFe-Zt{v@*rurDz
zdpR1Bs@Fw>^|$)jzLGqq{r+?)Z^sxU<))hCyYAND&C8&-&9LF&R_`{Iq~D(`HvesC
zOiB(P!#`e2zRde**tui4L0jKL|9*xkg)Teu7km=k`gI`-E|-~?uj};XdPeA;tB{vH
z?-fW9`j*%A;?79}e(^LH7u)eGBGIv!nDCUuRdIJ}z`i<u&nG!t+7UywI!m%el__9Z
z8ogkHiTJsFty`7_N!MZMIOQe@2HUzi%1lp7Mj#={N(HzUQVnaTTK!nkj8sZP%}BKj
zJlxp7v5l?QE!&MpwjK!G##^^>Y+>UalGa%+0Jp|XeT?@mew8N|a6ND*xn;W5j`6zh
zqe^1-nVFiB#N+^5sr|`$!?H1Na<|+1W%(}$%2uL#e3g*i-C)%%aW93(`l!nH7@2gm
zXIuPg*oQ1VYw;<qiRzJpo#n8#<x~FC71AHo6d6ZsF{TR^z_*2;3|mSWJ_ojmGAEqb
zKkO>MS1kV9u@g2bH!vRK-IvYAgtE8{^S%y5m!FTfN`8XA!_t5RHwh@d5%mWP{{%h!
zmHVvAfILbQ!z9OBVP)-~Agx%Aai=8pCH~mR#ceyusXo#dhh*ZnL{x{#xSun}&E8pm
zUInVD)Jek*>4&{X*)6QqxGn--TC}b|Ae+X$Vu<0hlG=dU?hl3;tzj-67wXXiB(h+&
z{S!vc6-DzXK^{5V6B*pAsaX|2C2@0JY!W){W5TFM=g=ecU|H5w)ImD|{nlP50j)8=
zCw<ybI*aWCsG)EXIIE>cY_KKgu%1{hYu*y)=T4@6y7{<wp&7RaV>RfnaD&G4(dj1^
z$aa6I`yy#LgWKL=%kcJk_fYz9YWObs>nE?Gd!rrRuVqc7>wpMty<b5%&|CAygR*z6
zvRiEH1%EKPmzP);&Mi&&`<eQs*<WX-xf%0em*3}s?_&j79xH7&vBBdjgZnp76wx<`
zAH6nkA}cA)Y7IY)Z2P{z!%dbDu0`9BP3u-Zaxkit*EXv@T3MU0NZA4*aFbI<8OLO~
zX{9;xhZRU$JKA$bJ?U|H0qtpVfAu6hrfs-^t$Rs;(h|!*Sm+e0Jz=D4m_yzABktby
z_hcEg*L~Q1iP))z>h+6|$<Y_j&>rOUMnld<T<uJBk+$hAn#!@3B)iVh7FWd9r`%1#
zdP1`5zo5NRt=Q<1E^(Pw9_icSW5w5^P^Um^S&-d4(B#_H)M8G*D^ZTd`JxEZ$qxus
zNqUUb;sqtiCSAOCoE1MGzmC~+ZvOX)2g;mL%AC<r%D2kjXI?AMYQKNjzPk@pHF}r8
z=V!nUI`k3lHXyfx54er-GT1pFVes$0@Ve$tkXJ620qY<;P7fseZIoC9khcuorr-D$
zbQB)+yB+fy8!B4Z@we1`wC8@!lNm;s3W`RP98$hNFQ~6vnh(*b$ebdU)*~3^RHv~|
z+my!?;!!Bt8^et)|8)vgsJPUKQpfQ2x?-=UGPi<X%e^G*>i-%!+<Q4^U&D+wToxVI
zB6qz%j8V;)we%=R6hC(!a(A3otBHCHq=r7Swl%2Tg7y&at8-gZVX3wvmlj6D`zvGY
z@9*5GQsI86ur%SWhzAlfyNc7N49_aCQ2C^xJMAFpug%)H0v@Y1XvH!OVXmWe7Aj)C
zS)QD0C*iNbSEuzAI3D+Y%vU3HQ06?nRadTX8)xKys2{q^?aBaOUK&uP6$k!o&UMx4
z>U_oy&mTMiW1Bs|*rc7jQ9qF6zle!(f88H3G$_v|ccG0AYgW8Wmc3~(H<M3e>;};a
zy28^^!>laNLg{_Hy$yK(+ToJ~Fe>zqMd9*aMPbsv2Sm7RF{^UKax33|Rq={01F&E7
ztw+QElHiHpY#sd%pX)#N*cvoJAnRk=56cj$jbwV{>M8&hFH9>Rq7qXZ&NVl;i?bad
ze!I>9S{IpTc3XdlB#5X^MtJk_#jA5ey4^{|#@^yTQ*O!=T=vOG#)sOYNSE51=b6#{
zZL;j;mDS^`B}KYR!+R??NITkNLvOUDy`G|K$qDj?G7=EwuZv!Def6#c&O3Q<3gN$V
zBn@9sZ?p7fajR+7*YVve6ty;TK^5{J%|4h@E=cz?je;a4jh`lKd@s%6F`bgX`5<1d
zj6TT803PHa21E*Va?g-uk+qs=ZEKfMk+#PKkKIh2i87g#M{;{Y59;Y7*Y*8>q+@7M
zhydy5$Z?|&&qnrE=xN+kx?O69i`sk2W5!+Mvv06#)vqzeRK3~+!U1nyDE$ajCf&N4
z@}sR2f@NF$dH>DNd0(84Om;d+*>6K987tBhUNbkoAUC!XwzrQv-tbB(c~WNmfIOEM
z^4(L`cC36D;wk$Ht(9&BeSs!4HC={W-JD~HJQgW03L3h{k3n!hA+`BnRkR|cKuUYv
z`!z4*`GdUFH}72yjc>+O%=TV%W)LL-;)(6xK-uF%fmS#jTC%$dM!8gloT=w5r32vF
z{`(Jpf&#tLOuC}U-&0(z-j94o!V}$pquvi~y?iOWWcSet_$-?m+`gMa$Uv<rM!7%+
zxIN~R=*EL?wd@X)&lz=@1$1*s2uN)vGH%HR(P~H5oj+}PD(P^udzzt;$fgBff~*!?
zi*xj{%bj*Qwe1ZfuyZBp7l=!r>2+=D0qa+j^Y^ml|9K@#?#yDfta#9ag=gR!U$5^-
z+~dTXtAc#Qa-r-3k9MCx=-u3*a*pJKVMOB`Jx|S$%6-FaD4&TA+?Zq9bJmdqo<<Hq
zk~C#bBq5NXm&)c@<hd!f3Qrn6bj3sWw=kQ(Z|3ex7i0-Hp&SU>k!1%&mYTiEeEa%a
zbZ%T54W4dGR_VyUOWVCmXhkV_-?U+dccBw2tbQSEr9;>hT{cA<9&%h&9?JZ-Md>HV
z&g`)zZV}&ryRP(SDl%??7Bwr9ojzw;QP<*8%+s;`TPnSZ&2#v*#mH(aYy7@otNm-^
z^e>ejt|%x)uRY_?z>Roe(bI9J-Y`!Kcixo*3Y2#c7M{y~vF^8q3k&TE)2wO8RAcW)
zE!`5^dh!lu`n}Knlb@g|bu0<jQt+j@Q+Pn+yd@Jf-m9*EN<;MGX`v}~H&g5bt<Ocq
zl$eq@j!dZak^vweJyaPez~^*fdCb^yONHo$KX)dW+a}~EsHW3d+9xB8-IuqgK(sgg
z$Ffc?^Dq1+`v_Eh$&SSBTfbg;t^KWJ6YHyR+U8ao0&_#$u6FygIF<?=(xJM9h4uDn
z8aO(y#rXQ5iDy5xWJaecOU33|08L)oOTyDErARF`7kU#MqXu+JknA)P0CU-IRF5LZ
z%Qp0xYrdpb$DO`@NmiU!!cARdls|ir!SZr^7=nL9>4#-OpNi19sV!?DgDp$vXOx%R
zMpw$pschX<9v@sSrj~!EApgu2WX%Na)2^#~YU%jnX76}m_S8Iq>@}4<x9msgz(u=H
zah0;T@e`!1SKKc+LR|%-9?A}utx}khUlb3lq-2chzO;}%UQVH?m%#9rBg6f7FoF)%
zwxN!~y1J8!+1;Q@b2E)XKSzdHK2!dYj{5SQE=S`L>L(qin;GeQLELY?<&T^#pPT{>
zz1JwpOXwuvk{;;Pz5yTL4#T#m<Swu1e8A5A1RcDc1A<X_g52fpC#Ru!Sb2TBOHak0
zpwys4%b%e75kN~5CIPk%25OV0gg_P#B*mN8NCBteq1gIgZI{0^hw6m>Qc9I)`}%vb
z4jc(1p?_`;@pfSUTuQz6UwxTB=aIkP{*S%x(_s~|Wf312OY!jMPiG|7YK_&yD|NtS
ziE?C4mjnhiN;YKUQ@LbxbO)yqluvKJ36OK^JV=SjQ+l8!&^>76-(E4OB&jj--@^L8
zhy6HfDP{AR1lLyB#kFI*OODt&ZtsnAo%ny=w*BAXp_zbRHzXAa)a7%f^nl21m#0p5
z{Ij(MR>-r7EBx$N`Bh*j2X9oxbS%C<Iq~qx3<bISy1%#RhU{&WLS33ooNCIGA9Mx$
zhn$weab{B~Vh^c9{oV?b-Mw<vQY?1$_7ml*f^bQ69O~gyRHaWnyCs$#ITCNRDO6Ap
zG8Gg^XPxa5bmRLp2)bCr5E@2&kCXC?h!Me@9Nw0!%mBtc(~1i1p)}<s{^W;ycKPeq
zf{!Ij@F}V0yUop-DI;_c7#p<SvU;F56N*okXSD2sF}0d|vA0qT#hZ@Ur&(v{4Aiv!
z2}-=@{U9E5iE<5uT3?8xr!Jb5Wf1;l<oDAPG)F|<*}fVnAPXC$?FnPLQSy}T$^?!}
zpM7=h`A}JO)WA8WKEz7%Gq*Cg%dGy?bRHsVzSFQ5T~LVT_vS;p0s@tL0Q9T8!SZ#m
z)|+%A*`@nMLSKU2pI?cn_U--f8Rto);g*|S8<T7B(Ka~=bGWQqES8%w<^?gWGPK0+
zo_bs#h%)hjB-${xG<&nUu<rYohx3-Y1*b-*?O;}$e74&whL@T+4{RmQV$d7}HSqU9
zDg9e3(8H0lfWg(BdVzc@;hyn*k=q3v4cA^)uYREJ!Nuv2DsmHW%{}gmE6VzzKYUhW
zea3MF26R-?3pT(LY=kI8U|F5Q+@adErQO@K)sRkhrS!;{hh)W(q)H&iqBMzH_341t
zMgiqw{a2YVKReE=>f%Y)V_(O7*(FJLboA5G!Yjh94LGqyRtD6<(kx+DkJa&%zoAR`
z#{$K^nMEdak%Fg8bd*lM;&St^=HB#r4^N}dS<duyTHc$%v@<`KED3Sit2W**X1`Vj
zppdNG8~<S9IL41r>R1(v#{Tn50KhbR(_{Ca0GNQ&*f;-%r~I{@u+0DaKE5?n`QPFI
zo)J3#@e=;{zbbD$08(o585Z*+gcqnhFMW9+&r0wHuWJu#vzP*QJJm(j&HI~;iVBdW
z8#mq@t(H6sqQ5j&dmiXaq6?bfw6KH+0{vLm7P+hS_}1RHhsx&BkN3huFpsVCM3Ax;
zetTw7a)N{6pOuB_#-3%KM}S1n@lX$!2*QymjZFQ)qN&@ardKVcKb<OH=dxlyzWIqI
zk?ar>CJre#F10UoJJl{XsH){ynthGB!$(_N71a5T%WdVIbnmpV@5j3m?S!<QdO2-F
z_w1=dOaPYg_2pi5-67FzYgUiOV_pNx{Ha652)YA`Rc9ew@%J>6whipm8Ptx3^ubOX
zi}2FvDOHXhx<}Pfy10HEYn<#FVhg?*QL}EiMaO1zr>;~bu~D;0wLe&7m#acv7P@m%
zB~s(8<*K|J2esa8IaJ7eAYD+|Z5U=yFY7o|>sjH(UzxjeF0&VxRp_^DWW`&jqLb`$
zL(0Wa!R5x|HN2ELXl4Z_<UU!p1UysjwYoo>;AI--dY)Tyn-V!TCGYVyjlN(O%~sAw
z%8VLf1kZ}?8YGl6Kr10`T+QKRZWe9z%!7}|gZZF>xzkTJo$bCiO86T%OplqleNg65
zE-p!B;Rz8sf)9jGh%IX(ZL^mI0t7RdQvCUD(2wb_mWanWP=5ZT^);B*sT<T_YDpTy
z;ye@}GUUlRa=lkdCOqp1{BiE;pi>fC-+0rxXrjKM|I!$*im1}=nw-}%!<n*Nhlh??
z`n8F`8!5)sDrtKevB1jKWc<IGXs-?bXNfk+|JeYH@6q9XKGza;@K6D*Y`n;n0Gq;E
z@M7bVk%CIqQtZs$83#LNLW7cz)-mzUZaX{EBsu^^_y}#Zu&drGd;N#C46|B@vb~Uk
z*-KJ<5D!q!AoCpWgp{T1PG-wBkqhXdC6+vS3rtS5lP#*=yG|P@wxkWcDu+07b=|-D
z@anx@=f$E?>ptnoYTG%8`yFLl4@w|9u~8?0zn6yG`C6d2;mtc_LQIiVpv<Hq5;A>1
zR2(|v&+zu8k1kK*Fv8q!%2WZ}j9k`2rF^o<$H?VuG}@G_8gEixi4y*i{@Q_n0Mv0M
zYxy9G-1`|u2R&$dp^DLGyD=&)Zkd7h=M}AVcltVFutnVh|GG+DDC81$4chG|(I9%%
z-hjZtJVJ3bSnre0qebnY0$#bTGP`rD<<{Eobu3>C*=RI=DHU1-=`?cTZCRU(<SYW+
z5f{~hsX^z``n>Lk_+pdiX&Kwv`X>oCIhNysa}zVEQOuthgp{(=2&?i^*J^_o#2aR%
z7Fr~OHtN}HMxB^fLnfwrE|z_7&fKT`QQIa}Tx-fsA(a3wTW>+2wdAfqRSO>q#N^Z=
zCi0c;*~vm8bv+AJF0YQaELmwb=k`jqNzR-IVQAlW)2a~O_dMqOR%cMcgTY5%TA;Fx
z(C<xcP4VPMd&_gfGb;RC+PREfr5(X(`X@}7RG_AC6V_CNzDRGDt~XAC<7F7)4Lf~g
zP#^Do!MMXPzIEv~sUW)i4s(c(w!~T^<>XP6{+7tLcSUIDi`*um@J@&B^`3;YRG<ex
zEqC|#BGa#<G*H|C;#ONU9Owxo12~!thw)5-S@4rX$dnycwPp!%8RSukjqEo5jBxV$
z2dL=(DIgp{Q3u9)9nf!o*phthGB+OrLGdMDSD7c<rk>(UK)g$tVzQL;^y@HZre9^#
z<%qK0#2czjHv}#@85s^f<g%7Khhz?wPd-t-6{l&jRA0Gl1b#X^->5HBQS5SRV5<Rz
z7B7d17vj9dos?3PGRTCk|B@L~Ss#|>VokxQu=Mh_dJok;>vr0rx?vLJAdink<KJ&w
zst+0~OJO#IJ{yEPUFwg43EFY!W7P;wVF}VUoKa8RUN+nr>#%7COSnxpm14|?Yo8<c
z+x3U60KWSz&r6|2&I@UtpvZ(zE#FfMH&z(%)q0!Pp`x!e_V_tQ8>*V}d89<jy^e>G
zvb^YNR5~zMKm|^|k<2y5$2Uo>WGbXiUS2khHL-Y0bolwP2_;Et&h<c9jx@LT@EyCI
zV<T08WOF4`gK}Y8!t2RG?Sd>{imtu-Ocf!lywq#07PS3rXv{sU)c<wpYlFnjqHkO?
z%rACAN!G!FSJ=`SJTCh}L~fQ>#EQPjq*g6<QN4bBAP+<&eTCB>$HBg;7*3~QGh2Ez
zCBi-I8^ZDC%yQk!?$T8R154MlJTH%__Z3L)Qqi3?USj8rRn5<~`o5^dvq7NFn|Rnh
zh6UB3;{0XrxCv;YWInp!HI2Omx&<=v24w||iTPy6oX`w+jvy{62iLnS1XgF^P2*!s
zuFU{-mRHBfgZ05Jl}*AuhR4iN95!7w;~t%H%q4l1={n<(B6wq{#j-h~#bBSduFK6;
zKk(tKVp1i&@3XF-AYtfEs+d78wNTXkf?NTQZpXg%-iH*cI*Xy>$#6`?wCOe&&wFT2
z3}x}&$;-pe?{q-jE7$AugHS>34<<q#3E#;34)E?$VLt9P64ZwoM(+a4%cquu&<Dej
zf%cwSvi>5nK{tCd5!0%mwD1nphZ_b0QQ$lANHU##i`P&2I+i5SeQIBVt%=eY(imm<
zuT<?9XyeaeSj=|$UtyU5T?+tU{*EpGm2Wx1s9`I~=oD<DE9Cs<HrE0K6A8nCO(3#i
zn|xXYC@!q>K9GX!&;#wSZ^k9w06Mdi9!Y-*z{{~wt@5m+{|Yc7TH|HusLILWD9>^l
zwmx1^%aPl;_{F4Z{1IfMxIZ+rM0mtUZMFqho)e@*d#D&B%ZPb^&O}8wEVo){4wofZ
zU#qH@w7)ea;oG4`@l@$d_dSOYfoah$LEUzH_;T@5KN%&NqRBZ`0`^vcgzBRB9dA^h
zm|a_+t4)7*{2~I7FnK+^AR;OASqx?~=B#r$p2RFd^@w83WvVf}x7^!sm%Iuo(UzG%
zQ*k%1YjpjaG%)6g_To|NVjg2XRZ9iXscurw_%R|%8gjSHSHX#4rt#S-PPfb3FnM_A
zIEqmP?-ff?FthT?!RM)c=BEYMZ&UK%(Sn67uAJP1TVAGnUpIgnpliIArqfuzcWs|s
zC}rI?b(X)WI~m9yksqW+zak}1h7UcaZ4px2c^#q1KZ4*(kX?j5AYfUrV7E6m(yNZM
z2sTd7UpY4{cXYIO&YA7D<K;^>KJK7kVbi6F0yW4PniN>6JIkh?GR{Tq{isxNWxg*M
zp1`LJc0#JT?Y5fxzpjdcL^LhLycw6f_jUn)Gde;@Fl=-lb9?w`pBcT}gk`L~xLHuQ
zmJ@s{(}gtuZq2TRTQ^S0wORIy+I0znA43}s6GwKF;f?iRbU4a;OpZle?CZDYH)cQL
z=NPpHp)m(1ZyBfP->{ewyq3lty--93G<aAv+XYN11{`@-)(`Wi^Iz%rA^CAf45ul(
zw}Ax*!Zd^731|i+`T0D!KE4&PyE?IjY!{>_ijv=4ZJR#hu+?THRi<K()xz=KyqKH1
zRvE|<NOx}l|HX5k@Ap6yGdcwiQf|1;oZkI>Mx*Xvo~@~!WE9}`tMmW=^EmFIYUfF&
zX*=yYg0vhAo;N~=LhZ8kERpT*l`3K(lwaw4lS3R*-!`-6E5FTYxb@jKp}xV)dpI=3
zkJ=){TL1dyPtbEu$$s?yg@PrFWLGrw&e#&p(icAR<7(q9exKEf(!FPf-=1`RWe)P|
z;MD0xRLbqLx-JP^ar@ribRKu&60>*_tdd})PsOcr(XsBUv~-cCM3&#(skuV!b^{?N
zD}){MmP2Rep4D^452;$sal97gc$@dGICSg9OHk@rXd)R4_m(h3G0yC;rB^b+G8<BJ
zs34pZHL#1YgiI&2L&~&bc4Jlw(%#@}Fo)ilayej)PJ7eY$k%R97@>pnN&hTG9VK&;
zVD6l)`0nPgt&lOU@`GkPj}T-Fc}CbfrTl2_*BbCD`_HC>igoM}J0$ryvL906gBu!Y
z0a>|?`n*^|3|Kg_Vs>&5Tsj|qWQV73eJVIL!xgT#i0vH3V5SWelxcVss!VnmKn<8w
z!R173HMV<T3lPOZSa4&+m*)utJ}#f}f2@A{U{pYpl)dUPsBm|IT4BYTsDqQT2Qd-8
z*yWbeT1h>N5g&oV?AVf|5A^q<Za<3>dtxj^K|r{7SQRUEzt?*N%2!`I;?0Kkw%#Sh
zQYD0!<ijTK@$iod<)o>RgnrN9W(4`T6V!v{3W&O6dFSf(y`TV2*N7rWdC$^Kpz$2`
zURpT&jigBz-&>P|Z^E*t9Gp?@BT?$F`avW=3ea}SD4B2i+mc4H1y*4<bp;BB%yd9L
zjk;GUh#5~szv1&k4eKrrtsP5fVkEflPZs8!*`2&V()){2UWLB$gi~0G^P4UgJYZIc
zZ{rf%C=|<gheRsAyo>Lp43YNklsjNW0|;_cEC3EqBROZQovG%2wQN3rXfbIYsvjd9
z^xeU>i>{`wuEX!Y<9U9S2>%y>jN(r~#PHkbC$ObW%s-l6q)Uhvbt*Kh?exRbk*J~O
z`=vwskf#?Sw$2Y-p<}zuQn;OdsyU`Su9Z>&-Fi%fEgjPMsNiM%j5$`6TABG+J}1qF
z2j_SZ9P@-vVa(|Y!LpRcLhimiSmC*`3P5V61rWRs1OP%Y=iW~cI$mz^TRvjr@h>*?
z$0Xnzppj=Qf9wtr?6l%8i4XwAIJyRyoC92*e-HoyNTUZx($uanj4H@uly(Jy9Mpy!
z-M|37m?kx#vl|GIdwIICA0Ys@S$P&<ev061ztD*SngfsZU<1LwxX8p(2<$C@vYaPy
zPUU?aw#eoB6Vx(5Sn&VR#kKuLbo}E-TU3I*2?j7eLcf1UO+GS)$<~dsw=qwU^Ud%n
z#YR`Q>!TdQLw3YlC<Q;g6%#wBv5O$$wh<RD1{{VHQHwSROTKeInN>Ut!vUspGIh5d
z+lGPt_MWF%oIS*~?^9alkZ6UrFRN2bSVQP!X<G#Xq-}C$H{uo2_)aCm{RE^tmsKvf
zsi<*vYwZJXK4bg@5p-yY;4!c^^$^UyRhLmFJBv<VwuBNRhg;aVODn@J%ccqIbSIG^
zLE<zMiFXX}V%rEgb(So`>+ibtFS+E}FZY`2M?tOz;(sGGFsD2*)4P&p2#!?zl2_)d
z<h^LUqmI5!zYFCr+a+ycG7etiCsrYUg0VV(T4B3EtV>1J#;UBR&JOSV07<~!7+bCz
z!PDPd>k|gYu~7S7i22B#D06|>h@L0n7~AqV*mkF#Op=p0<yMz#mrPFXRxUwM-q|_x
zXx|eXedqd-I}@|D`7c&0J{(Hm-KlvH8I};BsYmZ3ewUGojW&XZ&;J&)p02q^@_LF@
zp0k_n`bVStyyW@1k`lZ%DZ&ymsPLD<YO#8ScM6hKRKA*j{Ag+RK7%mghr~LAF?|J5
zA%%8-z7_}TT!OPvZCT~=iU*>}jvDRt2>{?^%tAI@A9Y^=q$(d(?4(V`tnqZwJuHhZ
z5Aw%<vNFEWzGJx{aCIKtHs0#-LKtn`P|VRXWfmtS9rc7`NnDSm>MZNE)HwQC`m16H
zsdv{DfY$S?7usa1JN1=*3{Rq?$?4A0x3rTkZ7&`WhQIifcb&ufpb9KbdmfEkUIyT9
zXkm2~u%EFcaz~T<Yd$}e*Qq1F{pJUJ<`3^2<nM8?Q@!7Km0Ot~9@1A_6uLP)p5Q%o
zVjxg;PIjB!H4~3^CH*9@1bq0r!C|;zf3{xZuPCnnuWac*GjyE@jHVpQQ<efYz6bPZ
zvNn0t|Fj#7(*P4JKD5JFWoeoz18P;%nLR@_9^R@+S2bl`SC6n!u=lq`vn<d|`N)Q|
za$EDZBy$c!85ffbAY-<%&5A?$tMzr&&V1K>IR!W(QloHlrE%ThDL`9rMXuEDl9pfY
zCkRvs?0vl;FW$%AX4p|Vr1m`w9*Vwc7{ff~B*%(2R^h^qm(-Rf-z_DUHHx|#mamEE
zLF~jIsrw?3y6PY0u1W{XF->mCQIsn-mBsPcy;WgrdNb~}UelIp0XGDBttzSU3O57N
z?HJ3{Nox;+T+4ztr^RO#<0mxv13f?5v_>M_uXx?3&6>T97mtouz{sG)|4wFw{E5t(
z*HtDERL_CRid^FLGIv@l8iPvYZh5@bepco%$tl!nYx4DLM?}%>>7p+-+aVw;$cL0N
zm~R>4V?9Y(vZ`m_heySM7&xg_$(_~85Eh}Sh~}`=2+wDPgD-TVVa@7p^cPs5uhygj
z0BKg9$Sr2ql3oIxbKu*$3H&+-H$fh~v@7$-;W>OP0F!_rqeg#%&ZxuDmixFrL9iA%
zw6fJt5GGKbt@N?GI7a03Jq)n~#&DLyu#W&|;MstfM^R#_^$|ZoH=YsZ4;rfiE=Xnp
zvi}Q6P0SAgaMiZ~-;#_JIK?~!Tu>}fN$CLS!Xp3a#a;i6vHz#@!Bd_M+2}vM;MR%{
zkI-_Vbr)hW8%p9f|LYo$fop`bpy3xDZ?HJ1lW+@5(;SGKw=V5o&lE+P9Z#vSk4iW7
z6}*@2UDF5695%OhfAHs|ys+N&x(v)=z1<`#Xva0F+5lu>Wjnw~g}pYfQb>XvGs3dK
zu@zjBIROliArio-+$tNR(+{5J@rn`+y%qJ~+Ff$rSNEnU7HcfGw^GWocTGK)>NxHX
zVd-Yw;)nIj40az#*->r<J6qHE<UCceCAHx*xUyrD4F_S!f%z_q=npxYfobUVb|gA*
z_YRQ}!Oa?)X+|1*cWr|=pKQMziVISX<5q1eZ$()U+;Gr&{`3Gr<2GlcspwDhy11@7
zrmBWOUCg9MPp@yOro;n8G(8e!9sSA2HmS=<d7WN6QZFJx+17nFF|0mSvk=~`)N{f5
zS*clU&)6_R)>e|Dy_i<N4<kQp*m;XdkMUEi_!h&e8c_7#kTj`nrSC3YQJ`M2ZTpY}
z2Zgz}jy&mTWM{u4Sgw;ME!gK?INc~qGQPin9UBPk3Af$<K(Vp@!lXEgB+T{BF$poJ
zZNoj$-i^Du)=>?iUiMTyC7nXksy7~_DIF#~qq|~yXR<+qG6y~YpPZX-swYQ-Pvio?
zH1IuX6~`Rri*B?uSDwi2CH{&*?A~l&%W6|GPh+1V%cfdL)`@8511+|k3r!#*G_0pV
zvn~=H%ZTqVtxr6h<tz4cn+?-@fzM3ucU>NDsxH#Q5yIr%522S<U+gt~F<Q@!9@ZO6
zUK5J2JV|C4T@91fA#<6?unvWX1`$&pQa|?0xy+U(kqiGXV=(TKrA^xhKsm88W*Xzs
zl)!47|A!eo|BD6i^s4K9t4;Wmx%~BcxA7wXJwWb2aW@*(-83hT#xK}pGO#8%(A(AX
z9$&mv7?S4eP&134z4}h7sQ-SiCq?kZQ#2bnTbmf{s;v@-W;lt}irMWw{&z5vf74E<
zH2+U7lXpn=_HjIKCtzxS+DyQI-bZ|7UI!i6{dqG1bQ}J-kEBxS!G-?}kslTK^I2Lj
z3&gbvx8W8r{c-5snFQW&x(777E{^)02OIJFiLF8$aQ5Tpk-kDiA~-uvz;M|u81fk~
zvus{rr10osKCt4dvHnw}DphIqO;+7J0V-2pqEyQC+wi{qsS7Mg++sf=+MroKpkbb2
zWA64?TW+PsFkIwpimU7(5i=btB>OHg9k2I3(f~_(e}R&Qh_9X#ZT$>sw0LIle7F>5
z=B4);hX+<or(OW^4$-in?Z(_gr!NWw&oPC!u#uuDQkINZXWCB+WMAD#_aRnfeeS~^
z^OAySIX(?#+FG6B=j6pB!)Qa0m$y|hrB!>1s*N!Cs=F66$jaFCy_kg6;hi`|8BXtC
z!3wL0Yf$47BlU&wV2l<Y(}Tjae0}3uV$O*CO-pJ1RX?%|#)!zo1vXU=`OCa|ddjnS
z8o)o=BG9A1X&k|jqw}PQjW_BT93Wd;egSLNfdbf;v@|&+!S+v3C{TTSZVbRM$c>OS
zSpSc}bW3J9R#Xuq>e{q_&h(F$S_Rl<|L48bWLDzJcvQ+%&7zCk${WbvKBHDrq4zj2
zo~=yv)`dnw%f({Z`*r7_ri~hWjdwVo9MM7ARc!B3^h<X_k<HEhdE2fKi;q8ou1)or
z$C=)hzeY325FG*{mL@sl(7(*|mfV!1s=et|@J5ymY1bTlHh9OG-1w32b+#9!jGSIu
z^86uWve)36dI)ORLM99!YJhar9!Zc#jN7qS>FylytMxz+$(A&hJ#J*(mykI7Y`RW+
zW&tF8h>&{0SpzVG)v09J33H2po5MgJ%bAi`XpoQ_-hdNseL7$FsI$PEIBUe9k<Hke
za8kJPWhtPM6)X^HEIJ|*PGC9c=Gow)bI@yJ_r?Xl)b&>7)O>c~LpT$XP(F=@NJ!_K
zlkM~-UZ0`Rb=uw?%2VcfLb`v3ZfJ*XYFwu2sjzT!?ZNLu?EG{+=(E*S9_8;}S~;qG
z-I9a#5s2S?9y58alL^H^gY%&t=!6WkxDqjW>rq#f>fN)KPghrXN4|eAW_L7n^~Z3)
zmNav^O=As69qoBOWtemjF)&{}>~nZ`Z+i%#1<-X7fe2!0;l*&YlJo8rH`8|1WP8@U
z_3(XH!BMeTov(rvBYdVBzz9VhE(zLLi?1rg3L5^WmU;!UI-_?#N_;(m03yjq4>?~j
znG=Le=E$S>IOG<i^K+}~?h#T9!?`xac|;?^f+XqjM@76H;{dD45Clek7QljLVW{sA
zwAH@f{E^7tZ(_0>W0WPjU0ip5Nr3%NYx)IF*I$bZ^<tL^va)FUCXe>kTxTO^8_gG}
zq|3`Ru~I|=UL{bI=d)lux+goji}>#rqqV;J6SOyYhJAZM0i+(0y>?2l&8p-dkKMt4
zvN!ObPTT;qdHla@fdt|Il))Zz$hAv)p$Zirm&L(68Gc)<P-<-KW}#W>D_1;Ic*ZvN
z_<8hIJkBs>-1>xn5Z(ha`LE&hZ(;F~SVY05M_cwcUC36oDSb&@)q7(D6ZbA)jHi#=
ze($>!VpC0&%Cds|z43fPxf6n7xV{W;mp3)6^933xf4{n*ycE_2)EC{Af0OKp26RC*
zH&O}y_uXWO1_QDpuL6a<A7j324yC_tW+P6ekXnKOv$>WE1L$J%Nk3Kw*}p5W+RA6(
z<pv8CvFF|0j7;<;BB-~!gge8T-6#PCak~IIL75}(tE0Q?RS~QCyMA+DuK1MZ_C3gj
zMx_OVT5upb+oxkp-oc)-*)X=a?EJU4YLo3rnlfHi9LO8jd08LCwO2CrH_C9eMNVO!
zBl|<f_T>>3!>xt6w3EKvqB8f?8Xa7oq82_rdp9T?<``lnWpt-K`@@Ia+p}}MdQxS3
z{kPDY%NPBhQm*nRv{ui=Cnse+yfHnG_-b!o{2B2XN2;XY;L2NvgbX};IQ}@GCnj)p
zE3T-G<HBZVIODddV|fN&kP?W6I^72cv=psB3U_{CS7un#_QUNfkyltlaWl=qo^*u-
z7Z>dVWqt0)wLt?lZH<d~w#*zL(|N)aeWW=TzxtS^MkxN9nww_T{4gM4As=z>`u>!Q
znDh067DdkY;#q2|CW13BwZlTiQ*txQPPI>v<sH;L2h^#NKCOW(+<cB<lrr0QY>JwK
z*?oMP<)Cp3zIaZJ(kwbZuzhm4ndn~n5L-4SOdY)%b*GaZ-E|u3VugoF57yUAXeS?D
zVW(2#?#u`*&=z4|*R(FIJNj|@Iug0AjPA{L<7kDE@Rjqb&^U}qmW2xaDF1rJk|UOn
z`6lU;k^pDEt2^B^5SN?P-*egy2*l+$FIA!47nGY*lui8s#TZE&m{L88PqWiYCi$yF
z<&7JrAwrDLADOq!?6Q21>GMd*9?$d~iAnd)US;%C<S9(rXQ;F&%q?!>{3Ur4&izZj
z=AOn__7~x<U)nW_pMNRNq&x=wFWfX%L_MbU2E?42-ebRhKLQ_9MAdoM1}~-j64gmL
zzW5HnDyyG>jy>~~1a@zgSNlsPSPr!F3A39;5;bZQ`Vw(owT=hMBB~3pa~dd+YWr5n
zp@X&pp6G+nr6)iM(bv`zT(;k0N3s=L-5T&7DwC-vQ#V@?otZs#XEtNDjpW;ceU(1|
zH>lb7O+l*7zlY#q`yI30B*9eHcXv5-P~7yxd#sGleS*aLB_3(+57ozf^I`f_{Ox;=
z)^nfKmpD%pEZ?M3-W{)}M_V+q(V^yYzik48mUB1bUN1e4Rb=4i^Lwrr)RH+nlkw_B
zsW4TPsuh&%3I=EZa$z4|${{W#+w}pI+rRE0zQCE;J(Wvh_Ud0(c?`NW?|3#?IfHd7
zdxmTCAKg7fwFi@Q=5R_S5XZ{}hg!_Msi5}=txp#XEj#zCE_vw6$uq1_aO(Sm5p;^1
zhpr#j8j%*xo!W2>%(gl-sMeoVs)^+$I@1jYP_6^xL6{c_Ey=8CoI78?Tf@DUCfK;S
zJc+06KlL93>fv^-`ZbjWeOD8HAMd|A5cp)4f_K@jRjK|eMia$`_~s_1G2%RJEi+C(
z?$~+4NzNjFFI3@$#6kKYV+9_wg)On%7oe>L2_0Xi40cXo?fq`4?&Znd>z5;I%Fqzf
z$$390_h4MAStv2bFfnpBrA)4cn;#>w3?WMi)UPOY%wu-9J?gbFqo)#apyDLB&I{^d
zeSgNGQ4~gembY}N9Aj2xTGW<iN_ywAh2kn1mNEp%+gYIb_25XF=h$$yd*IU}Z2|s_
zh+Pdf@fnH!^IDunyy&TDRlj)6E1E6&aW|o@uU{4*R$kt9V~LXQ?@{3H=oR?#h+e28
zL)bl_hYyFj#Ek<EdB@ZK@yK)6cw<7pQM6}B?O=N9K~KIC=Up6E)(OV#rd-)$|JwX@
zomCjexHkiauMQ;^Y42>l#LG*0w;jI&LH^0am4KU~<!$a;vOBubhqri-qfewEAUC<i
z)^J}a{}R8r*0_nUO6UWddp{_zI~c5)<2evMzAac89^(i}LX@aCS}Q}1GW5~a2U1?7
z#z87h6~iaW?{@26DYJ>$Y%x6`K67|Mz@VY(orR&hOoY-PZ}nUFADxQ#GWZ)k*bIr5
z3}0l8@dDxb5UAn1zWO#^8zsZX$$A#0Fy92hx1J*!5-1P_YWFkf_$BLTK1VgfFT-yb
zx{D=DNUu^1D3gGeYU=9O4|9g$<toNO+A=Tl|FLQ{84`entN^THfYSq(_MTe?mx_OK
zmqQ&|0326h<NZ_tj8fU0I>u9R{xZ^->cSJ~mY2E(VC&xkT;(+0IT6Y;981LUE*R^}
z+nbHO(8LI0R=f%Yu*xCeOK+f_lkrU8ABF*-v59|K%ddO*uYvyX-oNg4UP~cHtazjQ
zfmVz;0{f*b{;2D(7&DNExx*na+QX;?&R2hRz$|8>RBAIK*2JANsWyJ=b}Hp(#9P>e
zx-yWK-&`!flaG6hWSZDQ*wl{jmwmQ02H$!#@jXGDwTF_Akaa7d(GGpHvLWT;Ytp=3
z0tlNGYT**zvd8s_Et{_EitaLcG`!Bs{V_;q_-NbcRu`!h^P9K%3(q1ArrS8eL|Jij
z7ARlo84Dt9W0ngT%j%4YZ&n<dCb5wQ@~`>7c-hJ6(AI^jjvz8;51Iu4LsG5vEJetG
zmU&+$v1nVqvTg2T-Isnw309%954)-L0;K`QT$Gyiw1%t%5aD?-9oYR7nm1Rhi~;ss
z#NBihv7Ziz%rUQqYXcDYP&VCTyNiwCUm|qBJ*4=z=lv3?`|Xjzzd!Fc6}x}_64&LW
zDld;LkJ{d9yu9lpRrC||o(X8A!7snTWv>}^I%aG$v1Mkx8Q|TXW2{c1TiWD|yjsYr
zY(9BQDLiJYA-C~d$2j(!p9!ma=XRl}Xhp=-Ln#}@FWeLPp12cN#uw#wYh0I&9G7C@
zOW<p^!?w0}@DryzaHv~QA<&M(2Xulf&NCpJS<tBiltnNmK4(a&FC$UgpT1pS%GDTE
zSLSgurnN4`-z3?fTa|5Y-b{=dpjy9^HGAemJ5}dEe5;mx)iddUj&Fxhh`Ke*U1X3a
zqVFP@##hC`>+E!2xojQh>v>_T9DE3&k*Scx0Dm1*TX_r=AnzHxWppJI@`o;4-jnE9
ztM2zlkYH3%77!zFQ#|b)-22RwHJ|@h1JTNY{Rmkig-1}s;E~0c-LB0yvPZ+M1ba0q
z)j9V%j*sC}^Jp&Doz(@%$7;6XU-XX(2=x|IZ$ZBJAVY1{&KH4rQ^OorMsJqzFSoai
zWnX<!#AAFi9urf2xi=g7ik_nlX3;;X51H8389EMUnK#;sa4VTJy5&Wv?DyV0Qj779
zVFd%I@uOvwK_s>ip&-q*$S=?+NnRhGZcv_;Vym0+S|?)lODT1aiXfjdXR@RI+>?5t
zYYQu8PO{}UvZgTOw86Q-&{6U2bITRyHXcdP{jK@a!OVm9JlMR#XEKd(s)x|xUw5T<
zvlaYWg3i|(&gM^^$UD!6Ucy$qM`}{`NzR%7{@~g634n8s*asv|F2KhEmqa=P$Nw}-
z3H{p}g!6g)T3;hNsZh81-~NB|w+DVxY5dz8O8@ru+0W(4|NA-52)}v{D-rGhqFB*D
zIXSriqlksB!k+?ZLd+WCpD-Jq;ST;?O+FygUA;;Dj3?y}8k?qnRipc>#^yiZNSX$+
zWuWX#=*5NM6!cN`AoQL$YcovjTU^Kxf8?t^`d4qS3duqlDdWc7R!rx=R5FN0doI}s
zwJRb<3k6b*q|}u;e^e<_6caTH)Y!T+NU&O&eUABB&J^}}^by?lJbeu^B{^?^{<`=R
zgbNiKcLv5#+qi#=)>V0-Nn|GqjpbUK!)u!ibr8DGcg^u^|Kc5n0vWf{kCJU}PG(Ng
z8@p9ENVmI&s4|=uta=gp;zmeBv6$<6fClg0e3)DrXXR9ErsLv1?MAG2Y_)-k-L?1E
zM{TC}x@4UXZDuNPpl@{#+B3ahp-Y!k140GLN#QijIopym@z7%ZEeGSZdr6r-#8c9x
z)WsQjW#T=0$_)%~$+AUH+a0f114Zg@8HK6w)%sTR#$(6QeKvV({$Fnj;=*f7d>USY
z4N(zN8d5iFc{zm?xj)fs^mXY835OfQ*mykOgX8!dIA4|&w_#i{Z9<OGecVQkjBfff
zIr7gsW3}6ReRQfe2=Q*zcBeR6RF}fWyZ4X9p*OR$Dur}A9j$E~uY0CQD%upMD!lUF
zlGq|}{GmoEdhT~Y3dX~zqFtBN3H00qouuBZ`tB{iD4Hd!q;9+MW=gyJRkMWI^-t!V
zJlX76Pbka4kpR7+@q|oFTV}yiKc_rv^T@|N--+18h}#+~EnkJ-rAo>|Jdrc&51}GJ
z0^RI=JgA&1mty(c`N)@Oo)w?he89{KO&agho2ReT=tXD!RpQsX*M+0gyRYpf?K>5e
z)}NobCkFyUaOu#1+F@HgcL~yOXDa-YsaN!68THs8?=``;=?C?UL8ZW;Oa@fYIrsfj
zwzlG<6oQR-+0q<qI!UAQtn8eKaaTbFye`Gl@bP5HWL2BDwA+t43rr8Ej*To2J1dUn
z?VACX0z>Ba|Atci4Y7J45Cr?B7xH`8jd$GoFFI?0h5;+LB_R;1|4wXN!~T1v)jWeG
zbxia;Q0AMs15^Va)tO;P0osGWg1`m+(f;MlARnpk|4dKH`bcEU^a#*=ZHHVfQxA|d
z)c3D(IvG1uS>cK!ebO_n0*<+Vk0ih4ghUnEd~x5c5|}=7hSJnT7fr>cd7=mo9}4MO
zUJ)#g7R(nci{i0;{PF`yAN}=DIAPX5E^DB$Y&X0GT1<6Vn6qP!VP;IdsW#mZt%b1p
zCuZMtd`9Lk-`C?Yo#=8Dpo~kAv}CUOq=i#+b7_%ks!=cY`89*llNOHG8bK3Kyu_C}
zv>vt9(0(ZzJ<Z`Ycky7TgD54P>XOUnt@ygxi`f}PPf}^y)}+Z<#tHGBdHYX&!oI{@
z{>kpQNZAiY=jMCNh}oKpFjddoWb69tJYXTBU_3ehJ2po$Ob0UeGXr1!sCh+PF3-TA
zM|1n2BY{*&M=H)2r`6rR<@ME84s{1&oK)4claPR!8C2{Q(;_%`vT$AdL!#Kn+7?l_
z%tJF@9Rg^>D};OmL?Gu5_!H!#q1kO?iXkX#Do~Uq2A^b(miLVHJujTy6K2BUtk+)x
zN58}KH-EcSRmag3x};)bY$FqOZ>QiiJ2o@<kw9YfK{Cj+#`bRFa?p9gqIy$s3czqA
zE1TvAH=mw(iw_FGmn-N76ux15W<`bs4_&H-zOv%jGJMN1m!QqOcX-SC$sOhCj!S8@
z7C`7pCS-d3LoA%y4ZgE=>c|{;ha+q?T`OjJPK|=<+1+)koK1riT3IF|sA7Op*mR4I
zH&_f1vWp*~Swn(0y8R8!l{*+EG!5n{+sdRV7x&T1H~hg@FMTO@i54UtRUNc{p`UiK
zPH*+J<kdZAXuUlVCYv2X5*vN3{Pp|46KV+i!T*DB5BT3w4-?ccPqNXbXB`)Y5KJ_K
z-1#NYvQ$ci09bIK%kjIJ@b+M7U>%pJbZlEU>{8HDbTbAJzx@@+0yU?<2eP1}u0L3U
zbnstfYJtpK<rfQS{v7i+7Scj0{$G`N9?FwGSF#URJ6hw^28T!+WucjynNmK9=Jr;^
zWlh($wsiS*eGikzi&B>-<V<`P9bF<1#Y459tf7A_FvFPLJer%QwBfm%$qvTzn-|@i
z&yqUK@rgC4khJqj^KSM*nC!7y<?q|LS|g&&b@I|{Iy6So-_6$7qyjtHlRXZsx8$aH
z+hy_5m8cIb9w%Ryc)Rfm$_`X)oyN$iC}g#tAM78DUuiSo^C>ngcdLO)4BQ&c8Z1Ll
zv8o+B(z9{q=Fjk*O<)wH@@?kap&-VuVi)L#<h{K8ym`obHIoybKK!FuLablNs08;1
zCpB*F(K*Qp<;N8Avfw&J#S!Oi%kpjJi7d*j$<hWJa{~Ef=ts~C9%7;^1Z1gV4g^O(
zK_zxADdcbJ=}^-8tgCx*`A<##*VQWAmw6*3bJk%!*X$E5I7H87vc22H5JY8gQp;rU
z09;xleumr>Zdhwg^ub+y?n*bq<h4XQ3whJcAEjF1Jvo{oCfP3gXo%u;ry&PH|5YS?
zgT;VIr~+@VtPIaQfU?qwZxlJQwhkB8DVJ9<|11(&#D(AZOpF%){iQfE{Jpf?g6^G#
z0g(hF9_Ft-hc`=X$gqg}12_#VAl^<{e)O&W#rd6M)5U4xy*oB<okM9g3}W%8UCQ6A
zB!WQQEchTC9|C;O31jtTNW2M3qh-Wl!$m<lM=M(cCrE$sF%YCY{UyslnJj0Paz=r%
z1H?psf&kj;fS(uTKSj4;5SLSOa=j_9S`h~l1)G$Y;#YwcJs>On`-=W`JXHTDBnIAI
z*gq40`md#m^UuWj+W#7U0b2imFo)k<)bF>gnVbcygFr#@`FA~7K0x@<hlYUD-Px%C
z3VyYMbZM?jc1%4fucMAl;^)|h_;A4QNL+EB(P^*vM?}V-lP;+he|Lj5OpRrKYqpck
zw0t|PQ2F%A%R8ikozGkfmwn~$;c-a7Be9ZDCt$(`a{K{G+z(hdL`GW`Og24JS=o*&
z33b#DKWl5nK8~7*IeU-O`S$jemx)$S+B1DKB@la+v-}!Ez+j>pumE-#z)CpD5VlLk
zeJmHiwRAb3`<f~0*E*LVoG&hZV<u}oz@tg$?e~C}Lbx)r4^dgguk5qXfeW>Ym34s(
zA`U9WF$1|g>JppDb!cfFi{+o7Yi=J}IcK(zeep@H7b>#p(1Wt^M>g8b84fChoi<h5
zo%a|P9^KC<Km6R|XEwu1*g@N-BtZx$GD4o9%%!@v&$0R52`ZF3xwmC(thdu$4V+bs
z5admwF_rB=s@B!RNbl*Jkyy+U<{3YXTnienjNNs=*_#>MQ*kgSS;}J(=_6rMD>k}L
zkkh*QkU?SICITn+CKm@6ABB8J+g-`EI-Tc8igbaG(lpL_v+Y@E75yKP1^cGzWx+4}
z<)djBuQ$j~PO0}!EaR5y#62wh7Rm|dcQHDzqmRRg_NU4L;6wlCP$_iU0^<K_?>(TR
z+On?EA}Cn|$*Ck~kR(b*B?|~h7D^=NoI@c<77!3nKtOU1l5<Xyvt$ZHl1LRG6i}4^
z@%FX*UcKF~yZ`UK@xM2oV>pf$)INK!z0cWeuQk`4<XaDbf(x~SlY!b$Td(K_B_>=J
zQtp#Ox{3$)4X-wC=I;n^c0h34Oa-D;;I$d9&UJ1YxJgP>2_xh2&Iz9}Brx=$Kt8Lh
z6bshZ2_>>-U}&+oRE?Q)V47y{@Xh85wf3SinxoQa2VI$-@)~TP2gg<;?P~TU-3hF6
zuQ3UUk=v0=6yjV`!31_}?5obkOI*ey>>CZCBBtDj;tP=UvUys#;=A6m=k!!&H*RAy
ztWMPsRSPV1#&rkX8oyJ-(GGF~k&C#cOK1KSt&;A~T+zl&Ki8Rpd{zPaV=>J!j32v|
z=>J7+g2Bqaj7$C{zmpa?SL!wU-J?w6*M7F}PsF&xg;oZl-2{W+YseU+%>(_>dg{*8
zCM_S+O-skb*U8ZMa9MmgKXyde`e;}yzgKxrmo8j8+t&W^jI-pf5=}KctM60Uko4<=
zS3{O~)f8AFXSWgMhLdikz6wp_e$si!s=i~sQlhXF<DKQjFH8{5D{D;^SR~K*3<HS*
z?gUJu`6rq!0jCZK2Fp{D7^xR7$~jdo<hG8!lE>&W`M_bu8svtZkT&}!+%N7#otxs6
z$$JT1a<&|tw=Ez`!}ppc8Y}01x|!BZ)R$hF#X!Qe1A&!WK5{i*YCp}-k(UuVI+5f(
zzZx}0Y100P^kGISy^s!nNy^0WI-rcjQOA?Zv`khnF29KNLUB=0{AsVNLP)cud(jTD
zW3HjP$9M&F@sPQ4WIV=c$2AZ`Vs%~QlksNESDzh!Jov5}ysk6ja*x-1n(ngrRGT|V
z70HtR+`_4yv8EYep2hpNq)hUAb|(GIN6=$(_#<N)By`RGp6zD0CrxaGh)6ubT<|V?
zvbR_SNd(WrVf>Jbj0WoZYzTT#8m>;jE3W<yLD?N#r`7yUCHSonw_<99#UTOTR{zC^
zc6RU$_B7pW6fqLA7K>?XkW{s*b$o7EPO<9~kWYTD5WX6#X|7}OzAC<8(`m7N4`k2I
zP=l+?wa4&@A#MA7LgB-1q`!PVz4dyoCqsDkAGmb<5dSiy*8vc8Ku>QvUIj>+hBMGF
zS$%t-0^An7qUe@vV26?OuKosEv4Mt4r^VLv0HHyFV1T6o0Erz%UOMB*YGGar`avZ0
z3)8~?jij}<bXtY6Y0ffa0Hcn$?XE3n*=o$eszB({hIYJBb8QbhZ>&j=*K9<ag&P6_
zgg(NPTY0B)CrL*?yCRM65nGC#n&YR{N>sh+Q6|;1blNuRi;x09**#KHW6co1Wa;^N
z!H~LxPYbjruajNY*{v-kNYW1D+7mhfHZwo*wG|rMWs1rC*I$jO;#|t{9@>{y){6R<
z+(}QLBX6dS{FJ+3bh9;MlCtph0x^5cIu;-nFu8mkpdZVeM1yOR(yF?gP4lp@H=cwS
z#E~Z^v-S(d;8KC!)p$ZKe*;-S@U73%g8@!TB7G|<#^gKZ!9DY(OWAU+JW<?OtW|YG
zPn8?F?>sz>NZN<^g}+dQJPBjA_H$8~DX?Cqo2a%O+-)KfoOr);idtycTYc!gLbdpj
zu<}G?TScgp%jV5e{G@S<Ay6WqP)HYfLJM2DvW+Gix2t}`w`gE}>>U0!MDI<J<S2W&
z)=c#=NC7h)hpfJwd}(KxnC&7TU=C^fL4aO@e~|HKmh?A16?s?%zte03B2_lU`xI7f
z*k6YoJAA6=)v9tXwOKRXb7jFpHz>L@V-QG}_jAwM5S&wqh|@-i7bN)_hR;(!afo?-
zqy7A<s;NP0vDyQ*;nH)4B*Ul}99jkPTb#Y5sy#VV@jiL!Nw#+n4v$rl(Fpb)FwJK-
zdhYJJo3$y&C1<_$npCkFvCC>7Uy7@Q%nLkCB0o!nFa-eQ;Z;Kb@&o0K;MSVTGUbgv
zv@&peM<~>e|9--s-e58Kb7yN)IW6m3ofz7a;ibAZwG6oWM6>GfjoV2cNk+IuPS4yU
zRbE%;ddwWFDj1aj)o8w(A5H#H-T&Vf3jmx|f8|-Fc9e7f`<(ChT_gP!u8)4rB&J1v
zMc|9aVtVa%qA90%SP?m555qnuBNgMQb%$9$cFWChirQK1Xn9~+v~f#7;fHIKUE&x8
zHph0!VjEG5ITecv5}3D1vOP$-m80LpJ#82mFbToFaiM}lSi;JQ7HV@{S6{pD?)fLH
zHZ)YS-A<IBd2|Ek<XP6GRR=DN$Cm<J(bL8^ka2L8J`ZE;>8~P2VD(zlJoRg#@p_v?
zpWeTFag)-5%haU39|tVr{o#$SkW?RQGJk_Aue;~2!|1?*ir;8DBO<BBdi6rBQ=D5x
zRSd<WMOB)@c3nB1#YM;g#NBi4`PWsmPtDtmLxVidxYr|Yr&XAON3y-?z1hLBGmTNv
zPkeWq&QlkHy=mTC#B-KY_3Ni@Vh1N%-%P6*0bzTT*$kL8BknsK3iaVxELMg-&dk)7
zb8P*vZjCydO{qDLtt%Y7WLDz>Nm<d^-#(+y=xoD~MX+~|<_x$s>e*VSaF%_-!+$ka
zspA@A^gbS_V|m(C>K}Z5D`0?fg&alK=ocV5;V!P?O|qhDS#O#b$9MeVvfLGj!_YmM
zyTJ|70Ka+{KRpt14=%SK;>VuJjZ84o4&B$7G90!l?Q+0#WLuOslV~!L-<rlu`_y))
z&2*m%$=*c+4wGOthaKi2ThWVoxt`V_t55YZU-VaeDR_z=y)(xm#lyjDEVv`+1CZA$
zjDD&wI$GVhWa%%g?$Byzy?r3{45_`U0iT2&hrvRcd=YsIxKd-MgK6>Vw<XUHd)!}1
zy^Qpv@%P0a&<CdtG>Eb{k;q>X`vax(56YYG*Z+>*nHA)k@h=>mA0?b^e_g@Z_g591
z0ByKkJns0bBP@2h*>oiFdTeL_qqVMX?FmtC4^c;LZyuua(H<c_svDdbs<3SQ9Am8o
z1efIg<QNT{4bb%H31g-f`0J^1Hz3bu6({^Yse)@kap(Q#5^5);VBSTp8VfZ$775!#
zqfkQpj93O4dVW6fIQ3kqwFzDaX__f>*ISE+eAZ1|CP>nij}>^{FkkRq@J=Wet-7>`
zS6zoOBhq9Y`5DcMbk#CU-BLv*%|}+G=xwqK($R&H`gI4n^y|JYx1BiJ209R#l%gBa
zEWdmuxl4(+I;E%jj&8K6<_KXSqdUeu=R(#xHU%uq<`Yfi+cik!GJeCDpXwSdue>A^
zEN{|mdV<30Szxaw9zEW@D;kkWom|iTK9tjVcsjan?uf80jMr6ec%+!OpyG#xBq#Q*
zFK<n(NZ))=abz+e@t8h4-S$WtjkkL)CfZd_JMT4UJpK7`W80)f!#GN;j%T)v{?6-2
zaH3NoS2@dzJ_(TRdRtHc#*90JbU-Rj!nq@#Ye&%D41bIpG-3ixfyB#e(<L+oDX+CD
z+N0!(Y-!p;P)hK=j(OdPx0BVywro?p`AHl0IeOI8iif6z193F*4V=t3>@f4-<En=(
zXEY|9Ij8eSVt5kZGC$|W)HR&YoGBE}TB=HHB~`h(BbQ<lX-@H};Zzx-L7?ItNy3Y-
z+ig`rf=I0%uDzDN=kD;K%c7#k&rfgdB_u=wI=dom2*)Tt*0sTwC;N)^6PY^o6OZi?
z5@Da*iR&MeD%ZVVNR+?%wJ75!&88Ns;zuWp42J1N6iG>uRWg4F++c0SF07zcQ@f?V
z{+?1rWJ3E1Q~@^$D62XmF(&?({?|WCsgBE$Z-dExJZt2?#G8&957mEDM*-5fKh#lx
z#OdElo3-?7$8h3*#f$d0a?qiE*9oZmO?=}o$gQFQ;DfAy-nykrL8PdVKs(5jy~eKt
zaqV~ksR|!`IJI!MT8b4x)@MGstuu(<zx#s1Fciy)J~FJwUnO!ifZ6XdoVdp;15N5k
zsJF6M(cW_JpiFn0%cTB@ong8sGKO`smO@EddiTh3(oE!?!L3}LgO@v6dw8rTd1bRX
z**kvTtByzxhsrhns53@{xSp3nJN{&9|6XZM<I7kkbq?_mOX=o=Q#nY05JV2z<$?md
z*S8DbK%BZOD2oO#c0h+QJs_s&dk^6dS3KySHCK%eh$>#lIq&LVv8|O2^r#9B)tW4`
z-GU-zB=~`Xx#c*Zs$Bqz5ZQQKXyNoM(Yko0$T4evSl?Uk0xJU1Oavj3*Y{w^XUa?d
zvO!}lWo^wW=IWAI>g_5#oqQL7LN^590^ai8H_*$}eYAiAN|e6?d`91Pkl+vK18~nk
zVIX+h1%ZzO8U#4e-9Xq77bgTD7B`##aJKwtww5c8JP*6RfiR$`IK=Ww2y_cf0*D3|
z0wo&3pVVW%focFG05wX=UPu99xxlYhnEU7yGFNAPUhht8ooiPu<4ou9VMOhQpd$Ge
z^YI3f86fbjLu;0|A$#;LawRBX=irc1-rA5n!@-NHtTJTDx=xqPj7`^dx9czbWtm#;
zd0};dk9fDiNGwX=1*pI$ehMV20^87vl5P0Fz@&f%akK!zdz=Jd*K$Dto%_w+qARE;
zu}B9<ko1u_bc5|2p#Rb>0m@A<V9+y)qgWJ1)NfUT0KGNPNL>zel@NkbrK{iH4EwI<
zc6$97#r+K=gN#Mds{^KE03I5mLAU}s%n_Bd0!2igW_Py1HTg-c#J=vew&ds|vjOl2
zfp3%F+>*CpOs5yu3g4~y{HfVxFg(x;f(~f`x(Rv$6g1j?iUqpeA=XTLrNy_{t4h0h
zHo9jhq3n&7)PP;&km8+($#$01gc-_*&Tdso$_%}>rJqY$JsrM*oR1g9V^+n;oVf+N
z9$FX7sVTcS8Xnxblfk|3t-t*Nh$KCO*7SA7K2J_M^GGOC&EIvs80Ob{)+0n`iQmPs
zM2rz}Ty9zB+5x@d#}D7>`W!$#W+$wO+ZA<-oJQT#vAJ(4LS%flL00F1a+8{|^2+RL
zfPU)3L7OMJJVj|Rm8`Uv8LNrON#(;pC8`ll%#Sy~kE?h7U*j(SMTRtS#H+8@dKJmL
z;SbiI?jCu-7W?GH?iKamrSt6NMj=%JZg=XlF`Upvz$59N0kBck{tzwot6R$$Qz?_`
z@1E}QtYIGOdL0`nFr_9E_TDI^#upl;7I6f)150?Ngiw&l+zk7@k%q^JZf4g+bZ_OR
z6RpDCDL_>`1GXRPAfbU1VKL^m-7JfV9rZANj%G~+6iD9R4|^5BRv-JUI3p55-K%!F
z&7>E)Lu4YuP*O&dp-K?x{&%geA44a5ww9DK1q#_NK`&04v71~E^tbJla7F}cAqk3i
z(M-s=qFz68*ITs{yhrCIBE4&#gj@wC;a!f4!wEt4beaho)1PgX(~4U4_7{q8x6RWY
zmrqC#tu2&?b)Ferd(cq#5WOg~+5al`MpjWFJ1&_J*2_cfz*{1xzUMk#Yx_NIWc<9F
z_3x$~cwg(RKdR*ws5hm+y3D=_Va=xIy7Hy{%V<Nu2eG3za81iKKq~}XH)i+M*1WoQ
z%7GxH;q-mB;t*M=re79jBWiBbl7V%-d~Q=8P9B|9lfAplfOr_;Fl!Jo_7&SSe~4O{
zGvqaEoHrqhoY@mKjQx+gX?gR8;7s#ZK2(a6X>W4Qyvs%2P1>T_V~qr@Q=VOG)=(l9
zY-DP>G{}#Ie>b{*6Nj4WSbT-hkEj5ph>SLYT~O#{PnC`Dm1MJac%PKr6ntLzvBI)_
zbu*Vg)w&oNe|Bce&3j|80X=|Wc8|r*%Jfha)E9&S?iK^t?nCB+VPzR`UjPMw=ci=?
z)2|sNw>@JhzMM^4nOHK<ea_+6U;7sKef&Uu6S!I7Re*-xc>XB2o8<jb%CA0@b<KPB
zZjH0XNn=M4fFSVL2WfpSYSV~?l4%Nsh6^mzjBLM9GJir<{|pcF;vy~$Q;~<R06X#w
z(kq2IA;pg*9hQ(Rmd!Qn*Ri!%PzxltI#7zmf3U~nB4cvM!G7!d0R!U%V>QB~%^zW`
zih&aG=<SkAvP#|~DVBm4Y-QcUkl`8XBW!xAVLS>eC$?Uu80Qr(Wt+2iJ2&Gdv+1df
z*NazTjrdMx9k1$pHBu#!mw2R7FJdaClv7mn#IHr1!p-;c#8H!qLPHW_HH67FV#Mra
z(bJY#7w3d8HW%`lAEy#)O1wSm|0+)S{j*;G7Taz#GcpJ;;QD^2EiU_tr>$pEb>*qU
z&DhV|;&H)KZtSJL){kZZsWNOoTjVW4Cr5W<p>LoE+kk2<*+;EHvd4c5BLaNNJ;2eO
zLRb>03COxHqE&#S7?$vMW%%+Ymg9ar7K4;YLK9Zn`sF4A+XD0Jn>djmxHO*EZg(py
z(s#X-zqjN#ovU_&tsy}<L774xmw_|Uh}!bi{g7-YFqca{mzb{gxuh*XVGp<M;5LEU
z`Tz{|TN@OUC80OZdHilpIQd5l=^$M*k=NRdg_0Gj;<=-i_|``o8k$meEC~oxb%$Or
z1@Wl?EaLGpA7^6QbFo(20xLj(PYYE8c1N|!F&ytd9G8EKjQ%FNBTyN{z2O)s3Y=Ox
z+n&$2=H?hEt<kIW)ZUq&m=L%7{GdNqak14zPM?ZPFdoe3bje?{+q8_kOTrUw*ek_7
z>)xWPfQ<4qwf3#qD@n{H;R9h1Fh>{RF5*9q@0Nn~Sc<s6O?)^%hU!T<?3_U6?RTX(
zhSjLFQSIdNK5tA|cvWJF&2f<C(t{Pl!iB{_I7fF9AaxOH0*?tl8S}e0287_%-kmE%
zxk=ZR&$~@o>RuYxpLeb=V4POD`by$c>rileE4>&GKO$>p@D4(!)Q>UfY<Ng(so-Ap
z5-Mvea*t8R@#PIO-cV)+e}wq)ZMf0Oo?x9$Ix~$JGLIr2$U45BfA9sCIK_A;*PNtm
z$`d54_E2EE<Fj4a^va@lhVF^GF4R4i<03=qI_lD-8!Ug9fo?X-UnG8x$}x^VYM7~5
zIn4GBqW|+-Q~RhF-ptIq`=JnG_wq1Tuh7Hxq?E^Sxi7Dem#(P^3Rve`O}||neN7pE
zUU!?y9iI|MO5t2A;JtxoNPytZESk%oV5sIDMQu9SOIeq5Wv0^&73qOjCs;N5DN59V
z!~VAs(!C7K@JC`aNXO0zuMh`^dqO#~m6)I#H?lWI6bV0QFN|s!k!7vZ4jA;Q;<|LD
z_!w4Z!`FcAr7<Iz_lty~3B{XIffo`2fo<1KD_`Hep~D^YP{?$qp>1vwEmuwf;0-p{
zz!$gGjWgKKYN<Zk4oUx{$>z>0ES?nER}3J4djGyZ00+|lr=p$sC!QMm9V~}KFYeML
zd$T+)%5-N*2c4APK(ZF;?;7(X`+W14I-u74QP=03ZMg<$*Y8dMq{`P1a8caT;W(uL
zQ|$xFzBcN1Dlk0B5CC0&7xYs7{aAsSUS70d<zv45)|)YMVume6AG69{j+z}GPr?I~
zg7^la_47#88n^$5bZ^sdiaBPPqOub89JaL^WE2d?k{LbZicLf=jm_4?rF0{7gG(gL
z^Gajh-pnS%R2LHS$aghmyv2N3k)Emy?^LM+NF0C>ZBcH6fc`v+V0NU`obR<KTehH$
z__M3ZwD(=8JuBylr8!w*wIWZvSe4fGnICVplbZtxlV>ytM=;V?b-a}WMKb=m<Z-Y{
zRfbj&flOc@4PWYYZ4i*$tsi3wd4Tye5XT1O|M#=WUn|)B52)AVNOA)DT>uxadg+Jl
zm8$aYEr)&kQu79QZ7H3=uwb_Gis?%e+1sY+oh>Hcsd1A7rYIh$pWi}hd7H#2#CE1s
z%DWRjyofQDS0LZv{=2vh5aGuDwajF;_*ZUwD?nTO_weT*Q>~n(YI!vv`QfL=BJ1=4
zUA-%0xk!_>Q9SGUj4*zl)=8`#J!rLHf&Es=4X5scdCx+lMNA@JZdWw<LH^$DV|vFm
z<LE1KrSn!Pdug;H^zu8dgdTCxWg)L?gLH12+sXU2(>Y=Ek2nm_zfvrma`sPeX&#M1
z3v9RI0|H?rYpOVMV|>k)Sql;ggSpCPL59}a8`d+N**qIs_zjQ2gBBh0O|c_4Y#AsZ
zq+YG|@*~HSDk;mW);cqX_jEX4bR$@vciBZ$Uv-kTz?Bz*Xrml}!_{3Fq)(DtZyY5P
z!p}1$x7I|*6R&XnB`4v|+`&42PF1}8m>Dh7qV268-bAxPu^02B%2_r<Xoi(LE%<6S
z)ufhZt<u}d8;W}2bNE@5BM|26A><p%ln&dx>TTKK(YNH48jQL1>ilV%YzDjy11?5M
zkks(<y9n@ei^jIP>5Q%kfSIs2=;|z!Oa6eaNvv>l{f5FK4h#O-3GC$A*U{(3{BTIf
zM*Mn5#g-KH^u~A%x89@E;)}N?bptQ=F5kcmi5F@!_4~l0n~cK0$ntZNFf7?>#f9r6
z+q-Yv8h4YmV2dA5eow?>7<#ZrI*``?@Zt+Ok$<#kkRwXvTp7ha>!)_0@0Bv!p?gA^
zR4ZgbBH`&lt$jpY@4pZL*T#8jL}kTo#31DyQA|B=vs9zD!rxW7L*v+K%d6fY#ul=E
zr<JF%!XkOkleL3KKW0qDfs5=}*R^`$REgQQIW~cpsOY($B6Z=158=2o^7(7F86^IV
zyCdO?UoOTr&xclo%q$%!H>fm*eL&kr2pxs~sRWmRud}d|%ku^tMS_1yx&KJLf4~0M
zNU>OCT`Su^)3yE2`EW5UQieDUb2Vm!H>ZW07K9s;hRaO6LWUc<<0v#m3(@cqg?h4Q
z&Kb1A`((CYVs~EgPL1px(d^F?L0j>-bb|e+e0Rb+s94~F3fJ*bXJKoOJ<?C1v}|zl
zq|^eN6cokYF}Jv}z0t>5SE{!Jm06EnngXS4FcKdHAD^^?F_8PP&-3WmHC0V|EtwdX
zvqy8Za6Ndot6<q8DSgDUA()Rl{h|PcAHYK-)YZy>*zvmCd()P6z7MN$RHQ+xi3mJ`
zzxwX!3#Yk9bTK}0F3KwMF51B<o2q%SDd-z0S;R^oG47U+XOsjtacIpFPlJYJ<z<zM
zVOr9%+^p04EEo;i%YugD0!O3md+KUHLJdY$fjAOSMVQ{Ned*X3dRN%_vPH!G=b6+J
zNeYauPIU2?Rb4ZxbfRFq+^dPb*=xMYhdno2!Be>{hI>A<&Q*@Qv{(6Ga}xBkddAa-
zGb*;9s9%o7M~>(<JLc%gvVNViw{gegZ7zO65?SpS_u^rpCb5KRE{E)rQcGtV{prGV
zS|31~XUR|KmA@A7dVPSUwR)x`S3h!LNTN{R<8k8ky(i3aJkKb!=e^iko(SwnZsE(_
zmgj!Uk{-UJp}fc2{W4(tKN8cMCi_M59!wap0<NaLYhLch(Ey(sQxTox+xrTw!J?}S
z)fZ%*OD`IjElN3wopU@6a*S<dm~vo*FV3^ZyfCSvBwfM_=}r~wkdckf<dpaZqAI5W
zs*w`?glUpP{3O>?t*WF_1t!bs71*c+#}hu6;hqf|@1Jw}0fiMIhH&mKv4@r31|dVz
z^P-tCe%lov!R|(YJwWBJ-hM6LGl+ZHGL<Y>TMJ=ZSq#`rg_=MMZi!WM8Z_s#B2<*-
zr6vG^{jE+KyN1MgNsma5@_cE+zdwP$Ryh3sGRFUojIpXi=o?iVP>{6_nKyk96VcF+
zQ<0?7Q8`iDtQ3Kt!Bn1aRH>Az&W=?nQ|sc)?6n*0gI26mi#dhbacWlt(t}!P{1v*u
z6x9$Cq|1ty>K#@h(|{zGM6WtCC+D3jxc#h(o7lSDU<&9He@EDUcP>Ic7J)h01-%j>
z1X+SCR$uG<Ja;`unL_K6om*$%&DH0GGa`oPTw7mnJlwuHZ4g7cZH_ibKVNSW2hSOH
zpg9rt3y-|AyH1E5ZE?cN9w|85g`ic38J+GB72s-w5WH|QC{$ll2cgUnj-8G)9dNY0
z$L<6CnX?L3gN`H_Ld|EF%nn{TGWKK0Z!Epw-YkYZJDxSBoyvswbrY;arPHP#*m_2(
z9?DeLr>r;_Q0VJk!C;b+aZw)$v}nykdc(l<D6(33Mu_mR)U4t77f04q?J3cwSLA7g
zjvhn@nTyoH6u0h!FiNsE_)`2g&Goc$8O+5;wf#+VOE-Y<p1rzo41efK)&3rjgNUz@
zt|7vq$U|?b%OS4IoVKtZ?-LqPt5oIY97u+Z#aOikp_9OHM}XI7gQ^ErT{gQ8<Y4~B
zrjivbt^C!ML0Fvq(t>HLH?V_lNnP;0ws&<V@2(vi5Qe(wM(ZbRDfR<WOEsBLSU!qL
zECYGB=DRS!ZkE7CqeNqU@@;wBH5LEkgW9mX1pxWY!ZV^F!?UDLHE^=8qoDj4M{2H+
zYvelMEK~in6ZiiS=p0Q3TlOAf_IGgLph;w_X{ka>Wng{Zx}i2!cJZhPjgLr-jr9|<
zEVFL4s%}^&2f$4wWg3ZrEM|gn9n3L!xdB0~wKp5-W|Nn87%(6)NjE1ADiH%U7t{Sp
z5)~5pT*TrMqZ~@Xb>g0c&(~U1sEdeU7b0KHrVTphYVL)1Cih+vDlz+*>F2Ppm!@f@
zDqu0mQ^sTQE@c9bXH!R?clm~?(~6hiI*#PFn>|yX?Br_2Brjt{<x`Iydq=7mVTPv)
zn5|A11*?eOr|V#1WM2oGXj(#rFBb1~wLvFDkF@Iq@AWJCZmFfFpg=WHc~}4^pfo|&
z%Jj%cZx?;NdD1>u*v0xLm)#oVq5C5te-<9w&OVDHC;lW2`!jw2{iBAnI(#qBSBU%m
zEkLb-dYj<OQZ#Pu<UvBjISBXqRTtu1UcXBC;X1TuSh}5oy5=2{$h}ZsHqW5v(i$=z
zw;NHSOre^`r0WD)JsNfc!&_U0pySQUP?$P^knAn4y&Xa=C$0j#!!v+#5-*W@1%X_h
zhf~|asgQjt_qv4Q{I^Fe|3ul_`JJCP^1a5>i}bSgj!iVFFBC2Eh_Yk!c?Q{$|Fv$C
zZ78-wO+g-luhLbxcn$Z-fo1%$ZW1BY$bAQWQ8)VrkVWzCk?)Uto1%*>Aj1G)8~+AC
zrc`}S6M4Fhrkw3{JQGbFMR5Xg5#;pG-7h%1U;t2v?d$+yQrKDHYp4hq#Q|gp4kovJ
zxHn^w5D0bK*0Q5CJiqQY-j2>Q<S+oZI`js%Gs(7ZAe!tybO5}QXO5bJ!n29Lfd<vz
zz7u+WxUB+t1+6IuvJv5J@aksi(&a6h<jy4<q8oCAI`j1@O4)~L`zut&f{OF5lgmw`
zb1TR7$n`bf0yo6!2{O*?nf!@!*Rc}wdf+SwR{=$6b~c+z_a(S&OIh$>9IexM9$Q-q
z^ROn&OSe^SxtY?OkfvSZtNwa2{_vB+)+KT+rN#z;YqgA@CquO{4ok(FC5kbksceG<
zTu|(O#JThc<)Go55Vd`X(g_ufLb2u|cf0C*7;5bJg$GO1?Rdv)8ykUfzbA$n3Tr5E
zwTWuJQL5)sZGE_)lI1WPQ1%`j4jjNN{tnIfRhRYtucR~oAJk?2-#og$7{i<dzs;-@
zb|B;+ALW1pINg&>q}E!`XpC@FR1`HIlX<fGbM$sv&=tJfcuD*lPwTvSwWY1NEfIVf
z*~mM)9kUGVV>JWao>hiT*#`D*A9l8`^gT)@?B-);W_dweUeUffi?c>d<&~<2eEoLI
zgMm8!q-!?Lp>aCdgHigXH<)X0zJX}aJJ@kagj9)}-l<sAo$TY6iGBOp^O$SL&PXg*
zII#}GJUY^#1C~0V{W;OX+S0#dfj|J1iF;>nz-W$(j84}Zp8XI}Tb#GUzwXbsaKA|<
zt9H;aHeJHSiTWKzKcOPeZLStBXatyCSUM!h$A6@n?=?T$jb7d-SL82$vKXMl_+Xaa
zK5J7%MXbzBR&9h?YLMutS=5vaQI&@EDV)f24~qPxCvIGHnfrasPao>Iq!+9FUb6Oo
z^3H{EX}Ve0Nr@1VGuEGLe49+LLQ)ndh)bM+z@Be}Rz=*al-I;4t^*VMp2is0(?H1(
zeUZC*4NWx@`xcdTl*yHy`<JSuI)tApJzQE~!APIWG`0xLsxJ=-U_^*U__5RPM^?L!
zi)aaD);%;^qGGoFY+-T#;D*w9!<MtrEB2XqPwX#YPwMx>!8j7+2(=GgYZ*j(HhXn8
ziz*azV?I2^VUUgxkB1LOND3MBi_3+Ii6hE`3yMwhU?Npf*5&dwoK`84bz#+pL|g;P
z`X3hhY%!}Sn1mk`;YoU7r8LQrE3uS3XXR8=)AY16`zITKPtHI2fuOqX?@>Ce5JXt}
zHxQ7<K$ypdqf}t$_O4p*>L`E=n*Gp=EC=49d!J2{Bq~Ja_2&$?XDSWmWywpcl}T@z
z$b?6Vfp9<%r*xe2aYZ?DQdfp{i=TJT=HrXlC~Jhqsn%vSCk=iC;+j@20Qg_#Pk;Yk
zpm7B#Nd5qY{>7D8_^*Yen`i%t+5*S`h+B|8pMivnoU0RBd-|kjbKf<!N&Ohd?2T)T
zvm~Q;;EQDpS(lC;c800%m1FNvcw+WxPwthkO&tv;t`PN1J*bFvj<Q|5bJwrkH1T#O
zu^C%)K8zieC>;$x23G(gxFXS-$!jC6ZP!8~`|@K608ngs=<~~z=hm-F(l_@oRnI*x
zKS+lp$LF*nqVtJ6R>&miSD{fWRHc4$Yr_Pxjj4teNw-8)HJ9h*)j#`^)U(9kQDa+D
z^-F~YN(E*$AEIg4VU`zUu>eY7^?Ox8j0TYEz6P&CM=Cs%hC{6Z(KimPv8{68;2Ii%
z)c*#OW9|ear2+8m`b%inJroZ>WgiD4MuF#{i1(l)G44@-**Aa!I<5#0Llf^wBR9bC
zacKhStsJkrfG>ZXfpQ6yyb^ghinPiI$vD!KU<BX<=j!O&0Qs7F=ja(H1CYY+xXAwo
zqRS+{n<KJOiJk-#vd(-dfx;epshS2YQ}=Yt?%izgcu59s-f7iizd-0FABQ6iV^4wa
zqTIi?B}0#D>Z~1d=;-ya0MrLZ*YhR-&kH85BHVJWaV|Y~6I$Fpy2xU`^rqpocHL8i
z5+yAC+r6*;xHrHdNeP2|w+r-dX3?z8Wj9;5AC+B5k(b>@&Vr<^pdrC%n}1kIx>~fe
z>KjPpANJ`NH(cVI)QS;qst(#_cIM(iaECaL2QmkVGt{_9rGMRAfsd^KaDYG9=IQx8
zKOXSmKg=TYRg$zh9|*)JyoJYJM4XMHldZM$pjNRV07FK691Q$7%OBEPu9tU(U1a-l
z2DDw3;FxhnYUw`xa=FP$!>HV?SDW|KV^WfEoU)m|yE0(Kabqv(-&;udF?T;t8_0*A
zd^6+cxdRxK|2%Cn3}JHKn?L66kM{e0UHxvq2RLuXjR7C<=e6}0%L-%5O*hE~Ts_-G
zt9n<hRvnB~Gi&mcPxO<uz&13sz(yZ7P@!xN(BohN+1tp3A0_}D8T{`rzP}FuSbTqc
zI({F;Up^eWzZA5Vmj7%fWB)a1L^=s#wom$*lb36c0ed)m(5800UUiEaLB1S%<b{KI
z^Z=HG8KNIR$!7K`8`!vreR}h^H!K@0WwTiq;E7GY?CXV6TC=C^+IJ}#D=UVQSH9#E
zCBM?;L>uZn$yD##_T15Qo!$t4J0<K{CLAEBt(tOMNJ_Du5uM+i9OP2q(vAjsw2@MQ
z!&=$<E-=5e9jVOd{!IKDF^*>1H5P_b#@hMG<>~{X!l7};rL~kq!n{O1*#lII@2`CA
zV)kGGq7_Dt?Ze9B0fb(-v;`R75h&{G!;sVC*yZofc=1IsfB}<sg028jL>lQD;8n5t
zh4>uVwk|_aP}DY`1U)>#xj;b>+2@+0=l}p$-WG)x#;^gtSor%Ffm6UwUn~^TZu>(^
zs`hG=%oluD+773oy@9+zM2zGSuoa5!R>tvCcMhQLlX~i1Njx7cNCDtA%fBeJfNlU?
zIBQ#1#GAtC6*$U&QVg}8`vw|a1fP6`_Qvk1NS~euqtX1xNz7?--9L5=Y6HCezW*NL
z`<*njp?~Ox!f|c}>Y@W+rlmw#VS4{f%^W?_cEIaZhwhUGR@L@<pQ8p56(L&UnvOBx
zu%iu(?4WIKMYCF+$g4{90U;o7N;Opc4J6BXv4!=4<L~yl_|NR~uMC@Tt7zpPR?n-=
zx9~Cg-e+B)&&#eVdMTEXZWr>059?IIr$6&iEZ>>nqkgXO9(Oa36&x;-{ij(0)yQe1
zr-1ieM!obtkU)(F|D)whf`{6We^_$-Klq~duXsZK?=77Hb5f3+O+zu&$3W;&mR3tf
z>QTIC^T!IP!0IB<SA0&4VO{{3qqyRB!2uekE|Wn$fDeCpdowN<nJU|g09l6e3Z3hK
zMe-hP;1>%$zv>#o@)*(MJ&F=$Xp-*QC)k+@pnBJcxKc`VF?D7=wZxklIaHUfX?kUo
zrKkuqDjt;xN&(FwbAk9^)$0C5fKA((Lwt*qZaA{e5TI4LCUt4&xPR9%;4FH|YW)-b
znP~+M{V8Tk#jPXhxJ;aJ)n&<VAZB^Q{wWQ}oos1+<v`ex0ie>$#L@WEkJzjUV(*^P
zj7+;1xV`4lF@N1U>ry*@YNbZ~^m>NuK?-Ox>lK}8c7S}c*9j^b5X*M>-lS+Ow0{h%
zAaDPBli9!g1m_o(ZEuCW+sR1H4($l^!dM?Zbc^58xP;CJ479TK&szDZ{rH6M_jxe$
z)vH>P;asy`U$rHT^pPU-9lat>)2F*nWoU#STC+W*yK#JdvhH8^@6RoJ*ZmV9Sw{6j
z@VTg;F7Tr0&oB0nKHTK>(ad-W=vz$z33f*EL9Y|or)UJfoDn-D=FDEwGUlU#dEw%m
zfY%2>Qn=!Ck9Wl&CF%#xFSv_xdO%*s^-}a++-vl$`Ti!QbrFm)Z2D{OrX5XqH>gN0
z4#r{D&3RU+nW3k8K_A!}TA;m+EmeMUD!ZyU+Ec<8hN_cCQH4iuBJhnBFd!0?rtC@P
z?AP<0F5?)Sku*9R1L#V8_Ks5jHQxrehWzC2B1&z;X!#wzF9LPeA0`szjiPE^a6ZVy
z<iL#Wyj9w)1>1#{TGI&6=Oq^zTMI4DTW^!;UdDTkT@>c5IvAwu+~oj@Ga5xPo>5+4
zxb35`V*&VL4m>|Zc;&Lyn6-gi`5cc4-}_L7xYtXAO-&6vUxt7`Dx^v*DRY^GP~_bs
z{xi(&6(k<`JBaen&6GxRV1dzBF9^_qjh<*sh2LSI0U-bY(nuVDegfFVHQOhVpg!OQ
zFMS74zU6xZKrrI~nFW#LHA7+oq@iMlR_nNZ>%f(zZy@uD=%KU<jqtVTHx0~LY3>qO
zM&_&_zYPR&U&SloH(H%O;5b;6KD#|svjOfFFOp_e@cz^7`K2IxK?*lgQ^%!prz&_~
zSUGQ2Ixo9w5+{ifi;5z={dv%>E!m=0VWj6<l#GgA&7l51@7~}O?3Q$@9HQt!Ci$Ir
zh4TPIvKV4uREbc9(3Xbd-UD)Nc@S0w8AdVyYyI<h00i{UW2uw_nF1Ie@LT8@r5X_6
zQv91t1@Tzh7C}CAl?4!=u+xyC0vJ8Rwjf8BCfneo(%&T|{$yK2vIm!M!$Y6ppq>JF
z`M=FmISrIlIpdn~$~m(v10{rnKa?nZl5$U8>q#Bc=J3*U2mf^A(RxvbfdylvZ6XjR
zGlzAoLy;=bD#~+T2dT+V19hIs8>i{U`}{)(?Xg1(ZI20>ta?D!{Lh4$G64m+Z4_`&
z$N~Dt|I^GmPb38j0FLh;#{m9Q_iH)+i{&v1>7=<zO~VI-FaNd-YE+%nl|k@PDu@x%
zah)x}#5Mbabt`5==m_f+PS|?A(!P0;n-zmI*DI;L+R2R@Lt#8ZeWeO8dcuS^KWyDu
zRg)3gc%^MEiU_Jvm2~)^^1~P&-{t|ySf#>&=&4-Cle;f@Zf!n|$;H4)0loia{)C}F
zP2GpA|B<nipqSrYNxAv^k`M8c&sZBJn?t7WcivQo4f0=AS@SLBS}=^2<s`gWx$OkG
zWOc8->KcD?lNS|j?PlcJ&3inq+s4EQp<~CImN5}uX`F41zCzpN&VzH!shR~q@B{Cr
zozNY>UT((wY!3@QWL|B*lu4|HR7YSgta)}VVRbvbV$OI_QQOc^9jd`tlccDrb<Oof
zyJ9nglkC2czjwxGU`e*_!}z<JjPMWiXiS7gOlo+Z1baw(<Xn6<WvviYVj)!l-~a9G
zi@PK^w^pyJ3%in9gt`NzG}=k2YT6#n9n2L0g1VLicJ2G}Q5I@Si)x=g0s>J`MR_Z%
z&6UeLt(fr95DE2szuSm_xV4OHDrU2c?r-j|P)6bgDbPK=(aR!p6=U=^fK@yky=rZY
z`40XK1fbZ6gT8^Tu;X-jpxI`!Gh}PQ4RP7(Y#+jrey<H(KY}6!4$b@_Ymh5FYyOwd
zmD6_)CTRN&C{uc2Z(n0jvC_HZFY02hRCzwj8*i4(d(>L)YoFC_w<ao<R&qkoG~QFa
zmo|LWYAnPd)qg~WE$Y|~DR&(QP3{;3mzVduZE;0<e;NpN6OO{8&=HP7ZSXgMzp}2q
zwUz2SxrH?hg}p|TK*xFEy69`QP?Sw9(gOMna-`x%4lJ%cU~$uwD+APNvuNrJ807`&
z$|WdF9Pmqf3xG3=B~lKcovG`9Ak_fx5m9th#DO)maBPjdlg$ou45|d4%HLWj(*j&r
zVyGoQhS6QeHGVwQ&9O3z=fiXcq0uJS#IS<esm?3`AUNNSNfa|Ra~*ah=+w8v_!827
zO+CUVFB%GOgUw{*R4KY?JK}Ju6m}NUp28QKpWubkzl+-lq`pPIt8^}mVm2A=T)A}Z
z$4yJ?rKqfJ*C9w{K8G4fd9)C-L6^VJ!NMG+QQ6~gRDtq_i@4C0PmN!xGKoe1VldJ_
z8K#8`AZ393Vx8=t4O2W(kV!1^Z#DR03?~NJw5kiZpLkvny_t_ljn#qmNxM(YR=Qm?
zGWF2}_Bij?Z}r;7Am!jyFaW;KG7Ui7Nl=VPPwV0jthpn5{9QA>I6<HI1qBX!)xL5w
ziQW>w3le?oPf}>@G2atCocNL3+1mBmU12^cU3qN5j3;<y01;Ie5iRUZ^iplSBde$;
zPr@U}lH|}33B|2R-*^uuuA*`ooI;jBli0Zo7|AzKis5QGirwye8iXGuB;Cn>#@Y&;
z*E>B%0f$#NC(_`5+AM!*=RE1plkjK-Fo7R=TI^{uaEwAu``C-jdXixr0Q*#Vm^3k(
z@oquS8uZx;adouAC8e0~bV3SGipr|~BFG*I@olvsJSJK^qEY3qzpnApQCvUaNk7IC
z8=4Xzf>l_!oYi@fQnS0H7{@KdqiFc$x~1vUsdOJg_bH4|ZSWyrD{b3KLUIEWw;=tU
zas3y&<Z8$;*#7d^0SxX!C?+6U^bec*Xp{X1O%fq2SJk^l@qky*mAXtnPNX|qe%_pl
zH;qj?UG0q<(JB?<%tY~IPlKl-`{BCX;?mf=I`hyl31-+Rp%<5`@u*Tu-9%|fN!vW7
zfuCr)mtJSD>BD%kC-UpnX@Qx#^<JADwdvCE+FaN<P@HK<e}@@+KYy{NxmT!*BhDN2
zJUFI%c4!57H&~Qk^@{2?@JNOYif>gDK(0T6?D%Y^Bp<S<&^#K&vmbd99dw!5_tZ<8
zD{qy!H0$vel?!e^Q^o`R?#H!}LO~js9|^BqQvdC2PpvCOU+FB=;Yj{wA#}kLTOcCa
zG~<nurl@PW?8)V3Lt`ZW>L?~kV&W)+6m@snEs9&EIDAi}R9EX=bsF#c8|hPuCil!<
zP~Mt<#GsvQ6SrSI@I+spS{uNxTLek~^~dlK<TsE3(jK;gR6x<MkAjw4hI4C%CRk6a
z(g`fNKSq~R(_YP{hlM(iiu9oPk^b<uz5uqHo-3D{Nlf#0Zfe>1h;-j1QQ}D&f8@<R
zszrsrm*_<H0_$;FjQ?y;2ElcNV<bwXT&kwT+I34vc;>j#zS~)r`vw=<=GBMV2k*Yp
zG4!Rx0(=_6>KMCcp{4nfpW_#K;$$xkk09+#yzfuQVXoTAzxdLzC=bHs0D;W-KUcxs
zVozC@f#<J782sI-5$ppb(*N;+PvmoacaXQ`&!2<ggJ2x#tz<92g~c8NYNmGF9^fQF
zyLkNQfMsU{#EGuS{>`~1tc1IwBRro$;i@=*Lu{Nc2lCdxND8<@e42~?eqRwFQF+Z+
ze_fp*U}T1YZYBp_7lfwvs_L%wI8brS(wDV%b{2Zug+a!3@TNJQ9FI!EqBYCSf<1C%
z=18d6T35%}XmLA!Bvt3QBaS_Mvq(S(bRoNAh1K0k=|_OPyPkn-u#@0-6Ha(?|3FS`
zd;SLAwc@K9NmlXqoB@rVtG>D>!1GfH_xwXAjU0m{@XS|L1$|X+JCuEDZ15dy+TFgF
zOsUn1rNj)vxTXCF1i}Z==_-o>qXvqh{=H%Uw^p1G1?I_J0TsXhJ#w5v43vs#rgSj{
zN|^xC(P6NRVDSBlu>gD|rNLAgtg+RqhDeO+i5{=%l~oej07Mj5ITcR4SJ`eCv?{*?
za?MK2q1+naxOlSgpf>W$^)q3NTE(Zc%#M#}c3WBeI3`-z4d?m_Mxmy><tY)ad5fo{
zuX0rr?x_*??{=(9VB*K6?3VC6nY4dgQyX%<Hb^9rBOyfY0SM#)!jNIG|IL<voN$Go
zmyXXHGK`IkpVp7?FH2{(a9oA~hywkze*V4YL^dI`twespNLN@Y_a1n4G}v9q!Evqn
zb^aKQRcV%aT#BxW7t4zRj^fVsP3#xk+mRgz&(KYT{41W-8FxuMR1dUfTQJA<vE{~K
z_H(*34?1ACaKr})#4r2F!Akib#=_d3ewjF!;p{#ZyU&lE`jW1<2ZMVQJJ5NS94OfP
zsnbTA&rkv;tD{8Q^9;#&!Ri%H?P2mRdo>E0eM#(v?1d~dE&t=s*e{C>=U5L395L@T
zbxI@If`Kz_xmJ!O2iAsqB$QZ+9L0~|K#w6sRt-l9fL&L*$Yw$@#SUV-Vov&gd*f9t
znVB6{)9t;;<BU)#$?0a5LfbR;@al73>24aH3f|7`s^*!`3EiyEi^Yn~2#d|!AH@&}
z^+{h6UI{nmn*934P;2@*MUp24QO;eLD064R-FI$@67PmvJc2{PZl-jb5Wwx|@&$+0
zo@6b5`38E}(wb=2IOT5mRB~wHDvL=P7rX%TX{>9!Qs$L<x2&Yd-`EQ|fs_!T=D6Ii
zMPfjV;*SLZKmVJRm+Zk0KAzvMN<fMVi>&&mEZfgnxBuq<_m<EvHDrj5f$Dh<(C<mZ
L|LH5%x4HiXI!>k0

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/packages/gluon/normalization/normalization.md b/docs/python_docs/python/tutorials/packages/gluon/normalization/normalization.md
new file mode 100644
index 000000000000..81d59be56ee0
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/normalization/normalization.md
@@ -0,0 +1,274 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Normalization Blocks
+
+When training deep neural networks there are a number of techniques that are thought to be essential for model convergence. One important area is deciding how to initialize the parameters of the network. Using techniques such as [Xavier](https://mxnet.incubator.apache.org/api/python/optimization/optimization.html#mxnet.initializer.Xavier) initialization, we can can improve the gradient flow through the network at the start of training. Another important technique is normalization: i.e. scaling and shifting certain values towards a distribution with a mean of 0 (i.e. zero-centered) and a standard distribution of 1 (i.e. unit variance). Which values you normalize depends on the exact method used as we'll see later on.
+
+<p align="center">
+    <img src="./imgs/data_normalization.jpeg" alt="drawing" width="500"/>
+    <p align="center">Figure 1: Data Normalization
+        <a href="http://cs231n.github.io/neural-networks-2/">(Source)</a>
+    </p>
+</p>
+
+Why does this help? [Some research](https://papers.nips.cc/paper/7515-how-does-batch-normalization-help-optimization.pdf) has found that networks with normalization have a loss function that's easier to optimize using stochastic gradient descent. Other reasons are that it prevents saturation of activations and prevents certain features from dominating due to differences in scale.
+
+### Data Normalization
+
+One of the first applications of normalization is on the input data to the network. You can do this with the following steps:
+
+* **Step 1** is to calculate the mean and standard deviation of the entire training dataset. You'll usually want to do this for each channel separately. Sometimes you'll see normalization on images applied per pixel, but per channel is more common.
+* **Step 2** is to use these statistics to normalize each batch for training and for inference too.
+
+Tip: A `BatchNorm` layer at the start of your network can have a similar effect (see 'Beta and Gamma' section for details on how this can be achieved). You won't need to manually calculate and keep track of the normalization statistics.
+
+Warning: You should calculate the normalization means and standard deviations using the training dataset only. Any leakage of information from you testing dataset will effect the reliability of your testing metrics.
+
+When using pre-trained models from the [Gluon Model Zoo](https://mxnet.incubator.apache.org/api/python/gluon/model_zoo.html) you'll usually see the normalization statistics used for training (i.e. statistics from step 1). You'll want to use these statistics to normalize your own input data for fine-tuning or inference with these models. Using `transforms.Normalize` is one way of applying the normalization, and this should be used in the `Dataset`.
+
+```python
+import mxnet as mx
+from mxnet.gluon.data.vision.transforms import Normalize
+
+image_int = mx.nd.random.randint(low=0, high=256, shape=(1,3,2,2))
+image_float = image_int.astype('float32')/255
+# the following normalization statistics are taken from gluon model zoo
+normalizer = Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+image = normalizer(image_float)
+image
+```
+
+### Activation Normalization
+
+We don't have to limit ourselves to normalizing the inputs to the network either. A similar idea can be applied inside the network too, and we can normalize activations between certain layer operations. With deep neural networks most of the convergence benefits described are from this type of normalization.
+
+MXNet Gluon has 3 of the most commonly used normalization blocks: `BatchNorm`, `LayerNorm` and `InstanceNorm`. You can use them in networks just like any other MXNet Gluon Block, and are often used after `Activation` Blocks.
+
+Watch Out: Check the architecture of models carefully because sometimes the normalization is applied before the `Activation`.
+
+Advanced: all of the following methods begin by normalizing certain input distribution (i.e. zero-centered with unit variance), but then shift by (a trainable parameter) beta and scale by (a trainable parameter) gamma. Overall the effect is changing the input distribution to have a mean of beta and a variance of gamma, also allowing to the network to 'undo' the effect of the normalization if necessary.
+
+## Batch Normalization
+
+Figure 1: `BatchNorm` on NCHW data | Figure 2: `BatchNorm` on NTC data 
+- | - 
+![](./imgs/NCHW_BN.png) | ![](./imgs/NTC_BN.png)
+(e.g. batch of images) using the default of `axis=1` | (e.g. batch of sequences) overriding the default with `axis=2` (or `axis=-1`)
+
+One of the most popular normalization techniques is Batch Normalization, usually called BatchNorm for short. We normalize the activations **across all samples in a batch** for each of the channels independently. See Figure 1. We calculate two batch (or local) statistics for every channel to perform the normalization: the mean and variance of the activations in that channel for all samples in a batch. And we use these to shift and scale respectively.
+
+Tip: we can use this at the start of a network to perform data normalization, although this is not exactly equivalent to the data normalization example seen above (that had fixed normalization statistics). With `BatchNorm` the normalization statistics depend on the batch, so could change each batch, and there can also be a post-normalization shift and scale.
+
+Warning: the estimates for the batch mean and variance can themselves have high variance when the batch size is small (or when the spatial dimensions of samples are small). This can lead to instability during training, and unreliable estimates for the global statistics.
+
+Warning: it seems that `BatchNorm` is better suited to convolutional networks (CNNs) than recurrent networks (RNNs). We expect the input distribution to the recurrent cell to change over time, so normalization over time doesn't work well. `LayerNorm` is better suited for this case. When you do *need* to use `BatchNorm` on sequential data, make sure the `axis` parameter is set correctly. With data in NTC format you should set `axis=2` (or `axis=-1` equivalently). See Figure 2.
+
+As an example, we'll apply `BatchNorm` to a batch of 2 samples, each with 2 channels, and both height and width of 2 (in NCHW format).
+
+
+```python
+data = mx.nd.arange(start=0, stop=2*2*2*2).reshape(2, 2, 2, 2)
+print(data)
+```
+
+With MXNet Gluon we can apply batch normalization with the `mx.gluon.nn.BatchNorm` block. It can be created and used just like any other MXNet Gluon block (such as `Conv2D`). Its input will typically be unnormalized activations from the previous layer, and the output will be the normalized activations ready for the next layer. Since we're using data in NCHW format we can use the default axis.
+
+
+```python
+net = mx.gluon.nn.BatchNorm()
+```
+
+We still need to initialize the block because it has a number of trainable parameters, as we'll see later on.
+
+
+```python
+net.initialize()
+```
+
+We can now run the network as we would during training (under `autograd.record` context scope).
+
+Remember: `BatchNorm` runs differently during training and inference. When training, the batch statistics are used for normalization. During inference, a exponentially smoothed average of the batch statistics that have been observed during training is used instead.
+
+Warning: `BatchNorm` assumes the channel dimension is the 2nd in order (i.e. `axis=1`). You need to ensure your data has a channel dimension, and change the `axis` parameter of `BatchNorm` if it's not the 2nd dimension. A batch of greyscale images of shape `(100,32,32)` would not work, since the 2nd dimension is height and not channel. You'd need to add a channel dimension using `data.expand_dims(1)` in this case to give shape `(100,1,32,32)`.
+
+
+```python
+with mx.autograd.record():
+    output = net(data)
+    loss = output.abs()
+loss.backward()
+print(output)
+```
+
+We can immediately see the activations have been scaled down and centered around zero. Activations are the same for each channel, because each channel was normalized independently. We can do a quick sanity check on these results, by manually calculating the batch mean and variance for each channel.
+
+
+```python
+batch_means = data.mean(axis=1, exclude=True)
+batch_vars = (data - batch_means.reshape(1, -1, 1, 1)).square().mean(axis=1, exclude=True)
+print('batch_means:', batch_means.asnumpy())
+print('batch_vars:', batch_vars.asnumpy())
+```
+
+And use these to scale the first entry in `data`, to confirm the `BatchNorm` calculation of `-1.324` was correct.
+
+
+```python
+print("manually calculated:", ((data[0][0][0][0] - batch_means[0])/batch_vars[0].sqrt()).asnumpy())
+print("automatically calculated:", output[0][0][0][0].asnumpy())
+```
+
+As mentioned before, `BatchNorm` has a number of parameters that update throughout training. 2 of the parameters are not updated in the typical fashion (using gradients), but instead are updated deterministically using exponential smoothing. We need to keep track of the average mean and variance of batches during training, so that we can use these values for normalization during inference.
+
+Why are global statistics needed? Often during inference, we have a batch size of 1 so batch variance would be impossible to calculate. We can just use global statistics instead. And we might get a data distribution shift between training and inference data, which shouldn't just be normalized away.
+
+Advanced: when using a pre-trained model inside another model (e.g. a pre-trained ResNet as a image feature extractor inside an instance segmentation model) you might want to use global statistics of the pre-trained model *during training*. Setting `use_global_stats=True` is a method of using the global running statistics during training, and preventing the global statistics from updating. It has no effect on inference mode.
+
+After a single step (specifically after the `backward` call) we can see the `running_mean` and `running_var` have been updated.
+
+```python
+print('running_mean:', net.running_mean.data().asnumpy())
+print('running_var:', net.running_var.data().asnumpy())
+```
+
+You should notice though that these running statistics do not match the batch statistics we just calculated. And instead they are just 10% of the value we'd expect. We see this because of the exponential average process, and because the `momentum` parameter of `BatchNorm` is equal to 0.9 : i.e. 10% of the new value, 90% of the old value (which was initialized to 0). Over time the running statistics will converge to the statistics of the input distribution, while still being flexible enough to adjust to shifts in the input distribution. Using the same batch another 100 times (which wouldn't happen in practice), we can see the running statistics converge to the batch statsitics calculated before.
+
+
+```python
+for i in range(100):
+    with mx.autograd.record():
+        output = net(data)
+        loss = output.abs()
+    loss.backward()
+print('running_means:', net.running_mean.data().asnumpy())
+print('running_vars:', net.running_var.data().asnumpy())
+```
+
+#### Beta and Gamma
+
+As mentioned previously, there are two additional parameters in `BatchNorm` which are trainable in the typical fashion (with gradients). `beta` is used to shift and `gamma` is used to scale the normalized distribution, which allows the network to 'undo' the effects of normalization if required.
+
+Advanced: Sometimes used for input normalization, you can prevent `beta` shifting and `gamma` scaling by setting the learning rate multipler (i.e. `lr_mult`) of these parameters to 0. Zero centering and scaling to unit variance will still occur, only post normalization shifting and scaling will prevented. See [this discussion post](https://discuss.mxnet.io/t/mxnet-use-batch-norm-for-input-scaling/3581/3) for details.
+
+We haven't updated these parameters yet, so they should still be as initialized. You can see the default for `beta` is 0 (i.e. not shift) and `gamma` is 1 (i.e. not scale), so the initial behaviour is to keep the distribution unit normalized.
+
+
+```python
+print('beta:', net.beta.data().asnumpy())
+print('gamma:', net.gamma.data().asnumpy())
+```
+
+We can also check the gradient on these parameters. Since we were finding the gradient of the sum of absolute values, we would expect the gradient of `gamma` to be equal to the number of points in the data (i.e. 16). So to minimize the loss we'd decrease the value of `gamma`, which would happen as part of a `trainer.step`.
+
+
+```python
+print('beta gradient:', net.beta.grad().asnumpy())
+print('gamma gradient:', net.gamma.grad().asnumpy())
+```
+
+#### Inference Mode
+
+When it comes to inference, `BatchNorm` uses the global statistics that were calculated during training. Since we're using the same batch of data over and over again (and our global running statistics have converged), we get a very similar result to using training mode. `beta` and `gamma` are also applied by default (unless explicitly removed).
+
+
+```python
+output = net(data)
+print(output)
+```
+
+## Layer Normalization
+
+An alternative to `BatchNorm` that is better suited to recurrent networks (RNNs) is called `LayerNorm`. Unlike `BatchNorm` which normalizes across all samples of a batch per channel, `LayerNorm` normalizes **across all channels of a single sample**.
+
+Some of the disadvantages of `BatchNorm` no longer apply. Small batch sizes are no longer an issue, since normalization statistics are calculated on single samples. And confusion around training and inference modes disappears because `LayerNorm` is the same for both modes.
+
+Warning: similar to having a small batch sizes in `BatchNorm`, you may have issues with `LayerNorm` if the input channel size is small. Using embeddings with a large enough dimension size avoids this (approx >20).
+
+Warning: currently MXNet Gluon's implementation of `LayerNorm` is applied along a single axis (which should be the channel axis). Other frameworks have the option to apply normalization across multiple axes, which leads to differences in `LayerNorm` on NCHW input by default. See Figure 3. Other frameworks can normalize over C, H and W, not just C as with MXNet Gluon.
+
+Remember: `LayerNorm` is intended to be used with data in NTC format so the default normalization axis is set to -1 (corresponding to C for channel). Change this to `axis=1` if you need to apply `LayerNorm` to data in NCHW format.
+
+Figure 3: `LayerNorm` on NCHW data | Figure 4: `LayerNorm` on NTC data 
+- | - 
+![](./imgs/NCHW_LN.png) | ![](./imgs/NTC_LN.png)
+(e.g. batch of images) overriding the default with `axis=1` | (e.g. batch of sequences) using the default of `axis=-1`
+
+As an example, we'll apply `LayerNorm` to a batch of 2 samples, each with 4 time steps and 2 channels (in NTC format).
+
+
+```python
+data = mx.nd.arange(start=0, stop=2*4*2).reshape(2, 4, 2)
+print(data)
+```
+
+With MXNet Gluon we can apply layer normalization with the `mx.gluon.nn.LayerNorm` block. We need to call `initialize` because `LayerNorm` has two learnable parameters by default: `beta` and `gamma` that are used for post normalization shifting and scaling of each channel.
+
+
+```python
+net = mx.gluon.nn.LayerNorm()
+net.initialize()
+output = net(data)
+print(output)
+```
+
+We can see that normalization has been applied across all channels for each time step and each sample.
+
+We can also check the parameters `beta` and `gamma` and see that they are per channel (i.e. 2 of each in this example).
+
+
+```python
+print('beta:', net.beta.data().asnumpy())
+print('gamma:', net.gamma.data().asnumpy())
+```
+
+##  Instance Normalization
+
+Another less common normalization technique is called `InstanceNorm`, which can be useful for certain tasks such as image stylization. Unlike `BatchNorm` which normalizes across all samples of a batch per channel, `InstanceNorm` normalizes **across all spatial dimensions per channel per sample** (i.e. each sample of a batch is normalized independently).
+
+Watch out: `InstanceNorm` is better suited to convolutional networks (CNNs) than recurrent networks (RNNs). We expect the input distribution to the recurrent cell to change over time, so normalization over time doesn't work well. LayerNorm is better suited for this case.
+
+Figure 3: `InstanceNorm` on NCHW data | Figure 4: `InstanceNorm` on NTC data 
+- | - 
+![](./imgs/NCHW_IN.png) | ![](./imgs/NTC_IN.png)
+(e.g. batch of images) using the default `axis=1` | (e.g. batch of sequences) overiding the default with `axis=2` (or `axis=-1` equivalently)
+
+As an example, we'll apply `InstanceNorm` to a batch of 2 samples, each with 2 channels, and both height and width of 2 (in NCHW format).
+
+
+```python
+data = mx.nd.arange(start=0, stop=2*2*2*2).reshape(2, 2, 2, 2)
+print(data)
+```
+
+With MXNet Gluon we can apply instance normalization with the `mx.gluon.nn.InstanceNorm` block. We need to call `initialize` because InstanceNorm has two learnable parameters by default: `beta` and `gamma` that are used for post normalization shifting and scaling of each channel.
+
+
+```python
+net = mx.gluon.nn.InstanceNorm()
+net.initialize()
+output = net(data)
+print(output)
+```
+
+We can also check the parameters `beta` and `gamma` and see that they are per channel (i.e. 2 of each in this example).
+
+
+```python
+print('beta:', net.beta.data().asnumpy())
+print('gamma:', net.gamma.data().asnumpy())
+```
diff --git a/docs/python_docs/python/tutorials/packages/gluon/parameters.md b/docs/python_docs/python/tutorials/packages/gluon/parameters.md
new file mode 100644
index 000000000000..57ab5304bf9a
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/parameters.md
@@ -0,0 +1,243 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Parameter Management
+
+<!-- adapted from diveintodeeplearning -->
+
+The ultimate goal of training deep neural networks is finding good parameter values for a given architecture. The [`nn.Sequential`](http://beta.mxnet.io/api/gluon/_autogen/mxnet.gluon.nn.Sequential.html#mxnet.gluon.nn.Sequential) class is a perfect tool to work with standard models. However, very few models are entirely standard, and most scientists want to build novel things, which requires working with model parameters.
+
+This section shows how to manipulate parameters. In particular we will cover the following aspects:
+
+* How to access parameters in order to debug, diagnose, visualize or save them. It is the first step to understand how to work with custom models.
+* We will learn how to set parameters to specific values, e.g. how to initialize them. We will discuss the structure of parameter initializers.
+* We will show how this knowledge can be used to build networks that share some parameters.
+
+As always, we start with a Multilayer Perceptron with a single hidden layer. We will use it to demonstrate the aspects mentioned above.
+
+```{.python .input  n=1}
+from mxnet import init, nd
+from mxnet.gluon import nn
+
+
+net = nn.Sequential()
+net.add(nn.Dense(256, activation='relu'))
+net.add(nn.Dense(10))
+net.initialize()  # Use the default initialization method
+
+x = nd.random.uniform(shape=(2, 20))
+net(x)            # Forward computation
+```
+
+## Parameter Access
+
+In case of a Sequential class we can access the parameters simply by indexing each layer of the network. The `params` variable contains the required data. Let's try this out in practice by inspecting the parameters of the first layer.
+
+```{.python .input  n=2}
+print(net[0].params)
+print(net[1].params)
+```
+
+From the output we can see that the layer consists of two sets of parameters: `dense0_weight` and `dense0_bias`. They are both single precision and they have the necessary shapes that we would expect from the first layer, given that the input dimension is 20 and the output dimension 256. The names of the parameters are very useful, because they allow us to identify parameters *uniquely* even in a network of hundreds of layers and with nontrivial structure. The second layer is structured in a similar way.
+
+### Targeted Parameters
+
+In order to do something useful with the parameters we need to access them. There are several ways to do this, ranging from simple to general. Let's look at some of them.
+
+```{.python .input  n=3}
+print(net[1].bias)
+print(net[1].bias.data())
+```
+
+The first line returns the bias of the second layer. Since this is an object containing data, gradients, and additional information, we need to request the data explicitly. To request the data, we call `data` method on the parameter on the second line. Note that the bias is all 0 since we initialized the bias to contain all zeros.
+
+We can also access the parameter by name, such as `dense0_weight`. This is possible since each layer comes with its own parameter dictionary that can be accessed directly. Both methods are entirely equivalent, but the first method leads to more readable code.
+
+```{.python .input  n=4}
+print(net[0].params['dense0_weight'])
+print(net[0].params['dense0_weight'].data())
+```
+
+Note that the weights are nonzero as they were randomly initialized when we constructed the network.
+
+[`data`](http://beta.mxnet.io/api/gluon/_autogen/mxnet.gluon.Parameter.data.html) is not the only method that we can invoke. For instance, we can compute the gradient with respect to the parameters. It has the same shape as the weight. However, since we did not invoke backpropagation yet, the values are all 0.
+
+```{.python .input  n=5}
+net[0].weight.grad()
+```
+
+### All Parameters at Once
+
+Accessing parameters as described above can be a bit tedious, in particular if we have more complex blocks, or blocks of blocks (or even blocks of blocks of blocks), since we need to walk through the entire tree in reverse order to learn how the blocks were constructed. To avoid this, blocks come with a method [`collect_params`](http://beta.mxnet.io/api/gluon/_autogen/mxnet.gluon.nn.Block.collect_params.html#mxnet.gluon.nn.Block.collect_params) which grabs all parameters of a network in one dictionary such that we can traverse it with ease. It does so by iterating over all constituents of a block and calls `collect_params` on sub-blocks as needed. To see the difference, consider the following:
+
+```{.python .input  n=6}
+# Parameters only for the first layer
+print(net[0].collect_params())
+# Parameters of the entire network
+print(net.collect_params())
+```
+
+This provides us with the third way of accessing the parameters of the network. If we want to get the value of the bias term of the second layer we could simply use this:
+
+```{.python .input  n=7}
+net.collect_params()['dense1_bias'].data()
+```
+
+By adding a regular expression as an argument to `collect_params` method, we can select only a particular set of parameters whose names are matched by the regular expression.
+
+```{.python .input  n=8}
+print(net.collect_params('.*weight'))
+print(net.collect_params('dense0.*'))
+```
+
+### Rube Goldberg strikes again
+
+Let's see how the parameter naming conventions work if we nest multiple blocks inside each other. For that we first define a function that produces blocks (a block factory, so to speak) and then we combine these inside yet larger blocks.
+
+```{.python .input  n=20}
+def block1():
+    net = nn.Sequential()
+    net.add(nn.Dense(32, activation='relu'))
+    net.add(nn.Dense(16, activation='relu'))
+    return net
+
+def block2():
+    net = nn.Sequential()
+    for i in range(4):
+        net.add(block1())
+    return net
+
+rgnet = nn.Sequential()
+rgnet.add(block2())
+rgnet.add(nn.Dense(10))
+rgnet.initialize()
+rgnet(x)
+```
+
+Now that we are done designing the network, let's see how it is organized. `collect_params` provides us with this information, both in terms of naming and in terms of logical structure.
+
+```{.python .input}
+print(rgnet.collect_params)
+print(rgnet.collect_params())
+```
+
+We can access layers following the hierarchy in which they are structured. For instance, if we want to access the bias of the first layer of the second subblock of the first major block, we could perform the following:
+
+```{.python .input}
+rgnet[0][1][0].bias.data()
+```
+
+### Saving and loading parameters
+
+In order to save parameters, we can use [`save_parameters`](http://beta.mxnet.io/api/gluon/_autogen/mxnet.gluon.nn.Block.save_parameters.html#mxnet.gluon.nn.Block.save_parameters) method on the whole network or a particular subblock. The only parameter that is needed is the `file_name`. In a similar way, we can load parameters back from the file. We use [`load_parameters`](http://beta.mxnet.io/api/gluon/_autogen/mxnet.gluon.nn.Block.load_parameters.html#mxnet.gluon.nn.Block.load_parameters) method for that:
+
+```{.python .input}
+rgnet.save_parameters('model.params')
+rgnet.load_parameters('model.params')
+```
+
+## Parameter Initialization
+
+Now that we know how to access the parameters, let's look at how to initialize them properly. By default, MXNet initializes the weight matrices uniformly by drawing from $U[-0.07, 0.07]$ and the bias parameters are all set to $0$. However, we often need to use other methods to initialize the weights. MXNet's [`init`](http://beta.mxnet.io/api/gluon-related/mxnet.initializer.html?#module-mxnet.initializer) module provides a variety of preset initialization methods, but if we want something unusual, we need to do a bit of extra work.
+
+### Built-in Initialization
+
+Let's begin with the built-in initializers. The code below initializes all parameters with Gaussian random variables.
+
+```{.python .input  n=9}
+# force_reinit ensures that the variables are initialized again,
+# regardless of whether they were already initialized previously
+net.initialize(init=init.Normal(sigma=0.01), force_reinit=True)
+net[0].weight.data()[0]
+```
+
+If we wanted to initialize all parameters to 1, we could do this simply by changing the initializer to [`Constant(1)`](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.initializer.Constant.html#mxnet.initializer.Constant).
+
+```{.python .input  n=10}
+net.initialize(init=init.Constant(1), force_reinit=True)
+net[0].weight.data()[0]
+```
+
+If we want to initialize only a specific parameter in a different manner, we can simply set the initializer only for the appropriate subblock (or parameter) for that matter. For instance, below we initialize the second layer to a constant value of 42 and we use the [`Xavier`](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.initializer.Xavier.html#mxnet.initializer.Xavier) initializer for the weights of the first layer.
+
+```{.python .input  n=11}
+net[1].initialize(init=init.Constant(42), force_reinit=True)
+net[0].weight.initialize(init=init.Xavier(), force_reinit=True)
+print(net[1].weight.data()[0,0])
+print(net[0].weight.data()[0])
+```
+
+### Custom Initialization
+
+Sometimes, the initialization methods we need are not provided in the `init` module. If this is the case, we can implement a subclass of the [`Initializer`](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.initializer.Initializer.html#mxnet.initializer.Initializer) class so that we can use it like any other initialization method. Usually, we only need to implement the `_init_weight` method and modify the incoming NDArray according to the initial result. In the example below, we pick a nontrivial distribution, just to prove the point. We draw the coefficients from the following distribution:
+
+$$
+\begin{aligned}
+    w \sim \begin{cases}
+        U[5, 10] & \text{ with probability } \frac{1}{4} \\
+            0    & \text{ with probability } \frac{1}{2} \\
+        U[-10, -5] & \text{ with probability } \frac{1}{4}
+    \end{cases}
+\end{aligned}
+$$
+
+```{.python .input  n=12}
+class MyInit(init.Initializer):
+    def _init_weight(self, name, data):
+        print('Init', name, data.shape)
+        data[:] = nd.random.uniform(low=-10, high=10, shape=data.shape)
+        data *= data.abs() >= 5
+
+net.initialize(MyInit(), force_reinit=True)
+net[0].weight.data()[0]
+```
+
+If even this functionality is insufficient, we can set parameters directly. Since `data()` returns an NDArray we can access it just like any other matrix. A note for advanced users - if you want to adjust parameters within an [`autograd`](http://beta.mxnet.io/api/gluon-related/mxnet.autograd.html?#module-mxnet.autograd) scope you need to use [`set_data`](http://beta.mxnet.io/api/gluon/_autogen/mxnet.gluon.Parameter.set_data.html#mxnet.gluon.Parameter.set_data) to avoid confusing the automatic differentiation mechanics.
+
+```{.python .input  n=13}
+net[0].weight.data()[:] += 1
+net[0].weight.data()[0,0] = 42
+net[0].weight.data()[0]
+```
+
+## Tied Parameters
+
+In some cases, we want to share model parameters across multiple layers. For instance, when we want to find good word embeddings we may decide to use the same parameters both for encoding and decoding of words. In the code below, we allocate a dense layer and then use its parameters specifically to set those of another layer.
+
+```{.python .input  n=14}
+net = nn.Sequential()
+# We need to give the shared layer a name such that we can reference
+# its parameters
+shared = nn.Dense(8, activation='relu')
+net.add(nn.Dense(8, activation='relu'),
+        shared,
+        nn.Dense(8, activation='relu', params=shared.params),
+        nn.Dense(10))
+net.initialize()
+
+x = nd.random.uniform(shape=(2, 20))
+net(x)
+
+# Check whether the parameters are the same
+print(net[1].weight.data()[0] == net[2].weight.data()[0])
+net[1].weight.data()[0,0] = 100
+# And make sure that they're actually the same object rather
+# than just having the same value
+print(net[1].weight.data()[0] == net[2].weight.data()[0])
+```
+
+The above example shows that the parameters of the second and third layer are tied. They are identical rather than just being equal. That is, by changing one of the parameters the other one changes, too. What happens to the gradients is quite ingenious. Since the model parameters contain gradients, the gradients of the second hidden layer and the third hidden layer are accumulated in the [`shared.params.grad()`](http://beta.mxnet.io/api/gluon/_autogen/mxnet.gluon.Parameter.grad.html) during backpropagation.
diff --git a/docs/python_docs/python/tutorials/packages/gluon/text/gnmt.rst b/docs/python_docs/python/tutorials/packages/gluon/text/gnmt.rst
new file mode 100644
index 000000000000..c716c8c7f2a7
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/text/gnmt.rst
@@ -0,0 +1,489 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Google Neural Machine Translation
+=================================
+
+In this notebook, we are going to train Google NMT on IWSLT 2015
+English-Vietnamese Dataset. The building process includes four steps: 1)
+load and process dataset, 2) create sampler and DataLoader, 3) build
+model, and 4) write training epochs.
+
+Load MXNET and Gluon
+--------------------
+
+.. code:: python
+
+    import warnings
+    warnings.filterwarnings('ignore')
+
+    import argparse
+    import time
+    import random
+    import os
+    import io
+    import logging
+    import numpy as np
+    import mxnet as mx
+    from mxnet import gluon
+    import gluonnlp as nlp
+    import nmt
+
+Hyper-parameters
+----------------
+
+.. code:: python
+
+    np.random.seed(100)
+    random.seed(100)
+    mx.random.seed(10000)
+    ctx = mx.gpu(0)
+
+    # parameters for dataset
+    dataset = 'IWSLT2015'
+    src_lang, tgt_lang = 'en', 'vi'
+    src_max_len, tgt_max_len = 50, 50
+
+    # parameters for model
+    num_hidden = 512
+    num_layers = 2
+    num_bi_layers = 1
+    dropout = 0.2
+
+    # parameters for training
+    batch_size, test_batch_size = 128, 32
+    num_buckets = 5
+    epochs = 1
+    clip = 5
+    lr = 0.001
+    lr_update_factor = 0.5
+    log_interval = 10
+    save_dir = 'gnmt_en_vi_u512'
+
+    #parameters for testing
+    beam_size = 10
+    lp_alpha = 1.0
+    lp_k = 5
+
+    nmt.utils.logging_config(save_dir)
+
+Load and Preprocess Dataset
+---------------------------
+
+The following shows how to process the dataset and cache the processed
+dataset for future use. The processing steps include: 1) clip the source
+and target sequences, 2) split the string input to a list of tokens, 3)
+map the string token into its integer index in the vocabulary, and 4)
+append end-of-sentence (EOS) token to source sentence and add BOS and
+EOS tokens to target sentence.
+
+.. code:: python
+
+    def cache_dataset(dataset, prefix):
+        """Cache the processed npy dataset  the dataset into a npz
+
+        Parameters
+        ----------
+        dataset : gluon.data.SimpleDataset
+        file_path : str
+        """
+        if not os.path.exists(nmt._constants.CACHE_PATH):
+            os.makedirs(nmt._constants.CACHE_PATH)
+        src_data = np.array([ele[0] for ele in dataset])
+        tgt_data = np.array([ele[1] for ele in dataset])
+        np.savez(os.path.join(nmt._constants.CACHE_PATH, prefix + '.npz'), src_data=src_data, tgt_data=tgt_data)
+
+
+    def load_cached_dataset(prefix):
+        cached_file_path = os.path.join(nmt._constants.CACHE_PATH, prefix + '.npz')
+        if os.path.exists(cached_file_path):
+            print('Load cached data from {}'.format(cached_file_path))
+            dat = np.load(cached_file_path)
+            return gluon.data.ArrayDataset(np.array(dat['src_data']), np.array(dat['tgt_data']))
+        else:
+            return None
+
+
+    class TrainValDataTransform(object):
+        """Transform the machine translation dataset.
+
+        Clip source and the target sentences to the maximum length. For the source sentence, append the
+        EOS. For the target sentence, append BOS and EOS.
+
+        Parameters
+        ----------
+        src_vocab : Vocab
+        tgt_vocab : Vocab
+        src_max_len : int
+        tgt_max_len : int
+        """
+        def __init__(self, src_vocab, tgt_vocab, src_max_len, tgt_max_len):
+            self._src_vocab = src_vocab
+            self._tgt_vocab = tgt_vocab
+            self._src_max_len = src_max_len
+            self._tgt_max_len = tgt_max_len
+
+        def __call__(self, src, tgt):
+            if self._src_max_len > 0:
+                src_sentence = self._src_vocab[src.split()[:self._src_max_len]]
+            else:
+                src_sentence = self._src_vocab[src.split()]
+            if self._tgt_max_len > 0:
+                tgt_sentence = self._tgt_vocab[tgt.split()[:self._tgt_max_len]]
+            else:
+                tgt_sentence = self._tgt_vocab[tgt.split()]
+            src_sentence.append(self._src_vocab[self._src_vocab.eos_token])
+            tgt_sentence.insert(0, self._tgt_vocab[self._tgt_vocab.bos_token])
+            tgt_sentence.append(self._tgt_vocab[self._tgt_vocab.eos_token])
+            src_npy = np.array(src_sentence, dtype=np.int32)
+            tgt_npy = np.array(tgt_sentence, dtype=np.int32)
+            return src_npy, tgt_npy
+
+
+    def process_dataset(dataset, src_vocab, tgt_vocab, src_max_len=-1, tgt_max_len=-1):
+        start = time.time()
+        dataset_processed = dataset.transform(TrainValDataTransform(src_vocab, tgt_vocab,
+                                                                    src_max_len,
+                                                                    tgt_max_len), lazy=False)
+        end = time.time()
+        print('Processing time spent: {}'.format(end - start))
+        return dataset_processed
+
+
+    def load_translation_data(dataset, src_lang='en', tgt_lang='vi'):
+        """Load translation dataset
+
+        Parameters
+        ----------
+        dataset : str
+        src_lang : str, default 'en'
+        tgt_lang : str, default 'vi'
+
+        Returns
+        -------
+        data_train_processed : Dataset
+            The preprocessed training sentence pairs
+        data_val_processed : Dataset
+            The preprocessed validation sentence pairs
+        data_test_processed : Dataset
+            The preprocessed test sentence pairs
+        val_tgt_sentences : list
+            The target sentences in the validation set
+        test_tgt_sentences : list
+            The target sentences in the test set
+        src_vocab : Vocab
+            Vocabulary of the source language
+        tgt_vocab : Vocab
+            Vocabulary of the target language
+        """
+        common_prefix = 'IWSLT2015_{}_{}_{}_{}'.format(src_lang, tgt_lang,
+                                                       src_max_len, tgt_max_len)
+        data_train = nlp.data.IWSLT2015('train', src_lang=src_lang, tgt_lang=tgt_lang)
+        data_val = nlp.data.IWSLT2015('val', src_lang=src_lang, tgt_lang=tgt_lang)
+        data_test = nlp.data.IWSLT2015('test', src_lang=src_lang, tgt_lang=tgt_lang)
+        src_vocab, tgt_vocab = data_train.src_vocab, data_train.tgt_vocab
+        data_train_processed = load_cached_dataset(common_prefix + '_train')
+        if not data_train_processed:
+            data_train_processed = process_dataset(data_train, src_vocab, tgt_vocab,
+                                                   src_max_len, tgt_max_len)
+            cache_dataset(data_train_processed, common_prefix + '_train')
+        data_val_processed = load_cached_dataset(common_prefix + '_val')
+        if not data_val_processed:
+            data_val_processed = process_dataset(data_val, src_vocab, tgt_vocab)
+            cache_dataset(data_val_processed, common_prefix + '_val')
+        data_test_processed = load_cached_dataset(common_prefix + '_test')
+        if not data_test_processed:
+            data_test_processed = process_dataset(data_test, src_vocab, tgt_vocab)
+            cache_dataset(data_test_processed, common_prefix + '_test')
+        fetch_tgt_sentence = lambda src, tgt: tgt.split()
+        val_tgt_sentences = list(data_val.transform(fetch_tgt_sentence))
+        test_tgt_sentences = list(data_test.transform(fetch_tgt_sentence))
+        return data_train_processed, data_val_processed, data_test_processed, \
+               val_tgt_sentences, test_tgt_sentences, src_vocab, tgt_vocab
+
+
+    def get_data_lengths(dataset):
+        return list(dataset.transform(lambda srg, tgt: (len(srg), len(tgt))))
+
+
+    data_train, data_val, data_test, val_tgt_sentences, test_tgt_sentences, src_vocab, tgt_vocab\
+        = load_translation_data(dataset=dataset, src_lang=src_lang, tgt_lang=tgt_lang)
+    data_train_lengths = get_data_lengths(data_train)
+    data_val_lengths = get_data_lengths(data_val)
+    data_test_lengths = get_data_lengths(data_test)
+
+    with io.open(os.path.join(save_dir, 'val_gt.txt'), 'w', encoding='utf-8') as of:
+        for ele in val_tgt_sentences:
+            of.write(' '.join(ele) + '\n')
+
+    with io.open(os.path.join(save_dir, 'test_gt.txt'), 'w', encoding='utf-8') as of:
+        for ele in test_tgt_sentences:
+            of.write(' '.join(ele) + '\n')
+
+
+    data_train = data_train.transform(lambda src, tgt: (src, tgt, len(src), len(tgt)), lazy=False)
+    data_val = gluon.data.SimpleDataset([(ele[0], ele[1], len(ele[0]), len(ele[1]), i)
+                                         for i, ele in enumerate(data_val)])
+    data_test = gluon.data.SimpleDataset([(ele[0], ele[1], len(ele[0]), len(ele[1]), i)
+                                          for i, ele in enumerate(data_test)])
+
+Create Sampler and DataLoader
+-----------------------------
+
+Now, we have obtained ``data_train``, ``data_val``, and ``data_test``.
+The next step is to construct sampler and DataLoader. The first step is
+to construct batchify function, which pads and stacks sequences to form
+mini-batch.
+
+.. code:: python
+
+    train_batchify_fn = nlp.data.batchify.Tuple(nlp.data.batchify.Pad(),
+                                                nlp.data.batchify.Pad(),
+                                                nlp.data.batchify.Stack(dtype='float32'),
+                                                nlp.data.batchify.Stack(dtype='float32'))
+    test_batchify_fn = nlp.data.batchify.Tuple(nlp.data.batchify.Pad(),
+                                               nlp.data.batchify.Pad(),
+                                               nlp.data.batchify.Stack(dtype='float32'),
+                                               nlp.data.batchify.Stack(dtype='float32'),
+                                               nlp.data.batchify.Stack())
+
+We can then construct bucketing samplers, which generate batches by
+grouping sequences with similar lengths. Here, the bucketing scheme is
+empirically determined.
+
+.. code:: python
+
+    bucket_scheme = nlp.data.ExpWidthBucket(bucket_len_step=1.2)
+    train_batch_sampler = nlp.data.FixedBucketSampler(lengths=data_train_lengths,
+                                                      batch_size=batch_size,
+                                                      num_buckets=num_buckets,
+                                                      shuffle=True,
+                                                      bucket_scheme=bucket_scheme)
+    logging.info('Train Batch Sampler:\n{}'.format(train_batch_sampler.stats()))
+    val_batch_sampler = nlp.data.FixedBucketSampler(lengths=data_val_lengths,
+                                                    batch_size=test_batch_size,
+                                                    num_buckets=num_buckets,
+                                                    shuffle=False)
+    logging.info('Valid Batch Sampler:\n{}'.format(val_batch_sampler.stats()))
+    test_batch_sampler = nlp.data.FixedBucketSampler(lengths=data_test_lengths,
+                                                     batch_size=test_batch_size,
+                                                     num_buckets=num_buckets,
+                                                     shuffle=False)
+    logging.info('Test Batch Sampler:\n{}'.format(test_batch_sampler.stats()))
+
+Given the samplers, we can create DataLoader, which is iterable.
+
+.. code:: python
+
+    train_data_loader = gluon.data.DataLoader(data_train,
+                                              batch_sampler=train_batch_sampler,
+                                              batchify_fn=train_batchify_fn,
+                                              num_workers=4)
+    val_data_loader = gluon.data.DataLoader(data_val,
+                                            batch_sampler=val_batch_sampler,
+                                            batchify_fn=test_batchify_fn,
+                                            num_workers=4)
+    test_data_loader = gluon.data.DataLoader(data_test,
+                                             batch_sampler=test_batch_sampler,
+                                             batchify_fn=test_batchify_fn,
+                                             num_workers=4)
+
+Build GNMT Model
+----------------
+
+After obtaining DataLoader, we can build the model. The GNMT encoder and
+decoder can be easily constructed by calling
+``get_gnmt_encoder_decoder`` function. Then, we feed the encoder and
+decoder to ``NMTModel`` to construct the GNMT model. ``model.hybridize``
+allows computation to be done using the symbolic backend.
+
+.. code:: python
+
+    encoder, decoder = nmt.gnmt.get_gnmt_encoder_decoder(hidden_size=num_hidden,
+                                                         dropout=dropout,
+                                                         num_layers=num_layers,
+                                                         num_bi_layers=num_bi_layers)
+    model = nmt.translation.NMTModel(src_vocab=src_vocab, tgt_vocab=tgt_vocab, encoder=encoder, decoder=decoder,
+                                     embed_size=num_hidden, prefix='gnmt_')
+    model.initialize(init=mx.init.Uniform(0.1), ctx=ctx)
+    static_alloc = True
+    model.hybridize(static_alloc=static_alloc)
+    logging.info(model)
+
+    # Due to the paddings, we need to mask out the losses corresponding to padding tokens.
+    loss_function = nmt.loss.SoftmaxCEMaskedLoss()
+    loss_function.hybridize(static_alloc=static_alloc)
+
+We also build the beam search translator.
+
+.. code:: python
+
+    translator = nmt.translation.BeamSearchTranslator(model=model, beam_size=beam_size,
+                                                      scorer=nlp.model.BeamSearchScorer(alpha=lp_alpha,
+                                                                                        K=lp_k),
+                                                      max_length=tgt_max_len + 100)
+    logging.info('Use beam_size={}, alpha={}, K={}'.format(beam_size, lp_alpha, lp_k))
+
+We define evaluation function as follows. The ``evaluate`` function use
+beam search translator to generate outputs for the validation and
+testing datasets.
+
+.. code:: python
+
+    def evaluate(data_loader):
+        """Evaluate given the data loader
+
+        Parameters
+        ----------
+        data_loader : gluon.data.DataLoader
+
+        Returns
+        -------
+        avg_loss : float
+            Average loss
+        real_translation_out : list of list of str
+            The translation output
+        """
+        translation_out = []
+        all_inst_ids = []
+        avg_loss_denom = 0
+        avg_loss = 0.0
+        for _, (src_seq, tgt_seq, src_valid_length, tgt_valid_length, inst_ids) \
+                in enumerate(data_loader):
+            src_seq = src_seq.as_in_context(ctx)
+            tgt_seq = tgt_seq.as_in_context(ctx)
+            src_valid_length = src_valid_length.as_in_context(ctx)
+            tgt_valid_length = tgt_valid_length.as_in_context(ctx)
+            # Calculating Loss
+            out, _ = model(src_seq, tgt_seq[:, :-1], src_valid_length, tgt_valid_length - 1)
+            loss = loss_function(out, tgt_seq[:, 1:], tgt_valid_length - 1).mean().asscalar()
+            all_inst_ids.extend(inst_ids.asnumpy().astype(np.int32).tolist())
+            avg_loss += loss * (tgt_seq.shape[1] - 1)
+            avg_loss_denom += (tgt_seq.shape[1] - 1)
+            # Translate
+            samples, _, sample_valid_length =\
+                translator.translate(src_seq=src_seq, src_valid_length=src_valid_length)
+            max_score_sample = samples[:, 0, :].asnumpy()
+            sample_valid_length = sample_valid_length[:, 0].asnumpy()
+            for i in range(max_score_sample.shape[0]):
+                translation_out.append(
+                    [tgt_vocab.idx_to_token[ele] for ele in
+                     max_score_sample[i][1:(sample_valid_length[i] - 1)]])
+        avg_loss = avg_loss / avg_loss_denom
+        real_translation_out = [None for _ in range(len(all_inst_ids))]
+        for ind, sentence in zip(all_inst_ids, translation_out):
+            real_translation_out[ind] = sentence
+        return avg_loss, real_translation_out
+
+
+    def write_sentences(sentences, file_path):
+        with io.open(file_path, 'w', encoding='utf-8') as of:
+            for sent in sentences:
+                of.write(' '.join(sent) + '\n')
+
+Training Epochs
+---------------
+
+Before entering the training stage, we need to create trainer for
+updating the parameters. In the following example, we create a trainer
+that uses ADAM optimzier.
+
+.. code:: python
+
+    trainer = gluon.Trainer(model.collect_params(), 'adam', {'learning_rate': lr})
+
+We can then write the training loop. During the training, we evaluate on
+the validation and testing datasets every epoch, and record the
+parameters that give the hightest BLEU score on the validation dataset.
+Before performing forward and backward, we first use ``as_in_context``
+function to copy the mini-batch to GPU. The statement
+``with mx.autograd.record()`` tells Gluon backend to compute the
+gradients for the part inside the block.
+
+.. code:: python
+
+    best_valid_bleu = 0.0
+    for epoch_id in range(epochs):
+        log_avg_loss = 0
+        log_avg_gnorm = 0
+        log_wc = 0
+        log_start_time = time.time()
+        for batch_id, (src_seq, tgt_seq, src_valid_length, tgt_valid_length)\
+                in enumerate(train_data_loader):
+            # logging.info(src_seq.context) Context suddenly becomes GPU.
+            src_seq = src_seq.as_in_context(ctx)
+            tgt_seq = tgt_seq.as_in_context(ctx)
+            src_valid_length = src_valid_length.as_in_context(ctx)
+            tgt_valid_length = tgt_valid_length.as_in_context(ctx)
+            with mx.autograd.record():
+                out, _ = model(src_seq, tgt_seq[:, :-1], src_valid_length, tgt_valid_length - 1)
+                loss = loss_function(out, tgt_seq[:, 1:], tgt_valid_length - 1).mean()
+                loss = loss * (tgt_seq.shape[1] - 1) / (tgt_valid_length - 1).mean()
+                loss.backward()
+            grads = [p.grad(ctx) for p in model.collect_params().values()]
+            gnorm = gluon.utils.clip_global_norm(grads, clip)
+            trainer.step(1)
+            src_wc = src_valid_length.sum().asscalar()
+            tgt_wc = (tgt_valid_length - 1).sum().asscalar()
+            step_loss = loss.asscalar()
+            log_avg_loss += step_loss
+            log_avg_gnorm += gnorm
+            log_wc += src_wc + tgt_wc
+            if (batch_id + 1) % log_interval == 0:
+                wps = log_wc / (time.time() - log_start_time)
+                logging.info('[Epoch {} Batch {}/{}] loss={:.4f}, ppl={:.4f}, gnorm={:.4f}, '
+                             'throughput={:.2f}K wps, wc={:.2f}K'
+                             .format(epoch_id, batch_id + 1, len(train_data_loader),
+                                     log_avg_loss / log_interval,
+                                     np.exp(log_avg_loss / log_interval),
+                                     log_avg_gnorm / log_interval,
+                                     wps / 1000, log_wc / 1000))
+                log_start_time = time.time()
+                log_avg_loss = 0
+                log_avg_gnorm = 0
+                log_wc = 0
+        valid_loss, valid_translation_out = evaluate(val_data_loader)
+        valid_bleu_score, _, _, _, _ = nmt.bleu.compute_bleu([val_tgt_sentences], valid_translation_out)
+        logging.info('[Epoch {}] valid Loss={:.4f}, valid ppl={:.4f}, valid bleu={:.2f}'
+                     .format(epoch_id, valid_loss, np.exp(valid_loss), valid_bleu_score * 100))
+        test_loss, test_translation_out = evaluate(test_data_loader)
+        test_bleu_score, _, _, _, _ = nmt.bleu.compute_bleu([test_tgt_sentences], test_translation_out)
+        logging.info('[Epoch {}] test Loss={:.4f}, test ppl={:.4f}, test bleu={:.2f}'
+                     .format(epoch_id, test_loss, np.exp(test_loss), test_bleu_score * 100))
+        write_sentences(valid_translation_out,
+                        os.path.join(save_dir, 'epoch{:d}_valid_out.txt').format(epoch_id))
+        write_sentences(test_translation_out,
+                        os.path.join(save_dir, 'epoch{:d}_test_out.txt').format(epoch_id))
+        if valid_bleu_score > best_valid_bleu:
+            best_valid_bleu = valid_bleu_score
+            save_path = os.path.join(save_dir, 'valid_best.params')
+            logging.info('Save best parameters to {}'.format(save_path))
+            model.save_parameters(save_path)
+        if epoch_id + 1 >= (epochs * 2) // 3:
+            new_lr = trainer.learning_rate * lr_update_factor
+            logging.info('Learning rate change to {}'.format(new_lr))
+            trainer.set_learning_rate(new_lr)
+
+Summary
+-------
+
+In this notebook, we have shown how to train a GNMT model on IWSLT 2015
+English-Vietnamese using Gluon NLP toolkit. The complete training script
+can be found
+`here <https://github.com/dmlc/gluon-nlp/blob/master/scripts/nmt/train_gnmt.py>`__.
+The command to reproduce the result can be seen in the `nmt scripts
+page <http://gluon-nlp.mxnet.io/scripts/index.html#machine-translation>`__.
diff --git a/docs/python_docs/python/tutorials/packages/gluon/text/index.rst b/docs/python_docs/python/tutorials/packages/gluon/text/index.rst
new file mode 100644
index 000000000000..7fe503ae279d
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/text/index.rst
@@ -0,0 +1,116 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Text Tutorials
+===============
+
+These tutorials will help you learn how to create and use models that work with text and other natural language processing tasks.
+
+Word Embedding
+---------------------
+
+.. container:: cards
+
+   .. card::
+      :title: Pre-trained Word Embeddings
+      :link: http://gluon-nlp.mxnet.io/examples/word_embedding/word_embedding.html
+
+      Basics on how to use word embedding with vocab in GluonNLP and apply it on word similarity and analogy problems.
+
+   .. card::
+      :title: Word Embeddings Training and Evaluation
+      :link: http://gluon-nlp.mxnet.io/examples/word_embedding/word_embedding_training.html
+
+      Learn how to train fastText and word2vec embeddings on your own dataset, and determine embedding quality through intrinsic evaluation.
+
+Language Model
+==============
+
+
+.. container:: cards
+
+   .. card::
+      :title: LSTM-based Language Models
+      :link: http://gluon-nlp.mxnet.io/examples/language_model/language_model.html
+
+      Learn what a language model is, what it can do, and how to train a word-level language model with truncated back-propagation-through-time (BPTT).
+
+Machine Translation
+---------------------
+
+.. container:: cards
+
+   .. card::
+      :title: Google Neural Machine Translation
+      :link: http://gluon-nlp.mxnet.io/examples/machine_translation/gnmt.html
+
+      Learn how to train Google Neural Machine Translation, a seq2seq with attention model.
+
+   .. card::
+      :title: Machine Translation with Transformer
+      :link: http://gluon-nlp.mxnet.io/examples/machine_translation/transformer.html
+
+      Learn how to use a pre-trained transformer translation model for English to German translation.
+
+Sentence Embedding
+---------------------
+
+.. container:: cards
+
+   .. card::
+      :title: ELMo: Deep Contextualized Word Representations
+      :link: http://gluon-nlp.mxnet.io/examples/sentence_embedding/elmo_sentence_representation.html
+
+      See how to use GluonNLP’s model API to automatically download the pre-trained ELMo model from NAACL2018 best paper, and extract features with it.
+
+   .. card::
+      :title: A Structured Self-attentive Sentence Embedding
+      :link: http://gluon-nlp.mxnet.io/examples/sentence_embedding/self_attentive_sentence_embedding.html
+
+      See how to use GluonNLP to build more advanced model structure for extracting sentence embeddings to predict Yelp review rating.
+
+   .. card::
+      :title: BERT: Bidirectional Encoder Representations from Transformers
+      :link: http://gluon-nlp.mxnet.io/examples/sentence_embedding/bert.html
+
+      See how to use GluonNLP to fine-tune a sentence pair classification model with pre-trained BERT parameters.
+
+Sentiment Analysis
+---------------------
+
+.. container:: cards
+
+   .. card::
+      :title: Sentiment Analysis by Fine-tuning Word Language Model
+      :link: http://gluon-nlp.mxnet.io/examples/sentiment_analysis/sentiment_analysis.html
+
+      See how to fine-tune a pre-trained language model to perform sentiment analysis on movie reviews.
+
+Sequence Sampling
+---------------------
+
+.. container:: cards
+
+   .. card::
+      :title: Sequence Generation with Sampling and Beam Search
+      :link: http://gluon-nlp.mxnet.io/examples/sequence_sampling/sequence_sampling.html
+
+      Learn how to generate sentence from pre-trained language model through sampling and beam search. 
+
+.. toctree::
+   :hidden:
+   :maxdepth: 1
diff --git a/docs/python_docs/python/tutorials/packages/gluon/text/transformer.rst b/docs/python_docs/python/tutorials/packages/gluon/text/transformer.rst
new file mode 100644
index 000000000000..c44fb37f6b8a
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/text/transformer.rst
@@ -0,0 +1,607 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Machine Translation with Transformer
+====================================
+
+In this notebook, we will show how to train Transformer introduced in
+[1] and evaluate the pretrained model using GluonNLP. The model is both
+more accurate and lighter to train than previous seq2seq models. We will
+together go through:
+
+1) Use the state-of-the-art pretrained Transformer model: we will
+   evaluate the pretrained SOTA Transformer model and translate a few
+   sentences ourselves with the ``BeamSearchTranslator`` using the SOTA
+   model;
+
+2) Train the Transformer yourself: including loading and processing
+   dataset, define the Transformer model, write train script and
+   evaluate the trained model. Note that in order to obtain the
+   state-of-the-art results on WMT 2014 English-German dataset, it will
+   take around 1 day to have the model. In order to let you run through
+   the Transformer quickly, we suggest you to start with the ``TOY``
+   dataset sampled from the WMT dataset (by default in this notebook).
+
+Preparation
+-----------
+
+Load MXNet and GluonNLP
+~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code:: python
+
+    import warnings
+    warnings.filterwarnings('ignore')
+
+    import random
+    import numpy as np
+    import mxnet as mx
+    from mxnet import gluon
+    import gluonnlp as nlp
+
+Set Environment
+~~~~~~~~~~~~~~~
+
+.. code:: python
+
+    np.random.seed(100)
+    random.seed(100)
+    mx.random.seed(10000)
+    ctx = mx.gpu(0)
+
+Use the SOTA Pretrained Transformer model
+-----------------------------------------
+
+In this subsection, we first load the SOTA Transformer model in GluonNLP
+model zoo; and secondly we load the full WMT 2014 English-German test
+dataset; and finally evaluate the model.
+
+Get the SOTA Transformer
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+Next, we load the pretrained SOTA Transformer using the model API in
+GluonNLP. In this way, we can easily get access to the SOTA machine
+translation model and use it in your own application.
+
+.. code:: python
+
+    import nmt
+
+    wmt_model_name = 'transformer_en_de_512'
+
+    wmt_transformer_model, wmt_src_vocab, wmt_tgt_vocab = \
+        nmt.transformer.get_model(wmt_model_name,
+                                  dataset_name='WMT2014',
+                                  pretrained=True,
+                                  ctx=ctx)
+
+    print(wmt_src_vocab)
+    print(wmt_tgt_vocab)
+
+The Transformer model architecture is shown as below:
+
+.. raw:: html
+
+   <div style="width: 500px;">
+
+|transformer|
+
+.. raw:: html
+
+   </div>
+
+.. code:: python
+
+    print(wmt_transformer_model)
+
+Load and Preprocess WMT 2014 Dataset
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+We then load the WMT 2014 English-German test dataset for evaluation
+purpose.
+
+The following shows how to process the dataset and cache the processed
+dataset for the future use. The processing steps include:
+
+-  
+
+   1) clip the source and target sequences
+
+-  
+
+   2) split the string input to a list of tokens
+
+-  
+
+   3) map the string token into its index in the vocabulary
+
+-  
+
+   4) append EOS token to source sentence and add BOS and EOS tokens to
+      target sentence.
+
+Let's first look at the WMT 2014 corpus.
+
+.. code:: python
+
+    import hyperparameters as hparams
+
+    wmt_data_test = nlp.data.WMT2014BPE('newstest2014',
+                                        src_lang=hparams.src_lang,
+                                        tgt_lang=hparams.tgt_lang,
+                                        full=False)
+    print('Source language %s, Target language %s' % (hparams.src_lang, hparams.tgt_lang))
+
+    wmt_data_test[0]
+
+.. code:: python
+
+    wmt_test_text = nlp.data.WMT2014('newstest2014',
+                                     src_lang=hparams.src_lang,
+                                     tgt_lang=hparams.tgt_lang,
+                                     full=False)
+    wmt_test_text[0]
+
+We then generate the target gold translations.
+
+.. code:: python
+
+    wmt_test_tgt_sentences = list(wmt_test_text.transform(lambda src, tgt: tgt))
+    wmt_test_tgt_sentences[0]
+
+.. code:: python
+
+    import dataprocessor
+
+    print(dataprocessor.TrainValDataTransform.__doc__)
+
+.. code:: python
+
+    wmt_transform_fn = dataprocessor.TrainValDataTransform(wmt_src_vocab, wmt_tgt_vocab, -1, -1)
+    wmt_dataset_processed = wmt_data_test.transform(wmt_transform_fn, lazy=False)
+    print(*wmt_dataset_processed[0], sep='\n')
+
+Create Sampler and DataLoader for WMT 2014 Dataset
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code:: python
+
+    wmt_data_test_with_len = gluon.data.SimpleDataset([(ele[0], ele[1], len(
+        ele[0]), len(ele[1]), i) for i, ele in enumerate(wmt_dataset_processed)])
+
+Now, we have obtained data\_train, data\_val, and data\_test. The next
+step is to construct sampler and DataLoader. The first step is to
+construct batchify function, which pads and stacks sequences to form
+mini-batch.
+
+.. code:: python
+
+    wmt_test_batchify_fn = nlp.data.batchify.Tuple(
+        nlp.data.batchify.Pad(),
+        nlp.data.batchify.Pad(),
+        nlp.data.batchify.Stack(dtype='float32'),
+        nlp.data.batchify.Stack(dtype='float32'),
+        nlp.data.batchify.Stack())
+
+We can then construct bucketing samplers, which generate batches by
+grouping sequences with similar lengths.
+
+.. code:: python
+
+    wmt_bucket_scheme = nlp.data.ExpWidthBucket(bucket_len_step=1.2)
+
+.. code:: python
+
+    wmt_test_batch_sampler = nlp.data.FixedBucketSampler(
+        lengths=wmt_dataset_processed.transform(lambda src, tgt: len(tgt)),
+        use_average_length=True,
+        bucket_scheme=wmt_bucket_scheme,
+        batch_size=256)
+    print(wmt_test_batch_sampler.stats())
+
+Given the samplers, we can create DataLoader, which is iterable.
+
+.. code:: python
+
+    wmt_test_data_loader = gluon.data.DataLoader(
+        wmt_data_test_with_len,
+        batch_sampler=wmt_test_batch_sampler,
+        batchify_fn=wmt_test_batchify_fn,
+        num_workers=8)
+    len(wmt_test_data_loader)
+
+Evaluate Transformer
+~~~~~~~~~~~~~~~~~~~~
+
+Next, we generate the SOTA results on the WMT test dataset. As we can
+see from the result, we are able to achieve the SOTA number 27.35 as the
+BLEU score.
+
+We first define the ``BeamSearchTranslator`` to generate the actual
+translations.
+
+.. code:: python
+
+    wmt_translator = nmt.translation.BeamSearchTranslator(
+        model=wmt_transformer_model,
+        beam_size=hparams.beam_size,
+        scorer=nlp.model.BeamSearchScorer(alpha=hparams.lp_alpha, K=hparams.lp_k),
+        max_length=200)
+
+Then we caculate the ``loss`` as well as the ``bleu`` score on the WMT
+2014 English-German test dataset. Note that the following evalution
+process will take ~13 mins to complete.
+
+.. code:: python
+
+    import time
+    import utils
+
+    eval_start_time = time.time()
+
+    wmt_test_loss_function = nmt.loss.SoftmaxCEMaskedLoss()
+    wmt_test_loss_function.hybridize()
+
+    wmt_detokenizer = nlp.data.SacreMosesDetokenizer()
+
+    wmt_test_loss, wmt_test_translation_out = utils.evaluate(wmt_transformer_model,
+                                                             wmt_test_data_loader,
+                                                             wmt_test_loss_function,
+                                                             wmt_translator,
+                                                             wmt_tgt_vocab,
+                                                             wmt_detokenizer,
+                                                             ctx)
+
+    wmt_test_bleu_score, _, _, _, _ = nmt.bleu.compute_bleu([wmt_test_tgt_sentences],
+                                                            wmt_test_translation_out,
+                                                            tokenized=False,
+                                                            tokenizer=hparams.bleu,
+                                                            split_compound_word=False,
+                                                            bpe=False)
+
+    print('WMT14 EN-DE SOTA model test loss: %.2f; test bleu score: %.2f; time cost %.2fs'
+          %(wmt_test_loss, wmt_test_bleu_score * 100, (time.time() - eval_start_time)))
+
+.. code:: python
+
+    print('Sample translations:')
+    num_pairs = 3
+
+    for i in range(num_pairs):
+        print('EN:')
+        print(wmt_test_text[i][0])
+        print('DE-Candidate:')
+        print(wmt_test_translation_out[i])
+        print('DE-Reference:')
+        print(wmt_test_tgt_sentences[i])
+        print('========')
+
+Translation Inference
+~~~~~~~~~~~~~~~~~~~~~
+
+We herein show the actual translation example (EN-DE) when given a
+source language using the SOTA Transformer model.
+
+.. code:: python
+
+    import utils
+
+    print('Translate the following English sentence into German:')
+
+    sample_src_seq = 'We love each other'
+
+    print('[\'' + sample_src_seq + '\']')
+
+    sample_tgt_seq = utils.translate(wmt_translator,
+                                     sample_src_seq,
+                                     wmt_src_vocab,
+                                     wmt_tgt_vocab,
+                                     wmt_detokenizer,
+                                     ctx)
+
+    print('The German translation is:')
+    print(sample_tgt_seq)
+
+Train Your Own Transformer
+--------------------------
+
+In this subsection, we will go though the whole process about loading
+translation dataset in a more unified way, and create data sampler and
+loader, as well as define the Transformer model, finally writing
+training script to train the model yourself.
+
+Load and Preprocess TOY Dataset
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Note that we use demo mode (``TOY`` dataset) by default, since loading
+the whole WMT 2014 English-German dataset ``WMT2014BPE`` for the later
+training will be slow (~1 day). But if you really want to train to have
+the SOTA result, please set ``demo = False``. In order to make the data
+processing blocks execute in a more efficient way, we package them in
+the ``load_translation_data`` (``transform`` etc.) function used as
+below. The function also returns the gold target sentences as well as
+the vocabularies.
+
+.. code:: python
+
+    demo = True
+    if demo:
+        dataset = 'TOY'
+    else:
+        dataset = 'WMT2014BPE'
+
+    data_train, data_val, data_test, val_tgt_sentences, test_tgt_sentences, src_vocab, tgt_vocab = \
+        dataprocessor.load_translation_data(
+            dataset=dataset,
+            src_lang=hparams.src_lang,
+            tgt_lang=hparams.tgt_lang)
+
+    data_train_lengths = dataprocessor.get_data_lengths(data_train)
+    data_val_lengths = dataprocessor.get_data_lengths(data_val)
+    data_test_lengths = dataprocessor.get_data_lengths(data_test)
+
+    data_train = data_train.transform(lambda src, tgt: (src, tgt, len(src), len(tgt)), lazy=False)
+    data_val = gluon.data.SimpleDataset([(ele[0], ele[1], len(ele[0]), len(ele[1]), i)
+                              for i, ele in enumerate(data_val)])
+    data_test = gluon.data.SimpleDataset([(ele[0], ele[1], len(ele[0]), len(ele[1]), i)
+                               for i, ele in enumerate(data_test)])
+
+Create Sampler and DataLoader for TOY Dataset
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Now, we have obtained ``data_train``, ``data_val``, and ``data_test``.
+The next step is to construct sampler and DataLoader. The first step is
+to construct batchify function, which pads and stacks sequences to form
+mini-batch.
+
+.. code:: python
+
+    train_batchify_fn = nlp.data.batchify.Tuple(
+        nlp.data.batchify.Pad(),
+        nlp.data.batchify.Pad(),
+        nlp.data.batchify.Stack(dtype='float32'),
+        nlp.data.batchify.Stack(dtype='float32'))
+    test_batchify_fn = nlp.data.batchify.Tuple(
+        nlp.data.batchify.Pad(),
+        nlp.data.batchify.Pad(),
+        nlp.data.batchify.Stack(dtype='float32'),
+        nlp.data.batchify.Stack(dtype='float32'),
+        nlp.data.batchify.Stack())
+
+    target_val_lengths = list(map(lambda x: x[-1], data_val_lengths))
+    target_test_lengths = list(map(lambda x: x[-1], data_test_lengths))
+
+We can then construct bucketing samplers, which generate batches by
+grouping sequences with similar lengths.
+
+.. code:: python
+
+    bucket_scheme = nlp.data.ExpWidthBucket(bucket_len_step=1.2)
+    train_batch_sampler = nlp.data.FixedBucketSampler(lengths=data_train_lengths,
+                                                 batch_size=hparams.batch_size,
+                                                 num_buckets=hparams.num_buckets,
+                                                 ratio=0.0,
+                                                 shuffle=True,
+                                                 use_average_length=True,
+                                                 num_shards=1,
+                                                 bucket_scheme=bucket_scheme)
+    print('Train Batch Sampler:')
+    print(train_batch_sampler.stats())
+
+
+    val_batch_sampler = nlp.data.FixedBucketSampler(lengths=target_val_lengths,
+                                           batch_size=hparams.test_batch_size,
+                                           num_buckets=hparams.num_buckets,
+                                           ratio=0.0,
+                                           shuffle=False,
+                                           use_average_length=True,
+                                           bucket_scheme=bucket_scheme)
+    print('Validation Batch Sampler:')
+    print(val_batch_sampler.stats())
+
+    test_batch_sampler = nlp.data.FixedBucketSampler(lengths=target_test_lengths,
+                                            batch_size=hparams.test_batch_size,
+                                            num_buckets=hparams.num_buckets,
+                                            ratio=0.0,
+                                            shuffle=False,
+                                            use_average_length=True,
+                                            bucket_scheme=bucket_scheme)
+    print('Test Batch Sampler:')
+    print(test_batch_sampler.stats())
+
+Given the samplers, we can create DataLoader, which is iterable. Note
+that the data loader of validation and test dataset share the same
+batchifying function ``test_batchify_fn``.
+
+.. code:: python
+
+    train_data_loader = nlp.data.ShardedDataLoader(data_train,
+                                          batch_sampler=train_batch_sampler,
+                                          batchify_fn=train_batchify_fn,
+                                          num_workers=8)
+    print('Length of train_data_loader: %d' % len(train_data_loader))
+    val_data_loader = gluon.data.DataLoader(data_val,
+                                 batch_sampler=val_batch_sampler,
+                                 batchify_fn=test_batchify_fn,
+                                 num_workers=8)
+    print('Length of val_data_loader: %d' % len(val_data_loader))
+    test_data_loader = gluon.data.DataLoader(data_test,
+                                  batch_sampler=test_batch_sampler,
+                                  batchify_fn=test_batchify_fn,
+                                  num_workers=8)
+    print('Length of test_data_loader: %d' % len(test_data_loader))
+
+Define Transformer Model
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+After obtaining DataLoader, we then start to define the Transformer. The
+encoder and decoder of the Transformer can be easily obtained by calling
+``get_transformer_encoder_decoder`` function. Then, we use the encoder
+and decoder in ``NMTModel`` to construct the Transformer model.
+``model.hybridize`` allows computation to be done using symbolic
+backend. We also use ``label_smoothing``.
+
+.. code:: python
+
+    encoder, decoder = nmt.transformer.get_transformer_encoder_decoder(units=hparams.num_units,
+                                                       hidden_size=hparams.hidden_size,
+                                                       dropout=hparams.dropout,
+                                                       num_layers=hparams.num_layers,
+                                                       num_heads=hparams.num_heads,
+                                                       max_src_length=530,
+                                                       max_tgt_length=549,
+                                                       scaled=hparams.scaled)
+    model = nmt.translation.NMTModel(src_vocab=src_vocab, tgt_vocab=tgt_vocab, encoder=encoder, decoder=decoder,
+                     share_embed=True, embed_size=hparams.num_units, tie_weights=True,
+                     embed_initializer=None, prefix='transformer_')
+    model.initialize(init=mx.init.Xavier(magnitude=3.0), ctx=ctx)
+    model.hybridize()
+
+    print(model)
+
+    label_smoothing = nmt.loss.LabelSmoothing(epsilon=hparams.epsilon, units=len(tgt_vocab))
+    label_smoothing.hybridize()
+
+    loss_function = nmt.loss.SoftmaxCEMaskedLoss(sparse_label=False)
+    loss_function.hybridize()
+
+    test_loss_function = nmt.loss.SoftmaxCEMaskedLoss()
+    test_loss_function.hybridize()
+
+    detokenizer = nlp.data.SacreMosesDetokenizer()
+
+Here, we build the translator using the beam search
+
+.. code:: python
+
+    translator = nmt.translation.BeamSearchTranslator(model=model,
+                                                      beam_size=hparams.beam_size,
+                                                      scorer=nlp.model.BeamSearchScorer(alpha=hparams.lp_alpha,
+                                                                                        K=hparams.lp_k),
+                                                      max_length=200)
+    print('Use beam_size=%d, alpha=%.2f, K=%d' % (hparams.beam_size, hparams.lp_alpha, hparams.lp_k))
+
+Training Loop
+~~~~~~~~~~~~~
+
+Before conducting training, we need to create trainer for updating the
+parameter. In the following example, we create a trainer that uses ADAM
+optimzier.
+
+.. code:: python
+
+    trainer = gluon.Trainer(model.collect_params(), hparams.optimizer,
+                            {'learning_rate': hparams.lr, 'beta2': 0.98, 'epsilon': 1e-9})
+    print('Use learning_rate=%.2f'
+          % (trainer.learning_rate))
+
+We can then write the training loop. During the training, we perform the
+evaluation on validation and testing dataset every epoch, and record the
+parameters that give the hightest BLEU score on validation dataset.
+Before performing forward and backward, we first use ``as_in_context``
+function to copy the mini-batch to GPU. The statement
+``with mx.autograd.record()`` will locate Gluon backend to compute the
+gradients for the part inside the block. For ease of observing the
+convergence of the update of the ``Loss`` in a quick fashion, we set the
+``epochs = 3``. Notice that, in order to obtain the best BLEU score, we
+will need more epochs and large warmup steps following the original
+paper as you can find the SOTA results in the first subsection. Besides,
+we use Averaging SGD [2] to update the parameters, since it is more
+robust for the machine translation task.
+
+.. code:: python
+
+    best_valid_loss = float('Inf')
+    step_num = 0
+    #We use warmup steps as introduced in [1].
+    warmup_steps = hparams.warmup_steps
+    grad_interval = hparams.num_accumulated
+    model.collect_params().setattr('grad_req', 'add')
+    #We use Averaging SGD [2] to update the parameters.
+    average_start = (len(train_data_loader) // grad_interval) * \
+        (hparams.epochs - hparams.average_start)
+    average_param_dict = {k: mx.nd.array([0]) for k, v in
+                                          model.collect_params().items()}
+    update_average_param_dict = True
+    model.collect_params().zero_grad()
+    for epoch_id in range(hparams.epochs):
+        utils.train_one_epoch(epoch_id, model, train_data_loader, trainer,
+                              label_smoothing, loss_function, grad_interval,
+                              average_param_dict, update_average_param_dict,
+                              step_num, ctx)
+        mx.nd.waitall()
+        # We define evaluation function as follows. The `evaluate` function use beam search translator
+        # to generate outputs for the validation and testing datasets.
+        valid_loss, _ = utils.evaluate(model, val_data_loader,
+                                       test_loss_function, translator,
+                                       tgt_vocab, detokenizer, ctx)
+        print('Epoch %d, valid Loss=%.4f, valid ppl=%.4f'
+              % (epoch_id, valid_loss, np.exp(valid_loss)))
+        test_loss, _ = utils.evaluate(model, test_data_loader,
+                                      test_loss_function, translator,
+                                      tgt_vocab, detokenizer, ctx)
+        print('Epoch %d, test Loss=%.4f, test ppl=%.4f'
+              % (epoch_id, test_loss, np.exp(test_loss)))
+        if valid_loss < best_valid_loss:
+            best_valid_loss = valid_loss
+            model.save_parameters('{}.{}'.format(hparams.save_dir, 'valid_best.params'))
+        model.save_parameters('{}.epoch{:d}.params'.format(hparams.save_dir, epoch_id))
+    mx.nd.save('{}.{}'.format(hparams.save_dir, 'average.params'), average_param_dict)
+
+    if hparams.average_start > 0:
+        for k, v in model.collect_params().items():
+            v.set_data(average_param_dict[k])
+    else:
+        model.load_parameters('{}.{}'.format(hparams.save_dir, 'valid_best.params'), ctx)
+    valid_loss, _ = utils.evaluate(model, val_data_loader,
+                                   test_loss_function, translator,
+                                   tgt_vocab, detokenizer, ctx)
+    print('Best model valid Loss=%.4f, valid ppl=%.4f'
+          % (valid_loss, np.exp(valid_loss)))
+    test_loss, _ = utils.evaluate(model, test_data_loader,
+                                  test_loss_function, translator,
+                                  tgt_vocab, detokenizer, ctx)
+    print('Best model test Loss=%.4f, test ppl=%.4f'
+          % (test_loss, np.exp(test_loss)))
+
+Conclusion
+----------
+
+-  Showcase with Transformer, we are able to support the deep neural
+   networks for seq2seq task. We have already achieved SOTA results on
+   the WMT 2014 English-German task.
+-  Gluon NLP Toolkit provides high-level APIs that could drastically
+   simplify the development process of modeling for NLP tasks sharing
+   the encoder-decoder structure.
+-  Low-level APIs in NLP Toolkit enables easy customization.
+
+Documentation can be found at http://gluon-nlp.mxnet.io/index.html
+
+Code is here https://github.com/dmlc/gluon-nlp
+
+References
+----------
+
+[1] Vaswani, Ashish, et al. "Attention is all you need." Advances in
+Neural Information Processing Systems. 2017.
+
+[2] Polyak, Boris T, and Anatoli B. Juditsky. "Acceleration of
+stochastic approximation by averaging." SIAM Journal on Control and
+Optimization. 1992.
+
+.. |transformer| image:: transformer.png
+
diff --git a/docs/python_docs/python/tutorials/packages/gluon/trainer.md b/docs/python_docs/python/tutorials/packages/gluon/trainer.md
new file mode 100644
index 000000000000..c154a56bed26
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/trainer.md
@@ -0,0 +1,168 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Trainer
+
+Training a neural network model consists of iteratively performing three simple steps.
+
+The first step is the forward step which computes the loss.  In MXNet Gluon, this first step is achieved by doing a forward pass by calling `net.forward(X)` or simply `net(X)` and then calling the loss function with the result of the forward pass and the labels. For example `l = loss_fn(net(X), y)`.
+
+The second step is the backward step which computes the gradient of the loss with respect to the parameters. In Gluon, this step is  achieved by doing the first step in an [`autograd.record()`](http://beta.mxnet.io/guide/packages/autograd/autograd.html) scope to record the computations needed to calculate the loss, and then calling `l.backward()` to compute the gradient of the loss with respect to the parameters.
+
+The final step is to update the neural network model parameters using an optimization algorithm. In Gluon, this step is performed by the [`gluon.Trainer`](http://beta.mxnet.io/api/gluon/mxnet.gluon.Trainer.html) and is the subject of this guide. When creating a  Gluon `Trainer` you must provide a collection of parameters that need to be learnt. You also provide an `Optimizer` that will be used to update the parameters every training iteration when `trainer.step` is called.
+
+## Basic Usage
+
+### Network and Trainer
+
+To illustrate how to use the Gluon `Trainer` we will create a simple perceptron model and create a `Trainer ` instance using the perceptron model parameters and a simple optimizer - `sgd` with learning rate as 1.
+
+```{.python .input}
+from mxnet import nd, autograd, optimizer, gluon
+
+net = gluon.nn.Dense(1)
+net.initialize()
+
+trainer = gluon.Trainer(net.collect_params(),
+                        optimizer='sgd', optimizer_params={'learning_rate':1})
+
+```
+
+### Forward and Backward Pass
+
+Before we can use the `trainer` to update model parameters, we must first run the forward and backward passes. Here we implement a function to compute the first two steps (forward step and backward step) of training the perceptron on a random dataset.
+
+```{.python .input}
+batch_size = 8
+X = nd.random.uniform(shape=(batch_size, 4))
+y = nd.random.uniform(shape=(batch_size,))
+
+loss = gluon.loss.L2Loss()
+
+def forward_backward():
+    with autograd.record():
+        l = loss(net(X), y)
+    l.backward()
+
+forward_backward()
+```
+
+**Warning**: It is extremely important that the gradients of the loss function with respect to your model parameters are computed before running `trainer step`. A common way to introduce bugs to your model training code is to omit the `loss.backward()`before the update step.
+
+
+
+Before updating, let's check the current network parameters.
+
+```{.python .input}
+curr_weight = net.weight.data().copy()
+print(curr_weight)
+```
+
+### `Trainer` step
+
+Now we will call the `step` method to perform one update. We provide the `batch_size` as an argument to normalize the size of the gradients and make it independent of the batch size. Otherwise we'd get larger gradients with larger batch sizes. We can see the network parameters have now changed.
+
+```{.python .input}
+trainer.step(batch_size)
+print(net.weight.data())
+```
+
+Since we used plain SGD, the update rule is $w = w - \eta/b \nabla \ell$, where $b$ is the batch size and $\nabla\ell$ is the gradient of the loss function with respect to the weights and $\eta$ is the learning rate.
+
+We can verify it by running the following code snippet which is explicitly performing the SGD update.
+
+```{.python .input}
+print(curr_weight - net.weight.grad() * 1 / batch_size)
+```
+
+
+
+## Advanced Usage
+
+### Using Optimizer Instance
+
+In the previous example, we use the string argument `sgd` to select the optimization method, and `optimizer_params` to specify the optimization method arguments.
+
+All pre-defined optimization methods can be passed in this way and the complete list of implemented optimizers is provided in the [`mxnet.optimizer`](http://beta.mxnet.io/api/gluon-related/mxnet.optimizer.html) module.
+
+However we can also pass an optimizer instance directly to the `Trainer` constructor.
+
+For example:
+
+```{.python .input}
+optim = optimizer.Adam(learning_rate = 1)
+trainer = gluon.Trainer(net.collect_params(), optim)
+```
+
+```{.python .input}
+forward_backward()
+trainer.step(batch_size)
+net.weight.data()
+```
+
+For reference and implementation details about each optimizer, please refer to the [guide](http://beta.mxnet.io/guide/packages/optimizer/optimizer.html) for the `optimizer` module.
+
+### KVStore Options
+
+The `Trainer` constructor also accepts the following keyword arguments for :
+
+- `kvstore` – how key value store  should be created for multi-gpu and distributed training. Check out  [`mxnet.kvstore.KVStore`](http://beta.mxnet.io/api/gluon-related/mxnet.kvstore.KVStore.html#mxnet.kvstore.KVStore) for more information. String options are any of the following ['local', 'device', 'dist_device_sync', 'dist_device_async'].
+- `compression_params` – Specifies type of gradient compression and additional arguments depending on the type of compression being used. See [`mxnet.KVStore.set_gradient_compression_method`](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.kvstore.KVStore.set_gradient_compression.html#mxnet.kvstore.KVStore.set_gradient_compression) for more details on gradient compression.
+- `update_on_kvstore` – Whether to perform parameter updates on KVStore. If None, then the `Trainer` instance  will choose the more suitable option depending on the type of KVStore.
+
+### Changing the Learning Rate
+
+We set the initial learning rate when creating a trainer by passing the learning rate as an `optimizer_param`. However, sometimes we may need to change the learning rate during training, for example when doing an explicit learning rate warmup schedule.  The trainer instance provides an easy way to achieve this.
+
+The current training rate can be accessed through the `learning_rate` attribute.
+
+```{.python .input}
+trainer.learning_rate
+```
+
+We can change it through the `set_learning_rate` method.
+
+```{.python .input}
+trainer.set_learning_rate(0.1)
+trainer.learning_rate
+```
+
+
+
+In addition, there are multiple pre-defined learning rate scheduling methods that are already implemented in the [`mxnet.lr_scheduler`](http://beta.mxnet.io/api/gluon-related/mxnet.lr_scheduler.html) module. The learning rate schedulers can be incorporated into your trainer by passing them in as an `optimizer_param` entry. Please refer to the [LR scheduler guide](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/learning_rate_schedules.html) to learn more.
+
+
+
+## Summary
+
+* The MXNet Gluon `Trainer` API is used to update the parameters of a network with a particular optimization algorithm.
+* After the forward and backward pass, the model update step is done in Gluon using `trainer.step()`.
+* A Gluon `Trainer` can be instantiated by passing in the name of the optimizer to use and the `optimizer_params` for that optimizer or alternatively by passing in an instance of `mxnet.optimizer.Optimizer`.
+* You can change the learning rate for a Gluon `Trainer` by setting the member variable but Gluon also provides a module for learning rate scheduling.
+
+
+
+## Next Steps
+
+While optimization and optimizers play a significant role in deep learning model training, there are still other important components to model training. Here are a few suggestions about where to look next.
+
+* The [Optimizer API](http://beta.mxnet.io/api/gluon-related/mxnet.optimizer.html) and [guide](http://beta.mxnet.io/guide/packages/optimizer/optimizer.html) have information about all the different optimizers implemented in MXNet and their update steps. The [Dive into Deep Learning](http://en.diveintodeeplearning.org/chapter_optimization/index.html) book also has a chapter dedicated to optimization methods and explains various key optimizers in great detail.
+
+- Take a look at the [guide to parameter initialization](http://beta.mxnet.io/guide/packages/gluon/init.html) in MXNet to learn about what initialization schemes are already implemented, and how to implement your custom initialization schemes.
+- Also check out this  [guide on parameter management](http://beta.mxnet.io/guide/packages/gluon/parameters.html) to learn about how to manage model parameters in gluon.
+- Make sure to take a look at the [guide to scheduling learning rates](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/learning_rate_schedules.html) to learn how to create learning rate schedules to make your training converge faster.
+- Finally take a look at the [KVStore API](http://beta.mxnet.io/api/gluon-related/mxnet.kvstore.KVStore.html#mxnet.kvstore.KVStore) to learn how parameter values are synchronized over multiple devices.
diff --git a/docs/python_docs/python/tutorials/packages/index.rst b/docs/python_docs/python/tutorials/packages/index.rst
new file mode 100644
index 000000000000..f6aa17e6b6c2
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/index.rst
@@ -0,0 +1,140 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Packages
+========
+
+The documents in this unit dive into the details how each MXNet module works.
+
+High Level APIs
+---------------
+
+.. container:: cards
+
+   .. card::
+      :title: Gluon
+      :link: gluon/index.html
+
+      MXNet's imperative interface for Python. If you're new to MXNet, start here!
+
+
+Shared APIs
+-----------
+
+.. container:: cards
+
+   .. card::
+      :title: NDArray API
+      :link: ndarray/index.html
+
+      How to use the NDArray API to manipulate data.
+      A useful set of tutorials for beginners.
+
+   .. card::
+      :title: Symbol API
+      :link: symbol/index.html
+
+      How to use MXNet's Symbol API.
+
+   .. card::
+      :title: Autograd API
+      :link: autograd/autograd.html
+
+      How to use Automatic Differentiation with the Autograd API.
+
+   .. card::
+      :title: Learning Rate
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/learning_rate_schedules.html
+
+      How to use the Learning Rate Scheduler.
+
+   .. card::
+      :title: KVStore API
+      :link: kvstore/index.html
+
+      How to use the KVStore API for distributed training.
+
+   .. card::
+      :title: Data APIs
+      :link: data/index.html
+
+      How to use MXNet's data APIs.
+
+   .. card::
+      :title: Visualizations
+      :link: viz/index.html
+
+      How to use MXNet's visualization features.
+
+   .. card::
+      :title: ONNX
+      :link: onnx/index.html
+
+      How to use Open Neural Network Exchange (ONNX) with MXNet.
+
+   ..
+      .. card::
+         :title: Optimizer
+         :link: optimizer.html
+
+         How to use optimizer.
+   ..
+
+Old APIs
+--------
+Currently supported, but not recommended APIs.
+
+.. container:: cards
+
+   .. card::
+      :title: Module
+      :link: module/index.html
+
+      MXNet's symbolic interface for Python.
+
+
+.. toctree::
+   :hidden:
+
+   gluon/index
+   ndarray/index
+   symbol/index
+   autograd/autograd
+   onnx/index
+
+..
+   Basic
+   -----
+
+   .. toctree::
+      :maxdepth: 1
+
+      mxboard
+      gpus
+
+   Advanced
+   --------
+
+
+   .. toctree::
+      :maxdepth: 1
+
+      symbol
+      record-io
+      sparse
+      control-flow
+      distributed-training
diff --git a/docs/python_docs/python/tutorials/packages/kvstore/index.rst b/docs/python_docs/python/tutorials/packages/kvstore/index.rst
new file mode 100644
index 000000000000..4cf15d93ed3f
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/kvstore/index.rst
@@ -0,0 +1,44 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+KVStore
+=======
+
+.. container:: cards
+
+   .. card::
+      :title: Distributed Training Using the KVStore API
+      :link: https://mxnet.incubator.apache.org/api/faq/distributed_training
+
+      How to use the KVStore API to use multiple GPUs when training a model.
+
+   .. card::
+      :title: Distributed Key-Value Store
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/python/kvstore.html
+
+      How to use the KVStore API to share data across different devices.
+
+References
+-----------------
+
+- `KVStore API. <../api/gluon-related/mxnet.kvstore.html>`_
+
+.. toctree::
+   :hidden:
+   :glob:
+
+   *
diff --git a/docs/python_docs/python/tutorials/packages/module/index.rst b/docs/python_docs/python/tutorials/packages/module/index.rst
new file mode 100644
index 000000000000..a485b7f523c3
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/module/index.rst
@@ -0,0 +1,52 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Module
+======
+
+.. container:: cards
+
+   .. card::
+      :title: Module API Intro
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/basic/module.html
+
+      How to use MXNet's Module API.
+
+   .. card::
+      :title: Converting Module API code to the Gluon API
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/python/module_to_gluon.html
+
+      How to transform models written using Module code to Gluon code.
+
+   .. card::
+      :title: Data Augmentation
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/python/data_augmentation.html
+
+      How to augment data with the Module API.
+
+   .. card::
+      :title: Predict with pre-trained models
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/python/predict_image.html
+
+      How to recognize objects in an image with a pre-trained model.
+
+
+.. toctree::
+   :hidden:
+
+   module
+   data_augmentation
diff --git a/docs/python_docs/python/tutorials/packages/ndarray/01-ndarray-intro.md b/docs/python_docs/python/tutorials/packages/ndarray/01-ndarray-intro.md
new file mode 100644
index 000000000000..26e9c48bd8e3
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/ndarray/01-ndarray-intro.md
@@ -0,0 +1,176 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# An Intro: Manipulate Data the MXNet Way with NDArray
+
+## Overview
+This guide
+will introduce you to how data is handled with MXNet. You will learn the basics
+about MXNet's multi-dimensional array format, `ndarray`.
+
+This content was extracted and simplified from the gluon tutorials in
+[Dive Into Deep Learning](http://gluon.io/).
+
+## Prerequisites
+* [MXNet installed in a Python environment](../../../install/index.html?language=Python).
+* Python 2.7.x or Python 3.x
+
+
+## Getting started
+
+In this chapter, we'll get
+you going with the basic functionality. Don't worry if you don't understand any
+of the basic math, like element-wise operations or normal distributions. In the
+next two chapters we'll take another pass at `NDArray`, teaching you both the math
+you'll need and how to realize it in code.
+
+To get started, let's import
+`mxnet`. We'll also import `ndarray` from `mxnet` for convenience. We’ll make a
+habit of setting a random seed so that you always get the same results that we
+do.
+
+```python
+import mxnet as mx
+from mxnet import nd
+```
+
+Let's start with a very simple 1-dimensional array with a python list.
+
+```python
+x = nd.array([1,2,3])
+print(x)
+```
+
+Now a 2-dimensional array.
+
+```python
+y = nd.array([[1,2,3,4], [1,2,3,4], [1,2,3,4]])
+print(y)
+```
+
+Next, let's see how to create an `NDArray`, without any values initialized.
+Specifically, we'll create a 2D array (also called a *matrix*) with 3 rows and 4
+columns using the `.empty` function. We'll also try out `.full` which takes an
+additional parameter for what value you want to fill in the array.
+
+```python
+x = nd.empty((3, 3))
+print(x)
+x = nd.full((3,3), 7)
+print(x)
+```
+
+`empty` just grabs some memory and hands us back a matrix without setting the
+values of any of its entries. This means that the entries can have any form of
+values, including very big ones! Typically, we'll want our matrices initialized
+and very often we want a matrix of all zeros, so we can use the `.zeros`
+function. If you're feeling experimental, try one of the several [array creation
+functions](https://mxnet.incubator.apache.org/api/{.python
+.input}/ndarray/ndarray.html#array-creation-routines).
+
+<!-- showing something
+different here (3,10) since the zeros may not produce anything different from
+empty... or use the two demonstrations to show something interesting or
+unique... when would I use one over the other?-->
+
+```python
+x = nd.zeros((3, 10))
+print(x)
+```
+
+Similarly, `ndarray` has a function to create a matrix of all ones aptly named
+[ones](https://mxnet.incubator.apache.org/api/{.python
+.input}/ndarray.html?highlight=random_normal#mxnet.ndarray.ones).
+
+```python
+x = nd.ones((3, 4))
+print(x)
+```
+
+Often, we'll want to create arrays whose values are sampled randomly. This is
+especially common when we intend to use the array as a parameter in a neural
+network. In this snippet, we initialize with values drawn from a standard normal
+distribution with zero mean and unit variance using
+[random_normal](https://mxnet.incubator.apache.org/api/{.python
+.input}/ndarray.html?highlight=random_normal#mxnet.ndarray.random_normal).
+
+<!--
+Is it that important to introduce zero mean and unit variance right now?
+Describe more? Or how about explain which is which for the 0 and the 1 and what
+they're going to do... if it actually matters at this point. -->
+
+```python
+y = nd.random_normal(0, 1, shape=(3, 4))
+print(y)
+```
+
+Sometimes you will want to copy an array by its shape but not its contents. You
+can do this with `.zeros_like`.
+
+```python
+z = nd.zeros_like(y)
+print(z)
+```
+
+As in NumPy, the dimensions of each `NDArray` are accessible via the `.shape`
+attribute.
+
+```python
+y.shape
+```
+
+We can also query its `.size`, which is equal to the product of the components
+of the shape. Together with the precision of the stored values, this tells us
+how much memory the array occupies.
+<!-- is there a function for that or do you
+just do it manually? Should we show that? -->
+
+```python
+y.size
+```
+
+We can query the data type using `.dtype`.
+
+```python
+y.dtype
+```
+
+`float32` is the default data type. Performance can be improved with less
+precision, or you might want to use a different data type. You can force the
+data type when you create the array using a numpy type. This requires you to
+import numpy first.
+
+```python
+import numpy as np
+a = nd.array([1,2,3])
+b = nd.array([1,2,3], dtype=np.int32)
+c = nd.array([1.2, 2.3], dtype=np.float16)
+(a.dtype, b.dtype, c.dtype)
+```
+
+As you will come to learn in detail later, operations and memory storage will
+happen on specific devices that you can set. You can compute on CPU(s), GPU(s), a
+specific GPU, or all of the above depending on your situation and preference.
+Using `.context` reveals the location of the variable.
+
+```python
+y.context
+```
+
+## Next Up
+
+[NDArray Operations](02-ndarray-operations.md)
diff --git a/docs/python_docs/python/tutorials/packages/ndarray/02-ndarray-operations.md b/docs/python_docs/python/tutorials/packages/ndarray/02-ndarray-operations.md
new file mode 100644
index 000000000000..0f2520f2a322
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/ndarray/02-ndarray-operations.md
@@ -0,0 +1,260 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# NDArray Operations
+
+## Overview
+This guide will introduce you to MXNet's array operations.
+
+This content was extracted and simplified from the gluon tutorials in
+[Dive Into Deep Learning](http://gluon.io/).
+
+## Prerequisites
+* [MXNet installed in a Python environment](../../../install/index.html?language=Python).
+* Python 2.7.x or Python 3.x
+
+
+## Operations
+
+NDArray supports a large number of standard mathematical operations.
+Such as element-wise addition:
+<!-- keeping it
+easy -->
+
+```{.python .input}
+import mxnet as mx
+from mxnet import nd
+```
+
+```{.python .input}
+x = nd.ones((3, 4))
+y = nd.random_normal(0, 1, shape=(3, 4))
+print('x=', x)
+print('y=', y)
+x = x + y
+print('x = x + y, x=', x)
+```
+
+Multiplication:
+
+```{.python .input}
+x = nd.array([1, 2, 3])
+y = nd.array([2, 2, 2])
+x * y
+```
+
+And exponentiation:
+<!-- with these next ones we'll just have to take your word
+for it... -->
+
+```{.python .input}
+nd.exp(x)
+```
+
+We can also grab a matrix's transpose to compute a proper matrix-matrix product.
+<!-- because we need to do that before we have coffee every day... and you know
+how those dirty, improper matrixeses can be... -->
+
+```{.python .input}
+nd.dot(x, y.T)
+```
+
+We'll explain these operations and present even more operators in the [linear
+algebra](P01-C03-linear-algebra.ipynb) chapter. But for now, we'll stick with
+the mechanics of working with NDArrays.
+
+## In-place operations
+
+In the previous
+example, every time we ran an operation, we allocated new memory to host its
+results. For example, if we write `y = x + y`, we will dereference the matrix
+that `y` used to point to and instead point it at the newly allocated memory. We
+can show this using Python's `id()` function, which tells us precisely which
+object a variable refers to.
+
+<!-- dereference is something C++ people would
+know but everyone else... not so much. What's the point? ;) get it? Put it in
+more context as to why you care about this and why this is in front of so much
+other material. Seems like an optimization topic best suited for later...
+###edit### we just talked about this, so I have better context. Now I
+understand, but your new reader will not. This should be covered in much more
+detail, and quite possibily in its own notebook since I think it will help to
+show some gotchas like you mentioned verbally. I am still leaning toward
+delaying the introduction of this topic....-->
+
+```{.python .input}
+print('y=', y)
+print('id(y):', id(y))
+y = y + x
+print('after y=y+x, y=', y)
+print('id(y):', id(y))
+```
+
+We can assign the result to a previously allocated array with slice notation,
+e.g., `result[:] = ...`.
+
+```{.python .input}
+print('x=', x)
+z = nd.zeros_like(x)
+print('z is zeros_like x, z=', z)
+print('id(z):', id(z))
+print('y=', y)
+z[:] = x + y
+print('z[:] = x + y, z=', z)
+print('id(z) is the same as before:', id(z))
+```
+
+However, `x+y` here will still allocate a temporary buffer to store the result
+before copying it to z. To make better use of memory, we can perform operations
+in place, avoiding temporary buffers. To do this we specify the `out` keyword
+argument every operator supports:
+
+```{.python .input}
+print('x=', x, 'is in id(x):', id(x))
+print('y=', y, 'is in id(y):', id(y))
+print('z=', z, 'is in id(z):', id(z))
+nd.elemwise_add(x, y, out=z)
+print('after nd.elemwise_add(x, y, out=z), x=', x, 'is in id(x):', id(x))
+print('after nd.elemwise_add(x, y, out=z), y=', y, 'is in id(y):', id(y))
+print('after nd.elemwise_add(x, y, out=z), z=', z, 'is in id(z):', id(z))
+```
+
+If we're not planning to re-use ``x``, then we can assign the result to ``x``
+itself. There are two ways to do this in MXNet.
+1. By using slice notation x[:]
+= x op y
+2. By using the op-equals operators like `+=`
+
+```{.python .input}
+print('x=', x, 'is in id(x):', id(x))
+x += y
+print('x=', x, 'is in id(x):', id(x))
+```
+
+## Slicing
+MXNet NDArrays support slicing in all the ridiculous ways you might
+imagine accessing your data. For a quick review:
+
+* items start through end-1: a[start:end]
+* items start through the rest of the
+array: a[start:]
+* items from the beginning through end-1: a[:end]
+* a copy of
+the whole array: a[:]
+
+Here's an example of reading the second and third rows from `x`.
+
+```{.python .input}
+x = nd.array([1, 2, 3])
+print('1D complete array, x=', x)
+s = x[1:3]
+print('slicing the 2nd and 3rd elements, s=', s)
+x = nd.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
+print('multi-D complete array, x=', x)
+s = x[1:3]
+print('slicing the 2nd and 3rd elements, s=', s)
+```
+
+Now let's try writing to a specific element.
+
+```{.python .input}
+print('original x, x=', x)
+x[2] = 9.0
+print('replaced entire row with x[2] = 9.0, x=', x)
+x[0,2] = 9.0
+print('replaced specific element with x[0,2] = 9.0, x=', x)
+x[1:2,1:3] = 5.0
+print('replaced range of elements with x[1:2,1:3] = 5.0, x=', x)
+```
+
+Multi-dimensional slicing is also supported.
+
+```{.python .input}
+x = nd.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
+print('original x, x=', x)
+s = x[1:2,1:3]
+print('plucking specific elements with x[1:2,1:3]', s)
+s = x[:,:1]
+print('first column with x[:,:1]', s)
+s = x[:1,:]
+print('first row with x[:1,:]', s)
+s = x[:,3:]
+print('last column with x[:,3:]', s)
+s = x[2:,:]
+print('last row with x[2:,:]', s)
+```
+
+## Broadcasting
+
+You might wonder, what happens if you add a vector `y` to a
+matrix `X`? These operations, where we compose a low dimensional array `y` with
+a high-dimensional array `X` invoke a functionality called broadcasting. First
+we'll introduce `.arange` which is useful for filling out an array with evenly
+spaced data. Then we can take the low-dimensional array and duplicate it along
+any axis with dimension $1$ to match the shape of the high dimensional array.
+Consider the following example.
+
+Comment (visible to demonstrate with font):
+dimension one(1)? Or L(elle) or l(lil elle) or I(eye) or... ? We don't even use
+the notation later, so did it need to be introduced here?
+
+<!--Also, if you use
+a shape like (3,3) you lose some of the impact and miss some errors if people
+play with the values. Better to have a distinct shape so that it is more obvious
+what is happening and what can break.-->
+
+```{.python .input}
+x = nd.ones(shape=(3,6))
+print('x = ', x)
+y = nd.arange(6)
+print('y = ', y)
+print('x + y = ', x + y)
+```
+
+While `y` is initially of shape $6$,
+MXNet infers its shape to be (1,6),
+and then broadcasts along the rows to form a (3,6) matrix).
+You might wonder, why did MXNet choose to interpret `y` as a (1,6) matrix and not (6,1).
+That's because broadcasting prefers to duplicate along the left most axis.
+We can alter this behavior by explicitly giving `y` a $2$D shape using `.reshape`.
+You can also chain `.arange` and `.reshape` to do this in one step.
+
+```{.python .input}
+y = y.reshape((3,1))
+print('y = ', y)
+print('x + y = ', x+y)
+y = nd.arange(6).reshape((3,1))
+print('y = ', y)
+```
+
+## Converting from MXNet NDArray to NumPy
+Converting MXNet NDArrays to and from
+NumPy is easy. The converted arrays do not share memory.
+
+```{.python .input}
+a = x.asnumpy()
+type(a)
+```
+
+```{.python .input}
+y = nd.array(a)
+print('id(a)=', id(a), 'id(x)=', id(x), 'id(y)=', id(y))
+```
+
+## Next Up
+
+[NDArray Contexts](03-ndarray-contexts.md)
diff --git a/docs/python_docs/python/tutorials/packages/ndarray/03-ndarray-contexts.md b/docs/python_docs/python/tutorials/packages/ndarray/03-ndarray-contexts.md
new file mode 100644
index 000000000000..f6e365974d64
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/ndarray/03-ndarray-contexts.md
@@ -0,0 +1,89 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# NDArray Contexts
+
+## Overview
+This guide will introduce you to managing CPU versus GPU contexts for handling data.
+
+This content was extracted and simplified from the gluon tutorials in
+[Dive Into Deep Learning](http://gluon.io/).
+
+## Prerequisites
+* [MXNet installed (with GPU support) in a Python environment](../../../install/index.html?language=Python).
+* Python 2.7.x or Python 3.x
+* **One or more GPUs**
+
+
+## Managing Context
+
+In MXNet, every array has a context.
+One context could be the CPU. Other contexts might be various GPUs.
+Things can get even hairier when we deploy jobs across multiple servers.
+By assigning arrays to contexts intelligently, we can minimize
+the time spent transferring data between devices.
+For example, when training neural networks on a server with a GPU,
+we typically prefer for the model's parameters to live on the GPU.
+If you have a GPU, let's try initializing an array on the first GPU.
+Otherwise, use `ctx=mx.cpu()` in place of `ctx=gpu(0)`.
+
+```{.python .input}
+from mxnet import gpu
+from mxnet import nd
+z = nd.ones(shape=(3,3), ctx=gpu(0))
+print(z)
+```
+
+Given an NDArray on a given context, we can copy it to another context by using
+the copyto() method. Skip this if you don't have a GPU at the moment.
+
+```{.python .input}
+x_gpu = x.copyto(gpu(0))
+print(x_gpu)
+```
+
+The result of an operator will have the same context as the inputs.
+
+```{.python .input}
+x_gpu + z
+```
+
+## Watch out!
+
+Imagine that your variable z already lives on your second GPU
+(`gpu(0)`). What happens if we call `z.copyto(gpu(0))`? It will make a copy and
+allocate new memory, even though that variable already lives on the desired
+device!
+<!-- wouldn't the second GPU be gpu(1)? -->
+
+Often, we only want to make
+a copy if the variable currently lives in the wrong context. In these cases, we
+can call `as_in_context()`. If the variable is already on `gpu(0)` then this is
+a no-op.
+
+```{.python .input}
+print('id(z):', id(z))
+z = z.copyto(gpu(0))
+print('id(z):', id(z))
+z = z.as_in_context(gpu(0))
+print('id(z):', id(z))
+print(z)
+```
+
+## Next Up
+
+[Back to NDArray API Guides](.)
diff --git a/docs/python_docs/python/tutorials/packages/ndarray/index.rst b/docs/python_docs/python/tutorials/packages/ndarray/index.rst
new file mode 100644
index 000000000000..9f8d04741c6c
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/ndarray/index.rst
@@ -0,0 +1,88 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+NDArray
+========
+
+.. container:: cards
+
+
+   .. card::
+      :title: Introduction to NDArray - Part 1
+      :link: 01-ndarray-intro.html
+
+      Learn how to manipulate data with MXNet's multi-dimensional data format, NDArray.
+
+   .. card::
+      :title: Introduction to NDArray - Part 2: Operations
+      :link: 02-ndarray-operations.html
+
+      Learn basic array operations like math and slicing.
+
+   .. card::
+      :title: Introduction to NDArray - Part 3: Contexts
+      :link: 03-ndarray-contexts.html
+
+      This guide will introduce you to how CPU and GPU contexts are handled with MXNet.
+
+   .. card::
+      :title: NDArray API Quickstart
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/ndarray.html
+
+      If you are familiar with NumPy you can start here.
+
+   .. card::
+      :title: Imperative tensor operations
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/basic/ndarray.html
+
+      A more detailed look at NDArray operations.
+
+   .. card::
+      :title: Reshape vs Transpose
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/basic/ndarray_indexing.html
+
+      A guide on the difference between the reshape and transpose operators.
+
+   .. card::
+      :title: NDArray Indexing
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/basic/ndarray_indexing.html
+
+      How to use the indexing features of NDArray.
+
+   .. card::
+      :title: RowSparseNDArray
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/sparse/row_sparse.html
+
+      How to implement sparse tensors in NDArray.
+
+   .. card::
+      :title: CSRNDArray
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/sparse/csr.html
+
+      How to store and manipulate large sparse matrices.
+
+   .. card::
+      :title: Sparse Symbols
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/sparse/train.html
+
+      How to train a linear regression model with sparse symbols.
+
+.. toctree::
+   :hidden:
+
+   ndarray
+   sparse
diff --git a/docs/python_docs/python/tutorials/packages/onnx/index.rst b/docs/python_docs/python/tutorials/packages/onnx/index.rst
new file mode 100644
index 000000000000..a3483337c9a4
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/onnx/index.rst
@@ -0,0 +1,51 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+ONNX
+======
+
+.. container:: cards
+
+   .. card::
+      :title: Fine-tuning an ONNX model with MXNet/Gluon
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/onnx/fine_tuning_gluon.html
+
+      A tutorial on loading a model in Gluon and fine-tuning it on a dataset.
+
+   .. card::
+      :title: Running inference on MXNet/Gluon from an ONNX model
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/onnx/inference_on_onnx_model.html
+
+      A tutorial on running inference from an ONNX model.
+
+   .. card::
+      :title: Importing an ONNX model into MXNet
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/onnx/super_resolution.html
+
+      How to load a pre-trained ONNX model file into MXNet.
+
+   .. card::
+      :title: Export ONNX Models
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/onnx/export_mxnet_to_onnx.html
+
+      How to export an MXNet model to the ONNX model format.
+
+
+.. toctree::
+   :hidden:
+
+   export_mxnet_to_onnx
diff --git a/docs/python_docs/python/tutorials/packages/optimizer/images/momentum_sgd_animation.gif b/docs/python_docs/python/tutorials/packages/optimizer/images/momentum_sgd_animation.gif
new file mode 100644
index 0000000000000000000000000000000000000000..759f35c3aa9f759036070f127fc794ea6baeb002
GIT binary patch
literal 45436
zcmcHBcT^K^+b{YFgpdHCcSJ(3Lg*kZbVNV|rHS<3Md=8kcL+tgbfv2FCSvHlDP2LD
zfPfN;NYC;4?RW3<?6uE(&U@OGm49YtWmeXj?{&|*@A-`89Ss>-YXspn=nsJ?@UH>^
z_`hEe0YLD#2tkB|ATXE!OhiZo0Yjh=C<&B=jD(DWoPwH?nwFZDfsTQRk%>t)hFK%|
z#@$?27FNVf1iO+ayLJkPK@lfAC#QN4mqsWzCpRw-FR$uTz6VA8y!`xniToxNLb?$`
zww<>GZ{4zZC#>Oybij&=h>G5i6t#RKYSSQo_nEj^p19*j2{8!?qa;bUVJQhI8EF|A
zSFEgKqpZj9ZO;KYSvh&neg%&%1+QU6c||3MN|Z&4vXZi@{Yy0!HBEI*Z7ppbk5{^P
zb#?Xb>v~q{IcML~y?0Mf-$38MAY{nUA=)rv>Vd%nBi}d14~<Qn<IGIV9(kpjo0(fa
zwzTw0u?{G)v9htZwYN{1bF_DKLRY#txws^Bxx2c1x_f#?zxDR=_D;q=jj8r|=Hr{Y
z;2&QZ;1>{(_$~lb_dMu%==0FfmlNm^G&(d4UAhvM*%potkBW$jPRfgoj*W|ni;s;@
zN=U*aW705bFVbJUc(<6<xRafgon87q=gm}Z%}8$3dS2&weokKD%fiB@#iD9#aba;;
zN!hEiSFbzP%Io_pURPAM&sA4ezv=$*wte#L;PJaR?`o&;4Yl<RJu8ja*`|i3rs4gT
zrk3{B_V>MW9qk`FJ36tQ*luih_t<*R?4Q1#zW&d<{a>yJ`UVCE28RcSM}|g5hDJw+
zNB`E?;=%an`1shw`26m~?E2*R<kaNUr{&|%Q=g~5<7Yn4%udhF&(1H-FD@=Dep%W5
zvhn-t;@7Xs>&su4SC&^+zT;NEt*-9l*H_oSuWx+c*!=$Ec=P+#)(L)lbNk2kj~_pe
zceZ!-cJ_Yl{rYuqw*PA%w|{VOdVPpHJUTc!I{bb7`|S78@88$>ljD=qlhd=4v$Jdb
z`N`S&>G?l(d46$yadvTWb$xk$d3kYhd2xODx2~?QF0THm>#J-0^~LG+KSx|&USD70
zudgouZoR(3UteG2ukiSPZNmR8c08$``U68%WqoxqVI(2wZyMOoz(gbf0l<EJOV!)n
z`kB2Q!q54c6T&{w(b+-R*3%u~>uhK5iFoFT(7dC8u=VutwfBBz?+pn3D@*^%6#&Hp
zXRsSuwMAVKP#OXA!P?@U7)nm<e66~Yz63^bm#>3$r2{Dl)%Y8C>dS`G`3$Sfhw5LA
zWFc*b^Y1jg9?O$?y7P6Yp?snc6+yzP-B>YIs+BJAc(}3hbGd%0_Dk)is+nq&2A8Gb
zrs}zORz01Kn$0x}4Njk{9*;D?`O@OGKK$}-%iE>*{zp4YBQ5W~eMA$I-qdNWUG0gb
z5wsX>ty>?!aNaG@X{-M}k}d8kCD`1sHBqFRa8tLv@yF*1!|L(Ywx->=THBEV-S^Eu
zzqCHxT^@Vivi}VmLCU7r(R#2xm@a6k7~FKUHCcLBWUQn8_wIay>$mX_@Bi$t^d#Wf
z?sayY9c_QEww&nvaPbGXK2mt^W9QYy>Cx`DiH{%gUq2J^t)Rj5PAg$ho{E+5va79?
z2ucmU)ks<+r`4!=(TdgR8@}7CF^Fiswb&ACr?oh~*A;8=Le1M8Azb}@>xmMxPU}fB
zoAav)@+aHtDJTg421bqEc_USer*b1rN9xB$y1oYg_lyTd&fg<%J5+wpH23}SJ<BRu
z_R|ZyOy|uUr`MI6xo*urcv5Zp`M2^VI-R#(`fpZl6$GEFZxx~;?N)`6^e)@Qu{=9v
zMeww~?NSVV*|s~OuFH>CTIYo0Li@OgALT_Sr{hZ9T!*7dFQcn=s(21d$$4XA@rM-E
zjkC<F6(5mnTAX7=F1znK)deyYE{oD(oT*6ac%@{L-MxlMZOJNR`|WzycP}-n-LtTI
zn*zKuJc98``_Ei|we2PC+~s&LwI<9tavJ}u<DGjRUEO}L*idJxj`tm|a^&5b58!WF
zBDQ@FrDIZa6`zu%NV9@)y*YHjbai+;w-h1#X?k5b;*pS89Id#Ku<Sd`MRG59%bwWN
zzD}{-5sA5A^U=e_nxpZK)lsnt6!9&VaTNym{Ge$u=Mh#*TA^erL_k4&`oa45#)k&t
z&wtOF`#q+rGYz`c9R6@${n>)kgye~#^}C-ZUtC+@f4)w<a`&0_-Fow9%6}jKQ`+$v
zC%4~9WZp@Oxg*=#(>3?A(bIKI)(iiQ47OkX-?R6h2W;k-ehAnqVo?a#MlpCyue>h5
zX8KW>{;Pew^rJ_^j&j%siJz^0{ej==|6F_T<-HNQl-r^83_jTAdUts^!nS{jQ@Xge
z{H3^{=&Eh6?0I>(@hnBJ?b1%@`g9}9^ZIN<{BH1{4GGIj9NJI>f5}q(Hq}(HIwJYc
z%aYJerH%P$$=3&D_O$B^ALjdRxv`TP)!5Kg$@KASDJvUj>C;i#^$9vWNqCIwX6Vz>
z5Pz)<cSrZ!ycTCuTCV$e@<AlzxYUruQLBe}5f?2pT1@UY*mLt77lRV|b{lVXkn()n
zi%m;nO>t!^*(bx6^(Q)p>PnN`UB8s6FFT4)xGPraYZ%-b8%~QsMY7GWfH`njA}#<#
zEMWzL@d3c5*1+w>x@4F>itq*wfSFqnvdM&z`69raGF>E=T0J)|(Xn@e!|6Wi_e<1+
zlKD$g;`Aa*XwzeRIUon=MP#q|H17-><Q`^J(0>1w$&+>Y;a(`85L;XOlSE_xaH?Q*
z4`YpfBy%i^fULC?8N)vcVoV1HB>=H37+_0%l+*J71(re(vTLo9x=j*t2tSFpG*M-}
znf~f_tY*i)`n|wy@c6cXdgkR%3f5J@LWK<WgnKe26wbBbl!^d848jNGK9%PIky!y?
z1Of!rtx#qdo{YYITukX+p~6t5Lm)#_1hK*r3R@BKCbCn+4fYDePQj1PR9S`1b2d~m
z$1}%ye1+UBg$DkRJK@4<6Va;lA-E9;>M1~^3#c==+($G2uF!hUqt77lAcCZ+V&Vao
zu!28I0@_W}qTesw`$J`@jTR+39XCB3N1hlr!8WG_;ipWE=9yn1AVoMR=bTT25Og5Y
z6<aXZN*K)^8me2QLf?ssmcXu(2d+f0U87^wkR?>fv5|J%u5UDu;Z(7dy=<qoF>-_K
za0e(ML$6aL+u#ZzeJqNF1p~Zi_5lbLoDAVRi*k5!6oDYJo8cS_v(!;#UAN7=?yqdB
zWp1%LF=3K1=}Pf(&|w`ojb!WA2UA=cW)r>?R8EEI63DC&i1887wPPT1r(slCr;!5Q
z%4E*ZdTx&Ez9Q)oO|rv<(-jX!a$aMIk!D>`IUJUdO#ji|_(PB`>o$@bM=1PtmAV5U
zK8yX+z0Se+)Q;|(@bwe8sSW|TZY%oUKI5Z!`uSuE92%a8CKBHZqbk7kiSjTd`@hyl
z{G=^7Iv$Y!UN<qIpfPY1$=h~%O~Lx5C|b;$JzncH3~uh!O`%5_Ml+XM1kJ8~0jfnq
zp<QHgGJTa!F5~Z-`@021Nwpo%Dco^ucxqEbH&M8=CINC9=y5|6=^?<v^U9_zeEkxG
zPu4e@kpcVZjB@Z#La`EoQ_vyNC<=w~&GQtt1+9KzJ*qf`#4f1dJeDxp+41M15rHI>
zkkd{yX@%$Blv8q3=H5Z)!F^l0U`#h`E%18;a|Y1u22|AnF>1lx^dpQfNM@*#y&Cst
zhLSAq<20>r`ib1IS7(#-V2+b}rVM590&0J@PDd?2AyoH|;7z-wXcl^Jw(5-K(9JFB
z-Y5u0U0W&!uNsvTMuBya5o|Jbq_7dgfC0lFepMgj?bB1(T-1A6Y7m6PU#Uh^`ku0~
z;0VQaIGEO1-sWhMGtif)2Cw4v)!8sW*lkDIku9EXhD!v=8^y1Nu{<7+<!4^L`1#Hs
z$89O|dMaM63<P^WE1e1JIAE=KKf`3pK12oCPgqI0A+Foc-HHPxP1F^En>Yz3Uq;{g
zO0cuuFfd`NAytq*v%2;^T15T{@#B>2WH3C4^%fSahO-IY(2ps-`S^GD+|Qu3yAY1-
zN$-xMFUjN;plp_6_~AlNRPT%XatRFnhsCxGS!h)%jujsuBlpKsb2R0iS3>&4&Qf&B
zbC=lf9tZrYlo~}(#eXKEpG;5@Xkb)%GKyD2p-hYNmX%3q?-f|^oGfL3FMcWgf%%>1
z$zoO?PL~<P7A$p~RK~OMQas8;2@?m$KEbGVb>*bSkAyd>5yo=FJY4tS48OkAs<V}O
zg$YKyOorkJLjiBRzR;G0PX^&VrgTzg)bp)!9Sco>7e|!iMYi5UA;wL)JxMZm<fIjD
z;3W+&!$IV{{7^pk0UTgLNiuw*0A-YwVNkrp!gSC8sOj;uMnDQfT(uHdLGAykUrwf<
zi-||E<8Qu^F489Y&mItlTfv!*JVcyiT^JNMCZUpOKf>>3Py{%*)|xHC$@M^XXpZaG
zhSvxd`aQ;Pc*Bg|irAJX%2118S(B2`P`;rX%Ig(DBBd#4MchHD=VENM6hp}pE1(){
zJHF;YUQQN^*9XrLJ*prEc~83ZsRa4V(@Lxd7GR=YYwExK4VWTHB8|`+UT9nYSS7>g
z2OK`kCy<*c%~3Ewy+X{p75-F9@~xOaFN$a@M(3>$K`<(q^=Z7^oOF#Ak5HN0HzZ80
zD2lm-pqMhMF)JZy{kc`TXt-5K!zp39e!NQNEozX|4HI)t!yu+o5)%wyN&Mg`E#wQ1
z!o)E-M~0R*A`Fj&8%!oM{~^qwjNZW{x|GKIhCdzlB{fBQ)JkfyVM+QOlTc5+n<*3M
zB|SKhFzb1SzV5J!K_358ig#%67BL`|4J4ukSYfHh210mB62&M-|7O6ClDxve=su@7
zKmJGRTWMIGOd8-tEL)n!9g$K9q>mpHvTRaFoiVU5Wdw%De4&JFZJBxFD8hL&xSx8r
ztQdYJ0^C+aTL-1TJMqamKdIn_YV%`EU_zy7a1H>_kp`0V(V)uAz^RPbZWTO+^s4*`
zp%;l!TjsI1w=F7dYXH_ytmvD=;Ge@6`I?Lt=EjR6Kjh8cBrtD4JHGu6Jv2#*n97a~
zPwAfY{<Td^5%(gB*xVAx@|S}2`%%YerFg0eNd&+^L;wfEdd(!I!<nYk8&18FOg-s>
z<H}=EPL9pK-FQNw2W0t5k{B)Ip`Hk1xk-GZ9PMxvlF^J<)lB>fj1xv&j(5&!R&k26
z3dHh&_3khhRr399DP3`n4<>U|Ono9ZiMgDO;VYz5&eU~x<m?V8>IRe55h<Snio}(j
z!htNZ_*C3R;df}!H4c8|7@>#Dg$~nfd3)!e{Qw+MSODYC96oFxiBznk1F~S_j$mmT
zS?Q(@2S$K{IWIjt#Tm%LMyDEArajCu$6JBPhV5?{W^o-9Mx`gcr!>aWgC;TgOjR`7
z1m^jZI%F=ntH{zDIec$zUdZ9%0L;s~L!}DwD(WlwfE9>di=5k9$!k5ZbMP4j;Qjur
zOh!~Bd&t#)6LiTR6DC>`yv83HO@0yUh~<F_w3jq-P(~VM`{NxVC~+_%#@9M`N~6MK
zh9+M{k^ttl1+iv=*xxW?DNQe=KyXzm_zvGI85k91+1NAEn8nH_F@f1he??QmD#|Fa
zVFultbeDRS?G@2s43VW@9+f^BJ4y9Ycm8Wl&0nX*V5h`(?MnY{l~qFW;7M<+e;&)E
zOGkW7wzv0ltL(QkFDL-wC#|J^J=%VU<gY5o6LFd2OkqGRLB_(HWfy+l7Lq+DV{I)f
zW(JTa3T@ygZF|NulrH)O0~JJSLMLk^YpbMA2?qk-H5&*`$2f{jLg{fn&$j_<B<WhZ
zyl-8#JHcJ1bh3cS^dUQA7!I<CECR6LWB%;?2MtpApaz+|hTHEO<i9j1UNoRs8dc;P
z)gCoUeIdtViJ~ikK7iaA>k8Eeq@Vzi6*wbH^rGkv>VjeyA+B-JWW~~KBiC%V+n@`k
zU?QX_nIzI?Y$94Q(_N|hTwApVE!ov?_RnhxeBToMr6uH|MOsdjmjPA-1ES9<Ixs|r
zd4A9AKn^I9y2!BGb21%Ixp&vSI*N!%FMQ_y5Ec|!(>q3Z;;Lxh3t(qRMPPtI(Mu*<
zQd6w!(1wp7lDHl)pWv=^ntFM^UP(%WEEWZBp9`|!1><7{BmzkGVjW+{y?^yyp!Cx#
zDJRgPmqGIvQs>&LF$rUz2ZV05B#-EPCh@-X?bTc-e(`HZ<R2%AHN5;xHqA&;MD+&g
zE`KOk+iOyhbz>s}yjhSjl1lsEsh@Gg1*dUHO-(jRNcyB8`-RHj0+)xh=h|m@F)G}m
z<#DGU$PMRrNLqo%LLIb8P9GI68^0X0i+E_7|8_0##EZnTw$3uk{?#8q4MSWy8M4LV
zzd>32tUll;p?^IZ`hYUO-pS+XG@y%wy@(MAH|CF|f4c_-LN&{twbRfZ6&%0-r4<VK
z7+WD)z0XLP0jiqIRG-Kfq=6+arxaP^B)vqtpHy+|9#yZ~!kf`hQv|_WUoFx1AW{Un
zw-!6(@9k!lO=cD5ng5aNoFW2G0+1of%~a{nZG)0gL^h*AhIcio^}%jlux_vFwTP!E
z8Gk+%qi|`(Z5&w%hS&fH($;+RY#LCRByOvHZao}d`iUso8T2)N@cS9Xsp$jWwhYzQ
zfaVK|>A&3=ct<hlNhIZKQcIlgMK<pE0$T1xv8Fd{YsbL{wbl>!f%tZ6COkku`vc!^
zx6|VhX;S_Kk9-C>v*JqYNu``3OmP^}y2Q03cs+(#1LHY*?p>j&Nsa?OC?d|mkvRPs
zQj*qlV_=<rtz=@XxPm1uL_jU|2`RjWEVG`heg)*LAjN23mzG{KWuYA_!ss)?O0hnL
zDKTCjf?ubg1x&sRJ~fy}T5%W$^TZL(%k{q6^t>@c)bWCJ0`Oz%5!#n%+q|#0^jYb<
zD*QvOksS)mW)<QCeh@?!Ac)3(TrnTv8}=;bpLUF>4jS)pYsXtbqG3SwWTCHV^!Ep&
z_#(0b46zm^h}k``(+F$)93+P#GNL2twt~Mp^%|X%k9W9HAm+@kPOlLyuvkP^H3@x6
z>Bxq4wfW<{Dz8aiOK3kwI_?uNE6-5x^2A53Av`DU$Tg2tmbm<fQ-Xr|`e%4Gc1+M}
zMtb`JGVw9B6`=tV61V~_og|&Z%@^L7lk1<XW~7<k)RyD1bK{xnt0l==A$CR+snmi3
zf@e%J?H}XsXu`Y*Olye?F_RIky^((?OAW}L*hjcc<_Nth<~}N*Oz2~8&-SqPc6&|o
z2}KezNu;zgaqjE*$RQ98fNmndCK$-`6=*>%$p-`+BCXmzqq4lNs0vYFeycDvrm#oj
z?;xqVkAn9iNQ;rMP=NTc6_^(RBFFVM+XK)heWMdV3`OLJh4twh6bgAi2bos^ZrEAk
zvY^>w0`na*fE-84p3iVjNPa_HaR^6}iiKz)2q<gq>$o4NJX|5W0O%0}Vkoc)3gU-=
zrsA~TqsWF4@C8cp{#%w08sVR7$@;LQ^$3!5G&BeUF}DIcHi9V83w;vCx(@?=@EZUN
z0&I(hmg|#AFnNn{Gu{Zy`!mv^K`y}CQe-`l0~*SZt;x8cS-M1#m90R1(L`d%?H;Z;
z@o{|%4^22ymsg)Kii^l(lE@AR@tY(L$3RoPVEG7=@>-I2lggbaGWHCz;Ucmz9N9<_
z*${?ozzfxZA+4VzsX~$zU|{KJ(+~`?7Y1UDA$ow)kn<wAxw00!yc6_BALOnX@EfFS
z^{^02Dj#CR|9#x&iFa&m4S-B?c=;punxci$*x!LDR;JQ)IQR+c()A?#!wM`M3z`23
zYIn3+y7h?m_tG8vN3|~=2N3trlRad+XS2Y4Uwk>bHxi&<`C>OooP|e_PFqd)b=zt`
zXN;60j?MvC`uD+>eAk$$sYd|?&QhP^=|z8gd_N*zm?Ue#z(TPQ)5*IfCuUFk^u@1t
zxKqM;@9Ff@9xlDqry<(ac;zUF0za^V1S4V97_!L6iYGBnZHtTvB*5Mv@czbejf#_W
z)sxt`AAYOl?sdoelv^7}crTK)loIwF^;=`*E(0Y9KKbhow?0SVZ>l~|I{W)|9uG)?
zO*YJlkrgBm3oV=^`A|!CR-m}Is35Dv#Op}pe=lJPf!r9%8@WTmO$InqW)H+=(WWZc
z(fL2Q7M;~6Yw;o}K*HiMckQr52Krz}I}jxf@Mr``Y8Nc=6#8HcK#B$6IDirXvJnRB
zVu-9Ux=!P;G6d;Iq!a(~xk~@!up>?KVd5Si;fh^8&~<MG6;1@dsg|w3suB%nRg7f3
zzor&XcgvzT`~JE{GOI!e_zrIAqbm!*2zaTyR0?99YC4}wD^&>EpI+*X`@F1Fpz!?U
z_zyX&jO$43PZysZn)17;jO^4K<r4++ew%|~N6|^M(w-~WRPn9xn~EXV2<;6g>hgPK
zMoZ_f+?Zv2_D`>RN74&#Ka+jZ8N@5jHTZJL2+#tsgG!kZtV+>0Ob_jbiJUF^^G%N&
z#$PIgLRig?oj$$NE!21!DBd1w4j=#|YF5eT692$)^N-l(&(G+y5>-ud|0OKJ)!9g%
z`I+~o;uWmMnMOu-^?BS;e*x=}mNUHt;dcF(X5Yi_V}+W97FU5MKbJnnuzi!?)dD)d
zoN0Q<pL|6Uj_|^fd0qr>Q9I;n$QwiqYPDTE(HEphfcIGEWoZ0;KonRWOi`gw0*k6q
zX>!%6(J0?=W+v{W>AsoSxVvA7*DI^j_$@Tb-As0|J*mMK3KrA)D!^H<#hDUW@jA3j
z23BX`w5}!Hg(<7o7NoElqXD3(q14dHHZdL10j_j0_6fDms@~Vi8(bNer_>tHEEaC*
z%I%j8bxYkRKxD`qfF<;miMbmm|7dVH;MM12i*)s}$)X;peKAQdy#w)ZmN3wj{?MRv
zPl-s9oixs=9%NZGttI_l#lC4KZm(`qa-IXNp<(RVCu#iH?|ai+v=D8tE<kza&B^~<
zJ@esg{HX`wc3PB_nKRW|qnYZhjaOkj34<9eU$+~j%>RI^lvP>mXr&+f%-Od*o)My5
zlOvA(VbsL9)F<6Ut8kheA9zG0vod|c-f9&=p&?@(#o*9t9m5taV-v^wy45B@#KQk&
z=p|4D%!AJySrbXgnKDB)G-T~x7(29mPry5G6<Xe$L?%67d?M@k(r>fPu`m=8$QBlJ
zJ=twZKi@q5rSoB~Q@Mz(JKcp<gdzTA`Mdtxu5Z3w#F)<0Q$m(rZMg-yqD@EC=5?+m
z-n+Mai+<D~Da(TffRpVhi-+s|Sxrivge$boXFMHVJxi?oOz!s=LG5*23g(YVKcD^z
zGklXQ|8z{6_gNWFF4!tFRQG+>k;^SR>Sp+BsxQyxLfwRY7aYq!_<r#mQ1Dyw+fsP<
zeE4i2HQCyB-EZj+EJtr6$gwkEt1w3KyznG!tFT8*o}u9TtF6wU{g1>-!3R%66^>}R
zPk%*tm?^zEK7>CXDD>!v7>n!pHc;5>{AWvveYSo6698ag4<1Ik8SYpTu#2Zz@R0z{
zIO5xs6(|xTeVsG$8?Gu;EE%WOc&W;`sYE1{5573yfk<D0K5SK?KO|<2QH~>w@9O;2
z`Yy`Z2F|I+fxU<N5v>Hap%m}Qm4lLc^1FRoj%4H_T~Jm9{HZrHWz+@MJh{59P;Le)
z$qd|7-VYn6w9F%@Vbj`?R%+``LaqIFT{~<KfTcyOTB>c`R5FuuoI(ihB{Q0n&kt;e
z;7r#juZyPP0RuEn6&n0hhidp!$hj3~)RgR}w5(n;4{}Zv!MsgsaUDC4Qe;IhmIwZa
z#$JG^ZCHPy$MXgl#kfYBnt22BqQx#Ys<Dw9D5A{z2zEe51qgEMauVozqr{kBaJ|;`
z%C2r4`05V>?$hFO=I}Qh1SU=>q@W4#2pU~z(^;`()T$h*en0QP9WA%!Bhg#R(`x__
z<}fe7;q$B<;e1HJDKU~VKI(`fx~(5hA1P@m6SuUYFN9_;%UlttVAZrqq6j92t;IQM
z(6s6WTa!8y1f#`%b`NSkp+(ViK-_dp@1!?Rzlu8lmZ>iWiT*Vp{)c%GY-x7$ad(=@
z`GT{4R6H-~@3qp{u4!WBg~1Umr-}`0-J7{~fI;nXrEy<XnTajL`qpNm^bFi`C;Ub~
z^n0}_E8MjP#~N5g^Tso3xA^)X(Uu(Rku;d1E7wEeU1P(m@Iynqh$^{2OaI-IrCm9@
zqGFm>y1KK^PIx>1`qv+sP-v=(@)>##TA#f&wy|tAM!UZ=!6+1gI!;x=mBZAcn3mw%
zP`4P%u-RhsP926i79MG@RPM3KBhNm-R_~>ALEF+wbxL?IBk<8!1_r3T^OiP8?(vR8
zjATtMi^vg<`$snkE9cpO#I?Tg1ta!v!@>r9&U-9q;#4;C3g&uRs|q}zi^KJKhG;hM
z@E%rX4F*hF;zdctDd%*rB|Vn(Rqll3|8B0hNCsytJif|(+st4A)%`^+0)|oowp!))
z{KKH^Tn=}Aj=Z$XP|>2aAZK6BWg*eaO0!%pQ90CHqL0of$+sbuks`|{?C3~|vMv&_
zNuz*5{3COUjxf4!xFO-S7dA>uS#9*WM4uc;#&7y9jI^B%+_Ly$4Q7!302vepg?Pcd
zj@4wkQ|~KHk)i2|N)u98CvqvN(6aZst1KWA!ChYyXmsEpn3hP%Sdh8cAc0|9mwCY*
z8){*ALh<J&N)_vLbnSha$wGPw*Ur>vE|!*7QUn2q$sY=%8Y3jLh2i2}ZrUF)Kdy}m
zK5aXc330^Y+^GGSQfwVDyE%e@0)vIsprIDDQa7%Le)tOY*0h6GsLi8ywY<Zq=Yv6C
zK0>4mq6p^gC~artMP2#aT?P*c&}juBySe9-C}3q~c(wme#D&Ui_T{EnamLWc8IUR-
zkg$rzjaNnG<5(fF65l9d-sw6tviaCTMuW7RL_Q|Xq{^>1@lka@MJOJM2&Rh94|BND
zN8qZ>wyDL4u&h#ghc!RvH<`N1*S&A2u}bh0&=mX}WA9-W3LXHeYhO>k2vN=@uF5I2
zhWBp?Y4&;#$nnW4e}?%{y!Bh!wImLj?^E=x&sy4Z)C^jj>GW-GS^DW2Blq>`dVFZM
zf(r<W1pz3?V(-#^C~@!#5#D!JhX5d+l#7`b_3xKnK1hiP-r!aWXck#IgjaGm(v+0B
z#jeN+4jz2xF6H9VF{I0(e!lbY<GJAXOT^pY(hj{Qf!Ua{3RAT>xW$8@WeU8(u0KJm
zC!R`ZgaA8X6)5OBk3_2<Z7+YEGut=z9En<Sasf_Xhm?IweQBaf&=7*U?fmii;RN{H
zm!#7{Lj}%4h=vBJRd?9hN_#5dFaHd<oX_A>qIxz#vwV3)F14Y9(WEP{Kt(i}Js*jJ
z`L9M2B?BUOn9!dMu}{(@WYDt9Hdy$m((7}wEY-4&`j44w#NBC8WCzHyY81Jh^8LWu
zlZ<gTs9X=#SKQ}GYiM$xqU7C4vhEVT?Dg;**REjxk0VbNT;hu2!{O|VmCH}Lkx!EM
zhZ7(ynD&)8JChpO2=ue6B#z^55wL0u3H#_5m`pevhJgebe2}lB#51KvsaX*U@`ba`
zgE)%9MF%4^5K%Tzslp<NvJB6Asv@nyYNUi(JfjNB6D<3A495-<z?IPjzkG5*Y^p^_
zg<>`omn8H@lg@|9=@9;16ItT`HhUBow0j?zm#g<CcFTzZR$%sER^&yDp%Vc<Q21CI
zAkqTVRMC5%dJo0=C=%k#4Et<*xs0Zfq-{h2VuYs8SZ6ViIlg!5q#74iofc{$M~nRu
zJuJqm{azhh_GNLWXOMj@GLO{WQRmv1E8u-CK0Gr(iUF}x60qQa+%^qERRS+jjVE9&
zB*%dFB{BKkICrm_v4w>9d?l=FG_aNMJCxDRr;uD|48CNANN*(qi414Pp=}cY3d(Tl
z>?9@9!E|s**t1emOcq~v(FYVcP$CVqqLs8%3n^na{9~N8Al7^kK&^#J?T)W(FKcOM
zv{jNqU7o;uS$`FB85NkKHKF@TTqh;Q`#9+vTBUHZs~y^20v@R~Zy!yFi?K?&kLISL
zz5V_)rX&KMQH#D`p?zZ%32KYOQ;u|G#1<b@zK~$f4TGDSX?8Qgo~}s&wPB9g+?{r#
z)zrLG$N`mzBsSzwPaS1ty!Ibg@eS9}aaw^FkqQoX$VgAiP0eX~SO7yVg6>?-iDzRF
zdBi6xxESiS!*0_TOfnWo{&IsBIZ}Ts431|i8rBLLTid8$piL^v=OLgTK3O0It!od6
z(uOg{xeiLU>hQ{m>qSl9oxQCRRf{py8X<oIE5Q9-B?@D9B2YtVjh$=XWP$PUs{<xL
zw$s?J-ie`ky%S(8RVJG0>-%9L_$-ddK(q;N!m`|vE}0{uT+?#*%RS%GiP*r3`rz0d
zA&CqVGI&qy>3PgO6Dr{a4aF{&_Tv|3=fn?DFvG|qp8QFM=jBk{lmRYyh)br|DW|7t
z`aN`=OHcn^ciwlBjMRC^M0F#Z3^~(7gNI_WQ+(w-%H@$d<Ds=gw~lJzCLq1T+I7wh
zol+5KHv72wCsAF_jc1=ivs?DkM(|zoZK1W05dKckN`w?C(SG(lLn?hXS>0Rp6iS@L
zVlVU=>G<k{?}gW5RM)s|ayZqVl?oNT1DUWOIU#aQd6<iq$7glzi()<0(|W(Be{zl-
zxW)<!ydpl2@oI${(;`6e>Rz>^-DXi_tK2fC)8<b&d2$+PoWwgWr!AJCC~gYoC>^ad
zF251OaTBF|WS^rNN9Q7AAZexTOko)rT0%}ilxWuNF~{UIG6<=~WWq-DGjU<G^>)LH
zpWDpVFbXL;DpFZUuBiGYAmIMWn0?KN8-&0=g7CW=K0?DOX!2*x`$OlWN(lfXj1d(R
zjK?4TegWivyC(3(JXQM~Ub0JO9^D)Hfh>xKJ~vPix%>K9n=9$(-B~Sgpdnf-fBuC#
z`)ev=4K>~O>}tMI3inZEP%BpAJU%(PB9{_VU?Cw?AL8G{qEMln>CR&5UQTjrI=>!c
zJShg4RG9fdx!GT6mp+X0f2Dm?^;$Ab-ZU-o0`$^wv3>?x#?4AmbKix}NZVz6=2YM0
z9PJ5>pTH4&Aq_4n#1N+G=h^qbxUv@YFI@^ZtXY#%WfFTaVRCX`pIxZ~T43lB8In^G
z)Ls!Pk$QAT_kF5y_wTQVqF=2a3(nU92kB#>wF{<lAmQ+j<H&&yBA|@MoT82rFq2@a
zNGiNCFOm4-)?JC`dQ!?^c0YTvhKy4y`lYzd86pZd`O#EN=rmnyh}ta?PItVx+_$_p
z#>;S~_c!EOaw6+@+s9bqj90jTQ$VR69=#$@c$w=UXd%iJ=TX5&udeX3&(dshX`<Cw
zIxVWFm*P(c|2zw424&Z0AGLX_SsIY|aE>??x_)i`8!S2hnl;YB%hL9R8tDO%dmRfY
zfX(6_v_F*rutI4MDUgq%XbFLPICc`Lyo1zLrq`>?&8uHciNCk8?wrLGj9_Sr3Vz-p
z-<ZnAL?y}16Q@?PCKZpV5(0|Qob<NU0z}U9$y+I)6uyGBTbpabCu<@Q+vPCp@omfb
zrw{N?Tk#*5QMPNZoak1Qu42pwDKjX;LgP(}Uc5_82WF?1MD?;RiC+I2tChF7U&v}F
zK}T<@&2DCsG);vNZb~cplsOFsM9x0%5^Y@mwjF~$o~p&%L(5{?>?UwAUW1RogG3>8
zop6IOpbV4B_d(mROmTU`jAxjK8%~<qwZ9@qR7PP^&$h%`S+GvCdqwFuQ@rPm)@%6g
zptm@`mlQe~O~Z)h&KMu((+b=zR-6^juKqZyP}x=``2I<!obM)j$j+!r|NisXM{jNb
zZhWNdL~4s+!YaL~gAh3)G|o_!!+AQ<^+v+otuO~m$6t0+h#GCv`y@B!=JUimn7>b3
z!SQ6l=A|!}jsS5rIs{=6jhn(&3Qw*Wq@giq#`B2J;t*)>BQH%Yr@&50^H2nDG8kUI
zPJsN#qt6mEqP9on_CmX3J^Dgds?_B;o+>Ui?T9<cr%0|=4b056_x-5QaI6peF^Y=`
zyC7$ppSg04wpD>a=Z(!~YpJz!t4`==vmMuW*i$lhnvED<`f}>3k~ui2DWGLLZCz(@
z#7k!P7m`v=*<<>un$N{uTBO``!*)Fi8ggb5L+2sdrT0#`Df%>Eo;0qXyo{pgi%5$g
z4;9^9*|#53Z&)%R0XRj~!*SV$aQnqrJ9VCebh25eD7xnxDr7&u2yZ(?yZR3|;WL1A
zrno|{LH!HL{1Jy<kE+I>1UtvI34W7rv>&;zU+?|6PF*_rM5k`TrR@&??Iy_#v-B4|
zR@Z8fN|K~n9wFK7e*Md<%N=!l*tiaJr&chd*6HsYdq~bi(+RZpR{Po!b=4JgR;KGL
zqVJ~jK4Ce1gwdDJwHP~`3s4buLwrARo&Jy<K3luMQ>;T8GYS#RQV(kReUlmNSo$Y=
zKa@{l9sxvN-LlEHyQruVzJziwa4H_qFn#mXJ~K$=IJntDMug{>u}aba^lbJLLQnJ6
zBP@PwCR)C2w9=y>y&v_^{^1bafncGYlTp%<^SbRy+{fY#ND%|ix5U$~M|cYr^aA2(
zI5<Y7T0(oxODO9p^MX4Ct4jvjLhsagUWVh^E-Bzqqi1w#E@Eyi0T6z$FS|v{_O58E
zb25}Nnt>SL%C>v2{OR{|GT{~$2$Y4U2};ZDe2YWvr|q$U(pH2Nd^Q%9*w@e4p0B2~
z8ybt%eq{Bt7UO^_bb+7|z2@qcSq8=k&5x4?!dYMh8jM8uB7m#R#9mV>kT)RWxD}*v
z5KRP7v_^*lC+=G(&en_MO>n<kf7hu2hGfe@w8El!qv82y@}zd>lD`k0PI@Te3@yDr
zu*1aX(a?Fc2yokEBDpB(nME4=t7XAPVc`suih$q0qy5^02K?~OJkf3y+9cNSOft({
z8~;yr7-uAwo3zdM*=Z`V;w=h)?aMTokMNAcmBA3}&sKM38}f){WYD~4d2CeT-&4Gg
zhh39XAmgpunNA)hBChFv`I_g5$|E)=81<&%YoYy6gf}Q&G)(ur`d#WPJk@ONu55bP
zX@ITc!>HcL-+i?KHJU+ZlY#SR(dSWcK8s%w49%#<yqSQr#(|(W0{l;&^vSt5cl5(a
zi=)Zt5Rhzm-gr=Xsh=~Iuhadss7Dg)SiUz`#9otcG$jH7%V4$^KRo7!R-s*#(qbr0
zlYgBMXf3t%-sMao<;!>Uj&Ew#+MB(y&4Um%t_TN<a~?d%;cD6QdF(s-iQ;z<)a}Iy
zvQA2=AvbE%D%dx_>u=vKl$t4>il(0%b?w*3NuI^%6ftGg=HUjXwixh#OoCoHCK=+c
zbqA^oO(2oEcT7p}2U+;vUe8Y~XjTLI)D9z^vmy5Zs5s@{2ToDwmAuf)>4SzX9D8pp
zHLpR9(*Z|+BD1vj-u40KAcjLTP00C>>rRM(alPA*L+;URF~?6e&PP1s`M3QJ_kMgX
zS9<p5df~$Pcv~-M`<k7@$qEaElQUBa$p9cUI4-tRb?5irvCn28?Uw6_kXaeuT)q45
z$*r%goM$j@fj@5tqIa%kwE>_DAbbd7L?9Tkad>0=G+p2Bsp!UF8n5A7w==QL(OhZO
zGP#T++Yfk?m$BM1pzt1mxdZkm<;M1Wz2oP%?iW(OzP5i0Y<_=X*Yj%i)QwR`KRm}0
ze_iZ)dLetf{i)pWoyX&+`@0-dXBU5droR-i{^eiaeB-h0i^aJH&;4tK%hU9RkpaeF
z*^tYf<(d6`yb>Bj!pHiYP_K+!7M9jfs5;Jciio7De?cEbXXeiyZ8tOXFq$>+ls%R`
zUWOx%JCmC|{@3q*%lKREryPmQ-=#T|Bp3ZTlVx{WIa3tQPdPElB(hwo>YrMZZ7vww
zxYBiRpK)c}*Ouje@z5-QJJa->ku&Qf-8^?T-a1~EC&ykdHrd9Zs*NYly&blm<vA?N
z`|?SSKW{-mn#qI5>OanSi^525^A$%0KIARody~yqnsob|ugvi^6aT9hW`X==PizE=
zBJu*y`75ZWB%Ld+WapeKE30yUL|30|Q78$u-xhr1k-YQh&CdhEWCeT&r|a7;5>npW
zPV(v{Is8U<oY>L=;a*)V^X*$rGwsvmg;RQ2d$(dGsI?o+P345!KD%6OzQI3h$yfT>
z{^f1^emhH9$M40v`yWo;R&#&6S{s~DdXHR#a()vaL{@Dzn+o^9^J2#ZR-PS=NiUfM
zi}usHc0B2047wB@WO`cjWRN>g{;;1W@AAn2Ug+JW*r@1;y!aTqXT-P=p`3~rX9?Hm
z{6cb41&Jwj7KLMrNiVSkAzisEiRt@y6(naKK6)-WYwG$za_(`^mE?R&u$UKTvf3vo
zN0yH2^pyMis~6oa{pTr?Rd(wl%K^I|T9V##)r)f`+O3hwUiUtiS^NA`GD+Zz&`I^}
zi>uqR-(Nfm3E0SS4Uye?i9fH|%nMSy{o_?$$nBlV>dxDn_^$?D9687J8LM$$LN45g
zgd5^r6MZ1*CD=SJLyK4*S5l7p^is;kRyn-91OCR5cD+y)!?Ess%Y*wmi=TU+>%sLh
zYEK=;b#;XA_AN3V|9mg9p!9L{)31y7w-|Qc;Wy?ew!U0lhy-K3iz)Z7-l2KoV?i_(
zsI{M-sQvBaTY+KK5QExq`r~`QEtC^s7PS#KFRh+!Lh9@oNhAMGf05XWw|G>jFmvm$
zliBk13~=o-mm}{TIaKtp9@hE~+2l(FzxsHSddh%Qo};bccZz)l!3`BP4%$+_K7NC`
zc%zPGrT8flpVJ6c<4X?49=`r8+9|2=vl!&&TpxR8U6MznwJ|}3a1eGU$+v@(jD0Wx
z^cI{Fa%oGHZchRCCc;F^mtCYqksD!<F|om1TrqUjAA)V^CQNj=#D`vsdV340-!YPU
zU|j(5sn2*V|D8$_#_-~@_DMopfq*R&5B|X?G5&Vfk~){lk;jMi=JgMzRqF@iKRo-M
z)ekllNpJgLaiv0kiOd%(<sWnY^&|T!|FDujJJ!&&A#Yjj!Fvz(F3+^j>eJKq^{2{O
z*ZB=Ee+Bc(ub+KrON~fv%ivW!s+`Elc*;3+9xYIG&>ceESY%#REKnh;A0gIQOzC1z
zx$t8$Zq~8T@LB9HjuW*6@5a)bS56>Sjt|!L@G>3+X9<1*18bRdcY){pIuA3(9^ph^
zOMP&LvTaj5A*U=?yyEu&^C!R+6OvF00`B@QjYZrEf1hA0KFtuAsr>Y*Xow(2KzK;8
z%)6;N17xpa`D3PTExL;QVneB-I36K}@2~NAE@-|UNXspyTDq4ZXmQkz_2v;|sxgV@
z91@uC%H;|fuVJ;Osh-D3fUP=;oNXBd7lvFma$4qX?f9z~#uU{mx>pd6s?{b`i-sj7
z3b(AcqG#@!Q8c}M{L11t*eGgw{bt>VTOLn?3}TlJTRN|PPTJ(uIL^;hyi@Sdxi~jw
z=V)nDQsBHBL}#*PkX45rqm*hIk|(fedB1#Xnv><z%aN1D_RWWt{%7n769p|FuxG+%
zw5Ddix?4I=K6rxOxeO((wS2s0O=a(DZ~oEVf@L{-XPowG7NJ4cMIPc6buD9VPX8H8
z&*Nxm3^G}_Xzf{#K<Xh5DQpJAlkpZ{(RjnXA?f~ux*d+%7~4y0x}yfUHmnf8bIm&I
zMQ;*C<3J!O|2n&7T4T}%GBtg4{pVxJ>dVJs8Kvel9OJqy)=^K=8q76_s@IrDXT-7(
zWUP5@WJk3(>!{O<S9w1f4|{}&=ccPC8Ur<gK9S-DM;$#m%_HNr65>UE^KV(iClbOG
z8_8^EgiZN~!?ZM>#XQUx@@XWfavv9eU7Fv`BW+uZxju%a+kXc|v`_QqKU(@_WNp}R
z9!_%|UH%B}L|h)wMm8kz_UYC3R`CJdhsCOzh%3bbmFCgzoAse}0SS*YjOWHfn*G#=
zQ%os;f12!Uj^~$=_NiTCtW(5GwNlJcUI`O6t|~S@Pj7-hBx|jk50UOzuj+juF1>uB
zadeSJ1b>LlRo%Mw``Uj+At^HP4IuXMbCt6(m9K4X`XJrw(~%Nq*s(_cQKnz~?LpGh
zj`f>(nL$-y?^Jv#gTxiF{I%%ei-wNx!lAOGw!%j_pF1|CKFW?ieS7rssAEg<Tia3Z
z?rjH#-z#sfe-h4oadOq{_<{eh)ck1Gz0^>_tm?6PTlHHXYwm<7*Dtr{o%`$`#(dbT
z{3QMHquWW7t$^vUNyGlUu;1NVUwfZ^yeJ)aI%L>q-VZ6{mU`N?_xtJtZrh+yiP7Do
zN4!%f-9cTnbIJeK(VN3eV}lU9!LMn(9>Swcy!^JR$oW)y=kaSLg`HO$0>5Q6m-hPY
zzVV7&;Ctic)E5%WKDaF|9CfN+BQ*JDn;D!dN?6kN6%L6Td)3T2Vh2pryV6CT(_XF9
zh8?Lf$H`v(RN7|ylC8GDC>jTU`tcIbrWd(}$Qa0fzO=q}ijXgEc*(s++v3pv`6$Ix
z|3{D`_--%OD_o_G4b(rNR71c49_Pk{i<lF#(phB&(6WZ9^7x0}l8>ONOSpDN5DOUI
zM@IzICe%Dbz%mg6Q&h$#DX_Oiua%2PKiwqlFCz2)L^@jpU$&YYD<c1epj$1XxJFPy
ziYdw2spyNTZn9JJ6jKZ1*=eMTX%yLMHP|^05OhYx1b5i!9g67#<LG>g8E%fzM;9|D
zTRQ|7GZnBizb<C>re$hg)vRH^(O-OHoSoGom1UXz=4SEDU+ior#pcuO2#Af@DLXrT
z3HwbB4xTj{5)Mu&4ii2OE)5Qv+Z^0RCEOMqJn>On9vr;BCA`BHJkce5*9*LvCHw^(
z0yn?#z2y*WE)g6q?&&KL8t1q*TXJieLwK`9_!o!BNr}ic2NF_>B<B>RFBQGXDaKPO
zCd?@=RVuE?DWOp+p~opnZ7ZO%D(Fxu<-sYPwjvqADHB~PlgufbSt{%DNcwdtQ$Z<J
zRjHgWyL@k{{5Yq=?7dQjWlqJ-QpI1KN++dC*PJLw8H$`snZ8W<CYK6NnTjx%s#KY(
zBA1#*nVKG#x>1?B1($|HnT7|Krf->M2$xoLnN~8_oy@X31sn1Z@c%$rLc@Qj0~J8x
z{}?MG=>KZ0616=3OU6n!jL_pBV>Jj?{FkxH;M50Ef=#Tl%PamGE6orpukw{Q6;dTG
zOU$1OfgsYUCo}UkMeTJ?j|1tve67C3T79<37ijsV?XwR#Oi%dp*doXCC&|rx`mnGV
z8`v-IQ5kFV{sdzV6oL|^>g~_sx=hx@(AAr-now{jXC-B-!tgVfzE|>_i7ML>@}b2Q
za?pUo?lSzawtKK6g49xL;jft*WTmcn+0phi0!%Jmc+_#*QwB`I;wK2S{@%}hPlxBi
zd@i~jlG0WE?xLl*`8Q5|D__a$;pIhclF)JTNBrzv7O1|3@;+|sTbL^MNdc<=e#}b5
z{fBn?dZCwut5Is|&3DlRHrvI9tF#*|PHdS@>0t!Ft=Hmt>$hzllyv;dSaBHV0$j5d
z>&gF%v0^d{)Rqd+57PT@#;R=+ZS;?^g2bu)HCFta*>;(-kCGk!!&q(RImZ)k<v*MK
zYpi%|4!8=`m4DEr2a_NYQFCCIZ8c3+moj4~fgQG@WDS8I!~~+n<z-ir%sge;YXi>Z
zyT#C*in737^U8|Xe~nd5^Uh9nZ9iUMx2AE{W%o_%X4UT7j+33;cUXwvUTrVE>t0>t
z^Z32`QK^3!D-FS)jnhW1Kbx@e@o)49%_@F0%b7xcwcak!{-qCb^Ao=3{Z{aS)vs%&
z{f^^V*ZmKto7J8^I(saYk=GFU{ARN>|Mlb$4<_Sc!Z<<Pof8~kemg}e*sG864&P4s
zG}$v`KlN4VcncyFOwU2E9biVqlxkkDNxmMtJVL2^f@ve<zuSP4S#P{y5930kCRO(H
zr-%q=5aDVtmLz?ykI)SUnYj#g4A~75HLx#1+g_ux>l+_^4}xbxm~l%-&dEOFmqhP?
z{h}kiUQ6l7f^lq9O!Mar3H;+0&5gaQKkAM!#a6X#EuBBBYzONUU5R&T{rxP}$Em(#
zdPa_~<@o8DO%yx@2W*o@u}@k=r+Z9FTPhT^@0gI_T6gGWwPb(NBqhtKkdD@0>|<$O
zIN^F3JTDIhdH)(Kq4REeRa&J9tG!>oU53ZIJe&%T2s+q-lOd5!QbK}S9s(KqW!DdP
z6Yk&d#J1-N=_i6jP&zt43eTJ0r;pNNE8`j%7@CbSpCV%=&WGck+fX;{uZ{q0t}ri3
zuc|p&L4JiccqjcSHK_+_u<$_=E$%z;b4pF_4*{!=N2AhrctzN#Z5j!T4?{1IB5QHi
z!ow|AzK4t}2Fvmw1`pj<UN=pOcBMfXVxZEYjS0~Qwk5RXT73d$2k|DOCG;?Y<P9HD
z_VF(zj00NzVz~#2PCBK`bAuY|bX0y%0@n8(`qkc@x(AIot>;~K4Je);qy*<f5rndX
zDD5&#EUn%AY+6DXQUzfn_?d=D>`r*_dRlgeou=5(o!KX4>Dl?QJVZrzya&4x#i$a_
z>_Jl1%Jmnuk=&YC>Jc;Fvdp?;sIdJ|rtz8<kVHmX&ZRMG6Ihl#xCa$WlBK?x>nt)-
zwys<P8*}m<$b}WZmTJivb2l^0TjJ6(_q{Jepd#*gf+^MU1>FkTh{_I3ESKN9GZDCW
zRB$?4uJ~t2V<P1Is1Sfxpoq06!<mnZ{u(Ql|8A_3D%5zjr{ZUS#2R#zs!I<~B?lgt
zBH)!;n%bYza*xaSd~$C)w-_uE{ab7O_P4ddKy3e6YrU<;`(NX$x~Wj`M#n#VmHapG
zKk-$q5WRo#Rms)&RVEGX56b`Y)mXyLc-FiBulVZnXxqQ@RcLX^^@z_ueASW*@t^b6
z<%OF^G2?}gxOk9x&%VgL`-iWZQxP`!<2@Gx|4;cU9)SEezG~%H#rl`8l1Xj<J6~mV
z_@DSHSLVO*)wKlVfACe#XxbM897?d||0}-w!sOrh>OZaZ-}vfgp4Y$l>OZa3e{;b^
zhVw9IQX~)}uwC>Ye6?!3BvESTFJJxJTFWww{_@pqhpHd1^L=;z^3`a8zpXXXW#@lt
zt)zcjtEvLcx`N2R5520O#{dRU0RU(@Km$c0ivBNnvv?Kh|1odQ?)Yy*?|+py|3A4t
zMDo<&(Et6mVGLvofd75~C>$Vysu61zDThJG{{7#Lf$RVXK={uQ-=K_IwN@fvA|mj=
z<zw>iZ~T2t!rvpnz|6k~=M%{Of9weO_Z|Pgp7mdGWdw2mr*Y-_FUIx1Yjekb`G4+&
z{-w<&r`Z3c&4HkQ;yP2@{x`16532sgH3|6tI<6IlwvkoL2k&S9rOjEN0{@vdAMa>8
zYJFGenkal#iVT~py3i~7N1F?V;wcBgcTa1~C;l^S{y*aSAKHASuK)jjT>qub|G5)t
zrB$E);gk}g_>VRp&m>Y_<O<;QBm8kNod}uFq?Fk(?^{>GpO)GufN#)l5?a@$!esGf
z#Oop~P&*dDs{l8ov_p2rAwz&5K>vN~1{(Fk>$b{P!v5vaC<LGc91DX8YO%r*&9UQ3
zz7oTHrBDE)UM6%{noJfa%uHVjm>|cLlwY5wNKpVtbcJ;n9#$@r|JuY!>P;t!U}a-S
z4#$JS==R-e>ik@R_;jOObWI(J75a5kmC3dQ{VD(MD-s;Fd;F!2HVNTdYMf)?cqa!|
z?y?cOr__9jbt?=edG@ef=<NjsuVrFaG?>stwzT~El5kv!0Ek*wn#P5be~|vgiIy6|
zZQqj`D$6NVz?*QAb=^L1fJ<C&%eaeF_|}X`uy2`ENU+d2uPe>T_dH3)>lhv+OMa`d
z?f*0yY+F1`YbZ80XOb?K9L2-gI3mdrTs4U2Dovb_`rq0+4~HhxbnU++frOTXDxE;+
z9qBa*9R(7K4k|h{5fBg*6?^ENgrXv#(iB8Q5Jg1L&^rV{2Njhjf>J^?KqOxrXLg<4
zowMicx95D{o}K@Im-l(|+}Cy8_wN?wa_n~)^!M*4Y|}aZV!P8}-sgJ4Dm_Dn_+z{;
zb#oeeFrMx1{yIJ<&jpTX3GeNBW1`8d)*ouXHrKp(7Nip~%%nA~_VxQ8s~us|EqbXK
z=CPi4CW@K86I$LQ-NO;h)BQScB+k(^G8d*1x}(|Wea5ptOyjq|3)QI8io3m)JXY|y
z1A*zf{{)SBJekISjeiO3Xjt)MjH$&FLiW7Io8kG#K)2RiG-hE%ww~Og_3lja$Tmx-
zWgX)YxaND<s8-rF{UUg?!HF};YesDtX6qKYI@x97lb~@%73O7n^EURDbWGC#clhxX
z3BJv%vy?Dy=oCq!n=E1&gnPP?ZYe5R&j)6wnrd93#()gRIMoZAglj5gHRRlR?i?bE
zwvly4D>*c;wUa~WtZDOE7?K&U)FQ)?j<#WV9dchx+(Uy&HjMINC<Q6xhKZyfR$#&p
zvs+W^<C6Ey!bGY{TeYPQYu&!b{vNWuSier2$a|_t+)TDz|3q=x39(c&VY+_w=pu=*
z?qdN-Y%dvdmLWP6OF(aR7>F&A)A#Jsc;SvtIX58^`ud9gd0gksgVDZ+^3pMNku_JH
z!7c?^14`_d^@DHRJGMoQ>p8G4o8lNPa<!cE@<yii)ka_G&Dwaa1wN8@n`Jf#=5=oS
z?j4sW(bDsFP}5i1q`*r)lArS9fotnM3<g-NwpiY-PMH`%7>xVkZi}hCLfWKHh~dxJ
zo7B3cx~LVxgO#GiISQBwUI`P@^HhAnrAGU(I63L%dK-x-EE|`@XaS-Z%b8V~c-~#`
z%GwooQ)dt&f>9yG#M%fcAftpwM>&k`0(d;AQ9MKd`8fh=G80{O5Z0S7ycqAaJcc-#
zH&OCIBwI#n6eL9-EmdBPgh!B{@llB4%!GKSfe+Vms*cL_55=2fH%P2LzT%q^I9Mn(
zT1sNI)O56w*Mq_)&#1`mSG##uizD@6qqD5>gHppC1u#p(_}#0N7!eC>B$q$HuH?_g
zWtShxNyz7_OMsf+G=9F$@DX)w4zL>DLpqh<BcPxa$>oNP7Rh(x2_LpVx>MQUSwwbQ
zY%{u#4-vr(P02fUNAw*Q<ZyK!Ej@#@6$}mCEj=>I*)#e~FFZ1NYJ0P&f-h8j!L`)K
z&7m63b`u-((pA?B1T*i5j35ycFZ47^?%!2lJQcfd)3-bXU9jzPc^FJS!2qgK8e=-;
zObRddX_)henoUQ(SrbeWxx$`kR@k}A&ol~oi(tib7JwKhfOD2mAG8+6(unizm-1gH
zU;?>G5w+mr86Zv;a8ACk8_lJl6s=RLOzIN0DycVZ+of*Ncj#ub1WL$O4JO0s!O*$7
z1dHL1l1V$Y0xkjt962)wzMp!aID1|z{)dLhBnvxPk)`Q&(`N%-ry$Y7n})V^DWV7v
zuP*aA_mvs_msb#H%cFM=D_P4(@ul9aYi^k*$D7nAk<O)L_deyE6q>7@E{R#JKpJVi
z(8zGoFait4<Xh=6)JSigc2%^$EaC(|>MqGcK0`KU>q<Zat~Q~iJuKQ3U;~Mvp#hS6
zB`3p%!Zei)y)tTSVuX0wN#=%Q+?;Ux0+6j`RCxBzx>AUYL!<)U@ukr;2+9v=C0-b=
z;{Yh{F-O^m(3H7kK4(ANZa>p_hUlv#B`X!rFQgNb6X*D|;FzGRFhDM9XJ;q?xHZcA
z0~EOML_22aT>8O%FHvEs?Z#`3H{6gQ{@T%u><hLZPV9y{W&&8L+94=Rgw?9aT(Sh8
zj;lC%wmFJx>}`{X1~Nam4<&i-X8u(MDLgw0d6C-s!zZ?n%MsjwAKlg<C|?m5(*q8&
zb1kT=1lP6(+hxiQA0EH?@WG?<o)4o&`o_))jwnm1{qm=CU%Hm=2Z&tE4zN%;8(_$2
zMhJ@x3o+AUiw9lCCTW&@DHq8WUqDYFqKl@}w-PP<n?$|fS{{$=$%lP5b9uBth8kMj
zp@1h3GXneY;u8EgMC}SUE*_o2Dx(}UE@E#>nLKKL?oWt9-XMlxDl(3d9>(h-=r|Gm
zi@Y%3F>c3vfxAb%AfgT~k_*`O)6Zqjh37X1`r5ULNLA_&Gkv!COeixg78~V#Oru(d
zr$3s^%OK~P+NEMa&p9FS@RNxY_q9>IZ2-#fKtD~-!<%E|@=i<d)4eoa5be-`RRqNo
zrgp-YU0AAqH)KRXi%B_MNLH9}QF0-G!<P`PWX@|8&b(1hg$_=<j+U<$aiGfU(?N>{
zE%<>4h_?i8Vm^$54cnx`rCJe}pwdh%*P9a>OcYlh)~HmLE#E*Z5nvM!Wg|qei6hxq
ze@{(K)Eh<QBH;+PJ1>REoloSVP<UntTo*lg&MWN7Iw?Kl5*Tb}6!$~qO(>kzNOB?~
zeh3SnIT||^r_wrvK(+Fc01itnFbWBgCxCET?8i~zADHa^k`JV}8z8TZt7blfK|)nV
zDG0U+3mMkGhRngIrMy6JKwae8MJ0$~LF&{b^?WuVDjQl0wnRXD84aWnQT|7v0Fv{^
zP*)$T#33rrJU`b<Ec~fFVkRFVISM*>(Cttw*bE7lA%iY?9<V-%3VsMOL2{KAI*8K|
zCq2J%LabTvl4|%2kylF#WK7(xEJQM=f;EV2am3Wa4@G;mghvMD9~Xx_U5-<njNZbY
zCjuZ*EJ!01tn?gwI2I(M#kQBov;X4Zt9+4mi(;QWPxJD*4t<873O~XGxLmbBqJT4>
zH5(2IeGr|_(6&vvkF?y5Ujle?wP45bqZ}awD4D=b*mXq6;uw4^`lGMqGR0N%nd1V%
z@%erDawXif7V(9Mz(mMC^>N+{^Y4#AxXql=7!ROKp4S99^jlq$Y<Kh%J$T@hC>z_K
zWy9ARTk@YEiCHzafh@98=Vzi*;1K#ZL??%J+|NX(q=kyWH(YL#ETWSo9QIF@#4MuI
zf!>Y6p%-ZlO<##l!Pg-ne~ak!wZ>)<oo?4CenGE(luP97iMtxnKmU#Bw6}$#Hrd?e
z^tXvle_3O*Byqm;U9(?_PX9Sc{HGfGt0a!I9XO3$67V8_Pjm_pHv+-X@4u78PxeE<
zx{WMJOgUautm|Jh^IJ*$mFToK^OflI7m~Qn-O#`yhpL(OSCUxe5|e=@-@=?Inkp?1
zS?jxf=h>b^r0lBSx{dn=1n{QS9Jdga9rMro%t3R_LHF8QtI5^WuWn=A10QE|yE<3&
zMPA9ox!PTCEaxA2g(c6|&DMMhxmsAiIi#~QC)pSKXW*FHH-QcPS<(Fq2yE=1_+fux
z!2EpL>i#}I?3dHF^SV>RU+}|r{5^iy-#Bf5rRd;u6K>!Cd;eS2`2RZ(K=wcQefqC@
z044n?r_TRMoQp5q`IR^qy>{&@aW2sM`0nb!)konh;+!lS@t<qieLXRb1}cX;77lzR
z&aDlw)%;E3+%G3)Uy8b!-0DY#SkC&mhKQ(dzfYQ*(O3odj?B5Tze$|?n<pmu5yvmY
zxqsmIi9=b=UU0ubF|cMA@iTGG_qYf0esAVZSu9lwByt4}f)El3a^~Yx@;GnfJyY5K
zFG_uLg3JSOxt4*PEXyv#pU7r0??SkYWDNIegRjVtFP@tnEYgR}-6N8R7#Z@ohz)vw
z7WDWjISgn|*F-Uhajf`on*K`%i*&A-8Bzn%_w6mcE@kBI<892OLd*+7QKs3LmYsvs
z$H|SP8{R_xyiSbL<$g?42YDv4Yc!@0(?Dn>0^o#IiVo<`jI^%4sQ3<+<wdyT4xU^@
zlrx<T932-Ju03EtPD|s#n9b1&BJr6osp`iTRctL2#QK_~i4&w6?HZF*cECU)r1l!4
zSt_+{Z9J4RCJRucnFQaWs@8<}v}Unmvx}*h37eX22rzQ7MOP#9R);#GzR_T7)}Kg$
zq;OOE07KcTw1{zG4K^$o)s9(^yD|anKcCJH9uq?~%U^ra{4q6RD>O(mkFmJp@j|UJ
z8dSTgOsQtNPbl{oaHt}U*XqH2DxmzwtI^X*Og9xHd*NJ%KCz}d=^%(uq}=h=x8tOj
z+b^jgM{kx*aF2<~_6!t)2;qTMPd(ELrsN8abz_R3D$%f^%gavND<%>SiE%DQJE>lK
z1=F*vP!-rD*K}u?&^{uU>2J-2yOXCa%Bs0P-umDkq}|>P>%(@4`);Z>XGWZ}c!JDo
zSX)qPu1^`rJ$>dgj_dpuDfKC{=sonuGN);Wiet$@9Ee%RFncAnNpw?NJ{K6=wDaBh
zr5l;vq6pkmVxB{m0hVrbH>%SWx_U{_^rj?ipUKRd1+M!~krkxqk0KHt8#hG`tV~Lt
z26^r|T2j(FL{NN3wb6Leekml}Cz!h4qQ;zh2Kz8VvzxYSd!li?>7gA}r@|GwTT7@l
zvfeU@Z{{jbcOj*>cHX_6pR$dKv(^f!LP<(EKRyx0iFcUrpe{jnzr`gvq!fy;9<y6q
zg30<+d4qw`77S$(p#We-nKY;@h)VPxxh>OmCNF``mv&)=4M?R8=>w<g(s(JHpDm!=
z2$%Bxb}m-^fe>ILv>czH>dmA<0H?6KS{lBmOLzvVJv!rj^w_w;o-I|`sF=NLUi?z!
zlE$^RvOb+4U=KZB>4ayM8KX#u7x}h2gN<mwbLYhSZux{)$5S9$W6~x_D?`@UR`~fb
zkG7~w`dSGM3UNyITR=QGMT?mzz?cx@7O_;2!*(3HT8Oxef=cscScIJ~4PnxV5GRyf
zXx=Df8?so5DZ>p)AO<)u#xoq^>-@GRpa9IWeG3NxR-XAV=<OVD?bFW9HcKy7LNRM>
zq-#^n(sR?Tm~mX;iN*rh5P29gIvR5*F+C4w@`LHkk7*}h`4aDu9i|XZxK~HBq?5R_
z!<9DDJp~tFtMUy-ferXEulx8aAwhjDeNf{-X;697r^zjC4o*WM8GmGg90cHDb6>Tn
zu#C?$KUCb$SsbJ&4>A;~%JSm$1c{<x?2w~z&K=Ft>oC=5Euq{Ku~qJSlVMK*6vPVY
z3sFMC5E0a9aV8Alz#{F;ueqZVYHn|{LB_gdiV6+LPD;!qw8<ZpL9jtAP{!GF*zi%X
z^a_egKATM+wPJmkZYlmFwh5jlpWsk0l*%<@J3lo*iegCub0ikAR||Q1?S*@$J5jn^
zn=4>8-!%v+w>$4et1WVpM+|8P<GHsdavCkY?oSmPq_A^KB#Z3!|4C<WSX&Gg#j!LO
ziKU>Ba2{VyP@@e~!k2VG&PV)?JCP8q&2ckq3`Fg|w4a^!HgW|mIRxXtRTdy3-JeUz
z9;a5_<YTj9fcZ|-UvWxw?u)mIh_Dx?xRq?69MUJG^+%g9d7*XpUSi24vFjGtJUH7*
z(d#nTgHV&VVWfy~5nTVrW^kP+N21v$XcVngJ7WeZ#3Lsu`##=OxYs}Z)ry6BO&ezl
z?cf>HWCYyK(oo8FC~3&26!{{)bTy3RayiGsZPv{o&H5_N#zM?D6^0}a_}!oKDZAOX
zYL)@<f|-<-|JdVF_N6f}a!5>eo?2xx()d1-F7~`UDjqi)2@kM@l-SJM?%5nbz%Z|@
zZlv9|vimZpcE-OQ%7&^_ciWwS6tkH-pHQ&I=OuUNvE`ipNsE^KMMB*l-?2KfDNrX`
zlsIElTkK+HS$Prhy9)7-h>k^={kMteE(ivHi|ESE45a=V(S@yceC>Wf{@VSp*Zhy1
z?*IFo?mvpv|7$h6y$<5n*mT-H+$K{+GVsXHRA^&2MHUr0+wN}Qo4tI%S1R;B5}STc
zh5k;}ojyGH?EJfe1HCL&mj?d1M*rJX=wr8?wnB}LB{xA1oH<~^_A?dw=_U>(p!WKz
zACvd*P@#XR(fK3xexpMFBe6P+&${o9e}AO@-=#u-=OO&kvGjYfx&}vD!Wo6ho!9=#
zL->nW{iC#X&bL_o)q%9aUt;yZeWn#ZcPt^keY1xz^-}Btlm60Y`j5`YAFUUEB8UG;
z$Nej{UbOVe&J<U}w7AtWEV{%^X|Q~_2{UGcWLlhrh8ujiKiC()G3|=S3}cxVP|3id
zy|=eTdxG(1i!Fm*w^xVIeX>fW$mS~T+E7dN@%jxf^Ys;UkRE<yG#X(<2U=v9J<S2}
zo3j&m)2(UOG}CyVbjup;p|M`mWyS<Pp1QUdUpn9k;@~w}p;_rI)xQdiUt3$o>pmEY
z>P4&2Lm>)VJ%gX(s9Zx%r6W|AoqFR8A16T#!Ewh*fkPcdz`Cadx5_|AHNt#~;bSj4
zoG^&TO!!6go2}0-BaCXPAji_HT%jcMVXFy}iAin`!i<&PIcb&J4ThP|cc77Ow3r+c
z_J`H!e2nkr;3{SsVwtS{f#E|kUygEg`spKmydPt}DIgi+`;N)zHwqM=NCh@MtWVU=
zBy8ZtcpR2U#xL@V49*H3oV=Z;%oK1?r+7}_G3JV}{??{hXD6kzvtup&+sRAxsP{vz
zX#mrMf!KZ{d<&0hxMoT<VPY-WfitDccqNZ~$0YqNk5*62z0rN#>cg|w(o80Ho01nZ
zOD)>C3XV3lVP4LTwSR&q=``p(zTT|c|06dIZrsO>?8BsBX+4+=44DUlCfL`}3S%jy
zVO`Q6lx&UH(h=tYO}7PN(qcj*k-$K6*T95See+W<SWQcRINA=5{Gs+jxi?QI-4^ox
zGl{z=QgC;~2qs4+%G=H{h&jfF;A#RzCRj>ROsvH={1uyZ0W1<E2pDfQLV=F_Z6xqp
z&M8#F5r;mkIVu6ltV+88i-bMcwC&0mi~kavdg7I5vnq8BKEc@pF2Vj7o#Y&278A%G
zg~S?}(y<TuCM8e0j$vnI%5Kl58pT_68ubmZYXC$6reWkK4Fv$|0*POl80eH0!AXeO
ziMP<>1ro4u870fuM2BMR>{wodwe3ardv7p)e33g_2-a8zlz+Epyj-DwVmbhz&z_PU
zJ!c6D8yZNgi;LOWp>54nvJhi9KN4i?=k!8NCC-kK{5+aTLQBP#FwQ<9qtMu#9VNy;
zb@rX8TfRz5`4D%Fujov@66(7$_nQ&A#)^oT{|cQ(|BXiI;s3G`S}2S+z4&Drf%(j8
ze>%7}?0Us~17($Lkk&APx7Z{fduc?rcO{tc24;~g7PQv-aSYnAJ*HEog2+F|9{)?7
z(*Nj&{i6!|%NzDzwh9x@>5dURm_-6Uz={#($`ylYMxf`J0Y}>I+p%$jc;Z}oF`)Yb
z{R!qU3K*w}T<JFhy{skyZo#F|aL}X{ffL-&T!ft{aE}4r&w~50-M$GH+#okBRU6Vt
zpjOk~i?~2JZWK`~LC00R1=w#jBXE;R^t4AZOVttrx-T}z%vLv&$Lnt|+0c@<+?7Hb
zc?@Q0%}86d#TLOI7%n<m<H_DEC-v7oGirZ``aeYdzqzR2#%jK;2P}TCmvx5+-S5|_
zo^-FfY2P*{p`tSWuQw<C6E*TLo0ERl$bWBh((fmqe|K}zpY+c!y{!L;M)pS^-UoZw
zdt`Lw7*FU^-z9{}<Cn{LgUqwbB$LOzXrf{RqX>>!Y{`6$sXx2aZ?ZKRooo6*;#8;M
z7A>>?%7LyaR^0ty8KKxh_aR*gcy+T6^C;$CamZH7GG0G3Wf^b2MUQ@Mw!p~b)ZVvr
ztlzAL?{tY;p<O2_Byq0}r`i6GCvFwC>n7!zfkryHl$n%?fWkosS;~#gO6IBe^4$qJ
zudkNn-MfK}ZQHxAu-`#e@z%8sokU$`ec9`)CFS1AH<?Pu?C(8p^_i9*BqWO$Ofhu2
zRGHN3eTD6?b@-93QXNjcn(-4QN;R`raxo$NXgSk$<eA*u35N{F#}D>!_hSksL7|k8
zwWiKSHYT`F?lq%GGuVG4vsAI3G3a!8*Q+*sVM9b=Rly_CL^(sIyUF6+BU}O@1+|OE
z4r%NyLsPC4Z58#(O@`ZVZ#}eONh(aKIi{QwFwWR(9^z7`8WQVgY;Pndz_lE-YihY)
zzLV#4z{UC6qVU5Dd7tnP@8+ML@2dKOzL9(9$+27QjLM8)<9({jy1^>T%*HnLt@5Zc
zBaF|NeuGmnUL3M{v&(4@YSxV$#r%pFTP5r1nX@C+ydFIwStYnQ(M<Q|^&rRS1E<8i
zQnYuNe|UDuUn##tyG<lP&XQyn^z_6bIiGB84(Z3#V+gbGv|T6gB_`pg3onFvwJT3x
zRYLYKYVWA;SSX$N^7(E1d1d2}_IdK_Y2+lr@VrN1>eM^~jc@mQIe{?ll+a1dQxd)P
zg&87{V8(x(*LSrKWfK*b@oG$dGctNVQ!D-f*Kxf{ITZ;%Ei8H~-8^HYQ{dv`tFS>7
zw=(Cbbd*w5bx<1_AuPjxrd!Ii=cKS7o2RhE?5IgcV^b=50RdD}IGIkUD1o5SI3_C*
z?5G6$7JwzI&cwqD?8sJM#vy053b6hO!b>wUpr5F3zuZ@Rm|CgAcA9=2l|UnfvJwOS
z1d(-6ghgNgeh)}ST3d=xJ#{1YtOGLY^=UrN!=8zJhLeTZ`uKJmsp23evI&|oZ6k`Z
z*s(yvN=dYlc7+y-hD38H-@Bab;G2eGg!+gCQE(4MlqJONqN_?r0h7=|7*K1e3bjyS
zauR_0iK{@YsDnHxY+Hy)E64q?CcH?#h|7kp96*CUd_TsfB+d@Bq2!OZ%<t!}jTQbW
zL&+P7waWu+w@HPvq5#kp4+l3@!Y(B6iZ$E?V1=2B%vepsS?N$R(O7K_ao&)~8?qS*
zs@t6co&M;Q>MaD)A8vdw62K1zZoE#SKaJS(5rYHRpMq@6t;(@5j9tKvSQI#E%aUU|
zJrkA}drvJdKObZ_#9m7y{g5{bDx=3sB~g3rt?WZS`+P8bZt+C>ERa0_0?--Lj-LE2
z=O^<)d|SsWtg4z23CI}t)}#bXOY+&X09IeN3aqSL1q+rOy?TNOInOl?;ocWnZN33^
zd#UEkhiY~i@Ftnup>TD+5@QotO#_SFI>Rq+S2+7}z-kTzq;*6|AEhHS@;sAIW<>Gr
z#<7Rn5e3e|qO+O`QhaIscC2kE8fUUEIWYB5d`wR_F%9B&>)q+n!j?CTui1@^+brIY
zBI9a`=?Eh$;?5q&XlN;NNWiXu^96N8H;K%?Q=UsqY~;pX*fdL#7PyFe`dAB%?~7B?
zX0^y$sxGW>f@*_mnKUq<zfO__h(Z$e0{oN7@bhF%K^Rgom@~K3J{<SMXl?W*Dqjzo
zhbH}$(&P;FFXf%)m#7_YGH~jE;)=P;#ItvAjL>7n>}H65i|;P!K9m<ZV%1hvZEKIF
zX>vVwbjp+$>d*&z6}?&5L)ZA!?n1cKQna*_O=srg_EwYh0uJi1kl^(mJ2vuNKE-MF
z?aZ-cfmyH0@GH5}t{G-H5jG2tDU++W^eOCN=f`7TKkhC<t@2xaQQk-93dhYaI?Hon
zBln$lJK(05d6eob10Hmj{HZZ#O|0c?_e6Jw>%EzV?<ZCgSQ9Ju00-+@{lJ=f`SY->
zKS8qjzjYzu|LnDTU`f*DT#MP3j@Wk^Hjy=yvjYIJp8frAo;`xR?SIp=JFz&b#h)*I
zKgsm%twUH(KK+{~_ecKx)@@?Yzs&LZ`g{IU*HHe?zV)BiP`Z9GJNJ_=>d#^-iB(D3
z1MIAa|MZ)OR~qI0`L&x4A7)jO2tLn$_72Yf@rMs<|9H*M1pZ8V@cV@L-}#gOCZwHM
z@1FUacmK=&RpUQAp8qu-kKdn?HvXh2Tq}_6V`DJliG+fBwF(iS73OHO9y7t!5!c7C
zEL5J!T1IFu@vQV?KIDP><14vPtcZZ2j5VQ_S>jC=Co>Vd#Gau5IOqx*FieN}A~1|;
zG+^2umz{>aj<gCf-<q}4z?ixWr(tcCR4L}*2h08DCVNdZ>Q|0tlODcu0QF&L41xZ%
zas5_OT;oTsDxir`4S&kzDB)#1?|z$Pz6dMEyFP@)^qaE=m=N*XxB+cUx3$GUt<*8Q
zd92iRF1)#RyvTiEYG|LV9;j4cA?dQ*@~rvv-rR8$jcx(8B%{Ladr2?Gqe(;RA(9^h
z2UCLAUWVy|CvN#aoU7U2_eA~kNcc~|pWj3*`-Q(f`+4`==Xb<?JCd*~F!FLDgATrz
zq}d!{7)_;>98SVaXLW9;zmXY?P_J{42Kq-@YR)B=57$0cvWd?W6{i|-rMk`4k59;R
zIGQAt%<Uhq_kzy9oE!Uovh~}irun;{+8=@CUw&%B{{KpW<!@)j|7Ih~FBlHA$|tX9
zJuD1|-PcBxS{8=GrGcUH3x=Zubo`>%_d)l+`waOuRz+S1SpMF3vm?CpKZmdRb!huP
zy(0X7ygd1~BK&{I=qe1km!;H=WhLx86Vbw}eI|4F(EtXt*xYZvKC;|rG+&>v*L;20
z2XDTaCy=V$25ENCvR6@tV=P`p=a8{o%{(eqEfza3?Fm66$YpE+{iz>Dn{HgWkBas&
zUCf>$Vf5DqNvUg$Xa{wVHt!VEqZ^rG7uPA(B+R56*x}Os(W7Y??;WSaa@XkraD$Oz
zSI0{aJd5z;v?z|7fDhNuP2d9|FfDa<)Ph@QKEWOxV9CL>FESZ%^GO4?T3F#j+_lU;
za~kkbxr9ENi8px{Bmln(vOfhkBE%W*&8n@jR5o1F-7L#G-(=)da?QFMkD+^71{i}F
zJ`>ordLI&IE!+o<d3(Y}5wlba?$e)SBu!M*Hhn5dZ6eF20t;lFsoPmImITvBuV79F
zqqT#GQj2@4?+rTZEgiSFPs$RF@4ACO`ptEqNk*^FHV0>org8RZ6iE0?=++YhQ;iAU
zYb0!p%V~H;s$Wk9K=oMOsS0vrP4P|374>vp`BLquH$&m99+*3^kp}eUfzp63hcA5^
zicyC}<;E#9X(sz@Uk0}=oVV=K1<^86ufObIHdXs6!}9Sa*eNtM&tl_Ps^%zlSfk>$
zppRppPh4}C0i!lqlp8CqO4MuW=_HLcd2D}B<f5?Lsrtrq7C+g=a1=#AI}MZ7^V2i{
z!!EVQ8d=lG%-)nMnGH#LwCxgCI3TG$JGbjTDeS={+8X=do;@?utp90eTN5AaqDN>p
z#6+D`RTvD`#iiM*-q`%4=ywko5RBn91*Pkp(5R_La~qBvzI4Cxr-qb<9jB5-U*pOZ
zf#5s~vB#df12(F7{YBt1qTQ{kN$DofD6AG?qKyflKs<vY?081E(lIv7Np_wPciRcW
z425xJ-nkw7CZD5}>ttglyFC#1xV4cvG$ndw#Yd8w5Wfk3rhH*!eA|$?qd8qWHAGJH
z{BWj=##z4Ru3Af}O_8Bo2ylyIJotctRYD6jy{)TwF;3qiUiWPzC6;<SEo+bT$G3XC
zfVZ=Z7I*LC`?%D}0Y8VM68S;3&ut@ud-c);D0<JJH}vnt?VBXG)kwX}Ii<Hf<btVV
zse-+p?%tz2wqWrJi}W0Ad$h!P*}}tbkEb3sTq17)?YfM`2QRgDicyVyA2YmC4~J{(
zFM#58L3ZLw_F&0}dA(*3Z0eZf;N|15Z;;&|<!Cp6#Xfo}1huVb1O}mVI<j00?kjh{
zNU@iEMt)=HHAPbKPT4!L(ymVvW_E?VfJsvGCz|}4+mFbRa?8@6nJ4~)e@<0R=g*Tj
zEAb+%hTXYZXrHHle`crBIyfJ+e^|LBs}{dQIpjj&A^m6Z&O_MF{S3mvoh5SiM<<=c
z&PyDcN|Q+mVnk=5zX)Kc&M61z-lBu~2Q01amAnieY-=61`Mmea&L0hTi9DsZ>Kxje
zgWCe##;>w3)f%f`AT)1lp~q^TEl6o|T+IMQ#5tcy=LxP?C421oxU*vi_QX0`w4R)2
zoYnQit$NM^$$-m`$A#^U*Jfq(KORhdo1PL7bIfc11lLsx8(cr91rI${o<-$zh^IwM
z2x)sx1)ZiU$>^)R6>L+w7Un==o=p|oqxW&|ot)Hb>z1N5mGXPmBe?y;!xFvMbM!2<
zO{F7u9Fthj*=`!fwcGuqP#G*%`{^3@9<@)_XM%*obKjFfSg1j5wb@-yPNcY2hIuZO
zrJoj9st6^68o+zn)SYgoxSqXp`L38q1;=dZ`0)wB2=OC(o}5T#EvgWwR?g_>6!3-x
zsx=U^GSqkTsb9G7E&6C5`)SpqsR48ii5DV+<_`<%+gD|S1FjUP#4I8LXybyd*>URT
zjkwTVE<|1L;RxyWcZ9s<V`W&|K^r#8Njg}pAUg+0!zCrDT~4NFW$4)zq`r!?zFJ6m
zt^0O>TjHsY=m)pg*$Ro+(~>WkHb<WA)v!+IiCih%h86&{og(*~AxDYSX0R)_all=Q
z5kFK|w=3;7{PA`^K@f-;QEBrZ(@u{Tyxwsue4lo*(8bchyY6E@9a!Wlz2p7()M0D#
zjpC%dgV91|S}(Oj73Bil!*b8hFYb9VmAq>RCQ%41dRv~+-&eU<ywwpm)YUe7FTf`b
zWM`uUsBj-RA8ehLnW47!n)_h6_vJg4@&$g4E*@8-k~1WC_V2Aq{$%eXD0H!)7Pfsb
z<v3cfe>C>xlQ!<%Faq$XwpH)MA{?R!?zb2ZDtTqiwf}PKvtmG-=RyJ3wFiQCL1)w3
z4AP9x*Lw@yMM3MI)EqjxV`kg!m&38zFB_6SXEX~pg!-PdWgE9~eSfvM)><>Xlxwd+
z%hYUdizf2Ky?_KzT<cB_MMEWE&)KGI(X(O?ZfbMgdUaOCEYn8QVJ%JK%qg_gT${$T
z5!|j&>i(9=SYsE1hG4GI*NU5aHFum_Ez?pvqp)Wc`=o~FGyfOjVydr!n|4jgOZCRP
zb31<WhrGReI40BfQ$@!O!DlD5%`3`>Ig+~{CmH{APJ6*6j<VC95_G?=d$5*0*jC5!
z*+yrKf-bB;MHU#kM7wjX@kdL*#^OzLmkc-atZ&TqOW&Ijbu14~6j)*P@x=Wq$Rq3F
zDjvTG@*R10|2d-6|8=#a*SP+g4)?+S*(dsMZE^Xgj%<Ig%KO8J`0|(P=wGne#39FW
zCd~A}t9>_Bj^-3%;2U5SI|e~W+FJBIns`g90o@(-9>&zdn@uFvjjL^jTJ@CicMW=T
zg3gYoS};W1f-DB*JE8$IBqRzb6OO^-8ooLYCMqkvRj>_mG?v0e!<#=+P(_+<tJ<57
z03Fw2uY<f<lHpW`5st7!dcoZnjN*nGu{&YmX+Q%_wk!4kEGrsrJVJx$fI58Uu|<Kf
zg@+(23Q%D72q27@f<sOwkb{=w68<=eqb;CvG++jr&7L#Kui1|5;x5x7o6f`X#Y~rq
z(IitQqZ(fU!8Oa6F2g)ifuba?vOBP`y!>vE5{{}5N*Ay0;dlxDsB3^v=raE>D{%PL
z=A^)#*CFD*$=EOY(|FS_Fe1YI(b1cd%VtC<JmRN;uETRN8`C;SJ;D#Z$D=>Wb_U13
zC@-IRPcRD^f1vKyEnB0Vk!o~=X&Stql>gZ9QPOSFTb`8TB#jRdYH=fVv$Y>8Hs|X<
z&u`8tf_^N-EYQ%5gsRO7#!`;~3!Fr?Wfo&{c0PTnEv|Rm6!#8N>ae3s2#vyQ1tA8u
zbMER%ij?HLOC14aFL<qTc3#-OWW30a-Wq#}7huM7eg_KsqZ#MluNmh+)C4}|hseNy
z@SET9MFoDHu&JH;IH;rjcKJ6=TmSAx#_z)Ll2~`XJ%IhIyU+iBOYU|g*EFdRI}jBE
z@Y)!`^1>!dHSBXC3<3Z75|gOMJ2+7Wn%4*s@7FFeLGpJGV9sq2Z+?1M@uT|E^2U4c
zD@W|p)0=O*bAQk*#hm&49-3sxSQo&XFRu^vn=<Paoy=DAKM$IH9{FjMIMq5KNe~8A
zcSe7qPv8lk*C%8WWVd2G6BR8=WfE5zgWfSS>-W7=+T4~r%`{(SlVXidPmIT5_H;PN
zTf`X_;>F%7_NQAc*~#G@Uo6XISZ>J`W)7hCjAvM{Wjg7Rt**$%ucqwb)c<Th?SuJ}
zV0qKv^Qy>A!(nWb0%@zXsLzD;BTKn{fy;O>DU7x8*7QANVj8nhx~x>Jt0!7wkUTrn
zkGT^*V7)!9Lbg?8h@g^rV>~bOR_u9X6&N_nU^XZ4>;_N?m}wZX&qS*LT4Fd}{)%LD
zo`_DmbC16n0i-aZ`-&pXTkr+aUvm1&R)aSBC4rcwX(e6>o&EZmf|fI8AgiQ#?DUi6
z@>`N$KH_g~cG&h|_5jpw%qU|yR*)I9jF$rv#>=s5h8xmNYjzv`YW0*{zQS=vb%G9a
zMWEk^Sxy5Cp9%Ris`qVG^C-S{Fqp=aR}Q4!T3}f6Yy(!pXf}dOaH0;TEm{?#$l`b*
zLk(}IsT9DLNybs;-5uZ?OJc`lZK!!2y%Jx<`dcw|%cSkxsIgKZ>(ZWnDIg`R()f$<
z(MH`Z^0H$0Yj-dp>waYAc3+1tbEFUR1T`A7-C~xyFwzNxc0PNxmD$P8X~I=n^kn3!
zfsK*nGXv8L`z8PY>C0DbT(>m{16o*!B|Ue+u@)|F!GRd$DYbK&+Lu1mrQZ!(nnRc`
zGS1mc-7D=|Ik9yBxQF?vUYkSQMwb>V`lH8ONzWtkvXcuJQOEEmVm427?}CQWZ@3%`
zcWe}qa>2nfwG)y~>wAt#_h2YzO5n!D2*G7t9(uEh+af7$Rh9opa_*1>%SO;`a-N!i
znbR7pc1({;&)IFW@IJu{3(DurBMM^jricLq6~8C6Xg&ZHWlpy67KwCak6x5MV?{-+
zCA?G~C39zg@!57^`tmxHFb%abl;t}wV$G&kSipHSNT8MuHPw|<f(Tg=sN?a%gYT7a
zr?mYtk7n5_xN9plA!RmA-eT!|w^W}*OH9I`63=TJ6m141VN14ATKqWiSD`3_dRP=P
zEA0YNR+woA6^gt^IxEPsDhS0VL02+y#!J#Su-3A3lSSdPV8NWM;!Bs@IvnQFlBXa7
zPHM`?yB7-u6Z7(=?o12((6l50d-fJTzKC$k0!hted*ZFKWY5r+F3uaqt1XA!%tdBq
zU$A6X+6se<>t`x{9{y2NPh0I+UXkMcS=+WPTylW%4j<P#djV!7k7IZfKQW<MMydej
zO1Io$#K(6&yownsGD75Fx-}xT7D=Z(suje_Snx77PN7LG)R-2@^${uW^diRMbK{^u
zNPe^gZIt5y3Tghs6$I}^OR0Df3;0<gSQALTXl(C~$z$UXqq4xG`{N^z-Xonu`r?)V
zc3y7+$03Tnm0541u=1qFsp4jCb*0vGQ(<Wl6tI;2Mf=^#F|8MXPe@m6oQ~Kk!hDaQ
zg5AL>OlTEMOO1^~#}kLFAwUTcXdhYrB8Qq4M9`=VM4*(&VZmaJ#pHtorzw1akvRG1
z5GiH?m_wHsExtm58jnUIPKQPd6HqW`i2w)W3L=E!31OWVOPQ`$Lu~*!iQI17_bS6C
zNz9*1GPIG`rF)+=jR-Y_;kX>Ct{-Y^!D>Ha7X*oa#qT|89iTwAS0*eOUQ$@6@6&@7
zu#037K?2zTyPp=sbksu1Z1t97wkX<`y+|;pv_%2S#uL;5j*jjrbDXbAI~(JCeLgha
zbh1hSvR_Z~gPj($XDp2-s&n;|dVncN4GD2}zO4OyeV4;B;0=E6G+p~ul19S>6s$@r
z3!3BE$FScdDHrd*UVnhg+}PXR#}60YQKFB98<3?jxh?D>{Z};KIi0C{r~j05|7gP*
z12E`&3H%QeZ@*Q^j(>H@|B<xQCc3ZVpFa8DAaH*{MSn}$11`U#fzi9J@6Pf04J!Kd
zpQ_~XAqQgdz3{+_TSvKO{RVjt2a(qpgX78|+OlfW$6<82#hc)W?o!aKQd!dLn%aq~
z(Kyxl+R00YE1c%*v-=;-zEHwHrt!Psf2Qz12CYxvljtA2$IUlW61o&JP+9$TGHc5d
z50$0VkeD|bLf`dz{k`M(M;iT?e#^gp8vVCL8{tpK@tbHX`hF=yhr;c@S_&~-G1Z4P
zEvvl)yt$W+H`ja>9as~c`nd2B=*`4ow?CrmKVKKWtN4BwefF&v&E>CdiGQ_5#LT%{
z=+ElPnBxmuUj%^OQZu$`-|P39Z^i*25WutR^WAJdpyL8m5mv9b_#;1A0E-1E0GlI3
z4d^1hLjc?vJDJc*kVMt>79F-y4Uc*@;4JKkYl+V1%d2zHamA&(xs;P8>C1RNAUx|`
zgfwG<fPZA~>=`<j`mhNz2BY-{8>gd*l630?2@r4==cS3`9W6MnaLr;2Z^GD}&!$lE
z!&Olx;NMN?cn`yhFz7`&fcXYQV%v}Io<trmk0k_Vfv$rjns6#owh0FoIcmR9qQfyD
z>b6OzpE!LH##;=ZULA*{_JWtozGU)f>%s5mn~8u9PU(hm#q?=_f^G@Oa<s1brHW`M
z5p&GLP8~{35Ej~}(>|$8Ryzk@?bq$<iOVV+)YaKn1d8l(4eA#}&p0zZqDx#zT*nGr
zn7+(t#Vp%7%h#tihm*3d^!w)w-RPv(ebP-=IfyT(diaw{E%t9DSK6)*_Ubw{8K-LQ
zY^vkceJ(2`t{18yba?v~HK|O$EBx_oy(guIvoc2=z`ic8iA6nXqiTBGswKM5dr$4S
z)!eV&HT3Ay#d)z+l75q%hahHDt$CKv?pNJ?IX4R3+j1%JSk>Y9xIvzFi}mGHO*!a$
z(kZyWgC%Ws?t}eZ^)PHnqs5|*j!`dt;+QGujmuEi!;ZoICoLO`!c9Jti&B8)9yHkq
z_rQm$1;vTGiG#ALhG5uGs+-`B$$U#5&=@%iz%qTKux3^R(SVW21O1T$J^PW+9sbO@
zOn$9eViPWFX$Xvf1TRM6+_qVBYk};?9WkH)B|+-r;l0Vg7rU!z&QFPh{c50I*M8@g
z9y9{C+R`(Ksp+v?zq_u4Ud4rTu=iu`!t!q)uF=#xHd_JKtJZpm2tV!2q-8MfKI$pD
zZSYt)QxjJ$u!>MC+~#)HN$2&Gx}_k*^S-rr^gHLaFV_k<x~wy*yA7UB_zambW426O
zwvga7lRjdM!6n<QH*X5XHz{#046n!ijUR<yhQ`-svB9CTt&&UCai%S7w|!WcVOyh1
zTKCy3UQcR@&xpLM$Qt0~Y*N`K=H_+(+9FO=_`Un%{5b9l&v_t8J4M8FcZV?vN>&Gv
z+R9by*7ssyyurf+uLfe9K)eEPFii6Tby&LWQo6j1g3qo>6%LULdZ~v?@k)yVaV0I*
zsX=*Fds^I!9`uxE(dB&-X`8k>#fxqEEIRn0-Ag<~Ui0Rt1?noS<*rLrjCd&!Y&Qed
z=LiGe7@Cl7E43C3B|O}3h8A~N_X&nP>nt2y5N)4sS75QUkq1<gC01TYUmqaxi^U2@
z)Ak~>wnIb+SU}Ewhdgu~+`@ZMTn1&QoNQrKb^ONhcgvkD1Xypx<V4PTzF$SS3uknB
zk>nyR-ee?<bkZZ!e$MliS>+W{UNKtgUX`exx^MNH)@3PE`*zIu4JI#Zzr6I8yRCk?
z0`V}xQhbWuu6Nub&l@?$c9LwnVLC>{cf<)OR#M~y?`P`Yf^swMq{RUujO&XJlzo)F
zU?@^22gU_B(rt}<WWc~kq_cQ}@;TpS<+$(<lIK-A=ku$Gl35edmklGqwJ32HU{r`n
zh`;YQ))uLiXdc<cTeO2eH`oIUc}<7%NrZv6c<i{GdwHb+n%ahz@-@gQUX1re<xF}f
z2Z9c@VTOVD7!v|;m@t9K$7N2mbZyyVjfM-A4b!9f`0c>9k2VkrB7oR-&lq=k8iH>Y
zhhw1PwbH`el{lw)6%7T63CFk;kT7_rd?K+909Zt1oW4gTR^)_MZpE_~2nqRiuMa42
zI7{_L^R_Be7Xkg26LO#^Fd#v^b0HK4swl*V*suXEN(m**QA=Km$uKLOXY11w3};5d
z=(EexDF9Y(3kTPD?}y-_#sy_Ha_CUJpnP^<@it20{T$AQe#yiuN5>R(Ze{I$q}>#e
zh~_~HMskXW68TapqpkdBC^LGAg}SXbw#Sy?x3KY$HdL;{;tG5_a5wNv*ntmwE95+m
zs`~U==k4dA2(-=w8<u~{)!1`fA6aRc%>IZ^9o`c72p|fUqqx}a`$)e>0^;T<WJJ)Q
zSWyHRunJ8S4|Y{xOoN5oVs?#tjH~gc%(;dQW8-v-y?EsHwmD4IO|~^X?;oAhaZI^=
z;Q7Z#_(@oNVY9P;^l#@ke1V;S!(UyXhO}?Z><&LSv-ABcG_%+EA7KGuBRV<H{-on!
zb@a)#uC4XnH~@?|ay+CrtH$p*;rSxJC#lwD(j|A>{!7$Tgo%B3push#UTO}HHd``_
z)#qlOmb9q`{@8`D->m8OZMcsAk;vL#zHbw3+c5JqL<slbnhV&b29Mi-W$kp{r09?_
zppi^B)A>p?)zl7mQb>$l66R(E^01`xNlM7UT?}a`3NMI_Eecpi-6(W3hYd9f!Y$aj
z59WHJ5iav0lQJ&-SoU#OB$rk(OI)9H$Jp;$DRLdnQ=}w1KNr|#ipO1<^zCtAzj1KW
zZP|_N^vkldo4A?!uN!%s4)VHLzc}?w+u1(j%BAE|nj6tk?^ucJDdQUs+d%`U(ra>&
zGDseLEJcvhP}e#D?ilt+%rt+h1D^?cFHq#n&^<)L`0r30!mR76+mTCb(w7nD^!sQs
zhUtqYn7DErjK6qY7s-X&21Cxeuwg&4mYu<~Y^=U7T7S1_`zD$8{?*a?L&5()Q}72p
z7_@9z3`eWqI0i(qM1qvO?bER^|Ch3CuI34TIf3w&gGqi|R=W~k!R}9C-LLs$ach&E
zg%{=Kl@)UlX|~e^2?~E&_Vnx^@wlo2QjvV#Z#Wi+GS0e{bUjCe)ovJ*G$zR#JIH-a
z^1x|oY57rRM2SU>)vLGJ7`Nh_TS6zwsCiO-Nrh|&%zR7ZiDF+1!1`v@m@_KmgetM9
z#Pn*>C>_E*j?^nYQLhvxlPw(WeUO!^8#>6Enx*)mTsajqA?@~g?(uL?sA}P5a3lCG
zmXR^+fSbNIyrWt{xM%{~Uz)p+G{GM}oA4S&({zDn`fD_S9MuZ-`%Uw_T?efU2yGij
z{Zm_^F7NSOVjG#gxVxbeg@Hp#XLvAcHQd+hIkZ0&Dr!zed2iR`_aXF}#nYAh`%k!+
z;T-pMsAHT-Fy0<H?{nTaD=$sQ<KTERvYP6Y{jL`R8!lBit`7-o@uO!qc&pv3-*~xp
z)0mlvTf%9b)U?kd%Tw<l>q|#XrD`UQo6`LE^%-Y!=fkykou~~ok-hFgR^KGjohaZt
z&PXlhQKecy(ia6lL5`z!KtY6#H%uK?Yy~$Ev`R3QK38OLMit?k0BF!qQ~}633dxNY
zQhR5<1YoG#uo(&%ewTaHDM7{!+i39~a9_35EyYFy#u7t~F!md0tY(3SNG#TzHd>GU
z0wXZ6H51g_ybsZ9oW2q-(00rmJyY0wEIW9{)*!!1C91qgdeL(v&Km`wU?|Bgdb<%s
z%Oa}y>&9cx*wU)hW}y~o(Lplwy&^dPh6;c0p1(>DBb}vd9TzNY#W05#5oNW_huN>h
z16&_uP7gOr57FZQ`WT{Es8|a0GCgj40!i#d0BgCyYx6iSS$~+EE0!l+(5pocd5d}|
zG^I%5gI&AvtHrd4yl%>Y@F%9MJ$gi*VieTA!-BOkCc&Xtc3Y?2mzK(Oa*FScOER6b
z>;dZ>BvBqH;MmEck`|co@t!VuBA`y@fXF5AJKujQh%0i!(r|DUd&yW%-*bv}@z`sN
zi1kynGZ0Ss1dB^HY%9@{8%lhFo?6k-<vvz8_YRQJWFE2-e`<ZFGut_8SG1sjr6h}B
z8j)}Mp40P!80s}zTi0AfP!23eyl$9cBuf`6<~10JH$T~OyZ`)>tHo`J1O+)6q6@W2
z0<t(}e!05n<ZRh>{-|vtuAjH7bHyZzbfIv4jRX8#DDhX%N`Q|o^1<#!;+}#tu_I&f
zbCs4rI}%oQ-}-)(JH=aa9O`1IO%CN`ahza3RN1Yh8NF;gS{4>H4QT?{B7y`#*xdH*
zL%fl0dNPylSL1T-<(1mwEsxz}?MP{YvXOnRO1%=qJ;8R@bhUZ~3l50CsT04bB9Hnj
zXvpt7uzy%y|KC|&%?BUK0vckJsb81%ecMc+XfdX=(tZ8!E$i!&vA!0k5|}+o7ns;p
z6e70&*FC7a{%m=D-Gl0~>X7nz<xc|`f6ManyFFL)Y;m~op>o?230R*jlGUH!^6f8E
WT=RiHOn6gN^oQ^D-?R45<o^KmefX9D

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/packages/optimizer/images/nesterov_momentum_animation.gif b/docs/python_docs/python/tutorials/packages/optimizer/images/nesterov_momentum_animation.gif
new file mode 100644
index 0000000000000000000000000000000000000000..e8ee688f5980395d18fdfa5aebefa72767148111
GIT binary patch
literal 38179
zcmeFZcU041w<h`vsRTlTAcBB|UKEXh5Q-3bktSUc1pz5ir79?)cL+@pQ9_eup-B@V
z6zS4L1XOyHW+>7j!*}M)Ip4W=&dgo&&s{V3?!S_?SK40hv)}jGd7f8OTLUj^g9ZNr
z?SL)=CqV;10O%y>06H)Td=d~k2owxG2^a(hhr;RM^bGV2C`J^93B$t7a+>w@8P!;J
zHukgZXE`+!xXyBM>m;A!KF5pY<u}X_;1dwImMEwlCM+l{Y>*;+<Bh1WsHj<%*xk?P
zO)|vKi(l08xoDDf$^J9$A`WNq0(ZMu!v3S=Wl2fH5J}S%N$1Whjtw$bWMnMUWo`0h
z?^VdUw8*)4$;-;iyEZErK2WfJrQqJK;NGd=*`;_@k>Js+LQqlh=}}WrQ*+GIP}9(K
z%+k`((sFyJeLGCsE?MXC;5F@Q`nvl1HjfST3=H)RjSP*9{mM*?Ow1g@EX*u!SlqC@
zdCM|-#L6e<c0|t|t2=kC@7mki+Y`&~MK(Czb98({zMuBX#o5Ir{EeH7n_F_P$MZ=~
zch3h=@4P&{yu<UnW6FKKef@p?10DrD34HRTU@hozP*Cbe;u9h<h!{c)2@MVn&1eow
zuL}<gkH{N~iinDhj*ZXwkPw$ZN+6{qr94Y}mR|hjdHvsv=NTDqIy2r6W>iePcwO_N
zi1IS?W!6h_*885U+TYn(+1VdPbF*`ETaWW|^YiP*3-Sw!3W|!V2TO`d-Zss=d-Lvn
zO;=fIS$V@yWqD;)=TddckD98Qns3K-H6K5H{M69Y(b)Zq(m-ixYH1zXX#IZhx#e?P
zYg_y0_Vypg9c`T*U+23zy1E9&yF0tTbbt9WM(z93*WcSe&^K@rgQN3b2flv$`fYfL
zHZnXiHZt~e?B}nkrC)Pf<3Gp87Y`;@k0-|`rzWPRCa0(8eot@GW~OFlr)Ov9R_15t
z7v>ihm)3tT{9gXOyt2Hqvi5gno3^&H_Ge?Cx<=igZfvY?Y;5gsZES5F)3&$%ZvWlc
z-rn8WJKo*i-P_sQ+uPqeKHlHm-`_tt*gH7f-#gqtJUrMtJUBQ!IC*e<cyx4lbbPeG
zeROd2FL8W)uzP%PbbNSxd`LS!Iyyc+rX3y7j%c)hJg1!mFO5N8-NaZ`*-%~LA`T2X
zfdlUz1d2R?1n;r9>H~WlZ+knepOd#E*8Y)$)4hwf?ygv0Cp&w0thYN>Q(FUT>+a@j
z|G?Y+0dV0TVEPBH00a%#qvz5pB{zj4Sj291lxDX?V)%73waRin$FfU0|LiEsZBN9i
z#&Btu=XIvA{wccES)Skh3}@Swsa^5<%L}~M($CI{g1#(5D3V*JvTz_*D@DwztFq{8
zfnl!BOPvqJ-%2bhoPTwFDEa>0x+RADTGgA;3dgU-R^3%^e^h(SbiKS*{qEOC|IMXe
z-PP|W8i`<rbGkL9Q!P;}=WqAal+Co0_^-Xvtu3GHPM5qt-cwt#&_`B{J*QV!`TJ|3
zamnp3bsv_$m)dr}()(Do`lH5cdHl=A>OT{dPzE0T`kM8bj+FCvdh2U97y5IrW$Ax<
z<eoZGaet!s)5o1ZlP$3{9)pJZhpEF~OYZbFd^*@!>+8-kXlyt-*xg*7=%Xnf(g2vq
zBoV^qI2nu(TC@yelwO<+#b}63g|X@s_Jy<GE1Zhp@?D&Y#72lrM+rQ2oQ@WGT{s<c
zp=xnD7S|>+6DKw7I1`VbFPuqGcwIP|NPvmXlGNCoW|OppejA1BO8=fsG1L&9OEocb
znoF~|S2XwZmUF@EGwTS^`E<LdPV>(lUl+}1xKwTRz3^xgUC8tvc3ODpKVP))DscPv
zLKYDwwnz?Rb6(7j5-MKIiIZMh%q3}v{mx5;jm_t$-z)z8I@5RQcR{U>=u%<cQ|G0k
z!q>%1#c!&XmP+P>X=2N7Du<ny-`30*FTbnbURr)nft_C|ZDqT^Qr00<vQpk7y}VM<
zuW^30a>(rd>W7hgC973GeV13OCnL_U)yzJ<zgD~Wx@4_xrD}QY`r)n@TIr88!}2GV
zUo(mL`d!sa4FLQ?>C^hI&Av;C)|-h6e=o{YTcAa=iE^(lG<P+NDZg27<Jzn)X`<jH
zv;~K)e^NVDH$oGHlT4aB#DsD$c!gjkjXDLy&ns;9!Z%dc`BUP0IzB6$5hph_TsYM^
zn51wjTIw>V_?3V|1xZE;N@hsh<ra}1>X3Codt#G&amBSvo1L+Bidbh$BEIG@xA*(~
z@2)$)#{Vw$;XOan^xVgY1=W5NbkKL_$3f@@+;p7G+U^WV^G(gfriR<zT>6>b<=G6*
zHCgXN>Zjd_tY^Jk3wbZz?R#eLeA-{`SPh8nywl=#;2zL-`(U+x=V`#uCwStIKOY&~
z6<o`~K?*OP=NA9L*U^3*ZVml-E3*~rP=C1nPOtZ9=T~g6$I|38rJMC0wz>^_D?#%|
z`>XBmM;qR?T%sMNt-gre-`z<Z9Y5gAqQSJD?=Vw|BoLN{>7ab>cMmp7iiFH%Cm_u*
z-0VGMm`oItmp?U>XO@hRbZkQEm-?gPCcsb;)<M1g5RNUZ+)p!6rOs>I5(3%u*V9$T
z-(o~g)sq=%b5n^5+5DVetzkHS&sg)!W|lMyHS94Nj<3vSv|emxdxL|DC}h*?B4(Hq
zFd9NqQ}ovhn@`sO5tj?6P=1T5SR^yOJ}KmM)g%=6lo#a^l^f+>2WR>coWQ+N8X0_$
ztrt?5z?7lNeOQ`;e_h6Sr?r_SakquD%@8VdNPzKnP#|Zq0Fb_sTBw^ZX5QIl_9_{~
z+(CpJ5kvWPvl(0`Tlk~~kaxIL&yEALnTNb+jx|h@ld*uV$cVDf&rtnEN`#~@bBy{=
z8%ASx6$S|^U|yUIKt(7Z43X$527r)S3v|Psy}or7FMpd*=vZ;RU@k%?6iZaR_M7&_
zSeim7xU0-s2f&p*LfE>~+whCClIY%5Qpb?aU!4!lW7kQf{p7D#E1MY2OhVAMQ6Y>H
z{eXMO*UXhX2gQTV!KYOw0EFE^r~A3Vlk!s_Hb)Rkokyf*W;6SUVSDD<K+l-oR^jfT
z<vz}liLXnWtIq<;KUo^iV@CkJjT5CCzM)T7nF=5riD0aC@QrCKLO1H9oc-aKYsygW
z<WTN4GF)*a4gIKNnR|r_lPSz*v;|t&aw%bahh*rvQUJo$PlU2gmPl9;%q?#K43iY3
zs|ZB&={ic5`D-@wC!|rWHyZiK*4Hz+T~hx#;)+xgSkTHIRHb9^o_5a^gzg|PBp5<(
zjq_p>`&&hBmc`oYhH_3gIt%bzk@gB~;T$~td_k)zS{o69b^}6BrxU?wf;FhhJ|940
z4WO))*M+wTuozqfA#9p`zF!2^E;F{DfHcQ@3uXFFq3<}Vpv@4<F2*;2cM;}k3=|?`
zIwf3si}zG687fgah4Q9$-U&~^!Z?eRz2atCWbK^c@`wF#^u`7o#CMVplw)*&X_QqI
z9g{R}(mmM^1h)_bOHjjPOs3h!ql{G@*l)UAIhE`w7a_bq31b`@NrjVE9-8kFPqErC
zxMRYEJ*F64EJpNYyajk=4*Th=;zfn+1dQXgRppCRX`BPp&m!!~@QXNJD6_?!KiWfo
zi;e(d!_#p|O~pDiINjY8->8@kR#v5fxoe|Z*^T=304Y#c@>NQ(-VJ*p;uJb_<g;YW
z^@6?x+aCLf!W;S`YASrs^(ROXmsFS$Qfk@Ihx++|U52fRGzDylX+0kjxTsNlb3nH2
zeVEw;JN%yQnZ%J0I6J8eZUO=l)de9&m~a{0yKLRqDvkIX(`hD7g3jAy<2flNhe;I{
zzrqkkA+nCwy&tKN4gxD3b8Pt2x1`PD%yRg-Y~3pddVN$i4kN;<an9HJ*Y(~Fo<M|p
z<uv=TZ<U<Bl@{_Q>#qd*oTz6E1p)X_wiKG7DzkL|ePbjsQydO}h*r@ysr54*!>4n@
z_l2xkL+H1B@v_Do!8}_w%$3BOnTN{rwP_k(7Cp9Jl$J5*R`Md)wFG6EiCu8^Uu@l<
zG>yG(pJ!~QXD1?>iK|7x);%(PAU<4jc8URixsIeCiGs==+T*ozPlptSz}bj8=~+gA
zI0-EN^oLe?K+HhIlQzB>g1^XDmNd2zzw=tC0{5RGJY63qT+(lJ;GfU1#f~(?ny-zg
zcazZ+{MHC}U;SX)4$m<jdIsyEqZnBY!q<RB%coXiyZ9fIR&Kd#89eZ6rNd}We(JvD
z-UTHnv%Gv<De06Yq|q478B1!SiFO9KBXonAr6a#4f#1E~+SA*r%l@9EeGaAT&AWW@
z(t_w!3f!Tde)6Ze&>d;iBVrIh4tfm%nOoC;_3*l0t=QEn7b2zVF^$T`!C+dqtDqo9
zOhDqiGzcNIAC7v2@wT?NL{cD$WbX%(0hgJByUV<Yt*A^7Xj;|%(P6;WgVFsi6R*EO
z(58JXF=)Cn$lK7*z1gw7>J)B~>)wt~F9sow0}O}nGa15_rDdo)q0ni`J~aj<!2Xtl
zjxv$HRl_9JmPK!bYpdMLy&0W{gO2(dysiXgQiCiz!a+F_?m6&8LH(+~kOFMLH3W<M
zjNI?*x)D_jR)!C~X$ZiI;5<)?e6)Lxyn`Mzgcv?`RVN~~*?kl}qq>Z_<F|zBOQE~c
z_PnJ^iWbqZ=E!;yXymEuIWoO{#bc$ih`}k9JHtquC|B8G;1$*JofbCGpP~MY{_7#w
z%WwWFQ&*|iV`x}}V167xq^IGcKO?Y;o9H%@Lzg2cj~GD4v$nEeukWB6uut~wbaJVV
zsWtHf%4##XaBmTCA1NNI#oE8ivvumuk_QyP-39=VLR9QV$GLSV93VRZR4_w+yv9J{
z&s}?Kte5OCBsZNj#A`w^j0X(qkfBWLg)DXKJX&nf3Ixz%k-+7|y*>v+$&$(laoU~<
zP#T}fh&2d~05nUJF70u-<uZx(r`Q6CI>9Mz?03R(DNK~maQ4&=UXw;3If)d1Ejtx*
z7uQR2xa|{pQ8caJ#-<EMO0<VQNJ(3Z#KQQWSePO#MpYr-xmErm!8df+Fm`_ym{&FN
zUN=su{YB#eAWRrBy8A5ZIkyU*$w!jyAw&`(n{?j^3kyY+?^=woKfU{#3zmX9XZ;i)
z!#$%}h3R?L=jbIg(;ZlzBU01HY;10oChL-(Vh5h(&Sda3;X-6n#HnbG&=(YrQ>uI>
zZzdlAlK@I9IZyUEc=|CThcRG?(5-&{%?4+N^|}p((Z~{l(JcA3=WK0lVrkYoiKcY(
z*h@pBN#h*>bcQ;bvKfaRnLr4WIxAxLo=#}t^WVR@o{hjHG)cO^6#&ng!=Co_x-Nqx
z?BarHPEqE8EVqgevT)Xv#Uqku<fTM*nn@^`Mqm*4c=kFE@M+ILcRi<Bqqf=%Mtz~(
z{h_do>>M!9XA`wiDlEt`li8Y)a3kmPE?VrlE97COkD<iav)E~}{A!b<dLC0aHkRg;
z$vJsNL5_PJiYOYruZ{rNC~%aB%zjbC$YxF_Z~P1uAxL;-mYyL>%r=(eQt@G!k%1{M
zkN_OWeKJ@~HAgZ#ds8bo9f9B_x_gho1E~&Z*_W<zi3c0cWobf)F%RgWox$rNzzl~}
zsD5G{lQ-z;=OMvhkMSlt>Zlv0JRqb47^pPI^`@e9Hs2%*Q;2&rQnx=q{2ARN3WG1)
z18MVO>q9a4a`59w#&BvZDjKNzMpv~fWMy4wOGfw3mh_J?Yu>d<BDw+?(D9B?0j}h3
zhXDfy5;7V5V^1rxwV=%=&|`z~DF&M6OSDA*5{C4BVPvsRA0JY2v{)*wlo88;+{ZB~
zoMEt>1opl^U^0XdDGUi_y87n$iCWGeW8KF)=oTvCI_W-&-gBf4^2$(6EcZodr%%LQ
z{$3py4HI$89M)=~?0%Q=5vFif`|*en*p+~^qTQ69y-9BMyw36hRptSAmU)6G%y-7f
z{J4>@RSuGLiWRUd@367Sk1-X;V(#$AFJh3E#4-pOfNOvZ`{5O2TyGnysx-H%$x-jL
z@C1b++ykm3|3u;kU|>k!x{&WSjhaLNuSP2M?{l$fAekad5S@<QS5PT9=-`<g#H6n=
z#!v00UzYm2ssSxMpSz<3hTu{t?ni*@j*dDOA-5m&#tGL(D3y?re>7Izv5x*pNlE;G
zh#EWPHRG373S}kgKz)HlC`h#*mPTP1HGhB)^D0uVmGr(lZ_Sti!1X98EL|V0;Ls4#
z69(0C6?F#NT|oL-UGe>smEwAf7FoPM8j-&vG=@XEV!?l6T$96f3<1D>0hR*Ty@I|`
zYvah1krCcT&rC9;W86cZ>0D>?;+2JEP(pMNL2S}UFnyyT|Lb2rICV!;*bOBVT4NQe
zUs6gDRvxcwECDnQdQ%3Z1i*-s^bMGL4+-hE7k;tIkMJ_Wi#WzyEW*(c!e;m|QpVuL
zQ;;cusKqiZ?Py8c*rec6L~+p=E~Tr3t@?+p3WH2bTCRy#5>_d^Gz#yk2a|I*1J?yG
z3J&~v0nnEMFtQGc0K!wCfkgTeYsOo;!khg}s}v^R9;Q_sdK373c|(X(qTR=(-TT64
z?;La=jv)g;*wQc%4(qx?JAi@EwiDqh=m8BTFpOb@ee2r=n?G-B`zuk=<~{9sx4K>j
zb`@rJ71wvY`Ps$0f#%5ofJqpQ!ekNHy<hry1tYv_&6qg}yN-ELWCz$~0x2#!U;xBH
z1Y2U^u^40}g>lwH*ekBxkE_G`O?&38t}%rU@4zpkZ_vIH=sprdE{6UQ08^a=F=M-n
zqyT%}wqiSwG7*+G$uKR{wLoeYE$`0PaWypTey7k6vFe9C?mv6#?Fe(ayLI6wQDqpf
z2ZVP(tjR`r7+G&fFSY<$J|D3-G*FNSFqeYF`ysYuc-$m?356lIN_ay?1@~4}ko7B$
z_BK3V1LeuakZnl+m<TiOw_?Gx(J2_r&N@&nn`3bd16X0sdv2n<Chw5In07MK>Kl8u
z02><;RS(oGkrh4d#yzAxLQwS>WS|FB!Wsl|(V0!vSN*8NL;!J;=#+3!TSGVzL!a$|
ztej+MqcV<o1Y8kl8N;I5CmAYBk@DgQKO)SGtjB8&VzM48fS6f2yeW|aiQ=F>WcnIP
zNS0Q;iVf~B64>dgX0ETGkx&ZHZ9PU~l#}Gi<_(^wP}P(4LH$rA3|+LP`+B8MYJ8jB
z2aq5Z?C=+2)(>^Wm<Ixg5UfNxnf?_9nMXht5RoOMU!(EJg8pB*v`P9*B7GW$J{&+i
zwuXC<VIilX5rSaBNf0{*l=8)BW@z|vn-01Zg!h0xBGV7(KFI!6nx@K1cRBrC{QII$
zCn$P(YX{=A7dQP?I#5&EQ-Z0Tij2Mm(<9NbQ-K#6)^mRcwx#SZnobL?=>=B#tcTh1
ztlbApA=VW5QzDW=>T}mp@tzrK6lF~mij^Hsre6!AQ4v>VL1@OQsKrybL+1y{C<+Cc
zX^410fof8wFjTvDl@7d5^tXrRc3AcIAo@0n^VS#VcZTNX><rLnroFq2PymSCkWSSD
z>V`$6dm!rxd9ObEU1U6+HmY~~FA{xj7I}=O5K0qipXtk9#4_Jz;fwTRz1Y%M%1}tu
zzQks$+)u}j1EKqYmjTn{+tW6G0cOBZ34o%8;c;a8JR(D;y>LLa&8gJWuuX<E;hYm1
z+GT?iIsdJ8jfGW~WtnBU{M@DBSyV4^qyU3VFGYlSjJp}atURE`{gBmPbb=T<P7;XC
z8pMJFF$18fb0B6Mh}9azj=d&Gq7$crRVfg|6HbPMxf0+HDiLXu^aVJEdYlc5rSPeX
z%Ud62Zt$9vPz+cmgRZ?<uH`j(+@HdUV{$>VH1(eP%>SrJRW5bedFw;tLka2p&yF)#
zrfDilC}?AvYQt~v?#|5m!|6-;pKS_h(#w~W<+>tIv%RJ-;-Gt8Ph||^{LXoMkK;%D
zIpx-i_aqHAAya7GiLJ=tEyF~cj7=4ZAm>6eHy`FZA^j<M>?K9oW;HKbJ=)a0bn)tX
zDQ*wVsO?`7J!VbgWSh^rRsEXf&nd?{+;5!=t$-}|Kh-pB_HA|GO6MnMkt~UQv_XH$
z1LEZ4#)@MqN+;ITkFqOtW9zt(X?mwFbz-;K#s=BtBQg1c;JGujjvl9^<g+3h6LihK
z6w@e_+dhD2N}y<&%3B_0*e0XkoN3|tfoj?TidQI2H1^haa@?!gfn9W!wIvgg@m}{{
z1M{73<bDo%CF0LHwup>;*8Dyi`UrF0<mRd=6n8NEzKCXHW0_@(FHIZ|dRL-%`1;F^
z?7BSNsXHh=f6(eS9CEnGd<Ou*06IW)cKu_!!IVk_igSEZW^K4Jlu7({Yx?yWjRfwi
zPaqsdv!4SNk3B)90EPYW{HcU64&!;<j4Q6=t<Q}YdPzHh$B?roi-tMs@#oYtOnw`^
z)_bWKzVvlg^yIw($G-+vLs2Yq>QGLz6^nBF=TDBzAruf0c$_WCX})%=&S!P}^9%Do
zw;LWG9sd>W%c{YELF9+srkUoHcy1-8!#+zJIzUV?BJ;-Pz228sgJ9fsWwk+Wj1H3;
z%uCj7IaZBqVt=xv;HETC{UFkE$8G!*Z3A-d)-H8wXXq%`=YxcB(4v}3s#emj_tLlf
z=lXoE2ul1O{uBh~xqaxr-0&sKd><V-C0d*1YZ`xB(K;m)Fc~L`9|xYyu4!-_p;wCm
zLZky6DeOBLcU_f0Jgl*mN{k0hJWm=zaH=So%vc4q66AVrfSuQT@Q1NSB?V0$zb~qt
zxf7UBdD)0=GN#qK^|B_n-|PS%Q;_#cjX2|Z8?GRnowGVT*zQ5$%qxHZ0>*dS^M&5G
zN?a4KPVE*jdRN}X(v!>@sV-0pF{*Mm!eNN&#cGoJvI}`E*TQe&R-%7mZWcF5E(aP`
zIqKFspMJXwk)mB!KVvT?m4bZphG@lRqSL9wIfs^CWwp4ct*ZpRVsgDbSuX>j&t==A
z)4k<AG_Kp6_XTD2amdut{j-nBU_WjJbWcRRtMArh{?!B#kjQkF%`^L|QMtrTb)Bk7
zVugWOqsq{Yeyxuo8o1(b(ziU@Pg85$=wk>OX3^y)ybt8%wO;*2?1O*${rN02(Vy^Q
zPONIy@NZ#MkFM684xN{jDnsIxnkk`xF#O%jxC4h67G4jH5AkGvi@S+ehqlBZtZx==
zQw%g@?Gw1>Nq0&{bWXp^gRLdlQxaaNnYj;ts9gX|VRDX7bk8QgrE`=uQ4{=j9nS4F
zo7DdMZP;r$n$X}FzKY)dPI2A+_l+=lmoinkwKFh%M!tqP@<(V+(0Igw>kRT-fJ@AC
zav2K0Jpa+XQGVlw>fpFVNjYJKTj!il>y&!A=~`({6qfAK(oX~n|K)K}aFk5$z6`(W
zJz#qf`2sa&NA_GeCA{itFow619}P_ppBi{6mvE+pvFG#lk0AKC&7eY2aXD+-o&y&-
z=jkesr`SV2Bo1vs072NSOT;u3(dR@{E;xM+d3k@eA#k(hiZ&T6c3vejXm<<lX1x|=
z^(f8Ge2oB9-8l;ULz_xVt$Zp@JKayLNsgD%vBs99Qdl~25jZyH@CvIr5Zgx`B&9!+
zP#H|~+Nnjm+4)jhk7nRfbR!7u8WlDPXGa<}JQ``p0oY*?>Qn-_(hCSVp|8w_E$46U
zNPAM3)I<r9N;eUyGq@R(Zta0n`S{QHPb4->4VuokS{1s82Jn=yC&vR^Ua9M@_BCir
zpy?_&!;aPU0p)#%rn=nlWz|cNwnvAbTmCx1=`0E1vVlnoKAGCwY|Si|7sy$%`+Wi)
zI7X%|WrDofV6u{Av=SR{=!#Q2ot}kSTc$(oTj*T)9p@D6$YSG6rlH`Y<q1K1*>2rm
zws9Z(!?1MMqs<h+Zi^&%NmO&<3|A9QTttu|(Uy^ej=*v~&t58j^hM#Qgqu4}6)sn$
zQgQQBzoL}XkeLOdBzEsX#=?cJi6Pl@bsWMkMlZ}>!w=_#3(R6CCb^Wiq(As%^azOX
zYL)8@8-%b)xhxK$RBA+7{hmYayNCwMi9BpzNV^D*C^~cNXSiY5*`ZvXj0FORQF0Ku
zS(+|NeNeHE|57j&$eUfzv6uTA<!K_rVDG5@Dain^^9~n!&a6ywGR|Q$l|pjEn}{J8
zKl)Cb?&J-sT?&eUeV(ab%GvD0KKrh>u^6p`Ls^IvUwE9530DDPTWd|Ls(dctujCs%
z@KXXZoBf*3&qqHY=~i!ldw=gb;#Oo4leBhc0aSrWoNw;6>e08dL?{DI>4Fby4tF=w
z{`4<~kJju+3vdMq>gxD7hjjn?HcvQy0M?VOX4Y&d=;GNTrY>*U0h2DW*L&m2N{#Ki
z+bi(v?lo}4faMt5v$w(GZ?Efw2TR+iR85DR9ZC*Sn0{Hv*muRvH}Skh4wo=OD(7k1
z+UYA^TC8rG4>F|lIoTg#(d-rqn6{`AtNpRmN}Ve)+H?q_NY8guN*t$LO%lqD@v@uF
zEc~a3=Kvj-R+`uw_pl#gqd%lKN|LnR9&|vTRm*}|Tu(-$DwDydDv9)#{w@4wWsELw
zp7M-SKXdkK03lo7n@_cQYSzlLri?U8^KV{yP2N`2SId^(Rox(5p!J7fAx+?Ye!$X*
z9N#uS(skq4<=;6;r8pZI6QMo0Fcl&M6|sIyhb4gCeg7=*UNAty@R{0sg@v4ZvLYil
z6H|ZkjoUw;<uGO^m|`YBOW)T7oHYQ{dkNU==j7f*MTZUQebzxnZNXbqiPt|n<`n5D
zAWg?mEN+Tk4;O;Ac2q$k!j%}kK~AO--f!}q7RaR6XyIn%`iT6RAjGs7s+Wvad#G3Q
zaE7BE0p+IyMQOFF#GOs^sdR`Q(mpeX&jxHD?6PlWs8iq5q6xfjMA2Rp$rtvljAcQ!
zW=_=y$(8q}l_I7-kCz=Z(rT`NAE#e_qM%nBbAL>J=C>OyW^ib-m0ncSE;RnD>AXa*
zY)zDI$W06#!95_>zwJtcXM*2PtDm=?yA1IO5}&uspg3MVcsI>9llgP*(hhsm^SeKs
z(Z(85br-_Ab80vb=EFZ0qCjt^*{t<V<~4=I9^BVwrBffAy=vYSo{XqY1{U#SlUyJ%
zaCa#7*YeH$hwh1+8YODhXO+*ngzVWc-nzj5WO=nuDeu`mz(o1R_VJ@}d9l%1K6UA~
z#o0XSaq5}<T#PujgU-W)!y>DFc&A90K}*vAllkooWbLYvU2c4=SzE1~Ul+s6^-yu=
z0;If`x4V|@ptQ1e4Y@jSrQl0q0}oW(nK$Yj=FFoMMp|esswxK9-whq=7?o+a@qYne
zg+{u^_tNsHgVD)~XI*V}^kfsH^q(!3{7hUk{6u;v764S$H}SLuJ=quM@GYE}SvM*+
zwCI-zYX$+}bbk#2UDi3#EA5NfyeP(*#!vf7e<cZ`A=TtUG^UGKabGDLi_)U2Iz`Q|
zu7zI6;Wer$>BG_Ab54P?U+j{nFxzEAZk=Nv<E8^(l>yo2yH@}*F^(hBdLM2gN5Xhy
z!2=mbDKF?sk$p~zr&v|M5=~j@#lm#NaOS35LKGW2ai#41GXi3;KlB0}QH~-u;3#KZ
zktE2~d`T*c?=YI-`5C|>7keQ_T!BPYVWkzW!#VItFbm~NJ#pm&+2Ar_#d&tztSa9d
z+-nc9v|TnuW2Ca|X;I6UE|=-gC@|})w)9|R=Vrn#thDH?oV&b5KUfMr%MpUgYzXo%
z>|%%NcsB+aW)%~Q`WLHjOOn99Lv$-zEh;&51&VYWQ!v<)?UF*9e%=sA^j2)OZQ;rA
zL|)D%ZiYR=rn1pB<@3Em?wh(7j*?UjOejH!;t!a5sY7^k=efMgZH=UOA&c0H0cs9X
znvbXCfAb?pOC#>7K%EbxX8;)Y-B{&ArBVL~RR}S2GtM+HOo|TBJ!tb1(0ZV)Nvp$1
z^G2z3NTm??t1N)=4V6JssI)AR<}w>M)6q_q{TO_qBhiH|fAi94Dc#xV=PHMAJ6zJE
zz>BADXal=XAI!#149Z-qQ6#l$hvt1Y$^+|fm21i|LmjznrRcBVnc;Yte-DFuaSYfA
z{k*4>5?ouf66L!dbyI;EO_oS{5XHO)zh5X;Rm%K)6fp`!R6Kvm$&7yA6Y*7+1Ycy8
z&F!kqyB0kV`LVUTeyID?Y<I&}cO#^S!rId$(9<l{)1ubXYSPmf#`$3J<wIPL?{4G`
zEn*2t^|50lMIkas1Ou*#8SCt6vWrXsh+UO{Yd^f>R?oNA?k}^a!FWu>Pu=0XFJqNo
zezbo1IrQb%?3eMaFBA1&XkY~`?n*%y;w7M;q8S$jJw@J=OR%7x0SpCI4jED`!q2Rz
zO~-Yua-H4|Knn{*NlJ-R2kHdE)MG9Si8=*eb&2zDN&#O7;%0mISf5R`au24SS`JWS
zkz?aR#J#p4)G<IG6Lo4W)ZM|-zjF2W?fUIUJB>0iX{6vnIi_MDTeC2k&aN<w0H22d
zSJyjgaP+qgqrpzWs(1TfrURDi`oIy~rG$~8BVAS^S`44WIKolrpUNUj%!agU4Q2W1
zww@Low0zj#_(KV>hE4>tIe~y*=ld_KrL!o<ooxEEH^L6^q-C8?e1DA$ANCK3F^Y<Y
z&0EU>b?F@ONZOG^9K8pn0ng+>;j8KZ?i~)wL%%B3_j!SUWzKq|0nLlh*cl463L6Cv
z{Td|F2`-IsC^u3FH@QG>oB}{vv_K8}^eHu0s%o_IE31Mqn%PUSrbA)H*0J4mZCa+^
z<em?mt&E4Kx5ytvx{oLxJJ%QB=%<<GHdh8~E%5Vvh`pscQ$aI1dgIabh%nwL?J!&l
zrF2DGzbxt*^lt32j%gja0gNZ9Q{gG+huz=i;Qn^~Nj`O@L!~oBHDu)c>TFq1p-JV&
z^^g9%F7iqI<R}~H_rMP+oz35ZM%4a#lWv780HvK6ab%#v=@8s^&8xA4vM(o-wNT%_
zM;~#}4pzR)6vx5X^FAcUv+zZoq7IjPbV!??@imP}f>vEY-iWck!Bl2ZU{O<ivxAeq
ze?;JOA$ABXOKP#P$iwHK`B}p3G@AVllVoRpw>Z5(E;SVU=FMTmPLc)T5H5mEzR%6@
za++&5GxfIM*t?hLT&u4^{$`LH$`8E0o7r6`fik2JulLFKrF_t+{3Xe-K2~>>xTyNE
zgY=bU;Ofq0n0;z+VSG%GmN{Typ28jTFkYX_`fYsqk9Ku*z536IQ$PqIzWO@DQS@*v
z7Nvnts(WOai%)PW6np#Eq(l8zAL-|ZBX}`h3W$2kF{PyWhM|cXar1*&1?~*EDDKgX
ze%cW9_6WwD{}66_mjTKyss6~g&Yu_Z3F%%x*6XTc*ZE`eD7CJ2{1IJkIxc#)u;#mZ
zFDocv<r;fczrnj;`jp-9F+2v2Xj%!M*gZ;TPOV(}GE`y30O>f_cjLzV6?(VUwy%0z
zoi`H|r|&d<7>D#SX7DnM?uMQ#=AHk_FlrI;EYGU6<}8@`8pv#t2^P1nev3a9b!QPQ
z69p+>8ZE%0a)(Au1QZ5EzDaii<q{U?HrBoT?yy|?7<$~+>4X_f8_S+L&l(~Hh!B7R
zs=;`!d(%R`)57nVxEq*U#p*<)>Aw<HK5QXIagjT3;uJd!Mc8I8ZznL%n$vJ<v`=^6
z<`WR3?8$W2vD*k6&et<_fF<xy!8#yi;;2<DPaaL4l4G0gwXg!1<2*$etF+GK3&dxF
zvZ2<@isxpv=VRp?EK@{Gr<RTV1yDwXjA!>1Ag^byOZTfVk!DKMS3KVxhf{+5p=l$g
zIDH$V*K-|mwj~E}bYS;sySXfEycwbntbMwWdG4ketHgNCv(m12yzv*HRjh=ok&YQb
z?9fL7Z97vscEj_o!}MU60k~{}`c)F&<Xy(=k;B>e<NI`$#WuG3tZu_K_G=kmuFZBK
zLMrYj%qPK82|t8LsOXRdBHLmH&i3kK(wP|EaVWr081`~6(snmAOk0vxQWLT}b<?lS
zv5x)rh{b0!vgX+hBL6N(1yptX@mrjRQ@r#dBX7Kg7CT-@YNG<X;9Q$p<@9l6F^n(X
zo~)EqbWXh@q$YFbg^;sv(I<Jj3N5YR!2qI|e|x|9QXyM<+Qplj8<7?w5Jdo@Ur13A
zyH^BT&gpQP@L;-Yo56}&u6%+nh9!Gl<hM3ju3?HTd%8SxcJX;g)cxA!PxH&WS7vhG
zEmL}peOOnTVX2nVE3Nvht!682Pg&Z0S2~7I)jeJ5(m;1rt@O;JI;1bwe02$FS;^zf
z%|2ckop$+pF7l2?7Qg7KFN15s**Kkk$Zd}->it!Vl!b=jr2647nx=KwsN2gQ5tUE)
zUHa>Ub)&*`q97WRklWOV_iSz*XMY*iKrKtxmfO}=hSyf-*VeYz{=oiF+5W5x{n?QI
zv#IgNk_?@D6{b@N(GY<cvBMW%+fG*gIcWQHIQ*wRGxEkus7uW?9~LS|m`W!@1#40v
z=2WNy73N2UeUF?S2%Aj^)1-#o!A1s+{6Q^HnRcjX_<EymV@z{|d8zUK*|_n0Zg)EJ
z7JDz-y$oM(DjS)Hu(jVi(|$QH4?2i-y}${##-fIOZ*lcq9L2*diBa!z|IFw_4LlX*
zE4WnA1Ff^#XjDn&jeIbsgS*2n@S}I*(wX>BYNVBgGET;eW||rwPzqgRe=F&?`DOC%
zO=|wRpiMb=EF>m;?!)HW!PU?VF_wY?#WP#V!dogkOzb>7uO+tB9kxQ{<+x?&<t8?@
zzt^YcL7Pb4I^VZ)cj4CQtd}OX48J?FFFmMa`)iDjWr*gHn8*;*{A(_J`-(ZnqTsK|
zGn6+uJg(@kRb<S}Zo~}_!i9yuHh0>CI$#bn)^{WS4#Yz_%(opbZ99;mvz^|KJ6ReN
zuA>B)B`MyeZQC90SKm;spY>q4YWqZQ#_r5zuC#MiDJsbNukW)RsmAa;7q5rr+cC{P
zR_%WG%~LEyMxO+2$8^C6?|p;))(7I(?KF4g&ZIg$OQ1#Y_#!nP9HjY63i}1&c48-d
ze1dq47yP}EJHJZ{Oz$YG^X#RX@7YPa3D$+*K=0jb{Clf<H@$s5=+X_}>W5Pea)hjm
zmr6dqf#ENm9LSA(K~WJm$iBJg{U8gNB|ah4VZTFY=Q7=1QP8IN9lxUX{bn;C{{``v
zfA+h~e4h9})a4G4c`xEa-pesK=y@mN3HU|?1=LI^*!dmQy?60-*^c@iP-?mN3d5R@
z-lE7Hw(Q_vB%|(;!wYE#a?fOLMP&s91&TO`C7wCxy0qe#==TM_S*{e&_vd`;PSS@0
zsI1DvLGz<;eKn5BsM<uBeP`f67T@q4#ONf9P{2w1v2Zj_E4*>DCi~grvB(W0-oQY?
z<Hg&D*Pb1X8aydY2<)8RUubmdDR^>bA#l9zc(DP~*$hpec+!Xrd~X-<Wans8Gw6A8
zPRv>O&8VZ_=petgBig&Y-DgXa1*Z=#xy}^$F2JJ#=-{U=I8w3AVaPKQE{jy&)~Hj$
zMsFO~`P$;n$~;(ET<7l~@o6Ssa7yM=dPqwjFcSSE(339VF!;u4Q>Zsn&TnJox8#D7
zZT0R^z}}|FV4lu13D>19(V;@a=;N`!24cf+ZdN~7UHWjoq4aKhviSY&3uBee-^*Q>
zx5a;!9#Wa-{x0A7S^sEfV|97w(j+AW&T`RZ7dK67jX5Lf_GbIMUC74aUW@&T^K4Is
zjMv)Ap5)?dQCd;%=QoavL&fG5ZmauO?9yiLsjH$6q}P7d`E9Pf^^mHz7<M_h=yr(T
znC%KGZrzUIT%Q{(Fn;fL^hh~v@Lv4+F0}kEb-F!e;>{q3xX*ZZh5Mi5BlRa63t!*6
z(-e;n_jfk`{Gllk59nEk439flup#tfHP}#;+%7hZSqIM>e)^_AZv=;P4R0j(6Zjo9
zo@3*0N}|F3d@-WMHPgxydz<J>4XGV`@mI$DXS_xCTF@6M))D+9Wu&Z~qgYy%uD6<4
ztw4&NkRYe{2aY*`G}D_8^R+1S&jg-YJ=zmYr@`VQ^e*&svkPT775~ojt%#}^&+zP$
zEr?n8&?@-y;Zjjv<m3I(31Zu%%FxxIvyVh_qEGhJ+<3Wtk-Q|Kj+`ip#hgKQfODN_
zLHjpB+k)&EIp_N+rt`T3p5!{QlDBTAtm0btmc-tE7>gNFTI}Co%&gwuFL6soo+s<1
zKiNr?sbF*%D*YmNaG`QQM_&BHH|LR8IxSkNV@h_;p~9t8G4dC6GOee-C=IrTtfkJ@
z9bBx37j5YZ#D$eJiZ5=Rz0`Pg*Iqntr*H_#pQaEJ-H>t`iEBa0AL3e>uPLlQUfJ73
z2_@+oX$uq@4!>*Ujs01yLt(w8EttYuexgj^|9DtlNFz0CeTuU_g%qDDL`wEueP8s8
zFUh043pY|<Q_-h=K0aTkZRAvZ1ML=cW6;#{i$k_iQ2U!e^MIodqzyxq^r-!%S@tof
zl26h<Tt6O3|Mcv>D)Y<d$7=fM!{tvh6ORv$WF~_*wJ%SFb3VaON1t!ltq74n#?K~Q
zQ<R-cvwR{u|NMS~?83`{W7);*SVg(t`7fTxEftkC$SuG9cr3S4)_w8Z%!eOO<kxDK
z8$PyH930D2n;4W7)<1IwDQt9}Z&cX)B2QD;8n~u(_3t;!psU-X_k*tf`4vFBx;quC
zq!{$$MUdkD?~+EvgVm2GqUQB(C8eXkKZ2BwUG|n6mHr_$R|3E%ipf)@M3@4Wj-`+S
zmGBVV^v6Q@MVjc1N<-M{u`tQPCe(w{&~t}agsMn0b8=~zkOD8#_(Tm_UK)NekeAU`
zq=jR!G(x(bm&vQJ<sb4-#Y0|9s7NdCsj?_$M#R4<)-O1LSV1S#0suh30D8<x{Cmpr
zBt`cxDI@?!APmVsF!A5D?iV(Ir@%)b>*STRll1>xx*n)y{oj?Yix?N5NY}ILpZ@PF
zUEd#<8~zvRdVsdcc#xU+%GD=3Akfr*lde-|{!O}mFt_lc@tAfy9z03B5$)COc2eyB
z<`(#$SJ2{rRKclF|2tRkw*0?Vu+w-v2q{jrO7a8I{dX()c=R6?w2J;m1>-~iV+9>S
zY$pXhv0xw|02%`M_tNr!fBstn{LhL1_4WRreijO}4BP{P|056>x-8TG)x!~1c#+Z=
zyC|mr4-bcC=;)h=ExTnW9u7#ZNp!z;3%f0?KRZ*K<?e}}3RoQ4BbxMN5JF)D?1^al
zg$6U=0jy+cWE)=7VSr#AzP;3`J&f!6#xM3#Hufyk8uUZyq19Int{VtKaE_VJDGOh-
zx4$ChkM$k|eE2iy;PVH0v2ZQxJygDh4hu}e8=FYrT~3Q#B#?ii;Y+W)vhgc`Ztm#C
zys`tR4GOr>6Nf7nng?Fvq;;^sGeE*hbKZP+;w?d$Zjupmt=jEjI2d?A7^!g>Xn*sY
z-UCaqTzE&bX%Rww^com=@{s2D#2`Q_81*JG@zCIG&)>Qa*Ip=4<pMwC<Z~|v-YNB^
z%ri>e3UZ2%(6w61Bap4eWLZW+7;mbY-^Uc^b4#MlI?^0R@LJgsw~lPT1Y>VTp{#kH
zvZp!@Q&Mwc2uHanYn+DQm{f}$;<?;nTuY+Nx)aM6rC00s2Go1whlgUF)_n&=9Ca_N
z`;2<>qmn{pJA0Vrtxgjjkwl~Ot!3Ow*+I}ep?+7JM_zLYG!GDw&U!BI2IS(n&rGu2
zv27gK_F?C<bBCuF=hJ<&H~U0lgXbKb4Oqw0q5{YB9Yx}A+Ml-7J!BzU&3Q@+IQpHQ
zXLuQs0V8rIM?~ovd)%ZAINph4CdC>bd#c*nIYJE2gB+K<;qQ(=73W&xeMBjxr11SX
zrMyQ~6twsxx$PqDXN!ROoK1>tWym)+3XkILS=JiA&P8iGc>QU0px(O0QvGm9ene@s
zeqj_wC(ECplnBL8KaG(JUnn&=hKdA0zb@Rk-4{4h>ae?XkZCh|flEk+eaY;0D~n9*
zpXztf+p#wmndTE^dczdb9sB%e%B|<@znXMCItDX#!2>qmc+&9vlKs;6e=hWDFwFPK
z4D@7HNRHuOx=A-`#ZM@;r`!J=+ys5mZBsiL-&^H~=l*TdwR~wd>flfif63WTFhkNc
zU_KE)z{vI7vBQGqwc;RQ7B5*yQ)sEUe@!V@#xBr6Qabb}z_OD!IK(|VD9*EkmT+%d
zh@T}G*vi^nla#EDx5ypY#1&{>8Cw(!3e{aUbZt04J;7e#pPr-iZg{W`{3$Ndk-y^L
z(~rGZ!(A<0e|RKY1AO~_y$*QuYbxM9W89`4JbtRm_m@hyXf=D}wexQ#b6zNdqWH)6
zL28@2z~*9GAHG*W_Dk9%Z7u%NPg_H&VS6R7o=e`UV`DObNdiu4@3mk<6W~kLgqfd?
zA*3ly2-t$`6!^z_+8PTN_Q`4dr5*~8>yu&{YMcu<v|bmrVW>R~c;Rh5!d^tgdW@Xz
zk83$23F*2@Tf3ad*W1D)DUod@Rm1pk63o*v8d=+OC0^&W6W27yl-eXHG0^`qk3k+>
zq2~k2Q7V_CAEAlw31Jdf3FleF!x*pUpdYh(a{J$kq+B$L*9&Yy2u}iVEpphUu)CZz
zfB4nKGEOgmKMGxxONhaL4kGb$)e%(S0)&v${tuu6yA*m~a_p0pT%OpTWZ5`gw2X!T
z53NvL2vZg*>1abQ9i`0Z-w)>59ek!>(F83tL>a#}!g<&U8s>>abCUsRU1c!Jf&%8*
znuIBI%%cM7>9|={dH&+(=?B)e%;oHo{uXM=YyjXp+gLaOBf#M75qf(A3%k%ygd7^u
z5`0!vGq1(jp1(hxcOL*DWNLz$)s&+JZcWmijw)13^FRddg2^+7p9$Cb+I$Xk&yw-r
zQ*J~$b?+ITbz(U01_dTPtIS*qz@OICaQa-xO-t-VD9!S+c)`f$j&>t2GG32$>j{Rc
zkYNhMo1LOd15XyI;c~u9iLqN6(Xi1-)^=rjoxmBMIcx+oVm;A^P{zHg6?yY(u$h*x
zI(H9A^I-#XwDGP6#>Hjo>X;|9sg%kY+G4w0n@FtlE<X?5dgR?RB^E1`q%&L7YOEeu
zlyV;V)ck%w3zYBVKVnsmXNziAj<2Kraf9d1To+J=Pk#-gwsE}WYFA<#C<>y*WuK-|
zA(zh0ss{rfxWs(atw)1Lg6l&c3+}bA0UCzh1hxd4NT``(a6~NY^yyY4h3~^Y+L^$|
zvWt&Zkd{wr2Yz4Po{q$i;WblMwm9Cheqm*2E_fZrhY5aoS8>EM$$%?|V;^%tkKyj6
z#3&6k-cd(7r!*$0k`KCjrCmd*`Ncwv5W-j_)XM8c2xY}2=Cfm%XkB)qzsEZF9}zx1
z<X0p>p5`eViStDg>AkMl6&QaiQ?UN{HOTrdqh69LcASj%_PBhGwiqGGIKc2^EBA?w
zot++XGY;l4&EpLK*yHSIo?8%(k+G*af$cA>W-X-5OjA!wO{E$itJ2uxcU#q?&Q}Nd
zgXy07YoxZ3(Lx?M#eUYNZV0}_;<I2D=O61D-)s_MM2eBdBdbc2Nztl?Gdu@~f>fEU
zL`K$TREc2=yNV68K-c!nJ>>*BvrkKD>N!x(j(5tw`?o^t`5nt@v@S8)#5~yIKS%f5
z)tuEdjMd~BTHLxh`1pGCDN0_^ndcGW8RGZp;MjK`m)AArJ)`fEd@0fU?X18gSqqr#
zn%;$r>5$x<GU-lJ6`|royk`yk$zzbwqDv#3M7c);6)N0nVr_*?fjIqNoYHM+5BJf8
zA!#-}lF{i`{Sg{od*h*IhzF$g)YV5)T+fNE(p%ZzV%Jvr9?;bHt|D!r%Kpf)nmxFv
z9HV&1Ph0N8PY7H{-5p>5EuMF{u#0Uq=ulzbGT~c3pGf?q=0ghdDK)bAx1Rejmn|cd
z5IzHGOMFNgh^rvBU3qO0-=DCZWhC_8Pptx}SeU>IUWW?-*AaxnX&%k3I%%vjj4zW6
zHo}9Ln{kM{LpjXNtD5rCdRVE%FY9NN7nDqrAoL~#$oY|ARs#J1M|yVgrpUVp|FEyb
zZ_8VYxX2}jU+(^xZ182Ovb6mleV#%h>{)N5($MYP2-fR7@BR=`{-l2A?LoLSMo<d(
zD{)*?Z6l}f5Q?K|GcSIHChWrHGaH$M$WPBkBEZ@xm50GB{X}Z#Zsb)uO2C?oTA3_O
zlOu(f(b4dvrx*qW|GLENUmwc7i7_#5MQ@X}47SvgTZ8DQ)Z0zeL|)$=`|MA@c=qHV
z^lyh`X%I8$B4BZHx(fIwnvrwu#38F1ymanAb;$l#n(_a@A-k1Zo6caP;rf4Z$Qo==
zFK>oWbg8#tmK^`YivH7+<v;k)DA4yGkAeSW<^3P?p_4BPnr-^u#mX!A7JKKmQ7{1n
zO;=^betOgi9_BPN8o|o9EaDHfX$yY+wL2lRa2?~qwR+0m0aTS=mIvF>*4E~-fs;#+
ziq|o;#*lZKK8i?$FcYi*8rC1nihyBwLYURGFusU2K1>7~Q`p4A-=2In$~%iPSr6&b
z7I}QRlFi_H18TcAvD`3#thu=L)K*KKwG#Vyd7TZS11#=PYzo6nnZvF~|IYJMQWUj+
z@)$dES9<Li{w{D1%gzn1g;2Boa@C4DpS)!>Ng~`tMkU1TG2?8aJ@lO%Jk@VLEO^14
zQ2P%n@6FV#V`j0%?1uQz#hk`yFNS}$@|xA%3I+e(G!31d6*LCEdUwR-|Fe}>%mSZL
zr)nMygLztq5mx%Yw-96?hN@8TH<l6~MNF&edwpNy#JG3=WaVYJt~7kF<gIZ!lD#B*
ztEoc#{v?v^pBCKzlxP2cgeA7b3jf0*zPGvfA9%LXa`*Vo@^YV;TO+M_X0qIp@J};}
z{}inMuO@UriXfH~88d)bJZWjE!ljdhQ0N|%x{#RF5{6=CJAAD_r5ZHTx|dS8cBk;v
ztK%cp%>|wcp<ArQ3}nM|N?z!nhX-wwF<M-f$Js2}IEp@pmBPi+jlP@o)^ZLv4unhj
zQ$%TPl}+n^n9e4BjoIC=e&Fy-G-Hj7w7Gz&3Yv-AGaA|A;bAbZTrKZc`>OPCIwN&}
zV{*J#2f!bB!K~x-y=Xbqfro!y)d#iVjy88tDb*i$j??g;{m5FuheF-UQ4H|vkFdGy
z^HII_nnrB<D=Ok;)-yqqbWR!V@2V(mjWYXBJ1*TN>bKlyIMcz0{r7%u0@4K01BjC!
z&jWxV1mRL_W5_>lJYWa1ngc<zml-|YpWqE)`_H8Pze1qlf3daxpEGJ|iS_#LLg2j8
z#0dnx%l!ia(?2&Ri2r&^Lg8e{$t?-w>n)Ii-ca8;P2M}T5S)$c{vrzz53(-rmE(HE
zPEnw3J?&E}f=p@wcW0kY6E5ufs=CYWnuTJ{jLr3#{_|e^|F@#vPXQ+btN(La(SO<p
z9Y8W5VSwYGAg3I%)X#l#F$u5S!=>Y+l<%c?TLM4=$1Up4IgJ3jcueYo!EFKCws(2{
z+Xk7n0INDO&y@6<V(BMAko74>3g0$YST7$n%u)=|L?y(|YezJAIyXQG`Qikl2<EF2
zEz(I@b(W(#8Um-M?_9V_i#r|t*MM8qf#c=FgPoGF_d$!tATWS%Z?zJwDJl~jC{U_~
zzrD&pC~i#+N{X38O&Z+F{<t*zP;X%RdUudDUeC?{emd0FgKuh1^G41*z^qzyT>Xb9
z<6_BiQm&sznc78S7uO3smDfIaRbW`>TZyIU#cqM^+;&bo;4`T8sKg}1z%uwU>NV{^
z*a?oo-rUAMfD~IaJl>_E!Z-&;=nN*#7`^}g-ux-23<cLrye;yzPjQH&6|JPsxM?6y
z_t-8?@y+^-{?)(Up>U+)&q-C3MOaIp!XB%)ZrBn8#OS$~itkt0T-;1lI^LRYM_t_|
z(s6Vx8lx4c0#W@+y{V-+mPcEXX!-43{_8U3E=GD+nlB`QqlQvj5@H$lHsdc{y1tX3
zc%)SsFIt|O`(N7o&afueZQCS--U%R0dheZpfduJ2R6$YcML@cOBE1s`U7A1u5kwFK
z1Qi7Z>Ai_G=^}#Cn<2Sz?Y-A>?X%Cm_n!OQALs1-`+L3zo|!r4H{bD&IR>SI6Ascv
zac#fE1SUV)JgjPb>TlwOU-wTIpFr#-$cD`G4Z%yCFouAGV-0jP<zfb{vff6$k?eNJ
zZjYkuA(0aRyT3f7IBqSRno9aQdI<ihz}%3pHo&0$GmMlV$>TwtY&HPj%Oo=3dp`~t
zf}hk{nFdk$a?=SEMQcz)@!QKoFdU26*hMw60Ck3>#6;TE@%Oi@bxBe@B-K}+_}A$E
z$5ztvOMd-X_~xHa%m0&FdeVQPr4xj2oHd5!NIyE}LrG5ft|*QOmo0jIA=@_r40b;s
zKpgBZFB(+5Mk0klTYRZJP42@$hEy`JQ5bOc_K<4LK^qaRGRwY6DA&8mDB4#C2oANT
zhjkXePEP-B_5U)h{uWRRNC!AiD8-TAPS`#R$v-DD{vek{L@=&nV8^Wg)4RL)5GIQ9
z?*$s6h0u{#w@olDVPnjzr$XL~dY4hPXa4K756Y2pS8<Nd$yf{?REXZIciSy$(yIz(
zK4|RDHH*PrbWiUx5mMBCUdXV2+hh)Y1?`lr3H|n|KcD>ML5o6fZe1aMoM-=~0kTX#
z4V7E1zs)bLoh7K1x8i<o;>`t8lXq3=>l&FsJKDRs!$nGlX|i9v5>ks)j8Njrs|DL!
zkMzg<B=?mJF5AWtHHtpbXFHsV`Hu~!|KANK$??C|a0p6U7xA+Y0G!1k{z@pD@!u<L
zIp|6qxL{&hR&~t(-AH~@I6M9IlJj3+W{d*r1FZopzn55Q5iDS1yeC8FcQHHqlDA<j
za;M8mNc;B$F}p~MBX28i!>SZ}h|o|m3`M2YTOh(P*Q!Ohnw(m|5Z8v`aLMluqyz^_
zbtW)!aXvhH*XV0yo<#GtwR&Hjhc3q%mzkF7$R##zNVE&1E<x>|nCXaXq+l8SqRAM=
zjZ*H-l0B9e{U9HMPaLLaBX+D{MAT{RS_aqx(zQM-?jQB%!(YjrrJXL5pt3~NH^Ar3
zgL*%PSuSAZj--h5!X71dCrvs}Y{c&B8rM~)EWCXxud2;R8IUKRo)(Fk-W_rwH^#j=
zs~=ta#6*RCW>b5H{$!i87CD7~xpgMc9|hzOTZykz(tNnSbVcHnHYbL=KKzbY#70qb
z7!m+7WHiJQ#k1pe_j;Xy$IH};c(?XwBNp;~v>e7~*#wE#3_xCLC0`kWrWx*`??UGI
zhE$I{St6LKu1<i&a5f)eIWZP8Nlf^;-Dy?DWCl$pL+fpA9>OMwv48{$GbE=m+&!#*
z1a5QCKC*LPg<}kXi;0%cbkpDsn&ymp@SvrkFmpsWN=m?b2O=F4k~<IW+#c1TNU9hi
zmP<Y}kEg;L(i2CmT|G>f3E1ADm18|^Pc@1R7%4E6_B}wKe|knr#)n^7QWs-kU4AR8
zMczTHU1}?11yJ0<W3+{d3s2C3S5SQJdLf3XMm!P#(74vv!h+#r9&(*Y`sb#?Lr}UD
ze-EbnG#%4i*@;mlMu9$c#L=%m@{@vR8Q)*#9>>YW3Y|-Ve1Jm{AF>R6(wzEPP?#|@
zq`pm@ULz=NUzcBqL35Oum1T^>mLs>l*a%K$pwH?d(tnvU3jiB?1#}w2+&+KS-oZja
z)U=_22V;6}Y<LL|8%&j@3Z0wUhUiOHK7YR3qVqmbw)@M~l|Ipp3MIN>AbwddLYU+{
z!tl@mK*Ba;*AdMvBfAWAG;&-^4c6vLzG8Q8_Ey6PiTg9ZxMi%gVRBwW_>&Lx0*t0p
zK=D25d75g&=?6y5igENb;5|d7*KQvsZcmXBw@tI_Z0^!3Iku3@F7oiG4zD^Kd0}K?
z#51Tlk^=5?u2u9o6)tEsW}bfKW-WWVS^GgUBZr>&7god1q3jc&@Am@xd$q`aJ~R^`
zX7<(pHW2gQsYSj#`1-3_Wc**hVaXr#ep8Fwb}UW&LoKqk6_5W_Et2INd`YHaA*vvm
zXfgUe0nGhpYLP4-*dyXU#3q&W@TaQKj8-^aedQUBOqodFOI1FzuPt?3=crssmvC<{
zb3OWxAk{xP(frHcv^xKA!1yyx`}ZK#-=sXP-0W>{k}c)43;mk-|0zuI-|WH90{-&<
zaz`XB)SL9*Q||Te3ivm<*S}%;_fAy4{l=LKy=8IuQ||Te3;3@<W(2nO++q~tJ4NFd
zmKwo9!qVg44WYsgv|I1F>0qDV%M8O;2_H;&2^c{Q`?0UV3IB0CW+?pO*nd8`S$el0
zzQ+<Ul!2W0pUc2@m+A?dsj#kQ6RDEVW#D1;@lyDHi{WfHzb^&J_*&TnC2iv==ez)y
z7qc0-c=H|+`CW6NOkhy1i{`DRPC@A;4)gLNTby!5rm5h{LyTuiB)N*V%47Gk8-?WS
zUY;(H9&zWJH#UmF7}4QJEJl|<Ccluz^BUzw6yEsyC}8qb81vFhdsVd{$YJGi)3m;4
z&C6Sl1<Eo?SXtr2^TGo;4Q$}Q$-UlWM>Nr2`{C8Je1~$&0XcuH&p$ofjjThzyDMHF
zDERfUgWH-rxkyszE4r4!Ne`(uP-*@*cqczwc<%RU8_&msgMA`zpNNud!SS<uv?4h<
z%R{g6G%X?>X@jiN^$h6#oXkFP0<S8q#cV?}oMOYL!=E$Szgnd0ii`uc0s@doO7)}Q
z8^JE43!*0r1fCV9+twAUdQ49xzY*sh<}afvrEcfS+AMqIuqWO)fRwsbDucElh{mGD
zBPpir&)q2><mPkQoUk{xVn~=tL%kcQ6_3Em)m=`sRdmGLIre-)7dWK6l34Y+{aED^
z^%qARs#K33U0jj^4-h)|Twf@@n+PkDtF|ajk6_golh6J({vt!`nCk0>#OuPXtG~94
zzW^8m|M|D_U#1=ZsniI{%D>T5|L)rCAGKq$Nns!sk`Lbn`wYdhQ)n6hM#)xaJ&yue
z!r|*+>E8He=Watl+xj7cXjTXxGPJ&D53K}z!hnc<arUk|>BU^TiEv&;eZB%Pq?uhZ
z&9FW{9kabg#n-z1Twh>z{SckJ&?t;V_08U*O~QhU^%CnJ_Rzw+p%8!~u-{=u6(Q?K
zA(9rIQ|{G5WkD|5we2<|okqn-&i7<NIKLo+Y+n~JJ?mm508Wm|SIPm?^@y%pyRpMJ
zeBQ;!hNYa2iixk46F^M>nDtKw(m<dlkQKo5M`GBihmk-;4b8XbRb$9K$grfQJntjk
z?^{O4OCw~$4nPabO=;ZlyDuZpGVV)V*Nfc6X;lSme!37RMUE@Abf4}m5Nvhl4Mcy^
zD2AZ5K+n_pM@uTC-8F(3*WsQyR;=&I<;$Py_J#6hb(vfhHd#jTQq!4V7Pul-b$L~U
zK>06Of+q8d9XT61WrUo4rOae1_r!6i+zTEmdj14EE*TqyF8QF_y=ev87?^>Fl|k>x
zv7M~K?FxK=RncFC7NceEYu(|OwtC+El=^uiqupp3z~;44O^~HIf5e;HX#-z|@*nAD
z72{8pT6p0Br72g~6roBT=1KC-9NArsEA3%b(yCwISaH_^Hb1DW3OzkTq|b|y{4#Y0
z5N_-s0IuHyhO}#qaJq9Q)-8=7EaGGmTYr#lyKo1HJ=<$azWhEC+etlh_`OIql#8hU
zN0T^1eb~mgIfGnZCc&Gy?E$RtcP=Hv3csc0rh5CK>uD)x8A5Ub`ep@$+fE(1wpRBn
zx3dhJ@pZUuT8Y=zbSjsM31*mH;7aYIdfk?6#cJWb>tY5|oh46%hl-Sp9!-vJl8cW@
zzW{>eR=U1t3VP$#o>BXJ=X$PR-#IvI;Nv)SHKUNH@Iq>l;>{}>!DdxCb|%;JJa_UK
z?~Bb8c5-SZMziL(Hagu^tI`ZHD+5%tG;#(pnV0Eh1^rl>-Y!x!ZXvRhJw)#<`C<Yb
z`R@b|U3i<jt1Gj_pDj8Nmh#A}x|by<q<kMDC4W85cj+<c*A{dWsQc$kTR_|*{>mzT
zY{IP?w7q`NcS#}~9!N=!yL&FYsO|32@y_14h4Vcq(9UB1XOoAz_fEg{Xy^@pYK`Sp
z-)PbCFK%^jpWT^xVQ!}p&r907*VLO14bu!hZ0M$jh*kjOjkAtut@)ksD@+fj)3`*8
zKNY8J^-NqAxNP5;e%2&5THowpI=>e;Xf7P6p+#{xwO2%lDXwSh%gCoH{i$u5LH)6x
zF^!ch@0qBQY2hZmlbN_nh-spd>q=tvXjQGvUJb6RPtt9aRfodjTrWf=)WMP#thP1N
z>Xzm=mo0I5PQ>_?vCUGsb-i(>?Ij6^eccQx@5x+B%N2bwvxj<Vy!J~>g}WDP8ijh$
zjJ<iSZIP~bv+(hmJE_eBhyB561-!;LFh&O^9;hb{gL*LHoDuk}+cSzw7`^y(_$NSA
z3S2I&arhqZn$^<9U%@?RLZsON{J*DYt)04kIcQ(5yVf4yL$pZT+%<&J3OiissqdyQ
zYPm~59#*ELS32(UpHI~6@x>rQj!9~q^cXl>!f;NBh9{$3P2NndqXD{TPIzFM_1TZ+
z^v^9}qk<n#Z~F-ig*Av8T~U!+RSjE)CZ~6yd0Xp&qUo7Ov^M<LZL6<cjnTU@SR*8L
zqrE}2Vzv<<qNSqEBsFAO(7F0VWt?}0kl`ng-i)nky-}R0G5I9n*9gTcu5?Y>@L$WL
z&e+#Emk%!&WIqQJ8xj(<a8^QFJ^J<uZkL8K>Yp3u?hczm?(}LWW;FLW59vRxc)kGl
z&f#>=3+S~CA2)KQv|VwP952;N6AdnLPHu3E($Ukp2v49F@>noU6zWD>p6a8bBa>ZT
zT-E9$Ea}K5DuMGdN8v3nh8__r3VAc<R+(qL{_I<>Bk=xVhd~e6+s|m_;;eOKpePOF
zFQgQEpb?M>!23tQi`#C0rxIm6mIA2WIr=71K-lQgjNB*Do()Rlh6S-JUn7)fQEMvm
zxxqalG_yn?7HQmaBz5JX`H@39RonL^__eeG;?#3~Wi80U{`ZW^J4Fq8%{Y3aFBw(>
z9vVr;D@rM2!s*JDqM1kDHvD!r??w3&C?HDNhUZSlV%;a;xsV=EQiFJPM;cJum+c(j
z`@66)!I5;f4|YRgO}u*SFB_!Q6X;h@<UWgJ!XEJ7J><|GU4gr#g~&|m`_xEz;H>T6
z-SQLp3cx+He>Z+O-Wv9t{m0i{VKaWChJu8QlS|z201U5X9-}*bxm%S4WJT!vc&UCC
zuRT=umYIZm*wZ&$*GXD-7j4lg@K@}L-&?OI!Sr6S*YnvpVyxS(b%+KY8flc$uNS~!
zBD#4|;9vX1RUncOOzuB|X%`+wLj6oH<0r*!e|_%NedGg`i1jJ*5Fy{CjyT}%`Tg7I
zBE3)`QU$BzWvQ6XDWZ~jgl6XRz&k{)C>f6l=Wue3+IEsz1A<k!a2l39?8rn}$myPy
zyJdKeDTJUR?q)MsMst?D<dkw9nJUirQH5fX8=lw1YxE;-e_11R9U3D$Gs$h_iVC^9
z>d|D^a~E%Axs*NT*&Amrfq6N7zqlo;?d2$SUSM%+n6Qq!Xp#a?tMee%IZ6I&k;=OV
zXk5|(*+ix8D8f-fJpV(9Zki}YWzC4wz7-!%Tm&vKt(qftmras)H+!gC+VGZfDtE0x
zf08v#g5ksQjrPi%@TammANZYu^Ozgcn^YCmGccElw_Rspy1_#&E?iE+m0&Z#uk~;9
zTLgbeM{e~H92J{kyEef_14LNAnyPuw7Vrh!n@{Gu&=HM0-uVjkDeK7K*XdopZN}RZ
zHmK|VqbY5<Jq>@&sQ2)xvrgE&`~!yshLV=BHeO)qmr*xB=p*@Hz8iZOxM!Bbf%xgR
z@Ke2^9L3iOl9@vO7FBF8Hi=QmauFhIzGct!WWo*A`5#9{kje~kofM0p@f@=}UOkfS
za})WkdBNnI?_DY<;E%OmYdhS!?WrDycgs6}f074o*V?a_A8qi&qzCUNv%6Ah)-as&
z0k?M`Y*wa^xn*>vCvaiJK11+l7MhB61IIZ7pN9mOSuJDd`n<58<PCfH1|-ZLsqP|*
z^Fn5(Ip_S<L)T!cO9G?Vy8(~ppM~7G&J7Qof<0tWKgv|H`%?rtfEGYkfchUf_*Wy6
zpkc1R7?B(!uZ7=b{Aon8!_|71MVVkklFqH<k3+<Y_dFA551WT>5pIJX_T@u~wlj`s
zS@<0Bo@*Sv>0EHaxBVfbenX{p;gsM=NzZttWdiKBR=W1y;}0n8cre97<JAxKba>UI
zjwI3PCW_!UkE4@C;mtwhy?a75W0ensiLS3GZ_*=^ZhxnU{ff>k_vRXq)TV44tt#dg
zYPUQx`6}0+w-|e{qBqwy?E3vNC7py>O-;q?SnBQXT-CaPCSA`@hge0OrsW^ViUuDc
z3rARtmKfWzTf+>h*rdn_Uwd7kc=FP!#A-l^w1PdJmd@R@;v37uuyZ{_1&?$xL{(K3
zB-c+d7xc8!TPYqK{eaFL42hyNcYCiLt&<cRzy{vMOK96<=9*)R9rIjZJAO$z8>>#n
zUd%?ZX;AGiD@-QMs6|T83}wcWO9fkIZHeb3b08{spAjuDe1#(>nF<$$A{P5*cF7Cz
ze1DO}65HOx=m_N_6T$N<kE(Xt*co_5XfE_H+wQZ3!`92<kh(`XQ7JG5n}lU{(||@G
zmu0BAfC;BW0|eb@Q^68`w-R!Ze+<i~OJPC$eoP`Q=Zm9_+cmH@W8{=ynU)j9IL7XF
za-Ej=Iuh9fUq`XF#n}NC1|Ed;mX>SbyYb=eOzaJG@A>f|*3QXe6IMks-l*ka1WZQG
z5>Vi~L?UXt(+yz^2(32&-y3&9u}2&qJ#fJJSUn{0o;x5Ceil3Jj5qC`QJGxXJSHH(
z<FOPpg&|R!MMu>x!^R3rCuqeAtgjX;S2i#vRhD}XEze$XQ50Ug<MA*7T<Mt7su~)k
zLx;DZ*Yk3;R^L!^bTbn$YCMUO3KzTNnc@zm7}XT8Cy_QLFI+htqV>XRBz6z3M2>b)
zEq0TGwe0q0C0SNFO(+YnV;3n^RcrNYBuJ$<I|}A@0a;7Oo50>r);@+jI??VrIovlM
z)3|>0sYBhf+Lar0-SyE<q*6V^XGXH+%+hy@m?I2(4S9Bh3@C{!JHI}^y0f1|Wq%@$
z;Kp0a5VNJSvgxC5zXtWgvrr?Yu-hk9V>336x|{GTEB!-qG<Z2prlGU3&WeZgmfCbz
zf&iT|(08H(nPrhvciirR{NGE%9xtP1=$4P$E2_wksYL}4Bbs!kVL?<}tQ>o2p`4Hl
zBT3g6$@*jAqqjCr48tXnck!C$9sbWz#xC9h$DN{V;bQmborFSf6kvsKW%Z7%nB<(2
zLO6HYek@in9JHHW@^MNK=HMfKQp<%7Sdt3cT-Czbkp#@YD}Qq4NOdFUO1x-O{KaEO
zqY8~l_&2*4u?3I^G%=bAzws{DemH`rfSi3J$c+3#g|{r{Vkac<83e*fr=za>cDCR5
z90yYg_+xPbJoGIYsdW@RlI%V8Q8WpKnxr(H#$2zN4&6~BBF2`3tiviEt`JSmZrN5a
z$*cfomNAh@kYTEuHjmW8I&w(N^yJ_gfuaMElv{5*`HLcmTv_~N1!PV8pc+Xc0i--U
z@8|`OwG$;nNLa24TY6s}W+u8pay`M_#w%Nij$_20$j3+HYPczR%p%!2_6o`$h~TzW
z9v7bOkq(PO2nFrtss;)%E~}nvAUbb?cD&5V>u%1TbVfuqaj`2dvY6D2)wKWvBa=GN
z6%5?&I0tVtoia|l5OLAVT+>CL)?85ma}DQ64-X<vaZ8IF%1U(OJ-IK4TBKI<T$H7a
zS=R<z<a<{PhqA5eo-=K#3i|cX^cVO6`ycx0-;AC9IaL2YiQn>%;fH@pS<JZ-`XbGg
zPIZ65YVhVYaIuN!a0o-28qsU{;d{8@WfPAXPq$R~47d$_;b@j9A11>PK&uAoA>#4C
zb_XGoX`T_&!d@Ie2*ysaoirC-zh5uCiN*St+{DHEqm;~D!2GVeSUE%fHZ0$g7p^!`
zPZ0-8NXyHe+oM&=d9q8JUmr+r$ln>CFr$hsh$PPr=sh%1%&L`jEv`FTHsJ5Zmd>a?
za@#e^YYI#-5~${2Ys-$5e2FS;-r=JaDagrKOQ}C|G!m%qf8h#f(Znwq0vspT(6CaU
zNSenDm6ypS?o0~~8N32-qY&}WZkgO~h+Rsk?Abl0l>B4}A+K#5jP2vi3zF@TOWCEE
zkxC=8&=YR4gYdzZp2ZsSXBqO+@l|YYICx%FgLw02XfjYJHjS>K6>`WJ6NIMXKYqJ$
zbiAA_xXLT;(onVIm?8x9N%yJPjV|fwY}yeqksLMbuY3J*^w#xIN}(^i?hV^(v51Xr
z!KdC&bK3I<Ax6tr=-xQ*`CX6Tzc1kV9BR?w0n=a|9q2QKO^asHNL*rNuM`Pz#Vzm!
z^ggHIKe*^f*<mt};oD2ER9N|de%foM+5KZh<(KE43Ts<@bLCqUs<$h|KK0%F2Tan9
z|GYf@jn4f-YX3**+)v*#E4RfMmQIBs*sD1m!m;CwZ$P+@keu*WnvwnR-GxrH3MMEr
z1%5Jdh*roN-W^iKu5=pk4}yo6ys)zxiGoYRLHRJeoq3Pgg^fc4{w}iIeE1`~CF=O*
zlXhys3eDgdgHBBlD$9|imO^m4U_Dvzk?;#Az@X+5WnR+>#6VzjQ;|jpGj4q)X-M-~
z5<+$I7MDbpZhgKA2$SJ<Gh-@!Mgj=XcLf}6ThBZodho0tn$bB~B15FbfZ%_W<9H<%
zY-tEGOlwFG9_9yj_f{6yI{Sa+Z|T;{hcEds=<z=vhB|qzgWGh)cQf^83`z?KKycr5
zIgRwJkKT$w;cnjI-+a#uGPwhOJyTr*ni5D&e;@+L;u3%-1;JQP8UVl9+OXvzyj@hM
z_XaoT(Jj?hr9UZ5vQ0Q)R@}uZ%3_5i7;3_HHP`a?#lvA)6epGJ?|Ta11r%rYRr~s-
zx`d<nN5-Fr&oIWb;APFk>s_(<0<ERSOnbx2S!Kk4%c)#1k%i+|y`a?$)nU9u#}u`O
zWfSIkEoU7YMq?tSR7#U)3fY^}fI3jLtsC=WWfRP}!hWEE9e;!q=@P|)yG3}Oj2vl@
zeC<R-CS@E`ho(o_yO%^_S~p&A+XzHFv+23uxyPkAo2GqlS#@{@VKP-Lxqk!=FL(7x
zxIjfGIx3x9B9rlwIEbfrXt@kRy6L9i(jE@@gMx1bG$BZxi~Lc13)Gy%1PXqRG_^*=
z4$e2};nreXSPRR^hN}z!ZjsXxxc`~(kB4H#asJE+AK<~X=F@rn)qL>aQfThp?eCaY
z&#Y@Z7VhE<wE$_>!w^gIe0UPBKbS%QeSIiq7pIr+^!3w3TnkA0YtYcA%2bnk*|4l^
z9>eT6-6YMHl`cAg$|DaO;s-{nQb2L4vauG69{|<T_U0Qh;n0X(8leUOpV9WhCQvjl
zQ(G<~^SpQPk0zx>)5yXlNloVB^i=@LcuwrWcB6|Ip4qrv9%<fipR&=NLb`HP>M2#L
zZz5xYm3|L+gxEUZ28UP7?nU=^B({s!f~!%1bgt_roX<aot6npqWx4b2&p|Q*G6E!i
z4^nC|n!t+Ntxc=Nw6U3iw%2!t7ZC#fsDN7QRNG4Zph+M83U8+JFh0`Lz49;PumF*d
zXLp(1KePtp%y2UbUp~TvMNW1u#C*wby~}q}y`s5MX#QY?ck#3*?K&Xz0-the-Z+=2
z;e1N@{!#K5)lgj>Tn4kqht>v;wB5uG@rS)n?H4ce_gPfTHF|wY3E2=sTn)C;xihgM
z5k4Qgdz`KhW2k7*d$CEguXSnf_50Zll@%o#_m8Ho=JO0XbnCj>rN&we6@zsyb_nyC
z#s<+ml~(FxBQ|LMI5ZCNfVx1wa<sOJ`JoNPFpNP0lL>KX=a(L>sI57FLvf6sE@LQj
z38wXUh0<9+CkcsJKBvYri!7*pVRFZtNxDX_k}p~Lqa{hfRTI^RA0vrW70rI7fL#I_
z6Y@=n5X$hstnmGnU>GkHo)AOSMVuq=Gx+Y#8OUvvR0i=f<)_J|%{C)El-FOq<s5=j
zFPopV1f^HMT$#sHnzq8!#yVJp@Nl5m9&#QomEe)(C4;!DCzf8ca$xhzz8H)rF{W-D
zUe^sH650V>*u6FulLt!Oiq~JTiT)Kb_`kn7<^Zw+{Qx1qXT;yw7ZphLG)AhwdUZzO
zaaVuy>Wnl8ImroqFYU<?hTJjlk|MA#I46QNJ}?lxI_b}n9TVywnM5982cDeNuN#Ff
zt<Wp<GCB`qj7kez;xZqNS8<$>KYYPhbtwV+#^y{vo5!-kc}%cOG^KpHiKH^rpR%b2
zQ5LZECM*Av_vJTN&qAGNE^RN=t{u<G6)=0-b(*cF+@(?$ZS{>2)5*%W-uuv(tID4B
z#CqQ{bWJ5co9))_jmfo(t=|#?3R>USCzn`x?Of}bWol0pQ#GA`T@3UgBF)e9o6W<4
z-0IiR*9|`ReGmN+dw$*5b-rZ>pQvE_;N{9k$aINTpA~^(v`AX3CCc!~rUCl;-9xVX
z*nN^T4PyYSyL@T{@fZITg$Aq~t@Iu^eBc5Ybj+dBPCPNH>LqfkKyh8%DqZ2JuA3oX
zejzd$-fE*e?EFbn$yjf`<DC&;myD@bmSEU9K#)qwFaqGVc?aOVFM{qXFM;g_#0djb
zzr^}ZDNRt*c_9<=23XX*)aXLRN|NDeX|hsPkR$4Tb7Q8!ow1;gCU78jsV|W+g_S^G
zynHTPst*7u_3OkuLaTwi{09_Q?UwtXAm>~YHRcklYk<)XKdK}Uy$T&%U!>GPq7lqT
zB|nUWq`))GC>EUCSMkRKB4fpINaV>eWU!(vJx^bHab;`>M*k*p&?#xoKS`;{D@fR~
zd2Y-^ZB^bpN$fR+Mp=>MZZ5{*IaQu*de=w>2Yk_bMywXt5;gSd*rJ!e0vEqWQNKS-
zL<{RWe+~g(bt5u@u6ky8L9xv$f;VPOCuo#97+3)%cOREhD{Xsf(7=9x5h%&G7JDY}
z4jW;#Z<lEKl-KSG8$k7&Cy@#C19yuTa$<dH5PrDQzLm4&Gv*E0ldqyt#@cEmU)ZJ#
zvaP~4*FSy%Y^f58V5^Iof3t|FPgV=arpgS&U6V+qzvlTY8L@3E=i`2=*@R4YA@xxQ
zTxc^JQ2R24ujd*0G5H!v3p}Q4qgEo*)KUO$meQ(D?nId^MDj%+Q>*e9+O0AH-)aDg
zDET81a{;!BpD7+lx05nZ1t{@ua_cE=kR~`L@&?zYX>%s$_>p%VSm?aExKo^yJnQLg
zHc&8!d6SYgVpwmiaxe}_QOg$|GwEs<TG@k+%&rd=EMn{RWS-o5nhKwVW$?`4!_E5O
zo4Pfjw#M2~DTt1Dd$i)U$E2_)8Bc!%_UezeX{)d7pfBgV<62$M1;PV!UESKbMuIP?
z;nY#a2!fRd%<hM0eNM>MGS~a54tQ_fYr1a<TvPGd7f5z%?(LVd<qMNi#wnonSdr&P
zYf1d@AZZcyTgwdBFR<3}D#|4K)<jNoQ>d-fr34ZT;KcIA8bT`64qwm)?+GKZ(WcZ6
zWKkTa3riTc+q9m#j^g;^P9e#<I4Oc2gPC;uat;&*zO3PpvD>9w+XiA`VIaAX+5z7%
zZTn`K%I?>9NfkGeB;E$_mF~VMb!v+4QcmaEf^>OFL)FBtfr!<%ffU%`<U7!m%zG$|
z8JTS5y#x%2QaDh2f3dg7H$`+ewNpMtBQxZ^23Jith$0(6b1H&LzMs%Ze5CpI-uQC7
zXnZ?-`mL5u?5-jx_*~tg{d>P!bte2&+{8F(yHcXp^^<p9aEq+YZznD$ZyU7Th1>Cn
z73%Mt^TV+!{r2i`)AC@K<v7IxdumTu)CC9sMMT9rYFF4Y>&SK|Wt`xMG5PzFbIBx%
zE*&E&B&+P_j4I`7O)FB28AU{lDwXJNn2c-kl~3tr8(hDU;$}V0I)?1stZ^v7Xs>au
zr^aeMl*wfdet&%dYalJvo7_s>Y1kHCX8Mp94Xw*8Z?K_KGJgAH>|Q6^Xg8&`J?Js~
z#G%wB85Qj!3VR0D;&6BPg!{HTez$);##y9Cs)RrJD)u4=)xjg%rs>SAWJjhh$~IXm
z8S3jT3N{v)HT8J2@qQNq@su1OU80#~RUiz2u`bQRl7_M+NpqeC#VUgqP&W7i4~@;F
z*xTs==dW+RWBoWK=5oT8*2wHEbIak2Xh79i`f(&<<yodT#VYOL_7W5F4oT7q^Q95D
zRoEv$r}%Zz6wd~JoiRYC?kdTkEeBEKtTnMLnBkgBzz4q0E!}+UD|C}L1lT(lVkB+>
zsodioWm<y7%7b|5JcoHJIQ55+aRwY=6-4aa1gF<jb^+hvC3YZ7ml)%5{(H~(x~KFb
znpy8Yrn9uGwrra{U=gPcJmxh_8`BS^mb(BqIx6gIdoRcMB<0fh2CM-Pn{mrj$_u50
zp`>b)xAINuIOb!2QrzR~MlX0+Ovc2|xsk$C&*JrD3C(vWX2g1j+I3+GU){M)e0xaC
zkUhyv@3YpV14}d?rXR#IBHbxl`!=cAdRbdA+gj1Bj<LTar|b6HWTYF9phrQZP7C*T
zFsKc6nI6K<cCS;aGfTW=|LaJo>7c{Sf&yP;7Z*_qhjfbt!^-H=%Qwfd#->CT#(@;j
zE_w>7oJLG7Kn~Dg_YEPAO1`wod#<U%GNr>8WfgACHiCSjiJW6(Q{Qz;V;Gb7k*P7x
za^h%m(^Krd%y9QA3)A$2xyqeO4`gQer@T)a5q|XF-Lm-ER<2P&@voD-J1#~bAG*}?
zd0ClG{}2?oHzEaQvJ6vhXT?`ECKB~idc{}r4;9B`$2d?qf(3Lm)+qz7@<62WI+9|)
zKjTtga!#@dx8~dtO^|a~^a~AYXRY8~mn<j9j|{TuNV!rqpz`?HMnGmY1HOKPL{!RE
zj_39yZY$oTpZI7SXA8}Xk@MmIZdO{#6c#W({(Z^b)bGN?RU1RaTRiNunVM{w&ts+X
zR;aIMmR+kKeq7j4@-e!(nW-Jn>G_(T`rAr8`_tjwiDHE&H%@Pvv7ng1fSWTt_h-1@
zYt!B)?&I^BS<*B<pOLdXDutQ4C?eplNR9~+dmNzkxuLr<+GX)eQz?zYC|#dSP{j5F
z<IFtqmwWdFL`yVvY<Yeimvn%tKy`o>Vfd;3MTUW<zx;P@_um<n^X>~hE7MEeZYK6Q
zCuyr5A|!J9gE_oVJyb?ygL2#ZGvdCo$Y`mO_qE|)jml%62w5)GKmI>9D!(n>Q@c(w
z|JC4kqw=85h0au&JP3Q=6WE^yzMqKPM*<z>zou9B-%Fq2pOa^QB68Smm9^jY=<m5$
z{Kma23X%Md1<xmO3WPuR1r`(bJdPNx9~k~1PUQ}qtth=3dZ6_?<nYsKeO3LKJC6Gn
ziCsMZWdapX;5V!F$=hi7QiE`*(R7a7U#-^5f^*KpId4D!u!$4CRKnx}30E7GfTmtp
zA#*B&8!IX~sBhER48wayG=|lHEbgf}{R!klp=yUNrCMs`%IDQUUzP>EmAAQ(w6Oi*
zcIu#c1QI^5O4mjTTuIfS1x>Vp)DNhjuD<#KwM3-nSpsMR_QEyj*g^Lg5XxtprReM)
zbqGS`G^BI@9?rQkRQi@4FGB~rPth=lu#NpltLBrfHz;So3wFtIViOutJ#~W&K%HJ{
z3|AiwFAMmc5++wgU@ZipcGhqinbpVIjW#r70N41Pyk-km1(HD!H6uIACUqmMP`W1r
zCqa^d`=U<XzF0CnWb25QV`@1u505syjLjhR{vLMOQ(e1%dfV5=B{;Q+nd@`%TYQkR
zmpX?xxs(r`{_=wQ>M8f|g{B)%X5SEREpPeGuHT<iBH!|t@duGe52+tBD<!}9coJnd
zDsOQAld9}h(#_jid+RpiAFB(lqKvw#>~3A{qI8-Xw0vElypZRm;0&HGJ)DkDqi%B9
zZP`klt=8L9xcy?uKDl6W%3gv_s$Y+YYXsQuw=X`h+JZz04`LtW{NR;4S}i~OdXV$n
zXsb!{)_szdD$IrPDmmc3%9Stl?v4hAbxak(x;R_bSA%50l(enab)l(kv!<1EGC5I#
z8Hy+kreBBxf5E|z{`0E)mjU|zMp^%7>J`5Oeb2I}Rmc_Bf3l*x*_B>@*(02OHV;hz
z`1N{r)qP#zrFi~qx1|K(->UBU`cjf~r|>dLe$wr)RR=p<Uq&NHL{?HYncWFExFCVD
zZm8(a6lM4;NahGkCiHk9;l}|3zW@0F>h}i|e}3@C8z+B$@W&e?e}3@C8~?xl#{V|S
z{zarn+mrBIQZ|Jufl|JG8lS%x-Hjx6_(WM;bEah1Qes~<Kv=sB2+1U%T#z8TaKO&{
zwIT&5K@%&A)JlE^ZU_hu2hdOy+XHAfLF_8{AP@pb3!(!6m>L&M?o{XQnVb_=3o1#V
zd9zEKRQ1AI6D2<y1OQ;LpV{IqQB%t(mYQ9(O3gh;xFCQ`dKm=<qyiQ3{hz1hFS)S@
zWWhnaPM+)}$TlDgsP;X)LEyM8ihX-$Ao)3KdxAh&NK}f9=k(fq(G+x#7XG|C&iLLu
z)tKu|T!?>v$<K=w>GW@kmEN5>%V@LcQR053e8CBKTP${&N(r-eh?cQlT7MK8Ac|Ko
zw%x_nql#-rt;7_^I__Ml9@)<IFkPBuxDTJ#%@DnbciQVqe7WN|DAzYHxFuPtEsB<F
znOmlnT64PM1{g$@I^S$AV4#$2(OWLN*~keo%BFF5Ou2#Ab)+tf!R=AW?K`ZX;M?Jc
zw2GtK9`vo-1*IR~qAv%avm47s4HSmg83<s*z6Px-<kBHpvEPDnMD;6{uZIRfwiCk|
z2L<U%;Wwjb)v`|b2BnwOG)({p`(p;o(u~O<D!w3$A^af3Hn#yL#kkRNO4Un?A)I~z
zoYMQlQFID+b%gc%6@OMtTeqX1`bp0DHr3sJIg__r2TO6(O2+^q)?P54jvyZZJ=>Qu
z6qNUcpRLHDRr7IU$>RAI=E>3qD|Ctq_}Y3!C&(wqDk#2RB~@j*Tkg{k(OLapH3Uw5
zIDTpfjGDl3#VH@*Z9b*7h`r7Ba<vcLkvl@Yn|yS$`1te~S!~(z&RT{IA59l=#1vX>
z21+y}RODG|(iQ%x-Gm@!tU(R`Mkfk&8SXDJfD%<h5O4pYCXhKM^O58QsoFFjy3k9T
z98?*L=mjQeGnR7hkRjBz?Nwr^lH_>l$moT45`2(E0U(*HpD!;HaZz=U-=l1UZ||hs
zrrWsjS)ZErCLMs2kw0>~ncKqwNfe1K<-)y41L7^)fe|b`fbRtB=C(z&C-<_p3e1QN
z{ER$olaE0nh)AiIo9KG8kR?>b#H|_g4nU>BwM-eVRDvOqtk7UHpNgq$spAccehmN4
z>fka;AMOmc3z?vsXT%lLUd~+9c1BtNI^HH5pS7RUXP{Qa)g^gRm05^b$EdZnC50_G
zFadq&g-+>I!h%YOZLQPYu+BXZn|vUQ50T*PSga&A0pjC>hL+WlU16?~+aha&;bf>h
zsLE6gSwJE<nMaDLqmUZ}6R~M-vLk8Utk!+Q>M~4n#5xh@dTm$w4W<|?7z2}E^E*)o
zQ!xXSdREb5+~=IP5)tcs9JmFY3$Y1!YF6>31bmw&`z#-=Zv~KatXt4^<%HJ5u$21{
z`T<U1N)|Al$Xs|Fmt^cqdcnFR95`I+G`^_o&Z5D6A5b`Bar_7{t{*QIh}OLwN9_Ve
zL;!`mR3_6p0mVe;L0@R~G#=CVqZ}*bm~s`v>(DpB0Jhe<$xUx|s5a@_D?tf2F4ib^
z8Xz3FFSC%TP9LV0nF(?oQMFN!J&TH%^;9g8jwB}n5Jk2ODI842NrT!zQR7inwRLf7
z=_C3P1aIowUeqw$P7)ajH;K|j*9724o}RuVn;r+5@)~`3(>p<~rYg2G+&bsopj!%3
zCpp`_bqP`|>cpbmAkF}p$q+n0vqIvJE`>6?NXmEwn42I=X&6oxG*znbxVj6D<&I!m
z7$Cj&3P=tuP6~U{?)Xw}VB(PHwSomVsodsU>Rs(9itcwWzt*l(4*4r+L}bbpY{hNI
zVnF2a#U#g9BS=)Xi31mwO{4|f3}e<=<=-rGy|Zqof+8a*3c|?}$VlPXx5*d5OUwoo
zrrKPr@nNT;a+SSZ5qNd#t8F0Wy&rLEDL>FE0J~zzNCeqbbTR{go7sTD(e<WF;o~#%
zc;L3q1salY84Is-0O1HIwmq5|F``PL)qQ8aiskKM`*~{`_nCEHJ5PB#if<SVB3bFD
zuJYj!)|6<0bxq+5v6*;oZr%zT@@1Q61R2t`?lxNN4vKd_0?>9G$Kixnpej}`RcDHW
zs%Qxx*%8r2q?9;b+ok33T;OsW)({6QP<k7-6{vS~LRE-MR4Ral*tS@17yxh?)gybf
zJp>W2O=9qgVg^hRvv?}gbP}+MkIiW+YaPVW=w&262+YN-9&W51EGr4pAc~}m58GY>
znx;l5MH^wr!}m?xbRQaOoS=>Rc0Q*2j+_VhfRA+Bfq+_i75)P>pY0-iZ2`;&r3Z7k
z@iOGl#laB)iMH)~vbY^EO-oRra6Ea1C<iI{aW##%1%_BX2LKM`j^ed*FD21Aa?;nb
zi=^m)H$SEeuM)ZXSZ4N>YV>pkz^|@MSxKAIo`WnNOb-lPaNx-IOY5@~sXXil0YXX&
zowS=_5_3eFSpWp&I8`9H*^Lr;OuL7okLLz(QzQjxw`sT223UXw@=K0Hvso{iTV8}<
zhQWdiR(Yu}gq*yF(z8jhLVCB?kUY)}DWnbuMY!?r#Td^r&0e>uso5PWb(lM27+p`s
z=iQv4mNliJlfk>Vy-<2(QxNEn`?&w@`0_6^Bgg)qZVnzZ!hyw(-1bk!b!`+WRDL^|
zPR*;5ovtO_WIc6UjN^{|FU`nu+RcBGLHYND{69;q|L25Eo6EJExS~*6q?!y32Ye5E
zBn^KB_|6C1WFO~3JotXUuDU5d{Kfk?y6`2y6k7!Gi4r?OuE{YKPN5Q?A6b9mN~5xG
z3Qdh^WL>tuc|^CL4sFUkN<!=&%cXb=vuSZ%70x><LHW-un*ipqe5ug2O)Cd)jd{1s
zG+$K+B0-(v%S`Gzx4@vp7rld_)K|GpeBN>{AM=&3_}Q%H#JP|0=em)s8GEah>#<Vt
zF_zJ~pEii3raY$Z8OkM{i*nZVzt(AzL%tYgqIR<FXacy!$ry#$_Bo`Lr8yZ}zZk)g
zn5&p4D~g1J0xOXU$Kzw}uCXMkDF`(|4O-2xMEv0m^*J(mUeE92H|XGXy(GqfW3V)>
zrz&F~5hak{)DUoX+)fxGvHqer6v<YI)PI2KggqxHEQl!islXdIzk+>Gh^Z-;UV-IZ
zW@CW<tq5cJ=O+~?glSOTOm>CsjVmu&PLlHB`#ED`H%ULapeveI$Jk*%YWGr}-?VD6
zQrqsA^Yj>F(;RwIHP=4WMWtSqmyYX?`h4-n`k0J6i=ahKPqmEUup-@u(xK-SBd&7l
zU!IK7_B?sL@~D|8;n^xIe(AXez+d`~yhl`dB9i#k0Udz$*~81|HsaS$E|Zc~iyXSY
zrs7vuzXeK9^4NeHBBdE=o#MQ3o|L;@9^WiHc0PrZhMJ*BwGaoIcDG|O6(KKUSBnI2
z9Bs)bqb><TMDN+;+%(i~E@cC7FHSs688T}ZB2RkIEA<a<=6`K4N<Q6e_;B;bR`chY
OAKyqKa(*_93;zpRil4Fo

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/packages/optimizer/images/sgd_animation.gif b/docs/python_docs/python/tutorials/packages/optimizer/images/sgd_animation.gif
new file mode 100644
index 0000000000000000000000000000000000000000..12d558239c1603b49e0b0cf76f20c7a6fe8b4f34
GIT binary patch
literal 44001
zcmce-XHXPR->^H&F1t$x$q15>EEz<}84;E!Ai^pj8I~YP!jiLO1O#1j6v-?}mmDRC
z1Qn1hl7r;1umAfA_j%ts&#801oUZ;bRoydFHPwCfZ@Pb%hL*a_-KT8eNze&M6!=^4
z00;mP00dwV7y=ML2q1)DLMQ}E1S29PCZ!;wxIuY?hMI<+mYz}d6%zv!lWHuJRt7T@
zGqY;ME#_P7tn8c$D9(rJTpV27+R@zlZ+Urm`FZ&TP4k2Vgzi{Xi3p2`i;9bDI7pbL
zNs3EJx{gR0gi1NIOF8#ROG-=I)X2)n%IbN`B6DTk2JXA{$=#Eab8b^G@>7&oRP^ju
zwn|o2R#mmdsC#_UP}9)X)Yf(`df<uG(bmz`(bd<}HwYdwGB7eVF*WrpGIL3JZ1xyw
ziF_9P!RDEb{d0SVptsJB&MqN&ZZ2+au5Kt-l)Ia|2g<|K-P0?+!Q1PFclw}D)>mI|
zU;n@e|Dv6jelLSw1_T8J1qTMBpPPiDL&Fk_BeL2e!y_XxBhiu3F|i5pvGIusiD@Zm
z>F?@ar@ziD>CCEIc$4)er+O+k=WT9fe{S_S<}Icmub?o$sHmW*xUjgSsHCL0u(Y_e
ztZBTwexUr*az$B1W%piHWmRo;ZSBX&#`?ylzLn;t=H|}H=Ff+%pZeNb+S(?sKed1A
z#C3Lec6Sd>_jL92boKU*E%r_w^mTXj_4M`i_7C(84F0$nT)_`jV}=HXJ`a5!86Fw^
zJUaGycx+^BY;tit4K+SGK0Y=+K706O^?D-s!NmB))a3Nc`t`TzZ{L1y{`mf5?#JAG
zbJ6_l{QS!9&$*vJH!c_E7Z&HfEiEi9?c<l1R#ul+*Vop6W3xBbH#TctZEkFDZ*K2w
z?d*=U?e6UDP51Bb?(hG(I9QxM*grgM&Hi(6c;wA<bol4!?E3i6@yXBe)84Yv<I~gY
z>$9zuv$M1F*}?OJ?eo*~^YhD#vx|%Ki;IiPi;L^a^OMW-%fDBb7gv{8f9v(t?7-E<
z+116>zsmLXT-Wv7;PuM*_1@a`;nwxp@!xx{FR!mJ@z+;>dtd)k|0#I94=X-J8{ZO$
z@6N^#zsJw^<LCPE7uWc!OZ*ic|F;7k&yFY2RWmYFQ9`JR35$S1f2RZc8HAAd@04J_
z7E<xBed=Xv!{+1QWzS~oZ|Cq_*xJp7&D+7o){V`}jZH&Koz2?K)!Wv?%hm%B{AV)#
zGp_(R9=IT4)~v>Kgu$u#E&8kTx}qpKwR1FU@_XVK#2sh*YYO@j*;HbgwQ36oQh5!_
zEe2}2d%U+T26MFPia)=R@!XIgsw)|Nrx-@eqFrA)UZ9!E|75VfY@!4acz%<<p?tE!
zw9avEu%Tl51F|cY<w0ZRw>tZY@`tiJW(ws_%Y(TOnyThneGfzD2AV$nj9b25G+&`u
z``HyuEnqo}71eA{;(Snbo=BDR1mAn~X1KZT*C<9M?$*PWdIt>NdoD}mw0?8C+Ir~i
z!`4QZ_vybD@=^s3*M8u_NLY2-u=~rJ;N4nlyBAI?V+9Z1>9)5VeF$Ov+}xGpv$na=
z6^Cctovb-K6ab$%j<8T{28)glz0>>j@#=zlU{m43E%~ObCm_?u+=OfT83N}iT?i#L
znH-fY$K=QI##P^V>PGgwbTN|In|JQ{)g|YWcu6#GeiUzU=~Aqqx73KtHN&CS^E<>}
zml9;wN|#?fy0RYT{khM(lB7ykR*<OZ*vM$1F7=x$jz^tuHO<JxVKv?KdD&`)h4=5(
z*T_h|wM?GDLkcnO6wdtn&r^P{y+QTz{mSv0a`=_&yH@t=ZNRa^YAVw?Z)^yf&T&02
z8hV>IFGgx(y&y@w(Y`Rv#F05D^Et8py;R4I-zAtxetzwYNNI<%(qg6`rIky4WujqB
z_^FM``YA_&(sC0Y$EvpD4X5(fYyPcjuQ|ucXCKJ5Cu@eKHn-}=)GwTC@hIwd^)t^a
zwi{=?A5b?;`3vk|SLU@V-Q&dkc3QR?E0*%7S_F36{%~Zvwx6z5xOwgEZ0>#nhP-#(
zK=jUg9Z5%Vd!3}xIdmyMskZjIOEa8A)j#k!EcY<`Z0+~4*=5(bu|+x`4Dgmz9%yq4
zG#?C!^a&mg6E)}@ewJC*akrxsXg(ZOgyqt7FP0xI$;@&EewLh+2z>ZOV~9UV!RV2T
z?W7SkWMW)feEZMDY#Z;N8LQQRn$O%1yxuEqx6Me-JT)YI{?x^@?vJ?dulG&!lW06k
zssgex;m;4m9jD_(+0=wbqf+v#$3LcVncU*7)ndflF_33bHyzSI>a;q{Ps1tYXDL;P
zI0D9$pmaI_2tqD_!)=jCETF-ZItV2wO8}cPk?8CKm{NtgqnpMuEjjY$3m^r~m0zC{
zCuaVt?$1$#Kvlb5bn1imUrAgN{(K1pTXKsv<q&AG`xlNAQmd>Zl%cdTI5Ytaz~HYh
zPj>NmfD{9OkZ6b?4%A8=LLP~M2p2ArhuL@YIX;cAaPM+(7q>W(;;q{L#%*e_IdEFu
zL3XJ1o3RxU!9!3JDa5Q4#EixfKtvR;iO>L422JkM-*xMJFG`Vgi5yR+hz5O43jW&y
z(?)?gk?dq4)#3ae`=tC2i`<@WYAxUI?-70$;Vxfui|8(Qk<)2D?f3moDsQ$>VlE^a
zjLQS9WvW0Z-2pIWk?N$sPmUM~c-{vHyfifkD3NG*rXt)M1rf%wQ&q03(H8y5_*ga+
zceaFSQlFCitUACtnyJPyg@|CU#t``bdB;u|gEpBLO_!7a2yS2inCSu-HxWV+j)W|W
zvJ)HQl<9Fmq!>dTshwuAXrH3a9b_J{0V?!{>g{mWbu^Uw6ivunjfTunWe{90Ge7nN
zIOs&tl*kau;-pCBwopcPv2mlV47G>f58s|%W;_oa$HxR`?y*ubL737|VZ>|zTGeKi
zIe|eJYJ-kZlnJHAqM<UU?4kL-J;IS8q|fhnnfq__5x&cdf4ap%SKzD4ehI)Nyx57g
zC_Bm4rqL?IBCnS3quD#k6d;OdsJ5aC8$zFqkDJWVX{f~DSJ*&Z{4Nv6228-fI_4fy
z2}X;+;o*w?cMrURNf!W;0Ax5nrh`0Yy<1S~JU;jtpVKG5oVlv$`j+nvRnuW_W|mYW
zg+snDw3hbp(S^Vykc)8nzfWPtA@D@#SE2O|cp8zAn>r!n={TLVb-#gP|6@DN3Miu*
zO}yb)2V9~Q4Z57}`oNDNV8@2s=v9nT!^d;bb-;%wRW}+w{+4ufxchyf|Ah3$;I{S^
zLnM;XJxWYO3C2;4BLMm*J(z$TNGU*)iHy>_<j~$0*|F;zswR1L%-W3XW(dH61~w?7
zWW9E;=@20|7&=HQ@Ae4K@{f`Tzel}%SRNOCA=6?~fc=%8atBcZzlUCgm*iC8ZL~D;
zwn!M#R8?4qVcs|Sr0cZSlIUT5oRUQY=<4w*h^h4x#AX3zY^uU;B&x;N!1)15g7PBH
znT8}r)5f5{bZ8vt{jnPm%JmG-fC}S>DA#e+zIm>wWBcq$(oG)!Ue3X4Fmxsf@V2SB
zW<!B_3fZZe&>Od-F6u|1Fue7P3%8kB{{#!cXJiOmIlnF6Pr_NYu3?enXlsMj+d{^M
zE-$lVP<c^Yra;DBBQZQ12Fy3MNYfE*XY`}vTVcN7$dt#X2;%7^i|InY$Cef0D@2qT
zT-IaZUVWt<nb>C88EYjfi~`a2NwL!d#39qk%+vwbzJ6r#_Ei@A>J8E=`Wd-4Z^-Kt
z*xzB02$D4o<JU>6x0yKq!O16~->Xk{I7dJ(D<TGzZ(Bl3aRcSix1|yLiHV&NE(uKR
z83o<3iq0W~Z9%)t+;7k~D?27R$|K+OeBNW>c2Nxx>8uAOZCtUlK@0#$r=C$l5X4%Y
z<j$l8Cw<XlNIo$K-q-pA*<CAkfMaa(0M%s|#Hohoq(iO)4ec$Tpm27wQiko*r57JO
z-KDh4O}i+Wt3UNIVQLuHI~&D4$+ADdXJbMJy6{h9I7=Uwq%`2DfN>J){V7{1ZTcw|
zk7XzObNZ0KL@WJlEXRX`_Y}7@O^=*>{L$yn@hqI;@8BG$YN70){KJr%AoWSW#)()u
zS@cF;I5Fj|sEY|D_WWV8zL!CUM)UF60;0ZGLPo(vdm*<lg0@|hMRpgp4KYC=I6K2x
zO-j4ce1))R#f9a4H=<#o6x7+3EkJ4rv!{!KR<NBp1@QQ*F?wBF60DEI4QU;AgGaA^
z-`7-i$~?yF9?^sE;o6RL`22A;R&ENj$klU09HZ*W@y?H6Swwpe`Fxzl)o(T`jwK4c
zI?><L0fx^-+HB?H_Q2<!DpG1-!=9HXC9LnHsdJ7gB5+=QHin`AdavUpostlfQOF(r
z7a=9!OCL>Q04|{%DvS-Zq+xKv1)p1keAqaq>eLW<L1fmRXvIMHLIqoZ6($p+ZhF(y
z1S&xj4lF>rFg~)_FzBgxY%i&)Vi-_OIN}p-amw`bH5Ay>h*|-{6ob`fI1RDl=`_A2
zCaUTZP+&5`!~Smv_&IerKY|2!5owhc>4=SNR|HGbL;|?bBk8E<Q^pV~QWsxMSv*3G
zg`V|sN0`E4;1vL&u~Q{OlRVChehX(*dl8`r1NaeYQ8TpF(o#QvQS8!(YqL=ppTyK=
z(wjz-SbA%cE;#0^*_W<IDIW&5BK_3UfFC@bb-wZLAY<307$6CHbbM1JI#MfN?h0Y_
zf-*8nBDf|xe%4+BkAy%V04+jE{*bn;RPGu<Cfj5#kK>&)y?LjfI){T`6ABQE7~7vP
z#8W3GhS=%=z*#D28o_Xh^?tZYQF5#SR72ev=zf*Ts2M{6B7h8`w(FaG*#X-0)>Iiw
zLhdKofQ_GsBvX!=F!-fBqhjFo)n!cbWuK0kz{>G6z7nL2;Pp#2#qS3qDPNr%5=|~8
zeLRgrND@~xnCtJQ%4DUPh1h0{q21t!@tLT>e7QRZ6r`CN0D%5cnr1;nt9eKv;Gims
z5DJLCsbm%-eN16T8L3`K;5CtsBBC`lAtKXMu~*Dk^Ub&i_MO&@2CDrYGrkTjqd`a#
z9`T0%p-<OWilI4VLyLzw69GEtv@o+QXLg3B5(O<}Y;Sa=A(6xilvN)UCBa2tdYqDx
zmCYfM8daFYsz~%lI>tXn<c+>7evgQsGE%}z6S+r`T$+u$OQW_*7^UU@RE99-HoCNo
zCcasOr<C+bl7gvOA}M2LhhMJwBSwuz^QCnt!Z=~bkLEMpOk{<Q<4O_c*%yD=6`C89
zo1~v~pNgRi09_qwkkS%(SGy%shl0WH$U(kYi?HUb7}9j@>TiD6Ss3DM))3IEsIP!1
zE|~NP<b+T{V13}@z9i+6`6Z7BMPS^hC>>w$<Av9!*gW3sd?I}#B-R{;AVRSP{o;D{
zI4NK*Lu#=uk(Y@&%v!z@fc}orkZJ%>_OQiLASW}Czon44MG}u9hBcV0_>$;iJY;MP
zi2#WIUO0Y_?db$1Q#odtT5pgNu8i?;w=slZAT?|p_nOV=_MT`D7G|qjzNvWyRD*58
ziM7zhf+Y&HI7l4AI>U&DjqlDg8;)rl+z^j)EIuiLA|YW0ZukWVH6MX7&D)*3nSbN(
zvpOJC-w2N(*2Ottr%hSd2;-4?Sv9`0zNH_(yj&@MjK`2Bq6t~Yyp~}A2?nC5oM9>j
zq4on4<dwZ)dW|52_Ywgil%9@}BzB5{n>-HZT_7z0-k#?x2CMkwVarob5`u$#(RTlu
z4YEd~^p*=HM3Pw?D*K3EcSb!sTZVirdy(%?79s-KkOJVyK;$ae7D*(zM&U`zDS9T*
zIOS*7OJoN?Kt3w6lh1^uwW`%c@EA{65#;E+s*j0bGh2Zh;i{7&v6caqz~$0B(k<fx
zo;5+=Agu8sq*sNgW-*Hkd<H6a0jiM%5_nnkl@4q-R{Z3==qW@y<F$hMDJk>IN(6Uu
zg&NQUr+I44QI3F0U_5Rz)?9c3v;f4u8s3T^GmHo`?dIZE;i{EYZsOr+#lam!Aap21
zc!^<x?cZNbXbGj>R9D>7KNL;qXHhHhQ_c0|M8-*!S(9hfQp&Cc>0bA6Ug57*4kSUx
zMX+4J7Su1{vgWUx-{`tZQh<T!Q-Z0-6p|xr0i2Bkig*qDRyD-7U{77W5SFHe?O<f+
z#nqjGA-Bq~FR1-EJV?-NRUQ|XQ>zMeieQ}uXf~QeWK(a&q>(?rvEGqmvYI4U1g47t
zGqODoS+RuUlR(-Fu$$9Qhlq^ha0+pV)<%*X1mUX+V^HSe^Y#;~=O;u?NC+dkg@DrP
zAiafO1o8w8%NZldC$`jUXg=?FZYIG!xkr+}07D`n{FJEV3YBUJfK~+JP)#KLKrj4^
zA_$QPMR4qV|0ok<>s#%rUYI@$=15uhXUnn8t%hn*5|lo0M;6y!*8zW*Y13+G7>c-(
zpIb8(_l=s_na6JOCU-xYIBZN=g^fUbMFB0Lc_0a}q96_!A`$8O-`XOv->B6ZpjRXS
z5t@-VKG&3v#QcS(F_8X{-InJMX$=5>r1p*vYE`hVKr{xh0}x#l)V~^Dx<Jxz>okM+
zG-9No0gKW=cxj~g21EqqEj*26imOcyNXl{WAT$)g2H_Y3Or2Ej8tLL6gJeYr{c*%1
z_v{fMV{4IQaz#0(_}p7BdpX8pbg0aohREX4#4p$grISEK&TS<DPXJXb7Dcdb8Z@XH
zo-;<AQpkP1N2g<G;md6oA7G@RgM2OcXzsqp3u6nbK`SEzvX6Vjxi~m_Oj$z_%<}gg
zZN~Tie1avl>4?DIpqnT2amM3i)QkkH-a|f0C2XQOy;*=_D8{)@s^hk8!4wakk~E(Z
zZi|F)EC4-23TUrU0vmu731X)N%cCI1C@2bm$L$f7SDSR~kv`nIZGsmgAEqP?kz|hp
z;3yO{Y85O>Nx(P;oKY)Is_EiMlF0W!DgZQOj3~OzY$+nok(!}1TR|H28QyMWB=vf?
zA*uZ_E%qK~p!FO5T9S+fm~9e76aW$L8BfS+PDvJ^>H%$|FR}oneFb6)K<$#CC?pI`
z36H_S(=bH2DB|)l;twJu4JZ;U<u@#fq(OwFZjZPeMf|RsC=CbC0>ga(m=hX`L=);H
zLDT_=EG3u&2cpISS}r{yg@|Xp3dBjeS_r5=K$K&uK0n;_c%0&TuLc-P#<`5S*G%4`
zVi?BNMSXqcX-*|YZRaAz?cE0V1fW_NFl#l?fS>b{Hq!98(nif`IA>`%nEOUVX{^p^
zJcC=kmzbY8Qy{1YSWsXM09v_EJo<)F+fa|2ih354eCC-3*Pa}FC~gcj))q-7!}RuD
z(adfP<?(Q1x(6Zv7!m_frUWyO0o&VY9uhxfCFVDU0AAO5kj*;>|G|BhpHI|O*{c-D
zX~BvZh~>iHVc+j!yBH%iX-~y4xL|uG9?l}&+n<|;knB8Khat)&4ScKBsm!g7xR*sp
zu{h!koH7znkj4-k4g)OjmtMqZ2$7qq8fk9HElYX<qzDiThCrGPqBBP51i;fV#8^sF
zr<>dy-0d%@80s*_6@)Oow<~4%GG<kBfa&I))Ha&gY-UbX<!p8CD>P~I7;y=LC<ZrS
zzd(pUL6k9IK}v!OYfyF9U(^Hey*Zna5B1#u)*94CO(2K@QxOtAK|x&?U@?kBC1b=`
zBx$-lcd%unY$Ux-v$@3A%97}H$K(Z;aVZmr96Drj;_y2KWiiH@?4${4d2enE4QVri
zq!LYBP)(GCgU2D@VU+NY1=v9q%ooSvgM#^DU<W&}FgbVxVkQwol#eE^6d`G*oalMQ
zExq=5*10hz#5M$@@i!8h<k&tjvPVdUNi}@Ty`9h&&vHLwAfB08Vp}h#w_m2WdmxBu
z&@_<*oj6vN9fA)b4kriP+u}4`X_DpXJ0lMGem?ej@kn8`zf+P$O>(GW70_ci#4$1m
zQoiC~t4$oq`4QBKH!ylj3@|Rd(ox8KIFx#2BoTM_4Vm>MwHDao=TZMT#T%4n)C%Fp
zyGO$(S(qcbZWjpahhx-lia(hOf@4KgmS``$xLs(8`|Ml)(R4%)Veefa?90A2{#2=e
zd7{DG=PowpLz+%Z0w5ATx<j)(AQI9+Y_+|T`R+`njB%8bfcosjmhaTh%JM4j<I{w5
z3o|2QWDZP(Gg|K`OivV{1`96*xW8O9CZ?ErV;9%Y(v*%z&M4{vG!JfG^s&y;eVWB|
zpI0;tapFs*9DT0L4UNd&DzE?v3!_TnkBqoCjx%r{33*p(!fZ)>5%$67lZltkZvJMb
z7fF^!z_{*$VkjZmE!9lKqH-h|i+nhP{!(}7@}-<12nV1!@m2Z|MuQd26z+#_RkI9M
zwKIgCd<<tiM1Bh51}FhsUbaFqK0fALT6YbRuV(h@qNyTO9YEe^ieNI{Fe*2GZ`qSA
zx!MqH3t505`mGg`<TVH<vrSyIUGb(YSR%~GM8k{i`JQ`eYnC4audWH1P4|$U5oE0D
zIi~y1dLxp{wE@62C3yC`sH>fnz4b`0Tria7{BX4~0U%`n(E7IH@1J~%WO;n-{JjAW
z`_+@%%Ke2!=jN931N}P0T#tKG5w$d^e6TlHA?od;d9RJh_f~yxpImtF%(Vx@SS>Go
z4^~_8L&tZTLlRua@B(;>Z)L$N!$14pS>c1OFDG6mlL93EWgis46sjUhg!DGmO0ecQ
zVPzr;<#KvBLE+-Nq{uhbDrHzE4n<n~gKAZ3JylUPTB@bk7%VVTc(;~g38i-Pp`!rw
z@5ch7Yg{2>n$#|wwVIrXTa~$uydX3mPp^Ss-8(6jT5W+3_esl;Vw6A?AEsMON3@Tt
zPUlCsYAS!B*KB`?MQDM$_R^O^&%8z>Gqx(p_3}DB1w2IJVby{RS&77Hct8F&W{vc#
zUKPV#p$0Wmr+R}2wqoLj52ddj3=u7zqi@#UNcGUy)l*9sty)9Iq95@-lr%B3D|%7?
zz>O?MK+yFD04Qe1dFosIc6ORh5InW5d-~dzuF(8lgh_oL#{(gi5K4jcLHnl9MgzeC
z@I7eoIaTaq&rvn$CoiVCCw_2R>V!9R)v`oAc{%Fb_m13qWxebro=|4N?+y3NNU)&8
zjS`jxFHQYO5_PIFGbUttd~i8C)w2ZYHSBW8cY@GIz|z$^dz8>2R<(-Gp>|pkqjUFp
zmfdlkkmmAWl71xfRMzufP}@PkWGs8!t{_p}yt6R~%O|N2<Q)C0FxSZm9}5T|)VuOa
z)>@oC)Hzn9@JMegFb4apYdX4?H%Gp4s^U9wdVk#<@-qVV-Q2bPWbI{9<2*N#vqa>T
z|DfIZ*=_e80hMOAvdgDy%D0O4q}}@N^xgLivl^&-1VOj-dW2mPtyK=ngSZmJzrbxW
zx`n8p9Hu92g!d&ZU{7mjY=5<%v{(G*Hvmi*h_(|$@V}^a3s2c<dEVG4Ml<{no%cya
ze^{1C5Cl#6?|#C6&$m;?65SRQ-tl9`QscQ3DBgY=K!eGF2WZi2cKXZr*JT`vu=w$$
zYe*@52yK#yONi=Fc6h6I{b!fW2eW*IsJw6%Hpx+HGzgauA)6e+qghvmY%letv9oMw
zHr1!Z##m*#kw^g~B_l9aEz?5?rcjuH10M-8c=#{`ez7#VEkH5d^@9hb@Ub)GBUK)$
zaUHM))snw8+LCOPEWTQmeM$rZyPpVH1z3{yB4eIFtm(s$A><Xj87WFsTv87hfVWfX
z(o^HFAe$PGBbDU&O_Hq`NM{e37s4N&hDF9a2;Gyx#YfH6u;T<&Ea-4K8+X5?P`W?k
z8U0F8b*ikf9~MXc&AQ`P`T}W!CiCrPQF4==6;@eZ8el|G^`S?cKb}$<EsLX`oBZ<n
zQ_g2@xo)jQBzu%&I4i(_g4A4w$>;rG6204(Mhq!P?!-&L4FYP?5_pwvd38URVIk1*
zh>etpyst(hgHr+9EWlB|hD)LQL@wKEY`CzeY;)kx9#iE)iIhba_TThE>Aa9j@s!Qj
zgG9UfP^vIww;;n=OCv+r+g_DH;T9|vodtCs*(D5(s?FI6{91ThPXE>q56vbrRYy4J
zEw!Z)k!mgtpTxo=qLE00w{+bYg;mm{-VU<-8+Jy22;!ThM5O>!o`>R`(PPs*pg7<+
zC!)vEq@l%lbgbfa;J~zuSZy+8PEv(60bd1ttio4lztZi>R?p>+L1v_cFnnEAOQA)g
z*c6wYp5}xkBw=Z}RVW0v)@J$`^QG}CLCnqHaJ&pG4uLhv5HV<3f7CHJMk=BPSFICF
zQEZ9)Ao1JQmCVJ_Oy(Ok?Nl{hb48sEO`2$VQ0!iY6LB0epFj2%s*;57!6T?amBsE$
z&T%~c4av8)rkY+Gxp--H*LAS1(j<TDCl%#+@}oYQ5AY3d!``z5%ljzfB;jUQ2tDVz
zai(U6+FR5Y{lls0kDjU2D&L<$ioMXu=!j<u=sv=P1sih7_Fa~No8}+LBLy6ggMc~c
zJ7L(G*nB794fhm~P0ssH*6T;1@51IoD@n6H$yRM?*53=I6i-IxJY844Z_U&f-VIS2
z6p|uF3t7bXc--6o$CXpo-d|=78GGs|gKzu6B>iombKs6j(lcu8C9d7y@Fzc+R#zR>
zk&7D3#*gcon31Aha>Ppyv{Hac;f-P;?e!0?a;8tYg(Td25fR)B`_a1f1n8(jcD4fv
zAm<eB=A`b`@+JQDP3mv~r|3A4k^5>HnWXziIE2PXB!rL+Jw}97B$Njrf`1PM-bAYj
zquhxV&;l8>fNm_koRj&k;2?>gQ3^-|4$*VTD?xUaq}+8rWygSMGtq~-byS3?{ON{9
z%lxMAYaR8r&)jPm6pDrhgJ+eAkVLZ}%IYagA|$XR54`lv6d{u$BFLuvP`@hY=Fb}Q
zJ+!7@Qk4?JPAX9^kPE&c`3#LGx$$x){P`$w$qTVKZJnpkpsk<$Vjl|Jd42zj!*ujn
zyifXWDCwS388|sw`SdB-V;KT6F^u!65)QtfpVn<F?-_c^RY&H>Zpelr;qlSI;rrFp
z5nwu6Y~CVT*WzBNKFNd3OHx~O7bl!1&3}A$L%ifv5laBy2BBY$Bru9vKLsVgF^}l5
zT^W5HQu*>pF3~D28n~brEedxxUoR5#6O_YPZzg2cEvykHve{vU!qY8-9{*S}@#!5s
z-&xTfERp(R%{U>Lh+ij!NqBUqtnTzB(DQVY`MY&kJZF|I7mFDEo?%z${=P)XeNs)~
zf+kd+--jOlXrAyjIRua5R=c?G)gPKhNwpAqrvi$0`E*&aeElognR+x`A-SPl=!}0!
zn_kyY?c<}cv!5`9o%Wt1ODZ6}_Et=(sqEG4WzJ@Qy!&K_0=_rylTfK9neN)sRcd_^
zO9+yHjUiNwfhm}hmSU0EWSu$3UieRef_r{@5QkjAMby)E4(wiNiFQhL4biC<TF9Qn
zMVvm40XE%5ME#xbb1aV5rh|@@XG4tiizX*_y~*Quc_TW^lD*!mm5r&IM_-A)s+wsT
z444|y(W@$>{S-4P6?}-2C>dU-_|~s3u}A!X)Alo+dA@q5B39>=qQ#!WP1Cj3Dj9it
zmm!n{dMc32#)IdYkOU(`0*QE4tUuM;$5~*LzSJ^Y-|Uj_ZV|JAFEoR<mCy|CIQN!R
zrLGu?g}2`5Jelt*>x!`!1O*XgDhgf&Twe=`XyUs<<GwE3qvE2?9;4!*0nHKT1}lQ=
zCX{@^opy`F(lhb^3LW`U?O}J1#n&Fc5T&lM;`<?S7yBQ3`r8-s-f;v_$LqffPU7RR
z0WGA`enF9xA9UXR-s7Z7uVWO$Lz8#1NHvI64aHJBI;2dN-ggzj1%eCY7Q5OgV;{wG
zlgp~Q6!tMK_iEV?Gl|H>0$B%(HM8#ZVdF{w8d!j9lmWNkoSmcqCakhADo4<DB~}Pd
z3Afk8$FLPEjWtlr#JmN)WuJ}aSFCHn-Z#dSlW=4$g!QMd2)ROTnS6s2Fl2`n620kX
zuaUup+LKtFrhP@AiQe>8qBTT9Lo=-h3UowbE@2tt?0}3=%tGu6n*?xLne(SiBd%zz
zCGK6A#8&`ayDwMR&EstG-ttdRaCAgp3O8#PT`)HCL@NP|p-9^&3E7KsL_YvD(OE_v
zVl|CQPAQE{w6$Erox^Inf^r^<H*W10Z_CCT$7qoKiF<7!1&nFyHWwv<3T`*6b^RH(
zr+L6joe^yxH_w|#?jP;H2tCkBXfLd%YlR+k5D}tSqQZ%`#w8=Sn#Wc?zg<#ytI5VE
z^rI!c;8nP=%7Dfj7ci5o!9xd)%KlPe1DO)$fqAdO(Mt^U4Sj$70MVPeAN}zXj-R!}
z%W;^uaWnKey#4;l0&|Cb{n=DoX`|6~BdQ?PoC}!dzH*Lpbi57!Abb#Qp~g_5TfRV?
z5Fd9`|M}boaqs+dqdg(n<Duq?Xtx*=0!#_O0xvv`D8dnui@YIxNK}Y`ett73UN8nV
z)^|Vu?C%G4+j{-WCOIsofISID)^l@V{NwNLFoFo;XB(^{2UN^Y#yQ#xenR+AA#tw^
z<mZMkcXV{a?c+=_{5k!M71;aQywZRv`caKGC*cH2`AbY;u{1~!E~f=x4e&AcaWnjV
z(_F+J18@;p8b-2-doQ@STu9`6sZ_Yc#E!I`Yz&!fV_&fuWcYq$F_@_Co>2chap4@@
zC=(&Gp-=ptl7bL;rK1$1NNmbf_K0fy&jqaCUYh{bNb};WQNt~+&%8QUDSqAB9Py$_
zGqNn5F{Jeqc?)F0UgdX;C#?usx!SpJn1A&fE@<-ttY$ukQ&#_>Ns0?69?Th}fa@b0
zOs=R*7Tl?@r96q{lV4<6jd~h_Wx&COVZ3Tz?-eqng=aY(y*;C0=;k8rAO7^=BJQd2
ztE{bfX2T%{kqVA|x@$*ia(+f%$(gVsX}|ZvcF*`{snnjeWF^?&`D&1k4@TE9%YNcq
zj1}HWC)T8kRhvnoHQO;TC6<8J{(uAThpzaA_EM5D+Ep^Lp>AR~gxFh2{Ij1jf6G;?
zu%b0_8{o#CqIoM3267#?F44NSvG2kS8&6^Mpn`I*>9VW0?;;9|xb;?@)nA(l@?F9d
zswayTAEmMttJ<f0MWW5^&C6{cx1X8Ge0d8l;0#J2p%URwRUE}uqbp;G3Xze4`-6c7
z1C89Xg=gP}6(dO?0Pr3gjL{VFFipdnbEG}aTp;G-6#>RTg+j9<lG$C{@{UW79oh(q
zKBEOyiB3}x=N?4Sa*qK7TI<kwwCG9IU5f>Fp=$ezXU^(5V^#Q$!01L)GBZck+-cOO
zgrcAC=T?uhyIA^w@xjJZNDe~myFtv*4@k}oyv&xUDw-fzA`f9Zzu%MI)y|#sJ=*n2
zQJzjh;4`9+U*?-Fh^j<>eBme?<otPA#CVu6`4yL2ncP#xb?ddvSha0vl^jw3&B&2#
z?bS)E;YBkw@u42X69nk3E}O=r@3(20(VC?%-<=a|?-Qs=<~hYIP`lE<i!jnzBF;L{
z7L<w5^ZNpR`y_24c8d``0Pf#BgUL%u&!o@6=$5iXt=#gy6w?5=_=L#OCd;<a&#6LT
zf-ys$f;JwBBFsyIYndE8#hM&NgF0vizWWP3nhR!XABLsyV(QU8m2XN{ErgEm(KK?G
z{pRl`%KA$S2(o}Kl}vB^7F;u9m#bI&GWM(K=t$bKs)<c0|2GEJSSNIyfrnalbSIMF
zt>|8##184XG3JRURt;+V9hl<yn34WHT$~|H**@;^*5lz;${Z9BX0&Xe8(Y->6sYEx
z8OcFZrK_uxx*9Trhu`d+!*s~G1~(t{C2<7XfNrd;+VQ05TP`M1itQJ{$+xwH69@~!
z;ZGcxW>}cqCs*C8Xben9su&PC(UoG?uzQL;cbOi0yk)X~)k|bT-x>hL1x`fKk}lLh
zu)rf^-mj1hGKY_MX%>mAa|dtC^j)vSn)%vBj3?olGJbtvUyrW)^`e<uuO~*1dAN{+
z$J&=Q6pSW4orzQbod#vMTBEC?jL@5<8sn35D}&~6C=+H5C7S%sU2`DHNF`VzCIE9%
zVWAWxq*V)0y(a~%zke5dZ@7$)P!36d>n1|c*xUfUwJUw|8-fPk?6nEnsD=vA-ug_t
zNc>PUfz_Kh|9f`B1<WFIf<HS?<^6igHMQr*xbOffpgObl{{0oR@JAon;>@^PrM8}T
zY_^`hx$Sa;N2IoApK$o|Y=hudS0u9L<Bbe;`a=7tmT%FPzX9p!h$l#hAO`bzXJZiS
zIQ)G3CHJN@-N!~G^pA@f1kspR(9>A^eCW6tKSlRGo}8h|H`3w&`)eG-d;og8tnl;s
zPNDzy+(_KeCh@P|DR<~7XZQ8zr*;&zw$>Zt^^FOLH}Y<1hJ6a%*;d~@%zjSG9v5*C
zp_~bp1|VyiITjCh+Z|jvV;pHY%2gaOlOwypD1-6#&Jv>35!rfSviF#8j}WE+QlMUl
z+#~wH?I7#~>qGTD-X;CDcj3544%>flzDId`UzTbAMoBYFf1lQFpU%g9Cn>S6I+4C)
zpQ&m8X5T*Z^ghe4{adeL#K9f!pa<+H+$2#hG#VbFb9=jw+qitDsXvHzFLK`wKHzR@
z7d4C+?q}t{?J*PWSl#Pfs0oo~fSB0F;qMb13fs|h>UsVgiC4=6%VvftiW~-Kdhoj$
z1uH`R%OhcqZ@R&Mx(_{-DE=t2{!!NWqpaYi^5~B$UQ8p(OVP?p%gsyU-5*UmuLmFg
zsDAR&x&5d62a!x)*uBiKyS#^8N*<Cv360nYrPJ_-=p)nDM`k5Q=1oVB`;IK8kDmNG
zvOGDmf*m92kDuVf6*I%`RfpZff@RQQ50m7KA9bp;d<=#|A7F%nXT<I$-gEg?>oR)m
za&nB?J;q*mqcmDPSWkMCd|pWVpsY@i@`XNn%zkb@u1%iZR4?vDAFB2f3UzyOe&&5}
zdPkItybygD*hk2x=V8BF&O8CB0`~B0AL&G+!qjABHyk`8r%xx9V@*-s$B=^nCBlZD
zckw@cJ&7h587J%5UYk8{E4;TGY}M)S&a;N{`w{1t6|5My9;L^?luPkBbu<Mt18Hcd
zOQk<Ad~}XhinXhTh^w6!y*@9~qc2p1K7iYlPy4^r*B8nQk8Jb9&|eG<lxmCA=CEGW
zYh*LmL82dBG)3hH`x5HHE2^U|TBR+FW#S(8Tznj5&Q>5?+q>v^&EmWp{_e!5^HIPD
z_Ktjoyq+k{yiX#L{{HV{&foW4+L>u(vyE%BQPxLYj!~pFo<@97@EF$!e9m`)uf6zM
zaxZ~C+I0egpZGo86j(K_p8oCvAG9YjeG-^BkePn_`_J3g35@C4ch08n1XUP%zE!yF
z`*gKVktrYE<~{}~u)5y5lUe*BU~Kw&H!*4Ck;yI$e;`dRR2I%7g`cI^BOy7hdwqR6
zs(WtzvdId6@oDqX45XVq_!1WFO#r(gXurqS8BR<u=Dfbg-W`48Hu09-K1XjnleEXy
z`aWlW5{E{Lpu+)|c02*TjOl39sx?i_?n|Y^A<syTtk1#L@59?;80DxNLXLlUza(y7
zUfqk>u=-MJP-5`j@rZw_(%i>jYvV{@rutc5D9f+WpMMW``fFV_kA-Hj9>2c4cRCTC
zZ}UGn*xr1!iw$0u_<Un__s$Z&JC<JDb?a1grSI`=Lvp6NxleW(9*nQd&cxTp@-<S0
zU1V)mzLx(mC#|eZ`7lypH&NwsA+<Bx;&WK_dE2U5yLal=j?1OY!OGxk4f*X^zgAqU
zpeX0{rR?$6_a@Kjk;~SfxF)G5sbtsp&;M*p*SYP^Z!Y3KNC~xHUd#WJM0~iL=wqQE
zm`jizc+Kd`7D~jAUGM|RHkgEvYRj-k(DF~QL~8cfm?$_Xt(!%%$I5WTaA*5+#HzUP
z&hSJSr~Qqq24y%CB)`j;d1bjAFDA&HpK>NC(b+Bfe_~Y8a$crv=1P4idsY~61)-Zo
z0m^>d8D@@rEAa$cj|Rl?k?wbSvTU>cax>a{PDX{YjSin?yAR&Io%>?Rcvj&9sx~d%
z`|mCvCWJVa@<G00(F!JpzlFDe_SV{hp#k|f-lBAK{^=~v>{Y$nj_sLzrFpTkjs~v}
zxK|Zs>v>JXi(Ah5D{JKGGN0%0-7TwZ-e?i{;7S-t>Rfp&D_GORR6$wZfe>Ss?vuR`
ztgmT29u-Dr(+f3zbG)!aV(ks)8y92mxn|(IYqgN?Q(A>vw-4ws{NT<N;r65N6}Qk2
z)%@>#x;k%tjLh*b-}yv%GhnYQ#9bw`8h-Cmq}y}6WmzU#JwUXVQIBROS<Adqw4Xh0
ziyM_j>bc#|SK+*ujh`?+*&h@ga;7XxTb+6C2TZkzkIG$aO|*pc)Rrg&js!@2S?RnN
ztB}HTCGmB9?E<L=(Fl~Bs&f6#mt*!aVPD~CoSf7*+c$wy-yJI2rGB`yUP;Zmq#jb-
zee?Ao@xJ?JyY$b%i!14c5E6Nr#fY0hGD|T6A7z#kq^q{ObIh(JSJPqVPTcKYE_Z+B
z{<v$)ef#G2-QPvle7W3DU%AR|zBi0^eB)NNBfH&jlUE{8ZQ1eOZhKRd-Giue`TP4@
zO4`W%0f7&*#kYj<_y4qL{Z1^8=wOyR{&tVfuj~6ux8I}kPpg_o7T*NRpZ^}}7Ff^U
zy_UP&pN=>m+8qj3xW?PW*u`v}UEmeWhSlmfLDgueGyH+Yvrk~LJ*|y<1B#gkY?=3~
zL+RSspif?87T^64a_bVQM)SOB=Wm`UUS(38|7itbc6G$O=fmpfzd9KmKLm0WvXj?D
z$xg0TM}7NYH4?z9y1J;P7r?}EBlS^f@k8MlU9Kl+M9qiW1~p#M*|*HUc6R<vICV(O
z$G~^hal$G44k1cC0tV@f4`<5bUE{wO^ioC3ZY{BDaQvkES=u*OdPf6`0kZ(2Z;@Wm
zu*myd9Cy}Vsr}swd=<dO#mP6IWl)=v(Z<EY&d8uS45D~Zs^VNVo~V;jn^t_Eo8Pc(
z(73iXy()lP(3)=uZ}z1&qp^)!*t2X%ZEApZa1YF~g@nnHa_GFgzcx-#5PNsFgWLxT
zW+vW`zt2&ZO}VMTzK(*)4nHOLhlj6J^64gMmAvtSt8$t?jSoVga(3cPk+P!E`ddp6
z@A%!8zavHdTx6A-_NKKw`1_qqzz74C<5i|EABzyH&m#}yRZUFqk1?pvr+aUvD$ZXO
z=TTp9>&iY}g8xf$N_`<Ah)>(F{7ZUDZ=rA?pN=*EMAn!3VySjMUC;7~+=KcO`71s|
z82{Hig7hMFIevpwZJK<sNqy}=exm|@<7A13a+7v`le+TB_Z|%umRJ0yUHnruDGim+
z<pj(p%BLD?@eS`!Q(+d%{L{@}8mhe81uPHCr`r!2J_KC}Ai)AN9XA@QBjp62QCH0L
zh&9%{3KX>A6!<n^&{!Lq%l}-w;#*-$LtXqgL3<T}@3H=k^~G}UgVZa&Pu5NaWCRL1
zTMPX7_NB40v0cd3v*O3xL1R;fix4VIV0Q6F6Sh}QSPM}xyDHYyJQ67ERUk0u%-+;8
z)xP6dTrs!p(bPKMPU+nxFu$MD)V3z~A^u~<{84RF`%WO-e_7z?S=Da)al4z(?~0#S
z2Th-@ukHlz3oL*X=y6ba5pSZ(1;W>K9ppiKC`Q3WB13E^-A9o~@mUqPC${U>wMeu|
z4j)-6w%bsF8y0I=xkOus?GX+VjkgwDW}LwGN_`Z4<ypDRa)|AdzZOjj6I@}ZZtho?
z7fVU4T;UdP9?%UEODhmu<uhy^H2El&QCGPt<k>uAslLwvCirhLLy#5x74%nG0suG!
z0RPQ*`!8cdFbLoWtUxCJ5-`B<{~H0Lw3_vQ7cjVFRxz>3{~=%$JxI2+{I`HHJMeD-
zW4%XA{l5i_0_|MwhVn1&%aBH=sIh!pkY$H|u0O8)Te@aMC<|6f@o|k+m-(=Hk}J?^
z3R~jFzTW)y2~EKd2f9~(bh&>UY~bgrUe2{v>&y+9;jn0O>T#^fVK{s?ieZf-4|xB*
zVZ79^VydO3!3p=qYUpkIp~UtNY=p-U)7_?t`HyAiWPb&W;rG3%0>PTd=EGlO1*6Qu
ze+7)t&qU{~Bkiq9#`9f|@bpMF9KrtjM1|Gp$M%bpz2*NDFisCQf3nW%0>C99?*bY^
zXTR{D0*2K3LKvkw?_xNOiTz>(!+!`E>x)rrk-SUMTp5{$LWs<nJo3@fbq;lx_RM!K
zVveWrPnbZDtOLJp+Z!g1FP0Xlz)xislGL@M3zflKe8_lR^%;sshCD_O(_rR}HmJwX
zvQlg*%pfaP%c!o0N_PHQsa6(b)8tQFTqTRMPeQ&?riJuyOj3Ep^5D#-$7byG!IyW}
zJwpd0aRk)N<>oXJxzhT=UUZRGLa(5&d6Z%}X2{=WF~J>6^;*=*L!UG@{w@=HqCTro
z*@%~Qth^}Fb_#5q=YLnxG9~Nu;dqr;@Z~e8$gk>s_X5V+^Zh}=s!#GiEVDb$r^@On
zS>jw?j<}NiR9Nu8`Lo0-M&PG+v5Awg&-bd$x!RqN(=HyLx;K*)j2ky+8;(adExeD#
z3U@s&q4!a^$+FFxXt>iO*RIVolA0HLx`J=J=~SYsI(xV&t1ZT#SI(Mx$L3T&V}7$`
zUh|`e&(d#asno-lZ}h(C@IwM;aWB$cizlhj?Ipx#c@1OD@x>=G^?|Z?guZv^Y6u;D
z{VQM`O`6)hKbo@e**=;^MhP9y7#`J?tDutJAAfgl+CKi_C_r{H>ox6iGUxm2VcxL6
z0zQKFYaoo<ZXulB^>i_s;)rxSX4K1jSsg@vwvzV9^=viMuIg+p$7kp47bZ&he7(?<
z%jfq5ZT$I0Wz)|2W_6#dQC#h`>%}(qSJlN%+sV$wE)MpNdaTp>tJi-2?GKj+!_vE#
zhhrLduKs*|<aTv5WB1|ec-Ck4>SQ76&h_caYq#q&h}f%-d%16)uU+g_%qLy!^|>W&
zOixkZl}>-MqOW&TSyk3yxMM!9U?3m|Ez^O6iXqs^z1Sd}r;6c-e^y7NvT(Fg7z!1}
z3SlQyW9T4yB@)K;HBpp2v?>f&&UVM<DR8OO#nf2ME-KOQ?X1}qv694oe`}Ep|I|K-
zRaP)s<<C=P?|v0-c}R?S8^ur3&~$#2aq*g%XxjJDJ+VqQamKUp^ehCuZPB7OFg{MV
z5g97Uw^Rv-j26l(sv3743txGyz;A75Cd+YXBnClNZjq&_Zp07-Y=A8}ru+KUWmBAq
z18nx6jmHEr?E7!?+8pimw5y$uq_$lYaA{}_8e|`&mFg7nKN=V`X*o!*jxQ3l(;71W
zevpA3E)w<`7_$5bXPhetzw-n(_>`&cX@1VU0(rBXE^;GtEWTKxMC-Go`C<0VaIsX=
zz-L$g!#4}0B{F?lBOcj@IqNzlvf{O!FIo<B_u@<L|I!-u|0`fnec_VtFd-ifJU@H~
zkd-R_oNPz`TWb4%gGiSD1Cgey$e4d4^2z^*Nb&y@kp`}_kXJYVL8SLTh;;l1k=Bv_
zMkMo4qpL-$E77Zl{-esDpJ3FG)5U}Li>=W=3<wFZ`?|q=i`r86U)Mga$y^;wXg%|=
zZF!~gZq-|;Vtu025Vl^@(y;j{>mNkcG5tkkRL01gd;IJ3MrL4E-GJu`i#@3VZl>P<
zLgbL@e;^We{VznC{DVkVMK<*Rgh+}uT>$j|6_FD{x|uVJ<e$pcIaFiYqu)7*IhsC<
zqfs&`2nyVeHcXrx@Bg6+JC$L3^)E!Oax?x#r1A{8lOd1SUqqVF&83)@OH8W-#PohP
zAq0I}eb8AGIqObP=EwZPZsWvO1#x{8Yoyi9_#G%9D!Z)@**p8?**ND7fN4pynf{?8
zm9YDb$e)6-1U|M(K^!u=x#@KM4kdczM2;EVPh>ZAP*f$$Wf6gc<vAai@cp)xM@8BK
z!Hx4RKPy{4+9hYVulbNY?-=~;ShKepTN&{BK-;2tNEu>Rzt8tTDB!^6rAxq7c*FaE
z&d+B;*yVFam*$P!#}*>%vR{R&=aqg`wG0t`7gm{o`rT1J+u6*m>jACrDlU?@-D-zE
z&~WZtVjr&dBrJ%+wP%AaQEo3|{6)Q9vb2h#K2`CEy;+pJTh+$;*v@^Bp8m`V_*Qe7
zgA;pTC#KXaQ}6NFn5?tTr}+B5)v)rtDYtRW+wcE;(UJb^qapsUh-@%_F16A-^)E!)
zyngs%?0Lz5A(D89^hD2XgvEHy``7!E`G6Dr=ZS=%JCiRKRLOMzBJy_C=~BG(&gpWJ
zhVXwOQYGAbP53BG>^~9t+O<V*IH&5r5h;AJRX^?8sxAFfo@%`1KM;B6a<7}-?ehNt
zkpi=Uf3lvx_<XvOiuY4ldTkYSN~}~7bOs9?xxQGlOOwCay1gQQ^@apHfCnz$DHAZ@
zKsOL*_+2&v>QY=dFDeA;hXHfTbbw8g(9n7|@Xb<I8)ln(aI;B8Rnw&H%jIxhNS=zO
zCM9+8Ujr<>pG1!VV4w?+5d5l3W(QAZChm^lvxuhHrxH`4p0_i}%F?;PozmSPq3@~}
z&q4iWJ!?jFCC1odhPFUc4YZhG<wndoAi>cq;!zlHH4JC!)9jUeJDxyT`}9CvNv`>9
z<kioif?FqWsjmGR?+**A_adX)9x9Dm_jDEP&^X+ay53JtiZA54tu>%+evpzmT*xCm
zFre#ykcuHK;{9(#u7<`_wQHA(dBq152{KB(Dj*zX%|*}i@JbF@o*%r%krs<YX$?PP
zI?U|XDHeM@F#KHhFzde&X_(!G{ofJE$No4a)v@M&iR|>ih<D2&a-0PI??CZ?j3EF2
zXb=ar{Y~J`_#3|BfqzA*Y@pE5ssw?5X~G~V<)udQ&F;Skw#1Z_iW({6HP=Tsr|%SK
zg*pJGG?*LcP*MD4!@Zb&Gr;w_+zgIO0YX6h>6bUBU3nfx&t5+SA=%K{B?RcHGoNkV
zZg;$+5P<tqW(YulXspzi@-8R*1^^H*Y(4^D>Q{C48BZI6?K2zyl#2;+mcQ7(ehl{B
zHLL;zLF76jzY8)V{Rq(2PQ2EkYWSY9m%c&(pHYG(U8Ywms95kvIo%6SiH;z9m?(P7
zXek3QenOHH(@2*_45MR?{yS_P=D<%4RV6pSXEpY-dbCD9Kvolt-^n*nmu~G!X|9UF
zACFY8l-XT-q~3wQ-8zrF!saRCKVHgM>}-BoD=s(Jx*Bkwm<<LF=;G9t+oyGP%h}*a
zplz-e5xRSJNXmaE2Y)QgCC8f-QR21!RAF-E1`q<6N5@7D$a#{)^2rwv$Df-q#3=|u
zk&A-(>X{LR!O{ag{afbI4-y5ht%vzCXwP(nHRVs2Q&jXd=R@_#vkFB^`|Fa@u6aEl
zq*7dqlM2`nl&%OkOi9}cxlEntiP#63VHElm|2oC^Esnz6bD?M*+n4?)1@GKHme|<d
zJs#k}oPQ?bE+js_%TpA)<H1!NaIRb$Wx7akyJY2zdiaihhTl->t!oAy5p#e1>8Rjq
zp5G*u^3RLxQd&<_%7gNb51a~{M6zv!yGSo?nS3x(FZT)LDA@8hEjuWyY~>B(EX9{{
zI2SZ)4sn=NE_`=1>FqXOP^gK%V3WQs6C0M<0L=_=q@xVDUX`+?T9=NFVYd|Wu>z|K
zSj;1PZ_|YQ;rh)hPm(~b5m-$gzWyM-#dV`pH2nR!_|Hbpc>}Tm;CN!xk5_@=EpF(Q
zvS=I@1s3<)&s}1T+n;+T9{0N}<tawGVF?pCJ$z7?N;bds#Q(;5>C*UlnZ;6!mOS+{
zEnUvnsWAoubsYT^=Ds9TwftS$)8Edzh6VU<59kQlvN*@{U*G>BCEi?*XZ|fxYLPRS
zI7-<f;!y<+^7V36`DQN?{J+|J55Fd}?d|(c0YXivh8}u{(5of^q$pLYf>KpbRGKtf
z=tZgmB1P$4q<1tF=}iSeK|}(GNCy=a$QwN~XY|a>IWzMs&+~lV`3K16&d$EC{k_(@
z)}6Bk_pUc7oz@G8voum&srOR7u}0V&RF7`o98jL}Gc!Wh^*hn4X#0;DtG+VPPLQh}
z-y2e&?w3Edu(sXKaIh88M#<W3#;3zug5QLz6LjHl!?cH5gXxy(nK&ri9^VRKG;t^Q
z!tS)gF=@KSS5$X)oDMU>-O9l<as-oybP{+Jjy5Xgq5tvClyH^3^>AA9%@<~14|c!@
z(OSReeI^>d+RsFIo##l&1mI{Ese(BO_&|wq0>vIW5dgdMY%FwQu&oe!69B>)@tP8i
zIIwRG#Pwz~{FMPy%R5{<qcau^C%I#_1+77hwQF{&nj#3{6V8HTCf(Pj=in0;A)K?Q
za1FQ9jJ1kTiP|)3WNTK8|AG<Eh98x(_?!4A-XJ=SLo_^-*n|@)AiBT>Q+-WkwJQ6Q
z`xF2yPsQsvN;H9YtON{%WFrE%+9NHWr|<w+s;CGsz`Ae{*uzl)&@yokCj&RYoifVr
zH9(JjigB5S^qFXDBgFK5IX2;kflM0MbsRgBRyouyNF3TnusiiUzPPy(rSD1sie7_@
z+6OWNq8%7-cW80uNk(xjvn(yRQ*FWC4AOcC%>slRciv!moC}58*TkPrcN0}^??r{h
z0}z!G6bX-|i!R>@Jx>kL@fA8NdWTTbZr$YEipti^(YxV+cVKbB;_m}Vi31(>PEF+m
z{y7K6(vhG<!bpbRKFtIL6kx%!8%{1{jcjlifUu2^Yu<2-1npGvKCsr}@M(Oim>H-`
z98b|VjWRahdBR^4@^ok=D2yUA>bQa^h&I1B2pNKBGS`pN7wL_++UqLGHFc#H<wqpE
zh&}78-wyIgQ2yZVlzg14+g6egdj5E+Ad5#ZxI9QehQ^soH4qDn&7>-hcH#@CCphRw
z(O^OVz*-cl?i!S&$;HPQN%ICnQ$c}_Zl$#c-srCd^jimc<X)v8R;;Dw^|K(>j4qg9
zztiMepaJsf3?{!2V89m&q+a<k>@Tk>ZP3uye3oypql@BSV29J6f*smFz>cKfg&p0$
zf*qs3h8?&59qicmFR<h8Z^90qD?h;wr3K%bcVn^2?RN~nym|Yg-ivcCS+Vkb!Gmje
z&q<NfCQ@zUB<v0(0AFFp;fMN{8{fZ!WiW4rw>0^N7C*R7`Y27n;wgx~2|I=!KF9!o
zEs;M1J6y5@YOo&J5o#J$GuOFlD01+11KY3VeOy`6V?JAS33w9c>dCNUCxQ$+PJDwM
z{+ZE6$9{$#bjNCQ$*|-3SJ*L2{26vcl3_>9v!7tc&M&Z|_$%xf8Gmzx3_A)h1`U3L
z9W*v93Rz^>5zCfLh8^NxVTbTn*dhH7*s&OV{K+@iQTsFOfaV`6xmr6F@DuF#GDqWf
z`5Wx`@D+AIHon0Q3A@xbp0RJR<KE@L^EO{$$Jwv2;}Wkg8FsAry(lZ0s$6+8CguGT
z>`;G6h8^k|Ut!06?{BbUs_2p}9(I8YJJvqYuKTVT^^;+T9T|4?{{wb#z5E$=*xFP~
zKI*ySzcOFA5g<0?<^`aYsn-Uc$@Y8s8gf)$*HR=ISn%nT_?~?7dAZ;F3>D^gV25*D
zGZ}Uy{;cHPC91uDh8_2OBji>|+^m+SU-l=a2aQpBy*|PoRc{U`__xo*bAB{<OuI{T
z1aMT--n08M_8*x~zr^CVP_(PtXm&E=!N}|4Iv%^lGI(AjmP|ap1hBEV-kGVRP5Lz1
z^3EK$h8@)d7COkVBa}kM&g}M}6Ld$%`xE1?_OgPEspY~6yQfKh{Hra83XaZx+Fp35
zvw|znzt42zetq1Ng2cW!({8EbcP?LeKOw`ow@2=8p&HLzVO+z*49F$V3egm5XvLXJ
z_PR<Utu$rY#%%reCT%&bG02=?6kQffSFepJbuC2HAdAMTyN#`JEfgJ{Mb2kv=XkLe
zrqZ9q03UzIonLNNAbS%*3NPFr1~o+$$Y$bFtvW){9+5Pzjw}V%@gk1eQJb$r*y;)o
zhPo$QKe6MukFzaOJ7gu8(v%<fqOLQ<y^Gg9I){T5ETKX<&70sr#BRAD4jk%;J0}T4
zeN0Kf(R9RxH9PTrRO#B@8bDb`It%U>NQ=-rawDL}RDepMaSI{M=SedsqR8KCawbnL
zBhd;TSJlH7)fQgTZ!dX8j|@9*_`PWoqIDuO62XnoWtTkK_0aaCmcuD<gIMW*D*1W3
zc78JKkiXK~oUgQ8Cu??&Iiz>{6wI{YfCm|NTy@fs+729KuFUxcJMIP3eHh16X`_Ns
z5ukjA5Yrh|+nzaWy#u{_q7%<*ye|D*Yn*Shyi<3`p|Z|3w7=+9*cu|Arb4#D?TI9?
zhj6jddmm>dCz^zwf#$O~H~Kej<)KfG3OFr3y#2&GP_HEGxOSsvnE^apsikO#zZiQm
z+dM>@b#dlK@kqYf`y!K=V)KGSqs7PG7mH{MSl0E9mIb^ov8OMw?mG0W(nmYzloXfQ
z)81#bjqmTdAD=kJh(!EyrhnzsR{#F!{t?0ilzNtjbpI3-l-DgS?ev?R+I;4A_Afy}
zUpe)ti1K-GSB#d6seMzKU6YZbNsU{#tky#Yb58s@>-+XnQO6JOJO8p4`smo?ncDky
zfz;p~_qVm>FWcRUz@Pf+^B*Q&5ZITj-f(Wb&x6=D>UMi$vNJP}tmPG}-#*Ryl67LB
zQD**Gxzn3c;ilT<%-r><JD>gQ%tjvs)5blAZ?6%%_uViYY$ih(g&3{7Mpvdc0!hVZ
zUm3--eq0@*LXcpYK=Y@~-{RCC?=fTXdnADJ4|8h%kUAAv{xJ0|pP48Iq0X7hc`eos
zisDDMQlj6lhZ2p{g!ko~XXY-?xvI$U+&+OO=Ct5dY3T-g^<v!ZVMQYvz31L~mh3ld
z;mapEl#j8F<RpuO3Q|2DQG1{Ac#?3G_ZGe4r!k2mLa+1fAX^tlGqf1vMecS#_L%_D
znFFFOCd1}a+VzsE$g5RcXE>86c2lNRJP*;>=cme1I~Q0!(-A9FYO$Dz$sKe)Q%pM9
zQ+&DX0qOHpapeo$v-d8{oH+}eUOe<l@z$r$!Ub)WzGo|zOgYY;OXfdpnl{LB*sc0a
zkCRl*(t8M7?J|f;r0!)<$%*orSoL#mt%;lowJn-srH%d>XD9BisX_0>y%+bs@L)vh
zY}&OQw%K!pY3{>oUb8&58Lc!T%I+6oYiYM1()Z<$6|f2v+-hd;I%RtQ?2&@p##Nu_
z@*Yvz=XStY)@3e&w7fEzzf>bPU4thLUKXm=jd>28TBh9^*4}q+>sJs);N@TneS_)G
zoLZvaYux$Pxew1>3hveVv)mtGPCGA8;rr9k9jxs%rR_i(_-K5UB(?n$r~V$o_&;$n
zK5?oi8GcZx>-Omoi;0YIqr}>5Ez8gPFM_THXs4HSM8CEwdyu#I1E+49l_yC*9%}pd
z5at(7-DJ7<e!%hKz~`l^I92T=QcKUy*3f;E-S@BJ9`1$<c$xUXaHCkLb^Ou}5f6h-
zYr#^KtsHdZ;ECN3n_IOFd?AL&YI_~Ih}I@ClYpFYp5+kbw$0n*qucq|^pW}|EB9<}
zx<sD*GTl~U(y*`c?D16VpM!$35$)l69sC^Y5qbvMOt-o_gw@s~P5R^HT-IAf!(w?Y
z`?J~V7MPq$si;)2L97Ue>*Ak_nFdV_cVJKG-Qt6}HdIjW_Z;r|CQ5|Qd%=*+{0Kin
zaG0je6vq|CSgdCkKgpWUs(Log2(6bO`9eFFse@PgD})I#6=n|aG7ONj%NWVv!2AxU
zKD<nq^G{Cw4Z;ZDrW5PW53T$e!W=&?gZh<_g^;x>9WX%_vTMi4Yy$k9bNAPj?(agG
zfBd<>`gz;H5MT&g|AoW+6xUYh&h-c4+Vc8tp?~`n{xPoYA8z6A#I+sotGzc-=l)Av
z+v3YUa$MU(JmdnG)xGl9XU3o(IDMDx9>(tOcyNA9A<kS&kw2ZUG5G4?=|m|^d;h)4
zPBP(X&=1a6OjQWn^Z2rFx}WU@=!Jt@_%ZlR+e(2lGnFqa00pm#I}9}4{jgIjMT6F4
zzyUZv>1o$j_Qm0&s?eET66e@{hXzmZVLrF9mAf#Z4Y{tu5ccZ~14Prfq5cE>tJho-
z*Nm^hJ`sofUk%q%<B)Xb0~mWWq2CSPfj^37unu}I<~01#m@jgtik8D?n$6U8%o*tR
zqH5V>P?^r`G*;-E4jy*P2FhWlbTR|kxwy=q4q;jq+_f*0!=-M+6w2%4wUYL`gdkcO
zp+iY)@yf*gT`U3&8dSTtK+GE>{B6>p9mv6((Ypd245W^1{0&key89Gm#h8)eo%bJ6
zNbtN)yvFKf0ff3OTYDUKfqMkWhS>4aVY=J!DcB7zo)wgfRbYo@*iR-Sad8e|baM5I
zwP6%F^c`@_FrLIr5X-s@idNi%x`%7Vn1TtynjtDL(o*)GQqbNPeH=kS++Ks7O4dl@
z#$`|h>%i_p$E-O@_cjMgDmHG$%6DDn!(&HHt`9qL4+Gkm-Du&^(2?CL9co?4pu-f_
zlONzH15x=qxZPn79g0-w$7we3M!Qixkw?Hl)r$^2EK0ZEngVdwQ0C-eMH7dDLjghu
zrXIisYmv}xco~vk*ekcakW2?3IM|^B(}xu(lW#Prip^pfc-F(%I{a)(9>H?tt)xCs
z5Y19xPD6!A$?$+6Fo1_v*WT8X)`2QM2#$ZVT>kv<VmE|d37jDT*CvyT)r&wB_Sh*<
zH)(R7(K=fBut@f0{$AmFBr<w{<QIIKMIBr9L17UKp;uq^6NrE0^>n|HPkt8VBSh8q
zroYQg>Mc0EOZgMz)yiEWEi`dQD^}KN2|+)cIOO#y8x&7uJV{!R4U446Qlg+Yx<u4a
z9aMkN&B#rc)uj>W-kOI#r&Tp{fDCu?bbSj{@3>O1k1yhO_2g3(mrA|j<R{x3jSod;
zU5kI4E6xUJq!dv$EK<d;g<iO<q8f))+hmsp<(!)q)h)X`!+)-y$or1aCCjr7M<%r&
zRP=bPu10m=cdb1L*+C@}k`T)f<ir!w7^#GYFzgGY<q{jAKp#Wme7m)HC{ufqM(jHn
zF2bC+(NPu*z6-VIo7;KqX|UU;z|uYI{4DeMPB}sXjyoS>xdiZl8IjEUa76A8=!0@g
zpqO*yv_m*E)%<MM!&^HhC~0uG(l{f1fS>^Tdp`km_IkNhAVS2dm|sovwQj($0Luc<
zwW__%AonDMYZmVqw6w0Pdw_{MS>D0;s!PK~Am?U}d#>10E8KlSI6_6$xpd8e#yEf}
z-Yz6T=(J}SsWpd-WTGoO92o1uax-P2uv2}osxO@Y2}ehrQXoAR72WFyyWcxs;>h%!
z+@uESe$96!A9Pdzi2{a}M*S%%`eVsNK)R<%=s%^Xp3Lt_QO93N(Kq!T;E9^AQuN_h
zDZ2lw6wT56MT$B~sVHM#mOxgSMw&*+eju0?3d}sY=TLu^n9K^U_b{yBe|1R7ZT*~0
zjf1?I&gG*XZ-!#EtXeFWQ^|gy@khgf#*0S>%AEQJT?1;DC$J3;q0yY;t5Zf+WIwPu
z@7YZEO=ZE%uTpgQEKQtIOCxq3x_k81U`A%~%5)Yj35~GFZU3eeefROK2!P-H6H;_0
zg7F!cB9eV^jV?;^NQdS1_oI|Kp+h5ik1xn~Dj$zkS+-7(QBey4;=WMm=LYMDD-aSs
zw{$xP@4B&`)Ew1NwLhuPbSp0jW$_{Y;DOJI49b%%=EAo+PqGruEce$WS$1XJzI%&a
z^|`g=5pKRWcHr%cG0~U6irBk>!c+@xFiA8nE9)Tcra$vso1nv|(YA$ir<uH6f|keq
zi|}`@zHqk5=BUqex}E&ly0CONXR)~80g1z|w6cvXMVF6;NnCAQ33zqy!5ymk)YfNR
zhNm9A7&}{8RoPn_a2ovXMgEWx%c+`0sq0dgNZw87E@cF~zjx_Hg7|X%<Kx#?e7eh;
z?$p-r&YaiYGbobw7#U0(ZzysoxUcn*;nwQIGfZ4O5AW@scE`=}H>1z59oVsLVzSxi
z@u)|!`8b%eYfiGWJ@TMP_dw*1Y)>tH$;&hlT=&icrEJ@bb~&pr8#nfzMW?r^Dc9xV
znpCvChV)rAy)Rnu(Ay0jN*ne3(GUDtiiXo)#1yGP!^g=|^lQn)agQhGyQF?9nRxps
z+V_aneo~9FvHRXXq^O_X5Z#sfLnmbz5?{ss;s<_LGEt^5nO!w9NXVnLnCmIJ;G8;h
zuZuxtsZvDy(lX`60QD8BioqvQiL15htC5U9_<@5Yy8Q<YjDM$OVxi#v-sh#d)je{x
zgbZnCYixCF?E~k-B3I`RtC&T&YdTQ{M*uUK@KGv|{Xjo#A3G5>rxsB{BZq@g5_pmQ
z_E7F4xURPYC@X3Qm^vgaBElr(CataJbcZJEncPrG$w|sbA~YXf5BVwg-z8^OYUNRv
zhYwsoC_?)DxOJn(DU4XM@3UvOy6`h;BW!dwOM%`a@nh>z)?cM4IkPfg{klC}4w7sF
z%FTzf*Gz}A=Y(}+mlH4Ode1P=rKoj>I`hom4A)?(1YO}1P<z!)sRCZpIS!rXPDe%1
z`eC7Rupne`DVtDgPLgkPJNJrcw^}A2;%a<5=N^HmJ{#CI{|Uk~i|AIP<Yx%1cj9t<
z?gSiZkXAFIi1SBvvr`837}Tyn7#nnPx$AIp0+X%}olTP74Q5Toj(uw|EH~v$j0f(q
zNUm8Yl!}qrNP3o19Y(mlv#vGa@s?LnFsga2EjnmgUpRGp2-<~?H>Gnvc(<JGmWsox
zqHJ4v8D{<2gY!r5zz&g`_ifzR<uRtsEq<V>ya3CErZbd^wXEKOvBCI*f|hR3?z_@W
zbxS$ceSfY@k9M~J><DP>IV>4``=Dm^W)zU(jFK#z(38Dk+Ju7Y(FeLND`<_>**=mB
z;Dbr@hYB?<v79r$Xlh!aV3A#$6LH|Up?QU_>`F}$iY|1@QqQ+yMZYKctU;Q7&&TXU
zH5prnffKfmXl^y+zc2F}C^_-5ckIFE_xJJiZWBDB<82%t$|DR*9ryK(cdGeBoxAMz
za#;L%@39Y+DFbBJ$fogMYe4?S4}ar_zwyKW-|@rXAHomz_nB-HZ5i5O35f0$c2GQc
zs*_J4y#p?05q&dx=W%3q?sab$x&a!y>PS7-li{4tLrk`rYdEeBaX#lHuJp4o(c0f<
zdvHnjM80w>%#$KKX{jIn@KN&+(lY@x&E9_JqrP0DVZwDc;)bJD%Mr&XN%{X$CRdc_
zu=ZmONOZdR%^pQ_xu0r4{6$aCkX7w3vi84527cFb`yV0^zx#RHz$w5O*!lrcP_Y+j
z{;@>)|6xQy>+_=Q)yP@n33_s(d<C(KoG5?CprLZEFGc?B)c1+<Z^_kqC1g-c_V|8E
zlqXl~dA*w&XsTQ7S?;9fmA18Sn(o@qcAyODtbPr~x{m=}X7_NOBbaLKmTkks)Ai>^
z$?Ei-rOe9M=@!k0>z|}8hQRi3wkNImbdoo04uu?gfH5z&eLAEvaPd~U1M^&EkNo6*
z1!Glk9ymd`cn5MENT(Apk;sPHzx3(CDQ93|I=SOgX|~<N;h^SCLm-nbHxL)~s12qZ
zwcHko*)@L}5w?qzKaAenetKO7q+xbgS}0Z2PN>Pm3Zb!9@06!L*S{I3^0;twK$wai
ziI=^;!;cWJfrT6vBKma?2o&xx<An?S3iU-c_AA(>6^^Ld0p^37hDy(&<%Vi+JE)Ab
zhAfV}mQU5lK}x8f@$D7Wmy?USn1=fF`Yy~e+}@MyQV2#mQmz@RJuWrNR;H^VJ?|9&
zjXdF12HA?+r4{3k_KEqe$kBK&&iNNROu9Rzq|{@Nyu|Ji%m%ODjb;R-NY&H{j;)e%
z{bG6>pL)tb&6SaI#ZWDM^#TMt0j<5~KdD&f;%IQ^+?1yN#i*8>bC|JQ`2jc#ii*S1
zLkV$Op+}!Y-aS`&BRjF)1lcbiU%e3(-c;Tu6VosWjtq;c`4lC**Su-uuo}@WuDCku
zVA>Hlt$94HK_k1=@c~Qau>5s*ITLgY%;h*ngYBp_=0mT@yx6X*ym8-Rb#JT^6>Dh$
zrZe-%z(t<eO}+Y8t#9DUr6{fS@){qxDAAnDL9}tB)|#FUFEyC@F+267^g*>HjqX9L
z8J&5j7EIGO!yA-YpK&dWwOd4DW4}}X+%1d~QYqDCI~H3ST`5$uxVAZ@u_MU^NI(&@
zk=i}GJ-uS7@^2h`_q>nL$`cDkO@c?UOa{O!J^4P-A@k#ED%0db-CEwzls-xOrwee2
z43z%d2Xo3bTJhA(%0A44;UH1PrS!7O-j@gCjt5`ME|$8^b3&Iv6<^yPqqc}{XNZe>
z%X0{pC=$j+h3oT7y|_cp5E3I^+f=c~q*KAu%3(bFJi{HjO%=C13a&^;-o10DD()7&
z(Sh0~YOmDx7I;fe@C=Ngp`e%NiCqv<Qh+_-scz${Z4cQSo`z0X3AZJU#~*t6c0#Sg
zA@2e<f?LL>3qv^@ODw-1A)Upv`HV5fu{4-#I-UxnCi<wTB_~W7nq?4QU+k(Nz_H`!
zpio#5>yH<>c(AR<qkbP=y^P59G^JCudmHXPxq4M~GP1X_I@WPPi}wvow`58d{!U??
zQ=B;haINfOYk_bNsC21rCBS8ZOk>o=ZcG>Q>6sqbhyjUqRT|~B`AB;5EV>H{9uzy|
z9no$#hZPc;z~vbXYH>|ihpRnn?LXxMF=3^Px;W1Vm)g}b+8llY0x2f41~6Zjp#H8d
zj}vr?Y>l~)&$|o2zAPH-pPQiZSI@iB<?k6ajJyV2VC9!H7lqR)ru(B)JpSq$ewgC%
z8(8>VE$>$>Ov__XU;g^1DIS0L%!Qm2u@Rs_f<4}Cy}4jt_k4M(1wu8Lfw>CFQk{8T
zmeK4xSx<r3hdKuMT!rjh>WaIo5nOFo`&vudBm?UMeyA^DWumg3Gz7uZPKyDzfSs%R
z6y(>qSFi4N3&8P`1N#U{BP=;u<On4+974BcX`;MFaJnfsOD`COZHYFE_g37`j8%eU
z@ga~@2<>3FdV;74D@SM@Q{+85=s_TTR@)GGb+OAtrU1c0XSm7o#>k76qdHV`Oj}Su
zZ%$j^Hj(+{J~J;<r2>Q?)B<IqZaKdtsBC<o!R)ryW47a7rznSo{VphNG3dHIb3Lt2
z)ay3MV7x67k0HZFb6@jV7GYgdzd*dZ$I8g@&_H4KME4a6RMBB_6VX3ghqTExIFO%e
zaLCq0eaZJVIA6D=1mH^E65jud+w#6W^@p?1Wvsp{2I3$uL=redUWgyCi$21d`KKn#
z{8#qo==1YdeI7rUIbRne^=&Z$z<VgUZ0Ub%F-P3ShEA59m6)KX`|j-c`y2ex`v2D%
zLBI12+5^wO9d-Y!-{3C;i+<y%`^f?O;iy|5^x18jZ=A2mApR+N`Rd!iqV#MHf2-C&
z&aUQ953ZQKZk4cFx%>Xz_^b7tDf7=wt0Zz@jd;?bWPh952&uG>Ek&4XpchxZjCCGl
zU{?)*QfubMDEY4LJbmdsSH2l1rJa&#Pea3>5T&u?{>EH&v%HF4;Zo)rm=2w30+D1r
zZ)Ie*yQRgTy0KGbz$;3~M8U7^2*hd#$SE4Du6jFh%Z7A#M6gMW#0PN=m$wgTY)a0>
zs=r+D%jCPjwb74BK~%J{4kv02s_X05=Ups<ZG`cU?X(Z64M*TYq+XS45swh-osNrL
zYW9e{xEsC6pt@OFNu#pk9%=48x4jvsajUsPS1hcJIB;WSYjZ$@v_*_lKPXkir5sk7
z)6E%_>v%l&Ny{6!#;X`+d{O?MAos-?>#4eaz6(f4S`AX6c$W9!BuH!mY0bJsinQHs
z6rw16anWNFUu6_kxy7d|*a3VtR88joIAuCfMlL<xB=Fx89Nq~yR|m;D%yMxhx_t;U
zU79T_HoVh5n8T!$<P<fG-_YSjy`VL!G-$b%Q<_i>D!;gdmml)z7rvbsIlHu=$9WCv
z^it&o-pujR;rC{PmyLBI4WecXIg*rKG@rem5^jCdQ91djQj*lAZa(U~4~M!WEJEU$
z@VC2X*x4;#!UAj~kDU})w-6q&A6D-^Dg>dUJeebHxzs{*x5nIM`OXXZdX(D&Prq&X
z|2>8ApWg1j-tu(S+<)apHL+ap@#VwObGnnVR{oS`tb)QNwok9_!Yb9r1-iyqprn&Y
z2`zOWSIMpw{Sa+jiO0*zwuhH|<-T)-zCIyUV3PcVCIR-M|1Aynt0!a=vpe<ADQ5q)
z#eX;yex-ldH`0Li^LzgDtEXzOL0|PWeJXq3lvZf-Dul||=S|l1Qs>KtruinHWd!AS
zg|vUaIR4U$<L_7h_fz?PUFPw(cq=gZ^;2p8ZSnSh!^Q9G8eGXWL0|i_{tq5}zg-XX
z+Xvr;hrNFkww?2wRH-J<$K>>laccs`&c9?Dy!X=MO;<zhm+;c)1m57Vo7`_?u$h!L
zd;3YWgqVr(qk&9l)02@X5hMFu)-HieOMIVGm&ccn4&4uD>^{wNd6N1Q@Ny*Wu2Ge?
zT{a-N4vpx>%%2;D2e+HZO`iM`YSM`r)lf82Bu>{2W3bL^@T;RcUJxXTZEhNOi@Jat
zPqq9;hn?NoYEqH3J>7XtTQ(xzi!^y7Ok9aS*A6J$sd~gfTK-5&+a##M65IeCW>9`;
z_+|)m6+lF2)I&F-F@s5`bKHxugxM79%j4<Bc06^wC~LZ|NsKOmkBgRB&N0M)zLot^
zxD*ZFBgJ~8fM^=cytXZHZC(>1-R|Mpn@JCdK?P8Evir$BG;&b-<IPXt@%BiP$)f_&
zb_acYo0Ml?%4t#4camK01bbE>GjOIL8Xd29Lw(zwO8%Pk%t4XXw_|aUDze*gvS%Xz
zJZ~F2h$v6W1QWPO?yo~B>W~fw?pg#Soruy2BC0`RPeEAxkis;8RLlGlhcLs#a+z3n
zayq1yhlfQPPQo0B_v9(3;T~ff9n&%g(1>=fVetf=dW`v}A!+J##z&ZF7KtfLz$E^<
z5DWiBb_CE5p1~L(zr=AeAk8F7!Fm-lNQ0HS!<5SCqaF!uNdOMrs_zlzUQbSPZy|}c
z<M*jw=(ZOr>^93D!0yddxL<s5IV5VIT*M)n_UDI(L;Fu&HuslGC0**koFI(PNK>H$
z=*yDWW@D70xVpJ}O10MA4QE5BKB1t)rmN3kfj14+jY%Q0>4%Vq&vQU0*IvhwSlU_{
z1GW9hm^9IBaH{P+b2kGQlCKvtYrhF+0|<UG8l=cKlx)*su|>E_7H3-7XZRd5_oisR
zpzxz-x-!&HxX2Af*V7baVqQ|L4d7I_GH+gh#>l^9V+CZh+&_FE8eyjU1tzah@-d_c
zJA#9{orD!>uo18gxJER;?<A^Az3gO@=Vd34@kSU8Ob&j-Hb)Z#b-*FbVQ6ys`(!Ii
zXng`Mh|tbE4TLYRISVYP=-LdqPF}rAo>DkD!?C>31@yI;BPe>bcx2OcsV>dHPnEV(
zpLKkF)e|HQbl$hUh@_OJ6y(1K)uM(i+;R8G0#m}y>G;tS<SF@+b~OY*9E}vTwfHcc
zn<KKO_pwUiW0j)jD9G7uU6x^cKnNvs1@?AOG9TQCV&4elS{s#T((Ndz)}s+GXG93r
zV($ag4D9xl@#Ux>jjJay$u7XnJyR;G;Q@c^+p%&?K%`YOn04vNpcsh}!pg<Rw%Z*#
z1>TA?+L>m&dd9&j|7j-G!SEY4jG4GZ*TnB_Us*EZFeMub0POKkeW$HHls}KBrSP=~
z{<q_4|10p|pW|tNzU|#u7Wq1!mZnR#Im}8(^Ikwh>?WK2-KRbgB%07SaoHL{MU+f8
zEzVCFdNO=@Oiw7YkK@u{zhuC-W@pZx=lbB%<x%ff|1cBM1PhA42RUC)@xNKk->l}R
zG0NYk`1~+N`Pb6qZ^8EepkR9hZ2rF_*#4wD`VV~db>4ROO$ocb3f^xWkax+oi)4IN
zb@mlG?&;$yDBX+HWND~U2aujHLx-Dq>$U=;hfNsqyXQZWJl_HYUOe!nF=jJPjt{^B
zAP5~m06>YE6N)5r5COEiZp8~u-0?bb4!k1Q8U!xYMSuXotgLgZ7BjD|y{~w~3-IoE
zafU=A7iMBeN2iU|mq$KKiL%s=&jbM-sGM`l(aZoXNVJ~k8cXu}kYc@SC4l4mpzA>K
zU?(T{Tn$12n@qJ)fE6%X)FHp7MPUF~J=2r_0xjB6pFJSr##GpYT_=M8;@a~Gd7_J^
zOI+)FuRfJQ>Ni88lM1N|645kcv6w;_O**Dn`;04)$n>$WpzXcc;k>rpcIqO+`})Gd
z67rDuw?&nopABN-qh8Pyw|exx%fJ0>F%6SQGci%vx$$90YL?E|RU~yUf8qYaPmpt!
z9|xGu-G%URa4AQJG$7(;liw}l=95cI8W)8-q#NEn;9PC6)i<?OFV<*BDDQljSk>I4
zq;n^bgtgsUDS4aE0WjaBh=b|ck`*1ahHf5@*mR>kI@8_KWpUhIbH+f<@H@_&`)W;R
zJPMessKw10x<JyxUphe?ow7$Kp`~;M_u-umIIM?(o93iqlY+wF6Ur;>geOKfKNzY)
zsTarrlKol;m1&k);{dr20v|Ag?;#&tSLz{fjn^-445&<9-i%S2&V6aDShwUDjoIBM
znGHxM%>?zyjOZwlo4fIkO<t_|RSjSY@EJUq8OdF^oE8#zgz4WQc7h7KiEs|ukofDj
zZaqpjsRG4s(g%<viQVGW?V5BdZ!0$Mhy&Ye@Ml_pNjGK>2_euI+O?mtYc)vp9-!DY
zi^Wf?XdYzLbHRcrIi|~@><@Re2JuwG@poi+pCsH70t9aeXYQ2_DA_^uM+CM&Ywd%W
z4rg8v<$m{88tilsy*&ynve1s%_MoS|Jk7qT(oW9;gl!3CK~HdmV`yF<XdKWwoAOpy
zeS2L+Q5%4Y;&F%o0D^4J3>75+)b=<fHUljC6aplK%nZ3!9R!L_d4eY);zRuPa?av$
z?I1ONxPJ&ASOSluKeHAjZdnn^GfWVlU5E3b2;3(TZ5Xa;bv`<r7Wi|xu#ql<XJQW5
zs$bWM&aB3uYKRSe!a=}|ks9FwKzOiObe3o=TyGle41_D}wL$^&8A!rAJX{nMxYqK9
z<vb9gxX0L413JYOrS<Zl=Ws9=G?NN9-AXNp4W!bTzNUl?;_IyImIYAI<9cuedD8fP
z1AS~&As$k$bl@7&%h^}V7<ge(AK9FVs@?7shgDFiF5wUfFv^v2M*JB+2Av&8K_Dew
zIBYkV(s4`2&O}Iw(UW-CFEK*nSp}wNPMUMhC7r|9o)r0NR|~GSz?B1KiKa+@&D%?7
z&?w0d2`P2rnH~;PDs`Zbd{BWJ1WjOvglj*#fPuJ_n|4#ILlF_d+}Q#Vvh<ZtP71qn
zeLPF0ys{5~W#K?|K}9Z8-Dbu6wJn!R-(q6z1I-e%IJ%+q8iy9+b#O2^O=jy_b{M;$
zCh-W=KWAK8oUwv^mbcy{OmsgncFnD$#O{69hFY*?5`R@5n*+z*Gfkzn(s@1omRs&A
zI7I%7i}Ep=VYvCvRs@dli=p3Y7p+sa6IFO6W39&45jNeyO1DnF)!g>TebrHcLJO6i
zl{*`MzBESHv)K@m?1~WXhH&1a0k~tUf{V?bE2+#eQh2b1rQrkxvzn<vxWvdRDc}u9
zc1;XLaq{GZ+hLswmw23`eaZZbd>iQj>9_zbp@O5zN($#n{iwA@*MmCm>uHYoo(?{-
zc_hCj@_SeMD<D(@kmUD_?zgt4pN%d(cPqs(gutQnKPH^#dyz`}Xu5BkXn*JE{Vi9c
ze#e;ln=k&&7yrD7|6RT)2Q&hT-zS3xjxqe7F6#KT?bE;gmft+)uN2b#C>6hTT$95x
z{zWR1Z66Y^@q=G=T)#Tk^!aNt=+MK5q(9Sf{WTfXWhPQIzi=i>vUXwSI=Wai7|Thg
z06))y*cx=3reo1PPukw>mj@3xIiFM3WTuiF9y!&9eX^qk<C}Ha9KzIAY{Dfyu<Z=$
zEA?ia>hDCU`!Gw*#5hdFOC+FTX(Se>1e&y(IAGJcK5%PeOWr1BX|0NmvlUN{1KQoD
zPSe<c4#y=0aka%Mg%47~HCDZEiYUy+Z!&09vG)wA2QxtilxL9*^Qre_8-;Uk1dz-I
zHA*TYIdZ_<p9hq8TpA8>Z9oHD-J+At7%&@&G_mEGy*|7uq$NIT)ul;aLAP5UJ`ags
z_UnngSIu{rjx#?>fI*okx@d+Wx;rJ@T_~0qRrk`7=+Q_$@hL7tqUI@s@)%TdjX)gn
zw^JXpeJgYDBZdD!tBFu=oXRG?vP*fUoB6tM+H_Ff;m=``o{gYr`9Vypo92f0t~-P*
z{OX%V^4cj8!Wh!HX4x&0;1dwwwR3a$kO)MMg3UtkIK>ldZI51z@W;&|4us0OY~Li1
zfJ2!mUvw?VI3~7?neDzgfH4NpQNxl#8kB%Lxy?nujpWq<4z@K+7;#6E0G+W7{*y}`
zBx^27%p8^z0`0|eqm`FV1WW5r?gY~Sl9Nn*5*5{z*Wp4cdvGM+#_!#CgV~$DhX`%1
z%mJCNPyv4BFU{4Lx^p(=rndqr<!C0?j1@r(Y2^=guFhUiCq)1RX+FrqP7HboOhAr|
zMPAO>vsGo-oM0Yd=+{95n{9gc{6!V^);y>&%jf_lAz<gqNp7>tn!6kZ(BtI6mS0X}
z(A;Pao)mG&^=e<UPo|{UarDc95F{Oa<n~89FwkpJawvER876}0ix*NC_gZ1V0?vg(
zf#x-TE0sbEgljtD2?x2^G?6}#5^NtRdwH!&V4MNpzu841K|Sw!m=nnz*N%!jmJP-L
z7RIupOC1fgp2d>qGN0}0p#cG4(O~~%0L$u_e4RYp2_Z|QR8CfAxMog_GU4PmWl9p2
zS{^h}rvX%#gz+{zTC6VHU2t)9$P0E*jGVk4W~Y@VrDxrl|N1Oue6ACY0s=@Xj{tE{
zt0jP<X=m1J^R~y^>fNIQOu+B=Jc1eQi#&NH#(VGtz^;Ql&L(99GmE9E9E~<)gMrev
z<}_4b%ZJM4_1aAQLv-k8ol8aA+4J=c&m{BF54jUD#Q=b@?V;u)9BDfg(Gs#H;GjeP
zr|#k)z|QocIxKd$__4eK|19sJ_-Uz_N>V;6O}14-)8n4=AiN;mea{*_JZxrr|5l`o
z!vO$G8UMKE+O89V7LSK0^_o&)GszQwUD{9}KwR7j97s2gSpXDRC{Yg903fgj3g-i|
zAR-Vg$`7NlXC(1aXO~=|sue@oWW1{K^r#Zyv6w%Bm`)<c8%2vXTnD4#<qnVFj(vzT
z1_IAUvgFBjlEX4$M@>a8g;8)2Vz8ow<a;?{z@ocaVLk}3rEL{XEC&)5Q5Vo(bh|R=
zMFJiSFkx7F^A=9#sU&JfbaKG<J4Hq5@F4)M5L>PBy!*l4#y9vl_2#kxyb;U+CQB6G
zH)P)wqF;mXj%dZ?1HfgHX2P9fYX~X@RMsmC0!l^6<JU!Euc@>lTpV?AMzs)1K|1y$
zIUq=8ms(hP35SDFL{Dws%jGFA1Tc&vPY3Ml&Cra_ihhY{tEXU9nBhICN8peHT0&m8
zfoc818jXaZ6tkKkTDOyuof)Z+^OdLGZiK^x@z({RnmpZl0PT^I8%pBs;1mJ3o;}%U
z2+-=2WEKhn2%!>hK>#(NE&8@7n0tpjJcuCH6qbG9DprD_XsxS`U{Mg_4<ZkUhwRr^
z>kLsz6x=JeJ;1tME;zA#<G4DzG=c!)-wn<+z&dc=ZN<ysRc?+QmNX%l#`>~+tlvaX
zVdhO4g0}TZddX`}@$u_43&Qg50_-j*4&C_P&<dZ=^mt5JLIic~V`Qj(XP0;#H4y)i
z<7&`zHh`Q?_BnUo1@SKBy#$*5?^nzQIJ(pB3sw!zJQuv$$doo~p~@>sK)y{%4QmqV
zXfrYdwJ&h=o0BKX!jwh!P`ZE-E*^`6aCyWZEaxwY_v!KQlyQBDGh(BoEyO++46emi
zFWXRMI?_Gg>5?k5);J7#WJ^LrJx3>t^4t}X7(8Cq@L7B>NE0MGeGEew287c4C$W#s
z#ka8xvZ{+C2VXB9d`WN$6<y?Qz$-TO5_;H~=k_VR)2#$8vG}yg-DU`ME(-n7n2ACG
z8>EzQKCX4RB5m#|BM>?1GNmzdTVMnfuLB%n48OJ&%d61te@1f`1cCyLXUY#^9xw_|
z;9KeBt)UO59(#`=-*LA|Gs!|30hd`hrLDyzmE4MGj~=jL{{ec{R90^%DTv`aARJ{~
zcIsZE@GZ1+J1_a_d1Nd7h`*a*HpBb4lTZ+aGqPT4uQhJGRG()Rdz9Wj9*iW1!?iX(
z8-C6ZcVYm-hQ_zj9vOy65lT7DmAB_3)#LOsQCny<h$aG0?H?hqTuA+NdOXXMrFr_J
zXkjal8y~!*hFvlI5KH=a1-(!n_Jhp$c9*?Tsv<kJQQKZ4GDwEfVB3{zrT!D?3{#Sf
zqjnqO8n%t5x}2^kQowXX#;|sK1tvZYKp6c9lp0ttC#sckj^JnE%W=z7&e<KrR1QLw
zgXv&kR5^&r9b~x%pF>>BPLMjy2I2uO$)EztsUSQEP?-Ix9edv#5BW!-FvfDQ;&5QL
zm4B8MdFlb+hL#nIaKGyzI*p*yz=C+n!DwWFTsfEr3zD`!T&RmT9)-U|!z&S#oLCSX
z1wyU?7Dy0s7}}exZY1QL%Ljjj_b*4m9EYLW<<P?@%FFJ;Bz(YfJ_o3qA+^nS1IsW+
z5VhiGXn>qa^A!!Bp`RM2e`zm0?@0fAI^5qmFw0avLhp(d{FZLLFGo$AI4v&HpOKs(
z_s<egPLL!J3>^4QBVuJJr05jv%d`G94<cbE5a7kpumG7kJwK)L2ptT8-?g9}9?Ba?
zQ}J3Hc^*44co!>DOMB?QB@g1a{NMgt3F3d)|NYu*@uxK+ytL@Y^!N&zZ4H7v=y5zu
zVdRwVY&gp$O5wt3$E0XRf@tD<krxJOd3-~P;L*0{`0UHidV{*I!1z7(qOG}t(vue0
zqlBjvj803Qf0lKcBcN0Ny2{H4VyvxLyMU46o~WQj9Ubh&^_zU`1gS$l^USBk>25QH
zno0H?Wu>$Sy*|Ah?Ud`x!oZs2wDhS;AK35{j!_3WGXpA!UE=My=$B98RZ?8n0BUI{
zK}W`2$vn_xndQYTDjtnBEO4#4D&f{{w5k;YxD{ne4N{m!$Wd{v8!Ot)oP2vIG?`=3
zzOZoHL~$>r(F#goK?2k3hU##*p72!YIHhB}QWJyOgrZKDgIk`)VAaewQWQYeo2P(P
z?RPG?0uJH0YB0x{_}q{rGX{+{y^ec{vu8$TPmLy*tTZmwzFTQp>r`hG-#8KCaC+1P
z_TuXMBTCE8K9i_nWilK~kPTdBA}G(6%Og~(#KY)ScSUdGu`}h6*z}?u@|d-=(P1%I
zj~$3n_03Nk`RS=woXfF+K_%y$+y&Rxv5yB!Qxf=qathoW3|i(mS&NUr%rqMu&3v?{
z{#*_`;I-*w0B50A1^cUeJ2ApLsM2TSiOWtM99!gA4mYWzG;M9RHK)iz?qYpRAQOR>
zvSKGi<@lSM+<5gSqs}lXC~I-8^DN=1vHD)2+H<9;Gncj<1^v*7RwHAE=Ni)slgKHV
z5d@wN%%>?i9UmC0OanRB&afC=arGk>1sgdGEQ#JjQkK(%v_g<{BOb~L1+++2S2lBP
zW=P(r0Szar%un_k%{y^ZppfGlz1JPu_v6wp*=U@axy7vH<gMi`;Y|D>fw;8n#rj<d
zpDfviWdpLvIOYrhhoZ44d}}c5XCL#A<Cy$!ztjKd5Aja8PFhI)P#-5vHW<Hte9}K`
zRC1yy?4SJsoIa||71=)~sB`I8=Kg$4{6`x#v;j8y<Ts6&|4=l0q5G;VIri_nif9h<
zgOUWY$tz9$X{D_I6(xhp&)s7z5vTquD?PsW1TJ7<@M|Nj{XeYqYrqX>?F5CFE&h9d
z`s;F~epoIp2>Q=EI(0zgAH!_F_q{F`o-TU&S01cPr_Zh<x%Rj+gYf-w|K5f_yZJwq
zY5u(p|9vdffA==zB>!q$frH<v7V?iAj6}Kri)uNs<f)U>6^GJ_`cucz@!sk`@vE`9
z$SB_ExA_OlbsMVQ3}tFv=P_&~X9@m(i|8xD(K=f4%$si}D6h5DvQ&^bc$@faXsHG`
z1V+;hufM8UbZXcor4a%04-^EH+rN*S7(Yt{bsOL3#!}cWx5s|5I!nIQFAVqsfKD~l
z9jlob5FWHaLC!>`F5DPWv2U$wzeaz+`YsPi;Unv1S^E^e0J^rv4xuC@hyc*n`c0vx
z0)_5y)7CCzf{UTkQ&z-nFGFg83q|mszt?@-kCb`j&Sko65&Y4a0e)>TTX!8cU?OHT
z&X1Hn6V4f&c?5TCGNP^zL!wgvZl~Fr4FsMU1s5D6M$ZhnyVX2B1hg+lSgJ2iDsqu?
zka|UIMqA`gA0u9_TsS(~^VU*5jqAlTskl3%3?j+JtgiW|MyZRBmPU&|%Txt7T3#$n
zIrL1jEQ&N*h`HeK!V*JtV=A#8YI)RC<}fg0nOczh+)`b2iUTiB2(Wl5HgJ`d);2?X
z!a{wBF_rCJ`tag2sjHP1u!8f{@1Cg?G>4WpmU^c?t9s@`QBpZ-6m`AW-!o|S*!Y7{
z7j>Qh4s6o}a#p`h0c`ylm~VL9k`JWu)YALHuIHT1b(kRBW~doFcIQGQ)5@`@49=0q
z0%v+<?iMj96doO|VY+Ww|AHNhQ5iBAEqJh4$|~)?tL9pJK4!+tQtqKpmd(oKT(WXC
zfwjjbGoBGEGhI5W1H?EFu*q)#%WO)sjTU{KFwO}!377kgOOLH68fAK)p0QaFSt%A$
z$_BwmyUUYjMs1{RhaXrhRxDSaRn7%rY3nTJYb^|)pA&<OvAAC`>9HxS9$+iomAv}w
z_-r}3@nWfg8O!g|3W2N6mV*c1x+a+oD!-Z(8d86iGN+a~jelB#o#<!A3!L$qYs9WL
z69-jd3u|_;4Y2B<EpUcrzfC#9Iq29Do(W5Xh9w3aokk8Bi3dSV4%p<@3k_k4$lj!e
zt7|3>j;x-;0XUaFTF>}k)D{6MQwXAYcI2zE$v07Gbg@h}b}^dB!E)Kw*cpMRqO=br
zal%r?>+doqyN7U$P*Vt^;;HPXL11bvvDu0+>G9XRQ`=P11XR{FmNr_K`bQz8^2!h)
zx>?T6rw~ALE$;MAu+DisW(g($ppNMmBVjpJdvWpepNPC{<#>heb+{0@?V0<tE(JcG
z_igT)$R1%7V@|-!zs`mL_TW?=5O4@7YRJOToh<GWFV|8bRL(qvpd;LoQptj<VV@#=
zhI!V}uc@@4a2kpriEY7E(dBLj764Ck%GrLO>^1?T)Jp!~9B#2T&8xs1+<e3_0wn4n
z6$^xYh>#XtQ`KTEaSyq+1b|5qt!&Lzd8Y|)uwSxT<<hF|9`_5R017pu<B!B#n+5^)
zh(JV=NuXQ^f%oaBaB&|5SRyuu<E3+;((S<3Yv>ueCOm<q)}O?(&f<8`3v0#>B>+a%
zZ-Pknn&9ZiJPKKnlr7#a)DSdCZ0)J&%xfrcIlfaJK+#W3!Vxa<Iu@A@^yj<r3=4Ql
zWvBpV2#;lvKErYi?Zo)N#YG1B7=A%t1YoevXQpKYvvpuWh|EBkH%j~bwm?2xi_wu6
z>yP-_Hh8yR#EAnyj?Y$iR&U3d)MgIZ+deS&gRgZ^UhLA_{d}OI@ki)p)&W-<CPOys
zQ>hG*%-aO=L)L3U3o9_JUnWo+vP59wD`03C0UWRZl`h(VJGnW~%FxeV9YnzV>rZ1I
zs<g8a07}Vi2lD*LLMTOL&!Wm!O-OYwg_w0b4$c9+GwP?Km|M&n-Y<sw;btohnElH;
z0tQAPe|$$UwPdz-SNu=BBMiuq1;3kg_**yIe-|V0;`~E$-fZ6;Lvr5iw-^D#q%6W2
zOKR-0vJrn>{&LyTD^lg#xG;Wjb<<+n+pP$yM57BI1J1QdflElF!vPs6uOLcTvToyv
zY2nvKUuu}&$7ca~#Qaxhj1l;Yd*B-R3@@rlkztN1uFyWMZdf(q65P<2%abKT76H6v
zrLe~l({Ck)GYiR$TznT;cn90dUGv%%Y$U>K(SKWSG|KqB0MY#Z#h}p`vxF*EiF>l)
zG%*Ixk=P(B#xB-`gOugeam@$CPC6PWJ&LrfLpJ1CTFB)I;0AG{C(pR+vG!xVBt?c%
zAkhM-<sKCA$ecMqk>@P-)ccdHRxNVz`%>K$^G!5`#p3Up37<JCb?kB1oaLMzRLJbP
z0&6~Q#&*#p&!(b1&SaC0<*j*^W71*eC~3uBOW*vc5y{}IB%j0SRxwrjmeQ1B!?|Uj
ztJ&^nis`?z2<#N5$SKd*I97U4mE0O;U6bQ#zEK1oQU<Z`C*fs2n=z=Ec}xt7f!Bk@
zarcxiacit;^c5<BBC29FNEG9P>MvC|u#F`04OsP+r1hvqmZL;G9e9~<2y?e`jG>9L
z1%anFs*s4&SgB{e&|&haFIEMjPiN5y4xBV8GH~cKZifzNty=78K7vql#-kDSCJhKI
z98_x0+Gf?g7DSCUPYS}K1-XGXQ||qUr#6awhx-ppISgaW1%%)j87j2#Gpo^~tSw@W
zp@#Q$Tk_K*>|V=`VtS!#*R8>tbLlZ-ARF`OfE>#G*_Lm<PtpHd#s971|H6^?->u^R
z-iCi~!~YAm;n&NgIFLyuMmK&TM&Df~L&oR&$)l;{&rJR`p+Mu!D4zdYU19%$7_E-`
j{8;1v>t%kgXF?Vz2L4OSOicKT%e?yiGXKx~sMY^JqY8Gg

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/packages/optimizer/optimizer.md b/docs/python_docs/python/tutorials/packages/optimizer/optimizer.md
new file mode 100644
index 000000000000..dfc56abedd02
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/optimizer/optimizer.md
@@ -0,0 +1,429 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Optimization Algorithms
+
+Deep learning models are comprised of a model architecture and the model parameters. The model architecture is chosen based on the task - for example Convolutional Neural Networks (CNNs) are very successful in handling image based tasks and Recurrent Neural Networks (RNNs) are better suited for sequential prediction tasks. However, the values of the model parameters are learned by solving an optimization problem during model training.
+
+To learn the parameters, we start with an initialization scheme and iteratively refine the parameter initial values by moving them along a direction that is opposite to the (approximate) gradient of the loss function. The extent to which the parameters are updated in this direction is governed by a hyperparameter called the learning rate. This process, known as gradient descent, is the backbone of optimization algorithms in deep learning. In MXNet, this functionality is abstracted by the [Optimizer API](http://beta.mxnet.io/api/gluon-related/mxnet.optimizer.html).
+
+When training a deep learning model using the MXNet [gluon API](http://beta.mxnet.io/guide/packages/gluon/index.html), a gluon [Trainer](http://beta.mxnet.io/guide/packages/gluon/trainer.html) is initialized with the all the learnable parameters and the optimizer to be used to learn those parameters. A single step of iterative refinement of model parameters in MXNet is achieved by calling [`trainer.step`](http://beta.mxnet.io/api/gluon/_autogen/mxnet.gluon.Trainer.step.html) which in turn uses the gradient (and perhaps some state information) to update the parameters by calling `optimizer.update`.
+
+Here is an example of how a trainer with an optimizer is created for, a simple Linear (Dense) Network.
+
+
+```python
+from mxnet import gluon, optimizer
+
+net = gluon.nn.Dense(1)
+net.initialize()
+optim = optimizer.SGD(learning_rate=0.1)
+trainer = gluon.Trainer(net.collect_params(), optimizer=optim)
+```
+
+In model training, the code snippet above would be followed by a training loop which, at every iteration performs a forward pass (to compute the loss), a backward pass (to compute the gradient of the loss with respect to the parameters) and a trainer step (which updates the parameters using the gradient). See the [gluon Trainer guide](http://beta.mxnet.io/guide/packages/gluon/trainer.html) for a complete example.
+
+We can also create the trainer by passing in the optimizer name and optimizer params into the trainer constructor directly, as shown below.
+
+
+```python
+trainer = gluon.Trainer(net.collect_params(), optimizer='adam', optimizer_params={'learning_rate':1})
+```
+
+### What should I use?
+For many deep learning model architectures, the `sgd` and `adam` optimizers are a really good place to start. If you are implementing a deep learning model and trying to pick an optimizer, start with [`'sgd'`](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.optimizer.SGD.html#mxnet.optimizer.SGD) as you will often get good enough results as long as your learning problem is tractable. If you already have a trainable model and you want to improve the convergence then you can try [`'adam'`](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.optimizer.SGD.html#mxnet.optimizer.Adam). If you would like to improve your model training process further, there are a number of specialized optimizers out there with many of them already implemented in MXNet. This guide walks through these optimizers in some detail.
+
+## Stochastic Gradient Descent
+[Gradient descent](https://en.wikipedia.org/wiki/Gradient_descent) is a general purpose algorithm for minimizing a function using information from the gradient of the function with respect to its parameters. In deep learning, the function we are interested in minimizing is the [loss function](http://beta.mxnet.io/guide/packages/gluon/loss.html). Our model accepts training data as inputs and the loss function tells us how good our model predictions are. Since the training data can routinely consist of millions of examples, computing the loss gradient on the full batch of training data is very computationally expensive. Luckily, we can effectively approximate the full gradient with the gradient of the loss function on randomly chosen minibatches of our training data. This variant of gradient descent is [stochastic gradient descent](https://en.wikipedia.org/wiki/Stochastic_gradient_descent).
+
+Technically, stochastic gradient descent (SGD) refers to an online approximation of the gradient descent algorithm that computes the gradient of the loss function applied to a *single datapoint*, instead of your entire dataset, and uses this approximate gradient to update the model parameter values. However, in MXNet, and other deep learning frameworks, the SGD optimizer is agnostic to how many datapoints the loss function is applied to, and it is more effective to use a mini-batch loss gradient, as described earlier, instead of a single datapoint loss gradient.
+
+### [SGD optimizer](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.optimizer.SGD.html#mxnet.optimizer.SGD)
+
+For an SGD optimizer initialized with learning rate $lr$, the update function accepts parameters (weights) $w_i$, and their gradients $grad(w_i)$, and performs the single update step:
+
+$$w_{i+1} = w_i + lr\cdot -grad(w_i)$$
+
+visualized in the diagram shown below.
+
+<p align="center">
+    <img src="images/sgd_animation.gif" alt="drawing"/>
+</p>
+
+
+### Weight decay
+The SGD update step can be modified by introducing an extra term that enforces a penalty on the size of the parameters. This is achieved by subtracting a fraction of the weight $\delta\cdot w$ during the weight update as shown below.
+
+$$w_{i+1} = w_i + lr\cdot (-grad(w_i) -\delta\cdot w_i)$$
+
+Introducing weight decay modifies the objective of the optimization problem by adding an implicit regularization term to penalizes large weights. Weight decay is discussed more extensively in this [paper](https://papers.nips.cc/paper/563-a-simple-weight-decay-can-improve-generalization.pdf).
+
+### Momentum
+The convergence of the  SGD optimizer can be accelerated by incorporating momentum. Originally proposed by [Polyak (1964)](https://www.sciencedirect.com/science/article/abs/pii/0041555364901375), SGD with momentum improves the approximation of the gradient term by incorporating the gradients from previous update steps. To achieve this, SGD with momentum stores and 'remembers' the update at each iteration to be included in the next iteration. In the equations below we denote the momentum history as $v$.
+
+For the first update the SGD optimizer with momentum performs the single update step:
+
+$$ v_1= lr\cdot -grad(w_0)$$
+$$ w_1= w_0 + v_1 $$
+
+For subsequent updates, SGD with momentum, with momentum parameter $\gamma$, performs the update step:
+
+$$ v_{i+1} = \gamma \cdot v_{i} + lr\cdot -grad(w_{i}) $$
+$$ w_{i+1} = w_i + v_{i+1} $$
+
+This is also shown in the diagram below.
+
+<p align="center">
+    <img src="images/momentum_sgd_animation.gif" alt="drawing"/>
+</p>
+
+
+The use of SGD with momentum for learning in neural networks was introduced by Rumelhart, Hinton and Williams in [Learning Internal Representations by Error Propagation](https://dl.acm.org/citation.cfm?id=104279.104293).
+
+To create an SGD optimizer with momentum $\gamma$ and weight decay in MXNet simply use the following code.
+
+
+```python
+sgd_optimizer = optimizer.SGD(learning_rate=0.1, wd=0., momentum=0.8)
+```
+
+### [Nesterov Accelerated Stochastic Gradient Descent](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.optimizer.NAG.html#mxnet.optimizer.NAG)
+
+The momentum method of [Nesterov](https://goo.gl/M5xbuX) is a modification to SGD with momentum that allows for even faster convergence in practice. With Nesterov accelerated gradient (NAG) descent, the update term is derived from the gradient of the loss function with respect to *refined parameter values*. These refined parameter values are computed by performing a SGD update step using the momentum history as the gradient term.
+
+Alternatively, you can think of the NAG optimizer as performing two update steps:
+* The first (internal) update step approximates uses the current momentum history $v_i$ to calculate the refined parameter values $(w_i + \gamma \cdot v_i)$. This is also known as the lookahead step.
+* The second (actual) step uses the gradient of the loss function with respect to the lookahead parameter values from the first step and the current momentum history $v_i$ to obtain a new direction to update our original parameter values, like classical momentum.
+
+The NAG optimizer with momentum parameter $\gamma$ performs the update step:
+
+$$ v_{i+1} = \gamma \cdot v_{i} + lr\cdot -grad(w_{i} + \gamma \cdot v_i) $$
+$$ w_{i+1} = w_i + v_{i+1} $$
+
+<p align="center">
+    <img src="images/nesterov_momentum_animation.gif" alt="drawing"/>
+</p>
+
+
+The effects of using NAG over SGD and classical momentum are discussed in this [paper](http://proceedings.mlr.press/v28/sutskever13.pdf) by Sutskever et al.
+
+The NAG optimizer can be initialized in MXNet by using the code snippet below or by creating a trainer with argument `optimizer='nag'`.
+
+
+```python
+nag_optimizer = optimizer.NAG(learning_rate=0.1, momentum=0.8)
+```
+
+## Adaptive Learning Rate Methods
+
+The gradient methods implemented by the optimizers described above use a global learning rate hyperparameter for all parameter updates. This has a well-documented shortcoming in that it makes the training process and convergence of the optimization algorithm really sensitive to the choice of the global learning rate. Adaptive learning rate methods avoid this pitfall by incorporating some history of the gradients observed in earlier iterations to scale step sizes (learning rates) to each learnable parameter in the model.
+
+### [AdaGrad](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.optimizer.AdaGrad.html)
+
+The AdaGrad optimizer, which implements the optimization method originally described by [Duchi et al](http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf), multiplies the global learning rate by the $L_2$ norm of the preceeding gradient estimates for each paramater to obtain the per-parameter learning rate. To achieve this, AdaGrad introduces a new term which we'll denote as $g^2$ - the accumulated square of the gradient of the loss function with respect to the parameters.
+
+Thus the AdaGrad optimizer update function performs the update steps below to obtain $i+1$th refinement.
+
+$$ g^2_{i+1} = g^2_{i} + grad(w_i)^2 $$
+$$ w_{i+1} = w_i + \dfrac{lr}{\sqrt{g^2 + \epsilon}}\cdot -grad(w_i)$$
+
+The $\epsilon$ term is a tiny positive value introduced to avoid division by zero due to floating point issues.
+
+The overaching benefit of AdaGrad over SGD is that it ensures the overall convergence is more resilient to the choice of the global learning rate $lr$ especially in tasks, such as natural language processing some data is sparse but the parameters influenced by the sparse data are quite informative.
+
+To instantiate the Adagrad optimizer in MXNet you can use the following line of code.
+
+
+```python
+adagrad_optimizer = optimizer.AdaGrad(learning_rate=0.1, eps=1e-07)
+```
+
+### [RMSProp](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.optimizer.RMSProp.html)
+
+RMSProp, introduced by [Tielemen and Hinton](http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf), is similar to AdaGrad described above, but, instead of accumulating the sum of historical square gradients, maintains an exponential decaying average of the historical square gradients, in order to give more weighting to more recent gradients.
+
+For rmsprop, we introduce the term $\mathbb{E}[g^2]$ - the decaying average over past squared gradients and $\beta$ as the forgetting factor. The rmsprop optimizer performs the update given below.
+
+
+$$ \mathbb{E}[g^2]_{i+1} = \beta\cdot\mathbb{E}[g^2]_{i} + (1-\beta)\cdot [grad(w_{i})]^2 $$
+$$ w_{i+1} = w_i + \dfrac{lr}{\sqrt{\mathbb{E}[g^2]_{i+1} + \epsilon}}\cdot -grad(w_i) $$
+
+The $\epsilon$ term is included, as in AdaGrad, for numerical stability.
+
+RMSProp was derived independently of AdaGrad and the name RMSProp derives from a combination of [RProp](https://en.wikipedia.org/wiki/Rprop) and the RMS, root mean square, operation in the denominator of the weight update.
+
+
+#### RMSProp (Centered)
+The MXNet RMSProp optimizer with the `centered=True` argument implements a variant of the RMSProp update described by [Alex Graves](https://arxiv.org/pdf/1308.0850v5.pdf), which centres the second moment $\mathbb{E}[g^2]$ or decaying average of square gradients by subtracting the square of decaying average of gradients. It also adds an explicit momentum term to weight past update steps. Representing the decaying average of gradients as $\mathbb{E}[g]$ and momentum parameter as $\gamma$, we add another equation to the non-centered rmsprop update described above.
+
+The centered RMSProp optimizer performs the update step:
+
+$$ \mathbb{E}[g]_{i+1} = \beta\cdot\mathbb{E}[g]_{i} + (1-\beta)\cdot [grad(w_{i})] $$
+$$ \mathbb{E}[g^2]_{i+1} = \beta\cdot\mathbb{E}[g^2]_{i} + (1-\beta)\cdot [grad(w_{i})]^2 $$
+$$ v_{i+1} = \gamma \cdot v_{i} + \dfrac{lr}{\sqrt{\mathbb{E}[g^2]_{i+1} - \mathbb{E}[g]^2_{i+1}+ \epsilon}}\cdot -grad(w_{i}) $$
+$$ w_{i+1} = w_i + v_{i+1} $$
+
+Here is an example snippet creating the RMSProp optimizer in MXNet.
+
+
+```python
+rmsprop_optimizer = optimizer.RMSProp(learning_rate=0.001, gamma1=0.9, gamma2=0.9, epsilon=1e-07, centered=False)
+```
+
+In the code snippet above, `gamma1` is $\beta$ in the equations above and `gamma2` is $\gamma$, which is only used where `centered=True`.
+
+### [AdaDelta](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.optimizer.AdaDelta.html)
+
+AdaDelta was introduced to address some remaining lingering issues with AdaGrad and RMSProp - the selection of a global learning rate. AdaGrad and RMSProp assign each parameter its own learning rate but the per-parameter learning rate are still calculated using the global learning rate. In contrast, AdaDelta does not require a global learning rate, instead, it tracks the square of previous update steps, represented below as $\mathbb{E}[\Delta w^2]$ and uses the root mean square of the previous update steps as an estimate of the learning rate.
+
+The AdaDelta optimizer performs the following equations in its update step:
+
+$$ \mathbb{E}[\Delta w^2]_{i+1} = \beta\cdot\mathbb{E}[\Delta w^2]_i + (1 - \beta) \cdot (w_i - w_{i-1})^2 $$
+$$ \mathbb{E}[g^2]_{i+1} = \beta\cdot\mathbb{E}[g^2]_{i} + (1-\beta)\cdot [grad(w_{i})]^2 $$
+$$ w_{i+1} = w_i + \dfrac{\sqrt{\mathbb{E}[\Delta w^2] + \epsilon}}{\sqrt{\mathbb{E}[g^2]_{i+1} + \epsilon}} \cdot -grad(w_i)$$
+
+As evident from the above equations, AdaDelta is similar to RMSProp but does not require you to specify $lr$ and instead uses $\sqrt{\mathbb{E}[\Delta w^2] + \epsilon}$ as the estimated learning rate. AdaDelta was introduced by Zeiler in this [paper](https://arxiv.org/abs/1212.5701).
+
+Here is the code snippet creating the AdaDelta optimizer in MXNet. The argument `rho` in the code is $\beta$ in the update equations. Notice there is no learning rate argument in the code.
+
+
+```python
+adadelta_optimizer = optimizer.AdaDelta(rho=0.9, epsilon=1e-07)
+```
+
+### [Adam](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.optimizer.Adam.html)
+Adam, introduced by [Kingma and Ba](https://arxiv.org/abs/1412.6980), is one of the popular adaptive algorithms for deep learning. It combines elements of RMSProp with momentum SGD. Like RMSProp, Adam uses the RootMeanSquare of decaying average of historical gradients but also explicitly keeps track of a decaying average of momentum and uses that for the update step direction. Thus, Adam accepts two hyperparameters $\beta_1$ and $\beta_2$ for momentum weighting and gradient RMS weighting respectively. Adam also accepts a global learning rate that's adaptively tuned to each parameter with the gradient RootMeanSquare. Finally, Adam also includes bias correction steps within the update that transform the biased estimates of first and second order moments, $v_{i+1}$ and $\mathbb{E}[g^2]_{i+1}$ to their unbiased counterparts $\tilde{v}_{i+1}$ and $\tilde{\mathbb{E}[g^2]}_{i+1}$
+
+The Adam optimizer performs the update step described the following equations:
+
+$$ v_{i+1} = \beta_1 \cdot v_{i} + (1 - \beta_1) \cdot grad(w_i) $$
+$$ \mathbb{E}[g^2]_{i+1} = \beta_2\cdot\mathbb{E}[g^2]_{i} + (1-\beta_2)\cdot [grad(w_{i})]^2 $$
+$$ \tilde{v}_{i+1} = \dfrac{v_{i+1}}{1 - (\beta_1)^{i+1}} $$
+$$ \tilde{\mathbb{E}[g^2]}_{i+1} = \dfrac{\mathbb{E}[g^2]_{i+1}}{1 - (\beta_2)^{i+1}} $$
+$$ w_{i+1} = w_i + \dfrac{lr}{\sqrt{\tilde{\mathbb{E}[g^2]}_{i+1}} + \epsilon} \cdot -\tilde{v}_{i+1} $$
+
+In MXNet, you can construct the Adam optimizer with the following line of code.
+
+
+```python
+adam_optimizer = optimizer.Adam(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08)
+```
+
+### [Adamax](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.optimizer.Adamax.html)
+Adamax is a variant of Adam also included in the original paper by [Kingma and Ba](https://arxiv.org/abs/1412.6980). Like Adam, Adamax maintains a moving average for first and second moments but Adamax uses the $L_{\infty}$ norm for the exponentially weighted average of the gradients, instead of the $L_2$ norm used in Adam used to keep track of the gradient second moment. The $L_{\infty}$ norm of a vector is equivalent to take the maximum absolute value of elements in that vector.
+
+$$ v_{i+1} = \beta_1 \cdot v_{i} + (1 - \beta_1) \cdot grad(w_i) $$
+$$ g^\infty_{i+1} = \mathtt{max}(\beta_2\cdot g^\infty_{i},  |{grad(w_i)}|) $$
+$$ \tilde{v}_{i+1} = \dfrac{v_{i+1}}{1 - \beta_1^{i+1}} $$
+$$ w_{i+1} = w_i + \dfrac{lr}{g^\infty_{i+1} + \epsilon} \cdot - \tilde{v}_{i+1} $$
+
+See the code snippet below for how to construct Adamax in MXNet.
+
+
+```python
+adamax_optimizer = optimizer.Adamax(learning_rate=0.002, beta1=0.9, beta2=0.999)
+```
+
+### [Nadam](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.optimizer.Nadam.html)
+Nadam is also a variant of Adam and draws from the perspective that Adam can be viewed as a combination of RMSProp and classical Momentum (or Polyak Momentum). Nadam replaces the classical Momentum component of Adam with Nesterov Momentum (See [paper](http://cs229.stanford.edu/proj2015/054_report.pdf) by Dozat). The consequence of this is that the gradient used to update the weighted average of the momentum term is a lookahead gradient as is the case with NAG.
+
+The Nadam optimizer performs the update step:
+
+$$ v_{i+1} = \beta_1 \cdot v_{i} + (1 - \beta_1) \cdot grad(w_i + \beta_1 \cdot v_{i}) $$
+$$ \mathbb{E}[g^2]_{i+1} = \beta_2\cdot\mathbb{E}[g^2]_{i} + (1-\beta_2)\cdot [grad(w_{i})]^2 $$
+$$ \tilde{v}_{i+1} = \dfrac{v_{i+1}}{1 - \beta_1^{i+1}} $$
+$$ \tilde{\mathbb{E}[g^2]}_{i+1} = \dfrac{\mathbb{E}[g^2]_{i+1}}{1 - \beta_2^{i+1}} $$
+$$ w_{i+1} = w_i + \dfrac{lr}{\sqrt{\tilde{\mathbb{E}[g^2]}_{i+1}} + \epsilon}\cdot - \tilde{v}_{i+1} $$
+
+Here is the line of code to create the NAdam optimizer in MXNet.
+
+
+```python
+nadam_optimizer = optimizer.Nadam(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08)
+```
+
+## SGD optimized for large scale distributed training
+
+Training very deep neural networks can be time consuming and as such it is very common now to see practitioners turn to distributed training on multiple processors on the same machine or even across a fleet of machines to parallelize network training because this can reduce neural network training time from days to minutes.
+
+While all the preceding optimizers, from SGD to Adam, can be readily used in the distributed setting, the following optimizers in MXNet provide extra features targeted at alleviating some of the problems associated with distributed training.
+
+### [Signum](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.optimizer.Signum.html)
+In distributed training, communicating gradients across multiple worker nodes can be expensive and create a performance bottleneck. The Signum optimizer addresses this problem by transmitting just the sign of each minibatch gradient instead of the full precision gradient. In MXNet, the signum optimizer implements two variants of compressed gradients described in the paper by [Bernstein et al](https://arxiv.org/pdf/1802.04434.pdf).
+
+The first variant, achieved by constructing the Signum optimizer with `momentum=0`, implements SignSGD update which performs the update below.
+
+$$ w_{i+1} =  w_i - lr \cdot sign(grad(w_i)) $$
+
+The second variant, achieved by passing a non-zero momentum parameter implements the Signum update which is equivalent to SignSGD and momentum. For momentum parameter $0 < \gamma < 1 $, the Signum optimizer performs the following update:
+
+$$ v_{i+1} = \gamma \cdot v_i + (1 - \gamma) \cdot grad(w_i) $$
+$$ w_{i+1} =  w_i - lr \cdot sign(v_{i+1}) $$
+
+Here is how to create the signum optimizer in MXNet.
+
+
+```python
+signum_optimizer = optimizer.Signum(learning_rate=0.01, momentum=0.9, wd_lh=0.0)
+```
+
+### [LBSGD](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.optimizer.LBSGD.html)
+LBSGD stands for Large Batch Stochastic Gradient Descent and implements a technique where Layer-wise Adaptive Rate Scaling (LARS) is used to maintain a separate learning rate for each layer of the neural network. LBSGD has no additional modifications to SGD and performs the same parameter update steps as the SGD optimizer described above.
+
+LBSGD was introduced by [You et al](https://arxiv.org/pdf/1708.03888.pdf) for distributed training with data-parallel synchronous SGD across multiple worker nodes to overcome the issue of reduced model accuracy when the number of workers, and by extension effective batch size, is increased.
+
+Here is how to initialize the LBSGD optimizer in MXNet.
+
+
+```python
+lbsgd_optimizer = optimizer.LBSGD(momentum=0.0,
+                                  multi_precision=False,
+                                  warmup_strategy='linear',
+                                  warmup_epochs=5,
+                                  batch_scale=1,
+                                  updates_per_epoch=32,
+                                  begin_epoch=0,
+                                  num_epochs=60)
+```
+
+LBSGD has a number of extra keyword arguments described below
+* `multi_precision` - When True performs updates with float32 precision weights regardless of whether weights are initialized with lower precision. When False perform updates with same precision as the weights when initialized. Set to True to improve performance when training with low precision weight represenations.
+* `warmup_strategy` - The warmup is period where the learning rate is increased through the first few epochs. The following strategies are supported:  ['linear', 'power2', 'sqrt','lars']
+* `warmup_epochs` - How many epochs to perform warmup for
+* `batch_scale` - use batch size*numworkers
+* `updates_per_epoch` - How many updates to the learning rate to perform every epoch. For example during warmup the warmup strategy is applied to increase the learning rate a total of `warmup_epochs*updates_per_epoch` number of times.
+* `begin_epoch` - The epoch at which to start warmup.
+
+### [DCASGD](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.optimizer.DCASGD.html)
+
+The DCASGD optimizer implements Delay Compensated Asynchronous Stochastic Gradient Descent by [Zheng et al](https://arxiv.org/pdf/1609.08326.pdf). In asynchronous distributed SGD, it is possible that a training worker node add its gradients too late to the global (parameter) server resulting in a delayed gradient being used to update the current parameters. DCASGD addresses this issue of delayed gradients by compensating for this delay in the parameter update steps.
+
+If $grad(w_i)$ denotes the delayed gradient, $w_{i+\tau}$ denotes the parameter values at the current iteration, and $\lambda$ is the delay scale factor, the DCASGD optimizer update function performs the update:
+
+$$ w_{i+\tau+1} = w_{i+\tau} − lr \cdot (grad(w_i) + \lambda \cdot grad(w_i)^2 \cdot (w_{i+\tau} − w_i)) $$
+
+The DCASGD optimizer in MXNet can be initialized using the code below.
+
+
+```python
+dcasgd_optimizer = optimizer.DCASGD(momentum=0.0, lamda=0.04)
+```
+
+## Online Learning Algorithms
+Before deep neural networks became popular post 2012, people were already solving large scale optimization problems to train (shallow) machine learning models. One particular area this was done was active or online learning where the model is continually learning and updating its parameters after it is deployed to production. In online learning, the model has to make predictions on new inputs but moments later may become aware of the true value of what it tried to predict and use this information to update its parameters.
+
+The class of optimization algorithms designed to tackle online learning problems have also seen some success in offline training of deep neural models. The following optimizers are algorithms taken from online learning that have been implemented in MXNet.
+
+### [FTRL](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.optimizer.Ftrl.html)
+
+FTRL stands for Follow the Regularized Leader and describes a family of algorithms originally designed for online learning tasks.
+
+For each iteration, FTRL algorithms finds the next parameter by solving the following optimization problem which minimizes the total regret i.e the sum of the inner product all preceding gradients and next parameter. The optimization objective is regularized so that the next parameter is close (proximal) in $L2$ norm to the preceding parameter values and is sparse which is enforced by the $L1$ norm.
+
+$$ w_{i+1} = \texttt{argmin}_{w} \left[\sum_{j=1}^{i} grad(w_i)\cdot w + \dfrac{1}{2}\sum_{j=1}^{i} \sigma_j \cdot ||w - w_j||_2^2 + \lambda ||w||_1\right]$$
+
+Due to the similarity of online learning and neural network training, there is an [equivalence](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/37013.pdf) between variants of gradient descent and FTRL algorithms. In fact, the $w$ that minimizes FTRL with only $L_2$ regularization (i.e $\lambda$ in the equation above is set to 0) is exactly the $w$ derived from stochastic gradient descent update.
+
+The version of FTRL implemented as an MXNet optimizer is from [McMahan et al](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/41159.pdf) and encourages sparse parameters due to $L_1$ regularization. It performs the following update:
+
+$$ z_{i+1} = z_i + \dfrac{\left(\sqrt{\eta_i + grad(w_i)^2} - \sqrt{\eta_i}\right) \cdot w_i}{lr}$$
+$$ \eta_{i+1} = \eta_i + grad(w_i)^2$$
+$$ w_{i+1} = (|z_{i+1}| > \lambda) \cdot \left[ \dfrac{-lr}{\beta + \sqrt{\eta_{i+1}}} (z_{i+1} - \lambda \cdot sign(z_{i+1}))\right] $$
+
+Here is how to initialize the FTRL optimizer in MXNet
+
+
+```python
+ftrl_optimizer = optimizer.Ftrl(lamda1=0.01, learning_rate=0.1, beta=1)
+```
+
+### [FTML](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.optimizer.FTML.html)
+
+FTML stands for Follow the Moving Leader and is a variant of the FTRL family of algorithms adapted specifically to deep learning. Regular FTRL algorithms, described above, solve an optimization problem every update that involves the sum of all previous gradients. This is not well suited for the non-convex loss functions in deep learning. In the non-convex settings, older gradients are likely uninformative as the parameter updates can move to converge towards different local minima at different iterations. FTML addresses this problem by reweighing the learning subproblems in each iteration as shown below.
+
+
+$$ w_{i+1} = \texttt{argmin}_{w} \left[\sum_{j=1}^{i} (1 − \beta_1)\beta_1^{i−j} grad(w_i)\cdot w + \dfrac{1}{2}\sum_{j=1}^{i} \sigma_j \cdot ||w - w_j||_2^2 \right]$$
+
+$\beta_1$ is introduced to compute the exponential moving average of the previous accumulated gradient. The improvements of FTML over FTRL can be compared to the improvements of RMSProp/Adam to AdaGrad. According to [Zheng et al](http://proceedings.mlr.press/v70/zheng17a/zheng17a.pdf), FTML enjoys some of the nice properties of RMSProp and Adam while avoiding their pitfalls.
+
+The FTML optimizer performs the following update:
+
+$$ v_{i+1} = \beta_2 \cdot v_i + (1 - \beta_2) \cdot grad(w_i)^2$$
+$$ d_{i+1} = \dfrac{1 - \beta_1^{i+1}}{lr} \big(\sqrt{\dfrac{v_{i+1}}{1 - \beta_2^{i+1}}} + \epsilon\big)$$
+$$ z_{i+1} = \beta_1 \cdot z_i + (1 - \beta_1)\cdot grad(w_i) - (d_{i+1} - \beta_1 \cdot d_i) \cdot w_i$$
+$$ w_{i+1} = \dfrac{-z_{i+1}}{d_{i+1}} $$
+
+In MXNet, you can initialize the FTML optimizer using
+
+
+```python
+ftml_optimizer = optimizer.FTML(beta1=0.6, beta2=0.999, epsilon=1e-08)
+```
+
+Here `beta1` and `beta2` are similar to the arguments in the Adam optimizer.
+
+## Bayesian SGD
+A notable shortcoming of deep learning is that the model parameters learned after training are only point estimates, therefore deep learning model predictions have no information about uncertainty or confidence bounds. This is in contrast to a fully Bayesian approach which incorporates prior distributions on the model parameters and estimates the model parameters as belonging to a posterior distribution. This approach allows the predictions of a bayesian model to have information about uncertainty, as you can sample different values from the posterior distribution to obtain different model parameters. One approach to close the bayesian gap in deep learning is to incorporate the gradient descent algorithm with properties that allow the model parameters to converge to a distribution instead of a single value or point estimate.
+
+### [SGLD](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.optimizer.SGLD.html)
+Stochastic Gradient Langevin Dynamics or SGLD was introduced to allow uncertainties around model parameters to be captured directly during model training. With every update in SGLD, the learning rate decreases to zero and a gaussian noise of known variances is injected into the SGD step. This has the effect of having the training parameters converge to a sufficient statistic for a posterior distribution instead of simply a point estimate of the model parameters.
+
+SGLD performs the parameter update:
+
+$$ w_{i+1} = w_i + \dfrac{lr_{i+1}}{2}\cdot -grad(w_i) + \eta_{i+1}$$
+
+where $ \eta_{i+1} \sim N(0, lr_{i+1})$ i.e $\eta_{i+1}$ is drawn from a zero centered gaussian with variance $lr_{i+1}$
+
+SGLD was introduced by [Patterson and Teh](https://papers.nips.cc/paper/4883-stochastic-gradient-riemannian-langevin-dynamics-on-the-probability-simplex.pdf) and the optimizer can be created in MXNet with the following line of code.
+
+
+```python
+sgld_optimizer = optimizer.SGLD()
+```
+
+## Custom Optimizer
+
+If you would like to use a particular optimizer that is not yet implemented in MXNet or you have a custom optimization algorithm of your own that you would like to use to train your model, it is very straightforward to create a custom optimizer.
+
+Step 1: First create a function that is able to perform your desired updates given the weights, gradients and other state information.
+
+Step 2: You will have to write your own optimizer class that extends the [base optimizer class](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.optimizer.Optimizer.html#mxnet.optimizer.Optimizer) and override the following functions
+* `__init__`: accepts the parameters of your optimizer algorithm as inputs as saves them as member variables.
+* `create_state`: If your custom optimizer uses some additional state information besides the gradient, then you should implement a function that accepts the weights and returns the state.
+* `update`: Implement your optimizer update function using the function in Step 1
+
+Step 3: Register your optimizer with `@register` decorator on your optimizer class.
+
+See the [source code](http://beta.mxnet.io/_modules/mxnet/optimizer/optimizer.html#NAG) for the NAG optimizer for a concrete example.
+
+## Summary
+* MXNet implements many state-of-the-art optimizers which can be passed directly into a gluon trainer object. Calling `trainer.step` during model training uses the optimizers to update the model parameters.
+* Gradient descent algorithms minimize the loss function by using information from the gradient of the loss function and a learning rate hyperparameter.
+* Stochastic Gradient Descent is the backbone of deep learning optimization algorithms and simple SGD optimizers can be made really powerful by incorporating momentum, for example `sgd` with momentum and `nag`.
+* Adaptive learning rate methods compute per-parameter learning rates to make optimization less sensitive to the choice of global learning rate. `adam` is a popular adaptive learning rate optimizer.
+* Certain MXNet optimizers like `Signum` and Large Batch SGD are well suited for large scale distributed training as they consider challenges specific these tasks.
+* MXNet also implements optimizers from active learning like `FTML`, `FTRL`, and optimizers for bayesian learning like `SGLD`.
+* Finally, it is easy to create a custom optimizer by following the patterns in the source code implementation for the optimizers that already exist in MXNet.
+
+## Next Steps
+While optimization and optimizers play a significant role in deep learning model training, there are still other important components to model training. Here are a few suggestions about where to look next.
+* The [trainer API](http://beta.mxnet.io/api/gluon/mxnet.gluon.Trainer.html) and [guide](http://beta.mxnet.io/guide/packages/gluon/trainer.html) have information about how to construct the trainer that encapsulate the optimizers and will actually be used in your model training loop.
+* Check out the guide to MXNet gluon [Loss functions](http://beta.mxnet.io/guide/packages/gluon/loss.html) and [custom losses](http://beta.mxnet.io/guide/packages/gluon/custom-loss/custom-loss.html) to learn about the loss functions optimized by these optimizers, see what loss functions are already implemented in MXNet and understand how to write your own custom loss functions.
+* Take a look at the [guide to parameter initialization](http://beta.mxnet.io/guide/packages/gluon/init.html) in MXNet to learn about what initialization schemes are already implemented, and how to implement your custom initialization schemes.
+* Also check out the [autograd guide](http://beta.mxnet.io/guide/packages/autograd/autograd.html) to learn about automatic differentiation and how gradients are automatically computed in MXNet.
+* Make sure to take a look at the [guide to scheduling learning rates](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/learning_rate_schedules.html) to learn how to create learning rate schedules to supercharge the convergence of your optimizer.
+* Finally take a look at the [KVStore API](http://beta.mxnet.io/api/gluon-related/mxnet.kvstore.KVStore.html#mxnet.kvstore.KVStore) to learn how parameter values are synchronized over multiple devices.
diff --git a/docs/python_docs/python/tutorials/packages/symbol/index.rst b/docs/python_docs/python/tutorials/packages/symbol/index.rst
new file mode 100644
index 000000000000..20af8b0ca8a6
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/symbol/index.rst
@@ -0,0 +1,41 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Symbol
+======
+
+.. container:: cards
+
+   .. card::
+      :title: Symbol API Intro
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/basic/symbol.html
+
+      How to use MXNet's Symbol API.
+
+
+   .. card::
+      :title: Data Loading
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/basic/data.html
+
+      How to load data with the Symbol API.
+
+
+.. toctree::
+   :hidden:
+
+   symbol
+   data
diff --git a/docs/python_docs/python/tutorials/packages/viz/index.rst b/docs/python_docs/python/tutorials/packages/viz/index.rst
new file mode 100644
index 000000000000..5bcb2ac1ca8b
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/viz/index.rst
@@ -0,0 +1,38 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Data
+====
+
+.. container:: cards
+
+   .. card::
+      :title: How to Visualize Neural Networks as Computation Graph
+      :link: https://mxnet.incubator.apache.org/api/faq/visualize_graph
+
+      A demonstration how to use ``mx.viz.plot_network`` for visualizing your neural networks.
+
+References
+----------
+
+- `mxnet.viz <../api/symbol-related/mxnet.visualization.html>`_
+
+.. toctree::
+   :hidden:
+   :glob:
+
+   *
diff --git a/docs/python_docs/python/tutorials/performance/backend/MKLDNN_README.md b/docs/python_docs/python/tutorials/performance/backend/MKLDNN_README.md
new file mode 100644
index 000000000000..d8f78ecf71a7
--- /dev/null
+++ b/docs/python_docs/python/tutorials/performance/backend/MKLDNN_README.md
@@ -0,0 +1,291 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Build/Install MXNet with MKL-DNN
+
+A better training and inference performance is expected to be achieved on Intel-Architecture CPUs with MXNet built with [Intel MKL-DNN](https://github.com/intel/mkl-dnn) on multiple operating system, including Linux, Windows and MacOS.
+In the following sections, you will find build instructions for MXNet with Intel MKL-DNN on Linux, MacOS and Windows.
+
+Please find MKL-DNN optimized operators and other features in the [MKL-DNN operator list](../mkldnn/operator_list.md).
+
+The detailed performance data collected on Intel Xeon CPU with MXNet built with Intel MKL-DNN can be found [here](https://mxnet.incubator.apache.org/faq/perf.html#intel-cpu).
+
+
+<h2 id="0">Contents</h2>
+
+* [1. Linux](#1)
+* [2. MacOS](#2)
+* [3. Windows](#3)
+* [4. Verify MXNet with python](#4)
+* [5. Enable MKL BLAS](#5)
+* [6. Enable graph optimization](#6)
+* [7. Quantization](#7)
+* [8. Support](#8)
+
+<h2 id="1">Linux</h2>
+
+### Prerequisites
+
+```
+sudo apt-get update
+sudo apt-get install -y build-essential git
+sudo apt-get install -y libopenblas-dev liblapack-dev
+sudo apt-get install -y libopencv-dev
+sudo apt-get install -y graphviz
+```
+
+### Clone MXNet sources
+
+```
+git clone --recursive https://github.com/apache/incubator-mxnet.git
+cd incubator-mxnet
+```
+
+### Build MXNet with MKL-DNN
+
+```
+make -j $(nproc) USE_OPENCV=1 USE_MKLDNN=1 USE_BLAS=mkl USE_INTEL_PATH=/opt/intel
+```
+
+If you don't have the full [MKL](https://software.intel.com/en-us/intel-mkl) library installation, you might use OpenBLAS as the blas library, by setting USE_BLAS=openblas.
+
+<h2 id="2">MacOS</h2>
+
+### Prerequisites
+
+Install the dependencies, required for MXNet, with the following commands:
+
+- [Homebrew](https://brew.sh/)
+- llvm (clang in macOS does not support OpenMP)
+- OpenCV (for computer vision operations)
+
+```
+# Paste this command in Mac terminal to install Homebrew
+/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
+
+# install dependency
+brew update
+brew install pkg-config
+brew install graphviz
+brew tap homebrew/core
+brew install opencv
+brew tap homebrew/versions
+brew install llvm
+```
+
+### Clone MXNet sources
+
+```
+git clone --recursive https://github.com/apache/incubator-mxnet.git
+cd incubator-mxnet
+```
+
+### Build MXNet with MKL-DNN
+
+```
+LIBRARY_PATH=$(brew --prefix llvm)/lib/ make -j $(sysctl -n hw.ncpu) CC=$(brew --prefix llvm)/bin/clang CXX=$(brew --prefix llvm)/bin/clang++ USE_OPENCV=1 USE_OPENMP=1 USE_MKLDNN=1 USE_BLAS=apple
+```
+
+<h2 id="3">Windows</h2>
+
+On Windows, you can use [Micrsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/) and [Microsoft Visual Studio 2017](https://www.visualstudio.com/downloads/) to compile MXNet with Intel MKL-DNN.
+[Micrsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/) is recommended.
+
+**Visual Studio 2015**
+
+To build and install MXNet yourself, you need the following dependencies. Install the required dependencies:
+
+1. If [Microsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/) is not already installed, download and install it. You can download and install the free community edition.
+2. Download and Install [CMake 3](https://cmake.org/files/v3.14/cmake-3.14.0-win64-x64.msi) if it is not already installed.
+3. Download [OpenCV 3](https://sourceforge.net/projects/opencvlibrary/files/3.4.5/opencv-3.4.5-vc14_vc15.exe/download), and unzip the OpenCV package, set the environment variable ```OpenCV_DIR``` to point to the ```OpenCV build directory``` (e.g.,```OpenCV_DIR = C:\opencv\build ```). Also, add the OpenCV bin directory (```C:\opencv\build\x64\vc14\bin``` for example) to the ``PATH`` variable.
+4. If you have Intel Math Kernel Library (Intel MKL) installed, set ```MKL_ROOT``` to point to ```MKL``` directory that contains the ```include``` and ```lib```. If you want to use MKL blas, you should set ```-DUSE_BLAS=mkl``` when cmake. Typically, you can find the directory in ```C:\Program Files (x86)\IntelSWTools\compilers_and_libraries\windows\mkl```.
+5. If you don't have the Intel Math Kernel Library (MKL) installed, download and install [OpenBLAS](http://sourceforge.net/projects/openblas/files/v0.2.14/), or build the latest version of OpenBLAS from source. Note that you should also download ```mingw64.dll.zip``` along with openBLAS and add them to PATH.
+6. Set the environment variable ```OpenBLAS_HOME``` to point to the ```OpenBLAS``` directory that contains the ```include``` and ```lib``` directories. Typically, you can find the directory in ```C:\Downloads\OpenBLAS\```.
+
+After you have installed all of the required dependencies, build the MXNet source code:
+
+1. Start a Visual Studio command prompt by click windows Start menu>>Visual Studio 2015>>VS2015 X64 Native Tools Command Prompt, and download the MXNet source code from [GitHub](https://github.com/apache/incubator-mxnet) by the command:
+```
+git clone --recursive https://github.com/apache/incubator-mxnet.git
+cd C:\incubator-mxent
+```
+2. Enable Intel MKL-DNN by -DUSE_MKLDNN=1. Use [CMake 3](https://cmake.org/) to create a Visual Studio solution in ```./build```. Make sure to specify the architecture in the
+command:
+```
+>mkdir build
+>cd build
+>cmake -G "Visual Studio 14 Win64" .. -DUSE_CUDA=0 -DUSE_CUDNN=0 -DUSE_NVRTC=0 -DUSE_OPENCV=1 -DUSE_OPENMP=1 -DUSE_PROFILER=1 -DUSE_BLAS=open -DUSE_LAPACK=1 -DUSE_DIST_KVSTORE=0 -DCUDA_ARCH_NAME=All -DUSE_MKLDNN=1 -DCMAKE_BUILD_TYPE=Release
+```
+3. Enable Intel MKL-DNN and Intel MKL as BLAS library by the command:
+```
+>"C:\Program Files (x86)\IntelSWTools\compilers_and_libraries\windows\mkl\bin\mklvars.bat" intel64
+>cmake -G "Visual Studio 14 Win64" .. -DUSE_CUDA=0 -DUSE_CUDNN=0 -DUSE_NVRTC=0 -DUSE_OPENCV=1 -DUSE_OPENMP=1 -DUSE_PROFILER=1 -DUSE_BLAS=mkl -DUSE_LAPACK=1 -DUSE_DIST_KVSTORE=0 -DCUDA_ARCH_NAME=All -DUSE_MKLDNN=1 -DCMAKE_BUILD_TYPE=Release -DMKL_ROOT="C:\Program Files (x86)\IntelSWTools\compilers_and_libraries\windows\mkl"
+```
+4. After the CMake successfully completed, in Visual Studio, open the solution file ```.sln``` and compile it, or compile the MXNet source code by using following command:
+```r
+msbuild mxnet.sln /p:Configuration=Release;Platform=x64 /maxcpucount
+```
+   These commands produce mxnet library called ```libmxnet.dll``` in the ```./build/Release/``` or ```./build/Debug``` folder. Also ```libmkldnn.dll``` with be in the ```./build/3rdparty/mkldnn/src/Release/```
+
+5. Make sure that all the dll files used above(such as `libmkldnn.dll`, `libmklml*.dll`, `libiomp5.dll`, `libopenblas*.dll`, etc) are added to the system PATH. For convinence, you can put all of them to ```\windows\system32```. Or you will come across `Not Found Dependencies` when loading MXNet.
+
+**Visual Studio 2017**
+
+User can follow the same steps of Visual Studio 2015 to build MXNET with MKL-DNN, but change the version related command, for example,```C:\opencv\build\x64\vc15\bin``` and build command is as below:
+
+```
+>cmake -G "Visual Studio 15 Win64" .. -DUSE_CUDA=0 -DUSE_CUDNN=0 -DUSE_NVRTC=0 -DUSE_OPENCV=1 -DUSE_OPENMP=1 -DUSE_PROFILER=1 -DUSE_BLAS=mkl -DUSE_LAPACK=1 -DUSE_DIST_KVSTORE=0 -DCUDA_ARCH_NAME=All -DUSE_MKLDNN=1 -DCMAKE_BUILD_TYPE=Release -DMKL_ROOT="C:\Program Files (x86)\IntelSWTools\compilers_and_libraries\windows\mkl"
+
+```
+
+<h2 id="4">Verify MXNet with python</h2>
+
+Preinstall python and some dependent modules:
+```
+pip install numpy graphviz
+set PYTHONPATH=[workdir]\incubator-mxnet\python
+```
+or install mxnet
+```
+cd python
+sudo python setup.py install
+python -c "import mxnet as mx;print((mx.nd.ones((2, 3))*2).asnumpy());"
+```
+Expected Output:
+```
+[[ 2.  2.  2.]
+ [ 2.  2.  2.]]
+```
+### Verify whether MKL-DNN works
+
+After MXNet is installed, you can verify if MKL-DNN backend works well with a single Convolution layer.
+```
+import mxnet as mx
+import numpy as np
+
+num_filter = 32
+kernel = (3, 3)
+pad = (1, 1)
+shape = (32, 32, 256, 256)
+
+x = mx.sym.Variable('x')
+w = mx.sym.Variable('w')
+y = mx.sym.Convolution(data=x, weight=w, num_filter=num_filter, kernel=kernel, no_bias=True, pad=pad)
+exe = y.simple_bind(mx.cpu(), x=shape)
+
+exe.arg_arrays[0][:] = np.random.normal(size=exe.arg_arrays[0].shape)
+exe.arg_arrays[1][:] = np.random.normal(size=exe.arg_arrays[1].shape)
+
+exe.forward(is_train=False)
+o = exe.outputs[0]
+t = o.asnumpy()
+```
+
+More detailed debugging and profiling information can be logged by setting the environment variable 'MKLDNN_VERBOSE':
+```
+export MKLDNN_VERBOSE=1
+```
+For example, by running above code snippet, the following debugging logs providing more insights on MKL-DNN primitives `convolution` and `reorder`. That includes: Memory layout, infer shape and the time cost of primitive execution.
+```
+mkldnn_verbose,exec,reorder,jit:uni,undef,in:f32_nchw out:f32_nChw16c,num:1,32x32x256x256,6.47681
+mkldnn_verbose,exec,reorder,jit:uni,undef,in:f32_oihw out:f32_OIhw16i16o,num:1,32x32x3x3,0.0429688
+mkldnn_verbose,exec,convolution,jit:avx512_common,forward_inference,fsrc:nChw16c fwei:OIhw16i16o fbia:undef fdst:nChw16c,alg:convolution_direct,mb32_g1ic32oc32_ih256oh256kh3sh1dh0ph1_iw256ow256kw3sw1dw0pw1,9.98193
+mkldnn_verbose,exec,reorder,jit:uni,undef,in:f32_oihw out:f32_OIhw16i16o,num:1,32x32x3x3,0.0510254
+mkldnn_verbose,exec,reorder,jit:uni,undef,in:f32_nChw16c out:f32_nchw,num:1,32x32x256x256,20.4819
+```
+
+<h2 id="5">Enable MKL BLAS</h2>
+
+With MKL BLAS, the performace is expected to furtherly improved with variable range depending on the computation load of the models.
+You can redistribute not only dynamic libraries but also headers, examples and static libraries on accepting the license [Intel Simplified license](https://software.intel.com/en-us/license/intel-simplified-software-license).
+Installing the full MKL installation enables MKL support for all operators under the linalg namespace.
+
+  1. Download and install the latest full MKL version following instructions on the [intel website.](https://software.intel.com/en-us/mkl) You can also install MKL through [YUM](https://software.intel.com/en-us/articles/installing-intel-free-libs-and-python-yum-repo) or [APT](https://software.intel.com/en-us/articles/installing-intel-free-libs-and-python-apt-repo) Repository.
+
+  2. Run `make -j ${nproc} USE_BLAS=mkl`
+
+  3. Navigate into the python directory
+
+  4. Run `sudo python setup.py install`
+
+### Verify whether MKL works
+
+After MXNet is installed, you can verify if MKL BLAS works well with a single dot layer.
+
+```
+import mxnet as mx
+import numpy as np
+
+shape_x = (1, 10, 8)
+shape_w = (1, 12, 8)
+
+x_npy = np.random.normal(0, 1, shape_x)
+w_npy = np.random.normal(0, 1, shape_w)
+
+x = mx.sym.Variable('x')
+w = mx.sym.Variable('w')
+y = mx.sym.batch_dot(x, w, transpose_b=True)
+exe = y.simple_bind(mx.cpu(), x=x_npy.shape, w=w_npy.shape)
+
+exe.forward(is_train=False)
+o = exe.outputs[0]
+t = o.asnumpy()
+```
+
+You can open the `MKL_VERBOSE` flag by setting environment variable:
+```
+export MKL_VERBOSE=1
+```
+Then by running above code snippet, you probably will get the following output message which means `SGEMM` primitive from MKL are called. Layout information and primitive execution performance are also demonstrated in the log message.
+```
+Numpy + Intel(R) MKL: THREADING LAYER: (null)
+Numpy + Intel(R) MKL: setting Intel(R) MKL to use INTEL OpenMP runtime
+Numpy + Intel(R) MKL: preloading libiomp5.so runtime
+MKL_VERBOSE Intel(R) MKL 2019.0 Update 3 Product build 20190125 for Intel(R) 64 architecture Intel(R) Advanced Vector Extensions 512 (Intel(R) AVX-512) enabled processors, Lnx 2.40GHz lp64 intel_thread NMICDev:0
+MKL_VERBOSE SGEMM(T,N,12,10,8,0x7f7f927b1378,0x1bc2140,8,0x1ba8040,8,0x7f7f927b1380,0x7f7f7400a280,12) 8.93ms CNR:OFF Dyn:1 FastMM:1 TID:0  NThr:40 WDiv:HOST:+0.000
+```
+
+<h2 id="6">Enable graph optimization</h2>
+
+Graph optimization with subgraph is available and enabled by default in master branch. For MXNet release v1.5, you can manually enable it by:
+
+```
+export MXNET_SUBGRAPH_BACKEND=MKLDNN
+```
+
+This limitations of this experimental feature are:
+
+- Use this feature only for inference. When training, be sure to turn the feature off by unsetting the `MXNET_SUBGRAPH_BACKEND` environment variable.
+
+- This feature will only run on the CPU, even if you're using a GPU-enabled build of MXNet.
+
+
+<h2 id="7">Quantization and Inference with INT8</h2>
+
+Benefiting from Intel MKL-DNN, MXNet built with Intel MKL-DNN brings outstanding performance improvement on quantization and inference with INT8 Intel CPU Platform on Intel Xeon Scalable Platform.
+
+- [CNN Quantization Examples](https://github.com/apache/incubator-mxnet/tree/master/example/quantization).
+
+- [Model Quantization for Production-Level Neural Network Inference](https://cwiki.apache.org/confluence/display/MXNET/MXNet+Graph+Optimization+and+Quantization+based+on+subgraph+and+MKL-DNN).
+
+<h2 id="8">Next Steps and Support</h2>
+
+- For questions or support specific to MKL, visit the [Intel MKL](https://software.intel.com/en-us/mkl) website.
+
+- For questions or support specific to MKL, visit the [Intel MKLDNN](https://github.com/intel/mkl-dnn) website.
+
+- If you find bugs, please open an issue on GitHub for [MXNet with MKL](https://github.com/apache/incubator-mxnet/labels/MKL) or [MXNet with MKLDNN](https://github.com/apache/incubator-mxnet/labels/MKLDNN).
diff --git a/docs/python_docs/python/tutorials/performance/backend/index.rst b/docs/python_docs/python/tutorials/performance/backend/index.rst
new file mode 100644
index 000000000000..fbe40c0ed684
--- /dev/null
+++ b/docs/python_docs/python/tutorials/performance/backend/index.rst
@@ -0,0 +1,52 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Accelerated Backend Tools
+=========================
+The following tutorials will help you learn how to use backend tools to boost performance.
+
+.. container:: cards
+
+   .. card::
+      :title: TensorRT
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/tensorrt/inference_with_trt.html
+
+      How to use NVIDIA's TensorRT to boost inference performance.
+
+
+  .. card::
+     :title: MKL-DNN
+     :link: mkl-dnn.html
+
+     How to get the most from your CPU by using Intel's MKL-DNN.
+
+  .. card::
+     :title: TVM
+     :link: tvm.html
+
+     How to use TVM to boost performance.
+..
+
+.. toctree::
+   :hidden:
+   :maxdepth: 1
+
+   tensorRt
+
+   ..
+      mkl-dnn
+      tvm
\ No newline at end of file
diff --git a/docs/python_docs/python/tutorials/performance/backend/mkl-dnn.rst b/docs/python_docs/python/tutorials/performance/backend/mkl-dnn.rst
new file mode 100644
index 000000000000..1841c1c05b32
--- /dev/null
+++ b/docs/python_docs/python/tutorials/performance/backend/mkl-dnn.rst
@@ -0,0 +1,33 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Use Intel MKL-DNN to accelerate CPU performance
+===============================================
+
+.. container:: cards
+
+   .. card::
+      :title: MKL-DNN Installation and Verification
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/mkldnn/MKLDNN_README.html
+
+      A guide on using MKL-DNN with MXNet.
+
+   .. card::
+      :title: MKL-DNN Operators
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/mkldnn/operator_list.html
+
+      A list of operators that MKL-DNN supports.  
diff --git a/docs/python_docs/python/tutorials/performance/backend/mkldnn_quantization.md b/docs/python_docs/python/tutorials/performance/backend/mkldnn_quantization.md
new file mode 100644
index 000000000000..b2eaa2e44d53
--- /dev/null
+++ b/docs/python_docs/python/tutorials/performance/backend/mkldnn_quantization.md
@@ -0,0 +1,258 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Quantize custom models with MKL-DNN backend
+
+This document is to introduce how to quantize the customer models from FP32 to INT8 with Apache/MXNet toolkit and APIs under Intel CPU.
+
+If you are not familiar with Apache/MXNet quantization flow, please reference [quantization blog](https://medium.com/apache-mxnet/model-quantization-for-production-level-neural-network-inference-f54462ebba05) first, and the performance data is shown in [Apache/MXNet C++ interface](https://github.com/apache/incubator-mxnet/tree/master/cpp-package/example/inference) and [GluonCV](https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html). 
+
+## Installation and Prerequisites
+
+Installing MXNet with MKLDNN backend is an easy and essential process. You can follow [How to build and install MXNet with MKL-DNN backend](https://mxnet.incubator.apache.org/tutorials/mkldnn/MKLDNN_README.html) to build and install MXNet from source. Also, you can install the release or nightly version via PyPi and pip directly by running:
+
+```
+# release version
+pip install mxnet-mkl
+# nightly version
+pip install mxnet-mkl --pre
+```
+
+## Image Classification Demo
+
+A quantization script [imagenet_gen_qsym_mkldnn.py](https://github.com/apache/incubator-mxnet/blob/master/example/quantization/imagenet_gen_qsym_mkldnn.py) has been designed to launch quantization for image-classification models. This script is  integrated with [Gluon-CV modelzoo](https://gluon-cv.mxnet.io/model_zoo/classification.html), so that all pre-trained models can be downloaded from Gluon-CV and then converted for quantization. For details, you can refer [Model Quantization with Calibration Examples](https://github.com/apache/incubator-mxnet/blob/master/example/quantization/README.md).
+
+## Integrate Quantization Flow to Your Project
+
+Quantization flow works for both symbolic and Gluon models. If you're using Gluon, you can first refer [Saving and Loading Gluon Models](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/save_load_params.html) to hybridize your computation graph and export it as a symbol before running quantization.
+
+In general, the quantization flow includes 4 steps. The user can get the acceptable accuracy from step 1 to 3 with minimum effort. Most of thing in this stage is out-of-box and the data scientists and researchers only need to focus on how to represent data and layers in their model. After a quantized model is generated, you may want to deploy it online and the performance will be the next key point. Thus, step 4, calibration, can improve the performance a lot by reducing lots of runtime calculation.
+
+![quantization flow](https://github.com/dmlc/web-data/raw/master/mxnet/tutorials/mkldnn/quantization/quantization.png)
+
+Now, we are going to take Gluon ResNet18 as an example to show how each step work.
+
+### Initialize Model
+
+```python
+import logging
+import mxnet as mx
+from mxnet.gluon.model_zoo import vision
+from mxnet.contrib.quantization import *
+
+logging.basicConfig()
+logger = logging.getLogger('logger')
+logger.setLevel(logging.INFO)
+
+batch_shape = (1, 3, 224, 224)
+resnet18 = vision.resnet18_v1(pretrained=True)
+resnet18.hybridize()
+resnet18.forward(mx.nd.zeros(batch_shape))
+resnet18.export('resnet18_v1')
+sym, arg_params, aux_params = mx.model.load_checkpoint('resnet18_v1', 0)
+# (optional) visualize float32 model
+mx.viz.plot_network(sym)
+```
+First, we download resnet18-v1 model from gluon modelzoo and export it as a symbol. You can visualize float32 model. Below is a raw residual block.
+
+![float32 model](https://github.com/dmlc/web-data/raw/master/mxnet/tutorials/mkldnn/quantization/fp32_raw.png)
+
+#### Model Fusion
+
+```python
+sym = sym.get_backend_symbol('MKLDNN_QUANTIZE')
+# (optional) visualize fused float32 model
+mx.viz.plot_network(sym)
+```
+It's important to add this line to enable graph fusion before quantization to get better performance. Below is a fused residual block. Batchnorm, Activation and elemwise_add are fused into Convolution.
+
+![float32 fused model](https://github.com/dmlc/web-data/raw/master/mxnet/tutorials/mkldnn/quantization/fp32_fusion.png)
+
+### Quantize Model
+
+A python interface `quantize_graph` is provided for the user. Thus, it is very flexible for the data scientist to construct the expected models based on different requirements in a real deployment.
+
+```python
+# quantize configs
+# set exclude layers
+excluded_names = []
+# set calib mode.
+calib_mode = 'none'
+# set calib_layer
+calib_layer = None
+# set quantized_dtype
+quantized_dtype = 'auto'
+logger.info('Quantizing FP32 model Resnet18-V1')
+qsym, qarg_params, aux_params, collector = quantize_graph(sym=sym, arg_params=arg_params, aux_params=aux_params,
+                                                          excluded_sym_names=excluded_names,
+                                                          calib_mode=calib_mode, calib_layer=calib_layer,
+                                                          quantized_dtype=quantized_dtype, logger=logger)
+# (optional) visualize quantized model
+mx.viz.plot_network(qsym)
+# save quantized model
+mx.model.save_checkpoint('quantized-resnet18_v1', 0, qsym, qarg_params, aux_params)
+```
+
+By applying `quantize_graph` to the symbolic model, a new quantized model can be generated, named `qsym` along with its parameters. We can see `_contrib_requantize` operators are inserted after `Convolution` to convert the INT32 output to FP32. 
+
+![none calibrated model](https://github.com/dmlc/web-data/raw/master/mxnet/tutorials/mkldnn/quantization/none_calib.png)
+
+Below table gives some descriptions.
+
+| param              | type            | description|
+|--------------------|-----------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| excluded_sym_names | list of strings | A list of strings representing the names of the symbols that users want to excluding from being quantized.|
+| calib_mode         | str             | If calib_mode='none', no calibration will be used and the thresholds for requantization after the corresponding layers will be calculated at runtime by calling min  and max operators. The quantized models generated in this mode are normally 10-20% slower than those with  calibrations during inference.<br>If calib_mode='naive', the min and max values of the layer outputs from a calibration dataset will be directly taken as the thresholds for quantization.<br>If calib_mode='entropy', the thresholds for quantization will be derived such that the KL divergence between the distributions of FP32 layer outputs and  quantized layer outputs is minimized based upon the calibration dataset. |
+| calib_layer        | function        | Given a layer's output name in string, return True or False for deciding whether to calibrate this layer.<br>If yes, the statistics of the layer's output will be collected; otherwise, no information of the layer's output will be collected.<br>If not provided, all the layers' outputs that need requantization will be collected.|
+| quantized_dtype    | str             | The quantized destination type for input data. Currently support 'int8', 'uint8' and 'auto'.<br>'auto' means automatically select output type according to calibration result.|
+
+### Evaluate & Tune
+
+Now, you get a pair of quantized symbol and params file for inference. For Gluon inference, only difference is to load model and params by a SymbolBlock as below example:
+
+```python
+quantized_net = mx.gluon.SymbolBlock.imports('quantized-resnet18_v1-symbol.json', 'data', 'quantized-resnet18_v1-0000.params')
+quantized_net.hybridize(static_shape=True, static_alloc=True)
+batch_size = 1
+data = mx.nd.ones((batch_size,3,224,224))
+quantized_net(data)
+```
+
+Now, you can get the accuracy from a quantized network. Furthermore, you can try to select different layers or OPs to be quantized by `excluded_sym_names` parameter and figure out an acceptable accuracy.
+
+### Calibrate Model (optional for performance)
+
+The quantized model generated in previous steps can be very slow during inference since it will calculate min and max at runtime. We recommend using offline calibration for better performance by setting `calib_mode` to `naive` or `entropy`. And then calling `set_monitor_callback` api to collect layer information with a subset of the validation datasets before int8 inference.
+
+```python
+# quantization configs
+# set exclude layers
+excluded_names = []
+# set calib mode.
+calib_mode = 'naive'
+# set calib_layer
+calib_layer = None
+# set quantized_dtype
+quantized_dtype = 'auto'
+logger.info('Quantizing FP32 model resnet18-V1')
+cqsym, cqarg_params, aux_params, collector = quantize_graph(sym=sym, arg_params=arg_params, aux_params=aux_params,
+                                                          excluded_sym_names=excluded_names,
+                                                          calib_mode=calib_mode, calib_layer=calib_layer,
+                                                          quantized_dtype=quantized_dtype, logger=logger)
+
+# download imagenet validation dataset
+mx.test_utils.download('http://data.mxnet.io/data/val_256_q90.rec', 'dataset.rec')
+# set rgb info for data
+mean_std = {'mean_r': 123.68, 'mean_g': 116.779, 'mean_b': 103.939, 'std_r': 58.393, 'std_g': 57.12, 'std_b': 57.375}
+# set batch size
+batch_size = 16
+# create DataIter
+data = mx.io.ImageRecordIter(path_imgrec='dataset.rec', batch_size=batch_size, data_shape=batch_shape[1:], rand_crop=False, rand_mirror=False, **mean_std)
+# create module
+mod = mx.mod.Module(symbol=sym, label_names=None, context=mx.cpu())
+mod.bind(for_training=False, data_shapes=data.provide_data, label_shapes=None)
+mod.set_params(arg_params, aux_params)
+
+# calibration configs
+# set num_calib_batches
+num_calib_batches = 5
+max_num_examples = num_calib_batches * batch_size
+# monitor FP32 Inference
+mod._exec_group.execs[0].set_monitor_callback(collector.collect, monitor_all=True)
+num_batches = 0
+num_examples = 0
+for batch in data:
+    mod.forward(data_batch=batch, is_train=False)
+    num_batches += 1
+    num_examples += batch_size
+    if num_examples >= max_num_examples:
+        break
+if logger is not None:
+    logger.info("Collected statistics from %d batches with batch_size=%d"
+                % (num_batches, batch_size))
+```
+
+After that, layer information will be filled into the `collector` returned by `quantize_graph` api. Then, you need to write the layer information into int8 model by calling `calib_graph` api.
+
+
+```python
+# write scaling factor into quantized symbol
+cqsym, cqarg_params, aux_params = calib_graph(qsym=cqsym, arg_params=arg_params, aux_params=aux_params,
+                                            collector=collector, calib_mode=calib_mode,
+                                            quantized_dtype=quantized_dtype, logger=logger)
+# (optional) visualize quantized model
+mx.viz.plot_network(cqsym)
+```
+
+Below is a quantized residual block with naive calibration. We can see `min_calib_range` and `max_calib_range` are written into `_contrib_requantize` operators.
+
+![naive calibrated model](https://github.com/dmlc/web-data/raw/master/mxnet/tutorials/mkldnn/quantization/naive_calib.png)
+
+When you get a quantized model with calibration, keeping sure to call fusion api again since this can fuse some `requantize` or `dequantize` operators for further performance improvement.
+
+```python
+# perform post-quantization fusion
+cqsym = cqsym.get_backend_symbol('MKLDNN_QUANTIZE')
+# (optional) visualize post-quantized model
+mx.viz.plot_network(cqsym)
+# save quantized model
+mx.model.save_checkpoint('quantized-resnet18_v1', 0, cqsym, cqarg_params, aux_params)
+```
+
+Below is a post-quantized residual block. We can see `_contrib_requantize` operators are fused into `Convolution` operators.
+
+![post-quantized model](https://github.com/dmlc/web-data/raw/master/mxnet/tutorials/mkldnn/quantization/post_quantize.png)
+
+BTW, You can also modify the `min_calib_range` and `max_calib_range` in the JSON file directly.
+
+```
+    {
+      "op": "_sg_mkldnn_conv", 
+      "name": "quantized_sg_mkldnn_conv_bn_act_6", 
+      "attrs": {
+        "max_calib_range": "3.562147", 
+        "min_calib_range": "0.000000", 
+        "quantized": "true", 
+        "with_act": "true", 
+        "with_bn": "true"
+      }, 
+......
+```
+
+### Tips for Model Calibration
+
+#### Accuracy Tuning
+
+- Try to use `entropy` calib mode;
+
+- Try to exclude some layers which may cause obvious accuracy drop;
+
+- Change calibration dataset by setting different `num_calib_batches` or shuffle your validation dataset;
+
+#### Performance Tuning
+
+- Keep sure to perform graph fusion before quantization;
+
+- If lots of `requantize` layers exist, keep sure to perform post-quantization fusion after calibration;
+
+- Compare the MXNet profile or `MKLDNN_VERBOSE` of float32 and int8 inference;
+
+## Deploy with Python/C++
+
+MXNet also supports deploy quantized models with C++. Refer [MXNet C++ Package](https://github.com/apache/incubator-mxnet/blob/master/cpp-package/README.md) for more details.
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/python_docs/python/tutorials/performance/backend/profiler.md b/docs/python_docs/python/tutorials/performance/backend/profiler.md
new file mode 100644
index 000000000000..91a74e4f49cf
--- /dev/null
+++ b/docs/python_docs/python/tutorials/performance/backend/profiler.md
@@ -0,0 +1,333 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Profiling MXNet Models
+
+It is often helpful to check the execution time of each operation in a neural network. You can then determine where to focus your effort to speed up model training or inference. In this tutorial, we will learn how to profile MXNet models to measure their running time and memory consumption using the MXNet profiler.
+
+## The incorrect way to profile
+
+If you have just started to use MXNet, you might be tempted to measure the execution time of your model using Python's `time` module like shown below:
+
+```python
+from time import time
+from mxnet import autograd, nd
+import mxnet as mx
+
+start = time()
+x = nd.random_uniform(shape=(2000,2000))
+y = nd.dot(x, x)
+print('Time for matrix multiplication: %f sec\n' % (time() - start))
+
+start = time()                                
+y_np = y.asnumpy()                             
+print('Time for converting to numpy: %f sec' % (time() - start))
+```
+
+**Time for matrix multiplication: 0.005051 sec**<!--notebook-skip-line-->
+
+**Time for converting to numpy: 0.167693 sec**<!--notebook-skip-line-->
+
+From the timings above, it seems as if converting to numpy takes lot more time than multiplying two large matrices. That doesn't seem right.
+
+This is because, in MXNet, all operations are executed asynchronously. So, when `nd.dot(x, x)` returns, the matrix multiplication is not complete, it has only been queued for execution. However, [`asnumpy`](http://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html?highlight=asnumpy#mxnet.ndarray.NDArray.asnumpy) has to wait for the result to be calculated in order to convert it to numpy array on CPU, hence taking a longer time. Other examples of 'blocking' operations include [`asscalar`](http://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html?highlight=asscalar#mxnet.ndarray.NDArray.asscalar) and [`wait_to_read`](http://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html?highlight=wait_to_read#mxnet.ndarray.NDArray.wait_to_read).
+
+While it is possible to use [`NDArray.waitall()`](http://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html?highlight=waitall#mxnet.ndarray.waitall) before and after operations to get running time of operations, it is not a scalable method to measure running time of multiple sets of operations, especially in a [`Sequential`](http://mxnet.incubator.apache.org/api/python/gluon/gluon.html?highlight=sequential#mxnet.gluon.nn.Sequential) or hybridized network.
+
+## The correct way to profile
+
+The correct way to measure running time of MXNet models is to use MXNet profiler. In the rest of this tutorial, we will learn how to use the MXNet profiler to measure the running time and memory consumption of MXNet models. You can import the profiler and configure it from Python code.
+
+```python
+from mxnet import profiler
+
+profiler.set_config(profile_all=True,
+                    aggregate_stats=True,
+                    filename='profile_output.json')
+```
+
+`profile_all` enables all types of profiling. You can also individually enable the following types of profiling:
+
+- `profile_symbolic` (boolean): whether to profile symbolic operators
+- `profile_imperative` (boolean): whether to profile imperative operators
+- `profile_memory` (boolean): whether to profile memory usage
+- `profile_api` (boolean): whether to profile the C API
+
+`aggregate_stats` aggregates statistics in memory which can then be printed to console by calling `profiler.dumps()`.
+
+### Setup: Build a model
+
+Let's build a small convolutional neural network that we can use to demonstrate profiling.
+
+```python
+from mxnet import gluon
+
+net = gluon.nn.HybridSequential()
+with net.name_scope():
+    net.add(gluon.nn.Conv2D(channels=20, kernel_size=5, activation='relu'))
+    net.add(gluon.nn.MaxPool2D(pool_size=2, strides=2))
+    net.add(gluon.nn.Conv2D(channels=50, kernel_size=5, activation='relu'))
+    net.add(gluon.nn.MaxPool2D(pool_size=2, strides=2))
+    net.add(gluon.nn.Flatten())
+    net.add(gluon.nn.Dense(512, activation="relu"))
+    net.add(gluon.nn.Dense(10))
+```
+
+We need data that we can run through the network for profiling. We'll use the MNIST dataset.
+
+```python
+from mxnet.gluon.data.vision import transforms
+
+dataset = gluon.data.vision.MNIST(train=True)
+dataset = dataset.transform_first(transforms.ToTensor())
+dataloader = gluon.data.DataLoader(dataset, batch_size=64, shuffle=True)
+```
+
+Let's define a function that will run a single training iteration given `data` and `label`.
+
+```python
+# Use GPU if available
+if mx.context.num_gpus():
+    ctx=mx.gpu()
+else:
+    ctx=mx.cpu()
+
+# Initialize the parameters with random weights
+net.collect_params().initialize(mx.init.Xavier(), ctx=ctx)
+
+# Use SGD optimizer
+trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.1})
+
+# Softmax Cross Entropy is a frequently used loss function for multi-class classification
+softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()
+
+# A helper function to run one training iteration
+def run_training_iteration(data, label):
+    # Load data and label is the right context
+    data = data.as_in_context(ctx)
+    label = label.as_in_context(ctx)
+    # Run the forward pass
+    with autograd.record():
+        output = net(data)
+        loss = softmax_cross_entropy(output, label)
+    # Run the backward pass
+    loss.backward()
+    # Apply changes to parameters
+    trainer.step(data.shape[0])
+```
+
+### Starting and stopping the profiler from Python
+
+When the first forward pass is run on a network, MXNet does a number of housekeeping tasks including inferring the shapes of various parameters, allocating memory for intermediate and final outputs, etc. For these reasons, profiling the first iteration doesn't provide representative results for the rest of training. We will, therefore, skip the first iteration.
+
+```python
+# Run the first iteration without profiling
+itr = iter(dataloader)
+run_training_iteration(*next(itr))
+```
+
+We'll run the next iteration with the profiler turned on.
+
+```python
+data, label = next(itr)
+
+# Ask the profiler to start recording
+profiler.set_state('run')
+
+run_training_iteration(*next(itr))
+
+# Make sure all operations have completed
+mx.nd.waitall()
+# Ask the profiler to stop recording
+profiler.set_state('stop')
+# Dump all results to log file before download
+profiler.dump()
+```
+
+Between running and stopping the profiler, you can also pause and resume the profiler using `profiler.pause()` and `profiler.resume()` respectively to profile only parts of the code you want to profile.
+
+### Starting the profiler automatically using an environment variable
+
+The method described above requires code changes to start and stop the profiler. You can also start the profiler automatically and profile the entire code without any code changes using the `MXNET_PROFILER_AUTOSTART` environment variable.
+
+`$ MXNET_PROFILER_AUTOSTART=1 python my_script.py`
+
+MXNet will start the profiler automatically if you run your code with the environment variable `MXNET_PROFILER_AUTOSTART` set to `1`. The profiler output is stored in `profile.json` inside the current directory.
+
+Note that the profiler output could be large depending on your code. It might be helpful to profile only sections of your code using the `set_state` API described in the previous section.
+
+### Increasing granularity of the profiler output
+
+MXNet executes computation graphs in 'bulk mode' which reduces kernel launch gaps in between symbolic operators for faster execution. This could reduce the granularity of the profiler output. If you need profiling result of every operator, please set the environment variables `MXNET_EXEC_BULK_EXEC_INFERENCE` and `MXNET_EXEC_BULK_EXEC_TRAIN` to `0` to disable the bulk execution mode.
+
+When working with networks created using the Gluon API, you will get a more granular profiling outputs if you profile networks that haven't been hybridized. Operations can appear fused together in the profiling outputs after hybridization, which can make debugging tricky.
+
+### Viewing profiler output
+
+There are a few ways to view the information collected by the profiler. You can view it in the console, you can view a more graphical version in a browser, or you can use a vendor tool such as Intel VTune or Nvidia NVProf to view output. For most scenarios the information you need can be obtained with MXNet's built in profiler support, but if you want to investigate the performance of operators alongside extra context about your hardware (e.g. cache hit rates, or CUDA kernel timings) then profiling jointly with vendor tools is recommended.
+
+#### 1. View in console
+
+You can use the `profiler.dumps()` method to view the information collected by the profiler in the console. The collected information contains time taken by each operator, time taken by each C API and memory consumed in both CPU and GPU.
+
+```python
+print(profiler.dumps())
+```
+
+![Profile Statistics](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tutorials/python/profiler/profile_stats.png)<!--notebook-skip-line-->
+
+#### 2. View in browser
+
+You can also dump the information collected by the profiler into a `json` file using the `profiler.dump()` function and view it in a browser.
+
+```python
+profiler.dump(finished=False)
+```
+
+`dump()` creates a `json` file which can be viewed using a trace consumer like `chrome://tracing` in the Chrome browser. Here is a snapshot that shows the output of the profiling we did above. Note that setting the `finished` parameter to `False` will prevent the profiler from finishing dumping to file. If you just use `profiler.dump()`, you will no longer be able to profile the remaining sections of your model. 
+
+![Tracing Screenshot](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tutorials/python/profiler/profiler_output_chrome.png)
+
+Let's zoom in to check the time taken by operators
+
+![Operator profiling](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tutorials/python/profiler/profile_operators.png)
+
+The above picture visualizes the sequence in which the operators were executed and the time taken by each operator.
+
+### Profiling Custom Operators
+Should the existing NDArray operators fail to meet all your model's needs, MXNet supports [Custom Operators](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/customop.html) that you can define in Python. In `forward()` and `backward()` of a custom operator, there are two kinds of code: "pure Python" code (NumPy operators included) and "sub-operators" (NDArray operators called within `forward()` and `backward()`). With that said, MXNet can profile the execution time of both kinds without additional setup. Specifically, the MXNet profiler will break a single custom operator call into a pure Python event and several sub-operator events if there are any. Furthermore, all of those events will have a prefix in their names, which is, conveniently, the name of the custom operator you called.
+
+Let's try profiling custom operators with the following code example:
+
+```python
+class MyAddOne(mx.operator.CustomOp):
+    def forward(self, is_train, req, in_data, out_data, aux):  
+        self.assign(out_data[0], req[0], in_data[0]+1)
+
+    def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
+        self.assign(in_grad[0], req[0], out_grad[0])
+
+@mx.operator.register('MyAddOne')
+class CustomAddOneProp(mx.operator.CustomOpProp):
+    def __init__(self):
+        super(CustomAddOneProp, self).__init__(need_top_grad=True)
+
+    def list_arguments(self):
+        return ['data']
+
+    def list_outputs(self):
+        return ['output']
+
+    def infer_shape(self, in_shape):
+        return [in_shape[0]], [in_shape[0]], []
+
+    def create_operator(self, ctx, shapes, dtypes):
+        return MyAddOne()
+
+
+inp = mx.nd.zeros(shape=(500, 500))
+
+profiler.set_config(profile_all=True, continuous_dump=True, \
+                    aggregate_stats=True)
+profiler.set_state('run')
+
+w = nd.Custom(inp, op_type="MyAddOne")
+
+mx.nd.waitall()
+
+profiler.set_state('stop')
+print(profiler.dumps())
+profiler.dump(finished=False)
+```
+
+Here, we have created a custom operator called `MyAddOne`, and within its `forward()` function, we simply add one to the input. We can visualize the dump file in `chrome://tracing/`:
+
+![Custom Operator Profiling Screenshot](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tutorials/python/profiler/profiler_output_custom_operator_chrome.png)
+
+As shown by the screenshot, in the **Custom Operator** domain where all the custom operator-related events fall into, we can easily visualize the execution time of each segment of `MyAddOne`. We can tell that `MyAddOne::pure_python` is executed first. We also know that `CopyCPU2CPU` and `_plus_scalr` are two "sub-operators" of `MyAddOne` and the sequence in which they are executed.
+
+Please note that: to be able to see the previously described information, you need to set `profile_imperative` to `True` even when you are using custom operators in [symbolic mode](https://mxnet.incubator.apache.org/versions/master/tutorials/basic/symbol.html) (refer to the code snippet below, which is the symbolic-mode equivelent of the code example above). The reason is that within custom operators, pure python code and sub-operators are still called imperatively. 
+
+```python 
+# Set profile_all to True
+profiler.set_config(profile_all=True, aggregate_stats=True, continuous_dump=True)
+# OR, Explicitly Set profile_symbolic and profile_imperative to True
+profiler.set_config(profile_symbolic=True, profile_imperative=True, \
+                    aggregate_stats=True, continuous_dump=True)
+
+profiler.set_state('run')
+# Use Symbolic Mode
+a = mx.symbol.Variable('a')
+b = mx.symbol.Custom(data=a, op_type='MyAddOne')
+c = b.bind(mx.cpu(), {'a': inp})
+y = c.forward()
+mx.nd.waitall()
+profiler.set_state('stop')
+print(profiler.dumps())
+profiler.dump()
+```
+
+### Some Rules to Pay Attention to
+1. Always use `profiler.dump(finished=False)` if you do not intend to finish dumping to file. Otherwise, calling `profiler.dump()` in the middle of your model may lead to unexpected behaviors; and if you subsequently call `profiler.set_config()`, the program will error out.
+
+2. You can only dump to one file. Do not change the target file by calling `profiler.set_config(filename='new_name.json')` in the middle of your model. This will lead to incomplete dump outputs.
+
+## Advanced: Using NVIDIA Profiling Tools
+
+MXNet's Profiler is the recommended starting point for profiling MXNet code, but NVIDIA also provides a couple of tools for low-level profiling of CUDA code: [NVProf](https://devblogs.nvidia.com/cuda-pro-tip-nvprof-your-handy-universal-gpu-profiler/), [Visual Profiler](https://developer.nvidia.com/nvidia-visual-profiler) and [Nsight Compute](https://developer.nvidia.com/nsight-compute). You can use these tools to profile all kinds of executables, so they can be used for profiling Python scripts running MXNet. And you can use these in conjunction with the MXNet Profiler to see high-level information from MXNet alongside the low-level CUDA kernel information.
+
+#### NVProf and Visual Profiler
+
+NVProf and Visual Profiler are available in CUDA 9 and CUDA 10 toolkits. You can get a timeline view of CUDA kernel executions, and also analyse the profiling results to get automated recommendations. It is useful for profiling end-to-end training but the interface can sometimes become slow and unresponsive.
+
+You can initiate the profiling directly from inside Visual Profiler or from the command line with `nvprof` which wraps the execution of your Python script. If it's not on your path already, you can find `nvprof` inside your CUDA directory. See [this discussion post](https://discuss.mxnet.io/t/using-nvidia-profiling-tools-visual-profiler-and-nsight-compute/) for more details on setup.
+
+`$ nvprof -o my_profile.nvvp python my_profiler_script.py`
+
+`==11588== NVPROF is profiling process 11588, command: python my_profiler_script.py`
+
+`==11588== Generated result file: /home/user/Development/incubator-mxnet/ci/my_profile.nvvp`
+
+We specified an output file called `my_profile.nvvp` and this will be annotated with NVTX ranges (for MXNet operations) that will be displayed alongside the standard NVProf timeline. This can be very useful when you're trying to find patterns between operators run by MXNet, and their associated CUDA kernel calls.
+
+You can open this file in Visual Profiler to visualize the results.
+
+![Operator profiling](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tutorials/python/profiler/profiler_nvprof.png)
+
+At the top of the plot we have CPU tasks such as driver operations, memory copy calls, MXNet engine operator invocations, and imperative MXNet API calls.  Below we see the kernels active on the GPU during the same time period.
+
+![Operator profiling](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tutorials/python/profiler/profiler_nvprof_zoomed.png)
+
+Zooming in on a backwards convolution operator we can see that it is in fact made up of a number of different GPU kernel calls, including a cuDNN winograd convolution call, and a fast-fourier transform call.
+
+![Operator profiling](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tutorials/python/profiler/profiler_winograd.png)
+
+Selecting any of these kernel calls (the winograd convolution call shown here) will get you some interesting GPU performance information such as occupancy rates (vs theoretical), shared memory usage and execution duration.
+
+#### Nsight Compute
+
+Nsight Compute is available in CUDA 10 toolkit, but can be used to profile code running CUDA 9. You don't get a timeline view, but you get many low level statistics about each individual kernel executed and can compare multiple runs (i.e. create a baseline).
+
+![Nsight Compute](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tutorials/python/profiler/profile_nsight_compute.png)
+
+### Further reading
+
+- [Examples using MXNet profiler.](https://github.com/apache/incubator-mxnet/tree/master/example/profiler)
+- [Some tips for improving MXNet performance.](https://mxnet.incubator.apache.org/faq/perf.html)
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
+
diff --git a/docs/python_docs/python/tutorials/performance/backend/tvm.rst b/docs/python_docs/python/tutorials/performance/backend/tvm.rst
new file mode 100644
index 000000000000..8da277bd119f
--- /dev/null
+++ b/docs/python_docs/python/tutorials/performance/backend/tvm.rst
@@ -0,0 +1,21 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Use TVM
+=======
+
+Contributions welcome!
diff --git a/docs/python_docs/python/tutorials/performance/compression/index.rst b/docs/python_docs/python/tutorials/performance/compression/index.rst
new file mode 100644
index 000000000000..e2e4f2c73250
--- /dev/null
+++ b/docs/python_docs/python/tutorials/performance/compression/index.rst
@@ -0,0 +1,59 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Compression
+===========
+The following tutorials will help you learn how to use compression techniques with MXNet.
+
+.. container:: cards
+
+   .. card::
+      :title: Compression: float16
+      :link: https://mxnet.incubator.apache.org/api/faq/float16
+
+      How to use float16 in your model to boost training speed.
+
+   .. card::
+      :title: Gradient Compression
+      :link: https://mxnet.incubator.apache.org/api/faq/gradient_compression
+
+      How to use gradient compression to reduce communication bandwidth and increase speed.
+
+   .. card::
+      :title: Inference with Quantized Models
+      :link: https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html
+
+      How to use quantized GluonCV models for inference on Intel Xeon Processors to gain higher performance.
+
+   ..
+      TBD Content
+      .. card::
+         :title: Compression: int8
+         :link: int8.html
+
+         How to use int8 in your model to boost training speed.
+   ..
+
+.. toctree::
+   :hidden:
+   :glob:
+
+   *
+   
+   ..
+      int8
+   ..
diff --git a/docs/python_docs/python/tutorials/performance/compression/int8.rst b/docs/python_docs/python/tutorials/performance/compression/int8.rst
new file mode 100644
index 000000000000..a0cad0f03800
--- /dev/null
+++ b/docs/python_docs/python/tutorials/performance/compression/int8.rst
@@ -0,0 +1,21 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Deploy with int-8
+=================
+
+Contributions welcome!
diff --git a/docs/python_docs/python/tutorials/performance/index.rst b/docs/python_docs/python/tutorials/performance/index.rst
new file mode 100644
index 000000000000..6d9159eb2ace
--- /dev/null
+++ b/docs/python_docs/python/tutorials/performance/index.rst
@@ -0,0 +1,130 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Performance
+===========
+The following tutorials will help you learn how to tune MXNet or use tools that will improve training and inference performance.
+
+Essential
+---------
+
+.. container:: cards
+
+   .. card::
+      :title: Improving Performance
+      :link: https://mxnet.incubator.apache.org/versions/master/faq/perf.html
+
+      How to get the best performance from MXNet.
+
+   .. card::
+      :title: Profiler
+      :link: backend/profiler.html
+
+      How to profile MXNet models.
+
+   .. card::
+      :title: Tuning NumPy Operations
+      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/gotchas_numpy_in_mxnet.html
+
+      Gotchas using NumPy in MXNet.
+
+Compression
+-----------
+
+.. container:: cards
+
+   .. card::
+      :title: Compression: float16
+      :link: compression/float16.html
+
+      How to use float16 in your model to boost training speed.
+
+   .. card::
+      :title: Gradient Compression
+      :link: compression/gradient_compression.html
+
+      How to use gradient compression to reduce communication bandwidth and increase speed.
+   ..
+      .. card::
+         :title: Compression: int8
+         :link: compression/int8.html
+
+         How to use int8 in your model to boost training speed.
+   ..
+
+
+Accelerated Backend
+-------------------
+
+.. container:: cards
+
+   .. card::
+      :title: TensorRT
+      :link: backend/tensorRt.html
+
+      How to use NVIDIA's TensorRT to boost inference performance.
+
+   ..
+      TBD Content
+      .. card::
+         :title: MKL-DNN
+         :link: backend/mkl-dnn.html
+
+         How to get the most from your CPU by using Intel's MKL-DNN.
+
+      .. card::
+         :title: TVM
+         :link: backend/tvm.html
+
+         How to use TVM to boost performance.
+   ..
+
+Distributed Training
+--------------------
+
+.. container:: cards
+
+   .. card::
+      :title: Distributed Training Using the KVStore API
+      :link: https://mxnet.incubator.apache.org/versions/master/faq/distributed_training.html
+
+      How to use the KVStore API to use multiple GPUs when training a model.
+
+   .. card::
+      :title: Training with Multiple GPUs Using Model Parallelism
+      :link: https://mxnet.incubator.apache.org/versions/master/faq/model_parallel_lstm.html
+
+      An overview of using multiple GPUs when training an LSTM.
+
+   .. card::
+      :title: Data Parallelism in MXNet
+      :link: https://mxnet.incubator.apache.org/versions/master/faq/multi_devices.html
+
+      An overview of distributed training strategies.
+
+   .. card::
+      :title: MXNet with Horovod
+      :link: https://github.com/apache/incubator-mxnet/tree/master/example/distributed_training-horovod
+
+      A set of example scripts demonstrating MNIST and ImageNet training with Horovod as the distributed training backend.
+
+.. toctree::
+   :hidden:
+   :maxdepth: 1
+
+   compression/index
+   backend/index
diff --git a/docs/python_docs/themes/.babelrc b/docs/python_docs/themes/.babelrc
new file mode 100644
index 000000000000..69f50d59a72b
--- /dev/null
+++ b/docs/python_docs/themes/.babelrc
@@ -0,0 +1,3 @@
+{
+    "presets": ["env"]
+}
\ No newline at end of file
diff --git a/docs/python_docs/themes/.circleci/config.yml b/docs/python_docs/themes/.circleci/config.yml
new file mode 100644
index 000000000000..edc02a35d7f0
--- /dev/null
+++ b/docs/python_docs/themes/.circleci/config.yml
@@ -0,0 +1,37 @@
+version: 2
+jobs:
+  build:
+    working_directory: ~/sphinx_materialdesign_theme
+    docker:
+      - image: circleci/python:3.6.4
+    steps:
+      - checkout
+      - run: sudo chown -R circleci:circleci /usr/local/bin
+      - run: sudo chown -R circleci:circleci /usr/local/lib/python3.6/site-packages
+      - run: 
+          name: install dependencies
+          command: pip install -r requirements.txt
+      - run:
+          name: build
+          command: sphinx-build -b html ./example ./_build
+      - run:
+          name: deploy
+          command: |
+            remote=$(git config remote.origin.url)
+            pushd _build > /dev/null
+            git config --global user.email "$GH_EMAIL" > /dev/null 2>&1
+            git config --global user.name "$GH_NAME" > /dev/null 2>&1
+            touch .nojekyll
+            git init
+            git add .
+            git commit -m "Deploy to GitHub Pages. [skip ci]"
+            git push --force --quiet $remote master:gh-pages
+            popd > /dev/null
+workflows:
+  version: 2
+  build_flow:
+    jobs:
+      - build:
+          filters:
+            branches:
+              only: master
\ No newline at end of file
diff --git a/docs/python_docs/themes/.gitignore b/docs/python_docs/themes/.gitignore
new file mode 100644
index 000000000000..fd123f1c8549
--- /dev/null
+++ b/docs/python_docs/themes/.gitignore
@@ -0,0 +1,13 @@
+.idea/
+.cache/
+.vscode/
+*.egg-info/
+dist/
+_build/
+build/
+example/_build/
+node_modules/
+*.log
+**/*.pyc
+**/__pycache__
+.sass-cache/
diff --git a/docs/python_docs/themes/.sassrc b/docs/python_docs/themes/.sassrc
new file mode 100644
index 000000000000..ac7d2c97c6e8
--- /dev/null
+++ b/docs/python_docs/themes/.sassrc
@@ -0,0 +1,5 @@
+{
+    "includePaths": [
+        "node_modules"
+    ]
+}
\ No newline at end of file
diff --git a/docs/python_docs/themes/mx-theme/LICENSE b/docs/python_docs/themes/mx-theme/LICENSE
new file mode 100644
index 000000000000..74e4dc3ad961
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2016 myyasuda
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/docs/python_docs/themes/mx-theme/MANIFEST.in b/docs/python_docs/themes/mx-theme/MANIFEST.in
new file mode 100644
index 000000000000..b5ab8d3cb11c
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/MANIFEST.in
@@ -0,0 +1 @@
+recursive-include mxtheme *
diff --git a/docs/python_docs/themes/mx-theme/README.md b/docs/python_docs/themes/mx-theme/README.md
new file mode 100644
index 000000000000..cea79f43a9e5
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/README.md
@@ -0,0 +1,65 @@
+# Material Design HTML Theme for Sphinx
+
+## How to use
+
+- Install the theme by
+
+```bash
+pip install mxtheme
+```
+
+- Modify the `conf.py` for your sphinx project by
+
+create a submodule of this repo on the same folder with `conf.py` for your sphinx project. then modify the following three lines in `conf.py`:
+
+```python
+html_theme = 'mxtheme'
+```
+
+In addition, to use the `card` directive in rst, you can and add the following two lines into your `def setup(app)` function:
+
+```python
+def setup(app):
+    ...
+    import mxtheme
+    app.add_directive('card', mxtheme.CardDirective)
+```
+
+## How to build
+
+
+Install `npm` first,
+
+on ubuntu:
+
+```
+wget -qO- https://deb.nodesource.com/setup_8.x | sudo -E bash -
+sudo apt-get install -y nodejs
+```
+
+on macos
+
+```
+brew install nodejs
+```
+
+Then install packages
+
+```
+npm install
+```
+
+Last, build css and js
+
+
+```
+npm run build
+```
+
+## Acknowledgment
+
+
+This is fork of
+[sphinx_materialdesign_theme](https://github.com/myyasuda/sphinx_materialdesign_theme). With
+some CSS/JS modifications. Please refer to the original project for more
+documents.
diff --git a/docs/python_docs/themes/mx-theme/mxtheme/__init__.py b/docs/python_docs/themes/mx-theme/mxtheme/__init__.py
new file mode 100644
index 000000000000..a1c4e0783b6b
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/mxtheme/__init__.py
@@ -0,0 +1,13 @@
+from os import path
+from .card import CardDirective
+
+__version__ = '0.3.9'
+__version_full__ = __version__
+
+package_dir = path.dirname(path.abspath(__file__))
+
+def get_path():
+    return package_dir
+
+def setup(app):
+    app.add_html_theme('mxtheme', package_dir)
diff --git a/docs/python_docs/themes/mx-theme/mxtheme/card.py b/docs/python_docs/themes/mx-theme/mxtheme/card.py
new file mode 100644
index 000000000000..4d389a939ee8
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/mxtheme/card.py
@@ -0,0 +1,42 @@
+from sphinx.locale import _
+from docutils import nodes
+from docutils.parsers.rst import Directive, directives
+
+class card(nodes.General, nodes.Element):
+    pass
+
+class CardDirective(Directive):
+
+    # defines the parameter the directive expects
+    # directives.unchanged means you get the raw value from RST
+    required_arguments = 0
+    optional_arguments = 0
+    final_argument_whitespace = True
+    option_spec = {'title': directives.unchanged,
+                   'link': directives.unchanged,
+                   'is_head': directives.unchanged}
+    has_content = True
+    add_index = False
+
+    def run(self):
+        # gives you access to the options of the directive
+        options = self.options
+
+        cid = nodes.make_id("card-{}".format(options['title']))
+
+        classes = ['mx-card']
+        if options.get('is_head', 'False').lower() == 'true':
+            classes.append('head-card')
+        container = nodes.container(ids=[cid], classes=classes)
+
+        container += nodes.inline('', options['title'], classes=['mx-card-title'])
+        link = options.get('link')
+        if link:
+            container += nodes.inline('', link, classes=['mx-card-link'])
+
+        para = nodes.paragraph(classes=['mx-card-text'])
+        self.state.nested_parse(self.content, self.content_offset, para)
+        container += para
+
+        # we return the result
+        return [container]
diff --git a/docs/python_docs/themes/mx-theme/mxtheme/drawer.html b/docs/python_docs/themes/mx-theme/mxtheme/drawer.html
new file mode 100644
index 000000000000..f5f4a016c18c
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/mxtheme/drawer.html
@@ -0,0 +1,17 @@
+<header class="mdl-layout__drawer">      
+    {% block menu %}
+      <div class="globaltoc">
+        <span class="mdl-layout-title toc">{{ _('Table Of Contents') }}</span>
+        {% set toctree = toctree(maxdepth=3, collapse=False, includehidden=True, titles_only=True) %}
+        {% if toctree %}
+            {% set lines = toctree.split('\n') %}
+            <nav class="mdl-navigation">
+                {{ toctree }}
+            </nav>
+        {% else %}
+        <!-- Local TOC -->
+        <nav class="mdl-navigation">{{ toc }}</nav>
+        {% endif %}
+        </div>
+    {% endblock %}
+</header>
diff --git a/docs/python_docs/themes/mx-theme/mxtheme/footer.html b/docs/python_docs/themes/mx-theme/mxtheme/footer.html
new file mode 100644
index 000000000000..4926f4fcb5d3
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/mxtheme/footer.html
@@ -0,0 +1,46 @@
+<footer class="site-footer h-card">
+    <div class="wrapper">
+        <div class="row">
+            <div class="col-4">
+                <h4 class="footer-category-title">Resources</h4>
+                <ul class="contact-list">
+                    <li><a class="u-email" href="mailto:dev@mxnet.apache.org">Dev list</a></li>
+                    <li><a class="u-email" href="mailto:user@mxnet.apache.org">User mailing list</a></li>
+                    <li><a href="https://cwiki.apache.org/confluence/display/MXNET/Apache+MXNet+Home">Developer Wiki</a></li>
+                    <li><a href="https://issues.apache.org/jira/projects/MXNET/issues">Jira Tracker</a></li>
+                    <li><a href="https://github.com/apache/incubator-mxnet/labels/Roadmap">Github Roadmap</a></li>
+                    <li><a href="https://discuss.mxnet.io">MXNet Discuss forum</a></li>
+                    <li><a href="/mxnet.io-v2/community/contribute">Contribute To MXNet</a></li>
+
+                </ul>
+            </div>
+
+            <div class="col-4"><ul class="social-media-list"><li><a href="https://github.com/apache/incubator-mxnet"><svg class="svg-icon"><use xlink:href="{{pathto('_static/minima-social-icons.svg#github', 1)}}"></use></svg> <span class="username">apache/incubator-mxnet</span></a></li><li><a href="https://www.twitter.com/apachemxnet"><svg class="svg-icon"><use xlink:href="{{pathto('_static/minima-social-icons.svg#twitter', 1)}}"></use></svg> <span class="username">apachemxnet</span></a></li><li><a href="https://youtube.com/apachemxnet"><svg class="svg-icon"><use xlink:href="{{pathto('_static/minima-social-icons.svg#youtube', 1)}}"></use></svg> <span class="username">apachemxnet</span></a></li></ul>
+</div>
+
+            <div class="col-4 footer-text">
+                <p>A flexible and efficient library for deep learning.</p>
+            </div>
+        </div>
+    </div>
+</footer>
+
+<footer class="site-footer2">
+    <div class="wrapper">
+        <div class="row">
+            <div class="col-3">
+                <img src="{{pathto('_static/apache_incubator_logo.png', 1)}}" class="footer-logo col-2">
+            </div>
+            <div class="footer-bottom-warning col-9">
+                <p>Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), <span style="font-weight:bold">sponsored by the <i>Apache Incubator</i></span>. Incubation is required
+                    of all newly accepted projects until a further review indicates that the infrastructure,
+                    communications, and decision making process have stabilized in a manner consistent with other
+                    successful ASF projects. While incubation status is not necessarily a reflection of the completeness
+                    or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF.
+                </p><p>"Copyright © 2017-2018, The Apache Software Foundation Apache MXNet, MXNet, Apache, the Apache
+                    feather, and the Apache MXNet project logo are either registered trademarks or trademarks of the
+                    Apache Software Foundation."</p>
+            </div>
+        </div>
+    </div>
+</footer>
\ No newline at end of file
diff --git a/docs/python_docs/themes/mx-theme/mxtheme/header.html b/docs/python_docs/themes/mx-theme/mxtheme/header.html
new file mode 100644
index 000000000000..606c6f985f6c
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/mxtheme/header.html
@@ -0,0 +1,45 @@
+<header class="mdl-layout__header {% if theme_header_waterfall|tobool %}mdl-layout__header--waterfall{% endif %} {% if theme_header_scroll|tobool %}mdl-layout__header--scroll{% endif %}">
+    <div class="mdl-layout__header-row">
+        {% if theme_show_header_title|tobool %}
+        <!-- Title -->
+        <span class="mdl-layout-title">
+            <a class="brand" href="{{ pathto(master_doc) }}">
+                {%- if logo %}
+                <img class="logo" src="{{ pathto('_static/' + logo, 1) }}" alt="{{ project }}"/>
+                {%- else %}
+                {{ project }}
+                {%- endif %}
+            </a>
+        </span>
+        {% endif %}
+        <nav class="mdl-navigation breadcrumb">
+            {%- for parent in parents %}
+            <a class="mdl-navigation__link" href="{{ parent.link|e }}">{{ parent.title }}</a><i class="material-icons">navigate_next</i>
+            {%- endfor %}
+            <a class="mdl-navigation__link is-active">{{ title }}</a>
+        </nav>
+        <div class="mdl-layout-spacer"></div>
+        <nav class="mdl-navigation">
+        {% include "header_search.html" %}
+        {% include "header_sourcelink.html" %}
+        </nav>
+    </div>
+    <div class="mdl-layout__header-row header-links">
+      <div class="mdl-layout-spacer"></div>
+      <nav class="mdl-navigation">
+      {%- for title, href, isExternal, icon in theme_header_links %}
+          {% if isExternal %}
+              <a  class="mdl-navigation__link" href="{{ href }}">
+                  {% if icon %}<i class="{{ icon }}"></i>{% endif %}
+                  {{ title }}
+              </a>
+          {%- else -%}
+              <a  class="mdl-navigation__link" href="{{ pathto(href) }}">
+                  {% if icon %}<i class="{{ icon }}"></i>{% endif %}
+                  {{ title }}
+              </a>
+          {%- endif -%}
+      {%- endfor %}
+      </nav>
+    </div>
+</header>
diff --git a/docs/python_docs/themes/mx-theme/mxtheme/header_search.html b/docs/python_docs/themes/mx-theme/mxtheme/header_search.html
new file mode 100644
index 000000000000..6e1f9a7f94d6
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/mxtheme/header_search.html
@@ -0,0 +1,17 @@
+{%- if pagename != "search" %}
+<form class="form-inline pull-sm-right" action="{{ pathto('search') }}" method="get">
+      <div class="mdl-textfield mdl-js-textfield mdl-textfield--expandable mdl-textfield--floating-label mdl-textfield--align-right">
+        <label id="quick-search-icon" class="mdl-button mdl-js-button mdl-button--icon"  for="waterfall-exp">
+          <i class="material-icons">search</i>
+        </label>
+        <div class="mdl-textfield__expandable-holder">
+          <input class="mdl-textfield__input" type="text" name="q"  id="waterfall-exp" placeholder="Search" />
+          <input type="hidden" name="check_keywords" value="yes" />
+          <input type="hidden" name="area" value="default" />
+        </div>
+      </div>
+      <div class="mdl-tooltip" data-mdl-for="quick-search-icon">
+      {{ _('Quick search') }}
+      </div>
+</form>
+{%- endif %}
diff --git a/docs/python_docs/themes/mx-theme/mxtheme/header_sourcelink.html b/docs/python_docs/themes/mx-theme/mxtheme/header_sourcelink.html
new file mode 100644
index 000000000000..9e2a889f7d15
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/mxtheme/header_sourcelink.html
@@ -0,0 +1,18 @@
+{%- if display_github %}
+<a id="button-show-github"
+    href="https://{{ github_host|default("github.com") }}/{{ github_user }}/{{ github_repo }}/{{ theme_vcs_pageview_mode|default("edit") }}/{{ github_version }}{{ conf_py_path }}{{ pagename }}{{ page_source_suffix }}" class="mdl-button mdl-js-button mdl-button--icon">
+<i class="material-icons">edit</i>
+</a>
+<div class="mdl-tooltip" data-mdl-for="button-show-github">
+{{ _('Edit on Github') }}
+</div>
+{%- elif show_source and has_source and sourcename %}
+<a id="button-show-source"
+    class="mdl-button mdl-js-button mdl-button--icon"
+    href="{{ pathto('_sources/' + sourcename, true)|e }}" rel="nofollow">
+  <i class="material-icons">code</i>
+</a>
+<div class="mdl-tooltip" data-mdl-for="button-show-source">
+{{ _('Show Source') }}
+</div>
+{%- endif %}
diff --git a/docs/python_docs/themes/mx-theme/mxtheme/header_top.html b/docs/python_docs/themes/mx-theme/mxtheme/header_top.html
new file mode 100644
index 000000000000..3d16e1ee930d
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/mxtheme/header_top.html
@@ -0,0 +1,25 @@
+<header class="site-header" role="banner">
+  <div class="wrapper">
+      <a class="site-title" rel="author" href="{{theme_relative_url}}"><img
+            src="{{pathto('_static/mxnet_logo.png', 1)}}" class="site-header-logo"></a>
+    <nav class="site-nav">
+      <input type="checkbox" id="nav-trigger" class="nav-trigger"/>
+      <label for="nav-trigger">
+          <span class="menu-icon">
+            <svg viewBox="0 0 18 15" width="18px" height="15px">
+              <path d="M18,1.484c0,0.82-0.665,1.484-1.484,1.484H1.484C0.665,2.969,0,2.304,0,1.484l0,0C0,0.665,0.665,0,1.484,0 h15.032C17.335,0,18,0.665,18,1.484L18,1.484z M18,7.516C18,8.335,17.335,9,16.516,9H1.484C0.665,9,0,8.335,0,7.516l0,0 c0-0.82,0.665-1.484,1.484-1.484h15.032C17.335,6.031,18,6.696,18,7.516L18,7.516z M18,13.516C18,14.335,17.335,15,16.516,15H1.484 C0.665,15,0,14.335,0,13.516l0,0c0-0.82,0.665-1.483,1.484-1.483h15.032C17.335,12.031,18,12.695,18,13.516L18,13.516z"/>
+            </svg>
+          </span>
+      </label>
+
+      <div class="trigger">
+        <a class="page-link" href="{{theme_relative_url}}get_started">Get Started</a>
+        <a class="page-link" href="{{theme_relative_url}}blog">Blog</a>
+        <a class="page-link" href="{{theme_relative_url}}features">Features</a>
+        <a class="page-link" href="{{theme_relative_url}}ecosystem">Ecosystem</a>
+        <a class="page-link page-current" href="{{theme_relative_url}}api">Docs & Tutorials</a>
+        <a class="page-link" href="https://github.com/apache/incubator-mxnet">GitHub</a>
+      </div>
+    </nav>
+  </div>
+</header>
diff --git a/docs/python_docs/themes/mx-theme/mxtheme/layout.html b/docs/python_docs/themes/mx-theme/mxtheme/layout.html
new file mode 100644
index 000000000000..64a2e79a59a1
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/mxtheme/layout.html
@@ -0,0 +1,52 @@
+{% extends "basic/layout.html" %}
+
+{%- block doctype -%}
+<!DOCTYPE html>
+{%- endblock %}
+{% block htmltitle %}
+    <meta charset="utf-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+    <meta http-equiv="x-ua-compatible" content="ie=edge">
+    {{ super() }}
+{% endblock %}
+
+
+{% set css_files = css_files + [
+    '_static/material-design-lite-1.3.0/material.' + theme_primary_color|e + '-' + theme_accent_color|e + '.min.css',
+    '_static/sphinx_materialdesign_theme.css',
+    '_static/fontawesome/all.css',
+    '_static/fonts.css',
+] %}
+
+    {%- block header %}
+	<script type="text/javascript" src="{{ pathto('_static/sphinx_materialdesign_theme.js', 1) }} "></script>
+    {% endblock %}
+{%- block relbar1 %}{% endblock %}
+{%- block relbar2 %}{% include "relations.html" %}{% endblock %}
+{%- block sidebar2 %}{% endblock %}
+
+{%- block body_tag %}
+<body>
+    {%- block header_top %}{% include "header_top.html" %}{% endblock %}
+    <div class="mdl-layout mdl-js-layout {% if theme_fixed_header|tobool %}mdl-layout--fixed-header{% endif %} {% if theme_fixed_drawer|tobool %}mdl-layout--fixed-drawer{% endif %}">
+        {%- block md_header %}{% include "header.html" %}{% endblock %}
+        {%- block sidebar1 %}{% include "drawer.html" %}{% endblock %}
+        <main class="mdl-layout__content" tabIndex="0">
+{% endblock %}
+
+{%- block document %}
+        <div class="page-content">
+        {% block body %} {% endblock %}
+        </div>
+        <div class="side-doc-outline">
+            <div class="side-doc-outline--content">
+                {%- block right_sidebar %} {% include "localtoc.html" %}{% endblock %}
+            </div>
+        </div>                    
+{% endblock %}
+        {%- block footer %}
+            {% if theme_show_footer|tobool %}{% include "footer.html" %}{% endif %}
+        {% endblock %}
+        </main>
+    </div>
+</body>
diff --git a/docs/python_docs/themes/mx-theme/mxtheme/localtoc.html b/docs/python_docs/themes/mx-theme/mxtheme/localtoc.html
new file mode 100644
index 000000000000..7ae810fcbd25
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/mxtheme/localtoc.html
@@ -0,0 +1,8 @@
+{%- if display_toc %}
+<div class="localtoc">
+    <p class="caption">
+      <span class="caption-text">{{ _('Table Of Contents') }}</span>
+    </p>
+    {{ toc }}
+</div>
+{%- endif %}
\ No newline at end of file
diff --git a/docs/python_docs/themes/mx-theme/mxtheme/relations.html b/docs/python_docs/themes/mx-theme/mxtheme/relations.html
new file mode 100644
index 000000000000..e6922d48550a
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/mxtheme/relations.html
@@ -0,0 +1,20 @@
+<div class="pagenation">
+  {%- if prev %}
+     <a id="button-prev" href="{{ prev.link|e }}" class="mdl-button mdl-js-button mdl-js-ripple-effect mdl-button--colored" role="botton" {{ accesskey("P") }}>
+         <i class="pagenation-arrow-L fas fa-arrow-left fa-lg"></i>
+         <div class="pagenation-text">
+            <span class="pagenation-direction">Previous</span>
+            <div>{{ prev.title|striptags }}</div>
+         </div>
+     </a>
+  {%- endif %}
+  {%- if next %}
+     <a id="button-next" href="{{ next.link|e }}" class="mdl-button mdl-js-button mdl-js-ripple-effect mdl-button--colored" role="botton" {{ accesskey("N") }}>
+         <i class="pagenation-arrow-R fas fa-arrow-right fa-lg"></i>
+        <div class="pagenation-text">
+            <span class="pagenation-direction">Next</span>
+            <div>{{ next.title|striptags }}</div>
+        </div>
+     </a>
+  {%- endif %}
+  </div>
diff --git a/docs/python_docs/themes/mx-theme/mxtheme/search.html b/docs/python_docs/themes/mx-theme/mxtheme/search.html
new file mode 100644
index 000000000000..bea072f264fb
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/mxtheme/search.html
@@ -0,0 +1,56 @@
+{%- extends "layout.html" %}
+{% set title = _('Search') %}
+{% block extrahead %}
+  <script type="text/javascript" src="{{ pathto('_static/searchtools.js', 1) }} "></script>
+  <script type="text/javascript">
+    jQuery(function() { Search.loadIndex("{{ pathto('searchindex.js', 1) }}"); });
+  </script>
+  {# this is used when loading the search index using $.ajax fails,
+     such as on Chrome for documents on localhost #}
+  <script type="text/javascript" id="searchindexloader"></script>
+  {{ super() }}
+{% endblock %}
+{% block body %}
+  <h1 id="search-documentation">{{ _('Search') }}</h1>
+  <div id="fallback" class="admonition warning">
+  <script type="text/javascript">$('#fallback').hide();</script>
+  <p>
+    {% trans %}Please activate JavaScript to enable the search
+    functionality.{% endtrans %}
+  </p>
+  </div>
+  <p>
+    {% trans %}From here you can search these documents. Enter your search
+    words into the box below and click "search". Note that the search
+    function will automatically search for all of the words. Pages
+    containing fewer words won't appear in the result list.{% endtrans %}
+  </p>
+  <form action="" method="get">
+    <div class="mdl-textfield mdl-js-textfield mdl-textfield--floating-label">
+      <input class="mdl-textfield__input" type="text" name="q" id="search-input">
+      <label class="mdl-textfield__label" for="search-input">{{ _('Search') }}...</label>
+    </div>
+    <button type="submit" class="mdl-button mdl-js-button mdl-button--fab mdl-button--mini-fab mdl-button--colored">
+      <i class="material-icons">search</i>
+    </button>
+    <span id="search-progress" style="padding-left: 10px"></span>
+  </form>
+  {% if search_performed %}
+    <h2>{{ _('Search Results') }}</h2>
+    {% if not search_results %}
+      <p>{{ _('Your search did not match any documents. Please make sure that all words are spelled correctly and that you\'ve selected enough categories.') }}</p>
+    {% endif %}
+  {% endif %}
+  <div id="search-results">
+  {% if search_results %}
+    <ul class="search">
+    {% for href, caption, context in search_results %}
+      <li>
+        <a href="{{ pathto(item.href) }}">{{ caption }}</a>
+        <div class="context">{{ context|e }}</div>
+      </li>
+    {% endfor %}
+    </ul>
+  {% endif %}
+  </div>
+{% endblock %}
diff --git a/docs/python_docs/themes/mx-theme/mxtheme/static/font/Roboto/Roboto-Regular.eot b/docs/python_docs/themes/mx-theme/mxtheme/static/font/Roboto/Roboto-Regular.eot
new file mode 100644
index 0000000000000000000000000000000000000000..cd7a772eb27860dbe8c51ff95727215ffb3271d7
GIT binary patch
literal 56856
zcmZU4WmFqZ5N8qsBoN#^xVu{k9<)J=6@qIiR;(7>Dee+n+akqj8-hC&EmpjEk@jCG
zExn$6xO=zr_RY+1=gsb?*?qG!j2i&Jy>$Qp^pC)R|97MT|EF$<`U?VV+QI=S`2Sfh
z1p@ww!232qCLjI}^nWoJz!dNp;Ph_>{hI&)`9Iwi;0|~K@Bugi0{$T)0Ly<!03Z<H
z`Om`lpED_d7(n8Gks?6hA6Ncgm;^u)AoI_j_5bk*|Nr~}02*exs7iwW>k$7$0uZDV
zur3E!R|0H$(M}#FPk*AauFxVeI$Yn{txe>5rEt&leR*G>CK~kRXWTOx<@^%yLZ0h5
zbL$(1wc0gDWhFC(8bw*5@axWQhdUdS>g$0D7V;p@l{^#I)c=&fdJ$ByZ1VFe%IxzZ
zlV=*jOPYH+M@Y?$*V`6l;KCi+2(dvEzaeu%v(d}ngO%Uz`+V&jtzyZnAm{&K{m0XM
z7qcSdt;w%qE_n-a(>o&DKl@PB-iB!DUGjSCK`UQv_T=uQY%<$t_^=!FLNTzlzSg;%
zwM>Hv^)!xzk*e-93S*LY!_oRUfaCGov+R+-AA8AvPe-U|{YBgkd}q{Kk)R*5zEW9P
z>d4nhOQNQRAWbWvRJ$Wz&@yPsC;_U#87G&K$A{4^PPv0r>@Hbc{dLp*06)34tvM04
z*l;(T1jy?!kZ4}TQKLI36>Y;A6Qonyl|$NVtnwdYQF(0wRLb_kdgU8BKr3Ni)RVH1
zFhhuh!`;C8Gh6SeJ)-uH4qKX~!qArE^T?d==JI&w+_e%yEA`nxkz$QZP7b?aqJc2!
zceu9aR~E9G)ulRw2?QyayRYe?PYpx)627j4%!!)+1aXgcibd7{oA+PIyw&mAj--;O
z)@pEQI(ofc`AYCZ*5a6?2BO*m&Md#kBaKGu1quwuOe=e^t%rWoe)-`+Vt%uYm2cY$
z*hEHI;lRODthYMrz`Jp*X{y3=Bl@J|nKI!$2}B!L`%+hPw|^r8n}P0#19&kk<&UG(
zes;?x+{vEWl2L|kHiFw8qrEnp`Xl|Pm9fqnr=rT*Q>KuKiU!)+HtpKf=5L<=1-7hK
zII?_jbVJq{NoU%5>oi`i@w{!g5A0BYDKbt)c)QUh!(S5nx66VV;gp5_=3&fbER419
ztsW6sbpB@I^izmLjdM)(I04|?_7Z=Nr;TGVLtyepW%cPigc0`?3DApFx}-=0D5c|=
zJrDW2O<pseNUBBYei`@5&RBFItYguyn>m9q6w9LiFf>OHytzRcZvgNE=WOTQhopmf
zL3b@1*WXNQ+pNC%Z%vG+xG6lF;0XBvJI+60nz_?%b6itll^Ixjv0Gf?p>kHb;`hFD
z$whD0<@XmxZ&0)0s_CG5MWfo=rgwZdVnMp>0U_%*!(884s+mHQDxN6(#=Nfe;@h!P
zR!p>Y^_9=qOUW`xEgxuQK(rSLw423LR8r==S*l>d@lWXx^qZI-*;c$iyeBr859HHt
zk)5$5_uDbNc;PyP5ZTp?rb$v9lRJ*K9LTL_5GR~=g2+@7N3cvMrP`xx^W{>$&ZV6^
z`7?7#SiY(@R%d0Z@9ZwzO|H&%;EpA${yIxH+&Usx7-#pEqxnoyh}&3v?e%4oz3o{r
zZbov~U4lV^iRUDpr#L>sBpT5ExmEPF+_$oa)jzUgXMi*L13S<6V`59Kq22*QdM?CE
zKl5m^0z8;g4kTu(V`7uKj_w|QlI0Xz@$`&@MfNwK?o|jfK1AF_a$^Oj#1-N8>P9{~
z22S*w7WG#Q1cbvRA;N~1`=|={`_0I&4;BYpm`G#|ndHB7-TtB@!YNXkg)8yD$8ZNw
z%PZhr8HX;GUVG%pu5=QNJ=B7NI*CP`N6peK5Vy)@gC@g`C!!6&4^uXIPG)ZiV0IYr
z3HUTg;s9D8W+EhpIw8vDjb?rnWesiwRxV#K94(I-5Arl>`J}Zz_-+0+-TztS&<D+E
zE8b5A#dg+ej%JT4!NxUJ%gFi>p5MMK3oaD2*`<^4o|>cLsPG~;bUsYsJm!^04b+G$
zGS;35%`OS(h$g4rCuEnWpCEk~T)ocx-S^Y`J>sbwXA6l6qrlo2exdPWb#zT`-~?Q;
zJI-oN6JH|uYkSRRC0cF|MBBYzw6C-7C-~e?^gXU^T0G#_cP={FJL08;Za{eAw!^Xb
z!*TtdtkmnzDHG*xL#TB%uCpEIX}5l=f<}Zl8!r_tITx>Nm$dv%L(GqD@YBA?{Mfru
zlyeWD{r*JWqR*tje_HXaKJ7?(7Rk()lX@n}dfcT_;+0S47QgD<d8%<l-*y|COob=?
zJW?9=+iUyKQpUl!LA5RUv*WAO%aW+yCCoxu>*Nt#0#vU<7UFgH<3nZp>_w*-y?@ZQ
z&DBb&u#DDML^dxeGFxAV7lse}sT5?Hi(s0*UAovxX|@`<$ZQY7jv8Wkcx?Wrc{>0$
z?-oL!KZ8WH+v%rHO*ij4TO!_8h(1r$_%rqLr{asX(cnGLQ<?(t^VA^Y(sKJknr*jX
zg1CYZT5SB4OxeC;$|Jv5BCYM(MRs|gq{IK?`MKP(F~D~waO<QFaIUNFGn#_YmxBXh
z*=jfAI8HIqmY=abhNa2px$VpsYiXtgYy5O?xi^2iAe+82_@t@52n!#_1QZ^s-I~`A
z6ieYb#|d30KKk}`W*a#tMHq|-K~cb##A~7(XoMRJsmGAR=;~ufOXw}}5A|d}XbfyO
z8~?1#Ia-bASu-y=*JElbtC@pzn-KzJb4#D`HX>=jmSn(go=nX;Q6PO=_O405AKtXy
zhBAmatHFbpInaV-s$&!-n_%yCs;PMZV@uKHA{KO9o2`l<e~lolR`YyN3+#J1$NY%&
zfuqJb$gUS)<0Qv;<9NCWh`n+an{z_qN4;nZsgu(r7eCZyoQtKvLF8$migt|T-1o+|
z7Oz>;BTpXYe$`Jc{iBn^LWQXh1;Y2eO405=*2)!s-bzMuoy8Q1VtDoDzTPU~k890H
zp_<v$!i}JkuYNbYk|y$}DuY~(DnR@bZue$o+07qkT)Sje$+Rvel2CQKy?e?eGOm24
z>Jt5;65pW=^+c7+YkE#7j>puz8xtE@l(7OrJ;w^z&4_jLOW5b%k`B0WcO*?3%O&|!
zn%VKZ9f7d58+x0A90B{RkAnS0h8vrq3!ctcvrs@ovY|Vh0o}<-iP<KnFw{$M3fvgy
zvu8G`OlQ{0y%Xd4BSE*5@n;A7iqLbN>;y9V^xyj4ro;o4Kf9Q#vI<61-_wyMHs=;>
zB~%Wx6evY$L3qR?wY)rv>L7b2NzAs|lk|zB^nBz90A0_zl}sGywZoRd=658$rQ|MQ
z=VxC2F7VLMV+dm4)sH*VQ1VL_j%(E{xZ4?LGc2v|7=-S^qu*<Csc85{Nw73Dku6@s
zO7|3M`T%!2(gDC4T3~gQAwZ}SO2bDdNlD60$V*;x-)^EBqpiKR$p_TyD1PdXl$*3K
zNu?kJRNWV867K&l4##pIjVZmAAVDZa(WwBC7B-2vujnR*;w(Rkq#Fr`RaG{@j_34r
zvOy|}Ld@@F;ty_5UYXQbhCgHmwe>nRyltMB550pu&nJEckTc%sKcP|^Vr1r8o*E}1
zOPtke%OIikLdJ+2*;Q?S=u~t`A3reO3(T%jG*51eaMfPkNJtLd%OJNO{b8tiRCqJ@
zoOP$ZE_XW(>0Neg&u(&<q&_{ZD%?n6_3M>eU67ffJ0e1l+#Ou}V<0lExED>k`a10i
z|Im?7&~Cu?PQV`!MKP0_O$?p$?@wkM-o^*fIZTM;BL;=`Rr*^bllD5$F8cfzVw>>n
z!kPe7%ycKJ!({xaDNirfr0WaMi2+KFov+!YBn-!?7Nr(YGa6di?N`F)9egWI&BEU^
zX3Mo;EB0+X^Y-$4&$@MHZ;R0L*82|o@{P!>>#FUbCd{`$p+UHPO{J8a#+Y*dxn0Vw
zP~Ogyb_;)0im0Cbk15c=f$(Y&uo(7S_0YLJphLC+!rI)ZE%3`jZ-Zd;3vk;xLPF?|
z4Oxor?z2h$5{xNlT*V_&adk9D8F*_YH?bt@LzA{X`#$pv_P2e*d8)4whv+?egx~qo
zkYmF)OQIcl=#7qD6HqkV<l*K^j|)2iy1WxB0;8;DbRLr`a0mvi;;CQHccmgnoCkOR
zNo0;+bDWB5J`m7N1;t<T@H%ZhVgl;{#&qI!lUazW2L%CAB{v{LCitQzs}_M6%{sZl
zIN7TOSOpU$&Cr6?<&6oSy{<C@O}T-vK!iy}TB(~$LL20l*a-U~Zu@UQ?^8KVxHmT=
z#{P08=~EQe?R|t*#E=h?q%WkhMUSlZUQpO;#~C`GCYlM;MkAhsHo}E(HD4ra?#;?d
zg_DdFnBBt&UkC|zM(zwWXTA>NCzhXLIC7(C%Tw@%NxF9zj(Uj|%yFApCkaGLEP)B0
z^btIm%3XB)ZTe09oT37L-S3A_$mvC~yc;#;*XklY^o<`P)Xb1(Ox(AkD*rXOB}ex#
zA(yj;^~<eS8F|HR6gA}AL}`KS1-9z`=vR)5wf`iN>fr`ik;AXI7>{YG&1XF5HJM>h
z7sa;%jJbc90Oj%BK`tJANYHju?HiGa&BzJV=7#=s-%Oe^F%lI_o;6`~?CHT9mdB#%
zrum1`o^GsR`PB(bKI<(j&b<T}wgs7S@MlFC(?1glxz@?-tB#Fhh<fa>5*sO@onG<1
z>{_~}`+(gyWA;=}iRlR;ZjG`a`c#AV54P`j>>Q<{3|N|yPBJ?RPNbj8OcS7BqD7w`
z@kz<!qPo_=C1N;{m?)x}7a*sS)`F0h-Z8M7QB+U{_arA8n3>LRKL}{&)Q4y?us`ue
zO-e)?aI#UZ>gu2y`~(*7o7REia1|lT_sc^LX7~HKx;QRq6k^t_OSm4;%hL-dzf5Y<
zEBD?tFnMTlTCO;{yXujzJc~SP^AKvLgJ8Phk3}3}?>9f<DlLzrd>~;Rf6q^&lSk3B
z%Uh=MQ5_<`WH$^&Eu=Y#?Asv{($UlIZ)Wmzl&KgMSmk9%K!=GaSt6OOQ5~;g7)w6;
zM!ccMO}2>ZA(nS^OllX(t1_<VqMPO4Euo~VR3>2~vN9zar?;LRQ}~fjpje7(n38E(
zWGr7+-?2VNfyFt(QB*tm^!EYJfxtmYP#dE*v1y#4e<IeCk{eL(#|ampw1vozQ1AYW
zmLg8<l7z|kz}!FxWmFdY4P+XOh~$Jbq9Nxn0L%|)R1%Bm#~ounAhL0e2!}M$c=}Dq
zZ6o(XyWr~sC-XtwS8amcFXnYG%3q6|zB&~mH4f;{7_*jYq;Hx;7rA3qm=fu!<qnk%
z)Ij|3&57(xI__r%J3ciAS`8NVuA2!RhM;AlCa9yLZ+doq^QBu~RlzYq!xI0(aTN7v
zH_k8lal{(`DeeAYa)YD?MD&u69NrON(wb4uI`SqE{%|5~U!y-?3U4wI|5M5}Z_#;q
z2mVIf$MyZqXVcbMrRB@tVfQ6piR1Ha+3%gzP8zqX2zPzc^AH1%-KKBOyz)@|&fy`U
zH=Wi`tN|>7Kqx6ba%&NEG8vEom1;UsQHGn;l+#2!bWOe`?2}QH5nA`d+X)(BO)np)
za^F9StO-cLOUIfO?MwD9(iBZ`46}Wcq^bZ?)s(z{#Z8pNx(LS`Kx@&9!|73TMB~i$
zVdfvr;dLh_EL8Xy&9UgmbAM1fr-S<U%B_u6Th~$7sJ{X)=bP#xa_km}NrDA_n$A>2
z7_Y>)QN|D>1yQWny;KmMxBPG;;<=k|gA?0O0J4YqaiDN;AZ1MDb_wZZQCOFc;sD}q
z$hNleFPCI$=WF(&4R*%KmX{Cp+Qzz_qR#h38aS$-lAZ$Uyq0-HH}zp(l1rW~s6(7R
zKNAmdphh(1)m_!yV*Y?K$ZGxA5!opCjLpnr1OgK)S=p~S3iD#zl}0+xe99Yhj)>8e
z(xEe?GE9}`b7n;+Pn6|oKflIzMQT#bmv=9@L1W#B-1rc%W(iFRW48+!2+kvcoJe~L
zw}GS7zeni71hFmAsRhXu3d6R$F`dc&1nC6nOnOXSbg^mua1ZV4JTyZZ9b6oT0BnFR
ziLN<5*%Ha#H5$EqXES!J4(N^O&4JuiS7qos0-SMk|MEQ>;4G>?PquJ>feIjf9bUi=
zFrBiDmb&x;jf+ThF+nj-Hrww!rsoODBFKZe5M23KWCzl@K02m%3KW`{A;7@osf~X+
zJCGMGQt}aJ-6QPu-`AKhKCx~P(Yeig3fotX{le@kZ^v=|d}uAh-Cu3w8%)eYAvi#B
zQkTYVd5<8D$v3hy+PZD>C)0-MHOH0Ua0y5uXuBrwfy7#Lfb0-E%a#6MzXRoOCA4tt
zw?Td)d$qL?1%)leC<5Rbd$T6)DP<@UXG_fbB*m*@Vy(X0Qms?P?<rayvriitnSHIY
zHJ<3pX7zV#GSQ7FXse<G<7&57|BlPoe04GIJ$(@1FtvOgV9toT(Y4R}r7Un-o|EWe
zw^rL0!!~b=C65Q{J_jVK(1v<&mHme?0=y%f-7)r}qpT&y7O>`8U2$Zw{&zg_q~Vld
zs+4Jnh%yKDE0wg2Yh^|S?G#JRD&ft!_8k3wAXSO;G%BBKPCMznw5B>|mOpI!Nf?G%
z#+aAwO&UQ4Ij>#8_RmUWR%8wZ6MUm-0SUsYMuKRY(CQ{+p}_M&oICIt=oTTc&AWgD
zYEM0noZ6(-kSt3}_Q;K-#>OK;c`;N`4Vq5U<<{yH<!sY*ODdf45?4|COy{~~MnwNo
z1j?ytD`k3w)CAW8jtvwL@Fp_qJFTCorVaCf8kQDgxeVOSgpE|<+$M>aA=6J;h@1Qb
zDCq00Dq>Aj3w}DPM;)ZQ!BcsomlH-^B~_hJgd<KfktWdwm={blRtN)Pjq?TS6hJKb
zFY-H(?_xb)M6^lY<LVy!k~kfzGYU-!jl^7fDf(+jr}9c_A7z}&@;&U=SRojkmTkCz
za7KBg&y#F|Ve`DEm(-63aejgoi&#0CuT#$<_Y>_m$7^yn_PM=|Jcf$IN&I#kn#xTs
zZrXG!pCW=)ThWSaK6I79eHd-QolJaj0s2*+SAx!c$dl-^CB%fz#RPR4CA3ydnYp^k
zg&8e^<M%0d*V@@a*}C!G)-hdZpRxnX8Uu?ktsVigWOfpoOtV7%2?Ybn;?j0$mGUK7
zO>u$~{#FkwG)d$*IN71)*`Txx4Aqj_zuYD=@v$X-bYClv8$HsiB;nSt2(I(`2v7Ns
zOr$DL)=DMsh4jx#TmeaV<5I*I^a=Aams86)29PrYPw%Bf#xZ2Yz^y?eLzYtgB3LRM
z*s9q?pzeg3q%TU7x&=GioK4-rS;lDvT;2rE`fZH0!kH?^3guejf_8D4kRibsH1q|A
zI)1OwMyhr&gcZS}<u*jls2plt>(tw1-}EERP^?7>mqZW4x_V|3QKto}izR*qyh;0y
zt4Cd0yE4)})@Jrg;bGF`WkhU7IQBq}W(LASkwE-!*AdW9tv>4*mff9A9o0|g6(bTp
zrjOF$+DAOxXbZw<WGvShXvC*nCu&>dK&>2!!BZT-u*QjoXuFEU*me2>(Mks74wzIV
zve|>XPTE+qQ=W=A9;URdO~9oX1q?TnxRS*42`fulvFz!}5~DW6-7msfB(Ykl*>v0A
z`5`b)S|*R^bU!W{EvfjcI*wvQYaREItPYDn3H;A(*d8=3%>(v8OJnv$Br_NB{Q|LC
zITH#l4&6f*>X5V#&#(>rnVsAWOh@o^xhO#zT4=hI`g7`9+8v1F5L#=N82L$SDVdXj
z@jayshlzrwAaM|DSruqNJv}^1i6>oAlMXabmcFwCt=*A^qa(Iu({S2@M2Te48tDPC
z8U!9BWzq3oWCA7F`S<`fqIZNdj3exhJkN-@h*^}k!@yuK>rZU7`cXei&u77cVSmBS
zs*Vvtt|rrcHD9a0a7hcuoo2VcG^S+cmP-D`U7N;@faaAAV*$i!szR|JvTgn9DQrIB
z^OO4DE<m?{scGLM<tq8~o04$^c3DQml!<?(jhX^viDocz^kIeX$|7zj)(JQ1>by){
zQtAFhrMu9#O2e87^wCBpLh;)W4~ek+>e-}bQRY(687FJ?*i@DmG<pVJ(XlfUAq6db
z_CU)7<---&@rV&_!OSnrHB|kX*wE|YZ+Jqmtk~X&M7nqnbMCV~tRYZU+WY)W|Ksbz
zUz%Cq^AXUao;rAaDuTz}{Q2mW4`;+8xK`>lU%i4rR@o~CMgo#gPcyXvQy&piZ%#)B
zlSc`bvq_h;SIuVY1P(`oOx{H<DD{m~@$me3wB$~%EMB}@mlA(bxx)3ECvNRO*}wCk
z0Sv19o1g%~Q5G4&H;~mrK41fRxBj>L;cWNY|Ar3*oxfl34Dd->O6BZsD@5P~<@JoY
z^iNYuv`^{22HGm;K0-uC=Wxl5yefB&vg>K&b`j7ZE_p4J{an)gq5c!3(oBhH2R`3L
zsHUDp-NTJu7K)^=E;Y(`{QYTKL{B?FlEj$A{ac$QkgwBoG>wG+ndw||?vor8IA&5@
zHMpVZQx5imSvv$Qa1Lgz*T~-P)^8HOz7E6bge|0GeA^ei2Y$E|&^gv4oPPhMs7N1s
zHxa_N3R0=|BbUoRN*~kWOE0VegG!HTr7!e|et5Kevo(INg%i)KS{lbL>)K8vqU|wY
z(~L!U7;wSly+m!QxTp34MiN^dA{)81vcPM6`%;S3GZ-H(|E=_IeZ?PO;peXYQHP9j
zq|}vK8q?!gs?6!QpU8zc<=vf>c~+^bY4)*Ao*W)Gutq^|w?$=w)Hwz!*n1#V?L<F?
zt$a4I{{d|=HCq0Hddq3%xxyGy1A|VGWF|+;$hFGHyov1WZrI?@5d)qYKDnYXS14$}
zc&g!&%3Nef+?|%gKlpfk7)jz?PP80A$8wvrW9T*bR4|cJjyi{iP7#iK(Le-%1VCDz
zGu3SW0899q^=LbqnYHHg{mGoG0E^#QQt;iUz!d6CS84T`JU}Hc75e~<_9@TnO!x-0
zlF$Dwh0nL)bAa^=%G#cU${Mg5?u6hcXx=!(!-L~j(AO64{uY^;6Zb~>=H^$sUdGu@
zTPe;NWqP0e0gpSXWl#@Pq5_Drh=hnAsl0`rtMCt28;aK@zn;Jx;ZTOOMfxuauSwp6
z-ly&q3Cs5`?iU?TLRF+AA}NCK`xE#2^*O8)Q+<P-A29bWF)i#j$)=Z4Af0H!lw>Zd
z4K=(fqChAn<<|F%<%=2dW2M<cLhCOFi3Ez1z$m{P^yy`Pn6*c<d!Jw>rkil<GAd0e
zjlFN?<*prL7aDK$MEW^MTLC+oNbOf$O87DW|DTgtkq^dnI)OH6rs3SwkmQeGuSFB~
z$Z;9y3saGeD-^yt5S<=fFaD}Ivv1N~WWjhZ%E715PByckDu8SBCXW|nk@W_+SWQRt
zm0$v?hs^vaL06Y&HHW=ddVelmX_#;9*FfDi?~f9}C1df2dTSH*-mT5bWtfNba1iz@
zM7E1Y2eMyLCiOg*ODc;&5a-`}E2gFEzqLc>#QT$uB<?xLhKV#Y<!SAU!mlsyNPgLH
z6O+l#-4817=2HC6Xy7V>!liE?Cm~pt408%hXHA~hsgTu)MGP*pHh*r;`y;G(FT0jJ
zyEenv^lNuFItc%R5uwQT3uDVVjR;iXtczY8_$`1EKsE09^d8dNJ+=Da-qux0Umwk(
zPZ_6AuQSHjNx=TxDNYaROiw2JDpH&)PmAheP!`MhI90?Lv+?%>|LFPjt0E`wqr4>%
z={ZOFj94+h%`%3JX#R{?5x0BK;6^T#pb7U;O`8$j8s2T~E_Lc|LSnM{7FvTwOfrQQ
z?!wb=RbRWp#E&W*XKU;H$IrT#dEW`~@dz_CK-{M6Zmm;etpgKKV(-&pQjgdrbX9>6
zfGnoJ@JV=kup&NpFpeMTC4HL%6xt12y`iREl37wIRvcxuoH1%Yxs}lP;|8(Y-{-R`
zg$h6%r~3){l9fWy1Sh2~1O4|?M`Yco0y$V|SLI0Ui&(!@_sa4{Ztv$v)=iwe_|%%G
z7)enq|FskCmAfU)l=|5b%|#Img8z5&f;wAD_+1q%e&sd}nvv;E+{9g6K*EQ?1BI%b
z_W1*=qDq<8m^V*d_&#Lul_>{8i>``UJSXw70w3wP_Z~d~w)Gq`fAAJ^RM+u|b=(27
zGA=l-5Gqjqsoca#W;Br}iVInd_1T#=L1~0N|BQXMR_Id$X6NjTdRT|RlYqGK-L)t#
z15tva%A5MXFKJ5zTpX{=q;7acsu%?+X|W!)urV`z9~Yv>`&KT$F|T^VRlD^a4qS}=
zzC_+6hzEEQD>wy<mYi3oxlo+CiAzw-g_b4KeV6P^;FlCl$OXc5L^u*ii*`SepYK#t
zHPT0`pt+wHq)*BHqy=SikOiY#Pgv#}F%kcXi2Ap>afHrX1=y+kaLR@LyI~{3>kX~;
zlLMT%_auP}93H!F6O!q#Vi$cq%jo?yxZdBX30;yb{lGP3QO3w6NecQW$;Df>1wslK
zP?}i6B*QZB@XG=FnO|EZQ?H*-#fB;iBp}p{nrtZi-V0TgccpW<^RiKHH<xp3>42vw
z<P3-UL7!dkhu3b^m`L<h+!r_^irriOm6J>#*ia_;4#K`%FX-}ME3?;i_pARii$FvO
zeeId3Vw;LUE}biT<)E?Efp#Vnsz8@oWJ^xuD>JIOib)0=L#2|1bdJwPjSb9r4H12%
zVx`CS5n9Ri`I0a2MN#t2N|J?U#bW$43F$lvqlawlb?F3(wDNc_(x3t&dCfbL3}tEf
zI;bL8zq&eG0@)l5o6#>%W-Tj7^~;XVk$4mCLqR4-?p;h^NSS8*s0qn6#6$Ue(n)!Z
zFNS~>1Za}rWI0+xVIbMi2*^`_*1H(PZ~}TBFz3+QCZMU$(Iak4TRpxNy@?D(jcJ<V
z@$Nx9ho^OH8dY!E(UfpPQy{&Yb469Z?K7@j%T|aU5R)u}E-xqT6w2Dy!tO}KurS>%
z1-*S*VdP7d`b&Z^j>)PU?*|(R)TZTi6!pmuUmohL;(70}>gMvD)F%tRDyb+p6v^Ua
zT;Wq2YaQ<{*hzIM=Gn!><by`Arn^8ttV2^DWyNaDYAoe4;96sI^l+=kVR)g*ygH&~
zkMma`dp{AbWowPmj>Kw3ycI7aG3URGS)?hrepf+fCP^faeZiI*>q3wx02CyR4n$`C
zX^5+03XhDCA%pf3?ai)>{R$sw3=FsN86?&_8#>V}cyN;-=^x_^9A}g<ecxwnlW#dw
z2JKO?pgePbj1A5oLlZucx=*+;`0l45&tfrMk_sfi#4x+aVfY0i2=C{1CHX>U##q+8
zn&nX=`uy8|sEX}}u!g}L0N(m~@xkmWV^tda$M!Vy%EwyQviFLAaOdjk1hgI0P<q;g
z2(F2G&r9-#Xt4?r#R)u9^!i<Pm)oWvXp#nYJS^imvycvu|Jf{Vij}{u{{&9K+#c|8
z9tEt;6gn9XXM*P0tMPLA9Zz$$UwmQueQm*6GiiYJ7j6qtwwohVx_?jJfB2G;wWwOO
z`A>~nhEM<jtiw6b!!k(M{=wj@8StbqtqACRtG)rHZ*oQRz8f^iJ}Q@?*?ITp)t&|k
ztE{{(s7~I)a0IPqt>X5E`^&OXx(H~qretrBzFst7Us%oKj?-KxpH0*iQuW=PR!8(l
zb^^_|riHL-l=AXG<x5T_IVWSsUZ#S;cC~1_#x;>s%i(*U^-02PbpRJiHVR}6q=8EH
z$xV}n<ew1OAs-XdvWUV*IFV$^Y6~io+{)T6Ax_ADQ<B&3Wg(3G<gFF@0RojbB7XgO
zKNKoiIh<F#$-WhA^|Ir$PWZo-@aO5rz+3rV@p~3&CAY-CduBsq()yN5%cn-Oc%u?7
z9_r4xqT8S_88j^PvnQ|_O>>RNQ63nGO|0FDcXe#B+ka|k@!mb8#?!bgfn3J$bx7rG
zyLufNwUaYE%Y|0asHCh;uU>Z18glmNQF-zYqH*NeL&a4oQp#53@r352u&kSUZ(H%?
za&~o}++{k2^4Pyfelpt>_5HH%zZ(A+R&(zEQ8d41;ccecNoq`@&L$o(sfuc$k9MdS
z<R7rNs#i|}!~Ic2ZSd<f*G^uAPMOJAm5U<<vzU8Mgp>EN77AT#BRzn7X^QBtGtv*J
zm$SW`<J%vUFDx-M>ez}7eqA1)MSn4{{q^NmL-F}AeUnx_OOgSZY1m*+GTkZ;f6wwi
z;@NqM&X(-%HZc)pB~w8sB{IlsdAcJJWtl$yP<MUX&PHmDjv|X2{hTwQp)wP1U`<OU
zvAIqxK)`srOx?V2CDcR5EEnojBFNzjOj9Y-S*8LutF~t`fr|gjV$i=Ytq<MEo-<hW
zF=&8#hUWcRFD@M=u(2149(^s&OdZDjP=fYMrkh_dQlgC)60&TQ;l-;%@1aO_MJkPs
zh{R8rWAz<flSiy_Duxd=iyWPGXPfh^QnQ3lM6@HR8%3b+D6HfU-7Lv|n)3UwoN58b
zSuk8|2Be-@fJORs58_7Ace<)p)*dN9E30=zGfh+}fWDZR<ferNlDG?^g(g~^RHd~<
zKyyV7^BMP1pn>Nq_iCdccA|Dtw4!SHrjj=B?rni}8+|w7ei9*z5Sg_Prwo#?0D~mc
z*{`6YOi+Z)SfzkrgwdKvv=g3cO-AS1k&$4xqe)O<{z=?&UcUjwYf0?mtK$Q|s<>fP
zfn40v%!FPWjy15MQTf9<LfAXsAg%%tMXtf=k0%9@*{?IR5$W=)DI&&IapVV?A%{^-
z4#MOlBMv#t+K5XLnGQ^4?cc+YXD}Suj4-LFV_6~@b-zkYR2~4jhj@2Zrl!EE)Gun8
zFag=SA@y|3J>YVR5@Mu3isJb$o=v3J<K}AjIURK}O@emYAeuZ5Mw)nHA6|3_sJX!O
z&siuPMB_7Qnw6hA@hWt@8Tj2{XXLfp`Mm!>QMVJDVnbiUlq92OANA!yzPhTX`BjpE
zu?%2`ddT`aT*OUQW}dU{9-$?c@?8LsHm0QukkU&j<)DmHC3r_bU9d?nVKYt{Krn}<
zOfpLZN=uQcf7%7|5<eIL3BkMt(qjB>X=qt7Bl;+H>K9$$n%6UCaqx_&F&CIQ13ltX
z6w8MgLWOJ(7m&CQTnBeojre3c!fX(v0}!T#9T+u9x>>`WcAO$!d3q6vcuPn+mEi`g
zA_(YTAm&<3LLJ5t`b!K|38bIKwLBk!Xd8SgRXgt-Y6t1YWZIIAZcTCmYsYWq(Wh;6
zQ!9!5+PWF<b*oIv1?x->r)#Uf&flLClD!fVUrS8Q5Y=i*GI=ym+xoa(l7E4{XC+yE
zPrLr8oVKgB*>2v|SV){No0;9hLYLQ5N=zq^t1d}EVY+;bT&d5Mm?d6OeR!W0yW<yo
zpp5m6!dQR~6gWw-J3${#_N`m5#MQfU4Sik13i7RY_ifS@vzmFOKD}|dCj;v|#8_9j
z0fm$Eh!~~N^a7(8dr&p-F*FbSfJOp_CY69`QKOkLV4SK46r=NTf?)d$%@!93XuHiL
zJY2&ml~e;`10^Vg6ENPXe870634)zt2?%i`P7<I+1ALO4q@;uLWHNh~oQghfp0$dO
z-V!R90Hv}Y*Rk?OBbGJht3D_6j|MyW;m;-vnR;63shFQw?M*vZ5ETatl*xz<(ke4J
zC9<qrHPXJ|&NYAHWUOkWPwxuZqY#SrDC>^=WfpD-8SO@!2q!o~ffTbEFR7m{fFI?e
z0t;&;TWYybQ|~0`Y1S?DBHHV<Ex6+JqROL<>kHsXY|_Ce)#9sy@rtgJ87+C;UIo&b
zMGKJP{jyOb%}6gsNl|jBhfkMBfX_SmXYbUy%UCtT7udS!pkad=+N#zXzK%?h(Vy-O
z*GmD9lAGpNn)GRq-0zPtA|AC2yXG*;8GZq+ivx_K#_uyK#SmY8jL;)6kh+iuDES=(
zKl#7~CCdQht%+(7w*#gxZvZRwd`wELF^-MNHQ`e1z7?P0MwLnJ(VuPbU*xr_IcTU@
zjsPMNS)?{t&L(rPc&I9cXzaDw$N&i?E22yVNoB1tLXGi2xegCN!)d9-CJ?!0<_<m)
zVnKrM!)b@Xcuj%;7%B93g!rACgC$`a@9*T%>kfaAgU3YNU|THpFD1&SYXuSs8Qerv
zlKq1C3J$+yicTHkHEx14s)(<U4W%tc(p0J<f4q3-7DJI=`~+z-uV|4Yy_5vrSV<!D
zQ!>v==PiFVqgFk$L_EGs6Dsk{k>8n7FPi`=9gBxVrNqDvn=)v0@BrQ+d~DFN9`var
zVbA|YnSFR-3!$9Vc%;59L^NyJ00d=;ro@JWsh=xRn}zn3lMc>l=skADL>EJWG(C`z
zp2IwibK1dW#A-8lDOhYBwqO`6;EIb`<qW}4LvqpNyOU2LWNGJVgi9EAM-WtM2Bd{W
zLyLm}q#ro_Fgyb0mJzH%0A$FdbYNd~0Ia}Wu)%Pz*|Vsl2MbcDdnoPdSrLB_)#C${
zT0pj%D<)F{C5*}-Sje(L6Cmvz1=4Dtkd1datBz`_j_O0c#4(UMKLTj={~<$|d<NJR
z!1j!weu8BpbecHQ7?UC#4Or^~4wCo$oN&kj*ZGWdeUsGnmm~#b9tM<*V@A)+5k-pt
zv>p>l@t*OENNWnu5m?3jdpbWp1H@|?!y&cBXo%6_zvqTIKFud-gN92>RSin2!x>Z1
z$;82}!zn_xagTCiXN#m(K@PRW4ru3kjLD!?0rqxnYQ7I8c<~k^?_!XgG?_#&`i|!s
zk~NK^+<yx&W0n?TAI5Oyn&D_+nmDG^Z#YBsO+KDb;>4s9r8SmmPLRtr0q7`{!0c-T
zjBHaAch2eh0xbVAf_xIE3Gt(x>IL(K07-#3&8tn$_;X>;EgT7BCXRh{lOpe&xKDxL
zv<P^a3b+Zxs7&J+{%n#y@gq#N1ES9W&2KT{eK>mJO^cy4uIN1+B*y{!#<CaC>HrYC
z!Z7^Ckrtx~2bw_Z*T9!nz(7Mlb2Y$<10eDQL!mDtQ_+wjFjI;a;*P_~hxiE%JK3NK
z2G9i4n*^Vt3*Bi2G}hELqD2D9-Zm!GwgaPrvvMU>cpd|Wsv)O!l*65XHoZ{{duFa=
zFkkCtNu~!%(5MViC6_k#h%qL($Xbw1Mxrf5>=z&rnH>MOys)zsr%L-ylhw=OMbHz=
zFbxN?UO1o_wl&xx9wxBdcKt<<C1QtKh>w>jPB2#!Ldi+}p0n*`d^BfQO7>#nZaQ7f
zBC(%~_5`OO+mn|fKnn)JQ}SaL69dV+DWTS?RmUuM|0wRBnwqEac!{B%Qr5Ub!BGSw
zHLX;d4dXC@QkkF~M~$|38-UsdLHo=L!)A9_1xYjgqeUOl>i6J=QR!)3D~3x8Jw%ah
z-Dv6+FTS2MXN0Z1Sfzh?01xVTqq^u(_G4~+gHiYC&gcPzJxu<d-oxqHZwAQzy-0IT
z5n?Hf*l9Zgyh`AU*Tj5%D_oZmh{n8Sis4lu+%WPnl!Us_0{~}_&D*%_mT@Dwa4nws
zUr9#V>9o(5glf#t1ao(jwdy9<k3p1=CD@tX(;Vn(Wq8Ja{fY%@_A=fN5kQf(0Yp!T
zYyzvxBq&T6V)3s%!CJFK%7BZQSG7MJL{zv8qnK4+$}5Z~E9`bpNplS~!D2`GyIsnX
z3?tQzXnYEk4r=Fvndl1{9WBE@K5kGB@R8H3cs>CsrGlCG$9lu8XoTv^-b;P(NPyuS
zodp(WT$n~^wBEnkNK%)jO5s$Mv7DtZw9`tYJXzt=)TAYqnYvqXYb79>ye$-_b?9++
zA<S1#DWrE9EK}q9#@dMU`7($DyWGCmnt}e-^n=CxM>f)wwG*K+M(jvH<3CW=!nGE1
z+m4&<t7-kg+<Nnhv?EkGYkKd$^rIpsChOIV@gN-^ORy=u+;^i1*Z3)`QB-X6t5U}1
zw&^>8!Jt`XT1Tdl#3{}5uK>%E-2kfduQ@7W?gWG70dFEk*YHYnWVM9{@!k$V)`a6~
zkCvE<1ZLN?{~n0|<lu%*5*WwN+WluFlBj!95&jDdGC3zMUPRxYjm(GO)kqbtj=$O4
zxj0@rzpB=u`g4&UHyp)H!I1mngRh#!FGuR8K9%=+54OHNKbQEDt79N(H<_xJN4Uk7
z&Z>X;va~mUdk0`>yqCmv^K9JS@cPO0l$f6`?gcb<oAN$lykQe99})eSfJX)P@ENS_
zK<Y0Rucn@JN(2mtz4MUL#SR8X0lHxf2NDceJbTo=AV$OtI#(TYV(%gSSKa?1VOkIa
zY{p?I^70+SG`2;oEq!G%aPiHHHma3*?ql<$*QR1^PgWN4y;0PQw2~l#xwZ!5iZj1P
z?K*lQ{Z@??N#lQIi6-c`i6YeF<v2Faht5r!;ailep*gJ6{^o^I%#5z(5DiG_sJh}{
z^b%wm^65#W>$YoVIa8Jg4zR$7@V<<hC#iJ}TeJu?A&C!Hp<ud^3r2M_EM-#)Xd=2T
z9vs+v8%YN1n?!=RB8t&h8fZK6@3~r+21lH2Z#F9*dL<eg5)d=4^8~^2XSK;EavKdV
zka{J;muM&JdZ}m}(n-i68mG<S6R$%}=D9w)m)SisGDH1qV2O}ocTyo{kPjZg#Q9`t
zEN&;LDf;k6?}bi6q)UfRa^^(9U&)NTM4c4;M+d2AOLn2L4%%CM;zi2xR>uu)0HkeC
zS=fpJ)XY3Ybx%A<L@jW(<Uw%_=V28=eo;*fYF^0PZ7Pz2?vTwUTC?B&$rx;j((_pe
zU>0z`k^*t-U>I~d>|5pXj&?&wSshx>1nONO29{)%3*a`a1yak;Z|^()iq*^!LQuz5
zf05X{#VVQmr`i%<Q4-w<v{cKI5}~;<M5EG0POoxv?nl;RNN8{~Fg2k2;!K+2jE%mY
z^E&Q}1rE@wuATP7l~qa*L<?WKR>3Yxz_Ud}*wnE+w8PhzGd#6ipeW#H@ziC^*mhR-
ztag1Y%1}iuC*z4^#XAo#IT^`KEufkzVhrpL(5NJXxMovVJ+yt--K_;wnz(zN;s<f3
z^y1R|{9aLda6#t5D`rV-^U+1sJC(f+8~Md3f~ncsbTA#=lB+^+9=zFC{Eg*FIF(`@
zo-9~uNts)V4SdDI9G{L-o#l#>K>7)2%Q?>fHB4448*>H`S<Uy)y2|Mw*?Hab@Rrsn
zC;xtth>bNr1wOIR*EF0-!2Nnwvamb&X5ZL5^}cwdueOZ(L9KX+bvv?yr>ysnTNxL*
zrsv^ERLQa^R%1SiFTH9O0QMN!10exw)hd(%^|%PJ{%Y~~pwX8+9J~r<>Sl&=;g@U~
zwYd*G%k{aVB#)Z%wyum#tVzg8V)Fh(m{#L5{vaQtc@vtLd&@w}deh;?h}iMKXz35-
zV5yEjvh0p>p{A(@Nq}!-!~8b^5UM-9zonlqqs@M)^f^L|NQGs-BZX!7L{5w9O7b7x
z5BT@RQ2_f|O$FyE;`3x&E=BL`-fP)wcs=hv=U|iFlO+|=1@m)iMslp#G@H~WKZsl-
zp1~FnFSCw)|Ni}rpC$f_7GBbs7r~_;HKK7V&>}#x@|}~2WHeK}PgJ_BD`i)fsuY`}
z@Uq@f%zp6LW^$Lr!q2tw=ZCCpXrwv4&-htdHb@%EcN<A#vkA9j<W;xMJV|jR4P(JO
zwb~c_bnW3`@##YtLm9a*;6i*YgHeqZ&gnvgjU|tTJ>E8D6SsWv8UNOs4TJ~VmS)Es
zf0qy!_+-vYjUA{RC*jin1QppF_rzXc@{72{n3v@_gQJ|tM%XsPC@}BDYhUw+-#?v#
zoIa^4=*2S7b!#km<|Mw=Uaxa~M#5%Z`_h9JM(;u0TH7~1Q#g1XSWt(d15JR)lOK2}
z4uRRTN=W{YeKrbrK`jg!M+D;}oL4@Zf9N>WKk~MIt)n1#8Ol5IlhCmjwVkjq>O#N2
zS}zLJ+1MyFuNqWb;CVw`BqUTHWR9%@6-?_%OlEG*)d-`$G?)1(6;>M{XuD#LaeXS@
z4Nu{$L3{509rjim%|M%)8fowu7!pAm18nlguM$Ut)9A|MB8)6Da8xx<lGj6{SzmS{
z@Gi!$XqpV*?e^!eZo^RU=KIf0GZcUVh8_jA4ay&F=i_Tq%tb*chDIENBFcg;=2)#5
zg^miP{P9g(Wol4uZ@ykbh4S@ow$mSCAnWh|cdCVk0*1Kg?=LK=SojKf4+rPxyJbWE
z-aoxO^}-l`_OM<(74_qOcbS@2FGw`Sp}8b(n~U#aV5s@^btNHuB1vbU(;krvB?NSK
z07#R{UwtQ4;?Vy_<;U<^hM->kX>0lZjUT)93D!6KC@F)J{KspS{hnv5H7kM?xuh>j
z`<_laIl^rhVo+alX@P$da8&#Dch6olgj_K%n_DX$E_Nn@6Y)R)?#C|3kFW2ke*ib!
z<=hh=Qq$G&{63oA_fQ{t=>IM+<~sgo(^k*Zofss?c@RY_KL^I4!iB}%<qW}fLb6_2
z70iNsF>)?s&ezg-kwt7YPbH8SC-f!B^{g$)BgXbS@CD<6MP{nOk6`2Ok|Mr@cu}Dh
z==hJ{vUCG-=KWDB4W9Ue9u_{msFnj%#NbW12mTFR2*%a0LQuHv(W?s^%WFto*5?35
zlEPi#j%TU;1o3aiyy&DN`%vE0WvLC%b5X%cn{V`&BFy8o-pF?d(eUw;l&sSApS+24
zbwp`$+9-L#zi(@wk$?V+u8pTMh*8`T=@7}c=VijzbHzRxN&g%%<h&q1{K*n?4W;e$
zNgw(1-kt1W$n|2+8=9EiSq1R}ctK^I?icrkNIF?R*RJ}8CZo?FgTWTfSpDtAa|mBg
zw}m5tr=0xp#}9-m>d(R_Oazc1jgENU>RkI7A|hJO%zNFwi<`w9frLyO$9nS^627Fs
zpwYKwFZ47YC1~@$)(26?pir6*^%fT}=1iZeC41eOlfc21$EQza$%iEE32bFzYRVBb
z)a~=_b6eI{OdmEVp=zfIui{XZz@q|hqXFYm=LfPx5Id=ZPliLTiYvrM2=AtO;ZIaD
zE(XEM!`cSi)jyaMvrNm#J}59It|A_3dJsa`j+PC7j5Rw}TG8e|{8@j-*g=p+pv!s~
zRI;7~gN;sA{K^=q`hnvdBrNlg%IwhlA~25E(I)A}3H~VFbd!+{Z1n8htofo0mf1Oi
zD#Q#x555drOV#d=pvc{&eM~U!Ug)Gd-WWtYfQCqPf~pmmGP2tXry&mMu1BsSJ9hXs
ze9JGgIggtC9)D*xx$#oNd|RUU^y&}i%Mt;73d|jiLM5Ys)7oT9%zcl3O!o|lwnO2y
zsr8czRVVb)Mhp^&WO(~(r^=*b{d<<2{YG1#g?}OUK}4>6t}I$!1NR<%?<X5|$B+1A
z9Rt@U!4nz(khV?RM^{OR-|jt&XCfAP$3r|tjxGg(N*TiMJ;af71gW=1&w*Eee&U%u
zH7)5LTN}TrEnk)Jj4r=;G*vM>uf}jyJN<Epv5`5Nf6y)Bx=YD!Di|tqKc?8(^Gbv(
zL!?trb7UiJY{4I#Fr6ZT45KiFeNgf*KGrSH=$o<R&7p!s+{gs6MrD8fts>0WBcUd=
z;N+fvozBJv7OJ4ajsp1EU6S^Ll#*LOo^}eDf~jse8{y6thWtml6XmS|3kwOu3*uDk
zAgc3kg#ntz{SjlL?8astxxUBw<J8n$Q$dCjkwbU|0}wt+1e<oSmzH3aY699$oKkyl
zk_jRl5wW?a1@PCUe=D3v&0#SX+O_nFRXXj)raP=6WSx~dw?-*Q<9?cHM0`@bLRbw9
z!tW4FIBgZdlN&%fO5_6UQxJH|7*hF^pX&Zlzf4A2nULaxri);v-2J1L=JeNV7f)3T
z><&qSt%l2$dWesg-WU}*UIs?{`xZHpN8f4u+$-j&j4$5PU6F+iMrXg)ddu1L-ezJt
zU|JKgT`QugL1p(0Q*a_qaCyZp;cFGc!8Y_Kw56fYa6nRZ+XHV5<$tc)=i=04^+F(u
zVIarKc9*IWsBgy?75(jR`eSkItK=G-op#ck83ZG+;WZSeBEa;yjFH*6ZYj;`nj<xf
z=W6o;L6^JQh3{blWwAEIXc_RmVk4(6KYasylQ<X@^BdrCs}1Q)4`tBm8+g)j0$WV8
z{5;4hg{(CPb`XGH{8WBpOrd(9J%U8@+~ckKS5;35C>YX;85|7e0#K4NikP&}j?zs&
zB|s}Vk<(SYTur#Au6;5SYxA$cL-n)sv1`HP7gEJGA-q!BSa~P6jFKt|)3rDGqop+V
z=Eql-KGfRHBl&r*wP>~4&@L^*qrYCCU)^#-SKNv;TJg<%l*MGDm2&Q8#UVDaZl$UB
zSex0kOY?{FpxM7)NPh5bfwVxM26D(^<i_nBl`Ag2mz9848kmf7>#^S?o>bIzJ5qin
zy;<JmP@^ja#dPDpg?nzuu;jiw7Yo4SrzxXuIdhusrkP>0+KJtkl?G1!A{m3vUJ@QT
z1r`kK#Qjkw*E6W7r|n+jBFDEw6rYq~?-$FL0LBBVMWp{e??1=%0<C2*=L-1Z3BD9*
z{khH&qX!qJ8~7V)rk5(EeE}LCLbLtN{YLh;Q*8GLufa`%eCcX*rhU>Ea2`|qT7>x{
zf$O#?P~e7uN8&h%O2_sCV&c1r28dunt0*opB|PZkglK+|*NnK{J9vvX@-Hcs^0C&A
zN5and>hFNek83XZ;8$=NEyd(=l}kaK{KRn8b%aZ}c1-nCpC1B7!<<Ts!7Gz-p9b8w
zEd}#k4dnV^Z5<J%_++EH2O}SU2rrRAXd;srm0ei0om|QJjELQi=i{hk=5o2&5_Tss
zk(zbN3A+A>XILlLX_|k!5?xC6fd7cOX1+Z>@SUV3*&n=4^-G4UJ4#)N%WSgFGBs0$
z8HPbAjW8-uqHkZ!tC)boN>T>)<Vcm6wW_aj^NNhw@k4#niN}mkc|O+3Nrh?NxrU`R
zzwKb^R*KK9VfxA^+1iJE#3HV;zB(k);XF~iO>t*M6Vv>BqSN2ERhS*_Q!A(EY6W}|
zA%RXtt+~C{`6)`hK@_L{eWe3=?e{6<E6g_aE9}g;gA#SWOVT@RI}2-0NYX#jK43gz
zY>0f|Ml)#_ezZwD(as5(0H&Tqsn2v1u_s@=&m8#umz`Mq?kdhrLoHJzL^^L!#>Uc*
zaB<*Sv)^dKx#+M=dsskPaNm0JDCCpp1mNkgtTB?h<b0sp?IU@CW#IQQ^%Jsx9S<_^
zC&+{Kl3)BV{E_A8#MV=*3h5Yr+dJATMi*l2%g1*+BojZ7R|S>(7XRgA3YqhhPp<76
z!cs8*pP{p*E>+I}jBd2vqr~`XRgG2oN$i8ROqs||5RP6l{Jbvg^KeT_*H(Uk9V64w
z31$LC09WI8Kc?5*{DGnQQ5kjrPQCxhk!`VL!dtt`9{sK%EPCNi5m<4HGV3DQb!Men
z5`L(}k4g<1%XJ=rj&k{XL>|=G90X~S)t*bgWRc;KjPiZiZXk%sINoDiWl>EpJb^ZS
z<Xq~GVJLMqju-Re6ICT+jZ5a7P{0q5&>e>ot1)GW@$NXTB}LgZwfONRziWBX{YfFX
zaGpSe=tei^=6JeU&cCE;7J5RK!H(lYPpgRbGdC$Vri%PxiB7vv`Fymm5Oo#h>4$^J
zN8s`$8<n)jm2jfx#PI&n;@FI}N?ATx``T0q_Wx4T2=6k#u|DbJ{~rK3K*qm4hsI8a
z>Yx=EjXT^ZV?Dt93Y_Q*La$YyBgiVles{!UA-vu~p`1N*o5L}>%wu&NsDQK4GVP4#
z3Nt<c!P1MQ4iS;N7!ZCdmGsxH!2|<2%)M$b1R@9Dg{(KoT8j$>rU_`eX#p3HW;DQ3
zukIF-M0IyNEJSsXysV~fmMEYUO;=ql({ebGpJ_t-qo~D@dZU+ITh)1BtiD96#QO&i
zkS*Sh^mPNsQm{|Tk|c;ReS*-NB2)FIk&H8Q<&OBiv$GT~uhhdnrjoZMOjAEfz=grW
zO#PJ4hO@nwr9MPL`k+d|V*y5P6#qdYc_KKIPZa`l9u%YW(NAK17b2C2kCd@r^~ycI
zmR1t7nf;Ru|KMfF8+lb0eC(t*<A(m^Ai2{$h;m$8`ao^-{_!Bf6_w21<upJADoHLP
z-6g?6s31yynW1na@9;bkrk?;zlv~T@@?|^tZv9KF>kBl*A!*iBB_~Lv-8WaK-;X_>
zN$E?eg{yI(D9;WvXR$1&MGLYzfx8r=p9<qu6#uH7OdZdVqOet_NNIG11L7~0XZ0^T
zwgB>2SIfb{KYUdRMj=lgxYDXrS}|F}I9*p1y-qdPMpN`6#qDm{Q7*vg`N37X5p+>o
z7-4Ik)*6>XJY&~hfS4Y3sD3BBU!J!}hY4njWALi_ylu!Zci)VXZxDR=muu*WijS50
zFLm7u{*F01!XdRgdp%W4-l_V!{niINS}FjX^pae!Y6tB)5}iV~0L^{RACHlWL1eR)
zk&=T8=GU=@?Mt8Ry`8!QhP8Nu((JE82RfJe>3N|SU6Ap`v<Z}`^m~YKP8N=?aLD!B
zp@dKkl&S3K-c}p!zEUT{V-d>gO~j)OTUb_##y&wHf(3?p3JI(0dRBAV%LW+0{Wk)U
zmu#(LcpoZ86nFI}8a}UBiS*Y~Tm;o@NOYnas7}l^*<7SB2rU$GKR#%jy`HWYOD0W)
zf*)r%(Hr|kwUMZ6Gqpp9YN~6tw|LLTZ#8r)VL~T~7ic4_$neIo`k^eL&jY9L75R&O
zGS?L{WztDUw8vdoND<Ld`IWUFSsmI1zMJV&%S13nW}<AU{&P+V(c%P&xh2ZrIJ{+3
z^%Qv9;?(4`WCSyPC^{@i(=G%Gx=k2#mpU9=pF|;Q?_n`q;nkP<ma`n2M4xdwc<CQJ
zoPN-Yuo{wYJt^cPp@IrWGzia}YfL^yukcJ?Hz&uSNGM37(zO_UNsJDfaSm7I+lIyA
zD_?HHP`?AJKytKl=Hz!#TN#w<$5B83N1!cP>7$xwp;)?)2oNqDfX9ch!PoGtxuPRy
za<w|YfSnzcSU+&KQ;z~eZD2e+mDs5RDzCQmre7_y5EMOK@x^gMnxNS8v+Ztybp#f7
ze(Tn}tAMo;obU!O2oLwk3+o~3Jq&3+rH{tgr0iT42>%k3W3k}gR;{x4M_?y_C<Dmv
z6#iw^#7Odax+?_zV_`S|8od~-fr|3stMU(0siTO@Ml*#+!$nL=rU2eV%puN%`Z61~
z@GW#{PiyPSk~A(N7>ODxVol9TMtrM^o#KF4cAd6+9`GLp?5c9&@q{u>2Bl8F$MsW!
zm)(=p);Y6paih%yV4Bj3F%+ZAEUKai293B}=L}rC106|<G^jQ)g>>@k0<B$1(4j`?
z1O@>yraK$pk3fXmpO_{vm`;v(%-n%Oar_}vV+Z+(c&n4p@$&T6xbvHUkE;_y<Bpvi
z4>(&M!kZD<iVmQ96UAh)B|I;R1YxkB$ya(tGtm<dNesIk8X<}0wbvv!P@V?hEN2Hw
z(s<rX24jJDnGb!x9nTz!g|R8`-{sNRVq9N^AZy!og0&h!le|QolRKH@={UkP1T>jo
zAVsM?xs`a16C|1YM0?N(l#bU_7#_fyJ|rT@u!0kg8M(|>a$rx}*gv}{bU@4v$ah4@
z{4(EI5}h?`4B_F7ml)5O#TgFz=rvoYldhA}M(Z8e>hbA_bD&C<r9h2hr_uBDXastS
z$+Sl@XwI+%a5$Y7T02b4<b0hNKCFin6RMSq<MAOD(SY%`2rLUw$#?|EI=mqGF#nx}
zgCK-Ocn!!XdQ<__p8=IKP@mQI+4x-u&V7h13lNHjOkipjpEN4su;_A<kC8J6#o*{e
zK!^j0L#1CoyzT-Daf`czrx~iSj8n-d2_FO#j9JgPQ3nl9*H+3?({$*v3PTY28g;1I
zZf?U@9Aqe#3^O43bjewt#oh8_jyoc7>UW4p@zgP<Yfe=txIFwoAOZlgIs_gHfM2B4
z#mppWV9Rg@B?q`6_}#D>?b}j^1bBADq%*3!DN^f;WQ~fr8Ld<Rk$`<|FcjClg~AZB
zNKWuWX{fFJ8sRTSrqUh9PhU<XO(l?8wtfzfh(na6)$t5Sh=@21k9y!#1d+`(532#7
zysDu!C>+=uzbqca3d+aqip7NltPzq2&9o9!IX5R|V<gP<B0_m=i|pu{KjhkpX)GBk
z(36B{k*wjJdg$-Y&j!gMpd8^GI=^I`SY%=w1UOTh2X|E@G5+RzE$&*XmrdRV`>-bc
zM~drMt=@DKx%>EW`YOHMM(u1j$~~ZopOZnz5HYy!wC$dnA`4{1{*QiRh$0eb8B8S!
z=@3Iz;)K?MJ(UhD)V$vS8IZ$MA)~UgO{i0SdK;Z%U>=gkyijN$L_u!~92wGx?eSFh
zOg*v-+?ikz4-F8CskFQztOk%!DMA?FY;ir+?RpUd!sfevZlF%cr2;5Wq$x#7-<Fwc
zHC7Zc*2qqe&<QEAboxLt?+Yb_qXH}=VdA7@H15EhNCe|73Zi{NV<du@2<2C^a$tZM
zG$5RaCdS21QD;l<(S+m}RT&+MOg50au%`>@jK*eI4?HMn6%2?g0_>`>yeBF|BFqB$
zmW<k|sV6DNc6lirafeK%1W`c%ShPEZmL0;2dppruQkW&|)wm%h8H|_9)ozquGAJ;~
z7KGT8OSV@u;g1Adig2=kw3D4I%V}m!EkLSXPA3p%Wx8G`)BN%;uNk-4c7Si%k&IAK
ziWhj5DYE-6_Nrx7K@-<hUwkD9v_0!+jaW$yFCqfM|5h$$4ayaBsKs39r-b8bC>Ven
zt);|db0lL}45Sl~!>T4zOBkFxUfDe%O2#EuDcJL~Mm3mKya)$WC=zq!4RU=vQ(Mmj
z0YOtCN>U}ITMOqnQYGq!H7YRbx&`u~4IIEuUl7bxs}F+clHrIKVysmf0^ArLyAGO@
zlIHMp8(^D>BD)}t$W2U0ixHBhl_;v{?YGd}wS_$_iT{X(S|-TxQIxlT5124EAuWWe
zl-0=Z#Z!Z??DcCk(+;GP6T`nYx|i7_4DRY})}^A`kTo`v6=gD3!(j#slR+u?Hl3$&
zWmH<Tq|jG@R{PTgnURD@+N`lSWIG0>I%t%w`$VN~=lY*FrnGG0nL%G6AX((oYn7&L
z7_cQPS$|zGF)%aKQQ1(m_jJz&%}>z}`VwYjr!vCkb($fDBozkr9}_jBg6+5_NQp|6
z8HXe}HUifI|3G-QO%{%^7RyrRf9Jvvb#^jZVqy6PcZ=~eB%pLTr;Yey0V5%EMP4UJ
z>K&<525sRnuDepRLT)!3R*#?LlQi9I(*NMs!0o)xSuRh_lF0$8wspH+Rl=H+$1%G%
zrt^(#B!Z!gu3#`GgU`(+^Y3FdnsKz1#d`BbqHs{Y%KfTwopiqodeo&RV<=(vua>j_
z4IKq^=!1D~#x;#iT{-E}h={xRG$M{o4F62r_1|Kgb{L9tm?U9hNa46=<0Dt9p;WMv
zO6wQowW*_>{EAftQTb#gl<*H!eWYJD8x}yMB5Nnb5tN;VDHBog_*ot<H``Oh$rU>F
zL>hdOcWt2=c-2w^GgT9Tr(V&~PHL-FZ#nIf_k=@KlKhgPE&X8>WYhL>#F%@LO|3+F
zVG4m|N#=ihi4}<&%S60UGEZtmd9jiGTylMWj4``|@!r4IM6rOd(3sT*-uRzd>I05^
zM@-o>h>Jq4&CxHRm1vUQDcDk~n1%%!(LF>_CSnt|N6#CG-?Xu#?XdVq$97qJO~moy
zTOK&7traio@ka9)ki_yIq9)nutcZ|nSWa<HA(W<nM;<YaCe6x$E5!9G->T4fy5bof
zWR{`Y{#KFbn=AUp3)GhQsb5LYrH(hnqHCiuF_D;(SiWrcf-eGQ#HoGv<m`5;jz8kf
zmBnzzIrgjmzqm8x<~B|feh95Dp-BH#zEns4S&!qtmp++4YMq&NdF^i(Gi=EA>O`NS
z%yuyn#>)~+NKmcfSBkf)KCc!t6he!w#tlur9|HR_-=|fBjQmg>bp(2W=W=%(`(%jf
zD3!J%br}Y`HeTFV<<YL1r_MHwlkT9b4%T|nrf%^xdjT(@FqAlUm`esj+xK&o-9rn(
zT7Qd<A5CdwC@NygT9+FNRom|N;_Rj9>|V}QLGz#;TM23?2vpUnfN@uy-~2g?J_=SP
z82lY5Vhl+p$`*1TNFOHDv*&}IuZ4_4IaXWl@*nqjd%@j~R_JG2l|lpR)jT<_<+z2{
zg4q7?ql0_Yh*UKqsICzgWmTfA-A=7=RHm-gT;CYiGtvbm1i!OAY^vkbt!mbjn4R9r
z0&f9S&8S{yX7pC>_p#BF84u2Oq8|NG@ov}%BDCGe4FR_5>-)J2a@)A{P#)!>W8R`^
zMDE|3_7w-3?zr?s?%Q^-?#iXN$>>DV9aSXbhDs<dTom)Y@Ogar3`Pf=X2pHUlDONy
zfKhEwpo5G4!;&jral8F*7Z_k;GW5z~2y<No0dB4I4$=k$WkLjqXV#{n+`#kL2T<g|
zx$lKXFWJaIo@a3(jDW5Q=1aG%ewW~`9GNZ*sUr1>hTEjFau?xO564TE-2Uu?mq_r7
zji4l0gDI|@pmxYoelmSTm5<Qag!aBAqP%>PmnT(Vx6`M~Ru}oD#7zalPL$d=R1hor
zJY)9*7i-2ZG09Q^F)kk`{Z9#tkXH{$*r1q~?!s3xGy9eYdSZIvi0YUpBOpjTh_t$e
zSjq-41B6B)^Y*KN2(TW-_up{QF>KVA#(tsk6cEP<3)EFPT1J2vQkF0Zy<mo3t)rDi
zQ#cVnZgA@~RX)^kTWJ5sc+7M6D{<Qq4X5pHLeL0?%p87%>jmT~@uQANFlB8B_hS-t
z1nauO$6bjZT$!Z`0z1$h4rhH{pMV4?8#;sQZ9w66(1imoG;@RrL<IJ9JKuLp(yZsA
zX_VSp&&&8uv*Uq(_FmqNIp5pIFuJtk8|FZNXf)1R55B!9zUqXMY0=)hUT$Jz8+}Wo
zI`e)&Di+f$>+^M9qv9o<JMlLo;wN*s#|2Hzxf}~4k-Zpd7gw)x2=Ty4&LsvSa{oNV
zpaE+h#p1M7NHB1TQBV)$1*Fv11{;<dyPPiUESxNs1=CtG%k8K<`2(N26-Rrl)0ONF
za?<UkIYHOZ{oK9GTWJN|Ai}RP8J|E0V^%N9JuE(S)aj7=dCbSUZ{*pLFJe-cDRFF$
zf?u*?T~J2(AHz<5$5KHr*%Wd<1)=~m=R95;1c&|?=h|ajH_<vDsWXbRtbGxylI5go
zV$LL0OXOGvK*>>dKxz>xfKU*?;68CtxfVm^NGE*tgiSb_jD_+skKosuXs-g4c_EWK
z5zCK}dPX-&Yp_UD@F#*q{|Eow_b}HkxcHoww0*U2qzd=px1Zl~YYRXQ5cE^ClaO~h
z@`vYPR~e+G%Xk&po%U+1vR8Cq;dK`cbgw5-?ciZ9sOWTML<eAq&MG$Ix$fc`W6oyc
zhXF39oF-{(Sy|uu^K@fqG!fUp#15Cc02M7(yv}D_tqlU<+vpK^HC`H{RZKQD@<a=T
z2zAFYFT^w*l$26oqlyT4kT^RilT=+%yoiuwFS0!pxD|nY94N!c+s(XEYXytKW;Gw>
z?Ufh=TmHWHvfv$|n8rT{-2mV%6&;W=uk~Qr#B3aaKp5Ek?gUUpL@l}S;P(O8gW|Y&
zz2-wL>KL1N%-qPy9`G<n=D(n*R$9zr6bOPBP^q&P1$~9k6x2#Cg<Oswgj`4*EQ;`n
zg+^6e#ulh97rz~t;%J{EK15X)9V&*{A4H5)^xwF)3gJ$rgFSl3tSH~5<ubCHqwE|M
zq9Di#uK(lPBjyLc1arX2KVtAvY;E)hbS{WSUeses9q(j{{lPDD5|k{QcXlMZW94Uo
z8)G>ipZ;axl+o}F2SzO%LS_pYI9v(CCG95X+*W?7>Cz?~jOjyQ2O*gV{@laN;^7k(
zX(^ij)!rET3{ABjJOa3;5wFTzBF=YvHfMTvi&|}XML_^37ZI}-2*rkqNWcphM!eg?
zYv>*ME0hz4TneEgV>bCE&?ViJjVDOBfVX#YW1OC+fB0+g0UTlmL!y&j1`wPco0!IU
z#2$Yd%ZA`eXw>ADkRGA<Um#TLcoA&c5i}uQ|LD2K3Rsdl?nNPkRt$imqMh(>5A_7%
zCrSOQ)RVcALt1Xs!rND!vc`Y=@AyO%SYOv*IEir*Z4JT^Dp;M-5PV^;QiHF@7ZHHu
zMIJ5BK}Q#u(-Q*l6cLCi#@)zbZ3sP)D1ARa5MN&exxwIRoF?XYP+KTBK*c~c1K%AP
zOwNXlhaUz;GaDldCJV__Q-_C-c{-}B#FOC7<kze|ZFIv2g0Zy1&&oXbiwlv07$aIA
z%5F*TB9+W<L~F@SiaEY&{P1IXzh=xOYF7>axKETyH)#`r3;AMGnKgRiw7SfQo`+R`
zOMn;6L*?S!!F6<*r`&eMt>ICExT2R?w0UnnkhE&3w4g11<F)D{giKwKgp-CX@v}@Z
z;_F7W=P76%|5=DHL3&0JFW=-k4b~D^9woU7IvS)Mp=<@qDi~VOM2eC%jKBl@b>JkF
z;3yO@3KBrDlm}(_t5lDE2n649(sDwvsFVsXSV?QBI$g@oYf5TN_QnD@vF<9`%8ST=
ze8H^|n>!%ajrwrm$VH(nrnpq;J*5E;>+q0PfYq=yn1wZ04Iq7t^naBWphR+=;yt{O
z#5Do6J?SIGbQ?pf$X+R{-I<Ypd-)aOSUL~b@o5$t+!lRCNyEHZb98NCq4drzbManT
z%j%0~%KLY|XwD$4|1Z)lfQJGLH37Rpb4e^cqhX8WO4XSUvII}Dlo*q;;8;3O#1+yB
z0GOClOx*#0Is+xKL2aka<hbp)(tL<VNuOK+e-k37_#Kx|(e2y%M+^ql5=J@^jsV*l
zAn;@lhTl2q*rtKNJ#h>15IUYr3AY`1rZ+PBhhv65HNJehCm;;#;p_ED^K{gIZaCYa
z^-Cqi?JrD&hz5T3(lfDO?tAJW{KwD3n~1`6wCO#^xO~b8f`@K@GCf9$2ii;(t82&@
zVu2Yaxy4yilj9&x4%naQyf<3G(O|&Kh~)nlK8u{)Wx67#+=oCU5IY9zpZN=zRI&yQ
zmPy~v15`GIo`1aRW9ks;fa?X$l~W9^HC38g^a&uH%71Bpn#qF5*45PTG>_^~53%(w
z2Q7R@&Ta@NlK%YUi5+2cM#4x_5ET0m%Hx2-@XU!gv~-J1w4wiB9N2Jy(miknu%MS#
za-3euuiLC(k3>RF|MCq$zyOo=@LxpCYzvb~q?ZqeRR{#}je05kfEEGZ;y*A_l`5PA
zv8W<2d768t_)x>+wOTX5fE4&Mq(!6%HrB;0%F&2S0s*J(bGb{2jo?B0cQ#-X>l9uk
zfQGWNPgD~fs-sV_W|TU)l76^^OCJ{u_6%?9r|LAYdEY;2oO^w43w;N9!+GEtU`yTr
z8R8}r#@>wT!Q&f(H7vy4n-s7J98YP}yL)(Yg6z)w`NZMEn|fknSR|5zw)D^uz+(?g
z@r|8|j~v^!v9-XK8of|2!0VuUmY@{?0KxD)!LX?mBd*+O0AgV%m)vPV)<w0_%t0|h
zKyY~3+Q|N_*Hm`Bc1UEHS^FJP%+M*?rR@L>lVG_hD}fXa#3~?>oFn6Q=Ag){!K1*7
zUpjyYTiR*vBS^(`KogOm=ui>5fUVe@(*?%o3HO9uf-JqJP<9*_7l|rcjMk}ppI-tq
zs*!E{nADDQ^)+xFXBY_b(_o4K_^Bkb0)%i>gW~tQ$R$3&4Uu2E8SbxW{w7pK8_npK
z=?bJgSdSz+fOWN`Ke^P|@dY2;`h{%3xjvifq7BssK->}T_=XjcvB;(Dw|;foYB5c{
zuO4h0s_MjQMEl&jn<U+?PrXFbDl=${PERH!7)aVRsVc13RC??goE6H8BU}f&u#%He
zSx%P#>(1+oJOt&D_AybU93U)*ZIU=OB`n3Cf-d<rdwaa`E&%lWJ$Zwvil?s-1iIIx
zjVp)SY{{xRK)FrR2jOU4T)2iS3jqBM4T09H+BzYz0IBdKY&6w?X*ZHm5Py>3oClr+
zdnSs(ds=$Hv%0ikQx>z)7REsVuq>n*O_5OVnq?ZOMFt;l5um}NaL6^hMIsv2^b^S1
z$YVW1M`(Ctl(gp)W@kKP+D7><R7g-YgX9c=wHeD?6h@nDUPo{_$5KeG-%2WEGLLY5
zcssL2a#8!KMr)a^2T77*`i4)3xCTR6v3~7u=EoIi`wL#-Q`2-PF!!vi;D}bS5-o+4
zYmBkn8bj72qtPl1<ilj^-6Abuk|vzoMQu-sK>#9w?j}me`@7JUkCH%aM`axet9b8h
zK=~0V7;4~yR>OnIfE7$Sw~t}pzz;y8;JAUPbF+D;z77Rw<QE7?iTBRM#te}um(QUU
zNSq5zkI5Axa4!s#5CNcEo8Pm44v}EdR;U%93{aKQpSAKFmIZ;aX3rQ?sC{rr#Wx*i
zyfW6LiniT@*2J9@4xr+Tj)bD-r&@xg!CJ)x|F>q+*rKU$g%0kG49R60j~8?pi;ot@
zUbP1_l0yL1M{fAVYvA>a<h}T#u{{_%aC0Uu+I80gA16yHdT_3+>pUUMnUUIw!1R%J
zs0BTNe>56htpvbi+@al;goAn=`yV?O<d@>MEvXcShea}?>`<Vv(UV~`03Ae&xz~(N
zMMmu95d!v7%6UF0Dy@UTVJ0T0J7f3o^LC7}W7scV85wdwymdh{&PwM^yU@OjW`KA5
zA|~FDEUOtv5%!D7W9U3slJw$BW@&@CPpYRQa=esB@3_pB=7yEUN2$sd0V1hVRF%H<
za>d+H9c8^=NKX<ij2GY<y61bGfHyBke0SF6ib&KXEXO&<Ty<;V+IDs&$XtSWxG<;*
zTh2T(p%0#&lYHdLC}ibp#*q;sjOUR=Db%i<l9=Ek6pvCRA`d|!yeCv^yjGDO0%E)&
zh>=t%B9}c>f7g6)-$70(NV`=9bsj*4=SRYc`rUUww#v`nr351lsp$Rj(EO8fsVE*P
zXkDkx3!{&z)<8oZk=BJ5qxV=KfbnxZxt>bP#QNAoYq-|L1QF>pEJEZ$D0TZr9e8eS
zPH=?u+anyWhAw`7zx`M;y9&pL@*^GHYI}=M$q8u+*1ue?v~uv81nl(%B0A?M2y32n
z9Q@93-E*i>BWaL~sc+yhZ$fa0x#%zKVu;F+g^qt9?t5hm9gT=fEut50VJg6*nnKo^
zW&V*dU?gqu=DZAyv{(#Cl%S!`;(5X}(Q;RY`m<>-L`%S&iNvLmgCZj!FOzkkxk?ie
zkw|HzP0WCX<!_j1eRi9n2&M8hu5ZZ67S(dTnbpq0aWf4E-tS&<4ZNFas6G>V$_j**
z0-P*yLS()o6?Y)gq3>W7{zH2JSxTM0<!RXqg|Hf6O$S;#*PsK$M|8&I5`?(%N$q7P
z9@xH&Lv=v9YGY0Lp>{OJP(kSLTk=37&5{xkK8Hs49>;8=MTq9vXZbde&<_j#3I$(j
z27hh>t@tyOnWYWCi>$K}{E_=RU(`re0-$xq1!(BH)=|NRn-}O#$I$lM#O8@BNhM%M
zRcwO%Z1TLZnS79I^_59U$XEj8f$gfehm=Af68BCf!`W=hERD=n5f>@)1-U?4-z1Sp
zHbIbhMbN0#7;v^#>6Jg)(T96&h(NK|<Dp?`MbcR(s<qVFc3}}C&m7T`RxDLn=t)8?
zB0)=)^tV&5yRI5q7tje3v%s^b7yv`tP#|hggIW?`2CS@4raPA$B`r*tP7<h~>@p)Q
zEeURW__P<SWNfRpU7eX?+b~G?$<Oi3vAR8s{Ag<R-R!1wSwj(qH7XN;NYCyG6lp5@
zXGGYSxNSx*C8;b&5;)Of?TQPrD9+M&4D+)x)2fa(2}l?L`}L(sD+zR>0x<^00E$oF
z)mp<_*;tfa3xxsV<$73^FEH-73z{GY?;$YnC<9BG4@=t*H537?M-Z`$#x0Rgc-TO%
zLL-IGc@&WQU7Fa8a|A9_W|xh4HUdU;3tD)n>Wm<HfTH5`=w=8(MGo?LA;zs9kbJLX
z7@L<Z;Sl`v%aC?y4m+E<dnceg{bjijkaMD+y9`rk-d>qMzuXYOW&<*xh4#B3wCUL-
zMchi);DSfM>`6@URD9UX3k5lu(wU;1+=6&B%sGfM8GIT%WZ;AZx#&+0E)aOs_>e0Y
zTFin8FFhJMjPR^y;TYBuAK+3=-0keVaDT-S-oYMyZRwXZ4J%p>0P!!z?($P?8*xkI
zOFn{22OCj7AT2t&#q;03cO!-}!eK*}xdvwCHwj1<Z2F6w7XKhnC5r_}nxsW=<T8Ol
z5PTew`u;I}eMv-zB^g5(1h+pxTTiuZZxQ&4OMp@cmP=Yrh)~3@4g<su4c2B4tW8FS
zL<NUXDWnR}XO7)g<WIbP2bJ)z(mrDOa28CJWx+-+SH0Paqwg?YC~*hm63r+e30?(T
z;7E{9X@gxDyVt*dUR(>)ic7PZC%JhwmdikESRaM33cU<70q_Q4p9)wfp5O|F_xxyM
z3}m?^9Y7wQtJ!hVM?t<gWP)AOieqCHT2>(W&Q-7rHjZXi(Tr$ae)gyKX!(si44dKX
zUE^2>WJwdh1syVjG-&;KUeFIC8yA}J05DSsu+4%qOs0Hy-}4W(xJ1OLK+D}Nf({M_
zM=xUz?_{jl+HG4%Bi<0Tcz}%u1a}KT{H-CWLC}^kV_0A(<4CjscqF`nL`J#U488tg
zr2wl?2O8kNqLSt4nqx+0C95XAVi2|iCZ1Q!<yBS<PZy@pt<tCe2p|cN3v0^Cz(b*S
z<g}=*pl(ji`YO@E;R(oLD?bepL4E$LPsiASB;)@CP$$Rg=hK5dt|T~jL@|JNCrOO~
znVT{8WFMKyAY3DN_L|lDlP#MOlF9U(KV)G^!edEW_{WM<e*lM}s~7Q{FFU_yn99~K
zCxa$@X<?zGGNPjc8=Er>isPxrgfaRLeHJ`pkxxRDb7v#YVbU8s<Fw@<1nw0;fw{u_
ztij2waK|VObJ>UB4^OQAe2{|r<`kGWF6+FPVh%$fz1}cj*b967i8Nz&9J=lZIlsiq
z&JvA@Y2+Vind`-gzGWK-o1T6vte99EKeRtu@}CG}x_ekbvf`iFv<s1zFG$JW23%Fd
zb~+K`=j0f~kMQOWWFj$;6Bi}wXJ<Bf9Yyi&6$4}{phuYE)WDU&qbfoJK-UZx=oEhf
zfQKW)f{yNKd=BI3Dh#_l7%|MU6z<O~>N28&WdXX!Sh^>F2GHU7Wfz=d0KIsH(Atj7
z5eV6$pUC!Bj-EWw7V=q>u$V5)V~^_uhzxYa#~}g;d<KzRqC<jK!OG?naY}nlXBV^o
zECDwit6=3^`;gKPv-kknKy_DXoAN7;8Ata!TN^kk#H~*%*nL*gFun6jN~B?^wMG)t
znP#nZ%<rS9at8<zgxru8g6Vpcl(b<r<_lsn{d~N3a^~mYaw*ZyFy%O&elNSzWk~G*
zF}8F~J4pOD1w9Jo{JLmm071|B55dcZx9w8fdnh90c5v4&9}{_tiln{D$K%n>q6f7A
zJS0jT7`Y2aFhfMjn50IFdzGM(S_9nzGiQN3dBUV=b3GW~u5%|?FQDS#^ZGAJ$Ab8_
zr+~FueDkL$1TRc`c=5+i$3{B}pDdC&E*glNwQr>EIUVURUHHL3U~Sh!(Q9ruf!Mvu
zi3B8JTgK;Rfzu|4-^yY{|GlO^RgEs2X>)x*%Z%`NM1-p}6_wB<iLDl`4y#$v-Z+%w
zTyxM{d6x9lHKHz5R}oiIZ9lw;%-5_SD-?<vce~OB_K->81d$JP5#e#LXdJ|8i72VY
z%7_`Ds~WN5UcT@fx6&ST;|I27H3&(bR6TTuqyy@pshAZkFV^bDo`x~M1PYL%32Qq%
zr54~szZE!+i3gmt7ZP<1IaO{XA6DI278*&~Q?+mz@k@WD50ws^rMehh1nJ)0IXg{q
z>4;;(G;K)Db&jN)9n+O58LkUaY8Y=e;Q}P1b3k}&G~Dg>EnG*X7{M9VIgK3Z;!&H4
z)}9dL3AB{)p?ad1UyMv^uAfyCq38(gDNBvaT%ad$J+{xBZY`kxC*g%9l5L(o3(M3#
zfTd)$VX(*Uaez>c7nNWek&C_Jv{(+gi@i6jt-+c1)3VHC8C7(Z6!=9M^>|nHc%<V>
zhqKDKjT~NK{I;GTI&%qBu~epLu$JbiDr3(Qh{hsnW85?74q=<<-b5mG>-kx?zeaWC
z3p+J_GX)-bG<PoMx!d^=Q185I1I^Ew*Q8S#5(Gl0K6T<>Y?cw-z7wmDNMNrwI5q>?
zHaGx;26QEO4Yp|~={8ab0%d}j0qHg39l<6$+d1f4UMP%Jyi!h&EyyBbrTH;Vay{;3
z^A5-7cxP1|KqbxbK(l^zQ?+<5N@jwDXXmi98eT84ZFSLBHbi4JsgpS^9M9D7rNB`-
zLzyGiRxv)9$&Fqnt6?Mkw&qGX7ewnkZVsSKz~8#qc30=x(g(5o-k)^t9sbsCk?K^r
zgzhIpPn?o@W>I{EgPZXtwI*?!Zllbn7Sw6e+{u$FoN%TQ(jtC?N}NEd?_f9N`vZCe
z#<4%My0T=cmzU;BIV}{~-@aqp$`C6wZQ2SSon2*OXTz1O`zX<8ffy>)iI*Xn1%*)W
z2jF-+O7A0TBKKeCUiJ4*xlFa_F+w~(P<She4W-_hhX>no*cuOztOpOR=gIkr%;k4#
zQf;~@Ab)T9EJ0Fal?Ep7E_zo%L@%^dhxH0XNYQ#L-Qp&&aa;ppr)C9fA}U~|M0QNc
z0{2WLEfO)89=gh3z<4=0nR+b?WUpphUbIRrAZNtKj=C|?>~*3lAL}{$*TzfmALjpL
zA*zzWweu5d#Hp|K=;w@UY@g4}wKrrSq1Wcb)>YIwNJ}{#j5<f?<VsG6l)YuEp?Kmo
z>F|X82@(lJuRBCu96ZBvoPMG;FxI!BDESiZCrh@W<jbn8w``d%NxCLj$?lz%JJZ6$
zn6BtSZBH97Ox##m5TjkMfv(DsL%>FBMK@4L(3FRVkdZ1a*VOM0Zl|LCGND^0EG7fE
zzC`PzOOGA@Z)5|mpxG5B=aE^-%(&G?l4orX1{97em=#J1Nt7M9Y$z0w;Dr(b)7Ov^
zC>Y|?R1#>X_KukmQ8GE@GwMpUUMJ?ibY6%-0k!@nk;9#Z?rwDQSInCwdj}djt_`kb
zjn09)NeYHXi4KS-owE7_7<DaF;FbFb6bKR<3gN28-J{pRC8WYs(ip)hBIL{z2MOY$
z2C}hTX?lKkU7rKPvf}(lzcbz0b3dAUT2}UTb{AKwNP3xJM)u&eL?lFPx@qVP%KAg9
zg5ihXtsHEL5oA#t0|h&=*<dU4xPZY!hNS3F-W*X^E8qrD<beTLG<i}Hz};k006XpA
z9|M6YYLyepjWcs3Rt*+z0`M5)wy*m5HmrKUhfo9chc}1eciZO{EzB4`BoGUvLWi;n
zTW4pP+9(D=`j`<VNAb;4UF=fA7x3n8Z-0po@8V2TDp>3dhX9q0ot%Q-J!*I<I1dT{
zvjFXc0-C`mVWX59i6`jvrFaZ|nmuw~sLuiz6_DBmIuOI9+(RnM$vLaH>9>jYp2UAW
zJp*SHmnDnfa14;}#H3MaxFSwyKX<VqXN<bFBH<1ZXBHq8pUG|nDjAYyd<UG9{Qkiy
zzB3dLVErzKF#t7Om{v%4S2N)LzRIY99iTs2r9}D^%>aB`2biq?pW@WkXsC;<fyR1{
zlOfd7|KM`1UJ`9^>(7_>G0d59Z%)2krVG3mL6E%I1@q=x!gk-*c1&vmCE$s-17{pH
zUBDRuZxJ~XAOONe5GOnk>PgXbj!Uo2i}L;54SP2FI1sNVqFygE?*mJIS}hpraM(c?
z7IZ;yo^qErn|FB4IPjJR0gJlod{B{pH&%4I!>>tqF`Fi#?TgJ4S>AnBm^&QEd*qn9
z<sh_3<NxLcgeCr#pGvP7x^;PO(5I-gE5=#wOg}!9izx%pB0kbTTv+qQs_M*aIgJN$
zkIbQa*`T3y%}iy?q=#fEBBe;cIKA1d*yhB&R238eF;!emplzhb-tg$i0a~U3Pg=%a
zKN@A4v(J?x@=q|O01M)QPXglY$mxKBV^nA{Ej^2+Gs!sDQ^iGk$b@D_Zt7^aENos4
zqhnX~!ki^n_>6kF3_^9rS}Z74>ZSpN<AvXW*f6Rmf&d`$gZIrx+9Hw+{(H{sa9VUo
zGS(hjAOQ^soL(Q9bAOHcmM#DH=aD*PV`eyFV%s&zA{5mf@3~N!pW;<BCMZVMH#KP|
z63?yWSfCEBeVQOkc~%{v7N(#p)BNIt>O_2r!!|31@8Gww(eKf!seQ_0LC<zQ_+ZP)
z@=Pk{P4We`7)lCI9-KAy2IN^6I{83NikNP5u(M-h4nFY-g~+nOS{<)%^$ANzI}*ja
zGz=^9rJ<}yyp0*V`ACRYRL0YJyZDV?p&K0{bX(j6BMA}qxE#ig5h*cQjB!YcjtGV)
z*~<!Oinry&y)_DA>I;Yc{~~t(s6uiFS1l2r^ugFwKg{M#y(sCh`O1xas*MiYtQL7B
zVs+X3tB!OV_EO{DbKe*gR=|b<nF@hGvL2X{c%)pr;CeLH_B8oPnnfY*Bfprz@f!<D
zeEn?$GqRtx8pUf<w_!#Iw@0sj99S2r_E1$Gv+oHVv>jsNQX&U{??3byos;X0=O~7m
z$Z(2zuFb~P4{6v=9h=4sG0?!>cgFZ@{|wkwz7PzThfYZiG_kxcr{p)hd9R5CXvX-c
zRwN!HXrew79<@Rm`*Dqdk7bDae}aO>#I82U?^dmQ?^UsXXs56YWEc<@KTQV}m>Gz)
zWfiAU(G|&FhHFuqKZpjYQ_!4<FOS<*`Y;jb!k$?B!1`kNl9EdjxrFRuhln2wdsQmk
zFcqrX)a08%zOa%GytBcwd(BG=s}tp4j7N6BSf-U5jH*LF9b<;k_`sP;#>1c-0Wufj
z<xzg~t#gAKgw9g$6~J6Xk69NCMdi47NVN><nUa$%lg5UECuzzz85nQCvGfi&bc|tQ
zEJ#0L05O?zi^{=3*eOA+2St0Q@~8a3L*QT_7HeFCrI0^wC2t%gCxgV49HQo9!byiL
zTGl~|V!Hr58gd|NbVL#Qm_el5Pd`acsGR;FfF_joZ3NT7Z-HYQ#O`uiT!n7R06Nb<
z%ca2k9hx?vTywJKQ&C@FJtPQKmJOkI1MISuQ%26CcJo>2Be_Sim|v1lKynE4tPe`1
zNCNV}(Foo(A(wKIo>^_R+X)lzMP7;y+^!O=8K~&P<!w>a5^hi@I^~$DoKpk>OwC1_
zi6RN2_@s3?CetO199W+v*$*ngL{oK;-$kf_@Fr~5R01`r?VkoEm}g35dNM~Np<p=B
zNp+#HBhuIkY&jXQJRj8pVL8?nVYkeQoGP1T17*gogi=|c5|qpEa6aOy(L3`{hl;U!
zA@-RXK_*MLc01QF`@n$wirJ8FPiA*@Coa3rj7&%jup&bTAjx@@pbUXa=$lO?gTTMG
zG)}GYWHHFuaVp|`K-2PmzO=3yJMBnBS0R!Taf0auB2aZ2kG6vfH`P?us;m6#fVJF2
zp|xLEMS`y2gbxhB`qK@8m$z{DbjR^v4%f#Oai(8H!;}JD&hx6fXaoTq)IeBmS{MtB
zmPZAIIp|drJDY1DbO6POoeuF7+<w_PLsM(4V&%^F9)D2?go(8oc&QiHSctnzn=h+2
z@ZLMA8V&~`VS4scnmI(T={K>~O^gl)di+0adwd#_LW&ADjC^2glvCz$qDd5&N3-ZE
z?5mSKQ2RPmH8{&>%T{|SP)k9>XW1PJDUb$+fgW%#;QYc1v&Pb(*kM(nGTg2u^eHA*
z@C()BcrnWM(vG*=8%$z9LVW3smB`{3kjq=?T=b>HnRR@{liKJQ&u|4CA)z+JF)(Br
zcVh>h`CYt*J98-}T}>%-ep>Yy(!?a|)orWhCc%J$MSQ@i%3Viv^3s>kx=>hI=9yc8
zNf{`1z>?v}z{>-EH1|q=dB^6?(>mt<3h!8=b@US-fi+bkjBrr}=tF8EijaLrmadY*
zb0v{joG<k1l~D;;xYA)FR*(F3M7^|jc9CSPp?eL_dcGG(RqC1Eib5`-UuOcmMhXQh
z{Om+SSw{#XhbOLyfCxUmxlWT~|4*8p@k|FLEO+FoW4ziINKD~E7%_74GgEfp;8;}J
zSN*0j;;Yo2pd3~5Y|IlS&EtsFtC^H)uMRh_QN7;}P!ep2C`lgDM8gAvk;T-v8OaKb
zy%CfSOm!Wk7Q(sl<=~$ylE2X#d<mDWGBw3MS%r^yrvJ`+wYcsU+g=6&&V01muT6v0
zBaD(c6j`3flj9)l%wT|)bB;scIfAjhaCc#oImM6M6cn!vE@-P0vX&yd0Ty0nWOP5p
zURv4RZickR#MaPB(Kn(~*yq`<%&98<uMFgzCL;>(T2S#klPjL@!KAW37R$2dCS|!P
z)Lo=hqL70XBpOblpnS=ahsfh=xX1^UStK25hq3*t?$uY<AJlxcx%{JwgV$N{krL`m
ztGgY5jKutTpR7E@fqq>~>JydO5{os!O{XUH^TY$3y%(&$d*@VKJvFzb7<Ezj3SR^?
z0|%}G!nhdsnO+w#V~EDi#jJ<)E89rm`Inmqo`^jY6G=<yV3hF)yi7Vz`i$O%X&xU|
zSg5FEW7!cg9fx(a`yi2#z73j-vNgJ(55<!7TenTIH2lpIw%u#8yG?CyvR$^o$gZE~
z3)C^>?wMvdnuY@g)=nUdv!%FE0uLU5)~}Qv$z^u*JP99zn1QnAATxr<v|kgLsV5&8
zxv%_3MP-|zV`cilguy8i<H!$lHMvAEc@1eo4>Q3&U}JScX*8-CwmVV)F&MS$wJ>g4
z+-%P#xvXM1UPvxlXG}JXKMhTc%a=9T#k@ew<Pb;ryFPrg=a#zB61_2^O91d}N(Rer
z_{Knn5TqGYi-2J}p0menUs2QG6{^)Jwg;aj|Eu_mGgx`w!dg_9M&|H|J(f(8Swt3r
zG6a-;1#5zjiC24h0U*6~<9lKXHZsUyFsbT}b9q0;#sq^UM&@c7jYpwwtiYXKw9{FK
z_4I}QMHB^;4Jf<leR?JQWL=o4c0M9Kr|C>XO^eKAkk5??QbWGnw*(M>=N{?IYGtXw
zL9iJ+20>+Mkv&Frh?F1%dHa#z<^MK070=iDqA8Mbr|Za%>{_bWFrX-8QW4YvlS%U_
zC3}3^@_qjGm`CSD^At}%YZYS0)wDY3f&<McK*e9LP@);@TMzEFt{DZA9vQwYtL%y<
zFo|NbK@-<QQN@8IsF>+Lvj9tvX~k@hy;4JMnvAsX)C%<RS0@2=f$3NyB9Sky0`v)o
zVlo;oib)f$qc_p3IsiqlHZuk}ikI;L=)8P(1;&9l37}|-JQ*zWX?4+2R0tU4f*b~c
zno=PG4Pa%1u^WaR@!CwUEI>iD5IOo24ulGgBe$4PpXYRR4_MkY4;ORWz4rx-3Xk?;
zJZ6R%op4WE(Tq>WZ&SdfNUbg0PMBsv{p%Er6xK;H>7Iiq)e11GtkmBiuVRrhNJMVg
z1rGMZOS!EAh`{Jk!B@6V-tCd$dK94x#|V!2(2wX3nz7GI2qg|~ui5B8#8&Hh09Tc1
zkHpVJQF02HYB3RQxj{U1Y7{~+SENqjA{c^bj{uASYM?QscSzbsuMi%<lgZ>9Q46ae
z=beBY9VacU@IYodVWFa6wwpLFiv{ich9?!O56o2<4BDqQI4e-IgOV|CV7&*6s+cIZ
zzRE@hz@GwLe(tzxJ=g}m?gIjgSZ>2<=^s-!7aQx8>>fQ+9^#eCq_T+dqeWQRRsC+_
zvx)|YPcgFHR)j~bqZ9=|OFYgmXIR$&L@0x@;03qt8?$3DZd0&hya4VqtE}sjt*RtI
zRaxd4u{we+%&SBt)oMpuf&P^q1Q2)z3IKX}RxZJu^GN(yim&)}*z5$3?*bePEz)WK
zVf3K0U;<o3qj+kXATF696c4ij6*_bkGuRs27LaaZn+Oyg98a|9V-S$*U(1MoKQutA
z`rxhyf{k4xpm1<2_{D>-PF5{M^=2I)2Wi~;4dtFGF+D^6diCfr5*3`Cv_!`KU^C7r
zLUR$uy81w!BK-A91*)xfgHpNOdC<l~YMwVnSX4u(`Yz??Vc8oM(o$p69d|Paz-4$K
z@{Q*1-4em#ypY2Fq&$RnDvR2z4eJb=<=k-rn3E_IR7e8rO`r8?A2hl|(+NyTs)KU2
z7YXf3q|uWZsmiexH-JCydg|F$A6PT`&O@pv3owX;1&>FV>w${R>z4u_7j;2?Iye?c
zOo1k9JB<a@&eX+0GNsbR)*)AKI9c=E_V3E5Zzz};>})dn{P*a)WH*f{D8V+ihZ8^p
zR5u$@@A2*yTK2Ha?va`^#M@kHlTJUx=NmL@v^_t}Nj%L$K!r}5$u5}8JGn1IPg}Bu
zMpKlCF9_tQ3x2>S|3bKoe|=LDBzgcHni&aw;Y51ZQb`WF6aTj8{V?(oD)9FE!iNzv
z4%lgxaz6J3X2w2KB=}b+!K@M{=msm0<d!9KeJ7g{T|V|zO6QU-FfR&$>U;-v`<Ind
zq6KFF4{F5|v@bbE&g%+pfL&<wN{qUy2?(lt0KnR@QW}$GOrmPgqJ*Z7eytP9H#?F%
zl65Ltbm}m>trG5)X}}hmt$lBR2^&~_({|*wamC`GRl^k5?1*Qbd44npwZ&dLCZIP!
z;@rI}MM3~gdRg!dzlGaco41ke%a4+Rcc!QuA!cPT7t*s}YxE_M8d2=|<5fiPh>+D&
zkS*+aZK6b=tZ3mp7*+VWh-VXlgn31<b^}17)s+2&3H;b+9+u7Wpc5v-)&b@17LR5E
z3XxNg69@Bci*&&<yhH1>AsTQzc#=Y!wqRl#`8KMRK>Q+0^!NOaE%KJaFR+Sv5@V|W
z)ta)EjI=tq0CN3W3TstT=JGqy&fM`Qpt4vBcexUdTpUR_D`ybozQjUCT){Y4+mT1I
znI=5g6G0>|u=2jx0Rp?wc1`tEssy_GU`svs515G2u~H6Ol>s;(;6TeCT(J9og}<|?
z#8|wN1@WHaN?INwe`&$fj-Pu+?jBw6dWL$+3Fqk+*1_c-I3Vt{@^SQm9R9e44jc;r
z1SS^COb!jpS9LV}0Xx2{5tBNRIle7}icfwQP3BlRaS^mjwAt&$#0me7h$m=QYzo$Z
z!Jp8eLVHMOJ=_m5GLNd9gArYFT|&pF4%p3sCf&z$)BACEZBljN4k_Os{iI3gw+>Gr
z92Umt@*xh^D{v3AJ%^ej0}U2IW{CibnL!4E041U`DF*Z<q&uK1Cgw2TwiCvF_S}@X
zp-&ei#;;P7Hs>#8C-<#I7ZG6Hs3JnVioO+5@T!d^BQt($VQdOrb1R$#bgXBTQcek_
z!||Z;UMvg54hr=BrY?%N|Fl#^qB<%7^!M}uu_0mAQF{Jyc!|6;oWxVh<g9Z<i{;pa
z9p_g1H;O6>LTxQrwo@mc1Y8WqOl=}qz!M#Yt~(y0%n5+i$wS60J;4g9oUJR6oD3(}
zM5LR>SqFYZ_+&0eWsPN!K!Fhv5uEk5E_3HB=1N9j$2QtsFC6-}BpL3qt3EJGdME~D
zF%Zo1|F3PIpK0lg0IH~}4}2Un2#4oSe|EEryQx(IfteEn#P@|`l#iAh{k!duySYtR
zNrr_b{)GRcCc`2DjNZAiYG(T=he`zpZ(QGNcEQB7f%Xg3u}@t6%pQ})uLkW_K-aa_
z!zAQW(OL75s2NYtBW0e;IOg}rB7LS20>lfIg7nIs=rL#^RoepLBiCbY3Z9%y7b=Fa
z9tAdwgY_6Qi-oD%1$q393Ed6*V%O(#;tM%odo5ubik`K^!Q(Xj(UMa&?*ZtKgCun}
z4JCIXNg@G!3x|pjOa?H2eZpCUi3E}(AIy?}H71d6h}eW!h@w`)j14JzMmJ7fk^(O=
z`_6)WMN>vrX#_*GAPJ4K$a;Em8yRhECAeREVNsfpj>reFB`~oou!~Sy8HRLXQzNF)
z7J|fw7ztLkxuL#@P}B{2FOe{kd|*_CP$G0m-_(a7>G+^3I^tbixM23dDKG>jlt-_#
zjWXEc7NAWiv*lfiE&*|V3FHj|45ICqYlN^)syg;49l_rEBfzOX2^65w+^Bk>khkC}
z+7sIj2h%Nk@ajt17kJb|2Ew0?uLmdZcC9ncLA@9bEkyg|vXZB)!F2SRRj~v*A@&H=
zYbHn?9G-y4@l_o(BRkD6P?RDpgi+>QsZO?3H+*5eWSlzIvFw#cJDMcvM5924dw5N{
zK`7n=nr*NWtfJ*;sIwogs&>O#Jwyp)CrCg4UU4Ovx!GDk@E=nIsFO<L|D70sQM0(u
zT=?FVN)Y2`E94@0m9hI&y)sx$?$}Fz7M%yN`KiX#d=u$dHEp_-&sWJc|I0Rm6G7Y;
zP9mTCgc6z?dV^JLA>~sc<pkeNBedg51(6dznb?$wk4O`O&cw{awrJ?`luT|@`dnux
za>ts+m`*V}x6o}3-!qI~L>Q@EN2eN$=hk?+5)Z<33w<|za>BTE2V*=%HpmVAzf~Pv
zrlee+gt}EhK>89wP^X35>Mw=}pAsJ4!)7`axQD}I$=D-Gr|!^V*8U{QATzqF%uNc%
zCbq;xUoR120}>-Y|K`B%N!h+8cw$6iI(UtF`m(%WKo*@W*p{(@yELr+5Cx^kj-T~P
zKxuF$NOVZG7Qg0^OGg0)Az?{!RzCn2Umr_%pMIJn92jZ%A!r0ney<_EX@xAGsqC#q
zLGTgD1uqst<1c8KXllVrBrz%{J;Nb$Fru(j6hJIFfd)}crMDEBAHF4M2e|rqchm!R
z+0~tw=FQ5CA&W#5!2Bdu3mU^hwICy;Zz7lxI)OD{Z?&hGl@+@|?UEvCfH4l#8Y=1&
zx8o!j+LQ_i&cv!xTo!YtFTNS0`M9m=9cS`1GI2YphH56m8=3^nRj~!(FvM_Oae#dW
z?PGm>3JRz*zy#vDvw7$8$&)c)f^lGp90qtx3sl7%B**4URm@UtG$Q=^fvLOk)AM02
zo%<kLHw+!GI*@zxJ4KFjxz~VI4u)=3R3BYyP4G44Kr(>pj(VWw5GE!f(Z`&XNs58k
z2ubHNzW=H5yK&G5bjP!xg5&-U`?!X*t2COG6><<0nF+J=3oW69@Ab&#A476HygZ|q
zlFyEj-6kugAf#zu9S6Tv@g-|fh$+@%CXEELS&Q<~>+r!`wK}EJLQc$Imf1<H5JjZ&
zCC8{V6zQ=%9AiD(!su}~6GY>KSLKL6?jyc~R=8UcR%9c02f{2;G$f1&ErYdRa_e$r
zUXlD606Rd$zgP!PrZgv%r8J-t@^ER;x;y|L>0>ChfOBOFN4!PM>28)*vbqeEg7wrs
z0)e1~p2USg1g5E{%c%u(^uKXw*L4M4CE^yRDu9Jl`rxR-UEMYmDN`t`3rZZbNJP-#
za-|NQ);nR=YX($QRQ~@@kn9V~>Ym*Q>m=B$$D7loxH-Zz`qHIKtxsNhX;cy>NC3bL
zu2>-;w^m@8kqJFt!PK`3RS~Mzsx?st1|gAE#<XHss})XuJrJ}JY7|7fk68g4FAl7D
zakST~1>j1(YITt|@Iy4{VN)KK+nCfcCa^bms*E6-%(t4&f1=7Gd8IFm*@?1nTvMiD
zZ)-h>16I<S8!A%C$^jUEdH`Mh>&qX<K4GQX^Sn}?t<}xDdB86F<T4*)p*DTo;b0mA
zLKVc12XAa$UCfL~abdB`R)EBCZB%@uk+0xGuH_krB7?p=84uIZtVT`fRi6JJYl_Vv
zcVQ90V-+Yepx?$;sYO&X%ZVoAa5Es&;w}%7psC7$1RCm_()P`6%&YNQr70So0?>gu
zY>o>3nbsv1)`bhSG~rs7Tfx>U4egU5LL7^z#YV=cm%xObWS<weJM*RE)B1HmGT(j3
zT{!)-H+b$N23WQ57FCc#EvAMI#C_VkPPzayIFZrhzM17?riPps6k{I~cUz;@(hVLW
z^oPdzYs_G}-heU$B37tFmXwRAs!g|^Yt=n$zUorA)7KZ>N!)GP4mMu~@{|!BECYa5
zEg-Im((~)GDW(A1p)&9`G$skp>9_?sSRS}w0){jM4r!r&5)cd>#N~wK6<xderTZiW
zf{CS$^?Y(EDlGwPmu)N+<e@aIzBN`9-wMb!H%QC*ub6UQx;4m4`D2Wia$v$A;9id$
z>2XP$U}nitM7(*4AUeM_6cR>K8e)a*!kvG{t|ciD10j;Q1V$-<VABID0GteuDNYqC
zUn$42*8su_!bFiK4&_9PDH7R=dqTbG_JoM=r<Ix3th@E$dnT(>8nJ*PrZp|O(TN%>
zUJ3O+Px+ExPUa85zv>=;m#T$Iz5vs(pQ&>e%~wO$r_>w(J#4<($EF5^J?JIkv5J$w
zHL+Um_r{S~`<uXIvaRm57+ix>PYnaKte}~!&JcqRTOPl4C7!rB;<;Yp$(j!)1X38S
zG7QK{(geg;EQR2pHq)Y`!qM31bP!uIBPo4Wm4Cb}IzV`2o2uYKD|?yI5$nc{0fbH9
zm3?KOxceH>eyMkLcAmR5vm9j9A6qA5D+#rcsvf;gTA=!BKsV3<6;^`eZ3}{WM~=z2
z0WQTs0(NkJ>3a-jV9SW?Na|J^f>8xxQ1L3TqXqah*C{Lyc2R+cp;KJAMVO};Nh?UN
z2aY18_ibm8#jZ|)AvtDg)F%w4X8?~4_^AmpCU>h;fQrd}cSrQZZFNdPmOu7ZmI@ri
zDr)YRYNVHRTyS5JifOHAl-C-DD;^oSC$<`Ewa9>f$QQW-!jq;K{SQ%K`R$M?Wfm#e
zN|>w`Ia<nzJQH77+o5iWPEM*@2guUriqP-f<lBcN+l9)O$S~%4$yMiVR$7d-2fbJd
zit+7_p#*l8{#QY&>pGU7K6V?}bp$vc^*ANZO~{fO1+L`Rc)yJrV&73jUr#H(@?(kA
zs;I7b<DUN-ofuVt$RiWH(Igf>Z+E2^{|dr2;d4gdp}GC9hbD|cS`n}d;J>4(#MQ4K
zOCQb$O=Kh@!QKWo&O8yv=v1wRL{beD$P*ie!VkYSrW?73WpRSm)-74PWT7<4=a?XN
z`NS1PMFvDM_JAKeEuP4>jz%N`;8oW;1(o=%%I!FS5z00rcXEr4Z82+;gag(&*iT^E
zFiW7tGAm9kEMmN7QDEKoIZ$Wx9<Q18#Jm+;s^E1`3@v^eLJT9lpWv<%plS|3E!-@w
z;Xf)QK}{^R%dk`Oqk>YXufjLV;JjrLx{Jvo$w||%DlmVG@o=Dn<2@Y+Hm{h#3l)rv
zrJT52f3QeW!6VFMD)OM!FUdq&w%Sh3S(tSH$?^|l>mQO+m?*tO!7$8$3}yzpx|uy-
za2FSTa7LiDe56C{Y@}*QY09K5L1!#9(h$-$=Mf^-@~Q-yS1rh|k%-Jyrln!XipuIG
zq``*D$+v-1FpoIg6Y?tnubMhr#>)BRh@##MxC#}~Br>ARShBo0St3ITmsY+}F$8D)
zlnPjcNj9+mTk>?R;Up=@hWG<pns^lzh++Ke=N2eb+3I+{EH7bkf5cJ}u|+NeK#p>=
z)<kiHK?0!o-}>S3dBN{Q>&I6K)RTg1ne?AInuIt>KYd-SD84!r)7AqhCErbb?Dv&8
z+f@ezC=AdFU@u@bR>@z^F8rY0h<d3=9)NkY{B7S!bo^nCt!IP}bE7X1m$rt@$T)%=
zMXgU-z+|~!{Sp1-R|g_9;~0dxrUz3r$L%6QddP$}d>N;7oY(t>jjG30WkB7V=dgrP
z$3c!*gEmekY63N>wuN*VK)C2;bpg2L$3z_tfpEtyIZ;cUF4b2m?N$Exl@~*T^K|GA
z52+jARZ?cb!Qs)vMUP_m{=YJ_jD=JA#_`bc&OpfueIV7`o73O(Yx;wZ(1nUK{Jq&#
zs&`vIxUk%OD^<4~CKrRKx)f<{v9d%Y#wKu!RmE)=SX#!doMjV0?M&epnHSiI-@X>Q
zEWF>FJR;lD`w4s3`2r0AF~i{u87xwTQx)DeHwv-%$k2fzvm5?0E><o`u)<>$#uG3;
z0|=N^Wp5*n0KqZp+{C*qd8}r!5d(m>=CZ7;J}kIgK?Rkg?lLc}(o6cD)LL#De9MAU
zzBw~uxWccBhLAQM%ofnLWZa3_OZln|y*s7OQ41=VK{hbp1yZq650w~fIN#Gw3lLXp
zz!)0JDySzWN*OVO_|_~=!)u7<ZLR42IhxE2UF)0APcrU4kt4Y$9t+jDgD$L-z%+|-
zLo|Wy5LF<a2T)-$%ZREo`OYpgSJ72p216n#N-%@dP<e)k1Riic_)LOdxrjUo`dAu+
z7^~3*spSdPY$dsz^r*MTn2Q%9Ph(t5XV)rkqa39KULC=c`92>|xYTY5O_zXEn}~UM
z1U~)AxWLUM2$+_4Ovzr~zc+lhBE30}bIyJ)!mb*V?cEPkh~#}MKk{#Wnt)8AJ0+Au
z%Jz`ew+c!)pAF^+8_g*Q;+YnT4SQVrV~x)6@mpk}bi(4F?4yiX7z()2xBBsRm0Sao
zN~s1dRx-WBoTE2jl}Srr=a<r^uww3=20Lk4AAtKfZE1yLeJJ@%GZIr+G4*(Tb5$BW
z5$Sd>>7vo5qPnm#BV>|bIOwVsI%88|u(MLlR;3(ts5_4<7~V<>KOkhETuE#Um&2a`
zpT%bZzD3a6V%91cse3bBLR!oo(GyZOxe<xA8!IQhC5%$$hFpsw%qGaj&gj7IbW76|
z<DP^J79QEcO(c*cybv2th7c@xKf#QVG7Jc^BXVA7m(2iMLBe8+t7KC+Y5?vr=YYDI
z)>JZV9n9k=KPMfh8yql=9&UcYL{izRAf_GXw?C8SEU<(l2}Tt4SU*Xn5jxrba9+j`
zD2NY}dq(+GS2pstV=|}%K-^iCoitA64;xQ%29+b1!AVMt#$}_-UXUUQ0gG*1Y}fK2
z&w<TZ4M&MHXBJBlDrPaFm~<~?J#X=jw^$+L1S(C{<%BMUv9P9_)=1-JqS^s0Xt+V-
z@dMD75KN}FlvyhQZcSn<VG7ewZHP^ZwtS5;UqWmwM6|1B>bVJtV8JoXS84oMQ^7aO
zJJyg$az<cRAt=FJth)sd1P1R6ny4kDw~w`D#o5YDH=$Mpu)yD0+Hnw;Qy_|FzUlN1
zytc2u9g1==+buWi_X(>?Mdsi$hC@!9M_#J?XVA+d5xHYL(2f`2?U<pSHZRp3s5bqM
zg&x}tTOZ)Xr#oCyov@^#uNo;QC9xPmHPkK<tZZ#zmUNUINl98Xb7~ubYl7<~B5-Pb
z6F`0AXxe0MEX-gB5Jub1-nebf^tJd=fubOm8bGs^14}MjFp3wbi^EWZ?9mnoaGQ0D
zrUSB!%0~aHUITHgASSbRUS7>7Wp9t;YZ&e3wYKuDLz$>%l2uxvYDy8uo|7x3>CGyK
zA5Sz3x&n>CyCNb^TCv5W&}<OVoTv}YO}eH?ClJ)>Sg})1sl15`(M%(PU3)TVPoyRx
z?KUTcu&ptWqX{_6Ba|Z1Au*d3(oiW=fwUzL!A)}ePr-9sO10n?5{d%h@d~ZX`+Km*
z+tMO3YEl}vINGrBi7o?y8}KR++ZyNv?y+sEmefz<a0?*)F)cphFB|cu_4@~yH^-d~
z);`B9qf>u=ja}~akGe1KQc4Z1vdHR{J>!rC!&Izpn!XLxnySzw9wQWgFjwwny-)(J
z7F{}vESg2UqyqqN+y+@C4tqvKHo$C(PCO)N+GIV`XIYwEbG6VF;wIQDPqP6yW7>;*
zrmB`s0^jN-4c?0NDRH%M)uGsA-$x{r&lHTpuw>D#)F#YV+><e1WW)p7|1HZ8HDwnH
z!hDPnt9ub?USTeB3wq#I#qua575gRbggK?w13bD#vkT%?dsVDl*h;J=zes~PdR3!h
zb~>jA*Mz8aVjqxVolsG8(8h0OMhA>TqET`KPC7cUl=WgnOb8Y(q<*sQ61!h0&tSh#
z)dPn!*1^m`qFXYLSW^RNJtRt$&+!0oM~jngC3~9$yDY5Bh;73Yn!QS{&&uIl8_U`X
z9EOR;W-o=fWZ((jM~uvAq=kYLx(MBgTMbkkq-sy0irm<dc1k{vDBL(FTd9m@&eY6w
zMg`-F<kUeBqH|WzSDCc)LvY;4YgdUmNI77VP_#6dATHz`x+$eGc0@uVR)=4Wn+1^?
zV_0+%v%n!jYdNE2VeRsXk?q*0=?=t9AnL(%CDJ7&jv8`p6*Oqa?<R!?mi;IG+R&Q?
zr6loz?WK}n9VbEm4?M`!;H{U#Rc7E6le2@9oeXhBOk|R4^EhNgiBOfHX<%hdfk<F1
zOK~SE#EZ7wI0jG>`A?@O%12wWeh9%8$EtqoVVIT-Ay0`k31fsOujaHZS2kY11-zz}
zL>AhF7W!*S<y-TjqrgFsjaq8^Ybqc~BsVEMhePO*e41Bq4@<PE8z?53eR6&;0F|sw
z!Ib5l-7C<s$+77m(Q#a59OJQ4sx$<{^BGT~*+!zp6`+wh6**E=GdyV3Ad5xl5YrNv
zg%U2dCoe`45`OMv%XRHs4D8rEMt`i%J%!!M0fyu!6~;6+d4e#Uh#OM?=on+xpN4BL
zCqB1#8d7}|lmumG(h(O^-2qWW0iS5^IOSa`x$fm5c8Sy-5i*Bud$X@43#3jU<cX9W
zv9J4LW8MOzz<3+ZuRhL&0y@M5NSV>h=$xL+5Cg<f-bA!>G4d5B@MKAeikWd7nl&JN
z8^WdQmV&242vBi#D3W%<qwF&Gd9Y4Of_GwOi%a5z8#{4Y#(IZahak7u3F6^XV1~V+
zAq%PR5C;tp34)W(1N`}Upyt{fuZ3j0?VZV5MLbk03gsV{I)>@*^T6Z0?uOQZW~QNW
zKhl~6(EoavJfC2{yFQOQZjCGQ^Pd=4Kx8<<L8G-4lVtco3ZJQzH8ETsqa;0!Y|ZAB
z7l3u3!(la!ofpC;x0+^xn<hG9?$fDs!x(;pfbHLD>(hf@Jap{LUJz9ZbyGi8sH)>{
z>$iwg;McyP%Ch8qn=10WB1}@ZuZf1JJEiS_5p5xAv`maEH<7#_I%KOgt!<n65APxw
zinSKSJD`EKOyn#e3<S`70i9R&A+aPQ;2Qz&RMDi~qhiucV4R8j2#RAFFx5&3Y!K_-
zn_owFWiWyu;CSX@w8mE}aepm{Vcl$z%g>94%F7lsVN3!PDFqxgg2GF+Ia7g+Hf$%7
zr&wmqITq<?w(0>?GaM&|Zh_FRUS5F}2Dp`;S=kc<gK6Mby72|v*jl%*>$klkutX{z
z6LC449;|4JUXN4v`K*P>c8Hiey(tQ;7B}kqqJu8)h*S$gY3NGtKOEGoLRDXP3R@6f
z>|;AgggcxW)v%(IExcj1a<H=r7%|p@?X|VXjZX^=^G9w7<%~wu3FVCRX$#Ebtm@WH
zVDlK;Xj`gnngz#cm4N(fX$6q7bPk@jkl5H5x>Do90#p2U`L)!snI$!pu-8fyi567~
zH6-d!VvNOPli8~HM-r7vCjJ6;YJsWtOt)yQJIPJ+&e62LjTJ=N^tFUP60IrD@mjx$
zTD4+=qcHcFmPmYZ8Gl}*lbO-9h15H?wj3j@SxQO>V}Zuav9?B#*O$Vql^r=}h6(20
z&z09*SKj3ES<N395^k!`;MVfv<wGt7fZ;$4A`@fCc^+h&+ZPtC;yu=Sx~_Gq0wAGG
zLI_z1K_$&(d_f*FAqA`Fai&dJGwNpa_lILPd)m2Vx>=akuK>M11e<cZaM{_(RQT^U
zlc*pXkS}e09pdE^_5<+Nm;gMIbE>Es$rwYh99EsnHzek%xvYCO@b>G?y<qMpHgp&-
zHBr6R=B<Mn-EL1I0J<zIaTCf8r0XcE4NvZpC+l(S(7U33(X}!7I=;m^wkteX@lnkq
z@*BISq+!4(2J?t<E9K`1X0gP<PK*L=*+9ub7lVl-{5)g8nU5CD#(ITPfrh1}ALH8&
zf`}-Iw=j{?oc~dF+3IPJi**a}%nfAANaiGI7|-WYgmI-CR?8UYgtw4ibuAA42bKA7
zeMBYdC@?WNomJRJC&3rEEmkk81On(aZC@~I%&yY}gg@I?cUkWpW44VeN%j}ZHn7_*
z>^+PfFD0>Op%>~m&!f)8XA>Ssv}x)EFC_+xPjd8R@}JCk9AcM)#izMKEcF=H?7W6{
znC-Ex)l}dyJQ6nwho_10A^J$OSF{{fe-VaymxlTA_~J=vur`w0fXh5%)jMiL5BF6<
zKCG1hY4d8|r2rjtV;^WvAjvn<CV~4i@eLj|!)Ki6xepIy9daECQe%<`XISbVpuOl)
zhT(F^&{LkxjAACATfSV|m>?Ca_NKJBu}wt&$Gk#w0Wh>R(bScS+iz6ZN!ei9_ZBXL
zjx5_DYPAaU8cdMQ`1kA_9*VY)Q<Gzjawj5b0rZT#VBeHLwbhdpL$4MDi|l}{^H3{c
zpjH)X>xy?&p$U)t15d1(mX=h3eJ8jQ+}0?jqici+a_&dk3SvoNGDJ8W2E}j&NDOO|
zJ7J*hjdj_wKxWkFB1S!kZJk!K2>P*}Y{cOt$l~ZIhABfc<#UsW#eSO=dN<8p#7cpC
zr3*!_k-rC*veL4Ym$t%C_zT|9LnfGMNh<Q$y)tJ0<2g6$qMy*ZGKoitVq{6~SH?l-
zi9IIkXnZ7vRGVF*c`{&$IMn5cbB%S6s>mvgu!TAY$$16BW1<$(p72b^n{H+{$f{EH
zF5%Jsc#i3Y!j>^wsYCj_K{}{|+>A#SkDvL%!7Buk3mMu+uu~TwSQeluxi7%Z%{xJF
z=6FK1x=-+(loN$-)?%vg{CQcgxQotFa__`&xARN#v6dXeDWOSyV)@#V-l^|mlzn8V
zys2H~bz~=^nk_gR%@uJrcub2GNzdvrs@BS{bb>;+VLJ;Ply;`JzraOuy!zso9_=r9
zFcMd}ODWuWB^>wWdrS^ICNC5^Flk{DP%e@}EJ4h<B+TgUw3rEtx^G-je5o$m?gIHe
zLu8|svJneXG`n*ysK}y0C=<`F-j1malN1~)cD#YNYnBD5i^VhPO**0^ksfa1I!Ul=
zHYHoAPv0M<g>K{n$cGiROB*$pe`#7KP(FiOuU)+HA;@DKG_y0??|)^$O(3!yr0#;m
z?Z?wvA)wSTm{oc(30Bd;^=pa7wKu4Crzk_19anzgmy-@AB7E(1$gSS{*z>F);<j6$
znngZ;NiDCAEn}yjqT=%bM%nd*Lj)!nLYQ*msLgg^kOcCR14}nd0s;}%U)A_HebU66
zvS#JjN^xnk_{2<v6LE~Tymjd@2}sy_6-4H%Y7avBTuAM3PjC+T?lt@}2^<xaX;NIA
ztb4UMUD>?uSU<qP0MM74#V9^lxL%lY55{uFG73<V$>Bteb~>$C(d?I4n$;fnm37gK
z3W9e~s>;YcG#ZaRRNbJkP@9^ht9?lte~RMDk1*);(<)6~qB0_1{ZuXKH;AHaq0P_{
znwG8K>*tZN6e(&zb4mg>5bVsds~Yi%7iI%FymjI^2+02r3*?ZNgnW6D|G;30B)o#2
zP_2p}O$LQ#7bI5KU{&UCFi3}(w7M%Y90h-|Rk2Bat{iMLwKMCcasA=T>DiuxlVK$O
zaLvX=(yuth(;YyL$hlEwKwP_S@mHa0q7k%Vw2qi01cVl^d;e=kCb;WEdM>v^lMg+S
zjk}367|J*L5bbchJ|^ACn*e(QMA`U)#XdGo9bJ2QPd+cjfSX~xkQT$>TN0Azd=%h#
ze#0;TEZKW#*cNp|7;wtefIV+)v%qyAfD1fMT;>E-fV6s0$E(C|70tX{kJBO@mJH=}
zwIaEnf|4n`+51owku!Ed1f))qhOdDs6Q^5Bg8@i{(U8W`kOh3Ywws#IrA6J3nB2gf
zAge);%Po$!=q9lCCYOA3aFA}0n7@8=v_Uh834lsxnHNiKB@&`*it<zvRppsbRTM2m
zOYJb+kyO|&w{9ByTD!N<#-G@_11^BAD{)gtjEcfq5EiPH=Rg6Wb3i`B^df7^4e}Z?
z>_?TyQYYX8q-=1)^>va>8-t7A*9n=Pqg>bq`$I@Zjp}B#WinG{lmWM1<}(}oQcc)M
zTOCuP#^W)AKtnC;?Yk_%PHV&T5*2?F+OX=Aj(fXtq1gAnxi*T_I>fAML`06Rvm07y
zx>X51t1?n9)kJBss+x9PCICJLsn*piYe@DLQC6yxLd_T)v5FMbvzLww1vnh~!lOlf
z6UAuMtm;aebK<7<=6`~m4W2lHAf2woNBi{^m-mzxvsRCf;PdSdLdgMd;|mXd>;=c!
zPZB^#m3w*ECK|w1xQjRI9E&-=j84rOkUUXfGfJM4yRzF&GZ&)R8Ue{n^1178+GC4Q
zAX2t5$Q!-plGejEkFh25Yg4RC7u|&H*>E+=lB8X9MKrDIK>yh+h*zFch7@!&D<@45
zDZc8OJkUa}PJRtpK|UgcJ2bB1@p9HvN;LHOQc`tt=h#I=;<qkw)OPIBy}cZGgwm)U
z(wIcuAg|U~)-?U(4#3?An3X(Mq*cE*BFvr~?^~dNBj1Fw#@1O<HY0-rahcp>xR>{#
zS{!kI$*-5{43`v@q3FKQn?oVbP8_1s5K6FPkG7p8jT3k2ilde(rPo7=cr7Fd4J9KX
zsGs(+b?k5une!e?%ND9S1cur&1Wc;yDJ?#RlWE}azF)oBaKzcv+31d~@xAV5#EH63
z0;P`JAC1?c*-U{Pt7**fD^HH@`<_|;=JiNQvJB4t1z?fO+=6t}aoTfn=$NMAD&LM-
zPtxygkr8lF`uk$ENnY+dX){Nq8~S25Z}gP^Y)NTms_~?*)0}K>3YJ`1S?o0tkcz!T
z()%CIicNexX~uuw@9v5ra)DCc)Ppc(O(Z`u%2%b|=jMc8oYdCQuTP<tC$o-ZqMZz9
zWDV2`5h-#ngzl^;n#F^(A%dDL$jd^te2ksWxn8q~YwIk@i2h*)gau_L$BD-iQ(L>C
zH){@+jMwdv>?dFza_PVb>`~WnIV_QUx^~>I2nTz>y8lfB`TJF#5whX3Z0}A4t*v%a
zG|&8)8W=rIfwuMOfI^0R!RvB9*C&NQRH5pDr}Xujx`0kku#B&nE@3+Zeo^q4Td7mp
zhC6E@t3vcM3$Wtk7)4BL<@9?wz9`NF0+ep*c(L!J&wrKukymfD{g4FxKo9&UCDzX(
zirZ3*K#wkNXRBYyprrCZDG&;MQvg;BwqQi>8Z0xjrAijj>qMt}6J#k#@WOiR%!D^?
z9UO*n#S;sLp@<+YEw^V)a8u6YVHXy+EOhjGKH^~t?2>&6%htc#<XGdwO!L>PqoTWm
znTwv_-6PUwsrkC~!g(oNPmVdf#7oS9wCrCfZKVY<A}$0q1UU+vHwVl_h7a``+Hq+t
zEf@-d#M)V8Gs8n;m>r9Tnn9~O=~I8=%Z1Bg76U~~YZ!o^5)lHXn1HAPQC0?cdN)`8
zWzfTs5$v1lz}2XRSU_N6n42sEsP3>T1i#78LO;EU9Ozv)Cg@u>+P@>Z4N+4^0$-8q
zk356!<t3rkAwRxIDA1GfYs&fElR_kJ#Lw!r*E=g+$}taP{R@uBnGqu#jH7YUFWa(p
zii%GYgDLBH#6HyQh*Gy~g*zihp$xPa<$p`l(1%$-*CkQ_Mwgb-WKHc_<s?+HdZP;u
zzcgao0Q{(F=gIFL>npz5H`ha&etK+i0LLsOw@nzA!?s>cEygCbVQiJ~vo5`MS{BM0
zlh!hk&7Zb@o`}aHFY=yCR0V;s9sfCSe3|Qzo-?q>a73jE;{RoH6z-dIIea@wd-CXc
zTRHPF(3nZU<vD>Ao#F3MkfnA%|Aht3_4V1DH)xb&D6xNy{n4^b&BDh(&Em4Mj$5Ij
zl{Me}0%0R0%vs;+jLOL0{U_uPR`6{1q9R$hjsR^jby(48r@mX{NmvWY6eOC^o>cwA
zr3fl>EH<#vEZn+t-a1qeYN6H8=x6P&y)u<F+%QcZO}zpv9s}{C&kAa0e%fD{o~<+|
zvYApt!d=aqsbLXMi)x8&ic+y_;Gf`9v}JjBEV6!4>#ZVi-|DCw{jbs9#B$m_qjLQ3
z5eIb>(>j!Xjzo^Vtu%1DfQzachbmyC3`}d9+-1`4i{cG)<#X{RgB2CT^p%j3Xw2_A
z`K6IBQZ)lB$La^vF!Zj#uMpCi;P6t%%Q?fXlM6?3?6|l=yy$|Y(&BJ=t8mrUs~Lwh
zGMOVJ=h^W87}I~xd3_4m#fZY1K;sXjD`LdGFcrYP0DqZv8RH{0jS0&8tmlLTrmBw7
zmD5)$ku*^eS0m;4==roY;{gWU5>U;`gDoWDCF=OA8~Aahr=n1&4K8}?8F3I)*_oPS
z`n!yR424JKo$izqD8dyTkxh;y(B(^}mgT$#G*?+tk6r+`#MqeM8-wwvmD9qKit9~Y
zzC(-{`7v6d-Jvk*J)%XAVXW?$?*d4&4AUvhgnd{gFe(ut-;GTqhVUkY)PQ2418o6w
zY0B^L#JkWmf9xy+_Q5@1mh)C*IpDEO%dz*DaTnKF?&(QDUFIb04Adad!{PrVg~U69
z(07T~&<<!2a?E#&_O*C~NVC5-v^IvbcD2T@4hGHe1)duY5=zBXmWEck3<Z3E*g>+y
zb`Y~msH>HW0i)Q6cYyNNk^9Y2Y49AjW2sHZn(tvsL?xoQiPt2y(bOg9#`*HI!}Rr4
zkUm;C<beIZ3vgb8PYpi+!Y`Y?9%kGx>ig9`-19T#FQiH4hA3{t8!T4##76PmJWHs1
z8gbOCeyM&tZfzm>{XRa87qfXHVsee~@r*U+^9RFtWBl7AKz4xZ2HxGoh78w%6~y(P
zcP}renHNh#@j9@Gvg&R1wU#B^(*GbgQ~`ED2uklo10}4@Ibg(dghyD!s%p->ATy{i
zwSt`tUn)bWS2D-)`6NS3;j4qXnTnvDz#y5JOBv4+IS2CvrcaXK3VG85hYdH&b=rf`
zt;I`p<{po9Ppz<Z+-lk?hMDm}E%;M*W>AdDoQnJVT;0R_^`5h#KWZyi%xC5pKn1Vf
zhPam;_SxjQQoOJYh($kprRt|obbv6pafnEDESA;oEkw)Kro5_vqbBa*f0PZc^fibe
zd=oszh*Oai#(BZI>6a-pds|TYq^?yvccYT091gH>viYcMh|LX4E=Tml3e6?;;BFEs
zi9^hKOC&-cs0&gaDfgk6C$7ISCjbu$-JwMdhLAEp%!SUG==wHtA$6cG2GIsjfWh<v
z!4TAhZ-amsEISF*EZ2!k#rspE{h6Q}iwL$Dw2RUumsE+0vkU(27AxV1oG6$D>{yPl
z1$oa~#-MvtELY(A1L>*%*&|op*Kog2TQKxVtF6E3w|8_zw`abZTiAY^g+kV6d!k8O
zGVxMp5xF`S42I2oShN??!R>bo^!v`*r|eNQDbYP)V!o!hm#H|32|~Gn5k$qKj}Wgi
zaau`1Yr_^TzOj}TEBCDYyEapGE9F?T3+0aS+sWVe0z@^A7sf@TN+7}YNvg-tL1R*p
z6GW$SOY445D+GOF#{AO?2L)boZzczdr&vz}P$s50LMPEj;3Qv1vhJ$w!sTY<FPaK3
zvIk}gfE+!1+812gtjsXAEo+nui8K9E<h!MP?EomG*@aiTfapxL;<CG}4ThZB_6Jys
zW;wf0V0XY4EZj;)2Jqh;8<<0bY=gm=b;aV;Xl;3zR3a$dYKYb_CDqGP%%RGgwpgiD
zVLG`!7E3jj3uhn9uOeuxiI}xB6few+Q785_3sY^ni|I9WRKRZ)$Sp=8Z9-lOC|i^h
zB`6n3N>NOhN@n+Z=i5|AP`r>Q3v$d`2n>Zf>vZH-1q(GEQ<i`#7p4H1sZ$oV1?4Cq
z9T{Corr3D@$p@m4BNeWHEJQ)OGLuSTymtVy)*9)uJ*SG6Sdo1^2viZ)iWw#Q@n17N
z>JS<+^7VIkwFN{M^hQ5X(HP>HC62XV>k$Zg&yKVf)X+}-c9~H!##Ss!BAy_RBtwa{
z8OoG*j9dbYTtaqeA!3wTJ=Ib20C$dj2!%ySNxc;XT2T+Bu+<wvVah-usP$|svVJb1
zPc&A9!6{TWX6a00D+PqW?8U|kRCEL?Dmk{;R9v6|c+55x6(uWlAyHAXDaJE5%9hss
z9;PI@WXp9(S#_A;Ui;afDx4+V4`lKYklj3I8D1*M<-#m0Ho}Y*`RX8*Nsdv5M~H`<
zxW8`so|E5yQHo@ZGtmU}Sgb(2eV-!kIT>n33)(Nk9ChfCr{6G(8pjb02ByxKc#jel
zBQUw3Fsq^ayE91C1x==Y@IU=NR<<VR)ney7-a=<jKC+3=`Zq0;qR1EL-<G{QTcY3z
zBm5NBz9|GMBT^}x#mc5tuzv#NarMnol!UKq_DMoxiii1@@_?i&46Pc+0!jp~3*JeF
zyANx2bW)b*2v)XSjMGjE$-Kp;1QoOw3xzz5XDN=GT|dH}G1C_q5LVOa?)D!}cvu3;
zcg4S!5H(yPG=SA~+v`lLXwr<7C{G=Hee<R!j9gUb;B6lk;cP~X4p9TG`UD4ST>J}I
z08Cd^PlMjY{(_DYfE5xPJ2ZKMjunlAPbg&QK9XS`O3v%ctYB!5Uh*p6OpIqllC;J2
zHp@XEviOB?fn>~bjRq;Jo5fpkjRWdXKM}4^q^+kL={deFS*+j!$%zYB$P|c-k#`8R
z8#O?)TPfXfZTdYZwYSka+(`k24v<)h@Gm+FDOrg`Ou_vuWGMI57{Ks&yAx=yPAGsS
zQ&W+$3uPR~L@w6XPYN7}twbwod}8fZ2E0PjZH2sZL}=>>8c<gU`sXF-G7R$)h}HWP
zEfO@SI>kvgf%HL%LDngGu>zYwF|?f1LZe^e<><h5G*oL8uR{jJQR^6D2jT8BwIS5u
zTWu1mdBSs|{5e{^alm@Tbz;MnnTLid&s#P$)~te4n6%ESOPi_SY*atq<AavtEtZKW
z*+dCm?zV}x8XPny$Hyo{rA{L>_$W|W=jfHknx8MfEBdUFsYGU4?0ptXgn}`7X^+Wh
zQrB%-ppl@~<mP{rQ_3)@`&g&vL}6*HwfX`tWp@ubET0sH)8j+lakfh=x6-~ZS3KkB
z2Zb;k13cpe`R27F^dl?f=hm)8knGTm$yk2&$TTw}K`t1geP9y0eg-1mJMmmrnH`qP
z!EzlplI3&psd$e##9Cw6!-RuFosB%>b_scZW8<~X`C{S$7&=&+zd;bNbfNH{tRjM(
z_J;0^sa#Q&^4sRtddU)8Mqw-xkup&i5sRw?d$W{_UAx?(jPKlP_?BhMNAx0=OM7z=
z<G|O30@hu39GsDZq-8q2AlVxGCmC#YUdhVujFtcToLhZw6iPMS(B>k7BZ8TE!;4;1
z1boBp<++It*1~Kei#7?9V0K#kAnGA@IrJWgm<BCe)*4WJKLQcCeIi+~9T~IywdwbI
zG$H3L5yFY>W3z+KDOZC^g2A?n(iLgW;fp&+khw3dt5$R_MBx(JWYg-rJO3{dUq2|V
z%Vee_T{FS8E&Zx=YqFqs*7IY*J_25#`D#XQI=mCE_ue@FGam+X2?tlTb;T0L$Hd!f
zf&C2bn5p2N#v}PnEZ#ILA3O57kx102)_NZ*s(D_uKSsmG5fYUawMX6Byr?SmW-GQ;
zL#TJ0Hb#}1*;^!)>>E$VfGXsYb0b!pi}}F_eoO&UA~J6;Da!YMnbzcDLplqQ7S!~k
z=GU;y+{lgB*{jS!YUT3jo;Azm11?F%;;;uF^?*16#R63ifV)X}X7PT#&|YS$<r}UW
zm%<4kCjFBemez~d0BIHJ=C2;{t*tL^_45+Z=(C+C=u<-`Et{BXOg3~0;KNdMvyQK+
zfDjPZAP16FCQSNk5Ollv6kkMS$>8S)iSO!>IF0D&UJbBdIwBuEKY^KnIF&&6(|hWR
z0Cmd`7#3JIP|MhKVSWH*FOn<y7nefAc=JDpPr=UoNs_!AZ^46c-Mut8IH?QCSOQ(v
z3~#8U@V58e)aqF{ElZb?{R`Z1<BeWu4Klm@UD{f(U*Pe=c2Kou&#I^j$^ltxY6S4Z
zKPHB^04xA=MSKEbpA5@U(UswUW9Dq=<CyjA!aq9TABMWEiqRtiQ@iq#R~%|9&Y_fR
zCYFq26BnR0n;*$WG1wpPnnk-y)HO?z=-vLmjO7OuQ3@wQB%?$_{B$+x((Kaz*V_@c
z^-(}79K_8p_e_zzJBJapRxo7&2LB5aAL%TIb!%W+C>KYk=9AX7=rl0A0*xg>K3t1~
zc$Ea~i08DudS(Z#FiL#KuRpP_SB5o|<w1I46bXcU4{zqKdK@Z0?I^|?F~GQdsQ*C9
z>yze*g<IC##47jb33OE!<3|GLCb5HJBIlp4h^-`#iH@(`bGDlHKk<*M_<0T25Noz%
zpLDflinv?g+(dESLc2YyIg^eFTyc)oAC1zEIeW>Ra1CwD<Kn$n!}XVNcvqHRZNvLe
zDwO@pO0cvWajb2_W1`ZJfjy3SH!|r-o9S78LHYg-xws?1OW4T9&xccYkx{dT$eeF*
zsa8yF$rf-Eys8wtMhW-mk|4^4U>YXAjm&sSRat5W#349&5hc`6;<0R`tabO9v^za5
zk(=yE&^*iA>du2-0By;WYo-~KuN!Vs_&6H}5by_NxdqR>x<z}po?;|a4;2o`lsX+R
z4JVsIQ9~9tVAGkht*}`L$ek9ax;qaUh~u_nAAu&EQ~0w5hYgT%DOD!()U@K6Uu1CT
zg1E+P31<Wy7LR#|Zd(N_DUNtQwBQFVGYA_No~LQX87nxxID{St=m{FE3kIAz6<H!V
z^Tko1EGrNb0&nMwZsv(c!SEl2H%458mVeOz^o@uo-jgyG!G^=VtlYu&hPPg&MP`BT
zF%3&iGOG0H8oy<{wBjJ&uQq|$btiaKu;yM!%1<(84Z5MBX`CvH{7b%M@xu;fVk`I)
z8SqsS5RxvE2$)}`@-c(J9v#&He^cVX2KY@-V)ncx;?i9^!t)IHd6_0Crc6$!>*o{l
zpAq2MhGdjvQ?}8lSH2ho_gNgThF(DAecb5-#)e9nc?qNXU%OQrx@(ZYA&OrCrp!B#
z95g}&n0X2EtEQw)@5ahU#v{@dkBmH%YL+$(JcbbOKK@bWY^(OvmkJ1abGVt|yWb!F
zOu#{xVkt8QS~x~C@-<*^YUoH&sSYlKxhe)?PA3%knCP>3p=1J$#x(9HAkYh8*{Yia
z#_8kWz~TCqAEq(!s(8w(0te>;)+xuOKxB+BzCfQm;K>-TlCf$OEdvilk<32FqlE{2
zG(ml=H^B(2O@@pgyG*0k`S_#tFDVB+G(R}1pPF%fG5e}*IEf<#PvHJo7+xe6DFgpP
zWhxjdp09oHUwfuyyU>}a;x4*o7o3YlM9n27qT4qonqwPI@^ANQ_a>9Pmqij)5@@|p
zAUI@nT9AAsua_XjN1}-;fC#%7B1)j@S;(ryJ^@@4dj(daW{M#b^eQld!7#|5jwKBH
zs1#|80-;KyQa8K>DhKpwRqW`HI?Oz|lNt1GpTsQOfqCzERLhQoer8P@Y^xjc-V&qW
zEX${Jq%-#GZl<+t5^>Ow*zOTgwRw?x#Q_se5GUaf_y*xVMNmNm$$|<WY9P?Sf`u!3
zs#HL~v(=KHVi0dSpKPcUN)shv$#EORbix*t8h^qvq!ap(emp)JZNsnU9|}`6ehZtf
z!Lp7mqXjq=?GL!@U>S>KbF&a!KoKs0A~`<>a8>@Ufc@YeR-6v?u7b~p6mKZ6`7O<a
zQAfih{v^o;jSsPFs1C4Ni6~PP{=^U4odjaD_koMaVXn)j(~BJbOd#>6IpJ>PL3zOS
zJqtTrw8LGHk<4pXsLL?E?iv}f{}|6C*d28z1q!4FH6O0971Y=&wz-u7hMm@GSbV`_
zE|8;2^$T~vH#s#6cjUrdnru-G&9#m|Xh<xWwK4)y@=S*Mg}dvo=gdi_Lfte8$+t=N
zX%_CX08mjGx=mqLTnIIvmvjl2e|^!3*nszNb`H`Tq6_8$)VJ+4SuRPoDDaL(NecTT
z%;8?l7job)H~{t;|1k{0%p{`skMi_cXmyOSW(>Pg06+M{PtR=jPP>yXeg}>bO}C~U
zAcs}9csIb%(sd!pZvJ+@OwNYzSws{bf&gTmP#rsXJC4a_LQIPAw-6IM7maKTEie&=
zj;!Nb8<s)*OSsE~0Y$Y?F$__qn>BV936i*HVv%EDxg&{Kuk%M@+E0G`)5gMzTZMg&
zf)?I_7}Nrs*^~l=lN($Sbc*wql5np33;pd0N2Yk?6#~A3_u`uPOECQ!`*hKhru%#E
znouV29{Y6tcz%+N_rF(EzRQOaPl5r*z^A&<E!Uc9j8f%Zd&yEm;KUC#pcpSVK?q|&
z67t(n1rkKzM1)MRgXmEpUFJ(A9m*PVRjm<j(m-Bk^2q~t<f!+%7`W|RfDNY8s#@s$
zSO<4$dVZj}ncTeWW>1!|Q{y9$6JS>As7yTTosaO)8I|)p--fDX2(iw%B%_s#Raj6n
z=56WsM4yOr;4G8>Ge4^gG~^o(z$VS~fS_5%oNlz{8v{5d!V)PHqUB<V5xDQKF+fCo
znVM1l5M#~`(8Uk#N-I(@jW918R;BV%|9B06&tY3&s54bD&9(pobKMiZ^|LVKns79x
zwOkB=WC#?N4FS<ZT))8!ARq9=l4^J9uYl*v*O9ypE=470jHR2Q^av<<V3OAePIkFV
zmYfppIt@I-K$wsL;NV(>807@k<vB(Z3BMmx*`N|=U->ZBKC2V)`!7G~GEvC66fArQ
zW%Ib$Ik?yb`DhNyuIh}JO~s1Tm49}0{t+n^pKj)$B*4O$Kc3g%g*6@2gH9O?gt`nN
zl5qGizugvM+!#Jm7KKLJ+JRM=Z*H!=V|hCVu<z@e1qZ4sR8p|)t5hA7DJOEPc67fV
ztlMXD{Yf2FLG=b+)ZGrCtxM8uDe3W@(`ASk+nm}-V_Au+NvEQqos$yUf-B6YxO)!o
z254`OoeIgRGve}>>Cu#8q=RoJMCY%2bn!5=cn~BB(rB(?)XynUa(Poo>wL)!X#Wh%
z`^09*l#2m?4W>$%JuX|Ih#2na9gD#I{CTG|4z+LXLaEDWK~=wqKW&63JeT`zJ|?f3
z%~Z`h%xXIjT)*TcbCF9bnvj(szxZa<xfw|9IrG0^5T+Z}+2cH8<cG57X8nYKwRhv%
zxtaW1O<nk-0VuAyK?tmNV(p@c)xUfW{6KLr;f<?r6?uM$HkZ*q?c{H$`DOWhJ|A}m
zGv?u^@=9bV@jDESJBC`bLB;qba=a)gCAurN!B;?{-bC)TEMe`pF=Af5Vy=NvZz5`I
z0^;7*mo@gZB&x~>Wj%!0Nf|qAz-pe*QfyU1T%xi`DiVQ{S@Pn|l}W~&q{%?diOYys
znM6yX^y6b$i{_S`{z|%3whBHJ(pp)Zx4MR$>DRNU<8(6RfI_ILu#=Z(uVKV=t>q+?
z_is&k4+_rNP#ukw7P!9=N=!=+IH<^~icy7n+ElDzcc#ujUuo^is$4Npp|>SN>JHtv
z;^O$F=zJ|R*>o8rk=(oe?7<p_E#|TfPW80EquYJh!?UepD{Qj7)Ezp#64`H2KCe!X
z<VTUt1ZVEl9?U%pA-c(Tj=`>M{s$8=?VO;?(55k`BLE<pIHL5At{N05h)`E;P~xjh
zpk3*d*Y6zt{%}h?OOeZ26r}5Nl~FaBeRvw;nsArEj3UZ<P~|0P$~>o~Mx2^-jT{RW
zM)4@!X+C4URxsVF4g;u^l3vtLA{*-4Ew<nhJVi9b6R%42h2k(R#95a$I0D{$xLl_j
zSSbp@L`_L!C>e(fegN!-Fc#+u5M{--tys6MczdN{wKVKibOASkK=NrdV^shdI(Y6w
ztU$EA!ZFwYPw|IGyOd2+-t{eQo29fs16hk1q+@t5LtW`p()Fk9VL8ET#d!n}!!3r3
zLyIT_6CpH$BdYT8e=(1qp*jf1|FX!w>?7xS)i+cFeZ5h-VEiJ~#qBr>z+FFbCYm<P
zxm}uQ_WCADyM<^A%#WJ;5v5G%OCvp3a(a_3%U$@YjDsT}5hN8-)B!t%M=IJObxltR
z0r6IGXFU`-9P9#w>#zJ@t?LCT*cI&Ncd~LKaq9^9<5t;iC4q|aL&+-IIEp?@@d30p
zKXwkOWIbzssr|9C*8OVJv%FmE0J=ZsWUSHfWmM5{?RjbW*&jFP3CSB~!MrSfyJO|7
zskjNz{4biCTM*z{aVeIyG`Z&Z*~y&9>$#%4H1j}i2(E2x{j3n3>aF)UVi7&@9M=cp
zbMfFX&9JHXc=1q~zWIS%LiN}p;Vgym!YF)RHPR#%qeqZEj)1gfdVzgltRd^Tk0`NS
z&5YA(hqB)IlJbM6!U77<O2|6t0IPQ%!3UOs=!^tjlv$?Y^-mo<1tqG{e0n{z_gt(F
z*yfn>1QZE?sRB+VUBpi|!}87&?n|#EY}_L56bDk-^3pS9>bsYxPK2L5l=Vy3eD>H9
z20e&MTRMN9ra=Vez~kqrrS_#WIG2ByXLru=y@D)S173m-HPGDYWF?uTxQ$-UOYqj(
z2B!I#npZ;k2Bgt?&P9LHhAL|lm*+OL>(+6#X*Wqt3Jzaomn=AH0@@z3{HPpjo6Gf-
zUwGPSrd^KQ-d_99XOm%Lu$BIE6hVO!??U(rm&|8VRj^aOb6HU|{`PnD0`lDv23#*)
z%1IYmo)(HI%=<|^pKvcw{&S)+asM>ZmF(s(hE}*8U6N1oHPEMZcY`}M_iH&-56dkd
z%Cq#8ckT;7vC5D>*I+m^^>G(`)^mAY0;PfVoNz4XKqsoF1kV=?3ucvN6p#S%n$W7=
zsm4tA#8f^h=ZI~{bWW;wa=UEYE7h-R93FIcU_%M<rHj5Ux(yAim{_)r^TNeUPtm}9
zb=oNr{_@_Kn0;phDnmrE6dl#~(BXWx{;)H6r<=8DRyhiPHCR|qXYsMu@P~6}%0?~j
zZIN;TKT(O!Q>|m=+q*mwer|N(!k*KBsG!E(xwCI(dpe&xDAZ5OL{RsP6P|TxbEi2$
z+x^rg->N)#TwMjUdY5aNxb}$DTI+h+1O=L;%CUu~SEK>e2fi-B935Ve2T&gPx`wSt
znjN<f+0-NI3e@BU)MUol`<9KtM%ItPivle3(5?YPRoKtNl%RUtDR^kUg{*u@RXzbE
zZ(1;WgGYR%T~pu_PmxghoTfbd$k$%Ds~mcwMpGVscfs02!j6dZV4+FXL7?HFJkjYo
zQ+<#Fkz19a$f)-HkHhw|JdfTxW4KW!2cw{<$fW6k=yfTAEar)+{GMXxrw@?=o`Ukt
z9|Pr&xM;?0tg{ptAt-!|Ehp@w6dFq$e1|O|NW<-Tqu}DwF(m`btWof3X#fdDNOyU+
z5@XM~=EOO@Snm5y{X&gID}bGSO)15rt2h|6ch|XOg?~W7sCy|iZPQXA53{G(zO{!K
z>CiB;Rh(UP3@ewUa>8Q`HWSd)>QdNnxNDXGXQcWM@L&o<K|MS*9>VItVx8S^8x=#A
zm1pPZzNzRJBKUX&kdyTy4edi>@Ebm2fG$pWaWVxZ-w!_mumy)njwbLMJ!1E}ffRwv
z61*N{6e;$M-n<6R*tk2FGC%D6w}0R^4|_U}PxgLWv-oO41vnes07ZTpw9QNse0At%
zVy+caf%BY8)p2N1lCC^|=~PE|l4opX#N@FFMCe}sbZ<4!_Eb4&O?bfP!tgXs==z5&
zK#K>Jf90n2d^yl%6T0qVK3ZS6F2~oH0iNeJ`*(L-?T*HD{JE|hPyPW5958V3DD)~@
zt^kF!L1h|<wMUg57_v}0=z0Doz(88Kt1BfUT@OF*x(LHs<#asZ_)8BF<At^n2P|G1
z@EGKHD!3kA6(|WrS~}>1hAq<N$cyZHT5l_&5;W4W|6|bN3aTXpgio#^LIC>=(TA&W
zpQ2A#W{n7o+q}OYne2sUMX7WcS<z}+U$<Y(gCX^G{}REnvWaS<jF{?2LCUD1M7z{V
z#f&K36WP0JB(HLKFQ3HuEa+JpmjKC#<|D#$;At0RJ99g5Xh7ij>6u-kQg){V7U4D<
z4Tn)It3MUtLu%j>!(rD(R(g}EfclVMsM?sU)vD8YAeC1R6y6n*TU%rZ9z*%EJAcJw
zc^7T1p_Th9HnR*kdZf@jUqz`6IojTv66Nf2_k#V2aQw0Ji-*xN^5%S#EL_K{X;(3Y
z8bP4m6D-xjt0EK_BY#iP4b1NL+!uc*3sId_KW{UZ-cSL-*<ejCKnif6Z5SCVb^xQ!
z_Wrw66;wO=*vZ&DT&g?jt40FWIB7{8J%vW0=D;46R~F_#8kVCN)-i*8;>L3yopo^m
z&z*v?%4GlYO|Rgl@DefTwhYTTWSnVu_L~P*sf2e0yK_6U>5h!=w;TWr61WZCSR&V<
z@lNV}Q&{jRbv-U(J5PAEXlO8<GUI4v$i-}-{{6X*A$XI<zs2Tw=UA>);OB=R7H5%0
zShle8VYP;5A!SQG)bLr1x*?njGbjqDfDB>V1zd>R7CVhDj6D|_kZ4@$b+*w2U9MJ5
zzM=UmonyQw`zo$QQ$Hnf8V#Z@hg4)n5tKebuo8V1(ODqdl3|U*c7No?DHJlse>C_^
zVyEV7C-$zQ4&N1$BtlmjRky2kS{X?OWip6Kc49-_BXZCHBoCKd6PA$-f$r(@(i`|T
zU2sksI0yq>w(lNr%eBs|(@-L>$TG&oS+SSfUPDqx4C%C~O`r3KuJ4$ScX0_86>-a=
z_E1!4VBa1!>l<(daA6^cQ4-$<Q~<z4%7t5c4K*xIs!+NEk{TuioHP!LH9KV-W;Vhb
z({LH=Hs^w^=17OJIZNQn84-+4R^}$^FCA%z%eB*`p}=!yo`WG{>vAHgZ7-@L!)+p*
ztnT?hzV%$g^@AF?7HpEu4nv*Ymez90Q33WhjWMa}*u@aXt5Jic1vE@mt2tIVfIRhV
z)Kwb|vuA{9<E2n(7xZA*fNn*`0TvzU;%Zzqh%%1|`4q8oKG=%%z!+tvr8Myv2c1Ng
zG@@&HfO*rCZ7GWZsnmFbKx)e#3v?Kpf-|a_NHIbTE!{Os1f~L5({r>T*HudhBLOUG
zmM=JIKi@kdCd_CZNlx*uPGP?jLo6J?!O($XH4U!{T6FV3=xjFg%e*=UY$1*%IE8p2
zOd>*aup<IIG>B~018`cL(CLjU9zGNzhTvI6p*jL5C5f=}Qy-E8L>~Aq$ejl90FXcq
z<Q>fI!EfBLMkNF3dEv6hFj=OGj<iF^7R3ecLXa>C$y1o*q7(ue-d(;?m&{&sbR{*)
zvPgX_pt8QN^8^?AXg{YS;n!0c=0aU{D3fxKQZ$5RVobi~nTmH%>rRW*Y^pDtGoiDE
zNiN_KgP3sU33s!q1PJ{9uuy+SC9!R;<lu(*E7vPIWB}A`5Mm@3QklYRHuDdnXnX8*
z#-`PEJ2RYAUw>$cP#%exAXERI3UX9CYeCXc2vM-S&4A#bk;j-=U;BNTrifD*ukmS9
zbcGO&XoZECS{HEA+FKvwL1BpqVt`YUpy9mdfyg74#|<Km#akX09HcoFCuS>nThP11
z8$y_i6fdZ=d<czX7PH?>TL!s>N(RviZ8m~Z32sV$e93zWl88)+P{@TjT5U%`deJPJ
zMbe}pE&papmXn);5X6kxuTZhC|3r02kt>~vXu%L9{6Oe}pE7`+=^<YtMzjbctV$y~
zfeT2*;yRuK<4DvR5L6(smEu-(C7|zwVsMFb^tHeV=qH3@@4uwSu^6~%8^@p_G$ZC{
zu)y<yio8(-ajMD0NK|7d6sK<p;}u0ECr<#P1BHPCEKtDlG9S3<?t@i{!-FzS&nyz)
z)S>V;G3R3bSZgGKz$(nFgdQ=w?B|)F>12q(vIR##`PFtKzAo_50gJ$atoInNFlq;-
zq?|wpbYzQ$E)*OMn7jmxsJxe?7`Pzs-mKDZp9wI#$LVth;dPbb1&VC>I3M3NaR?r(
zf<_9wEQMGQ_+k$`f<>c6Vu1o5iF1A*fT1%T)?q;iVuUmb)DYsk$smN`AY(?4-^ix|
zEFn6sBREoJe&n_0KtQ!fTDw<40fHe3mPR5FAgc^@rd&-g!o(>&lH3ViL5w8XNwChc
z8;ri@Ya|Gw5C?X_s{{yv7sOlr;-bM3tk@9ZJ@bPHj2NT3qBKVw1iTG`c?NjzBiM*c
z5#XtkD+827CsVR?8?;FGS@3iLvj<Cjia#z^u>j4F9M(Jlm4luKh#als{1L{3fdn84
z5HqTFK16_)tQf^LiZnizydnS)E)?KK8X_sV7(W>WPnDzsh@oIt!S4j3uJ;5;6F@L-
z+sA|$xCZDIae)f#Uc9!XE(6>Ma07y7fGq$afopRY0i*HRiUP0!ClJ5{U5Wq^ibkrF
zE8rAArT&kVGxPpgNLTw4_udwBTDOnF5?eSc+g=boFic1D)j*6MH-8J@BWV}`P-XFR
z!2=lOB!<sSvrc<NEOqMu5xyq&QNVYdQGVOhiK*zAM%2d=kr~r$yNM6$CQD*d)THSr
z-(RuNrR9Obt!gl+&kK7?%)-$$1L?0!51sv{NesZWyFeH5gwjd-70#^)q$b-6uFMeL
zFBn3`|2laOpzXOXyVyy3Zo7KHLFV*W6+!5~v9Q>&AvZ=b9(s;|hXA@qAeHyzEW@(m
z1pr+&BqlR%##%vBz^6fIA<l@v8W7zq*G7Nc)+aJk&{8--CCosBgTr%l0x$+x`v8Fp
z(1~m5Is=z#ona*aO0D2j$JeK6=_;Mi&B6efblqA6tkZ<_C1GZs#izbmoc*;oyZF9S
zbW|3#)luW^dq=lHcu^`0A-5MyCb6`}D3U_W_Rt#jnBY62_g)KFd!iQa^i6DA*hKx1
zT%qLwpcj^3lPtFaT(`+p@P%YY%dBs=1!Z?0NkDtfzC)P+KRc70LQepoDkHSu{<2w|
zuf$kOEdK)KCMc2!<UwBO>zrWF42J$LE<WHwd4v5gd^sH!QTzyi{V06_#!d{Jf8w7}
zKE@$%qroxw-yVM>1%^EYyhIlWN7T8-au4Q)huVby=kD;Shg7mef0Sdm6A{Z|1-gB)
zgB~^!E|K%#S+HW9RZDgQCv3iMr*%IBg%7k+3HK<*+rPs>pU&m7GCPjR2{A&2|2+y?
zPsP0GiRzh?>i|QEd&7RLo!c$*@)+F;1zrbR+-~f(5jLVhTCi>)I_?j8kGo^7T1YK7
zL-39OJRm&w4DmecPN*213y~wA!ddVsBn-O-6J22%ML<TBWYGbnY1=vbMoXSLz$S|d
zNu4R{)_#z)?^T+-gp^v;P^HidSUQ*Ucm|{prU*F_7nI~#&c8Ir0phvG!-Q!!6$v<9
znlI{?!N}8-#~n^OoL?pq=!@s5C?p^AdRIMpBeX=3Zw5p}LIa`F^8}9TLVEb58Jg(R
zpTY;2fpTw+rT?3!{)=V6gTQa7l5f6E3Or7F_7;@Y9&-jYd^o%FRe=%bV5UWf^JxYC
zy(Mp}lH81uZ5fG8FG5$iw4HlbNZe-iNkeGFo^k}lPvJD=&><!XQUY4{^?2&EqZCpF
zJ|asG>+Gf@x34a3>!3wba&QsDfj)64{ph1XK+kw~qW_&t<!$k6(G;5+YB*=XuIJIZ
z2A8o<GiM?Ox`*bYrcBxR<wNsO#q8#xQDo-CrR=rOP9E9nmNG<;7@K;q4jxv{Y>jqh
z=FN%Oj$c`fRY?VTu`$khawP|)M<W~8(Iguck_Bh|OWx|lYDw&)BC(LD!UsD=Hk*rz
zvXZ9XsOK7?@GT)gD0mzm2hS06!a)c?#C@1m?L0+#ng=8#o`B<i<r=X3`>5?AM8ba)
zVe8rN!0=A<<8=Q#OS7`dNyW%}1%*W=N?|@BvK-I;CMq5(GyeSdux)bzn62Z(d)EU6
zS7cNwZZ6*U0cg8x0Hm9xK>#9EQbLFrXQC*_>Ba@>WQomP6N~@2FsLA?zpKT&PAlsD
z`<uidqo0>{w)K*3$$*hRi`LW?X8W^X6h|5XxAL4K!IYMxljBnp?9G2-CHpZe*1u7+
z60$H&ox9=<7ky&mn>v;2Uvj%Uuq7ODTyfHKii0L<!F&a$Hht%dnLFmYm^sro?l@|H
z{NI<>CU;FQq*y~tb2k?uNNRC@P88rEdz|t-y+A8eiQ4}gqpk%L3q2(vs*-6@Wprp1
z17sSaZ;{G^SZ0We;h-`YmkKg-O!`!{5o(@hyWJ9H)9%8Vbi2J42thtZH)WuFhYpZb
z2nOPB3ZFd7rN!bc4{m2h1;Q+#!N{}T^~xY<9npGl-W7h7fxSA61213!s0)#`=|C|R
zJ}sB5(;qc4P5)b=hG6s%)yJpZIWa}Mt+|RXz3Bk=QxRABl^CL*6qbf0Pm9>(l=kmB
zZUx=BYkVaRrVDoX|BvI>b5lwJnsU>pEv_=9>~I`;pV)Z=KP*LtSNd`Ui2q{X#OShR
z-JONL@Ob|Rg;^<RxV{DA9K7hb|956s&MM-Hn2(k<@=d(pxnWDuPvBRw@ESGb$iD`Q
zxZ`|o&rBoNv#=I4<g+Z9Z&3geMfggTzO5m;zn4bVSDUdfP`*P<kWc_tzL#kfk_rZ%
zDdVT2fvmR9H9Q|FJ{TE#F=-;MQB+wO0}0cDlTsmyRNO`I))R-ki)E}h@EpUmy90W;
zFhkxPFhkVR5iw8f%7KvJM^z^Q$<lrSZ&Jd~V|y^EaEHGAWmF^<0=m5})o8LvF2iE+
zD~XQ$oIlZxume~9G!gwj_J#TbU=WT!BqUrPpMo27n=N94;w_;7kx4O_7L(A<MS|Dj
zl0~slY6F9ktfy$g8ER1U*2%RJecE~wpj@9vMI@Yeqo;Xi8(BL*%1=EfD6R0wAeN-G
z7LNvHDUk)r6v;{s;OZCUB|vvafNO*t!C^#;w-~=2POp`WD+xiQxl;#1z6?E=>IJlE
z2{iXYHisY<SoWfdP9jfq$AD1>iROhYsLh+T72Aq3^5;nx^Nk?f8I9SLP&hDh)KQV0
zH)r4eZaKqEm#NvPx#kPkGH^ls%(~wxNns3Kh1PactACnmQm9Sf;mWp=*<>TF+HzTA
zN$Cyh0H#O338x|<n`cL@yQ7!kV`9coeEgd$e)lyC6aU1EvN+0Vk|e`CGbu`I{ZW%|
zi9UgBzR^i0hR*q&GwPfOq=WOBe1?Y-8RI;|KI|`=aHeIUKUT^dg&8KyKHikBJbZPP
zZI@yZ<h}M?KJPNS_U<PzD?7-}JA5VbgH514CEOjb^609Njbc2}^zuxiZNqrH1p^(d
zA~YUJjYzzZOI+aM0Nib-xq-&*w?Jh+bD60<#NrgLVo5cCe&bIzxT@w4rj(&ViAwVb
z8zAhINi2$1oUd&O?$T{Zz6;sHgAD)(1~V$%%8d#~<YD+D>gp`CSL0A#9FkJtD8Q1x
zIL%oPW=xUYbJi&D=<Kmxx~RBD_fAHWXq&|n72lBHfdG9GxQ&X#5;2_^m3a>YK(wC&
zNEtU5Xo+b{K8$lbrGn+^(~*eaOa-VG#RQbdTdXVwr99+MSOXIx^o?xU@48eb40i^@
z&dnG6@En#TIGC3hVGl?H7Id{#qT^k+^IMg<e!BrZuE;DTWx(8gzR~YxT0aH(YVDvy
zM%UUppvJ2s1njkZlc=5kX;`<(#dzOw<$*WY+I#0wHC=wy)%W{Fd+g$oY#iE!&66*+
z+r?hI<#FniCEq)5T#p=S@`2f?2JJN7xym*?>3^loy;42-RU_u!E_;uO20d-swhgOZ
zJ5s#;U1t5Zk*oZhQt@@V&pG$-sI#k9>gjDuc8f!;D7K}yO}TD0Dv5V#OEzi<do(K1
zYgG-awAR-v*DtB|=9>O%8hF%^TO4U=+P+#VOwC)gRMf*nZjg!(X|1NmeSW`wwbm$Z
z(Dk#1>of6y134_nGa$#C1<X^h9b?6lhi}eOnPz0InWi~iiX#Ud5Sm=h5joD{_#V4g
zg>qVaT<f2xrGV=zuCjEsJ3(xXh>`1}y;wRr>`st$*&L0Z_XOQ&n;N4nZdG7u+U^Fp
z$a7nkl##H&<)$reHj09WDi><F)=t|nG;^%;;Rt8_8RN*qTAwi6VrPnmx9sM0ux7W6
zG%>GZ*uc8U9k&&%5c{^bcJCJGxo!yZw|b^^@0#7}tN|_4Z(e3Eua0P8!>+s?^_9q2
z>~5ue1Y@zBuDJE3+I<nXbxTIu7vzItcGsJ5ddsbibFO`+*dover_}CX0PUg9(8Qj)
zuIZQktyr@OObR!DbZ*|-P^)K)zbQ7@zbqFF0vk=RT<T{kyQ^R}LNR5JY?@>1AQ-U9
z1~Ryh8X}-PTV7!&jS+RLpkAW!*EGEahZaDgLF4XhPajp)7HeC%b!Dg5l3U?zWzN<M
zTybQzz?SC^D?uXD1&KVbn-=C+p<%^|)_|&}!!$KE%i4@1EENY-@=)ziwMf-qQ0+z4
zCKX##%|I%zYICbmRYgeER#hRu)+Osdq8V!vt3>e01LbQXYk{nqX6;6@U#zYg$zaPQ
zwUP238EYdw%3@!(vJk#zHIP>b)<tilS-(l#HnVfwziTpPwXD;%t9G+S#<iPA(R^z)
z<?_j_)xO!wY`LltF3gJ6GN<K0DRooIWmo#FRLYh-byeW1)laKAEO>)ij}bxQs!ivU
zGkN4zYdqZ;!REO^B??rxRVhX_Iu%PXHXkYIVrGqwDdufc^%s=KP>`P3#*kt`Nj0R>
zml8y&45Un&3PVX-q&k<1M?}-ih(%|cB%D7y(WTyy5jM2A)1aDSW9g=%gpA4p^an_r
zBS{}<`XfY((eEdH99(trz_Yy%HalGN^VQEEJ{<0YhbR6W(0)PcX95oTIN*Be&Bw|f
zNN^DO^NPnWjw_rfId*aE?J2^8^A5A0*nVNh<A<jj4lAElb|k`qkMk%7K8y_;mThr>
zgAJzo8JsihYUIEG%_*HDe<p?uKbQz_F`pQ1K@no9A6l0w(7f=+ANUNF7XS=%7*)JO
z+4uv$^>5vjO%l1E0@Pnva3z^%`3_I2!NT|ZCPMsne|(o@x|Ic4^RNuGx4P#yM+Z~@
z7-rmIf=0eFIk`4EwdPQ(EkT*9rP1^MVk$m{S55@99j%-7qW3F6zI-^uBaxuXAg78<
zm&g3-8j>s>>Y7_Q-{m%GPQ>&`K7K0k_A$NIh)9*dAH`z*i+om6VSg(wEbP_s*Mm16
zgDf5-6o)1Du!Vttz^AvQ3fNQ2{0ezXQVCHMu7VWVkF%RrohVicUl51scD#xa;Xs&M
z^>8iT-`g%ut;muEv>$?54%i508I+KQVKVfE9GUxll2=$X7uq`@f!Qk`n|fgqq8ZgJ
zT@mHE8k-Sau67meNXdn2q*AMWr7BtxihzNkofxnWuwWfvz%;>tSyCVoREPqY9;|Gr
zSjdm;NTp!VrC`jZsxqfYF{el|CrB=&NG+sDEFerPpiC;DOe!EuDj-T;phjIEK(#)B
zWPJj_`UP?2@~rYQr1CP9@-mF-GKA{+Jas8_bt!CdDP~<1u`Y^NmPIQUu&<F}N_GS+
z*jBH3S5&x!*NSp$#UVA~h>}u#l2UAvQecYlCPjFQBD_QqL^sH<8-!9DL{b`LQW@k>
zGe{s}4?w{jfqXdv>~aNEQpy~FDn}(LM<po6r6>laCkC`8MzRuvU5MeSNTI1nVW~?o
zsY@Y=O95yV!q6)Pu9ZvPJg;Exxr4IS4y#x<i%vF+U^WY2HHu(0tzg!%gG#oID$P+(
z260G(Fr-10QvsD*#xY4o6>kfck^D<R_)9@-N&?uLM^I>$gaWIg8mdhwA&}A)2`$i)
z4FxHX5vctHk*fLf79OM7;w+v{sxXuab{xE|L3#j6Q4FCbiBg=EAqf)DkswJDkc}FL
z(UTtlxqMuFzhCI_d5fS4>yhdNgj(t%nxTZDEagIaQDAJU#I^^#-r~~!qlf`}tI!(O
zS};0M3D$~r40#}eTH^C}>1&T}fVmwPv#lUp%{p1yWgU_kt($CqI&|Tc5i^GrLXV8=
zk<1VoYk+v|io35G`$BvytQ`Taa$@zDlSkSYjJP<2;EVUGNSTFR<Tn6*MdHdRKGINB
zZpD0H6V;mG+UV)6x|#<~cMwcXeo`y@7o=L=)z@8DTBL{!vWu*PkhnJl;}GUC3nLIp
zFAE~j+AR&jk(?<D;*f$R6CzOnAq>Ir@*f+a(NG%;L6CYE72!}a6Bl6dFby8!AO{m>
zCxR74;I+A}Ss4l{5oLy0a3xqPRY%cT!RcF~vx=7j(jsPx*Bd-a3{8?8H{Fd4+x~M9
znAtE2A~2o`gkj0hjH==hdbkjmCx}Z_%tTt0?rC9Qd!C>(YSuyuNC0qI*Qzu%sco(*
z_gAf*1hU|ux(IPMNo?ZBZ!tR<7#Ln*AxU235wa-OmZZ6+VI|Zl1=U@zc>+v+=Z72F
zmIQ0igK@Ai+0mh#Yiqv%t%-^O5ob;SuMkXP0GLH&6Zn`-6QFnqs7{O8<jKJz1P&fV
zf(zKE`{e~XFwjDuBK`_=1GE`H`rt;<$AJMAnHx%a@g+{Q^ddEf0*3$?ivfsW2%7&I
zlxF)?ryuYirI8OS_(#N4pgB31d_Z@ESVT*~5E75G#E9EzCs;bZq8^d2tMY>_phb#W
z6G(IPK{Nck27)Ai-<kV0hIi8SgfQ@CNLZD28g#ql+LuLF6{;F;o8ctk2OuUgD1L-O
z197(R9K$7z;gZKN(NN~YH*)N02Tff!=fd<x84|a9o`}N1n+q1yPAN|!7+`e(MEXO@
z6Wq+2N**X%Ld@w-zWhxQzai*}icmYI@UDLhBcE*vG}r+t8)!w@d*){Cfelb;t;lNb
z?Ulq(mEFCQkH!;*R+i;`h$#v;*%US`V*IdOV-+@puvn2yA$>s*BDGBpT^-&MJGpp>
zO@j{!q|x1+9Ro=S7N|WAWqr2h3>#*I2HBv&)<{9w;K48c^cB?o_|+hC!u}1g4F_Z#
zB2<HfO>rQT(g#3`styr3<|+}$u!Cb{r42iHMgijh4dNqIq4JDgP-}|kB0oH$q6)Yu
ze!IjQg1P=dT;{-=Z>T2PY6|D{71;TpDYYx;YCFRap}6v3L$@#j^iA&*VH*iQ6^n{X
zt_gv01y6uC+YDkMy^NCkh2Xn3ClLBs6&JfZ3;YdI3;Oo?AP|%m9#K_najRVwP++G-
z6ypOHRW#nf{t8^c!V#MhIlk8lDf<DJuTbHMTJ;XVl*w!OS#KCP+njXNC@8Lt;MC2y
z7@4;t=}>yy3M0%7aF#~68zWP42&~O{M<4;5Y!B~ukM9?ch~Tt&0Jfvb1s#;RUKF?9
z6qg|CP(;*njr%yV(}DqDnFFY&O3nf))&JCWe5OOim7FRkT#6gyCqSnX+4c*YJ>sB*
zsc>jq{ed%MZt7emBL*2Ks`V>4yeLo&!uRN8M3L4LGWpQaEPf%KkHH4TxZa`W5(0IW
z90pWf{fsaJBJ&o-ru<?<<7-v02qVpvg|W24HJF`aG?=~5964O^XCP+KJ2D!YIEY-q
zSe(qlQWN*DzI;t<*E2R?$mUtJp9{hv7cO3)$}X9yMX+*`+D=1aLZC>2f-s!4AxNxL
zP^2~vtYo<nT`qCf1iO1y)WLsh;2SMU$_y*iUwvH+sP#NmV$dzoF@t0)J;()NV)eA>
zX20w_+SJh3q*d*=A!Iq5IXViN4G(*@rnCWakiRSlW$H((!6l&{iwvPM@Ny!A7-sV_
z#m3RM#^X0@!6k&)tFRnObR9q`l$mjM-U))88ibH7paPp4(NCjw0Dm_D6;|7uTd}go
zF#xXik`||I(fQq|EX=86H`@e}DT(LB0E;P6V4x<3E_}Gfe*$ompvIWmi5pU@Vnk|7
zO-VTnRul*&XGU}jBJ!u!R04r80RTZzi-;5@3GyMa)q@=VV)3MYGys9{0Tf6!(Hzhe
z0x=e945pe+7<n>@i3>2OEV7KU$`Zk>J_NMBk(bdX`ZFPoXHSsp??BLhHdlu4^k@L`
zeN^5J3N*E@CCK%&z|U2E_FM-qlJ{`#k1p?!&%v@yhWn0S0Y1MBkAx$cKvk;ZmC40K
zTu0WLNnivjz9L&msP7^zpax$+Sqaj}Sd~J;Hqb*_3acSSWB?cXD+x-BVPUKcl|h!E
zekbWag%UpsBYRT<r3uOuCn!*qp+ZzjKzboLL_Zw}{~ZXw4iJ7Y9b=*hv%~V>bMdfq
z;6az*!zaOpKZ6Wk1{gjJCVUu5jGi9`621fyeF!7=5Jv1EjmSY4kcSr<<XrIJIG4b2
zFM#4-0mQAs2^)kD_X;1&0inGIFc~IQ$HLO*iSToRCTM(20*b7xkdTrPk^-VYMo0=6
z;F}y6<AMBe9E=Ez1);HzaN`c)#O=e0yN44u4khdyN!U1%uyG?$;zpsv-2;c32MY!i
z<3Qn#fx{gGhx!K(v<@ER96881Wsq>oAmNrl!t8^E*#`@<4i(}YD#SQdh;W+_;Wh!n
zOap^>2L|vC4B#9Wz&I^{a9aT2wc){Q!-Cg`1#b=t-W(LTI45v$O5os)!NCiIhj#}K
zt_&I67&EjmV`yN$(7}D7g8M@SmWB%L3>DZIC$KP2SYVd0!6jjW8p8+m1`p~CA5;)K
zs337rK;n>rr6B_f1LrK!w5yH`xW&`yu|T-TxnhC*P$Px{5awb~p@~9)fTHBEtyYj_
zHdDkoLX^xDJSYU&KmuV!!w5i3#9=c9tSPKWNofQniHJ!O5R6TN6r(JR5F{`p0Juaz
mAUY~go>*7_SZbJo!wErJEU2%At13EIBd@>!000000002K4fN0e

literal 0
HcmV?d00001

diff --git a/docs/python_docs/themes/mx-theme/mxtheme/static/font/Roboto/Roboto-Regular.ttf b/docs/python_docs/themes/mx-theme/mxtheme/static/font/Roboto/Roboto-Regular.ttf
new file mode 100644
index 0000000000000000000000000000000000000000..0e58508a64bf413a58c24bf60a0e8a90d663f304
GIT binary patch
literal 126072
zcmeEvcYIXE`u8*EoZS>4kRDPt+4SBv+4N-7d#@y<C!5|wdLZ=Ps|cZrfCvE*A$k!M
z5xI)U^&%n)B4W8*MeLA0`#y6vLG)gK_x|zzdBbO(Ic3hAnP=wtKF>VQoCOE~=)n-M
zP!=B(8#l>ek|U5@Z@iiipOK!u@><Gsc-{lB?o51kZj`-~<YgdV1_E&5>DgYsCy%(C
z03fwkzAP^$wV?Uyu5<u{B><vZl}+WX8?J8h1Te$<dbevDdzA-%FwzF_8UYyNYOBku
ze(qIN<FofL$!qb#BzK(>tj`|L?Q5Gld(L#^wc+^%fN5tMTPn-%{<7~nfKv*9c12Tp
zPb-&BT=9MambYmxZ>qi!diNC|ZpX2Gy{#=BozZ1Cp912I{jpa8?JK6!nVp5DQ$v3Q
z9c&8zox87(Z3KAt@MqsWT&eoIj<<FPp6UQYui_s*qt(by#!HEK|C)}sP?P4KKsn(G
z(h)F$9uWQEK8xopw~Va8J0RLBl3|tBnw)(PloPMfRCo&*bq3fJ;h7D+(%ReJ0SW-M
zN(?-JQfn)NH^`23Y!w$QLQdi-LmttxSTE)@F{@z-_Xy&++0ZPy0d?H3P)(|#hIs`R
zG8e#+Z3jzkDWs8gV8!@@17iVe*c33p@*S8CVk*W|g2^A#R&EgHW1fns6;l<t4~xiA
zaN+bJfvbWooF^<|--Ki>FQ^v<V%?A6fXE-ZMG<g-TY;$&&pV(`bQ}&a9`F)Z5B?$!
zucyHQ@fhC2>!Km(5=|EJChniGpL-QDxbxs6@`e>6Q?S&U4q^CA5O)ctau*?wv4(YQ
z0ytx?!xn&o-3A=fi}wqm3ER5@$73cpA4<6}C}!S-P`p;g)xrw$9jqi@sbA&vF#is=
zX|b>z+p~gO14Y7fE1;Bl5zlQf-wrxl9V}sQfmF<b8~Zc3uy270<^@<rLB5A0m>Y`P
zU_Kr%=gN`S`=AK>Q^#EcYw{0R&wUAn*gmB=5sKLLu%0~&O0F5^h?DU8LFi#rFprCe
za^_oz!W7QTgMMxaY-PWKXvPHA<9K#5r=XABgXs(?aXfRy{*WOKf);E`j8>oe0T%-^
zM025w8-;z`Yp`5=7Sgy8n9Z$)cpT><ZX0YAJq;4_5T52ba6Hq&j&TJ$(Q??0?^>(1
z3+l9Dpqz}sPUe*Qj%W^4bL+7mYmg7$!xDy7ALjy~f;|TZ*p;w?t%tdkkIeJXi}m=j
z=b@h&gA%3?W&^0?m=>XoFUK6^dCZA?s|sYM;J8nMHpUcr(g8<Bd%;@N4mF~1@Wpoa
zh+c$an8%7<hE&l~C=_Ku8@CXuxWB+ojsZ_>XBn3Pm7D{PFSeOm1%1qBcpZ7^z~005
zzJc?RUQfkiTGkyhxnfu?+5`Ebtxzi741L@#=;fl+*To5tBvON`s0#YEp8_B4y>L`K
z55jTWMnscfx0nN8t!S{Ia}ci`6&(OCkrt$iJs?~3Asi9)!6vQ6Frsw^M#Ki7Fa8G{
z6Nz9C*0)~E30@MJ!3>=9<=i^h&K4oR-v>{z4H$FNpnw~OZCn<no!~6`7PfQS@ffeM
z*YNxup}*U>8Bincfo<X)*q@(4B9>#h0!*n;C0>Gg6?k#A>Mu}!YQ%4WllTtSu~e<1
z^=N71b8aw~%TwRba)*<mU*Re43OI9dFjZ?ZTog}+SH$nZdeNt_Nk;@PX}yPY!X7f%
zk06DP2g+-aNEb{v7CeP>oIhFw>$Hd2S~{4!!(5Rs%)&Wa#i^hZ$F~L(;<QSMX)nOT
zm6-O@_lqo0j+fyas)F6@8tltQaFi3nT9FRyp|~Pmf#baZ!nNLo{mAFF+CIq7AMpM+
zIQD;oCM^%}6V1eV(h8fn2AGfeGO-s#aHrKD3w0F2R?#u66USY&7%^)<)MBdRUV>mY
z5kk11AYZ%zW*~p6#CaeWxq%Ot1lgQ6^lF`j81XEK&_b*d?}9a0wu`fZYSHs37d8-w
zW4VblLQUg>NIRaR90H?@7&{ZM&B0^TEMzwV?|TCLf=GdLCjzl_A50-nK^Z1*vKj2j
zR>)x9hg#+%2xZQKJz`26w;J-9W3UWmYJ;c{HfSA$7)A+=m}a1?haeWn*)p87h$rH;
z@GSDHRP-hEvzMS&I~aD02+EErY!N#`j`#!Y-@8yLuE%+|8ZmekV(Srz#rd}%<st&h
zS1^;+BiuZc4T{(L5YBnQLga%RlL9ZIzG!Ax#LH%|rajS?L6NpU%)~Ziu#?og+3(dQ
z?6=U&?u8<vf*mNAhA4~6xB-;2A?(9lOn=4mLnxo0!7}nCY-ZmDH!cUSAIC9c@E%_0
zUdHk>g?t8%!!pF!XwDJm<^XhZHDDw*#_`DkN6rnVaCV4YQ?b6O*xsp#XPV@V`4D1v
zJLXZC$}r_(yXIqC`!H!R6nTfoMVRt11!GFbl*n#^)ri-_Y&_!ETGWRQU_r*zC$PRc
zE*K<iFXXd7;BgBSa<Ak3MjR2Pp#0k-HW|V?Q5fb^QSa=4x$H2?80tjPY@C;KVYcWn
zruB#ew=n+<X0v}s+?t1avjFwgyU6bb<h>^(ai?G&8Uj7o_Ygtf_#)pCYpL9!{1$SE
zuZY)0g8o9?CFrf$I99U}6H%X{p6sSNPQYNQ>rsD;iZzKt)TKI;*{=S9>ft|4sG9}7
zOm*{kVm#EhP&bcY{uQRHD32PQO!e}UiR$AglAw>N&i%7Q^)T(n6Nx-jpGRG^3G;U`
zz4BQ9QXTwcLfuRC??e)GFX~^x#=!UeOHTWR@1wTIzs)KCrlU<j`%K&OA98B5Q2zgE
zjy$3^1@h}9?8kP@Phh?c^Lv<IL4N9x@6-=5KZyG0CH6Y3q_z*$ry^?qpbdf;z6*8f
zdTI+{o=<aTG2%JR-+^s(94LSOm~-{&OLSZ)zy6qWXg}dRUooC%kLQ(wO@(&Nf6RqB
zgtpI<`5T}^$DNMFf16XAjoM9gj?l52u-&NrL~Xbyb85#>yG5{LsLe8@J}V@O4-{|y
zEFlI7_(L&hJdrQerxAlzVg4qj<Bze2;?I+b;?9$a;?9%rq4@S>n!vgz-bLRj;2y=k
zi6mg4dLzDj1E!0ZKEw7bWq-rOc@!5XlF&Yij}sU<(MF1q6NzHwlZo<W0z04V!}zg)
zrL;d2SW58}lePk)1-ZGezArXY^UPY{L>thy_Eq<Ylptd^tDhF`qh1u`1ILNZJ)FZY
z(K-7^d!5b^I<9yxh~kDY#?)@3wi@bX!A3(nOt3qtY*X77?P+RD<Gb!s8%>aT%mo{b
z$|Tx{bdJ(@3U$);0%9<IKefXiw~v;?^4PwkziSuTlEPYo;sCafwoSk%D)ZF-qU{)8
zBM6v+ZJ~G|*lH*rD2FR4E(z-dwAE;tiMjm8d}3}3>npnMk|6G%L_Lo(PW3-+7hPjK
z#;PH8yp}(NYR$nl$#~lZEW&mPb}!nh8v9HANP7kp=_I0z)3p%VjuW^h@EC1k${%W{
zQrl7(AG-E>0v9J_Y69m38wu@rYRgYN_qcB4A>|F_rI1fx%Y;ohk)w@E?NB-nlrR61
zqirnMuV@ofyOr{ujx}A2Q9GKWcJCkB`TKMA#owR*8=U_?pGRHvA8?&pp}x<BsYm~;
z9OWhIr>8OBgL7fwAMy2x^LTEJ`i&>fe~-r#>!|;f{}ET$AkN~tYhwNNXZgh3`<Hwd
zVmv0nZo&C0=nlHhKv@vh8RKIj8Xv2PoXQ=Qfr+^>VQW(zClaaOrwPljY>|2l$7m8~
ztNz>L`9k#+zC(~hT-QvD^~79#JP#-?G=c%Olc}xAOog485g_yrCc`h<YS0F4H4i$V
zqgH_~=pvm2laNk^$w;Tb6!jSTt@Myig{erV!8D}$ps#)e24H~H5DbwTfe}(;qz}<I
zV~o@kOpuy^Dbnd+rv4SoVLDO^Fh^<$7D%nYQvC}^zzV4~NRZlqHBu?qsDB1qkRr7M
zTcq}2htvV=)jxqFI3RTbN2Jc+gwzG;kKhW<NZr5%sXMqL^#C{Z58w&zNWH)VsW*5c
z^#L#S_uvcONM+!I)DL`-`h!gU0Oa6@GywdO27(-E5Co{dgJ1|m8UjH`Lm?Px7}EO?
z4k1V*AQY(r!jMKnxEd{Fh(H<*3ZyX*i8L0X)ZaiHL?exd7^Ddhi!>49)c=Geh)0?X
z2}n~Q5oszUsqaA=BqL3S6r>rDiZm1HKOhUzkY+<V(j3S@nhTlguOSbzkmf@+(gMgq
zIs<amUqK<{AuWP@q%)xaX)(-De+eZ}h_n=nke0zrq~%bo{sJnX1ZgFdBCUclq}538
zf)dJ+)<6Z)TBt-?2UY6Np&qJ{Hh>aoBh(;mf?D+*XofnZEl`iN6&jGXL8JOJXon`G
z9ng%l6IzhYf>!ly=z=z+-O!G-2Re}UBK;Klpc837%tAT<T}TI^TaBv#=s`LMdXWx6
zAJVzduf7TMU;ycS7(}`NW+Pn)bJU-}A{aut80I2f0`rhAh571_VHqqyx*QfFT>*=b
zu0(nRR>5MVsBw_4fu%^-!ZP(ounv|ZT@NdeZh)0YH^M6Qb-=X`(#^02=@wXv^eI@U
z{t&jpdZbUo2BhdGLb@F`sjtB>Y(}~RwjkXJPa)lf^aI!pTaoU8r;+Z3ZAhPi?dq$r
z4~CIG3p<eRhn+|dz%KRQ;UMfr`W);*dI<I+eIA}szYm9DAJQZ6EYcC!kMt-UQ2z~%
z!9k=iz;j5C!y%+EBE149;CZAk!C|B);Rw=GFrt1BM&T&Zm*E)FSKtMte}Uub%WxWA
zMEWY6KzasVLV6ZXs^5jz;1tr=VHD{(cp2#%@QV5poQJ<4eG^V2y#TKweGBP3a1qWR
z{VSYB`Zm0V^c{Fz{We^Jb4c-Nq?h45()Zv^^<Uu%TtNCacnj(Ka1rU>;jik8a24K0
z`T@Ly^cq}3`XRilehaR{Wuza$dq{7<6{H^{y#SxU-;myf_mSR$za#w=uBzXJ+wcL>
z&)^!;JMbaW&*8fIJlutkkbVI-kbVgtBmD|KQNICS!%d|BfLloK!KX<73Afef;2Zc1
z>9=qP>3#Sd>32w9hX-&M>G$vj(jVYUq(8z}>et{W_!{ZY@DHTFz&)hD!avn#;URp3
z^bvfEbPVnzRl#@aGr+?Gq-yvcDFCZU+6xfr>7OND!6U{I#MGlg6~Oo%dZPIwkA3jW
z#HByUHFdI_7jPf~F=(L%(Lv2I2{pzP)D%-uL+GPsFhq@DjGDj{HNbR4ehX}e6(YSg
z*dV&wBC6XXnmZzjJ0p6#B5J!MT6-c&dm}pgA}aeK8p{!d0}*|L5p_cmZNm{|6^O1;
zh^jG&rg4a(35cFah?*&gmT8ER8HkQqh>AIghIxpB1&Dryh<Y;-?Me{k%8(a2j4m^o
z(PNAmb4J2Q85_oyv1jZU2gZqUVVoIP#*Oh{+!;^Ci}7ZB86QT*_%nV?03&AtnIO`L
zXjXy9SVNi-v*sht)saC&vRbknacC}C0<B~PS&4YrN#>J<WC5HdOA#M?QNk7=^2|nr
zZ6u4)=e!JMv;q1^E3k}+(PpM2!mK3~q>|K;dQwf4=>K2EgfIt5C+Q{aq=T$ut4I@c
zks8uK+88ZnDkEm5F;f@=#*i^$^chpe40~w8STa@^ZCHk=vl9_`8)D5cV(Ko$nWuph
zqqi96>ky7$JsvSQc2aEBPQ;DpQIgN2wB5xS%af@@pEwXF;!OgHf@G6YeD7>BhpZ#J
z$OySl9uYM&ov}d9%wS$&-elflK4!jW3CprN>{Ql{UB<3p*Ri|U5%v`O7JG@k!d~SZ
zI4>@iOXn)M7Ot0D#9iV(;6CMk6cN!hk)ec0A|wkX-$}lg{A!(QGs(u-Mq*=Y<7ne;
zBeMy%3AKr_>9m<+v)ATXn?q8O)KF?7wUs(bJ*6eKjIG#qs;z;oxvj+3%{IZd%(mL$
z;y27suRr2=wOXZC3v-(G)dpwaPUOY&h>d3ui7ulw-v>3BMhu7}aV9<_h(wYcQbuZp
zzV0MPu&)ov7-NPrM$^~x*w<^=S2WVb`?>)8x{6)T?qQFzqwGcYJ?txRj+{3a$7OJp
zTr1bdE#cnfu5q`ypG1sEUjmYF$&h5H<bmV|>?_z9+L+qd*f?NceQkms_jM2U^|{~m
zwP?Jr`cL$=3j0d1uLS!#hJ7WRJ6b@1+7l=)s9!~~kHw_G6pASrQy?acArR_a^~ExT
z|AGI4%tAiBh?=*LFXt6-Q1!m*8Pz$}5!D{mZq;_x7S%#5xj;Ria#JP3<EpV=$9_W3
z^oy9D$Fv*MHURz@rXzTaZ~eUPbC)|Gem?n5)91Q(B%kI1e46@c%BM-6Mt<h?Y0zg*
zpL%~vZv6~!3;nydX5Fg2rMy*ibM>u^TlP2Se)8<iSvNaxHr=ehS#mS#rrS;DkIosc
z(!GS!b_3b+7|ZTpb7;c;B8t=54FtW(1Y?em6YVp4lu0e#7yd}u_`#oKB!=D~Df9_4
zq$z~&1ab@Cj$uuV$pC}3GUqV8kLgQIncpaL<G17Aj+nQxET;eNkJ&d~4sn<8C~+c`
zc`ZZ>#4(t|5X3PPD)%Uf<}d_vQ68!EOBidkf#xH+NfF=d5aApUeHJ33`5>mr5YPP4
z7Fvv$7C=!A`>})pj)SEPdXbqrrk-hF8kr`h8Ev8!7=!9&dYE3OkLhOym_f9PRx`7i
zIm{3<mzl@Rr?|zeW7ad9n9a-<<|&3{I9SI_LcMbU4ziQsId%%G$4;dP!<w+BtQj0;
zr?cj8gtcHTVT8596_EsvvDT~&yueD)B6*RuMFg>D9au-!iFIaO;8oU*b%!&oD_R(@
zp=EInEsFE-ChLKA#9M4LD}#4fKe)vDvvM|o4MY^X4DYeQYzrF#SJ+Va8ym)k!~1Lm
zS_)U;16IKX!8JCLjbfwO7&exTW8=|QxXvbEOyhGzz%SrSHVL)+*YFQE8Fl(UQI~(q
zroeqRl}&^1*mQWnX0WYnCaBmf;Mr_8hs|a4P>^tu#pbhZYyn{iOE}J)ZD%_WT?~m4
zF=jj2S!_4kMeK+@XU*Ag(-C7G5oMi;3unPu5?A8J8F9vJ58KO$IW6K&JUA;(LOh8V
zr_Jdg8v77mA|rmppY3D&IbCiNJHQTdPMkA0nVUl7B!JW7rXpqskzme@gpg3qgfnGl
zvvb%X5=O!igB?i(QE*bumYc@ub9S6PiDZ`xc)b!)Y&E-vM3HFDfHP#DVfT?363afz
z?&n-MR}#k_;M~}Q>~mC4aUPsI>E=YJolrBejcn8Zw9Egr%m1_sMeP4+m;Y&(|7n;1
z&)VfllgScSd52Lu?!>jo4P4WCGI2}^+AQ;!HOyt^HfxSH!7BC=XNflWR_-qMfa665
zB1ciMXr9<e>@DsT9}|Bf{zXeqD@UtQt4C{C>lLlHwZ75jwBxipwO`S`r=!rx(V4BY
zPUjV!8#+Je>gq~#-F3rtvvkXJn|05it2BJl>`A95-I*LZxoYyz<inHSo_uYJ*%ZZ;
z*;7tW8Pl`WYt`GP_nO{Sy&t9;OjS(Ho4Ri5&Z#%18BOz@)-~;fzOKHXeuDl*{rd)0
z28Rqz8eA}pG0ZYtVR*>!hLNq2w^4ynyU`M(!$xO}cw=2-OJh%Ch4B*O5#uW+jwX2~
z$4os<mzkb4(=|&n>oglO+h%ss?6Mg@J$U-i^n2#I=5gj5%x_zmTVz<YS{%2yU~$6|
zEKMvkEPE{XTb{OjU<FosR%TYgRvA`Gt4^!kRtK$KvAQC$lz2;0Bqfpo$wkSSb(Hlc
z>w7kwjnd|X%|ofZR4H8|y<qEVn`gVp_PU*=U5s6q-D`GZ_G$Ke?Qc2wI@CHGbhz*6
z>R8~|>bTqSvg4Rjic^KtBBvA1oU^5KoO7A;kn=I;yDmm9c`m~)mt9?5t6aNW_qtwo
zvvHHVWw~{^t#do*_KMqO_dxdy_fGd!?uXrPd6;;_cr5UE+k^Lv@m%D2(Tnr)^Gfqt
z<8{Sb*E`31*!zt49UlXqD4!)h=Y0))3w&RZNn};B{j$rlhki+ZoBS>PclzIx2g_UK
zd*zqqcLKx#CIOCt(*l)&!-4MySqAwA#Rato?GCyS^i8l{uxD^murhc<@P!c9kYyqJ
zL(Yf%6lxoq6uK?+ZkSP6Tv%t=aM+!2^YEPTs_@S6-Qjm5k|K6TJWwbUs}%1?T1FN`
zZi>7XWge9gwJ7T9pZ>cQ^>x%w(R$I2(c#fa(RtAo(aWOuMqh~jI{K#=am?|U3o)O>
z+>7}sRuWqqdoq@f8;E-?-Yi}bKRbR+{O<VU@#hmP6TB1B5@sf}Cw!A=o46oxf8vS6
zbBVVT?<a9dmPrXo3CZrsvy+b{UroNBqMPEEl9E!JvM=RM>f}_%)X>z~shd)Fr(Q_C
zoBC6lS(;~BZQ7=^tLfV5h3U=dThd3;uciNzF)hO}qcEc}V{gV!nI@Tenfo$Fvn;ce
zStD7mWj)9a%+AYRlYJ%oZcboMU(S-8b2)c%9^{(m2IeYrf60Tq;Jih77xQiNmHG4X
zhx14CuNFW-ctJ(M?t<GhoM+_C*fQhxj2{YJ3uhMg6`m}7yU4Pru&BA{aM8V)?lXsG
zo}S4UhZgr0pDdYNGPC4R$(2%*(#F!x(t*+irK6?y%WTR*%Ua6@%9fOEDBD?fsO)6f
zxw6Y;H_E;&`=K1lCzqR*+n0Nnhn6Rl=aiR~H<tI5FDPGAzO8(J`SJ2I<!_f?E5B2I
zzkIAhyTYi#roz1<up*`+qhe-7ZAE9rP{oRhEfsq!Mk-#ZxKQza#jT2a6~9z+mD4IM
zE1fI-Dw``msr<U~rz)mOugbj2vC6k9yeg?Gud1S|xvH;fQPsMt;i{|Ee$|TVl<I=&
zs_NG2f$AmI8>)9!AF4iCeXja)^^NK;tA9{}a<WpcY*h{@mnb(VcPbAlPb$wTFDq{-
zzf}HE12vOt%xdgwylX;h5^8d4%4!;GdTJKbtf|>nv%lte&6%3FYp&JYskvV>R;yiW
zRBKb~UK>~&Q=3scv$nRjvv#O<MeUZ_y|p8?uhd?seZTfr?Y-Jx>bSaTb(VF`b$)e<
zx@C2j>aN$_t$R?%*X!1s)Z5m3)(6+e)o0b0)YsQ{)z7P6Rll`<U;VNA)AbkYuh!qL
z|EB(7gSf$<LDJybAa96jNNXr;P&TwT%x+lLu&H5p!{LU}hVu<q8a`?Gy5XlrrZKcJ
zp)seitg*4Nr*T2!n#OI7?>F9Ryw~_k6W27Y$+F40$*)P#RNvIqG_Pq@)7GYaO~;x}
zH(hMH+H||=o2G}&;%0+pNwaISyg8~lt+}vS+1%bdyLnmjrsmzvhns(BftJZFW-azD
z-Yuam2`xD-Wi5>@JuM4b*0gME+21nKa<b)2%Y~N9E!SFZwS3ufzvY)!Xw`0=)@s&j
z-|F2O+M3Xs(^}Tr*xK1T(7K>?MeByvovnvjPqv<Gz1(`E^~<)<wwShzwt}{@w#K%e
zwxPBqZEM=LwC!y>)ONh>m9}$jZ@0bQcBAc1+r73Q+M!*$eOh~3dtQ4<dwqLX`|S2b
z?d#gNw(n~{)_%JEV*Azh+wI@9KkN{97<8C-*mih!1b4)Bq;(W_C_CCaW_K*>*wnGF
z<5<V(j*A^vJ3i^S+wq`-@6_!y>9p<i><sRV>rCs+>n!Q4@9gTF-MOrDQ|Io^!<{EO
zPj_DIyxMuY^Xtx^W-+t$W|_}&oaH+!d{)w|yjc~qnrC&*nmudTtWC3a&pJG7bk_M<
zS7v=O>+4xRbunFfU6L-(uHde?uC%VgE@fAH*X*ukU7NagcOC8;?K<CerR$Tfue*Ng
z7I&L;+je_)2Y1JHXLXl!*LQbyFY4aZy}SEx_h|R|?knA&bbsCbQxDUl*JIw}*yGz1
z-jmi-(o^5l)ibYWRnOL*eLcr|PWN2wx!QBP=bN5~z2aVjUP-TKZ+LG~Z(eUjZ*y;7
z@1ovyy~Di+dr$Pf)_bY<dhgxd2fcisZl6h?ZJ%dfa9><sR$ob9eP37KyuMX^Tl@C)
zJ?Llp_4>{G9s7Oz!~2u^^ZG0LoBR9v7xk~}AMQWcf1>}j{!9JW`)?1J4Wtd!4=fru
zG;n3$_P~Pyeo%MNWYBidb1-<YaIj}^=ivFlOM}-3?+o6V&7g_F;Qj&w#!ieI$<enB
zgtasDmx%;Qj3q7T-PNO1)WdHjGuJIaTD6Q?CJg$df!i$_L60}?@FP-u#Lgexl$c~J
zO)_IUA=%-XlzE%~C1Z8Qw?rqF=ZRI;>MWw2wI=KBFJeD^l(A-*XvgxukgdFuY+Xt!
zmXn=)8BNRi;ibHiu_G1e{c2WwbNb?K=oJUz=gByEvi^SIOn|@4SVD~L@QR%+I=YP{
zjH!e*Hqv9XjP3lfj0~^He8U;GKoZ@MR=T-9(6@L&_Qw4$pR|c64vm-@DzS`e&Mw<j
zBbQgNF4&N|x+UtAZ)T-$bZvqo>Gnv83N*J$tz9xBduerq?ngIF@4XY<n&oL5mgnc4
zstB^MPE{@{$X`|!s`KirT7mU@+H+f@%%x%Zlt&_r2mV7eU-Sk>2;8vU`RJVoLu|Cn
zSHf5kY@?m6C-b<4VYFL9C&+(MM&v|H>s(S>V&hs;T%1#zF;8<@X5<{^;vDK^Xyg>;
z>=No^#Dr*yq`J8LyCTk^&PKde9rI=#w<WwO%iBAvDLgf-Dbw3Kvnk9@;VY5&MB1g=
zMdE>Pq}|v%Va@bbb68rqrcC(6tu(txAEEHiQCxcz_MOET={@dy(K(EDgkh{g=y!l-
z%xL%N2u&p<oM89GTBdeRdMssz$X6afwDfQQB@An1Vj2*RqpaadwM%ksxI?I$sbATm
ztc;;TZ!5n<w`LM!WB+Vu#N|)Q9Sh5qK21mZBFP5{%R>0=uJQ;Q9lQ9(QiZ(E+Q(Mk
z)FaB(J<7wBERJd@&2#t2%FJ@hT~O%do6{QZpPw2n&EyBe)@BdBpZ9BrtxT#P*0lLx
zu$&yQc1~8vrumn~yRe@JbVnr_yLtyXN&?+1tmW}I6>)#F6!2RL9BnWZ<VIyYmuwWR
z<=2q1Ie32|@5*cxH=vwQ9&iDsdJG+W0|_zJ@)Y?m^h~X8YVvjr>8*;0sOk-I^lEOZ
zPW5Ec*Q&1_TT*7;=P~>RfBCfDY5ww;yS>}Y%a)E@<JV!mj#zJire2&%oR(8KV}MhL
z@gz<G5|J?zKr3$cati6IjEGkD$eqS3j$OxJ8(CUr-sZ9EYvPL)6Th!_xnspkj$Ko)
zrSq16B~#AkqHOEoi9>*hm9Y+{B15f7=(<bduXqwqou_n(izoli`GL{ngX{-|n_Ec%
zzo&Ui5&vBYvEjeKiB9WDgLRxAcS1lGsj*bwP9O0>U&aiRO?>?*Ur#oTvQv)me&p>V
z<N%hp;y)pN7`a3wa-bsY6pl_jbpM%pC|v8jWG}{hRFoCiCPXdI+$_}Zz*u8M=md=g
z60Dh-!kdvV8Ed>rFe?2c>B0yq))n9<m-(6)8;R{~9oGZ}1%-qlDfd<=ygkFiX;}mH
ze0Cc47wa*#BeJ7An6lwP+?!8vt^?<>F3w{r!{}+(2rV*NBgV!~6GE#$4mAFpuA=)u
zeci#HsK}lJb@c~(6sPRsYNDfRV(snYYocP5v9?Up3qNj-irW0+3!l93V1q)j;lT^m
z{f8R7y&Deo^LP7>wEFqA9_iEY@E|tgL(xkZ!2+qBzR2G}#(X%+FJ}y_xc8Rse^cN?
zF=DX{+h`BqLj1{ZLW96K-cHI+BIRUbapdEue3_B0q9iP-DZ&0!SkJLpHGA4Z-IJ;U
z_+I9tg^WRBYjLKlyfn_mJ*_Ef=H9;ez?wDrig|PAgz*K!`%2XJ*kAE|lz9|f{rwyT
z;QHO$G%)--h@-OXM#!QC35^N%v*(7C$2y);bUjmDxw}JQ5s)96P#$a+H?Y65bpPzc
zQO5+OLZM7>bkA&$U%>i0Mi<Ek=i5aV2j#45itw-5s&pyLOP+3;JX7hT+|d~1U$eP9
zs5shQ8l#Mfsfx6rW9N^2$-r^d6=pJOBw+^PjFReWv5PKV997jZE8bAeC2yFJuQv0C
zNmdj4-B=KF(HXmm^I|KGy$3#z(GLrP6POLh?rOk`_RC2o8SlOZqO>k0F<+}*aChp@
ztlHHY8r1&$tb*-*X*x&r*K|cG;~gB6n&P}-<Q~(R>&(^X_zqL&*g3~r>W<D$^r_mS
z9Gv5s*Pjy8mG3*n%+46!&){y2dz^*nD8?*l^vam>SnKyqpc9dkvGfMTOwC|;GHjau
z!qJ{^?a*DtP1}_|!R`B7x<+ONPY<to`tK`l`xGVzP4!Aw1}Lgx?WIvwqNCe079N@z
z8<D;EP+{$h3sWn0T&T`h{`J(3Sy8H1Yk8t;OvT1(Kc5+WiIKg<7)+zQNk!f|Aa8J9
z>Y#%k#gB7PJ@XnfW9%M#O_cEPq{s{dNG$GjxyR|@T(QF#cQQVRaR0>2!08#_NvI~L
zjAbW;X+mlL@%&y#MPN~+UHssIii(2+@wO3#f#t1=_Tl2<?H!8EE=e`v5y}K7$Ap?l
zMNNW}tzBfHTwWMyXQwC(2q;w8aehq)z0DGe8pEo#*2(2{TdTqvXC|0=A8ah$sFcf<
z8^<1oDHENY6O~~R6)3K;70hSynbD3o>w*JkVm*-<Rl{+qQmfftuusCcIG_Lo=n-L}
zkN1Nrcg~IY8`4`73XF~qiQe>Dn<p2%tE_Q*wNG%{ftJCMRzE}k(ybqFHn{IqkQ8Eo
zupJPI<6;w8`E=&uBPFppxl0evs6Dwfy?Wo@TB1u|e`#lT>^AbbM4sT9RQXh`+^=XL
zF|xlb7-OM}5c8V27~F3#4Fi>0xQFd$wga!bAg<=(btk-XlkLDU)5FgX3GOL)o$bT@
zz&M6PWQ@pROthKyu}iEkvp{u@^sgg*G~fqY$ZGaF?loqyPM||gah$zAcAAZ%iS^v2
zI<ph&TLkCW8(gwxPSD!?SqoJIn>TilSw`5?{GxUI!ZTQ3vie813;E{&K3HEkE2#N#
z2nlB=L`+K$Lr70RH*OO!aE|o0jqtK;u5-_fa6Cm6%^UN}HY)vH;|sk59M1;G<$-~6
zG$FPoD09+%>>{GmWOd!9KFNN$i;8{xD_7>nbTyQE$cB>m*FD0*JUl`|gz>EBL&TRw
zn{ih~1-3^|gnGhBGosis+8k3>tP!pNIVxC`L&9i@QH4`^LyeBMi@hTgAUBZP%X~Q#
z10psN?)U{53Z)tGo2-dz<c!XQlHTd^<w;3RvF19HGrHEM%Cn<`Ed9hrPBOF5l1K+u
zCuaE3;qCm3wc0wVdw$&MUX-6^?O<0}sq{R}fACg?j?U1>ME1%V;`DmSqaXD$3(Nxp
ze2s|b^iVr_d5RzTDQtFUb+TJQMs9XQ`>x6b?{CP^w{kR5T}>?V)sJY}kn=Key42MD
zA%Eqim-(yjwN5g&GUzaonogSgfXw*d9a5}bGygk&KYw-A789>ze=$pbo-!@4bsvtI
zC7L)^DDPT`ra<j|gi#cGmVHMxy!1TdzMt`ULzPOt{e=wh3q&IiGjIpN0h}=h5GR)4
zb4DmF?f^1<sequ5QGnnDszDG!9W+OdcG3gnM(ap_WM@@aX{2rJ>|;&*>m)3;A<4-m
zwmy*`CAQ%xrYlzScSIu|rR$W+?QZ%}g{@)A?G19~fa(W_=<?9eiWrBjHlg_eJxu~$
zbt4X4!#PK_xW?Fgti(<2JaGi|s982X%G|ZdsY}kypL=CvX4aMu7x$lDm^SI`B+tyA
zobvURz8*7ImCu}=?KzoTszLBBJNnC0>z?}cXnDfi7aCLg@;s9lzuK{(;gyw{?%BNv
zlZ?QF^T>lK)C$yaKx6;2`gERPL$LP;$kS`?9Vjk-?uX46FLsV}g@ktQZxoG`9pi`d
zhWVqVB7;ZElb5~LGh^`e#c|jrlsC%TIII_K98_u4!V(gDOSP2wc&w6LukyfX_Ab@f
zcG1W%kK7-B#tWa(5uQPdj6TJ_t=fKuU8D+TDw+AJL0Vc5%l0A9ZXnOR&>j<bMrd0N
zG%Dy{xkgceyJ4KvJJP@K=B~ojjn`KWzOo=q@605xteza@hBB|vrl*S2nvy-I@<*6y
zuSyaX?j=VF+0(v<z_F6(?!Dz9b?Kf-%g*(8&OW~^!Pwei8j0WbxUV8@O<&OtqP8S5
zQeP(0KC7}lbB6ikEcUZhG@@F?v<v-w3H#TL<<X|Y@-otW<_w+@-0gageH)KV@xDxd
z+@5>92=iwrosH=~P*uIZFXoJMTt!GwMS`<v<k7E<&$b53>UK25Rz+GnBs8Mwui>Rg
zgmqCc)e>41KxC*58H;0Q&J2^UzEr)=)Zb;EImH`c%e+Sz8`a?0-yZjev&3g{-QYmS
zKt}4BV`D4NZpX12uUnjjhiHTAFfy#%5XH?9bKH@1gWUM_i~(=Ep0_i^UOk%0?L~PD
zV5%Nfb6Zq9RBzEL#@k1EhGQ+YBea1;!!f(FXPKL?autt;@#&}VOF0JjncfrW;8>5_
zxr~5+PmDJ)cx>xFmJ@%GPWF~-3+G<jmY28n+T4X7K9&8dzjA%?jCD1ELAC2<l&q`v
zV-D=)RVT{IP7rqQ%soVOqP+YB|7Z_0@4}L}xFr|nmCV1eG%jxGh56%hLPtrD$_Z_+
z1}o6!9)|`L6Dg@X!%QDIJvTaf?&$&kSu!K1H`U%gwKs=<mI+p&m_>E(DX)8`HDtSM
za!q((T?X#@!MdB&x*{?5&kR?jXoWCZA}t485P1H_E|NvPgqzR5laTRSKeuL|@vmNA
zGGi_Fvu2{7f8*cHd%O1sga4wd*q;3;yL6m1eHGS5z8tPmw9w8Yk0DXYyviRuOK$%}
zbk@%&d6!hp#3^l7nv+9XPd0y(Nmacd8o70K$wgn)rpe6LZi$sa5j9C@y%O9feUEF$
z{)XWR!ppe7M9{7O##@4n;KqeO&K|W+UsTh0q(3Tt-ig-!GxKB4I%V`^rM4&8J7vu+
z95_BJC}H8t9np*W>(boMhE&dt-?E#u%F-f(B-ZIQ3vzSUHiRbi?aU3UO>wo4$dh@d
zDCE=aavK-r&RC-inC_cY(U+Lq5GNfF<|tYK+-Bs1U|G;1M-17_yT8hNa7vNE!*4_e
z!$P?g*q?(~ZaSSA6Jm=PFk$uTa|iR*o~ds+voiCvPeFfj%0Pj)Xk^T>|72fSSl`Kh
zG*=vR2eX}>vj=nWvpJzo)M=aw>qPs<K~QHh2Axk=RCf-lK7UWm9eqd_56Da8q4=M8
zXn-fCDB?&AsrGZyLLwudS-KFL-DJA6_3g9#jwAg0rl$N`NBEs*$Y0Mq&;Bx|J#vn{
z^Du*RXlZ%$5v`Hd8HDe?geQpIIs#}QZr}m4fE3@x74Zjm_^tew&&V}i_9;7$@l<)L
zY?(mS1?CgxitzkYeEwy8UR!vc3JX45O$M&>x@UOZ8_eICkH%V5pEA}gUDOlYBMMv}
zmPah0c;ipaIAapTMn1a1O2)orf8M@*4L5IiB`v#-zraiq4+&#}pg}bRGwIb=`NgDH
z^yRNTTeMDU`rwS~=0L0yF_!Yfd4wq&5smzc2uDDNzd+_|>M+FT{Sm@+NFTrG>{;>9
zF9}=3OR$WB`H)3q6ztD&gW}-nL&R+kaea<^nU^phs9b1WoVt$1#RPqih+3Xo#v0iT
zn>9XwAnqEuF5>AuVKA=m;Q5<)%~6o~d+gWv-J?71s|6`~Ml_0_E;mAO(eZ~xt3EoM
zj0J1n-_BMnxS*VMCv)bJsfE7Ti%b1taIto>v&}Cr%yu-abw@?P#`;i0Lw=HPL4yC(
ztve2x`==U(NiBRFjD7QFMdvRp^vo^qu=EPI4Q?+A@+ewT#Onm~x=&ka=@D%GNoAE&
zjE@zzk<M!yoPbaG7aX0msCFA)=}kzhmc9gSQ8bk}o503xRSnzZItNe5x747~6L?Az
z-IylJiuBeu@QKWlCD+H;F#6qZt<Or18x(`;_2`6*wSOJRT-_L9?ip^+yW59*nuph~
z&P3kMM16P<d4zV6(0AGYWgIc#K83Mv+z~Fm^QpYP7kffNdXD#%KHa9kOHNTmfq8AQ
zmX<N?^KAlCJrd?uhU9NJ*W}uCZbN=(-I{FQ{7A=`mgRA-3Co*fa5NeG;^H2Af1>Xi
z&!~wh_8i@Tn)TF#_clOZW1$(BhP7@luiPL%bx;SpUb?+C?36UFG1(_A!pFeCJ0i_H
zr72FzI1c=EZANqi<M$9cqvEpGztx?&x<1_0HQ0`PCk=EljcCNK<NJ&8{oiBXt!eK?
zxT1e-5?T={<+hv}_rmI0iq2#vwe6*Oap?;V&g|UZ<fpP@JhEpcRV<OUnI}~a)drR%
z`RFhb)+(&bC~f(fcGm@$S0!hzdaXS>d0tsyMpS`wa)=#!(Iu@jSx;V{<A=Nt#YSAj
z?<&oxRcPlg)2xHhFNnPtTM_E8_~N<ed}c1lRz&C;`<oY5eDMYQ-0JrH#WTJ1H*uo6
ziuToGxl|SzDC6_k2iO-E+&ez*Wk&z$V+#kJg2L<xBY(!)vXGYH%9<^5odeN*`<1Ru
zElT?`+l0hqhsgS5XZgme^6f35qtaMpc6x;OII|lkn4J*Q^XTJ-XFEa--4gs;Tmx;V
zIwUp3mxM=6FyD+a{~KwLtA_amcUl5_6UUy~VdFC$F-%zSiv?SX#0KxKN*^lpIb|7;
z>0h|S@zg2yrT7fD(v9`15a!K@8G+_yX=Bv&D)gTMS1sy07Wd{07T@odj=85!U6@!M
za>2yfBRt&0BQz9$9OqR&nasfFrfO^s!Aa=kPnULbfBoOpk-TEHxx&=QOFul)Fu-3}
zY0A0aN3olRb+tE$*ywETWn%3fp{W?#MDV*B;3i`O|7ahQ(auw+DoN(2d=a_&A^EYF
zUm%9D(r!}4hpL453jRI@R*;L&PS~&513V+RM-{|9IL_#kBBqpaCIUsT)CBv`7c_Aw
zaFPUz0(aEc?Q)Yp-<7|p*!z?;G|w-nAi_a3Dh<u`uk4YW;(~jklCq1^$~?+8C{;ns
zg_uIQv3*#!m&#Ye>Acvnuki;B@cqKL;rr>h{qfD3*M*3K_bPuka9r?`#Oyx~9IX!L
zZ4u(1AJ!+}-+vPwDT<wQqOIelp_rJV6YcFMhGIt@Q#(>qI#ZmSQae*q+EX1Fr>?g)
z<m7C4tIMS8t@YX2>)-0iT+^gbG_A=r$ynPI8QHWpLwNTJzKoMhyj$bjb#y}B$tF7X
zuh=UIY}i(%d@A770iA$th`o&gr)(3NQ@m2csZ4~Yc_%j|*lMs>5vKZCbiF6$G4`4`
z2iT|{*#tP5C>qyhQvBn~*l&gRqnew*Ka^QJ{oe_x{5SXuZrQ1<+$7gV<lj(My`wn<
z-`<owv(M4cCp_6Br5WEXfL~NZ(?dsw3l10LZ*8L@OTide`#G9m-x2yW<H&5rckAPc
zfH#8Bjn61%vzd!|S+C^Oan^Nqp^=|1yMbeK;#K$)QG?NG_$x0&!0wmv`TyF!#7LJ0
zf>_4Dzapb>O|?9zc0*D5Qw@QmlHi<x;7mU=qrmdi%5`O4A<bJ$8lPzmKE?VZ6}SiG
zg-D3C#NW$V-_S3yDq7KzFY}+-A196Wms)rPNhID*Qhn3l)T*fXt{kt>$~n<Nsoq9r
zu4rPR9iqO+<cSm->86M(7>x1&<8c;K+|?(GFJ3f~x|y2F^i1M>WkoU0Z@eK=@DEq3
z%3WoaIy&qov7S{-OC~eCnxGWYdY7Q?xPfyR<-lR0s&Q)+#|y`hb(U>w&X`vsJ7pV~
z;Mlg*=F~_-XtYQ9Q;p2as_4Yh0OPy_*6`7JtV@bwK8kf=)#G+3`HoEf3qP=u@A=Ck
zHsaBFjqFh^^*203JaM1~7j?YKEQpLtx_pX%V-x?Y8aDCgPyT&uqnZtQbe{Dadz%X$
zyNdhB3F-x4FXFS4u<?d;kvd7RH_6ZUukpjA>FUFWACM+~_$qURELVM{`j~9w8*no{
z6>~7*li3k`mafY1y&4MvK~m5t5d=-(Z^q8^sZ+gE8xto<B<5P2-qb0aS3;nRz6(G5
zBALp%J+gK#$qe8`B4!i)EZ4v##<v-Ny@Wskw(A9~`}cNfFw;Bs43n!m&iZZMyo8<p
z)O^|w1;36N7Kh=tR5)i@joZZES1xF6s;O~dihd;`tWQmu9_7+3J2P2kU~83K)>5To
zNQRGbdO?1Mu}?+_zs{<pXHD8Ac|o}C+M%X*(^vNvTXh)56t#rCTfN)MYfsgs@aCcz
zL(Nzp;UAC~jh##AB06T{M*e~Lv@j10u}$sRCd6aYaM~t0I<$qRQI)G%DjRE@O9;${
zA(=kLd8MU!v=NTVrP;Q$fq|JaGu!N?%179M=I~3kyWQP(*S#Cof(_`fD(+pK{w}st
zy4E(lKz=E0O;3px@?t(&$9>D%q7U_PRLV(^V`HsxFEj?iHTL4S-g({e?#c17PJX$*
zX*s*R^12e-Qsd(sEWF&^r#nS>n6WykOO&AowtjYAvW5~SZBa#_p`D*}x`Wwd>%dfW
z|1hwe-$Io5oxY%74P}OQhJvc}7aX()_fL84tqJ|g9C~z5lro`V>A2OYCt<wEk$QV4
zY)nmfUNdquhR^=aox~(->`XSWcp!eXY*neJSIMff(bU00KlWcO&4^hgQB%J9hkp60
zqvft;M^}{_+`c(Ewt8OT?=4P3<L?}`5m1hZDNYrs7OqP$TIPfpz>ah6x4i_0xIQ|o
zEwN!fCXaqeHMcX-(SBy9PIUL4vhuBUfv-A8<$3z#ML6h?2&VZ)+>qDy?PC9g4C%+)
z30JeHH6%#6IzJ*maJoZ$Z8Sf&o$|0w{kf=!`%Z|3Xix)9eJ3FRA`fs17zf_~Q=G%p
z6JV+};gFn&A#mv_FW1$MrYxGb+*cmrzkc5O<YQG;$CEcM*z6Z9_g=kVtSq`?YeD|j
zj;N@Pt@#C8JEB`7Vq?OWEMA%XqOyK8b@P%<K@p0eElanio~p$M*DPWBOZN>VBn<2;
zE!o!}AK$+Z#|w9KaL?m>H_@yuak)tUp<2Sl7yXATO(kjkh%X_5{16%9htBeg=-Q41
z@g+A{J!ZGcd+?b7{xpdicxI5fjB>)jdX%A5L56UpZ>TSuP9<sm=;$c_fHfWaioL*K
z$O6k1^7>paj!`0h#i)rEAurGagx0x#z{FyI!l6NBZ6bK|+rVKdocj*chfh~=rWk6+
zjRyD(*IQ8#n-GWVxUyH)7mbFM^@Jp}r8?VYF0O8VzDH4-9~B=v^kPfLi8(Q+oYUHq
z(>jvuC25Q6TMzd{s6H)B&PbMIEN$?}uusde%UVW`lr3}iSyGk1q}0c~>X|+!y)nvC
z8kFYY85`^`v5Ko$R8qX$6$@l9F7pd&+Ep8@OmVjjPIL2(50Y6~$CWR5*381NI#!k#
z>?fIGre{<g>lq&!fILug_nB{T-^e7YT@0THfjB&ItelWHa(LoMR3t<-LPOmqq>n}W
z>K~%J$WJF+1LyyS6@uiAdxL~^@VHY??(gsflc`r6;e^A2=$q2xsGEo@rEyBS1H-#;
zC?lmm&ud+2k#Dlh++3FIJF{3tN_KtN-+ygaNy)Bj{rw;AD(R5ToHzVpMa7HT=gpMi
z@pgJVjK@rFdRLlLpmKFyXWlAhkW*S$T4k<dOljb(pwbw}oazOO?j9^JKX`Z1!p{$s
zl^yteVg1>CxDNL#UA*sXef`;I7nl3_l`nqwtY&^OFR)XwpY}pbX(E1u@HD>C7o+=9
z6J<1}3w;M%+u<^wZW%IR>L-h>s}n+tBc#tBY7C2rP2Yt<=Qu@uipyj@ey>}BWrJt5
zn=$^<nysUq%qBEP*3#n_6)TrbmgR=q*~Qkx^5=aD-20s^RvFm1n7R7`#@v+ni)B^p
z3*boFAM&7u-?YZyFHjJeL%P}1tPS=1q6AQH#_3U2KN~^1iS{o1+PDzM>vQaf1$x~x
z@LOJC*T|Fne^Ll&2SijZAIDU17}1Mi=gIit2s_$2QFz0d??wv$Y!o^)<F&iEc(-P}
z$eESID5*QgBQnC)%W8_2?pl$Ujn3G!%_i90+|V^vwwU?wPe!8t4<mM&Kg?#@M3gHc
zif!~<{UhuG9HUI9O>uU0oaCPnJ4B{QJR}Y>8yaiq5lPvoaUDy!5~Y$e7e&(56cgsL
zXP5!xi6PG7aXb09DEuG!G0!Eo*v}75IOmumKfmHwmqBAYFAEDVJ3~WFZp@vb+j(5#
zOa0}=vChu1#d1q8TSG%zFH1`wdm|%zR8tIoxlGs}@i$W@(e)$^{%hb5wLi8F!<h~I
z#%KB2WZttxG4>byt}cHIUqObqkZq6jHsb^MyKzr5&xu~fZ^7|t+>@d4H;I3@BV#I&
zGS8_F5SwVZA~ZREJ8ssBi>^;{vhay_A0exmryI%CumY)1SW1w*G~QK;`rkP|HqLo1
z)*sHSX10n(@f&gw)~bK>W(k(iW8{<>VMJ1nE-&1Ep_7x85D{nTZEv*QJ*_S@vN6>y
zE-E#_E2}j!upvh_oE{yQn4g%#Rfl<d1Wz%uHI`)tTU!O?1OyaKH!g_{%nP=*2r3A}
z<+CgtcUWLs`nhY2<+u-y`b9%;@jq-7@%(kXw-who-=KX*4ao`H`>~5hSZ$GP+t3Wp
z#sRC*eY$S*3KQF0aXqS9S2H)kJTsoPd33%rGTzRScN3x4mf#ohz$$Q_U|a-;glYvU
z)>3_DQGWNyL4{)QWH-@gwBrU3RaYGvjDMJc&#Y$}NH2cth_>&OXu5u4FA(z~-3;Uv
z5rJhp`BZWcdxh<lPV7{08jnyq1rV#rmI3bOPN{84TDD;x=7vt;t~P!mQ<pF|uhc+E
z!z43D^XXFK$=o8^);?x8`Bo(C-9cH`!o54>+wIp#r2(^>nGrNJi_Wt?<{0aQbNjzu
z|Mq#$n3I+<XNH&8jM?ewa|*rp;AaD#9?|Y5CLYn4dzf&Qsq;$w{7UAfW=tG-rUgk+
zHF;)uXbwE^duEDJTkzY<7ZIa6rr>@7{OZ15xQ;F*(OS{-;6%=<KVr6E-bu{R)2v0#
zF;|f{^jTa%i{28-bmAAI+Ctf@SXPU?hTpep%Cumaf1(V~=Qj&w&apUOvCQlEn}Sc{
z5d$&&9JUATO3hvXJyzKLCxp-a$6b+hfCEr|o-{a_6x<7-@U#i_wCrqh%L=D^0h%`E
zS8S;ZaEmSQklR1r3&8A8sLD$7wTp<&kTrEom!<gSE-v+zRje$G>1il;m-Q!;Fsd!x
z(Hn&A!C%1vt$dtcQ>nM#g|IZzjna?CASR4=YvqrvoT_^Jo!j_5>`SUtBPLCC(u6tt
z@CdnJP0INlqLD4U%VyO-Ne}ZeodYxQJKc}DN@4ElkN_QGszbDN$PC;`hch*iBzE&B
zNm4ieBL5<uWsvlK{-{9sk$#d%KP+c4Hn#_3@AohU?~K0}L8Di6#fe_x0LRAxS?cVg
zcB2UKpJ+VWQa?v0qG6>bQ1{&Yvejz}t7xpQ=|n=!iXG7jWA47*j9=A$8nR=Jtt?bG
z3i_W2+hsldMG-MLrU~3B!k$qV{kUc3;F%b9NsAD?+ZHjlS|LR5=veMUyRjH~Hw}LS
z1Eu|O^p0YuA)b!M`SJG#jJr+@C=>%Hy7+&LjuPX{_C#CT#P&?l2>+3yeOuAYZEXtv
zO+1TMed3u~F7vCzMF{%7&{tG1Ho#xRpdP*nb&hrknt9Yd5v;@sPbaE94yO(#j2fap
zpTwLBs##l*xx6aK(9+h@&@Z(p)h}4NGRLJbE5^`G-!CH0E+pH3x?fshnxC=6L1Jv>
z7w1yj&gD1nYYBADnUgQijFkHa+Il9%#Aa40^5$f_Og1vtdwP<&=`;&(g@ad0Y;3kN
z#yKh^AUMJaEu!fwH{q1R@k>+R6Az%TBouc|%|Mx;+e?J~uD_WlXozU+lL>d;xCdIZ
zzhojRLCqE1MMSMKsbJ84v-rGXdRoJ>oIKph`q{@fn`;AcpVP_kw&Ajl5#NZzb?8M2
zQsFn{nSI$Ir72<475d>Nv&S|$W+W?2KaQ-7vYqam%G!pO$9rfq=9Xa<My0vZNSU=~
zPEXSI!?_Lp)_$>0hH>3_KH1sn`4|Cd?Mhu#8DwnjsJ~>2i<9LhV|&l(|Fn_EInj^;
z>*tlz7G)h&or3s+^I<lQmxtILWjhS#gNXqeC*zj9Fm@)U7?KgzKR9L>Q!{08!j3!o
zCyY?muWNqX;B(b=M`p)G_v|gtT`M;)kGh_CCVn_CsQYsMUlMBx(esMR^-F3`b8$*;
zj;A}nM8<>s(aRP6@2tyAnSV^#@t1{(5wf~xqBc~{_lYsxVNo7Cw`%Nr{BF(M+tI|g
zcwts}PiaV4;|4U4>3GG%A^hHZR9Nd$p9fv?JYjk3KXJ@?jG41{RXZnC$wbZ+oFu`5
zRtXOu<AuGZs9vzfB6TnNJ5Qm6xYAvHzj^&Mjtj~k8vmgS8&uk+f^QCo{RvqX9H2o;
z&%}OZNi}tX`j@yR^;cCkjC%W!JgaC&!3U~zcjpfUL{Yz}#vf{8wDD5XS8D~AsDb{b
zw-dhIShORMd1<OX^@rwe2~=fFr!G<)w~go*bwX#<?_yFY!~aK2%4xzcdMaQ`)qix0
zYVA~ARK!fgr2fS(iuyye6mi5*uo}kqZQ}c>t4`SUK}STyEM83aek@!_cYlnPGoPqz
zxz(%3fNligU^S8QH;GiW>NPs11b+<%{po0jJocxLQjZ(jCx}@Qctm~lbblzmK?ui^
zR~tr0i^$5Gd?@+yEAkF6<AQlPxv2VHCB!%F@Hq=?BPvefU@Q{$(P;Uj=MR6;MvMQI
z?&Vmx@GP5>vpYxi{dT6wb+;?C3*U#&VQdwsJ|F?c)aj~us&U7TQ)||oVynY;hpEDt
zfq>ls%xx^cn@{Gx!}pkDc}?8gQG;;k_9C>sM4$W4Bd0IL#$TeRZDD1u9TlgN;f|Ph
zFcLeKH^0zRTX!of;+UG5Ib8jtO}OD>3;z6`z>~G;*Sn9ngjV%$zI;JMC*I-zL_~%J
zOp))X&skh5bI<NcwwB}TtRxm%kqOqJ1wkc)4#>}~nZ@499W7(3oOv@mv~{<$B9<wc
z$*6H7hDAcy*!#$5iVOH%gq9AjZ!Pe*kWj}?xKGDHf!f+aqF)2(Q=b;je`-2v>5LtX
zS)V%a&ic%Zb?*$MZp7cRpgWxCZl^~p0~%$r#-QA#Ww<C@radKp_{IX_z2L@h{uFK8
z`?Os(X0D~ht<=&EYJH}Oztps^4c)^y8_L!9v;cWPztBkiO&&ENsVBpLzikS9yjw_c
z^9jxS4U)^F23}}e`c_!vxz?6_&2p75i&lMN(?;K<>2amqiHqNf2!C@?`|*LOQ+7#h
zsVS{VcJ_&Fsi`eVcASTQVZ4_vqbG?d(}?V>DrHB)(9!xnin$RUv(3C5O<Suf+cXi4
zW`&~piHHVS?U~V{Fqc<R@FlPl89fhUi|F5%X;h`jcwo_3f2UXe>~#Bhds72<tHNUL
z5RE5J)>$PIw^TgJ0iwRcCo`|$v*<HMnfSwmKp_-NZVWs<4pcH_e--A0pQWCbg%!?;
z_~)MU${(cfFfsJCD$M;W``n{3&7@dQCq?Z{j?=_3C+n;i(Lg4h7x>MHXuCKN{ZF{A
z7`Jks*r0=|kwxB97w^B>i^To%<X)uSH<SGmJdB>5ztCIuU+hWBNambl%ft4(CCPn%
z=-=*5qH(HGj_$<{cw&~&_MkEre9q&lZ6ap<+vE@cyW#l=cYiD0jV3<nL3WP+A$FA*
zDRvCc@`-8751ktMbpHPt#=^12Jp^Ja{7rP~3pN&bgl+nhO)^-m(XdOws;YmpTjqVS
zTVh>w)waekk!s$bZk!QfAEIg8T8d><qa%n-1ndz$1<g%EAhac-X;yLFg(KV_Lm{D2
zQY~(6Mqjo&GdB8f!Xon+3;zscG*KVN_-r-e28~ncYofD4C>x`*r260%iJ#T@luN)^
zY}3JG+PV`HY`uUr6&R=aic80Fqhf)662S;@5`z6wjj@5>nXravoCZh*>q_1F;!teL
zoc%>qewlYzmz0*ofTEP}>4{W=tNo~us=h}d<<c|OyxHa6eSTFsiAOQ*O<hzKq;KVH
zGHB-QXg22Wnb92iSRM~>cUUpvKJM4S9bTy6{qZ9?@l%^NKJ`;8_l@qsgOkZLyoJBh
z`Wf?+i2h<J>Ia!I{xXoijGWoDlW%*pU6gxJm;Z(s=wi9K++Ue%q7P6<3u7RxgeRt+
zKXc6`ttB?LCCzp7G`k=d=OFuOq7OnU;+>u2D?%i47c(;#xv)sX7&Z3|#?4>*<Jx%T
zW1nLFgfEf7|Lfv7<0Q`b@BA>m(%>{tkMv-vG&tSEGc8zJsc-FSKHbe)U*Fnoy1A>h
zzG%oTCCJ7mD8<b^DZtt~Aj#dt)yB}!+SSy|RcdG`r6F$c<<s$hl?kN#zNGZSFzUy^
zSdV6Z2wfSH**4+cR<`a^*|aHUDWNWLGKp10d5oEFV2D)eWT4$U)ym8}-pe^7HQpNQ
z>*qdX!$n^TUo8sAMpp-qt#8f#Uw<}S8j|KAi;@Sr8rxe;9gqg4d$>i0ggfKeG|`QK
z!U$W5okU;aVx|>P6lpK9m;67@o&&6nD`~ym6}Vy3MF$~)=uHv=geU^hMen^AQw7+7
z4aNo=SKJHky{Fiz2B$#clImQNbIPS(a+gb-++A``{={aNf8MSHxFnbVCm*p`?e2ST
z-pst|W9IA$Q5?X-N~u4QKCpjWtUOplAbUzgl)T0K=WO%UzwG8uF>fP(=56-g6-*<#
zIR*V*FeM3Q^chx<;p|%2W`pOq!ofdMEK>Q|+xw|RVhDlHTWI5KE(!<`nLFDo{81d?
zV{7XZA;t-E0`G7)GiPg)0Ff9{Nnp)%F!ccw%+3k;IVoj=R}5T$5Gj1@(?irp%u&Eg
z68?ah(my?Ny3>fqd;~iF<VNblsZ@ZKG4IGEASe7Fx|I5m*=f`XbLJt)&tkPJ1<tO*
z34L@YN{+MhRk<4P+Gp<_;)tjZ_wt=2R!-(~7uV&u2a7GKcpfn+p}v|j=KoU<BQ}y8
zW<{K3ZhV2JcK>{X`U=fsen#`~cp=O&#$swI#!vn%xtk6#Am~p9{6x?X=AURk)B%RI
z0C9=Ff2_03C=#pCa79P`n;e9wGwi>@GCh1h`+c)5>*$N;YSuF2h5-nL;giui<`=j7
zP3&L`G1!-kpr$Ll8tRVnr~r`Ju#K0s8&UzZdV=g{;~6htI1(8D_#JQ$iM_tyK^3|Q
z5ixX*h`I9!G9pu$IvhiPkOEgH2{mTFqwau>hy*I|$L|E`m$L!PIvt%J8lg*$(~|^+
z!e*b150BH`xo6;yM(V;8u-%bQ!u(<OtQ#7lo*{wv24VgPVeuBV{w~B`gYAuk`g%M{
z$ga~E?;-G5)|NuTzun~7$-F$`M|xovxpT7V;6XC-Ai3}mh7ZJp!}}3DXUyvQEEM@@
zf({y|()3_N*bYEpzbwe^bFq;|J{LhyBf#-TCWc%ziO?5}GkJp+qszn%`mKXoh$JF@
z>meEn*pXP)<9EWdSP#Po4eJX5B=!dG42}WLt_Z#ZHn|F)3rdKcX2bPalqc0h_nW(O
za(2JjO}xbKdINuaql^F2_5AfEfq^CK^Z74P`Rhsp154NCQ~#5`^|hX5uWnC&{PFay
zuP*C(ZF~ACb0(yAYkt9&8U;CT@@GZu)`I*kwJ;I9_ClB&h8f@pPUgdhDtpR86dd~p
z__WNVq;>MLT>lB}#_^7cwy~k)alg#Y_z7bE{HVI&d}1N}WB;pLbLKO<(c<~JTVGv)
zujFm2i&{XQV8_q_>|O_)l7df`^;>g%kaYtI0F+YJ!OnS1*dx;z;y{TnVw$yHL1jbP
zS!+tAK54D7jO8b?>@b%MkjHy(?8tBl%a(o2I2<#{9=^7+W62ZU5fPAI*s=2ZaF)rj
zgF&ly9L>~i{`O2s$(e6A>oSk-SQT^-XrX%Q7S#nZxGV5^e9B`ktY`8DM~>cK1y(MD
zPjoschWw|AwWH}gb0dMX(j~6UQntStM3|6}#)GwsM`UL78!j=85Fv;IbOIx^{wa;I
zE)W&*y<eEnge2BmFVI4|jCR9#yVc5@`*O2Vhv_lx+UhWez*tZ8f`>-xpjmPt5AO@X
zJHo7_v;Z>)9Jz;O3GAiCzFJOe2W<@OO<0oPY))8h!GUDl-;fVRlvEsURaNFMAB^oe
zRh4llV$4OES=AP*E>$`>tBS%ErEwlUTT3;Ki4tLSdBhg}m9YA)c|{|BzAq0np42J*
z0vS2~Vn}UalFX6b?W)QS^373uy2lg;PhOQpdTSR)%IY$LM%exJ1H1cYAUA;=0b#{h
zQv|Q=JY@4cvu2E0{XDWH-S18@67)HFhh1Aa@ED1a1MnB1DnlF=Ncdo<Vq;+iv&=W_
zW+))sz=GW>eFlpJAETehDmUdz;?z;DgdjX2%`>;gLRuLA{@c;LPqw!|-K!2R>QSSr
z#t2bT8z?`CBCpgYwYDu;O#Kwwto1ZDv08X!u9ew>ga|9YfIyoh=G!-{bT4gAPv88K
z&Z=PlvP@IHWD{Mb-B71e)osvPMfKGunMBlYNMYAp1+w0EU`qxq6LGU<fsaLWj-r3l
zU#vt{gni#iQEXMDsw&QXG;QVatjbf}u?^Mvl@Yoll?8{oG^3K3Vp&v)%F!;gAo5N0
zX;zUaD!{=-rg1M=ndJfckG6D$i+EXL%F;AX->j}AafH8}gP+nxr1Y}c$<D8tSw+4K
zbnOH4vw|=h%vPL{6KMrzZ}u#8_lLL^*4_;~o_v`(rYul7n@hsIS1HQkBuFGz*;o2Y
z6EX_(x)X&?F^f}>fF9&u=w@;>%a^ZC35b)rE^s%s_eoTF<fg@1XUlVZMWCk%RC!C%
z6zdM0FF@hAlIS+3g4O#$rkc_F^}&}Mqd_6_Lyp#AU4Im7_krn=0j8keLl~b&*?}c@
z&r=!6)#0`_KH+YY|H4W=$>s`~vwjRGHoig+b3NSxsX*j57G;d!170_~Gn9E$*SXob
z($Tb#aND?UXcOT^e>9ygz4GiY$J5h~|8jQp%+E)(+9N-ou~B^a!w+A+CDC2%jgIcU
z2+RzY0#&qc6Ui_gESucJMSz=|p<XV+%LG}+;}9<iFo6%ff*j_~q4pS=IL5SQkkG3V
zYVQAwQi5!Pbt#~50b)Zz4HBn>1osvfXM?o{#1G9>g6?zP(pNOsYL3wYZ-HN2XrRsC
znZMpZ8_ET8-?@%p?zgeG=l|2zFUsTYd8q^uBh;LQK51$qWAZ18e=EcfLFr@AiMch@
zFkR$XfMXntueW^wv;aT#bjachJ`P}b%x0!BW+oCsK(;G%s<ie*kvMI6-th3)8~&*^
ziWXIcS|paM>>33U2U9b*Ft4zx6HC=TnVkts2Q@36Y8A!Dh70&}7FjvVV%-8WLS04<
zoQ$bS@;9+x<m?|O_sB_&vCg*h_VRPIbQD<5GZ949?`=!zt4gr;8Y$0QneS&be<5%i
zfT8EqZR!N<1D3O<1MUnmJDa;Df-Dc+f%`UuX&XCBC6Gty_ItL47@jO6kM0HjyAAA;
z&TvjxvlKMLbvb(k;Fuv^6Zoz^_Q^ST@&4+Xy^68;Wygy;9_tJnby6=*iK~iq<f1j3
zG(vQ{{lyL1sBroh@(Z-;%IcWheK%L;9ay4?Z(glpZn^|Zti!5@w6LxkW&`;JjE^&j
zF!whM=ZH-C1}cV4ahI@enFxYV742(_eB3#rNR<Mi(x^&Vo8*N=GL>DWKx|`@nDyN+
zk=+-lRC)D|lAwHjbUgFVGE?UOtxA%e9%G$j=jG|==;LiJL+D*Lk{+K!m{%_c_;PR%
z&aysm<R&znk?^Npy`h#fUscpa_W>7vvQMq<eL_csO&(^sur5(7W=&CvBJya$J8J~h
zmp12M6O%P?-c0)a>Dbwn_4O)M{d$hqW&p4K5au8NSO^~=Hjq%s51C;|KDDwBF!S=y
z3kWTW5Hcspb>s=U!Bgfq-)!!Hi=|6Kdj^5MBnWy5d`JEa@53?=_c}N>A(R(_Mh%`F
zpy5QP;6$IqwiI_#BYoN0)7c_)>61Oh+v^nIF>XuO#k**i?@L{W$Ugtpft}f%?;I_Y
zwVYi-Y{s-j@bVZ}ehQ4?hNT`p=CtNGY|t18Ofbuxgbf-NKwaF~M>K7Fy+3SKe~oXX
zrzIiITvP6*E)J=>utuv|{r0ZW(dvzP9)u*hHPOY@%%uL%wWc%3IB$)lxVk<lZc|tG
zBH}Gsw$deI>($Qs@sXMZ48%^EWax%s5;rudz3f<eDPuhNG3>lx?J)4nvax*_=E<2=
z1ZJc|&89qeGG6|Yz)NTuHu%oSXxEEF31fkI%fS3GnYu8iYjgY%B9{BLtlE*){OaMl
z1xzS<bwT#-kG7+~Gd8WqS|aC@pq{X~okq4WCYHFM%CdnY8w@!20pB2oac9#NRXSxO
z%MK67Pzo;#l^Fry*)kg{UG=hEpqs#}bWc;)2`|qRT}^vRy{z2>>>+5FnOvWDV5!C}
zW1s|iGdD{HGTcC{%3qHt*r~x?*&Xj3DeGCXq^Io2J001(23tYPf&J<MJn!LO<MMaF
zObj&+^e4HQ0)luzNK|ogpq!X$1t~MEcnF)xc|fTi=Op(C-QkLwQ%j@AHuZK6v^}*V
zHmGn}LTX>0zewXu?{o(fY)Xntu!l9#vA~o%(t55#8MX9mJxV2*3tRSYA30@Oy!O$m
zMOmu~`~u517BYuPlQ}3eLRS)Nit==|R=x><ObEJP@TeC4bGQZv0_4GQ`J8#wn3cUS
zD>uT2Jk~P=g!l`apis4ktI}D_cQ$pATQTpy$b4WecQ$k4i=DzmZZY9<8`22fRU~+u
z%{Sdc@`ZkM7k!NgC)WB2`Q&cX1&cfrLjgw6ImHMd4E_PUlcE`E)L<eBRsgewqeLPE
z0P43Hs;U^yU?cl=jv`6U`pT{oo}S0MD%a<_iyb=oZ!-+x<vMxU)tj*Ni3{>K>~x^U
z$C5mn`b;ysV@sE^JJ$S6>R$TivS*75FO?a#`R-l)CIhZPj~f0(kbAJbPzVTa(6vNt
zU<C&nKy+Xl)K2<mu{Js0sb_pBKB8}|m&jd`;6fZfmb6VX!BkL-`Q}XMeDejyswL-I
z>QA<-c6(`?)SGt;1au+J6a@PY;0(@L#yol<>tvl4K@1_L#-Jk_mam{belXP=5|$y=
zyL@}44B?d0hva*2+k0mw-#c;5nC>7ko$yWI2(&pduwG6Qn(YS{l1AXW2UP%zKMD)%
zBL_;uOq%)`m33cQ5f$Bcshj?SF(wIV>l3SjJhaUkAwiK{s4Tq$JIOoJA(i-bh(fI>
zKi(C?H>Yo3XFhqc<Y3IsTc?S0v@bm#n9-n6)Uj!=fFD5Yb8y_HwI2Fh`Z-HT;AR1+
zgKjWA3Di#97{~MIi{|D*2Te?nn`g!*L&4p0NY%Qpxa44a6mfvAhS?zcC-1~^nY1(k
zEOr2PTo;D_XBd<9of`7kU?|QLk9iv2>QnT;6AssRUh0pXa8K5y6rD^PXX+t7*PKZ=
zw+_Uma6-}b99@iqwIv<FrGdh`FED$lDR?)I<zan+>>`90CSc}F)C3a*c8Blz7I)H_
zy$I(u9|O9?xW~mLz>-<D4cG{LoV9XFNULGtqp<K5umPWw{rK-`kef3pIP<LhNC(6o
z(Wdk#ejrgPDiZO639wP_gIE|m20jg8brAgzfp-c>e3WA53PbU)zj*i7IO?)Zhza7m
zkaaIi_RVWdA+9p!%zjkE>?25rhI9WpO(fm<o_O^RbD)ue=pYPBfT6J*-i__N*gq-a
z!uGKvlyg*4XAX>?WUBv-zGcF{M1v<G@qF;F;I#bqU&IJqkAF!Xae-b(bhE!1lT++_
zaenY(pcAYs8E_CQa5FW0W>i}cpQlaBID|}2lw~eW61k@?%Z_Zx@E=bbda`E83qy&$
zt$nN0H(y;+dTD#%xL;0J0+tHT9y>+K#nA=9vSN*gTXdnkWOsdtykTE$&8Yyd^DA-f
zp1`^zZHmf7FOT?&u(d0&0f@7q;eCd@0Laq^?G?LbsmSqhS~@X-ZA<#>bxiYcT0o}>
z>@SA%6Tp8jkx@U7)4!M*1=b$#k$c3IhdUby@bfdI6T1VG4!j4tO@U2>#apa1bL_(q
zPlEFth=VBYGny92)7o<uI6K<#jf`yU?dEuedODc6Gp6qoRiyTAjI=Dthk(WaBRJE+
z-do{NK!K+pbfm1QEBr4Zj_3btbLzHV@xkGuqQf7oupf1VkA;Qs(Q)*>%8`1xyndw8
zan!M51S;9pja2ZRbN9bLFaVkPSFb|65Bup&W<OK~sjNEE9zoVk?!^^CD&aFs4c7iR
zbCi$suxIEBETm?_$1z>)HGlN(`+B<i0fJb|;C>L=s8`^AtV06KIauih6>PmRqh6Wt
z$}MS70fEP00#9yuu?WMRC|Y2-&=}J<y3T}R`gXL18eVt~U>Y@oaqt{~G-1EFU!+4n
zy@-As|Mf%t99a5>``BlgqsEQ!dC}}=*r}Ttp4(}D>Q_kfX4wLM^dHPz1f?SIx(V!8
z;$MP4SpI-Bl^@O@%!q<n?BNTh4*xyP9xl+2O|u6Z`piA-0rnkinMMIH2F~@z^6jiK
zAP#<p`(kF?K6@D8#fCA+vhi`Ubb6F~m=KKvB4I7Z^}J4BQ($H#_l#2YTO<6&+cBIV
zh7$P2ZQvK!i^eHe3Si=T^}<-h9qO#15Sv;gD0_il){5ft;Syg2RWMZ2svYIo-5GvR
zMkRI43(MDjIFgyM>z!4DSJr8vjEc4=%O`!q*y2MS&#z7L%Uzbz^yI39ii>ylZrl6I
z#qz{e7wg!)dKP4zzrY;q*%)uV1)PxR!#3C{Jk$to^cQUY?t5onk<r`Z<9{OZ_TOCj
za3eTZtfKJj?S0s;LRTM|dZO&N>s1s0yx{-@+;adcHJm3Rkh552RA|PkU~mn1DtVl_
zz`Xk=%3QxKe(3ow^v>k>9uWDC1kAtEd(u4!1^p_~6nt&l;^S>$chbpUBOB9vhzLni
zJ>FA@PoO-E%)m+=1(1g=orbZB#HCnp?IhL*4SGxj62Ne917}`gwLhY6@u^OgLGg=U
z^;C0MakT4*s%B6V+fyF9DEi4%Y=@hr36hgZtDkHT=q85K(}yQ?0%Of3WI5huHP1#Y
zmsGY#IWJly%U@lZTsER8Sso)uS&waT>r(_V%S%}MH3>bKc+f#33~K&3VtLpga=?MY
z>2g5aY%sD2V!!1wq39qwRd=d0qA*{U5#kWmw6B7>fr2_aIy#vf|3VaV=X)wl3`$>e
zkIRBUb-K5<BSnHfV#ab)wK+RSU@(lI`+&j5!<f@P7c!nP86zGgk4~1sW%z;qrp!y!
z4*0_qb1eWIdqCp@1N1%M_V}D06sm&6b4-cRjia>5C_=CD?LB2q=tt(sJ(A2Nr+U!t
z$r8pJMKCX+n}kW(!Bush#4mV!IG>Fuc=vCf)8wsz(E;Y|Z_yxg>~kdOA6<cLKWC1j
z!Ee!fj2Hfejy3a`C<I~*8mE7v-)7g9KlMHNh~B38e?O<q!%_pRY-E|rfUF4dCADsM
zcImEqMSezUSw!pp(zJcck}rv)iWFf5N~h+IQDUjJXPC%E>E~eQA0bZa%JTJ0Tbh!%
zOzg6vRNI~D<B_?tz+EGEaSVtO6{SIjGOQc^Pa#<MJ?Au;FgnVZj6xhYWFcBj-U8k9
zTm8KcjkWq@?buikYWkeXMt{0BE7s~4`UV~YoM03QV@#j#3rTNi>%xlA6gkFhGclHA
zuB!{CJc2b|Rz{ZWut%BK_Wfgj>PaMRexwJzh2I0H!q0^FfWHXxELoufpIuVoE^7OF
zlsUGN*+2U4pGXZ%+G6s1Qap(hJoWR2)K`2i30Z96jwXsX$}EEL)gMOD4JHV}!@SYW
zdtfrlcSWI5X5koFqen+}O>9mj+=BrB*i4WM%&t-90Jf1#7LzC7goU%CnknZufRoJ)
zyi|z>%RIP$^kTK18%W>{dWFx;AZ6=a4?>n3%tTUE2UYE;$cY+t%?Pa6SrsIOeA^1m
zs7ta>+4d?amEssx9VZI5vI-GlBiUrLU3ht%i_FSW>JncOZb!_;P7+7-7UOMZj_qVt
z?c*D?E=&$Ovv3jXM8Sr$`Ew_Km@_x5daV|FQs&Je&F0MIyeNP#%!||j2TQ&D&K}kW
z#?X(WtISVLz5}>YLLLVy^FYx6d*J-!^OO?P6~JD<Mp!-<2mo%d=f^^q#h34IQjT`(
z;+BVwp>>PQ0&Dixqj2WRP><On=oO{kL;mO7xWIm9WQ$z1X%uZ7yYrl3T=F=VZVO{?
zV_r0BhG!loTO@POjhLUj^U=(slcnHNiH1kuhrqcX)Qj-Pe~~SmpyirH)t*|JtaeWo
zBV%5~e?yiLq$6jC16pmAm#j)j9xM+bk1?B=2AobTP6`JDHO3E&)BP~ULWoztU$($9
zGv>8=6%*yo9%hvC92>j){>(_kRl_)#e!!r1``)n#JQhi_W3iguZ^#$-j`HyL-~Vm|
zKEUtD&%w5e<%`*R$n<eHt%6{=1B!9X@ypYAU5Hz<CsTK!I}+@uI&5sc;HEB+#nr{R
z@u9rzP9qOZ4f%{SBt9gruAW_uWJ?ydw<^nf)gzIeg~6~_2SK**hw(YtLU_MCgGp3+
z&%w&Y{pBHIo_59gdP6~i#>e`T=P*;i6-FN@OVC<>p+8}|EJ4!XQ;m!VfO*EiI#Zm0
zz&Z_i9bnDM76>y4NJP*}7bGTWIIOU2aea*WX6hAWTHmT@*jxF9ATT8uB42+F&k44}
z+lWp=jfn~O!;9m4@`ISCgi(RcsLn;I2HZhYUqk%o=fGRQUI9FavrR$Sk^3Y53=9S2
zEQtNXZs02BulnK~2*>@o1UoJ=c5t$v6CH1;)p7FVC~~4k8HTIaF-3oYqyLQO?ZnZ4
zbtR;Wp?(K7y5|C}%LXwH<o|H{Z^#kGg5*PuH%=sx`1^%uv`rc@g;Urmk*4{#%h80E
zbVrRucNc@{4AZLw&hR0C`9thB7$*8N2h7kjG(~=fEGqZZ%H_3tDj6wr!J}YnT`=hx
zoDyhFybQRXB@+l_S(u8eg9>^Q;+Gf8Nd3vQ9|h|khm(nZgW2M$h;<c41pxu$dBB-!
zbQ<RI$80va_yC&CKj2J)Mw-)O6GzcrrYb0(8=aU(gPHyQG0_wa_=fATusr0UHXFm{
z_b_kVUzuggmQf_5USNKGus#d*!k+Wz@fpe-VC95ax*a}BfhYYq$~-#Cz<M#?zk7{2
zfU05bfRfM)_&cK|AYWsDsR1cxod9|#B0dZZAB~no>h={jpAVEhq08IVq?&LE)%wW`
zqs7-<BeH@5vsJDT|NKeA!Fp+4K|!9h{$K+oy8DT`GTOx@x>9}bXS^2>X9xM1kHUQZ
z_#F6;hSsdlDBbBBu)i{Ob$RfponN%OZ%T;En4>h_3*yrp0sfM*O(lMb$;pX+C7a5K
z4{1qAc8I+_{O{h+cn$<~V&;*PM%L5#FiU@gO~Gf`EQ};}!vSIr#|g$sI8I)CaSTP{
zOS4Xt3yP|>>1i8k!>F)5NW)yFS1}s5h*U30db9_u`O?()Mn@n%TMhsKI~HrQ*4N0q
z$96hhD~i>?Q1^8zchAk$v#Mp5Zm9|;2`Bom(<ao3X>03s&}XB!|MN_-p=@T{$|svg
z><ng9LuE}nIu9@aL8s{N_(y?1U}O73=H$fs`xN>aaVjCo8aGyVJ=#92Ko!Rqo?((O
zUr3$BL)stfQX7v0;nY`OT&*>ftD5EyhJscpt1nbD0W5j~onpE4E12DUoQ(*FE&K;e
z8fQR4UDWmGo;x&hl`uI^P*>^0XvcK~!{T4o2vSIZFo$U!WDCj-UN|45g!|tiF($rW
zGTPhqJQ_QUo_~R{MgA*oY>+4OKK^Cd`vg)JfEr3bS81NnPcRn?z!DyU6n%*F)6WR3
z8u;)Ml4t{3hh%s{yV=PdW}r?bp@F#(g=FrXbi)imR*i?+dFKpsd=6xcuV9WAxKh=9
z@8-5%NJ@;+QfQ+~;)yO}C*AWloPw`%Q=TAt;4IQloSQtomYX`veFw}v66Wr~QW<zF
zSO)=28crT>wz8&#J#Fz7T)GVw2g^Y}lkYO!w(U>#sVBq<ElJ&06DI4DniIw2YH)LU
zFx?-arKZS^zRW*sOfU0qIMc&yLAtEwXgd+T2UH!q=;kbZrRPk8KQTlvL;t{V1gV>k
zV+cg}G+-m$6)+U8<RQX#`dQO)gn3v9cZW9at7N_)Ut~Tn+ufiTbBU@@OXrUoMW`#H
zozby5&oUcNEIH8-M550aAz#*Ty#2(9>{hkC1;n*po@=je%|1aCBk=aa*w2Ekz8&D;
z1Kpv)o&cpp*qsCNmI{6cDi|2VHBS$w97w8=>G_EI4ZsjqEIg2|e5;HGqZ!>xtTz4~
z*pOZvO!k8fS_v>=_Xol_X6E&KkRgWL#LN(o)Z70BJY}0@47{FkMW6l`5CM$RnK<&C
z@eueJ;sF;8qAiGodTCT2kL9fo00Ji*i4*IT+^yJnJrh=aEUPP7BuZ#W#`Xpf&Y+-p
zQP|-6gjE;n<QcK9fg#pOekI8P$S=27>#J!=@i>2C7|r8iu}Osps*4BaM!Am!*K9AA
zmZnErdp<f?d#EWiGRPy`*LhyAMR;0GxVk*j$s?gkxqi(a@}wbr8`hBm>*z8HfJick
zw|Zb52HX4ZEW=_tIvlE9M>{!1lteM_BHz*@ozb;L4K3;=M@s|lUBq&&r?NEKb?27h
z32qHUgTPBEb`J5h+3S_wk&v`h?9!8u75xWRVC$=L%gNY7I!><uj-=_hR4eE-?Fp-R
zXiO@X8skTjLFNi5sz=?qKNOXICVxv^81(xf6gArlo_TG~<abnf{pQ~)9BUUdIMLTP
zF&J~V4e@R-8|#OTFff;yI$|z3{>Tu)HlsdZ7~=_8fYfXZ3d93-#L)MerW9Wz`mm92
zN>glXL$aSSQ98#jrST#4#F%z@ac_KlZ?T+|$_lz-W4j6<F&999xi9qz|7Ea!L2Q)*
z!Z)P?W6ZqRQlUhQP=t_eNPHed{g~K#ikucPz2_L?KcS@qt4Pn(HV6_qzYt0k-t7T4
zz`hUsnACi*|6r~TWk>O8JmXo$hs{R<+k;k$sCairnXMIHu+TZ&lzD%c8GIgvS636x
z>f$gvV>1xD1AHf+0vd+b!M=4+cZ}o!HDj>Q;S&|k2lkA3=nfisTud7l<)rl}{GhMy
z-^vFvZKx;Fz^@T#BRc=bV~Z)+%k^yEkO$|dkCC6*RfAt@CenpCiswd;{ns*aWE^U{
z^Db&d{(q=k96;;9)Geb{;|#FG=JF~5YMABRFPGvOT44otMkt*npJG+Y0z$dqA6(%P
zj+9`0)=8m01nZLrJfS|y+u5YT$*65l$YdAVuo$aq+Lh<0#kmrkG&_O)Xnxud7m!Q*
z>b>*Uwv_nFR4e;i!s}a>r_+-{@j*u)xefpE5Z$?U`fB@GT}V!Teolz)Y&)TBKc@>h
zBu;8crmsTO3N#w1RHH|jY%Bze6I+uBWpYcB7`@al36RJ`%*8ED@PXOt5$|4J>liNg
zF~N9ALT>LZ{yF{+|2J67iaX#k3;!D|)L4KHE?=Embs)h104q-8zucq!><m)%C?t^h
zx<}y)QSRq_Ff4pz5i9#!!|R)RF)T!fFf6E8Y2E%h=F|FpwbG>I<RoeBzIx;em!u;u
zQI%@uQ*~vO3l_j{-NK?czJ^@!HFUjS;xCa+Lt@AyUQ%X&1n6p5M=_>=l7Imqxlka4
z_;LRISTPF)W_?7zfE|;0`V1NS-?SE^k*21;zJL5-)rH{n7XHP1=k*7*D6|e5kTXx7
zA-?->nhaml)YRY4{Pcg(XAo}*vzmGi^a~s-gX5bXQ1V$$qeP_Ti%l0-Xfk%Z*|+@K
z#uPMxeDnIVas7s%(xIFVW;JSBSa<xrzB_BbI$avu|8!f*@*Ka!6=%yQE6=T9<!i7j
zF$1h!>4C9kMel+7s6@oE(b8>I(#Re#B<+Q$J$H416jzfKYMQk*%m8XLt=RHp6<5u6
z@s{lAc)f&;t+BxOJAtuo<wvkKYA7uZt)QTD68kG9;cp@z31gX0o;mXc^I0r%dz1Mz
z4vC&S`89G&U_M7LJhJ>8sA!wdFF%i(&-a~2$xQwEW#<4qlXcXoDG7fP_7P%+B?_TL
zU`3mpLWDg!$rtsZZ<^2tK#yor#}`ahL(V4lKfp-`rv%Gh*f=N<dM5X$^)}UqxAw1$
zkolCftj+V`@AmNbXlfCgE98LRE8sD@mT%2u8|AS+6tMFMjOp4Zm0JGC%QtnxA&ck}
z@cj<`_Y#FfYDNE`RidZ)*3q4tmh<M^8D_2!9{k5)4qU&pX5JugFYj@9RL{4kVQ%%{
z6~l@Bd-pu})E~L>;G5}Xj=`ycfvF0|w)p~gdwVy*JoG7C3J6S7IO^uvxZB&or9?*s
zT){W#ugqs&XK$K)iB7-gRq)(=S_==}`*!Bj2i_%Tugo5g`j4k3$iX2<<7p{&Ff(%y
zTQavExH9+tEA+4TUtzww{|evk{wvHMcn{#!oI59&D)c49;8Eb=S;%UDj*zDTx)EOh
zI8I6+(F!T}R&Idfaz0W(qcCL5m$;D<@U9_&g)zyTp1-U-E^Gn(k8m_WTIST<B__<1
zsE|BBpr}~pqsjje%(w=*dz<ZyzDAar?NVO}U&uU)DyU-%Q6_VQF<a1E8L!0uLywv6
zjeTWuv)NwFE5ug>iqkMRC%+_^%kjv2+D@YT4Lb?8T>f9|BzznC9fO?&ni)+@OdgwM
zCBbLl8KC(OKu{y~ws&I)KLjz#po+}yT0ni!!fb11c)#7dAhH(itYc~)-ob$6;aasK
zuz%=tz}#Rg+#?NVrDMsBtsMyBh#@3-rdqhDg?Xk0X$<uley?urqB^v+ZWFE>{@-fX
zLR|t_y9k)lT%c|dR~^etN{I+abR%9F<=YrR>HZ5S9rGsMHUL%(zl-HzSfd5^3mL=A
z8AHE}asDu*V(+^MmD_s(o~(BPa;;!Jc!~NbL2nkeIF;_{;ZOEusJbq866vk6PUwvr
zPqWE&jDPB`_tp)(zA<q}X#I}D*Z)jcU@02=alRs70NIzbZR+2PTjoIoFjndDl*kCi
zq_g$%I<2{Tcure<U|e~aO<-<ULMQ7(X8snn?4r&szoalF)MUH4D!p8uvLxM;ae%iX
zHtqt)DuK*D7ur>da0VIP6z)Gny~DJj=YL?1rSGhtn5f^WMGzcFr_uzm`SxuZ6n?rN
z@uqmhDm>SH09WkKdGK9;zfwdMLYuO#Tg<JXoi$@)H9Nf!I7VrPeg(bx(o0;8ikmkP
z7=!S;K(jR30P`|fr$9>vI}k4ZN^ZS4&bSl%b|d;<^q+~GTf}CVh|LJSf*7D9Xmjp)
z2o2)*!dxEmyxrO{riRG5$7xF*8(}MCuwGj{KG28pY8W3xM({5{)Fr$3*cMe!d`d>p
zgKX~>GJ<Fyx$_UMe+xTrz!&~v827>FlF<)@AG4A5yAn0%=HAIN&iBebcY*oL7|LFN
zP6IZ=hhh(&05#QfKmPP^iG^hJg9wCMP+<Eb;E)lnfh(MO_k9?|V$siMI$j=5102e2
ziw}q^54DvRuShw=`F@F^-$0_-F$%h=ulUD6Hj?7qV=D!*sm`2;G%LJ=EB%BIdNw44
zFt!Hw6fPb#qY3ekwO-BD9L(SJx8nusp0jl*qDmviRo>%5dR8l&_LLNlG=;a;*7wkT
z-AgL#okBBY(u`0i2X~Q+YvXgwjz`u&{l$~<y=SW)Ykzr5hDimoI`?Jgt2?qYx4+(V
z`w;VUnH?b;`93M(>)~H>Hdlv))o#j!eZaj|Zczsy=l-E<z{-(clY}BS)1n_-GJ41w
zr~^fZG@%1YEzhiqmzJzbb65EYywY?Dem<pDKAB4r6PITB#Hi9Lm_-?>(qJ>!AQzWF
zq1ggQ2Nzcej_V7neRO5QA^+^o*zn@Ro$C2%e_2^ydQcas={`}`(Ef6=6*{~4bZF?I
zvZnBcR9~DO58KO=K}Pfr*mbbY;#^z6TkJ4q{nY3@vz{Df`lqAfY(_@zu7RL3LtLCQ
z^CDjW{%XUsEVt7?8_4{Vxblm^(M=v78JWC2?dk@&f$fovsUuko#&iNW^<ZQjDcH3O
zU3@UQ4n+{pqo;o}!VdZg0w4Ym=7!G@o*RDu!#wK55WGI?KCk-Uh~UY;J=C=h@Y(?L
zeh%OTZ^b;1^B7Fmqlc>S8y=i2aJ`cYEKTOj4)k@hrIRuKL{5Fne;#ZoKDbK?*Ovj3
zAaFS(L#7WDW|@xQh71naC9GLvwVV8A_umg?q;;ozjHKt~?7Y@K^!hr@<Ul~((RNi`
zPOO8!S#-q!LlQ5zWe!zj4;K1c7oYn3`s#TRl>@1TeU|+7e?M7V`smMlbtM(&`V&oU
z92ag`Aau4S3xy@eyW_BO0Voc##wQS$RR!m<JDUm3dMfoLo4DoAE*(Q+`w!be;(6Rb
zqPu%nfXuX@Z*RsQ=_6t4`1p{f+OFY*F6{TZNnpG^&b&wA-c}A^J!X#|Fc0ZNt_+d(
zLR395v4%i@rGHsAz9ufb`>8G>qcz5nc=c80->@fJ2pzp{e(sLf`}<$toH7ztzb)_e
zcksLSF${(CMO@$y_MP`~KiGE&kL@qmxj2kFKm+2+!dT{a9+y$t@HzC5k7?dhF3v8<
zNeo`F-83}0ASk6-<6@|*gvYoDwT>8=KR*9ilG6(~hJqjX{D2VkH^y<jBWFXsqv!-$
z1`)w@3$gvaf=9$nx*uK#KbU_fLp{$hE?yOr^S!l1*jK~pGJ1Z*NshhQj&{cC1PJfp
z!g}thEJ8?dv=87Z^bg_VoJUuEMi{~s&e+;~*l|6LlUUHOV6BfBXNi@Ee>#&}di<ll
zU0>|glDB2L3%$`zg$Yjn=CQTww-#-!4>z8C%e(Rb|4RKB!|W^F&oHC4C6D|((resy
zZAXr&t&_=?1+FfFJJ*s{U8r#_Io_jz_0<E<^?}7=Jr{Tu<SowRO&N@2Z2eC;=}0dn
z8xhmkc%~yfY4GXByfZ_Y;~t51kqNC@k5h++A=bW+aWan99jdN9+zip#N2jB+4{qKA
zV>K{KD2QAD-2c6?uw*4C7trg-8pO0OKH08J9DHhV?wK{2W1dO%QHi=#PoM1NX~U4)
zx0ErP6WzMMss;~Nf21{}GT9p&&4JOo?t$%t8Vg~78#rIzo%cvA4=Pr^c0LN~NWR7U
zH{M?OUJ3iVo1N3wY3%opY-Ydz0)G#Cl?Obk2A=0)V`2Y=K(j160aU|`1N)H4<1f*l
zLG}YKBifD1n4{@jVZwB>gHYW`XKQ~S?Tn(mh!8(*wL;O50ihEFuIXol-|d2<SEk>K
zkqqiqDuUf{5*yUD6uHfeM8q4CEKvDGerr2PxHyfO*jmg(gr<E@HuE)eB)vVs)it4g
zdQ_OfEktZzp89C2Br`kPqwz?i{NSvK;5_<oj9QFrfN#baEZ}TE9o)PUq8(u(P-hQM
z#ArI7OP^z^Kajyf4v9k5ICZ{ki*yv(_(n<Mni3@hA#{4u-isho2OcR~a?De9v>m6|
z6W7H{3vfO?&an4SQ3aS21mZ%(Vj~Id-ROX{P-hfUx1+fFwQ5xPTHVIC*2NOJmHDMO
zR17JU$uS-K^Q^RMDnqO#!7dA(&G23!Uhvd6{C5GKAb;WnMnEGrTNtx=Ky9{_GlY_I
zbLQ5i2e1)D9uqH$qD#YH-kQ^sB7$_84Cq;3`y<oS8JQ~+SV|PGsuIXehxGuoE$1U5
zarx20lx2B=6zT|re#D1dqROK}i`C*oZ@<=YI>0g}zcF0nrt-B#p><pHt6#4Utg5M#
zZn&`_mJp})0aD@l22OoR{R41M0jDao{=8?;0n{wnNgybmJ*0#ZPCvxau#SlM8v1p&
zEhPz|^Hbb};yQL`u3c~3Sobb*xv$M3tT>XnV}use9%&9OFP6poSlsjoAoj0MJ)SB_
zOV9Aooo-Q(ZG*{)>AUljB+>r%yL~ddQ!?7ci~LndURszR!OLRaC$VqXo2^~-Tfk)N
zMq^tXfSE1p1lej}u)T1spjRt$P3p{GzDE}Sy1PUdnI{)GxxXN=T|h#MTotj{(4U*=
z8F2-FS9p1h+sHd_=uQP%stX!b>mpm+Y^|a+k(R5Kl^S>Y`m8SAkmJW(CqDq#P0vja
zCA~wBgA&jtE^3V@=RSLGFa3E{t9@v36!Rr=#EB&3rSb${3;sfZ`HPm;^N7ZJ!p$%@
zG`c%K1>j@1+dHc(C96ZUNE(yjTVd|)<-WkeBv9tRpe&=9>6$e+#EYH!#<&gO22>6D
z3+C(wRD{zB^NV|l3XqSIFfM^D7y9L%$&DHpvclRYQWD#c;7%YQZ<c@t&zEHm6wCRL
ze)7+kn9p7;J*e6F^>O0D-E;n0g)>srZY!KkES?3Ms#qM01lkS(IQesPSqN0YM!?GN
znaV6WAOo4_JqkA0g_2&u+5l_fX#vb7Bv0wg5;L$GKz>cfo~-o#A{hlSNpJ4^=0Ms>
zBx`-N8@)k0v!okTJdjZ~Ug}p`UJ<bJg}w*^rUQ--m;&!Cz(xnKfiOg{DnASsFflBI
z2+gQ(tbHOR8Vm-))#<XmH$kKgJW`$kV4%QUFhRTkK{>y@d?dlAI6mI-xLW}Z*gp>z
zDA*Hj0M4ZT04D$&gAsVC?u>FP4~LbzIzGWs8LT{;UAaEm7y_{W`<j=+8H?j$z`uy4
zQgC5JU_&dH=xK2I7^ojJT4Hq7dn4J|Bk!&1cucK~-jZLmwMs6p+FCTcn<N|sV;1g+
zNFRF4s`TVv*C3-ce?3|1>%BoJO6)yd%KTDxrZ17$J5=~gLH;vEy3?(|U10q+Q@0>4
zkd2dsx-AMwHkWcdofSdvoe;|*i3~X~4K#1w6Z6dw5n8n_t?Svo=*aHJbrl<Zef)aj
zQXmIjl+-zZYyd$*mxYFPDKmT0CHcFqG<h{$*_khL-fHixY2B#tir?6Zoz*-M@Mj1R
zVRd0nKYAzvAck?HRx%C(Z<Yw)6mbP2@Fv+)1Tul;6i6C}CKG-K@ewa>>Kp1=*l(<N
z&l+p?v5L7qc(C@78<|}K-$)N#7qHXmjqU^#UsWWhAfS3T{WL~v;^6wU(`k~7%uG++
znO21XsUfEcWcn%CU+|LtKkA3P`pji%#g^iX)n(F&@&nguSNwTz?(o{~p|ovpu4sB`
zTh4^Ms5icJU4fS*d$44b*t247SVg>tS7N22?MPi<Xw%`^mFK+zPSw=zZ;*#H>@JZP
zs@*&_#j@anXz?Ld#yLi8q&7j%mpC-^Aur|4C@lAEB{uS3;jXRb)z-4tOqeF}C}4{O
zYFf^n!WE{Xv6+rnP6eWFBMXQ%V*TT|s0BC)$_2jBo{IwlE>UNFtIG3T6Vw|sm$SGQ
zKv|=BhzW%IXB`MAp5v*3&+=FLRh8$l4}fZkcw**nNXdH|-;ZsRAot<}1Sz@u{YO(-
z?wY9Hn7)`@PddE64&EP+YN0+jZyZIzu&}0jDv-Dg^K?fwbHIcIjlV&jf$QOvH8BMD
zyQ5kN;Wvh-BH;l7#(kHl=KT!g3ho)1|FB6QPgt*4gTMb+s(*cTu{a@qNACGEGaq3c
zaUd&-0l)FOF^>Vf!;Fj)GfiV)%G)}Ic4Cta2(D-;1IjO6dmh+lW$+B-D0m|MGy3PS
z{eY4&i^rG_G>m`8&~AuM-p!PwBYVhs>`V4e{>;5+17wH0lAmEej=^sMWDHhN%y2e1
z%98y9QsfQ}^5g($(CtXtdMC=9oMegKDrmgt=qOL{1~~16{_6g`w?S4mr2tMmQ#8uL
z53W-;rmsu5>-c*z9|kvq^X?YZKEDdIrhxibRlFWvA1qDJQw-t3dYw!khiUM?%*^q@
zAZb444xs<cSf(Dl$3lVxq{1_`MU&bo7Imh^wQ4<~@S-Ml3H+svYfbf}P4Kte^_Dn%
zg<N6j5$@>`?qR|GE3rhc89o{Qy&H4?J)hPS*XvP@swE>ptx%`~GFnur5zQF^(F#R$
zKt^)}xhkSLGaw+dIYO&~%Wz3G`GzPu$jK=vT9o?RABew;qTr^$XpvT=#&@elci&S}
z?P_jcn|ON(7pS!l=!@&6$+2pVh+*bKBpD{$0V`Hppf!L;1-ubrA$Okz6bTZ|MIa$E
zA8tkj!;nui%PpAYXfxWv;+&G#LN4aJf<1?)0QeopD{H|+0uBU2{|V?13vnk`fJFAQ
z_b($h62Cyt2%r)RgTnw&l{KBPz5DJsiP{pe_;$|9O)gX>Tb|>eUml$(N(SMNLLUbw
z`&lZ3=o_R9x27VX6WmV;N012bZ>WTuDowXsF}NkHz&#Ewa?*;qNY3u8M4mfE9Xn|B
zKyxJ{RLq>EP!@VGcBaFUrf*%}v-h`HdbV#JOp4^427f#ObcpplI5AmLoHhj;)Ch0n
z2K7m&OVixj3$A<=E$e-}O(tu5ymw+EuSsQPrE1EXAm4qkM;sxiAzv8Jy8J{}%t%m1
zLrBQttRRoy>>S7T&4a$*Y)|!>9;2*@7XwTVzq3Mou-OQ=ULrG?m4vjYKQ*jbY8lcK
zx#Y^`4Dt#1;b(51=!{&VvJ7es)8g(}sGnJhRuYbNBQ=3$W=9s9#%+3cQ}6n|bvHI>
z%oZLqweYXlQQfy5?;i!V5^nJaS$QTJ^kdjf1EZO?%1--}IAsfnSpFcz8_Wwc^vwrs
zB+k0W*F{A)BuXTSb<xpv3GN%sTxDW#jhLH@<zkV{)fAb|kaR1McB<#b`>B<oFj7-a
z(8$FDlcE+Ar>CY=27Pt~n8vi2>TD$bHgr5D>1lGtr2GJ}{*Qqk32ZMR7sDAN#<7;1
zjS*NHbdysUD#5TU4N8sx^CH;_dEiU8BHpM4$OhJhgw^|bfa<HPO7ILUA1<<%W=DJ!
znCLGEa9?Z{)pfeoY~jKS=4OPjV7Nl&ld&|>v*7tB4un=}no~T9Du3P5rE>4glux*Q
zI0j7m8h;$H7uR<Ln<tiZ{s^3bkzf%RnMsXL(0hqGdPx8@1u--Sfqx%t4#GV52L1Tx
z>qPdm=)h(|rR^^Y3M%T?R%-hT0|N{DwSLLLPEJ7RmA>2uU+53ta(6pO=vycqoV9i2
z9t!t-Ucg4UL3vs{0q8-93lqRcs5L@<x=FUFux;zswnAq9j-s7tfG=d0qV-G{Vaw>y
z7W!-S3KN1}$$-TZyb+MOc7QzVjH9W!PKs>X2F|Y`WDDp8D?l7G22H{bQPw$QRS9c5
z%VXU<6RVc2RM$N<luGjyn^H?h3z)6R!=Z$Oe0`~0<&jj|w?(Vn(pQ`0k-7ES5`X5B
zRH=+4y`n>zTgXYK0v?$SbxEG&?`M0L{Dm08eM)$Lhglh;S4i-`v1exW)K5lP{HNf%
zmwE2QPPUiHN&P)f>F=q6dlLC4;Jd55Im8zBo<aRR=k)g!z*sbU&vl-&{vH?oJ*D^_
zfW>0aFM^;xfIC-T5c(sKT-6vHg3lTkIXQqO&sn}8e6&JFRCvj$u=%mItK!qw#GBfy
zl1suj+*q$s_dN|YpL#*!e{`i5*Ls3X+EiVVnC}+fOfuTomW#bHF7|sC*jkt@{eZb~
zS-Ih#k5{K}hQ!~7r&cGAC`y;dvN^s9eP=7wwux#`KmkEP|MRB)26zB2U_SP7V8`0u
zM(N;DD*Pt@`#Tm!Yq<L~%zvo!EWX@Zqmyi;FOzHT?jtJb!<6mhbs~<4rjtgt5N{Cg
zY#pKD)CB(_v5~w5vMro%m?FqqxYH7`5ky6VyBt#^@LmuQajgajc89i10qFem^k0X6
z8bwOvK<xxqNasFyx)S7?*8#^o!7f$;59`G#eG`spW(VcPS+Aj9UZE6nKR)YI(5Jam
z##T;z_6RY49h%5Bm+WZEa&X9O+g{T0RDWDt|C4Y<m+9b;rQ2E3{6v4;ujvwPTavOu
z8|G=t=i7QJwUtT$zJ&ij9x1H};T5S$!}SX2Lzm(~ENZ^Kry!<&Wkkfv`j~<}*PHF&
zLSak;zR(a;h%aPpYl`$sD9B5)_OeRMD@gE*YTBBSv9&1*Ure;}vQEMmBXNIOG<5;$
zxNnA-AXnHUz1U_jrO#FrjjW)QwhvfEoI)JUjjgP$<|Jj{>g{gvVjDAG$AXf^?7j?-
zdGodr&_*Dqgm0TSRK(SbZ?3%aHQ;RxvZ3aa{{gu%1|$m*6obKD;{?E*ZnFw)J&awA
z7U53MGvDbe!g1dj*9Hu#J(M!3i<5t#!cDy_AYhr=O%dqt<RX<RJ=B8%C}2Q;LqI@?
zo4N-s+;<1LMjD3ZTiTm00z+zS_zTjoc^0;dOdY_^7aRUJsgnE7&U}7s`1AVj?Ah-C
zpGJ5G$iGlG+4wCXG$jFa!ik0!0$lghSV;bb{(iWu@iDYu7gL6&_Lg?N%zU;JJ&m^f
zM|tVU)KO%leEsMY@RSH1L9U`yyjsX2tBB%-?gMTzG_{-Vg6}}SgPyVJ@SO{<WBR*K
z=%oNdZxCKS27KnT5f#`+KbJz!Z9+Slwqd3T(!;+&4#)=VAQIY>c%N8A|3LppfMXu(
zMNjiiqy5xRpzoQ2J?054(evCWF?>nzT8JJ3T2}xS0Hs>s!vcQq4iBS{;bEc&O)!yY
z0@G9RR2j<S{p)_+Ezex6aBFZEUp(TGmz(O85Z0yPe7q1#LB0vNWs7~4!2KW66#xV*
z?1p;n{CP#~j^f1iiO#+~30)1Jd_p9SjX@vt(&Eh(K_a^Y<}QtmrO?P6W@*hUV%m-W
z$$t#=A2E2=VtGl>X)2dj3@}^FYvHv+h2>?u6}%zvId0}{2aoAK9_Ypfik>EAD+>r8
zfuhu09KDeU+YNERE!PHZI>izD6tP_v4p1xDW}MxKll!bhSp0=GAuHBu>n;&lU^lS-
z7tQ)?Aq`^9yC6ES!W#yJ+pN!Is8E$HmF1{hUBh#vQusIKxyp7guOL}NF|#@;Iw~ni
z9hFFq6+_sUM}BijNPuVXuUB+e_#W3=e`!YYEBx<JaO-683Yj!8kpCxSO&1|6x)|I{
z^JYFjSF$uLykznR9s6VK+RFZ*KL@6VxwwR-2et)d;13xA%q7AtQR^OIY7*zGO+^Ws
z)KpCy{vS$XhCCvDY%;R^eS&=^Uq#B-bh_7=t47YqvGeTNPUg$Alh2&>k+}O2WKt*l
z^J@q<9C;<((}{U^a>boVt6x`e{{R*j(>IOZpVI+&=Lz=HK%7f9m*fN6!38T=jRS;R
zPS(ZSw2-tCh^(X}NagsKvLRobjGm087V#I^TDgY%+Iwd#O^RyC3P7(TGe@yyXo%bc
zL`bvOUVAN>q6msI%A5?G3?$$B!PZ0Qu+Ur<ndBK&mnd<Hu8Fgk*e-~Y$+S>>UaAf#
zI!C8a^A1r!8|hO&5eGq;v0`gu=}(0j9Fp9FG{ix#lxDTMIn^82ti^SZ55~u+;}g~D
zIHbv4k>%-`wIX*zPJgziXLf(iqUz$}>eDqPB{jf@k1?MSZ;;Vgr{_)>Jk)elQ|Zn7
z!%=QN5Qu`$Qevu<dYg<h8UP(~9^gD~B7mfPfEO8e_1w8D$aRF-vgwuU)Z2#+A>({R
zaP&`oMdcgq(mw;#&pt(p(Do7JdiCtNYozNduMYFSW$wUJ7a1D!j3Vk3F=f;P^R=C!
zt?B%0xiPerB&PiH`%+W;^8I&P`o_zo@xGRId)|RyIAuk=L=s=2bduvP19B(+4Lo<m
zJ4|gxyN#5XzhJwD-u86Z&NI-aQDi^LU*ubm9&MGST&(J5=M8ogs*M^3u7%f%lb>Pk
z@Gluh|I+e2`s@_?;5qsl+IAhvsC+{W-|aw;AsVm^%rVeYeHf%uR-y#64XgcOKLQ&}
z2IQ@G#JF*^?G0IRyQ>cC!prjI8Nv3-mID>c9c0w8%q}D^ocZzFrry5pPpDz{wC;?I
z6+)BX#C*T3-fS<zoLN(u<C7?L7|zKm!aNb|Yg7NG0;q$Sm;YAnxoJI0K?Tf>?RrF~
zduAXme)Z+f=F>}J=Z?;CkFAb}kv!cXyezq1BbkeOa<|qiwVPk>tLb}vORBPAdwytf
zq-${Pj*{AfZ4IF=szN2+Ggvp&Mjhsv;63A3%_=Y;pzCcf?xGE-p%+?Wr9<JJ6DP{I
zmIpllEH#|E^^FygE8x}S?aAYc`W+?F<8Q)RFdQF)_i;8bV18Ur9x{*qj%b|xm~^7|
z6W|eH*6*Q)_cBkg>&MURhi9;{k(CJXQ)Dvz%P46^pCRU;|1c)_Zd`-73ScD%Sb;d{
z1|G>7>otfD`drf)SjmC)QgVx!#=Z5OFKtTI47||Yda^^k;L;pPY?UT!S-J<PE@=@3
zD#-%oYxMSRo3J3)<SjQ=XRm&1OR}PJUAkYIQmClgU6xz1r!IJrwcY%GZspcU<#TI<
zg9^_WyDOwr{wRH7bd)F_g}N^bsbQKXKmvvPvEZ2j_rqWKU_%+uLuweVdr$qF)WRpQ
zs~C<JFxDB=VmYz>va)VVcEO&O$T6WZTk4w>>_`nyUd<XT4)DquERZDySlfd^&j3Rz
z58ed^PB!xqe8^lss`qw`jt-#VFBu_$z95V@GizaHH@Bkw^sn?Xc$8ZYxe=b_15ELb
zNl^@uFgZ242lhz+J--V+g4i&ZzzIJVKvEX)FItAyqBTEUvS8ZRF&!3{V0Jok3((Xy
z!fLXRJVyVEX~nOY1vA)V;k5Qi<zJ$Y1H?)$kxi33;prV@GvFkqheFt6E7n`{aQ-?S
z3>GY>3KP~m*P*+-HaThSm<~5>d-jq2N!>Yqez`qK2hI}W)o*W0P2KkP>X_AU??_GE
z@%C!s+B;C}tV&b0w?6pY>o7l#4(!=A+QW3QT4J#NxCycOf^{OJm{Ig@<EeI~vi(#e
z^LJ#Ysf=`TimcSYXPg{7tf*L<o;_3&IN}_f;_H<f&dE{*Q^Bw|2Z6#cg~O>OFy=H?
zY}&-GAP87x4~^N2%4w_ppqKg8G15hMd0ley&}CTJ5UlJ#Qg^nWUk<G793ejSpSUBf
z*Nu&SZ*|<-++b~NJrRz=^vn&)L(eKSPZToWjG}Wm<f69~xm=^a`Z=;J%EdXdGUf*?
z@lnJ6MQd`F9P(nE<`MbM!AahJsR|J2@czaCE`I`R1g3&L!62L)Vy>obLD(b$;yuew
zu~nKw^k7nC6VZ@Saj+#kqWwr^!^w{DQ5Wbi7+D<Q<Pz7GUa`AQ9@%-ky5Nzu*<=3c
zjo~W>ke6FnpqHghL~dI`+|rzY(27-wV0Lh}^NkR@D*QbyZPod@grp@IKE6fki^DQy
zHejQIIfA7C>X6{GfyY4sbVHiajWQkNP2ge+$lH6_`#J&t=D~gV6yq{-V75TxdV93o
zjGC9&ce=Fl%<{NrJrZgoB5D#OfV{V>ch>|3)$Fb&7fjxcE>$@>sY;_sAV@qm*afI8
zcqizcIKK}cN>akk)(%BoV-$}vit9*RT1+<H?SOmm{SY9+{{cRmv-ev_Eg*>xKLq*G
z_(*$v{-R$_-Zoiq?Gp3eV+^#cU~WIky#Fk6PmdzICWn;iWX;_Uau=UJ*@$0=aLf%A
z2p@qjgWikP1hya~;enh#W|pA6A2T<Z4?YIxLdQqMU(l2E3-r_IGNU1q2~erwc@ucv
z0iM_E&6xP5ge4036O%d0<o}6CB4X%yOanSb++y#K2Uvd&_XE9So+4+RaLAvyI9Ug^
zC3R%?o;|%}_ud{%d%)Al^DMoKV5o=F`?L^Fo>w;SFE2aL60U67UsisgIee_UqrH1s
zd&g4r!{9`JL`46@;A4a1D^#i#<AV;Tjvqhux6^0NoQ5@wz+eZdt=N_he3%b?b%}#x
z&_DH-9r<|GVQig2yi}O?YcLm}Tu-pJ&j_7>#(GkP2^xb$@!I5JbUrlM*8(SUsLLaq
z#=>+b8j5$+Dm*mBauAtU45EXw?0A)vL=)ub5}Fa1vbIJUSg<@vQCm}|V7}qTgS8)q
z@xozn&ddxN2l#~V8SO!$!Tbn%GM6ulC{Z~#wFPE{3rEA7_7oQFYE%mR69dCDf~=#V
zBWljxPR*!mL_u&{zq_VNwT#$r9~k42Rc_-Ao^>D{@2uVoabirUrFlqvmTyLXp08)-
z@+^tk->$Mqn&b~B6YLM1FyvRTm-Fxm040F2!A>XvEEJj<lk&$OAEPgkg!*NA0<Ez_
z2ludI5NS!9qDgG@LA;f)#;fqW7msHT0hkaoKNDsRi~xJ04HkC1cH<Ko;tYM;P1u}J
zw6i`$T6d^1ZCiVy@e>PIR)*z=Il8LL!o4B_BvwQWkpwcbsY`Ipw!)0z29;Ol@{FQf
z5flxTLWlMF7Pc^7n14Hrf0^0=(s~F?2hu2E486@}7P3)n%NEXXtLOO8^k>PbW`&U;
zJRV{`hKqd_4g0DZjx;IkRTaaRdv16pM+PpE#N-DA=Y~V8>s)Hb0Y&ASR9hbxi>RjI
zl<aLys)XK?CD{chw=K=_r$4ZAmpOSP=~5)(=zLjFz8Z)U!v`pg90&LSUSb~)2rbZl
z=N~T<0y>UdL5c5v%rYhtpNr(9fGf_x9W?NuUQN|I`5?VsU*`vyJE>Qd*v3~8v;O2<
zYUI|coXQS$`@a0beeKb@%A9g#{g#}Zt&77Ch$8dl!FeiSe~Uan!qv$MGDWe+&@nj0
z-%lItL>A_*b$8!UnbDi=?Va75QMFy-K9rl<o$2kJ*-anvNsu`>$P#>ts_g>fi7-iY
zfSs+M+S4=I-`3VY8ulV*>JGV&U4slb1Hf6K_Xz5#ixoa0T*d-_Wl;F=$$>;&CmN)D
zpS30BTT1*Ci}zJGAFdAy+j4u%{By752>*rd(YXPNT&1%^Xzq4dNq<s6L{#JE)a;$D
z(MhWwsnQI5bFqE|^;=71PU`sPL^lsjku;!C;|X&bq&_6xG5Qn88`vYIM~N9Z%tC(!
zECKpvyBrYZ#5<na=GfR4t><o2*ANd$h^y(at&d9V8SLjTbPdR|fzPhNK~fhpCxHq7
zG5^$1XQ(OVFYv})%De?=o;W(tQQ#<$x?72(rB3k4#6}1T48{o%&CtI{D?P88-2<HK
z0FLhgydln$F1TJ8%c~HW6!0Wx^N#=0EB-e-#pCx!fS$V#-X8>ia7xd8tcf#|=<)CW
z(JLOk{s()-6S(IsaT(w-&4VCWhUGz0Gh-sXvGE|Y-U#&^WmbP?hP(&Po~<Q}7;7Yi
zkY)sV;Xe_l;aMOzmj6J(OV-N6O?-y^u$<SpEePsK9Je-4_FK6{1c}Wdbs17}KR;P1
zmI+N$lk7d51@qA$+P?bS9IN#`!Ig<VPGLF0GJk7#nRA48&Vog7L=wDQ81p=IG<Rfc
zli)5K?5H5=&0;y=A%g3A8imaxJm#<4wZOSl6<HSP;+fnO6R%763T->pyhW-t%ZhZ7
ziY?<wr@`hnp^s;Lg(9>n(bFfRGeJ>*LKn`o__(-c=UBVRoFY;<9D;Zh;v|PddcH!O
zWR9OgC5)12Brek$V#ffm9$p&E^)T$SGsve30NC&H6&Ax-^B1&T#@NPwCN@TfL?!#C
zH$_G^rTZpDg+zKL)r5!FCV8$;jfhN6iHuCKj0#aiMuvn$5;0)~cJ>wO(40^Q2l$`5
z!rrbREI2d7(J>^GX_SS9$>d>SXrrG(9^e-OEE~f!aq3rM7tGNS$4o20Li}57BN(^_
zyOEe_e7bJUeCJ$wWQAG?;EaufS{$;ble&e6gEVGo;VwZUb2E{&yRz0%EYVabL+es}
zp)Og9wEkF2DCAaoIfiGY+juIRon#Vgz@shTSC~J`uP~opP4u#U1(J7&iXvK!+5wsZ
zR%?+Oi;QOp3|LXUw=*BQ`25H10}lQXB3BhoLRYzpBK#e4X1C`vYQOZpp+lcI+}p;+
zJ6vq;EHDWayF-UQ7Zxt7s5as_|2@#%7QxP0;LeZGlTL3YK|=*st`snLi*pmbJkL-A
zC}sgOnw7iRH_W#_Aq}2~`e{(_TnO2=pqnDBn+nIqn7ssi1>e~-)#xSQG9U+o{hvcz
zHWrv=25@lyB2Nhth*eqsiKULt<|;{4ptacFMr|J*mnf2^%IqB%Et_K@mDmURd0K)!
z>lx_DSqD-WgzLJ2@eb!8a()G9NnCeNPtK7aa7RvkAK)MG{x?7$%<qie0)A+RXUbZm
z1Q0019ud=x1j)E_SFfH$rq8{AqLo09j{KEF&`QD%QC~q(A*0?w%1&r*XKGxWpD6Tq
zHYY*qf|Wag>ShLho(NtJuMxj}x-$xncgA=GWng9~7b`aUqjn|}Ngzb7;26R;Ps=h7
zhO1C!dmNo2+76a^C5x<_EnWTWAFUW!9Fnm9`bynnOTrxkqr`SjmM&ganZ=R1gJtRQ
zsFmL2?PlZcY%YXANXR?!vzF&YG3P~5g&~T9NLNu*fkIIbB_dCIl?PiV4pfHDpJyg@
z7r0o=5hb)HhxSxO`KDBd1f;8_=GNg7NkovxqM(sN>(td{!IpP_G@9?~8|)-h`r3&V
zF)rX{w;)bRlEOryuq27KS8}LO7@7=H8~6(HU=xP;&jS1{9yRPooY9}*Z{Vuzez-{$
z(+4F8u!a~878?3}@g02G)<-FH5AyPMUgTu6P;2Y06gmg_1vuirsILwky8Sn&5Fa~h
zdutOb`-K)xVZIL5cGe55?6Ba;D`ZlrPAU=Gek@==1mHeK7T_-m1OJf1m}3ZRrzLXQ
z0s2xxrP|x9W9l+<<n2XHB8kl$TYK9%{`1{T!m`y0$1Tj)SJ2>@g(B}@R}=3AXs)e;
z6P2jQD3L%bZi@D2T!jh`0R*QXGM*!ft%*VL^oyh^8WgFe&J@~7@cB?ApY21J5BS6O
z)wx%*hsJYK(gI?SIkW)RAlR$`I^=+KbhDw|+{S06t``>TF3?MGKzw9rjPJtvUa{p-
zI7I%3-VM`D=GOImPydzv3f;!#QKOrOQ^ZNbtuZrw=E}hjEaBtW+6Vxb6%?%jb(^#1
z8SG?=U~mpg<QDq*fc*jM>JUeQ=kydKGYtxlc1#^8l^ZHQM|7QUwi5VNmgS3m48@+E
zgQA4ifexA7Y3}?BJI(y%avROM%_G^??Oe;XcI)C3{aFhL(knYV%cEmbtK~WXX{}X_
z`5J){KdfpX0pNBKbjmOJkAqjs7v{^_TsYqgp7iXn^4TVs_6cI)zwa*Vh=OQx2gx;S
z98Dfuse*9*&^$JDzdfCKn&_lA@gINZjgFHLiVv~mLn@t{jb${%k`E}$HEsu8qst<~
z%3`p22Ji~3kbFmg8@8Qc-G)s{f)kSvT^t|3H`VpROr|RWKN63!1fl)@=g=D~$HrD5
z<wwjAdh8?g69bLgCpSZ=sRjL{emv;QsjpW*GaPCkA74OJ{XjPo&wo!mO(%obu7_Al
zD`{{5;OAgG@(0M%cwoGA3b-5E@NcLjJoOc}mKxbiE<uknCiEz#)m7AI#OFpEe#<T|
zfwPME+$*guF1{_zYs6F><^`R9&C2(lsO4YqgXnT+h1O4iTkN`c3I1{o&jr8pbK*5)
z65i<vQ+q&W_6L%@mo@({d*1<HRq^b9cK6&HQV5|+C$tb+NV#bc0;vQN5_%U1Ng#oc
zKnlHs1+k#gn*k945kV0F3q_Ik3nC!4&=h093sUTod;Z@oxw#a9|9k#?{@xQln>}al
zoY|S#nb|#iw$Q`ge0!wKgyzqAG?ty^L!?V&tyXOtI?Z2Dt3^;<g3=OrX$icvD*Qm-
z%2V{3^*kPOH}^=4tk=Ab2jhm#&*0RtA)^=@aW_>Wje~<~6KpPq!QMs>KRG<sw}D%w
zU6pF|nYK3V=2z?!Q_ZnQ-rcrsXUg*B+pyBUrZE`<0i4ck_{(X`sp8gmORtOvms<yu
zabgfBf4a-#yYpv$@xp+fi$0z@bI&7P-FLgT={coe?|H+5!bUCVw`Q&FZEB{KC*i2h
z{_B5OzH8Zy4gI6WuT5^3k>nFIV@J+A`MYLB@88#E1uviPg;P(=qf+DkgbxT-k#EPh
zF@Pq=RM)lpy#3Q3{bv2pfiHeNcglhJecblCh76k7YuL){u!y`DhV+}(Cji^xr*E0n
zdBi)!c}>_F;{0xU+{|s62{`k!$AY6%!lxcx(4$qaiBO>|zrLJPCdbn6T|S@BYN}tY
z$ILgz&HH|9+QTnhc=VA2bGudA<L1|Q_QQi$=5**ZWn)U`u|0#_ZChxi_v&@&*edNk
z;<2gDCgSm4TA3PpEIK*6-ead0B~|xq>Oo?cOMBuzSQ&L%k+0$t-y!6xII~{<CBla`
zew}Q6?)M!T>2G6&+qtvj-<=)XY37^RrY$KuY`?wO;zir<Z>5;3{5~&f{*lS8W*vE|
zi?CI$=ONHD-(LhZEmzt($a>e!|4l=Nto!DXSr;}AOWtr{)+67nAF>-N9z0;>xagR1
zs|E~tA*UnVS77^b@2F9GiEBa20^+(iGjp%)#{xR-=z{Lu7aX0|dipVbacnyLWtyQ2
z9%}Q^6mD&N5Exyc`<}#W8q&IFe%x=fZT|`lyAL;6`_|5C_5Q>gZlm@P*VWAnNJYWX
z4R9nGnby+I5wHIOIPxJm?oc)!c4((<K6Pop*3DagSo#Qj%~LPnb3y)aJj{&YFx~U!
zW8_kB2Tv#7r4LJq^WGUfaeYeavW&n{!)#B}3A4!#zxeQ=dQD@3Jex!f4ePaNOoV0N
z<k;jl`q>Z_IX{(bFS0Y>#}hZOwd(e@h^o`chvCIBG8anoSdMK*&*!r`_Fi;!(x?L?
zEnPPceE9japq+cdFjz9qmgyRoyL{;E!%xHoSmrfv!^h=cd5NFv6K>nC7oZIB9@$il
zW${_Hd@SqB{rh*7)}yBml!j54x{DUu-Xfhx>kV%@)0=_dFU?k<oAf97IC9xIRxqZe
z+|n`TJi<=-5YwZ>F$gv-sP)tV!(Yq^k9zdOStAb(Y8&-xTGI3ZzRkOiiH+zR+PEUg
zV>Mr~<<)LRNbHR5*;%`v?hzC=&*B%7`dGJ^asB+OHMMx6>@K$L?7FP0vN59Tr3Jfa
z-_km4z25k@=S>~>@TN8k^IkjXt1;GvH)hja$5({?3ze!t7W?ZGIos=*(Vbk|E$g4T
zX=2B?DeH%1zcst_fK{JOi(Jsb%P;fY_QSdaxUYV$-Ege4>@sS1+q_qEEP-7f-H|nQ
z@0{qECqH@S#unR+?GM-We7ahFa<iUqheiwb@Anz_1UAhZinmelHvutsF8EWvC||)H
zKZ8i+*}vfh>T|n3v3u;;-Sd(X=e@`C#9hAqro?xd($CMo|CBBXll!)zVb7gen2@mW
z%yU(rJF_SuVbPiA-1#V7aArY9>x|Vn(>HT<26TvT6YMx*44UjR9Ud=zZx`)Y+LSFe
z#r?JeJ6UiZpTyik3y>Um^uh}V`UPs>vTRy%`EDte@Z1+uN3RO1RIoE>$%x@E=7#U|
zO2~;1=n>&txr$#zkAQd_K1i!zkCdKW<7S$*()Xj{lAb*>rRV&S?Q8plH?g(i$G(PV
zK7;vJm{ViLx9CJ3t?s#}wNMi6D7K^BqIb$)+Go=C31MLqwoejU*KX09MGg$^lM_>~
zZcOeUZ)P2xJukLJ+`O!4Q9pb?M6MtqyIe^i)`B+1n-G7AwGWHg&ru%81AEGXpq;DT
zgO;Zcd2xL7&Sr@@I61k4UzIAp9TI&Laub_TuPH~K?H1L6T7J_eM91}b{?PdD&x{JM
z*`jS@at#|7KGc5H{GMF*18seD4YD`owN+MrFuuJ=OW`X~@%Gssr~3T?Hxc93ri@%3
zRB2^6mMVD<8ZxL^aEBL?7CsTQC^h-TagjTlbp>xplD8!A)>ZMA5I5sywTW-fjH+16
zyUr6W{I{(4?ht9`vt_%+<hw?pEozqWIRJW!2A>XkayVnft|igI7QO2MakF-e&Dk}l
zQ>WQGbH?tR9k;W2_x!F2<GQtI(S2N3zAMaK_KUQip;^AgJGW1)8SyVGTlQS;x0{cD
zc<!3i;Y)*ezECM-#mM1nbHaBvPs&SZ)0H1@?Hiux8=sfhoK`O1!)I!2d^cgAquST-
z4)!el-7~mZjgEYNAg`mL%n;?H@8?iQ_FW;=m%VZmi5RzTc<Rz1cX-Ewp=mFTi`dyb
zF)x1LWUs1z;qk2#@{*d<rc;hQ*DWR@=Ql5^w+-*{{K4^jdSZ_VTDgJ6t44>>&%r#D
zZxo^IGT*>14%`sz5rl8t%JnUMBiyfdM03-I3eltI_n!3Tgm&#FY@0Or`Hc1zHdMfN
zJ;8m(#?-AFoin>(NPG*g4tf17eHVN%T5Q!8I&^xBmtXIWP0PJQtlMWdP<8{}=aqB6
z?W5Ls*@9hS+=cNWy@tFIpkpWQpBJArXX7BTUqch!GH^z>K~MUIKGdO4=G5FJm8oal
z@RVxZ=6^V@)%1f;bnY@|+sH}rvyy{@noO_e-LN`4T(GEC@Nj-2AHIp(`XK)Q*ZpEB
z-@s+$vLKI@Si(1^NzXn#8V3hI8M|<9(8APYbRTy$jLGDF!OemDEY6*kRg%zY`c0bm
z=9CDR>J4i=(a`(lRc*uJ7pR`6x&Oj1t~3m70snng`0pd|3%*W4v_GVLk>j1CpXlex
z(a-V`6S}|MSg*#ly9W1|ml5R>@cgjUg0W%Y`5Q((ykUC$fECBd(8s-e64F9qhDUie
z?t~>I)ha((p>n&tSH}2;@=Xo0-=3QgIrpQdPQFg6>>5&<lhk2h#**YV?NVmP#ZK%W
z!1V~fW!KOb6#U)g`-6RBP6V1en#aP#*+YYA$g{oDaqLjcgn~gMw@i!08RIz}=7xE-
zPMaS(Af}D`;>UxBbn<Q*n>Ncba!po%#j$y`ZC!Y7VSH`TJf6kaU!%nzeCW_Tg1-&u
zZ}B}*D)Lz&x6j8x+{4)B2C{tq#g_*3Uw>`!;lmfdI*;Scx-R{C;ZF;{TGEwwe!n{X
zYw@RC*71gm$&9+FDfXGDX76&AdkaQs?r3j+o;2py*|uYGJ!gKhq-W2?Cucr#YEie{
z{=?=E?*DX3U{LDQ{Rhua_M`px+J5Qs{={pQ5?7Qgzq#zviln3!mzL29uV!2LT43A6
z&HQ5XMDQ^hI==+lZKbuxmsji*AbpmPqqW0ZqU?UyKU(03mWQf~nh6wv6GIiu+qEKl
zC_HhHrRQTqEFGr4Gj&7GSpJb}SNkz94C}XWWLVXzHdp_Cae<ZQb)VV4P3^$0HDa37
z!&EZwZuyDW3p6-4xknugv9`?}7}BKMw1K7H1y5|{@l?%bezktC8<^<t7uCE9!VAW^
zX6i@iXQof7nV+VBd-V_Z7$L=dQ`&%5q}y?i@m9#z6!pdTa<2@FLVaNhr<gf{@Ph`6
zALNKGm}msBU0A~dYu2FdtQ#JF_>~_Pce52A&f7AsU0B}w%r8l8x@<9>KJ<0lsy?f1
zTZhnPHWvTHz6t%OzW-RPP0X?QE6XBWY*vbX0E$M}SoM1_HX`grEc)WJRr#SFQ*7Vf
zey+pDjXOy#{kZL$yPWHAeDPww7YODz=!>B@K58u&fIxA4f-Lqt+W776Q>SdL_2ITw
z-?HGFhgk4p61{4b?H!CE|6o|@7*j{CZNk@LVgBu1*JJS&QI*MKaFC1gxN!FxvHbXU
z?=QaCzu(jmE3I}dw!PjWpsuqsTV!^x7oFXmbX~mb83LJkJ>$cXsh*RCdhwDlV<eLJ
zlAyc5bIEQ8c#s``nRdtl-pCFw(DswIWpvEH+u`ZjuMYUn+Tp86Hxf~X@3f!{j_Ire
zNF%_<+oG|T;0J)i62T{qi8Fo%oX3$^r;dO(ll*JH6TZIhc6ctPF_yz4?fA>I1rBg0
zJ3L+cfWwV*(Em(9A1yK0kMXj=-vX!T?f}o#0tNm>z+;^_eTl!!=qTW)03YJS=}UOJ
zF-Fh<pQZ{<TyJRpK)(R|bG?nj-Z{=rT+gr|Q7=76$X(#M<h%ns#E!pA`_2L0#11de
zz9nI0bj+o8csi*g@cBEzMqv;5LMF;>DeCKXcrMxH0Ox#4{AJJ^w;zEo>RrOqwI2i=
zexScC=%e2Y`nfoAf#ZwVX|jP%lq^^KMA+x;@LZBw4mWEG->fam!ee$iRy(|4r0xLU
zZ>OKm=f#)dn+MwAtF#|AS37)28Jrve@@2pW*`k?MPXs(h@Hw7z5O#>Yj@S*sKV}4R
zy=nUe{oCQWq*pmy@XztV|6s|##OHiUI_a3lkD`qaVTU73yuO|g_>oT`W`mzau&ZDP
zcy1j5H$DQqmB~=}PTDfV3sel_9l&FRA8|TPSm)P=gS96S2hW<g-nt3;1<6uxV&8Zn
zS9gIQsT~vL-VV>jsi&MD0T*&{e8`o7tPC#bOL#$=BYiHfq@Rv&bR7S2=(9+a+gFu)
z;I8mo(x)6Q%H{Ydcc?5^(&6+aopf!!z^{S*khTc=0|otDQpW+#@j2YArT3O}M4U8V
z0bIt(EbRaZ2cObDcL2`q)~sb^;<Gs5GOoJY@mIk9gYEdADSRiizDv<cP_3>lNsP_c
z*BXkqcK(Ttuf5=3IK>+vK6Wxb%^QE{RodFiGu*dcqgD+AJSx^q2*as4^}F?dtaR1f
z4{56x$@jnTLsV+|hqP<huvwJ~6D!rL>D#GI<jhclO`&me$w-Q@gZ_#wN%U8uv>tc^
zf1v(K<Gb+HOiW=BpUBaLX;Q(J)sE;oz@K+vqNi`m-jO%f(x-hRhb~ORJ9tm}RCH>V
zy|4*g8mDTZ^|5@lx__VisJ@R3@adhL%e#<ma|VXC9{Bh`n=|jz+#%eI_v*H8+u%$}
zYO`oREV<!zFdnh&GpF9rwQMtT$%mvb;C$S{D(YVPa4tcY*^b}Wj=xMhR*o-hi{rz#
z(7h|iuVu$yi8=P=^!EWS>916_CHX0*zg#OO{mS@ppufB)`Xzv)lJpNnTjGTRowViJ
z3nU8gA!r-=qy6^cJ27DW)Nmc0I)xu={te|e6Zl!$bEFI4GNy1Fm9ZvkkhD=wKh~+X
z9e;(kThN#I+(u;#TA|HFkEE<k)$RBzaT+ggQzbr^SK_ZEVJIJKAZ<x{Ec!n181${M
zZ0Ih=gCkscXQ&}NS2~xT?tF)3bm?CDFnhkVH64!)P;aihz1llW4K{6AWow1<@ZM@m
z7w@fs*w=%vm36{#M|XOW*d{4l%to$Pw!~Z>!hw&L?3)GyJCZxNXcLhA#<UpwD{f<o
zvFa|v9uKhX3mM$S&za)$WBZ}+`u3@RJAVPGZD<n4^;*n1_H?|%{)BWj9~f{C8rcie
z!|Zh@>RQ&_a&2RIT?pHdb+<fE+QuZ<c99derRE@j_^urkwkO`71%7A1VXG>BN&Gf;
z{1w^_VS57K#Os>l^LR8v)V0Ltwk_%74BB$~CSKPPf2H!BNjNsGNVF>gpFB%mv@M2h
zNPeC#!zas0OO4z72GQ?{1iXdf|C+ER(GEF90NzEx>+yb}@fIq0l9LbMvj0?IgppXl
zR{}oNbO~_To)s94uoB2HwgWyx_=3dGGQznp7%KoDZrX0g&oY{5X27k0M+sk$_$!Q2
zT>r*#z?<Vesf_*#gSS&ebH@2Z<6+yFrP1)Nc8xhE;~U*!NIeGax!pvfW`&ww0!?cC
z2AX`oh}wLv!5>j&{74M``M7Z-FVefx9VK+_z4KsjP*6xnP*CvgMMX>t(St~X*5To;
zTZh3%Q4bp-%p*?Rr={F0wR)l+aF(>`SIAceZ$<I-5cp4_-RuG$y|j2yKMCZTfCs`J
zXE_Ce4);aaBbh=1p}%cdV;d^$i+_yd{g43TGhY9;ekK$4-|*sJB@x4(#Y)a!1^pP^
zP1r9!-3fSQTQT6P5v%Gtar?!*DA38pE;oLFKM%!O#QQ>`g4ZOA1bt_W&v5zzAM`VA
z8v*Z&!_9jO`U0OkYy2+Yu*X~QhtsOY7T|Mx`BKDMiH}{#?f&XOUt3AuDc!H|3(D~U
z4=LTw`4RTc-?L;tKbQO{`^lo;#rwUy-^KgG=yz~@o3p8c;9uZ_|6_vBCvDE`tmuCV
ze82<B_{Vz$?}tnJ%dt5M?{5fy67q6<v$lM)h#$sLUapy{a?ND;Dolm~zIg92Uj^Jx
z@ROxoBpm^l{XO1>aQjC;XM}7+<a>&T9e)L>K|<~LJg#tj9#<BKxFYd`?f9$7DDmDQ
z>GS@o#9ysl6#T45y&Mwn3p`MA{mdxS8$B-CaP+5zJ;38p?s}WEen{v|;6pztLO(ri
z&UCTRo4`lC|198=KG&P1zg#O2dP_mQ7Ye;`eCX{{8F#zE&U1vG0~J45DvWZ`CbPE%
zKRn=aJ|PF-5<dj=CH^vPaXG%A&+!qbd3TfJ+q&vGB92P@@#F;oA7YEa+;0BfA?c4N
z4;#O5{8GaaAM)ng3OGNdKRMzHJK^#8c7B#?uZsE-{5$Zoe5{n$5A-p9fpUYmT%a>t
z(0491Xde@Q??(Td!?pba?g#!I@bR8LU-0j2>q?iF<7?xwmYU1!hx%PC^eO10kF!ST
z-x>O>Z61#}Bk2z(C$#avm+zsCP;M|US1Z7_Sp0oi_VfAsi|qdw43h5`@;#LI+a-RM
zwomliB|f)jiJvuC+Owq3`|T2ch4#JRN8)pP=J?#6=Lvh3_<S5g;;$m{VjM%@i}y;2
zze@X7yjMzm9w#OKY7#B%8Th)JcwdwFtF?0|hu|B?WPKyzFnXwBjSgQ4e8-DHBQyp+
zw=Lb-=-nwLtbRz#+PAzUcr`b<nX>mh)%`ucev{)9ru6g0>ws6!xmn%o)bI@qY2fg>
z;4`o#X+LSlWcI~PuijI8dVBYr+M9jBd3qf04aHcEj>y{ZZtRqOLMvNJggs*dDzoEv
zG*yZH^nevv-DV9A*vSuV9y`}_=j|<4<n)lVu&CC_OEWQ#H>&fH;Oc$iSk>P@6t;tI
zxcRcMolsFH%gBDAOSELB6T+7JD|i!uFUKCZueXzM^pE-2gLoGd`0|}^SyTKBxWE@<
z7!p4nw!`sZk@_SNlXyRu`}$)7-VZVL4G{wdDR@IcN8p=(LAertB@Wf%bR_;hz$N}l
z;#`g|<dXQyv3~)N<pN*u$?*}BB4jMT4kr4Fv8d^?TzW{f;fdJmX^OC^ISM{W#o!k)
z<~I%H&QyF}A~QgLpfM49wDGp+F$E9d{1|&7FV@adFCL5F>xT`0W5OT9%hsO(|25#7
z$>ErDLawTyGsavO@D+l7B*u8^qukr!e2k|YUSHuOMrPG_pyOhP7mUQLjWRl>ukG~t
z7*9E!iFP<2<7r-oZ=O;H=VLsY3*bXBPZsNx0mm2->UTWJGfDwR50h;c{1+(x*9-c$
z!*fZn1Dx~E@xgzD<X_+mJ|#STtbiNu!MTPA`fCM#<RcRQX|zRc9N@WCCH?`xn_%-|
zFT(M8->N<@*VqPldqH2q(~ZP3ez4Y^>kYjT;1_5MwMJZCz7AE;M{f}DNbQQiza5@S
zraHiRxe|XF?1k%D;&b{EUVt6-?eqmd5}uBeJUPBte<;e`qRRc~uJBwk*a6PV<@nHN
zy3~)LBj`&y>6m-X=~slk)fM!i5y-{g^Eq7Do9Ptja69B<Ht28L;Xf(39Ghu?_ez{a
zD%RRR(Adlp^4XnZGd&+pm&@`eU`0M(jemQbi=G`yT=+u$(uP>34_>$~eFg8U6z>^=
zcM0b@;Be?5SL#5(P3IKc3H{FQcfsdl2y?Xtp#KE-X<<9urx8o|;o1_;HVfSp2zXWp
zsT<$}&UGW<D>5DNdAS_U%WZ&lI#?f%HiEB@f1r1Nv1q>i9qj|&{SP}Vq`zHfc=uOp
z>&y5S@<{%c_m%vKb@T$h2K+%^($_Qr7xHj8uj>^;FA~n>k?@ryN$4@9^d?`AUj|1%
zD9N@`>9L=n|DvF?4C9NiF=HWoQ2Ltm!2+W;_XT4C;Dd#JB|OWhtMQT9!+<vw{7Co;
zqcgrq5#wvT6Y@3n_j=Fle&>5;?;*QxS69rOP<@)H<GF}4slrxxS!QkJO=&||r>|EO
zvhep&{to9PWPieD;%n|9`#KJX?A2`RAp0!X#5`f6FAF-kPLj?OHYdK$9{3#2@jYzo
zRUJ9Phm&_~Pu&TJ4Gk~l?H#w#euBP?SGm~Nk^AoxHpcfQ0DS=m{Y=|hJ3jXfiN9QX
zP1LD~OG4)xg<M(KX$U%(@k;1i!dGC1H1`dGZ>|SA627`j58$V%;OA9ACzmu+{5a`Y
zcMg032R{|d<OM$Ghr{7xouo}hVSVFiL1(j~6E5gTILGI3*lL{AXOyigUvsYDyglkE
z>hX5`<%1=CZyVw3zk$!&Us+#kAeT4lt5onK+GTBdcZo0KFpt|Zt`?*=67*$U<#AHN
zvxdu9AmKa~$auQ~Z{b{Tk`8ZYC43e7_`IFv@M=Pz624k15iugqmM7Mq``)u}dwa*$
zrBVCd0rf*$)w!i(+p-OosmI*1Og$=lKva!~`~pKAd$oQ;TL+}xzH}Wt;yuK4P3Y!z
zQ76l=Q*U`2!s~>$A*hq~vQ9+XBHA1YPaiGeouK1$qHf<5bjsQs0T(_e;e1S()0g<1
zK8M3bdfIKosTAu+r7bv-&AO+kle*xup1A}1Xo#%9-vGuDw~ec?&5h;`cwcIk%6)f(
zfj%+dHsEKPBLHuq;GOvRn5IMLUz$4t-crH4ioRicjQfB-;C|Hv{YNGJ382$Jz}rcD
z8+wJH6M%Bh3izAA=jBcj<t{gVps$+qz~_4);G}IdS{PQ~3;yvwY3BUn9-gW{F7BJy
z?=fbK-<yeh(RUX2VP(IIaS<~gH^lRAK@N<=0Uy17Nl%O`nfbVqyf^m{zqcv7SI@)h
z@Vgj~!27nDTY$vZ_XvD{aWCFa#eI;tuLOSizOiQh4gq{<SwAZ7+wyzk6yo^ZzVnLb
z?(!~?ig}{r&26El?Lto*NWSqq=%(;*6y{LCcPV&F<2S&cwI#Ew0{<Naj}`duL*Mb{
zrb^$iix$O#zh^+_Cs7X{0^h7{Y>1Ig&{+n)JLqj8$2@T_#$PbLVCLg5{CCWKyCQzi
z6ZZ{a*EsA2zvC?gzc<JIVR1iz-y6j!Kijm`Uf=1)3{l^rd;yo`r;nHA%kP9-Zj{IE
zhC~ZFB)?n^j?dwZK-<tT9&N5==I{N7B%Jm%z6PAzPptT^4toK<`fe}Q-1v%jqD=yx
zOXibE^h?C}2gWb)?)YH)cj)&0JGM4RdUQnQYm*~8&DuU@@}Akzdz*Ee(4%WkmnO}6
z%p5RfTYhNVBX8$MJvuofvDMyoDKk4QSwqGIBu0eRZ;&)%PVe5&jtc8Csi1fJ^n?~o
zI`j$fN$40<t8wqlM|=05KRmdWf0tpCI>(QSZ7BTbcDw#VyTZ6D!<j~?msX-)5WV<!
zL~7>eo=W|gOip~<9R2|P<dVt67x1pIyCY(}`*Q(bp?x8~H{8!Zw?f|-GeCLJYnlGV
zz4TZ9yCLX)D(MnGK^OJ(2Yx91_;x?!a$unh$^k`Hj-y|SZ)w7B@v@HJ{ZK9+cQW&F
zD%{`Zmt}La@OzZ_U3{|=_KpY){1tefh;g^?ls?vo@BO#?1)q~zUp&_XcybOi<^%BP
z^8j<0!^Irt*Yr+#n8U2*X^J__ukks|A!YM4acp(#xY;S~?9(*EGUoU6?;GJ&K1-AH
zEXFN_4`SQ`_qX|^`Yy-6WkW{c8+elh9(<p565mX|74TfGQ244C0~E6L7I;rVKzxsY
z&f@v~AI%*wc4NJ3e}R`Ep5wagV7iEMt&%=zXM9)2ulN=q$5~c%BLZKJ<M6R@)EfAV
zq^-pG2y}{bZft<}mvA!guK2n1?YrXV(%R+tBKAu9>1ym<(&ze-^wZVc3=yjx=BPxf
zIZL<C8HnVg>yT@N-cZ9FGU&-!+&`{Yp^e48F76Ak-#kBO(o07hq&ewrC`!O{VbD`u
z>=Egu#R>Tm=>ZNG-%L6<d^1^YbT&Q*zB+FJzCBz#FMLYyDd=jsbg9q-v}B$j^p+&(
z=F(RMeqGxr5gW?pFsvcd1$?w^6rTqGJ-|1h|23NAqZ6NlWPTBFco+JS|FV4uOA&mV
z7nJi&o|AlYdCXti;mx!-4hOwNle9bVd)K*IGG_3xWEnFe$!Le~%DnzN2|8tSYNW4T
z0{lA#KO^iHEs?3Ahym!6fzKf}Z&Q@;kAvdA2FgEeE<s-lT@~>4vL33TO*MW8+|{%f
z@ahuIP8uih{6JfLg^uv;vNmd<K3~uoXN!0C72m*wUbKOx83NwT)?FVdVhDNx&{rW@
zVe9~%ezqQLlin5lUQ+NVV<+I_f$uP<4fx*({6V(vj&qCv?;+r@np^rFK4_!Q3i)=3
z@^9}8)d3TMA{Kul<XS;~5wRFjnsveFMFoFK#Jby`uglvKCq5R=`<nb6`5tpvAdkQo
zekSqD=K9GwGThIkeW|%u#&r<wE9y(yp%||e@n?>ZN7~DS&rd*1H&rozj(Us`{HALs
zNHy>^3~l5^JqiA~RL~XorVqgn?jcvY_L%7VN`3J;dQxBMq*U0U)EDpDNPVS~=Y+lS
zzQjl;jAv@1zCq_L@r}HnahYv0ad~-tV-C?^WZ*;Cx%eh6?L3R<Vt$6S=e5A+_KfkU
z{2$8kMc+o^r;{MTkHqKvNc&IcyZVE^XhZKZr%!%c<NS12@=8670RO-7xJ{~v^6YIL
z!3Y`XiQk>@jgbG&^+dc0-xplcI}bSUg#I4a>H_{e=>6b?_h#)Yg<nz3F-V2}&k23}
z1~~LF1sU)~A4KTmbAg|QZ&7@{ij1@T+l*`{vKq^8GqTUZ>qp|JYiET%BtDl<;+M_Y
zlK6Zsfy7@;+Su_w7USX^-wEHAG2Vf47s0;{3Vjy|csg+w_#@C>zAO5$s29$ED%v&R
zzm6MUCnv;*^Ki{(T1$PWdhUdH4s#Ps(@k6P`>%jE)1J`L*AaSzN8tHTTfE5~_V2|{
z)Iq%Xh`lW6Hpf|hI5`<`L^KYkZwfeXpRbB`C05+G!SkEM^XThwJUvFhz483p;`z&p
zZZ83^Yg=<m{9kXP0zTTd=9c)sUhg9Dh5zpYAJU)K=~wui3)z=4&jDQe^E&-`%>(ed
z0)JH*K3S^0Pn;#bzQ>%Q@Xcgt`bh`+%k20|FsH62;_+d~dC8n+$6u29wFCY~cKl<Q
z^TO)^+SL1-IUUZAnH(E^!~y>cJN^mKH{1Dt$&P;_^J9t6^&`H!aCw`dpM<&1sIOb}
zB)-4!a=gTxQ~b)c<xt*ndwE6LB@$;ZZ-pw)i4<wiXjK50`Z{69U#49l1MK)1?~(c?
z%P_vuqzu0TZznl_PUJN3xj%~d>V)wdg?}3OPImn2cKkiSk14}9pS9!f0e&TpFXS(a
z_swLyzg>ROFX8g@-}&4{r2#y@!M2Id_X6E!oIhx0kNBSQFzgHKi2#@F_<F1@<@F}x
zJCDN>f1SP)EuETAChSY%uhVA>`;z!RcKiZuzOY9*|BQe8<@jc;V1Vp*ar|PZjb-?-
z$EQhkNnhV0>`~y8rTv6GiZPT}r*(GxCHOuFy&Jn=k2i!pO8g~*zP9r}-RVm^{xR)&
z;-=_#7WOFdk3B5yQSx7G$3Fr3+ztf)W^RuX|HL3^k3ygJIbqx$F<1R|y$XBe&%qwi
z&Ty?myMjO7p8w*+_1h8vCv6=|75ZHc_!;voz@Jd?E}A>|c+vI>nI`H_;;(0;a6T6H
zWw&h-?*V0Ve%7<DqF%?rUYtcg!cWjyZ>lNE9cWvlTSR@!_GZ1lO2FmZk9Sb6#9wFf
z5dI<B6H(t1f1UocsBezn-l?DrANBVQ;d8S&{@bGdI6mS@ii{_szv{&OOwcDwFn6Uk
z>Tf^rEu#J;{*n>0{v<yi+wqUVU-{S=r{7D|pTs|wF6&R?7uoSofPMvqZxQt;@lTBS
zSjx-w;}oLsdHrEdKCdB1J&F3`&!PS@Wc@)~;`ublzf#P3S^xllcRwTMJaPVZiT?dU
z1y8#xek4h{D}E&JuYhk!|4r~G>8~?Y6mu5@eNi8h{yKeus1J$H+jogypk3f|yoA1X
ziMibzAO1H=`d=F4Uuov@OZ@dFXHo7z+fsd(&igNt{(616m_z?0>;`ik01p;&t=F#z
z{KpYLibQ+XSHYn#lq=^_@b*dKuhTDzcp&Fq@b*dKuhSn9`~%-yPmBqnr5F1AM6}bu
zH+hKn;l&D$(Kku|7W_yOdsqBO?W6-f_cuv@ok<t|Cg_{T050jT)1NbL+UfInCh-fj
zFU$A`Kc6Xl=raSOyBy!P%v?{**^>Csr<el-{g^!TXYKUY>yR7xm7q^PR#`URVZHvg
zfY-2@*cPEr+5cH@s%xZheExpn#P!)u=wY4ymf%P9uY^5F{B>yYczZAL`Iwx<FF<^4
z3%Kl$@p#Pf!GA;81HPl8|Dta)YuMWiqsDwSl$t1_7PVsiHC9GptyO@18==Z-8zFtu
zI~$#`;C#r+G40!rS;?1S+kPcZJKwpP*)SICIm_2zkB)7qAOGDaFEW;|X|66;V-qtL
zW8GZe{h5*B57}2@ci@ZAg+7MZ3I$(sK6Nv4n5?FqU>_0M3q~sBdEK_e6wUR>$JEg7
z7V3==RWN1*JDw@@i2fjlv!R0C2HR582BCM+-vj>R0?zT*2>kCA{!9V)1KeTG6e*+~
z1l-%URMpcpiBB*ligH7Nj~V{Zuk1G#vQGuPIpAhNU-lad={Ewt2>f7+8{mt6E%=fB
zMC^&C57yfO{)6HNBQc;4eEpy!KD7xr@Y%bL_~b_c-wJ#_CWy1AB^@=lYZ>r634Xp+
z@J|GO9otf;SppyTTyN%N0k3XbYHly^FG{!xJtXkc!Pc9fcMtswJA<Ch#yhBh)3$#6
zjCsJrBt9j^Zs4Q+!`wzb9>aTP)Hn&d-iq;;8ob{`8dtY(d5ta2#p1pIhQ*9(M+@5R
zG8r;q=hTRZX*(vAp8x2hJ(mIKmfN`zPD}2lce~E*Z)iboN4?wPLx)N~FFoy(m=+wI
z+?m()Us)F6D>b>^`8)^AuZKVJMVWjuJ)Lg5v3K)kHoY{GRHU0qd(lmc>F8B9Kd^{5
z-J8%4>g6uaO|TX1TD6K|M<vpPoV7J1XBU$XmfCnVp{M7H{=FZ{tA-o3mXWWF6g#Q&
zt(Ix<);({(y$72#8@KExr3Wwm>Z`@JpXsd9@KXLN2_8;>cTdCuPRmL1&OINC9MyjA
zNAq?bJ-YM9A5%#8^R|KH)aT^KiMBcV-o@+JFD_j*o}}8sN_qDM^}W@$-1NGA%o+Vs
zKIRPnb=RjE@8b6rw&nVpqMz~|9^m~}jP3H@*-Y{K20Sn0r0`cf_n7^;ZRP$7{268V
zX6b)zKo4|G9|-tL+j6t?KeSt-{Nn=N2K3hpdl&Ho^z#JV8+_sHR``v0PXqng#=Fq7
zh^x@Ed%2$ZR@MO|fUP^f=gS4Q?nT&<e##fN?#rli>Ce=8G4m*GO252`W&)3BYFvmv
zkF%$QU6eiFrFd7-Zrkd2Ndr>zUE7NXU2UVt!o_U!%`ceO%^r9*6!n7dS$yw8Y|+bi
z&*n$Bls*2zTC(|qZJBM!*W|T=XKka-lOg2cuWX|h(N(3xXh`Wnx@s|<TzZ`Pls+PP
zX%1V%ccL<Su4VN2o^}B&><toe>bKuMB@r7}*-o9heDyQish7!lTLZEXjGbRh_F>QN
zdA4xSLOqIaVi-p=^KW9fhbif8gkHUYz~`mSwoM28jx%4;_FxQ_Oom+8>X=K0LqQzp
z(GPsN;=V&=v*vMDQ1#zlZQZ_o>(=dfz`-UsU7#8H4_{~1H9^aU85#miQQJ+`H3MI*
z)U}Q}2v^r8#GeFpZ3cXpx^~h$wK3}288rFH3;dre_U>7vuH7(asX$#<#JtNl)U`Xl
zf}U5`RWx_v3i*hRUX~V4YN>0Yx#8p@38%nGQP)gsLdL3VU8_qLs%w+hifmQaX24IY
zYbUKE`BhyzYi=}DUAt=4XpFjc!x?b>)pbQ}5S^~B-L+oy%h>F!JkQkRykyUm>@gE_
zGDf85d3MUq9+8>m>F?`j>5)AwJ1@IO+KBwj<QxfaD`9QRfA&<r_esmi&B)I3#GZ=2
zzM&z$?How7Ryguu&x~BpWY4^u<kYm$$vLAuvxlofePkK(uTM($=&1CZjNH78<Sb7-
znU<56o1K-Ak&>2`o0jUCpOu=H<C&M9<{2?2IR%;erIn`xC+T^4W7@W9Gk*MdpJajM
zlbti7O{S!g+a{rNR7_&enAYG~v8EW+Vzq1>-joMJNY#>&C*vvwurWAdHb=|QMqp@>
z|Jn(#Y}{pPX}I&pRDVBATkiqbFr+-B9{6R1mJf_%Jn4wv_7=RhcYWIPw&(kRS`L<4
zWPoy(DA5OH^8caOV%!(qzPoeL`ZhX_9D0IUE}r8u=7D-LWKPpY3w}odmaXyQzZ^OD
zx#jNGU$~Y=W7s2I=qVQzGeC*+D$C^j=b=2#e*%6>L0MV2PeZAm&{LKuJqOo$pux2o
z0j(zkPu|`7Oe_3;cb)M1$-|TU0QxqV+|K{|fKE9rAMlWax@m*DapWTxkOVBIio#;S
zM6D<Ownp7>J>wt7=lDNkB-p&mKOIOo$B4qqOo$v#IJLkTQNvYpL)-3-NaumLTLrPB
z8ust0ft@yLVvpO}*m<rl`r!2uy*#nnv=MqmO%T2KD2zA0A-2G1K`Suf1ID<4`J=aP
z!Rmz|yk&);;@hIy!|?q)9A^x6gt{WZdNkOMMK#4ikS_RIn1H<t6SX9)Y3YuN=&AL>
zs)asy8|#NJZ3FODFbL-i4%UWfL!qJ<wK<rN^)6yv3C=Bh8uJlWVq|Eo_5@*=Kl;3O
zUAv(@g*T_iiLRZ|u3=8^M$F&+0q0q-L9D#0J&V?JE~4WlZ3jkjuMm^=DKTRN`fK7u
zDrj4@t(aB4j<^t4;zlZBKc!0AXNZ|!qAz?}JBN|sv&4f`CRIpPQVnZnYLJIWO;U^0
z#(Ma=IR3jnT1roXO>eRGvN1YEO^Fw2hH>!bqy=e7T9MY;a^iz=Wnc6;{L!XaaF{|6
zc3%u3p`<NoN5XK#cR1-lI+6$yNun^@KL&4+ok$$%OuAsLKmzGX67inijdUkHNKev>
z^d@~sU(%2C$Ib@>$sqDD8H`qTC`l&6ND4{CY2U-i2$D`R$VjZ6$;7v^EX*e#L&jn?
zW-iGi`D7d!PbQFw=p#(VDy*qw8kvq&Gc(C7Z09|PJVGADeD%l4T<nN2k3319A~-J@
zvm>4*&ynZJ0<w@SB8$lqvXm^tYP}U?C0RvYAO&PKSwmhVYspJk3HUNuPd1Q^<Q1}s
zyoyz?uaPZeD|wx~LAK${zwP8LvV-iz4gqhIcgVZsJ?tH^hwLT$$ou32%+ddl93ThD
zA-rcDA^#$U7)O7D9L4;Xx3G7>N7^==*S8;Y3-@V{kz?8`m<RHnwp)9Ld`ym$6XYcM
zgq$Lu;!W&H@;Ui}d`XJXLM_4A%2K>_yo9|&S7<L|e02fF7ItF1<SaRd6(Z-!SLADQ
zfqX-X$+x70TqKvscf?9AlPlzVa+O>o*U1g?1HKFYM1ICsvR}z><acrt-+yexfP)cA
zDWf_yVbe<|T7f!K7wSsgutSPFtwcR&Wm<(+#d%NFX$_oDUX#|MwP_t%m)4{8X#?s>
z8`4I!F>OMdQZL$!dei2#1#L;OrjGj1Hq@8;QGXghEi{k@QEa?JLup&uj)u|pG@N#z
z9ccuOq){}Q#^4yxPBe~o#yKSM+9sMnyJ{C{B2A**v{x}R=76>tO+gCnj(Bkd;q0Jx
zSo=u(msY49MOZz7wfi4qPUHxT|D41(u5^4q8i`;u3L!L8%c4AFjz!4gAwM7CY#f5v
z1nkg1QJajD&ZlV8akAhHtQoLsyJ%0^i}t2{XkXe-E7feYKOH~^(m|Sm#q1B$!E^{6
zN|Wg@nnF`)8XZnY&~%zXM{2)lzhY1JOgfro(QG<~j-@#?m*&xYIu1*GC*U;3Npv!H
z8=p$2(dl#sok?fW*>nzlgg#0iqmR?M^a-5O{Um*gK24vY^XaqnIr=<ZKo`<QbTM5*
zm(pc8*?R?DNmpsVYd7f&6r)tyH*_^!Ltmt8=}UASeVMMO8|X&*3f)9srJLz%bPL@|
zU#D--ZS+mLU3;6pMR(AhbQey${g%E>-=Xi)_vmiAhwi2O==<~ox}SbX572}35Iszf
z;7Ha&dX#=dkI|3mae9KDq@U1JIE3;u`Z@iAeo2eyX?liYZ(4euenr2g7w9*%n0`x3
z=tX*oen+kJGQC2-$Flxw^g6vkf1p3opXkr@7y2vxjs8w=(o$-p2GbZ}lrg3=6Eia>
zR)IM)7wpvT#ws#*R*8AA%B%{j%Br#ItOk3C)nv6;ZB~cXW%XEn)_{3p?|??EF>At_
zGB4JQd9&uM1#8J#vDVm0x()MXe$1cuj9DNHV!<qgg|fD+9SdXaal&&4){#Z9NEXGS
zSqzJ1omd>}%(}36mcY8QM3%(5vF@w~>&bet-mDMn%lfhYYyca`2C;|PU^aveWyx$9
zOJS)jjSXibSUSsKBiSgH$wsp*md(bnu`Gw>vOJd0#<B5i0-MMtvB_)-o64rK>1+m@
z$!4+HYz}*bJ<1+qkF&Y#2{w;C$(~|QvuD_R_AGmjJ<k@fg=`U9%$BgFY#Ce5R<M<9
z6?=gdu+?l0dy%bWFR^v(WwxGeU>n&hY!iExZDy~rEo>`$oxQ=fu{YUv_7>a0cCuaU
zZT1d(m%Yb!vpsAt+sEE#AF%!GLw0~2WQW*cc7*+l6|$r3BX*2^%#O1Y>?Dp^I>kO^
zpRv!`7wk(`#7?s_>?}LS&a<!B*X#oOh844KSqZzyF5v`UE4$3Du<zMbc8y(UH`ovC
zNA?r@nf=0kWxuiC*-ci;Y}koT(+Q4pWV(*sInBD0UO{)p{8?AsO|PiC>y>m5?CxGg
zuc}wmtLruNhxD3yExop02j3FwVa`$m>~GpoZ=^TYn_#xGm)=bG)|=}s^p<)ny|wP6
zx51hhKkT;`fU)~P?Ponm57u<2{HzS#!#0mjPRYs6avzhHlaZZ@PH|pZPFkuSH!L~F
zZPbXIw6v_u<gC<;6c&>;f~94RFlA<EjmR}6rDx}4nX<*--r{dQ|0}5a2LzjPQ_{yL
z%e&YB=Mg!{<I>O(ALg8#lAkB9@-i}0#g(3(ojppR2KdHSNX^b0mX?`4-jtV}m7QBL
zH3RJAqW8}4oRhP%^U^ZYGLlW5l1GnDHbtjp<|UhYrKja3n-h{p4@*s^{W55`3{%gH
z5u=k?xAY9wEjPoIoH-^vSsw;0(+KfgDu2!?ZA@+kq|qng2gZNITR>j2o{<e|nUdOs
z43?9gZO-N7{7fQadCB=s`SMXV1|{Iqls!6agcLU*+E2<7?XU76kz0J_y~Wp}?n6}`
zq4G$T$I9GK(y{m{d_P6UFIfE^qH^_|e}KIASNQ%4-(TVTD|~-duD>eRzwG%a^?bC-
zV^psA2~hk5C^-UDc>$`t099UqDlb5l7of@sP~`-uaspI20gApw(X}Xg7DdmZ=vfp!
zi{js+=vowAi=t~$bS;X1i=rQ>=m#qLfr?I`q7$g-1S<R>Unzf(k~>Jr8>F5O3RJ%<
zK7$mWL5j~HML$T<4^s4l6rVwg&mhHTkSaG=@fobj3s&U?tMY;sy<kNzSkVhs^nw+=
zU_~!P$q}OD2vPJy6#WoIKSa?FQSyW+IYN{iA&P#8q93B@hbsD^ihih~7pmxmD*i&%
z^AQR^Lg7a!{0M~~p~{a?`ixL~L?}KY6@H|`k5s=$mi?~u6RFCLROLn~d85?xQHp+)
zdOlj^F^az!#b2zV7c2E0=qrB@RQ??3E9nL*zYg@3d<FVSIRbrUIf1^CexR@9GtgJk
z4fHLeEBOudmHY<!N<9bqN<9bqiE{lBv$@?N7yKa?^@&{Yfn3OkT=0Qh@PS<Lfn4x`
zT=0Qh@PRzCq8JOwP02<BFf6m;*n9-roV0NmkV#9GW%?_c!4~JN+<ehw<mj0hImzZR
zX}LUh#^mQ@3sgT<yq}+B%g-;!ISpelqm%Q}Qk}E2($e@SQMy}RI+_@Ho$ES0V_ey_
zTP_ODQa`w)WFt=dWM<`$b{1TLy$P;pPevyvr|>3=7a1E9D>M}w8!I%4Ji5Z9w47`o
zaO#ecrx9r3c$sKzs-|S*q~wnto|!hm-ZVMD?2VNBFfP9xrLsejIcO#F(##P)xua9Y
zm?LEtC9`Om#mFpHW}RdfC$r8n>msvwnI*`qtIQH*mL#)oGV3n09y04GvtBamEwer{
z>npQ<GV3q10U~pjY60UFP!j@FA#Xes1&E8N5s5ZDJ3mKVp=KmrZpH+OCn`u@ifWRG
zSs99;RGqz%cM=8Vk8u*^<d1Py#pI8vAWPvFq7dMSGJvCsz?CWiNU{K2px2U{i(ZOz
zPDa)UQDe@j$+;QH*%LC7T~ja$nTheayo?l&vTI)PnQ6oG+#CT_$}NHa&ig2)5kAS8
zc|tSX;vDRvT$+^h436Y1dKThJbSe0isfYL*FaD0=f6e0B-z;wYrDE(RR#BK(S=mZt
zPNtK16c&~18)Sy@@C=5<GnfR=U<=3uquF3oR3X1mRmd-$g>vN;*RJ?ks#O4mHpP`N
z35TD#=;mRvpcI*<%4~$pU@n}AF)|w~Gl3awhOOlK2Ac(Du&7Q*B&!Y=dbCeUmfoHJ
zNyeXwW%VI6!Ce_KDcT&33Qo?`(^18GFa9SFe_SH$we6B@zjTU_omi)2nVTa7DRZ)<
z7?C{!);G#2S>~=$<#q0wQhqH;_ETZm&o>erB%^0+P8FF`j4A_z&Ah{Gj*(@giOeMi
z{Z<)^T+-~9PBE(FG?|-YB*iq5xppcq+jT_wwdBKJHZy+yer}x{!j9Vrha1<p@~2$W
z%dgFyg{7J^MCR04agN|CIPWYu&yd>bta!|jxl3m|`?PZxnlaL~OF3cJk>%HJ@eZ80
zanC7>#^{RkUOxKJJabEM_{lBP;l`XG)sZPOJpl#lnfPN)l+S0$=Mx<sam#YJF(=CB
zv!pt+lE-A{=H+CMNl$Z1QX*$7k&~pz*&?f$l%9`}k&{0<GdVx6Vz%R*xrZz}N0!~g
zp*Xi3hZ}PbS$2-hddjDAMdsSGTuZLG<=3KO{rzM=z~4{y1N{ADKfvEl_5=KaWdF=B
zD8SrH63>%aZ&|7NvQm313G<bNy`_ZtA~W^ozG2Gee=GKOl&@mG<DFA)Rr&ccckNrw
zt?PJ)>;4Yc6U(p71Ek!OMCLNUZaOZL?3YdvG2&OJ<TR<Bq}<Hp+;n-J?Qks}UbZqZ
zdFiq};hrz9Tq8s?tggk=;@TskoE`O}P>{HGk0^V86t^;Zh|5alfkfO_j7mvMMU#;%
z?p$LW3X|*$xJS%wg}KMvQh;k`hvzywFxmO`=Q`i|Tw*!Tu1O9wk{oCx-A2PTsr(`L
zq+99rcA(MQfkyA!Xt?*j^|}7#xNa(_N`=UXDz4pBJe5C0l|SL(5LhK9kL8G{3R9S|
zyq2L=BB{VCe~?jCTvsZOsR~gVsX{EL=HU=X<&T|&7s#gs5B|R5rm|xw6%Y@H5GtRQ
zu~R;)BB%Pnfv5_a;)hD*5mVfII0Q@qsO%UiB`p~t<*$zMQT<vjn=_A!5-Ovje8w>*
zs$a{WaZ}Mx{vbo2xUO<b;FCYgcqgtaJ4QS8YdKX{8RG=p!y&>6K*gT7xKGdW$13-7
zh?<h93~TaQ1~qZ*rb3$fp<LT8BAN+sm0N<Ddd$HrJRAa;_|?N9atVNY|646yMlkVH
zrSkYC?p+6zi%|)052G;<HY_t&{N@tD$CSjVlv|7(3=%gLV(?lZ#;Q8=7X*HV>d4^j
zuDt2(fb5dQhm8chG9Ps+<4izIq69q>uT@T}1bqN*7)sDNaZ`Nqm@&z;Yd%fPM}sHI
zU_H{aSuYGwsbW}EdWIWEE+bt@{ZMxAB6_frH8mb4ZYrQ8apBAv6Ia#LpjX+W0%#h=
znQ+RM#fug$ATo+(<ui1V-5iCgblc-@>KQ@CP5mftT*PRaxD*5}@>SQ@qF%Kv>J{6f
zUa>9e72Bd-u`Q}ewfL$b0QD+uk+0Cc7C$uvBHvAMuZ9v-lWg%*&-tmL13xw7;irZ?
zRNu_vuZ9Tx6+QWS>}v^-eK$*hq8p%wCIS>4i~8N7p0}u>35&uHRPaCr4=jVLp^-o}
zbP=eAECLn3fidR3BJi8X%dD>m&gStVbM9MKbI#+-uFU-<;zXH^&dA~&w_J?NW~Dl(
zO-R8oC`_K$LTscO@bHV3-22M`7QYxN6>^~+zZf|{fm}%87b^$k@Vt-)xf*~`11T0Y
z;9&_=S_xEI4_5cVN*lpS`d}q}u+nO<(n4riuHX@B;3h&1ltn1*N2pqiQuLz~{U}w7
zQR??-^}8C-wWtAIOSGz$XeCFqs-0-XU$o*cT9qHI_=#40M6240R&quwzM@sRF^Z2E
zRZfg5Cq|VMqsoa<<-{ntV^ldYs@xb=UW_U)MwJ(%%8OCusrt0UDm}!i@?%x`v8w!7
zRer20FIJT&hg*Ctv66nE^437*Wr5Pm{9@$5A986IF%hC1+>3IMi*k^Qa*zu-kqbGI
zi*k{Ra*+!;kqbGI3ptSsK9CDJkqbHfVj@L-B3I={s`4XM`Ep<pzpL^iRr!&sd^ymF
z-&OgMs{BY*ewjbW0Y~7e@*`FG(%$@Hq`e_m<;#Id+$;H`RQXY=d^u2w-<AAPs{ANb
zev~RdO35Fk<d0J2OMCW<k@k#S$uI30_ey?g&$w6ROMAw>l3xyx;$D?6?Hl*1d}-IX
zSLI8)#=VkX4y^jc$bnVlN`7hQxL5K^JIB3}U)nkDmHg7qaj)c;c8+@`zqE7QEBU3J
z`^88*N3P_Tc8z-_Z>%aWR>>>v9KVNR2!M}8@G$7_TNX}2Me)9tP$9mrMVd2m<u=kB
zaWCD%BF)X$BFzoCC=R(O&etNV5xH_#$qw8~{T{0DL)E=BFFdc@Sel`)B}6^1-Xkrc
zLe1b)@f9l6f_wE`h>{~j$sx@Pd?>gyC)}&&q<P}LjDHm-rCWhtg(pX2eFNopEKKwh
zYR2%jKcDYJklfV7CyLokhw-0aPEHg2?L(6BcN`gqznJHOzthMx{CyrX842c<{7cP?
zETiLs8Bds(86BI@3zYbZ1U`q9mq13P<zxXS%Om)W&%46h&BO}5YUiEP$do}GpRB`I
z?Lqj0osO@%7uZkuuIjIcVFvvS{Zaj-iJF?2VojN*=S)k?v&@g1H=9p71vwpbx>TV;
zg+>)xRcKowx<X=weidd_SW#hPg@YB2R<Jsoo!y*|x^#3I?(&(-8J7~5D=t5{{N`HI
z)yuV;>j2j=u1~nmcirf^#dW)@)%CjTO}7?q-Q32xEpglBR#efYVrIoz71vh$(A~q`
z-@TXnqwbsBkGmJS7gy3MRjE|BQc$I^N<AtS;Dp$dl`eU>dH8t5c_ey_@p#T-TV=1x
z-71f(yrA;tDr2fls&cSujjEniy{fjV>R&aaYIxPus##ShR9#ecebrr6|5f!=)vMKL
zHScPP)$*z>t3IdtPc_nOeDP4iL+{n}t+}S=wwi})o~!v&Ew@@ewYt^HskNZi_F6@?
zX>IS?iM8`;PpUnm_M^3*tW&>ESe*fNCe&F}XIq`)b*|R=t*&0zy>89Ajp`=U9ap!Y
z?%}#W)T>g@x89n1d+Phw?^b_b{d4uNH_#i@XyDx-u|aBstOj!$Jl$YzgUt;-XmGT_
zg$7qVJv?iBhItP5oZ-2~^8?Sr4Qn=xZJ5_^O~YdiuQ$BesD2~wMqL{9YBadfoJN})
zoor-l+@x`0<3Wu_HlEOASknZrAg?g5NUu1rM6X_61H5K5tJ2K3*}i5Myv^R;-pjnV
zcpvxvp?U4*n_ARw;oTy<#n2YBTC8nxy=AqQK`jThoYg9`m96#A*0X%7`PBD`^BL|l
z-)CK$^fphl+0^EB%#BR+UE=5BSIzH~pVi;p-`Bs7{}lhH111D~5%7zJT3T6REvc4~
zmORTe%L>arOG%)6U_sz}fgc1O4m=ikJ@95wR?w3{uLm6rIv(^(aE)N^;9bF=1^*T@
zC}dd3oRFVFtA#!qx;AuI=m()kLqBWVwr$6@!`sepyQ}TVcJ<qZwR^PP+ICmM+`_WL
z^1_PS>+Q$2Ul48%ZxX&Jd|L;v4$&R9cR1NGwqt6?QyqVZ7#=Yv;-|=Jk&i~Mjj9o4
ziRu#7D{5p^Ueps&3!*kgZHqb_bu#Kw)DO`v(bb|`L|dZ!L{Erb6@4)JN=(g|@R;<N
zr(<@+oQbUv>l52Ac3SNE*yFLE#h&Rjrqk1Lo^j!E$KtMZ4(mL)OI(+YUADyM#7~Jo
z6MrH8N<y`SQ(c>MjqREa&+Ctwie-N*8=jbz*wA=i>tlSc^*7FHi;OGU8^$-<HsfQ=
z|EjF*Hx6RfQdRA|v0uAp942jy7qF9ZckEs~8T*SI#;G=^v3l(~>1^bac-;3il1MM(
zNwNkrzc(ViW*i_}jB8}8QAl0~?l$8COzd*gT!C2(|8~XS4#p2yrSv^$UB*nx>EL3E
zaYK6@D?@i1m$1s~EJ{BI{BzjZ;WBx{_!09i<G^Qs;}Ymz1>MV_dlm7cmr)FyJ%}nN
zG54}Ws}5c-fY)!p>jm)mHF*3AlrB5~1_7n-AbEf6#QFxL-;W(WzcD_A7IqTdc%3|C
z94B>*gV0C;O4tuAT);Z|e~|>FUd9p98xpNDN}!2iXyRM4$-rL3ptctKUslC0H}K0f
z{Bj+?T!Wt4K~G;mo?ejWS;*7j0eHN>oDZf?XUr3Ihvr{^#?NT`Q2!^5^;!w)GZs2q
zVSGqVLF>>6u-a<O_zaqU6S5r8M%i{?mnd&M+f|!mJgq%utcJEWYrBkT+Iz+bZLjed
z=%qn>tF!}{QG3wH)eaehLH{Mp2dxB+UeJn-0+f}fT{31syRU1%7*nxB$PDeKF%c_6
zr$XD)q3tEm_7|j?F_SdMb!))e7;lgOq5UbS-(|3$Ow>X#cpinCcn&r3D(MD0?Wt8l
zjXaAQxei%ggYI`i_dB8cozVSGve|eC`o9LfUxRgBCMS&zkoORm7Z%`*TC0Xxx0NCD
zSC~Cp70_*<@i}OG4jP|>#^(a760qEW^&PO>fpr8}7lHMyz5Z)LHz&1sVdv7f&Os;N
zLMPuqCs$yzRiKZH(7~sW?NeCQ3CMK{a&hbWmUPxCl6b5u=mi*;<yPt!!ShwjDXsu6
zImd1|ji>`C#=(brp~m_k2lv`2)XpTM5H(hS8rzE++XJqSX>Z~8T{!XRZOmeR7hJrD
z-LLi<C8)h!sJ)Be^awcp0-PQoS>SAi@d`L7gnUOJ-w|lx2()+uTD$=*eh)2{fTJt0
z;{K5D4e|5^SWrbgbs0~6ho`J~%8D7(*WkrEv|k6$wn5$nc>;9T3oDk)DE$)b;|j30
z;8!c4R!$xMehm?3xWU(#$U3SbB;@ct0)DqE7e#Leur8t0t0?sv=zfP%e-yMq5%e$J
zqhy9T*OlNi{)ncP!G9cdwii6?!&CcV10{$H4}q%m8eH86=Z|G1Tt>_Y?`4dJ9-q`k
zmG0Ii*`{jKabDdVqa|uD9=c6N%~jIgvz4I68fg1$-$2G#?SSpFcF<O;9kP86f9nB%
zGr_*zg}-&yN^CaxTUAg`hrhYQ->SmjoZxTn&`D)*9|pZ_g1=RVzlFiy+@Yh9@VCye
zOE=hM2NGtx2D{utVr}0;cYdg`A@DaB_?roO^o5On0zLMJ7M}SZXk{KO^8NL#AA#>z
zgzdgz?8B7@+Tz{tSOVL*&aV++?`=;Eb$eM@IMzR7-N7!jpzk8)yaylMg9xw}5oN#e
zHT?A`{PiUK^-JN0<-TA7z7-`^0xs+oEi#WnW<-jM_~nY=YA-PM8=k1YF9cVWZt;d!
zZt+8Iqn`k4KRlo!e8Ell0{j3mVmG|~3cTG4Z@<jF9nluO0>sCyhy>e^PC*(@;WOdS
z8&TqU&^ZszuNjA6i{HQwzeaf<pe;X-HvFXU(|E+go@jx4Ax=93`+Z=41gxXL`WDuC
z7MLHSwYLJR09YSCAXWv?Is{s0QSxP!{2geX1-*+ZI-Gl;R{1>Y<eG6z@lyg`z6CFr
z!AmiCIR^PoB0{5AgFZ@CAy+7RH(`)30wr}uiSfo6V4vl29_vT`-K~a6tA%pTBVu(%
zyAqGo6ZiM;UECj|3L?*DM4l>$I*`_Q6;bCDBF<Svo8yQ!=MZU*BFdabl=%u#<{To-
zIYgL?(8d>N$=^jpIg5x=`A^}-P4#7qwXe|UECE;7P%~|iLSSd1Xt6q@hT@TAO?~@-
zR`XyMNTCba`{Y;Wp^XdhD%rAKfxZ2pTB7e^Bj2NMd<_sC-qJ?2OFzOtO5hO>!9(#B
z?B^Oh=VwrC1Ir3U>cu^Yw>yZx55aO*A+3QXHzMC8`m(&8+Go6?y^l8k0DQK@*a*z8
zNFDfU7xc*!kRHru`8oH0)Gn8CcU8Lyx^2+Hh6=mCiuR`j{l{y<pT7e1Yw-LvYWAA3
z3zB{W`M(5yH~g{^{&p2D%5`8QJOIWYb3-8I_YXkf&*0*(Z%rP&H}hcq*n77<f2`&I
z{2ofVcm28Ve19&raxTl>;T_-S%def`{nz3B*WvyA4f8ts=6mplX~i4n0rb&7!yD#q
zyoql`>-;);nA9F6oN)C$p16u9-d0b%1)NXt#5OF3cnwc%MN9DpXl+CPfkQ3=@*Qxl
z0rHK2To*W?49FE;j%@7_HR90v?LzBv0CDONe^0>M8GldsuZ{&gSU>O2#QMMU2K?8@
zvwuqbcZ|6Gqt$<hR_}~hcORGh??dn3&;Gk${F!m~ucswP{O<hw_ZwBa_n6s#;;-eS
zdH27Pqk9kD_x(@7ym#;W-oO6!G5+3G_76Yz*3X@-$A1m-U{Uh!ZBV(V{ZIdX|MshV
z{PO;|e-{kdlKi=Efd7Br5dS{d_pg)xpBnzJ;g&!3U|#d!?Z)4o-mPD(?sfNn=k4|%
zN&mNgv%j+k?+D=|q4)n*|99;3-|IW`-;dw>fBFA2F#h^}=H2^B_ix|skMUrw$ltG~
z|N1&8pF{RXwQ;{$C-<+7`(ymMJ-hptcYllrv!Dl?@AZGDef;}*)W83m|NU+0@28dW
z{`-UX%<nyF^5Ast9%ugZzkA-_4(^Zf?|9Gsb7SkDHxK21vaS2ix3;@`(fuv#{uqB|
z%XjBU-2a`n@ZaC^+<RP<$9Orb_rYWHAJ0p<cl4LDrOHbzpIve9=N{}0^3TNj`?baQ
z*3g}^Q||A<<*oZ)A7k%rlMfbK?jGC9t?SR2fpuqO`cJcr``f|4KLSa=zdt8`H#_{h
z@pC_mFSq^jtG}vtAIxVTyp4Hq%s+08{=8o3y`%5{wsQU|oB6x>*FXH+yIuZAKmX|G
z-fNBj(a%5n`R}Y%|If0Y`}N2ExBq_s9{v3>{_K9^KlJXlgTsUOrvL8xdvHk{=OFy2
zy|w?t?|*k6gnPfS{kdN|?jPm;8PERn_S}%8N&orZX39su|El-x@)6$$TS5Jwuj9Yk
z&+i`z{(h02_$}f)zhNJYnmFfj2~IMO(_hzrz+M+Y*d?M6>0_*4^+CctU4XO*X^HU}
z>tuYUyWscgI5V>vPR*=>Q!{IjYB(#mfmVaA)2h(*$Tw;=SY;$nq{c}7wJK~N(!)qY
zaG$4DV^fgZv>ITehE9>1;Uv$0Y3@c1%>${i(MM}#`vs?SR@0)eV^R!O*T*7tLh6jv
z9jPZ$U#ySqhn;E$B0Y=yg-DA5dlk>WhO`~`JMi24$Ui{(5Wf|I=Eq3aac{$_+6qP=
zQWbf1>`qV%c|9Z_tr`hLj`9i0BymVRkVY6a$S9<2q%lSfYBKszGm;Y$=ul@Q7bI6C
zPvZ=2h|~zFF;Wwxrbu2$&5ZA;H&Sz?7Dz3T{E+;S0+1|7D4zxymuN6j2vR6gTcd=w
zLka`^_Mj7vyaV!%$Rm(P;Th<Pf@can(Rie;NJ&W08HLVhZ=}9R{gDPDJ&ZKU_6wbk
zd?wOtqy?zaMMz7KmZRL2NG~9*MtTwHC8P~T33D_0uxdz6jPIDYafv||)(Y@8xc0@h
zAMy~}FAV&$PN?<H#u?TH@C2kpq$J>iFNQi~Ly<CoHxf8w0L#byIOLO&rs8@Qew&To
z<{*Cr`CP!~Aw7w-2x$q@G9>H?#tM+uAZ@_?Cg8sc{LRQ;L%s$1R^+cEe*^h8<ZmM1
zj{Gg;JCN^0z6<%=$lpQ!F7o$~WA`q$2l-y)`;fnnd_T%KfOH7y2vQ-^M@Szdok02=
z=}V;3NN17GBYlnZ4brzr7m>b0x{UNa5_U~uH;{fr`Wfk0q~DQBkqo0mCrAv*1vcCO
z9{Y)gJ=nBR>?hOCI1TUp1m61zsfOLse30XJ+Q2vsFa88x{0Y4H6S~f@()ECCG)}Y1
zNS;WIkvbWlu>OXX4MchvX$W9>#sxM7xy?AOYe*Ey#rQ<Wjv_d7Hwb$}g<w~VcGz>J
z5Z51LXQyh|DZ>XjenZK}Q1TX(yagq1L8-@3>M@jh45c1JsmD<27L;@hC0#~I$57HS
zlynRw9YaZ5P|`8%;?oK%*n^ELv<^~Tq<Tp8kwzgcLt24^op5LY(i)_-#uXNVbQS43
zp6!H)=YicuJP=Dt5KBrBOG*$+N)ShQ>?lF(C_%j7v7!XAq6G1w1o45#gc8Jr62yZN
z#DWsUf)d1n5|XBQkl{$_NEt{YH4pf83H-VQeq92;E`eW{z$Z)KlO^!U68K~Ze6j>S
zSpuIdflrpeCrjXyCGg1-_+$xuvIIU^0-q{@uav+~xQ~>uWX*#OLqeJG5AGW!@Qo7q
zK?(ez1b$EgAK*4$0-G#>O_snWOJI{Fu*nkGWC?7t1U6X$yTd7sNZ%oS4>?Io*&;O4
z`r+K(P2@wI20M-A=_5_irZW{LxP-fUxS1>dtD?=_*L}0cg32zHi>lhHZL3kc#>s~U
zJhY?cq?%W2(ORu)PpkV*-81!msGn1RMg0#OB;Y*d7}#YjQYWO&NIj9BMOuio9qE0f
z50I`S*|6h5Eu?x#fk=1;+O|U5R%q4=&03*ZD>Q3`X06by6`Hj|vsP%<3e8%fSt~Sa
zg=VeLtQDHILbFzA)(Xv9p;;?5YlUX5(5w}jwL+U#XwwR9TA@uVv}uJlt<a_w+O$HO
zR%p`-ZCasCE3|2aHm%U67232yn^tJk3T;}UO)E~M2352vR>X8GV!9PE-HMoQMNGFM
z&RP*?t%$Q$#7isUq!sbdiWp`^{IVimSP?I*h!<AG3oGJ<74gD~c!3i&LEVg2at>O_
zIcSaMpf#F<)@Tk|Bf#J#R(OdOUSfrpSm7mBc!?EWVuhDj;U!jhi4|UAg_l_2C02Ne
z6<%V6msoLrwvL@6oRRurhsc3QJFrt+b)+=xa4{Sy9Vr88q;Vas(G9dlH_#H?K-9m1
zXnq5c{01WV4VG+NXTy;249my;R3z-lh{${ck@*H9*$qUp8;E2#U?olQw>gp@QUKCS
zr1?k-kTxS71qZ++Rgh{R)kLa|R2RvD)E(&@(s`t>kuHEMHzeTFhme3zYa!to8i^E*
z6pIvx)CUP%(*a0>kftHcK$?a01~~OVs)rPXglE_&r140TkQO2>Mp}yW8PXR>MM!6m
zzz_Qh3B0gkq!Oe{NLHjPkcmrO45^DDbupwahSbH7x)@RyL+WBkT@0y<A$2jNE{4>_
zkh&OB7ene|NL>u6iy?I}q%MZk#gMufQWrz&Vn|&Ksf!_XF{Cbr)Wwjx7*ZEQ>S9P;
z45^DDbupwahSbH7x)@RyL+WBkT@0y<A$2jNE{4>_kh&OB7ene|NL>u6iy?I}q%MZk
z#gMufQWrz&Vn|&Ksf!_XF{Cbr)Wzu0HAZXI);Nwf;{^I?7tl|;pnZb9swx;4(4V@1
z{?rBZr!JsBbpidU3+PW>K!552+L9CK_gp}q=K}gW7tofRKwEMGeUJ-iPfnm8ash41
z3D(d!j&|h)`WzQn9Il7pIzzN#C)jk{&%*T_q^EHGGSVhoZ${dJ^g7Zur0qyMkai)x
zgY+KK9;AIpHsb`^rxR$OPUvpNalIN+GsxlvzpM|xtPcqbAz2|LD}+>q@YDM6)B5n!
z`tZ~Gkg^a`7DCEGNLdIe3n678q%4Glg^;ii5*9+TLP%8zNeUrBAviAt=Y`<75F8hR
z<3ey;2#yQEaUnP^1jmKoxDXr{g5yGPT&S%>6fQy(F48(7bw+}xYTb~!W3R9t$a@0T
z7cF`}?3y(Y=~>(_L|P2kcH}z%dk1*$<N5=n4*~xc_unDGo;7aI-y>Z`x{lv(Apa5R
zC!}AIen+wyMMQ&dR>kg`)v*&>E!@{bg3lw`7m<$0W01fjqJ9ygei5R65u$z(qJ9yg
zei5R65%q;%`WdGXS5G0Xo<dwbg}8bOedW{WE1yPR`84{<rxBgbBRZW&D^Y}2q6n=-
z5n72Nv=T*VC5q5W6rq(ULMu^(R-y>4L=jqvBD4}kXeEm11e60Gr&Ev+y%4ugA#R^S
z+&+aq@oDslPoqzK8hzr^=o6ntpZGK);d!(!Ma&C6?u}kk3*!_b<9YOlPb1EsLVx%)
zTAm^nkKYq;-xYZx?vsE6UKr|v^+iHGAc~%6>9`&P_*mrmxF3h>$w*UiJs0^rq$iP{
z!|#ZdXhTk+)ha?eathJ*JS#w2gR~C6Z2){f(gCDHNJo(VUw7vnpGB4K@l#$PByaN4
z5|U5_r7dz{LBzfah#*L@BDx~$D$3enR{<f&vJ2O~t6rCDiO8-X?X_V+V~r&i47w~O
z7(&YAC5D+dMXq|6x!-dpZwiF9+<)%p&iVXi&h&Gh=l4A4oM+~J=M%6M6oSvePR`p6
z_JCqg0!l$SaM&LMRiK)6mt~y#)Pj0&5PSo^1OEiRUxJ4(!NZq?+TrQjgU+Bg=mU<0
z6F1Y|?4rHd1w%a;>cLPChI%m4gOM(bEP|0mFw%pO9*lHhqz5C5V50{cJ=o~NIuC|<
zFsle=c`(a^RUT~eV3P-%Jo#(>@$PCc8(a&n^LN284~BU#%!6Sb4D(=;3zJ-!<iaEu
zCKbV?BADdCBo8KeFv){S9!&CJk_VGKnB>7E4<>mq$%9EAO!8oh3tL>+;=&dewz#mx
zg)K#}#e*p>EGdE|E(~#DhzBb?SWyHkJlIeK8;WRCchRQqqD|ceD?Auc1S32c;lYR^
z7~#PP4@P*fz=HurFrWwq6rp_&?R#k7L;D`u_t3tF_C2)kp?wbxduZ50!yek@yWNzf
z7Sw}-;2ZEA_$ToFBD7nCc8kOaw3v$)bJ18Fjpd`Ud^8nDOZjLgj#l!KelF6_Mf$l&
zKNsoeBI!7ijw9(fl8z(kd?cNZ#NtRSj>O_fERMwDNGy)T@{v{?Y2_oWIMRwErF<lm
zkA(7(P%aY6MMAkqCm*TgBZYjVkdGAdsZpF7#i>!88pWwmoEpWcQJfm(Q=@!plyCd3
z4t#XrqXQou_~^hv2M#*$&w+am+;iZa1MeI-=fF7!&N*<-fpZR=bKsW)za04Gz%K`W
zIq=AVM-Du4;E@B59C+lwBL^Ni@W_Ej4m@(;kpqt$c;vtp2W~iU!+{qL9B|OTgZ>?K
z@1T1J-8<;sLEjGgcF?zjz8&=Kpl=6#JLua%-wyhA(6@uW9rW!OYvjL0+k?Z$NK=WM
zm~ql2{IYWfXg*$gnf1fROr81X-*o=#@aSWwFk`1MW2Z1<r!ZruFp@fQ>_q$kKffP8
zzaKxpA3skFM5L%wz)u`k;m7ym$M@sMiD8Hoq*{re-meFfAIj2<xhAqS<F3hUGh;96
zt!IKOMVK*Im@!xw=~wD`Y)g#8?jYZMEcP(hFypb6EFWiIE?5PA$oOn0=j;Z1Krtu*
zrJx)%AG=kv&iGCHA|)h&`Jd*nxReq3WgzBPtMy<bh%&mK0&>8;V2xiLN(Jd0XU?eM
zj2h0U;fxy2sNswn&Zyyx8qTQUj2h0U;fxyM4wvx%p609>Ea^34>2>C8EWHjZuEUD!
z)LO8fmXjVlS2JUTI;^$MUX}B)*gCAV4lAu|yvyU{bHOUW9kI$ftg;TPtZTgc?f9Dl
z{7nJzhizgM|76P@ZGUny|3f<kOat_8DWOXVD=1wB{g*;w6x)bVY@@$fCw5ZvBKkEB
z+hZJ8#quj~0Jy*dwV)n+4ZZ>2@t?N>bm3oe)9G;!q2D@;b;?2irI7whA%3m^KUaXC
zE5Oec;O7c#-*u>8h-4~|Ooh6Q{Cuzw+yNf+w-F=RMvP>e?boj57}K{k{o0^UTW9;U
z{rp0tSfNirgX8GCo`aOmW!oe$ne9{X%+#GeL?J%0!uEY<vb@qSz%Lfy7Ypc96w;q4
zB;N7`(yt)q@;Mr)pl@un;L<OyL*FiaibDDnh59k>^*H;VV42JENtXQQzJAK~D+=jD
z8%?>UUyQaYY~NV2H2q^nFSPxIwEcy|a<<W5Hd=H;Y4iy?fX;01!nSm_^(Nm391Bc;
zx(C|LLyJa(E*f;vULM-ZL-H<?&qL~YNYq85E)vW`f_X?V58j#f)I~yhNX11GE?je|
ztxHW^YU5H9ms*&1%!OYrC3Pub9&Oo3xWa!N!>JlL<-nmhcdp>h74W78-W-58m2f5w
z4;)HTK}jmmu|p{;C`AROs6dYnI&{#XgAN_WB{k^KL3alzSp~W?_7z7*4!UsAgM$u?
zy~WXivAH<1KY-j1AoByryAoL&JB%Z12RS>)*rC)F$ko_p9Jy8^*Ggn+>@<!X9kE>W
zB{FavB_0Tdf#F~z{}wz7@K4TS3G4TR2f+r8-N<qi%TL(<DfzAB3(4<bX)LCiWgY82
zQ7=s-EQw_&mR<N~;S83UARBZCL)bQgCHIq)I1X<r_kt2o3d->MZHSz;1?>RWQbg%g
zGDrbo_H#|fvz3bLGM=qeTpMW@t5ey3I`{=Ri)~{7kv+z@mFgmJ3Ahwo1{Q%Fa3^@s
z-;0*^qNTlPsTeI4Gv+0lO=Jz}p|N7!g*;I|G*^t)iZ%CQEL_Q0xDxH{MQg>Hs2}6v
zO0>6^v2i71<4UwxtnXwy(HO?bm5h@s87Eg-Ygx^4&#-+RSPwRWD0mLM05*e{!K>gk
zkO$rbZ-X6NV;6vVjI%2lXIC=Lu4J5D$vC@`adsu+>`KPjm5j41VMs9yDTX1%Fl4W_
zxCl#QbsbqACpv`sf&S?Kc-Rn!4RsBE)I;-ev6X)<Eo5nY<qq;Y8Jl6NXf%#C<8qQ;
zj}|<n8AqCNq!~w=9@6xXpoio<IB)!x2j^>%THN}pD_Eb)_Ju5s&oTbyG4?+Jo&?69
z@MRNf;86pQ8hF$oZheRG703DYl-lFV0DbwFiNIPb_}Ak!xLg5$BXBojEp{v0tktsI
z0d~?l7V-b7)$Ff>*FOJ@ngqI7Pj0Okj;6uK2z-pd#|V6kz{d!DjKIeTHLien5qKAY
zcag&^-gxZ_coabb6>unm6e{#FA`L0vwQ2o1H?V%TUBR|%S)a>tAy~||J6ZodxQF~>
z9RCD(5<KOX>(%J>X|_KD)^W^wun|PTbKnKA8N3W$1+RfT@FsX09J1coc;<@0Ylt*>
zQ67p|uTc&!@EmMA4)hZdq+B6}AmLJYQI2%WkZLKsC`YQL@S+@El*5adpdZ3#r}o2*
zQn*nHH_DMz8PX|*8>Mig6mFCu9TTfDahfu?QI1r~;6@CIl){a2Bx0U6l);N~cu@{7
zV(=mcFJkbb9A1>`b@X`-`DJjUjGB~DlTx@*j?XKH8|9&X{(eecCXS)x`zX07^*%~n
zMydBv>M}}Q)==syO3l+g{-K*=DYYr_K1#fg67QpgiPG((bf#1$YE*Vusmdr-8KtVC
zB>N~y86_#BBxRJOidgVS=J20VR8azRH=Zl<jI9^Fq6b;>?25ZpA)i|0Q;U3RkxwnM
zF*Z|$OlpxyEp@J?&b8FJmOE8Z$6CHyLHkCJ$EC;P(o%W!a$I^eF0GSCkH)1(<I<yX
z>Cw3KXk2<UE<GBT9*s+n#-&H&(xY+d#kllfTzW7r5f)e72Ihl>z_gkDz`0-&n1bbU
zKYAH1y$P4zgiEXC(PDY@8eDn}E)fTpmdc~`cWM1yS|^Xz-lbLYXz5*AdY6{oZ4q&<
zr=_o_rLU)jucsf?TEw}Y*1nz=zMdAop4PoyUjk-=mJ#QATKalg`g&UWdRqE=TKalg
z`g&UWdd4|*^kP=i|BZl-^aPXXQ`Xa`H1{>Xj;yEuR!{$}p8lJ;c0K*4diqcG;sQoT
zEn^it!L;#;2iR_66ed2Ah)pEo66Oi>QQ{J<#vq2lh{K--|J3+HvnRVY-e8{6ny0fS
z&Ja9_^=zEM#1s<ogcalyv4r61s%M|Bu4Vi8#S#AX6VIj?L-54Y6ZfHyRK}gDf*K;-
zC?oGEBkw39@2HJYp3kyr>={MMQKTFdX6*Sc%W8iUBk`y>1bn_gWMhnW{266b9+f8m
zuE(f6%BVcbv%6HrpQ(&LQyG7zGX6~EdA-N;dXMMz9?$DNp4WRkulIOf@1db6qxdMJ
z_$Z_JD5LnOYAptt7@MBPIcI<~!Dx=9PK@@WjP|39_M?pUqm1^WYAT>^>Oycam=0!u
z%R%#aV=zvQGEOC)NNZqXiR0141fHFmIO2IMgRyF~*;qA|v1%$~)l{CTdyH434dc})
z5re2U<JF~XUj|mNeiyhK1Tn;D5JTjcrg3W&FSki=;P_48S@1k~5xfLm0b9W9;0^E=
zcn9Qj&G*5F;3L3uVxkaHq7YG{5Yg|6IYx<4M2S#Dbr5rmHXrNaVK?#RfuKL?nf@p?
z?V0`+cI{!;p6TO=v81p#8;k?v!TI0;@DSJx-U07|YTk&cVOi%N6o**)B2#7&pXdQj
z0Gu!R{)(Jt;uT;37zhS|pMlYUdnt}n+(+>>DK!a922%m|RTqMb!E`VKTuv<ESTF&c
z2RKgO1bzz^fEC~_a5s1ato9G;wJg_(OwBi7^!wmL@Dca~Yz2kjbKrm&r~<ACBb9?l
z<seeYY#hUTc-=a@t{K03SW+z(WS;r87{}vn6JvK1k34+r9{B#2V|VmGtJQM+?pgm&
zblm#e?fBd6_}lIH+wJ(<?fBd6_}lIH+wJ(<?fBd6Kf*H~BPNL6ViGtHOyvt_7y9?(
z3v#g49K2}`aikpLNIArja<H!)wFE2$clr0Ld%%6*0q|%4erzoVJIleoa`gGEKg@C^
z*a>!nJ)jtrfKpHns=4Pc@n$J_vlKkp;cqR)XeX+~1Hingv>7DcOiH|s)S3VKPrQX>
z-aw)UfY%D%KKeBt?P@R^Tnnxv3f7-?;>b6NV$?Q9ZDZ6nMr~twp`Z3<&|=PA0+!MP
zGjI6}VmTB9Z}G(NJXN$ERYb;8@HQ!Un-n}v741my=1$AEbYeur%v(A!qF_~chVOfG
zCWfSANIHh3V@NuNq+>`rrdz%_6VtpoBUDNsWG_;`PQQrWUk~1J>*#;Vx3N0;^mzQ`
zex1L?UrY-g_SgEW_zKaFn&{~=`FtUUdF{VAwIln>`JPVWL|@rXei{8v!8>!!_B9?v
zhLQDQQ^P&?^G=W1U*+%jk=<bjwYr+PXRA9~Y^0ChIMELhq6@vUBPP42#a90#|7Cxn
zzsS}}_>VEB>fk@gx2`&nnEYo!Kk%6Uyr1d++4ubW{JVYj`G4|n@xy+EFQu8|#_-je
zcK*5kA9x?Jo!xS}5QpZQJ^povX8FVYLVvK|-|Pt{-aoVx?<*W-YHg3d!+(lvS{XOU
zwG~MkY4~rpl(E0S+{S;yFE|usdGn!aB96kJ>IZu4e3U{6m=fW*ox%PiIphD<FFEv@
z&$|JME5J?Rk4mf^eaUF+sFS${SFzV5(rNOBbqy0=l@xrzPS_gkHXDOu$aM5eh;S0E
zvRkVf=V>O_G+7DR>o(hFHQ|@?1PD37?~eXX##)_7O#Uj7>K||R_%Z)u^x*j~`R|!y
zTTWItY0a5tc<(O*XbomD5v^#L8gyvci|w0zhP{a?HOK^e5*y8usIj0NTLK+h4%$+(
zD=6(>Vc+!}13Uc{{z~Q#BxFQ1q+trUn^^lX%RoOr{0z&98gqP;O*fUJ={iSlYsM>k
ze$%l(Jm)|1!;dvbno`h<FuB{z+)TlGkWb8y)QdgxA9jD_$hz3Q4{@y~TWQoqpf`JF
zht@2gvkZqC*^%%c_Ke0^>u1{g9@WnTC%0C=@7rUq`w(B%J8bfoH=LPokKoR<V0fLD
z6K^*Eh^aZ__%0dKp}<Fc;BWA0N4ba97|SEtM1N1C|GNEO-Lm;TI8G6D4w<%?eo%Y9
zK8QzPTVFANFNqEk!`U`coXWRSM~gAMMLJHL!}lvEka~*AVj4Zu3rKw#@m<3ARxjm=
z<8fSXx#%zcLK+~}i>MeVo)a$+o!U$q#yId5z7@Jfyhi*!PrS+1-xlxle)=b*Gl*aB
z6u%I=N#}|^qF7AidwHeYsa(Xk+gGHEMO@T!x39%P?)HuNPFya0Da1_KMz#^NWD;@0
zU&+oq)4WRdmjlH$a<CjMu9HLL5HUxNm1D(k<ge+QT`y<TE4xuX%6C|Akx$4c#I5pg
z@@X+wJ|mwIzva!!4dQol6W?dNU4FrrndZyAa<5n*_sLSRQ1KZju}Ep9MUHBt`iUiK
z0Hcbh)KGPTcv_vT&Jr8c7&S(0R%ffT#Y^fOb&hyhO;8iWD{3O+idWSy)lBiOnx$rm
z_tjPED)E83T3szZRI}A=QK06iIpQOAy}Dj}tZq~{ici$d>Spn&x>emOwyJq*p7>1t
zPAwIMYMELlzEpoye-y=Pg<2u@st0)|yhJ_BTj9jU)FYx)tyC*TnR-+`D$3R4>Tyw_
za#gNy)GD<~RI1f#wTP*|@tc7CYK>Yas?-LxLAYv@+9W(3=Jx?{-GyHV9Mt`EKiN(X
z(t~7@9;^q;_IjuuDwFkaJwm4Fk@^gos?XG;Wmi2`PmtMqqMjss>&beW?4vKy(`7$B
zL(h-{^yT_;IZ$7rXUReOYCT5|(KqND<OqF}zDb^-=jwTKq@J(m%TaovUM5e{%k>I5
zTHmGbm1Ffo`e8X<uhc8$x%yGPN>0>I>oxLxy<UGIFVx%hcDYFJ(B*QmuGZD^9_?vQ
z-m7bLjl54E3Tb(NsBNgNd^nU8N|OH(N)9E<M?&FHs{Bi+Qz%{jXDBn2DW3>shq}q!
zP>)a#xhix_=otA_sBfsRTpj8k>M#F_m9)b;lHvDoSbQ2bAz}O^zJfa)HvR?9KPR4t
z^IKr}8*n@yhJPSFgyDtabMb}PF1{4|;B^_iuHZX>`{A`=_1CcZknAC^hHpyT*1|5+
zh>9!QW7F7MfA$T+jsolKgmsP;Sy<<|2J7r$t+S`K&OX*U`(mA!VWWX{t|#(709$-n
z48{^)<GI4yq#@YfXX4+)HqtQhchb+rKd{K*)*?q@k$c36SY<K4DJdbHBplMo*k+7h
zm(-9>#VQXXk#EGe;&f}5zp!?BmbJ^#)-K0byBufja)PzXbFE!Y#4hKEN%BT{leoZI
z+eOyeF1FS--CElWtnFvwGHYi`v9k$cg|(-Z)}B^ddwSa1(;90}Ypp#!W9@03wWsyg
zo;Fx}+Gy=*leMR)wWnvTJ>^+@dc)e&o7SG*vi9`0wWoKiJ-ut~Dc{=Dd)A)bxAydb
zwWklQJr!V2QSp(rp)afr?J_nb`1OSzB>4RW5hC6y)<Z?Lu_1B5*bp{{4T*1!4T*nZ
zL(`<T){|_lCv2@J)ml%QwVqDadNQr`WLfLU#(I{??$&MwSi2cy?PiFzo1xZjhFQBA
zW$or<Yd62Lc5{`rn_H~i+-mJ+uC<$a)@~M9yIE-M<_>E&i>%$`SiAYXwVP$uZvJTP
zX1TSS71nP49jAG+<#Z2t+n!OM;WY1Il6u0-W3i&+NFm<O?1z2zC$)j)KZEy!Nnu!e
zJm(D|CBxF8Y#&C_u=MAQ;YX6%!p{>qaug{QexAg&PY#|q!O#f&JeBrsG^sP~*%*;-
zBTikc<z(1)tt*x@#k?0z%B0Pi#xbUi%eGdOg%w>Yy3z8?z}_w+b+;CkPD{5QudtD%
ztzBhVyXtD~D&5*uFKbuI+Ep5MWxn#elhnytm$KHStabIW*44{eSCX}^Zq~Zet#$Rr
zy1o&;v9NC`n`!%cTN~?*b#*2R)<t$9PMS^&n1!YF7wK|<93a}sfpVZovR2pLT3vf<
zbxF20>>;m_*NC3BP3$4(V28W|iyii~b{MjDsI48+<|!@Os;~-+kV;h@L>tvnr3<Ap
zRJPEno9aoVtQWsBi>ThTjGa|~;%{AS`<Q{<4i#P1Fj~n>toIa=rA||)am*R&4CZI5
zGnt>I&f?0W)o9K#ZD>f1B~sVLwxl67PVws`+n$Ehcr~89X;n2|U?L@%L<}!Oou|$t
zKbcrwhMJ<Lke{lilAorgkw2f<UWU3rT|oXq+Tsj+$1f@W73vDgHj{Q)SwEtzA5qqi
zDC<X*^&?^HN7`FIl5G7*vh^d$){i9PM}Ef@Zd12$wcFM0<mapT<QJ#~<QJ+t$S+ch
zDDz^q7&+vq9P&%l67qMdJIODlWzV#pDBF6XZ0m`#ttV=0JyBchiP~CE6t<oyY&}uf
zdZMuPL}BZR!qyXYz!R-OBWu-KWd01l`-`Y`wDld;dbM73w=I50wNY&(Z`%EiDvIao
zu2XfYXrt3~L@3P@ccFDh`U4T2uG59o89IZ#U3FK{QD^Ebj>*>9qP^~>yOZysdvJVD
z-IF*|FWpPD)xG(hVMHHepR)GReK@AC=E<=2iCwKv%(Ol+%lgDjeByAfKLXF#4$pWZ
z$B)vZh>xD6Pv$PC=u`NW;%WLcN^-hBony|xd$z%Qp2d-)@u3<YI+nYit<M%AJx-6K
z4Cm-`cqThuj~AWw1Uzg89(E$}*-3hmNZ04-^Z4!CWFqugdWxPRy6LGJ=ICj9nn=P6
zU&8k3dOFc-dNR!E$uPfy-%3XGOg)pmSL#`!2Y&f#=GW+JD9LO+TXfae>Fc=T96g6p
z(c7V%^me%NTs@bl|2#d9bLsO?&IQDh`PC)AyNu{Ni2Y~jMS2m}S!|y==ja?twL~vr
z%Tm3RYcA8vm@n7Mx#kMJf@|KT?`D3FeKLKozL(=4(hqUphxNnMl)e-5NA;uB_c5Ma
zchZmZ)?1c-f+yIWbgs@N|0GYcJLy$=75S&Nd6K<Gui-iy^ak#=NpE8QtbUgH^ZI$_
zFX|VWzocJc{)&Eu`4+u}`Rn?1=5OdXn7^gpV*ZYPhxxnuT}qozR6Ik!r{5$0KGE?E
z{ek{~{D(xzGjxG2Apa53@(lg4{+RqHM9nkwr}|U!TZx`$=+E?L<O_+SXXtHu8~M+P
zrf29cG(9NOsxjZ8cTo48dMCBsrFW6vt#^|z(naL==so1W)L)V>*2U!a>b>MkbP4%=
zdLQ{xT}r-8mys{m<>cvGGN*3|8|hmzKg4fjBiiSgM^=a?jA!za_x!R#ir>seLYnve
zvP=sq+M5<sDAR(9wzQyOp5Uj3Qbjk~(N3aWsB@^ZND6haPxsS9>1?M*D!S6LW{X~-
zZlP`*L%)>k&@bgW^h=r3FQs*pA`}`V+A)?3rlwqHTC}4^zY>SrrHPcaY1k4hyEpDP
z>jqi0zinw>+tYTX@!Q7cQ#LE<HkoD9lu2uv1kAQ>AcfYCUlcPBHy!l9%q7a*`eoWQ
zlqXoGw0=;lTRT!~BbTPh)WVeCOqs!=VQ%VfYS(;9=6srIq!vs;6id=KF%yx;Rwi@R
z3}ECIvU9T~ot(C7<`lCfxO=)i#%ybnLCX@GeP(YGdQ<i&BZokv&Gi%<clbOgd2lat
zUZN(=j|=viEhZPpA=q!~Zl;6=g0<G>UE!(uwR=0*YUXAcOr0BZt)2J(VOyI9Z8T}{
z$JENmEisuGPE%^_dgfk1YKtYCltvG?c?xW}xfYH6CQ=FvH;FDGcgk)=z$wG40D%uM
z{A!8B{=_m-@*|f`-skXrhGhXVxnEKvnje>iuQ?osy9u<V4y~a*$C&Z9@owQpm@Bs?
zO|$*~?v(HYZK#czg7xNJJE&8djn1Z7O&PfwAC?3HKbJzjrB@3y)1l#d9B8NDT5XVU
zCwO7}Rp9mc=LoxIe7~78xvr6N0!G@#-h;_3GcBu{<ZZ5HCgZ;X&lPwSbEbJBV7z65
zi{`il=h7SYnJocd0=AfInEDC)u!*b$h~Qt%aprpTaq+8(YyQw>;=V^;+M71=wK;d2
z@=Z3BC~bnLfo6;@nh<c+@U?lXlxcZxYGP{Dyk<cOTFwL6n7W%<H-DDYwW0MIA>I>m
z%-It!KyD^t%<BiX^_JXKv!xBT)Q)}axo?UH|K{3jt`rk~bHlvr#grS({Gyx8{IXlG
z|J6-m)~&Y=9VTWo|GBt<`EW6p`3Nze`3WM2`AG2x<|m4~xskLbF!wQe!x?Go7PKtV
z>dchdY&VRNl*G&pkEGQyzqz2S*`kk)o1M(RsZ3yOf2o)yt`oO#j5%&*a0QhStgGG)
z%RYy#_iR}2-mpxpCypQ4uzgI!@@&R^6GRcQ=EvmY#F}&ElX8`OO0JfFH8E$oMy@65
zyw1d&<wj!8QTeQVPChSRkT3p}b99Q@tX>2!kzQ7>s8`h%^_qHJ<*7H+o9Zp~wt7ds
ztMb)*>V5Tr`cM_9kJQKN6ZNUusy<VNYTHjcOSe;>lfEGRowS|w57JK3F4AsN5or(U
zOHwguFR6r7N-86jlPX9Ksge{^`$<)#uSnIT10<K^k>aEpQZ1>DR8RVvbddC4q;E*y
zlD;GTxB73NNgX2j%GXdvOOgp^n8|d}+mPCl+UX=xds4DaRxhH(FF_Ir$v?rtbHO&E
zS=(ea+dh-a?Yx(rKgGV!*tbLOB=;V<U2-=$-dQ4+vQL)CQduSsNLP9?E^B11tYh!n
zL}tE{<+4ILvQoz6ep$uV8yfDRcyglU*Ycn|$ktTizo)|2(L|rJ#ZuA;v5a&Ajj<FX
zsnA$4iU`Scu|QqM(}a77S}YVRi8<US)~i3MKMQ_;!RY?Y5EHQ{V_PPIt>|H8*|qU@
zzS+)4*geM+-ImzTaYW_@5_20)>})LWX^kgJHjQ_*jC4A2eiwXZ27Nfw<{GOUC&tUT
G5dQ-=cGtK7

literal 0
HcmV?d00001

diff --git a/docs/python_docs/themes/mx-theme/mxtheme/static/font/Roboto/Roboto-Regular.woff b/docs/python_docs/themes/mx-theme/mxtheme/static/font/Roboto/Roboto-Regular.woff
new file mode 100644
index 0000000000000000000000000000000000000000..5a267b0f7b7c8418e0caea00e60e50c5b2cf214b
GIT binary patch
literal 65388
zcmc$_WmFu`pZ1Le2oT)eAvnx1z~BV;;1VP-xJz&g65QP(xVyW%1b26L5Ag8&@9uqe
z_qli9-aS`+s_N=*cb)EYW@@TVm9v7lI21J0`|)N8h5DXBpSb*6{U7hY9ulHr;_m^X
z?=Js)l0>FKrj_`rp#1LnLqV~nLqWxKhA+sLNGPj*fr0{1LqWm#LP32Hpr#{jQBY<D
zKtXX7zF+qq5B0sVg1%l&R8HgF`wImHYYznlw_$zcqyVwfw}pb@eoyeKP*A8_adQbQ
zCYG+oP*4JcP*5pXP*8tA6y0EdG&RyUgo0XZeZLpzJ#pT<3K^NcoA2Jud!6DvQ6n=U
z>Y7^paDjqa!iR!FeD}~hI~~;QENvj~aoc=QPzWzjP#+EU9`cv0^j+TDJbw3}NZu3q
z2L)&bYke!DcTfBMIk?}S!*!5vuhrJZ;Rh7dX&e+3#{7E|FCyR9lU<e|-wnTi-_ZXF
zMGKD_pe!^v_<5iZhPnqI%g2xaLJ(DITnZiI80IKs57<^AcMZ7w1WTnt2MYza_EGD-
zF_)#&bVLu8*~R{eHWL_%<P$`HD8y7~3=H&WxvxF#!P1H=w>2Suv@9mIL`B6)%IfMf
zm5u@l>FYw6sFcK1M5(I@DA1KU5h<vXo20Chcr|P5tj6y<=%9T|wWKFg9{C-{SKQC|
z&+l5#!OrkF0qB&{xuIv>_&gpbq8Rji#2C_u_hIa(&(7;Z?2{M44zy(wy%CXVNt~V)
zY{t6#5nQ?nu{(}exL~ofguE-D!Z!t*3GxY)jKz!rzDEpW(fy!J48foWtKL>)G%okn
z#VO;*v1iWpKdqDiKsChheh@lZgla%Sm*mlQ=kK6cG!dm>aP2n^i%qLbshK{-2C>ty
zB`hPc)5zlRCrTa=576z)4QSF??2Yp2@IY*`{eUay^x){g+oi>o&gJ}e{JQ+=36gE#
zg??>2+MN_>unln^kPq`@a<V(A*YLI^m+z(NuM0mW;`)3R(s<ISYWHY|bGi0FdP%iC
zFtCpEw)}vDn2d;pc_!yVt8ccY6AZ-UqOOxQ&x}i+;V*Jl>d5VqYM@<B{-HOulB*u0
zM&p^L6178}I;0ajK&PPK#bP8sY{FbnyRvF-Su$J1uZ)*lF1Z@l%ynBcw>HPGrC{pm
zd~BlCvAC3JobM<{l*!3&CBAf0J1~`D8M|iuHPvEiW!~62700T+&Th$La&DTl-pYJA
z!fK$tt-;D^S<cF0nWp}D(OS3#bl5gcms=sXo~oO^Ib@nV34(a^mk~G{j}EyoK=k{k
z!W;=Q4daYYY{=FH%3iYWxN0+3y@a0~Pq`c=PuW^~ZcveVEKk3#ZjzBq^E#b!H*fbw
zaQ#S|wlf>}8*!V?<4m}I*etKiyE^}vP}6UGH%1)iYPL5xdYOy`tQbmLrKyscV6kY4
zbF!MNvzXs+ermQp)^c}RS`2N;b3R_OvTaCh=3lYWZ#Z&@nK*PGciiV&c|lk`o@hH<
z33R8vmpo^$9<buiIJ>-ZRUUuta}AyNwx)bTyzZb|QK&oAhUB)`264YX;V-%$T~kQ+
zUVm(vc(Wj@QMecAd0tsEG0!~oG`>w<H?bl+_FX`(X4UZQYDVVNsl4?)LVlgRA8%K<
z(^+#CS-0|hbJHI|ZqaQ#>Q2Y=nmljzYPd<AKCgKldLcXRsC+AaJ$~|@^4|Xm4a-7m
zyZo~Np_DL6kj--=%O?^=o7?t{{5*>nL5>W&anND14UZq#xVSOyw~wmJPVF%Ghr$MR
zsKe!<uR&Ma*6x(1K?krOGr=O)ep4lbXIwXW6bs_9FqoSuJ=E+NbfdLUoC_(H(|pv#
z(X12637|zg<*c3fBPDW9^wj+aMdTh$hJ@2)Ax>Wb<r~;?;$m=<KR}P)W9~vdz=o*Z
z;UNRVIo!qE@HyH~!kVPXdnQYfD~uHEHfg$^)G{ziTw@}8nDhdP9l#Ac=Cgiiq`$DN
z4ib3C$XW`m&-Q!zDyu}nu89-eWZvN-5+97ipwP;SDH^dvekB(3Sq)5C%1N+agct60
zWS`T~ypBJ(FI@bmQB;*EXNJEYUqG*j1xQ;=7Y<iEuJIwE=r%y%SNObYc~x2!;|z~J
zp)SVQAk^Q8J<p(G9Ij&Y`(jMyUO&lAWspIWeeZMem!0b-jZ>q)x{&r_z}i<w$}eG}
z<3q*1&pay)A<sNJk--d|Ng%^`k7!D!Xy4MjL9KvyX!wRbH!2@Wg|Rg%)1)Y<k$sOQ
zW~%xUSJd(SR|86<mUXStau}yMEbh?W6LINR^Ds3sB1o5L-Qoi&VOtgx6GkVdFr*5}
z;CjgXH@hCSU48&ie_X|ZHY^?9pkVa?h~eqwCm<ppj7w)Sfol*GWV90+wm>zv_2zLK
zQhBj&dj`_;G{j~@3S~3@8D&nF@Cs}$#hX4-4<RUKoR7eAm{H!^KcXqEQz=!d={0w3
zc@TO@d-fzdZX=&7DKhYT3SKC*-gx!K?xREvO$hlpY+Qbr{bj7bd8AKo7Iq8y>Fui^
zQ1%vT&I{dQqGzckoaKmO+wy7O{x9EcfL(k2tL%+e#KR-B?nm-#4!+B8d{w<kLTS46
zTNi^i7m}c6rk*<+$a6#0%ksjj6{N@w?>Vr_9p6HQ;PFWHW_1A{n)yPj8XpFaw%Bt^
z+=EN6Y=g9=ae(ElD%AaCDrXCS*GPpX$AF+NS#dWEiS)*sp?(PO9Ah74Whqq7cOmyv
z>d8Q2@InqT(O<aLS6|)b$r7lxl>;Qg9MLd#ahJ(Fp=0}p)sg5$jC3(-rDja`S==2d
ze|nt3TC4!|#;H&<7n6YDKFFfW2`pbdd{KQ!19ykCC-oWzyQD_MJ<>(SuX{BLGjNQx
z_|&Ro^I9X;&|b-W6geIuCsmg*^+FhqMWq3X+9!Q6<lsB3oY={86i-Kb4D23NRc+NF
zRZjKAY%n3O3pgy$;tyZgz4?VMIK>)oEUC@+VPWDknTb`z(%I^!d(tO|)GNfrgB;Pa
z-|ima0$#PcD%&q=_PM^AB*rKX>(a($)Az>M_Z8gZqe@}D%dw(1SEevmk-f%b<ULyb
z`{=N^uD@}dt-hnQVTFP3WCKca4uTQ9gGe&5r(Yl2lN8^6x>UaEv^t}%5&2o!^gGxY
zIJW&70Q4?B0nWIzODifciH_drIp@xOZPYs(K5iZJKX6KG+}>U=2e&Br*i>CGBesJA
zZK@Wip@-o06Z<Zh;o4DrZK?)AIPKw-EN#8J=#a0TNsZc&PZenTExIlz;t#lR>y%L)
zbnMNrB(yo36>qSrdpm&j!^Dw|arWMAB6H?8mwR9sC@9q2ljZJ16=ig>w1k7eN^tiz
z^n(_n-bf!I$IH*Km>lViV>&h#vUaaxSC=4YjIf4e`iQC(vg8$}nJ`(!@#Gh+Vl@2d
zoF(9ZHM8o9KKs)W279yLJu!8Juv0|>NmJF;bET)?U1gO-{D@OEvlE42yF9H1G}52`
zwjO!rP0BNk(b+ypQZ=IBKEPI`|KptgqXAQTg)2`$nllZK3#Dr_T#lgO4X(IncOiK@
zy@4TVj-|K}kl~zKLJVL|l@pTG6q3wHpwl~e)qhfXpIdCtE`V5477o-*L$R+P*15Kg
zEIUY4Xs5(&Qiz!=5o8Ak-C-IN_Ju)aNX7A*Twgg<!aq)?xP}B*Du&_1mhW5_X?Rn2
zH|H5<h*qO671J1|!vN$l7LAD(jaltp5H<6om}(-KY7#%yB=W{ny;(Cnx*Nt(0;Id?
zqx+W!_kJ!6q)dM)W>e!vyMg@hJ}rXEfep_<%9vUJREhZ!TUX#)#B~?Li=Wc1nG-Q8
z22E1T_3KBt1T|m(*qOD9hojPB&nOAXWW%@wJM%>j9-+}T%TCSG`rb?P=>!*yT>hZR
zaGI~S*3~T0#UfL>p6}5_Zm-)ywF-)yEFBu~jomIi=LQ}B{1NehaL*r!w5<1SIvf~!
z3(>fCC6kch*79BNHpfY^7JgFz1ATxKJzRkyN@GD)bnII$I5XWT1qKmVk=!q{J3Hz?
zx`v3C=Hob<SEOC^7WvbR^O?2NsJOuMosPe2zKkVJ)y;N+nsPU#6mFK;Cj65%u?Mc1
z=?}+jo=vIWP>qB4Zob?Qm%9{PhBV;U_%W(|M3cC8=v}2>Brf_RF$`8Jx>sNfa=w?W
zBkf5CSFfiRP&hP}e9HYM=BTNVv2<{TwUFL)99!|sv~d&pbiN3k`RZ}E8n!<6AeVeO
z80L7hAM^G!sa$o(&rdZzljdokLzwYE^60h4ugd!htg+cz6j8XAUwJN8xN4`TitKn2
zcwL;%e2X}rINt%c^GT>hRygnIJ@=nLmAp>xD`;z?UcRO(qr6?OQ)Ga16v_fS))qwC
zVj*MduFdww>*p6Xr4jds9VX7t>G|t(=O>YGUiRy=nZXvX+huR<!RvoBLof=wb?(oX
zb27E(1h4sCGXJ71R<+t~4L;4kIGU?FnJk^Ry}B3oCl3^Qzd66sMdSe|81(iRCoW6&
zhSv8FT=!Dn+V+#1C;01}c^x}M9u?-AZM<q<3g+eM)E^@}hVRyuk>6h8_YFG`xN$mW
z_><T7IA5$cJ1ZC?yjO=#$rW}BZI-NW2K911;`e`;EY&<IUx&S@{DnCl9KDeX4Z|Af
z>JWqlUA*?jc-)iBUG9Pvd&$b2n_JzBGqW;7%zqrI{T|p~Xt2td#sRKhzVN!X$L{|J
z!jG$Lo*$?0i4T{rN21QpoSX09OrBUnIy5_29-M~Un>(al&s^_{)_A>AU*UERt2L+B
zAsM3`S+9G`52Me0gUN~X;kpjD-t^^QJC~v)Ixbgnw*+_dqFT>r2E3i3baAv!lWPJt
zyisM|-H&zCSbdM!lW3k#IU_=oQ<Att#sQ{g?M#?s>ZW+X2>|F?g!E3s=+6~!^L|8+
zd^*%4$+frHGzPUdNm-$$^a8k5#d$#p=`+-mZLbyCtTl#3{08_qp4|A*;yxo?@f!?G
zKR~!mxPJpCCVbcJED`5fVQsElY4!zjbr1x$v{~*)VP`$cO>i)ZXbGfh=`{*KR=j^i
zsEP3Y06MzT`Xz1Q8g=S8@_opBT2y3!U*rI(K6^i(lbD6DgWykRNo!yaKJxgCZ!kq6
zHWy0QFme<9fZhM8o5jUVb^|?XbEk^4_bZF+01f{02=PaFySLEBiSJ7kwRqFIUsC%(
z3&WzuSiH%QPHtO*PGee5U<X1uOl()(h8tViYsH>lEt-!+C(z{wOA^9sq-!Put8Zc_
zStnUn$#9G=NQ%~PyjaOe6$ewt&^aPgqJ!>5xAx`ejUie+qVqUsh7_E#4e4^RC4fZB
zY>BvHHTu~p6FA@voNZpW)!zFd90-m!NpN!o-YG88algu3P$wbp`RqdxCoF!4c8Q+|
zCFt<Klrc-3d@|K5(2;+JKSf#+oNkwT#yyo@YA{ouY!`WEId!isF<URt5qX9_)mlO`
zb4Y^lFMQ<m)2@Wu@j3mD_kfM<OZoMav=G5ZU;D3!#X=t|hvPw6s2-@V@UN(^h;h|?
z@KNL;_~SpTct0dd+^E;%7b5AOqb{`cNYWzTX6?2n|3=ckMAc}8#XV=!Q>%YO8o(|3
zk_@%vC;#1liGq&tn=W!W0++pIGebPk>{#`;1QeEN5lIHiXng;PfN_PvUfJjg#Fz}I
z&R!Fl>^a&R|Bio*8f&AuveD*v%?7}+n(;0Bw3>0TL4Mh2ZG3V);JtG7?qIMs$2PF2
zJdbf^F`FVxb#Yt}C!y&=y}uug!n+Z#PUu-}Uf(c01)MOtM2#KHP;G-~8K$eCR9+_g
zK(TgPhhJjlm4j%wk6mMkxZo~IGL0RWQ0?#0$V}VMlxvMBve3>yizrETzg;Z(&f~V$
zZ`8rjF>{-BNg>N?*-rV!;-=MZ<o<W7dLsl*K|4a@${2&y26_}xP}^F=w#OF%m#=ce
z2M%E+sUb)zG^zH#gEzFXCWtpQsm9+<UPSDGvYfM@C^S5BRMH_K?JSv)ox?z3$5YWS
zXS;38Kp>(h@|2*3<2Bj8@PoIK=T%688T0iwooREUqcD|%y2WPqrPBKI`qk-j&X=>B
z$3rrsFdRI6D<X?!jphC&&bj)k`tUldQY*;+WX<(gb0*YPm4+KxF<C$~iAI(*{_j@(
zVZWmZ8(|niSWgP)Ty0f;ILNBh3?fHlv7|BG|G!z2Ay0<N%9i%ca`FvBhn(P5+JfaB
z%~U>%SQD$OJh_b8p+HXXD6RM64sxo8ek`ly)i1gB${{??$8K7Qg=_*<d(?N$n*JX}
z7zd-sSZWybU=k%PRQ3kYa57ct|2M1t2mc>=7nM3w=yKyXFJ_IQB?>r?OvgQ|??{cX
zvZ}KxIfTWEDDhbVrFtTq6%?`aQ9~9xW5rr)FpUASx+3ru6u|kYWQ)O3kYP0;Kocdo
zG~hEh^u9)z)mFgeBOgizv`iPBWTzw*M(u|pa+F#)&(7WNte@d&vapL<e^wbYcDmGn
zHVChk*swn#P<2uNoY@0y@US6|!lU_dMRAbU!fO7s*x|TPW`pHJ_CoMNbMjs7M7x99
ziGGHv9)zkCf~)L$6)%G|B@sG?(n9#d^K%Zm7B$Ne>i?<L8B!kG!mGAiNU*F~5MM3N
zh{LOqDp9O7r<_+>Ag!qopK`X3OO7P!_o*)3QGwnfLurmTZzksSU8Ta(MRd!WfpJR8
z^&_a}M2<$h77<F|nAh-|^FOMVEgn%5Xw#XnUSe+w-`QXCIuR25a5m&BF-u|2eP_R;
zXdoZWIvq{_qeykDYid81gBV2C=}jXZ!o6%wmXzIWRTe(Ez=FdI4w8)g93vL3z3l&t
z_b7dqe}!u8W=pXU?ohh2#Ozbrv0(ns2uU33_NuPTWICZkeuk69!Rp*S`COjW76VaN
zoUBmmt87pDbV?`ktSN&RllNliV{pl`Es)!R>d|{ip2-M($+Y5v3XP!R&>m2a>H+`H
z&+JkBI=U?|SjQrSfp+?N=*A0nJofaZzpu6j3J`PqV(2NTxvnsGAN%h`n>+u+HWg^s
zx}U9DLb}Z46}XjZEQV#;gIY~db@mjCMM)<rvhf$nG=EhP1y#Eh{+HsO=P2yJSn|&k
zu{9QQ8@#9nWDP95YFZXUIqkue#wd^-g~iqvfIj<dA1iRvMO{s9RRub^D(Tji!nOV9
zJL3mf2W-8VG6yCj_OFe?gmHsecT{;oabv|ocd?(G?fwqb-d{ZGKK-jV6Mla0!ycIT
zhvH|VV2Tqmd6b0enQ51CW357WN7CHmx53~4OW!o_iG(DK@z0m%(+98~DuXairbw;+
zO%b%?hQkHdh4Y0%mI^z;``gw#F_X`?fsPS!|AX;^mexzQZBg!B{yd_EBL9LYkEmf#
z&<$3!7v&TMB?%=!^xetLH2o1I9%%Xr!x0r@4_(uXa)hFqID42Pg*bcY9p($x|5=oy
z6y3!CftW9%N<orH7*Z6JLkiT7!PJ40_CfzU63{0xDYd?c$^@ArVlY!o-%6V7qSI4k
z_bSBs2SuS{Y@y?zDsIv-5NCHOa6A+hwfvickphD*urN?DHqkM>D7z^riQj=lOxdaM
z;UO4GaLxIm-S3{H^?bt<;hfiHz3UnE-Kz8pB;h;R&_Os4xUl~CUr<hV{&ZQccheQR
z=)z7E`Rh^xm2{*_S-&;T&$-)$2I1HQ@?u$<8Gc(+<;9tR3OQu^H;i{pfi;-I`nvx!
z5G;OGsiDpxLCV<FhP%FiH9jLC8$o_sGR1{E+E+@WsttA_fi)-)!n;ke#dFvQe@%W4
z6W8ZJ%27lm+|5#q&~1PZ9w5KOrzZ95g+$?h!t@i~VwF6B3vMH~!=pa)?bVJ-LdJ9u
zHeit)g$r&Xzr>+#_w7}WdO*U|6W;nPNema9LvDvn9pT%n7{!f*De?a^H4rh0gbk=9
zwLb*=kiVjBy^*)0Z9V@RUQxHs$^RELTVm_pNv=`dNhX#cwJ9`fiH2GTPrpn_3wXOu
z#N{%CVPklvwZ$g>C*2pi7w8uI^63d@RFF@oN9{9?LQ^gge{A1t58=}lCaoZkR8Oi1
zF>RYT>R)T-eaWaj%dlW7YdLQYgPg4)x9!Sp7;hS(?Y(Q(z0#fA={wmS)Jwj5Rntn(
zY9znJGt7Q<7mO|7xkg9(n*Q?#u`K+!pMXC<&ei%%?rfBTO(UUK-sEf4wR<A|@_a2X
z9_{;1e^J$v0cGr0(8*Xlj0LNNzptw}hBK@EKY<c9Vg9J3Jg-gu9Ls#QMBj~>sr*-8
zU1eQD%dBk!;o069hPl2)Sv%u=&w`eQ7P5>5+V~aBi)AGg8&zrPulTyk>bs2-L8^62
z%5_UjPfdwC<;PMwi(9uVK>R>6Z6rvJ6cav*&EFq77rD}F2&*Y<0+tuQ19|MF)WVmT
z)kK@74CT%b-Bt*t5H{1N!>6N!Z;s7Xyn0w-`sbvdqXnw~6~b{M-yEy!PLte!mfPu)
z@~(a|R03jU{UF3DdSHn%vQ8}HF>J|r9PavOOSxw}``OZ*&OB$SnZ<1i=X-G9PvRg1
ziApDup-XmRA_SK0hYtW?b4|ctPd9YiU0d*-ab0k0<39q4ksb&0fpvBvFF$qh1cjcn
z?!E*$+A(K*tjZsR$VJm-nv~;lzfoFop32lBo4#eu<G7ao)=~Ji;>f;+bWm3%d|V#6
z*!K1Ly70v{<MZ5*?rC2XT>?m$PjbrzFrdUem*FMxhoJHJ(8<Lar2B&(zuS+ZwSn-w
ztpAFFse`bjqCcC0iLl$Zq-w*Qsgdxw>6XL)ml`A<J>jGNFZ88=KOFv?6dZ`Ze|;PH
zB1!aR3LUdNKjD@}?I{`s0o@GNtm1dXu00dl$;JXpU1nodP%@hxx@Zhk%!dm*bb1&`
z451JxuTNV&<O2UfX@8)wM+4aq%)|JuIzAcPr5L*4hfAN41~viPMfCo==c;Ef%XTvN
zZwpJi`uN`vsvOXX5Tbd0>gZSpRF(YFVYWO1(1elrZP`sq_14VSE?8)tfSB)6aJA0F
zM%qj@dfTTB*3GKUYkM*KS@LwmQ%4>Z4s3pBN_f+ijU+KFX$df5OuWtjD9y0UVrO;l
zig2G*LOqN71ZKN=+!sr<G)yblBkT;7?@Dpq)k678`Z8t#RHChmpEwX!Ml7MTg*Yzk
zGu*2)>lP-oPE45ZYH;UR00?$oY>oKZ!&Se37Wci*8F*V%b<{1aYn|9I-!<Sqe+zvn
z`;~aNmDlEC#;VKEEhBc~%6y1(dE(B@r$!xnsN8g+jiJjJGuo%SV8Cj*+1BP#Cb`yC
zW#*AFzMnZY4h00ivJLD*F;G7nhI&@#EmlX_g>t_VYJ5t7A=5xr`^nWMU5V6+tmX&$
z&b%<h?*vO3=~>?=uxyZNRW(t+VDjyxhWTz0_t`SkvtG;}8-qejR1yQDS{=v|F=wO$
zbE-+X7K_-t@#YX5z&81HIZ1+qj|}&l3W+Y-v367JE6bg+Ely;>rSSrO!O?}UAE-y;
zeOj?cW4bE^757JF+H_I_u|Ye0$uGzD=IDuOGn1)i@W2??gu|=Y1*Yxw5T$A$JSri#
z=|+~Y#y%>SdRpF5)DlK)@NuJfJ$x?|M(7Uo7FWbV_;lE6H~W~@Qu=qi*~yCGpmpx8
zc}lPCpjGZ#B;xPv?1VCW{TPvUy%6F{e2M=k5+Ve(aBs0hG=@H7Zm*V-kVkA)3jM?n
z=|O^C6z(;EtLB`|v>;lTs3I~u-?;vf{%fd{*ga{&p|j#ZaN+^4OJ)PDIP8G}*46{V
zC7eC9OKAgkqzmbSRs&JZ$GOYA6227fmWQ_`8T=;-B+6u`D6ZY8nG@DX?qGYu6h*&P
zX)kos*-g_cMli?RSNu<|#!SI7925%J%GRz##RviEp{e@f$OMH0m{da?r7PtT=jgk5
z@-zlK)wTr+=xI2IE2ZhH6eGl>{|VXp$NKln(;7e1u?z?Qb0^vM{*(gU_-rZql(=ju
z#41H`%Qy@oqYPP9pPjsH*qCz>d-WqvNu7|H=cs1vA{nB*a?vB{+_k{>`ck|L8Rogj
zmZzkPP_w7xzVxmDh~XthC2wl>2&5WJ{T!8xEhQcO4hAvC+`?WbkfdVphcnCgQMF)C
z^Q5wvMTX=u%;%Z!nS>|GexBx);Ume?mP&qd=6{;t(=Glq1tc>}R*Byj_F)<POW8``
zQu{BZre9R(0&(Pm{&%U$2I2fm@zOy!|E|Fw7RI#ru>C%WY>AH=M*bRd|EN$46=y_$
z7Tw@oM(FR2sJx-i3W_?~7v516$B5*iDISWbJpIvENINdPaa4KYVenXG&)}X{JUuSF
za8!BVDRw8BZHqY4z0ac?|JQj>lKA!rX+4R&ikzu=#luzaUDXi=V~XQ-?x;>_HYD&-
zR8RW+OBsaZdXC+O<huF3e0$AtrA1zrZ%b3Gr(tru%LM!EIv93>7L_m2wnJZ_-yt|h
zz>j;Wp-b=nQ6$#J>Gm4_{+rhQvgSQlq=&02!fSj(K$-i3@AqMie}IiT$;;`{5T6A1
zD_2Hf20Mqx_F<6Tb|n5?Iy($XRXN)6Q{_Ln0S+^op`G%3YAgoEd%B%BMbt|(_e=>e
zo%&6V5N5C+y%eB@K36vW2Nt~hE8oKO-w1kMfdMZjVNz`X-k%XaQ;ZDgubHBX_;-3l
z1o-|9yhp%6Soi7BM&pl-9}L-X(=SrcDjxqwpt`RuR4Uwp_!MTIXJDj0YzSw1MQ~@-
z>sK6ERI7*UR}l8-g2d%#*KR>)>YgqO1@|S9wtp2*t;p8yHm&rwfuednz}}v_GJ)z9
zK$HLFNayhe%7w`Vt_x8s>>BL3A6jQ1kCumMXr$>S2D3nRpG56L5H8u|7H=0&;yt6%
z@(7<G_LRGCHe4{GthVDnYCKltK5aZ^bUE#`7|_`c(r;}w_}2s=W0y^5&4%0sg$6>g
zXmMeFnQCUJn-+~y_*}O|Ei4{XIhVLE<H|lx*1yl&F?DLwZp-em5B#*-0N8@R>t4HW
zFt8D5BbVdpE)s;nZ+GpU)xg=ZHj?o{dm(0gxbFON+vE{{2a0BJFrD`c{|+34`zA!X
zfa;q?tl}GWVGuu2HN3AubGt#hHtGHl@X`VsYySP~SmOPq+}+u@;@ufa)7wQGuh*aT
zYtU*3FVC9^ATZSid9VR)XBp<o3i`4Br^h#;c4qQ*+^B!qdK5rs^c*pFcGNJ0#FE7=
z_aig=F3Uz9qVn;8lA6t<e~MmX0eUr?^Oo0%M`qXR8}l1n8{V_iwpN^csVE)BBaDxH
ziFe>iWRL$d{83@wM>*@Dn0v#09MofuKh|R=dB}@-KF)>hkmb3s$fZ{Gx=@!qV~t9A
zeL8qHtX=NJwna#i&IdoS1`kix6I+e^3mtc?exy4~ft^&Pjw^bPKXOhH3BCH1%f0)y
zUw!wD0lmynk2e+0sDJJEFZ2%MxL&0TwBG>xy&Y-v15>*i&BQW^kVAVWort`Vj+>EW
z`};&-ci7p%YY4?&O`KHk7hVcc-zU)*Eoufez9T!;k~Kv%Ogi1$Vy!IBkcyiD>3PqF
zmE)jw$Q#W;6{1`@lbNNArZZCX!p(|45St7#j#MYo5Pws-Dkb&Je##9l<)*|%##q&9
zr?8kR!cvWKRu&|4Og(dbQnWQIV@~qOsY~WcV%TglL&Tu^Qei(+F&i7=aMW;Qy{l?>
z#LsVC7=%7Hi+`JBIzGY=&<J`WI3obnz2Z8y4=b;#Cei}WEiF$WM?d0#<^@kZ&_rBI
zb$<`$zFMFP=A6x+pO?tp3M`S$s~34Jji~yO#YWKgiCa?6l=mx0^oBL7<IH!Izyyw?
zr+&HRMa)zs-q&9Jy*N`J&}d<QH$e{DNa*2a8n!20HN4HgfNp`X4v94QZ6#6Dz@WHT
zo*s1mp)Rydjgs~L0R|&yd={5UoopLHqAgFret?wb6_P5RLUeyL82}VbaL+xES^3Bj
zU2hiF&k<3~XOJLQzCZL~X@CbFi%0<^`7Dur9*ANxgLMR6^L7x@!x3;vyzia=YjH85
zPBd|Lz$Q-?lQYyv6#)t^IjUejZLQ!YrPmN^Y<or}b%+3zUlppqiE-IUOh?vCkmgVS
zW)DVizm%9j<OwKlPZN|+1Iqu{TX;%8ko6Nd>Y<9@Ut8ECPD)1F<29Jctst^94}aEO
zkD}r%q+vL4+xnR%F(<0(!sh*Q!YEotPg;Q4((&1{Ys3}^Gr$vE{w2hE%Om0z$8RuA
z)1XiiqAH+NYjnz~l^Z(Ji&H8~cXiYoac(}Tsohh4&UAL)ctGhjQBtD8<gsO0rCo~7
zuhG^)?^cE8@$ClpJjhv$tV*kN^UR{pZBlRNijcBCNN3C7lD>jH`zv;ERklVPn{x7J
z6VB9mRZ1rFq*LG}xaLLfuZ}ir97V63xIYGkH9KXv<fWiEru*=>aF+hXEvKB8NgY{`
z8$N$82PBDI_HXBdfJF@%p|VE&%V3htD4sJBD<Nx0CO#{XH|BJ>SU;s*Mb&}^rI$Gq
zFd;aT;8DCiN&f_N7xEnLDIT0;hBS>{BWotjz8uB|>=Q9ocf#>zb5Fqw5=@32mFdHD
zC<LptnxS$9hBk~w?@3lo5nDyz+N44j)k^_3Y&Mw3qu)lq^}1cFU8_}fwT`vQS#lI4
zDfiMkq=4ltIlWC^zj|1{4*rcE1bJJ&YIqpD_WliCFj#}EQPrmig2*sj_j2}f3?d&L
zuJ^MCJ-R)<oPaXfGco%1v>nP9OvZux!I;<JYp|mEIM;qC;2Q244uh>`%o2kGFA3S-
zh=l_$#Qxgo+DK8q-JqS8VT@)E69bT#JmOE)1`zf)sy?9>NK6Jyo3gYFS&QeMu(S{{
z8!9YL2gU4<2#D97Fb3kYUtuO2)yDMfm{Q3_#BYb)T>)HwO)Ba#(-9?KX}Ok7s_L><
zaE&K$4Sx%>zTyOr9Z;OdITL#B=v=XE!+>?xAUE;AoliNLY4l{Er;&kOEb)ZDc^<>~
zw3+b^;fhz_$24~F@kHdXsw;&noNehQjXk3ESh(uWBpT;oz_zd^O-Y!Zlmt(|a<!Hk
zQAffpsTtp?*`As4=&h(5W%#;lHlJcYU*tR%<nLPSffW1eMC_3iNcSe!bqRkl!zsh5
zA4UN|0bv_0Lq0>k|BC{i0^W9HWiI$6h_ZcxE`-$=3m!vIUKTSpxPKk%dZdr2_b;#L
z!*j0-Dn`4oC3W|8uTwNDQZH#$WFrerIczs3PH!F2Y$nFotvK&C=hhhZJxTwFhHk!Y
zmDI!BFW~BiA}lYK{yy6zp;LsZp$YHU_>Zyih?Mm7@B5CnwLa;geSloOp%PwM5A`%;
z<oWJNW`h!L1<UPOnUjs#Z-3#$Gb#uc@x*aMkoAqC4-i;G^{t}jp&B(=;H!x$#1kf5
zbw03mp!<PXS?5|?RjvUt*Y<^byC26!CQ}k29IOM~BW4C^?+ff$*cEH6VvLtnQ><b^
z%jLhU_JfW&CP;lua{91R6ei3Bm}fkmV)v#QQxq!91(>Hi+~ls!s`jQa#|rXN6h=5Q
zJ>E}P2$aovxanP+h3`!xq$pT`G6iNlv<e>MavpD@_om&(3Q$rMh(MW~U38fXrQo;<
zaP9JLRG}@Hnn4E@@t>kSnEI}T*w?{U(qq$J@-G&uj=tnr#^*883S)&ZV}(*<g=}Mm
zVq*o1u?&ud>%vL3>->^EIj*r((iAR!kd2$!+Rbz;L6+;aU<y}*`PxdE>owL`>R}4k
zz3JLYg)30`dJlRmRWpUF24u5my2e-RdhIZlIsmfC_|y6bXHA?1X-<zgbmGaMc9bo^
zN=^^Hs&R1@rDc_hbJFjaluHv?ao*phk)GIXCT-0Qn#v$P?~}pIYg_EO-<=J6XpM?~
zap^<Pd}+G>8DNv<(ua}hS7@Wz8dGQ@A{OGc6<3-(tpqH<8BN}JWl2+KAa3?$X9=C+
z2-WJzwh$#^n5es{*q&7bGQf#5(=syc(ts+oEOhIpTBRpjhySz!Kotg?@z36mt9^`$
zH6t@808k5n;~!CiT9!<m@=TrT0(HE%Uyya_Qy{0Hfu){u>S%Lnq9MwDNguN^eJ#9b
zgCC&gkiGuJN*1M>@?XX}$YaftIw;Eo4MYv0KXkB`38pvnoA=)JTjKhwIjQtmk8%F|
z4N@clq(Af{uuB#re{o87+mZ;V<1+RMDAJpJS`;Q_j8_vXId@w{(|Ss;mE88-hT(+d
zvsNlzG9J5e5ZM1-W>m(dBkt&sy=IiD+}@&afY^A8N=J%+@1w&9&xQ=QQN_K_Pu8mO
zhCWr2fN|mhrtPuI8&)7|j$l`s(3}0W{gZl<`k#)e)|gjO2g;Z8XkM)tUTdLOqA{ZA
zQ`)PbqU--6_5EmEo=DL{KFqvp$+&SU#hvI(z?HzYz}X$}+T_M033#w>v!>vk{|NP>
zxyNMa^~VF9ca+m)+I!F==(T6B=Z1^|EAL1)E+43vGA?Syg9VySE=(R59s5ChbtJnp
zn><(oj8-?HtQZ&7O%L8060O*XRv%NZdZcu~c$qJ}McgxRs4^{K^zv<*(Ux8>dq&KU
z9Gz9`AH>I?fEcB*8MWr#4SulKOp}eK?~Y<Nk{Pd$DHHXTjV~jJrJWW<UKFp-TbHkg
zcr?r6O#(!WgN&=&EGOznZW>BSj3oMhP}21urY%VC_o}30eLGG|l9uPoe7>gua?$~H
z2OqquGcSf;?V)Nu#lebYw@T{zQ~T|H@Bc*ME*{0tYGjBDR*4Y_WPi4m+8M;K{{RS^
zS>KO{$q|hqmJ6_w3LqKL-rnJkrW_c#q)f&cF&bnOmPfbR*^L>DRv%;(rPfUPL{2v{
zM7cKB*BTq@+M&1>gv&LQ%I#j$DppIy+C%T!(n=zgKS*!Nr7CaKZ)Q)Z6kQP=>v8&u
zECYUDDdfJxuRW5206tH=Z+SaX8sEu48${q1kQv_+Ks&NYHyK%!yp8e*qKX`pXxz`a
zQd}cTjC7cw+re77PP~#@^Umu?_L3IpdobCp8Rb<U7gIKeI=|laxbf5#h(ytgVr3f%
zy%Kv`1f;&oT+uus@4&pT9$h!=pw!zGklDf9)1pnhd~NnfzvtQ^YJJmuO}=l~`PSOO
zyuSBB@`!qVK6LNmt=B&BjBo|JCKyL{)h=HyvP7Khmqs=ec~Fsfd}tFzbYK%lbZ8Tk
zdSH`~dg#q0n4ih$aOh1b_`5^Q`gez_bzUaj^?^<7@_|j_^5MRW+E;7UZO?+0;Ioz;
zmfAID=jk^UFX?sGmm6dwiWh!+v=>TEQSXWsnGOxy>2<pMN!DnZ8yO?|7l5XCCgBh-
zD8@8b{7RSg_%bm@x^`})o;z}r{UDVzw*5x(N#*NgcUe~V55_`MaX810#mLLvAkLXd
z(aDwA9VyJ{48xaGuMoOl+bU|4A;T%O06r@|D|`{pU4a?(v-d3hN!k(UKQLXkIh6Lw
zJ0p{gNs~T<JL9Q@JGo$Ex=hN4HyJcHvu^z4-YLqUd8I#J;Dh~wNr>>O<SGVpuR(+^
zSB5pC9c%7c{7NZr%b6!d^n!Di+6f92M3kT8-G@yf@!A_~k+5s~y_~Ng92QVeaTYs}
z^Ec1@m}6lqXI=SrRasLnmvcPX&GrtPf6tbPns^pI`eDrS#&EdnPSjgbj>B<4f<$CX
zq%Drt8PvYx19_|4qPdygrG8oa`+bHs-SDrM!y6@G8^&c_xwWH4E2pOzF{C^|y{2EC
zY1HaT8vMYsGdt*4anO$^^Tk+_Hz**9=Y!oRV(^)Uw^gJo?Jc>Iqc)k)bjRFI3M*AH
zdxj2&NMO$IUtTHJM<;vyL0J1qz)RN6tO-b}hr9r#P5)6Vc1c_GQc@2k+Fq0C=`9sM
z)>|GQ*5m_e-|EzlcJsj}+QreAnZ2xgS*u-G+p^Wa-}}tVB{)Q9evhUsJ$U*oCN&WP
zq)QXn6ir58?OZ;~v|y~*o_!tV5S1S8G7X+rP$NW_@NyiWHg_#54D7of5+bHMOP%Y@
zXLqMgxysqR_F2G|5Edy-39skQHB-qk(k@Feck&Qu(&BnyDVlwQZPjf)_}Bh~^l42?
zSw&*_i}SaR>G2;tx=c7SQAmivqUw|xrX|dn->7rZX8f|t#9f6&DSKg~Rknmr>KM(<
zPE}Rwl<S-n%g&yQ>p1$k3byki+d5XxOP!N>_l2OiF+yM@MQ41CY&-i&8&_?s&_6TX
zJ#^wZMLgP-3u*@+&y|Z?8eAn%Gc6qq?b2t2$;fHN`aaCe912T*ho?#=@EsfdYH1Xp
zPX~OT@dH*)@Cmn0T;^s<6-Xu3$}Y)1CEb9`xDMHdxVlY07%=#<BRimn(r5&`ln&6I
zud3g!3&TDw^K^ZKM>UPSPJkfPzC{MQk9@QtfSc8b8^QoH4D2+fMa9}nPwm#GMc3HN
zkB`7LeNLnnVHv?`Vo0`^D<45=qSy3m>c5(|9NTgYwLT_kE4jaE7x+W$S=jU>d3{s0
zcZ|jSr{GC8^BCE)sN$xseX>ET@VwU4R$bG{Q-SP&$}@XHMV3ZI+UO>J_>f5TD~+lm
zX!a}ftRg?ilnSH>tozDXr^xI0mCjL->m`fyC@=FS)N7jTXj*^^?M24g@tZavAu49S
zG8*kMX4O8549r|$e5uF8RT9J~JJkrkf6e489bH>l@%1i+*anae`9=Ip*;k5FsYaPs
zy4yp8bEU(OCtaMUx|FNKldIgEo0{WlD_JumSvDip#vz%*QN^|R{7H;cuU^6}FY%Tl
zDQHn!-2r3Zwl(xiRcd$KG{!g?c5pIb8Dpi~HV5>xqo4JF{_1apsNZI8=wLTVV6?@y
zk5WxT_ShO>fMPxl1h8yaNRS-xi%BQRe<;b9+@BmKI%sROZ$(UIY@T5FI1usyhArlv
z0OGXE*DE<77#;`B`_CR3$amvIgS5`h;m%H;(Agv(A4SqHKs`fAh=CA18h+9%0^4U%
z*kmMfFJU7^c0X9yp0^ImPjC46Qwt{S&@}k_&p-fMrAC!CR0#wIs%##O=lz~IBg)^=
zKjDjSPV3ZLE&qog<`$OqT4h>gv-On^FX!GY87;86RI6{%$+b7)Zd1#GfT*t5nCLJ7
zR<fZnV1&uAO3;v|WK^6pIsF8H06yUY579eB%k0rMj8b_=C-MQvT#dZ0hG2FYGO=@i
zGKmUnCVi>?_=`C_8#}u1GefAdY(iG!OBOYaA-(kQR9uYFXRCF383N6S>T14SL%7HA
zkhsM7LUKlBMYZ2Co_P%MMxf%sBb9hip>fenZgZ&^ie_p`HEJExujZMA+d`^iWp&Q1
z44Ubvr)mroLkQt8?G>_|8FlKU24_LArLgj|tW3&~8b@N88O8FSsTflk#>I%aYJMw2
zgry}qo+){!?6wnGgvDPG36knOL)JeMRu3uN2P1ECOHRbN7E{lv(fChbJrC)hCuQym
z8i<9+WWJMo?vg(Zb=@S2;D0|S7#0GD3EOk~?S2ec;1vg`L>MFykJ*C7+l`p0cDTid
zFT-N6@U1Ro4a3rUHNeSs_DI{OD9Xv?1?-CQCNXJUhVEms`GcCW@iob``)sRQNh6=Y
zk;(P~H9IhmA-_fwLX)WQfa)yz8GY4pkM;z}JNCH4I84>}YSkL$2Ep26N=*SqxMNxg
zBaWp}$C~t9mBZN!gXT@aAbX(2E-t~4+T4&;Y2xxCxpU<t<-!ncQ|OpI*S8(gdM$4E
zMcVr+oimd_zD{}MZSs~K3a62tlW2(ri{HdXnSA|Tu4%AW{+~9f5q9ZOho*@WOt=lY
z(sm2^2H0IQwy$95n1Vvn;u5)OSRJ%<H^boU<7=)6_jeq}M~Jj5p{(r{*S2C>$BbMh
zo*WVxuSnH*J<Ep>w7CRH?L`E(@;yeGAN`m2MwHJ6O;+MjS7IYp8WOoLg?O&NHeW~z
z>{mT*3}YSkD?j?0OzF67`}p1TD!Oe_x7l&Bde}xc=dqSgp>bXJX5s;$oC$&gKXp&@
zee9PB*kBg9jMi`EaPGd0$9Nc${A(2FA<O{{2oNHWiSiJuY{HNT@{lX^5J?K7ThVrv
z@%NC(^-zeDC11+8_Q8LQ)<_;?d<^Dxpa`D70^020Z5dVnp#9YVh<CnXJm2NQ91TAA
zh!TAGJS`a~_-GjMms09=ujX$L-eaPn!v-1slq4wd4a(Pk=<Hv6-Qq25<92Zd-uBZ>
zL_Q)mt#6LCq#8PO#4<u-N}^+YfKv<=+sQ`%m__|1o4U(CR2#(2tYsx!{JAhI0qyh$
ze5p#vbhbwE!m@EZw!3ocWR<bAJEvix6OWbP4J0G^ZM>%C{PbR6;*kW(x6A+TCpt0Y
zCUtxv932(;RS5rSKhv*M1laFQsy>db`~eo>3meRD{9v}QFWi)Dgwa7?I1JE3xqGRg
zgC)NJl%k3!*l}^EzV{&FO-1|2Hb(!l+#$zv9h87ZQuH^7%fdFo542aw!c;93N9oVO
z#bU#V{Tk>d+mFqWg`sSRlE0}XJK&h)liWyJ!$!olAwJrrz2fiMNYJpM<^g+cN7Nn+
z*GPhlV^1Au#~r!nBCj8sZdnrw<W2{ILmjwN9l(*u+?mKtL7`1?GL2y)uEgs0aO!rT
zvR-qhZi5xEl0PBV91yTorxw%u4!K_Or4Uf9pOXQ-R$(fqBAV`Rgh_u+ViC0ZELi97
zG*(@tO9A^;FsHTf-KeO$hJ5~kW*E%*yECFPhw_&BI(!*uipX`7{Iv;gMu?zo-R4lW
zKdXGcRAB!cZeIv@!Zvpb7FY@q%jHw#u8L(3WFb)1g;-&uMI-R#fwUbKt6E6?QsH0<
z$C6O{_M=WbD*qMOGh^EY#byKT;iFebuIX?t6GJ{jaSr%L%UXjUPf_CCv|_WUp3=`)
zu<H6~*<Xl2U%{jE-=e6XusBJnM5}5MH=_>Xc(P$LFqO^FGVL^WvN&*r2~Z-Jx@b9`
z<Qz8daYil?M>R)j5mPtxwD2_ZL}``JKOE7(ylm8RYS1!TUS)9#-_mzh+hIdio9V!l
zRyF>iVC2vVmj|zL0cR`ZM_;3ob^_xBXsqr}9mn-8+T&G;9-%k+2J(jhCl~)S;l%1Q
z^*KCSn1TyhmMu&eY%%tod`pJk=EVg~wNVKu)n_?W!%xnH8`P`p>Kb~=bJxQmC8hks
z%`$g`(5{MJ)D<I|M!)P*4S=(woS|cc)7JGVT%)en8BHhOC2HX;vS*D>YXqtUl+_lh
zoU*N!lZUm;)%A?{vq}Ry`gy#AKu?UNGI8<mA4eh0p&bMP1ekL(dD94sOHT76j=B^=
zx%m7q%BA$Pkioa)H_E!BZ#)6FruXZc6+IN|;C1Ep*mkOR*mgQkg@?N*&ckSapR<jT
z8}N^I!;Mg%k<L!`vFj8%P$cYYB8S9}a_3XMwM>W2)z^#n1r_>uOy_)^ywkirysO{d
zyrLVPUYKmE5}#h-Z_;OL@0gEUYHZp~oYzm<KTY(nlik$1Y4MF^&`!Wi7%F7YJ#*1P
zoPVWH=6U$v={hd{`u&;p3Er9Ov%K;{{D)+lX{MIqnqO~X&M1DFKwp!u^M7pIL2d}B
zvpeQD#U4ry^4DD*x<1~|=j9`U9WifK7t<nGw=9o!Tf|z|R5Q3o>^}fS1DL*!74b>g
zpf<&0c8wR@f=%cjC97#i4L?BI`Aa6Tgk_@XYWV_;1ICt$1*-66`NZD{`Zt7)Ew^`9
zdKq14xULy22T)IT99uJ%8`ICXpIu>{^tqgE>6|acy?(a%t!?S9^{j8%T<dzp{X%hb
zjk`-Xv6Jk!)1Wl06@^EkLX7z=<suwI11CYP+P_g4))#89Nh5_t#2Shv*vW2ZrfMXa
z-$)}<O@lB%qi|*S#r_h>%U9hFQ&3esJqG6Qrvjt+xJ^6&s!6UkL#|TsjDgKo>CB)x
zL1aTNZbuv<0At?})9Y8mWyFjWXk4aEHi3SMJRG1}-qrF@CmU-qSpnY~gl5R|TIhZ_
zVNdev+p#6$K@GH(G9JEt&@kS_N@lj$fq&Q$e=v#|jZZbqz{L#OF>d;1O+iZmQQ9HZ
zEO83X;|@v0HH&w&XU$U%kOJ0AsSPyGRxidul*aIf-$x=QTSjfQ$>>1etD?>@5H5~V
zG$M#<w*R01svHr+c{*o6edD+lRwCJiA<j)&Uxvb|FwW?s6Z2wrDUJ+tIbW$b)WbNI
z6d|nHF6$Ig$9^+y+F{YuAF;x>)t-Omd1nn;b}3S1m1V<%;|q4o%(KM7pC^)tV`Y?g
zHDl=lcWf&al3`*Mj>AM+Vx4F;>Bo9jjT&1%IVWzO0akPZT1MVFTK>YiRTHKBYmAcS
zi6HcC_SJTr1bXSq$VYvVkLbqunx&s$^aI=O*HRPYoDZwrh0K1pI2GoV&G|0*=4eTf
zoEJGp?tjy|Jyg!bwSOFM;(~6Tc~eJOp}x<!=Iv-3_xV-DiGhosIc5-D+Q)?3`Ivx-
zA@%lwxWM@0Bb$ie5Q{lJ_Q~1@Z#7H(E^mCsls?LcXb6*g0?;D@foxO8C4uh&bMt^^
z>*5fPtbJR*Lb)UVMDmd|$@DW4cmAm)Y`+=rwC-EMUg%+5dQPW?$O}?H5d&K*L7Www
zL?PzQl61IreleTCX9%>~v?Y}OxIk~n&zj(0NkTy~y-2dO7<Oq;foOu8Eh+lMjC5hg
z{tl(1Y*eUvFy|Ou3ZlYDpC;@=N);GLOOa6-v@_e!Z-DCBalbZ)eQoIw9F^UMUf(xv
z*h`G-o<<nox_mWv{c0#zig0ivqk^yuHn-s1&9^C6VR<@$oSkw}ew*?yzhbicsfJ^4
zURp*tIJBmOVQqh!qG-E%SwD(fN1~7sZYx@CZqh+9AI)0D%|!OmnUgWIui9TI)HjVy
z0!n|U1*r=BaksPb;|1Lr1>DbX$9YzBqFi)tuI8y5=hPL<Y8m#d6Sh8i@j_>ZI4qUj
z-AUhSb#>92`+zIcA%NmO-Ex`$>~J$^XtQvnvQ~BwIeDGpxH=pd3@3h3_xGa<26k7b
ztH_#vyLk*9eUdwNKt;?0hR@B|n=+P_d81RBD%d^sf%TD6W-z8kDammJKjn8G1rjry
z5cI~>g1B~j=O+nd8oyk?cf$ZGo-MUG?PM5%uF}RS#^2(bq-Q-iiJx-wO2?6Hu?HSG
zd_LnfNc6<pcxoL*t83vbxU|#3w34;Zgb`jQFzCoWu%q-bn3XwXm4N3JPESkjwtd5o
zp0=3uI%;P)GDmM+Uxf*nk)-N5V-lrrN1XHS-$2I=PU;vF+H2e{oGM7;-0rl6q9fFO
z?79!@M(IZ`5zazEfy$MUZ8aS5)s!VE8&yAQ|5h@YHBH*h{q)>nF{k^i3D>bM<O+FS
zxcw@Rj{1>c{*#D*oh^(u6$Skw`HUJTC%lWlqx$D~oK=0&SGn+bEsBIzi@Ui#IT|N^
zubj8swPtgtc(-G{0T``QY0k*iY^NSO{*6X}vGyRXf3%Q~rqErdzYw_tB^Z6tc7xVi
zSVN%+P+cs;@CU7QQEruRS=(U#$oa+s{R0C0Rz>7fw-1+2E2stelKDIMd!7qzoE7k)
zUq^h4awT0xi;x)}23?_z>cf{<*wCMeQEuCA>%Flr56`ZC%;bSQYaDS3FJC`s7kcjj
zx2da~une{UWtuL;8Q^u|BLoKwPNX|!M=TYR1wo(GjDp^c+v|-_t?SWG*g=|Pzn@p5
zKO$obp^6HP-YUD!KZRz5Trx5a*@dgx$dTj@I1i+;E1v*%IJw*X1Cy0FH}sOO$)BwE
zPbRYbZaE2MXcIzwOTlWE?Ii=_QBhHqY!5jcLU~B&4A=*{p^J_WfJR+z)I>B<JX8ox
z&Woj>)<m6%fbNG!{2{gx@+f=1Ao~ySJ{<F*h5j13M|{fa{~rK5K*Ya$wo4t+b*8rN
zY?o@BA6*@(uGYA@#Z;@Ksx+=x<E5Vm)#|~YFMWFH$Gs}m-XAYH_dZr15K#YEFZGX}
z^DSa=%lRG#hi4%WACOP8AdB(%=A^`3gnckUZN)4ciMKbOewn_f5b9zPgwc(~@&Xbt
zFEmgw&dZw-;1QykXz+<TTV&;`Dw4-H#=4EmyDqM%KHeJc8($%#y0MQoU>0#Lg{j`s
z;%G15q(*hYiJq9?>YdrDwQE+%sT_K~qS@Q{Z*V?@JbKMykNan1)bL&E3xVYWz`*+0
zhFG_iYr;x39^<Nxla=Mi+Ew<l?C{vqP@Cw!)8)mdSH?|v#8#<PRk0qvsckXq@gR@L
zd};qWeneqN#_mR?q<Xl@D=#a-mYYye6<Bq+Aw*I=SQ=6o>Bftyii)a;a6z<_K)j?t
zx-OzQ8CoNn10hH8%#HAYS6`jb)nbplq+0`CvIhSiq@Dn2jrb2UA=s;)4DN>^?fu~M
z-Yk|qVqjbW49i375{R6DVOePXMmOWJuXUGv1-+>iM_a-}+8$q#Gt!e}a^8Grhq@}p
z-6Os++CNI_XN!G^y?2gk=Xz>ZJ=t7)VNG0M#ZXoMDq&V{VpK<Vkf{xyL-XD`IKMr4
zfyHA<Avhd*j=MoXj7W%Z^aW=DlJO+-4HI1o<M5+}jU!cop>3y|JH}Ro+A69KzWd0R
zfq4lbOZ}6pWU2~{8&6$9UKmN)@K}LHnZEI{yqc#rXv+@2Qkh-#+W6rWYF&%7G|oG!
zY+t1~Ft;ZzqPsAd!HpK;)*a#o@}&vjBk?2Lbtj+4a%XPix5(H#S4f*7WCvQ{8q6TC
z@L4ie0+Jy1Le8MA0x(zwe1RFKjrP6oZ?~5P=ST2k`p=Y=o$ZTpRptekwy4@h3kyfu
zRfAsf)e2=*tiU6-Izm+)D{$pU<VmG@5q!QXPbSM#@d<I`*#Mi^{04c&aII8YJ6s`e
zD2TNQINMOXuSzPd+Bb7YUKQu*8CNA&mO*jVlwp697DReLt_uw=fa63I&~&MooyC8J
zYoh7m4gr&y0mE_(hMVvK5=-=zWWgdp<o@Sdg+%Dll7^AWz|ht+&HZC7VoOQ!@F#;7
z|MAa>53_)3TNVN7;u2AQFm>bkB27l-<|lG%u53=OJoT66$l@2CKGLZ<1pe+MjrEQ%
zKTsnT=l8`$^p=Fq&awue&TAy1SRSlpzpz+FtYx$R!`tEKUQn-Q!p{Zplh5#WNHen~
zh=#GO7w{e;9?=kx9H==sz!+<rxrqm1>vb=J-rb<*MYP@_umk@Pe-l#Eh|#o2kK-TC
zT*cMsg$s}BCXc}J23RlR9}@`-PoU8xxV^3(&zd=lZ2|aIYG60DVG@p)F#9v!0rBU~
z3WVb+aN5l4Rfqzg(;#MmgD+6spb}VkMg+Jj{T-TWeN&Yl<3QE4FS}%4mBc$HFF@uo
zC6h{ngQd`g7>=#VNDkyHBa=k6o!r0#appi_prm|Tc2q}wv7cylJoSQ~T<+%=7Dm&v
zjtXPHMGmr(S!EC&GZIcFU??hMz~SO-h680%Xka0r05f7+Fwze#I`m!KJm7a0Qa4c$
zVQm4}*7P@GnI(NU#(o*M_KwJ19$VCHE8Q9&->9)OS(4JRODj!}3~>;%tpp;Q@S+HJ
z+$3sr`iT+hsTyMw?eU+F_~vIPIlJ@o%BzG|srPS`nV78p1c;uU1cDcerhhg|&9MuX
z1z7>1Z8%?AnkWXp$X70}Oz_D`$xK(a9W7t~*S#s`jvm&!_u}${%$1FMGoAs0n~j|x
zQ13kb4E5gIEsHsh7VXwN?&38+g53At0EM$V*Zn}9rrz5=WbL0IVdLO`rptm`PC*<w
zKo`dm%DWNNrnJ8g)hHBu9DhSMy7@Zhdm8h5Nv8$h{|fr3_2k%{6wD4g13Bgl)DxTF
zb5>AVd|51!IgcjqoE8A^1HB4$=jm+;?+p03Wvn-1d4;?<!d0{KVk7kekZbDW1umMp
zIBEj8DxjD?vV;1H9P=yQT~#&WW3J9?kynk>OR+P$pWGu$!^6v>+=pGlvt?b4w7%+u
zdgvD9Ib_8dXLHVobNNCXQ3;Bzp320nCE86du3PiYzSOj#4>tB*-H^0+YOyf2E2DHz
zd5~Yh_R@lt>B1%8W;ImaB^Q1@u=~Jo7fNH-Txv+{$r2`Pyw<+A{@HD*zUkds#9s&p
z*C7r}k+;TRfbs2d^LelS3^=&+_L;)Mb3YBf`s(trj<E3MryIzzl8e-6)+lwMn6#MQ
zny}^huH60?Hbyf#7>yeZ$EE!%bL3&sFZ^@eX6%!ha(s`@59`E^>SjjBu~Eu*?iqjh
zj0yb=U;o7Gx{*nIKo^RYW9xMN=x#H3Zx6)T#}H@!P|MLc0|-_N#suAuHKY}Yk2&$(
zW4#+bJDR86_u;nwXV)j0O)mCN>&mFwTjC$yc%U$;F+sSLI*%>8<`k#$ExG`}@wVfD
zb+IV2^F(P_ZL%<a%Zt6sS6<%|%W-yJ24W7)t(7!p)(ZU~1UW>cxrj8L(z#AfVxLaI
zbrzFjy6spSegD(&M<?Uc!TUv^b8-^C11t&jU0#P*T;vg$-{+y=i>IP`&s0>N?unZ8
zj4lfcDU0<a$EJU4c(f%{RC~ByQxW0p9@`Kz2NMa$Ldw*LhDZiP&>CX)7bho2!N32d
zdjYHa2X=CtvVxEWjA1Ui{+Ykb)6M}t3*!cNL<141!!FKjn;L<%(tpoRV|;KEOauqF
zgyLoie}kjoWAO2BlNOZg9*S=XS2bNgoPhEs!z!jLi6Px#-3>Iv0-Tv38~_9$U@*p?
zn!-N2MwCsD!lw@q)sRO!W^a=w^L{Qy>%Rr`1{QO^?i|G>q)GbFtqp5#9m>iYzO`n<
z2M5xxNvie~=I*Wz4yoClTeQ1UjGZ|_=`NR)Tn6}wg5!X^Tv~dWnm&%LePvU0^rlzV
z7Oi__b9D6PSJvs}WRhMpifRyZ8eARsIVHgZwUd}_-_<pdk!!B@QICS$jBc%)o3=ZH
zdK3%QK`~Qz9xtsu*%CJ5olva^u1yJ`b$sLOB9aZ)X9FWrv_2!!$esOXmIwyA0kMvH
zBQ|Azox|yqlFB_rxx3&xs}1Y?3-xB!>)k(D{E4MPcuvE$-k4h}Er~${(u>f~1M{oJ
zu2E;Fz?Z)OlRfJ|)=ga#5G1Wg61XRIrBk0_THPgb?DO|Fy&9z3zXbc*C$2n1Ssm|*
zXrBUQw+*gWepkMPwyh7ymOw-hdLfYF7o3v^svFMts$<q(Zt0y|7d0hF=}ObK#k&d8
z*5vg)xgsQX!!zxX1HH9LzEff4Yhs3uffi9xM2M4fa`pPm%w6^2@jXW}<u!@kZpti?
zFi|D7<!3ewWajRylGz5um-WOY)JOCBXdZ<g05M406+IT94jH6;uTg$P6=`wjJJMp5
zzV8vZp0n^iTf`X#v4s+6pp!XqHfz^pUGwC&)T@Cxy$OkZIRWI@j7RU49=W{dN-uO*
zJTm*!Jw4O=Gw~1gaW0%&$M~@Bv^f)DTfvccbYGp-{r&A(;=-L2MiwCs+26uT3;2c$
zMI8RHM7Il!&@<p~4qm|J7_jwp{&I>se4hF%mrLC^PaT;AuT4IV|2ktl_9Fh(ofN{o
zxq12{S~TI9A#m=Sj6O4=l?MI42wM*dzl0I-`(IJR)X?9+ElTtSz7`YeggRF&SoaF{
zDfSNi{8IS*Gw^w1`gtTQ@Zn0(_aU`tl3MgJ_Ad6(OtbC_%o*QB-=78V?_qQS(l-+1
z#&JLh9x?qf?lki~{=botoy6MFZRp<J)GOFx_G+3YP-!3wfi1pvjoJvh$$$OUHDq*!
zS%W8xn}Zo0i}2w&hLwzwW54|ceP=?w0@g8OSR#KBsxTAKLk&z#u~+{ZJH*}u?@?hN
z;8qO&f!?7wd-XBkvkG{hBc7q0u=jOdXe?s379XOsN-Pq2o<lPY_})P#2M|Ks0w0nT
zodZBae0Z3mK7*fm&@z7;|C&gGeh2jPctW7hG9f_-1NQ~3=2q5R4(-iL_^xa^1?9PD
z31JtZ%?nE3SS*f$!P=GOt>P@X>x8`Ja9Pg2x^PQNYH?6btYqo%;m7PGS}QrvKG2;L
zl(ix<dqbWuv$Wm8U*Q_smLKAmzbT(Gk#+kn+veaG>ilVWg&-=>5yFW0+65wDL4LtQ
zV1#UmKGHKttC6`A&LkLIf*TH1)E|<XoE?`ARYRvIc-$$nAxV@L5nyf+7?CDQsEcyJ
z%sX%FNlS?CXR~xKM8>A<daW;YM}yK%sBoiv-4sGQMcs~6h`$1equUTiKKe*T^n1<`
zrU=Z__z>rbnB@nudY<YE3+sBar}$v23VtL|=LcuCY8)J*+Sa)QYyD!^m4{{TeX-HI
z@x{H_;k7%{gR&z$qMElxd&h2VilTLLF`WB9hP5-9QEM(-IbZL6&zWVJIRgZzSl%*H
zTE160e%1soy?CTWKF*77NC-?)23lAID3byb8>4xcN8f9^QX-X@_zr8iIy!C7jn32^
zbqcO`C?EX53-;nF8{pF6{DpA-|H8F9qxs2&t9ArDgl#Eu?&;{7ClTV9w)N??(aG!2
z7A!yADAw^Yzw{OHWt&8;cJbw_Yl4g715Gd|+)-X)m9%xT&3pY@+Y?f^Ki`&~u(l*P
zMV;fB5XQ$}^-5ZvU?#205JTLDLm*ydbD&LvULjv1Vl*_@2n7vnM>=8g&vWMj3)ZKr
zl#4hLyS%c0{u4j9qb+-5fxr2Ff~+lT+cA^L<l{@IEc{2f7B7}^9+$E?4@V$lxJM2i
zhT4*_=F#%%A*sok$ez<x-i^&wZY8d<aS84bbqSu*eHEo6&EXR~4McXbGC+^)1_QEV
zqq?R)seiOR+|nmj((E1Vy3{?sKBh>aHXz>yA^$sGh&O|LsL!D|e+Frf{4hPILmfu*
zI-B;TfF|@<Me^#rz;Opzsw8id$M`t@bWDm*@xD4;81}L<H`uNuX$GZU>GdPURRirE
zjx;0k`0ffFGsnkYF+_(%C~)>uDE$1w!&%tuH7Wt*!snJUJ_mifK!QR$sl@!QF%llx
zVW;9+`I{>uEM*dUEGZE>t=T`i$apV_N2U|c0B2t%Gcc{2AWXy(2;jZ`A(&V`K3)z|
zzo7ELdmn(GyQ%eT)=Y6HsG!1iTbO5|+Y2(;tU3P`t^lb?)>deXx*(msku5@qv*zA|
zKBOuZViJUs!H4T=kNQX-@5mk~3>fEyXNf~{l<wpNFFaFH-sLn-gm$Up)AN%`{7Uv#
z=|ZqqqVl91H+j0hE{MT&mS*N_B9vvI8=RkOnEzhFfiQOHiK@E@M}%&QOTU+JXgEl>
ze5n7#aJ&UN;P)#Z`I=RiTic&r9Tm0ua$DQw)tU(pZM!ycd7?m|U9L@R(|TZnjvIS3
zGWOo+u<p3ACp~@7jgHivjVe{+&Q$A^U5ybDjk{9lxgViQ2q(kb2HlJ3#C@plbqcOO
zR8e(6Hh#uLb_nX;2HCi4Y*V7YR)J(fkra^780*UDUX@(;Kk~z_s5#wh?J0B7O}oeh
z)~beGsYw4(CHVLB{9bp-EZ_VtA(hUnztH9*RptAo#!&O`EvY=*6b5H+OepB_unbfr
z_$4;MxoLS+D;w{4U__{bmcLyMsx%d5;VkyBhHFn{>FJ0K!nw`i8(QBO%`M!Tiw)X%
z*_CuVjX#NdugtR&FT(c{ct(tlMTGW8CYg}%d8E6afzSVG{}Qf-K_ZS>NXk<3c2-J5
zYWC)r9;gqVa0<<kg{F#atb$9m<-1G#!<vSR8cw!^j^lyxIlduTVNSr=N#gHmZYhqd
zh*Y&_izEfT(Y#0r&)zS@$tgg<Gv|hCE7UO^8UEqrt0F_R0aiBNA+#SddmGClRg82a
zjjBf}M~`Bpcg?}ZS6{W_`Ea=+GwbLeQGS%?OD~Zs>dp>bskg|%#01~ZHgk+>PQ^xd
z05&6^o8bCChCIwgu`#&I8-?_OG{ikchniB>7Kz4PgJV5fH@l3F)rUv=l^$rop3z0d
z70WnTvAE^*bvPCe%G3lLYu*n9KY%6AQGMH}uIHw4<@9w%_K=nO3%rE-#2q<YNR^1}
zfryBIYn*y%KlR%zYd>}U%DXfBX7RA;>$rI4bs}`;J=_~U3posb6+Vk{1(rw&EvEfV
z@W21uqDDdEdw1@<4;rb__ptL|tM1>rPryE^9^+$3%vsh{0zL+xwWpuer%#}gq{*oS
zaFp+6u2b5j0osPR#ZFFkMugc?Q^G$s*vs6DTKOtiiu+7Edlse22$ID11NhIvD=Mgo
z5W=x@AY7N=*mwJ-p;-6KNi0+MBrYBt+=Sa6Scl+HQM<8Gww#3m93jrA1WAyTw%W*2
zJJQ)@K)WhjtgC8s8sA2g8r)LBPupDO5tb6jNzTbm;RL3HQM(<Bx^^brl;$X0cdc%G
zGkHgMp<}ycRDQGk&B|l`{>LkBDw^`6EE!s#r+x%cjGv4AVGy-%AN3>qD*c@$gsBa}
z1obgjfnbu#gn$MOnOr80<S_md)FYN*sezoV;^Hg>gh$opbXNpmaH`10HGOl{Gz6eY
zakJ)_ug|gCH|5O`fOf~i?j6Z*LO6N5TopOen@KyniX7p*>%eZ}d)$@fK3|m*&~nT%
zGI(wVo%MY{R%eWFLX1Wr&J0S*IO?C(5$mIkiFUX5_w}_EDE(}36YZv|a0^#4-(OT;
zge47>1zYmPJX?30CC<TGf21q7QbVAM&@%SbQe?@uq^+WawvcB}n?8TTU|+Gvrf-vp
z1`CUDR6DyE|BduOv7W)lWXkgl<xmYDzuV7DVEjxxxUer~qGWrq(7$MV$%M8)PmJI1
zX)0F~sZIa=jd|(z3#HyA7q*vLeEHcDP379SyFE^T>hDC2J_pE!tipqCok)R81W*Uy
z_h~On$cIzLPA=FdVER|B9pBo-_~a3j$j;*>rNgzs*F4o(!oV!0y9rQYO&>?E_8%Ex
zOJY-apNs&a`gluNNY##PWp=QwdrVCvH8X;6SUdZ7GN1T?u7xmK1G@SGx&R`T31oWZ
zE8{{QMwtMv5m->uK`;@?iO<y5Oe7Ah-5Mkflk8c$C*fj6#ghs9)(?t9r2#wE&y+;A
z59eeLx2x6d!`V5*?U5}?O_XBO#%&2tRn<*s2RH2xQK~|QHV<pZYv6-BH(|ZSr}|=J
z`%V=Xo$8H=={*JU<P7ojIOKP0CbooP$(bwPqW@r|Sp^zCqKZH;wHmCZR!>m_D7FJ3
zRME${8Fow;(0{Uzx(d{NC;PFtXj{7n%8-_pArujsi)@i3?U|UEpnk-;Gyle4!G59d
z%cIPRZb+jzR*}A11m+BgYymoGgCku~Ji6EHxT73CbB!Do^bFBmmZOP{hH+fUvwQL<
z<Rx8Uv8`H9*VK)bO^<h}inG-*n$=G=w_jcrHSU?zmXOpQ@8*=Wv99HbE~V~^yo8hl
zr<Be0ekpEA8T_;@;C#sz&%jL;*_(<3eJf7(V95<?2VO{$pHLGjadM0<8z?H=>J4v5
z-&i6JX*^mJT9xSQ8k*!26cZwHbdD}v|EP_<Wu-<G7b<o#wK21*)Cgn3We^8d#DB2w
z$(Nw6<|4mf-UoGGa9uT5?Rdg)Mds60jqq?EgY@Chzxsy!2gIiU#=zMRs$lb(I9Q+Y
zl1kj~;W;BzVGMF2AQcFdCL)Saof36m1mvwwN$kz?-(8#^lpwOR6D0%{6zV|H(GPli
zZyhZvI(n<O_k*KF?V^IUqfeEUJvFknKm@Nx(Df+1#xj#Tk_5q3JF=E%ZLbOuBy}W}
zXL>{x2d@Yzj`GN;TtD!Sv!$hH|1q%P?`KL%&is8t-P9=<hl`6ho|>wwn|gF(saRaP
z@zE)UzpzXAQo06?YD$Ls4P+60q|fFE76$t6&`L`fZn4Jdmav^GW5WxTyhk5vkSjIG
zM}1S<qE&T?UQ5iV6FxZ(^}<LWPB=E@>cJPeglCAFyTodZRJ25tso?W9)f(!0V2*FE
zr~P&d7cU#%AlB?GtBN&<SKyae9-!?G#WDhIAZnI^vb73y;#YAOl=lU+K;c&>biKF|
zbOPg}EP|B>>GgNG4tthAD+N{dE$_j<>>m1%b9qu!Q-Nf(=5qK#ObjXP!54^_W&j_M
z_b@87GxR!ESa^(~7ns}ztl~Q}{34XD{*I<bi*}K0JTm3@A(v2JJ4<hkXe0K)Lnu*q
z55?Z1p1@OGl%*<Vp^KTfM9G(VsJY8bJ-s~^OJX&v!7?X5CwGwxsx@?xJY21}u?Q<_
zofLbO<n2JTU5ig*eGn&>kc;PH1cO$%7awcAG=*X@bm2Us^2OppjaNU1?{9DK&$qN>
zeshRP)Xw7-Q!J4dYCJtPg;EE9S4&G*e+P#^H!CZ*Ks0A53*bK!|AjKR^zQBzy3=F}
zsHgTlO05KI9|fwJ=kQ28dx$CnqeI}(v>DxhqQ(wl=g4PRPV{<ErxU}4{JT1!c-T4J
z8Q>BrRfQ+SjCg6Aqa*9$1@?h4zGGkqcCY~~mFMsR<%uEE;uvopwEvzlnrP2maC`-}
z0~;nMSV43ThV=dxjJq_a$jX|z$p-+FjQCh(v_pWK)rfCWZFoe3)+bu6jrC7!i3qOG
z5RE2BM#p8xWe}C}0KZUE8&{4fHPqQLBts_4x8)RRg0n)M?L%_pP`!v06577^61Ol1
zeGU$}ui>9lKkXwa>O<5+0b`u+7(W>-bdLp$`9lF?x@&lfu%XX!;?yFawRv%^-Y{U)
z?XF%EYnK{>yG&nS9udR$pnS*=(Oi+xUsN%55l9k4(Vb_G4P<v-=~t=xuXF-)%s9IL
zvC4|a`eW{-z-RVg^`IM%L%#1qZMw(M3xs`unt}Y4N_g*bN(;`i=)45aP@@zWY>o^#
zE^(0g+6lC+@kXw4KRZi-!rMhma=qj}{@P%t`o%ULcD6ju5@G<s+JhYf-!r{CP}Yqk
z?jrF0$SqKAfgQtgLsqXqeAa_q#07@B%b$sVdjfJ-C8ey&_4m(RnVh^TFW|VfUzE@<
z(%0JBFB1OtvnI;5Ym3C<qP5x-!-X&@ga=KNFvX9#@MAGLY6$PbpMpB7-IN7&R6D4n
zKhK15#xC-4mH<r6euNFd-^+mw%NXAABK96~iQzpkf+la!_bg}KhToxk--GuWf#-22
z{XNa_o^PQHpyvnadtSsLzruT7U<G0aX}ujqt-`yYU&-_WK*tbu|Iy`hiD*7uD}(a0
z(BZ@qfrY0^=n^h;Sl;N9ra-*_jr+38hH7O#njAl=+gvXIb~?5qEh&hv)TD?S+igXO
z;>?Z3L87v4c~M>UrM{xx1RzJ&(wFvCvDqIXc-fG@mZEIG7r;@a8%n<={Q^APD0^nx
zQr+use2KT<PwTW+Sd#9FH8yqUJb1+!lv0Puu_4N9Q1>n9!ahMfkjuJ>e?pYgyk`Pr
zCV*=Kj7&f-h^HW@#(}s_>I#VOq@JRlg6~p5axZm({y~lPf>i1}$^doD9w!~i+bjnd
z`H1JLa{?o%PUosuC~<%!o`m`zFuH&z_7DK;1%<kE>q>U)%&R~(tj5c+)sGyGjGgff
z3c$n_r%}lc=Q!HyKF;Y~P_|1u_|JS`agnKJspKbT^M4*H=%0+jH#O50twYM09V(`z
z1!;K-`i+GUcgt9gNZRMBcSv_y&e!=t3E*^G?o+AyE_YDhOiTbyYFnJEYg}6@IYxb?
zYCDu)aHv&9y$s()>OK`}OGV;x7=!@U*4f)c9ovGXp>tyMiWJ&L+CJWu>4Z}ekaY;g
zF0eTuYCNQRS5E5IiV#Z&R|iY6HeV|at=g91m6sM}>0>TdM)SkcCAQ+Eyd*Kl$=!+L
zD3123X(O_mPBjO6W~|DVrbb95!LGviC{1dGDr;4`*Ago`vxAH6xXbJVRPO$X8cljt
zl&3mO7OE6LkH~i0eimJ|NSeLP?jw>|;VdoW2NsFiOYpnh6EjTjpBU14`V2G^*cz%5
z`Ys|SA&nf`?>1gn*(TL*$;eut=l{1)K5ME8&QtNPC|XBL+Q)*FPt-P?YzfhM;$Epe
z>0!l*a$A+TqG;vJevgy{759^fa<!{%kQR3hFOBgt#_SyAWmd(Rya<uAFrzDe<cZAs
zUT3jJU>V(+6_}o$oE^35$(9c7KzRtq*~5I3sh7ZEKgUgI`>l&KT7XIlxY$3VRqY&7
znaK1`u0;KFY+oqbC`)8*F|XG2OBDhp{(&?DFz&%2!|F3#bM<&}$GYhLbCtE{S4Ky6
zohZ%RCABM6e;79zGny6B`BwIGaWw##`KvR<@oh<7f`q0R)cHkn`l$<VmG!={J2i3L
z#j5t_HpD4KwI|hk%hv@)aSz*<YSvWD{MXFE%r3ygI;e0%nxd;XOy00Jo1s?>>oLNQ
zn4n`_d&VCD3+#sZf@{KW#t#37?ufyZ%$by&D<G7%DhkC6MfaLQ^FsUM-GrDJhZ5qA
zy2s|TeoW%RQ2#-JjR=V{(?GOPmgxj&NR=?Ix2mWTB|#-cKJmR3<@FN*fgsB<(u2-{
zR{8o;kIB?1FUsUYt*!RmjQ{s8IwfjhzW?>u@Au^&4#u8dYL4=unM1+46kC)cg>>6T
zl@Nk^Yf|RFTaywP@}lFip^86Diy9r#y{d|OXkHZB4{|fqBbH2;2Gh3*Uno^acYPp=
zkl4nJsP|*T2GspAQ;L16b0v1{m|>wt5Q4P>@TkuKPq+PfCWmtn<<p@bGM7)EKp8ja
zpFo|ZWO8bNPp?tL<mj)}PfX;4ZJ$x$;9vg+Z%`s4l#+s1b^q1T^-Vr}&Yte+VF7mz
zN%zqhNuX;3YJm~;8|vlQuwe>M%s7^z`|k+W=zYu^JIXwVErz&7c{mvdC0&`P8V(;G
z-??)fuaqB?>*QFU?3fJu65fA|N+5oK^VrSRy*(HWCzS(zO9%0TUq(-&i^R#y*xu33
zSRJhsO-^EOkYm5iWUb2+8ZR2gNdl`b$RO&b_mgAOLqrXOlU-B-{vW891PgQdcg-PL
znAn$@N)Oj%Y%CV}rgtScOW|~mPWDC-vCiQ+Aw~V}5TC=Tg#lHEn`d+xYjgdK7meT~
zjujPPvkBD-`&jwRU-dcv+SzZ6OkjL#&+<by){s8ci^5zV6G>;j!CZT_k;%-3s6E=g
zH}<5a?0%z9yDtjYpbjV0?KHhj)*uo!gk)|mfuYD2W7F)>kJp2M^&gLBn;MT&z9YIB
zJ0l}vn~`xy%gILSX5*>W5L(ttXKx#^==x^_>S?unkP?<MJ$@edmL(T#*_czwrRu&*
zt($MiBVKH2KGh`E1>w-Ek89i)6mJ_{+!?p=4W;7cfwm|6)Z_g4R&8QSJl`#@RjX}|
z=M#RCycqvQn3<EZgb~>l6;<uAt1r~`Af2o9TWRC(!ELE1Z)GYPO)6E>f{F&%Axvpj
z*-0yq@&&9T5YcPd9F*S|88FKf7CGie{IgdkyT!P1EqopG3W>*1J#mT2c9Pv(Hl4u&
zvtLmO*t2Z9DgkBU9)_5!Q(Dg#DzTE+Xr2%|m>JnSLY{~@cg{b%AI)KH8RVFk`5Jz1
zdWK<&J%}l43J8L!V=gh-L!v?^;)}}J+vEs4nB|N*x9{&kf>goj{UXrKxxZ0|_;q0~
zQuoUV;#fbcN7ro#5dDdsq?820Q&TEGe#0sAud5%}n}q6A69nqTmM!2C1P|(U2Y*<x
zs()PRD{)-3&pI$UgzxbOwX3)Ywud4uFsd~>d}+kN?B6Y8F}*QtM>a<1f;k459?~QO
zhqW5@%GqA=fNq(;vVG!eBP$Ly$VuJWhc?d8wGVO`u?y)kI;%ZjuwPgLL6DHk9HTq)
z#)x}MA>nGC5wR<!C*2pDnRq~1WG!YdNr8w4vrke9pc3j0^ZDwmffGUH*&!#fqn_nr
ziO}iBvlr(XY!9t#%4Tol|0a?l-H=!qVk-fi0^>2xhLJ#l)2-S(+Pj)<d1|#Lan<R3
zB)`}jxL0CxoGd?4VH<}exKfOSRQF#fq(pMc&X+rUJFjm~1~E`fyR`!qA?A*r*8MgC
z9yT))VM<fPoIIW(zQWm1?;F#d{LsQnEJZl`!2W#)erX}TTXgp960nS>CGj`x7jl-q
zPbT8vd`M0(x&H{&Iz2*Wo?S$J2P_uB`_>SzVYkTlp^awU$9fX%mRC}<M$??+J-Cb?
z;^i6Qwv2o~tSrXUGo~!eN$O=|<0W;{*Qkl_Kn?l)y|MAOxtwD5T&<kdO+1MS*eQQ5
z57W&HO%nPghw^x#$$rA5P+qyYv$vhCkF&YCvyZKvx3f99+9xr@#U&)s$2VT)>@17-
zwf1(gv~>37+IaIUEqSQK%?hHD!ERPC>igp9LqnvF=lbnsV5N&9z|qy0Ct7A|lNjz5
zEpl>HmPXkG1&8r?0t@5rrH(cMG5($@+8Ad#SMdR^Apb@0TGT_n!233MoeEx9lAlN|
z4ff`^*)Q$mg(Um=M1(0k;k#wz$Fe-7s}tYJ+{w$vNR}Vr=H%vN?(Ahl^IH+|7VZn_
z$MnjJ03R3Oe5~+I>c3|iU;1!AIZM3_{HeDY|Cp)=@6J92@d+h~`#^je5<4p${FQEe
zxzOHTDCfH={T;H+Y@99l0RemqXB)F0-4wpIw!R8Cv_X#R6Yg&AY;79AcMCGLfos-D
ziCdJEhKYsXp`<I<juE#2GW)s#;v?!P(`Nu6q`IFUdvrOQMSTS0_nEE42eU~KS4ONO
zGt1~c3!p0^KA`rpmqVCYK)|Hc`7*>cD4nHS4icj6{N%1i`-bg(f*k?z!2!}qz;UuL
zsjJTPkh)nBF)VCGKzuc8M1H5o5ud>lg`J^pegS6p|8kS~3M`_20jSYj7fc<aEN43q
zf07IMQx|Mt(4P(b30O}<UOvcHGW}>c|Jx^)x3HBcgodBA692*1u{X{6^zeJ~A(?gX
z#j}+gsmZwvg3NHm-bDTCes79>c6Khm#AbESGIas^h5<}(FVoXV1nAUL_$s=eag6C#
zf&53d)A>#|%_lr}9>509Vv%<q18h8zszyEZBZMb{y&Bq{J9KZ0E$YRlz286Cp^H!I
zW-tPT{&An~-odqhwo{juO9($czyAl45F4hdZekXXVU}+ao2NSw{1D<Uhzp*+?t}K?
z>2oPKe0AYxX0o%fA9ZHw_}-a@kr7-uf}4#pdg&q@A25BP`hIU738r*GlSHx}jR55V
zh$yE2+5{zOBql%te*x@ADiU8h1Hc!QGk#O&2A;ug>fRjLfyHAnJ4bbYML31N9=RMo
zi{daDokL|r&j)H}$$~CHdUF-B3t<4o1_lL(tFf*(_Gf19f1?X~p6q-bUB2E)KL2vg
z=EA_h!p%A4^F+?3qQJnSO*zE>Qg*)D-E(7i%K7stJ8$%Kzq&hRoH`v`wKFGoN2Ls3
zH1mtBYG-cFjw%_m_B_<*2w`)kcgN61m1*h`;(80qH5cL52|Zc<Q`)VQ?Ncoi0|}GD
zwB<2V*y6>i>TNlgneN9`H+E(&ruKun#aTOVtVEw=KT@q)f}f!0Xoa|Y4f2$T#d25R
zbIuA=JBO$g>8rDI=}03HhlhO;*{JmnDjvv4-%up-O>T~&tUeKEgu0|cf4uMJo>Z66
z4DrX5!!gs0ZP!<~uejKyP;^~f(Z2fSZRw`RMuOJtIhxkG{p-_(g{QyX-kNrF&zhhS
zdOy`SL?`sYU7?<z&k}Q)3^AkX^{~J|8Qj~rhJR;j?YL}_1)J*}<`P|OB|g{)U6|nD
z`jM)-A+fpcb(hHcguo;ei3TdP{)zQbE=1_ex57}KDUNNnUZTxO$<%I{Y~!qccQ`9O
zX`60ByRjnFAu!4dT=mq595gEqWiv8+gj!8#A<i67=N`Bx%T7ngVg+Hjzy@;6=XVJD
z00~xDdbn9$meac~s{2%V>ZoGEB`mGHB}82m=HM*P50@21d;0Dy($vQZco8Lv9psCl
zH9NEOhlGAFtgS!U8YT>+B;<wQs=Royqi(;eJR`_2Q|;vuSs<NxNv!nI)+xo+sX;?D
zeT7sB{u%U}?&=R-1Z<w8HcU|Kp95C7$Mh^E0H5P`nEpYiV+3pms9!YmwjFx-xV{&b
zyC8kTl<fsVV7buO6`%!wA})I*M-Z)6xnkV#*krG)N=s2*%=>Rg^j&Ied#X<@&F@x&
z@_Ge7z6Az9aeVKj2DP>&!HxJis!{7@WXdr+V!|<B606_{0|IU0sjpw>v_8K*C1v~b
zt(@G0J!#8G!6UkI?Urh}yn2h4qw24TH&xVZNu<|YPWQ5ES*Tftfttx9&t;t+S}zU*
z9L&CdH9x9cDKC%q7*Ae(JiY8xS5$39PMM<hNLlV^r)FFbSs+#w${p=Oa+Pm@Pt)`H
zssINUvBo2Jb-E{vf3!s_UEF%&5<8N;{L(w)-4y<I4#F@Oeweq-UK+keY7PDZlxtt)
z0|I87yEAaYIcB&KZA(4;f&N?rsN?Y$sAFNda%T%cxX&6{akK#NC35>Re^G2|UUpX;
z&ndDl5pZ?u$gwUeQ`6hKJ~1F#<hsOTnZ0kE+%qdV$~r@m>BomM!<A>RNS19nbS@W!
z57BXdDINFQKA?w>Q8XZUaq!V<6zh+p=sqw-8L(`EZsYSTKD1)`9Fdw(5pHYa8}2^y
zJqqe^dasZLUB_^G@J?xxr>}sKygAAc7OKv(3-?M#=y;g>)=dMCVD7q)=C(_(KJ)AG
zl$7JYo*6&=%Mq>i$S<dDWPkhNhrhikXuZ%E5z%*{mC>6o%>INWKyWPY)gmY|0ewt|
zmnpE0K`mYas02Rn4stLtCI;E2j*-o&xCh!(KKnfp27MEEM#8y(ZUr?ABfwGHKhrM<
zqXs~Rq=i8Zb<WC<Z^AKVFY)0DqeB91{y}|t6KpBrO8iV5O@NV&y*>Gztx)ATeNH3*
z*brfC=9{d>QfGc9$Zvv$0Bpx#$il3llIq0IKs-k4`|{@aQ!j@!zc8EQ>B{aL$INp!
z7jKib%9GpT_-@I)+1s{FyzZY=DQl9Ks`+jbxm`V1;IPcxJ=8n2{6vS^H*I-r$2!f*
zC!6^(QQ=(DcqzwO9OWLE8sai^=wxJNyuYdaQfL2YiDza~q;-a!kGIg#%8_fe$ds$9
zIna{WUmk1iJyepmI!DM}Y-UWa!E5#>;)K3mjDH^?m`omzXgzcfL8z9AGej(YRM&PF
zS+H$0#rV+!P)F^eN(g7jx7M@-&1>EJMS3nT=`5`~SW$UEHWAZvJiq;k<)PzF>bk_}
za-}1amTb`Qz)x-0wrEx1x?k}xz?v7=M`jJbyE^;O3QbJodO7v3i&S78S}~xdX|o0Y
z0?segYk&Tn(OgCvq!{gF6?iawkID0gTa@RW75Q@1Dh<lRs^Yx?Uo5vP<GR_H#--o>
zRoOL0BuOgv6b9w!dzGl~ikCSDXyt;8lt}ALJ8v(cqpy#J7=XXfo%9A>-3#%{;DNUw
z`@o1B58Ri7AbP2<N~<IKp%%W>uU7Y6Y{f!n4%1q=HO|eAPDRD>@uRWtY~WU0*`A5A
z4c1Wers>|FOFATOu93@YHZ!_573$g#^mSd{{WO;F@DHeM=8hg5`v7xq|LlN}d<Bm>
ziEqMB=xV*hj*HDr*1A}^#I`~5?nmS8faCoF$8(;OT}VC;w8wtFfz%wtIZ5Mu<606u
zaE<5%YcFTZkd8~;1-q+dM5wMMr8UMy+dG`J3E=(y&1?5&EPv-{vAF5X3T!)Lzq2V<
zG7-+<j=UZc$&Cfd>>1Gx)xxt2%=wm(kc{oa8g{+5Ds;`NN<XES73P+<p~PKX5L`aC
zK`USX_P+7)imln6m>{7!&c)T-v}W{r!)aiYy+Ke=Q4<vXNN2`U>`ie-m`m!;mzLK|
z4plCpL|Bz+YU@B`+?EElw;hekV#<?@q~U^VhZD~)`yQtccc^?M+XIi0JkRy!S%t29
zXK1|h+Cc0?V0KR|RA)(MnVs8X1_0J8Y+AD?z46B3>LpYNxUnQ-|3|yQKPa2#V@=A%
z_yrcTx07uP=R_VC8#9{A5Q!d${P}(wWSd@;rw}&cjPQWeFy1q~u+)I?46zN7B7eay
z(4FgDG}zF2!rSXaXTxBTx3x!rJrUYZ&1}v-)S+=tU0VozsCNt3rn*D7DrYkwAf?vr
z%V>Y+NOAXy72U;0-f7R+x31ZrrdK!Q^KSAwvwsJe0i$_NkeSn&06AFTrgF>`fOZcd
zF`1GY(d``Xp=do^T6wA?V&aj$<!f7>Tp1OV*Atu6pY6}rIP3O$B(}sSCb~#Ht+Dnc
z%ZMY*XWPS69cOAl5=M>fIJkT0)Utw&kC!h^Uy~~gEZ&+&9mY+KL7JkqFlrgdZmr_@
z#fng#>O<&HH<8bxGdO@H+BI%Sj~dawm%(H(TW2x1FKmKB)Sj+k&TgdhG8YMldjA@A
z%Ua@W?nJsdh4S4a!zDI28%)b$easgx8^lSTP}ixuj)h|zg*+19zii1;uecD1qj*S1
zh+&o;O-cT|H#*k<C4<Tq&M)9W1kULNk--MD?Ke5{1(}=6I!}0c9q%mLoaN!>u$+9G
zqA+jQnP*&mFe~5aAb$g;12w)@_|c@xjWoqNI%tYDUQRlo`=$8l{Jbm01~N}i>xm3`
z&3pD|@&fdG__VH=-{H?RR?Kyuqpg2?b-%c26JngYCkJ8_{S$py){0mc?D(<xU79JX
zl&B*usF1}LON`_z&NkJYY?JTz);6fO@8@!Ld1$8~VD;+vEDImL&)W#KGtO=G{`mf_
z-jj>{Q@b)sc9)3(+EV&}{ocC{+?n6+9k*ekbp)H+@QtBP2IEM$Ud9tzSO@3fY#6u0
zP=KD>a>3?z;ulcdb!DY0qW?;l?hDEY$3)E+*93WL8#O$Pz&k;4N;@i&x2KR}y4NB*
zkC&Dl?~EiZbU(dJeR8dEBy#UJk78$ae!9uP)LNOWn%?#bVS&PKtfhN78SIPh0*5Ie
z%~$5zu$z+zj{XP+4qedH6uy0aZaf6*)Q!rUhYJcv+EmyfT?K>&=zjKzD-nx|V!at3
zMgg_!U+J^KXnH?jlL#X9Dcyf!57#WevMOrIBcU}h|77wcRYTN33a40D2cn~JLcq(J
zt&tAaRyqZ<4HS+&Mjaq#nSGO%bS;p{4UA1e$SKtn6-18R`IfZ2lR_N;XkYU&D3=%J
zdJ=edt$rc;c{(**_&8knO1SXP@m1*k9Lg=IM6~m)<Vd>;E_j*l6Cw1dWTkQ5G>(I)
zVe$q&2bbyUGw0(X1unBhrAnw=Q7!A`Yt!FMf==7m$RN@Muf96dzo<SDdx^534uVQ*
z7{eWE&;IvOEdI{7*o`~Xp?YEkvtEcpBMBUvqpwf$nYMk=T%nRUeQ5F|Rq-G2H5L9h
zu<qpKC|LI;6%M}s9vjltpcl@<#&nyoF8Y-^Gs`mU53Yl9f?}nG5$i)6$PC$C4;082
zr**{hJ(7AdlufDrlgR^@Dpy<`i0f<aU!Su5r4>b2cIQnBGdp9ES9s>wDO}<fkt-D!
zXgu8`@+5`(Yl0=U!&Q~10=&<yMrZc~Rv&4RmBo2`#*~I`Tsb<ILz~r~Q-F5Fpmt$%
zLOD696HQH_+>(CxI+A&4Er?&}dNH$W{B9v5ewoz$YR(R<I^Heuj4laxW^>W=^P&^A
z2Z;_k(`NP?;x&eE5svly9AxwR9Iz3P{FzS2l_a-iE^&6WA=zvjdpl$A5HAN)4{F)_
zSUIkpjuaKg`(hZ54WXS5_C7L)TtZ6cAk$}GB>zU<{{PN7b=j}HbvQr&@U4~h<Bsq$
zFArWij=xtnR3nkp43#;KJC+VrOQqFArKEG#!S~m$y>%$-rI)gXZ_!t8PzRN5N6X5O
zv?=iFnFHvAkTQ5}*yC`N9K1`eL^wp{c_dc{EFPbJUoTfbP%Kak?}tts@rq$THs4&|
z&$6KYL%cHOomE&XhXNjn1U#YkT0VU)Nd6KlGb1G5;AJWV$+x2|cA1fF7K{D9{yYo4
zXrmWs1wUT{KTiJkfgT20zV&Auj8R98>gj&11<%l+&po%-;?!@t1&{uVG6BVgJghm?
zRaXr9!SbQ{!5qt7pdP-Us?qxe8u^Ji^<bkreV2NGbiWAK6`<eiPoI`JKZnS6ezQNl
zx1??1G?NQ7Z}H?LUi4rkq(cpna4pC6x=w$lz(Zy!f7lE~4^?rfhZ5?GpP;@##Vmbe
z5>(=bl!5-(p1C+g05X;c(^nRhY%BBwx)(6HsC-XJMpvqE@zmm^4Oe?NelV1ly6>Ge
z>t5WXg=SUTo$i~mWuk7h{ke_t!mOUehD&Q=OE27kcJ|i`C2?ymRMWJ28u~gP(!J4v
z^Yvj&pl{otL3m&ioc$q6fA_yLoX>uHa`G)K`{28)fA6r0yfZ%yqr8f);>hg9;)kDC
zkqhx%LGzvi>PF=OKF~+ukonN$@G}Sqew-Sk{_+M$+q^4g;JHrl&dj%-$sI|6OMRcx
zo#KhfbT_aD>GfT8$6G@0q~O0P>yv#kg&@9$?n8!n_hg$xjiFjlzoh*d93}o`hI%iI
zV-$e9aNTtDhgH{|S}r#PelcsFYz!@ka2=9YuG2(ymqaa%xRit<zXg#XJ`=zGQZ2W2
zYFkRmwy9RGk>(1pnrz`LvT>6L%Gy-UOP7js))yrd56KF9Be{v2Q7&$CA~&+PP{#24
z2*isQ^hvFtOu0Se%pbb2<t}oOh<W@GaH{&$az$Q_I5pTIv|+fEx(R}ox3@2+Zhj94
z>dv=Bs2dFW61rWM1gcYfwC#xk@DVkUm88wwJ2av{s|`*Z1LvHpx!^IB=>+yTesrdo
zZaaav6jIL<d*GX8NNdUT2Ab(DvP7+?kX-9{@*p?kW!U)Eah>Tn05?c`FR>H&k-9V}
zNLz8L8|<Gcq<nyadLF!snHG<%srJHtMe{@Z%qHO254F?yH*3e&Qg43^)=|ek2i#TT
zD}n9j)KReRYw#ZBjb2y)wun-JN%YeFtb3bYSI+FW_+$E<=KtMJTZASBaBReBVFBq1
zw=3$_{TW63YGgU7Ma7EdgGI^1JqcIbRQa;d+%Tuc_HnGk+AEar5+-!8^H;dVcc%M!
zC3hso^|-mLEYfzR`Ff_U&h^kpTpR;b{QTrYhJDHeVD|6L+i5&@e4H{JA16Ymuj8DV
zZ(wZtwf^1*_F8?iabltyG<;5FfVaL`&};Rp?k0WC1bhxR<n<6~&2S%}1dMgZC#YSf
zMuN0W)l$M!s`2Kqt>|fwQm+pG>tNDJAZmQ98@!3efviHN>Gx0Sfu&ohuxZfp&vEM5
zYU<$le}2X_@M+7LZ*jMokC}a!2yPa$cXHv`Lj-G_S~@WSei#QgsUQfl4|w-3oGhqa
z5n!A$o4_me>`1SP-cz~Y+!K6^+BZ%eLOGI|0{p~&T9zuP60#O;422#;oFOyMoomI@
zYXiajo?&-`m#r^7@aFaqGugz7pz=MXnW}Nu)WFic<w2tI-6f@(ahC+&;@#yUBGFM*
z5zUu!IKh0BB%5ip3onUw5p%3WE-|Iyc9;n&k~o4lDIaqSlqch~O>WV;P?_Mg*;2ZQ
zf)ZzoO=f;DHVLiRs6~~OMaH<fu?bT}fw-Zr5o;M-F7z`FO@JTA*I1mIc?a@JA$}Z6
znJ0)q=c_^_JV%5fx#G>O5xE)GOqU7%+(bxEOv%B9u<@?e=-!YCuxY7zVC8`t5Kg@~
z&~3gHvop|y{9M2Pb)j#O1s)j(TPN;3YnT^5&TO}(^W@jqjqv(GzC{A_+>ph|J0HzI
zI#WamK!icxI6*$RM(dm3^(~yh9bj2LSS1!$4VF`4>Kb|<o+09LMH%6ct+q-E*CZya
zD+$JrQIAlysE5WaKHPmy*OIH?9A+$w`+N&<Pjr{HaGG(#Yhq&h{rQ=&mkjeD`5{7e
zKixeShUUU`^jyE^U)(**gU7#jY{06Ry^ViPB+>rGLj87sV-@l{gg9m_%+`31MXwl4
zYaQ!SD!ay7QL=T+U7ahAu8wvmsXDNi?Ww86FFS`M3FFpREIf^5M>@*4hL!ZGhm_0n
zq-br>xA2GaGrk4yUVny(l<)8R%LdpY?aFgCh64%epIDV(OwB^AFn(WaS98tSs@S<>
z3F6m1Sx<RFRJ{Lyt}YD4H2BLj?>QK>-kf)VFYqm1o}m6c`wCcA(=4k!Q1%5kFi{G4
z)Gy(gQaeOOaFWUgQyz!wV!CsJs3&=<Kxa_xB0^_0gV}#l3Hawww*)ST(;W?`qCKbu
z6!re0GH^NdrT%aZfcpMiq>f9C9GvWpBVr6kKAb!`4xET_iegUenALrOy8nz8?M2O5
z)rGjr+%XX2gJY<cUk&O1KZgFldkzgs-Z<e{-0vSmqixW*5oilL!PhiC{5YDB!Y<WN
zL{~vD!`}jE!v`SFACR{(r^7Vb=19yJ$Q1q=uq+#_l1Qos%P0{w=9#;*T8eu~69cWW
z7a;Gai@98JI+Egwpxo}*nBD@h!7?2L($@3PGU;BYcDTx-TzRS>X9f<atJsgy`TakW
zv&ljRXrcaqHVHdBKR0$19H7dBa+uk%MY>-g)7>*CnAPdeV{v|H&c;CeZtC@WPiEP%
zV;qQytJH7zpU*;E9XxlA*87KOo47##@)`a6<2d#BIQ1x_M5nJ)hd_m1{~m%exk6tr
zHNQKpxv;5m!6kczvUNDG@m!$zVr%xk2Kkgrh*l`ci*S3{RgoSPm?3v1$EH829jOsz
z=jLXMYDQ`a{`4p6vIrNKh%)uv*Nk3TGy4tsIE3fV!$3YBQn@*`Xs=(u!Lp?FCDL&_
zVT6ZYVzA4EqbSDPD_Z0T@h>QTq)-@_kPs&<e54rrKqm;!2)4I}KkmM!`C;~BNShh9
z^&CGe+*rf3#4q3nY(F$07Uc|2LUy=zZ30A~j}};zF<JS>l;kZ{p+x8)&`{6l)=(OE
zMUuB5CBhS~`O56K>?7n$bQi8je*)@*a;I}LN;`9)>t!Nq@ZI$bPRlIXQ7*+XC*7BG
zDb%UCZ0pM~o{iZ3-RS~D8Z~<LrN$vULmJhPX|>a3)4e_iW^a>6p?(lO7$$v4s)vXP
z2|@a#k-GEow)^zLWhx%&g`@>Kr0t1Lwb3LLoSKRY>$Ugk2ZIJlTt8Mp1sLo*@=L#>
z^A%<WuYdkr)Xis~9UXcJGd+h9FX;}0Jujna(Th%k<Y95l80Z`~cUmr%-8Y872>W)$
zcwgspVB#=%?kZ&q{8!u9059r&^rF@K6bQ@BMdQ2*b;i$h4$K<y2;EbsAI*|c89W?p
z0h@pr0koe6?l5(6auU-(*swf2>rRRRLg=f;kYh7v4KO|neT=VY|J&xivHAM31xBC)
zECo0Mw1)<|`)z0iZ!jQFVcif|J>U%Rx{VCzXzw<JT?t|Lq@@g<FEFoseCU&t1WZ+z
z-t-lQ4jh3~f?x2zP+hidPxh;)++v&JyEs#(o8lYe+$Pm9+V!NmJ^~%ffSv9c@{EzL
z$G`S;H?;$_rZ+~|@pXeR)Uk_bOh=z|pRV=C26R2(UkDcHb(5%vfRC(!NZ*SA(8g-M
z<Zf^rAsm*x{UP<kWz-k=HR|)?{k5_Q7gecRw0NAYP?ttHgJZ@|Q(I50I8hsfgU=`)
zDXsumK&QVw-ge?dMzh-9l1c{8nAoeEGfrRyAezZ<uvo@zw7v^K`2?|4rjIp%i2FNO
zLCC<adwEhNP^9vx?vIFvAO_>m_yDaXJH@(sa!l8UtvC7|+~D2}P7bnIMqv;qG<|rI
z1#tZyX0QQwH);Ti)Z2f6p5pCd3Jy=Xf=~YdMG%%0DjGj)G{91@VirWi&?THZcOEb)
zRejr%kq{TqOfwf0&$m?{OYcnJ^JAM5P`&{=XECnI{LpoqW7mvTOH!j;1B0#Og@p+L
zK$z91_0u#ZdY(J64J=}Mv2htXT|6*L<uN3!++83lN{O)cdVF2gXhVoH$TQr}c~PHb
zcyeX9x<u*Z8CxE<dBY%n($KyQ*O3U<(a8>Ag`<9}`_{q!<7HUREn_&gD#FQ0QK+K+
z0{n`OERU$luWeGVI9e2N_ab_=USUNMu6uWEn_||0)pEVV+?<2GYz}y*w8zGGxVd!a
zpg{k=71;X8-7`}MamTq8EbL3=uw3Z9A`k3IWwyo$aXiRE2Guli=iXLS-Q}De)u9+}
zg~=*+<z9Z(c;+?{UbFpS2glk)uAA!bpIR3=-2zxY^-QdaU@`mZ4CjcsJa{KHoi2a?
zSnA=q?;J7kt)?N-kF7h*_DgJtimFWz8ev7o!o>OqoF_)LNecR6V)_asxJaDa85Px;
zD`xx!sC|h~$QM{v%vqt(QgMxFBSjxC8p-1WU>h8h9V`T<wqAUvrBvTp%IGc7v33pa
zmDCc=1Lw?m!CuqdhI2xNaJ<EYme%TA**%qIWGFu3+KzB!su&MPu`P$>nmLCrqu$>}
zt$PlHS5#om>Vi-^BXj6>2l&lANgFN#(yg7i!<NuJxsJ3&<<ogE(Vso{7q=dV|G-y&
z*i}qi0&9O$fUV%%pYJUuAT2l3e>dcQ`2NKHc<d;GO*ip}$Hd{$pykeAKr`_FQ^&<Y
zXb#PO!)`W8WvN(M^hxmx_scEZ#KyVn4}vW84H1x*9A?u2n^v4sdeZ@*|I!Jqzle{r
z_cqAUX4IC8V)0lDa%1HU`?7^vv{!=hW~WdcO{fiafqsdw#wUAYQ=y+&zIs(tcuiAp
zif)GIHsa_ju_4a~x1Md&z0`K5H8?XTCo{P9OdA%~cD6Nm)GfX#LHANZbDSG;cEMv*
z26BAe;+hk%u!N>~H}L!_L4ZIKY~j|_057PWo-rOJRgU2jUsDDbE1_R}mi*!WhKmJ#
z2Y(J1YUDsmOV%fq9}4h4RAzYR5h1iQc-7-BA&Gt-D)fQM<E$^jg|Aq#dR23HO+z2T
z1%DLbf`}4TAFQT6tr@No#U~`hi>iiefGhk+G~}WxQ&XR+%TzAN>Hg*$<bI>ifGhe8
zyu3=_FA&dRV!$&-P;9^i#>6N_1msjP?YnS!9KMA7bCCl0754$uQ>XE$KZ;t|Lk$i6
z{r~#Yp$o>uE#!r};rf#i4A_MG;MAql*zG@xWJpaz!>U!(&;OTr2C!C8>xpM!eBnk*
z_JR(cyAA;h1Gv?-h6^h-se9h&@4db?5jwkm*{d?p`3*ru1DWmAdeC52ef+)tI~%`x
zv?yxTQ!R<TnZmf0XG&(u&aRBp=as0nbYAJcxfXQq(UW1v#*229i<I4Ct@#I9l;=Iv
zxgvC$EKk#@t)$k1mSv?oE|oLsgSs6VbNza;Tbt3@V#}f8+DR(t95rd=4n1Rc8A-oH
zIDC)g0A3XJ$>q~uP@hEs_cy3dqXGZflm7(nvDD|_>SMiUVTiW<T<<y1c&`5(NT6!Y
z^_+!6%~TVoW(DL7ohRl9WIO?vqcc4PFne&4<oAPrH-KAcoinZE|KsgDz?(RhfN$?k
z5*^%oFLH0PCHF4(-f-^?+zViX!C=$DbZ|GO*HDs>U=tEZC<&yJgpl5oM?wlOBrgH$
z^zZJS&cZf%`QP{bKi>yPqq)7=+1c6I+1VLAWvNgHpFeouP<cG1a2yo07RLG$+o~)5
z>)KZadOGFRuAA>9*kk8vS6yqZDf5Ezy#n2}DHrH6X~OyV3<YlW^+byDXZ+#?pDf$l
zjLNPl4#MAea=(jZVu_C8e!L$zC(sRP-n@*M_OOe&LfQ#V^YzYZh)$xHIE^wyP7KhT
z4AJ@rIR6`Da?6UPCej3V_XL?q{Y*VuBO@EV8Q?Pra&u3VnKaDMw>2_^plA~rgdiji
zGLyN9k<>woq{o`Vpm_M;<E=BFO=`;vL)70#|7I`uFgEs(+iP1JYiJl-YcoGif=r(X
zQT#Ot=bMQTf#Lts4wS2D4-YaW;A_D(xGGPFyIiybG$Aa}-bkPaWt<X2@dh%1jt!J?
zF9DF*=m~&GAhw~zluQfgX^AN2$jqgM5x%qFKcvZQ5YHTbv}iW-49KPiNDv&(d_49W
z$y|~HTStvuA=jt^joqPF{O2%Ffg*bU9FW2cGa9qniX;8dKj757-tbq)wrKRqUnRdG
zL4=%nf9z|Lxey5)c|S?R6Z|B|dG&weClTl?ZmIkv0{x!Lm&f+2c}dV3h;sPf!FQWj
z-@6FNo>aAfzEjI=uVIKM*DeSu2fHem@~JBrP&iku71U$g9WWdCE}MUpvDT`Vd9D`7
z|4H83xfNh>#pZvKyB6TMDl4dz(7B%#n;lC-LR*oF(97hjBLaOPx}yb>*mD%ftT4fn
z@v|-s2^Gdn8wG!kvgctSfQ|WKKBwFGxUq>*f{s9SmF`^Er+vu*OU^cvNp<0-;Pp4p
z;mdW5Yr^i^>pO04ir(o{xikCbdx|19fB$yo8|p<awwQ01Rz=XW?NWiv?B==)>*F<T
z{nP6s-6INp_1!a;L^b0xWaiu8rDq##GV`+Id}i;^3`i>Uid&Rq&luygyaQaUk~XT<
zVFlS?<;5Lu;v-J_7E=!{-e>kF?W(+dxpG%L2!ng?1O-WMdGJ61H~flrKqv`v6?cwJ
zx8t+(iETmog>7Xsr@)dQnIAoNm5q*;?Q#GV?W<rEuYxyTewjTtegA!MgI)XJ&QiFM
ztK(<I=P$#D(c8{mVr<F2JwWk`;yp6`M{)}!M88L|g6vQPDm2IA_}cJ!&!p!K*Nrk|
zWcp)=w$%Qf@l#v$Je(h*)m)AYHm3u3dI~DZ&!|8}C$8N>1(J<D5C6s1Z&AsMU?F_(
z<mY0+ebR;5gwI{cGVp%ySOI(X%EbbGE_wss=^Kx&dR%(21Swl5%1<kMv5*RxEI_Ke
zhERrt=)BfqyzL1Rgr(?@qfM`LB|;g>sE>4uDD*Lq<gSQ2%AWm_Yo8!Q>M;rp%5Mbw
zp=~5VqbHbf|4l8tLuRX<tzMfH=}J)Qzd)c?BXob=)eQMy=H_n?WQDdKs{nx|a%+_3
zJ;J+nwO@62UT#mde_eTHt73V}qT)(ZpJY!-vX7~;t(CcD)eFqdlWV>^UUVq3?O4gF
z##go`&n^Nw$G>iVZD(4_j+?CyhL}GJ3`x(PpJJlE@A@l!OR2YS`Q{X^*2<6c0G&8>
zA9U3EfHURteGpF32YMi9Nbb`SQ~TViNJ-wRMB4x-J%_}GC>N*v5~q|!(b0=jox%bV
zi<r5|2@<J>rH8q>yM@Lq6JvAB?7erE`<6eoGHS>*tvTF3cW-m(%*2mYR^|^j1j$<t
z7A$CdB~}L<TX@9BXQ-gse?fvXx*d<?$dlP8+{wk1p>)I+;|IUs1hat}VcPkqIQ^cU
zM{CsL;>?(r1bVa||E!hTv+m5#<dr{F2i??xo}RG>{82aSd&Dty6qaCo%rjMF9i_K>
zH#j>vx()=A7s0tFM%a<i8Y8F=A#G?4A#AMh0rBJ$b#g6iK6S#W`nRNX?As}i)?uGF
zLb_jIV*!!I;{v?Z4v#iY6$+?0G)sH-H1$AVQv*dT;zN+~2f;<bD})oOlET(yz@>b3
z^~Njgjh{m3&7pt=9~VR2-Sgd0a$-x8T~E^d^j+5)*WO$&AM0?d*w+|PksfaBsu5Du
z!BFIjHYsb1(mJzUb#o7Yx1n@KU~xx6_Hu2(hVKsL=0EjEZ$n<u@%HF>`X+O>&ayDm
zrLryZ4zxs|%>{cV^QrP4T>{r*TYM9mrIAh9q&hmDs)EEtR6(MpWw)E>ysYKD$^WE|
z#5|MJYvrYe3l28;{#`W*G!6%t+cc_eWz6O{Lb@8Lr!aH@Ru-W2^5r!o_)78T(o1V1
z{9B$~LMGRRnUJr&#{3G$WGmUASU)pk=gs!^n_J>~d@Hxlzj+I<OAJG!`y%Ez{%?}@
z!!IM-vvw`?WfxGlhyq`%{7#@WN*li*OXq~ubQfBuWu-?;XYH8h6Px7`S0gu9#rm-8
ze=N;#>pz}06Aq%0iUp12>z7czBQsULqmU?t=VeHGExBVt!Xxs1MZ3z6hH@SK4(T`G
z+ZIl08m=xQCwff1&}p`bp(r#8E;DRc&tsWINHJb<aQzSgy7Oqs-3SBdiY%#8cDzhJ
zo?nDqx%c;@8Tki3UcURw-gxSPXTzDckm~FxQ&-LK@(tT^wpIFz#@=!)9uQoq9A%ik
z{C<WRDbG9kM^Brm{@Tv;c?PDlx6ZOO*L!#^X4RQ8%e(`va@fC0sB@iQXRTSiqX(v}
zzlt2kl!;<7RRx%;s-sQ*F`ehC<{w>~a>*{bA}FdZ-tO>Fmq2r9Ib$jcX&5Rk-&^D3
zUa^ml&K}&-jq-Uji)cH@0o%WQ7rydL%~GtV$^g^2@KB>)bmy}RGmfrF8MTk8432I{
zuy;yZme@t@>s`!fObe;&FDXN>s~oQLE{=6XPIIW}ExXxVlD<$j(0zSd;$%2M*n%4;
zf(a3Li};nxHR%V(cMqoN`$YWv$u0QzFS+_4^P!F^8?P-U(XdeEp#wOO0;?11z{`ry
zTi7(BEuetem&7I}Orn~|fQLyq_xHZ$V1)ze?Gj%qlPyTbxni@RorT&qN6`xw<2gws
z8nQqjliH$tR3fOyZN}e3Myf7ZfWpg}b&V8hZaO;KKx+md<&E8G%=gT2Qe%{*WmF^o
zR!G5*fZVYx;i&|1N?Mv-)o_*9pc*0Q9{pWmtxzB8&9KMn>7e43ffOP{W{S#dTM2m&
z5O>FPyojF*5SOB>^95TaBS7CdSR7FuEhb3|(`SqvNHU@0WWl2S_MZD1(G`30y7l5L
zbe|sGuy>6MaML77z=nv02a)lQLfjI4%z$^r&fL-)rJ(pm#iqB`hm#q_nfWMG49NUq
z!<zc%>%^}q_SO|k&F7eDuya+ayeoJY@-vXFEx|?d^9Llle5zx<UmSylE~J=z$ton@
z|CMd&t#MYC(M`!rA1MDf)7l)A;i;!BmRSbm1zV#2KyGW#1Z4EGec5qK=eyIO$+t*u
zJ7gYQ80?c9YCZJMjiw`R+F_Yh{&JfDX9M6<u`RRoW~qBgS%qZdn;XMP>%`^rS$f@-
zU(^4BvWK#|3w0%rjX7jBo_1oK%LVm7te&lz{2qv9^@}Oe!t7a=popeDDeE?fsw&<k
zFD$P&_RS4q9ty#%^5Ghv!d%ZtC$0DG+{pe72?r9yiAl+J4M%EaRDEY`bkd&87;%WJ
z(H^Iimbm0b>$$E0F%I!q_EVV;D0DXLh;vsxiJ3TWG?xhijF;a&vp=gX-pVqjIhpwh
zX#MhNQA5ytFFjM+7xfHgQP%R}0CJ(KJ~!FgV;Nc)<X;$O({t<fhQsdKp;=V{>w{`-
z40M9!LE5YRisiP7>uOcJNurgxPThgL<7tylJP4z>oD?i^w(7LK{NAcMBcI%0=4)Vr
zE|L`Hdqp{G3Fhc&zND=?gRH6~ZB(>@kv*AlkUxfd98;IXr8Zg3m4qcb7il^=*v`_L
z?e6J1s~|a-S)xW8Ai|Y*MfH$x&g>o6@k=FWz0jUCsi5%DV#eIP_Ds8%eQcH7oGQ|F
z3KEAeh_WRCoZff>b)G0l>B#jG2s9Nxzs!98QvRTP*Y^j=GmnnD#>>ortbBX6`ed=s
z+FXKVEC|kRZ^$QCmKL>WBz02M<^ko-T(ryDQsF~6NaNjf$#Z&;7Vr6fW_U6~RRd*B
z-qf9%)Slx>!yWD$eRl^E4*}1*r&_@43Nt))J#sse3ohlm<QEpXt$cBLAR?!jt6vFu
z(*Sv6qq?s<i5GmJEPYqkDNrm&Y#=R7FBmzJWMaq3!eqz>S`m8rGWnw5@464fXcb4x
zJ6qTNt`MEii#cBc8<aUU2f`TP$Ow2i!uH%B_T19sX?B*uo~PlSbJ4~Q;P!v-^J2Kh
zqPQ5YTA0^kdG%Ds9IDxm_&|=Vy4{nO)^mGR)2UFukgb_H+e*B=O19;6?V(5$y-_V&
zKqjp{rIUZ?t2IEl=Bq>b&W;-`tfJeF<THO399<qw_O8u-E-Ul7oQ5NHGB!Ve@<-}A
z7bgdM?5TRyV@Xm(YFeF4_JT?S%$V`?ObtN#l&nu&^8E6UpqA4OMVp+RTv{XIs^u0|
zG0h!7AF|13sgLgxzm(P_apvwT)ehBHc4b<bZ8I{H*KLwJL~g2+<J^}}KS#o`x8V0j
zQwjiJ7#n&eW31<hCxIA^{-}{7g;$Z}jyx|KS{g%RQGX-&fQV4urJv{a-D>Xw6UG{x
zWcsL0%x*pBAiIVoP@VDv-3&#1RgM=;9o(}=aSjbJIk+M5NTN77CB?qsXq`+oz<}5T
zZF(gf-~TOt$ZOACkQ8mrO<r9fxm-AKt$f9My%}BWTGl3Re`7`U%iGg0d*!r6=C99k
z5T|wKjgZ|d*7+7i+BrlQ%j$<K+<mI|majbF;C8sIynlh0_kulnUfH2G_VQd$X;z5!
z5Vmpllbh(xlqC@X)_zFDy%CIH+sI9VS6SF<qP!f#W;4~)KG-8M5kgE?q6||Z$W2ES
z((a%|sKw@b!#gKn0;~%4oI~svy1AXFk2#mH+An1p=7$@t3nIB(rQ=E$M6x>7{dvJk
zmy*Ky_yJs>$C~*T_z~w2zKt5bAB*ogl7O{qbm*oeHqUqxY`+4w9|_6<H1jTjVAhta
zq}|C2kWO1rHVtJ9F9Ts1?oaEIYlX<VE1!kn<sb;JZ`RDaWGV3nTQ5WH&4Q_j0QRuI
z=+kI|Yh`J!byVcej1x!4KO&ifwxTs(SDEvBX;{$KO!X*J54MehUC3nv4Og(3;p(-`
zX9{Ug_70*w;GW^0L;eHWlUX>*G=VPkcNFYl7Q(-UV7Qx_fg9-^`-9s{lX|2j^*K7n
znH<Bg(Zn;>)h*7;*w`!9%{A7`gfei9_B1v1jCKrgjPf!u@rrVUeA=b_opL4KL3QA1
zTn8SEVE#ck{U#3=vvBk~l?neB!u9S)(R?WXD92?yQ9`s5%T;GL;~7yOKS7*)B0o7u
zS}-|mdJ;rS4D=|OeL}4~q1oE1IU%Ah-rhd0PM)v`ev6N&OR!hWM!&M~+6V-pR%qM#
z+uQluX|ex{wZRS5C)NLt!Y1N;R!Ls31Z4rW$!?)CS*TlbZ9qa`O|n~vOcvsnToXvG
z3am+Sb4#fSj1Pcd2nrZ`-73Vx)YK!yD&ff=$nUIz;V1VHt9YwW1PZl!bUT!83}wf)
zn&>6XsdK7)X<le+4#+O%Lm=)VZJ|`G)&gsAzW5$;4vVJ+bkHRtXdyBmZUH32Q0JIs
zTFf%A1#HD_`VqC%LV+d9cPE4VJAisti(U!GT1xhl7X)WW5x4@UNI${WBiKa#DHz37
zWu~m%_t4a;J{p;C$8~JHEc{}%j|)yL3yHRhg~lHZK9R<{X#066-lWXgKB?*{p$H9&
zi3OGL-$}X>W%t#ypmdnClzL?)>iA)L|DbSEaU~(hWscDx72FOVudpOvzJB@h6H6>T
zzkEGDlR2WiCnULjo@_!DY4P~R)aJ>x;0i~;(zes}o}Trm+b&<8Umc*M6Hq<>GWG7{
z8gZnT;-=sXqPp<llCU0+<OSZ|3sXJpo>(~!*Uh7i*_!I(9wP1@qnE_X%1fda;={$;
zT2RT%N>Y;3p5R*}(e`c)T6ASgGW9h4BS_hDxjAT2fVM}SFRqRS+EGO&(xjrN%w0oc
zc+R|t&F^k*+pv87n;YdCbN0{EaxL0fx_kqH<7}2Ge-w0Ldj_4>=1g53XkzToKZ(H+
zKxrh8*+y0bhb)K|i=!(-LMozcH)&XUT3dNpX=++~SzCEp&I9wtr*xehP3cV&XDdS^
zp{7ke^W=7DD#oM9X)n+BG;+7mLvt)1%{qOttG*%<O=*5Qqp3V;j{ZkAA4%jdp%%h5
z#u~+1vi>_b3|`0s`dHRG)-MpqZapk@$k)puXWc8jeJfq;;MUi#B+A~suq#Jbk{0-}
zd$g;bo9#lK;3Y@OHRjAYqp3k!WOWsJIwdcTw$Hlw^ng#Xye7_`EOBjEyx7YzCGJyp
z9QH#cd`)nPJKOV7Wt{)-d<J|~chNybFIk~j<d(G}QB@oS{y(`m2=l`0iqj)E$+YLe
zz!owfzCFjoBd0ySIKDmG-95WK-X&IQY6>TMu`~O@`B5)j`)CKW(6`bJkZ%=KH|>pc
zE^6bd%@iWQ2;HV*gO|N5sLwWgZqBaXwyi#!nYlA(7w8aJFpI$kW(jG)G=Qy&@4>5#
zH+VG}k3$cf4?A%UUOQDKMO?Ol^Py_6>9+vVPZk<3fYwbPEs0v!To`U+A6>F&WoX5-
zYZDZNY;!{XNEWj#w#$b!_S%r|6<`-rzI<zZ{MO~=F?K22t}Sw9&P)9Kf+&X&ALd73
z>KOoaWSa7K!83w>T)X5WaxJc9@*S>E@+zgx`ISnQzY9|Z&%)ncA#BNAxR%Kw4(C}8
zrv&0e3l75Ht`gJ8tr(}1!#U33WWjeSFwS+tjKeYKaPm1H)k3(x@DSXC>od+TsH=xF
z5Fz)WgmW&cP0tBeeO)NhKku;b%<%G6kx6SJ=NSdW=J{`YbAvo|`MJi%XWF2}|J2HO
zbi+5gJ}#jl##&$#Swk`L;k9Sm!px1jXBlYCUVMjn^Mc>TpHG)2ZMoc_x8T{;u|2Z<
zW#RaaZ`AT*MWF`Kp{}a^pCkP(o1aslI#~O5L?Ioa{ofb-^iWH<hC72~expxJk)xBU
zQe2?cJnAEh6nkldvFl_68KQ{k*-E}n-rClq2*u}z<R<Dz%95`M^&_gZL~g?Mm7wKE
z_l(y+x1ylrk#69`Ma5TLzmEVvU`+3Nm_#=B!KaI%U2_xHMh-(2wfIY26R#L?2N#^Z
zLd(2d1YGe2Jl|-Mj;R3aK+(;J_GfGIcGjmF8>iIo$g6#}JtCt083<`eF*Z(Z*p*lF
zbbG{KN#gkW7{8);UwZ?Az`))wzSs{kUo7~KU0hw1e^G*;3SQ(F)evV#&aJuLofTHO
zGB9vuWms1C^%_G6$PQb801Lvh5g>Vcb&yL`*8CV<2c78oSy3**)!UMjw^autV6={d
zZVUnj;WK*W877w8BGe%);TTQ4)Ae{pcu?k)x+Koj+eA~OqpLG5CK+XKw~4gY*Kjt;
z%BxCSo@_T`#&%Lblj(T^gZXQ7*nIId#SgzndE0?LJ(Kzk+KtHX2n`D<SACLSTE#Au
z$Wk~LJocNO`fs9gx6V*IKTnCdsjIupCUmKr+tN@QnY*j0xx~}YF0|7PxOH#{Zf@Q-
zp{)=w0fSm2@dY!rjpofY(3%nM|DwcqhL*wHdB$)b81DbA#E<>WP;+Lu|3&UMBm5hb
zrygQ}`iOoX$8TAP#3*SJx@f4Shw`3^EU1qZKXnyUodUCVGX+4|o4@20=JQ?P9N7As
z-}zzXJ|OhFxlak_c_2YjtLOltoG>Mdf(f5KIK$T}_b8UY-=KX*eL^R}-^__7{<lvR
ziy;pkq4Dww&KZnvQbQYYIt^af40bW~T}(B&&D;gXKwof1Ay(LvACPku_Z9yp=dilv
z9B~Bn)4wB&p0FXFMixC`LtA5=QcJdy5?uEJ)m>!I{3MUMx`20A7ugCfGeO`of$P8*
zfceBP6ZdZOHLYbf3v8{=4%^MoNHC4^T_R6W<u9h*fU;$P&MGBSRs}$3pYUZjPMnaH
z@63(f5N+n%8ntA>r=OD1qodS`t@*iIiae|g2Q<yAs`9svrQ;iKIZUJIXTd4B|FI@K
zh;Sl?NFp+bT*$M9L@m)sEGCu`D~PqkMq&%GgV;m#5kphiEm|59&b5h3X<FgMTZo*T
zNc#CJj;gw7s4}nwCA~%VAKb86A@djZ3FxpHxW!glNNI6@f!b#+i3eL%jWga5BW%<@
zQ$7{}X%f%$082~%bcqE1ANE3Vql1Ho=Ym{jbxcTbOiXBSG&P!Q>>;<ytjY6svzPvL
zrQwRe?t0yyHNfkN;2x0HjpeTJl(@SK-Uhmg9H67f1={3{@y{>hE%x=#8@u0t|A;K>
z@E`D=dy=oYxo?tty<0N+knF~sCvBqRZ3E}cj&P1o08#RU1bIFB2P86U?Sh>2lT%%t
zq)ub60lym!4L6vpLNj2}eC${=^YyW@=Z-mvZCywzrWybF%bR^2btTE(lzDe-#ltb3
zzgDpSK`G8vyf6A-S`(Bzd%~G;M|a7lQvzJdi{cN)SX&nj(;P}i&q_x^L7_o^X?^OY
zv5>KFdahuufsUoWvyo%+;+WvtR5x%FXqZ@Q`*?fVK@(Er#*G`XG)>a9FlEer%$<7c
zzJZ;E@f=Ohpcwn$ifFNENLhrD*kD$$r)RvOfukhUE$6r*j-D|@qq`)^-^oF^nbA>I
zGI(5u#E%ILIp`2q6B=5R;D~b8qCCihkzt{c(V?LcK%TK8)!sgJMaIVT_B4C@wD$D5
zrMbDKN6PZ@%Ag+J&wNh4PKBU7JvBKlCI9`2;VAD<3PfSgob%~-s0d*P+#90Q=%|PQ
zkO)wM`G~8>k6!_nJ<Qh4uU@C$85#njOhB^ppZ<o<6z=Apk#J8@R$xaDu)KQg_%+J%
z)z`WNKQIsBQ)d|kuE|axCY8cgLI<CgPCD5W!HpEDbj@6zkgz<{b&s}lq^Bg(S-Yb9
z)*ZnazoJO7II_sk)C*M^@G=!}u|Rw2Entt(Z-Vb>uc5uWewCHch+wXBR#J#gs^7wZ
z7ECwP!*o5}1+^BU+hY7)U4-J#+Kb@x!{E*fifdr|b)d)GrMn(Afm1*MrEMeR@h|W>
zZPucEQtlBbfHta%h#SG4lD!T71({ySQX{|Gfg<K15H>9}^q%j}{QHOMw&g9K(p|QR
zEy>9%EM`liGhI^K(i})lW=(OrQ?$gmD?K$Q1^2N~`77;458~UyPq@B3e$Ym{O%Gpk
zvZ3L*j)=(B7n*C1EDE1KGR-!;G!nka-X?BQQf#GMJRP)VY^(H(-*R(#+47rP6Z{tJ
z$n?n#vXqwZ%q!2@zQD&kAlnb|3&miV!G6zXqcAM^odc!8?HO#XHi89hZ|q1;+I?%)
z!Gnd{3f(R~Pj@A3dwoUF3fMIDVC;aba%W!1r8n4^4(b$aZzdmuh5u6X75B)hu}>&d
zML(%c&S5rm(_OvH)2cWSsvkZxUFA2!PsA$z9HBH6N6Bg6H)b}%?!md5f2F-3uTU)G
zf8`Y_I!t?Mg;n)dHov?%LEiCVOWmQS&{^lFiNj0esY{dW;MOHEFe^Yji}@bBvq#_8
z!!ma3o2%1SzqK`1R=hsRCDG49R<Wlbqo}(=I#<_l=Fi)>xRp${kB{Z`$Q)4|92p^V
zN5H?CIdqpoK~lJX$nZ>t_^KLG$)lt28z3B&zf$q=6RuXJF}WJYjthPjThp?-YlB8D
z{L&=OF;Ww{YwT)jXRez=dS{krjGL~JSF9T!OEVv~g>s~bFhw#pN6PJ;BO@K4>ubh>
z1YeS(EzCMd+56i-zv3^&Qa%oc+612!u(i}7g<)jWm~x~WjuH2}U<v#<2gMem3pHA>
zT`d&(uoSEVYwn-dVj9;oO<Lz6bq&;3IH$IgI%C<?e#I|L9cn_2XVVc6RJ%_)^St5!
z<XGN$s(Ne}e0nEUgY(q>N@v3{*1@MWQ|ni?%ENSa)S4HX8ZNAhjafI^fQq&~f3iQO
zCEdj(qcvvW7-_xwo$Uz;+uvCow)&l&2?;yjSxsKMC9UWwk(cyVO5eRssOEtY?xWEJ
z?~htEE(jX|?^Ydd^z&;xT*Z6`4CTc^rlvu~a`+it4(^f_txHN<o9EtRCXI7;NbqNE
zsVt=wj?EyPVUBEVYCVz7W9jLq>Qs7t`JFcAFOwkih70RsW7l4QJzEQVHW1U2=HikL
zdv=_(KKxt6aNTv0@b7xZ_RR+NO_j@Vyznp3u~JJkPi8ZBN5F9ua?w@?%&#fF`6H+x
z*xW3rIP5;M_~<UzoHgl-h8!5v8DyrJG{(^-LB{3*Ai|(5e+v8P$DX~5+3eC&71*8-
z@*$<H^aB?p7Y)|>2R05DFF4fXKVlwI5*m~nXlfo&pH#G`!YioxKxx*=b!nrnNmc$U
zI)H<Xue*b`eqcs@RK((RH=m+a(ay0_GehS<YfG7{y|zJUW<ylWqGTuMoDI3YDW3W{
z1eXtpQsA{=Ja>+QX&7OesP~~3n?*h7#dyt7{$@Zt1OD#*#BxY8qL&}ZFFv{~;(5EM
z^1#5dC@~b?2c>(;JUq(wlv1<C9)#ovn3@LUhfuRv{%FH7NQG@qWAFD#@U=ce149se
zjgdXY$gTtH{9LN)Q4=>`+~J(N4?k<N_xB`P=pU3soV~qwdgk0e4?UPY>)Ltd_9<oz
z0Or9{%m>c{+oWJ>$=F)IB&zID6SZ3)7^`CEuq}MQJFBy%v)crmXo|E0W}h&NK<_8a
z`^=qBz#gXQWAY>LjN(PbIdFlIld&Yu`GI0;X=9fD@wx!o!24|`WrWFmn~WjD6f>9w
zU_bdI#*c)&{(_zFNT+z=Lpb0{o*k<odlU^+OLuo0)zaIF<_Fa2)CoM_|A9S4o$#ya
zFDw|S_4ljoFDM+S@gHqzYHV5B*t8hj@4Va|7}$Qf^Hk@h6#)S&E_E6oK5*dhw?~d1
zJ%aaY1UX1=Lq2k-hXr0X5^`{qIR}0;q)wOYJ%s5_fOOx0v{<9_7%wnxbUs#rSp+SL
z8)JLH37=SJEp(A1v@p<g)VJZ_g4~_uGCO&$7c`kybb>+8w8#KcvE0MN+$Y&RZe5w5
zd)BfTS$SE7jJeCc5B7a8e4js#55}=plixOZ(y$X26fO<S3oxs$cTe@V81b*}&d%9g
z<)`Nw?e3rKp&QaTkdxlqEFZB9%#zl(+saD<mXiHO?qPPRh5C+xVmX{Tj;U?Q*3n@;
z+M3>xsm{sm^PTNemZgeAT@8zKBr&dfSbxD4hWZAM<qRCr&DB*DqwC`uBFgLZ=~2ab
ziVS^4aT%;J1cTk!45G9Z^T3!Z?&C_>$E)yp2ZAs{z8oqLs8=jPC((~pjz1_Tk18J6
zSky%2?5gyZR18%mZf}ehJw0b-v2UiYiDhVkzeAv#SceQFW1x*Z&s<uzJv+H;L4ZTb
zvgDi$tB@=ys<1v&%U}+szY)Iw0=*MTmp87Wh2Ei-O_&ol8mG?`>8J&wQN-Luhiha)
zJ^#pL=c2A$XW4?jk_E%nGNXW^t_!Vi+Q$aD&lQJdx=Azq%}jhU=$!+y;x!2dPUc#{
z)m?FE+p7bj+79KVWgXhSINeooN5|IF)GnqWPHY{L>FJRfYRl>dIK!w*kU!iR4^m5)
z6#sgK)Kf%ID`@LSpJ2sAp>>gZ3d)KZ)DCiXB~eS^_Sh*i#p_&L{F72s!T9^OlJw%H
z(8j*Z?7qg3hT`->zsjxY>Dw0i4_F0ddP(O8ShUxAWd>TBntI2(pc6wAX`HJ|ywsG+
z&RAz_yR$gCEzQv}tu47^huC&)MnX%9qhm^oV#q1V)7aQE$|<MR&^?m$6^FPP8n}eo
z+lROs7`TSOu}D`wr223lJh_ZL;~Bzvw>7$^j_o%!vVql-g9{`Ar*(OSTk~9G3;RlI
z_EviMZhbJS`GrGlpz9plkPJ6jhM$?SPsR?<y!IHkz~HJa32D3PLSj~(ERlEIJzLoW
z+I7XArlFBF(KdGS9En@D+#ba@(;t$zgl|LrBbjR7qQ%$y5<Xt;i@aqYUlSf)8*jg7
zo~5^)*xPbmmw{7&wY}8E)xy#(RUdw~lzK?aHB9wp3r@Kv_?VgbB)IB1qAF#MdSHfi
zh`WiNiJrt($2vq}3O~)(x6oo`0-9#v7fOfIRq8ds*$UvC+>j^WKGc-cRsTb+_$OA1
z$M-nkKDQ6H|NpF3Jh=G}YsHhO<}G;v@`KkwI#ym-2T3$UWKvbtAhSUTTK6%lKi2?`
z9mkH9lR`!pSQz8;M%a%d@L5|<|A=r^$jRylcTZIB0X}06=xWBgtlVl9T^H+Jku0J5
z1+jkCo)+^GVvOv}^k#xiuw(V{X*wHPrNz-sroQP?Pgh-APqV=IX|v`|L-c0Acb=e3
zp~dCI>KviyajnO$Zxn>9>Xv)jl6EuK@1A9r9}rX!WNsf@9TwS;<ls|(xMr&)UL!Tg
zTw<*qNtt%m)LS^&M;6I^N}}zZlAEJsl?NOAnOY}v%d~V|8&A`~I95J<At8rY8RGN`
zd5Af1800a2WEFWqAt!fsAia_ZY26FQ*^G^*`+L2DB@7R>`e$-ekauvbb5eCsP<4`X
zOt5#5eN36Ze|e1khJ?VN*tnpeIPG9>Sx}I-cMuuon`LNJ6zY@iV{8on2`w@*%<`3{
zc$=7br!ZBXzP_GbzP@0Si_FW-#hc5q6|MY>+zn|oK{3-Z59t%thE%4KQ#gM=Tfb(e
zS%z0oQK$uEXLv+yoSkR&p@unoJ>(jR{^lN5ni^J;mf~^~Yq7k@&!-~J*(tRpPExtQ
z)~6j14krGoN&5COGgD8oE~+&_ZlY$Yoh!^#tR~y=xdKHD(ZOV`un~UJ!@CxV$V&9L
zmHEKgi=Q-h7`p~qSq7j>=mC~ifv(2s>gD-_jpsj5Rp_(!chuK+^taYD)0^#XZR;^x
z-yHL06<tpr5Zs1)H*|AE3+48(@sx6I60kPw%9SkUQEo=GgZ)vu1BA_DMp84jIQu$R
zMkQhysbG461<}ja_(K_aMn{=muBMC;Qwm;&S_Wl39k1vhGX<OhBL34W&O^^SAk{TG
z-^5HaKpgC@YwfBZY7`O?Z6!(YG%}gHbefg~>KYe&Z2~BtV`4!Cw8Au?=GRe=qYnU}
z;<1{XJ@--EJXZ(!KCb;se?#39z6E93koRHfO)4ZvMI66+^%$7<!iylp4^BuE!OFF|
zwr+-i{$_L(2-`YoLkmsAd7|9RXbV>}P0Ami{hPwAn+Bag1QO{)6<ZBKEnbC<(^4Bp
zwX!+mH<9!#fTfr%&BWU>Q<PKD=r%D8tRF0Jh_%u&)3$Umda9^rp?B1V>nj^hE%G;U
z54JWm)i!s~Ny!ar7%WJN1a*qdjyC#^W||iA1yO08*)F<X^MjcaR>9fcvaBFWtKck|
zEGyWGI^s|$)s5~b_L(_DLt?9Et~JdnsxH>2wItX%uGHHtDO94V>n|1udf3hN=*iYi
zSY061e)MnQOiO2}sfC}jp|vc`+|tupi##Nb@wKw@jS=fQ#QIoR_{2KkvjS8=X070P
z$bYS;RO1mLd=GxN<;KHCB71USOm<wHJ6|zy^0Tn@aBwu6YpOpd-oVk%!py_P%>;d;
zzZn{O@U5x0lcBDW?ra^SIa;Q^&c?ciy0df)=ivHwOdQ=zM<d^l7V`HPLG`Z%1;`gJ
zeKL_+r6r0#0DU>CIMh+2NqK=8erK+!l~{k8fsw&9*O@l6ebYi^CR>^BuYk^@bF3Vt
zma`paf$0XurgXF{IZw>|MbZja#?nG&r$+*ENHonVyetevDqf=IfljMXi5U%ckpcn5
z)uGE&)fcjvSa?oCX<3bXq*TS{IwT`oRORM2Juk65v#{ZeVi5|656Ta7o-@-Syf7Gr
z$lq_<$QPMg-};l{E5$e90ZNY=QqvV@9n)18HeP405Yi#Wv9%}y9wj%^!%@)c)z_qY
zE|l2o@d9jI7%KHF<2ld6Kg1-VBi~Dv`8jaOi5eX}m*Rp<YbRB*XETpr3te~Pl$JzW
z!I@neu3ldH^7UJK(sUcyl535+xd+=*XOWacT3V`I(^z~wTLCCuH=rt0t|t`umUKk1
zH74k<1*h5j53J8+>acQk7fj=2a?Y=hmNo^?_tG;KU(-d=<l)5uOds%>k3;txlbCa4
zvtqO0^sU#M4!LQEWmN^N4QQ~@*A9^fX?OS)%54TMLka?Y3&O0&Yet%|XU&i07bdOF
zUb^)7Ro64)H(dd-1JbVz7uQL7iC<qiI=TY*eax%{r#=S1Gc$$6*cNb-(Na9ay^nsV
z%2%)USmUKjv&fSBiYoHrPvkj8EPPulxlZAy&|&F>@1yPu^(VhCiH6z@Z2XmR1j=ua
zw^XP<wg^1M%vOxBYsBZ|7s8EC^2>|iT19^0kXRoPS)b_8Gtb)B!Oq88qp<g2x!{aT
zJSwpp@1loF?3z31eRPfB)}1fN8zPEet9?&u(-llq&EyR`lpd)%&VI){E7n+k4a9^R
zIQd$Lw(T--kQxK{wm9^*IP^A6RM1zWjn+4rr#DBKk`QWQZ#0iWf{ki$(w2~aXb|QI
z<^n63)DTF~u`6wvx4AsR&1}}((Yd-AD-M>8eMP-5&~LbT@!4lbO8Wbs1u~$oT-L+#
zvlGsjPr2Om6TJ=-oXJvjFUx}dG-mxvi&lNKH#>FDJIhyHS`$C#rJ1g&ZJFttilzQl
zyE6A5WG;|GQe)|j9Z6Y7AM}s*{dO!XY{|hQzXgdd@|EWsUsyD{GW_bxt^?@(C>M?i
z8yC*{{nhd(P2tTJ7|%@JbhUl$w?_+e4t%|#?fSNiS(j&e=dMaC9<1{ZY2I6qxgtX{
z3%pr*rX#xI1u%WT|9&w2VrA5-XHlX=*OXmvwgoPKb61K}T5G)O{ibY8naw|VKk}Ce
zOtn*1J+pYzFK5g04t=wB&GijQb1%(upWm66KiC))*>=1nx*=6MhdD#eebFQ)*s1JA
zFz=+%NigrlG8G$A_Pp0=vhMbtL>+URc_5;nl_xX@HSnEpBkml6y%i^<$4?@lI6V!{
z?s#;5LFEO8>HFgIB^Ns*B3GTO6PzhI&pbTfaDaLAe2GBw(Wb<0H<vke-0X^B>m|c}
z7C=7hu>RM7ma9roP;mI$H67m^FD^RvO~;yVj~2WHS)89exHw$CcqqGIZ(}g|@-XxJ
z<*KU7VCJynFqnC{y81Hn`!KoU&0WdKyWU*k-2OKD@OC?<fyOi#B5w+g5YrRc2>%fs
z&Z&!{9(FRX`}qGCYHD9T*x~d_>u<BFE`gc*?1#Z@Own(UB9@R9jyy$Z{D&cPUSs_Z
zDu38(Bg{5(h8cBu)6pC39D#GqoE}E8AW!s%2}QtyHXmCDevk#3i;zk0UlL_E65e{W
zq_l5=S5+~yg>2~r=iOrir6x9VskwDnp?}(*`Vh~YWf4WsWHQgN^KmY7fcgy5W6s6w
zg9%0_pPv33MKjgNtW4cm8=Su9&81b>7kb8@%E{YVCLOutPd(^e%v8^eYU(fSys<G#
z;<?G*75U{)971({0-5LNVU;h-hVx~4alWjNuU;Khn2@)xEBwhB#(TP%=RtH0Z80{T
z9P`3;{Dx@(y&3tDRemg)RAm>c(a5>6)|OM+iWO4l<=N#28Uw@DUh7!+TCQ){Q)P+m
z*>3jn^${WSeXM4KW=j7A(`+aPAY$cnb+s>TNs;<*@^tquU6&+Zoav!$<7p1x9m70F
z{rhnr(Z3bLqvU*r5p|Tddbm>%gxs4%2Xl-LqW2M2XvZv=(!WBo1#%&|oRfv$E`iU}
zw$?<>^y|y2KG_-^)poR??)lE>?4dg=LU#q(x>sKeD2|cL*|)>5Jl4@Jrs^f%=BFAx
zy<*m$uU&q5b+~-<`&)lI!~FJKp0W8BZBsC2;ud7N>*`gPoQ*m1_CjAg7h9mt-2my+
znY!BNWcjBz#BY45q2Z-Xi3yutME@s@x@ES-#<XR+dt|l6#4VfeO8W2kY<FDT?$35;
z?f7g@T-=_|cFaLuI$8Db0_O$$ppjL*Zvl>bCa7EEwWkpeZz*0JC4&_<RJS1N;dy~R
z=HX_P6A&V)(P#?|Tt0dJ6xmWd1Dg(%)(lDK4v$ECD#{Nu1&-LpHO5L(LfkYo-9u6&
zvGsAbq~?;>cBaNhtr8N7SHhwax8H0_-L^2m&?V5CaYAKZ1FN?t<CvOz)GyGUbo?(l
zt=W4z!8jf589H)NZ^qK+TKxT6o?}y5pFyc>Wf_ff6JvSPlapC<*{Ev6@%W&8h=i)|
zYIi}cRFV6ViFK57kYX>{B^}u}N7`Rma9~OJh&Z7!);%G}T~pI7D8VhRDM3uyw%y#G
z6c$8!J|tSgqf&Ohwj_CLRiM6uuNAm&;cc%MP_-=;zn{sV3D|cVG+J7`4Xrwc;dY-m
z=yqyqLKYt^S=cX~I~dq<y1_2DAXh94+MBq0qjXPc(SgOGBjR{STOvzaBBU*zqb)9K
z<(PKsg;im*4eX3II(VEpY8MpB)9L7E1%9;janM)MnGN|Aj`K!NnKg29%x!j5$N7fF
z(bbWWog<A6Bb`wr_Q{Ll;}$15I3zEQM+rTq$QL<PLO~Me^zl%=8~meeJ|2Nh`*9wh
zy>q<>7M34u3>>jfY*x?X)0|*WYK-UbSsoFagypj;U_uTbG)9G#62IU6u`%MaLP+@9
z5&WPWE-&qs&VjRIS7F(q#UUdo13}I*TP^p%Sm(IrM0?Vv?dFaoc}U|!Taxw-OxgMB
z5)_`85<+U2dD`j*)$B+m2ySkCqBb}5Ce96!`rHU~PY<yd9Gezivn^ef?md6!f`Dnq
zrrG+Z%Q70|#>U}|ofh7)4z@wfnV$UQ@F{GU+q$O*+l<c+d~cun4aXeYt75;$^Gc{?
zZ4ip^9e%Q*_3EbB#MQ@halQs?+B0WmQtoCqAHATA>g7#68l<^#V2O6pwrh)>+F#um
z9kcq`g-c^Qieysjc5OQg9qPvL9s^l9s>lcTc1#A;|Nr~EC`1Wd7WPT!4F<KGuD4Fj
zNU@U1Hb?B<Al+SB1kJ}$3wbq~7jE2~t&Xr@ha@hteT-~A*A_BE$3l0bh24=M-@s5j
zJX_HGh4bQP3m*qKf4Ad#v4*>+fY%?Ob|GPs_Y>MYtoL)=V?ti_Yp}I4jF+WsS`aov
zva`5!xWPYg(Xpz$W9_lo1GhlodRv#cGH-c#n7LJCrQnLT#^z}ne$6N9-F#3|gSrbF
z;zBpP)%D)fKy$P}(U=&tdqGc;t6xcHR77i*gqye2eQ1Ya`ldL4&>2r2F?o4>BcZcU
zMtW~gt32EsC~q0gU3g|i1lsThZSc2sF5BjlEq9&My<S!jX=f8r*5SEuf33t*eR*Wg
z?*3wTtRY?=KR0#_wCuieO>KFE^c~}JqRd87Ars?qq&OVs80g=2_fU4$(fi#uZruIq
z%MS%-;(Nc|{m1UFdg77s`_-p^efI%sYl(7(KnTY|ALp6S=1tDOUvK}jwEnM7=IyA|
zRqywtrgp!#YR&CENiTU6Z_CfxQsN~o-IA5Rt;n6sy3G6;^GfUexd{XR>L2U-VIVPa
z;D<i4<*7PPbQhRuJ%v7;YQ=ed?omHL*>xfUprz>oxBzOdYTd+B1r4<I$0OsVVmxIx
zb2g<Hn2_t4x~{-8X!#4vk2N-+L#ok$`n|=OyBGRvX)!ZBGNZiaZc1L2<!a~^uPe7U
z@v_&qOInnWu)0vzRFq=m5@6}ul;drkv?53Gqpa0wUYEYu-QYjQUI`xVVfLDAOzbN9
zCV5Bj0Vzb)6qHJW`vbpI_`3~hM$RUmgWu=k@v`A^xoe=aYaF_%HfE?!lxh)h1R56C
z_wR>eckftU-iZg@Nz8XQn$Ilu^KU*{{V^~k`?|^Y!l#*`j3MT10olh;u^V4*$y)x(
zx(J4=@9_WcT>vL*Rs5s64sFc$6YDx0KRyBs=v&OUQ`B|1)!p6A#y8O2T&^Vc0*FL|
zy{9Xoj(`8s?c0nqUCuatPszq!qh#G6d}xSyfdI+}%6nwJAQ(pqpu1Q)Pq>eT18KoJ
zI6>|{e*5a9kFH)71Tzn}j&?IoKX%s&#CMP4+g50tjge1C<Vo71HxVKt6u1HD6!0eS
zk{Vpf!~2NW)Zmspe3-Zjd{uDaBOYE!{H2D!ore#BBoM;Gcd6juCddYs5Wa*7r|$@^
zK)4OYUjl9^{{!Jjk3@)qAzaMTPdoylQ^1?RMm2aS5AP#(sli1&ypp(r;L0x`|5xF5
z9NG4jM=(ArD447UZz8-f{#^)X+h^f@%3ut?4dDg2eHLD+tXI8PFb(s~jpN%R%(unL
zdxGhh&+go~qyX<J;7#C5HMlnq?<0OxgIn|PVd8t>uYwB|JiHPZVSKbFIF83%#=X}O
zkJm(a6Bt#4OL=%7<Qosi<1XX)`2fRFJ%<ap9Y3y}^%#zIr+|U*Fm4>*$MQK5-ULd=
z;X;Cg3yHp3LJj{O4<BAgtHH1G?Nky&YVG9k@FC)NVkQqSP{F}Xi2M_T=Q82cJvsrx
z<(wP_VL8M$6Q(ngqyH*ye<HjIq>aNd{gEvF`Ggw&RUW^R*n;57Yfug=1ZccAV*Jo9
zCC1+a<w~XoZ!*Gg<y#Q$Bv5j2B!{+2^ydW#m*aV~k(0v=1SU44`ZkzvNi5%rSia%B
z@tFQ(u3v9Y32y=g<8a)sWVT-m2sQkBJbbuJz5T0vJC#H)hObBVFK&N6_ulJM!ka+G
zI2^xsKKtH6LJj{ak6%e_!*E?DmO6vm$>G{DQiEUR;6kD@T}@94f9Ca7EpZ(L@^W>a
zm$%yLX=-|P4v#+o<zL3*-{IwL0QMKaHP4ty#J+h`l*s|^+R+i4uG%dibUaO=FmLz|
z`77ycYaZxkV(DZdnKxTM&fh*lVw#k-PBFCM8tHTZ{PHI%qN4BN?Pp;j)|}Ql*F@hf
z(lvCI4`^am3(kx!B#+@dgiIo~SHg%CXe0h3du1xR#GDO-GeS_-(`HhJA@SKB$b<=R
zf41&?bAx9_fTfxVV=;;L#1F7h)3^5|G-*WIKBh+QHaZ>|i^Arw$#zLEYC<NYZ)1*+
zbI$r4W;(KICUtJ*i^k5rW}lUmio@Ma*fCoIW$TV8onJGWz%?)*&0#nO_wd-uIk-d(
ze?E`jN4!0bk7Y~F%2t4S`vyGzAULJg{>yy(gPd$lY`>rQ4rETzet#;!_-IYKhSw5X
z4u3BQgYW{VyR%T92b75c<$0hrdHe|BLw*gbC3b)qUZ<Srbxm#Vv~hfqA&);myoB3l
z@$c|DXn@!NEfN(z&EpS(M!bfx_%HMLgTSA?1IZ-P5qw>Hiz$UirV5<@La~ATG<uR`
z#3U>7sGSODatXOlF*Z1H*n^hK1ZRdACu}$7F_n1V>P3X$^GT6psu!x|IOn&sK$U@z
z`qnHb7)+wVRZ{oN3OT>U<x>(eFRP^^yy%u;I*~p}sVkYd@udcb)o*W!Rm)FE?o&@q
zIE7u(PaGc~3$=BkznPVdrBIeW!Lnq}$?B_E_VE50<43cy%IPl_-<8K7Ab!KLhw<@P
zM|1F%c&u4`10H`693F2Uk99N$XXnmR#UJ!1cwNExU^_U#bo2D=RN;eu;7DZhb`R>W
z14sXTEK7JD5`{o`3<ozscA@flIKWFqF1-CTtn>#F5IzXug@PaW^=w#aNsthIy>(O^
zLDxP=LU0ek-QAPmPJrMRBsc{3;10n(!JQBiJh%jRCcz2rGT30l0AU1XW_jQ5+uc8Y
zXSYtDuBWu?_IXa#t?KGqDEipOQQzpX#&_7?y1vHtFtk`)(PvoKwQ<IhMvx4TTv^ya
zaI)Veq9@ErJxQQ%c^{rQh~{2^v{+4_I&6@Z@Mv*3(mrn*{g}!8<G`wro7I*<?vpYM
z^;db7bArk?@#sG5{SQ4FT+7$>&%V!=+{VLhznC}})O2<l@fN{qKT~YCy~vlu!IqM~
zzX$p#zr9`Am7Kl{CB3Fg8@bT~JgO6TE#6#zX@G%4|3t9=I%|&F1=0V?*t62Jl2Wm1
zV&DqcRG_>1^bnex#1Y;U_Rl*YQ6(5BO+Jf<6n949ml&5&PyT@S{l^f>+?^oehM_Q=
zy?Z^2*SXeIGus_lm!$pVqBG=Rs^=2F<W^%g%5>;}cb2~bY72ne{V@f^e|_%p_$}bL
zBl8c203ojSY$s;=Ka&8{l@{2c4VAq0($$_!kfKk{2oob9!P{%hx3@R+Iv|1^R??o;
zyfx+>K=tXk=|XG)Yr*^8lOE}1FC{LvP&3<%EEpZ);6>Qp8}uV1zQ$anO}lR?Z8w}*
z$Nx*X)+Xe7=dCBFvJY*!|6~mhR}}D}dUg}A9=sh<_cQ->x=<kiNP=OxJM!3<Ty#IN
zqgv+U?@tBn;M89A$Bda=!|oG%euZZ%qh3vk>!!=|y$fUWvhDxj14PlMkN}YX6U&h=
zUo+*`LU$vOMh8XPNNn_HdkvIPkB2>)l^TeK=Z;@rU>but6{YAz`l-}(sVbQo{w!i?
z=}O_~8jnDaAoq!vK4F^?Qgv0nrm`iYqYG)K86iIRDSgydgTHGC@=<=orBx5)dwkia
zu%>~OeB0PYm@)&?<Xh;A+gozoJxHOtZY1W0b>M1*_)%51TNQn$yk$AN*fVEz(a>;G
z9rYzK$YdnJujTH}kF)fg0l<|@I?!~%hQ@u&UEw5ug!WU<DEG!IcO7^%9LK*0Ey{i9
z|Cs+P7CKp95}DD7PVikrV)c+93jmEAR^tKEDUqds#_6)x=@}ORG+dnnHX)`D(hqWW
zQLC|s1!FriK$2+FbASz$O;QF$3X$D5X@ObEpyWu?A5HKtv^G7=9v`3ydS~1nbLRo&
zjJe){ekz7P@pFg&m;|P8yvc<Xg-FxNbx5tQ6FB0X3i@*loRoI)zitp%Ao9d<Hxysw
zqyu#a2N%@{Pzp+>GuwdR4vT8`9iU0Wi*tj#Dnllg!sU_hk-#>gzhRVyU&NZjh~Ia-
ztTxZet&P=sFRE9x_(h0DaV(wgb$*7H=-Ec!<ikjDN=V%wg5;)?rv*ByFMj+xp=IQA
zjwgs5ZUK(`lr#|$(nk#iHuF3Ac)O*}A{f8-g3C$P0R~H@B*50temOcjdZv5fhhKfb
zNopny_P!Tq?2(=gjU!iD)rMt5Qp|ZV=DPvH{a3n=z0YFJ-oVsjj|u|9@^vCTb?Apg
z6ColcUFY;fw*K$|md9;dDXvaCn*H%BSI-V*ZRv7rmgvKHs$xh6`#|`()?@Q<Ow%NP
zZ@$hdYrs&S&wbunPwRJqzi+$+xAj7}%}WVQw&Y{37J@vwSkO<Q1UqJww8}6HFi@oA
zX?mCp(A$vs(W`X5nA+VNArJuUeQJaUe9my{Eiq<%Ot_Qj)y7hjW3<EWFteYNb--oX
z_cLuZc(MnZ5vugfuRwGESC9C;xLZk2|Ced_Z^mA}4yDyv)ZPTu-U0961^tI`88_1h
z0dIki?_$JVnqey}o3x#gpId)w&4=d9Dg|M9qM46@Z}sMto$UG<fPTlGPvOQ8S))yq
zPlL6*{X^0fXOjhy=cCH^wKsLd>FUb%n{RKp)~t`GMJWIVL$P?G38CAam9gT{VXuDi
z;3PcMe}KpA{X&Xa+{;ok?HrC=$V`H7f`dsnj}EIk){V6zP`iiE;Eq#smZ85g-UMEt
zzj#&RY!;jsy`8T6(0JGxb+Iz<@sJcE*t>u;r9kzMf>J@zTgG>!jl`!-XgBygK3I74
z`fJkRlX!yuT?kG&QiO&Hlt~mz!$sgo-JiF_Q9bucsHih|t)8gmT?~3?1gCZhGO+6q
zNt&-H_r#9Pf^7~7i)@1e@{TYo*Y9`qder5|w#$0V<;N_}%Dl!8OA$GD!&gZDfz__@
zVV}r5Uwhdo`0((+?p_Qt*-f*GNc<(RZg-AY;{!HaGHzXda5~N-i1;$z_j8FGPFHvh
zOCf>AuTbV2kw}t{_qz5^gB`sTeqmJ}Gk4CW#<$>PTaU=HbQ_`r@23v_+)v$pnol~<
zY`iNs+3oj=vNuS+|9zS@B$CUO-UM#-u_pF8t4v&?OU2mgO1K)a*l8~qAx#%tavv5A
ziCxlU)fo_B^_wk?VS5Ok()UbV+&kRGs&o^jzwWhQ0S3EGoz-zR<y0gn1)jh2?(LL@
zt(wvUKOkQ+8XBWFkN)WjG-E@G|ND*-s{oKVPMNT>WRjBz$;xA%bVd=i4BRbglOYz_
z)_2G_*dxOxKCts;!H6Y2k6*0lFncD%6@B#PuG8n2AIT5$J1z!TKQ&ldrvZRxoi;k{
zqt+3V=_?US-Kh}WR@fg$AcB07x6UL%Q()=#)PvE=+_MfF9kbGj=}qN1-nlR_lJGUn
zYj&tzM1I!0<6@vG-Py4ag7obiX!qdSdfQ2;o|li1ppoUgN^clb%lpIBQ^{zY$52^!
ze0f;mxztT1rK!PXW$6TE`E;KzuIt+ZuIh--|LBH$jcl?1v==mOyO;BdpMD1iU>|zf
z{0e6TQY;O)j}4aaMAIMNA3CUf|B8d>IW@$yZgD}+nka2T`W&#?{Im9f;;*j-^)smE
zzQH0&<OfN-80o-!>Zg0e^fJ%k0Nh{FPnif}zC<R(0PHt}F)nAdy`?YLe<9o5?**C|
z#0a7Gkb=p4#$Q6^@_;Mfb$4kG>H%wbRG7vpAt;Ot3jOt0F<AGF{3a7V8M2tkK^EmH
zdx<l)GY0GsMqM@{81CQ1J`1%z4qnaRfc3DUdoUscjl-!bcfk$hrGa!%>p^5I>01ma
zN)r~C0YUPh1Q~Y7cfk=b=>t(DC+afg9{JsOo9lvOuB4Dd$r?A3cqAkG&4gMIgObxz
zIa0ZaA%+|hmN02U6=X8RdUuzM8cp#VY+6nP@RIN(-myh=3<sbFf!;(T8RnZK-bQKy
ziFa{+UbONF738~t%U<(<7Y6dcogzq}GhD9%7Rrh8%(%h%3SRrQv-4;m6>sJmXjx;C
z1fUyW8d-yy2zFHk=$uU6ho(n+Z<6F%(GfEZ3FPcFqnyj5NiXiL?e{*^UY~K5Z2+ak
zJ%e=pGK&&66u1{9SJ5oLKHa6G`6F?rkHHddxxYHTA#Pjdey1Ow=2@N+4)E)^zD@E<
zZKkZMpYRL~_@cX5NT6-&mM5U@-F<G-!rS5`U!BJu3(GI$Fs|3p-LX1kYW0(l{VF;$
zB*m?~|D1~dRg*@eRZ(gCPYcIgGQ-}NQjZpwzIpI=MLZgPQ;g_KhZJA=>ed}m)rQs<
zlB{}NPC;yGyko9{xC6zT2kw=15+Y+~=};RFqhVt^x;m29^I(n5OPpf)Ifl;rgWwb4
zLm3q$1`OJ2IHMVs8xQfQEUqFy`oa5}AcBNW-U+b7_V@}R3mEtF5eKklrZT8*F%>uG
z(u+vlJ>W|)5=ecdKtB>znC^Z#tvBYfPkpq&9lvHyQ3cnv%YPR^AcNyuS(v4p%%R|0
zmOSdjx#QcvAbw>rCa193>9@Q>B5j%!OrPR#h1ZNZk(Y1!IS#TPWQC1)sni=<JO$^8
zJeXYuUq$3bvwuATKn{=+<=+>6G2z^^wfr*8);8Q1|3Xzc>ZOFWY6LTdBD|Xg2kQ2$
z4<USAtEovMyycJ6`O*Any@R;;EBy6Odbs-M^k)NRx^9`qFzK@xdSjGMxl~ri%L#JU
zZ<c?$-1{Tk&A;9w^ndj|dk&D;70Y677t4xpXuDQUoL!=e^6d!=^BD2#{)oI7^HUPU
zGZ!vNqJQ974zQ`c+B6<xKgYQ^-~12Od&Exm0@+_+{JT*TCM1tvMHSx;`4I%BEf57X
z`Psu(WGu8)SR_vKY*N-~jt@))jGdm{NU^<GtS;fxO|P*%J|%guAmWf4xhD8wWfOh;
z&{{mZsY4onJW_Wa7Cjrz`#{y!-Ujhib7p$lP&1)MDre|TGwg3F@vrpS@+Guj)n({c
zJVTG_;at5?s0g|A7ExJrvlF%Cr<`K2T!B^MF~DOgPkb=yTIPB~glF-s6ytE@?yl_w
z8T~?TJ>vrQ-j3aa1*e(pIPS2aaM-tty{9WDvp(n<T^9nHp@hCB!HBYju}C{h!cd;?
zkWlc)E5Z6RPC0ZW#mKdIbA)bUpB*w67G6(p7s;_KUx%)OJ+tuAfw9m7NH1l<Pd#vj
z;$eXxl~qAH^Vk7`hN0t7$zFe0aIQ6LsdbJ*^x@~j6|+ZVgOe-%L6iRlP2D&hy#0zM
zxbm9ol>HJ+Qa?R17GNt0aQa(cyz-P5;M8;-_BqSx*X;XS1rGrV2rNe-8DtBYy}q_i
zYC)j2wRW=X>Uo(TSpIG(-1yLuI;WupSy|*?b={pSVC-z?kv5FAca{pPIyIA`6rMqY
zM@{Ha?)3J0_;4gv1X?+l$%9|MiRcp?!qO=eiGLHb)Orl}|F-P9E+KaWNZ1UO2RsBB
zOi`vV4FkcL4KgU{RiV<63pJ7&JJ5-V&wZKqtu6pY;oaAp2uDVF4BVDZsFHOjspye}
zNm7BN)k0!JFD)N7nTts-KDAy!2b$zI)up`$R(2uRwsu&)O>%c*HxB}*xIO7&&21uS
z?m%}+ol^UMGzY-OAj_n|9m~BNF33%wsm%MC3$#6m00uAn6|f+I96*a8BaIY3A|lcD
z2sAfC#cQ-$@WaUMEcel;lr)QJAX8=F^P8Xt7P)}VydW$sCkRM$7^{|Aa?TuH`u;v#
zCW6F=;e^ea+H)Wk0Ios3CqfG_qCNKC4Xy?F<u+viB72T1X;Er2sGg>97F{$8BYNvJ
za2!4Yc%8DgdXyBlZ?EIwGsOcCXoqg???CY&n2Gy4;)8(92qkxpm%$H#!4Go#PM;rF
z4z6M0qdLRy<KzYM0q7U|IMr8g#DX4lS^5(JD|-+j#W2Yo)UgbTBT+sNjHd5Lo9^hI
z%Qr&DEYA0T_0rqx?Ds`!STp?)+Ple?fo*!3_OnXD9A6<L(g5;W8G|`G6gO>!k1)^<
zZU?apda$*l%T)`I{RIH16FDt}-R~r&*Ikv%cs`NhmkY<9v}fnbC;&s`vT^5a83z(p
zpD^#_ROFY6G7fM--TILM^8gvt#;A^Pikm#`BwGqPU|YK#6#)Pa5Nxa*+=nItBE_L4
zJ`vX<wsx<L!&$B2tHS2=%2Pghoj9n`2M1_sLXE-HP6RGmYjtP?l2Ai!M;yy!qZ36D
z^kDi>VMGyq1g1S=-^Qu7i%2(sz>q2By;=b8K%KIa)-&8zrbf2fe@EQ1-zoX7pJqgw
z3X{x|EK@c8M?SA1VA>U?%1AGAO*4$Z@gt1t3dYe2G#Nf6cW+F_4tikR7Zi>K22ylB
zE{s8q!hWjGN<WI?-JfZ3@A99Ack+4F?nMs&N0PGfg~&%qI`<i7bT%6)vxFS&ueq+1
zGIBb`n*Y*wUV$C1!eb<kT&OxT5QG4ka%h@ePmnl{P|<Vw7wkGoOv&dIjA;7*w2uxT
zXcH#G*wY1BjRVAtiSI!YlDza%sa8<3#7r{-yaA}3AruLIU+_c4Cy>QJC`cc{zk|~6
zW&+H4#Xm@-AL@<|MLI$a^=9OU&ep<+OY14?L?#~D3$_a%9Ji`Jf{-k@2_s3Zoj*Zo
zLM8h9GFDs?h4K@i<XiN;&l+dRA<BxQ*|74_P*>PW(8I`6+F9{bq59*;V$#|6GW`S@
z@>*f_-<Ucg6GjpqEUx&DLr=&Hck_SB@<!}aGyi59k_nN1Y*2_?Pe$BBZxkE^7!vZ1
zz<hE?SAAYq@zmY{Zvf12_*mVneaI>-aY+VcTMa&jz;b0!!i$aly|Muroq%`&XZo@f
zSLu=$&tvh@9h>*T?{6ey=cc)ZlG2}P^)9_F3)_SFx#+$bvI$pEKr(m7Pd{L(unDEU
zGgmRH+Zk#|YWnIW`_Y9sO(C$*e|2M3VRdUuHsEFO*|35viZg(;J9g%Rd}q+F8M0{S
zjd{+-W*e!L*f(MD;aEiWllQ3F;JTUDoH1->6eAn>E<XcmD)H_&=)!79WGWDcI8wI9
zD-L!qf)b)f@h%5q?VXAkdwo_cW>}d_TL|}L`aRwUQ+SYHIv*FWCXwiA&ZDedOY{1?
z(-I)?y^0<fIXpkIo<u<^Uv>vPf4`^`g<lr&j-l*>5p3~|f7q3Dk8U)~lN!xVaBSCu
zV2)s~JPswE*#?Q&g}h;)Mu$@h@(+76RGLsnLBK8E(IW9UBB1JR*@FQlwJJUMQ~D^~
z0xctZ#$lwSRP;4Rd%hY=ItyoxxBu5!{Fy<dyi{7p-#tSsbML%+_H693;3O>s-ix+W
zeiA%0pZ+(HLvt@n1m4rkLcQ{{N_HvGjyeiIq39Fn`{jz2VOTY6k)vFTlP@^{hJFx?
z@^oeGuW02iKBn0bP{BF<AbqoOXjoMp@P<2IyVU6W7p|T^g)HL?JHbZjENG?+=`mk{
zDXt1-<paYhcN-!(C;q|Xtz8YF8@W)EkDP5>TYL9y0I=w{qAwCzQZsBE(xIT?I86-U
z2|&x+`IgJZ{!>R(#RPYpa}d>`d9<%i(Ijw2L=B*kGmD%)`XatV4@W|ue}3S|!t{*9
zjqq|XUdZdoV7xFU6nOF@b-<3Tv(XWOrF$diBy#2(e-lXDrxZ*!+Y^DH2X9cc_FLXc
zx8tKD2<0Me!}^Dhmm#yV(sI@;C=H7K&n1!dJ09t~`0*r464L01qxG)1y@n=a1@QhQ
zuGhG?^eXD+5w1p5U9(P*j39!&vGvkt6S=OKPOtLv<hIAyBcJAe>d44Hko(xZFMn|B
z=~_A*{}6O;5>PBLKpJ8tya&$*tI7u_tp$?hKS8EUMVmN@zOxAw_jf&Kn+V{?lADB2
z>t8BK;Btq))(d@@Q1m{ftIPnwX^R=RXN<z}@D;XAw3Zk@s_Sco@L6#rbpLTkHvgXT
zsm^N*{{7v>v{AcL?8Z$bcI;*Z><W|gV_0HW_<fZ%ShzDIN{B|RiSGHGgLqJZ*|XOo
z(X~=`zgHMZj{k9^h~I$Fsc)F*8))bLIZdNao44Tj^`Vpx=m}GzTPm>+T!Lb!Qizko
z-UOXt<7u%OM>1r)@yfEX$;x`+6O3WBGBpN9(7L_N2OArV{qSfGeaORWYZ?hjYK7h3
zIEu2Q;SpQFjDZle)#1xsZtRbeH2DhOFboq3&oTa=a<g#2$lxFdUIzse>0n}i#TB&?
z!N7=%#VY!5o-`9YZ{sBzS#V$I&OO`Q3%xnT=-jQQ@pxWyYK4xcl~D@Uts3gMtP`wC
zbDw{>vnVJD<FfNdm&ev{*bXKd<h=j-mkj&IPYP*e9Kf(M!HdfD&$s`=#DfdU2DBHZ
ziX|hrLv}ig!4_wXQ)T{*#CMeSCQxAm2iq^Gr!=S1Q>{lVTXIRS?!4xg%s#0tVQV#Y
z?2AZ0u{;mp_8Tv!XtdU#=X4|$OL}Hk6+#qz02$R(sr4s!`^kH&Cm{0v$3D>#om)vO
zENKOY;5IAlPS)t;S?@P;{c`-oxQ9p{(SIUFt9^PuDVY`d7|WvYJL8XoES&7SJUwAp
z|HSDHz-(_z6y>+5u(|~p|1l2PY~N%{WtjRC8=e!do~Eh>eoPf3QZ6yz4&|7wx<ak!
zeMQ)HPmJ)tq82-Er5?3!T?%_)+S&A{`*NVMU#&mRV0K8gp-O>*)%}uhkpEtyi>-@L
zgctLygbx;d#ykV3lr7Gfi<e1TA#C>q;&!!LrOKeTtB-`2ambFKAF5~Ph7h!4r?dvY
z`d<bT4(o^{ml&!WrZlZHOac>{ha28B(8fvL5fQS*uXdUKShlb7Wf-}5&Fv_cx5o7u
zbj~HGKzjqPFN$J^^7Q_0PrVXFMpl!lFH4#>Y!4~^b9498wf$79lFK{HeI!}00qNe3
zyIZzAH_$AYb(;GVL0@vKntfyWW?Sw<5SuWdcF5{J7X8C2Ex0y+NB_6P=6J{kB%_82
zD0wVi?ftrZ&@75RsRGGqd@Tu?p{Z|i6JF3!q}1CeGbLDbXP;#cZ1r4}bLq6%F0yUj
z$`vt7<_*VSxPAWpj3x0{%H#wnp%L+ZAO#YFX>hB;Z|wHDHX^QXLe;Cmw?kvtVkr)O
zSh(c)VSNL_V!t*9k3W;04#@@r;pPn&NoBP(tKXpZwcAN%mjNGY!%b`A;blD)BF6VY
z7FBldoN*9w#9t?-$4HNU8i*@*)scdU)TC<^8SDO1oK(oB28P;ZqR7)vw6cTF^g!WJ
z8l4pHyRCuM%d4};6ro#sKbcfT-6GQ0lSR3vf~=LiI@uTvb^tHi>3+DfHdAf=Bg(E3
zMIDJ8i=oiX@NI*fRKGwhjV;-SQm^^#i`lbEGT@if2Ck}a8HHeU4l?U9BG&UVuh%KY
znr{}O)GI_R>6(m=TOs#412-v$f4X#Kx9dBSIDQRy3uR6IXRY_VLjS_@gSMY)rY{YU
zov9w(6oKK99kYEDxt!qyDJc1JIQBV|RSf`iSaz?i9TPngFEO36s3OCO>M2UZypGsP
z4=<jj)p*lo_}i;1mG|(QI>2V&B-wm03{9xqOCT{SK^2D(DGdxlfv1pFK|^P8OdwEw
zR-WFsQZ+`&or@Tif2C-ro75NQi)wJKBg@7LM~2}rR^@m0QDWGbOY$O1Tk#*pxt2;k
zp`?hBi>6uzEcLfKwz2o3&t+Y6dPw4W1G+*8Z*GguQ_`vbSmajYMC*|-+uX67<5V{<
z1iQbAgdT!F;)BPekW0#D(n*VdW1MX-2Hn3kwlkFcEKzR}vh5G2i8(hs(bA@=>9DB{
ze*r(Q@sPsW=pCW9t+HkLGG)c2E^q>#a-25M-DG_zh7|s2w9WBlyivx~YY@28G4I_A
z82bRtc<3j{KNGas@+imls_bAKTBsjTK2K4xLKkmsHX3!E6V0FHQh!6(5CKV@GDdu6
zQr>hZ_gTaeUo(_*krH#*NUeN6czH8fvVqb%#-^0SA?<K&P6%Hs?8|Soawv1Sq3dUj
z0H7m>P`$}vXS5yCRHRj$H!zW-sUt~76Yw<x6rWX)5axRBy_C}q=gqD0lR1$aeb7=h
zp-7;tC2!};olQ9mv9EFRKhgYP*pv4;fvh#jsjESz;Z>`mGL1B=mKF;(=`5*z@pJcb
zD{z3S{y?$>Nzd1kcff(MV~6S|&fxk{qhsR8fy5X8Sk8*1z5$xi(uPIv?duq|(hoP+
zV(6X3LsP}{otg!GKmUk)`@#^X;d3MFBwW^?FVEMP_IL?v{62WRUwpil{P)rze#E_k
znAbi{pzF_DhI{ch`qniowqgSkJyV+#b`34nNlA`!{j6d$&P`JxGhVV4#L2aMVhe&d
z;NSF~FN&uf1=R*G53Tpy8`!P6P;<gr6)ZOrPaPR2r#uLU0#ydP&&u}rd0DKPbzV>H
zID6lz4o_JP2NBBM%bWJ6fZeh=A~NR4e~VAPFSLR1KS!$;h&<OF3VL@VJ@MP?i{_u5
z&+@i1_Vwl{%WK=2GS_2Y_vc`?n?)E?2&pSfZsxBYMCqVr_)hAy>k7dA3TzYh%}n5G
zR#T2$XPzJRCn#U4)M(Z>t?FB&OQtr_{JqMx%V>U>7ztV^#ff}}{S|94L2nVP%9)ah
zt$z$g4dGuy@nFv5%omL-5Q!qlpPUt<8DBYz!TEja6+^$Vbd%%FmsC6LB%I&xkPn_%
zv8Q4q8p$^jeMjFlI4Q?+9wiH63!nASmVbGo!iJe>tg5vC-1#^%dfotDs-SsBMw`4>
zY3+#+-%3D6uD#I<q3iSS9Z;&;!wc;h;Jdo2B*`xu?9`lAz4xyn`coI=J5Q8l5AoPv
zlE_O_s~{2UH*_`Z6+pHHx%8j$`Rb%+rKx2F{$<h-n2??pa~Mz{%7vzoxS5t&nAj{E
z`tNi<8UwOYLRj6A)<#$}_X(i*t`Li{A@V!QGc=9k2=UNor<rS9b9H}SDGI3h>TNhi
za@;rEEh0N5kJo^rXa~y{fb(3ndt`QAl;XDQ<=fL0#`;N^RmhSQ`f!JUgN<G+9?2a3
z7EFDTpClsNJgOE}8>E?4#w|-2QmZ&S$~F%m_;gQODsZ;yaa);=sf{ONJ9ztRUzvkM
zHKn3pdlhC9Vh8#8Je1t}a%@zMZf7Gug6?h>3BJ+xT|iv!7{wq1X{Cjlv-_S0%e&cX
z+e>LO{=i2rq?MA~>1+G6MJ$7R3is75`!b;VBX~e_&gEIsHK!)3DY*(cF2#nYxe=L+
zxUOth>!QH6J0ZNFG+-=}cD%a)QI2*|*~$;hmpka^{Ozw-N951k-9-9xlq+SW&Au7B
zlV!`q{a+o)f5=OnmIM>S=D0Y=ZMjg=>8Q}H;hk?oyYWny@yt|C*HL|!aR?~5BqXr(
zI2gsj%4(;g@;fo{b0Rg_|2h%&$GBLP)Wk&Gq(oSF=s@4Bas@IYs<KLZo4Z%!qy6c)
zV9A@jxp%jY-7(vW;0nMAr0AjO_w;sAB=ytyY?0p*ABsg9yc{;4?__SB+I-A6;2bJ;
zyc7}2Tz!jRS9_lGvbgPY+ZVsgDRpBG@;?NB06d30h-`0-$O)wh&k4ztvF9!LX86Xk
zlcP$enw>746rCfTR9;NqCkzE@E^4xB=xU?`g90bOv0(n~E<1u%>d?f#scpxyDu?em
zRWyGQ3#xwu79>G$Kpzn2mdjiFJ*Pcqfx2S+G~_hCG;Yvr=qVW4EAHc!$F{F7`FNhm
z!gRp_zfXghS-~f*w~oP%fk#G1oF-GRysN;Pm(a~JU~>>H<B;1i5#%UHWcwfHTFe~|
zAdJ#_jA<9p3N!2G=Xc|D6L2$8Aos?Sdcr|X#g9MJ;<9$`Zq>nWo=4<JzBYfpvqrF1
zq!g{kTTk|yAZFNqZ)qes;Y$KQGL>G~>Gkoo{w3R$KD6SMe*bUg<V2JlrHrv0CzFtT
z|HRT!`q8C4PsmeQIP$=#Sjh8*{eHCsXMf21n-@Xr|NLHOeBt_Scvx_~ep7ivT;@|s
zQ%WSCa(H}fe*NcR{=rmHNAay<_!F}^>#H7{a%N-G_tkvt_@M+`1kf_yGQP4vxt*X0
z0ey{b_A?=Bp=4`k>)XE7?3V8#peDb!Ay=Y6hq1}ayt+3uZ_?fnG{!daH;!A6Sy@uL
zC9^eRHL6)L<^9%Z%AC(M{WC;&M0ZK&;}XJSDrM3Aj+2{@*Dz<#VZNokg`CEm^NY)e
zukH#RcdyYNvm21kIxq5%Uo6;|GMV~;y@q{k+e8D>dRG)7ac>m4za@`A3!(E+r^Ca;
zzC&}SXH2y6DstyWSA2dHmJ}OkXvCQQn{SzxJPk%huDnD!3J$u5&WG<0^&OP{7|n36
zWQR`JJM33nvEBMy0^513-#6^P%Q1<oL)3nIC-$z+HTr$+a&6J#FU0rvMq6gGR<h=O
z1`LgIOTsUoroFE~8NqlSTo4O)RSUGn+CJLaL`Gu9zIsLP`5JOo;@#NV=hxfUN7iF=
zt8$!KJXCrt^DLa|l}{s<w3jXxEl>XX+m+}a&!wF{FY78JDg(&Pt`M9KA!-nZh*qH=
z)`ZrGe)kB;#k^A_!V{T{Qi#0ELAT%X!Tt>^i2WwCjQ@wNkGVQGFT5$d)3l}TX7t#m
z`EgihuUEX+K>yf43EfSaw(5Y-%rA)L)fS8aE(b^cs%fg}b7=-45*`xJ01IDNU&9-f
z8%Ki9v3aGoXXaAoN9H=(lAK+5Vp!T+l}{EDU4~yey%!nN>Zy#Z^c9;fc0DTaoi=FQ
ztbctr&DlDlRUkbbe#CO~D|o#3!?rLupJb})h>w|O7ei8kMGv3*3B%ko1&k+cnDWuq
zgWSSc{1|S7luab`m{dPgmv4;~-K^Yv`2KdUge(>|%?Y>@?67vcFnd9nHfHmLKXJ?p
z)N*ScFR-y}=hoFezV6&^D&jV`?!nNMZpu~fyG-FW)$N9KlErbc;Wd3~XG409jWbB*
z{j?a<WRUt70Sa^0<E43Rn$u+aTaS-L>y@1a?!<HsXKnx1;@yEAG#i;7BOUhAO(p9M
zUq2*b9xBA?@qCZ<(<6VT_-v6F85^NkG54Yk<ENteoSYw_Wt+(Q-;HM;!5(8ChaR#X
zk?W`qhK`bU%Mt#3f)pD({+M^(vYUh>!gzs$eOJsf*t#PrS5m!1fIV{9-<dY2)wsK`
z(4@ZMhIYD(uMC|D7qWMWvaV%qvl}KCMR&6IUu5^bN*@rnD}#k4>4_)iK_|Y1k{$H-
z>GpuQtUawj{NmslJEt!4fXo?Ur$*m7$)n-`L8+CJjhSCJqrpkSUkFta57;==vA(@Y
z#ZBB*rv@pqs=p{unbD^$OunA8FiM`#e^sldU5)u;PN$88K?yL=<40_%v@%EC9dlWM
zo-@3CT4DRgRBC+WqX-WtU@@Oj%$7KH@RKRm9XZ{gY7?G2vH6hQs^Hx$M<9=-lu5<p
z!N*_RCOvf<OUH;qT7T_djur$B6py^0Y(JCoX6Tvmp)F0V0>0mj5(f(F?A!JdfB`rV
zkuX~8(G`e?OnTXFo@b@>$aG-J=JU>}pC8?}RK-umw`>D5r1u$pb$h6`&MrNmrT25;
zNBx|p&+<sgo-3(V;|ka^7vkkJCkf9ht0Z!Zyq$WUGAh-qJ(|SEx>p{$%Gj@V&U4Kz
zLz^*#8AM6~!V3+NA=6zH0~J8)Lte{<ma^%7`kv)(pst}#Q^VT+{mZkr#e`Y1A7WE8
zvkqjsG(0my56B9xMEfinfAc{mdU?J2I@bt7-+JYV@27=u_1$d016Kylh8}3^`6X*Z
zctqZ*@Nf$(X$`%2>#x_h$oxuG+c0HOs$F}mhWzv|;MM^aE0?6Zw*VuQ8%%&b^b1-K
zc&$P=#Lvv{kPzfQ2E<I*N<?ni85Nu(qQLsF5h{LfD-Qx+K@m7edsHii>>%+K=Dvcy
z0@>U%!Y9m8&mN*16e4XTuB3(OOo<=x9*9O`dcVHL#p(G(J~VA36GYid+^n?unVTK&
zkJ30P1ufpscvLJZo>&RT77=M~=QREQuA_+9tPV1d>kSP~U>vYKal}@|V3U3l@jv=O
zrmh>gBK3ILRwbT@3-P;W`rgE+ikaH9A<l-}xtX~dCoDP1yAIuJoSIoB64D&E2PRkF
zoCZF@d`4rKMv7)H`{%wd^0(fEmO;T&*n@^(0ht#A#}-(j+zY&`YFI*XD@{q2-grdk
z1dTuu>|uvfs$mN5skpk^FtUcdC;fOU5YnAEanju?Z_GxBS`ax=<M^djjfUG!DC>Fo
zWgpcY4LN?}UL5r8uUMyTlRf{JeN2y)48-vT0g)69-!8%UD_6h41S^mUG;?os2Fhs<
zdmBEGuC>3YF8xzUY@ituw7=La0(AM_UGnpyyi0h7umbYWXhR81@gBVVb2O51?d9vF
zQ|uBQr@*S#PNQ!eBAH}W8hlRwxC>oEe@y*&NtCReIyXhk^;*ZWo_uWTclRQDj6`bY
zOWK;V4=>CPIloBdpM5S0Ds*+pO!B?4>ghA(e_Eo3HJ-k)z$)rNqC<RN<%FbUGa-p#
zqh|MhU`zel<z}GnkxpkiN^PGRno&dZ-cY5j`^1J|gW8~H_oIb!$oMbESjVG}VO|^!
zfnFWmS&=%EtcTCm!ps0sW^1h}4E<1)*qbw*k|jnFzS@V>bh&9UwfsXLlSM*7aPdpi
zW@FhkfobpG_szkUfX*2wfe^<V>C?3bOoS3I-bJD%#j)n~<?Aws%$?IGUY24FO~$^3
zMwbIw=l2qhcXLOrzssWhJ>9TUJBxd!|Kzz<8evQ9(I6zoXm);nJCau|_{$Wu?qTL{
zLkC!3keC#So0!_Hq%;dK^VXpMxIe0$_05j2<GI;Kta4GY|J&U(-j;Zl?YG{;GeTR}
zx1f%c;Boc?6jHh@<OB_GEle_1SFec=Gty+MPCu=Qrmaa*{mV_OZSTx4pf<UH-^Th=
zg>QkkjrXT&-2!bJ=g&883sP-@Kh@3_u-hI-T)G9$HXaYvq6NyA=^fu4uMML<z4#{F
zt{3~hCavS!PZzrN+>VM17o22+)ctJcqRf}2oO;sQ-`~D0sGKu8{p#|vM9Kww`n%xz
z7sq*>mqkZ&Gi?MH8a?xuZ8$$pSf%ob%R^6$5XHnl>_Oa82@PLUK<dU)j|Z4Afw-jN
zbt*>Iz6FACr1BcRTc0=~as<BvL7`I7{pD;YVTer6DuNU9hVqqFHKxC67L#lzA>Gku
zCk742*q#TRjDIRl{1H*x-#S4IA!XZNp+PS*_L_U?g=YD#l6#*wPSsu2_R==axLp<Y
zQZ`PXU3L30HUiFWuzOj&M(J)idwIOZi*6|WC^LWpT?I(`UFHOdDFOODV^84EAh+%K
z&clnK(Cz37dY}Z?>JfX!G2Z43dGK@HBOaL?=^$F&W1Z3=v96TB1G8T8*(1qbG4OED
zwP&w1c!;NTa%*$Mr4-P&37{#RC6Jgz76WBSu1YO-S@V8P5d@P79dYz#L-+fFnYQzw
zKqMSP?lZ}L44hOhn*=xl$Cb;>*sp+7%jIS~E`B|e`=Yy_1%DxzqYGSs6Ut@H?zh6}
z<??2MH}I$ZpM~~Q;LrQBg@BWA{Qk_w{W>^pe{Li2436FZ#cRI^PT8O11>ArW_h*6k
zd*F=yd0^l@{7J-Tru_ssSwuDy5CF%E$Sm8hg41+=@!WR;8F!~K9vz=JcE>T6V}cZ=
zQp>99PV~F;E-QNASP@^W_w(Qs5jobt6_9g8%+5h4%p)Q}r%DV&h)5SKCkMHAr>0kA
zfnIdS>>NU2;oW8OyCiTnRKEP4E?f*%Jg_SS=R*|^?0LbZQKgZ)OmHq#LFAq_TpU$`
z-bF*o9;ndM=&OMlY<n{O=I^Ru27GMabxwaM(*Ag>I>AjNVE>_>pKEdwzvWv!JKrR4
zOKm-8-6U;GWj%-6q*O~oJ=fVJc1yWCJKZE_ON~2c(Ilm)PDi#QL{s6@^US6;b(Qx&
zv^z3?I?$~dcVt~S;3R2i?PrpjXNp>Js`qGRzx8X^%H_F@)5;+(P+gfLzooX+^sO<k
zls+Ca%&r80-iD-?Ryv)S6{-o%-`qa6RO`Ft?bB-1V+wzB!=p@4UDlGFr8BWm`ceUl
z*PuY7?K9=P-Fpp9XQB0kyGo?9^?Dic*G7;|0gKoV&46sP7ILIE`nP)loAjjMtx(oE
zZ_l60mW1yg8vHafzB`oOYLtEWAcxe6{QdNg#=rr)5N1o&i|^bVJd;Xh>?saO{5gFe
zyw=q2a*!V)B^8LXV-9R2Rfuy&4!k9m(z7cLoF!G$bASgyl8QRnSqC2^-{|Bl9QZx{
zDV*JU;4Y~)^FtmaB&j=7l>s8}QV`5eIk4+e5zKkiTDp`Pvg;09yHp!;&JIGl6g{(x
z4jjAQc>d@)c->`r`B@rdh|s#s>^WrYGP%sUKYS9b$C#OLNEU3wm<2e*3pOattU9C#
zHZILNKEw*vx5~^rqzHazm9=t66l}Pi*?GtiY_gpNJ;a3TJ(B&99B%Z;>q7##!6Sta
z>EOnXY(2z*>pv3nkP7}TB5UW61YY*Q6ZvQ7I^&`4d;PcC+~sOrH~-jWm%i7|6|X_+
z3_mLTQ`46Y{emqOO%`+f94z0MEG_x@TPm+Cw)=Tls;w;j^9$cn5MNC5v)@t?Uz+yw
z-BJR8a@Xr4h@AD5X_qO}oa;Is>EG^Iv2hyE-FNv_Kh4F0+||thaQ4h&B}wWn*V&7b
zS_UcYDz~bcxq|f{(x?1eZ=KHQx6A_PE&au}ETMDRGfSQq^KCN=Ru>DYGb>nyOA*s9
zRycmT0?zuH@0a7c)v3;&Th@kxw!1Sf>+V6QkSNA#9MDimB4cd=XfGs|u|^FvACk&g
z2LM4rK9yE;f&PRfm)2H+Kq2v^HBO+lM*`LzgWw_2R@GFXv5+LI+C0!<NSsxT31}%K
z&8lt%^e-f4yILGH9g?zL+X=b|N!YFl1Z{<6Y}Y|SNJNxeH3?`0ktkQ20Rkdo<!W?6
z3y4&?x>?W-;!}UM5NHyS++W)WIzz<w*LZ<85b6DOV9-4xI-;5h1VAK3)Ruvc5pfYU
z)}R$cT14Fr2#Sb7Rm+2B5h<wJ0T39GfYL=T4%~Vn)zIy~?KJt@`Rm57&Dxi7>t<>@
zY3o(%{?NLlwWylg*EcVJT)tk$Urt`uSe{tsU9MerT|Ql=T`pXHzr4OIwcNcNvW!^9
zUXEW@T^?WNT&`SpTs~T+T$XUttXsP_f5PV+S1*ioY&Ruqah>ea^7ZvCYLs8H{C%uS
z{GFg6OgB)<M1$(D>~d^ZjS}$FwvtrIWaUTM0?&5ezQUP*%O7sKw(YdltouLL{ppz%
zgqk(TSS~9F8yKEnnrDmt_|VkQ`T2eR<qUN8r>1|N<~XlIi)@3#zr!1X&B~#pgT&IX
z$Kx=Htc?sOj<3fmOH+Y5B8GmN{xuf;*)x@5HHDBG6v{}OwM5{DtK;1dO`e?tBlKoz
z_IdUfmx4l?f^Af`&e`Nin_VSm4#SD2nQw{lt3J<xQjLN2TLlrIioJk8Z<~QxEvZ>S
z??~<|_Y|@BkNXFcDWoFP2;OVtxJ_2p5li5wn`MD@p@rTtc=Ey+?mi-m^ce9`9*;9g
zY%SW``)Fq{PKNwbcdT#L%(yD|80$E5a#43rPO!hyRo`V`k}J^RMt-{DN|g9CN1!Bx
z+sAzSAHaoM!tD2H4)2rvvu#qC(k?-?(6fHh2a;&<$8eDHF3}L06gIL;5+hGoDjYPt
zi&qh@DMsqk$-GT@>xGB8=S1+}9$hA|>-8j(0A{}Xzrne*!CBl}*0~e~p{M=0FxK6a
zsQ-fZWNLi-&RCIrF!P}ue3bF%LbAkv;{GT2`l>T}fGEsk*DWT}<AmwWJ@>lWyj=2~
z{)w7MsXmM>j!C2~qdc7OU$d+1I2ZO7wI481JRRk81(=tA(zaJdgwEX6EQAn?i(_IA
zG7CQ=#59j0v*BwZqQ)`~1`a-(!!Gn8zPH>`@b#rY4q^!75<VFjq_laqitmo88r@v^
zv4P8yJK(s4z{az~W%gtLM=E~52n}fd>nGl-O8#ATYki{l#;*<4;n;u2Ce~c?2!cD4
zn64utNmmW>1an1^H3r>Pf@PXjVtwI1`o|5*e8;GJR)vLHn_R^y8TRP$|7`^yH!<W*
z=iHxJG}1#U#U%pOB6*iAyIOdkzm*218;KUAVv@7vRuaH{(yQ27Ym^#+o^dl>9=T^T
zFS;j@H!YyC$p+3@uSvn0FTQ*bAve)+HZyO}B!hJ-29t9z<JB19C=om1Dt0FJI9<NY
z*R4Sn%q&8!*TQ3iMwkrPl{ERDUl#@mF~x}dXjbP3`7xzw@;z+Q3H%*R>D^+*$j7+_
zL<bgo#Ow;EC-VrJiVH+8k_*NvOQaRY{i^=FoN`cbj`v*Sus*}j!Ev=LxmIBKFoDUI
zsxjvMrucP3`~vp#bS$i@FJ#_?$3K6V`fVGW;ulynO56L(*4T08SQo#2<tN1Rp=C`g
zkY_=*+tq|quBg<+^%f-@;JwT@mR$F$4`{HKDJ_~qaco8vGw%m=;7vQ-6en2uXwAcG
ztX*|qd>}_<R4TVZ4BJ#1B%EsDSU#YvTgUDC>)*SvWTroMlsmCsqcE(H5DgOim43va
zw#8FVoj;uHQaNXuGw)xQ7JpwMUD$9ah^On%;kRSKw&?K{pwJlI8~p?`G~~T%m<y)o
zWTJFQ4w?TL{fI{D*^Jo&Uq<$gbL~um^z);>mxWgx#dtb7^>R+hKMQiy{sb?o<xK~K
zyB3(yy%K6}=ZWdB);!dt8F%hqp{c-n3==_yXgp3B+bkXA(;Xsxry$JlSk8RPm*Z7w
zX*=fCd-S}d!9R~|s&$3L+&s9eh!E~0`2pp&y?~7Ey@+Rp(Wr4qseM)RilKA<uI&k3
zTlmGL#y<y(YLA3tnxJh@nc4h#mxK(On12_On|XIhnFTw-^K7D#bDqTq{%+A#yS>RK
zj$^?I4GO>yUOnfZ0~{q<1WmITZ28V)!VI<1#US~m4f*2>`K8<0g1xC9*w=54Ef`NU
zR|1$1WRxz2S&l88Pfz%y`Kt6TXDcUuRQ7froV{Gfs@MW82?n)Grv(fR5-!x8D2))#
zCQ>A5yJLVao<!0K<G%zyLFHlypA@<L*2M0f`kp)eJ%D9cl=WPcEZ4FcRC}IQ`0;RU
z@fG`eOQ+FB+#ex(x2fx({@?bqcdME*1Rm*0p*3q?e6huc{tjuAwoh|v7brKDSLAC`
z+^3d6R~<)+%*O@)72JPsxv{KcIkg&T+G1>wm6AFk=G}TCny1%>KspQZLT|mN^<!|7
z%srC>t3rznxg8|jGx9IUS`(7e+7fVIwuK_vA1@N{id@^yqGB1HlngxsV|w~tbU@J4
z3+joL!@ZMhZ$Bbvg`4xhT8sysPqm%imVX?6tEtwT%@IbX@$E!%3V|&L_w&)OTh5{W
z=ZYozF;2X+A^sPN@$<lhjH#fEP6_R;VQR3xD?*2QyEEL|h;E~=p*>W6L$)E+mFe{O
zBx!^jpherTE*$K=IIe~Ay0CHg&oLx(8(nZZjYMeuOLb0Ixky*+1Q1KYwEU|Q+Hd6R
zOG+h+_6k5WBTDXtA|LLjsLx+J9!07<g{b$)RBRpk_qfenAQ~Jt0^C<3S)I?Xr^Zgq
zM(F8eLt)8fvVLQ;G^wtO#pUE{!cKGZ`ZFuak-W!YK}H#w6Gdf3Ij(KmqQ3<sxboob
z4YDMY1?gHGf`%v8FB`zs-_tC7wK&dRvah`Z@|ubqx7JePFBfZ6PUtuB9=ysjz{KP!
zFvzVreDCb+WNqbCInn-kKFGSVc}rOqpJM4_xT|n_n|Qk4t8}eb>4%fbJ+wkpM#bKj
zYpGpnyflrb79Sn_t-jFpDW=gb_lbZRa$5pTuP5WNctpaIfriqty61YnuiE2v;YY3-
z^@Rq?_!wn>S>)v|km}K}p<^7ThJ(*c$Om->SRL}q^<a+~H^#uzU4kgCM1)N{;kL~y
zjyMD<B4Jkz_hJMlo4PZyv_s}a4T*oeq2fJu_@TQ{0a4*ei`kT^<N5m2xfOxh_x)>?
zd(SbE1;zKYz_5aYkkh{@J*_nnPJepJ7DU)(9^L}O2iZ*@I+7-azAI*Z^`1)`E_;b5
zSrtiYsvTwb;`0@5oIbG$xf!>bFfEx1lj1E|s_V0gIpa4EjAOistooE=kHcHa;~+M~
z1mGXnFH?|x-p#`9r;InH@zjT1Sb9mt>nV!kYgv>$9_-7YI|r%L(+G~QZpwi8bRq^i
zsn>q)C#y@N6p!BeTx+?L@@#x5k?fQcx0!a3qC6gw{Wfb(S4jx3j~$k^G@8SN$H)GA
zO7Lbwzn&lq<|~X*znl<dYeOLXs5o>ysTiatJrgO!u(MQc7POzsP?X0;Qs4fcpzfaK
ze-o7DiIEbwz`2b7Z=kCn{{;E$mVEC22XvNuDeuJh6NS?uUf+70Y|Q;15xPqM6((~|
z^S{E}W_t89LKj94<mv!#={n5J{{;<oB-w4KGz)xN4G>ub=rN7FPJeeZx0__rNq(AD
z?WNpBbV%LdL%1OkkNUZU7E);ZnLOvAJkW89#vxU6d&jgPmA(U9dXPkn`4EoQp@6W&
bcNrvYn#MQFzYT$z9-hPxOcI3xcNqT{{TaB_

literal 0
HcmV?d00001

diff --git a/docs/python_docs/themes/mx-theme/mxtheme/static/font/Roboto/Roboto-Regular.woff2 b/docs/python_docs/themes/mx-theme/mxtheme/static/font/Roboto/Roboto-Regular.woff2
new file mode 100644
index 0000000000000000000000000000000000000000..91d210a0ac669627199fcd50e895627b55e91b3a
GIT binary patch
literal 49248
zcmY(pQ?M{h6Et>g+qP}nwr$&bj&0kvZQHhOBkz|#RY~S%r{-#FtGc&mdfes3m;e9)
z{)>JC0J#4Sz-y2Ha-jcH`(OS4A6OFDK_vn>;e22QXi)|LIYv<b-a-C#umZ9HkpLG6
zK*t*afDnL&dxB5+YEKU(z$}<t-~MGBE^|`lH}ZvT=>sCvE$IlJ)eCDtOx8J1!=V$9
zShMx^x0>z&cG)7R)WP|QZ~XnA_)46#P}NFDnLLl2=m|=tXv9VePrNL>3GQmT1&?kt
zrr{nMCMipwA^%ry*vcT28j6~8l|ZUsmrNASC}U&p8Iu$Dw(HK@{wU08#Gi+<6jyWJ
z9}i3+=f<%$88ab~pd@wZRO_o)ts_%93pEWiq0H1!v;7i}HUVZ+lcGb;zhaT~&`=JG
zioBe;+8X*WNPfw6UYg4}Wnoe&m1WKZy_7A%jEwFzE4eHpMRtoK=12;)lBp3cNw<N5
zL?Q>KtOZO7Q@p9{j}Rexi)DQ>Ac!zl=C*0y0zM+QsmIJbGsAErGAki?6{S&n5xGtz
z-X6kquK=Xe;u&J+aiQLUTGvA3gxCvX8?<`~lBWdCAAYnp!knu7vrre>^|@|4jL`=-
z_h1qYy_!O}!smzo8hxbgH~jK3QIzm=I?R5V$w8&8OzU&H30WuVz85*>4)z%B5<7d}
za;no(x>4NuZ~evb^pof?uP^AbOP{vrt=)3CnIflKgld7urv37&os$Dx4ychGk$1g9
z+02#&PG;q*zoIeyXN#w`vBECGp<<il>dfNLSWmvz(}20wp<)vo?C6u1z(ldiv~|Gd
z5FzcST>fhlerM<HRyW{Y&g`%-z&02pIUyr@&c!FBqMB6uH3v$Zz~p?-e5~u!$y~hh
z6?{}=NoIaRxAXdXcMF=%1XgVn{(IZYf5Je8Ycv3{2N4(yC#49c5O$JiGash1coE3D
z979q&45tuc6jCh6<ePB5i8*Q!tIKq3TeN0)oscLTMw3Vw&1e|Ps`Prk(b%1$Qnr^?
zYx3%Mv7z+$j!)*Gxenm^TR7(Ds^g2`;e!rJ69^4c2Nw#pgo+x{H0IpJlkZpW-F>FR
z`}8|?@fi?|As-c3WrPT2o(yR|7SQ?!?k`??)qkUel7$0a$bBWUnLN2;g9HG~BI~b>
z2K~g0z%bsoe*lp-a=kc`K#|NW0CP#N!wFluZOyxni>*veKmn630JI(eE59*s{C1wc
z#Qpamt+85S71gZ1Rz`~)+*4jaVgBRi(<5hn)~&K5BnYYl2UZW@PvLL-DQl<WO!`$c
zNhpdEfyP@K00<O;fnhFws9$*SuP-mt9N2DptWN0m`Q6lOl+LQy3|PRHALRTb>_AAs
z6_`WUlz}l_g@<*cvZ6G#G__T7W>wWiyKEf{W=UAi(0sp)Zx*J#i(M6A8xzdT63+IA
z{vOT0sSQUo5lsWqNYuQ3D`Nf2Yn1y<k6uNNa@mK$%w9}h3*=~{9Wq92HtzBucgNa4
z784AlfQ50szHasYK<*Tv|1W?$?@i`${Lw&LbIaM+_KV9g;i57+-32@<5dabfHAD*h
zFKc&3Z;u`61OX$NkPsq-0qDZwYs~K{%A(TZXKi8FRp?(vaI4ZVLIX#f6}>FwJgRo0
zw89S@0#uv>(t*j?_iL-W(AnkfuL;vsh+3tcsAjZaAtMxJyS%yY3XrgB+ves64lV%b
z0%$_N@m%aoW6cSmbfrk=b+P+~xJEEMMD7D(bJMNqKA|Zd%+TfE&5v)2g?@JJ7Z)11
z&cB1>UWJ&^;HYtIW>L5rq*K>gJlH6^U3YLy=tYBpUL!RZH2~nF?btJSAhTi1CMm0a
zJO<piXYoHc%O~pKcN!VkTI?esIAlC$KlA*6NNcw9+Aqxgd_%7>RP$j>F1F|tXJV%B
zzK%t*F8^KVR0&%hz>un%delat8v!Toj%g=2&J#{L04fz<RH&Cyfa8GZS{}{hRw1+?
zF3Dd=QZ#LX=}@=R66b`mLqlkTFwpDoE19*wnxjPAL1#vgp~ESL6gIrkSp7%Rmg@lA
zRNVR@uCQz9i`sF53Baid>*qO1YEWAJ0jk`nUZg{iZrAlr8C|neL(I---<`>)Wv0Ip
zU%%}@plf!Nr&PcQ6qmV5l}`KYPQJ}W_jqUSxTjTNIm`u(Z6~y*d`K2Tv*kbY$_*n(
zD*f?f<>ee_MJCRZ*_y1-r@!lJH*jc(z(e3v1Bp2U4qh{4sir8<7L5QFfP-0t6iVIk
zWIECCG>R~UN#~5i6NRmktj)7dr<2b5(kRG+`cNAAAk{6qHDsZDgxQuJ@Z7U3@1-T}
z=u2vB?7PP+Co!~nKrSL`H3^^>chtanX$<Z~;6WAzY`CGZe&ox`-?Eu&ItAqY$i0%)
zub->iOL#Oh*<F#ZLWY6+D*JAZr1!F4yisTm0QCFLAA8ohMs~mu&5xC4cB?AV#w(I4
zol5&;WCAb+5bXR6MQu3Vxlh8jo}*;bvna!95Y;e0Tmf){$!j?LhrFTF4jSG_L89os
zS0UIR<xF9TU<49iXokb(3)-Pg#V3=+zgW&qWERw)FZ|9?hyp<j<RC~f4EdZ%J?8>V
zlQRD;l(Mg^I?kDNQ$!&eBLpc+1>?{gi^@6y1-eVrkDW!U9#)!N4W%Fu!~_H(go~&B
zJ<Sw9{T=4A2{JImXogaT5XRC*x-tak(>~SLgT)^&e=|rj5M&tyv2a0{LtMHNNC*)B
zSLLZ8jiyv%R#gE30Z`Veqv^ewxQ^eYlpT$`7x%Rj4HAxlK$(zbEZPIrVkmVx$<kA`
zKT*;1q}g9SS&v{rkxYUC)P&N6`mia52DWd9QJWU5(2)UxKsd@oANy8_h{#>?+r$x5
zNEbZ9bB>%6uCKJ~Ro15nr0<-q(p<8)#jSRf{6UADOwdN3)0iyoxv}|R%N8`ZlC``t
z9q>QFUEfAXl<slquE}VG7H>b*ekF>97)zj}RAtW{44$5wPk}U-6BC(Lot(8a*Sy`m
zM}xis%Kcz|2Au~oU{<-y<AXNJ2pO-AB(%;2S8=ntHrA46#M(N=)A6;6y>fe9b|rW{
zp%hYWY3}8Bc$kCu=qP1pujyn@1H6pB8StkSpx2&Px;)hO#I^mznwb~7vJduX+n3kd
zgrD<#u|jg6(b=7~uhl+$=mcF*BqmbmMl$Y4$_^z8-N!T;DYZ+Y&Ggqw-=$e=e5Vv8
z*$>?ysh?CqoEy}g7{3rc`8@_7qxY`n&ID-U$$REe4CrX^VTq_ks-5C)m29d-?2Df5
zw6;fIx@lccj<lw6YfyC)NvJ7RN{WA^x?V97%xm8)g1jPk&)t=mShMS+*P{}t)+?`p
zKW2bYTDt)o>v)jb9%7(=f+&`IJMuhpVLj)y_VJU9dJG{NYRvsqYwBJ-6&WdN(Bcq7
z?e`Hi9O2~>;mPCSkqfxuXJUV11M8*6pE5sqSNPO%FQcZT{^XxzqhxMmdgQ^#eX{KW
zEG}cw#vu)@j-&5?&G(aRDYZ~4hE!oxwOGo^E54V+mzuEiBo?J4rjJan8N}0)x`4Xu
zJGz}koj^S#=Q+EPd}4c&dvbm1_!V(W_-kvI?pJ>-2n-l39wzhF_uKpY|2Y4_t`ZvK
zU2$DeUZGl#C=A9kFfG9@;qLXtFwLMjhkdvk>rN>~4HGq0Uw3uh#rE~yAg%+B8De&0
z8d$Yx#%YnR$_HKscC_Z-n17+Zp|6kOObV{XI>&mJ#Vj59TyMif0NC!Y_1`E&<_q6@
zr`O(>D-bQBUx*4O2p*$=iuxA9EhJm$H4|>4^dQ>~EgKlc11$<$mZNC}tQN={2{#Dy
zkm4cbq46h{i&#iECYGBhKV}`r9wSbNh83SGg*`(r$~OvVD$=FwCC^c`M~aQSq0pn`
zC3jBNRaC3;sh+&n2$CFiUDezop*&(E9!V(Kw44ZikkVH6N|L)E8yj%FKV(EP&z%<3
zGyG+0dv)Ul*FYD&eg5u;Y(?mH<Z<t}FHuyFVD=dz0{N?B@8%N74Gd}(bDVeRd2G8N
zF{!K4#BR34`g-7LCuSf+SpXg&2OfF|MkANzO>+S=G5GJQkQSA;^-J=zq%C|vOvGT~
zxU~Vwk$B!GI#FSMCr9*vQEiM)31@(w$uEivcP9eqE!yMa2ID2q`<l?V#qHSM>ASr@
zTttAZ0?}zEY5-Am<F~lE3X}gma{L53s^RB-#^Xf5yRnHzOGruVIgRRBerDed!`u97
z+6lZs<Mk-7vAgco-ocCAJv<lX_6%J0brE6f?ElK+@t?k>^$YIhwY|>#{5^X{lk|B^
zs8_tQ((hrM<Br3J;xX0TICt1Xz4e6hwYNP=9{t(p+9JdFcJI5T*%dPw4vIt_oj$kn
zeD^f4{(5I3{aK>T8OW`}b*dEh`@PSp@k5nx5sr^z0AeOb!)bh?a@$o~HE%{L#sG9X
zS}eK=J<)<<51^Y<b$At2pE_dtb2S#p8M88l6)FeDnqRI+C7BDRLx7itm#!yO>yUqe
znjF)7PHe`{2zH2;lwP)DDH5;ftVz1aV+CJ|F^_JMA@`|t{{3cLnS;SoJ8yA|FuRSK
zSQ(QDc8H+441-t*f^{&6|09}U;$658hMLP)1T~$1UI+UkhKb9a=8?lV|67Jn8zU5o
z4GQ1C?A>zg*l$nHFiA)RtYTBqLps1>x2`Wo(eoMr6kHFX!w*LMS8)_Z$6es<-z0_W
zce_N|(P%jSmKjkuK;*{h#noSbv@>_s6Xu7f>=T#Zg|F~~ljGN5*G==bYv<wO-`efe
zxo7`x)h)MMxu!4c{u<ZXnO9aJ$o^}-%JunaCuon_3Jta`2&#XR-5r>8G31mnzI%vt
ztmL=LEDamD2lyumcKd05BcsNV98J9w?c0H$0-W9M+f16x=R7}T^ZoK740>8?{)Nws
z6|z7}Bz^mSt2TJ#QelU#+mIwjpn6EDk@EC=MS^%qrUf;nc0J#a#lG9BaVe-ufnV0>
zUk92dxqF!+3c#=uPxDnhx)8w|J!YHfP9v}OkNr8;GJyQCs9x4Kr_t?po?9L~ZJGPF
z#USAda@KZ|RvPZiiPTYEL#TTTRB0lNvKWG|7_}-dRn37Kr<9>?3`7~iaozxDiAp8v
zl6u&SvI<uLlNFVmWMhg^0gE#VjCd{B<9rna%`Q>`5}!g12MX$g97eSg?_wSbqZQ(}
z7PgdB&DLWd`gVVL@L8>OAq}dP`gN~4yylZvf6DhAyzkYYx89rXL!S>{RoZb~MxS6`
z(nrDH40H4RufZvHiL3`AAx8&%$8Hngj17*>3TC>kq$tD>_qy6ldJ^jRT|}n1V25Q)
z&aobZ?YRgX5DT*8tLmQ4a+v3(ysnYH#z?uzM<ux0DPHnFu@Qdp4SZ4`E|5A7w0Nb8
zFZ~|Dq86ebWl4GBvcsu$mUq^*-*%&@9>H~iR1gYCv?#tuK)u*YO_2n{KZ^sCMCb}R
z2gTE`NQ?FbPvNbDjz4_vr;sF38VLasV1soeF-Cdvq^9skyoF-R0olmCUkbaq*QJ{u
zGF`S90=fdv+<&BQ-)(IH5U^*|M&6grj4e+Lg-bTRQlCittbfAnsX3YI2dA;vd?kCY
zxWHKQgYN`<E4Ab$<~a?X(($rVdcfg17D}I}!W(nbwt&@edXi}P$<B5C^GLOT<X}?u
z^ykw6Eg*DF+zf%;$D}BSnmSa9p5$P|#q<eNt9;1<oKU|ME>m=3lJKOeria~<q$qFj
zvNV#(J3)6#qydZ!^+q<;G1{)%)`<;0xa6KHJ@mN!Wo^oL_-lS{x_^&?4VkgaxxgTM
zGO4CV#^M<gXsV92;gwv}igjd^p_pt5Uq2H}Ow7a%*GUR8^BCYr#=B3=CXNweL=pMp
zm)RdeNwrx3mFNJiWETV-mjXNikMVX~sa@|->h(koaQ!@sG=?kTV1_6dg-t;hkoR7m
z?^Fn+RiX(rfKCsX+46khtDou>M)35cOwyopQz_xaK(hB$sP2S{bZwdbzv`z28qn|I
z;NcVqO2|TlHTclY$t?^G3kHJ3c$LXfKG^x!hS*#8*WwW$T7~Hs=JHv2`pkxi%tRlw
zk3W0BpCm@6l`-U(8NRK;IJFZ2xNLr*lk-Svz4$lzRWA6^JlUIlF&tvyhS3mY5+dvk
znWPJhVRAmr%4jX=O_dj<5uuQ?(^uI+jQ*)k1QY06#J{BVx{$o`j_S!z&okkMJyEuy
zy7gz{L$AhlWV?`ll(X56Is?a%)SI)$Y3>MKOkTKe?3e_j3a2`D1vFIWsQ6q(mxPRw
zl5%Oohc$I_t1-#J!VlGkUDjW?bc$<Wcss5T;Xjd%68DEy%{Qq$ppoav6zVCaB~me5
zuri&`=Wi=rMm3tD7>u>oq^3-=Z;{4oALPb-8uihxjiz%6%yQfJ^*eHUKJSp_Uwf!B
zR*TZ7G)3N%S(D2OWrVWL9B(13BmxPry6CH+DlfdoX=GC7M!}qT(Z4^sdEwb~o3!M@
zbTL=|urK9^@1z{~a1EuMsV5b9s06w22<Bmackkj)>@n&G#iZmoTatPiM-$R++tl~E
zFGBOEB+%8+R8iH_mXpa$F&{f4Kbx4UN<z_)a2YJd>lyESpC^Lpdp{1+_)BHhG<~1Y
zXti7HnhL3L1AXb}!)5Jg)W5$Q=OhARslU3g-H@NSZ!YuPCNF4`9+f!fuzNhZt(!=q
z#U9poS@DhifRIc^KW@&`wL66eeFD*Y^hS~};z$ilhy;!&x8Wg?nQ2i$6cLcnA`2-Z
zA*Dr<C?X5*Jw%){&3tC;tSL+sZ`5nldc<Z<t`ie7nBD}A_k-e9v_*m?7O;ib6q;zq
zL9BeX{3ZddFrJ@`;XGD-pW=e3%I0f(Y!WJ$%a;sx)<*6QD+`<t9F7dTa=PE|sR5;3
z<g%#wGqITXh4kOtZ1nP}skl2R?W<_s#PY~<Jq`Qw35WdNPvkTFz&qqw-|caqdV{&0
zeoh8(=<f=8)J-sc6Ql_3=jCKwO=G68_}x2hxJow|3rk-F&dSz#R?<IF|G{i3doljN
zFFuONP4`@*UyGA%snBtN>#;=rd$(*ABML`>El@23MX!EW^P@{5o666_cUIs*S1ng6
zlKbb{$CVqg2AA{Sl^51v+z&^>_OuoY*yVJ*V+$o5L|Sz`sah_dhMDPMFj!31;rqTR
zm&{?@4h##0$vU4QR`1}kQlz|?s`!mqfYDDXV?6JQ<jEdOe5jT0f|wxk$t~zm&H9Gu
z3x9orPn~?y*4X~{$aib8m7s}UF`3S2#LQRMce0^bLX(#>_tfsG5o^9K1%sl=<JOHC
zWF@YWN!ovADAl3!Zf{Rrhni#MYvmpJQwc%7{Wem(Rty)CZBtJXp3?4^sSuD#CJN_I
z40eNQkFE*ZbFic@O~RHx-}6w|ZaCM6GiiNCizdm~(n2Ph?nOT&QjTIFR7JZV`+t6E
z3+BC_w`F@|{Lk)#003AWmRJCpU;qHHHWrzsmI_N4S@di@^$IMb=+wrIQ0iv2E9kp4
z;v?lqlWx_%mCM)FW>l~!B*Viy$#Io-<*esSb!d}XFU~{dkZmUv>m5cPe1kGA;U?L{
zv8JUNto8$zOy=25$38z5P60lu;!|bb=2t+bAgkiznvNaf6QpnXzH}R(k+~WnzBd#F
z9aLKj{4te<W@Q3`q$Y|vcKmS82$gQbjLc>;+76l1m}fL8PvR#0F%2Oi;V8Pun73(o
z0VbnvX%x7YOcpJyi<k{G(b;NqEMARD9q)F%X5WTRv?nsm%x7Y~X`<3<HEA_N`w0<2
z5mQOK;%wweGY0kHHS2R66uD7UDiw-lj?)HAf&cLfb2&{;Xf%d;Yqexc=;kZ+QB&Bg
zL;J&dDECO!!vT3d8KW|eYjr8mlIbc|jw=?6rQ@w<jgw0{leB6Ym&6lBP6T49G<iDn
zl&L-^&6+VgCw%(ZDQW&AC%T>3K%w-vOaN|zv2yG7aRzC1Bioc<iW09`kB+ewAK#o+
z$cyJ;209oF7L(2xSkK!6Idbp2nm^3fiG1nebM^}BT%l>zs;!#KjJN?cVoOqZp(4dI
zaUuAZNJ}X4^}2KO&$=`zY>-@4c0!~vn%J#_V4dF{U0Z6zkYtpNaeprGT<6@GP)HO-
z!kP+U<$stcYE*&6WURF*muw;1e7!pS37w4?iV^R-b0Gg8fwosMRu>5-p`EST;4Dd2
zT4gR^8k)Hhb&S$}rTmW^R}k`UZr}WiDU^6<2%SmJ7&Tgt6U!P`hp<@J)Rg|eppU!Y
zs$#QWXjHvoqwYLR2;v>IZpS6S_13(91>icom_0(LMSiRIg@Y#55sBy~n<^wE9!<Fs
zOj?$}h0XjQXRE<iU|GU(86K{##RNHrax{0npccLR#+Lktt*h!1&#2TItv1btmjj7`
zTr0IO41j>r|J(KWpS$s2ni*PZnK>F4TIy-I!|{+`i=Q5MdzO=Ms^sOXuQqw~4g9p2
zr}iAd%r3e3WzVF(cClcturckAm0@LmSxWrWyAsX1Qf6hj%23Evpwm8DbK|_20uOcv
zgW0+DwOjU`^8U*9??mIL7spM%cFw3GcKL1TJk$T$hHY&14G-U!=^)B}FKvq{MONNr
z38yfKHz>L;V*1hTp|L8Y>zGbfu%kV@FFa39S&FhGSxKr-Ga;+ZjnJ>*!#o)M=Fsji
z)5g5;#V147$eM4ym8~HU7qrV`%+6lISGI`d&9rO21sCx3_0;##psw8M;3zl`+RbPp
zg90$^c#0(i2G!-5<!O32Wnr5@8z(NqgW$=lSJe~$gCNi6b;O<j`?l_Jj>$u3+VpU;
zIcId*FhbNu_03ba+yRmFakwT8MzXaKQnFHVKyl0}Bk6=ny|l+(wcRZRJJ+{`x0a@)
z!o+A&FAYv>RtcxiLD@;Y>vcTJ+djk_8Hah;x@*OqDo1(2$2nX20Sfs{!=?gTVx>bh
zUz14nG|jd2G6ieCT=Hpk5aq1`TH8x8<9(^X`rwcN5STl{f@`90I4W0ecfVH;U<RG2
zuqV7_Shz}&;W?$GZ^MK}yt}O|ms<UCDE<g+thopu*92ac$CUM?d|RuiIpa09V__~X
zh;*0n1&bBkg%iko?22E=p9Ylw?_F8uJIT;c-~eoHqcA$dGr+@IlbHa_9`HtSX5USu
zv-qn9vRye!Yq|_nT~rUw02>nXq^5@jf=wPoyJPLP<U@`_`f8NPAheeyh~svk#|4@X
z;Df%R;C$J)BaJ*Dami%Kbz<pJ#h~#u3PFmvJW#T(vDC=LhAG4)n8-9uy|p9KftC)u
zvRekDI+SVhGF2uaL}y+XMF9!bLS5JDfF?qM6EoSO?fI*+frFTb$&Sp2QojxrFBlH6
zVGc74M9&A_<k@@%n^ZE7Q!10sUEQfF`9kp*F-l_~6gTWTh+Y3TRys+s!VA1bxAO|5
zL|J~0uyQD$tPo9fXENBc`rNuyVR(a^xZ+TY^L%0|DNf@W@fku{mW$Ttxmqp*sZzN|
zW(sj^I{p7;@ScAjbHH&t57W?f-4C<id6)8&fRArh<(IA3u`7-XMnXtj*neNGD9r}4
zi=B8vb3ppJfqaD8j2ZMVHFZ7VGZsmhKg<XB)Z)$479CYSS40b~qK3`bCOtGP2bS`M
zR$z#uj{eF0Gm)8^V@(6BH546IM|mTF3~$}#!=sTAO-TMn_UPHc0`nE1v=G^d>YLqP
zm#Z+B$Ln|>&;CClaeMKULEi-+%MkEc7{l^#eiA<9{OFpgrkZSO=F9!#{IiGP?YNu7
zD+5$Cofa!ZfT;LDhfmuvPu|oNoSsp@_n4ZsCKTinezH3jSUOAS+5)7vcRkG&dRCqr
zsyb^sE>pN~Jogf1x3<vJuph__yRe_g0tYvILran8t%{9jROB912%Gh0{dmJB0fANw
z!jEJ{Je;XaD<C`bV<8C(!iVsz+|9<#Q?BMj_R4jZwE?F~K+BQ709GWx^$`KdYvIYg
zeqh@QqbZcJWf{L9aPj<V!ZFI?2Nyxvw&~jXfX5eCgDwGZ-Ea)RIlXk!W&T2IMvR}G
z2=~1J06OMnp_gTH><FsYwhSbOFP2R3d+VXM5LMM?JTTTw4SBXaA=sOx<7v#uX|bs_
zj`0lUdMqpOb6qSL&dPyFJj@XAlUE<9_vXjId5;<Ny<Z3J{we;W`G$=Ytx+(NN;tYN
ziU0lM;O>i+3F=Vjx=RwE?xbR-*?)tgPKAz%_wt2^_!f!|;%`6o8P30f9B0|izrESc
zvspoI-;Us8gUaTz=r*G7q|5y?ggc~X*>y)wbVhdO;>ir4u7$us<jdMd8bO4R4rw<E
zR^Rc9kh_HEf-bIztQePI=4`hjIvRB0f-z2^NHQ3KTvd9K*o4IgZcrY9NL6}JArAM#
zkA5A4A1aXC>(1Xhc%w$#2dZnD=}0r&<@bjZUO)iXvaXMX0*E<_rP;Uv`f&Wan~M%3
z*(d*4^~h^u)iFmqJt7hb2nvdYhkWp@x*N*zgOTK)9}B~fAsCtLY%aYEH?lK+5UM7t
z7H8d<mhK7P?^*ZkHQzGT#7{Ea*cT7%x~_V|@UAw!pg;lMUH8R2cIFy7_S}-kL2|X8
zQ9o3Wl(cVhcgl6v?~-FSdijXmjOG^HMrPYf#P6QU%-TD~X?)D7na;^_7seB4Vb+}~
za;5(twW^;KTTCsMGjK3kREM(zNzeB!)i3xdSoW`$2g8-&U_asC2Dmf&lget-ZOf{3
zlGO_GTsMQkyh7;C|1DjAcn^13`U8@E-=JD<i$W;DF5fa}yDhe`k*L1VMEJl)(p||z
zp5w;0WUT(rdVoEYh$lUNS%zcw)e)ZCb#q>M&a_Y(o-deE$y9a6>nXL0j#OQV9r{1%
zR`dVl?+Gv8r^z7t-sCbnB1gcT=Quw}{WH~GXmX=LTS4&_!6LbcBLF3skN&;IZC08B
zI$fd2F9jh{G~@4$0|iCJS)v{36Uu#rS8zcVz+Bb#+$~hbncd5d+(6FO{46}gdG{=K
zn3Lc1Jj^JDu}t7+d*t6s*mu~VLk*6>_fP#|JSf-Ho$%c^r?<7evi>o0OWz-pDAYgP
zkF%L6$a{--Or%&1Qc3pQt3Z5^=Rq=MsG4mRXV(0B_5<1<Df6-XW0Acob3orY!FFvo
z7qg?hC!n0kk6oknxgrSJ2?a0{`1;3!uDh}kzVF=|`2D1~^-VW36}c+_voo)uv-D^u
zpmxfmAGf)kn|WH>zMZD>U7yp@i{eJ#%&!#vj3pcjWKobY`@JSWQ@hWHsUdeMR0l>#
z=T9bWNkWKbAnTs(0CEd?E}%MIzJ5iWis_~ydKNKE3diH|b3{#;N_Uq-<w|DTlCLh~
z{Cb<DoNCkBKEP4GL{9L7mQrFSTeuT+<F6)nn$GJ?WNhp)&1eegQI!o0*8VF4jb{{v
zSwD>kr3%TsPlns-{&kiBJ1M<oe6{gh<L_E;gzeF-zJ$1n*7z;@Q5f-(3^AK@>l8OM
zD+I<>XGLXwjJCzM6LtGU0HYCQ*obSI1oqMDsLg>BXD%V1k*uLx@bbI>(<b(tXICU=
zn^e&7vJQUyj4S(`?}oxWMZce~NmS0HO)+R7S98dTAAd+xE13qIR*EKg@3m@_6Mdin
z0%*~Gz<gJ$b>h}875!L1(aQRKIe177nTW<wFw9pDfrDb%lTdOeH<1MQtXXvaWRnrK
zm9J|ZGcfP9jIj|~{li;0C7$gw7(Kj(VevNBFBl7OY!`KiyRfK}qOrsDdmW;^?v!Z~
zQ5l8lK^~7Lkg5GKs%l{Xi*N!sC7{(ZB|~AZ;jJv%mO-b&ah>MAdn{rmsp)cyhRc$4
zFrkJRO!`{Ng5X#VEu%5Y4%w3DVz*-RUKbYt)N5=gF$S1Z`0{s5XBnabK{D#gKLjyp
z3~JNv?#%LNmJSxZXFHNxQqJUMhsC!f&-m-1W3eQBX+s}{>~^UONtZmajF?Us+mBZx
z>gbh#mj0LgnEjb}^MX0>(<pnxx_-|ZDRky>je{AaDXXdE4qOe_vY-{0N{!JI<)O<H
zSMG-DAbDVG%x4%V`qStr>fdQ;nhzRj>K_{+XE(#kjj$*OIK{#|>5gpmYlq#F6n{Qc
z>>T|dtJ(I>#<|W>sM*%p$+-q$xVetq%(;>Wy4}~<XD;q8(qj+z@6=_!#BVblLFWUg
ztwrg22}KBklY>B&;5MQZ!lU<u%5afxD<V=AVUxxhEMjR^alIB1#hjSQBG*_P1VHHS
ze5p7-W;lZUpdmURi2O)Qvkc>0eU34r|9URXw<>D8cFQH&IG(3%X!@SVQEvQ2+Vee9
zjWAk~R-r3aPl-d48!tdrnU+^B8VaRAVAsxR8lnbKujyPGs*pmCJ9yqS&fPu5Ag!f;
z78iEb*w=k7-B3$eQJuSNsd(7XRmj45MkYj~e!(~DGSef=kD|;~tKUPCSJi@S*PM=Y
zj_?;FV}o=dmj7w(>gx4{@jPQv=dlXw`#zn=_9nXa^Tesz_RGvGyY`FJ|Lk4eJI_6E
zzGhiin3!nUNtZI_qqsHVyG%d!AInUeOsmf=mZfI4mDAa3@kf+&;r}L5T^7tHD$(v%
zVh1p){|!DFFg=B?JQ@y%xev>YW4G?=iDNl$hc_fK+&HV0%lx9Ceq0eYgNk@HxHdgH
z7XXCq$Dcz){Ik*kF$^%UXod#I$`LSdnT!YL)^!Qj?Un){m<R_o%tuSP)>Y6~?@6D<
z=X%0O4>fWBRg=0VK1dJt*{!SSkcVceI!GaLc+>n$kbj8fq=#78*Co3`aRFMl4AD3D
zDvps93(b<g6ZZzY6ibJTw}LQQQQtGbK~|(nwRgx;tFK=ioU+;e>zo$kPUKL-_cN%E
z*LM1Wmib^5+NY)gl_50=x?vL>Pl*BzDxnl?Ui3g%8ciGC!y>6#2~aVtkyx{17igCA
zrh@`)T!oMF^E0vKv`6hR(eG?|PBg9{KyU?Ag?kT8!A33NDd%X(ky7HWATszz&<vv!
zh!hlFu3E8jsYEQ#XM+T>18EbvV#gJywW&vt3Dxi=$fAo2Qkc;x3Nc{SvdB`iV%4H$
zi|H}k9d2tabct-1*1ukc$7c11`2};e`Ym1QS4*d#MFS_{Sq;a)ZvZxr2r?9luHywp
z92H`gJ3|3%23o1eJP(_VCU$m^I&`yp(|hx~=8J5ke=79T8R3Wx{YY2+fxuxD<{|cD
zI^Y*I=mS!(zyAex3l37vDNsi|4K@%ZoD5)&fkqr3EyM=4%aQCQ%*2jivQDQ<24{WW
zN}w6hziO7=0stGf4>R2gE>sD>9GmCqGKyX7b%uR`Sl<q{Y8<}}&4TK+MBoizK{k!o
z;EqGl<bTjp#B;Y)>jDb7@g<<a7yf9kYeqX822DmUa5>Fo?lmcZd133^@I_7OE#dPD
zq!J#fz(^%nQVx?+1f>!|RfJMhKvfdl#FbK*sS;|X%vmXoTF$3h7^)J2g)O=8%5tU&
z9YZt0rDkpNWECZ&=xvh8MZ~;}iJ9yl=Y|^xo4>iK051g<0DBmXlDY_{a_I@+KA!~R
z`wqy7OjekT_@sNNA~HV7w=p+?mC}mWJO#a-qZ+Tsp6JjK)&$Ty&B6kNvhef>`y56o
z;AH;{5XW#qyVwuqAdW*GU2KtjnD>?q2n)pH*P4pEJuL#uvGeh<Ea4o30<wDykexij
zuN=*zpd9HewL766u(mTOC5*U#7Cm(fi@5LNI3xI+*ku@X!OjDh!ZmHwpHsFn4?ZIX
z-!7@ar*V)t3%KJ(ZMJ8oG&BmB`msI0Dci)DcI$7ks7f~L9+Dl%G*+auyrZC1%HPpe
z>kz$Jl$gq@SlI6+G$oloN-)CI8Y{gi5xD-Su^26Lvn^SgiN)8m+AR)xt~gV;gAc&;
z)JRGO*!c)X@iV{NK#5z#zT-M}5e%kq>DZ09?NVCU4$Etn!DN*bq%p=m4(`>OL|ob5
z_VO-H3B&VJPIl_DrhAC%M%&p_h6jiYk7Z&3_o9@Z3#>2+wZ@mvPJ;;kO_~$5?6%3K
zZn4{pllh!Exa!7Y(m5TPaG8vSEZJiQ3XvgE;vz(SqZPk-MSLOHamsi9u#tjh4G4-<
z9N&DK6r)tKY9ajAq`#p_aJsSWLl)3C&sssx$Nz*%mH-P;cfTEefVfx?e<UOc$x7R-
zEPvq%Kyr5|696SaD9G$|JekDbK9g$&HEv^oo|S?z!GtjHAWpYhYa1rA2fO(}2}*Ry
zAjKj=)A%t;_i?Zc-17O1{lAGeB2rO%hYr3>`nM;eY?SfelzBFT^=x<2)Lkoi94Iwi
zn@Zas{PHS0TifK#Xl#6aczJqzaB*^Tbai%jP+{UD7JLJyn~bWguCTQB7}HZH3RMhX
z07A5mV(c4O(L^;;)9JK!y~RW$YSn6WT8u}GvS1p7FatFxINm{I_=zA@cTI1=1;j&F
z0G~cnN-*QU?l(g(Q>tNYP27%)RE9s<bg`I_2O^ofZule3<56F>gQ&XSOg87&?GI>%
z+~K8S`~P~KGJU@<WUyF&>?1-O=`?Ey3{tIhAe#vj8s@^gV|*5j{2)c(sD>GPj_7A8
zvD17qtY^aWc?#?qT|C>Oo<F_p=nSs6HSq&+2umtXDbx$SoK;T^@$V<?FDgDbh<L#W
zCRQ0QoB9zrah`jVujy`?U<$@V5(%s8H-Z3AZ=5w68oIJb3c%vaRp4Z9|4`Thlw$wi
z6rRi2SKfXY0J^`?;ULiYZ}xgnht?|ic>>ry8US;4_lzeD00aaqr1AQ}e?ai}`P%UT
z!FGet-Ml{i=uGi(D1Ts`fdNRx!4!tqSR%<3q+)F+;dfRf;YlQdktQXQH&iAPTVh}%
zt&@e<QspKS1%(M!Je#!wfjpv6=O|Udy;F*%A|Z`w#P|O#vh@oMCvV6ign2IZv!2NN
z*oJ16n>C<Grm&Xi_Q1?6(;C0FWt*dITR#N+?n@EEj3z)m(=bAnMPNOeP{y8SfW2aH
zMx=#Ma;qpNxqL*QG4ED1vUKeZjh9_CZol%y7F$DbpSP6K0jeW~QzF0`A`H<T#(SUu
z>LMabX)whG)k#6H5akRLtQE7+P-Tr4teGzJlI4*a$hT_^BRZ3ImwsE3#Y3YOrXEU>
zDoV39wm$C26{usW;I1@D8n0@(H2Ia(S-sa|<BaB(PMErB^Q`u^KA698my5_LfF2Yj
zhDWAn7Go`f(_-Rs@{VAn#w5$AOfR?0lTg!Q<8uGtgA2Pn?i(GN)rOIJXnw4}Z(#4Y
z!#hDaLORQvxGUl@>N)n3$pkJ2RUlv`>&a9^P!p@f2PpM}!43vA#5>J%!4vmKgeRya
zC7__7{@C<oRK~veeS9+X#J)f|=DFp^sXOBNqomoqP?+XX<wIV?V3ed&8cJ>?)ucP4
z=#bW-_9;NK2-TG5aP%duOXHgGPCAo^G(GC*b$$G)=JZ%Pta$}I>70Uk4)IC!S$vK?
zk3D;Ut67<3ahMfCXXtdWO+oQO!5EP1YRRd1I}m{Y5txF&(A(5KCas>deWVzHr>C*5
zAJ?_;hAvg;Lj&?d1mq8BrnD|7#WhbK4O5yZP81}R2_Wj?{!s0*1k|SFY4}jdE0lS7
zIaSoeb-Z@O&3k~BlrO_uKyl+2VyDzK6nv&z^9P~nkG?(m{gL^JCzWpD&y_x%7^R?k
zYT91j*!lBh)17U-{6eI_4GRe*>)L%(;v_3u@+@OXt7_^hvLd#~>#16)tdVv}h{|yt
zG#@oGRMs?y)~?raxIqEO9SlgQ7jpm=;;__NMg!yG02akW*&)*9w#Ec&3T~Wp&|EjD
za64el6fdQO6#;5Et5~~8)N#Ea!CEQ+x52^e`&&Y^0;(pe$;M*4Y@o)yu&=+BZc&9p
zC>I6|goA=?OJy^iY{FzboOro(_fC4%1yArk70vna<T(?rudN|MQirQn7j#QmMQ-in
zek`iTa$uI653|{A<P1s9R&-Bq$(S@Cb}P{1D78F`_NiQU*J6QlBMJ(FoYsfK+8jW5
zQiOr{AM)=Fqwl(Uy+`va=c1tslTnd-it$Vg#rP|Y(EMET+gUHngrT`#JOalAiG^}Z
zGNx8{+_umMOhTqeP^MWiPPNlmi-W4av&hD{);zRqawv&|y$&tN1ds<q=Ps6=So+`c
z&gB&p;_fF8h3jrNkt<3{s(~?Q&S_e~=)D6g^a;v-&A%Jg{@R;z<okw@NlI%)RCcl$
zuP44Kb>^L<?dydLfY?<d3|Q8}v&<DLo)l_=SU_6<T0o71u>hF>G6A>>61Tmy)!-ij
z>M2W1!gioF=d6u&EP3m2)0TZK=S(^y&8yg>0`TkuVlDouv8Mvua^vU)&YWuXAGzMt
z0p8jM>hJxj!O`_4^!>hLe|`!^0g`y-9Y#_<cbI*v7zIcF+4t*nTK6fHgqLczuNLPy
zK2H3Dgrza$DD3nWJ<|g-%%^vq(&)-N{=?h1HpFLgAl^3&BH!=P&84CB<I^D5+uW$r
z?S$sv_RjXjzRwfL614EoM-J?TeiqL6w?>!tU)J2qs|IBeKnI>cZE?L6&0+w7K^!7B
zTruS5<W6~-8IPCDG^Tz!f}A8Bvdr`Wg+iq~4Ss@YxeQ*m-cO#Qkqs&pNv}45lN;@?
zKwK=IinpXg{G9y}fu?O-M^aDqynokd-iBJ5VNh<QXcFNQ(KjqnHDcu8NP;htsUrUw
z{&#a6ow%n<mbkR2n`hQ};2)GFxxYGj<C@EjO&@6k9Th1pH8t7e?7iKEx{Hj4h>Dz6
z{#>#A0n}T0a1|;y!w7Vs^e$9&xAiSx5~f-P>>48x^>>#rpBtvo>u0>#5Lk|gCkFOo
zWF$<cX%o9Yy@AOlefk|`%qFb8aIE4MKgmCC&~uo~<_QwD@4#dm!G4n<hDYk66nd!6
zP!%!Ddlwx+p5f9sF~{W{8I$89MUAS9DK5fN#&fo>qTGirE`+fp$8Pb7xjEv_inCYL
z_uQHmBGYU<^E|^LIBbzRP&UW4RZ-T`_DE&MHa3zr%{i<w5XS#_jy|JWq>{$c7X(F}
zxst!eS8<h-H!}##fLQA`;v=h9MBZ7fNaX(eo>~`7QngryIT~I;g8|U*3kbl&5AX#>
zlSmv_{}uT76%45vZr>@C_)iSZ(5aIAeFCe67x-!!V@iaJO$|&1t;{D&$+oOxf=W&`
zD{G>_;DwVDY2LlARX|y(l;*aQLMtSyUBthNB_{)t>ttiN0u)wRFe*|;l6mEOt6%GM
zT16;>OqaZBwD>q|ivJC0w6q<KD2f!7Qc1~lG&U(jq$<1;1bas8V1R+bUMVnwysuIe
z3|$Q(9$@bATDqmos$kjKEp0@J3ZcO^yq=Q{hF<B`MEB*-NOqDID0>8-1|xJfz6pIE
zDk?=qfR2}=s~B&~R6w@LC5N|Lr~(0ifgb4Oyv=&u^FW1mHeZM^NSYz)VC0`Ic7)zl
zmlOcw;a`X^P6E4R#4R2np>)S|q!RtJjk!@^to6U0e-Pj^k)BuyFs0aX0KO#aWIhsL
zxP0gl1T@>MHsbwH6>E(BeGrZ%QGwM$>>ygi%joeGG9XXkzl^EjfWG{o98OP$q7-7#
z0Ey{!kq$;M?4jCtU-hD6hRa;c0Gs?v^B9TSwgZk?HU?5QmEqH-V<nES-wfv0@p%4H
z(`CTKj72ziW~|4m{#wd1A=41azyt8Lbd}MgFh2n(f>C>@*Gs9y;u?Yfy6`N8w(y1!
zFzta+=iG@lVP>DYk6uXejw1M0$*s=~!6fNtlZJLH_C<3PO!(INToF%hHrg=2Y@19U
z0CV0QZ_>}<YIV~ol%ysrdtkn@KKM-fH)Nz~vWTu6G^ngBV)gCFmtEby99eHRUjcDz
zV?qewf&-3~6dUrGeht3JohI036K#RalT3BDte~;P8*ZdR(bDNO*zO;63@q21*O>Pa
z7I@4pRR&eiz@F0`h*FI_uymyS-I1-39Z4^t;ZswPO3i=z?KFHsXiz>$%?w{R*h*nH
zmWUba^{z{MlBYSNY99<}KSOjcJg8%$#V#h=iQk~pO0phtf*r_@-d-NcF6$WQ9v+`<
z=FiOMlt&!rP)e=`uR;j-T{XvSuc5HsLvSy|C{)yQVXfrJ`ho1wmmLL1F|C&B4cHEY
z_5lF7iWDFPA)SJi%VYy_&Pf7-=F1AXbR3^Rr4iLNE>jgd9EOyaYoD4kw!k8Tc5jND
zj3nb1S%ZIm0)FUjMl;%2i)0x5h0EAGhL8i^F}f*D5~5J)W&U1BX~b0dUaN2{JxqVp
z&_h=3q=plB3ST%_HypXY=>`1&<MC56AN9&KA?Q5W9k*BavDx+tDCD%{!|D#M#esQX
zz;UhOzJE*CEWxV<uq>nsWUwro*)*6{($wSVX<pkmgM9u?;);^lRj$MEGBk*TMbB14
zu(Ev}lGrwjWX~|i_k+UDGjp%X<O(ZdVYFl#nRtqGlEp(R{ES^el!|QhUA?ey+_0x7
zl*BKLog?wx`5DBkC09dQZiScG9z>c5`oc#_#A^?p;EbEA8sst>nvO-WQD1M&>RPAK
zV`hhObcUk~FN~m7AleYy<A=#GSQKiVRrrV%$A??^T3~ya+sjdExYxM(xwU$;5#`np
z>meoi6Y0BuO}42GVI@aZ%O`sG#jhw@51sT)HXr-{#MI55^4Hp}tf5nP!?QuBhaUR9
zeM_v>5oEJ5m9*EjMx>w>UQD8;USj?+@S-GkV>z&*z^e!3IhPE|&0&e`L|$uPX<Y{y
zpR0^%^ij0mwt?l!`+4sr3o&on42>_cRXDuK!1h{JM$_>QNjDCryD8)Bu!v=+_5|%n
zGjLlGGTSL&qgx}Lg&kJSVd{UW&?+3sz{!?LAzOZW;7-7s?881m(wz`iR%+8roz9b%
zf=5jxBgH^3=0M{n9zOU%WDrR=478Y0Rw>`K7x*D5ffG|c3YR{UcR+ydzTpQMXU;e>
z4tWSpr6WP#M2pekAcEw=L{kQusK%L_r$`C%G{!K_Fp~aak5G2Rjpga$L?g-w=l5I7
zQlrM;R2gVAM3_XzI}64VvUY@|uQ{%k@U2y{)Y`glPUw>!BUx%8#`*33QJmRGG3KW{
z1WLqs_MFAtc=@>4f}RmfNZjg!QtEp$7K;7~x5ow@hS2v2vH=1BuG*!_3m;Zr!0%B}
z^@`@l-kf14mByi>z<B@^hsr=@En~7m9D@f>Dn@Xqx2nKo4SaL`#MOo%#+<Dy`|$Bm
z?%!f1YqD?nCjDfkEnhS0l}G^vHm2&Z+OJ&oHLX!K(Yg9cT}d2-HTK<L*VEs1bL7?S
z3m)js7&4)6R+bSAVs}URj8~ab7oT-Y1)_!t2B_7pTGzQ}LzxScZ&HjO+tUk~B%G$U
z8|jsS#eTw_?cL8Mg&x|~Y3^8}OB{FcYEJ8J*4{>hsX4I09xz-IEB+gbz~%InFbOcJ
zDo@|m>iDA^2@Pbi=ci(2v*g-Chs8F`^@(0RIsgoH4?_vX41H7sitF#Pt_JmRMY`%F
zse%AXT*dB|AjVmY+D>E2J((5Jqf?FZlH7ZBE??e76h!|V?FRsLC9B+(1>G)d0eC40
z@<doPv_AK4n%C(~b3~1Y9VM#GjyD06L@hfHVjF85;MyE^C2^^cPR~&so_?p;bi14X
zW&K5BD{+ksJnr^s9SA`kG78?q<Wa=5f&8E-s*%~%oy+rcd#Mg#G(|Qegy3QIuIuQA
z%@P&TCUi=<eBC!a8hO)Ysh|D*64y_(g4v<G2!Ib8;vad!EeN2CcDEBLV0A$mkDaH1
zBic5})t~4rvBGMIUA9Y~(vm(f4a9Vz!$s@~j?|^pQGC8tlu-@Wsto^_DU{_6j!>At
z#wFG!9SoExdj#!*HL+#Rc1n5Ow3c~}o0WZA{vH%mZ4BFUHD1OUweU6)p;7FoH4*Uq
zqtK}3i`lKh*3v{!$6<5;3+mZ?+S$NWV_dj|(A#T(o-GACE1z9<W6umt8poIKzDWP#
zn7=iAM=2s)=~qZ3HZ8<*g07U;YK(?mK`ZWn_DJ&HQZ;NUg|${JkWl^yml}LlIt%e%
zcl)p|<$nTHHUlcCh18`*DHa>MzmPK<KP1zC@*iX#`shM6>N-_|ED!TDul6BC+|u7)
zh8_wioBuf;lqU`P-#xIE#rlLl$xLy@WpB_NT32zq0oU}HH83AJKYVPDUIcxb=i$mm
zUlO<ZVHg0}I233(hf0tMXC+%4Fq~QhkJhCNF38M}5T8SLyIXn_HAe{F)9`#P7V@!E
zLE=ko#FrRRh$sf$bgmU*VyAksJ5?RHJoQuZ*8rvY1{WVDoQ^>m+MRV-#bW*yZW~6W
z4AJZBZGAABhGg-m`CQ}=toJW397hwRekVq*-4^84D-P1CY-r{0<9V9WHr(|j|HBE#
z-^WW$9!3h`47=$K1#BX};AJ~%3U1`|sJw4%pAY-bR!Rj`e;r6$nPYITydMTJ*e^Io
z3InmdhEVN|dQ4x|y?$gP#E2z?A`_OU$OhKa@|doflnhx}TSJW$-5QB$v6wVHb*P-{
zJNILbp4ANc{n4P^*pG+($bK#e$B;(9Qo#NZVn1!q#G|IFfuu6GZ?^BCSD%0irSK}3
zG3((+s@QORpwVEad5l4XqV~bkJ2vGgkyIIFoSp#A&i=WI{!*mQVVN+MYD&nOVQ6Ai
z7!&|z2vLI3wP5VA-oTHYFT|eONRvaPA66L8dx~~t0D{UOiQ7zylsYDuO(4|!1IJp2
zD<%QU3}bP0ky_x&(NV?(-;)NVJommh2$>L^)C8Jn;c-i8RLS`I9T_k%W;|lquF+vI
zG6CFN0xV8PWS_=XNUh6}r65w&@Rny!Lg1E}7)W5`SiubUK~q1Co*`z$ql?*dmM{lT
zO0(p~{MOxr!5X0Ttd8NG@%LUfOb;;Z4$~dHv3}&=Bn>1NoNU|&7RizE*FfVmg0itT
zP>zS!)J)Q|+O7IXM@xZ<<t90-dw7}p5xvwYn9Ir8P0T^?t6x^Xjj<&kAGkrKN^%bp
zXHwgL_n3`9C@-s#f+f6}>8izGPw-P<v;Jj2Gy+ccr7)W?3hAq_HpCqP8=5Gf%jAkd
zpW1hNpL_c((y$>h6QLwJUY9UT%TIz-N`a8-SYzG$s%wQvo9cud?H@uevW8L_=F>Z@
zWq#W;fsy#3YYku*fVr+|G7?PNWpcF5Nn>iBWtiXyPzP74zBUJrfFTpm=?<L}BK8tw
zo)+ODS1~hT?EFHd<8>}NN(U`)YJ?8xa_7jG^UE;0J816EQJJM_Mqj%ibTQ;H!_!(}
z+<oQcgv!N5vW}2gg9yrZuro)?_Z3uCXnfWD%yfaV1xJFNRp!&i+0WsmX;TBIM2rj%
z(wl_Go8ByKUk*1z+&RQ>U5X~NWoRx;zC?a}C5iYb1-+#Uo>69r!)TXPM>uy;p44J0
z$VzOW<Gf4>auzy9D@iw_bTZAHO*qd<LVzko$#NE`A7I$dj<nZeB4tN)$S`%E-Mfq%
zh|NH>fCZ`0<x|qh{UJ)AJkHo>E29F-eq`bkhH|<n?V@(Bdz~2&9j{zU%qY|<1kR*z
z4o+or54CO~>-S&~W=3)UK^a@M!FILXunn>iReecsG^Jq&<OTx$!aK~XZ}6nIeQ};;
zUD7f2OH82_C|$*4VNITHTaN!FhoWw*HdGF~>wbNztzAeI9_+X4{#2oEfMC)Kagfoq
zMP~B6f01^$A)Ar{&aguNzW^sd*uMzLLkLc3V114z)wp^R5RvUwfz{zZ<Ggz^nVO+D
zKpSYvq(Nn#wK~e0i+FQgD(X>OUE=2QMwj&24K(KmA3qEZifZSEb>wR`R(xDq*XQ>G
zrkbM78)>82a?$*d42jo0ViSjwQ+p$8o-sDL%vADYv@R!r@)4eJMjWg)ds(TJL}gez
zSMJ5BhwnqorBKh4*0&XLV$<a?XH^p$Xv#K5blBO>)vYI)o?weKAQdM|B5~FYnb!pr
zBnXrdxie3JJQP!+!Saoq1lpjvR|{OLp+b}59?_N-HYr-f)xyjwW5&7daLk>v$qlAc
z)}%)LSbQnUR!kIJb+@@fkZ<4%!NA;_RH|)Jvo@8qGCtU4N2WT&Q6(eJVBL^zxY;@D
zV{`~Bi&f5@uOp|b6L_)uuXp@nU1|7VLTyJX2AGdFPdX0^cgE7j;qNT8AK<XaHF*zV
znv+^1hXlCRsXNxLMo=VH?@Qvl2MmLlx>9Ed56Efr0=NshJAajP4QUY@$Ap<_$RbPJ
zMGCbNOFfP}xZ!h(7o2?s$ct=|NtnHH#b)FaV9bbk_1QMo$M}BN2i?QZ1z%LmTL)SO
zivjPOup}Hh_=-xh@z@V#tgZ3iJuFKt<;j9nxVM&hBg1kY4E+*}CZ(rYi_}9o`K+B;
zv7m#{y5C@W4&k|2Tq(C9gu9T~K;@=oX_2k?)H|GDkJ9lL<zKI{+zeVWL>Z!x0Rb57
zx#`xBZvLIZ^=0tvrK5$MV%AQN8hR?U+g=zhPN!p-r5Fm-orO~}V2Nu4o`TfAyps%q
zPzOF_RVu71-@SGs6xlObGgu%91A<IXGKfVu-_Uhrp9D0#+3>94Q#J}}c--*OVE^uF
z2D5w0Sn>3|0~jWE4aI=(wF#-1+}96A+rQ|9o9WN(Lkd`mip`Q|gv?}AK5TCqj#s5g
z)NGAb;$n=TMKQEV+?J~T?9?$-78SkqE@JQ|DM_#{6ei!0=EY>>u&IdIZ<0Z=k9SIW
zsu6;KB94qnp_PJ_yL%*9t$$@4S4`V3PE=(fY&EC>Gb&=`!~m*L>IT12vCKWy#Fz!t
z%rCA9Bw*!%%(I^(oi+eM-A_LO%7y!dPKW?$TKz-Vj}k7#Qn(-+Dh4Tk9-xqS`50}A
z)DHDzMD|n+Jb;)i>C>dz`BH3|1wjTy(9xPoZ?M!q3sSz)mGvLb1m1ZbTd$Y%XaYFI
zl6DU|YY*k}fQXRu*4XJIh2|XTL1RJN(m<=(w85D{1VG*?gTgIAdr@#fPg~u(+>UVl
zvCGq5A`~&WJ`z+Nj$-MX%S!awp@UQvM-3m6*Tm>4R4U6WK*NNh1;ZANC(-TTHGg!6
z2S_}}ihzslyw3{92W;2F{zp3)xshO@xB-oaT$NM&2~=Dn*Yn<vUTb#pNohgQSP^Rs
zU$8s}T*ivvn*Ipg3PA5>TVWUAWsP1BBIlk1TwYhea}lcHu^|tIuHFKk*WzLE<UE}P
zOj)8C;POD*ZRCwLuhxvk_pr7SDh*(&CILBfPJYuhDZtWS8jxI$uiv<MmV9z@Isf4O
zPn6is=LHqCN|roTWtTSZ^$rJv&7Om-RT{<_ikl6uU|BA@**ElBIMfjUA;BL1tqCW$
zOfI8y=AiXZO-9*C-&jp%m!v#SwJ0JS{7}1HtnULsuetwSjI(D#VD{8<H2WYRhs+{%
zset`>f>RL@PhgBIyW~pHU*klpzKMBtpw_}VOS=g?rLIfWuWVBTr?XlrGjP7N?Y_cf
ztmX-SK7c}0aq_J{nuk0sHz!sWw%i(;wRSmmDE)=`HX`PBxbjRvaJV#4afhAJM~J3*
z`x{|AhDy_d&}{85%R3T_J|eR$VxUc%U9xbh43-JSZgQftL=S+LvCQ`svJz$uZGKOp
zLV7qewgngF3WlXFJ%tfVdffj~;Lvt$=0+~}#RjzYmaAbACWPBKC@^+bf3u-m>h7Q=
z=;{q)G9fD}ib5QdrA&jzP$A$xm>J!8rFMywKVbaI&~b{-)u$gGV*~JR;`_C~{qFp1
z|6^kby<ViZUaz0Tju(U|ln&=R?)n~q^c2&4hKqPMk9%m+1l^fWYYjdyIm$(lPE+(n
z)BKw-gdJjF2RH4v1jsv$AN}e9G%Zq4bN&b>BUcvS8;6uHakYnuNkG7Y1R0sB0MAP(
z14RN<tzoem#>-}KPR?|<HB%*u8Ny^>sI^@N?;cQ^WN8tJ6yCqFw_SM#=!u&`bv<)Q
zXnl2IN#G;U76E1tIqleMixtS8iLJ+B3_Ogej~!x~e8Htuvtzs_vyOSI3lD>ZJ=N7g
zZ2-{CclT#hN2j`Mp@omkr6wKSmq_CN!W=22maHa0lR}3K4Xvhw@24=ZSy$Kp^jgo(
zeGyh0I$ga9q-Nck?vp&jv6o;VjA*vE^EOX!ph-e}8@kvM)4JunMd3jhWpGg{6w_%o
zfi7x$J3Kppk-sAk3y=xmJ{>~HdOus%y|`Fv4MdNckZl5e$DWtGNk6Ur@#=f-TD^=;
zoQ^L(GtgXEB+itHby1D=z8I0)644KuC1ydIYFmLnu5c>7JK5F$8Fs7Ht0y>fSDEkC
zE3X}F*4~tUWxe}!-e6j_V+As}=QvYc+5L;36HrNhaD^q%0~-n&@h0^?Rlv?P_wVMA
zWV2n}94v>7V{A9fWd{TW+m0Hl6R|Q=@F@qj8E8kl_i^-1?hqiLkU>miGJn<WDv;iV
zEJ9M?M5*0MupqcbGI^)rOCUEIeE_#hC~GjX4!2&A{CtM76ndFcg1NCjaN={{3G&0e
zcB%vqyA8;<xZ3lI`NhU15fPVI+U1H|gO-ekNgjHxF3bDLjxIqPhXpTfZv_A+Xj4e7
zRY7o`gBgc#M)S2ljX>$t#rVCIAFSuF$X9G-)FNpf@FT`+yiBn9B3^2abF*wJvcDgZ
zj?0=TblrGA7;pHiO3Ke?TSdhM1GT%3g-K5Ak%&TkL}3+ydPvF08?Ah3s}6ht*Mg~4
zn?0IsKBY>Zfq$9Iea|t%qgQrj;m*^7oL+iMVYzZK+B%A7mI0hXP`P;^A6tf0`2jj4
zJV^Y)+qX&UZjl;e{q2if@;w)p65|Ro>$>!%mI*aTQq;{<BM116v88#i^0nx3Rdr$?
zy8D2<Ku?6)KMCC_4fyJFOX6X;K7Tj-{-kMOs39osY+>wI)N9+i&&GV$Ory`<09`ev
z<`36D_vK8RJ{^U0!T@LE!`R3u@Gdk0PFhJ=0+o0$_n5UQ#aOu1FWhC;rR_g;t!W7;
zg|Fk|Yusz4P3L@2YuUb?CTQ8uHXeS4r>mAo?tO4pQ?ZQ=cUBf@4yVmuw)e1zn@?8M
zBJCk-O|kJLb+&)Y4IT~C+MG7XhxMOr@t!9i1UB#1c~y^paUnY#PiJ#jY{miIO7;1j
zk#E?V=RDt;5_(06TrLwFTls0i`&vDAs!ep@K|h6_H!ZYyExM0W&c)3(ta>b6iZ7S2
zQIL&<*3<I~t-sf3sOa=ow>jFvg~>Ekg$>eWsBm&O^clLTj2(u_DZ0CIZfAepNz^t!
z9)Gk2%}!2k?A|okzG$mR-Pz1|7*}DG!b_StB+^2@(2m=IUK*3X)rK&n?A?xDTqN6V
zPw7+_a&e*Bu!o*Wqv?$;)sX}3)s(ksw<-+(Mr=|%?!fJm(>4arFR+1jyL@hA(xC|`
z8Bcd%1iLMn><ekj@+;kF*f5Iy4Vq3<5+?KqSOyCjO5|a~iCoH}52(j7lvY4%BOrCc
zyE8!<&0|U^#XRsXww{C-H-C~VZ4s)@sB3K^Yf~ewP0eWwk14{8lG`nhAkla~S&u~5
z><hp|+ki%Gi>KBw06ytaNvw(uzTelY!b0{onj~F}2^<FTF+S7y8kY6L-hck_#``Q)
zc1j-x-j&PF*FvbkNmnsS*%Nq91-(0mAodhU?Y!i^`<)NQ^<}@lavEypap($Ij&p+l
zf`il(Sh$PtHtU&X;5bgPQ#-RLYHyu5#76Zlb}YszAN0+g7A*UvdG4O5z;c=7Zl%ID
zWE$u$>khJ;#dS4!u`fa@Vi=c0Wk=8ATK7PfDk|6BCk$TtEYt4IvNkAG3C|*x&tmM~
ztkepHh!>&#vN>G4AOf9^cJ-Q88Fe}OX0~Qw1*$<AUG$<J{vS)!(463w{7eMu2ZVOc
zU};6(x0L}pg_f!t%Y)iW>7BUbXfx52z<V52b{bM*!X-Cc)b<~%mc)2dz?XxNpZ~`r
zJXdETmGkVw#JOiUMYG_<*CwOtnWNQ-1yqpy|H78Hpy-oSV-9?O=^MPmAefT<<cOJy
zjcMz)z&LB%1%99(0Svuq)ny$3O6B9g;e=M~PnX-a6Ruhp?w^pNr)vAGuXwI+Z9XTt
zwV&4xZ9~|<bmLq-w&Tj%*#Am^5q9??=ao9{s&nIhiDExhH)wgwb5|{edD?#&87#6f
zb=yu1`dJLfBWRMYoKUiQQB_AWRo%)-UB-teO9B!pb96x-kU!VxI^K0zb~zTSNg045
zlou-FWlrN=m614st}iCu0fW>x;y2(d-YsU?_pHX{X~pg|@SI=(rR>}fAj9rC{&qsw
zW+<&{OTeEHY|lNK3;D`#-we)~+2U6i{?{9)O-lr~yfg=1gvM`P&<x_%ZsU64T_4Lq
z7JBStXd&(HF7Qp+jl8-|t&1iAk{x-V6^iA1NW~<rg2pd8#giCra5ftz5H2)P9JsPv
zbYGHRu}o(-*|AJWc59%*TITB7xDV(=T?a--!YHE9*tDmuH2^Pw5M;3>kqjQ*&RFG<
zYd0v|CV#qVe<f$kOUs|a`sG1p5zVf3co!Pz8w_G;gHqa*PMBp(NWm|(BS6HS3cHbZ
z50q_<g1!{^FksI5<va04vP>^_+r9U7)%N?7)eRK*>~8educqT22b#rbU402+McEG*
zav1lPDsD&R7t9fgYO2Y@<ZGt|XQKr|J64j)sK+5>D3I?WO4Oo3RCfo^zdX^WRkNPR
z@8C}NpiQ7lJj~pjp4>&4&QqC>mZ~@6SmAAR-U4%tjB;FhxBqmre<p8J(DcJ*_jFKh
zU|_$52)MB@$xfytH3F1H)CB~owXR|-9YB#%<PLTo*-6)3`v!2VcrmwZnO!4!7%WV$
zw%dHujnqvlp}IvanL0{r`_tNvq8uD4$9o=k;rL@q<asJ)a*oPvNKqe;u-~o`kUG#j
zXG02OD7&36uJ|DbLV%zI_MpuCANg2LAVL$8X3PmdB)u4B2qv5$PPz#vA0Z_G^w7jI
zoZW>zwh?0tUCPC~<_x4B!qFU+`DC$fGlA8#ZDXyGe`7(*lHPhP6bGgY9zf!x-rv7)
zMR8;XmY*T7VW;qH8<p;=k)b@IAs|SpN6IWrpeK{_?~13z?z@q_>jS_(q6)h#3=u8~
zfp+#}hx|G3tX(52GrM<zdJo{&vP*Lj^&hl9xrDJ)P}N+xDo0EG0VGDB0FuF-bD3iV
zRC$a-u2Kdvu*{IpX)65>0n!}Y<v7n8X{$=?t`g>Vuo+;M^EW5GJ@NozE071Wq{Uj}
zC5^#wQXeCC@t~#4OT7fXs>GY2_IP4EO+PGTcfRat-xVcVSV_HPn{<G)8KWBzJTj=w
z{AYhNj?Mp9F#nBg=R*rBvN-$YFKn|0b29o<#Z>GcT64uFo`q`fcR44kxcN0J?eHy?
z%jKEh`JYGg8%s9A_r73I;QcHO5nNng9GWM}^#|phf6rUA@+q4X3*<Gxc0)kuX}Fla
zIx}IpHM2Tv^!bko@Hpt=K42fuDK3J;4oYCo>adFq1x;xlyTdcrM7vLA+BOIGhtFil
zP*k~%!>xEkRqdkeolXyGaHUps+Ejaot2r4Em7m*5F<{)x>9ih)s`R0t4zBwjRV7s(
ze^ziugKLkRwXaU&%<|Hgd7Yh;qe#B<j!K&o;Q;edY{!15`+iON?pc|oj-GMnqyZOg
z_-cy&1WmO2|5v}OKQ(FN&SNXcqayLQ<BmJ!*R#8l(n|xnV-H-gWq=_0rbNY$b#?TD
zrS-$F6G7nl%so||S?y2N_s{Kt_@&33=%~WrYZ^z6{2wsL^#LQ?5tskP?PCF=W4~Bc
zCB%>L<CogB!(#~=#iiNqh^yD`Ig(cv(08D|(jJq*&~*Y|PunN;3W2r)x_<t@No?WH
zm`5?okeHM%Fivv2DAx{#^?E&PlSyi9kcy^63)M?vTdNl|UuRn?BG`|v_5)1G^QS*h
zV_fe${>JQ0pinZRd<CX^AN27GILm;k_Z(+Ma<czLAnvPG<NV}syUAv^Hr`alD!agW
zlR$VwDZ_a?Ec-POZQGJ0%ZpFm#?`b&j1TW1<t3RTd5S$JZ{sUF@8G+4k`+hJ$qBvI
z$h9Nk7}Kzr(oJ6zVyhTy*>#sQy_BM)9`afhnA*qU|3+yfcvDaj4qG<o2v=KJXIX;m
zoGGO?QyD}!91P(UuBpWvazIpj>&rE6g{gg)goPe|w%5|uYv?g7Lej#prg&aTpSR*t
zRjv74diJ}@<6CuYzY5)v)5ov2zO_x*mZic_77Jh0BxDhEE+4RJEkf6cFxbhUaH_$(
z;bMoNJr5~~x6)3s*}GrMgmuuxyk)LUE24;G>Iol0=0Kz%oFo+Ew;0mt4T6WsylO^g
zDjkk>kYzNUyYo`9wlbhHXV(SZDEysOaZ76C+Kmzr$WQtT=n`3U!r$ztfZ*u8dnL#x
z@uEczZ9!$FX(#+i!@XDckZz3`eLY%%H=vDid}9h@_aNt|F|PfUd>^+>zJogipVQkO
zt6mntu6*Ir729jn9s$J{T@vElYa+c$5{JBi6eqqWF?!UiLJ#^oe||AE^6~3F-!Rjc
zw;%r<nt;8VNAA6o+jEy(r<||s0~qyM_R))&Uo(X3mqkE3`SURlvL06^gB^FePyQsA
z`x*|I0gzUtTrnx#T^Mpeg_YcYIB{8hB-MW85*Sxhl=){A$Vx^V5XfUtZGXV1=9`AN
zDMcnSDRGpDO_76cqo^aA{t(N0EiWtJ_fhg#pEI$%0S7KE6fQ6b*|D6QJiB}14`1z-
zvD1Ia^FXoSaoO*_cW7XmKtf-3Bg$j|mDl2%KBl+LCXuRV<=*06K^xEw(*54(c{tlK
zr=i8^8v?1$WC*2K^VE=z0V@BC-PF-oXvd5kn&yh8Q-)%|F4U%TAyXD^+uj&y*A^g5
zu~K(E%IA-=E-)S3i5{g7Q;W-H*z75OY-92@qD|Tb79ttxMG;hUwa@1u1<%d&iGU)c
z(e{qjGMAWT9Tj3J2{mFdrNT21oEK-8&FNSpoOQlbXFlZ3spcxQD(Wv)wZ97e@v{XI
z@L8U?ow`a9T*vo{s<JP9(eKa^zSb_9jL8gcXc>d#WiH~odtx+O<G3JQUhUR+2o{Z#
zvKR}iS#Ypw!w(~q5rjZ*4D}>r@3bwqL<W97J>5LbMG?9t(Cs(`@`EFtP}r5iN~OjB
z^VsjC03}Ex{4Td;K9N{4344or`K<^#x?acKU{e&X(He!_Pbr(f44b5U`DRGybcQ^D
zcKS<8#snnuoGy2Sz0Q}lg7CDcrv36S?N6G2{z`98Ga2zJGvcy_q-oAF6$xWAtBJMX
zJ=vD_GBtm!45HfDF=3@3C1puJHg%pe()K8^yXVZb0rEWj%eltBX5+uYIw;Syf)F(K
zd6Mb)ya8Ky0$AVX6*g8C8X^>Etv_77u$6yTh?UzrXe0@&$Tt*RkkP7^^bUmv)l+2!
z#?G9~5`MI~mRN_Gn_kY-rC2h;-&iMyKlf}c*gX5H!H8O0&btOt6EGmn!%DULqOR&G
zO3U;Id7H53<7oR1f)jY0TRtjU59^0>xaPK<WUfC@DTda=G94s_GyHrN9^flxQL&i5
zRuab<0Z^s%feJ##qrmlS+!|;Tb4>9WeNxc%Zc>HM5<NJtK#$X=$zsS)ck`5LCFSeC
zn<<(7J`%4-2+i{0c3M0(*l0@+3M?j4tm6XgAx2wuQK@2jDHq>Z&@o3?W38kXaX;O~
zR}|`WWl6Y=TQPcAd=7+NRlldmdG`i{SMV#j*CfEY|NI7lnsAS*@Eqe_Kx4rL$>9oG
zGi(}-K_rZ7b%P9lD}R)Pu1ryA6ZCrDvosU*-duA!9A$D=!0XN(<y7Ucg*t4(;W#S_
zvqE#Z(>UzxDw|bhM%@XF8y>(LNja7jZR;C=o{t>dXx<ob@EDjdFpzzz3mJaG!!yqo
zxzRPTg%6rnm4Qb^+i@1n)wbqf%A~2;UlM1dElPL5=TJ+>^UPr6NO##>m{~jBWByRe
z2F{l{!F~ufgXoC>sr@+RLa}-wtbW%>kave4;UL(feg>AVPUnn<=pPV_td_a$ZK2~G
zfvIhOr?N)>HvNeoIsrJG?&CNx{JJcpi}BxhCr&4FBuQr7p~|GwwV#7=SF+fTJBK<X
zf}y;hQUxqC?a4ipNs;te_$tjHqtx;lcP$c|-JjQ34#f(4ipj~yEmt&9LLs@v{&{l3
z4WmoGN^`|_m8UzWDU67g0k=jc=zbJ^M*nVxlkm75pQ9pglsc9wroZYMS9cy<NNybs
z7m{n!h9cJh&ufPBvHQ)&__+j&Se)NeLQV$4-&Nyvx>MvZ8y=UBNca$BaTs)@aKc%-
zv?oEkGnEyhOUIO>5`h>3AN$FvUyN*jw`LHh1|FZD0AJBPuihDV_pKxNBvhjE-Bu`R
z5J--{mA%Jl4wkWl*qMq$x$54e=(;oF&iqLBIBuuF#$+S6Zisp$e%8Fh+;Km`r1^gj
z!9yy2@tqlZ{@z6SLk~v#22~WYt(L>AWfs0N&7d~uLzzXsZQ4c8qvNzG5zi;8a359(
z?gKnu*s;84o}9{rD3;rKtnxrZlY5NeQIp0>=;Q95sH|=4jPbkgAk`o8W6WdctLnOX
zJmV}+r%2R?>X<XJs#<^fk|5!lzhoLtGe$k!2EkexwjO`+v;<Xv>gR@V4UiBw)0@6M
zbj`1H|5}BhLbxm}ZmeIcFJ<2N`~z^ehJlQ>F8f^u{H@#gtX`unq=ApwP~z?=_91~)
zexBV{%UG?K?nyMQE(s!6+1O&L(u^jQIKC&!?9!7sZuV_2-^}kUVoLk+?FAaiF&hHv
zYQ{Yxv$pOq%4S7Nl~R^%gHZUg6=8px%z_}sNE{!*=+fn|J>VTLzns?&j^zyGOwFjg
zE+${YBFr9$#`#R50hb`<*2TDVUyR*r1l{}m{wh$m+|<)q{VLb{4Vj1yrBwAOWwe=Y
zYT$>`dHMLkM?o_~i`S#_2wW&UV#OMYNOCdB%_ud8EQug9;R}Ua`~RvAU|udw-ockN
ze7(HTA8%I;=Ec&)ZCpviH_AYS^H)1PcG07)2l}w{WI{EZy+sPpqogHT5g41Z7oXi%
z24nBN1k+3Rk<V-%Qd=?i*X9Vw^GM`~%S&j3oZ>K*d}+YbF&X9h1QMhwUE#qvye#4X
z>U~jhwo&@*4BbUIvW*$$JP%l(@@0o>hJr!vj_gfxb6R5>+R9vb=8GG8zP^?$(&@@~
zq~tns$_O7U{Qlb8m8(|1lJ3y*)2%ov6E)x+g+kip=H5RHob5G*2rhawJZk|xg#T<y
z1n8wNdD|5|l41-+pO=Yz=u!hsT5v;!Xo|idg*=$zRcdmW|D!#7eq`aoX7hwU{(h1F
z*eV2&jb!Lk)%_d&!fs*jxUS+is8|G^ID}%PoD4IiX86San8x}P%z2|-5tr0tO59bc
zGCL|`#4vpt$R~cX-O>z(I~pl+m_tzhGGgz44BaUYLyl2`SqXAn?PR4nD`BF0^MXrC
zGCV0};8HQB2Nal{%v>`mV_#4vSVcZ5o9syIWl4av`t!+7NGCw?g6CUlE!N1$W!&s;
zZL);-3?A#_se=b388UDB_*c0*`{D;jRZxjf#DYfttp+KARs~Na{4>f#=ezG{a<W{X
zNz@sDGxrkZ^qixs1jp%Kj=Y<u@NTWs*oasE_NZA6@VlYD^xKSzYF-l1bCwv7Z_Q$&
zWOcNm3c$4za24)bwI;GAVl++SrAKgj#7&VoPKolKQsPxwrAG+C$J40<LgRp6%PQ45
zRbTwmIcFG41G%^Q-Pt<&mmslnw~DS=>=+SMog`5!w1GS<E8=k)%Rkjy)rWpq`wo$#
z;dVB#br)X$Gx%q8rI}ORb2_&Y#8SPacad7PH?Kbc*(jL?R)_1QYU!_Puv(^Z%C){K
zrD?*qgh{xWSo5p&4C|Q(>dUOTPx2#ej%x$`Kf^NMzjW^pDKrsZtD!>Cacl0T5;Y+`
zEPM7DOX!l3-*6;_c|M5Vj+gl*OZ?f1%;4;1@9+yK%p0}+H;(j-rCw|aE5*~wAqu~W
zDSbE{;E@`08EehlI6W=ahbAT%(F`pq$^t2mq?RfufQt$trIu_(@|n?wegKmqHw`3-
ziWpeM1O5A6h3~csh%+^3k*-jPfJ~4|Pquq7=Um^C+*R9>*g>6`o;kHNFag3(&eeX1
z4L0mog*ZvQU`o0jo1VZ$g7ZH*`tUK5?yj$7n7}_Pz@$$`BkbYDym<nC>$cuq?@&LT
zpW|8;v7(HJiv{1<8>iYCCDbu;t)*lI_(4T*?cg1ZO%scD>6}d>SuXr<_{Z;t5>f<)
zG_N${G$)Qh66$Sbl_Zhl?Fp4hv9RhkMI{G-!YQHEHuZH&iTE>(tvBK1s=JEPHt`7u
zPxvBIy%Lp`7w082D$A>a&~J`L5ni`NxXrtf7Z?|I7=Iq03cYBFA|9;L*tJvR?&na5
zfkFu`5mg0DM?46%94vLSV8p?J+o;hDKz}QE_4E~PAB|HQ%eEWN4i|b~g~&J=rk(QT
zsE!8mXG@}OzZe!APd&9{WGT?uK>XAa>HOWe4DGtmgS_>*Fiq6^N-25SldmF<SBCMF
z+X+yv0C|$=X_We~&WlK%H)<U_=B+gi_(z&+ES4-DSuF4S7W3%sBao7@WWZaO=~w=Q
zzm0DC<naad*G_K%wH;SRG!L5Jkl<x@WDe8p(7S+of!xm-uIGYO&`cXHm1j~A(Ypt9
z<5D%-66tkVB@h1Qi5{-xy;hYlTSu`W=PNXtaRNPMkxB(fdGPTbZ}^2`nxPAiZIBig
z6jC%=<7tr`1=sk^6TRG(FAePowJ$0Df2=y*G#WFDtFSWX84MO%fC%(Zn|l1w&*Znw
z!|!*vRY&d*_g%pP)!1<;G2BE$_Ze)?&PcZ_rgpcDNJ8(~hdj=}Es$5|JlxP)ncd3(
zWt*_;<A(MJ2u|<Ed4X2M{+^%hS%aC))O$0<pQFLE1t-)nWOHM@y@5U5VwX~Jm?}H=
z8CH<hZu!W2z(1!i84;|9r8>)4fa&F{a1UQLmoD(<3gZ~VTaT&+l)2I`h_EllywjDE
z2(IShNn;;Q&7mme0GE!ST)NWeCY(kj8Sv9TO+LxVDAZVP0}_SlONw<+KYe{u8$t56
zuU$p)e%X#nlh#;G?{1MUoqXGap8^q~k>(-0?xH3}PKKp99#Xk|<En7I+Qc=f`*MZR
zW);Ny@V)xBGzffIeC0?IbKIln)#QiSLxK?TOhySJ^&NqmTS*QCjHJoBiQDXEGxR|Y
zv9rtoa;J|ew!-(;dG+CJQ%3}MA5C32RR>Rm8t+$5Gz6$A@aPkhC%R32Z#5^dpN>(0
zyY0q)q$!U<7sF1G&<9b`MnVR#Fe)K}oTATWhlqlv!eIsb>ork=Zn5i=pLP)Og3_h~
z-F+ea2N@dD?~e9#PIgT8sQ=i_2DTFNk~ygMpBCp|T55ejS@ZP(y9yzxL5sb?r}AZ*
zt&=ncby4-S+Bh_$MJgPA<0Z5ucE_1C4Vv>B$4pzxm0L-39@C^bo^v(T(&m8WG{>r=
z1ZlcNM!NYgNz#xs-YhLsGERcye+(@@mzS5&Z2!)d$w_6gdrj4BX*#lYYE_Dx!=)Sn
zH3jm^4~8_KXE+vGdX^l|Jr3fGD?g=<bQ_xcnk~XsS{fFdmXE69jH@e#fZRd@WbMfV
zE`4L#<#6mP{o$XVbj7Fduh5k)z3W$`fB$`dm$mT!@gGDK(H7CHqR3?8BcMn|k5?*l
zfwlmb$LMi@-O3nL#wgmFsEF5fJyjq?bXJ8TZO*mCUI05__Tp<RlQgkKDqmE62~T=e
zzxBID%&*46*}zmk>%ra~r_Jf&ZC>LbsFKJzXq1lcnS3g+BR|(hH{k9J0ZO$lMrEqN
zkuSrEar-Kbm|itWw)9Z@QeKIBS>Av<2#LL|b**?_@Lb)TlHRVe`Z93W1SDJ{S5MS5
z7b7?Db>IO*unvulJ3g^%d0GGAo(52?moZ6~-8u_*{F+>xvVNxc6e*Oy)_o1nzvPp<
zo>G+ZD?%+_>27zwS6ZE+f5WZNJKe3opH#>4?&3Yqi%Y9z1%ND{G-Cj{HYEXf!hn-n
za`4WkOP=?xE`ijz^_;@TJGg3*%aJU#yE|(Noz{=<>J;#D&h?=1g6#&*cER}1R_X^3
zaZPG{X|eh4?ScFnvmJ?Qvw&rf_IO@}M!##*p=79f^JE>rFFbTd>}R%d2jiR}?c3q;
z%b9}4&lb0BP!5JT$tg>gwnPQ-LEj7Dc;T<3x&^;{T1`8N+Vj(C^N6DItP#>YU|`y2
zmGoZv;ugIY(<ZHF4oPuN>OED%15h}T(uES8Oodj(`hqKz{)*=2`I2%E9-o0k!Iaqy
z0e)t#Wc4$1Tbx)1ZJsnb*N?ZoC!|AHYuRfoVr(9J{gMoHwfmVhU{g$HA$!=2X|yEm
zHVdJ{f=QbokKvdm#bOj)WMsd1HVEr=W=U~Wm6O72BHA({@9Y}r6V*B-IZK=GxGQ^N
zaN+N>VAJa#Fi%%S*^fz+in~L}+a_`Y)OlD_Jz$?&hk56f>YPSG`&#t%^OyBF$eRYy
zgG8?Xk*3rLmVJ6A!%V)YRwp>6NKkg(!R;k1djk{?hdFfTNSpmLoX`X1jYLJYOmIB@
zFjw9}^x}Z|u(VuXguuJi78c)L<%%JsN0P`TG&k#)(TM6=Ha{+ks7foFtSLsJd8bUt
z4K1n{F@)TFPC*<B%M%$)6j#KcAcd=+-7vR$;6$1M|FaHY^L}mxbCU(j)^qB?cHHfZ
zw|f6p1%y?+A|69w_CuCPz1k+aZbtlsl%hoVwOWdanoo_`r0LXPgnsQ9dbnyzk#>Q-
zXAK}#!)mCDx}?uGbfK|D((HC*LKl18`nu7Osg2Nn{m-@*Qx<SpwmcsEN5b${;u5Wd
zhQBQMV^TU1@`FFH8TZ4cEOg>F@yGyT`f<aTx&w`0o?Bmq*e(h0`AQt15VC_%L)yJ1
zKwI&*RcJLtg{AL<bI6F}hb$EJIfep;k-uXm(eEtWzA~Tk_|Qr<B>I4OZrs4dyQ$GF
zfhPE=R_n(*uNq3aaK9_!u}79F!0D{K<r}~R>CKHg$BisqmqO`oF;N%|GwQj<(^DeZ
zrNg687aJk7;aqc9K8H6IsM5U#j4-KJ*@X8w5mnB_e@%1;BTA=x3HmF}{FY$K8<*V}
zvjkxHd`RAQ9-j;~HyO3rHx8ks3(9*o8~Al@JaiEEb9^v9BpaISi2|;#+g$Hg)AciP
zB>NPf-Ya`5R~d1ku4*|99k`Rnn`UlSNKq!UuDmKpDX~a+E_S=!k^5H;PJ=cRs-<=k
zK)9KJo~E#ByphS{B-4x?%p<vYTO_VYHNi+GRg2qpQ<*vww;!}bK$>b`nNzBmwVz#c
z!W8<EUwi%m!wyV`j=SXBslY#XV=vzC_+qC433<{edXU2PKhO&}8%E#fBJAM%NgNh!
zRL<pOGdyN5Upk8zK`HDBWP6GFyJaiA2M6}{YdmqL!sQITtAF6Jul#+g4#AS=a{WAC
zY_O=S2ar49mZ*u>Ai+{{0t-q-J#soMUkj7>5WHV%R|B?=wkPu#U(6qTfGsK>CR65U
zo7)cpZO!=LMt0debCogCqFlDi*70EwT4p6xj7Wt2v4W0V+{tTR)(ID@h|8!wtF4a3
ztpur@*N4Q{Ngc<oNKccA#~a|OUUa8hE=svKNUssdQZF+B?TY_wU86B2HH;ke%hyE6
zc@@9sws-SdNNouw^J8KE4T|{Kn)KI#o#zlW<B|79Udi6D+-{_qbv(HL1YpBp&T3?z
zLLJIr7RpaR970sYit<_~9iNY3RxDw7G=pGEc*sH8Y*wKzQ6W(9{(@VssEyi@<Yj&l
zGcbW=L{@6;gR<bq$#=azPce&`H+U)M$HYQ)?lXYDE%08p8`0?5zqQC+U}ZJd`1#wx
z&B}zt9(ZB@Awgd2aQ7H%_Z+&kOLWA#8^LVJrPX1j;YLD!5!aW`N{0qt)023}#Za+F
zBs+zQR@jxPhSl>h1PGwrPyQ}&hffMLF@h(4Z`FmqIBkv)m>zi1W_xrpB<0Lk!mP%>
zf7l9wZa(c5wE|S;Zq)6K7wpXOP+v?L=)n?jcG`v9c7=K@J*CJ+<Qc$3?6BguO_yi_
z9V9ud+GBo(H_fDY(t-6lJ3E%|g-H0Csq)V+|KMpop6noL76^nV9bNN}8<7u05KQ^L
zIMi&5*s7s&S64X+j-aD6H7^s!Q>xKDqT$Zrn3fAL>N;aR90M0<fLLA)2Cc353v{Mw
z-!SE2X;ueW3M~51!}2S_G9u)ARNTrFVQHAZT)~6OjRnV5m{N>he_?jYUG%V=f9rl`
zC8<vqKP!YrQ{TyrDNOouzYOBEeu6ZbR%xR1o0!Ir*8(&v%jft@7C8JI!8|x!i~;8w
zIZycV%zdtRS=HzF?0=kwKTC?GmFda+NeuR9N|PWt>GX4ctpy5o+Y>_BjObRw+M6{b
z#2XZPuPP^2&nJ-HE@8rUU!a149DJZl^y<l~NMU3zS6l^9y`;BZ!WBN{2o}IO<e9}L
z?hWlceuz2yd#j>YtdvNzq3AASQh;0F*k9ZA9Pa=d5_4gyaEtg2H)a;yf)|@}W{+E{
zf6<5v=LexrKu|c<+r891dRYAB?f-Vqh5XUlrP}^=#d-GLG1HNb&p`a$AbC4NhNKr5
zDdGtNszV!`ZjlzjnGCy_gz?VoSvq<9OG?dD)U<=#uvX*RC~tqFf|9r}NMB*(7oQMX
zo9T<*CAxDZDGg^z<IQNJ4_gj*KUO-t-VfYAW~$eN;PjP6X*;2ls4KE2%BG6uPNj=(
zmW{WTGmEJ6vWSw1Jp#T{8q=Qy9wO-ene#Z+>&>cHX;|jV&KrEX%ndythO?LADh>@N
z7cN24xchx>15*um&)%=+_0MG7Y7=JMUO0S$%C3GOT{ZDBPN&~aOILF3v(haRVUm}<
zL|Lj5DT^)6B$vRcSg$ZKt9i;2b*V<6E;e_S1@terj)-87*8f*MdjPM&Im9yhaGp-_
zCMscn7v~WQD5hT}XV4!5G`&PF(H|$c*zVhoA+Uj8YapbURL98g=W%#KB<R@eTrwHZ
z+yvX#lSP5;rCAjdZVz|q%id%7Q)gG~M1bm<LPsER<yf3nZCu}!U0fc}WcKDrf$9AV
zcAoA<^}t9p-VniRQ`oxx*sh~=&V!DMvymm;-@uFSCH*y+PYe8yj+q;{%JvIUwJmWo
z$0#s2Ea!%5x?$?s%=oU<bE6cao3q~Qas#P951rV=R&ex~SAF5*w9k_0J=B&DTWJx?
zovbrV{+Du#m%Hab&24AG>QJ_t#v6&Qpfmw=1t8)5hc+tXwR2{t4SdLMV-xR(0@b-H
zSn?Sh3cn-KX?c5&#W*)TVE_uxN!O}@zB<1Rgl&cj6zY{_MC~Ur7V-zB@smmxO<G8=
zcVAQrdc;HM#Y6Pq1|ng7px8?T9`+w=jUsp$msOT^Q>`(D9o5iXEX|n8;%_UaQCZq=
zMR)|a<f&DDLy$Q7d&TZP<&QwmyZq-Nng3DXV3%@Wk~Sz@>nRlMl#VoRNrS%sBL%*)
zuC9ocnUhsOlyCg$&v@WC@ipNsX-_$qt#?9KOy3jz#eGYOr82|UKftO9UEYC_d{{;K
zhoe>MF1wD!*2Ed#+i(3^Ff{DdkLRQm$1vGpS_qdJ>$HdxZt0#<6*n@ko5{vTrqM+)
zd9&#$%F6SX3^O{MWp97q3O%b1B<6s=8kV|`Vg^xu`>EhNmut06+7;11`N8y#Ywqs>
z;>Ch*TxO|zEBDyEPCC>bpH15*Vf#S3%vtiSuEL(B;$Pba3gJIkQj*?}bt<R`QN}v_
z1pvDG^(qwZSHEsaLC4*pZ?%w6GGJAZ&?N~8sjCOnR4Uvz2|Xjoti1nRlWu9kM~Ibs
z@(9z!%?drP?sc_(I1X=yQf{qJ{1>y!3HQ>6&`Vm}0^2>0V$VABYLEm`h@FZ|LP)Ai
zUX9&Fw5yD|ti%#i!ejbJ=rW$a46%a@l|_fXUVquuZzIx?O0C;tp)SCivbuvsns9lB
zK(Di=rUDbT1IvXzN&V>|rVOBv;FVL!iZqVM{8JDBUJ!5djWgV>b!Rj*SsU;I)A7~+
z?|2>SISRQzZoy;8Z4KN6o%rc;=wCeX*}w!JP4!DMsDZIu5eG=E#9~=ew1d?!I|bTy
zJn8BrnFe0fD=w)ZWX>d1_X6SVLE;1}d1*VJKgzS4$QLJHhdD*lY}OQSC^18_&bYt=
zB*oWciAxy`ouTDZ9a!6d$7G`d>D;}l*)H2nWjA5!lDbg!9a&kMeC`kpxr6MMe9g|B
z<g(>a%X9g<!OyenyO**dH5Z(bwAv}-6p`=)g@p$QnbP>J{=gKjo*@7vUhAN|r9!X&
z<rW*<czF4@PPP-~zci$x?QoQ&+5y}GNJqt^GtwDTfH{xoL9`iA0TFSIuXQSfC`3F(
zE;?Y}X7`c^=ZNGir51xJ^^KwlGU2luaGx3J>$F2Ls#mr5P=!ydfv`xqyYt#xPY#I_
z=hLWeLE3^=4|W<m_{Sb;U&r}~<P148K}Ik~zpB;SdOx1@Ku9~@^4ejiQS4l^xOrMS
z?M0L%0Vk0^qMop&vw#-z9wNsvx0pRm%9y*E?^Xa6JfF`oj*;js&fK)s8>=v`*$m<-
zI75yLA_y~z_{b`ZZ?=Q+6p}W<<skg;dIR}Q)?J)me?7AYn^YW?I?2`Y2c!gbI~G@<
z_LuG^?)n)ez786rlp$t>n`{22?5^M5gjB-Jl6#qGwucGiBv=qlvYw-ntCX&2BBi%M
zo?z1I3)bTK#c-K86pTJk3QUNwR6>EKMs#2T%>igtuC;xw$d41_o9Jq1!Rx;`BqlUd
zA8cSyr$A%TaJ{?4%pS&Tle6HBGM>GAkZ5@?Ty;B++uBtguPv;Pnrg(h+))&?3yvQo
zc7DP1o&a;}D^m++7W%~}SIxI(rp?2j`v9Ll`e79Kja|MxAs6hxyktq>0oJDfxt}e$
zmctjbUKlu$UUwFnj@h;IWZJ&{rxT<ox~&oEH~;pfQ}TtWI_%8Hw_^82`vSY(EPPHW
zF8T38H*0H*xG&-aQ-X~;&Kn;B;qrs-%kmXr)tm5YPYmO-6hXKWzFl9DV55=x0LPVe
zAGx1jbK2<V;ozJk-*07xI!(;lVESm$>=9P6(~~+yTizmhCP^`?aSF;;gwXQy(qGbo
zggZLWb-gCJdIPPp5S|Zw#KB`?TL;JSJ&IIk9B9AqgN7$9S5YwMs03#{2o--=p1tsU
zH%uSCsbpH25<Al6NV>^L6r}uow^*T@(ovtKmenQ0#e({4&Y{VJ<85|zPgPMO=UeE<
z8QBJ%n@5td2FrY84PPn_kcaWMfiSJI6t97!Qi|u$3P0Ln?R}zJA`)wkTsH;3xVoPj
zWxoK*zh!Oq^*fmcpmJJ?y_m_1fPSgm9x6msa@{KhDnTTuTgt|23IO%DP0pUAgX78K
z)IHkfL9x1y$$m>lhTS)Lqm2sA2q|sMB{_lEc&KLWl7#UvrH+<`U;jVq=_(YNBwA&I
zpam4~hrw_<MKO`n&=nB^T4;x_rtF96K~rRT78~w{1|EX)e*RE_lMJBwsjY&FVI4W0
zFnN69{8Yvlz!MrqBHrychOlEd%{rgE4!#wjU!z+ex~}Uo7X35A2*ok(C%2=dlV*$m
z(Qd~@7dQRS0$S3G3855pcS{?k&!^10_uvX0-?~={bZJQ!q+%DVFZsmDZB#p*zR20y
z65zLOw%Ma})Z%@Er_l?ZHL#6i{)SDk1;!?S0T(A^1i0=HTfwa{OL4?lDs=nJkuYmB
z0-92&(KYB8x5nf)$*^1RHai-RqL}Jp30D7spZ0!H?%wJePQ_I$>LuEZETUC}r2Jur
z$*1$N0>EPph1*HECUvG|S!tLqk*_flYO^v|xk^6m1b=}Bb$ZIO!Z1^!{NPzbg>b)b
zl!c}Xv)A`Qvq{<eYw3HxHQF(SF;?>bu%Q2!<UT5Q!UCu&=jgu-J*H}PL*qq6%Lr-N
zg?ni3fi;FB(2i}|Z1MtsF|*uq(rdho?7ehO^Jeq=qjSuKNE^EP+C@JxLn&Q`wwnH~
zg+Ct|yNoGq_(0SUjeP_xECV5@`m`a6HKe+v*e2p;`Co$j)?{g?6d3?6mrA{uQ!RMH
z5NWo?7ZHVE3QX5b9w$M1lJQxColTlDJ-IHC$_`}CrcS$y)3;s@nfQUod}@cwxcMo=
z;-*)Se)IL1(VvNL)&8QCEkNxp+;bfO4tqdb0(=7)k>@le=xSAkl(W$3oQ#%H->-N%
z6}muHmjpE)1>~dODVZDa=iwm0`ynx9umZpxCrf~g^k@6Mo|x(?Q5%ysvmv>cywZkW
zy^6F)&pp)juSn2_tNF;IyA4;eqyA>7w8_vK3|Kkm)eTk~Z0>PZF;>vS(ZGbZdN^zt
ziyN(f1-%&q*?>Dclgq{B`y|6>i;~L4Wy!z06jx#~Nu>AKe_vRbZ;qJ-ueqTaAGzJ+
zYVy}SFyu!L&*j>~L!b|`y#FjK*8vNayq5*!WV%4bZQL+Gm(=3g;<9WxRQGK0>E?<M
z@|lgAQ5WU^J=X8Tqi&~|p|VhHtx&(M=up)A(gx*qG0r+%3g|sC*R&!Jc~;Est(cu2
zLml}aI6Y<VTUi;V&l4KB4ta7V?zw)}Et3*ou#p*l+0n3~7I)9*P4j!=7G&n9ljCNh
zA;Ml_f2&V2mgQ$R$Y{0I3bGly(Si&vnz&SAft|y)7qVwW4uo6S{$TTKNxu7b*#fly
zO7van;h+O>!YaU4!uIG$@ckq9uWol)_deNAeBV~t8+FVxFqSW9FVJa7->a<7DBq}@
z*67;t@eeSE24uutax*e~xEp!oP^%n|xIt5U5-xx5#EL<X^v5lip6x@5;DG@m-$RQk
zxfw+ROLh}*5{_>2&hwjgCRCM35w0diguYT;a6;3BQ9kw)CG-DKdGq9j50FR!#GT`(
zo%#uF_<7oY?MG@tU)D};;<JHkK?EPvfY_`Z^ZUQeJva|1N*Zg++s{q?IM4rMdfNm7
zQ#`4o53@Ilw)=Bc$$=G@>kg8;mp=rf&2Yuf2LD2u44R2iIt7Ya7!6K;90Zn*qwA7k
ztk?mInE4%3#X`jGCVz+ScA>llPFz4;bwp4D9!{}*A<Xj%q_}6336^iRP_fx_F?NtA
z3xptjBzvWl;}^c=#Dve8|1d#=udDnphKEZG&gm(>ik-^qF^?EFTLI=tI)5$EU0I^j
zI%bm2)zR(`y8l#Hb59alj${1I&Y5{r*9m;&MW;_E#gnBRcQ^@WaST$4;M!$>2XM4d
zyMBu9TZ6ysB_$l;n`tWO)^K-WVq^Z<1NA8{M<uIhZr3|Z?QPhP9!^<_8NbymTat3Q
zj-eDu5&Na;;6Mni9BieSeL3S?8b-7n<9CX)3~kc%CfsHjyy2+-h(a^Mjy%)V8SZMe
zoT7TKG_t3dNml!gdaux*Qv)nMJH$^ux`?bE3EyZgXTRf%9>((bh?8=e!d7xTBe$`B
z<5x|vmsoJ3JuJt?oG4qLNr(%qhNRR(l?>2iGrO@y%pko$H==6-YD!T)>A-Vs>pyt{
zXprabzTCECnUl%Q3bl8C^)r<yP+AAdY{IlpO}R-JBX6BF&%bTm5oSmCqw)u2N-G1~
z+2aYM+#&4Y61m~i_sFH=KgjI975AfCu)Tw=_W`?YJ4GO<K9w#7bH3wpkOk%2xjj@>
zC4cl;S#}HicsGfeKB-u)6wX3GyQST5feuzXen?Xu58-;*^L2`Bp^Y)mpl3D+HO-_Y
zqcFd11(P~5`Pe*K=|fA^{B=>kNDMR=h6;P^V7usdIXfgFoEp|ljVw)9ZH_@ptE_G%
zEqY`OyGlS_kp%|4Xq6vhv1-P=3AjE@la_@&x7y#9mbNhWLqR|}i@&tW(_s3GUuEE;
z7Id^#dzm|hX?+BTKLG^OKv$XDP}+#2fO$Di@!n!2UhXz0{>F!fNu#-a2%;R#X3<BR
zprp{}%y>BIFySYSPp#L#&%d%Ek&9~m^=^ZNoBy*72im^UCTgSujSy*{m=Iq8m|5~q
z<S0l({MFXlX(%qZX9#I$hK!d=xGU+&f05?DJ!n!og(82-Oh!+|?Lf1fbcI?$`S6FC
z2?zi<*FU|CKqW}d`~5A*!W4xwy@W8+CNO~?p=&NUzWsN7gIBm7aQ!hdjBKPjZDKKJ
zb}D^0(~sZe$2w4GfOr4N+}JfFkZk=Fj9h)xmHPT6Xog}>kz#(B(6})bezv)TW$5Y1
zuijfH7f)LT<6rvFFoC4$hZE)4w(EZ8I4$|;-a3}DFgDt$srd4q&R<GdMmhQ_a-qnW
z->uZCPHD(y#267=SLQ21@O-KlqtOZ#@N8lCa(YRc(v@E3<nT)YVIV_Cnj(Gv`^IHX
z-kR6#$El%|0;<#A<20E|UVc??J_gf1ci3uG@jzxIciMb+@f*pzr@J;Qb5Tn47355j
z5y+X~P%dQA<BTw#TjnoGSNlmh=%`j{9+iL7zUS=QHuyc))o~uqqRNb(oHR!BlYXFm
z-pzdOt!+#>bzUAj13{xHuSRCFQt{{USAJ&;1*Ux91~T4}4mGp3v|Kl~)lOZcb$cr9
z&{;}<CjlUx%WlsZ>6^5}g9+Kpy^<E6I0!#axxI3E=c${1__g2JveQJ?1lZaEN3gWl
z9L$TKHN=dT_2UKQ%57fy1ruCEWKWrDVV2y#F$=<-_lmFjJzJOwA<DxWgu;<PPF?U?
z(03z2<yGImb<1t%+JN5uv%jxM{iR>~`<ArmbhcUkH5>Cn9+G7W?`yi1U&V-55l=gn
zYZ5)^Qc@W>niHyEoj}Zo1Zfj7DWQR~UR1&9Z&U?3`_Th?I=kg-<kBfS)U+E(DDe)E
zmt(uFcjg|YfoLTdaq+oId7o-g<%3Dhf_}LqquQ#b8<n{H*eDnSFSs0B`bO!yD2}-=
z{i<X0w+c-qcr^rZ)2G(!01Ns8xd8~^zSO?oU(x^Kw5vY;NV{}E)d{h_4*5Yw0U}cX
zm=`3JuKq51Y!d|D{GA=Uqo9_?>q(%kvlCvZ2Ts!UC-DT>Yy@&AXe|IM43vc4>{^hO
z*0MaxWyugaGuEDo#Ykq;8#Zonma<Bq?ENyMyNz6MR18cVu;;5guahb2nK<<_rEK;M
zy;P=*mn>7ga(O{R23+jMq<J!n^fXB}0k$D9>kHF&Nhh84tK(&ev9&4^FHrZzOP&Jc
z{QWqePR@K1wP_eu^#s82WDBBLRbP~rc;=`tw8pK@qI;4Qlc+}0(+RurXA30Y89<>o
z##|M!T6$UvWZ@1#-vPfrK)`iLHYgH{)I>d^SH)e^U20vRx5dbjjX`n_UK8SoSkf`>
z7|ZXGCll1z2vLX)j{y*;!NtrG$b>=RfPjW#U22wsJp@-^!a;CkS472BVDQj^d}l(3
zS!k<-Hc;~+G9+&LkG;qX@MZdvwTG_gO??udUG?bs6kqz8RdCxy+Cw{+4iX4Yyl}_{
zHh_w797so#$fk;9bNDpa9NV$T_DaskiR2D~E6nN7-$Uo7O^OBJf>g#S69|JDf!Keh
z9{A`62~iq52orTr@PIbyYZ`f|1m-5U7uMD^wy$=$wC})zAB^yUh0IdpzFGt*c~RG&
zP2OU`T_Gz>UM}OwD}_#Y!S3WTicaK2$(3dXdD^Defik|!b(6`g%DA^SXMYDpcQQ|k
z64bD3IF)p9PvNQH{P)G8e)w;D8)pTxy|bmW`)iY~4Aw8f^7z5w&*}_ub4OXkD$Z0U
zk8B{OHnsFt07VDrB`)|N2O4x$;-yiP%dT>yY)t3C3tSEi>_L|3`&oeOBN7t)GcI|B
zDO#e9Vxs;}YWL~eVzqCFHn+hB>%YKV|Gjh-%JwI^l=FPj4#Ij<>jZ`0ZwAgD`GQ5d
zbRdLxk|C>Q431(#flQO;Md`%A;S4{E_q<D|NMLy1BJ7Ha!iA_ILv~!PkT|~!><XQ$
zI9SoZiC0Ing-t|FG)k;NxF}&!r7vr0A`BD|3Uc_U@Su9|Ly?J-v-4$C7G*CX1RoSt
zp7XRmgVdd$zgF@tL10V1CxHE>MdJyt#^d!FM&7eSemQ3w75-(deI30%mLvpR_)-HU
zVIyK)XkMVqE^5peBH}San80EW%9_!_)<a!QEPgfQ3SfkORzmS9E6s6nqeJW|u<ra`
z(0!Z(gcp8dMwR<S{Y>k7_L3H1q_!g^f_bWIdOb$FcrdWxv;4ZR!`z=+8(be7k3Av=
zRGLZ#!T44=9xhkijwzp1v1!stM7`&tQrLr0JRD`%3(Fc2o0TwWhSVo;;easQ&%-sf
zQJpp^=sp<;jI%D-PG>izx%^W3jkr;_CHzgmF?)w(B}VE!%$M#xYw>bMw9S*w1c24q
zQ%u_rqg@Zvm?a}ZR)xO#<;$e<3OOvq)I2OGn-E`g8&Ax6kZxjE>YT~t*hZ=7zHjo;
z0cMg(EtYf-Pi`$&D*em?Sw3Cn&4TDsSw38V!)7cyu)tTeQLz}#3&d<=yJ!txYAaW&
z{H*yJk$SSkbQvf&Vv9V@(D4|k(+t^-acvl_1nX{{X{o?+1&3qY4J9Z7O42CR$z2%&
z7dNq^HP;{?w<_yXU=YTg!QkMYnmM7d)#8+%I{N!3CIVu$6Z+4ixgujLam0q7|D!Ib
zEh<cC{SSO~)7i5aS7+<N$|m4JJflKKv!O!Kyg4j8^3|)2<c_VOCUgpq@lUwj(<EQI
zbrQC%a`Q?@3rF%xN8SiX?ApfZT*E*E{9Pz|Bl2CUl?<c@Fj{G0o>4_q!X`x6tG^H2
z76MNrHj_5kz>g3!13l?cjO!1+TWBU-u5?zHemC}TI{G)UOsMrM?&gtOxn^X2lKY88
zqSmWPi>~jEWT;mWTqE=&UsO3#6>9$CUQe%5HKi(UfW<PE01TJd+cFA4X-cziL^A6!
zuUIe|COfOmEx|WvGE?K$_vrykx8LU^w*X6P?^pqK#uScW+Xq|NrL8=lYYUIFpuw%E
zSGH8LNlr;aAtvrZ@On@$crB<K<gMT2==-kw^nBNRy1s!6E0_Xp=DZD~`J)!+Kb$h(
zzc}xGe{r~}(S`r85dRnJz2`3$a2?PG{CThv!wc6!zI-)`{6T-b*2B*kN6SHlBPtD{
z<cRy49>LoH@1>*4>jAo!eR>Ov@5Hc1hh3UaGB$2*#LVrN8#wgW`FdfgnN1<R3nN6k
zGF3dQDczT6A0=|DF+#TsqfBH*Q*$Rtc10qg?kctodsMMdC@!`GU0*`Cj0^!=Ku`MO
z2QI@{eJd8q-E7prM{;X`0r^$8h0^N87<m^xES|r)E-wP=AKU<!az@gKQinUg(T6Ly
zPJmuFBMc~zlJa9O(`Kjm#+5suZ*jM?QC6nWfZLq<tSi&@+I2vmMLW!tc*)t$A2X>w
zeP-X$yDt6<aztIkek420j>L4OO~zDZluE^zMpZ7{Fqb=<nbpM_#^)uQ^Kvd@Tk@}R
zHZ{Ftlo)c08g;eb1>FBJloUwoSUCF@i?g(&tQ-vAXd*XB-nAc;#pk)#S$R{U;lrrA
zwU8n=&&r1*5vsO<8JVP*GU6d>kf9A#q(C-Q?&J=9U&s-BM%5aFUy24tBk#rz-iWU}
z3SGcR%A0Ap7Jg(hboa&OQ^3I8tjljtW{AL^M#4Ejf8x(7-=!k0o-Fk+fWjxxVHT%$
zicj74M&39Sf2AX(8oJtg@N7(>XO+gS2)d6Y-f6N?yIB^VI8jH_G|}UwR5M{BM~PB3
zj)KjrIkMjwwR{m|rGLu&CKX8)GO<z+lMTv%-1_wE0SLIQi5|kI<R&tiNnTq{MllaO
zo2y4w2ABr6TmyVnxlL5Gh)#}`XPXW^f20g*o&vN>D&8ZK0-EHsA@A%Z#MwWJPPc07
z(tkWwXLxg1Tl22hL?C+_d6U6Nq9u>Xd<uAnGpha9J6e8;=lPzg4#Z<ld4X2Tu6MOb
z<2ivBs_K!b>m3Vn7pHF=j^R@cP3&YX+=`t_W!F;W4{iRo2GEm_rJ|ekQ5vVr>WZeY
zI+rrn2z16IGrV&BrzWw=EDrH&vz3?$c#|{M7rv3hzrI+Kd+_4Lm*$&Ft3~PQY?JBC
z8rYkWOC;ge_lw-p#bv2ziYCEv@~n<je#3l?eaa8jF7847Ef@>_RW5oA2NFLsn~MGj
zblnx=onNT5*OJO<b9QEm?ZG76(2`*hPnce1aLeqLfD~&MM5q=}=qzG<=b^Rb-)vnx
zG7YJmKJKCzDPBy@3vSgeXOnWbat`DXJ;gGXODsZm(l~n$6aweX?dEOiwy0+dAd*3K
zlEZi-X`P6zZV-#>;&Ge$DfLj55<kbz;W_FY*oK;P9S25M^hrh$c^|z$x#RTFYO<|?
zXCP{c)2FKVqo<%L?~WysiTj==NlqWrgx<GbQS(CQQSFY?r-1aX>FjqJLc8S;vSd=(
zcRFL51or__v`P4dJ3qwI;qiCZfvW3yagR$|>{ptn3yT2w#2>5tml$O;TzqMk%iPP}
ztvXiazc^Pq!@-qjIgCB**N3Eg#+HWzT;j0)a=d8V&kJpAfQMzrV+f1Ck{E;ASyeZC
zt6~s2lJV<OQ~7Te$wai2@(mNX_8GpfYmv0V$|;Nx$XQx_kdAJ6Y()+Ud?5;y4%G)0
zGj~nHWV!7!FHui>w#ioN4G6Uj6ZnjYu`F7g)ey_LfT@HSW8Vq5FVRQQD|JZJmp_|>
zPEBEWF{nN_fwpjcufT}5xYt}nMg}}M3zfSIfRY~G79UrZDMw+rbOibRpwBh`_JgZo
zFxlp^y0!TbLryv|O3kj*<M|}gK$AMpp-1AGoS3x!ApH7?jMO{}%P3$X>;fa1pLB6c
z1suh<zER@=jbeN6?)-B!MupbV6(9awVx}F%R!;HJ?VJHTg%+QpPjiUVi!i-`wmO+&
zy0U6(Dd!IuWVoWw(Dp&{PZd7e#YnuU!iXXmQ$jWZ8ucW0#w<u70h^+=#bMC}3*KL3
zJ!Qb6kg?0sWq_E){<|$nf(BpibXHoP^2$ko4LYlL*lc4EmmSGZU`1NGp)6!7mX#A@
zM;<ggrTc$u+(R{{S?`%gju-8<)=-U%FIYrym{Jb1Pk9-q4$@U@fjpCq6cv~7Dx6-*
z0Rchl@(7t)AVoW)A9wA?l!y44GuXj!3T<>ccN!$Q7mQ$-#OYdi8}rS<neW-Mr0(yR
zImjtrd;ajl_A={2q<I+xQ%cJ6fQ1l&l`}&r44{NOqqZY(h6{u|?(0f3hEPw?{ubiO
z&|8N8CE3(xZHPSg8EgCWcYbDJ`6uOMas~}~;j`A3X*P;C@}2CZv*7o>wHw`&g5pm?
z@ADWNai~66{gL+>$X6mK9T+Nk`+bX@fF&fhey<v%w?&H~<MyAaEAmONVA7gwh1tQ;
zT1ZLptUCtB78$y}n*|ZL{A%RaHRFJw3alEa>tO6;jh8rbokbbX$1OK8W!0dx8YA+1
zvo+Do07kDguPm_F*vvD-o^fNREK}kJeyUgxfev2?xF*Y#t!K7<Af$tnVg9`A&VR+=
zh0L5}gi+1nJ0q4Fb57DC43z(?D5wCY$zlPzjm@z&*fJ9)c6`h$-okSREfhn@!gnh9
z&z@BMZshwDH<zFL`TZPxmtWVBrAD62v!Xi75`ibR+{%<SgNo`L$VcBZ9i8L0iMVGt
z)U=-|F^b-t+K-Yva<VBRoES)xRF#mUVwE94n9V&ZJ_`2e$GrLec1-1n&y{ghQK8v7
zI=$xI69vV$<{efd|0h(Op(`tX3{s?n-eD``9=mi!&s4N%r;~%FRy7PuoB$f-2Kbq@
zZ0Qsm;%-ZI5b7y%-uE=tah%-ylA28GR<YQivd7*2GZs+9v+}s~S{_X1SI{T@q^<WZ
zpRKKeN@pG(0m2Z4R(TPhl8-FTui~Uvv_)X?YY88))VWacOg6|U;b>1koYiX-q;Tq2
z3;B_<q`Ao3ZC66X3K4$aL!o{4D|Ndsnx{n{)r<=T>@hPaZW_x!G0(hLQ8AmXa7}Xe
zJMXLS>d1`3_8J65BXkZ+Z8mF!?;4sEr^@r%IE6DDl0rovqm~s<VY@q3+af7ZQE>dH
zkAjh*x8Rq&qf|m&!Ra5~G(*Gax1!*r*l$~1`)J;(IiK16m3sPqtGu#!^*K4g4{`Sd
zT(qg#moLf7iH|MH3cioKD*$AAG?wLK8FldQj#1u<`$5<L`#3(L5=!Th9!iULsXK44
z!A1xy-9CHSR%h%CEk2Oa6S|P`O2P6<pe26`7fLHn+<XN_=Cis-Udt{JJe^YGt<$<m
z{7G(vNfe8lGcS8OEzSTWjNz2WA8XC)XrE`Diwg^pKFvfx_;Y3d&QNb!cs=33Do+Q9
z^ecAg#~{l`MIlZ}!O={{==<AS63V7L89OZrcS8_Z1S;7#c0<n%j%Ai(c9>GoQp?dd
zqeg1IoHpG%6)DNf`&_G#bO*rU<t+pLn!p#cYD=+2k{H+dT~;Ee%(g|<4g0qnTEsy{
zR**Y-YqNb18GEf*I3t#U4z_$C7RIr14*^S2VBIFUor!2)^wJh(K!AJPGXG2OlRsQ>
zxew9v9kWQCCKC?KcJw~@Jq2#xaPvyOlq?sizdW-!w<Y-s+Dcq)HJ7l@VbIPWs$Gq0
zE~-k&Rr3;c)eLizB<`D4Eq$TDX_Tw2;vDkX|2jknI2%=+RV9+EI?*}-;Hu<PA!vwJ
zur!brI`#03Jb^PrQqgF9*tMPqaI+^s801o6vHu<UjCc+|2c8}v2pp1))Y6+v`83g1
z{zodlKJ`^V{C1`|@JV;;xy1a|Z>IHcq<=pwJCyvbbK-hhY3u8->Tul33H;-mF4VZX
z0b7vjgR+{bPqv&11tez5M@4DND=X(+!yh^bl9oTGmhHaXSGf{Z-t`m8_;Z}faNux0
zMbA9Tu5Y{uH@6_?j=7*sqhZ}dUJEXzI;YDWWnU2lH6fo3v@ay#3YxO>5+r#-QpKLU
z3QP*L@Mp&g?)V*P<|S$Okv&HWyCr9zlMZxV466$#uAIboK69g{)ooZzaZbk>=iRI_
zsr79&bZ#XOW!`gouHpL<`0Ob#d;TwSC))ldoO(i48gUqDDasb)2-PQ*wDZ3oIinWF
z!Z#Y6+I$-=P4m$#AocD1AXaU3=>@H(?Ky37H%o2Maj>j5vGklS?0i8V-^Hp8o|7xv
z*D!Ew;hZ*qRCFck-0b=MBQ_7uC|l5SM)}vu*AY3_=}q!Tuo8CsXScA>^G~^fD+8~y
zhU_iX33bcQFZ#8pWAfdSj<FgxRev=DyrM(DJcSRE|CelnWO<Gl%$T;S5Ju5uwnP#-
zUd!45iMDAnHBYJcnVk>JD4((xP(lCF)Wa=k9@VS0MKb;TnR1oKWvbL)i7c-Ekdy<F
zPvUlWROrbO8mUa}icUY3ECpr*_L+05;?-!G?77?7#{b9(`ggT|=fM%4GEwT>lLx&U
zZwYKnKm%L}=QMM77IKBJIz_8VLgzXaZDpQBJ#TBsktp3%!;|vim=JFC5GAhsvmL@~
zz-cX%bTRKC^PNBKo&euFmvWHF*ebbwl+!Zpl|Fk*R@liuHD3zvHdJjuah}JJus>=u
z_I)N@z3=4h%rfEV)z1*oM!M{afA7y?%hb3pcB~Grv;~lr@_bhBL^wJcD04Catc}x#
zKkV%|7t8U*SR2L?9**d=5fA%1&kkgb#|KC?a&#6mU$&fzU_&LJusH2prCrL>8|ZSp
zo}S;vElmSE1QU%%^G1e};r(tgaEB#D>>qR6mXk^Dd8IeJxndB<W?oiv<J{%Ug~UQR
zK90hmZC>Ci+Q>|&;T{o$I|eqiTJEHCP4x6qTC&l@^n?7e(RbmNl?X!`d;`$~Ftu!1
zS1=+{^V$p%G@m6ccne20gNh}VHX-jqKB99*aVV!-dEIY{_CH&$xEsd76b)jfilk0s
z?=Sn^6YuLiK4FKwPe3v+>+(kgD<yC-HSJew+V8dg{FPv!2xY{aP1yf&Yx^k#$Dhcp
z8jWxyL)pEp?KC!DbQg|9QomGZ7mG52^M?4E*-W!M9%Y2gK~Uquc-)p}PW3u(xP**#
zV3)v9{GgoXSkZ7YhsiD$rUf|Kyp^wNV>;%okM3Hvt|d}zQ;;{fiPMM>qgM~Z4X#CY
zVq640z*kJv>yY3GLo9eJPd$qnPO$TC{}di*!UO}G$TD}pWsH%L>V8~0!l0Ho(H_`E
zBwK8F+h9#s7m`Jtx(_rBv6$7cQ*-#E_WH@F%;1KM(N3P5DgMsB7`@g!zL};i^i#hZ
zmDPTpXI@P{baR6zUexU58JSoOyQYc<O4d+2*!rw+PAj}6LuGaU%L{t6Tyhs=XXyKo
zVmcnuc%s~hj<~O9tluAIXIQH|=%HEstqn&!lNwKeapVVVHc2b!KOd(_kDp{ww+e3_
zVd;#R3H3!8`%moTYOuJ^w$*LM(n~f4Wj*q%gPXJITC?jnu;4VlR*fC6a-Iy~Z8(Ov
z!4g(e+OUZ9T3<m03HY=!K}g0;({6MyW8&nLsWlm|llx@zL%WfxMQEmT8!GJ=i$Aeb
z^LVghIfl#cVJr&weUeG+d;8K^km|QL#4BB^b-q}oB#%}jWkkJYHYU_`Qg7lUQ|UQ&
zJOB7y<LgR=^LAq5<7#<i4A@zOYkE_+gXQ>-mv4ex|6sohk^Yr<@oe(z*?$iuznwUI
zGoi9`E1`2X@$JkxxkBdcN^Xpd%?*+X^OV~wKa0HY)N(JC>BSfsKTo)`6UasfVT!J=
zsO*Ypl*CaqHbM@Dzqv(R#XYB$J|4*O>3p<&=4aLMj&KX4H;u@Nu$v~%KOtza1pLL{
z8<Q%ors;8nj>teU<r(1PL&M_XazyJAXnL;vw^M)nhsJXs2<ggiMtj1*cmHil>{3#(
z5r9WbuYDIyx5T$#xXuCnUxB@*+3mmVt@v$1FpFRHt;u_`pcckeX@-VNa85R0hVjBk
zOMze47I1D%&9e)dd%e<y^zbfX*_pZVH+9Hx^VeQ-HD%CkPSFcZ;s;i>v=t2QE#!c|
z`TGpyf8}O<<dIc1RZYiiLTN8_&PTS~nSIGErle)d6+f&*vDP9#pzGWcNbR0}R06!o
zK&PQ5fwcP2A#8hmOpEr}SIZ@nX3=XNC(fTdFr$PZ$?Z$0_F5pKM*2Py#;_B}t4~{H
z?cEOJp?F)dUqzn*H^hHryOr&sxpOa#PP3Ng7CURB#mV+kzvvCC*+UAIud$huLgd#f
z<|j_V!-S|PY4ta|$8}R`FpJvDgO~enL2JF#n3Y!#Uh!Eo^a!q<%0w~odA|S;jIFcS
z*2{n>x__J#Kt4q7pRX8v*7<B|QrQb~+UJj!P4kEO{$Z|GTs>Ic|7egO5+Bh&-TPEu
zQQv!|YTl6!wBKF#$MAl|{m-4BkqSih*B2jvrrzJKmlZEB-o3aeUL>GXmt+(ACP<y6
z_Z4L_^Fkkz?}30x>zk<t{XxE0XYQT(Q1MuN{^~>Fd1Wxhorw=$eF~;w7tUU(!v|Nm
zTGv=nT$b`Q(3vXSR9kd`-txo^izk%LCZ@Di)T?!kOy<Bho0xI|d=Fiy6gR(yoXq$x
z(tJrMg#IBQgV3(%SSk@o`8o(7Kn5rk^PepsA~9vNN+?(C=YW}Gm18RspjOwuuSzTy
zS5kO@i1eNjDH>;z!+X-m;0~nwq0*YFcth`_3x3cZ?x)5MX#6h(5Rd?%xcmc@dj2zu
zWDNT9y5`3>*ZjO-(Wd}**Qq}&i2K+3=M_Jp{QCYYOGqdd2w?dIu(v|10(e&zpwzyP
z0CJC&HTJ&bDD<<fp=F*Eim_C8kyR_)f4qU(x(TSJ9er1)+LQ14_30xP9`>*~WrTAQ
z-Q3+r1yh)1QrosC%re^l2?RKUD&#vD|I`lT)OP(pkx*!2UsfNU1YO%Hxo-c7f0T{@
zqQ4+dfLeYKcD>+lD^mo>5N8dW8y2fli&f{@{-_Uo2>z1yCBcOr)C;~WNonWaep}0;
zg8kM%t*TT*)hR2$3TagB9Iq1Zs+&jwlMRMqS_iYykj#&a_$iF|4~+1GDHPfx!?1!l
zwhcqXGG@fRD8M#MvZ&5m)10jyz#1)yT?o<hF~p}2Weh=o{*%NaK6X$qLe~Ua!znnk
zm(>!cl1j1HME8pYI7m6cez9{CZFN@nB%2ILg?Cq)JOqym>3ZhT;>aR;D)!=I`32~K
zS8=??j5ZY((Su?ETHu${?g>T@ZX5Itt@+2)fX}ML&Rb}R&Z~k9Z%19^1*JSIvs^m=
ztqJb*F$^6mK+wb3&%X1{`4$M}@K+5%vtb7y7rJW$D#}A!F+B@-uS)2=$3Z)&UUpzv
zL)=+l3`2p{0&6R|5%|>uYAw;z)UmdYMfK)&J2=)$Q&5J+9N45<KBi(Lv*JND9IF8J
zi)2<GoS4Wi5}xHQPFvXxS{J7PMpm3bW-#{Sv_?=r+lirm#&j?)hU%NFI=y#j1t^?#
zW?!?sW|&IA+gGxw3GFDfn#nunDmcis<`vq@DYuCUS4<2mmu=RBG4r@t@%ndQ91}Wp
z7I$B$j^G%fGe@6A@H?U_PN^Sj1@0th)bh=WU9S%JK%QKpoZiUu<WBe<c-}4aUsqp;
z-m;k0fKRB1oll?f<aS;eBxxFbVKOxJD?p#9!tY=`3*lzPi0W!sYv}J>qO4JCx+D1+
z2GvA#5q2`}HOr+_u_m~Cj&5%QLBYPgVb-6oRV;r-skW?@l_<N?vfoL`OJL;)r6Aok
zS04kbHm@kZi^e{)d=zWWw>JuPv&1_@qs|+QmtKhX9ugIuUuKLF?^iKsI`g7jPMQ=u
zT?|e9xI-A42I~q#Q{#Z~B*-ekhlw>(ld%$BxIMQk7T`QgyCStL^B0S9ma8FFHKeNS
zLMsk80-ukRsx*FG95}&diwkM`RPUtWG`9E!BMoQW^QF5<JFW$YnVXNc_Lgp`<j}z3
z6VYf3G8{gU?{Ha-Im|Sm9xHk4KcEpZQNmPGzLui%aRe{<voZP%n`nze50nf^09uD>
z_6((HyO2I*ZE<O+I;V$GW=R1~fm<QIMn;Rpg%CX{7NEPmd6#~|z7n6c>AWg(7AcjJ
z<Jg_xUOB!a3nc|**#*8~L@pf<Y*)auC@lJCC081o89d8(L~0fXcdlgqOC!4crNq)f
z6-%5Uw3rnaS*~$1!E8Uyy_D6@Kt1l;DEyb68@B`JYv;Pt>M!&+*@O#X(42O*FVvZt
z|Ar9dxajtPYVLh~|FlV2*)<Iu6xHZs487|zQcbv0FS2D8qM^gljlfqF;A##9Il2?v
zmB*Xe-VgzCmhaDRueA#!vmv>O$0)={3(kO#+l<s5uT;jvEHOQA%ry1$a$GmI4H!Q6
zVEe>5g6H1u7)nm=@z%gS*A{XC{iZMN=G)YN4OfnBpip@f$gWHaK%vf#_LJ96<9!4<
z?U)+{$>*ub`2VgA9UHP*xrKX=0J5v|!%TTdfEJ)NwEoaxf^I$f3>adlVMZBGyLjBQ
zsM(!9V{Ox|oma|V|KJ|W9oJg+Kq6%S$bc92m&)~@vsbsNrPDNzcG1#!d1E=uil$^1
z-U#rhOfdAPS<99DGkp6%tmiu;F8A+Z3)qa$Xl_**dea;-Dncvs)e>$)xv^3DrgyAu
zZm*ktWp{5g#<|A>*0aU&9i!tmyB%~5gFj?SmGsYD&GWbR(EKx3e8zr;ACD%vG77Y)
zow2z!JDCp5e8psIAz=NvpM$><-s)95oQAf>8^229mmv*f8Svy}0JcgV8(4n;eoUJ5
zNx_frGX%;~CqRz(4M7kn956q7?0Z!Cex8AM6Oho?I`<|TJvHIR;-g;1@Lc8F@%%Y;
zgo!8r&Wi(Aj4C<Dips%W++Q8sjT)!IzYYDETU4`8;}6NB9?<>d)tK1*OUr$&Ar*hJ
z*j&Q~`n46ymH<y>U9qaDmW({pfZFvM@S9DnmvncukZ*Ekd(jik9Ji0B|LX4E4Cnek
zuiEr0h-kp#9>c!C=KC2g&!^l6UBmt#@e!LofB#%yS&-i(#(TZ}QNK#<1?ii){<9Uo
zn6#~c+5}_Hsg2UnngI9Donnf+pZpO^d~I7HpIYgkvGLDvaz5oY=o%6EaY;0~v(8zu
zW0ve|O|RCW4COw{qOk${H9br3f`y^zC{n+r9fw5)kMSu_f~ib%wQSWn2_G&IRrm$`
z!=oRRO<aPgG?sVYU3c>rv#|<L<t!ZpVYZvFZ#KAHsONpz%IJvZVDf7^I=9<WI@RBH
zjK=WtN`9h-mzOcJX3I~zIjq^FJs~=(s(O$vgr!dz?Xa%0C!}gBfB&!hucx5qzd!15
z{tCjG!@v2eOKGKX{-XMMrSH;*esdtKy^shtRri;#yso-Zbm`*?GwI_BqSU#<R_Y80
zs=}$V0tn?*kff_v+P)O;^psy-Hp%O<rBt$!EvpQsFrp&!0q2Hz_o3rms;VHp*Hv{V
z*Hu=Mbxq~d|8@UWntIKD&(z`k720PGKjo{gt(AJMxQb#6q;k+~K>~7Eh-nw?yu6^N
z3TH@s9j4*sm7$L~xV-FN={IF7&3>}1+47(*HIAyPZ}@EUOD{4S;TIW{l^_3&@EZo@
zf1ix-3lFvpf8=1D<ImD)>8+=fQkhbZTp^rcF<GwK606oN<*N96`1f^&p0}()hBorT
zp>pT~8vo>&znJ}*3-qEjjxY>a{MT7E$b)4)@2_!!7s<(sP_9?rEF)9|*|TC;u39a-
zsui#Gd~khd>_2bG{;Q4LzsjKtX#D-B!>X_S7*rtkdR!Rseb2YM7|Px%>v?Ze=0{Ep
zZRDVrlKL%t!?~^hqSo_jI`)WQNy8%@<ywn65Ji96PqMiun4m687BGz{-On-Nb<JZA
zvu5=J`}pVWGCpPCd|UUp9$Uj=0SB1L%bYE<7U8n%CW#+xzpV#AnmHlx%V>&KCNm!b
z`T$I~ISBgr5Wwky3v~-b5WpSZnL9P8k5wX=*mQR5Gz#EOGnh89X`o}`d*QE%dxb`m
zaWJK0GaCtT4`o&U%&D1g>i_e$`&it+JJ7=v04~I<5?Nr_${*K|@z4FS*$e<O5pC9(
z&%)cduAXn&)(@Hrmg!dz+GWC@o+kaJ0NlJE>tJlCy}#`obB0pUd!HM3tL&tw0}@EZ
zeFZ$!XDaK>ruzQ>avfXO{;9q8SMuYC{mwA1xdP%2T^_dP%1z#*peXcy`l0x@e$-6>
z_LZox0l1KP?vE(bNSspx@bvv?9Yb5XFYA0xjf%~c^8DQB8l;eBd*4Kqa_qCx^=y8z
zAQ)_V%$!6H`yuW+7+*D6<OS$d-&X01Gum&Zv06NGhaYpxUXWz3J|Pw;gtOkF;;}2k
zzc!9`uStO0T?;~1G7%jIFkOlZkcYDrpxL$#P>=+zfW))n;u0PYiO6b@5zuE4HsvkP
zwyO`U4Q8DnfZtE$;CcFYZ&5hV7n3CV{w4e<E{OlN>B+bXaEAHbqHuuj)VKWrhV`a$
zfDGw7=x8>IKKK!AyH^vyKJ_H@<LdX;RyaW3(1bp9dVkol2?v_`KKj2U&c#~MmlN@K
z3gQk4fqvX5{^O3`o&1AOCL{2&2SEDd-95DUbAb0>D?wp=sM&7TrXJiO`^unEgv)jB
z@LIsrj9(l7Fne9_-PLnF=-54qq?K;%ELq(fPN_u#D^0)YwcIg4DOY>H2Nc}D_ZFbE
zS=(Abp?zyV0Azh-whWM3zcmHm$%}*AA!W6>MDN22PTk9Uxf!>w9Dbe4_z9iYJNQwY
ztz-hgGk{ZeluH59zhz7$;V5fRmxS!pg!JUjgS{7M#5;q2vA3@3cpjyx8~Z7)BVct7
zpjVr~SY6CmBe>JsL?JYa3<sld5#Z16(Dh2Wn1)dhI;Mh+()b=W92kK8$BoGg20h;Q
zdkc(+XqTgPuXb&<xRwtp)>+!wS-!0Jisn+?T+FVvz&uN9dK9ZlG=NvBq-L2(^sG>@
zb}(7(`3raKD}Y9!*dD)xL5yTP3T7atW)IZ>X?Oqu0n001eSulG3-FYy&l|8Cz4d0#
z3UuQ$RNip_{RU*OYr*yLIoc>BykA@P9_RO-_VQNS1#r3Kw>j7W+gvdn;88<CRNxK!
zOA*O3c1HzYCmE^J0I;h=c?0SO{be5k4C?h20Hj0TghL<d4s)}aYQy*P9$pB3Lf@eX
zqPinI%JWcpcT$0hS*roylqZBeQmU7(3dT&tXl3+x@8@VzwXzs#Xel@TB)(@fYf$hJ
z6J=aG8%zs)6^Xi^1G-WHEi)&uNA@|FA9b<;+{+#1^WU|^#pAq^PsnqLaxM{@n@QPO
z!!yaK6^rxkU2W_h36`WZvNceeW#N>tWGd??`eTnZEbVf}r>;@;m6o8^@H8zGc$<wM
z5lctO<PF6}789gdG40)YgZ?5Cu<@b1g+1QW6}tCk>Y_n5UjcZV-FZEb4vZxn0N;-V
zGjx=C3M4s+9`H|j@VC2NS0?W2%;uUDSxvR0-e8p?Y|SYE6D7d6<HK`AtIG(yQxpLh
z>P5`ReZXHfmjyhJZo2m|6Nhb26v@VlZ%EH%-m_ceJW2|CH(HuF)c`jS8-W|dRrL+-
zzZS^@yQe-iV9ym6otbjD+i}pd<zZJmOG)V&=}};oIiMZj4EJeOt8s%-{;&3%)kaEq
zqBhR1o}c^0mC+1mh}uD~q9e96f|=C_YK4(ZdQ3C9(uik_LSk|)z!7|}2A;g2R1kea
zVeArgNUiq$8{KykSNQ;jcW>EjAHUAar+a6%n)k06%{_|PYW)&=t0zzi^syLYm&#$v
z+av%yQRFBBpVi7hPICoXn~O)VWIpH?iL#GQg7oloasiRlNO`wwn_&*}^wORgk$%fn
zEy`$Bzj!Rlw9Z*D9MkB|zhwYPzPWH$wXMUB_X7Om{M>cG`S4xP*ZE@+R|(hoi_yK{
z*TcxzFztHw<!~fsvXe|^Qdab=JksS|+n>Vr#FBeJHn=jxId0TD+9|7fjLrI>FLJEr
zGDg^uwQxugnK1;Ke#K3~?!OBWtJ@>`Mv<4o`&oUt`?LrNYZ{2N+6ZEwYiDeaNqIK=
zuUU%r*2T>13GLCsm9-ajoO%GyX1fmV^jJ*Xk$Ekb9BLHG0sb^T46ujYZWa&>oH{^C
zz2ClfMg;I&_U{ZSVOzCtW&oB*x!hcKJ@tMw<vnMKM6;{yvh7~YiMmsNX6HnFR`15h
zzQy3F&s5KMS8vYEKEA9z;Fa1rTwC4oM;86^PVqWF&1wQ0=Dfa)hD%Joo8NWoU+umv
zP5Y=Pai8@4wsp##2*j_lyXRMSHo0$7>V4m><5XXT@AxY8F2d$SFvOC4MbB~=!2c)1
z$i1|VH&@NQjD&)MT?v{pXw(9XJ>fxVPV|$_Ze!>DiQvgjV(qSu#J;o1q2?V^UB!p1
z!MWKM(P-uXoKCL0I_Gk)w+eLFa{^?wp2V|^y2(_Tk#<dzZOv78)#eXjv!l4jl8F<u
zM)q1<NQC-#g?@XYK%1~>{#GjvW_DV8itS!SP|&FV=Af4$5bj}PJt$;Dte%9s1N-Fx
zcyTLZ(_FnfuQ)wyt+y(z0-FKT8UZ#t)|s?1jumg1&PuSA%T6fFt`HcLIenVl4`DkM
zMf4$Z?ZdV$duIT>>@gEJe;4Ip_w2{(?Vbq9&m`m-v6@`JGHP#)9Z^nZ$NiNM$ITTx
z*31fYUr=Jx(J2>K?9bj#gijzGBEWT;`zpkZ0DKdD^YhoQf&@+fE=WA^KGmQ8B>Z&%
z;PWrP$hG|Q=7RB2EI{i=a1|mzKJbzOS{)C-=xTmcv|7%nb*fR{2B}b;^s)1}L-`=b
zRr}UHVf8>wJ!SL6pd?$uDq(UAD4lJJ+J#7tiv_m902l}j*lLf-gysOFV!3zCIJMf@
zN0@_qRjTb&KM}iiM8*gu>sn(VG3H$60S*k8SSh(lw6xqdl0oC3)%VWXKphoInu3r;
zTc%Scd<<?s_h!h8B+8X$);KWEOS^H;rIwtMcA#2>NLX(cT}u&@e1z-kpvUCN;BO|J
zW&-Moz*Z{QaCvK1{`$3#BcO@Rs@+(~HBz2M&{x<zxat7XqN$^bi;&ghECvf2&oZ^o
zSpcEEkJ*ql?;5O5N6V~2xrT`FJald%G|H_0X!oa#-Ecz3kzbc;?=BP%Ngb!2RGY%u
zl%c(lnil42f=-4KvmEMQpe3Pnt&6L>io2yqb)w`0<QQ-tV~6)hBy6Xws|}x|EAXB%
zyERUi1i8vA7HKxLwK`M7wAR!Zt6DdTN8Q=lxrWWsx|`%|8zE~$W8K{sNwv>X=oD#k
zURGhzn=!Pzdrfw}rjC@Zp|7qenjx+G^ed?if#vHs@W2v#3UHqrFi<;{n*P7h;fLGu
zL3BwfHCB7hGV2&&`G;<b6{f>%iBEX$i%+(Uc#8HUk`E1v0qtbL>u!%q4z3%O1&XV}
zg&d;vOd{LI{)pyO>Reb&9tJ2(3skD%CgNXglOnCQJr10Lf_m?D<Wt4{qq9y-hM>M_
zCVbF5IMBV;w%Lztlkr4axi%3vqh))>98Ze!ecH_KG&3TE=a1Rop(T5jZWXs9#m56e
zEYa6~s(sC6Xv8DL+j_lvI=7KsPwTU;l5+KEb__ff!SfL|NouD-tIMdvQm41G9D64#
zg7?x%_+ma0oo8(4t6sDBzZtI0bLQ;QknXNswl&?!sw!Wxiyc8C8J-Ur9?X#TOz8T;
z!|grfuypSIUfeE1ng<<TkO&sEdywtg$UK(r_$CD+ogQ8B+S}`<IN(0a&C$R^sQE15
zHs5~s?rF+47tPV+Ud>&k)%Q+FfZ~v#1u2-5Cp;$v-9Zz%srAFeorNRcUmSn8klb4I
z+n-!VDR;ljD4sJq%=2g;UI)H?a@~oYb8;HSi>NN0<8A(rBa<xgnlkC{8}+2wh1D9E
zak5<=NZwsw6VLpvYn>)7W8o5%wJ%p#HH}^NT8UzyMGdqObQcishr0~$R3OH~{LKKq
zNG1!-W3H&r(?F`=Jyvd2!Mdu!>osb<7kIi@CxV@YP<35H_v(mmG`HKpD%1gSpKcLw
zrujU<Qc$A%6dU)rp!`(QFHt{i%|Re6k}5~pj(LkTgNPH!$3nCenDm-(OH9}&c(nNL
zBBR`7AfsAiVgE$ONqIoyxag=txrGIP50L{viyps^0soo?Z$9)lCM6sAOv~-F&}&nJ
zC+Wmpjz<NHY3T0`USN0`(Jva-PvRcrm>oTE(>WNF6!Jd2{>_xG6Aq2F%2abyoMFU9
z9CKV)IU4wJitH7HJq9b&T_J-_QvhN_TLFXVojVPys%gdeB;RkOK{QN-{xA_gy2~|J
zhyJ#gzKUg+l5MrIw`K`N$VPpVdG$iYKg`!2B(vb-qCIri6;%?m0D?ujL1ovgj6;yX
z%%m&JRcA}hW?Z%}N~PZzG((l%1aX6cvfn<nIeC{b09VqOM2%aQKW1I#iCZtjd`;F?
zD!M#o0wRk!?xfj$qqJDk@bz^NzwUJq-JLt2_Q@$AF8iFi-HzZjRy+n0BYBmAI9KJu
z4VvPO1?q8ZDWAlUhf%y|z77g|f+%a(+V%&PhdM~$LG)V6Cq>A@*d-uekh#_ko<vsJ
zE1;brE$F$hMmiOE1+=$UhTk-4xV-n3I(7xMPSU7UD{;@Ob08tJyO!KG6;Aa#BJP1G
z-OxC|A5(PIv}Tot)LP~BsyeOLGl26wk(SbB<mPt)8d(-#C*&7emLWKir}mB}NYFlo
zRK?SWyWS@evIKYym<a@gaT~CcWdnu*W<dd7G17MV8=wJDW2+cP1*1olCL8vVpo5`4
zA#f8rmIBBz35eBPb8$(#g9#$fB#6J@o<S;;o_oIJtH+@#WO)Ow#>6CmM^*rnCIL+t
zHd7VEC_IIUT)RIOti7`2%+8F|uCHQ>_%i$Ze+jnfP<p3g5A!>EfP8Eiv+pE0RtH<a
zPP_0pFh&OaMP`e++Y`yiXGVZR<^)>;*OK3*fuqS9{^}TxNFblVeX&^WHUz<WkQK=H
z-wnY522)mpGdypV%o8iA9b-On{=od8<1zvG8rX|*ew@Ld<u$cq<TQgOC?AmTNth9c
z!I;_DvN2m@wt~zxlB&@=0{soq4=X{wQ1EnjX&ZSBnBG*TYqN^X+gc6BUW=gXA0K{S
zjuJdces5$5Lw#cy(AEYR6}S@qTB4BoNkJL#QOwZpZ|sUP7gm7#Sz|b1JjhCHuTlnu
z9Uz7+tC6CdUCHmmz^tG<YMaP}ORIo+ZJ`_6dD!$QO-U2&EB8xDJ6S+S$VntFSIe4p
z60rnNWNEuPVi_u}Vu?;UbEQ>GNHt5W3GN#*i|!d8kTJ;D0gAj2wkOuWOJILy3&z<H
zZ(YHoufIKRV_~s=yEaDna0*+o49-UnlD}!#o|#P*3jh*g2Z%88cLV?=2#v@AI0F}b
zNuT?MP~3rAzJ0u$isI8CjS>hRg)y*z2uf@qLX#Z?;2%yP!6glcc5?=rsI=89i-6mK
z7s}TiAgbj7fKJeM1Go#+O?3AWK==R)2#y4R)sjvb0W#v1V(_$wu|ThA#sQ_yFdj6k
zJtZLe$V9MMt4sni#+dm)?AS6Hl#dVdgMX#|UI2E~``gNA7n=?uG@8ddPrZrF8|L0(
zbKD=!-?`<;-?2{?{H%C#ee|TPY}|Xl(FbDJ9JhPBe*9?vw0gO5L+Wk{4K&e1hXeSl
zhA(R@wn3PAta*50JcUT$8m7<C$H49*X><X*Cb<rM6Ad=cM$jlMq;vkd(c<$=jN_Bt
z?#9Afpcu1d-|s9{g|Peh?!~LmKVC(DI?pT`vLuMWk}Hq7y|Uz;zqGkMSlEhi<;X_G
zMD4MhL^cMpH&qt{lRi&S3EOk#VM#_laz2JETrbm~Cc8hQo4FTFMfMGz9Nj>gs_nrs
zYRt&b#Y6))XU{@>Gy>0o_Nj3wnuX+XCXGr*{P6#2Fln9XlCvn3_6#sVQB9=sK8ze$
zdQy3yyPD?#43Cq8h#adtKS5$HehZR0R}W4#Xw(uLbYjkeUUITw4(OZVdD#5v9KDI<
zE<(g4TpY%ABv+HTrFdMqhz1od0m9dvA&hbOY|S^4|1<m1fCCi$qymBe6<J1{aMDBN
zes)TMTy<KVm6HM-R%*_?d=;`%gx}4o*JjazCC!S=C^l8LM!&nG$zOQ*f&l;ff{5_I
zZSAQ-mKwr`kP&`F01<?*P4vDGeQdn3J`o};OaD+LNRlE=#x@kPsN~SdQ>ek17JzBs
zr5^xITGix2N0kOkno80zFr-0{4hco0?^7`0B1DQ3Ek>+3@e(A0OOgyB1yU-MH0d&+
zWx~M1!OJ>}AR(j3Mr|2boQ{Dh7YiF_$Ig?l0AHaZ#eW>MO6d<%u0kcDD%ENnP^(V8
z2927&fcYn0vlgw|wCm8Ri&(cFhaB$D_;p)w%4t$Ezc}Npa~3V>(yd3YKK-6}>Y3+W
z_}4(!-RNq!+}3Nbn>zHBp}W&v?~?wIp(P)5!z;sHdt<~~Z$P2$HeLLZeDJYK)v8Q)
zPKjC<6e@GZ-5&X5yiOAtIz@cVx|qkLsnm<`#juOTR<2&Ve&ePgeq&T0VuS*Te+69{
zre!DT1tKUeaN;IRe6w|y7qJJbzUif);sPoDXZ(79zQ2E0w7aLbuRk_0IAo_ucq&Im
ze^N(3O9Y091CmR(TTd6Vx%?e&7E4Pbx2m<fyuU{dIwpqPAqxllS^Hhim!|+<p<+c!
zloBXcrlJ_`xXHOwBBw@8v(DOQM)XV@WPsSX_*n^wNy#a*Q|F{vX>iM*l&tKW+`M`7
z7vSY4y8Or4v2)k%J$v`<KXCBS*m%agee5`7e&5cVJ$L?sHRcVM0C3agD_5^wzj5={
zn{U1S&bznYd;fzEKPvb*$<&1l!3`gLxY7d}J^1MO`#ul;f1F2j;^*<NVIKOADi8iA
z$%Fsx@!+#D9(<w1gD+=z@U;dHZ5r4N@E{u<EC!3k;>tT<V@X(EEFYFEr{5`7sF6k^
zMUJAU!(b0$oWQv8;;ZSaQ+(BwsZys&n=U9lmY$R)IBPa1eV&{lLOS^9BZ?`uI1_!F
z5`&jiGKU@t6=1Zg3^j8qcE4<VSrH;eiX5fvsO3b99wTPCv0}%G8?U_bD~NBQgMAM2
zIhD?jQ+gR?cA}GY_f)hhqg9xSa-Kv$NJJcGRDq0~f|BY!o`#l=o`I2xSv?2yJjCIh
z|M%tQ;pO8O5EK#?5fu}ckd%^^k(HBIP*hS@QB_md(A3h_(bdy8Ff=kYF*P%{u(Yza
zv9+^zaCCBZadmU|@bvQb@%8h6zTThj@81vOG%ssmqd34JI1CPpvwl2|fFp|RAhCnO
z4hB1d>oJjtO6_pEA{FV}qgA1BD&P($rWm@#jzs&<NiFKph-S2+9i1@3jBfOz9|Oxc
zRt({YceKYH?d@0nSS+ADyV-`GxYOt&dhGI73%;IukLWD8j&ugD&l=YkGrK+%WZ1hx
z>+yUqSKTOZ%j4-`acXk|xiA0vRCXS&QS;E2N~ajV>LS|><UR8h**A9DzrLtBwrKgL
zxY%)+)})FNC^+|4D#sBm5)>8;!(3~kji<y0zh2?O&~`A%XyQ{5Q9nTD8;VMS3S~zF
zd5LtqW2veWvKa%lB$)rd)Up!o;u6gV7F0baeJMK(i_;AsieOB3L6Ud1V73XDwjMoT
z-_%{^`)^IbAz8$!kuYt%_B%5ZQiY{NfeER!8LSpubfKk!C=*f{&q73(2{0j*Hl76+
z;Jm!Sc%iKzz?5+zRU9FDEV#(56hs*}o&^_CRRow&#)Z=0B3kQegE-X-VXYy+j0>rv
zLp=ko2F}>XtX0HhSSO$#MYs9`TT_2K5fI9_kV=~&(t>OMkm*N=029i%kV+f4@T1n#
zNsy(3W{+KKr+eav9<M(~u*2&|aibod%Qd9e#W8$7lLxV3cy4w>@?5ST>W&`{r||21
zP&Ogb01>-VJ4EVDH1%lqn7%crmab2!Pt={eXjQRgx3w+}rA9=xW8bQZhXFuqbP>8=
zzk2;TY%=`?eCd}LfAqW!X`IdAd-S}%2J_WDO<t$r(&FuFNxW8GP@!4bcBS08tnbTt
zju26ZZ5Z?@9>GU2n81_e1=$=fk3@WdP$b3@sZ3sR*(KaVP|t;S7)7p|D_->mCFyNB
z`M8;`*Jqq`i`JnD+K3XJv67?Rro7;}ti?#9oX68x&Rf%XF^33OO>ns)czH!)c|{25
zibRW7o<nHCmj}7Nx4zr5hCsl#1gg68WG(4Pda(zqU&7*LtUozcJJ`T#RMBn0Mxz{P
zT^h!O(2`hXh=7;BDlo>M69C~N^u12i#Kksz6t3^sDa!EjPMKV}xlqk(jWD($>>msq
z2&&C$@&>4+#ArW=WPpSMjRCNXibp_0BH(ZkL9!81*5a#z>bknC*43MHr)qsD8bII=
zZ3)K2nn*M<H@Dj5!9`O3X<wib=x@@_XD@|0PllNHE%-dXNY|8`RidY7l}fA>KVYp?
zp%H?Q5h{2i1^4XmN-89j(VIe5sV}uWJd%5dk@O5W<#pgBh^Gbtnr#Ig$A`kKY7!?w
zDmGStFldCbwr5qRgFEiS%R_BFa%A0Qq<S$cy%9J{jTizaQEM6yVQ>=l!(mpT>Nh9&
zelq;8?7cF!YDXn?GQ#Z<Bj%u0yEOV7bOp@g+^uz-&lf8sE3TT<R+O-Is1-&I(er3f
zQgcZof&ObsCb=Gb8Z4wZ)j2Cdxp-)yc3@Z>K@l;)DOF^wQ>Rr+p5!<CMz}*7cmobu
zD|v{Rb8!U)TNH0%x{=|Y6ntMyira5bxux3eBwHSDsZy<vKdG84KP!qh!(UXz4WRsu
zc!gO>h2gHEU?G@P)xpgke8co1nykF*{gqMT6&$8MwGU$POqH<2?;IbdGx4vZzX_fk
z{%STC;5++MLt{tQ!cr=zNrgX-z3MaIo(fqI={tfZ^-V?WNbE~0YUE0(7?9boVa6<$
z)1CT(S{=G~^`$lfHw3d6r+%6i)!r!uv(+F2z~;}>BKpWQV4fClAeE(QJxMoQ%bnQh
zTSJ4k8r1^K8r2x)sN=MF&u?6QkQmA}*w6AK=GCsJd?KpAB|ZNPvel$A(7k%-smw!x
z{5yoHqF!b{`ZT(AELzXYpByoEHnd&pa4l}B_?9ic`lWq-&HYep@kgm*i+}4PvwXDI
zDvHbtE9R*}$-9|%=m&6JYab)<s}J8uMQWG`!VGz8(Smr191~kq5#2kPaIm5QdvDc}
z%oAO_5dKd210%>_US!k^w4C!@7Y>XgOuRIaNp82!Lc>`MNiG-63_~^Y)#qQbd|sv6
zP^VemEFX&em#R?>wMl`9)OFLhUBjQhn5-$Qq}Ca%sut9`OX}P!^lm8AVUr~eDG#7s
znv<W4<`qo>Lb<juSlcflz$79{2}WKAl@Y=;wB-zR6bY&9=P@tRhyJtp1lQ?y`P!hD
zGVS)4@)JLcoapA~d@ST>|36W)>&oU<TNsA>A9*{%>3@?QC;aI~CRd*MTC@t=??el}
z`IB)g)~RSrexeBnB`<q&kgRNo0d6}?QO$to+V4x41P1^e%D9k5dlp<|)(N7F3#oLV
z?tujtS?SAf`-})>Jjk!VFea@U=MHQB%Qu8D892s;RN8nJT>JG1FrkbKskHGdxX8BF
zT5GMf)>><=1poj500000aLzgBoO8}O=bZEX`UYG`b=iOa*MCd4?>)z(q<X^Sw?lb_
zsBP#^s2J~{=Yw%8&4A#l1Crkktrhaas`4-7i#4JyEWIYqc4(~}1C9^fC&74hxbe=q
M@eRWVkpKVy0CY3lUjP6A

literal 0
HcmV?d00001

diff --git a/docs/python_docs/themes/mx-theme/mxtheme/static/fontawesome/all.css b/docs/python_docs/themes/mx-theme/mxtheme/static/fontawesome/all.css
new file mode 100644
index 000000000000..8e62fcc5d582
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/mxtheme/static/fontawesome/all.css
@@ -0,0 +1,5 @@
+/*!
+ * Font Awesome Free 5.5.0 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ */
+.fa,.fab,.fal,.far,.fas{-moz-osx-font-smoothing:grayscale;-webkit-font-smoothing:antialiased;display:inline-block;font-style:normal;font-variant:normal;text-rendering:auto;line-height:1}.fa-lg{font-size:1.33333em;line-height:.75em;vertical-align:-.0667em}.fa-xs{font-size:.75em}.fa-sm{font-size:.875em}.fa-1x{font-size:1em}.fa-2x{font-size:2em}.fa-3x{font-size:3em}.fa-4x{font-size:4em}.fa-5x{font-size:5em}.fa-6x{font-size:6em}.fa-7x{font-size:7em}.fa-8x{font-size:8em}.fa-9x{font-size:9em}.fa-10x{font-size:10em}.fa-fw{text-align:center;width:1.25em}.fa-ul{list-style-type:none;margin-left:2.5em;padding-left:0}.fa-ul>li{position:relative}.fa-li{left:-2em;position:absolute;text-align:center;width:2em;line-height:inherit}.fa-border{border:.08em solid #eee;border-radius:.1em;padding:.2em .25em .15em}.fa-pull-left{float:left}.fa-pull-right{float:right}.fa.fa-pull-left,.fab.fa-pull-left,.fal.fa-pull-left,.far.fa-pull-left,.fas.fa-pull-left{margin-right:.3em}.fa.fa-pull-right,.fab.fa-pull-right,.fal.fa-pull-right,.far.fa-pull-right,.fas.fa-pull-right{margin-left:.3em}.fa-spin{animation:fa-spin 2s infinite linear}.fa-pulse{animation:fa-spin 1s infinite steps(8)}@keyframes fa-spin{0%{transform:rotate(0deg)}to{transform:rotate(1turn)}}.fa-rotate-90{-ms-filter:"progid:DXImageTransform.Microsoft.BasicImage(rotation=1)";transform:rotate(90deg)}.fa-rotate-180{-ms-filter:"progid:DXImageTransform.Microsoft.BasicImage(rotation=2)";transform:rotate(180deg)}.fa-rotate-270{-ms-filter:"progid:DXImageTransform.Microsoft.BasicImage(rotation=3)";transform:rotate(270deg)}.fa-flip-horizontal{-ms-filter:"progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1)";transform:scaleX(-1)}.fa-flip-vertical{transform:scaleY(-1)}.fa-flip-horizontal.fa-flip-vertical,.fa-flip-vertical{-ms-filter:"progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1)"}.fa-flip-horizontal.fa-flip-vertical{transform:scale(-1)}:root .fa-flip-horizontal,:root .fa-flip-vertical,:root .fa-rotate-90,:root .fa-rotate-180,:root .fa-rotate-270{-webkit-filter:none;filter:none}.fa-stack{display:inline-block;height:2em;line-height:2em;position:relative;vertical-align:middle;width:2.5em}.fa-stack-1x,.fa-stack-2x{left:0;position:absolute;text-align:center;width:100%}.fa-stack-1x{line-height:inherit}.fa-stack-2x{font-size:2em}.fa-inverse{color:#fff}.fa-500px:before{content:"\f26e"}.fa-accessible-icon:before{content:"\f368"}.fa-accusoft:before{content:"\f369"}.fa-acquisitions-incorporated:before{content:"\f6af"}.fa-ad:before{content:"\f641"}.fa-address-book:before{content:"\f2b9"}.fa-address-card:before{content:"\f2bb"}.fa-adjust:before{content:"\f042"}.fa-adn:before{content:"\f170"}.fa-adversal:before{content:"\f36a"}.fa-affiliatetheme:before{content:"\f36b"}.fa-air-freshener:before{content:"\f5d0"}.fa-algolia:before{content:"\f36c"}.fa-align-center:before{content:"\f037"}.fa-align-justify:before{content:"\f039"}.fa-align-left:before{content:"\f036"}.fa-align-right:before{content:"\f038"}.fa-alipay:before{content:"\f642"}.fa-allergies:before{content:"\f461"}.fa-amazon:before{content:"\f270"}.fa-amazon-pay:before{content:"\f42c"}.fa-ambulance:before{content:"\f0f9"}.fa-american-sign-language-interpreting:before{content:"\f2a3"}.fa-amilia:before{content:"\f36d"}.fa-anchor:before{content:"\f13d"}.fa-android:before{content:"\f17b"}.fa-angellist:before{content:"\f209"}.fa-angle-double-down:before{content:"\f103"}.fa-angle-double-left:before{content:"\f100"}.fa-angle-double-right:before{content:"\f101"}.fa-angle-double-up:before{content:"\f102"}.fa-angle-down:before{content:"\f107"}.fa-angle-left:before{content:"\f104"}.fa-angle-right:before{content:"\f105"}.fa-angle-up:before{content:"\f106"}.fa-angry:before{content:"\f556"}.fa-angrycreative:before{content:"\f36e"}.fa-angular:before{content:"\f420"}.fa-ankh:before{content:"\f644"}.fa-app-store:before{content:"\f36f"}.fa-app-store-ios:before{content:"\f370"}.fa-apper:before{content:"\f371"}.fa-apple:before{content:"\f179"}.fa-apple-alt:before{content:"\f5d1"}.fa-apple-pay:before{content:"\f415"}.fa-archive:before{content:"\f187"}.fa-archway:before{content:"\f557"}.fa-arrow-alt-circle-down:before{content:"\f358"}.fa-arrow-alt-circle-left:before{content:"\f359"}.fa-arrow-alt-circle-right:before{content:"\f35a"}.fa-arrow-alt-circle-up:before{content:"\f35b"}.fa-arrow-circle-down:before{content:"\f0ab"}.fa-arrow-circle-left:before{content:"\f0a8"}.fa-arrow-circle-right:before{content:"\f0a9"}.fa-arrow-circle-up:before{content:"\f0aa"}.fa-arrow-down:before{content:"\f063"}.fa-arrow-left:before{content:"\f060"}.fa-arrow-right:before{content:"\f061"}.fa-arrow-up:before{content:"\f062"}.fa-arrows-alt:before{content:"\f0b2"}.fa-arrows-alt-h:before{content:"\f337"}.fa-arrows-alt-v:before{content:"\f338"}.fa-assistive-listening-systems:before{content:"\f2a2"}.fa-asterisk:before{content:"\f069"}.fa-asymmetrik:before{content:"\f372"}.fa-at:before{content:"\f1fa"}.fa-atlas:before{content:"\f558"}.fa-atom:before{content:"\f5d2"}.fa-audible:before{content:"\f373"}.fa-audio-description:before{content:"\f29e"}.fa-autoprefixer:before{content:"\f41c"}.fa-avianex:before{content:"\f374"}.fa-aviato:before{content:"\f421"}.fa-award:before{content:"\f559"}.fa-aws:before{content:"\f375"}.fa-backspace:before{content:"\f55a"}.fa-backward:before{content:"\f04a"}.fa-balance-scale:before{content:"\f24e"}.fa-ban:before{content:"\f05e"}.fa-band-aid:before{content:"\f462"}.fa-bandcamp:before{content:"\f2d5"}.fa-barcode:before{content:"\f02a"}.fa-bars:before{content:"\f0c9"}.fa-baseball-ball:before{content:"\f433"}.fa-basketball-ball:before{content:"\f434"}.fa-bath:before{content:"\f2cd"}.fa-battery-empty:before{content:"\f244"}.fa-battery-full:before{content:"\f240"}.fa-battery-half:before{content:"\f242"}.fa-battery-quarter:before{content:"\f243"}.fa-battery-three-quarters:before{content:"\f241"}.fa-bed:before{content:"\f236"}.fa-beer:before{content:"\f0fc"}.fa-behance:before{content:"\f1b4"}.fa-behance-square:before{content:"\f1b5"}.fa-bell:before{content:"\f0f3"}.fa-bell-slash:before{content:"\f1f6"}.fa-bezier-curve:before{content:"\f55b"}.fa-bible:before{content:"\f647"}.fa-bicycle:before{content:"\f206"}.fa-bimobject:before{content:"\f378"}.fa-binoculars:before{content:"\f1e5"}.fa-birthday-cake:before{content:"\f1fd"}.fa-bitbucket:before{content:"\f171"}.fa-bitcoin:before{content:"\f379"}.fa-bity:before{content:"\f37a"}.fa-black-tie:before{content:"\f27e"}.fa-blackberry:before{content:"\f37b"}.fa-blender:before{content:"\f517"}.fa-blender-phone:before{content:"\f6b6"}.fa-blind:before{content:"\f29d"}.fa-blogger:before{content:"\f37c"}.fa-blogger-b:before{content:"\f37d"}.fa-bluetooth:before{content:"\f293"}.fa-bluetooth-b:before{content:"\f294"}.fa-bold:before{content:"\f032"}.fa-bolt:before{content:"\f0e7"}.fa-bomb:before{content:"\f1e2"}.fa-bone:before{content:"\f5d7"}.fa-bong:before{content:"\f55c"}.fa-book:before{content:"\f02d"}.fa-book-dead:before{content:"\f6b7"}.fa-book-open:before{content:"\f518"}.fa-book-reader:before{content:"\f5da"}.fa-bookmark:before{content:"\f02e"}.fa-bowling-ball:before{content:"\f436"}.fa-box:before{content:"\f466"}.fa-box-open:before{content:"\f49e"}.fa-boxes:before{content:"\f468"}.fa-braille:before{content:"\f2a1"}.fa-brain:before{content:"\f5dc"}.fa-briefcase:before{content:"\f0b1"}.fa-briefcase-medical:before{content:"\f469"}.fa-broadcast-tower:before{content:"\f519"}.fa-broom:before{content:"\f51a"}.fa-brush:before{content:"\f55d"}.fa-btc:before{content:"\f15a"}.fa-bug:before{content:"\f188"}.fa-building:before{content:"\f1ad"}.fa-bullhorn:before{content:"\f0a1"}.fa-bullseye:before{content:"\f140"}.fa-burn:before{content:"\f46a"}.fa-buromobelexperte:before{content:"\f37f"}.fa-bus:before{content:"\f207"}.fa-bus-alt:before{content:"\f55e"}.fa-business-time:before{content:"\f64a"}.fa-buysellads:before{content:"\f20d"}.fa-calculator:before{content:"\f1ec"}.fa-calendar:before{content:"\f133"}.fa-calendar-alt:before{content:"\f073"}.fa-calendar-check:before{content:"\f274"}.fa-calendar-minus:before{content:"\f272"}.fa-calendar-plus:before{content:"\f271"}.fa-calendar-times:before{content:"\f273"}.fa-camera:before{content:"\f030"}.fa-camera-retro:before{content:"\f083"}.fa-campground:before{content:"\f6bb"}.fa-cannabis:before{content:"\f55f"}.fa-capsules:before{content:"\f46b"}.fa-car:before{content:"\f1b9"}.fa-car-alt:before{content:"\f5de"}.fa-car-battery:before{content:"\f5df"}.fa-car-crash:before{content:"\f5e1"}.fa-car-side:before{content:"\f5e4"}.fa-caret-down:before{content:"\f0d7"}.fa-caret-left:before{content:"\f0d9"}.fa-caret-right:before{content:"\f0da"}.fa-caret-square-down:before{content:"\f150"}.fa-caret-square-left:before{content:"\f191"}.fa-caret-square-right:before{content:"\f152"}.fa-caret-square-up:before{content:"\f151"}.fa-caret-up:before{content:"\f0d8"}.fa-cart-arrow-down:before{content:"\f218"}.fa-cart-plus:before{content:"\f217"}.fa-cat:before{content:"\f6be"}.fa-cc-amazon-pay:before{content:"\f42d"}.fa-cc-amex:before{content:"\f1f3"}.fa-cc-apple-pay:before{content:"\f416"}.fa-cc-diners-club:before{content:"\f24c"}.fa-cc-discover:before{content:"\f1f2"}.fa-cc-jcb:before{content:"\f24b"}.fa-cc-mastercard:before{content:"\f1f1"}.fa-cc-paypal:before{content:"\f1f4"}.fa-cc-stripe:before{content:"\f1f5"}.fa-cc-visa:before{content:"\f1f0"}.fa-centercode:before{content:"\f380"}.fa-certificate:before{content:"\f0a3"}.fa-chair:before{content:"\f6c0"}.fa-chalkboard:before{content:"\f51b"}.fa-chalkboard-teacher:before{content:"\f51c"}.fa-charging-station:before{content:"\f5e7"}.fa-chart-area:before{content:"\f1fe"}.fa-chart-bar:before{content:"\f080"}.fa-chart-line:before{content:"\f201"}.fa-chart-pie:before{content:"\f200"}.fa-check:before{content:"\f00c"}.fa-check-circle:before{content:"\f058"}.fa-check-double:before{content:"\f560"}.fa-check-square:before{content:"\f14a"}.fa-chess:before{content:"\f439"}.fa-chess-bishop:before{content:"\f43a"}.fa-chess-board:before{content:"\f43c"}.fa-chess-king:before{content:"\f43f"}.fa-chess-knight:before{content:"\f441"}.fa-chess-pawn:before{content:"\f443"}.fa-chess-queen:before{content:"\f445"}.fa-chess-rook:before{content:"\f447"}.fa-chevron-circle-down:before{content:"\f13a"}.fa-chevron-circle-left:before{content:"\f137"}.fa-chevron-circle-right:before{content:"\f138"}.fa-chevron-circle-up:before{content:"\f139"}.fa-chevron-down:before{content:"\f078"}.fa-chevron-left:before{content:"\f053"}.fa-chevron-right:before{content:"\f054"}.fa-chevron-up:before{content:"\f077"}.fa-child:before{content:"\f1ae"}.fa-chrome:before{content:"\f268"}.fa-church:before{content:"\f51d"}.fa-circle:before{content:"\f111"}.fa-circle-notch:before{content:"\f1ce"}.fa-city:before{content:"\f64f"}.fa-clipboard:before{content:"\f328"}.fa-clipboard-check:before{content:"\f46c"}.fa-clipboard-list:before{content:"\f46d"}.fa-clock:before{content:"\f017"}.fa-clone:before{content:"\f24d"}.fa-closed-captioning:before{content:"\f20a"}.fa-cloud:before{content:"\f0c2"}.fa-cloud-download-alt:before{content:"\f381"}.fa-cloud-meatball:before{content:"\f73b"}.fa-cloud-moon:before{content:"\f6c3"}.fa-cloud-moon-rain:before{content:"\f73c"}.fa-cloud-rain:before{content:"\f73d"}.fa-cloud-showers-heavy:before{content:"\f740"}.fa-cloud-sun:before{content:"\f6c4"}.fa-cloud-sun-rain:before{content:"\f743"}.fa-cloud-upload-alt:before{content:"\f382"}.fa-cloudscale:before{content:"\f383"}.fa-cloudsmith:before{content:"\f384"}.fa-cloudversify:before{content:"\f385"}.fa-cocktail:before{content:"\f561"}.fa-code:before{content:"\f121"}.fa-code-branch:before{content:"\f126"}.fa-codepen:before{content:"\f1cb"}.fa-codiepie:before{content:"\f284"}.fa-coffee:before{content:"\f0f4"}.fa-cog:before{content:"\f013"}.fa-cogs:before{content:"\f085"}.fa-coins:before{content:"\f51e"}.fa-columns:before{content:"\f0db"}.fa-comment:before{content:"\f075"}.fa-comment-alt:before{content:"\f27a"}.fa-comment-dollar:before{content:"\f651"}.fa-comment-dots:before{content:"\f4ad"}.fa-comment-slash:before{content:"\f4b3"}.fa-comments:before{content:"\f086"}.fa-comments-dollar:before{content:"\f653"}.fa-compact-disc:before{content:"\f51f"}.fa-compass:before{content:"\f14e"}.fa-compress:before{content:"\f066"}.fa-concierge-bell:before{content:"\f562"}.fa-connectdevelop:before{content:"\f20e"}.fa-contao:before{content:"\f26d"}.fa-cookie:before{content:"\f563"}.fa-cookie-bite:before{content:"\f564"}.fa-copy:before{content:"\f0c5"}.fa-copyright:before{content:"\f1f9"}.fa-couch:before{content:"\f4b8"}.fa-cpanel:before{content:"\f388"}.fa-creative-commons:before{content:"\f25e"}.fa-creative-commons-by:before{content:"\f4e7"}.fa-creative-commons-nc:before{content:"\f4e8"}.fa-creative-commons-nc-eu:before{content:"\f4e9"}.fa-creative-commons-nc-jp:before{content:"\f4ea"}.fa-creative-commons-nd:before{content:"\f4eb"}.fa-creative-commons-pd:before{content:"\f4ec"}.fa-creative-commons-pd-alt:before{content:"\f4ed"}.fa-creative-commons-remix:before{content:"\f4ee"}.fa-creative-commons-sa:before{content:"\f4ef"}.fa-creative-commons-sampling:before{content:"\f4f0"}.fa-creative-commons-sampling-plus:before{content:"\f4f1"}.fa-creative-commons-share:before{content:"\f4f2"}.fa-creative-commons-zero:before{content:"\f4f3"}.fa-credit-card:before{content:"\f09d"}.fa-critical-role:before{content:"\f6c9"}.fa-crop:before{content:"\f125"}.fa-crop-alt:before{content:"\f565"}.fa-cross:before{content:"\f654"}.fa-crosshairs:before{content:"\f05b"}.fa-crow:before{content:"\f520"}.fa-crown:before{content:"\f521"}.fa-css3:before{content:"\f13c"}.fa-css3-alt:before{content:"\f38b"}.fa-cube:before{content:"\f1b2"}.fa-cubes:before{content:"\f1b3"}.fa-cut:before{content:"\f0c4"}.fa-cuttlefish:before{content:"\f38c"}.fa-d-and-d:before{content:"\f38d"}.fa-d-and-d-beyond:before{content:"\f6ca"}.fa-dashcube:before{content:"\f210"}.fa-database:before{content:"\f1c0"}.fa-deaf:before{content:"\f2a4"}.fa-delicious:before{content:"\f1a5"}.fa-democrat:before{content:"\f747"}.fa-deploydog:before{content:"\f38e"}.fa-deskpro:before{content:"\f38f"}.fa-desktop:before{content:"\f108"}.fa-dev:before{content:"\f6cc"}.fa-deviantart:before{content:"\f1bd"}.fa-dharmachakra:before{content:"\f655"}.fa-diagnoses:before{content:"\f470"}.fa-dice:before{content:"\f522"}.fa-dice-d20:before{content:"\f6cf"}.fa-dice-d6:before{content:"\f6d1"}.fa-dice-five:before{content:"\f523"}.fa-dice-four:before{content:"\f524"}.fa-dice-one:before{content:"\f525"}.fa-dice-six:before{content:"\f526"}.fa-dice-three:before{content:"\f527"}.fa-dice-two:before{content:"\f528"}.fa-digg:before{content:"\f1a6"}.fa-digital-ocean:before{content:"\f391"}.fa-digital-tachograph:before{content:"\f566"}.fa-directions:before{content:"\f5eb"}.fa-discord:before{content:"\f392"}.fa-discourse:before{content:"\f393"}.fa-divide:before{content:"\f529"}.fa-dizzy:before{content:"\f567"}.fa-dna:before{content:"\f471"}.fa-dochub:before{content:"\f394"}.fa-docker:before{content:"\f395"}.fa-dog:before{content:"\f6d3"}.fa-dollar-sign:before{content:"\f155"}.fa-dolly:before{content:"\f472"}.fa-dolly-flatbed:before{content:"\f474"}.fa-donate:before{content:"\f4b9"}.fa-door-closed:before{content:"\f52a"}.fa-door-open:before{content:"\f52b"}.fa-dot-circle:before{content:"\f192"}.fa-dove:before{content:"\f4ba"}.fa-download:before{content:"\f019"}.fa-draft2digital:before{content:"\f396"}.fa-drafting-compass:before{content:"\f568"}.fa-dragon:before{content:"\f6d5"}.fa-draw-polygon:before{content:"\f5ee"}.fa-dribbble:before{content:"\f17d"}.fa-dribbble-square:before{content:"\f397"}.fa-dropbox:before{content:"\f16b"}.fa-drum:before{content:"\f569"}.fa-drum-steelpan:before{content:"\f56a"}.fa-drumstick-bite:before{content:"\f6d7"}.fa-drupal:before{content:"\f1a9"}.fa-dumbbell:before{content:"\f44b"}.fa-dungeon:before{content:"\f6d9"}.fa-dyalog:before{content:"\f399"}.fa-earlybirds:before{content:"\f39a"}.fa-ebay:before{content:"\f4f4"}.fa-edge:before{content:"\f282"}.fa-edit:before{content:"\f044"}.fa-eject:before{content:"\f052"}.fa-elementor:before{content:"\f430"}.fa-ellipsis-h:before{content:"\f141"}.fa-ellipsis-v:before{content:"\f142"}.fa-ello:before{content:"\f5f1"}.fa-ember:before{content:"\f423"}.fa-empire:before{content:"\f1d1"}.fa-envelope:before{content:"\f0e0"}.fa-envelope-open:before{content:"\f2b6"}.fa-envelope-open-text:before{content:"\f658"}.fa-envelope-square:before{content:"\f199"}.fa-envira:before{content:"\f299"}.fa-equals:before{content:"\f52c"}.fa-eraser:before{content:"\f12d"}.fa-erlang:before{content:"\f39d"}.fa-ethereum:before{content:"\f42e"}.fa-etsy:before{content:"\f2d7"}.fa-euro-sign:before{content:"\f153"}.fa-exchange-alt:before{content:"\f362"}.fa-exclamation:before{content:"\f12a"}.fa-exclamation-circle:before{content:"\f06a"}.fa-exclamation-triangle:before{content:"\f071"}.fa-expand:before{content:"\f065"}.fa-expand-arrows-alt:before{content:"\f31e"}.fa-expeditedssl:before{content:"\f23e"}.fa-external-link-alt:before{content:"\f35d"}.fa-external-link-square-alt:before{content:"\f360"}.fa-eye:before{content:"\f06e"}.fa-eye-dropper:before{content:"\f1fb"}.fa-eye-slash:before{content:"\f070"}.fa-facebook:before{content:"\f09a"}.fa-facebook-f:before{content:"\f39e"}.fa-facebook-messenger:before{content:"\f39f"}.fa-facebook-square:before{content:"\f082"}.fa-fantasy-flight-games:before{content:"\f6dc"}.fa-fast-backward:before{content:"\f049"}.fa-fast-forward:before{content:"\f050"}.fa-fax:before{content:"\f1ac"}.fa-feather:before{content:"\f52d"}.fa-feather-alt:before{content:"\f56b"}.fa-female:before{content:"\f182"}.fa-fighter-jet:before{content:"\f0fb"}.fa-file:before{content:"\f15b"}.fa-file-alt:before{content:"\f15c"}.fa-file-archive:before{content:"\f1c6"}.fa-file-audio:before{content:"\f1c7"}.fa-file-code:before{content:"\f1c9"}.fa-file-contract:before{content:"\f56c"}.fa-file-csv:before{content:"\f6dd"}.fa-file-download:before{content:"\f56d"}.fa-file-excel:before{content:"\f1c3"}.fa-file-export:before{content:"\f56e"}.fa-file-image:before{content:"\f1c5"}.fa-file-import:before{content:"\f56f"}.fa-file-invoice:before{content:"\f570"}.fa-file-invoice-dollar:before{content:"\f571"}.fa-file-medical:before{content:"\f477"}.fa-file-medical-alt:before{content:"\f478"}.fa-file-pdf:before{content:"\f1c1"}.fa-file-powerpoint:before{content:"\f1c4"}.fa-file-prescription:before{content:"\f572"}.fa-file-signature:before{content:"\f573"}.fa-file-upload:before{content:"\f574"}.fa-file-video:before{content:"\f1c8"}.fa-file-word:before{content:"\f1c2"}.fa-fill:before{content:"\f575"}.fa-fill-drip:before{content:"\f576"}.fa-film:before{content:"\f008"}.fa-filter:before{content:"\f0b0"}.fa-fingerprint:before{content:"\f577"}.fa-fire:before{content:"\f06d"}.fa-fire-extinguisher:before{content:"\f134"}.fa-firefox:before{content:"\f269"}.fa-first-aid:before{content:"\f479"}.fa-first-order:before{content:"\f2b0"}.fa-first-order-alt:before{content:"\f50a"}.fa-firstdraft:before{content:"\f3a1"}.fa-fish:before{content:"\f578"}.fa-fist-raised:before{content:"\f6de"}.fa-flag:before{content:"\f024"}.fa-flag-checkered:before{content:"\f11e"}.fa-flag-usa:before{content:"\f74d"}.fa-flask:before{content:"\f0c3"}.fa-flickr:before{content:"\f16e"}.fa-flipboard:before{content:"\f44d"}.fa-flushed:before{content:"\f579"}.fa-fly:before{content:"\f417"}.fa-folder:before{content:"\f07b"}.fa-folder-minus:before{content:"\f65d"}.fa-folder-open:before{content:"\f07c"}.fa-folder-plus:before{content:"\f65e"}.fa-font:before{content:"\f031"}.fa-font-awesome:before{content:"\f2b4"}.fa-font-awesome-alt:before{content:"\f35c"}.fa-font-awesome-flag:before{content:"\f425"}.fa-font-awesome-logo-full:before{content:"\f4e6"}.fa-fonticons:before{content:"\f280"}.fa-fonticons-fi:before{content:"\f3a2"}.fa-football-ball:before{content:"\f44e"}.fa-fort-awesome:before{content:"\f286"}.fa-fort-awesome-alt:before{content:"\f3a3"}.fa-forumbee:before{content:"\f211"}.fa-forward:before{content:"\f04e"}.fa-foursquare:before{content:"\f180"}.fa-free-code-camp:before{content:"\f2c5"}.fa-freebsd:before{content:"\f3a4"}.fa-frog:before{content:"\f52e"}.fa-frown:before{content:"\f119"}.fa-frown-open:before{content:"\f57a"}.fa-fulcrum:before{content:"\f50b"}.fa-funnel-dollar:before{content:"\f662"}.fa-futbol:before{content:"\f1e3"}.fa-galactic-republic:before{content:"\f50c"}.fa-galactic-senate:before{content:"\f50d"}.fa-gamepad:before{content:"\f11b"}.fa-gas-pump:before{content:"\f52f"}.fa-gavel:before{content:"\f0e3"}.fa-gem:before{content:"\f3a5"}.fa-genderless:before{content:"\f22d"}.fa-get-pocket:before{content:"\f265"}.fa-gg:before{content:"\f260"}.fa-gg-circle:before{content:"\f261"}.fa-ghost:before{content:"\f6e2"}.fa-gift:before{content:"\f06b"}.fa-git:before{content:"\f1d3"}.fa-git-square:before{content:"\f1d2"}.fa-github:before{content:"\f09b"}.fa-github-alt:before{content:"\f113"}.fa-github-square:before{content:"\f092"}.fa-gitkraken:before{content:"\f3a6"}.fa-gitlab:before{content:"\f296"}.fa-gitter:before{content:"\f426"}.fa-glass-martini:before{content:"\f000"}.fa-glass-martini-alt:before{content:"\f57b"}.fa-glasses:before{content:"\f530"}.fa-glide:before{content:"\f2a5"}.fa-glide-g:before{content:"\f2a6"}.fa-globe:before{content:"\f0ac"}.fa-globe-africa:before{content:"\f57c"}.fa-globe-americas:before{content:"\f57d"}.fa-globe-asia:before{content:"\f57e"}.fa-gofore:before{content:"\f3a7"}.fa-golf-ball:before{content:"\f450"}.fa-goodreads:before{content:"\f3a8"}.fa-goodreads-g:before{content:"\f3a9"}.fa-google:before{content:"\f1a0"}.fa-google-drive:before{content:"\f3aa"}.fa-google-play:before{content:"\f3ab"}.fa-google-plus:before{content:"\f2b3"}.fa-google-plus-g:before{content:"\f0d5"}.fa-google-plus-square:before{content:"\f0d4"}.fa-google-wallet:before{content:"\f1ee"}.fa-gopuram:before{content:"\f664"}.fa-graduation-cap:before{content:"\f19d"}.fa-gratipay:before{content:"\f184"}.fa-grav:before{content:"\f2d6"}.fa-greater-than:before{content:"\f531"}.fa-greater-than-equal:before{content:"\f532"}.fa-grimace:before{content:"\f57f"}.fa-grin:before{content:"\f580"}.fa-grin-alt:before{content:"\f581"}.fa-grin-beam:before{content:"\f582"}.fa-grin-beam-sweat:before{content:"\f583"}.fa-grin-hearts:before{content:"\f584"}.fa-grin-squint:before{content:"\f585"}.fa-grin-squint-tears:before{content:"\f586"}.fa-grin-stars:before{content:"\f587"}.fa-grin-tears:before{content:"\f588"}.fa-grin-tongue:before{content:"\f589"}.fa-grin-tongue-squint:before{content:"\f58a"}.fa-grin-tongue-wink:before{content:"\f58b"}.fa-grin-wink:before{content:"\f58c"}.fa-grip-horizontal:before{content:"\f58d"}.fa-grip-vertical:before{content:"\f58e"}.fa-gripfire:before{content:"\f3ac"}.fa-grunt:before{content:"\f3ad"}.fa-gulp:before{content:"\f3ae"}.fa-h-square:before{content:"\f0fd"}.fa-hacker-news:before{content:"\f1d4"}.fa-hacker-news-square:before{content:"\f3af"}.fa-hackerrank:before{content:"\f5f7"}.fa-hammer:before{content:"\f6e3"}.fa-hamsa:before{content:"\f665"}.fa-hand-holding:before{content:"\f4bd"}.fa-hand-holding-heart:before{content:"\f4be"}.fa-hand-holding-usd:before{content:"\f4c0"}.fa-hand-lizard:before{content:"\f258"}.fa-hand-paper:before{content:"\f256"}.fa-hand-peace:before{content:"\f25b"}.fa-hand-point-down:before{content:"\f0a7"}.fa-hand-point-left:before{content:"\f0a5"}.fa-hand-point-right:before{content:"\f0a4"}.fa-hand-point-up:before{content:"\f0a6"}.fa-hand-pointer:before{content:"\f25a"}.fa-hand-rock:before{content:"\f255"}.fa-hand-scissors:before{content:"\f257"}.fa-hand-spock:before{content:"\f259"}.fa-hands:before{content:"\f4c2"}.fa-hands-helping:before{content:"\f4c4"}.fa-handshake:before{content:"\f2b5"}.fa-hanukiah:before{content:"\f6e6"}.fa-hashtag:before{content:"\f292"}.fa-hat-wizard:before{content:"\f6e8"}.fa-haykal:before{content:"\f666"}.fa-hdd:before{content:"\f0a0"}.fa-heading:before{content:"\f1dc"}.fa-headphones:before{content:"\f025"}.fa-headphones-alt:before{content:"\f58f"}.fa-headset:before{content:"\f590"}.fa-heart:before{content:"\f004"}.fa-heartbeat:before{content:"\f21e"}.fa-helicopter:before{content:"\f533"}.fa-highlighter:before{content:"\f591"}.fa-hiking:before{content:"\f6ec"}.fa-hippo:before{content:"\f6ed"}.fa-hips:before{content:"\f452"}.fa-hire-a-helper:before{content:"\f3b0"}.fa-history:before{content:"\f1da"}.fa-hockey-puck:before{content:"\f453"}.fa-home:before{content:"\f015"}.fa-hooli:before{content:"\f427"}.fa-hornbill:before{content:"\f592"}.fa-horse:before{content:"\f6f0"}.fa-hospital:before{content:"\f0f8"}.fa-hospital-alt:before{content:"\f47d"}.fa-hospital-symbol:before{content:"\f47e"}.fa-hot-tub:before{content:"\f593"}.fa-hotel:before{content:"\f594"}.fa-hotjar:before{content:"\f3b1"}.fa-hourglass:before{content:"\f254"}.fa-hourglass-end:before{content:"\f253"}.fa-hourglass-half:before{content:"\f252"}.fa-hourglass-start:before{content:"\f251"}.fa-house-damage:before{content:"\f6f1"}.fa-houzz:before{content:"\f27c"}.fa-hryvnia:before{content:"\f6f2"}.fa-html5:before{content:"\f13b"}.fa-hubspot:before{content:"\f3b2"}.fa-i-cursor:before{content:"\f246"}.fa-id-badge:before{content:"\f2c1"}.fa-id-card:before{content:"\f2c2"}.fa-id-card-alt:before{content:"\f47f"}.fa-image:before{content:"\f03e"}.fa-images:before{content:"\f302"}.fa-imdb:before{content:"\f2d8"}.fa-inbox:before{content:"\f01c"}.fa-indent:before{content:"\f03c"}.fa-industry:before{content:"\f275"}.fa-infinity:before{content:"\f534"}.fa-info:before{content:"\f129"}.fa-info-circle:before{content:"\f05a"}.fa-instagram:before{content:"\f16d"}.fa-internet-explorer:before{content:"\f26b"}.fa-ioxhost:before{content:"\f208"}.fa-italic:before{content:"\f033"}.fa-itunes:before{content:"\f3b4"}.fa-itunes-note:before{content:"\f3b5"}.fa-java:before{content:"\f4e4"}.fa-jedi:before{content:"\f669"}.fa-jedi-order:before{content:"\f50e"}.fa-jenkins:before{content:"\f3b6"}.fa-joget:before{content:"\f3b7"}.fa-joint:before{content:"\f595"}.fa-joomla:before{content:"\f1aa"}.fa-journal-whills:before{content:"\f66a"}.fa-js:before{content:"\f3b8"}.fa-js-square:before{content:"\f3b9"}.fa-jsfiddle:before{content:"\f1cc"}.fa-kaaba:before{content:"\f66b"}.fa-kaggle:before{content:"\f5fa"}.fa-key:before{content:"\f084"}.fa-keybase:before{content:"\f4f5"}.fa-keyboard:before{content:"\f11c"}.fa-keycdn:before{content:"\f3ba"}.fa-khanda:before{content:"\f66d"}.fa-kickstarter:before{content:"\f3bb"}.fa-kickstarter-k:before{content:"\f3bc"}.fa-kiss:before{content:"\f596"}.fa-kiss-beam:before{content:"\f597"}.fa-kiss-wink-heart:before{content:"\f598"}.fa-kiwi-bird:before{content:"\f535"}.fa-korvue:before{content:"\f42f"}.fa-landmark:before{content:"\f66f"}.fa-language:before{content:"\f1ab"}.fa-laptop:before{content:"\f109"}.fa-laptop-code:before{content:"\f5fc"}.fa-laravel:before{content:"\f3bd"}.fa-lastfm:before{content:"\f202"}.fa-lastfm-square:before{content:"\f203"}.fa-laugh:before{content:"\f599"}.fa-laugh-beam:before{content:"\f59a"}.fa-laugh-squint:before{content:"\f59b"}.fa-laugh-wink:before{content:"\f59c"}.fa-layer-group:before{content:"\f5fd"}.fa-leaf:before{content:"\f06c"}.fa-leanpub:before{content:"\f212"}.fa-lemon:before{content:"\f094"}.fa-less:before{content:"\f41d"}.fa-less-than:before{content:"\f536"}.fa-less-than-equal:before{content:"\f537"}.fa-level-down-alt:before{content:"\f3be"}.fa-level-up-alt:before{content:"\f3bf"}.fa-life-ring:before{content:"\f1cd"}.fa-lightbulb:before{content:"\f0eb"}.fa-line:before{content:"\f3c0"}.fa-link:before{content:"\f0c1"}.fa-linkedin:before{content:"\f08c"}.fa-linkedin-in:before{content:"\f0e1"}.fa-linode:before{content:"\f2b8"}.fa-linux:before{content:"\f17c"}.fa-lira-sign:before{content:"\f195"}.fa-list:before{content:"\f03a"}.fa-list-alt:before{content:"\f022"}.fa-list-ol:before{content:"\f0cb"}.fa-list-ul:before{content:"\f0ca"}.fa-location-arrow:before{content:"\f124"}.fa-lock:before{content:"\f023"}.fa-lock-open:before{content:"\f3c1"}.fa-long-arrow-alt-down:before{content:"\f309"}.fa-long-arrow-alt-left:before{content:"\f30a"}.fa-long-arrow-alt-right:before{content:"\f30b"}.fa-long-arrow-alt-up:before{content:"\f30c"}.fa-low-vision:before{content:"\f2a8"}.fa-luggage-cart:before{content:"\f59d"}.fa-lyft:before{content:"\f3c3"}.fa-magento:before{content:"\f3c4"}.fa-magic:before{content:"\f0d0"}.fa-magnet:before{content:"\f076"}.fa-mail-bulk:before{content:"\f674"}.fa-mailchimp:before{content:"\f59e"}.fa-male:before{content:"\f183"}.fa-mandalorian:before{content:"\f50f"}.fa-map:before{content:"\f279"}.fa-map-marked:before{content:"\f59f"}.fa-map-marked-alt:before{content:"\f5a0"}.fa-map-marker:before{content:"\f041"}.fa-map-marker-alt:before{content:"\f3c5"}.fa-map-pin:before{content:"\f276"}.fa-map-signs:before{content:"\f277"}.fa-markdown:before{content:"\f60f"}.fa-marker:before{content:"\f5a1"}.fa-mars:before{content:"\f222"}.fa-mars-double:before{content:"\f227"}.fa-mars-stroke:before{content:"\f229"}.fa-mars-stroke-h:before{content:"\f22b"}.fa-mars-stroke-v:before{content:"\f22a"}.fa-mask:before{content:"\f6fa"}.fa-mastodon:before{content:"\f4f6"}.fa-maxcdn:before{content:"\f136"}.fa-medal:before{content:"\f5a2"}.fa-medapps:before{content:"\f3c6"}.fa-medium:before{content:"\f23a"}.fa-medium-m:before{content:"\f3c7"}.fa-medkit:before{content:"\f0fa"}.fa-medrt:before{content:"\f3c8"}.fa-meetup:before{content:"\f2e0"}.fa-megaport:before{content:"\f5a3"}.fa-meh:before{content:"\f11a"}.fa-meh-blank:before{content:"\f5a4"}.fa-meh-rolling-eyes:before{content:"\f5a5"}.fa-memory:before{content:"\f538"}.fa-menorah:before{content:"\f676"}.fa-mercury:before{content:"\f223"}.fa-meteor:before{content:"\f753"}.fa-microchip:before{content:"\f2db"}.fa-microphone:before{content:"\f130"}.fa-microphone-alt:before{content:"\f3c9"}.fa-microphone-alt-slash:before{content:"\f539"}.fa-microphone-slash:before{content:"\f131"}.fa-microscope:before{content:"\f610"}.fa-microsoft:before{content:"\f3ca"}.fa-minus:before{content:"\f068"}.fa-minus-circle:before{content:"\f056"}.fa-minus-square:before{content:"\f146"}.fa-mix:before{content:"\f3cb"}.fa-mixcloud:before{content:"\f289"}.fa-mizuni:before{content:"\f3cc"}.fa-mobile:before{content:"\f10b"}.fa-mobile-alt:before{content:"\f3cd"}.fa-modx:before{content:"\f285"}.fa-monero:before{content:"\f3d0"}.fa-money-bill:before{content:"\f0d6"}.fa-money-bill-alt:before{content:"\f3d1"}.fa-money-bill-wave:before{content:"\f53a"}.fa-money-bill-wave-alt:before{content:"\f53b"}.fa-money-check:before{content:"\f53c"}.fa-money-check-alt:before{content:"\f53d"}.fa-monument:before{content:"\f5a6"}.fa-moon:before{content:"\f186"}.fa-mortar-pestle:before{content:"\f5a7"}.fa-mosque:before{content:"\f678"}.fa-motorcycle:before{content:"\f21c"}.fa-mountain:before{content:"\f6fc"}.fa-mouse-pointer:before{content:"\f245"}.fa-music:before{content:"\f001"}.fa-napster:before{content:"\f3d2"}.fa-neos:before{content:"\f612"}.fa-network-wired:before{content:"\f6ff"}.fa-neuter:before{content:"\f22c"}.fa-newspaper:before{content:"\f1ea"}.fa-nimblr:before{content:"\f5a8"}.fa-nintendo-switch:before{content:"\f418"}.fa-node:before{content:"\f419"}.fa-node-js:before{content:"\f3d3"}.fa-not-equal:before{content:"\f53e"}.fa-notes-medical:before{content:"\f481"}.fa-npm:before{content:"\f3d4"}.fa-ns8:before{content:"\f3d5"}.fa-nutritionix:before{content:"\f3d6"}.fa-object-group:before{content:"\f247"}.fa-object-ungroup:before{content:"\f248"}.fa-odnoklassniki:before{content:"\f263"}.fa-odnoklassniki-square:before{content:"\f264"}.fa-oil-can:before{content:"\f613"}.fa-old-republic:before{content:"\f510"}.fa-om:before{content:"\f679"}.fa-opencart:before{content:"\f23d"}.fa-openid:before{content:"\f19b"}.fa-opera:before{content:"\f26a"}.fa-optin-monster:before{content:"\f23c"}.fa-osi:before{content:"\f41a"}.fa-otter:before{content:"\f700"}.fa-outdent:before{content:"\f03b"}.fa-page4:before{content:"\f3d7"}.fa-pagelines:before{content:"\f18c"}.fa-paint-brush:before{content:"\f1fc"}.fa-paint-roller:before{content:"\f5aa"}.fa-palette:before{content:"\f53f"}.fa-palfed:before{content:"\f3d8"}.fa-pallet:before{content:"\f482"}.fa-paper-plane:before{content:"\f1d8"}.fa-paperclip:before{content:"\f0c6"}.fa-parachute-box:before{content:"\f4cd"}.fa-paragraph:before{content:"\f1dd"}.fa-parking:before{content:"\f540"}.fa-passport:before{content:"\f5ab"}.fa-pastafarianism:before{content:"\f67b"}.fa-paste:before{content:"\f0ea"}.fa-patreon:before{content:"\f3d9"}.fa-pause:before{content:"\f04c"}.fa-pause-circle:before{content:"\f28b"}.fa-paw:before{content:"\f1b0"}.fa-paypal:before{content:"\f1ed"}.fa-peace:before{content:"\f67c"}.fa-pen:before{content:"\f304"}.fa-pen-alt:before{content:"\f305"}.fa-pen-fancy:before{content:"\f5ac"}.fa-pen-nib:before{content:"\f5ad"}.fa-pen-square:before{content:"\f14b"}.fa-pencil-alt:before{content:"\f303"}.fa-pencil-ruler:before{content:"\f5ae"}.fa-penny-arcade:before{content:"\f704"}.fa-people-carry:before{content:"\f4ce"}.fa-percent:before{content:"\f295"}.fa-percentage:before{content:"\f541"}.fa-periscope:before{content:"\f3da"}.fa-person-booth:before{content:"\f756"}.fa-phabricator:before{content:"\f3db"}.fa-phoenix-framework:before{content:"\f3dc"}.fa-phoenix-squadron:before{content:"\f511"}.fa-phone:before{content:"\f095"}.fa-phone-slash:before{content:"\f3dd"}.fa-phone-square:before{content:"\f098"}.fa-phone-volume:before{content:"\f2a0"}.fa-php:before{content:"\f457"}.fa-pied-piper:before{content:"\f2ae"}.fa-pied-piper-alt:before{content:"\f1a8"}.fa-pied-piper-hat:before{content:"\f4e5"}.fa-pied-piper-pp:before{content:"\f1a7"}.fa-piggy-bank:before{content:"\f4d3"}.fa-pills:before{content:"\f484"}.fa-pinterest:before{content:"\f0d2"}.fa-pinterest-p:before{content:"\f231"}.fa-pinterest-square:before{content:"\f0d3"}.fa-place-of-worship:before{content:"\f67f"}.fa-plane:before{content:"\f072"}.fa-plane-arrival:before{content:"\f5af"}.fa-plane-departure:before{content:"\f5b0"}.fa-play:before{content:"\f04b"}.fa-play-circle:before{content:"\f144"}.fa-playstation:before{content:"\f3df"}.fa-plug:before{content:"\f1e6"}.fa-plus:before{content:"\f067"}.fa-plus-circle:before{content:"\f055"}.fa-plus-square:before{content:"\f0fe"}.fa-podcast:before{content:"\f2ce"}.fa-poll:before{content:"\f681"}.fa-poll-h:before{content:"\f682"}.fa-poo:before{content:"\f2fe"}.fa-poo-storm:before{content:"\f75a"}.fa-poop:before{content:"\f619"}.fa-portrait:before{content:"\f3e0"}.fa-pound-sign:before{content:"\f154"}.fa-power-off:before{content:"\f011"}.fa-pray:before{content:"\f683"}.fa-praying-hands:before{content:"\f684"}.fa-prescription:before{content:"\f5b1"}.fa-prescription-bottle:before{content:"\f485"}.fa-prescription-bottle-alt:before{content:"\f486"}.fa-print:before{content:"\f02f"}.fa-procedures:before{content:"\f487"}.fa-product-hunt:before{content:"\f288"}.fa-project-diagram:before{content:"\f542"}.fa-pushed:before{content:"\f3e1"}.fa-puzzle-piece:before{content:"\f12e"}.fa-python:before{content:"\f3e2"}.fa-qq:before{content:"\f1d6"}.fa-qrcode:before{content:"\f029"}.fa-question:before{content:"\f128"}.fa-question-circle:before{content:"\f059"}.fa-quidditch:before{content:"\f458"}.fa-quinscape:before{content:"\f459"}.fa-quora:before{content:"\f2c4"}.fa-quote-left:before{content:"\f10d"}.fa-quote-right:before{content:"\f10e"}.fa-quran:before{content:"\f687"}.fa-r-project:before{content:"\f4f7"}.fa-rainbow:before{content:"\f75b"}.fa-random:before{content:"\f074"}.fa-ravelry:before{content:"\f2d9"}.fa-react:before{content:"\f41b"}.fa-reacteurope:before{content:"\f75d"}.fa-readme:before{content:"\f4d5"}.fa-rebel:before{content:"\f1d0"}.fa-receipt:before{content:"\f543"}.fa-recycle:before{content:"\f1b8"}.fa-red-river:before{content:"\f3e3"}.fa-reddit:before{content:"\f1a1"}.fa-reddit-alien:before{content:"\f281"}.fa-reddit-square:before{content:"\f1a2"}.fa-redo:before{content:"\f01e"}.fa-redo-alt:before{content:"\f2f9"}.fa-registered:before{content:"\f25d"}.fa-renren:before{content:"\f18b"}.fa-reply:before{content:"\f3e5"}.fa-reply-all:before{content:"\f122"}.fa-replyd:before{content:"\f3e6"}.fa-republican:before{content:"\f75e"}.fa-researchgate:before{content:"\f4f8"}.fa-resolving:before{content:"\f3e7"}.fa-retweet:before{content:"\f079"}.fa-rev:before{content:"\f5b2"}.fa-ribbon:before{content:"\f4d6"}.fa-ring:before{content:"\f70b"}.fa-road:before{content:"\f018"}.fa-robot:before{content:"\f544"}.fa-rocket:before{content:"\f135"}.fa-rocketchat:before{content:"\f3e8"}.fa-rockrms:before{content:"\f3e9"}.fa-route:before{content:"\f4d7"}.fa-rss:before{content:"\f09e"}.fa-rss-square:before{content:"\f143"}.fa-ruble-sign:before{content:"\f158"}.fa-ruler:before{content:"\f545"}.fa-ruler-combined:before{content:"\f546"}.fa-ruler-horizontal:before{content:"\f547"}.fa-ruler-vertical:before{content:"\f548"}.fa-running:before{content:"\f70c"}.fa-rupee-sign:before{content:"\f156"}.fa-sad-cry:before{content:"\f5b3"}.fa-sad-tear:before{content:"\f5b4"}.fa-safari:before{content:"\f267"}.fa-sass:before{content:"\f41e"}.fa-save:before{content:"\f0c7"}.fa-schlix:before{content:"\f3ea"}.fa-school:before{content:"\f549"}.fa-screwdriver:before{content:"\f54a"}.fa-scribd:before{content:"\f28a"}.fa-scroll:before{content:"\f70e"}.fa-search:before{content:"\f002"}.fa-search-dollar:before{content:"\f688"}.fa-search-location:before{content:"\f689"}.fa-search-minus:before{content:"\f010"}.fa-search-plus:before{content:"\f00e"}.fa-searchengin:before{content:"\f3eb"}.fa-seedling:before{content:"\f4d8"}.fa-sellcast:before{content:"\f2da"}.fa-sellsy:before{content:"\f213"}.fa-server:before{content:"\f233"}.fa-servicestack:before{content:"\f3ec"}.fa-shapes:before{content:"\f61f"}.fa-share:before{content:"\f064"}.fa-share-alt:before{content:"\f1e0"}.fa-share-alt-square:before{content:"\f1e1"}.fa-share-square:before{content:"\f14d"}.fa-shekel-sign:before{content:"\f20b"}.fa-shield-alt:before{content:"\f3ed"}.fa-ship:before{content:"\f21a"}.fa-shipping-fast:before{content:"\f48b"}.fa-shirtsinbulk:before{content:"\f214"}.fa-shoe-prints:before{content:"\f54b"}.fa-shopping-bag:before{content:"\f290"}.fa-shopping-basket:before{content:"\f291"}.fa-shopping-cart:before{content:"\f07a"}.fa-shopware:before{content:"\f5b5"}.fa-shower:before{content:"\f2cc"}.fa-shuttle-van:before{content:"\f5b6"}.fa-sign:before{content:"\f4d9"}.fa-sign-in-alt:before{content:"\f2f6"}.fa-sign-language:before{content:"\f2a7"}.fa-sign-out-alt:before{content:"\f2f5"}.fa-signal:before{content:"\f012"}.fa-signature:before{content:"\f5b7"}.fa-simplybuilt:before{content:"\f215"}.fa-sistrix:before{content:"\f3ee"}.fa-sitemap:before{content:"\f0e8"}.fa-sith:before{content:"\f512"}.fa-skull:before{content:"\f54c"}.fa-skull-crossbones:before{content:"\f714"}.fa-skyatlas:before{content:"\f216"}.fa-skype:before{content:"\f17e"}.fa-slack:before{content:"\f198"}.fa-slack-hash:before{content:"\f3ef"}.fa-slash:before{content:"\f715"}.fa-sliders-h:before{content:"\f1de"}.fa-slideshare:before{content:"\f1e7"}.fa-smile:before{content:"\f118"}.fa-smile-beam:before{content:"\f5b8"}.fa-smile-wink:before{content:"\f4da"}.fa-smog:before{content:"\f75f"}.fa-smoking:before{content:"\f48d"}.fa-smoking-ban:before{content:"\f54d"}.fa-snapchat:before{content:"\f2ab"}.fa-snapchat-ghost:before{content:"\f2ac"}.fa-snapchat-square:before{content:"\f2ad"}.fa-snowflake:before{content:"\f2dc"}.fa-socks:before{content:"\f696"}.fa-solar-panel:before{content:"\f5ba"}.fa-sort:before{content:"\f0dc"}.fa-sort-alpha-down:before{content:"\f15d"}.fa-sort-alpha-up:before{content:"\f15e"}.fa-sort-amount-down:before{content:"\f160"}.fa-sort-amount-up:before{content:"\f161"}.fa-sort-down:before{content:"\f0dd"}.fa-sort-numeric-down:before{content:"\f162"}.fa-sort-numeric-up:before{content:"\f163"}.fa-sort-up:before{content:"\f0de"}.fa-soundcloud:before{content:"\f1be"}.fa-spa:before{content:"\f5bb"}.fa-space-shuttle:before{content:"\f197"}.fa-speakap:before{content:"\f3f3"}.fa-spider:before{content:"\f717"}.fa-spinner:before{content:"\f110"}.fa-splotch:before{content:"\f5bc"}.fa-spotify:before{content:"\f1bc"}.fa-spray-can:before{content:"\f5bd"}.fa-square:before{content:"\f0c8"}.fa-square-full:before{content:"\f45c"}.fa-square-root-alt:before{content:"\f698"}.fa-squarespace:before{content:"\f5be"}.fa-stack-exchange:before{content:"\f18d"}.fa-stack-overflow:before{content:"\f16c"}.fa-stamp:before{content:"\f5bf"}.fa-star:before{content:"\f005"}.fa-star-and-crescent:before{content:"\f699"}.fa-star-half:before{content:"\f089"}.fa-star-half-alt:before{content:"\f5c0"}.fa-star-of-david:before{content:"\f69a"}.fa-star-of-life:before{content:"\f621"}.fa-staylinked:before{content:"\f3f5"}.fa-steam:before{content:"\f1b6"}.fa-steam-square:before{content:"\f1b7"}.fa-steam-symbol:before{content:"\f3f6"}.fa-step-backward:before{content:"\f048"}.fa-step-forward:before{content:"\f051"}.fa-stethoscope:before{content:"\f0f1"}.fa-sticker-mule:before{content:"\f3f7"}.fa-sticky-note:before{content:"\f249"}.fa-stop:before{content:"\f04d"}.fa-stop-circle:before{content:"\f28d"}.fa-stopwatch:before{content:"\f2f2"}.fa-store:before{content:"\f54e"}.fa-store-alt:before{content:"\f54f"}.fa-strava:before{content:"\f428"}.fa-stream:before{content:"\f550"}.fa-street-view:before{content:"\f21d"}.fa-strikethrough:before{content:"\f0cc"}.fa-stripe:before{content:"\f429"}.fa-stripe-s:before{content:"\f42a"}.fa-stroopwafel:before{content:"\f551"}.fa-studiovinari:before{content:"\f3f8"}.fa-stumbleupon:before{content:"\f1a4"}.fa-stumbleupon-circle:before{content:"\f1a3"}.fa-subscript:before{content:"\f12c"}.fa-subway:before{content:"\f239"}.fa-suitcase:before{content:"\f0f2"}.fa-suitcase-rolling:before{content:"\f5c1"}.fa-sun:before{content:"\f185"}.fa-superpowers:before{content:"\f2dd"}.fa-superscript:before{content:"\f12b"}.fa-supple:before{content:"\f3f9"}.fa-surprise:before{content:"\f5c2"}.fa-swatchbook:before{content:"\f5c3"}.fa-swimmer:before{content:"\f5c4"}.fa-swimming-pool:before{content:"\f5c5"}.fa-synagogue:before{content:"\f69b"}.fa-sync:before{content:"\f021"}.fa-sync-alt:before{content:"\f2f1"}.fa-syringe:before{content:"\f48e"}.fa-table:before{content:"\f0ce"}.fa-table-tennis:before{content:"\f45d"}.fa-tablet:before{content:"\f10a"}.fa-tablet-alt:before{content:"\f3fa"}.fa-tablets:before{content:"\f490"}.fa-tachometer-alt:before{content:"\f3fd"}.fa-tag:before{content:"\f02b"}.fa-tags:before{content:"\f02c"}.fa-tape:before{content:"\f4db"}.fa-tasks:before{content:"\f0ae"}.fa-taxi:before{content:"\f1ba"}.fa-teamspeak:before{content:"\f4f9"}.fa-teeth:before{content:"\f62e"}.fa-teeth-open:before{content:"\f62f"}.fa-telegram:before{content:"\f2c6"}.fa-telegram-plane:before{content:"\f3fe"}.fa-temperature-high:before{content:"\f769"}.fa-temperature-low:before{content:"\f76b"}.fa-tencent-weibo:before{content:"\f1d5"}.fa-terminal:before{content:"\f120"}.fa-text-height:before{content:"\f034"}.fa-text-width:before{content:"\f035"}.fa-th:before{content:"\f00a"}.fa-th-large:before{content:"\f009"}.fa-th-list:before{content:"\f00b"}.fa-the-red-yeti:before{content:"\f69d"}.fa-theater-masks:before{content:"\f630"}.fa-themeco:before{content:"\f5c6"}.fa-themeisle:before{content:"\f2b2"}.fa-thermometer:before{content:"\f491"}.fa-thermometer-empty:before{content:"\f2cb"}.fa-thermometer-full:before{content:"\f2c7"}.fa-thermometer-half:before{content:"\f2c9"}.fa-thermometer-quarter:before{content:"\f2ca"}.fa-thermometer-three-quarters:before{content:"\f2c8"}.fa-think-peaks:before{content:"\f731"}.fa-thumbs-down:before{content:"\f165"}.fa-thumbs-up:before{content:"\f164"}.fa-thumbtack:before{content:"\f08d"}.fa-ticket-alt:before{content:"\f3ff"}.fa-times:before{content:"\f00d"}.fa-times-circle:before{content:"\f057"}.fa-tint:before{content:"\f043"}.fa-tint-slash:before{content:"\f5c7"}.fa-tired:before{content:"\f5c8"}.fa-toggle-off:before{content:"\f204"}.fa-toggle-on:before{content:"\f205"}.fa-toilet-paper:before{content:"\f71e"}.fa-toolbox:before{content:"\f552"}.fa-tooth:before{content:"\f5c9"}.fa-torah:before{content:"\f6a0"}.fa-torii-gate:before{content:"\f6a1"}.fa-tractor:before{content:"\f722"}.fa-trade-federation:before{content:"\f513"}.fa-trademark:before{content:"\f25c"}.fa-traffic-light:before{content:"\f637"}.fa-train:before{content:"\f238"}.fa-transgender:before{content:"\f224"}.fa-transgender-alt:before{content:"\f225"}.fa-trash:before{content:"\f1f8"}.fa-trash-alt:before{content:"\f2ed"}.fa-tree:before{content:"\f1bb"}.fa-trello:before{content:"\f181"}.fa-tripadvisor:before{content:"\f262"}.fa-trophy:before{content:"\f091"}.fa-truck:before{content:"\f0d1"}.fa-truck-loading:before{content:"\f4de"}.fa-truck-monster:before{content:"\f63b"}.fa-truck-moving:before{content:"\f4df"}.fa-truck-pickup:before{content:"\f63c"}.fa-tshirt:before{content:"\f553"}.fa-tty:before{content:"\f1e4"}.fa-tumblr:before{content:"\f173"}.fa-tumblr-square:before{content:"\f174"}.fa-tv:before{content:"\f26c"}.fa-twitch:before{content:"\f1e8"}.fa-twitter:before{content:"\f099"}.fa-twitter-square:before{content:"\f081"}.fa-typo3:before{content:"\f42b"}.fa-uber:before{content:"\f402"}.fa-uikit:before{content:"\f403"}.fa-umbrella:before{content:"\f0e9"}.fa-umbrella-beach:before{content:"\f5ca"}.fa-underline:before{content:"\f0cd"}.fa-undo:before{content:"\f0e2"}.fa-undo-alt:before{content:"\f2ea"}.fa-uniregistry:before{content:"\f404"}.fa-universal-access:before{content:"\f29a"}.fa-university:before{content:"\f19c"}.fa-unlink:before{content:"\f127"}.fa-unlock:before{content:"\f09c"}.fa-unlock-alt:before{content:"\f13e"}.fa-untappd:before{content:"\f405"}.fa-upload:before{content:"\f093"}.fa-usb:before{content:"\f287"}.fa-user:before{content:"\f007"}.fa-user-alt:before{content:"\f406"}.fa-user-alt-slash:before{content:"\f4fa"}.fa-user-astronaut:before{content:"\f4fb"}.fa-user-check:before{content:"\f4fc"}.fa-user-circle:before{content:"\f2bd"}.fa-user-clock:before{content:"\f4fd"}.fa-user-cog:before{content:"\f4fe"}.fa-user-edit:before{content:"\f4ff"}.fa-user-friends:before{content:"\f500"}.fa-user-graduate:before{content:"\f501"}.fa-user-injured:before{content:"\f728"}.fa-user-lock:before{content:"\f502"}.fa-user-md:before{content:"\f0f0"}.fa-user-minus:before{content:"\f503"}.fa-user-ninja:before{content:"\f504"}.fa-user-plus:before{content:"\f234"}.fa-user-secret:before{content:"\f21b"}.fa-user-shield:before{content:"\f505"}.fa-user-slash:before{content:"\f506"}.fa-user-tag:before{content:"\f507"}.fa-user-tie:before{content:"\f508"}.fa-user-times:before{content:"\f235"}.fa-users:before{content:"\f0c0"}.fa-users-cog:before{content:"\f509"}.fa-ussunnah:before{content:"\f407"}.fa-utensil-spoon:before{content:"\f2e5"}.fa-utensils:before{content:"\f2e7"}.fa-vaadin:before{content:"\f408"}.fa-vector-square:before{content:"\f5cb"}.fa-venus:before{content:"\f221"}.fa-venus-double:before{content:"\f226"}.fa-venus-mars:before{content:"\f228"}.fa-viacoin:before{content:"\f237"}.fa-viadeo:before{content:"\f2a9"}.fa-viadeo-square:before{content:"\f2aa"}.fa-vial:before{content:"\f492"}.fa-vials:before{content:"\f493"}.fa-viber:before{content:"\f409"}.fa-video:before{content:"\f03d"}.fa-video-slash:before{content:"\f4e2"}.fa-vihara:before{content:"\f6a7"}.fa-vimeo:before{content:"\f40a"}.fa-vimeo-square:before{content:"\f194"}.fa-vimeo-v:before{content:"\f27d"}.fa-vine:before{content:"\f1ca"}.fa-vk:before{content:"\f189"}.fa-vnv:before{content:"\f40b"}.fa-volleyball-ball:before{content:"\f45f"}.fa-volume-down:before{content:"\f027"}.fa-volume-mute:before{content:"\f6a9"}.fa-volume-off:before{content:"\f026"}.fa-volume-up:before{content:"\f028"}.fa-vote-yea:before{content:"\f772"}.fa-vr-cardboard:before{content:"\f729"}.fa-vuejs:before{content:"\f41f"}.fa-walking:before{content:"\f554"}.fa-wallet:before{content:"\f555"}.fa-warehouse:before{content:"\f494"}.fa-water:before{content:"\f773"}.fa-weebly:before{content:"\f5cc"}.fa-weibo:before{content:"\f18a"}.fa-weight:before{content:"\f496"}.fa-weight-hanging:before{content:"\f5cd"}.fa-weixin:before{content:"\f1d7"}.fa-whatsapp:before{content:"\f232"}.fa-whatsapp-square:before{content:"\f40c"}.fa-wheelchair:before{content:"\f193"}.fa-whmcs:before{content:"\f40d"}.fa-wifi:before{content:"\f1eb"}.fa-wikipedia-w:before{content:"\f266"}.fa-wind:before{content:"\f72e"}.fa-window-close:before{content:"\f410"}.fa-window-maximize:before{content:"\f2d0"}.fa-window-minimize:before{content:"\f2d1"}.fa-window-restore:before{content:"\f2d2"}.fa-windows:before{content:"\f17a"}.fa-wine-bottle:before{content:"\f72f"}.fa-wine-glass:before{content:"\f4e3"}.fa-wine-glass-alt:before{content:"\f5ce"}.fa-wix:before{content:"\f5cf"}.fa-wizards-of-the-coast:before{content:"\f730"}.fa-wolf-pack-battalion:before{content:"\f514"}.fa-won-sign:before{content:"\f159"}.fa-wordpress:before{content:"\f19a"}.fa-wordpress-simple:before{content:"\f411"}.fa-wpbeginner:before{content:"\f297"}.fa-wpexplorer:before{content:"\f2de"}.fa-wpforms:before{content:"\f298"}.fa-wpressr:before{content:"\f3e4"}.fa-wrench:before{content:"\f0ad"}.fa-x-ray:before{content:"\f497"}.fa-xbox:before{content:"\f412"}.fa-xing:before{content:"\f168"}.fa-xing-square:before{content:"\f169"}.fa-y-combinator:before{content:"\f23b"}.fa-yahoo:before{content:"\f19e"}.fa-yandex:before{content:"\f413"}.fa-yandex-international:before{content:"\f414"}.fa-yelp:before{content:"\f1e9"}.fa-yen-sign:before{content:"\f157"}.fa-yin-yang:before{content:"\f6ad"}.fa-yoast:before{content:"\f2b1"}.fa-youtube:before{content:"\f167"}.fa-youtube-square:before{content:"\f431"}.fa-zhihu:before{content:"\f63f"}.sr-only{border:0;clip:rect(0,0,0,0);height:1px;margin:-1px;overflow:hidden;padding:0;position:absolute;width:1px}.sr-only-focusable:active,.sr-only-focusable:focus{clip:auto;height:auto;margin:0;overflow:visible;position:static;width:auto}@font-face{font-family:"Font Awesome 5 Brands";font-style:normal;font-weight:normal;src:url(../webfonts/fa-brands-400.eot);src:url(../webfonts/fa-brands-400.eot?#iefix) format("embedded-opentype"),url(../webfonts/fa-brands-400.woff2) format("woff2"),url(../webfonts/fa-brands-400.woff) format("woff"),url(../webfonts/fa-brands-400.ttf) format("truetype"),url(../webfonts/fa-brands-400.svg#fontawesome) format("svg")}.fab{font-family:"Font Awesome 5 Brands"}@font-face{font-family:"Font Awesome 5 Free";font-style:normal;font-weight:400;src:url(../webfonts/fa-regular-400.eot);src:url(../webfonts/fa-regular-400.eot?#iefix) format("embedded-opentype"),url(../webfonts/fa-regular-400.woff2) format("woff2"),url(../webfonts/fa-regular-400.woff) format("woff"),url(../webfonts/fa-regular-400.ttf) format("truetype"),url(../webfonts/fa-regular-400.svg#fontawesome) format("svg")}.far{font-weight:400}@font-face{font-family:"Font Awesome 5 Free";font-style:normal;font-weight:900;src:url(../webfonts/fa-solid-900.eot);src:url(../webfonts/fa-solid-900.eot?#iefix) format("embedded-opentype"),url(../webfonts/fa-solid-900.woff2) format("woff2"),url(../webfonts/fa-solid-900.woff) format("woff"),url(../webfonts/fa-solid-900.ttf) format("truetype"),url(../webfonts/fa-solid-900.svg#fontawesome) format("svg")}.fa,.far,.fas{font-family:"Font Awesome 5 Free"}.fa,.fas{font-weight:900}
\ No newline at end of file
diff --git a/docs/python_docs/themes/mx-theme/mxtheme/static/fonts.css b/docs/python_docs/themes/mx-theme/mxtheme/static/fonts.css
new file mode 100644
index 000000000000..872c990e123c
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/mxtheme/static/fonts.css
@@ -0,0 +1,390 @@
+@font-face {
+    font-family: 'Roboto';
+    src: url('font/Roboto/Roboto-Regular.eot');
+    src: url('font/Roboto/Roboto-Regular.eot?#iefix') format('embedded-opentype'),
+         url('font/Roboto/Roboto-Regular.woff') format('woff'),
+         url('font/Roboto/Roboto-Regular.ttf') format('truetype');
+    font-weight: normal;
+    font-style: normal;
+}
+/*
+* Noto Sans SC Sliced (Chinese Simplified)
+ */
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.0.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.0.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.0.otf')  format('opentype');unicode-range:U+2252,U+30c3,U+5c83-5c94,U+5c9c-5ca8,U+5caa-5cac,U+5cae-5cb2,U+5cb4-5cb7,U+5cb9-5cc0,U+5cc2-5cc3,U+5cc5-5cd8,U+5cdb-5ce0,U+5ce2-5ce7,U+5ce9-5cec,U+5cee-5cef,U+5cf1-5cfa,U+5cfc-5d06,U+5d08-5d0d,U+5d0f-5d13,U+5d15,U+5d17-5d1a,U+5d1c-5d28,U+5d2a-5d2c,U+5d2e-5d4b,U+5d4d-5dc4,U+5dc6-5dcc,U+5dce-5ddc,U+5ddf-5de0,U+5de3-5de4,U+5dea,U+5dec-5ded,U+5def-5df0,U+5df5-5df6,U+5df8-5dfd,U+5dff-5e00,U+5e04,U+5e07,U+5e09-5e0b,U+5e0d-5e0f,U+5e12-5e14,U+5e17,U+5e19-5e1b,U+5e1e-5e25,U+5e28-5e2c,U+5e2f-5e36,U+5e39-5e3c,U+5e3e-5e44,U+5e46-5e54,U+5e56-5e5e,U+67d1,U+6cba,U+9569-956b,U+958a-958b;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.0.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.0.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.0.otf')  format('opentype');unicode-range:U+2252,U+30c3,U+5c83-5c94,U+5c9c-5ca8,U+5caa-5cac,U+5cae-5cb2,U+5cb4-5cb7,U+5cb9-5cc0,U+5cc2-5cc3,U+5cc5-5cd8,U+5cdb-5ce0,U+5ce2-5ce7,U+5ce9-5cec,U+5cee-5cef,U+5cf1-5cfa,U+5cfc-5d06,U+5d08-5d0d,U+5d0f-5d13,U+5d15,U+5d17-5d1a,U+5d1c-5d28,U+5d2a-5d2c,U+5d2e-5d4b,U+5d4d-5dc4,U+5dc6-5dcc,U+5dce-5ddc,U+5ddf-5de0,U+5de3-5de4,U+5dea,U+5dec-5ded,U+5def-5df0,U+5df5-5df6,U+5df8-5dfd,U+5dff-5e00,U+5e04,U+5e07,U+5e09-5e0b,U+5e0d-5e0f,U+5e12-5e14,U+5e17,U+5e19-5e1b,U+5e1e-5e25,U+5e28-5e2c,U+5e2f-5e36,U+5e39-5e3c,U+5e3e-5e44,U+5e46-5e54,U+5e56-5e5e,U+67d1,U+6cba,U+9569-956b,U+958a-958b;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.0.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.0.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.0.otf')  format('opentype');unicode-range:U+2252,U+30c3,U+5c83-5c94,U+5c9c-5ca8,U+5caa-5cac,U+5cae-5cb2,U+5cb4-5cb7,U+5cb9-5cc0,U+5cc2-5cc3,U+5cc5-5cd8,U+5cdb-5ce0,U+5ce2-5ce7,U+5ce9-5cec,U+5cee-5cef,U+5cf1-5cfa,U+5cfc-5d06,U+5d08-5d0d,U+5d0f-5d13,U+5d15,U+5d17-5d1a,U+5d1c-5d28,U+5d2a-5d2c,U+5d2e-5d4b,U+5d4d-5dc4,U+5dc6-5dcc,U+5dce-5ddc,U+5ddf-5de0,U+5de3-5de4,U+5dea,U+5dec-5ded,U+5def-5df0,U+5df5-5df6,U+5df8-5dfd,U+5dff-5e00,U+5e04,U+5e07,U+5e09-5e0b,U+5e0d-5e0f,U+5e12-5e14,U+5e17,U+5e19-5e1b,U+5e1e-5e25,U+5e28-5e2c,U+5e2f-5e36,U+5e39-5e3c,U+5e3e-5e44,U+5e46-5e54,U+5e56-5e5e,U+67d1,U+6cba,U+9569-956b,U+958a-958b;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.1.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.1.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.1.otf')  format('opentype');unicode-range:U+8f7e,U+987c,U+c557-c57b,U+c57d-c5b3,U+c5b5-c5c5,U+c5c9-c5cf,U+c5d1-c5eb,U+c5ed-c600,U+c602-c623,U+c625-c63f,U+c641-c693,U+c695-c6af,U+c6b1-c6b3,U+c6b5-c6b7,U+c6b9-c6c2,U+c6c4-c6cf,U+c6d1-c6fe,U+c843-c845,U+c873-c874;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.1.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.1.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.1.otf')  format('opentype');unicode-range:U+8f7e,U+987c,U+c557-c57b,U+c57d-c5b3,U+c5b5-c5c5,U+c5c9-c5cf,U+c5d1-c5eb,U+c5ed-c600,U+c602-c623,U+c625-c63f,U+c641-c693,U+c695-c6af,U+c6b1-c6b3,U+c6b5-c6b7,U+c6b9-c6c2,U+c6c4-c6cf,U+c6d1-c6fe,U+c843-c845,U+c873-c874;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.1.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.1.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.1.otf')  format('opentype');unicode-range:U+8f7e,U+987c,U+c557-c57b,U+c57d-c5b3,U+c5b5-c5c5,U+c5c9-c5cf,U+c5d1-c5eb,U+c5ed-c600,U+c602-c623,U+c625-c63f,U+c641-c693,U+c695-c6af,U+c6b1-c6b3,U+c6b5-c6b7,U+c6b9-c6c2,U+c6c4-c6cf,U+c6d1-c6fe,U+c843-c845,U+c873-c874;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.2.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.2.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.2.otf')  format('opentype');unicode-range:U+54fb,U+573f,U+574b,U+5f89,U+647d-6486,U+6488-6490,U+6493-6494,U+6496-649d,U+649f-64a3,U+64a5-64ac,U+64b2-64bb,U+64bd-64c4,U+64c6-64cc,U+64cf-64d1,U+64d3-64e5,U+64e7-64ff,U+6501-6511,U+6513-651d,U+651f-652e,U+6530-6535,U+6537-6538,U+653c-653d,U+6540-6544,U+6546-6547,U+6549-654b,U+654d-654e,U+6550,U+6552-6558,U+655a,U+655c-655d,U+655f-6561,U+6564-6565,U+6567-656b,U+656d-656f,U+6571,U+6573,U+6575-6576,U+6578-6586,U+6588-658a,U+658d-658f,U+6592-6596,U+659a-659b,U+659d-65a3,U+65a6,U+65aa-65ac,U+65ae,U+65b1-65b8,U+65ba-65bb,U+65be-65c0,U+65c2-65c4,U+65c6-65ca,U+65cc-65ce,U+65d0-65d1,U+65d3-65d6,U+65d8-65df,U+65e1,U+65e3-65e4,U+65ea-65eb,U+65ee-65f0,U+65f2-65f5,U+65f8-65f9,U+65fb-6601,U+6603-6605,U+6607-660b,U+660d,U+6610-6612,U+6615-661e,U+6621-6624,U+6626,U+6629-662c,U+662e,U+6630-6639,U+663b,U+663f-6642,U+6644-664a,U+664c-6651,U+6657-6659,U+665b-6665,U+6667,U+6669-666d,U+6671-6673,U+6675,U+6677-6679,U+667b-667d,U+667f-6681,U+73c8-73c9,U+78f1,U+7a3e,U+8866-8867,U+957d-957e,U+95e5-95e7;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.2.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.2.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.2.otf')  format('opentype');unicode-range:U+54fb,U+573f,U+574b,U+5f89,U+647d-6486,U+6488-6490,U+6493-6494,U+6496-649d,U+649f-64a3,U+64a5-64ac,U+64b2-64bb,U+64bd-64c4,U+64c6-64cc,U+64cf-64d1,U+64d3-64e5,U+64e7-64ff,U+6501-6511,U+6513-651d,U+651f-652e,U+6530-6535,U+6537-6538,U+653c-653d,U+6540-6544,U+6546-6547,U+6549-654b,U+654d-654e,U+6550,U+6552-6558,U+655a,U+655c-655d,U+655f-6561,U+6564-6565,U+6567-656b,U+656d-656f,U+6571,U+6573,U+6575-6576,U+6578-6586,U+6588-658a,U+658d-658f,U+6592-6596,U+659a-659b,U+659d-65a3,U+65a6,U+65aa-65ac,U+65ae,U+65b1-65b8,U+65ba-65bb,U+65be-65c0,U+65c2-65c4,U+65c6-65ca,U+65cc-65ce,U+65d0-65d1,U+65d3-65d6,U+65d8-65df,U+65e1,U+65e3-65e4,U+65ea-65eb,U+65ee-65f0,U+65f2-65f5,U+65f8-65f9,U+65fb-6601,U+6603-6605,U+6607-660b,U+660d,U+6610-6612,U+6615-661e,U+6621-6624,U+6626,U+6629-662c,U+662e,U+6630-6639,U+663b,U+663f-6642,U+6644-664a,U+664c-6651,U+6657-6659,U+665b-6665,U+6667,U+6669-666d,U+6671-6673,U+6675,U+6677-6679,U+667b-667d,U+667f-6681,U+73c8-73c9,U+78f1,U+7a3e,U+8866-8867,U+957d-957e,U+95e5-95e7;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.2.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.2.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.2.otf')  format('opentype');unicode-range:U+54fb,U+573f,U+574b,U+5f89,U+647d-6486,U+6488-6490,U+6493-6494,U+6496-649d,U+649f-64a3,U+64a5-64ac,U+64b2-64bb,U+64bd-64c4,U+64c6-64cc,U+64cf-64d1,U+64d3-64e5,U+64e7-64ff,U+6501-6511,U+6513-651d,U+651f-652e,U+6530-6535,U+6537-6538,U+653c-653d,U+6540-6544,U+6546-6547,U+6549-654b,U+654d-654e,U+6550,U+6552-6558,U+655a,U+655c-655d,U+655f-6561,U+6564-6565,U+6567-656b,U+656d-656f,U+6571,U+6573,U+6575-6576,U+6578-6586,U+6588-658a,U+658d-658f,U+6592-6596,U+659a-659b,U+659d-65a3,U+65a6,U+65aa-65ac,U+65ae,U+65b1-65b8,U+65ba-65bb,U+65be-65c0,U+65c2-65c4,U+65c6-65ca,U+65cc-65ce,U+65d0-65d1,U+65d3-65d6,U+65d8-65df,U+65e1,U+65e3-65e4,U+65ea-65eb,U+65ee-65f0,U+65f2-65f5,U+65f8-65f9,U+65fb-6601,U+6603-6605,U+6607-660b,U+660d,U+6610-6612,U+6615-661e,U+6621-6624,U+6626,U+6629-662c,U+662e,U+6630-6639,U+663b,U+663f-6642,U+6644-664a,U+664c-6651,U+6657-6659,U+665b-6665,U+6667,U+6669-666d,U+6671-6673,U+6675,U+6677-6679,U+667b-667d,U+667f-6681,U+73c8-73c9,U+78f1,U+7a3e,U+8866-8867,U+957d-957e,U+95e5-95e7;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.3.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.3.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.3.otf')  format('opentype');unicode-range:U+25bd,U+51fc,U+6bd3,U+881d,U+962f-9630,U+bd34-bd7f,U+bd81-bd83,U+bd85-be43,U+be45-be5a,U+be5c-bed2;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.3.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.3.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.3.otf')  format('opentype');unicode-range:U+25bd,U+51fc,U+6bd3,U+881d,U+962f-9630,U+bd34-bd7f,U+bd81-bd83,U+bd85-be43,U+be45-be5a,U+be5c-bed2;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.3.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.3.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.3.otf')  format('opentype');unicode-range:U+25bd,U+51fc,U+6bd3,U+881d,U+962f-9630,U+bd34-bd7f,U+bd81-bd83,U+bd85-be43,U+be45-be5a,U+be5c-bed2;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.4.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.4.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.4.otf')  format('opentype');unicode-range:U+3050-3051,U+5e85,U+c7b6-c7bc,U+21ca2,U+249a9-25d30,U+25db9-25ee8,U+25f4b-26412,U+26488-26cc0,U+28eac;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.4.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.4.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.4.otf')  format('opentype');unicode-range:U+3050-3051,U+5e85,U+c7b6-c7bc,U+21ca2,U+249a9-25d30,U+25db9-25ee8,U+25f4b-26412,U+26488-26cc0,U+28eac;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.4.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.4.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.4.otf')  format('opentype');unicode-range:U+3050-3051,U+5e85,U+c7b6-c7bc,U+21ca2,U+249a9-25d30,U+25db9-25ee8,U+25f4b-26412,U+26488-26cc0,U+28eac;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.5.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.5.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.5.otf')  format('opentype');unicode-range:U+88ac,U+95fc,U+20e4c-20f4c,U+20fad-21088,U+2109d-21c56,U+21ca5-22c38,U+249a4,U+26cd1,U+2808a,U+2b36f,U+2f945;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.5.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.5.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.5.otf')  format('opentype');unicode-range:U+88ac,U+95fc,U+20e4c-20f4c,U+20fad-21088,U+2109d-21c56,U+21ca5-22c38,U+249a4,U+26cd1,U+2808a,U+2b36f,U+2f945;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.5.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.5.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.5.otf')  format('opentype');unicode-range:U+88ac,U+95fc,U+20e4c-20f4c,U+20fad-21088,U+2109d-21c56,U+21ca5-22c38,U+249a4,U+26cd1,U+2808a,U+2b36f,U+2f945;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.6.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.6.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.6.otf')  format('opentype');unicode-range:U+36e1-387f,U+2b300;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.6.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.6.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.6.otf')  format('opentype');unicode-range:U+36e1-387f,U+2b300;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.6.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.6.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.6.otf')  format('opentype');unicode-range:U+36e1-387f,U+2b300;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.7.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.7.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.7.otf')  format('opentype');unicode-range:U+339f-353e;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.7.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.7.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.7.otf')  format('opentype');unicode-range:U+339f-353e;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.7.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.7.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.7.otf')  format('opentype');unicode-range:U+339f-353e;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.8.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.8.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.8.otf')  format('opentype');unicode-range:U+94cb-94ce,U+95ab-95ad,U+95d5-95d6,U+b02a-b097,U+b099-b09b,U+b09d-b09f,U+b0a1-b0a7,U+b0a9-b0b3,U+b0b5-b107,U+b109-b10f,U+b111-b123,U+b125-b154,U+b156-b1c2,U+c728-c72b,U+c761-c762;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.8.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.8.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.8.otf')  format('opentype');unicode-range:U+94cb-94ce,U+95ab-95ad,U+95d5-95d6,U+b02a-b097,U+b099-b09b,U+b09d-b09f,U+b0a1-b0a7,U+b0a9-b0b3,U+b0b5-b107,U+b109-b10f,U+b111-b123,U+b125-b154,U+b156-b1c2,U+c728-c72b,U+c761-c762;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.8.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.8.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.8.otf')  format('opentype');unicode-range:U+94cb-94ce,U+95ab-95ad,U+95d5-95d6,U+b02a-b097,U+b099-b09b,U+b09d-b09f,U+b0a1-b0a7,U+b0a9-b0b3,U+b0b5-b107,U+b109-b10f,U+b111-b123,U+b125-b154,U+b156-b1c2,U+c728-c72b,U+c761-c762;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.9.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.9.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.9.otf')  format('opentype');unicode-range:U+3bc2-3d62,U+65a8,U+8d32-8d33;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.9.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.9.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.9.otf')  format('opentype');unicode-range:U+3bc2-3d62,U+65a8,U+8d32-8d33;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.9.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.9.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.9.otf')  format('opentype');unicode-range:U+3bc2-3d62,U+65a8,U+8d32-8d33;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.10.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.10.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.10.otf')  format('opentype');unicode-range:U+4c0c-4da8,U+4e23,U+95c3-95c6,U+95fe-95ff;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.10.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.10.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.10.otf')  format('opentype');unicode-range:U+4c0c-4da8,U+4e23,U+95c3-95c6,U+95fe-95ff;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.10.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.10.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.10.otf')  format('opentype');unicode-range:U+4c0c-4da8,U+4e23,U+95c3-95c6,U+95fe-95ff;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.11.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.11.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.11.otf')  format('opentype');unicode-range:U+c726-c727,U+cd79-cd93,U+cd95-ce57,U+ce59-ce73,U+ce75-cf16,U+2b37d;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.11.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.11.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.11.otf')  format('opentype');unicode-range:U+c726-c727,U+cd79-cd93,U+cd95-ce57,U+ce59-ce73,U+ce75-cf16,U+2b37d;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.11.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.11.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.11.otf')  format('opentype');unicode-range:U+c726-c727,U+cd79-cd93,U+cd95-ce57,U+ce59-ce73,U+ce75-cf16,U+2b37d;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.12.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.12.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.12.otf')  format('opentype');unicode-range:U+519a,U+b851-b85b,U+b85d-b973,U+b975-b977,U+b979-b97b,U+b97d-b983,U+b985-b9ab,U+b9ad-b9af,U+b9b1-b9c7,U+b9c9-b9cb,U+b9cd-b9cf,U+b9d1-b9ee,U+c7ad-c7b5,U+c83c-c83d;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.12.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.12.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.12.otf')  format('opentype');unicode-range:U+519a,U+b851-b85b,U+b85d-b973,U+b975-b977,U+b979-b97b,U+b97d-b983,U+b985-b9ab,U+b9ad-b9af,U+b9b1-b9c7,U+b9c9-b9cb,U+b9cd-b9cf,U+b9d1-b9ee,U+c7ad-c7b5,U+c83c-c83d;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.12.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.12.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.12.otf')  format('opentype');unicode-range:U+519a,U+b851-b85b,U+b85d-b973,U+b975-b977,U+b979-b97b,U+b97d-b983,U+b985-b9ab,U+b9ad-b9af,U+b9b1-b9c7,U+b9c9-b9cb,U+b9cd-b9cf,U+b9d1-b9ee,U+c7ad-c7b5,U+c83c-c83d;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.13.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.13.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.13.otf')  format('opentype');unicode-range:U+00c5,U+03b6-03ba,U+03bf,U+03c5,U+500b,U+537b,U+5834,U+6703,U+6727,U+6c7a,U+752a,U+767c,U+8f24-8f65,U+8f6a-8f6b,U+8f6d,U+8f71-8f73,U+8f75-8f7a,U+8f80-8f82,U+8f8a-8f8f,U+8f92,U+8f94-8f95,U+8fa0-8fa2,U+8fa4-8fa7,U+8faa-8faf,U+8fb2-8fb8,U+8fba-8fbc,U+8fbf-8fc0,U+8fc2-8fc3,U+8fc9-8fcd,U+8fcf,U+8fd2-8fd3,U+8fd5-8fd7,U+8fda,U+8fe0-8fe4,U+8fe7-8fe9,U+8fec,U+8fee-8fef,U+8ff1-8ff6,U+8ff8,U+8ffa-8ffc,U+8ffe-8fff,U+9007-9008,U+900b-900c,U+9011,U+9013,U+9015-9016,U+9018-9019,U+901c,U+901e,U+9021,U+9023-902d,U+902f-9037,U+9039-903a,U+903d,U+903f-9041,U+9043-9046,U+9048-904c,U+904e-904f,U+9051-9052,U+9054-9056,U+9058-9062,U+9064,U+9066-906c,U+906f-9074,U+9076-907e,U+9081,U+9084-9090,U+9092,U+9094-90a2,U+90a4-90a5,U+90a7-90a9,U+90ab-90ad,U+90af-90b0,U+90b2-90b4,U+90b6-90b8,U+90ba,U+90bc-90c0,U+90c2-90c4,U+90c6-90c9,U+90cb-90cd,U+90cf-90d0,U+90d2-90dd,U+90df-90e0,U+90e2-90e7,U+90e9-90ec,U+90ee-90fc,U+90fe-9101,U+9103-9118,U+911a-9130,U+9611,U+c838-c83a,U+ff14,U+28482;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.13.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.13.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.13.otf')  format('opentype');unicode-range:U+00c5,U+03b6-03ba,U+03bf,U+03c5,U+500b,U+537b,U+5834,U+6703,U+6727,U+6c7a,U+752a,U+767c,U+8f24-8f65,U+8f6a-8f6b,U+8f6d,U+8f71-8f73,U+8f75-8f7a,U+8f80-8f82,U+8f8a-8f8f,U+8f92,U+8f94-8f95,U+8fa0-8fa2,U+8fa4-8fa7,U+8faa-8faf,U+8fb2-8fb8,U+8fba-8fbc,U+8fbf-8fc0,U+8fc2-8fc3,U+8fc9-8fcd,U+8fcf,U+8fd2-8fd3,U+8fd5-8fd7,U+8fda,U+8fe0-8fe4,U+8fe7-8fe9,U+8fec,U+8fee-8fef,U+8ff1-8ff6,U+8ff8,U+8ffa-8ffc,U+8ffe-8fff,U+9007-9008,U+900b-900c,U+9011,U+9013,U+9015-9016,U+9018-9019,U+901c,U+901e,U+9021,U+9023-902d,U+902f-9037,U+9039-903a,U+903d,U+903f-9041,U+9043-9046,U+9048-904c,U+904e-904f,U+9051-9052,U+9054-9056,U+9058-9062,U+9064,U+9066-906c,U+906f-9074,U+9076-907e,U+9081,U+9084-9090,U+9092,U+9094-90a2,U+90a4-90a5,U+90a7-90a9,U+90ab-90ad,U+90af-90b0,U+90b2-90b4,U+90b6-90b8,U+90ba,U+90bc-90c0,U+90c2-90c4,U+90c6-90c9,U+90cb-90cd,U+90cf-90d0,U+90d2-90dd,U+90df-90e0,U+90e2-90e7,U+90e9-90ec,U+90ee-90fc,U+90fe-9101,U+9103-9118,U+911a-9130,U+9611,U+c838-c83a,U+ff14,U+28482;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.13.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.13.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.13.otf')  format('opentype');unicode-range:U+00c5,U+03b6-03ba,U+03bf,U+03c5,U+500b,U+537b,U+5834,U+6703,U+6727,U+6c7a,U+752a,U+767c,U+8f24-8f65,U+8f6a-8f6b,U+8f6d,U+8f71-8f73,U+8f75-8f7a,U+8f80-8f82,U+8f8a-8f8f,U+8f92,U+8f94-8f95,U+8fa0-8fa2,U+8fa4-8fa7,U+8faa-8faf,U+8fb2-8fb8,U+8fba-8fbc,U+8fbf-8fc0,U+8fc2-8fc3,U+8fc9-8fcd,U+8fcf,U+8fd2-8fd3,U+8fd5-8fd7,U+8fda,U+8fe0-8fe4,U+8fe7-8fe9,U+8fec,U+8fee-8fef,U+8ff1-8ff6,U+8ff8,U+8ffa-8ffc,U+8ffe-8fff,U+9007-9008,U+900b-900c,U+9011,U+9013,U+9015-9016,U+9018-9019,U+901c,U+901e,U+9021,U+9023-902d,U+902f-9037,U+9039-903a,U+903d,U+903f-9041,U+9043-9046,U+9048-904c,U+904e-904f,U+9051-9052,U+9054-9056,U+9058-9062,U+9064,U+9066-906c,U+906f-9074,U+9076-907e,U+9081,U+9084-9090,U+9092,U+9094-90a2,U+90a4-90a5,U+90a7-90a9,U+90ab-90ad,U+90af-90b0,U+90b2-90b4,U+90b6-90b8,U+90ba,U+90bc-90c0,U+90c2-90c4,U+90c6-90c9,U+90cb-90cd,U+90cf-90d0,U+90d2-90dd,U+90df-90e0,U+90e2-90e7,U+90e9-90ec,U+90ee-90fc,U+90fe-9101,U+9103-9118,U+911a-9130,U+9611,U+c838-c83a,U+ff14,U+28482;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.14.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.14.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.14.otf')  format('opentype');unicode-range:U+527d-527e,U+5280-5287,U+5289-529a,U+52a2,U+52a4-52a7,U+52ac-52b0,U+52b4-52be,U+52c0-52c2,U+52c4-52c6,U+52c8,U+52ca,U+52cc-52d1,U+52d3-52d4,U+52d6-52d7,U+52d9-52de,U+52e0-52e3,U+52e5-52f9,U+52fb-52fd,U+5301-5304,U+5307-5315,U+5318,U+531a-531f,U+5321-5329,U+532b-5338,U+533c-533d,U+5340,U+5342,U+5344-5346,U+534b-534d,U+5350,U+5354,U+5358-5359,U+535b,U+535d-535f,U+5363,U+5365,U+5368-536a,U+536c-536e,U+5372,U+5376,U+5379-537a,U+537c-537e,U+5380-5381,U+5383,U+5387-5388,U+538a,U+538d-5394,U+5396-5397,U+5399,U+539b-539e,U+53a0-53a1,U+53a3-53a4,U+53a7,U+53a9-53ba,U+53bc-53be,U+53c0-53c1,U+53c3-53c7,U+53ce-53d0,U+53d2-53d3,U+53d5,U+53da,U+53dc-53df,U+53e1-53e2,U+53f1,U+53f4-53f5,U+53fa-5400,U+5402,U+5405-5407,U+540b,U+5412,U+5414,U+5416,U+5418-541a,U+5420-5425,U+5429-542a,U+542d-542e,U+5430-5433,U+5436-5437,U+543a,U+543d,U+543f,U+5441-5445,U+5447,U+5449,U+544b-544f,U+5451-5454,U+5456-5457,U+5459-5461,U+5463-5467,U+5469-5472,U+5474,U+5476-547b,U+547e-547f,U+5481-548a,U+548d-548e,U+5493-5495,U+5498-54a7,U+54ad-54ae,U+54b0,U+54b2,U+54b4-54b7,U+54b9-54bc,U+54be-54bf,U+54c2-54c3,U+54c5-54c6,U+54ca-54cc,U+8df4,U+8e1a-8e1c,U+9004,U+94e0-94e3,U+95a4-95aa,U+9641-9643,U+989e,U+280bb,U+2b4e7;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.14.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.14.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.14.otf')  format('opentype');unicode-range:U+527d-527e,U+5280-5287,U+5289-529a,U+52a2,U+52a4-52a7,U+52ac-52b0,U+52b4-52be,U+52c0-52c2,U+52c4-52c6,U+52c8,U+52ca,U+52cc-52d1,U+52d3-52d4,U+52d6-52d7,U+52d9-52de,U+52e0-52e3,U+52e5-52f9,U+52fb-52fd,U+5301-5304,U+5307-5315,U+5318,U+531a-531f,U+5321-5329,U+532b-5338,U+533c-533d,U+5340,U+5342,U+5344-5346,U+534b-534d,U+5350,U+5354,U+5358-5359,U+535b,U+535d-535f,U+5363,U+5365,U+5368-536a,U+536c-536e,U+5372,U+5376,U+5379-537a,U+537c-537e,U+5380-5381,U+5383,U+5387-5388,U+538a,U+538d-5394,U+5396-5397,U+5399,U+539b-539e,U+53a0-53a1,U+53a3-53a4,U+53a7,U+53a9-53ba,U+53bc-53be,U+53c0-53c1,U+53c3-53c7,U+53ce-53d0,U+53d2-53d3,U+53d5,U+53da,U+53dc-53df,U+53e1-53e2,U+53f1,U+53f4-53f5,U+53fa-5400,U+5402,U+5405-5407,U+540b,U+5412,U+5414,U+5416,U+5418-541a,U+5420-5425,U+5429-542a,U+542d-542e,U+5430-5433,U+5436-5437,U+543a,U+543d,U+543f,U+5441-5445,U+5447,U+5449,U+544b-544f,U+5451-5454,U+5456-5457,U+5459-5461,U+5463-5467,U+5469-5472,U+5474,U+5476-547b,U+547e-547f,U+5481-548a,U+548d-548e,U+5493-5495,U+5498-54a7,U+54ad-54ae,U+54b0,U+54b2,U+54b4-54b7,U+54b9-54bc,U+54be-54bf,U+54c2-54c3,U+54c5-54c6,U+54ca-54cc,U+8df4,U+8e1a-8e1c,U+9004,U+94e0-94e3,U+95a4-95aa,U+9641-9643,U+989e,U+280bb,U+2b4e7;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.14.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.14.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.14.otf')  format('opentype');unicode-range:U+527d-527e,U+5280-5287,U+5289-529a,U+52a2,U+52a4-52a7,U+52ac-52b0,U+52b4-52be,U+52c0-52c2,U+52c4-52c6,U+52c8,U+52ca,U+52cc-52d1,U+52d3-52d4,U+52d6-52d7,U+52d9-52de,U+52e0-52e3,U+52e5-52f9,U+52fb-52fd,U+5301-5304,U+5307-5315,U+5318,U+531a-531f,U+5321-5329,U+532b-5338,U+533c-533d,U+5340,U+5342,U+5344-5346,U+534b-534d,U+5350,U+5354,U+5358-5359,U+535b,U+535d-535f,U+5363,U+5365,U+5368-536a,U+536c-536e,U+5372,U+5376,U+5379-537a,U+537c-537e,U+5380-5381,U+5383,U+5387-5388,U+538a,U+538d-5394,U+5396-5397,U+5399,U+539b-539e,U+53a0-53a1,U+53a3-53a4,U+53a7,U+53a9-53ba,U+53bc-53be,U+53c0-53c1,U+53c3-53c7,U+53ce-53d0,U+53d2-53d3,U+53d5,U+53da,U+53dc-53df,U+53e1-53e2,U+53f1,U+53f4-53f5,U+53fa-5400,U+5402,U+5405-5407,U+540b,U+5412,U+5414,U+5416,U+5418-541a,U+5420-5425,U+5429-542a,U+542d-542e,U+5430-5433,U+5436-5437,U+543a,U+543d,U+543f,U+5441-5445,U+5447,U+5449,U+544b-544f,U+5451-5454,U+5456-5457,U+5459-5461,U+5463-5467,U+5469-5472,U+5474,U+5476-547b,U+547e-547f,U+5481-548a,U+548d-548e,U+5493-5495,U+5498-54a7,U+54ad-54ae,U+54b0,U+54b2,U+54b4-54b7,U+54b9-54bc,U+54be-54bf,U+54c2-54c3,U+54c5-54c6,U+54ca-54cc,U+8df4,U+8e1a-8e1c,U+9004,U+94e0-94e3,U+95a4-95aa,U+9641-9643,U+989e,U+280bb,U+2b4e7;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.15.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.15.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.15.otf')  format('opentype');unicode-range:U+353f-36e0;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.15.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.15.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.15.otf')  format('opentype');unicode-range:U+353f-36e0;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.15.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.15.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.15.otf')  format('opentype');unicode-range:U+353f-36e0;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.16.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.16.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.16.otf')  format('opentype');unicode-range:U+2609,U+273f-2ffb,U+3003-3007,U+3016-303f,U+959f-95a0;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.16.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.16.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.16.otf')  format('opentype');unicode-range:U+2609,U+273f-2ffb,U+3003-3007,U+3016-303f,U+959f-95a0;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.16.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.16.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.16.otf')  format('opentype');unicode-range:U+2609,U+273f-2ffb,U+3003-3007,U+3016-303f,U+959f-95a0;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.17.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.17.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.17.otf')  format('opentype');unicode-range:U+54a9,U+62c8,U+6c82,U+8099,U+809b-809c,U+809e-809f,U+80a3,U+80a6-80a8,U+80ab-80ae,U+80b0-80b1,U+80b5-80b9,U+80bb-80bd,U+80c2,U+80c4-80c5,U+80c7-80cb,U+80cd,U+80cf-80d5,U+80d7-80d9,U+80db,U+80dd,U+80df-80e0,U+80e2-80e6,U+80e8-80f0,U+80f2,U+80f9,U+80fb-80fc,U+80fe-8101,U+8103-8105,U+8107-8108,U+810b-810e,U+8110,U+8112-8115,U+8117-8119,U+811b-8130,U+8132-8137,U+8139-813d,U+813f-8149,U+814d-814f,U+8152-8153,U+8156-8164,U+8166-816f,U+8171-8178,U+817c-817d,U+8181-8189,U+818b-818e,U+8190-819b,U+819e-81a7,U+81a9-81b2,U+81b4-81bf,U+81c1,U+81c3-81e2,U+81e4-81e9,U+81eb-81ec,U+81ee-81f2,U+81f5-8205,U+8207-820b,U+820e-8211,U+8213-821d,U+8220-8229,U+822b,U+822d-822f,U+8232,U+8234-8238,U+823a-8246,U+8248-824e,U+8250-8257,U+8259-826e,U+8271,U+8274-8277,U+827b-827d,U+827f-8281,U+8283-8286,U+886d-886f,U+9496-9498,U+9522,U+95a1-95a3,U+95ae-95b3,U+95be-95bf,U+95f3;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.17.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.17.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.17.otf')  format('opentype');unicode-range:U+54a9,U+62c8,U+6c82,U+8099,U+809b-809c,U+809e-809f,U+80a3,U+80a6-80a8,U+80ab-80ae,U+80b0-80b1,U+80b5-80b9,U+80bb-80bd,U+80c2,U+80c4-80c5,U+80c7-80cb,U+80cd,U+80cf-80d5,U+80d7-80d9,U+80db,U+80dd,U+80df-80e0,U+80e2-80e6,U+80e8-80f0,U+80f2,U+80f9,U+80fb-80fc,U+80fe-8101,U+8103-8105,U+8107-8108,U+810b-810e,U+8110,U+8112-8115,U+8117-8119,U+811b-8130,U+8132-8137,U+8139-813d,U+813f-8149,U+814d-814f,U+8152-8153,U+8156-8164,U+8166-816f,U+8171-8178,U+817c-817d,U+8181-8189,U+818b-818e,U+8190-819b,U+819e-81a7,U+81a9-81b2,U+81b4-81bf,U+81c1,U+81c3-81e2,U+81e4-81e9,U+81eb-81ec,U+81ee-81f2,U+81f5-8205,U+8207-820b,U+820e-8211,U+8213-821d,U+8220-8229,U+822b,U+822d-822f,U+8232,U+8234-8238,U+823a-8246,U+8248-824e,U+8250-8257,U+8259-826e,U+8271,U+8274-8277,U+827b-827d,U+827f-8281,U+8283-8286,U+886d-886f,U+9496-9498,U+9522,U+95a1-95a3,U+95ae-95b3,U+95be-95bf,U+95f3;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.17.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.17.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.17.otf')  format('opentype');unicode-range:U+54a9,U+62c8,U+6c82,U+8099,U+809b-809c,U+809e-809f,U+80a3,U+80a6-80a8,U+80ab-80ae,U+80b0-80b1,U+80b5-80b9,U+80bb-80bd,U+80c2,U+80c4-80c5,U+80c7-80cb,U+80cd,U+80cf-80d5,U+80d7-80d9,U+80db,U+80dd,U+80df-80e0,U+80e2-80e6,U+80e8-80f0,U+80f2,U+80f9,U+80fb-80fc,U+80fe-8101,U+8103-8105,U+8107-8108,U+810b-810e,U+8110,U+8112-8115,U+8117-8119,U+811b-8130,U+8132-8137,U+8139-813d,U+813f-8149,U+814d-814f,U+8152-8153,U+8156-8164,U+8166-816f,U+8171-8178,U+817c-817d,U+8181-8189,U+818b-818e,U+8190-819b,U+819e-81a7,U+81a9-81b2,U+81b4-81bf,U+81c1,U+81c3-81e2,U+81e4-81e9,U+81eb-81ec,U+81ee-81f2,U+81f5-8205,U+8207-820b,U+820e-8211,U+8213-821d,U+8220-8229,U+822b,U+822d-822f,U+8232,U+8234-8238,U+823a-8246,U+8248-824e,U+8250-8257,U+8259-826e,U+8271,U+8274-8277,U+827b-827d,U+827f-8281,U+8283-8286,U+886d-886f,U+9496-9498,U+9522,U+95a1-95a3,U+95ae-95b3,U+95be-95bf,U+95f3;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.18.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.18.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.18.otf')  format('opentype');unicode-range:U+b50f-b52f,U+b531-b6ac,U+c7c9-c7cb;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.18.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.18.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.18.otf')  format('opentype');unicode-range:U+b50f-b52f,U+b531-b6ac,U+c7c9-c7cb;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.18.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.18.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.18.otf')  format('opentype');unicode-range:U+b50f-b52f,U+b531-b6ac,U+c7c9-c7cb;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.19.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.19.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.19.otf')  format('opentype');unicode-range:U+48cb-4a6b;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.19.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.19.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.19.otf')  format('opentype');unicode-range:U+48cb-4a6b;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.19.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.19.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.19.otf')  format('opentype');unicode-range:U+48cb-4a6b;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.20.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.20.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.20.otf')  format('opentype');unicode-range:U+6a56-6a57,U+6a5a-6a60,U+6a62-6a70,U+6a72-6a7f,U+6a81-6a8f,U+6a91-6aab,U+6aad-6b1f,U+6b24-6b26,U+6b28-6b31,U+6b33-6b37,U+6b39,U+6b3b-6b3d,U+6b3f-6b46,U+6b48,U+6b4a-6b4b,U+6b4d-6b61,U+6b68-6b69,U+6b6b-6b76,U+6b7d-6b89,U+6b8c-6b95,U+6b97-6ba9,U+6bab-6bb4,U+6bb6,U+6bb8-6bbe,U+6bc0,U+6bc2-6bc4,U+6bc6-6bca,U+6bcc,U+6bce,U+6bd0-6bd1,U+6bd6,U+6bd8,U+6bda,U+6bdc-6bea,U+6bec-6bee,U+6bf0-6c0e,U+6c10,U+6c12,U+6c15-6c1a,U+6c1c-6c21,U+6e3b,U+8716-8717,U+900d-900e,U+94e7-94e9,U+9592-9593,U+95c0-95c2,U+9a6e;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.20.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.20.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.20.otf')  format('opentype');unicode-range:U+6a56-6a57,U+6a5a-6a60,U+6a62-6a70,U+6a72-6a7f,U+6a81-6a8f,U+6a91-6aab,U+6aad-6b1f,U+6b24-6b26,U+6b28-6b31,U+6b33-6b37,U+6b39,U+6b3b-6b3d,U+6b3f-6b46,U+6b48,U+6b4a-6b4b,U+6b4d-6b61,U+6b68-6b69,U+6b6b-6b76,U+6b7d-6b89,U+6b8c-6b95,U+6b97-6ba9,U+6bab-6bb4,U+6bb6,U+6bb8-6bbe,U+6bc0,U+6bc2-6bc4,U+6bc6-6bca,U+6bcc,U+6bce,U+6bd0-6bd1,U+6bd6,U+6bd8,U+6bda,U+6bdc-6bea,U+6bec-6bee,U+6bf0-6c0e,U+6c10,U+6c12,U+6c15-6c1a,U+6c1c-6c21,U+6e3b,U+8716-8717,U+900d-900e,U+94e7-94e9,U+9592-9593,U+95c0-95c2,U+9a6e;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.20.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.20.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.20.otf')  format('opentype');unicode-range:U+6a56-6a57,U+6a5a-6a60,U+6a62-6a70,U+6a72-6a7f,U+6a81-6a8f,U+6a91-6aab,U+6aad-6b1f,U+6b24-6b26,U+6b28-6b31,U+6b33-6b37,U+6b39,U+6b3b-6b3d,U+6b3f-6b46,U+6b48,U+6b4a-6b4b,U+6b4d-6b61,U+6b68-6b69,U+6b6b-6b76,U+6b7d-6b89,U+6b8c-6b95,U+6b97-6ba9,U+6bab-6bb4,U+6bb6,U+6bb8-6bbe,U+6bc0,U+6bc2-6bc4,U+6bc6-6bca,U+6bcc,U+6bce,U+6bd0-6bd1,U+6bd6,U+6bd8,U+6bda,U+6bdc-6bea,U+6bec-6bee,U+6bf0-6c0e,U+6c10,U+6c12,U+6c15-6c1a,U+6c1c-6c21,U+6e3b,U+8716-8717,U+900d-900e,U+94e7-94e9,U+9592-9593,U+95c0-95c2,U+9a6e;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.21.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.21.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.21.otf')  format('opentype');unicode-range:U+6ed2-6ed3,U+8dde,U+d73a-d787,U+d789-f9ea;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.21.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.21.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.21.otf')  format('opentype');unicode-range:U+6ed2-6ed3,U+8dde,U+d73a-d787,U+d789-f9ea;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.21.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.21.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.21.otf')  format('opentype');unicode-range:U+6ed2-6ed3,U+8dde,U+d73a-d787,U+d789-f9ea;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.22.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.22.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.22.otf')  format('opentype');unicode-range:U+7ea3,U+7ea5,U+cf17-d06b,U+d06d-d0b4;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.22.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.22.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.22.otf')  format('opentype');unicode-range:U+7ea3,U+7ea5,U+cf17-d06b,U+d06d-d0b4;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.22.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.22.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.22.otf')  format('opentype');unicode-range:U+7ea3,U+7ea5,U+cf17-d06b,U+d06d-d0b4;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.23.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.23.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.23.otf')  format('opentype');unicode-range:U+00e0,U+00ec,U+00f2-00f3,U+00f9,U+0101,U+01ce-01d0,U+01d4,U+0261-02cb,U+24fe,U+2501,U+3012-3013,U+3041-3043,U+3045-304a,U+304e,U+3053-3056,U+3058-305e,U+3064-3066,U+3069,U+306f-307d,U+3080-3088,U+308c-309b,U+309e-30ae,U+30b8-30c1,U+30c4-30c7,U+30cc-30e8,U+30ec-30f2,U+30f4-30fb,U+30fd-31fd,U+55ab,U+5739,U+5b6c,U+6242-6243,U+6f32,U+758e,U+7ebe,U+86a8,U+8bee,U+8c27,U+8deb-8dec,U+91d0,U+94e4-94e5,U+9504,U+9572-9575,U+95d7-95d8,U+9698,U+989f,U+9c90,U+9ddf;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.23.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.23.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.23.otf')  format('opentype');unicode-range:U+00e0,U+00ec,U+00f2-00f3,U+00f9,U+0101,U+01ce-01d0,U+01d4,U+0261-02cb,U+24fe,U+2501,U+3012-3013,U+3041-3043,U+3045-304a,U+304e,U+3053-3056,U+3058-305e,U+3064-3066,U+3069,U+306f-307d,U+3080-3088,U+308c-309b,U+309e-30ae,U+30b8-30c1,U+30c4-30c7,U+30cc-30e8,U+30ec-30f2,U+30f4-30fb,U+30fd-31fd,U+55ab,U+5739,U+5b6c,U+6242-6243,U+6f32,U+758e,U+7ebe,U+86a8,U+8bee,U+8c27,U+8deb-8dec,U+91d0,U+94e4-94e5,U+9504,U+9572-9575,U+95d7-95d8,U+9698,U+989f,U+9c90,U+9ddf;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.23.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.23.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.23.otf')  format('opentype');unicode-range:U+00e0,U+00ec,U+00f2-00f3,U+00f9,U+0101,U+01ce-01d0,U+01d4,U+0261-02cb,U+24fe,U+2501,U+3012-3013,U+3041-3043,U+3045-304a,U+304e,U+3053-3056,U+3058-305e,U+3064-3066,U+3069,U+306f-307d,U+3080-3088,U+308c-309b,U+309e-30ae,U+30b8-30c1,U+30c4-30c7,U+30cc-30e8,U+30ec-30f2,U+30f4-30fb,U+30fd-31fd,U+55ab,U+5739,U+5b6c,U+6242-6243,U+6f32,U+758e,U+7ebe,U+86a8,U+8bee,U+8c27,U+8deb-8dec,U+91d0,U+94e4-94e5,U+9504,U+9572-9575,U+95d7-95d8,U+9698,U+989f,U+9c90,U+9ddf;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.24.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.24.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.24.otf')  format('opentype');unicode-range:U+3880-3a21,U+966b-966c;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.24.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.24.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.24.otf')  format('opentype');unicode-range:U+3880-3a21,U+966b-966c;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.24.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.24.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.24.otf')  format('opentype');unicode-range:U+3880-3a21,U+966b-966c;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.25.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.25.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.25.otf')  format('opentype');unicode-range:U+039b,U+03bd,U+223d,U+5575,U+569d-56a2,U+56a4-56bb,U+56bd-56c9,U+56cb-56d9,U+56dc-56dd,U+56df,U+56e1,U+56e3-56ec,U+56ee-56ef,U+56f6-56f9,U+56fb-56fc,U+56ff-5705,U+5707,U+5709-570a,U+570c-571e,U+5720-5722,U+5724-5727,U+5729-572f,U+5731-5732,U+5734-5738,U+573b-573d,U+5743-5746,U+5748-5749,U+5752-5756,U+5758-5759,U+5762-5763,U+5765,U+576b-5776,U+5778-5781,U+5785-578a,U+578c-5791,U+5793-57a1,U+57a7-57aa,U+57ac-57ad,U+57af-57c2,U+57c4-57ca,U+57d0-57d3,U+57d5-57de,U+57e0-57f8,U+57fb-5801,U+5803-5805,U+5807-5814,U+5816-5820,U+5822-5823,U+5825-5829,U+582b-5833,U+5836-584b,U+584d-5850,U+5852-5853,U+5855-5857,U+5859-585d,U+585f-586a,U+586c-587a,U+59ab,U+651e,U+663a,U+6a50,U+6d65,U+7395,U+7817,U+78f2,U+7c9d,U+7f17,U+8233,U+8347,U+83f9,U+8764,U+8fc6,U+9506-9507,U+96e0,U+979e-979f,U+9899,U+9935,U+9e32,U+9f2f,U+fe31,U+ff1e,U+28090;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.25.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.25.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.25.otf')  format('opentype');unicode-range:U+039b,U+03bd,U+223d,U+5575,U+569d-56a2,U+56a4-56bb,U+56bd-56c9,U+56cb-56d9,U+56dc-56dd,U+56df,U+56e1,U+56e3-56ec,U+56ee-56ef,U+56f6-56f9,U+56fb-56fc,U+56ff-5705,U+5707,U+5709-570a,U+570c-571e,U+5720-5722,U+5724-5727,U+5729-572f,U+5731-5732,U+5734-5738,U+573b-573d,U+5743-5746,U+5748-5749,U+5752-5756,U+5758-5759,U+5762-5763,U+5765,U+576b-5776,U+5778-5781,U+5785-578a,U+578c-5791,U+5793-57a1,U+57a7-57aa,U+57ac-57ad,U+57af-57c2,U+57c4-57ca,U+57d0-57d3,U+57d5-57de,U+57e0-57f8,U+57fb-5801,U+5803-5805,U+5807-5814,U+5816-5820,U+5822-5823,U+5825-5829,U+582b-5833,U+5836-584b,U+584d-5850,U+5852-5853,U+5855-5857,U+5859-585d,U+585f-586a,U+586c-587a,U+59ab,U+651e,U+663a,U+6a50,U+6d65,U+7395,U+7817,U+78f2,U+7c9d,U+7f17,U+8233,U+8347,U+83f9,U+8764,U+8fc6,U+9506-9507,U+96e0,U+979e-979f,U+9899,U+9935,U+9e32,U+9f2f,U+fe31,U+ff1e,U+28090;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.25.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.25.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.25.otf')  format('opentype');unicode-range:U+039b,U+03bd,U+223d,U+5575,U+569d-56a2,U+56a4-56bb,U+56bd-56c9,U+56cb-56d9,U+56dc-56dd,U+56df,U+56e1,U+56e3-56ec,U+56ee-56ef,U+56f6-56f9,U+56fb-56fc,U+56ff-5705,U+5707,U+5709-570a,U+570c-571e,U+5720-5722,U+5724-5727,U+5729-572f,U+5731-5732,U+5734-5738,U+573b-573d,U+5743-5746,U+5748-5749,U+5752-5756,U+5758-5759,U+5762-5763,U+5765,U+576b-5776,U+5778-5781,U+5785-578a,U+578c-5791,U+5793-57a1,U+57a7-57aa,U+57ac-57ad,U+57af-57c2,U+57c4-57ca,U+57d0-57d3,U+57d5-57de,U+57e0-57f8,U+57fb-5801,U+5803-5805,U+5807-5814,U+5816-5820,U+5822-5823,U+5825-5829,U+582b-5833,U+5836-584b,U+584d-5850,U+5852-5853,U+5855-5857,U+5859-585d,U+585f-586a,U+586c-587a,U+59ab,U+651e,U+663a,U+6a50,U+6d65,U+7395,U+7817,U+78f2,U+7c9d,U+7f17,U+8233,U+8347,U+83f9,U+8764,U+8fc6,U+9506-9507,U+96e0,U+979e-979f,U+9899,U+9935,U+9e32,U+9f2f,U+fe31,U+ff1e,U+28090;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.26.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.26.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.26.otf')  format('opentype');unicode-range:U+59aa,U+6683-6686,U+6688-6690,U+6692-6695,U+6698-66a6,U+66a9-66ad,U+66af-66b3,U+66b5-66dc,U+66de-66ef,U+66f1,U+66f5-66f8,U+66fa-66fb,U+66fd,U+6701-6702,U+6704-6707,U+670a,U+670c,U+670e-6713,U+6715-6716,U+6718-671a,U+671c,U+671e,U+6720-6725,U+6729,U+672d-672e,U+6730,U+6732-6733,U+6736-6739,U+673b-673c,U+673e-673f,U+6741,U+6744-6745,U+6747-6748,U+674a-674d,U+6752-6755,U+6757-675b,U+675d-675e,U+6762-6764,U+6766-6767,U+6769-676c,U+676e,U+6771-677d,U+6780,U+6782-6783,U+6785-6788,U+678a-678f,U+6791-6794,U+6796,U+6798-6799,U+679b,U+679e-67a1,U+67a4-67a9,U+67ac-67ae,U+67b0-67b5,U+67b7-67c3,U+67c5-67cd,U+67d2,U+67d5-67db,U+67dd-67df,U+67e1-67e4,U+67e6-67ee,U+67f0,U+67f2,U+67f5-67fe,U+6800-6804,U+6806,U+6809-680a,U+680c-680e,U+6810,U+6812,U+6814-6815,U+6818-6820,U+6822-6829,U+682b-6836,U+683a-683b,U+683e-6841,U+6844-6845,U+6847,U+6849-684b,U+684d-684f,U+6852,U+6855-6862,U+6864,U+6866-6868,U+686a-6875,U+6877-6880,U+6882-6884,U+6886-6892,U+7c08,U+94aa-94ad,U+9511-9512,U+9e22,U+27870;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.26.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.26.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.26.otf')  format('opentype');unicode-range:U+59aa,U+6683-6686,U+6688-6690,U+6692-6695,U+6698-66a6,U+66a9-66ad,U+66af-66b3,U+66b5-66dc,U+66de-66ef,U+66f1,U+66f5-66f8,U+66fa-66fb,U+66fd,U+6701-6702,U+6704-6707,U+670a,U+670c,U+670e-6713,U+6715-6716,U+6718-671a,U+671c,U+671e,U+6720-6725,U+6729,U+672d-672e,U+6730,U+6732-6733,U+6736-6739,U+673b-673c,U+673e-673f,U+6741,U+6744-6745,U+6747-6748,U+674a-674d,U+6752-6755,U+6757-675b,U+675d-675e,U+6762-6764,U+6766-6767,U+6769-676c,U+676e,U+6771-677d,U+6780,U+6782-6783,U+6785-6788,U+678a-678f,U+6791-6794,U+6796,U+6798-6799,U+679b,U+679e-67a1,U+67a4-67a9,U+67ac-67ae,U+67b0-67b5,U+67b7-67c3,U+67c5-67cd,U+67d2,U+67d5-67db,U+67dd-67df,U+67e1-67e4,U+67e6-67ee,U+67f0,U+67f2,U+67f5-67fe,U+6800-6804,U+6806,U+6809-680a,U+680c-680e,U+6810,U+6812,U+6814-6815,U+6818-6820,U+6822-6829,U+682b-6836,U+683a-683b,U+683e-6841,U+6844-6845,U+6847,U+6849-684b,U+684d-684f,U+6852,U+6855-6862,U+6864,U+6866-6868,U+686a-6875,U+6877-6880,U+6882-6884,U+6886-6892,U+7c08,U+94aa-94ad,U+9511-9512,U+9e22,U+27870;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.26.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.26.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.26.otf')  format('opentype');unicode-range:U+59aa,U+6683-6686,U+6688-6690,U+6692-6695,U+6698-66a6,U+66a9-66ad,U+66af-66b3,U+66b5-66dc,U+66de-66ef,U+66f1,U+66f5-66f8,U+66fa-66fb,U+66fd,U+6701-6702,U+6704-6707,U+670a,U+670c,U+670e-6713,U+6715-6716,U+6718-671a,U+671c,U+671e,U+6720-6725,U+6729,U+672d-672e,U+6730,U+6732-6733,U+6736-6739,U+673b-673c,U+673e-673f,U+6741,U+6744-6745,U+6747-6748,U+674a-674d,U+6752-6755,U+6757-675b,U+675d-675e,U+6762-6764,U+6766-6767,U+6769-676c,U+676e,U+6771-677d,U+6780,U+6782-6783,U+6785-6788,U+678a-678f,U+6791-6794,U+6796,U+6798-6799,U+679b,U+679e-67a1,U+67a4-67a9,U+67ac-67ae,U+67b0-67b5,U+67b7-67c3,U+67c5-67cd,U+67d2,U+67d5-67db,U+67dd-67df,U+67e1-67e4,U+67e6-67ee,U+67f0,U+67f2,U+67f5-67fe,U+6800-6804,U+6806,U+6809-680a,U+680c-680e,U+6810,U+6812,U+6814-6815,U+6818-6820,U+6822-6829,U+682b-6836,U+683a-683b,U+683e-6841,U+6844-6845,U+6847,U+6849-684b,U+684d-684f,U+6852,U+6855-6862,U+6864,U+6866-6868,U+686a-6875,U+6877-6880,U+6882-6884,U+6886-6892,U+7c08,U+94aa-94ad,U+9511-9512,U+9e22,U+27870;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.27.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.27.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.27.otf')  format('opentype');unicode-range:U+2105,U+22a0,U+51fe,U+58ef,U+5cc4,U+62ee,U+6391,U+6c24-6c26,U+6c29-6c2d,U+6c30-6c33,U+6c36-6c37,U+6c39-6c3f,U+6c43-6c46,U+6c48,U+6c4a-6c56,U+6c58-6c5c,U+6c5e,U+6c62-6c63,U+6c65-6c69,U+6c6b-6c6f,U+6c73-6c75,U+6c77-6c78,U+6c7b-6c7c,U+6c7e-6c80,U+6c84-6c87,U+6c8a-6c8f,U+6c91-6c97,U+6c9c-6c9e,U+6ca0,U+6ca2-6ca4,U+6ca8-6ca9,U+6cac-6cb2,U+6cb4-6cb7,U+6cbd,U+6cc0-6cc3,U+6cc5-6cc8,U+6ccb,U+6ccd-6cd4,U+6cd6-6cda,U+6cdc-6ce0,U+6ce4,U+6ce6-6ce7,U+6ce9,U+6ceb-6cef,U+6cf1-6cf2,U+6cf4,U+6cf6-6cfa,U+6cfe-6d00,U+6d02-6d0a,U+6d0c-6d0d,U+6d0f-6d11,U+6d13-6d16,U+6d18-6d1a,U+6d1c-6d1d,U+6d1f-6d24,U+6d26-6d29,U+6d2b-6d31,U+6d33-6d3a,U+6d3c,U+6d3f-6d40,U+6d42-6d43,U+6d48-6d49,U+6d4c-6d4d,U+6d50,U+6d52,U+6d54-6d58,U+6d5a-6d64,U+6d67-6d68,U+6d6b-6d6d,U+6d6f-6d73,U+6d75-6d76,U+6d79-6d81,U+6d83-6d84,U+6d86-6d87,U+6d8a-6d8b,U+6d8d-6d94,U+6d96-6d9a,U+6d9c-6da0,U+6da2-6da3,U+6da5,U+6daa-6dae,U+6db0-6db1,U+6db3-6db4,U+6db6-6dbf,U+6dc1-6dc5,U+6dc8-6dca,U+6dcd-6dd0,U+6dd2-6dd7,U+6dd9-6de0,U+6de2-6de3,U+6de5-6dea,U+6dec-6ded,U+6def-6df0,U+6df2,U+6df4-6df6,U+6df8,U+6dfa,U+6dfc-6e04,U+6e06-6e09,U+6e0b-6e0f,U+6e11-6e13,U+6e15-6e16,U+6e18-6e1c,U+6e1e-6e1f,U+6e22,U+6e24-6e28,U+6e2a-6e2e,U+6e30-6e31,U+6e33,U+6e35-6e37,U+6e39,U+7a9e,U+8e1d-8e1e,U+8f9d,U+94d6-94db,U+951a-9520,U+960b-960d,U+9e20,U+26cc3;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.27.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.27.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.27.otf')  format('opentype');unicode-range:U+2105,U+22a0,U+51fe,U+58ef,U+5cc4,U+62ee,U+6391,U+6c24-6c26,U+6c29-6c2d,U+6c30-6c33,U+6c36-6c37,U+6c39-6c3f,U+6c43-6c46,U+6c48,U+6c4a-6c56,U+6c58-6c5c,U+6c5e,U+6c62-6c63,U+6c65-6c69,U+6c6b-6c6f,U+6c73-6c75,U+6c77-6c78,U+6c7b-6c7c,U+6c7e-6c80,U+6c84-6c87,U+6c8a-6c8f,U+6c91-6c97,U+6c9c-6c9e,U+6ca0,U+6ca2-6ca4,U+6ca8-6ca9,U+6cac-6cb2,U+6cb4-6cb7,U+6cbd,U+6cc0-6cc3,U+6cc5-6cc8,U+6ccb,U+6ccd-6cd4,U+6cd6-6cda,U+6cdc-6ce0,U+6ce4,U+6ce6-6ce7,U+6ce9,U+6ceb-6cef,U+6cf1-6cf2,U+6cf4,U+6cf6-6cfa,U+6cfe-6d00,U+6d02-6d0a,U+6d0c-6d0d,U+6d0f-6d11,U+6d13-6d16,U+6d18-6d1a,U+6d1c-6d1d,U+6d1f-6d24,U+6d26-6d29,U+6d2b-6d31,U+6d33-6d3a,U+6d3c,U+6d3f-6d40,U+6d42-6d43,U+6d48-6d49,U+6d4c-6d4d,U+6d50,U+6d52,U+6d54-6d58,U+6d5a-6d64,U+6d67-6d68,U+6d6b-6d6d,U+6d6f-6d73,U+6d75-6d76,U+6d79-6d81,U+6d83-6d84,U+6d86-6d87,U+6d8a-6d8b,U+6d8d-6d94,U+6d96-6d9a,U+6d9c-6da0,U+6da2-6da3,U+6da5,U+6daa-6dae,U+6db0-6db1,U+6db3-6db4,U+6db6-6dbf,U+6dc1-6dc5,U+6dc8-6dca,U+6dcd-6dd0,U+6dd2-6dd7,U+6dd9-6de0,U+6de2-6de3,U+6de5-6dea,U+6dec-6ded,U+6def-6df0,U+6df2,U+6df4-6df6,U+6df8,U+6dfa,U+6dfc-6e04,U+6e06-6e09,U+6e0b-6e0f,U+6e11-6e13,U+6e15-6e16,U+6e18-6e1c,U+6e1e-6e1f,U+6e22,U+6e24-6e28,U+6e2a-6e2e,U+6e30-6e31,U+6e33,U+6e35-6e37,U+6e39,U+7a9e,U+8e1d-8e1e,U+8f9d,U+94d6-94db,U+951a-9520,U+960b-960d,U+9e20,U+26cc3;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.27.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.27.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.27.otf')  format('opentype');unicode-range:U+2105,U+22a0,U+51fe,U+58ef,U+5cc4,U+62ee,U+6391,U+6c24-6c26,U+6c29-6c2d,U+6c30-6c33,U+6c36-6c37,U+6c39-6c3f,U+6c43-6c46,U+6c48,U+6c4a-6c56,U+6c58-6c5c,U+6c5e,U+6c62-6c63,U+6c65-6c69,U+6c6b-6c6f,U+6c73-6c75,U+6c77-6c78,U+6c7b-6c7c,U+6c7e-6c80,U+6c84-6c87,U+6c8a-6c8f,U+6c91-6c97,U+6c9c-6c9e,U+6ca0,U+6ca2-6ca4,U+6ca8-6ca9,U+6cac-6cb2,U+6cb4-6cb7,U+6cbd,U+6cc0-6cc3,U+6cc5-6cc8,U+6ccb,U+6ccd-6cd4,U+6cd6-6cda,U+6cdc-6ce0,U+6ce4,U+6ce6-6ce7,U+6ce9,U+6ceb-6cef,U+6cf1-6cf2,U+6cf4,U+6cf6-6cfa,U+6cfe-6d00,U+6d02-6d0a,U+6d0c-6d0d,U+6d0f-6d11,U+6d13-6d16,U+6d18-6d1a,U+6d1c-6d1d,U+6d1f-6d24,U+6d26-6d29,U+6d2b-6d31,U+6d33-6d3a,U+6d3c,U+6d3f-6d40,U+6d42-6d43,U+6d48-6d49,U+6d4c-6d4d,U+6d50,U+6d52,U+6d54-6d58,U+6d5a-6d64,U+6d67-6d68,U+6d6b-6d6d,U+6d6f-6d73,U+6d75-6d76,U+6d79-6d81,U+6d83-6d84,U+6d86-6d87,U+6d8a-6d8b,U+6d8d-6d94,U+6d96-6d9a,U+6d9c-6da0,U+6da2-6da3,U+6da5,U+6daa-6dae,U+6db0-6db1,U+6db3-6db4,U+6db6-6dbf,U+6dc1-6dc5,U+6dc8-6dca,U+6dcd-6dd0,U+6dd2-6dd7,U+6dd9-6de0,U+6de2-6de3,U+6de5-6dea,U+6dec-6ded,U+6def-6df0,U+6df2,U+6df4-6df6,U+6df8,U+6dfa,U+6dfc-6e04,U+6e06-6e09,U+6e0b-6e0f,U+6e11-6e13,U+6e15-6e16,U+6e18-6e1c,U+6e1e-6e1f,U+6e22,U+6e24-6e28,U+6e2a-6e2e,U+6e30-6e31,U+6e33,U+6e35-6e37,U+6e39,U+7a9e,U+8e1d-8e1e,U+8f9d,U+94d6-94db,U+951a-9520,U+960b-960d,U+9e20,U+26cc3;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.28.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.28.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.28.otf')  format('opentype');unicode-range:U+b36a-b36f,U+b371-b3c3,U+b3c5-b3d8,U+b3da-b417,U+b419-b44f,U+b451-b4db,U+b4dd-b4df,U+b4e1-b4e3,U+b4e5-b50e,U+c7a6-c7a7;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.28.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.28.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.28.otf')  format('opentype');unicode-range:U+b36a-b36f,U+b371-b3c3,U+b3c5-b3d8,U+b3da-b417,U+b419-b44f,U+b451-b4db,U+b4dd-b4df,U+b4e1-b4e3,U+b4e5-b50e,U+c7a6-c7a7;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.28.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.28.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.28.otf')  format('opentype');unicode-range:U+b36a-b36f,U+b371-b3c3,U+b3c5-b3d8,U+b3da-b417,U+b419-b44f,U+b451-b4db,U+b4dd-b4df,U+b4e1-b4e3,U+b4e5-b50e,U+c7a6-c7a7;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.29.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.29.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.29.otf')  format('opentype');unicode-range:U+529c,U+5b84,U+6026,U+6598,U+20b1d,U+20f90,U+246d4,U+26d22-26deb,U+26e00-26e12,U+26e42-27785,U+2789d-28048,U+280bd-28473,U+28501-28e97;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.29.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.29.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.29.otf')  format('opentype');unicode-range:U+529c,U+5b84,U+6026,U+6598,U+20b1d,U+20f90,U+246d4,U+26d22-26deb,U+26e00-26e12,U+26e42-27785,U+2789d-28048,U+280bd-28473,U+28501-28e97;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.29.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.29.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.29.otf')  format('opentype');unicode-range:U+529c,U+5b84,U+6026,U+6598,U+20b1d,U+20f90,U+246d4,U+26d22-26deb,U+26e00-26e12,U+26e42-27785,U+2789d-28048,U+280bd-28473,U+28501-28e97;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.30.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.30.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.30.otf')  format('opentype');unicode-range:U+c7ab-c7ac,U+c88c-c8fb,U+c8fd-c8ff,U+c901-c910,U+c912-c9bf,U+c9c2-c9c3,U+c9c5-c9d0,U+c9d2-ca36,U+2b363;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.30.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.30.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.30.otf')  format('opentype');unicode-range:U+c7ab-c7ac,U+c88c-c8fb,U+c8fd-c8ff,U+c901-c910,U+c912-c9bf,U+c9c2-c9c3,U+c9c5-c9d0,U+c9d2-ca36,U+2b363;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.30.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.30.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.30.otf')  format('opentype');unicode-range:U+c7ab-c7ac,U+c88c-c8fb,U+c8fd-c8ff,U+c901-c910,U+c912-c9bf,U+c9c2-c9c3,U+c9c5-c9d0,U+c9d2-ca36,U+2b363;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.31.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.31.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.31.otf')  format('opentype');unicode-range:U+57cf,U+6300,U+6ed4-6ed5,U+8287-8291,U+8293-8298,U+829a-829b,U+829e-82a4,U+82a7-82ab,U+82ae,U+82b0,U+82b2,U+82b4-82b7,U+82ba-82bc,U+82be-82c6,U+82c8-82cc,U+82ce,U+82d0,U+82d2-82d3,U+82d5-82d6,U+82d8-82da,U+82dc-82de,U+82e0-82e4,U+82e7-82ee,U+82f0,U+82f2-82f8,U+82fa-8301,U+8306-830d,U+830f-8327,U+8329-832a,U+832c-8334,U+833a-8345,U+8348,U+834a-834f,U+8351,U+8353-835e,U+8360,U+8362,U+8364-8366,U+8368-836a,U+836c-836e,U+8370-8376,U+8378-8388,U+838a-838d,U+838f-8392,U+8394-839d,U+839f-83aa,U+83ac-83b0,U+83b3-83b6,U+83b8,U+83ba-83c6,U+83c8-83c9,U+83cb,U+83cd-83db,U+83dd-83df,U+83e1-83e8,U+83ea-83f0,U+83f3-83f7,U+83fa-8402,U+8405-840b,U+840f-841c,U+841e-8424,U+8426,U+8429-843c,U+843e-8456,U+8458-845a,U+845c-8460,U+8462,U+8464-8468,U+846a,U+8869-886a,U+9527-9528,U+9606-9608,U+960f,U+9637-963a;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.31.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.31.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.31.otf')  format('opentype');unicode-range:U+57cf,U+6300,U+6ed4-6ed5,U+8287-8291,U+8293-8298,U+829a-829b,U+829e-82a4,U+82a7-82ab,U+82ae,U+82b0,U+82b2,U+82b4-82b7,U+82ba-82bc,U+82be-82c6,U+82c8-82cc,U+82ce,U+82d0,U+82d2-82d3,U+82d5-82d6,U+82d8-82da,U+82dc-82de,U+82e0-82e4,U+82e7-82ee,U+82f0,U+82f2-82f8,U+82fa-8301,U+8306-830d,U+830f-8327,U+8329-832a,U+832c-8334,U+833a-8345,U+8348,U+834a-834f,U+8351,U+8353-835e,U+8360,U+8362,U+8364-8366,U+8368-836a,U+836c-836e,U+8370-8376,U+8378-8388,U+838a-838d,U+838f-8392,U+8394-839d,U+839f-83aa,U+83ac-83b0,U+83b3-83b6,U+83b8,U+83ba-83c6,U+83c8-83c9,U+83cb,U+83cd-83db,U+83dd-83df,U+83e1-83e8,U+83ea-83f0,U+83f3-83f7,U+83fa-8402,U+8405-840b,U+840f-841c,U+841e-8424,U+8426,U+8429-843c,U+843e-8456,U+8458-845a,U+845c-8460,U+8462,U+8464-8468,U+846a,U+8869-886a,U+9527-9528,U+9606-9608,U+960f,U+9637-963a;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.31.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.31.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.31.otf')  format('opentype');unicode-range:U+57cf,U+6300,U+6ed4-6ed5,U+8287-8291,U+8293-8298,U+829a-829b,U+829e-82a4,U+82a7-82ab,U+82ae,U+82b0,U+82b2,U+82b4-82b7,U+82ba-82bc,U+82be-82c6,U+82c8-82cc,U+82ce,U+82d0,U+82d2-82d3,U+82d5-82d6,U+82d8-82da,U+82dc-82de,U+82e0-82e4,U+82e7-82ee,U+82f0,U+82f2-82f8,U+82fa-8301,U+8306-830d,U+830f-8327,U+8329-832a,U+832c-8334,U+833a-8345,U+8348,U+834a-834f,U+8351,U+8353-835e,U+8360,U+8362,U+8364-8366,U+8368-836a,U+836c-836e,U+8370-8376,U+8378-8388,U+838a-838d,U+838f-8392,U+8394-839d,U+839f-83aa,U+83ac-83b0,U+83b3-83b6,U+83b8,U+83ba-83c6,U+83c8-83c9,U+83cb,U+83cd-83db,U+83dd-83df,U+83e1-83e8,U+83ea-83f0,U+83f3-83f7,U+83fa-8402,U+8405-840b,U+840f-841c,U+841e-8424,U+8426,U+8429-843c,U+843e-8456,U+8458-845a,U+845c-8460,U+8462,U+8464-8468,U+846a,U+8869-886a,U+9527-9528,U+9606-9608,U+960f,U+9637-963a;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.32.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.32.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.32.otf')  format('opentype');unicode-range:U+4e4a,U+54ab,U+66f3,U+775c-7760,U+7762,U+7764-7765,U+7767-7778,U+777a-777e,U+7780-7783,U+7785-778a,U+778c-778d,U+778f-7791,U+7793-77a6,U+77a8,U+77ab,U+77ad-77af,U+77b1-77ba,U+77bc-77da,U+77dc-77e1,U+77e4,U+77e6-77e8,U+77ea,U+77ec,U+77ef-77f2,U+77f4-77f5,U+77f7-77fe,U+7800,U+7803-780b,U+780e-7813,U+7815,U+7818-7833,U+7835-7837,U+7839-783f,U+7841-7844,U+7846-7854,U+7856-785c,U+785e-786a,U+786d,U+786f-788b,U+788f-7890,U+7892-7896,U+7898-789e,U+78a0-78a6,U+78a8-78af,U+78b2,U+78b4-78c0,U+78c2-78c4,U+78c6-78c9,U+78cb-78e7,U+78e9-78f0,U+78f3-78f6,U+78f8-7900,U+7902-7930,U+795f,U+963c-963e;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.32.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.32.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.32.otf')  format('opentype');unicode-range:U+4e4a,U+54ab,U+66f3,U+775c-7760,U+7762,U+7764-7765,U+7767-7778,U+777a-777e,U+7780-7783,U+7785-778a,U+778c-778d,U+778f-7791,U+7793-77a6,U+77a8,U+77ab,U+77ad-77af,U+77b1-77ba,U+77bc-77da,U+77dc-77e1,U+77e4,U+77e6-77e8,U+77ea,U+77ec,U+77ef-77f2,U+77f4-77f5,U+77f7-77fe,U+7800,U+7803-780b,U+780e-7813,U+7815,U+7818-7833,U+7835-7837,U+7839-783f,U+7841-7844,U+7846-7854,U+7856-785c,U+785e-786a,U+786d,U+786f-788b,U+788f-7890,U+7892-7896,U+7898-789e,U+78a0-78a6,U+78a8-78af,U+78b2,U+78b4-78c0,U+78c2-78c4,U+78c6-78c9,U+78cb-78e7,U+78e9-78f0,U+78f3-78f6,U+78f8-7900,U+7902-7930,U+795f,U+963c-963e;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.32.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.32.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.32.otf')  format('opentype');unicode-range:U+4e4a,U+54ab,U+66f3,U+775c-7760,U+7762,U+7764-7765,U+7767-7778,U+777a-777e,U+7780-7783,U+7785-778a,U+778c-778d,U+778f-7791,U+7793-77a6,U+77a8,U+77ab,U+77ad-77af,U+77b1-77ba,U+77bc-77da,U+77dc-77e1,U+77e4,U+77e6-77e8,U+77ea,U+77ec,U+77ef-77f2,U+77f4-77f5,U+77f7-77fe,U+7800,U+7803-780b,U+780e-7813,U+7815,U+7818-7833,U+7835-7837,U+7839-783f,U+7841-7844,U+7846-7854,U+7856-785c,U+785e-786a,U+786d,U+786f-788b,U+788f-7890,U+7892-7896,U+7898-789e,U+78a0-78a6,U+78a8-78af,U+78b2,U+78b4-78c0,U+78c2-78c4,U+78c6-78c9,U+78cb-78e7,U+78e9-78f0,U+78f3-78f6,U+78f8-7900,U+7902-7930,U+795f,U+963c-963e;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.33.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.33.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.33.otf')  format('opentype');unicode-range:U+03c0-03c1,U+2030,U+2042,U+2109-210a,U+4da9-4db5,U+4e02,U+4e04-4e06,U+4e0f-4e10,U+4e12,U+4e15,U+4e17,U+4e1f-4e21,U+4e2c,U+4e2e-4e2f,U+4e31,U+4e33,U+4e35-4e37,U+4e3c,U+4e3f-4e42,U+4e44,U+4e46-4e47,U+4e51-4e53,U+4e55,U+4e5a-4e5c,U+4e62-4e65,U+4e67-4e6f,U+4e72,U+4e74-4e7d,U+4e7f-4e85,U+4e87,U+4e90,U+4e96-4e99,U+4e9c-4ea0,U+4ea3,U+4eaa,U+4eaf-4eb1,U+4eb3-4eb9,U+4ebc-4ebe,U+4ec2-4ec4,U+4ec8-4ec9,U+4ecc,U+4ecf-4ed0,U+4ed2,U+4eda-4ee2,U+4ee6-4ee9,U+4eeb,U+4eed-4eef,U+4ef1,U+4ef3-4ef5,U+4ef8-4efa,U+4efc,U+4efe,U+4f00,U+4f02-4f09,U+4f0b-4f0c,U+4f0e,U+4f12-4f16,U+4f1b-4f1d,U+4f21-4f23,U+4f25,U+4f27-4f29,U+4f2b-4f2e,U+4f31-4f33,U+4f37,U+4f39,U+4f3b,U+4f3e-4f45,U+4f47-4f4c,U+4f52,U+4f54,U+4f56-4f58,U+4f5a,U+4f5d-4f5f,U+4f61-4f62,U+4f64-4f68,U+4f6a-4f6b,U+4f6d-4f72,U+4f74-4f7e,U+4f80-4f82,U+4f85-4f87,U+4f89-4f8a,U+4f8e-4f9a,U+4f9c,U+4f9e-4f9f,U+4fa1-4fa2,U+4fa4-4fa5,U+4fa9-4fad,U+4fb0-4fb4,U+4fb6-4fbe,U+4fc0-4fc2,U+4fc5-4fc9,U+4fcb-4fce,U+4fd0-4fd6,U+4fd9-4fdc,U+4fdf-4fe0,U+4fe2-4fe7,U+4fea-4fec,U+4ff0,U+4ff2-4ff9,U+4ffb-500a,U+500c,U+500e-5011,U+5013,U+5015-5017,U+501b-501e,U+5020,U+5022-5025,U+5027-5028,U+502b-5039,U+503b,U+503d,U+503f-5046,U+5048-504e,U+5050-5054,U+5056-5059,U+58ed,U+5b82,U+5cda,U+6f31,U+76d3-76d5,U+79bc,U+9580-9582,U+9615-9616,U+9670-9671,U+28083,U+2b4ef;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.33.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.33.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.33.otf')  format('opentype');unicode-range:U+03c0-03c1,U+2030,U+2042,U+2109-210a,U+4da9-4db5,U+4e02,U+4e04-4e06,U+4e0f-4e10,U+4e12,U+4e15,U+4e17,U+4e1f-4e21,U+4e2c,U+4e2e-4e2f,U+4e31,U+4e33,U+4e35-4e37,U+4e3c,U+4e3f-4e42,U+4e44,U+4e46-4e47,U+4e51-4e53,U+4e55,U+4e5a-4e5c,U+4e62-4e65,U+4e67-4e6f,U+4e72,U+4e74-4e7d,U+4e7f-4e85,U+4e87,U+4e90,U+4e96-4e99,U+4e9c-4ea0,U+4ea3,U+4eaa,U+4eaf-4eb1,U+4eb3-4eb9,U+4ebc-4ebe,U+4ec2-4ec4,U+4ec8-4ec9,U+4ecc,U+4ecf-4ed0,U+4ed2,U+4eda-4ee2,U+4ee6-4ee9,U+4eeb,U+4eed-4eef,U+4ef1,U+4ef3-4ef5,U+4ef8-4efa,U+4efc,U+4efe,U+4f00,U+4f02-4f09,U+4f0b-4f0c,U+4f0e,U+4f12-4f16,U+4f1b-4f1d,U+4f21-4f23,U+4f25,U+4f27-4f29,U+4f2b-4f2e,U+4f31-4f33,U+4f37,U+4f39,U+4f3b,U+4f3e-4f45,U+4f47-4f4c,U+4f52,U+4f54,U+4f56-4f58,U+4f5a,U+4f5d-4f5f,U+4f61-4f62,U+4f64-4f68,U+4f6a-4f6b,U+4f6d-4f72,U+4f74-4f7e,U+4f80-4f82,U+4f85-4f87,U+4f89-4f8a,U+4f8e-4f9a,U+4f9c,U+4f9e-4f9f,U+4fa1-4fa2,U+4fa4-4fa5,U+4fa9-4fad,U+4fb0-4fb4,U+4fb6-4fbe,U+4fc0-4fc2,U+4fc5-4fc9,U+4fcb-4fce,U+4fd0-4fd6,U+4fd9-4fdc,U+4fdf-4fe0,U+4fe2-4fe7,U+4fea-4fec,U+4ff0,U+4ff2-4ff9,U+4ffb-500a,U+500c,U+500e-5011,U+5013,U+5015-5017,U+501b-501e,U+5020,U+5022-5025,U+5027-5028,U+502b-5039,U+503b,U+503d,U+503f-5046,U+5048-504e,U+5050-5054,U+5056-5059,U+58ed,U+5b82,U+5cda,U+6f31,U+76d3-76d5,U+79bc,U+9580-9582,U+9615-9616,U+9670-9671,U+28083,U+2b4ef;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.33.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.33.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.33.otf')  format('opentype');unicode-range:U+03c0-03c1,U+2030,U+2042,U+2109-210a,U+4da9-4db5,U+4e02,U+4e04-4e06,U+4e0f-4e10,U+4e12,U+4e15,U+4e17,U+4e1f-4e21,U+4e2c,U+4e2e-4e2f,U+4e31,U+4e33,U+4e35-4e37,U+4e3c,U+4e3f-4e42,U+4e44,U+4e46-4e47,U+4e51-4e53,U+4e55,U+4e5a-4e5c,U+4e62-4e65,U+4e67-4e6f,U+4e72,U+4e74-4e7d,U+4e7f-4e85,U+4e87,U+4e90,U+4e96-4e99,U+4e9c-4ea0,U+4ea3,U+4eaa,U+4eaf-4eb1,U+4eb3-4eb9,U+4ebc-4ebe,U+4ec2-4ec4,U+4ec8-4ec9,U+4ecc,U+4ecf-4ed0,U+4ed2,U+4eda-4ee2,U+4ee6-4ee9,U+4eeb,U+4eed-4eef,U+4ef1,U+4ef3-4ef5,U+4ef8-4efa,U+4efc,U+4efe,U+4f00,U+4f02-4f09,U+4f0b-4f0c,U+4f0e,U+4f12-4f16,U+4f1b-4f1d,U+4f21-4f23,U+4f25,U+4f27-4f29,U+4f2b-4f2e,U+4f31-4f33,U+4f37,U+4f39,U+4f3b,U+4f3e-4f45,U+4f47-4f4c,U+4f52,U+4f54,U+4f56-4f58,U+4f5a,U+4f5d-4f5f,U+4f61-4f62,U+4f64-4f68,U+4f6a-4f6b,U+4f6d-4f72,U+4f74-4f7e,U+4f80-4f82,U+4f85-4f87,U+4f89-4f8a,U+4f8e-4f9a,U+4f9c,U+4f9e-4f9f,U+4fa1-4fa2,U+4fa4-4fa5,U+4fa9-4fad,U+4fb0-4fb4,U+4fb6-4fbe,U+4fc0-4fc2,U+4fc5-4fc9,U+4fcb-4fce,U+4fd0-4fd6,U+4fd9-4fdc,U+4fdf-4fe0,U+4fe2-4fe7,U+4fea-4fec,U+4ff0,U+4ff2-4ff9,U+4ffb-500a,U+500c,U+500e-5011,U+5013,U+5015-5017,U+501b-501e,U+5020,U+5022-5025,U+5027-5028,U+502b-5039,U+503b,U+503d,U+503f-5046,U+5048-504e,U+5050-5054,U+5056-5059,U+58ed,U+5b82,U+5cda,U+6f31,U+76d3-76d5,U+79bc,U+9580-9582,U+9615-9616,U+9670-9671,U+28083,U+2b4ef;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.34.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.34.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.34.otf')  format('opentype');unicode-range:U+4e28-4e29,U+4e8a,U+62f0,U+7eb4,U+88da-88db,U+20b0d,U+20e1d,U+21096,U+23781,U+25f1a,U+2644a,U+26484,U+28e99,U+28eb2-2b138,U+2b410-2b413,U+2b4f6-2cb73,U+2cb78-2f921,U+2f96c-2f9df;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.34.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.34.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.34.otf')  format('opentype');unicode-range:U+4e28-4e29,U+4e8a,U+62f0,U+7eb4,U+88da-88db,U+20b0d,U+20e1d,U+21096,U+23781,U+25f1a,U+2644a,U+26484,U+28e99,U+28eb2-2b138,U+2b410-2b413,U+2b4f6-2cb73,U+2cb78-2f921,U+2f96c-2f9df;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.34.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.34.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.34.otf')  format('opentype');unicode-range:U+4e28-4e29,U+4e8a,U+62f0,U+7eb4,U+88da-88db,U+20b0d,U+20e1d,U+21096,U+23781,U+25f1a,U+2644a,U+26484,U+28e99,U+28eb2-2b138,U+2b410-2b413,U+2b4f6-2cb73,U+2cb78-2f921,U+2f96c-2f9df;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.35.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.35.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.35.otf')  format('opentype');unicode-range:U+2243,U+4e26,U+7617,U+9665-9668,U+9672-9674,U+984a-9874,U+9878,U+9880,U+9883,U+988b-988f,U+9892,U+9894-9895,U+989a-989b,U+98a1-98a3,U+98a5-98cd,U+98cf-98d7,U+98da-98dd,U+98e0-990f,U+9911-9934,U+9936-9964,U+9966-9969,U+996b-996c,U+996f,U+9973-9975,U+9977-9978,U+997a-997b,U+997d-997e,U+9980-9984,U+9987,U+9989-9995,U+9997-9998,U+999a-99a7,U+99a9-9a16;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.35.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.35.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.35.otf')  format('opentype');unicode-range:U+2243,U+4e26,U+7617,U+9665-9668,U+9672-9674,U+984a-9874,U+9878,U+9880,U+9883,U+988b-988f,U+9892,U+9894-9895,U+989a-989b,U+98a1-98a3,U+98a5-98cd,U+98cf-98d7,U+98da-98dd,U+98e0-990f,U+9911-9934,U+9936-9964,U+9966-9969,U+996b-996c,U+996f,U+9973-9975,U+9977-9978,U+997a-997b,U+997d-997e,U+9980-9984,U+9987,U+9989-9995,U+9997-9998,U+999a-99a7,U+99a9-9a16;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.35.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.35.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.35.otf')  format('opentype');unicode-range:U+2243,U+4e26,U+7617,U+9665-9668,U+9672-9674,U+984a-9874,U+9878,U+9880,U+9883,U+988b-988f,U+9892,U+9894-9895,U+989a-989b,U+98a1-98a3,U+98a5-98cd,U+98cf-98d7,U+98da-98dd,U+98e0-990f,U+9911-9934,U+9936-9964,U+9966-9969,U+996b-996c,U+996f,U+9973-9975,U+9977-9978,U+997a-997b,U+997d-997e,U+9980-9984,U+9987,U+9989-9995,U+9997-9998,U+999a-99a7,U+99a9-9a16;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.36.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.36.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.36.otf')  format('opentype');unicode-range:U+574c-574d,U+7b15-7b1a,U+7b1c-7b25,U+7b27,U+7b29-7b2b,U+7b2d-7b3b,U+7b3d-7b48,U+7b4a,U+7b4c-7b50,U+7b53,U+7b55,U+7b57-7b5a,U+7b5c-7b76,U+7b78,U+7b7a-7b7d,U+7b81-7b96,U+7b98-7ba0,U+7ba2-7bac,U+7bae-7bb0,U+7bb2-7bc5,U+7bc8-7be0,U+7be2-7bed,U+7bef-7bf6,U+7bf8-7c06,U+7c09-7c3e,U+7c40-7c4c,U+7c4e-7c72,U+7c74-7c7a,U+7c7c,U+7c7e-7c88,U+7c8a-7c91,U+7c93-7c96,U+7c99-7c9c,U+7c9e,U+7ca0-7ca3,U+7ca6-7ca9,U+7cab-7cad,U+7caf-7cb8,U+7cba-7cbd,U+7cbf-7cc9,U+7ccb-7cd4,U+7cd7-7cd8,U+7cda-7cde,U+948c-948f;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.36.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.36.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.36.otf')  format('opentype');unicode-range:U+574c-574d,U+7b15-7b1a,U+7b1c-7b25,U+7b27,U+7b29-7b2b,U+7b2d-7b3b,U+7b3d-7b48,U+7b4a,U+7b4c-7b50,U+7b53,U+7b55,U+7b57-7b5a,U+7b5c-7b76,U+7b78,U+7b7a-7b7d,U+7b81-7b96,U+7b98-7ba0,U+7ba2-7bac,U+7bae-7bb0,U+7bb2-7bc5,U+7bc8-7be0,U+7be2-7bed,U+7bef-7bf6,U+7bf8-7c06,U+7c09-7c3e,U+7c40-7c4c,U+7c4e-7c72,U+7c74-7c7a,U+7c7c,U+7c7e-7c88,U+7c8a-7c91,U+7c93-7c96,U+7c99-7c9c,U+7c9e,U+7ca0-7ca3,U+7ca6-7ca9,U+7cab-7cad,U+7caf-7cb8,U+7cba-7cbd,U+7cbf-7cc9,U+7ccb-7cd4,U+7cd7-7cd8,U+7cda-7cde,U+948c-948f;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.36.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.36.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.36.otf')  format('opentype');unicode-range:U+574c-574d,U+7b15-7b1a,U+7b1c-7b25,U+7b27,U+7b29-7b2b,U+7b2d-7b3b,U+7b3d-7b48,U+7b4a,U+7b4c-7b50,U+7b53,U+7b55,U+7b57-7b5a,U+7b5c-7b76,U+7b78,U+7b7a-7b7d,U+7b81-7b96,U+7b98-7ba0,U+7ba2-7bac,U+7bae-7bb0,U+7bb2-7bc5,U+7bc8-7be0,U+7be2-7bed,U+7bef-7bf6,U+7bf8-7c06,U+7c09-7c3e,U+7c40-7c4c,U+7c4e-7c72,U+7c74-7c7a,U+7c7c,U+7c7e-7c88,U+7c8a-7c91,U+7c93-7c96,U+7c99-7c9c,U+7c9e,U+7ca0-7ca3,U+7ca6-7ca9,U+7cab-7cad,U+7caf-7cb8,U+7cba-7cbd,U+7cbf-7cc9,U+7ccb-7cd4,U+7cd7-7cd8,U+7cda-7cde,U+948c-948f;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.37.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.37.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.37.otf')  format('opentype');unicode-range:U+6c71,U+9131-914b,U+914e-9151,U+9153-9164,U+9166-9169,U+916d,U+9170,U+9172-9174,U+9179-917e,U+9180-9186,U+9188,U+918a,U+918c-9191,U+9193-91c6,U+91c8-91c9,U+91d2-9273,U+9275-92ea,U+22c4c;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.37.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.37.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.37.otf')  format('opentype');unicode-range:U+6c71,U+9131-914b,U+914e-9151,U+9153-9164,U+9166-9169,U+916d,U+9170,U+9172-9174,U+9179-917e,U+9180-9186,U+9188,U+918a,U+918c-9191,U+9193-91c6,U+91c8-91c9,U+91d2-9273,U+9275-92ea,U+22c4c;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.37.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.37.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.37.otf')  format('opentype');unicode-range:U+6c71,U+9131-914b,U+914e-9151,U+9153-9164,U+9166-9169,U+916d,U+9170,U+9172-9174,U+9179-917e,U+9180-9186,U+9188,U+918a,U+918c-9191,U+9193-91c6,U+91c8-91c9,U+91d2-9273,U+9275-92ea,U+22c4c;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.38.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.38.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.38.otf')  format('opentype');unicode-range:U+1186-2012,U+201e-2021,U+2025,U+2039-203c,U+2047-20de,U+210f,U+2116-213b,U+2162-2191,U+2193-21f5,U+2202-2229,U+222b-222e,U+2236-2237,U+2245-2248,U+2260-2299,U+22a5-2307,U+2329-23c8,U+2502,U+252c,U+309c,U+4e8d,U+4ebb,U+5155,U+55c4,U+570b,U+575c,U+5cc1,U+5e11,U+5f73,U+5fbc,U+6042,U+6206,U+6265-6266,U+6426,U+6b38,U+6c35,U+6d44,U+72b4-72b5,U+755a-755b,U+778b,U+7f2f,U+83f8,U+88fd,U+898b,U+89dc,U+8ba0,U+8bf3,U+8df6,U+9577,U+961d,U+9b2f,U+9fa2,U+ff3e;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.38.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.38.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.38.otf')  format('opentype');unicode-range:U+1186-2012,U+201e-2021,U+2025,U+2039-203c,U+2047-20de,U+210f,U+2116-213b,U+2162-2191,U+2193-21f5,U+2202-2229,U+222b-222e,U+2236-2237,U+2245-2248,U+2260-2299,U+22a5-2307,U+2329-23c8,U+2502,U+252c,U+309c,U+4e8d,U+4ebb,U+5155,U+55c4,U+570b,U+575c,U+5cc1,U+5e11,U+5f73,U+5fbc,U+6042,U+6206,U+6265-6266,U+6426,U+6b38,U+6c35,U+6d44,U+72b4-72b5,U+755a-755b,U+778b,U+7f2f,U+83f8,U+88fd,U+898b,U+89dc,U+8ba0,U+8bf3,U+8df6,U+9577,U+961d,U+9b2f,U+9fa2,U+ff3e;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.38.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.38.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.38.otf')  format('opentype');unicode-range:U+1186-2012,U+201e-2021,U+2025,U+2039-203c,U+2047-20de,U+210f,U+2116-213b,U+2162-2191,U+2193-21f5,U+2202-2229,U+222b-222e,U+2236-2237,U+2245-2248,U+2260-2299,U+22a5-2307,U+2329-23c8,U+2502,U+252c,U+309c,U+4e8d,U+4ebb,U+5155,U+55c4,U+570b,U+575c,U+5cc1,U+5e11,U+5f73,U+5fbc,U+6042,U+6206,U+6265-6266,U+6426,U+6b38,U+6c35,U+6d44,U+72b4-72b5,U+755a-755b,U+778b,U+7f2f,U+83f8,U+88fd,U+898b,U+89dc,U+8ba0,U+8bf3,U+8df6,U+9577,U+961d,U+9b2f,U+9fa2,U+ff3e;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.39.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.39.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.39.otf')  format('opentype');unicode-range:U+d0b5-d0bf,U+d0c1-d0db,U+d0dd-d255;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.39.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.39.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.39.otf')  format('opentype');unicode-range:U+d0b5-d0bf,U+d0c1-d0db,U+d0dd-d255;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.39.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.39.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.39.otf')  format('opentype');unicode-range:U+d0b5-d0bf,U+d0c1-d0db,U+d0dd-d255;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.40.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.40.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.40.otf')  format('opentype');unicode-range:U+2113,U+4f8c,U+663d,U+9f30-9f3a,U+9f3c-9f4f,U+9f51-9f7e,U+9f80-9f83,U+9f85-9f98,U+9f9a-9f9e,U+9fa0-9fa1,U+9fa3-a97c,U+ac02-ac03,U+ac05-ac0f,U+ac11-ac18,U+ac1a-ac6f,U+ac71-ac77,U+ac79-ac82,U+ac84-ac8b,U+ac8d-ac9f,U+aca1-acaf,U+acb1-acbc,U+acbe-acc3,U+acc5-acdf,U+ace1-ace7,U+c6ff-c700,U+c7cc-c7d0,U+c841,U+c875-c878;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.40.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.40.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.40.otf')  format('opentype');unicode-range:U+2113,U+4f8c,U+663d,U+9f30-9f3a,U+9f3c-9f4f,U+9f51-9f7e,U+9f80-9f83,U+9f85-9f98,U+9f9a-9f9e,U+9fa0-9fa1,U+9fa3-a97c,U+ac02-ac03,U+ac05-ac0f,U+ac11-ac18,U+ac1a-ac6f,U+ac71-ac77,U+ac79-ac82,U+ac84-ac8b,U+ac8d-ac9f,U+aca1-acaf,U+acb1-acbc,U+acbe-acc3,U+acc5-acdf,U+ace1-ace7,U+c6ff-c700,U+c7cc-c7d0,U+c841,U+c875-c878;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.40.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.40.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.40.otf')  format('opentype');unicode-range:U+2113,U+4f8c,U+663d,U+9f30-9f3a,U+9f3c-9f4f,U+9f51-9f7e,U+9f80-9f83,U+9f85-9f98,U+9f9a-9f9e,U+9fa0-9fa1,U+9fa3-a97c,U+ac02-ac03,U+ac05-ac0f,U+ac11-ac18,U+ac1a-ac6f,U+ac71-ac77,U+ac79-ac82,U+ac84-ac8b,U+ac8d-ac9f,U+aca1-acaf,U+acb1-acbc,U+acbe-acc3,U+acc5-acdf,U+ace1-ace7,U+c6ff-c700,U+c7cc-c7d0,U+c841,U+c875-c878;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.41.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.41.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.41.otf')  format('opentype');unicode-range:U+55ac,U+6347-6348,U+6c23,U+6c98,U+751b,U+94bc-94bd,U+94ee-94ef,U+9a17-9a6b,U+9a72,U+9a75,U+9a77-9a7a,U+9a80-9a81,U+9a85,U+9a88-9a8b,U+9a8d-9a8e,U+9a90,U+9a92-9a96,U+9a98-9a99,U+9a9b-9aa3,U+9aa5-9aa7,U+9aa9-9abb,U+9abd-9ad2,U+9ad4-9ad7,U+9ad9-9ae5,U+9ae7-9b2e,U+9b30-9b3b,U+9b3d-9b40,U+9b43,U+9b46-9b4e,U+9b50-9b53,U+9b55-9bd1;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.41.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.41.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.41.otf')  format('opentype');unicode-range:U+55ac,U+6347-6348,U+6c23,U+6c98,U+751b,U+94bc-94bd,U+94ee-94ef,U+9a17-9a6b,U+9a72,U+9a75,U+9a77-9a7a,U+9a80-9a81,U+9a85,U+9a88-9a8b,U+9a8d-9a8e,U+9a90,U+9a92-9a96,U+9a98-9a99,U+9a9b-9aa3,U+9aa5-9aa7,U+9aa9-9abb,U+9abd-9ad2,U+9ad4-9ad7,U+9ad9-9ae5,U+9ae7-9b2e,U+9b30-9b3b,U+9b3d-9b40,U+9b43,U+9b46-9b4e,U+9b50-9b53,U+9b55-9bd1;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.41.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.41.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.41.otf')  format('opentype');unicode-range:U+55ac,U+6347-6348,U+6c23,U+6c98,U+751b,U+94bc-94bd,U+94ee-94ef,U+9a17-9a6b,U+9a72,U+9a75,U+9a77-9a7a,U+9a80-9a81,U+9a85,U+9a88-9a8b,U+9a8d-9a8e,U+9a90,U+9a92-9a96,U+9a98-9a99,U+9a9b-9aa3,U+9aa5-9aa7,U+9aa9-9abb,U+9abd-9ad2,U+9ad4-9ad7,U+9ad9-9ae5,U+9ae7-9b2e,U+9b30-9b3b,U+9b3d-9b40,U+9b43,U+9b46-9b4e,U+9b50-9b53,U+9b55-9bd1;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.42.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.42.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.42.otf')  format('opentype');unicode-range:U+9e3e,U+b1c3-b207,U+b209-b293,U+b295-b2c7,U+b2c9-b2e3,U+b2e5-b2e7,U+b2e9-b2eb,U+b2ed-b2f8,U+b2fa-b2ff,U+b301-b369;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.42.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.42.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.42.otf')  format('opentype');unicode-range:U+9e3e,U+b1c3-b207,U+b209-b293,U+b295-b2c7,U+b2c9-b2e3,U+b2e5-b2e7,U+b2e9-b2eb,U+b2ed-b2f8,U+b2fa-b2ff,U+b301-b369;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.42.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.42.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.42.otf')  format('opentype');unicode-range:U+9e3e,U+b1c3-b207,U+b209-b293,U+b295-b2c7,U+b2c9-b2e3,U+b2e5-b2e7,U+b2e9-b2eb,U+b2ed-b2f8,U+b2fa-b2ff,U+b301-b369;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.43.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.43.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.43.otf')  format('opentype');unicode-range:U+6894-6896,U+6898-68a1,U+68a3-68a5,U+68a9-68ac,U+68ae,U+68b1-68b2,U+68b4,U+68b6-68bf,U+68c1-68c8,U+68ca,U+68cc,U+68ce-68d1,U+68d3-68d4,U+68d6-68d7,U+68d9,U+68db-68df,U+68e1-68ed,U+68ef-68f0,U+68f2-68f4,U+68f6-68f9,U+68fb-6904,U+6906-690c,U+690f-6911,U+6913-692c,U+692e-692f,U+6931-6959,U+695b-695f,U+6961-6976,U+6978-697b,U+697d-6981,U+6983,U+6985-699b,U+699d-69a7,U+69a9-69b3,U+69b5-69da,U+69dc-69fc,U+69fe-6a09,U+6a0b-6a20,U+6a22-6a29,U+6a2b-6a30,U+6a32-6a43,U+6a45-6a4f,U+6a51-6a55,U+710b,U+7eb6,U+9494-9495,U+9515-9518,U+95f5-95f6,U+96e1;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.43.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.43.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.43.otf')  format('opentype');unicode-range:U+6894-6896,U+6898-68a1,U+68a3-68a5,U+68a9-68ac,U+68ae,U+68b1-68b2,U+68b4,U+68b6-68bf,U+68c1-68c8,U+68ca,U+68cc,U+68ce-68d1,U+68d3-68d4,U+68d6-68d7,U+68d9,U+68db-68df,U+68e1-68ed,U+68ef-68f0,U+68f2-68f4,U+68f6-68f9,U+68fb-6904,U+6906-690c,U+690f-6911,U+6913-692c,U+692e-692f,U+6931-6959,U+695b-695f,U+6961-6976,U+6978-697b,U+697d-6981,U+6983,U+6985-699b,U+699d-69a7,U+69a9-69b3,U+69b5-69da,U+69dc-69fc,U+69fe-6a09,U+6a0b-6a20,U+6a22-6a29,U+6a2b-6a30,U+6a32-6a43,U+6a45-6a4f,U+6a51-6a55,U+710b,U+7eb6,U+9494-9495,U+9515-9518,U+95f5-95f6,U+96e1;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.43.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.43.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.43.otf')  format('opentype');unicode-range:U+6894-6896,U+6898-68a1,U+68a3-68a5,U+68a9-68ac,U+68ae,U+68b1-68b2,U+68b4,U+68b6-68bf,U+68c1-68c8,U+68ca,U+68cc,U+68ce-68d1,U+68d3-68d4,U+68d6-68d7,U+68d9,U+68db-68df,U+68e1-68ed,U+68ef-68f0,U+68f2-68f4,U+68f6-68f9,U+68fb-6904,U+6906-690c,U+690f-6911,U+6913-692c,U+692e-692f,U+6931-6959,U+695b-695f,U+6961-6976,U+6978-697b,U+697d-6981,U+6983,U+6985-699b,U+699d-69a7,U+69a9-69b3,U+69b5-69da,U+69dc-69fc,U+69fe-6a09,U+6a0b-6a20,U+6a22-6a29,U+6a2b-6a30,U+6a32-6a43,U+6a45-6a4f,U+6a51-6a55,U+710b,U+7eb6,U+9494-9495,U+9515-9518,U+95f5-95f6,U+96e1;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.44.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.44.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.44.otf')  format('opentype');unicode-range:U+31fe-339e,U+94e6,U+9612-9613;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.44.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.44.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.44.otf')  format('opentype');unicode-range:U+31fe-339e,U+94e6,U+9612-9613;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.44.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.44.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.44.otf')  format('opentype');unicode-range:U+31fe-339e,U+94e6,U+9612-9613;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.45.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.45.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.45.otf')  format('opentype');unicode-range:U+60ea,U+6e3c-6e55,U+6e57,U+6e59-6e5a,U+6e5c-6e7d,U+6e80-6e82,U+6e84-6e8f,U+6e91-6e9b,U+6e9d-6ea1,U+6ea3-6ea9,U+6eab-6eae,U+6eb0-6eb5,U+6eb7-6eb8,U+6ebb-6eca,U+6ecc-6ed0,U+6ed6-6ed9,U+6edb-6edd,U+6edf-6ee0,U+6ee2-6ee3,U+6ee6-6ee7,U+6eea-6ef3,U+6ef5-6f01,U+6f03-6f05,U+6f07-6f0e,U+6f10-6f12,U+6f15-6f1f,U+6f21-6f2a,U+6f2c-6f30,U+6f33-6f46,U+6f48-6f57,U+6f59-6f5b,U+6f5d-6f6c,U+6f6f-6f83,U+6f85-6f87,U+6f89-6f9b,U+6f9d-6fa0,U+6fa2-6fb2,U+6fb4-6fbf,U+6fc1-6fd1,U+6fd3-7008,U+8088,U+9570-9571;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.45.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.45.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.45.otf')  format('opentype');unicode-range:U+60ea,U+6e3c-6e55,U+6e57,U+6e59-6e5a,U+6e5c-6e7d,U+6e80-6e82,U+6e84-6e8f,U+6e91-6e9b,U+6e9d-6ea1,U+6ea3-6ea9,U+6eab-6eae,U+6eb0-6eb5,U+6eb7-6eb8,U+6ebb-6eca,U+6ecc-6ed0,U+6ed6-6ed9,U+6edb-6edd,U+6edf-6ee0,U+6ee2-6ee3,U+6ee6-6ee7,U+6eea-6ef3,U+6ef5-6f01,U+6f03-6f05,U+6f07-6f0e,U+6f10-6f12,U+6f15-6f1f,U+6f21-6f2a,U+6f2c-6f30,U+6f33-6f46,U+6f48-6f57,U+6f59-6f5b,U+6f5d-6f6c,U+6f6f-6f83,U+6f85-6f87,U+6f89-6f9b,U+6f9d-6fa0,U+6fa2-6fb2,U+6fb4-6fbf,U+6fc1-6fd1,U+6fd3-7008,U+8088,U+9570-9571;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.45.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.45.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.45.otf')  format('opentype');unicode-range:U+60ea,U+6e3c-6e55,U+6e57,U+6e59-6e5a,U+6e5c-6e7d,U+6e80-6e82,U+6e84-6e8f,U+6e91-6e9b,U+6e9d-6ea1,U+6ea3-6ea9,U+6eab-6eae,U+6eb0-6eb5,U+6eb7-6eb8,U+6ebb-6eca,U+6ecc-6ed0,U+6ed6-6ed9,U+6edb-6edd,U+6edf-6ee0,U+6ee2-6ee3,U+6ee6-6ee7,U+6eea-6ef3,U+6ef5-6f01,U+6f03-6f05,U+6f07-6f0e,U+6f10-6f12,U+6f15-6f1f,U+6f21-6f2a,U+6f2c-6f30,U+6f33-6f46,U+6f48-6f57,U+6f59-6f5b,U+6f5d-6f6c,U+6f6f-6f83,U+6f85-6f87,U+6f89-6f9b,U+6f9d-6fa0,U+6fa2-6fb2,U+6fb4-6fbf,U+6fc1-6fd1,U+6fd3-7008,U+8088,U+9570-9571;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.46.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.46.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.46.otf')  format('opentype');unicode-range:U+5769,U+71d6-71e4,U+71e6-7205,U+7207-7229,U+722b,U+722d-7230,U+7232-7234,U+723a-723c,U+723e-7246,U+7249-724b,U+724d-7258,U+725a,U+7260,U+7263-7266,U+7268,U+726a-7271,U+7273-7274,U+7276-7278,U+727b-727f,U+7281-72ab,U+72ad-72ae,U+72b0-72b3,U+72b7-72b8,U+72ba-72c1,U+72c3,U+72c5-72cf,U+72d1-72d6,U+72d8-72df,U+72e2-72eb,U+72ef-72f0,U+72f2-72f7,U+72f9-72fb,U+72fd-730d,U+730f-731a,U+731d-7329,U+732c-732d,U+732f-7333,U+7335-7383,U+7385-7386,U+7388,U+738a,U+738c-7394,U+7396-739a,U+739c-73a8,U+73aa,U+73ac-73ae,U+73b1,U+916b,U+20158;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.46.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.46.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.46.otf')  format('opentype');unicode-range:U+5769,U+71d6-71e4,U+71e6-7205,U+7207-7229,U+722b,U+722d-7230,U+7232-7234,U+723a-723c,U+723e-7246,U+7249-724b,U+724d-7258,U+725a,U+7260,U+7263-7266,U+7268,U+726a-7271,U+7273-7274,U+7276-7278,U+727b-727f,U+7281-72ab,U+72ad-72ae,U+72b0-72b3,U+72b7-72b8,U+72ba-72c1,U+72c3,U+72c5-72cf,U+72d1-72d6,U+72d8-72df,U+72e2-72eb,U+72ef-72f0,U+72f2-72f7,U+72f9-72fb,U+72fd-730d,U+730f-731a,U+731d-7329,U+732c-732d,U+732f-7333,U+7335-7383,U+7385-7386,U+7388,U+738a,U+738c-7394,U+7396-739a,U+739c-73a8,U+73aa,U+73ac-73ae,U+73b1,U+916b,U+20158;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.46.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.46.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.46.otf')  format('opentype');unicode-range:U+5769,U+71d6-71e4,U+71e6-7205,U+7207-7229,U+722b,U+722d-7230,U+7232-7234,U+723a-723c,U+723e-7246,U+7249-724b,U+724d-7258,U+725a,U+7260,U+7263-7266,U+7268,U+726a-7271,U+7273-7274,U+7276-7278,U+727b-727f,U+7281-72ab,U+72ad-72ae,U+72b0-72b3,U+72b7-72b8,U+72ba-72c1,U+72c3,U+72c5-72cf,U+72d1-72d6,U+72d8-72df,U+72e2-72eb,U+72ef-72f0,U+72f2-72f7,U+72f9-72fb,U+72fd-730d,U+730f-731a,U+731d-7329,U+732c-732d,U+732f-7333,U+7335-7383,U+7385-7386,U+7388,U+738a,U+738c-7394,U+7396-739a,U+739c-73a8,U+73aa,U+73ac-73ae,U+73b1,U+916b,U+20158;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.47.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.47.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.47.otf')  format('opentype');unicode-range:U+3052,U+653a,U+6654,U+70d9,U+7edb,U+8b47-8b65,U+8b67-8b6b,U+8b6d-8b9f,U+8ba5-8ba7,U+8baa-8bac,U+8bb1,U+8bb4-8bb5,U+8bb7,U+8bb9,U+8bcb-8bcc,U+8bce,U+8bd2-8bd4,U+8bd6,U+8bd8-8bd9,U+8bdc,U+8bdf,U+8be4,U+8be7-8bea,U+8bf0,U+8bf6,U+8bf9,U+8bfc-8bfd,U+8bff-8c00,U+8c02,U+8c04,U+8c06-8c07,U+8c09,U+8c0c,U+8c11-8c12,U+8c14-8c1a,U+8c1d-8c21,U+8c29-8c2b,U+8c2e-8c30,U+8c32-8c33,U+8c35-8c36,U+8c38-8c40,U+8c42-8c45,U+8c47-8c60,U+8c62-8c69,U+8c6c-8c78,U+8c7a-8c8b,U+8c8d-8d1c,U+8d20,U+8d30,U+8d36,U+8d3b,U+8d3d,U+8d40-8d43,U+8d45-8d4a,U+8d51-8d53,U+8d55,U+8d57,U+8d59,U+8d5c-8d5d,U+8d5f,U+9609-960a,U+962a-962d,U+964e-964f,U+97f4,U+c7bd-c7c0,U+c879;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.47.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.47.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.47.otf')  format('opentype');unicode-range:U+3052,U+653a,U+6654,U+70d9,U+7edb,U+8b47-8b65,U+8b67-8b6b,U+8b6d-8b9f,U+8ba5-8ba7,U+8baa-8bac,U+8bb1,U+8bb4-8bb5,U+8bb7,U+8bb9,U+8bcb-8bcc,U+8bce,U+8bd2-8bd4,U+8bd6,U+8bd8-8bd9,U+8bdc,U+8bdf,U+8be4,U+8be7-8bea,U+8bf0,U+8bf6,U+8bf9,U+8bfc-8bfd,U+8bff-8c00,U+8c02,U+8c04,U+8c06-8c07,U+8c09,U+8c0c,U+8c11-8c12,U+8c14-8c1a,U+8c1d-8c21,U+8c29-8c2b,U+8c2e-8c30,U+8c32-8c33,U+8c35-8c36,U+8c38-8c40,U+8c42-8c45,U+8c47-8c60,U+8c62-8c69,U+8c6c-8c78,U+8c7a-8c8b,U+8c8d-8d1c,U+8d20,U+8d30,U+8d36,U+8d3b,U+8d3d,U+8d40-8d43,U+8d45-8d4a,U+8d51-8d53,U+8d55,U+8d57,U+8d59,U+8d5c-8d5d,U+8d5f,U+9609-960a,U+962a-962d,U+964e-964f,U+97f4,U+c7bd-c7c0,U+c879;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.47.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.47.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.47.otf')  format('opentype');unicode-range:U+3052,U+653a,U+6654,U+70d9,U+7edb,U+8b47-8b65,U+8b67-8b6b,U+8b6d-8b9f,U+8ba5-8ba7,U+8baa-8bac,U+8bb1,U+8bb4-8bb5,U+8bb7,U+8bb9,U+8bcb-8bcc,U+8bce,U+8bd2-8bd4,U+8bd6,U+8bd8-8bd9,U+8bdc,U+8bdf,U+8be4,U+8be7-8bea,U+8bf0,U+8bf6,U+8bf9,U+8bfc-8bfd,U+8bff-8c00,U+8c02,U+8c04,U+8c06-8c07,U+8c09,U+8c0c,U+8c11-8c12,U+8c14-8c1a,U+8c1d-8c21,U+8c29-8c2b,U+8c2e-8c30,U+8c32-8c33,U+8c35-8c36,U+8c38-8c40,U+8c42-8c45,U+8c47-8c60,U+8c62-8c69,U+8c6c-8c78,U+8c7a-8c8b,U+8c8d-8d1c,U+8d20,U+8d30,U+8d36,U+8d3b,U+8d3d,U+8d40-8d43,U+8d45-8d4a,U+8d51-8d53,U+8d55,U+8d57,U+8d59,U+8d5c-8d5d,U+8d5f,U+9609-960a,U+962a-962d,U+964e-964f,U+97f4,U+c7bd-c7c0,U+c879;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.48.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.48.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.48.otf')  format('opentype');unicode-range:U+627d,U+75b0-75b1,U+80b3,U+862c-8637,U+8639-864d,U+8652-8653,U+8655-8659,U+865b-865d,U+865f-866a,U+866c-8678,U+867a-867c,U+867f,U+8682-8689,U+868b-8694,U+8696-86a7,U+86a9-86c6,U+86c8-86ca,U+86cc-86da,U+86dc-86dd,U+86df-86ed,U+86ef-86ff,U+8701,U+8703-8711,U+8713-8714,U+8719-871b,U+871d-8720,U+8722-8746,U+8748,U+874a-8752,U+8755-8763,U+8765-8773,U+8775,U+8777-878c,U+878e-87b9,U+87bb-87e1,U+8bc2-8bc3,U+8d4d,U+94bf-94c0,U+9529-952d,U+961a-961b,U+9717;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.48.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.48.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.48.otf')  format('opentype');unicode-range:U+627d,U+75b0-75b1,U+80b3,U+862c-8637,U+8639-864d,U+8652-8653,U+8655-8659,U+865b-865d,U+865f-866a,U+866c-8678,U+867a-867c,U+867f,U+8682-8689,U+868b-8694,U+8696-86a7,U+86a9-86c6,U+86c8-86ca,U+86cc-86da,U+86dc-86dd,U+86df-86ed,U+86ef-86ff,U+8701,U+8703-8711,U+8713-8714,U+8719-871b,U+871d-8720,U+8722-8746,U+8748,U+874a-8752,U+8755-8763,U+8765-8773,U+8775,U+8777-878c,U+878e-87b9,U+87bb-87e1,U+8bc2-8bc3,U+8d4d,U+94bf-94c0,U+9529-952d,U+961a-961b,U+9717;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.48.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.48.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.48.otf')  format('opentype');unicode-range:U+627d,U+75b0-75b1,U+80b3,U+862c-8637,U+8639-864d,U+8652-8653,U+8655-8659,U+865b-865d,U+865f-866a,U+866c-8678,U+867a-867c,U+867f,U+8682-8689,U+868b-8694,U+8696-86a7,U+86a9-86c6,U+86c8-86ca,U+86cc-86da,U+86dc-86dd,U+86df-86ed,U+86ef-86ff,U+8701,U+8703-8711,U+8713-8714,U+8719-871b,U+871d-8720,U+8722-8746,U+8748,U+874a-8752,U+8755-8763,U+8765-8773,U+8775,U+8777-878c,U+878e-87b9,U+87bb-87e1,U+8bc2-8bc3,U+8d4d,U+94bf-94c0,U+9529-952d,U+961a-961b,U+9717;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.49.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.49.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.49.otf')  format('opentype');unicode-range:U+2100,U+6b7a,U+92eb-946a,U+946c-9487,U+959c-959e;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.49.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.49.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.49.otf')  format('opentype');unicode-range:U+2100,U+6b7a,U+92eb-946a,U+946c-9487,U+959c-959e;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.49.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.49.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.49.otf')  format('opentype');unicode-range:U+2100,U+6b7a,U+92eb-946a,U+946c-9487,U+959c-959e;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.50.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.50.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.50.otf')  format('opentype');unicode-range:U+541d,U+94a1,U+94c2,U+94c4,U+94c6-94ca,U+955d-9563,U+9647,U+967b-9685,U+9687-968a,U+968c-968e,U+9691-9693,U+9695-9697,U+969a-969b,U+969d-96a6,U+96a8-96b5,U+96b7-96bd,U+96bf,U+96c2-96c3,U+96c8-96cb,U+96ce,U+96d0-96d4,U+96d6-96df,U+96e2-96e7,U+96e9,U+96eb-96f5,U+96f8-96fd,U+96ff,U+9701-9703,U+9705,U+970a-970c,U+970e-9715,U+9718-971b,U+971d,U+971f-9731,U+9733-9737,U+9739-9751,U+9754-9755,U+9757-9758,U+975a-975d,U+975f,U+9763-9768,U+976a-9773,U+9775-978a,U+978c,U+978e-979d,U+97a0-97ac,U+97ae-97e5,U+97e8,U+97ea-97f2,U+97f7-983b,U+983d-9849;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.50.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.50.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.50.otf')  format('opentype');unicode-range:U+541d,U+94a1,U+94c2,U+94c4,U+94c6-94ca,U+955d-9563,U+9647,U+967b-9685,U+9687-968a,U+968c-968e,U+9691-9693,U+9695-9697,U+969a-969b,U+969d-96a6,U+96a8-96b5,U+96b7-96bd,U+96bf,U+96c2-96c3,U+96c8-96cb,U+96ce,U+96d0-96d4,U+96d6-96df,U+96e2-96e7,U+96e9,U+96eb-96f5,U+96f8-96fd,U+96ff,U+9701-9703,U+9705,U+970a-970c,U+970e-9715,U+9718-971b,U+971d,U+971f-9731,U+9733-9737,U+9739-9751,U+9754-9755,U+9757-9758,U+975a-975d,U+975f,U+9763-9768,U+976a-9773,U+9775-978a,U+978c,U+978e-979d,U+97a0-97ac,U+97ae-97e5,U+97e8,U+97ea-97f2,U+97f7-983b,U+983d-9849;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.50.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.50.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.50.otf')  format('opentype');unicode-range:U+541d,U+94a1,U+94c2,U+94c4,U+94c6-94ca,U+955d-9563,U+9647,U+967b-9685,U+9687-968a,U+968c-968e,U+9691-9693,U+9695-9697,U+969a-969b,U+969d-96a6,U+96a8-96b5,U+96b7-96bd,U+96bf,U+96c2-96c3,U+96c8-96cb,U+96ce,U+96d0-96d4,U+96d6-96df,U+96e2-96e7,U+96e9,U+96eb-96f5,U+96f8-96fd,U+96ff,U+9701-9703,U+9705,U+970a-970c,U+970e-9715,U+9718-971b,U+971d,U+971f-9731,U+9733-9737,U+9739-9751,U+9754-9755,U+9757-9758,U+975a-975d,U+975f,U+9763-9768,U+976a-9773,U+9775-978a,U+978c,U+978e-979d,U+97a0-97ac,U+97ae-97e5,U+97e8,U+97ea-97f2,U+97f7-983b,U+983d-9849;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.51.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.51.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.51.otf')  format('opentype');unicode-range:U+57a4-57a6,U+9bd2-9c7b,U+9c7d-9c80,U+9c82-9c8c,U+9c8e-9c8f,U+9c91-9c9b,U+9c9d-9ca2,U+9ca4-9cb4,U+9cb6-9cdd,U+9cdf-9d72,U+c7a8-c7aa;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.51.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.51.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.51.otf')  format('opentype');unicode-range:U+57a4-57a6,U+9bd2-9c7b,U+9c7d-9c80,U+9c82-9c8c,U+9c8e-9c8f,U+9c91-9c9b,U+9c9d-9ca2,U+9ca4-9cb4,U+9cb6-9cdd,U+9cdf-9d72,U+c7a8-c7aa;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.51.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.51.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.51.otf')  format('opentype');unicode-range:U+57a4-57a6,U+9bd2-9c7b,U+9c7d-9c80,U+9c82-9c8c,U+9c8e-9c8f,U+9c91-9c9b,U+9c9d-9ca2,U+9ca4-9cb4,U+9cb6-9cdd,U+9cdf-9d72,U+c7a8-c7aa;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.52.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.52.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.52.otf')  format('opentype');unicode-range:U+c75c-c75d,U+d598-d5a4,U+d5a6-d603,U+d605-d653,U+d655-d68b,U+d68d-d6c3,U+d6c5-d739;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.52.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.52.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.52.otf')  format('opentype');unicode-range:U+c75c-c75d,U+d598-d5a4,U+d5a6-d603,U+d605-d653,U+d655-d68b,U+d68d-d6c3,U+d6c5-d739;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.52.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.52.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.52.otf')  format('opentype');unicode-range:U+c75c-c75d,U+d598-d5a4,U+d5a6-d603,U+d605-d653,U+d655-d68b,U+d68d-d6c3,U+d6c5-d739;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.53.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.53.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.53.otf')  format('opentype');unicode-range:U+c75e-c760,U+d3f5-d557,U+d55a-d55b,U+d55d-d55f,U+d561-d567,U+d56a-d573,U+d575-d597;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.53.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.53.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.53.otf')  format('opentype');unicode-range:U+c75e-c760,U+d3f5-d557,U+d55a-d55b,U+d55d-d55f,U+d561-d567,U+d56a-d573,U+d575-d597;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.53.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.53.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.53.otf')  format('opentype');unicode-range:U+c75e-c760,U+d3f5-d557,U+d55a-d55b,U+d55d-d55f,U+d561-d567,U+d56a-d573,U+d575-d597;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.54.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.54.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.54.otf')  format('opentype');unicode-range:U+53e7,U+c3b6-c543,U+c545-c547,U+c54b,U+c54d-c556,U+c763,U+c846;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.54.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.54.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.54.otf')  format('opentype');unicode-range:U+53e7,U+c3b6-c543,U+c545-c547,U+c54b,U+c54d-c556,U+c763,U+c846;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.54.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.54.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.54.otf')  format('opentype');unicode-range:U+53e7,U+c3b6-c543,U+c545-c547,U+c54b,U+c54d-c556,U+c763,U+c846;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.55.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.55.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.55.otf')  format('opentype');unicode-range:U+306c,U+4589-4729;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.55.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.55.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.55.otf')  format('opentype');unicode-range:U+306c,U+4589-4729;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.55.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.55.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.55.otf')  format('opentype');unicode-range:U+306c,U+4589-4729;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.56.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.56.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.56.otf')  format('opentype');unicode-range:U+90de,U+ac00-ac01,U+ac04,U+ac10,U+ac19,U+ac70,U+ac78,U+ac83,U+ac8c,U+aca0,U+acb0,U+acbd,U+acc4,U+ace0,U+acfc,U+ad50,U+ad6c-ad6d,U+adf8,U+ae30,U+ae38,U+ae40,U+ae4c,U+aed8,U+b098,U+b09c,U+b0a0,U+b0a8,U+b0b4,U+b108,U+b110,U+b124,U+b155,U+b208,U+b294,U+b2c8,U+b2e4,U+b2e8,U+b2ec,U+b2f9,U+b300,U+b370,U+b3c4,U+b3d9,U+b418,U+b450,U+b4dc,U+b4e0,U+b4e4,U+b530,U+b77c,U+b78c,U+b791,U+b798,U+b824,U+b85c,U+b974,U+b978,U+b97c,U+b984,U+b9ac,U+b9b0,U+b9c8,U+b9cc,U+b9d0,U+ba70,U+ba74,U+ba85,U+baa8-baa9,U+babb,U+bb34,U+bb38,U+bb3c,U+bbf8,U+bbfc,U+bc00,U+bc14-bc15,U+bc1b-bc1c,U+bc31,U+bc84,U+bcf4,U+bd80,U+bd84,U+be44,U+be5b,U+c0ac,U+c0b4,U+c0c1,U+c0dd,U+c11c,U+c120,U+c131,U+c138,U+c18c-c18d,U+c190,U+c218,U+c2a4,U+c2b5,U+c2dc-c2dd,U+c2e0,U+c2ec,U+c544,U+c548-c54a,U+c54c,U+c57c,U+c5b4,U+c5c6-c5c8,U+c5d0,U+c5ec,U+c601,U+c624,U+c640,U+c694,U+c6b0,U+c6b4,U+c6b8,U+c6c3,U+c6d0,U+c704-c725,U+c72c-c75a,U+c764-c7a5,U+c7db-c815,U+c81c-c837,U+c847-c870,U+c87a-c88b,U+c8fc,U+c900,U+c911,U+c9c0-c9c1,U+c9c4,U+c9d1,U+cc28,U+cc44,U+cc9c,U+ccb4,U+cd94,U+ce58,U+ce74,U+d06c,U+d0c0,U+d0dc,U+d2b8,U+d30c,U+d3ec,U+d558-d559,U+d55c,U+d560,U+d568-d569,U+d574,U+d5a5,U+d604,U+d654,U+d68c,U+d6c4,U+d788;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.56.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.56.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.56.otf')  format('opentype');unicode-range:U+90de,U+ac00-ac01,U+ac04,U+ac10,U+ac19,U+ac70,U+ac78,U+ac83,U+ac8c,U+aca0,U+acb0,U+acbd,U+acc4,U+ace0,U+acfc,U+ad50,U+ad6c-ad6d,U+adf8,U+ae30,U+ae38,U+ae40,U+ae4c,U+aed8,U+b098,U+b09c,U+b0a0,U+b0a8,U+b0b4,U+b108,U+b110,U+b124,U+b155,U+b208,U+b294,U+b2c8,U+b2e4,U+b2e8,U+b2ec,U+b2f9,U+b300,U+b370,U+b3c4,U+b3d9,U+b418,U+b450,U+b4dc,U+b4e0,U+b4e4,U+b530,U+b77c,U+b78c,U+b791,U+b798,U+b824,U+b85c,U+b974,U+b978,U+b97c,U+b984,U+b9ac,U+b9b0,U+b9c8,U+b9cc,U+b9d0,U+ba70,U+ba74,U+ba85,U+baa8-baa9,U+babb,U+bb34,U+bb38,U+bb3c,U+bbf8,U+bbfc,U+bc00,U+bc14-bc15,U+bc1b-bc1c,U+bc31,U+bc84,U+bcf4,U+bd80,U+bd84,U+be44,U+be5b,U+c0ac,U+c0b4,U+c0c1,U+c0dd,U+c11c,U+c120,U+c131,U+c138,U+c18c-c18d,U+c190,U+c218,U+c2a4,U+c2b5,U+c2dc-c2dd,U+c2e0,U+c2ec,U+c544,U+c548-c54a,U+c54c,U+c57c,U+c5b4,U+c5c6-c5c8,U+c5d0,U+c5ec,U+c601,U+c624,U+c640,U+c694,U+c6b0,U+c6b4,U+c6b8,U+c6c3,U+c6d0,U+c704-c725,U+c72c-c75a,U+c764-c7a5,U+c7db-c815,U+c81c-c837,U+c847-c870,U+c87a-c88b,U+c8fc,U+c900,U+c911,U+c9c0-c9c1,U+c9c4,U+c9d1,U+cc28,U+cc44,U+cc9c,U+ccb4,U+cd94,U+ce58,U+ce74,U+d06c,U+d0c0,U+d0dc,U+d2b8,U+d30c,U+d3ec,U+d558-d559,U+d55c,U+d560,U+d568-d569,U+d574,U+d5a5,U+d604,U+d654,U+d68c,U+d6c4,U+d788;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.56.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.56.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.56.otf')  format('opentype');unicode-range:U+90de,U+ac00-ac01,U+ac04,U+ac10,U+ac19,U+ac70,U+ac78,U+ac83,U+ac8c,U+aca0,U+acb0,U+acbd,U+acc4,U+ace0,U+acfc,U+ad50,U+ad6c-ad6d,U+adf8,U+ae30,U+ae38,U+ae40,U+ae4c,U+aed8,U+b098,U+b09c,U+b0a0,U+b0a8,U+b0b4,U+b108,U+b110,U+b124,U+b155,U+b208,U+b294,U+b2c8,U+b2e4,U+b2e8,U+b2ec,U+b2f9,U+b300,U+b370,U+b3c4,U+b3d9,U+b418,U+b450,U+b4dc,U+b4e0,U+b4e4,U+b530,U+b77c,U+b78c,U+b791,U+b798,U+b824,U+b85c,U+b974,U+b978,U+b97c,U+b984,U+b9ac,U+b9b0,U+b9c8,U+b9cc,U+b9d0,U+ba70,U+ba74,U+ba85,U+baa8-baa9,U+babb,U+bb34,U+bb38,U+bb3c,U+bbf8,U+bbfc,U+bc00,U+bc14-bc15,U+bc1b-bc1c,U+bc31,U+bc84,U+bcf4,U+bd80,U+bd84,U+be44,U+be5b,U+c0ac,U+c0b4,U+c0c1,U+c0dd,U+c11c,U+c120,U+c131,U+c138,U+c18c-c18d,U+c190,U+c218,U+c2a4,U+c2b5,U+c2dc-c2dd,U+c2e0,U+c2ec,U+c544,U+c548-c54a,U+c54c,U+c57c,U+c5b4,U+c5c6-c5c8,U+c5d0,U+c5ec,U+c601,U+c624,U+c640,U+c694,U+c6b0,U+c6b4,U+c6b8,U+c6c3,U+c6d0,U+c704-c725,U+c72c-c75a,U+c764-c7a5,U+c7db-c815,U+c81c-c837,U+c847-c870,U+c87a-c88b,U+c8fc,U+c900,U+c911,U+c9c0-c9c1,U+c9c4,U+c9d1,U+cc28,U+cc44,U+cc9c,U+ccb4,U+cd94,U+ce58,U+ce74,U+d06c,U+d0c0,U+d0dc,U+d2b8,U+d30c,U+d3ec,U+d558-d559,U+d55c,U+d560,U+d568-d569,U+d574,U+d5a5,U+d604,U+d654,U+d68c,U+d6c4,U+d788;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.57.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.57.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.57.otf')  format('opentype');unicode-range:U+6b77-6b78,U+79d5,U+899f-89c0,U+89ca-89d1,U+89d3-89db,U+89dd-89e2,U+89e4-89e5,U+89e7-89ff,U+8a01-8a78,U+8a7a-8a88,U+8a8a-8a92,U+8a94-8aab,U+8aad-8b46,U+9620-9625;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.57.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.57.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.57.otf')  format('opentype');unicode-range:U+6b77-6b78,U+79d5,U+899f-89c0,U+89ca-89d1,U+89d3-89db,U+89dd-89e2,U+89e4-89e5,U+89e7-89ff,U+8a01-8a78,U+8a7a-8a88,U+8a8a-8a92,U+8a94-8aab,U+8aad-8b46,U+9620-9625;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.57.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.57.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.57.otf')  format('opentype');unicode-range:U+6b77-6b78,U+79d5,U+899f-89c0,U+89ca-89d1,U+89d3-89db,U+89dd-89e2,U+89e4-89e5,U+89e7-89ff,U+8a01-8a78,U+8a7a-8a88,U+8a8a-8a92,U+8a94-8aab,U+8aad-8b46,U+9620-9625;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.58.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.58.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.58.otf')  format('opentype');unicode-range:U+7ce0-7cee,U+7cf0-7cfa,U+7cfc-7d09,U+7d0b-7d1f,U+7d21,U+7d23-7d26,U+7d28-7d2a,U+7d2c-7d2e,U+7d30-7d4f,U+7d51-7d6d,U+7d6f-7d92,U+7d94-7e40,U+7e42-7e4a,U+7e4c-7e84,U+7ea1,U+9490-9492,U+94f7,U+9513-9514;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.58.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.58.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.58.otf')  format('opentype');unicode-range:U+7ce0-7cee,U+7cf0-7cfa,U+7cfc-7d09,U+7d0b-7d1f,U+7d21,U+7d23-7d26,U+7d28-7d2a,U+7d2c-7d2e,U+7d30-7d4f,U+7d51-7d6d,U+7d6f-7d92,U+7d94-7e40,U+7e42-7e4a,U+7e4c-7e84,U+7ea1,U+9490-9492,U+94f7,U+9513-9514;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.58.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.58.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.58.otf')  format('opentype');unicode-range:U+7ce0-7cee,U+7cf0-7cfa,U+7cfc-7d09,U+7d0b-7d1f,U+7d21,U+7d23-7d26,U+7d28-7d2a,U+7d2c-7d2e,U+7d30-7d4f,U+7d51-7d6d,U+7d6f-7d92,U+7d94-7e40,U+7e42-7e4a,U+7e4c-7e84,U+7ea1,U+9490-9492,U+94f7,U+9513-9514;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.59.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.59.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.59.otf')  format('opentype');unicode-range:U+7980,U+bed3-c072;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.59.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.59.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.59.otf')  format('opentype');unicode-range:U+7980,U+bed3-c072;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.59.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.59.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.59.otf')  format('opentype');unicode-range:U+7980,U+bed3-c072;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.60.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.60.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.60.otf')  format('opentype');unicode-range:U+51a3,U+51ee,U+604c,U+75bd,U+8002,U+20e16,U+22c9b-23766,U+237bc-246a5,U+247e0-24994,U+249a7,U+25d43,U+26469,U+26df0,U+277cc-27858,U+2b372,U+2cb76;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.60.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.60.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.60.otf')  format('opentype');unicode-range:U+51a3,U+51ee,U+604c,U+75bd,U+8002,U+20e16,U+22c9b-23766,U+237bc-246a5,U+247e0-24994,U+249a7,U+25d43,U+26469,U+26df0,U+277cc-27858,U+2b372,U+2cb76;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.60.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.60.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.60.otf')  format('opentype');unicode-range:U+51a3,U+51ee,U+604c,U+75bd,U+8002,U+20e16,U+22c9b-23766,U+237bc-246a5,U+247e0-24994,U+249a7,U+25d43,U+26469,U+26df0,U+277cc-27858,U+2b372,U+2cb76;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.61.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.61.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.61.otf')  format('opentype');unicode-range:U+0251,U+52d5,U+6427,U+65d2,U+6d0e,U+86de,U+8753-8754,U+87e2-87f8,U+87fa-881a,U+881e-8821,U+8823-883f,U+8841-884b,U+884e-8853,U+8855-8856,U+8858-8860,U+8871-8876,U+8878-8880,U+8882-888a,U+888c,U+888e-8895,U+8897-889b,U+889d-88aa,U+88ae-88c0,U+88c3-88c4,U+88c6-88d3,U+88d6-88d8,U+88dc-88e3,U+88e5-88f2,U+88f5-88f7,U+88fa-88fc,U+88fe-890f,U+8911,U+8913-8929,U+892b-8943,U+8945-895e,U+8960-897e,U+8980,U+8982-8985,U+8987-898a,U+898c-899e,U+8d61,U+8f98,U+958c-958d,U+95b8-95ba,U+95cd-95d4,U+9649-964a,U+965c-965e;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.61.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.61.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.61.otf')  format('opentype');unicode-range:U+0251,U+52d5,U+6427,U+65d2,U+6d0e,U+86de,U+8753-8754,U+87e2-87f8,U+87fa-881a,U+881e-8821,U+8823-883f,U+8841-884b,U+884e-8853,U+8855-8856,U+8858-8860,U+8871-8876,U+8878-8880,U+8882-888a,U+888c,U+888e-8895,U+8897-889b,U+889d-88aa,U+88ae-88c0,U+88c3-88c4,U+88c6-88d3,U+88d6-88d8,U+88dc-88e3,U+88e5-88f2,U+88f5-88f7,U+88fa-88fc,U+88fe-890f,U+8911,U+8913-8929,U+892b-8943,U+8945-895e,U+8960-897e,U+8980,U+8982-8985,U+8987-898a,U+898c-899e,U+8d61,U+8f98,U+958c-958d,U+95b8-95ba,U+95cd-95d4,U+9649-964a,U+965c-965e;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.61.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.61.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.61.otf')  format('opentype');unicode-range:U+0251,U+52d5,U+6427,U+65d2,U+6d0e,U+86de,U+8753-8754,U+87e2-87f8,U+87fa-881a,U+881e-8821,U+8823-883f,U+8841-884b,U+884e-8853,U+8855-8856,U+8858-8860,U+8871-8876,U+8878-8880,U+8882-888a,U+888c,U+888e-8895,U+8897-889b,U+889d-88aa,U+88ae-88c0,U+88c3-88c4,U+88c6-88d3,U+88d6-88d8,U+88dc-88e3,U+88e5-88f2,U+88f5-88f7,U+88fa-88fc,U+88fe-890f,U+8911,U+8913-8929,U+892b-8943,U+8945-895e,U+8960-897e,U+8980,U+8982-8985,U+8987-898a,U+898c-899e,U+8d61,U+8f98,U+958c-958d,U+95b8-95ba,U+95cd-95d4,U+9649-964a,U+965c-965e;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.62.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.62.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.62.otf')  format('opentype');unicode-range:U+c073-c0ab,U+c0ad-c0b3,U+c0b5-c0c0,U+c0c2-c0dc,U+c0de-c11b,U+c11d-c11f,U+c121-c130,U+c132-c137,U+c139-c18b,U+c18e-c18f,U+c191-c214,U+c816-c81b,U+c842,U+c871-c872;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.62.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.62.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.62.otf')  format('opentype');unicode-range:U+c073-c0ab,U+c0ad-c0b3,U+c0b5-c0c0,U+c0c2-c0dc,U+c0de-c11b,U+c11d-c11f,U+c121-c130,U+c132-c137,U+c139-c18b,U+c18e-c18f,U+c191-c214,U+c816-c81b,U+c842,U+c871-c872;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.62.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.62.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.62.otf')  format('opentype');unicode-range:U+c073-c0ab,U+c0ad-c0b3,U+c0b5-c0c0,U+c0c2-c0dc,U+c0de-c11b,U+c11d-c11f,U+c121-c130,U+c132-c137,U+c139-c18b,U+c18e-c18f,U+c191-c214,U+c816-c81b,U+c842,U+c871-c872;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.63.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.63.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.63.otf')  format('opentype');unicode-range:U+8862,U+8864,U+bb8d-bbf7,U+bbf9-bbfb,U+bbfd-bbff,U+bc01-bc13,U+bc16-bc1a,U+bc1d-bc30,U+bc32-bc83,U+bc85-bcf3,U+bcf5-bd33;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.63.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.63.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.63.otf')  format('opentype');unicode-range:U+8862,U+8864,U+bb8d-bbf7,U+bbf9-bbfb,U+bbfd-bbff,U+bc01-bc13,U+bc16-bc1a,U+bc1d-bc30,U+bc32-bc83,U+bc85-bcf3,U+bcf5-bd33;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.63.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.63.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.63.otf')  format('opentype');unicode-range:U+8862,U+8864,U+bb8d-bbf7,U+bbf9-bbfb,U+bbfd-bbff,U+bc01-bc13,U+bc16-bc1a,U+bc1d-bc30,U+bc32-bc83,U+bc85-bcf3,U+bcf5-bd33;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.64.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.64.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.64.otf')  format('opentype');unicode-range:U+846d-8470,U+8472-8474,U+8476-8481,U+8483-848a,U+848c-8498,U+849a-849b,U+849d-84b1,U+84b3-84b7,U+84b9-84c3,U+84c5-84c8,U+84ca-84dc,U+84de-84eb,U+84ed-8512,U+8514-8519,U+851b-8520,U+8522-852b,U+852d-853c,U+853e-8548,U+854b-8573,U+8575-857d,U+857f-8583,U+8585-8586,U+8588-859a,U+859c-85a9,U+85ab-85ae,U+85b0-85c8,U+85ca-85ce,U+85d0-85e3,U+85e5-85e8,U+85ea-85fa,U+85fc-8610,U+8612-862b,U+8bd0;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.64.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.64.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.64.otf')  format('opentype');unicode-range:U+846d-8470,U+8472-8474,U+8476-8481,U+8483-848a,U+848c-8498,U+849a-849b,U+849d-84b1,U+84b3-84b7,U+84b9-84c3,U+84c5-84c8,U+84ca-84dc,U+84de-84eb,U+84ed-8512,U+8514-8519,U+851b-8520,U+8522-852b,U+852d-853c,U+853e-8548,U+854b-8573,U+8575-857d,U+857f-8583,U+8585-8586,U+8588-859a,U+859c-85a9,U+85ab-85ae,U+85b0-85c8,U+85ca-85ce,U+85d0-85e3,U+85e5-85e8,U+85ea-85fa,U+85fc-8610,U+8612-862b,U+8bd0;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.64.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.64.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.64.otf')  format('opentype');unicode-range:U+846d-8470,U+8472-8474,U+8476-8481,U+8483-848a,U+848c-8498,U+849a-849b,U+849d-84b1,U+84b3-84b7,U+84b9-84c3,U+84c5-84c8,U+84ca-84dc,U+84de-84eb,U+84ed-8512,U+8514-8519,U+851b-8520,U+8522-852b,U+852d-853c,U+853e-8548,U+854b-8573,U+8575-857d,U+857f-8583,U+8585-8586,U+8588-859a,U+859c-85a9,U+85ab-85ae,U+85b0-85c8,U+85ca-85ce,U+85d0-85e3,U+85e5-85e8,U+85ea-85fa,U+85fc-8610,U+8612-862b,U+8bd0;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.65.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.65.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.65.otf')  format('opentype');unicode-range:U+3a22-3bc1;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.65.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.65.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.65.otf')  format('opentype');unicode-range:U+3a22-3bc1;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.65.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.65.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.65.otf')  format('opentype');unicode-range:U+3a22-3bc1;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.66.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.66.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.66.otf')  format('opentype');unicode-range:U+011a-011b,U+ae8c-aed7,U+aed9-b029,U+2b404;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.66.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.66.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.66.otf')  format('opentype');unicode-range:U+011a-011b,U+ae8c-aed7,U+aed9-b029,U+2b404;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.66.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.66.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.66.otf')  format('opentype');unicode-range:U+011a-011b,U+ae8c-aed7,U+aed9-b029,U+2b404;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.67.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.67.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.67.otf')  format('opentype');unicode-range:U+2032-2033,U+2312,U+57cc-57cd,U+7575-7577,U+7b7f,U+f9eb-fe30,U+fe32-fe35,U+fe37-fe6b,U+ff02-ff04,U+ff06-ff07,U+ff0a,U+ff10,U+ff12-ff13,U+ff15-ff19,U+ff1c-ff1d,U+ff20-ff2c,U+ff2e-ff3d,U+ff3f-ff5d,U+ff5f-ff61,U+ff64-ffe4,U+ffe6-ffee;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.67.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.67.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.67.otf')  format('opentype');unicode-range:U+2032-2033,U+2312,U+57cc-57cd,U+7575-7577,U+7b7f,U+f9eb-fe30,U+fe32-fe35,U+fe37-fe6b,U+ff02-ff04,U+ff06-ff07,U+ff0a,U+ff10,U+ff12-ff13,U+ff15-ff19,U+ff1c-ff1d,U+ff20-ff2c,U+ff2e-ff3d,U+ff3f-ff5d,U+ff5f-ff61,U+ff64-ffe4,U+ffe6-ffee;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.67.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.67.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.67.otf')  format('opentype');unicode-range:U+2032-2033,U+2312,U+57cc-57cd,U+7575-7577,U+7b7f,U+f9eb-fe30,U+fe32-fe35,U+fe37-fe6b,U+ff02-ff04,U+ff06-ff07,U+ff0a,U+ff10,U+ff12-ff13,U+ff15-ff19,U+ff1c-ff1d,U+ff20-ff2c,U+ff2e-ff3d,U+ff3f-ff5d,U+ff5f-ff61,U+ff64-ffe4,U+ffe6-ffee;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.68.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.68.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.68.otf')  format('opentype');unicode-range:U+5b6d,U+67ce,U+8d65,U+8d67-8d6a,U+8d6c-8d6f,U+8d71-8d73,U+8d78-8d80,U+8d82-8d84,U+8d86-8d89,U+8d8c-8d9e,U+8da0-8da2,U+8da4-8db2,U+8db5-8dc2,U+8dc4-8dca,U+8dcd-8dd0,U+8dd2-8ddc,U+8de0-8de7,U+8de9,U+8ded-8dee,U+8df0-8df2,U+8df7-8dfa,U+8dfc-8e0e,U+8e10-8e19,U+8e1f-8e21,U+8e23-8e28,U+8e2b-8e43,U+8e45-8e47,U+8e49-8e65,U+8e67-8e71,U+8e73-8e80,U+8e82-8eaa,U+8ead-8eae,U+8eb0-8eb1,U+8eb3-8eb9,U+8ebb-8f23,U+94b6-94b8,U+94d4-94d5,U+9536-953a,U+9559-955b,U+958e-9591,U+95e9;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.68.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.68.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.68.otf')  format('opentype');unicode-range:U+5b6d,U+67ce,U+8d65,U+8d67-8d6a,U+8d6c-8d6f,U+8d71-8d73,U+8d78-8d80,U+8d82-8d84,U+8d86-8d89,U+8d8c-8d9e,U+8da0-8da2,U+8da4-8db2,U+8db5-8dc2,U+8dc4-8dca,U+8dcd-8dd0,U+8dd2-8ddc,U+8de0-8de7,U+8de9,U+8ded-8dee,U+8df0-8df2,U+8df7-8dfa,U+8dfc-8e0e,U+8e10-8e19,U+8e1f-8e21,U+8e23-8e28,U+8e2b-8e43,U+8e45-8e47,U+8e49-8e65,U+8e67-8e71,U+8e73-8e80,U+8e82-8eaa,U+8ead-8eae,U+8eb0-8eb1,U+8eb3-8eb9,U+8ebb-8f23,U+94b6-94b8,U+94d4-94d5,U+9536-953a,U+9559-955b,U+958e-9591,U+95e9;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.68.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.68.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.68.otf')  format('opentype');unicode-range:U+5b6d,U+67ce,U+8d65,U+8d67-8d6a,U+8d6c-8d6f,U+8d71-8d73,U+8d78-8d80,U+8d82-8d84,U+8d86-8d89,U+8d8c-8d9e,U+8da0-8da2,U+8da4-8db2,U+8db5-8dc2,U+8dc4-8dca,U+8dcd-8dd0,U+8dd2-8ddc,U+8de0-8de7,U+8de9,U+8ded-8dee,U+8df0-8df2,U+8df7-8dfa,U+8dfc-8e0e,U+8e10-8e19,U+8e1f-8e21,U+8e23-8e28,U+8e2b-8e43,U+8e45-8e47,U+8e49-8e65,U+8e67-8e71,U+8e73-8e80,U+8e82-8eaa,U+8ead-8eae,U+8eb0-8eb1,U+8eb3-8eb9,U+8ebb-8f23,U+94b6-94b8,U+94d4-94d5,U+9536-953a,U+9559-955b,U+958e-9591,U+95e9;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.69.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.69.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.69.otf')  format('opentype');unicode-range:U+75c6,U+1f100-20118,U+20164-20acd,U+20b8f-20e11,U+20f5f-20f8d,U+21c70,U+22c55,U+25f23,U+2f9f4;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.69.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.69.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.69.otf')  format('opentype');unicode-range:U+75c6,U+1f100-20118,U+20164-20acd,U+20b8f-20e11,U+20f5f-20f8d,U+21c70,U+22c55,U+25f23,U+2f9f4;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.69.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.69.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.69.otf')  format('opentype');unicode-range:U+75c6,U+1f100-20118,U+20164-20acd,U+20b8f-20e11,U+20f5f-20f8d,U+21c70,U+22c55,U+25f23,U+2f9f4;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.70.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.70.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.70.otf')  format('opentype');unicode-range:U+d256-d2b7,U+d2b9-d30b,U+d30d-d3eb,U+d3ed-d3f4,U+24706-2472f;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.70.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.70.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.70.otf')  format('opentype');unicode-range:U+d256-d2b7,U+d2b9-d30b,U+d30d-d3eb,U+d3ed-d3f4,U+24706-2472f;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.70.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.70.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.70.otf')  format('opentype');unicode-range:U+d256-d2b7,U+d2b9-d30b,U+d30d-d3eb,U+d3ed-d3f4,U+24706-2472f;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.71.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.71.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.71.otf')  format('opentype');unicode-range:U+224c,U+2606,U+306b,U+30c8-30cb,U+4e93,U+5e5f-5e61,U+5e63-5e71,U+5e75,U+5e77,U+5e79-5e7a,U+5e7e,U+5e80-5e83,U+5e88-5e89,U+5e8b-5e8e,U+5e91-5e92,U+5e96,U+5e98,U+5e9b,U+5e9d,U+5ea0-5ea5,U+5ea8-5eac,U+5eae-5eb4,U+5eb9-5ec8,U+5ecb-5ed2,U+5ed4-5ed5,U+5ed7-5ef5,U+5ef8-5ef9,U+5efb-5eff,U+5f01,U+5f05-5f07,U+5f0b-5f0e,U+5f10-5f12,U+5f14,U+5f16,U+5f19-5f1a,U+5f1c-5f1e,U+5f21-5f24,U+5f28-5f2e,U+5f30,U+5f32-5f38,U+5f3b-5f51,U+5f54,U+5f56-5f61,U+5f63-5f65,U+5f67-5f68,U+5f6b,U+5f6e-5f6f,U+5f72,U+5f74-5f78,U+5f7a,U+5f7d-5f7f,U+5f82-5f83,U+5f86-5f87,U+5f8d-5f8f,U+5f91,U+5f93-5f96,U+5f9a-5f9d,U+5f9f-5fa0,U+5fa2-5fa8,U+5fab-5fad,U+5faf-5fb6,U+5fb8-5fbb,U+5fbe-5fc2,U+5fc4,U+5fc7-5fcb,U+5fce,U+5fd0-5fd6,U+5fda-5fdf,U+5fe1-5fe6,U+5fe8-5fea,U+5fec-5ff4,U+5ff6-5ffc,U+5ffe-5fff,U+6002-600d,U+600f-6011,U+6013-6014,U+6017-601b,U+601e-601f,U+6022-6024,U+6029,U+602b-603a,U+603c-6041,U+6043-604a,U+604e-604f,U+6051,U+6053-6054,U+6056-6061,U+6063,U+6065-6067,U+606a-606b,U+606e,U+6071-6072,U+711b-711c,U+7f07-7f09,U+8bbb,U+8bc7,U+94cf-94d3,U+95f0-95f1,U+9617-9618,U+965f-9660,U+26e40;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.71.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.71.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.71.otf')  format('opentype');unicode-range:U+224c,U+2606,U+306b,U+30c8-30cb,U+4e93,U+5e5f-5e61,U+5e63-5e71,U+5e75,U+5e77,U+5e79-5e7a,U+5e7e,U+5e80-5e83,U+5e88-5e89,U+5e8b-5e8e,U+5e91-5e92,U+5e96,U+5e98,U+5e9b,U+5e9d,U+5ea0-5ea5,U+5ea8-5eac,U+5eae-5eb4,U+5eb9-5ec8,U+5ecb-5ed2,U+5ed4-5ed5,U+5ed7-5ef5,U+5ef8-5ef9,U+5efb-5eff,U+5f01,U+5f05-5f07,U+5f0b-5f0e,U+5f10-5f12,U+5f14,U+5f16,U+5f19-5f1a,U+5f1c-5f1e,U+5f21-5f24,U+5f28-5f2e,U+5f30,U+5f32-5f38,U+5f3b-5f51,U+5f54,U+5f56-5f61,U+5f63-5f65,U+5f67-5f68,U+5f6b,U+5f6e-5f6f,U+5f72,U+5f74-5f78,U+5f7a,U+5f7d-5f7f,U+5f82-5f83,U+5f86-5f87,U+5f8d-5f8f,U+5f91,U+5f93-5f96,U+5f9a-5f9d,U+5f9f-5fa0,U+5fa2-5fa8,U+5fab-5fad,U+5faf-5fb6,U+5fb8-5fbb,U+5fbe-5fc2,U+5fc4,U+5fc7-5fcb,U+5fce,U+5fd0-5fd6,U+5fda-5fdf,U+5fe1-5fe6,U+5fe8-5fea,U+5fec-5ff4,U+5ff6-5ffc,U+5ffe-5fff,U+6002-600d,U+600f-6011,U+6013-6014,U+6017-601b,U+601e-601f,U+6022-6024,U+6029,U+602b-603a,U+603c-6041,U+6043-604a,U+604e-604f,U+6051,U+6053-6054,U+6056-6061,U+6063,U+6065-6067,U+606a-606b,U+606e,U+6071-6072,U+711b-711c,U+7f07-7f09,U+8bbb,U+8bc7,U+94cf-94d3,U+95f0-95f1,U+9617-9618,U+965f-9660,U+26e40;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.71.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.71.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.71.otf')  format('opentype');unicode-range:U+224c,U+2606,U+306b,U+30c8-30cb,U+4e93,U+5e5f-5e61,U+5e63-5e71,U+5e75,U+5e77,U+5e79-5e7a,U+5e7e,U+5e80-5e83,U+5e88-5e89,U+5e8b-5e8e,U+5e91-5e92,U+5e96,U+5e98,U+5e9b,U+5e9d,U+5ea0-5ea5,U+5ea8-5eac,U+5eae-5eb4,U+5eb9-5ec8,U+5ecb-5ed2,U+5ed4-5ed5,U+5ed7-5ef5,U+5ef8-5ef9,U+5efb-5eff,U+5f01,U+5f05-5f07,U+5f0b-5f0e,U+5f10-5f12,U+5f14,U+5f16,U+5f19-5f1a,U+5f1c-5f1e,U+5f21-5f24,U+5f28-5f2e,U+5f30,U+5f32-5f38,U+5f3b-5f51,U+5f54,U+5f56-5f61,U+5f63-5f65,U+5f67-5f68,U+5f6b,U+5f6e-5f6f,U+5f72,U+5f74-5f78,U+5f7a,U+5f7d-5f7f,U+5f82-5f83,U+5f86-5f87,U+5f8d-5f8f,U+5f91,U+5f93-5f96,U+5f9a-5f9d,U+5f9f-5fa0,U+5fa2-5fa8,U+5fab-5fad,U+5faf-5fb6,U+5fb8-5fbb,U+5fbe-5fc2,U+5fc4,U+5fc7-5fcb,U+5fce,U+5fd0-5fd6,U+5fda-5fdf,U+5fe1-5fe6,U+5fe8-5fea,U+5fec-5ff4,U+5ff6-5ffc,U+5ffe-5fff,U+6002-600d,U+600f-6011,U+6013-6014,U+6017-601b,U+601e-601f,U+6022-6024,U+6029,U+602b-603a,U+603c-6041,U+6043-604a,U+604e-604f,U+6051,U+6053-6054,U+6056-6061,U+6063,U+6065-6067,U+606a-606b,U+606e,U+6071-6072,U+711b-711c,U+7f07-7f09,U+8bbb,U+8bc7,U+94cf-94d3,U+95f0-95f1,U+9617-9618,U+965f-9660,U+26e40;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.72.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.72.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.72.otf')  format('opentype');unicode-range:U+95b6-95b7,U+c83b,U+cbd8-cc27,U+cc29-cc43,U+cc45-cc9b,U+cc9d-ccb3,U+ccb5-cd78;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.72.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.72.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.72.otf')  format('opentype');unicode-range:U+95b6-95b7,U+c83b,U+cbd8-cc27,U+cc29-cc43,U+cc45-cc9b,U+cc9d-ccb3,U+ccb5-cd78;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.72.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.72.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.72.otf')  format('opentype');unicode-range:U+95b6-95b7,U+c83b,U+cbd8-cc27,U+cc29-cc43,U+cc45-cc9b,U+cc9d-ccb3,U+ccb5-cd78;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.73.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.73.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.73.otf')  format('opentype');unicode-range:U+3f03-40a6,U+c83e-c840;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.73.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.73.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.73.otf')  format('opentype');unicode-range:U+3f03-40a6,U+c83e-c840;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.73.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.73.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.73.otf')  format('opentype');unicode-range:U+3f03-40a6,U+c83e-c840;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.74.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.74.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.74.otf')  format('opentype');unicode-range:U+541c,U+ace8-acfb,U+acfd-ad4f,U+ad51-ad6b,U+ad6e-adf7,U+adf9-ae2f,U+ae31-ae37,U+ae39-ae3f,U+ae41-ae4b,U+ae4d-ae8b,U+c7c3-c7c5;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.74.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.74.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.74.otf')  format('opentype');unicode-range:U+541c,U+ace8-acfb,U+acfd-ad4f,U+ad51-ad6b,U+ad6e-adf7,U+adf9-ae2f,U+ae31-ae37,U+ae39-ae3f,U+ae41-ae4b,U+ae4d-ae8b,U+c7c3-c7c5;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.74.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.74.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.74.otf')  format('opentype');unicode-range:U+541c,U+ace8-acfb,U+acfd-ad4f,U+ad51-ad6b,U+ad6e-adf7,U+adf9-ae2f,U+ae31-ae37,U+ae39-ae3f,U+ae41-ae4b,U+ae4d-ae8b,U+c7c3-c7c5;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.75.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.75.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.75.otf')  format('opentype');unicode-range:U+b6ad-b77b,U+b77d-b78b,U+b78d-b790,U+b792-b797,U+b799-b823,U+b825-b850;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.75.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.75.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.75.otf')  format('opentype');unicode-range:U+b6ad-b77b,U+b77d-b78b,U+b78d-b790,U+b792-b797,U+b799-b823,U+b825-b850;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.75.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.75.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.75.otf')  format('opentype');unicode-range:U+b6ad-b77b,U+b77d-b78b,U+b78d-b790,U+b792-b797,U+b799-b823,U+b825-b850;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.76.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.76.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.76.otf')  format('opentype');unicode-range:U+24ff,U+587b-5882,U+5884,U+5886-5892,U+5894-5898,U+589a-589d,U+58a0-58a7,U+58aa-58c0,U+58c2-58e3,U+58e5-58ea,U+58f1-58f2,U+58f4-58f5,U+58f7-5903,U+5905-5906,U+5908-590c,U+590e,U+5910-5914,U+5917-5919,U+591b,U+591d-591e,U+5920-5926,U+5928,U+592c,U+592f-5930,U+5932-5933,U+5935-5936,U+593b-5946,U+594a,U+594c-594d,U+5950,U+5952-5953,U+5958-595f,U+5961,U+5963-5964,U+5966-5972,U+5975,U+5977,U+597a-597c,U+597e-5981,U+5985,U+5989-5991,U+5994-5995,U+5997-5998,U+599a-59a4,U+59a6-59a7,U+59ac-59ad,U+59af-59b8,U+59ba,U+59bc-59bd,U+59bf-59c5,U+59c7-59c9,U+59cc-59cf,U+59d2,U+59d5-59d9,U+59db,U+59dd-59e7,U+59e9-59eb,U+59ed-59fa,U+59fc-59fe,U+5a00,U+5a02,U+5a04,U+5a06,U+5a08-5a17,U+5a19-5a1b,U+5a1d-5a1e,U+5a20-5a30,U+5a32-5a33,U+5a35,U+5a37-5a45,U+5a47-5a48,U+5a4a-5a59,U+5a5b-5a69,U+5a6b-5a73,U+5a75-5a76,U+7ae4,U+8f67,U+94ea-94ec,U+95b4-95b5,U+95c7-95cc;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.76.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.76.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.76.otf')  format('opentype');unicode-range:U+24ff,U+587b-5882,U+5884,U+5886-5892,U+5894-5898,U+589a-589d,U+58a0-58a7,U+58aa-58c0,U+58c2-58e3,U+58e5-58ea,U+58f1-58f2,U+58f4-58f5,U+58f7-5903,U+5905-5906,U+5908-590c,U+590e,U+5910-5914,U+5917-5919,U+591b,U+591d-591e,U+5920-5926,U+5928,U+592c,U+592f-5930,U+5932-5933,U+5935-5936,U+593b-5946,U+594a,U+594c-594d,U+5950,U+5952-5953,U+5958-595f,U+5961,U+5963-5964,U+5966-5972,U+5975,U+5977,U+597a-597c,U+597e-5981,U+5985,U+5989-5991,U+5994-5995,U+5997-5998,U+599a-59a4,U+59a6-59a7,U+59ac-59ad,U+59af-59b8,U+59ba,U+59bc-59bd,U+59bf-59c5,U+59c7-59c9,U+59cc-59cf,U+59d2,U+59d5-59d9,U+59db,U+59dd-59e7,U+59e9-59eb,U+59ed-59fa,U+59fc-59fe,U+5a00,U+5a02,U+5a04,U+5a06,U+5a08-5a17,U+5a19-5a1b,U+5a1d-5a1e,U+5a20-5a30,U+5a32-5a33,U+5a35,U+5a37-5a45,U+5a47-5a48,U+5a4a-5a59,U+5a5b-5a69,U+5a6b-5a73,U+5a75-5a76,U+7ae4,U+8f67,U+94ea-94ec,U+95b4-95b5,U+95c7-95cc;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.76.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.76.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.76.otf')  format('opentype');unicode-range:U+24ff,U+587b-5882,U+5884,U+5886-5892,U+5894-5898,U+589a-589d,U+58a0-58a7,U+58aa-58c0,U+58c2-58e3,U+58e5-58ea,U+58f1-58f2,U+58f4-58f5,U+58f7-5903,U+5905-5906,U+5908-590c,U+590e,U+5910-5914,U+5917-5919,U+591b,U+591d-591e,U+5920-5926,U+5928,U+592c,U+592f-5930,U+5932-5933,U+5935-5936,U+593b-5946,U+594a,U+594c-594d,U+5950,U+5952-5953,U+5958-595f,U+5961,U+5963-5964,U+5966-5972,U+5975,U+5977,U+597a-597c,U+597e-5981,U+5985,U+5989-5991,U+5994-5995,U+5997-5998,U+599a-59a4,U+59a6-59a7,U+59ac-59ad,U+59af-59b8,U+59ba,U+59bc-59bd,U+59bf-59c5,U+59c7-59c9,U+59cc-59cf,U+59d2,U+59d5-59d9,U+59db,U+59dd-59e7,U+59e9-59eb,U+59ed-59fa,U+59fc-59fe,U+5a00,U+5a02,U+5a04,U+5a06,U+5a08-5a17,U+5a19-5a1b,U+5a1d-5a1e,U+5a20-5a30,U+5a32-5a33,U+5a35,U+5a37-5a45,U+5a47-5a48,U+5a4a-5a59,U+5a5b-5a69,U+5a6b-5a73,U+5a75-5a76,U+7ae4,U+8f67,U+94ea-94ec,U+95b4-95b5,U+95c7-95cc;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.77.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.77.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.77.otf')  format('opentype');unicode-range:U+30b0-30b6,U+4f35,U+5c80,U+70da,U+7600-7602,U+7931-7933,U+7935-7939,U+793b,U+793d,U+793f,U+7941-7947,U+7949-7955,U+7957-795a,U+795c,U+7961-7964,U+7966-7967,U+7969-796c,U+796e-7976,U+7979-797f,U+7982-7983,U+7986-798e,U+7990-79a6,U+79a8-79b8,U+79ba,U+79bf,U+79c2-79c8,U+79ca,U+79cc,U+79ce-79d0,U+79d3-79d4,U+79d6-79d7,U+79d9-79de,U+79e0-79e3,U+79e5,U+79e7-79e8,U+79ea-79ee,U+79f1-79fa,U+79fc,U+79fe-79ff,U+7a01-7a0a,U+7a0c,U+7a0f-7a19,U+7a1b-7a1f,U+7a21-7a22,U+7a24-7a32,U+7a34-7a3a,U+7a3c,U+7a40-7a45,U+7a47-7a56,U+7a58-7a73,U+7a75,U+7a78,U+7a7b-7a7e,U+7a80,U+7a82,U+7a85-7a8c,U+7a8e-7a90,U+7a94-7a96,U+7a98-7a9b,U+7aa0-7aa4,U+7aa7-7aca,U+7acc-7ad5,U+7ad7-7ad8,U+7ada-7add,U+7ae1-7ae2,U+7ae6-7aec,U+7aee,U+7af0-7af8,U+7afa-7afe,U+7b00-7b02,U+7b04-7b0a,U+7b0c-7b10,U+7b12-7b13,U+8338,U+94af-94b0,U+9583-9589,U+9889,U+c7c6-c7c8,U+2b461;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.77.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.77.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.77.otf')  format('opentype');unicode-range:U+30b0-30b6,U+4f35,U+5c80,U+70da,U+7600-7602,U+7931-7933,U+7935-7939,U+793b,U+793d,U+793f,U+7941-7947,U+7949-7955,U+7957-795a,U+795c,U+7961-7964,U+7966-7967,U+7969-796c,U+796e-7976,U+7979-797f,U+7982-7983,U+7986-798e,U+7990-79a6,U+79a8-79b8,U+79ba,U+79bf,U+79c2-79c8,U+79ca,U+79cc,U+79ce-79d0,U+79d3-79d4,U+79d6-79d7,U+79d9-79de,U+79e0-79e3,U+79e5,U+79e7-79e8,U+79ea-79ee,U+79f1-79fa,U+79fc,U+79fe-79ff,U+7a01-7a0a,U+7a0c,U+7a0f-7a19,U+7a1b-7a1f,U+7a21-7a22,U+7a24-7a32,U+7a34-7a3a,U+7a3c,U+7a40-7a45,U+7a47-7a56,U+7a58-7a73,U+7a75,U+7a78,U+7a7b-7a7e,U+7a80,U+7a82,U+7a85-7a8c,U+7a8e-7a90,U+7a94-7a96,U+7a98-7a9b,U+7aa0-7aa4,U+7aa7-7aca,U+7acc-7ad5,U+7ad7-7ad8,U+7ada-7add,U+7ae1-7ae2,U+7ae6-7aec,U+7aee,U+7af0-7af8,U+7afa-7afe,U+7b00-7b02,U+7b04-7b0a,U+7b0c-7b10,U+7b12-7b13,U+8338,U+94af-94b0,U+9583-9589,U+9889,U+c7c6-c7c8,U+2b461;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.77.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.77.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.77.otf')  format('opentype');unicode-range:U+30b0-30b6,U+4f35,U+5c80,U+70da,U+7600-7602,U+7931-7933,U+7935-7939,U+793b,U+793d,U+793f,U+7941-7947,U+7949-7955,U+7957-795a,U+795c,U+7961-7964,U+7966-7967,U+7969-796c,U+796e-7976,U+7979-797f,U+7982-7983,U+7986-798e,U+7990-79a6,U+79a8-79b8,U+79ba,U+79bf,U+79c2-79c8,U+79ca,U+79cc,U+79ce-79d0,U+79d3-79d4,U+79d6-79d7,U+79d9-79de,U+79e0-79e3,U+79e5,U+79e7-79e8,U+79ea-79ee,U+79f1-79fa,U+79fc,U+79fe-79ff,U+7a01-7a0a,U+7a0c,U+7a0f-7a19,U+7a1b-7a1f,U+7a21-7a22,U+7a24-7a32,U+7a34-7a3a,U+7a3c,U+7a40-7a45,U+7a47-7a56,U+7a58-7a73,U+7a75,U+7a78,U+7a7b-7a7e,U+7a80,U+7a82,U+7a85-7a8c,U+7a8e-7a90,U+7a94-7a96,U+7a98-7a9b,U+7aa0-7aa4,U+7aa7-7aca,U+7acc-7ad5,U+7ad7-7ad8,U+7ada-7add,U+7ae1-7ae2,U+7ae6-7aec,U+7aee,U+7af0-7af8,U+7afa-7afe,U+7b00-7b02,U+7b04-7b0a,U+7b0c-7b10,U+7b12-7b13,U+8338,U+94af-94b0,U+9583-9589,U+9889,U+c7c6-c7c8,U+2b461;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.78.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.78.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.78.otf')  format('opentype');unicode-range:U+5a78-5a91,U+5a93-5a99,U+5a9c-5ab1,U+5ab4-5ac0,U+5ac3-5ac8,U+5aca-5acb,U+5acd-5ae8,U+5aea-5b08,U+5b0a-5b4f,U+5b51-5b53,U+5b56,U+5b5a-5b5b,U+5b5e,U+5b60-5b62,U+5b65,U+5b67-5b68,U+5b6a-5b6b,U+5b6e-5b77,U+5b79-5b7c,U+5b7e-5b80,U+5b86,U+5b8a,U+5b8d-5b8e,U+5b90-5b96,U+5b9f,U+5ba5-5ba9,U+5bac-5baf,U+5bb1-5bb2,U+5bb7-5bb8,U+5bba-5bbc,U+5bc0-5bc1,U+5bc3,U+5bc8-5bcb,U+5bcd-5bcf,U+5bd1,U+5bd4-5bdc,U+5be0,U+5be2-5be4,U+5be6-5be7,U+5be9-5bf7,U+5bfd-5bfe,U+5c00,U+5c02-5c03,U+5c05,U+5c07-5c08,U+5c0b-5c0e,U+5c10,U+5c12-5c13,U+5c15,U+5c17,U+5c19,U+5c1b-5c1c,U+5c1e-5c23,U+5c25-5c26,U+5c28-5c2b,U+5c2d-5c30,U+5c32-5c33,U+5c35-5c37,U+5c3b,U+5c43-5c44,U+5c46-5c47,U+5c49,U+5c4c-5c4d,U+5c52-5c54,U+5c56-5c5d,U+5c5f,U+5c62-5c64,U+5c66-5c6e,U+5c70,U+5c72-5c7e,U+5c98-5c99,U+5f09,U+6da7,U+8ba3,U+94a3-94a4,U+94a8,U+9542-9546,U+9550-9555,U+9598-959b;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.78.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.78.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.78.otf')  format('opentype');unicode-range:U+5a78-5a91,U+5a93-5a99,U+5a9c-5ab1,U+5ab4-5ac0,U+5ac3-5ac8,U+5aca-5acb,U+5acd-5ae8,U+5aea-5b08,U+5b0a-5b4f,U+5b51-5b53,U+5b56,U+5b5a-5b5b,U+5b5e,U+5b60-5b62,U+5b65,U+5b67-5b68,U+5b6a-5b6b,U+5b6e-5b77,U+5b79-5b7c,U+5b7e-5b80,U+5b86,U+5b8a,U+5b8d-5b8e,U+5b90-5b96,U+5b9f,U+5ba5-5ba9,U+5bac-5baf,U+5bb1-5bb2,U+5bb7-5bb8,U+5bba-5bbc,U+5bc0-5bc1,U+5bc3,U+5bc8-5bcb,U+5bcd-5bcf,U+5bd1,U+5bd4-5bdc,U+5be0,U+5be2-5be4,U+5be6-5be7,U+5be9-5bf7,U+5bfd-5bfe,U+5c00,U+5c02-5c03,U+5c05,U+5c07-5c08,U+5c0b-5c0e,U+5c10,U+5c12-5c13,U+5c15,U+5c17,U+5c19,U+5c1b-5c1c,U+5c1e-5c23,U+5c25-5c26,U+5c28-5c2b,U+5c2d-5c30,U+5c32-5c33,U+5c35-5c37,U+5c3b,U+5c43-5c44,U+5c46-5c47,U+5c49,U+5c4c-5c4d,U+5c52-5c54,U+5c56-5c5d,U+5c5f,U+5c62-5c64,U+5c66-5c6e,U+5c70,U+5c72-5c7e,U+5c98-5c99,U+5f09,U+6da7,U+8ba3,U+94a3-94a4,U+94a8,U+9542-9546,U+9550-9555,U+9598-959b;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.78.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.78.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.78.otf')  format('opentype');unicode-range:U+5a78-5a91,U+5a93-5a99,U+5a9c-5ab1,U+5ab4-5ac0,U+5ac3-5ac8,U+5aca-5acb,U+5acd-5ae8,U+5aea-5b08,U+5b0a-5b4f,U+5b51-5b53,U+5b56,U+5b5a-5b5b,U+5b5e,U+5b60-5b62,U+5b65,U+5b67-5b68,U+5b6a-5b6b,U+5b6e-5b77,U+5b79-5b7c,U+5b7e-5b80,U+5b86,U+5b8a,U+5b8d-5b8e,U+5b90-5b96,U+5b9f,U+5ba5-5ba9,U+5bac-5baf,U+5bb1-5bb2,U+5bb7-5bb8,U+5bba-5bbc,U+5bc0-5bc1,U+5bc3,U+5bc8-5bcb,U+5bcd-5bcf,U+5bd1,U+5bd4-5bdc,U+5be0,U+5be2-5be4,U+5be6-5be7,U+5be9-5bf7,U+5bfd-5bfe,U+5c00,U+5c02-5c03,U+5c05,U+5c07-5c08,U+5c0b-5c0e,U+5c10,U+5c12-5c13,U+5c15,U+5c17,U+5c19,U+5c1b-5c1c,U+5c1e-5c23,U+5c25-5c26,U+5c28-5c2b,U+5c2d-5c30,U+5c32-5c33,U+5c35-5c37,U+5c3b,U+5c43-5c44,U+5c46-5c47,U+5c49,U+5c4c-5c4d,U+5c52-5c54,U+5c56-5c5d,U+5c5f,U+5c62-5c64,U+5c66-5c6e,U+5c70,U+5c72-5c7e,U+5c98-5c99,U+5f09,U+6da7,U+8ba3,U+94a3-94a4,U+94a8,U+9542-9546,U+9550-9555,U+9598-959b;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.79.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.79.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.79.otf')  format('opentype');unicode-range:U+03a3,U+03be,U+222a,U+23c9-23db,U+2466-24fd,U+2503-252b,U+252d-25b7,U+25c0-25ce,U+25d0-2603,U+260e-273d,U+5b78,U+5f9e,U+7d93,U+91cb,U+953c-953f,U+95dc,U+961e,U+fe36,U+ff11;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.79.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.79.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.79.otf')  format('opentype');unicode-range:U+03a3,U+03be,U+222a,U+23c9-23db,U+2466-24fd,U+2503-252b,U+252d-25b7,U+25c0-25ce,U+25d0-2603,U+260e-273d,U+5b78,U+5f9e,U+7d93,U+91cb,U+953c-953f,U+95dc,U+961e,U+fe36,U+ff11;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.79.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.79.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.79.otf')  format('opentype');unicode-range:U+03a3,U+03be,U+222a,U+23c9-23db,U+2466-24fd,U+2503-252b,U+252d-25b7,U+25c0-25ce,U+25d0-2603,U+260e-273d,U+5b78,U+5f9e,U+7d93,U+91cb,U+953c-953f,U+95dc,U+961e,U+fe36,U+ff11;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.80.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.80.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.80.otf')  format('opentype');unicode-range:U+43e6-4588;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.80.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.80.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.80.otf')  format('opentype');unicode-range:U+43e6-4588;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.80.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.80.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.80.otf')  format('opentype');unicode-range:U+43e6-4588;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.81.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.81.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.81.otf')  format('opentype');unicode-range:U+0391,U+2200,U+2234-2235,U+2423,U+30c2,U+4e0c,U+4e57,U+51f7,U+5497,U+56f1-56f3,U+5c95-5c96,U+5fa9,U+625c-6262,U+6264,U+6271-6275,U+6277-6278,U+627a-627b,U+6281-6283,U+6285-6289,U+628b-6290,U+6294,U+6299,U+629c-62a1,U+62a3,U+62a6-62aa,U+62ad-62b0,U+62b2-62b4,U+62b6-62b8,U+62ba-62bb,U+62be-62c1,U+62c3-62c4,U+62ca-62cb,U+62ce-62cf,U+62d1,U+62d5,U+62d7,U+62da,U+62dd-62de,U+62e0-62e1,U+62e3-62e4,U+62ea-62eb,U+62f2,U+62f4-62f6,U+62f8-62fb,U+6303-6306,U+6308,U+630a-6310,U+6312-6315,U+6317-6319,U+631b-631e,U+6322,U+6326-6327,U+6329,U+632c-632e,U+6330-6339,U+633b-633c,U+633e-6345,U+634a-634d,U+6351-6354,U+6356-635d,U+6360,U+6364-6366,U+6368-636d,U+636f-6376,U+6378-637f,U+6381-6387,U+638a-638b,U+638d-638e,U+6393-6397,U+6399-639f,U+63a3-63a4,U+63a6,U+63ab-63b6,U+63b8-63b9,U+63bb-63c8,U+63ca-63ce,U+63d1,U+63d3-63e0,U+63e2,U+63e4-63e9,U+63eb-63ec,U+63ee-63f3,U+63f5-63fc,U+63fe-6400,U+6402-6404,U+6406-640e,U+6410-6412,U+6414-641b,U+641d,U+641f-6425,U+6428-642b,U+642e-6439,U+643b-6443,U+6445,U+6448-6449,U+644b-6453,U+6455-6457,U+6459-6466,U+6468,U+646a-6477,U+647a-647c,U+73fe,U+7481-7482,U+7573,U+7a92-7a93,U+7e4b,U+80f4-80f5,U+8aac,U+9678-967a,U+983c,U+ff62-ff63,U+25d99,U+2f947;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.81.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.81.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.81.otf')  format('opentype');unicode-range:U+0391,U+2200,U+2234-2235,U+2423,U+30c2,U+4e0c,U+4e57,U+51f7,U+5497,U+56f1-56f3,U+5c95-5c96,U+5fa9,U+625c-6262,U+6264,U+6271-6275,U+6277-6278,U+627a-627b,U+6281-6283,U+6285-6289,U+628b-6290,U+6294,U+6299,U+629c-62a1,U+62a3,U+62a6-62aa,U+62ad-62b0,U+62b2-62b4,U+62b6-62b8,U+62ba-62bb,U+62be-62c1,U+62c3-62c4,U+62ca-62cb,U+62ce-62cf,U+62d1,U+62d5,U+62d7,U+62da,U+62dd-62de,U+62e0-62e1,U+62e3-62e4,U+62ea-62eb,U+62f2,U+62f4-62f6,U+62f8-62fb,U+6303-6306,U+6308,U+630a-6310,U+6312-6315,U+6317-6319,U+631b-631e,U+6322,U+6326-6327,U+6329,U+632c-632e,U+6330-6339,U+633b-633c,U+633e-6345,U+634a-634d,U+6351-6354,U+6356-635d,U+6360,U+6364-6366,U+6368-636d,U+636f-6376,U+6378-637f,U+6381-6387,U+638a-638b,U+638d-638e,U+6393-6397,U+6399-639f,U+63a3-63a4,U+63a6,U+63ab-63b6,U+63b8-63b9,U+63bb-63c8,U+63ca-63ce,U+63d1,U+63d3-63e0,U+63e2,U+63e4-63e9,U+63eb-63ec,U+63ee-63f3,U+63f5-63fc,U+63fe-6400,U+6402-6404,U+6406-640e,U+6410-6412,U+6414-641b,U+641d,U+641f-6425,U+6428-642b,U+642e-6439,U+643b-6443,U+6445,U+6448-6449,U+644b-6453,U+6455-6457,U+6459-6466,U+6468,U+646a-6477,U+647a-647c,U+73fe,U+7481-7482,U+7573,U+7a92-7a93,U+7e4b,U+80f4-80f5,U+8aac,U+9678-967a,U+983c,U+ff62-ff63,U+25d99,U+2f947;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.81.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.81.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.81.otf')  format('opentype');unicode-range:U+0391,U+2200,U+2234-2235,U+2423,U+30c2,U+4e0c,U+4e57,U+51f7,U+5497,U+56f1-56f3,U+5c95-5c96,U+5fa9,U+625c-6262,U+6264,U+6271-6275,U+6277-6278,U+627a-627b,U+6281-6283,U+6285-6289,U+628b-6290,U+6294,U+6299,U+629c-62a1,U+62a3,U+62a6-62aa,U+62ad-62b0,U+62b2-62b4,U+62b6-62b8,U+62ba-62bb,U+62be-62c1,U+62c3-62c4,U+62ca-62cb,U+62ce-62cf,U+62d1,U+62d5,U+62d7,U+62da,U+62dd-62de,U+62e0-62e1,U+62e3-62e4,U+62ea-62eb,U+62f2,U+62f4-62f6,U+62f8-62fb,U+6303-6306,U+6308,U+630a-6310,U+6312-6315,U+6317-6319,U+631b-631e,U+6322,U+6326-6327,U+6329,U+632c-632e,U+6330-6339,U+633b-633c,U+633e-6345,U+634a-634d,U+6351-6354,U+6356-635d,U+6360,U+6364-6366,U+6368-636d,U+636f-6376,U+6378-637f,U+6381-6387,U+638a-638b,U+638d-638e,U+6393-6397,U+6399-639f,U+63a3-63a4,U+63a6,U+63ab-63b6,U+63b8-63b9,U+63bb-63c8,U+63ca-63ce,U+63d1,U+63d3-63e0,U+63e2,U+63e4-63e9,U+63eb-63ec,U+63ee-63f3,U+63f5-63fc,U+63fe-6400,U+6402-6404,U+6406-640e,U+6410-6412,U+6414-641b,U+641d,U+641f-6425,U+6428-642b,U+642e-6439,U+643b-6443,U+6445,U+6448-6449,U+644b-6453,U+6455-6457,U+6459-6466,U+6468,U+646a-6477,U+647a-647c,U+73fe,U+7481-7482,U+7573,U+7a92-7a93,U+7e4b,U+80f4-80f5,U+8aac,U+9678-967a,U+983c,U+ff62-ff63,U+25d99,U+2f947;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.82.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.82.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.82.otf')  format('opentype');unicode-range:U+472a-48ca;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.82.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.82.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.82.otf')  format('opentype');unicode-range:U+472a-48ca;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.82.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.82.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.82.otf')  format('opentype');unicode-range:U+472a-48ca;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.83.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.83.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.83.otf')  format('opentype');unicode-range:U+c215-c217,U+c219-c2a3,U+c2a5-c2b4,U+c2b6-c2db,U+c2de-c2df,U+c2e1-c2eb,U+c2ed-c3b5,U+c701-c703,U+c7c1-c7c2;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.83.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.83.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.83.otf')  format('opentype');unicode-range:U+c215-c217,U+c219-c2a3,U+c2a5-c2b4,U+c2b6-c2db,U+c2de-c2df,U+c2e1-c2eb,U+c2ed-c3b5,U+c701-c703,U+c7c1-c7c2;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.83.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.83.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.83.otf')  format('opentype');unicode-range:U+c215-c217,U+c219-c2a3,U+c2a5-c2b4,U+c2b6-c2db,U+c2de-c2df,U+c2e1-c2eb,U+c2ed-c3b5,U+c701-c703,U+c7c1-c7c2;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.84.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.84.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.84.otf')  format('opentype');unicode-range:U+40a7-4245,U+94de-94df;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.84.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.84.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.84.otf')  format('opentype');unicode-range:U+40a7-4245,U+94de-94df;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.84.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.84.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.84.otf')  format('opentype');unicode-range:U+40a7-4245,U+94de-94df;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.85.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.85.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.85.otf')  format('opentype');unicode-range:U+2016,U+5741-5742,U+6074-6075,U+6077-607b,U+607d-6083,U+6085-6088,U+608a-608c,U+608e-6093,U+6095-609e,U+60a1-60a2,U+60a4-60a5,U+60a7,U+60a9-60ab,U+60ad-60ae,U+60b0-60b1,U+60b3-60bb,U+60bd-60c4,U+60c6-60c9,U+60cb-60d0,U+60d2-60d4,U+60d6-60db,U+60dd-60de,U+60e1-60e6,U+60ee,U+60f0-60f2,U+60f4-60f8,U+60fa-6100,U+6102-6107,U+610a-610e,U+6110-6119,U+611b-611e,U+6120-6123,U+6125-6126,U+6128-613e,U+6140-6147,U+6149-614b,U+614d,U+614f-6154,U+6156-6161,U+6163-6166,U+6169-616f,U+6171-6176,U+6178-618d,U+618f-61a8,U+61aa-61bd,U+61bf-61c1,U+61c3-61c7,U+61c9-61d1,U+61d3-6205,U+6207,U+6209,U+620b,U+6213-6215,U+6217,U+6219,U+621b-6229,U+622b-6232,U+6235-6236,U+6238-623e,U+6244-6246,U+6248-624a,U+624c,U+624f-6250,U+6255-6257,U+6259-625a,U+6268,U+626a,U+725c-725e,U+79be,U+949a-949c,U+9509-950a,U+22c62,U+237a2;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.85.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.85.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.85.otf')  format('opentype');unicode-range:U+2016,U+5741-5742,U+6074-6075,U+6077-607b,U+607d-6083,U+6085-6088,U+608a-608c,U+608e-6093,U+6095-609e,U+60a1-60a2,U+60a4-60a5,U+60a7,U+60a9-60ab,U+60ad-60ae,U+60b0-60b1,U+60b3-60bb,U+60bd-60c4,U+60c6-60c9,U+60cb-60d0,U+60d2-60d4,U+60d6-60db,U+60dd-60de,U+60e1-60e6,U+60ee,U+60f0-60f2,U+60f4-60f8,U+60fa-6100,U+6102-6107,U+610a-610e,U+6110-6119,U+611b-611e,U+6120-6123,U+6125-6126,U+6128-613e,U+6140-6147,U+6149-614b,U+614d,U+614f-6154,U+6156-6161,U+6163-6166,U+6169-616f,U+6171-6176,U+6178-618d,U+618f-61a8,U+61aa-61bd,U+61bf-61c1,U+61c3-61c7,U+61c9-61d1,U+61d3-6205,U+6207,U+6209,U+620b,U+6213-6215,U+6217,U+6219,U+621b-6229,U+622b-6232,U+6235-6236,U+6238-623e,U+6244-6246,U+6248-624a,U+624c,U+624f-6250,U+6255-6257,U+6259-625a,U+6268,U+626a,U+725c-725e,U+79be,U+949a-949c,U+9509-950a,U+22c62,U+237a2;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.85.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.85.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.85.otf')  format('opentype');unicode-range:U+2016,U+5741-5742,U+6074-6075,U+6077-607b,U+607d-6083,U+6085-6088,U+608a-608c,U+608e-6093,U+6095-609e,U+60a1-60a2,U+60a4-60a5,U+60a7,U+60a9-60ab,U+60ad-60ae,U+60b0-60b1,U+60b3-60bb,U+60bd-60c4,U+60c6-60c9,U+60cb-60d0,U+60d2-60d4,U+60d6-60db,U+60dd-60de,U+60e1-60e6,U+60ee,U+60f0-60f2,U+60f4-60f8,U+60fa-6100,U+6102-6107,U+610a-610e,U+6110-6119,U+611b-611e,U+6120-6123,U+6125-6126,U+6128-613e,U+6140-6147,U+6149-614b,U+614d,U+614f-6154,U+6156-6161,U+6163-6166,U+6169-616f,U+6171-6176,U+6178-618d,U+618f-61a8,U+61aa-61bd,U+61bf-61c1,U+61c3-61c7,U+61c9-61d1,U+61d3-6205,U+6207,U+6209,U+620b,U+6213-6215,U+6217,U+6219,U+621b-6229,U+622b-6232,U+6235-6236,U+6238-623e,U+6244-6246,U+6248-624a,U+624c,U+624f-6250,U+6255-6257,U+6259-625a,U+6268,U+626a,U+725c-725e,U+79be,U+949a-949c,U+9509-950a,U+22c62,U+237a2;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.86.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.86.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.86.otf')  format('opentype');unicode-range:U+0000-0020,U+00a1-00af,U+00b1-00b6,U+00b8-00c4,U+00c6-00d6,U+00d8-00df,U+00e2-00e7,U+00ea-00eb,U+00ed-00f1,U+00f4-00f8,U+00fa-0100,U+0102-0113,U+0128-01cd,U+01d1-01d3,U+01d5-01f9,U+02d9,U+0392-039a,U+039c-03a1,U+03a4-03b5,U+03bb-03bc,U+03c3-03c4,U+03c6-1185,U+201a,U+2035,U+2160,U+3060,U+634e,U+6baa,U+6eb9,U+824f,U+948a-948b,U+9564-9568,U+95bb-95bd,U+9a83;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.86.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.86.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.86.otf')  format('opentype');unicode-range:U+0000-0020,U+00a1-00af,U+00b1-00b6,U+00b8-00c4,U+00c6-00d6,U+00d8-00df,U+00e2-00e7,U+00ea-00eb,U+00ed-00f1,U+00f4-00f8,U+00fa-0100,U+0102-0113,U+0128-01cd,U+01d1-01d3,U+01d5-01f9,U+02d9,U+0392-039a,U+039c-03a1,U+03a4-03b5,U+03bb-03bc,U+03c3-03c4,U+03c6-1185,U+201a,U+2035,U+2160,U+3060,U+634e,U+6baa,U+6eb9,U+824f,U+948a-948b,U+9564-9568,U+95bb-95bd,U+9a83;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.86.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.86.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.86.otf')  format('opentype');unicode-range:U+0000-0020,U+00a1-00af,U+00b1-00b6,U+00b8-00c4,U+00c6-00d6,U+00d8-00df,U+00e2-00e7,U+00ea-00eb,U+00ed-00f1,U+00f4-00f8,U+00fa-0100,U+0102-0113,U+0128-01cd,U+01d1-01d3,U+01d5-01f9,U+02d9,U+0392-039a,U+039c-03a1,U+03a4-03b5,U+03bb-03bc,U+03c3-03c4,U+03c6-1185,U+201a,U+2035,U+2160,U+3060,U+634e,U+6baa,U+6eb9,U+824f,U+948a-948b,U+9564-9568,U+95bb-95bd,U+9a83;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.87.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.87.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.87.otf')  format('opentype');unicode-range:U+4246-43e5;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.87.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.87.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.87.otf')  format('opentype');unicode-range:U+4246-43e5;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.87.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.87.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.87.otf')  format('opentype');unicode-range:U+4246-43e5;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.88.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.88.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.88.otf')  format('opentype');unicode-range:U+309d,U+5491,U+64ae-64af,U+89c7,U+9d73-9dde,U+9de0-9e1e,U+9e24-9e25,U+9e27-9e2c,U+9e2e-9e31,U+9e34-9e3c,U+9e40-9e44,U+9e46-9e49,U+9e4b-9e4e,U+9e50-9e63,U+9e65-9e6f,U+9e71-9e7e,U+9e80-9e92,U+9e94-9e9e,U+9ea0-9ea5,U+9ea7-9eba,U+9ebc-9ec3,U+9ec5-9ecd,U+9ed0,U+9ed2-9ed7,U+9ed9-9eda,U+9edc-9eee,U+9ef0-9f0d,U+9f0f-9f12,U+9f14-9f1f,U+9f21-9f2e;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.88.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.88.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.88.otf')  format('opentype');unicode-range:U+309d,U+5491,U+64ae-64af,U+89c7,U+9d73-9dde,U+9de0-9e1e,U+9e24-9e25,U+9e27-9e2c,U+9e2e-9e31,U+9e34-9e3c,U+9e40-9e44,U+9e46-9e49,U+9e4b-9e4e,U+9e50-9e63,U+9e65-9e6f,U+9e71-9e7e,U+9e80-9e92,U+9e94-9e9e,U+9ea0-9ea5,U+9ea7-9eba,U+9ebc-9ec3,U+9ec5-9ecd,U+9ed0,U+9ed2-9ed7,U+9ed9-9eda,U+9edc-9eee,U+9ef0-9f0d,U+9f0f-9f12,U+9f14-9f1f,U+9f21-9f2e;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.88.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.88.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.88.otf')  format('opentype');unicode-range:U+309d,U+5491,U+64ae-64af,U+89c7,U+9d73-9dde,U+9de0-9e1e,U+9e24-9e25,U+9e27-9e2c,U+9e2e-9e31,U+9e34-9e3c,U+9e40-9e44,U+9e46-9e49,U+9e4b-9e4e,U+9e50-9e63,U+9e65-9e6f,U+9e71-9e7e,U+9e80-9e92,U+9e94-9e9e,U+9ea0-9ea5,U+9ea7-9eba,U+9ebc-9ec3,U+9ec5-9ecd,U+9ed0,U+9ed2-9ed7,U+9ed9-9eda,U+9edc-9eee,U+9ef0-9f0d,U+9f0f-9f12,U+9f14-9f1f,U+9f21-9f2e;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.89.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.89.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.89.otf')  format('opentype');unicode-range:U+3d63-3f02;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.89.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.89.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.89.otf')  format('opentype');unicode-range:U+3d63-3f02;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.89.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.89.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.89.otf')  format('opentype');unicode-range:U+3d63-3f02;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.90.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.90.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.90.otf')  format('opentype');unicode-range:U+2027,U+54cf-54d0,U+54d2-54d6,U+54d8-54de,U+54e0-54e4,U+54e7,U+54e9,U+54eb-54ec,U+54ef-54f1,U+54f3-54f9,U+54fd-5506,U+5508,U+550a-550f,U+5511-5523,U+5525-552d,U+5530,U+5532-5545,U+5547-5549,U+554b-5560,U+5562-5563,U+5567-5574,U+5576-5577,U+5579-557b,U+557d-557f,U+5581,U+5585-5586,U+5588,U+558b-5597,U+5599-559b,U+559e-55a6,U+55a8-55aa,U+55ad-55b6,U+55b8-55ba,U+55bc-55c3,U+55c6-55d2,U+55d4-55db,U+55dd-55e2,U+55e4-55ee,U+55f0-55fc,U+55fe-5608,U+560a-560d,U+560f-5617,U+5619-561a,U+561c-5630,U+5633,U+5635-563a,U+563c-563e,U+5640-5667,U+5669,U+566b,U+566d-569c,U+5767-5768,U+7fe8,U+80f7,U+8bc5,U+8d63,U+8f87,U+9502-9503,U+9530-9534,U+9548-9549;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.90.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.90.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.90.otf')  format('opentype');unicode-range:U+2027,U+54cf-54d0,U+54d2-54d6,U+54d8-54de,U+54e0-54e4,U+54e7,U+54e9,U+54eb-54ec,U+54ef-54f1,U+54f3-54f9,U+54fd-5506,U+5508,U+550a-550f,U+5511-5523,U+5525-552d,U+5530,U+5532-5545,U+5547-5549,U+554b-5560,U+5562-5563,U+5567-5574,U+5576-5577,U+5579-557b,U+557d-557f,U+5581,U+5585-5586,U+5588,U+558b-5597,U+5599-559b,U+559e-55a6,U+55a8-55aa,U+55ad-55b6,U+55b8-55ba,U+55bc-55c3,U+55c6-55d2,U+55d4-55db,U+55dd-55e2,U+55e4-55ee,U+55f0-55fc,U+55fe-5608,U+560a-560d,U+560f-5617,U+5619-561a,U+561c-5630,U+5633,U+5635-563a,U+563c-563e,U+5640-5667,U+5669,U+566b,U+566d-569c,U+5767-5768,U+7fe8,U+80f7,U+8bc5,U+8d63,U+8f87,U+9502-9503,U+9530-9534,U+9548-9549;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.90.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.90.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.90.otf')  format('opentype');unicode-range:U+2027,U+54cf-54d0,U+54d2-54d6,U+54d8-54de,U+54e0-54e4,U+54e7,U+54e9,U+54eb-54ec,U+54ef-54f1,U+54f3-54f9,U+54fd-5506,U+5508,U+550a-550f,U+5511-5523,U+5525-552d,U+5530,U+5532-5545,U+5547-5549,U+554b-5560,U+5562-5563,U+5567-5574,U+5576-5577,U+5579-557b,U+557d-557f,U+5581,U+5585-5586,U+5588,U+558b-5597,U+5599-559b,U+559e-55a6,U+55a8-55aa,U+55ad-55b6,U+55b8-55ba,U+55bc-55c3,U+55c6-55d2,U+55d4-55db,U+55dd-55e2,U+55e4-55ee,U+55f0-55fc,U+55fe-5608,U+560a-560d,U+560f-5617,U+5619-561a,U+561c-5630,U+5633,U+5635-563a,U+563c-563e,U+5640-5667,U+5669,U+566b,U+566d-569c,U+5767-5768,U+7fe8,U+80f7,U+8bc5,U+8d63,U+8f87,U+9502-9503,U+9530-9534,U+9548-9549;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.91.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.91.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.91.otf')  format('opentype');unicode-range:U+ca37-cbd7,U+26cdd;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.91.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.91.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.91.otf')  format('opentype');unicode-range:U+ca37-cbd7,U+26cdd;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.91.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.91.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.91.otf')  format('opentype');unicode-range:U+ca37-cbd7,U+26cdd;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.92.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.92.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.92.otf')  format('opentype');unicode-range:U+3067,U+505b,U+505d-5064,U+5066-5075,U+5078-507e,U+5080-5084,U+5086-508c,U+508e-50a7,U+50a9-50ab,U+50ad-50b1,U+50b3-50ba,U+50bc-50ce,U+50d0-50e6,U+50e8-50f4,U+50f6-50fa,U+50fc-5111,U+5113-513e,U+5142,U+5147,U+514a,U+514c,U+514e-5150,U+5152-5153,U+5156-5159,U+515b,U+515d-5164,U+5166-5167,U+5169-516a,U+516f,U+5172,U+517a,U+517e-517f,U+5181-5184,U+5186-5187,U+5189-518b,U+518e-5191,U+5193-5194,U+5196-5198,U+519d-519f,U+51a1,U+51a6-51ab,U+51ad-51ae,U+51b1,U+51b4,U+51b8-51ba,U+51bc-51bf,U+51c1-51c3,U+51c5,U+51c7-51c8,U+51ca,U+51cd-51ce,U+51d0,U+51d2-51da,U+51dc,U+51de-51df,U+51e2-51e3,U+51e5-51ec,U+51f1-51f2,U+51f4-51f5,U+5202,U+5204-5205,U+5208-5209,U+520b-5210,U+5213-5216,U+521c,U+521e-521f,U+5221-5223,U+5225-5228,U+522a,U+522c-522d,U+522f,U+5231-5232,U+5234-5235,U+523c-5241,U+5243-5249,U+524b-524c,U+524e-5250,U+5252-5253,U+5255,U+5257-5264,U+5266,U+5268,U+526b-526e,U+5270-5271,U+5273-527c,U+8c24-8c25,U+94b9-94ba,U+9602-9604,U+22c51,U+2478f,U+25da1;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.92.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.92.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.92.otf')  format('opentype');unicode-range:U+3067,U+505b,U+505d-5064,U+5066-5075,U+5078-507e,U+5080-5084,U+5086-508c,U+508e-50a7,U+50a9-50ab,U+50ad-50b1,U+50b3-50ba,U+50bc-50ce,U+50d0-50e6,U+50e8-50f4,U+50f6-50fa,U+50fc-5111,U+5113-513e,U+5142,U+5147,U+514a,U+514c,U+514e-5150,U+5152-5153,U+5156-5159,U+515b,U+515d-5164,U+5166-5167,U+5169-516a,U+516f,U+5172,U+517a,U+517e-517f,U+5181-5184,U+5186-5187,U+5189-518b,U+518e-5191,U+5193-5194,U+5196-5198,U+519d-519f,U+51a1,U+51a6-51ab,U+51ad-51ae,U+51b1,U+51b4,U+51b8-51ba,U+51bc-51bf,U+51c1-51c3,U+51c5,U+51c7-51c8,U+51ca,U+51cd-51ce,U+51d0,U+51d2-51da,U+51dc,U+51de-51df,U+51e2-51e3,U+51e5-51ec,U+51f1-51f2,U+51f4-51f5,U+5202,U+5204-5205,U+5208-5209,U+520b-5210,U+5213-5216,U+521c,U+521e-521f,U+5221-5223,U+5225-5228,U+522a,U+522c-522d,U+522f,U+5231-5232,U+5234-5235,U+523c-5241,U+5243-5249,U+524b-524c,U+524e-5250,U+5252-5253,U+5255,U+5257-5264,U+5266,U+5268,U+526b-526e,U+5270-5271,U+5273-527c,U+8c24-8c25,U+94b9-94ba,U+9602-9604,U+22c51,U+2478f,U+25da1;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.92.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.92.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.92.otf')  format('opentype');unicode-range:U+3067,U+505b,U+505d-5064,U+5066-5075,U+5078-507e,U+5080-5084,U+5086-508c,U+508e-50a7,U+50a9-50ab,U+50ad-50b1,U+50b3-50ba,U+50bc-50ce,U+50d0-50e6,U+50e8-50f4,U+50f6-50fa,U+50fc-5111,U+5113-513e,U+5142,U+5147,U+514a,U+514c,U+514e-5150,U+5152-5153,U+5156-5159,U+515b,U+515d-5164,U+5166-5167,U+5169-516a,U+516f,U+5172,U+517a,U+517e-517f,U+5181-5184,U+5186-5187,U+5189-518b,U+518e-5191,U+5193-5194,U+5196-5198,U+519d-519f,U+51a1,U+51a6-51ab,U+51ad-51ae,U+51b1,U+51b4,U+51b8-51ba,U+51bc-51bf,U+51c1-51c3,U+51c5,U+51c7-51c8,U+51ca,U+51cd-51ce,U+51d0,U+51d2-51da,U+51dc,U+51de-51df,U+51e2-51e3,U+51e5-51ec,U+51f1-51f2,U+51f4-51f5,U+5202,U+5204-5205,U+5208-5209,U+520b-5210,U+5213-5216,U+521c,U+521e-521f,U+5221-5223,U+5225-5228,U+522a,U+522c-522d,U+522f,U+5231-5232,U+5234-5235,U+523c-5241,U+5243-5249,U+524b-524c,U+524e-5250,U+5252-5253,U+5255,U+5257-5264,U+5266,U+5268,U+526b-526e,U+5270-5271,U+5273-527c,U+8c24-8c25,U+94b9-94ba,U+9602-9604,U+22c51,U+2478f,U+25da1;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.93.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.93.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.93.otf')  format('opentype');unicode-range:U+306a,U+620d,U+7009-7010,U+7012-7019,U+701b-704b,U+704d-706a,U+706c,U+706e,U+7071-7074,U+7077-707b,U+7080-7088,U+708a-708d,U+708f-7091,U+7093-7095,U+7097-7098,U+709a-70aa,U+70af-70b7,U+70ba-70bb,U+70bd-70c0,U+70c3-70c7,U+70c9-70d7,U+70dc-70de,U+70e0-70e3,U+70e5,U+70e8-70ea,U+70ec,U+70ee,U+70f0-70f8,U+70fa-7108,U+710c-7114,U+7116-7118,U+711d-7125,U+7127-712f,U+7131-7135,U+7137-714b,U+714d,U+714f-715d,U+715f-7163,U+7165-7166,U+7168-716d,U+716f-7171,U+7173-717c,U+717e-7183,U+7185-7189,U+718b-718e,U+7190-7193,U+7195-7198,U+719a-719e,U+71a0-71ab,U+71ad-71c2,U+71c4-71d4,U+7d50,U+8337,U+8f9a,U+94f0-94f1,U+94f9,U+94fb-94fd,U+94ff,U+950d-950f,U+9535,U+9556-9558,U+966d-966f,U+ff2d;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.93.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.93.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.93.otf')  format('opentype');unicode-range:U+306a,U+620d,U+7009-7010,U+7012-7019,U+701b-704b,U+704d-706a,U+706c,U+706e,U+7071-7074,U+7077-707b,U+7080-7088,U+708a-708d,U+708f-7091,U+7093-7095,U+7097-7098,U+709a-70aa,U+70af-70b7,U+70ba-70bb,U+70bd-70c0,U+70c3-70c7,U+70c9-70d7,U+70dc-70de,U+70e0-70e3,U+70e5,U+70e8-70ea,U+70ec,U+70ee,U+70f0-70f8,U+70fa-7108,U+710c-7114,U+7116-7118,U+711d-7125,U+7127-712f,U+7131-7135,U+7137-714b,U+714d,U+714f-715d,U+715f-7163,U+7165-7166,U+7168-716d,U+716f-7171,U+7173-717c,U+717e-7183,U+7185-7189,U+718b-718e,U+7190-7193,U+7195-7198,U+719a-719e,U+71a0-71ab,U+71ad-71c2,U+71c4-71d4,U+7d50,U+8337,U+8f9a,U+94f0-94f1,U+94f9,U+94fb-94fd,U+94ff,U+950d-950f,U+9535,U+9556-9558,U+966d-966f,U+ff2d;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.93.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.93.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.93.otf')  format('opentype');unicode-range:U+306a,U+620d,U+7009-7010,U+7012-7019,U+701b-704b,U+704d-706a,U+706c,U+706e,U+7071-7074,U+7077-707b,U+7080-7088,U+708a-708d,U+708f-7091,U+7093-7095,U+7097-7098,U+709a-70aa,U+70af-70b7,U+70ba-70bb,U+70bd-70c0,U+70c3-70c7,U+70c9-70d7,U+70dc-70de,U+70e0-70e3,U+70e5,U+70e8-70ea,U+70ec,U+70ee,U+70f0-70f8,U+70fa-7108,U+710c-7114,U+7116-7118,U+711d-7125,U+7127-712f,U+7131-7135,U+7137-714b,U+714d,U+714f-715d,U+715f-7163,U+7165-7166,U+7168-716d,U+716f-7171,U+7173-717c,U+717e-7183,U+7185-7189,U+718b-718e,U+7190-7193,U+7195-7198,U+719a-719e,U+71a0-71ab,U+71ad-71c2,U+71c4-71d4,U+7d50,U+8337,U+8f9a,U+94f0-94f1,U+94f9,U+94fb-94fd,U+94ff,U+950d-950f,U+9535,U+9556-9558,U+966d-966f,U+ff2d;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.94.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.94.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.94.otf')  format('opentype');unicode-range:U+b9ef-ba6f,U+ba71-ba73,U+ba75-ba84,U+ba86-baa7,U+baaa-baba,U+babc-bb33,U+bb35-bb37,U+bb39-bb3b,U+bb3d-bb8c,U+c75b,U+c7d1-c7da;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.94.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.94.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.94.otf')  format('opentype');unicode-range:U+b9ef-ba6f,U+ba71-ba73,U+ba75-ba84,U+ba86-baa7,U+baaa-baba,U+babc-bb33,U+bb35-bb37,U+bb39-bb3b,U+bb3d-bb8c,U+c75b,U+c7d1-c7da;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.94.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.94.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.94.otf')  format('opentype');unicode-range:U+b9ef-ba6f,U+ba71-ba73,U+ba75-ba84,U+ba86-baa7,U+baaa-baba,U+babc-bb33,U+bb35-bb37,U+bb39-bb3b,U+bb3d-bb8c,U+c75b,U+c7d1-c7da;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.95.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.95.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.95.otf')  format('opentype');unicode-range:U+4a6c-4c0b;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.95.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.95.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.95.otf')  format('opentype');unicode-range:U+4a6c-4c0b;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.95.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.95.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.95.otf')  format('opentype');unicode-range:U+4a6c-4c0b;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.96.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.96.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.96.otf')  format('opentype');unicode-range:U+3061-3063,U+64b1,U+7e85-7e9f,U+7ea8-7ea9,U+7eab,U+7ead-7eae,U+7eb0,U+7ebb-7ebc,U+7ec0-7ec2,U+7ec9-7ecc,U+7ed0,U+7ed4,U+7ed6-7ed7,U+7ee0-7ee2,U+7ee4-7ee6,U+7ee8,U+7eeb-7eec,U+7ef1-7ef2,U+7ef6,U+7ef9-7efb,U+7efe,U+7f01-7f04,U+7f0a-7f12,U+7f19,U+7f1b-7f1c,U+7f1e-7f1f,U+7f21-7f23,U+7f25-7f28,U+7f2b-7f2c,U+7f2e,U+7f30-7f33,U+7f35-7f37,U+7f39,U+7f3b-7f4f,U+7f52-7f54,U+7f56,U+7f58-7f59,U+7f5b-7f61,U+7f63-7f68,U+7f6b-7f6d,U+7f6f-7f71,U+7f73-7f80,U+7f82-7f89,U+7f8b-7f8d,U+7f8f-7f9d,U+7f9f-7fa0,U+7fa2-7fa3,U+7fa5-7fbc,U+7fbe-7fc0,U+7fc2-7fc4,U+7fc6-7fd3,U+7fd5-7fd7,U+7fd9-7fdf,U+7fe1-7fe7,U+7fea-7fef,U+7ff1-7ffa,U+7ffd-7fff,U+8004,U+8006-800b,U+800e-800f,U+8011-8014,U+8016,U+8018-8032,U+8034-8035,U+8037,U+8039-803a,U+803c,U+803e,U+8040-8045,U+8047-8049,U+804d-8053,U+8055-8057,U+8059,U+805b-8069,U+806b-8082,U+8084-8085,U+808a,U+808d-8095,U+8097,U+89c3,U+8bec,U+94b2-94b4,U+94f3-94f5,U+954a-954f,U+9578-957c,U+9626-9629,U+9708;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.96.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.96.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.96.otf')  format('opentype');unicode-range:U+3061-3063,U+64b1,U+7e85-7e9f,U+7ea8-7ea9,U+7eab,U+7ead-7eae,U+7eb0,U+7ebb-7ebc,U+7ec0-7ec2,U+7ec9-7ecc,U+7ed0,U+7ed4,U+7ed6-7ed7,U+7ee0-7ee2,U+7ee4-7ee6,U+7ee8,U+7eeb-7eec,U+7ef1-7ef2,U+7ef6,U+7ef9-7efb,U+7efe,U+7f01-7f04,U+7f0a-7f12,U+7f19,U+7f1b-7f1c,U+7f1e-7f1f,U+7f21-7f23,U+7f25-7f28,U+7f2b-7f2c,U+7f2e,U+7f30-7f33,U+7f35-7f37,U+7f39,U+7f3b-7f4f,U+7f52-7f54,U+7f56,U+7f58-7f59,U+7f5b-7f61,U+7f63-7f68,U+7f6b-7f6d,U+7f6f-7f71,U+7f73-7f80,U+7f82-7f89,U+7f8b-7f8d,U+7f8f-7f9d,U+7f9f-7fa0,U+7fa2-7fa3,U+7fa5-7fbc,U+7fbe-7fc0,U+7fc2-7fc4,U+7fc6-7fd3,U+7fd5-7fd7,U+7fd9-7fdf,U+7fe1-7fe7,U+7fea-7fef,U+7ff1-7ffa,U+7ffd-7fff,U+8004,U+8006-800b,U+800e-800f,U+8011-8014,U+8016,U+8018-8032,U+8034-8035,U+8037,U+8039-803a,U+803c,U+803e,U+8040-8045,U+8047-8049,U+804d-8053,U+8055-8057,U+8059,U+805b-8069,U+806b-8082,U+8084-8085,U+808a,U+808d-8095,U+8097,U+89c3,U+8bec,U+94b2-94b4,U+94f3-94f5,U+954a-954f,U+9578-957c,U+9626-9629,U+9708;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.96.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.96.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.96.otf')  format('opentype');unicode-range:U+3061-3063,U+64b1,U+7e85-7e9f,U+7ea8-7ea9,U+7eab,U+7ead-7eae,U+7eb0,U+7ebb-7ebc,U+7ec0-7ec2,U+7ec9-7ecc,U+7ed0,U+7ed4,U+7ed6-7ed7,U+7ee0-7ee2,U+7ee4-7ee6,U+7ee8,U+7eeb-7eec,U+7ef1-7ef2,U+7ef6,U+7ef9-7efb,U+7efe,U+7f01-7f04,U+7f0a-7f12,U+7f19,U+7f1b-7f1c,U+7f1e-7f1f,U+7f21-7f23,U+7f25-7f28,U+7f2b-7f2c,U+7f2e,U+7f30-7f33,U+7f35-7f37,U+7f39,U+7f3b-7f4f,U+7f52-7f54,U+7f56,U+7f58-7f59,U+7f5b-7f61,U+7f63-7f68,U+7f6b-7f6d,U+7f6f-7f71,U+7f73-7f80,U+7f82-7f89,U+7f8b-7f8d,U+7f8f-7f9d,U+7f9f-7fa0,U+7fa2-7fa3,U+7fa5-7fbc,U+7fbe-7fc0,U+7fc2-7fc4,U+7fc6-7fd3,U+7fd5-7fd7,U+7fd9-7fdf,U+7fe1-7fe7,U+7fea-7fef,U+7ff1-7ffa,U+7ffd-7fff,U+8004,U+8006-800b,U+800e-800f,U+8011-8014,U+8016,U+8018-8032,U+8034-8035,U+8037,U+8039-803a,U+803c,U+803e,U+8040-8045,U+8047-8049,U+804d-8053,U+8055-8057,U+8059,U+805b-8069,U+806b-8082,U+8084-8085,U+808a,U+808d-8095,U+8097,U+89c3,U+8bec,U+94b2-94b4,U+94f3-94f5,U+954a-954f,U+9578-957c,U+9626-9629,U+9708;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.97.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.97.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.97.otf')  format('opentype');unicode-range:U+0060,U+00e1,U+00e8,U+2015,U+2161,U+2464-2465,U+2500,U+25bc,U+25cf,U+2605,U+3008-3009,U+3014-3015,U+3044,U+304b-304d,U+304f,U+3057,U+305f,U+3068,U+307e-307f,U+3089-308b,U+30af,U+30b7,U+30e9-30eb,U+30f3,U+30fc,U+4e1e,U+4e4d,U+4ea2,U+4f36,U+4f3a,U+4f6c,U+4f84,U+4fae,U+4fde,U+5014,U+5018,U+5029-502a,U+5055,U+5140,U+516e,U+5180,U+5195,U+51a2,U+51cb,U+51db,U+51f3,U+5201,U+527f,U+533e,U+5364,U+536f,U+53a5,U+53e8-53e9,U+53ed,U+5480,U+5490,U+54aa,U+54d7,U+54e8,U+54ee,U+54fa,U+54fc,U+557c,U+5580,U+5583,U+55c5,U+55e3,U+55fd,U+560e,U+5618,U+563b,U+566c,U+5777,U+57a2-57a3,U+57ae,U+57d4,U+589f,U+58a9,U+58ec,U+592d,U+594e,U+5955,U+5993,U+59a9,U+59be,U+59ca,U+5a05,U+5a1f,U+5a34,U+5a6a,U+5a77,U+5a9b,U+5ab2,U+5ac2,U+5b09,U+5b5c,U+5b7d,U+5bc7,U+5bd0,U+5be5,U+5c09,U+5c27,U+5c4e,U+5c7f,U+5c9a,U+5cd9,U+5ce8,U+5ced,U+5dcd,U+5df3,U+5e37,U+5e62,U+5e87,U+5eb5-5eb6,U+5ed6,U+5f08,U+5f1b,U+5f6a,U+5f6c,U+5f8a,U+5f98-5f99,U+5fcf,U+6020,U+6055,U+6064,U+606c,U+60af,U+60bc,U+60eb-60ed,U+6177,U+618e,U+61a9,U+620a,U+620c,U+620e,U+6252,U+625b,U+627c,U+62c2,U+62c7,U+62e7,U+62ed,U+62f7,U+62fd,U+631f-6320,U+6346,U+6390,U+63b7,U+63ba,U+63ea,U+6479,U+6487,U+64d2,U+6590,U+6656,U+6666,U+667e,U+66a7,U+6714,U+6726,U+6789,U+67ff,U+6805,U+6808,U+6813,U+6853-6854,U+6893,U+68a2,U+68a7,U+68d8,U+68e0,U+68fa,U+6930,U+6960,U+69a8,U+69b4,U+6a0a,U+6a71,U+6a80,U+6aac,U+6b79,U+6b7c,U+6bcb,U+6bd9,U+6c13,U+6c2e-6c2f,U+6c40,U+6c72,U+6c76,U+6c79,U+6c81,U+6cbc,U+6cf5,U+6d95,U+6da1,U+6da4,U+6dc6-6dc7,U+6dcc,U+6de4,U+6e1d,U+6e3a,U+6eba,U+6fd2,U+701a,U+7076,U+7099,U+70ac,U+70c1,U+70ef,U+710a,U+7119,U+7172,U+717d,U+7184,U+725f,U+7280,U+72c4,U+72e1,U+72f8,U+73c0,U+73d1,U+7405,U+7409,U+7426,U+7436,U+745a,U+7480,U+74a7-74a8,U+7504,U+7578,U+759a,U+75a1,U+75b9,U+75d8,U+75f0,U+762b,U+7656,U+7678,U+76ce-76cf,U+7729,U+7738,U+773a,U+7766,U+7784,U+77aa,U+77b0,U+785d,U+78ca,U+7901,U+7934,U+7960,U+79a7,U+79b9,U+79e4,U+79fd,U+7a23,U+7a57,U+7a79,U+7a91,U+7a9c,U+7a9f,U+7aa6,U+7ae3,U+7aff,U+7b03,U+7b0b,U+7b77,U+7bc6,U+7be1,U+7bf7,U+7c07,U+7c3f,U+7c7d,U+7c9f,U+7caa,U+7cef,U+7d0a,U+7ec5,U+7ede,U+7eee-7ef0,U+7ef7,U+7f06,U+7f24,U+7f2a,U+7f2d,U+7f81,U+7fe9,U+803f,U+8046,U+804b,U+8087,U+8098,U+80b4,U+80da,U+80e7,U+80f1,U+80f3,U+80fa,U+814b-814c,U+8151,U+818a,U+81b3,U+8231,U+829c,U+82a5,U+82b8-82b9,U+82c7,U+82d4,U+82ef,U+8335,U+8339,U+835f,U+8367,U+839e,U+83e0,U+83f1,U+8469,U+846b,U+854a,U+85e9,U+85fb,U+8611,U+8638,U+864f,U+865e,U+8681,U+868a,U+8695,U+86db,U+8712,U+8715,U+8718,U+8747,U+8749,U+889c,U+88f3-88f4,U+8912,U+892a,U+8944,U+895f,U+8bcf,U+8bdb,U+8be3,U+8beb,U+8bf2,U+8c0d,U+8c0f,U+8c1b,U+8c2c,U+8c34,U+8d3f,U+8d4e,U+8d58,U+8d66,U+8db4,U+8dcb,U+8dfb,U+8e66,U+8eac,U+8f7c,U+8f84,U+8f97,U+8f99,U+8fe5,U+9005,U+9050,U+9082-9083,U+9091,U+90b1,U+90b5,U+90b9,U+9102,U+914c,U+916a,U+916e-916f,U+946b,U+949e,U+94a0,U+94a7,U+94b5,U+94be,U+94f2,U+950c,U+9523,U+952f,U+9540-9541,U+95f8,U+95fd,U+960e,U+9619,U+961c,U+962e,U+9631,U+9661,U+96a7,U+96cf,U+9704,U+9706,U+9716,U+9774,U+978d,U+97f6,U+9885,U+988a,U+9890,U+9893,U+98d9,U+996a,U+9a6d,U+9a6f,U+9a74,U+9a7c,U+9a7f,U+9a86-9a87,U+9a8f,U+9abc,U+9ae6,U+9e33,U+9e3d,U+9e4a,U+9e93,U+9eef,U+ff0b,U+ff0f,U+ffe5;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.97.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.97.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.97.otf')  format('opentype');unicode-range:U+0060,U+00e1,U+00e8,U+2015,U+2161,U+2464-2465,U+2500,U+25bc,U+25cf,U+2605,U+3008-3009,U+3014-3015,U+3044,U+304b-304d,U+304f,U+3057,U+305f,U+3068,U+307e-307f,U+3089-308b,U+30af,U+30b7,U+30e9-30eb,U+30f3,U+30fc,U+4e1e,U+4e4d,U+4ea2,U+4f36,U+4f3a,U+4f6c,U+4f84,U+4fae,U+4fde,U+5014,U+5018,U+5029-502a,U+5055,U+5140,U+516e,U+5180,U+5195,U+51a2,U+51cb,U+51db,U+51f3,U+5201,U+527f,U+533e,U+5364,U+536f,U+53a5,U+53e8-53e9,U+53ed,U+5480,U+5490,U+54aa,U+54d7,U+54e8,U+54ee,U+54fa,U+54fc,U+557c,U+5580,U+5583,U+55c5,U+55e3,U+55fd,U+560e,U+5618,U+563b,U+566c,U+5777,U+57a2-57a3,U+57ae,U+57d4,U+589f,U+58a9,U+58ec,U+592d,U+594e,U+5955,U+5993,U+59a9,U+59be,U+59ca,U+5a05,U+5a1f,U+5a34,U+5a6a,U+5a77,U+5a9b,U+5ab2,U+5ac2,U+5b09,U+5b5c,U+5b7d,U+5bc7,U+5bd0,U+5be5,U+5c09,U+5c27,U+5c4e,U+5c7f,U+5c9a,U+5cd9,U+5ce8,U+5ced,U+5dcd,U+5df3,U+5e37,U+5e62,U+5e87,U+5eb5-5eb6,U+5ed6,U+5f08,U+5f1b,U+5f6a,U+5f6c,U+5f8a,U+5f98-5f99,U+5fcf,U+6020,U+6055,U+6064,U+606c,U+60af,U+60bc,U+60eb-60ed,U+6177,U+618e,U+61a9,U+620a,U+620c,U+620e,U+6252,U+625b,U+627c,U+62c2,U+62c7,U+62e7,U+62ed,U+62f7,U+62fd,U+631f-6320,U+6346,U+6390,U+63b7,U+63ba,U+63ea,U+6479,U+6487,U+64d2,U+6590,U+6656,U+6666,U+667e,U+66a7,U+6714,U+6726,U+6789,U+67ff,U+6805,U+6808,U+6813,U+6853-6854,U+6893,U+68a2,U+68a7,U+68d8,U+68e0,U+68fa,U+6930,U+6960,U+69a8,U+69b4,U+6a0a,U+6a71,U+6a80,U+6aac,U+6b79,U+6b7c,U+6bcb,U+6bd9,U+6c13,U+6c2e-6c2f,U+6c40,U+6c72,U+6c76,U+6c79,U+6c81,U+6cbc,U+6cf5,U+6d95,U+6da1,U+6da4,U+6dc6-6dc7,U+6dcc,U+6de4,U+6e1d,U+6e3a,U+6eba,U+6fd2,U+701a,U+7076,U+7099,U+70ac,U+70c1,U+70ef,U+710a,U+7119,U+7172,U+717d,U+7184,U+725f,U+7280,U+72c4,U+72e1,U+72f8,U+73c0,U+73d1,U+7405,U+7409,U+7426,U+7436,U+745a,U+7480,U+74a7-74a8,U+7504,U+7578,U+759a,U+75a1,U+75b9,U+75d8,U+75f0,U+762b,U+7656,U+7678,U+76ce-76cf,U+7729,U+7738,U+773a,U+7766,U+7784,U+77aa,U+77b0,U+785d,U+78ca,U+7901,U+7934,U+7960,U+79a7,U+79b9,U+79e4,U+79fd,U+7a23,U+7a57,U+7a79,U+7a91,U+7a9c,U+7a9f,U+7aa6,U+7ae3,U+7aff,U+7b03,U+7b0b,U+7b77,U+7bc6,U+7be1,U+7bf7,U+7c07,U+7c3f,U+7c7d,U+7c9f,U+7caa,U+7cef,U+7d0a,U+7ec5,U+7ede,U+7eee-7ef0,U+7ef7,U+7f06,U+7f24,U+7f2a,U+7f2d,U+7f81,U+7fe9,U+803f,U+8046,U+804b,U+8087,U+8098,U+80b4,U+80da,U+80e7,U+80f1,U+80f3,U+80fa,U+814b-814c,U+8151,U+818a,U+81b3,U+8231,U+829c,U+82a5,U+82b8-82b9,U+82c7,U+82d4,U+82ef,U+8335,U+8339,U+835f,U+8367,U+839e,U+83e0,U+83f1,U+8469,U+846b,U+854a,U+85e9,U+85fb,U+8611,U+8638,U+864f,U+865e,U+8681,U+868a,U+8695,U+86db,U+8712,U+8715,U+8718,U+8747,U+8749,U+889c,U+88f3-88f4,U+8912,U+892a,U+8944,U+895f,U+8bcf,U+8bdb,U+8be3,U+8beb,U+8bf2,U+8c0d,U+8c0f,U+8c1b,U+8c2c,U+8c34,U+8d3f,U+8d4e,U+8d58,U+8d66,U+8db4,U+8dcb,U+8dfb,U+8e66,U+8eac,U+8f7c,U+8f84,U+8f97,U+8f99,U+8fe5,U+9005,U+9050,U+9082-9083,U+9091,U+90b1,U+90b5,U+90b9,U+9102,U+914c,U+916a,U+916e-916f,U+946b,U+949e,U+94a0,U+94a7,U+94b5,U+94be,U+94f2,U+950c,U+9523,U+952f,U+9540-9541,U+95f8,U+95fd,U+960e,U+9619,U+961c,U+962e,U+9631,U+9661,U+96a7,U+96cf,U+9704,U+9706,U+9716,U+9774,U+978d,U+97f6,U+9885,U+988a,U+9890,U+9893,U+98d9,U+996a,U+9a6d,U+9a6f,U+9a74,U+9a7c,U+9a7f,U+9a86-9a87,U+9a8f,U+9abc,U+9ae6,U+9e33,U+9e3d,U+9e4a,U+9e93,U+9eef,U+ff0b,U+ff0f,U+ffe5;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.97.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.97.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.97.otf')  format('opentype');unicode-range:U+0060,U+00e1,U+00e8,U+2015,U+2161,U+2464-2465,U+2500,U+25bc,U+25cf,U+2605,U+3008-3009,U+3014-3015,U+3044,U+304b-304d,U+304f,U+3057,U+305f,U+3068,U+307e-307f,U+3089-308b,U+30af,U+30b7,U+30e9-30eb,U+30f3,U+30fc,U+4e1e,U+4e4d,U+4ea2,U+4f36,U+4f3a,U+4f6c,U+4f84,U+4fae,U+4fde,U+5014,U+5018,U+5029-502a,U+5055,U+5140,U+516e,U+5180,U+5195,U+51a2,U+51cb,U+51db,U+51f3,U+5201,U+527f,U+533e,U+5364,U+536f,U+53a5,U+53e8-53e9,U+53ed,U+5480,U+5490,U+54aa,U+54d7,U+54e8,U+54ee,U+54fa,U+54fc,U+557c,U+5580,U+5583,U+55c5,U+55e3,U+55fd,U+560e,U+5618,U+563b,U+566c,U+5777,U+57a2-57a3,U+57ae,U+57d4,U+589f,U+58a9,U+58ec,U+592d,U+594e,U+5955,U+5993,U+59a9,U+59be,U+59ca,U+5a05,U+5a1f,U+5a34,U+5a6a,U+5a77,U+5a9b,U+5ab2,U+5ac2,U+5b09,U+5b5c,U+5b7d,U+5bc7,U+5bd0,U+5be5,U+5c09,U+5c27,U+5c4e,U+5c7f,U+5c9a,U+5cd9,U+5ce8,U+5ced,U+5dcd,U+5df3,U+5e37,U+5e62,U+5e87,U+5eb5-5eb6,U+5ed6,U+5f08,U+5f1b,U+5f6a,U+5f6c,U+5f8a,U+5f98-5f99,U+5fcf,U+6020,U+6055,U+6064,U+606c,U+60af,U+60bc,U+60eb-60ed,U+6177,U+618e,U+61a9,U+620a,U+620c,U+620e,U+6252,U+625b,U+627c,U+62c2,U+62c7,U+62e7,U+62ed,U+62f7,U+62fd,U+631f-6320,U+6346,U+6390,U+63b7,U+63ba,U+63ea,U+6479,U+6487,U+64d2,U+6590,U+6656,U+6666,U+667e,U+66a7,U+6714,U+6726,U+6789,U+67ff,U+6805,U+6808,U+6813,U+6853-6854,U+6893,U+68a2,U+68a7,U+68d8,U+68e0,U+68fa,U+6930,U+6960,U+69a8,U+69b4,U+6a0a,U+6a71,U+6a80,U+6aac,U+6b79,U+6b7c,U+6bcb,U+6bd9,U+6c13,U+6c2e-6c2f,U+6c40,U+6c72,U+6c76,U+6c79,U+6c81,U+6cbc,U+6cf5,U+6d95,U+6da1,U+6da4,U+6dc6-6dc7,U+6dcc,U+6de4,U+6e1d,U+6e3a,U+6eba,U+6fd2,U+701a,U+7076,U+7099,U+70ac,U+70c1,U+70ef,U+710a,U+7119,U+7172,U+717d,U+7184,U+725f,U+7280,U+72c4,U+72e1,U+72f8,U+73c0,U+73d1,U+7405,U+7409,U+7426,U+7436,U+745a,U+7480,U+74a7-74a8,U+7504,U+7578,U+759a,U+75a1,U+75b9,U+75d8,U+75f0,U+762b,U+7656,U+7678,U+76ce-76cf,U+7729,U+7738,U+773a,U+7766,U+7784,U+77aa,U+77b0,U+785d,U+78ca,U+7901,U+7934,U+7960,U+79a7,U+79b9,U+79e4,U+79fd,U+7a23,U+7a57,U+7a79,U+7a91,U+7a9c,U+7a9f,U+7aa6,U+7ae3,U+7aff,U+7b03,U+7b0b,U+7b77,U+7bc6,U+7be1,U+7bf7,U+7c07,U+7c3f,U+7c7d,U+7c9f,U+7caa,U+7cef,U+7d0a,U+7ec5,U+7ede,U+7eee-7ef0,U+7ef7,U+7f06,U+7f24,U+7f2a,U+7f2d,U+7f81,U+7fe9,U+803f,U+8046,U+804b,U+8087,U+8098,U+80b4,U+80da,U+80e7,U+80f1,U+80f3,U+80fa,U+814b-814c,U+8151,U+818a,U+81b3,U+8231,U+829c,U+82a5,U+82b8-82b9,U+82c7,U+82d4,U+82ef,U+8335,U+8339,U+835f,U+8367,U+839e,U+83e0,U+83f1,U+8469,U+846b,U+854a,U+85e9,U+85fb,U+8611,U+8638,U+864f,U+865e,U+8681,U+868a,U+8695,U+86db,U+8712,U+8715,U+8718,U+8747,U+8749,U+889c,U+88f3-88f4,U+8912,U+892a,U+8944,U+895f,U+8bcf,U+8bdb,U+8be3,U+8beb,U+8bf2,U+8c0d,U+8c0f,U+8c1b,U+8c2c,U+8c34,U+8d3f,U+8d4e,U+8d58,U+8d66,U+8db4,U+8dcb,U+8dfb,U+8e66,U+8eac,U+8f7c,U+8f84,U+8f97,U+8f99,U+8fe5,U+9005,U+9050,U+9082-9083,U+9091,U+90b1,U+90b5,U+90b9,U+9102,U+914c,U+916a,U+916e-916f,U+946b,U+949e,U+94a0,U+94a7,U+94b5,U+94be,U+94f2,U+950c,U+9523,U+952f,U+9540-9541,U+95f8,U+95fd,U+960e,U+9619,U+961c,U+962e,U+9631,U+9661,U+96a7,U+96cf,U+9704,U+9706,U+9716,U+9774,U+978d,U+97f6,U+9885,U+988a,U+9890,U+9893,U+98d9,U+996a,U+9a6d,U+9a6f,U+9a74,U+9a7c,U+9a7f,U+9a86-9a87,U+9a8f,U+9abc,U+9ae6,U+9e33,U+9e3d,U+9e4a,U+9e93,U+9eef,U+ff0b,U+ff0f,U+ffe5;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.98.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.98.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.98.otf')  format('opentype');unicode-range:U+2318,U+306d,U+4fe8,U+707d,U+7583-7585,U+7587-758d,U+7590,U+7592-7596,U+7598-7599,U+759b-75a0,U+75a2-75aa,U+75ac-75ad,U+75b3-75b8,U+75ba-75bb,U+75bf-75c4,U+75c8-75d1,U+75d3-75d4,U+75d6-75d7,U+75d9-75da,U+75dc-75ef,U+75f1-75f3,U+75f5-75ff,U+7603-7616,U+7618-7623,U+7625,U+7627-762a,U+762c-763d,U+763f-764b,U+764d-7655,U+7657-7677,U+7679-767a,U+767f-7681,U+7683,U+7685,U+7688-76ad,U+76af-76b0,U+76b2-76c5,U+76c7,U+76c9,U+76cb-76cd,U+76d9-76da,U+76dc-76de,U+76e0-76ed,U+76f0-76f1,U+76f3,U+76f5-76f7,U+76f9-76fb,U+76fd,U+76ff-7700,U+7702-7708,U+770a,U+770c-771e,U+7721-7728,U+772a-7736,U+7739,U+773b,U+773d-773f,U+7742-774f,U+7751-775a,U+956c-956f,U+9594-9597,U+9651-9654,U+9656-965b,U+9663,U+9979,U+20ad3,U+22c88,U+2b1ed;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.98.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.98.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.98.otf')  format('opentype');unicode-range:U+2318,U+306d,U+4fe8,U+707d,U+7583-7585,U+7587-758d,U+7590,U+7592-7596,U+7598-7599,U+759b-75a0,U+75a2-75aa,U+75ac-75ad,U+75b3-75b8,U+75ba-75bb,U+75bf-75c4,U+75c8-75d1,U+75d3-75d4,U+75d6-75d7,U+75d9-75da,U+75dc-75ef,U+75f1-75f3,U+75f5-75ff,U+7603-7616,U+7618-7623,U+7625,U+7627-762a,U+762c-763d,U+763f-764b,U+764d-7655,U+7657-7677,U+7679-767a,U+767f-7681,U+7683,U+7685,U+7688-76ad,U+76af-76b0,U+76b2-76c5,U+76c7,U+76c9,U+76cb-76cd,U+76d9-76da,U+76dc-76de,U+76e0-76ed,U+76f0-76f1,U+76f3,U+76f5-76f7,U+76f9-76fb,U+76fd,U+76ff-7700,U+7702-7708,U+770a,U+770c-771e,U+7721-7728,U+772a-7736,U+7739,U+773b,U+773d-773f,U+7742-774f,U+7751-775a,U+956c-956f,U+9594-9597,U+9651-9654,U+9656-965b,U+9663,U+9979,U+20ad3,U+22c88,U+2b1ed;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.98.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.98.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.98.otf')  format('opentype');unicode-range:U+2318,U+306d,U+4fe8,U+707d,U+7583-7585,U+7587-758d,U+7590,U+7592-7596,U+7598-7599,U+759b-75a0,U+75a2-75aa,U+75ac-75ad,U+75b3-75b8,U+75ba-75bb,U+75bf-75c4,U+75c8-75d1,U+75d3-75d4,U+75d6-75d7,U+75d9-75da,U+75dc-75ef,U+75f1-75f3,U+75f5-75ff,U+7603-7616,U+7618-7623,U+7625,U+7627-762a,U+762c-763d,U+763f-764b,U+764d-7655,U+7657-7677,U+7679-767a,U+767f-7681,U+7683,U+7685,U+7688-76ad,U+76af-76b0,U+76b2-76c5,U+76c7,U+76c9,U+76cb-76cd,U+76d9-76da,U+76dc-76de,U+76e0-76ed,U+76f0-76f1,U+76f3,U+76f5-76f7,U+76f9-76fb,U+76fd,U+76ff-7700,U+7702-7708,U+770a,U+770c-771e,U+7721-7728,U+772a-7736,U+7739,U+773b,U+773d-773f,U+7742-774f,U+7751-775a,U+956c-956f,U+9594-9597,U+9651-9654,U+9656-965b,U+9663,U+9979,U+20ad3,U+22c88,U+2b1ed;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.99.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.99.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.99.otf')  format('opentype');unicode-range:U+5233,U+56f5,U+5c50,U+63a1,U+6c9a,U+73b3-73ba,U+73bc-73bf,U+73c1-73c7,U+73cb-73cc,U+73ce-73d0,U+73d2-73df,U+73e1-73ec,U+73ee-73fd,U+73ff-7402,U+7404,U+7407-7408,U+740a-740f,U+7411-7421,U+7423-7425,U+7427-7429,U+742b-7432,U+7435,U+7437-743b,U+743d-7454,U+7456-7459,U+745b,U+745d,U+7460-746f,U+7471-7475,U+7477-747f,U+7484-74a6,U+74a9-74db,U+74dd-74e2,U+74e4-74e5,U+74e7-74f5,U+74f8-7503,U+7505-7517,U+7519,U+751d-751e,U+7520-7527,U+752c-752f,U+7534,U+7536,U+7539-753a,U+753c-7544,U+7546-754b,U+754d-754e,U+7550-7553,U+7555-7558,U+755d-7564,U+7566-7569,U+756b-7572,U+7579-7582,U+8278-8279,U+881b-881c,U+90c5,U+95d9-95db,U+95dd-95e4,U+95eb-95ec,U+9a7d,U+9ca3,U+9cb5;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.99.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.99.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.99.otf')  format('opentype');unicode-range:U+5233,U+56f5,U+5c50,U+63a1,U+6c9a,U+73b3-73ba,U+73bc-73bf,U+73c1-73c7,U+73cb-73cc,U+73ce-73d0,U+73d2-73df,U+73e1-73ec,U+73ee-73fd,U+73ff-7402,U+7404,U+7407-7408,U+740a-740f,U+7411-7421,U+7423-7425,U+7427-7429,U+742b-7432,U+7435,U+7437-743b,U+743d-7454,U+7456-7459,U+745b,U+745d,U+7460-746f,U+7471-7475,U+7477-747f,U+7484-74a6,U+74a9-74db,U+74dd-74e2,U+74e4-74e5,U+74e7-74f5,U+74f8-7503,U+7505-7517,U+7519,U+751d-751e,U+7520-7527,U+752c-752f,U+7534,U+7536,U+7539-753a,U+753c-7544,U+7546-754b,U+754d-754e,U+7550-7553,U+7555-7558,U+755d-7564,U+7566-7569,U+756b-7572,U+7579-7582,U+8278-8279,U+881b-881c,U+90c5,U+95d9-95db,U+95dd-95e4,U+95eb-95ec,U+9a7d,U+9ca3,U+9cb5;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.99.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.99.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.99.otf')  format('opentype');unicode-range:U+5233,U+56f5,U+5c50,U+63a1,U+6c9a,U+73b3-73ba,U+73bc-73bf,U+73c1-73c7,U+73cb-73cc,U+73ce-73d0,U+73d2-73df,U+73e1-73ec,U+73ee-73fd,U+73ff-7402,U+7404,U+7407-7408,U+740a-740f,U+7411-7421,U+7423-7425,U+7427-7429,U+742b-7432,U+7435,U+7437-743b,U+743d-7454,U+7456-7459,U+745b,U+745d,U+7460-746f,U+7471-7475,U+7477-747f,U+7484-74a6,U+74a9-74db,U+74dd-74e2,U+74e4-74e5,U+74e7-74f5,U+74f8-7503,U+7505-7517,U+7519,U+751d-751e,U+7520-7527,U+752c-752f,U+7534,U+7536,U+7539-753a,U+753c-7544,U+7546-754b,U+754d-754e,U+7550-7553,U+7555-7558,U+755d-7564,U+7566-7569,U+756b-7572,U+7579-7582,U+8278-8279,U+881b-881c,U+90c5,U+95d9-95db,U+95dd-95e4,U+95eb-95ec,U+9a7d,U+9ca3,U+9cb5;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.100.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.100.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.100.otf')  format('opentype');unicode-range:U+0021-0023,U+0025-005b,U+005d,U+005f,U+0061-007a,U+007e-00a0,U+00b7,U+2014,U+2018-2019,U+201c-201d,U+2026,U+3000-3002,U+300a-300d,U+3010-3011,U+4e00-4e01,U+4e03,U+4e07-4e0b,U+4e0d-4e0e,U+4e11,U+4e13-4e14,U+4e16,U+4e1a-4e1d,U+4e22,U+4e24-4e25,U+4e27,U+4e2a,U+4e2d,U+4e30,U+4e32,U+4e34,U+4e39-4e3b,U+4e3d-4e3e,U+4e43,U+4e45,U+4e48-4e49,U+4e4b-4e4c,U+4e4e-4e50,U+4e54,U+4e58,U+4e5d,U+4e5f-4e61,U+4e66,U+4e70-4e71,U+4e73,U+4e86,U+4e88-4e89,U+4e8b-4e8c,U+4e8e-4e8f,U+4e91-4e92,U+4e94-4e95,U+4e9a-4e9b,U+4ea1,U+4ea4,U+4ea6-4ea7,U+4eab-4eac,U+4eae,U+4eb2,U+4eba,U+4ebf-4ec1,U+4ec5,U+4eca-4ecb,U+4ecd-4ece,U+4ed3-4ed4,U+4ed6,U+4ed8-4ed9,U+4ee3-4ee5,U+4eea,U+4eec,U+4ef0,U+4ef6-4ef7,U+4efb,U+4efd,U+4eff,U+4f01,U+4f0a,U+4f0d,U+4f0f,U+4f11,U+4f17-4f1a,U+4f1f-4f20,U+4f24,U+4f26,U+4f2a,U+4f2f-4f30,U+4f34,U+4f38,U+4f3c,U+4f46,U+4f4d-4f4f,U+4f53,U+4f55,U+4f59,U+4f5b-4f5c,U+4f60,U+4f69,U+4f73,U+4f7f,U+4f8b,U+4f9b,U+4f9d,U+4fa7,U+4fb5,U+4fbf,U+4fc3-4fc4,U+4fca,U+4fd7,U+4fdd,U+4fe1,U+4fe9,U+4fee,U+4ff1,U+500d,U+5012,U+5019,U+501f,U+503c,U+503e,U+5047,U+504f,U+505a,U+505c,U+5065,U+5076-5077,U+507f,U+50a8,U+50b2,U+50bb,U+50cf,U+513f,U+5141,U+5143-5145,U+5148-5149,U+514b,U+514d,U+515a,U+5165,U+5168,U+516b-516d,U+5170-5171,U+5173-5178,U+517b-517d,U+5185,U+518c-518d,U+5192,U+5199,U+519b-519c,U+51a0,U+51ac,U+51b0,U+51b2-51b3,U+51b5,U+51b7,U+51c0,U+51c6,U+51c9,U+51cc,U+51cf,U+51dd,U+51e0-51e1,U+51e4,U+51ed,U+51ef,U+51f6,U+51fa-51fb,U+5200,U+5206-5207,U+520a,U+5211-5212,U+5217-521b,U+521d,U+5220,U+5224,U+5229,U+522b,U+5230,U+5236-5237,U+523a-523b,U+5242,U+524d,U+5251,U+5267,U+5269-526a,U+526f,U+5272,U+529b,U+529d-52a1,U+52a3,U+52a8-52aa,U+52b1-52b3,U+52bf,U+52c7,U+52d2,U+52e4,U+52fe,U+5300,U+5305,U+5316-5317,U+5339-533b,U+5341,U+5343,U+5347-5348,U+534a,U+534e-534f,U+5353,U+5355-5357,U+535a,U+5360-5361,U+5367,U+536b,U+5370-5371,U+5373-5374,U+5377,U+5382,U+5385-5386,U+5389,U+538b-538c,U+5398,U+539a,U+539f,U+53bb,U+53bf,U+53c2,U+53c8,U+53ca-53cd,U+53d1,U+53d6-53d8,U+53e0,U+53e3-53e6,U+53ea-53ec,U+53ef-53f0,U+53f2-53f3,U+53f6-53f9,U+5403-5404,U+5408-5409,U+540c-540e,U+5410-5411,U+5413,U+5417,U+541b,U+5426-5427,U+542b-542c,U+542f,U+5434,U+5438-5439,U+5440,U+5446,U+5448,U+544a,U+5458,U+5462,U+5468,U+5473,U+5475,U+547c-547d,U+548c,U+5496,U+54a8,U+54c1,U+54c8,U+54cd,U+54e5-54e6,U+54ea,U+54ed,U+54f2,U+5510,U+552e-552f,U+5531,U+5546,U+554a,U+5565-5566,U+5584,U+558a,U+559c-559d,U+55b7,U+5609,U+561b,U+5634,U+5668,U+56db,U+56de,U+56e0,U+56e2,U+56ed,U+56f0,U+56f4,U+56fa,U+56fd-56fe,U+5706,U+5708,U+571f,U+5723,U+5728,U+5730,U+573a,U+5740,U+5747,U+574f-5751,U+5757,U+575a-575b,U+5761,U+5766,U+5782,U+578b,U+57c3,U+57cb,U+57ce,U+57df,U+57f9-57fa,U+5802,U+5806,U+5821,U+582a,U+5851,U+5854,U+585e,U+586b,U+5883,U+5899,U+589e,U+58a8,U+58c1,U+58eb,U+58ee,U+58f0,U+58f3,U+5904,U+5907,U+590d,U+590f,U+5915-5916,U+591a,U+591c,U+591f,U+5927,U+5929-592b,U+592e,U+5931,U+5934,U+5938-593a,U+5947-5949,U+594b,U+594f,U+5954,U+5956-5957,U+5965,U+5973,U+5976,U+5979,U+597d,U+5982,U+5986-5988,U+5999,U+59b9,U+59bb,U+59c6,U+59cb,U+59d0-59d1,U+59d3-59d4,U+59dc,U+59fb,U+59ff,U+5a01,U+5a18,U+5a31,U+5a46,U+5a5a,U+5a92,U+5ac1,U+5acc,U+5ae9,U+5b50,U+5b54-5b55,U+5b57-5b59,U+5b63-5b64,U+5b66,U+5b69,U+5b81,U+5b83,U+5b85,U+5b87-5b89,U+5b8b-5b8c,U+5b8f,U+5b97-5b98,U+5b9a,U+5b9c-5b9e,U+5ba1-5ba4,U+5bab,U+5bb3,U+5bb6,U+5bb9,U+5bbd-5bbf,U+5bc2,U+5bc4,U+5bc6,U+5bcc,U+5bd2-5bd3,U+5bdf,U+5bf8-5bfc,U+5bff,U+5c01,U+5c04,U+5c06,U+5c0a,U+5c0f,U+5c11,U+5c14,U+5c16,U+5c18,U+5c1a,U+5c1d,U+5c24,U+5c31,U+5c3a,U+5c3c-5c40,U+5c42,U+5c45,U+5c48,U+5c4a-5c4b,U+5c4f,U+5c55,U+5c5e,U+5c71,U+5c81,U+5c97,U+5c9b,U+5ca9,U+5cb8,U+5cf0,U+5d07,U+5ddd-5dde,U+5de1,U+5de5-5de8,U+5dee,U+5df1-5df2,U+5df4,U+5e01-5e03,U+5e05,U+5e08,U+5e0c,U+5e1d,U+5e26,U+5e2d-5e2e,U+5e38,U+5e45,U+5e55,U+5e72-5e74,U+5e76,U+5e78,U+5e7b-5e7d,U+5e7f,U+5e84,U+5e86,U+5e8a,U+5e8f,U+5e93-5e95,U+5e97,U+5e99,U+5e9c,U+5e9f,U+5ea6-5ea7,U+5ead,U+5eb7,U+5ec9,U+5ef6-5ef7,U+5efa,U+5f00,U+5f02-5f04,U+5f0f,U+5f15,U+5f1f-5f20,U+5f25,U+5f2f,U+5f31,U+5f39-5f3a,U+5f52-5f53,U+5f55,U+5f62,U+5f69,U+5f71,U+5f79,U+5f7b-5f7c,U+5f80-5f81,U+5f84-5f85,U+5f88,U+5f8b,U+5f90,U+5f92,U+5f97,U+5fa1,U+5faa,U+5fae,U+5fb7,U+5fbd,U+5fc3,U+5fc5-5fc6,U+5fcc-5fcd,U+5fd7-5fd9,U+5fe0,U+5fe7,U+5feb,U+5ff5,U+5ffd,U+6000-6001,U+600e,U+6012,U+6015,U+601d,U+6025,U+6027-6028,U+602a,U+603b,U+604b,U+6050,U+6052,U+6062,U+6068-6069,U+606f-6070,U+6076,U+607c,U+6089,U+6094,U+609f-60a0,U+60a3,U+60a6,U+60a8,U+60ac,U+60b2,U+60c5,U+60ca,U+60d1,U+60dc,U+60e0,U+60e7-60e8,U+60ef,U+60f3,U+6108,U+610f,U+611f,U+6124,U+613f,U+6148,U+614e,U+6155,U+6162,U+6167,U+6170,U+61c2,U+61d2,U+620f-6212,U+6216,U+6218,U+622a,U+6234,U+6237,U+623f-6240,U+624b,U+624d-624e,U+6253,U+6258,U+6263,U+6267,U+6269,U+626b-626c,U+626e,U+6270,U+6276,U+6279,U+627e-6280,U+628a,U+6291,U+6293,U+6295,U+6297-6298,U+629b,U+62a2,U+62a4-62a5,U+62ab,U+62b1,U+62b5,U+62b9,U+62bd,U+62c5-62c6,U+62c9,U+62cd,U+62d2-62d4,U+62d6,U+62db-62dc,U+62df,U+62e5,U+62e8-62e9,U+62ec,U+62fc,U+62ff,U+6301-6302,U+6307,U+6309,U+6311,U+6316,U+6321,U+6324-6325,U+632f,U+633a,U+6355,U+635f,U+6362,U+636e,U+6377,U+6388-6389,U+638c,U+6392,U+63a2,U+63a5,U+63a7-63aa,U+63cf-63d0,U+63d2,U+63e1,U+63ed,U+63f4,U+641c,U+641e,U+642c-642d,U+643a,U+6444,U+6446-6447,U+6458,U+6469,U+6478,U+6491-6492,U+649e,U+64ad,U+64c5,U+64cd,U+64e6,U+652f,U+6536,U+6539,U+653b,U+653e-653f,U+6545,U+6548,U+654c,U+654f,U+6551,U+6559,U+6562-6563,U+656c,U+6570,U+6574,U+6587,U+6591,U+6597,U+6599,U+659c,U+65a4,U+65ad,U+65af-65b0,U+65b9,U+65bd,U+65c1,U+65c5,U+65cb,U+65cf,U+65d7,U+65e0,U+65e2,U+65e5-65e9,U+65f6,U+65fa,U+6602,U+660c,U+660e,U+6613,U+661f-6620,U+6625,U+6628,U+662f,U+663e,U+664b,U+6653,U+665a,U+6668,U+666e-6670,U+6676,U+667a,U+6682,U+6696-6697,U+66b4,U+66f0,U+66f2,U+66f4,U+66fc,U+66fe-6700,U+6708-6709,U+670b,U+670d,U+6717,U+671b,U+671d,U+671f,U+6728,U+672a-672c,U+672f,U+6731,U+6734-6735,U+673a,U+6740,U+6742-6743,U+6746,U+674e,U+6750-6751,U+675c,U+675f,U+6761,U+6765,U+6768,U+676f-6770,U+677e-677f,U+6781,U+6784,U+6790,U+6797,U+679c-679d,U+67aa,U+67b6,U+67d0,U+67d3-67d4,U+67e5,U+67f1,U+67f3,U+6807,U+680f,U+6811,U+6821,U+6837-6839,U+683c,U+6843,U+6846,U+6848,U+684c,U+6851,U+6863,U+6865,U+6881,U+6885,U+68a6,U+68af-68b0,U+68c0,U+68d2,U+68ee,U+690d,U+695a,U+697c,U+6982,U+699c,U+69fd,U+6a21,U+6a2a,U+6b21-6b23,U+6b27,U+6b32,U+6b3a,U+6b3e,U+6b4c,U+6b62-6b66,U+6b7b,U+6b8a-6b8b,U+6b96,U+6bb5,U+6bbf,U+6bc1,U+6bcd,U+6bcf,U+6bd2,U+6bd4-6bd5,U+6bdb,U+6beb,U+6c0f,U+6c11,U+6c14,U+6c1b,U+6c27,U+6c34,U+6c38,U+6c41-6c42,U+6c47,U+6c49,U+6c57,U+6c5f-6c61,U+6c64,U+6c7d,U+6c88-6c89,U+6c99,U+6c9f,U+6ca1,U+6cb3,U+6cb9,U+6cbb,U+6cbf,U+6cc4,U+6cc9,U+6cd5,U+6cdb,U+6ce1-6ce2,U+6ce5,U+6ce8,U+6cea,U+6cf0,U+6cfd,U+6d01,U+6d0b,U+6d12,U+6d17,U+6d1b,U+6d1e,U+6d25,U+6d2a,U+6d32,U+6d3b,U+6d3e,U+6d41,U+6d45,U+6d4b,U+6d4e,U+6d53,U+6d59,U+6d69-6d6a,U+6d6e,U+6d77-6d78,U+6d82,U+6d88-6d89,U+6d8c,U+6da6,U+6da8,U+6daf,U+6db2,U+6db5,U+6dcb,U+6dd8,U+6de1,U+6df1,U+6df7,U+6dfb,U+6e05,U+6e10,U+6e20-6e21,U+6e29,U+6e2f,U+6e34,U+6e38,U+6e56,U+6e7e-6e7f,U+6e90,U+6eaa,U+6ecb,U+6ed1,U+6eda,U+6ee1,U+6ef4,U+6f02,U+6f0f,U+6f14,U+6f2b,U+6f5c,U+6f6e,U+6fb3,U+6fc0,U+704c,U+706b,U+706d,U+706f-7070,U+7075,U+707e,U+7089,U+708e,U+7092,U+70ae,U+70b8-70b9,U+70bc,U+70c2,U+70c8,U+70df,U+70e6-70e7,U+70ed,U+7126,U+7136,U+7167,U+716e,U+718a,U+719f,U+71c3,U+71d5,U+71e5,U+7206,U+722c,U+7231,U+7236-7238,U+723d,U+7247-7248,U+724c,U+7259,U+725b,U+7262,U+7269,U+7275,U+7279,U+72af,U+72b6,U+72b9,U+72c2,U+72d7,U+72ec,U+72f1,U+731b-731c,U+732a-732b,U+732e,U+7384,U+7387,U+7389,U+738b,U+739b,U+73a9,U+73af-73b0,U+73bb,U+73cd,U+73e0,U+73ed,U+7403,U+7406,U+7434,U+745e,U+7483,U+74dc,U+74e6,U+74f6,U+7518,U+751a,U+751c,U+751f,U+7528,U+7530-7533,U+7535,U+7537,U+753b,U+7545,U+754c,U+7559,U+7565,U+756a,U+758f,U+7591,U+7597,U+75af,U+75b2,U+75bc,U+75be,U+75c5,U+75c7,U+75d5,U+75db,U+7626,U+767b,U+767d-767e,U+7684,U+7686-7687,U+76ae,U+76c6,U+76c8,U+76ca,U+76d0-76d2,U+76d6-76d8,U+76db,U+76df,U+76ee,U+76f4,U+76f8,U+76fe,U+7701,U+7709,U+770b,U+771f-7720,U+773c,U+7740,U+775b,U+7761,U+7763,U+77ac,U+77db,U+77e5,U+77ed,U+77f3,U+77ff,U+7801,U+7814,U+7834,U+7840,U+7855,U+786c,U+786e,U+788d-788e,U+7891,U+7897,U+78b0,U+78e8,U+793a,U+793c,U+793e,U+7956,U+795d-795e,U+7965,U+7968,U+7981,U+798f,U+79bb,U+79c0-79c1,U+79cb,U+79cd,U+79d1-79d2,U+79d8,U+79df,U+79e6,U+79ef-79f0,U+79fb,U+7a00,U+7a0b,U+7a0d-7a0e,U+7a33,U+7a3f,U+7a76-7a77,U+7a7a,U+7a7f,U+7a81,U+7a97,U+7acb,U+7ad9,U+7ade-7ae0,U+7ae5,U+7aef,U+7af9,U+7b11,U+7b14,U+7b26,U+7b2c,U+7b49,U+7b51,U+7b54,U+7b56,U+7b79,U+7b7e,U+7b80,U+7b97,U+7ba1,U+7bb1,U+7bc7,U+7c4d,U+7c73,U+7c7b,U+7c89,U+7c92,U+7c97-7c98,U+7cae,U+7cbe,U+7cca,U+7cd6,U+7cfb,U+7d20,U+7d22,U+7d27,U+7d2b,U+7d2f,U+7e41,U+7ea0,U+7ea2,U+7ea4,U+7ea6-7ea7,U+7eaa,U+7eaf,U+7eb3,U+7eb5,U+7eb7-7eb9,U+7ebd,U+7ebf,U+7ec3-7ec4,U+7ec6-7ec8,U+7ecd,U+7ecf,U+7ed3,U+7ed5,U+7ed8-7ed9,U+7edc-7edd,U+7edf,U+7ee7,U+7ee9-7eea,U+7eed,U+7ef4-7ef5,U+7efc,U+7eff,U+7f13,U+7f16,U+7f18,U+7f1d,U+7f29,U+7f3a,U+7f51,U+7f57,U+7f5a,U+7f62,U+7f6a,U+7f6e,U+7f72,U+7f8a,U+7f8e,U+7fa4,U+7fbd,U+7ffb-7ffc,U+8000-8001,U+8003,U+8005,U+800c,U+8010,U+8017,U+8033,U+804a,U+804c,U+8054,U+8058,U+805a,U+806a,U+8083,U+8089,U+808c,U+809a,U+80a0-80a1,U+80a4-80a5,U+80a9,U+80af,U+80b2,U+80bf,U+80c3,U+80c6,U+80cc,U+80ce,U+80dc,U+80de,U+80e1,U+80f6,U+80f8,U+80fd,U+8102,U+8106,U+8109,U+810f,U+8111,U+811a,U+8131,U+8138,U+8150,U+8170,U+8179,U+817b,U+817e-817f,U+819c,U+81e3,U+81ea,U+81f3-81f4,U+820d,U+8212,U+821e,U+822a,U+822c,U+8239,U+826f-8270,U+8272-8273,U+827a,U+827e,U+8282,U+82b1,U+82b3,U+82cd,U+82cf,U+82e5-82e6,U+82f1,U+8303,U+8336,U+8349,U+8350,U+8352,U+8361,U+8363,U+836f,U+8377,U+83ab,U+83b1-83b2,U+83b7,U+83dc,U+83f2,U+8425,U+8428,U+843d,U+8457,U+8463,U+8499,U+84b8,U+84dd,U+8584,U+85cf,U+864e,U+8651,U+865a,U+866b,U+867d,U+86cb,U+86ee,U+871c,U+878d,U+8840,U+884c,U+8857,U+8861,U+8863,U+8865,U+8868,U+8870,U+888b,U+8896,U+88ab,U+88ad,U+88c1-88c2,U+88c5,U+897f,U+8981,U+8986,U+89c1-89c2,U+89c4,U+89c6,U+89c8-89c9,U+89d2,U+89e3,U+89e6,U+8a00,U+8a89,U+8b66,U+8ba1-8ba2,U+8ba4,U+8ba8-8ba9,U+8bad-8bb0,U+8bb2,U+8bb8,U+8bba,U+8bbe-8bbf,U+8bc1,U+8bc4,U+8bc6,U+8bc9-8bca,U+8bcd,U+8bd1,U+8bd5,U+8bd7,U+8bda,U+8bdd-8bde,U+8be2,U+8be5-8be6,U+8bed,U+8bef,U+8bf1,U+8bf4,U+8bf7-8bf8,U+8bfa-8bfb,U+8bfe,U+8c01,U+8c03,U+8c08,U+8c0b,U+8c10,U+8c13,U+8c22,U+8c28,U+8c31,U+8c37,U+8c46,U+8c61,U+8c6a,U+8c8c,U+8d1d,U+8d1f,U+8d21-8d28,U+8d2a-8d2b,U+8d2d,U+8d2f,U+8d34-8d35,U+8d38-8d39,U+8d44,U+8d4b,U+8d4f,U+8d56,U+8d5a-8d5b,U+8d5e,U+8d60,U+8d62,U+8d64,U+8d6b,U+8d70,U+8d74-8d77,U+8d85,U+8d8a-8d8b,U+8da3,U+8db3,U+8dc3,U+8dcc,U+8dd1,U+8ddd,U+8ddf,U+8de8,U+8def,U+8df3,U+8df5,U+8e0f,U+8e2a,U+8eab,U+8f66,U+8f68,U+8f6c,U+8f6e-8f6f,U+8f7b,U+8f7d,U+8f83,U+8f85-8f86,U+8f88-8f89,U+8f91,U+8f93,U+8f9b,U+8f9e,U+8fa3,U+8fa8-8fa9,U+8fb9,U+8fbe,U+8fc1,U+8fc5,U+8fc7-8fc8,U+8fce,U+8fd0-8fd1,U+8fd4,U+8fd8-8fd9,U+8fdb-8fdf,U+8fea-8feb,U+8ff0,U+8ff7,U+8ff9,U+8ffd,U+9000-9003,U+9006,U+9009,U+900f-9010,U+9012,U+9014,U+901a,U+901d,U+901f-9020,U+9022,U+903b-903c,U+9047,U+904d,U+9053,U+9057,U+9065,U+906d,U+9075,U+907f-9080,U+90a3,U+90a6,U+90aa,U+90ae,U+90bb,U+90c1,U+90ce,U+90d1,U+90e8,U+90ed,U+90fd,U+914d,U+9152,U+9177-9178,U+9189,U+9192,U+91c7,U+91ca,U+91cc-91cf,U+91d1,U+9274,U+9488,U+949f,U+94a2,U+94b1,U+94bb,U+94c1,U+94dc,U+94f6,U+94fa,U+94fe,U+9500-9501,U+9505,U+950b,U+9510,U+9519,U+9526,U+952e,U+9547,U+955c,U+957f,U+95e8,U+95ea,U+95ed-95ee,U+95f2,U+95f4,U+95f7,U+95f9,U+95fb,U+9605,U+9614,U+961f,U+9632-9636,U+963b,U+963f,U+9644-9646,U+9648,U+964d,U+9650,U+9662,U+9664,U+9669-966a,U+9675-9677,U+9686,U+968f-9690,U+9694,U+969c,U+96be,U+96c4-96c6,U+96d5,U+96e8,U+96ea,U+96f6-96f7,U+96fe,U+9700,U+9707,U+9732,U+9738,U+9752,U+9759,U+975e,U+9760,U+9762,U+9769,U+978b,U+97e9,U+97f3,U+97f5,U+9875-9876,U+9879-987b,U+987e-987f,U+9884,U+9886-9887,U+9891,U+9897-9898,U+989c-989d,U+98ce,U+98d8,U+98de-98df,U+9910,U+996d-996e,U+9970-9971,U+9986,U+9996,U+9999,U+9a6c,U+9a71,U+9a7b,U+9a7e,U+9a82,U+9a8c,U+9a91,U+9a97,U+9aa4,U+9aa8,U+9ad8,U+9b3c,U+9b42,U+9b45,U+9b54,U+9c7c,U+9c81,U+9c9c,U+9e1f,U+9e21,U+9e23,U+9ea6,U+9ebb,U+9ec4,U+9ece,U+9ed1,U+9ed8,U+9f13,U+9f20,U+9f3b,U+9f50,U+9f7f,U+9f84,U+9f99,U+ff01,U+ff08-ff09,U+ff0c-ff0e,U+ff1a-ff1b,U+ff1f,U+ff5e;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.100.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.100.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.100.otf')  format('opentype');unicode-range:U+0021-0023,U+0025-005b,U+005d,U+005f,U+0061-007a,U+007e-00a0,U+00b7,U+2014,U+2018-2019,U+201c-201d,U+2026,U+3000-3002,U+300a-300d,U+3010-3011,U+4e00-4e01,U+4e03,U+4e07-4e0b,U+4e0d-4e0e,U+4e11,U+4e13-4e14,U+4e16,U+4e1a-4e1d,U+4e22,U+4e24-4e25,U+4e27,U+4e2a,U+4e2d,U+4e30,U+4e32,U+4e34,U+4e39-4e3b,U+4e3d-4e3e,U+4e43,U+4e45,U+4e48-4e49,U+4e4b-4e4c,U+4e4e-4e50,U+4e54,U+4e58,U+4e5d,U+4e5f-4e61,U+4e66,U+4e70-4e71,U+4e73,U+4e86,U+4e88-4e89,U+4e8b-4e8c,U+4e8e-4e8f,U+4e91-4e92,U+4e94-4e95,U+4e9a-4e9b,U+4ea1,U+4ea4,U+4ea6-4ea7,U+4eab-4eac,U+4eae,U+4eb2,U+4eba,U+4ebf-4ec1,U+4ec5,U+4eca-4ecb,U+4ecd-4ece,U+4ed3-4ed4,U+4ed6,U+4ed8-4ed9,U+4ee3-4ee5,U+4eea,U+4eec,U+4ef0,U+4ef6-4ef7,U+4efb,U+4efd,U+4eff,U+4f01,U+4f0a,U+4f0d,U+4f0f,U+4f11,U+4f17-4f1a,U+4f1f-4f20,U+4f24,U+4f26,U+4f2a,U+4f2f-4f30,U+4f34,U+4f38,U+4f3c,U+4f46,U+4f4d-4f4f,U+4f53,U+4f55,U+4f59,U+4f5b-4f5c,U+4f60,U+4f69,U+4f73,U+4f7f,U+4f8b,U+4f9b,U+4f9d,U+4fa7,U+4fb5,U+4fbf,U+4fc3-4fc4,U+4fca,U+4fd7,U+4fdd,U+4fe1,U+4fe9,U+4fee,U+4ff1,U+500d,U+5012,U+5019,U+501f,U+503c,U+503e,U+5047,U+504f,U+505a,U+505c,U+5065,U+5076-5077,U+507f,U+50a8,U+50b2,U+50bb,U+50cf,U+513f,U+5141,U+5143-5145,U+5148-5149,U+514b,U+514d,U+515a,U+5165,U+5168,U+516b-516d,U+5170-5171,U+5173-5178,U+517b-517d,U+5185,U+518c-518d,U+5192,U+5199,U+519b-519c,U+51a0,U+51ac,U+51b0,U+51b2-51b3,U+51b5,U+51b7,U+51c0,U+51c6,U+51c9,U+51cc,U+51cf,U+51dd,U+51e0-51e1,U+51e4,U+51ed,U+51ef,U+51f6,U+51fa-51fb,U+5200,U+5206-5207,U+520a,U+5211-5212,U+5217-521b,U+521d,U+5220,U+5224,U+5229,U+522b,U+5230,U+5236-5237,U+523a-523b,U+5242,U+524d,U+5251,U+5267,U+5269-526a,U+526f,U+5272,U+529b,U+529d-52a1,U+52a3,U+52a8-52aa,U+52b1-52b3,U+52bf,U+52c7,U+52d2,U+52e4,U+52fe,U+5300,U+5305,U+5316-5317,U+5339-533b,U+5341,U+5343,U+5347-5348,U+534a,U+534e-534f,U+5353,U+5355-5357,U+535a,U+5360-5361,U+5367,U+536b,U+5370-5371,U+5373-5374,U+5377,U+5382,U+5385-5386,U+5389,U+538b-538c,U+5398,U+539a,U+539f,U+53bb,U+53bf,U+53c2,U+53c8,U+53ca-53cd,U+53d1,U+53d6-53d8,U+53e0,U+53e3-53e6,U+53ea-53ec,U+53ef-53f0,U+53f2-53f3,U+53f6-53f9,U+5403-5404,U+5408-5409,U+540c-540e,U+5410-5411,U+5413,U+5417,U+541b,U+5426-5427,U+542b-542c,U+542f,U+5434,U+5438-5439,U+5440,U+5446,U+5448,U+544a,U+5458,U+5462,U+5468,U+5473,U+5475,U+547c-547d,U+548c,U+5496,U+54a8,U+54c1,U+54c8,U+54cd,U+54e5-54e6,U+54ea,U+54ed,U+54f2,U+5510,U+552e-552f,U+5531,U+5546,U+554a,U+5565-5566,U+5584,U+558a,U+559c-559d,U+55b7,U+5609,U+561b,U+5634,U+5668,U+56db,U+56de,U+56e0,U+56e2,U+56ed,U+56f0,U+56f4,U+56fa,U+56fd-56fe,U+5706,U+5708,U+571f,U+5723,U+5728,U+5730,U+573a,U+5740,U+5747,U+574f-5751,U+5757,U+575a-575b,U+5761,U+5766,U+5782,U+578b,U+57c3,U+57cb,U+57ce,U+57df,U+57f9-57fa,U+5802,U+5806,U+5821,U+582a,U+5851,U+5854,U+585e,U+586b,U+5883,U+5899,U+589e,U+58a8,U+58c1,U+58eb,U+58ee,U+58f0,U+58f3,U+5904,U+5907,U+590d,U+590f,U+5915-5916,U+591a,U+591c,U+591f,U+5927,U+5929-592b,U+592e,U+5931,U+5934,U+5938-593a,U+5947-5949,U+594b,U+594f,U+5954,U+5956-5957,U+5965,U+5973,U+5976,U+5979,U+597d,U+5982,U+5986-5988,U+5999,U+59b9,U+59bb,U+59c6,U+59cb,U+59d0-59d1,U+59d3-59d4,U+59dc,U+59fb,U+59ff,U+5a01,U+5a18,U+5a31,U+5a46,U+5a5a,U+5a92,U+5ac1,U+5acc,U+5ae9,U+5b50,U+5b54-5b55,U+5b57-5b59,U+5b63-5b64,U+5b66,U+5b69,U+5b81,U+5b83,U+5b85,U+5b87-5b89,U+5b8b-5b8c,U+5b8f,U+5b97-5b98,U+5b9a,U+5b9c-5b9e,U+5ba1-5ba4,U+5bab,U+5bb3,U+5bb6,U+5bb9,U+5bbd-5bbf,U+5bc2,U+5bc4,U+5bc6,U+5bcc,U+5bd2-5bd3,U+5bdf,U+5bf8-5bfc,U+5bff,U+5c01,U+5c04,U+5c06,U+5c0a,U+5c0f,U+5c11,U+5c14,U+5c16,U+5c18,U+5c1a,U+5c1d,U+5c24,U+5c31,U+5c3a,U+5c3c-5c40,U+5c42,U+5c45,U+5c48,U+5c4a-5c4b,U+5c4f,U+5c55,U+5c5e,U+5c71,U+5c81,U+5c97,U+5c9b,U+5ca9,U+5cb8,U+5cf0,U+5d07,U+5ddd-5dde,U+5de1,U+5de5-5de8,U+5dee,U+5df1-5df2,U+5df4,U+5e01-5e03,U+5e05,U+5e08,U+5e0c,U+5e1d,U+5e26,U+5e2d-5e2e,U+5e38,U+5e45,U+5e55,U+5e72-5e74,U+5e76,U+5e78,U+5e7b-5e7d,U+5e7f,U+5e84,U+5e86,U+5e8a,U+5e8f,U+5e93-5e95,U+5e97,U+5e99,U+5e9c,U+5e9f,U+5ea6-5ea7,U+5ead,U+5eb7,U+5ec9,U+5ef6-5ef7,U+5efa,U+5f00,U+5f02-5f04,U+5f0f,U+5f15,U+5f1f-5f20,U+5f25,U+5f2f,U+5f31,U+5f39-5f3a,U+5f52-5f53,U+5f55,U+5f62,U+5f69,U+5f71,U+5f79,U+5f7b-5f7c,U+5f80-5f81,U+5f84-5f85,U+5f88,U+5f8b,U+5f90,U+5f92,U+5f97,U+5fa1,U+5faa,U+5fae,U+5fb7,U+5fbd,U+5fc3,U+5fc5-5fc6,U+5fcc-5fcd,U+5fd7-5fd9,U+5fe0,U+5fe7,U+5feb,U+5ff5,U+5ffd,U+6000-6001,U+600e,U+6012,U+6015,U+601d,U+6025,U+6027-6028,U+602a,U+603b,U+604b,U+6050,U+6052,U+6062,U+6068-6069,U+606f-6070,U+6076,U+607c,U+6089,U+6094,U+609f-60a0,U+60a3,U+60a6,U+60a8,U+60ac,U+60b2,U+60c5,U+60ca,U+60d1,U+60dc,U+60e0,U+60e7-60e8,U+60ef,U+60f3,U+6108,U+610f,U+611f,U+6124,U+613f,U+6148,U+614e,U+6155,U+6162,U+6167,U+6170,U+61c2,U+61d2,U+620f-6212,U+6216,U+6218,U+622a,U+6234,U+6237,U+623f-6240,U+624b,U+624d-624e,U+6253,U+6258,U+6263,U+6267,U+6269,U+626b-626c,U+626e,U+6270,U+6276,U+6279,U+627e-6280,U+628a,U+6291,U+6293,U+6295,U+6297-6298,U+629b,U+62a2,U+62a4-62a5,U+62ab,U+62b1,U+62b5,U+62b9,U+62bd,U+62c5-62c6,U+62c9,U+62cd,U+62d2-62d4,U+62d6,U+62db-62dc,U+62df,U+62e5,U+62e8-62e9,U+62ec,U+62fc,U+62ff,U+6301-6302,U+6307,U+6309,U+6311,U+6316,U+6321,U+6324-6325,U+632f,U+633a,U+6355,U+635f,U+6362,U+636e,U+6377,U+6388-6389,U+638c,U+6392,U+63a2,U+63a5,U+63a7-63aa,U+63cf-63d0,U+63d2,U+63e1,U+63ed,U+63f4,U+641c,U+641e,U+642c-642d,U+643a,U+6444,U+6446-6447,U+6458,U+6469,U+6478,U+6491-6492,U+649e,U+64ad,U+64c5,U+64cd,U+64e6,U+652f,U+6536,U+6539,U+653b,U+653e-653f,U+6545,U+6548,U+654c,U+654f,U+6551,U+6559,U+6562-6563,U+656c,U+6570,U+6574,U+6587,U+6591,U+6597,U+6599,U+659c,U+65a4,U+65ad,U+65af-65b0,U+65b9,U+65bd,U+65c1,U+65c5,U+65cb,U+65cf,U+65d7,U+65e0,U+65e2,U+65e5-65e9,U+65f6,U+65fa,U+6602,U+660c,U+660e,U+6613,U+661f-6620,U+6625,U+6628,U+662f,U+663e,U+664b,U+6653,U+665a,U+6668,U+666e-6670,U+6676,U+667a,U+6682,U+6696-6697,U+66b4,U+66f0,U+66f2,U+66f4,U+66fc,U+66fe-6700,U+6708-6709,U+670b,U+670d,U+6717,U+671b,U+671d,U+671f,U+6728,U+672a-672c,U+672f,U+6731,U+6734-6735,U+673a,U+6740,U+6742-6743,U+6746,U+674e,U+6750-6751,U+675c,U+675f,U+6761,U+6765,U+6768,U+676f-6770,U+677e-677f,U+6781,U+6784,U+6790,U+6797,U+679c-679d,U+67aa,U+67b6,U+67d0,U+67d3-67d4,U+67e5,U+67f1,U+67f3,U+6807,U+680f,U+6811,U+6821,U+6837-6839,U+683c,U+6843,U+6846,U+6848,U+684c,U+6851,U+6863,U+6865,U+6881,U+6885,U+68a6,U+68af-68b0,U+68c0,U+68d2,U+68ee,U+690d,U+695a,U+697c,U+6982,U+699c,U+69fd,U+6a21,U+6a2a,U+6b21-6b23,U+6b27,U+6b32,U+6b3a,U+6b3e,U+6b4c,U+6b62-6b66,U+6b7b,U+6b8a-6b8b,U+6b96,U+6bb5,U+6bbf,U+6bc1,U+6bcd,U+6bcf,U+6bd2,U+6bd4-6bd5,U+6bdb,U+6beb,U+6c0f,U+6c11,U+6c14,U+6c1b,U+6c27,U+6c34,U+6c38,U+6c41-6c42,U+6c47,U+6c49,U+6c57,U+6c5f-6c61,U+6c64,U+6c7d,U+6c88-6c89,U+6c99,U+6c9f,U+6ca1,U+6cb3,U+6cb9,U+6cbb,U+6cbf,U+6cc4,U+6cc9,U+6cd5,U+6cdb,U+6ce1-6ce2,U+6ce5,U+6ce8,U+6cea,U+6cf0,U+6cfd,U+6d01,U+6d0b,U+6d12,U+6d17,U+6d1b,U+6d1e,U+6d25,U+6d2a,U+6d32,U+6d3b,U+6d3e,U+6d41,U+6d45,U+6d4b,U+6d4e,U+6d53,U+6d59,U+6d69-6d6a,U+6d6e,U+6d77-6d78,U+6d82,U+6d88-6d89,U+6d8c,U+6da6,U+6da8,U+6daf,U+6db2,U+6db5,U+6dcb,U+6dd8,U+6de1,U+6df1,U+6df7,U+6dfb,U+6e05,U+6e10,U+6e20-6e21,U+6e29,U+6e2f,U+6e34,U+6e38,U+6e56,U+6e7e-6e7f,U+6e90,U+6eaa,U+6ecb,U+6ed1,U+6eda,U+6ee1,U+6ef4,U+6f02,U+6f0f,U+6f14,U+6f2b,U+6f5c,U+6f6e,U+6fb3,U+6fc0,U+704c,U+706b,U+706d,U+706f-7070,U+7075,U+707e,U+7089,U+708e,U+7092,U+70ae,U+70b8-70b9,U+70bc,U+70c2,U+70c8,U+70df,U+70e6-70e7,U+70ed,U+7126,U+7136,U+7167,U+716e,U+718a,U+719f,U+71c3,U+71d5,U+71e5,U+7206,U+722c,U+7231,U+7236-7238,U+723d,U+7247-7248,U+724c,U+7259,U+725b,U+7262,U+7269,U+7275,U+7279,U+72af,U+72b6,U+72b9,U+72c2,U+72d7,U+72ec,U+72f1,U+731b-731c,U+732a-732b,U+732e,U+7384,U+7387,U+7389,U+738b,U+739b,U+73a9,U+73af-73b0,U+73bb,U+73cd,U+73e0,U+73ed,U+7403,U+7406,U+7434,U+745e,U+7483,U+74dc,U+74e6,U+74f6,U+7518,U+751a,U+751c,U+751f,U+7528,U+7530-7533,U+7535,U+7537,U+753b,U+7545,U+754c,U+7559,U+7565,U+756a,U+758f,U+7591,U+7597,U+75af,U+75b2,U+75bc,U+75be,U+75c5,U+75c7,U+75d5,U+75db,U+7626,U+767b,U+767d-767e,U+7684,U+7686-7687,U+76ae,U+76c6,U+76c8,U+76ca,U+76d0-76d2,U+76d6-76d8,U+76db,U+76df,U+76ee,U+76f4,U+76f8,U+76fe,U+7701,U+7709,U+770b,U+771f-7720,U+773c,U+7740,U+775b,U+7761,U+7763,U+77ac,U+77db,U+77e5,U+77ed,U+77f3,U+77ff,U+7801,U+7814,U+7834,U+7840,U+7855,U+786c,U+786e,U+788d-788e,U+7891,U+7897,U+78b0,U+78e8,U+793a,U+793c,U+793e,U+7956,U+795d-795e,U+7965,U+7968,U+7981,U+798f,U+79bb,U+79c0-79c1,U+79cb,U+79cd,U+79d1-79d2,U+79d8,U+79df,U+79e6,U+79ef-79f0,U+79fb,U+7a00,U+7a0b,U+7a0d-7a0e,U+7a33,U+7a3f,U+7a76-7a77,U+7a7a,U+7a7f,U+7a81,U+7a97,U+7acb,U+7ad9,U+7ade-7ae0,U+7ae5,U+7aef,U+7af9,U+7b11,U+7b14,U+7b26,U+7b2c,U+7b49,U+7b51,U+7b54,U+7b56,U+7b79,U+7b7e,U+7b80,U+7b97,U+7ba1,U+7bb1,U+7bc7,U+7c4d,U+7c73,U+7c7b,U+7c89,U+7c92,U+7c97-7c98,U+7cae,U+7cbe,U+7cca,U+7cd6,U+7cfb,U+7d20,U+7d22,U+7d27,U+7d2b,U+7d2f,U+7e41,U+7ea0,U+7ea2,U+7ea4,U+7ea6-7ea7,U+7eaa,U+7eaf,U+7eb3,U+7eb5,U+7eb7-7eb9,U+7ebd,U+7ebf,U+7ec3-7ec4,U+7ec6-7ec8,U+7ecd,U+7ecf,U+7ed3,U+7ed5,U+7ed8-7ed9,U+7edc-7edd,U+7edf,U+7ee7,U+7ee9-7eea,U+7eed,U+7ef4-7ef5,U+7efc,U+7eff,U+7f13,U+7f16,U+7f18,U+7f1d,U+7f29,U+7f3a,U+7f51,U+7f57,U+7f5a,U+7f62,U+7f6a,U+7f6e,U+7f72,U+7f8a,U+7f8e,U+7fa4,U+7fbd,U+7ffb-7ffc,U+8000-8001,U+8003,U+8005,U+800c,U+8010,U+8017,U+8033,U+804a,U+804c,U+8054,U+8058,U+805a,U+806a,U+8083,U+8089,U+808c,U+809a,U+80a0-80a1,U+80a4-80a5,U+80a9,U+80af,U+80b2,U+80bf,U+80c3,U+80c6,U+80cc,U+80ce,U+80dc,U+80de,U+80e1,U+80f6,U+80f8,U+80fd,U+8102,U+8106,U+8109,U+810f,U+8111,U+811a,U+8131,U+8138,U+8150,U+8170,U+8179,U+817b,U+817e-817f,U+819c,U+81e3,U+81ea,U+81f3-81f4,U+820d,U+8212,U+821e,U+822a,U+822c,U+8239,U+826f-8270,U+8272-8273,U+827a,U+827e,U+8282,U+82b1,U+82b3,U+82cd,U+82cf,U+82e5-82e6,U+82f1,U+8303,U+8336,U+8349,U+8350,U+8352,U+8361,U+8363,U+836f,U+8377,U+83ab,U+83b1-83b2,U+83b7,U+83dc,U+83f2,U+8425,U+8428,U+843d,U+8457,U+8463,U+8499,U+84b8,U+84dd,U+8584,U+85cf,U+864e,U+8651,U+865a,U+866b,U+867d,U+86cb,U+86ee,U+871c,U+878d,U+8840,U+884c,U+8857,U+8861,U+8863,U+8865,U+8868,U+8870,U+888b,U+8896,U+88ab,U+88ad,U+88c1-88c2,U+88c5,U+897f,U+8981,U+8986,U+89c1-89c2,U+89c4,U+89c6,U+89c8-89c9,U+89d2,U+89e3,U+89e6,U+8a00,U+8a89,U+8b66,U+8ba1-8ba2,U+8ba4,U+8ba8-8ba9,U+8bad-8bb0,U+8bb2,U+8bb8,U+8bba,U+8bbe-8bbf,U+8bc1,U+8bc4,U+8bc6,U+8bc9-8bca,U+8bcd,U+8bd1,U+8bd5,U+8bd7,U+8bda,U+8bdd-8bde,U+8be2,U+8be5-8be6,U+8bed,U+8bef,U+8bf1,U+8bf4,U+8bf7-8bf8,U+8bfa-8bfb,U+8bfe,U+8c01,U+8c03,U+8c08,U+8c0b,U+8c10,U+8c13,U+8c22,U+8c28,U+8c31,U+8c37,U+8c46,U+8c61,U+8c6a,U+8c8c,U+8d1d,U+8d1f,U+8d21-8d28,U+8d2a-8d2b,U+8d2d,U+8d2f,U+8d34-8d35,U+8d38-8d39,U+8d44,U+8d4b,U+8d4f,U+8d56,U+8d5a-8d5b,U+8d5e,U+8d60,U+8d62,U+8d64,U+8d6b,U+8d70,U+8d74-8d77,U+8d85,U+8d8a-8d8b,U+8da3,U+8db3,U+8dc3,U+8dcc,U+8dd1,U+8ddd,U+8ddf,U+8de8,U+8def,U+8df3,U+8df5,U+8e0f,U+8e2a,U+8eab,U+8f66,U+8f68,U+8f6c,U+8f6e-8f6f,U+8f7b,U+8f7d,U+8f83,U+8f85-8f86,U+8f88-8f89,U+8f91,U+8f93,U+8f9b,U+8f9e,U+8fa3,U+8fa8-8fa9,U+8fb9,U+8fbe,U+8fc1,U+8fc5,U+8fc7-8fc8,U+8fce,U+8fd0-8fd1,U+8fd4,U+8fd8-8fd9,U+8fdb-8fdf,U+8fea-8feb,U+8ff0,U+8ff7,U+8ff9,U+8ffd,U+9000-9003,U+9006,U+9009,U+900f-9010,U+9012,U+9014,U+901a,U+901d,U+901f-9020,U+9022,U+903b-903c,U+9047,U+904d,U+9053,U+9057,U+9065,U+906d,U+9075,U+907f-9080,U+90a3,U+90a6,U+90aa,U+90ae,U+90bb,U+90c1,U+90ce,U+90d1,U+90e8,U+90ed,U+90fd,U+914d,U+9152,U+9177-9178,U+9189,U+9192,U+91c7,U+91ca,U+91cc-91cf,U+91d1,U+9274,U+9488,U+949f,U+94a2,U+94b1,U+94bb,U+94c1,U+94dc,U+94f6,U+94fa,U+94fe,U+9500-9501,U+9505,U+950b,U+9510,U+9519,U+9526,U+952e,U+9547,U+955c,U+957f,U+95e8,U+95ea,U+95ed-95ee,U+95f2,U+95f4,U+95f7,U+95f9,U+95fb,U+9605,U+9614,U+961f,U+9632-9636,U+963b,U+963f,U+9644-9646,U+9648,U+964d,U+9650,U+9662,U+9664,U+9669-966a,U+9675-9677,U+9686,U+968f-9690,U+9694,U+969c,U+96be,U+96c4-96c6,U+96d5,U+96e8,U+96ea,U+96f6-96f7,U+96fe,U+9700,U+9707,U+9732,U+9738,U+9752,U+9759,U+975e,U+9760,U+9762,U+9769,U+978b,U+97e9,U+97f3,U+97f5,U+9875-9876,U+9879-987b,U+987e-987f,U+9884,U+9886-9887,U+9891,U+9897-9898,U+989c-989d,U+98ce,U+98d8,U+98de-98df,U+9910,U+996d-996e,U+9970-9971,U+9986,U+9996,U+9999,U+9a6c,U+9a71,U+9a7b,U+9a7e,U+9a82,U+9a8c,U+9a91,U+9a97,U+9aa4,U+9aa8,U+9ad8,U+9b3c,U+9b42,U+9b45,U+9b54,U+9c7c,U+9c81,U+9c9c,U+9e1f,U+9e21,U+9e23,U+9ea6,U+9ebb,U+9ec4,U+9ece,U+9ed1,U+9ed8,U+9f13,U+9f20,U+9f3b,U+9f50,U+9f7f,U+9f84,U+9f99,U+ff01,U+ff08-ff09,U+ff0c-ff0e,U+ff1a-ff1b,U+ff1f,U+ff5e;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.100.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.100.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.100.otf')  format('opentype');unicode-range:U+0021-0023,U+0025-005b,U+005d,U+005f,U+0061-007a,U+007e-00a0,U+00b7,U+2014,U+2018-2019,U+201c-201d,U+2026,U+3000-3002,U+300a-300d,U+3010-3011,U+4e00-4e01,U+4e03,U+4e07-4e0b,U+4e0d-4e0e,U+4e11,U+4e13-4e14,U+4e16,U+4e1a-4e1d,U+4e22,U+4e24-4e25,U+4e27,U+4e2a,U+4e2d,U+4e30,U+4e32,U+4e34,U+4e39-4e3b,U+4e3d-4e3e,U+4e43,U+4e45,U+4e48-4e49,U+4e4b-4e4c,U+4e4e-4e50,U+4e54,U+4e58,U+4e5d,U+4e5f-4e61,U+4e66,U+4e70-4e71,U+4e73,U+4e86,U+4e88-4e89,U+4e8b-4e8c,U+4e8e-4e8f,U+4e91-4e92,U+4e94-4e95,U+4e9a-4e9b,U+4ea1,U+4ea4,U+4ea6-4ea7,U+4eab-4eac,U+4eae,U+4eb2,U+4eba,U+4ebf-4ec1,U+4ec5,U+4eca-4ecb,U+4ecd-4ece,U+4ed3-4ed4,U+4ed6,U+4ed8-4ed9,U+4ee3-4ee5,U+4eea,U+4eec,U+4ef0,U+4ef6-4ef7,U+4efb,U+4efd,U+4eff,U+4f01,U+4f0a,U+4f0d,U+4f0f,U+4f11,U+4f17-4f1a,U+4f1f-4f20,U+4f24,U+4f26,U+4f2a,U+4f2f-4f30,U+4f34,U+4f38,U+4f3c,U+4f46,U+4f4d-4f4f,U+4f53,U+4f55,U+4f59,U+4f5b-4f5c,U+4f60,U+4f69,U+4f73,U+4f7f,U+4f8b,U+4f9b,U+4f9d,U+4fa7,U+4fb5,U+4fbf,U+4fc3-4fc4,U+4fca,U+4fd7,U+4fdd,U+4fe1,U+4fe9,U+4fee,U+4ff1,U+500d,U+5012,U+5019,U+501f,U+503c,U+503e,U+5047,U+504f,U+505a,U+505c,U+5065,U+5076-5077,U+507f,U+50a8,U+50b2,U+50bb,U+50cf,U+513f,U+5141,U+5143-5145,U+5148-5149,U+514b,U+514d,U+515a,U+5165,U+5168,U+516b-516d,U+5170-5171,U+5173-5178,U+517b-517d,U+5185,U+518c-518d,U+5192,U+5199,U+519b-519c,U+51a0,U+51ac,U+51b0,U+51b2-51b3,U+51b5,U+51b7,U+51c0,U+51c6,U+51c9,U+51cc,U+51cf,U+51dd,U+51e0-51e1,U+51e4,U+51ed,U+51ef,U+51f6,U+51fa-51fb,U+5200,U+5206-5207,U+520a,U+5211-5212,U+5217-521b,U+521d,U+5220,U+5224,U+5229,U+522b,U+5230,U+5236-5237,U+523a-523b,U+5242,U+524d,U+5251,U+5267,U+5269-526a,U+526f,U+5272,U+529b,U+529d-52a1,U+52a3,U+52a8-52aa,U+52b1-52b3,U+52bf,U+52c7,U+52d2,U+52e4,U+52fe,U+5300,U+5305,U+5316-5317,U+5339-533b,U+5341,U+5343,U+5347-5348,U+534a,U+534e-534f,U+5353,U+5355-5357,U+535a,U+5360-5361,U+5367,U+536b,U+5370-5371,U+5373-5374,U+5377,U+5382,U+5385-5386,U+5389,U+538b-538c,U+5398,U+539a,U+539f,U+53bb,U+53bf,U+53c2,U+53c8,U+53ca-53cd,U+53d1,U+53d6-53d8,U+53e0,U+53e3-53e6,U+53ea-53ec,U+53ef-53f0,U+53f2-53f3,U+53f6-53f9,U+5403-5404,U+5408-5409,U+540c-540e,U+5410-5411,U+5413,U+5417,U+541b,U+5426-5427,U+542b-542c,U+542f,U+5434,U+5438-5439,U+5440,U+5446,U+5448,U+544a,U+5458,U+5462,U+5468,U+5473,U+5475,U+547c-547d,U+548c,U+5496,U+54a8,U+54c1,U+54c8,U+54cd,U+54e5-54e6,U+54ea,U+54ed,U+54f2,U+5510,U+552e-552f,U+5531,U+5546,U+554a,U+5565-5566,U+5584,U+558a,U+559c-559d,U+55b7,U+5609,U+561b,U+5634,U+5668,U+56db,U+56de,U+56e0,U+56e2,U+56ed,U+56f0,U+56f4,U+56fa,U+56fd-56fe,U+5706,U+5708,U+571f,U+5723,U+5728,U+5730,U+573a,U+5740,U+5747,U+574f-5751,U+5757,U+575a-575b,U+5761,U+5766,U+5782,U+578b,U+57c3,U+57cb,U+57ce,U+57df,U+57f9-57fa,U+5802,U+5806,U+5821,U+582a,U+5851,U+5854,U+585e,U+586b,U+5883,U+5899,U+589e,U+58a8,U+58c1,U+58eb,U+58ee,U+58f0,U+58f3,U+5904,U+5907,U+590d,U+590f,U+5915-5916,U+591a,U+591c,U+591f,U+5927,U+5929-592b,U+592e,U+5931,U+5934,U+5938-593a,U+5947-5949,U+594b,U+594f,U+5954,U+5956-5957,U+5965,U+5973,U+5976,U+5979,U+597d,U+5982,U+5986-5988,U+5999,U+59b9,U+59bb,U+59c6,U+59cb,U+59d0-59d1,U+59d3-59d4,U+59dc,U+59fb,U+59ff,U+5a01,U+5a18,U+5a31,U+5a46,U+5a5a,U+5a92,U+5ac1,U+5acc,U+5ae9,U+5b50,U+5b54-5b55,U+5b57-5b59,U+5b63-5b64,U+5b66,U+5b69,U+5b81,U+5b83,U+5b85,U+5b87-5b89,U+5b8b-5b8c,U+5b8f,U+5b97-5b98,U+5b9a,U+5b9c-5b9e,U+5ba1-5ba4,U+5bab,U+5bb3,U+5bb6,U+5bb9,U+5bbd-5bbf,U+5bc2,U+5bc4,U+5bc6,U+5bcc,U+5bd2-5bd3,U+5bdf,U+5bf8-5bfc,U+5bff,U+5c01,U+5c04,U+5c06,U+5c0a,U+5c0f,U+5c11,U+5c14,U+5c16,U+5c18,U+5c1a,U+5c1d,U+5c24,U+5c31,U+5c3a,U+5c3c-5c40,U+5c42,U+5c45,U+5c48,U+5c4a-5c4b,U+5c4f,U+5c55,U+5c5e,U+5c71,U+5c81,U+5c97,U+5c9b,U+5ca9,U+5cb8,U+5cf0,U+5d07,U+5ddd-5dde,U+5de1,U+5de5-5de8,U+5dee,U+5df1-5df2,U+5df4,U+5e01-5e03,U+5e05,U+5e08,U+5e0c,U+5e1d,U+5e26,U+5e2d-5e2e,U+5e38,U+5e45,U+5e55,U+5e72-5e74,U+5e76,U+5e78,U+5e7b-5e7d,U+5e7f,U+5e84,U+5e86,U+5e8a,U+5e8f,U+5e93-5e95,U+5e97,U+5e99,U+5e9c,U+5e9f,U+5ea6-5ea7,U+5ead,U+5eb7,U+5ec9,U+5ef6-5ef7,U+5efa,U+5f00,U+5f02-5f04,U+5f0f,U+5f15,U+5f1f-5f20,U+5f25,U+5f2f,U+5f31,U+5f39-5f3a,U+5f52-5f53,U+5f55,U+5f62,U+5f69,U+5f71,U+5f79,U+5f7b-5f7c,U+5f80-5f81,U+5f84-5f85,U+5f88,U+5f8b,U+5f90,U+5f92,U+5f97,U+5fa1,U+5faa,U+5fae,U+5fb7,U+5fbd,U+5fc3,U+5fc5-5fc6,U+5fcc-5fcd,U+5fd7-5fd9,U+5fe0,U+5fe7,U+5feb,U+5ff5,U+5ffd,U+6000-6001,U+600e,U+6012,U+6015,U+601d,U+6025,U+6027-6028,U+602a,U+603b,U+604b,U+6050,U+6052,U+6062,U+6068-6069,U+606f-6070,U+6076,U+607c,U+6089,U+6094,U+609f-60a0,U+60a3,U+60a6,U+60a8,U+60ac,U+60b2,U+60c5,U+60ca,U+60d1,U+60dc,U+60e0,U+60e7-60e8,U+60ef,U+60f3,U+6108,U+610f,U+611f,U+6124,U+613f,U+6148,U+614e,U+6155,U+6162,U+6167,U+6170,U+61c2,U+61d2,U+620f-6212,U+6216,U+6218,U+622a,U+6234,U+6237,U+623f-6240,U+624b,U+624d-624e,U+6253,U+6258,U+6263,U+6267,U+6269,U+626b-626c,U+626e,U+6270,U+6276,U+6279,U+627e-6280,U+628a,U+6291,U+6293,U+6295,U+6297-6298,U+629b,U+62a2,U+62a4-62a5,U+62ab,U+62b1,U+62b5,U+62b9,U+62bd,U+62c5-62c6,U+62c9,U+62cd,U+62d2-62d4,U+62d6,U+62db-62dc,U+62df,U+62e5,U+62e8-62e9,U+62ec,U+62fc,U+62ff,U+6301-6302,U+6307,U+6309,U+6311,U+6316,U+6321,U+6324-6325,U+632f,U+633a,U+6355,U+635f,U+6362,U+636e,U+6377,U+6388-6389,U+638c,U+6392,U+63a2,U+63a5,U+63a7-63aa,U+63cf-63d0,U+63d2,U+63e1,U+63ed,U+63f4,U+641c,U+641e,U+642c-642d,U+643a,U+6444,U+6446-6447,U+6458,U+6469,U+6478,U+6491-6492,U+649e,U+64ad,U+64c5,U+64cd,U+64e6,U+652f,U+6536,U+6539,U+653b,U+653e-653f,U+6545,U+6548,U+654c,U+654f,U+6551,U+6559,U+6562-6563,U+656c,U+6570,U+6574,U+6587,U+6591,U+6597,U+6599,U+659c,U+65a4,U+65ad,U+65af-65b0,U+65b9,U+65bd,U+65c1,U+65c5,U+65cb,U+65cf,U+65d7,U+65e0,U+65e2,U+65e5-65e9,U+65f6,U+65fa,U+6602,U+660c,U+660e,U+6613,U+661f-6620,U+6625,U+6628,U+662f,U+663e,U+664b,U+6653,U+665a,U+6668,U+666e-6670,U+6676,U+667a,U+6682,U+6696-6697,U+66b4,U+66f0,U+66f2,U+66f4,U+66fc,U+66fe-6700,U+6708-6709,U+670b,U+670d,U+6717,U+671b,U+671d,U+671f,U+6728,U+672a-672c,U+672f,U+6731,U+6734-6735,U+673a,U+6740,U+6742-6743,U+6746,U+674e,U+6750-6751,U+675c,U+675f,U+6761,U+6765,U+6768,U+676f-6770,U+677e-677f,U+6781,U+6784,U+6790,U+6797,U+679c-679d,U+67aa,U+67b6,U+67d0,U+67d3-67d4,U+67e5,U+67f1,U+67f3,U+6807,U+680f,U+6811,U+6821,U+6837-6839,U+683c,U+6843,U+6846,U+6848,U+684c,U+6851,U+6863,U+6865,U+6881,U+6885,U+68a6,U+68af-68b0,U+68c0,U+68d2,U+68ee,U+690d,U+695a,U+697c,U+6982,U+699c,U+69fd,U+6a21,U+6a2a,U+6b21-6b23,U+6b27,U+6b32,U+6b3a,U+6b3e,U+6b4c,U+6b62-6b66,U+6b7b,U+6b8a-6b8b,U+6b96,U+6bb5,U+6bbf,U+6bc1,U+6bcd,U+6bcf,U+6bd2,U+6bd4-6bd5,U+6bdb,U+6beb,U+6c0f,U+6c11,U+6c14,U+6c1b,U+6c27,U+6c34,U+6c38,U+6c41-6c42,U+6c47,U+6c49,U+6c57,U+6c5f-6c61,U+6c64,U+6c7d,U+6c88-6c89,U+6c99,U+6c9f,U+6ca1,U+6cb3,U+6cb9,U+6cbb,U+6cbf,U+6cc4,U+6cc9,U+6cd5,U+6cdb,U+6ce1-6ce2,U+6ce5,U+6ce8,U+6cea,U+6cf0,U+6cfd,U+6d01,U+6d0b,U+6d12,U+6d17,U+6d1b,U+6d1e,U+6d25,U+6d2a,U+6d32,U+6d3b,U+6d3e,U+6d41,U+6d45,U+6d4b,U+6d4e,U+6d53,U+6d59,U+6d69-6d6a,U+6d6e,U+6d77-6d78,U+6d82,U+6d88-6d89,U+6d8c,U+6da6,U+6da8,U+6daf,U+6db2,U+6db5,U+6dcb,U+6dd8,U+6de1,U+6df1,U+6df7,U+6dfb,U+6e05,U+6e10,U+6e20-6e21,U+6e29,U+6e2f,U+6e34,U+6e38,U+6e56,U+6e7e-6e7f,U+6e90,U+6eaa,U+6ecb,U+6ed1,U+6eda,U+6ee1,U+6ef4,U+6f02,U+6f0f,U+6f14,U+6f2b,U+6f5c,U+6f6e,U+6fb3,U+6fc0,U+704c,U+706b,U+706d,U+706f-7070,U+7075,U+707e,U+7089,U+708e,U+7092,U+70ae,U+70b8-70b9,U+70bc,U+70c2,U+70c8,U+70df,U+70e6-70e7,U+70ed,U+7126,U+7136,U+7167,U+716e,U+718a,U+719f,U+71c3,U+71d5,U+71e5,U+7206,U+722c,U+7231,U+7236-7238,U+723d,U+7247-7248,U+724c,U+7259,U+725b,U+7262,U+7269,U+7275,U+7279,U+72af,U+72b6,U+72b9,U+72c2,U+72d7,U+72ec,U+72f1,U+731b-731c,U+732a-732b,U+732e,U+7384,U+7387,U+7389,U+738b,U+739b,U+73a9,U+73af-73b0,U+73bb,U+73cd,U+73e0,U+73ed,U+7403,U+7406,U+7434,U+745e,U+7483,U+74dc,U+74e6,U+74f6,U+7518,U+751a,U+751c,U+751f,U+7528,U+7530-7533,U+7535,U+7537,U+753b,U+7545,U+754c,U+7559,U+7565,U+756a,U+758f,U+7591,U+7597,U+75af,U+75b2,U+75bc,U+75be,U+75c5,U+75c7,U+75d5,U+75db,U+7626,U+767b,U+767d-767e,U+7684,U+7686-7687,U+76ae,U+76c6,U+76c8,U+76ca,U+76d0-76d2,U+76d6-76d8,U+76db,U+76df,U+76ee,U+76f4,U+76f8,U+76fe,U+7701,U+7709,U+770b,U+771f-7720,U+773c,U+7740,U+775b,U+7761,U+7763,U+77ac,U+77db,U+77e5,U+77ed,U+77f3,U+77ff,U+7801,U+7814,U+7834,U+7840,U+7855,U+786c,U+786e,U+788d-788e,U+7891,U+7897,U+78b0,U+78e8,U+793a,U+793c,U+793e,U+7956,U+795d-795e,U+7965,U+7968,U+7981,U+798f,U+79bb,U+79c0-79c1,U+79cb,U+79cd,U+79d1-79d2,U+79d8,U+79df,U+79e6,U+79ef-79f0,U+79fb,U+7a00,U+7a0b,U+7a0d-7a0e,U+7a33,U+7a3f,U+7a76-7a77,U+7a7a,U+7a7f,U+7a81,U+7a97,U+7acb,U+7ad9,U+7ade-7ae0,U+7ae5,U+7aef,U+7af9,U+7b11,U+7b14,U+7b26,U+7b2c,U+7b49,U+7b51,U+7b54,U+7b56,U+7b79,U+7b7e,U+7b80,U+7b97,U+7ba1,U+7bb1,U+7bc7,U+7c4d,U+7c73,U+7c7b,U+7c89,U+7c92,U+7c97-7c98,U+7cae,U+7cbe,U+7cca,U+7cd6,U+7cfb,U+7d20,U+7d22,U+7d27,U+7d2b,U+7d2f,U+7e41,U+7ea0,U+7ea2,U+7ea4,U+7ea6-7ea7,U+7eaa,U+7eaf,U+7eb3,U+7eb5,U+7eb7-7eb9,U+7ebd,U+7ebf,U+7ec3-7ec4,U+7ec6-7ec8,U+7ecd,U+7ecf,U+7ed3,U+7ed5,U+7ed8-7ed9,U+7edc-7edd,U+7edf,U+7ee7,U+7ee9-7eea,U+7eed,U+7ef4-7ef5,U+7efc,U+7eff,U+7f13,U+7f16,U+7f18,U+7f1d,U+7f29,U+7f3a,U+7f51,U+7f57,U+7f5a,U+7f62,U+7f6a,U+7f6e,U+7f72,U+7f8a,U+7f8e,U+7fa4,U+7fbd,U+7ffb-7ffc,U+8000-8001,U+8003,U+8005,U+800c,U+8010,U+8017,U+8033,U+804a,U+804c,U+8054,U+8058,U+805a,U+806a,U+8083,U+8089,U+808c,U+809a,U+80a0-80a1,U+80a4-80a5,U+80a9,U+80af,U+80b2,U+80bf,U+80c3,U+80c6,U+80cc,U+80ce,U+80dc,U+80de,U+80e1,U+80f6,U+80f8,U+80fd,U+8102,U+8106,U+8109,U+810f,U+8111,U+811a,U+8131,U+8138,U+8150,U+8170,U+8179,U+817b,U+817e-817f,U+819c,U+81e3,U+81ea,U+81f3-81f4,U+820d,U+8212,U+821e,U+822a,U+822c,U+8239,U+826f-8270,U+8272-8273,U+827a,U+827e,U+8282,U+82b1,U+82b3,U+82cd,U+82cf,U+82e5-82e6,U+82f1,U+8303,U+8336,U+8349,U+8350,U+8352,U+8361,U+8363,U+836f,U+8377,U+83ab,U+83b1-83b2,U+83b7,U+83dc,U+83f2,U+8425,U+8428,U+843d,U+8457,U+8463,U+8499,U+84b8,U+84dd,U+8584,U+85cf,U+864e,U+8651,U+865a,U+866b,U+867d,U+86cb,U+86ee,U+871c,U+878d,U+8840,U+884c,U+8857,U+8861,U+8863,U+8865,U+8868,U+8870,U+888b,U+8896,U+88ab,U+88ad,U+88c1-88c2,U+88c5,U+897f,U+8981,U+8986,U+89c1-89c2,U+89c4,U+89c6,U+89c8-89c9,U+89d2,U+89e3,U+89e6,U+8a00,U+8a89,U+8b66,U+8ba1-8ba2,U+8ba4,U+8ba8-8ba9,U+8bad-8bb0,U+8bb2,U+8bb8,U+8bba,U+8bbe-8bbf,U+8bc1,U+8bc4,U+8bc6,U+8bc9-8bca,U+8bcd,U+8bd1,U+8bd5,U+8bd7,U+8bda,U+8bdd-8bde,U+8be2,U+8be5-8be6,U+8bed,U+8bef,U+8bf1,U+8bf4,U+8bf7-8bf8,U+8bfa-8bfb,U+8bfe,U+8c01,U+8c03,U+8c08,U+8c0b,U+8c10,U+8c13,U+8c22,U+8c28,U+8c31,U+8c37,U+8c46,U+8c61,U+8c6a,U+8c8c,U+8d1d,U+8d1f,U+8d21-8d28,U+8d2a-8d2b,U+8d2d,U+8d2f,U+8d34-8d35,U+8d38-8d39,U+8d44,U+8d4b,U+8d4f,U+8d56,U+8d5a-8d5b,U+8d5e,U+8d60,U+8d62,U+8d64,U+8d6b,U+8d70,U+8d74-8d77,U+8d85,U+8d8a-8d8b,U+8da3,U+8db3,U+8dc3,U+8dcc,U+8dd1,U+8ddd,U+8ddf,U+8de8,U+8def,U+8df3,U+8df5,U+8e0f,U+8e2a,U+8eab,U+8f66,U+8f68,U+8f6c,U+8f6e-8f6f,U+8f7b,U+8f7d,U+8f83,U+8f85-8f86,U+8f88-8f89,U+8f91,U+8f93,U+8f9b,U+8f9e,U+8fa3,U+8fa8-8fa9,U+8fb9,U+8fbe,U+8fc1,U+8fc5,U+8fc7-8fc8,U+8fce,U+8fd0-8fd1,U+8fd4,U+8fd8-8fd9,U+8fdb-8fdf,U+8fea-8feb,U+8ff0,U+8ff7,U+8ff9,U+8ffd,U+9000-9003,U+9006,U+9009,U+900f-9010,U+9012,U+9014,U+901a,U+901d,U+901f-9020,U+9022,U+903b-903c,U+9047,U+904d,U+9053,U+9057,U+9065,U+906d,U+9075,U+907f-9080,U+90a3,U+90a6,U+90aa,U+90ae,U+90bb,U+90c1,U+90ce,U+90d1,U+90e8,U+90ed,U+90fd,U+914d,U+9152,U+9177-9178,U+9189,U+9192,U+91c7,U+91ca,U+91cc-91cf,U+91d1,U+9274,U+9488,U+949f,U+94a2,U+94b1,U+94bb,U+94c1,U+94dc,U+94f6,U+94fa,U+94fe,U+9500-9501,U+9505,U+950b,U+9510,U+9519,U+9526,U+952e,U+9547,U+955c,U+957f,U+95e8,U+95ea,U+95ed-95ee,U+95f2,U+95f4,U+95f7,U+95f9,U+95fb,U+9605,U+9614,U+961f,U+9632-9636,U+963b,U+963f,U+9644-9646,U+9648,U+964d,U+9650,U+9662,U+9664,U+9669-966a,U+9675-9677,U+9686,U+968f-9690,U+9694,U+969c,U+96be,U+96c4-96c6,U+96d5,U+96e8,U+96ea,U+96f6-96f7,U+96fe,U+9700,U+9707,U+9732,U+9738,U+9752,U+9759,U+975e,U+9760,U+9762,U+9769,U+978b,U+97e9,U+97f3,U+97f5,U+9875-9876,U+9879-987b,U+987e-987f,U+9884,U+9886-9887,U+9891,U+9897-9898,U+989c-989d,U+98ce,U+98d8,U+98de-98df,U+9910,U+996d-996e,U+9970-9971,U+9986,U+9996,U+9999,U+9a6c,U+9a71,U+9a7b,U+9a7e,U+9a82,U+9a8c,U+9a91,U+9a97,U+9aa4,U+9aa8,U+9ad8,U+9b3c,U+9b42,U+9b45,U+9b54,U+9c7c,U+9c81,U+9c9c,U+9e1f,U+9e21,U+9e23,U+9ea6,U+9ebb,U+9ec4,U+9ece,U+9ed1,U+9ed8,U+9f13,U+9f20,U+9f3b,U+9f50,U+9f7f,U+9f84,U+9f99,U+ff01,U+ff08-ff09,U+ff0c-ff0e,U+ff1a-ff1b,U+ff1f,U+ff5e;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:300;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.101.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.101.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Light.101.otf')  format('opentype');unicode-range:U+0024,U+005c,U+005e,U+007b-007d,U+00b0,U+00d7,U+00e9,U+2013,U+2022,U+2103,U+2192,U+2460-2463,U+300e-300f,U+306e,U+4e18-4e19,U+4e2b,U+4e38,U+4e56,U+4e59,U+4e5e,U+4e7e,U+4ea5,U+4ea8-4ea9,U+4ead,U+4ec6-4ec7,U+4ed1,U+4ed5,U+4ed7,U+4ef2,U+4f10,U+4f1e,U+4f3d,U+4f50-4f51,U+4f63,U+4f83,U+4f88,U+4f8d,U+4fa0,U+4fa3,U+4fa6,U+4fa8,U+4faf,U+4fcf,U+4fd8,U+4fed,U+4fef,U+4ffa,U+501a,U+5021,U+5026,U+503a,U+5085,U+508d,U+50ac,U+50e7,U+50f5,U+50fb,U+5112,U+5146,U+5151,U+5154,U+515c,U+5179,U+5188,U+51a4-51a5,U+51af,U+51b6,U+51bb,U+51c4,U+51d1,U+51f0,U+51f8-51f9,U+51fd,U+51ff,U+5203,U+522e,U+5238-5239,U+524a,U+5254,U+5256,U+5265,U+5288,U+52ab,U+52c3,U+52c9,U+52cb,U+52d8,U+52df,U+52fa,U+52ff,U+5306,U+5319,U+5320,U+532a,U+533f,U+5349,U+5351-5352,U+535c,U+5362,U+5366,U+5375,U+5378,U+537f,U+5384,U+5395,U+53a2,U+53a6,U+53a8,U+53c9,U+53d4,U+53d9,U+53db,U+53ee,U+5401,U+540a,U+540f,U+5415,U+541e-541f,U+5428,U+5435,U+543b-543c,U+543e,U+5450,U+5455,U+548b,U+548f,U+5492,U+54ac,U+54af,U+54b1,U+54b3,U+54b8,U+54bd,U+54c0,U+54c4,U+54c7,U+54c9,U+54ce,U+54d1,U+54df,U+5507,U+5509,U+5524,U+5561,U+5564,U+5578,U+5582,U+5587,U+5589,U+5598,U+55a7,U+55bb,U+55d3,U+55dc,U+55ef,U+5631-5632,U+563f,U+566a,U+56a3,U+56bc,U+56ca,U+56da,U+5733,U+573e,U+574a,U+574e,U+575d-5760,U+5764,U+576a,U+5783-5784,U+5792,U+57ab,U+5815,U+5824,U+5835,U+584c,U+5858,U+5885,U+5893,U+58e4,U+58f6,U+5937,U+5951,U+5960,U+5962,U+5974,U+5978,U+5983-5984,U+5992,U+5996,U+59a5,U+59a8,U+59ae,U+59da,U+59e8,U+59ec,U+5a03,U+5a07,U+5a1c,U+5a36,U+5a49,U+5a74,U+5a9a,U+5ab3,U+5ac9,U+5b5d,U+5b5f,U+5b99,U+5b9b,U+5ba0,U+5baa,U+5bb0,U+5bb4-5bb5,U+5bc5,U+5bdd-5bde,U+5be1,U+5be8,U+5c2c,U+5c34,U+5c38-5c39,U+5c41,U+5c51,U+5c60-5c61,U+5c65,U+5c6f,U+5c82,U+5cad,U+5cb3,U+5ce1,U+5cfb,U+5d0e,U+5d14,U+5d16,U+5d1b,U+5d29,U+5d2d,U+5d4c,U+5dc5,U+5de2,U+5de9,U+5deb,U+5df7,U+5dfe,U+5e06,U+5e10,U+5e15-5e16,U+5e18,U+5e1c,U+5e27,U+5e3d,U+5e90,U+5e9a,U+5e9e,U+5eb8,U+5eca,U+5ed3,U+5f0a,U+5f13,U+5f17-5f18,U+5f26-5f27,U+5f66,U+5f6d,U+5f70,U+5f8c,U+6016,U+601c,U+6021,U+604d,U+606d,U+6073,U+6084,U+608d,U+60d5,U+60df,U+60e9,U+60f9,U+6101,U+6109,U+611a,U+6127,U+614c,U+6168,U+61be,U+61c8,U+6208,U+621a,U+6233,U+6241,U+6247,U+6251,U+6254,U+626d,U+626f,U+6284,U+6292,U+6296,U+629a,U+62ac,U+62bc,U+62cc,U+62d0,U+62d8-62d9,U+62e2,U+62e6,U+62ef,U+62f1,U+62f3,U+62fe,U+631a,U+6323,U+6328,U+632a-632b,U+633d,U+6349,U+634f-6350,U+635e,U+6361,U+6363,U+6367,U+6380,U+638f,U+6398,U+63a0,U+63c9,U+63e3,U+63fd,U+6401,U+6405,U+640f,U+6413,U+644a,U+6454,U+6467,U+6495,U+64a4,U+64b0,U+64bc,U+64ce,U+6500,U+6512,U+655b,U+655e,U+6566,U+6572,U+6577,U+658b-658c,U+65a5,U+65a7,U+65a9,U+65bc,U+65ec-65ed,U+65f1,U+65f7,U+6606,U+660f,U+6614,U+6627,U+662d,U+663c,U+6643,U+6652,U+6655,U+6674,U+6687,U+6691,U+66a8,U+66ae,U+66dd,U+66f9,U+673d,U+6749,U+674f,U+6756,U+6760,U+676d,U+6795,U+679a,U+67a2-67a3,U+67ab,U+67af,U+67c4,U+67cf,U+67dc,U+67e0,U+67ef,U+67f4,U+680b,U+6816-6817,U+682a,U+683d,U+6842,U+6850,U+6869,U+6876,U+6897,U+68a8,U+68ad,U+68b3,U+68b5,U+68c9,U+68cb,U+68cd,U+68d5,U+68da,U+68f1,U+68f5,U+6905,U+690e,U+6912,U+692d,U+6977,U+6984,U+69db,U+6a31,U+6a44,U+6a58-6a59,U+6a61,U+6a90,U+6b20,U+6b47,U+6b49,U+6b67,U+6b6a,U+6bb7,U+6bc5,U+6bd7,U+6bef,U+6c22,U+6c28,U+6c5d,U+6c6a,U+6c70,U+6c83,U+6c90,U+6c9b,U+6ca5-6ca7,U+6caa-6cab,U+6cb8,U+6cbe,U+6cca,U+6ccc,U+6ce3,U+6cf3,U+6cfb-6cfc,U+6d3d,U+6d46-6d47,U+6d4a,U+6d4f,U+6d51,U+6d66,U+6d74,U+6d85,U+6d9b,U+6da9,U+6dc0,U+6dd1,U+6deb,U+6dee,U+6df3,U+6df9,U+6e0a,U+6e14,U+6e17,U+6e23,U+6e32,U+6e58,U+6e5b,U+6e83,U+6e9c,U+6ea2,U+6eaf,U+6eb6,U+6ede,U+6ee4-6ee5,U+6ee8-6ee9,U+6f06,U+6f13,U+6f20,U+6f47,U+6f58,U+6f6d,U+6f84,U+6f88,U+6f9c,U+6fa1,U+7011,U+707c,U+707f,U+7096,U+70ab,U+70ad,U+70d8,U+70db,U+70e4,U+70eb,U+70f9,U+7109,U+7115,U+711a,U+7130,U+714c,U+714e,U+715e,U+7164,U+718f,U+7194,U+7199,U+71ac,U+722a,U+7235,U+7239,U+7261,U+7267,U+7272,U+727a,U+72ac,U+72d0,U+72e0,U+72ed-72ee,U+72fc,U+730e,U+7334,U+73ab,U+73b2,U+73ca,U+7410,U+7422,U+742a,U+7433,U+743c,U+7455,U+745c,U+745f,U+7470,U+7476,U+74e3,U+74f7,U+7529,U+752b,U+7538,U+754f,U+7554,U+755c,U+7574,U+7586,U+75ab,U+75ae,U+75d2,U+75f4,U+7624,U+763e,U+764c,U+7682,U+76b1,U+76ef,U+76f2,U+76fc,U+7737,U+7741,U+7750,U+7779,U+777f,U+778e,U+7792,U+77a7,U+77a9,U+77bb,U+77e2-77e3,U+77e9,U+77eb,U+77ee,U+77f6,U+7802,U+780c-780d,U+7816,U+7838,U+7845,U+786b,U+788c,U+789f,U+78a7,U+78b1,U+78b3,U+78c1,U+78c5,U+78f7,U+7940,U+7948,U+795b,U+796d,U+7977-7978,U+7984-7985,U+79bd,U+79c9,U+79e9,U+7a1a,U+7a20,U+7a3b,U+7a3d,U+7a46,U+7a74,U+7a83-7a84,U+7a8d,U+7a9d,U+7aa5,U+7ad6,U+7aed,U+7b1b,U+7b28,U+7b3c,U+7b4b,U+7b52,U+7b5b,U+7bad,U+7bee,U+7ca4-7ca5,U+7cb9,U+7cd5,U+7cd9,U+7cdf,U+7d6e,U+7eac,U+7eb1-7eb2,U+7eba,U+7ece,U+7ed1-7ed2,U+7eda,U+7ee3,U+7ef3,U+7ef8,U+7efd,U+7f00,U+7f05,U+7f14-7f15,U+7f1a,U+7f20,U+7f34,U+7f38,U+7f50,U+7f55,U+7f69,U+7f9e,U+7fa1,U+7fc1,U+7fc5,U+7fd4,U+7fd8,U+7fe0,U+7ff0,U+800d,U+8015,U+8036,U+8038,U+803b,U+803d,U+8086,U+808b,U+8096,U+809d,U+80a2,U+80aa,U+80ba,U+80be,U+80c0-80c1,U+80d6,U+810a,U+8116,U+813e,U+814a,U+8154-8155,U+8165,U+817a,U+8180,U+818f,U+819d,U+81a8,U+81c0,U+81c2,U+81ed,U+8206,U+820c,U+821f,U+8230,U+8247,U+8258,U+8292,U+8299,U+829d,U+82a6,U+82ac-82ad,U+82af,U+82bd,U+82d1,U+82d7,U+82db,U+82df,U+82f9,U+8302,U+8304-8305,U+830e,U+8328,U+832b,U+8346,U+836b,U+8389,U+838e,U+8393,U+83b9,U+83c7,U+83ca,U+83cc,U+83e9,U+8403-8404,U+840c-840e,U+841d,U+8427,U+845b,U+8461,U+846c,U+8471,U+8475,U+8482,U+848b,U+849c,U+84b2,U+84c4,U+84c9,U+84ec,U+8513,U+851a,U+8521,U+852c,U+853d,U+8549,U+8574,U+857e,U+8587,U+859b,U+85aa,U+85af,U+85c9,U+85e4,U+8650,U+8654,U+8679,U+867e,U+8680,U+86c7,U+8700,U+8702,U+8721,U+8774,U+8776,U+87ba,U+87f9,U+8822,U+884d,U+8854,U+886b-886c,U+8877,U+8881,U+888d,U+88d4-88d5,U+88d9,U+88e4,U+88f8-88f9,U+8910,U+89c5,U+8a79,U+8a93,U+8b6c,U+8bb3,U+8bb6,U+8bbc-8bbd,U+8bc0,U+8bc8,U+8be0-8be1,U+8bf5,U+8c05,U+8c0a,U+8c0e,U+8c1c,U+8c23,U+8c26,U+8c2d,U+8c41,U+8c6b,U+8c79,U+8d1e,U+8d29,U+8d2c,U+8d2e,U+8d31,U+8d37,U+8d3a,U+8d3c,U+8d3e,U+8d4c,U+8d50,U+8d54,U+8d81,U+8d9f,U+8dea,U+8e22,U+8e29,U+8e44,U+8e48,U+8e72,U+8e81,U+8eaf,U+8eb2,U+8eba,U+8f69,U+8f70,U+8f74,U+8f7f,U+8f90,U+8f96,U+8f9c,U+8f9f,U+8fb0-8fb1,U+8fbd,U+8fc4,U+8fe6,U+8fed,U+900a,U+9017,U+901b,U+902e,U+9038,U+903e,U+9042,U+9063,U+906e,U+9093,U+90ca,U+90e1,U+9119,U+9165,U+916c,U+9171,U+9175-9176,U+917f,U+9187,U+918b,U+9489,U+9493,U+9499,U+949d,U+94a5-94a6,U+94a9,U+94ae,U+94c3,U+94c5,U+94dd,U+94ed,U+94f8,U+9508,U+9521,U+9524-9525,U+953b,U+9576,U+95ef,U+95fa,U+9600-9601,U+9610,U+9640,U+964b-964c,U+9655,U+968b,U+9699,U+96b6,U+96c0-96c1,U+96c7,U+96cc-96cd,U+9709,U+970d,U+971c,U+971e,U+9753,U+9756,U+9761,U+97ad,U+97e6-97e7,U+9877,U+987d,U+9881-9882,U+9888,U+9896,U+98a0,U+98a4,U+9965,U+9972,U+9976,U+997c,U+997f,U+9985,U+9988,U+99a8,U+9a70,U+9a73,U+9a76,U+9a84,U+9a9a,U+9ad3,U+9b41,U+9b44,U+9b4f,U+9c8d,U+9cde,U+9e26,U+9e2d,U+9e3f,U+9e45,U+9e4f,U+9e64,U+9e70,U+9e7f,U+9e9f,U+9ecf,U+9edb,U+9f0e,U+9f9f,U+ff05;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:400;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.101.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.101.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Regular.101.otf')  format('opentype');unicode-range:U+0024,U+005c,U+005e,U+007b-007d,U+00b0,U+00d7,U+00e9,U+2013,U+2022,U+2103,U+2192,U+2460-2463,U+300e-300f,U+306e,U+4e18-4e19,U+4e2b,U+4e38,U+4e56,U+4e59,U+4e5e,U+4e7e,U+4ea5,U+4ea8-4ea9,U+4ead,U+4ec6-4ec7,U+4ed1,U+4ed5,U+4ed7,U+4ef2,U+4f10,U+4f1e,U+4f3d,U+4f50-4f51,U+4f63,U+4f83,U+4f88,U+4f8d,U+4fa0,U+4fa3,U+4fa6,U+4fa8,U+4faf,U+4fcf,U+4fd8,U+4fed,U+4fef,U+4ffa,U+501a,U+5021,U+5026,U+503a,U+5085,U+508d,U+50ac,U+50e7,U+50f5,U+50fb,U+5112,U+5146,U+5151,U+5154,U+515c,U+5179,U+5188,U+51a4-51a5,U+51af,U+51b6,U+51bb,U+51c4,U+51d1,U+51f0,U+51f8-51f9,U+51fd,U+51ff,U+5203,U+522e,U+5238-5239,U+524a,U+5254,U+5256,U+5265,U+5288,U+52ab,U+52c3,U+52c9,U+52cb,U+52d8,U+52df,U+52fa,U+52ff,U+5306,U+5319,U+5320,U+532a,U+533f,U+5349,U+5351-5352,U+535c,U+5362,U+5366,U+5375,U+5378,U+537f,U+5384,U+5395,U+53a2,U+53a6,U+53a8,U+53c9,U+53d4,U+53d9,U+53db,U+53ee,U+5401,U+540a,U+540f,U+5415,U+541e-541f,U+5428,U+5435,U+543b-543c,U+543e,U+5450,U+5455,U+548b,U+548f,U+5492,U+54ac,U+54af,U+54b1,U+54b3,U+54b8,U+54bd,U+54c0,U+54c4,U+54c7,U+54c9,U+54ce,U+54d1,U+54df,U+5507,U+5509,U+5524,U+5561,U+5564,U+5578,U+5582,U+5587,U+5589,U+5598,U+55a7,U+55bb,U+55d3,U+55dc,U+55ef,U+5631-5632,U+563f,U+566a,U+56a3,U+56bc,U+56ca,U+56da,U+5733,U+573e,U+574a,U+574e,U+575d-5760,U+5764,U+576a,U+5783-5784,U+5792,U+57ab,U+5815,U+5824,U+5835,U+584c,U+5858,U+5885,U+5893,U+58e4,U+58f6,U+5937,U+5951,U+5960,U+5962,U+5974,U+5978,U+5983-5984,U+5992,U+5996,U+59a5,U+59a8,U+59ae,U+59da,U+59e8,U+59ec,U+5a03,U+5a07,U+5a1c,U+5a36,U+5a49,U+5a74,U+5a9a,U+5ab3,U+5ac9,U+5b5d,U+5b5f,U+5b99,U+5b9b,U+5ba0,U+5baa,U+5bb0,U+5bb4-5bb5,U+5bc5,U+5bdd-5bde,U+5be1,U+5be8,U+5c2c,U+5c34,U+5c38-5c39,U+5c41,U+5c51,U+5c60-5c61,U+5c65,U+5c6f,U+5c82,U+5cad,U+5cb3,U+5ce1,U+5cfb,U+5d0e,U+5d14,U+5d16,U+5d1b,U+5d29,U+5d2d,U+5d4c,U+5dc5,U+5de2,U+5de9,U+5deb,U+5df7,U+5dfe,U+5e06,U+5e10,U+5e15-5e16,U+5e18,U+5e1c,U+5e27,U+5e3d,U+5e90,U+5e9a,U+5e9e,U+5eb8,U+5eca,U+5ed3,U+5f0a,U+5f13,U+5f17-5f18,U+5f26-5f27,U+5f66,U+5f6d,U+5f70,U+5f8c,U+6016,U+601c,U+6021,U+604d,U+606d,U+6073,U+6084,U+608d,U+60d5,U+60df,U+60e9,U+60f9,U+6101,U+6109,U+611a,U+6127,U+614c,U+6168,U+61be,U+61c8,U+6208,U+621a,U+6233,U+6241,U+6247,U+6251,U+6254,U+626d,U+626f,U+6284,U+6292,U+6296,U+629a,U+62ac,U+62bc,U+62cc,U+62d0,U+62d8-62d9,U+62e2,U+62e6,U+62ef,U+62f1,U+62f3,U+62fe,U+631a,U+6323,U+6328,U+632a-632b,U+633d,U+6349,U+634f-6350,U+635e,U+6361,U+6363,U+6367,U+6380,U+638f,U+6398,U+63a0,U+63c9,U+63e3,U+63fd,U+6401,U+6405,U+640f,U+6413,U+644a,U+6454,U+6467,U+6495,U+64a4,U+64b0,U+64bc,U+64ce,U+6500,U+6512,U+655b,U+655e,U+6566,U+6572,U+6577,U+658b-658c,U+65a5,U+65a7,U+65a9,U+65bc,U+65ec-65ed,U+65f1,U+65f7,U+6606,U+660f,U+6614,U+6627,U+662d,U+663c,U+6643,U+6652,U+6655,U+6674,U+6687,U+6691,U+66a8,U+66ae,U+66dd,U+66f9,U+673d,U+6749,U+674f,U+6756,U+6760,U+676d,U+6795,U+679a,U+67a2-67a3,U+67ab,U+67af,U+67c4,U+67cf,U+67dc,U+67e0,U+67ef,U+67f4,U+680b,U+6816-6817,U+682a,U+683d,U+6842,U+6850,U+6869,U+6876,U+6897,U+68a8,U+68ad,U+68b3,U+68b5,U+68c9,U+68cb,U+68cd,U+68d5,U+68da,U+68f1,U+68f5,U+6905,U+690e,U+6912,U+692d,U+6977,U+6984,U+69db,U+6a31,U+6a44,U+6a58-6a59,U+6a61,U+6a90,U+6b20,U+6b47,U+6b49,U+6b67,U+6b6a,U+6bb7,U+6bc5,U+6bd7,U+6bef,U+6c22,U+6c28,U+6c5d,U+6c6a,U+6c70,U+6c83,U+6c90,U+6c9b,U+6ca5-6ca7,U+6caa-6cab,U+6cb8,U+6cbe,U+6cca,U+6ccc,U+6ce3,U+6cf3,U+6cfb-6cfc,U+6d3d,U+6d46-6d47,U+6d4a,U+6d4f,U+6d51,U+6d66,U+6d74,U+6d85,U+6d9b,U+6da9,U+6dc0,U+6dd1,U+6deb,U+6dee,U+6df3,U+6df9,U+6e0a,U+6e14,U+6e17,U+6e23,U+6e32,U+6e58,U+6e5b,U+6e83,U+6e9c,U+6ea2,U+6eaf,U+6eb6,U+6ede,U+6ee4-6ee5,U+6ee8-6ee9,U+6f06,U+6f13,U+6f20,U+6f47,U+6f58,U+6f6d,U+6f84,U+6f88,U+6f9c,U+6fa1,U+7011,U+707c,U+707f,U+7096,U+70ab,U+70ad,U+70d8,U+70db,U+70e4,U+70eb,U+70f9,U+7109,U+7115,U+711a,U+7130,U+714c,U+714e,U+715e,U+7164,U+718f,U+7194,U+7199,U+71ac,U+722a,U+7235,U+7239,U+7261,U+7267,U+7272,U+727a,U+72ac,U+72d0,U+72e0,U+72ed-72ee,U+72fc,U+730e,U+7334,U+73ab,U+73b2,U+73ca,U+7410,U+7422,U+742a,U+7433,U+743c,U+7455,U+745c,U+745f,U+7470,U+7476,U+74e3,U+74f7,U+7529,U+752b,U+7538,U+754f,U+7554,U+755c,U+7574,U+7586,U+75ab,U+75ae,U+75d2,U+75f4,U+7624,U+763e,U+764c,U+7682,U+76b1,U+76ef,U+76f2,U+76fc,U+7737,U+7741,U+7750,U+7779,U+777f,U+778e,U+7792,U+77a7,U+77a9,U+77bb,U+77e2-77e3,U+77e9,U+77eb,U+77ee,U+77f6,U+7802,U+780c-780d,U+7816,U+7838,U+7845,U+786b,U+788c,U+789f,U+78a7,U+78b1,U+78b3,U+78c1,U+78c5,U+78f7,U+7940,U+7948,U+795b,U+796d,U+7977-7978,U+7984-7985,U+79bd,U+79c9,U+79e9,U+7a1a,U+7a20,U+7a3b,U+7a3d,U+7a46,U+7a74,U+7a83-7a84,U+7a8d,U+7a9d,U+7aa5,U+7ad6,U+7aed,U+7b1b,U+7b28,U+7b3c,U+7b4b,U+7b52,U+7b5b,U+7bad,U+7bee,U+7ca4-7ca5,U+7cb9,U+7cd5,U+7cd9,U+7cdf,U+7d6e,U+7eac,U+7eb1-7eb2,U+7eba,U+7ece,U+7ed1-7ed2,U+7eda,U+7ee3,U+7ef3,U+7ef8,U+7efd,U+7f00,U+7f05,U+7f14-7f15,U+7f1a,U+7f20,U+7f34,U+7f38,U+7f50,U+7f55,U+7f69,U+7f9e,U+7fa1,U+7fc1,U+7fc5,U+7fd4,U+7fd8,U+7fe0,U+7ff0,U+800d,U+8015,U+8036,U+8038,U+803b,U+803d,U+8086,U+808b,U+8096,U+809d,U+80a2,U+80aa,U+80ba,U+80be,U+80c0-80c1,U+80d6,U+810a,U+8116,U+813e,U+814a,U+8154-8155,U+8165,U+817a,U+8180,U+818f,U+819d,U+81a8,U+81c0,U+81c2,U+81ed,U+8206,U+820c,U+821f,U+8230,U+8247,U+8258,U+8292,U+8299,U+829d,U+82a6,U+82ac-82ad,U+82af,U+82bd,U+82d1,U+82d7,U+82db,U+82df,U+82f9,U+8302,U+8304-8305,U+830e,U+8328,U+832b,U+8346,U+836b,U+8389,U+838e,U+8393,U+83b9,U+83c7,U+83ca,U+83cc,U+83e9,U+8403-8404,U+840c-840e,U+841d,U+8427,U+845b,U+8461,U+846c,U+8471,U+8475,U+8482,U+848b,U+849c,U+84b2,U+84c4,U+84c9,U+84ec,U+8513,U+851a,U+8521,U+852c,U+853d,U+8549,U+8574,U+857e,U+8587,U+859b,U+85aa,U+85af,U+85c9,U+85e4,U+8650,U+8654,U+8679,U+867e,U+8680,U+86c7,U+8700,U+8702,U+8721,U+8774,U+8776,U+87ba,U+87f9,U+8822,U+884d,U+8854,U+886b-886c,U+8877,U+8881,U+888d,U+88d4-88d5,U+88d9,U+88e4,U+88f8-88f9,U+8910,U+89c5,U+8a79,U+8a93,U+8b6c,U+8bb3,U+8bb6,U+8bbc-8bbd,U+8bc0,U+8bc8,U+8be0-8be1,U+8bf5,U+8c05,U+8c0a,U+8c0e,U+8c1c,U+8c23,U+8c26,U+8c2d,U+8c41,U+8c6b,U+8c79,U+8d1e,U+8d29,U+8d2c,U+8d2e,U+8d31,U+8d37,U+8d3a,U+8d3c,U+8d3e,U+8d4c,U+8d50,U+8d54,U+8d81,U+8d9f,U+8dea,U+8e22,U+8e29,U+8e44,U+8e48,U+8e72,U+8e81,U+8eaf,U+8eb2,U+8eba,U+8f69,U+8f70,U+8f74,U+8f7f,U+8f90,U+8f96,U+8f9c,U+8f9f,U+8fb0-8fb1,U+8fbd,U+8fc4,U+8fe6,U+8fed,U+900a,U+9017,U+901b,U+902e,U+9038,U+903e,U+9042,U+9063,U+906e,U+9093,U+90ca,U+90e1,U+9119,U+9165,U+916c,U+9171,U+9175-9176,U+917f,U+9187,U+918b,U+9489,U+9493,U+9499,U+949d,U+94a5-94a6,U+94a9,U+94ae,U+94c3,U+94c5,U+94dd,U+94ed,U+94f8,U+9508,U+9521,U+9524-9525,U+953b,U+9576,U+95ef,U+95fa,U+9600-9601,U+9610,U+9640,U+964b-964c,U+9655,U+968b,U+9699,U+96b6,U+96c0-96c1,U+96c7,U+96cc-96cd,U+9709,U+970d,U+971c,U+971e,U+9753,U+9756,U+9761,U+97ad,U+97e6-97e7,U+9877,U+987d,U+9881-9882,U+9888,U+9896,U+98a0,U+98a4,U+9965,U+9972,U+9976,U+997c,U+997f,U+9985,U+9988,U+99a8,U+9a70,U+9a73,U+9a76,U+9a84,U+9a9a,U+9ad3,U+9b41,U+9b44,U+9b4f,U+9c8d,U+9cde,U+9e26,U+9e2d,U+9e3f,U+9e45,U+9e4f,U+9e64,U+9e70,U+9e7f,U+9e9f,U+9ecf,U+9edb,U+9f0e,U+9f9f,U+ff05;}
+@font-face{font-family:'Noto Sans SC Sliced';font-weight:500;src:url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.101.woff2') format('woff2'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.101.woff') format('woff'),url('//fonts.gstatic.com/ea/notosansscsliced/v2/NotoSansSCSliced-Medium.101.otf')  format('opentype');unicode-range:U+0024,U+005c,U+005e,U+007b-007d,U+00b0,U+00d7,U+00e9,U+2013,U+2022,U+2103,U+2192,U+2460-2463,U+300e-300f,U+306e,U+4e18-4e19,U+4e2b,U+4e38,U+4e56,U+4e59,U+4e5e,U+4e7e,U+4ea5,U+4ea8-4ea9,U+4ead,U+4ec6-4ec7,U+4ed1,U+4ed5,U+4ed7,U+4ef2,U+4f10,U+4f1e,U+4f3d,U+4f50-4f51,U+4f63,U+4f83,U+4f88,U+4f8d,U+4fa0,U+4fa3,U+4fa6,U+4fa8,U+4faf,U+4fcf,U+4fd8,U+4fed,U+4fef,U+4ffa,U+501a,U+5021,U+5026,U+503a,U+5085,U+508d,U+50ac,U+50e7,U+50f5,U+50fb,U+5112,U+5146,U+5151,U+5154,U+515c,U+5179,U+5188,U+51a4-51a5,U+51af,U+51b6,U+51bb,U+51c4,U+51d1,U+51f0,U+51f8-51f9,U+51fd,U+51ff,U+5203,U+522e,U+5238-5239,U+524a,U+5254,U+5256,U+5265,U+5288,U+52ab,U+52c3,U+52c9,U+52cb,U+52d8,U+52df,U+52fa,U+52ff,U+5306,U+5319,U+5320,U+532a,U+533f,U+5349,U+5351-5352,U+535c,U+5362,U+5366,U+5375,U+5378,U+537f,U+5384,U+5395,U+53a2,U+53a6,U+53a8,U+53c9,U+53d4,U+53d9,U+53db,U+53ee,U+5401,U+540a,U+540f,U+5415,U+541e-541f,U+5428,U+5435,U+543b-543c,U+543e,U+5450,U+5455,U+548b,U+548f,U+5492,U+54ac,U+54af,U+54b1,U+54b3,U+54b8,U+54bd,U+54c0,U+54c4,U+54c7,U+54c9,U+54ce,U+54d1,U+54df,U+5507,U+5509,U+5524,U+5561,U+5564,U+5578,U+5582,U+5587,U+5589,U+5598,U+55a7,U+55bb,U+55d3,U+55dc,U+55ef,U+5631-5632,U+563f,U+566a,U+56a3,U+56bc,U+56ca,U+56da,U+5733,U+573e,U+574a,U+574e,U+575d-5760,U+5764,U+576a,U+5783-5784,U+5792,U+57ab,U+5815,U+5824,U+5835,U+584c,U+5858,U+5885,U+5893,U+58e4,U+58f6,U+5937,U+5951,U+5960,U+5962,U+5974,U+5978,U+5983-5984,U+5992,U+5996,U+59a5,U+59a8,U+59ae,U+59da,U+59e8,U+59ec,U+5a03,U+5a07,U+5a1c,U+5a36,U+5a49,U+5a74,U+5a9a,U+5ab3,U+5ac9,U+5b5d,U+5b5f,U+5b99,U+5b9b,U+5ba0,U+5baa,U+5bb0,U+5bb4-5bb5,U+5bc5,U+5bdd-5bde,U+5be1,U+5be8,U+5c2c,U+5c34,U+5c38-5c39,U+5c41,U+5c51,U+5c60-5c61,U+5c65,U+5c6f,U+5c82,U+5cad,U+5cb3,U+5ce1,U+5cfb,U+5d0e,U+5d14,U+5d16,U+5d1b,U+5d29,U+5d2d,U+5d4c,U+5dc5,U+5de2,U+5de9,U+5deb,U+5df7,U+5dfe,U+5e06,U+5e10,U+5e15-5e16,U+5e18,U+5e1c,U+5e27,U+5e3d,U+5e90,U+5e9a,U+5e9e,U+5eb8,U+5eca,U+5ed3,U+5f0a,U+5f13,U+5f17-5f18,U+5f26-5f27,U+5f66,U+5f6d,U+5f70,U+5f8c,U+6016,U+601c,U+6021,U+604d,U+606d,U+6073,U+6084,U+608d,U+60d5,U+60df,U+60e9,U+60f9,U+6101,U+6109,U+611a,U+6127,U+614c,U+6168,U+61be,U+61c8,U+6208,U+621a,U+6233,U+6241,U+6247,U+6251,U+6254,U+626d,U+626f,U+6284,U+6292,U+6296,U+629a,U+62ac,U+62bc,U+62cc,U+62d0,U+62d8-62d9,U+62e2,U+62e6,U+62ef,U+62f1,U+62f3,U+62fe,U+631a,U+6323,U+6328,U+632a-632b,U+633d,U+6349,U+634f-6350,U+635e,U+6361,U+6363,U+6367,U+6380,U+638f,U+6398,U+63a0,U+63c9,U+63e3,U+63fd,U+6401,U+6405,U+640f,U+6413,U+644a,U+6454,U+6467,U+6495,U+64a4,U+64b0,U+64bc,U+64ce,U+6500,U+6512,U+655b,U+655e,U+6566,U+6572,U+6577,U+658b-658c,U+65a5,U+65a7,U+65a9,U+65bc,U+65ec-65ed,U+65f1,U+65f7,U+6606,U+660f,U+6614,U+6627,U+662d,U+663c,U+6643,U+6652,U+6655,U+6674,U+6687,U+6691,U+66a8,U+66ae,U+66dd,U+66f9,U+673d,U+6749,U+674f,U+6756,U+6760,U+676d,U+6795,U+679a,U+67a2-67a3,U+67ab,U+67af,U+67c4,U+67cf,U+67dc,U+67e0,U+67ef,U+67f4,U+680b,U+6816-6817,U+682a,U+683d,U+6842,U+6850,U+6869,U+6876,U+6897,U+68a8,U+68ad,U+68b3,U+68b5,U+68c9,U+68cb,U+68cd,U+68d5,U+68da,U+68f1,U+68f5,U+6905,U+690e,U+6912,U+692d,U+6977,U+6984,U+69db,U+6a31,U+6a44,U+6a58-6a59,U+6a61,U+6a90,U+6b20,U+6b47,U+6b49,U+6b67,U+6b6a,U+6bb7,U+6bc5,U+6bd7,U+6bef,U+6c22,U+6c28,U+6c5d,U+6c6a,U+6c70,U+6c83,U+6c90,U+6c9b,U+6ca5-6ca7,U+6caa-6cab,U+6cb8,U+6cbe,U+6cca,U+6ccc,U+6ce3,U+6cf3,U+6cfb-6cfc,U+6d3d,U+6d46-6d47,U+6d4a,U+6d4f,U+6d51,U+6d66,U+6d74,U+6d85,U+6d9b,U+6da9,U+6dc0,U+6dd1,U+6deb,U+6dee,U+6df3,U+6df9,U+6e0a,U+6e14,U+6e17,U+6e23,U+6e32,U+6e58,U+6e5b,U+6e83,U+6e9c,U+6ea2,U+6eaf,U+6eb6,U+6ede,U+6ee4-6ee5,U+6ee8-6ee9,U+6f06,U+6f13,U+6f20,U+6f47,U+6f58,U+6f6d,U+6f84,U+6f88,U+6f9c,U+6fa1,U+7011,U+707c,U+707f,U+7096,U+70ab,U+70ad,U+70d8,U+70db,U+70e4,U+70eb,U+70f9,U+7109,U+7115,U+711a,U+7130,U+714c,U+714e,U+715e,U+7164,U+718f,U+7194,U+7199,U+71ac,U+722a,U+7235,U+7239,U+7261,U+7267,U+7272,U+727a,U+72ac,U+72d0,U+72e0,U+72ed-72ee,U+72fc,U+730e,U+7334,U+73ab,U+73b2,U+73ca,U+7410,U+7422,U+742a,U+7433,U+743c,U+7455,U+745c,U+745f,U+7470,U+7476,U+74e3,U+74f7,U+7529,U+752b,U+7538,U+754f,U+7554,U+755c,U+7574,U+7586,U+75ab,U+75ae,U+75d2,U+75f4,U+7624,U+763e,U+764c,U+7682,U+76b1,U+76ef,U+76f2,U+76fc,U+7737,U+7741,U+7750,U+7779,U+777f,U+778e,U+7792,U+77a7,U+77a9,U+77bb,U+77e2-77e3,U+77e9,U+77eb,U+77ee,U+77f6,U+7802,U+780c-780d,U+7816,U+7838,U+7845,U+786b,U+788c,U+789f,U+78a7,U+78b1,U+78b3,U+78c1,U+78c5,U+78f7,U+7940,U+7948,U+795b,U+796d,U+7977-7978,U+7984-7985,U+79bd,U+79c9,U+79e9,U+7a1a,U+7a20,U+7a3b,U+7a3d,U+7a46,U+7a74,U+7a83-7a84,U+7a8d,U+7a9d,U+7aa5,U+7ad6,U+7aed,U+7b1b,U+7b28,U+7b3c,U+7b4b,U+7b52,U+7b5b,U+7bad,U+7bee,U+7ca4-7ca5,U+7cb9,U+7cd5,U+7cd9,U+7cdf,U+7d6e,U+7eac,U+7eb1-7eb2,U+7eba,U+7ece,U+7ed1-7ed2,U+7eda,U+7ee3,U+7ef3,U+7ef8,U+7efd,U+7f00,U+7f05,U+7f14-7f15,U+7f1a,U+7f20,U+7f34,U+7f38,U+7f50,U+7f55,U+7f69,U+7f9e,U+7fa1,U+7fc1,U+7fc5,U+7fd4,U+7fd8,U+7fe0,U+7ff0,U+800d,U+8015,U+8036,U+8038,U+803b,U+803d,U+8086,U+808b,U+8096,U+809d,U+80a2,U+80aa,U+80ba,U+80be,U+80c0-80c1,U+80d6,U+810a,U+8116,U+813e,U+814a,U+8154-8155,U+8165,U+817a,U+8180,U+818f,U+819d,U+81a8,U+81c0,U+81c2,U+81ed,U+8206,U+820c,U+821f,U+8230,U+8247,U+8258,U+8292,U+8299,U+829d,U+82a6,U+82ac-82ad,U+82af,U+82bd,U+82d1,U+82d7,U+82db,U+82df,U+82f9,U+8302,U+8304-8305,U+830e,U+8328,U+832b,U+8346,U+836b,U+8389,U+838e,U+8393,U+83b9,U+83c7,U+83ca,U+83cc,U+83e9,U+8403-8404,U+840c-840e,U+841d,U+8427,U+845b,U+8461,U+846c,U+8471,U+8475,U+8482,U+848b,U+849c,U+84b2,U+84c4,U+84c9,U+84ec,U+8513,U+851a,U+8521,U+852c,U+853d,U+8549,U+8574,U+857e,U+8587,U+859b,U+85aa,U+85af,U+85c9,U+85e4,U+8650,U+8654,U+8679,U+867e,U+8680,U+86c7,U+8700,U+8702,U+8721,U+8774,U+8776,U+87ba,U+87f9,U+8822,U+884d,U+8854,U+886b-886c,U+8877,U+8881,U+888d,U+88d4-88d5,U+88d9,U+88e4,U+88f8-88f9,U+8910,U+89c5,U+8a79,U+8a93,U+8b6c,U+8bb3,U+8bb6,U+8bbc-8bbd,U+8bc0,U+8bc8,U+8be0-8be1,U+8bf5,U+8c05,U+8c0a,U+8c0e,U+8c1c,U+8c23,U+8c26,U+8c2d,U+8c41,U+8c6b,U+8c79,U+8d1e,U+8d29,U+8d2c,U+8d2e,U+8d31,U+8d37,U+8d3a,U+8d3c,U+8d3e,U+8d4c,U+8d50,U+8d54,U+8d81,U+8d9f,U+8dea,U+8e22,U+8e29,U+8e44,U+8e48,U+8e72,U+8e81,U+8eaf,U+8eb2,U+8eba,U+8f69,U+8f70,U+8f74,U+8f7f,U+8f90,U+8f96,U+8f9c,U+8f9f,U+8fb0-8fb1,U+8fbd,U+8fc4,U+8fe6,U+8fed,U+900a,U+9017,U+901b,U+902e,U+9038,U+903e,U+9042,U+9063,U+906e,U+9093,U+90ca,U+90e1,U+9119,U+9165,U+916c,U+9171,U+9175-9176,U+917f,U+9187,U+918b,U+9489,U+9493,U+9499,U+949d,U+94a5-94a6,U+94a9,U+94ae,U+94c3,U+94c5,U+94dd,U+94ed,U+94f8,U+9508,U+9521,U+9524-9525,U+953b,U+9576,U+95ef,U+95fa,U+9600-9601,U+9610,U+9640,U+964b-964c,U+9655,U+968b,U+9699,U+96b6,U+96c0-96c1,U+96c7,U+96cc-96cd,U+9709,U+970d,U+971c,U+971e,U+9753,U+9756,U+9761,U+97ad,U+97e6-97e7,U+9877,U+987d,U+9881-9882,U+9888,U+9896,U+98a0,U+98a4,U+9965,U+9972,U+9976,U+997c,U+997f,U+9985,U+9988,U+99a8,U+9a70,U+9a73,U+9a76,U+9a84,U+9a9a,U+9ad3,U+9b41,U+9b44,U+9b4f,U+9c8d,U+9cde,U+9e26,U+9e2d,U+9e3f,U+9e45,U+9e4f,U+9e64,U+9e70,U+9e7f,U+9e9f,U+9ecf,U+9edb,U+9f0e,U+9f9f,U+ff05;}
+
+@font-face {
+  font-family: 'Material Icons';
+  font-style: normal;
+  font-weight: 400;
+  src: url(material-design-icons-3.0.1/iconfont/MaterialIcons-Regular.eot); /* For IE6-8 */
+  src: local('Material Icons'),
+       local('MaterialIcons-Regular'),
+       url(material-design-icons-3.0.1/iconfont/MaterialIcons-Regular.woff2) format('woff2'),
+       url(material-design-icons-3.0.1/iconfont/MaterialIcons-Regular.woff) format('woff'),
+       url(material-design-icons-3.0.1/iconfont/MaterialIcons-Regular.ttf) format('truetype');
+}
+
+/*
+* Noto Sans Japanese (japanese) http://www.google.com/fonts/earlyaccess
+ */
+@font-face {
+  font-family: 'Noto Sans Japanese';
+  font-style: normal;
+  font-weight: 100;
+  src: url(//fonts.gstatic.com/ea/notosansjapanese/v6/NotoSansJP-Thin.woff2) format('woff2'),
+       url(//fonts.gstatic.com/ea/notosansjapanese/v6/NotoSansJP-Thin.woff) format('woff'),
+       url(//fonts.gstatic.com/ea/notosansjapanese/v6/NotoSansJP-Thin.otf) format('opentype');
+}
+@font-face {
+  font-family: 'Noto Sans Japanese';
+  font-style: normal;
+  font-weight: 200;
+  src: url(//fonts.gstatic.com/ea/notosansjapanese/v6/NotoSansJP-Light.woff2) format('woff2'),
+       url(//fonts.gstatic.com/ea/notosansjapanese/v6/NotoSansJP-Light.woff) format('woff'),
+       url(//fonts.gstatic.com/ea/notosansjapanese/v6/NotoSansJP-Light.otf) format('opentype');
+}
+@font-face {
+   font-family: 'Noto Sans Japanese';
+   font-style: normal;
+   font-weight: 300;
+   src: url(//fonts.gstatic.com/ea/notosansjapanese/v6/NotoSansJP-DemiLight.woff2) format('woff2'),
+        url(//fonts.gstatic.com/ea/notosansjapanese/v6/NotoSansJP-DemiLight.woff) format('woff'),
+        url(//fonts.gstatic.com/ea/notosansjapanese/v6/NotoSansJP-DemiLight.otf) format('opentype');
+}
+@font-face {
+   font-family: 'Noto Sans Japanese';
+   font-style: normal;
+   font-weight: 400;
+   src: url(//fonts.gstatic.com/ea/notosansjapanese/v6/NotoSansJP-Regular.woff2) format('woff2'),
+        url(//fonts.gstatic.com/ea/notosansjapanese/v6/NotoSansJP-Regular.woff) format('woff'),
+        url(//fonts.gstatic.com/ea/notosansjapanese/v6/NotoSansJP-Regular.otf) format('opentype');
+ }
+@font-face {
+   font-family: 'Noto Sans Japanese';
+   font-style: normal;
+   font-weight: 500;
+   src: url(//fonts.gstatic.com/ea/notosansjapanese/v6/NotoSansJP-Medium.woff2) format('woff2'),
+        url(//fonts.gstatic.com/ea/notosansjapanese/v6/NotoSansJP-Medium.woff) format('woff'),
+        url(//fonts.gstatic.com/ea/notosansjapanese/v6/NotoSansJP-Medium.otf) format('opentype');
+ }
+@font-face {
+   font-family: 'Noto Sans Japanese';
+   font-style: normal;
+   font-weight: 700;
+   src: url(//fonts.gstatic.com/ea/notosansjapanese/v6/NotoSansJP-Bold.woff2) format('woff2'),
+        url(//fonts.gstatic.com/ea/notosansjapanese/v6/NotoSansJP-Bold.woff) format('woff'),
+        url(//fonts.gstatic.com/ea/notosansjapanese/v6/NotoSansJP-Bold.otf) format('opentype');
+ }
+@font-face {
+   font-family: 'Noto Sans Japanese';
+   font-style: normal;
+   font-weight: 900;
+   src: url(//fonts.gstatic.com/ea/notosansjapanese/v6/NotoSansJP-Black.woff2) format('woff2'),
+        url(//fonts.gstatic.com/ea/notosansjapanese/v6/NotoSansJP-Black.woff) format('woff'),
+        url(//fonts.gstatic.com/ea/notosansjapanese/v6/NotoSansJP-Black.otf) format('opentype');
+ }
diff --git a/docs/python_docs/themes/mx-theme/mxtheme/static/material-design-icons-3.0.1/iconfont/MaterialIcons-Regular.eot b/docs/python_docs/themes/mx-theme/mxtheme/static/material-design-icons-3.0.1/iconfont/MaterialIcons-Regular.eot
new file mode 100644
index 0000000000000000000000000000000000000000..70508ebabc9992e64f1314f866b2d7ab90438c58
GIT binary patch
literal 143258
zcmeFad3;;dnKyoqti`)5$yc%^Tb3=$a&*1Kl4UuYqioJ%CnO|N7M40831EdRkc1F6
zmC_Kl5Ei)<C{5Y1fTk1zWoSn)Wf+D)hcd`N-j=o|KnQV%Qa&xi+aII6-{%}%OLjtM
z+P*WN_m5Zh-OoMCbDs0;=iGbRHPYK{8i`37iGCVMSHMqj=OClA{^k52NoHJV=}z`q
zx_!ehZl&vqzZz+&v|idNZIUjPF2>mw$t5k8E|9iKTcyj8v|KtH&n}d9;mLZWk)#?a
z%2Qv4^pwgabx3W}RO~x&PS5{*jz~gPs=yBQ>+M~&bWi)gluDYm=jr8z^B44LnY2=3
z|4@qS#)ZqzS~*(vyZi7wj_a<4D<gA1c<b>Mcs^B<F2>V#fAOw{3z6x5JRi8=lJ(nv
z|7pjlBo*BvNqWr%yLY-I6Pqtd#)EjSy>R_y+fSX63h+Kbr|`lpS8TrIm8#n$$u?DD
zyS}(+)B272cW=G``BC{@7vYAsQv)bd9#Z2)m+aiLEx7sLaD76O3=eGCcES38?_G<$
zjd$VtzDw5c*{=0U@8S7HNblOZ{*p~qw;!J)v0IzbUx&7DyKLv0AHM&z#BO_4l7iDY
zESToaTYs_P_OBUd{!uCsJ^R+LAAAk(OX7GcqmeYczS)jP_Q*|pDkA|K=!ZQ)HUFoj
zI|Xk@hovfhn-VgfDvS=JpB9*OP}1RcKiiD!h1ugDrE2N=eC)zAHjYsACjH{l)KjOj
zrE7V;@@wFd6g)FgLP{gj(bfNfpCT!eEQPTT<1fOu;P_Qcit%TZiqa{lDV-(xp3dk^
zdY|5n;3+*9X~eUbl;BV4ssSk}mHH?194VIw&M0@vOO{%YR-B2uVg4+P)<&eu@hpa;
z$es3duJC%&TQPp6NCBRQ0MI#jKgKsoDd9}K)q^uCm40+5#^0elrSY7oWpszioRxj9
zNcGurC?%y((@_56O0<Tq)xQKFA^xZwg0wOFNA0It<^CwYLjESTo$AhzM&ud5eHl-S
zcnZQb1^M2<%gxnC)GB|UKst$+M0ewoOA6wSXtiix40oyI2sY88%lR3#ooX!pR^up3
zeLNlI5W_}w$o&Xw)K*iQMB4;jP}y{rl%C_I#d%6<qxus)A+AOJsg6_!>NkNGR3C9h
z<xuS@pD<b^Y7`%bEv2JBQ72Kl$dzhLXOwI1kIJE(=sh~4?J=Y|(;uah@SL8tfMkpM
zCHULaql)CjIX(69{M522|NK%#4dbXIjjZSRQ@ST=LVNn7cgzw&pU$er?@~L~;+`j)
zO4Le18X0%gdiV4CP@P2C^cLmh!Jhu&JZ+fgAxfi^v{8RhZDkzA74;vTsi~)D-=#aU
zG?0b$be@9V>fv{&mna40LT!ujeFV5m_)D!^%v(==NwA>Q5h)E8c|ZOry{PBq{D}&a
zTs^48;wp@`(Ype2V%$<`be|yN1MU-SD7ApHfKnW9P<<&M+AAoX$`SccTEb;I(mj#C
zXbrtD-c_$D2T@1bi;~o9#`6&Os1!wVXVd0dKu;+T37f$Fa$X~<gIXVgNElEn;%U{~
z=}2{>_hZ?7=`HHDc=lb&LzFK{q4eTCx)ya3@S-v#+#~3g;3!HFu#kEFVX2irr+$;c
zr3e=Eg!a^ebv%{0t4N*5f$q|tYA5bT_>syHrBe;441opI9(ALdirUhfqBgXryyzV@
zhoI!;SVXz3!$!28>LfwnqO_tVl$sz+<4)9;_Eam{(-VoOruSy^w?vtu2K1i#hXnY6
z|3e;<Kn*0R7@WF>%LtP8OOlfK{etX$E&CMLdr*!}>XxQSUz7HZ7K~Pp){i!hwv2X+
zP8(f0x?}Xk(Z3&kdGy2gE_pBU-hJ==^u1T#d-J_tzIW`{#$zuZ`=?{CzhC%%)%)G=
zkG}uO2bK@oKA8Do*N2yW_^act;|q@e!|`7o|IP9DKKl3sJ5hc@J~92oiW8TeICA1=
zA4?zWKQ?}>jP*lylOSw<O<F%Hp=8IXd$f79Z8R`CV>B}Q{n4L{{&e(}_ZGZ&5G6l|
zlHYjmo%cqMH645L*efX6@P6_8_V>T@{;~Ie|AG9$)DK%foc7^QkCz{JAAeab`J+!x
zl$<b~=sdCX#L5%9PCS3&&5t!77k+FSn{n#Ysed{3)Tt+3%`S_}>@vB^U1hFPSBa~*
z?v1+F>;9?k)w+x87S_$Fn^7043)HpLH9J3a9&q03yxF<Jx!ieH?O5%}+7q=O)gG_;
zyyg!z57q3fxzzCo$FCgkI(}(?+y18gAMO8Of7G6^-(|nkeuw>b`$79{_5)Sdirzr0
z|NDO=0ZW?EY{4^egv^b87x?lu9BC`VwjSGFY~_+Pio+=36Xe~|YOxV+jXJQ^<9;iS
zZfuP>@5GVP5$<*22)T5$1Lrev3}B<OX5&cLD{<b7Bk+542hLIU==ZU`i1Vv({0X+d
z$N5b-{uJBGIKKtQSFnAE^8=Ff-U4iw;QT2ZA+x`i!1@1&<9%Y2eud+M*zmseYaAh?
z(<Yt5@eOQm3Nib=cX0kooNI9$#dZwmg_3lv3EM`Tm*9wc9YY=15*$&7WB-J6wBgw6
zVq>c%>3st>yvNqyh_UxRBz3k9M?1D|oL`LNcd(%h(r?~JIq!dhbL8><?<MI23(o&1
zju`770Cwz49MMM~%tXIgCFw)J`a{4~19*Q3SbhivPDA(5z7Nq>4fLkt<=9*}zYa&F
zI}R9W0GH#a`|*Fk`SUm;Ep3_?aQuzfG=GQVdt%d|tsi|XHqDQ5L>*6nc4*KiCjg5R
zC`0oWj?f2Apstz`9MShDrsEuNJh2qp3Y?>TCjg@pm*M;)9Cu+m0(1k6PM{tqeui_v
z^91?<#YqLgyN@;4^f<o*N2L4Mi1TmZh_XHgJuLXHB#i-o#`<yof>d$p-}xpL0EP_j
zp_=^1kdH)L{!e|FcdtOJ(;Etlic3oKZhrM?nN)79Fqtitvei~qZFkhvI_q5Z4emxy
zllQCN`tN<h^<zjF^yinROLL_qQ2W<O8>B&~{ClPAFsAO1?vWmlhNY*a7o?Y@*QB?k
zUr5KKlhS9>pO}UfGc&WZ2IgaaHl597OW4_LJG+}5W+yZinsu6|G_PnrEzlHH7ECX=
zrQj{CM%$(x)IO$tQ&*^)tGh;bSoaIvC;C!-r#`G-qu;APq<_btHB2|`HQa4T8IBbe
z7TOEj3O5(-EPSN!4@C_{^NY3=T~~BR(VInI6gL%bC_Y&HVo6KMXzAS2r^_xXd%av!
zzN-9Cd8+&~qt@7LJlA-W@hRhp3Twsr71vd~Tyfk~Z1S5{nRc2UFuh`~F)uRjH6Jh!
zn^Weu&7WDEmTt>t%R`pWDle)$Q29dT+j6noFYl9|lHZj-x7w{e)<Ns*Hmhxo?S9*c
z?N3!TRpF`)Rd-aqSoMeMn(9^6_f?PBm)ozgKX3oQQQ=tb*z7pqc);<x<F_@|nk6-R
zLHU1D^GR*Ec5m%HwJ+9=fGd3I9B|&`{E72}y5hQ~y3V@(x~+BB)g7vPx$aZfM%QlF
z0oN0*S6!di`|8iFKUn`-{hu0|8_sPw*zkPAXKt6f*L|t`9`_6GUo_eqdm1loyua}s
zkKQxgv%#~^bHww9rpBg?O^2G^^454ad++xC#QVDUSaVbJ*5+H9-=0!4<*F$!P5G_Q
z=j-;J?c43U$@h@&8AYo!D`zVQl-FBoT2{3jXnCg9-a6F!LYuQ~XWP5&<?TD$ho_pS
z?wNZ3)R(88>@auqbZqFjuj8eTQGc<&)4#-jssAbe$v{=WA6ORH5;zbz9C$NuqO-Dd
zZs&&1`#XOY)C8M?bA#sxuL<52d?fgJS9#aeu0>rtyPoL!NvJ&385#&(8afbqJ@o7D
zif(`R`Q7`wpYHyA+SF<5rd>L1|FlP@y*^!<-ZFjj^e3jjHT}yO%`>*n7@G0ojAJuP
zXKtLid*=R`ch7um=1*q6J!|T$MYA@}IymddthalLd;C4u^c?DWwdeEMO|v)8etPz=
z!<ukUcp!Wzd~#0tobEZB=Ny=GWX><<N^_lax6FNF?g#VA=gpkAeBPG%n)yQucJ^M>
zySMkQ-luwhy->f<xA6Rhw=8^S;pdB57HwJd(4tQkw=CYh__4*GFL5qezGUx`BTL>{
z@?~E`-~7JKeK+;}q;Ispw7;$Y?EWqN@%}^oBmKW!>RdW=>2*tATKeU(-ep6}-aM=P
ztms(}o%QSGJ<AU)Ke3`>#j+I#R-{(EeYXDW&1b*5vUFwJ%Knw-uY7D(@v6a9uSTjO
z=SS{~yd3#$bVGDsG!^}Pb@A#&tM6I;#OmW~O4oF**}Ue^nwQsnv9@XL;MzOZKC$+-
zb<#TXy2f>vt{YnS%(@fj*w0yZ&ZXzvf6m+I>d)P9?tSOJdhVC!b)C2UynD`j{ruAN
zr=LG~{v+p)d`<dVd_WrT4_q_w^1x^7o7Zn$zkmHt){kze+Hn4c0~?;cz<j};3l3c{
zveCJ5%f_cRe!9uGY2BvyrYANRZeG6muFW4@*m&Wp3-7w{)eAql$b8Y%i#A+z{Y9@|
ztiQPN;{J<Yy!f|+_QCCgFAip6&9VO29kHilzunTZCAwwzmixE7w&l}He3vZ0B!0<L
zmwd3bVe8=5Lt9_jrrB1xExhfbZA05$-u9>M&h7r~L)%~9{>2X8j+r~Q?|5Lx>z7tt
zy5!RPF8$!LhRX&od*HI?FMIj2cP~3}S!QSB&aRz{cCOpGbLYODckg_0=dqn%?rPlC
zzia2NgS(#C_0q0)c73v2ySrw0=kA@mpW6M-<(kV|F5iCnBbR@$$G&ILp20nj?Kysh
z|B8#Q7{21my)*aj-g{{8D|<h^(t2g@l{a1a%9Wp8HT9~US3PyrTUU2qJ#h6cS3hy}
zudgY+rstaLuX*X3%(eb&2d>?J?PJ#-kC(@n#1F<_{d(coyT88t>lb}}|JR@X`n%WZ
zud`pb`?}Y^q5Z~^Zyfx_>(>`v@4tTY^$%Tt?3?y)F8}7xH-EOzx372K_I(fSduiV%
zH?-Wa=!WZVc;<%B_qXlezW>GjpWj$>WBA7NZ`^m|{Wqp=JaJR>rad>^f78g#g*Q*V
z`MR5r-2CY+b8p#m%Llg>-`aEQ;H|^AetN)nVB>-N4!nJv_O`a$&c1E%wwrEy=b+}G
z|6uRI4F~T!`24}Q4`yz!xxMH1!P^ht{?hHo?l9lcd&l4%!*?9Nv+&OTJ1@QSkvreL
zYx-T+-*w+z&)jt)QIqIT>`fd<Jd=2BNITRpv}9<{(9qD~p|`%(^{oxxy5(E1+^xNP
z{@wfT9=`jXZ|lG9|MtMQ@A>x2_Y~eU_nss7d~k2oz5Vyb@4f%tSMNQ3U-^AK_ieuK
zj{DyJPUm;}zq9u{4}9mf@BHEZ=>0#tKl5GxcQ=0bk?+2BsQl2@Lq9w8#rKwf@22m)
z`n}8pJrC@C;GqZJebD^iss|4~_{@VN5B}+)s)v?6bkRfCJ@m*!BM*J~@P>yUc=*kS
zGmp%FWa}e`9{Jg$_D5Gey5-Sp9vyo0sYieI=qHakAM1YX?8mk|cGqLiJof9y^^Z3`
z-u?Jhk3aVKm&4}{-!%N%@aM_q<jmym<kQKwo+y4|`V+gKIP}D;Pki>I`N_FYZhUgz
zlSiKX(^D@!b^QAc-|zqaecylgaP#50hp#&P)Zur3;QT?)53c{gLr+&e-Tm~|r(b;f
z%OCdsaL*6#_~Dz+6hCwQGp|3Zf41k@OP_t_*)NYwKeFY>V@JMxZqIWsKKIG<RnLc?
zKm7a$KN|Yci!W5XFz~`HFMJ891hS0uZ5Dw>W{}!`z@+w>PaCw-zxscm*S$OQ=>iQ-
zr5_Z~Z5`cy+Ms7&&U_jo&A<8srhv&CFu6_pFW<bGMKVbf3m~@%l1<0|yZ(Rh$dft$
z3VbLas{%{2G{;fM;GUpcwqeuwt(E$QrcNe9%9wZf>Z=c5Jr<8g6@`Uydi7xxs~vyW
z02`=9vOyX2cXc*3=qt_Hw?hHhEn|D(mQ9;(*|d^plR}2R2rf6>!W=v=OI#rfNCDYG
z*jTnM(5uwC7#xgOV`OYB9gRj5CCn0;7}CRn!%|3JBGtjVNa;~?DlibNV{&~k6zuGx
z&2Oz`P9_@y)Fc)O`@&)0HLaO=Yq+Dbrm`da<5krS)hCs382>9;ZMN2{0xL?4<&~A?
z#*!6*w8;*|j_RkvirS0Ts;$&#Ta9u!%>K<>T52w}Ebs1K-d)Js%w*gsHD8Py-OK+y
z3qUREA4dICpbkbZ!%JOMSLD&f^Xc+?10u^{XDDbhc=d9n)!!BDZ1T$P%52hjskyAo
z{0F|jQC_}%`=RYiZ+GsjUwzf;C0#yVtf-HZ*D8tU+m{baW4Ajmn-c6<y}GA+RpFH1
zP#&Zi5lkbfQxffKCmo)lG=(+jYgwgr2J?5#Vt{ZEUHTRGLw>7ls?@vd8=8Wq&aME9
zmllPK%F3`Sbt%d&e!_3z>NP;tlo>HsSDTq^Eh;T7iqg(n5-yo|HDouPk|;65Ui1a*
zVl0Vz5^aU9%21_x)QjjQi$tTRq^K{-!ehxKi)MyFi&a>cNDgWhumspb+i5ip$K#Pm
zWLVUV7aCSZ6vd~+sj~crk7}WT%`VQ4vN+Ck$kS$Un`}%zawK(x#p7`%#VJh?X_9Ih
zOUUH*hW>~YNAUQR6i1T0aioQPwHzafx5jG-*<^PCat(S6-e7<Y?AisnHyn3`!@HP_
z6Bdrs4RJwtMgJ-2zvCRIg22%%nt1h<HbYZq$O8PqFmN|`o5UX<9<rN;ty~HHi2wc3
z)lyg2a%p$BbHRe+rfS9St~5?D8)~Oi+E!l^Rc5X0F_kwqmz3EGnU7M|{kZ!O(!R7H
zv(0XD`weo)YIS<1_-03=vs-(5T3vMxhqIxnR7-tFcrS3Q09fXR{|qBH0IHLZY3QDS
zzso@5nfFydKCPdEiYtp-6|Rb+(u7NKx$;qJG#oDB_=KIyRazPk20>`XCZH1ap^W|+
z1Kew=9yUV|nA;h$(O9zpnJW!m8gXvf8-imj(!0q~XY31R50OYb9EpZE_4=pOIqHLb
zOc_7LeY8~rn=YsoXq1uT9YAkDi`p1E0Ye$&XciOnsgmtz><jvWeU0<ZowvHPsj0Jx
z$z3Z7dwUC4bhXc#Wooj=?M<{J7#8H^TSGQGHYsN>st*7LL)5X>uPEkbV@a`*Ra91V
zo?hM$t}iy0mt0?BG`^8nGSP=Hr@7VeOu`gmVIWJnCI$Q<FOUnZ^6I<-wQL-M=dewV
zwbpvxzP9QrccI?iL_-A4j9BbNES0&}v9?^7ZEcBL(`2uR-+8T;H`!C~sI)=$fn^@N
zDoLJk7RaMAGng&l4Tz1!Bh{wNu&KJ*Wc7u8J|(OW!$@X`jC1-Cr5}f(O3#TVG$Kz>
z1C@VF#x{egUgk87B_c{BqQs~GL6f3PLFUy=ru&gdO1%$SRUOHsebpvL><u(REl<!a
zwM@=$Ln!&rl$nml)9JC(%Z&MaNksum{EAX@JgZQ$O0aWU)OP4lV3QlX8fZ9+O)ff;
zZBrytZnnBW4f5K{;@L*YzFj3mD6G2&^V>{qC0<?+Uj7?SpFwd|UhXm2jLd6jXCZIM
zJDb@;b<Dt-JCiyMOPd!jZeD8W96u8xM{LGfrulVs^UP<>n7NG3=9|vqWvKAX@?#Gl
zs0{`SAtCU8T!y)!aZ~Ti){AGhe%iRn%o*~xT4(ldYRvqs@l!fsaaE?#fM~~9oF#a@
z3Ck2l90mQFj6mubVZIEkpm?g5O?{b_n+SWx%M62IRPnNs!}u|uT0|bbF97G_aud-%
zRf<rh3LEYLr}pKEP6-8hMe{|cCx?eIE|REo0(I7)e{F{3$&)ORpewbG8uA3qX5OZ(
zfC6l4<yAX0FtBU9Ub!|=ix4<6S(>v2ZTW)SaD0Lk_k3zV@G=^saX=_56Nb<ZE(ZcU
zV7LJ<1T!5rWjM*lixN47CY+LzBo{`(wSmp}&xi4U&-fI4iRxa)eT(pdkW?WHSuEhU
z>8K^Bxd$7I4TeFxn(T$;nRGJS1Qu6PAXuGE+9sb5yoNVp99PCTrqmOi0WCpZ+N|7I
z;ISDX=;g^xAXmd6cbWg6)8z_QUVivb=Bg@lX4qUsTu4!pRCp)TY;9avZ3Z0zHDZVX
z&|%!gFzyn(tU7R4&}a|%7fEnkVzhTo9_)nS7U-I+WbVwlmo?SaHq|aCIqZ}~f*T9_
zKtU4~R=eF=@d}>RHm&B*kb+4?rD9BE$At#`f#i=gB1WM75?<GXbIL(5y?SD<Eap3f
z2KcBIi72>dBs?MxfRp;J9=HP<?;(r{1!y2l%0vdIm!r9yQW2}1oR}NH4BigH58h4^
z6|r)#Tfui#e!g%c29we^*w+_~VF+#PUAS@M!eAe6L}M`wbJR(dGxZ!RB#$j5yFt4G
zvX^-oNLh%@oY%6uWu9$!OXd$wR(h>-oAVgI^HKKBJlvtYh_=K*TWD;M2ZPfV8$)NN
zpJfmRNnp&S(NG%5i%8}^`s^tQnioloG*a+WdC5DF7)^-#ss%TeEC7^&MDvhJOH_>n
z0%VBlnu;%78eiIX)oN%53!n%b+RhS=4UVH592-Dtn${`awW&4qG_&LW9XsytuV3kM
zo$WfCV417CWT`jR$EhMzYf%@$mjz!lLK_>W_e2we5av7+@EdwTa%3dx8;KH=RQYw9
zj~GS4$VpC+((=nNNEN`1dc?qt>tD96POpI^wWjQVGLQsE5-;_7o}3W)U4z};V1LN(
zuWRyL)9d&5`gKhnr{B*SbG2X!lGqz=@*hB|&MuE0sr<bmI(6b6Yn)WI@$sET@0m%;
z^8i046`T_vAoC|r<KssM2A1Tr&~dSW;y2<LPjQaJAsJ#F*R4ou4Ai3#7K=p6XrzkK
zSjJqDlaW5U8lfHG3h{w3ACp{b<q{j|XOzbb$Z0ZZnYFB!?RCu8>qS--(^=wo*B6zs
zbY`DpzLw`UeZdF6D=q3neu&gyH$x870sACNAY}5o8!HXLSR@vUFjp4~$1=mQD|b7d
z0Nx~!7MdE;W>Y4jgM-k_D2dPosBS3^J13XI<aWk|%3xQ>D|^8+ygF#SK`8JfNqVsh
zTA9?k2NGqAXG*p0oLFSjCKj2|K4+EHRT5;KL6%(CIu`<EOKowB!F<laSbWpNfo0__
z<rmMNI;GxGTcqu5w{v>40A>FjxUdm)Q-X~E;(4%#p*SnrW8f`g%A8fJ=Cse4(H;zr
z@fQAF`NjJ$MsCYCFDqZU$Nq#>ZHp?Os`4El1zwB*vZzePMV5@pV`F11nnS&KDuwRE
zKX7oO!ke=d6o3v@OH%}G^~#X((T`2;d`*eemVB&dL8Xr?Lh&=GNCQyN@h~p5BPLDM
zadlFI)WP0j|BRk4l`5gHLH+P>8bek9U7(!>Y;LdH;I;(}7I%m(zwXF2SN|Y$-%6j!
zJ>adpq|*DN3;cmi{>K^|&owx%+t&7n&Q7<VZSfOcBEki{DkXBw!bgE>WU4ozh^z&{
z60)%r$?lOzCLW2<88md3;t;9^BRM6Jhz-L)CKita-?e#kzY{SDkQF_Z0sm|UNP%m*
z8;lIh&>E`55dtt&;WmgyfFe==oM7}$jm;`U?>2z@7%<SdR>&n~(w$*KvKQ+#T8*Yi
zQ&6BSC}`H`4O(0jX*GHr4EpSaL95lQt(2Pz?%KF87VGTqzo=gUzj@K1(P=e`R;w#O
z0!3>n)0XKq8m48sYNL9tDb<$*d-cXrNAE`PqA2H4;7*g;=am*pH%e0I43!N1`8GrS
zKds4bKS4-g`i5HOMwT2HAPo?v-3<m<1~3B4nIR8qzyWLJ4Azx>F&LT9(tpeYlSbpq
zg2_;!0ni9!g#<E!(hj3WTcXigOAM@#m6jF)xVj?j028eNAT87|q&4$I8g1FwM3_{a
z5unJZsL)uV7qBIGmcfCo(P|5g8okzt;<Y+mK_SXvdWOz~KU`PAQ=lf0=z*WA{NSeu
zmXIDOL4vHo55+bJW0=j!@<76|2>6l3w073y_MV|z{$$XU=>i^&2AyA6fTB@8AYsG-
zEj6KzIt}5+;}anH>jGBLZnS6^V+8<8Q8|(q5?D~_@?tbfQ>N8v$|k~P60ZF1V}ZEZ
zX+qS*v7mPHna097G!|?^=E7L$?~KL%vaxX6X#!eqtjn;pT`t|6H`Y5L1)OQL|JPvt
z8DZlrA%sFdX$x>r>BQeY00L(MBJe^pfu#IxBjDM|z|M__PHEAf9S;RWBF-=#f@i>=
zFAW;wY2!hwW15o5BVt0E&p05o1x$?N+>m%~aw|xt7k-8m=lh7AVo>t3%BgGv?;%c9
zP^njNKd5q{tk)o!iL<!L&Z<qZ;TX4D!?;9lgCQ;tD?%4j6k<qbD3JQ`vdX|cotVMl
zr-(UWRVN!Kj8<KSklWyO+h8w)shv3bWMSC`szXedrOf4hu4=Q(V)<c{%PPA(b1h|k
zePtFVQ(ZD~+Pk1B$k<Bcax*09)}pTX;>9o;<I4OA5@jve=ITS?Fod3*&YpxOQ44)B
zM6!jjyOQ)S?EWOhs1|dQHN13bn_N-$ti8%n<tQtPz#bck1cSjYdzalFVn$Vatum)$
z1ATo1F-WIt9dU=vQMD=F*N6Wxf*l7bhn%Qa47LQaWaweRKr|W@We=2RQo_lE0401d
zlVWltGsFfnLu27EDO2z+aeor&NC~_jgG5jXouNi@0c*Vm7}i0>;MB-Uz<w)fn53cF
zAk&Oa^*B7w+D&m1%h1&dS#O3@;i;~vaa_^^yZuFlJOXha<XcrgG{9bxwY3Ygh%l7^
zz9i>FAm=2xwP%8qsm;E@V%>`tbvL!QH)V!}dFSEsb9bLx-cmMq)!eeb=O&;$ye0bt
z^gWaZVFh*PHVPGU^CiDzlig4rgQ4Pg0&gMQ@isgH6UFt)kliDb-p(Qugua|#kMtQU
zm-xK0XOiZ@%{cYJdKQ5ysA_N|%#tlcf%zhuTyXQfT-8YeVlR_HM8&l+=rg1{P&*{}
zO}rr+SqjFx^{6Yc-Zdwa9)=r-kE;^b(10s!_4$N~D8{f$BSCi5ArIKUNyEh-!6G}9
zL69NE1fi>nds%T(BO#}->t?tB@sFx008LmCqdCr>_3<GcsWuM}L$(|r%sIM2@;!Nx
zP?YW~>BTUU&p<4cGZP9uVr^T1t8W8GOpu3bh2=&X*GGcZf{@iFLotCH#~acc++DMo
z9{LarsColSZ(TvYmKC2kJ?*n2`WojH{n8q}qMvfG-Qx_*R%Xwh!_IzaZ=q7SKE59N
z%xzA3{G;~9y1?vf@J^tCzw~f>I}~fe*_@9C))N|Zr|B=;8fva4+@WC$1NCWY3wlS@
ziKw?pZ$TU@Q5Qi!h`y*~kNiE8)n+1mEa?b399{cd3K2i0+&q?}C7^XFK|SK}K2Gl}
zPv(=6n~A{D_#?k^3L}WO#VwzvA*E6&8rx!YK#iC*Bu>xc4Dd)z9AmL?cyccJyoYcI
zK4kJll6H6+C6!PmVl`@|U^w%^{SL#3O2LH>azfiEw=?84As;D`fbNu+3E2mP{YC@Z
z3a~mr^wF)$4w!lt-sKt};1auew-uwB<$SS(E6~R<rpXUay^C51FZ^tF28#teGGLid
z<?`(oV>BAW;ov0e#c6%_AH$u@-|~b80tZUb#xTYg;edhL+#RSZSw|K4xdRj(@4;w>
zZ7)c@L1cus0-V#ex=p@@)fB2K%)W{eg&zh#GGgP$&#_CUGtqPkb}gT8I1P`00)3VD
zBUlvfO}faC@fj2^jJl>!*BaPNthP{>&B`q_<hF%O3!@FmQ8ugL)~V;U7ne3Rlci@U
z4ciP(v_;H*-qiMD9q!>eJwy+i8_SB2FCyqQ68Is^k*vW0WlnF<H$YM840X9|R?>&L
zTNp|pyfJO1u4=tmAJpqFW(^nXabnh2yGs2mY@BP<8LPydjT=RpDx<D^ZUxF9ewYBy
zMohN_APj;oyHjc37tSwkWr_5yIHDRDPnSze*^TU3q(j^RbbPmrltBwv0h|(jap%tX
zV&`HO85r0fcV2dR=8Hv6$ebKDw*odX@@Np}KnW<t#vX8{;2qg;Ig8<BLsGe%(qc{z
zj}bs!7&nZ9$FSrVx1u`i)-Qahji`&t!wX>tk}L+e-Gg2OG&~u?LMYv_!?&qpB;L{E
z>|x1{9nU<lsUyy>@N$k<3Y!o43_vRhUxE-&#_5?s#6rN!k%V2JTYS>+Q<Cu)$P<Ho
zU6}tO7^=tvSQ>`xLhTg!!saU18;~<m;siP8U?T!^^s$(d1oHWOUs)F&-k{B6@~S}@
z^?s``IA_@_EGuweqB>yTRHZQgAAz}rkMinjPRAvd)?l(UVDz8#E0|EKiHm8A=P$mt
zxu&MM=E|J0i{dg8t}3Uq%JoY;scBxFZ}ZBa8X!y%8Blg^Jh<S)<PtQf970ki4G1F>
zqa_6J3sHdMN08$P#0xhpgA>O}d;9tAMP-f6RynL7q=MQVwLUv(<g}mL(O#r29kp6H
z=|WoBstEr;G4NgHJ_g}~tOG@9hXg$vb{W{6R9~Jo>v&V`@#?kekn2LE6+P2VN~fk*
z^AYxIZlAW_!)@8(d^10Pk?%7v=RL?z%EtKBW^wPd^OyM@vA>6(i+x5MXw0g7g1k(?
zbBsRQIEH7r1xwN|ub1H}k12z~S_fv94d9`?MBG^!$(QI!h4C>0)Rb-5U{WUeoMim6
zqI3kVCb_5#T18~6sFca%cY@oU`Z8Qvlu6;ak^-urjw0S71&yH$G(BMOlFJd)1xyJ#
z9NbO51O<RWI~htuhKBGCjWrj>+MA$7Mv4M~^*IFF4w@I_eq^|)DX0MoEksO^l@8}w
zS2UTe_incL|Es|<y?y3Ff16>-3<p!#om1&qto@#Uxo~rX(%J7QbWCsZw|~Pw!;wj6
z%aT!68W60e`BjX3)(E``@c!qP$v|g_G_0(Q0=N(;L@?EyN;o5Y=$tZpwXeF`$Agq2
zP<y!E<f*K22Lf)$3=L%tM}0|y;|}P76b^ydhj19c2jvm{r~qfv;DgY9!C?&Ea=5bE
zwB$7j*+LBvE5UHMxgE9p(<SpAiz*t6i}jVAUYB;h<LN?M#e$Y_&+5jdraD%#Wxiu!
zRa;4k!M0$|*>xpWB)HH#zioBT)CJ3oRaS@uqF)pq2V)@5o;mBN0shOC<r-1;Nghd5
zbk*v*7R6Y>3u7gSEvjotuI{N@-aE@|5=Hd<hqP{7Rvo9iT#X=?)kNYeC><#<6w4u~
zF9`HFZgRgN*Qi8J0<$^kPUpXq!)Gmd&OHzdLX#?y%nU1erGpP-(h7s=I#)8mDInFd
zpMv+7P(IvOBQ#a72Yyua$}+aArn$6m#8lnQHhktE0>yqZb4$$>z1{S??t=gInV-i8
z{a48l6eAFX9F>+DK!@j|hBm@a0}zzQT-Xc<Z6e<r%m(VYZ4y4&L_pQ}-Ch}<oI^|t
zvr0pePp)-WI_ulKa>ulmmbBuj^Uqpyb)8Gz)Zu1+cahd@wIj~zs1@#=??DLFHkd0r
zsy(I|?xwoBwyw6bY@V76rZ1V+s;jK4yL!zmf1P(q@1|e6wMFncxQqINcJORT3PTP`
zVYJ)8%i7Uj5P<@ywE;duun;hR;W0OVmB;+*!a}#p*J9M`jV(Uct~JZo+{0b<C%M2L
zrF~j%k$p;iCk~zUiZ3G9dOWqDTyQ}&b#9&{pu#(rt&@leag&D!(tIshipY2WRW;GU
z8)gf!^zfOgz!G8n;Jb~-zq)kj6;{q?2`=*hmoy+mQW-=F3sg;Vc*6mLlL->yWcl&N
zB^qb{R^L1Q&i;rH4Qpyk+b)_QMY8rKtge6m;NUrZ&i;QFf}_10f}?SQNQruArE@R_
zABOLqbT$XkDbgCjZHViuwwpkcAKEYw$<`%K5D|HGad~<1UfQ`+3!L5ecRQ!ITmvGq
zt}c>Hrpt@}nI8Rfv5_8jJ7=}DIO{;6NY4;)6I@4RkhVOqu7DeHDT4&3Z1m69AtsHh
zWh^#=fmm(gJ5(F_VCFaIoTxYGfZH@q$5kSp|4xkCU#<m-=6O=i|5w$8Xd&6)l4NgU
zV54OICx;{9BxkO~w<tK6hXy0~E({I@21T4Udj>~m!tM9F6ErHocN4^<#KoEA=bY_v
zT$dHp7jiOGC}!p21t-EXbS(U`(Qkr&7oZ;!z~eZVRe?E;S#G07MKqF>MILafJGfi`
zN)_AEqbNNsCwp32dKA{aVgCG%%*l@V^EWJT3^-D_hlhNB7j7+}$BN6PWNjrf^k1@R
z8Nk!Wqo&AJL{djN1N$aRo%MPW%&H&!l@d9xY%U@p5$-uR%_Xgw(MY7J9X>wLBJ_V4
zIFteoQFN@zfdtM|fHUQa{A>mcN(@UHp^SqQTwY11;W~~)B61`uV;_a#5u}DAS6E?D
zC2X?aqdr0c!dZ%O5#<>m{RA=Uvor@)o-o=(JZDzoJFPxPEq2Q}#TBNKUBwj@#h$jt
z?{g}s)``ik(u#^wS4Bx5CF(0Sx)4}sJu<m=g15`SIWdOp;526(M>@FZ5e^XIhfXh3
z@`fT)Dl28Pd2VyFwYD}wng&A%d2WooD0f<{)m3wwVjgSVSx~bJXw`-+%<H68Uk!Cl
z7MDN`$$jkAV^5$6A)4BtZzm^m(Az|wWE%{0q^irrQM&3F)ayyz)YMR2-C)1Urq#$A
zZB4mOr(=o+?mb<(Q=^kLRaNvr=O{D49ir%Uy7C&0R@T@~@6pUjjzxNlR^uVRi9@Gl
z4oy*!M%Sp-nyjYr+d8_-w2fK~46iwRCRx`ZHo$*Rg7TRR$!Q;AwxVNX;UZB}cp4~%
zD>8?*WZq^`ax8Em)JJX)PQ!v26Ng~sMbxP$*aQc%j6qP~w~;snI|FXeMh<vLw|Hdh
zVySxHoX*ZUUT?q8pe@i$nKh?VS<*F?e4YlSZwclPd|vGOql>v*nSat67!b(D^jbE}
z<#JJbRQ*JQkw~_+P(4>t#sx8z0!xs^V4ud+fILM-j-8UygM+6VF*RsU1oR|{Tt#$`
ztT&*45tSAU2q%KdD3~0LUTjk$Ly?RWAu}Z};pp<^ZN{zB_rj8FP7{zYP?0C`TL<#}
zCC<#CuO4_O5b~n;LZVW04tW`{9C0vua5-=zxd2opQQOhr+R7ewOwU}6Qp)SukFC2K
zq8fu{J2RQ8D`uEd&7}=>A7svPOou0G#8O(HIayu0+jUkeW813WW+Am2icDA<x23=W
z6XI%e&=7V$a5*d}3APj`u&E@FR>Z-5kA08RRP-=$HB1_ClS_||GS^?IFC%NNnzgp)
z#-Gmo?hZ$@-C?P8uqjuq=~?^iPjB4u-S;dGd$YrVvdK0>cA^qs2d9%kC~9(mCE~s~
z{t@|$Bd3L7j7yS-MHDfl0AX7^P4kQ)b~Nhaq)y=C7!N0nrYFf-Ia!^eZztF*RehX^
zye{EEve|4-s)4eEXF_)+ogcND948y%wnLD9G$}t|_9R8OMkfT#T6mqKfNxk5?W9#(
zV4t(pd+ROe5wzBmAI(FQrQUD5gf&&3lD@ucNpuNQibVs9YZ1&Z8H-uMni<`tC<jZ8
zna_y^vtnauV<}(wfN(hsoFjMw2R%S7FcGklkS!~xfG;9A8v%NWeu#3I@QnZd^C_E5
zs<QmD%gQT%NVl^8Wbr3cvgOGq0ZK6jbFje}^k5DGMdx`dZ6OSIy=u{565_8vZQUjU
zu*J-<2t{ds+L+{V+PA6To`@@K0@u6za;N&wE?3WRi_-!@&q<yGfj^TZ27eReGq6&Y
zILQTfz9XMcwJA(CHdS{Pnsb!yD17L0dzfe^$;uRw2)>DEVHgVhoeip>p&|xm1-W^I
zB7`TB1fXHiND<-(g!F{svRe1$nQg9MhH%1fpvXhayz%>ul@OqM{VXL9DSg%>)s5BF
zjUVy7Df5M#P@fz8>N}z@cswn-obVLX1u!6i#Pk)q#0=I0N@-&W--z#iK3Oa~mZ<aB
z8eBe?0jx9}2JOV8Gn(Qy?iBzXwNzCB14woecuX^yxFw~_U&pMKx)3HZu@}6{y%78T
z`b_+{%YVC^4bn9(&7-)aI;%D&;{BvY13h&)$0OzoAoB0<;lu2kqUg&1@joiZwqAIl
z78Dg4S9R%*9i@CM#X&RTIOB7}LS+4fL{F~LIMi-D%ES}VF`qA?q`B(Dag1VxlN8ZN
zQQz=SxnbE4>=`QD<HHMSUjoKhq*HCh6v0bzVX!H}sGG!`vt%+lEZTt8`#8-ilos$2
z5GqIPqP3lMVO(*E6N;u4Q)R?8Ik~uy2>B}1rUg$dM+_Nc(w>4)m$!*Em)B^WuHuxZ
zxX@8&ao5z8=yiofMyq4$G~bkhV5h#PzNUF*h%ud^ytrnDsjRBR?JqG`7@4WWu+!62
z*i>dH`d^Kug$`$(v)S8dsxn&}XNDSr3wpec`bEWs2$9k)mW`DKB~u%XjSUVftI<M;
z&hJZ5e6l?ANlU>TrO2A45}9-qYHfs5i8S%OR60#FcVJlI0SB1hC*%U6oe9WxF<>OO
zJ^|xMpCjiVML!_qE+98j$N|Owk*fno7HjG0X^BQ1UauoMIGoAg5Y66VvDs^8Pk|gb
zCGPW0#ept!ctmlhGWFEoXse8H9q4b*i-FH4dHZQ}K(ppV(ctZ20YAI3KecRXPxo4r
z-@0pYggKX{mNw7nte77qyo*VQ>Sr==TJn(FoqJ!$pri9KBWMxX4!E8?PGw^wmMjnR
zeFgu*pjF9~YGo#b%;#H!BO?=-);x-P5dLA|=Rq7CpCFgMXoq>}{fPuzL|;GWlGS_;
zijbWs9kp0ZX=FB<P$y?!fHN@fEz%wFZaz;`IkgNula$X&iw)6u-XOB@6<bY^ako%3
z`*w&8=bEe)6;|e=%R}1%pNaVZlgUe(%O^ixf`!vpayw3vheU9J7PU!pn+<D6Xak`*
z^Km9%9;bDJ&WQOg6lKzghy+@tu?ws6^hk3NJ=h11j({1$?t<(V!qF?vEA@23AA&$5
zK`k^2%=jePiaZ$fgo67i6~z?Fsdy6cCtMrG-flwB3#OidH^HEYT^y6bFkO|Nz#@XQ
zBhcfOOp?dfV-Eq)WKZ&`W8}Mp9a14r4>sh68#LPhkB5;J;72w9;Xl_wLJhXEpboRZ
zK}AzwTuLH^Bpk+Pq`5Mx9OlMX(A@Yq$))*=B>yFWy*#o9{y8H<cqM{~4ItG_YF>U`
zWy>blrk2XrxQ!$pYib`GYhOpWhc)!rZ$Lu`qrkCXa|F>u@D4PHlEpi1e$(3So~g@H
z{j))p=T~&jX~swjp^iDZEgQ#N;4TP+0+Z<(gdw)7^^)dGTC4VK%V`MLwzTYG*38K;
z%elkB`2M(icRr5kAq}`tFYb7NBk(JUCUhrXbnrCF$&@H~Y)qw}LcGeOpW!TT$&(W?
zdkDA{2>p3EaSj&~&DD8g;%-QZKQFCNrNoL-H%W;qoG7-Yh%6A8IR>fABnM^z+A#a%
zjAF0^fkcxvVk{D|hQk0yo=O$1HtmW=;8tVB@Q7ENeRk6rfpg4crxBF1xtVB|63@Gw
z)v|h+A877q0Nyaw`bGuO6`qle%u;nqs<HrO+aWXwy}rQ?XDGa=Z3dUz(jvPIZKmo$
zyD3Hm#%R=r!_awi@hAqw*^m#0{5_<|0rhBP3gaS=a0eBXv5}uOEJv(K82nK*SyK}U
zgy<(=H??NQ(j<D^jv*JPu|ob};uQ%<FBCZiNgh#JoQolT9H1hJp@B?8D2s2(ifMg)
z&E4J2C77-}?fX31Z1}B9SNO1200JT~AP*^rh|Kf46P<qtQVq>l;B{ANw@UFtGOR-I
zUVB5Z42NFIo9S~jQn6_sP4roW89z|c#%K9Bw)^cf;}>9_&)JyolPl*Je{DIYjdL&H
z<y=scEr-)$`;LIUC%$2YYsCuJDwLz*SsE=X<6N58UA3^N+=!J7RBZ3an+3^Y+ow6^
z$1^AMCqhnu4{-|--$BtV=tiN7<-~GYW1*>2ofSos=>h5~dBDm|i$WLB|5zTta?_%+
zyqElWMs7g~Vcg4LZJ0R#sttJ+lUabuAmm{OTkg0LMhxHZwXx*<M`0~v2OXKij=g8O
zTrwuK?F#yqJ7PZh(Pd>83w)P(c}oNxi!Co_byW-7|NJP)^BhKgNfu57ca7&3+eI1e
ziXkh&Isu(EhI$gO5}GuRQpnOiGGUR65g1CdP7%R!L8-}9dLQlF(KP1u!0xJ;N;5wq
zBOY5=YNCh(s-v2(j%=T^8JJZd&q6q!=NR#){5&Ivb{`r&Ekib*ALJ6^f8fp&>$WVf
zUMx8|gTM9t9Goa(6#iL0r#Rm~3s3Dt_iR4kI*gDMTv(f~sIs!bY;4|;SJ~eiWfF<e
zCw4rwRhZXLnF4M-5v0Ty6}A`sL~BX_1|Ba$C@>3!mVpq)UdRQrnww{xd+uI&18Kh-
z<cFrLV1+BDoO|vS4~RPt;9YetCCxP~f;9|%PVq88&(rebvlzk3eQgu-jghe(1V}me
zv;xw&EAR>{({f+324mQY`iA)It!!;E5)k5ydNaAX`2p7L>^PSyeQU}m@15gpxT<-|
zuQRVYJI<kc-&(XliCr<r35G*7NUgtsg9CE&8DRmZ6s*9oFo6428knWId1fADUazaK
zmgQ(iaq;Oe+UVcgS?ycni*{63A;N{m2v$jFzh}S42+7I^@N9xHKy}5e56nbEfD$eO
z2FM2;Qx^CZOfk;CvA1{Q!i5{f{^jcGWGdymbo@qd?~j%(6L8Aq&F4p|)J>H`5!pPU
zj9>BDi7w1c9qeODejzMEW`W)Z@t|+;s=T789L<C_j7#>q33<!5quG3ORqu`S{LI;w
zP=OQk<GK&5Ik5F1J}M6yvW-dP%ScYj>>u;9BE6n?*W2K_w9bYf3v7_x7aFh3m19eG
zdLQwACM{~|4sTg=^|lpVOIEL3vCZ6Fyre7EUBCM3H7l<u3>U8I?pe()F1%vpRBwHa
z9Shii@2YV)Wf(QpBu`L8u^=9^pwNeagfMe)cL_!MLA<b$KmqkV26NAdsoEEIZrI?Q
z_ndRyJm-eY$?VOTk88CN-wJv<AOG{68xnLAa{wZo20|mt+K0*3<f4*K_f&eohahE8
zxd>rQM`9Ga5~umqnUf<Bz{#ta7#d2I7NzNCx~Nnc3x^>oDS2}Ua(V{ZXF)5-HH*F#
z$FGP)hMeMKzLOLcnIDhL6&c9+h(ZKy$37)yFo<b&BIsGzy2wu;!<J9;*D1bf9IHr5
z!gMf{j|p@TW_lRuG)|_^MllKLgRN?5F$~P}%^NVNC%@##9m6E#)mN>|f51}|aQr4*
z&M)^rEcGi(`>JvXzw_Hx%_9N46uJOE1#xnHu5Dc*9a>8P*T7I{8{RI_R(8(M>sI&p
zKXcG*)Ygz_jNZdmwzTLfTqUb}oDNr=2e)tf`4m5}cDjS+rF!%N6rt9QK5wnd;p|ym
z;;PWm&Hp>=^xx4JIhvotb8d?>V0{7_%{Cmpv>FF^RfM!w61t%;MF?}T=F>16&WH=H
zBMO^{2IH}wVIFgoA^v6<ea>Ku=we^66u0$(J53FF5kJ75Vb4Cx=3kMyg4x*UC|gC|
z(VUD*^G6^LFbeo3#Q<X*90y_~!d+-oV__9OacF`$0rTS{zTvPH?m26<X^6Z|Iek)u
zU}ApBR2WjBpj4Mm#T?c&_g2R9O{=K0eq48@brr}b>ZMh^xu=0#P`WI!BikGiHFr>m
zxLtlS?bjDzjB+&u5pM_>ii5b(iEk(hvC(4x1_WH_{B(D0h(F^`8EP>!<hz=ZSmI|*
zi!g=XM}35C@Ll#UxIWY>NEQGJt?fZNO#wD`!4+4Wmt3}oq5;{vSKzp3S@OKhuaZg7
zYBkn^{LOqOKiNjetb;fr()5T-QafmavyhZ%^&DP%p5l0?(P}gi5rr9?4udq0g}|8g
z@+wwqvC()Ftq@geHC4mrDNe~2k`34x;i0yuPkzjXgH{$elefV3XX`j25@f~X%84+K
z_;}@@N2bnPKfspYBeOpNaS1+Nz6#r)Et_oX4NcyFlX<csItb~8qX>p~a_%5PfNfqZ
zJFOgCSg?n{mHA`Mit>$j7P|Bc*!JS${YCcTQ&Ms94MmRPPG{>CpY3UPvZEy>Mp?dx
z&krxLVx6B7eu_8Q{tITTzXfS;C@w}UroDK7F<xug^Vt>cPSl?PBj_7LH0Og_0KGu1
zAz~E_A@~D$ta-MYBp||!<8EVzYO|G^|MUH)r2Y3ZCaRV>#g7NaA9wLO%p9=nXVU)f
zW=@GJFvgFL>_ZMqRX!CoC2RfTel@|R$7jl0La>F&_)8KNk)@AaODoCIFE<>eIOiF;
z`6T4sKEu$r2;n9`27enipfT3CiswRyFp<pOR9~f)+iHATM$jD;o^)aH5mv^`Sd+cF
zQKNVqh=W9PCepPK!|Fst)H1LT=hG??za>HHh7SXSk?e*@7Ex$s0vnEJM!@tj5@{U&
zpNThA<vXFb5S1)q^_=L&Gas>Pel{=jQI@AuYzJv2Jif6$<V9%uX12}Y%Y5_4H?UpL
zu|t`I4&}%DH)n2tj<;E**YKEo3W!uoUw}+^u^<m%DL_t$6$d@1II(F+BO3}rJqEv1
zj{!hVM_O8r2P_GKLJDP$%*d>-@89(LGHbP~(c9YA>TPsYTg&wN7JWtQteZgjqJ?E;
zg)!PW+v<*XpU=8<+M@DOtevLQ6_*%G%Zs$SZq2NvE>gE$<%JPS8YwJy*_?IIh|p$H
z|My_&#QYkIRRY2r3gGI8i3a%5Tgn`Z91B-|QQxYtH+KH2wdGp&UTNm|634;|S<q40
za?b7V79$_x0m2WF<B#yL3cE7R3_#F3dAUF<Xr=%ZZeRus!Gs?^x*)wi9E}dqKE+Rg
zPn)GC*kQ*wZzIM=B6A*RgOHBtAe<gUze3hLMg#{Ij>y&Ku-(z@u!k`Vtfr}^T9`^?
z@}8Tk=ako2YVcn^r`p_7?x=J)Dr?GGhQX#GHIvLteU|1l4h|c2jUo({GsGNusrL*F
zvs4UCEKPkvG>ps8buF&0mY%*-(sr2qr|FZT7x)a+QcOcFbMc|o(=zJ<28(MyNE7jN
zKXegVccPX2w$R1UY;wYIJQ)vN)zQstd^T23q{vpVB~`k8?C{moI;`IIjR!XJXjK+*
z*UCP~t`x!wW$JeB&t<8r)=s~m#$!9Ht#{+b-u7iYK6UL{_Y9M#x`V>vMD!{&s1gJN
z*&Aq#CqVxZQAU&#stXq~V4D$;q5i-<=#4;OU^VC$&Vy{gg}}5xuOIMmVJ%M_aKYk#
z>{zg%gB4TA-6%og=uv{)D1}~SKBv1>9=_<s1^|y{V3V_eU;#c3nLYqPnB4bVPXi5s
z_r^`*Nv{0TTI(`RX$aRVns)7sI!Ab;&lRqBvvy6Vy^h)Rr83~|)wi`7eD$q1V`GJF
zPVE#!TYF(kZL8H>%k}#(_mSpsnfqmIgvHqCSb({khzH@3AQ1H6Kwxq_KCJ#@RpdC(
z2=Yf5^-rPxxxC2Ukw3pHgmpk>OY=x#atCFM4$=Whrle^iq(PAY#oa47Cwn1u+#pFa
zw5jKo>~)Oll0;-Z*+w!W;sh6vMrljKY!b(o;=YtLp9~hKz(u%hFi|H>Z^65Q$$WSR
zCrROOfeJ=;zW;n%7Q(A^7QVR%xC!Tx0Y{R($h=JFMEdEP+C|rNRL|*J-BcDq*Yun=
zMwVx42ii1DJN`?H+VgYxTeJ5`9v>FoB(#V(Yutx~R-(<r{3co`8ZEAbk4d#7p(8c;
zLm!rr=O0~E59uW&q6gp_3WhXNMq)4cmiu@F!Ge+HBe22`(=|m9(Dh%43)KH4uTp!t
zH;!-t1rr?Tnxtmxd%_vQ3EC{#^fX=Egmbh#E}%pXHi{F7Q=ei_T^=EC8cnAXeN6}<
z00lUaWeo=oF}#a}I0cH3Hx4k!#{*g;@N|3z%~YL+s5sRx7~u7zd3WS@BiyC?)Jnnw
zsx#G}?ogj8kj;I`B*X$<73fo<LU9ETQ5hDe$3CoiK<9LaT7w@|F<VpA^)DKKDz4g4
z2aao~4#AONPIaTT=y+e?oa(HeQ(C&FbR0LS&h#TNqk`E>KwkWEIEmg!bB`vqNFu}}
zN`kvUN^*(f3{UV+UQZk(!vxsV+E4=i+~Pv`Kmn3On}~)Gd<j2NK!RbS1w^G#E!2Y8
zE#)kx*k^zm_+bG2#WYLBIcht{OB`vvK7t9|%Rdrasee_t(K)?8#=$}W1EheK*?~+)
z0v!R6z>4|~k3%L4CsaAf|1pqp!XXCO3Fx6Qg?aE2bNYpE3Km=HZHl}xxm8@H`cj<&
z*(3Uiq+*qv((oZXq`Jh>J?I=1k6MY}UxsT`Cc%U7hG0N2VW^D`$9$MkooJ(bl%9@M
zN4kfK_&7x;EaRO^mC4aTl?npBNfp0%g@FWsBogB`{z3a_&;QXVe)X5~E)|B<9$rsW
zlg2sK6M{Y;Hw1BdPWPxC1ZO%|@A0w2kwBpz!J7;z{6ZiCmPi(D5tUUb7PTFB#0Z+u
z4B~_MKQm9Kd8-spLJ}#Tg^Keq&kI2%9G+NB7CLzfU=}cn(*ViM`ONWjJ_nX~H|3*l
zJg;m%adAXmR3jC0zB(_OpX$M;u1K1f7YsMt9GX%|1R#|Bvu(_)6V;996-TfcDn)tG
zadO4bPVzU!*_AxDn8%WG3p*rmteGfm?IfDg8njR$nXBB4H4wt2XLPr<HhgPeW5=@)
zhQp~a_UFi*h}=~Ci;yiCDaC9g`8;gcXV42U*@xWiFl|Bizyg*aQdc#!8Ur(@>shDc
ziu!QpT?j;6-qJWztIHg9T(RuRyC@%MT;qJX9HS3o8jY)Gs_CmM<iYGpuFkvpOmf5u
z2u7X2GK$r5apuD}F1)3<cvo>*Y4Psj;;n%?Fzu4|8q%>#n@U?ZFE6$i?Lq=*?pp(J
zms4^0jwXAIy@+Tgc)-MZh1|~O)xp^la%#4-ZpSoFd$6x&AyW#Tn_jLgWCt^^I=Wqb
z!G^Ad-!+y!-&fIfCGsLY0~ta#%*L5mXvI1UU$KtjOy1ZWkbS$R+V)jV-Noe#>4s^U
zbQm)#N#6=jk}Julj|dnsvN&A>Fg$R%;}!w|(pd^YBPE)~C;}Q15rZfg8Mbo5|DVW{
zC>42wkaaFktRsLJDMkS_JX7LTDJ&I8nIa+qI1~pCl>k~a*08}*<xVGOE^4QZCBw;Z
zI0;Z_C709(-RIe?tPH<0MMo0G5@m#4lj2sO<E1(JOJW#Af=l{>!M-IZ9rTX*d?QPi
z;2-(vp+5~k_CA!&FFRL@M!h;x`D`0_+jAZ-VVX?v2wPAe9QSh*BanFfAK?`!3jaZV
zi&@}QHP>wAdtNT-!YQ;_&<7dv5wxZJwh-0hkO)DapmS_E(s?ky?DSyR>kT)VWN5@;
zpG=%mG%{+fY{7pxY>lR@QERakN;t)TiuG+_j)5K~^kuG-K@@<IN3~DZL#Jf%a1s-f
zu?T}azL+j1E@O&xij=Ui2&Ob=6;(6|7#c%tB!!uZxyw8rA7hnQo+-X?+&89#A)H0=
zAr9L3JNULK`jh4+(AovmMzk`7@s7}DHz-3m%;dpv3;}zHsY}Al3k{gY1-&QWM-mQ)
z959-Tk(3M3uO#yg(qf%yNPEZ!VK8BOm+C2CK&x9}<((kn5rbYeToc737nb5{A}2}1
z#Q;Dw8qK<nl6s6qFOi38m*aV$z97T1VV4oXG9nL5k;P{mqCSxaxKqN1V7$CMVi=zZ
zuJ?gXP#%~^pVN+^C3Cy$YlD5kmY$YiOo_ltnXFTkx?~b+KaCp<DKTzfDkB><mTd3_
z;fWg@Oq`N#z8M2277HtJ&Xe-j;H0?XY!ro%0nf0$Ck7|#OcUN!lU^bk9T^!KQiJA&
zjSpi|jY|`_%x#o84#%S)#+PoGux<n9GOH^$fGR*_!arY(NnN-h56)t37rtJn>Y}i8
zb}@PP?nDxXX>RYVrlnruIL)pFk&6XVV{~dMj=YhK&H5(lEN~@!T!0_d=o)pLGtZJ4
zWxpu9pAYJ9@cj?+qIhneQeVG*e7IZe|1u$(M~&H}IB?Q@pzG8_<^@yC4{{UXDglzo
zDIWHuA`4hk+@`DyTVpYJJ>%Ia3If_7o3wI60briMxcRS-Lk?$o2(3XTg$(sD$O|&T
zXY>=9cy<Z%4J?^3OJ#H*J`kj~P&lU@4E(QOFGa;IL=>@!D}ba`+=;>^1SO9`K&k7+
z7+354*T(V3Ft#YZljfF@{hufcnI~fio{MvxC_OY3kB=yvrh*c&FlZRj)OwJgdUPAj
zmzOAh6}(sjdKKq3o;rNPSfaH}VXN~tv|-H!V1^sB%^{NTWe2gmBffe87cI<Gn0klF
zNkQ__!O)5%ul975nHZMWe1pYT7BIs+xw^vGSm}K#&{$GXQBl4@Tj*-&2s-OdbTw9#
z7iu&ZP&#HZ&$8$X48~gjo>r?Ki;5QOFeR<b=D})rH85UM{UYe+B&S7aFVP}Imyz%-
zW*<tl@I*{WMx*I8r5AH9X>D?f;}jB_`bGhtg%`|wagg4IIynsL7tO%z!M|TIsZ1_u
z=e-)`FF`NOFEb1ns<DX_U&XDxUbqptuql?d=Vpx<5nV*S!6gam^&vSW<4gwmrf4|0
zGl@r%8-cfdHQN3-+Fl92Wd0XHxB(F}BZFSP1^`J;+=O^33pv^?_2K#!SPy4<%KKI>
ze#TN>S8fiw$b`rqhb%YMWDwTF$|cn^7T4?R3_4*x<gga_LiCbzE?R4c!z)D+7i6MB
z?n9%P1tIsD5sBG=nTXSoo|Xflm0WGuf^G}A6%?f)C*v}S32sD41%f&r{7I}#$PBm@
z_@KMS(`R%qb}nidQ1F8q2M`dEN1^c%vHSGwOMI!+Nis?x1)h(PUPUP)*%YXt7PdQu
zJ)4ck<8m&87te!DSnptkBC0mn>TJ#Y)Y<C1%;$@NhsW8@R%ctAv$ZZB1|bU%4iX<z
zq@$o^LE=cfPcgmHjisF26k-E!s^1oX(<IQv4v_`8v8<<T5w^zs3l@Zf$%S9`KsZYH
zl@DhUpMcj9XluZRF#wv<1R+NpRpk$GQ6%C6oy#BWDCC9_W{p7rA^9U5%L*Vle^CRi
zGeA*x+{#RKMewhi2L;iPMiablEPNLj|FDL0-nwo_RV$Vgo-wV~vv!)TCaCol%$v5G
zPjtAZtZSC7++nP2o@Vs<^lnGDwXD8OrYrR;AL`sgl9{+>M3NFDg<bfPk}4n(OyitC
zfZy>5UNKfw0i^+p9@CJ4D~ZSTVwQ?BzhjgJ&9h`%KlDIR(Q`C(M(bKy>W&`mcv&^h
zD>BUdG?SbQViAo5*umaO-zUrR1;j+rbPy~1&Xno1TQXz8zPVH7{?_Shn6F@#uVr>$
zuw${#J9V+>BbC0J`FuW6CyG#k>w>D5gWCZ0cZuKaY&8^6AYuWBR}vlW4tQ$difzjK
zMu`TQi@ND(y%vf9_ZmE>*D-yob6bm(nJ3jWajSD{i*w1u>gMv)Q>>_mk2qWC^h{YS
z91X`Nw;mDZvEhk15*-zOB<f2G`V4cJMTano-^oUp>2k4U;1=2N9RT=Y5B1lZnNKNr
zNBL8*Z&83XOqt$VSKnlJ&F`qQxxDRwx<ZbW3X0qD^#wbZXcSZ;<f=0_*_%o_=9kq~
zqbG80v+()Oq91r!dIPUg;0(}X3F-(G)!b8Jq`I)$Nq~%_TTsYU59%<v@wqYuJOf?`
zuz@qdQz0ofdg-LR#~|7#CIOUWd*a(E&X@A}m<VC;5%-QOV+pur1pb|tJ|tX(JUvb8
ziNuvCBIe_qPDQw`Li~=L;%an17lqPryhH93pKfAFNNs5@0YQEy_>#UydJaWwacK~g
zF8_Ue1B!n&1;UJ)32g9b=&^Yj0YQZ9C-Nel2^J02_n7y6l*u_KW^~RFi)@AS^G{eU
zHk-wogsG#tVcn#Ha&<1@F^`~&ppIZM0a_RCv=S%;-51TRZApeu@LK5Y*;(VXqA)dp
z;zLCI0oA{f^j-_ZNInZY{|Eqy@M&65A7mMvKwf{7vg53tHOlN3dlTdp0&pS$P6g3V
zxoQ3K?aJz&me~p>?oq4?=JhHJwL%9_^rtA3=uEvdMQY=`W^w_BYz1H%hyO6BUdnV^
ztm2<7PT@6S@jaDan;jjmP-;S{393<SWcEO%dnj8kfpaQtqVLDSDqkN&NEW0<QvT4$
zdI4O>12hYtx{F6$!8em8vq`p>fLzFo;@T9%Hd0(X(Ihaqaj=nE5h4=8rwFIWxWnP)
z8}=pWgv^!*B_ho!EX!dbJx|J$bi`bA2<&=U8^l-Vn&gHT1YuFWkT9rgAeAFu^8bf1
z254-O{z-lbtQSI72ro7+H{eJs(qcwE&iFP_*}&`_TD2h_i^cgKhRHO-!RQCji$wqx
zOwWO%bp$jc9EG(fGs1lYToV!chz~W0XMNNZ^Gfr)f!l<IdGY!IE=Wd(F^$Y_61x<}
zBbt^gC<EDu`ZT8l|LxyEgqQ?ck%H_8s6iX0kp{XiBFq2)npp}&Iech)6PBq#5NafB
zZ(?IR&Kj~eB^CN4Ccds@A3BTbMS00QFGMZp=S5OsHmD>&zc^+F<>feHZ&KvRdD2>>
zWV4|ej%q*V`}by`SU{6J;HoD){~W?PywX^FJA2HdB^>=}Vv^5FJvvPLaQ12_N4H>0
zqTAvh?v){tO1(q(^CRTQPV#rKldJ_50NsX&>b)Pa&~e=12LY#~bTeDbZl*NQe2|DO
zMiS?S$N65Qc~1H!z6JA5N<(&Ho8dX0!Gj_jD(5+r6QSp{tPRTHFFz-;puUKsFXE8y
zB?h;GC?=J8tDh3|LqS?fCreS7`W>Yk0plW}BhZ{En<0);sP0b4<8Sa2_*xb8F^wyA
zopE*U7HJGVP&zC)=kW$Ye@mxGkzutbzOrBB=t<Rz6Na_`dl$ci_JGUme;)M`GAh9X
z8Yk(^0w4&PP%<wHNFeem7FU5nw2oxjIADTle)M4}4&)F`Cr<i&&v4*ne$Lv-$aIWf
z3H>ex8kqom{+e%CO5@+K?CF{K4a=PT^0(7*k^@Oz6|@odOrqp8YU(*>KpY8{e4(#R
zDH*h77<33E8(LWy@8#a5(df>_w-<2Ek|m&%xQqCv?htqk=qA>C%kh~QUmJlrPoB&m
zeBW-en@-XJJC2vRdV)ITco)7pn$Nu4lergyp5b2@QxrK51Bh;h!4p!v-2tpS3fe)G
zI7|PzcF&~_OgcpVSydL20H5bY<_Ru^1xV>7FE*lB`GlWpOaumH#+P65%UvXWk$7Tc
zgo0ZpKN9KSbc1-vVH&}hO0thp!%}YFfPB(bfKH)kCYtQw9vfP{n$gD$Q?pkOv5z00
zGm!ZWeP4AG+g3O*hvyMP9u%8Kbdq=s6gBG8$$W-wAH6rqKaM&~@-$pqaN)=(zR8~9
zcUaVpFio0UC4;_De!_Z-{17`uWW4gUJR_-<+-A9()TSiuc)s{pDV!ASNrX=gk)p>2
ztu9_y(XS+*{#VQa(V`4D@cUQL6=jL&5^4H^vysCFoC1;$CYdlET?k=_R$<g(5g9f{
zz6Nc$Euzp|lMge$TfvkDhd;AVDfY5|%zUJ<`3&o)V%kY~Xq&ROsMPZzTeKqcHqwO0
zjHTYW#Gohpt?D2F1_Z8U@r%|SS7lnlWR77WzQhv+IbwkTnlynEc%WTA7epZ%6=E6k
zw9<iff?cx7**v9VO2^bGrq)?Kt)S5Ki(x%8N{U@1vd<(VRas-BskIf=0<^=N*9%^X
z5k}ZgHG;O4Uz<R^2aBXZj$t!OjB2s0H1oC*G*(e=D6p07$`*17qTqPGg71_4lk{^N
zFc%?!vv7iWE|`obGby$8?6bF~W}ID!#S6xw@wInWR^GWbp7?vq#q(aAyO-)`825AJ
z^Lme{r^?;cBh{N(RDq2a`^4(bWMrafn>Zkb^)5*&qz}lY@DWHjsu0qMACmYmBcFft
z7W@r_h4eUe?7%m2rg-)V5$i&`o45`k#l;idrx;BmWGNUrRonjfsNp6C8bXjlCdhur
z`7#QXYi46T4*sDOKzAL9APBmLKrU9n{3agGUc+pB^)fpNhN213;Q%`wU`I4$4E%ol
zn|~Jd%V1XFbjYo>qgG{dO(6E;TPOU3)@3yBC~#qez1nQ5Zm+P}Dt=gDwN}7)K<Xxo
z96{_P%wR|p=>4R+v8t+ZzO5qj22T^H00jbEW#HQw*F!m6$G;Xso<E@}(8?S@R?H6*
zQxPC?!0UzrsPDKmFTOn|u@qR~IBl!Xr)|01?#;aRN;FAy?UY3O7{VTtMBUP$Zk*^r
zR|7eqoG?<ed1mQb{bc%<%Qulp-N^~u5u$CGH0^obVoC^m2R8@lCL6NWfr`uM3$mx`
zKs)IR6|`nES`4;GdL>!MNW%mcFloU+@7UA@3#QVBb0%d^#?%J_jzXX0ykFIkPW$Rk
zR_hYq+ac&B@_-5MM5rX}18@?sB;ZMQAjwQIv7qY|nBex(WQc%GOmG~XBM-}SXvczl
z5@g#$oqPBCAM@|!<CSF0Fz2VRdht~Nykv)k?3a!HB`SabXZlEAEE2U;!9G)ENyg@^
zTQ?^bnUA#xsE@Gvz<i<8q>xvb_c8afQUnioZ{SGI5EhA@lJGw`2po*1h!l|ln{YoL
z&m0(gAwe^;(*Ae|yXtid$>je)Rr!1*%#tFlM7Wqh$0CD;<>iHO3QWJ8clr{mH6qI(
z9~>jGL`X7SR+y$l>B6$tIFH-VROzUxY-+O9)>xV#5DG;U^AQObz(pZP5?yc*)=60|
zs@4V+jp#5GQ2yo536z-KL7sf`TwV`u;81x%TnrEH<3F44F)5SGA_-N0<T4cDxdJ&h
z&AE6Z`aQ3-?C2lI^W29w69|a^tG?TvE05YsRDduaxURNVrHTw6+QHhs19@IfX6JNp
zY=UzWIgme#_zTfi;#!zBZDe(9TnZ7kOKx~*UbCi|wgf>Z5)Sh&`&WLec@2Z6HgR=t
zfCF?JwjIzYXg;Lwe&t$LaikM+Jx`Zih6>}ErdT8*Oxxw5l_^X{EcG(h*nt%(^r(CJ
zIQ#`XXijKu&HL5T*Q5)jZM5pDi1V=d>Fa@5rYeggDztRj`|5Kq(gbi%jjva)4U^Ml
zudUhq$p=mS!5U~yvNCINamnWV9th(`3AP~i7M_Ie^x<j=wk~XQ%w>p}$Gg~%@v|O!
zp)bGF+>}X#ZpUfo;;CE4ufuefpfpQ(1{?Tuf|W{71gyw^J_R$(rb%<8#nMu6xbvh<
z+@sFfSrf-AYo&U$H2@@)DkLc4NT)(+QcCp=60tSeX9N`$I&@@CwUwf+*oRSLs&(cl
zkDXI+`UE8^J&zAhP6#Z0EH2a?uGwTCC3rg*^IO;hcB`${DCM<F`6JI%K$4H+c|4=c
z7tpvQo_0NdZwtQ-l!Am6l!EXh9R=kRGq~$HR;`lONgJd==~8L06qoi%w@7zOhe$({
zD;t6;2H5;Su+F9l_Zs<?ic5SpuZUUW*p2n>i9Ev7;vK(R$9Oj8NkCJ9r~=rE4)Duf
z-Z25bhg3+?H=&_h{g6Y@YMsmBI0%3YsTT}n_hXJ`H#^ulHOm>n0$)m;sWF$9St?3q
z;s%6u{ln;!i}~vU;$r_C-6_LYC_cwy!e3)?1tSQS74bYr|2K7S0@&DD-;3&#EXlhp
z%aSbFmSoG8JUZUSwk*$hY@f`Y@g%cFp2=jvStbx0$b@D{NC=4Sh9p4QJha>kgtVm8
zg3`-v=t2u>N@*c1g<`)hw|(i}H-3e_zS7>y?N#pY_y5k3WREA%SK#)|SVz*4biVI=
z%YXm>{up&rzB(5QUBOJ4G6T-aY31!If2`}2vkn?Td$Zct)D~!IZb$2Nh(p4Oe+-2b
z7B)V-Q+}xz@kiv$SzP6b5dN*HwnjHWECNgNfkUtl_1xL?U;ueF6VOEuz{`0CNZxlj
z?gd4-<aosK7RTd`_c%V__=w}@9G}9xTYJ}@NqfFzFfkE&X6<)+)>GSlWB=Cvmi@Us
z6?&>%HESd%(`HYso=}@wd1T$#*V=w<6|316^D^r7at5J|O_)T?&r_k$+*MO2Bh~yf
z^3`4PHIga(<J$NqJSn{@04Vec`SFy!Wb`lfQ7y=R(VOW8Irp5L`MXf)n7rjImE@J)
zl6vm+Y=-5lkISc6AW&aJtN1$BQ}wsY^65?JT2QuW?eD=?Y`*IipQy}(AuyfDg~BuD
zlf1_O?^(w#rdI-6q7Qjh2-TQczxmNeZ{9n$d(WQTV`?2=@9w_qfxEi9@tcCBXI%B0
z(z!<;J=Yi8f9A~onEa-p<NkNu-_Zd6Y2#@y!;~na;?|P_&LNoj814!;G$Iaw*P-z#
zqFueaQ2hC~Yq2ya6E^$ikI(y>e`gDlM)mq)d6=?z7czxvuKT|MN23;_<VTAmV)LyQ
zi7KG>OPLDlFMxts7YQg+Af@oNAj(lI)MkEh6%Sa`C2UDGn|DxTc?VJs|CEQt>-e6s
z5B(UBj3J*Oo>r29ndlP+-WQwcOEc+A{hG(~`I*ln*QflapzX=YUSF5`5Z>~n=dV)H
z<hX$)jWt)zjrYRV)DBHG#way7(FJ5AJplq8K;l|<(oGo|=(b5XNdbC8Ip>>oHwJH<
zIUfAwub&t>ka*=&b?TYA>3cl?k#GOe$xk9<-nS<o8(V$Mf5h?PX9JyQ2Jx8v#ik%m
zoNzui=0+Dp_sWY*N_3?f8)5)B(ONaOMba5%tFGS??bs-!4_0mS2a|)-gM-tnS~FJg
z%2c(+-{B{NkiS<jYOA8_Q1*iS9SD;D1c}e4wQvC@g<Z(f0qht(ss*bk#>Dn6n}{9}
zZ|3U-ha5V{`U@<_^a3P(+GQY{i}xu5tU%Gw%YRLCFz(fEzaG)Vl)tUr5vr)(+nQ%k
zvV@jF^L}(|+n3R7LENX&!d1<d1z^Q%(H(6E)PAoptxwy%B(@!exiK_->YIlliq<Gv
zr)W_r8u*vVz@~!Cw6(RUU%bu$2SBc95L!Fz?9jt%|A3oP5~GFbk4nUDkp576e`q|0
zBxixzow%An_%sLu6*7rvMdqw_6WN>vMtb7(!^M}k5c~1^LmC%FP7{q0`U-Rwd}lq+
zZ?9@#l&UR`{I(O`*bGO*rcrha3`+lgz0vig>ZRE$J9J1_C%8x2{grEV%U=IlC#whL
z2SsLOydX^?JBB>M4hI`8tXQUUEY5#kJ3F<b6LRP}EJ4Nm@`*034|43;Ms>_>Ib#xM
zt+ki3o^36|e&&*twLn-Gp)Qb#)hmP(56kgv&Fj?@_s<Uvw6?dkx1PLs5e|2%s+!;G
z(DYZxnP0g3$k1G%rKzL2wOwP3QlB%`m&6W__yoqw44^;&JTgGSk`fJ|Z}0!au1}~F
zKHq^|vDoJ?d@dH-bpZ5i1!w<JpZwf~&&x><_L$ZWH!+%RL=mj)Y-FD?o6n0NZM?8I
zxUMX)jEIiqWy4sOfh+0#R6^XIQ7#n<SlV#6YT~?g&N!a%RNE`a*3jT-gbt5JU`?*Y
zgMA4c^EzDEbUH8&`kHC#f!e}uuS*0ag@Yro5a)s(_%JIiC}$1+UVbFain!^25&d5y
z_>=`ju>aik?IRs`KYVw`NP9wJCQP(x$A=a$a<t1q3)*In2FN$;a-d~;RIu;Y*4DS!
z6?lz2_>#8a*ZS>k(tXu63N_X!T-dfn*|xGadY};tDuZY{U?vc8m<1*3f$ELmcGyFC
zWX-fypu@vJnprHb7r6zj`(m6v?c3IU2_>_+F<9{S`+|dJE5Zry3tB#6oh#S)eq)2x
zQq+}>33+MtL-TBXjcQE8HOEGqltIO`jh{nb>#wruCRes5mRQl6V8OdB#L_9G6NAUq
zP2&w{;5w<0P)3FIjdSIq&?W0reh=N1_3&>UH|{}w&&1Q9JrEJ2aX?#Kjq{UPxUx6%
z%+~ewy$i=D4(Qy^tz$sjytcboWBCKs2--T3jLpo8jpy3)0h=8cq%A@0xK?S~dJ=NW
zlUN({e(D@MP;UMgWD%lA$m2=4piS)$w)Y9K`4|x}(Cd2pLl2dIR~1T(vO(mwJqLRH
z{=dbLS7Soxmk+_~g*~YPFez>~@&!ntU~A+?qoX%e5QeS237f+2k%o()Ri$Gas&0m|
z*jzJMFL~HBSFz6|ULCQCK=7O(<QKvz%)341Qn6V0i%@@mf!}3psNRzaaypb~fY#^*
zsJoBmwrmc#?u=(m_QC0yn*zd<np~{&*7?J;Z~T>;+grTO#_o}k&IlvA7h0PFp04oz
z{=^+XC~9kQM|bykMy&oYf6qhjlhq%QA}oRoLB_n>y8+sPTp;!=W0jJy`p5W^CSpWk
zz$!GBnV2=*Lx?;(sAJ=G06p#k8w#6kL%Leu)YuVhZ5rup^|goE+dI7Bwq|#0gH<16
z@pq)#ExtBiOKVeSe`l*3(DdGpWM{O)-3IF;`tos%)y?Qjkp3fuQAwj<7lsNpVV1Sc
z0mi}$BUhM2Ko_LtSF*}zs%!Lyyp45D5pPqlsrjwvuJe0UXKQQQU|VZzry3medpvb@
z9*=);&$O@3-#)m<`@meZduS%!=Jt5pZC&$dzv{Q(K8ffxboB-nl8g$eT4eD9sb`&)
z9<XmEB<iw}FK^QC;*YLM*4m=|3BN^Z4d1+WA#G`s7@~uMCxu}bm#9VgDUkrq?COqG
z&DATw9KtlPzP3@^+MT@a%BF!&jZ9~|(H_a58YaH9rC*EHF4c?!<;4M_o(za^2ps`?
zr_!w?#HH#m?9f5AB2-s;7HDY+pivnYY`ZN(w#lwR(C%y(K$a)0zR(f2y`LDen*MQ~
zx+N-eht?~@;|3`XZ=WV>T-5yW%^8$yxT+z0K<>YLVf?B#dn9gmhc@-vAbqJ0%(nGW
z%;Q$=k=UVORO^MSW#=_QG`AR|DQo5f`;|&#wY?p>r6(d^Sd<m`5|e;v4HRepKI2b+
zllJe?A4i`KWNSn#W&tdwz4^LN;>mgGg^REnO6-g57-Wd8%fg-_BwOautM*R81;<*s
z_H5Ox4PB=S+Vz0Wiu^tuLCvZHa-3>+vekYG;ZBHj*}f9(1wxxbbv(Rcv)|*`uSW#Z
zi9IS-OpsPCA(By&!PEEY7Bx@QOTp&B4sVwiFEvhtUx?^klwfqQ1~_`@Ap>8o_P;ux
z*&19@?s`49J3<#$b{Z985+N`JPHS<;@#L5CotRH-`UvO|;MrelbhUp|uQy#yItN(`
z`aD22&_}It0Ixw?Gb$2h7+ps4oWWR5tM458V0tUv7P=#gPgCzj3C=amAlD-f79~e!
zS$Vx6h5*RA`m0<j#URNlu&shjBQ&c-v?>phM!|B$dOBD2(F=im74oVr^WhC7VT#jL
zIk+hDQLR2pO`(rA1Y7s|L9}-rW1-Cl6nv|O1j`foscWk2PS8Kb%N4QPA=6o?13EW?
z!C4X$6?(~>g}wc|vIZ>!*UVZNZ8|grh^ebH7qq<$_5yT+`ERfnD6n2=wosS1OV=|n
z<5^IdOUD-Os7263ikre+D`R0{`&I@roWdT^erk9vRiGD!%d|2|<xF9zZy&E%EJRLW
z9I6<wn)mFe`BnGBHrj7dlpUykm6{L<amX>Gz6Jba9X}C4PU`^XdpG_1^C(*$OhFC{
zd%QSKc)Tgg!|X+I3zT3*3JLIM@gfvX<9#*+0V+L}dtGc`bpGDa{`kcFYq~v#XV(Ss
zKmV4eJGRSngPc(}>NonE1F?zuTVniRKu)iuhUX?eDjw>*T;>h9;KXZ2`Mr17e2;S6
zZiioeU3~{y0(PoxDe%&RhRBF1BwC6)O)_mDP-2_1ic!poI@&=GBs$t&V24Fi8e~5W
zZvF7^KzppuG#l!j$)291v%bMJ`(h}4<Z^ku&TXF*#Rhd#>QHk-`%Le^K%KYM0}M6P
z{q?kZ>jnmTXWCVrODR{K+UavlGv=U56RGReqADSa5D^)091y9}_8?JO&LL_z4T?mJ
zbXxN^Ganj>jXV<@iM>_Etyn1Z;6y%?#wk?*KG~b_7JsqdmC0~t-iSAM6$<acDJ1d9
zdZTUUkKiL=ZX!%uY62|_Bt@=w!aL#0Ce_QwBSFH8`*s~FuS4fuHPvl5W@wcxz33=$
zKIn^KY?d27W>y=?0=a?M9+2hIYNhwtjm5946J6(SFqf6F=h>09Rg@aI`7NarE7%vS
z`0WaQi+vH$4X#3wxQyfi6hFh&Iaap)Nn=U2A<u-U+6FTYVCU2oYtX0w>kyz9HqT(P
zS8ME6^a<^uE<D86iLe#|>A;)-m7VN_v<c8L1*nx$jAl(EFcs8BIiv6mesRM<w5vYv
zz*w1<K5ip2u{RTf>vjZV0GkhWK+y!jA%dEdkek>!9W|Up=S!qTX_PNcFrJ<O7__HQ
zawVI_L95W3cH?akj81=A$e>Uh6D1f9i?6Qo$dKY%N?(w3FgFXIVP2}Ox7#*MJ9eT%
z`9s_Mn-!m^<(P(-#G(%R+5Y|)h#nZZPi*=33HIQOyw*5`W%(H2;GcYqoNsxzPhbc4
zi4A^6&U~TvSo5i@3mnv6slUPsq_?F_I3c|Nvsf3%K+7W71ocOB`U3K3F{((j*6;;s
zw+yk=vGl`OZw0Pe&nh2}XPS@UZ*ehxNBj@fm%EJeXAox$5Y`tRaYG#gU~$|ywx|#I
z_Oa&00w0TuDvn>Mw(?XLBf^duaj3-zjQNZa&n)5{4_rUC|FSj~Q{$@DcoP_5j4de9
z<J(_bR@llqLTO|Eg9=_b*77I9$D()apks$u(D1632<kvFM@)ScF%TW#h`pk_hnU5P
zbPq0KC}~{W$X-s1`H*SbSr);PcTmkiLb7+Nsm`a5CK9YpTz~1G>l1@5fy9zw)ORkx
zX&*=gI@R-cmA@K(`ufDZ*({dlrPV~BWmlph7+eb98)!iZmSKz)Ws?$Y945G93wC}F
z#t!?wR(|xNJ9f9+_+&!&fZYTsAov+^ijJAm7YK+8(s}~jc)>K60xf_sLh}W7qA?ik
zXdK2Hse3|xT#YY7yp_#uYzwmOt3NO6Tf7$&s#l`<Y8@80Nks$A-?v<EyG7cj_v)y`
zhw3Z+jJ(!tN7q_FTqwdv8DW@lWW?8}YqfI)r5(pHKB!h`=MdWd2iq^fLYpCmsKOe~
zdWb9@48SC>$3{)E5t}W<L$A8)#v5;lNm#)35)u%X*Znu#aN}p?{e`^8bE~JJ(Q{KC
zUWD5`o;*(6hL`!9JOO;-!S8Q;xF#4NF0bP_n-u5%<TZZB>)eV<kPpyyY+@ahusZ)O
z_!YI-wN<{mqol>OJ1%xfzr*wPtj?nF5yU=UZF?7LyJ&;#)#gUfiq?l2!r~13QFl*v
zQkD&E7vR;($_i568jXZuB)(fC$0q>OQQr72PaDQkEs-ZltgKlK+e^rrwZ6M%jA%C~
zC+uRZH6H}7>I9#WC?RhU42$x*Q)IUC44)=O!_Ls$tfXre_^N<hwz;zz$?HUF<;9b#
zAaCV0ay`+<s=lFRP{!yuP);_8+G+5G2m4$dgu4YiH!2^{FlZmqIt!L&$LCp$cq*%o
z?wKFW?C<M}H9y`L50#icJsZfxfvhu~-M@G!8{akF*LAL`&!rmrjL`nRCa}r*J@L$F
z7qh5myZXj=#j*z%_h+Yp#S_m2&c(X=nvjyjakOc)x2g;2=n{$lc=Q)**CefL;QLI&
z$BZ)El!g-l#!Ze-T{W|am4sCS_0{~5_SV*RJn7&`IBpUN=zL`N(6j-v_6ysl7yBRx
z9|~h$n-~EKElul4oUEGHA9)T#|I|g}7al&Bfd?Ou15ey>;{N+j+>t$pyxRF8B$Czk
zzX6n0s45pCL@8X!#^#aGPdec9keKY=P)aMn(r3TdK9%Yo6?`^&#cKuv+Va%{!Mp)K
zqTpHLuW~UHvKa0k+PhJxoD{DOX{b#HShW*gJ+wkhX4W**1HMB$fh_S8(NNTn%WQ3}
zfP@9wrKn?H)gse$YsZP!3>l{##>iMSR;xF$x?vQrJw6>0tx#Dn0tpTxfF*PF5K%-0
zigkepxdlso55gK^7@h4{NS(j2WtdzwYd1j5BAahvwa_lafP_hs9^1lE@u|YBa_-S9
zB1cpTZJvWe@X=qi>qArC*SWu4B7wv5Ai-5g8zZ%#i#3^*42bQk2<r0$GUJQGb92Mp
zgM;0C@HH-Piywzlg%rR=LbK=RQiFTbkK>WT>mXBgE72#oX={Hn&ZS6y7%~V!lIY~e
zYDND2L>j>^e5gX+zIKRdrc7Qm5Rwcd3)TW9Y7v;^dIj890sIBAUv!>8O_W<=1act{
z+rc>MY^YLx1MmokCoCGl7_*;N-=ywj*VU<p=R+bdZxp7EpkUkakDtk8ZfZD_n>%-I
zZqH4!_efv<5Wl5W&tV69!uCqv3XD&cSP^U@JLdn@60ojrK0Xa=5TM*pQ6gBB22@aj
z>N@t^<UNyF7Etm<(`pd3tOz;gde96v?u~V;bLx6Ybyaujj8~j?{4&0i*WU^7E9rG4
zr`~y|0_-)O(B&GsZR-sGp5h*Y@Ix{mrYau*Hr-wnj1RN$t%$>5mF56N`)&o`3X1^%
zS6ZKCOh50jcIU`?sO%Jk4OVstV-7h!{H<P-Vfqc!IEW~M$mnK5+__(zOYk!ZI8;z#
z+rQM@?5tb*;!>T{e$jp7ZRgIMzWLl4RctMPvDKMOI$PCVdCqlq{%t2#JKC`^>lpV0
z93kxHB<vcDD#HyEV+!sq1hMyuH?ZR!_}SlfDz|g%74v6)ku9xM8@GC~^<IR6w2#M~
zkQJSf5}gnbogv&4+-A6&$@G8u;B5g<d%GuaoBiVN-?8ZPE%Km>ZLYxn|FAy*Oe^a}
zSL>eF>}lmew`2ZL<T~P5>3C1(?Mt*n8WDEET=*)kQ&5i6=7^b{5^}BS%F3%w=e}J`
zBpB{cs|tbN=sD$j_)*8xRq6JRE_TU|VI$52wdN=|(HM9t_$cg7w*8t$-yGR@76xv3
zptWE#K7~+A5pj0pR7P-<wswS%1Ydx%mB_ZYX0xsBD}k15BG?ju23?`zO{RHCqN`vr
zMJRWokV&9uEA+5HE`(zy@X5+oHRJ?#wY^Sg8<m-WS8BV7b|Mhe$zOJm{PviX@0RxO
z+kc$Qr+bI6kn&%lU~@ET_N01xQd(bI0!?{JXaUxgZ4;;N#9UoST;WzQ)AW?Kfa@&7
z?hhBby9+#6)^D9%_?2@<kDkMW*KNmIT~~h(+r3m~g&HPAAt!!l>|K~Ha=~#o?8I4+
zV)dcv>D;N^Mxv{CZpbi(=FXo#RsO$jyz#~-o_JEdX8I%3)1C2_A>;K!Lx;}aa_DpT
zVBZr@JV8DFtm7<VGd5wF>p)AO6+m|ahsjDHG}>^%kQ;<ev})Xie0=uo{K)L=$Q^gQ
zAs&v6?Qyrk0DE0~odGH9=j)f=yi{-0AG!TV{h62DQGQq4?>0Q|>gkLPjJzz`HJn15
zd9?ZSmHa%kIY3LYPid=ilE!0LXIde#S<zQn^$K34`!%s}ynpQdhT9+SK6?kdz^;5I
z92*%3@to&#v2bkQ$ji=1^M-U~KWRu+Cam~Fu3j0az_NWYEJMS(Ixc2KYPo2P0?OG^
z9p297)f%D5O;^Wt5jtA`)mGOj0xVDe+ZN`9vFdb;sXtM_4bIwv*};;_Wy5eN+47n@
zgmGgWvJ2E4b(EM6HKOKK@X=#&e^k<pn$-bsYuktF8@ij8UC|FW)OR(Nzuea9oxJ6-
zPe=cUWKTTP*6Y2_g?hpK5HHG~_9ogMEmir|Iw&S%*Tuf<#opg0glfsnTv#aJZ>1`(
z9ZVO}&n_%XP2o|pqkAY+!MaRd2+J<wEw){I9^j7*f3&1EVazIs3m-dHz|f;06iN;@
zHz=Uh+Sfj|SiZ8jAZLME+stqdjF_#T>6*PiE<Dhx?n*ol+J8Y8FeBa^<?5UQf&oQP
zZ+0LQR;lzCzxNxE%`=Eh$mF9xGy6TY6!DZl8upssoBa(~oAU}rTz~X?v!C^b%TIfu
z&{cVWma(pqK>BHHMLt2TB%&IQA6rhH-y)C<9)HF0)cFF(im{Mya5~hRL3d)<pSMzn
zA(a6Whduz$$T(uu(^l{cbL0m4f*mpU1h6die;d&fCII4Hj=11R!IEUW4)xD;Y9PlT
zP?SAw^oASj!%o#URPU*K<jk%>==2%Y9aBzcoztrt8XE%jt@UpAP;>hpzk64z*W+nT
zdg_}4nPz`)Cez+rzW##8F#me0-|us^PG&nB+=;g4_9L(A?B28M)vwNmU2d1lN#}~w
z<7#j>H8u_S>e9`Pv9PDX<!Nxb>RZC8*1A|vu+Fu2;6=w*;T^?VyxH*v^&IYh+HpH*
zAa4%F>m6?o6d3oXcz;{(&<o)TMzR!!fZ;-ubpAs8$xaHuC!?1z79HxfakzePuO8LV
z(9~Yv>h<7W1Ji!@V91Y~^}5@-JKFWl2AV&b=?otmHJV!dnUQED(cnx)8(o<(e_JY}
zdc&?dmrJ>>x>cW#_lh<=EzO;BuN`hztSvLpj%>Y-`o`0}VRsY!b`8S;cR#od=U>;b
z-2@-AYk^xFY=G`COV0r>pedCs)GA?VWEkms^158Ct@TSpPL|r!&t~_-l<?vEjd+Xy
z6NQ2bQ6Y0fi(wuq+t7A3`e$gaLAWyw?;vAbF=-Jk&;;l)M-*0^3JxALt#toaI?ldX
zJ<>HbK7I0HV&;ozbt4wbB;p-s=LY){V|x!|^TDt5?V3A>FuJ#OO(pI>v3G3d=GgWB
zr(qnA#k2eOj>Ylm*^YREE-v^vY&t=?kQH4Rg+Sae@+BD8ap(bWLNT6+6y!zg;PR6}
zRiGX}=5)UQVzcXwiP+rxoz8c^(bar$E_Mv;sZR2Z);(<@)9((=)wPvEZ5|WsYObTL
z{AqQXOiI00Qj;{j4T2^TM*vzmtP(on6p*wU?c9#cMMzQ7-@6X=1Y-{1HCjgy_J9{C
zEzjuFLO~$e5H$>S)_f87S!>U$tvZegr7|0aaY3@kX?MhfCtdsi4`m{Via|kea3N3y
zH5ZEO>rnm>mq4=*X28g-<DrNk$}w%mdeXH0YX61hnG+2f&9Eu^A)d_%)PKV{t^)f!
z{02ZnK^IDkrF<#BiRZ=Td{ExNufCdJ*X%Q&UtYvX<hzT~z9@{EyuY?D5;q!#2d)=a
zMR03*1&R9Q6@*k4)(`=)%Kp3X$g6*WuIL3lgr){0R;jN%Faa+H{v;_A<_utfhGDb-
z80vo=JZKz*8&uV&hXxXflrwmG_mR8rK5}}ZEt=>{ls6)sEVz8R^yW7=7?a4?H6MHI
zu~w8^R%{Ej_uqo8$rvEOr>Gaub1z=g3xZzT7_Djgx=ESf1Bde!<I2&c4_RG|rD8$T
zT#-6p$)3Wdxsmx&DwcY*Cl&j$@mBxdB+7kdlY9LyFTuGvKYu12gGr74X}0%|(w^<(
z)K#p&>rlNI*SGfuLsPwID`5Gj**<;M$I;B2GRLg?&ZaqouHX;nE}M_w#RF(6J3p_M
z#W|8N9zvJY0=|Hg-!hEX;VXKss{5DTDBAmi^V{8D)u4#tN_MKMXh<+3K&2hUV&d6a
zl>*UN(Dal6-VlWTxe2?Ewi^HzJB^_9E#^-MiA%?(R8w15R~sz{U2Us{CG&Z_*=Xxx
z8bezbB<$*SbY>9iw9+5B3puE}9aBL3h0zr&4O*#=?qs|!vGU+lLna7b&|A&nWc#u+
z;T292hJ{e_a>86cm`yJ!R0x9Q%BsN7rDo{W1xPjY-?lCoxQc*j>1w0HcjcBtBljS=
zKvxJefE`*i004|F-POi@%^N^<7!Yt@rT=<Q)6cH&AdQHTF+}0*foJtPP%UfU$~;TY
zae^mZ)+0bFrtPCF&@Dbg-b9wTPCS8v3N-K<u-1z=8f3>G-B_2d7wn!ZEJ)l53&2RP
z;e6F~eqfk~k_n?|LCH-Jo~+FKUX$_QnyJp|mYm7$)B3CLx*H|))QT8wF|8o0vKGI)
z#h8lgdEry*Rlj8I2RrWzc`D4OadDqvy(7J50(P@eP8Dm+EMoy5#6#`J;dPnJoAC5D
zI*T%kah&bP+wy;prDG4ztC!*RE%R3j1++z34L9I)KsS$or|w28+w(TUS+qsg;nKxQ
zv1G@m(2qXySXN&JbfaOc(L8LRNS#0WMyaoQFxzv=RsS*`T>3;2Ul1MwJZPA}mGSnw
z_ANzFw6tuOHLa1eiG9<LwZM2xEYJzCZ4d#i2xA#nMS7|WFbb!$A{(ZVJRyMs3ejxc
znVvUEuHnYAZD)ZJ(H;q*g38Qul5mJxzwJGl?(*gwzxaVoZf5Efh8cNz_d<5pO!>vV
zgD?zRV@1r&O;9Yq*fEHZOpz4i(Dr+RyMX2Kq-7(X1>fU#f-0B&#$b03F6LuXbB4K!
z1V}6chF62F8s@K{rJnA=lyv3II3r39I<!&X$0p<++df2I;u9~Bg`nZJku<Jpo4$8w
zsG|dq3o|oa(V3ZOaYtLVlJ9g3MWbEvJu$IZ#%q)R*ay?Hp|KBrn4ho@*m<DTJV4Mw
z{7~)mfYS7;uDyG^_*h{V@CeiLC1&wb30U@B@?qEBjP7H{P>CZ$KM#n8jJ;hunRJts
zLTDT7=h_1;pf5@21ui@`*AGsmo@JSl7e$9#7U$^d`GX_BE+t8*^5Fw!uE_PBxF;=7
zx^aDQ9V~LM-oc1~(ttASrP$NQW9(Dllb;MAkxJfgU;_W|VCw@9v<6j&l`}*h@+YwA
zHo*#_^<3?8naCkG&h~t>o~v=}*h)ilX7OiRTTAd@mpu4u_dJiPGpLVZ`r2?-&ftkM
zm&Sa4LFS%V16f9&Z1?J?g$mjlJbdo(aE>Z_>r`S1D20m#F*gKM?w<2=bLVFdjb;ut
zH}9(t1$?^_L-WJ_uK}-nZYXws7H$!Bs|4CC;Kb0}6ZrP*`P+BDbs67oZ{FA3yenZ0
z`+;;({q###T~FF*-~_>V=(qvU7{)BwR;}vJVTYg~da-&m&QG<<Op0-f-xPnuFUZk2
z_slsYuOcsXtp&&CkO~6ZRlE{9W6`xf(bqSz4kRDy@SzZoDNL=_PfRP1g|aIE)dIZ$
zTepad-Jb#Ge(Qb^y-&Z&F{2g?I&tMsBz^M#kfYi)geDrAUv$XRkSB5lNiItoPe^y?
zxk9npk75CSJ&eY{i)Ho4peJ?MMaWDD(0q*5Hm@^xA@vB^4P(*3C<8UzBm5fAX>^DB
zAS~h`I84gl8uSJs8khwW;zH0nsCEwwEDa3oR^ZyhyYl&6!<o#;VEWedAS7QfuJY!)
zI=l^cG1ocjzpKI9@h-4N@a5ppP|)b^>caM-j#WEvL;zwY3*;etq!7wfd2RXnz=Baq
zM&hegVP#gPD!(GhqwT}boCC<%iw<DzS10RL_N=MlzKQ4KMxv=HVZ@(@haTgj+h_i>
zAW7G*8fM{Z_oCe-SBQ@PDqw9?S~8ZGFH2mP=Y@%6a^eN6iGK(8N32%F2&xyrc_Cj1
zT)=FA2^<E?i7caI;Al?5JBPd_Yp;G|-*9@!7;YIkGS?IG1zk<9`;63*M7F8@<=-zF
zg{KTNo0;qjI!Et5>bu_Be@@X!iMix})P=mYbTAAggKUnuUe+*%|L|{$#=^18+8VYH
zN|LN*FC`MeU?OoTn_0QUfTl|;_&Edx?e|)R1WF78tx-Z^fG|0c71f?ZWi>e=bs&oS
zBv@3fOe9ODsm#)40=uUsaUByxR^vdy13CZ`NDj7bO7`#;IvU=SDwR_2Zv0TPR7w`T
z_xG%>_FSBQq-%8*Vi0-Yyy&$=0f&c*mD2Dtn6C**1NcWo3FiVhaajFi*U9Bl@<R);
zVO;Xid9~U#^YOW#i$~&WvwX4Z)ali(M`mJuXkhC6yMrSiA1iOh15^ZbJO=v*k}<H;
zfT>c=fE9Ndt}3slofs>6M-Kvpt7=p~_dI29y|f8^h_pUcgN}}rEVyAoW}qtT7w|7J
z>mbtpqJu#NL`yGU31vVW@p-87LfZqhzpz1ae$pZ`pzy{N$|YDF8%PRbA;GesskBG+
zspH|p=X-l^ITAizjQF(})!xZhjQI0^@a$gqO*gssp51-8FDmf@$Zvu9fb2WZHf>pf
z2p)*K&Fxa!<BWx#?fuAesq#OlBSoUY72ytF$&{9n#$BSzUFKQ){;4C-#|Ez92h9P~
z*Kq|EvP>-l(G*At8&4LJ6DD*M>Y+$uXzw&x?U`$z5!~eQ-w}F3x<3u1o*OCg{%fdz
zN>pFOuEWx_<IC^M9=x4bIya+%w)*<S&E4&MI;Y=lE<LYFr5t~&utotUDA)^Yg-`~7
zM?H;=18{Eb@Jf%DJ-^VXHLcbWHl)<DuCUs~bP9RYkFZ|_FuV{J5|FQrb#rZT&4k6r
zu>va({z5@B7&2iCUsNGe=0n@{$j4i`?^LcAK3T4pQ3bo3tJl>EQ4TtRdaR-RLc^;j
zQ;D`V6N;1J>}=a;>vS?M^hzok*c%8OHKOWZ`H{9@unm{qVl!lJ0OT5n6*cIc%+b~W
z>xf0Gw8%|!9uyE7_sMRki-a*9kO%SI4vz{A#ZltpbMbg0<n!T`&lgJYjlTtNTKu8J
z<0mfy>)_-?^`u~A9`5z|dP9lEx)9K9>lzawc^_}6BNk*`L;NrAKXLyF{N0bcCtrFN
zIy|H4v*@WMIe0P31F|&5vW6k1ZJvu&?`Mpa?kEkDIrX4~Gs0GsV*e5lB%wP6{ZWJ~
zV&lg9h9MtH=wkEo@{BM1ydYIRMTkwnK}P*cBm*C;Lt&fNVVa%MFJk&@nwkWStRT?=
z1QL5W$k<B*_%A5oA_x_5@uZWsk5TEM((?M@4z;hL*>$`*@{xdY|1(toh`Z+oLx)al
z#1eI1rlrB{ej=1~xuK&pHfEoOip-$Yn*DCqiNlz1?OmO`3`qoeEQ`yc0s(df5$}AT
z&u?y1h<{i-hw_Z}*=t`%nv&m?HFxdSYO~{5w<{L#6l0-1FZLx3CpW?AYgr}D)s*+(
zLy_^rdC}=rleoe(yk4%ey}U-^L^@vn!s{YO#$}x1)?6W)$6;}J?-ZjF5z;hn0VtX$
zFWz_R)O{CKUijwy3s;UGzan#O+t`Up3hfm4kwu^+c0jTN_W*r4yj3-t0rCXS0DH&4
zrHr>trV6r>tO$QZ?QPDXI7TWu^np|q{}=&f>B5ZQLz`Yp9xGv`aph}E9o89TLKq3;
zK}uu4Cek|I%0uJ>ECUj1{Dbuf|7bVRFH!OgBnP4?v;OQC&^_hb@*B;+4Dfx%Mu>M@
z8w1=DR7`<MCv1*~HOh&4pt<>SbANNG8JIsnd0P?X;1bSUZpO*>%l=3+=AnSSyefMc
z^I+9BB8Q>Gj~xu?VT{5dlZHz(uYwRI7mJu<P&EumWxa1=JkTfjZF$3by$8J_41>+l
zW&<J%gF5bw_oRIB@lLU+n`SA4!ZIkem{|-QfZSoCKPB`BB6uJaMn+M{5(dPfA@qdx
z-MsG{yKS_wxqpyU;PQHN109xi4Jvxu*l430=kR|0GCv&bZ)w1c*|eK$A2N--?o$?G
zY-&8*q`o7vq$%QZ-(TNQDwSkcKP`%%39lk*6#Va!BkWiwiHTHMFJ!Xqdlb=r_{aIO
zVrl?;x&mpU)>n-F6JNoWE4Yej<%QN#S3|{dWu)cfB*QzfW+7ln=y<cSb|soZh0i=n
z#lSa!WrRKc(dA`uHMkTsAh8p+*S%^({U&xMT&xlS2Z1!j@>U_Fhe29J1XWng#@`ov
z-}_>}*X)nPkB>COBj(4?M<SP1qjkR1jKmv8Ziq+Py3R)qM^?GsY?<=M5g$Yh0UhI}
zv&NwP*|r9FvL@{Df}U&>9w9#*vEHD1jp%-6_GRUd%PX~DlJHRe2hUK(CE1Rj%swg4
zU(K(O_VBR@TeHXmdLLA52LCD@JquFvgXrnmLiYP<>JtCS{X^N2<1RL@9cm}h5M^L;
zxltvIxp3~}xt|*A>l^F4MZ{s#giSKiUJUdM4D<y4vTv-nckC6~3`RX1Nw^>(RhmLN
z8zfChBeoS6hat+R7}y6(TMpi`?>5yxRCtX`jw}Cj_<yRwrt;_Nox6sIRFm2-FXReA
zu~~X0Tcfh?>7Bz$1aXGS1Qi6JaTv3hva_x%m+VbLL*AtWnFC8+`^6tK8Dv}|6P>C3
zK)&I!az&J53481*j2B}-;jBSek>e@fd*0(y>*a@irU_+$cxpPAkFjBt@8PHbg0<9S
z>_aA{PmMT5=993Q<)|O|*nRIY6A9G41_{ms)Lt@SkjyhM;G2BC0U|BqXJ6M#DYf0&
z*T{AWcC<Qi(Yoy96_E}bUVk<|Wcb=V9h2#VYJwCNA1a=mi=R9^+wS*<4_$XU4krrt
zoaQ_1CoOte)KQ$sO@Zb3RK?8i)pLHySjxVyzCKm&eIF;3(+fi@oDT1+_olqw_dT!u
zLI{23Z2u@}MKy*^*P?(%08Ixj11&1R1&tTmX{dHS9h*A~LtZp>r7D~i;Dbsqnt$cG
zbd*JtL}b&E_#7h>w4wyRqYG3|R;^Uyu=sx#ngXlMD@Q?@K@@}pjyuxRQCk!KRT!`e
zAk<)uSSt9f&RK%B^Cj|0Y@1>Uw)l2<*sqs)h9&~hM{_hOKF6u&3q;F6>*T>U<RNZa
z;+(BRMNsE=zTF>*$RAf6Uh!$8zuWBZZfSr@*4=OFKY53<Onsz$=YN}U&bNx)<>z9<
z^_{Vy`UrV&q<$#YSwD=&P)sb5A$+1^Yv&vxu>nPCNf*R4adnMD%h9%56wbviP1{DG
z-(Ri2w=bQ!57wWcFW57GWTa)-7`|Ru^j$qh@RPpl?;dpq`zDO6nR%*^d0q?VTlPg0
zr3*-H6<KA=-mpZKGg~$X%5YPFS-Wa?)b3+uMv|NAodL@NK;$WOR<@m*^LRQuZeRT5
zk@ykdp4IHWm$~tYM}HDu-`-4BE+Rc~B}Y|Ol|CArelM_43h$l9g4@~;KSy(FJSPi+
z@-P<#mPc)8zVzx?rMlg{wS8G12}B}+kIU@0`wwpKOLbqdUU>&rAENP-oH{t=!n9z)
zElxWYyy7^jQepR8eD>>w-Lvtz6E&}Ke%l%SDer}L6I++V^&z{9EX~*ju252KdOhK5
z3ab2dne5bZW~49MHv(U)D#UW-#WmC$?%VAwm7Kf#tU!dS-pUxZ9K9Tfp+bpN%c=GQ
zYpzA$BpF{3?RDl_s%v3&<*5bHUPr&5>Pp%{pEzY`iT)0BAV8EXbVhk>%ur}X<Jusf
zo&4^uZ?r`HA8bnqSm@D7vp3EH357j4FFvE#jpFq?l5TpVKY9tQ23*Ld;l$HQ0e5DK
zbX=$dY)K)+F-#!z0!AsgHWA=}SYL!UOpSZNChac3*{3~=h5d6ghkC=E{fT{vL}$3S
z&KM~#ju^(%pIu*X6p8S0QU5;qK1ww23-@-`Cld9Yy=tWG;BfhGRp;=*Hvj61Rv@*a
z$T_dt+qHHTW+tpHaWQ9@_600suIY||wt>;*jbIX3b;RaA=TxV)dj!dRmf5{<hQ@Fs
zb1E|i%X@b=yDAdbTNveXSzcC5?}j19_pbgn`kHF_bCaND)U*$Znw+RK&eb?M#r1^o
z@?!i@WaT#L$g@3Br*6D2oH#h+zh~oc+x&IwD=SOVw68mrXv}09``rEEU)mLSCh*T0
z%ZQB5*d=WnA7t*&R{J<iPN3{QLiJSa;{il3gQ#KOF`)U8nc;yEW2j~LNY8FxM^mK1
zdoh!`L+E5No4u61bSay)%;p~*GIN=p&b|)U$lXWUwB5Y?%LCb|RN{OBPmEE?gTEDp
zzg2Z#okNXvgq?0Z*tnOYfCrzsLtS{#^tHGlpk;Vs%G1VTB}-azq^$!prm^8G?bYLr
zUE&PJ;D32ARxHLIM4ItKeDM(GNdeaYD2K`b)gZ3H>IPn~Xgy8KEyP=ZA2JE@M-#y$
zuD+(r;I1#OS<zzpI_tQO$?HJITwPhgJZ<j_30rTr7vdcIQT`WI_aODI*h3liMf)14
zqjN9Aat;;%2Id7>v?F-b$_+Qf?7xpmK-+(@-{QP7?G7n$bz&JlP8kyYfoZ?==dl+Z
z55|5@KgqrSaB_|8+Q_TucSU2POF^wZ_+ZTbQxDp&a0=sIQ@2kbPcbM=UW%!_JW2lw
z4jQ(v7de<?7gkbz?@p^sAihIf;}ysNtN4yxV}=VT$+#zQeja<Ae5s6Ovf<^@GBAwQ
z>aw|EE@v*|+#*h1^Ev?5z>j5xLgtfeg2lrlzHF{#mX{IYmsu`v;3`*e5@$^Gi8Q%@
zu`l2i*Sp3?g#F!(hbc7^3M>o?(+n=OV(CbE2QuW8Av%)I7M10u$tDy(Yx!w2!w1FK
z@Yixg>Y_Z?9NC9;>^WHM?CaUrsXSX&Gkrb%+kJ8!jqvb6IdxEuG8c>19){$FT<By@
zWJBs3xrs^)o_gK*<wL1S+IuqlTZUS1ojH8>-G`6p)6hCbQ`1Kg$AjxR9UsQ^?nM7M
zZ^R<OdSQG{+AE1}2M<1jwQ%sDdZ#><e^;LL?@auzq<_b_y7avK+N&G~xqc4H@%3{2
zf*jG@R+<ptuTaO$n=cmQ4`v>!*^c_V#M&gi!p=vW5Xg`}iC=8SZuo-n9aKhCX6%NI
z7ae#d*QE_izRMbx;>?5KvWl9L-!Z;$L#%`s%CT`nY-=B+&wxP5kpNUXq3S@TWPA!(
zLyyZEY9M9uCv}BP8o(M@W)azbFFIoKO1}RfzK{Qyd3?WGr;)K(e_Hvz6llB?-{-!e
z|MbsfeZ*-6wJq_s4?hQ)rdO=(VT4;??_u?FNvfPkFAvD^le6WG+2QuxiGzvV?d88#
z85A;ArndLU@$4|Nh8Bj0{kz-RcKgeJoX;b#lKk41(TE4iEFoxE@D=)lIm&;Ktw8<Y
z(Vqw--zJUBHE#~0FoM<x2lw52>%+l2NmuR+ekI-;2?iH;H#Yw4u2ZW1)UI1^ecjXg
zv#0MKe|R#IIhR@79qEkFI6&K#_VIW{&%(Ki{Knk{+r#7(gZjA7H8M(ufumT%sv9nT
zjT7<fh#18hHdkP9<vz1zBW)KaDi-4Py`gkRShk40-<0F<g@Nyky#|jxhcBEF(w)C>
zc+c2(28ujf_(_{|?{H0VEB1K<R*ZIq8$8y3;qboUd-o0hegB1ax#S0i_Z_~_U;bME
z-}8lCy`J}8)iHlGaT|2ZDdQ!baF_!vuQK-BWz9KO72G_vfk>4)lf+LV7pY?8(gYNQ
z)jR&igW=&<tX#Q5#Pg~)ZQ$Q0<Rdh-tu3r@B0OKKEtjk}5C&}Yom%|W@Z==^Lj2pY
z-SWErh8$(QP4EETC#^?Gv<@aj4sfj=P%}}+WrRRiAgAry4Q{N1<Lq>|tKc4#8}zzv
z5Piz_O*8bCzFs?jht<yC=+7PP={cJ7&#zQ%y#-_S$e8EA0ngZxfiGI6LM4u`Rwk)~
ztw3xb)wnUS+GwN4KUJ+Yg(dT;Eq0nZ%!TQ6fM)AGlB!DPHm$LrD+VFBB#@<{-Yy|R
z8f_i!UbuEuR?I5Tg<5~&Ct5pR5N9vX+kaov>w!^bdJWL8NBzhP?HU`_wmnv&1Vr|q
zLg`EFRFvpJmJR-TRS<-(gp20sMqcIt7Ow2zbg+CyU3Pkl8c`9EW;9XYO9^@ym!=0j
zfgV#`{!N9(ZC!U07K1o6Cuk(NKba9&IknY<?g$^C+~})F2a(m4s}MdRgGwTwsy>R*
z(c4EGTKZib9UbR=Gc&&3jk6;ovyIPg^I44Ex_h*tuK7O?c3d3nc%5(GKHu(B{NgE&
ziH<Aam~_j!e91m#A&h5%1;E?9x*G`azedwbkgFj7+WTG4Jv?D3>jDp*j!ES+s=Z%(
zM39sj9h%RaojZ9EVQM>c014tL)TM{T?c&LWoyVZs2fYUNq7P#0vOUYNJ8xM7{+I00
z2Sc&odG=+6hxkxp=YAC%eW8%AvARAQ=^AH@+ZNxFHBX!qhvR_kksd~-4rsqz8i`G_
z3Q6hAXR=^_a#IOX60j+nOVsx~o-D#1yL*~PUNO>~h~RbB<GEM$mA{uLEG!j*tN%%#
zinh92_xpVNeQsZ;{>|M_E)){@#$~i!ohO$gf%sfV5NP0~M#4pM1@a>%xvN%2Ck!yF
zuig1>Rg=!`pdC{Cl(n1;eoSwuW%i^WvBDJsIS9)^{71O2_DHEWidG11u!@KUouZms
z&TCq>RQgn*^VeQ22!6g=z1Aj*AV9DJ*o)=;!lguBux;0r<G1y%LaVg#8ge{>09B|$
z2vI>n{*`KpVO3tR`e*xpEB(_~WB*FkOCi~Z{d3e_3Tq}08Lo(Z+)B&g05y0L7C=J6
zWXB6qbfTRG8a>oj)Jg7&`xdg+6O6~GyklNCJiaU5)tg;VM&%{W%bJ*V%!(Y`3_1Z1
zRy7R_5pZ6C{PAW1=7{FPq7pFkRZu2XabhBx@TEq--336w@y^crhEL#K0cY>=G{z$+
zsWLHfXe<+t^&kMI5bFZa;CQQN$mRZoKBGT%dJ;bO7TJYW)(60cu>Nxr#li$>3S3z#
zYH8S=f~`og(cRh16vxMpPNzl(#`2kU{h}~FzF8=gCdLoX+}b+SvOkj@7%h~qApfG~
z{RE+&v6i%qyou=HM)6&EVM#(l6jTRmx=1>-zX}E+6LyIW&Y2uGkr}m&6asMbttE10
zT~YSlg#DAK)f`#9It`?uLefW?Hywc>jY!(S?D3hjl5da$ffj~c{_8dvN49*&<C*(U
z0INxy4(bXP@G9W%2r<~?`VeqyAHVa&{lIy;Oz#8k>EDYSNE?|c^p5d;%=6PS(hfr#
z;9P?mxjDgjsQlWqfLm%U@$g;H1f#rS;J;1}Z*MKj2g+=1FRyQG6p^NaLdXTQSGBKL
zlz}$En%El42?eDP?j_szR6KaFc<8fR_FWYpz7e*y94azmJ(7iRBZP7j*mu+=IE9wk
zRXis;FcGJ)B@sFbL*%tM5O5h@Ubk2_)T8HE|191MIQQlTqkv*V@y4OiU@UlZtyFRy
z(g8yEw1uv_<Cndy{K2_L8>f)j-Oz$iBeQ4pVC$!M<VF}0tnm!=Mp&*yHVeTM)QYrq
z_}aZzCd&XkPZQ&zA}kOJk2k8d!dd~f9X3nJapjkDDs>T*Z#h#ym?pW$iuxP%vC8#4
zyj%>?scvr?%Xq*Gojs<0X>20o$Kzu@wc2{eni}(!|J(3q`eJIgoc52seDJ-4eSJ%H
zgD-pC{$>+%r?IqFuoEGNie$^Q!L4z#C{<GvHCx!YY=_Ls+?(p#kU_;tKEP~!I-A82
z0B2EdDl|Y`s-Qln{>IzbeB%1mXsWQ#)pWG8IZ}QAQ3{2jxy-wpqn`fNRJ2&=Ty1jK
zUl0ML#Ie+J)q153>XZmIue1U|XG<H;G8S=(ZD3EAH{|;BMgc;nTpnh5#6&>4;l=f}
zKZ$EMQBw}o_BF&-a@A&nUQF0Z3TdcA^M;yt1$;|ET#2n@V$A_dW-67R@r6yqViXH#
ziFSVSCdPLB0mhp3p-{U-9P+Rh!^krQk6I3}ym1*L<W6B0C!CyIyzr>=k0egLH=K0$
zxWWMy@+HS6$L2zJT^`)$0Vk{L@0{=UbR=6_y&a{cgxM2*Lr0>{)!a4EI8xttHg8O%
zzwzZTO4)WKyS<G~$!_!|@5qApY=GYPNnfNZR+Lg43ktzrY2@P848)Yq%J8USeBqVR
zR8K$!+G74a(YfhkpSK2gRKQGciZ7(1j|6(!dIO!KqbE>)nWN0`B>ZW<uk|S{mp~q~
zxilq-9HZT?5%Vh$mbl?7Bp_OtsK)!Mg-m8i{+>cR5<sToeW~Wj_-kE-7{4BJbl9HU
zR|BWah*li-mQX<6K|7)-L~?xkurHtY9ahF-9>Rhh+04E$vRQw~c@AQpH?fx34sI9_
z2zEOz9lFIuYw@*8Bf6e8`>KNZBI*TIudi!rw?jax=JWk4YIO6OXlAGD(N1i_u2o{(
zmF#+|$z+eVl|U`FMPXWuwjo<DqgJ}wE-jem!lkw@aAeLUWlTHvgf+1%@q#qw(>jAf
zFiA;Gi7W7!S}-s58u4WRp&Xwz&8eoCGuAX^mcN@ozKB1XKmr^KzQfOrWBI*>r(?0F
z3r}6*gj{+`#+fld9|bKWZli<s3cZl#X-|V>mXJ3qbDx^c4838<h#rrcn|G?f9mdHQ
zPHxW58bdgTQ+M29oH&7Q%buXTya^f*z+Q#KC$e&oWDt8nc#18v1G7VK77P`|c*`+;
z_O0ou^mDf03ut?|0gKu_y^b``bo#I*zCeaC7`upO7&XTGSi>vY*dx?Xps+$EnGk&O
zoE&>W3A`x}GQWZ@2w_}75C^AL%v-y64{S8~niHKydUw;IiE?J<Kc0xpJ~*rbO_Q!p
zUwg!TpKH@}&3Xo!8t2+NGwF_fk%^hn);;yRh6|15zf-Lpk@lv>|5mT*AY+L((I?nc
z0zniC1+}v{Da14KRoGU==z#%%5%F_r_sp)L`Jg}gClBD}l{4B|?_pMMcqoc8g6Y92
z+-1c7>Ho^xFTX$9>BJC3nhk6Wd;QQSLm3cQKv;$1D4QTDu+0_}PjvjKa1?w3Odxa%
zt~}E$qBww{v9@ev*1$pyfAsRAX(Ciht(zwDe@(L>j?9S+L%1S-2^K({m!5OtkF$N|
z05Fdg`R~N+))EHtHIjhgL`x9$CsqMld_g_cJu`LS+@q;|YVp_y<<(vB&CF-ZOI`a?
zkDfa)HS>`V%Jbd4epM!7tQc1mgATN#Yh@!KHpNZQGut@ngkQmgU!jEl;QLZFQFf81
zZyklmxD{0CMaRo!Tj2UteTSG1;4%a77I^(J1>j*n3%aS$=e@5ye*Bf*&l{)iyYC8K
zs5PAUe0j0{;b$HeSy}5~br^rCq&|Rft0Y1%!+sA2d8A?t4emYTeSc}uoWJ+|-ZMWZ
zJwNL`vv(0FNC0=JfB)Vy6Z$U5Tk@jU&>jV%)o$H^$9e``z>rdi`_q}left*cWq+?T
zzZ6NRD#CL!7lkU^Iz5<Xf-7@gUW<8J)lrL~Fu;@G%b@)-1=JDF5iFVO*9~|LPh>G)
zNIaFFoylK66FYkTo+GpBmDkT4ofx@e5$6{VADWDrCk|Gx`2^^Pj&1?fSimno5PLTN
zT6}XY_7$zCi7g!_gf2lg*K`tdXh8)N3dN!YP+meveWQRX1Ojj191P)**4s5q;T5|g
z73pgp8EH;7505nWMN-j~%nhMfr~{BcYYokTa4ZxK2j_y}1%K4r-|vmkP}7T;VElxS
zh`$#+)mRO++)4Auoh|%&F#Nfwzo}_{zNtx4JwX$zv7|N<iAFBa80~woT0rcfS!TJ}
zu*c-#!fU>_AKTxvilkr1N-q{g0|!IqKdp`<MtZ%xxL%S#>9ut#?MT~@;OV@a+x7-U
zA_}Afb`EW$Xl?!I+jvZ|2;C&Yi{vGoC3DaRWf9rMQ&qlu|8yba{nhFHx$^q-LrmZr
zK>Rr9z-C*2IGY`cDR~a}@5MR$50uv7C^#KhPugahq!(WZIa(R88ctb_h5P6s(Q6hm
zHpYuayNTf6ojeDg+Bz_8zDF)?0MaMW&<HB?9g`{1o5>!-=!tvcVZ-P<s@ryT9qe-9
zWr$xeO>_hM&cvQ457qLJj$PLp8!URH8Jvsl^H3Yj4g!fe$-;@WiitpCrTo*+>u`n^
z5Xyb^;IHA7HBE{!ROm}J7Ujo{C1v}0YTCtogp*fIxy=;p?2tb`Hq-J)Eq!9iev2Ic
z^E)oUiKKl%>$`e;@0D;4o$LDh_~gPnRF_3?Y2zW!e;ynk3<>eVND&1~=Ywny;UcKE
zM7U=d$bc&`F%0!#cmjF)i_xMH3>wSh$C`XpG<;3R#$g^n;G}IMdmd{```s*UG`GXm
zK|64Dl^?lrgf;r?29VOSxLVki%k3(Jc5DW7rQ0b<-&qF&{ymUxAeLFycImyoKEj4T
z+vL{3u~p&ou0|)63XfCn=GFa?#kY1o51{_?9~=ICUBB}s<py$7^|m_O+tuF4@9pV=
zHDF~|Vy3&oH=f6e(REWC{c<Nc<Zo^t!m!9M10GgPQSJQ^*xIUhnsFbFopG1Hdc<wG
z&p=LS|K{@c8%{4y#LSmJK7RJ)T%tAOOFT7D3Th7wlFBBbJZ*2hGJo0SnVIF8|4`Gk
z%%U1irLIq<X0|tKO(xp2{R|~w6|piH<RX!jQHLZO04|AWp>UvzZtJAv97H_;*2i24
zEiQ&ukUO;;S`-DMfVZrX$>;Jq&#jpH8*B1y+?$QD$r|6FN!s5@NK<H`A?QXhzD;QF
zMyl3=D-mf#6?P2GJBRC_o%T*vUOg!GmNgy;RJS@m<b|pT7P3)!g)mXdY>4#6k)GEd
zKTW39Xa#bxO_Sr$CUz4k#?H3GS{CBZ7&FG2+3&;HUl0PYUBNBH?jwF6P;!a|Q|f+E
zJe!&QKlHtt*DoCDkEWUuM~)s*hOt!6EE%J_cdJVhB6}-<2aGu#Zlzy;G}_qR+|m-o
zQ+H!@DIMRtH=Yi`q(d4~m3Jy!#14x6@+&Wu*S;o^>FcB5+NtvZO`bT+b*$z7BIpD?
z^vsEqkN{%c*i5Fv=o)z$B#x4A{6)>x-X@G>#kwZ-G2Iv9fJpB4RF!#Jk|$~&NIUj8
zzF;1oFR#sCFIRojxkXiXAA<v<o9!7U;b*={$mheK>1_^&oBs@=OC%-t3?M0q3u10k
z4%t);c&l_=WtTEjO9G#*-ViQt{&|y=6)af^wz{7*zhjIlb}+cAJS<zS?NyynE86le
zt5f@;R}rqZaryE#Q1PlP9JXv4G|n>#6WLO7fYeD%hGu-rnplx;-!MEg26V!fhD-rS
z(y_PuqxZNiNV9j29mp;Q8lbQ>G{a74-n0vP-8~)c*Hz)nFdnwf?}6L}9R|1Jg)Ju+
zWOBi#QQBN&q$}cWi|@a;aP#TYi>Jg_sKEP&g!jX1OOJ=BIfVI#ekaRU5=^zUjIamP
z^O%D8*gr5npgs_Yh(Z&%?4)cF?d-VhbpBNP&ksbR0|U{<x^sS<_MdHZ<6D!d_H&Jm
zjpvxqYV*8*fQFHP<SP*saDUOMQNhoG3=82^2Jm~vOmqC;zQF_L0s=D*4fQ5=)z_`e
z7(<y|tu5JZWYkWs-o!wpSDvo#9U1QHnDo`i5%xNSWkC2j^z-DD9Vu$_@a<u&<mt)C
zosncqw1$^2>)=p1g0E*Xy=QB=`Is<&&FgWSHGR-Bo>;)`h?Hx!%%Iv@JjvQN-%u!Q
z!Ii9S&`i{{x8<H0zeOyf?|08%=k3n$#_O(e&&5pdu3f!_AMBpp*#ATVl{`|&QSc)%
z;zvhd=%aRp?<`Mw!}tfag8%*#57z6)Yx+jn>Lz>u*kXcXh7ZV*Uk!!6Q`^hhxv*^<
zu(Cp8NKbe~i<25(Y47&Fr~wh~ekK$i9tmGJGK@>Vrltc=q5}_w!^6Y+5iV_MRNHh*
z(4{3WuNF6_60<R31Lx32R^AXxSt$-E9Wd!?+>5r0P)097qebAub{geIkwjhY-To1S
zOvvf&_ZC=?6Z~E4+gy{C_$Yfd)~>uQOO5NfO5gN)zUtT5Ut(FPy~@u^wE3261tc^b
zH(}|A#JqJ3cykE!BS4aJyfyj2Pw(6J(+?!yee&{!mUq6h<-%pQ&%V=s&AcgE8o~^O
zK|l{gAN&-*I~hE3d;j_a$+zkY{PNf>bM=b{E?hpTFSGR;4ID>(4Ii#Du>Csyw;u_r
zfo&ICte?B(mufHQ#$7nni%v#C)AMkUm<gDja1yCw-gFq)q%af#n=~!ejN#^oEnm<!
zLGWlon;@Cf2&RB9g$qy@UWUB~dqhKsDF-3K+=qY}cHd}6puf>Mnhm&vq2%t}-KeeX
zi*&}ie9hhQwzi&Vq}$gPO19P=?K#z|fyJDT$L^XTlGy*)^GKi9fAeT_s6G`8t(|G=
zZftC>Ywip;wSpdo{l1p2`sVN-0&<MdV=~@$44=d+tEA3Y#<ACqRs}zSf?ZIBZ-`i<
z%rcy>aEvco@G2HfqCKx#GY)odNXZPuW4IKdFDY<*_xUnhe5Q$oySfRJnF$j%IB)(k
z;(3+h^5x4|f2PboV@;aqCAQ=L8B5OWr3%aLC7-owkxZzgLYK4aLGA{zZv&>P#1}fe
z=*Zfd0qdcL)B<Y-44W7*sxcE>??4I7q1%-2be2JA9PVhrOtvdt6qEy5jU_U}$v=V_
z9nI(_SA#*MH9jCpI>d5&S7MBJ*i*R?)#s_yjIu}iQ%{eob2pw-AC!yyl$gKnIn<pR
z=(!^8-J=ys+A$3ETNrOmdtqbAZNW6i0%{k^*u)f;7uKRTWbIkIom^Ji7BC~hpRPh2
z9Pa3F_cvZ%@D7|N_(4f>3xA!H9>Gju+J4(cJAfUg;fCPbTZ5~i<Wb45;+G<Qe<YHl
ziF>wn{6D}v66=r{niX1N(-7!!$Q2@OhF>wKGcFGS0r#nk&*$4N9JsUVPIa{F&Mpmu
zTLuK~{U7eSI3C?sxa)M{^y%I+iQ0C!ek~7lt&C_>d%I0W;4&srLCnQ1?Rx!LM7!!n
z0giKL*Yj*uOTgx);P2{&kE6K+nw5J?3Vr<p%q(%4dWjlHJkEePk0-`mAqQ?uwmzgR
ze30h-d9?{q%)FF&itX{hH^13;c5d>Yj;qK(*J$p@$%~`@&RA!U`ZRj-6J4DUKTci@
z^fsJTzvhei%1?bWlLZj(!^g{ii?5H~f8ub?-w_Y=jG%Al8WJ7%pBNfUbav03tsNI_
z>yYsW0YyJ-8NnWr9L}+Se|B%Ua4_Vn8IV`lGw|hQe)M$BaJ->11H@&cMSa~_hw<=2
zcTG9QSrv^MGv#dr{q|xQQgA*}Rf3n63z7k2V$u|a#ClC2Sxb`UXSyg1o)O1q1{Z@*
zW?ytncGtIg6VZ4qngH7Kb+YZtKQB*tMffquW*IyX(xg@?G|Z;1+dt5a?<QJX6W-SP
zpANLFGU{NJ1{LeobL#*>U*OpVeS~;jyKi}4RvZ%cPBp4&r#K(lz!03WCE7six!PaH
zSP5D)c0@y>NDuoikI3)a>KH_p0`eY~)lm6cQt!Qze_AkC4F0jtKPtV4vEHWmY_vWV
zfZn{tqiW-aTxBnqBd0_<6qP9*Zw`xuO&xFcCNc@Hf(Iop)>{I;5H!U8T)+l9I|G&b
z+7x;cro?GSf!ZzGn&vfnz+`kySat0CRlQh=F)cl&971a!xkg87#+ZE}ege3Zonvj=
zvjfnwY>pvT6B&hnmT-HTO~HKZTeOB?Roq8kh4jxDHv7ZM{o8zsaZIhWN6~KxU|{>^
zuW{3e?ObV)R<F9<5>jARz;&;EpI5gD*lz6UYe2-zAdG-h?SQ$-yv9tk<iPs1TCBE5
zngt7(mZo32x%Fb~ow?ZKJKy7KG4_sJ>{<;{M-7TER$}i6Xyp;FA63#(Fld;;kcSBY
z08x4B-CY+B=kwVG-~D*R<&C;!*ZAQJ=3?Gl$i`v^7qY!wsDLMDxECQXT3FN`?0Bh?
z3NLR?)L#)}U;-A;r^5EdpuJ|--ua>OW_Dr!Sn&Ap-g69STy?>*Jin0LeZ4z9c70W^
zUdN7rH(C%Ax6S=TAzNE&2e~PUtLyAA-%I1ZG>R-6^rx*5NYs;AMXUkUiXx=4y!P`r
zE8njZxg0TGIa%cP&o)*Nn!;Z~G%Rn9D*x8s(~iu1oRaVe<lfrR7_ey4UJ?YYr&=s(
zET|u;KWHEdEoC9bK&O=S6_mwBTm%d=!%o9+4)62N9G>y-v)=eRld(~-$<bJ{(??_-
zkd(DdYdu0vy{$%15{{BqFW%I*BGxJy8*#ZtVo72UDD0>f*aW{#L_dit@&L~(h5FVx
zK<Z6Vmh!5<^s4&ZKHqcwr(%h=j@!*!Q|A&fzi*{3;N@wbKbAO`y4Ae3y)_X#)sMNi
z?QXKRkd>HbjYq}~KxQ2X!_^GzE+8FZttAUvV>p8~0iD>OcFbqOj{VeY%gz^I?hTOw
z0^VNbpjYhes!c%i+<zo%(AEXHr|cbN$%9%iofSEARW#Cd%VmLUYFiWW4D8rGaI8wu
zWJL@QW75E~#I2_Qm?MNuh|N0WdHh@9dJu{RZ#m>|NZ~Ieo=5}}7$fZ$`M0iR>Wl^G
zj0N<X{%<Z9?YH%+++BAeaq1mRp1SM9$SlJ%A9r#LDVOK*JKm!pS2H_mhwP3CjX}5-
zhmbj01nu8~J9st!g$~Qb?Z*%?)J<X^gUA*u7jS@P^`e8&|0n<;b7k*I<~9i?4|B3z
zaD`GRf)Io(nq#5m(DSUR1{R`<Kr9WGOANcg*z%rqtV|L5Gvk691*Hy^gF&|W2!w5>
z6u@Fs!PmalTw4QVwH1zyZEs$B0UHM_D37j5(9xoRvQ*^?j#Qjp1DofG9o%+C<FeN}
z|5ni?6hIlZ2fDdo^r|yhC)6YEX>LHEWD5?><)WyWFPArR8vFGcd9gju{Dq{)%G@@<
z&i~($JIh=;S^1#hOrZ@^-ff6I7*prtFFGEI-~8BPvB&<L?_=`go4@e(*xT_dvm)<M
zs{p_44FW-aVmo8Uq)t#?<J?@)DyCLIk>V6uVU|$=VIVaY8l%LV;;P*!O#3Jpr!6lq
znh};BToPj$ApPTVwhcc38E1?vR^2OeU;A1vhKfq0)FKO_cteKeJ<I>^bY>c#a6MFY
zPmLpl-QiQVB?w};*mBh9sZX666f*F9M@vgb%QrrC>QgE&wBS0tZRx<6la6dc=fupT
zw-tN0<qUy{j*c@oAGQw(?9#qXj2GgTo(&;!A`$VQ0u<0Ge<ab_+uMn^i%7@8@=`(l
zY@fEjW&McZ5m?tY9V6~)jQE|fvchjqGAedu`=(H7MsCVhc44UU26|p?_MTAMTdNvF
zZEc~pi*iKzUMT85e=_N5K5RK`&-nNW;1Y3^``!Z@A+c)%=@FOZ@8LS=KopE}#Is{F
zp#$T4Q>ne<2PS6^?dcvHIhgC&eQ<dA;O_KIb90z$!=bOyDRS!^E<!Hgh5ZOmsE*@A
z*rZ$#=san`{1Km$b8KWPXe1&={b0QjNf^PYkum4e_<_*OST-I%G&?EHK41*(8wn*c
z-Jwu7>bi~W8#0{g#)G?iatBAoy1VA)Zc4K=Ti1f<Gd2%q+d>$2(q~XI=^A+pI`6~F
zSXLVv0`-JtLD1lZ6b9iEAT`KZ(-xE8KHW~k;848#_NG=6Ed(SM3*QiX#|@{u<3n@6
zes6zSy#`k;XnAK!eHk>mo$WB1l`{@&ke9p@mClgv<%I*Enk6%u)m<--zhZC<ez?L^
zd^o+Q-l+S@^j_3ZyJ>n>&i%$K;xF%!S7QVHKRY%sbxhu1jkQ>NUdt$Lmeqo28H{C_
z1X}7o(gBTUfM81jk@O>^y~9JgP79Tr*1iBaB8D`{Fh>__HjqG|cUdYrC?-LA-5vo4
zK}<N`V^im;5*P&pvY;$3qHdTd0+$pD;?i5XVfYaVAUYD?vWmdEd;_ONZvu_e^h)=g
z*pu``3`#8t$+TcBNscT)tKOA-DMsc43MlAj&v_esp!rN;eVzPzoi;RtC9F&NTXc=L
zVBkaYApH$coPzG}i#Uf{KuietEM>+#{CDgVq)eh)!TQ3E=s5q(>MAs4V{>)2fSCqa
z!8mK%?+<1xuF?c;s|J;}t%V3J6Pyqm_1z{8YJMmIw9`BYPJAfOLt;Yc-jj0XH|5NW
z4tb*tOBkxsP};Gu3YA{utlAA?15{B6u#<4qvp|DIQLpO&5LgBw!Y$EACKAaUkXBS%
zM>p!(CNZ#mEnQu`O}$<3$z;^BZm8N0?*|@BP&k?*JM7-OC9@Um3ml=={;ry1xt#sC
z#SHJV&*Edu2W2|aGPmF#!O#FCKIyTRMA;aE91s@{gp9;O+bw%(a+{6~%_86X^=HjR
z=NTvj=ZB-Yb=c^p<3li=ot;~BojECUM_H;QGYvHnC6%z?lk3ao`ntKim`AyuB4*j;
zD1eVZS_4HF>pWoBAe#UuX~NZO14a^_16BOiu*UMl8qBMv{K7&hpI<<AT;gR($w0Je
zUkmFcQ2BMC@~idgm+g2NZ9hn34lTxITf?A4*?G2GV{Z2&-gLKEzKh%sI)H&8x7fc3
zHtjNi5sB%1=IWT+>U>hZq$J2&tz9L|5VKYWwp9`0qz(R2UE7*(15JWE)wX8ZD+0;&
z!OhGqHSVes>uVdEwN9$v!uG259RuGRgAVPg7C>@~*s-~>VU-_;B#-IB{zkb6*h}*z
zc(9P#!h-5*Xc58&_ATZ3N$7UJ2Z<ARIp7yJui(X^cCAv5^Qq&X9EV$X{B^gh?0IO^
z$N&5IJMX^xo#U^=9NP7yS}{Iit`@5xSjM<|=(gKhLRUEIS3>G7Z+T0*j4|4?e^UqD
zgz`S1*0%jL513fhQuBL&xomv9atW+bTb|Hy-4VzXNt_$gH6;cn5R8|ECmfZ^JPgcB
zdOBdfMA_f;RL<iKhQh$#kvhP@R$~Y?!jEvy-u<^7^>~iDcFiW{8zU~)-7eR7cgyLP
zJ&hf+iNtJ2<H@5g*HKTA=N{twPYQ8Z?~Zi`ho0=19y}c!3gYjLyM~4~2Kzgv*QPuA
z2QxU+9s5<B8{*r7rWsW^vYr%!x&~nvl3XlRX9;s+0I#@ed?WUa<I9*!%L@tGym}nx
z${VYz9A=uEbiEPQH^ZmMDbn^n9WPixL7_BEPDq|73<FTL>(&Fj0RIqCQ4~%9WkSEe
zm9)P1<K2UHn=Vjp*euy|$gkV?Iw>C*H9xQ;egyTDdX=^3p93M-g?)csh(Ue>*%LK;
zo}@rx?KL?dKeKf);s-Rz>e5<7@TH^tYulgOA=JOxK8AUxXn(s0Rr=EgJrFE;0PKXA
zQ7lCXu`^yNigGoCu)5e54~2bgsD2oV`)>K97}&QV3jbOgP{rHYeEi}qs#$^}w!sSv
zUw|*#>5!OeTh4^6Ay!eW9vBX?s#uL30~O35_@oAXxsV6ru-6Xt+E&fI(g6@LFqt4z
zCw9<m>E6kN^ra|gLbomd6VI6dd9T%Oxrg#t>qXEn&_;x{lXF>CHK;SdiANiB<f79P
z;4Zkt6+m(N^ZD{dULZ|jl`0efU{3B}tgnmEX3<7d^hn0_{V?}uHGi=NgO5fgB<Bfj
zhwRA&1`v93Vr^kmp%ujF!q%9ly^$|Wlg3)6`LCEk9=L9~ZnRybMLdk8uvM|1h!nQ2
zDg#o{BJYXK`KC`!PY*^TZ>dh`6C!cFJ~kJL4o>4^-~VH8C`JLUKmto30J+-c56>23
z#}Y8#Zj?J;$L&!xtWGX6kFB&1WFy#<tSj(Zm`svLdU<%xfJA4^4PWN1F~V^Z+z)F2
z2P?I7xc<l}Yb}f(sUN18j$kf0#&~af4OHl!-IHq?@3Ai^CabE?LfWbr7K{RY%{@WO
zS8<PR0TB8Ps=zk!PnEInyr}VTLsc@)5vzjsP2k`|3CIGTz>Bx2xig7aFuX;&4>%p(
zf&(Md0RB284FcW*>;$S7OyWu9O<ABG_k~@~psOzG^3{1Vp1S(RsM{HIy248F1E)LM
z?2!-ZS}Px%*nhk8pCK%?E(pDF`dxJcZfBFr+f!H9Q|D=Nx(Di9epO%RRqgnw&e>G&
z>aMHnb~ZFQ@z&Mu@;d9+Zr@+us*3;Y1dUx03?$N6ZKtfYQdUyCD+(C=P@F$Gp!8-&
zYa&~cyEh62<mqGhNWXs^agLlT!|4#2I7C|$QEG|MVl%0%R-r2@=PJs$XBS@J@?-5Z
z(6$1#N{DHQc~~-+R;u_fkqj6!?A;1CRFxI#M2i7U^M<WfK-{RNV{6xdnJ7o8U~WeZ
z0qi=!OCUIyQ`?~-0KE>t5?On^pAueqX?qJGYMOFyU_9KQrjUx1$pqj2(2LN5Ukf1`
z_D>bOrTj%TbDrx~+nxDa&gYI^Qa9-HcJC-M%6D*$LRY{x!2Zvx?}EOgFOV?-Rq9Ft
zoz#mg*VUz^{L=Kt^ZH2u)bA?)18lQ*DF~#sV<x?kZNRaDaRb4Nv_PSVEqe3%gV2Z$
z#&Xbza@S|nd-tAWZH;q#W5Y-0pd%d_x<O35SW{ZZ<^BCAu-B_RU;F)GMTU}>y-E*m
zT57wvyGCtLD;M{49R!F%t>Pf}Z_CLol4VM}pIC`{u~e{l;5QP}UvE~ev^1=vp14A}
z769txH{pN>fof<*QBazEO;~+P_}nOQTxm}>z;o9nGNhM2)SMrIgO&gAM^*k)W^-E)
zGqLkOmC$w-+Exs;R{N#$V$Hkn+OB3DBSC$59nrLukP@u73BM|QG%zVbh?YieB5k`N
z?HwbzSKKq=A(ZrU8hwCoR(#cP(yXsHlf$n<(d0Gl3f0G&LPn#ApYQExYU)6mRo!}F
zU*v34)7i+r!ZHj3rUpfoV=U4RVM7iKr|<RwGBoPt;<WdQ&gp@L#m?gKb}8UvqI~e?
zCKCD}fWy;QUcvtV(dqxR^=K^fA2#Or4{IHpjs>e`4P1V<Wd*T_2ta~GQG)(4dEuXd
z0CRHsh2_MuCpEeE)Tswf_IlPS)tsEhIlrg(<O8Qp?VU^k{K5%quAzP(>&NSuuU@l;
z!_sR5-fDALR6VHjS5jMW)(U^^_%q2JxH9Z@8+8rYps(IpzlcY`pKYi!+}>fOuKm%o
z$T}FBYfQQuj(Gu#?U`<9n67ULy1d64+(~b@_gWud4Qbh=1PqKLjuVg*Ces$MJ2YGv
z^Pw(aFQjmcD-ZnPcnZy{5RT|SWCh$UjzIb%EvRaoh(xw5VHZ`xT^DnC23nduPWL_}
z1hq8`;7px6p|1T=ic4P0JK{Hr{!@p$v(7W=cKRCKX^&^n>-9N36P~(G_q9IwdaYEn
zDn=U4nZuID=|zAK3%#@*v6lG@j#}OIsqtTAIrjY%vB6OgaT>t#=B8Xol|b$XV_jkZ
z%jZwdi%y7Zd$122>R~jaF%dN?%8sarRMF`=6-jS)aB#K;6VWs_V1oVn;7R2@Ie5#h
z4+9VJAz&pwU3Ngaqk!jz?F$?q?bvb;eN<KdY(?_1TVYG!k+3q0>kx1eur?QrBI%_7
z@QHPc?*2*})G>6-61~9@iTD9z{$$FVO#{3q2iI>HY6c=;uLd}PnuLsi`x1$){rAGw
zXeQKeK%C8L<zu|K?&{Dc+^64hNqt8>iC6UF>53v?X^5-C9nq*RHV8ug<<uh&JkWdF
zZTF#wPSJ?oc3bZQ52z=fe6s7}MN|UT|K4`-V%L*TYTDT+{w!j_BNrYkf!I7^25^&S
z;(A$<cG7ehb}Ao!<hdsdOg`-M*T+mVc0Dkq<3I8Gvl&<hGHUaQJIMF%c<*8T;o+8k
zV#@XZ>RCYd0OJhngSp`JqcsXD#xUwUA0p+pam`b%peze%Sv%KtCupQcX;31c6FJF7
zL9hL~S}Ak}4BUNzb+b?oF#`@#Mu~YN&<Us$#Km>yG3SWCf!83I@SZ>KQ)g*Tg|GE?
z{nSw2K*dbo`SaccIEZ}5n^5!Cm$<(F|K0y8pOT820oi@rfv|=$z*yjhZBe+GLG{3l
z5j59K9Wbn51I0f?E*IoMRe~CpJtnpvB8?pG8R}d%A<@s<t+zIfoYL-n5x`LdK$wx5
z6O+rV96u&Ou`he>i6@?W;(c<a*xm|oBp%)(dK6&}+q)l?*METJdGRSVgD*ev+(+eu
zZ^@4ym7~mIH5M_1olR`R9VP7moB_c!y>3wZw#6fELXyg=Y&Kgk7IzT6%bQbERHO{!
zGgDgLGa>I$?oA+mjd(0r=e97lqxzA?Y|s?56GENVLOMYtm1!*PtcX0x#zB*bEAN2H
zNja&4QpIQt7nRx_q<Kbb2)#6o#$&KsAB)Wbon#I*WNF(}ADukMOr&#@vD8t*Bp)3(
zv%X$tV+Q2f^Vr|dfG)Fscc<K^1`J70c`fLW8kYlEW`$HM$6W_v(@#yu4mAwDekc|{
zAD<DnvO@70p%BdtPRF6xG(LaR5I&B7)Q*)viDv{$P}au1lK&8wOj~S|%-Kb#ttK4I
zM1aoXvhEMrgY<QX3dnP66`0T2h5iG#9@q`y;TE@S(NM<J>H5Qm>rYRO&drU&6NrDC
z0OJ9H$D#Zk4l?x)ElXQ?G>F{;vI90%MJRyPQ)`%|r=AKfW(vA09G)N)BQ9_SS1oUn
z?IX8<)kH9sLC9C6mHL{{5nzeR>aA)AoK&vr4))sTl;hGRl)j>?qP=rD(hd_Hf@Jb&
z5Z+A0J+dh+Q^TnV@8%|(qXj|pJIZs7$nL91iD^X8P_~D^pDu04MRF@S%%_Il4i?P&
z{ugy`0v~5p?+?#8=b0_ZOgfXjNs~$1CT)^#Nz;wercn0EQfaLSOedL1GR<UWn3=Q`
zQ6nG%0<zeGh=_`a6n8~L#fa-w#N}G93wjY1m7*6F5xw5}|9;PTo>|gX@B4X|&*!CN
zp0hpYIm>VVon_1)s9NIoM~*%p@vjj9|JWs-W{(j&ZoTwUTtjwiT}Q{)q~))<(a-zH
z*tc0NJ1)f76=?;|WTI;I|HWN}UVPW=6DqgyW0V~~6?OLV699^qKeJANOPFxY0tORE
z(Wfnbyy}N5qu_$V|G*X#s66yg!IvJvyv_nzi6zv@;nCzM^D2kB!8-Bn_HU)y&%I+;
z|BjZ+o0m;3nZkM5-<oPa@3VWK86M_>GN3h}!f8ME!tt#A6}a6Ix2_buuopF-pIo{$
zd4BUnyR?gCX8bh&?wIei@in5u@wqy--~W5}_Rv-|2^w<%Giz1_7kwo|SouZ!(+(<w
z{DbG<058JB@PH4KHumA&!+4jc)0lg32Abeh#u6Z$;zsmDsN3i$ymM8%oo5>t3?5i}
z${lB)|9nUz>RvjpJC1nTmaiOYS~RaQGJoaD`MdRE=96qIE60q!vsrwLc4M9z1wuCj
z?<>v4xY6z4!KV%$+#!Z(<DS7EC@0>T1a89{WZtPW_VFfdZ5{+V@!))(oM>Lhr5bpr
zWQX8)xOQICa!fMNchL4a<06LXAzWH@FMpQl8JdNY_gnZo+xERvXB`5pd>WLSwt1jN
zk)V*Az^~Pb%}PoyD;N~B+9n1w7`DI9z*(&OAwJE60GUQ8?WLot_vn!wv$MKy<uWcc
z=plH@D!+ni4`Fp-sp`qYF#K1ZT8*keah<vh49vp{-DdFC2xxFWJd3L#>z!<D42Hi@
zj)U}ya~}{O0j99dKV{}zfj8+j8Fce1_#TS>He3`uL-^^NfAlU0Bv^{rhb1CUp_53%
zX^DDaiCL-v5IclCV&zc)9iOm}0AXRfhQ&}X*gzU3|Go!tHM(Y*fZ-jO^Fx|n8+fMu
z3v}v)v_ap`126~2{(t1IOCwWJUf^9#s9g^VFM$Ko;z@ZpE>9m$H7KxE8?6uA*yL|`
zD|#x_mZ}9j!N@F83Eb4=4|qpfLUO6kcMuM1#}|G?&kk?^(lqp2@?o$;o~5$@X#<UJ
z9PJO#)71g6ocfRU!lGsBqMg;L{_oVJP`3suJeus4axmhu%{b!J9Ph{Tv<9(Zyz-0W
zE%>B(CK&eb33*ku$oOmIDu4a?{JEt(RrJ*b-WK#HL*A<3YHy9dRrxNf-=TjiGJvhe
z_2E+202=BO@^O&~{J7?iVix{`npX7X!c+?tk*QeHqankw7m!=>Omx_G%@)5oIOCk`
z)&Rfz@lHd>1uZ*{cNRK_kGzp*l^ueGRG(RZJMo$tc+k|^29F~=L2A*G@-XkCz_GA*
z>zipeXp^Ssy-?p=v6rXAz5vnfoITHIyCwjs$TNlzG4h|LBSlegh-c4IN`fK8NyJet
zrBrdpCK9Jz4UI4bG`1~*Td1YI8(%qj`DX4FA3y;d--$9G!y!tbIxRKJaP1k_{dBgr
zo0~P@BI@()-g0^(aXK!;wfjt2hyKkf?tNH{WuJBWYNI-U5BMW@cQ;`L0?0LJXv0D{
zaKx)mTiCd}uQ}1&xBFG0QzqM|mNfS@v@LJ^uY<h@H_wOQe98KMxWF)7ss2;;v`_XA
zE$JBC+y{Db{z<}&#SHk)48BOoicA>)U?x~KvE!chnH|@znkj`?ei(PT9L61}Fn)n^
z0C<K3Na3u&7sv+C4~}oKPDuEuR9B1j#H+5wtuXiOm;hOJf-LXw7PLXbkh7uS|DAmZ
z<bMs|e@7sDV6BSTVN%D7wY<E;$?PVXnN4rso;I6Da2?UiYj3Zwzx}l{=pJT=a_%hX
zqTVfv7CTNFO45VwIdOC5`q2Xv-i4Oq?eJ`Ah4bOErOkl-9T-3IXTw8a6(0=ogf<+#
z6go7EO^Zhl55r%ACH3~7;kA?>4ms~ov!Z^}L;{92P)85#Xn0D@9653X+S1Wk_T!lE
zkizmC{3oz$2p7_0ncji9-!Xl+eKS~d)j7Av3e*IKVC?hN8jI;JGz&31jB_n?8h(x*
z86Jk*b3&Pk)_&5ZqReDuC&*3EC><{4(eIbW>R+r;nboE)n!8*_!7=ckII!dJjvae;
z?&Pi{9ROrkj=x<eMwANyuzCwaYyWZG3rA`O19b%dK<EzOCP)662gjLxET2!n94RL2
z(W8y$;Uh<8CXXJ_!_Bujv2<Gj*2>>4%9A?;O5>WU11yYUm`lo!90$TAJl!@eb`(qP
z)+<*u7aIA-$;U<j>W{+&(x><{%qaXf3=wb-)cB!dGNgZxA2xJ*H)z<sF4{q0Wp`Go
zmC@`by}@VAb6R4p*XIVb-80kHHp3sBK*#jAaEb7)Cw85A<}Unk45=$Uul0onc#1%|
z>%c1)n6sL(&{!Mr;gf>}5Lm2ZSf;gHG}$|Dn|Pi%B<TN9^Sj?uv$?;ixzp#F-!1QG
z6<4&nhk|_r=gqXWtgY$$etq5Yp5^DgsjYd{Cf{Zrh-VEpH}!9>>6G2`J-$0yk6zpA
zUL6eYI&)KhP5)Zo^4eWzp47Xku3n$Z%{|Y<z{CIl`jZ@einK@HkXhUhLn?=34q$YJ
z<fTbn<))aOSR_s@THu)ONl23oP^iN#dvN1P3j)nr$1!6=uV<avzp2;RXpxclPFtK~
z4=Os>Y?}f05F8qYU^`4Y0F2|O%$@SX(BH!?hw01jHNG=QD<zkiwj@Aob=HE=285mH
zlwvgqIVJ<j0HtsB?R={Ag`L_$fo&YesaEs5b1>(#kn;8FbnQ*5SWas;enFJLu@{qj
zIws*)>h6rx)$7$TXYblRw0-@C3%Wdq_e3V=c{=MleDpRg*|B5&_6s%)_Gmwob3YpG
zAkdaOaj<u%aNT?qo6>kT{Q;bSXww0XG0#q@)iT5ip^w{e?2dJ~;IUG*IY|gt>xK=j
z_&Y-*W8X~AR;|gKgS-x$)H?s9ljgUczj+r{<_qk0vKPZ~bqi@vX`L!1HEnI8CB5DQ
zd-*0@UTh5N4gqio0?FS`PahcWt#7V}5Y<~-=847v)|r8Yt?QebI%^w)Y|FyGmOG_?
zs1+3Yj}6T;e3En(gVF#va(KsZ>6SLS9x8bU<yhknq>M?MhXzI?Tsh-)talLNO#}8^
z^<8otFCotNV&>#=orR_E(eKZ5p}_^5VLA3YkeY+X`vi8uAGdl)GYz-{5&P@_-#lTp
z1GGp(Is9@q?ix>t-A#!9cAN?uIoY@R`nV&$FEYJ55;=&!9g#?1BqFLC8iwiu4S~A8
znMfqE8yUNISNF}(>}$`zfjQ%y0Z>5Gw+($f{$nxzI3rXHu?rk}IXq0WkV*GIl$jMf
zho3s4eNu=HV*xb80^sTp=;*gVN6k&ja6}GuNc@j|m_gCwCh+%dzp|^Ne$UdAE(-*d
z)fhN>r#N_Y&l63Ns<dyQ?PJxm?N(DDP@_)OxZi{Nek?v^>T}@^dQYvyd4bs7iN7_v
z!XJ17fh)IP*5zH*(Yt5Y(%W-^qnEQvv3Y)@)!EoyRdb;)()P~k>)Wkob)7TNQ2<n!
zO`Q9^))-cy%6E0Ig?NsIcr$vv7}SEv-g9>EKIbsye^`tEyl?kzj2rKz0LR9B`7u|J
z_W^$xDNY5rGYmd7lK_ourF6|nvH_?44fpj$cH^jmvwI^pZ=S{sVJ~IhOpU*KIHDI?
z?DW^nU<`-R=ULDK%4&LL0QTvX+fH!i8CI}rqlVr+23_xPxyO`kboc>%Z?Lc#X_;0*
z2a%tCd;4We+n=%z89uCIchi_)n{Pm`E^=)b9~B?eG${-yZRR@2MP_3|W7u|(zzV`6
zXSC@+3~k*S+qyN4EeAVyrgrQQJ9g~2jw!LNqKau(?i!{@%Z#!%lIB77Su<zPL{k^Q
zpSx@aaW0SHUH0ia3s?8xYM%0a6yIO6q_)1hv7vnl2vc`wT|>8W%c_bE6|EaQ8$yAW
zy70<g&(fyWx`sBdr%F}<|29EJ`3himjq7Yz!gZzVCSn3U0AwIR1g?V`a@QBv&^3B`
zuoysZ{<d8ZB$P;RYV>+~^r>jrN{=vn)2IQ*$>CY7tIN<;ySM_#t)VU2yv%*eUVLRS
z-oSx{;5Mee9<fO+?_A!!rUyp>zixQ>wz`_2s#o)B8(y>etnr>T-8gh_Nn5X9%9GbF
zl|jE>$m*KllKF$xRqHL)I<M)B_!*5Gwr{Az5kfw7gV$q4Rt{V~u&Tr3uX498Tb^3J
ztWAl2OPsWE%Nmd0z2S^4!(OYergQlg9u*gAZVqkSxuU0K;gZEw9{;j)+n22wJ#GG?
zMbM91LhF{aRf`(m-HX>q<*t^pZsCUN_p8?SC979#-MXTxs%P!mo|Roq3%ouhWR+)m
z^ZfbE%c|TSe@|1BXl`0!WwCoxd8@5R#NzRDT4rnj&Ko-gWe%Ri4|In<3Mp|hXvp@<
zH}3x<{w~@mmTx?|PiqHj_HVrWkNCT2<I(SLoDV!Fo<g0$yas@$TtCifG^?k9qI3+y
zDeK@Vri$h}6BV!r?Ap9Myc2h84{OzDcn8oF==s~oT6%-mMgpr={q;4Q&h1}C;d)j7
zxtoqY-q3(Bzgw~;7T3WoHqwe-b=!$A)2XAqa)*Gp+CE7u<xYDcoMZ583BB3q2^Pks
zPrA?-zxKeb1J%JII3BGvXzrN<3UcQuDG@j!u*Ix+2ErHFh%NoO{;_4Ke)UlO(3XZ-
z+Ek&naGn1F?Z27Dp%dtk{(%s^si$XC_b}Wq)GFbz>($Gn!_7x$4j!DX@z1g>en7Jl
zXwe5iv)W)U#ZG*f;et4)91a(Pa3hXuH_j^T^(*3n*Gz}Ft!|zO*W8MG-e4CKN}L5(
zXb6_EKJd$4Z29`Izuvm6rk2^F_E&VB&d7nb)eyp#47Hw5Cw<vmQ$B^3!E??z2sAut
z`w!>{&Vz!ZnCoB6<rSJ(>!I2xj7>^}`+XaF`ZujyT-V$^zoBzYZzB*AcA|9y!x*Z`
z(0qNw`pjZ%QpC>0eh72(yPLn)+!heZ)6vtr^eo&wuxEbgN6z`{lwGOc+ctanIXFSy
zA$^8DO!Ygkfet@M^zB864nr6$-Ob568qJB^-~xK@Lpc{H+UW>OWIz1lUS=*OCOhDS
z^0r1yFU-t4jvUcv&*&{cu%_b_*WTWP{DC8*#4kvTLFIYMJCBNoD%LZE1F3p~xM|$B
z<cWrrXL*Nx6X(x(&sur@gzx!*bI%>X>G2n&yl1XFKk0pb<(b|s$bOS=XKJT!3lEa_
z0e(y0pkIN~2KIpR6Wz!8LTlhoU-G;qHEri5eb2KytpCft$g5J`=Oey5y{T6%@pdig
z^4@7`uLjTD0iNmDQH<{Ij2?&qB}}ck1INoP0ggprEz+IvFs0AK+~@xw^cF6)ndy6e
z^3Qwz3@;Iv<mp{8eDFV5SM>;zZq04Ozm#ha?S$-fu(1)s0msMXngovXeA@={LuW^1
z4;rSo8*Ao*)5mSTv(}3<Hvr@m*;q!rUaRIG*|^c$Sx*mdeW&XmOV@3>=w#1H3w`T*
zmTlU!>@R)WE<K=Cb$4G~69Ub*sf~ThPCj{AefXrsD^I>?lh#LQzZifI;YmmVkRH}{
zVy&##Q#7h>hWO;_zUZRvznZ;ahu3yrdP(=01E=fzTCq2b`gE_>XZrMdic?+ZV7}YE
z9`Hr*H4p}H(VlJq8U6<c90ZYQA{%TM&VFR$Dl;o8C~A1L>&(+7ax!2ARE_W}x2o>(
zzPin7d$qTxTGfXdt2(Q~b(Zo}*SM8c7xs54y&d#T`u9C$;V+{OT*&8{=M7oCK2cRA
ze7*I(;l}DE)r}r0g!CYIf!_Wwtv8l}48ak%MyFt&r0WoP%MQ(3=ph5)1^yX%q0wnM
z?)(_;#Sr%%&=(jS!G&Z8IW3_#Ipjg}x?$Ky=(jeqKP=n0O9I>0c$h1n9+D{>Z0C%N
zcxcn6h5h{tBaubH;3GWscPGr)1E=G5gF~CT2CrVZc;T+dqPk^u59-r|Z`7yuqREhU
z9l6rz@y>dR9?aDW%#sTpIZ$L8%izrbK4@z-p324zuGZoot!LOejeCwD*v|HK&)>7B
ze>3(Og2>peEh2`{-es|4&mP|IbM%W$qj<?*{2bD#1CwAcKp(Kb1K+1@mp;@UjHp@M
zIn;>l3;=Jf2_ebjZ5ni3#HtDF*I=6*zQY@szb@Q94;KzCsNYt<;M&Lqdhx~XHGStq
zhKGkEci>K~mb%V)ZS}98U%zctC~&Qq)N28c{%y_K5sr-j#?3$no8dUPz^k!OovJW7
z2y9fv80^H@tWLI~r3ZbqjgE;U^FuzhO`=-4)>Gw)F~p>pz#$l7jhd%3#aLI1*yeFl
zaB_RrN*P0#2k?oQ@{!E>)EXhqVIpA6nJ>5YpX$}%O$$Mhy*%G~t&xi%DxxvC+@kvI
z+SeVv^`!pS_xJ6*vcIoy9hRf4%ignYgW2}GXW7Pn(bs?ZuKvE0&s)20-P(6&*KNc%
zN_ql(%3Pzdu%WmY@}ki0h}q+Mf7^!4jC~6U$I2y+NEbQ1FviIhaExJSI%tUDTybf~
zu}^ViaN)ZawLH)&yj5P$#bEOdSmM-xCy#y;Z#(+Ug##^z=6lrYqts^)ev7>RTMsk*
z@Lv3Ko}4iujOcR;y1GgGAS&ZmpCzD==^<=l%`1M}g&2m{dd=CJM(fYnBE*(+>PI)7
zy#_v+o>RiFzh27gUmxBw-POJ7%yoTOZriu+%vIf8OS}4l!C+q(q()84`asJLL;vAw
z_br6g#c<sB;haXG0l&mGjQFFF(b%*JrkjNw5_}DtPaBLNLcqas&wBfwCgYIG%nBkd
z!l$gp>wzIseZFOB)2mvmIyTg9s0;NS^w%8p*VfFFYKPYys0pr<7p_o^^$qRq4RrxI
zU$%5E8XR1-c*SD`s6!2UgVRji4$+9U`;Co_J&V@QTVHolU(LtsLcs+-U(>u5)wQQr
zTb(ua9lad^(SjbFIk@s}IN5P!)-V=;G~$6iQ{HrmUx{CVE``wVWz^1$!nd}E@+*Cy
zUfhL)tw=b45cr}IML#wR2z}<nn4iyz4u6f>R`c~#-$0!Z+geW!G=%1d+usvvaW7bL
zY3qz$2lbZOfZy%Dp|P)Tp>ltq^<-=oZVW7#AF8Sv8od<42<hz((Az_pgC<&haO%B2
z(!Hl)(7tyA2#cAXvy~8M-EZ#S5NX}fRlmHWzaPi3FRmHdR@zoL1Jl^5wjEtd`Zo1P
z7A%}|a1O`7gL(f6Mu4jUA{Y@fY22%aLs5Y$SX`t}=uudVzR(-$;daR8(1GMT8a{OB
zjY~=D65`R;D`s&Y*kNsdYk{k_p>OFMXK`g>pYR+#z4eM6Vh>3mXy7FD{sWMqdEPrM
zo%-M*eIYxYdt5gQ4_om>p>xjGM+&`p`Lgz9_W!RgKV9excE<Ad<;x$YiUj(?@6gX@
z;2EC<%?IdWZ@ZUE_Ml}AfvDOi*WgyGwUm{hdEx9c9AtC&wWqa@_Qg9+>piU_-Z$EQ
z+H2<~VDG|-vWjKd2ChY&1ihuNjNFL42ojF~L^+l<QX<Ei$FauU8_sS&l4Ua6K`}hJ
zvtfx|8ace<Tzw051jdEF8QewJJ&ixueMQ60XFy%*=I`6N^9DNrb2Aw+|7Ky~=9su#
zH$nEwllQnZqyva?GY^d&Fge*fi)rnH2C1npcE?|``_xl+ceO@Z8aMX#ZfrQCZeyoO
z>1-~hi2T+|w{|tRH8l2a>}}W-9_pME2W)S~yw8fS>rrrlS9x)88N4BDJ$QhytsYzh
z$;itYC=6V^X=Q%Z6{}Wx8X^NHZ{93UAGu)NJ3FrL5L*W#4W3oV%CFpXHL^a5JfCOX
zBONzj@eO5fW9y?0o%4>-wXNu2_>f+VatNIQ$5w%}W7-}2`XF}5noV~*XLy?)4(ack
z*?GXM@rO67w^!fBMjQJuv<h(a*#^C{7G#p^7}z&s*QRZ=2fGz8!nP5~es<4!Ixt}4
zTk*s)vAuNDc-YLh`PD0BG1X+D_JK{qI_>-V_uR5ylOmQE3qo50jdyf*H|Ptc)mPIp
zNRJoV0$L5qu1!EV_Xl>ME|_Nk1xOrhTnrUPd&ZzCX~U3~XJG<@*3?+Ph<p%ogd@G(
zJ6p4Qcy;I4c>!VJpgpW>vP9s#%BYyMB2A6a+@+08otBhVXH#gwnzJ@sqN7K@st-7N
zoj&8}FpkE1)y5S9vW8f((FvT>yx6;9^X3)a#m&ANA0pk&D>tpwu~vO@5{^*9|Ck2Q
zdD4PeTmD=^I`3#54(=_c90c&_G0FzzA#mEJ&iWn@!-e8fjeYheb8MUKXzR6>Sxa#q
zP|vazXV;6hht7x}+M)5z+^}<6Q)frt;?~y1eI5Ocm-h9o|7-m8GMYr)(>^N&gorB;
z_>c4tskp%CGwsXT+dGzZw0CU#%(nLSirAN#&XjiKp+tGi$e0cec&UH#R2#y6(LjQZ
z8l$c4yY4eCsDXtu3kN6R7{iup!g$Ltw+bKT_!+*$@gohx%8G`PcGfT$a&9)m>i*>$
zVNV|JGmE{&Ri}JOHqRr|ZmC%eb@L4xuDSBq(B@g}ly8S5+>O2e%U!3yE(yJ$RxDh*
zhP#jk^;RAo)!Tm&Z>l2_m;J$%T|{7#N=P3i#bkY_M=VwD2He%RRJ!Y4)8&y%@Rat`
zPb9|wSO41Fp*g&uu72JEYg%8mD6WpI@Yl39bS?J>TI)sUazyHdi5>l(@u1^aJbf;}
zAsm`BuhSjZceQc6xSreaamV*#uS3GRGkDgEKFe}D$KW9<*oU1xg514NUIxkz7UCLi
zn4CQFr7sD+CYb{&;LOx;ZRVOQoi1>_8Q-=sQJZ5ah~Y|S?29sXTF`j6ZFHsCmd4lO
za|og~q<&!goM*$Fl1{Yzh4S|VARb!etu+Cw=Cp>$(9+e-*Lzf;raI6#&=?S*CLHx1
zsBUVW`#fG6%giaavZ*={tZr&5EAAxW5cz;GitG-ba-Y^18LDf(&JsRfAmG~_^zynN
z^Ve4j2EA`{Qu(aN;arw%TeP;ga2Hk+?w~9xN7^;hvw5d=bjI4b*#e2e3_f^tr?@B(
z+p;AVsPb~Qyx*bw6MUi%YkMeTOJL?w(4kdWafx-ogFUowz+T5ayG9SBlDcrMa|auy
zQ7b}#AAtPA#k>|B^hr!^-n?c(Z|{OoW7E9O_I?~$x3PV3WYwa1O_3hGuj%OC`M9q1
zz~KWAZGK(fhTcfSywLofwyxItrtQHEi~9Q)4Mv(m?T<r$JluNpK<mL_wv%>^N!`vy
zdtE3kvD9>o>Ri@Xo)&A8braEM-XX3}O4SV|@!8pdBjQ4xPCGBLTi7&jL9f$DuKE$(
z-3u1B)i*iK913n&xN_y90W=WZ>0QwNRkU)L4J_$%=2T3Nx4UP}+D5GOfmQ++7pyOI
zG6;B~BE7y-Pd)nUt=Z1b?AGYEP)kF@wkVdccXc&IPd#<~l)=GMF1)tY<5s@bYqfmT
z2l?o*zCS@9oV5^JBfC~eZxhcVz*8Sduom0W8@1WjJh|Jfd#JN<ZTDKZI6%C5fFuxT
z+6C?q#yd^oA40T9S?iU48Eje+vg)O*uWGRxtfsdv^hr;>5^bPLo<=Ls&?LeuSNMW$
zKCfT;7YSJ-tp!WHmh!rV!~w=iM5Jo8taf+yI!j#;8d26;Puc30V?7()a^3b@{0*Y3
zF<9@ms=aMN0ppRi7$+F`YJF~~vY@vg(Y-_v>wQP;Fwd9$xE+>|%fD-fm20W!x5Jhz
zC<=DijqrQzFl1cuG$b!!`BCR?{F$(Tjr?{y%o9;&?XYrfckx_$mbYAOVy7L(iC^Ms
zJM4i~?PHKnGOny^zbofTy2f1-t^#zE0knD*EZ-6A1<l~!xGM#ld_Ur6xzesta1rj2
zPT^k!-*Mi{>sZ2-N7<ZfFMi`F={hx&-JeU2PZS~pD_0Fg&d6lOQ;Gh_S?ST`ku9lI
zWIOZnk?o0mBDXgY#~ZJ5#n22^l0=9fISU`n;L~|z#tMmCGM0**HJV8yz8wX}UDGHQ
z!watMiSg-F41v@23kChmp+o}J44^$L@!WvFfA$emVjU>Qfd1oXVDr-HnRMawOl~|8
z8CbqDvLRC06eO%$Hn0qxMA3g%`!QYEsr#BoZ`sEPKE`IQ!oP^iwKI{+Co}2Ds^u$J
ztwMZm&84APHg_zX_UVyYRvZ~ZDE|A$)#x1vj*gW5sBi032|Q2`DBZZb4`&_=zo-)M
z&AGN>pW^9&i!(7PXG6B#h8CTR9mD5?Gj4~Zdm*^;PS<N(yIik@&-Zn%i(Qw9fCyqo
z#yk-cVNoaQaUMV;3>3{U8O#^0q79^tI2;k3qDw3ki^O8lEqcTfu@v{(^of4440_B8
zu~MuO17c9D7DHl<SS!|v^<slKNo>S|O`1uD#TIdjI8|&Fr-{?W8RATFmN;9SBescG
ziF3ty;(T#|*e+fzE`+Uhr+AInC0;8o60Z{%i%Z0%A}V%^m>3bGA}$hQOpJ>OkraEd
zz%C`GL|SA-R$L}>A}<PJTI?13#N}eYxI(;MTq)il-YDJ#GxnRsRpM%Kjd+W=R=ibQ
zC$1Mah_{Iw#ZBU7@pf^GxK-RHZWr%>UVc#AA?_6K6nBYtiMz$S#XaIZ;$HDy@jmf>
zaUXnN_lpmT2gHZOgRoOPBt9%Y0^j+^#K*-a#3#k4uz&qw@fq=m_^fzTd`^5`d_g=W
zz6i<tOX6|d2l5s1Rq-|Pb@2`HZ#XgYTjB}vZSkb|j`*(lp7=iYss8{x=PB_Y;)mi#
z@QnUMJS~1IekT4?JR^QCo)y0kzl5#kIq_@p8}VCMlaGiO#P7uKvG?{x@dxoo@h9;T
z&d_*S{6)Nib9MhF{#*QA{Es-wdvO3VQYvXlxAaIH&jK=BC96SN12QOU<vbaZVOb~Z
zaU@KmY?8Q~8CIuO*(MiA9DOPyvQu`+g>sQxEW2e7q{yYRSN6$%xlAsXE96SKN)E_D
zxmpg%HFB+7C)djj@+7%Yo-8-X&2m_7k*CO0<yLu`JYAk4&y;7$v*kH*n|zf#SDq)&
zmlw$G^40P}xkK)huaUdtYvo1qb@F0)iM&)s<!%|1BXU&6WkQb0aXBHAa*v#pDLEz6
zG9$C{GMSTkS&-9muiPgum;2=v^7Zmc`3Ct$`6f9d-z=|^SIcYUTjaI!t@1i~y}Uub
zP2MPPk~hn@%Uk5F@-}(9e1|+B56U~_o${UXF8MBbw|uv}N4`hiE8i>MC*LpclOK@x
z%MZ#2<cH*g@{oK;epr4)epG%;eq4S+eo}r)ep)^(KO-NJpOufw&&kirFUZH_7v-${
zl6+i#S$;)+Renu=U4BFUoBXEymV82fTRthjBfl%ZC%-TMUH(8GmQTt5kUx|^l0TL|
zkx$E?%Ad*ql+VbY%V*^;<S*r~<a6@Z@;CCg@_Bhgz94@me=q+_z9|17|0w??Uy^^8
zFU!BkSL9#i-{gPGzsvuTN98}1OW`6<rIZDB=uuwfQ+`#Ys#T2&sGzEa)jEV@d+JoZ
zYEX@;Nj0k$HD9%=Hnl*ts}2=WovKSMREyMN)vbEe617zIsy@}PmZ{}xg<7drsR1>p
zR;wYkMy*xr)Oxi+ouoFZlhr1*Sq-Z#>J)XV+Nw@dr>is6nd&TcwmL^`Q?F9zs`J$O
z>H@W0y;@zUcBq}|HENf7t-46PPF<`nQJ1Qy+O1-0M2+H{zJwZ6<7z@B)gCpeQff-2
zRYqmiWh$rgs-ULTUbRnMuJ)@d)a%ui>J93R>P>1!y;)tQu2$Emx2S8?Th(>ydUb<(
zo4Qfmq;6JkSGTBJ)oto_^$vAF9aMLyJJmbYUFu!xZuM?;k9v=~SG`xgPrYB=r#_(W
zS07Xls1K<J)gkqe`mp+l`l$Mt`ndXp`lR}l`m}mjeMUW^KC2#8pHrV#Ur>*!FREGf
zCH1)avigeps`{Gxy84FtH}y^RE%k)@wt7;1M}1d)PkmqgyZV7Tte#T;p?;`-q<*Y^
zqMlYiRX<bzsh&|kSI??ns9&mIspr(M)o;{q)${6zdO`h8{a*c-dQtsB{Zai%y`=uE
zURHlmuc*JOzp4LLe^>vbj;eoHE=yR_QkG@8Esy23e3sv;vZ}2b3;r>y)|zL9tguyQ
z)k7d_w3@7DtHqjcwOVaB@TuMEup(Bc)nzTT7U6uSZmY*yVlB0Ltv;(CHh|^U3Tvgc
z${Mf+t<~0$wZ>X&t+Uo!8?2M8jn>K5CTp`bY;Cblu}-zNTBlj3TW45jT4z~jTjyBY
ztXEm*TIX5kTNhZ{tyfzYT05+r)@!U?)@!Yctk+o=TbEduT2X7a6|+XHQ7djGtTAic
zny`}A9&6G{SyNWp%2-+JGAn20t%5ae?X~t<ms|U-E3DUBS6Xkd-e|qa^)_q9db4$v
zb+vVk^%m<|>#f#x*7ep6*4wNbt(&Zyt+!jZShrfYS+`s7unt%Utvjqct#?{?S?{v$
zw%%>sW4*_^*Ltt@KI{F~ebxu8`>hXJ4_F_v9<&Zw4_P0!K4N{;`q*)KVAP$+<ubW|
z{*7j)3#nu};oTR@rIYD#e=Hu4#!`t~!5vGDOi%e@d!vQqR3aA~O=a?lcyu(DEkM5T
zCoY?grIJ@9a=ygn*;H~gS*RI{<qOfVOb%7Wt92xo*q2Pl-Q%&TMBY1*h~@Bwnh6MS
zQT=vdzm>{lvT7<h>cd|&ok2qw$c&A-r!sq!iRx5xBp1u=kD^%tCp?nLOy-NRsp))j
zv?iU{7tJM7iC8|752iDPXfh4CFOe@K;_hs0I-jW1ztPcTZZwsMjwMs6M7+Kt%^r!W
zY$}HDVTksHb+F=<y7DCZjT)w<lIT2I>Aq|_F`cN^zfs*bcP<uBW&*jyXd;awqlHW^
z@5v>yiCCe^Jfi3~Q&5dJKQTQvmP+{ZlgVr}ow&RZ(4lNDu{W8S&U^CdOy-J&l`mwn
z{`~YvAz4VF$N6crcRC%9=J^}gn~WziQG2|6IyM@cs&N7w-|D@Y)bvy$8qe%YJK?G6
zLZZq}M-jgroX)C!i4i#w59BksLUaV9lgduSys>1CO|G)1Ji2OSF*vx=@1zd-P0x_+
zga%jo?6jd8!doE&c)+On$C4w7Tr|EnXNNGWf#TpY;E$mJg<NcOvd)y9ilqUEnEKI7
zE*>^%`6-q%N!5A^qPph+j8}d%m(&29*OTkaT6A<GF*;dmW{n@xLm$c|0EK{-TxQC&
zFqBVa_C=>ML;+K&A43z3<@1Sx-R5jE9R|#%<AC+(K58Fm&SY>jmP)asn18$*9>eTU
z=YUW8V{AsSn4}wP0f;dfdLsF5wF#zTBZ-u61TddY<ntDDe7fpHI_}qjsaQNwWkLz)
z9@Qq8L<{2q9VQ?qp*fjCc_44W8l8w0{6LIY4s)CGj%KE&0IWW!Oa(MJUloT2g_+4G
zvt9<XvA8=i6-%bvnC0oIKyoTe$N(k4Ba}?<1^tMQW>SE3!kRyY??opPsccOuGaA!_
zM-VJU(q2%Ae2mRSOQIvwBO|FqU0K+nQ=W7Nt(bCWCx9~{elCWR(S6CWB)=4}TO8H-
zHBO98ttRn_8jud;fqj$ckS^AkFJQt)fkH&k6s9f@Dwk-eNam+<v1Gv%&gPPs)%|k|
zJIPMrs{FoK79=y3%6kjI<@{70NR=GbW9QwQO-Cma`)hS0K!hiwi5%!xuoTAtO_oL3
zgR;_qx#?^Wlp)V{6Sh*RT6-#-nX6&fox!UC^d}POXnrD=ON2;5qU9;|WuU@W#xwYy
zFPG0p#}bLS0;YP2ts|MstN1i@F<c5dpYg_WqZ62M{|N9jpN)+s+#~FXTLYM$6gGGi
zihecQsUy<`J$DRe!ABAvG!@9>iRWTtg?u##HZTb!Enl$4Qn7KbO<ug?pz%N?cQTE3
zSsX=+U9KY1O{Y;u+ya_qKwgQ;pu!rXP1_qPVovkjq)q!Zts<pMkuqcQt$b{6qACv_
zGYTr6N?927xJ8Os&8a2hn~H(gr{kHx7<#2CYbq0q2lN*)J-Uhh>9jLEr0&suiNvIr
zKd7xHj@k=}XdZE*%DyN7Dw9hV_SeKlM}Z*tZY(zyD39c;?HIYV&Am~k!4T&(x*Jd6
z%OpE{6Ll4-CM_@$D`05ZRbXv{69ew$#)&aw)2URAlgdc76Ui6iwN7|CJ(&h4@sFgY
z6NOBsFi}?wVWQJ%3|b-{E~Ukjc_MDStXLk@2XF%rAJO(761Zq=x{xW4Q+NoL<ztXS
zWznfbJUKm86^|8S(P`3?U_7xmk;-HNI~)lwqCmgAB99r*0<x2rgDx^ep+U#IjDePf
zisZ>@Y`B<GY-`ncE|#5u$db4WGDu-xCN~)!O<=^F2q}|FkB$2G0cNrwD>?7JWPChP
z$T!%yN7N`0lKS#ghmKSv<}w9MLTl}kPP{4)_7C9#y>13+C}1S>(foek(o{4a+aCum
zOf}?_<7x0qHj#F(=<4W5EY~+TwK!uK6(dQZJEm;o+_Hus*B_G%NU58f$WgV^G{x3m
zcWNx9L^&k2<puGg*Y2B|&X4$YS(XawP$5y6fR!&jUJY1|jZRR$OVrrmDR7GYRU?oO
z00fwXfE^sk6bhNC8arlalM^B93fYkfVBi&Ggg^sRNfPj!tF|Ml#8|<}0MSh3oQMWu
zCz3DhPbHj2fbiDZZ<A9dL3is6`9S+HL6{y&uk{sad#3Y+<k<dDMIt{Qu1L{et+ta!
zGO2it9o8Lm-qyTXOTo_DCWXADP+5q?t<kOn!aNCdJVD}|Ps~Y9rcE-)A4y>}i6Kjl
zIz6M75hXXkG&w!f;@1cu1ynCyk(i#If+(0Mwmut!K<{+rvT4XhP9O77LY!Cgg<NtH
zumD+je4^EE20IW>Lh8|ShZe$e$Ir`=iKd+HgOVq5Y<1i}Hl5aQ=WCK_n4Q79KphJ~
zCt{=}$UQnQo};uw2&}bJK!h>LnY2>^0usiC&8fCakZ(D4gZ&pWRZzLc6Vbx{Y@$Y!
za9bqv>j<b^!JC~P!T3%1HHRe{*H&~tns0D`%Z4fg@N-iQtcB;4rFyXsq%F$P!$*Na
zT5AA19*<5yL`o%`X@)eBP2~Ng%mChrLJh|Zqy<70Kt2!jfpCIaY&Mxpq~iHn)>ueh
zVatMPx7K5-48~IQgshO6j@Rg4Thgd9kvQI@GCCbEa#%>NjDkp1n~;{{?U$#snhA40
ziZ4){NJLGJXADRJbT*GDSVbB=W0H}R;vpl6KqJ@(CeI9r-H_M$Q{ruqm8Y^q|LS-m
zKS|MQDmGf{gsBE+_T>?GxY0-kTCx=f3G-4thD1#TIS%xzwx3a|#gGqm$vP)sG*jJ^
zxoIQ@^>fN;auAx6IGqg_Q;ITLsSt!;s5zsPfnuQvs3En*SPpV$E?rZMLj(^NBU-%m
zq+lh;WJBfwY=HumpxRz#r(kS?cF-Og&lIpEx8FSlB{vb!B5)L7mdhmLH71tS55N8;
zzL-J`appb1d5pdnP)F+Q%O@bvLvr##TYv<fi&ts2P`~Z9#s8ujFpihA9<53gg683T
zyvmNE#a1D+ALiV>ZfJF*6IDi(h)zt8RA)2$04~Wi03cvX7*WVTiCnGL+Bm^RM+{I}
z29ZHC3Yoko7AIBrLw$r?8%q~ziy>2@DmD$%f!22ekezZG6Ueu#b<7}|Plw4NO(0!U
zCJ?PEj|`MY2FoL>%OgYOku~L!wTNUOsYapwj6<r=P3I>(uq;4tfi}qpNKK)}4nl2C
z7odOYbTEuuF`R*HVCW7oYIGW6N&#iZLltTJprS!P0CO<3x5}`nR3e=RvVlQ^@KaOG
zLYdLJLoSnwW}&kbny6J2W6?Me9Xz&>@Q>zzm#fx9*ZXv6Fgjo{U}8hj)h4huI%EP+
z;ms>!P@97Km7;bS&`~r3LUJr+Vve|0t@HA*apj=EBqm~LXgpDXT9UU&x@j`Yz+{sM
zIsp)u@#w0`=s;~TH=Dv3t(p@b2o&>ShRgUs(|`ccRZd{Q2@HBnmJ?s?1cscz8Yi&U
z39NGh>jQSZ>tU*x&Ij!nY#y*S?SmCYf3$q!YV0(yim90M_820Ge3hLw5eL0BZ_?%$
zvcm-=f#@d3U<@(2doi1YG^l*JL=}GX6Ivzkn;?MRXNS^I3zJzN#PN8N`~!hx8quWY
z3tkgQd+}vWE%Y;8#fO1CAA<xrl3^g$xLd<`SpNcfvsC+X<X0-0&-jaQ;Dga*l7_!(
z9WWZDMIiOUwmF%P4!ZTzj;*$zL-uox-;}~A*dgc;cw;)72!Tgt4C%D#MG%@~G7;7M
z2*N{^v2D>%<VkCDQouZ-*+eD_@gp7En;Zw+M@JHng%ZfM<J3I8S+Hkle!grh4W(f!
z7IFfRT`<gv{j`~ytY}FFE|qd5hEgdgHlTB9`0Ht(DP*ekFGvCm$t2<+e1;m3bW_wa
z#@%2xIZ7QKqa4k~$9xVN1#R`5W-_#<K`T0m==zlqK*xcR`RM5AzG^#Fi{W-yGd+Jk
zh2}u~*jI%aj!`m6q}^H$59IeHLBZ|D)Yvgis{H`B=om~E#$bb1`KefgSLG8vZ3u@Q
z(`y2QkXk_7bO8-jzCs4H08Gb^52o{!Kdr)anr1x64j@%y`(cAMK?uSH?>?Xr6kI=^
zT5#E4ix9+U;8r3}obn-_pH8Ru*Ow7OSYR@fv~M(<VWb2cbqo_2$yloC*bE@hc;Yze
z;EFII0xlqr6%qk{A2?&aZs?d9RM^|oliL1Ia$Ze77BgT8`IJy;FxH-C6R8@R&Ot_^
z$m$z`;Ry^e0aKzaibEPI2BRa8+k@qCkeae67<fhLOso(p%Z6chUu=K4ECmyunv<mG
zBv~l02Riwr2Xq6l;RZw|QXZIov4En+JTNKXS^>yHP?>2ufhY?s)v>}<&<@eE!Z|ll
z$l+BcG#VRA*k#asn1b^YO?FNO7Sc4x)F|c+8avz^x>9Hk5Nk|$j0`Sc&_;+<bR4Jw
z34tm@oymvDGD@qBe&7DgL?)l5(q&htd(>cxr(#fNSR9LHU;wc{Vk4m4U_yd6(55hD
zn9{sm52$Ry83i!gNf1Z-gYW`C(;MAy7X;L$oxWs~S6mUZE2fRx?g;o^fdm+Bs5LEv
zi7Q<Ini|`1Dhj5H57?ub2j4Ztz!tJFt&iJP7Gjqtt6)sY!w!#RH5JQNPr)8*o0qF>
zQit*KAv+2=GV4Y&MibSb;R!egh=;Xw5XGi}s{qnOdfJ~yUJ3}DsMo^^5gtZ+167bs
zVZzOkt9SuP>_Itz=BCrymEklXKt5k=j#n#0Q!rQ;TWl*eHCJTdTY%__Z)M>Qa@tds
z*y~I<$yXv#V1qpd(A}hGX$!woEgGsZKQMt8U_1j-+HhjUve|r<_G3XgggwOsITBiX
zXq*$b8x$x@p3d4aP%=98AnE~RQ*P}nslo4FNNrJ4-hdO)q?gKDK2Z(vghB%RQ+X#0
z5g%+E3cs;k)s}*A9>5R5S*T(m!*QT^B1lH#%_Sg&<tM7A)6~ICrFNKwYD}}IvutM*
zhN(Dfw!*|#HXkQlH#lf_8Wv2x4>5&wD+|d3jNT5x{ykC)3EP-ALAN29`(YUz)fUol
zJfkg$NB}DXSx*&$l}UbM7l0U83ndT`7uyROMI$KYnsGRF_S-?S^q`}JLh^viMNLiR
zE%r3%SQY_lAjl-?FszgsX6rV_PvE#?VdFIA03jd{leS7_s<0zd+DBwZv}ZK#g-MaR
zQ_xYO&@W7ef*%9w6s)J{K>|kb{pryFe}F5-tXrdF*<1$Qn?ja0LQZF4mrc|ZU{xSo
zkSYYoLABK-0qeSdF9iY;Id~yKA!ueP_~<5KLqnjz)ObL{U|fROS7<&a#Yi9V1r-_4
zy+NVl^b_W%f!y;ea#N5ch=UM`a<SIqW<dx7C1^Wt9)y$W(L}tmPH;Hg(&N<WWSKha
zps4`ZU@b$ZqsHjv)!wqg-e3k&0Q{SA<7f;T+JR1JLB*|gPPuQ?q6M@L%$2waK?z2<
z{o2DBZ~#T4YXElIQl3Az7Z{eAge9O7N=chTbCc2m#}5&umB*i@5K0SJxEOMrC-EBN
zyEFN<Q`0F3^_(<ap4K1;LVOiAlu0N@88~aw5Y{2^`t`5z^xI?{Gzn`2rZSnhk3XPe
zar6UbP^xnnv3$59rWX)|DwEKk`pOjJ_L!4s;2lc^;7_UolPm1kVvEN}(inRR)J`Xn
zO2*^xAIL)X<TwR<T44iTT3P6?9&M=`_2fq}<SDoIzWMe6v@sg+Cj$3@1T?Dx0nx4e
zSTYe0m6FqmX+S27>BWM@Qmv&l5Xt=&HNXepRIKxgFZne*(2dtr3^@%49f_qU8}*6>
z5F{AL(^1$y=^TfFwj$Y1#7cx%&d(p11yf_yI%JI7)r0Y<79KHE0pq1gjsO}8`)d)y
zLIuz~IJ5w^jdS7<mS_{EYs(bNTpo17#&s8>07|KWG6886*Q-Dn@#|j#ksZSP`%GFU
z?FBMvgAz<cs26Tn3{g$FZdXN5em>811|bzQ7%T-LoyWD6#^k_nQ%Ho$vW;!5?wAye
zURfILXTVjM7byP2?F`mV9zC74DBJrS0UurhBk5zd=)QnJJr3E0a;SS`I+-Fz#yrG>
zK&dp$#S`E@5LC#T;Zw35w6+RKS17~;3gh6<XpA+AAVhpEsgTP1;}B7`wGl=On&fH9
zbk<9#csdVrWjf*G5BNaJt=SehI|Lmla}YPY;P?<*6MoP=D8QI@i>j1wEVdWIY#~wW
z1nlnk=vQXHVKM-j18*Y!nTTE>GKvVadREV30Vkp@OK|;yiDt7@^|9o^P!BrbEUq*N
z6z$O@X<mC|tO*z*YbP?(xp59bG%=Me><^aWlt<hXg+exOB_U|k@W;UoxR`0*t61IO
z*6T3>`qwGv14xfgqa#ozh&ymf!)Z+pMxhqQN?4dFW+P;;y-H>FLtfY)F8Qn>4JK2-
zP#;Ja20I?F<y#yf7~u9nh_80y03E*$Q~rZ4KdvQPDEIpbxwW8FG=FLn9PG&rCQfe_
z%o#M$r{l34#ZvkR;WV`!hfO9$J4^9Z<4GYJ>vZUcAT5RcB!E+poUtT?1aOM_AYfxE
zKW?J5#p<OQ;1fo}=M<Zu7nBiA%!Q5ZwCJ!zf81In2*8LBlBU5NhE&Ah{{lIJVhv*}
z<Uy=r$rj+8k0%mSFclCf;4jw_2XeIlZ;`Y6KzG5UQwfjp+QKw!oTCj+fKa3@gs4B?
zR7^D(34GIag-hv<5Ev*X(Vq$eZ982{_E(T`J575i!I44l?Gz18=nhaN9V>ZkO)~A8
z7|S$d*2?laBM)RMea^fw5uZwydSue_T2v@C0xKYLoRna(Qaxj3Q5v#qi@7wwKoTv+
zwNz%2?E3f<g{`W#NQ6gXdHCJ}#W;y_(ZWtRhRm47IBDYKCZEXT7v#bVv3w8AYvDYs
zUQkc5rUJ{PH03u7Xtedouh-I07pO0W=sC5?T&P0Gr2MVum%twwm@$qd;y&2qL9lcA
zs=@>uj9L#u8iQC#qXT(_bh;{JM%{%BJm;|c!)pv4Oq!|*ZQdAoNc@ow93m6qO1e^v
z@@aK357JGM<_C%)Y77DN2=WZL0sJ00tnz|+2uKXHG*%;MCJ>W#G6s~+5iSMOHYBXy
zOT%)5FdAu!&~-#{3z9UFNq9_QP<<ePsqr!}4m+8eWo*h6<{A_f$lEMBSPGJWV(C~e
z9vz2imVsIwgU1FwS}Nu+>?0Q=1n-tb5?sF@v~7As%j#Ivf!=|HxD%XZ_{}smvL)6U
z66+!*%q29R^J=Xa_FG3gwn&q0<%r^cwK3Z0WfMU=T!28ymg*IC_*|3?QD|}#m<zaB
zZVHxqP*cMkL0qd*TFa)8A-vS|(Po&X!ANtO0%6@M`V-RTAvmc=oK`j-OU(#xA+z*~
z#su33<S%5VVP*t%sRBGhu1G>13fe9UqnBc79oqyG1lGQRv%(OdEp>KSTj#2DBemrc
z>tR7)_a$htf}RZQ8Ql*fl{Z7bIz;6x)|GHY7#4Y;8T%kzr?H5UYa_ApjT#jxKoDSr
z5rJx#ff$m5=L|l$ac$jX^&X?ARckMo7I1R35rYY5Qq_s+92Vg1pTf$5>T%9^I!`R9
zw@tMb$X{bqPR-Rrwq8n@hvgPSAGB9HVX7cW<h-#_U@Gi$P_r~G$4b}&7PkgCGq8!m
z`2fFq%}9dlJIrv_I_n_q1rv~lur4xFg;hi_5dbDJ*g80tgRw21+7GuvK`$#Z{)!;|
zI^d|V;B&ch^>`*VhCzYRGU0`29t8w@Cuu28q#?*>QZ-spv`uJGfZ+<m+R#KIz#vHg
z^YaQ0d21XNO1()+{No?k8OdE!IG&HEBO@lUqpk;OOVkqx^Q;f<v0|Aj?7Z%G2@Gi(
z`?2*&D0rVa0$><Jnkgrc$6SFFG4XfoTV3b6(D!Wcg{FJkZs~Yn=`(%lW!qMKVpV$m
z%_pxMt{Q&u>_^YJ=lt-c7mW;z+&6aH#0?XV;9W~TixV(H`i#OhoG%l^UnAPsirreb
zxt_!>u3_w2`iS@?_TOy9UY2{XN90B9!`Os<57%O^!4v8Qtk&<xiun}QtUqXd!+Opw
z-R<rT?w#(O`)2n8?#JCvyI=Oq!!6HSJr{clo*O;)c^>tA*Ylj`@7}O?iFcECyLZfc
zh4*If`@9c(zv+G2`+M(EU(na?TkhNBJJ)xyZ^Bpbz0r5Aybotb@Rs8i{I=rk)PO63
zU$!`)Q$qM%h`$hat_M+y`CW*2y2A2<=+lYbZ9y&dsJ9LO=IfCN>pJEkJtXhPxBj<p
zwxWg()Pb+d5B-z(I#BbxvVN4m_W`{Bzx`eqr$uz)i$VFoKY45J*Gg}_ALq)P=v#GY
zpBdXAdQy)VM`MpJ&#_vBkqYTiTcATsX+>zBF2UY~<*yK)hktFDhgLmLwR&b)BS$xY
zcQ{k5yVLa(oPxpDEI^zkc%xApzQJ;w@gUwIbS^~AFQY{f{A<Ls73ISCB^-3(OC9=M
z!ZhBJf5z_c<CWy7l$U-Q<xceVvM*ylE}?_9m-Q4e_OTN!*RUynqsy>V1iflQp9s4g
zN4^(C8$0DoDDy8`Cf|oL|7wPQVz=aY<JN{=)yY5Me2C+H$;@yVcVs&G5%hs$&#_#f
zTi)qvlS$-v;NOX6{b}T%XzV~s)aQ!%#1GcZQQ>%T#KQ8I$p0sBB7d#tz8sFq#$LWj
zk7ucM1`-Kb9e~(B;(5Hjb2dWi^Joj<&p;TVwhN^Vj7=i-c)jj&EfCis&p-jj2jSva
zxgs)E@m?MBJ2afLHNQX}dm6Ue#Bn3cbmFXyA6DcA&~DBT=}AOh3Eoj^3tHgRi2b!S
zdK?L(4XA-QL)__hjUlJJjXcpK%s=^G$cdm1QX$R;C<vaQ`||h5X~jQ-v23S}Tk>_q
z+IZ_DVc6jm|ACxRpUT=Tp9621JFjh^SwFJV%_tCS8~GjCp?MLpCWyb{n2Kto6IPr)
z5>p8`e*m{UW^UyKQcGh+ii7(U)%%cIYGorJj#TGkNIeFd<%@Q`rhWDZh#QgGrSXxR
zl4FK?#o0(LwauYKd-Unth9cP|<q@PgGfH{^%*1&PJRjTinC&5U{J0`bLx6m~;@u$M
zk$*uNaft9m`WJRB5;q{tj7Sla<qMDy%D>x+9qK+%(c-wfI@Db_UBKXhp<|ywyaW9s
zMFzcfeH3xi&RTqLvHTt4z#o($Uk$mUh?gipw|W%udH4e9KA>A%uTS!9LLBXqzr{%w
zPK)|=xE49{<v1i7QpyNUL*T7?A$gU4*5cHrtm|&qS6nY(KlnQAzn;Oq=f}mf*gf2Z
z9lRshMSCxHwf<1Pgngq+)G64Zxfi=9AH>eapQ%@{hp->}`*vgR-8--=?rYZ1te4$2
z?k@K__qpy7_g?pP?z`L%xgT>s30dVg?w4>?!q3oiLO5gx^syT~;aG4k>d_xV$Lr8$
zj@d6TkE9#qCzK&NH6D^$5t9s#k!SqsxaEut<BT<Hp4Q(a6(;RB-y$_LZJ~700lxi9
zlsfjSd?SJq2D(G8-=dBVq}8KMow_xI4pIrWsF9;CIIn~ua@r2$kzeth9^C_CU5os0
z^q+le#+&B*b2y&sIehbYUkc(I7#H;z@(pd}n3z_OzL3}8yP_HS<*gvp5gRB$kP`m-
z1oaa_(K~rH@{b)4ex3bnbNw9ol`~4%<eBoCU!aHleq|5KTl5><?_)=j)Nqj?y((`#
z-z4-vq7*kFk1gZaI(&<e!*Tp6(kffUzUrQfFz9M|orDFBU5B^>Ima98Dlu=)JDfFr
zORuOsCWqfa8^pmA)Q0POPLSgh?>Iq@SKNMr9FMr|1UYVT>j`o!`6zO_Ff#_8OIX+`
z?$qyaz6e9)--OJ1S6F=kIqaF?hkV-nRx~1~d_+kjyY+bgCvqz1!NzXUFz21(xQqHZ
zIfim_er^W8a%SAoRyfjiNH70BXP^#kcn0Z}UuVlgq6xgEygcD)fe6e=CB(GIziaM9
z`I6jVfqoCCBk0r);2U|7BTql8xk)9(`XtgcM?={KIuw=*!6(A_*9w|Im<-FmA*BLK
z#M?D@G0<&z+LcJDz;tm7xPdbspo;Ppa0Bx#%2MwH2k6jmG|2x!m^xbvu=Z1$Gf>uO
zmVX6jFyGw?-taS=4zdm>Z`^>>Eq)ATdJ#@;*n^W39>6IEzr=|G5$LBEW2gO{*!lil
zXsABy5Ff^_?aQ&3`XSK&XRN=u>)p%Ur@Jo&^}pGDKWP6`?%#Vno_5a~&o<A9=W@@@
zT2Fn_^Q`A(92eW}9q^v+-R0fmy%L)0z1~lFzv6w$`<(Y>pXCetBG6Sg`p)!S=-cf}
z`7ZZe<-5^$(08xzL2*CMWg{P;yg*E(R6~wwBy4J`l-doAqn`RS%5$uF19U)Bq{Vnv
z=oUJi)C1H@HUZ*UFCocjNBkNo{9J1G5K3xDK^`?sQeg4`r*_t3xXtZ=NyGhEOF8yc
zv_rfH<%;wh^-$8Cr{|IKz3DNymwZs?wBnZ%0<qMQ90O=8I1s)9=u!f|4SJl&oeUgP
z(jnv(VOP8cDTHv=!cto9vA<5JA^zQ~pEhT)^PH9~((g8-Rfa>KuvO|hw2L&FW5gEO
z?L^<ma~6nm^>}hjSb{v18ZfCXS}p&9UydUAH`~c~I4Z>FZXDOcx!?`2W)8^X7C7Ug
z-j5h<5yV6Gl{~_cXd~jah*75@Mjs<(s4pRp-{M$sPDrmfOCh9mtDE$@><y*TfX*?k
zB#qpwbB!Fxmh!trs1hGRj8IH{-@qUBSJU#a_yAH*0O>M|JVNzC{XVI6k&3z=DZXc5
zi5Oyf7gldYo-+fKyGSefJ<iD;#rI4b9en}tAU>+UU|P!YCf6oxbIh;Ob(%hMY}s!k
z<A!jy6<cQBvneRzLF9M3$Zt&lobM5mG0x%w<Zvzx92kt&Fd*(mPEf;+8EwvnspH{d
zZBDN_lM7tkVp~ORj=TW;mKo$!j-t&!aCE=PF;t`M9d*6tECz;+&O*7WMJ_<P(^Ebv
z<pG~?dKfv>G4w>-qw6Wb4c|d2@$O^NwM+rqVKv&yIxA_&XLS7rGYt(SUUrJ}kzN|Z
zqNXZ7j&!G;5q%~lXFsegoo-}Ug9ipG-i-9)P&qXVK3KvE14Cig)kt+plj~_sTu@VF
z?+7`Bbk0yjyb7tM782VDY46fCl*Wa%b-H*js#CAYBMo?q=DKEl2-h6H4<pU#AE5)>
z7B?Q1(kz4nt{V9hJm>HQ^)94on8ACtTq@2(TB((WAHNA{rPdgV`c}=yIRgfoBjWu!
z#q^r8e;Bt}_=;_-LMwdY!Lk$|QoQ1UvJ~E{;T8{-rD**{T!a)u&8P{G^1Kl#PKz{c
z68AwuD5rSW+HsB^S}CWr#9*w3E$WLpMmr6q_s=56kuwl{m(H_iL>|>KN`inDagHrf
z5K}*`!+JK9)M0HWl4*qPk(QU)<w2#zhaiENnYsoc4ckg-sf0Y|4G8HT$uH;-q*Ad<
zhe)Sa;QYO7T@Sgw?|KDimkmR5xd!KTeN8-vQ?I&l@>E>wuaDterN7~vqm?*^XgAIZ
zx&`O?JchG(o`<2o5$D=$!dWzNoF#J&wAlAqAG02_p0J*REb|-rX|#aUnxjE%Fj}Co
zZq_1htblL%7p2(#PT7Z2<kNHI2}3<7TYwtM-V>BE)Urier@G}*-I8PLqFuh$F7q(T
z{PVWA%C~?@lY<!<qi9V-TgZpNkI^1O^^V_az=>RPf)bQr$XmNn!dNlq)<H=MuqFFX
zkZ)|*&2sSx@>wh4;S%JN2U2e*-txOfPSdnYT&(jWD68ds<So(=ZX}C{ya=s0-po*T
zZk0VJ$ThU?2y&18wyh7y?i0MLHFkL|a!)uz>a)6LQb{9^8GEPsR)@G2y<nIUF13Eb
zBJCsWJ8wJXSU7J+&|hjB3)KCf;-s&n7nCK*X-O?#hji9b8X@M)Q@m%M>GM!4Bs09F
zeOT%P74;BSXuk=IQ;<$brM6_GA4*4Ixd?Rom=PA&Ahpy7zCq}{PM2l7%sdh=7O49)
z<u@b2FBs|Xc{`O77d0cTr^>g2F0;La2#ykHvAhOploM$?vo%n~JM=V^<Ew;3aC23q
zX)0x3hm(8~6jRrM_ghfI3p!pJ`RfsH#M><xAMgP22~bAL+d<NL`50oPzl5+>*Q1EB
z^^~_F%A-r#^Q83YpePd-mul(<nM3(tr6Xnd9iEi&+Ht2{+I0}J-H%;=!-<U>aB|{a
zoMiYB@lBi;_&1#C*Ne0FcH*48t8lK}gRtB^iSy*1moGzi3qnTadHiNx2sMsmXC%@R
z9pZdqq~*ua&hql46XY6%hVBwHh|eHpG2)cEsjsqTYC?oFLZ(|h{4Yvak~#tB%g8Q4
zgtZKaUu~ffpT%?THz-#)UwGu7=38iE@R;{G%Y;<6@5H&5cob#kej9U(p0>%0kw>mT
zd?NKBRdBShrARZ{1?f?Rjwf@#6xM01kwzMV=BR3Px~b366kxfP`8nlDVKCC_Q%L8S
zla53%E7YYRW6A4*8Kv<w7Q$|Idqt|Tkao(LPBpq^vGwwXin4|ShQV?DwzNo@PLF~L
zB8~o1&K|K96h?d+cvLxNEA14cm7|9Wa*i%zWTUX_21o@4Q(DxG2sfcdXi2IXVW%%k
z5Vpq@w})Ex*k9y&*!6qSjq^mW!Wo@U!E(_nx8p>}_u<sV7s2^A+Ftc1)gQs}*Ws+W
zD{$J|BjEBcxx?<2uvVvFtG>@Y3p@2Io_fzp*eb?6GoHIVpYlBE`HffN2AS2~ZQeMn
z6z}jp1RKTE-j{q;uurV<ZS#%5GI1lU5})#Y!}qlBMVw+>@9*}n^Pla%*q_D)>$myu
z^FQqWhX05D=ln0>YRX{M{HjG&%f&K`7iW=uCj4*&IgW&<O5RR5Vm@cNMfCq)c#oD5
z^InG_%_{#MF_bcv!8yi=*_7NUm(f~u;?jJtymU7(sk}62*=W*)J)`%qe<9H~rzA^}
zTGQT04n|n+LYW59GpA&!&NAzUu5<pJlBC9z+^G@Uz7fRhpam|*@7#78YVTO*I1crq
zWlp*B8c91jW}pDL-NX3IbPQiB%R{VWxtY}Ev^@stoN@MnwQ_DbmTXl>5GTvq$8>`q
zrX2G-al$fXNvAf$$BqBDysox6b(Qy_sM)yop_GyF4echCHy&?#XY4T-PiUy54raJA
zX&hsu8r1*5(F7s)L~ZALfNepDD=$;tCUQd7vwTh&`V+MMNw<cx6%y3E%j+p0Co>~0
zsK<<|gI~wgqe3V_Uc;J5bIG|X=rl^uR#jPo6qet|NXTjA8~8R-lCkQNPcCMS;%j)@
zjFcHSY7Gv}U!)EoY#^0hBw}h%<AK&I)Z_Zya_>?(bX|_zx$|aL=>USnxAnWF`C<-X
z)>!htj3*(5{iSz_^)zGbiTR!KZFmyu%$ReAjYnHcIpV8$r@Tish})c4Y`61RuaT~c
zb~~K0&apMLW=@{qnwRp~<Xo7MnTPYznOEK#N-%75Nc<b}%G*Ls-`IsBsu6h{TUyl&
z1uR;L#HqT@66GZf(@qFkS1g6}T;3MiOekB26+IS}HQGLK(LU!Ly-J6z6yHEjc?)S{
zWebC1$((m63+Opg^nWl!Ok_U|He&8oU0DuGaOCxj;(&5m#0<qX6wY3!A-)9(p}Ym;
zImDsQ%}KX0UEQtIjeN~DL&OZsn&?G(`M8kNga3;iI=wVMMgwbA??8GvY*VU4p9C#r
z<@IQJ1$`Yry3+&p+Sbv;tC7z36Q;^+fU*Gj<XFt3EW_IE-s2q)4nzJXQp?An0pIHo
z-$5!{#n~rTYJVy$3Uh0)TuRdNM6PT`*dBH9T}VEq8Z_1lN=T$MMf)z^DIl#xug!|3
ziHbDN$RgZNK+98k>wP#czx>@0Xi<lF9I{k-f2zcn=A`&>H&bO9pZMaO6t4;+g;c;$
zIcOZ>3v<eN#OLRvxW(t@q*&s6NGTtORy7YP6?9X4WzJhQ;>&YVs$E}zL|E2;xeT*c
zgduSuVns?Is9BZ9;BAl#`6m7DPa@3L0E)!-AtM@`{k#qVTU0GVw9ZjW>&JS`-LA`B
zH@ohGR`mqdC%h<JI3GWP^L>YLUi~hdL7&CB^4GflOJf`L8OIt#KC_skr@o?Lnlgj&
z9FUWOQ>#xSoz%x@tj4NBF7qcq6Vtd7hZ;QjMZ0RzspLPTS&MXkn&m9!-^fHvrIj({
z`aROPnu8LC-3Hv@L8~IyjF4Vp4UG7Aox(Sar=n9lh4C$wX;n>#Q=VYGTp#k7juXbU
zEFif0#L!bhe8}}8;^w=CLp5t@oH=3jZiG4Gv^)&zFl~hq@dLXIsEGWc4(lFbeJ(wZ
zkbKo0&~7O~G~ud+?YKGO7FhJY2H))~@J4pwj)AR^>*F}<{~Dav|32utkK$bYC&dpH
zam?6fi!g$okakczLQ7OTxEsGl>x6+%jv4c5%ji~|gJbg@`+)I&bA?BF{krG4k%Kgv
zd4V#0h;`nK9F75Ji**;*E{YhSft;4!9W(0GlIMvZVcy9Xj0VQJrUbfG<9)@~#g8$g
zrljp{!!3rKLBlKYmTE)1%pWFp+xhl4)mITWwoJ}(t!uTNZ>W5|_z%R*7Yq#j5Lo8K
zTjg@hpXP<G78OCb6aN4YYCh&t_gUSBkWN(#^!ySAF2Ne?kjAx(;U&7?^-bt%Va)4=
zm{ViB_@y|CI|x?d{(v)aC%~oZPHm~6{6TCXpC%8YmP9UKtj=YgT=7$+Q_3?QK~i)h
zu@JI3$66y7184;`RMyBG_RFyn8JRKcx=EKI6q_<7xss4$pq^e(LJ;W<<rGJzDcQ0j
zV)dMFvHg_6Sl+RsEkaM2dZKT&x*os`mA>S7C0Pf#OGFGJzr05V@>xII-6~e;)|Gn9
z)cN?Q^%q5{>qOtb5qafbraWu+Sakuy>2;<yS_$E6bWZ7erIm&&=j1r6zHn0;u%f&V
z#5H)})k45mE51n#BHd`!xV#v5?-(lq=2~?E#&k=>&p<;Wc8gBeHnPvVp23|d-MD`w
zj#X`U!7h-{@X@X7A$K69<dYc3u^==M-#SGcz2izxQjg=6bTpOGe^JhsbHzAnn_HhP
zJ8D?f+(?X}MRQA)w-0<lOdO{Sbr@1*V}ELv`!M@+>(TIlkz`+6#2)l)ZVA#&L-$)n
z3N_Bnrw&B83X3GhYHlw17+NnTHTA0;Il^nL7y<37cuRc^w1yaA@VON?*W3#&;qPJ*
z?uto+GCYhsQI6s+l2hQvxfb_ud__Ksn=RU5&AAAx5O2dx4Bu4G;huwb+&!=pcLLmi
z)A}E?erWv>YY-Q?H@Yu`Rp%P_-R@7hpK$-u{Wt8D==N;%Z1+rfuJqi3RVa^mp71>5
zdCBX8b!WME*n7Trw>RsZ@!o=UC?D~D9(yL9@;-}Q6R-FrE?jJsYeAKag^yah8Bt;{
zS7P#)qi$$8DMMIp0xXrvlm8eE)U10nSVEeB_vA?$pE#4O+w7WPeNI_wFr1-3;;+)m
z$S;xul&y4>XJRf&UpAImQar=E%ojIQl;Ii{QaJMdB877Op;*SIStwzAV8xY?>K>h<
z@di*uiQIU`$~j*eWeB6}1^FUpoRCOLL>$9vGkLBa(Q^CgoV6Wtuvmg)#8w)~swip8
zvnomybr;<)y<0$@T~UG~YI;l=K~r+n!4^3)QL2Ml0$MNEpj;`R<;c0IZ=eL)Se|R>
zG<gi+yjyOo$YqVxPwgEHa$`j<zeYZ0>%t<7-10H3^kHqSc$ajS?IKT{hwrXO3rlV0
zs#-#mmJ_7Wg-iYAd)&POX+pN+Jxz@<PNbGB#W}-R$;}mYl4{eEz_+_yC+qi0aq<#c
z>Ir!pkIAzk0ITu8RIIZEZ<O&kcB_hcX7;I#IUI?WDncY>kU2cb^>$Ds)Av%HhL88#
zIb0D|!N&k4&O5A+5KkQEh>(v3(L;^_@ujHgyPneTkdGLcF(b^DZ>gxw$fl$i5pf!F
zSg*lfVxO6ZFl1nA$&8h^x>cTDOlQjo9X<FLl<O+ejby`BxF;dKd}cUK&>+Omk<PEO
z@5BOIVwNXY)I%7g%+(_4*(*Vip(7Xrd0Iuf;rHl|dI0IBXT%L!D>a2sAB3#yq!aV>
zDp5I%)Kcw)T3C$bP(>=o0$f>SkXnuh8pb78k(6p64TJqno>7r%TQ%i5kgQ8(Nj<}I
zbw!$9pC$(?(l`_I<(rX4OeZyUtN_2&<+x&&5Yb7^O>zGLzhPjD(rByrO+`6vg%SU$
z>!Yp5%sorqj5J3srAHUKgm^}CPcvV722~GINU`dHS;nfj4E(eWBg)ak$ckU<xYI7b
zc&;Mu)3?BzwV!OaR~|yX^W9F@Lh*}=@*cSYx<C>3)e;>umW~Et){)p0UOW#OjTnXA
zh+pckZiQNmu#KnUS*<fr@|#77Fwu;g{mg3A1!}2I!#Jy6oyL*u!OeDiU3WoCeFit;
zErMPchrj1u+`9IZcoFxTb>R-O^W_+B0lN(=RzHthwtgvJ!M#^)xVLH}?w{HTFHj1S
z!wl|%x()X|EyvgzYR6s>3pldII#0_2>C$3x1ZkWr&Sr^garO;%kV@Z<GUOzLCC&(`
z!yK9dx(7;()JjTe)+v}d`V&f!e-h&z&PMntt+hY-1a%att2k4)niPe#aP2}R{osx<
z=Nm@Sb~GxEM5lNGZ%}t8eIj);qvF(Y5^|5%2Mv*~@992MB62LQoK-!Ft`BLt$x)p%
zE@GXgjvPOJuS`FY5uM|-#b{_+ACz3xX4+x!*lFJs(j7h%(7j;2ZIFnKO^@H;tdaNi
zh_y)NOc||_w21KfJEWHCFt(ns>v5zW11su7x?Urjadyq_%*!>NbL7|texXyW*J-Ag
z<fWKlu|=nGuGkktQ*T4s97vM1lMzn|o1{!3>`10<k9H%+n!8@(E>lcBw<2!p(X>Ha
z25dD{9{gG4Z2Tm=ZqQ*p%JOj=|3a=WA*4MQ@+O^z-pIWO5x%?RdC;~%gXN8QViz5D
z5h>lC`M}`a@Z)~W^#j+-xLI;FZh1^&HTnJ6Q}i>ePpHT3foICyxU=sD+_m=*?#%lJ
z?!o&8W`KN#HX!z@1hb?|q_Uh3jGFxVKP_33w!-qOD1YKQ?NO6o!&siUlpaZZlTdl0
z)=|rTQO^oTkW#d<QBW2!cKEQo8L7la+oFd)*_s;r<l2l})^8*&4c|y7hhdLDsb`e>
zmhsM(t-Fw4(Rs&hnfwIuD!*y8EZTVgfIM32%G*mQr0@AJx=u<llxXR<<@l2;bqHEO
z**|R?M3||R=cx^E0<O}6ZKMgNkV~>3f7UtWy{F{LnSSY*9BMrL<}1Ksqm#32NMmRx
z<iiUz7M5DB*BZF~UE>T}Z)A-ytOqMJ&X~E-)Ja~VF@iWu3Lkd80~ldeO<;$M_`T+4
zPT%%xnCIIs1G4J?0pxtkal>ldb*1YTwGuPV-Wv-ce<|y-#q4v3d<dn<(~VV%p)zWu
zR-t#5Z*%k)sdplsdlyS|S!)K0a)?u&;{h+ZdJw(g%-GT~Zh8Hf>seUUPQksX@5B9~
zf5-aAbAe~~VCTjQxa)Bp?p3@7>mHv_FXGleu7J1__uS1|&$wOgh<h0OHLk>7y+^QH
z<0VfR>mSbbq&(L_N1pXO?Rmvp?_G)24`a}c@A7`i`=s|b(1_>z2Cy^ZQk)@hgT40Q
z$G#VQNB#5sUH;Ynt^N!Baeu*omH!t1J^lx={^4=|ll~uL1;iiyf3Nb$X7F=|e^Tz%
zz9fti$B)oMosQ7Pr}mS}7HHr9`VC|E2Su0j|5xRUYvAPD?ed%p&L`Iv5ras1xh{ci
z<1A1H!yK!1C{NnXlMOi2gi2!pAlEavPubMwP3k?!X9)+!489YKDpR%erq<w#PVJ8E
z#*Bv1!Z=2RE5h?Bn(i1H$y{niMlYGeYjIz&Iemcq=AV_@f^rVkom<lIVSYa({-Vo~
zno{#)Z;6505`>VEPEk_utN_N!%N6-BQjKT2MQui{#3J_GzyfuAjweqgAh#op*@|Dz
z^Rc}sYn%KmN;y)D(S`|86?z`VFH@|GSZCyVBh9eioC8oyb+WGOU(B6atKq<b2+lmm
zgAiTpg}l@*LtCZs-?DcVV=P|Q^IADtj+NN(#}=#sB<C{rXJ-$eLsxAXK=2%dxuebz
zGX73V9bt?mA?2TpCE(oHp-u?rfYnlo<H9OhLatufBc~<09cl;|SAzGVl>sB8eI%~;
zBcBjM2r%@Kk}==Vd`8|<mOpnjGr8>jW%;G|&D=mwksn4qtj){|aj=YHyFP&2W4}%*
zk~V=S^t%oxoC9O>I~6t0$yK!ZIQ=#nAfdsYNkw_v$t~*EdQ2j~i2IOxY^xmE;)BSg
z49Y$l4+Uu%u@W3s{2kA^bjL_rAy=Iu|Kht`LB)A9oYkyjZIz!vE$q9IqbWBVOC2<P
zwH{%o#fA>>ZEe9(2a)H%nxT*EDQjsKd0mH@Uqd-)!=S}EB=(l&mFCXKxwJ)_I!pL#
z>-plp?e|Q-nP0R>$y>|nbSN?LwFQ<)?!k4&wNn1%9^6t6A=Bu5A;~?s|EIBYkFl$$
z<M^4GcDs*z*X^}5v|X}yu`Y&SLn41P6%nEGD5XlFr9dOKw6)ZV1qzfxq4H2H3C0Al
zf)r3f41|Z3)W$TXK_j*eh}Epx2%>)&LkKaIKL7#!e&#oG@407hD<oyl`OV|Z{O0$b
z-^^{7<TXG>ZtN%_-k0jRcT<Ut4-QGOHT|kPnI>>$dsb$}jCQm!c^7R)_l~RwdTy^W
zp59D%XR@VO6`QH;shlZWDz65s9qpGePtd%6j0R6-(7%+RS!&r>7FD3pTJhvs(4Wx-
zaV2R-zgsp;MH;*71j{%}>*^Or&juc_^*_$NA1yihgV#k#{$)XO=5M4J_isTu<V*Nz
zf|w_&J&*9I2*KAwQ)6$j_X&=fr>NA;(>r^jhVx_G;aHBQLw+~HX?iFBz*>y>gR%;9
z{ULuqZ!LS+zZl<=JPD`E$1-U#TJW57t9x6|Y0do8dQQ5}U#Mr+2Dy4)sw2tg_vzVo
zIh+1>(MA<7hZo`#b9_=yjJBzf5@}sk+#xBkS2v)1Pv0S<(;`xxQX0OG>#T3G_S>bc
zk5(5wk0-xDxWY!_AAFLj2^lSGJe-lr(W+*t8)w^jgpu$$wW6)<anKjNVoQxED+5nH
z_uYzb6lV(q8Gcf6sZI+#r)W^WJk4dw(%9jCAwTmy{g(Y)?IUlHm-(1}kA2*~sb4`K
z)-RTyrJbXz^vmG)>Q}vY>6f|3_3PSiX$NUYdq^i~7wH`RBK3+%>-;Mk2{g{Au?5-7
zfUls-6!Fs7z$F~=YvHyPK~;^TSXS=8X}z<bSd)KY_9J^f>=LpXoUET6c1ix0`%OC#
z(PG3RqF&@Q|Ek`OG~SjKqrRpVS4aG&Xs>#(oH%rQ>ug|nD%OEUY>%|H^QT%duR<Qq
zEAiXiF=T%865-AKSg0M|H9cb7cD%-Hv5amw7N!HIVzJiDkHUkp5kXxe?gN#UaRctC
zW7fpyV`<)@hc=OJW+%-o$Y3nDEw9&tSO(wOXwBvZ6U{&_AKn1iLaUBsn?L+aB{o-T
z8aG5f^)9xtJvHD+0oyCX+cV1+O9Q#WiS35JNbpP@^p&KQuCc=>ZubPQDqGf*uQn0S
z3nSG06|;N6+i#+$45y;Aqa`32LBy%ey~>v>uBgDsfO}3b?zTZ*5w9e=yIL}}+_txP
zN)U7+lm#_U4d0P0LN2C^*>F~TM<zsEg)`$j7)Ljb`WxfBo!Jz{hi4>7k&A|_^P*hM
z^V*l*=xJ29*yI?OtYvpYYzOCRrNaDJhHPQw&rj`4V!a@lVzU@4&W#c_(sejH$<>*~
zFQZ(=;=WEd<;<6)dD^|E(>C+vVL$H2!z9U;72%$+M`sGBPdY=tQU8E$LpVHnST`YT
zkYD|)$#3XX!Kpo~dbagE+w%u`)Wh0uUNz+|oh5jA%HiH=I!Cay_j=uJu&Z~x_pQE0
z-^qQ8`quZ|rIQ1D`~K4RcK@{gllvF;ukF91|B?PZ{p0=r8t5OGJ#gy4;(@gTw+!4n
z@WjC0f$@PiYQA=CZBA`|ZCP!7ZENk`+K+2{YA@IRUVE$FQ$MzTVtrnHLH*MDy87n&
zw)*z^<Mrq2&(~k7AFBVOez^YLV9(&t;LO3<gL4Mw4xa7`-J%4wVgq6ivqnT`h#w<&
zj3Ybm;<1QiIhF<8L4i~B&-PXRtR9WO;_Xz4)6BzxF2O!DWWIA#l!U&XT6_@YZOfO6
zRK}SXi<Oywf<DJ`z)znUF6`0*Ix-8$#&20JL?F=di$8RWGzWhuwlFI<SrVE<do~m1
z;crn3>;~q^NbxJ7VB^Uy`6zyiVhtarMUn|&VT?_5VhY@&vEW;a(zSTdIgRA#D~<1l
z)YFQE3-tezPaD^vj8&8LtT2@GkZL2fn4Rs6Vn5hyp(Esbd++PPQf+oaz($tE6KcE3
zs$Cv;+XHS<o4%T?aX+rQ6Y1^<)E1URt=@^l@+_NCG9yuj!+Blm5;aAtn#Z0kpr&ry
zBG=#WQJrDdSjo~YuolYmo5+FfdG09nTg7v=-*;5k_IAKW3q$d#vLen)0vvq|oMe$)
z@E5CQlIrS-RYqDEKX=mke^j&k_tDd)<IG}w_lPwsa|&NkO=8wicQsl9r?;YFj)?Bj
zXA~`%@P(+uIw=4TlD4H=zrsab>Xj*lY;*aF{!g)9RZ>il58-^#RF%Ib#3REh)rmW-
z8WA=Ez8NwZF2_>BavF7ZLar^tyK%^q8XxNh=HNuu%IE@%6JBkEFRD&;OuSo^GRk=W
z$5^L2Kg!U;(DaP3v`at9rc6>`R@kjNrAVtrh4ev>V^^5pe~s$E*X%f8&1U{8MQsAP
za%JrRwup$)lUYBbT4Q*sbB#?RB#TINC0ksr%5>F1Px=mfe{C()`5kuBPnEO>16@wJ
zX(Y|!J(ZX7Ug3hk33u$SR<4j*A<sFnt<k$4y_G4ty~;+(m#DltOIF!1*YNkNFLW{U
zV!g4X;a+Uq(c&UKn<UQmIAc!FVVO#+eZ!kEiW@HOTAIB6aA}v)mbc+2YZT1HG6ZdG
zXjoBFxLjpNpb@uaLYd{$u`XXynOTcqFk8XIWCp{2m4T6z{iHY60eipcf1tM`tZ|^v
z>gpuVOc@LH*dNq`R5p7a+%=V@iZ{s(v8;Wxe)WI6%w|}mvMN@S^!E>_Y)rM-?KN6$
zW#cJ3qKcPRyvg^-7b)WtsS)*;e^4d4#|~e~|4k*xV<ZcsZe_C<PpG6!vn^SnHry&|
zZP@+KmOq{fYlc>6d8K~4pzI;zF<4vyuVJ;Ind`tU_&Fi$i*;EcIM{rIhPx?3`5xKm
ziMEOZg7U5QUJ~DZUr?w!+ddXed05c=HCB$AO}|o5xYnd`_QOJgGFH<Al9hgspnMs5
z#N6U&+*t|A$cRMQ9_xkjq*!-kR=lTI-+E23j5Av6EQ$AmVLz}&=KIeMYXr+9&03DT
zX`oqX3~8&_i~F=-wm%4_%<f()m`Ok87wuhlWAxz64goUeHVe`r3&L_ijJ|Vr(>a3J
z3Ubq$a2yuIeoAsQTp^2}S*BCm#Ui~EgR&jYhQCkm=GB9%sqSXImpIyO(mQLB?`E8$
zce5_cv;Mt&IK}{n&L}R>8(LymIWLWHZuIZyS2g?<dSfKuZ7h6Q9-d*k={cK{G@PFl
z;V~aAtc=f4U88t0dVyL&|5rW5mf<oz8Adq8g07tEzpp3bq5XQYlQ4_*6g8{ojEkr|
z9ME6Z<&(v7<@oJXzVn-|<~|{xa)rFg?Yei*ZgLpX{_Q;N;I53h5<C1(Wj~Mmf9f8a
zTFPfysGIoKDVymw`JE4h$CM4dJ3Oz9=!1TZ@L};5d?(~(mZMmc*n74w#axo!4A%)$
z%ob~$Iz*Q`HTPAj&H8b}0Njc+MgBnZv)rvJ%~A+jH!BkPDf$iTQmIAHh@E7<gx9Z8
zea4$(ldK3M5Y_t~Dl;!J=M>R0VmEA38G4CvLP<8aXe)$`vCq!=q_JwQ=KqxEYWt$2
z{vk;<)Acq3q-l&2gzF{Msu%(XF9~jZjYNWGuzW||*&T2^4`Q3mus+HJ!($tNoG&cn
zk3NRNqvBYkE_4n~rT=|hFvEz+Wbu>lYrz;RX8{_1O5}Fd5xI))X!?%`)~1X&)^LMh
z6)c8pOZDgx&-iOm3gmHX=6+BRv=!1S%8u~WScmmN)+uJ@xlg4%TE&sVvn>d%w06E%
zoNX9n91WcU$nWgVH~l4gXVgdzc~gEs@9jQ&{IXadPZ60s%iSV=Z{gAr(MfXF*Wz=u
zA^a*mrxsy@p4q36d~n|o&yq#i^sDtmjhTK(PuSCqaCIz|{0v_gCoB6bT&chMn!8P>
zoIV!6Jf>`z#ma+P=Wh}XSS|o-Z`Oro=r>p3&@>T`gTg!b%(?uM@Zl%wvu(HjR^Nwi
zhaGprG{uV_S60d<MfvwDGBxPt=*;3;os`R2k-a)UF;`iUYyDQg-9IfUa!5B~bVkGP
zS6;}^l^OD?<HUm5eCD;{z1iy8T?$YU_Py%3&-|adXnyp8>?ikGwK!V8=6x&?l_rZs
z-^GVPPnrIXc9G}2KAdD)7R$^y4oMcHyY5^1oBJRp&RR1Euq%5id{fW(O6&!)+Ysqi
zHOx-FRZqEpFhNW`QE9OxmPLC>Pe5C{iQ(o_JG1=E!V}Mj8&$^KE$7K3mE~%Tcy1!j
z-($YSqTtn0<?~QtU!zBCa~tFBqMh6ZD+Z1s*0rSsH{8LivkGW1_hfse_yRKmH~Qs%
zuzIs5hc+|ZtRv`cuvjK|D@v`=Bxzb)Qh~EqgS@y?5^Vye1GZYbcjMWyVsnIM+$Iam
zKO$&4bENMf10`2P1MMCRXS&xT+*X3Jlfl}KYJZ3xQe-`2&_66L%d405pWP2-l!fO)
z4%yaN&tR-K*fxm_ci=sly?UAImA`^)oE4=UTw_*zBXBogo_Uls#nK2Vt+9VpkmJ;*
z9g+E=WSc>-dB<c;oh*NBqq5g_O7py~Jm~4}M0c9<EZ6AwWVY#?+Y>q;yI&d6uesOV
zVcoULw~UX_%kV?xzEyvWD48=mNMp0O7!zJe<aXT_iv8H`qjF=YLvtB*n~j^J0`xb{
z+YTDN!vmc?e_LNI_iRxD?AjS&r|1ejV!AJBA@zZ7;?z)ytCh8wZ(@Xhup&p?>4G7>
zu~Lv&SjDC%(IPDj`)R62Z{)$iwTc;d&QeEe6Be-ZM*Va_sKpIeSfpdhQA7*1a}z(R
zcN-P-KdcN`w3z5f|KOk#^^RmuCrRAx(m82<VO!KH%7gIF>dA1qT=HG>^v_7I3>ErC
zp>VfsBJ;tpW|3>iw5%;mvY0#!8tw7LDgJUv5%XZP_XY(BC*ixQ(NPk(OFBbXIj@m$
zLs_Bv)AjvhC3X8X?-N9WPEdYN>6Grjr*!R=o7owfq*{;YU->^;76-4ndsF=T>W}uN
Hzx)3OGG_Ot

literal 0
HcmV?d00001

diff --git a/docs/python_docs/themes/mx-theme/mxtheme/static/material-design-icons-3.0.1/iconfont/MaterialIcons-Regular.svg b/docs/python_docs/themes/mx-theme/mxtheme/static/material-design-icons-3.0.1/iconfont/MaterialIcons-Regular.svg
new file mode 100644
index 000000000000..a449327e22c0
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/mxtheme/static/material-design-icons-3.0.1/iconfont/MaterialIcons-Regular.svg
@@ -0,0 +1,2373 @@
+<?xml version="1.0" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd" >
+<!--
+2016-2-8: Created with FontForge (http://fontforge.org)
+-->
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" version="1.1">
+<metadata>
+Created by FontForge 20151118 at Mon Feb  8 11:58:02 2016
+ By shyndman
+Copyright 2015 Google, Inc. All Rights Reserved.
+</metadata>
+<defs>
+<font id="MaterialIcons-Regular" horiz-adv-x="512" >
+  <font-face 
+    font-family="Material Icons"
+    font-weight="400"
+    font-stretch="normal"
+    units-per-em="512"
+    panose-1="2 0 5 3 0 0 0 0 0 0"
+    ascent="512"
+    descent="0"
+    bbox="0 0 512 512"
+    underline-thickness="50"
+    underline-position="-100"
+    unicode-range="U+0030-10FFFD"
+  />
+    <missing-glyph />
+    <glyph glyph-name="3d_rotation" unicode="&#x33;d_rotation" 
+d="M256 512c134 0 244 -103 255 -234h-32c-7 80 -57 147 -127 180l-29 -28l-81 81zM353 260c0 34.9356 -13.6691 57 -47 57h-20v-123h19c25.6434 0 40.2719 13.9518 46 34c2 7 2 15 2 24v8zM306 341c38.1819 0 60.9327 -19.3318 72 -47c4 -10 5 -22 5 -34v-8
+c0 -26.4057 -7.57104 -45.571 -21 -59c-13.4122 -13.4122 -30.5519 -22 -57 -22h-49v170h50zM207 258c15.3033 -6.12131 28 -18.2465 28 -39c0 -8 -2 -14 -5 -20s-6 -12 -11 -16c-9.37815 -7.50252 -23.5479 -12 -40 -12c-31.0959 0 -54 15.7441 -54 47h27
+c0 -15.4518 11.6022 -25 27 -25c17.8699 0 28 8.53939 28 27c0 18.8434 -12.3951 27 -31 27h-16v22h16c17.3039 0 29 8.03066 29 25c0 16.5246 -8.84017 25 -26 25c-14.4441 0 -25 -8.58131 -25 -23h-28c0 14.8936 7.55163 24.5516 15 32c9.92709 7.94167 20.8987 13 38 13
+c25.6475 0 40.3859 -9.7718 49 -27c3 -6 4 -12 4 -20c0 -18.1878 -12.3353 -29.6677 -25 -36zM160 54l29 28l81 -81l-14 -1c-134 0 -244 104 -255 235h32c8 -80 57 -148 127 -181z" />
+    <glyph glyph-name="ac_unit" unicode="ac_unit" 
+d="M469 277v-42h-89l69 -69l-30 -31l-99 100h-43v-43l100 -99l-31 -30l-69 69v-89h-42v89l-69 -69l-31 30l100 99v43h-43l-99 -100l-30 31l69 69h-89v42h89l-69 69l30 31l99 -100h43v43l-100 99l31 30l69 -69v89h42v-89l69 69l31 -30l-100 -99v-43h43l99 100l30 -31l-69 -69
+h89z" />
+    <glyph glyph-name="access_alarm" unicode="access_alarm" 
+d="M256 85c83 0 149 67 149 150s-66 149 -149 149s-149 -66 -149 -149s66 -150 149 -150zM256 427c106 0 192 -86 192 -192s-86 -192 -192 -192s-192 86 -192 192s86 192 192 192zM267 341v-112l85 -50l-16 -26l-101 60v128h32zM168 440l-98 -82l-27 32l98 82zM469 390
+l-27 -33l-98 83l27 32z" />
+    <glyph glyph-name="access_alarms" unicode="access_alarms" 
+d="M256 85c83 0 149 67 149 150s-66 149 -149 149s-149 -66 -149 -149s66 -150 149 -150zM256 427c107 0 192 -85 192 -192s-85 -192 -192 -192s-192 85 -192 192s85 192 192 192zM267 341v-113l85 -51l-17 -26l-100 62v128h32zM169 439l-99 -81l-27 32l98 81zM469 390
+l-27 -32l-99 84l28 32z" />
+    <glyph glyph-name="access_time" unicode="access_time" 
+d="M267 363v-112l96 -57l-16 -27l-112 68v128h32zM256 85c94 0 171 77 171 171s-77 171 -171 171s-171 -77 -171 -171s77 -171 171 -171zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="accessibility" unicode="accessibility" 
+d="M448 320h-128v-277h-43v128h-42v-128h-43v277h-128v43h384v-43zM256 469c23 0 43 -19 43 -42s-20 -43 -43 -43s-43 20 -43 43s20 42 43 42z" />
+    <glyph glyph-name="accessible" unicode="accessible" 
+d="M274 128h44c-10 -49 -53 -85 -105 -85c-59 0 -106 47 -106 106c0 52 36 95 85 105v-44c-25 -9 -43 -33 -43 -61c0 -35 29 -64 64 -64c28 0 52 18 61 43zM213 318c0 29.9891 33.3578 56.5098 64 39h1v-1c5 -2 9 -5 13 -9l28 -31c21 -23 53 -39 86 -39v-42
+c-37 0 -78 18 -106 41v-73h64c23 0 42 -20 42 -43v-117h-42v106h-107c-23 0 -43 20 -43 43v126zM213 427c0 24 19 42 43 42s43 -18 43 -42s-19 -43 -43 -43s-43 19 -43 43z" />
+    <glyph glyph-name="account_balance" unicode="account_balance" 
+d="M245 491l203 -107v-43h-405v43zM341 299h64v-150h-64v150zM43 43v64h405v-64h-405zM213 299h64v-150h-64v150zM85 299h64v-150h-64v150z" />
+    <glyph glyph-name="account_balance_wallet" unicode="account_balance_wallet" 
+d="M341 224c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM256 171v170h213v-170h-213zM448 128v-21c0 -23 -20 -43 -43 -43h-298c-24 0 -43 20 -43 43v298c0 23 19 43 43 43h298c23 0 43 -20 43 -43v-21h-192c-24 0 -43 -20 -43 -43v-170
+c0 -23 19 -43 43 -43h192z" />
+    <glyph glyph-name="account_box" unicode="account_box" 
+d="M128 149v-21h256v21c0 43 -85 66 -128 66s-128 -23 -128 -66zM320 320c0 35 -29 64 -64 64s-64 -29 -64 -64s29 -64 64 -64s64 29 64 64zM64 405c0 23 19 43 43 43h298c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-24 0 -43 20 -43 43v298z" />
+    <glyph glyph-name="account_circle" unicode="account_circle" 
+d="M256 102c53 0 100 28 128 69c-1 42 -86 66 -128 66c-43 0 -127 -24 -128 -66c28 -41 75 -69 128 -69zM256 405c-35 0 -64 -29 -64 -64s29 -64 64 -64s64 29 64 64s-29 64 -64 64zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z
+" />
+    <glyph glyph-name="adb" unicode="adb" 
+d="M320 320c12 0 21 9 21 21s-9 22 -21 22s-21 -10 -21 -22s9 -21 21 -21zM192 320c12 0 21 9 21 21s-9 22 -21 22s-21 -10 -21 -22s9 -21 21 -21zM344 419c37 -27 61 -71 61 -120v-22h-298v22c0 49 24 93 61 120l-45 45l18 17l49 -49c20 10 42 16 66 16s46 -6 66 -16l49 49
+l18 -17zM107 171v85h298v-85c0 -83 -66 -150 -149 -150s-149 67 -149 150z" />
+    <glyph glyph-name="add" unicode="add" 
+d="M405 235h-128v-128h-42v128h-128v42h128v128h42v-128h128v-42z" />
+    <glyph glyph-name="add_a_photo" unicode="add_a_photo" 
+d="M209 213c0 38 30 69 68 69s69 -31 69 -69s-31 -68 -69 -68s-68 30 -68 68zM277 107c59 0 107 47 107 106s-48 107 -107 107s-106 -48 -106 -107s47 -106 106 -106zM128 299v64h64v64h149l39 -43h68c23 0 43 -20 43 -43v-256c0 -23 -20 -42 -43 -42h-341
+c-23 0 -43 19 -43 42v214h64zM64 427v64h43v-64h64v-43h-64v-64h-43v64h-64v43h64z" />
+    <glyph glyph-name="add_alarm" unicode="add_alarm" 
+d="M277 320v-64h64v-43h-64v-64h-42v64h-64v43h64v64h42zM256 85c83 0 149 67 149 150s-66 149 -149 149s-149 -66 -149 -149s66 -150 149 -150zM256 427c106 0 192 -86 192 -192s-86 -192 -192 -192s-192 86 -192 192s86 192 192 192zM469 390l-27 -33l-98 83l27 32z
+M168 440l-98 -82l-27 32l98 82z" />
+    <glyph glyph-name="add_alert" unicode="add_alert" 
+d="M341 234v43h-64v64h-42v-64h-64v-43h64v-64h42v64h64zM403 153l45 -45v-23h-384v23l45 45v124c0 69 48 128 113 143v15c0 19 15 34 34 34s34 -15 34 -34v-15c65 -15 113 -74 113 -143v-124zM214 64h84c0 -23 -19 -43 -42 -43s-42 20 -42 43z" />
+    <glyph glyph-name="add_box" unicode="add_box" 
+d="M363 235v42h-86v86h-42v-86h-86v-42h86v-86h42v86h86zM405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-24 0 -43 20 -43 43v298c0 23 19 43 43 43h298z" />
+    <glyph glyph-name="add_circle" unicode="add_circle" 
+d="M363 235v42h-86v86h-42v-86h-86v-42h86v-86h42v86h86zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="add_circle_outline" unicode="add_circle_outline" 
+d="M256 85c94 0 171 77 171 171s-77 171 -171 171s-171 -77 -171 -171s77 -171 171 -171zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213zM277 363v-86h86v-42h-86v-86h-42v86h-86v42h86v86h42z" />
+    <glyph glyph-name="add_location" unicode="add_location" 
+d="M341 299v42h-64v64h-42v-64h-64v-42h64v-64h42v64h64zM256 469c82 0 149 -67 149 -149c0 -112 -149 -277 -149 -277s-149 165 -149 277c0 82 67 149 149 149z" />
+    <glyph glyph-name="add_shopping_cart" unicode="add_shopping_cart" 
+d="M153 197c0 -3 2 -5 5 -5h247v-43h-256c-23 0 -42 20 -42 43c0 7 2 14 5 20l29 53l-77 162h-43v42h70c13.5017 -28.165 26.8327 -56.5006 40 -85c17.0313 -35.6353 33.8016 -71.5317 51 -107h150c27.393 49.9404 55.2665 99.4002 82 150l37 -21l-82 -149
+c-7 -13 -21 -22 -37 -22h-159l-19 -35zM363 128c23 0 42 -20 42 -43s-19 -42 -42 -42s-43 19 -43 42s20 43 43 43zM149 128c23 0 43 -20 43 -43s-20 -42 -43 -42s-42 19 -42 42s19 43 42 43zM235 320v64h-64v43h64v64h42v-64h64v-43h-64v-64h-42z" />
+    <glyph glyph-name="add_to_photos" unicode="add_to_photos" 
+d="M405 277v43h-85v85h-43v-85h-85v-43h85v-85h43v85h85zM427 469c23 0 42 -19 42 -42v-256c0 -23 -19 -43 -42 -43h-256c-23 0 -43 20 -43 43v256c0 23 20 42 43 42h256zM85 384v-299h299v-42h-299c-23 0 -42 19 -42 42v299h42z" />
+    <glyph glyph-name="add_to_queue" unicode="add_to_queue" 
+d="M341 299v-43h-64v-64h-42v64h-64v43h64v64h42v-64h64zM448 149v256h-384v-256h384zM448 448c23 0 43 -19 43 -43l-1 -256c0 -23 -19 -42 -42 -42h-107v-43h-170v43h-107c-24 0 -43 19 -43 42v256c0 24 19 43 43 43h384z" />
+    <glyph glyph-name="adjust" unicode="adjust" 
+d="M320 256c0 -35 -29 -64 -64 -64s-64 29 -64 64s29 64 64 64s64 -29 64 -64zM256 85c94 0 171 77 171 171s-77 171 -171 171s-171 -77 -171 -171s77 -171 171 -171zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="airline_seat_flat" unicode="airline_seat_flat" 
+d="M152 254c-25 -25 -65 -26 -90 -1s-26 65 -1 90s65 26 90 1s26 -65 1 -90zM43 213h426v-42h-128v-43h-170v43h-128v42zM469 277v-42h-277v128h192c47 0 85 -39 85 -86z" />
+    <glyph glyph-name="airline_seat_flat_angled" unicode="airline_seat_flat_angled" 
+d="M156 294c-32 -15 -71 -2 -86 30s-2 71 30 86s71 2 86 -30s2 -71 -30 -86zM32 253l15 40l405 -146l-14 -40l-97 34v-34h-170v96zM475 207l-15 -40l-264 95l45 121l182 -66c45 -16 68 -65 52 -110z" />
+    <glyph glyph-name="airline_seat_individual_suite" unicode="airline_seat_individual_suite" 
+d="M405 363c47 0 86 -39 86 -86v-128h-470v214h43v-150h171v150h170zM149 235c-35 0 -64 29 -64 64s29 64 64 64s64 -29 64 -64s-29 -64 -64 -64z" />
+    <glyph glyph-name="airline_seat_legroom_extra" unicode="airline_seat_legroom_extra" 
+d="M487 144c9 -16 1 -36 -15 -44l-79 -36l-73 149h-149c-35 0 -64 29 -64 64v171h128v-128h75c16 0 31 -9 38 -24l72 -149l24 11c16 7 35 1 43 -14zM85 256c0 -35 29 -64 64 -64h128v-43h-128c-59 0 -106 48 -106 107v192h42v-192z" />
+    <glyph glyph-name="airline_seat_legroom_normal" unicode="airline_seat_legroom_normal" 
+d="M437 128c18 0 32 -14 32 -32s-14 -32 -32 -32h-96v149h-149c-35 0 -64 29 -64 64v171h128v-128h107c23 0 42 -20 42 -43v-149h32zM107 256c0 -35 29 -64 64 -64h128v-43h-128c-59 0 -107 48 -107 107v192h43v-192z" />
+    <glyph glyph-name="airline_seat_legroom_reduced" unicode="airline_seat_legroom_reduced" 
+d="M107 256c0 -35 29 -64 64 -64h85v-43h-85c-59 0 -107 48 -107 107v192h43v-192zM426 102c4 -20 -11 -38 -31 -38h-96v64l21 85h-128c-35 0 -64 29 -64 64v171h128v-128h107c23 0 42 -20 42 -43l-42 -149h30c16 0 30 -11 33 -26z" />
+    <glyph glyph-name="airline_seat_recline_extra" unicode="airline_seat_recline_extra" 
+d="M346 192l123 -96l-32 -32l-81 64h-146c-31 0 -57 22 -63 52l-29 126c-4 26 13 51 39 56h1c15.1794 2.16848 28.3139 -2.05108 37 -9l35 -27c27 -21 65 -34 100 -27v-46c-40 -7 -76 7 -110 26l22 -87h104zM341 107v-43h-150c-53 0 -98 38 -106 90l-42 209h42l42 -202
+c5 -31 32 -54 64 -54h150zM114 392c-19 14 -23 40 -10 59s40 24 59 11c19 -14 24 -41 11 -60c-14 -19 -41 -23 -60 -10z" />
+    <glyph glyph-name="airline_seat_recline_normal" unicode="airline_seat_recline_normal" 
+d="M427 84l-31 -31l-75 75h-108c-35 0 -64 29 -64 64v123c0 26 22 48 48 48h1c14.8367 0 27.399 -8.39902 35 -16l30 -33c23 -25 65 -43 100 -43v-47c-41 0 -88 22 -118 47v-79h74zM128 171c0 -35 29 -64 64 -64h128v-43h-128c-59 0 -107 48 -107 107v192h43v-192zM162 397
+c-17 17 -17 43 0 60s43 17 60 0s17 -43 0 -60s-43 -17 -60 0z" />
+    <glyph glyph-name="airplanemode_active" unicode="airplanemode_active" 
+d="M217 320zM448 171l-171 53v-117l43 -32v-32l-75 21l-74 -21v32l42 32v117l-170 -53v42l170 107v117c0 18 14 32 32 32s32 -14 32 -32v-117l171 -107v-42z" />
+    <glyph glyph-name="airplanemode_inactive" unicode="airplanemode_inactive" 
+d="M64 400l27 27l336 -336l-27 -27l-123 122v-79l43 -32v-32l-75 21l-74 -21v32l42 32v117l-170 -53v42l127 80zM277 320l171 -107v-42l-68 21l-167 167v78c0 18 14 32 32 32s32 -14 32 -32v-117z" />
+    <glyph glyph-name="airplay" unicode="airplay" 
+d="M448 448c23 0 43 -20 43 -43v-256c0 -23 -20 -42 -43 -42h-85v42h85v256h-384v-256h85v-42h-85c-23 0 -43 19 -43 42v256c0 23 20 43 43 43h384zM128 43l128 128l128 -128h-256z" />
+    <glyph glyph-name="airport_shuttle" unicode="airport_shuttle" 
+d="M320 277h107l-86 86h-21v-86zM373 139c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM277 277v86h-85v-86h85zM128 139c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM64 277h85v86h-85v-86zM363 405l128 -128v-106h-54
+c0 -35 -29 -64 -64 -64s-64 29 -64 64h-117c0 -35 -29 -64 -64 -64s-64 29 -64 64h-43v192c0 24 20 42 43 42h299z" />
+    <glyph glyph-name="alarm" unicode="alarm" 
+d="M256 85c83 0 149 67 149 150s-66 149 -149 149s-149 -66 -149 -149s66 -150 149 -150zM256 427c106 0 192 -86 192 -192s-86 -192 -192 -192s-192 86 -192 192s86 192 192 192zM267 341v-112l85 -50l-16 -26l-101 60v128h32zM168 440l-98 -82l-27 32l98 82zM469 390
+l-27 -33l-98 83l27 32z" />
+    <glyph glyph-name="alarm_add" unicode="alarm_add" 
+d="M277 320v-64h64v-43h-64v-64h-42v64h-64v43h64v64h42zM256 85c83 0 149 67 149 150s-66 149 -149 149s-149 -66 -149 -149s66 -150 149 -150zM256 427c106 0 192 -86 192 -192s-86 -192 -192 -192s-192 86 -192 192s86 192 192 192zM469 390l-27 -33l-98 83l27 32z
+M168 440l-98 -82l-27 32l98 82z" />
+    <glyph glyph-name="alarm_off" unicode="alarm_off" 
+d="M171 442l-18 -15l-31 30l19 15zM351 120l-210 210c-21 -26 -34 -59 -34 -95c0 -83 66 -150 149 -150c36 0 69 14 95 35zM62 463c131.276 -131.057 262.677 -261.99 394 -393l-27 -27l-47 47c-34 -29 -78 -47 -126 -47c-106 0 -192 86 -192 192c0 48 18 91 47 125l-17 17
+l-24 -20l-30 31l24 19l-29 29zM469 390l-27 -33l-98 83l27 32zM256 384c-18 0 -35 -3 -51 -9l-33 32c25 12 54 20 84 20c106 0 192 -86 192 -192c0 -30 -7 -59 -19 -84l-33 32c6 16 9 34 9 52c0 83 -66 149 -149 149z" />
+    <glyph glyph-name="alarm_on" unicode="alarm_on" 
+d="M225 202l105 106l23 -23l-128 -128l-68 68l22 22zM256 85c83 0 149 67 149 150s-66 149 -149 149s-149 -66 -149 -149s66 -150 149 -150zM256 427c106 0 192 -86 192 -192s-86 -192 -192 -192s-192 86 -192 192s86 192 192 192zM168 440l-98 -82l-27 32l98 82zM469 390
+l-27 -33l-98 83l27 32z" />
+    <glyph glyph-name="album" unicode="album" 
+d="M256 277c12 0 21 -9 21 -21s-9 -21 -21 -21s-21 9 -21 21s9 21 21 21zM256 160c53 0 96 43 96 96s-43 96 -96 96s-96 -43 -96 -96s43 -96 96 -96zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="all_inclusive" unicode="all_inclusive" 
+d="M397 371c64 0 115 -52 115 -115s-51 -115 -115 -115c-31 0 -60 12 -82 34l-27 24l32 28l25 -21c15 -15 33 -22 52 -22c40 0 72 32 72 72s-32 72 -72 72c-19 0 -37 -7 -51 -21c-30.1331 -26.5336 -60.4726 -52.8608 -90 -80l-60 -53c-21 -21 -50 -33 -81 -33
+c-64 0 -115 52 -115 115s51 115 115 115c31 0 60 -12 82 -34l27 -24l-33 -28l-24 21c-15 15 -33 22 -52 22c-40 0 -72 -32 -72 -72s32 -72 72 -72c19 0 37 7 51 21c30.1331 26.5336 60.4726 52.8608 90 80l60 53c21 21 50 33 81 33z" />
+    <glyph glyph-name="all_out" unicode="all_out" 
+d="M343 170c45 45 45 120 0 165s-120 45 -165 0s-45 -120 0 -165s120 -45 165 0zM366 358c58 -58 58 -153 0 -211s-153 -58 -211 0s-58 153 0 211s153 58 211 0zM90 338v85h85zM175 82h-85v85zM431 167v-85h-85zM346 423h85v-85z" />
+    <glyph glyph-name="android" unicode="android" 
+d="M320 405v22h-21v-22h21zM213 405v22h-21v-22h21zM331 466c32 -23 53 -61 53 -103h-256c0 42 20 80 52 103l-28 28c-4 4 -4 11 0 15s11 4 15 0l32 -32c17 9 37 14 57 14s39 -5 56 -14l32 32c4 4 11 4 15 0s4 -11 0 -15zM437 341c18 0 32 -14 32 -32v-149
+c0 -18 -14 -32 -32 -32s-32 14 -32 32v149c0 18 14 32 32 32zM75 341c18 0 32 -14 32 -32v-149c0 -18 -14 -32 -32 -32s-32 14 -32 32v149c0 18 14 32 32 32zM128 128v213h256v-213c0 -12 -9 -21 -21 -21h-22v-75c0 -18 -14 -32 -32 -32s-32 14 -32 32v75h-42v-75
+c0 -18 -14 -32 -32 -32s-32 14 -32 32v75h-22c-12 0 -21 9 -21 21z" />
+    <glyph glyph-name="announcement" unicode="announcement" 
+d="M277 192v43h-42v-43h42zM277 277v128h-42v-128h42zM427 469c23 0 42 -19 42 -42v-256c0 -23 -19 -43 -42 -43h-299l-85 -85v384c0 23 19 42 42 42h342z" />
+    <glyph glyph-name="apps" unicode="apps" 
+d="M341 85v86h86v-86h-86zM341 213v86h86v-86h-86zM213 341v86h86v-86h-86zM341 427h86v-86h-86v86zM213 213v86h86v-86h-86zM85 213v86h86v-86h-86zM85 85v86h86v-86h-86zM213 85v86h86v-86h-86zM85 341v86h86v-86h-86z" />
+    <glyph glyph-name="archive" unicode="archive" 
+d="M109 405h294l-20 22h-256zM256 139l117 117h-74v43h-86v-43h-74zM438 400c6 -7 10 -17 10 -27v-266c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v266c0 10 4 20 10 27l29 36c6 7 15 12 25 12h256c10 0 19 -5 25 -12z" />
+    <glyph glyph-name="arrow_back" unicode="arrow_back" 
+d="M427 277v-42h-260l119 -120l-30 -30l-171 171l171 171l30 -30l-119 -120h260z" />
+    <glyph glyph-name="arrow_downward" unicode="arrow_downward" 
+d="M427 256l-171 -171l-171 171l31 30l119 -119v260h42v-260l120 119z" />
+    <glyph glyph-name="arrow_drop_down" unicode="arrow_drop_down" 
+d="M149 299h214l-107 -107z" />
+    <glyph glyph-name="arrow_drop_down_circle" unicode="arrow_drop_down_circle" 
+d="M256 213l85 86h-170zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="arrow_drop_up" unicode="arrow_drop_up" 
+d="M149 213l107 107l107 -107h-214z" />
+    <glyph glyph-name="arrow_forward" unicode="arrow_forward" 
+d="M256 427l171 -171l-171 -171l-30 30l119 120h-260v42h260l-119 120z" />
+    <glyph glyph-name="arrow_upward" unicode="arrow_upward" 
+d="M85 256l171 171l171 -171l-31 -30l-119 119v-260h-42v260l-120 -119z" />
+    <glyph glyph-name="art_track" unicode="art_track" 
+d="M224 192l-48 64l-37 -48l-27 32l-37 -48h149zM256 320v-128c0 -23 -20 -43 -43 -43h-128c-23 0 -42 20 -42 43v128c0 23 19 43 42 43h128c23 0 43 -20 43 -43zM299 149v43h170v-43h-170zM469 363v-43h-170v43h170zM469 235h-170v42h170v-42z" />
+    <glyph glyph-name="aspect_ratio" unicode="aspect_ratio" 
+d="M448 106v300h-384v-300h384zM448 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-384c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h384zM149 320v-64h-42v107h106v-43h-64zM405 256v-107h-106v43h64v64h42z" />
+    <glyph glyph-name="assessment" unicode="assessment" 
+d="M363 149v86h-43v-86h43zM277 149v214h-42v-214h42zM192 149v150h-43v-150h43zM405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h298z" />
+    <glyph glyph-name="assignment" unicode="assignment" 
+d="M363 320v43h-214v-43h214zM363 235v42h-214v-42h214zM299 149v43h-150v-43h150zM256 448c-12 0 -21 -9 -21 -21s9 -22 21 -22s21 10 21 22s-9 21 -21 21zM405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h89
+c9 25 32 43 60 43s51 -18 60 -43h89z" />
+    <glyph glyph-name="assignment_ind" unicode="assignment_ind" 
+d="M384 107v30c0 43 -85 66 -128 66s-128 -23 -128 -66v-30h256zM256 363c-35 0 -64 -29 -64 -64s29 -64 64 -64s64 29 64 64s-29 64 -64 64zM256 448c-12 0 -21 -9 -21 -21s9 -22 21 -22s21 10 21 22s-9 21 -21 21zM405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43
+h-298c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h89c9 25 32 43 60 43s51 -18 60 -43h89z" />
+    <glyph glyph-name="assignment_late" unicode="assignment_late" 
+d="M256 405c12 0 21 10 21 22s-9 21 -21 21s-21 -9 -21 -21s9 -22 21 -22zM277 213v128h-42v-128h42zM277 128v43h-42v-43h42zM405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h89c9 25 32 43 60 43s51 -18 60 -43
+h89z" />
+    <glyph glyph-name="assignment_return" unicode="assignment_return" 
+d="M341 192v85h-85v64l-107 -106l107 -107v64h85zM256 448c-12 0 -21 -9 -21 -21s9 -22 21 -22s21 10 21 22s-9 21 -21 21zM405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h89c9 25 32 43 60 43s51 -18 60 -43h89z
+" />
+    <glyph glyph-name="assignment_returned" unicode="assignment_returned" 
+d="M256 128l107 107h-64v85h-86v-85h-64zM256 448c-12 0 -21 -9 -21 -21s9 -22 21 -22s21 10 21 22s-9 21 -21 21zM405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h89c9 25 32 43 60 43s51 -18 60 -43h89z" />
+    <glyph glyph-name="assignment_turned_in" unicode="assignment_turned_in" 
+d="M213 149l171 171l-30 30l-141 -140l-55 55l-30 -30zM256 448c-12 0 -21 -9 -21 -21s9 -22 21 -22s21 10 21 22s-9 21 -21 21zM405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h89c9 25 32 43 60 43s51 -18 60 -43
+h89z" />
+    <glyph glyph-name="assistant" unicode="assistant" 
+d="M296 237l88 40l-88 40l-40 88l-40 -88l-88 -40l88 -40l40 -88zM405 469c23 0 43 -19 43 -42v-299c0 -23 -20 -43 -43 -43h-85l-64 -64l-64 64h-85c-23 0 -43 20 -43 43v299c0 23 20 42 43 42h298z" />
+    <glyph glyph-name="assistant_photo" unicode="assistant_photo" 
+d="M307 384h120v-213h-150l-8 42h-120v-149h-42v363h192z" />
+    <glyph glyph-name="attach_file" unicode="attach_file" 
+d="M352 384h32v-245c0 -65 -52 -118 -117 -118s-118 53 -118 118v266c0 47 39 86 86 86s85 -39 85 -86v-224c0 -29 -24 -53 -53 -53s-54 24 -54 53v203h32v-203c0 -12 10 -21 22 -21s21 9 21 21v224c0 29 -24 54 -53 54s-54 -25 -54 -54v-266c0 -47 39 -86 86 -86
+s85 39 85 86v245z" />
+    <glyph glyph-name="attach_money" unicode="attach_money" 
+d="M252 279c48 -13 100 -32 100 -93c0 -44 -33 -68 -75 -76v-46h-64v46c-41 9 -75 35 -78 82h47c2 -25 19 -45 63 -45c47 0 58 23 58 38c0 20 -11 39 -64 52c-60 14 -100 39 -100 88c0 41 33 68 74 77v46h64v-47c45 -11 68 -44 69 -81h-47c-1 27 -16 45 -54 45
+c-36 0 -57 -17 -57 -40c0 -20 16 -33 64 -46z" />
+    <glyph glyph-name="attachment" unicode="attachment" 
+d="M43 245c0 65 52 118 117 118h224c47 0 85 -39 85 -86s-38 -85 -85 -85h-181c-29 0 -54 24 -54 53s25 54 54 54h160v-43h-162c-12 0 -12 -21 0 -21h183c23 0 43 19 43 42s-20 43 -43 43h-224c-41 0 -75 -34 -75 -75s34 -74 75 -74h203v-43h-203c-65 0 -117 52 -117 117z
+" />
+    <glyph glyph-name="audiotrack" unicode="audiotrack" 
+d="M256 448h149v-64h-85v-235h-1c-5 -48 -46 -85 -95 -85c-53 0 -96 43 -96 96s43 96 96 96c11 0 22 -2 32 -6v198z" />
+    <glyph glyph-name="autorenew" unicode="autorenew" 
+d="M400 347c17 -26 27 -58 27 -91c0 -94 -77 -171 -171 -171v-64l-85 86l85 85v-64c71 0 128 57 128 128c0 22 -6 42 -15 60zM256 384c-71 0 -128 -57 -128 -128c0 -22 5 -42 15 -60l-31 -31c-17 26 -27 58 -27 91c0 94 77 171 171 171v64l85 -86l-85 -85v64z" />
+    <glyph glyph-name="av_timer" unicode="av_timer" 
+d="M128 256c0 12 9 21 21 21s22 -9 22 -21s-10 -21 -22 -21s-21 9 -21 21zM384 256c0 -12 -9 -21 -21 -21s-22 9 -22 21s10 21 22 21s21 -9 21 -21zM235 448h21c106 0 192 -86 192 -192s-86 -192 -192 -192s-192 86 -192 192c0 63 30 118 77 153v1l145 -145l-30 -30
+l-116 115c-21 -26 -33 -58 -33 -94c0 -83 66 -149 149 -149s149 66 149 149c0 75 -56 138 -128 148v-41h-42v85zM235 149c0 12 9 22 21 22s21 -10 21 -22s-9 -21 -21 -21s-21 9 -21 21z" />
+    <glyph glyph-name="backspace" unicode="backspace" 
+d="M405 179l-76 77l76 77l-30 30l-76 -77l-77 77l-30 -30l77 -77l-77 -77l30 -30l77 77l76 -77zM469 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-320c-15 0 -26 8 -34 19l-115 173l115 173c8 11 19 19 34 19h320z" />
+    <glyph glyph-name="backup" unicode="backup" 
+d="M299 235h64l-107 106l-107 -106h64v-86h86v86zM413 298c55 -4 99 -50 99 -106c0 -59 -48 -107 -107 -107h-277c-71 0 -128 57 -128 128c0 66 50 120 114 127c27 51 80 87 142 87c78 0 142 -55 157 -129z" />
+    <glyph glyph-name="battery_alert" unicode="battery_alert" 
+d="M277 213v107h-42v-107h42zM277 128v43h-42v-43h42zM334 427c16 0 29 -13 29 -29v-327c0 -16 -13 -28 -29 -28h-156c-16 0 -29 12 -29 28v327c0 16 13 29 29 29h35v42h86v-42h35z" />
+    <glyph glyph-name="battery_charging_full" unicode="battery_charging_full" 
+d="M235 85l85 160h-43v118l-85 -160h43v-118zM334 427c16 0 29 -13 29 -29v-327c0 -16 -13 -28 -29 -28h-156c-16 0 -29 12 -29 28v327c0 16 13 29 29 29h35v42h86v-42h35z" />
+    <glyph glyph-name="battery_full" unicode="battery_full" 
+d="M334 427c16 0 29 -13 29 -29v-327c0 -16 -13 -28 -29 -28h-156c-16 0 -29 12 -29 28v327c0 16 13 29 29 29h35v42h86v-42h35z" />
+    <glyph glyph-name="battery_std" unicode="battery_std" 
+d="M334 427c16 0 29 -13 29 -29v-327c0 -16 -13 -28 -29 -28h-156c-16 0 -29 12 -29 28v327c0 16 13 29 29 29h35v42h86v-42h35z" />
+    <glyph glyph-name="battery_unknown" unicode="battery_unknown" 
+d="M305 241c9 9 15 22 15 36c0 35 -29 64 -64 64s-64 -29 -64 -64h32c0 18 14 32 32 32s32 -14 32 -32c0 -9 -3 -16 -9 -22l-20 -20c-10 -10 -20 -25 -20 -43h34c0 10 8 24 18 34c6 6 14 15 14 15zM276 129v41h-40v-41h40zM334 427c16 0 29 -13 29 -29v-327
+c0 -16 -13 -28 -29 -28h-156c-16 0 -29 12 -29 28v327c0 16 13 29 29 29h35v42h86v-42h35z" />
+    <glyph glyph-name="beach_access" unicode="beach_access" 
+d="M372 324c-66.8504 66.8504 -159.585 88.9016 -243.437 61.1868c63.7841 7.27512 145.981 -25.7308 212.437 -92.1868l-122 -122c-66.456 66.456 -99.4619 148.653 -92.1868 212.437c-27.7148 -83.8524 -5.66368 -176.587 61.1868 -243.437l-61 -61
+c-83.9541 83.9541 -84 220.85 -0.137673 304.862c0.129398 1.10582 0.166618 1.14572 1.27547 1.27547c84.012 83.8623 220.908 83.8164 304.862 -0.137673zM280 201l31 31l137 -138l-31 -30z" />
+    <glyph glyph-name="beenhere" unicode="beenhere" 
+d="M213 171l192 192l-30 30l-162 -162l-76 76l-30 -30zM405 491c23 0 43 -20 43 -43v-276c0 -15 -8 -27 -19 -35l-173 -116l-173 116c-11 8 -19 20 -19 35v276c0 23 20 43 43 43h298z" />
+    <glyph glyph-name="block" unicode="block" 
+d="M256 85c94 0 171 77 171 171c0 39 -13 76 -36 105l-240 -240c29 -23 66 -36 105 -36zM85 256c0 -39 13 -76 36 -105l240 240c-29 23 -66 36 -105 36c-94 0 -171 -77 -171 -171zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="bluetooth" unicode="bluetooth" 
+d="M317 164l-40 41v-81zM277 388v-81l40 41zM378 348l-92 -92l92 -92l-122 -121h-21v162l-98 -98l-30 30l119 119l-119 119l30 30l98 -98v162h21z" />
+    <glyph glyph-name="bluetooth_audio" unicode="bluetooth_audio" 
+d="M275 164l-40 41v-81zM235 388v-81l40 41zM335 348l-92 -92l92 -92l-122 -121h-21v162l-98 -98l-30 30l119 119l-119 119l30 30l98 -98v162h21zM417 369c20 -32 31 -71 31 -111c0 -42 -12 -80 -33 -113l-25 25c13 26 21 55 21 86s-8 60 -21 86zM304 256l49 49
+c6 -15 10 -32 10 -49s-4 -35 -10 -50z" />
+    <glyph glyph-name="bluetooth_connected" unicode="bluetooth_connected" 
+d="M405 299l43 -43l-43 -43l-42 43zM317 164l-40 41v-81zM277 388v-81l40 41zM378 348l-92 -92l92 -92l-122 -121h-21v162l-98 -98l-30 30l119 119l-119 119l30 30l98 -98v162h21zM149 256l-42 -43l-43 43l43 43z" />
+    <glyph glyph-name="bluetooth_disabled" unicode="bluetooth_disabled" 
+d="M277 124l40 40l-40 41v-81zM115 427l312 -312l-30 -30l-49 49l-92 -91h-21v162l-98 -98l-30 30l119 119l-141 141zM277 388v-69l-42 43v107h21l122 -121l-65 -65l-30 30l34 35z" />
+    <glyph glyph-name="bluetooth_searching" unicode="bluetooth_searching" 
+d="M275 164l-40 41v-81zM235 388v-81l40 41zM335 348l-92 -92l92 -92l-122 -121h-21v162l-98 -98l-30 30l119 119l-119 119l30 30l98 -98v162h21zM417 369c20 -32 31 -71 31 -111c0 -42 -12 -80 -33 -113l-25 25c13 26 21 55 21 86s-8 60 -21 86zM304 256l49 49
+c6 -15 10 -32 10 -49s-4 -35 -10 -50z" />
+    <glyph glyph-name="blur_circular" unicode="blur_circular" 
+d="M299 235c12 0 21 -10 21 -22s-9 -21 -21 -21s-22 9 -22 21s10 22 22 22zM299 160c6 0 10 -5 10 -11s-4 -10 -10 -10s-11 4 -11 10s5 11 11 11zM256 85c94 0 171 77 171 171s-77 171 -171 171s-171 -77 -171 -171s77 -171 171 -171zM256 469c118 0 213 -95 213 -213
+s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213zM363 309c6 0 10 -4 10 -10s-4 -11 -10 -11s-11 5 -11 11s5 10 11 10zM363 224c6 0 10 -5 10 -11s-4 -10 -10 -10s-11 4 -11 10s5 11 11 11zM299 352c-6 0 -11 5 -11 11s5 10 11 10s10 -4 10 -10s-4 -11 -10 -11z
+M299 320c12 0 21 -9 21 -21s-9 -22 -21 -22s-22 10 -22 22s10 21 22 21zM213 352c-6 0 -10 5 -10 11s4 10 10 10s11 -4 11 -10s-5 -11 -11 -11zM149 224c6 0 11 -5 11 -11s-5 -10 -11 -10s-10 4 -10 10s4 11 10 11zM213 160c6 0 11 -5 11 -11s-5 -10 -11 -10s-10 4 -10 10
+s4 11 10 11zM149 309c6 0 11 -4 11 -10s-5 -11 -11 -11s-10 5 -10 11s4 10 10 10zM213 235c12 0 22 -10 22 -22s-10 -21 -22 -21s-21 9 -21 21s9 22 21 22zM213 320c12 0 22 -9 22 -21s-10 -22 -22 -22s-21 10 -21 22s9 21 21 21z" />
+    <glyph glyph-name="blur_linear" unicode="blur_linear" 
+d="M277 149c-12 0 -21 10 -21 22s9 21 21 21s22 -9 22 -21s-10 -22 -22 -22zM277 235c-12 0 -21 9 -21 21s9 21 21 21s22 -9 22 -21s-10 -21 -22 -21zM277 320c-12 0 -21 9 -21 21s9 22 21 22s22 -10 22 -22s-10 -21 -22 -21zM363 245c-6 0 -11 5 -11 11s5 11 11 11
+s10 -5 10 -11s-4 -11 -10 -11zM363 331c-6 0 -11 4 -11 10s5 11 11 11s10 -5 10 -11s-4 -10 -10 -10zM64 448h384v-43h-384v43zM363 160c-6 0 -11 5 -11 11s5 10 11 10s10 -4 10 -10s-4 -11 -10 -11zM192 149c-12 0 -21 10 -21 22s9 21 21 21s21 -9 21 -21s-9 -22 -21 -22z
+M107 224c-18 0 -32 14 -32 32s14 32 32 32s32 -14 32 -32s-14 -32 -32 -32zM107 309c-18 0 -32 14 -32 32s14 32 32 32s32 -14 32 -32s-14 -32 -32 -32zM64 64v43h384v-43h-384zM192 320c-12 0 -21 9 -21 21s9 22 21 22s21 -10 21 -22s-9 -21 -21 -21zM192 235
+c-12 0 -21 9 -21 21s9 21 21 21s21 -9 21 -21s-9 -21 -21 -21zM107 139c-18 0 -32 14 -32 32s14 32 32 32s32 -14 32 -32s-14 -32 -32 -32z" />
+    <glyph glyph-name="blur_off" unicode="blur_off" 
+d="M64 224c6 0 11 -5 11 -11s-5 -10 -11 -10s-11 4 -11 10s5 11 11 11zM128 149c12 0 21 -9 21 -21s-9 -21 -21 -21s-21 9 -21 21s9 21 21 21zM213 75c6 0 11 -5 11 -11s-5 -11 -11 -11s-10 5 -10 11s4 11 10 11zM64 309c6 0 11 -4 11 -10s-5 -11 -11 -11s-11 5 -11 11
+s5 10 11 10zM128 235c12 0 21 -10 21 -22s-9 -21 -21 -21s-21 9 -21 21s9 22 21 22zM448 224c6 0 11 -5 11 -11s-5 -10 -11 -10s-11 4 -11 10s5 11 11 11zM213 149c12 0 22 -9 22 -21s-10 -21 -22 -21s-21 9 -21 21s9 21 21 21zM53 400l27 27l347 -347l-28 -27l-80 81
+c1 -2 1 -4 1 -6c0 -12 -9 -21 -21 -21s-22 9 -22 21s10 21 22 21c2 0 4 0 6 -1l-60 60c-2 -15 -16 -27 -32 -27c-18 0 -32 14 -32 32c0 16 12 30 27 32l-60 60c1 -2 1 -4 1 -6c0 -12 -9 -22 -21 -22s-21 10 -21 22s9 21 21 21l6 -1zM299 75c6 0 10 -5 10 -11s-4 -11 -10 -11
+s-11 5 -11 11s5 11 11 11zM384 363c-12 0 -21 9 -21 21s9 21 21 21s21 -9 21 -21s-9 -21 -21 -21zM384 277c-12 0 -21 10 -21 22s9 21 21 21s21 -9 21 -21s-9 -22 -21 -22zM384 192c-12 0 -21 9 -21 21s9 22 21 22s21 -10 21 -22s-9 -21 -21 -21zM213 363
+c-12 0 -21 9 -21 21s9 21 21 21s22 -9 22 -21s-10 -21 -22 -21zM448 288c-6 0 -11 5 -11 11s5 10 11 10s11 -4 11 -10s-5 -11 -11 -11zM213 437c-6 0 -10 5 -10 11s4 11 10 11s11 -5 11 -11s-5 -11 -11 -11zM299 437c-6 0 -11 5 -11 11s5 11 11 11s10 -5 10 -11
+s-4 -11 -10 -11zM294 267c-14 2 -25 13 -27 27v5c0 18 14 32 32 32s32 -14 32 -32s-14 -32 -32 -32h-5zM299 363c-12 0 -22 9 -22 21s10 21 22 21s21 -9 21 -21s-9 -21 -21 -21z" />
+    <glyph glyph-name="blur_on" unicode="blur_on" 
+d="M299 331c18 0 32 -14 32 -32s-14 -32 -32 -32s-32 14 -32 32s14 32 32 32zM299 245c18 0 32 -14 32 -32s-14 -32 -32 -32s-32 14 -32 32s14 32 32 32zM213 149c12 0 22 -9 22 -21s-10 -21 -22 -21s-21 9 -21 21s9 21 21 21zM213 331c18 0 32 -14 32 -32s-14 -32 -32 -32
+s-32 14 -32 32s14 32 32 32zM299 75c6 0 10 -5 10 -11s-4 -11 -10 -11s-11 5 -11 11s5 11 11 11zM299 149c12 0 21 -9 21 -21s-9 -21 -21 -21s-22 9 -22 21s10 21 22 21zM448 224c6 0 11 -5 11 -11s-5 -10 -11 -10s-11 4 -11 10s5 11 11 11zM384 405c12 0 21 -9 21 -21
+s-9 -21 -21 -21s-21 9 -21 21s9 21 21 21zM384 320c12 0 21 -9 21 -21s-9 -22 -21 -22s-21 10 -21 22s9 21 21 21zM384 149c12 0 21 -9 21 -21s-9 -21 -21 -21s-21 9 -21 21s9 21 21 21zM384 235c12 0 21 -10 21 -22s-9 -21 -21 -21s-21 9 -21 21s9 22 21 22zM213 245
+c18 0 32 -14 32 -32s-14 -32 -32 -32s-32 14 -32 32s14 32 32 32zM213 363c-12 0 -21 9 -21 21s9 21 21 21s22 -9 22 -21s-10 -21 -22 -21zM213 437c-6 0 -10 5 -10 11s4 11 10 11s11 -5 11 -11s-5 -11 -11 -11zM213 75c6 0 11 -5 11 -11s-5 -11 -11 -11s-10 5 -10 11
+s4 11 10 11zM64 224c6 0 11 -5 11 -11s-5 -10 -11 -10s-11 4 -11 10s5 11 11 11zM299 437c-6 0 -11 5 -11 11s5 11 11 11s10 -5 10 -11s-4 -11 -10 -11zM299 363c-12 0 -22 9 -22 21s10 21 22 21s21 -9 21 -21s-9 -21 -21 -21zM448 288c-6 0 -11 5 -11 11s5 10 11 10
+s11 -4 11 -10s-5 -11 -11 -11zM128 405c12 0 21 -9 21 -21s-9 -21 -21 -21s-21 9 -21 21s9 21 21 21zM64 309c6 0 11 -4 11 -10s-5 -11 -11 -11s-11 5 -11 11s5 10 11 10zM128 320c12 0 21 -9 21 -21s-9 -22 -21 -22s-21 10 -21 22s9 21 21 21zM128 149c12 0 21 -9 21 -21
+s-9 -21 -21 -21s-21 9 -21 21s9 21 21 21zM128 235c12 0 21 -10 21 -22s-9 -21 -21 -21s-21 9 -21 21s9 22 21 22z" />
+    <glyph glyph-name="book" unicode="book" 
+d="M128 427v-171l53 32l54 -32v171h-107zM384 469c23 0 43 -19 43 -42v-342c0 -23 -20 -42 -43 -42h-256c-23 0 -43 19 -43 42v342c0 23 20 42 43 42h256z" />
+    <glyph glyph-name="bookmark" unicode="bookmark" 
+d="M363 448c23 0 42 -20 42 -43v-341l-149 64l-149 -64v341c0 23 19 43 42 43h214z" />
+    <glyph glyph-name="bookmark_border" unicode="bookmark_border" 
+d="M363 128v277h-214v-277l107 47zM363 448c23 0 42 -20 42 -43v-341l-149 64l-149 -64v341c0 23 19 43 42 43h214z" />
+    <glyph glyph-name="border_all" unicode="border_all" 
+d="M405 277v128h-128v-128h128zM405 107v128h-128v-128h128zM235 277v128h-128v-128h128zM235 107v128h-128v-128h128zM64 448h384v-384h-384v384z" />
+    <glyph glyph-name="border_bottom" unicode="border_bottom" 
+d="M107 192v-43h-43v43h43zM64 64v43h384v-43h-384zM107 277v-42h-43v42h43zM405 320v43h43v-43h-43zM405 448h43v-43h-43v43zM107 363v-43h-43v43h43zM405 149v43h43v-43h-43zM405 235v42h43v-42h-43zM363 448v-43h-43v43h43zM277 448v-43h-42v43h42zM363 277v-42h-43v42
+h43zM277 363v-43h-42v43h42zM107 448v-43h-43v43h43zM277 277v-42h-42v42h42zM192 448v-43h-43v43h43zM277 192v-43h-42v43h42zM192 277v-42h-43v42h43z" />
+    <glyph glyph-name="border_clear" unicode="border_clear" 
+d="M320 405v43h43v-43h-43zM320 235v42h43v-42h-43zM320 64v43h43v-43h-43zM235 405v43h42v-43h-42zM405 448h43v-43h-43v43zM235 320v43h42v-43h-42zM405 320v43h43v-43h-43zM405 64v43h43v-43h-43zM405 235v42h43v-42h-43zM405 149v43h43v-43h-43zM235 235v42h42v-42h-42z
+M64 405v43h43v-43h-43zM64 320v43h43v-43h-43zM64 235v42h43v-42h-43zM64 149v43h43v-43h-43zM64 64v43h43v-43h-43zM235 64v43h42v-43h-42zM235 149v43h42v-43h-42zM149 64v43h43v-43h-43zM149 235v42h43v-42h-43zM149 405v43h43v-43h-43z" />
+    <glyph glyph-name="border_color" unicode="border_color" 
+d="M0 85h512v-85h-512v85zM442 426l-42 -42l-80 80l42 42c8 8 22 8 30 0l50 -50c8 -8 8 -22 0 -30zM379 363l-214 -214h-80v80l214 214z" />
+    <glyph glyph-name="border_horizontal" unicode="border_horizontal" 
+d="M405 64v43h43v-43h-43zM320 64v43h43v-43h-43zM235 149v43h42v-43h-42zM405 320v43h43v-43h-43zM405 448h43v-43h-43v43zM64 235v42h384v-42h-384zM235 64v43h42v-43h-42zM405 149v43h43v-43h-43zM277 448v-43h-42v43h42zM277 363v-43h-42v43h42zM363 448v-43h-43v43h43z
+M192 448v-43h-43v43h43zM107 448v-43h-43v43h43zM149 64v43h43v-43h-43zM64 149v43h43v-43h-43zM107 363v-43h-43v43h43zM64 64v43h43v-43h-43z" />
+    <glyph glyph-name="border_inner" unicode="border_inner" 
+d="M405 149v43h43v-43h-43zM405 64v43h43v-43h-43zM277 448v-171h171v-42h-171v-171h-42v171h-171v42h171v171h42zM320 64v43h43v-43h-43zM405 448h43v-43h-43v43zM405 320v43h43v-43h-43zM363 448v-43h-43v43h43zM107 448v-43h-43v43h43zM192 448v-43h-43v43h43zM64 149v43
+h43v-43h-43zM107 363v-43h-43v43h43zM149 64v43h43v-43h-43zM64 64v43h43v-43h-43z" />
+    <glyph glyph-name="border_left" unicode="border_left" 
+d="M320 405v43h43v-43h-43zM320 235v42h43v-42h-43zM405 64v43h43v-43h-43zM405 235v42h43v-42h-43zM405 448h43v-43h-43v43zM405 149v43h43v-43h-43zM320 64v43h43v-43h-43zM405 320v43h43v-43h-43zM64 64v384h43v-384h-43zM149 235v42h43v-42h-43zM149 405v43h43v-43h-43z
+M149 64v43h43v-43h-43zM235 235v42h42v-42h-42zM235 320v43h42v-43h-42zM235 405v43h42v-43h-42zM235 149v43h42v-43h-42zM235 64v43h42v-43h-42z" />
+    <glyph glyph-name="border_outer" unicode="border_outer" 
+d="M192 277v-42h-43v42h43zM277 192v-43h-42v43h42zM405 107v298h-298v-298h298zM64 448h384v-384h-384v384zM363 277v-42h-43v42h43zM277 277v-42h-42v42h42zM277 363v-43h-42v43h42z" />
+    <glyph glyph-name="border_right" unicode="border_right" 
+d="M235 320v43h42v-43h-42zM235 405v43h42v-43h-42zM235 235v42h42v-42h-42zM320 405v43h43v-43h-43zM320 64v43h43v-43h-43zM405 448h43v-384h-43v384zM320 235v42h43v-42h-43zM235 149v43h42v-43h-42zM64 320v43h43v-43h-43zM64 149v43h43v-43h-43zM64 235v42h43v-42h-43z
+M235 64v43h42v-43h-42zM64 64v43h43v-43h-43zM149 235v42h43v-42h-43zM149 405v43h43v-43h-43zM64 405v43h43v-43h-43zM149 64v43h43v-43h-43z" />
+    <glyph glyph-name="border_style" unicode="border_style" 
+d="M405 320v43h43v-43h-43zM64 448h384v-43h-341v-341h-43v384zM405 235v42h43v-42h-43zM405 149v43h43v-43h-43zM235 64v43h42v-43h-42zM149 64v43h43v-43h-43zM405 64v43h43v-43h-43zM320 64v43h43v-43h-43z" />
+    <glyph glyph-name="border_top" unicode="border_top" 
+d="M320 235v42h43v-42h-43zM405 64v43h43v-43h-43zM235 320v43h42v-43h-42zM320 64v43h43v-43h-43zM405 149v43h43v-43h-43zM64 448h384v-43h-384v43zM405 235v42h43v-42h-43zM405 320v43h43v-43h-43zM235 149v43h42v-43h-42zM64 320v43h43v-43h-43zM64 235v42h43v-42h-43z
+M64 64v43h43v-43h-43zM64 149v43h43v-43h-43zM235 64v43h42v-43h-42zM235 235v42h42v-42h-42zM149 235v42h43v-42h-43zM149 64v43h43v-43h-43z" />
+    <glyph glyph-name="border_vertical" unicode="border_vertical" 
+d="M320 235v42h43v-42h-43zM320 64v43h43v-43h-43zM320 405v43h43v-43h-43zM405 320v43h43v-43h-43zM405 448h43v-43h-43v43zM405 235v42h43v-42h-43zM405 64v43h43v-43h-43zM235 64v384h42v-384h-42zM405 149v43h43v-43h-43zM149 405v43h43v-43h-43zM64 149v43h43v-43h-43z
+M64 64v43h43v-43h-43zM64 235v42h43v-42h-43zM149 235v42h43v-42h-43zM149 64v43h43v-43h-43zM64 405v43h43v-43h-43zM64 320v43h43v-43h-43z" />
+    <glyph glyph-name="branding_watermark" unicode="branding_watermark" 
+d="M448 107v128h-192v-128h192zM448 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-384c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h384z" />
+    <glyph glyph-name="brightness_1" unicode="brightness_&#x31;" 
+d="M43 256c0 118 95 213 213 213s213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213z" />
+    <glyph glyph-name="brightness_2" unicode="brightness_&#x32;" 
+d="M213 469c118 0 214 -95 214 -213s-96 -213 -214 -213c-39 0 -75 10 -106 28c64 37 106 106 106 185s-42 148 -106 185c31 18 67 28 106 28z" />
+    <glyph glyph-name="brightness_3" unicode="brightness_&#x33;" 
+d="M192 469c118 0 213 -95 213 -213s-95 -213 -213 -213c-22 0 -44 3 -64 9c87 27 149 108 149 204s-62 177 -149 204c20 6 42 9 64 9z" />
+    <glyph glyph-name="brightness_4" unicode="brightness_&#x34;" 
+d="M256 128c71 0 128 57 128 128s-57 128 -128 128c-19 0 -37 -5 -53 -12c44 -20 74 -64 74 -116s-30 -96 -74 -116c16 -7 34 -12 53 -12zM427 327l70 -71l-70 -71v-100h-100l-71 -70l-71 70h-100v100l-70 71l70 71v100h100l71 70l71 -70h100v-100z" />
+    <glyph glyph-name="brightness_5" unicode="brightness_&#x35;" 
+d="M256 128c71 0 128 57 128 128s-57 128 -128 128s-128 -57 -128 -128s57 -128 128 -128zM427 185v-100h-100l-71 -70l-71 70h-100v100l-70 71l70 71v100h100l71 70l71 -70h100v-100l70 -71z" />
+    <glyph glyph-name="brightness_6" unicode="brightness_&#x36;" 
+d="M256 128c71 0 128 57 128 128s-57 128 -128 128v-256zM427 185v-100h-100l-71 -70l-71 70h-100v100l-70 71l70 71v100h100l71 70l71 -70h100v-100l70 -71z" />
+    <glyph glyph-name="brightness_7" unicode="brightness_&#x37;" 
+d="M256 341c47 0 85 -38 85 -85s-38 -85 -85 -85s-85 38 -85 85s38 85 85 85zM256 128c71 0 128 57 128 128s-57 128 -128 128s-128 -57 -128 -128s57 -128 128 -128zM427 327l70 -71l-70 -71v-100h-100l-71 -70l-71 70h-100v100l-70 71l70 71v100h100l71 70l71 -70h100
+v-100z" />
+    <glyph glyph-name="brightness_auto" unicode="brightness_auto" 
+d="M305 171h41l-69 192h-42l-69 -192h41l15 42h68zM427 327l70 -71l-70 -71v-100h-100l-71 -70l-71 70h-100v100l-70 71l70 71v100h100l71 70l71 -70h100v-100zM231 242l25 78l25 -78h-50z" />
+    <glyph glyph-name="brightness_high" unicode="brightness_high" 
+d="M256 341c47 0 85 -38 85 -85s-38 -85 -85 -85s-85 38 -85 85s38 85 85 85zM256 128c71 0 128 57 128 128s-57 128 -128 128s-128 -57 -128 -128s57 -128 128 -128zM427 327l70 -71l-70 -71v-100h-100l-71 -70l-71 70h-100v100l-70 71l70 71v100h100l71 70l71 -70h100
+v-100z" />
+    <glyph glyph-name="brightness_low" unicode="brightness_low" 
+d="M256 128c71 0 128 57 128 128s-57 128 -128 128s-128 -57 -128 -128s57 -128 128 -128zM427 185v-100h-100l-71 -70l-71 70h-100v100l-70 71l70 71v100h100l71 70l71 -70h100v-100l70 -71z" />
+    <glyph glyph-name="brightness_medium" unicode="brightness_medium" 
+d="M256 128c71 0 128 57 128 128s-57 128 -128 128v-256zM427 185v-100h-100l-71 -70l-71 70h-100v100l-70 71l70 71v100h100l71 70l71 -70h100v-100l70 -71z" />
+    <glyph glyph-name="broken_image" unicode="broken_image" 
+d="M384 268l64 -64v-97c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v140l64 -64l85 86l86 -86zM448 405v-140l-64 64l-85 -86l-86 86l-85 -86l-64 65v97c0 23 20 43 43 43h298c23 0 43 -20 43 -43z" />
+    <glyph glyph-name="brush" unicode="brush" 
+d="M442 413c8 -8 8 -22 0 -30l-191 -191l-59 59l191 191c8 8 22 8 30 0zM149 213c35 0 64 -29 64 -64c0 -47 -38 -85 -85 -85c-32 0 -65 17 -85 43c18 0 42 14 42 42c0 35 29 64 64 64z" />
+    <glyph glyph-name="bubble_chart" unicode="bubble_chart" 
+d="M222 324c0 57 45 103 102 103s103 -46 103 -103s-46 -102 -103 -102s-102 45 -102 102zM273 128c0 24 19 43 43 43s42 -19 42 -43s-18 -43 -42 -43s-43 19 -43 43zM85 205c0 38 31 68 69 68s68 -30 68 -68s-30 -68 -68 -68s-69 30 -69 68z" />
+    <glyph glyph-name="bug_report" unicode="bug_report" 
+d="M299 256v43h-86v-43h86zM299 171v42h-86v-42h86zM427 341v-42h-45c1 -7 2 -15 2 -22v-21h43v-43h-43v-21c0 -7 -1 -14 -2 -21h45v-43h-60c-22 -38 -64 -64 -111 -64s-89 26 -111 64h-60v43h45c-1 7 -2 14 -2 21v21h-43v43h43v21c0 7 1 15 2 22h-45v42h60
+c10 17 23 31 39 42l-35 35l30 30l47 -46c10 2 20 3 30 3s20 -1 30 -3l47 46l30 -30l-35 -35c16 -11 29 -25 39 -42h60z" />
+    <glyph glyph-name="build" unicode="build" 
+d="M484 107c9 -6 9 -21 -2 -30l-49 -49c-9 -9 -21 -9 -30 0l-194 194c-49 -19 -106 -9 -147 32c-43 43 -54 107 -28 158l94 -92l64 64l-92 92c51 23 115 15 158 -28c41 -41 51 -98 32 -147z" />
+    <glyph glyph-name="burst_mode" unicode="burst_mode" 
+d="M235 149h213l-68 90l-54 -68l-38 46zM469 405c12 0 22 -9 22 -21v-256c0 -12 -10 -21 -22 -21h-256c-12 0 -21 9 -21 21v256c0 12 9 21 21 21h256zM107 405h42v-298h-42v298zM21 405h43v-298h-43v298z" />
+    <glyph glyph-name="business" unicode="business" 
+d="M384 192v-43h-43v43h43zM384 277v-42h-43v42h43zM427 107v213h-171v-43h43v-42h-43v-43h43v-43h-43v-42h171zM213 363v42h-42v-42h42zM213 277v43h-42v-43h42zM213 192v43h-42v-43h42zM213 107v42h-42v-42h42zM128 363v42h-43v-42h43zM128 277v43h-43v-43h43zM128 192v43
+h-43v-43h43zM128 107v42h-43v-42h43zM256 363h213v-299h-426v384h213v-85z" />
+    <glyph glyph-name="business_center" unicode="business_center" 
+d="M299 363v42h-86v-42h86zM427 363c23 0 42 -20 42 -43v-64c0 -23 -19 -43 -42 -43h-128v43h-86v-43h-128c-24 0 -42 19 -42 43v64c0 23 19 43 42 43h85v42l43 43h85l43 -43v-42h86zM213 171h86v21h149v-85c0 -24 -19 -43 -43 -43h-298c-24 0 -43 19 -43 43v85h149v-21z
+" />
+    <glyph glyph-name="cached" unicode="cached" 
+d="M128 256h64l-85 -85l-86 85h64c0 94 77 171 171 171c33 0 65 -10 91 -27l-31 -31c-18 10 -38 15 -60 15c-71 0 -128 -57 -128 -128zM405 341l86 -85h-64c0 -94 -77 -171 -171 -171c-33 0 -65 10 -91 27l31 31c18 -10 38 -15 60 -15c71 0 128 57 128 128h-64z" />
+    <glyph glyph-name="cake" unicode="cake" 
+d="M384 320c35 0 64 -29 64 -64v-33c0 -23 -19 -42 -42 -42c-11 0 -21 4 -29 12l-46 46l-46 -46c-16 -16 -43 -16 -59 0l-45 46l-46 -46c-8 -8 -18 -12 -29 -12c-23 0 -42 19 -42 42v33c0 35 29 64 64 64h107v43h42v-43h107zM354 171c14 -14 32 -22 52 -22c16 0 30 5 42 13
+v-98c0 -12 -9 -21 -21 -21h-342c-12 0 -21 9 -21 21v98c12 -8 26 -13 42 -13c20 0 38 8 52 22l23 23l23 -23c28 -28 76 -28 104 0l23 23zM256 384c-23 0 -43 20 -43 43c0 8 3 16 7 22l36 63l36 -63c4 -6 7 -14 7 -22c0 -23 -19 -43 -43 -43z" />
+    <glyph glyph-name="call" unicode="call" 
+d="M141 282c31 -60 81 -110 141 -141l47 47c6 6 15 8 22 5c24 -8 50 -12 76 -12c12 0 21 -9 21 -21v-75c0 -12 -9 -21 -21 -21c-200 0 -363 163 -363 363c0 12 9 21 21 21h75c12 0 21 -9 21 -21c0 -27 4 -52 12 -76c2 -7 1 -16 -5 -22z" />
+    <glyph glyph-name="call_end" unicode="call_end" 
+d="M256 320c-34 0 -67 -5 -98 -15v-66c0 -8 -5 -17 -12 -20c-21 -10 -40 -23 -57 -39c-4 -4 -9 -6 -15 -6s-11 2 -15 6l-53 53c-4 4 -6 9 -6 15s2 11 6 15c65 62 153 100 250 100s185 -38 250 -100c4 -4 6 -9 6 -15s-2 -11 -6 -15l-53 -53c-4 -4 -9 -6 -15 -6s-11 2 -15 6
+c-17 16 -36 29 -57 39c-7 3 -12 10 -12 19v66c-31 10 -64 16 -98 16z" />
+    <glyph glyph-name="call_made" unicode="call_made" 
+d="M192 405h213v-213h-42v141l-248 -248l-30 30l248 248h-141v42z" />
+    <glyph glyph-name="call_merge" unicode="call_merge" 
+d="M160 341l96 96l96 -96h-75v-136l-128 -128l-30 30l116 115v119h-75zM363 77l-73 72l30 30l73 -72z" />
+    <glyph glyph-name="call_missed" unicode="call_missed" 
+d="M418 363l30 -30l-192 -192l-149 149v-98h-43v171h171v-43h-98l119 -119z" />
+    <glyph glyph-name="call_missed_outgoing" unicode="call_missed_outgoing" 
+d="M64 333l30 30l162 -162l119 119h-98v43h171v-171h-43v98l-149 -149z" />
+    <glyph glyph-name="call_received" unicode="call_received" 
+d="M427 397l-248 -248h141v-42h-213v213h42v-141l248 248z" />
+    <glyph glyph-name="call_split" unicode="call_split" 
+d="M213 427l-49 -49l113 -113v-180h-42v162l-101 101l-49 -49v128h128zM299 427h128v-128l-49 49l-62 -62l-30 30l62 62z" />
+    <glyph glyph-name="call_to_action" unicode="call_to_action" 
+d="M448 107v64h-384v-64h384zM448 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-384c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h384z" />
+    <glyph glyph-name="camera" unicode="camera" 
+d="M210 48c34.3543 59.3123 68.2469 119.086 103 178l78 -135c-37 -30 -84 -48 -135 -48c-16 0 -31 2 -46 5zM52 192h207l-79 -135c-61 23 -108 73 -128 135zM99 400c35.9996 -62.3337 71.7933 -124.873 108 -187h-160c-3 14 -4 28 -4 43c0 56 21 106 56 144zM465 299
+c3 -14 4 -28 4 -43c0 -56 -21 -106 -56 -144l-102 176l-6 11h160zM460 320h-207l79 135c61 -23 108 -73 128 -135zM201 288l-2 -2l-78 135c37 30 84 48 135 48c16 0 31 -2 46 -5z" />
+    <glyph glyph-name="camera_alt" unicode="camera_alt" 
+d="M256 149c59 0 107 48 107 107s-48 107 -107 107s-107 -48 -107 -107s48 -107 107 -107zM192 469h128l39 -42h68c23 0 42 -20 42 -43v-256c0 -23 -19 -43 -42 -43h-342c-23 0 -42 20 -42 43v256c0 23 19 43 42 43h68zM188 256c0 38 30 68 68 68s68 -30 68 -68
+s-30 -68 -68 -68s-68 30 -68 68z" />
+    <glyph glyph-name="camera_enhance" unicode="camera_enhance" 
+d="M256 149l-27 59l-58 27l58 26l27 59l27 -59l58 -26l-58 -27zM256 128c59 0 107 48 107 107s-48 106 -107 106s-107 -47 -107 -106s48 -107 107 -107zM192 448h128l39 -43h68c23 0 42 -19 42 -42v-256c0 -23 -19 -43 -42 -43h-342c-23 0 -42 20 -42 43v256
+c0 23 19 42 42 42h68z" />
+    <glyph glyph-name="camera_front" unicode="camera_front" 
+d="M149 469v-224c0 36 71 54 107 54s107 -18 107 -54v224h-214zM363 512c23 0 42 -20 42 -43v-298c0 -23 -19 -43 -42 -43h-150l64 -64l-64 -64v43h-106v42h106v43h-64c-23 0 -42 20 -42 43v298c0 23 19 43 42 43h214zM256 341c-23 0 -42 20 -42 43s19 43 42 43
+s43 -20 43 -43s-20 -43 -43 -43zM299 85h106v-42h-106v42z" />
+    <glyph glyph-name="camera_rear" unicode="camera_rear" 
+d="M256 384c23 0 42 20 42 43s-19 42 -42 42s-43 -19 -43 -42s19 -43 43 -43zM363 512c23 0 42 -20 42 -43v-298c0 -23 -19 -43 -42 -43h-150l64 -64l-64 -64v43h-106v42h106v43h-64c-23 0 -42 20 -42 43v298c0 23 19 43 42 43h214zM299 85h106v-42h-106v42z" />
+    <glyph glyph-name="camera_roll" unicode="camera_roll" 
+d="M427 320v43h-43v-43h43zM427 128v43h-43v-43h43zM341 320v43h-42v-43h42zM341 128v43h-42v-43h42zM256 320v43h-43v-43h43zM256 128v43h-43v-43h43zM299 405h170v-320h-170c0 -23 -20 -42 -43 -42h-171c-23 0 -42 19 -42 42v320c0 23 19 43 42 43h22v21c0 12 9 22 21 22
+h85c12 0 22 -10 22 -22v-21h21c23 0 43 -20 43 -43z" />
+    <glyph glyph-name="cancel" unicode="cancel" 
+d="M363 179l-77 77l77 77l-30 30l-77 -77l-77 77l-30 -30l77 -77l-77 -77l30 -30l77 77l77 -77zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="card_giftcard" unicode="card_giftcard" 
+d="M427 213v128h-109l45 -60l-35 -25c-24.0378 32.6288 -48.3187 65.0147 -72 98c-23.6829 -32.9837 -47.9614 -65.3719 -72 -98l-35 25l45 60h-109v-128h342zM427 107v42h-342v-42h342zM192 427c-12 0 -21 -10 -21 -22s9 -21 21 -21s21 9 21 21s-9 22 -21 22zM320 427
+c-12 0 -21 -10 -21 -22s9 -21 21 -21s21 9 21 21s-9 22 -21 22zM427 384c24 0 42 -19 42 -43v-234c0 -24 -18 -43 -42 -43h-342c-24 0 -42 19 -42 43v234c0 24 18 43 42 43h47c-2 7 -4 14 -4 21c0 35 29 64 64 64c22 0 41 -11 53 -28l11 -15l11 15c12 17 31 28 53 28
+c35 0 64 -29 64 -64c0 -7 -2 -14 -4 -21h47z" />
+    <glyph glyph-name="card_membership" unicode="card_membership" 
+d="M427 299v128h-342v-128h342zM427 192v43h-342v-43h342zM427 469c24 0 42 -18 42 -42v-235c0 -24 -18 -43 -42 -43h-86v-106l-85 42l-85 -42v106h-86c-24 0 -42 19 -42 43v235c0 24 18 42 42 42h342z" />
+    <glyph glyph-name="card_travel" unicode="card_travel" 
+d="M427 213v128h-64v-42h-43v42h-128v-42h-43v42h-64v-128h342zM427 107v42h-342v-42h342zM192 427v-43h128v43h-128zM427 384c24 0 42 -19 42 -43v-234c0 -24 -18 -43 -42 -43h-342c-24 0 -42 19 -42 43v234c0 24 18 43 42 43h64v43c0 24 19 42 43 42h128
+c24 0 43 -18 43 -42v-43h64z" />
+    <glyph glyph-name="casino" unicode="casino" 
+d="M352 320c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM352 128c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM256 224c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM160 320c18 0 32 14 32 32s-14 32 -32 32
+s-32 -14 -32 -32s14 -32 32 -32zM160 128c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h298z" />
+    <glyph glyph-name="cast" unicode="cast" 
+d="M21 299c129 0 235 -105 235 -235h-43c0 106 -86 192 -192 192v43zM21 213c83 0 150 -66 150 -149h-43c0 59 -48 107 -107 107v42zM21 128c35 0 64 -29 64 -64h-64v64zM448 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-149v43h149v298h-384v-64h-43v64
+c0 23 20 43 43 43h384z" />
+    <glyph glyph-name="cast_connected" unicode="cast_connected" 
+d="M448 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-149v43h149v298h-384v-64h-43v64c0 23 20 43 43 43h384zM21 299c129 0 235 -105 235 -235h-43c0 106 -86 192 -192 192v43zM405 363v-214h-120c-27 84 -94 152 -178 179v35h298zM21 213c83 0 150 -66 150 -149
+h-43c0 59 -48 107 -107 107v42zM21 128c35 0 64 -29 64 -64h-64v64z" />
+    <glyph glyph-name="center_focus_strong" unicode="center_focus_strong" 
+d="M405 107v85h43v-85c0 -23 -20 -43 -43 -43h-85v43h85zM405 448c23 0 43 -20 43 -43v-85h-43v85h-85v43h85zM107 405v-85h-43v85c0 23 20 43 43 43h85v-43h-85zM107 192v-85h85v-43h-85c-23 0 -43 20 -43 43v85h43zM256 341c47 0 85 -38 85 -85s-38 -85 -85 -85
+s-85 38 -85 85s38 85 85 85z" />
+    <glyph glyph-name="center_focus_weak" unicode="center_focus_weak" 
+d="M256 213c23 0 43 20 43 43s-20 43 -43 43s-43 -20 -43 -43s20 -43 43 -43zM256 341c47 0 85 -38 85 -85s-38 -85 -85 -85s-85 38 -85 85s38 85 85 85zM405 107v85h43v-85c0 -23 -20 -43 -43 -43h-85v43h85zM405 448c23 0 43 -20 43 -43v-85h-43v85h-85v43h85zM107 405
+v-85h-43v85c0 23 20 43 43 43h85v-43h-85zM107 192v-85h85v-43h-85c-23 0 -43 20 -43 43v85h43z" />
+    <glyph glyph-name="change_history" unicode="change_history" 
+d="M256 427l213 -342h-426zM256 346l-136 -218h272z" />
+    <glyph glyph-name="chat" unicode="chat" 
+d="M384 341v43h-256v-43h256zM299 213v43h-171v-43h171zM128 320v-43h256v43h-256zM427 469c23 0 42 -19 42 -42v-256c0 -23 -19 -43 -42 -43h-299l-85 -85v384c0 23 19 42 42 42h342z" />
+    <glyph glyph-name="chat_bubble" unicode="chat_bubble" 
+d="M427 469c23 0 42 -19 42 -42v-256c0 -23 -19 -43 -42 -43h-299l-85 -85v384c0 23 19 42 42 42h342z" />
+    <glyph glyph-name="chat_bubble_outline" unicode="chat_bubble_outline" 
+d="M427 171v256h-342v-299l43 43h299zM427 469c23 0 42 -19 42 -42v-256c0 -23 -19 -43 -42 -43h-299l-85 -85v384c0 23 19 42 42 42h342z" />
+    <glyph glyph-name="check" unicode="check" 
+d="M192 167l226 226l30 -30l-256 -256l-119 119l30 30z" />
+    <glyph glyph-name="check_box" unicode="check_box" 
+d="M213 149l192 192l-30 31l-162 -162l-76 76l-30 -30zM405 448c24 0 43 -20 43 -43v-298c0 -23 -19 -43 -43 -43h-298c-24 0 -43 20 -43 43v298c0 23 19 43 43 43h298z" />
+    <glyph glyph-name="check_box_outline_blank" unicode="check_box_outline_blank" 
+d="M405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h298zM405 405h-298v-298h298v298z" />
+    <glyph glyph-name="check_circle" unicode="check_circle" 
+d="M213 149l192 192l-30 31l-162 -162l-76 76l-30 -30zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="chevron_left" unicode="chevron_left" 
+d="M329 354l-98 -98l98 -98l-30 -30l-128 128l128 128z" />
+    <glyph glyph-name="chevron_right" unicode="chevron_right" 
+d="M213 384l128 -128l-128 -128l-30 30l98 98l-98 98z" />
+    <glyph glyph-name="child_care" unicode="child_care" 
+d="M160 213h192c-16 -38 -53 -64 -96 -64s-80 26 -96 64zM256 107c68.0018 0 125.232 44.8128 143 107c2 0 4 -1 6 -1c23 0 43 20 43 43s-20 43 -43 43c-2 0 -4 -1 -6 -1c-17.7643 62.1749 -74.9972 107 -143 107c-68.0018 0 -125.232 -44.8128 -143 -107c-2 0 -4 1 -6 1
+c-23 0 -43 -20 -43 -43s20 -43 43 -43c2 0 4 1 6 1c17.7643 -62.1749 74.9972 -107 143 -107zM489 242c-5 -32 -28 -59 -59 -68c-28.2678 -59.1055 -94.8338 -110 -174 -110c-78.8834 0 -145.522 50.0487 -173 110c-31 9 -55 36 -60 68c-1 4 -2 9 -2 14s1 10 2 14
+c5 32 29 59 60 68c11 24 26 45 46 62c34 30 78 48 127 48c80.292 0 145.109 -49.147 173 -110c31 -9 55 -36 60 -68c1 -4 2 -9 2 -14s-1 -10 -2 -14zM176 288c0 15 12 27 27 27s26 -12 26 -27s-11 -27 -26 -27s-27 12 -27 27zM283 288c0 15 11 27 26 27s27 -12 27 -27
+s-12 -27 -27 -27s-26 12 -26 27z" />
+    <glyph glyph-name="child_friendly" unicode="child_friendly" 
+d="M363 85c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM171 85c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM412 173c15 -14 25 -34 25 -56c0 -41 -33 -74 -74 -74c-38 0 -69 28 -74 64h-45c-5 -36 -35 -64 -73 -64
+c-41 0 -75 33 -75 74c0 28 16 53 39 66c-5 7 -45 94 -45 94h-47v43h74l20 -43h311c0 -39 -14 -75 -36 -104zM277 469c94 0 171 -76 171 -170h-171v170z" />
+    <glyph glyph-name="chrome_reader_mode" unicode="chrome_reader_mode" 
+d="M448 107v277h-192v-277h192zM448 427c23 0 43 -20 43 -43v-277c0 -23 -20 -43 -43 -43h-384c-23 0 -43 20 -43 43v277c0 23 20 43 43 43h384zM277 203h150v-32h-150v32zM277 309h150v-32h-150v32zM277 256h150v-32h-150v32z" />
+    <glyph glyph-name="class" unicode="class" 
+d="M128 427v-171l53 32l54 -32v171h-107zM384 469c23 0 43 -19 43 -42v-342c0 -23 -20 -42 -43 -42h-256c-23 0 -43 19 -43 42v342c0 23 20 42 43 42h256z" />
+    <glyph glyph-name="clear" unicode="clear" 
+d="M405 375l-119 -119l119 -119l-30 -30l-119 119l-119 -119l-30 30l119 119l-119 119l30 30l119 -119l119 119z" />
+    <glyph glyph-name="clear_all" unicode="clear_all" 
+d="M149 363h299v-43h-299v43zM64 149v43h299v-43h-299zM107 235v42h298v-42h-298z" />
+    <glyph glyph-name="close" unicode="close" 
+d="M405 375l-119 -119l119 -119l-30 -30l-119 119l-119 -119l-30 30l119 119l-119 119l30 30l119 -119l119 119z" />
+    <glyph glyph-name="closed_caption" unicode="closed_caption" 
+d="M384 277v22c0 12 -9 21 -21 21h-64c-12 0 -22 -9 -22 -21v-86c0 -12 10 -21 22 -21h64c12 0 21 9 21 21v22h-32v-11h-43v64h43v-11h32zM235 277v22c0 12 -10 21 -22 21h-64c-12 0 -21 -9 -21 -21v-86c0 -12 9 -21 21 -21h64c12 0 22 9 22 21v22h-32v-11h-43v64h43v-11h32
+zM405 427c23 0 43 -20 43 -43v-256c0 -23 -20 -43 -43 -43h-298c-24 0 -43 20 -43 43v256c0 23 19 43 43 43h298z" />
+    <glyph glyph-name="cloud" unicode="cloud" 
+d="M413 298c55 -4 99 -50 99 -106c0 -59 -48 -107 -107 -107h-277c-71 0 -128 57 -128 128c0 66 50 120 114 127c27 51 80 87 142 87c78 0 142 -55 157 -129z" />
+    <glyph glyph-name="cloud_circle" unicode="cloud_circle" 
+d="M352 171c29 0 53 24 53 53s-24 53 -53 53h-11c0 47 -38 86 -85 86c-40 0 -73 -28 -82 -65l-3 1c-35 0 -64 -29 -64 -64s29 -64 64 -64h181zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="cloud_done" unicode="cloud_done" 
+d="M213 149l141 141l-30 30l-111 -110l-44 44l-30 -30zM413 298c55 -4 99 -50 99 -106c0 -59 -48 -107 -107 -107h-277c-71 0 -128 57 -128 128c0 66 50 120 114 127c27 51 80 87 142 87c78 0 142 -55 157 -129z" />
+    <glyph glyph-name="cloud_download" unicode="cloud_download" 
+d="M363 235h-64v85h-86v-85h-64l107 -107zM413 298c55 -4 99 -50 99 -106c0 -59 -48 -107 -107 -107h-277c-71 0 -128 57 -128 128c0 66 50 120 114 127c27 51 80 87 142 87c78 0 142 -55 157 -129z" />
+    <glyph glyph-name="cloud_off" unicode="cloud_off" 
+d="M165 299h-37c-47 0 -85 -39 -85 -86s38 -85 85 -85h208zM64 400l27 27l357 -357l-27 -27l-43 42h-250c-71 0 -128 57 -128 128c0 69 55 125 123 128zM413 298c55 -4 99 -50 99 -106c0 -36 -18 -68 -45 -87l-31 31c20 11 33 32 33 56c0 35 -29 64 -64 64h-32v11
+c0 65 -52 117 -117 117c-20 0 -38 -4 -54 -13l-32 31c25 16 54 25 86 25c78 0 142 -55 157 -129z" />
+    <glyph glyph-name="cloud_queue" unicode="cloud_queue" 
+d="M405 128c35 0 64 29 64 64s-29 64 -64 64h-32v11c0 65 -52 117 -117 117c-54 0 -99 -36 -113 -85h-15c-47 0 -85 -39 -85 -86s38 -85 85 -85h277zM413 298c55 -4 99 -50 99 -106c0 -59 -48 -107 -107 -107h-277c-71 0 -128 57 -128 128c0 66 50 120 114 127
+c27 51 80 87 142 87c78 0 142 -55 157 -129z" />
+    <glyph glyph-name="cloud_upload" unicode="cloud_upload" 
+d="M299 235h64l-107 106l-107 -106h64v-86h86v86zM413 298c55 -4 99 -50 99 -106c0 -59 -48 -107 -107 -107h-277c-71 0 -128 57 -128 128c0 66 50 120 114 127c27 51 80 87 142 87c78 0 142 -55 157 -129z" />
+    <glyph glyph-name="code" unicode="code" 
+d="M311 158l99 98l-99 98l30 30l128 -128l-128 -128zM201 158l-30 -30l-128 128l128 128l30 -30l-99 -98z" />
+    <glyph glyph-name="collections" unicode="collections" 
+d="M43 384h42v-299h299v-42h-299c-23 0 -42 19 -42 42v299zM235 256l-64 -85h256l-86 106l-63 -79zM469 171c0 -23 -19 -43 -42 -43h-256c-23 0 -43 20 -43 43v256c0 23 20 42 43 42h256c23 0 42 -19 42 -42v-256z" />
+    <glyph glyph-name="collections_bookmark" unicode="collections_bookmark" 
+d="M427 256v171h-107v-171l53 32zM427 469c23 0 42 -19 42 -42v-256c0 -23 -19 -43 -42 -43h-256c-23 0 -43 20 -43 43v256c0 23 20 42 43 42h256zM85 384v-299h299v-42h-299c-23 0 -42 19 -42 42v299h42z" />
+    <glyph glyph-name="color_lens" unicode="color_lens" 
+d="M373 256c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM309 341c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM203 341c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM139 256c18 0 32 14 32 32s-14 32 -32 32
+s-32 -14 -32 -32s14 -32 32 -32zM256 448c106 0 192 -77 192 -171c0 -59 -48 -106 -107 -106h-37c-18 0 -32 -14 -32 -32c0 -8 3 -15 8 -21s8 -14 8 -22c0 -18 -14 -32 -32 -32c-106 0 -192 86 -192 192s86 192 192 192z" />
+    <glyph glyph-name="colorize" unicode="colorize" 
+d="M148 107l172 172l-41 41l-172 -172zM442 392c8 -8 9 -21 0 -30l-67 -67l41 -41l-30 -30l-30 30l-191 -190h-101v101l190 191l-30 30l30 30l41 -41l67 67c8 8 22 8 30 0z" />
+    <glyph glyph-name="comment" unicode="comment" 
+d="M384 341v43h-256v-43h256zM384 277v43h-256v-43h256zM384 213v43h-256v-43h256zM469 427v-384l-85 85h-299c-23 0 -42 20 -42 43v256c0 23 19 42 42 42h342c23 0 42 -19 42 -42z" />
+    <glyph glyph-name="compare" unicode="compare" 
+d="M405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-106v192l106 -128v277h-106v43h106zM213 128v128l-106 -128h106zM213 448v43h43v-470h-43v43h-106c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h106z" />
+    <glyph glyph-name="compare_arrows" unicode="compare_arrows" 
+d="M320 235l-85 85l85 85v-64h149v-42h-149v-64zM192 213v64l85 -85l-85 -85v64h-149v42h149z" />
+    <glyph glyph-name="computer" unicode="computer" 
+d="M85 384v-213h342v213h-342zM427 128h85v-43h-512v43h85c-23 0 -42 20 -42 43v213c0 23 19 43 42 43h342c23 0 42 -20 42 -43v-213c0 -23 -19 -43 -42 -43z" />
+    <glyph glyph-name="confirmation_number" unicode="confirmation_number" 
+d="M277 331v42h-42v-42h42zM277 235v42h-42v-42h42zM277 139v42h-42v-42h42zM469 299c-23 0 -42 -20 -42 -43s19 -43 42 -43v-85c0 -23 -19 -43 -42 -43h-342c-23 0 -42 20 -42 43v85c24 0 42 20 42 43s-19 43 -42 43v85c0 24 19 43 42 43h342c23 0 42 -19 42 -43v-85z" />
+    <glyph glyph-name="contact_mail" unicode="contact_mail" 
+d="M469 256v128h-170v-128h170zM299 128v21c0 43 -85 66 -128 66s-128 -23 -128 -66v-21h256zM171 384c-35 0 -64 -29 -64 -64s29 -64 64 -64s64 29 64 64s-29 64 -64 64zM469 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-426c-23 0 -43 20 -43 43v298
+c0 23 20 43 43 43h426zM448 341l-64 -42l-64 42v22l64 -43l64 43v-22z" />
+    <glyph glyph-name="contact_phone" unicode="contact_phone" 
+d="M381 213c-5 13 -8 28 -8 43s3 30 8 43h35l32 42l-42 43c-28 -21 -49 -50 -59 -85c-4 -14 -6 -28 -6 -43s2 -29 6 -43c10 -34 31 -64 59 -85l42 43l-32 42h-35zM299 128v21c0 43 -85 66 -128 66s-128 -23 -128 -66v-21h256zM171 384c-35 0 -64 -29 -64 -64s29 -64 64 -64
+s64 29 64 64s-29 64 -64 64zM469 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-426c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h426z" />
+    <glyph glyph-name="contacts" unicode="contacts" 
+d="M363 149v32c0 36 -71 54 -107 54s-107 -18 -107 -54v-32h214zM256 368c-26 0 -48 -22 -48 -48s22 -48 48 -48s48 22 48 48s-22 48 -48 48zM427 427c23 0 42 -20 42 -43v-256c0 -23 -19 -43 -42 -43h-342c-23 0 -42 20 -42 43v256c0 23 19 43 42 43h342zM85 0v43h342v-43
+h-342zM427 512v-43h-342v43h342z" />
+    <glyph glyph-name="content_copy" unicode="content_copy" 
+d="M405 64v299h-234v-299h234zM405 405c23 0 43 -19 43 -42v-299c0 -23 -20 -43 -43 -43h-234c-23 0 -43 20 -43 43v299c0 23 20 42 43 42h234zM341 491v-43h-256v-299h-42v299c0 23 19 43 42 43h256z" />
+    <glyph glyph-name="content_cut" unicode="content_cut" 
+d="M405 448h64v-21l-149 -150l-43 43zM256 245c6 0 11 5 11 11s-5 11 -11 11s-11 -5 -11 -11s5 -11 11 -11zM128 85c23 0 43 19 43 43s-20 43 -43 43s-43 -19 -43 -43s20 -43 43 -43zM128 341c23 0 43 19 43 43s-20 43 -43 43s-43 -19 -43 -43s20 -43 43 -43zM206 349
+l263 -264v-21h-64l-149 149l-50 -50c5 -11 7 -22 7 -35c0 -47 -38 -85 -85 -85s-85 38 -85 85s38 85 85 85c13 0 24 -2 35 -7l50 50l-50 50c-11 -5 -22 -7 -35 -7c-47 0 -85 38 -85 85s38 85 85 85s85 -38 85 -85c0 -13 -2 -24 -7 -35z" />
+    <glyph glyph-name="content_paste" unicode="content_paste" 
+d="M405 85v342h-42v-64h-214v64h-42v-342h298zM256 469c-12 0 -21 -9 -21 -21s9 -21 21 -21s21 9 21 21s-9 21 -21 21zM405 469c23 0 43 -19 43 -42v-342c0 -23 -20 -42 -43 -42h-298c-23 0 -43 19 -43 42v342c0 23 20 42 43 42h89c9 25 32 43 60 43s51 -18 60 -43h89z" />
+    <glyph glyph-name="control_point" unicode="control_point" 
+d="M256 85c94 0 171 77 171 171s-77 171 -171 171s-171 -77 -171 -171s77 -171 171 -171zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213zM277 363v-86h86v-42h-86v-86h-42v86h-86v42h86v86h42z" />
+    <glyph glyph-name="control_point_duplicate" unicode="control_point_duplicate" 
+d="M320 107c82 0 149 67 149 149s-67 149 -149 149s-149 -67 -149 -149s67 -149 149 -149zM320 448c106 0 192 -86 192 -192s-86 -192 -192 -192s-192 86 -192 192s86 192 192 192zM43 256c0 -60 34 -111 85 -135v-46c-74 26 -128 98 -128 181s54 155 128 181v-46
+c-51 -24 -85 -75 -85 -135zM341 341v-64h64v-42h-64v-64h-42v64h-64v42h64v64h42z" />
+    <glyph glyph-name="copyright" unicode="copyright" 
+d="M256 85c94 0 171 77 171 171s-77 171 -171 171s-171 -77 -171 -171s77 -171 171 -171zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213zM253 317c-30.7612 0 -40 -27.2286 -40 -58v-6c0 -30.7691 9.25381 -58 40 -58
+c18.7788 0 35 11.4624 35 30h38c0 -19.5209 -10.5691 -34.2021 -22 -44c-12.8931 -11.0512 -27.9049 -18 -51 -18c-54.5845 0 -82 36.0734 -82 90v6c0 25.8562 7.26023 49.4403 20 64c13.1991 15.0847 34.3422 27 62 27c22.1368 0 40.6281 -7.62806 52 -19
+c11.0024 -11.0024 21 -27.9871 21 -49h-38c0 5 -1 9 -3 13s-4 9 -7 12c-5.41728 5.41728 -14.6761 10 -25 10z" />
+    <glyph glyph-name="create" unicode="create" 
+d="M442 362l-39 -39l-80 80l39 39c8 8 22 8 30 0l50 -50c8 -8 8 -22 0 -30zM64 144l236 236l80 -80l-236 -236h-80v80z" />
+    <glyph glyph-name="create_new_folder" unicode="create_new_folder" 
+d="M405 213v43h-64v64h-42v-64h-64v-43h64v-64h42v64h64zM427 384c24 0 42 -19 42 -43v-213c0 -24 -18 -43 -42 -43h-342c-24 0 -42 19 -42 43v256c0 24 18 43 42 43h128l43 -43h171z" />
+    <glyph glyph-name="credit_card" unicode="credit_card" 
+d="M427 341v43h-342v-43h342zM427 128v128h-342v-128h342zM427 427c24 0 42 -19 42 -43v-256c0 -24 -18 -43 -42 -43h-342c-24 0 -42 19 -42 43v256c0 24 18 43 42 43h342z" />
+    <glyph glyph-name="crop" unicode="crop" 
+d="M149 149h342v-42h-86v-86h-42v86h-214c-23 0 -42 19 -42 42v214h-86v42h86v86h42v-342zM363 192v171h-171v42h171c23 0 42 -19 42 -42v-171h-42z" />
+    <glyph glyph-name="crop_16_9" unicode="crop_&#x31;&#x36;_&#x39;" 
+d="M405 171v170h-298v-170h298zM405 384c23 0 43 -20 43 -43v-170c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v170c0 23 20 43 43 43h298z" />
+    <glyph glyph-name="crop_3_2" unicode="crop_&#x33;_&#x32;" 
+d="M405 128v256h-298v-256h298zM405 427c23 0 43 -20 43 -43v-256c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v256c0 23 20 43 43 43h298z" />
+    <glyph glyph-name="crop_5_4" unicode="crop_&#x35;_&#x34;" 
+d="M405 149v214h-298v-214h298zM405 405c23 0 43 -19 43 -42v-214c0 -23 -20 -42 -43 -42h-298c-23 0 -43 19 -43 42v214c0 23 20 42 43 42h298z" />
+    <glyph glyph-name="crop_7_5" unicode="crop_&#x37;_&#x35;" 
+d="M405 192v128h-298v-128h298zM405 363c23 0 43 -20 43 -43v-128c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v128c0 23 20 43 43 43h298z" />
+    <glyph glyph-name="crop_din" unicode="crop_din" 
+d="M405 107v298h-298v-298h298zM405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h298z" />
+    <glyph glyph-name="crop_free" unicode="crop_free" 
+d="M405 448c23 0 43 -20 43 -43v-85h-43v85h-85v43h85zM405 107v85h43v-85c0 -23 -20 -43 -43 -43h-85v43h85zM107 192v-85h85v-43h-85c-23 0 -43 20 -43 43v85h43zM64 405c0 23 20 43 43 43h85v-43h-85v-85h-43v85z" />
+    <glyph glyph-name="crop_landscape" unicode="crop_landscape" 
+d="M405 149v214h-298v-214h298zM405 405c23 0 43 -19 43 -42v-214c0 -23 -20 -42 -43 -42h-298c-23 0 -43 19 -43 42v214c0 23 20 42 43 42h298z" />
+    <glyph glyph-name="crop_original" unicode="crop_original" 
+d="M298 250l75 -101h-234l58 76l42 -51zM405 107v298h-298v-298h298zM405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h298z" />
+    <glyph glyph-name="crop_portrait" unicode="crop_portrait" 
+d="M363 107v298h-214v-298h214zM363 448c23 0 42 -20 42 -43v-298c0 -23 -19 -43 -42 -43h-214c-23 0 -42 20 -42 43v298c0 23 19 43 42 43h214z" />
+    <glyph glyph-name="crop_rotate" unicode="crop_rotate" 
+d="M171 171h256v-43h-43v-43h-43v43h-170c-24 0 -43 20 -43 43v170h-43v43h43v43h43v-256zM341 213v128h-128v43h128c23 0 43 -19 43 -43v-128h-43zM257 512c134 0 244 -104 255 -235h-32c-8 80 -57 148 -127 181l-29 -28l-81 81c5 0 9 1 14 1zM159 54l29 28l81 -81
+c-5 0 -9 -1 -14 -1c-134 0 -244 104 -255 235h32c8 -80 57 -148 127 -181z" />
+    <glyph glyph-name="crop_square" unicode="crop_square" 
+d="M384 128v256h-256v-256h256zM384 427c23 0 43 -20 43 -43v-256c0 -23 -20 -43 -43 -43h-256c-23 0 -43 20 -43 43v256c0 23 20 43 43 43h256z" />
+    <glyph glyph-name="dashboard" unicode="dashboard" 
+d="M277 448h171v-128h-171v128zM277 64v213h171v-213h-171zM64 64v128h171v-128h-171zM64 235v213h171v-213h-171z" />
+    <glyph glyph-name="data_usage" unicode="data_usage" 
+d="M256 107c47 0 89 22 116 56l56 -33c-39 -53 -101 -87 -172 -87c-118 0 -213 95 -213 213c0 111 84 201 192 212v-64c-72 -10 -128 -73 -128 -148c0 -83 66 -149 149 -149zM277 468c108 -11 192 -101 192 -212c0 -31 -6 -61 -18 -87l-56 33c6 17 10 35 10 54
+c0 75 -56 138 -128 148v64z" />
+    <glyph glyph-name="date_range" unicode="date_range" 
+d="M405 85v235h-298v-235h298zM405 427c23 0 43 -20 43 -43v-299c0 -23 -20 -42 -43 -42h-298c-24 0 -43 19 -43 42v299c0 23 19 43 43 43h21v42h43v-42h170v42h43v-42h21zM363 277v-42h-43v42h43zM277 277v-42h-42v42h42zM192 277v-42h-43v42h43z" />
+    <glyph glyph-name="dehaze" unicode="dehaze" 
+d="M43 395h426v-43h-426v43zM43 288h426v-43h-426v43zM43 181h426v-42h-426v42z" />
+    <glyph glyph-name="delete" unicode="delete" 
+d="M405 427v-43h-298v43h74l22 21h106l22 -21h74zM128 107v256h256v-256c0 -23 -20 -43 -43 -43h-170c-23 0 -43 20 -43 43z" />
+    <glyph glyph-name="delete_forever" unicode="delete_forever" 
+d="M331 427h74v-43h-298v43h74l22 21h106zM180 259l46 -46l-45 -45l30 -30l45 45l45 -45l30 30l-45 45l45 46l-30 30l-45 -46l-45 46zM128 107v256h256v-256c0 -23 -20 -43 -43 -43h-170c-23 0 -43 20 -43 43z" />
+    <glyph glyph-name="delete_sweep" unicode="delete_sweep" 
+d="M299 405v-42h-256v42h64l21 22h85l22 -22h64zM64 128v213h213v-213c0 -23 -19 -43 -42 -43h-128c-23 0 -43 20 -43 43zM320 256h128v-43h-128v43zM320 341h149v-42h-149v42zM320 171h85v-43h-85v43z" />
+    <glyph glyph-name="description" unicode="description" 
+d="M277 320h118l-118 117v-117zM341 213v43h-170v-43h170zM341 128v43h-170v-43h170zM299 469l128 -128v-256c0 -23 -20 -42 -43 -42h-256c-23 0 -43 19 -43 42l1 342c0 23 19 42 42 42h171z" />
+    <glyph glyph-name="desktop_mac" unicode="desktop_mac" 
+d="M448 213v214h-384v-214h384zM448 469c23 0 43 -19 43 -42v-256c0 -23 -20 -43 -43 -43h-149l42 -64v-21h-170v21l42 64h-149c-23 0 -43 20 -43 43v256c0 23 20 42 43 42h384z" />
+    <glyph glyph-name="desktop_windows" unicode="desktop_windows" 
+d="M448 171v256h-384v-256h384zM448 469c23 0 43 -19 43 -42v-256c0 -23 -20 -43 -43 -43h-149v-43h42v-42h-170v42h42v43h-149c-23 0 -43 20 -43 43v256c0 23 20 42 43 42h384z" />
+    <glyph glyph-name="details" unicode="details" 
+d="M136 384l120 -213l120 213h-240zM64 427h384l-192 -342z" />
+    <glyph glyph-name="developer_board" unicode="developer_board" 
+d="M256 277h85v-128h-85v128zM128 363h107v-107h-107v107zM256 363h85v-64h-85v64zM128 235h107v-86h-107v86zM384 107v298h-299v-298h299zM469 320h-42v-43h42v-42h-42v-43h42v-43h-42v-42c0 -23 -20 -43 -43 -43h-299c-23 0 -42 20 -42 43v298c0 23 19 43 42 43h299
+c23 0 43 -20 43 -43v-42h42v-43z" />
+    <glyph glyph-name="developer_mode" unicode="developer_mode" 
+d="M363 107v42h42v-85c0 -23 -19 -43 -42 -43h-214c-23 0 -42 20 -42 43v85h42v-42h214zM213 188l-30 -30l-98 98l98 98l30 -30l-67 -68zM329 158l-30 30l67 68l-67 68l30 30l98 -98zM149 405v-42h-42v85c0 23 19 43 42 43l214 -1c23 0 42 -19 42 -42v-85h-42v42h-214z" />
+    <glyph glyph-name="device_hub" unicode="device_hub" 
+d="M363 171h85v-107h-107v65l-85 90l-85 -90v-65h-107v107h85l86 85v68c-25 9 -43 32 -43 60c0 35 29 64 64 64s64 -29 64 -64c0 -28 -18 -51 -43 -60v-68z" />
+    <glyph glyph-name="devices" unicode="devices" 
+d="M469 149v150h-85v-150h85zM491 341c12 0 21 -9 21 -21v-213c0 -12 -9 -22 -21 -22h-128c-12 0 -22 10 -22 22v213c0 12 10 21 22 21h128zM85 384v-235h214v-64h-299v64h43v235c0 23 19 43 42 43h384v-43h-384z" />
+    <glyph glyph-name="devices_other" unicode="devices_other" 
+d="M448 128v171h-85v-171h85zM469 341c11 0 22 -10 22 -21v-213c0 -11 -11 -22 -22 -22h-128c-11 0 -21 11 -21 22v213c0 11 10 21 21 21h128zM235 139c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM277 256v-38c13 -12 22 -28 22 -47s-9 -36 -22 -48
+v-38h-85v38c-13 12 -21 29 -21 48s8 35 21 47v38h85zM64 384v-256h85v-43h-85c-23 0 -43 20 -43 43v256c0 23 20 43 43 43h384v-43h-384z" />
+    <glyph glyph-name="dialer_sip" unicode="dialer_sip" 
+d="M427 181c12 0 21 -9 21 -21v-75c0 -12 -9 -21 -21 -21c-200 0 -363 163 -363 363c0 12 9 21 21 21h75c12 0 21 -9 21 -21c0 -27 4 -52 12 -76c2 -7 1 -16 -5 -22l-47 -47c31 -61 81 -110 141 -141l47 47c6 6 15 7 22 5c24 -8 49 -12 76 -12zM427 405v22h-22v-22h22z
+M384 448h64v-64h-43v-43h-21v107zM320 405v-64h-64v22h43v21h-43v64h64v-21h-43v-22h43zM363 448v-107h-22v107h22z" />
+    <glyph glyph-name="dialpad" unicode="dialpad" 
+d="M256 491c23 0 43 -20 43 -43s-20 -43 -43 -43s-43 20 -43 43s20 43 43 43zM256 363c23 0 43 -20 43 -43s-20 -43 -43 -43s-43 20 -43 43s20 43 43 43zM384 363c23 0 43 -20 43 -43s-20 -43 -43 -43s-43 20 -43 43s20 43 43 43zM384 235c23 0 43 -20 43 -43
+s-20 -43 -43 -43s-43 20 -43 43s20 43 43 43zM256 235c23 0 43 -20 43 -43s-20 -43 -43 -43s-43 20 -43 43s20 43 43 43zM384 405c-23 0 -43 20 -43 43s20 43 43 43s43 -20 43 -43s-20 -43 -43 -43zM128 235c23 0 43 -20 43 -43s-20 -43 -43 -43s-43 20 -43 43s20 43 43 43z
+M128 363c23 0 43 -20 43 -43s-20 -43 -43 -43s-43 20 -43 43s20 43 43 43zM128 491c23 0 43 -20 43 -43s-20 -43 -43 -43s-43 20 -43 43s20 43 43 43zM256 107c23 0 43 -20 43 -43s-20 -43 -43 -43s-43 20 -43 43s20 43 43 43z" />
+    <glyph glyph-name="directions" unicode="directions" 
+d="M299 203l74 74l-74 75v-53h-107c-12 0 -21 -10 -21 -22v-85h42v64h86v-53zM463 271c8 -9 8 -22 0 -30l-192 -192c-8 -8 -22 -8 -30 0l-192 192c-8 8 -8 22 0 30l192 192c8 8 22 8 30 0z" />
+    <glyph glyph-name="directions_bike" unicode="directions_bike" 
+d="M405 75c41 0 75 33 75 74s-34 75 -75 75s-74 -34 -74 -75s33 -74 74 -74zM405 256c60 0 107 -47 107 -107s-47 -106 -107 -106s-106 46 -106 106s46 107 106 107zM230 288l47 -49v-132h-42v106l-69 60c-9 6 -12 17 -12 30c0 11 3 21 12 30l60 60c6 9 17 12 30 12
+s23 -3 34 -12l41 -41c19 -19 44 -32 76 -32v-43c-45 0 -80 17 -108 45l-17 17zM107 75c41 0 74 33 74 74s-33 75 -74 75s-75 -34 -75 -75s34 -74 75 -74zM107 256c60 0 106 -47 106 -107s-46 -106 -106 -106s-107 46 -107 106s47 107 107 107zM331 395c-23 0 -43 19 -43 42
+s20 43 43 43s42 -20 42 -43s-19 -42 -42 -42z" />
+    <glyph glyph-name="directions_boat" unicode="directions_boat" 
+d="M128 384v-85l128 42l128 -42v85h-256zM84 107l-40 142c-4.78212 14.3464 3.99001 23.6633 14 27l27 9v99c0 23 20 43 43 43h64v64h128v-64h64c23 0 43 -20 43 -43v-99l27 -9c10.0202 -3.34008 18.7871 -12.6386 14 -27l-40 -142h-1c-34 0 -65 18 -86 42
+c-21 -24 -51 -42 -85 -42s-64 18 -85 42c-21 -24 -52 -42 -86 -42h-1zM427 64h42v-43h-42c-29 0 -59 7 -86 21c-54 -28 -116 -28 -170 0c-27 -14 -57 -21 -86 -21h-42v43h42c30 0 60 10 86 28c52 -36 118 -36 170 0c26 -18 56 -28 86 -28z" />
+    <glyph glyph-name="directions_bus" unicode="directions_bus" 
+d="M384 277v107h-256v-107h256zM352 149c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM160 149c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM85 171v213c0 75 77 85 171 85s171 -10 171 -85v-213c0 -19 -9 -36 -22 -48v-38
+c0 -12 -9 -21 -21 -21h-21c-12 0 -22 9 -22 21v22h-170v-22c0 -12 -10 -21 -22 -21h-21c-12 0 -21 9 -21 21v38c-13 12 -22 29 -22 48z" />
+    <glyph glyph-name="directions_car" unicode="directions_car" 
+d="M107 277h298l-32 96h-234zM373 171c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM139 171c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM404 384l44 -128v-171c0 -12 -9 -21 -21 -21h-22c-12 0 -21 9 -21 21v22h-256v-22
+c0 -12 -9 -21 -21 -21h-22c-12 0 -21 9 -21 21v171l44 128c4 13 17 21 31 21h234c14 0 27 -8 31 -21z" />
+    <glyph glyph-name="directions_railway" unicode="directions_railway" 
+d="M384 299v106h-256v-106h256zM256 149c23 0 43 20 43 43s-20 43 -43 43s-43 -20 -43 -43s20 -43 43 -43zM85 181v224c0 75 77 86 171 86s171 -11 171 -86v-224c0 -41 -34 -74 -75 -74l32 -32v-11h-256v11l32 32c-41 0 -75 33 -75 74z" />
+    <glyph glyph-name="directions_run" unicode="directions_run" 
+d="M211 99l-149 29l8 43l105 -21l34 173l-39 -15v-73h-42v100l111 47c6 0 11 2 17 2c15 0 27 -8 36 -21l21 -34c17 -30 51 -51 92 -51v-43c-47 0 -89 21 -117 53l-13 -64l45 -42v-160h-43v128l-45 42zM288 395c-23 0 -43 20 -43 43s20 42 43 42s42 -19 42 -42
+s-19 -43 -42 -43z" />
+    <glyph glyph-name="directions_subway" unicode="directions_subway" 
+d="M384 277v107h-107v-107h107zM352 149c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM235 277v107h-107v-107h107zM160 149c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM256 469c94 0 171 -10 171 -85v-203c0 -41 -34 -74 -75 -74
+l32 -32v-11h-256v11l32 32c-41 0 -75 33 -75 74v203c0 75 77 85 171 85z" />
+    <glyph glyph-name="directions_transit" unicode="directions_transit" 
+d="M384 277v107h-107v-107h107zM352 149c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM235 277v107h-107v-107h107zM160 149c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM256 469c94 0 171 -10 171 -85v-203c0 -41 -34 -74 -75 -74
+l32 -32v-11h-256v11l32 32c-41 0 -75 33 -75 74v203c0 75 77 85 171 85z" />
+    <glyph glyph-name="directions_walk" unicode="directions_walk" 
+d="M209 322l-60 -301h45l39 171l44 -43v-128h43v160l-45 43l13 64c28 -32 70 -53 117 -53v42c-41 0 -74 22 -91 52l-22 34c-9 13 -21 21 -36 21c-6 0 -11 -2 -17 -2l-111 -47v-100h43v72l38 15zM288 395c-23 0 -43 19 -43 42s20 43 43 43s43 -20 43 -43s-20 -42 -43 -42z
+" />
+    <glyph glyph-name="disc_full" unicode="disc_full" 
+d="M213 213c23 0 43 20 43 43s-20 43 -43 43s-42 -20 -42 -43s19 -43 42 -43zM213 427c94 0 171 -77 171 -171s-77 -171 -171 -171s-170 77 -170 171s76 171 170 171zM427 363h42v-107h-42v107zM427 171v42h42v-42h-42z" />
+    <glyph glyph-name="dns" unicode="dns" 
+d="M149 320c23 0 43 20 43 43s-20 42 -43 42s-42 -19 -42 -42s19 -43 42 -43zM427 448c12 0 21 -9 21 -21v-128c0 -12 -9 -22 -21 -22h-342c-12 0 -21 10 -21 22v128c0 12 9 21 21 21h342zM149 107c23 0 43 19 43 42s-20 43 -43 43s-42 -20 -42 -43s19 -42 42 -42zM427 235
+c12 0 21 -10 21 -22v-128c0 -12 -9 -21 -21 -21h-342c-12 0 -21 9 -21 21v128c0 12 9 22 21 22h342z" />
+    <glyph glyph-name="do_not_disturb" unicode="do_not_disturb" 
+d="M391 151c23 29 36 66 36 105c0 94 -77 171 -171 171c-39 0 -76 -13 -105 -36zM256 85c39 0 76 13 105 36l-240 240c-23 -29 -36 -66 -36 -105c0 -94 77 -171 171 -171zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="do_not_disturb_alt" unicode="do_not_disturb_alt" 
+d="M256 85c94 0 171 77 171 171c0 38 -14 75 -37 105l-239 -239c30 -23 67 -37 105 -37zM85 256c0 -38 14 -75 37 -105l239 239c-30 23 -67 37 -105 37c-94 0 -171 -77 -171 -171zM256 469c117 0 213 -96 213 -213s-96 -213 -213 -213s-213 96 -213 213s96 213 213 213z" />
+    <glyph glyph-name="do_not_disturb_off" unicode="do_not_disturb_off" 
+d="M149 235h74l-43 42h-31v-42zM48 464l416 -416l-28 -27l-59 60c-34 -24 -76 -38 -121 -38c-118 0 -213 95 -213 213c0 45 14 87 38 121l-60 59zM363 277h-74l-154 154c34 24 76 38 121 38c118 0 213 -95 213 -213c0 -45 -14 -87 -38 -121l-99 100h31v42z" />
+    <glyph glyph-name="do_not_disturb_on" unicode="do_not_disturb_on" 
+d="M363 235v42h-214v-42h214zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="dock" unicode="dock" 
+d="M341 192v213h-170v-213h170zM341 490c23 0 43 -19 43 -42v-299c0 -23 -20 -42 -43 -42h-170c-23 0 -43 19 -43 42v299c0 23 20 43 43 43zM171 21v43h170v-43h-170z" />
+    <glyph glyph-name="domain" unicode="domain" 
+d="M384 192v-43h-43v43h43zM384 277v-42h-43v42h43zM427 107v213h-171v-43h43v-42h-43v-43h43v-43h-43v-42h171zM213 363v42h-42v-42h42zM213 277v43h-42v-43h42zM213 192v43h-42v-43h42zM213 107v42h-42v-42h42zM128 363v42h-43v-42h43zM128 277v43h-43v-43h43zM128 192v43
+h-43v-43h43zM128 107v42h-43v-42h43zM256 363h213v-299h-426v384h213v-85z" />
+    <glyph glyph-name="done" unicode="done" 
+d="M192 166l226 227l30 -30l-256 -256l-119 119l29 30z" />
+    <glyph glyph-name="done_all" unicode="done_all" 
+d="M9 226l30 30l119 -119l-30 -30zM474 393l31 -30l-256 -256l-120 119l31 30l89 -89zM384 363l-135 -136l-30 30l135 136z" />
+    <glyph glyph-name="donut_large" unicode="donut_large" 
+d="M277 108c64 9 119 63 128 127h64c-10 -107 -85 -182 -192 -192v65zM405 277c-9 64 -64 118 -128 127v65c107 -10 182 -85 192 -192h-64zM235 404c-64 -10 -128 -73 -128 -148s64 -138 128 -148v-65c-107 11 -192 102 -192 213s85 202 192 213v-65z" />
+    <glyph glyph-name="donut_small" unicode="donut_small" 
+d="M277 195c21 6 33 22 40 40h152c-10 -101 -85 -182 -192 -192v152zM317 277c-7 18 -19 34 -40 40v152c107 -10 182 -91 192 -192h-152zM235 317c-21 -9 -43 -33 -43 -61s22 -52 43 -61v-152c-107 11 -192 102 -192 213s85 202 192 213v-152z" />
+    <glyph glyph-name="drafts" unicode="drafts" 
+d="M256 235l176 110l-176 103l-176 -103zM469 341v-213c0 -23 -19 -43 -42 -43h-342c-23 0 -42 20 -42 43v213c0 15 8 30 20 37l193 113l193 -113c12 -7 20 -22 20 -37z" />
+    <glyph glyph-name="drag_handle" unicode="drag_handle" 
+d="M85 192v43h342v-43h-342zM427 320v-43h-342v43h342z" />
+    <glyph glyph-name="drive_eta" unicode="drive_eta" 
+d="M107 299h298l-32 96h-234zM373 192c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM139 192c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM404 405l44 -128v-170c0 -12 -9 -22 -21 -22h-22c-12 0 -21 10 -21 22v21h-256v-21
+c0 -12 -9 -22 -21 -22h-22c-12 0 -21 10 -21 22v170l44 128c4 13 17 22 31 22h234c14 0 27 -9 31 -22z" />
+    <glyph glyph-name="dvr" unicode="dvr" 
+d="M149 256v-43h-42v43h42zM149 341v-42h-42v42h42zM405 256v-43h-234v43h234zM405 341v-42h-234v42h234zM448 149v256h-384v-256h384zM448 448c23 0 43 -20 43 -43l-1 -256c0 -23 -19 -42 -42 -42h-107v-43h-170v43h-107c-23 0 -43 19 -43 42v256c0 23 20 43 43 43h384z
+" />
+    <glyph glyph-name="edit" unicode="edit" 
+d="M442 362l-39 -39l-80 80l39 39c8 8 22 8 30 0l50 -50c8 -8 8 -22 0 -30zM64 144l236 236l80 -80l-236 -236h-80v80z" />
+    <glyph glyph-name="edit_location" unicode="edit_location" 
+d="M318 351c3 3 3 8 0 11l-20 20c-3 3 -8 3 -11 0l-15 -15l31 -31zM223 256l71 71l-31 31l-71 -71v-31h31zM256 469c82 0 149 -67 149 -149c0 -112 -149 -277 -149 -277s-149 165 -149 277c0 82 67 149 149 149z" />
+    <glyph glyph-name="eject" unicode="eject" 
+d="M256 405l142 -213h-284zM107 149h298v-42h-298v42z" />
+    <glyph glyph-name="email" unicode="email" 
+d="M427 341v43l-171 -107l-171 107v-43l171 -106zM427 427c23 0 42 -20 42 -43v-256c0 -23 -19 -43 -42 -43h-342c-23 0 -42 20 -42 43v256c0 23 19 43 42 43h342z" />
+    <glyph glyph-name="enhanced_encryption" unicode="enhanced_encryption" 
+d="M341 171v42h-64v64h-42v-64h-64v-42h64v-64h42v64h64zM190 384v-43h132v43c0 36 -30 66 -66 66s-66 -30 -66 -66zM384 341c23 0 43 -19 43 -42v-214c0 -23 -20 -42 -43 -42h-256c-23 0 -43 19 -43 42v214c0 23 20 42 43 42h21v43c0 59 48 107 107 107s107 -48 107 -107
+v-43h21z" />
+    <glyph glyph-name="equalizer" unicode="equalizer" 
+d="M341 320h86v-235h-86v235zM85 85v171h86v-171h-86zM213 85v342h86v-342h-86z" />
+    <glyph glyph-name="error" unicode="error" 
+d="M277 235v128h-42v-128h42zM277 149v43h-42v-43h42zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="error_outline" unicode="error_outline" 
+d="M256 85c94 0 171 77 171 171s-77 171 -171 171s-171 -77 -171 -171s77 -171 171 -171zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213zM235 363h42v-128h-42v128zM235 192h42v-43h-42v43z" />
+    <glyph glyph-name="euro_symbol" unicode="euro_symbol" 
+d="M320 117c35 0 66 13 90 34l38 -38c-34 -31 -79 -49 -128 -49c-84 0 -155 54 -181 128h-75v43h65c-1 7 -1 14 -1 21s0 14 1 21h-65v43h75c26 74 97 128 181 128c49 0 94 -18 128 -49l-38 -38c-24 21 -56 34 -90 34c-53 0 -100 -31 -123 -75h123v-43h-137
+c-1 -7 -2 -14 -2 -21s1 -14 2 -21h137v-43h-123c23 -44 69 -75 123 -75z" />
+    <glyph glyph-name="ev_station" unicode="ev_station" 
+d="M171 128l85 149h-43v107l-85 -160h43v-96zM384 299c12 0 21 9 21 21s-9 21 -21 21s-21 -9 -21 -21s9 -21 21 -21zM422 358c10 -10 15 -23 15 -38v-203c0 -29 -24 -53 -53 -53s-53 24 -53 53v107h-32v-160h-214v341c0 23 20 43 43 43h128c23 0 43 -20 43 -43v-149h21
+c23 0 43 -20 43 -43v-96c0 -12 9 -21 21 -21s21 9 21 21v154c-7 -3 -13 -4 -21 -4c-29 0 -53 24 -53 53c0 23 14 42 34 50l-45 45l23 22z" />
+    <glyph glyph-name="event" unicode="event" 
+d="M405 107v234h-298v-234h298zM341 491h43v-43h21c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-24 0 -43 20 -43 43v298c0 23 19 43 43 43h21v43h43v-43h170v43zM363 256v-107h-107v107h107z" />
+    <glyph glyph-name="event_available" unicode="event_available" 
+d="M405 107v234h-298v-234h298zM405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-24 0 -43 20 -43 43v298c0 23 19 43 43 43h21v43h43v-43h170v43h43v-43h21zM353 276l-127 -127l-68 68l23 23l45 -45l104 104z" />
+    <glyph glyph-name="event_busy" unicode="event_busy" 
+d="M405 107v234h-298v-234h298zM405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-24 0 -43 20 -43 43v298c0 23 19 43 43 43h21v43h43v-43h170v43h43v-43h21zM199 149l-23 23l52 52l-52 52l23 23l52 -52l52 52l22 -23l-52 -52l52 -52l-22 -23l-52 52z" />
+    <glyph glyph-name="event_note" unicode="event_note" 
+d="M299 213v-42h-150v42h150zM405 107v234h-298v-234h298zM405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-24 0 -43 20 -43 43v298c0 23 19 43 43 43h21v43h43v-43h170v43h43v-43h21zM363 299v-43h-214v43h214z" />
+    <glyph glyph-name="event_seat" unicode="event_seat" 
+d="M363 235h-214v170c0 23 20 43 43 43h128c23 0 43 -20 43 -43v-170zM43 299h64v-64h-64v64zM405 299h64v-64h-64v64zM85 64v128h342v-128h-64v64h-214v-64h-64z" />
+    <glyph glyph-name="exit_to_app" unicode="exit_to_app" 
+d="M405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-24 0 -43 20 -43 43v85h43v-85h298v298h-298v-85h-43v85c0 23 19 43 43 43h298zM215 179l55 56h-206v42h206l-55 56l30 30l107 -107l-107 -107z" />
+    <glyph glyph-name="expand_less" unicode="expand_less" 
+d="M256 341l128 -128l-30 -30l-98 98l-98 -98l-30 30z" />
+    <glyph glyph-name="expand_more" unicode="expand_more" 
+d="M354 329l30 -30l-128 -128l-128 128l30 30l98 -98z" />
+    <glyph glyph-name="explicit" unicode="explicit" 
+d="M320 320v43h-128v-214h128v43h-85v43h85v42h-85v43h85zM405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h298z" />
+    <glyph glyph-name="explore" unicode="explore" 
+d="M303 209l81 175l-175 -81l-81 -175zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213zM256 279c13 0 23 -10 23 -23s-10 -23 -23 -23s-23 10 -23 23s10 23 23 23z" />
+    <glyph glyph-name="exposure" unicode="exposure" 
+d="M427 85v342l-342 -342h342zM107 405v-42h128v42h-128zM427 469c23 0 42 -19 42 -42v-342c0 -23 -19 -42 -42 -42h-342c-23 0 -42 19 -42 42v342c0 23 19 42 42 42h342zM320 149h-43v43h43v43h43v-43h42v-43h-42v-42h-43v42z" />
+    <glyph glyph-name="exposure_neg_1" unicode="exposure_neg_&#x31;" 
+d="M405 128h-42v227l-64 -22v36l100 36h6v-277zM85 277h171v-42h-171v42z" />
+    <glyph glyph-name="exposure_neg_2" unicode="exposure_neg_&#x32;" 
+d="M43 277h170v-42h-170v42zM351 405c51.6805 0 86 -23.4377 86 -75c0 -8 -1 -17 -4 -25s-6 -17 -11 -25c-10.9564 -17.5303 -25.11 -35.11 -40 -50l-61 -66h127v-36h-184v32l89 97c13.5256 13.5256 22.2329 25.3882 31 40c5.07755 8.46258 7 15.5272 7 28c0 6 0 13 -2 18
+c-5.9589 14.8973 -17.1504 26 -38 26c-30.4379 0 -46 -18.4676 -46 -49h-46c0 25.6678 10.3448 46.3448 24 60c15.6925 15.6925 36.8462 25 68 25z" />
+    <glyph glyph-name="exposure_plus_1" unicode="exposure_plus_&#x31;" 
+d="M427 128h-43v227l-64 -22v36l100 36h7v-277zM213 363v-86h86v-42h-86v-86h-42v86h-86v42h86v86h42z" />
+    <glyph glyph-name="exposure_plus_2" unicode="exposure_plus_&#x32;" 
+d="M171 363v-86h85v-42h-85v-86h-43v86h-85v42h85v86h43zM342 164h127v-36h-184v32l89 97c13.5256 13.5256 22.2329 25.3882 31 40c4.82857 8.04762 8 16.6883 8 28c0 13.988 -4.98597 22.9813 -11 31c-6.38798 8.51731 -15.0381 13 -30 13
+c-15.4008 0 -27.3364 -6.33642 -35 -14c-6.99604 -6.99604 -11 -21.7099 -11 -35h-46c0 25.6678 10.3448 46.3448 24 60c8 8 17 15 29 19s25 6 40 6c14 0 25 -2 36 -5c10 -4 20 -8 27 -15c13.4025 -11.4878 23 -30.9925 23 -55c0 -20.2222 -7.3586 -36.1738 -16 -50
+c-5 -8 -10 -17 -17 -25s-15 -17 -23 -25z" />
+    <glyph glyph-name="exposure_zero" unicode="exposure_zero" 
+d="M299 296c0 25.8021 -1.44348 42.6652 -11 57c-4 6 -9 9 -14 12s-11 4 -18 4s-13 -1 -18 -4s-10 -6 -14 -12c-9.53524 -14.3029 -11 -31.2267 -11 -57v-57c0 -34.3202 4.66098 -58.7966 25 -71c5 -3 11 -4 18 -4c16.7682 0 24.8052 6.20777 32 17
+c8.87006 13.3051 12 34.4084 12 58v57h-1zM168 289c0 68.5293 21.3782 116 88 116c46.5088 0 71.845 -23.9186 82 -62c4 -15 7 -32 7 -54v-44h-1c0 -39.7402 -7.68654 -69.0255 -24 -90c-8 -9 -17 -17 -28 -21s-23 -6 -36 -6s-25 2 -36 6s-20 12 -28 21
+c-17.4464 19.6272 -24 51.2554 -24 90v44z" />
+    <glyph glyph-name="extension" unicode="extension" 
+d="M437 277c29 0 54 -24 54 -53s-25 -53 -54 -53h-32v-86c0 -23 -19 -42 -42 -42h-81v32c0 32 -26 57 -58 57s-58 -25 -58 -57v-32h-81c-23 0 -42 19 -42 42v81h32c32 0 57 26 57 58s-25 58 -57 58h-32v81c0 23 19 42 42 42h86v32c0 29 24 54 53 54s53 -25 53 -54v-32h86
+c23 0 42 -19 42 -42v-86h32z" />
+    <glyph glyph-name="face" unicode="face" 
+d="M256 85c94 0 171 77 171 171c0 17 -3 33 -7 48c-15 -4 -31 -5 -48 -5c-72 0 -135 35 -174 90c-21 -51 -62 -93 -112 -115c-1 -6 -1 -12 -1 -18c0 -94 77 -171 171 -171zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213zM320 261
+c15 0 27 -11 27 -26s-12 -27 -27 -27s-27 12 -27 27s12 26 27 26zM192 261c15 0 27 -11 27 -26s-12 -27 -27 -27s-27 12 -27 27s12 26 27 26z" />
+    <glyph glyph-name="fast_forward" unicode="fast_forward" 
+d="M277 384l182 -128l-182 -128v256zM85 128v256l182 -128z" />
+    <glyph glyph-name="fast_rewind" unicode="fast_rewind" 
+d="M245 256l182 128v-256zM235 128l-182 128l182 128v-256z" />
+    <glyph glyph-name="favorite" unicode="favorite" 
+d="M256 57l-31 28c-110 100 -182 165 -182 246c0 66 51 117 117 117c37 0 73 -18 96 -45c23 27 59 45 96 45c66 0 117 -51 117 -117c0 -81 -72 -147 -182 -247z" />
+    <glyph glyph-name="favorite_border" unicode="favorite_border" 
+d="M258 116c102 92 169 153 169 215c0 43 -32 74 -75 74c-33 0 -65 -21 -76 -50h-40c-11 29 -43 50 -76 50c-43 0 -75 -31 -75 -74c0 -62 67 -123 169 -215l2 -2zM352 448c66 0 117 -51 117 -117c0 -81 -72 -146 -182 -246l-31 -28l-31 27c-110 100 -182 166 -182 247
+c0 66 51 117 117 117c37 0 73 -18 96 -45c23 27 59 45 96 45z" />
+    <glyph glyph-name="featured_play_list" unicode="featured_play_list" 
+d="M256 363v42h-192v-42h192zM256 277v43h-192v-43h192zM448 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-384c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h384z" />
+    <glyph glyph-name="featured_video" unicode="featured_video" 
+d="M256 256v149h-192v-149h192zM448 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-384c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h384z" />
+    <glyph glyph-name="feedback" unicode="feedback" 
+d="M277 299v85h-42v-85h42zM277 213v43h-42v-43h42zM427 469c23 0 42 -19 42 -42v-256c0 -23 -19 -43 -42 -43h-299l-85 -85v384c0 23 19 42 42 42h342z" />
+    <glyph glyph-name="fiber_dvr" unicode="fiber_dvr" 
+d="M448 267v21c0 18 -14 32 -32 32h-75v-128h32v43h25l18 -43h32l-19 45c11 5 19 17 19 30zM269 192l38 128h-32l-22 -73l-21 73h-32l37 -128h32zM171 224v64c0 18 -14 32 -32 32h-75v-128h75c18 0 32 14 32 32zM448 448c24 0 43 -19 43 -43v-298c0 -23 -19 -43 -43 -43
+h-384c-24 0 -43 20 -43 43v298c0 24 19 43 43 43h384zM96 288h43v-64h-43v64zM373 288h43v-21h-43v21z" />
+    <glyph glyph-name="fiber_manual_record" unicode="fiber_manual_record" 
+d="M85 256c0 94 77 171 171 171s171 -77 171 -171s-77 -171 -171 -171s-171 77 -171 171z" />
+    <glyph glyph-name="fiber_new" unicode="fiber_new" 
+d="M437 213v107h-26v-96h-24v75h-27v-75h-24v96h-27v-107c0 -12 10 -21 22 -21h85c12 0 21 9 21 21zM288 293v27h-85v-128h85v27h-53v23h53v27h-53v24h53zM181 192v128h-26v-75l-54 75h-26v-128h26v75l55 -75h25zM427 427c24 0 42 -19 42 -43v-256c0 -24 -18 -43 -42 -43
+h-342c-24 0 -42 19 -42 43v256c0 24 18 43 42 43h342z" />
+    <glyph glyph-name="fiber_pin" unicode="fiber_pin" 
+d="M427 192v128h-27v-75l-53 75h-27v-128h27v75l54 -75h26zM267 192v128h-32v-128h32zM192 267v21c0 18 -14 32 -32 32h-75v-128h32v43h43c18 0 32 14 32 32zM427 427c24 0 42 -19 42 -43v-256c0 -24 -18 -43 -42 -43h-342c-24 0 -42 19 -42 43v256c0 24 18 43 42 43h342z
+M117 288h43v-21h-43v21z" />
+    <glyph glyph-name="fiber_smart_record" unicode="fiber_smart_record" 
+d="M363 421c74 -19 128 -85 128 -165s-54 -146 -128 -165v44c50 17 85 65 85 121s-35 104 -85 121v44zM21 256c0 94 77 171 171 171s171 -77 171 -171s-77 -171 -171 -171s-171 77 -171 171z" />
+    <glyph glyph-name="file_download" unicode="file_download" 
+d="M107 128h298v-43h-298v43zM405 320l-149 -149l-149 149h85v128h128v-128h85z" />
+    <glyph glyph-name="file_upload" unicode="file_upload" 
+d="M107 128h298v-43h-298v43zM192 171v128h-85l149 149l149 -149h-85v-128h-128z" />
+    <glyph glyph-name="filter" unicode="filter" 
+d="M448 149v299h-299v-299h299zM448 491c23 0 43 -20 43 -43v-299c0 -23 -20 -42 -43 -42h-299c-23 0 -42 19 -42 42v299c0 23 19 43 42 43h299zM64 405v-341h341v-43h-341c-23 0 -43 20 -43 43v341h43zM340 292l76 -100h-235l59 75l42 -50z" />
+    <glyph glyph-name="filter_1" unicode="filter_&#x31;" 
+d="M448 149v299h-299v-299h299zM448 491c23 0 43 -20 43 -43v-299c0 -23 -20 -42 -43 -42h-299c-23 0 -42 19 -42 42v299c0 23 19 43 42 43h299zM299 192v171h-43v42h85v-213h-42zM64 405v-341h341v-43h-341c-23 0 -43 20 -43 43v341h43z" />
+    <glyph glyph-name="filter_2" unicode="filter_&#x32;" 
+d="M363 235v-43h-128v85c0 24 19 43 42 43h43v43h-85v42h85c23 0 43 -18 43 -42v-43c0 -24 -20 -43 -43 -43h-43v-42h86zM448 149v299h-299v-299h299zM448 491c23 0 43 -20 43 -43v-299c0 -23 -20 -42 -43 -42h-299c-23 0 -42 19 -42 42v299c0 23 19 43 42 43h299zM64 405
+v-341h341v-43h-341c-23 0 -43 20 -43 43v341h43z" />
+    <glyph glyph-name="filter_3" unicode="filter_&#x33;" 
+d="M363 235c0 -24 -20 -43 -43 -43h-85v43h85v42h-43v43h43v43h-85v42h85c23 0 43 -18 43 -42v-32c0 -18 -14 -32 -32 -32c18 0 32 -14 32 -32v-32zM64 405v-341h341v-43h-341c-23 0 -43 20 -43 43v341h43zM448 149v299h-299v-299h299zM448 491c23 0 43 -20 43 -43v-299
+c0 -23 -20 -42 -43 -42h-299c-23 0 -42 19 -42 42v299c0 23 19 43 42 43h299z" />
+    <glyph glyph-name="filter_4" unicode="filter_&#x34;" 
+d="M448 149v299h-299v-299h299zM448 491c23 0 43 -20 43 -43v-299c0 -23 -20 -42 -43 -42h-299c-23 0 -42 19 -42 42v299c0 23 19 43 42 43h299zM320 192v85h-85v128h42v-85h43v85h43v-213h-43zM64 405v-341h341v-43h-341c-23 0 -43 20 -43 43v341h43z" />
+    <glyph glyph-name="filter_5" unicode="filter_&#x35;" 
+d="M363 235c0 -24 -20 -43 -43 -43h-85v43h85v42h-85v128h128v-42h-86v-43h43c23 0 43 -19 43 -43v-42zM64 405v-341h341v-43h-341c-23 0 -43 20 -43 43v341h43zM448 149v299h-299v-299h299zM448 491c23 0 43 -20 43 -43v-299c0 -23 -20 -42 -43 -42h-299
+c-23 0 -42 19 -42 42v299c0 23 19 43 42 43h299z" />
+    <glyph glyph-name="filter_6" unicode="filter_&#x36;" 
+d="M277 277v-42h43v42h-43zM277 192c-23 0 -42 19 -42 43v128c0 24 19 42 42 42h86v-42h-86v-43h43c23 0 43 -19 43 -43v-42c0 -24 -20 -43 -43 -43h-43zM448 149v299h-299v-299h299zM448 491c23 0 43 -20 43 -43v-299c0 -23 -20 -42 -43 -42h-299c-23 0 -42 19 -42 42v299
+c0 23 19 43 42 43h299zM64 405v-341h341v-43h-341c-23 0 -43 20 -43 43v341h43z" />
+    <glyph glyph-name="filter_7" unicode="filter_&#x37;" 
+d="M277 192h-42l85 171h-85v42h128v-42zM448 149v299h-299v-299h299zM448 491c23 0 43 -20 43 -43v-299c0 -23 -20 -42 -43 -42h-299c-23 0 -42 19 -42 42v299c0 23 19 43 42 43h299zM64 405v-341h341v-43h-341c-23 0 -43 20 -43 43v341h43z" />
+    <glyph glyph-name="filter_8" unicode="filter_&#x38;" 
+d="M277 277v-42h43v42h-43zM277 363v-43h43v43h-43zM277 192c-23 0 -42 19 -42 43v32c0 18 14 32 32 32c-18 0 -32 14 -32 32v32c0 24 19 42 42 42h43c23 0 43 -18 43 -42v-32c0 -18 -14 -32 -32 -32c18 0 32 -14 32 -32v-32c0 -24 -20 -43 -43 -43h-43zM448 149v299h-299
+v-299h299zM448 491c23 0 43 -20 43 -43v-299c0 -23 -20 -42 -43 -42h-299c-23 0 -42 19 -42 42v299c0 23 19 43 42 43h299zM64 405v-341h341v-43h-341c-23 0 -43 20 -43 43v341h43z" />
+    <glyph glyph-name="filter_9" unicode="filter_&#x39;" 
+d="M320 320v43h-43v-43h43zM320 405c23 0 43 -18 43 -42v-128c0 -24 -20 -43 -43 -43h-85v43h85v42h-43c-23 0 -42 19 -42 43v43c0 24 19 42 42 42h43zM448 149v299h-299v-299h299zM448 491c23 0 43 -20 43 -43v-299c0 -23 -20 -42 -43 -42h-299c-23 0 -42 19 -42 42v299
+c0 23 19 43 42 43h299zM64 405v-341h341v-43h-341c-23 0 -43 20 -43 43v341h43z" />
+    <glyph glyph-name="filter_9_plus" unicode="filter_&#x39;_plus" 
+d="M448 320v128h-299v-299h299v128h-43v-42h-42v42h-43v43h43v43h42v-43h43zM448 491c23 0 43 -20 43 -43v-299c0 -23 -20 -42 -43 -42h-299c-23 0 -42 19 -42 42v299c0 23 19 43 42 43h299zM235 320h21v21h-21v-21zM299 256c0 -24 -20 -43 -43 -43h-64v43h64v21h-21
+c-23 0 -43 19 -43 43v21c0 24 20 43 43 43h21c23 0 43 -19 43 -43v-85zM64 405v-341h341v-43h-341c-23 0 -43 20 -43 43v341h43z" />
+    <glyph glyph-name="filter_b_and_w" unicode="filter_b_and_w" 
+d="M405 107v298h-149v-128zM405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h298zM256 277l-149 -170h149v170z" />
+    <glyph glyph-name="filter_center_focus" unicode="filter_center_focus" 
+d="M256 320c35 0 64 -29 64 -64s-29 -64 -64 -64s-64 29 -64 64s29 64 64 64zM405 107v85h43v-85c0 -23 -20 -43 -43 -43h-85v43h85zM405 448c23 0 43 -20 43 -43v-85h-43v85h-85v43h85zM107 405v-85h-43v85c0 23 20 43 43 43h85v-43h-85zM107 192v-85h85v-43h-85
+c-23 0 -43 20 -43 43v85h43z" />
+    <glyph glyph-name="filter_drama" unicode="filter_drama" 
+d="M405 128c35 0 64 29 64 64s-29 64 -64 64h-32v11c0 65 -52 117 -117 117c-38 0 -72 -18 -94 -47c54 -15 94 -65 94 -124h-43c0 47 -38 86 -85 86s-85 -39 -85 -86s38 -85 85 -85h277zM413 298c55 -4 99 -50 99 -106c0 -59 -48 -107 -107 -107h-277
+c-71 0 -128 57 -128 128c0 66 50 120 114 127c27 51 80 87 142 87c78 0 142 -55 157 -129z" />
+    <glyph glyph-name="filter_frames" unicode="filter_frames" 
+d="M384 341h-256v-213h256zM427 85v299h-96l-74 75l-75 -75h-97v-299h342zM427 427c23 0 42 -20 42 -43v-299c0 -23 -19 -42 -42 -42h-342c-23 0 -42 19 -42 42v299c0 23 19 43 42 43h86l85 85l85 -85h86z" />
+    <glyph glyph-name="filter_hdr" unicode="filter_hdr" 
+d="M299 384l192 -256h-470l128 171l96 -128l34 25l-60 81z" />
+    <glyph glyph-name="filter_list" unicode="filter_list" 
+d="M128 235v42h256v-42h-256zM64 384h384v-43h-384v43zM213 128v43h86v-43h-86z" />
+    <glyph glyph-name="filter_none" unicode="filter_none" 
+d="M448 149v299h-299v-299h299zM448 491c23 0 43 -20 43 -43v-299c0 -23 -20 -42 -43 -42h-299c-23 0 -42 19 -42 42v299c0 23 19 43 42 43h299zM64 405v-341h341v-43h-341c-23 0 -43 20 -43 43v341h43z" />
+    <glyph glyph-name="filter_tilt_shift" unicode="filter_tilt_shift" 
+d="M121 91l30 30c24 -18 53 -30 84 -34v-43c-43 4 -82 21 -114 47zM277 87c31 4 60 16 83 34l31 -30c-32 -26 -71 -43 -114 -47v43zM391 151c18 24 30 52 34 83h43c-4 -43 -21 -81 -47 -113zM320 256c0 -35 -29 -64 -64 -64s-64 29 -64 64s29 64 64 64s64 -29 64 -64z
+M87 235c4 -31 16 -60 34 -83l-30 -31c-26 32 -43 71 -47 114h43zM121 361c-18 -24 -30 -53 -34 -84h-43c4 43 21 82 47 114zM425 277c-4 31 -16 60 -34 84l30 30c26 -32 43 -71 47 -114h-43zM391 421l-30 -30c-24 18 -53 30 -84 34v43c43 -4 82 -21 114 -47zM235 425
+c-31 -4 -60 -16 -84 -34l-30 30c32 26 71 43 114 47v-43z" />
+    <glyph glyph-name="filter_vintage" unicode="filter_vintage" 
+d="M256 171c47 0 85 38 85 85s-38 85 -85 85s-85 -38 -85 -85s38 -85 85 -85zM49 137c0 49.2164 31.3621 102.121 82 119c-6 2 -12 6 -18 9c-41 24 -64 66 -64 110c38 22 87 24 128 0c6 -3 12 -7 17 -11c-1 7 -2 13 -2 20c0 47 26 89 64 111c38 -22 64 -64 64 -111
+c0 -7 -1 -13 -2 -20c5 4 11 8 17 11c41 24 90 22 128 0c0 -44 -23 -86 -64 -110c-6 -3 -12 -7 -18 -9c6 -2 12 -6 18 -9c41 -24 64 -66 64 -110c-38 -22 -87 -24 -128 0c-6 3 -12 7 -17 11c1 -7 2 -13 2 -20c0 -47 -26 -89 -64 -111c-38 22 -64 64 -64 111c0 7 1 13 2 20
+c-5 -4 -11 -8 -17 -11c-41 -24 -90 -22 -128 0z" />
+    <glyph glyph-name="find_in_page" unicode="find_in_page" 
+d="M192 235c0 35 29 64 64 64s64 -29 64 -64s-29 -64 -64 -64s-64 29 -64 64zM427 94l-82 82c11 17 18 37 18 59c0 59 -48 106 -107 106s-107 -47 -107 -106s48 -107 107 -107c22 0 42 7 59 18l94 -95c-7 -5 -15 -8 -25 -8h-256c-23 0 -43 19 -43 42l1 342c0 23 19 42 42 42
+h171l128 -128v-247z" />
+    <glyph glyph-name="find_replace" unicode="find_replace" 
+d="M355 189l103 -104l-31 -31l-104 103c-25 -18 -55 -29 -88 -29c-41 0 -79 17 -106 44l-44 -44v128h128l-54 -54c19 -19 47 -31 76 -31c52 0 94 36 104 85h43c-3 -25 -13 -48 -27 -67zM235 384c-52 0 -95 -36 -105 -85h-43c10 72 73 128 148 128c41 0 78 -17 105 -44l44 44
+v-128h-128l54 54c-19 19 -46 31 -75 31z" />
+    <glyph glyph-name="fingerprint" unicode="fingerprint" 
+d="M318 43h-3c-34 9 -56 22 -79 45c-30 30 -46 69 -46 111c0 35 29 63 65 63s66 -28 66 -63c0 -23 19 -41 44 -41s45 18 45 41c0 80 -70 146 -155 146c-61 0 -116 -34 -141 -86c-8 -17 -12 -38 -12 -60c0 -17 1 -43 14 -77c4.57857 -13.7357 -15.4693 -18.3268 -20 -7
+c-10 28 -16 55 -16 84c0 26 5 49 15 69c28 60 91 99 160 99c97 0 176 -76 176 -168c0 -35 -30 -62 -66 -62s-65 27 -65 62c0 23 -20 42 -45 42s-44 -19 -44 -42c0 -36 14 -70 40 -96c20 -20 40 -31 70 -39c6 -1 8 -7 7 -13c-1 -5 -6 -8 -10 -8zM265 199
+c0 -48.7573 40.2888 -91 96 -91c9.9641 0 32.7732 7.36098 35 -6c1 -6 -3 -12 -9 -13c-12 -2 -23 -2 -26 -2c-25 0 -48 6 -66 19c-32 22 -51 56 -51 93c0 6 5 11 11 11s10 -5 10 -11zM208 47c-3 0 -5 1 -7 3c-19 19 -29 31 -43 57c-15 26 -23 58 -23 92c0 63 54 115 121 115
+s121 -52 121 -115c0 -6 -5 -10 -11 -10s-11 4 -11 10c0 52 -44 94 -99 94s-99 -42 -99 -94c0 -31 6 -59 19 -82c14 -25 24 -35 40 -52c4 -4 4 -11 0 -15c-2 -2 -5 -3 -8 -3zM75 305c-8.81177 0 -13.8926 7.8456 -9 16c21 30 48 54 80 70c67 35 153 35 220 0
+c32 -16 59 -39 80 -69c3 -5 2 -12 -3 -15s-12 -2 -15 3c-19 27 -43 47 -72 62c-61 31 -139 32 -200 0c-29 -15 -54 -36 -73 -63c-2 -3 -5 -4 -8 -4zM380 417c-2 0 -4 0 -5 1c-41 21 -77 30 -119 30s-83 -10 -119 -30c-11.6907 -7.0144 -21.6392 12.0165 -10 19
+c40 22 83 32 129 32c45 0 85 -10 129 -32c9.10556 -5.46333 5.20303 -20 -5 -20z" />
+    <glyph glyph-name="first_page" unicode="first_page" 
+d="M128 384h43v-256h-43v256zM393 158l-30 -30l-128 128l128 128l30 -30l-98 -98z" />
+    <glyph glyph-name="fitness_center" unicode="fitness_center" 
+d="M439 195l30 -31l-45 -45l30 -31l-30 -30l-31 30l-45 -45l-31 30l-30 -30l-31 30l76 76l-183 183l-76 -76l-30 31l30 30l-30 31l45 45l-30 31l30 30l31 -30l45 45l31 -30l30 30l31 -30l-76 -76l183 -183l76 76l30 -31z" />
+    <glyph glyph-name="flag" unicode="flag" 
+d="M307 384h120v-213h-150l-8 42h-120v-149h-42v363h192z" />
+    <glyph glyph-name="flare" unicode="flare" 
+d="M235 21v128h42v-128h-42zM120 150l46 46l30 -30l-46 -46zM316 166l30 30l46 -46l-30 -30zM256 320c35 0 64 -29 64 -64s-29 -64 -64 -64s-64 29 -64 64s29 64 64 64zM363 277h128v-42h-128v42zM392 362l-46 -46l-30 30l46 46zM277 491v-128h-42v128h42zM196 346l-30 -30
+l-46 46l30 30zM149 277v-42h-128v42h128z" />
+    <glyph glyph-name="flash_auto" unicode="flash_auto" 
+d="M359 349h50l-25 78zM405 469l69 -192h-41l-15 43h-68l-15 -43h-41l69 192h42zM64 469h213l-85 -192h85l-149 -256v192h-64v256z" />
+    <glyph glyph-name="flash_off" unicode="flash_off" 
+d="M363 299l-33 -57l-181 181v46h214l-86 -170h86zM70 448l335 -336l-27 -27l-88 89l-77 -131v192h-64v79l-106 107z" />
+    <glyph glyph-name="flash_on" unicode="flash_on" 
+d="M149 469h214l-86 -170h86l-150 -256v192h-64v234z" />
+    <glyph glyph-name="flight" unicode="flight" 
+d="M217 320zM448 171l-171 53v-117l43 -32v-32l-75 21l-74 -21v32l42 32v117l-170 -53v42l170 107v117c0 18 14 32 32 32s32 -14 32 -32v-117l171 -107v-42z" />
+    <glyph glyph-name="flight_land" unicode="flight_land" 
+d="M299 204c-68.2362 18.7638 -137.045 36.9549 -206 55l-34 10v110l31 -8l20 -50l106 -28v176l41 -11l59 -192l113 -30c17 -5 27 -23 23 -40c-5 -17 -22 -26 -39 -22zM53 107h406v-43h-406v43z" />
+    <glyph glyph-name="flight_takeoff" unicode="flight_takeoff" 
+d="M471 306c4 -17 -6 -34 -23 -39c-68.7788 -18.2212 -137.501 -36.499 -206 -55l-113 -30l-34 -10c-18.76 31.9067 -37.3464 63.9869 -56 96l31 8l42 -32l106 28l-88 153l41 11l147 -137l114 30c17 5 35 -6 39 -23zM53 107h406v-43h-406v43z" />
+    <glyph glyph-name="flip" unicode="flip" 
+d="M405 64v43h43c0 -23 -20 -43 -43 -43zM405 235v42h43v-42h-43zM320 405v43h43v-43h-43zM405 149v43h43v-43h-43zM235 21v470h42v-470h-42zM405 448c23 0 43 -20 43 -43h-43v43zM64 405c0 23 20 43 43 43h85v-43h-85v-298h85v-43h-85c-23 0 -43 20 -43 43v298zM405 320v43
+h43v-43h-43zM320 64v43h43v-43h-43z" />
+    <glyph glyph-name="flip_to_back" unicode="flip_to_back" 
+d="M320 149v43h43v-43h-43zM320 405v43h43v-43h-43zM107 363v-256h256v-43h-256c-24 0 -43 20 -43 43v256h43zM405 149v43h43c0 -23 -20 -43 -43 -43zM405 320v43h43v-43h-43zM405 235v42h43v-42h-43zM192 149c-24 0 -43 20 -43 43h43v-43zM277 448v-43h-42v43h42zM405 448
+c23 0 43 -20 43 -43h-43v43zM277 192v-43h-42v43h42zM192 448v-43h-43c0 23 19 43 43 43zM192 277v-42h-43v42h43zM192 363v-43h-43v43h43z" />
+    <glyph glyph-name="flip_to_front" unicode="flip_to_front" 
+d="M149 64v43h43v-43h-43zM235 64v43h42v-43h-42zM405 192v213h-213v-213h213zM405 448c23 0 43 -20 43 -43v-213c0 -23 -20 -43 -43 -43h-213c-24 0 -43 20 -43 43v213c0 23 19 43 43 43h213zM320 64v43h43v-43h-43zM64 320v43h43v-43h-43zM107 64c-24 0 -43 20 -43 43h43
+v-43zM64 149v43h43v-43h-43zM64 235v42h43v-42h-43z" />
+    <glyph glyph-name="folder" unicode="folder" 
+d="M213 427l43 -43h171c23 0 42 -20 42 -43v-213c0 -23 -19 -43 -42 -43h-342c-23 0 -42 20 -42 43v256c0 23 19 43 42 43h128z" />
+    <glyph glyph-name="folder_open" unicode="folder_open" 
+d="M427 128v213h-342v-213h342zM427 384c23 0 42 -20 42 -43v-213c0 -23 -19 -43 -42 -43h-342c-23 0 -42 20 -42 43v256c0 23 19 43 42 43h128l43 -43h171z" />
+    <glyph glyph-name="folder_shared" unicode="folder_shared" 
+d="M405 149v22c0 28 -57 42 -85 42s-85 -14 -85 -42v-22h170zM320 320c-23 0 -43 -20 -43 -43s20 -42 43 -42s43 19 43 42s-20 43 -43 43zM427 384c23 0 42 -20 42 -43v-213c0 -23 -19 -43 -42 -43h-342c-23 0 -42 20 -42 43v256c0 23 19 43 42 43h128l43 -43h171z" />
+    <glyph glyph-name="folder_special" unicode="folder_special" 
+d="M383 149l-17 71l55 48l-72 6l-29 67l-29 -67l-72 -6l55 -48l-17 -71l63 37zM427 384c23 0 42 -20 42 -43v-213c0 -23 -19 -43 -42 -43h-342c-23 0 -42 20 -42 43v256c0 23 19 43 42 43h128l43 -43h171z" />
+    <glyph glyph-name="font_download" unicode="font_download" 
+d="M340 117h45l-109 278h-40l-109 -278h45l24 64h120zM427 469c23 0 42 -19 42 -42v-342c0 -23 -19 -42 -42 -42h-342c-23 0 -42 19 -42 42v342c0 23 19 42 42 42h342zM212 224l44 118l44 -118h-88z" />
+    <glyph glyph-name="format_align_center" unicode="format_align_center" 
+d="M64 448h384v-43h-384v43zM149 363h214v-43h-214v43zM64 235v42h384v-42h-384zM64 64v43h384v-43h-384zM149 192h214v-43h-214v43z" />
+    <glyph glyph-name="format_align_justify" unicode="format_align_justify" 
+d="M64 448h384v-43h-384v43zM64 320v43h384v-43h-384zM64 235v42h384v-42h-384zM64 149v43h384v-43h-384zM64 64v43h384v-43h-384z" />
+    <glyph glyph-name="format_align_left" unicode="format_align_left" 
+d="M64 448h384v-43h-384v43zM64 64v43h384v-43h-384zM64 235v42h384v-42h-384zM320 363v-43h-256v43h256zM320 192v-43h-256v43h256z" />
+    <glyph glyph-name="format_align_right" unicode="format_align_right" 
+d="M64 448h384v-43h-384v43zM192 320v43h256v-43h-256zM64 235v42h384v-42h-384zM192 149v43h256v-43h-256zM64 64v43h384v-43h-384z" />
+    <glyph glyph-name="format_bold" unicode="format_bold" 
+d="M288 181c18 0 32 14 32 32s-14 32 -32 32h-75v-64h75zM213 373v-64h64c18 0 32 14 32 32s-14 32 -32 32h-64zM333 282c28 -13 46 -41 46 -73c0 -45 -34 -81 -79 -81h-151v299h134c48 0 85 -38 85 -86c0 -22 -14 -45 -35 -59z" />
+    <glyph glyph-name="format_clear" unicode="format_clear" 
+d="M128 405h299v-64h-124l-34 -80l-45 44l15 36h-51l-60 60v4zM70 405l6 -5l308 -309l-27 -27l-121 121l-33 -78h-64l52 123l-148 148z" />
+    <glyph glyph-name="format_color_fill" unicode="format_color_fill" 
+d="M0 85h512v-85h-512v85zM405 267c0 0 43 -47 43 -75c0 -23 -20 -43 -43 -43s-42 20 -42 43c0 28 42 75 42 75zM111 299h205l-103 102zM353 321c13 -13 13 -33 0 -45l-117 -117c-6 -6 -15 -10 -23 -10s-16 4 -22 10l-118 117c-13 12 -13 32 0 45l110 110l-51 51l31 30z" />
+    <glyph glyph-name="format_color_reset" unicode="format_color_reset" 
+d="M112 400l312 -312l-27 -27l-57 56c-23 -20 -52 -32 -84 -32c-71 0 -128 57 -128 128c0 26 12 57 28 88l-71 71zM384 213c0 -10 -1 -19 -3 -28l-183 184c30 43 58 75 58 75s128 -146 128 -231z" />
+    <glyph glyph-name="format_color_text" unicode="format_color_text" 
+d="M205 256h102l-51 135zM235 448h42l117 -299h-48l-23 64h-134l-24 -64h-48zM0 85h512v-85h-512v85z" />
+    <glyph glyph-name="format_indent_decrease" unicode="format_indent_decrease" 
+d="M235 235v42h213v-42h-213zM235 320v43h213v-43h-213zM64 448h384v-43h-384v43zM64 64v43h384v-43h-384zM64 256l85 85v-170zM235 149v43h213v-43h-213z" />
+    <glyph glyph-name="format_indent_increase" unicode="format_indent_increase" 
+d="M235 235v42h213v-42h-213zM235 320v43h213v-43h-213zM64 448h384v-43h-384v43zM235 149v43h213v-43h-213zM64 341l85 -85l-85 -85v170zM64 64v43h384v-43h-384z" />
+    <glyph glyph-name="format_italic" unicode="format_italic" 
+d="M213 427h171v-64h-60l-72 -171h47v-64h-171v64h60l72 171h-47v64z" />
+    <glyph glyph-name="format_line_spacing" unicode="format_line_spacing" 
+d="M213 235v42h256v-42h-256zM213 107v42h256v-42h-256zM213 405h256v-42h-256v42zM128 363v-214h53l-74 -74l-75 74h53v214h-53l75 74l74 -74h-53z" />
+    <glyph glyph-name="format_list_bulleted" unicode="format_list_bulleted" 
+d="M149 405h299v-42h-299v42zM149 235v42h299v-42h-299zM149 107v42h299v-42h-299zM85 160c18 0 32 -15 32 -32s-15 -32 -32 -32s-32 15 -32 32s14 32 32 32zM85 416c18 0 32 -14 32 -32s-14 -32 -32 -32s-32 14 -32 32s14 32 32 32zM85 288c18 0 32 -14 32 -32
+s-14 -32 -32 -32s-32 14 -32 32s14 32 32 32z" />
+    <glyph glyph-name="format_list_numbered" unicode="format_list_numbered" 
+d="M149 235v42h299v-42h-299zM149 107v42h299v-42h-299zM149 405h299v-42h-299v42zM43 277v22h64v-20l-39 -44h39v-22h-64v20l38 44h-38zM64 341v64h-21v22h42v-86h-21zM43 149v22h64v-86h-64v22h42v10h-21v22h21v10h-42z" />
+    <glyph glyph-name="format_paint" unicode="format_paint" 
+d="M384 427h64v-171h-171v-192c0 -12 -9 -21 -21 -21h-43c-12 0 -21 9 -21 21v235h213v85h-21v-21c0 -12 -9 -22 -21 -22h-256c-12 0 -22 10 -22 22v85c0 12 10 21 22 21h256c12 0 21 -9 21 -21v-21z" />
+    <glyph glyph-name="format_quote" unicode="format_quote" 
+d="M299 149l42 86h-64v128h128v-128l-42 -86h-64zM128 149l43 86h-64v128h128v-128l-43 -86h-64z" />
+    <glyph glyph-name="format_shapes" unicode="format_shapes" 
+d="M228 240h56l-28 82zM293 213h-75l-15 -42h-35l73 192h30l72 -192h-34zM405 405h43v43h-43v-43zM448 64v43h-43v-43h43zM363 107v42h42v214h-42v42h-214v-42h-42v-214h42v-42h214zM107 64v43h-43v-43h43zM64 448v-43h43v43h-43zM491 363h-43v-214h43v-128h-128v43h-214
+v-43h-128v128h43v214h-43v128h128v-43h214v43h128v-128z" />
+    <glyph glyph-name="format_size" unicode="format_size" 
+d="M64 256v64h192v-64h-64v-149h-64v149h-64zM192 427h277v-64h-106v-256h-64v256h-107v64z" />
+    <glyph glyph-name="format_strikethrough" unicode="format_strikethrough" 
+d="M64 213v43h384v-43h-384zM107 427h298v-64h-106v-64h-86v64h-106v64zM213 107v64h86v-64h-86z" />
+    <glyph glyph-name="format_textdirection_l_to_r" unicode="format_textdirection_l_to_r" 
+d="M448 128l-85 -85v64h-256v42h256v64zM192 299c-47 0 -85 38 -85 85s38 85 85 85h171v-42h-43v-235h-43v235h-42v-235h-43v107z" />
+    <glyph glyph-name="format_textdirection_r_to_l" unicode="format_textdirection_r_to_l" 
+d="M171 149h256v-42h-256v-64l-86 85l86 85v-64zM213 299c-47 0 -85 38 -85 85s38 85 85 85h171v-42h-43v-235h-42v235h-43v-235h-43v107z" />
+    <glyph glyph-name="format_underlined" unicode="format_underlined" 
+d="M107 107h298v-43h-298v43zM256 149c-71 0 -128 57 -128 128v171h53v-171c0 -41 34 -74 75 -74s75 33 75 74v171h53v-171c0 -71 -57 -128 -128 -128z" />
+    <glyph glyph-name="forum" unicode="forum" 
+d="M363 256c0 -12 -10 -21 -22 -21h-213l-85 -86v299c0 12 9 21 21 21h277c12 0 22 -9 22 -21v-192zM448 384c12 0 21 -9 21 -21v-320l-85 85h-235c-12 0 -21 9 -21 21v43h277v192h43z" />
+    <glyph glyph-name="forward" unicode="forward" 
+d="M256 341v86l171 -171l-171 -171v86h-171v170h171z" />
+    <glyph glyph-name="forward_10" unicode="forward_&#x31;&#x30;" 
+d="M282 188c0 -3.2653 6.32517 -7 10 -7c2 0 5 0 7 2l4 5s2 4 2 6v43c0 0 -2 4 -2 6c0 2.75261 -8.21183 7 -11 7c-2 0 -4 -1 -6 -3l-4 -4s-3 -4 -3 -6v-43s3 -4 3 -6zM322 209c0 -6 0 -13 -2 -17l-6 -13s-7 -6 -11 -6s-9 -2 -13 -2s-9 0 -13 2s-6 4 -10 6
+c-6.74403 3.37202 -9 19.7461 -9 30v15c0 6 0 13 2 17l7 13s6 6 10 6s9 2 13 2s9 0 13 -2s7 -4 11 -6s4 -7 6 -13s2 -11 2 -17v-15zM233 171h-20v70l-21 -6v15l38 12h3v-91zM85 235c0 94 77 170 171 170v86l107 -107l-107 -107v86c-70 0 -128 -58 -128 -128
+s58 -128 128 -128s128 58 128 128h43c0 -94 -77 -171 -171 -171s-171 77 -171 171z" />
+    <glyph glyph-name="forward_30" unicode="forward_&#x33;&#x30;" 
+d="M85 235c0 94 77 170 171 170v86l107 -107l-107 -107v86c-70 0 -128 -58 -128 -128s58 -128 128 -128s128 58 128 128h43c0 -94 -77 -171 -171 -171s-171 77 -171 171zM284 188c0 -3.2653 6.32517 -7 10 -7c2 0 5 0 7 2l4 5s2 4 2 6v43c0 0 -2 4 -2 6
+c0 2.75261 -8.21183 7 -11 7c-2 0 -4 -1 -6 -3l-4 -4s-2 -4 -2 -6v-43s2 -4 2 -6zM326 209c0 -6 0 -13 -2 -17l-6 -13s-7 -6 -11 -6s-9 -2 -13 -2c-10.126 0 -16.1493 4.57466 -23 8c-4 2 -4 7 -6 13s-3 11 -3 17v15c0 6 1 13 3 17l6 13s7 6 11 6s8 2 12 2s9 0 13 -2
+s7 -4 11 -6s4 -7 6 -13s2 -11 2 -17v-15zM213 224c8.11936 0 15 4.95913 15 13v4s-2 2 -2 4s-2 2 -4 2h-11s-2 -2 -4 -2s-2 -2 -2 -4v-4h-22c0 9.89385 8.81974 23 18 23c2 0 8 2 10 2c8.67272 0 17.6319 -2.81597 24 -6c5.06488 -2.53244 8 -12.109 8 -19v-7s-2 -4 -2 -6
+s-2 -4 -4 -4s-5 -3 -7 -5c4 -2 9 -4 11 -8s4 -9 4 -13s0 -9 -2 -11s-4 -6 -6 -8s-7 -4 -11 -4s-9 -2 -13 -2s-8 0 -10 2s-7 2 -11 4c-5.58093 2.79046 -9 13.5084 -9 21h18v-4s2 -2 2 -4s2 -2 4 -2h11s2 2 4 2s2 2 2 4v11s-2 2 -2 4s-2 2 -4 2h-13v15h8z" />
+    <glyph glyph-name="forward_5" unicode="forward_&#x35;" 
+d="M250 222c-1.79204 -0.716817 -7 -2.40231 -7 -4c0 -2 -2 -3 -2 -3h-13l5 47h51v-15h-37l-2 -19s2 0 2 2s3 1 3 3s2 0 4 0h4c4 0 9 -1 11 -3s6 -4 8 -6c4.43641 -4.43641 9 -13.6284 9 -23c0 -4 0 -9 -2 -11s-3 -7 -7 -11c-5.62284 -5.62284 -13.295 -8 -23 -8
+c-4 0 -9 0 -11 2s-6 2 -10 4c-5.5956 2.7978 -9 11.277 -9 19h17c0 -7.40446 5.29508 -10 13 -10c2 0 4 0 6 2l5 4s2 4 2 6v13l-2 4l-5 5s-4 2 -6 2h-4zM85 235c0 94 77 170 171 170v86l107 -107l-107 -107v86c-70 0 -128 -58 -128 -128s58 -128 128 -128s128 58 128 128h43
+c0 -94 -77 -171 -171 -171s-171 77 -171 171z" />
+    <glyph glyph-name="free_breakfast" unicode="free_breakfast" 
+d="M85 107h342v-43h-342v43zM427 341v64h-43v-64h43zM427 448c24 0 42 -19 42 -43v-64c0 -23 -18 -42 -42 -42h-43v-64c0 -47 -38 -86 -85 -86h-128c-47 0 -86 39 -86 86v213h342z" />
+    <glyph glyph-name="fullscreen" unicode="fullscreen" 
+d="M299 405h106v-106h-42v64h-64v42zM363 149v64h42v-106h-106v42h64zM107 299v106h106v-42h-64v-64h-42zM149 213v-64h64v-42h-106v106h42z" />
+    <glyph glyph-name="fullscreen_exit" unicode="fullscreen_exit" 
+d="M341 341h64v-42h-106v106h42v-64zM299 107v106h106v-42h-64v-64h-42zM171 341v64h42v-106h-106v42h64zM107 171v42h106v-106h-42v64h-64z" />
+    <glyph glyph-name="functions" unicode="functions" 
+d="M384 427v-64h-149l106 -107l-106 -107h149v-64h-256v43l139 128l-139 128v43h256z" />
+    <glyph glyph-name="g_translate" unicode="g_translate" 
+d="M448 85v278c0 12 -9 21 -21 21h-188l25 -86h41v22h23v-22h77v-22h-27c-7 -27 -22 -53 -41 -75l58 -57l-15 -16l-58 57l-19 -19l17 -59l-43 -43h150c12 0 21 9 21 21zM298 253c5 -10 13 -23 24 -36c26 31 33 59 33 59h-85l7 -23h21zM282 237l13 -47l12 11
+c-10 11 -18 23 -25 36zM237 286c0 4.92043 -0.140331 11.2807 -2 15h-84v-33h47c-3 -13 -16 -32 -45 -32c-28 0 -51 24 -51 52s23 52 51 52c16 0 26 -7 32 -13l2 -1l26 25l-2 1c-16 15 -36 23 -58 23c-48 0 -87 -39 -87 -87s39 -87 87 -87c49 0 84 35 84 85zM427 405
+c23 0 42 -19 42 -42v-278c0 -23 -19 -42 -42 -42h-171l-21 64h-150c-23 0 -42 19 -42 42v278c0 23 19 42 42 42h128l19 -64h195z" />
+    <glyph glyph-name="gamepad" unicode="gamepad" 
+d="M352 320h117v-128h-117l-64 64zM192 160l64 64l64 -64v-117h-128v117zM160 320l64 -64l-64 -64h-117v128h117zM320 352l-64 -64l-64 64v117h128v-117z" />
+    <glyph glyph-name="games" unicode="games" 
+d="M352 320h117v-128h-117l-64 64zM192 160l64 64l64 -64v-117h-128v117zM160 320l64 -64l-64 -64h-117v128h117zM320 352l-64 -64l-64 64v117h128v-117z" />
+    <glyph glyph-name="gavel" unicode="gavel" 
+d="M82 310l120 -121l-60 -60l-121 120zM263 491l120 -121l-60 -60l-121 120zM112 340l60 60l302 -302l-60 -60zM21 64h256v-43h-256v43z" />
+    <glyph glyph-name="gesture" unicode="gesture" 
+d="M296 116c15 0 40 18 46 75c-46 -12 -62 -46 -62 -59c0 -10 9 -16 16 -16zM98 365l-37 36c5 6 10 12 18 20c5 5 30 27 58 27c19 0 54 -15 54 -61c0 -29 -11 -46 -28 -70c-10 -14 -32 -52 -39 -75c-8 -23 -3 -41 7 -41c9 0 19 12 24 18c5 5 37 42 49 58c16 20 57 60 105 60
+c63 0 83 -54 86 -89h53v-53h-52c-9 -102 -66 -131 -101 -131c-38 0 -68 30 -68 66s34 101 115 115c-2 17 -3 38 -38 38c-27 0 -61 -41 -87 -73c-24 -29 -42 -52 -65 -59c-49.7758 -15.7187 -85 28.2434 -85 73c0 52 56 126 61 135c6 10 17 28 6 32c-7 3 -21 -11 -36 -26z
+" />
+    <glyph glyph-name="get_app" unicode="get_app" 
+d="M107 128h298v-43h-298v43zM405 320l-149 -149l-149 149h85v128h128v-128h85z" />
+    <glyph glyph-name="gif" unicode="gif" 
+d="M405 288h-64v-21h43v-32h-43v-43h-32v128h96v-32zM192 320c13 0 21 -10 21 -21v-11h-74v-64h42v32h32v-43c0 -11 -8 -21 -21 -21h-64c-13 0 -21 10 -21 21v86c0 11 8 21 21 21h64zM245 320h32v-128h-32v128z" />
+    <glyph glyph-name="goat" unicode="goat" 
+d="M511 318c1 -1 1 -4 0 -5l-25 -32c-1 -1 -3 -2 -4 -2l-15 3l-7 -22c-3 -5 -10 -5 -13 0l-14 30l-23 5l-47 -112l17 -136c0 -3 -2 -4 -4 -4h-20c-2 0 -3 1 -4 3l-20 81l-10 17l-25 -98c0 -2 -2 -3 -4 -3h-21c-2 0 -4 2 -4 4l23 135h-135l-35 -66l8 -68c1 -3 -1 -5 -4 -5
+h-20c-1 0 -3 0 -4 2l-28 102l-34 -39l6 -60c1 -3 -1 -5 -4 -5h-22c-2 0 -4 1 -4 2l-13 56l21 82v144s-23 9 -23 30h274c0 0 48 -1 95 33c0 0 -8 22 6 36c0 0 28 -20 36 -25c10 -6 17 5 14 14c-2 6 -9 28 -45 42c-4.57884 1.52628 -15.2106 2.3682 -13 9c0 2 2 3 4 3
+c44 -6 70 -42 77 -58c1 -2 20 -15 18 -38c0 -3 0 -5 2 -7z" />
+    <glyph glyph-name="golf_course" unicode="golf_course" 
+d="M363 386l-128 -66v-193c61 -3 106 -21 106 -42c0 -23 -57 -42 -128 -42s-128 19 -128 42c0 16 26 30 64 37v-37h43v384zM384 96c0 18 14 32 32 32s32 -14 32 -32s-14 -32 -32 -32s-32 14 -32 32z" />
+    <glyph glyph-name="gps_fixed" unicode="gps_fixed" 
+d="M256 107c83 0 149 66 149 149s-66 149 -149 149s-149 -66 -149 -149s66 -149 149 -149zM447 277h44v-42h-44c-10 -89 -81 -160 -170 -170v-44h-42v44c-89 10 -160 81 -170 170h-44v42h44c10 89 81 160 170 170v44h42v-44c89 -10 160 -81 170 -170zM256 341
+c47 0 85 -38 85 -85s-38 -85 -85 -85s-85 38 -85 85s38 85 85 85z" />
+    <glyph glyph-name="gps_not_fixed" unicode="gps_not_fixed" 
+d="M256 107c83 0 149 66 149 149s-66 149 -149 149s-149 -66 -149 -149s66 -149 149 -149zM447 277h44v-42h-44c-10 -89 -81 -160 -170 -170v-44h-42v44c-89 10 -160 81 -170 170h-44v42h44c10 89 81 160 170 170v44h42v-44c89 -10 160 -81 170 -170z" />
+    <glyph glyph-name="gps_off" unicode="gps_off" 
+d="M347 138l-209 209c-19 -25 -31 -57 -31 -91c0 -83 66 -149 149 -149c34 0 66 12 91 31zM64 421l27 27l357 -357l-27 -27l-44 44c-28 -23 -62 -39 -100 -43v-44h-42v44c-89 10 -160 81 -170 170h-44v42h44c4 38 20 72 43 100zM447 277h44v-42h-44c-3 -24 -10 -48 -21 -68
+l-32 32c7 17 11 37 11 57c0 83 -66 149 -149 149c-20 0 -40 -4 -57 -11l-32 32c21 11 44 18 68 21v44h42v-44c89 -10 160 -81 170 -170z" />
+    <glyph glyph-name="grade" unicode="grade" 
+d="M256 144l-132 -80l35 150l-116 101l153 13l60 141l60 -141l153 -13l-116 -101l35 -150z" />
+    <glyph glyph-name="gradient" unicode="gradient" 
+d="M405 277v128h-298v-128h42v-42h43v-43h43v43h42v-43h43v43h43v42h42zM363 128v43h-43v-43h43zM277 128v43h-42v-43h42zM192 128v43h-43v-43h43zM405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h298zM149 320h43
+v-43h-43v43zM320 320h43v-43h-43v43zM235 320h42v-43h43v-42h-43v42h-42v-42h-43v42h43v43zM363 235v-43h42v43h-42zM149 235h-42v-43h42v43z" />
+    <glyph glyph-name="grain" unicode="grain" 
+d="M213 427c23 0 43 -20 43 -43s-20 -43 -43 -43s-42 20 -42 43s19 43 42 43zM299 341c23 0 42 -19 42 -42s-19 -43 -42 -43s-43 20 -43 43s20 42 43 42zM384 256c23 0 43 -20 43 -43s-20 -42 -43 -42s-43 19 -43 42s20 43 43 43zM299 171c23 0 42 -20 42 -43
+s-19 -43 -42 -43s-43 20 -43 43s20 43 43 43zM384 341c-23 0 -43 20 -43 43s20 43 43 43s43 -20 43 -43s-20 -43 -43 -43zM128 171c23 0 43 -20 43 -43s-20 -43 -43 -43s-43 20 -43 43s20 43 43 43zM128 341c23 0 43 -19 43 -42s-20 -43 -43 -43s-43 20 -43 43s20 42 43 42z
+M213 256c23 0 43 -20 43 -43s-20 -42 -43 -42s-42 19 -42 42s19 43 42 43z" />
+    <glyph glyph-name="graphic_eq" unicode="graphic_eq" 
+d="M405 299h43v-86h-43v86zM320 128v256h43v-256h-43zM64 213v86h43v-86h-43zM235 43v426h42v-426h-42zM149 128v256h43v-256h-43z" />
+    <glyph glyph-name="grid_off" unicode="grid_off" 
+d="M341 85h31l-31 31v-31zM299 85v74l-12 12h-74v-86h86zM171 213v74l-12 12h-74v-86h86zM171 85v86h-86v-86h86zM85 372v-31h31zM213 244v-31h31zM27 485l458 -458l-27 -27l-43 43h-330c-23 0 -42 19 -42 42v330l-43 43zM341 427v-86h86v86h-86zM171 427h-31l-43 42h330
+c23 0 42 -19 42 -42v-330l-42 43v31h-31l-43 42h74v86h-86v-74l-42 43v31h-31l-43 42h74v86h-86v-74l-42 43v31z" />
+    <glyph glyph-name="grid_on" unicode="grid_on" 
+d="M427 341v86h-86v-86h86zM427 213v86h-86v-86h86zM427 85v86h-86v-86h86zM299 341v86h-86v-86h86zM299 213v86h-86v-86h86zM299 85v86h-86v-86h86zM171 341v86h-86v-86h86zM171 213v86h-86v-86h86zM171 85v86h-86v-86h86zM427 469c23 0 42 -19 42 -42v-342
+c0 -23 -19 -42 -42 -42h-342c-23 0 -42 19 -42 42v342c0 23 19 42 42 42h342z" />
+    <glyph glyph-name="group" unicode="group" 
+d="M341 235c50 0 150 -25 150 -75v-53h-128v53c0 32 -17 56 -42 74c7 1 14 1 20 1zM171 235c50 0 149 -25 149 -75v-53h-299v53c0 50 100 75 150 75zM171 277c-35 0 -64 29 -64 64s29 64 64 64s63 -29 63 -64s-28 -64 -63 -64zM341 277c-35 0 -64 29 -64 64s29 64 64 64
+s64 -29 64 -64s-29 -64 -64 -64z" />
+    <glyph glyph-name="group_add" unicode="group_add" 
+d="M277 235c43 0 128 -21 128 -64v-43h-256v43c0 43 85 64 128 64zM419 231c43 -7 93 -27 93 -60v-43h-64v43c0 25 -11 44 -29 60zM277 277c-35 0 -64 29 -64 64s29 64 64 64s64 -29 64 -64s-29 -64 -64 -64zM384 277c-7 0 -13 1 -19 3c12 17 19 38 19 61s-7 44 -19 61
+c6 2 12 3 19 3c35 0 64 -29 64 -64s-29 -64 -64 -64zM171 299v-43h-64v-64h-43v64h-64v43h64v64h43v-64h64z" />
+    <glyph glyph-name="group_work" unicode="group_work" 
+d="M341 139c29 0 54 24 54 53s-25 53 -54 53s-53 -24 -53 -53s24 -53 53 -53zM203 341c0 -29 24 -53 53 -53s53 24 53 53s-24 54 -53 54s-53 -25 -53 -54zM171 139c29 0 53 24 53 53s-24 53 -53 53s-54 -24 -54 -53s25 -53 54 -53zM256 469c118 0 213 -95 213 -213
+s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="hd" unicode="hd" 
+d="M309 224v64h43v-64h-43zM277 320v-128h86c12 0 21 9 21 21v86c0 12 -9 21 -21 21h-86zM235 192v128h-32v-53h-43v53h-32v-128h32v43h43v-43h32zM405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-24 0 -43 20 -43 43v298c0 23 19 43 43 43h298z" />
+    <glyph glyph-name="hdr_off" unicode="hdr_off" 
+d="M53 459c135.424 -135.909 271.816 -270.85 408 -406l-24 -23l-162 162h-72v73l-32 32v-105h-32v53h-43v-53h-32v128h32v-43h43v43h8l-117 117zM277 288h-8l-32 32h40c17 0 32 -15 32 -32v-41l-32 32v9zM373 288v-21h43v21h-43zM373 192h-8l-24 23v105h75
+c17 0 32 -15 32 -32v-21c0 -13 -8 -26 -19 -30l19 -45h-32l-19 43h-24v-43z" />
+    <glyph glyph-name="hdr_on" unicode="hdr_on" 
+d="M277 224v64h-42v-64h42zM277 320c17 0 32 -15 32 -32v-64c0 -17 -15 -32 -32 -32h-74v128h74zM139 277v43h32v-128h-32v53h-43v-53h-32v128h32v-43h43zM416 267v21h-43v-21h43zM448 267c0 -13 -8 -24 -19 -30l19 -45h-32l-19 43h-24v-43h-32v128h75c17 0 32 -15 32 -32
+v-21z" />
+    <glyph glyph-name="hdr_strong" unicode="hdr_strong" 
+d="M107 213c23 0 42 20 42 43s-19 43 -42 43s-43 -20 -43 -43s20 -43 43 -43zM107 341c47 0 85 -38 85 -85s-38 -85 -85 -85s-86 38 -86 85s39 85 86 85zM363 384c71 0 128 -57 128 -128s-57 -128 -128 -128s-128 57 -128 128s57 128 128 128z" />
+    <glyph glyph-name="hdr_weak" unicode="hdr_weak" 
+d="M363 171c47 0 85 38 85 85s-38 85 -85 85s-86 -38 -86 -85s39 -85 86 -85zM363 384c71 0 128 -57 128 -128s-57 -128 -128 -128s-128 57 -128 128s57 128 128 128zM107 341c47 0 85 -38 85 -85s-38 -85 -85 -85s-86 38 -86 85s39 85 86 85z" />
+    <glyph glyph-name="headset" unicode="headset" 
+d="M256 491c106 0 192 -86 192 -192v-150c0 -35 -29 -64 -64 -64h-64v171h85v43c0 83 -66 149 -149 149s-149 -66 -149 -149v-43h85v-171h-64c-35 0 -64 29 -64 64v150c0 106 86 192 192 192z" />
+    <glyph glyph-name="headset_mic" unicode="headset_mic" 
+d="M256 491c106 0 192 -86 192 -192v-214c0 -35 -29 -64 -64 -64h-128v43h149v21h-85v171h85v43c0 83 -66 149 -149 149s-149 -66 -149 -149v-43h85v-171h-64c-35 0 -64 29 -64 64v150c0 106 86 192 192 192z" />
+    <glyph glyph-name="healing" unicode="healing" 
+d="M355 78l78 78l-78 77l-77 -78zM299 277c-12 0 -22 -9 -22 -21s10 -21 22 -21s21 9 21 21s-9 21 -21 21zM256 192c12 0 21 9 21 21s-9 22 -21 22s-21 -10 -21 -22s9 -21 21 -21zM213 235c12 0 22 9 22 21s-10 21 -22 21s-21 -9 -21 -21s9 -21 21 -21zM156 278l77 78
+l-77 77l-78 -78zM256 320c-12 0 -21 -9 -21 -21s9 -22 21 -22s21 10 21 22s-9 21 -21 21zM378 256l85 -85c8 -8 8 -22 0 -30l-92 -93c-4 -4 -9 -6 -15 -6s-12 2 -16 6l-84 85l-85 -85c-8 -8 -22 -8 -30 0l-93 93c-8 8 -8 22 0 30l85 85l-85 84c-8 8 -8 23 0 31l93 92
+c4 4 10 6 15 6c6 0 11 -2 15 -6l85 -85l84 85c8 8 23 8 31 0l92 -92c8 -8 8 -23 0 -31z" />
+    <glyph glyph-name="hearing" unicode="hearing" 
+d="M245 320c0 29 25 53 54 53s53 -24 53 -53s-24 -53 -53 -53s-54 24 -54 53zM163 456c-35 -35 -56 -83 -56 -136s21 -101 56 -136l-30 -30c-42 42 -69 101 -69 166s27 124 69 166zM363 85c23 0 42 20 42 43h43c0 -47 -38 -85 -85 -85c-12 0 -24 2 -35 7
+c-29 15 -46 37 -59 76c-7 21 -19 31 -36 44c-19 14 -42 32 -61 67c-15 27 -23 56 -23 83c0 84 66 149 150 149s149 -65 149 -149h-43c0 60 -46 107 -106 107s-107 -47 -107 -107c0 -20 6 -43 17 -63c15 -28 33 -41 50 -54c20 -15 40 -31 51 -64c11 -32 21 -42 36 -50
+c4 -2 11 -4 17 -4z" />
+    <glyph glyph-name="help" unicode="help" 
+d="M321 272c12 12 20 29 20 48c0 47 -38 85 -85 85s-85 -38 -85 -85h42c0 23 20 43 43 43s43 -20 43 -43c0 -12 -5 -22 -13 -30l-26 -27c-15 -16 -25 -37 -25 -60v-11h42c0 32 10 44 25 60zM277 107v42h-42v-42h42zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213
+s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="help_outline" unicode="help_outline" 
+d="M256 384c47 0 85 -38 85 -85c0 -53 -64 -59 -64 -107h-42c0 69 64 64 64 107c0 23 -20 42 -43 42s-43 -19 -43 -42h-42c0 47 38 85 85 85zM256 85c94 0 171 77 171 171s-77 171 -171 171s-171 -77 -171 -171s77 -171 171 -171zM256 469c118 0 213 -95 213 -213
+s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213zM235 128v43h42v-43h-42z" />
+    <glyph glyph-name="high_quality" unicode="high_quality" 
+d="M309 224v64h43v-64h-43zM384 213v86c0 12 -9 21 -21 21h-64c-12 0 -22 -9 -22 -21v-86c0 -12 10 -21 22 -21h16v-32h32v32h16c12 0 21 9 21 21zM235 192v128h-32v-53h-43v53h-32v-128h32v43h43v-43h32zM405 427c23 0 43 -20 43 -43v-256c0 -23 -20 -43 -43 -43h-298
+c-24 0 -43 20 -43 43v256c0 23 19 43 43 43h298z" />
+    <glyph glyph-name="highlight" unicode="highlight" 
+d="M362 372l45 45l30 -30l-45 -46zM75 387l30 30l45 -45l-30 -31zM235 469h42v-64h-42v64zM128 213v107h256v-107l-64 -64v-106h-128v106z" />
+    <glyph glyph-name="highlight_off" unicode="highlight_off" 
+d="M256 85c94 0 171 77 171 171s-77 171 -171 171s-171 -77 -171 -171s77 -171 171 -171zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213zM311 341l30 -30l-55 -55l55 -55l-30 -30l-55 55l-55 -55l-30 30l55 55l-55 55l30 30l55 -55z
+" />
+    <glyph glyph-name="history" unicode="history" 
+d="M256 341h32v-90l75 -45l-16 -26l-91 55v106zM277 448c106 0 192 -86 192 -192s-86 -192 -192 -192c-53 0 -100 21 -135 56l30 31c27 -27 64 -44 105 -44c83 0 150 66 150 149s-67 149 -150 149s-149 -66 -149 -149h64l-86 -86l-2 3l-83 83h64c0 106 86 192 192 192z" />
+    <glyph glyph-name="home" unicode="home" 
+d="M213 85h-106v171h-64l213 192l213 -192h-64v-171h-106v128h-86v-128z" />
+    <glyph glyph-name="hot_tub" unicode="hot_tub" 
+d="M313 387c22 -21 31 -50 27 -79l-1 -9h-41l3 12c3.05626 17.3188 -3.40602 35.406 -16 48c-22 22 -32 51 -28 80l1 9h41l-2 -13c-3 -17 2 -34 14 -47zM398 387c22 -21 32 -50 28 -79l-2 -9h-40l2 12c3 17 -2 34 -14 47l-2 1c-22 22 -32 51 -28 80l2 9h40l-2 -13
+c-3 -17 2 -34 14 -47zM405 85v128h-42v-128h42zM320 85v128h-43v-128h43zM235 85v128h-43v-128h43zM149 85v128h-42v-128h42zM238 256h231v-171c0 -23 -19 -42 -42 -42h-342c-23 0 -42 19 -42 42v171h64v16c0 26 22 48 48 48c15.3845 0 28.1542 -8.15417 36 -16l29 -33
+c5 -6 11 -10 18 -15zM107 384c0 24 18 43 42 43s43 -19 43 -43s-19 -43 -43 -43s-42 19 -42 43z" />
+    <glyph glyph-name="hotel" unicode="hotel" 
+d="M405 363c47 0 86 -39 86 -86v-192h-43v64h-384v-64h-43v320h43v-192h171v150h170zM149 235c-35 0 -64 29 -64 64s29 64 64 64s64 -29 64 -64s-29 -64 -64 -64z" />
+    <glyph glyph-name="hourglass_empty" unicode="hourglass_empty" 
+d="M256 267l85 85v75h-170v-75zM341 160l-85 85l-85 -85v-75h170v75zM128 469h256v-128l-85 -85l85 -85v-128h-256v128l85 85l-85 85v128z" />
+    <glyph glyph-name="hourglass_full" unicode="hourglass_full" 
+d="M128 469h256v-128l-85 -85l85 -85v-128h-256v128l85 85l-85 85v128z" />
+    <glyph glyph-name="http" unicode="http" 
+d="M459 267v21h-43v-21h43zM459 320c17 0 32 -15 32 -32v-21c0 -17 -15 -32 -32 -32h-43v-43h-32v128h75zM267 288v32h96v-32h-32v-96h-32v96h-32zM149 288v32h96v-32h-32v-96h-32v96h-32zM96 277v43h32v-128h-32v53h-43v-53h-32v128h32v-43h43z" />
+    <glyph glyph-name="https" unicode="https" 
+d="M322 341v43c0 36 -30 66 -66 66s-66 -30 -66 -66v-43h132zM256 149c23 0 43 20 43 43s-20 43 -43 43s-43 -20 -43 -43s20 -43 43 -43zM384 341c23 0 43 -19 43 -42v-214c0 -23 -20 -42 -43 -42h-256c-23 0 -43 19 -43 42v214c0 23 20 42 43 42h21v43c0 59 48 107 107 107
+s107 -48 107 -107v-43h21z" />
+    <glyph glyph-name="image" unicode="image" 
+d="M181 224l-74 -96h298l-96 128l-74 -96zM448 107c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h298c23 0 43 -20 43 -43v-298z" />
+    <glyph glyph-name="image_aspect_ratio" unicode="image_aspect_ratio" 
+d="M427 128v256h-342v-256h342zM427 427c23 0 42 -20 42 -43v-256c0 -23 -19 -43 -42 -43h-342c-23 0 -42 20 -42 43v256c0 23 19 43 42 43h342zM256 299v-43h-43v43h43zM171 299v-43h-43v43h43zM341 213v-42h-42v42h42zM341 299v-43h-42v43h42z" />
+    <glyph glyph-name="import_contacts" unicode="import_contacts" 
+d="M448 117v246c-23 7 -49 10 -75 10c-36 0 -88 -14 -117 -32v-245c29 18 81 32 117 32c26 0 52 -4 75 -11zM373 416c44.2046 0 90.7488 -11.0375 118 -32v-311c0 -5 -6 -11 -11 -11c-2 0 -3 0 -5 1c-30 16 -67 22 -102 22c-36 0 -88 -14 -117 -32c-31 23 -75 32 -117 32
+c-31 0 -73 -9 -102 -23c-2 0 -3 -1 -5 -1c-5 0 -11 5 -11 10v313c31 23 76 32 118 32s86 -9 117 -32c31 23 75 32 117 32z" />
+    <glyph glyph-name="import_export" unicode="import_export" 
+d="M341 149h64l-85 -85l-85 85h64v150h42v-150zM192 448l85 -85h-64v-150h-42v150h-64z" />
+    <glyph glyph-name="important_devices" unicode="important_devices" 
+d="M255 320h65l-53 -38l20 -62l-52 39l-53 -39l20 62l-53 38h65l21 64zM427 469c23 0 42 -18 42 -42v-107h-42v107h-384v-256h277v-43h-43v-43h43v-42h-171v42h43v43h-149c-24 0 -43 20 -43 43v256c0 24 19 42 43 42h384zM491 85v150h-107v-150h107zM491 277
+c12 0 21 -9 21 -21v-192c0 -12 -9 -21 -21 -21h-107c-12 0 -21 9 -21 21v192c0 12 9 21 21 21h107z" />
+    <glyph glyph-name="inbox" unicode="inbox" 
+d="M405 192v213h-299v-213h86c0 -35 29 -64 64 -64s64 29 64 64h85zM405 448c23 0 43 -19 43 -43v-298c0 -23 -20 -43 -43 -43h-299c-24 0 -42 20 -42 43v298c0 24 18 43 42 43h299z" />
+    <glyph glyph-name="indeterminate_check_box" unicode="indeterminate_check_box" 
+d="M363 235v42h-214v-42h214zM405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h298z" />
+    <glyph glyph-name="info" unicode="info" 
+d="M277 320v43h-42v-43h42zM277 149v128h-42v-128h42zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="info_outline" unicode="info_outline" 
+d="M235 320v43h42v-43h-42zM256 85c94 0 171 77 171 171s-77 171 -171 171s-171 -77 -171 -171s77 -171 171 -171zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213zM235 149v128h42v-128h-42z" />
+    <glyph glyph-name="input" unicode="input" 
+d="M235 171v64h-214v42h214v64l85 -85zM448 448c23 0 43 -19 43 -43v-299c0 -23 -20 -42 -43 -42h-384c-23 0 -43 19 -43 42v86h43v-86h384v300h-384v-86h-43v85c0 23 20 43 43 43h384z" />
+    <glyph glyph-name="insert_chart" unicode="insert_chart" 
+d="M363 149v86h-43v-86h43zM277 149v214h-42v-214h42zM192 149v150h-43v-150h43zM405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h298z" />
+    <glyph glyph-name="insert_comment" unicode="insert_comment" 
+d="M384 341v43h-256v-43h256zM384 277v43h-256v-43h256zM384 213v43h-256v-43h256zM427 469c23 0 42 -19 42 -42v-384l-85 85h-299c-23 0 -42 20 -42 43v256c0 23 19 42 42 42h342z" />
+    <glyph glyph-name="insert_drive_file" unicode="insert_drive_file" 
+d="M277 320h118l-118 117v-117zM128 469h171l128 -128v-256c0 -23 -20 -42 -43 -42h-256c-23 0 -43 19 -43 42l1 342c0 23 19 42 42 42z" />
+    <glyph glyph-name="insert_emoticon" unicode="insert_emoticon" 
+d="M256 139c-50 0 -92 30 -109 74h218c-17 -44 -59 -74 -109 -74zM181 277c-18 0 -32 14 -32 32s14 32 32 32s32 -14 32 -32s-14 -32 -32 -32zM331 277c-18 0 -32 14 -32 32s14 32 32 32s32 -14 32 -32s-14 -32 -32 -32zM256 85c94 0 171 77 171 171s-77 171 -171 171
+s-171 -77 -171 -171s77 -171 171 -171zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="insert_invitation" unicode="insert_invitation" 
+d="M405 107v234h-298v-234h298zM341 491h43v-43h21c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-24 0 -43 20 -43 43v298c0 23 19 43 43 43h21v43h43v-43h170v43zM363 256v-107h-107v107h107z" />
+    <glyph glyph-name="insert_link" unicode="insert_link" 
+d="M363 363c59 0 106 -48 106 -107s-47 -107 -106 -107h-86v41h86c36 0 66 30 66 66s-30 66 -66 66h-86v41h86zM171 235v42h170v-42h-170zM83 256c0 -36 30 -66 66 -66h86v-41h-86c-59 0 -106 48 -106 107s47 107 106 107h86v-41h-86c-36 0 -66 -30 -66 -66z" />
+    <glyph glyph-name="insert_photo" unicode="insert_photo" 
+d="M181 224l-74 -96h298l-96 128l-74 -96zM448 107c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h298c23 0 43 -20 43 -43v-298z" />
+    <glyph glyph-name="invert_colors" unicode="invert_colors" 
+d="M256 94v309l-90 -90c-24 -24 -38 -57 -38 -91c0 -68.7916 59.1998 -128 128 -128zM377 343c67 -67 67 -174 0 -241c-33 -33 -77 -50 -121 -50s-88 17 -121 50c-67 67 -67 174 0 241l121 121z" />
+    <glyph glyph-name="invert_colors_off" unicode="invert_colors_off" 
+d="M256 403l-49 -48l-30 30l79 79l121 -121c52 -52 63 -129 34 -192l-155 154v98zM256 94v103l-102 102c-17 -22 -26 -49 -26 -77c0 -68.7916 59.1998 -128 128 -128zM441 67l7 -8l-27 -27l-58 58c-31 -25 -69 -38 -107 -38c-44 0 -88 17 -121 50c-62 63 -66 161 -12 228
+l-59 59l27 27c116.495 -116.495 232.353 -233.66 350 -349z" />
+    <glyph glyph-name="iso" unicode="iso" 
+d="M363 149h-107v32h107v-32zM405 107v298l-298 -298h298zM117 352v-32h43v-43h32v43h43v32h-43v43h-32v-43h-43zM405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h298z" />
+    <glyph glyph-name="keyboard" unicode="keyboard" 
+d="M405 299v42h-42v-42h42zM405 235v42h-42v-42h42zM341 299v42h-42v-42h42zM341 235v42h-42v-42h42zM341 149v43h-170v-43h170zM149 299v42h-42v-42h42zM149 235v42h-42v-42h42zM171 277v-42h42v42h-42zM171 341v-42h42v42h-42zM235 277v-42h42v42h-42zM235 341v-42h42v42
+h-42zM427 405c23 0 42 -19 42 -42v-214c0 -23 -19 -42 -42 -42h-342c-23 0 -42 19 -42 42v214c0 23 19 42 42 42h342z" />
+    <glyph glyph-name="keyboard_arrow_down" unicode="keyboard_arrow_down" 
+d="M158 345l98 -98l98 98l30 -30l-128 -128l-128 128z" />
+    <glyph glyph-name="keyboard_arrow_left" unicode="keyboard_arrow_left" 
+d="M329 169l-30 -30l-128 128l128 128l30 -30l-98 -98z" />
+    <glyph glyph-name="keyboard_arrow_right" unicode="keyboard_arrow_right" 
+d="M183 163l98 98l-98 98l30 30l128 -128l-128 -128z" />
+    <glyph glyph-name="keyboard_arrow_up" unicode="keyboard_arrow_up" 
+d="M158 183l-30 30l128 128l128 -128l-30 -30l-98 98z" />
+    <glyph glyph-name="keyboard_backspace" unicode="keyboard_backspace" 
+d="M448 277v-42h-302l76 -77l-30 -30l-128 128l128 128l30 -30l-76 -77h302z" />
+    <glyph glyph-name="keyboard_capslock" unicode="keyboard_capslock" 
+d="M128 128v43h256v-43h-256zM256 333l-98 -98l-30 30l128 128l128 -128l-30 -30z" />
+    <glyph glyph-name="keyboard_hide" unicode="keyboard_hide" 
+d="M256 21l-85 86h170zM405 341v43h-42v-43h42zM405 277v43h-42v-43h42zM341 341v43h-42v-43h42zM341 277v43h-42v-43h42zM341 192v43h-170v-43h170zM149 341v43h-42v-43h42zM149 277v43h-42v-43h42zM171 320v-43h42v43h-42zM171 384v-43h42v43h-42zM235 320v-43h42v43h-42z
+M235 384v-43h42v43h-42zM427 448c23 0 42 -20 42 -43v-213c0 -23 -19 -43 -42 -43h-342c-23 0 -42 20 -42 43v213c0 23 19 43 42 43h342z" />
+    <glyph glyph-name="keyboard_return" unicode="keyboard_return" 
+d="M405 363h43v-128h-324l77 -77l-30 -30l-128 128l128 128l30 -30l-77 -77h281v86z" />
+    <glyph glyph-name="keyboard_tab" unicode="keyboard_tab" 
+d="M427 384h42v-256h-42v256zM247 354l30 30l128 -128l-128 -128l-30 30l77 77h-303v42h303z" />
+    <glyph glyph-name="keyboard_voice" unicode="keyboard_voice" 
+d="M369 256h36c0 -73 -58 -133 -128 -143v-70h-42v70c-70 10 -128 70 -128 143h36c0 -64 54 -109 113 -109s113 45 113 109zM256 192c-35 0 -64 29 -64 64v128c0 35 29 64 64 64s64 -29 64 -64v-128c0 -35 -29 -64 -64 -64z" />
+    <glyph glyph-name="kitchen" unicode="kitchen" 
+d="M171 256h42v-107h-42v107zM171 405h42v-64h-42v64zM384 320v107h-256v-107h256zM384 85v193h-256v-193h256zM384 469c23 0 43 -18 43 -42v-342c0 -23 -20 -42 -43 -42h-256c-23 0 -43 19 -43 42v342c0 24 20 42 43 42h256z" />
+    <glyph glyph-name="label" unicode="label" 
+d="M376 387l93 -131l-93 -131c-8 -11 -21 -18 -35 -18h-234c-23 0 -43 19 -43 42v214c0 23 20 42 43 42h234c14 0 27 -7 35 -18z" />
+    <glyph glyph-name="label_outline" unicode="label_outline" 
+d="M341 149l76 107l-76 107h-234v-214h234zM376 387l93 -131l-93 -131c-8 -11 -21 -18 -35 -18h-234c-23 0 -43 19 -43 42v214c0 23 20 42 43 42h234c14 0 27 -7 35 -18z" />
+    <glyph glyph-name="landscape" unicode="landscape" 
+d="M299 384l192 -256h-470l128 171l96 -128l34 25l-60 81z" />
+    <glyph glyph-name="language" unicode="language" 
+d="M349 213h72c3 14 6 28 6 43s-3 29 -6 43h-72c2 -14 3 -28 3 -43s-1 -29 -3 -43zM311 95c39 13 73 41 93 76h-63c-7 -27 -17 -52 -30 -76zM306 213c2 14 3 28 3 43s-1 29 -3 43h-100c-2 -14 -3 -28 -3 -43s1 -29 3 -43h100zM256 86c18 26 32 54 41 85h-82
+c9 -31 23 -59 41 -85zM171 341c7 27 17 52 30 76c-39 -13 -73 -41 -93 -76h63zM108 171c20 -35 54 -63 93 -76c-13 24 -23 49 -30 76h-63zM91 213h72c-2 14 -3 28 -3 43s1 29 3 43h-72c-3 -14 -6 -28 -6 -43s3 -29 6 -43zM256 426c-18 -26 -32 -54 -41 -85h82
+c-9 31 -23 59 -41 85zM404 341c-20 35 -54 63 -93 76c13 -24 23 -49 30 -76h63zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="laptop" unicode="laptop" 
+d="M85 384v-213h342v213h-342zM427 128h85v-43h-512v43h85c-23 0 -42 20 -42 43v213c0 23 19 43 42 43h342c23 0 42 -20 42 -43v-213c0 -23 -19 -43 -42 -43z" />
+    <glyph glyph-name="laptop_chromebook" unicode="laptop_chromebook" 
+d="M427 192v213h-342v-213h342zM299 128v21h-86v-21h86zM469 128h43v-43h-512v43h43v320h426v-320z" />
+    <glyph glyph-name="laptop_mac" unicode="laptop_mac" 
+d="M256 107c12 0 21 9 21 21s-9 21 -21 21s-21 -9 -21 -21s9 -21 21 -21zM85 405v-234h342v234h-342zM427 128h85c0 -23 -20 -43 -43 -43h-426c-23 0 -43 20 -43 43h85c-23 0 -42 20 -42 43v234c0 23 19 43 42 43h342c23 0 42 -20 42 -43v-234c0 -23 -19 -43 -42 -43z" />
+    <glyph glyph-name="laptop_windows" unicode="laptop_windows" 
+d="M85 405v-213h342v213h-342zM427 128h85v-43h-512v43h85v21c-23 0 -42 20 -42 43v213c0 23 19 43 42 43h342c23 0 42 -20 42 -43v-213c0 -23 -19 -43 -42 -43v-21z" />
+    <glyph glyph-name="last_page" unicode="last_page" 
+d="M341 384h43v-256h-43v256zM119 354l30 30l128 -128l-128 -128l-30 30l98 98z" />
+    <glyph glyph-name="launch" unicode="launch" 
+d="M299 448h149v-149h-43v76l-209 -209l-30 30l209 209h-76v43zM405 107v149h43v-149c0 -23 -20 -43 -43 -43h-298c-24 0 -43 20 -43 43v298c0 23 19 43 43 43h149v-43h-149v-298h298z" />
+    <glyph glyph-name="layers" unicode="layers" 
+d="M256 171c-64.012 49.6547 -127.813 99.5201 -192 149l192 149l192 -149c-64.1875 -49.4791 -127.987 -99.3464 -192 -149zM256 116l157 123l35 -27l-192 -149l-192 149l35 27z" />
+    <glyph glyph-name="layers_clear" unicode="layers_clear" 
+d="M70 491l399 -400l-27 -27l-80 81l-106 -82l-192 149l35 27l157 -123l75 59l-30 30l-45 -34c-64.012 49.6547 -127.813 99.5201 -192 149l69 54l-90 90zM448 320c-28.8803 -22.1197 -57.4315 -44.5685 -86 -67l-168 168l62 48zM423 192l-31 31l25 19l31 -30z" />
+    <glyph glyph-name="leak_add" unicode="leak_add" 
+d="M299 64c0 83 66 149 149 149v-42c-59 0 -107 -48 -107 -107h-42zM384 64c0 35 29 64 64 64v-64h-64zM213 64c0 129 106 235 235 235v-43c-106 0 -192 -86 -192 -192h-43zM213 448c0 -83 -66 -149 -149 -149v42c59 0 107 48 107 107h42zM299 448
+c0 -129 -105 -235 -235 -235v43c106 0 192 86 192 192h43zM128 448c0 -35 -29 -64 -64 -64v64h64z" />
+    <glyph glyph-name="leak_remove" unicode="leak_remove" 
+d="M328 265c35 21 76 34 120 34v-43c-32 0 -62 -8 -89 -22zM425 168l-34 34c17 7 37 11 57 11v-42c-8 0 -16 -1 -23 -3zM299 448c0 -44 -13 -85 -34 -120l-31 31c14 27 22 57 22 89h43zM64 421l27 27l357 -357l-27 -27l-61 61c-12 -17 -19 -38 -19 -61h-42c0 34 12 66 31 91
+l-31 30c-27 -33 -43 -75 -43 -121h-43c0 58 21 111 56 152l-53 53c-41 -35 -94 -56 -152 -56v43c46 0 89 16 122 43l-31 31c-25 -19 -57 -31 -91 -31v42c23 0 44 7 61 19zM213 448c0 -20 -4 -40 -11 -57l-34 34c2 7 3 15 3 23h42z" />
+    <glyph glyph-name="lens" unicode="lens" 
+d="M256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="library_add" unicode="library_add" 
+d="M405 277v43h-85v85h-43v-85h-85v-43h85v-85h43v85h85zM427 469c23 0 42 -19 42 -42v-256c0 -23 -19 -43 -42 -43h-256c-23 0 -43 20 -43 43v256c0 23 20 42 43 42h256zM85 384v-299h299v-42h-299c-23 0 -42 19 -42 42v299h42z" />
+    <glyph glyph-name="library_books" unicode="library_books" 
+d="M405 363v42h-213v-42h213zM320 192v43h-128v-43h128zM405 277v43h-213v-43h213zM427 469c23 0 42 -19 42 -42v-256c0 -23 -19 -43 -42 -43h-256c-23 0 -43 20 -43 43v256c0 23 20 42 43 42h256zM85 384v-299h299v-42h-299c-23 0 -42 19 -42 42v299h42z" />
+    <glyph glyph-name="library_music" unicode="library_music" 
+d="M85 384v-299h299v-42h-299c-23 0 -42 19 -42 42v299h42zM384 363v42h-85v-117c-9 7 -20 11 -32 11c-29 0 -54 -25 -54 -54s25 -53 54 -53s53 24 53 53v118h64zM427 469c23 0 42 -19 42 -42v-256c0 -23 -19 -43 -42 -43h-256c-23 0 -43 20 -43 43v256c0 23 20 42 43 42
+h256z" />
+    <glyph glyph-name="lightbulb_outline" unicode="lightbulb_outline" 
+d="M317 233c29 20 46 52 46 87c0 59 -48 107 -107 107s-107 -48 -107 -107c0 -35 17 -67 46 -87l18 -13v-49h86v49zM256 469c82 0 149 -67 149 -149c0 -51 -25 -95 -64 -122v-49c0 -12 -9 -21 -21 -21h-128c-12 0 -21 9 -21 21v49c-39 27 -64 71 -64 122
+c0 82 67 149 149 149zM192 64v21h128v-21c0 -12 -9 -21 -21 -21h-86c-12 0 -21 9 -21 21z" />
+    <glyph glyph-name="line_style" unicode="line_style" 
+d="M64 427h384v-86h-384v86zM277 256v43h171v-43h-171zM64 256v43h171v-43h-171zM405 85v43h43v-43h-43zM320 85v43h43v-43h-43zM235 85v43h42v-43h-42zM149 85v43h43v-43h-43zM64 85v43h43v-43h-43zM341 171v42h107v-42h-107zM203 171v42h106v-42h-106zM64 171v42h107v-42
+h-107z" />
+    <glyph glyph-name="line_weight" unicode="line_weight" 
+d="M64 427h384v-86h-384v86zM64 235v64h384v-64h-384zM64 85v22h384v-22h-384zM64 149v43h384v-43h-384z" />
+    <glyph glyph-name="linear_scale" unicode="linear_scale" 
+d="M416 309c29 0 53 -24 53 -53s-24 -53 -53 -53c-22 0 -41 13 -49 32h-62c-8 -19 -27 -32 -49 -32s-41 13 -49 32h-62c-8 -19 -27 -32 -49 -32c-29 0 -53 24 -53 53s24 53 53 53c22 0 41 -13 49 -32h62c8 19 27 32 49 32s41 -13 49 -32h62c8 19 27 32 49 32z" />
+    <glyph glyph-name="link" unicode="link" 
+d="M363 363c59 0 106 -48 106 -107s-47 -107 -106 -107h-86v41h86c36 0 66 30 66 66s-30 66 -66 66h-86v41h86zM171 235v42h170v-42h-170zM83 256c0 -36 30 -66 66 -66h86v-41h-86c-59 0 -106 48 -106 107s47 107 106 107h86v-41h-86c-36 0 -66 -30 -66 -66z" />
+    <glyph glyph-name="linked_camera" unicode="linked_camera" 
+d="M341 384c24 0 43 -19 43 -43h28c0 39 -32 71 -71 71v-28zM256 107c59 0 107 47 107 106s-48 107 -107 107s-107 -48 -107 -107s48 -106 107 -106zM363 320h106v-235c0 -23 -19 -42 -42 -42h-342c-23 0 -42 19 -42 42v256c0 23 19 43 42 43h68l39 43h128v-64
+c24 0 43 -19 43 -43zM341 441v28c71 0 128 -57 128 -128h-28c0 55 -45 100 -100 100zM188 213c0 38 30 69 68 69s68 -31 68 -69s-30 -68 -68 -68s-68 30 -68 68z" />
+    <glyph glyph-name="list" unicode="list" 
+d="M149 363h299v-43h-299v43zM149 149v43h299v-43h-299zM149 235v42h299v-42h-299zM64 320v43h43v-43h-43zM64 149v43h43v-43h-43zM64 235v42h43v-42h-43z" />
+    <glyph glyph-name="live_help" unicode="live_help" 
+d="M321 293c12 12 20 29 20 48c0 47 -38 86 -85 86s-85 -39 -85 -86h42c0 23 20 43 43 43s43 -20 43 -43c0 -12 -5 -22 -13 -30l-26 -27c-15 -16 -25 -37 -25 -60v-11h42c0 32 10 45 25 61zM277 128v43h-42v-43h42zM405 469c23 0 43 -19 43 -42v-299c0 -23 -20 -43 -43 -43
+h-85l-64 -64l-64 64h-85c-24 0 -43 20 -43 43v299c0 23 19 42 43 42h298z" />
+    <glyph glyph-name="live_tv" unicode="live_tv" 
+d="M192 299l149 -86l-149 -85v171zM448 85v256h-384v-256h384zM448 384c23 0 43 -19 43 -43v-256c0 -23 -20 -42 -43 -42h-384c-23 0 -43 19 -43 42v256c0 24 20 43 43 43h162l-70 70l15 15l85 -85l85 85l15 -15l-70 -70h162z" />
+    <glyph glyph-name="local_activity" unicode="local_activity" 
+d="M332 154l-23 87l70 58l-90 5l-33 84l-33 -84l-91 -5l71 -58l-23 -87l76 49zM427 256c0 -23 19 -43 42 -43v-85c0 -23 -19 -43 -42 -43h-342c-23 0 -42 20 -42 43v85c24 0 42 20 42 43s-19 43 -42 43v85c0 23 19 43 42 43h342c23 0 42 -20 42 -43v-85
+c-23 0 -42 -20 -42 -43z" />
+    <glyph glyph-name="local_airport" unicode="local_airport" 
+d="M448 171l-171 53v-117l43 -32v-32l-75 21l-74 -21v32l42 32v117l-170 -53v42l170 107v117c0 18 14 32 32 32s32 -14 32 -32v-117l171 -107v-42z" />
+    <glyph glyph-name="local_atm" unicode="local_atm" 
+d="M427 128v256h-342v-256h342zM427 427c24 0 42 -19 42 -43v-256c0 -24 -18 -43 -42 -43h-342c-24 0 -42 19 -42 43v256c0 24 18 43 42 43h342zM235 149v22h-43v42h85v22h-64c-12 0 -21 9 -21 21v64c0 12 9 21 21 21h22v22h42v-22h43v-42h-85v-22h64c12 0 21 -9 21 -21v-64
+c0 -12 -9 -21 -21 -21h-22v-22h-42z" />
+    <glyph glyph-name="local_bar" unicode="local_bar" 
+d="M159 363h194l38 42h-270zM448 405l-171 -192v-106h107v-43h-256v43h107v106l-171 192v43h384v-43z" />
+    <glyph glyph-name="local_cafe" unicode="local_cafe" 
+d="M43 64v43h384v-43h-384zM427 341v64h-43v-64h43zM427 448c24 0 42 -19 42 -43v-64c0 -24 -18 -42 -42 -42h-43v-64c0 -47 -38 -86 -85 -86h-128c-47 0 -86 39 -86 86v213h342z" />
+    <glyph glyph-name="local_car_wash" unicode="local_car_wash" 
+d="M107 235h298l-32 96h-234zM373 128c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM139 128c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM404 341l44 -128v-170c0 -12 -9 -22 -21 -22h-22c-12 0 -21 10 -21 22v21h-256v-21
+c0 -12 -9 -22 -21 -22h-22c-12 0 -21 10 -21 22v170l44 128c4 13 17 22 31 22h234c14 0 27 -9 31 -22zM149 405c-18 0 -32 14 -32 32c0 21 32 58 32 58s32 -37 32 -58c0 -18 -14 -32 -32 -32zM256 405c-18 0 -32 14 -32 32c0 21 32 58 32 58s32 -37 32 -58
+c0 -18 -14 -32 -32 -32zM363 405c-18 0 -32 14 -32 32c0 21 32 58 32 58s32 -37 32 -58c0 -18 -14 -32 -32 -32z" />
+    <glyph glyph-name="local_convenience_store" unicode="local_convenience_store" 
+d="M341 256v107h-21v-43h-21v43h-22v-64h43v-43h21zM235 299v64h-64v-22h42v-21h-42v-64h64v21h-43v22h43zM405 363h64v-278h-170v86h-86v-86h-170v278h64v64h298v-64z" />
+    <glyph glyph-name="local_dining" unicode="local_dining" 
+d="M317 266l-31 -31l147 -147l-30 -30l-147 147l-147 -147l-30 30l208 208c-15 33 -4 79 30 113c41 41 99 48 130 17s24 -90 -17 -131c-34 -34 -80 -44 -113 -29zM173 227l-90 90c-33 33 -33 87 0 120l150 -149z" />
+    <glyph glyph-name="local_drink" unicode="local_drink" 
+d="M391 341l9 86h-288l9 -86h270zM256 107c35 0 64 29 64 64c0 43 -64 115 -64 115s-64 -72 -64 -115c0 -35 29 -64 64 -64zM64 469h384l-43 -389c-3 -21 -20 -37 -42 -37h-214c-22 0 -39 16 -42 37z" />
+    <glyph glyph-name="local_florist" unicode="local_florist" 
+d="M256 395c-29 0 -53 -25 -53 -54s24 -53 53 -53s53 24 53 53s-24 54 -53 54zM119 293c0 21 13 39 31 48c-18 9 -31 27 -31 48c0 29 25 54 54 54c11 0 21 -4 30 -10v4c0 29 24 54 53 54s53 -25 53 -54v-4c9 6 19 10 30 10c29 0 54 -25 54 -54c0 -21 -13 -39 -31 -48
+c18 -9 31 -27 31 -48c0 -29 -25 -53 -54 -53c-11 0 -21 3 -30 9v-4c0 -29 -24 -53 -53 -53s-53 24 -53 53v4c-9 -6 -19 -9 -30 -9c-29 0 -54 24 -54 53zM256 43c-106 0 -192 86 -192 192c106 0 192 -86 192 -192zM256 43c0 106 86 192 192 192c0 -106 -86 -192 -192 -192z
+" />
+    <glyph glyph-name="local_gas_station" unicode="local_gas_station" 
+d="M384 299c12 0 21 9 21 21s-9 21 -21 21s-21 -9 -21 -21s9 -21 21 -21zM256 299v106h-128v-106h128zM422 358c10 -10 15 -23 15 -38v-203c0 -29 -24 -53 -53 -53s-53 24 -53 53v107h-32v-160h-214v341c0 23 20 43 43 43h128c23 0 43 -20 43 -43v-149h21
+c23 0 43 -20 43 -43v-96c0 -12 9 -21 21 -21s21 9 21 21v154c-7 -3 -13 -4 -21 -4c-29 0 -53 24 -53 53c0 23 14 42 34 50l-45 45l23 22z" />
+    <glyph glyph-name="local_grocery_store" unicode="local_grocery_store" 
+d="M363 128c23 0 42 -20 42 -43s-19 -42 -42 -42s-43 19 -43 42s20 43 43 43zM21 469h70l20 -42h316c12 0 21 -10 21 -22c0 -4 -1 -7 -3 -10l-76 -138c-7 -13 -21 -22 -37 -22h-159l-19 -35l-1 -3c0 -3 2 -5 5 -5h247v-43h-256c-23 0 -42 20 -42 43c0 7 2 14 5 20l29 53
+l-77 162h-43v42zM149 128c23 0 43 -20 43 -43s-20 -42 -43 -42s-42 19 -42 42s19 43 42 43z" />
+    <glyph glyph-name="local_hospital" unicode="local_hospital" 
+d="M384 213v86h-85v85h-86v-85h-85v-86h85v-85h86v85h85zM405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h298z" />
+    <glyph glyph-name="local_hotel" unicode="local_hotel" 
+d="M405 363c47 0 86 -39 86 -86v-192h-43v64h-384v-64h-43v320h43v-192h171v150h170zM149 235c-35 0 -64 29 -64 64s29 64 64 64s64 -29 64 -64s-29 -64 -64 -64z" />
+    <glyph glyph-name="local_laundry_service" unicode="local_laundry_service" 
+d="M256 85c71 0 128 57 128 128s-57 128 -128 128s-128 -57 -128 -128s57 -128 128 -128zM149 427c-12 0 -21 -10 -21 -22s9 -21 21 -21s22 9 22 21s-10 22 -22 22zM213 427c-12 0 -21 -10 -21 -22s9 -21 21 -21s22 9 22 21s-10 22 -22 22zM384 469c24 0 43 -18 43 -42v-342
+c0 -24 -19 -42 -43 -42h-256c-24 0 -43 18 -43 42v342c0 24 19 42 43 42h256zM196 153l120 121c33 -33 33 -88 0 -121s-87 -33 -120 0z" />
+    <glyph glyph-name="local_library" unicode="local_library" 
+d="M256 341c-35 0 -64 29 -64 64s29 64 64 64s64 -29 64 -64s-29 -64 -64 -64zM256 266c50 47 118 75 192 75v-234c-74 0 -142 -29 -192 -76c-50 47 -118 76 -192 76v234c74 0 142 -28 192 -75z" />
+    <glyph glyph-name="local_mall" unicode="local_mall" 
+d="M256 235c59 0 107 47 107 106h-43c0 -35 -29 -64 -64 -64s-64 29 -64 64h-43c0 -59 48 -106 107 -106zM256 448c-35 0 -64 -29 -64 -64h128c0 35 -29 64 -64 64zM405 384c23 0 43 -20 43 -43v-256c0 -23 -20 -42 -43 -42h-298c-23 0 -43 19 -43 42v256c0 23 20 43 43 43
+h42c0 59 48 107 107 107s107 -48 107 -107h42z" />
+    <glyph glyph-name="local_movies" unicode="local_movies" 
+d="M384 320v43h-43v-43h43zM384 235v42h-43v-42h43zM384 149v43h-43v-43h43zM171 320v43h-43v-43h43zM171 235v42h-43v-42h43zM171 149v43h-43v-43h43zM384 448h43v-384h-43v43h-43v-43h-170v43h-43v-43h-43v384h43v-43h43v43h170v-43h43v43z" />
+    <glyph glyph-name="local_offer" unicode="local_offer" 
+d="M117 363c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM457 265c8 -8 12 -18 12 -30s-4 -22 -12 -30l-150 -150c-8 -8 -18 -12 -30 -12s-22 4 -30 12l-192 192c-8 8 -12 18 -12 30v150c0 23 19 42 42 42h150c12 0 22 -4 30 -12z" />
+    <glyph glyph-name="local_parking" unicode="local_parking" 
+d="M282 277c23 0 42 20 42 43s-19 43 -42 43h-69v-86h69zM277 448c71 0 128 -57 128 -128s-57 -128 -128 -128h-64v-128h-85v384h149z" />
+    <glyph glyph-name="local_pharmacy" unicode="local_pharmacy" 
+d="M341 213v43h-64v64h-42v-64h-64v-43h64v-64h42v64h64zM448 405v-42l-43 -128l43 -128v-43h-384v43l43 128l-43 128v42h271l31 86l50 -19l-24 -67h56z" />
+    <glyph glyph-name="local_phone" unicode="local_phone" 
+d="M141 282c31 -60 81 -110 141 -141l47 47c6 6 15 8 22 5c24 -8 50 -12 76 -12c12 0 21 -9 21 -21v-75c0 -12 -9 -21 -21 -21c-200 0 -363 163 -363 363c0 12 9 21 21 21h75c12 0 21 -9 21 -21c0 -27 4 -52 12 -76c2 -7 1 -16 -5 -22z" />
+    <glyph glyph-name="local_pizza" unicode="local_pizza" 
+d="M256 192c23 0 43 20 43 43s-20 42 -43 42s-43 -19 -43 -42s20 -43 43 -43zM149 363c0 -23 20 -43 43 -43s43 20 43 43s-20 42 -43 42s-43 -19 -43 -42zM256 469c76 0 145 -33 192 -85l-192 -341l-192 341c47 52 116 85 192 85z" />
+    <glyph glyph-name="local_play" unicode="local_play" 
+d="M332 154l-23 87l70 58l-90 5l-33 84l-33 -84l-91 -5l71 -58l-23 -87l76 49zM427 256c0 -23 19 -43 42 -43v-85c0 -23 -19 -43 -42 -43h-342c-23 0 -42 20 -42 43v85c24 0 42 20 42 43s-19 43 -42 43v85c0 23 19 43 42 43h342c23 0 42 -20 42 -43v-85
+c-23 0 -42 -20 -42 -43z" />
+    <glyph glyph-name="local_post_office" unicode="local_post_office" 
+d="M427 341v43l-171 -107l-171 107v-43l171 -106zM427 427c23 0 42 -20 42 -43v-256c0 -23 -19 -43 -42 -43h-342c-23 0 -42 20 -42 43v256c0 23 19 43 42 43h342z" />
+    <glyph glyph-name="local_printshop" unicode="local_printshop" 
+d="M384 448v-85h-256v85h256zM405 256c12 0 22 9 22 21s-10 22 -22 22s-21 -10 -21 -22s9 -21 21 -21zM341 107v106h-170v-106h170zM405 341c35 0 64 -29 64 -64v-128h-85v-85h-256v85h-85v128c0 35 29 64 64 64h298z" />
+    <glyph glyph-name="local_see" unicode="local_see" 
+d="M256 149c59 0 107 48 107 107s-48 107 -107 107s-107 -48 -107 -107s48 -107 107 -107zM192 469h128l39 -42h68c23 0 42 -20 42 -43v-256c0 -23 -19 -43 -42 -43h-342c-23 0 -42 20 -42 43v256c0 23 19 43 42 43h68zM188 256c0 38 30 68 68 68s68 -30 68 -68
+s-30 -68 -68 -68s-68 30 -68 68z" />
+    <glyph glyph-name="local_shipping" unicode="local_shipping" 
+d="M384 117c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM416 309h-53v-53h95zM128 117c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM427 341l64 -85v-107h-43c0 -35 -29 -64 -64 -64s-64 29 -64 64h-128c0 -35 -29 -64 -64 -64
+s-64 29 -64 64h-43v235c0 23 20 43 43 43h299v-86h64z" />
+    <glyph glyph-name="local_taxi" unicode="local_taxi" 
+d="M107 277h298l-32 96h-234zM373 171c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM139 171c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM404 384l44 -128v-171c0 -12 -9 -21 -21 -21h-22c-12 0 -21 9 -21 21v22h-256v-22
+c0 -12 -9 -21 -21 -21h-22c-12 0 -21 9 -21 21v171l44 128c4 13 17 21 31 21h53v43h128v-43h53c14 0 27 -8 31 -21z" />
+    <glyph glyph-name="location_city" unicode="location_city" 
+d="M405 192v43h-42v-43h42zM405 107v42h-42v-42h42zM277 363v42h-42v-42h42zM277 277v43h-42v-43h42zM277 192v43h-42v-43h42zM277 107v42h-42v-42h42zM149 277v43h-42v-43h42zM149 192v43h-42v-43h42zM149 107v42h-42v-42h42zM320 277h128v-213h-384v299h128v42l64 64
+l64 -64v-128z" />
+    <glyph glyph-name="location_disabled" unicode="location_disabled" 
+d="M347 138l-209 209c-19 -25 -31 -57 -31 -91c0 -83 66 -149 149 -149c34 0 66 12 91 31zM64 421l27 27l357 -357l-27 -27l-44 44c-28 -23 -62 -39 -100 -43v-44h-42v44c-89 10 -160 81 -170 170h-44v42h44c4 38 20 72 43 100zM447 277h44v-42h-44c-3 -24 -10 -48 -21 -68
+l-32 32c7 17 11 37 11 57c0 83 -66 149 -149 149c-20 0 -40 -4 -57 -11l-32 32c21 11 44 18 68 21v44h42v-44c89 -10 160 -81 170 -170z" />
+    <glyph glyph-name="location_off" unicode="location_off" 
+d="M250 267c59.1765 -58.4901 118.284 -117.049 177 -176l-27 -27l-72 71c-36 -53 -72 -92 -72 -92s-149 165 -149 277c0 11 2 22 4 33l-68 68l27 27l178 -178zM256 373c-16 0 -29 -7 -39 -18l-69 68c27 28 66 46 108 46c83 0 149 -66 149 -149c0 -36 -15 -77 -36 -117
+l-77 78c11 10 17 23 17 39c0 29 -24 53 -53 53z" />
+    <glyph glyph-name="location_on" unicode="location_on" 
+d="M256 267c29 0 53 24 53 53s-24 53 -53 53s-53 -24 -53 -53s24 -53 53 -53zM256 469c83 0 149 -66 149 -149c0 -112 -149 -277 -149 -277s-149 165 -149 277c0 83 66 149 149 149z" />
+    <glyph glyph-name="location_searching" unicode="location_searching" 
+d="M256 107c83 0 149 66 149 149s-66 149 -149 149s-149 -66 -149 -149s66 -149 149 -149zM447 277h44v-42h-44c-10 -89 -81 -160 -170 -170v-44h-42v44c-89 10 -160 81 -170 170h-44v42h44c10 89 81 160 170 170v44h42v-44c89 -10 160 -81 170 -170z" />
+    <glyph glyph-name="lock" unicode="lock" 
+d="M322 341v43c0 36 -30 66 -66 66s-66 -30 -66 -66v-43h132zM256 149c23 0 43 20 43 43s-20 43 -43 43s-43 -20 -43 -43s20 -43 43 -43zM384 341c23 0 43 -19 43 -42v-214c0 -23 -20 -42 -43 -42h-256c-23 0 -43 19 -43 42v214c0 23 20 42 43 42h21v43c0 59 48 107 107 107
+s107 -48 107 -107v-43h21z" />
+    <glyph glyph-name="lock_open" unicode="lock_open" 
+d="M384 85v214h-256v-214h256zM384 341c23 0 43 -19 43 -42v-214c0 -23 -20 -42 -43 -42h-256c-23 0 -43 19 -43 42v214c0 23 20 42 43 42h194v43c0 36 -30 66 -66 66s-66 -30 -66 -66h-41c0 59 48 107 107 107s107 -48 107 -107v-43h21zM256 149c-23 0 -43 20 -43 43
+s20 43 43 43s43 -20 43 -43s-20 -43 -43 -43z" />
+    <glyph glyph-name="lock_outline" unicode="lock_outline" 
+d="M384 85v214h-256v-214h256zM190 384v-43h132v43c0 36 -30 66 -66 66s-66 -30 -66 -66zM384 341c23 0 43 -19 43 -42v-214c0 -23 -20 -42 -43 -42h-256c-23 0 -43 19 -43 42v214c0 23 20 42 43 42h21v43c0 59 48 107 107 107s107 -48 107 -107v-43h21zM256 149
+c-23 0 -43 20 -43 43s20 43 43 43s43 -20 43 -43s-20 -43 -43 -43z" />
+    <glyph glyph-name="looks" unicode="looks" 
+d="M256 384c129 0 235 -106 235 -235h-43c0 106 -86 192 -192 192s-192 -86 -192 -192h-43c0 129 106 235 235 235zM256 299c82 0 149 -68 149 -150h-42c0 59 -48 107 -107 107s-107 -48 -107 -107h-42c0 82 67 150 149 150z" />
+    <glyph glyph-name="looks_3" unicode="looks_&#x33;" 
+d="M320 288v32c0 24 -19 43 -42 43h-86v-43h86v-43h-43v-42h43v-43h-86v-43h86c23 0 42 19 42 43v32c0 18 -14 32 -32 32c18 0 32 14 32 32zM406 448c23 0 42 -20 42 -43v-298c0 -23 -19 -43 -42 -43h-299c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h299z" />
+    <glyph glyph-name="looks_4" unicode="looks_&#x34;" 
+d="M320 149v214h-43v-86h-42v86h-43v-128h85v-86h43zM405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h298z" />
+    <glyph glyph-name="looks_5" unicode="looks_&#x35;" 
+d="M320 320v43h-128v-128h85v-43h-85v-43h85c23 0 43 19 43 43v43c0 24 -20 42 -43 42h-42v43h85zM405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h298z" />
+    <glyph glyph-name="looks_6" unicode="looks_&#x36;" 
+d="M320 320v43h-85c-23 0 -43 -19 -43 -43v-128c0 -24 20 -43 43 -43h42c23 0 43 19 43 43v43c0 24 -20 42 -43 42h-42v43h85zM405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h298zM235 192v43h42v-43h-42z" />
+    <glyph glyph-name="looks_one" unicode="looks_one" 
+d="M299 149v214h-86v-43h43v-171h43zM405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h298z" />
+    <glyph glyph-name="looks_two" unicode="looks_two" 
+d="M320 277v43c0 24 -20 43 -43 43h-85v-43h85v-43h-42c-23 0 -43 -18 -43 -42v-86h128v43h-85v43h42c23 0 43 18 43 42zM405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h298z" />
+    <glyph glyph-name="loop" unicode="loop" 
+d="M256 128v64l85 -85l-85 -86v64c-94 0 -171 77 -171 171c0 33 10 65 27 91l31 -31c-10 -18 -15 -38 -15 -60c0 -71 57 -128 128 -128zM256 427c94 0 171 -77 171 -171c0 -33 -10 -65 -27 -91l-31 31c10 18 15 38 15 60c0 71 -57 128 -128 128v-64l-85 85l85 86v-64z" />
+    <glyph glyph-name="loupe" unicode="loupe" 
+d="M256 85c94 0 171 77 171 171s-77 171 -171 171s-171 -77 -171 -171s77 -171 171 -171zM256 469c118 0 213 -95 213 -213v-171c0 -23 -19 -42 -42 -42h-171c-118 0 -213 95 -213 213s95 213 213 213zM277 363v-86h86v-42h-86v-86h-42v86h-86v42h86v86h42z" />
+    <glyph glyph-name="low_priority" unicode="low_priority" 
+d="M43 267c0 76 62 138 138 138h75v-42h-75c-53 0 -96 -43 -96 -96s43 -96 96 -96h11v42l64 -64l-64 -64v43h-11c-76 0 -138 63 -138 139zM299 171h170v-43h-170v43zM299 288h170v-43h-170v43zM299 405h170v-42h-170v42z" />
+    <glyph glyph-name="loyalty" unicode="loyalty" 
+d="M368 186c10 10 16 23 16 38c0 29 -24 53 -53 53c-15 0 -28 -5 -38 -15l-16 -16l-15 16c-10 10 -23 15 -38 15c-29 0 -53 -24 -53 -53c0 -15 5 -28 15 -38l91 -91zM117 363c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM457 265c8 -8 12 -18 12 -30
+s-4 -22 -12 -30l-150 -150c-8 -8 -18 -12 -30 -12s-22 4 -30 12l-192 192c-8 8 -12 18 -12 30v150c0 23 19 42 42 42h150c12 0 22 -4 30 -12z" />
+    <glyph glyph-name="mail" unicode="mail" 
+d="M427 341v43l-171 -107l-171 107v-43l171 -106zM427 427c23 0 42 -20 42 -43v-256c0 -23 -19 -43 -42 -43h-342c-23 0 -42 20 -42 43v256c0 23 19 43 42 43h342z" />
+    <glyph glyph-name="mail_outline" unicode="mail_outline" 
+d="M256 277l171 107h-342zM427 128v213l-171 -106l-171 106v-213h342zM427 427c23 0 42 -20 42 -43v-256c0 -23 -19 -43 -42 -43h-342c-23 0 -42 20 -42 43v256c0 23 19 43 42 43h342z" />
+    <glyph glyph-name="map" unicode="map" 
+d="M320 107v253l-128 45v-253zM437 448c6 0 11 -5 11 -11v-322c0 -5 -4 -9 -8 -10l-120 -41l-128 45l-114 -44l-3 -1c-6 0 -11 5 -11 11v322c0 5 4 9 8 10l120 41l128 -45l114 44z" />
+    <glyph glyph-name="markunread" unicode="markunread" 
+d="M427 341v43l-171 -107l-171 107v-43l171 -106zM427 427c23 0 42 -20 42 -43v-256c0 -23 -19 -43 -42 -43h-342c-23 0 -42 20 -42 43v256c0 23 19 43 42 43h342z" />
+    <glyph glyph-name="markunread_mailbox" unicode="markunread_mailbox" 
+d="M427 384c23 0 42 -20 42 -43v-256c0 -23 -19 -42 -42 -42h-342c-23 0 -42 19 -42 42v256c0 23 19 43 42 43h43v128h171v-85h-128v-171h42v128h214z" />
+    <glyph glyph-name="memory" unicode="memory" 
+d="M363 149v214h-214v-214h214zM448 277h-43v-42h43v-43h-43v-43c0 -23 -19 -42 -42 -42h-43v-43h-43v43h-42v-43h-43v43h-43c-23 0 -42 19 -42 42v43h-43v43h43v42h-43v43h43v43c0 23 19 42 42 42h43v43h43v-43h42v43h43v-43h43c23 0 42 -19 42 -42v-43h43v-43zM277 235v42
+h-42v-42h42zM320 320v-128h-128v128h128z" />
+    <glyph glyph-name="menu" unicode="menu" 
+d="M64 384h384v-43h-384v43zM64 235v42h384v-42h-384zM64 128v43h384v-43h-384z" />
+    <glyph glyph-name="merge_type" unicode="merge_type" 
+d="M160 341l96 96l96 -96h-75v-136l-128 -128l-30 30l116 115v119h-75zM363 77l-73 72l30 30l73 -72z" />
+    <glyph glyph-name="message" unicode="message" 
+d="M384 341v43h-256v-43h256zM384 277v43h-256v-43h256zM384 213v43h-256v-43h256zM427 469c23 0 42 -19 42 -42v-256c0 -23 -19 -43 -42 -43h-299l-85 -85v384c0 23 19 42 42 42h342z" />
+    <glyph glyph-name="mic" unicode="mic" 
+d="M369 277h36c0 -73 -58 -133 -128 -143v-70h-42v70c-70 10 -128 70 -128 143h36c0 -64 54 -108 113 -108s113 44 113 108zM256 213c-35 0 -64 29 -64 64v128c0 35 29 64 64 64s64 -29 64 -64v-128c0 -35 -29 -64 -64 -64z" />
+    <glyph glyph-name="mic_none" unicode="mic_none" 
+d="M369 277h36c0 -73 -58 -133 -128 -143v-70h-42v70c-70 10 -128 70 -128 143h36c0 -64 54 -108 113 -108s113 44 113 108zM230 407v-132c0 -14 12 -25 26 -25s25 11 25 25l1 132c0 14 -12 26 -26 26s-26 -12 -26 -26zM256 213c-35 0 -64 29 -64 64v128c0 35 29 64 64 64
+s64 -29 64 -64v-128c0 -35 -29 -64 -64 -64z" />
+    <glyph glyph-name="mic_off" unicode="mic_off" 
+d="M91 448l357 -357l-27 -27l-89 89c-16 -10 -36 -16 -55 -19v-70h-42v70c-70 10 -128 70 -128 143h36c0 -64 54 -108 113 -108c17 0 34 4 49 11l-35 35c-4 -1 -9 -2 -14 -2c-35 0 -64 29 -64 64v16l-128 128zM320 274l-128 127v4c0 35 29 64 64 64s64 -29 64 -64v-131z
+M405 277c0 -25 -7 -49 -19 -70l-26 27c6 13 9 27 9 43h36z" />
+    <glyph glyph-name="mms" unicode="mms" 
+d="M107 213h298l-96 128l-74 -96l-54 64zM427 469c23 0 42 -19 42 -42v-256c0 -23 -19 -43 -42 -43h-299l-85 -85v384c0 23 19 42 42 42h342z" />
+    <glyph glyph-name="mode_comment" unicode="mode_comment" 
+d="M469 427v-384l-85 85h-299c-23 0 -42 20 -42 43v256c0 23 19 42 42 42h342c23 0 42 -19 42 -42z" />
+    <glyph glyph-name="mode_edit" unicode="mode_edit" 
+d="M442 362l-39 -39l-80 80l39 39c8 8 22 8 30 0l50 -50c8 -8 8 -22 0 -30zM64 144l236 236l80 -80l-236 -236h-80v80z" />
+    <glyph glyph-name="monetization_on" unicode="monetization_on" 
+d="M286 126c37 7 67 28 67 67c0 54 -47 73 -90 84s-56 23 -56 41c0 20 19 35 51 35c34 0 46 -16 47 -40h42c-1 33 -21 62 -61 72v42h-57v-42c-37 -8 -66 -31 -66 -68c0 -44 36 -65 89 -78c48 -11 57 -28 57 -46c0 -13 -9 -34 -51 -34c-39 0 -55 18 -57 40h-42
+c2 -41 34 -64 70 -72v-42h57v41zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="money_off" unicode="money_off" 
+d="M114 425l311 -312l-27 -27l-47 48c-13 -12 -32 -20 -52 -24v-46h-64v46c-41 9 -76 35 -79 82h47c2 -25 20 -45 64 -45c29 0 44 10 51 20l-75 74c-50 15 -83 40 -83 84l-73 73zM267 365c-13 0 -24 -2 -33 -6l-31 31c10 5 21 9 32 12v46h64v-47c45 -11 67 -44 68 -81h-47
+c-1 27 -15 45 -53 45z" />
+    <glyph glyph-name="monochrome_photos" unicode="monochrome_photos" 
+d="M427 107v256h-171v-22c60 0 107 -46 107 -106s-47 -107 -107 -107v38c-38 0 -68 31 -68 69s30 68 68 68v-137c38 0 68 31 68 69s-30 68 -68 68v38c-60 0 -107 -46 -107 -106s47 -107 107 -107v-21h171zM427 405c23 0 42 -19 42 -42v-256c0 -23 -19 -43 -42 -43h-342
+c-23 0 -42 20 -42 43v256c0 23 19 42 42 42h69l38 43h128l38 -43h69z" />
+    <glyph glyph-name="mood" unicode="mood" 
+d="M256 139c-50 0 -92 30 -109 74h218c-17 -44 -59 -74 -109 -74zM181 277c-18 0 -32 14 -32 32s14 32 32 32s32 -14 32 -32s-14 -32 -32 -32zM331 277c-18 0 -32 14 -32 32s14 32 32 32s32 -14 32 -32s-14 -32 -32 -32zM256 85c94 0 171 77 171 171s-77 171 -171 171
+s-171 -77 -171 -171s77 -171 171 -171zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="mood_bad" unicode="mood_bad" 
+d="M256 213c50 0 92 -30 109 -74h-218c17 44 59 74 109 74zM181 277c-18 0 -32 14 -32 32s14 32 32 32s32 -14 32 -32s-14 -32 -32 -32zM331 277c-18 0 -32 14 -32 32s14 32 32 32s32 -14 32 -32s-14 -32 -32 -32zM256 85c94 0 171 77 171 171s-77 171 -171 171
+s-171 -77 -171 -171s77 -171 171 -171zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="more" unicode="more" 
+d="M405 224c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM299 224c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM192 224c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM469 448c23 0 43 -20 43 -43v-298
+c0 -23 -20 -43 -43 -43h-318c-15 0 -28 8 -36 19l-115 173l115 173c8 11 19 19 34 19h320z" />
+    <glyph glyph-name="more_horiz" unicode="more_horiz" 
+d="M256 299c23 0 43 -20 43 -43s-20 -43 -43 -43s-43 20 -43 43s20 43 43 43zM384 299c23 0 43 -20 43 -43s-20 -43 -43 -43s-43 20 -43 43s20 43 43 43zM128 299c23 0 43 -20 43 -43s-20 -43 -43 -43s-43 20 -43 43s20 43 43 43z" />
+    <glyph glyph-name="more_vert" unicode="more_vert" 
+d="M256 171c23 0 43 -20 43 -43s-20 -43 -43 -43s-43 20 -43 43s20 43 43 43zM256 299c23 0 43 -20 43 -43s-20 -43 -43 -43s-43 20 -43 43s20 43 43 43zM256 341c-23 0 -43 20 -43 43s20 43 43 43s43 -20 43 -43s-20 -43 -43 -43z" />
+    <glyph glyph-name="motorcycle" unicode="motorcycle" 
+d="M405 149c35 0 64 29 64 64s-29 64 -64 64s-64 -29 -64 -64s29 -64 64 -64zM167 192h-60v43h60c-9 25 -33 42 -60 42c-35 0 -64 -29 -64 -64s29 -64 64 -64c27 0 51 18 60 43zM415 319c55 -4 97 -49 97 -106c0 -60 -47 -106 -107 -106s-106 46 -106 106c0 13 2 26 6 38
+l-59 -59h-35c-10 -49 -52 -85 -104 -85c-60 0 -107 46 -107 106s47 107 107 107h247l-43 43h-76v42h94z" />
+    <glyph glyph-name="mouse" unicode="mouse" 
+d="M235 489v-169h-150c0 87 66 159 150 169zM85 192v85h342v-85c0 -94 -77 -171 -171 -171s-171 77 -171 171zM277 489c84 -10 150 -82 150 -169h-150v169z" />
+    <glyph glyph-name="move_to_inbox" unicode="move_to_inbox" 
+d="M341 299l-85 -86l-85 86h42v64h86v-64h42zM405 192v213h-299v-213h86c0 -35 29 -64 64 -64s64 29 64 64h85zM405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-299c-24 0 -42 20 -42 43v298c0 23 18 43 42 43h299z" />
+    <glyph glyph-name="movie" unicode="movie" 
+d="M384 427h85v-299c0 -23 -19 -43 -42 -43h-342c-23 0 -42 20 -42 43v256c0 23 19 43 42 43h22l42 -86h64l-42 86h42l43 -86h64l-43 86h43l43 -86h64z" />
+    <glyph glyph-name="movie_creation" unicode="movie_creation" 
+d="M384 427h85v-299c0 -23 -19 -43 -42 -43h-342c-23 0 -42 20 -42 43v256c0 23 19 43 42 43h22l42 -86h64l-42 86h42l43 -86h64l-43 86h43l43 -86h64z" />
+    <glyph glyph-name="movie_filter" unicode="movie_filter" 
+d="M361 257l44 20l-44 20l-20 44l-20 -44l-44 -20l44 -20l20 -44zM240 187l59 26l-59 27l-27 59l-26 -59l-59 -27l59 -26l26 -59zM384 427h85v-299c0 -23 -19 -43 -42 -43h-342c-23 0 -42 20 -42 43v256c0 23 19 43 42 43h22l42 -64h64l-42 64h42l43 -64h64l-43 64h43
+l43 -64h64z" />
+    <glyph glyph-name="multiline_chart" unicode="multiline_chart" 
+d="M469 364l-65 -73c23 -37 39 -82 44 -131h-43c-4 35 -15 68 -31 97l-86 -97l-85 86l-128 -128l-32 32l160 160l85 -86l61 70c-36 42 -86 69 -144 69c-50 0 -96 -20 -132 -52l-30 30c44 39 100 64 162 64c69 0 129 -30 173 -79l61 68z" />
+    <glyph glyph-name="music_note" unicode="music_note" 
+d="M256 448h128v-85h-85v-214c0 -47 -39 -85 -86 -85s-85 38 -85 85s38 86 85 86c16 0 30 -5 43 -12v225z" />
+    <glyph glyph-name="music_video" unicode="music_video" 
+d="M171 192c0 35 29 64 64 64c7 0 14 -2 21 -4v132h107v-43h-64v-150c0 -35 -29 -63 -64 -63s-64 29 -64 64zM448 107v298h-384v-298h384zM448 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-384c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h384z" />
+    <glyph glyph-name="my_location" unicode="my_location" 
+d="M256 107c83 0 149 66 149 149s-66 149 -149 149s-149 -66 -149 -149s66 -149 149 -149zM447 277h44v-42h-44c-10 -89 -81 -160 -170 -170v-44h-42v44c-89 10 -160 81 -170 170h-44v42h44c10 89 81 160 170 170v44h42v-44c89 -10 160 -81 170 -170zM256 341
+c47 0 85 -38 85 -85s-38 -85 -85 -85s-85 38 -85 85s38 85 85 85z" />
+    <glyph glyph-name="nature" unicode="nature" 
+d="M277 168v-83h128v-42h-298v42h128v84c-71 12 -125 73 -125 147c0 83 67 150 150 150s149 -67 149 -150c0 -77 -58 -139 -132 -148z" />
+    <glyph glyph-name="nature_people" unicode="nature_people" 
+d="M96 277c-18 0 -32 14 -32 32s14 32 32 32s32 -14 32 -32s-14 -32 -32 -32zM473 316c0 -77 -58 -139 -132 -148v-83h64v-42h-341v106h-21v86c0 12 9 21 21 21h64c12 0 21 -9 21 -21v-86h-21v-64h171v84c-71 12 -125 73 -125 147c0 83 67 150 150 150s149 -67 149 -150z
+" />
+    <glyph glyph-name="navigate_before" unicode="navigate_before" 
+d="M329 354l-98 -98l98 -98l-30 -30l-128 128l128 128z" />
+    <glyph glyph-name="navigate_next" unicode="navigate_next" 
+d="M213 384l128 -128l-128 -128l-30 30l98 98l-98 98z" />
+    <glyph glyph-name="navigation" unicode="navigation" 
+d="M256 469l160 -390l-15 -15l-145 64l-145 -64l-15 15z" />
+    <glyph glyph-name="near_me" unicode="near_me" 
+d="M448 448l-161 -384h-21l-56 146l-146 56v21z" />
+    <glyph glyph-name="network_cell" unicode="network_cell" 
+d="M469 43h-426l426 426v-426z" />
+    <glyph glyph-name="network_check" unicode="network_check" 
+d="M107 235c43 43 100 63 156 61l-28 -61c-31 -4 -62 -19 -86 -43zM363 192c-8 8 -17 15 -26 21l12 62c20 -10 39 -23 56 -40zM448 277c-26 26 -56 46 -88 59l11 60c44 -16 85 -41 120 -76zM21 320c80 80 190 110 293 92l-25 -57c-80 10 -164 -17 -225 -78zM339 405
+c6 0 11 -4 11 -10l-52 -275v-1c-4 -19 -22 -34 -42 -34c-24 0 -43 19 -43 43c0 7 2 15 5 21l111 248c1.57297 4.7189 4.56186 8 10 8z" />
+    <glyph glyph-name="network_locked" unicode="network_locked" 
+d="M448 171v32c0 18 -14 32 -32 32s-32 -14 -32 -32v-32h64zM469 171c12 0 22 -10 22 -22v-85c0 -12 -10 -21 -22 -21h-106c-12 0 -22 9 -22 21v85c0 12 10 22 22 22v32c0 29 24 53 53 53s53 -24 53 -53v-32zM416 299c-53 0 -96 -43 -96 -96v-6c-13 -12 -21 -29 -21 -48v-64
+h-278l406 406v-193c-4 0 -7 1 -11 1z" />
+    <glyph glyph-name="network_wifi" unicode="network_wifi" 
+d="M436.25 278.586c0.284113 -0.222489 0.53415 -0.418301 0.749753 -0.585993l-181 -224l-181 224c0.215604 0.167692 0.46564 0.363504 0.749753 0.585993l-67.7498 84.414c10 7 105 85 248 85s238 -78 248 -85z" />
+    <glyph glyph-name="new_releases" unicode="new_releases" 
+d="M277 235v128h-42v-128h42zM277 149v43h-42v-43h42zM491 256l-52 -59l7 -79l-77 -17l-40 -68l-73 31l-73 -31l-40 67l-77 18l7 79l-52 59l52 60l-7 78l77 17l40 68l73 -31l73 31l40 -68l77 -17l-7 -79z" />
+    <glyph glyph-name="next_week" unicode="next_week" 
+d="M235 117l85 86l-85 85l-22 -21l64 -64l-64 -64zM213 405v-42h86v42h-86zM299 448c23.3225 0 42 -19.4985 42 -43v-42h86c23 0 42 -20 42 -43v-235c0 -23 -19 -42 -42 -42h-342c-23 0 -42 19 -42 42v235c0 23 19 43 42 43h86v42c0 23 19 43 42 43h86z" />
+    <glyph glyph-name="nfc" unicode="nfc" 
+d="M384 384v-256h-256v256h85v-43h-42v-170h170v170h-64v-48c13 -7 22 -21 22 -37c0 -23 -20 -43 -43 -43s-43 20 -43 43c0 16 9 30 22 37v48c0 23 19 43 42 43h107zM427 85v342h-342v-342h342zM427 469c23 0 42 -19 42 -42v-342c0 -23 -19 -42 -42 -42h-342
+c-23 0 -42 19 -42 42v342c0 23 19 42 42 42h342z" />
+    <glyph glyph-name="no_encryption" unicode="no_encryption" 
+d="M190 384v-26l-39 39c6 53 50 94 105 94c59 0 107 -48 107 -107v-43h21c23 0 43 -19 43 -42v-178l-221 220h116v43c0 36 -30 66 -66 66s-66 -30 -66 -66zM448 47l-26 -26l-24 24c-4 -1 -9 -2 -14 -2h-256c-23 0 -43 19 -43 42v214c0 16 10 30 23 37l-44 43l26 26z" />
+    <glyph glyph-name="no_sim" unicode="no_sim" 
+d="M78 429l373 -372l-28 -28l-40 41c-6 -3 -13 -6 -20 -6h-214c-23 0 -42 20 -42 43v239l-56 56zM405 405v-249l-242 242l50 50h150c23 0 42 -20 42 -43z" />
+    <glyph glyph-name="not_interested" unicode="not_interested" 
+d="M391 151c23 29 36 66 36 105c0 94 -77 171 -171 171c-39 0 -76 -13 -105 -36zM256 85c39 0 76 13 105 36l-240 240c-23 -29 -36 -66 -36 -105c0 -94 77 -171 171 -171zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="note" unicode="note" 
+d="M320 395v-118h117zM469 299v-171c0 -23 -19 -42 -42 -42l-342 -1c-23 0 -42 20 -42 43v256c0 23 19 43 42 43h256z" />
+    <glyph glyph-name="note_add" unicode="note_add" 
+d="M277 320h118l-118 117v-117zM341 171v42h-64v64h-42v-64h-64v-42h64v-64h42v64h64zM299 469l128 -128v-256c0 -23 -20 -42 -43 -42h-256c-23 0 -43 19 -43 42l1 342c0 23 19 42 42 42h171z" />
+    <glyph glyph-name="notifications" unicode="notifications" 
+d="M384 171l43 -43v-21h-342v21l43 43v106c0 66 35 120 96 135v15c0 18 14 32 32 32s32 -14 32 -32v-15c61 -15 96 -70 96 -135v-106zM256 43c-24 0 -43 19 -43 42h86c0 -23 -20 -42 -43 -42z" />
+    <glyph glyph-name="notifications_active" unicode="notifications_active" 
+d="M256 43c-24 0 -43 19 -43 42h85c0 -24.8376 -18.6125 -42 -42 -42zM384 277v-106l43 -43v-21h-342v21l43 43v106c0 66 35 120 96 135v15c0 18 14 32 32 32s32 -14 32 -32v-15c61 -15 96 -70 96 -135zM426 288c-3 57 -32 106 -75 137l30 30c51 -39 85 -99 88 -167h-43z
+M162 425c-44 -31 -73 -80 -76 -137h-43c3 68 37 128 88 167z" />
+    <glyph glyph-name="notifications_none" unicode="notifications_none" 
+d="M341 149v128c0 53 -32 96 -85 96s-85 -43 -85 -96v-128h170zM384 171l43 -43v-21h-342v21l43 43v106c0 66 35 120 96 135v15c0 18 14 32 32 32s32 -14 32 -32v-15c61 -15 96 -70 96 -135v-106zM256 43c-23 0 -43 19 -43 42h86c0 -23 -20 -42 -43 -42z" />
+    <glyph glyph-name="notifications_off" unicode="notifications_off" 
+d="M384 199l-191 201c5 2 10 5 15 7h1l6 3c3 1 6 1 9 2v15c0 18 14 32 32 32s32 -14 32 -32v-15c61 -15 96 -69 96 -135v-78zM256 43c-24 0 -43 18 -43 42h86c0 -24 -19 -42 -43 -42zM167 381c93.6174 -96.7159 187.67 -192.997 281 -290l-27 -27l-43 43h-293v21l43 43v107
+c0 27 6 52 17 73l-60 59l27 28z" />
+    <glyph glyph-name="notifications_paused" unicode="notifications_paused" 
+d="M309 303v38h-106v-38h59l-59 -73v-38h106v38h-59zM384 171l43 -43v-21h-342v21l43 43v106c0 65 35 120 96 135v15c0 18 14 32 32 32s32 -14 32 -32v-15c61 -15 96 -70 96 -135v-106zM256 43c-24 0 -43 19 -43 42h86c0 -23 -20 -42 -43 -42z" />
+    <glyph glyph-name="offline_pin" unicode="offline_pin" 
+d="M220 213l143 143l-30 30l-113 -113l-41 41l-30 -30zM363 128v43h-214v-43h214zM256 469c117 0 213 -96 213 -213s-96 -213 -213 -213s-213 96 -213 213s96 213 213 213z" />
+    <glyph glyph-name="ondemand_video" unicode="ondemand_video" 
+d="M341 277l-149 -85v171zM448 149v256h-384v-256h384zM448 448c23 0 43 -19 43 -43l-1 -256c0 -23 -19 -42 -42 -42h-107v-43h-170v43h-107c-24 0 -43 19 -43 42v256c0 24 19 43 43 43h384z" />
+    <glyph glyph-name="opacity" unicode="opacity" 
+d="M128 213h256c0 43 -14 69 -38 93l-90 94l-90 -93c-24 -24 -38 -51 -38 -94zM377 341c33 -33 50 -77 50 -120s-17 -88 -50 -121s-77 -50 -121 -50s-88 17 -121 50s-50 78 -50 121s17 87 50 120l121 121z" />
+    <glyph glyph-name="open_in_browser" unicode="open_in_browser" 
+d="M256 299l85 -86h-64v-128h-42v128h-64zM405 427c24 0 43 -20 43 -43v-256c0 -23 -20 -43 -43 -43h-85v43h85v213h-298v-213h85v-43h-85c-24 0 -43 20 -43 43v256c0 23 19 43 43 43h298z" />
+    <glyph glyph-name="open_in_new" unicode="open_in_new" 
+d="M299 448h149v-149h-43v76l-209 -209l-30 30l209 209h-76v43zM405 107v149h43v-149c0 -23 -20 -43 -43 -43h-298c-24 0 -43 20 -43 43v298c0 23 19 43 43 43h149v-43h-149v-298h298z" />
+    <glyph glyph-name="open_with" unicode="open_with" 
+d="M299 192v-64h64l-107 -107l-107 107h64v64h86zM491 256l-107 -107v64h-64v86h64v64zM192 299v-86h-64v-64l-107 107l107 107v-64h64zM213 320v64h-64l107 107l107 -107h-64v-64h-86z" />
+    <glyph glyph-name="pages" unicode="pages" 
+d="M405 448c23 0 43 -20 43 -43v-128h-107l22 86l-86 -22v107h128zM363 149l-22 86h107v-128c0 -23 -20 -43 -43 -43h-128v107zM171 235l-22 -86l86 22v-107h-128c-23 0 -43 20 -43 43v128h107zM64 405c0 23 20 43 43 43h128v-107l-86 22l22 -86h-107v128z" />
+    <glyph glyph-name="pageview" unicode="pageview" 
+d="M358 124l30 30l-62 62c9 15 15 32 15 51c0 53 -43 96 -96 96s-96 -43 -96 -96s43 -96 96 -96c19 0 36 6 51 15zM427 427c23 0 42 -20 42 -43v-256c0 -23 -19 -43 -42 -43h-342c-23 0 -42 20 -42 43v256c0 23 19 43 42 43h342zM245 320c29 0 54 -24 54 -53
+s-25 -54 -54 -54s-53 25 -53 54s24 53 53 53z" />
+    <glyph glyph-name="palette" unicode="palette" 
+d="M373 256c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM309 341c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM203 341c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM139 256c18 0 32 14 32 32s-14 32 -32 32
+s-32 -14 -32 -32s14 -32 32 -32zM256 448c106 0 192 -77 192 -171c0 -59 -48 -106 -107 -106h-37c-18 0 -32 -14 -32 -32c0 -8 3 -15 8 -21s8 -14 8 -22c0 -18 -14 -32 -32 -32c-106 0 -192 86 -192 192s86 192 192 192z" />
+    <glyph glyph-name="pan_tool" unicode="pan_tool" 
+d="M491 395v-310c0 -47 -39 -85 -86 -85h-155c-23 0 -45 9 -61 25l-168 171s27 26 28 26c5 4 11 6 17 6c5 0 9 -1 13 -3c1 0 92 -52 92 -52v254c0 18 14 32 32 32s32 -14 32 -32v-150h21v203c0 18 14 32 32 32s32 -14 32 -32v-203h21v182c0 18 14 32 32 32s32 -14 32 -32
+v-182h22v118c0 18 14 32 32 32s32 -14 32 -32z" />
+    <glyph glyph-name="panorama" unicode="panorama" 
+d="M181 245l-74 -96h298l-96 128l-74 -96zM491 128c0 -23 -20 -43 -43 -43h-384c-23 0 -43 20 -43 43v256c0 23 20 43 43 43h384c23 0 43 -20 43 -43v-256z" />
+    <glyph glyph-name="panorama_fish_eye" unicode="panorama_fish_eye" 
+d="M256 85c94 0 171 77 171 171s-77 171 -171 171s-171 -77 -171 -171s77 -171 171 -171zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="panorama_horizontal" unicode="panorama_horizontal" 
+d="M457 427c7 0 12 -5 12 -14v-314c0 -9 -5 -14 -12 -14c-2 0 -4 1 -6 2c-63 23 -129 35 -195 35s-132 -12 -195 -35c-2 -1 -4 -2 -6 -2c-7 0 -12 6 -12 14v314c0 9 5 14 12 14c2 0 4 -1 6 -2c63 -23 129 -35 195 -35s132 12 195 35c2 1 4 2 6 2zM427 372
+c-55 -17 -113 -24 -171 -24s-116 8 -171 24v-232c55 16 113 24 171 24s116 -8 171 -24v232z" />
+    <glyph glyph-name="panorama_vertical" unicode="panorama_vertical" 
+d="M140 85h232c-16 55 -24 113 -24 171s8 116 24 171h-232c16 -55 24 -113 24 -171s-8 -116 -24 -171zM425 61c1 -2 2 -4 2 -6c0 -7 -6 -12 -14 -12h-314c-9 0 -14 5 -14 12c0 2 1 4 2 6c23 63 35 129 35 195s-12 132 -35 195c-1 2 -2 4 -2 6c0 7 5 12 14 12h314
+c9 0 14 -5 14 -12c0 -2 -1 -4 -2 -6c-23 -63 -35 -129 -35 -195s12 -132 35 -195z" />
+    <glyph glyph-name="panorama_wide_angle" unicode="panorama_wide_angle" 
+d="M256 427c58 0 112 -6 170 -16l19 -3l6 -19c12 -44 18 -89 18 -133s-6 -89 -18 -133l-6 -19l-19 -3c-58 -10 -112 -16 -170 -16s-112 6 -170 16l-19 3l-6 19c-12 44 -18 89 -18 133s6 89 18 133l6 19l19 3c58 10 112 16 170 16zM256 384c-52 0 -101 -5 -156 -14
+c-10 -38 -15 -76 -15 -114s5 -76 15 -114c55 -9 104 -14 156 -14s101 5 156 14c10 38 15 76 15 114s-5 76 -15 114c-55 9 -104 14 -156 14z" />
+    <glyph glyph-name="party_mode" unicode="party_mode" 
+d="M256 149c59 0 107 48 107 107c0 7 -1 14 -2 21h-45c2 -7 4 -14 4 -21c0 -35 -29 -64 -64 -64h-85c20 -26 50 -43 85 -43zM256 363c-59 0 -107 -48 -107 -107c0 -7 1 -14 2 -21h45c-2 7 -4 14 -4 21c0 35 29 64 64 64h85c-20 26 -50 43 -85 43zM427 427
+c23 0 42 -20 42 -43v-256c0 -23 -19 -43 -42 -43h-342c-23 0 -42 20 -42 43v256c0 23 19 43 42 43h68l39 42h128l39 -42h68z" />
+    <glyph glyph-name="pause" unicode="pause" 
+d="M299 405h85v-298h-85v298zM128 107v298h85v-298h-85z" />
+    <glyph glyph-name="pause_circle_filled" unicode="pause_circle_filled" 
+d="M320 171v170h-43v-170h43zM235 171v170h-43v-170h43zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="pause_circle_outline" unicode="pause_circle_outline" 
+d="M277 171v170h43v-170h-43zM256 85c94 0 171 77 171 171s-77 171 -171 171s-171 -77 -171 -171s77 -171 171 -171zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213zM192 171v170h43v-170h-43z" />
+    <glyph glyph-name="payment" unicode="payment" 
+d="M427 341v43h-342v-43h342zM427 128v128h-342v-128h342zM427 427c24 0 42 -19 42 -43v-256c0 -24 -18 -43 -42 -43h-342c-24 0 -42 19 -42 43v256c0 24 18 43 42 43h342z" />
+    <glyph glyph-name="people" unicode="people" 
+d="M341 235c50 0 150 -25 150 -75v-53h-128v53c0 32 -17 56 -42 74c7 1 14 1 20 1zM171 235c50 0 149 -25 149 -75v-53h-299v53c0 50 100 75 150 75zM171 277c-35 0 -64 29 -64 64s29 64 64 64s63 -29 63 -64s-28 -64 -63 -64zM341 277c-35 0 -64 29 -64 64s29 64 64 64
+s64 -29 64 -64s-29 -64 -64 -64z" />
+    <glyph glyph-name="people_outline" unicode="people_outline" 
+d="M352 373c-23 0 -43 -19 -43 -42s20 -43 43 -43s43 20 43 43s-20 42 -43 42zM352 256c-41 0 -75 34 -75 75s34 74 75 74s75 -33 75 -74s-34 -75 -75 -75zM160 373c-23 0 -43 -19 -43 -42s20 -43 43 -43s43 20 43 43s-20 42 -43 42zM160 256c-41 0 -75 34 -75 75
+s34 74 75 74s75 -33 75 -74s-34 -75 -75 -75zM459 139v26c0 12 -55 38 -107 38c-23 0 -45 -6 -64 -12c7 -8 11 -16 11 -26v-26h160zM267 139v26c0 12 -55 38 -107 38s-107 -26 -107 -38v-26h214zM352 235c46 0 139 -24 139 -70v-58h-470v58c0 46 93 70 139 70
+c26 0 65 -8 96 -22c31 14 70 22 96 22z" />
+    <glyph glyph-name="perm_camera_mic" unicode="perm_camera_mic" 
+d="M299 235v85c0 23 -20 43 -43 43s-43 -20 -43 -43v-85c0 -23 20 -43 43 -43s43 20 43 43zM427 405c23 0 42 -19 42 -42v-256c0 -23 -19 -43 -42 -43h-150v45c60 10 107 63 107 126h-43c0 -47 -38 -86 -85 -86s-85 39 -85 86h-43c0 -63 47 -116 107 -126v-45h-150
+c-23 0 -42 20 -42 43v256c0 23 19 42 42 42h68l39 43h128l39 -43h68z" />
+    <glyph glyph-name="perm_contact_calendar" unicode="perm_contact_calendar" 
+d="M384 128v21c0 43 -85 66 -128 66s-128 -23 -128 -66v-21h256zM256 384c-35 0 -64 -29 -64 -64s29 -64 64 -64s64 29 64 64s-29 64 -64 64zM405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-24 0 -43 20 -43 43v298c0 23 19 43 43 43h21v43h43v-43h170v43
+h43v-43h21z" />
+    <glyph glyph-name="perm_data_setting" unicode="perm_data_setting" 
+d="M405 75c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM484 96l23 -17c2 -2 2 -5 1 -7l-21 -37c-1 -2 -5 -4 -7 -3l-26 11c-5 -4 -12 -7 -18 -10l-4 -29c0 -3 -3 -4 -6 -4h-42c-3 0 -6 1 -6 4l-4 29c-7 3 -12 6 -18 10l-26 -11c-3 -1 -6 1 -7 3l-21 37
+c-1 2 -1 5 1 7l23 17c0 3 -1 7 -1 11s1 7 1 10l-23 18c-2 2 -2 5 -1 7l21 37c1 2 5 3 7 2l26 -11c6 4 12 8 18 11l4 28c0 3 3 4 6 4h42c3 0 6 -1 6 -4l4 -28c7 -3 12 -7 18 -11l26 11c3 1 6 0 7 -2l21 -37c1 -2 1 -5 -1 -7l-23 -18c1 -3 1 -6 1 -10s-1 -8 -1 -11zM405 267
+c-88 0 -160 -72 -160 -160c0 -7 1 -15 2 -22h-247l427 427l-1 -247c-7 1 -14 2 -21 2z" />
+    <glyph glyph-name="perm_device_information" unicode="perm_device_information" 
+d="M363 107v298h-214v-298h214zM363 490c23 0 42 -19 42 -42v-384c0 -23 -19 -43 -42 -43h-214c-23 0 -42 20 -42 43v384c0 23 19 43 42 43zM277 277v-128h-42v128h42zM277 363v-43h-42v43h42z" />
+    <glyph glyph-name="perm_identity" unicode="perm_identity" 
+d="M256 235c57 0 171 -29 171 -86v-64h-342v64c0 57 114 86 171 86zM256 427c47 0 85 -39 85 -86s-38 -85 -85 -85s-85 38 -85 85s38 86 85 86zM256 194c-63 0 -130 -31 -130 -45v-23h260v23c0 14 -67 45 -130 45zM256 386c-25 0 -45 -20 -45 -45s20 -44 45 -44s45 19 45 44
+s-20 45 -45 45z" />
+    <glyph glyph-name="perm_media" unicode="perm_media" 
+d="M149 192h299l-75 96l-53 -64l-75 96zM469 427c23 0 43 -20 43 -43v-213c0 -23 -20 -43 -43 -43h-341c-23 0 -43 20 -43 43l1 256c0 23 19 42 42 42h128l43 -42h170zM43 384v-299h384v-42h-384c-23 0 -43 19 -43 42v299h43z" />
+    <glyph glyph-name="perm_phone_msg" unicode="perm_phone_msg" 
+d="M256 448h192v-149h-128l-64 -64v213zM427 181c12 0 21 -9 21 -21v-75c0 -12 -9 -21 -21 -21c-200 0 -363 163 -363 363c0 12 9 21 21 21h75c12 0 21 -9 21 -21c0 -27 4 -52 12 -76c2 -7 1 -16 -5 -22l-47 -47c31 -60 81 -110 141 -141l47 47c6 6 15 7 22 5
+c24 -8 49 -12 76 -12z" />
+    <glyph glyph-name="perm_scan_wifi" unicode="perm_scan_wifi" 
+d="M235 341h42v43h-42v-43zM277 171v128h-42v-128h42zM256 448c108 0 189 -40 256 -91l-256 -314l-256 315c67 51 148 90 256 90z" />
+    <glyph glyph-name="person" unicode="person" 
+d="M256 213c57 0 171 -28 171 -85v-43h-342v43c0 57 114 85 171 85zM256 256c-47 0 -85 38 -85 85s38 86 85 86s85 -39 85 -86s-38 -85 -85 -85z" />
+    <glyph glyph-name="person_add" unicode="person_add" 
+d="M320 213c57 0 171 -28 171 -85v-43h-342v43c0 57 114 85 171 85zM128 299h64v-43h-64v-64h-43v64h-64v43h64v64h43v-64zM320 256c-47 0 -85 38 -85 85s38 86 85 86s85 -39 85 -86s-38 -85 -85 -85z" />
+    <glyph glyph-name="person_outline" unicode="person_outline" 
+d="M256 235c57 0 171 -29 171 -86v-64h-342v64c0 57 114 86 171 86zM256 427c47 0 85 -39 85 -86s-38 -85 -85 -85s-85 38 -85 85s38 86 85 86zM256 194c-63 0 -130 -31 -130 -45v-23h260v23c0 14 -67 45 -130 45zM256 386c-25 0 -45 -20 -45 -45s20 -44 45 -44s45 19 45 44
+s-20 45 -45 45z" />
+    <glyph glyph-name="person_pin" unicode="person_pin" 
+d="M384 171v19c0 43 -85 66 -128 66s-128 -23 -128 -66v-19h256zM256 399c-32 0 -58 -26 -58 -58s26 -57 58 -57s58 25 58 57s-26 58 -58 58zM405 469c23 0 43 -19 43 -42v-299c0 -23 -20 -43 -43 -43h-85l-64 -64l-64 64h-85c-24 0 -43 20 -43 43v299c0 23 19 42 43 42h298
+z" />
+    <glyph glyph-name="person_pin_circle" unicode="person_pin_circle" 
+d="M256 213c36 0 67 18 85 46c0 28 -57 44 -85 44s-85 -16 -85 -44c18 -28 49 -46 85 -46zM256 427c-23 0 -43 -20 -43 -43c0 -24 20 -43 43 -43s43 19 43 43c0 23 -20 43 -43 43zM256 469c82 0 149 -67 149 -149c0 -112 -149 -277 -149 -277s-149 165 -149 277
+c0 82 67 149 149 149z" />
+    <glyph glyph-name="personal_video" unicode="personal_video" 
+d="M448 149v256h-384v-256h384zM448 448c23 0 43 -19 43 -43l-1 -256c0 -23 -19 -42 -42 -42h-107v-43h-170v43h-107c-24 0 -43 19 -43 42v256c0 24 19 43 43 43h384z" />
+    <glyph glyph-name="pets" unicode="pets" 
+d="M370 195c28 -28 63 -58 56 -102c-6 -22 -22 -44 -50 -50c-16 -3 -65 9 -118 9h-4c-53 0 -102 -12 -118 -9c-28 6 -44 28 -50 50c-6 43 28 74 56 102c19 22 34 40 53 62c10 12 22 23 37 28c2 1 5 2 7 2c6 1 11 1 17 1s12 0 17 -1c2 0 5 -1 7 -2c15 -5 27 -16 37 -28
+c19 -22 34 -40 53 -62zM363 309c0 29 24 54 53 54s53 -25 53 -54s-24 -53 -53 -53s-53 24 -53 53zM267 395c0 29 24 53 53 53s53 -24 53 -53s-24 -54 -53 -54s-53 25 -53 54zM139 395c0 29 24 53 53 53s53 -24 53 -53s-24 -54 -53 -54s-53 25 -53 54zM43 309
+c0 29 24 54 53 54s53 -25 53 -54s-24 -53 -53 -53s-53 24 -53 53z" />
+    <glyph glyph-name="phone" unicode="phone" 
+d="M141 282c31 -60 81 -110 141 -141l47 47c6 6 15 8 22 5c24 -8 50 -12 76 -12c12 0 21 -9 21 -21v-75c0 -12 -9 -21 -21 -21c-200 0 -363 163 -363 363c0 12 9 21 21 21h75c12 0 21 -9 21 -21c0 -27 4 -52 12 -76c2 -7 1 -16 -5 -22z" />
+    <glyph glyph-name="phone_android" unicode="phone_android" 
+d="M368 128v299h-224v-299h224zM299 64v21h-86v-21h86zM341 491c35 0 64 -29 64 -64v-342c0 -35 -29 -64 -64 -64h-170c-35 0 -64 29 -64 64v342c0 35 29 64 64 64h170z" />
+    <glyph glyph-name="phone_bluetooth_speaker" unicode="phone_bluetooth_speaker" 
+d="M427 181c12 0 21 -9 21 -21v-75c0 -12 -9 -21 -21 -21c-200 0 -363 163 -363 363c0 12 9 21 21 21h75c12 0 21 -9 21 -21c0 -27 4 -52 12 -76c2 -7 1 -16 -5 -22l-47 -47c31 -61 81 -110 141 -141l47 47c6 6 15 7 22 5c24 -8 49 -12 76 -12zM384 358v-40l20 20zM384 450
+v-40l20 20zM314 309l-15 15l59 60l-59 60l15 15l49 -49v81h10l61 -61l-46 -46l46 -46l-61 -61h-10v81z" />
+    <glyph glyph-name="phone_forwarded" unicode="phone_forwarded" 
+d="M427 181c12 0 21 -9 21 -21v-75c0 -12 -9 -21 -21 -21c-200 0 -363 163 -363 363c0 12 9 21 21 21h75c12 0 21 -9 21 -21c0 -27 4 -52 12 -76c2 -7 1 -16 -5 -22l-47 -47c31 -61 81 -110 141 -141l47 47c6 6 15 7 22 5c24 -8 49 -12 76 -12zM384 277v64h-85v86h85v64
+l107 -107z" />
+    <glyph glyph-name="phone_in_talk" unicode="phone_in_talk" 
+d="M320 256c0 35 -29 64 -64 64v43c59 0 107 -48 107 -107h-43zM405 256c0 83 -66 149 -149 149v43c106 0 192 -86 192 -192h-43zM427 181c12 0 21 -9 21 -21v-75c0 -12 -9 -21 -21 -21c-200 0 -363 163 -363 363c0 12 9 21 21 21h75c12 0 21 -9 21 -21c0 -27 4 -52 12 -76
+c2 -7 1 -16 -5 -22l-47 -47c31 -61 81 -110 141 -141l47 47c6 6 15 7 22 5c24 -8 49 -12 76 -12z" />
+    <glyph glyph-name="phone_iphone" unicode="phone_iphone" 
+d="M341 128v299h-192v-299h192zM245 43c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM331 491c29 0 53 -25 53 -54v-362c0 -29 -24 -54 -53 -54h-171c-29 0 -53 25 -53 54v362c0 29 24 54 53 54h171z" />
+    <glyph glyph-name="phone_locked" unicode="phone_locked" 
+d="M410 427v10c0 20 -17 37 -37 37s-36 -17 -36 -37v-10h73zM427 427c12 0 21 -10 21 -22v-85c0 -12 -9 -21 -21 -21h-107c-12 0 -21 9 -21 21v85c0 12 9 22 21 22v10c0 29 24 54 53 54s54 -25 54 -54v-10zM427 181c12 0 21 -9 21 -21v-75c0 -12 -9 -21 -21 -21
+c-200 0 -363 163 -363 363c0 12 9 21 21 21h75c12 0 21 -9 21 -21c0 -27 4 -52 12 -76c2 -7 1 -16 -5 -22l-47 -47c31 -61 81 -110 141 -141l47 47c6 6 15 7 22 5c24 -8 49 -12 76 -12z" />
+    <glyph glyph-name="phone_missed" unicode="phone_missed" 
+d="M506 156c4 -4 6 -9 6 -15s-2 -11 -6 -15l-53 -53c-4 -4 -9 -6 -15 -6s-11 2 -15 6c-17 16 -36 30 -57 40c-7 3 -12 11 -12 19v66c-31 10 -64 15 -98 15s-67 -5 -98 -15v-66c0 -9 -5 -17 -12 -20c-21 -10 -40 -23 -57 -39c-4 -4 -9 -6 -15 -6s-11 2 -15 6l-53 53
+c-4 4 -6 9 -6 15s2 11 6 15c65 62 153 100 250 100s185 -38 250 -100zM139 395v-75h-32v128h128v-32h-75l96 -96l128 128l21 -21l-149 -150z" />
+    <glyph glyph-name="phone_paused" unicode="phone_paused" 
+d="M405 448h43v-149h-43v149zM427 181c12 0 21 -9 21 -21v-75c0 -12 -9 -21 -21 -21c-200 0 -363 163 -363 363c0 12 9 21 21 21h75c12 0 21 -9 21 -21c0 -27 4 -52 12 -76c2 -7 1 -16 -5 -22l-47 -47c31 -61 81 -110 141 -141l47 47c6 6 15 7 22 5c24 -8 49 -12 76 -12z
+M363 448v-149h-43v149h43z" />
+    <glyph glyph-name="phonelink" unicode="phonelink" 
+d="M469 149v150h-85v-150h85zM491 341c12 0 21 -9 21 -21v-213c0 -12 -9 -22 -21 -22h-128c-12 0 -22 10 -22 22v213c0 12 10 21 22 21h128zM85 384v-235h214v-64h-299v64h43v235c0 23 19 43 42 43h384v-43h-384z" />
+    <glyph glyph-name="phonelink_erase" unicode="phonelink_erase" 
+d="M405 491c23 0 43 -20 43 -43v-384c0 -23 -20 -43 -43 -43h-213c-23 0 -43 20 -43 43v64h43v-43h213v342h-213v-43h-43v64c0 23 20 43 43 43h213zM277 337l-85 -85l85 -86l-21 -21l-85 85l-86 -85l-21 21l85 86l-85 85l21 21l86 -85l85 85z" />
+    <glyph glyph-name="phonelink_lock" unicode="phonelink_lock" 
+d="M203 277v32c0 17 -15 28 -32 28s-32 -11 -32 -28v-32h64zM230 277c13 0 26 -12 26 -27v-75c0 -13 -13 -26 -28 -26h-117c-13 0 -26 13 -26 28v75c0 13 13 25 26 25v32c0 30 30 54 60 54s59 -24 59 -54v-32zM405 491c23 0 43 -20 43 -43v-384c0 -23 -20 -43 -43 -43h-213
+c-23 0 -43 20 -43 43v64h43v-43h213v342h-213v-43h-43v64c0 23 20 43 43 43h213z" />
+    <glyph glyph-name="phonelink_off" unicode="phonelink_off" 
+d="M491 341c12 0 21 -9 21 -21v-213c0 -12 -9 -22 -21 -22h-4l-64 64h46v150h-85v-111l-43 43v89c0 12 10 21 22 21h128zM85 378v-229h229zM41 477c138.107 -138.226 275.985 -276.682 414 -415l-27 -27l-50 50h-378v64h43v235c0 10 4 20 10 27l-39 39zM469 384h-281l-43 43
+h324v-43z" />
+    <glyph glyph-name="phonelink_ring" unicode="phonelink_ring" 
+d="M299 85v342h-214v-342h214zM299 491c23 0 42 -20 42 -43v-384c0 -23 -19 -43 -42 -43h-214c-23 0 -42 20 -42 43v384c0 23 19 43 42 43h214zM384 303c26 -28 26 -66 0 -92l-21 22c11 15 11 34 0 49zM429 348c53 -51 53 -133 0 -182l-22 22c38 41 38 100 0 138z" />
+    <glyph glyph-name="phonelink_setup" unicode="phonelink_setup" 
+d="M405 491c23 0 43 -20 43 -43v-384c0 -23 -20 -43 -43 -43h-213c-23 0 -43 20 -43 43v64h43v-43h213v342h-213v-43h-43v64c0 23 20 43 43 43h213zM171 213c23 0 42 20 42 43s-19 43 -42 43s-43 -20 -43 -43s20 -43 43 -43zM252 245l23 -19c2 -2 4 -4 2 -6l-21 -37
+c-2 -2 -4 -2 -6 -2l-28 11c-6 -4 -13 -9 -19 -11l-5 -27c-2 -2 -4 -5 -6 -5h-43c-2 0 -6 3 -4 5l-4 27c-6 2 -13 7 -19 11l-30 -9c-2 -2 -5 1 -7 3l-21 36c0 2 0 4 2 8l24 17v22l-24 17c-2 2 -4 4 -2 6l21 37c2 2 5 2 7 2l27 -11c6 4 14 9 20 11l4 27c2 2 4 5 6 5h43
+c4 0 6 -3 6 -5l5 -27c6 -2 13 -7 19 -11l28 9c2 2 4 -1 6 -3l21 -36c0 -2 0 -4 -2 -6l-23 -17v-22z" />
+    <glyph glyph-name="photo" unicode="photo" 
+d="M181 224l-74 -96h298l-96 128l-74 -96zM448 107c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h298c23 0 43 -20 43 -43v-298z" />
+    <glyph glyph-name="photo_album" unicode="photo_album" 
+d="M128 107h256l-82 109l-64 -82l-46 55zM128 427v-171l53 32l54 -32v171h-107zM384 469c23 0 43 -19 43 -42v-342c0 -23 -20 -42 -43 -42h-256c-23 0 -43 19 -43 42v342c0 23 20 42 43 42h256z" />
+    <glyph glyph-name="photo_camera" unicode="photo_camera" 
+d="M256 149c59 0 107 48 107 107s-48 107 -107 107s-107 -48 -107 -107s48 -107 107 -107zM192 469h128l39 -42h68c23 0 42 -20 42 -43v-256c0 -23 -19 -43 -42 -43h-342c-23 0 -42 20 -42 43v256c0 23 19 43 42 43h68zM188 256c0 38 30 68 68 68s68 -30 68 -68
+s-30 -68 -68 -68s-68 30 -68 68z" />
+    <glyph glyph-name="photo_filter" unicode="photo_filter" 
+d="M283 283l58 -27l-58 -27l-27 -58l-27 58l-58 27l58 27l27 58zM363 299l-20 44l-44 20l44 20l20 44l20 -44l44 -20l-44 -20zM406 299h42v-192c0 -23 -19 -43 -42 -43h-299c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h192v-43h-192v-298h299v192z" />
+    <glyph glyph-name="photo_library" unicode="photo_library" 
+d="M43 384h42v-299h299v-42h-299c-23 0 -42 19 -42 42v299zM235 256l-64 -85h256l-86 106l-63 -79zM469 171c0 -23 -19 -43 -42 -43h-256c-23 0 -43 20 -43 43v256c0 23 20 42 43 42h256c23 0 42 -19 42 -42v-256z" />
+    <glyph glyph-name="photo_size_select_actual" unicode="photo_size_select_actual" 
+d="M107 149h298l-96 128l-74 -96l-54 64zM448 448c21 0 43 -22 43 -43v-298c0 -21 -22 -43 -43 -43h-384c-23 0 -43 20 -43 43v298c0 21 22 43 43 43h384z" />
+    <glyph glyph-name="photo_size_select_large" unicode="photo_size_select_large" 
+d="M64 107h213l-68 91l-53 -69l-39 46zM21 277h299v-213h-256c-23 0 -43 20 -43 43v170zM107 448h42v-43h-42v43zM192 448h43v-43h-43v43zM64 448v-43h-43c0 21 22 43 43 43zM363 107h42v-43h-42v43zM363 448h42v-43h-42v43zM21 363h43v-43h-43v43zM448 448
+c21 0 43 -22 43 -43h-43v43zM448 363h43v-43h-43v43zM277 448h43v-43h-43v43zM491 107c0 -21 -22 -43 -43 -43v43h43zM448 277h43v-42h-43v42zM448 192h43v-43h-43v43z" />
+    <glyph glyph-name="photo_size_select_small" unicode="photo_size_select_small" 
+d="M149 448v-43h-42v43h42zM235 448v-43h-43v43h43zM64 277v-42h-43v42h43zM64 448v-43h-43c0 21 22 43 43 43zM405 107v-43h-42v43h42zM405 448v-43h-42v43h42zM320 107v-43h-43v43h43zM64 363v-43h-43v43h43zM64 64c-23 0 -43 20 -43 43v85h214v-128h-171zM448 448
+c21 0 43 -22 43 -43h-43v43zM491 363v-43h-43v43h43zM320 448v-43h-43v43h43zM491 107c0 -21 -22 -43 -43 -43v43h43zM491 277v-42h-43v42h43zM491 192v-43h-43v43h43z" />
+    <glyph glyph-name="picture_as_pdf" unicode="picture_as_pdf" 
+d="M299 267v64h21v-64h-21zM85 384v-299h299v-42h-299c-23 0 -42 19 -42 42v299h42zM192 309v22h21v-22h-21zM437 331v32h-64v-128h32v42h32v32h-32v22h32zM352 267v64c0 18 -14 32 -32 32h-53v-128h53c18 0 32 14 32 32zM245 309v22c0 18 -14 32 -32 32h-53v-128h32v42h21
+c18 0 32 14 32 32zM427 469c23 0 42 -19 42 -42v-256c0 -23 -19 -43 -42 -43h-256c-23 0 -43 20 -43 43v256c0 23 20 42 43 42h256z" />
+    <glyph glyph-name="picture_in_picture" unicode="picture_in_picture" 
+d="M448 106v300h-384v-300h384zM448 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-384c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h384zM405 363v-128h-170v128h170z" />
+    <glyph glyph-name="picture_in_picture_alt" unicode="picture_in_picture_alt" 
+d="M448 106v300h-384v-300h384zM491 107c0 -23 -20 -43 -43 -43h-384c-23 0 -43 20 -43 43v299c0 23 20 42 43 42h384c23 0 43 -19 43 -42v-299zM405 277v-128h-170v128h170z" />
+    <glyph glyph-name="pie_chart" unicode="pie_chart" 
+d="M278 234h191c-10 -101 -90 -181 -191 -191v191zM278 469c101 -10 181 -90 191 -191h-191v191zM235 469v-426c-108 11 -192 102 -192 213s84 202 192 213z" />
+    <glyph glyph-name="pie_chart_outlined" unicode="pie_chart_outlined" 
+d="M277 87c77 10 138 71 148 148h-148v-148zM85 256c0 -87 66 -158 150 -169v338c-84 -10 -150 -82 -150 -169zM277 425v-148h148c-10 77 -71 138 -148 148zM256 469c117 0 213 -96 213 -213s-96 -213 -213 -213s-213 96 -213 213s96 213 213 213z" />
+    <glyph glyph-name="pin_drop" unicode="pin_drop" 
+d="M107 85h298v-42h-298v42zM213 341c0 -23 20 -42 43 -42c24 0 43 19 43 42s-20 43 -43 43s-43 -20 -43 -43zM384 341c0 -96 -128 -234 -128 -234s-128 138 -128 234c0 71 57 128 128 128s128 -57 128 -128z" />
+    <glyph glyph-name="place" unicode="place" 
+d="M256 267c29 0 53 24 53 53s-24 53 -53 53s-53 -24 -53 -53s24 -53 53 -53zM256 469c83 0 149 -66 149 -149c0 -112 -149 -277 -149 -277s-149 165 -149 277c0 83 66 149 149 149z" />
+    <glyph glyph-name="play_arrow" unicode="play_arrow" 
+d="M171 405l234 -149l-234 -149v298z" />
+    <glyph glyph-name="play_circle_filled" unicode="play_circle_filled" 
+d="M213 160l128 96l-128 96v-192zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="play_circle_outline" unicode="play_circle_outline" 
+d="M256 85c94 0 171 77 171 171s-77 171 -171 171s-171 -77 -171 -171s77 -171 171 -171zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213zM213 160v192l128 -96z" />
+    <glyph glyph-name="play_for_work" unicode="play_for_work" 
+d="M128 213h43c0 -47 38 -85 85 -85s85 38 85 85h43c0 -71 -57 -128 -128 -128s-128 57 -128 128zM235 405h42v-119h75l-96 -96l-96 96h75v119z" />
+    <glyph glyph-name="playlist_add" unicode="playlist_add" 
+d="M43 171v42h170v-42h-170zM384 213h85v-42h-85v-86h-43v86h-85v42h85v86h43v-86zM299 384v-43h-256v43h256zM299 299v-43h-256v43h256z" />
+    <glyph glyph-name="playlist_add_check" unicode="playlist_add_check" 
+d="M459 267l32 -32l-149 -150l-97 96l32 32l65 -64zM43 171v42h170v-42h-170zM299 384v-43h-256v43h256zM299 299v-43h-256v43h256z" />
+    <glyph glyph-name="playlist_play" unicode="playlist_play" 
+d="M363 235l106 -64l-106 -64v128zM43 192v43h277v-43h-277zM405 405v-42h-362v42h362zM405 320v-43h-362v43h362z" />
+    <glyph glyph-name="plus_one" unicode="plus_one" 
+d="M309 382l96 23v-277h-42v226l-54 -11v39zM213 341v-85h86v-43h-86v-85h-42v85h-86v43h86v85h42z" />
+    <glyph glyph-name="poll" unicode="poll" 
+d="M363 149v86h-43v-86h43zM277 149v214h-42v-214h42zM192 149v150h-43v-150h43zM405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h298z" />
+    <glyph glyph-name="polymer" unicode="polymer" 
+d="M405 427l96 -171l-96 -171h-85l96 171l-56 99l-168 -270h-85l-96 171l96 171h85l-96 -171l56 -99l168 270h85z" />
+    <glyph glyph-name="pool" unicode="pool" 
+d="M299 395c0 29 24 53 53 53s53 -24 53 -53s-24 -54 -53 -54s-53 25 -53 54zM185 256c-12 0 -17 3 -25 8c-4 3 -9 5 -16 8l69 69l-21 22c-24 24 -46 32 -85 32v53c53 0 84 -10 117 -43l137 -136c-3 -2 -6 -4 -9 -5c-8 -5 -13 -8 -25 -8s-16 3 -24 8c-10 6 -23 13 -47 13
+s-37 -7 -47 -13c-8 -5 -12 -8 -24 -8zM469 160c-24 0 -36 8 -46 14c-8 5 -13 7 -25 7s-16 -2 -24 -7c-10 -6 -23 -14 -47 -14s-36 8 -46 14c-8 5 -13 7 -25 7s-16 -2 -24 -7c-10 -6 -23 -14 -47 -14s-36 8 -46 14c-8 5 -13 7 -25 7s-16 -2 -24 -7c-10 -6 -23 -14 -47 -14v43
+c12 0 16 2 24 7c10 6 23 14 47 14s36 -8 46 -14c8 -5 13 -7 25 -7s16 2 24 7c10 6 23 14 47 14s36 -8 46 -14c8 -5 13 -7 25 -7s16 2 24 7c10 6 23 14 47 14s36 -8 46 -14c8 -5 13 -7 25 -7v-43zM469 64c-24 0 -36 8 -46 14c-8 5 -13 7 -25 7s-16 -2 -24 -7
+c-10 -6 -23 -14 -47 -14s-36 8 -46 14c-8 5 -13 7 -25 7s-16 -2 -24 -7c-10 -6 -23 -14 -47 -14s-37 8 -47 14c-8 5 -12 7 -24 7s-17 -2 -25 -7c-10 -6 -22 -14 -46 -14v43c12 0 16 2 24 7c10 6 23 14 47 14s36 -8 46 -14c8 -5 13 -7 25 -7s16 2 24 7c10 6 23 14 47 14
+s37 -8 47 -14c8 -5 12 -7 24 -7s17 2 25 7c10 6 22 14 46 14s37 -8 47 -14c8 -5 12 -7 24 -7v-43z" />
+    <glyph glyph-name="portable_wifi_off" unicode="portable_wifi_off" 
+d="M70 459l21 -22l357 -357l-27 -27l-160 161h-1l-4 -1c-23 0 -43 20 -43 43l1 4l-34 34c-6 -12 -9 -24 -9 -38c0 -32 17 -59 42 -74l-21 -37c-38 22 -64 64 -64 111c0 26 7 49 20 69l-30 31c-20 -28 -33 -62 -33 -100c0 -63 34 -118 85 -148l-21 -37
+c-64 37 -106 106 -106 185c0 49 16 95 44 131l-44 45zM256 427c-29 0 -56 -7 -80 -20l-31 31c32 20 70 31 111 31c118 0 213 -95 213 -213c0 -41 -11 -79 -31 -111l-32 31c13 24 21 51 21 80c0 94 -77 171 -171 171zM375 208l-35 35c1 4 1 9 1 13c0 47 -38 85 -85 85
+c-4 0 -9 0 -13 -1l-35 35c15 6 31 9 48 9c71 0 128 -57 128 -128c0 -17 -3 -33 -9 -48z" />
+    <glyph glyph-name="portrait" unicode="portrait" 
+d="M405 107v298h-298v-298h298zM405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h298zM352 165v-16h-192v16c0 32 64 48 96 48s96 -16 96 -48zM256 251c-26 0 -48 22 -48 48s22 48 48 48s48 -22 48 -48
+s-22 -48 -48 -48z" />
+    <glyph glyph-name="power" unicode="power" 
+d="M342 363c21 0 42 -22 42 -43v-117l-75 -75v-64h-106v64l-75 75v117c0 21 21 43 42 43h1v85h42v-85h86v85h42z" />
+    <glyph glyph-name="power_input" unicode="power_input" 
+d="M341 192v43h107v-43h-107zM192 192v43h107v-43h-107zM43 192v43h106v-43h-106zM43 320h405v-43h-405v43z" />
+    <glyph glyph-name="power_settings_new" unicode="power_settings_new" 
+d="M380 402c41 -35 68 -88 68 -146c0 -106 -86 -192 -192 -192s-192 86 -192 192c0 58 27 111 68 146l30 -30c-33 -27 -55 -69 -55 -116c0 -83 66 -149 149 -149s149 66 149 149c0 47 -21 88 -55 115zM277 448v-213h-42v213h42z" />
+    <glyph glyph-name="pregnant_woman" unicode="pregnant_woman" 
+d="M341 235v-86h-64v-106h-64v106h-42v150c0 35 29 64 64 64s64 -29 64 -64c25 -10 42 -35 42 -64zM192 427c0 24 19 42 43 42s42 -18 42 -42s-18 -43 -42 -43s-43 19 -43 43z" />
+    <glyph glyph-name="present_to_all" unicode="present_to_all" 
+d="M213 256h-42l85 85l85 -85h-42v-85h-86v85zM448 106v300h-384v-300h384zM448 448c24 0 43 -19 43 -43v-298c0 -24 -19 -43 -43 -43h-384c-24 0 -43 19 -43 43v298c0 24 19 43 43 43h384z" />
+    <glyph glyph-name="print" unicode="print" 
+d="M384 448v-85h-256v85h256zM405 256c12 0 22 9 22 21s-10 22 -22 22s-21 -10 -21 -22s9 -21 21 -21zM341 107v106h-170v-106h170zM405 341c35 0 64 -29 64 -64v-128h-85v-85h-256v85h-85v128c0 35 29 64 64 64h298z" />
+    <glyph glyph-name="priority_high" unicode="priority_high" 
+d="M213 448h86v-256h-86v256zM213 107c0 24 19 42 43 42s43 -18 43 -42s-19 -43 -43 -43s-43 19 -43 43z" />
+    <glyph glyph-name="public" unicode="public" 
+d="M382 141c28 30 45 71 45 115c0 71 -44 133 -107 158v-9c0 -23 -20 -42 -43 -42h-42v-43c0 -12 -10 -21 -22 -21h-42v-43h128c12 0 21 -9 21 -21v-64h21c19 0 35 -13 41 -30zM235 87v41c-23 0 -43 20 -43 43v21l-102 102c-3 -12 -5 -25 -5 -38c0 -87 66 -159 150 -169z
+M256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="publish" unicode="publish" 
+d="M107 213l149 150l149 -150h-85v-128h-128v128h-85zM107 427h298v-43h-298v43z" />
+    <glyph glyph-name="query_builder" unicode="query_builder" 
+d="M267 363v-112l96 -57l-16 -27l-112 68v128h32zM256 85c94 0 171 77 171 171s-77 171 -171 171s-171 -77 -171 -171s77 -171 171 -171zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="question_answer" unicode="question_answer" 
+d="M363 256c0 -12 -10 -21 -22 -21h-213l-85 -86v299c0 12 9 21 21 21h277c12 0 22 -9 22 -21v-192zM448 384c12 0 21 -9 21 -21v-320l-85 85h-235c-12 0 -21 9 -21 21v43h277v192h43z" />
+    <glyph glyph-name="queue" unicode="queue" 
+d="M405 277v43h-85v85h-43v-85h-85v-43h85v-85h43v85h85zM427 469c23 0 42 -19 42 -42v-256c0 -23 -19 -43 -42 -43h-256c-23 0 -43 20 -43 43v256c0 23 20 42 43 42h256zM85 384v-299h299v-42h-299c-23 0 -42 19 -42 42v299h42z" />
+    <glyph glyph-name="queue_music" unicode="queue_music" 
+d="M363 384h106v-43h-64v-192c0 -35 -29 -64 -64 -64s-64 29 -64 64s29 64 64 64c7 0 15 -2 22 -4v175zM64 171v42h171v-42h-171zM320 299v-43h-256v43h256zM320 384v-43h-256v43h256z" />
+    <glyph glyph-name="queue_play_next" unicode="queue_play_next" 
+d="M512 128l-96 -96l-32 32l64 64l-64 64l32 32zM277 299h64v-43h-64v-64h-42v64h-64v43h64v64h42v-64zM448 448c23 0 43 -19 43 -43v-170h-43v170h-384v-256h320v-42h-43v-43h-170v43h-107c-24 0 -43 19 -43 42v256c0 24 19 43 43 43h384z" />
+    <glyph glyph-name="radio" unicode="radio" 
+d="M427 256v85h-342v-85h256v43h43v-43h43zM149 85c35 0 64 29 64 64s-29 64 -64 64s-64 -29 -64 -64s29 -64 64 -64zM69 381l270 110l14 -36l-176 -71h250c24 0 42 -19 42 -43v-256c0 -23 -18 -42 -42 -42h-342c-24 0 -42 19 -42 42v256c0 18 10 34 26 40z" />
+    <glyph glyph-name="radio_button_checked" unicode="radio_button_checked" 
+d="M256 85c94 0 171 77 171 171s-77 171 -171 171s-171 -77 -171 -171s77 -171 171 -171zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213zM256 363c59 0 107 -48 107 -107s-48 -107 -107 -107s-107 48 -107 107s48 107 107 107z" />
+    <glyph glyph-name="radio_button_unchecked" unicode="radio_button_unchecked" 
+d="M256 85c94 0 171 77 171 171s-77 171 -171 171s-171 -77 -171 -171s77 -171 171 -171zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="rate_review" unicode="rate_review" 
+d="M384 213v43h-117l-43 -43h160zM128 213h53l147 147c4 4 4 11 0 15l-38 38c-4 4 -11 4 -15 0l-147 -147v-53zM427 469c23 0 42 -19 42 -42v-256c0 -23 -19 -43 -42 -43h-299l-85 -85v384c0 23 19 42 42 42h342z" />
+    <glyph glyph-name="receipt" unicode="receipt" 
+d="M64 43v426l32 -32l32 32l32 -32l32 32l32 -32l32 32l32 -32l32 32l32 -32l32 32l32 -32l32 32v-426l-32 32l-32 -32l-32 32l-32 -32l-32 32l-32 -32l-32 32l-32 -32l-32 32l-32 -32l-32 32zM384 320v43h-256v-43h256zM384 235v42h-256v-42h256zM384 149v43h-256v-43h256z
+" />
+    <glyph glyph-name="recent_actors" unicode="recent_actors" 
+d="M267 149v16c0 32 -64 48 -96 48s-96 -16 -96 -48v-16h192zM171 347c-26 0 -48 -22 -48 -48s22 -48 48 -48s48 22 48 48s-22 48 -48 48zM299 405c12 0 21 -9 21 -21v-256c0 -12 -9 -21 -21 -21h-256c-12 0 -22 9 -22 21v256c0 12 10 21 22 21h256zM363 107v298h42v-298
+h-42zM448 405h43v-298h-43v298z" />
+    <glyph glyph-name="record_voice_over" unicode="record_voice_over" 
+d="M428 469c83 -86 83 -215 0 -298l-35 34c59 68 59 166 0 230zM358 398c43 -47 43 -112 0 -155l-36 36c18 25 18 58 0 83zM192 192c57 0 171 -28 171 -85v-43h-342v43c0 57 114 85 171 85zM107 320c0 47 38 85 85 85s85 -38 85 -85s-38 -85 -85 -85s-85 38 -85 85z" />
+    <glyph glyph-name="redeem" unicode="redeem" 
+d="M427 213v128h-109l45 -60l-35 -25c-24.0378 32.6288 -48.3187 65.0147 -72 98c-23.6829 -32.9837 -47.9614 -65.3719 -72 -98l-35 25l45 60h-109v-128h342zM427 107v42h-342v-42h342zM192 427c-12 0 -21 -10 -21 -22s9 -21 21 -21s21 9 21 21s-9 22 -21 22zM320 427
+c-12 0 -21 -10 -21 -22s9 -21 21 -21s21 9 21 21s-9 22 -21 22zM427 384c24 0 42 -19 42 -43v-234c0 -24 -18 -43 -42 -43h-342c-24 0 -42 19 -42 43v234c0 24 18 43 42 43h47c-2 7 -4 14 -4 21c0 35 29 64 64 64c22 0 41 -11 53 -28l11 -15l11 15c12 17 31 28 53 28
+c35 0 64 -29 64 -64c0 -7 -2 -14 -4 -21h47z" />
+    <glyph glyph-name="redo" unicode="redo" 
+d="M393 286l76 77v-192h-192l78 77c-30 25 -68 40 -110 40c-76 0 -140 -49 -162 -117l-50 16c29 89 113 154 212 154c57 0 109 -21 148 -55z" />
+    <glyph glyph-name="refresh" unicode="refresh" 
+d="M377 377l50 50v-150h-150l69 69c-23 23 -55 38 -90 38c-71 0 -128 -57 -128 -128s57 -128 128 -128c56 0 104 35 121 85h44c-19 -74 -85 -128 -165 -128c-94 0 -170 77 -170 171s76 171 170 171c47 0 90 -19 121 -50z" />
+    <glyph glyph-name="remove" unicode="remove" 
+d="M405 235h-298v42h298v-42z" />
+    <glyph glyph-name="remove_circle" unicode="remove_circle" 
+d="M363 235v42h-214v-42h214zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="remove_circle_outline" unicode="remove_circle_outline" 
+d="M256 85c94 0 171 77 171 171s-77 171 -171 171s-171 -77 -171 -171s77 -171 171 -171zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213zM149 277h214v-42h-214v42z" />
+    <glyph glyph-name="remove_from_queue" unicode="remove_from_queue" 
+d="M341 299v-43h-170v43h170zM448 149v256h-384v-256h384zM448 448c23 0 43 -19 43 -43l-1 -256c0 -23 -19 -42 -42 -42h-107v-43h-170v43h-107c-24 0 -43 19 -43 42v256c0 24 19 43 43 43h384z" />
+    <glyph glyph-name="remove_red_eye" unicode="remove_red_eye" 
+d="M256 320c35 0 64 -29 64 -64s-29 -64 -64 -64s-64 29 -64 64s29 64 64 64zM256 149c59 0 107 48 107 107s-48 107 -107 107s-107 -48 -107 -107s48 -107 107 -107zM256 416c107 0 198 -66 235 -160c-37 -94 -128 -160 -235 -160s-198 66 -235 160c37 94 128 160 235 160z
+" />
+    <glyph glyph-name="remove_shopping_cart" unicode="remove_shopping_cart" 
+d="M149 128c23 0 43 -20 43 -43s-20 -42 -43 -42s-42 19 -42 42s19 43 42 43zM332 235l-192 192h287c12 0 21 -10 21 -22c0 -4 -1 -7 -3 -10l-76 -138c-7 -13 -21 -22 -37 -22zM158 192h108l-43 43h-50l-19 -35l-1 -3c0 -3 2 -5 5 -5zM485 27l-27 -27l-61 61
+c-8 -11 -20 -18 -34 -18c-23 0 -43 19 -43 42c0 14 7 27 18 35l-30 29h-159c-23 0 -42 20 -42 43c0 7 2 14 5 20l29 53l-47 99l-94 94l27 27z" />
+    <glyph glyph-name="reorder" unicode="reorder" 
+d="M64 405h384v-42h-384v42zM64 277v43h384v-43h-384zM64 107v42h384v-42h-384zM64 192v43h384v-43h-384z" />
+    <glyph glyph-name="repeat" unicode="repeat" 
+d="M363 149v86h42v-128h-256v-64l-85 85l85 85v-64h214zM149 363v-86h-42v128h256v64l85 -85l-85 -85v64h-214z" />
+    <glyph glyph-name="repeat_one" unicode="repeat_one" 
+d="M277 192h-32v85h-32v22l43 21h21v-128zM363 149v86h42v-128h-256v-64l-85 85l85 85v-64h214zM149 363v-86h-42v128h256v64l85 -85l-85 -85v64h-214z" />
+    <glyph glyph-name="replay" unicode="replay" 
+d="M256 405c94 0 171 -76 171 -170s-77 -171 -171 -171s-171 77 -171 171h43c0 -71 57 -128 128 -128s128 57 128 128s-57 128 -128 128v-86l-107 107l107 107v-86z" />
+    <glyph glyph-name="replay_10" unicode="replay_&#x31;&#x30;" 
+d="M282 188c0 -3.2653 6.32517 -7 10 -7c2 0 5 0 7 2l4 5s2 4 2 6v43c0 0 -2 4 -2 6c0 2.75261 -8.21183 7 -11 7c-2 0 -4 -1 -6 -3l-4 -4s-3 -4 -3 -6v-43s3 -4 3 -6zM324 209c0 -6 0 -13 -2 -17l-6 -13s-7 -6 -11 -6s-9 -2 -13 -2s-9 0 -13 2s-6 4 -10 6
+c-6.74403 3.37202 -9 19.7461 -9 30v15c0 6 0 13 2 17l7 13s6 6 10 6s9 2 13 2s9 0 13 -2s7 -4 11 -6s4 -7 6 -13s2 -11 2 -17v-15zM233 171h-20v70l-21 -6v15l38 12h3v-91zM256 405c94 0 171 -76 171 -170s-77 -171 -171 -171s-171 77 -171 171h43c0 -70 58 -128 128 -128
+s128 58 128 128s-58 128 -128 128v-86l-107 107l107 107v-86z" />
+    <glyph glyph-name="replay_30" unicode="replay_&#x33;&#x30;" 
+d="M286 188c0 -3.76553 6.92157 -7 11 -7c2 0 4 0 6 2l4 5s2 4 2 6v43c0 2 -2 4 -2 6c0 2.33563 -7.56075 7 -10 7c-2 0 -5 -1 -7 -3l-4 -4s-2 -4 -2 -6v-43s2 -4 2 -6zM326 209c0 -6 0 -13 -2 -17l-6 -13s-7 -6 -11 -6s-9 -2 -13 -2c-10.126 0 -16.1493 4.57466 -23 8
+c-4 2 -4 7 -6 13s-3 11 -3 17v15c0 6 1 13 3 17l6 13s7 6 11 6s8 2 12 2s9 0 13 -2s7 -4 11 -6s4 -7 6 -13s2 -11 2 -17v-15zM213 224c8.11936 0 15 4.95913 15 13v4s-2 2 -2 4s-2 2 -4 2h-11s-2 -2 -4 -2s-2 -2 -2 -4v-4h-22c0 9.89385 8.81974 23 18 23c2 0 8 2 10 2
+c8.67272 0 17.6319 -2.81597 24 -6c5.06488 -2.53244 8 -12.109 8 -19v-7s-2 -4 -2 -6s-2 -4 -4 -4s-5 -3 -7 -5c4 -2 9 -4 11 -8s4 -9 4 -13s0 -9 -2 -11s-4 -6 -6 -8s-7 -4 -11 -4s-9 -2 -13 -2s-8 0 -10 2s-7 2 -11 4c-5.58093 2.79046 -9 13.5084 -9 21h18v-4s2 -2 2 -4
+s2 -2 4 -2h11s2 2 4 2s2 2 2 4v11s-2 2 -2 4s-2 2 -4 2h-13v15h8zM256 405c94 0 171 -76 171 -170s-77 -171 -171 -171s-171 77 -171 171h43c0 -70 58 -128 128 -128s128 58 128 128s-58 128 -128 128v-86l-107 107l107 107v-86z" />
+    <glyph glyph-name="replay_5" unicode="replay_&#x35;" 
+d="M252 222c-1.79204 -0.716817 -7 -2.40231 -7 -4c0 -2 -2 -3 -2 -3h-15l5 47h51v-15h-37l-2 -19s2 0 2 2s3 1 3 3s2 0 4 0h4c4 0 9 -1 11 -3s6 -4 8 -6c4.43641 -4.43641 9 -13.6284 9 -23c0 -4 0 -9 -2 -11s-3 -7 -7 -11s-6 -4 -8 -6s-9 -2 -13 -2s-9 0 -11 2s-6 2 -10 4
+c-5.5956 2.7978 -9 11.277 -9 19h17c0 -7.40446 5.29508 -10 13 -10c2 0 4 0 6 2l5 4s2 4 2 6v13l-2 4l-5 5s-4 2 -6 2h-4zM256 405c94 0 171 -76 171 -170s-77 -171 -171 -171s-171 77 -171 171h43c0 -70 58 -128 128 -128s128 58 128 128s-58 128 -128 128v-86l-107 107
+l107 107v-86z" />
+    <glyph glyph-name="reply" unicode="reply" 
+d="M213 320c149 -21 214 -128 235 -235c-53 75 -128 109 -235 109v-87l-149 149l149 149v-85z" />
+    <glyph glyph-name="reply_all" unicode="reply_all" 
+d="M277 320c149 -21 214 -128 235 -235c-53 75 -128 109 -235 109v-87l-149 149l149 149v-85zM149 341l-85 -85l85 -85v-64l-149 149l149 149v-64z" />
+    <glyph glyph-name="report" unicode="report" 
+d="M277 235v128h-42v-128h42zM256 143c15 0 28 13 28 28s-13 27 -28 27s-28 -12 -28 -27s13 -28 28 -28zM336 448l112 -112v-160l-112 -112h-160l-112 112v160l112 112h160z" />
+    <glyph glyph-name="report_problem" unicode="report_problem" 
+d="M277 213v86h-42v-86h42zM277 128v43h-42v-43h42zM21 64l235 405l235 -405h-470z" />
+    <glyph glyph-name="restaurant" unicode="restaurant" 
+d="M341 384c0 38 48 85 107 85v-426h-53v170h-54v171zM235 320v149h42v-149c0 -45 -35 -82 -80 -85v-192h-53v192c-45 3 -80 40 -80 85v149h43v-149h42v149h43v-149h43z" />
+    <glyph glyph-name="restaurant_menu" unicode="restaurant_menu" 
+d="M317 266l-31 -31l147 -147l-30 -30l-147 147l-147 -147l-30 30l208 208c-15 33 -4 79 30 113c41 41 99 48 130 17s24 -90 -17 -131c-34 -34 -80 -44 -113 -29zM173 227l-90 90c-33 33 -33 87 0 120l150 -149z" />
+    <glyph glyph-name="restore" unicode="restore" 
+d="M256 341h32v-90l75 -45l-16 -26l-91 55v106zM277 448c106 0 192 -86 192 -192s-86 -192 -192 -192c-53 0 -100 21 -135 56l30 31c27 -27 64 -44 105 -44c83 0 150 66 150 149s-67 149 -150 149s-149 -66 -149 -149h64l-86 -86l-2 3l-83 83h64c0 106 86 192 192 192z" />
+    <glyph glyph-name="restore_page" unicode="restore_page" 
+d="M256 128c59 0 107 48 107 107s-48 106 -107 106c-38 0 -71 -20 -90 -49l-27 28v-85h85l-34 34c12 24 37 40 66 40c41 0 75 -33 75 -74s-34 -75 -75 -75c-25 0 -48 13 -61 32h-37c16 -38 54 -64 98 -64zM299 469l128 -128v-256c0 -23 -20 -42 -43 -42h-256
+c-23 0 -43 19 -43 42l1 342c0 23 19 42 42 42h171z" />
+    <glyph glyph-name="ring_volume" unicode="ring_volume" 
+d="M137 303c0 0 -74 74 -76 75l30 31l76 -76zM277 469v-106h-42v106h42zM451 378c-2 -1 -76 -75 -76 -75l-30 30l76 76zM506 156c4 -4 6 -9 6 -15s-2 -11 -6 -15l-53 -53c-4 -4 -9 -6 -15 -6s-11 2 -15 6c-17 16 -36 30 -57 40c-7 3 -12 11 -12 19v66c-31 10 -64 15 -98 15
+s-67 -5 -98 -15v-66c0 -9 -5 -17 -12 -20c-21 -10 -40 -23 -57 -39c-4 -4 -9 -6 -15 -6s-11 2 -15 6l-53 53c-4 4 -6 9 -6 15s2 11 6 15c65 62 153 100 250 100s185 -38 250 -100z" />
+    <glyph glyph-name="room" unicode="room" 
+d="M256 267c29 0 53 24 53 53s-24 53 -53 53s-53 -24 -53 -53s24 -53 53 -53zM256 469c83 0 149 -66 149 -149c0 -112 -149 -277 -149 -277s-149 165 -149 277c0 83 66 149 149 149z" />
+    <glyph glyph-name="room_service" unicode="room_service" 
+d="M295 346c83 -17 147 -88 153 -175h-384c6 87 70 158 153 175c-2 5 -4 11 -4 17c0 23 20 42 43 42s43 -19 43 -42c0 -6 -2 -12 -4 -17zM43 149h426v-42h-426v42z" />
+    <glyph glyph-name="rotate_90_degrees_ccw" unicode="rotate_&#x39;&#x30;_degrees_ccw" 
+d="M413 370c75 -75 75 -196 0 -271c-37 -38 -87 -56 -136 -56c-32 0 -63 8 -92 24l32 31c19 -9 39 -13 60 -13c38 0 77 15 106 44c58 58 58 153 0 211c-29 29 -68 44 -106 44v-69l-90 90l90 91v-69c49 0 99 -19 136 -57zM79 237l78 -78l78 78l-78 78zM157 375l138 -138
+l-138 -138l-139 138z" />
+    <glyph glyph-name="rotate_left" unicode="rotate_left" 
+d="M277 425c84 -10 150 -82 150 -169s-66 -159 -150 -169v43c61 10 107 63 107 126s-46 116 -107 126v-83l-97 95l97 97v-66zM151 121l31 31c16 -12 34 -19 53 -22v-43c-30 4 -59 15 -84 34zM130 235c3 -19 10 -37 21 -53l-30 -30c-19 25 -30 53 -34 83h43zM152 330
+c-11 -16 -19 -34 -22 -53h-43c4 30 16 58 35 83z" />
+    <glyph glyph-name="rotate_right" unicode="rotate_right" 
+d="M360 182c12 16 19 34 22 53h43c-4 -30 -15 -58 -34 -83zM277 130c19 3 37 10 53 22l31 -31c-25 -19 -54 -30 -84 -34v43zM425 277h-43c-3 19 -10 37 -22 53l31 30c19 -25 30 -53 34 -83zM332 394l-97 -95v83c-61 -10 -107 -63 -107 -126s46 -116 107 -126v-43
+c-84 10 -150 82 -150 169s66 159 150 169v66z" />
+    <glyph glyph-name="rounded_corner" unicode="rounded_corner" 
+d="M448 341v-106h-43v106c0 35 -29 64 -64 64h-106v43h106c59 0 107 -48 107 -107zM64 64v43h43v-43h-43zM149 64v43h43v-43h-43zM235 64v43h42v-43h-42zM149 405v43h43v-43h-43zM64 405v43h43v-43h-43zM64 320v43h43v-43h-43zM64 149v43h43v-43h-43zM64 235v42h43v-42h-43z
+M405 149v43h43v-43h-43zM405 107h43v-43h-43v43z" />
+    <glyph glyph-name="router" unicode="router" 
+d="M320 128v43h-43v-43h43zM245 128v43h-42v-43h42zM171 128v43h-43v-43h43zM405 235c23 0 43 -20 43 -43v-85c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v85c0 23 20 43 43 43h213v85h43v-85h42zM412 369l-17 -17c-15 15 -35 21 -54 21s-38 -6 -53 -21l-17 17
+c19 19 44 30 70 30s52 -11 71 -30zM431 386c-26 23 -58 36 -90 36s-63 -13 -89 -36l-17 17c30 30 68 45 106 45s77 -15 107 -45z" />
+    <glyph glyph-name="rowing" unicode="rowing" 
+d="M448 64l-64 -64l-64 64v32l-151 151c-7 -1 -14 -1 -20 -1v46c35 -1 77 18 100 43l30 33c7.60688 7.60688 20.1539 16 35 16h1c26 0 48 -22 48 -48v-123c0 -18 -8 -34 -20 -46l-76 76v49c-13 -11 -31 -22 -49 -30l134 -134h32zM320 491c23 0 43 -20 43 -43
+s-20 -43 -43 -43s-43 20 -43 43s20 43 43 43zM181 203l54 -54h-43l-75 -74l-32 32z" />
+    <glyph glyph-name="rss_feed" unicode="rss_feed" 
+d="M85 297c117 0 212 -95 212 -212h-61c0 83 -68 151 -151 151v61zM85 417c183 0 332 -149 332 -332h-60c0 150 -122 272 -272 272v60zM85 132c0 26 21 46 47 46s46 -20 46 -46s-20 -47 -46 -47s-47 21 -47 47z" />
+    <glyph glyph-name="rv_hookup" unicode="rv_hookup" 
+d="M363 469l64 -64l-64 -64v43h-171v43h171v42zM384 213v64h-85v-64h85zM235 85c12 0 21 10 21 22s-9 21 -21 21s-22 -9 -22 -21s10 -22 22 -22zM427 149h42v-42h-170c0 -35 -29 -64 -64 -64s-64 29 -64 64h-43c-23 0 -43 19 -43 42v64h150v64h-86v-42l-64 64l64 64v-43h235
+c23 0 43 -20 43 -43v-128z" />
+    <glyph glyph-name="satellite" unicode="satellite" 
+d="M107 128h298l-96 128l-74 -96l-54 64zM107 256c83 0 149 67 149 150h-43c0 -59 -47 -107 -106 -107v-43zM107 406v-65c35 0 64 30 64 65h-64zM405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h298z" />
+    <glyph glyph-name="save" unicode="save" 
+d="M320 320v85h-213v-85h213zM256 107c35 0 64 29 64 64s-29 64 -64 64s-64 -29 -64 -64s29 -64 64 -64zM363 448l85 -85v-256c0 -23 -20 -43 -43 -43h-298c-24 0 -43 20 -43 43v298c0 23 19 43 43 43h256z" />
+    <glyph glyph-name="scanner" unicode="scanner" 
+d="M405 149v43h-213v-43h213zM149 149v43h-42v-43h42zM422 284c15 -4 26 -22 26 -39v-117c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v85c0 23 20 43 43 43h268l-300 109l15 40z" />
+    <glyph glyph-name="schedule" unicode="schedule" 
+d="M267 363v-112l96 -57l-16 -27l-112 68v128h32zM256 85c94 0 171 77 171 171s-77 171 -171 171s-171 -77 -171 -171s77 -171 171 -171zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="school" unicode="school" 
+d="M256 448l235 -128v-171h-43v148l-192 -105l-235 128zM107 231l149 -82l149 82v-86l-149 -81l-149 81v86z" />
+    <glyph glyph-name="screen_lock_landscape" unicode="screen_lock_landscape" 
+d="M230 299v-22h52v22c0 14 -12 25 -26 25s-26 -11 -26 -25zM213 171c-12 0 -21 9 -21 21v64c0 12 9 21 21 21v22c0 23 19 42 43 42c23 0 43 -18 43 -42v-22c12 0 21 -9 21 -21v-64c0 -12 -9 -21 -21 -21h-86zM405 149v214h-298v-214h298zM448 405c23 0 43 -19 43 -42v-214
+c0 -23 -20 -42 -43 -42h-384c-23 0 -43 19 -43 42v214c0 23 20 42 43 42h384z" />
+    <glyph glyph-name="screen_lock_portrait" unicode="screen_lock_portrait" 
+d="M363 107v298h-214v-298h214zM363 491c23 0 42 -20 42 -43v-384c0 -23 -19 -43 -42 -43h-214c-23 0 -42 20 -42 43v384c0 23 19 43 42 43h214zM230 299v-22h52v22c0 14 -12 25 -26 25s-26 -11 -26 -25zM213 171c-12 0 -21 9 -21 21v64c0 12 9 21 21 21v22
+c0 23 19 42 43 42c23 0 43 -18 43 -42v-22c12 0 21 -9 21 -21v-64c0 -12 -9 -21 -21 -21h-86z" />
+    <glyph glyph-name="screen_lock_rotation" unicode="screen_lock_rotation" 
+d="M358 459v-11h73v11c0 20 -16 36 -36 36s-37 -16 -37 -36zM341 320c-12 0 -21 9 -21 21v86c0 12 9 21 21 21v11c0 29 25 53 54 53s53 -24 53 -53v-11c12 0 21 -9 21 -21v-86c0 -12 -9 -21 -21 -21h-107zM181 75l28 28l81 -81l-14 -1c-134 0 -244 104 -255 235h32
+c8 -80 58 -148 128 -181zM496 240c13 -12 13 -33 0 -46l-136 -135c-12 -13 -32 -13 -45 0l-256 256c-13 12 -13 32 0 45l135 136c12 13 33 13 46 0l52 -52l-30 -30l-45 44l-121 -120l242 -242l120 121l-47 47l30 30z" />
+    <glyph glyph-name="screen_rotation" unicode="screen_rotation" 
+d="M160 54l29 28l81 -81l-14 -1c-134 0 -244 104 -255 235h32c8 -80 57 -148 127 -181zM316 60l136 136l-256 256l-136 -136zM218 475l257 -257c13 -12 13 -32 0 -45l-136 -136c-12 -13 -32 -13 -45 0l-257 257c-13 12 -13 32 0 45l136 136c12 13 32 13 45 0zM352 458
+l-29 -28l-81 81l14 1c134 0 244 -104 255 -235h-32c-8 80 -57 148 -127 181z" />
+    <glyph glyph-name="screen_share" unicode="screen_share" 
+d="M277 203l86 80l-86 80v-46c-83 -12 -116 -68 -128 -125c30 40 69 58 128 58v-47zM427 128h85v-43h-512v43h85c-24 0 -42 20 -42 43v213c0 24 18 43 42 43h342c23 0 42 -19 42 -43v-213c0 -23 -19 -43 -42 -43z" />
+    <glyph glyph-name="sd_card" unicode="sd_card" 
+d="M384 341v86h-43v-86h43zM320 341v86h-43v-86h43zM256 341v86h-43v-86h43zM384 469c23 0 43 -19 43 -42v-342c0 -23 -20 -42 -43 -42h-256c-23 0 -43 19 -43 42l1 256l127 128h171z" />
+    <glyph glyph-name="sd_storage" unicode="sd_storage" 
+d="M384 341v86h-43v-86h43zM320 341v86h-43v-86h43zM256 341v86h-43v-86h43zM384 469c23 0 43 -19 43 -42v-342c0 -23 -20 -42 -43 -42h-256c-23 0 -43 19 -43 42l1 256l127 128h171z" />
+    <glyph glyph-name="search" unicode="search" 
+d="M203 213c53 0 96 43 96 96s-43 96 -96 96s-96 -43 -96 -96s43 -96 96 -96zM331 213l106 -106l-32 -32l-106 106v17l-6 6c-24 -21 -56 -33 -90 -33c-77 0 -139 61 -139 138s62 139 139 139s138 -62 138 -139c0 -34 -12 -66 -33 -90l6 -6h17z" />
+    <glyph glyph-name="security" unicode="security" 
+d="M256 491l192 -86v-128c0 -118 -82 -229 -192 -256c-110 27 -192 138 -192 256v128zM256 256v-191c79 25 138 103 149 191h-149zM256 256v188l-149 -66v-122h149z" />
+    <glyph glyph-name="select_all" unicode="select_all" 
+d="M192 320v-128h128v128h-128zM149 149v214h214v-214h-214zM320 405v43h43v-43h-43zM320 64v43h43v-43h-43zM405 149v43h43v-43h-43zM405 320v43h43v-43h-43zM405 64v43h43c0 -23 -20 -43 -43 -43zM405 235v42h43v-42h-43zM235 64v43h42v-43h-42zM192 448v-43h-43v43h43z
+M64 149v43h43v-43h-43zM107 64c-23 0 -43 20 -43 43h43v-43zM405 448c23 0 43 -20 43 -43h-43v43zM277 448v-43h-42v43h42zM64 320v43h43v-43h-43zM149 64v43h43v-43h-43zM64 235v42h43v-42h-43zM64 405c0 23 20 43 43 43v-43h-43z" />
+    <glyph glyph-name="send" unicode="send" 
+d="M43 64v149l320 43l-320 43v149l448 -192z" />
+    <glyph glyph-name="sentiment_dissatisfied" unicode="sentiment_dissatisfied" 
+d="M256 213c50 0 92 -30 109 -74h-35c-15 25 -42 42 -74 42s-59 -17 -74 -42h-35c17 44 59 74 109 74zM256 85c94 0 171 77 171 171s-77 171 -171 171s-171 -77 -171 -171s77 -171 171 -171zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213
+s95 213 213 213zM149 309c0 18 14 32 32 32s32 -14 32 -32s-14 -32 -32 -32s-32 14 -32 32zM299 309c0 18 14 32 32 32s32 -14 32 -32s-14 -32 -32 -32s-32 14 -32 32z" />
+    <glyph glyph-name="sentiment_neutral" unicode="sentiment_neutral" 
+d="M256 85c94 0 171 77 171 171s-77 171 -171 171s-171 -77 -171 -171s77 -171 171 -171zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213zM149 309c0 18 14 32 32 32s32 -14 32 -32s-14 -32 -32 -32s-32 14 -32 32zM299 309
+c0 18 14 32 32 32s32 -14 32 -32s-14 -32 -32 -32s-32 14 -32 32zM192 213h128v-32h-128v32z" />
+    <glyph glyph-name="sentiment_satisfied" unicode="sentiment_satisfied" 
+d="M256 171c32 0 59 17 74 42h35c-17 -44 -59 -74 -109 -74s-92 30 -109 74h35c15 -25 42 -42 74 -42zM256 85c94 0 171 77 171 171s-77 171 -171 171s-171 -77 -171 -171s77 -171 171 -171zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213
+s95 213 213 213zM149 309c0 18 14 32 32 32s32 -14 32 -32s-14 -32 -32 -32s-32 14 -32 32zM299 309c0 18 14 32 32 32s32 -14 32 -32s-14 -32 -32 -32s-32 14 -32 32z" />
+    <glyph glyph-name="sentiment_very_dissatisfied" unicode="sentiment_very_dissatisfied" 
+d="M256 213c50 0 92 -30 109 -74h-218c17 44 59 74 109 74zM167 256l-23 23l23 22l-23 23l23 22l22 -22l23 22l23 -22l-23 -23l23 -22l-23 -23l-23 23zM345 346l23 -22l-23 -23l23 -22l-23 -23l-22 23l-23 -23l-23 23l23 22l-23 23l23 22l23 -22zM256 85
+c94 0 171 77 171 171s-77 171 -171 171s-171 -77 -171 -171s77 -171 171 -171zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="sentiment_very_satisfied" unicode="sentiment_very_satisfied" 
+d="M256 139c-50 0 -92 30 -109 74h218c-17 -44 -59 -74 -109 -74zM189 300l-22 -23l-23 23l45 45l46 -45l-23 -23zM277 300l46 45l45 -45l-23 -23l-22 23l-23 -23zM256 85c94 0 171 77 171 171s-77 171 -171 171s-171 -77 -171 -171s77 -171 171 -171zM256 469
+c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="settings" unicode="settings" 
+d="M256 181c41 0 75 34 75 75s-34 75 -75 75s-75 -34 -75 -75s34 -75 75 -75zM415 235l45 -35c4 -3 5 -9 2 -14l-43 -74c-3 -5 -8 -6 -13 -4l-53 21c-11 -8 -23 -16 -36 -21l-8 -56c-1 -5 -5 -9 -10 -9h-86c-5 0 -9 4 -10 9l-8 56c-13 5 -25 12 -36 21l-53 -21
+c-5 -2 -10 -1 -13 4l-43 74c-3 5 -2 11 2 14l45 35c-1 7 -1 14 -1 21s0 14 1 21l-45 35c-4 3 -5 9 -2 14l43 74c3 5 8 6 13 4l53 -21c11 8 23 16 36 21l8 56c1 5 5 9 10 9h86c5 0 9 -4 10 -9l8 -56c13 -5 25 -12 36 -21l53 21c5 2 10 1 13 -4l43 -74c3 -5 2 -11 -2 -14
+l-45 -35c1 -7 1 -14 1 -21s0 -14 -1 -21z" />
+    <glyph glyph-name="settings_applications" unicode="settings_applications" 
+d="M368 256c0 5 0 10 -1 15l32 24c3 2 3 7 1 10l-30 52c-2 3 -6 4 -9 3l-37 -15c-8 6 -16 11 -25 15l-6 39c-1 3 -3 6 -7 6h-60c-4 0 -6 -2 -7 -6l-6 -40c-9 -4 -17 -8 -25 -14l-37 15c-3 1 -7 -1 -9 -4l-30 -51c-2 -3 -2 -8 1 -10l32 -24c-1 -5 -1 -10 -1 -15s0 -10 1 -15
+l-32 -24c-3 -2 -3 -7 -1 -10l30 -52c2 -3 6 -4 9 -3l37 15c8 -6 16 -11 25 -15l6 -39c1 -3 3 -6 7 -6h60c4 0 6 2 7 6l6 40c9 4 17 8 25 14l37 -15c3 -1 7 1 9 4l30 51c2 3 2 8 -1 10l-32 24c1 5 1 10 1 15zM405 448c24 0 43 -20 43 -43v-298c0 -23 -19 -43 -43 -43h-298
+c-24 0 -43 20 -43 43v298c0 23 19 43 43 43h298zM256 299c23 0 43 -20 43 -43s-20 -43 -43 -43s-43 20 -43 43s20 43 43 43z" />
+    <glyph glyph-name="settings_backup_restore" unicode="settings_backup_restore" 
+d="M256 448c106 0 192 -86 192 -192s-86 -192 -192 -192c-44 0 -85 15 -117 40l30 30c25 -17 55 -27 87 -27c83 0 149 66 149 149s-66 149 -149 149s-149 -66 -149 -149h64l-86 -85l-85 85h64c0 106 86 192 192 192zM299 256c0 -23 -20 -43 -43 -43s-43 20 -43 43
+s20 43 43 43s43 -20 43 -43z" />
+    <glyph glyph-name="settings_bluetooth" unicode="settings_bluetooth" 
+d="M317 207l-40 40v-80zM277 430v-80l40 40zM378 390l-92 -91l92 -92l-122 -122h-21v162l-98 -98l-30 30l119 120l-119 119l30 30l98 -98v162h21zM320 0v43h43v-43h-43zM149 0v43h43v-43h-43zM235 0v43h42v-43h-42z" />
+    <glyph glyph-name="settings_brightness" unicode="settings_brightness" 
+d="M256 320v-128c35 0 64 29 64 64s-29 64 -64 64zM171 171v53l-32 32l32 32v53h53l32 32l32 -32h53v-53l32 -32l-32 -32v-53h-53l-32 -32l-32 32h-53zM448 106v300h-384v-300h384zM448 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-384c-23 0 -43 20 -43 43v298
+c0 23 20 43 43 43h384z" />
+    <glyph glyph-name="settings_cell" unicode="settings_cell" 
+d="M341 171v256h-170v-256h170zM341 512c23 0 43 -20 43 -43v-341c0 -23 -20 -43 -43 -43h-170c-23 0 -43 20 -43 43v341c0 23 20 43 43 43h170zM320 0v43h43v-43h-43zM235 0v43h42v-43h-42zM149 0v43h43v-43h-43z" />
+    <glyph glyph-name="settings_ethernet" unicode="settings_ethernet" 
+d="M379 395l116 -139l-116 -139l-33 27l93 112l-93 112zM235 235v42h42v-42h-42zM363 277v-42h-43v42h43zM149 235v42h43v-42h-43zM166 368l-93 -112l93 -112l-33 -27l-116 139l116 139z" />
+    <glyph glyph-name="settings_input_antenna" unicode="settings_input_antenna" 
+d="M256 491c129 0 235 -106 235 -235h-43c0 106 -86 192 -192 192s-192 -86 -192 -192h-43c0 129 106 235 235 235zM277 207v-70l73 -73l-30 -30l-64 64l-64 -64l-30 30l73 73v70c-19 8 -32 27 -32 49c0 29 24 53 53 53s53 -24 53 -53c0 -22 -13 -41 -32 -49zM256 405
+c83 0 149 -66 149 -149h-42c0 59 -48 107 -107 107s-107 -48 -107 -107h-42c0 83 66 149 149 149z" />
+    <glyph glyph-name="settings_input_component" unicode="settings_input_component" 
+d="M363 171v42h128v-42c0 -28 -18 -51 -43 -60v-90h-43v90c-25 9 -42 32 -42 60zM277 469v-85h43v-128h-128v128h43v85c0 12 9 22 21 22s21 -10 21 -22zM448 384h43v-128h-128v128h42v85c0 12 10 22 22 22s21 -10 21 -22v-85zM21 171v42h128v-42c0 -28 -17 -51 -42 -60v-90
+h-43v90c-25 9 -43 32 -43 60zM192 171v42h128v-42c0 -28 -18 -51 -43 -60v-90h-42v90c-25 9 -43 32 -43 60zM107 469v-85h42v-128h-128v128h43v85c0 12 9 22 21 22s22 -10 22 -22z" />
+    <glyph glyph-name="settings_input_composite" unicode="settings_input_composite" 
+d="M363 171v42h128v-42c0 -28 -18 -51 -43 -60v-90h-43v90c-25 9 -42 32 -42 60zM277 469v-85h43v-128h-128v128h43v85c0 12 9 22 21 22s21 -10 21 -22zM448 384h43v-128h-128v128h42v85c0 12 10 22 22 22s21 -10 21 -22v-85zM21 171v42h128v-42c0 -28 -17 -51 -42 -60v-90
+h-43v90c-25 9 -43 32 -43 60zM192 171v42h128v-42c0 -28 -18 -51 -43 -60v-90h-42v90c-25 9 -43 32 -43 60zM107 469v-85h42v-128h-128v128h43v85c0 12 9 22 21 22s22 -10 22 -22z" />
+    <glyph glyph-name="settings_input_hdmi" unicode="settings_input_hdmi" 
+d="M171 427v-64h42v42h22v-42h42v42h22v-42h42v64h-170zM384 363h21v-128l-64 -128v-64h-170v64l-64 128v128h21v64c0 23 20 42 43 42h170c23 0 43 -19 43 -42v-64z" />
+    <glyph glyph-name="settings_input_svideo" unicode="settings_input_svideo" 
+d="M331 192c18 0 32 -14 32 -32s-14 -32 -32 -32s-32 14 -32 32s14 32 32 32zM373 299c18 0 32 -14 32 -32s-14 -32 -32 -32s-32 14 -32 32s14 32 32 32zM256 64c106 0 192 86 192 192s-86 192 -192 192s-192 -86 -192 -192s86 -192 192 -192zM256 491
+c129 0 235 -106 235 -235s-106 -235 -235 -235s-235 106 -235 235s106 235 235 235zM181 192c18 0 32 -14 32 -32s-14 -32 -32 -32s-32 14 -32 32s14 32 32 32zM320 373c0 -18 -14 -32 -32 -32h-64c-18 0 -32 14 -32 32s14 32 32 32h64c18 0 32 -14 32 -32zM171 267
+c0 -18 -14 -32 -32 -32s-32 14 -32 32s14 32 32 32s32 -14 32 -32z" />
+    <glyph glyph-name="settings_overscan" unicode="settings_overscan" 
+d="M448 106v300h-384v-300h384zM448 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-384c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h384zM299 171l-43 -54l-43 54h86zM128 299v-86l-53 43zM384 299l53 -43l-53 -43v86zM256 395l43 -54h-86z" />
+    <glyph glyph-name="settings_phone" unicode="settings_phone" 
+d="M405 320h43v-43h-43v43zM427 181c12 0 21 -9 21 -21v-75c0 -12 -9 -21 -21 -21c-200 0 -363 163 -363 363c0 12 9 21 21 21h75c12 0 21 -9 21 -21c0 -27 4 -52 12 -76c2 -7 1 -16 -5 -22l-47 -47c31 -60 81 -110 141 -141l47 47c6 6 15 7 22 5c24 -8 49 -12 76 -12z
+M363 320v-43h-43v43h43zM277 320v-43h-42v43h42z" />
+    <glyph glyph-name="settings_power" unicode="settings_power" 
+d="M320 0v43h43v-43h-43zM353 417c44 -31 74 -82 74 -140c0 -94 -77 -170 -171 -170s-171 76 -171 170c0 58 30 109 74 140l30 -30c-37 -23 -61 -64 -61 -110c0 -71 57 -128 128 -128s128 57 128 128c0 46 -25 87 -62 109zM277 469v-213h-42v213h42zM235 0v43h42v-43h-42z
+M149 0v43h43v-43h-43z" />
+    <glyph glyph-name="settings_remote" unicode="settings_remote" 
+d="M256 512c65 0 124 -26 166 -69l-30 -30c-35 35 -83 56 -136 56s-101 -21 -136 -56l-30 30c42 42 101 69 166 69zM150 383c27 27 65 44 106 44s79 -17 106 -44l-30 -30c-19 19 -47 31 -76 31s-57 -12 -76 -31zM256 192c23 0 43 20 43 43s-20 42 -43 42s-43 -19 -43 -42
+s20 -43 43 -43zM320 320c12 0 21 -9 21 -21v-256c0 -12 -9 -22 -21 -22h-128c-12 0 -21 10 -21 22v256c0 12 9 21 21 21h128z" />
+    <glyph glyph-name="settings_system_daydream" unicode="settings_system_daydream" 
+d="M448 106v300h-384v-300h384zM448 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-384c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h384zM192 171c-35 0 -64 29 -64 64c0 33 25 60 57 63h4c12 25 37 43 67 43c38 0 69 -28 74 -64h1c29 0 53 -24 53 -53
+s-24 -53 -53 -53h-139z" />
+    <glyph glyph-name="settings_voice" unicode="settings_voice" 
+d="M405 299c0 -73 -58 -134 -128 -144v-70h-42v70c-70 10 -128 71 -128 144h36c0 -64 54 -109 113 -109s113 45 113 109h36zM320 0v43h43v-43h-43zM235 0v43h42v-43h-42zM256 235c-35 0 -64 29 -64 64v128c0 35 29 64 64 64s64 -29 64 -64v-128c0 -35 -29 -64 -64 -64z
+M149 0v43h43v-43h-43z" />
+    <glyph glyph-name="share" unicode="share" 
+d="M384 169c34 0 62 -28 62 -62s-28 -63 -62 -63s-62 29 -62 63c0 5 0 10 1 14l-151 88c-12 -11 -27 -17 -44 -17c-35 0 -64 29 -64 64s29 64 64 64c17 0 32 -6 44 -17l150 87c-1 5 -2 10 -2 15c0 35 29 64 64 64s64 -29 64 -64s-29 -64 -64 -64c-17 0 -32 7 -44 18
+l-150 -88c1 -5 2 -10 2 -15s-1 -10 -2 -15l152 -88c11 10 26 16 42 16z" />
+    <glyph glyph-name="shop" unicode="shop" 
+d="M192 128l160 107l-160 85v-192zM213 427v-43h86v43h-86zM341 384h128v-277c0 -24 -18 -43 -42 -43h-342c-24 0 -42 19 -42 43v277h128v43c0 24 18 42 42 42h86c24 0 42 -18 42 -42v-43z" />
+    <glyph glyph-name="shop_two" unicode="shop_two" 
+d="M256 192l117 85l-117 64v-149zM256 448v-43h85v43h-85zM384 405h107v-234c0 -24 -19 -43 -43 -43h-299c-24 0 -42 19 -42 43v234h106v43c0 24 19 43 43 43h85c24 0 43 -19 43 -43v-43zM64 320v-235h341c0 -24 -18 -42 -42 -42h-299c-24 0 -43 18 -43 42v235h43z" />
+    <glyph glyph-name="shopping_basket" unicode="shopping_basket" 
+d="M256 149c23 0 43 20 43 43s-20 43 -43 43s-43 -20 -43 -43s20 -43 43 -43zM192 320h128l-64 94zM367 320h102c12 0 22 -9 22 -21c-17.3126 -69.0207 -36.8524 -135.814 -55 -204c-5 -18 -21 -31 -41 -31h-278c-20 0 -36 13 -41 31l-54 198c-1 2 -1 4 -1 6
+c0 12 10 21 22 21h102l93 140c4 6 11 9 18 9s14 -3 18 -9z" />
+    <glyph glyph-name="shopping_cart" unicode="shopping_cart" 
+d="M363 128c23 0 42 -20 42 -43s-19 -42 -42 -42s-43 19 -43 42s20 43 43 43zM21 469h70l20 -42h316c12 0 21 -10 21 -22c0 -4 -1 -7 -3 -10l-76 -138c-7 -13 -21 -22 -37 -22h-159l-19 -35l-1 -3c0 -3 2 -5 5 -5h247v-43h-256c-23 0 -42 20 -42 43c0 7 2 14 5 20l29 53
+l-77 162h-43v42zM149 128c23 0 43 -20 43 -43s-20 -42 -43 -42s-42 19 -42 42s19 43 42 43z" />
+    <glyph glyph-name="short_text" unicode="short_text" 
+d="M85 235h214v-43h-214v43zM85 320h342v-43h-342v43z" />
+    <glyph glyph-name="show_chart" unicode="show_chart" 
+d="M75 118l-32 32l160 160l85 -86l151 170l30 -30l-181 -204l-85 86z" />
+    <glyph glyph-name="shuffle" unicode="shuffle" 
+d="M316 226l67 -67l44 44v-118h-118l44 44l-67 67zM309 427h118v-118l-44 44l-268 -268l-30 30l268 268zM226 316l-30 -30l-111 111l30 30z" />
+    <glyph glyph-name="signal_cellular_4_bar" unicode="signal_cellular_&#x34;_bar" 
+d="M43 43l426 426v-426h-426z" />
+    <glyph glyph-name="signal_cellular_connected_no_internet_4_bar" unicode="signal_cellular_connected_no_internet_&#x34;_bar" 
+d="M43 43l426 426v-128h-85v-298h-341zM427 43v42h42v-42h-42zM427 128v171h42v-171h-42z" />
+    <glyph glyph-name="signal_cellular_no_sim" unicode="signal_cellular_no_sim" 
+d="M78 429l373 -372l-28 -28l-40 41c-6 -3 -13 -6 -20 -6h-214c-23 0 -42 20 -42 43v239l-56 56zM405 405v-249l-242 242l50 50h150c23 0 42 -20 42 -43z" />
+    <glyph glyph-name="signal_cellular_null" unicode="signal_cellular_null" 
+d="M469 469v-426h-426zM427 366l-281 -281h281v281z" />
+    <glyph glyph-name="signal_cellular_off" unicode="signal_cellular_off" 
+d="M102 416l367 -368l-27 -27l-42 43h-379l189 189l-135 136zM448 491v-367l-183 183z" />
+    <glyph glyph-name="signal_wifi_4_bar" unicode="signal_wifi_&#x34;_bar" 
+d="M256 54l-248 309c10 7 105 85 248 85s238 -78 248 -85z" />
+    <glyph glyph-name="signal_wifi_4_bar_lock" unicode="signal_wifi_&#x34;_bar_lock" 
+d="M331 203v-56l-75 -94l-247 310c9 6 104 85 247 85s238 -79 247 -85l-44 -56c-6 2 -13 2 -22 2c-60 0 -106 -46 -106 -106zM469 171v32c0 17 -15 32 -32 32s-32 -15 -32 -32v-32h64zM491 171c11 0 21 -11 21 -22v-85c0 -11 -10 -21 -21 -21h-107c-11 0 -21 10 -21 21v85
+c0 11 10 22 21 22v32c0 30 23 53 53 53s54 -23 54 -53v-32z" />
+    <glyph glyph-name="signal_wifi_off" unicode="signal_wifi_off" 
+d="M70 481c122.302 -122.698 245.474 -244.526 367 -368l-27 -27l-71 71l-83 -103l-248 309c5 4 32 26 78 47l-43 44zM504 363l-116 -145l-221 220c27 6 57 10 89 10c143 0 238 -78 248 -85z" />
+    <glyph glyph-name="sim_card" unicode="sim_card" 
+d="M363 192v85h-43v-85h43zM277 235v42h-42v-42h42zM277 107v85h-42v-85h42zM192 192v85h-43v-85h43zM363 107v42h-43v-42h43zM192 107v42h-43v-42h43zM426 427l1 -342c0 -23 -20 -42 -43 -42h-256c-23 0 -43 19 -43 42v256l128 128h171c23 0 42 -19 42 -42z" />
+    <glyph glyph-name="sim_card_alert" unicode="sim_card_alert" 
+d="M277 235v106h-42v-106h42zM277 149v43h-42v-43h42zM384 469c23 0 43 -19 43 -42v-342c0 -23 -20 -42 -43 -42h-256c-23 0 -43 19 -43 42l1 256l127 128h171z" />
+    <glyph glyph-name="skip_next" unicode="skip_next" 
+d="M341 384h43v-256h-43v256zM128 128v256l181 -128z" />
+    <glyph glyph-name="skip_previous" unicode="skip_previous" 
+d="M203 256l181 128v-256zM128 384h43v-256h-43v256z" />
+    <glyph glyph-name="slideshow" unicode="slideshow" 
+d="M405 107v298h-298v-298h298zM405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h298zM213 341l107 -85l-107 -85v170z" />
+    <glyph glyph-name="slow_motion_video" unicode="slow_motion_video" 
+d="M469 256c0 -110 -84 -201 -191 -212v43c84 11 149 83 149 169s-65 158 -149 169v43c107 -11 191 -102 191 -212zM121 91l30 30c24 -18 53 -30 84 -34v-43c-43 4 -82 21 -114 47zM87 235c4 -31 16 -60 34 -83l-30 -31c-26 32 -43 71 -47 114h43zM121 361
+c-18 -24 -30 -53 -34 -84h-43c4 43 21 82 47 114zM235 425c-31 -4 -60 -16 -84 -34l-30 30c32 26 71 43 114 47v-43zM278 303l63 -47c-42.8195 -31.8471 -85.479 -63.8543 -128 -96z" />
+    <glyph glyph-name="smartphone" unicode="smartphone" 
+d="M363 107v298h-214v-298h214zM363 490c23 0 42 -19 42 -42v-384c0 -23 -19 -43 -42 -43h-214c-23 0 -42 20 -42 43v384c0 23 19 43 42 43z" />
+    <glyph glyph-name="smoke_free" unicode="smoke_free" 
+d="M363 172l-63 63h63v-63zM309 326c-39 0 -71 33 -71 72s32 71 71 71v-32c-22 0 -39 -15 -39 -37s17 -43 39 -43h33c40 0 74 -29 74 -67v-34h-32v27c0 28 -20 43 -42 43h-33zM402 408c40 -19 67 -60 67 -108v-44h-32v44c0 48 -37 87 -85 87v32c22 0 39 18 39 40h32
+c0 -20 -8 -38 -21 -51zM384 235h32v-64h-32v64zM437 235h32v-64h-32v64zM43 384l26 27l363 -363l-27 -27l-149 150h-213v64h149z" />
+    <glyph glyph-name="smoking_rooms" unicode="smoking_rooms" 
+d="M342 294c40 0 74 -28 74 -67v-35h-32v28c0 28 -20 43 -42 43h-33c-39 0 -71 33 -71 72s32 71 71 71v-32c-22 0 -39 -15 -39 -37s17 -43 39 -43h33zM402 347c40 -19 67 -60 67 -107v-48h-32v48c0 48 -37 86 -85 86v32c22 0 39 18 39 40s-17 39 -39 39v32
+c39 0 71 -32 71 -71c0 -20 -8 -38 -21 -51zM384 171h32v-64h-32v64zM437 171h32v-64h-32v64zM43 171h320v-64h-320v64z" />
+    <glyph glyph-name="sms" unicode="sms" 
+d="M363 277v43h-43v-43h43zM277 277v43h-42v-43h42zM192 277v43h-43v-43h43zM427 469c23 0 42 -19 42 -42v-256c0 -23 -19 -43 -42 -43h-299l-85 -85v384c0 23 19 42 42 42h342z" />
+    <glyph glyph-name="sms_failed" unicode="sms_failed" 
+d="M277 299v85h-42v-85h42zM277 213v43h-42v-43h42zM427 469c23 0 42 -19 42 -42v-256c0 -23 -19 -43 -42 -43h-299l-85 -85v384c0 23 19 42 42 42h342z" />
+    <glyph glyph-name="snooze" unicode="snooze" 
+d="M192 277v43h128v-38l-77 -90h77v-43h-128v39l77 89h-77zM256 85c83 0 149 67 149 150s-66 149 -149 149s-149 -66 -149 -149s66 -150 149 -150zM256 427c106 0 192 -86 192 -192s-86 -192 -192 -192s-192 86 -192 192s86 192 192 192zM469 390l-27 -33l-98 83l27 32z
+M168 440l-98 -82l-27 32l98 82z" />
+    <glyph glyph-name="sort" unicode="sort" 
+d="M64 235v42h256v-42h-256zM64 384h384v-43h-384v43zM64 128v43h128v-43h-128z" />
+    <glyph glyph-name="sort_by_alpha" unicode="sort_by_alpha" 
+d="M336 168h130v-34h-182v27l126 183h-125v34h177v-27zM106 221h83l-42 111zM130 378h35l96 -244h-39l-20 52h-109l-20 -52h-39zM219 99h99l-50 -50zM319 413h-101l50 50z" />
+    <glyph glyph-name="spa" unicode="spa" 
+d="M330 307c-27 -15 -52 -34 -74 -57c-22 23 -47 42 -74 57c5 59 29 116 75 162c45 -45 69 -102 73 -162zM43 299c95.1702 0 171.746 -54.2229 213 -117c41.2966 62.8426 117.856 117 213 117c0 -113 -71 -209 -171 -245c-14 -5 -27 -8 -42 -11c-15 2 -29 6 -42 11
+c-100 36 -171 132 -171 245z" />
+    <glyph glyph-name="space_bar" unicode="space_bar" 
+d="M384 320h43v-128h-342v128h43v-85h256v85z" />
+    <glyph glyph-name="speaker" unicode="speaker" 
+d="M256 256c35 0 64 -29 64 -64s-29 -64 -64 -64s-64 29 -64 64s29 64 64 64zM256 85c59 0 107 48 107 107s-48 107 -107 107s-107 -48 -107 -107s48 -107 107 -107zM256 427c-24 0 -43 -20 -43 -43s19 -43 43 -43c23 0 43 20 43 43s-20 43 -43 43zM363 469
+c23 0 42 -19 42 -42v-342c0 -23 -19 -42 -42 -42h-214c-23 0 -42 19 -42 42v342c0 23 19 42 42 42h214z" />
+    <glyph glyph-name="speaker_group" unicode="speaker_group" 
+d="M128 405v-341h213v-43h-213c-24 0 -43 20 -43 43v341h43zM245 245c0 29 25 54 54 54s53 -25 53 -54s-24 -53 -53 -53s-54 24 -54 53zM299 160c47 0 85 38 85 85s-38 86 -85 86s-86 -39 -86 -86s39 -85 86 -85zM299 448c-23 0 -43 -19 -43 -43s20 -42 43 -42s42 18 42 42
+s-19 43 -42 43zM388 491c21 0 39 -18 39 -39v-307c0 -21 -18 -38 -39 -38h-179c-21 0 -38 17 -38 38v307c0 21 17 39 38 39h179z" />
+    <glyph glyph-name="speaker_notes" unicode="speaker_notes" 
+d="M384 341v43h-171v-43h171zM384 277v43h-171v-43h171zM320 213v43h-107v-43h107zM171 341v43h-43v-43h43zM171 277v43h-43v-43h43zM171 213v43h-43v-43h43zM427 469c23 0 42 -19 42 -42v-256c0 -23 -19 -43 -42 -43h-299l-85 -85v384c0 23 19 42 42 42h342z" />
+    <glyph glyph-name="speaker_notes_off" unicode="speaker_notes_off" 
+d="M427 469c23 0 42 -19 42 -42v-256c0 -23 -18 -42 -41 -43l-149 149h105v43h-148l-21 21h169v43h-171v-41l-126 126h340zM128 277h43l-43 43v-43zM171 213v43h-43v-43h43zM27 475l442 -442l-27 -27l-122 122h-192l-85 -85v362l-43 43z" />
+    <glyph glyph-name="speaker_phone" unicode="speaker_phone" 
+d="M320 85v171h-128v-171h128zM317 298c13 0 24 -11 24 -24v-207c0 -13 -11 -24 -24 -24h-122c-13 0 -24 11 -24 24v207c0 13 11 25 24 25zM256 491c64 0 123 -27 165 -69l-30 -30c-35 35 -82 56 -135 56s-100 -21 -135 -56l-30 30c42 42 101 69 165 69zM149 361
+c27 27 65 44 107 44s80 -17 107 -44l-31 -30c-19 19 -46 31 -76 31s-57 -12 -76 -31z" />
+    <glyph glyph-name="spellcheck" unicode="spellcheck" 
+d="M461 265l30 -30l-203 -203l-108 109l30 30l78 -79zM137 277h88l-44 118zM266 171l-25 64h-120l-24 -64h-45l109 277h40l109 -277h-44z" />
+    <glyph glyph-name="star" unicode="star" 
+d="M256 144l-132 -80l35 150l-116 101l153 13l60 141l60 -141l153 -13l-116 -101l35 -150z" />
+    <glyph glyph-name="star_border" unicode="star_border" 
+d="M256 183l80 -48l-21 91l71 62l-94 8l-36 86l-36 -86l-94 -8l71 -62l-21 -91zM469 315l-116 -101l35 -150l-132 80l-132 -80l35 150l-116 101l153 13l60 141l60 -141z" />
+    <glyph glyph-name="star_half" unicode="star_half" 
+d="M256 183l80 -48l-21 91l71 62l-94 8l-36 86v-199zM469 315l-116 -101l35 -150l-132 80l-132 -80l35 150l-116 101l153 13l60 141l60 -141z" />
+    <glyph glyph-name="stars" unicode="stars" 
+d="M346 128l-24 103l80 69l-105 9l-41 96l-41 -97l-105 -8l80 -69l-24 -103l90 54zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="stay_current_landscape" unicode="stay_current_landscape" 
+d="M405 363h-298v-214h298v214zM22 363c0 23 19 42 42 42h384c23 0 43 -19 43 -42v-214c0 -23 -20 -42 -43 -42h-384c-23 0 -43 19 -43 42z" />
+    <glyph glyph-name="stay_current_portrait" unicode="stay_current_portrait" 
+d="M363 107v298h-214v-298h214zM363 490c23 0 42 -19 42 -42v-384c0 -23 -19 -43 -42 -43h-214c-23 0 -42 20 -42 43v384c0 23 19 43 42 43z" />
+    <glyph glyph-name="stay_primary_landscape" unicode="stay_primary_landscape" 
+d="M405 363h-298v-214h298v214zM22 363c0 23 19 42 42 42h384c23 0 43 -19 43 -42v-214c0 -23 -20 -42 -43 -42h-384c-23 0 -43 19 -43 42z" />
+    <glyph glyph-name="stay_primary_portrait" unicode="stay_primary_portrait" 
+d="M363 107v298h-214v-298h214zM363 490c23 0 42 -19 42 -42v-384c0 -23 -19 -43 -42 -43h-214c-23 0 -42 20 -42 43v384c0 23 19 43 42 43z" />
+    <glyph glyph-name="stop" unicode="stop" 
+d="M128 384h256v-256h-256v256z" />
+    <glyph glyph-name="stop_screen_share" unicode="stop_screen_share" 
+d="M149 192c20 28 45 44 78 52l-34 34c-25 -24 -37 -54 -44 -86zM51 475l421 -421l-27 -27l-58 58h-387v43h85c-24 0 -42 19 -42 42v214c0 13 5 23 14 31l-33 33zM469 170c0 -16 -9 -30 -22 -37l-118 118l34 32l-86 79v-45c-4 -1 -7 -1 -11 -2l-112 111h273
+c23 0 42 -18 42 -42v-214zM453 128h59v-43h-17z" />
+    <glyph glyph-name="storage" unicode="storage" 
+d="M85 277v-42h43v42h-43zM43 213v86h426v-86h-426zM128 363v42h-43v-42h43zM43 427h426v-86h-426v86zM85 149v-42h43v42h-43zM43 85v86h426v-86h-426z" />
+    <glyph glyph-name="store" unicode="store" 
+d="M256 128v85h-128v-85h128zM448 213h-21v-128h-43v128h-85v-128h-214v128h-21v43l21 107h342l21 -107v-43zM427 427v-43h-342v43h342z" />
+    <glyph glyph-name="store_mall_directory" unicode="store_mall_directory" 
+d="M256 128v85h-128v-85h128zM448 213h-21v-128h-43v128h-85v-128h-214v128h-21v43l21 107h342l21 -107v-43zM427 427v-43h-342v43h342z" />
+    <glyph glyph-name="straighten" unicode="straighten" 
+d="M448 171v170h-43v-85h-42v85h-43v-85h-43v85h-42v-85h-43v85h-43v-85h-42v85h-43v-170h384zM448 384c23 0 43 -20 43 -43v-170c0 -23 -20 -43 -43 -43h-384c-23 0 -43 20 -43 43v170c0 23 20 43 43 43h384z" />
+    <glyph glyph-name="streetview" unicode="streetview" 
+d="M245 384c0 -38 16 -73 41 -98l-209 -209c-8 8 -13 18 -13 30v298c0 23 20 43 43 43h154c-10 -19 -16 -41 -16 -64zM277 384c0 59 48 107 107 107s107 -48 107 -107s-48 -107 -107 -107s-107 48 -107 107zM268 206c32 24 73 39 116 39c22 0 44 -4 64 -11v-127
+c0 -23 -20 -43 -43 -43h-149v117c0 10 5 19 12 25z" />
+    <glyph glyph-name="strikethrough_s" unicode="strikethrough_s" 
+d="M200 232c0 -38.1216 24.1545 -54 62 -54c26.1564 0 49 10.6702 49 36c0 23.9161 -12.6848 29.3424 -30 38c-3 1 -8 3 -12 4h-205v43h384v-43h-83c1 -2 3 -5 4 -7c4 -10 7 -22 7 -35c0 -46.2545 -30.1882 -69.4824 -67 -80c-14 -4 -29 -6 -46 -6c-10 0 -21 1 -31 3
+c-21.9156 4.38312 -40.6035 10.4527 -56 22c-22.8363 17.1272 -39 39.9131 -39 79h63zM311 352c0 30.7771 -19.602 45 -51 45c-22.5729 0 -39.6285 -7.25692 -47 -22c-2 -4 -3 -9 -3 -14c0 -10 6 -19 16 -26c8 -5 16 -10 30 -15h-98c-1 2 -3 3 -4 5c-6 10 -8 22 -8 36
+c0 28.7417 14.9303 49.3443 32 63c20.5411 14.3788 47.8126 24 83 24c35.0657 0 64.1326 -10.0193 83 -27c17.5603 -15.8042 31 -37.9468 31 -69h-64z" />
+    <glyph glyph-name="style" unicode="style" 
+d="M125 91v135l74 -178h-31c-23 0 -43 20 -43 43zM168 325c12 0 21 10 21 22s-9 21 -21 21s-21 -9 -21 -21s9 -22 21 -22zM470 172c9 -22 -1 -47 -23 -56l-157 -65c-5 -2 -11 -3 -17 -3c-17 0 -32 10 -39 26l-106 256c-2 6 -3 11 -3 17c0 16 10 31 26 38l158 65
+c6 2 11 3 17 3c16 0 31 -10 38 -26zM54 93c-22 9 -32 33 -23 55l52 125v-192z" />
+    <glyph glyph-name="subdirectory_arrow_left" unicode="subdirectory_arrow_left" 
+d="M235 320l30 -30l-77 -77h196v214h43v-256h-239l77 -77l-30 -30l-128 128z" />
+    <glyph glyph-name="subdirectory_arrow_right" unicode="subdirectory_arrow_right" 
+d="M405 192l-128 -128l-30 30l77 77h-239v256h43v-214h196l-77 77l30 30z" />
+    <glyph glyph-name="subject" unicode="subject" 
+d="M85 405h342v-42h-342v42zM85 192v43h342v-43h-342zM427 320v-43h-342v43h342zM299 149v-42h-214v42h214z" />
+    <glyph glyph-name="subscriptions" unicode="subscriptions" 
+d="M341 171l-128 69v-139zM469 256v-171c0 -23 -19 -42 -42 -42h-342c-23 0 -42 19 -42 42v171c0 23 19 43 42 43h342c23 0 42 -20 42 -43zM384 469v-42h-256v42h256zM427 341h-342v43h342v-43z" />
+    <glyph glyph-name="subtitles" unicode="subtitles" 
+d="M427 213v43h-214v-43h214zM427 128v43h-86v-43h86zM299 128v43h-214v-43h214zM85 256v-43h86v43h-86zM427 427c23 0 42 -20 42 -43v-256c0 -23 -19 -43 -42 -43h-342c-23 0 -42 20 -42 43v256c0 23 19 43 42 43h342z" />
+    <glyph glyph-name="subway" unicode="subway" 
+d="M384 173v147c0 56 -57 64 -128 64c-64 0 -128 -8 -128 -64v-147c0 -31 25 -56 56 -56l-24 -24v-8h36l32 32h60l32 -32h32v8l-24 24c31 0 56 25 56 56zM380 452c57 -22 89 -69 89 -129v-280h-426v280c0 60 32 107 89 129c38 15 84 17 124 17s86 -2 124 -17zM150 320h213
+v-107h-213v107zM160 171c0 12 9 21 21 21s22 -9 22 -21s-10 -22 -22 -22s-21 10 -21 22zM309 171c0 12 10 21 22 21s21 -9 21 -21s-9 -22 -21 -22s-22 10 -22 22z" />
+    <glyph glyph-name="supervisor_account" unicode="supervisor_account" 
+d="M192 235c14 0 32 -2 51 -6c-44 -24 -51 -56 -51 -74v-48h-149v53c0 50 99 75 149 75zM352 213c39 0 117 -19 117 -58v-48h-234v48c0 39 78 58 117 58zM192 277c-35 0 -64 29 -64 64s29 64 64 64s64 -29 64 -64s-29 -64 -64 -64zM352 256c-29 0 -53 24 -53 53s24 54 53 54
+s53 -25 53 -54s-24 -53 -53 -53z" />
+    <glyph glyph-name="surround_sound" unicode="surround_sound" 
+d="M256 299c23 0 43 -20 43 -43s-20 -43 -43 -43s-43 20 -43 43s20 43 43 43zM377 135c33 33 50 77 50 121s-17 88 -50 121l-31 -31c25 -25 38 -57 38 -90s-12 -66 -37 -91zM256 171c47 0 85 38 85 85s-38 85 -85 85s-85 -38 -85 -85s38 -85 85 -85zM166 166
+c-25 25 -38 57 -38 90s12 66 37 91l-30 30c-33 -33 -50 -77 -50 -121s17 -88 50 -121zM427 427c23 0 42 -20 42 -43v-256c0 -23 -19 -43 -42 -43h-342c-23 0 -42 20 -42 43v256c0 23 19 43 42 43h342z" />
+    <glyph glyph-name="swap_calls" unicode="swap_calls" 
+d="M384 427l85 -86h-64v-149c0 -47 -38 -85 -85 -85s-85 38 -85 85v149c0 23 -20 43 -43 43s-43 -20 -43 -43v-149h64l-85 -85l-85 85h64v149c0 47 38 86 85 86s85 -39 85 -86v-149c0 -23 20 -43 43 -43s43 20 43 43v149h-64z" />
+    <glyph glyph-name="swap_horiz" unicode="swap_horiz" 
+d="M448 320l-85 -85v64h-150v42h150v64zM149 277v-64h150v-42h-150v-64l-85 85z" />
+    <glyph glyph-name="swap_vert" unicode="swap_vert" 
+d="M192 448l85 -85h-64v-150h-42v150h-64zM341 149h64l-85 -85l-85 85h64v150h42v-150z" />
+    <glyph glyph-name="swap_vertical_circle" unicode="swap_vertical_circle" 
+d="M373 192h-53v85h-43v-85h-53l75 -75zM139 320h53v-85h43v85h53l-75 75zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="switch_camera" unicode="switch_camera" 
+d="M320 181l75 75l-75 75v-54h-128v54l-75 -75l75 -75v54h128v-54zM427 427c23 0 42 -20 42 -43v-256c0 -23 -19 -43 -42 -43h-342c-23 0 -42 20 -42 43v256c0 23 19 43 42 43h68l39 42h128l39 -42h68z" />
+    <glyph glyph-name="switch_video" unicode="switch_video" 
+d="M277 181l75 75l-75 75v-54h-128v54l-74 -75l74 -75v54h128v-54zM384 309l85 86v-278l-85 86v-75c0 -12 -9 -21 -21 -21h-299c-12 0 -21 9 -21 21v256c0 12 9 21 21 21h299c12 0 21 -9 21 -21v-75z" />
+    <glyph glyph-name="sync" unicode="sync" 
+d="M256 128v64l85 -85l-85 -86v64c-94 0 -171 77 -171 171c0 33 10 65 27 91l31 -31c-10 -18 -15 -38 -15 -60c0 -71 57 -128 128 -128zM256 427c94 0 171 -77 171 -171c0 -33 -10 -65 -27 -91l-31 31c10 18 15 38 15 60c0 71 -57 128 -128 128v-64l-85 85l85 86v-64z" />
+    <glyph glyph-name="sync_disabled" unicode="sync_disabled" 
+d="M427 427l-51 -51c31 -31 51 -73 51 -120c0 -33 -10 -64 -26 -90l-32 31c9 18 15 38 15 59c0 35 -15 67 -38 90l-47 -47v128h128zM61 397l27 27l335 -336l-27 -27l-50 50c-15 -9 -31 -16 -48 -20v44c6 2 12 5 17 8l-172 172c-9 -18 -15 -38 -15 -59c0 -35 15 -67 38 -90
+l47 47v-128h-128l51 51c-31 31 -51 73 -51 120c0 33 10 64 26 90zM213 377c-6 -2 -11 -5 -16 -8l-31 32c15 9 30 16 47 20v-44z" />
+    <glyph glyph-name="sync_problem" unicode="sync_problem" 
+d="M235 235v128h42v-128h-42zM448 427l-50 -51c31 -31 50 -73 50 -120c0 -80 -54 -146 -128 -165v44c50 17 85 65 85 121c0 35 -14 67 -37 90l-48 -47v128h128zM235 149v43h42v-43h-42zM64 256c0 80 54 146 128 165v-44c-50 -17 -85 -65 -85 -121c0 -35 14 -67 37 -90l48 47
+v-128h-128l50 51c-31 31 -50 73 -50 120z" />
+    <glyph glyph-name="system_update" unicode="system_update" 
+d="M341 235l-85 -86l-85 86h64v106h42v-106h64zM363 107v298h-214v-298h214zM363 490c23 0 42 -19 42 -42v-384c0 -23 -19 -43 -42 -43h-214c-23 0 -42 20 -42 43v384c0 23 19 43 42 43z" />
+    <glyph glyph-name="system_update_alt" unicode="system_update_alt" 
+d="M448 437c23 0 43 -19 43 -42v-299c0 -23 -20 -43 -43 -43h-384c-23 0 -43 20 -43 43v299c0 23 20 42 43 42h128v-42h-128v-299h384v299h-128v42h128zM256 160l-85 85h64v192h42v-192h64z" />
+    <glyph glyph-name="tab" unicode="tab" 
+d="M448 107v213h-171v85h-213v-298h384zM448 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-384c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h384z" />
+    <glyph glyph-name="tab_unselected" unicode="tab_unselected" 
+d="M363 64v43h42v-43h-42zM277 64v43h43v-43h-43zM448 235v42h43v-42h-43zM448 64v43h43c0 -23 -20 -43 -43 -43zM107 405v43h42v-43h-42zM107 64v43h42v-43h-42zM192 405v43h43v-43h-43zM448 149v43h43v-43h-43zM448 448c23 0 43 -20 43 -43v-85h-214v128h171zM64 64
+c-23 0 -43 20 -43 43h43v-43zM21 149v43h43v-43h-43zM192 64v43h43v-43h-43zM21 405c0 23 20 43 43 43v-43h-43zM21 235v42h43v-42h-43zM21 320v43h43v-43h-43z" />
+    <glyph glyph-name="tablet" unicode="tablet" 
+d="M405 128v256h-298v-256h298zM448 427c23 0 43 -20 43 -43l-1 -256c0 -23 -19 -43 -42 -43h-384c-23 0 -43 20 -43 43v256c0 23 20 43 43 43h384z" />
+    <glyph glyph-name="tablet_android" unicode="tablet_android" 
+d="M411 107v341h-310v-341h310zM299 43v21h-86v-21h86zM384 512c35 0 64 -29 64 -64v-384c0 -35 -29 -64 -64 -64h-256c-35 0 -64 29 -64 64v384c0 35 29 64 64 64h256z" />
+    <glyph glyph-name="tablet_mac" unicode="tablet_mac" 
+d="M405 107v341h-320v-341h320zM245 21c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM395 512c29 0 53 -24 53 -53v-406c0 -29 -24 -53 -53 -53h-299c-29 0 -53 24 -53 53v406c0 29 24 53 53 53h299z" />
+    <glyph glyph-name="tag_faces" unicode="tag_faces" 
+d="M256 139c-50 0 -92 30 -109 74h218c-17 -44 -59 -74 -109 -74zM181 277c-18 0 -32 14 -32 32s14 32 32 32s32 -14 32 -32s-14 -32 -32 -32zM331 277c-18 0 -32 14 -32 32s14 32 32 32s32 -14 32 -32s-14 -32 -32 -32zM256 85c94 0 171 77 171 171s-77 171 -171 171
+s-171 -77 -171 -171s77 -171 171 -171zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="tap_and_play" unicode="tap_and_play" 
+d="M363 490c23 0 42 -19 42 -42v-363c0 -23 -19 -42 -42 -42h-45c-2 30 -9 58 -20 85h65v277h-214v-128c-14 6 -27 11 -42 14v157c0 23 19 43 42 43zM43 256c130 0 234 -105 234 -235h-42c0 106 -86 192 -192 192v43zM43 85c35 0 64 -29 64 -64h-64v64zM43 171
+c83 0 149 -67 149 -150h-43c0 59 -47 107 -106 107v43z" />
+    <glyph glyph-name="terrain" unicode="terrain" 
+d="M299 384l192 -256h-470l128 171l96 -128l34 25l-60 81z" />
+    <glyph glyph-name="text_fields" unicode="text_fields" 
+d="M459 320v-64h-64v-149h-64v149h-64v64h192zM53 427h278v-64h-107v-256h-64v256h-107v64z" />
+    <glyph glyph-name="text_format" unicode="text_format" 
+d="M256 384l-40 -107h80zM203 239l-20 -47h-44l101 235h32l101 -235h-44l-20 47h-106zM107 149h298v-42h-298v42z" />
+    <glyph glyph-name="textsms" unicode="textsms" 
+d="M363 277v43h-43v-43h43zM277 277v43h-42v-43h42zM192 277v43h-43v-43h43zM427 469c23 0 42 -19 42 -42v-256c0 -23 -19 -43 -42 -43h-299l-85 -85v384c0 23 19 42 42 42h342z" />
+    <glyph glyph-name="texture" unicode="texture" 
+d="M198 64l250 250v-61l-189 -189h-61zM448 107c0 -22.9739 -20.0355 -43 -43 -43h-42l85 85v-42zM107 448h42l-85 -85v42c0 23 20 43 43 43zM253 448h61l-250 -250v61zM416 446c15 -4 27 -15 31 -30l-351 -350c-14.6749 4.19283 -25.8059 15.3206 -30 30z" />
+    <glyph glyph-name="theaters" unicode="theaters" 
+d="M384 320v43h-43v-43h43zM384 235v42h-43v-42h43zM384 149v43h-43v-43h43zM171 320v43h-43v-43h43zM171 235v42h-43v-42h43zM171 149v43h-43v-43h43zM384 448h43v-384h-43v43h-43v-43h-170v43h-43v-43h-43v384h43v-43h43v43h170v-43h43v43z" />
+    <glyph glyph-name="thumb_down" unicode="thumb_down" 
+d="M405 448h86v-256h-86v256zM320 448c23 0 43 -20 43 -43v-213c0 -12 -5 -22 -13 -30l-140 -141l-23 23c-6 6 -9 13 -9 22v7l21 98h-135c-23 0 -43 19 -43 42l1 2h-1v41c0 6 1 11 3 16l65 150c6 15 21 26 39 26h192z" />
+    <glyph glyph-name="thumb_up" unicode="thumb_up" 
+d="M491 299l-1 -2h1v-41c0 -6 -1 -11 -3 -16l-65 -150c-6 -15 -21 -26 -39 -26h-192c-23 0 -43 20 -43 43v213c0 12 5 22 13 30l140 141l23 -23c6 -6 9 -13 9 -22v-7l-21 -98h135c23 0 43 -19 43 -42zM21 64v256h86v-256h-86z" />
+    <glyph glyph-name="thumbs_up_down" unicode="thumbs_up_down" 
+d="M480 299c18 0 32 -14 32 -32v-139c0 -9 -3 -17 -9 -23l-106 -105l-17 17c-4 4 -7 10 -7 17c4.95149 24.3818 10.0081 48.6586 15 73h-111c-12 0 -21 9 -21 21v27c0 4 1 7 2 11l49 113c5 11 16 20 29 20h144zM256 384v-27c0 -4 -1 -7 -2 -11l-49 -113
+c-5 -11 -16 -20 -29 -20h-144c-18 0 -32 14 -32 32v139c0 9 3 17 9 23l106 105l17 -17c4 -4 7 -10 7 -17c-4.95149 -24.3818 -10.0081 -48.6586 -15 -73h111c12 0 21 -9 21 -21z" />
+    <glyph glyph-name="time_to_leave" unicode="time_to_leave" 
+d="M107 299h298l-32 96h-234zM373 192c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM139 192c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM404 405l44 -128v-170c0 -12 -9 -22 -21 -22h-22c-12 0 -21 10 -21 22v21h-256v-21
+c0 -12 -9 -22 -21 -22h-22c-12 0 -21 10 -21 22v170l44 128c4 13 17 22 31 22h234c14 0 27 -9 31 -22z" />
+    <glyph glyph-name="timelapse" unicode="timelapse" 
+d="M256 85c94 0 171 77 171 171s-77 171 -171 171s-171 -77 -171 -171s77 -171 171 -171zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213zM346 346c50 -50 51 -130 1 -180s-131 -50 -181 0l90 90v128c33 0 65 -13 90 -38z" />
+    <glyph glyph-name="timeline" unicode="timeline" 
+d="M491 341c0 -23 -20 -42 -43 -42c-4 0 -8 0 -11 1l-76 -76c1 -3 2 -7 2 -11c0 -23 -20 -42 -43 -42s-43 19 -43 42c0 4 1 8 2 11l-55 55c-3 -1 -7 -2 -11 -2s-8 1 -11 2l-97 -97c1 -3 2 -7 2 -11c0 -23 -20 -43 -43 -43s-43 20 -43 43s20 42 43 42c4 0 8 0 11 -1l97 97
+c-1 3 -1 7 -1 11c0 23 19 43 42 43s43 -20 43 -43c0 -4 0 -8 -1 -11l54 -54c3 1 7 1 11 1s8 0 11 -1l76 75c-1 3 -2 7 -2 11c0 23 20 43 43 43s43 -20 43 -43z" />
+    <glyph glyph-name="timer" unicode="timer" 
+d="M256 85c83 0 149 67 149 150s-66 149 -149 149s-149 -66 -149 -149s66 -150 149 -150zM406 354c26 -33 42 -74 42 -119c0 -106 -86 -192 -192 -192s-192 86 -192 192s86 192 192 192c45 0 87 -17 120 -43l30 31c11 -9 21 -19 30 -30zM235 213v128h42v-128h-42zM320 491
+v-43h-128v43h128z" />
+    <glyph glyph-name="timer_10" unicode="timer_&#x31;&#x30;" 
+d="M275 230v54c0 23.1679 -2.28229 40.1372 -10 53c-5.32529 10.6506 -15.2041 15 -30 15c-6 0 -13 -1 -18 -3c-19.376 -11.6256 -23 -33.9937 -23 -66v-53c0 -13 0 -24 2 -33s6 -15 9 -21c5.59145 -11.1829 14.1701 -16 30 -16c15.4236 0 24.6161 5.23229 30 16
+c3 6 6 12 8 21s2 20 2 33zM152 277c0 64.1175 20.6864 109 83 109c25.9331 0 47.8484 -8.37646 60 -24c15.2136 -19.5604 22 -48.5586 22 -85v-41c0 -63.9076 -19.6435 -110 -82 -110c-62.0914 0 -83 46.0788 -83 110v41zM436 289c-16.3185 0 -30 -6.67853 -30 -23
+c0 -14.5467 7.81248 -15.525 19 -20c5 -2 12 -4 19 -5c11 -2 19 -5 28 -8s16 -8 22 -12s10 -10 13 -16s5 -12 5 -21c0 -30.9206 -20.0931 -44.031 -44 -52c-9 -3 -19 -4 -30 -4c-35.9519 0 -63.4589 14.4041 -74 39c-3 7 -5 14 -5 22h41c0 -21.2888 17.0766 -30 38 -30
+c18.6968 0 33 5.89308 33 23c0 13.5739 -8.51234 16.8049 -19 21c-12.7341 5.09362 -34.9088 9.09079 -48 14c-8 3 -14 7 -20 11c-11.0546 7.36974 -19 18.5758 -19 36c0 27.5628 20.3776 44.7925 42 52c9 3 18 4 29 4c40.9316 0 75 -19.1355 75 -59h-42
+c0 12.2774 -7.84356 22.4609 -18 25c-4 1 -10 3 -15 3zM0 347l101 37h6v-256h-43v205l-64 -22v36z" />
+    <glyph glyph-name="timer_3" unicode="timer_&#x33;" 
+d="M374 289c-15.9409 0 -31 -7.57315 -31 -23c0 -13.6828 9.30955 -15.7238 20 -20c5 -2 11 -4 18 -5c11 -2 20 -5 29 -8s16 -8 22 -12s10 -10 13 -16s5 -12 5 -21c0 -30.9206 -20.0931 -44.031 -44 -52c-9 -3 -20 -4 -31 -4c-35.3954 0 -62.613 14.7638 -73 39
+c-3 7 -5 14 -5 22h40c0 -20.4241 18.5387 -30 39 -30c18.6968 0 33 5.89308 33 23c0 13.5739 -8.51234 16.8049 -19 21c-5 2 -13 4 -21 6c-18.808 3.76159 -34.9505 10.3003 -48 19c-10.5283 7.01889 -18 19.4522 -18 36c0 27.5628 20.3776 44.7925 42 52c9 3 18 4 29 4
+c40.9316 0 75 -19.1355 75 -59h-42c0 13.019 -8.2055 22.3014 -19 25c-4 1 -9 3 -14 3zM215 259c24.7243 -9.2716 42 -25.484 42 -58c0 -25.5996 -8.85495 -43.6412 -24 -55c-14.0356 -12.0305 -34.4533 -20 -60 -20c-46.8771 0 -84 25.2474 -84 72h43
+c0 -11.5043 4.1483 -22.8612 11 -28c7.35188 -5.51391 17.5339 -10 30 -10c27.0755 0 42 14.0756 42 41c0 28.626 -17.0664 41 -46 41h-26v33h25c20.5078 0 35.1083 8.27068 41 23c2 5 2 10 2 16c0 24.8875 -12.4571 38 -38 38c-18.723 0 -29.7305 -7.46097 -36 -20
+c-2 -4 -3 -9 -3 -15h-42c0 34.3341 22.95 53.98 48 64c10 3 21 5 33 5c48.7123 0 80 -23.4793 80 -72c0 -28.4847 -18.378 -45.189 -38 -55z" />
+    <glyph glyph-name="timer_off" unicode="timer_off" 
+d="M256 85c27 0 53 8 75 21l-204 204c-13 -22 -20 -48 -20 -75c0 -83 66 -150 149 -150zM64 427l379 -379l-27 -27l-54 54c-31 -20 -67 -32 -106 -32c-106 0 -192 86 -192 192c0 39 12 76 32 106l-59 59zM235 311v30h42v-73zM320 491v-43h-128v43h128zM406 415l30 -30
+l-30 -31c26 -33 42 -74 42 -119c0 -39 -12 -76 -32 -106l-31 31c13 22 20 48 20 75c0 83 -66 149 -149 149c-27 0 -52 -7 -74 -20l-32 31c30 20 67 32 106 32c45 0 87 -16 120 -42z" />
+    <glyph glyph-name="title" unicode="title" 
+d="M107 427h298v-64h-117v-256h-64v256h-117v64z" />
+    <glyph glyph-name="toc" unicode="toc" 
+d="M405 235v42h43v-42h-43zM405 363h43v-43h-43v43zM405 149v43h43v-43h-43zM64 149v43h299v-43h-299zM64 235v42h299v-42h-299zM64 320v43h299v-43h-299z" />
+    <glyph glyph-name="today" unicode="today" 
+d="M149 299h107v-107h-107v107zM405 107v234h-298v-234h298zM405 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-298c-24 0 -43 20 -43 43v298c0 23 19 43 43 43h21v43h43v-43h170v43h43v-43h21z" />
+    <glyph glyph-name="toll" unicode="toll" 
+d="M64 256c0 -56 35 -104 85 -121v-44c-74 19 -128 85 -128 165s54 146 128 165v-44c-50 -17 -85 -65 -85 -121zM320 128c71 0 128 57 128 128s-57 128 -128 128s-128 -57 -128 -128s57 -128 128 -128zM320 427c94 0 171 -77 171 -171s-77 -171 -171 -171s-171 77 -171 171
+s77 171 171 171z" />
+    <glyph glyph-name="tonality" unicode="tonality" 
+d="M421 213c2 7 3 15 4 22h-148v-22h144zM389 149c5 7 11 15 15 22h-127v-22h112zM277 87c22 3 43 10 62 20h-62v-20zM277 299v-22h148c-1 7 -2 15 -4 22h-144zM277 363v-22h127c-4 7 -10 15 -15 22h-112zM277 425v-20h62c-19 10 -40 17 -62 20zM235 87v338
+c-84 -10 -150 -82 -150 -169s66 -159 150 -169zM256 469c118 0 213 -95 213 -213s-95 -213 -213 -213s-213 95 -213 213s95 213 213 213z" />
+    <glyph glyph-name="touch_app" unicode="touch_app" 
+d="M402 173c11 -5 19 -16 19 -29v-4l-16 -113c-2 -16 -15 -27 -31 -27h-145c-9 0 -16 3 -22 9l-106 106l17 17c4 4 10 7 17 7c2 0 3 -1 5 -1l73 -15v229c0 18 14 32 32 32s32 -14 32 -32v-128h17c4 0 7 -1 11 -2zM192 272c-26 17 -43 47 -43 80c0 53 43 96 96 96
+s96 -43 96 -96c0 -33 -16 -63 -42 -80v80c0 29 -25 53 -54 53s-53 -24 -53 -53v-80z" />
+    <glyph glyph-name="toys" unicode="toys" 
+d="M256 256c0 -64 -53 -117 -117 -117s-118 53 -118 117h235zM256 256c-64 0 -117 53 -117 117s53 118 117 118v-235zM256 256c64 0 117 -53 117 -117s-53 -118 -117 -118v235zM256 256c0 64 53 117 117 117s118 -53 118 -117h-235z" />
+    <glyph glyph-name="track_changes" unicode="track_changes" 
+d="M407 407c39 -39 62 -92 62 -151c0 -118 -95 -213 -213 -213s-213 95 -213 213s95 213 213 213h21v-176c13 -7 22 -21 22 -37c0 -23 -20 -43 -43 -43s-43 20 -43 43c0 16 9 30 22 37v45c-37 -10 -64 -42 -64 -82c0 -47 38 -85 85 -85s85 38 85 85c0 23 -10 44 -25 60
+l30 30c23 -23 38 -55 38 -90c0 -71 -57 -128 -128 -128s-128 57 -128 128c0 63 46 116 107 126v43c-84 -10 -150 -82 -150 -169c0 -94 77 -171 171 -171s171 77 171 171c0 47 -19 90 -50 121z" />
+    <glyph glyph-name="traffic" unicode="traffic" 
+d="M256 320c24 0 43 20 43 43c0 24 -20 42 -43 42c-24 0 -43 -18 -43 -42c0 -23 19 -43 43 -43zM256 213c24 0 43 20 43 43s-20 43 -43 43c-24 0 -43 -20 -43 -43s19 -43 43 -43zM256 107c24 0 43 19 43 42s-20 43 -43 43c-24 0 -43 -20 -43 -43s19 -42 43 -42zM427 299
+c0 -40 -27 -73 -64 -83v-24h64c0 -40 -27 -72 -64 -82v-25c0 -12 -10 -21 -22 -21h-170c-12 0 -22 9 -22 21v25c-37 10 -64 42 -64 82h64v24c-37 10 -64 43 -64 83h64v24c-37 10 -64 42 -64 82h64v22c0 12 10 21 22 21h170c12 0 22 -9 22 -21v-22h64c0 -40 -27 -72 -64 -82
+v-24h64z" />
+    <glyph glyph-name="train" unicode="train" 
+d="M352 149c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM277 299h107v85h-107v-85zM235 299v85h-107v-85h107zM160 149c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM256 469c94 0 171 -10 171 -85v-203c0 -41 -34 -74 -75 -74
+l32 -32v-11h-43l-42 43h-81l-42 -43h-48v11l32 32c-41 0 -75 33 -75 74v203c0 75 86 85 171 85z" />
+    <glyph glyph-name="tram" unicode="tram" 
+d="M363 213v107h-214v-107h214zM256 117c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM405 151c0 -36 -19 -66 -55 -66h2l32 -32v-10h-43l-42 42h-81l-42 -42h-48v10l34 34c-30 7 -55 33 -55 64v180c0 59 60 72 127 74l17 32h-102v32h214v-32h-70
+l-16 -32c73 -2 128 -14 128 -74v-180z" />
+    <glyph glyph-name="transfer_within_a_station" unicode="transfer_within_a_station" 
+d="M123 322l-59 -301h45l37 171l46 -43v-128h43v161l-44 44l13 64c28 -34 69 -55 116 -55v42c-39 0 -74 22 -93 53l-20 34c-7 13 -21 20 -36 20c-5 0 -11 -1 -16 -3l-112 -46v-100h42v71l38 16zM203 395c-23 0 -43 19 -43 42s20 43 43 43s42 -20 42 -43s-19 -42 -42 -42z
+M416 91v37l53 -53l-53 -54v38h-117v32h117zM352 181h117v-32h-117v-37l-53 53l53 54v-38z" />
+    <glyph glyph-name="transform" unicode="transform" 
+d="M213 341v43h128c23 0 43 -20 43 -43v-128h-43v128h-128zM469 128h-85v-43h43l-64 -64l-64 64h42v43h-170c-23 0 -43 20 -43 43v170h-85v43h85v43h-43l64 64l64 -64h-42v-256h298v-43z" />
+    <glyph glyph-name="translate" unicode="translate" 
+d="M339 149h69l-35 93zM395 299l96 -256h-43l-24 64h-101l-24 -64h-43l96 256h43zM275 191l-17 -44l-66 66l-107 -106l-30 30l109 107c-27 30 -48 62 -64 97h43c13 -25 29 -49 49 -71c31 34 54 73 68 114h-239v43h150v42h42v-42h150v-43h-63c-16 -50 -42 -98 -79 -139l-1 -1
+z" />
+    <glyph glyph-name="trending_down" unicode="trending_down" 
+d="M341 128l49 49l-104 104l-85 -85l-158 158l30 30l128 -128l85 85l134 -134l49 49v-128h-128z" />
+    <glyph glyph-name="trending_flat" unicode="trending_flat" 
+d="M469 256l-85 -85v64h-320v42h320v64z" />
+    <glyph glyph-name="trending_up" unicode="trending_up" 
+d="M341 384h128v-128l-49 49l-134 -134l-85 85l-128 -128l-30 30l158 158l85 -85l104 104z" />
+    <glyph glyph-name="tune" unicode="tune" 
+d="M320 320v128h43v-43h85v-42h-85v-43h-43zM448 235h-213v42h213v-42zM149 320h43v-128h-43v43h-85v42h85v43zM277 64h-42v128h42v-43h171v-42h-171v-43zM64 405h213v-42h-213v42zM64 149h128v-42h-128v42z" />
+    <glyph glyph-name="turned_in" unicode="turned_in" 
+d="M363 448c23 0 42 -20 42 -43v-341l-149 64l-149 -64v341c0 23 19 43 42 43h214z" />
+    <glyph glyph-name="turned_in_not" unicode="turned_in_not" 
+d="M363 128v277h-214v-277l107 47zM363 448c23 0 42 -20 42 -43v-341l-149 64l-149 -64v341c0 23 19 43 42 43h214z" />
+    <glyph glyph-name="tv" unicode="tv" 
+d="M448 149v256h-384v-256h384zM448 448c23 0 43 -20 43 -43l-1 -256c0 -23 -19 -42 -42 -42h-107v-43h-170v43h-107c-23 0 -43 19 -43 42v256c0 23 20 43 43 43h384z" />
+    <glyph glyph-name="unarchive" unicode="unarchive" 
+d="M109 405h294l-20 22h-256zM256 309l-117 -117h74v-43h86v43h74zM438 401c6 -7 10 -18 10 -28v-266c0 -23 -20 -43 -43 -43h-298c-24 0 -43 20 -43 43v266c0 10 4 21 10 28l29 35c6 7 15 12 25 12h256c10 0 19 -5 25 -12z" />
+    <glyph glyph-name="undo" unicode="undo" 
+d="M267 341c99 0 182 -65 212 -154l-50 -16c-22 68 -86 117 -162 117c-42 0 -80 -15 -110 -40l78 -77h-192v192l76 -77c39 34 91 55 148 55z" />
+    <glyph glyph-name="unfold_less" unicode="unfold_less" 
+d="M354 397l-98 -98l-98 98l30 30l68 -68l68 68zM158 115l98 98l98 -98l-30 -30l-68 68l-68 -68z" />
+    <glyph glyph-name="unfold_more" unicode="unfold_more" 
+d="M256 124l68 68l30 -30l-98 -98l-98 98l30 30zM256 388l-68 -68l-30 30l98 98l98 -98l-30 -30z" />
+    <glyph glyph-name="update" unicode="update" 
+d="M267 341v-90l74 -45l-15 -26l-91 55v106h32zM448 296h-145l59 60c-58 58 -153 60 -211 2s-58 -150 0 -208s153 -58 211 0c29 29 43 62 43 104h43c0 -42 -18 -97 -56 -134c-75 -74 -197 -74 -272 0s-75 194 0 268s195 74 270 0l58 60v-152z" />
+    <glyph glyph-name="usb" unicode="usb" 
+d="M320 363h85v-86h-21v-42c0 -24 -19 -43 -43 -43h-64v-65c15 -8 26 -24 26 -42c0 -26 -21 -47 -47 -47s-47 21 -47 47c0 18 11 34 26 42v65h-64c-24 0 -43 19 -43 43v44c-15 8 -26 23 -26 41c0 26 21 47 47 47s47 -21 47 -47c0 -18 -10 -33 -25 -41v-44h64v170h-43l64 86
+l64 -86h-43v-170h64v42h-21v86z" />
+    <glyph glyph-name="verified_user" unicode="verified_user" 
+d="M213 149l171 171l-30 30l-141 -140l-55 55l-30 -30zM256 491l192 -86v-128c0 -118 -82 -229 -192 -256c-110 27 -192 138 -192 256v128z" />
+    <glyph glyph-name="vertical_align_bottom" unicode="vertical_align_bottom" 
+d="M85 107h342v-43h-342v43zM341 235l-85 -86l-85 86h64v213h42v-213h64z" />
+    <glyph glyph-name="vertical_align_center" unicode="vertical_align_center" 
+d="M85 277h342v-42h-342v42zM341 405l-85 -85l-85 85h64v86h42v-86h64zM171 107l85 85l85 -85h-64v-86h-42v86h-64z" />
+    <glyph glyph-name="vertical_align_top" unicode="vertical_align_top" 
+d="M85 448h342v-43h-342v43zM171 277l85 86l85 -86h-64v-213h-42v213h-64z" />
+    <glyph glyph-name="vibration" unicode="vibration" 
+d="M341 107v298h-170v-298h170zM352 448c18 0 32 -14 32 -32v-320c0 -18 -14 -32 -32 -32h-192c-18 0 -32 14 -32 32v320c0 18 14 32 32 32h192zM405 149v214h43v-214h-43zM469 320h43v-128h-43v128zM64 149v214h43v-214h-43zM0 192v128h43v-128h-43z" />
+    <glyph glyph-name="video_call" unicode="video_call" 
+d="M299 235v42h-64v64h-43v-64h-64v-42h64v-64h43v64h64zM363 288l85 85v-234l-85 85v-75c0 -12 -10 -21 -22 -21h-256c-12 0 -21 9 -21 21v214c0 12 9 21 21 21h256c12 0 22 -9 22 -21v-75z" />
+    <glyph glyph-name="video_label" unicode="video_label" 
+d="M448 171v234h-384v-234h384zM448 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-384c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h384z" />
+    <glyph glyph-name="video_library" unicode="video_library" 
+d="M256 203l128 96l-128 96v-192zM427 469c23 0 42 -19 42 -42v-256c0 -23 -19 -43 -42 -43h-256c-23 0 -43 20 -43 43v256c0 23 20 42 43 42h256zM85 384v-299h299v-42h-299c-23 0 -42 19 -42 42v299h42z" />
+    <glyph glyph-name="videocam" unicode="videocam" 
+d="M363 288l85 85v-234l-85 85v-75c0 -12 -10 -21 -22 -21h-256c-12 0 -21 9 -21 21v214c0 12 9 21 21 21h256c12 0 22 -9 22 -21v-75z" />
+    <glyph glyph-name="videocam_off" unicode="videocam_off" 
+d="M70 469l378 -378l-27 -27l-68 68c-3 -2 -8 -4 -12 -4h-256c-12 0 -21 9 -21 21v214c0 12 9 21 21 21h16l-58 58zM448 373v-228l-239 239h132c12 0 22 -9 22 -21v-75z" />
+    <glyph glyph-name="videogame_asset" unicode="videogame_asset" 
+d="M416 256c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM331 192c18 0 32 14 32 32s-14 32 -32 32s-32 -14 -32 -32s14 -32 32 -32zM235 235v42h-64v64h-43v-64h-64v-42h64v-64h43v64h64zM448 384c23 0 43 -20 43 -43v-170c0 -23 -20 -43 -43 -43h-384
+c-23 0 -43 20 -43 43v170c0 23 20 43 43 43h384z" />
+    <glyph glyph-name="view_agenda" unicode="view_agenda" 
+d="M427 448c12 0 21 -9 21 -21v-128c0 -12 -9 -22 -21 -22h-363c-12 0 -21 10 -21 22v128c0 12 9 21 21 21h363zM427 235c12 0 21 -10 21 -22v-128c0 -12 -9 -21 -21 -21h-363c-12 0 -21 9 -21 21v128c0 12 9 22 21 22h363z" />
+    <glyph glyph-name="view_array" unicode="view_array" 
+d="M171 128v277h192v-277h-192zM384 405h64v-277h-64v277zM85 128v277h64v-277h-64z" />
+    <glyph glyph-name="view_carousel" unicode="view_carousel" 
+d="M384 384h85v-235h-85v235zM43 149v235h85v-235h-85zM149 107v320h214v-320h-214z" />
+    <glyph glyph-name="view_column" unicode="view_column" 
+d="M341 405h107v-277h-107v277zM85 128v277h107v-277h-107zM213 128v277h107v-277h-107z" />
+    <glyph glyph-name="view_comfy" unicode="view_comfy" 
+d="M384 405h85v-85h-85v85zM384 107v85h85v-85h-85zM277 107v85h86v-85h-86zM171 107v85h85v-85h-85zM64 107v85h85v-85h-85zM384 213v86h85v-86h-85zM277 405h86v-85h-86v85zM171 320v85h85v-85h-85zM277 213v86h86v-86h-86zM171 213v86h85v-86h-85zM64 213v86h85v-86h-85z
+M64 320v85h85v-85h-85z" />
+    <glyph glyph-name="view_compact" unicode="view_compact" 
+d="M64 405h405v-128h-405v128zM213 107v149h256v-149h-256zM64 107v149h128v-149h-128z" />
+    <glyph glyph-name="view_day" unicode="view_day" 
+d="M43 448h405v-64h-405v64zM427 341c12 0 21 -9 21 -21v-128c0 -12 -9 -21 -21 -21h-363c-12 0 -21 9 -21 21v128c0 12 9 21 21 21h363zM43 64v64h405v-64h-405z" />
+    <glyph glyph-name="view_headline" unicode="view_headline" 
+d="M85 405h342v-42h-342v42zM85 277v43h342v-43h-342zM85 107v42h342v-42h-342zM85 192v43h342v-43h-342z" />
+    <glyph glyph-name="view_list" unicode="view_list" 
+d="M192 405h256v-85h-256v85zM192 107v85h256v-85h-256zM192 213v86h256v-86h-256zM85 320v85h86v-85h-86zM85 107v85h86v-85h-86zM85 213v86h86v-86h-86z" />
+    <glyph glyph-name="view_module" unicode="view_module" 
+d="M341 405h107v-128h-107v128zM213 277v128h107v-128h-107zM341 128v128h107v-128h-107zM213 128v128h107v-128h-107zM85 128v128h107v-128h-107zM85 277v128h107v-128h-107z" />
+    <glyph glyph-name="view_quilt" unicode="view_quilt" 
+d="M213 405h235v-128h-235v128zM341 128v128h107v-128h-107zM85 128v277h107v-277h-107zM213 128v128h107v-128h-107z" />
+    <glyph glyph-name="view_stream" unicode="view_stream" 
+d="M85 405h363v-128h-363v128zM85 128v128h363v-128h-363z" />
+    <glyph glyph-name="view_week" unicode="view_week" 
+d="M277 405c12 0 22 -9 22 -21v-256c0 -12 -10 -21 -22 -21h-64c-12 0 -21 9 -21 21v256c0 12 9 21 21 21h64zM427 405c12 0 21 -9 21 -21v-256c0 -12 -9 -21 -21 -21h-64c-12 0 -22 9 -22 21v256c0 12 10 21 22 21h64zM128 405c12 0 21 -9 21 -21v-256
+c0 -12 -9 -21 -21 -21h-64c-12 0 -21 9 -21 21v256c0 12 9 21 21 21h64z" />
+    <glyph glyph-name="vignette" unicode="vignette" 
+d="M256 128c94 0 171 57 171 128s-77 128 -171 128s-171 -57 -171 -128s77 -128 171 -128zM448 448c23 0 43 -20 43 -43v-298c0 -23 -20 -43 -43 -43h-384c-23 0 -43 20 -43 43v298c0 23 20 43 43 43h384z" />
+    <glyph glyph-name="visibility" unicode="visibility" 
+d="M256 320c35 0 64 -29 64 -64s-29 -64 -64 -64s-64 29 -64 64s29 64 64 64zM256 149c59 0 107 48 107 107s-48 107 -107 107s-107 -48 -107 -107s48 -107 107 -107zM256 416c107 0 198 -66 235 -160c-37 -94 -128 -160 -235 -160s-198 66 -235 160c37 94 128 160 235 160z
+" />
+    <glyph glyph-name="visibility_off" unicode="visibility_off" 
+d="M253 320h3c35 0 64 -29 64 -64v-4zM161 303c-7 -14 -12 -30 -12 -47c0 -59 48 -107 107 -107c17 0 33 5 47 12l-33 33c-4 -1 -9 -2 -14 -2c-35 0 -64 29 -64 64c0 5 1 10 2 14zM43 421l27 27l378 -378l-27 -27c-23.9841 23.6825 -48.1328 47.2006 -72 71
+c-29 -12 -60 -18 -93 -18c-107 0 -198 66 -235 160c17 42 45 78 80 106c-19.5086 19.4914 -38.6537 39.3463 -58 59zM256 363c-14 0 -27 -3 -39 -8l-46 46c26 10 55 15 85 15c107 0 197 -66 234 -160c-16 -40 -41 -74 -73 -101l-62 62c5 12 8 25 8 39c0 59 -48 107 -107 107
+z" />
+    <glyph glyph-name="voice_chat" unicode="voice_chat" 
+d="M384 213v171l-85 -68v68h-171v-171h171v69zM427 469c23 0 42 -19 42 -42v-256c0 -23 -19 -43 -42 -43h-299l-85 -85v384c0 23 19 42 42 42h342z" />
+    <glyph glyph-name="voicemail" unicode="voicemail" 
+d="M395 192c41 0 74 34 74 75s-33 74 -74 74s-75 -33 -75 -74s34 -75 75 -75zM117 192c41 0 75 34 75 75s-34 74 -75 74s-74 -33 -74 -74s33 -75 74 -75zM395 384c65 0 117 -52 117 -117s-52 -118 -117 -118h-278c-65 0 -117 53 -117 118s52 117 117 117s118 -52 118 -117
+c0 -28 -10 -55 -27 -75h96c-17 20 -27 47 -27 75c0 65 53 117 118 117z" />
+    <glyph glyph-name="volume_down" unicode="volume_down" 
+d="M107 320h85l107 107v-342l-107 107h-85v128zM395 256c0 -38 -22 -70 -54 -86v172c32 -16 54 -48 54 -86z" />
+    <glyph glyph-name="volume_mute" unicode="volume_mute" 
+d="M149 320h86l106 107v-342l-106 107h-86v128z" />
+    <glyph glyph-name="volume_off" unicode="volume_off" 
+d="M256 427v-90l-45 45zM91 448l357 -357l-27 -27l-44 44c-23 -18 -49 -32 -78 -39v44c18 5 34 14 48 25l-91 91v-144l-107 107h-85v128h101l-101 101zM405 256c0 68 -44 125 -106 143v44c86 -19 149 -96 149 -187c0 -32 -8 -63 -22 -89l-32 33c7 17 11 36 11 56zM352 256
+c0 -5 0 -9 -1 -13l-52 52v47c32 -16 53 -48 53 -86z" />
+    <glyph glyph-name="volume_up" unicode="volume_up" 
+d="M299 443c86 -19 149 -96 149 -187s-63 -168 -149 -187v44c62 18 106 75 106 143s-44 125 -106 143v44zM352 256c0 -38 -21 -70 -53 -86v172c32 -16 53 -48 53 -86zM64 320h85l107 107v-342l-107 107h-85v128z" />
+    <glyph glyph-name="vpn_key" unicode="vpn_key" 
+d="M149 213c23 0 43 20 43 43s-20 43 -43 43s-42 -20 -42 -43s19 -43 42 -43zM270 299h221v-86h-43v-85h-85v85h-93c-17 -50 -65 -85 -121 -85c-71 0 -128 57 -128 128s57 128 128 128c56 0 104 -35 121 -85z" />
+    <glyph glyph-name="vpn_lock" unicode="vpn_lock" 
+d="M213 65v42c-23 0 -42 19 -42 42v22l-103 102c-3 -12 -4 -25 -4 -38c0 -87 65 -160 149 -170zM404 256h43c1 -7 1 -14 1 -21c0 -118 -95 -214 -213 -214s-214 96 -214 214s96 213 214 213c22 0 44 -4 64 -10v-54c0 -23 -20 -43 -43 -43h-43v-42c0 -12 -9 -22 -21 -22h-43
+v-42h128c12 0 22 -10 22 -22v-64h21c19 0 35 -12 41 -29c28 30 44 71 44 115c0 7 0 14 -1 21zM452 427v10c0 20 -16 37 -36 37s-36 -17 -36 -37v-10h72zM469 427c12 0 22 -10 22 -22v-85c0 -12 -10 -21 -22 -21h-106c-12 0 -22 9 -22 21v85c0 12 10 22 22 22v10
+c0 29 24 54 53 54s53 -25 53 -54v-10z" />
+    <glyph glyph-name="wallpaper" unicode="wallpaper" 
+d="M85 235v-150h150v-42h-150c-23 0 -42 19 -42 42v150h42zM427 85v150h42v-150c0 -23 -19 -42 -42 -42h-150v42h150zM427 469c23 0 42 -19 42 -42v-150h-42v150h-150v42h150zM363 331c0 -18 -14 -32 -32 -32s-32 14 -32 32s14 32 32 32s32 -14 32 -32zM213 235l64 -79
+l43 57l64 -85h-256zM85 427v-150h-42v150c0 23 19 42 42 42h150v-42h-150z" />
+    <glyph glyph-name="warning" unicode="warning" 
+d="M277 213v86h-42v-86h42zM277 128v43h-42v-43h42zM21 64l235 405l235 -405h-470z" />
+    <glyph glyph-name="watch" unicode="watch" 
+d="M128 256c0 -71 57 -128 128 -128s128 57 128 128s-57 128 -128 128s-128 -57 -128 -128zM427 256c0 -54 -26 -103 -65 -134l-21 -122h-170l-21 122c-40 31 -65 80 -65 134s25 103 65 134l21 122h170l21 -122c39 -31 65 -80 65 -134z" />
+    <glyph glyph-name="watch_later" unicode="watch_later" 
+d="M346 166l17 28l-96 58v111h-32v-128zM256 469c117 0 213 -96 213 -213s-96 -213 -213 -213s-213 96 -213 213s96 213 213 213z" />
+    <glyph glyph-name="wb_auto" unicode="wb_auto" 
+d="M220 171h40l-68 192h-43l-68 -192h41l15 42h68zM469 363h39l-44 -192h-37l-32 130l-32 -130h-38l-2 9c-28 -56 -85 -95 -152 -95c-94 0 -171 77 -171 171s77 171 171 171c54 0 102 -25 133 -64h16l26 -135l32 135h34l32 -135zM146 242l25 78l24 -78h-49z" />
+    <glyph glyph-name="wb_cloudy" unicode="wb_cloudy" 
+d="M413 298c55 -4 99 -50 99 -106c0 -59 -48 -107 -107 -107h-277c-71 0 -128 57 -128 128c0 66 50 120 114 127c27 51 80 87 142 87c78 0 142 -55 157 -129z" />
+    <glyph glyph-name="wb_incandescent" unicode="wb_incandescent" 
+d="M368 125l30 29l38 -38l-30 -30zM427 288h64v-43h-64v43zM320 377c38 -22 64 -63 64 -110c0 -71 -57 -128 -128 -128s-128 57 -128 128c0 47 26 88 64 110v103h128v-103zM85 288v-43h-64v43h64zM235 33v63h42v-63h-42zM76 116l38 39l30 -30l-38 -39z" />
+    <glyph glyph-name="wb_iridescent" unicode="wb_iridescent" 
+d="M106 86l-30 31l38 38l30 -30zM76 417l30 30l38 -38l-30 -30zM436 116l-30 -30l-38 39l30 30zM277 33h-42v63h42v-63zM406 447l30 -30l-38 -38l-30 30zM235 500h42v-63h-42v63zM107 203v128h298v-128h-298z" />
+    <glyph glyph-name="wb_sunny" unicode="wb_sunny" 
+d="M76 116l38 39l30 -30l-38 -39zM235 33v63h42v-63h-42zM256 395c71 0 128 -57 128 -128s-57 -128 -128 -128s-128 57 -128 128s57 128 128 128zM427 288h64v-43h-64v43zM368 125l30 29l38 -38l-30 -30zM436 417l-38 -38l-30 30l38 38zM277 500v-63h-42v63h42zM85 288v-43
+h-64v43h64zM144 409l-30 -30l-38 38l30 30z" />
+    <glyph glyph-name="wc" unicode="wc" 
+d="M352 384c-24 0 -43 19 -43 43s19 42 43 42s43 -18 43 -42s-19 -43 -43 -43zM160 384c-24 0 -43 19 -43 43s19 42 43 42s43 -18 43 -42s-19 -43 -43 -43zM384 43h-64v128h-64l54 162c6 17 23 30 41 30h2c18 0 35 -13 41 -30l54 -162h-64v-128zM117 43v160h-32v117
+c0 23 20 43 43 43h64c23 0 43 -20 43 -43v-117h-32v-160h-86z" />
+    <glyph glyph-name="web" unicode="web" 
+d="M427 128v192h-86v-192h86zM320 235v85h-235v-85h235zM320 128v85h-235v-85h235zM427 427c23 0 42 -20 42 -43v-256c0 -23 -19 -43 -42 -43h-342c-23 0 -42 20 -42 43v256c0 23 19 43 42 43h342z" />
+    <glyph glyph-name="web_asset" unicode="web_asset" 
+d="M405 128v213h-298v-213h298zM405 427c24 0 43 -20 43 -43v-256c0 -23 -20 -43 -43 -43h-298c-24 0 -43 20 -43 43v256c0 23 19 43 43 43h298z" />
+    <glyph glyph-name="weekend" unicode="weekend" 
+d="M384 405c23 0 43 -19 43 -42v-46c-25 -9 -43 -32 -43 -60v-44h-256v44c0 28 -18 51 -43 60v46c0 23 20 42 43 42h256zM448 299c23 0 43 -20 43 -43v-107c0 -23 -20 -42 -43 -42h-384c-23 0 -43 19 -43 42v107c0 23 20 43 43 43s43 -20 43 -43v-64h298v64
+c0 23 20 43 43 43z" />
+    <glyph glyph-name="whatshot" unicode="whatshot" 
+d="M250 107c57 0 102 45 102 102c0 30 -4 58 -12 86c-22 -29 -61 -47 -99 -55s-60 -31 -60 -66c0 -37 31 -67 69 -67zM288 498c83 -67 139 -170 139 -285c0 -94 -77 -170 -171 -170s-171 76 -171 170c0 72 26 139 69 190v-8c0 -44 33 -79 77 -79s73 35 73 79
+c0 46 -16 103 -16 103z" />
+    <glyph glyph-name="widgets" unicode="widgets" 
+d="M355 476l121 -121l-121 -120h93v-171h-171v171h78l-120 120v-78h-171v171h171v-93zM64 64v171h171v-171h-171z" />
+    <glyph glyph-name="wifi" unicode="wifi" 
+d="M107 235c83 82 216 82 298 0l-42 -43c-59 59 -155 59 -214 0zM192 149c35 35 93 35 128 0l-64 -64zM21 320c130 129 341 129 470 0l-43 -43c-106 106 -278 106 -384 0z" />
+    <glyph glyph-name="wifi_lock" unicode="wifi_lock" 
+d="M469 171v32c0 18 -14 32 -32 32s-32 -14 -32 -32v-32h64zM491 171c12 0 21 -10 21 -22v-85c0 -12 -9 -21 -21 -21h-107c-12 0 -21 9 -21 21v85c0 12 9 22 21 22v32c0 29 24 53 53 53s54 -24 54 -53v-32zM437 309c-59 0 -106 -47 -106 -106v-61l-75 -99l-256 341
+c71 54 160 85 256 85s185 -31 256 -85l-57 -76c-6 1 -12 1 -18 1z" />
+    <glyph glyph-name="wifi_tethering" unicode="wifi_tethering" 
+d="M256 448c118 0 213 -95 213 -213c0 -79 -42 -148 -106 -185l-22 37c51 30 86 85 86 148c0 94 -77 170 -171 170s-171 -76 -171 -170c0 -63 34 -118 85 -148l-21 -37c-64 37 -106 106 -106 185c0 118 95 213 213 213zM384 235c0 -47 -26 -89 -64 -111l-21 37
+c25 15 42 42 42 74c0 47 -38 85 -85 85s-85 -38 -85 -85c0 -32 17 -59 42 -74l-21 -37c-38 22 -64 64 -64 111c0 71 57 128 128 128s128 -57 128 -128zM256 277c23 0 43 -19 43 -42s-20 -43 -43 -43s-43 20 -43 43s20 42 43 42z" />
+    <glyph glyph-name="work" unicode="work" 
+d="M299 384v43h-86v-43h86zM427 384c24 0 42 -19 42 -43v-234c0 -24 -18 -43 -42 -43h-342c-24 0 -42 19 -42 43v234c0 24 18 43 42 43h86v43c0 24 18 42 42 42h86c24 0 42 -18 42 -42v-43h86z" />
+    <glyph glyph-name="wrap_text" unicode="wrap_text" 
+d="M363 277c47 0 85 -38 85 -85s-38 -85 -85 -85h-43v-43l-64 64l64 64v-43h48c23 0 43 20 43 43s-20 43 -43 43h-283v42h278zM427 405v-42h-342v42h342zM85 107v42h128v-42h-128z" />
+    <glyph glyph-name="youtube_searched_for" unicode="youtube_searched_for" 
+d="M363 213l106 -106l-31 -32l-107 107v16l-6 6c-24 -21 -56 -33 -90 -33c-26 0 -50 7 -71 19l32 31c12 -5 25 -8 39 -8c53 0 96 43 96 96s-43 96 -96 96s-96 -43 -96 -96h74l-88 -85l-82 85h53c0 75 62 139 139 139s139 -62 139 -139c0 -35 -13 -66 -34 -90l6 -6h17z" />
+    <glyph glyph-name="zoom_in" unicode="zoom_in" 
+d="M256 299h-43v-43h-21v43h-43v21h43v43h21v-43h43v-21zM203 213c53 0 96 43 96 96s-43 96 -96 96s-96 -43 -96 -96s43 -96 96 -96zM331 213l106 -106l-32 -32l-106 106v17l-6 6c-24 -21 -56 -33 -90 -33c-77 0 -139 61 -139 138s62 139 139 139s138 -62 138 -139
+c0 -34 -12 -66 -33 -90l6 -6h17z" />
+    <glyph glyph-name="zoom_out" unicode="zoom_out" 
+d="M149 320h107v-21h-107v21zM203 213c53 0 96 43 96 96s-43 96 -96 96s-96 -43 -96 -96s43 -96 96 -96zM331 213l106 -106l-32 -32l-106 106v17l-6 6c-24 -21 -56 -33 -90 -33c-77 0 -139 61 -139 138s62 139 139 139s138 -62 138 -139c0 -34 -12 -66 -33 -90l6 -6h17z" />
+    <glyph glyph-name="zoom_out_map" unicode="zoom_out_map" 
+d="M448 192v-128h-128l49 49l-62 61l31 31l61 -62zM192 64h-128v128l49 -49l61 62l31 -31l-62 -61zM64 320v128h128l-49 -49l62 -61l-31 -31l-61 62zM320 448h128v-128l-49 49l-61 -62l-31 31l62 61z" />
+    <glyph glyph-name="3" unicode="3" 
+d="M0 0z" />
+    <glyph glyph-name="d" unicode="d" 
+d="M0 0z" />
+    <glyph glyph-name="_" unicode="_" 
+d="M0 0z" />
+    <glyph glyph-name="r" unicode="r" 
+d="M0 0z" />
+    <glyph glyph-name="o" unicode="o" 
+d="M0 0z" />
+    <glyph glyph-name="t" unicode="t" 
+d="M0 0z" />
+    <glyph glyph-name="a" unicode="a" 
+d="M0 0z" />
+    <glyph glyph-name="i" unicode="i" 
+d="M0 0z" />
+    <glyph glyph-name="n" unicode="n" 
+d="M0 0z" />
+    <glyph glyph-name="c" unicode="c" 
+d="M0 0z" />
+    <glyph glyph-name="u" unicode="u" 
+d="M0 0z" />
+    <glyph glyph-name="e" unicode="e" 
+d="M0 0z" />
+    <glyph glyph-name="s" unicode="s" 
+d="M0 0z" />
+    <glyph glyph-name="l" unicode="l" 
+d="M0 0z" />
+    <glyph glyph-name="m" unicode="m" 
+d="M0 0z" />
+    <glyph glyph-name="b" unicode="b" 
+d="M0 0z" />
+    <glyph glyph-name="y" unicode="y" 
+d="M0 0z" />
+    <glyph glyph-name="w" unicode="w" 
+d="M0 0z" />
+    <glyph glyph-name="x" unicode="x" 
+d="M0 0z" />
+    <glyph glyph-name="p" unicode="p" 
+d="M0 0z" />
+    <glyph glyph-name="h" unicode="h" 
+d="M0 0z" />
+    <glyph glyph-name="g" unicode="g" 
+d="M0 0z" />
+    <glyph glyph-name="q" unicode="q" 
+d="M0 0z" />
+    <glyph glyph-name="j" unicode="j" 
+d="M0 0z" />
+    <glyph glyph-name="f" unicode="f" 
+d="M0 0z" />
+    <glyph glyph-name="v" unicode="v" 
+d="M0 0z" />
+    <glyph glyph-name="k" unicode="k" 
+d="M0 0z" />
+    <glyph glyph-name="z" unicode="z" 
+d="M0 0z" />
+    <glyph glyph-name="1" unicode="1" 
+d="M0 0z" />
+    <glyph glyph-name="2" unicode="2" 
+d="M0 0z" />
+    <glyph glyph-name="4" unicode="4" 
+d="M0 0z" />
+    <glyph glyph-name="5" unicode="5" 
+d="M0 0z" />
+    <glyph glyph-name="6" unicode="6" 
+d="M0 0z" />
+    <glyph glyph-name="7" unicode="7" 
+d="M0 0z" />
+    <glyph glyph-name="9" unicode="9" 
+d="M0 0z" />
+    <glyph glyph-name="8" unicode="8" 
+d="M0 0z" />
+    <glyph glyph-name="0" unicode="0" 
+d="M0 0z" />
+  </font>
+</defs></svg>
diff --git a/docs/python_docs/themes/mx-theme/mxtheme/static/material-design-icons-3.0.1/iconfont/MaterialIcons-Regular.ttf b/docs/python_docs/themes/mx-theme/mxtheme/static/material-design-icons-3.0.1/iconfont/MaterialIcons-Regular.ttf
new file mode 100644
index 0000000000000000000000000000000000000000..7015564ad166a3e9d88c82f17829f0cc01ebe29a
GIT binary patch
literal 128180
zcmeEvcYK@Gx&M1)4R2eLU&)qiS+*?6)@#Q@mX+x!dpHRhNLkQ2n^<J;kbuez8wxUI
zr_5rp%WP?@?v|9UTluyHio*yYZVT-#T-r;O-}gC3uO&O7_qM(Fe(oPX+3!B@8P9pn
zv(I@YCP`A66qE9#?Q>?%nyr<jWfHqKA9ow(EIw!1Q04n~;kp^uU2~R&r@#I4N0#7v
zy(DeM)7Fl{UG?iEiS5Vp-gR5nZ2!Xt?L(4OfNQ;G-QL|!$;f7+fJ1n$-Lz)c_S2_P
z61x_cQvRk34{V%y__rw1S}n1ib1&GiX1)H`*KWkKPq1}efE!w-!E@G$>xK)q?B3sZ
zV)JZV|5B0+M=#vAZq1~o{wt7w4A*yUS+jq;)+-&y^A$+%+`4AVhU&7w+Y-AP^<@XQ
zZ`-x|^p#SF#I6~l=MuG@X?}XnH|mdkwrui;Qh^3HB<bhBx&P(N@fArroz_TH2Pt0?
zkMWL_ITbgsr<`(sXrm<My&^p+Rr1@EknvO@X&k?!)ky3nNr&4D7`1p#=J=shC0$PW
z>+*Oy+A$M$RE3dWOlmuQdZcu^om&H^q~Mv6Zi_T@_TTbTBt?>?5cVPbh4~g3xr$0r
z{)|#lIz@`{vjpGMJ$jSgr+346O3y_a@hmFE`BS>8M@mYi{>eN?$|a05%AN9(rDmiR
zXX0*%KMSF~VQC+pMR63l)1J;1UQc=}%C8j3&+`x<T8;Oke4~^S&cs{YIHOYOM|Yz9
z9m-Q0$%$G<cc{!MndgdBmnnx*QVKN<<u9&8Yv@}2ivtqkkIErPmu3E_{Zy;$ALW<N
z-=wxv-5Jt|JpH&Y<7oj;L71i>->Z1J+4_iD<?iE1C-IW#ZcK7Y0o)O-7VV4TE|na{
zCR((QpHbVX#^P@|j<VFl(@_plY*dHrkFZ8<HML2!P2dHUO=k(|d0twKr=&KjKhYE7
zTGXHFNOhoo6L>-O5oc5m)t>SRp+%xbu@Tr(I{FiJ5~Yh=sm63hxn}>U9LkB_qchsR
zgfwUSqf`=})3au&9ea8!&flgURU`+_>8X!DQOlzIb4wL9jG>MShYLNWd!i<^r$4%D
zk_h^ARylH)+OZP%+?iCORua-sE^56O@cK}l=xwSe;R3xSdNsz=(tWiwN=X~_2fZQl
z^mIl2NB7m#6LE)9(4Q>zW?(%ra~+nt`5o#dN<pPk+oF6Q2D}Jnl<Pd+dg@Dp1*Hy4
zDJde|gFi}7HI0D+&=a+mqExz~7K^J8&gosslS-m-OQq3$s;LjSFY{|r6BSA^q@`9;
zKD1X*y2yd{l$LN=lufx){-QN<<~_P6(oqidmiVK3spksDGF^##REi?GGEcKDpr@3F
zgiT<7DX$ULLF7zrqSEGp5{%BDYDG^(t3|m~4#8EtOL>TQL@AV>(uup2mi`D{REEUQ
zWT^;8^@)I4l&5ORq>Q0%Mr`yK<$G$uDx8bdly4`0gGv*%6RE>IHI+jcM5*by7`1ey
z^kSo$irUhfqBgXrGUy#Ohk)eeSVV8H!bY^7>Lf`Ucv{gCN=*=^aVO)P>OoJ$o}Lf{
z=vtDd;wWlIbx~_XrP3e$!22N!NuULiR0vKD83<>R_7jqj`2D=heJ%R{*ZYy5P8u&w
zkUlFN9LgK28mb#=7-}ABADS?<Y-q>OOGDon`p(ch$G04hAHVDPw~zne_)m|&di>2d
z*T4ClH-Gr%kKW3EtMaY!Zw<Zm`?t++k9&LaJ6-SWeCPEO&J(jveD}obC*C-5{N49X
zvXiAJ<&zUnE;+gD<Z~x~{GRll{=KsIl;H)C+a#$CC9fHhP_li<HPkpXZpc40X(&AO
z#L(A=zCHB4<Fk(6gp%(^$*&y$<?*36J#W7B=J!yt;jO~AY;WED)|+qr;cfZtws%_I
znefiHPn4c;o%oJg^1Hu3S#;8Pvh(D^lgm!-Ir+lLpT4JgFaJH`@TAkHPyfs5$4@`z
zY;>BPCa2L^>MU^<JByr!wXf9vsP>1oKd9YYJEwM9?WEdZt-rRpw$bs9;|9m|j%yuD
z9E%<2)C||0sySKnZq146kE;Jv{Xq5Z>YesK*<ZK6W`EW83)@d^-?x3&_OLB(yVZ7!
z?en&qZ8zC&wB1m7iRcZq`hWjdOhsyjG6he@u?^b<Y=keL&1@w&uEBN?Td5=s;V?wc
z8*!`>8{yWF9a|mlx8Uf))_`-!(?gVwaIXtT$fQH<IG=>09~+f56-T;WhI7c=L%{B#
z9XLn%Lr-9P3FnaOhrW*O8#uoP$8Tf%4$iN`@q5_b!TAl6bbJ=JEjWK1$D6RlasID3
z-X%8absX=<hWDl4;P?ZvNvCmy41WBlLcl!^x&Qd9IM?DhgzZh7LsxmzgKa&|i*Q7}
z-b5YPd>m1SH-Ct8wBgMkiH$9nq_+&%@E++2Z(;1c1u31a!qJ9pJkB@ccsDkb!H(dF
za^Ctq&XLDke~_fN%{c!Rju`2019t2a9MMN_Pe#94B<UT%`W?Vk19-m!SiZ9p=X4M4
zdk1aRY?7oCrP!P}zZ6HLI{_GJ0GAV}`-$)3`~@75mNv~-aC}2-ny=z`Tx=S&_1*Wx
zruiC<sN+e{4h{O`Bw%q8WoUkmBXoh2sH<iWNA&&4i8u!wPcFo^1m|eqNx<mjE}XxM
z;~s3!0o?$jlc>kZALAVGJc)ilaZ(=e?mZ1QJ<dOmBhtNBhV$ESL|N~F!sXp7NyEUO
z;RQJVid263uY8m807HiNP)+`0$VZ|r_oqJ0xtFKa=?(b>g+;|VH$VNfL@F&SH=4{9
zvc+0iWwTe;IBK1B^{xiD$NTAT{qH{Ey0O&6|JpIWr-3^!fpoS;+AQsm4oIJqu9j|=
zZkN6&Jt93Ny(oQC`l0kQ=~vKj-;@3z{h2XVz>KVl)v+el&L*&FY#v*}wz4>TjJ>TX
z)`T@*(j+yfG@s;^<oWY1&wDxV&)NoUul648ceS7Bx^z2q_vw!5exv&jy-VM!pRK<@
ze}n$J`VR~q!v@1;h5^Gd!w31Ad^vw%{_6be@_$ueC}=ELT(GfVZ^1JKuN4*)&MMqh
zcu!GDQL?zM_@<KCC6AQ6QQB5|QRy9}KPr8_jFkn-)|FjV_RX^Q%e%@~mfunSQu!N3
zgR#jt*SObsyYYRK-L%luYr4pEv*`iTH%-4a>&>0!9p<lBbXKgaxV+--il56B@<Mr!
ze82oPdB~DyX|>#J`L)$<?X`Zv`c>=el~QGW<<iPSm0zy>b(OJdWz{XV65B-EZri=K
zm+b|1hkdqvmHjgNefA&OPgjqtUS7SU`e^kZYLuG!H5b-gQFD9EfTPqAbVMCDIi7X=
z%<&t?hqcyPrFLHJg|)Xi3!QeS-?_xO#d)Xm$8}O&XWiDiyX#)AOV@YQudM%k{Wt30
zc9prhToKn^*K@94H<UDtZ`jdrPs7g|K6Y2T7r3u>zv%wh)9KmZdBXE&ug|;Kd%ky<
z_c`xh8|{s28y{&ZXj;^?zv<bgZ#BKzl=hi?if^LtYTu*2LB*sjR*ovaY&JCqo9}L^
zY?<70wB@65pBZ=KxIeY}T5oRs_qOV`>1`LZ-Prb(w%6M&?UUM9wqM%*X!|$YPj<LE
zKHG6!$AcZoj@LU*`|JGE{44yI`=9pzytA~kyK`q}f9F%3Kk6I~uz)$RHgF(tN8qWz
zYh4vxp02%JcXfTg>sMVL2K~WV!F|Cm1iu~p-FVCRRpW0R|Ml^y@xv1eCXAb~X2Nw7
zzBjRGV%x-(6EC0m^29$(vQC;jX~U$iP5SYqHzvJ5>Gb4^$-c=~PQGXIi<94;QZU6c
zW%ZOxr@S)d_uZE68Qr_OpYHza)W)ejQ?Hu($kdae_E0!<ap=*|FG8P8tDQD$+PY~^
zP5WTFclzS#ho|2>{m~iIXQXC+dDg?TUYPasS-+iKJ$uINO|$Qq{e#)>&uN{rVa@|{
zUY+ZnyKe5Ib6=n5o40h{W%C}JcXEEg{FeDk=kJ~$pa0_g-}aRDOzb(YC)RU&&!auZ
z7O(}@1@jhcTJY$C;e`zgw=8^V;fISl79Cjh{d3qkYtDIcalzuY#akCYw)l<3e_Y~P
za@mr%mwK1ZTe@lK{-xhq*0AidWyjBLKX>1`&z$>OSQ|bNzB@b^DT+8Et0Rv_z8?Aa
z<<-k)F5k2KiRJ&Y!muK+V*iSJSG=$ywX$es^~#o&2Up&+@~bO<zp8fC(p7s`eR0*l
zuFhY*ZuQ-(f4KUu=LOH(e%|fpy?lP*`Qy*ubpHM4|I7KGeCDFh{N=OFpWXM_uYGp7
z*V((V_mbXcdk5E)u35I`vNaE{HLSgG?JaA6u&#XF`RndocYM8V{k-*C*Wa=JqYYCw
z#5Vk7W9h~z8xL)Ka${=apEp@I&E3?u>FG_sy`bQNwhNA4@RJKZ*}Qb~-J9R&%kOLM
z+u3(>-^<a$XlHa;^rGm)(H~u?zfiew)rD7G__YiFW6Sg{`?lP@<yTvVw_3J#Z@qBq
z?OT7m^^e<h+b-C4_qHEw`^)yC?NhgJ-+ue{cXn8IY})boj^FO=+<D>7&+WW^=L0*R
z-1*&|r*{6wuHs!ayMnvs?pnF)@UHuIeRbDcy9;->?_Rk3g58IA-?ICW-Cy6G+Wp%-
z&3iWNxpB`6dyemI*t>G?ZF^tY`ycyi_O04?+rBsVSMFc6|Iz)!2O176I<Wh|JqNyX
z;NK1g5AHm8&%swOa$L0hqKhsXI+TB?^U%46t~m7cp|=h<9o}^KzQcnTYcFoTc)`Wj
zT>R9<zq`bLN#7-pT=L6H-jC^Hp4j5p!Po<_=Rarq+y$RI`nh*5^<BF0()%v`(WR#^
zn{wH~%bvRI-OHOVUw?V>^4G4=Uor8D6<1t-#W$~b?MnH|IaeOJGI<rdYT;E^UG>;i
zKfCJpM=VELjx0K|=g6B^=Uv@&b??<TU;W}Wh1Yanv+tT8UX#9d^R+iz`^{^AcU{YM
zJFk26x_7RxxjuaT<<~!c{m-sHd4u7G@C}#T@c0eiyy3MQOK)tyalwsyZ@lHkXKsAs
zCihKUH$`r`@}?JWdj00=n^)X?@aC`I{PyQ7KfmzviO>J(mZDqgZ;9M;%`IQK<>W1&
z+*)^Q*R9)cz2Vm9Zhb4x;`aEI_!r|pihtDK*1x6yvHtgOGv7Atwyn3_e%trHAbr92
zg)Lur_;&m4b8kO%`;)i7eTU|b<~!!yvHgyF@A%#wf4I|s=jZPnxbv5HNq2egT5{Ky
z?^f<!b@vT-C-2eRGwz<gdtScRckj9PK6>woqpqVXkKTSXb@cQXgJ0b8#V5Wvd|&B(
zZTFpf-_H9UzAt&-ukQQn{mu6;x&OKQKYF0yfu#?8;el^G@NW;+J$T`R4?Xzx2Y>S5
zyAP%xs(EPgLl-`D<wN&A^x{LuA7&3L4=;Uq+ryVW{Jn?&_@!B2df-bxf5i02lt;Eb
za_J*qedMD7%fO<6iw9mDcq>tq2qex;T%LF+@%_ZVKRW3#&10U&);@OaW3N7Le|+QP
zvB$si`0x`|Ppo?4;1l0?;*BR4J-Oq_ho1bmr#hZG^wi@|{orZ+(^H>*;px*~p77=E
zU%vm#Z$G0vv-z1jpZV8km1iG%_SAFL&&_&n%X6<iU;g|zpFi=!gcpvy@XU)1kV+uS
zNVl^v<ZgpB?kOg<PJY^;m44UplwSAR<frpAIF+8tquV;V{j@>PKAHS9M4I1q_>F#}
z*Kc$gkL=sHk%iL<BlC|4JzUOc&k8~R{~8eZ|M2*`@gt9{2`t4@977?2y8|xSicQmD
zsnFMZI+^VA`DQ$M`0$g5hhwpbqOcH74?ihsN8U8R`e~M|Py{==Iz9FJ3RC9YpkH>$
z*uHYzh7H$kSjIC+B0FCgmm98QcAk?trYI;KHV`(PsRuMFwH^kunO9+OcsLb_gcT*k
z;^`>T!#2W_NM9t?!m3E=QEMvBAFx{Gx<D|{*+pB2MV2iV+2E(<uyDv13i&Q>Nyl13
z?G@D(?V+!oTUB3mN(qJVzof-#Z8_v$QdCx2QBhh}w8Wn>+Mv>9p+s#(OVt+YGc86b
z99sWwDlRq^n-`BCzj%B;Z!eQ^qu8_=H^wjis{kEf7eZ^3ED5Sm2K!(KU`I7Y9$h@2
zt`4tXWEtoT2CN3JUaqiobOky+UfETVNg69Qm6VwN#P?Uri??q-x_#lzj@@<34=tbH
z<>SSQ`Z##45_rCSaqk3nvtw6NpnLi9?(yg5H@!i56mxinQKJM}*Gif@Ls>3Yy<SGa
zem{yP=myZ4pK?D4I2$YUuDW_pz}VU4XR+dfP(euvcEwIb*~3rxEnK|}s2bCQrm89v
zlPv|s#RU=CS&BkMqp$jH#?uldX4q3dz!Jt1s3p-;=%)-7sza@aR<dv;a$1V`A}lnV
zNU%tH0Q6UdbCG1HHUU3?C$ybW-%u<T4u=Os&3KU^Wl&LkN{p(?Z}_MV8q^`ij<Fcd
zbjZ_ca2c&ke(t&Cb1W8%F)2oA0!Wik)0l%smpAw!Qap#pr=?gdXY^=cPc6mx;cf96
zf>zm;hdcvrgE!!3y?geAdPAX@GZfmxWSp>2jBbbvx=T=j4H12Jf@4zv*qK2PufD=+
z@N@>v=suvotKRDoe_~j;Xt2r^R*U%i(AivD+q`r9c*m?+CyZ4}hpVEj$z-T$s<1A<
zIHF8h)om<oXe=tR<})9qto_>fqe%O$S?O&yqpQOp2Q3zdyU8~-5}Df4-QD7>wc8!_
zo?<Pz9%1X`*p&zTa=}}M@#+Vi$;B~r4e-ps@r?S(FQ3sz0mYfYs&Z#}L2=xvIGwr3
zG!zOIaZJL_=`1de1p**0!=sRh`cFpR3<K&lR1d2m0G#a%T4{`#fyNaEFO4*p><z*V
z7M{JqUR%}^$Q;7qSSTC`ZJ6EBRBNvb^e|=Q6!+2dQH<<3=u#QSIDp-NG2`)|12BL=
zf&v+uRKa#M^aOl?o`xCc%~;;)fvU&kt|j@iXXh{JYMnC0=&{9Y9@<e4XLGG4`y3mS
zua`zCdO1j4YWb8xt}QDnEMw&r<(+4i_SEHtWu--z7nPO0GOFAVr&B5Tl<Q#Omdp&K
zDBB=^N6-tjLYurguV|N*LvJ<PU|(se<E<N4Rq4vt+dMQh(8#daR=|?!JMAk=b(yvn
zxilVIdF+-;w7kLYI(vl`oD6nvaHa%#z?nanw)m}nZ)Rh$aFsDVV63V#T6`g&PYEf+
zA`)pL+?;Ae=m!X@e2yqV19AinQ2E7JJTn;UWKPFeJgkJnN|dq}bST0UWL?E%x*rZF
z)%&1NRpE5XS7l@{UFk+j6Ld-~k#p7{%KaN9red*FYWU0&qds3kQNRE{rOa##^CgP}
zd!|`!g$@OJK*~7>IfL+pGc5{-OHCFhXh2SDSuE2e*|(>N$b)5XUv7&DGi9j`eESWY
z83^N5zU?+x4F<2l>kZOh&>FN_4V;lPsnf8qao)Vfg@(?NGa*_;C!J%QSz9~9bk3y7
zi|A~o@tmBV%kW+|ADs0DGa(=Fene8as$s+I$t{~Fw|vmB!Ni&GZ7q{$Z)iyWxZwjj
zVKKpeH6YPZ7GrT<XTmL)i}(lnGZBV#G01%B!JKlbWf{qcaAu^u5ST+HFDEg8AM=fH
zcNN$2z@xZiMAS}|6;#>5ihIDLD|3XSxPqJ_xx&$70|OWd3Dg(r8K{e7wi*(rPO*5L
zuGDfg<mU+ny`GF10vu}L6+7D7yC+ktEPhaHOlL}K)_^rvP#cJilG0vC_7WWzV=@Nl
zWMsY|(P@(N02(k>zZasH4x2KN;3Gr{pGE^tO9_(uBH+%zVEhy2sI~v!7?FYlrNEI(
zxX%#&4U!#XA#M3PtU783>g~qHqJ1GyDvvF{G@VLh8o**o66C4VqxJZF;40JzwGG1@
zL+XgCfN~%wZALE4b6X7%<Z>h<kfs5Uw)B5+8eGmwOP~CUsj||P9xzoBw^5V?72U}+
zTN~z7nLu?wdl)?XI*ha^Mp~SgRSPZ(dh7<DA{nhqjPA~{bcF%0&A@L~G=1{)U7ng6
zPt9VIv`$MTnz4`%)G}UfvDqx;-@~&S&vO0@DVS7HEJgw!4VsLMkwO#*bXvmaI`Bt1
zfPSwd7RsW&)2P0W+7OR`M}|X#;s7WK`6MOjfi<A!Zo-wIp9a8~Ol0tLIg-sO8MerY
z(YaA%3J5&-H_1%Iu8FB~?yB<aIqNZml%Bqxo<I}>XZ`Fs>(|c-^x#G$8YRqArAR%;
z2FYy=$}UhTzwBjR2C@}olV>#VZJuG>+noNBgB4%m*yebX-+4E4X9n(&oEL+fhd<;=
z9tloKtPGu)dX_=ZBVjO`Mnh>J3sSOU&z_c`OOZ54qho|){1Vcj5!|*0{8lmpKn4=I
zgDUM%^$ZAyL8@mmws2u=Vb7uEkojjpyg#}fMx3?wV{7eeL0UYk6z|I93VNE}anFt&
z_bjMe=5#J~E=5&yYA%`UjCC=p2Gv>AMQ~ohy~?0rjnH+XfB{Hn?on6`c|S2Y81W58
zh!LtBImJhbqF}TnM#*5rA4LfUsT>$lN2>b>UF_=g8b}KBWCoFeq%)Fbskd|GfcNWd
zwtCwG9UZkE_r<e2I%aq1JZ?uv2W!aIfhkC0tG}k>2Bhlja_f<*V|I{E9k|CDMpbNN
zM5oYiCeF`*7h{UeiU*M76K8PhW4*oebD89bSimq2VvvGk9CL#*gf^isL2~lfp%4}g
zhf8Q|it$&%oZ(a99=aN&9pM{d0+0hqm(W7FG{!Y9%E9l|$)q*P@@#g{K2xt38I@0D
z@%Jw;C}FAemG+rhp4Y@#Z@*t$(1ZM<=!a_|W9fi*lGz_LdR+|_hCnnNjfR=Ci-n@;
zf#^kh?T-Ru;z$ea3u!Yc<B`5TXj_y-$oN#ZB!`@X%UH4(!^x{R1iFG=*$e*Q)j@*|
zKpiJ3(TiQc!lah{kQSTWO;y&_(eQ>1EIg@o+PM~IQGj&@SYlPnbO?*hHHFOv)9Ra|
zu?-LU7nL@bZl2lJRA;X#&~~=kIE9&ovcC#`TSn0n%mQ5+#ljxpwV*u)-ZG|4JNMja
zt&=9T1_Hypg<mb*d}K3nTeNXe>9YN{M=fewRQy!sH;(^a;6B+##^NDMMC9S&VHU}v
zT`ZYIXW}3Dm#e~NHUB)&o+^0mI4$+cT*U?f%hi8K8Og<W7EpS)dJ{W~;?oO-8xLbb
z=V8)ltyL%0OYQ9E>?i2wVyOby1GU1<X$xufI=>eZwae==xU7DI*%f4qFMaOf!%wB}
zTIMsldc74}D!ebQ>+o;r_)@+7`Fi`M+s6H=v(weVE`;eq1Bff&Oi7We3LWHYtTUnr
zkY}<8n1fc9B&j?cPRGJwI)l#5k{mu&U>v6<5}%>yr=u~_kh65Y6LAISpuQDQID#-m
zfJ3_K4F)hiORxe*2)Cr%Lc4`_g%kiLSh_=Fh26&$Fo4$>Pyw##2`N|@gKUL5jaH*6
z(B$Q5^YR)sdV>}h1zL?B2ZKIyVbE$dD=TDA-mUBBM5CPx7F@7E0e^YPpwVeHidL)3
zLjpx>F430gH5#U6x~ekuTvMzs3e47*729X82k(h+o&;_*s&!sz4*axI@GMmf{wFOy
zOM_h<1Rs}6UoXopWXVARq5x4DFoUj-v8UIMf|*~oRQUZ}nHK}$QSJPG4v;h&Uj|5q
zat%O60Lv$U5sY?}X|zQet)<Ao@>y|lK0vE0zzz`68UWCI4MSQJPo&Y743CCL<ypar
zj0*D0iu3}q1kDmHveatx%QSjz8H(5Hbb0wGhv^x*l4+Q(oTord%7B?F4s}ojN>C4U
zAYs+e0fHHTS<7n41&F{PzY24&*W>b@rBnW5(3I%>ZjA;VpPz?<Q99sIh6CDZL=ANs
z!i`5p!SOeFEU&f9tbx-#4<IQhMRGXDd6i2G(IicYR;MW$4V6)N^3RU~;$&xtOQT1D
z+QDZV1*>TkScP<jQLvyh8vXl5!Hr`?vFsR^VKLh$U7It;JHhqOI?De?aQ>{2aTF0M
zp^vnAIH>gDpGSTF*+2-K(2OD_{~Yc=I|kG_W1&-;`?tnIX&w=Wvy6qnS+M65gQo0^
zv7ps4P0`rVFsjXG9Sqt$CPr{}I6ObL6{?>g$vHiuo*0z4jOr;{!EcEB<Uy|DcQc$P
zWA#s@V=QL0u_|M9Aj)meFcp#6zn{y#iqN(cg_w{D3Zfo-jeHVGaOw(ZVB=t?M@*Km
zjFW*9W~eSh&}HzttgwE;TuxkktYB;fy&*=+lBUugXO+omHh<aUw8&2PbaP2hPYK)`
zRF`y&_D<*oGPWYQ)C8%yrJyS|Zyt=lxHA17i86L*Q`ON>2x5+^k0+or)Ic8$k~G0v
zPB0;xASy&si)!^I>B38w*0I%O&)O>OmG+W?Fzl+~a3B!qvUS;PK~|<}r<JBuw6~|H
zHwsyFr9Ec1+ABB2dJr4YGfa@<0OfF_T18>GBMXHdmI=g=K@E08H6{g{i~~@x`_f4!
zhtvJ6FWo;J3X#eLzYuh4(hcHxJBrp-KsTtCoWNEuY)L_qm$|hOL>YoE>5rs;S|Mo+
zwYlx?XKlt9iD2ktg)A}y$xxfKErv^aV6(lXkVQY{gDk6RfQGE+MVLE;353fuVf1~1
zTX06nliG}Rokhpbojcys+UiLU2$Ri&rRVKEue7;j`nl6fzQN5pkW8~UWF(yqejczL
z)STNMRE*7)@)91Kp)?8u#QOqYA;|F-JOtCj<x0QJEt5vh!lMMdtT&GI77Lf|ys~?Y
zhQW<9b%8n-hPtO}X(YCijYEN9B9fSO?VVhuNdR8oAw!3XWy8>0NJ}95i3G2QH)tg*
zz(|)KbH>*=r=?Q^aKiBMROIaMb%rcHpHKry@0KN}M#6Z~ArDxwNsGlF!6Gw+i45Z$
z`lz^<8NeC|Ifb0p!gYs#R80YBLW&s0G5)NF59M%`X<z_y<j5e-!arl51ic98K9x=k
zF!?M*LODBu&?7pw8JPMD;D`~jZ;i0YNF$m@z>*iVSY@anaKm_mdV{Mgh`qN9#!$V1
zrM501U&)f+JKU{P!}@ARlYU{fUePz*)arKlrz%sYPGd_SIGC^GuZgX}K7FHu9>3Vy
zQ0t$1G2Zdl^OqiMZH4+w78=#Z0?P;uH&qfJ@yT)9rm2cBhlVQ*&12LPKKg`aPCZTf
z38GGkrU<G*R7E9q<gFR28WXW&346e9@4C{d5W!PQO~Y9V0{WH|G$R)4;WW<tST6Cn
zR^}s5P~AMzld0@8^q^!iNn=`!3n&X?2E&<ooCOxi(W5IG3XRQWH10s(CjTMne`nB6
z2^AvNqE-pUFdtmyFnXvITX+stTEJUIxt%4aQTa&mIP{~OOvnNt`bPuH3Amr1Q$<~7
zpwzL@9_Prw#yrWr7K~=5`cDZ*pmAX&lh>SJi#mWEfFT6WW{-e31q>3(TCP=Mn8siz
z6ga~+F{*WE#lJByCquS8s(H{&$-<EWjiB*LwsSjbO14e~erZ3&u)8sWVXq5NPY~&#
zEf42(t!`tlVFQIK3KOoPMBqiikId2d@pJ4FsdOZjguTk=8%V*fuRtH=eF&C>dt)xr
zWJm^;3!$z_)U_HG5sNk0Wwn4U!D9~j3DPTPQsiGXT;FznYhiIiBUy3!Q?R_?L|edY
z=eM;M>TnO&seXFc*ice{d=cjkIvIt`A+dS`DQpIPJ=BrTV3*Shdj?%`W!D35%D7@@
zmENQe==Gaf{boH*O!_KkaR&>PO)t}xRf;?7*NZfjWxCSo<tT$VU>rOek=JH`FaTQY
zN~U}tJ3hXi#Z%YgNHk@iw2)oRo<%A|O+$ls$w(J4gZRU>&=Yg)j?Ht-W8vQ3BQeLW
zed&+qI_7e?To1TJ$tyve0=c6EE4$B;gok78J{HBv+Jv%?U>Jq0KpuV6gK=XgcnV8=
zd_AhduK(DFnovDdew`2dj$}5#NgnVTpux!y41%fl9lj0igR%B*M>k8f?|A0E4ec?0
z#U-R{d`l518n@9Co&+F>jL<KGh*^N2A_40>x8tPXStL^~kR}Q%xiIO4F+8h)n<2<3
z)Iwn&f(2EsGl1d}*2l@A2D=Z~ppQkB1W?ZB6I}ExHPPV>+T2F3N~Y^NEW&u4VWhB^
zz~zX_fKgM0Li~RaMif4-tExEFmRL%INz8!Hf6+H!M5#tDjLn-l?~=yq>c;AevIZ=Q
zpNKmv9ga%pt9Vk~xIEX6l}0r{ibz_^jsYjUj$A?}s&?iefbD@sND!bGET7{=fa3<l
z2p4R9qsK>U>t|XEN*Wq1a!5hw1GPG0d3MZbX+5vKwLn`uWU+8!g|xCoAuE3&a7N~S
z0^v8T1r2G1ggh127TA(hYqKTeGE*(<>b2@h>p~0^J=2a!r>0l)5w>V<Yqs6N?b70W
zBR_wM@6+GOd61iw4fCsw;@%nO-{E(}{tkXF_GxjT@u6}B@)7~VG3KlzxUFhI3Hs&q
zFdW-arBB%3z`A&d4do=Tw1i|xMiMt48$e3giVX&0<QE_rzN8=(hNDO>D1pup9xf<m
zGI@>QBBy=~6&IwFc&;R=ej<J$34Ndh6x?s{lB*EZ1Pln;8JtDFIE7|G^XQL<`}_F_
zb7F-36m+4C;xu4K4#HZ4h6T9S7w%*VJ%E}D5fWsi-EqzVkI8c9M%#kl8SE2VC(r2^
zXK0#aX9~Nejh;nY@A#KZ8|#(M1@?UVL{CTSWgV03X^MLb0Z+)LE`vHWqa^Y#!>Q)y
z{m!k7{>~t2PO2P28lMW(X%%oN_|PdOwkls$m5&Dyg`v=JeaKx=?ehCwkPPZe?Do2%
zdi&?0-BHK_;uAt403EbO^q&G;O@ZS<rv)!T<Mal@I4yZT;5@H~&`7L>%;u=wU$)G&
z&n<5#EYw$YdY#&t_NVi$<+GYY-OC#m8f#h6g){AQD#sNS8LYFWEv+rGAi*Zn%yG-R
z+h#2)tF(aiQ;#S-PQ^eTIa9{f0<4!SN;RV7Q#{J2;L!5gW~Hp07sZMY_fy-PSl(T`
zc=i;NQ54YqpHjCGNpytHautDGPNRvfplzg_P`rhpwjjtOILSSJTw4-334G?HI+goQ
z7LT>$>vn_v2gg(*kseTTN(bFfrxXSgbhcy-B#s*PZE*M^%0>8FIR1Ox@P4947O_3m
zjm7zc#;Wmb?H@b(L7^W@Usv6vw;A6bpZDiKcF-Wop^^Wcasqju1CW(cQa$MIbkxs^
zQQ|THHF;zNln&uJgCRgYw~oOis|a-(xjS2iFXkxI!c0<M;FD`y6^^=bUb%fjb8|{@
z*LF-<ak$ngZ)kV14p)KJWw9Xw>X-!%nlD1g)Yh9S+N<2gNiI)q?YORS=UCm<>n6^h
z(4woTtv$SAN=L1?Y4(O!UD^V84qOF20UP+UB!wXBBr(dZ;9RZfD~LIMG{69lA6N$1
zyzp_GKF!B{I6vRz^fj01^<~XI=bjadSKPs!>!-Lt9-)0oZkByYT_+Bmb&4-6*SOs^
zpjL1scse(Z5<%hJ%G5|iZ@9=uL$bR3pVUJKZ<aO4QUhnJ0E>t4gV!|{`}DG*HCVt?
z2_`cDlN8QK?t<`OhWbcOYPc|n4CYFJW97rE=W84bw)%d#z_B1KM8E2q;&B&@k`h_#
zd{(>QNMGOT9>;>e3c=7;3c;{!l*owkS7YQo2wyvCEOw$zq>mA2$+g9JI)Gk4A#0a7
zL5$+z!qU>hgS2xcXF0~-Gu|<=`C^ccRkh(nB2`-W6MFQM!ZLa|-Z7=Q*-^`>k{aV6
zG$cq>ZivyudsItCCO+qL5Qjz-E*2fc0IV|douF+pXq%`t#=grqLb+A4o%=?V+fyz9
zQRX>PzMzl)S877kFN#r~AnOqW%j5?93@&m;N_-0Nq4;2M(^xnJjs%88Ts3nB2W8yV
z(cy~ISOAZW6H^iw=wp?-3R#v*$XOfWh=wZYEhJ$mN6f;-2u^loXixZMqS93PSd!wv
z;24)jfi(>o{-VY)G>|k!o@-wB3WFbnie1>PDBaDcx|^H371p|T=FIl=srH#O*Uqx{
z+LO44hkSo4Zq1^{iqolZ%ZCiDmh4jolJC_hbaM2Ne4!_8jI3^!%SrsIy8m@0e16Gv
z#3myAa(ar(Q<Ed%u%{K?xmNVNkOziAkI23Z*b=|tRG$KkDNp2PMSL6S9yBf)2PHVV
zl1jnd8xDu%a74yF0uv&L3rDMv!Xiq@XuCswgajfMg`^LPAgX~61Ti0i+7Y+U9b+gH
zcT|~fY;BI2ZRXX5<;J2th2`ai?r{xIaH^-)h{?|4^73M5c~K7~>M1O9BGk|F+}OGa
zJ}v{>#MrTcvz&GO=s<$tzz_06r<W-?1CS{d6|%`Ry|K|!QxhgVf+37MJGNeuJI$7=
z%ITh{+fsWDROdWeErG*;IKa7|4s%Tgi$DR%=?fNtJwSoLH?KkO^}_WV@OsGqYwe=F
zze6St(q%<Zs$ongdFrdG>TQRtT8*sHR+s8@I;LpgnA4RyG&)&RSxFCc_7Ve}8H!$~
zE3MXOWsUXB{!E|Z7^F9AHE!~H*mYWF*Ax_JbPZaq(PA9At)sgP^Jg_Mpk{4LWFd!;
z0G~UF!)G%Hr+kR3iVTyziiAqxDWEv3@HEz({soJWV}OgBKDaH2as@CNj>1-pC{TC6
z1GldX^v~<C5+Xxn#tquYX%5#s9$C9svd%ZHvvZo)yTE7A=4qOyOzTwUceRnP(xCLr
zM+E;zh0YJ1%;`-3P-|d7A1l*q*#xK4Nv%=!4-N2`EM>tuu7s$gM^$YR%E+zE2+z+^
zMC9mcDb?3E))=V)9}I(vB#<XXmLi5kZ$RH7YAoPK&1o2BSOb$Ik<C^m+#gO$VKO)J
z{EsbOJg#i(#DlN~n^FWI3{lQq?a1|2oS8sV-S93TJVj5DDr&w#F9Uv4dEjbPL>_2K
zyr#Y0xs^R=pO`+3GD_>%*DQPMBN~HdJ2M)q$|o6<jm7n~Z>Lw=C&Gs`XfCcxpQ<X}
z>pZ80v2B%bk-(Ntvfzkq1oo65SAPSBkmJ66u!zLjLY%-xLb0i2^Y|kBB3fTYbd7iz
zLiSzchNGj*^%LsD@QOoIR(4<g?aBA<us7Q5<_bG&I<%sD<+I<uddI!T&30R(-HwvU
z(n1!WBEXVU#sHKs+0WuJUkv|<!Nrl&zYs<v$-E+-7aTuhBSgu$o(dL4JZHqm$(z8y
zVIJZcNsW@Iva&cu!;Z2#s#<m?r!72aHIvCH5x7dYB{Wik18StwcUAKaatj~`J(`mn
zz<P?JO(Ua%Sk1h?6b&AdL>p;^6j<5Jb>2EN`T{L==eCikNL`0@3-eT*mOi&<eq=sV
z3Ps}zYY<E?l@+qMB|Wr9QEn<OOMgUEmlc*3Hx$EMM)(^7rV%75+6D*(mI1~Qw5sw*
z9jtmNg2fj=TwCZ^i2ntjp-j@riqc)XN-Mrhw=(}^t0z;i`LV|U#?f%Wm~(^wQ{<et
z!WzWz)~gl@CLsp;gO+U~2wKd-3R9%?2Mq}xD}7@oLU<I0uu=SN-#&->&-STjxW#KB
zXg5i0Am(S2w%{Xz42IFl;-|P!&UfUesWOJhTBd5mLLZLM9fd6B<O>viPm(Z23W7r-
zZWr2dM`yh%OsEKfSvW2pIY{%?h^k>!V{`}+0|Izlaat@_=9pj(FheNbVW5aW%ysGL
zD64>wG`oW(<$k5d@?2FzRaL{gd~ZyDEXUR7h7R=|>IEL#imoQ?1T8`PN$4<snzrKa
zLcds9+&AdEhfmAOj2`Orl?JEJX#mR%g+L`Sy^AK3jac(RG0l~g=y{S*1oqO*A#NFg
z;0>)n7sSLN_7yA@0Fk~!pN{=@@oyKiKDx%GX$Y6}wxHF-;Yl+FQtDLUnu4dSh{${L
z$tT$rqTq^eezRhD>!wXw&`#)4RmD4Yh}mK>(1;lF;PbG8WWj{APL9nO6lpw4$KsJ;
zpD(VYpwe*aLs7d4iZi6hYxt88bkF?z`}6nvkUZs!!<>qAs->6WX(?h0c0m|r6PVqV
zNJIvx{#aj&)2DoC7RUOao~8kKy<HedpitaeZb77&xF(kr7XiU8g5$K{d$4clA#Zl)
z1-rZ+)>vAtbvO%??!tU~t=UywU8L9L7nE7-Z4-P=d4W!ScU^Vk<RD`@Luq04Bx6Zs
zk*lM~R9?o6MTXsOPrj$bQ1Iu5;(WWK*3sx~Fjkr@4U>cQfmz*Nd)?f^d;~A)=E-Fh
zc|~mvWexRq3#-=VjqXKIcd{JwAm%`pHi)=6XgsM16xA@N3n}7m$yADF%D_y*Ljo|1
zjyOM2gg9ikC@_)Rk-&XPawSI{MJFH-&M!AmPyof`VT90;MVq_3n<Ejs*K3dT4W!dJ
zL^8Knbn1$!O_1}NVm@CR4s@BtAc_S2=dF!luG{>xIWchZ1aCWy2x!Wj1VTmyO0cUJ
zBp0=Hk6&r*u<V%^W{!o)g^klX%V$Oi-=Y#?>X{7aNp5nDb06ujkB<{Ud&myJ_1+PR
z8XYueIF;|LTnd9!B}yunA~ek9PJM%eqgc}nib@b3T;Y?kSgd>sTIzxwriJ&!<8bGE
zZuOSseBOtUizpqnR!wPuTLhu&a^?lN?Q-5CZ4mF~az2$C%a)8>ZMGsl&Kp1$zCw!;
zvg?HuQNA65!FfhYdAWr->GJ6IF}Y+k#%wO5WQ0)aB5sXI@PGv_rlKw>Zh2v?2s|LP
zW<HR6(+FJ?R7K1%q4<vmL<`W0hh5S{=nB&j%hv;*4X-6(bY5l);^-CU6?!`14?&D6
zfed-kPtvH1ybkn)g6k<2Mfl0dSOSqDT(`yEW<)Rwrgwn{!61lT3{$T#5tN?5wt=*R
z(8`r`g2%UG57EwOOYjL=<YR;#Q6b+9HsppI6zh-21IP-!Odd{(D$+sn47l9^9p+Sn
zVkW_$lz39`l(SM-8PyGO!zrk1Y=pSd>_C$262Ms=Z391=fdU;7&}#ruW>Vwg^DCM+
zI5#v`yv%JKv8bnYc(`>H;T+bYV{d?F5GH{$!Da{&iI5uT1V!_9TRV&^$9K0aN-mfR
z3OuvCb6O)tPmt3ZRVvHG66d+{{6YU%>IGqko!hddaZ5|({%u*A|B~kBJ<O6m6=GR;
zH5lB7d-sa=3kt6X&v&9;++6_Y?x&JS=t90o-x+k0DG~6}s7ft`_%uQ-Lm57jBLiZ#
z4zS7}{M$0%G%f;~s&YiYy^sKZR$Q)1faS$5k^og1jrzKPtO=N-1-Z(IGDwO?xLDS(
zMcRy?cEjPYB@_ZMa#W;9m2pob3?~>XgwMLl<g*!v37Er18;zW-waQ4df(XxLFh%G|
z6mE|^;k_EMpy8UX@Qf^7=E~Djr5VWD3c*Nd>Gd`^F5&MSXK>2R&9c)l&RErFGe)Vv
zD2>)o2pTNOW`cGb5dA{F6Y|oKY6irkAt#I`JjNWfPsT<*(U2UrBw(sX(PRyc#}OhQ
zhuzbX9!`;naWe*6jBKDH_c*8mMKeK0r^qSdScu>Tphz;PCle1!;+wK$LQhZQ`0<TJ
zm;^lG2_FA7@bjHZe3*HUa0Lv+qsmd3QdODqml8>AnR=_#TBYzo8P=Tu*>_;o4Sp+U
ze$BCP`Gy%Zy=E@v*+B6cnOkGu-eH>@TZh>-OEJqPTh6cl(Q=IIr?2DXtgFtH!>O-r
zhu_v6Tf4-$WQp@!l%wKU3N0(){Fv8WwUwy+hZXgfZ*R|;YsjM8C)j7k(x-B#8|FZV
zxPyqjpePe`pwO_gLN{a!ND=BxB$}KKFgN9ZDmxVk;HUrL9B_?HMIw2WX0Own7P5l`
zG1_G?GDPizPD37*y@bL**^r$rwqFEegm2)IXkzBWuz9hY?CB@%2hVXjWlSC06Ywpz
zM}6|ci%QJqk_-o@oF#&b*_xYgW)xU|^=^XaIDp&|EEEsy8ObZUhqBoNsWcCBUlbNa
zPQ;mVX1S`=jvG?=0H!&eh$~rFY%~_%MLSm{g}F4anJUKO^owMMV{?j)6cL~q$yG=C
zeGvL5=Bc2es=bj^CQ{Ldi5KPO7(Tl9=+Kz#*hp@WK8OO0&4n$>sS`_#c^#ZUZR0=o
zeilX)wFy5epQk&@k2=<knlZ*x6Ipv1Bm$?Y+Eh?cQEw`1T$@wbKa|NN?xHtrcxo*-
zt!ZikUmguk;(`i0h(4lq8PEo|7vT+<`9X^*kTE9+<%CUXY@Bl5c?adSq|dIEA81;_
z@|QH7ciw^f#GU)_t~y(h=JXZ7nuY$RC>EgQ8TlEIF$3H7jT@bBl#JvcIm&rw6p+GQ
z!YHih%00dsj9Lq78{~7PGIa&gBfOY0mm3@JW8)p|=TVifPx|D8(;W4O8k>HT{(+-?
zHP!n1f>}!Rz%&QgOSbL;26jlrXN3c~ki0a{4xFySz|4(}lXIZ*quRPES&p<97M=;8
z^&JO0t9&bbk@l)eM4r$*;4=0H_6LlMj2r+DBv=4cQOvWzoG*k6;lgi#9MIl0%Qvg3
zZ06OoXRn_#XT8{er>ZKEO!{_?+?YN4#YKw<LfM@83`CWZsd6S_ibs|3I-ddP#H`Q0
z9;W0L!NR2C&%PfIdioCK6hvb%TMBF_Dj(2|%2~D^%jBA^_UstX&6H^c6_=YE*Jogn
zf!>8!r5rfORwj|>Au%Sa@8@PDXd*?HQd~DIJ6N28NDMSs;_DR_b7l%1@pmT8Z5|)G
zaK+(mOS<MSU$$hMX?)@QuITu><%d@+JCGmBKX-iha<)1Dz_K=PU9}C1zJR-`u`wkW
zDODshP%N+D*a4gcfqF1h@liwZb|6F){DCusHgZRsFXULe)-mIG$BY?{wdqrtn^7Ov
zQp3I_^mHcvXFAr#=_aD?!=QQ4vNASZvKN7Uoz0)<mbr<SN=4viDjcPFk{HccPM;ct
za816ycz=JgxFAJ0Qw7D!a3};>NXd!W&*~6pof$PJ_bK{S96u!j7?OyO`A$(>Vs0ET
zS5Y9tBN7ml9Q&l0F(9U{iC|;0SCLg;hHOvX9Evv@!6%Y}5YU0rF-Z;LN>>+YD;A4B
z6ICQ640djFv!Qo}Z$_^{J$aQQbrjQkmmgY|`+%p&<9JPYms{?CTI#2k_G#seZdn!g
z(t8OH;Z-1ho!hdYj@k<90^Ecq0jmseDO>%s+U4CHf3(wF&z7KQir&qZH8<<Zokh#L
z9d>7}8@I3dSyKn_b)ubSeY*7m5W$x9K5vcF?&w}#<Sf_G&HtNq`k(KKERD}%xI`8;
z1LpeED7ND0rS&Ypm%^lrl0XejC`>quHIfF{Kw4aI?N4ZN8jQp`hB?9!hNu`?b0S~r
zVjr_4x7UFawFSK}GO}mbv(K`b2hs<al?@HCbA=C6wbIgT4RC%&!L}sw!=4WQLi6)G
zX(=v0mL%a5bVeBnFy}n%8wgq8Mzd5I`^i6))e}V^BxZB8!4L}7qpG+K^FveIs~F2Q
zjbh}hwV)LW$aCqXRj;{s02K)602*!$Bbf~Y(G?H^Y*z;v<m>Wqi^MG%(Ps$aiGiTe
ziLXBb!O(2G4B{)ac)B~>&!6$940Y)5_Z_Ar=GZwC!c5`!F(O0IE?;A>fxAOlg8Tr0
z(CQeZtK?y0>kb?^Ke1>(#pJQq4&b<dQR4jc>xl%Yvl@FqK4CsLo@^cD7pB-AswOsS
z1#M^(DaKsq!#R1{D8-4+GE13}2qz5Kbm*fwBLu>XCswgo3d_o_q4kuCEygNXEyXF>
zHZq|UgA|*lg<O@5rm)jY{j=Hlt$(wIqarkxjIEjoz=(}h4Z30S?6u>tk=b8>t^^w|
zU#aYGmP|JBdXLv{vA7}gP~bE}d{K}L=H!flSjaZclN}ZgDlBnBph|yOy`*&gE%{FU
zEVjL{@JNBJ@U&D|cvXSDu+!0U;E(%T9qd?9QJE~?!RK5TS+Fur5kJM7?8v%FYpz4u
zs|pJd4{0krQi#`@_y6%gs{{3Czy|vA4$ZHi7C`P-Yluh!Ly(QBCO9$7GA@tjXicV4
zGk<pEv~=Vi#zfW9r}^=wk;h%U4wHM$N0@Zv-t=iv1+4W<Rk3Ftq9&>YD(FbYipPCm
z7`Lh(LihxoET+i#OA!8$#g1J<tF6&5I|!v1=YQF`8{~aH%YZi{M+h>0GS*wM0co)w
zR4g0LgUMPpPhF)}9#`$tGJ<WN@N*0Mjj$G`hds8c294sjBO(zX9U1)qZIBs3!|<Pm
zg*U4oi)bo5CC>wfAX)#AD6G&t05%Xy4}!g8{QdVt{i!mX&_{?SGOV*r1U8m_7i(_Q
z*^KnN8Qx717o=_Q7{j`t7vbO=**3c`eZ|+VVtbxvN7Faim9HJyn7;Y>9NMe}g!70j
zOCN(Icd-D-aUOC(Y&Ix2#cNGK3fYhs>^5{b^gwyAWIZjrMvKM(_Gbw(VLd(nuGg1X
zs+<kpmT@iK24|I}M6Ykwm$yv022?JRUs94ErJZA3?XmHnVO=_HL1{787Srhpi^_^i
z3$(iNnkfsNq&z!I^TU)hoL}m+I%+Lwt>7!iVX4IY6|+U6VVDO8JPa+sh}p%=KG!~H
z*~fJ)3VUVu>n+Wfu;az)6Z7qJHnD)cqIvbruN87yFKka)9ti1OScEAGA0g)CjRIw$
zsC=l;zy+9a2_t-TK{|RU66vRXlAi*q8zm2{sKcCt5&I%;k;A`801puA0&EoqWX&Ts
zaA2XZTxAN`?2UF?2(zoIJ=Imh;31P=+f+5JwAx&a|I%qyrsh(6h236JUD7-NR-BQD
zslQU3qQSkQuIY33?(tI385rh)7(6UR{XrCqOUSj&&aUR}p3~BH80shJ6QT$BjLu?A
z>nw5dq14?xWgQEL!wW!&Xl!)AYeFkGw2*HVIu@FZp2);NtAV3BepBELttlwLph~Y_
zdh+muc8j-l{SE7RtSAe+YGfZ|Qwku3nshVwxw7P;l@r%hyRGMpo4tPh?AAp*I&|eq
z*CeC6s-42qMC>TEqauXn*y?Fi$H99L+eLH|G7c9dU=<OFOZ|a+&<DZs2&0Ysob@^p
zAm*Rl0eCpEwj~a@V6hL|XU%G7g%tWWM36Xkj375efl%p>=q{Cq?^>~5z@rh^<j5mf
zfMY{u_e0br_c|wCC>1^z7mX#k;uA}a)7VrWs#7$r+DWzc(0ZRUROe!?noe6Sv+9dw
zz}>4KH_qUzYq6F!lv}6OG#SRV<~P^0SWGosXAg0IW)_!uys4G27#kh)Fe4Ii8azS+
z!W_*1Ope6{)PJlF9HZ~Gg;4t>YM;$%?EI-9R??U%%^<nR6_7DDNV_Ack)~vjc0^(l
zcZ=YhY<|#Q10=Q3rk<NK*HNlT0`cr*=|~TX6I?*vq%8#_NDNz&`#@5BvQ>=22jObL
zl$aE~1+NGu%HbWHB!r^`>J{1R{_Aa-18>kd`05~_CY(M797)C^^Dvzgv8QWl7hTg)
zJ*R7RQ<(x?({tJwS&pe4Xwv}g_%9`D&(Gl-&D<x6dq8-B&?4Tf5#JA5i8c@Ln`ohE
zw73$!Az>AQdaS`8da#7N^XQ;D=vQ1^A-MqBt42yo>?^*-KJMe6HMn>X7W4tSCLcdt
z|DBjXy-!jpwU%@>jtMB3pg`9o8B@;_#t=r(W~Ox5X!^AgN3=X9U_@>)^5(~=N3o|4
z50ej!rY(t{CUg*B0+h%~h69He-bF&30zt@!1{maG!I`rG37fg)g6f(lqa9SgfS=dT
zOqaM%m`nGmm4pRUXR1Hlp&nBpf%_5(hylDR(3eDoVhSFjGAu@qeONt!&gl-d20yA|
zrlzRt-!=MFOtqp81V@57!I9cQb)$9LcwgY0>a3nqTDqom95boT^dm5%f|*M|Ui`8c
ziQY(YKP0tCBD5qbg1bOTa%AERPw-E^N*pA^DA?1wN&^1emO}VIp^8M8h=LG&2|toR
zf&rogM4?bE)Ph(o<t!$xr-2&y0sa3@ELAa%TF$W&M_SpAAVT+Yj|5ffT@`9{PVWzM
zpb)qKCm>~J5Yv$WN8lr%qP7DgaLGUk6;AMf3}T#ccmZ+(c93bZcq(Sd3%?Squhi2N
z8Dn(OIHQ`<Mm6$hj_4zjepNzBLx*sXY7#@&pkq)t>Lh-DAD&T}1P#I&f&f8;p*AX&
z&xM?NPU*easE%|G74dOeP8h~JmMW8_fGYh1bQ3CW@d^V007oRoZTy4k(VqXKQT*!f
zZw=LmTElCJO410Yd$fWlZ(Zg&-Sc82D68+#k&haV01EvG+GHZ(7Xk^eV6bS3sH#e<
zsO7jL#?Gil5dXvf**Q7Q45io)l0*4CPn?H%UI+l;(8L<6(7BTUvVc(RZ{$QAn{rV%
zo>L|l(Kj*VMD<az=F@9M^FQ6#)D=5(@`6c*8#t3HX#ji@f3t--b)vfQykZChL!~G$
zI*zRvcw8KD`xo)}ULM&g?Bz5wS41pXNf@QIS)n8{XQ>J634}<i7(cG1{<bR{+Mk8^
z8%l<-UoCgmw=$(;F0oIFuSoEj&9J-x{xH7>U0yFujzUy~7li3heM^~t@<cuBv>&Jo
zb>52Lz{SlCleN0^G5di<7u`x$k1QuH1(sqYqgi!KHD`4N-I%|~RdqyE)68sG5;$v)
zW5K~HxiJ0CE1Rw>EZkFAQe3#VuyCut7HqnxwVE{OVo!0)#>IuUf;~t8t$eE=?roam
zJcWIUy@Y5Zc(24<Gg6hI8o^l;bZEA-@%9Ps)<9479H!(wKe1Gq!){9dz&_sD6R7W+
zb8lJ63q9ps7Xhxk?vMjB@gQbe-ObEbccXZaS2p@(-<~$>m6dIKc$KBACZtm#%vq#0
zZ?cq(BKv5iSa_#sWYK8ilnj7y!$FQqxa?CInn0r?lETOV@)6mB*cTqK0B8OSITB?e
zZw@lf=7<^jh+twA=EAcizLdn0dc-*pIRMOw0dtA~DH>ha;AV2A5|ih)(#8^@L?}eI
zG^f-94d>a6ObkCT#VQhx5*>t%l447s$)z~LO9Ju3f%!dwK+k-X4eG{xzQOtP@sG9y
zq+UqaM>Dx)=0wpLS4SqF*#f_K)>|dajBy_43R;8X5pFI7+K&7q1Of%&KfrG>GaR9&
z>aBdA(RPz)t&r<wX!g*Da@#?ajl&=aje*Xw;YjC!+>%p$A+I;&G0M<+Lq3@}qG({m
zQqhe6P{<NVS|XN0OCdp-EJWl#Cj$#I<fD;mU10b*w#GW>V=NX*V6rb3GLT1>m&IgY
zmPjN?%^D74ns7!HC0vgpQjr2a#e85M1&^`GtIiZ(DCQehLJ+_r_~Zm_cmv<><dK3D
zwZT_L<uV6GH$sbDpaP)~llwwZ1koYREdgUK*Kg@90Xf2D4m02|7ZVBVh)yM#ua6e8
zOhKAME(lQxi5iNw(<)F{sV0Efz<^f`n*?x?2TSs`ffJ;=0^t!MMst`Wr1)YvNYq)i
zu<<;Cge6!CdvqR{c8ZS{M0_F-aG<yk;c+>6L_y8sT&Dw7pgb@mJ*)RZ|K--xm-~7G
z&E3s`s1k;6F;S~1wTT22dKxJhL}H}C@I`iLEPLP$z=PJ;7e6gsdo6}aG#XN3;5)gi
zQ_|?qL^=rh?kwwGVlbk{G;v%t&BY^;!NLB1HB?>L>X5H$n->_&ZH-wj#-kNRmOmJ^
z_5o%GtE(S?3P2>nKVP~?UHl*i%3?(nzLKTtU@&)fF?sLacml>{ZnvzW1yW)-&8(-8
zjnh%%XKE;lyMau`dJlCKcn=oT=SMa6MIGDBJ%3WkuS@RX1Nkz(e<~-!=GvyZx-}z1
z+-&=oQIR%kBqqgSQ=AR-m^w(b+$yJ5Ukw29le|rlsizcKz?$MHWo5t;jlx$M%S;Rq
z&<2?ls~rDtMFWR2RtH+IO9~q5U{=o%2dY02hiB(AU+?@;vqFY?W4!@t3k6u(z^MPx
zwMJCT!ny)%^cor|6>}nR=s<m=kWA`$Dod^NKNdkB#h9XaOPaey_IsiyWN?fkoGr#R
zom78+EH+5do?!{}hlQX|5e-Fn9Ha;^NSLQ3iX{clrP@F`!Wgf`*Lfvc^Aq+g&>D)_
z2C;$>jx3Id0PxbHFTqZ@RbhC-)HX~53Xp^V!zq&dpu4@q$guF_D=fAwj~QmjRpn(3
z72e1F4Mln7<)v%2`Of<GfTQ+gS3`MezD9$gq+>?Y6th0hP*&5izr~`*Vw;6JO!_LZ
zy0IQyHIMcVb9suaO4M336ER;TR*SiP5-r{kRT7a%Dn)h+HL`$G3;9b;pC<s7^I9Lh
z3k7ii6fcs7Ap>7(AgUPx#4_b^`8nss2!927X12T#V5i0jQsfi2+j`;n<KY)#Nq26f
zh!T-SBpO_ZpjYpZ6EVhQkZX!ggZq(qAvpn1hvlflBd9|K{D?vp%P|CEzG1-2XVQ~g
z#7%)aS<v2Ut_#&Q!wNXrUD~s3-ZSRX+EP=<Nv1&d2;{Ujqd`~!E9O^CnpdZ<HRyyH
zkhfW23DHN+rD$~;4yh!`Q;>P`M|}K3sxu)bvK}-1CL%p8r6B@-gW&mQ@FoarVE({M
znS=osBA5ID9bE`o&Lsof^1nU4+TBy;n&+5X->cvUwG03tqK-migJSo=(k;GZ@)Q{u
zkOI#KNmHT};Y<otPzx)Z!k*2<$Z;8#!L#MYChTyqC=nIwYjL!szwKyo?DF}d;MOs=
zyTvhXoTH^S76Rc4_4N_gQlw+x_5tERqDSk!SkV@nn7Pnl#d5XS*io|NHk5Ri%*ED_
zd%*&53_0=39B7=WV^KhvfB|d{L;C_I3<J!RCIE@y7{)f2Gr%zkVl=q9oWYJkLKtM0
zC<G0XGeS`zXy84S4%O3qeTseKwqguSKSFR_JZwkkGiZeco{5pqNH}J!8gH*`!P2^u
zCe*lBPOw%7w7$F<6ZY~c1{arfO|h2R%PJZtl=*ymmwmjYq^?A!EA@L8>bxzgGuL-W
zB7#(~2VV)w2tpj9F+em*+>J-ligBU}BlTDSSj-X;@wJGvRc5vi(SUiDEaXS;D=2uL
zhRslIb93#nW9{EjP3(#cV?E8wMj2{s4=k6Mm7t1<aQ4wO3=6xvY2wu8^l+eOdYinU
zW#S6v%bVhBp4t;=pQrP-%_HcmP&DxwbD~BRM*^P&uh<CGda19AI$VwxLmmYO=5c5x
z(Ah4(yBa=T&&c;UG$ZxW(OM@IgY7lA&#YhSddIeA2Q!VSW&C=_)@H~2(N)dnsi)XY
zHy>8k;F+1SXebhjj%_(&yrTo7b0n>e{6N%;X21b6f<;#_im=H<f9bMuU*HXy02_ez
zK{xf(PtzYzc#ZOxK+jx1t8bdvQd{S-IcK)lTAkijz*`|lNI6B;_<91J^EC>p5Omg>
zJT^~J`^=KsD&7ZbFPi!MVbKS?EWJTg=`65gaq0vV)!1EBMs;B|W55_gm!Oa~H|j8^
z>F9U0OaV>57h)=+@Xtgcg=E#p&M|opLwt{q1}E|qT>4DDCBhAS#H(Y<a2#$G!n`w5
z2Ze)=r>3bi;g}LZyn2j}CE<M}{zfkBk=S|?cv5haL%I|nC1MH4W+^V`KuQ)EF5abB
zF)s6w^s5dPF7R3TH$ot^sF}cuo`QawlM&D(4>%%nB1#4Ogz7iU{T9fWeB+ZkCy52A
zLb<ueEoQ6LY)QayQB}WcR53InMJYu*5)rgeFcqvAcxJuh6awyxWY>EnQzm#TH1W&~
zY+6~Dcm@1Bd=3oNy@Iq^Gjijznsbi?8Xm?>OUZ)}1G@5>Ym^=5bgxjRHrqUq69}~N
zI5-o8JLQ@+i?=JwyPKyfm>fs(B$zF$Fw_a4r-)2ZCefBUsYx2gdCS-W44DeRtPQ_k
zK)s|`8z_7^#VNcdEVjSmvr{7@6-tgOHBL2(4o>Z@aP?>EML3{hJADle_Vl^{!lfV?
zl46&Un9*_I{xqANI*La`!K;!YBS@xyfK<py2G1SlJeQ~t*x3lkNM#5T*&v+C23W*>
z1HL%5f{cy`^dYS%B+DTo8;{D7w7;DA4Iw>1a`^N-6WoY`@F>a^vIKPsByMiO2!Z?1
zSQJ(zvxJp?$fn@M#^nPXX<b&#Ovf4D#wZb(sSa5)7LCUE9!A9!!nEiIkc$QK6ikPK
zV{{NSA{2q$COyb4`&`2idWH`*h-G}r6oE?frh(CfeH_s;ZR6r%cmNZ&Y(}w5Vg#aL
z*@DuLfpXWq`scsn2Vn?fEeUTo`jR~HG<wN`lL^-VPbLI;^5}LC7I#5NXgFl^u;Cr&
z^xHfMg+AzrZwJ}>&jDbOlgx8M^l)xYpORZF9?s2g(B@I((K*t(oMeBY8H8#N=K7Z5
zhf`NaRejdvw^q*~jKhPBSv#3yF6|(crzt=_3-#py?L(QX{w$S(Rfukje>g<?MJn|U
z-Or6$BfG~}VfR>xaSs{|A=G;hB9ddc!w&?bgmf*wcYiIVfJTEPY#tIg);_}bl;U~m
z3ViY83Q9rtU8~`F{__1I3o7Gzlo967>9O}7{_6801L}nsdLahcU1D$ph(eO-pD&;U
z3!wNcq?3ghbupxjv8w^y0wMoHMnQ%#ltHz2K-PYRpTH-opl@j`sjF+NGo(lx@PVpf
zIX1V~5B9}F2h=Y3yShUP52$_csXZb`PN^1|5HtZ;uJ|Q116*eQb7&RG^a2{tB1sb#
z;6PY|l730R0Z~!WSOz4V5|P9j157ZLjy{^iK^&w>x(T1}84kMi&sZxNjNar|q`5^w
z5#xZ)Kl1%WY2^Eh-QBt0U;OW**d*nJA>|252#X}qZ0edi&H)hRfdx|ND@sZl?HB;n
z0da<|6#^90H);I2va#iPoPT79<rp3!zW>?}P68T<mC&->B+6G8V2)F#(g>Wl8EwW>
zbifWUR7=VuN|fbK0ZxBL7F}_T<xmJYhCg6fQRJL{&$TJ~vHm6~1yS4#o#(o`&`CLU
zBLc;Vl$iNMW(F=_`3-o7Jj{qB<r8bFFtrvG7~d(yFME;jg=6u-K?*w=`$(jNV+>*+
zpegJW??DzR=5`ADSV|r`gJO(mdWCDafBAAoALC0-UEa^$dt_Q~`VIOT=mxeezjqpP
z$i~I;HE$>?mU?n5FJaq<?u_tHc@B__4EGb9Ewao&tJC}ri`Wn<De|LOCz@~0d9TZe
z*d`+W6=&obPA=p2#=WElC1}U<#m6<_RA7%Gq^Y0OHr8ixB0tfe<UjbIm~){;{x0D3
z>+luH5>X-2*#-9^=L)z0NIWKWFdpp(L5DlFu;dCGCf|TIG%l>r+>UqB?=N9Wy}cuS
zrBdi+-%r1*u$c^Nh+>*YsDGQXvY^=g4x76q{R^ZC4VM*rr=RIxs)c0d7dV!|E56FM
zDhX3n2&;m82_y<R#58-FrhpUphMhhqzQ8~uK`gqRQrfXbu2VKT8k^di+S{6pEmOK%
zK#l1a#adsK6uWTvN~4TaB@GS67A%=g)?MP|f?q=LC!DAHknWUQmpV5lkU>gelZwjJ
zLRoS87iFNPigHz+wPa7Gh%JpgSHaiGZb@3U6?suO9ylxJlwhKp%%tSjrAxOaCoRp#
z^#9>VY~?K#6}PO6#lK<RJmaP52Ptw|^-JXPa<{0X$}ZF+)tQ-9QH++n#G1xrCZgDv
z7+{5UA4%Gy51}RT`9e695L}2KlK3#Moqw7YTnvPSXtNC3Oc)Md#@V9CN5zQ?J#F;5
zd=!mN^qrz5X;mZ`C{=6x$XMZOGYuS)GIFK56MP|piWO6_js^b!2q3$PL<|JFL&z4Z
zL0*A}Q&%u6U)9S-BA;ji^fB;x)w(HGqY<(B>Nl<|!by-_mqx9~*m^*a#}_>K=ax%o
zevf}sy{*b*tZFT{TFbv&Zn2cZ)=!Ef3qOY#MwqdX#y|V_RSlJu4KuCf=~s9ff4P-&
z$uKkkF}6qKb@~Fz$eLTUq6JVCGq6PHKZFW+$B;es8<)_<7u3L&K>7(MNGgUbo=eR}
za=SDA^7kSMqGYEf+D8$5m>_zV0zKno4w@IIXAqAwIcDft-5K<3B-eO4c?&0K&k-$4
zr)bY}7Sk`-FLASvZnAz$E!Q7qw0amlBEG#qD;0w~f&F28LsvulG1AfhOq$g@d$?`Z
ztTx(k&ZNxAu=;>7Q`HT*My6^#XM9H{NzQH#Nqj+uU>DB;B{&fwk<jvC)@KPolWaMX
ziQ-a0$9V)Fj}`D+#q`0UY4U(Pjdm=+rx=cVp!49tjxTi_#5g7SF$5ZtB&!vlNAD#I
zDkQs1{4I@rgc#Fj=Az+<xe^wZN^>GQZPlu2(eO;n-lzV-{Qa3iPeD#xju7%YC=wSr
zNb%&+(kvW3E#bef57-w?68Rz1GkM5l&@vUr>=<)FK`T@#Ug#xVe$_t~l*wO#s*-Oa
zfVoIqbK%Y)P_J-beraibjKaeA@h+clv4mwAWP@WPme)w6O7c^bD3xFGGUsS(Jr(xq
z3XjKJQ*HJ@+!Kl==KGN)0X!2@BGCgoWK2oQ@JzKfpkzdQWr_t-S0*RC<9f&E$<J<@
z&g;Pq6)N>dH`CDI9{8nvUq!YJ7=2ZZ5FJf67zHwFigWA+bXiVW>Zn(7Jp0+mI0DlD
zfv-wuOQW`8jN(fp+%u`RRHcLrACJMhw!JyNNM_@-Z+Mgo5_m84M53m|qc8^<i^y0<
zqzz$r<OYPs6)PHPixXVJp%Cw^-|<_GD;RXI(JNY$O&Zo2&?V?IT+c@roOQjY7}5!O
zou{LDlE5d7Vw%<v4>N6-n^tu&mSKUE;f8js=AZ}fQ{gTkF?wzH<<e)RP0}`6byUP&
zSUc$3cUa0PgBdEEbea3=b1%~PaZinFSFa6Y(`BwLnf%E|Og+KsX~nJ*OJQNr#$4Ek
zaH9xY0DCh}LU(#_H6L3SwrQpkM5^Om?1%YTH@(o4+g&coBuuwsv~%**%_G+#I*U`9
z`8<QQ{J8{JsZ>P3iu~J6n8h_gnkLPY7J{RlFKyr+Z_d6v9HT51>d{&ckW{FUp!gr1
z3Z*eA)i+3p)?}U$R8;8DkvY^>ind}OLXD}`>0>;OO~L7-l&JW8J}CL{H}|lZP-VE*
zl6e&8?VQJNVGr0Xw^$;S*B<3Vo~eK&AH6epM(K~COG!NK8vfpe{5D85{5}EreU5?J
zi8;~qz57<IM!HH`EA>e`rGrvTx>CAM`hs+nbT7H0KA`r$wFBtY=^1sefnTYZ#AnHp
zHJji8%*KLjL^R<U@s8ZJifl|{K&FCF1+N9*?~uK`Tl{<v`A@8n0j8&Zt{`BsOy^MS
z133EC3kFjA9!Iik?QC`RVn&d_m;NSeOvNSU@}kMO0fAiqAbMmoe_g;^?4PGQCHT_9
zM|e!wTUJ=k2!166JkKG(Jj9cBTP&y04<QbJ&B$$Lu%{oiFVScr^_z;><f3wONwEo~
zYY`iS8-KD`7|<FI*YY<73g64`EJrTaSnwNQ<%K$03c}pWA4q&vK+$(9HE?4#iyxZi
z4EQ9MVGZkb(q_<uJ<>(eWzyBs&C+esz0$+d6T~aT$W?n%?JpH)MVF{oqSrlR-cjFG
zQ>o9@t`J?7mxCig-fe2fiVjt2m7e2`n%CI8nImUVOyy9|=XVfdScFbQ{~Wbgy3go3
z4yoe%dD14HjEEF|gc~2>zywxc8J<V^%UvqUh&RQa_3+UmhSEZQ?_2!N?<|)2{E~Vp
z$<M@q6n!Up)5?=x&7T56prBgfNDh3;zT3y2o{Or<e30h84|ym{^~s0H`$6H0S7HVd
zjz~e;K?3|3(IDVr7!dWC%Y<NxuGGrIhgVK)Yo9Qoy^W=ixVn1X?se7Gcqf+8D9(5%
z`TwYU7r@5O@=REtWJ$iuvMkAxZArdKwvKOOTb5@$_MFT;<4NWgc_xz!&Ser}1DVha
z2?+sl+du-8n}(KMAf#<dEht^Kp{2B-rj!=KrBLj@mu>&_-hcdW>EFL;ciFD8&+~rg
zNV3Nh=wD#}ow1~&Bk6qK`7ZDEdEfWkV~?Hdi|s#iW`9h6)6nt2dmiX$0N=E;Mlgnx
znK#81Cq;)tFxwGw3a2s90myuz^F2hndWTW4__u5GQcwnL_U${q&)57r{~Khb_;F?A
zu=!Psc>k&4>ZoQ|akIz^g#Q%XdZCHt;kKZjZswK>c)%Vma3a-g-a#?tT?p~}Q$8(S
z$M=-;4NIbKAgWbDZ6&yd`LSfNFvv^&n#c3Sxi2EVru?U%>iyHbzAp6<yyZ#HUnQgc
zlLi*Ecr~>2=Y<Wa9olG&-gP*;C0$2q3WOQR6_9y1<>3@i$Z%<TwXNaXbvFiYoI4);
zm2aLHJCJzQQ+4W@y4ia?|DJFE(aBFD6W(|9KR!`-+<)Yy&j&is4B;{VOHDz%KjD0Q
z!Y$HgPNYxfB&x9p29P7!QhjaVbIMjuza!eOkxw10+SU*D56un@%@(xgtKd(mYKFhV
zPlh08uVTCkqR~*=f}9-)atj6}lbLAQlB~1pAF@$%6#ok-F~s&Ro4|L7@9_<ie;c~S
z`U@;F^a3P$nMnp4=vjn25<!6FzXqIwwlUp$J@CAgvn@$3WDD5<HbV5SHcn8mfW4qu
zKen~y%V;z&uFh!wsz%F5QsTAf4DA$EUk7YT+dUk%jf8P1SUu$E;=twd<OI-k2?sF#
zrPHvNAOdV{E$SEVFoNw7kr{+82}?P2t=d229w~`I!t_T4H5;V&(%uz1yFou^fEpaz
z&4ASDlm=9~*mw}}soGs+a}t=@nVBALxtx`w9>*Wi*+t|uvlT)sfo6j5tmpXcf=(||
zMR1e9cEWd>riE?BnghE90>ZyvZ*-NUdTI8`4jt0j`0tT+fAw13;(D+-K|LrvC@|~0
z1-aIDgdf7X2AeDFQ>Jn(?fas3Pm19Ki5|-9u<;agD<`_N#>bJ@nUqY?y=|Fdx~f?w
ztvk2%3Hz0cQPu%dqX<2Lw5MJvTz6ES&(<6lPCT%0WU#fpt-bZ+#fz4zsd=jghQCq-
z*I&H*$jCyVrKzL2wVk;)HFohU;z0m{fM}LM5EXb+7##=~34;Yc_{rf<suMonf#F!}
zix<8Siwz$D{aVG@f7B<xaN&z`(u1+oI^ZTovW<E6DjsA+crjwm8-Vv6Dzvq_bsqf{
zATCx`3}ZzGt)%x+31~W_T*~LMsNo*f+qibNIF_(%+bhS`!{8l+evT%vfopT5zDx%1
zI?RUYbYK+pHB;2~Xk+no*y|B_0#SPtGms5>;CHOFpqw>1>T+W#R&h=Ji|F<`|4mu)
z><yf|zJ09Y?nmzK7;8@muECVC&tp~OF>176Lesg*q9FNWIV#$KTwGgQudx_#_GlO0
zX0Idtv`MwjKwG^+zQ)ERHVJKE3c{933s@U{G(cs_0Ah}06sH1wAyp_SfXiXu<TI;(
z6_67h2G8_TdA-PmFZV=@JT2AMJ=xZK_K4(Udyn8?*_tDScLg0D=(5T+zTdcDl@xVV
zUZ#SSUg-U1*@kyW6tit)wRsp+OAj>t`?PbJ7KgX#q^xIITv*4NK*1AD;yCXVQi*}%
znx;txG;f_$M<}7fs>Zo;QRtBMDZfWKLdO;STgHt0PTw)}QqaN|Mi|OY^&eDv@yed`
zGqB>~7VX>p-i6~+2XsuOeM*l2t?b&OVvXbvRQ+b_Fgjrs$cgpl+Oq*G9F3i}tgz!M
zC7pf}63UZU7v!W;Cou?0&Hs|0gBcm*@g!WvCjGbe{$K_>dhQ2%UGI4K;qvdQJoX*x
ztCZLD`0KIz|AODHMkCOJ9)iaT)@~JmdC-<7?5!9eMS|Usn~RRwP+l0b_6TeWUq@go
zz@tjz52~($ve-{~KRMVZ3)o$P6$efbIW4D{A`6fQ^KMVMR4nHIA~Z0N=XbS-oU1B9
zo`zxs&<4F8{P*HbCOeZATxowFoR!%bWJOZbOLg8le|Y{)zj||fi`UuMJvP=EA)<P*
zwJG4~3hy6C+=0wKZ7uHTu7OTg2Vs909p|C{$m)+s3$jP1G7wnIyS*Er7l^!J&(c=W
z1gn3<fHX0)5(cb7QyJk{(>=h`*+Gp<*Wh*B12z&i*@kqrzNxVz*xEGK+3IT#wYPV8
z!)?v()&{E%#M19bw_AK|zLwUe&VkNWH<Ge@JNi4L9qu;jU!TBT+>D+C=>bx}+NMx|
z3Ihe-S~$eq@0pAjhAXrU{5(I<*m-3%)iruU-p0D7h_@-&)cm${*ZIAwv$eHtsI9fN
zQw<IIJ)XKckH<f>d)8OyZy(z2eQ+V#Ju(+>b9+4Qwyu3O-UsfEh+aQe(<>ptsOzZ(
z6F(qWi2afcEMTR}My|X`--$n}Bea&Vk1H@HQfK(mwG*hOMdsEVk{nDJaFVZ#MdvAZ
zAobVP-Kd(KSCOj+6Tte<MsaIra=Ig%20k^?o$W?@B#k<b_|leME!iEa=@I3@LFNt_
z6yZ-gRMRQ&pz09p&^@&(R95;GXlV(cNf{Gtw=Ki9$&Nvg?rfK|CQn#Bp(AU1FUbXL
z?#LPH7IfqeZC8fIP13u4f~+x7`;KqUp*X=+&DewSz}<_J+gj`q<m?VD>NP={QXQ0S
z>!O&$ZQ7%-L$jzY3s=cbYlB(OVnj98%mj8Q#eiySJ9J7<K`y_9)oC}p<M0Djfui9r
zJM#1uX}N@+H2Py8HzR*=#%swEq!}WZV70vbas}*#viK6a1xaD+a<C@{fsCm6o^S7z
ztk_s1*Pf@EInp(kpfL~Xn8WYW5!7GGBbKOk_gd|j07L>F1)p7GpD^;z9uKcr-gi6p
z>k)wzQW+I{a44~1V62z#(=BS0s0o5igMHmD2QN2HOkohwyC*?}u1*j1@<Rq5T<w2#
zJ~K5n0Kp-t8ju+6l-{qh$Dk9Sld{=4t;HS3lV8epVm`6iBcMV^PX2PETkV~$dYpyy
z04*I!DbPc$PXOtG(X5rP!{{&r)X3ZIo^wT}wo+=LHZ}BmNkFou8RTljlSR7`!&P3-
zi+T@vwo1T@L6TDBg$goM&$J0=Rq7*Uf(?rGVy^0;7b157GN~=u;SHo+3d~hmw+K0?
zY8CK`9?*yVR>4F3Ao{pQL}-HmMcb-r!15t}`kG3(6B-ziY(?yIm}soneI1iP_>|~k
zp{bXP71%Q{oH3~DUo%=@yy?&gQZrp0F+j-@wl{Qwab~apD6m=Rt5AZk$}kBdtd&M`
z`Pkwewb>;ROr~(p%2-_7zJ-xVO=0b8-?9hS5A;H{PAQ{QPUn~V_VS9weB>0`ukH}5
z0@BMd;ce93q9Z%dd7Hg3Q{aeWM12R@fHm47f;hoJ-2X26;j>w4xsbKO9xtA!fCjR>
z!d@10NM#YUF_U%UAQVpFeI^8HC^eIPeQa=i-+ki)@u_{U?e-X+;S1t3{w+^;Y}j*y
zoKZLGH~O1{v8jEx#Q4FWoL)_iE=+w~yvjMb%o}mRsn?G4d+)9J9;NkN4!`=Q`Yv<;
z>`z<!_tN_XB*RP?l}<oDU>k+73!xF4lQnu`&M?k+AllKE;w9z*H{;Q1o*x+<Y>)Ms
zW<$NRzo)0)S>IrqeKDuk<8pbt&TXF*#h!Fi@=$X_`&{qfV4b(sgREnyQ|oE<)(sB!
z&b6yLmr|}ewbSREf$AJnkEzW>glIkBCt&o?;$i!KC=X|W;7x%FdGSiS+-CYCW3jPk
zVq>wl$*2|c`5v6erBgVi^2q1)X1v8;?001<-03&r&0YEY`)~@ua#(4!)cg^=8;k&i
zkxEUWT}kVZ?Va*YxibCg-pNRiDYkvXhsx{FWecXd?Zz~%i=~$wCC&x+O##<%!!yjv
z8X06jU}g-+Y$>(c`|QTjH`R%*b2peP%Gmwv*jfPz_HTY`>BK7bLjk{C#c#160=mHh
z6ot!x_M?~=uHGO$B!XS%T5LmX2eV5XMEk>9+2KKRl1PHOI1|<eKzRFmwH|KaL}=A?
z;UN}F08p5G1||Y1;$$AAM@YDmhq@>wSJrgKqP*HDrxm`zFK!sXpX&3h18-V-ww=L<
zy_u3MXh$#tu;Ea{6Fm<cA*eYAxkn|I!d3@mRxz_FQ3>UXQ$(~gjRb8ZluyZ&@uXE_
zO|9{^2)3p_&8JcJj6n*7sN$;yJ`><uOWX5u4n||)D~$cL^>N!8Y1gu^Q2Wp}uVlrO
zX}Oc(;jrk!R*$EYq>tP$*7*A+Pv4vz>zsXCD%Q)#h@=*~{9Z}Xw^!`wb8@D(O8u8=
zJ|zMK<NR0Zudw>)DQOeVM?3yJRs~|cGAIUyY8x7_j!0FEDZ-a^LV%Q823V>v`eAUl
z0HxNe%Eja9=41F<T8iHh|3mfFE~ETeAb*jb^d(2!P{)wMH*OqT(ogvIvF4>bA4^Lr
zj$f#@@=O<TTgQwzR9?VXK5N9&OL)g8t{&UcT<Z(abunr@0z}GT`w5bJ`>}0LwO0{}
z@$w(k>&kO2Phw(K^o|{L>~I7fu4-kVrW13-)YpMq=l~b&6}>#fctM0)a0x@m;nGHY
za7v_ZhDB#s<VnlmDD;D`-l=9fpFWyMu%2%HrGKtZ47CIj%Z5?kxd=ylAQ9+P&)-%4
zdid$<6Zd5@Se%y%i9pM6q9GVu4&N7OLBWwxj1{Gk60CqGC{+vgeGkSCyS`Rd^rAa<
zx7_$-3W~p!f|LF*RT%U895baa5C#{e^#r={f@v-XT9Ez+&F9&PMo7wyqj-Z#ALPMR
z6c~_EHjlAw!M1n(ysT~UKTN4!LEP249Bz}G{*)=UU2nT>*{1XAsNgsCm3~H!HM7yR
z27ucHypt%vv?DE^I$cwo>nG(nj?sbj-j3I^y$H5<f#o7Ygpk6n9j>MtqA5e?8?y5l
z+t~rtT{qr%Lrfg`*NYQBF2@5m+;HRP<^6@6$8)Qvq0w_w4&H#<Jf0j*+=j#4O`ZTg
z@!<D2K2qbpkIQi!XZyt+KYfkg@jAES61A4Oe_J*PX7i?RQ`^byNTUkAb&EW2ttbh2
z!!ZqYwHvA^90a)JtL^AQXP0z3x~k=vbwAq0s)OAV_M@7d?4b-7?sidKt*)*j%&gH!
z7)Ij78i_rDlo#cV7kSz+mTQUo4=7jGdfl3_ySqUzVe4W&^dRz%PVp5X3-X3Q)y(7!
z8LYh08<|mdB;;l#EHcldJfg=I&ZZ@P5+RHiPpZ7Um1D$I)@T)4-XxaBq1X~CLh!`}
zXZB16sj9sxG4$EbXqzQB@tl=!sH400ji>kbb;X+B*%uF$7@RyGNXL<#W;U~b=};y<
zJlWTEuBp$Z8v2aT{=OzK#(lfv>G3YcD9?BGO%BI02bcC|W|7Y(o(`Ogb@eqd7^p&(
zy;XfjV?YF_@z^ibu0&eQz~=$c0Ko}b4~!PiOwL?2qrfu4=77p!{z!XkYdc;vxDoEG
zL;^Y;**o-Tq$B&qEz=6_7K9gsSkxw>GvVFRS`eqH=J;dJVbGttX#C<M=hE=FBaHuv
zJ5D_Cz==CD=McHNZv^36w1S(Uq$Ti9cz5jB0p#$*b!yi>NF>t6K{~Q~LU}9?%boq+
z_6gY6lT2pxW6MBTg8<X*Ro5ko-w#wei`G>xW<u`5y+bcI@|Ba~cd6>NtUL*C9NNGt
zWr+wT&XvKxsuc=>NS@3FaFMNT<q<qUYZJBStJ-52Z0#t~4j`kn!w4BmMxlBOg$<*4
z?a}EFY(ZtE@CINb0B_o1l~5Q(Zs$Co<Q6Q|JP0d?VRW`*33dL)mO*mWtl9uG{@7fK
zD@8n0kkv?RY}-Y}7YcL9xyP=E%up#Bc@ECJ$9~DKXsnSf>sT>eB5T8{An+%IY>`IL
zHQJw%c!aCg5Q_C6;=DMzurS&^G}O%pk8ych)HsyPCy}ZnG=F{}IkYGBPCSx04l*FN
zf)v3`%f8f98~!Xr?12o~QV$?0DeIx~Is3{X26Qr5&;VGN2x9TdM@2Nk)$-T{dE66o
z`*2t)_(^<}gH>P>`MFgow}FHMho^)ttU^QiY4vStM|KsNDp(#;cX=Z}a|C6`j(_4z
zI(<{ane4*3a|^p~!j7Yy_lNi;t#l3>gb7P3eIqa@iLssYgso%a?_VR}adq?YS=e`w
z_6(I2f<?K!3JTb*WA{zoGwBsc(Y<6^&EdX^kXEh-op9s6=(*3T>m{UA-DyXb{tCW<
zyj}c8fL}<hBM9`)I~7tu<3-C_nnKtb!T*2o2|;jfMhjFW0HhDEECk@Ttb9<+XkZni
z00n!ok`PL~iq?#*L)+vX*6tNq1(m%4C|_ky5CurOV9O3+TK?4}NG5Y9!9fHRxL@2t
z@DNEtrJ&%<ztY_7tXux_a-GvY=)Uo`b7xN9eC~`YwwAx#>g?}#wyHhyn(gfT+s;n3
zVn<OClY|^R*t`9(R1oWhn}x9g^|fHL08ouSypDI_M}OO?+|I4n%bocpwzFDo+Ui5?
zeF(K^pNu<09X_Y0Z`_%j-scSAe&FoFolK|x(}!;hc-q@Nf!pkZzkk<~&$q-URcvzw
z_W!&60px(P4!T-*zjk*kpK7USb-rjGwKr~czJOz>njf#q-^GYZjlE<fykHViz`O;s
z;j8F=UO7&iV`gSX2(zXjtFJzt{kvi!K~O@Cf<u}~io#X!qsF*Z>GO{YRb(T})}dig
z4~~N0On}#eTf!`2+n;H;&5}iD$b7sOJDQvU>`_FR9r=+F+@z%(0FU4cP@fW+_SQ_M
zwS6_vl1T(x0?>&ow7SVOFA3@icF#~Kl*p$OC^!nuDv%A~IUV>^<*Q8IfPHLQ(g<zV
zG7Io0U1eqs1a-)j4OHKb8hNp_fA9X|WH8-3KsU;Ng#yFTsM(Y3=}Br0Y#CJKDcSw#
zgpC!VHe{iE!X7}VQ#0Czt)u$7Kbr6E&hyE#U+ZYTubn%3^c)_%ZaY@#y83(A-leK2
zF`z^VXT@ub9Sc)LHaO{qtrs;$Q|cqLv)NO-j6_%O!iZsvESx`os{DW5c;k&vJn^J@
z?d->9XFKC9BgPv>Mh>07<<J-K!QLmHc!Fbg)^Qeo_DxvXI?xg*1TrFln`8uzj5b_i
zxJ}g<X(9WbJ-csgetztZJKh)%$0l~W+h9n&uD#Ci^r&B`Uw+GSy-|PU_9OLYUU5hH
zJ#oL=@Vuv|Gd4K(ifGqp5^d(t<}XyD;?U-xScG9z4ST%Vej0&cZSho*Q5p40UZwlB
zv2c7~;)90UAMZYU2fM(od^Q{#8w~NB=L@lLZ1Bh{&PelybY(wBvvy^OH{<Gc5k_K#
zZP1++XiZmRS5{$jrwpw%d8uG-OTOA>Aac>wh%2T})_=7%WQs^Cr~hpMU}2Ox9TVzL
z)Ng~gwqRbc*s_^096`1;<_>vKCkRWzMT@gw7!-iK+2CWx;{K?F_%y2n-qyB{)HifD
zt+=8eZK&^RDu1=D)jNI5<DZHCcZpqirmfd|oeMQ?`5_L<pYbNz9xGL;`Z}m2vE$lC
zj6J_y1{b-^TwKiKZ?!6n9Zcm@&n_;`%-~V7(Kj?VO5Qp<OUL$WXZ<Nkn6*S*1##hH
z$MP6<lsrNaz2*jm{H^x2k1dt2EG^1e<Z*3=5zj3$5^1rnH2dSqlv(w1F*^pWxa2O`
zpf`&<>dz|V27ru<=fO}|B~xGi-fuweP6I`d&P9J_{(EXU;wgVT>@~kP{~NFw=M+q_
z{^<ASKkp5fpY}pgtI~ZfOI;;v4A5i>G=Htkp&E`KTS=bZB6<uRf93Jy`8;>O!|_I^
zL%jvmCWc*kE435S7O-qc`tWOjYtN)CfC^*N2K#~?G51smz7Y9Ok%2M`RC;EE9CN`9
z!sQ5Yg<54QIhZ9V6Qw&Fz2V0Cuv4{-)O+e4Ju@5#oj#+wW6J5Qb9z-nV?&_6wchO>
zX>Q-`cMm6fJ)YKnPknPB-R$p8r`wy$*I)1$=3mbY_<gR{=}c#XJJHtMe&p4i-Mfch
z^O{W9<#xH8bd5MYt_F8gW7D9oF4f!^3ws(|o(89@z9pP&t&8;p>s)&VUvhk%HGXb(
zyiq-eyPtL34!Xx%gZX*Kn*-GaSHrz+zdtXXL7?v#00MfZ>8>TLXIjRP=pu|nhk9Kc
zZX4XGM>RAwwb!?LJ-E}rtlvEp^5a&$?zZlZc73aX=8va4!^g&rrWSvCEE-8PIFr#v
zS9-$VmQ1VOu&d7HQm(6R)aT=!q76?=bEn*ChualvOAodqMy{j2@pNz4-2|Uo!)U-g
z01iWL$;`o<;9Pd)YKvzL(vc+!*<={hpT<x!O{*NEktXi+<aOCtTkBVtOIWHUKbzSP
z<G@EBFybx#Pv-L~M9tR?9VKYBp?y*Guh94cg`1{xtR1eFl!y{&0yLQ839Ai62s_(>
zBQ@}~j<au3k9N&W&YrxOnENtX-H64~iFn7^g`vL0#GV70T<~js!wcsCa(jE%OyYqP
zdnV>?j$QwM8piQhJhOk#L>!-<?T9Df0<N#n2TFjfUqZVbGi{L%!8p#RAfAxlJqgLs
zdU64A)e)*B_WL=f^Me<gU2jUn7Cz{7zV}V8=8FrlV_-*h663S(X$zTtcW9xmtrTkW
zm|#x}9c|^$sMBOh>U9zhq^WEWe0~$Xf~E~igXnG`^j5}iLKd*3B*&Y-cO41{MjVOC
zXzu_{4F@QKPDE%vFDcA`;f0cFzJ#4!YniL9l8x!4k{ZTkC0ZM=JmyIkKfpto06G!8
z1NRg_C8#q{TwjN32NVGfIT(K<y^e=sj!g$-M4k1Th`?$jg7!XGUNzB+(Fhx|AL5b3
zTfXNCu+PKlAB6Iq%axW&xl(QuuS+YrpuB-6ypUVh3^SKoS;9#~m5U<2M8+(<9=<<d
z?rN}{VR+9NKMufYc@@F%<yC+k^J~B>6!;4u1k}Gk6ZC=#LK8!tQmG9*I0X*`{;H9_
zQ(+h(kSg>)4;?fP!hNagQzL_kMA8{Nz3a%`cON-D)fP?kCCVF-P8JKkTzbn}8jNW~
z$C{5n{&*|O1uM1%id)30qoidsJGhl+NGZO5?nxqbkdQ>ZAoo|P-(lx3P02O6t7b5~
z^yhM9>GxF^W64<1G*_k8Rew)@)7(gZB^gUT){~5V)p(nKPd`dpW%~E{?=8V8xo_W@
zR15|(`jpw;KT3PHZ!)f}XY?iW`u46MVAP9q0h$8PHrvnQ_&Az*bNZN7o!B(z&=vgQ
z+-37o96X4oGW+(a6>)4NjEB)BwTLg^<aZ3?_4rCH|Dy&-ECrO0>~?Xa3gjuSW@f7D
zgun!mVA)YDCZ4TT9DtaDE~gBU=}g>d3AC{Ts{je2Q-p`tnuj0`E+3mwO>JFWZL|q=
zwH5Nq=JR;7(bmO4g0?P5(n07U`Z~HE4eO24k2s8Y&s~lg<idsV6srt+r$${8L(A-P
z7&Am-;DA164kp`|9SL7<Khv8qrMjFj*AHe=%L;XH{86|Da?F9yrRM01Mbc(yxNTi9
zR~3=|q^pf?+SOYQjoky)SXaR^kX^HAAT=(wa#tJoGj9NOP(ZeQmHz7;MDMu1gA@=R
z6F|!ChDY=|P%CT4$}CIIae^mZ)`KM!(cb`Xf^H8k3AqVoNa6{U6QHRU9lszxWRM$w
zbYoq*p0|6hupDtGEOsKjhSOBnvw?XXsw51Yc_lZ&<X&ao_nM3csjEE`k}JfPoVQQv
z=iYNSO2VmCF~4F`K}=;GzF2EWpm*#zcET@PJ3-GZIfsMMEH3UataJpoOwkg?`C=`V
z6)fF@c&Pn&dVM<mX1u(ePM-8q9A^jcw*22>sn{d?)GKg&%f2i5yvSwfywf3QsX?rn
zt0O1E8MH)Z;nHO{v6v=j(2G9uRMrtil0(B-qmkD@0XBd1O;RcJV5aAktNs;ya_JLA
zd_lMdawNl$t&DfvwRbs!@|$J5Kxd6a&3rNgSOr8&qVXxPX>5M2>S6)ci0)7eVA@S(
zIQP>@gfNI>Ujc2_o$h(FME7m1*fta>3+<5*Du&EGCn0{QSKHo`?k;aG@QWYX<Tj@6
zV3?76yB9OVbLE%z48goBBgM>;o1jyEu~JCZU^EH|#`aW#pMb@2u&k{-4?f3j1a&R*
zt)cE7T*}9W77Vk1fI~VGifqg@%wI)2J>5e|>Bw7fMpPMeXCu##O-MPm?T7rsCq5i2
zKZV!MQ*liT^L-;D9UXXFn49a0&do)OJ6fETe5Ye18tszri2=njL7V)?KA4v6gMH}3
z?1a5ogrLvz1S-9CazJ5vRo9+9U3{#v3wVTS(-Px$siX|mB_DR}N$Wm#jFiOg4W$Ic
z0wZr%|0T5~eb5wbJ3a1){O`hJbN%2<@>v$wcuDlM6>(=4&L156bt%L_wGJOJdIVQ@
z;(oN`=oVTGA2Z^|WCn3xI(~7z6npx3jGm*wr#=-xz@oh0z~uek!PW;KYz?XoiP)jV
z{7;|_Ho?B3^;qpNLE>I1v@2d}Rwp%%9b0W^PA~mzYikMK=8^}0?VjgRV+9pKOkW$$
z${D;+y3%=&Uyxa6B!7lDk?kJ%l+eA3h7KJe2*0?!Wh#DuO536*EQ}yWb<g>Qh4b@=
z#?yzIoA=g-0>0tI$i7kkH;}!0VI+2b9!?E)D?u=kMVuH}cmm&^KY#nKx2@pY?ah0e
zn}-v|s2^D*s-J$vs#Qtr3!E4j5AEXzZ6UVEwpUg6j5q@!jB`^9{<K<GB}r7_x5caQ
zi}Gxod*&QMMG*zM)`I8ekP0IIsrVLjjG$|Ms;_Tq9l3F+qlZF#PJ+qWHakjdlu?nA
zEYOR@;}%J=dow7})zS-QzSI4IYBe<phMc(aClapsf5@{M9zg>Q%`Z9RWyBM?f<TjH
zou5Z{=DB>a+KXa7h_(k`Dyu&R6{*ACL5x6v=3teAHAPf*@Gv2@VJsMEyHK({!kzJo
zBhuk4H02PS9_8;0d4muH%)ANVAm|-Zy9NiB2M2d4@aWOuTyA(YogN!X-I^MLgbOxR
z-h5Aox8W|thMQ6UT@Buj_ka<C3kOF=f<|{&7q%6%c~xhO8FiqkQ1-D$32>vzvF)P^
zL*7LR7kD&Pesx|ZDYq(tn(d>{oI|RvmmJ7AU!A5`+w-MH`=*|c8;Pc-gb{y!3S*;N
z-;@~=sjIqL7~zgh$tkfK<jT<atUT64r5$5s<+7l!JTFZ3_fNfGHSoXq-k8r6_&xOk
zEH#k(11>;tVa}$JHAD0YT*LkFt07{@+MnOrJDM6XMq9>?EcAqYL06OOej~Xoa5S~Q
z{QE^C|CC{7($jrG=lI=6eb-xi&M6va346`~stHe7Di}tFfJ~NAR@M-P|L|{$#^SN`
z+8VYE3UL%NmlBC!Fp;>FNv~ca-00G(mT2g;DnQC)W&jSp6yJcrIF%8lon)lYKP6QV
zihBjZsaB`@OQxyJ(q*PMPfiPc-3QH_{t9?42VvTP?bSos9bP_1!~2q@Qu4ixAL%cZ
z`itHNdJ2V}i~An!Dik2@kl*bSos~JU;X!2$F#HUrXrNyq_`5xL7r=?b>L<HSu9W&e
zvKSl1B@dlfg|4|zEc|>t5?7n$i(RKq7rGvui}j&_ne*=rj(uXHycrL~pe2!Jvv(j7
zgF6kDD%A{Dai^iGa%Fl0fDGBu7eFDZimvBAr*v&CX&@^Fqf^Zjj$kM_PeE9q1nUF%
zh=~1<w7=va1b}(N%U42at4Mw2Kx_Irj2bgdC3#m`H~<(L*aL1L+B^x4p$8mxLi)3U
zed>7l@cG`}TaJW}7bAWxF12^^h|nSbhtKYD-*l6E&)Hpv`=a9AN0bQ+17y@WwrNWR
z%!vUkY__)->zS%>CY9;^*mKG9Kd2)`=2I)efxVh8tsqpoWXUvu%R(2T4nR95c!VEx
zhU{G^aD@z0ivaQg!B~_1`Ti*rx(BsP1QWD(nygpMHD(Go|E|ywQu$fryt$E5?Z1ZB
zCow`$YqJpUkhEck!|%%syq#A%H=}{J`ufDp-R*oir{8TZKd*_SJpWdHje<&0vKp-A
zLusTA>S=5ogoA2_qgn}2v}H}5=?fr;ShO{4PH4gspHAftsezG7E`&vde9*?axwf=s
z!j9uuh3y7^p`aNInXqdwsgQ{=)0R4N>{jkKmF<N;l`ADhOPZ_K1q%hGe)V`m`Gtm8
zPbU*?Z6=f=!`a!k(bnlCR`W_S8rTyE95tfqVENIuV6bhg4Ui~($S)2H$>*KUa)c3@
zh-c0@trL(2#A4A$BR!WZb&W6%@DaY-;ZdQHI7(Z5As$bJd_Elce4zy2_*?L%#UDz%
z^W;Tj5jc5KJt=u55BK_fy`e;79kamJH6}vxKHgBr9Ex=f@xOfF!~-Yr_W<sleCS!M
zNLrY{qwV~8UW_m-mR64-)Jtv)c3JOZj+AtihQ_RVNFau=|0LPJ1jI$?MnQiR5JJc;
z@xD>WfdVINURjy*g`bxUk54f%CDJHH{mb0`AFe|&m)21bU?MOzrSifef{kM%IMq~`
zI~cW)F*RN<%9cpp2i9Ngw|#_4!#vCDhdb2XhGy6C=E%na%Kgt!=_Br*8w?F();U1b
z{ppqlxBH1uzsn6Bq_HvcG*n;0L~C}rT?q{%!c}*5pfF?(#F8wnh>C-RG{B$peJ;1T
zMb)L={KMcflw7p0U3)B2l<#IN*{<DMX*RBOyW#*ZFBaMJVl&d|*rqsjEd|n9O>GZ8
z9GN_v6J1?3i91WDr^|M>m)A&=6ly$_zx4XZkx3b)xW(~+x^Y+>-8)0PAV}_{m3q)T
zdGY>Jr|!R~a>6MeSiExl_?5~Y+{D`R6E}vt$N;{Gwcp=?JAft}#&p-3ihz8?8RW4s
za3SOE)5*N7Aq#5{MBU~BN<$>0BOgje@s9{4OUos?4y#<tU77;h)&{IFN`6rO<w5FV
zA2ahf-pWIy0W1KLX8eOa2>)mg(1$4M1u_Hild*R80klf_w){r(D|(<wy~VGsm3%jo
zg9@})Lg_#sblwp4U~}{3=7Hu?GqUF(N7||g|Cey)ax+f0U-n0uF$a0<;eza8%z<Rb
z=@*_tnI1bANqR8`hfEqG&72BCWLzp@enHEs+>CR89>M3z+tuql=oR@BOpSIJkX0DQ
zac8_E<%>^tif!C9OKFr+K?%Y1Qs4lj3=_R6p*Ik+10f_Np$A8^H_R)2b=<)a`rkcq
z+jwL1z!3NT<@M$Ux*O{nRP?rq@kTe!;r;q$emFGH(ok6|963rzl@*_~@~b8%!!Fl%
zMQSufDDL~~8%m{;?B=IMtux^jM81B?jX!>w!ERH~iYnuU{Iz{=0*8lxoGS|hgEXP5
zkQ{3LywIhX#Y)Q%T))&EAbQkU`=4}MqzNRI$5djtCHhSO+|9<?m1q)GD|0B20t-JD
z5O(&*S60Bq;22=yUMH-md(?*dP3%WFM+Kh-u`@|SddTC2EK9vUtmfk%h<)G#vEOU<
zN8-oF8sZW26Xzq5%c{{j-)TnT4P!UNBW+#hBZnh}DmUl+{iv}#w4!JfnvU0kW@lRv
z;Dnm8#|rwdO>BhZaI{cE<+Y;MnVDCVKOskI(Il~<j1L~6j7zc!Kb3h>Uca7OCB5Ne
z6E@?D?oA3q-5ZvGf0gc?0fG5J^zTeQ^Zhh%Se+^51TFe37Ob7>1d+b>*JOLmpF4T(
zrzZOPCi-p>k=Ha~UyQUD13iO-J%PXMo9OMGc%?RKQNKoHGzdqnR19rw5N7EBv3D>m
zdA$VQ!D^O;r|ZS0`iJwcb;<MU{}}xrYN)CFg?i`k=!j}k`{e+=wj=Jm5-U;J>-4N)
z4T2m)C4!PMLw8It6td%;ENALXBO~7B1L*_HUi;vW8HzEfGyI&X{Xo9qvLZE<V;Otv
zDNtu(GvRIl1jzA}?|tv{srB+BKGTG_&unBmhK?vM%J6VDAjh*5Q0rs#qIb-Rl{ASA
z*08y)ME!_+?t7n^NT6afh;9za-X#tNfi#1IzUem@Aj=p%`>I~bqV3jhMx;rw1JRJ)
zvAWFk6_ElP-f%WPV))uT9n-0VYJ#*CA1R()h@U(>-|qK@4_$XU4mSw(G|gw&OIqkM
zs1Z1ooq_)CwM>3cj=YlHH-E`k&U~Q0K3VVm04I}E3zI3_1|O*R;_DxHUVC-`N!2s`
zqoNVE-HN^<)@6Y8K>S6p!BZ@N>lg>ysit-w9a}gHvs^TJr7DEw;X_IgRlj;&D#|iJ
zBARJTJoiNo`+^ZBeylc*535pGygmb6fR)jeBd^RL3LPTD`BE^5ijnY(!XT9gVFn|_
zBEfGpVhNVZYeos%)<Gir+d=)Vml=kp0TD-YFDb3YN#_BOEzl?V#EnUf04Qd`9a~Qp
z6V>1OyMahV{<zX`#FveMZgZf!r2z_8_kgMY<Q>j3*pO13|Lwvh-zL_SpO1~!cg9BQ
zBjmS{`jJ>?{U{zIF|jFz@Ch-m3yzT3b)vL|OSUm_QcY5!(Kc8J3~)%<hMnIp)Zf>a
zO5YEQPS6+Z*>_~DWz-nGUYPM+Jx1_TzU%KEcLw{WjEtFnDxZE{i{3T6p@~uiWV4D)
zvSmkDBFUL8TLJ~7DX6UNuqUc}tXcS`-VF%eO?iV9D=S+~EdZ6^ar@#YkHn84V_40O
zdxaaHc=RXn_3e#Rr5{od7Yfg3RO#cv+4r*s*ZXI&(5m#qi<O6QoSDqZLZH0M#eii|
z+m)}pCRVB2c3*8z*2e;oNZ=DP>+Sx7+j~;oORTcpL5~`WnsL(LObgQ@1xGgRQqZRH
ztV;P^3-S4H=6B7<7f#e1<NUTW`cqBuZ7mH~S6G#lm5DRp+9i#qpAuf8pvqmB&djW&
z$NDmTWAKiud@NgDT0_O$zFp2z$+@dfKm%26lo+!t))<RgpxCG73KJ=k`+-tzWfNeu
z^uSg1(IRpU8DA5fbnaRTX}+-f)S~F5<KIsqCAFaMnKH7Vu>&25_SWehJ$7zQ=sc6!
zpq`n2arj#;QU8bA5|UK&=(O1zXSsm<Zk(4SrLh~uS9c`U^d^7wk`MgGrq|3!l|&ww
z8PX)72fRCA4u}YZfeG$NfDwSeCAeXdnW5|s*hz6!e*ePUq26%kKw@tq(HZWoGsenG
zV}|kc=hxR8Mdo{_sCA$E0OghsgnK*d6N&oHUNzQsaJ2lls&n*Uo4>HC6+^86*4oQ8
z7A4GRQ(LNHTrMR~EMKnWj)2Sw&DRp3ZrRKioa(f8Y#?mTGMnem(41|gPo*bdIq%M7
z3L;g#l~|O^a#%5)8-^Iqy9U~rx6t0pl(LwCqNa5s1E(rYa~0CQ1#uzR@5R`m%*buh
zjc0qJPTh20IB{^!f6vC@wtd&FudXgj!@llhqA{Ir>~jx<e|b3WOyHk0mKF&-2nk%<
zw}%*Uv(?580}|-C7gJWXaR-1W1{uR{BgFX8xzWKfW29yDNY5@`M^mK1doi87L+D~L
zlev_+bSaauOy3_LF|+BO&b|)U*xg6kwC%h6D}$MtWa4}RFT|4Mz{`rl%eLc$FfO!~
z=?5G4auo33D|e;~4;r$THYDX2UKnV!u~dmemMCQFi80C8=#}>B@y0IY1*7i2JQOPy
zV-F#a_hBA9jBgeY6TGU30%6X8!Um34YqenJGJyB6A0&@z|1_?>ri;0*FRfW<E?#9F
zS21}NNRfrrRm{=$evp)*I2Z&r_M!YQs_r4`Q?ZBB594?C?&AIxD=}m{xNoyz8)KtZ
zZ@3|5|NWeR&Hj`97Uz|zJEOq6S0OjNlM*8OqEgw?pT}NuJQVwR{epY`x5xL`HGK<M
z+Z7#*t^ied=%JYXryjD8a0>0#)T4u+T4Yy-3&m7UUgR4zNMA3~EypXYq^jJVR_Qye
z>{Z-d0e+BbWfd-$exi}U*ZJJz<e@TF$YxhcE6C}q3M=M@xstw&b4xgR&FdgZ1%52+
z5we_I6D%A)>lJe?y|MzxU3vu~bK1OulQ?5ypPP`cN-$K^;Ld`un!E8ZrDi~$Wm#Ze
z!DUuO<H=#Q6tU*Cb*vIQ00u8h%wB5RRPbRj82q(7BQ#K6Yo5uk*Ri``Z?mswU#IeF
z*}U}i^l$gdb#$(T56h{8@{BoHvi7Q2oWYPq;-mI}>@76>f~`%e*H2zPl$@r$CcVF9
zr1jRh!*}0(_=r9Y9b!B=dlc9jtm}{BYImY<oG+{tt`TBpQeFurJ9zLJ%>TiI>fQ2E
z{#|+D{`)BS*`2V_$nS`91E_(&_A19gu9<`K{04dcl00wQZvp-WHP5`cVln<u`r(>w
z$8RzVB`FeiH*h;3G=Ai0PHo0+_>%Em)c8|o?1qh(95}*vX^|`F@3ImjQCdiC0wiJV
zhVL3*x*=A=fpTozKo6Ep=}39lUnCL9a+_DXpz1(}aEE!Un|I2(X&~+K_v<Q;G+i~Y
zTp*(9UUJ0b2;Y7P-^PE;Hojf0Hb_j=pH{vtWd`q*)lL8ApUH|~EPusnZhO|@Oo8@5
z%Xk<N2<$VgRxUo36`ABgdH&RVd1HRGeOKaOVpn_luT>gFJ(Z~~HS&CR6cIX$qoe*^
zZEd^!<v-5l5U)s1Y<n)4C6h5Z(39ZL^9Qq(=pZq{3kFa76w?Nz5Fh5HZwFxUp`pFE
z-ug)JPEwFNgI|mHMuNemU5$-DJA6vjpBldP*4IC+KYRM_$w#Im>2v9&U6Ia61b1v(
zuPCz;9a+)Hp^bsta@i7C$33lcilhnL#Hv-@aJ=g*3%?G;CRVMv3KJ>!l}(eaeTp1X
zK*@VUsgAI03VVMk$KeZu-<^0Z9=i`;I3uJvcj55viSG^;`E=nYEk1Ge6~*n>=M7lc
z=nAcWeBi?2y`%T-9sT=(3+-~j4~_0Ud|{ycje)=Cfn8gjGPJEF{%CL%be$>VW!+>L
zDHA)S1nJXd%{5jNebig*;uv}Ib1!!VHcvHQEKN5-Sg7M~Iv5^(<?59y%=KN>g$?}s
zq<q9Vsci}CnmLZI)sjnA8OQ)O_D=16E<8PrzYza+Y_+_uzah_9W7W24_320JU^Jxj
z*XjXv3}svf@VJs3*S^`{#yYslPWM^>kEpc(Q!lD`jm2_`^=wDVAU66<{_N47o}*d+
zzSXK_Hg6P;On43)@Jt*T{IXT<Qjm1DvP~V)$YQ^!m@LHJq74@RRJF6@m(8cPSXk;n
zQB0=;^jYr>c(!dx+omw~YZY~wLM?+S^$vmS=uG2q#=`NcGGY>WF4X!HKhfIpg1BON
z-v0ZBUJXQhaRt!xMoq^H4O!%BQBJGgd#YdHQDWgjAsR%q;ICH&LEK8XWR5Q06+Xc-
zl^L21manMGPH$1?8wBEu1_pd7K@Z^a?2sqWW2(!)scPoG8?)a>?Sl746UbJ#fmiz!
z5L=4B3aJyqrv!mi^(Bmt-#*^ZGT`dy=s542oAd2zoF5yTZ+v!}Z(;n_UE>XP&Hr(z
z<Kj@q>wSCo`gWb-7f*3EP3%36N4KoVm+esof^`Pb^t{EZI{`rbH5y)q)C76f-hF!3
zN5F@m{?Q3cJSbmTjr^M9fsn`O$iDR1g_9Qn72BZ$2)It7ZaVB_7f&wkJOb4|==tA+
zK4>e|HRj*{vOW56C>A`=zO3>oK9bnEU&TgWDCBFbu8l^zt%)?-;sLT|iF4v`9FX17
zLtN;f<vUBJurd+m4ORz7g-(+*lOPQNV*(HMs_%I`89)!adz!~yIo6zr;27bM_o=?}
z_Y(QV<$SR4AM~kctGji-&$r*__I2vt-2LQYK7nssu9+p~X&#`ofXlz!IJiirKz78G
zcGbG*gbAhax}EP;H8GNc)<f;@)iN-+FTIsw6T@qTxL}(EvJixW_z$41_8O@-iFOCA
zu9`avvQaf&o7c4LpY(ZBbGP{lt`-FME>y3ziNya9ppYcR@=)PYA|2SaX6m2Y`d6V)
z+Sm*k9Y8!4s*pca4Um7OS`t|0<yQY}|7@jy`fBW7sd_1d)v$k#+Dl=*<lqA-Vh^{{
z>NiMDoFoO%ELc`}L5fMVwLmk6h>0q{U2)%H#(IIl*UT-M7Y<Jj$Gdtni^`}R;ygxj
z-Z3w-ZZjwZJWZ++Yy~WuSJLDl?pLC-!dVLZXgLc%+6)BOsi|nfmmL4QE+pNX?Ch*>
z_$1!tarPchV?2WLAyZR_Cera(&ooZQx{!=-veh%<a(_~v(Vsd!37=c&r!A|nCiozD
z4_1CwuqEh#2v->@U@2Hbf*#zv?#^bqI5~NAHaR{xkxQ@ZgZ$*=W<Fn<nmjyrYwJkM
z{&Z$=JYT+oc!!$zQ<J0@c2!rMRhPBYDTW{!r)2jN*AfO>{0uPZn6NEuaK7Ye6A?%&
z0PTZ+Z!PpHYl<@VCM=iC;LLHgRwe?OA<YMqL`hDOBmxM}d?(lt)*HkepoLMF|GEug
zx>oLZXZnE?$ZaGp0(Aw8w}2#ZOvBgY`UrBlzVpr#4<IknW%>%XjN|`0nGfCsO9CLy
zt|kN4)x#R#EQ1EQIkkAG+}g89Pt;oC(~F=5MtRl1e;sn&-ddIql-b%|Uf<X#A|M2%
zeDl~PRe4<K0quD;SQpDx1a%KiAlpM!Jb18p=<{248-1@q;h2rEjAc<{5bF>tAVW}9
zC_9DSW^;7QT*?z@3X_MYFxDx+oAiuagXbX2!M$}$WkWr7j#a(ly+~-@++gHUP$%9v
zG9HWtZ?2U=t^@o&bWdC8x;uWw+sYrDd#rH=@zM<~fc}_0;|E(mvm^iE+D=0&gyl)3
zFu;=9J)UF|esHf<Z(|TT9=ctGV_4zwCbgDd%cGvbW~qNt`Q@BSUIevUN#_BTB)?cy
zf1`e`dOf*Z$eVS0Q&_%(UTET>&@WF+h5UH@oKF>6?^sh4zVd$^{cK-M?UK{}iF=3M
zKh)Q^TsQQJ*Y9sOF>^Ze)GD-X#=mhO8J4#dxr&l3HMrIM#$_9{Dl>1Yzk{?Xw(UXq
z`L#2c*MMUuI};j&1sY3?(>SI6#@pC@;`%}~nP2Q`I@;MBDL)AOKz?K){odxNXP}Ub
z7W18jCU^Y>5jaY=6t!MyL3Bp&FS(wc<}EEeOGMx@Tfj~(Z^+g68F`48a&ef_fmMJk
zQ$pWO$Y-Czm7Ayq2WtBn!m`R_YZ~!lvR0D_@EqA^sC}-0Z#jtTu#I%AIbg|0rSdbr
zunB}jF^_h9m^F>J_ydeGYagLfhl~zvyfE3!!0!cOnhL|*45%QI9ECztPEIQhJnH<T
ziBs<n_q%&s;eZPH`X{C*7D9Jj9@^^x->Mtv+}G{t=x=THc9fPAW>5Hy9f>+ubJt+w
zSbg8woH3R9)>p%E)Zgy!_BJ;4ccU*kM+UrR1N6O5`eIF#_(ISXiGx6lYt1ms=oko(
zD#jOI6;1X8RG=;9-yL0;J@!RwV8;>j5RKjxUra_H4fM4220F*bPoR7-N0?wC{An()
zQ8QW!f#hZLWXcU$;?AyxxD_!XoxVcCp+$!(+Ey*5)64Sr6xtCmmqy!CmBSrteS}$W
zJ>=f7Cb@S=Kf+wN5b;VVdhXC=nxWMIf*AEbeb|@F`3@^%DF?y8MisLsL>21~xi^C%
z=W|7Q=r32^jNOh)=#yTqnvYc)K~-(kf@V)uFjqufoa*&;J?M4_L)Cb>e?@(1UK7pi
zbUj*nO<1c+L_x`Jry?xukgOLEwbT}cnK0Uhc(}A$?P|NUXqt<c+Pc7jZF#aJ7&KO7
z0&lf(do;n*?t;=M{SpWg#^TM?roU3J6<_lo%JZ|PInxw##+qi#@{0+?XZWKD1evi=
zIXuyLF26tjbS(CC{;5lxic3$)I1`!kanL~KHAKC<Nx0IGTad4*nS{JqUHJ5TdgP5G
zM)Y{p+`Ll-?l4ZiaB_2g-Wb6-oVw!<<HQNhnwE?=vHJtqr;zPLDh_fKQh1P$*s{4G
z!MRbi>Iyz7c($`|OU<O7vqfAacZbWas9nhG2%$`+4qL(sBo>1hLNr4R7w=*XM?@}0
zsD}XP2E_wm?O7<O^4tq5;7xh}w+Ol*aFG&0^qW~VZ|&YSxY6WmPIMZnT}_9k%IUd(
ze<Cvf(5MPDO}jdM?Gg9=u1(W5?-^`rTxjb|r#kjVrsl?5ch?V(<{QiZMYVQB+M62x
zE6kvzfI+bi1*xG1f~gr;kcF4x3Bnl10Jc&K3QwEl7gFxI;gNkofAmit#LX*bw6osB
zh}ZB)6vgpULo>L`i2pPHnYUm5V6@YTA&4{^LIpVD#4l3bLpB|(KyhqMkqFpE35p{$
zcUlx4pCGFaJEc}lvxwyQlA*L^BfSQ;Y51d;mrN7jDYb5zh^#fuyf_`F(gamS{Nm0B
z@=EVgdftfHmRe$rDQEs_Yiv{Qex#^GI}qrn3P|I7K|R$yH*?_JW68a0>DY(m=&tx?
z`t#-GuD!{}&K;PU``Cx&^=^)&EdkM|$hAaJfcOmHG7N~Fa1&Han;V_*3z+Z=l+YJ^
zTdDxc-tqLUqsSIFfGWM@xK}m<zGGjT*%1IE8icjq>koyH0N2klWh(SV@2id<f0g%(
z#;N=7zk&m`h7(^bFV#Qt%p)~ARvpG&>VFRc{L~NdW7zM(;Eq*{o54M2ydNwrnfvbh
zp!dwrORvv*&+J)3{vf1DsQ=)eGgJBwxO;M<xzNf4Le*~d-($U^TNq4=YkxYow0G}P
zz3l0AMs6a!Q$=`g?xIkFTW5z-2poll-f+AQGgQ#%LEK(4MOPH4bJ9*dkyY#04SEev
zWGR<VJe8ZD%UwSgJ9_?}BlGH2*UufD8oOf&=a&v2nvR(#4py)E1Zajvoq$p-;+G$a
zJ)3(SzPT3r8uc)-io-b2CHcKIWyI{IQeq9jleS!mdJ;l#8RbqP3VZ{>3r{J%MZ*+Q
zu@jP!zUHy9=Kki<vF5%=GP;_+AruRBAYsm0LvtV;3xVw~1jCE|sCQt%8=(oN7l>T^
zgpY{77d+G`gj(*T;p5I0emxleLe$^Xv~OQi6DyWAW4vrMr?*DZ*ZCc$5ECv|Q0R>r
zZZPaCdAM-Q_x5A^dsak5y>&P{jHRMz*N`{(Pmb|aTrV%JmjtA|woZi{VG;sd&dIrL
zZ%`gV^n5!uwNbRP0rYJW{&e(h8jv43gwtcjM*kq1L>7|Db?=|er@fz>-JdP5&pymh
zsX-vOvG+II2Ev)lNKDCVcwi6C*?*v|4oBYUz*^E)(0+Q_u_MK`!pahCIB7K!MyX%)
zLe?u}X?#Ru+*I(toID2}+B!IEzE3V~ASF(qp%IkjyCwsTH~V`GqbKf(hYh3esBYWU
zb+F5Y!w|n<n#==kVwT7;wd_-yuK{*CQVPz*_E|{P%n(Q-W+VewPyy3`utoW&U)SNn
z%mYS!_0X^3lr>3;xF(E=O-Fv*S(tWc7jqHrziPT|CSb>7{PD55mOpCg6T9?V<@rCp
z>jGRs+LN<B+}nGfz%6tX>F?u{3-3~0mQRPa8`{2}$KJqp0b&;cm{?<ca3x?Bu(l9`
zWRPrO&6pa6k}x`jxc8-K(Fg{OmC0jGKB^PGrel*({Q+RKrH1FRuC(vW(l@g^T%A;7
zszc?*ZX9FvIlBP_kt`MR!`bX`KD1*qRx2gxIjbPR%K(CnJYTl$oPF0<2cQQOO>PX_
zS>?azYIG`(@;K#QUNaC`dRyo7NK{|`W5d6<>vz7Q+{k)Vy{XRjcC{z+d%L@!>#q(c
z=DI7~g7xfmy%5KM+(#A>lG_I`EV9a=hm}H9`#=O1wCa7P-G^gm+~uzyaU1S4kO|tq
zy|VpwQ%h4Z^WJw(p1l`4r8>6EK?Vvz9f9B_U<j^mx-xf}mASc<xqnyFu*{$uPbRNV
zCg-*{X-%ZqYPc^;$n(ScSCD^19!B+zOaQqeD7a_f{-kbZ)q6xPeAath4J|E&RuQGN
z5?T^9A&<AL`N-GuI?t_|`WtKVZQPlii;|V8L5Z}#l1V?&N(lN73~Ez4z#&;{lNF#E
zv+O!X_BluEpnvvGSB@SMTgn=rncA(eZ^R3A5Nu<kas;?0Wi#Nc@l4MtEi9{$J`12r
zIy3l>mJZWCtlQIcI1Y_r7jv!HQEgboLg<P7A_B~QAI81{)3*sVEIQ)RA&;P;M?D~_
zWizAcN4{5c{KAoeXtF7B<meG)7|Z4KvN67Em%1cC)mstBZ!BmSls^7=w6VLnr6r1&
z?#AeHD!yk=9Qk@E&(+E|70zL^r?0;1QhDtgf(u_C2cJ%!M;hU&!&=IubYBF0pm!a<
zH_daHeZ*!g73S8+%OEt9yy7ovuJ(3e9;?<hX~WmD7Bgx{Tr}vHzU&N+M9M_If8lNR
zIKE&W-&bDScfDNo&F7X>-TegYMK{~i3~Wz-n@Nxlf3~+d9B%$I2rCiBZ{%RJDhPsy
zu|QcMG6_VhbX;YY(=*GGOj^A$T;BZiCMWAMvaYG^fu%%CJ3c+5*uCJS^04i%wr^Ce
zYD>PXP3=!E07kZP`SP|D+f~^&Y*{U6Y-g||%zpAjksbPhnB}#dup-UAadd71`TSZM
z(s|@pj=jSly~k}O1AF(xfy`2%0cu%8Gc17SO~cUM?&)a1u966>s(E`LX+cxLjd)?J
zLH0o4#5Rr6<`QwIz`hngcwheJ)2EkC!RM#I?MH;$!|%!!%gKS}CR&CpUE1(v(vY^m
z3-=S&ay~jRI60_36o`n@61eQ7ED`POxa@TPRQoRsMxuj*(Z;%Sew_B7ZFJ*X)5-R8
zjg5`x+GN(q<^BPqo`8%iNC-Hw=$^nLvD(KwW>d$|eb1O{jvw4RbiiB$pyJR-Z(_K<
zZgtKWNe{QSWV#WtI$gMlkfB$duJ0Wi?dzDXMVQ(v5PCmu0up*3NWYETw7K?nP${{1
zf8@?ce@nE6d#`A)raXg_r_;S>Yx(ztuzStjsWsa&giS|4uWfAawb~`XwKnr&ZHsTr
z=eJ~FtZmLr)U>zdj)}8^sc!1~-SIbhvva)dx@+8VG2J^n+?)SF?%0i8&y1N8sY$5`
zj9#0p!1*<XLc6kemY%#}{Db<yfB%6`*6}-QdPYg=CVUXWKYo5dp83^K=)1Ljtepwl
zegWGlB!twIM|3r*W(Bp$Li5^lqXtEk`<YO9bS!+`*eEXj+L{hLi4Hs*4v&uNN4T`5
zQEjOuua*cn=vvSVE51Q&F!D(|`3$XJ<qffcmEuT81IAoMtZ18uDSV%jxbS@2XUVI$
zQ36mGDz|?`fC#y}{az>A!M>|qkyow7+I6>Op^-<_{t}UL+t;y8(`&Es3xfIHa;1O(
z#7T3s9>~0~@S$OCWWzw#D979SAN=XPdw=@D{`a1|e4*vt?{2wpSz9WoH8M_#wuCSN
zEciM^9sW=`P6m(MKCu2^|J(G>e`Vs9h5Drf7cQUF7pc8M14mF_fpz2uw_j!8_9H<x
zxb0F)^$WNBa`kd<^xvUgauSP7AHqRqAppMsZK3)fM*dEa_v{SN>rk!fpod&0Zc-3A
zn#HC_+H{srr1*qK55`A+wZn_OA)7U%989d`K7>qL_m6i31{$5?nSeVO>fg1i8})&G
zk<M6`uem$k*47h^bo<&u{jGIJdrr0LbX-oy<9E$5|JHxs{b--pfAe^As6H7Ct(|G=
zZftC>Ywip;wSoqQ{l1p2`sVN-B2gC;c439sSUXx69jaeP1LL{Z#*u=1K!MJy{I^7e
zQDzygQ#iF(bea-P^@!f8Rz-sq8)7&CbA&fBJtReo7oRV~NoSf^tc6V&!At;8z+-cl
zfw5JN%a?8J0sScC&+zcts34-bC0fX4&b{QQb`1`7ROoPKJ;)s()@r18D)B(WfsU-L
z8L$RI#Kd_pQ7KuEHExR5tMMqvqnSmgX-(7^|Ij2H$&ygR-g|lFK;&SFjBomnU=o*$
zvB5$xh|s|YMFEHKZSTXKc2PEo1}asN>@oiI)8p#gjpx*d<q|(7My-1ebteaVu1IV5
zXmyd2v7vqoW36c|tS8wmIRY|Mq=w<C87wQTL2t;CyzF+e8ErMdYy^M0nlj*UM~8c$
z@$#Z~@HCSNlq7!d*E!u0%o3*Tx9#i!u&^}T5PU~#un_7$D$z{*Ql#&XL_RbtJn0WG
zgUkrXI$0IkVbcs~X^74tC5B%xt794uAv^3-7oX3yT{v)O*PZHU*PUHDMQj=AVITNt
z*Tu=`-uzvs6Q@u2o`DEq^V&baI-v|Jcsf+#t;m5kZMWNm-#+WbLqU|q4Gq8HEYPUB
zQKXl-v+H@bs-;|WQ*zVlhL5AU1e!&Q{bCvZ1I%g@?y6TJUvb5NIDV%lT_Fc<OSU^?
zDSVLP-FbCBT5fg0U=-Wqfp34i@9e_#KOI+*!LIS_k&_q4{hhJS9`zaY<0ra0A!eMs
z80c*{tA5QF^Oc|ab~=L;wT~Px|1G{g{=kXDS${`7&@+afooh&RJaA%UDAC!yaJF_-
zwDm+r9|RNqu%!WeJhC{){{7i~;rzjnuVy%2X^;O`SNPG>HG}cS%J{Q_l>-$@>o6K#
zXr@WWBrAT|xSeb$*o#3(&V<7xbXoY6u@njJ0x`@?i^5?YGs&tYDf2U31_iIc+nK?o
z;FFn`9Mj$PZQevQ9*ZWB1Nl1H?B!pOmz-k4E=XW$JODsa1&Rmr$?NtHcH_H=*4Bi#
zwf?6AEd`<l3N(~hN6)PzDflAKF6w6p&{eL-yAqTDJb&m@Mc>^Cl|#E0z$90p1c<An
zuEC7npe{RQ5~9EkFbSVCjG(Jc@s3ylL?5iEk@9z>{&FR{GjFaM{QJ>qG(=#VkUxmX
zB_$3(Bi`Z-wX<+k#>J9v5U>oc2yX(_B#i=xrNO3$H+vK5gjbnj@gt52DN~qw!~R^7
z@^y9wDw^6RTBk1nQl%Z&ZMSUekk{w|L%cOH)rj<~da)W~uy;&3guXs{jgD;T39}J^
zC)u&fwrx6qg>7>Pv4zMO{IfvdX#|CR#lAsn01D#%`8uR~i~-CaRjDn&ySMq$CVWt>
zv@y}^=M87<TffFF1An>NAgx|?vn2$ftb)g0>n^Wu5z%DOim#Pq#hPXZOi1Q6W|@ii
z*S~*zq*Kt6w6y&4&8-(>@6N{Fx$_+sim`WPW7lesR)ZRZoTADpK08rF3G$VAN3eTf
z=hS<<E*#F~GK;<k@QBMBb<6PN;S1(c&RopIVh0y9y<Mp8CTA#X+S!>s*y&R96aLw(
zD7NB&fjL)vmI~VzL-yL?J^Mz=o0-M^6T#!7d(IJbSa881yl*kH>w0%;;(A_F+lAM$
z0^voL%!1qJJ)fy9F@q?P#P<3!I!*=pKP+ili%3}@MO0EL03kq?p$O?KM_&zN^mU$<
zI+3~oam&i$wtuv-3MdJG2l21GIj;P*zouoBF)^fgUdFcC=m}USY5f3a?x3<O$|>j_
zX+5YO$_iy5u0ThWKoWqTfn<Q0Wz=aH&e6U8xx;h*z1ACFXMb!QEO9*6-|1ssnjCgl
zy0spFP;aZz(+^iis~2zTTY-t{kBzxpW3hfl^egP67Et5AWA;12|9Da<4*<2vY&e^m
zq0Hn}f9X~AyL`Uq22RBiZ5_9pw<gaeVt(IhUBJuJK7TB6E_thYYkO-Vc4`2l6%-$N
zQKK#q{g-0ZLVEilmkx#*&#w!w%w+oWTM?GQ8n`94rH#l;*vL%Ig&mQ?7#K$02fV$?
zL9f<3RGTE2bN`WiOM9!ikL+D!$$VPZoEKTLAX?_SmLjR2k~c9ofQ`LlrS=M-(H649
zm@cp<amgv9nh`=K#AO}QI{vM49S8-3cN@|*Wb79bPb7i~^z4VRemM$x=z)2R0X^1S
zCfeKTRoT1lLg3Q77`$}XM-kD5S3d64Ts((g@%|)(GozDsinGvp`dCks`2wumPoT+6
zS-&MW-_@KJS}AeSehd$xXo5=&B2KHE#}hQBmmExdkM#E#342#Dwn%c>Fw)rt2PVZH
zh&hO5ITl(8J<p0^U>2%~Jf6XFiQpKFD%-ZllGvR_$>oNcw;<4b1j07+31IoD;Okyz
zuB{<;vjvaFCO0p=fUN>nlS8)z7_@{<QMHD0jN-KT+ni0T*yx-%k8#m!oqwBX2ue~K
z(qr7*Fn-k;tPbi2_cS*Ez}SMP=5kRK$9v_CoW}mVM$~D~Gk+n$r<lnG*y#To;!=4e
zYaNuD;i+LtvJJ6^V(NVSCC9_@n;(BX_V}OkeN29O^OxQcdk21HV$ExbhS~ynYHtwv
z)2FsGZ%pPC<uT666|FRCl@cLFp;gBD<N?*Fa%|KdXRL13`V*#wv(}c(C4%Bt0x}Aa
z+Hn!v<{Kp1CT<n0?N!-td?On}<si~zk@ZltAk8wB<^Oj!J&RAa%BP~Q4??H0tyd7R
zDN;|X@l&5ZH6+{q`Hq&Bj+SqI`qZaYPWJwFc-zu}@uqcW6Y?Eq9ew3O6d_#HUn<t1
z&XVw@Ut^;zZzM-6sGbcWd?69>pF#qiQ~pSzv$wYsZ<i1dgN3Dn`qe&df6MyO64$eB
zTVgiS81q`fx&$9P2__6C!4CVDP*+86$yQ%sZ1M(rUSsy2P};ky8bfVup|*?ijG(zt
z)P4SBzo+@I<zPMI<0nWbh-bO$-JlDCD;rFWxhy{o?+Pg|5E>fKOw5H2Ozuf0_e>s`
zo<Fp^dt&ThwrAJD(b0puQa3FuV4e+!zDB1=r3<(S>Ae@0AetjOV$N_lzzZ^~O-eH5
zh%d-FF*Xx45)q?*sNRSqjNr`JgmZcFKxl3v6OSL7pO$7HG)DH0g%auRP^cSq%f|MO
z7*2KL!CgJsgJTojT?-30rP!IRD?v0Bo7=K&AqYEZDku(gjrajt=b5<*c2Yad0;=K4
za-iu7p#(w=NMfeK+5+<1r`u`V8;N({-qcD`1+ZW-|1Gg#+;F-(KC*!9=k2ek*GWh7
z+#@;1jQT3*ay#20&Xh9_+m07az<2C{BnDGGnJ9#YY*O8IZ~T=*6Y!tqXX2x&-StM@
zPp0;uO4v=a^K$MtUKzi)M~)^22Yz;9aORl20e#TBUCSbEmK}n5Ck(9kY2*>zOA4T~
z0{{joNf!M8n0I(c$!TqJV+%|L$p0{){RAMoSgU}f0e#C*i9rzs(&+XGqG*B9=6h`C
z90h(O56B5hy8;~px(i7qjiRpfaBdiW`0XjUEb%RK=&#E+a9Z#wpl-E&r<aPhjUlO_
zAQ2BlePl`a(J|Sy+^m?F0M08s{OmbzgAX*H&abbNJFn9o1`xrzlyF7YcnbzTB;V2B
z0IkXE{=S5BxCP)hIB%<vbSOo-C`U3M6s#%ihQ{2d3kB%E#%7_A$1Ib!Xxr@%V(G2Y
z0^4U&vH1b|CH8qc>$y!7)V`4fvVi75X5u3`J|(7v+C3>}epAl8<d8SY&>|0dZqppv
zq_FywUfirS4I<+O)xja$>MTrP(b4NVkTxp~&~8gKl8!{u2c#9%*3pfMto<0$zLu`8
z-lpEJ_odTnMK@G!hxY>y<955bTjEK<vcLsuZRe^vmd)CKTTJLK`z$`jTu^o+4RcH8
zAQ%t4DNP<>;}Mb#Dg;>+!l-g27Ta#wL-W~eY-Ap>)o(a!E;-LY+&@1W&91}VHX9#-
z8SL!BlIzS#nK{Z$qAgGX%%YwUUe;I4^>uS)DTm@TMa;0vkq7sHTn0)m)^)|@2;+Qk
z%GGP9RD@K!h8lHiSY0`0<jbk1+~Q&>ms>=YSLT=^QkO_yeI=}wK;^gj%5T=~uiCf^
zZ4pS}rxvTS?OIfhxEpMlrGkRp4+Q8gv0N9q3pCV#AXw~Lz(2bTWKhIZK65n+wmO%T
zBPsFmHfvW1qqD44fz4Ee*l4BEsNr$67E;P)m8J@S)LzR7Vh?VnZ>e!Il~@_t*sOIe
z{T8-Wt)~}7Z7|@_owg)c#FZ*y#^%O`RW=*aItCcK8ifvE_so^xcS3*(i-4<<B?uR|
za?UXg>i>I?Epd;7elp;YWKl&X#H@0hPagl&B;2r*ufJVo&cic&{J%}U`|i8nJ^6af
zpIyPJ6{902XNwpi$HT+7-PRJi!ZE)RQg40hTia!X(VqRAI*bctdL$;>_R}1ar>d5k
z-y<c;#@|&g0V-tgW{t9rK#J(cxd~lOVQ>mixqj?w07yNA&Gn;{Y#47sshO<D8w`b$
z*dMq%oX7jAB;Mnkz3*>3>hT<P4bLa`HAY;nyIroy?v~RnyBj;^6N&kb#*;@~uA`nJ
z&ppidpAxdL-W}@>jy%~hJ9IiY62#w|hDSy=h6Xxj*Je8ghSE6G9s3;4jqq(=Q;Vw9
zSWj9(je^My`ngoBwJa7T<~Ri>`Bv;($5$|umgf)@xo{lk${U3OhneOx*4SVLFMNi$
z9&NqTXg=<*US<}d<V)U1E+;GkDO}gB2dF>(0r^lA+7G2cAK*$_2l?^tKf6sAC^jsR
z>^UWCdu+({H2#~cnIBO8B|Vp%pwynM{r((?z%cgwc_9S34MZ~3?01p@LB4BJP}R6-
z|7?<#rS*lNZY_LuAFgVBVF%cKwRH^gPRM(^{VL^YgSH12JP4N*GcGaj5{*?z>!Y1i
zS0~n07u({Yu&)i3{X%iyEuRuI`L;Z}zt)Bv+ih(=e(@I7EC7aWNq2=Cz_#FYkapGT
zGqNJFc3>9BsA3i01^Sl;Or$0waXtrjVXqu&!mXNTr2-&dU@bw0G3=nf(m|6B=}S?n
zga%vwC!RA+m9Eucxqot4=|!x0P(`Krm2D>@iR?ui)MnUea1~tQ3er{jbGh<HPLh7Y
z+LX^Doj3V{vA!-snovbk^hF}?et0{x8ar5%vyO%%gvJR)hn&e!0+4s|UM2QI6<P&$
z7M8;tt%p1?O&VSqRKIEl`9z*<+lyMnH%Pi#Bfi^1Vq11XHm$oSw&0sRIXgQPjl8uw
zl~0I-^@i9&Bsw&UkA44-y^)B~TZP1x04%uL<`2)#Lj6k1H%)>;w75J)LHi#18S86>
zUm!Z5GQCn!*2-`sA)J>-7Ys;n#=_`j-Wu_To8WkueLPt~oul<f##wD({7C&M1#*C`
z;Ml5K3&{5&ZEW{G*nP3blfp0`s_J);mMW$LBTo-;Ptfu=++*wbLbE{)*e3p|GWMGn
zRSs^b`o6PBCGGXVUg`CAz+RL|0dj!1$t=<;P6Kj)1G?Gp7MZ7mz^w2senO>Io3{Iv
zH)$o#xIgT223>Vgm#@x~_SDrkM%~V!(-l^VA2{97W{-SO*IN1D#Qxiz{|o`4by4Vq
z)9<PqbUT||-k!R;o;pvH(>++{@~iqfuWH9fb<U=GS9e`qx3i(iiMOtHm)BXpcKiPN
zR#p6GCnO^~qT04TZm~Vuo3)~7!I#9@lP8pvOlzHEOE~pLJ`ZmauKeS{AabS*r$gl5
z5N%1794Izp(rR)=u`Hkzd1mniE<IKV14YYIcZ665l)|#Pyjsn$5=ny@!+Nc-BC66s
zooFneWZtmFdO*_1{P3<r=9-fZ2uUe#ZqLqvB;-gD0#U)7*`9;}iO7)_L{=W}r-UP^
z>k=TE83a0j>Q-t7AwlVM@Es4o1YP%a5Sn4vRKZ)yUsiMHxoWj7nZFe&cPB5W8)D6N
z?<S(ScW`Y&FTf@M&&{b9LDgaBgH(y6p00l6@$@3bw6MIKTb}(yPQOS(@w>|Z0GsPw
z3L<EA#H26c=ix}fd;ytOJA2rdHyniSb1;^L?vuSft=_-q9IH#5+Y=i-vH)%9$jA*a
z+=>jZX%VG>A9g14Dv#H`dRT^`%4KZEZfgjtX}Rs<t{MeFtzO*Sbr8u4YE=Z>xh)a5
zNOUJHdSU_U#S-D7@u$S7*PBtREe-3aiLFqk1j%Z0n{b+gEHyNv)Fn;0CZc~z_}nOQ
z1Z;E=kp#W;erEk)m|X4u{uIse`ah*JxAia+JO5J&Z8M?W#87LsUn(!vynE4h5o=5X
zXJH)(S4u+(){ulp6n>VJhr+TnYWqfQ7oxpSD(ax@7YX*3P2*L?SC96a_4Q`|=&Mow
zcTKx7^>d9oU>tb%-j1fG4um?@t>^bf&NeljjqJ^@K;<`e>QH%(McN@)$P?l1-99AO
zjCxxu`$I?8z<OfG(s%;_i5lM6+(bAX1Z{ZU%Bz^iADw<rJC4rm{lm_|{li(oreo2n
z)B=~CZ8r&XQ_xN*f<0V_;h%xHa&q>CmBflCIlbr9sRvK?de$k!oSeluzo+-)gQrgI
znNA|bgcCMeL;XJ1j@PlTdd(V+ifzJ7IyOgzPFUrqq_5zl6@J?BXM*IvGU|03bq$%I
zuija|gh#-iX{a;Y-chBl{n4|C0T@|m>~}XD^CDTaXSShXw!S6k@*Zn&_j|j&*ZKe}
z$h0KUtmBB|1muEgB*H?Uz1RTI2dEZcAK<Mg9EP6Dmrynv!ZZ30*#LKo=TvID%u<m7
zFRFyQF6QzKwlsU3?!5@iX=@n7nL2kuUHhXXm%Em4#19nxrw(^#ooCwZ^fkIu9?y{1
z>vMXhJawJ!Ykly|S}CX?W*E+y!@6Jk26T2y%+VI(*3`5%(alW$5{ruOpNb8QgK*Ql
zl`}WxLaGE3KNRZ{^Hwf*a-V2^&=cTBQIDVzom)_69@#OwAeC^a5L&LA9~zpk$t`Fa
z8!)VXbLgb<o*cU6)<=+6@L}W}e7fv_EJtz94a*idJle6P96<n8eXO>eW4FSVz!PCR
z7AGK5Gr)$NH;SZ`lF&}9S9H`@+MqU}F-G+0Mg*gS1oG2KZzhG*I9a%F!%!%IPu(G*
z0JA|P?@uH$_TLLz(MPCc0Ax&|@-YssyBdmw`}8|5sqd;MaYVnIuBw4Oo26YpNK?7k
z8JI*bs~&yu!QR_$yB`H)ibnLd+j<{-P(AtNlU)}tqPDI6_x6hyyPkYf%N2d%p<;$~
zM4y8nG7%26-~MSgIVG-_AyKCY1k+9B!;d}pgn_At)&2UIX~wQc*5&w5yy0vb+J9PY
zK5+**{T=T=tUo;5GQd1-1D`vK)Hui;hV@a+?!p`tqli#FM5<lM`RxkIvXGXwgIsrl
zrgxORWL9wICF+2kiO02HSF8EXfPuTuvsM(!{-wb?$|#g>1UivY1Q@o?9OfLT8TbN%
z3GeyyK6RF+Qg}{p*Dnp_4OE2moj>nQ!1yTN@g~$h>r1RJ`oDMot2~MrOW<B?%?j2|
zLVzVnuPyo()2Nh}HiG7wsUf`zHctElwm2^jDh|}A>@l%@3@JoV&r!p&$%uZnF{8HZ
zWmCu*N>gM&AgD-=FRVx{h+$=3o_|ijtFL(Oi6@?W;sbJ~*xrf+M0|RyXiZEV*xvn^
z9RC59=f$Vg9KQU-b03!vz9T<+OrB*9^}Z(U2w`V4W8jYX!GJfF3a02uL)hOo{NN^J
zsEo>FGI?WZ2T{AcIWt4G$uK@Uqa{5PmK4hI31H5c{RHdW7Nd4lH&U1lItX^k{id~!
zP7q0D8p}H?9#67y&<#2Q=zV1N5DUpmOofXI><-d9F&9EDO{4J`?9#_#^T-9VfC{O!
zUaF5zpJQaux#?K)C=(1H9XzwXUS?C&5YGb#_6(>pD^hpLUF!54sTr@8sH4`QU?DUt
z>(N~YVzW=p#tt=%ykR63KOdhHma<Br8L)=t2B+gtY!;utc?2KFKW^h5P`DVt2TItu
zS8|^M{O;N&+fUnqvPlJ_1fZk1touV8AN_hDZNUznQw3y8&MXcbxb?s;kPf#vT8jWt
z&YZ44e7OGf%=p5>IJ|rKw~53zAn$l8e;2onk+pqtR`wU*?T}LeTgt|cAavW(CreK~
z6<kW^H68{p5Qc%}Tg6q&n`HSw{u3>Ou?#}CB8EU;6S@IxP8qqXtp{f+S9J$_ZRd<~
zT)Kq9Pjp1IcdkU*VTJ?PC5Hy#p#)NqO=(#gj!JkeH`yF5v6|aamTLrMu1JU}U|}fJ
zdjK7P`v)?S+)5VnsZ&-5^XC2cG_*7hxf>GYD~W~~)zWa!ZJth#7CGK``|T*f^}awn
z{$*!fL-V^DSc{AIRuZ|fA7fXc6hFrLeBO#iS8K(`DBE5rYUs5Q_!S$i_WTowgfave
zOl%56Y<nzv!;H^xFyRloMIUTUC+LEIJb`(ohmpI48h9!j<|wC7Vka;aP+yNd|Ek!H
zA38UZi#*&p`s$%qBjfb*ua2Gg-0NO2O+vybUD^65W7BFf&{n*w=Q~f{H#~gb$<FiV
z#DS8(_fW&~9rcIqol$Yymug#g|7|sFXct-qg(+ZOiDrhE%l3t2fZn2|DMuBM4|oO&
zECGkX0T0$TfK_Opj74sTW{q{1orE<3zd<4hFbFwHT5bnBXtosUnJQ%Wf9TlBD}LFg
zucK2szUk>6o5+L*+Cquw#6)yipvQBTHI=ptfPc^uZNtpZ1R|G#Pn9NNR5QDLdE@fs
zoHGAsb>ALeS5>CH*IMVA<fO?-PVSd;(p=gkO|MBxDQya+l(yUoi4_406fA8A%2h<v
z2#8P-5otk0WE2^sh=_;`gBTG}fnk)f9fv`NVH6$8I1GafGEDfM=Y97+XP=V<{C>ah
zpRegTXYaMvUYB>h_w}x|>BAn!hwpjY4*d@+J^DnAdcW(%pS&1^#AD`pBB4Hv*G&i?
zfKMNI%{Ca{E*u<_3$k78uOlOZ=)ys~wCOf}&6ByAz_RU=_^k6+(`ls+0!O|Jj!nNi
zz>sGoWFuIw%3%wUlOTb`W<oq)aCTw)`8bw;Wen!KJY<h)#LiwWLG?}^7@e5FxWalZ
zE#^V2f0eX>SNS3?uu$>#eQ@a)pZx4$rh}Sv=Bp4(%XiLa!FT(yTDSz--685vP?<u_
z4f-lhTfhaGF4c2>oX)fZPnOsUF5Ef{HNT36*Wiv5Yx<fdZ-xqjAi_R`dhEvlk%lt}
zB|t<&E`V5jkw;3L1gh~D-VhKeJk#(AO5g?3sO$ILg_nJkgU5lxh3H!1Fg;wzn2{UE
zyJ~=@kkJ26-s`A36!inXu?V#bNDw&zeygX@A!Gmx##m>;Hfi)dbxnOT^J$FJxK(AX
zJS#{8O;Vq&Pp0ChHCEfXiNqd>JJwk`AaeuEry>nrP7{eWa!VbLwu|C0d?1}v2b2ox
zpX`O_O6#H@HK_h=T28<v{ImS2_ha24O{LF6SP%9a7+Q?Y+9fSQnxIMZLS;I+tvc??
z%{j>myD(XMEWfS`r<%T+)MqM_XI00`Dwo77lFcr0ZtbXi7iECvrd^k%Z2<z5v*q!E
z4}X0YE1kQ6IeZyqVSN^^SeS%NMPQELMc^HveB`O(IHyTB-IdZyphsgReY*3+@!FEn
z=}r?@OH5A-(4AHu-UU~m!49~g;Y=)0mY}7GUOd7#H{giXTjc$#%fxMIdQXsdJY2++
zR$qatwtm|S;=2SI<)y?RZdBhB{*hPx24i{8E%p?tJ~%WSN;NmGAXD0use{>H*V2gv
zpT@Rsv~tM6O77KOgaSAc6J_qjfkogpjTQ6o+Al`%<eJ;`9TZsn=}T;0wdRa7*5H+|
zfTU_RT(K<U?u0IcY}Uw&erkX^kcQk{T@U;Lg9hhBOnW?Tjz4^M-R8cAGaC9fA60$m
z_O`#6)6kb`8L0b@J-vHYHbF@~`Ji_^h(~Wn|DoI3w)YRtX<xju51iuIgN;IB9GrO^
zEmDu7$i*LA0t>f}-r6=kdga3L!WGMpc+i>gwokaZAS-}4g9a>c!k`7Ret~ViM(FaW
zQYu9h@WLzc#*|w}w}KT1m#i_6Cg_1+PZ0M1|9-CkWnBic?f`TQNMqgoQNx!@#k)cC
zy3=EP;_QtZ&(@6{c&*6z`@c|I`-S(zt)gp$6Oenei1F-eUf~4xL`&}Vyz<H2RMCxS
z>;CmbAtrfWC>R;@&od?{iB)RA=e@X^=bzz#qw2jA*g!bBZv<-~2z~cIs$o-4*c&`U
z>xotj-{4^o#WcBhG_&7~A2@IT7SZGcpD1aCJe4i*&tNYPUayV-yWOR&jG$)|cv@qM
z5YtgQUI!imH!t?uidCY61vfDhBREAu((pBTU}OY3{EV6rJ^A$L=QShMkf0sGW(=fK
zOr9@5>OCS&Cd8RVhn6=98G(Oh_vpUS(QRX6+$|&*z~^GP_;nJVpf|){;llqgdWDc0
z2cQn%53FrB-d)I#{!o7_txY&2YY|xEci({n<J%{93$XbtCsiV@!cXQk#!-W^cv;hK
z77gZ|5Fjqj_bEsH1!6YRG6|cJvrF!|41;reiJbfSHNso0$?%E@ySpL@1Cm$rJ^eNN
zk?}S|INs*b#{vI&!H1!NBH+;Vvh`-@CDBN0Wv_nAt>Y~%<NU(eZcP6aLwYwowQ23z
zP52Og<b^MbHITvj5BPKqhm`3dUxt5KT=S69@a}<j<*sK4o1E+YeXsQ~-S_&xuBrPg
z6)XGe8*+(QQ@6db*<8{b9jxqIbj)~5<8VdaGqp7XJp;#lqNQQsio{ADH5XajP~X3@
zB4@jsVu>4@C$DUdE~!j!TDzjZqJKCsFl*D=gL_xh)Z$EQ?gsw<d5tKrmB?@8eUOFy
zD&%Z10ALNMJ796~rWL$|T~@0Mj=0_qoiPJbIo-Go?*?haMFZw9fQ;&l^B`TQ68axf
zIelz_pC`Xu7I*_PaH&Ck9U=9D&I<cvMqrtz8~1TVzE@Ynpp_IB!Nf-Z^;CH^Ee=P2
z9<^0LpTHSFX<uloZ)|^|g)#9xV7tZCYN<D@2e+MoCNFX1gG9X2H3GzuLuJ4r6Wh17
zZ^v3ucP?8~EAcGrHf<c-c+j%rI%4~_Ww%$way9J<t{}}B9X)8{amyC>$l6ixt}yyH
zU<K!JEOyR#*G^)i7*BNHjdKMtDuwTa&>eM!9zEJ3@FmvZrG`Gq=YvIz*Su_5Gd@QM
z5%!JutQPxRkICA7aC6ha2RAhzyK)mE=nZxv`9W-qPEm_gZ8+|G7Y`DB<F#~*ufU6W
z)kPjCW&s-@!~6A`MI*hn4Yg2|dS``Jlo4_|zG!yyLG|^yS#_1{$?QLc7ngq}p0mI@
zRhbyFp#w=VQg~Y#mlBF41@%;2-J=#sFNPqYDo_Eermpp2qBGbH)pznVYkoL8i<#S}
zUHU7ur@Qrq>jyxY+77hh%ITWG4)kfO2gk|a&41YY1`Oa1<#ynKU^iFUlxB71!yhKp
zd;eZ24|40tzCP|o@5^4eIh);s&uBK=m(7~;OlGhql}Xj~jc2pj&B)lixx8<jZrnUI
z^F6y<YXLgIf+r23RUWs|g%9V0^o?EEu(z>ZGy$!18<ds_Gd8mSfUGd#b_C(dPys-p
zgW#P{f<pB@!QDMQgY6dou_rPX>xmNS`!-(M(O$c4?!o7#QZ7=Ln!L&EncVhNeYWiE
z#G;ma%O~0*^{G^aJ4`6P2lYK`?$`P}zEype?WR7<&yZC3%UCLP>Be(A;tSh*w{4pH
zh4WIA7qd#UvZ*eTt7|K(I3ba3`C|FiZIKtH&T&M90Hxr)!3prg>L`Vo-qAe_1snl%
z;}YowwSRl>`puiy@1uSX@9!T!ym>QbXglU=H|8pdc>;|B_W&oV5tPQbq8jhZY(Vp1
zo52}+BYl0@%{U@pU2oQx#TR0Bu(z>qydqgXl9gbIv1G+KAUJ{%PxxAy@K^4j3wuN`
z7mS<>);nRx?F+6M0p<xGQL5%T8a!sI6Vt~RknnS#Ydd#t+kXF`-hFa+3*7|jl!?K&
zz0rKj{FQK`H1M;5k&B%w{_E2IQU|;nJlsW`0%maa>Qh&*J{ubY#>RGxj+)WY(W{tp
z>S|NQv`aUQP;q5OsE5=rpy>>ioSszQ0mSD4UW;pCysK%=tvp<s>*?<44)1n&X3m^h
zwcT}@wmD!(-MN}fw~N}cqHPb&%VNu_Q;jw01--Gk_02VzmUyhpmVxqCKqGk!_&VgR
z^Um-t^*&1~Km(XMfL-H!7$?g>_WHV54;J;grzkKV$sm!Au&G#&oHz!}2-lDwr~!wx
z;WuAbhw@XuxC6Qk(XXrzqgZzwt#siDtinUW=&3$2v%(GJ2D*oOaHQ@BMg}(2R8+cJ
zS2Zj1z9mO~sAs4fN7>D3=}lUD$nacSnM@j6UQs!xX>obkK@rznRe!{mBkGoITvmgl
zdJ=9|JQm3=Sak8Ch3&CqS+sfHz>a}=Eza~u%)!f74aJhtWk;+UiAVY>as#V)2wQbS
zL-q2p`8|!Z=X90DlJkykn>Td&;Z2>Luzee=m(FP^Hx-Fnx`wQamRnmhds+F{Tyxu;
zCG%IWo?li5>D9BKqrNqsaK@I!1{#{08s?QnV@Vt<XBz6~M0R3drHhwGve^in!y3{0
zEG)T#To8XTtW9WyUEm%YFJAtU*YG)UxfxhK`C+jEhCZ_V;@9vwarxvk%bP&qq)`|x
znAH?$l(F9a{v5<QgFLywi^bJ|Mln$}d6G4-^f#>>NRQ#|(IaBujEsUrL7M-T9p<X;
za5lAY;h)~O;^_W`RHPU7AH8Do(M$%{Y`5Sc%*U^F`PvDChI43{%Z{?TwiiSzK1X^U
z$NT`cz=hdZ{YhE`5if`iOv*aiQfkb4=+xq2kq#g~?gu3gg8U<;NOUqtJuf_bUGJS$
zgSATrYX?_lCg>uCX~KZ~-Lecbfzuu^8u@~@yrQRPMfV6+QD`_~*{xS1nbQrE<9qf@
zR3s-@7GLD|XMh8K9o(t~K2Yq2hjT4PXB!k3QV9+^*F`6gZk`U}N(bipnktj7_&nZ#
z25*;f=144PR>R-b2PxT$O<CVe)<({)n8j=}>$hA09k+{GmO$y6GuV7Am)b)!U4zwi
z*b_V{oIntVl3Eo*IC%-ny>*OX$#nFn$_SapQtTWUze)Eemi6?nSkP6|(A|{D4fWQU
zcntoZrHe)YtL@cIazy!f7q$;#&tN~4x2EofUo^C&jElAR^v*pJ=k;%Es{ThkznpsN
zc4(Bo_Z@G{<MhM_>*<Dv(|r`1+wgTj-tdFl5v&aq-c!l<7E-eGU^L*ZGx^?#N1_i9
zpRm7}te;|l^T{_DVs_y;9zAeC&U=w<I`EC-Ow``qJ^aFg1ocgo6Xe3+?>r@)N3Fx;
z>KUx7tM9>!-2?xe$t*ZBK9bma?0Edh1;=hpyu9e>qZi@y_2YKL*Dg5rtoX|d*2Y&M
z`xA+=9b<`AJcvCJYJqD6)G&eurm4R<byMQ3W9C$}9CKFUWe$e5|0t0?YDfI#Y~rT)
zj-%$pJLYu6Z_>KUAt^^8DFZKw+V%nLzy`Q3BeprHJ8bC(7XL8PgX<CMoBO!q{w`Q3
z43-)1dwKgGxBU@oJq)Omy(|3TKln=pevv6X$XoU^>9Kpqpe^mGtAj#7e&KoBtp_||
zQ~{)5a6(xRy46joBO+zEaH?e-Ctd(?sid)t`KXxR_bgu?&((5`wl??9+@&i{JS2AT
z?8HGm^H!{w_uqXRPT4Kic(kvk9v2PQyXAfJ4mo6AZTjG@1&5rt0)_|Zc+^{jRjsFC
zolsxME$Qir$MR0n;o)(_nxA-L_n&m{*1qBHQ%>$)yJ(HPw-kG~XfyYU4b>;n5Qll|
zG1qPJ7-S)285ly0f)MD%|6mQ2<s$m(f*ryVk|%y&rO8-8o9Ee7B;_hh(nVc)PyG0n
zNXzl@o^n@PU00SXOV>nPth^%XA~oq`hm(z(pOEjbgsy*tI`EphSXI0_(wi`4WhT*E
z+ncT{pHp5Jv&PsME{~Iq3Kz<jPp}^>r4306ptBcrGAi<f>s(;BpgrYmbwR)JhK!M3
zz_)j|9Q=O(FYDUFDXIR1G6j)tBk+E3%~`d4c&T}i*Ah7vmA^5_2P`5k31DLGUa?|!
zfB)=kwzIPGL7tsE2AA}rHFzh$-W45-FJI6#dsDWvW?s!*awhLJa`vqUy*AJxgSDLk
zRm{iycn1B)9w1;4RwY0M;(5le^C^N+R{YQ>hK<ZvFo6;~(bwIyZCn3J?EeE}@fWB_
z4o236&FHpmd<)Lx512+(lHd5+D+B{aUvD2UeiW_KSIgB;aySjh9IPASVh{Byh~}Qb
zSiF95u(<2$5p@QC<H(KiRMXOQTNPf)(^|Wsw)M*FaS}AKd8luFc4TBEd!rdJjWxNd
zmfDXt)oxf=ox0L&m-xNOw}#eb(J-iN9Cp??Vc<b7W50Ny$PM@4<p=&9$P1gsDMUn5
zki%r;&Kzi}PPh#gmD=H0S?p9^G3iu;V<^m!tCCD}W=Esh5Q|bpipGYmJr&nH+RmKn
z5@c)%H)PCuCIYvDxr)l_r^Id7W4}7IL1>@DssTeOL}&1-+VXX?KCtie2ls!pzi;f)
z{=UAY2qIa!^VX%ybQ|urdCU7vU;o9M`uh$!W_an+;V<u8x*Tl;d|pm=VPwVZOn!&r
ziPf94y6^OkmSyLR`)}+ZtUPnTdX(!%osK<tWdK2w7wQTjppQVx|1}2|&;C+p<DJbW
zUKWp?1fiZmSW*VRJX+0sX7Y*IiyHSf#oUrf+N^t?q-1~c0bW0F5xzN30ZfP?a$Z13
zx9~(g;D>#PlRXkI5v7Xnx;it0HRqvqD^9Onzsi_Z>uXP6v2F-!D?Nv%KYF#bSAR6U
z>cWohg=?4gAwafo>Dq<e9dkSSDl04dI_3)hQc2k0F*1D>@w5xe?Xzds3vqB+2C67N
zFiNn$6KrgFcDu#m4K{>kROt}3fni!;+&~|JoP^8ER=0Ws{psPxx%Edim$fgOwXCMP
zZ%?vfPjXg8m35=>XsV)esXbx7tE<hlwPk8jw#hc;Iu|eQ?3(`w(P?i+wjYhxjG8(`
z+1J(8^>iLobx_U0eHGuXsjh5IB<ic?m(N;L9?4bIw)eKDOrxn^yms+|Td+WmS4xc_
zXrm54qFH+8x8}FtM%A$H;LB6K%cy1Qsa(U1L(0VCwK&?3QiqON{lNjM!+KuJzf73+
zWQE&M@sk~Wi)xJ7(0oWLQ{9wq`$~0Vv~~U|&Epce^r?wdG8)}g*Vi}OMZeyB2)6Im
zrCOV+%gP2%KLv^h<&T~kQBQvkPK%c<icr@4w^V>sF~=p_`*245%Kl~9=FyJYf%g7>
z9Aw^AF}R_y)o&b5uZ1n69dr6t^k-XV7av(85Qsr${S(H|m3%S?oiMln264zJhy=kv
zJv5sgUYmn05Ix+Y*igOutQ#`l*!%IhWN>Gghng>$z}vF+iD#`53$2;HxgVdvO9cB&
zY;sNWC8K7W$olQD>#=SEc-M&cQV#o(mymODjxnxSBg>!Tvwoc%<nzgad2RFj|Hp^V
z^!dJ@G0-+J@Bqyq@D)-{K<Q`_TplQkUFLBH*ToUy3P#FxMz~J!#;0NGL^wSLr;FhT
znSJj+yzTV9E$xT*9^SsC@AS6A-(Q-59R@RH<;!Z!=yvc}u6I%TFba<=8Nj5#V@B>1
zc<Bgbyr&}AfyWR%@j;o9?PHla62dq#dbGTMISbc8-#FgT)_oy9@Z*}y*bCq&HBBEL
z8{6eyz<o@i%g+Qn)>sVnJ_`-&e99V6bbX+1z4iq7&G+1pu>wST1|XD^VRQ24!w%cr
z(VT6pTi)BdJaa_N@|>pR8uBUT{MDzd?r3Pq)b%d!&8$cd=1T5?)5^tuA~5g_IQmc>
z_*VCDj6X}T#crq`SA_lri!NWW;QWP`EL<4NWEUN>a-~^w+Hp(2*nV}pS-mKmi7iCd
z`3qKDj;!w>FA-b%VEZlv%M?7u^oVoL0b7-#u)=UndIfieUmV9oL5^d}eR~wzBRu5f
zDdS_~e8U`$weK4r+pTf<Se5GSm3NfUV?F}A0rGnyBfDI|8X0{sB3`OMm+9dtZJzJN
z31+(6zYl(c^irF~Y%IKCJFT;=-xAM?iKfu>k4YMlv}fe|=+L*On1Osjy266f$ryju
zg`JS=z2oWe<JuMa0t;CVxqu|Wi8~M{9eLKhxUP#KfGF1kTS%NfqPH%Bw+8=fJ1Oy?
ztp=LfZ4(tsMwaBxJSJr#ILZ!jND-4druf!uk7VoXK6u_Kb@jQ3wUJzXb?eZP%TAWt
z$sfznLLZRRh4$h2xucfPH_$H3{N=&L`i8Ff{FN)`$GaL56$#vQH!N7OK<=95%px3K
zg#Uq8Un34@d2`6y1Ir?yJ6P(}e;`$Tdl{^KaB+REwg)V5wmC&8&)@EiZLIAry^(p5
zxj2WXXWsmEwPtwl5nJ|-3eD-8Xb!K>wfA*3H+S{5_t%}$*LTpLwyX(pBife!StVdW
z;B@47;ClFr<72+pHm|L%eO`N8`-bmrXlpCF`w`Qb(uO>g2;Y$c7|X=f8~Ti3Ve&*7
zQbFGRk$3d?tIvJ9oU~~6`0T~ovB-rD(8Tb@5pLbx7sw()kK7CK5SfDgm04UJy!Q+7
z_XEq}BOd9~aBOqgp+B?@RV1j!iY}Ow9}}Erbg=T|3G7&JgVx)PJ@^COq3}0C|Bqus
z;!qEE-7c1`HhLS}*N}iiAGoLU#7m+E-zu0N2jyaBu8U^y{<^s~TJye+n4N=P>;EQ6
z!1#ap@ARFLBds;HRjrW=<>iCs^6dO%MRTTOAem~eHMs%Y)Ed2;{DrQ7;{ZC@pT8GJ
z)>P%9TjWh<^jidyJMh{0aYKj`!@keL+GE&*y_e?mzF_wr_s~;*fuqB1;*DgsZ$I$E
z9~y}oCOCPb9;9`jKhKOzI?nqfxQ$PP;$)@Tg;yG5*OGc);X;l2u2ec>=~B)A4nnO4
z@Id?}zi<x~l?-mf4}_mHz;@V)pRY(oDh|(N2j?znxH{%i73HbAMRh4tU5|s(Q|0vy
zrN7gqu}n$11@+~r%JTa9Q1KuMN3f@inyRA-7d^Z#J6O|jRm3C`sZ?TfWt=a@(NACD
zvNHaeAeFyGImG+GYei2Aa1(q9YHnZ#$x=(t%CX4gcw}s41gr`3ch}^YIWcwWs#T|^
z%Ho`>_}{^s!1J6lph?C&aVOC{oNj#(H~^G!@m&B%x!x~wN(|9qP?(yegX;1J?f}_m
zckzYb;7exv%9TT{y}hl~b@f%bwtgHCx4f+@yRfsWKHDREjwUZ^!mB%X@7sO%$`AA{
z>&<4Ws+)RRI+|<iH&!m|?C<YfoUN~JdlXjUzUIl@&3i_OxjNo`*cGAioA9nuGHFl1
z96@4!SBpCc-IQ<7mJ>*&n`Aj-?KqIFIv4cvWWRs)Rjs{27a6MqHK28NOKpA7$-&BH
zvllGrT!ijnFukp9KSm!%Mr1Yu-yFFRf|+`ThU*ZY1KR_ORZw0inhaKyvb~AJ4x9Yl
z>YcgV&eb2>P~DixZ1^C8%R4&iKX}+-A3AjL;zLikvN;xYiRLRsBkF@jv`^kTAcs}W
zhO4JzzKz%OL;(EC!2rY99$qJoT>a%Pu<pTJ-Ej9X<R4g84~PRQ46vlW4bRk@cZ_MY
zc2?XbZDsxZ>PW4%wPlTwOr-wPvlBK}>r4xHQLHYK%G8_mg87NcmP9;hlbyy^*huT#
zc*Mn{#+nsy1!t|Ri$vO@JFkkk<El?{kxw7GI%?1CSst}ZH(r;_n2x&2+GwOa-co7c
z7K(J?9D&5FMAU*S#IbheJQKkwP~K*I^xD8fz1hF!*&&zu*UsDM?eec9p)yf;D4z4v
zW8m0tKzlgH+v$D8JI_1I`;fQQy8sr$BE<bJgx@)f-IV9x??c`Wc*Og0f24P|cRKhy
zcNp)$Ul#2I&z&!KXL#qM?0Mcr_}+q&M|r2BPNtlND?V`~YB&e=oR4SEK;0K2^;G1q
zk<<(1_dKMZfwGHG!vg#+!{^;<(h^HSlZ)iT{ttO$((d_aneAnf#@ZI*ue7d0pXQbH
zijbCn?_7?$r~hZ(c?Xznkcq_uMyKG;;<+BA&2dlRnkyd6=cf$va*1bloO;38kfO}-
z@Vw-nf7T^u_-VH>J^wFwu7CRHcAWL0Q}JBTM#OI~;hC*(gI6u}PDs31`AYq5E!VZ*
zIroLWv*&G?f8WBh54!e{1tVo6cddJ9{jJBQPdV|lMW@|<=Ji{5ZG8~EiP#rm=~T;F
zQwzKYmH5<QY|Y+U?|ScPlRy;Z#pWLK8(U{r+w<(L_NVq$*WgyT)7+KrF87psB~lUT
zk8FtSh+Gr7EArFGOHmtbi!O_fMbC>~8@)67X!N=08?h>!v9UUKQtX1*HL=@c55;~S
zdnxvIJRP4CUlHFJKQn$w{Mz_e;}682h(8zqLwqt(nP^K4BvvGjPMnn3nz$hG@x+z(
zc325KWug(^%~<_Td0Bk3$0<pv#`kP|s-dS;q7?HxaG&$i_OCFenVxM#Ew!k(1%FKf
ziL}&Fh4gBB2ip2`pKL}A?WhB-+i$$fbM2_PDm0Gpb6>~ve{Oqe*abPXSZVKkm#0cw
zD?Ifzcn)T2i)ZyKY%4L6THFyD+oU{U)d@&d3)EWWiYd*ws*(~MUE2N@*H!py!94K&
ziz#TOoEg?g=%(-t?^$=w`zLtq*qc_r1b3OVpbeJej920rV&`ns{04fI#a|tMn^7+9
z*Pla6?YQO)%2W1_&<Ca~NvMQNKZkNNwH|8O`?l20+CwA79mkkM&jmN_@6kU0h=UpA
zwF>SMj(n~XeazX{k^de&vtLD-_nM)9@_RBJ+*&ZI8v9>>`*bbo45zVYImpjq44fU#
zRjc$o=e5|gkl&8KnP&Yt<j({<@Dleo`Fzp`>n2nPFG4JBe}nvY!4vyCnfovvg<x+#
zA@D5pPBD>~)eek(4ZqWko%2-f9!6h?e~Mwm+76Uf9NUi6=|<h*wVJDtr}GQ=;5rX0
zFKc%cJy(PLcENM@=3kM=k*57V5pKkp94xC}73HPSZ_W?-N!DHpnN#QsdJxoTDv(1s
z5=S$rfiy$>@Al3_PPmV>-_rcp|3FR_b&v~jHo!sf3%+mvfShLhDaEp%K5f|#<ZI(D
zT;gybDgFUDg)xQtZC`?HDV^6A@T`CI(-jISsR`}ac9BJ-8k|yJfT<}*dU%Yw7dUPI
z6H<1{+}f>3Ex?K#2RmHdSCLxiWgRe%T<2b-DvZJy^{QX5_Roiaxdy2nLXVV`gc<5J
z>yTRLTfm97NrV+)n=fe(AT5|t@(WNVw0Ooi>4@1MQpdAJX@UXv<)UXR`HcN<q>+Y*
zU*vyjuhZ;8nnEN`$@UfK4B>X0p*tnOMe}g?+TG3Ke;^$wAG;6t?HC_9GWf0cE!=BA
zXQ4!w{de4heo%&Twc7h2?h72C+dYK)D%3{45A4Qi<y@QPTOrXe`){ycf*$qD^(^Ex
z*$+XZA*alG_du`W7%!9GS@6&8^ltGU^IkD&v(%h~2)M7BN6m}Y+75fDJ<VQbZ?g~E
zU)k4PrJLgpbtk!t+;z}T9&^vTHzR3iC~G2{BbP*Oh};u-Jo0?xjc7%*Bf2ztbo8|7
zMbWFGH%IS|J`#Nzy2|gPuOrCyd5oMG4&4D`?8Zn43(iF?#-n_^27M;)_*cv$`3B_)
zb%>nMA-NSPNokDo=(p3BQynINHEX_5+9Vey@7K1-&9pDnF4`fte}hs}Tjdj3lu+!h
z_WliZv?Hw+eacC1h#lk->=Dm(Xfm8v;t(ZmJMt*6_)L$CfSje#{tw2_u{GdHZ9l-2
zKpT4rZBExxCE5U7+#|?W-b$EgFUVggYtXJ~Kz_Iv#5z&~H3)LT-_1}zF%+Y-mm_~F
zJlHzN+2Z{R@{4DbxXH*skrx;t+b<qrxJSR2aZiOLxnZXvzY6!BPZE2eQJT*pk3A!7
z11>|%Asl~=wBlZItTJ+w244-=Nn9Z8+Rcr~nGV)vrmEx_&YGN>U}jCpVLRx9*)v0J
z*m5yLPQu(ULr&a$VTPQTxqgP6sQLU1IT8C1ayl?Giq8cq%$b|y8O|4Ri1M45S?i_U
z_mRVqsXXMbFK<m9a>5WLkL(tB|1)xm=fS6LlPP&74|h{rlB1lH^K&iaRWRcLeGt+$
zNDsHq8K^-YUO;+r>+D&zsfTO{mnS~8np8<DF{aVJEz*hlC8a^DJeRW_lG+Y>qbv&a
z=@&(s6mzWaAWbA1%C^c?+RlcYNaL>=Jb^fwwr?S&h)T@oM7k(;t4zBTDMgfSu7flP
z-~p~^--I;Kwx~;e5fY$Xp2*n$#WiiVMo{hjA{nS_G}u2uGHAPFkPXk9N=Sjz%r0}E
zc@{=^r(J8e*<JQd`?&p$vn~t!^dxtQyUE?}ehv#Y5$TMKL{5ra9JxMnH~9Yxk+*OZ
z(?E1h^b~OaYom97_wSGXAr_0Z#fD-VVyDF}j$JEu>eI0oV{af7pe?>Az9zmYzAb(!
zEY;iM_r)KJ?~lI}e<Kk|q!U@#s>>5=6DK4#Cw3$*PF$9_Cb1`RTjDNr2V@@Q0JQ*8
zBDESyOx3VysZwiK9!ER%Ig}@?c_s&~C2C8hoR;b29^hWK9vIJhiAic5u{Cn|Qf_uP
zN(!bRj}|65uv$rqx2#8{%@=@^D*aeXnEJG&kJ08UD3|BosFj*-mCPgcdmS;Pm%U4J
zn(<8yfm9l3j(op5BoJBwb~%IZjKGP~N%5GP4lyr}yXJjJA%?RSmJ+?kZ=F~}`nyej
zeaYhI1wHGOXB*HfmC!Tx%3Xzikw;TIV~_lPVr-N-t>$QfCt<=8l%ceM$!*bV`wqSd
zMapmXlg|(;q~~sUs5lqgf3I^u8OL)4#rNXAhCBKqNQWFNWkjISX3hI?N1KKeJw?lK
zKSUneA}ly3<X4;}?vdzrpOt4h8tShp$<bbtM_wekY7eoeY&VZp<{sP;i)rsG{?UHb
zo~O;%kvapW+nvZGR?n8_$*uET)O#@hoZ=EGL`Ro)pGICV1Jt|7E7>0B<i`AS+Q-0N
z06v&+NekLj!kbc?xJ{T}CUxo<30sa^b=+!mZ|FIngW_qvn<eK_-st#(_K3-VvpEhq
zoJ++6rD(wca|?1R1$PwMoDHqxfqZSjs5z6ZUbop$R9m1gfWGZH$SH=RFF)YA)Ewm+
zp=aFHB3TrN)e@mz)o5Fh9?T_wspWy5%wLLxn!-=atx`_`Z}<#KnJ-UC7o7sW!*cYM
zbr$oG?@9einaTr6mpOAR(hD%mTdL;UNDum%l@&<NK2Fz_bk$*%4iqasiS%i>oa37u
z3RIyul=d!1YEYU|kDM)MXes(y6M9b=gQJ?GkXq;=shybiC8?nR7uJ^ZxOY9MSM$gN
zJ|$9D;X}M8{Jx2_V0^?5NL%b%DWvhe5-G33{u6#n<bwMW(gbJloUfOfV~|$prOM+^
zAg$0F<*1()InEhSY|fgmNeZ83Z9VA6UZezlD?=|5=B`jm0x5BGXDEeH(ou7FC`IfS
zb0Sido6!;==lM8Nf*uKPGPgrR2y?tE{d+<Wy|jNJ8iRTb8{H4&j(!?y?|+RuLQXOG
zX36ts#7@c`H9_EtS?_BUq}1=qwakX&35euqkcsweT>Fr==lbQrrOh{>fhaVtz?I;(
zbE1_{=6noSG9vqZxq?<|HpvzF^n9$|T<P8IJ>$J;u)i3Z%N6Dh^SF7*#%#A;W4DO?
z`iOnbzUAuN0=L#}b{E5bz0*D7e(7F@qrWcF8(9(A7}*lJAaVt)*sn(JjXV;0DzYEC
z%<t`Y(F1a8LW9(xR-k$}XW?GGfS-DgQtW@u_MsHzbg4d}+=IFWXvFr;P)fOFqj;yf
z?Of@})Vk=GpXHZ%0A=32@6Gm8;L?;}s$=B6Y3Pg2S_b`5t~Y(JfhTs(3?-<;P_}lX
zgnBVc>!2nD+_L>M<f{+6!FJ7%&svEOCnKLSkaj!imhGyZCcMj>B>7pC6+It$or2-2
zS!C^r=*4t1L*2RA_RNs0yzT&Ur?&0e1GamHXT@T-S0Z=D8FGIuHIqxKKBoRoZL8f}
ziBa&H8ZNDV;v)Sc96Qf3CM<#{vluU}jaGLDxH$PM`2}@JN?LNu4<Mbj6d=T$D#x>|
zm|lfip_$<+)uX;%R1a~5{+qNp6zRlNT1%?^P&-Q7PVnt15H?pJwJ-)gLF~Os%CcWN
zkEDxMce`+Yg#=qr?eAqjl^Pc<Be7HMr@@!mUt$EI1YT^fKpOQ#`p$d{)Nvmqc~b_h
z5)&cKU77Gy>b`*_`3^Xy)Pd(4QTi3RFF^ik+}Gi0o?i_aVD1BFq`qBAUT+`49r-UY
ztl4`AckDg&t*nblNq?SPQg|L^-zjnhox^dj3^~KU<Ci*K8+In1?d^eX_pJApX)?>q
zCUcRw9_xrtm>11kHf?+Dh#j*#!1wmpyWqKd+CFbzwr{|8tAviqxJ#WEVojjgsYY7h
zL!3`Q+I}1T43{ULpwu8XbQiF}d=DvIxTn@ldzCfQ5+a@vGo$8#_b3suv<Wz0s=HL;
zT692si-*GeHGWImpk5KQ@Zh`VN6^R0DbI73iK*=0%()Ts5XzLcjk(20TkJ{5qf{V0
zk^7J<1XkEwq^Wg5epF=R+4DdttW&*_strL(x(Z3x`T|P<o?Cl<NqKS@K-%4pbi$l`
zB#T+0Ed?FRUJc49z*9Yh-R=uTsp=uk*;6G|ZOeS`?XIG-Dgo1wxJh4Iq)sPLaFs~o
zdMRg*)CvxRbK4RnFk9fKs8x;;I_No4Ms=e!A}$zTA@lC8!F4@qgq7sVaUG0h4z7Ke
zV*hu<hy97(1KuA@w>iOFX6`oo;koFw8|@|btM&=3s@J*Y{;K-Z?lnmKrI8civA#L-
zAf){3(R6eHywyA4tG+!t0YCMdIDd5kd=+QL#$z|f?vFhk`+eMEcfgYPhWHkEDQ<}0
z4IjmG@z)b&@J|dSHY84iXW|-oCGJoBH1S;GRYb4UCcBeMlk1WvCC|ojIM*j{Pd<?R
zY4TUemy)kzgK=e9Q(0%(fSCt)aTYmd;twH6I1-<VWjpbR`JClO)Bk_*96clYT)QF9
z3b#iJrH-XE$2(FsH8<*I^cKxrn$LwxcY~6`r8&!LNfY<f?&0|O+KJ+lEJbcje<LLr
zak&F!GNz}bWTDQGcSGuIDk({BOwFAZvA;Hg`x;n*UHC5Tr*iwiJ4ZOwn#PiH;Tp+1
z2{Uj2^AnV#T#!4o7Rtk2!E>`+%85S)>6~$nfwihXhE^)%k0DKl`^R*p4=u<193p<G
zO9r*69M|<<#T%`wrKGO#7zlA)yZA6lsgAF_n_OO3ytz8#!`wV2SXme+c^vP^HE91q
zq8VcDOnvup+YsZzWx{=;BxF4UC1tptApR%m4QH#`(DDw~6NZz{NF(Y|s0Q>kr5;y}
z5|lNpi9DB*tB6md1btP-CCFjfKIY$Eh<wFw)sob!OF7xa8qMQ)I*cE*8Umi*>2~8<
zF_o)Gq|{2G1F<XIqw;Kcbt#Np7bCZH-ZV-F7-ardo-NE5bBMF*$^Q|4i76Z}SC?2%
z17I)yA^REp66+M^oMBzj7A?p87|(=9RDpY65}S>F9_v-@I`6mhevUNt(M-uRjCl#q
zCg(ySQ)R{^FWehyFzj=+`5E%UeW9hVexa<ZLmpvEubOheyqCxvCUq7#FL9WDLg>0?
zF0|)xU+6QTZk={qu_&(5UjsL7CC^Bd4tr^Sikxr{>0@ONE6tpeXQ&Iv967Fk@QRek
zaVj-p?p;kNhb0JknNh^#(IciDS2>&?r(vFih7j%nWe#cRZ%WdAN_V$Ny6V@A86sr>
zb4)MN!*HRbhy2I+fJ`sUk6K{O?gpfXahqBt#$@Or3)dt13dXt!>A?s%YTrgP$0MEn
zCr*WYfc66DCsQepx(sXgM~`P>o-<D%H4K9c+G{s|hg9~8vrnp&^;CEiN^6LC&eHQl
zsjLw8p^i6NYUF{|AXICpA(7MM{kwSP0;CoAHKXmC5i1%_<BW8Ary;Gl_X$LDhRaoh
z7qy#5p-Y9wQ)Yfxl9I&UhvG5`^MjI<xJx62TtK-TEDrPik}@&#a7jwk{7p$p#QXv&
z1xNzFToqD^_@;TR<f#htqmq<z@B7dQL*uveFnf6%GAH0Jk7<UMRRIQ{fnLZbx$gcn
zuGt%4k$DC>qSEZcas_H}vv5W49Ido|#A9yuF7~eVZiiL%6yg(JHJ+(5S+fBCqz$mI
zwwRsfQrO%7A=E~DCh!JP&U6ua?lHk>>I}MaKuHQo?Y@h2av!x=)vH1&^IyOwrZKvS
z7Chxen`@L*${+HqP8m;w<w;Dc6ET%u#%k{mNMkeyH4MKGh~1`Fkuf9WrHFwse=jL~
zLRTtsW<TItDAVlfaZi1M^)f!>5xFOhi!NXoeWLu77+>wZihFHWB~*iG<IG9BFXNgs
zPS3+)xu&l$YkujM0nXbW$hC|RalKr5gy!pRgmp^|q8`!38xhHT9XxuEW9{}$tVVX2
zC1y4B`Yq-H`1Wo<bjm&EA)L+gwE2}Ijj0xs#}SN#yo1&edg9t3-Pjtv6N;aN8T0AO
z=ysfgsri9_Kv%!1C57vkkt4dBJeql_kUhjYuSE`Fz}aHm`PfBsDzJf)ma97oby~?)
z<{vTdlnZKsajvO>t`@p4YTZ1G8P$^hY8&>cat2ja;wjgH`_Our+3e^0ZMq-hUVWLI
z<5`HL*5{SW*P4I8y|$n@^ea$VaNlePFn=Noy+)VCbq;^P2iJtTlrg*OaV4p)RpysC
za55sedGc4kcM?{K?(m*~t(L~To`5-3-^Fk6R>B6mz%Ivn^9lA8cawN3sDF@JD5uFW
zX(dq#sMk5Pl52jAbZU9JB1n#|8VfO-b1W9QS%hBDLS>E2;kW`Xk?M?Tob<#p#9}Q|
z&?|{KiuGItB?gh-P)||&iM^$kMZS_XOG?^e|C!73ffub4W#6r>X75hSP@$z@Rg!g3
zx@65_<cCM3n9usz?`E@5dRG`RQ=9N7_KTy|HB<Z7ATQi9^;v(!t^*j(RcBhG#TY&$
zIfeG}(VJ#LNlp;;g<W}|itrdnYgmDIvw>gDXp<B~zR@hS+-07_wLjM`2ON{0nCHPm
zvVM=&h>z@H?*(kP>^5t_JI2k;@C%$F_|Yx(P&$xP@|P4xSP&b;CNf(vI!1budrVg{
zuvAWek8-{aY(9kAO6&7=N5NH*M&?ZPsI*kLe~=4i>ojF(!;mYh|Ea-#7_(nmkKh9!
z$+0$?Z5UZ;3Gz+l`^{ztYAnsC4J6oY&H}7Tb1BErd%O{v+^-mN#MfEoH1MvX9QQbQ
z4JktDxfyRByA4*t+osd3GiQS{Jb*L)CT$jRh+FKH_73})ebITY4c?p+5rufYyT?7@
zUW!<}Mr>JREV47QD{?#5ZhjSc4KawF(dE$-;MKVzdQ0^F=u^?(MBl<*iSF3)*v8n_
z*rl=S5QXw!?5WrbvDf1Xcy|WkBk^P7o8vp<<MHbdhjLH+VeFaMAAb?MCf>vw*eVir
zb{JeqJ$$s<6{6~wQu#`#D-S1UNZS?Qd4=+nKWc$$+@n&7&oS)5LQkAY)~&lHSYJ?<
z77S<THGGP_jBJq-AQb6n*J3UTEvsji98YDJ%%WXZRE9Au<ZzVzc@E|MXTFTjvrt0U
z!19rh?p8?=x&c;EBiEI&u;e=%Wr(931?3`VoR~;XL>fc1nLSz{8up)-#CF)l`4WT?
zd#RdLUemTm7L~}`E;26JEnwFbl^{fQ#MBXllcNsyD42;t9n|sBdpm@3g?yHy<fgrW
z66j+%SNSw$4Dr0%ZYauSjkHhI9;mg;i*nf-<(O{^o1MrF!?1V_Yjx4H<h$$_`D7K^
zJqSH4^qEn$#3s=btlGkb@$xzDUV%1Y+wh!lW59{rlBGCjfR$ZYR42JMJqdie8*jYe
zbA@}#5_;;1dCRBd`51sT;veSgEZ`e;Ji=~aK2K+#)>t5s=&2$`QU@uKN#5tck#y{Z
zI#rJM`#FpVE0SZtlHeKEM~r8*H6cPdR*4Z32Bep~rSI*RXDCM$XB5Kh`KqGYR5vBZ
z$eP2E!+Mo|NqssGY3RVTl6e>Ib+cWQPiN1F9X<G~v`dT9RkL9f?!ibeo(bxo)#hK2
z&Q>{gQh~2A+e3=#Ar4aKYP4M0D`1fF5x~G6UX-r#9^-L$B3(yD+Mu^mIE4Ev=(<5V
zDNmwA?Fdo}wG(UMF}8z6se}cjvN;E-VLA{Tw~Qhw)Ic5v|C>FcDAo6B+V#+^3uVbY
z({@Qwn#8BsMMY_xi6;9=q><9eO#?5$zezbp%n~DVwA>u`AFvI@Eo!69=J!SA#0z8o
zS?Z&&N9Ud;uSHs*mvTiHwuE^>q^Hi8%%JN*3OQCSC`-M1^B_-K08v5@kTt)P`=DP*
z^HR}$LQeV7*iZI5ZucTTXgBB0Hvd{wK4#~`7Rckin<IDX>Btz3Bk?)Bc^NtyDGH-8
zzmaR{h3mq#Pp9TZu^FiOP2h?+(SSXt8jafO=1Lmi?0O}QknHh}MI_zLuu@;Zj^Iw%
zg<G)RbDMeC>^HC4GVEAbW{<UJ+Dq;Ah**8t{@nh?zUj(bi|dEZu+EKP6=(-EhjF(H
z+QZFm0I*eV$5D_92wC;c)3ZRn)P+;YC`)Pchou&0U#Wv!`U@ySNkUxWjF3B&@D%Vp
zaAKqub4ra<&^h`ql%V`1#Rrm&_*00rKV*hF^4!&|m0pvhuolKH6!Q=67z^4^O*^ot
z2#K6|1y9g+CVwJ#Q>X-W9E{xQ#vmB!{X)h}jVSQAa#jV3-ZzAA5~?L|F-wIz5`Jti
zWS`iq<OF?D3r*}n%cwT(hthG-zrR3wAZJoC3f9{KjaYqpY=g5#+1F!+k;<7;tC75j
z`1<ciE!3gDp0xKUQm5dG`-ar3x*2CzcV}KKbWX^z4{RZ44w5t-C1ojQ*sPK?&K1X^
zJoS2{m0*&kpNw=$+$3kJ#*SqA_UJbXytxMn?J`B{`8@8m9^nn<Tu`fWdB|sTo==~|
z*JW}oP_~cy^jGcu5LdEtVLvNr7>&IMSH$lQdkm~C@L+olezA)VyNI0hrwJ6i8SA+B
zdcXAEFm<r;H$s!&g_x{I&GU#)sI>#I@Hg9w5L14Oz1u#7UC+<hpJE0mXXpdss0uhs
zzC<p|`2f`HPu{g;LEB2(AEW%tb^1`Vj{}x7ml8;#O=9Ity`z==s>})@NG)1@6x2o3
z51+QzB9-*$d-O0S-%{h4@YZNj9OVhAMerNxlrS9ecVtFsZ%v82u#ZXJv^}%;A+NYi
zwX*2r{ZHi4Qy1<pv5;%e|1EV=i=jr#bz8!pQmNh01IqD<ZxGi^r9Mw<cm-&c9&FVn
zm_jMZar{wo!lS3=%9(zBN)9a^w)rOLSZ#8a<w<NDOV%DIR9NV_#2R>iFEqp6tFDoT
z_h7!zjLwB{CwsC`1ZkKYKJDEAiqNPD>~JxE5NQ^S?IVKoeEJPwb`3Cql5fD<fHN5`
z^{#UZFykD(dI<SVU6(!PnA`2$C{3BJUMXIMxRG0k(G@>U=y$p=BAt5|3w&8D14lh1
zC{K7`mE7Hh(Qsyb?bv%CXzoRL)ebf1!AJUY^EToij|QFHik%y;xU^g9PH|Tt?(r%2
zYNS>oATEvE8kvZ^5cQ(j=m_>}T#CJV4`R2*>#;QAAC8Xgh+PF6c_Q{)?9F&>d;y{#
z&V+4zbNv4J)A8TKB5q17!p@9SaE8DxKlb6-#4Cx(WL2^wxg@zdc|vka@`B`L$?KB0
zChtQ0!=uTklg}ao;<e=4Wii_Tc^=45>b<g-1Sk=H#2(so#5VrQI=O9x_5I#YsNWy>
zVw?V~^7$Az`#HZn=YsRe*dk&bIWOZ9*f-7sbui4aTZ;1J?L66lGfk{i4*=;{X`i~O
zFPq#~kk1kUjw!v9ii%T3dvil*F{nN8-6%BF3L}h&SH$N-h3_bjWG*cuwM$B5E#5P&
zrw>rxyj!_dC>L<u(vm8N*?zV8Zz)G^O3RO<B?XEn2v@3|qNL$j0lW*B%gbS;>dJJZ
zTZvjpMI5=}0&RT4lcy3;+L6bs#y97A>L@~evww|Jffl3IFfppg&IA0;$=5}yQ@vib
z8IGHC0FLPnk-FYv?%c58L4XmQdBTGjogalg#V<o&rLNy{bVV>WZ^*nBLo4t|t9)!k
z3?L<!`k#Y6d;wqebpXS25K5s=2<d7iwT?7kNlbYcSOU-04>cp616K<rro~nCgi^hD
zL_traA8rs7SHSnYmjRHGH4^V@$S1}S1C&2fGv*T_XY6&M{L*M<O4&O?`Gx0oZeXX_
zZ=xR7rt?A?3~_Al>&TtjI<-jp1fG&-14&qdWA^WgYA(rj^!WtiRtu2W;LoI^z8&P|
zZEJx^78G$ia;Nqx&@KK7xzs^9MqQyGFC$e#!kV}7TgrD-+p6|z9OW0EWds%HO(m<w
zu{PW9p%#u^^=RtN>ZyZ;?+(Is&|~ETd|Es>ZV&PTTvPtYk+PNsoW-e{xpH5&NgoD1
z&ei6kP+no~RL`X^TI(#(uW#p@|M8#GaWg;fk+Po;)fsSN(rY6;k=%nDz_nQa<R08Y
z4lz@0zG}-oxP_cx4#*=fD9WL~&o5`*@^iF*q!8b?hBKTPX+o+jj7qhbT+uVi??h)b
ze}eYFnhUc`ORvd%%GaehD>_nLQ#lN}R4^NyZP8!cGNcCc$KKFVskBe~sR7s0z8qbW
zD%y%=tOe^+yr5qR($PK$9j1gEn+uT^z|5alyHP9~(tyr?tNCBi<X9hR9hhW)j+7ws
zS2%+CFQgRl7xx`MMJ-XRdAP-rLQRsAn)(*kJ_%!v6Ig1N;ht-v3iFJ4P4X$}$iLHW
z1@0Mtpii{hXP^}Z<*V$IxXbIq_HX6R=O=CiHkNXWoX2ZlwwgC_?a!IL2iLyrv&Z9_
zb3?wm+b;vkus_4Ku5xDWFGNQLExVt{700+2SE4DSr}VUL@vZ=un7|1rH=-RzbgD<H
zm`juHWuDO{=l&*0>vt<lIZsP|CtyV#i67EQfK8-fXyfisKc7-nHFebqbU#A7TZO0i
ztgdmeF4jt2svS`owB&=j^ZpH-sK$NFn%<8y-LArJ<`+yEEX_@b(zw+;0z30<oR5w3
zvh7Y-nYY_V?0)+P7lRjQkz0e6qqE&*?gn><d%!*J_Pdwd>sUdm!WvRPR*}|5PQYmv
z+w8B=6XG~~O<j;{8Kf)XrSI`V9|%iQ$m4)*-h+w~@1j|mcl`Md?8FM#7pgyU&4;Um
zoDJ@*?{p7<|Ay=JS0F@Vq(;PYLTUDS+|AVAycUDHs=adszP`5?HE2v6WqW>ap!=qj
zA&%%8X@2Dor6jHb7S6Aw?dc(;cJnCUrgki`owTcRM5(O<eO6z_bT9^_9e2f2TT?rV
z7L@jg*wt<(ksF2$VaGD6O&l({!3sTbliW@9q$ZCMjNxa)^R-U$NO#&>)wv0YtYa)6
ztpP%dQkCyxAw{L#_mHDwWl5z5p;K$*8C_FjI=O(ZmC@Q$&6b)5`3iSzr|k(y53qxE
z`P>SJ7}6##)I?fEw5(;k+Eh4ikW{r-RPQC+ekztSDU~u?Gy(7kdYlT>i+DMlFj$<%
z2)O%^#<Ok6ujsF&cbBK_g`Z7$-$V*!A~B2DT<tywUPQj=N6fl4a!;NhuyTjVJt0on
zn6n>|d)>1MjCbDxCnaB0SgjYn8jR~_{vB(|;S`&|#|3TKd{~|%<zlAXYRMyS;mjZ9
zKc^h)C*UbMif){)NDeD_?T+yIYLoXP;j7fRFT_bXtHEjRF)v`Z&1zWKci8<d0^YLO
zUF#mkp2E7wp^>w(yWnxGL$}~0gq^UfAB(<%T?NZyTVlIn_r`t+i@F8t&0FGEVK2eY
z<F6%Zv5#OZaS_focp$Mq@p>|yT#!6Exg&WMb`DG=pG&@3R$I29Y(v@BvMb7ND|@(X
zf7z?$W#yga%gZ;GZ!Q0L`3>cFl~0uKFMp-NRy0%$RIIMpRI#ICyyAw6J1ZWp_<6;P
z6|bjasfJW<YFTPs>crHx)Fr81shd)Fr0!2WntD3*Z0e=dYpJ&@W0h5vO_iOM1C>iF
zM-1LFCD=+Gkoqv^h~63ckI8qGB8$)BQIBNUmqolI2FCHxb(MbvZ7F^6Y>|M{)WRWN
z68gj;wVkuTB+Bb*Z&LVe-j)(9<DM7wm8pF~c}~rNc6yULewrtUN2&p7|24{m9tg_#
zjeqtN{uu1D@`P%+(~V}zIPAyq1oZ}vlRc$fNeot+oF*Q{ehj{bKj#UbC%6sbn<$19
zVNZ!gx>YY-o(7FUPso>Mo@v@{<a(GfcO1S$Hm%G<7^=$YIj^km%P38*#nI`?DD?*&
zE%Au_y|DK6rBG?Eh7cM7hD<4Kq9`5icfkVAV;frawQ;*2WvAlZ4A|yIMXDFWFg(hv
zaAtCp7Po$yvh<pgt5QNJCR2aN;<PQs`YSw2M;J&fU(>}492g<+Zu3$Y=dGc7OW|Bv
z@1Ias*LDbxJcQ(`WJZid`|sWd?qmU9u<t`jpR1!L&N8`$(sMDM{|`z|-#+EE$~e^+
z-<q*x$(Y=SP?A1tVt1*xgp{85iWz!z-+e@)!MI~Yh81G~El55aI`zw)Fip8IrZCzZ
zwxa!&lq=;FQ}}~B3OH3tUsL>%ZVSrD3M+a<9f7tPc`~V-ni4gqoY5U}1q_;wLiVD6
zoHs&_l*qYKyr9NOT1~rSQKqy{yjL%!@Ob+VQl@l#%%c=0PB*%-Y3lKHN}mffy9ZGw
z<Y`5($bBfssaL4oe;&$^uDRksZMI-x5Ee?6jFk&Z2SSS;G1jEoPY!cTsa8D4$`O2x
z2zUiu98ZPustBLlR+N~z9j>G=2e&5#rrG6&o@BkZkspS82^Bc*aHrmtj}^jG<mcS6
zjozCW?!p*dm}SM}_anb_l$>RST-xqIU6jQf7w4OrG^v+5Zq7Ra*UE_leVl#vuiYl(
zmex($6fdrO-?X{D)$dN6CO2<w*eH6zk!XaV_Kgx2nB+c)ycuxBd~8ad#?x{BPeGn)
zi-cgH1=A-}>7GCyA>v0r;g0h_eLrh&!QBjV>{w^%?D&=$A{J6oAF+pAS@n6sE{iBt
zT9Z5>mUA!KFTO=exTBF*3RPeKvNt2I8#KYyUd7dXG#;WOO5u|CH`y3$kuW^-lw!Yx
zoS?=cTgm<la>$R#S=j4*G`n{fa>6*9=M{K{r;6$`T>TF;e_AS>GfIWLRcdcSD%X%{
zF{odGR>K)c4XBQ=C473^&!jA8h!m_gLfU*(QrRA((S6+Vo<u6!Dy${3-Ty@@u{+Rx
z)SL1Zqz0{V=2$XoPeUrHR=IIthdDo$eN{OCUTGgjDqAK$;@G4&W{sc9p3xJfYpln^
zlH$CpUz9z}+82-(z>H60FNw8Cqy9i{rnY~lI}>R^PXj5(vuTL4#4&PP_+HGxNYnK}
zLQ3`SF{CN?<M>41H6IZRPW2F`bel_%Qp5|~Nk~!r4x*dZB1LDAC#_)wZk^N<;-l_#
zX#5<dzl$4h7U26VJ1}cpgGyVz-%_o5!m8R_iu({nn~QPJxd_f?jN@Ll3$?8O9yT2N
zKniWdyFX$m$?G2U=18&cST9Q0({ab15N_S>R9JWl>8$166ko#Gh@?wAnmbLdiFIl3
zZ^a744BCIjl|1P_fGdRvcd<}bR@*P)N@?f`T<K1jjkprYYPrV+Sni(0FXv?*_Cj3e
z-G_MRKbQ(L2R7wqSd};7+&w+Xp$hA_%dmobn#4-nYVSkz^M3n#oMThr$1`ofNqpN7
z&2$;;&YRush=zX9J&K6vXY84Phx%J+JCR=oe3aTG^*vpe;#hpS=`IAMI9h7sEJN>7
zvE)7*r8$2*VSv=Cb_8u=oX%!Gf!u%#5!Y3VB>x2dx@~^0de7)P3FwlvejduRzkzR(
zGr}H_E^bAhT8TkS5uX(3x{IY3MW>P@MRWysfz(+%9>1>`tJ*)|vFf^L&VCtOO=Z1~
zfZSBP1nwemwNeNX22Ueh>6#pgI77`hXO1XJr{zK4X4dTxo}h3f|5o^Me_<GYtfR{P
zB`B6$mv~N?LjKzn(i9@flR-~5nNMS1xfdX9<3jD6NAgw5j;x)9v;t;GU)_h1M(NJk
zCOu;I-T9$%tdW-SUkDcn`i+s>N~BO)ky{DxaNDH}=ZCxwJ~PYnR0_R?AIaUDPvKK&
z)h0mM3PJWGja>l2Jy++m_WihLugN)JP1$nX7wU}JO;VngB6)JN`8eo34@*Oj4tqzQ
zQz6%)L)b02_MdP&am{rK@CWlr&@7`Uv-S*Ju|$)t!WH%Dv^!UF!9U$Opkzd!xwG(#
z*34zt<uqGb?nX4sM#MpFvmXTxXe<EdUbPF1&|XrKPMoI4;~Bu6@C;)4!QtJvQD;GK
z{X4WzylpW&8zu`{>_Sw^#qjb!0nbz=-gUacY{gEwASyC}{S!+O6}i=p+nek?;3CiB
zM2uo@_#VWCJcP)Q=M8r(sLrQWE3G%3U0M*7Y@{feTXV>Jl%?dSJb?aWR^qvLt5>a$
zQPl72?$Q?ddcY?{FS6XPPfAiLOU+Cvj+{)qyXMpQ4eFpzoO8`F5W3K(+?BYdt;DrJ
zt~LnXqJ-+npTJd6KOsR+ppT_^qZRYSvcMHn^Q(#O($I6N`Kg8nns*;T9>=aRPfBAN
ztI=+G5^>NTZ8rL%NUJ%-^DswSV~y0!wU3trcY<fA#gJm?>-tzIopq@{x!EHQ1~utg
zDQ$s9#}oa6dZ_gVlAO31q^ovBe5>>}Aw8&-F!ec?_x_S}uGNrVd<q<G3Zxx%wL<Mm
zv)$e$k{ZC)*TC#;NcHy|p*`|I@D<=dp$3H+&h>DYg;Kea!MV+0eTX&qp7j8N_A8*W
zVD<v}r{_Z{X3=tC4B5Dp(=^UEp{*QQy#AOI)mJ}&a^Y6U8=Hm8k!n<nPdlE?7mQpu
z4c@QAl+Ya8zeGx_P+G8p{8jKaO`*=)4Q*-x?6C_Fy>=fY&&!B|t~0%OJJLpTCf+Br
z3;W#e!v5GN5E1<X-YWeX&RS(#N=KB-q=%C8RxQuJ;+`FHW7W9WCt4*!?Kmxz`eUJv
zk`qG>C6{8i>bQYdfc4c{T|r~*q=Dj^uSTokn$=4{y|&Ta2fU&jQQ7B9A=E+H#9c!n
zsz%gea1tZwhgxL289^GkH??ANENaCnCn-hpJ}+B~a;%MUFr-@e3@rCj3$_6Y)bnz-
z4k;|f6RxO{b|XfSQm7D{Sc7}*74g3X5wMhEz$1J}LA|&qXZLrKn9Ct^{PDS6B2^Fv
zVeiG2!tx~WcZ}113v#8(!yAR%XP^_Q4MuI2G)SHnNDJjG$`2iS+u<#-9|RXs3pTLc
ohyj3!`#ee%<v5-t9i(vPEPBEd$~=^>L;DTjx@8!5k5~VH0QmdE^#A|>

literal 0
HcmV?d00001

diff --git a/docs/python_docs/themes/mx-theme/mxtheme/static/material-design-icons-3.0.1/iconfont/MaterialIcons-Regular.woff b/docs/python_docs/themes/mx-theme/mxtheme/static/material-design-icons-3.0.1/iconfont/MaterialIcons-Regular.woff
new file mode 100644
index 0000000000000000000000000000000000000000..b648a3eea2d16b6ce783906d6b7d5f251b9eb56c
GIT binary patch
literal 57620
zcmY&<V~i+GwC&)GZR3n>^NelVwr$(CZQHhO+t!`$=Dp;-onGnG%1YJl`q9<dd$o&<
zurL51z;Em!1Hk^?03RKH$N$Ux$Ns+|A}A#MD-itU2!BEJ2LM1pL{>)OmoxnxQ~!cx
z7yTwvL_vxFmrDfzAms%BFq1u;FO!o|pk)96AY1*_{QHG2qyvG0ft8*u0022U001yH
z001b^-7WpDiJrqRN5%B30sjv_KLEfcmTtzs92WpU*)#y4J?2lST9B!co*@9hGW4&8
z`4=pp>u1uYzvM6XUw$aRAo>Fc^vBf7(e;<p1ONbH2LJ%eH9s~JwzM($)vf;K2Nv*)
zOgJC&K`TAi-*tC?^8mpA4}VAj_N?`+jDES_bpZx`pT`{=j``5m#=-G-uYM^2r2lk3
z!2k@dfCjpTy1Ki7`WX1eK>Ws_PPwU|4;c6vAY`D4U;s#9fGPn0SEC<mK&PiWDMmGB
zu&b-92jKtn)5j1wl%`ZBTL%LDn?e^ZsNe6zu7HtAz_G;K@FScU)yH~ojc_?u8_oac
zYIwY}-1uIyd{$C+Q7Myb|JZ>QP7GZX@2I3WUo4pB*5bE|8|@Fm_rEMeislDJkxA(b
z7tCUlVW`i$#DWbQZsJMnX?Wci4^U?JYSLP9^{854ZTD(mZmHb5Kg#0WKDy&x2*LAw
zTo>W>_}n7h_S_HghvODJCnAQCPwY%2)^GlIWGK?6;jNOlF0WOptuo*kv8|j_g}1_c
zE+(DP(B{zS(DhLNP{BA|<)Y%`;w0l_<D}#4<0#^~Zt=qM{QNTgZ2hAB*!}$O#`sy-
zQ{PkNQ|eRgQ}I*zQ}<KC+n<A07=RKm70?xM5D*ct5l|BF5|9(%-503KhaPmbcfNPL
zcfFUl7q-{7ceMBPu0|fKA;;k95KnS~sKXG<Pe9&1L8l<kmyTdtihA4V&3Es$Zk(GQ
z_>Q6WO2EZKL|*ys_L#EFFrpqv(C%GE%Zc>Y>~HgyL!|@;oHhHQP}pO{tpwUsv%B#6
zd!u<`WFA2+30r%fO!U*(zhn@xA;rJNv7)dP<fgYmI&dM<;)s<%!L(wcfBZ!X2M0j4
zXF<Mn(hWBJ=R_q?Clo71yhr0r>qcC&`Gkpup)6p#8t-&S<Pp*}IV&F>%`VH#+Vw47
z1ZrYVoekY6m!+MmkfSl@=(83Jh>RM=6@_BZ@#m2@gjSQDm~M#;i*tlcAUFkg;=PQs
zMJnWEk_2tyBE8hN<cB0~<M^QXk7eDw5MTG5yYO6hFb*?iIPBOcShezWpxaAA{AiCe
zPvc}!x33bl{>CL`jfI6N%DY2a%&bpE?0I6k{55d>M94FoUL_axD8r2MZ;xv-@Hvaw
zq9i|4u;P4|nOd?89&S@e7$fg9w5ik7{;s1p<$%{Px^pXA)ZiJ*T_`9A%ZsrKN$)%D
ztOb7M#2uWj)1nwnb0-iLgR~WM*q`jEA@w~(cU<3;TcGz6UD5z$GW#O`20df8;pRVY
zzoC4zzo)g|0FvRy)=K0+BCPi)KabsDwpTdF%AsoFeo@XLYf`R3tW(N(V4APa8VTqO
zYaFp!PT=^&)H+bv3U5T*5vk{AeXej$R;Oewpd^)uVn0)o;zmt7lRTM9REl*{mONZN
z<|S<4WFKxe0$E{<QwsdXC#G;vb7HKwW>t$xn2nCGWG0$W{E${W(Sw*BQ{1U**^A&8
zI$rVs&Q8tZEFBp*nancP<K_pt08!n3L+1%&`H1eUv_{0MQg*$cFBqyYZZwx9{a63~
z$L>z{--(mmK4uN7@+{1uq?=-Qk{v}Ai(*JQ<<i2r1+$=q)||#g!PC;a*jL%Nh>Qb)
ziI9oKiR_8ziS&uliH3<U<AhX4Mf*mFNIP1`OM6YnP6zNNhIW_A9m}KhOZ4;fi}fS!
zv-T6ui}%aBTS2E;_b33xNg(5CG!<SB9MP=Ql1pb?CSzotEXHYLEm|Z)`sqm4bQ))J
zarj=>S=!6yBgeC6Harr>SJm)-bB1PpopT0sz{MF16qoR^V~HVCLue&LV<fG^!7WgD
zW;Nw|q*om6cV9XPtR=`YwsIyvw@PWqoO0C8RSYR%=$b}h=|Ln%PQZvwMG*0n)!%A!
z;Fbo};mu&w#?gPm&)0Y~$!8Y59?yj3+pB%M%hBl#Yr~O0Xo{()a(P*EWQAQrGvLn@
z{^jSG68d*j<r}+WlHtpg3iKyEybPR_7dWzJWi4KiSqA?q!2Y>U6e$yTtP$;v!e<D=
z1+pRfZSI7$U^?mGiI!ep0YfYjh*tr%CuxR=KO5p-6^{{lS*@q6xIs_6rv2y`|NpVv
zCSo#t$FlVR*NCf@Y^0G~eTY4mM|wEG`Iz`x3(M`-rtLl~7qjkQ2Q_kEhfa6`f)&5j
z*TziN#409}J`ygz_vEJdf!ix0?SH6Jy1@z7wY7N>HTHBEyb|!?`@o*sevdTrHJeop
zwT0oAcEND0l*idnVa$A8P(K0ZVSeX`ivqs>8G5=X`&lYF5ee)Be(wuIckU$q*}<;@
z4r2#7nhUhaoUJcj*VC0s$-JYm=`HaJpLeRxTzn;J_aSv6KyL2}I@N-Vcnp-x5iQOX
zh|qORY8E5lSTmQTC|@~e(_QfIL@S-9IHiq1PS<HWhxX(ICJn+^yP<n+C)47Z#YWwL
zaZE;~abv^aD(P8nO!h#R|8_=qNOxoRc9;L|rv`K<1+yqk+#y97^~T5c%<}F_CWQxf
zz*dq?rB%1pz17N<?tU?3U}d1Dz@CXDojRRUokg8@U5;T%p<tqbv!JA)rJ&{jU;04O
zeyL~pVOV0gVi;?9W4L1kY*=KZWccJ^ZLvgK(GumZDJT?zDUhmxSn-wZu#8q7?^%60
zvw(PK0aB~ZuIH=(^pJ&K7y;ut`eup-Sy~nHQa5owL!CzJ(6o`={~Ys6P~7grllv99
z9PL{puG*DlGW#rCD4QLRJ0M*L26+@A?S?Jy5g?%U^J<7JOxk=TX>)wZ*$t!IY(~`<
z@a6PU3WzmFyeT?es(00UuAHM@*;!`}3SHx%=v)j#UpfM9*n2$NSKt9wR?y-h;`3^0
zlYNOTiCjHHknv2F8#vP^LJ`;lRH+t<dXlyv3!3!c=&%AvZ6Fug1M7GQrhj}$M~~K=
zKg|%XUsab!*K+fU3vUOrs}^AjgCa=jZb)&=A-8xxL6TYKjVjN%*0F(nIcMIr31TE-
zzUi$sk?Ce}smK}cZ5|2Wx*V#hJkdaOq%1m}wwHl6j&@104q6s#xtX(UWRjAwOn(zo
zM?F#j4)QVjQ*u9wbqByby)*T(6vx!Mn5CCgT&8Y=4JOHs*~z1#T7O%tQ}^%Ggg8r{
zwGA4xw<>>(JB&-@R!sXn&Y*hje6bmXmdd%}w>*#3>A))z4~D%XF*+~}&sYg%I=ANO
zz+0?E;B}3LCnPO}qgGQ!*}YM8HpXcy0t)~RdNRI{N?XQk$esPOG6h--f1AR(K<Ri?
zX^3Pdv4Y}aAw4E%ZLDn8Ms>2Yziif%z`E-CQd|Vjt8W*X++>o7Rd;B-r<c=d;ypVo
zb*|vIa|RmO-GM(hpJ^=ySOOKlSYFZaffgBgqg+fo+>q6B<{d^Zlfz}sJqYrNd!pa_
zv<n&i>~<S0@t@?5**tuoy(4mEo8s3{=VG=M@b0jg!qTaOtE!!JnT#m1$u7l(ZGT#P
z=)LynegcBqyVC_EdS1%;)1fbIU-7qV%^()(0nA1@QU)vu4VQIGP>xQf91*{23mLP%
z=BlE92usq)WUw6&Ro)nNR3PVL#>GlTLTK{`kJK^8KKJLHq&ZVA4;v&*36q<~QinCH
z8E8{4&WTw=(-taC8{*&Y)m>{mW;<|X=qQp<-?&t`l^B*7m*i@fXMII|Q+)w_3;ssi
z%qnt_Hr$~Zm1?=m@E-RRyV`{IWmoBEdvGCKTzT8TS91N#R<1Np$x??E36qMGdv<18
z-6C$)sM&E&c*s)~p)A_WQ4HKo+H)oAY8H!rC62qL1M);9P+;YW0|eykR*VC;U+M$b
ztVo>Ecpx6C5U+sWXwHg;;i@n-q2H3Oeh+`um{bho(vHgJ^=3xK-bvtgD!Q+M%U>PP
zQpY9F=}<8`)-ouvWJa~Y#!7b;#NGKhR^V@_k<EbjietkpfggB<wyX#lbsG}ZpDm*h
z4;q!Za<}b4@>;Io-OE|z-BG$LdgV;o>~$$`2S05D;l@z?Bzz6w^+;vkT0VL`Ae&SJ
zB7L8(p|q!#^NJ=dXA143B}42VU%KTfd%-Y_rKfmqA9`_DiO*O)<o&m*P%%oWAz+lX
zSHr0*exx5n?LmZM{XR%H@%B4b-@eG?y)=4BQWQkVm1(=THzogmQeQ)A!NsTpZjW1n
zOLLQE$mZ7B7&Q*ctfx{@9TV37BQ-d^X{5)ITA6=%<c7P=aZ}q)hdYUQ^Y+f_VJ%BD
z{x(Zy!3y8uT+gIU>Ij*dIQDvIVs0itZ>oVwYF~0%fjhehYKuIl;r$d0Z{9rb$9%=i
zll)UXq1#cW|ECVFN<j0A6WcE?(|7n}RcJQxx<z^OmE-)57(nF|+Vd*(%kNb6?!#^z
zW~s)(rgvpRr^kh%(xFjJ?&fM$@BBsK;U)Yp?+9at0-E7|v@R8c`vwfh;ZYiYgt{?Q
zj1Pdkf=AJ;5UH&(6GU5LE=&VnQpDFH<Q|cSARo1M=cIWqK?_wQ>qkfDd4YUbD+D05
zKJhAu2Ew|aP<Gn-pc1l&2Md&6zFJ%5EabEt<TC$<LKyPL25`}q$SqoPgm@c?LSdE1
zxQcwPM-P{l6y5`ocD&08)SH=rJsc|QM#jR_*Rev_r_YkgQ^?f`;@uBA6)noeL4XDE
zxR08+M1yFPyFi&zj#|6Y`QlD+lr3tRQYFqcCN7>fc~ZCwAyQQIaVTo!aw5f0++2`+
zfh+wx1C4~2ezj|#t5caIHkncw<$=cm+JOvG0#m%$7+%6#0!l(uf>y#n0%Jl&f=7Z$
zLQ4YeM6o70Tq0?r$v#Hbi&<t%H~vQ*j+>S>oK*JS54wtBrT`Vs1WpP4tXE5gz9&el
z<)-MSY1?K(>7M;TV#DV1BQd6`oqLQz>u%LYpC1Rvxm6ceTY_XuJ75~{Ri=3s%%yL4
z6#hikAX3@&grZH&61yjBtJqUC;@0^)_q%a0ZOcqWj3q!fZc&6{W!}EwL@8JOWf7;1
zoQZNbbVuXgqUc6R3poRBwF2_1*5G{UT9_g>p<URg`}cLlOLkr|N0%%E2%Y!+NJyXv
z5a&fLtHhyTj`Kn_)|tb*NQDVX?F$R8MK>DmxZ=^WXsVIr-I@^#YnJ7jA-{r=6I&hH
zN#!;#6L&mW<`MItoSS0tjqbmAvUogwxJflVDmDxZ*!<dhHY<>0wKp7%)JmTY3p!_`
zuHK_rDjtS~%J(<3mhcsP630pGaY|{xrTNUfkyAR2e)g|4d9Cps5uy_j7CP@6?Ks@&
zD@oo9BS^C+ub8IcqJ0ttGfTxPO*MC3*);KI7SZWza^_vsPrlMgp+5&xU}>sG!wO{^
zR|1U!mknKuS7M8-wzvmT<lDa2Nw_Kl>E^0?<lCvq`h#`Ne)P2nUJj=E*^A2&xSu0r
z_<L$aU?nFf_MZSwO=fhZug{<{)vR0D;2gKi+gTyt>UT`PZ<jP>#$+IFUc4!P(5pCp
z7b^|QjLrMQ$J5ibz-r3ga%PbOV#S%pE>P3v!h1SancBz>cSRYh9a=?~s;+s)!5DC*
zhs}NNBxPb9{(sAtkPxmn<jz@mMT2p^8WaApyJnk_HWGO#-|az3RTuM(>)jm0+ne-N
z2lo(C_W<2mr`PV|o*5!yugWoq57fBC^<~`xOZF1oV+Rm#!ZGsuSX|=0F%UyrA$%G|
zty?ztS=*)7-2(-Vb5h7{7p#o(s;ls{VtRUJRB1_!?*J5fg}XrBY(FT1<1q@kF3-Y^
zhnto$jkY<0=g>?wnXk=`bXj66^8t?xUgLvG)2^uBq_m?G_vxMFH=`a4q-<@Kqbmp|
zB>9l;CEI=+e-Y0nbj@oJ-|5m&y!eb})kCwC1|#U3#rTIz7s+a~y&WitVNrTy^J0QP
zwIFd`$;0bb+`Qs*0EC3WQS1V8ibwY_8okmt%#-<84>$><$U7m0&Sf-WAIODLRZMEX
z6z4JIJ>naiAf+1$V0b5G<jHGLw)|O^_3;XQWq;~MVnO^*3)D>Q)-z#?pw6t_le&)}
zV-DC~dpZj<`;$9K@y1FXhCI1<#^4?rl&@3QgD*^iA64x0!*B$+-7#UBWae<bsQ0oA
zg=l+r%HVdNHWF$mOW4aa%ob4I;0<vrmr{~rh+Ub_=Tlg7*{1v|KCzP!(AKqIUV9Q>
z8y+5zDNDMW@1WS~!l&nI3&`zv23(b{R@kq!TJ?G{OPeS2z68QOa^h?zb6Fm#g5F+o
z)565l!C0(>i90JJxK{xo!7Z9YB%l;G^8e{zs}Kk<Z#JQrxo~7$m&C9qIcUYVEUph_
zd^Vi1Hl+5XyG_L#8+lMYkt+{eEvx%2`;?xmT)q)!l6x{(x~=Z)-dQRRP$P$h^M_N&
zX&FrN?eaxU4I~9E<xp?|zi}!TjfRGo!_VwZ5x*C#jMZNKDUW7KdCZdF=18!2wSj7o
z=;4H-!n^MqYbf8bGbS_tQI=I1d4o%|F)`j%`>H=E%>ead@Px{N;^xTF(Aih(%-(+?
zaga~hD5!tGa;2Ed?Y7$VXPHjdNo>w;!jS;vL-J0eGAf_jEREX|t+DS-aJAM>a5*}7
z<JX3IrY|dfZoBERmL9Z_?_w-TD672V^bB`+TM@1X8jm9!`;H{z6Qhu;X!8(Z+$dRN
zZU`BhoMbXFHa>nxOS_w%Y_v2!zBtliWNgr))mBt4GFNwi!;Gh3WME*}6}k3xFV`x<
zLD6p(sai1gKU<~W5+)pyia28fSaQrTgkHOh4BzM%63Nh#v#v?$&}`kf48&L3fT`n}
zq#<i_GG9`hTpsOU{tO*0zjMz9t3#z3ad_R1OjVk?&NF<ideb07OJ@pcE;=|fcMsJz
zx}|l+v>E?+Nb_Xm?Xz(|{OZrxw>rH#%R1G<7`Fc2_ev)>5@uLnxCqhCGGIhAxt`=o
za^rrmYEHK@DluA_x=!V0@^BC3fAe}SyPQ~?ad?~UXb`nlw!Yfj+{|txbSMd7OU!U^
z31UYoXj2)e46Auaq&@O5RqM+HH=mYQ{FHa^371(K-{zS5*J4HcUZbAtFDM_a62_-6
zhtjg78Cbj7yhMLTeqNnor!6X?j?v`G^whuBA<@G&WVQfbwss6WNV-0pTo@PYS(Z53
zCa2LF9}m@0K*EJ7gjNp06~1p~Dy68fV_%EYSZFn8Gv{>>FAAwXWTt18!lvP?EY%Dj
zJ{}%)BNQKEpm@w2jH8E<n4p;}cY+X=LVdP)zNvWMV)|rQ<t}Xr3M?hDJid}HPug6&
zhGZl$;0SHuR&rA-t`fGNE0lmhWZt1T4rOK5W|%b`_TebpGn-LCE4;7@ek)m`)3ZvZ
z*JgX^_-FbD5EQvhd8kn_%N`)zk=Gv?PBF<jf^&zG^Z{4(Hj7~4U7@skLDqhx@#U3Y
zJ)wVdzPwq+2iZy`oQlNRS>jF{LIST~-emATQdZTNhm$@1yqG(mxH9+IGf>Oayn;ho
zgr3_1dOlpex`UYIRWQ*kUV$b(>T*L78OOW=L{D2zt8r#2)vTRS+NJPn4!cD2l=Qm>
zCDT3vdEa6wLRLjfiTICBfIoE$nOu4he>^|toeqZ@MbCguI=8ItwBIdT)m|eG?Oi6W
z`WU%V4M`Q~4ttQ(q8WLK<gHnwV7;WXzHfAVYTgBiE136S-~AN=^qv2D3+;1gYD>Zu
z)AEbW>s2UiCgjd}(H4BydS_(kb;>oqjG*>GE|Maax~k(xvc8e}G4&zh&cjs3^pD#^
z@PkjZ^}lIv7cOrzZHM!QMzVVPn}?c1-aE(K4e)59b(9Ah2J^b*sf$s;f?FSaq%4I8
z3a%*hEijojCk&wi*oT_EGG22(GR*KWRjiK#{>^|Cm^6fj&b4K1D;idpG`R<Q6jLCn
zye2abj>PFgi!&PcXzh}kwqAiwc$otwH-YVRm!q#YQJ%P&Lnt={ZWph5NFkx&SH>mQ
z9R<nxSO*LCG%+?P=BK%3!WlJjSSbM|?sEfCUQf`znu)NMHEP&p?{-dM_4f26IX~Ov
zNCtXh-u_7X516d9f~cOD`Xm{@_oaRM;$b<zM#;X1`>0T#;KyrtihYj6#PX~5KB7cR
z=?sG$Sp{=PnlU!0s;K<wfuy*OJajpNGnf+K#S#@6W4%-(xllSohJ)nW3ox=lgVoj(
zV6sBDpqd0bA>O#GxD8*}K%1W8<)k#|ooe|xCu5dRvXaU1MaI1r2So1D)!R|?Qa!}`
zxlhNyu~9KGrfH1xF|+c>b%|O~;B%B!EPI|KN`=_4Qc1Yp1==k*xOyE&NUkN5mlY&V
zzh$6;NIedWNI<4KD%EZtUn4p+(tYL5Kw7C7wed;|XI9emiYee@onsC2S%OA}siLnl
z!S+<^Lf(0UM<DLtVD+j!T+WZRKvA_UvnbrF#rZJ<FZ3r^>Ll|=aC01W2;u=7WzJ>{
zCOnJCQjx|}GGWCScuq%(aeLgQ0<^m-b0x;3!Lpct?iI=ul-&Z|^fH?u+=054X>(WL
zn>NGRNDmPHi=JT2!JkQy?1(1tP+uS`hCK5cv-^~R!vpy>lmEo-_Vuz76Pagjpc2=O
z8S)vwxs()yw7TDz!{?|Dp;-&H5|;V?vO8#9Mcg_)`w?WlyUHCt9hN)hQxnLf=!?t<
zE6X8qqtoFLWT<ZN-8R0Y=sM<6G)3q-Sw;xhanJ@s9!j^^rHTg<q#$_|Y@azrJ#y5s
zYhw$Qw7CDg0U;k<4%@3y0c!(wTk#UO2(aqJWL`#^kF0{X>?<j-I!tco#XPb<a5tGs
zt>@4biJW>>KM-xl#~fL_k$<mRfCKHz-pJqZ+#EqOOifnO=^V_`socDz&CyBJ;O^rs
zPhO{U|80!BnfNCBaH@{epc3S@#9JRmJHDRePVnHAdGq^3)H5DVe)D=DrFh=6d3$-_
zeOL1F?@8?G?CI{Q>Z$Q*^lA4g^YIGxaqaaP{?Q2aeO>(NjxFMOT>DrUj#tD|h-~DZ
z+t(`cessRx)1Ncd?Y_c+#?C6f3c5ebY$1a!M_9Mxg6KNWaP;(PFG1zj?ea>=6H#A%
zFd%fbE;F_1gl@k&tzMy(jZ(brs$XX}RmE7N_rRqzwf3;!xiT)Wm_%T1r=bt2Dbym9
zDkv@Hu6sKC06mUy>~J#@xR+c!LN+T@Ipx(Zh?Bx1*1&br5(;UX!y7!eZOmBYuvi_4
zF1nMcm?9z~krDCw_86JSPu>L|B5tq9rEZc^P_81~)Cze+Y+^AlYG9dB`W$e*2&=PS
zdcWqCi6MNFa;yNWi9V9Ml9b2}G&kWnF_OKStk{z*H<%VY{{6boH(=8aCKLAm5gN*t
zeu5{QWszDudu;9I2BP`!bZYO}%78#G&XA3M5hBZsU2TOta=alk=9kIC-U%ev>2H`G
zwQAymG3vN3mLIz&l95`39l1cts_>&+Xb?X|T_F?aXBtD7DJ@;Tk+V+WEVo*k9bz@#
z37+M5pP;60!T5spyVwhD2y$Zp;yl2OKub{etR6o}-ujDm#Pl(Wj_Q^%>Bss(C|aZN
zw3!88I9;>;cFcK2df{w^$}td)k#l?(&dU3{XD8=5CPU2DxX@V`E3NNYYb#}EVJ~x@
z5%F0$6Hk=+Og3eL2M0XWQik1p^l}Q(_CHg0<q~Gq4N+xOA^zTk2u8c)WT)RlZI?#w
z!V*a#PBa$zvB^e{f$HM@Xs8fB9<NvPtCwIsUOHx4hexy@G@);RZmb58*$b1as}c7Y
z_tl<zoQ*`YSe-d3iSeL<{$brs$oBQ#O{`PMheRY4Od>6Bisv6n-YagwuLAE)BW&(~
zY8&0+G6Yx>fbN)UsVrPj7#AY2KhbRCo>7vGCXS2@b3AkIqk^e;nS@q`S&wWC?ZG76
za5BaVGco-O%-aAm#v6jtTvZ$Us+wURw`iH9r|-CX<BHgFr8oxHGa@P_&C9&?q!e;I
zYM$r%8vZAyUX0Itrd;yMV+{zRUWZ_FpN8aW7($%R^B$du49%W@EyIy)Nwrx(8WPu5
zvCV0iUs8S2Zp?g1=H44gvqV+qxtYZA?2|)WA++OIkG9@Sc`|B_ckT(f#%=CTaNU;Z
zf*WPgh1AzHb}}xmB!7_2LuZ6cQ0;xI|8Z-VYCv5hIF8^hPNyfmiM?>vcZlnDsbGcc
zng6y^2tPHL_U$;kT_0(ghBIq8SGr^!hA-t~lnGd4ZR8zqWIYaN-d%=+kjtZ=gqku~
z{}H2TAxs9m!+!^fhaiBy84nqU;usmE9y}HW<H<QM=YfkZktoNiN5Phgc#A$20&+#_
zPJXE?og<r=QNxFYJs6O!q4j5;;`~Tct+9!lcnuiJhQDm|ZE5S;IrHnqnf|ph^+HCf
zs%j+2J&i1+H%*V=PoGxR5)WDOD6vP3=~Lm-n=rnxt}vQAoAR%V(jWnE<=m4<{?{`5
zPaD10d6e_&z7@hPA|JsJBZtJfMQHLqe3aO1XyR`Patbsdhv@f>{8mwh4Fac^pji`U
zeV7w>w55Iy9zV;rii7Xt!lbCS_IW>sXasYt)Z~YpA(fIcAIZMBHbnOIOTca63;grI
zhq0SOY1>+-q?3B~b4i6+BDc2x$$gn8TF=Fkt3&5j7gU!>Kii|M@z7*;p4OM_@s}lG
zB)3flH@%0&bJ1)*F66<~#<4WG14Q<OSGrB0+aIjsmzkW5A1iOBpEjKrw1<^PmnWIq
zn#Y@mpFJNfQ)E=iO6}D;Tg$!seyyOn{*fVmyF#|AR%^SF{k@@vh!wOO%4pObTDh{C
zMp3i3`Qaih36bP!8ZC|Xibu_}rTG&+mQyHH@Zvuepc$Z?U|?XRpeJA|U@o9=e`fyp
zKy5?wz;x4^Of231b1Uo=b(42X21G@+R`ObPH+hUoax)sMu%SMiz*MYMm>yR84(F>t
zJKwUP&P<M?V_kB#&WGY=c#b)hPz#i5TvS0-)1v-}?gOYvxz=OGj|iE2ZOuh?+xl^V
z(*pj2@yb$8&<4YyDJj`Mv<TI4rs9Ic3EqBt5k0J`iQZnMptzgBXK>z!#tg`QyL{BW
zq&#q%U5FDtB7@T!?hqtgrN+X*skIAOv;b=zZBB-ER?C=Y+FCc<bkl%h>$9q3kuEqD
zyIEA-9LCD+IH1UYh}kwjYYs2HlzEG!6@F2rl<bOvH;2l49#T6g;;>GiKC|oLYe}fe
zMNTJ;f{1#%58fp<BV<Bk-SwRklySInF`fMQC|i;5!6`5@m+Az@M2}-H=_0Q737T7S
z6AFh@h0;1xNc5HoTM<zakv>E1)P?&3(K7oMNPk%V$IYxgjyJXu-ppe86kDvmI2{o^
zEMV15dI-8`$+R`4U)P4($zoo{F4nC~b#OLQTC_sygyfj>?l!Q<rd$&FiTCNT7@xf?
zh&(N9RLg4K4@GEhK+(hI{fr|gu?@>leK$e;S!t1%o*pCm=VN~xwzT+le6Qq|bE&So
zAnwtuG&1RkMDZIpDfRkHp;s@sqvGRYoB8iS8WqLEw$ag{l&qbKnH(O!3Wv({tZx(9
zrVG-Fh}u!&`2mB;R|cyvJM*)x;n=-!**cN9;ew-;rIoC(ay~fUia@`{U-Sr(Nxic6
zV4+!?uwHc#lnM|i?eH8~?ehpzOPxQ~^F!dn>jtnR*b@u`>)?i+dT9yg511ZXTEk_9
z4;OQX%m{^K1@_@IiEYsN>B0wl{fq0=P2>^sk}{+`-U#B(f+NcLDzb>uk_Q;oB4*q5
z1eXenJkr(JGeUp^6c$xV;wJ^ZfKBLwHTVp+oXD4D4RJu;*dSYZ?)zFP0)>jFI5ns;
z`MbmMhaJ4&%i9DLOBwcR`xZ)8YlT&Eu?m#)tLu7|MMfTQffpqmvaz%=Y`E1ZO^%rf
zB^|h)Yc6*Y<Iow844x7V)@gib2%e7@xXgbG9-eO%=1r+zlSdX`^o#prY=@L@w&A;N
zD=&SGkFWzfWli}HCz!zqZ^nVPvE7btz_%J1?%M;H+n6spDeh@{?pe;I7O1Q%!!loU
ziTcAhvIx^apDB-J592c}U<IYm^II?+Mu}e{wFQ-r@v~_w1E@fcZo*0*C`N}<Ro6k+
zUbT?FWegMNrQ9JfCYrJnTMU^s2o0FiO7_k&e13w#&c-tO6vDZRQbRGd0Ayxnk#3og
zjJ3`XnhPHAMIpQqsXprISVqoTac1@e6c~CKCNkQNWgWWPv~=uC<s*t`(;J&~iD`;9
z&i-N+wVY|tkY}4YZD+b1fBhadNwkNQZ^qx;yuC8FP>tO0R>N_*kNd54@5&QbqB`3$
zGxc6r%uWtB(G2a(H|=GJbi%E8e)UQG2OHe4oej(3FH{(QNe$gC#%85G^mpwV2{cP+
zWYoo??vPGz|NdOn#EZND+(h6v;igqoGHaFCcrOr>ot@3Mb}a!vi_BdWF}Z>YMev9U
zdQFK-yTw$t1(V!_`xhBV_7KX6&dcoRv;lRCYQ?R*BMJiOkn1xm-CL>k90M(qla^>L
z7u)BGp}ZzDI#zoEd^%Iy^W1JYEW5HEUUeEBDK59j?{Ai96-ITV6O&f@dg?dhrrJb_
zTLx0aWXe*63u#&Z*o<#=K-e>24OJ^3v<;@J{kGa-BI+k6_eO^snJVy+#?&bOB0Uva
z9dt5nD|p`QbJK~8x!L52ZS*Ce0xJfQW@?;tRjzo!(FMyMW%b7I*fN3lC<Y!vS4On0
zror;b)d75ssc@d^mTY!^`=RYZaU;<RK3I*CJdT;{@yYIxzzHj8bF1Ni?Td5yy-2N5
z=_|8xrW;UGFvv)y3tB8hI}&Bdur3HZvc9)YVR_T&O!yp@yy_T(wrfq44NQP7aJD#U
zpJ#=+mo%GuS^An6#*Mbf-yF$FsV~y&bJJ^CBX549Q8-5ipGP2WEPl2e7>#Ubhqk!i
zBY@}MCB;}M@2vF-Gbzjo@+>|td`#wFyuaZ`g+8nDD(5;Klt#;MxCbvCbRvj9Tjam2
zv*QNjKO<mRdufFbk5??`aD~Q#Ww}v)C&$!Z1<_tfdE4e|TB{GGSw`mLFsJSGgelO}
zVaB12<i!><;Sm&Zv}doO!Y0diJcN(7VF$6@=f3p2mgmLp`=R1lNf5{9+09AGiB3xu
z9U0v^z3hM7sJ^cA4#(nPq^z-3iW+7qAcJi{dw-%NMFosfx`@mT3=|0pEASo#k9K%S
zs^G`yjm+Hfj+%+#otuh9U%s!RnH)HC1-QVZ;WqfD=`AyFWB^Zv9rHVMy%o6iN2aGt
zbsQ`3@O2m6)J%SKDV-;)5IupQM`&6Imt+kvqQt~`(=Q^+Ha{P~u2SZnhT4k!EszM~
zy!Rmt6>-*?KinXOMO>r!dX`=j(ML);EE`t2RWKb=a}R+b)yBKq+eo7bDg)FJu2@Hd
z)_C->k4dsxo^d_r(^h9b!bKN^(jh$2Me2wZAij(4l^ErF6_uF<8inX$N*KfrkZk1P
zLC7}t*nyNWX=O*><2XZwFQ>bGC1P3x&A{h8HTGUYx_PbZMD9YiN(xmKlUbq)euF;T
z!sNkeD-|>ry^R$@joo5C9RP<k)gy5C0qwNA;`m>`ou0mKW^eC!Z|~_q>TqxGE^JW`
zgD68I9UUEgEdygOKmmNLuHHW&7--O+A4b14Nm*vmdPwMXfIvmiFIT|9Dd1Qt737dR
zM%9guE0d{fMrRlOUke^q&}wr6zifDpRYpq(Sc?Ig|1=ubkW0Du(+?`6ilBHbKWGwx
zm;_>CVb5MmqTy<ecaF3t0YS>dv!}7Y<Aj}kk$4&GlE{+^2D7|VMZhA6d477nae1dt
z^pEdPEAK$g1?>~-E1#`B9b+mQ74*cwvn_vVe~i6UTeT(&FO83$w?ZG~rF^Q=s^Y5r
zZA6^(srpvF$0Oi7!B?<0wwNO3lF-2R4rjEG;UC(Z+`ts6B^elHE%U~6rI6B<MMGwR
zsb`nFo8zMsSGTyHWtF~OU46QYl!^ZFS@0b2fZGL432VV_#+|Gjxl-y!%E`~8qkWNf
zan@6McRa7yZo!_S6ydQ_E_2r&*60J2yNWx?ZQYtNi$}~(^_t;|=kiW*m$;7EtdnV{
z8Cw?z7h8|5yK7U{16b$#>8xp-X{%|#>F;Up=Z|NP=H>|JzW4F>e)sM6)%MxX{!K$`
zCRTLHsG?zPgXFvTJ72pVyBxb3yBNC`yA(T<52yIpDyOB`Ld56^{Xgw-{dT++eGsjP
zO$6e-J4SRHfTF?7b0OD;A9=jo!8no7+|gJ4qU|X-QP%F9&1hhA9rYo*K<{kN%#wvQ
z#-s+2UX+}`jAt8bYoiM;;jbOL*zZcu)?EK;^zgt8kv_1EXEWB?d<QQHs8GbfJ5aGs
znJVCCj|lqyWlE~lA<-vnCuBvrJ@Q@h1A3*3ss`J?+cqO!J;qh~K|EqyBFR2ZS^CDD
z23?jAw2`>uZ1~f>V>$n+Cm2(X^CTUf`&zZu6m_X*tPSIlDwKta>5jV!(K-cNO-mK(
z8L~#4y{Xms^Vm^In@bvwObEyw_9ZGvdOBu_Vt#gH39Np)bcy~ri?!-y3xHD#wnxxD
zs_oAzD1UURp(=SZMuQR-$m1uKpV*y3ErRm}zu~L*s6cS@qHpt#Qx?;MG7BYySOmYf
zS{S+umlE5fNuedLuB<JmrbPX7VqVx>-JMrg)>hP1)ippzz47LK4;d~#PEl@t4jljp
z0HBEy)ck8t1^o5p0=WWSx`ViGs5akrg;NjF58;zHBPHll#>KbSQBw+(iJv*jXJWY7
z{?G!SSzjD&O;b4uPfT9WFpf+_?%d$v(gZxDwrLwX?zE}cQ*oXdc+Z4Y7gkg_Omn~7
zqUg*1`TJ;YnNL6XS20YHz@C^uDBIyDjdAs|iJ;Y=&i*TT_Gj~F=8N~j8@fz%2xl{o
z0Zq6xSF95pOaXP@vRieiGoK8M*LJTTjK-0=qPl#w_1|@D$q$JaZLnaV`H^~4s>y-e
ziB?y?1Q&LWd*ARd6pMBKzjesZNtpQn1!Vb2d8OWILSPph4iZpD+d6b&y^4*i#f#!{
z%+@uFUNYdjR+xh?vH(a&u1JzoigdDjcBz$eX8S~tY_vbw74Y%3W@N#6T(zqWs8L0)
zj-F$$ms4S$`|;-Jw?6K2$Y?q8>{oCh`**UdKJD{iL<!b3eVSvEAB={-WrxfUJP}|&
zQ+#K0x3|T6wk+CX;KG|rhO>{NDUL(HbC}$2sXg*i=+26DI`coUniD8kh006JaS3WX
zG>I1KO=J)9n;7OG`F*;NV2xfhKId~W-U|gWJxpJ(o76IGN5Sd*bL)?VW*hz|F+5G)
zDBfo8b`R_0)Gd`%J6t?JB8OK1M<o!*O}fs+jc|XVTZzbL?tNS30T%;1)Ip-RKAd(P
zh^EEa@!938lxk6KF}ii()Bh<e%`8BzgShlSXhuCY=Z6U*KLDN8BnaPPeeZQdjEx!z
zf*P2Paisn;f@&#EeDxE@Cj(mzs~R-!^2S(7;$Ug7LalybL0C9pjFgIF_w)44j5Pr3
z3N}hBZZlI5mcu+xE~hAO+RB5+M9=N(&ly0~bOS6N6+=LH!23}BFj%dpilkx?4xq34
zPEEy)GyDpn+c&s<WvH+8d+tqw3zD78j+i_npp62Qtm++82dT;7wW-7kPGXLHNDYC?
zO>pduT8KDZFQc32DV#6#bL0RbXt0X|W{&J*P|~e-Ycu^>GyjV)cXW`i`}0ND5j#f3
z<?P1p^?Iu#jA*VF9&%E!Iq9j{dF0pbzI8<zH+z}MOBQVq6glbX{3b>B{DXVVO@R?N
zj$H%A-%eL^S+Vj$U0q3K%vh$#p#$w&+Q~W340=zT2RXL_N!xA|Mn*G=Byt3?Y{r^4
zzgS7Al&~hIlbfd0pw>e7Rj2oQ5e;C};OARprmNX*{Wt$&WMJLV?}9N9Hg2IbJxp*!
z-<M%}jF}&U_5wZa_*|F*NqZqP!Aw0^jg>`t;vr2@T4Uh+nfMX-5flgt<N~u85zO*L
zmuDzn`7fj-@_L<^-Dm0=v(((OG2FJ>ZL)ctDz$#Mv%9C0)2CyV<CasL{=7J9-e3D0
ze~zeQ=l&!=$NWeRHTgYA(QLeDnZP?k&-!+x4X_+~1lYHnUt3bo?(oH_3>dL2>=^!7
zY64g&U=d9NA|I)T5mu3Cn+w>s=oZN#**<p|xml(ZYM42II%D$Vp7Tvp*#^HhSci^&
z_?Xzm5u7{OK0gf#8iFtdgB_Tmarop4H5f`A0Vi1Vq+nqSh8Pinzdl*GdFh}~qkGo3
z|H{~mQC}EwDo7}e4?Ts6Ceg@(#U_)|f+7~cHbNeUGN2>S!z|p-)!@HIMB|zQA_7&R
z(TnGDn#je1v%^+~;b<c#j;$<otnhhxC0pN)1IH;_Y=}~Di=~h=kU$|>#&bSr$z{jg
z3}Z41!#>bf;|OXnuA0mjqzC*>m+2@Rxt^>6txplh;xfM-8e4*qu}rFqLm4zDxx-Sz
zk<xLT98Pz~Yc`dw?s|cUVP;ao5QemXxr7x>4}VRZ@XXCK4=6?U2hGY#g_c&FGA<8i
zgQxYOh7}rb6K6v4tQ$(S8m+C=D=)ie&O;!L<`1LTAk5W%DRIU)YB7Ru;N=D*e#g3?
zr0wPFxVXdUNN8JF1!NfuByZI-50{k;Z%hn1i;-wS5rRiQZ0-pZY-S~2MHeuUo2^Yj
z^d{eJlG%yg@^H<IdE!mW_zoW2U_r@t8Z?|)e1Qn$-Hhdx8BRr*2l{qKbtL9FoKh=w
z0l9V3^$R*i5mIX3<3Uf3N&jItM5js=41QW#>~rG?Q}9n6VRS8FY7lRy+i4OM{YRV1
zxLrT&@c=S^*TmW{Y8w%ar213h2Y_}c+udPyU@9egcHDC(_31ygMa>C=*6!iq`g3BI
zGkFqj>4Xjd9Dwm7dsnJ_hZF)1fD4UbaqA!KO??S$$nU)~`3eei+s2NNgh;u~;fDyu
zxa=N82tjSVlJw$)w6a?OQWo->7({>5Mp2&jJg1hg&tYRA>~VnKhQEPVa9uU+jEmVE
z!e2)wLfPaj$;!)FNP`UJQ$Lq5?q5;gp@nr#%SdK{>7^t2DkTP!Pq1G_v;&-G5YQl>
z&lqBBbWPKpZsUsUjB;jIpF5~zc|dHC)aEGnrSZ959e(>ki!31B%+N6HaeQB_VQJ$)
zYWyQm&tA`Q9(?voO%4_o>cGe++e?Hm+a7`%0nzRSd(i}H$b}6EPTKQE@CFzYsRsbV
zO<-u(8f;|SEwdkdm|(b)ycAz0jVCpk*#WZwrNni$LQj5I8i)u31kOC+)C8=_7SI8z
zm{9S0IUlD+h2^)IkSo0gpDg!)LJ&*>h2)^n`=X;&F~=AnxpA{=&Cz%*(KXyhsG)Cg
zJz<6bt!eF?Pi-9vE&=?=HY!IO>n-smT_c@)^f7J&b(>Oamr-k2eu`*EWXTbSRQ#ZM
z7^ZfOn_=}~jWCz(e?mYp)zOn0mzR~b*2<BZE<6rl=MZ1j@E5PqCxiS167Uh&<HM~t
z^3;`%MUj;qNv7gXBM0kU|D3cuj2ZI0HQTc*P;u3&)6^t)4e_^Y4mSW})}|8`WUF{1
zM3yf@TBboSju~OT-(HFa@70&dozFPFjSqvXDO-dPzi+<3!*pQ&g?88V!&OzZchWw_
z$;AYi0z}-To7(;N<IiTH&9E}Mg=!YsStiXT%=I^F7!;nOJ|!irZcpVdK|Vxv8I;M5
z+1mR91E6xjL-{;5q`&?BHpho^SEFFAJS-<hqn3cjS7nZ{o(b!uF?h{1l}%$nxS(at
z_1>%O1>i{v-D19Oder!9v#p(bFlzyEx~NR(#3&6kQe7&=O>N#+a8#GMFS^dilnJn4
zi1c4$t8A)Fs0-6%6pW>|!n#jG?2|=n`QGwX1Q@=mW@?)1ZoW%rp`KM|mpwrvJcozr
zjVBHB!GofNn7JM-@U@JB*%4p^{vgCUW-gL04|Wk+#fMF|o6lLgg?RdM5#y)h>7~Oo
zP$QCwbfC36|2?-qV+sO{?LOw(9AKxw^Mz;2#?X`Bs@fF`70IW;616T3O;jHK>076j
zgi&_!yl(I2n~bH&cZ2W(mPN{-$yUBujL``fI*dt`cA|*HYsITX?KB`V*qPrnP!lzg
z$BVLIXfd(cK2cr&5D`v}`}zoO>uulmg|$4vd^@&}pyu}>_tCiUo7UUn$U|8PxA_cQ
zxl&mqo;Hd67$J&_-A3^G32blFA%Smy9#3&Zs}vc-6mH@A;dt#oJTf0d$U0tefBUi(
ze2n^uX_YzV)8BSUNT2{14~iMUsNVt7BU@$>my~q`!`vTqIr4#?RAWKE5Xp34odH0=
z!2ve8S}kaCX;%!mf!EYJ`kB>L>;Ze+);l+JRB7ysO<P<z2aQbH=&t>3!YJXV)w&QI
zg}xroV1rIv;V0Kl16=!P5N^I?y;?92q`hxuB;Bud3M|+{<r|P4g%Tc!&bir3L&rbd
zXn(!X5TANHhC8HGDH7)52anxGZ?i~sk?|El!Tm)YV~j#vmG^>Ni{u@&7bo-FzSn)l
zY~`^@>=K}BBQ;}Q+#XZu4(=Fn`)2m+u)!k-G_>)UdJ*78UUl(<>*P2>@BVZQV5hAo
zWdV$`;yyP3TZ3{RTFtno>T&DA(sXUt+4TmfK_BXYdXVNN5I_(bXG|D1LSh^9VT;y|
zCpA&nrqT^h!G~aZWlz}4#k;5_=GaNjYLL@SqR-NUh5~Zl{)Hw@HTgsK$Y98DgS&r#
z7rj>}&o-<ZIpRy)9UF6NkQtAi?n_S37n1@3>u{u_3iYVfUxYv{`wdIo8er;YDxyMH
zVX!28fL8)SiwiLX+HepTd@VBLGF7d<_zh#^tukHsh1-u2Ye?|!@S~rvvlbOZm;8p7
z_!SdfyIusP<oLBy1eI4udFtN`731AUzlYPld#69lTMi@Ou&so_Wsr&?7ZMXQo!=uf
zTb!-U4-O|Uqy5K6pmPS@6>t5*6}RMk=Ui<yoXCz~e${ezy}>-?i*|lhrKy2hiCCH}
z{a@(TFv_2pG+_@}jHS$RHm6yAp=!JK!LfKU&a9(#Q(Y>cnBTL=nW-^ZO0c1BH6%jK
zZw3{1(BHzM5B(T|nmeLVO=*Y=+nWa&gtq&%LQN!wKMn0Vf5)FMS|o;K+Yr5zQ#$P5
zFg~G|Y?1Fk+3ZAhIV;!-LmP_7*dU&ibWyQ9Uk-$m(!wHBRdOY90tYPT8hK;Z@ca6@
zJ1{})hP<-4q?DDag~ja-ab^K@&~kA(pdz!`Fryzo(ZD{WdNj$ZHfJBtiiN@UrPkny
zJ6cCDpFD|>U-B`ilxv1+2wOV;0vXgig#$y$gQ3>PoVA+oXIybK!Q@rU3#xoj3<)7B
zOgDj;Q^M!^@b;zl1c4;sl!>DJTnlnw3*$fQ+6Vm<&Pzn_C^Jdb57e?<=#d0m6E15i
z9iK1zIz@_Sma~f2t31w|4#q}!F53sc-JfDx&3kc%DeNK8@?!QTFp4@t$~g*>Hd$au
z_?_Z=aec1!ZeVe^8ChBqD6XmTsXTxg#>5tIruKxle$imQ2u6155Gkkv?^5x8<%CgQ
zWRml$ff*laDKm9|_n!oQ5uNe&)qFLesnj~~u@dmO3tchZ6szr|t(^<Y<t+CwUJpBN
z3-ECL62InaV~oHJ08;+)RicO>UX`cNRK3<<&qNnWx&VOqIInKK3wkQr+F@BM>gLl1
z=JIi4g7!8DJ42l?txuQp1oU3_8dFjh`ksh5Sr=A#D)oO*y$>~nyptk=jLuS^RubVP
zk!Sv+0+0muLTV=LWyJ!ND~@u8?3-?fX7wue?;2<PW;tWhB`0eK3zg&3J%x&*D$ivf
z&sQU?-B)4<=dW7JTC>mEnItj1YUxvo&)fhviuaF2Eh*x$JdD-csIjW~)&=oKD=Y<J
z-pc?%$8nE?NogOMpN4V0=Nd3!npPV<_EI}Tt9#1g5=G`S<K=_9WqGD9x_rIeO;#Ct
zzlQ$dq3sJ6+?f-E1NA!ep)N)^^Og<ZE?+}EK@4HN%khZkT4!cxgFoY;m!ScW->@5D
zzWA(k@|86e<`*}GkT<Yq;IQT_8v_4Qzu$Y8h~kbRo5HnEzOY%QO<GW}_D*(4eOV5^
z&lxsQz<I0AS}ChLmIxICOp};3lTIlVVVdUF2YItk3S+0>9?1StV&jCI6!vG@n`co_
z<UzJW;wKcMz{(LOZj7L-VXXX?lnTnon*XI-P$zvNf0S_aD3_?*H1^4oj~j~wS%VP%
zcB;>?y3XSG8TvQcKAHIG`4%nm|6R};Ry3Wmk=OT(ciG+uh$H!}vG<on;FNau291Oy
zz^!Sd--4ccoe;ROv0^;}Y6fnP;BWpj`xk#AdOKd{2HschPE83(gzCJ%OCY?&?Puv9
zRgXE5J@nQ<RnaF!fJJ6N=!t&`BeTZ+vTYHQ2I(OHMuGz;`iwm6=PVBfph4BdIW9BL
z>-N{$SsUD>zWAl!;I-|wfQ|y-z)@~rFB28`08RtSLizn}dG1lpvbu(MM4b2fdt0Vj
zMn~rDo_`bcozzlB&xZ|vzol?Ps>$i)s}&H<rDgF00uHN`kDGqhuMHBX{9su|8@zBA
z?OPB~{Bm{4Q8>sCRyxp*0ZfjP7MMG$XoT$dCzR!Rad(iGWZZ|i7E3C%M_4yu=Y2%y
zDD6U}$xYoHzk+*+qZwr=!lY$84wBMXv5FKJC98E}ZX|&~z6&WS1_3aNa6X|};8wx&
z4Amf)I!IiBKA0vDf)cV*@kH0G0{A!_=D+18Xfas>fspz;a!CHr?>!(w$Q`|@xyo33
zumRun9>55_n0bAxa{?lGnHkyH8Q%33*6KG_EDZ{0kBZMP#bW~+o6-4ThIFBV7Bo1c
z`T011(VUflrkCOCzsx#3(^>-L?FEoATY{eo6yJ4-b!?rbcVUuPPb)9_MMN5l98cuO
zP9Q$(@MR4^4BYsL)A|K{a(32OCjn%{MMXYx*X`|Ptxz)^tPZ<ZZ(Oq7-d_k$c(BzY
zoWFGr+Ia*vW@ejTezLab@>(TsrrEX%R(^Jtx`&sZFOlrsKxnJH{TUwey9>m{ysJ@I
z{AAACnmx3%Ji__ZCkPP`Pr!+35kncGdc#)#c;O&v0^LCIPwP5+0Zt}p6>unz?V|(g
z)WFOvv8;bn<V(3|%I5Wt8CJ*^_Mx$ibw|$!4){(U=r44-#+j?49-dDq9+>zdBHBU%
zNlF%UbQ7$ia7qQiBkDCK^1Kb|E4p5#9oE^{msLot;F90$9oLBIq4aptx-FA+9b3S0
zC#Y16$RCtdL>$d8Oso{T<E*3@8mia=(k)pImCZ7fJb|??va4Y8Gj#JPyo~4x@cB7`
zlOTx`<XO%`k`gg76_O8@6p0J@cYD$$jlVsVrIb;yggJ!~!2qYiHiNQj+5p1P6UpfS
zB^a<<$AnyZp5|wV<km5~G98)3uqC#8+dJ#~N2wfDp2(gH9PA(NffyVmuN}leKl6Ud
zo{ui#_T(VJ-Sp5Opy~Ds6ofo)*4Mw>hTSH{)~N^%Nws5ffvoRZHX%bq!y6d<R*~=T
zxQ~)2{V}r(OKW)rI5b)+4W+BJ(V|wtmA|zwf<-PXC)#1KGy=X24*v&5K)S!Sw%8?;
zXarJg@OH=bj!TX`C<+n>?q45$wYRCdu(ya?SFth-rGjSg|D)B0Xn((j%D-ITWgS-J
z1U^4K7Z~4)B$n~r-z#4P3;o{S3#RAUWaQh+V?X^~Ir*;_Cy>1=jm|NT%IE;V7BNUB
z2QYP_Ban0ebb2ZDuf-8b5@{=K_pb7IBlRZifea|`Q}`Jvp3d!&`K7BC7CLGnQ@-xj
z3z;mxu_WQL<nu^EQd`5H6JUep%@AaI^3nKGq7b=8DrPc!YI{zKh3R{)zTv7xZ#<K^
zaO-vDj6A=(@q3cTCzY|}ThmD1ijYBaLdQmzV6Di9qd+ys0!u{5{2f0Qz0w}{h4FDI
zt1N_BHoTx@&)<6L*4NO@^-DC;tdq9GFYt<*tHP<8Qx%h+DHLXSjEZF7nV}HW+o#C+
zdP73<cC60-J#=_ESrc{K+1!l)+%(`vHVmyR<A$t3P*uv4gA5|FOXDsDR<;OMfCjzP
z47wx0(fIJWH!$gAk5qp9So)Z0hknyDfjti!iV|_@2AnzeoO9>ySW6%KrQMwjL0}jj
z3K;?a9Z1D*$6XrJr;udlV`S#;T1>GF;sqik*6a&xSQjQjp@}DvMrt2UFTY_qef7cv
zU^;Hkn5|YPH1Q>P1WlMcTuxuNu#nDBtK@v+;ABV;RTUiH)6Y$u?{l7-hzv3b+}PS8
zdQ2PJw(+>>Pz|~-MYb)svsOcIG-y5L!9+jlg7!ZUCD^H^wdnUHqGXp~9a*G~)cMp;
zpdaI6%QV0vfkQIP?JL}>H>Gk}Y7(g6W1HZVoSR)Ox2uL&7&e*>l_W=47?@pNrN8!Y
ze2h>NB-lcnU8S9M{0r-xXUl@kMM`^|tAKIB4_{H$m4!<y3kxeMF5P|T&{FTx(xF3l
zA5N{O+6(j{ZC?Rhhsk5I3A$uUjfJ}uTVn8CQ~#?ZmRQ*;F>lWx(Nf~Af1sKV2_8_O
zsH`amIy8j3wr-lm5)_$Bh;ib9E)ogl*tK5tLt_FHpotu)A}3Stj43O@qpO{cO7=HR
z-mLS`)=k{)C%cA<>#7k+zNY^OTKX-DgN=hIM*~gouk5gnIjgK+ftt_7lCe7`CL{jy
z6O)q@g*~(HAEF5J*}&vvAUo+_gF(=QvqCm2d~B39+mG|O<49~0<#(4_uRu5Ob$Y7G
zSak_8R^xF#8a*&KC(O*4B#*!slP-z=3}1~2iKzp{MnTA&oF+V2+2(i#-F#)9GyRn%
z*#s-eENNko4yKS}Wf^vbG`UE&hQu0aD`j4!?p6eYIkHH_d?JxgK1K8}JmZ-TdA(k&
zGGo}|4W$_`&rD5`2i{bW^S}ev>kUma9-a|*u4nHOl^{0eVG3l|Bjxqr6yx(T-dT?)
zB1E>ky`&d=W<5;AU0Wg*a$r2{xsz~sw}Nm-F-@i3CAE{mP60+BX8Z9%@9Ve@eYBoO
zYI{^0G=Tgj<U>VbuZef(LHx(cB7vHhNe4Opwz~fSY$Unvgz+w<21zi0K%)tOL?8%&
z>}Cc*aE3FSo*X#4lNOlS*&uG#5-aVjw6l4oR@@}{Buf~Dv!vDflnBdtC1=5sqt>!d
zI)Tpjt%Iz);hp94|JLdAVgB#E>IRA+Ig;-r`#us~9nh$%uCDOn?+ttCb)r0ap4F1t
z{<*pR+3ZP8b~znmd-u=jC+4S7JtOPOC%}UL?>ZB&C0HWS_-&WWp!=xI<6^rKi3B{2
zAeG{hvO<!W5Vl3WRV68NRkW30O8J^pUtMh%Mglu46&(^u>A5A2;*m+l2qtzkESeKC
zQ%a@#RlRtn*pP}SXr%mKIemJv_l>)s&_Qxr#|EnVImHo$T>qFT!zB8S6y|~4KuZ-n
z-$Ir_$HwwtRl_2jFqc$@W`+}QWS@%eZafWT^d#9YhaMR&Ib_Er=J$vD7X7tR-*Egd
z8@EJv>o67qzGUNS*!M`{)C6M>4uF(XmqghJ$x{m4r$RPjFFgtpkqWy34nRgyv8>cS
z$v#PQXc+G1Ci|(pwO5Eg!FO1^@YLR$m!A8|o=-d!9gRc-!6+Mh>cY~^FMs8^hd%LV
zfoNnj8s(A}lK6B%Teg&DAQd(>6FwW5nC(6j>FZc!vT_McI?a|H$_AXnr`|5JY+8B-
zHs@$_*;Y<(Aj?xLldEKR+Ge*J-NwsEX(<py1J<jQ%Vt+2Dg~^;1uL9RDTeZ!Y%)-w
zdZoo@EEKSdsc&GJ;5F%Omrs;VFujvMT;~uN(@VXbtP*OxdRf=s=j(0!9X)eruW!WX
z!*$7~B)hmihO}6)a1_**L>mmGQ80fJ$h8|{H^ArQ?bMvLV9%T1+!Op6xMY8r&Pxt_
z{__E88@p&&|Iu<T8&nHu?rlpbm~`YGFo1rL8$t87Dc*Ag&7`YjEB)_yLvQXOY}5Mo
z=%X?F!yXk*s1`JL(|R$u{b~U^BDhKlh^s^sQ?f?(J_bt5Y>t@o!zH|;lQu%&;=E)j
zm?yhkV8dqThFeCFe6KQepb52Xdbx7~Cox#XsOX7M=-q<w<#8q(m01~xB=mKeo$U>#
z(1?)Llq>pj=nLVIaCqd~l=>V0pj7Pd<yreTP2U?!@|MQur@rXliA%`MLv>VE(blz(
zlUtVA@;JI#PG|`kmQ2HdS<>{;_oA9EFfb61gb|9KLnIji!W*~(cL5xS*e_&HXMuX3
z^)$@?cKW}aW~+D(r~R+OX;W52Z>*nYRoUGV{1;$tWztXn<WI79@q&ZD`m4c>H{N%j
zi(XGX?0e`T?kz@o1Y7=DKnW($$f(#fnbd%<8fK-mp=lMpuIs#S86?5&usofhnLr|+
zd+dt$F%537YZX?8uLRp%iJ|2U$OR>kTd^Xn8l^R?|6c3qz0zUo^#u=dxLHuE5f4k;
z5W1%Db5u!rEJnL9>4J3+<QpD1TXGiWvl%wpGw6yXI&0}pZ!{iA4GwmB+}@7fP;`2i
zHs;LFd6trcqk9UBx#gbD!9{=fKv!zEtG}<8`MczY(;083TkiPB5uZ02i^oQ@BmRN@
z(8!*`aQ^U8Hkv%v=>-E0_i?2+=<J#v=^Yu4hS;FXx%n-IxxOSvG<;yI)~;Sc#~JD<
zbzI$Q^*VaWFo~N4SS!EN)|``VKBrx|9Ucx4*vs&CM#$BQh6U|ed3<SUyi|&2v(eJ(
zTB8BGl68rd_g&aG26Wz-scF-&!_)7hwDJ3x6N8n!O3&UywzVmm0)aS%F2J5US-Ww1
zY2jx7Z0PdwB8%NnyJ2+yT;HJ*@0Y;kF$H;+G^)mltJ}~dhv>z@`QGM?T3!!WE0wnG
zDizqqyQ0kxc6EJy)6#TMlNi_FS~?l9#vu!v`s*L+zv1JR3Nw1&cFP;iS1LALMEBv-
z+IPyb3Mo^pAAs6U_!V-4@LO@^vsYs!WYsmGf=y614_RoPAwSTr51>W)B_IrL^@sZU
zLM#EN@M+71I7Ts-&3={jCrKDmEjC>~p)Pgq<S+$>2TeMmU&s|_74k44y}}4s3ygz}
z_`I|mc!dLC%eM?Iq~xeaJFTq%Tb3UOJ$OK0!eoqJDrmL@<AJme^s|6<J6iZDHM6=-
zT0u-;nbm>j){C$P=~y$})T;26iQh28gnQSSr0Wgtj|J&932v>DgBCO43$%EETVX@%
zclut3uh$?e;^#T#@5XsEozA;;W;EcjVS&;sHEHMBRe|an+)lq?n$5}8$=7Y7zB~Df
zkdx84ONHeSe#WHH)3*i3?@8P<9{egv7|e2JYGY&SqDHl;vj4{#H?t%sgeejf{lF7+
z9e-Gz_20a(G<{?3<UW{z5B1IMABE9V5c|7rOYBTJbA?>{>;=RQyJ_MLqi>iPc<jN}
zAb#uk_+=JqTs2riT;N`N<Pk~eT^?BT6STsSdDqTUWDz-})h+WR(@U_HmhIURE{n6}
zHZ-6rlN)N``ZVDFPTrLT67UhA0Ke|*vnjwnU&`le(P%ZYoJo<77SL6GC<CZ?o5>eU
z_%Yci7DI*sjUli|rLg}pNDK^vb!r-LGg`#I0oNgkXq%)}eksfOX9X5TC5aB>n5S!V
zL2!oOAvYcvxF!t*pw3gnT!uyZD2;)>b5c$y<Ks#~p7e)T!9s#0RK)rkeo6G<;2u?^
zr9svVIvx%=P5_dPxuvE@ov5J{L)ALjXrVN;0@5f}0l8<Wu~A17<NYw$OucE@R^ir6
z_<rWC70ZJw7*Php1)e!=`k7tJ%cBbmqg~(xyz3{{=<geEI-`N-y@uLD{YCvnP4u*Z
zncA-Re4BqwK~X`Pi}bG>wl53*HLn!=?m39=HOIiurYQK<L@&)o_Lz5pcg<PwvT2p`
zNB_I!`0qY&7nO6@pj8gdzLAG=ktOraGl?^25;rA8KEN$?(<(}NI@;DB*0s`iX+~Ip
zSozYf=pnOlRa}LH*1^?*+g?aa;IMY`L}ChAg^X?MiUiG%pN;KYk!YReJ}o2%DKI1g
z6cGCt@~{=)&~hy{qU?0^Nf1V8Yi})A4}At?UhI9*#!p6{IF(2!N+NMNubqyTHRUrm
zb_W98Le3JlOF$L#wMK#Zhu^MJj%$!xl^K=%<z`;x%b3k&vO29mP9TMC_i}CLba0v?
zFW0+#e!uTWFiDl_;Ax_xs>#>*c@)F3qdq@c1UQ<xx}`AA4Jq_f*(S#gT#LjUi!VO%
z;;U`hF*`SsS8rFZ8-*NLwe9Eshrbuvh^#ipp^diaQAIUsd*(B}Fm)&T2m3pM!QuYi
z(L060{ztEZ6F9t$2z;yd_1`fz#<aEyI7y@V1X)13iL%Lpc>{QUAeaJYWPt+MJ36}e
z)?1%Y?nM6ePUSz0onhWHW4GS=_)GlCOOo66RwSRk4zfTZD;9a1{HW){vaL;S&bO@L
z3x~g3w-iu^t6c8OHNFlQwISlePy%J;ts-fn(y$sGeTgl^W^To--&@m^C-%pNpBf$e
z&yC-T&D`=5UhFummml9BOG!fAc^gEf<o4~LT7p}-fcz2*jE?RRYX3X&WLQy3Go77V
z+vo1tC+5Q11+6p_9telY54e~85B5J=bO2}|VF>_MR6#v?9?XT{BqtYCHZyiuJ3Q8V
z=(!_D?ml|-Zl3;HI9#pOv^Vh!l>YpUH%em8a1<9UHuwybZY$wW$pbL4iniiR7mHv;
za{BwxW&G|bp&%TCV*Q)*vwKs{iu#I`EB_g#Cgs-8Pbn31BYq}Le3#mm7n4x)P;JZV
zH^q!>-s78O*A4j;RGWiUh}jK<ni||Jla7rq7!O={`oS~vC(fTc^I-o%=ZX39Lh}66
z7tTHIHM}=1ES+cfdmleHolOo#0Q5z2HEm<Mp_hcEEcZfdiQEgQW|URxEon>P!A)~n
zStB{WX2kBiGj{Ncv4aO=cQ&qC7t0z^Uq$T<etQT$hhleD@FEMtRd}nmOqb+EiS6}?
zX@x|*B;Qq7y;vrnH}qI;TwMp^8Z~Z}D_3g1jyha~#HWJ^InYA7ZL=@(5QJM{*$ldA
zzq18!1px}Qt7w-f+5vMmlZ^k0l-9WELd4%P2(~Ro6}Exe9I`iR_NO!nAvkab`a#am
zBexO?d8|IoSAKTW__(}sP&>FH+XsJ4ow|G;zdt8_K?hFi*U<08a=&}2JC?RnIh&s>
zOj>#}D*&wmuGeB21vi!|x9kddne3LY$Ima#{%sU}Jtqo0XHS})8y|P~CA!Wp#iEIL
z8ZJNo^|4v#ue+n@^_lkYdK4z^*0Mv1Xl&_xSEA4Te{Y?B@NYs~pX?q^5;Ylo{RveE
z_F33)T`B@EN(432OGWInfRVJu)*Adou&i;Q^n)?5f@NzuL(B=UG|&Elq*Ju|O&78t
zWMn_fUVfP!dc5&CQ`xJpvYU!Ukpcy84YHsjzfbZyQ9_E1VudcC+i16#3ANJJj1cf0
zp|Jl-V@=czaZ@4i=9u<{aTJDq)1Y#zlUC6bIY-GO;Gg(ObD5Q<B?+|yG_Sa=367XY
zB4%wvJ`~6&QBXh(M)3+7@t827;emFg6o>%b@eUwgfs4nh8&~K%`j(k^s6CCh1k6*r
zicF{LmUQn=*q=20C5TPQVnWgicGu&N-&Vcxu`2wrKY1MXkKI_kt?{STs^k)o9)`#_
zo@5=^k>pL<V8xm@kuhd%tmI$K^@e)=IB|ji8+WVdGa{Il>!DC*Z}0Oy#N`5YK1eP3
zA<8yrGN%MJ!lDgBRGQgd#;;zthMTM$&a_vJn?0DKlDM{g?Wk=O_D>Fp+9pd#W!Ehk
zWa98eHWvz|EwdR0Y!?a4Q5gdZ9J}|p5(`m%0OAIBjn@Xx^xXXcZ^Cn!UFz(7wj<Jc
zP3r7?rX$)p7n^weZy%eAu~)jfdKKlP<a6B>0%V*nI)q=cXYX3P<2`WiGo77Gg5N&d
z2|pWu>~9~Rib4Gu)cBf1BL50}0;$lfp$hX>fwfgrM*IOamC3v~WL4_W*Pp#6J^OLS
zc-0!$X#c+E*Yi||Ju87{ne^-@8rOIg7^8jE`ciUn3UnvC4^avWJejF0@Q+SGBz0wP
zWyKQxwFaSNZt|E2koI|-0UzLmOpXiZNkrZ57ytlN$pM!#IjFf9w(Tm{bBkKV#zrO*
z9&zaDC|D%6&141U*J&DSl*HMItf}x@)I3<yKe(xgz5uW{8|#_|)=A$dt3MH)$4zr0
zV@aA|&BnJ`n2ruMzD+9zSzHjeL8T=NSuh8`mpvHO8t?k$XV~R0vKJfgi|W7p?7fZm
ze^Hp5UC_c#oR+13fGK&N<zYH@LM}RL9m!sc{3T>(VM(5id7#UqR9wBTi3wX?{(Fz7
zI}}cgWG5ykvLlIbsN3Ti_w-HdeI91HlDE6tTgD_d8GmKrb~f*Jb@ccETg>h5?CSOP
zbhz9Lj=eV|ka<q@c#Ci=Bs~c=77s}dtL}dWGPgdIa!WW2ouGhslL_JxAB!G~9zFLD
z$qAi(?cqP27=J7KGhgGcPDGF1$MVtO_$}}MlTOKF%br9@u`Ll!D&(((+9>NB*k|Yq
zAi{;Tq~Qtj=tik@1=AWGLaW{@WoVuoZ(;+b#Py4s368kM5@byl8?a+WQ3>}Ok?3eN
zVt{wmU}iAP1s)3Owfn>Sdj<o8@afqf?jP@o2BXp7VE6bMEXAB6ruS8+8IABDuJ?Mf
z_?67&6Ywrs9p4AdpqRwR6Z7Ls%h#k!AgJHvsq!~K9-Tf&x)U^%md5wqh4RvqjE{|3
zl25AA1R)3Fvys|_D$z6?b<;)b8ePK5*wpK1fAZ;FGokF%-9L0U*^07aYDm$5Hbnzc
z;BDT|jofQb-Mss*!L)j6^2ptHADOz5tV3_UIko6dhiA|{joXd_chnWeUvQa(!c4Y7
zQcYaH`5eMqhO;Up5au=W(=l5~fPq-<$Skx+0<bM%2Pe#YZRYUd8P<uWZ?9rYyz&aR
z%d2Pz)%ZKSisd~c8El2s&^s{@b1F8<9?Z@;Qm^BK6X}Liatd4HpF=|l|JbmR)E!f<
z#dy@XTT2+p6q|C)MdD2L_!MaOtY>mk){+xy??|7ze`rjeobrwjO@#V~B=<I2kEW+t
z*4jJ4Ba&52vd}Pf6#p~DG}r78D>h6?^0()-jsH|ZT7)(8pd=v|q~KVAJt2@lk9Whd
z+g6KMD*<`h;3gagtbG}4Qq>uO{50120c@H{TV2z26Sf-c$h}v`14!4&C8kb(SKP0P
z4oHzg?3E-b|AJ>ZDlLOY$2n{@Qu@&5v~bDrIA@*PN};T9EN;1N?qLR2lW1st4HNpS
z^V(ZqY1VaCfqUpVc#}|K>3&M|%xiS9NT>W3{_yk-%>}q{IPj<&*B*ouYw7o88Ms%6
z)R5ROXs0#O@gH74y<UU~4bDl&kLUlwo`7#ZWj$@d?g>z^Y@Iu;H(#J0!8coZmWN|M
z?BU5x-bSbvLv6l^4+SZ{@FJvS*Kg~~Oll@NW6egO-<ou|&UI)~xA+`8JOkCA-nby8
z#`*>DROre0luoP80Xn04LxrkUty%>#fT{xg5~Nh;3a_CFU&9CM#^^iKs%+h^Dg6D*
z+T8A`DsM+>bH8;B>xQ^(^e#l*rf@FXJyWwgAsjVK`&6_4>>f#7td4z=o(OhaiO4%%
zgMUv?ZQmowJ3NmRu=)d<D&oA9ruOCiqXwN|t+CE$C|(h!@SM5RdIP$KM&18k*h}pm
z*X)(BA+nu)P27`vz0hhYt5r9eE}t5`U_OXxZ_iE4pr>DJwhM11^5&&aiCWVhviu&&
zD?AC(^|n4NNpG5TxBisfPi3n{xmF)+n5~Hvh7R>XtceNPH)lxx_b(sYs@+;vi!i8-
zyRF6Kw$`IoYxOgY=5meK)3mBtZ=3%%_{=9YyAY#xEZQwsgztq3kIw$(PeUW!t|cGg
zyhW`M!|;3IX>xSjHfro~L#<6BlIBI>NvNvLxeA}WId<%a5O3UmB@ZASO6!p2=LyFK
z<pl;HxQDF{Ja<jktQIn_laqqE2pi?Jd1EzkiMdW*e#>9gM(h;wvi-Aa_S9fPdfg}7
zu3jdSAT!EqyNZ#<$Yf8lD!1&k<>iDgNJnaj=wClFi7e664|oCw(zFYc6T=^R_sGo4
zK>ivv18v`xx#20M&mOZe@~UJV<EPrC(6u?6RY|>4$eK)lYIveIw`aG9%|#zi8gn0H
z731{y$R3xw@k;dZ8=w3jNIis=xQCEC_*#rL;`}QpI=CZFihJG^vV3W-=-^|ZbT+>A
zwfo-F*?GCM+t>L>XXhJpaag9irUsFJ^<{h$_nz*IbX<lTIwJ68jQcq6WuIlgM4DHN
z>m<%2>qcYb7?>F^M0cg9^2>uqneP1J?jHRpdtc+Xq6>-T{P6tIPxN;G+;ZRilQtE>
zYPLN{0MXq7gzkp+AYZ#T2Y9~I>bnP~FH@DJXLdE}hG7&X$<zLZN&ha{GiloHHw7Ai
zpt7&G`d+bA-=OhsQn#rU*Fw7fz2q3cy(&ZJ7ILh@9-7Rwv%Dr@mc_EJH_<BzorS#D
zxpb|Ns#o_Mo5GD%jjCawEG;1|vpRd(DqX>nsgKe;m?94v<mBqq;jOKhKe3$8FQ0(2
zO;iYI>nBdY2c9J_0e8S&<hj{CQR2uPY4ZyXS$pydx3*_VTqu561q*^tp3YeS6ruft
zZ=}@Xc56*;SAkNqo1a5&B839S&{|{ixQQj#SB-4e$oP@{H8e$OK42*n9EXo#gi5tg
zDbyM2<hGbx(i|5!V(5~X@fNHI)2M>8FE}VFHoPo41G8$ihHTbGQNc^ZigLfG3PXcW
z?hjm`I;Z%K>6&3`8@d4mSjjX?xRE@Syr5{VAZmbU4jA2j_%~|kU8k%XWhNP5=T<fF
zMM_Tk6}24oJlm3r$VAA4=BX;vRuRY&sX55y8e~8<v@vT*lLyl`{AX>mNlx;x8es!h
zk$0_9r~vd~E+OL!aFCLtDPf~L3Q0n{Eo{!Civ10Y(kTyIfhro9#|e3m=QNk7@jT{5
zz8Cf+J^kwHa(;Yi99Xg<=oYJSU5{6*c|KB#_DEq$3gysA>?O>stgcqBNiP8Ur%^5&
zx`|ddZDTdM8Ba=-<cZ#3Qp<$7QYx*lU%A3`QC9aoc?T2x!FJxkgnrQO9oz&z5L%fd
zq7ag0Q*t>s&y+_VsZ>o%ZW%^^6eysnHjvzH_A^6h#XW)oSx?6D^AB13b_8#hKC#&S
zN8KN%A^Z+Xe@d{hd0{M>yh9k}|4Fp8vF*=Dt{&xREJ@^9a&3)FJ{mx8lfU6rU1>R6
zDEeBcTn1gGxv8~bnk<*4e?4npyU!3_msF6GAXXRZkCVg8Cz!T!Vv|?Mt1IS8o}Xa)
zzmGK{`i5`D(5Q>J8C3x;<Nwrxo=|fuWTQ^F>x5%~0>?6#vzf%{)URAI&2^pTP?&$1
zK}hpB_F!YCj=tv-#T;p&^3<RgEJzY!exhD~ID>BqCaWOF<+H&L3v-~t<Z(N|4JeNk
z0*5>Nt)-c6KLe<}uQBtSlgS5_a9{68F#F@VkuGOnU(cN`Z(?{RAB+E&`H{XJufw71
z%+37$djlS)+&eV;*hI+VML8~WvTijEcyNPbE!;qECrL9<n%iS?-#!V!8_IeWyngD?
z>uk#cx|`^)=KW6IP{PkvF=2|f1~Xo%v5skbc|=_bKP=HtfX{4}M{m-$6SR9dOtcme
zNs#VbNKwW~RyT}k8bja0>`bP>R14P-CK}g5R02R9&O@%BgE|DIVNQ#Qg1`d21@feC
zi2~om3el-R(nyYj6mU<vXZhUmpAGcHd-{z8iUQf^fKHqC%UlW=JP}?zp7g|JHx~m+
z4!f7&{79=5q0`U<bLp8Rd7GMLqUs1^d05IwyWxI>(jbFh*kEBJ!C|iHW+lTOO-|i-
zLKo>v;*I`tVKBYin>rplHoRg<4%T7gcFg8FPyXiY8?;*ODoJN__#QqwzoTf~L0;?2
zlFnXk&hdnCt;%WG3Ksu^O~_U!ViS$8#3o{I)-+tLP4@6aY;rO-5jPE(xQx|RuFZLc
z)mdJO+HZ6?oASVB`|_%}dED5GD9Ih^Ug|yu+lY9=@}L+>z@N2~+FKcGg)}`dV%W|b
z(9Aq?Pno@9(-}6pWY(fH*egIGtg}$rC^Mupj4}}#qPAxk{q@saR?KUfK`E|>My$f0
zBm|m?W*CXs!HWygfeDA^Sll&~zIm5An0IN;gS#G~MdU5r^Ly2vXm4<ISFfgdQqhG_
zcd}c-GdrFIYubOyG+wBtWu{#=E;CLD@fI~{HAq{QwA8AtnLn80!-ec?>56`6=2aXp
zFQbI~#g{rdzKFx-)%f^${FPT`e$5uK>k0_#(JxzKP1~M+@=D+&A~8$oh7n>P8{55a
zys?pAJ}|AEoY;MVY0kac_`c=*%yD;i`ncGN{ZgdK56*E{4ystQ)mBL7I-813$WAm4
zbn-wP@Um06^dJLcLOULZ;796~2DlA&R!(oNU;VwY2ghTqzpa*)_r~5h9y_tAszRO~
z^4_6gr53h%=(15V%I#0S0gTMr<{WK3P?aQ|I=o5iRWP(>v8=z`ExWH&N&xQoR2tvZ
ze{B2>nzHEslwUrUW5Z*+C*sLWByngat|qcm(B3*KLi*5(MO)6#op9(-g+e0UpNV9;
zW)5}7!^g$e;u>6wTHr5%S81EJW0gpTiW*(&>czUSp|(ec*gsgvbQ<c=_UKz$GnYj>
z{Owv(M_RS?ruOCp^1afYCtszvS+}^kfre|fsc(RzjJfUI1yb<wyNgA<TQ8Y#w-A{K
z*Ef~)bfUYqE;=h!b#gjyBJc^COQnU9C;mRCvg&3d7*j{2MoGv>7k#cN_Q>{lUv2qT
z7Uvc@AeABJUI_(MH4v&s&?o+)Sd38LE@`OU8+dE}gwI)O;XR@#lZ?Ns<blmW9$~Gp
z?F6XqL9)=Zy_&`SCMbDsprhRJy#COsr3?DL@kqvCRbo0S4J3h?{`?)MFX`u(#`o!&
z2(#k0=?v;bxGq+iG`A!{(>f_h+Y}&M6#%hz24-$~Q+;YeaXQt6nU4iux3AQ!P;FDG
z6|7Ntecwtjb;YWe*xQ|?wMOz}8=rPq{n4A<reMPiIspnn=>1S)Bk$9i8{Uk$m?D);
zY76pWMO)K25&{|e5LaX<ieYn8LXOeR_8TdM%M`AYCo!`&gc8sax|OR(Ky|d`hEtXw
zC~O5wf`e9>X)1=cHYP&JA<<}-%O<59g;B%5h@TVs=rpV`#axFu!YFA(hZB}#i_bti
zansT%JMGv^TTRl5Tr92;m={mL<q{rDD5wD$OW)TxezyPLh7{(Sc!l>&KCW#$wz;2t
z@lpoBUBE!FXhbq><DpYmBAKd=0UdR~4!&}V=cG_hLz{EjAvd#96!Tk;WOQY7j=m<Q
z^Y(K3#WtOF+i?Nri!5xawJ)?;t1&dZ4Z)_ZVQN@sSHw6jbQwP+LB7K85-u8o)f&k?
zevz5%W{Oq<41}EhI9Mc`aD{dfMb&zN9cM4#9ci+6X2;>1*qxuF6z}+=^e$Fp?;=mV
z0^adO`tgraN@aWz$|%zJSt^5m`bA2GcrRY^j8b_awZ=D2;teO6qTPT8H#B1eJxBT@
zqW`mWvk7HjSus=BzeWdAw}sGBYocp&&WCdY8q8`-XbGDu{GYrIskml*w>P4cuG$hA
zt~9IAfi7G$gt>|+P-=}%8Y5P7BvJkKOS~Oen3YX_Xrub@SYtjOTZx*ufKIxglK5G=
zukm#@g#x2Lr!%dIYghZ<ioYl|BO)#>3Go-dk2AJy|6XfFmE&lnNy^Wk#I+xzDCrG&
z4xDvha>k&$!Y^_BrCPSdPO1%md+jyi@n5e%y*LnAt8QgN7htigR~s8xIRa&%L~;mq
z42w^j-<)}>{dqBZVZE`T>x%HiqD;}&*dwk~bB=Gy7cuwdB*g_^w9(uz=Pi)X@;W)z
zg#9FY^oKW}RJEd6SzkA|`HD`+gx@rqa*F>7_45%Ohk+xU`6TIg(7htHapnAZhQau1
z`_5ls|MheGR~r8hMgzTvJ?LH8FF6IfSXolJRqS>?VeHbY|Gq?BX$=#T=?#3T3})5_
zU16n2M&kMLb%`XelwZ@Qx;@Wg?HoxJA3-*#iV5Xg<m!XZgGtjBWZT$kDDi{#xgcFx
z3iAI`n28jcg(PO@p3Tv;!wX<!?MJ16QVZN|*#?&4GA4^Umh-K~cg`?<I6B*Sx89j$
zzuEY<&JM966=_^~P`|mum;M$zcBb)7xX0M&^<@ui)i1??vde9jTc_LIY{|pLW}ejM
z0W<W3me85fl4sZT8NkR*DE`>!*v#0>^q7BQ@6v>208)<l$)XyPV)5O61@7t|8Szg{
zNX>Z4e7%gc>XQy_u1hjqfKj7sY_Y4?E|mEi-|Vem3C}py?#osYZy0T2m2MENfn2r<
zd7(KTOy%?Q=s>72srJURXWv*`JnOAM?<|=&e;^qAz|CgmOM&|j{?dUbBuQ>c%*C}l
zEyTDI_9XWY*rZs2I9e1Fkr|f>ZN<1`9Rs0(dJeuZi}Xk4Cq~mYIQ;!V!*dC^rM-kt
zzr`;sKs+j*wEI&270vR&3;RHFP1ydB?Zsws79!)j_Tl$T<q$F2CLaTLQ@ZJG#jOk2
zF|I;fNzlTju`AME4_Fo;f(M0L?nVcm+<n6ls7!dOPgVPVrY{ugW0eLnj;#2F@dVtL
zfO~RB{yID|FfejR?Q49E?#cD3LYxZTrnX;GJL*68z~T)_q5j&iwy<YOgk#g3&62`w
zCgk?-8(;m6QWfW|YZ9hqR%}#pKC8ogM$;UFl=xFSM|(a?w$Eo`9cv$;3GE9wmo@5`
zQqJ7{g8czpT*Zs*N+>S5nzB$gkG()h#eDfg9+6~QmN~O@c;(2(^x?zPxWO@#tb+~v
zi_O^e^z1vthp4qXg;loo10zWz%(vvF5P%*U<j+kOd}J$-Q@Q)#tNWETWj`h?PcDX&
z|6%TlCuTo8`@|C>ZtQ>+t1T;&nmcdV-;#MMD;Fu!Tq!UB{dXWxE$_d0aeujZNKTN~
ztdfuqaXtldVn%b!^BA6dBWr0^1Q<5>tgd2&{hDo8h8i-lk40h36}DeP?2cbRt7)t%
z*-dBd@xhmtT5;9e)8jSKEc{V=<tIX+qM}$K;h1RxKxntO4mUy4{Z)GZcV>do!C)p6
z7#a*@fZWq<`GiZreng57sw=f&O=bm|Mf*y?ei$|E{RgNX+)JG)V*CZtz@Mcw%;O$Z
zh$E!rUpa>D7Q`>fa$wq`mo#W5TM@neBQ*DIY*InmSeKMzg!>@NvZ`)}b3J<D!YFPP
z8{ZoD|9zBSW=1=r_4khVWLo;|UVfj_!s}F-R4Fpod>T<5{JpGZY>dnRnuAB`v0GwW
zZ1?lh>!kan2PMh2#ZYH44p@G!y`9|rdh`1%Y&kf#?b_{gx&1zC-;N#6hLNW34s~{R
z-7B`e0T;Sp%R?HVTky&<oSlGe9=84c-QDyBq#Ja!)P=W*uPcR_6!Ef&NrLVkZ=M^N
zR5~$J%HU4=UXps6vk0{n>9@yV-P$GXmySy}z)W?UbPu$Z^&FYDy*dm{5VTtYt##aX
zEA8+LB%&QctB89R<4-B11~v_BjaRtQC>;J6aV@tA_A$%M<gAQO_^Lt~9QRR5`JE4i
zN|@JW{WZ#zw<L|;gOpL@AJBY&?!AM4_W)gnDO$=)5W|SQBDpP}=p^;2o1}}<ozklG
zCg}<2sqdEFBYjwU5qU2qIGhhP`xkVFxy+_(BXnl>B=SfVkm<5bM6%XZm1onxL({d4
z5%P1hN|s(rj#3%rl>FY59j+iB3LT)PT7~AgVxKUWYX2)W{0mWb%iw8-Edep?_Bi@|
z-GRQYJq#PA!}BR<y!+v3^XH57z97=V-&wu4x3iC7`*n9vp07fi8+7YZD0CH?-=Ow)
zG%qtb+W1uTjKkGImD$tP7wCrX_rL}GJ^P=b5KGXVcM4tj6LjS~<nnw7{?=Gur@I^9
z{|bE}Ndxw~Yt9B~X+p{Yr+E-`lIH-!`Y!2ym=7*XPfG8ANSp7MJ|ulY`i%4wk|ecR
zX?fZjo?c1wjKB3v+i%)uf%eD|{8c%HK2o47o=q=w#I6C|F~39JEG7(!ebnk+&IiIP
zanSpDF%(*YgY7;)O1F+RC#f#pH~tQGjQ5IAsKE!{yxnEQ4_l^!`B1unu6>z~|9dEO
zqWP9;!hrmQ@HSPt^*OtPG@#@P-2STg+f_Qc396=S`MqH4Aw+G{X>R;1O|-P?aL%Ti
zGzz3`rBGb+^_!o5`sUr!GrM-pOtU)NJUDpQ!*>l1(h8)r%67l0U3mKG3&XJk=gu97
z(Qi6}5B<<X!43iE0em=Z9oeu<LE=K~$QK;%AreiFLBQf)7jY?&2K4prJGrmd_nZIs
zqf-2<A#&L3RX0aoY0_&dLf~g*_|Mx>atzKg8^uxuwxYqs{LE+Ef#k`1z_0H=V^Z3W
z=cIjW+WmwiiCk^T^v5-8spiqii~WMf^QFZvfdx?GKf{Pk%_V!I>|=0>7d_v~L{hUl
zbY{sT^hY18AYm!S(S+v-t|Oa+i5WDA=srhUTd+a~m8Q&P4c~CxsNA@CQu*TVotiwD
zc;H1B`?PD}UeCYB)BowfZ^F~^v#DpME6@0kUi-zsz`0S__Wop-0_Ue3&rG{*4Iq^t
z6(xd!oVvw|%w|r%N!+h)W)HO_xrb7t3!|e870&rGP2>!J6TcZHzFT4yhs2RBNI$I*
z50cL}HBNF~)DPKKb4dPIAjA-sbj1Ms4g-&#BK&ROHR`WokfB#~>rJAw0e_2C9^>Y(
z$VbvH-AibI60@E(RM??#Gzy05V;SM6H<x?`3N<5Cyt$>&Mp2Vw>%DGll8@xtH5|=7
z`JrsWGs48ecVkt{tOj?bwY7+!w8J6t$OKjc{Sj)LKTK)VNaO$tM6#MyB7)^TM>j~}
z8%S?~G>~l+1KC#aG*^xaA=3lTRIJkx9)FCZi_m3O#H+eaC-oxUQ{nI;9+841sfQ-z
zwqlv7-$QM9lq4?|dv%)%)p_hAD);Ahs+PzJdHD<+$XU$Qw&sVr#`&w7!KBi@FNxe0
zGl{*b7FSP2?Q3DbB(%3pQ_QtE%Z$Kbiu(eeMaV6bj&KC9*VC#yLFswnxN_>DedFn#
z{=WX6)0ZwWNgz}C=k;{u$L~Hmz7**03i^8b5qp!*kH1Z_3WZy<q+wD~2|Eh4P&l7`
z@W;k}jGgN3JvbJNed(TGiN(ea!u(Z+tN)W<{FQsYL>E1ROtBkeS}{>4uKLkqP7Z)x
zLJ)!w2e`V5Hq*MkiYK<xyJj*)Z`4eT&DpR=l=Oo@@D}36@I14?!L;>9PY`2oW(YG$
z6-r<Z0i&)snZ+os*?%V9zI?ugvo~_#c>iSZ?kDaJPWC6@OZW)!6Pqy(+a(GdKei=6
zuCA@s1&Kj>l+Jd1g!UY^7uSh6GksE+>{T|YP;vp>Vbv-O+6&~Hm?Da91=<e;o?59<
zuVTmNmh~j57UDKu|CuW77$FWj9^UufF3?M=6qT0}Vb%Yh+oils7+lG<WuBCnJskz?
z2`bB{qrDE>5T8|W8luUi&c#r0!fLc@RPl=aEgnhVmo{?>cGF&x@Tp*Lq;B`%+Va)i
z+NU??_fPkn%pKgW1w@a5?^Vj)mWdE=ap$)|R{9(dWT#$ABmV_fXD^6x677G&=V)#(
zVE8^w7#|KxbDvH+pMC7H#&0nbrABqIoc=$x-xgyfd!!JLal!)Ii0lG1miXL(irJ7^
zYf()bw65#ioSEzo1XV$U<fj_7q)C|iWGkNLaroUi>~orNx2I97R?WW%jf|KaaoV(c
zRf799rDr*uxy+q=<_lz3ni^J8VDt^BNNld;l3jjv?^}QF=KgNk(K$FdIS@vR>gArU
zfG4UR7)jg#*g1XO?#Rr@K-j8JmFm;qtdA^Ck5%2cTVAKBmujY2Q?6CNI>iT=hWZIV
zQa4vm_D}`6UAh{wo}o&@&2_4(x2rR#^mI)Q^z`<J`ul^j+UIlkbg(in>^G^}-MxLi
z-923cBLh8d0A-hhsewq)-G}_wXQ3uHLroNl&IN^LGs9R2j6s#K-}8BS4oiojPo;C)
zd8T){I^~eu>FNs0T}qelofr1|Wj4^$(>L1J(=)(ENBtg;%jNO-M|Umsy8Qj4yX1$L
zB7@_L@jkc5<L-;@6SNObk`xW^fOA7O*6F6yx5lFy*v3j%^kQQJH5~p3_s}YdW=uFc
z*Xx!NNGK7pHAe}9$)c_7d7e}iG<)nQb}pN!|1M(uXT7?)GDTjI4IRGKwSj)UKdhCu
z)~aMzNT|^O#ZbvZjhZ$_Vx?f$YJ%<NkuuG)RIfmHcL0i{s<5^iZeYc9tQ*0aJa5(H
znzXf+QrjCEB<slQHH{mmURK+-P1wGZ8;e?H=f-&G?&aCG5{IPB_4-8adi|nW)O0Ls
z=bljnRmpaJ!%tQ~lj55oQ@Zc%6%@@9<$j&!HxYW;yl%_8g?3O$A2Y`hx@>eVUL)Q&
zuHRi1T_@=45>><8_T><`0Mw~}fKaiak~_aAp`|G15=FD)K8N3>B3coeeB1JCRd9y5
z-Z=3H?IDxoeV25Aw@6lK6>DcV%=g+p&_Xn5U|jRjbDee~2!k*mJqfhU<ww_TvYp<i
zp!>6#Zi4r_ZhZ|MDoKN#y7~6?L`yO-8^+!ihFJ)}$-lSS@u<fI(qiWUlp@~>aI`f>
zeLkhO)f^i>yLm*?Y$MdLL`JfPLFz$BHtZThi<`vWSH((J6`V>H@X|v=1H-Pea}%8#
zBKmA=4P_u7E0q?p2Pb8wnVaItSJyUkseQB(=_Hl=p80WZ5mDcU6Ss7TKd}=NF4)AW
zlD64TKn{`3^mp|Y*gZ0q*JqDh$6H{k>+pCgx7B07<|!Q#+3OGS2#vt60u#KY3xX)p
zf{|P~v3v&;VfBke2G7j&<>mHHRxC=))-6*knm`g*>nzi24b5B`-b1m%&F~q?*|yeP
zf2G-Bk*Qp-mv>0x(m4Aj`=({>5GD<W4pcw^`G4ec3n+4wA<cm`kPT~z*j?EIIBkI6
z2pojeES%><vIia49*?D`_T4`<63^{>)1XK9jNL=;`zxNo*qG-Ay25VcC;ZNIEVu8L
z7=Dqa%jL|(Qtp$~e~OgNTi~|bo9Mpx3HKr0I3xMl@3HR?rc9Ijmr?r#mJIViB2wod
z-xla2FgP(rPt2jh6;C!pDl#6w76>^mRDNP2-5(n^j1I3OH8hlRcsmSZIOdQ&PNzq9
zw0%=0dD2ap!@iFG#bi3|l6yRWItEx{o*vniPA3=pnajzT)5W&?9^ZgCi+72(&lZva
zdbz=t5u&{yhB5^kfxQg-4eeu-vB^)zCS&j90Z~kI2rd-0EL>uyVw!J*Q~1Pwi(Z9W
zdn=sWWt#7YOW-VLNoxLx_!jc5WH~68U>yp{oSbv!Q|!Lku!0cVy<>+Pb>L+y2D|M>
z4dsfpYf_EV@Lb#Bwm2sMF(=@0^m1e6<jd=&-(#nu7j7_CnZE1g$+Zdq?BD#3+NrXD
z5DzakzzeJZqj1Gw^)<@9qbW<`Sc`p9kkF-dLg%&Pvfvhg4FPM(?IX$Q0?SS$ZK1W0
z#F9XH>KI}U81d%ZRD{b054p0&;aE(z-q0A_fj6$B#Vx-sNuA9((zaPAR2hyO#{JN9
zWUoP6Ub&9HJH1u%S!g;^67DI$ND#kID~7(sCtl<5H~d>ugRp1lq+s$}D?0r#L!8^q
z7K)QjzMnQf-fr(8=wRCRp6kW07w)5w^x+3d9R46lXBX-C{aYi})7N2ErL#R@N=c5s
z$m7$CsqiiI3ixB+V&B5(kkl(+<s&)%UheCa&w?x+6O}wwu}z&!=DyrnH#KlJNf-fn
zZcy6Xvtm51`A)#6REpmb|3miGsNVPjm}0SF>6#SR*$DvSjq4{$Jb}AU_(~>jr4oz7
zFIZn=K8ki*C-iu!gw}pv(BoR^1SQmaY+1n;zXw4hK$~-i<1OTNwS<3~kcw*(0;`(z
zVba#4Hqc`jXE7<pz6QGQAmL3RUmjwlgKNT`@lg9&A}+H9I0Yj#i$9Zb`pDnO<_BIn
zmPqLE@m~IWZ(_7NkN|gJ@4zz9h=D|4fW2~8<7?_m#}oJG^UygjR}z8lu|$WWtf=<~
zx)oKP(1{vp+L(0{{*~TlCq|e9cD3BF&ruEN9PU9X6^YUl2GxZb`ZATCfNH#I7%PEp
zfd7HwO{_#GWZ5|ZCxLD#PqYG4x9TQr?fOOP+oXTUu_P%bMU}&Cq>q%g=GQJ;ZpN)V
zMp^Nkew2=@f@U*8$EY*YB#rl?W?Yr5bdpEkv;FlvZQ6w_d>695Q(I6&vd6|7vT=-U
zbU=33jW^y9BSrpk($~l7c;to~Zu~_$zo+Q&-0JD*^xRYg@z`x1PZ2KM28YF)JOTK|
z1HZrV2|;}yr{g$WP0{(>4!<j6#&Gz3LxT_zUWF02Ep4R=NR<WH+Px>Mw1Q~bHWEsj
zXG_EyiGB(s8$+oM&hLI!;L8J<_H7M;S}ue9v{O&$dg3*KVo#i4aQ!v744)P8S-(fR
zQq;Qnpe+Zb5kiMW`&Npo0{av{Aw$(XsIGI?K81T`dqQqB-6BmqGQoRn>AXhnir~U{
z=`=Ixl#bz=z*TU1bAo0%EJ;?gxO0*VvWzxOB<QnKDBDU4bO$@OYu}W1U^p7{JvST=
z)zCS4Z$OL3b$ua!pmaDNADbPHUg#QjvW{UrbYQqkW6Hi=acwG!KFWKe!?R<t{Grl;
z{DQ8-L?5^iiw<}BB+(M2wE}V*5LUNs8wKmxQTPdRCN1AWM03R}v-4Zts6jFZy#!sT
zzo(}kcFo|V31F9L6%Y3#9NCYzOWqZ!LhrRpQmI~?3&PhV9Dm}=&+DLJ(trGk3mRzL
zH3o0x)I$%Qx+8x9UeUhsrEOwEa(X1U)fyW$*F{rQGvG4O2EQZZ=jWJ&n;RjO-`WT!
zQCc8VS!-nurNM|HzFarO5=3oA0I6as1D+PcG3;ClAzgAyQmwr#Q-jjNC<)l6JfiG`
zr{Z=k+qaNj(ko*7=+&KDCn${wJ%t{Cq-fjq5+p`J;ACP_6xvA9v5yS&mmv9Nv7k+Q
z(0~{nqUoWzlP(k{u?y|m&<mi<Yw1HBO%L0sG{vO~+z6f<#1nI#Cx(1W3t_xAR;^Af
zElmuLjt=66$AT04;f$FnGbyz9;!<XGclJ5hGH|RgO;n}py5Jc<KpuNyDQYfjZOkm%
zIfu}(kh}&BlsMY+G7&|VZLmp}KsNqZ(=ymB=1_2;gjlV(p2CIKCt3DiA7S4>?#S|J
z5{%`U0vPY+{80!)cJj05H0`F2bA_b~7nXM2Wbs9R2){%ron#wff+SU@Y*J0}TuNzX
z`9?AxXE&c*0QrtW0Sc5VWzQ7S;0JfzB%jk(38K4XSjCa&smYErlW^f>3iEWFJEz`B
zJMug=S&`onz#Fo4bSb@)nY8=A+CIVd77!=^_qG%Olf;M*uQf>k2~)`-S`BQq84&FR
zHdzRW7z--RcC*mkQ^TYn0;_F5sf9p8MC6o0z3I1oK8I`NH&$E@`(W_K+b*0td-H{J
ztlHD~jUGoT<>+C%X<rx^_%8zk0`3^vBuRxFjNKHpsv9<VSVUza2$)=wba=t{{1^Nk
zb2~QYS3LKVSWdZFw0$5xM}S+~#T}twufsDu<;cwKbA*N=S9lUE=Vv~8Tfo!b?+M&y
zAN<XGO1-@$Ojxzg894Cn0|95BeGu*0^`>1tn0((THX)*!i?3P*$S9jt3hI`5-(=ER
zW75daS6cex@*B<;{<@k-R5y8C{j1uz{ot*NWPzJRJ~#sF%`}%;=UVb-m4JFv7R@PJ
z%hBw7);ijDJ<^p8UY&~aDzHz9e1A_q-_u_XbmtRFcK~?eW(B(dZNPFWSq6jZgsCM$
z269$`LI_eV@OklBM4Jlo|JjKS4=CK_$~IJQw}5!9c3{teleoYPZew%M_!a~hjzo;1
z%+OGVb6_iMgT2W8{I=SfLJ6t|E@bCLufD;Ln}dTUCd?4L`F`iZv11ot!+iVc4g8HA
zRg{G|vRVPO#x!CHI&9VrG<hB-UiPttg~FK`JrPYVjqCdO(#4Bs8vpgi8*hC6`4`xm
z7Cy1CFc9w^*Pj|6KYa0)!@mMw?0NqA=dsPsOXtZ`um?s}greAzd2pUgi=hlB32w83
zkQQj)^XK<X?%g|i#~p8ptFh@_?miHt?(cW$o+0)*@5<X(yt?=3?MJ=mUUx_1hvI&>
z?)jmifmnL-b&=>q2Fff#nV+-0;>gpNB*HS64yRBE4AK@)%Q7m@UXQs9zA2{0N2Wih
zyZ!OO^LJnsuqt0rW0UC+Ui17)OpT?FzU~|quTxbHNbTB;9r!a<Ztn`K<?=RN0PGMY
zhs_%i9VvS8?&!@+(8>HG#*nG56|Fzf01MyDfHckil>It+dL*O_N^n(J3Y%8eArEJ@
zohWf88wLi3yanay6LEiJm|MahlzaL<=It2lT6IP~-r<dQHNM){BhTIP%uh%DU$R#J
zbYD{5?~E`Bzl4LvPs@qEr%3{9&uATFJI-aqFy;%}(PhT+vI(EEFp*`=?90o`^YgH6
zih)kJbv8;zd)@_qwjED}DUk7r6DBk}ggmMvjluxG)z14wsd2TmOjjjL|Lu@6-6Fa=
zN#H12sv;MVm?^NFjNJgzbO_#5_Ba0Nmx1oqK(C?|BR{(LpV&&+)A*z+8~?QTmr=39
z^dfY&f7<(tvf6k_R=4r1Yo|3vaF;D?vQ1<iCr_+qE^Z<{Mo+%}WagsD(WBqukl5P^
zrLQR63h%<Uj3EmZT(#%3+(3nV$<o70Fy_vMRQwn49`iXugPe4d`3fOD0w)Psr3+}3
zDYHPQtaf@;2kRU6dR$MQ8w-Tao@0YC=5V+iGVAE<2zYzEZuhvaf0y4qmPvX%Jt>dZ
z7tnnEq^9-z8prSP=*C~okNA6?J#+bi4tJu@*MIa41B1K9-uTA6>U2Au4pfaeJkAbx
zS7%qc*Om2k##B#-)6?N_db`z3k1IB$xSYGw*QBpujGvpOx3Dk6(=SN3OA^CJ1M%~=
z4;Lb=OL(^S=aca+a_J?5o;d<8Mf;+rbrGS0KN4rm2~X-_9UWc$-X7TlPa0V8yGKKQ
zcvRWlHyG^aj~eiOQX5cD098P$zf9>}-F|H{5>9kDGLcTFHtp}rXe_BZT}~%+Zh6q&
zUVKt0!_(~>peGHwov}VG-48BVL2u{Tr0VVhom<C5z&&z|ua-LLKxDqg2vGn94=|Yh
zQ5cGgrWFKG*U;|t^!`GuujjWl-Q+=^U(O!@;lRfq(&OF!A2UrB(n->q=6aT9RE#N#
z5=!w8odR+=krGe@%)w3IxF*_xlpXn<;Q6<+C!_PT3#Tt77JmauU5~}IL_BzYX>>R-
zz58IksQk|G*wO`7YP>5tpLpoh?&-ywW5@p=T|XI%=MU_jj>EU-gYkrhS_%;hsaxu&
zngP-ltwSIT$3%f7uK*@u)=r#$T#%Z;exGtUK6uIJd}|`M^g)N?eQ$O8E-l4Qz;fiG
zaaZ^Bg$%ztwB+imh59@OEKf_pzQ#|pv$!a+M+6>#N7eF5al(t{N^q4UehXkDph5E|
z>!@Hdi@IT;45CN}Ok=3&Hcf&sgVjTa{WVG2B$*SVWLuVkDr8IE+OUUXy6Chcpc{IT
zjCblf9GIF0zRvYJ8cdsn|F6TY4jV&^O+;NXu7|p0V`wRPNQBLf;)2JjaGm1WpkSv~
zsugR+4cM1fiwd1!7<M#%<$tTC5?(5o=&wvs9^2|-<NlNXxPdO?6(%*xCaaih75KAa
zy|RBMtyK&;v4RyFIGqCx1bjR#G*1;4JAexPlhH%^Ay8j3Z+1MLNMsz!*_oqv-+lCK
zt}l`pPBhlT0~jp$O6~1$@6hL9?lqoy=9!)wZ~Q)BPokwxl5Mb8>G_)RJ8b;YEak~_
z1eGavB}?ziF2yo21&qfj)>UfA+%VR)-_FD`PY-2cU)A5~-<?9&s?=`(eKk-@?%Q`R
z8_SYTnaca0SYF$krI>)2zdb6@U{r={0b8dGTLF$wLNRaCPFNmRhOr1$iP5zy#*=XH
zFcg*Fw~wuIb%g#HREaIa4RG<By8bxavME9yX_2^n&(7}a)pCA*tM9s0ttMXH)^WXw
zu0Z*7zA)XEdl?zbdDhhzjrJk^7VWE;E5<8uvfdX(@~|%|^$7XUSfF^L36hIXfQ`Yt
zv|C!DUIu(8uWZ29*rE)USvqEHOe~*9WEQpn3d-`<5H;$D^4S%p<Aao}*aK0vsKH+Y
zrU2lkqdF;c*Q0#|0&~mZ$$RTiMINH1FN%R&a5S6IK1g(Y0DTR?;(FU+6Qg#VKS5R+
z2R?8PY{T|S`+0<&oc)%m=2p-xr?mw*1UxCv?Vw!r5H#1rR-wpCI#m1~(jnJ%pnTTr
zRCUv?IjdQLY^qdi9>|3D671oTiYB9n(CIop2DOKXm$At|vHhj~{14p?A>mkA2<%Ax
z@U_kIR~a;6N%pfe62w`KFx8wm!q9>Ongk_bSqn>e6}s*r*w_I`9@n(D!R}qC<k3oT
zxH<x-jem?~V^8d3uY==T_FXkiA*u&jOQofmZ9FGb+j!N<!)|6}a0-N440}>MN@o?D
zXAOkBkecvRZ{<-p^FwEx-q&H`<QJeJ`KqyQq9B{;7<MxZ;Wrz0fKGv;Mm(7j=mm4!
zUm=HxlMY*Kms2*g1t8kZ=D9|}VTn8#Sep%MLvJH4czso17W%^>h#0c?WfFfdGu%I<
z4K_BG@Wu~q;5`JSVTA7+T+WXzHm>a+1@SJml+HE?X~<7f3PKHrLIr@EEVY*)hS}@P
zHO1Fo9~~Tmta`DaCEciG4^cM&V<$oc{W&OSXmB(`6?r=?upE_t-Ndhrc7#*X;aK<-
zvb7KFC}F;Td^{M0?ViQOXk>9QQr%YK%<vxAB9SQF$Mlv6+Q2`7!=*Y5QzT#X<SeL^
zz%0G_<tRW*u;}jHQQXQr`*BsXHAuA@(^)jiZ_wyV*lPAzfXPCL{_EoNgEgbI&&MgC
z|DCXH)DO*PUPd>;Ys9Cmk~*_;@zCTi`K(I}Qe?m(cMI`@WCXz`7BXcG&&6}D*J3Z7
zjA4BOpZ|OSIB7axhnM%?l%9tl?on9KAF<@Ke@fUV96Q8Tm;i7uMX<FM)J#8d0^r27
z(tqgbse#tG=JDX}6^O9{5kZXJz8<<lU*I~-agbl77Ee=L%Dr1P@L8JonjAXt2nv+w
zXxIDOvZzOv#sQ;x=;G4S#l458w8K8%9&aenJC+#VH{t)?C0$<{k6qjgN)2`^S-C9h
z_=D${@Y?M&?_7nu`+a+SzOjTp;cxtd@a=C~u_|30x4x<4pW(Jep>{MH8-7r3BIl%<
zM;X-qeuK0MKTfHB;nNquRTR8H*SaC~g_r{Prvj(!tmlS@b9KPR!51A0VVViHWOfy+
zHWNs%WmE07NvqAWlg*<7YC2#+PF(#{D&_YnWn<&M4#@wSM7wcM_-dFbD_<2V^JTNz
zszudQpzQRu2K!^O2OCBofdGnwSvFIkaNtdJKNUI*FoYiX(CQ3(I3kWO1Rv8h8{Zt2
z6(9r*(*WW?kw@7~I=zxk&oEe{C&r4!u?bC^9L?UE9c3nB{53XyC@6Q_#W88_>X3s!
z#I326@o_~Tj7DKtxy3g|oc|c7ee71s;&GdfPQ~ykBza<Hc`v=BV!5C86Q8~SP+iv~
zK%8&ArZ?CJY+^X~N?cELbtUxpD>*2Wm(KD2hV0%V^b)Z^>KWWV%e)|zqpz-BAp;iA
ztGQGv_o`LEzwxs<B$JTdUL_nADd5Z%2x=6Bta+RlV4_ILaG_vZr+t0TM0Q-C=$<^f
zG!*JpoL$Zb^~?%AE<4cpPgUJ~Q8)71+_2)9y8Bq~aX#kw97Om<lBA%fEUTyrNH`05
z+;|hJfB5aHzI;MkTZ5`rnNi7KP9zj1k+_`K%9qjH=yF+VYON|jWrmtQgGnM7b9qlw
zq@Bu96ol#?4j5X_rD}%3jM^2G#LYCn?xPyX6xj&q2avsbWI`^5dHDUAS}pUw&X1*P
zwNzDpXsA*dy0q`fXr&V6>)k%$S$k>br??Xck_wYF=96`M;4A<aj<A0hJ-u2>eQY^4
z0a+ft$STpr&n|r?9*(n(#--?)vz6$Ri?LxSVE*F!l*!LdH#Xvdn8cdx6@<EN*I2A3
zG*)%HrgCnw%*pNS=OS3wTbyyrQHgAH1e|bihB9dC(Ch=tS|{9pO|o&r8&^Y`6{Nm-
zAZp1v$m=G{i32c(2H{duPCQGJk0yBOFgvLpxtL7ea#TH84f{E9&_94jtOUtN&+m5M
zbd!7c`I#fb5$+I*Xo37l61`O^8da{4Ov;wJIh#*@;>(%F-?F1s#8ay>la;j^x=PoG
zrV){_!yN0^FWSg8r(p`PfsLcjrp#0h10Nxm3C;xl0|v$`#y-YZ^Y1ig`310Qy%BQ#
z7tQq<&ej%yxC?E2_+1wRdEn~6MkLVZ^(Jl}?8n^&ezvjl3QZvV^A&TA@C+18*UXRx
z&_P3;ooP@|ZF3}2fW$4gBGd!tO=*hkGe{Il_+t4aD=JDzFQPxDUN_cCYX;MpROWER
zA;nNa2FSHbEMyREN239bddOm-kW@p|Q?e*Yb0(c0YNjlErlav{#~bD{iM~F=WTx&I
z=v(g_aG<T`)l4L?I}kXgN7$jplYNTPw@C&_R{B7{NR}m^JXf&df>=Y26V<YXKVpTo
zhQo{s2k;f#8}zUcf<?xEB_2<NdVArhw>Ol)6Mr|Hbo)bz=T2WbeF;A71;Uj)lI-nG
zh7z4FM1gg6CPH)`?{Fc8qN^kRmk*tK=+r4ltaa#ROPZB$SrN#DR;utCQS%D07K#;r
z%oa2j*rTKvDVr>V^-HXiUpM&4z(p9R@!<)T={^ogwYu1=zCs9(FEScZfT_2FqyD2V
zh~LsP5#stk{%&NBbzxg@vYeWv29pt=PKK~0#OR|vWU8rc;AWnU`jH^p)8TWT^o2hW
zVD7(12E#pcgU$_^IR*%OQ0wk+yPprGoNnMjIy>_(HR|+@Fv>Z8<#n+Am{|m0lG3UG
z91G|0*$`RX@7pTl=DPN##v&_C2wDrPr#0h1w9m~2Y$c8z#NpU-lvet~_H29TvGDAX
zBJt|1O8{#t*z+~c-Hl&+JbZMPS}AV5DL?je{tz<R3&NB;Ye5ehr!PHt=FEeaSdpIL
zq2;S5PhM?~!41+_X6Y&NDSV5N&Xb*^+O8L)aRWMj9Wv72HJ333^DDHd+gKV$2cz*1
zWg-!>FR-~>w62q6P8qdDoYgnma%Y8O#%CAW=sm&4xP|^2rA(qjO2~nY``XzDjNT>e
zF_lES7Sd}swT?l~G}#VmD!0pF5Bq#qd?UV^4_t;p@mMB;>#}bIuENEB0A%+`jwXsC
zy#r>&Q7w=O7*?A_$d1cEL8MV+3eZ)hD!gBlna$OV-a)vnpDVJ;;{_&B4pSr?jH*sg
z#Cqei16FvCnr6Zk)6`0Vg92{pAX=k?eX<(jQwE<cXcj>&nEc-9+on2wBcnL>uhe}V
zsBUz1u*hxGQ=M)fo!776m!l)y9m0G~QA1iiK4amlW@c5VlS9lHL=+GI)eW^;jYjiJ
zH0BM^3bNwA5zSziN!E%iF9ZFxWge;GpXdyrm&-soY=TvA2{Z)sU*a9$CAoxoyFfFG
zZMR0=Z+r~vYgZ!~@ZBwDA`B$_HM;uA)m2!<Fsw!-1NGc3?PlxjS1nv!StTMggMxI>
zi~}u;e7(x{#y=4Izz1Ug(dQ4xPfm8k!^USXhQn7_r*(b62**1nZ-|Hcq8GzQ!WHRX
z8L!H=LgPA`v6cj(0A1VFqKWLuhEfau{7po!82Q&VK1)Yz*}%!hgpK0NT&6+z`TPsC
z|5~w(^9^nrATt*2Ww<2ZU&edW1oOS{-+43t-8gVv=U!vYQ8T=KoS=5JSM$Q@3m={y
z9-bb)#m0NZb)gy<N~ebce>pszOisVP9rIPBipd@~3leHBSdwKlyej}J!wmDaF7IRJ
zo1B!E|JTI-VxwJ+U-3G|CdOG8J3t45S0&+%2{L9N`aE_pK43EDtr&c^zmug*y=i=0
zUOA{8T#@aAKPJCHj_`9%{DKagmZt`jR^S<4BpU~b1+eQg>BZjnzrUB&8&C8aMlbYZ
z8-tvzxH$SwvfsiSA4cy*dD21D9T~Z-M*QISJp6vJ%7Tc^FzFUG#(k{7ktUt)oqI}$
zX<2dz$mRpBbs>XOWsd{0bmix+5*66-)cN?h-rMI1&Sev<D~=bd=a=HAkL>OD%j)6%
zXX8tPR)=cI5$NSqt}qWvj4U@r^)i3om-UtW2fW^lSN;Igxy5@ij81eP@XB!e2VUWt
zogy>gP5<EL2x08-xC}|#j?ngMP~f1jt@7x^(s><M{;Pr!Ip~lQXjNa{pN(j37V82M
zli#)<PdCks^1usVWOOE(%uw{=x}va>qBPb}e`>-XOw1S({d@D~u%&}!(ccfV-*I}w
zd?eB+M43qIpg?xVkk}IgMKBQ(n-r&e{(2-FrVsQqd$&F^Xp9VYcL2jRIAZV*oxxQ!
zUPmg<|1Mf3-x<dAGqDNpKy2I_#;GIh9ghuoCtw?o(OeROZ_ws-NeYo%CxSBxp^cG}
zYPKbVl}Rvb3wHif#e4s7R(lX6I!doHwD0I-_k=!ioEGD|hIHlgy~ppKawx+&J#T0)
zn%XOz^cF@#mo&H;S9{Zlut<_~n}&qWk-FEmjtOCm`DoqbqX9JvvNFFZ<0TJF)$ZQ-
z>7((Zj!oIW&JEvq_&4!-dm&8lN|2Z{mCfc^?UTyF4<mZ~4=*U2YvGrMac(P6N0_J1
zK;1&D%5T3h){NVBzgUy{WFQ<4e3ssNzyDBsO`2ngqHG6=hfz`#s3?VCn&kTnX(30^
zhFHy<S&HxdJ#%JneCd>MTobPd$MBW}iVSjRbMr(iqn$xB?v90b!ixK~{QRmmIh-G!
zBvZXup;20ch`GZvj#|wzGhBf`fg42|GxBc-J!sCJ{R`hSKUyv7Mg4b(-(1{@AvG)I
z7ng}Ao%(JJDd~Y|J?i4t*nyxbTcnD|rd4Dd1>Dhb?zOS6cSrmm?Mo1ma%|2>#vxl~
z?t<$y1I2D6%I0Xc>#hFC<z7J-7@`;|lhQn2t8G6Tm=FpGUV-Y!5KMqcC>+!)hzw;{
zVBXp@^T5*L;iNh+lGu|-45&$$KG`Tu>iSE+Sg&^y&G#HJbf5nK(k&lQlLOvF!aI;;
zlYNIK8vlh2OdRU-SIRj7r(2Yl%a%-ex<yr?O^qzblmktfGC8PQa^)olJIfUt;5KK8
zmd$f0TGq8Q+B8U)2lM#~kuvWn!rzt6;$>YY0dsVu&$DS2?ji&Vp>(ti%r%RKUPzKG
z(yAjk1uL)LMrFS|6mjsPht<TPMgP6)NBZ{dub0a!RB-QLCef*Box|=C^=HT8js*NU
zVw&XQ)<u?ldsLcgmvLuE$SrS@aR<Pjn1oddnixO1IFX*z$Gazv4$briyTTpvB`tFY
z&5PB1{&N2E<$T@}eSdP?C}=|i!$Ifd-ADVl?7Q*BbbdaQxR`*QBy|XQZS8hKgc6#y
zHXex=64-E`vwiJBkX2eI$S>G|M-ik=KV%^xPh?4Ac6pm4n^hbC{AjFNjXlZ~?J+!f
zj4%UgtV~uQh#62>hvTxy1v>~At&nQE)JnxQCpYyft#NBE%B2pu7?Oi*V=Cn`yrcGd
zSi!-vOu{-e{+YQRWmT+&_Lxv!7a`hZN%5)5Fby^>&&oI45VJp@q8j{+aD^FmwB6%`
z{r8;Yrn<0fq4wvoYto~!&+y&%!@tLl=}TB^Hho3QEvr2GXw3ewM}?Ek@#q-+gh`lP
zj1_4|cT^eF&AtPw4;6wht<ZOp%Pv=ltU|VI+>R`Z>5u~tnZAn4>}qWlkabyQ)mS%H
zwJUI~1Q&PA2QVY3|5I)XrK|`))K-l(ZFN;+MQydQ4!K-~i*SXcv^M6ZfFTGhlN&aJ
zVg}I0OdYZ*>pHC=z-Kevw&(5N0im6X3O-8dUs1|*NH%|Py{<zq(^=KdZwoxXb<FeQ
zpFNaT>Exr79^%=-2;zN~OPpar=A<7wb>x~BaqRKgD~B_4D6i2DbdUGkx_IR7yN?{@
zmw|_v$}AiM+ZyQCABWuTB&h=R6zn6;0=|6eY=;hgno{;&+BJTQb`t&0fZx^l@6x27
zD)3<}9g5*yls-l2uTk1I-U9d=K$nz@)oT1v?J;54iSa)=sfXtfLl*Aeh~4mO`gb74
zA2VV%tY4Ghh;lVph3=(Dj3j2uLRW{7e&5l5?S@zl4w$rlLu_*m=xG5&q`<0T6_^X=
zAuFchbJTA-$d@O@qdcPMs)KqvQs*%`g1aB32#j>M7;O-3qW*L9?musi64Gz}nT3R&
zZI3#`DU~EqA}W|bz&Nu)%drB{Bo9;i`Mr(xy%YU2i9?B*{>EQ14Ov%12#|4p0z7n<
zCno$eeSI_j#vd1p=s+mBn{<~0jss|AOZq%NOz<*NcYLw{<D3v0{mx}&)Z)l2Fgm*D
z)?1%Y?!+16PUUyv$*`i7W;#3n!`K<-Ju`Oet&hLNzkBKK*(c`0+6ApN6CMbMC6{1}
z<0tFLCPz+EM(Vb1%NCVmifCxK3B8sLYMRDT7JdmVHlf!VWsr<+V|SlM(l;xIvO$#w
z#e8!~IC4+=JJWB1ZP$^e&)<<<)89!~F}dg8(bT+Mj+Vi5fNY}$E8}NI?m4n&;{H7o
ze=~AVKV|r#i9JW|8EO3P$lu_B9b4UHbBRAFI|@>rG5xw~GTRD?Yz6qchGMqBTv_Y6
zOml$fa)a!F0>bI|TMwxduP<M{y4mkWhrb`AucWq;vez%k$(JM*m#6K@`+0S44n86L
zY+GuhPIvH+<Pyr3iFP5BOZWAjl@_8aOxjwwvR;Q(WxG-<y1q#Q92F(N+eEc~tG{q;
zXy{nMzpuP0YWC4-&%uM9>7(i2*c_SLA=uOQll(%k-jZ7ai@$5hSwK$lq9|c$!?#vZ
zN<Ff@SFd1onax6%W6uhnyf<_4WuBVm%b}h(zqu#)DqPw8`CV!uvi7nyZ(Q;=M0B+s
zF}N+-7uR2A@hkWwnF5nr4NXR{*REm$Y*&7CL1|oNR~&Mc;|+qv7cy3GQvw;Tm4#7H
zV8~!sew7LDe-PgOFme~^ZPf-c*C}L`0(_cN9Zh_!x>=VnHFf(`NB4*`7z|$QU0m#)
z>D)UxxwrG>Hr>M1tus>{F5gd$1}}{UAMf3>r+4NI-gw5AYHm=iQs1pc91M4-N`OKA
z4h63O)l_b`HXN5Eh6)I74@!IadZjZX11c`<{L<-5%C;3?QY51Tz{Gg~`dHq+BCR^`
z_rDwJaNYOsziy2_8j2|wv4}Dz@$tm=^{RIEhC;oat-jHTYU^v#4s|5#!Gkn9hR`lF
z&2?wwLX-zLZ}c3p4G`xOX>Lu8^A!6hk0%d?hJ!=C$=6T%5@9$7cgXwMaO0m6=JJZE
zRDOhCiuAa94)pdO=ymrF@Za41!m^owJFbXck5)7a%>H`qfHvC<rk&8Xk3XQZTIG@L
zpOtl0P%7FMu`}GVglGq)2^5&AEAU^neXH49iMxZp7Iu^bbdH#(E&ptf_z1ZvuzFWo
zd2ImjKf;v>S&4|++t#m5*j(laX`$xy#}u9ZYT^_q%CD(@ti67e8`ZDY%1SR5v3^pU
zyxNZ2*+YJj$cdAjNJXLmGqio96tvR9D8JEo?{ePSfxy=&mW+Fj%#OvQ$^0_Yn}={6
z>bF<gOEjrZN|BwNY-gC2IZO`v{SpPDBEe7Q3WUPQs<~Vw(VLn2>nMQk%?=EBJAMq#
zOt^Zlr!yW7;SGnUwRmi34lc){0LC}l;~96le~e$@-#R>rUbjfAP)zVN$0jUbZLk8o
zKFEM&DJVj-IvZMbcJ|mpW-2{h)av}eoSoe;&022u$l|R%<J||ee0s`kT-EimaGsl$
zggD5As!J3+F3sw8!1Ex|F|?9^KEDATNUP>HfnKRkQNDzIl%#gGv&&?GK36E}Sx)AL
z@F@lNdFzDHN<HE9?_Wp#?CKrQX%C$OY$H+;u&Yo~#UIuYhOf){F~D+t?#@#WnGoq7
zbp`*I(0m@70%%9T+S;rEOS0p(eY}cmF9SSLPYsjTbQoQYvJU^;<+{J8L0>SVr@v8O
zU$25g$hvNtqGbY~4`c!%D72}HfZa1&luPx{q3YpZ6h@nfzTHVEg*RY7#Ks{KypRhu
z=Sf>!$`ebLt3p35TzAa@ccc4UrH0O)zJO7^;z_`X^mXVa1k{Olj!!8u<z|rv*{^}w
z|Gj;o{das(?!g~ic)D{Q{G~g(!Su%%nmW|;)7!2;*ta!8yf4#w3>W%6o=gUGT(adg
zk_H|R>R3f99oXK=*331Ntu;1ksafX7Yp`9?bP!FLIf>SbGW$0BR4YHqE+iM+GCJ|3
zW#Gg^p`V@3h5WF6s+U!I?pR~fy^VjE_`-0E&ERF&?i>B#(c$40*XZjWKj1T($Wvu#
z@qRu|pknPdMGZ}~C^FZt*ycnQdeC398kcRSL5Ihc!I%dj%!Sg3<fQOx?Avmu@6>UC
z@imvDUB?D|;l{&YKVXh8Y47tzJR_A%q-qXSy4>D-h~TK%R8+lL0=G=b+ht&dH<j=*
z5{oNX0*vIwI^_;l05DW2a}cGQJZmfG&!d$V*&2(vgs_Z)P>2jkIRg%!kQv+O4D_xj
zCND#a`2tMhc{V=Xs~SbCoZhC*<{zL9B2mODwGPl1AhMYUy%$WTSyff&S`OY{&VjEL
z4m|AQlZi7wtft&UPBp+ny{YNB>7~$JS4Q`EVBK<!4D1{91XDdda<I0NFox8(1QRZ&
zFPiS0^bVgd>bdOKzpBPrAeb7IJG)YYv}yy9%hpLtpwVn=4-Qhnkq%DD$wD*CTaqeP
zjW0hC$qWTppfBd%6<Jz1@g=Kar$}mbQ+zoSc``86mkbO{O`TGBYbacxB?&SBzRHho
zwwWpndv>;-VTy)-SN-9wmNRTw(^ly7Vnno@A(Mk9Kf<GR43RX+y^YCnj<ce*3c8`1
zA`-+)M|z9J-Xlye74?=?F(+R7!~9Dao3>9Il@q~LJn!Bq5Ofg=5o1A<iLz3xu8aBW
zIxpdWFTOP8S=lx}0U0Z3jdq!Yvn|e;mHHweCXDnkt)#JXwD0n=VJu(ni^2*_^rT=(
z6~(nxY6~|SfNB+ZpGvtm!C$dAk*@g<DSg>6=DT8!Sl7JKcr5|`8U9FunG~ozOljkX
z&6i@am&_L_jQ!;oC8uSX^GOTWP(l|W8K`y@_u2Ubos^e;0^D=oGOkBXMvRR+S>O)+
z^sA>g_U_fk;Tl}J;|~4QsTS%G*URaft=F=!;X0zWA%<AXh+-*^&%ZM}pZ#(Ribw&q
zT$QVk>$)DzW{<Su_ULFsD8$5GYhrSb;*+Src;pCcyFh7j9xi1(U~Zu}6%y`}!&2F}
zb#NxV-qq_%4CvXJuEQ`lFaG;e;k}Peut3+GbD+0B?0(R>VL11C(p{ZPeFIuHxF?)j
zoa))-9h)#a8~>g41jGGZo&VsK1fMPiDTIIm;VWBu(JXHRCTDpAkWBJdvhKyP@qM5T
z{nLlx;h7^c;Pv3stK%5HJv%xNPZ{?A^q=74H$E5{aKO`teLBqoMNTCUz1L5clRWqy
zP6AEwXU;aP!XgQ)w?Oq_Wy7del_DXOcCTw|XjA2nTqzj_7*DafVd(n0VVEQV&1q;<
z753A+&<SOVXba*BsOOSTc~(F?aHq1CMRhh;0T|zV*o%XU^9L_Ho!P@mCq7C?cf~if
zUu>*I_hg>FaBzO{6Cb7h-GbzXC_mzenli}pdVu7F8!(HJY!L3QO9q2+#P6mkfYunQ
zmr7)j!2ospJ{k<0ysSGY{yIqeWq$~qOtXFj<6)sM$q$@7`GEW-{mg?8UWEg;1{c26
zD0!dw^b?Xx_<g4>-2^ZNFn(119%$Ujrf^f)eNO&htz_)G|AX?m&rq$;%jb5N0JH~S
z61*SWeJ;nJz$xNNlQpVUe@|;J$Z_%Re_kx@*;De;n69JeCb)O9FkV}{L^Hvy3!~ZH
zS&q&52;l^fWf1z%W-T|CCiFys)%T}m-4iYq&BTkvy^F=;i?L%D?>)MgJ#c*SSZ?x;
z5?n7GIXo9LP919H`8?E9vSg0gW%%WXVlNjTfjie?zf-d9LmiS7C46s*@o`U}xs(Y0
zC=?~AIVs=?5MGdE`4CkJFA!*h@UU-k(wFj0O!|hynMhf?AruP*0WfE+!xvCvAz1d8
z6m{7jkw-@4Fp6N3{xJRox3E76Yp7lcb>E4E<(=JlyQ2O|#NXAmZ(mmz@;N@yBV-G{
zLr&U7Qc&*MZTmbZBEmG^+RqWY%+KwVOH~dh&i{1luUc=E>NPS_UaJ#)5|hYYxk%UA
zP8xM)N`h}{Cr6|uN{)=!=fLEL4wKNr^KEcItT=dJ!PMlRUpP=`)E6E@sx$pA9+AFp
zM9t^NV~qCd$Zoi1e^5&)nGT6nEGcM8nj-BRm6Em!Zbd3bO$YC<S)~>K<G-K20BdMZ
zdck-<<*NfEPN1U`W|r@eqwz*+NY{trp17*(!^e2p&gh}26ZRo)-rPiy-xdda99m(A
zw~!T%VM>HIk}s&NqCwlz%dq!#vtgQGM!mJ^*O~`)vTORcLSfpzTqs3N(d)imxqnQ>
z4)0KG9g4kw$6}i}i?2ulk}i-vI`lEyWes|POfW$(Ty;Qb$W5TTVh;S?OOdLsDEjK`
ziLPE`CwjY1%mV9AvL!oDne-`58Fyiu+&z>#D^A`xSr-ZbCz4Xd94i#Y%+R*QSf$jc
z=3&yMWMRV2p|M<h?qJXZGa436zBJ~SoShElVIBt+^~RBK>74_w08oA7k9Gf^=x_cu
zb2F!-RoXy*KieJtkGrC}qL;@Ki-Y!RLGkQ)ybx)GN-8K@A5kS*CCx$T`bWaWlJK0G
z`$+7ZyYaQ7ZryzjXoCK4thPUHwv>w*_dPdz{yswz+7>a$Ml7^p86CCM>%6=C>f+++
z;=9}5Ae+i$j%PB9JG{u9<2@GSd?0Jbdz1@8yvM9c@gB>eQYlmhqp;ObiDOg1DXZ~)
zqmI|g2ESvC?iTFVyE)<#*H@-OR7$9T)_ZD>%YQT5qPa=q`y3N4;6Iad&7(&*L%UV>
zjmy9e!m_d6JTlr~-u~6+Vc9OPi8eb1R_#kIuQr=&$h4iST>Z*xMk5UB$?JxK9`+Ei
zmOk{RAO9!e_|>B$kxW<O=&_?r*H;?aias?n!!DEQ)2-v^u76xO{$!+c(AV7^f!$zd
zWF;Hly*r)_k#DF--!xYayIH>az~#o;?~+}3eG1m;%te3^&Ji!z^d2DXx-??_GMj5H
zEX_vk#B3CfTJaY`ZttSSqip5rYSyKL_=P0Z$Er{>D#x&gF4*n(s&R5(V{PAY%Jpp*
zO3d{j8tg?j`ZYAX*S?X%Z@!T9sjBbKfLIA<gou-F@&zg&Ox&gbkNFjCeucoK*;~}c
z#-DdNih2<~n~^?c+v{!TcqpvACDus=%*a5n#Pl7IuT@KGK~9!iWq-0QClplmZ<$z}
zj`%@Tr<s7S9DCLuxz}xhfxUP7V7?US0Jf~d2NFQzrZM2&?hW?uw}HR}&f6pTD4IMP
zM}S02LF1?my|z)Rz#prb)eqip-hB3K>C734Y<S0Mwa?JVf;w{Uq6t_<PMn-F&dQ*E
zg1&4<M$)rs_Mt$Sn3lj5K<R~pf#4N~<HP-*ONS%rbfnXD!4H@H=R4gI99;buIy*Zr
z^fgzxzm>WOO_*jDk4)-`P_ukE%W?nIf6^Cy@k4t?4;ss0P;q!XnHclB%8UBAHrCUf
z9|VupxynswGW5V%Z*p>CI5;O-nA$yX%v!-S!!Y%S+E(p$qf%VOQ{g+qsqToddarV0
zO-f-U*R<q$as8f#>-I-PkhJF!@&dYkxoF_}3p<qf7Sk;5RGg96>50+Kim-gXOUb{7
z54(tu?b@OIs+JrZOPb%y6T@gEnrXtOnhJvT1W#qUvOV=AtMC_6>F-B`<LBBashi-7
z5d7iy2PswQ%6G&%Zmw4sZyTG6%u;m<WJ9(C1^J^Pbz)N8KRE$ezp0Iad`wj*Cite-
z#6sKg`bF#+KBtJ_GbsJy#vGMe9In)M!0abVTxy@0+z<kg39$d9`Ouh{34!hWAE1nX
zSJcq?ZSwdW*?e!xDL@9|oX0hIUqz{I!GafJ(mPWRe`L>|k35`u-{~v&bien#-S=Fv
zCHD0GNS2_Y0Snxo<v(Q7Y31ncBlU+<@8m!K;`A*`-qOK)uAJtXh&gnSMXYL@z@*!A
z8M*za!qRP7mAp&0{A?>bH`HHZ*Blb%7MBho3IS^(XsL5F#{+(6mP4M(6b&eZ2XII<
zppEhg>97UxNl>BC5jpS{lMq<TeHf6o9-ImWMmil+`G8vqrDkRZ-QcPb9*9MIeS`77
zzM)8Xu(vOi>Tw+#I@819xE#_mcP%3R*8jWf$zj=l^OP^-%_yO@b6ta-oj#XuK<(;*
zIZ*ZYc1OKF^$#tKF2TovEQeW&yn!)IHcggmg!jhGuX7_(qXDW@1_Ue7D15B7MMaYW
zNDI43X_r)-77*QQuQbXGm^|pLl?@Pr8L)K08e6=w3P;kFE4J-H-SXB?x2%F>vW9Ad
z_*HD*0d|b$qkLVlO{8!H)bN0t107uhi>VfzyFy^eZT2W}7_$~}GH+2RSu98xdnS{>
zbFfBK;~()tc!3o~0oTEYiJ%n5<#wZ}kb%6LQIYI6{)v~S*o7M}u#Zv}AEwcC@8Q8r
zdgv;ZcCTfxN7{m~unlXj-34{tgb|R>;cTep01}%J1VU{#!G(M)=J!WhkO4=6LH9`K
zm1Q}77QqB+<iGbz97lkplfQ1G2WXn!af9+~k5UPxjz#D`O8<!H!xsPk2lT{*Gr2Wh
zRY5`T9Gr>WuyLQp!+;L^;-y!LefJ!^GkPaG7QHjdAz~W<5Bt!^qnBnQd(6AeCeEHs
zo=ZqVIU+`>KnHr-%0%l}88)WS1C0rVvI-RT3YKc{r`Qk*J_*Gopjap|WtGSgjgsW~
zN{}@kqFkIINo`7MX|;1>nIsf!*<e)r<AHB{WBB~i+~1vK;dFGWaP;)0DgQuhV2J%R
z)Z@+30U$F@UkW5U&a+?WjrBHO{DzhXh}tJkHvS9TKK0P4BL#mj9vGU0nqBBf1Rpvz
zKAIR9Tskkd3YTI~>(g3S2(`ZhtM&ive$_k_>J^&f^>+JzbrrvQNob6>G~3@plJUC3
zMYMDTD9KsrWXmoF404mu2pLcx5<NK|!men0H_eR*$EsytV4XMjU~gwACFO<uHEC|p
z+b1U?@mM4Q7|8o+us1$OyCOrQ131uN1LFf$3Bljq%>D!ELAW>3)02>UydMd4SI{V+
z(j90XeYp;x;LCWt%u}DZ>Iqgu1>CM@m4k9EFeYiY60mh*Bp-?I9NjCYP?~48&5FGu
zc^|B@@y0h<X;b_}qpaiS2glL4MO#(d!(7yc_V0kckwacj1#JRm%B{f7R$({G2`wSF
zKqQ#XjlspT0|NsA!B&#bn-fhLR<q>Hb!$K_-h47GY+s9V44u7WOrrVq$sH;p)`aAu
z>6Y(uQx?5#4gQ{r)!=V!O9NC${qr@T?$Oq)y->kM(IfSc^dnC=_ur+_!Tz$`vHio=
zzzL;nFlnc!+*)FR`q2FKOO!x_WbE*k5qQ7;UCX0+DrHm4*DtPKjlH)Jdv5#UD%IF~
z3bCCEY_pJK$a0d-ju_D_iMC`CZGr6^dtdaPBgJBVx%VO1;&j4p8Jj(Fk5MWb%lTOB
z&~iQ*jayeFAy%|U3iFtsu)-F$foX<e3r+T>Hn3(iI;^zeH9LfOGe}Qu8)#-zh#6Mh
z8eaz9kcFJmX>k!*%SaI-sZ_##Vi~H2!HUFnH1Bpvz1$Y75D<T~UbcQ`GNrLG#r)#0
zotuzn3a$A9q>~|qR_34#DKV!o-&u&Xa|KA}n~o$hbSoXb^(Gv;?wHu)Up%tt-(#Kh
z4y0mJup~~!QUkqA;)(;U$E)ay+@lYrK-JMB!-=;CnjsaNbUG(vDV&WNy!URl!Twqb
zS@u7kY}Nw?wHfqhpGTTWW`8L&?@Vv+mq*UT5`DqjjaxGp5;1>o*%grSa<4y@xRANk
zxV6705j!&?M1rC|6+qy15}wHD+>usOK|AmY`1ZG1SSrGa(Xz-)So^$)r{dsP4atC<
zWD;t%o@IRmFz5aw$suYj>``Q|@SNA&OSB~CGV8XkgVrW7`lMia*A@}j299O`HPc#~
z>R0HmjQxOSunis^4k9Ndo=+%=?^FMU=OYU>)Ar-a65oy~E8KNg%rxHvTkNinljEV~
z>?C6N5rQ*ePj2UD!EyRFWA&j&RNXW;WAklYX?wX{v>%!$Y1<_#;HT9vAz?Lerb6I*
zfWN0vC88JM{U9xO`jeKCBl?z{2(5-*VG{8rtg7pZ(x@?s8b-8_c92y9MW4$ymmjrh
z&P=4qBaawsYXIGBnKVO78kb)sH5)5Jwd}SPo=7HH)l_R`YmY&*)Ae`qkjVsT*jU4K
zYReU75Pxv5ufqg`MM!*&DlrZB(FtAN+3R%Z(|>`x82PQ0*+0S^c+}0QT81~ONXd4@
z9*wb!@oUm!@tdD{Cicvq<9UpJdh@S68+*3R^C!+de*!Q~Z{vDHR2jaNtGcqu>n2o2
zKOa-y>~d2pmqm$<DkU~cY_N*}x;z;f1#q^b?pv{Q)=_!`z3qX*@BVHfRxY>1II!$!
z7^brE|69-&;G50#DfjdRo~AuUHk&&06K6(g*uN6&?hbZ;{U^@+1S`_m-`|Z_NE*Yv
zV5X?9wxrrtV{o$;j<Y426y-2sFQfTrBJ4i{06J&<;lw~PIRK|6Fh)gtH-+~6vT&F6
zqmp1xPdITgDZ_Em=8$*dQepd%Tq66*g-vo`=#5756tm*gbcP_XR^KH`!D%iOabG;0
z^7xKe;-_=H_)RaR^whgZx<L4#lTJ%d!*RboGLz!@>BZ2&+1;7U?%9KLdk^m#oSr;X
z7@9dWF>z=nd(+aA<Y?wGKve0HlVcdjsSNIvV9cSa0UO^UJL#~>V2<g@c|}izb?>NG
z4<~eGesbEeGJ7zzIGvBj5AU6$VjtGW_e_Qo+F&R&s3k&^d&YGKyYbM>P~p(z^k8&p
z>831JM*6<{57>BnASbou!z%Hs+<CX4wx*bz$|WGbezqTld*kuJ+q-(Kxu$A<orvxm
z&JM=MmjL~||8-tI-<Z6ddG=L`Vv*!_rlep1oDR!BIExL5q!PE>XLsEffBon*=*-Od
z_(XP>S9krp>~62_y=h@DUHj$N$L|}Wqv`a>f0$0spP&<|d(&*)$2nodogk}|IcY)K
zBT057ezzU^!EJ}|m+>lGp`dRRvPb5j3FhXTVVDgaL+~>R7YT}_Lgz4?i%9V6CWX=E
z?s!P4KwNydhe_)g*Pru0c&hVQ{!GHlJW_K<J5NWljZuoBKpqb{D}Y|0&3c^T&BEr#
zn%j^FP`qZ=>$GO$EM|gNB86~;KLZo^l1b#@M@hrv^}PnyG>RV0>B1tbP>nh{9+c$;
z!ENrfN(J~|eWOw_&3~z+*R@4wB8{}+-Z|Q(^!vsWfC5@1WT+x0i5!>D)0JPPE7v4C
zVfq$%w!*am%z`J%aXd$ub>OgoJ^@YD-2Nb_B{dLvc1OZmIIJC{QdnPb5F)aspuvW_
zqtRqnGWvc^W2;n9o5U}=Rc`JUbRnA}Zuw$`g8kVfLU#&ZSQ@`NX&DBI27%o8^vG#V
z{!kc6Vvb3P<-S{Xqu^#CHokZ10!VU<lH;7giWes$g*wQ37UJUw8Mah%o;%%)Z(F0c
zj25yg$r0+SM!jyVmWmLZrfOnan(&M!xAh~bF?L5a3u;(r(uPz-po=w(P6r3YYp}7d
z;_@<p@+~vqdyBZi5AB@DhrRh*v-z94eQVPamfAJj^xJD;qU>Y^djKpzXE<EJW+PZu
zMDx$6ULy<ePq$maw$HJ>tvR-3il}LJuYkc+HBB2vLvppP)G9@3Qrb06DqP#pZV~!H
zO~b4<#18Nk)7+%#jltXDu9$@#$c&Bk^<q<!1P$CknxzVI&teVqPIY|FQi+Y1fViMp
zc@A3kKT00X^^sb(U`YuJOuCpk`T1E;!_Gc_JIh}LZuaCq&c65VyWczexWt-KlX}Qb
zwlx{$X4zx6-PRo<xU#Du_KtVFqn{dAax{PA;uK64T<F?<S_JS^)-C)VAW_!;x|xO4
z*=${IWgV7cgrhsnqba0w^Jusdl)-@1lf`3@)~Ik20;e>Ote{CymLl3hzd@5`IEQQY
zTfOa=$8*d%wl}e_GwgKU?R3r#cAxFu)fwEINbC)Eo<8Pu9`jW3+GBYBd9Ixtj14N|
zF9a7x&nn{zeBL@XKE6IW5?okY2#$<uaAh#|^Kfk(PtA6np;wAp7$S%e5y)?eyTM>3
z`FiZ@Cs%cwAVs}?<s@8dtXC?c+Rp>I!gs7JTJyD#MbfnKRgRVj3=Cpz9Qc)$5#N=E
z2jU0+M&r*e(@DB*+grb_93cq3(sT$iacypu_hqQW7?gRDDpFiuXOd7JR)fmqRe{kf
zl-xx<i}MTDvAAz9eQAr?y;!=wz2!YGUz~aRqycVGLY78ymJ^w(>evxjmtE?Mht%Fa
zi0l`N_ulgP?QnK~p${;&`}%tE##@+gJJ4N;@j5sp;-I&(NrX<$1T<He|1@Tbh{WJb
zCwk&Kq#nLzWdnj6!#4aY`7MMtaj=cQ)95nOL>|`B^kt-3k@5A)o)vM5OhOq=2NVfC
zBChs_k+o{97s&&M=_S)#=SAuDy3WneelR0b@EsH|>nLJhTBaFYR!A&a;A=0J7qU<p
zY-i!92pb;2DGFZU1BVJGqRgY4+peYyBKh7fdTH{C_r{ib7fvrMj7GxmXuZnkiNbm+
zwiJ$xF2L8l|3kFVE64HcABdbqi1gnjO|_){ND8)I>wF7DI|Kx|V1sBQ9FYs>m5C)C
zC^&s-;)-p5xIz9`m{?Ao6W*g!7;RwcsCU8+^e@V%X|~&{eJJdJ*dgd0ikksDOa=7~
z3X`}#w+*#}%7j1Ga7a+*LFono(N_&|d8I4|VUf%O5CEQL3WYhCZt{45YBo59;jgIV
zlaD_^rk0DgQ%ufSz!?v!PKV-jMV!4ZkLGcCJ0os~;&7^r;TH~f#OI+eTs_S%P93=2
z@%OCCdX{OPaQL0BwA<0;l!sidA(yAi;ZD1pe&%(_tRKE|Il8>gL6>XL(b46AQ)j<Z
zc6irrKj7_Q)xUQ%(S<f_>ErfZzfDG~EcjEKKyQ_|x>K*4CU8#wYBq>Y9>a;~-;fj+
zFi@1B$R;-#%L>z%^UJT=5yBWe2=b05K0$58SShy<R>GQY2Nv8EyFSV1Ao;pL3{0w-
zMmsvk^lbz}QL7m9?H~-dO%vdR{XCrG>_%C3KE-7TDr55-8vH5GK6VXw-A7oFMy+y7
z<2TsiMbWR2-sbjNPPdZUqTOW0wQW?JMb1HX!FzlS=Q5%y0n`(KMiKidz$z;%#g&E6
z7Ws|<#qVnTEvBqTY%!_}>3Ld62wd5Nb$RL#@IHrP1>k)O$2IoDyDwmLi3_`96GxYT
z8#+3E0|;(^z)0lIHje{|kyXSNZntZt@6wFOD3&kniXH;6f;Q_jJGXA~?j*!(+fYU&
zB@XxHhXK{yQ7?jE7JTu+A-uQ&N^=EcsFj$GJ;MOWZ4JKHYpqBhbsjI2Fc<u4q4f`{
zq2_;@&;GO-iS7Sbf@CJN>1<8>s!<Y7Ub9uilH_j66MRGsC8YhN5yi;z9$b>C!1k~Z
zTSzp^Azv+6#u%*nhKZEn^%|*(H{jaD)tEdLmZ>SQVowIUx`N>9<f@x{!solX&WHDy
z0LI4*A$CM8+z*5WC1}(>*bCsA5xJ*1J~$8A+47~40|8<m*Ecr6oema53NrhH<nO=V
z7~J3Y9&AX<JW`8@zoufAmSr-%IQ2(QBT4e=YGT!sncIEl%)_UXp85h^glm3J^7O-J
z&g`DccyK<LfExZMdg6VQXWNQOY-hH$q5@CYT2G0b7S_|?pON+c)d|_HyE^hpuh-!%
z!4~l6J6yV3o?vX}uU;O<A9tqQ9VcV}i}oyZbS!wg6{mcn!<~``<(<9|tkeq9QRx)W
z1#?*o=A3X?ku5+(a!6Gb+6#A~LLmex)E}OrfT!?-9cjQzEa7&=oSt;I&*O0K@i0$c
zM;flU+zGbxSDBU#E9rqEe;0HQxIA-iM{lP)>+y`ra<9Xa^SB1wJALtc;?!S>*ip|U
z{=B3c;OLgAw$7iMvyD)H5`&5#$i+sdme7I;HS`;l5vxJ>AB{z+`xlF+_fZ`skA%Rg
zPdKm~x2^r$9$heiJdRD*?HwK6D_{#6`ns-bzc+fC$)`tex%COa6?_bF1sjr1e~>pW
zWTr#fNyjRpo1|zXWD_zLp`@alnyFW5wk#6i02fi!ZkHk07`fpnOg1_SHj)fDy`W@N
zaq<9~A**h)CL<a|=D|cFZ+~8GTb~URjT!U89s9Sg*JSs7nSF=707s~o<5;MK`6^q{
z*>RucII&MY{BZKN+a838y{boUyDj<f!|a6@UWi`0^e)Z%d)K8)(HCCebj7fRNIfWx
zQa?~q7OU@8xCk|$$@57eBYgRZFF&uttJguN7&DC6@dV6XZ+_}LhR)I0#`AaJN`J=>
zAK_mf=^jCxwvnGdzl03<lH?K4T|&|@*;pQiH`l`ab}IC;sARQus4%>R?#L8ccW=6#
zmCb>G4o`1ltf(ryU|2gEMN`uQ16BA+3k(!B{H_~x0ZKx?c(IqANBJjcPH*SCj>fvC
zP4r&8C?^!U2ani3>n7>{>-86r@yV)!Mjzi)4v3g-#RsTrA^6u7W6e-3)w!X;pJA9L
zZOAi7l5Dq0Q^$~%a?&Eqq;0nB?b6wh{XHMARI11N1zRG1YA>aqBE!koefjz4zx@0M
z=t{M}2LOmL;jR=lvO|8Fj{o2i-p&@E$NN7?Uwo5(^faZCXA?~wf{{JAll@=-2mvLF
znlv@lPGN88dNI%P`Mjx@wjs3}8}swPHo@N)<~gM&qP~rO54dkxGBOmg-`cs30bNIN
z_R98*#|zd>S(GG>)Yig*N}_IV2kPB#&z6SXc>?6pCt`a63uI|R(@=WJJ~?**J%cXH
z#WKebVE9=2T)p0~XUvO|!anVgC?fR$Jtc?d$j;02{HQ6=Y)AK!?m8G-cyS?ixMTdO
z@mTy~e36zE!u~TcaY%<_3-JBh#^LMuCvCfjYZCT*q_8D7u0F*3l1!FI!)MK40y%n0
zr}cdEoOGo(fY(?B(311ZBL{CiI0Hk^O;U!c&h+`S-Xll6XXmGumZm_v2Y(yDWkfQV
zG`^z?aT&P<NrWtg3N)meovAsfYA?R1lr)nQz_2SpS}v9$*Tx11f}yaDlB<cN$@Ang
zr#$7_q`aHCx|O7TjY*d;7xgkKBKikb!~GzJYt&eA0MEl<sfa~!T3W}{+`!^C1WFp|
z=;0PMmjzoGECMw6943_aRRmt)kkyK5Ja{jlUX8jt5{;{gj(OJIaqn%O0S_lR-hcb;
z&@JB|h{u1!;p*_cr=#QliuinTle!kA`049Ja%)2}1E#oK=70!7&>M!V27OF^&~6Uk
z1pRn|Qx!ByEF^VoWsElv$OYKfVy`?9yYWL8#*5*{1}5Gx`Uch!d*uzWQ$PR6tA>Fl
zVK9%2zG)%?t)tmW1E=pF8@vDXz{Ly16`1!O?pV3Qd-%S27AKD2`xV26<U(auWtC=2
zHgzF+_&rLRiX@(d4P^5btxAnk<-W)|*H#f4437&l3-b}_+2&z^prdwZf?8-OS1BC$
z(23Jm{WMG;A+~D6GdMX7-p75RL}Yq8vO=Q3MJ`R{dNGP=?IF2`Ef6L^c~&c4xdJBD
zMP{H3+k~wsSeDT*0$lp7k~}$ABU!gmgdP7egqsjJHuwd#D7B@Ur8PF&)PP;i>-psu
zF`1xugKFDXU^~%7El{L9+h8w4kBo`h0U=JjA1o%aJe;6lIB1&8H0c@G%XZj!?425_
zpR~qCv4#j$B3;WdkG9gUwQ5~l?aK8c!vAgdqw8(v#NT|M6>~lzWyzjm4ydEOT%N$^
z+yZPe_t@vgApvW1@;B|YZ7Wo~2GwY4(O6kCvDfI4#zzT<1SVpTOx8)fYwDn3uuLwf
zV^!fh9ElC+YPi29!5$`nBFF^E@Pf?s;J0g}gp>a5<2rI0ipn442=de<Jgz>W&_TlE
z)w4<oHOt-aQ9G1p;5nIdf9DWl1#v@@?)R!4-SVAbzhlhny%OPha}44E%3DT4w5bp#
zv>Jl8a|0MY+u+&NTKPA$64QBJV)p+GoD*@An7~dYTenu7=jW-?yvo@vC3-wqBzv`|
zzhl)eJG<C}9X`iQNANTz?-Q>wJ<$C^Psja!xwB_Z_H{&^-iLxkN;iG6lU|l0m{{2I
zNv@xzjaBG9HO!WN7DTZoz9L&WyBX13rpP^z)AcaLL6g26o;cIX#qH3<MYI6!X(jce
z(j}82kkxjtA_F5g0L1E#Q2f>1B=lk0O%&td5kyw~ZxnX*Rg(Nj5^K&!`KGj%=8q=n
zm-jSjzk+>nUcAaaw1kt=1tkQFd1!D1r1;@j21?mGxetA{XW<5b#Dsf((ig@j3;QM@
z>=#<_B%=Y>A1L549)kjuKe~5i|B-v{IRYVHH(~O1N-47FF9cGw`pLw2qQfRgh?>51
zAV^~84yQsZ`oKK{`pOOd1LfEoMhA3da5D6rE83NP5g?Lp+jUJsN5==o53I(@<N48e
zZl4Y_#py*xEx|3FdAu01afW||6=z;w{CLbPzI(=O9dRH2D%@X`JhWCM;h(`2SYrlP
zbBZ|<EpNkAv7$Ni?xGp{c=7ejY#vJgioOCOrW^V9a=Z0_icBX)Gvb~eX6=n<&%Tj6
zb}*@A-u!`J@B=rSL|fS2(b(hLEdtza=NzxRQNfgzh2vVwCb=#cBa_D3WVI%}AQfQZ
zLHx5M?5j1!FdB7(9?RZ1g<TfVBZ+%LE=I*k%LILMwWzE!b7NyeV@9L4O(~3*K>w^*
z#_;M&nN`|LvAMLSO-K9lI$`wdC`@K%>tPjqSB6fU3MCEjz`Y)2<hEuZqKsM_tKi~b
zuSy>JJw3zsVrfDq?R;xgO8Cbr#d@*0S}K)`)&b>dw&%&)lYHd_c^T%3EoDMOZNPsS
zn#(jz-1v@YzqZ_HhQwT`tzlo^*f7hD3N<$Th+ZsNT#3JIK2wpwz0A7Rdhc{sFSns*
zZERz%?L5_X&Il5j4CdD{G4OPQjxb>rWFYB?((RA=oVCI>*o!vSoz0C1Gqg&sH}ii*
z6lsur^#?z04i1`_FoUSkcagvT?_4-`>;i0(#pPYKXt6ZT(*d#qx13%J*;b5n7`t=^
zMpl`ON`9|cDEE8)U(QJ86TW@p>Oj)#iDVofin1r7?tG6vd&(RP7kv6Rf`Q5G<f*p~
z4$Lfh_aW4rD>tBy@AD-cnTW^xp=jgXQTJR=|Ak{q<G%5Nce3}wu|;hu5FFq4|9Kse
z$mtDkwxi&_v00fzO>Qx!C>4veXS!(u<IEIbmHWdOaViW415F)f-5N;~n>|F`mQ~Z1
zrf4FfvZ|q*x`8FaIBPw$0i1b%xNd6j$DdT!_0|KDj6fH07@X3Og_gB*S$b)`RYHkm
z56s+}Ev;?Kq$NvmJMw&X8y$i57FAYWjh8*py_1PRknCAbTsWIQyKDEEVNZQEQSS33
z192}|!4!+T&Yszw%aZQMj`8K7HC9c^Fa<E_hZ=t=lL<nlu4_%n3SkHkt&rsg&z|c<
ze`Zp2_wFccN4E?ATv?1p78fJY)B7%f1wCVWxMh127A#AN8Y2l(KRzeQzLy-fHC$*L
zL(@M!GHYnTfglh{TJL6)Q*$;I-KDC@-cS$LA@z@<YVHq6az@<g&MSo&t&FzKD}~e6
z?hzG<Qj8XaVa~Iou8&E&qQmF5ZFGK2$8YrVcB((2JnpSevwmpDa)2uC*{%pfDQ%lQ
z&4xg~Faz=@nX=7`6`Es~Yd);URGQ~-51zAEbRE6X^~B|sMB)l;#Y93+fVNdp@_}xp
zJD{7O2wz#jtxGpij4N0qa9vB7ICxvc2AQWh^-ra+G7k#cs2K)ZHGt?~C1ZVqluK}G
zR8dHd{X7qOzQXF3j0Kd6`M3i<p^)tHWZl^5^|Mb6$Ahb5iw|{oJ6)mf#)sLJ#_I2=
ziOx5AX9s_>s}^&q-Q7OtK^pN{$nTHX&+_~vjF{Z($RO#7+dO6XO;30CQ)eF<mS^JH
z>V>fnys5kK7-q@#MMAD*DAwt_$(tDbNY`^Q*Pm0Krc}f(C3R8EAucG*Vb3n)Xt0}P
z=>=qeSzBINS*{~}52XETkFKmx3<v1B@UA*6oQRYK6hW&o88YSsPl`o{l42E@&k`#D
z5pY6FSZncRv|msXy6NlaG7>soDs}kGO_9L^mXvCX=l#0qbq{=8UF5Vj>(WVL#%W^Y
z<n1B+C;u+?&{%BU0_v^P*^i)@fD2_jAlp96eu905W}<#*`vJmsvs~r-Ema2eK?HXp
zp#aMtI&{aOLvIAbfl}##Vi5yaJPTLg!#eTWQx^;%T<ME8&xWb3o(<|Sbo?B>7Y=%p
zw^43Va~Qlv^mh2h=xA>+6H;QMFd=1<0VU&fJ32SHJw$hVcKf@-f&OXDGp0rZ%AoA&
zbaX=dEI~bf4eBv3osjO4o|4{+qW}uv!gA^w+$YO}+6oWF$$^U4>|<t_uB*_!vxGP{
zOUJ2U%mF~!VBgGZc+>4p=x!L4mY?Bm85v4R4^uc)PsVy)4_k6hCMPrVS%B2N#h5%9
z@bx%@&c0sd{M_;Tvhx`*BO4vmIvkF@g)v7@M+b9s`FchpxvtJ#E@!k)J$m=i(C)Ll
z0|3?Ibv`e9T#4z~$7W~Zo{mm;bYk*>$%#QH8+WnAJ^SZ99q!#n_ZzZH_a!IyBM6&+
zV8FkpG?fjfM$?_1j)@y%6Z3Z+j*N^%aB5!|9qeL0?~kPC9Zq+b!x2dB?)p(@G&VXn
zb?DGkXJ-~V9)yb>lD$sm==4kuL?Qzdoo-J@R#n-6I_kQ_Vlk)O<w_)6eRedAKVlog
zy1)^?vKSgB4TJscV|yO|Ti7n{VUv3rj}rYq|M;HA{uZ{2dm7)^6Vb(}(cOf<$T9S|
zt+O2!TL>4Pp9?gHEZaK?i|Ay338F_E#M>A}lZNJhO%zb8TS#=z%>3i|r5nd*aLmq(
z-?-HHvZBE84)$y5HlQKdwqL781gpc6Wxz(~Bw&9VaU4zSzz))*E#TV2L8o$LhYOjJ
zqlTqewHX0%@vv#VYy0!TxqL9cU#X#p)MN@u=qjX!sg;SBr39$urEGR7V}KR~8ApUe
zCQIi2frfeI3NX4gxD6AWOYe~+_9=McLBjS$;hKk=!4Tb>Q=877YI7XO{AI8o4)n2p
z-}}2!`qjyt>^SHv{UGVmVTshhWcc$PLDxgRUi_N%ehU?#rek(+4v4PNeDpM`+J!fb
z)M%a~h2sNTQF~}e0`d}Qk;sOH0zU9&qr2=N(Ea1y-P!S_>2zQq6H$`$T8POWkpC>q
z8qii{e}o{)%`~_Vg3sVM5O0ypz}E)`yP4Ay&uU}G0k3~G;{QXAU+&=iJD0wbz5-v5
z%!3*;5Tk>08zdVP;m5#Kj8o}sqFP@+b|F54wQUzsP$77h;>HGPYROH9fuLA}zbhL3
zwfmQGlyrnz2bL?F4~0}PuxZNYm@<7_HoUJtZOX@|Pru%Kb@s*^X90cv%mebV>C^Yi
zSErB3`{C=idP@(Ky!#P|-P@)kKnlYyV4M7--5>Vee`?e>c<O#Hx$0o<U-2exctF0I
z@P1f+;D%9ocyw6)FweV7(CE-?(S`_4G$rxoBBw8G1>u<J%{qGAgLO3;%RqXy{Mzbk
zAkjdBIvgs^KMRmrjDGYz(y@>kP)k=rA;Y%PE?b!0iZs=-(k4iYR;=3=s->K=!`|lb
z9`+=$-#@-*kDLsmjy9OQHny;Iaj$1F<=vH?SX!F+d;R3?72?L-dO(GPfgg76(I@uq
zoe1_Xrl<E_T+%h}O*_k~>~|#((F@5r#DFg}%Pp8p%3Qpd`A6=%RWD?2zb$iY_6Wr-
zoqe2mW{qe`ova}aO3U!BW3nfNYZ}^>(FzCM3qLS5;Mzt@UufR8m}uL3tUY^^qubT(
z^sx@7+u4<s&kec;&&k;?XV4$&Om_AMTux7y&+T*t`a6c5WL@}HN<SeSe+BY@Fm;|j
z+3(W4th1AOwV>7?>Kg3|c^r&6JaBl192G9Z{d557JRLymR3)7iS>4ieaXOsOW+A)2
ztY{b-w69hn;QtK>)^!D6iT|y5+C*`>Dtf0fJLasl_t>brcAh`Bw3HejPbCr~Jv~2%
z*tw-yv><2o{ne%6+&iYzsSAmbz(in;P;}ozcIT4RWz&%2s1R`SB}RHiLJ$lwKA+HL
zTMNj7oXw5LgxR5IBCD(8`x+)rEHpy+AJZr;uC8JfoW_@|t2AnwPG2RQjz~@^k*pT9
zpESd9<|!ZICX%#d!6lEZ=4|DzQw6It27Jed<nG8qf2=RS0z<)*!J&62&X7ueWnN!S
z01uM*5SwJ-K(cQz_+%t_a>n2NZdN9(eB+<yPb8pVcmpQeU?#SK!~tmqz*_rEgsmlp
zZr(m)8xg<PdC~!yItrdn&mDM#m+oMoq-XQaKKhc~I~-;wJ#L(H-JXKOaR(d|mc<=T
zub+M}&)70v+=TQQv#nm$6wJUf3YH3-0v-ms6Klb`PW0YVfB)k6ljC~nsc~Ij03(ux
zOCMj@&CPUIC-#gpef+Tt<NDrHg@uK}$1W}GX+w4)%ZikeY3k1XD+%s*`su;l51LlQ
z4n{V(8xDzLYjPoDHp>TYb5Y-R&o*+Ye?JobY?R5JvgcM<)D<vGQeYo~jlIzLYHBuI
zjd+}M4dhv`yn-wHD__9lFFXP%6lb>y^$@}fuwZ^Tz)uqxhaiB0Dx{$hGjcG&oLIUm
zxV)dS{ma3-mQKurZY6u5|H<bZj_022-+y^HGjnV~2LoAs;n+-Ocx+hj>FLpj#{`Vm
z0kTZrFBOq`!!e>Z)iUsAU_*ie^fl05Q*j5ZW8e^~aH7MK_hnlXw=JH{HU+pUDhhrn
zJf_|d?Tqj4-5v1jV99i)qu1Bxa292Ex36cxanqDD6jWj{CD84NIKs)1Ty7*i^()w&
zstUOunSmk;ft7tI6v~e5>f04q)O|k{@b?UPy=vc7SMQN7SJD@ZYw>OtW@_$OZu&<+
zBm^O)44?u+up`P+V&7ulA|x5YpJ<}_Wo@$*IhRGl6n6`WknajW-f_H^KdZ4gnWg;Z
z1Nv-$v6Iog-GFn_ANvH_r%c@*<)$g`s&UH{T?gBgPeu2F?`^1ih-_5ux;-kQMyO=_
zGs|5RfmkECFAY_A$8GL?5)$OQ6Vc*ua56qV4nXE<U;bpv)z1W>*UVsXcvN2+PYk6t
zL)K6Wc;KD?vE)ZhzJRoXHV-M>l&s3JahyzsmhflMMRCAix&MR8=c;cR)8X$P_6yM`
zYDMTgBv}iyimvEmZ>i}hK=m|^M4u?KRb1-@<emUYFNrWAGhvm)8OWlyaD0|e5RlK?
zCMRMO*5{mv>GR9h7n8Bc$uHRGK7tNZr&(TwYAcX%hr@gd5{?;@%R_=RkP1d2kg)pA
zhhul?cgGKFhvRqacf}6h+<t*6+kYiqfEI1y-*Z0~mE-@jTq`PMRtSECt(;cv07yI~
z!L(5>DWe>mx_Bc6eoPdLOgHCYiM<S)j>co9SIGwQ(NgJo>j1>Zxai_m1Bo?*cl=(5
z#NJGC=eg$tJUFij^lzEd8z{r$K3oMD*X*{Hg9lfJqls{6kEZQWjt2H5`IY2A^9p<O
zq1z7)4-6_HZI7lb_2-jq=gpb@H@ROLgBg831oKv=;KAJ-xY*6BaD=V@@a#L5rr$jC
z#LSFGNzCruw~rmU^UT6W;?Kp|q1lAunSrm~JiYV|`08iiJFjQq=i<-DBtc?~5^;UA
zOq_Uwrd%8x8etvJ3+8kvA*actWMYK5A}rQsslx5>K`W(c6r&6!=CH#hzow9vYZ2bE
zJwpptu!UA+fBQ{m#JzBRi~Y@6A;|WPLdri(5#Xr}y7mo9Zxm8~g-vd@C>N}M(nOV>
zlO&F5&YeJWe5UcF2uXLiId$hkX<$=G$CZK4oK3f)cn3bgkv9DE7i+#bV=j5`scz;X
zCLVU(r#7FmvMZs6UiYTkLu%6HaJZ7He`x;r?%U|J@#_RFbPJ&i)d7C)hCNdZ5t66&
z*ayo4X?bejz9~69;PrXoBr`C*G)-qw_?7)3slE`iZd97s8WBAW6Fgs4J<X{-5B7+`
zELy*_+iB!k?%v?W8XweL6Rt79;mJ-+od~i*_2}Kzq7aGZu$qkP>1Z^q$Hzmr>-w&L
zy!(hS8zFCLVU@@<)7gmb1)BZX7h@B#SbQQLi=X`B$yjXD*;n9*uEgLBu8C))`4(bA
zg*l?kX4$zd1F^KvI@kNmrp#2XtRsYP8GCrxK-b+mUyFF__42q}iV#&G=eOg2v9dY2
z2V}&C&dsse+YkJzW1x?sHu}=cY&=bU7p;SNE7YVODMq+KnlvdLkWL`|FUt@*5WR$Q
z<Z8oD?ehQ*U|}n?*}}rS)nY$(B|U7+=~jke=Wqwk4|^P=uy<Jd4jjPndB?Z8Zpau4
z1p9_umnrxldq-lb!xvSCCp)^ML6)3^lOW?~jjx;5U$sgU2td~KCEa)9@1VKeZSl5y
zZMz{qZ0Zg^+*JzXWUKG{!+ga?x9u{M45eJ&_|;!!WHro}YeDx|Bg>>S(%U3SvL2m;
ztc5IveOFZvNndexcUz*=RNEfz<FeIU`(A0gbGsji_cNWi%lG@bUB1IgB0n}a@SMll
z?d$3WW6o~oR{<!!yGtEt-;2lMzU|ty+ST3DrK;Op8h}0HtZLEgbRP~S@_~V8UCit4
z?)I+q$a0Up>3qkx7k2zc5~Nl<PC$raGB(*DQnh6Fm0S(eRodr*842FqTBF1+cHgmo
z{~g_(GQKPM_cV^@tZ)l)@+6v&W+*lr5aUE8E5jt2%h?96sY1v;%5#xF^qm51=>n5U
z&QadCZ+=MAhWsJ5FBuyL=(jzwbYf<tPfcXWov86hgq5R}dgaIV-J<W-5=x&xk{uk5
z2Gz4YyGO>yM{)_(ANw+JiS=ls61`$@U(hnuGQ{mSQM$^SbxMg<-CRN1g_Kq`v1v+i
z9jcYIYk8YhKeca2v#W@<IzR>tr3QnlUCDgU?$q@3$ShP39!49A{knmFVzdRCg*-Bv
zLWJD2$a{dYO2!MB3<Hf)Lh~Lx*!cFLOUdM=L$5pO4=c*a*Rh;BJgmO%;KBP2<Z=gY
zdUw?0c6y`lmRM7wd?UVNvQ)%ye^U-wMMMDD;|}aEWi>=RAK&N6Ln;|6WD2nU!IYJS
z!2u);^b$1&zfsvW#=;Iquk7e>^r%yQSJ2@Ic7|PwOMNEgb$EhKHVAW(C*<l@RMtN|
z<?R{t${mi55$5nYTtj2B%PG5=gDo*9OE{b%mn-JJ^sLKy#($^N^~`}oZpXdZJ#NRs
z+4prQY&g^tbi2Cb!5-!W*-sj91iZaox8wg0UtO_)004LaV_;-pU|?jrFlBo7vUq-*
zuMC__3?Ojn-r`eW`aa`U28RFtm>8H?fLsm+urvU78w^eW004LaV_;-pU}69QI0+O%
z1<U{d_W%q4004Lat&}yA7%>n<-)>@NtICO)nVA%tQkj`;9bi*sKEb3;O$YEv_B@8J
zS8dKbe?S^_|8D)3Gz+T$X8EtzUiMO`?4?p^@f^=yr^i@;!d^zSKH<L@Em}7QLmS8F
zOjP-|P-rA56fLtI-+i(M!ZY4LV~$Fv5$y49@$E+GG9HZ{aowTNDULzW>w^4%vy~H)
zDOinpKDF4KqfpZ(J=98wDbZDWh1g4rtP;VnkYF?S8Je6&gMA^3!s0mu_Z#zo`VUMo
z)278>Q`EVsT#wd>$f`?aF6Ulp;zne0HSCV76Y=2HRl<6LI*(Lm@QKe6ZD`f;%5{gC
z+K;GJ#)d65>T(}9qmkNLF>|s~eu;0P3Ux@k=JTHNC-fuN>|yhp%o+Bwff}QGV#HY4
z5@tB)><wM2^J?V!)@OC7Un#63_ZV=C%66h3AT=-Inl&<Zv|D(!z{qnTd3~zNhwA1t
ze#!g{;j5Yt)%uM6#s$xjPWY>Bk9Ui8IR)$Gn0;q3^k~d;owwi6=;k>WBW5XbUkk!F
zlyl#9+}BZ!O%$@qsnVcPoNWt>c^UGg1EV$hb0z9)U!8=J1T)m%&WWv#<P1cET>Z<n
zEi$_=xy+*E7v@pvzn$GfUu&85O|7h%SwqAiT7@MU#W}0$ek})uRTM{xD%_^0!h1QE
zYr2yEJ^#t*#TeaMw|s=2dlR0$$#9&V483oA*7u0<%&fJCVEdF+w|?#r%n~k!eNiVf
zO7InFA<!`Z004La9Kiz=<3JPuz`t=)ZylpP+qP}nwr$(C?WePC+qOLe0Qi3qOh`dy
z$PI;|G*pJ#&=^`nXXp)sVKhvJ*{~Q^!)DkG``{RygKKaPp20i#h6p4ej6`Ij6v~Wp
zqZ+6`nvPDQk3@cA60wRnPdp?(kuuqi+(O=?98@W41ht-eNNaR$dJMglK1@Gg>`aKs
zz*J&-FzcDCtcxwrwq>WVTiL7ZbM_aPoh!<9<MwfHcn_b8ufR9p=kW*mcS3rht}sIQ
zWLsqy?7bWr9FrYKoMC5E=Md*M=OO1am+C6(>gZbSy5iQ{h22Bk%iKrYZ#>wO$<x3y
z*mDI_I6E$do8sPh9A1qt;@4iqTid(Br}zr{+WCh2_V|AKv-|7&$NHE1kNRH(LV@0a
z{Xr^NDmW;(G<YcZG-M0q2{jAN4ebwo3l|Re4Sy0Xv7R_m+#<e{SSf>4L~1LIk+w-s
z<e*$ot}Ty}uPHtyi&9N#uMAeED4$hbt*Z7?=c~Kb!|E*!G@q7PtELUq-s_Yu>$&yn
z`cQp`{?t&68pd#Ai}Bc$%)(|LbESFG{9<WV0jq(vz}jzpiWrgFkr9#Ik-Jeknl0Kn
zx;T0_ro_s}X2$Ntz42V};_<ri1@T7-Dp4jeIq@!;BiSf9Ke;A(Ao)1?J>^STsm`fs
zsXqk41GH5E006LT+xFA7Z7bWhZQHhO+qP|Ym|cH6TH|+&jE#>SkNu99i;qd9PgG8f
zPdrWP$$rVlse-8isb@fDAO?g$KVT(r2KWzF0wu5`I2+smUWal)2Gkpx0<DM6K=0rj
za1B_5JHrd%gYb8xD$)=ciY!2OBe#$b$RE^3Tc9J+1?Xw?FP0Z;ijBs$VJEOBcqu%I
zH^YbHoA5jMe?le(6Kjbh#4X|zS(LQM-sD#D3sszwsLs?*>H(dOu1tIM8hS5%j=o2~
zqyI7mnXb%OW(9MZ`NZaB6}BV0hrP@G=i*!=ZXx%E&(9-#H+}|xT__=NLR(?Ba9DUP
zW)qX5BQ6l{OZg;HY9kGhX3H`8h_XnXrY=_xs<*YwT3idXk=l0co?cA%^vU`uBah)2
zvyC%mL6bH+nRCqR<|nI&MO%%nA=V1(w)NevXsdR6dxSmP-erGq(m9Y5IJ2EwZf>`Z
zyV`x?mGoM8+q@6H<<Iv&1;vB9!JOb+@G#63b_y>?`64I^qUO=YnrQ^V0{|2O006LT
z+qP}ne%sdBX0~nHwr$(CwG|v5AAWK~xe@LWb4DB)@y6gaD29E8&&J%w9>yugWybra
zoTi2*r)j!rx9PpPlG$U{%nQtW&7UnfEu}0zi)vYHxn|8{ZEtm1M_Tt=KiCG?6x&AI
zQ+pM=#V*)4**`g|I)*q#J9aysIQ`B?u97adYpLt9JFk1NJM5n8-sk@2>EMZb#(Um-
z4PMH-!TZD4%cuEH`m_6+`AvS&e=krg5D9D#d<)hJ27)t!dxH-{Swc-i!$Y$|S3)1d
zWy5-Sd-zGDeME^Ik9>%hjM}0^bW`+GtYWM~%pV&c+Y);hFA?t^Psf+WA1CT3+zBOd
zFmXBYFIhWjND9eq$y>>{si7$)wITH=^*LQ9ZAlC1v*~}CA(?5JD?mlS07L-<7z4}z
z)&iG+$G{gb7gz;s3U&j7;3#l0cpCf!m4jMAL!lr<K@*{6&|c^YToN7xWAFm_BvKIR
zhH%JM<T08H?T&KjDJ(129h-t(#tY)DaR;vAYw+7d6~acS#9=Zw*@g6zC&&*}MXDo}
zq-Ig4>0<N{dKA5ne#4YxS~4Irlex-PV2864*#qo#_8nK4Ys>k#G2DFa7eAEO`LjZC
zVX!bt*dja^Yl%K_rg&Z|DGiiXNJpf1a&@_@oRC+_N94as6D6apP+qF7)U-NP-Kkzv
z|7oSP)|yj0rM=dR>3wxV|6dS1Kv@w0007LkZQFK_*|u%lUfcFJH`}&t+qxNb>*sAX
zw~g5r+xC2WzwL{+yW6krD6wPs4r0eSAP3L^m<dq8F5n{Y0qg)y0V&W0kAmN!GEj49
zG_(Q|pc8NbxFXyUo(-$;F(f<E85xgQ$XPTq+5*MVyXY4zFV+#8k7?LN>?xiHuZR!D
z7vmCs27g6lBWe)ah$L~JEKLp~N%98yhpIyjrq)qm>Lp#29z?@THl{H%kzts#%xktd
z+k_p;ZehdhEv_85oWr<-+)KU?--hRfVnSD8vET@=#gbxwF)kIA+Dn9VUd|_Xk=M!l
z<QGaMWspKESCsE+X|=sNT!mFfJ)}O<ife5&NxQG-*W2oAbx*%z<S_;skYO3Valv?Q
z<}j<91I^jy7E?7(nXjy3R$FU`HQfqZx9tWtVDGknJ2jjk&U8n2?mPe8mhNO%b<en8
zyb4}>Z>9%%5${2uTHtlCV6b~|LGVZ@Tc~$vYDf!R31<m+4UZ4o;S-UTkzo-iawl3g
zIx$K`qtW-VS}`T|B3>mvG`=H#Hc>3mFR>wUAXzL4B`>G4ry8UNrH-b4rrq>;zluNC
z7k1{)08KD3UjP6B000Bc0I&cU0000000IC2009620000$04@Lk004Lae2z6z17QG0
zAMW%xE$&+3?hXy^?s@{wm~*7go5@<0wa<5cpo9Yo$SW)Zjv(N9)T^>QpKAUBUcd(b
z0WVB+il`+O@M2m?Gsz=QeDlIJmt65iGre@v!+>no^iltgbK2GOJa9^<Y+Ub7+=zCs
zs5lqTdEvwFnayR6&2+eyxnQ1C9(k~5n@iqxkIiS$wJWMF%{&R6bc>_DIsOzhhUsw8
z5uAUJ9c-IkV~b|JPE5QrLpKXyk}j&N0DosT5CC`qV_;?gga6G8MhsX004PKOxB#p3
BJ$(QG

literal 0
HcmV?d00001

diff --git a/docs/python_docs/themes/mx-theme/mxtheme/static/material-design-icons-3.0.1/iconfont/MaterialIcons-Regular.woff2 b/docs/python_docs/themes/mx-theme/mxtheme/static/material-design-icons-3.0.1/iconfont/MaterialIcons-Regular.woff2
new file mode 100644
index 0000000000000000000000000000000000000000..9fa211252080046a23b2449dbdced6abc2b0bb34
GIT binary patch
literal 44300
zcmV(<K-#}|Pew8T0RR910IduF4gdfE0r(sM0Iab90Rqqf00000000000000000000
z0000QBpWsyfznV0U;vB)5eN!}>qLaH4god-Bm<8i3y&NC1Rw>1dIum|RgzJoZ2Lrs
zpu7QWyVk0GD*tRm1RDn#*n?jf3b-+JGsXb`o^K4<|9?_)Fopu#Ks7Vl-V09HrK0t1
z8~Zi}2F+TgDCMZDV{d4SjNq*5tBjvq-#O>6QvbMhde0G@=1>WT6AD?FYHu0ikega;
z>#mApX-iw$(w6QH48JEw30FN{_sf5mTE?Y}D*r#_=EX+*uo1&#?f0LDsnA_;;~H3%
zLxCTdVy;vtIwBs?ZoLX9$L7>X+VkW~9@$mBGp(v>Ob<@a910>RNex5<Li^9H&m0#l
zKQ}CS5Rw4P_Ex7*9bZ*{8q@J>OognF)o!ohs!So!2}}rZG)$IL^H=v$DKWn<QRR95
z4?*vH?`P1Q6iNUC8j;bl1bfPJd9hgF2C!sn2+zDEFQe@n+V(aFdYVm>v|V>w-8hao
zagH}G<;94Yj2XA;q^>=(%^d5(wx|WmmDKWTsi$hebmD*KGM53NIwPkx<@V<0<%C7b
zQ3^@BU!oKcp8vnvoo~GfclBBJR-x#20u3VxJj}9%>0o@O93))a-xfrYnDq0!ZvFug
z2s1C_1qdS{Adq{*5`qetJRqzDWxe|t4%kYf;$S)Id$m@<w$`=@A8709<i*zhUt9ad
zHgrs|<A0LB8f|}nhxm<u*xG3Z=cPJac0T0VmQwtQaN{h&d-<iNwukG|`eYZ;`Yi`o
z>mtr~kQIgrpbIo%ngDG9Rlp690_YS-ueT}jfMY{APPG@P%2ZPKjR9shqiV}7sVy`{
z0|v~by%6)`bN^R5>(}h9YWLPb5@~{z33et(!V?KjfUCMN+JyUgbh%bvyWiYeEilYv
zi~`^ZS;_XKB%r!`_Dx<k9OTsgXY7wN(#N!21X*gkM2WHpjy;e>mpW=zm#clXua=#r
zyBzKU6?hrq`2FqYh3EGz-A>NUzmpIT-6)K?&8GByd21|V|7bvg!|BpeQ1st7wQTh-
zQdcdVvYfJt&avMWwy4fU>HOx+`yM_%esITg3*GE!fRiZVmevY}oC5z04;aqMhA1a;
zL?6fzWl+*xE=q@(%PXC`>ngkGT$C<r_l7$cb!8sDZlwDl$O8S?L@O3z6EyRu4T1#1
z#E$8JPESxz(9iPsrg-&Sp;G~((RwnY(`vLR^}FD-#_wI)&jFMIhzWuD`%<mCBB`dD
zY)jQR^~@UeTGcpnU++$HxbESS`ToaG<p1~y=1(RFWC{`ph!P2s5|ALPKu}aM6A2WU
zWOslNRlYY=u8pc<R;}(DV?j`?r76{rR9Aa{T4$@&sxi(T*B_2^oV}j+e3`>>PuGS2
zZMmoLz0@IMc!&`)-1+7gPM72-eaBTw3Bd$mgjNV4gjN`nH#1**`<)+suX~vNnf1TB
z?-~)&A|fJ6lqlsWCF0$$<@bLWLYYoFm#RV#0YwCT(`sH#fB6Slu3Fk^)pc*Gb)>IA
zA-n<dLIMdSJTvzrbN>I+4%<7Hwb-gv1XP@;u(M8*lcE1V4=X{;sOny%uTMRy_2PC!
z7{p5Dv!l%*wV%8i(2MD6gJlN%4&434HC}YXtI+FlpM2Q4twt9{w4nYk-Ut6sX_!U(
zf5p8!Pb^S%XdmFTu)gR}ULZPet=Kq%!{2oe>a8+P9c|k+c5U&T=RM7PKPX{+gg8WD
zcvK@9+BEZA%{-(WIlKIIx9ZJzTCd^eDb97y@S?eA8A}MIL0DyBc>*xs@VLlRMZ$!V
z*_w0VR}+_wyl`f46CWl~wnU<)8ZMIrq4CpItF2O_PJL~xq{TWP>h#qhIf|qKq5@Py
zOf*ialDL3Mh$@ggs9p88P69INp;4&7&|YJ=&rEHqHF*oSItB5^TW5<ZhE)^Foztp{
z>bbp6o(tNs-m%p#=hv(v3e?@xGt4L@*mnkUuN1rcwH9`shV5aEL7P2Qm0@9^aoCsw
zXw0bi+yZXLdsnfDJzNC^5eL>TQI=m`1$~pl50)}o0j`}UaMwC-DDA5ZM2gtJv9`#F
zEmGetQw|sTW>ag!tJvy=00=9g58EndtD<+y_eEf}SX1xjIGVj`iMKXRPy5W1U~3G^
zK4OeNuAEuF$*U%xo(=c5&?9-QZ@ScsXjc)?3YNPJJ>fl4(sS;}cGz$d$Bg)JSvi^a
ziIc6<!oL{>L~Q{p3eaB%`>}#A@9Z*mFo8CfPSY^|77lWWN%)u*A;1STVU<s#(OY#P
z;vo~Zv3Lg0A*Xf6h`^v3R<#7EuJrL}D~8|YO#tuB-j`uzGr@5iWqz}F&Px{6gYKl(
z#q5w&%Cauuy-7Of@}40}?{C%VZ3G4splHRa9luZOIXOabCl3ls(+~IvZJo>;>cpnu
zg#4PI>d?IC=Hws;eZX{JR2G-x?XYB2chll@H7~lfYzJJf*Uer7RVb8gJ++DjE&!Kz
z_LhqMui9$*((F6D+scmcfr4^bAjH$Xp|AI)_15Chd<S<`R0Jr)!dodNL?qrLZlcJ%
zW^cc;!~weP@LDSTtJh6ATbI_fv)^x-B&QOrZQn~`<zA07iG4jUJS98mMzK6jz5RDd
ze6oubPJ5Hhpkf~$E@pT!%u&_C8igf&s+dOjrn=YB)rUtXk3}IbL6v}1wbhX$Ss4T0
zahQNmJOC|J5-29Z?Fpf$3RL3(Cn79?DzXd@gP<3GoWdLun~g76KtW~E@j4A~pF$|h
zNoPRiJ?X$U;XCY+$b$>uX}M3NNbF1(>g+1_CA(;B3!V-e!$D0dUfTrzVUEotZ~*77
z>|yGpeoF{UPMy^44)+;PQrG@$-5j5*y6yzAt|d*6PQpNrAcPW&z-~Uru8;d>X{2aj
zbXZ3}<jq81UdriSQQB`_oXzIoFSxaqS|>*WZZK?O&mt_A3m6Vu!btFb(R(Z-odMIM
z(19nDmri#pXLuC#A%lZqHMQG+q}94|-N&;sq;a~GPUoXiay~M}=Oa>dK0Jk0)~RTh
zc$oqS%BYH^!pN`H%L`NlH*0*K$mqmhSi;1$=K|{J`-}xT*!zuo)f@*$Ri!9^HE|v?
zTP4vdk5Xy}1F4tJ(GL(YvO3O3t8J~d;bUQT1&3$9Kb=Xk(a{~U{5UG?unZZ<ia!D|
zxpW)oQO;1H=uU`J3$HB;nm1kQmmQGS@@bjW&k7EpLq53z>Uc}{gQQsqJ61_3;8oGz
zvwSBh-0e7KY~}sLDgSns*y?FkAyix=GRR92d0OozDk{~fK8&zUarRT!-)PzJuIAaP
zM6Z(7R7;LjRYW8z-l0?xP+|C<6`L&&hL&ADqkcPyxwG_gi<T;nNrc6hhZ%&11d^30
zUl8Aa(5ANq#g%Jxx95e$X;K8`3Cb>nOiU3u2(cUDMCBWtQNtVMIvbWf`JE}N2#&>_
zJX#qhD>w~f#fT)CcSGx13LX$S+8B;38K<qx>9WoT2s(I)941yT%WikbWo99ImmQBV
ztE(#dY?UpBMvv@HP)Np)4g@^W5Ea0~LLIJs+nSY7<pxj-jo5VsoNWyPD?Z!q#$1<#
zQS&mw8LzQd5=ZdpGLoGk2YO`rnFDqnMg5oY+$_+e1P8S!>eEL0gY}I}zJAS|0&G_W
zU8kF!I2(?}NgFWyTcpJBfauVXI_%_>c)4u?!-d>pO=s~(@5Rx1A)_7DULSY<ku;hJ
zvc5~OR@UWy!O|m8^+R`p-s0{SeJYbIY#T0}RX>bmP72$Zvs)fbSr%m**3Yt(l?H!!
zu$CN_mimVx3RHE7Z=i+J)6vMAvgjO!ilJInGtnM^Fq8e0t6`KzBe1>bPDU_W$~aCR
zDe*)y8pJ55dq?{KGKpcs+n0&dLm43QSt@4j)(`zog*BoqnO+?dQ7?dfS6jm_S8-Z;
zeiYw@B;R-7XN+cjO5M9bj<s6DF!+a6aC}7s6nNmFDB;?-00Tk5=YYeZ)D*tr*8~S9
zS6>i6Y5;?dE*q_e(gA7MI|LK!5dY{%FmCCN-Ci${#(~c;tbM<n>D&yxPU;C8R}K8q
zJ&wdifFbqb;e!DaOw-Y$X(xxc=ABVv|2C|f=D_{Hm+iVJb+$~05@+%B;Mt`$TRO?y
z(P+~_G#kvN>9tU4Cr54RJRb*;2^FfF-{5dDXWT<}gXXGCn-TQikijC_u^yq!+8u-u
z!NF(Ir3wplRSpV)zB7V#;*u^Mf&0332w=lhbRa&0@$B83+sYbK?5FQ*ok=#k=||Qm
z2gZsJC(v1#r<z4)*Dt5`t<|Q9O?zPS^ZS*iu9|%iyK@AN;xRqwkYk<G!=2tyPjtvp
zkJ&pZBf$`|@d^;}p-BMXu5Tw0ZT$qfFvrclW)66Gvhk`2AXBo^s;6J^<2P3}z>gZc
z19f{^wZtKbAT59cyQ?ArtYY{P@NW2`%LCvz@%ki1M4e8xgg%6?$IIh>$`chl2kM@C
z9SUic=t4ZUk39qBJfJ#&5?6jD+g|#8dZ6Qt5YH8V&6U-1>f?y#8LIUeyTc8~-(*&V
z_Xch(({a1Q{u8Ocm^?=%G5R|5XsIeeWUp;ONWjEWFlCV)>JC&Rd${j;#*q@LzcmM^
z&+-gR6)90fgb(xOdH|QU9!%~QtRKMOTz*O;rOsp~w(Ye*QEH0tldl4bK7EI%UpmL5
z>|oM?RoYutouF2q8;1=#f_Kp*I0EiAutdUP>N(Edar6z<_2^itR<^RFGeq)@fAAw{
zj<kxIP`j5+t5m4s;ph>y4j-_!$BuvC$EqP7pkxWZ6$_Jpye`Jr$s+qb^eYfdtV7dG
zCqa0s`U+IJ_r*1OUR=_oa_wd#2nmv_T##B2*ybQndTDe}mMVOqfD>LO?%23Qr=+W*
zARrGSEg*=GWGs4t^*mq>*%E0-uU*(yzDfRZoT==)pNQQ&%Qy!HOIBNtk(+0kV%6i8
zW3r#wt9f*9x?2_b&cX^qQ9hgx6haH=A5jQ%kxDozvxTLGz(_SU0(_L|R8c|Wc~vIt
zCBnhsc*Oy2c3sG&z}B*;_m-7L{Imu7Y88qg!s$TsNN#x$oq}{&X_S_JU#Q3zWb255
zyx6?fjw57$^Kwr8o-5i%2zV81-8A;IwGq7UKmQ7Qy-PplG13YvBF}1CwaW$#H%;D9
z|M8O<H`XLapHvMs6O+KBFoE$v==x_;I9l=Gh`E7BPHYCtHAaS@u(Z<(4UK58YOM~1
zm0K?Cs9tHqkuHv!vu>|TkMDSBlX)8sCJyO!4~IBX!VzI>8b^)haoSpsi9&@tD^2Lh
zjp;dMoTN7CY|BoV)KhiW9EotZuXA~1V6Z{j8MTN;_ym&(X5bPJctim|Y8yw4H=hkQ
zoa+@aATev1c(O$tg?l`XTbiV?4}m$vG?mf!l+6a~vTm2rYd02+@b)Q^yx{`;GgK)f
zbetX=D5(*%n*vAk-VV}CQZZDX|0t&P`fWrI?Jbq}5>#J<7)@RMp5BhoqO>1EfQ^^_
zEB0RMCVI{^M!X(U-1|)=E<5S8Q9mm_)-pJZyP+n6<?xT3tL4yT{T{<n^c5wxadEK-
zY75Q+0`t0O@ae5_J&TRoCqA#VbrDnSs2kK2!7ag&bW5kyKHSLAp#!ah+bxMfXhMiD
z%SsR%fjdyvQ$^J*8;$$iskjD!>GW3FteIiS1~Uy`1(4k>UP4MK_f6xnc}9F!LN?3W
zszgNPMSPo|C~*2T!lNOsvFxV-(csidQ9hNA;rMlgq0`~on?7nC*|hyVFqU-N{!trN
zb=SKh8opbyJPiF&U80?10+Z-j&r$~Ah7aB`0{wLiE>Xu#ZyObtMcVe?7t&MiU(NMM
zEvs4%^jb+kJA#Z+3p5&3K=b-a5Un-T+;7Y|#5{}!Xs_OBnDkjNvl?>%{~cC1oVt<q
zuyF^R9H-fWZPrE$Hn$HoZkLt1zjcon*iB6)tA{#%VF!g?II-&!)yguK+8f>ja5cJ>
zvfF$UXfN6T%8n|(Q)=!EFuf(Zm7+e2Un_N4SV?6*lB2Mo3@35kY`jQh=Cu;fbd}}M
z>cI*6$h2_gep`7^G-Ua8{LX*M(K95hi9VAvCvAw~Ir3q6Jn;yA<!<&I5L>V#d|vtf
zKTA|RQr0~Byh1P2wE1n!vcZ0rJ@p|7Ukh8rqMXw_1|=I7$NQmWQLC%Kod8r;=+Eg#
zj4603+$d62>wbpcJ2OFIpRmi(|At1y6Ch<fZvnhqcgmvgunt{*eW!Gv4JRQVe<>=`
zWixz6#Up*Ry4F<~z6UPC4_h!Nic6jQHa}35l>Ny^r|}A0EdjuN1OF+g;!X$?)#eMf
zv2i;%`g#17iyxX)ML!GlGsk9UJ@+FT;)qn#a~l*AE2rVo$s#oG8SV(9g~c&a9C8cQ
z*0D$iAsICl!qIDIdGT0LLIcH&NN&Qu(O@0lS)zpiPx8P^zP0os7i7AjfP?D`N^F<V
zHug%V4hpaDicZo-+l5`5g)eBBIws9l*wmb<a{jGO7r8m3#QRviJgw{;&^3MH3-V{E
z6PYmfnAl@udE;4l3{JZnSA+*z$E?9@?5q}XbN2OD8E2D=-eogVF%Q8O-;bWIbHyrO
z?cVl%edRKZd^y03&`+*~^_#mZbtpJAPWRtEeiAriHWVNPh_vg?cVhhdswFAA2oJvi
zHHC%MpmN3nLi<gs3{M&Rq`IE{xUhR$yM}*L)Up*uBqarq^ni^?m5JN2K8yWlJ1(TO
zcS7i*C}rt~e;htWl_rwglR=lVU50&PLECFGDy{v(JWKg;YD8OuAN+@7e#{NAaqJqZ
zc&FVHtJUxnJocR&_B_)NPc(zV1JcSCe2)nFEbG^HpN#J=OCpuBd$*64xaISbUb)5*
zGV45rOz4fx_l}djZP3qgK$PUR?BlZe@*mq7T|R&A1AUxjjVt@G^-*<{&yNX%S5Gi;
zr9b|y@s#*k{?Dk*wZ?USaz;1lG&9Ldy4I=|)u~WgK182Pezv1)uCriB5N4Y^UCK%b
zN!62L27VH7fU&q?M1)RdYs!H)$IM(e^30J{izvB9N|MWfWW#e9m`+H7kns2^O4Ok0
zbDGMkz&(??iAXAz0H+e~&O?esYLrm*NiL){;Mig!IcXa1AwZ38?L<{S*?=+C<v5wP
zkO={tUR!Rlb1`@!Rq<co$GHAVaY0tDPQOtH=O>&H1`6~fV<BU`DCDIn&rnjv_H|rL
zGHQ~uug|$C2GhB<Zt2<-7)<w;B6OJkLPzhxI{b5bpDlyJO%*w<vVO^k-fcixus8ji
zpo!jI7)<`=abOLrgWC({`j=u~DRrx2WL7SU&&SY6!~`Q6PWt_7VYG-PuSQswRyKFi
zkonuM^aM<<B{hevqoa|Z5dp5XLS|H$hW~(Ji|jA4ed`x5)u(qJ_`IEqOs=9`nUk4(
zw8BLwn4g1r(Y>&Ya-zEdJ?xR%)rTtI_eQ!Y=>n{<>VB0>C`(xi1kup)<Xe!Ma56Xr
zRSfPvR}-yDUDqfrDik?xWFsTBcbe2gu!k}<XBNwYDdcF3LT^v1A?lrjzWOz8?Gg&E
z10V><RF@sv*A7G4Av&Eh><*g!{n7ztmjYOjo&h&;)MoHjZT^8w>!pEaJ3VkAbB;h#
zAM~aTCUHHl))b}WX#k*Jy5x1rc1q?1Uy5lMGPoBhX!8}`2X3#nlYk_xkCM8z2lS}i
z;kAxeiv=n{2(hrNm*|t3k9$s)8twAz=ea6RtFqlx@_19-<Jg?2s3zL`$M*vvhcD(K
zp@FpaicqUpU{8n)o5!tjMq7J?n?=z>I8kMY6LrfTzXlZ55HLdjAaym*Aj=%}JQ(7N
zdQgnOkg$a9VUA*I+(=oQl}egbZ?PU>n$YB@yZgc6(eZ8XcwifV=~N&`r1qY<Qd3_0
zk^0TTvbU02P8aa30ce)ULGdUIrl&Js;AihTta`J>_Su`!&wF9kjcN0wax&z1<&Joo
z&relZLOg!Mag!nD4m~#`4S_U1@x7d%s3T@=pwBkCmg#7sEQnD$_StN0G7+1OIxLIj
zL1m0wX6xFHs0$Vd4~oKheXxPioGi*qRxL-W4!?!Z$?`nl5lEBPb;9wp8wz>}<7iOG
zRaXAc-`DabkCRG;_Q{A(3r_<i3R?DslVX^6hH0ry>2SE_FUs-gQz_&p4)GaC0R$v;
zHW#pB1a&xQY4*-=596p><>FFSBB%9o$VeRYW;wY8&`=<L=n6xuGtG9leajT7m?j+&
zA!g)MjTNbOL1{&A+UlZSzxyaxtYEJ-N<oIjD#zxnC)qo!W3Eas>ey_p2?^xv8h>5#
ziS$0$L(h>iH1g7(Rr9!phk2T^D5!Ysv=JVFMiQhTmWT7FdoE^bg{`WrA-0?bCguCc
z)+&pA%)jT$mfOQ(7gFT*egSH4h0|ZQQY9Lr!z&JT*a_Y7EBckGLe6UQe+jaEwypeu
zDuDQMmNJi-z^bXy=v7d;5SP<dexh2YmNxo~1LQ*AXmkeYm{^dLdq=hv?TbI$`I}h4
zBA@%6SvnYDPB$g%Tw5>=;~;mYReD|mCa-PFO`W**hXnrDuM*9z=44a_wHrYwmCv;h
zitB=~4JwR(%a+>iWj3Rle3r@5^r~TLr*-OXbErAanzU%(P|^MH<1kI7O9g=>yu%nW
zgCXqo<tJl%M%GPTBu3Hq9|;f0C`rs>1=ZU0y`<v(<BaRJn;n|5Vz)yhGj_PUVY}Vj
z`^NF%@t%*U0bx4%>eMz83Ni9W(=;PkJ!<qS$=ZkZiO29URcK27MOz@L+k|X?@$7hs
zP%2o`#(wTNwR1p&zOY_dRL_n!@9Y2yxAw^pnG=AYA^ITHc%$(IIY0KkIo~!}X6pBx
zqQ<9Z(<@l1knj7AP86oUXj=Q=w88%Snv%W=`S?=;rZjm$d+|o3L4z!sByFJAy8I>;
zhb?T9Ta3A#^SIV0afQW}M?3{Ew#k#l$v~b&yMZ9bc#O>Bq{9xS`zCZMd1F(~@;(?3
zVKk>|Y=5;cIXE;Z0^Y5HN%Y>wBOD5&_z_M9qv=fhBB=u3lP4{Ct^ottBbzSgCzIfC
zfW+r2s34YTemf(+`c+S*;?6l+FE<k4Yq9lVuH+s{87J?2XbC1_gueZ(iIK|5>z1W<
zNDp!E$-T0<H5j)J&RkIu$qKpJA(6%N9mY6Xi~n$(gF<VIORwh{XMMom>U0*_V&gX4
z=-L!+9~!B)F?q!>A-FPbHrH^p!MV9G_5;P*e=lDo+agKa!fn~vC5?Y^zu`r$(JO-$
zmQoWG^qR*d%$*=Tv&BJs2WD?Ymo4oE7k*`@O)B|yVQm)S$N0i9(%#t9Z9P=k&+cGD
z@BL5iHsVt=*(vcvI0$Vpv=5_gbhO7lPrC={OLZJz2ze}MOC=#C$OT_G0hqXS5n!b2
znbLpsNsyBLrMJa`4z^;u07}7Unp=Vme+gOMp*qP+B74E86-sGtola0xF`6amcPREL
zCW*U4I7Jj9DtX&=M84-(+av=t+jZTS_9+tx86GZ~+WSGAfm!P#Mzon3;r9ug8DG+%
zO|1WI*de|r=HL1sWmLB#l6}pP^{a0(!3M|Ow^$*NgiN*&LFsP4{rKm|(g=;L?ZWSp
zS$;v%5y7d(GKe40io^!jPlbIE0-@bx*u~ROUJD$@Q;E<Yb}uVTvD@=3LQ=5~%QR{}
z(iDoSd9xKoRm<vc7GaviH21vpjGa!{WX{N7;t(vd!*YZU=4hYW15Tn}$~+Tds){^c
zz1p=XaHb=ELIF?ji1-Tjf7GP8q5)m0lKGbe57o?-Hzv>7`>~_3?#XLSs`K1k1qm>#
zdoR$x-ne2(rk_STcg1yAQj9e70T#Tm0yet%VBCBB<4|9pCMLfo*_YyuG>rb^T96V)
zA;B6EWyyk84kglED?HAQif4q$V@c|R4eX3JnB!o!ao4=@GV2XGjfI;*rblgiZq2zK
zJM3<#gfl(LTqkxh)nous7HvNtmNV=z&kBeIcP>Y+dkWk}9m9x}O&^-vlLYGfwZIlT
zBFDn4o8to0Hq$BF%0Jpc!(a_^zUJ0$*{Rc{`qVl#s@u+XkzdSDNo7kYu3w`|*{9)|
zWJ|+OlOrB_j2!92qR68W{;7vU4x+=e$(rLQiH@vICkPpw7N<V4@aX?)oUx}jxeUuL
zU)`*FX}4vYMkMCE>d5}hrCnu8YbZxCD-~IWP+V_2@NeOsD;HUl1jS1$S>nc8y-M5d
zq^x3o%BJCYL(@lBoOqNooY=7rJmjzw{{7wg2mkiR{^H;M@vr~ncP}31E8XHgUVQmI
zz0xH&yZnkLZu8@w_qzA|5>I{NT|VKBp84M2_`!?cb834<kdR$rxWaWs<O=GFassFh
zDU-qkX6U?1>V`aGH5+4z_Bk18sl=D6NkS?9kh(F^T!w|)D@@6}#s8^LgHaVR87VGv
zoiI2E&MaArAB~#P8fUrQKPsllRKMTV)ng;cEi9He8YH_KViME6C`T_rc{1&+7wao;
zAY+b#0IoHEM;QdBA!im$Hv5?<>yObp=zt}E&1-X+qEc7}X@?H>IzN#umx=3V+C4bz
znzd%Kh}I><OFL6Gq#p0t&qC2)w!*x8y)<%D<||x0)&+{@lFuTH{Xb$Pc8g<Or08*S
zPW7j?so-`ey}zFx3?ePyF|V?cFFylY=qZFf@NaqPv_q-8`70^8o#uJLWxdIuBnhpI
zkATn&c>@ZKWCKk-lQsL9%SghbSMU_sg^YS>q+8iQnv5dX&s{plBtaOj9CFO@Xu|?-
zI^ydEBRye*MekXZpRrI6Y%_x259?fL4eAm`RGiK-hnACsKBjI$fUMmHoI%ZhW;X#D
zkNl1>+lYO{TUZRB6e789#9Cw|sfE~pj_nnDNhoDgX_oVrlpqs*EP2U>o73UpfB2p!
zPeA!O@UmZ-dd+qCaDW*wk$7bro*W;_bJ_e5cFQX#6J?R8#Cjj0ar#$&)?D63R<Zk@
zRm66PT+Q~<N+Srn33LbAaAc-AvGmw~31NwKSFJUq2pUEJqgN`}7L82aUpfYSm#`Z4
zt{q-4s%+gnHWwbfAt{fxEAN!!D#>pB1B7SDc7-^~ud0rNG<K3O|Gc(!rzgSzLoaP>
zJg#Q4**a;xhYSf*ybNPp$MD3P``44bCs(^uie#SEinLjU38;mLnjD3(2b?%<60~j;
z4krsIT{td)z1EGEc^2A8Kso;}<lK}9QnD8p5}UM&xgxDO<29Hd0Yg{7o2cq$cqF_9
zw?y;qcRRY_LNIbfqO3qp`~dWe$KtDL@GWUPb<A9To`8v>xqx08yKGKQtEX5?ZnpFp
zN$Wmt<fs_&5(IYDxLBJ9T4zsFo19INHa)zM3;b80OH(yCH;^Xx-S_4Yje@zk3YC&^
z1*R5Hk+!*LSC-B)j^F}0uJw+|@(%QBn*ZVDeS24MI%q#^GdJ`}l-9WrUEan{2*NT}
zh+`wMD<NX!dGEs+-+g@4Iy<}@@|+)Hi&u%yPxK7F1yfr92FP10cS$VXHdylgzqQI7
zJ6-oK%Mf4G1H#hJ;E2Qf+wv4s!$P0L=fCTqw4`RM4PK_V=zDXCv1)8z-wds@*~~rf
zyEiJgjqm`Awv1h@nyX#D!9~omK8u>Xw7tMr#+_@a?APUPkCQkC%JuL*INu0@Gs}GS
zz~WHW=|qzw3*eNx<CEG?1Wnl4(u-yflG6$(y>PY_s&oH~2<vm-t!kOcBFmm6Kv5)m
zfbFlnfSd=G;X6335X*Moox}5oJ?!e;*~ySjuRL!funI;TfryAf*9|RFFNXguY7bx)
z!bNmj$N?3s(UF4cEWIeu!O{x@`)ZRHK-UbvE)$Y7DP4O{>=&;?vNK)71VB}~&Cm^e
zkvUey1JZQbQ09`KjB7Wvp(=5G>yr@znJ*NzPHngivxy~=ecYT5!LgeW0sd%D?mKCV
z7hGS#fxnb%XM}m+(VY;P2D?}>A;7&FB)-hfM@;liNfkNVk)Lmj1={Eq4fz22)WMFy
zVnh1y$8BB#T3W}UCvT9HlHrT^=a)6Z15}lGFv}1dT=XWZkVy0si{*%1QZQRl4_~aj
zm+h2x+z^C6Jm-_PSTs2oglg*b=)tZP(vpt!j;{nRR32-KC1M0<QcmS<M2;<B4PkOp
z1H8R-{&_94P1~Bd`^I;Mop>CcByya@=0*w|Cw0tXGc(ypyyfDb&??i;x=3A&8EPcL
z5)wYiMWLe=v9LK_$`nG$OZ7cA4Z(#lS2iJJEK06w`&%_D3Y@YjsS0R`XJbRL7Ck2M
zH<Z!Z?ay2OssnC4<Zi<**KSPHncaCkcG(XO9bc6iNC)VZaB_=W+xUm$r3Pd(k><S>
zur6XsRqqatNcGga1;{^^P5vee7SfpNAq&h~X}W;Ri;5A6O~zrANM|BMS+Im2@BP+D
z%ZMYojQZl)*7$p@=x31u7TD>kSHTcX1fm$zL?TB71ZR;TBx>x$dlLQ^kn~fl?-aF!
z`E8hMt$~wXyEy6RDaS(FBLG@!ng#^O84)odnPHcZ^_)!BI-<FFLq_Wqlhz6Jr4cWg
z=yJaUr=lpYuS^!*mElvB8VH6|S~D4YyzqHrr?inaemXuHEeT;2#OULqKUluRc}mU9
z34u$mQ515gC+wlQSMC<c=F*7Pg3FSZ)w}_$?$%XeiC?AtB33Q7Z25&-yLo==D+?$5
zxc*4=@SvQ`DxgRc4HYNkE2OLKE1-73PXD_OBi4?L{kGPu)aKGvMMDApoPbI%6Tw*P
z+uN@y9f3voz{^m&Va`=!G<->*BRYOjKCP{%8YUnXL#(bEhEVjVocy0+$4giL%QWNz
z#)fD@_-w19Iq3pIB84<`<gtb(LYP5=s{icPrM|Jn9@!_GFwU>f3V-6S+I-Emy1vkS
zed}i5k}mAseHYHBVpc%{1(;!(z37Z7N<+djmc&Afvu0nv+AjdaIOza@o&-|KB%6GS
zA@rkSsrT&41-|ivJ@&?iOy&J^`8fPlo2$N{o~$1&`iq;}S-qy;hSfRd9n$|K4c}af
zOF`DfED@PVX5m%q9-m^r`2Xx*=YK(+sg6<0)Ra0(9jT5`hpWR>S5ynC4^<zi`c+k`
zQdO~Pl`3DQQ)MQ9oqRm`q2vdW8<Ojj>ymCHF^c)C{AK=P{n>mmEh{mh`is8199a%S
zfSvFGyay|w18rzQ6B<y58dT@cElCD1JrP{+7lk}AXvpF8j+l$sAOR1+dX#+G=%*17
z0v<3X`yq@I7b$#%$Pgh;jwCdtb(?0;WFdr;elz0DnU`hRiX3a6)_HiSAsz<kQ=rtj
z8a3`}w(On_8~)pk10)DsxEADrM|CJ3@d!L}7jDK1vo)KV+RkMJHRCaE;-LirI&B!B
z^sDv2m`C1Oh>!4uGX942gqnz7i52+=tN=U}CS{NcEmW3eck3;9Mk3GH9KuP1!-`d}
zx$CY=?z?ZcJuDOWGM>L&@Or#MdI7~7ctME7pOB;GAqC?f44C*QGhx0J5o3acny|+l
z2S_hLbmHZ(b<vkDyXrgFJuknVuU}AJPU1f$|JT6czxvX}?GH``Tpv+Mi`|v@VB_Ha
zZ+TvAuxaV5f4e6AJ-h!F_)sl7W7nFOOQrus^iA>Giu$o)-hGjQ2Wn>h!U(O+zeeeG
ziDKx%ycH?=7%cY*IOIjD1Eb_MNa5v-;KiYZx5kjc^2Yg+5;bChK7={3$*TvhCZE6y
z?*5R>n^9si6CoY|O6s6l))<3=IW<1O#kc}!`5AC(WX^3(Wf&i#vP0_<6WahPQRnNH
zz9#n;l&SX{N2vc(#W(M&VLSLhhmue#o-O7!X>2JaUN|B^pdN+Wmh7;qrK)r1a!t!d
z%<H!i;0`xdE!`NXkfjp_`ucvgvVU+PX<}49rYm2miyG#G<^n>OnsWWA_40VNj`>U=
z*{9D-O=LDvP0prTJVvwO+n8uGFxu1*_`1QxCC|UVTWe($8OWV-`C;tqOmJ3ct~3%S
zwaUcb1<XMQXRly=Y6jK_I*f}%%u?21E1BlUB?=;s&f=_lN}My|ob-0Mo3rH}6*r7c
zmNB?US8Av%ZH~JVa!dB1*~UdQNG+*uVBz0iD+g9#Y7l*bfkkEtO_s_8b5$zp)0K9}
zmNV;S54>o5*=qFfC-NAYB0Qx*m%&8c=iX7dXK}>+m=5jZ!RE}EoCX9FBMT*GXyiG}
zy+^c&-{8TUY2`2gP{N-m(UnKtIY#18WRXM`U+*LI$a&7$m$*^S$f{&#)HcL>VuJ`q
zDKEPqUPNsHBV5RVRINrM-3*^0I4~qHW@XKi^{z>UmJAK(^Jef!FDzx0{;qYKd*{Ei
z**UiBlrp#v9PZ7$8<y}a03-`42{vQ_NIpyB0D7o(1L=s&5`X1Ni+-^J+hTpbtrZh%
zmc^TwHMsBega&vh(rg#Ao~pKE?aAv?MUxixd_N@sD_o)iyimvjKgZuo>to!xjNm?y
z#=##A>CYm`E^Wp{dPD}vfc2P9hqDTfJjva+m;t!eKRpwvGCot!u2oUb2{n^1{3NNn
z5HqtNYqoX8ZQ1FDt;FH_l~Xc^Qkm164d~i!`G#If!_k=PQyv*$mK~C*xkOWK$V+}B
zorCnUWoP53UHoK_s!FL1+)?1>&fSMoVgP8BYY`x<6q+Uv?vpyPFV~}D?EK`@1|2Ts
z;&V?2oWENNn+zr@D;X@@@bX)Vq@%gHT;m-xf~8<H3gjaxomN^i6d2grQnI4C82HW!
zgVDqaY>l9h9_>5&_|@Tk@}qU7uIAD)IzZ&o1q-=^)TEI%%J9$*>f|0sH189)7Y>Jz
zD!*4~@fIf3jABrks&;$>2nE_XOyp%P7X~=%4y;6=jr&uc)$!Wq7*n1?XPj-{-5MDg
z5oCD8)sqKP+3+MpRG~h82sg6g@sKN!BFSB>3B;gsjAR$TP}IcO-%Zqt!(OX4!k)?`
z-@=Ba6?hb)fqQYSzYz~BkxN?!5q7joL52-Jt#8(cdq-;B3_F3fDs8XJRqGHjR>c9U
z|7v-l)LF^5Fjm<55S1Mc1N;?H#+jsPwPws3b3{cJ!Hr!+AZfu#sG_Z6hC{rCG91N+
z0yUQNuSui4@1m*?<(UzlOZJ53mW+7xvn_ln8tI0WqTzM)h*SjC*JqVPg*yYr%KQLk
zJzRT6mY&L0y?cL>gDOt$HGZ~VKcct-o=uB@a>{y?u0|U=ew0-TM?+GQl?<^3Zt#0_
z7q?rBnXquJ5tY_i=Nc+^l56iEbe5>`9U+ld32*XRk+J1dfx?Y%wpqeg2{z<A{=_p~
zfc7>`lSg23ex^!%#s?!GAnIq(Lw5*4Z7H^EPg4A;38F1p3J`y?kX~zJ;h>^kctt(g
zvrrNZ=CyuxXIv>)rC-fngI)PqFpdxz#XP~cH-d_z@>&W@jkb``gAV3kXG=Dw=_vz9
zZ7jic4})4A!B7mDbMQqNW_;#;d3K4X^*XoPpRWl|pagH<#q)eQ6f>3?a-(E{c`L^@
zeTZJoC_Ax-cE`R)J%WN;JPVG3j=qu6?%2V>?74YwRxuGlfwYJsFx6WOK1OuW=HxIZ
z!gCv{qA%KUC4<&Dr{1k$Wm@aeb97!3QQk6@v>S|xrXR=VJUDPZU?E8&JeG-MLVY_e
zKJ=ilBfVh~5tBvViC%z(%+&J))`*(`v{c19;yP__*t_vFqMhg2R>?^w;F}}Mm!gcu
zBmqX|gcqQ7xB^O{)Tq#rZwlmgZvJJrbp|T?!v{lN=)|ltVn?M*^q53^!-u9;Y{Tj-
zvyy?zG0(c<0FR|t<=~aeDA9)GI<TT>sT`!^14{9S=KxvHlBLQM&{DLXEp%S{XqOv+
z3&?kYq6e?!aWDMkm*l~L90;MR#(?`~ag8ZHp}Rt~Vo*a7_t8#khfML8F6cCKVi|m}
zx0%vHr^L{vo6HWE<1kGzft_#Bah@0h+IS8ARG#k1rb#AMvD7WO_&SjU-cWqBqGMYC
zH#FWYxz)Q^V<BFELCo+cCE4bCR||xJ+RY)~`MKYZYbEG{*>b-lpV`}beCQQ&3=JVU
z(QY<<(cxiaE%4v>o$`a8$}c}TD;}M0+h|Jx1d%TkoYp@Xz%5oj^_`cvI9DFPlAKeP
z;ZC}0eD_VF94VFQp681>|0m~(C0C5Agop7Q36!t@tK$o42Uh5WR$xo<)BQMSAP@v3
zE!o^^A_aVM8FdN*oJK30!%oww1E2X&aJyzVesU_pwLMEZ$JUYE7h&qARSjfeh@6HD
z_I*ysIBH~PK;H?G1<wJsqs7W!M|oJgF1RWLy(-E(g3F<xt-V)=)UM&W+dx&PNs7_}
z0Z17ukbt=d{Xx2^P*vS4WDdT4Lz*){^D}kAyxu^*P`&q1)NJENu^jDKe6H6-X!kL-
zQ)ia5r^eqx_7o6x{n3J%LsKV*UQCoGGF)HEa$w|Mm6r6u2)ML`ger)S^h9lAnpNug
z;Ap({C2%NLm$rNc2tN-HFm+R<-9^&rIUS%KtQkKWpyi6G<Jqns=}x7^$X3ohxrnDy
z7l$1)wQyGEx1umqqfoYEbHM_>WzV;j5U#vn8S2MC5%lbI^IJ$Tz^sY7(?luiIh*~}
zRm8;18%=XpSC#xcUM85I>&>zcVdeQ{t`JqZk|UY~0YSpH*<54$w@;?xZaWR(2t##5
z?ST;km9Rm8$_>B-#Ol&++g+<iTs@GE@*R%jIR+4xFZ_R<p4GM6VlBU1I1OPU9LIn$
zYGRg_Dv~RC#Z|JZawQ{5wfZ?8fH@!#hNyx$A>n<@d=X1o(&iG(SNq6y8fe;_Aw3uu
z5?O*i+$1!Mg$x;_+3AkD-f&%WuO%X}XJI8EQxx4xAvR<|>+)eEi~VA)L}$VL&c5i;
zbI4}n&~~|K4XboR>8OJN8YIazy$Z1Q0#6AVEikTKi;TTu^qZK+b2fw2`u3B4cn)`S
z21dx%>I4^%-`cj`zqQy_8u(Rt<O@mhczCG!*A56&P%=q<VFd~ZgGLvTW#eYfG9p1_
zJ^KN+{mkfAIz^1@88uT>8Z)Xvg@K~)ec+n6iR*i+NCuXNsZ6*)InxdXCgrq&r&U@x
zHHgbWwKOuX3kBhIc#&x*B(jA`F-t+YCAqhb>}&5t^rD`JwQmE|@vj2<Af^)faFxCb
z<4iy)2!^qCM(@LdMi}U#9<nMzy6T(yLdw;*d-qa3$nQ4fEXmsh$x7D$#~@VHEJtAU
z1lunqsWcTbtN>aKD$FJoD1dZ`dF(VW+itjz$JeQo7^(R@P_JpSvJ`o)D{wmEp1IlR
zb)hj(+qKnvH=(kCp-hxorT*Y#oafM#R1)RwFk}HXO$m8y$sVKp*&KhSdGg=AEEKUE
z1um(aw;<CX<=#5dP@_<p=7mK~EYSt|+@n{NxY<Zd@IyOR(!+yF2<TG8a$2aQ|3XCi
z;=O$iN2|%n^YFRx;#l<XasI|2z5cX+Fd6?ar_X##I|Kicsu%-|68vije_@W_!?K{7
z8S;_%`U~YtCGU6vPN3|auW0fvua*d|T?G(!3PX-2gF|E}as2PQJ6D1#XCpQ}RF4XA
z$VD)CmatogN+s)_gSN7g?%P^pVCmx*Sea{eUAmPVAo;u`*FcG#GFO77fOD28g|Qwf
zucq*B#Iw5U^{tM~(5T1F0)yi3gAjINOwcwMf;xgB7-9f?%;XiTC~%X1ukDA6LI*?m
zTi&W&rTAohff8&#+ldA|q4H8dA?ZiC?}ml<dJv>A=&t(jTR*q=Usqj5G0-k*M%%?I
zRg!8Y+sTN?>xG!J7$ckV`1_tc9lM_OM-4!G1N7OhXypv%%DLd_M)F7b2-1vM4#$WR
z)nIMS37clL-e@O4>NO%;YAX|7BM7E01D2?FBX*w1v7M-`BWwKRG_8hR6M<+OmG>i&
zh+bNFDYm%WT_#t9%Jk34(PEUk!e+dYgEgTJu8Y;W(?%1zdpF$xr}j1;BFn`(sGRz~
z4$7ZSwL2Mq1M|SC_};n!ONYpgFqL#S;0HICtpT1$+m9}Z=&Ob4amp{RZHtc6t04wn
z7YJW(@$|F!%yZd}mSaur{t|n02tC$VAVu!A<Jy4Vv3G|>Kif<3%z38}HSBZ|K)Aru
z7Le1aT%`)>$V+2Ds+FMKw~vsJ&;<BsTIbj~1V#WYRP-W#MC9L!GM6qrk@zOAmG)8}
zOmhQ8%o{{skG;@@U5tq4aw*Wj5VjyU-J+36?2L9!hMl|i8vxC<H^$f<jL7_CTFysM
zC$3q?pGb>Mk&c^LKP&Qa)5_+oZ(v=gRw{d4e9~7gqC;o>5>LC%)%II@g0hACrYboe
z>X))#ci5Kdja7A@P$EuZZE5P{O7IxwJV@7CZ>l2P@v6+yygk`<>71%glj?W>bjgDj
zia}hL8*I~0`V{A%kUL71tQ+vR=h6*hF=_;X-SzZ#J8t(G^lil=fKWY|CF<jKQ?l`K
zCcxsx!;tzC%!a9otdjG=ls7ogIIV?4Cta3>ad6YYTk|p#z~PUi>8ZJSEEcKMTzgAb
z%=|D(c8I4d%2}gb@N<}QpwnDtkeZ~PN)S}Y?l4o*ZO5`DRS7fpu|>z~CF9Swj)|+y
zMjx;6?r2uw{%%(;*siEJ)n=W-;pXmVCR$9|^w3dfO7TxuA$OCOCiBlz%5{}v2n!(u
ziVOt<T|q!q&QJvj^WMFYl|8Uxq(l7(@sGgKHwKqBi4Po^_=Na_Ap*4B$L!j6=-{w#
zOuFILln=f~_VT^0E&Yf#Ffjm@JvB@zRlP&e$BNuEl`tmA@c%;&0n`y~0(3M%0vrb-
zK=rBT#vyStK-FVaFdnD_#~mB_Ov@#ww1`fS#2z`#hFu{_3POf$@M0;V2(NZz0z<AD
zOS9as5wY5}mjcWK;R6jWpzh^Z7}S?*)Na;klJF4b3)rHGWD=~I3CmvMth8UYuA=9&
zhu^bf+*~EpUre5-S-y?6xO{Xiy4=-)A|F`ZWMXPs%AHboi)QN8mWJ;nETxH(&9R^C
z!b%MYu+vC-LO!Fw-AIU?ax=#XXKIg|00>)-s+~3#K<Uc9I3&QCI<DkV5BgaVN;&97
zh6az;+G|NjZv>VJ1Qzxex;K{_elQ!wJCrO&2KRso-iH+370hb0qE}z+O`--3Oa|x(
z*j)#W=!KI-pjP1Pqww1K5V74tt%&SuM!Z%ERhVX~LMVaWHsoSzvPgqsqI0w6bSj;r
zZz+XT4yeSnqP`dUuDBGxZH-Iw5E#kXNcc+TDlqCBL37N?SzIqThjNSixD7KO6Phhv
z53oUf-yTQDdHR`covILW_*5D^dqzFazS(m*GW3+?9+}rfq2&u5HXeo5)L!f*Fk_<L
z$^svE@Rl2Bh(;iXVeFx8NyScJ7&>Yka%AAL;&p*AQ~$jy@wH?zO54wbo%8x^i-BH<
z*mJ+_8IN}_g4R_u2>hH>xiW^;G-$@#<UvvF66@$Tc33f{VlXPVVHK4B9I_M|m}+Sd
z`2-C3=Pg=LWK7h?w9z#fTTG++kOuPvISD9qK$S5dxe0O<%+Q=tCfcx~^dgEnC&xgV
z)be8nP65^=1BC2%p6En3F!)v9EQKO{TeE?sdnbCAD5dIY!b4{;H}S<NFM0k2u*mlG
zd`7PYTB%3wK3MV`adgm!&h4f69`R0;wVl*OQK3<w3il4R1|6<)B`6MtzPtG|x4dFL
zKXzCSGQVe7-L;Vq`keP7JLgx_U6%%!eWpbFHG!y96Xk!EmWuh)GwHh6@ebUtxkFe$
zf(NyP=shqEP{6|x2&(v6Du(ec&^6(E`FU|;Rksi5vhfQ{20sna^7H#ohXWUNbd+6h
z-%nTYxDXGXK!4Zea5T<lD_#u>;x!onYEg8|@Ls0&p>vEzt2^~N*ggk@$GXG(BJn1&
z=XP*@7zrFr(@S`;on;e4Za%C8qJRPx93V8^<{0RJcpzPOl+K!RuZ5}03q=4ne14Vy
zuAIFIbJdOaxD<z9g8vNvq~#T;5ISs}9_bF}D25>Sd>$UjIUV)6v=pUPRBzrq-%Ua|
z&2AS~m9tL6F}Xyfijs0G8nPqK6C9{=#g!#*b$M1k7^wj2rJPfFn=>%($zfiDcs;J9
z&6K@Fe6D<;_9iP-OD-XtT`6zY3?$c{9}a6}9wr5m0u~7dNwA_hIGivLwvb$BaDoMB
za<?%MP*7V);N@^nj<As`(gK-sLdl|GBq_~D7Yt9jpmQOf1lu!U>E59j-H9Z<60bbE
zYcVn*H`d~3+jrSLeSuA79mg^;)kv}-vvHzZ-tnxp+KPGkz~^kY^38dQQ}mzVpAfGv
zz?X1r5<jZ$gbHBDUbs)lI62hXe4Ek8A0&#W>iqu&fUk{<^DrQnBy=*fOQvr{n9LN9
zAjOD4f}j58N#?+D`UZFr3zmgI6{?nvFPL@#{=>OoV4;m(qAknxa9V8%4{*kIAf`Y!
z2lq%BNabvRZfGB`Wu^5uT_r5=44biTBBPln_V>eNJ235W-}Rl@gfZG9Weog+#@T%e
zb&u5U#3eM*gn0PxV@vf~J^cr#$UI1GgoE@k0pa{o5i&2?_4L|`AyB)b9s=o#>3A%8
z3Z)Kaqz{_yRI)sDjVyPXcxDsu8u!6ZQ+A2ZW-et+9a5zXG@30TTVoE)D?M#<t()jF
zU^}R%T_vvWdscz+6bS{EO?KG;P3F5I%^^XG!~&sBhDg=GRz^f7M=>+Mn6Bk-B~xkM
zx@jFEZ0oRNv~i@ES_R@!-f{p$(Rwg1!;J~u`52k;IRe^dh+lgS30B%5`wTL<!!W(d
zfG4(jDLNW*;krZmh<v6XC{T91P1uq0(L-pnTJIJY@0rEo<_3(DPCK$2h-CY=agQa9
zOmI}^3#bS3(x8t%ug@~<E9P2J@{@53@flWUS*y@Q&olJxV62)s{bR8S0|QUIB=2#o
zpIF({$ksax5|(=hF$I@)zP7(K)BA^dO;Y|L#H@87Xh$K^@lN4lt+>`t-p2bbGSGX$
zB1+;X${@sw*$q{Iq;uv0AbdzU_9&m0f*_0rgXoovy9kEfw<({7@oU;E;7O!j)jF#7
z@)*bQp{KEsEz=GItvK-n)(8P*OnQLd>PpJ(I{q9mKFIu*jR)nDl#kSFV)=lO`c9s|
zLF^h?0Ri|xXG!JlP36X3NV0HxG+Yq@`N#@PP(c^t1g0Al%fjG7H5@zD(Tpk9Kyi+~
z;0v+|!6!7)m&j?Sb}0ZrkWB<V91%?=L7G-MP$yi)P`@seKJy`lTiD7Wb)>e`6+IHf
zN485}Zm4hAtrri>28&MoEC2lHzXh`~yj;2-q+y5XKMZ6T_;=XCOvg>)&z@Tb@^LR&
z$U*=5a&!A;;mS;*E$L2xMB$szLPOy_ELHv~t>4h+ULMuCS08dZYp1hvhx;p4Xh}pM
zSsKQH^wClcK3XrvH=-X5$<DX6N<~xc_0r&_^<Owp$LDy0A5CzuFd4$+c{wL5hw~x)
zJ@tnb)P%cWwIsX@ZIT_=8f+}zG2^hXD0+l0ye7ekk^4u=VG~RP;TEzS8f#ZF4g3~P
zz?v5&sxQIoUsV<V>x!yyN8@?h+)PAuW^th{9BFHr7y8%=&wpFCC{Fj5XtYI^06aj$
zzan1`;>^_y)=1*DB>dWaC<Tc++;@2BY9|b<9K(Xm9az^QE;0rMnzyc8h7yI3xX^-0
zLK5C9weU{TM#a8$))Sak;AJOwy{}+?a;$`RSh8X_`fs4^v~B5)fcRb-p+Dv4SBY}p
zM&>|O6-Itf(SfJooDW|Eg#BN+Cs6S49v4FphO5&19_G6QfJ}Uo?Ae)un^!B&l4r3j
zCI2R5GITlXY{{|{R%&5sPJi>V7Ej;xC&xp^x}oz28skSFi2LVuxOucbW9x7+(_~yT
zt`3a_k{q>g7|$6E|I+^V&oQi5rA4!dy!qsW6YN_|gXL7fm6nmM9|D(bx09dr>4g12
zJTVq^?RjeG;Eb%EKr~ArVXO=vYWhF;JqiaIl4y?zp0)VZ)Okd0(BW&IAuiYe7K%(A
zlkgOI?QfFQ#R{p5*^-YjNao<k{p-q-SsBZk+Jhh>(0YR~>7r#^W*-}$=w>k>pSy8S
zB`+13in3N6J5CA&TA&*Wt(somOfuw(ybe6i8TQ*$ha9v16nt&oJiH7i7|4>jnYE_9
zcV!4_gy6YXh*dLjLo(D0g7rC+>*nD9Jvaen^F&JifTmWXtH!zhg)(GSh#s#hQ(p*Y
z2dIyhR}W^r3>(xN<1UgH9!KW`Y^-s9P7hR;l#TS7*y|h_7$Vb_F(Ep+BVdbUCVJtu
zS))e=Lh0{!HPqLMCsx%>FtVidm7)_HoGAKeWeI2}%1s9jBasgA(}w_Rr~3vLA6{q+
zp&8RE2@Aa>&pDb<5UBz+v6*Or5pCej6GQQ8c1yO15%`U^NEi@O&d~bieFzBZC=v|+
znk2$Pq^xyR4_khMheN8(mU8r){Hi+-UQ80`R41Ceo*0(|l@N6eDxwC?@4iU7F|tRA
z>c}oor4=&57YNz9YdsH3Zsw12rGeOT(E7RRsVX+1<GID*T4>;UpXChZI*}Xm<1@8y
zpYgXx_?1gLlwC8`lU%>`(s=UVF(W#40Y9TUlcbH>HSL5KlZ}Vy;cBT4kbRP?KLC}X
zUfS*ZY3*3R<tqEzy{-+k5<t<A*Za!1qRR88Yk8hbrGqmAOtLc>&r0&`D9xQ0cfod(
z(iOs>BLNGGySU$w#l)!~u8C(MJjVv8ps^!Wu8rgg=gcTQOa#aP_fh`KaIjhgXpl$d
zJz}c3Nz>^O0|Ev~NwCa53ecOxWpaEs(%Rej?k7=&bm_bV3bt*gt*wYOJe+)rIA!KY
z5MJnT`cG=$Pw5Cfm&Eua;(#S&amkVeR5**`dgrai_u+9eE76Ikk=N2%A<i-4&`|ky
z^8BPz3_Gz(;z87u=_X^X8!kC=-Uzhm<K9sd6OyY6L?TC|`UX+8sf3z&zd6d5B&Yes
zJXt;HPz)sUEy1KD0hfpHl=&VBG-XTZn7(}P7$G<kB5k*g;)mmrNln*1U6b*bqBdEK
z8XGs#6Y>37@J26vJw74snDcfdts?q@V8A&H?Oqf8s)0LJx=jdRr#Vca<e4z&OEJvt
zlU^;WEQ%D<W29Xt(FmBYowmB)Xal$f^X#)PJ%tODA58@fD1uR$nC`I97&2)9{kQhm
zFwF&<DIgbA#Fv%G#Uu3d#uG_K`A-#-ns5><Nf3EEOb`ocpV^-2YvYN|+PTt~sH`aW
zAhj{IJrr@Jr;@lja`^Dnb}?YUSe9$@j>TyNu9x668<{?~i~+Kj4Jw=2GrRs`U(k!L
zleTfgC4t2+z0tSnE8;Qp;ICVcAA(lzFaMyyQ%_vs`uULHBsxe1)ou|hs5q6cMBStz
zux5R2nk5b*7Q%#+mNnrwFKM4`KL(6(dAp?_F{hIq;<M+i;yG-O_1fM))oqd4Feugk
z7SAS|2{>jPibe;+z7e69C-Nf$yge%Gx!Q;4oR+i6z9IO56#jYmJg~w!tXYOtAhn>-
zS~j85N<c_wz1*(bXzWr}rhG^|dT?+@IFNfs_-Kmkxqe~9uvd%$daDLVstVjeyaeX_
zd6t7x=BvzCqX*Ywg_EAVyNixFc4EY{SOG~<`KF@}w~WpUf?FnX7p(jlCs%5jVA)Iu
z&-F3KZDfu@=6T-xE^Q6j#i1soD>})+EoZrsj~8n$!+DDDJVAePvNww!1=AaL_k2Pv
ziCd~QAoOL^6VYZ&vLjAs!2Ad>GWpciq>L)a9q-K`f?{iv)A$lwgtA7Fg^t3gMHkp8
zo_rj0GHzWf&4)UH9(HTMdWsP6Kr<)B-fV5P`l+;xWTmbVHgQD)t~Xd%Jfk^7m9XG;
zG~I$i8WzJu0zTgf@Iu+$OhbZ4XeQNsFA-%m4U$BWWwyyeEGBoqp_yH}%<8NQ-)gCS
zqLQ>B+srDU?rcQl1PJY>FiglXg5H!SH}nz>2N`NdX|6mh?<sL2G(X`DgP2J+Ni9+z
zkza#AhuZB$tFm+eRzT-PG7u<J=}ADdSF&@oilU^9&uWjv=TI$(K;%>NXl?Ff0VyW_
zdsP)rXV#Lb^lkcd9wBG7$*du7^k?4>YJ6Uc=~|1C^{T6hc3q5lf~I3e-s$4-m!|6h
zI71nqgkIgij-CHl=OR-pqXUs|uR)D1d7Eg(Cb&iYu_^AmcYJhmYK%Vh<gGW)KXFUe
zUS=NP2Zuj4#S@R;eCy&<x9R?SXC}s`CdUpW4^#;+imyHFYn3ZB&m7y0v+HzRYz@y2
z=Ga&_)~f|Zj0y;~NDZ|bG=Tkj^Sh@`)k)|U;~x|7lbQ@j_^?s?8^6%8pP-!<lPBoo
zvRkFlDDkkVQHh{$`(dK5Rql!hZ8?R}Q&l;$u3wD^b&F?6;6*<_y=|PRZH$?d@<bWw
zfSQd7QOQl6Lx71It|>@F4q08=pft8G&9YAcV|wiaBHc6l?^rmVX@T)B<|6>cmKOLf
zhcGBj4&yf4w{1u8K`_nrgnX3WBX*x{ui|s+@nqN+(pno=?76u($(Wl9CT7r4VL=2t
zs{YzB$W3i<R(ve#xs8g*bhv@lZZo?jo(}#Z6x?@V7(Rp{R5=q&cD?bAkoXmj2QYiz
zp8ci$&jqwJvk|teM6DH4Xxtcvg$FuvddS|6T_VA?aJDZPv$Vg;w)3=;T0;|XHIvLC
z(d%QY1R~e8?6iKr&ALHFpK%SzrTjl_z^Jl~Lju?x5rv>P;E(W%Gmu?Ob0>_Y{XFlZ
z0lKTm64t#Ff&hZ$r}WzlGCvD!_YtIEsK29(8UG^ihwx_jrs&)MUxQLc$)G!v76Mgr
zO_40r!46|<F=!qJ;%s?#&SA)_>^rebORQr|qk<VrE%dXPO9~*q_jr$iC_3n|hyH|^
zo#|t;-xT%1x4}b)&CVCvKz0|GLV*8wb*U?`<HUlT?2!`Ly!@nIekXS%?8tQOQ)*jQ
zPDygYqP!U=;QuZ9<cy;cmJ>IuDa1`*xM>IHuj(sgG{|_Ff+8jpFK-mx)wR4`rMU@{
z-TEZ_g1q+}o3-WWsP~W;3uc4(!cC+}B0khoPm!l!8HuP4W(<3z&%vt0-!50B;pd@;
zY7ih4z%E>5VD!-W)9^zbm+*Ew4(!zI8(8ZiwMU8-jxKY%QvG)F6DWW8zPCu|K6MpM
zqNnw@M=@K&{_^Gzwb)Z8GSp*%am3gxnPH7i;BDZMLQg)bk$uk%sM$zngm9)=s~d8C
zCTh50uGtAIopRtn`#zG3J)|#GgABsTyne3NQ<J53K?dLROTKk{^(mEtVW0`Q;h^~G
zgKc-n8F0cGMscsVcGQOyR6F6tmO~lnRWCO<P>Vk3H#SSB`O?x9rIe?R^U`}?d|}2o
z!`pipFNdbr4xDf<p(G#SqR)KjX2sXBr^QN--L$!p5H#VMb2Ki|P!1yvWEDGe=ZwXj
zcK9+&GtA2(vzl9g4g;%s4>aL1lw;W^Hmqj_JAs)4Y6BYpCMfJ>JbM64gpmgk+It~1
zv~c!&P>U#U8jgWw#i?+FyuxOPvh0(X^(VaFan}=qxv>gWB?HQeHzn8dL)5U_mgK8|
zb}!WW7<aTlABc30Tm~lp&Uk8r>uIvQ?j)MEgPJyV+TJvc#W!(ruza1@3S^ZS$O}#b
z>C2in`#NyTPg*RQ;*nxDuBxJ0tD-Dt%7Uf@FsHERTB`?nMxN8BLp5QD+x!NBxI#?3
z&3Y{ol#?eP6wvj|?$ZV&^pik#Hye9qkY^^RmIz~GxgO1hgQLAe$n9L0T_j(Ac~6&}
zR$IPl(9LhTHh|m-LEu!tW+13R3n6p7ApuRZRliSazh1XiR{f{xq2i=qx@0AeRo(hZ
z3e!N%pYN1;Ux{~9PM9De0?N=&wrXH`CY*y0<Tq~GN7l4DC$rz_OXK_}g0&<96YE@Z
z{L#pAQ)@STP{&FKBpb{TB=XG`=$JOJKIn5UL0h;C@$*bX#N~i<c=FNn$ary?|7m~P
zeXaLJhjRX)^ZY(=@9?J&zdm@U_0>MTvUQmOVSd?y>(RGJ>J<T!(`9D4p@?BKDevrz
z-Z<R3g$7slEJyAPD@NY04m>yeL@btxn*Hg$DY&;|YGl;?IA+Vu6z{6{bmriLYpTh&
zA2wJIeMEMRmzp1_<%>15uXkzZ=ee)`6$#yIz><FabR)m0MxH-J@7BoQ2kljquD1|u
zkNPet1pVOz!qLGzs?C}WX{rAe-XyKCGKzA^ouWzykkrSW2>cgkdGef{pXzx5nYxW%
zV3RvGWeOYvHV_SCkS+0+@ZS3`?B-AN#M7?b$xL?_uN^H1zl7}O&t=~1K?D8TUV?bT
zRf6>8V-g>2H*T98y&c8w%gI!lD{JJy8C1J4ohfyQVKM5|yXsJLO2(!3x0tRjCK@fW
zA0F>_$=E&{Y3@YPkRP<k+mcpCJ|nB_1XgH%5k7$f;XhmblmovO&>H+F>Wj;DSRi7O
zwXEip<s(L~jSH@~%y%%CVtEAh>1<7`=t1OOUQ6@t8#*r5yC`RMlX%Juq;!>dF3Hpt
zGtN%>p$E!KcaxKv@x14M2d{i*dT4(}0_%scN+o=DmH7)D^XON}c<`;f(AADu+2Ij3
z8{V0glW%XaZCiqW0@$2^*q@rv`ECfm9463B2amlMrK5mM9%$Fhx9OpMAMoV|-Z#;-
zVO3|nS0$lkYn%RZl&+G`HIm=vFTi0V>lFec8L@?JO5=`(GEKWm(mleOMSU&@?XMGG
z&y>7(j7+17KDs!|O%5HEy@IjiIfX|3SCc?0r11<3W*H;PtaIh1&PyP_{-}mOzVJ;r
zg<U{c;^eo--4q{%H-&pZbOIqQ8m5UKu2}X8;lo--!EKEY$+*OH7Za!5(V`_Pk}7yT
zV5*K-4)%gs>q*@<uvbHo@l3?kvpg%MP;@=3HyVC}ehPL<Kl$M86SyZ3PMNQ1h488l
z>`{8zFL(q!t%pH9QH**M$W8F}xB0)Wl<>C{j}we!B55<rM=U+(TM2N5Dm9{bwvguB
zHk*zlVrG9`H}C8tE`myU3cfvZO>Hjj;nGlff>0--%)UlnA~G!b_e2Kfo7%a8u8|??
z^~Q(;nyv&wR$auw3zQR89i>c)p*n|ux&*25vsEThVuT2LB}(cZEoyGcO~yg!abO<9
z_u7vT#eF>G&b$n*u8@WsOUZc|Sv!3Btw%&SD!=I!5w3^)=2+=RNvKZ=5PiK|wQ$tb
ztHZBE{XQb5T^FZr+8L94uvFm14h|I$NTE!+@q1f@i0!!-vyh>qos!)V!n(_MFz;NC
z2UWGE>o=KHE6S)#N6*dwo;VD{5*eLU1GDR4VEpOpK-iMU#h_3NcqpejT+jHzZOac5
z@(c8XDl83>9+Dd`f4mvfeb4KP@i<~>M2{22o1j#^10yYBW{iF^8XX{Ck^v3OcnOtI
zqk3~Y_m@(|vsuzHp9CtwKu1&Nb2q-Vzt3XCgPzgRMfbzGG*_rP>U1Vwk5b?Js`oYf
zAjmd?3D&gJex~jZauZo-FE*Nr?qW()sV&h2=Y~kLxge9U2_nS~_NFF!jHo1Q9}UZP
zRB?kf9t{I%aqzrYeM^C4st=eiu7;HpWwy)hu~=1sal%Fud)(!0!=i$jSYj}61XZa%
zgVu!$mAxJs+HE{&5^^I^$z7zjRk8ipGE*qLA)1&0-9W5jiC-KQIAr6T6I&5yjcwY8
zrknq<DINr6JpR5Lyxq>n3*PIhWS{2ed&l<-Aa~@45xVm+W*gi;>=btK#Pi>j?JH3n
z90h9x;HLQ+S|4S01Yt5ydrteAETBBrwkI%)lZezeiT^M{whhxt`g)4MBkNmG-~x26
z$FC8hskrOX86gW&cN0A|-J#a#etBGV@`3R?t*p+|?;Zn9wPOqWO^(6kEIF4!+y(~q
zTh7*nPpmG85*gR}xGOoilAI;++>py|<4#k;-E|=x!5!5Ecs`WDB(e`)6a^KK4Z?(x
zi=>iEL0nDaPHHvkdDKo->2gf|Q|v3=@IqzD3F=juZUp&!cRp;zXj9N{&f;xjveyj}
z)wf6JMdRg(FHga{3vUe@FIxjgPsiUF<d@ici*F9?oL}M~v;7>(*9q{-7KRI488qa4
zKsEIb$Lqx-l5oeULf6CQs>$e3s*zVFG*7qfA*%YT#I05XVH2<}Z}S|3?bATTM|q;j
zjddfqz>F<$X2o+?24*f7*c51GqQ=Ol^Q3XOq=u#%T|&$RYH$gt36(@WC;-5ix>2O6
z3D!)EOD)A%Z5Vd(Z=MHxG)Zvu81YV8o>l$bqyD*8qyjc!s0DpOmC7;@f|2^7PS)iu
zcxZJiDm|%b%3=ItXP`QenJ+O?n*-|5CCBuTv;c?yX}4K(mPNCIEwO6f-i4s=n!PTl
z5UuTiEU3HGOP;INlD}W}NH$tz`g~Xq>4Cd<G?(7{5ETJJixG;(zhsyL#Ctir^d<_Y
zS-E3F@Y*k!;xfQV5k)U$Uf7QBK7+k&X7am2JdEH|Xqtld<ce|?B)OS(&E4eVea!~?
zv}ft5cl%I%VqL%XdX1@9?<Ahe_oSsVSrI3emQ=M{J>_;!yTZFQrd;MKcZxmS?5Z_a
zsFADQQqk|KsFzp7n0{qdze7Bx+p1bzdC<YW8RoU*<k2dcc&e%?eIX8RBn|}QavF*Z
zPW8u^Vw@n7)CklfpeSZ0DMAG|9@W`H&E{K(-ffPQwi#$!m2m$`iv!(hs9k7Wy2;m!
z`9Y=F#6AU_mxKH>v)14VVdDAz`yd6VnK=)w2N>+s8N>|x$=^aH`%R*7hN3mNyco5$
zbY5)tKWOl5{>;<%0Ld>T1D<K<Tqj6L)+<*o>etp9(<jn?5e!+ch7{9&3-woS9^3fC
zJlvq*EHF;-fMf_PK7hUOQaQeda6U1X4-3<y%0FaeLzcw`$BA)i4Z_g)YO3%DhCV79
zOiIJ{z?F|Io93`1gmwvlH6YZ0h6&Z`u3q9xfNJ#n`;HH1>b?w?w1kug(Uz5I7s=Us
zNZc$xRC0tIrU&T<29ZtXBDRL%8PP%|9y;~sJxE2-sPTEsE1#uE@w|LVrDz(5@j+5w
zR1e#V#4;eLCq$P(_Q}JfOz;JQ1@N4!mB4*Hz(H11v4(x~x}MkYxA5L`{{D)>Wmk1C
zl?doC>`f`Kgf($NH@q!;07)dvKOv5r;pfeHqYduV@|I0HQ3zzUK9yByawTWG?LHMY
zm%XBtJD)ql`<YM9f-Ys-OA~q1n6#ueT_%kd3=L6zP*xeU?FskV5LR%3VT)OwZhN8y
zf!yb(XJ2$)V}@etyAD{&d+-F!3$vj{;RexV#%JAR*u^mNx?x5A5r)~nkOnjsprv-4
z0>1LY8}uMSt1DTI21lAtuC<K!uMfJc8&5wV7tD9AP?dKx-QXcSB~B}t7#DiLBi<s_
z{ZmurS_rTY7Qu500)ZF!*g1e3n4rj&G`P%<59t3s)>{@H-^Q8I3!amqt+ej#YCt_$
zbbO}E|B^5CI=#GY$_6g<@<tJGg1rxMZQ-Mv<S?sRs3mfhAJP`bf>f+N|7h(PcVgle
zhIgozn@ax;?LY{@UpF_DZ7R19j2rLac9;4v#B{En_)aa1Gt4SToS9^@7Fxt=VTx_l
zvLnMjouF}3VQzfJUg7^_hSdC=g>|0qj{@rgZL=&2fEjg&X6}gPg^12wQ6@|}Ry@~9
z5`0$yQ;u%5+7oYRFIfYC8df1-)SA1ndA?NoMt&cuIu$kLFtgt~zL=t2Z7X({tz+6~
zkRCgfX|J``_4K!AzHt`58Y|vY?XBrk!Q_XdeY2~5jXB@<Ed3ke3#D7qx;Yo5xmaHi
zL#R$C_3IIAi7V$T_B|wSW*?9yVYy)YM^OM_#!uh!?9;bB%eUPQ#Lwr_QY%L(rFj*Q
z{u^aVvqYcv3W;K+W2&8EbcdWgBe@g5@U5J?%s#mYD|>2_Yqg9{E5T5zwT?6#ZyTw2
ziHen(2^$xO-}UI>a2n?F<Kc*O*7TQdgd~{RjEHAJN}K2T`oLmyIr;dfR`$ex0vmQn
ztTvL~VZtXh${*3V*Ng@N!O*E;;1!P88K~2-gC9CAJ)iGkr)YA9|I{{(-tfHqvwL?_
zdEs0?z}l7C`@_rknz><5Kav^}>~r<(YNqUjie#UlS8}u5qT;GQBc8oH5=-ePR&jD)
zq|+@cwyms-s;7^YfxMZ;I0qV<^H7=(BNvdo<*yKYW}Rz&EUVw-CaR60*49%SaphlW
zxU$t5lK8K9Y)i`a`Gnr+&mjHnAs-A*smu)fn04EaQuADpZwud<J&({{`q5tTeQ}rN
zW}X-lP+6>kQg^a;<Q%EFuy#;w_GYL0n86w78iCgK`ytU?fKpvTfciq6=fCNj)sBZ%
z34*E@MrS4((JlE~0X?@v(czZ92S;}e>7LQi2)JLvr!l!Jr!}x(<X|tOx6&YkTgHPV
zH~QD6oDL?|qs9`ZUaS~X!nP$|U(d%p6$7Vl6P4rx(B^j_PqaeNxNfEv^8}c3>KGR6
zk|(8_7A)9)espRwGh4_NXS4Ytg}Bo|I--HY;vfS_d;>zZL>a#UGI&jZA6BrD{Y39J
zY_}#Fn*Cp$iDI0~)Jw=jdON*zrq!7!)F!hHK&NAFoV!u{9Lyj0m&Nyuyg94>vvs3G
z)@*aXM5FE(m2b5RzVb8|Kp43a{?|hxhZhzEB+TDW$TfNCTl;(82}hg?(Ko(^i|+zk
z4%!}edeyN?Zq22=_#4s=#^2Skfu$errQXgVMczJRJDq4L{*9PbwXVb_Ts!%ippADM
z*-UMb+ZPIhQLe~qlbLijpXH;uNt|S72Qssn996FY&Px|o8B>M8(XZ-|GjqVz|0wIv
zcye$8>xZ-FM)nY8DWhkn`R=E%IaA6IXY2r@q*odZ&TYd8tmCVQ;r~e}b>eZZ$6Hu>
zUuD>hyvo<?cz?Z{G;IvUfhj#tA#F?23k!*A#`Zj(S3dJuFb?PH5K246V+}S_=b>)R
z@;cW6XyByP2OrK6mNtK!GEkGvg~W<~n2SV<bY<@<5TVJ)f)(NFbUiWp*7TT<{@L@N
z{KVA6O;_S;$(_J^(fW~#_b+m7?UE$bc6TW*ksyT<Z4Q*TO<rB02LhD+HAiQc^-Z;`
zYReb8+CcA+`_6q69e-E0Kb*(~nj&LlSlXB02b}cbdO8Y7&=QPrnen9q2lihBUWNyI
zdRiKlz9lXh5)elD((M9=I4zrX8<*^&7~pHnFo@9WuI{N{h!tWN4&)o_!=j_|6Z4~@
z!`AilFE6sxxMm!F*0R~{SID;3kF<vTTCjmX*R^?R{XgkcJ|@!j0|Tc&-=q4D+SLIk
zPI&k14%Hht?f}ObR@XE&Ytc2?Wx2(y3{Wd1vl2!!FX2FA(tL$F95Xn4NO8}(uxL=p
zhYo6r0!|~@czCa;$^a~PgM-yoPQ-B>Sc?UZfC(mu;2A#B!p#V1e8mjTfk?xT@}O_t
zc7nEcNEq_BxBLA;sN~NtldDSM#|qtDoewK_T^>0-;x(DxqTl&npP<GmoJyt2zd5Yv
zefOb)t;oocgM=7y)|EDImOAS1MfaR}*R}_0)i9X(nXBBGRIOG^%O6X7xS-Z!y;@LX
zj6Yne&Cik`!?G-4Dtme=S3d7sO3|Cm1?ZjWA9jSkI<3%LUsZ`Jt9(&mT25KXMw-J(
z3y4xslGgx_e{{!QIvnN;54#x-<4f+!@8&N6f9EZHPmkErvMFf4b9Veb&8}c2QA&B<
z|0sVjDB_kZzd}8IUo0RKOpQ|<OORD)2}6ewB1{B-Z3akVLh@D@H5P?Im1oVQ2#Ybo
zm0v(HkV9_7i&(&mEOz6iXi30JH7iGC=<hOxpG=RW7`FDuSlDPf`CU=)`dVp5d37>o
zGs<eI;&f4Q@H)>xd9AbnlctxHAUa#}_SQT$Z{6CqQas0RX^0@=L<E6l5sOH+#%jZ;
z4p_zgq){67bBz&Tx{gO6Ea2NORyWv~Q!^LmhHNmrDc2H2IL!Tau`Gr}b`3&ui9;)9
z=6(fXf7j4IqZP=BSU!vOSqMiUNpO%@Kt4K02SjfF^ZCl3Wur34LW~QYD7a8%>{3N(
zd<m5)q`TiuU)a(*UT1JTFrPMRk?p3%2!kUI3Bb4aVHwZmRsikQM6Gz5CSq6zp{nlg
z%Br632kEO+VZGUGP3t+Usf30fmzB*956{kq;+x01;o;u!@PlRE9Xq^GpVGaMM<~d9
z*CH4a<uw-4Yg^Z0A)~g%>^i_Tn;z~c({HB-cAkXSPIk-b&c^c}sX80Zi#-4$D5W@H
z4|cPd!)Vb2ZTXqsIp<73(P*YVVozo39jAPxpwM*B@=D5~mH%qqTHDmrI6?|Muv)Q(
zT;&(B>=MgbFnWAe;=%6uw}-uZ#q#o|;DA}uDZA-kKHuR+g$0}?Rx3wciE7_)+c_Z1
z^;<vp0>W(zBc(k(;%x1>?nq}_+lh`rp?9-?_UZhhbvJcPWYbntZp(kfTFJ8foEk8%
zJjKRTmWkBeY-)YanFWobHRqP-)Vl)X95*Mok{e{{s~ti0!=lhOw+nkXuHbnIDEWJl
zgg!~|;EF?<R}=1e^&ZXd6T0th((Z^%ZU28p{`UNg*EHSKN?R`l-s5N-5g4OpC26ov
z8|}RPaKSmRhJ-Dr3St6rOwi%LWt#ldK%BZeGG=3MP~g3Rl00dX^l*H1`O0nNM!NHg
z>F|~Ud1XcPhGmZ_E4#a^_-l+Su$ZkB**c`hEcj3XVo1C9VsnMF{-{$Oaz|R685$kF
z;x@7CZPu>n$RH{xD4aibL5k29LjraMM7**mIwU4AC@9c$Shi}pgo4`Y=6?s?8yHGK
zzcUX@Ws#%KdlVTBza8xgkVUS~k6s}Q3=B{Q1O<g^;88W@^jjl!vm?<y*8cMoI*JO5
z45G?o0wZHgd4bPIRIxVt{<0qJZCj~0-e}<sQjZsGCN6k2Bs?hF=>ahTfrEiTIQoOV
z`=3>>yZ{sZ1A%`j(NB1D8DvZL%f6UiD;RC-pBK>qV-y-{QU;P8qik5jHrW^jrBh_!
zGjtRcWf9akUa8h){z1QjSJTz<YIPc4s$!y2A}xX?GDUt<HYRHzRA?wl`mm`^VFt^M
zn8uI~Hf2YgCBYKckzkX|ONeCF-jYBBlAw>(^Xxc%kD#>Z%}U4>nxmG4xl|f;$H2vY
zBfeWk7SotrL{`+#Vk?Fk@2@*wcYznEDGGYWZ$E`*v4}n2$qX+d5#Z%ss~FtUd#W}J
z(^2>6HfEQy_uWX|2zidYtbiy({(RVmnF%FZ;FBW(@oe+wg1a^V^QH&<(@tuP;yCV<
zBp(v{HUeXK4s%e*_)8oe?S96HXe1)C*nJ5>RZfQc95XX$e_<j^P0B<p3Xn4x<p!8y
zxpJC(F2lo5Z{J%Vg1;pl0_pe`*N5EP>9u@~zh+<C{PNX9n``g6uo-VY^l-}87H{U)
z=xLvi$pu0;a%)vOk7_rIy^vrjFel9C>CHz3wSde7zZ{N|EuABWP#q)bReLAQ2`=o&
zwQrpf82+Y<LvzSQ@EqpF;HwnJoP&2c1zSBl^gWi71cdcjWg=oww+g<X^SN5jOTrQ_
z&&q`!V~j?~BV@+yTq!;rM#yiFB4%pYFM*I(sWJjeV(eA)yRj&^;qh~FHg3%EO<gF?
zl*~#jJUX$sIC0rS=F25xAhzIYS@eawM~RG>L~3idhN9O^kKVlyRi*+@ZZ~@9&K<89
ze+U*pyXkBh<9Y9%-6MQRb(L4_1r|B4%VoEBVW$&!4G#l9J{CuDb^(E*Z{G{(Y)=o2
z*(V5aR0%*9+lYDW#5N3xvG>|J%(B9zlpMyG72TviMF>SrighUb->@l0Fy`wDaHNi_
zPBKwhociG3GiP`0_Ho^3!HGEx$5n715xetcZ`hRU8+*Gr<C;RDFf$Csw118$B3PF5
z6$IH7HYi5@#ES1dwVjIh9PEAN?Av;=^Vi2u!JZ^20tPHv@v>O#7hQe-H*_MIm$+Gi
zHCh?0(Tp%Gd&5k_^c(=Gdie=tw>zJ$2?pfZXz%*<qoahfZl0d{^OoM;M{t3~@^9r9
zaq8=mD(Y(Rp?gIUA;?{xpO`AEl%*mHipquQV=3WCb1bfRYlLT5YLdd#6h-`gA{$@d
zlQ9|mmf68pUM^Nu-dQ_6%@yXG(iv5QDqO#RpML-QkiOXen#x~i3>;_3O*Pf7i;7eD
z;OmUe_aQ>XVeDO0$#uBm+?W4}8ET+#JLBhwwj6$39Ya+jBCX%-`_~NanH_y4)H7Ay
z8tDxD>A(M_CQ`jE;h&q^3l%**;;GXCxzrT3jJj8zH))zfsp*ERk%ie=>-$XMtGkNK
zuU%dY!sWi?wJiq@w5<K|Zl$)erziLCnq`tyDpiuJX;BU^KVT({41>DC)Ssqb`ij-D
zU%fQ_(;!PHHK)}#rzO!-{&9hIy|=w{(S2$m$QV%&fZh$e^{1Z{KmQC<uB)k$UbR@Z
zAGIRuU)n5YZ?f258*u+0ue80owSlh*7Mz0fz+#v;>=S1D+<jdQ)41kLChy3}5$_YT
z2WM~k+I&OGH9hE{n;N48YLfCuNf6-tUhY09jpHt@k$_#1S;9C{sY)u<!F5BFgMpcY
z5y{VE;I!yNzuVC+Qg`m*4Nik-xMaAF4YOWZ$Fd*mDlez3DNm6Huvynso`=tOoX2@E
zJ~Xpu9M_yJ_HvjIvgzH@Y@{Cz#gNPZMHwu)XEPYPIXA{k66}=4Kr?YaYwBlBdt{-Y
z)e$+$oCx)XFh8LsEINEB>_6caxf_Oxx@@E3#aA*K0|T5V;|?qkZ2ZJTvjqh!E8=2H
zONVTO<m0;evW%>tHRJeRPigiq@5-l4RM4frmYPigI4~6&RQ~m^l&L%@W~XAO|7(|v
zA9NO_f|r~1z-!Wc7u5kl44%6n!Ywg6LB|t~NMSCx|IGkD@CQkcQsei=(u{Of?Wt8k
zeL>5l_pdEAo;Mf%5P$(ey+LcvTg>OrgJ{vp5x-mP7yI4AmObkNsUvmSTcZ@)XNY4j
z!H}e~QJGuH=L2Ih_clQO{c!5;_OG6PTAaEs<l{qmV*UZImBnuu8d8z<S<(>czz&K!
zDvS2ZVG8Vh-ZN*0hx?jOn%xd?b<6(!Eo%)eErwUd-+F7jWY@`)yS|JOGp91e7`X@(
z1p$42EpQQWTw8u|*yMe5vD>a27Fw>$B0o0{dQ!R`##}TwXvQ2iqlX`l4og297XA3!
zMGWRKpiP!qjCm<m3kPbI+}ead#u)jZcAvwEKgmJBj#5@~>(<*l#BccZ*ESv(H24tW
z{kkKN#Y_0Q*arU5aH2DKHw|v2TYHAKJ4BUPp-|laie@rxlCAh}PHT-ygF|S>Zl`w0
z|6;=ato$2_`sQXsAm9+=VG#EuZ{957!>LJ%V~*V2wsze?ce>!^?tOK2eMCkmBIB>!
zxS?cOQ4bQ&Z$IB>GKZJB*<{QeUp%){{Ks4j7!eq27qDPo#2kj3aMV4qchrGwb0ENp
zq9}4s5w02#bwU4^?<1QhT|bsTJ|e1OvQ)_zUwx{+Dpc|%dFq!n=tzoQU$ETdO-US1
zNGY!B4_RK@yBL;OR2}s3p0h}m7X1|U^Vd-FR2PtUV>f4#EBL8N8NyXwHY!63{f#=^
z)t0L|PRk|q74{`?+I}91C?MyW;DQ79+`*mqX37PY+PS%PwRa4wTbN}kx_pq-5TJ+<
z;=?!CgJk@-m;N#j@<6a#qIL>YTkW=!&34-k^beCa3Rk#bvtEg0g96IWK+C2wI>YBY
zu$H*VzQu0mEyQe=h4zv1R<Hx2=t&~K)+w~?SrWm!1vMoV5k`w5MwsOveJm1~AXRAD
z-v*Bkd&GXNpYnhL%{XJQewq<s!Qpa&<%1rv-B=itbjYyQZ(L!}^(?6=r!#!mil9$K
zhOOB{6y>UAEzD}eoprTybC%j~;L(9u+vv<~bQV9lLpA;($Lzt|c*q<9Ff4g1h~b!i
zEAjvODGE2{-a%i%eEPVwPd5I=(#PKtabSPoX8ry!#3A*FBHHpBMbR6yW~jH@j;Kj0
zJDsO>a7`JXo_#mfubHB3y(F{scbhYap}-IVldB*^l)Eh+FMd?~Cj=}A4&)FBCSZ2$
zuCHHXL6*#s`jO0V<QE*i=0#&FJAHf-NQAe*I=9|O%AIW9Pd&v#S=XzmYOvP%M<v|Q
z`J?z<>`F=ZTA{SFt6mJ&SGk`ET}>{?Sa-Is{&}EW$fY^*63~_zK3;U@lBw`_nSDyE
zs}uL_tvjza%WLH7Q$sTa=wO{yDOypv{Ml#MM{1OsNH}1>v5N&m5u6$8Q1IL#(F!`)
zkZpvtMi+{JQ>!APBc5QbDs@Ul9D)e!DLgFX)?f76J#;?@<Zt-aN3+e(OLe3yQKlTr
z&yUqesi+QZrnXZXd3K{EqYa@AqiZ+VX?sWMvz?t%TMoD2SsKWQ;&j%A&;je>^v0k^
zjEtV~u3F`VmMxwu9(>RhS}|>-yQeXXR|cg8{6$N4JKz1~zGY)IEj5I|%(LSs;Re>4
zT!^Z)*G*%)Dk>|w9L39e;WhjAYjNu^14qCbD^zE#$oO+LXn&0RLID95Q=#fL1A^+;
zs>Js;ZdZMAr;*#HZ*SJLW3)bmX|8EnZQ!`Ztx7IkO}UDlk1OZKK+m)g(WgoYLdJS;
zr_FiG%3uAGLCJ?``{SG&vQwV+0D&gRgw-XPmAECBC4yujbeWgX=!S>E3~st-1PmnO
zZBxtktP^Mn$z3K7<@*9BYC?73Eyw5RbFHRE9nuAtwYQfAFMVafa^~x?{vL?b#wKz@
zi>aS}`rXRGR&M2g*N8^x74P%{j&QY&-KJ3atDlnr{;4O6{#&M)4TjSugQr|RcaSIp
z9On2L5s5qtiBiFcGc&Nc9P%|6u7SGs(NXs9C<}<7RGJ`B6q(!&@xsv^zaf_zryLWO
z?FcW}O9A4<1e%DM3Er`Dkb{3#s(Erisrh)CL%ebQ^F|hoiI9a3hez$e$R_8=`jL_K
zKD|lQ=x2b>jiNvi=2Q5j<C57)))~}T+EJO}?#lhN2YFckXs5|D;tAvR`-`Y|rL-Y*
zE`-;FN1Kva>6D>ggezv|c=+AB6?S{JzW&pmM~{YdsoP8)0}o6lOdUNkuAK7wCtd2u
z(ec+0mhYV(9r^EnM@D^KSWtUDYUPIV_D^L;kNW+beextIAzzY?s^^stE5QUHc{qKv
zL|&_-;FQT|9(?yvgP-MU|GZpDl<~`U1(~xG?L`3!pU$TMUNs|r<pqLp>v?ES<Q%2H
zzafYsUltb^51kk67!arTU%q_g=wR=_tFMC8zEi*xAA@@etIeB!(6db+Y<l2sQU5#H
z@AMblePGiEP3*-6Y1YDct-2#vL(PR=2_>Nmp*Ge?`UtCIz1cnm+<tsk8DCW+R#rCl
z-6;rr+quxT=fvm#_)y0KXoL}K<V22jn7&hDQtSHZ0_zAzkB-)6N}o)Oab`MW5}zz(
zJ|Bec$prbO^h@%@2X%~yjl@W>$RHX5mqJJ`TayimjWv=!4{C)^cUPhB*Liho&0T(W
zfK?B$t1b1g!oPH2e{0d|u5h+5dwq6gclYt`?#i63b=HTut!zswnlnx2jheB20?W>m
zC&Dz7cBEWeRDVD6UB_g~3rp2h%2L0`sbXF|FPWFkN{W-WbpG<d4?Fx(PUde%OVtrN
zo5>EIk>->XtDcQc^LJE~CQbg3&E$mOh@8X%<=3(#AT8Jdenv=YXU_eI72xcZnt(2L
z5n;r>F{Ii_TEV(+De;vS6^Lqkl$<qo6I(P9G$3L;<j1jJgTRu3W&{qTETDww%$W;%
z<`72dKn4<NS)8$idJUsY5Z<^jl0cfd7NjawFRz-atD7366q!tJcYQrOKsFA^R~IXk
zXJ?S0*y40#?a35F1pp8Yc_fd7j{^}TXGGrVM4ybslEmvI33sQ0u&8@ZlCTgZk~Ufi
zX^}%wQGNQXU%-O4h8$HGTApe?*8S%J@S342(wY7Z5tJe_>e%3X0-{ZFVg{iMq0~Tg
zNu+$F;YD#6K#5lpp(+c?p$mfrj9r`Og(>$YmWG7333q+<SXH*j8@fMn8IJ@ku>65}
z2<J`+e^6w6nI(}6LD3W?<Qcw5ye@9?ZUG6{d<nwQ2)-9EB?+>@dRWfUda#FOk+2xU
zKzxn^H6j@QhR=#zxakqmG6IRQqnyVfdc@xg>t2+Pk|||T7G{oN1j|3itJ)R|G#_hz
zhmWKMR09%b4y4r0f0aM`7@J=pj*hC=G5Px*dkj*QD$2Z=NKI+RsfdclmAWf^y${q)
zDJKU9ry?V!h6X2<Q`@&|ZbF}@R;?0`*5w#7KZcrDDl5^+Xx*z9*O;$8Q@IbeN|%*K
zs?i|I9XP`04jLg<Dn{f+>rRq9UzrjY%Zh~F`iA61KXyOaENk1I8`#N|REasvw+Ug?
zNAbO51sIj?)7R9PYxGhUvV|68B1}S!SJp^DcU~fsDN_<t*ph8OWq=|8UO`dXPZW&Y
zmf_x;4bcp(K%Ca4U^qsB!e|8@2FQ%lIcH8+c;g#xLerAV68P_egiPAFz>t<OlFuPG
z(5Zk3SRp~ofnALv%~&C)dMFzjLy;RXR)+g-^!womcx2fLqjF4ycGijT4U8dW1C!s`
zlqIazAD(3a`a=$*4_Uo9=EM@!CBr3^GeRj-j()2=%E3TV&7-v;F)WUFkOxn+^P`1Z
zwJqxlr%;|2v>hHAw5yyv58eCIr`a*MyxRQy+~4P(?9iCF?6jJf{xsaXN#vH$(sdqV
z+NwtBHkG1XHrp6`N^!oXrX98OuH9lmU4qO)wFx{e6vXtDb;0hy{|t#B2&@}n1Zc6q
z37CNT;LAcoUYhhuNI+>`;1w+3rhqhPSGu-LRuM1#XQ5%+$`?km^3$GK5gPsTPm5gv
zD+3P1uJ|c7PyhEDS^&pk&M&frC5#)n0W^m={|w8rEW;tLUwcji_@P%5-gKJgWf=Pf
z=c>1535f8BlT_8vZ)M>s@s>KcYnJ}FdC7`Dn`;{5imR(%R>!z~9(h&d-07bu06gXv
z*1R+D>50_|4Qbmf*Hf!q$yF{*`*pc?Y8oNWXVY}o_6Qy<2w(3LbRV$by;73pUAVfN
zM+~yMY|uljf)y6j(&)z1J~4b!&5P6S$^oJWdxYs_X4^zL!?>*q#<u_6bHS*xh{Cn4
zihB~GpNh+hMR6~pHR#>4gw-wdgDH_ciTYJ2vn&d&8Cow^;TSPPkW(zoJ4XH8eUU1w
zq*7l|+|~KZPvf%^T5^$^)cd2pP|X@Hspj!~9?Y#c^aRrRbhPZ+A+NOhcBLgJtEjme
z+Hy(fgr~|<li#taN9~IO7oXiKfgXI&yJP#Tm#T?>tGLJzjxbj16EmUCQnLa+`_t&?
z(Uh3^d0SFYRg;o}hWE4T6JJ2Ok|@>TdFADKs%>|-=DZq&zYr3T&%E|@bo^x{W<O>k
zW9`Q$#c<p%JCqbVOk>Gzfzk2(NtOs?Ux2`(a}4aYQ(hIiIXCh9?LiQMND=dF!Lu=n
zUQsipnZyejTLGHGN)3yMMt(9EuQWdhZ92!tJ8}KafjVqx<_uWp(_tl1GU8&>X%6f_
z0y9T)0q=c=kv;JX<*lAk!{+v{Qi&rQ0Z;=5^9&2i2hL0%Jc5V!kI-j2PSGNL%CQXU
z5O_{v#RKTtPauT<s}M~D!n<wu`n;7egg4qan_r7C;ulo`1(2Z~i92L?uSN*KyG8d*
zh8ku`1YcP95pd<Kdwo1Tu(NKWA{i&ai+^QdIGLR_QTIslJfVOcG<j^0v3;iyZ+B;E
z2^KN!xC{F!q{LyQ#wZVSZtK9jW>yol63o17<tFAW?*lI^_Ivfb7p8fQvMsf5vWfsG
zDtsZ*+FlUN647`aBYcW^`&MXKc)0y8?B@_gdg+E%1!M$MZ2@wy<AK$K<mNAbTvZOr
zfxfb0H?nr|+C{xVIm2LoSmrj;91W`U6eGgVZhB*;BWa0SqRI3tB<fv~mimDhon0sm
zvv6c4kMq-)$^_qaUa6>q_pm!a{Ay;RlxyeIgd>$5ZpyXe+p@ZJ0{S5S0#8F*!i!3x
z<e0eg01(gNMz*&u89Jx9rLaow+%&-CWkm?93i69gkO8svb?vmTzm`_j*CR}c)V{th
z(U14NR~j#x!1gX_HAbArSZ>9UEI4xa?<Sb!-Ve83qBg<NtF;mL<>lT7TN@h|v^nOk
z_!Wzeoc$(p2z;{$yzN_%=psVv_D36HP@ZqBRdCr|XB)PLlsPWjOZS2E1d~Bc2~Q9~
zY>{`f2rK!g<VHx9nKYskf=oo~pHH&qV-C0YKHOG?Y+N%g>xz@D+C~v|ivfwavAg+^
zqsXaObpC5@>3q6RDyd3YrKYm)re-qjsEj(AmR&CGljci%r7uf~n9oUp5R3w2Ase@s
zNZ^Lqjueu2N!TwgN`eksN^-_}lx#{~`HRA*m|%{#-9RMQWa_9e<=$}rdQ$}iJw)(i
zqHMuh#@UK%Sx+<h^1UJrFnQ0Tb0s(?Hd`s%im}r7G(uQYI&bUF(R{i|+i+|cj(c~w
zpLbvfcytnH|6=Ipq3%#`s1SNZjV^=^e+d0^tLaQ{N=R%<C+THLiDl_L4{1VhP%dD2
zMiQwHtF%FZZ~k!+l_j?~JubKnTC;80bBMc|_9eA7d(}718K_Xa*=@BO*S$pNPkrVh
z=XwW2ZUp4lub6%*r@n?uqU`b?a*S*W3lF1*5Z-If$uw#nDIJ^z$`|nA;g|V2vRT<>
z*@EmB--BkW#`vDs+rz^)22(Sl&5s)4onBkGl7S1Ta3i8xs(VOnzL5)8goi04B;m}0
zK>-Wsc8aDmES3z(jcbQcyo_As<`694AN*;^Ai_JMz@FQ}Y^YU}Y9_4I7-;sdEo8uP
zT_Fo)!kL;i0Z}5~vH22rJr*pswOy*K4+xUX{@g+mB%M{NA|f@B5&u0i`$T``QjpX?
z{r|93#8%Y{t|`BKik8QE^<+iOYh3!~_v66K0z-M!%n83_d1N^=k)iE5XW)W+U{~vC
z8ES)*A#Vyy_U|mLfSR;law@sjRSI66yA<wIF6*pfYh{i(Y*RKvVf}cC+o5FF?vijf
zI&kW*Z0aA<H=%EN@*?gBW`*WzhSqa&Ytb*^!@35kEc_Snp~YL#qWNoSOykbuLiSC6
zopAX>u+kZIy!LpM^PTr5a2h&oG>RpDmrmfE2mLG|#O`%vwv0?*CA>VB$jBRSh@_~G
zXv)6|h%%K*EeMN#Hbx1%t}k47v~1mx^R@J=_D|Ly`LwK3b=P+3^vbxVXELT~2YS!9
zP0M|q|F5SajUI+QB>OLiU`%(@RQ-fW^WN%_k5QoT#fn4y3teyigx`;?$cmYJYrnWa
zM^heTL6AzRG0o(AH3#^}!XZWyY`ej@>+2B0TJ_e2F_DXm{s?PLAqiC&C?qnSrl~0)
zCrR@Jv+Va-LhvH;T8rdjJz=Lq28v<vp%v2m^B%Z<G5IJMM56Wix<n%jX?Mf=H<XfB
zgToAT6;X0fXK}KVrQ72Fj@VzQ+u2>EyQy0dC5sIIe*~qX{s^uJo^wv;7`^lB|L^ma
zm5q75Z@k{y`}!MR?^szGkrAM=K?mzxKTlgRF$%%#H(E=%)xQyocKAutSiTeAo!Hct
ztm@9}JyqTNXkt%x=P#;$2s`tDSVW?B@js4S+{YiNi25CXI28mc1oK>&+xQEMvz5jv
z5AtZIkPae2{?D&Sf5(yQ068nJk4*#s3AJ9uvaecXb@zinIemdEelzzht+71%Oj*WQ
zZ{jSca*vDW=a__gj$g%8i&$iekqDD<WYIjCRY-6Vlgv$H;0U3ONI)**j2>NT4)ENE
z(dP~b(O2K6b*Ba!c_(s$(IOJ_XE;k#QI|ffucVYudrjTaLA`5}M#`rWv-7gkM#g{<
z$GBgJTT60Sx2FCvSknDoyfqF)OJ96KPJ6{T_<G)d(F&P-Ro~|(?x&Yc*T%qHm3dn4
z=vH()^rth|tUlfyPM`jL=9>G02U|)b`xA8m#Rsn~exLdM;<bH7@TPjAJRn5`&0K2E
zN{d_Gl1f~}0+F4--(HT(j>@oX@IjGC61K7=jxutXV1mf65p|>{l9FgV!UaWt3ZzuQ
zvi)8$?6h>>C^A11sZT_PfS!+n-Dt5aB}5Pqhr8bp8RDTZwYJ?;YVG0iqZAh>CTm{|
zkE;G+(jKuQK>}jkKnXn)6cbMfg2vRcqZDTKw(jDX70w!aLl^L#rN(5~aH?*>;=!^h
zJPTzZ#LHn~#Lh&dY1+ujCMgCpafF(b(E#tsC1V=U^1n5QU>E1vMf;2cKDSElJ+b(r
z4EI`{N{bA~3QRiu48HGx0DBcD9W`cacVaRWhSGDc1_sBf7atgO`8~YY&c_wkbD9G~
zTl`7Lb+@K{U3@e1>s{7YHsVc(dQR75#arxOij1$@wfTa#;15Sfe>akWBiwzx8+)75
zbtX&PXUde@x9=NH3Qk3Hb0{@9Y52bK3z?$)OxoS3RyTG_!zv+a0SQkCUTZv)<*fVO
z&)pD%j`|Z18f;hWPe1WlhW<uC(52PNxOFuqmv+gtq&2y)n{^Spe~tlXLN8p%_K=y6
z?d$8r=9bz+b4*>o6)1Sf4Ci<}Om?MQlAoEjD_i6}$is6*oKP+LA{#OVC4gWg90XsI
zBYJ%x?6+*ewNqL)#w<87RWbg8u`5+#2Hs)4=-iHC%^1M~V+`>T3TBBDrVO%@Ce>u}
zrLF*=@|`r#nmH{$N)ev35!GNv2XFD$=np>>MKd)<nMF4F$bpMi%^1_FrG?`j9bmw9
zkhj%o{nNGaAJ4B=p|eZBa`?=d>KcE)k>s932M2$!hx+*<HIZ6>+fW+Qs6BMJ-%@Tx
z$ENGlC=PTDgBWc)Xbhh<3qNDEm8D^n4BHmDHkML@RUBv@GDfAGE=j3WZzODw!<`)R
z=bW|9svgtO;eI<+Te~i4FX^vW^Ag<mMHp(MGV3mB!glJ(XIUa68#WS27-?dsA{z}E
zF{vgJ^Ej}fzln`(kX>L2%H<iMN};tak<UZJi$w1GY>sSdo3;jNwUXOvjQ_R0-M%?*
zWf#V33+V`ujo*N5&kPLIBYt5*n5V+>eZ!sqxz~tu9Hpg{n2aLE<i<E(YmQ3^p#P3s
zKpM32yPHp&Ft@7IRJw}86=vV@l}K~8xj9*+Pz0<9T+m#5ky<uJahN+2B|vkZilU+@
zNnU_v$pPE*@vVJebpfj^RMXmsRdu-Yg@S#}&4(6i0XH&$B@b?q9$6q9uT%q%2`>|f
zpeCFDCz2sN!^ePS&{ixH#X))x-xDz8;V^dEcQT}LTVr7K8RCR-lD+&h7_G}%h|BPn
z-#fE|)#X{Aw|TSD6Gw`M6URp^eJ)9hMm3yMr9HliHlfW|!GL(d_N1o3U{$H~2GA>-
z1O?U}*_O)2Rfgu~16;FVjim{C=|q`Q#zsp_K5w{*LBvXP_@_%bnsLUy58TyW+-wDW
zl;Q4VE3EvFr9$$nVz^}s+(KvgkRzgsq9OwG+BNUd%DljtwO(BpyQ!ry_Pd7IR$mN{
z!FREZFG=|sYbY~8)|i;t7)|?o$}`gmHu3bvXiXzkdPEF1YF1Cb;+FD368YWk?;L&&
zT$P^{9X#CA*x)hVbk?;y?OJUu(r*Y`TR%@X(_|Q$SsIM>dkD6h6|~|St!4x@QmfU9
zIwn#Ur5E&3GHanCQWL2c)QFDMymAhl3&g~X-d0NIoFkN2jG33yFEgfUyzp#s!u(0T
zIiU(IzInV$nA>mU)X0{GyyxzoOEJuf2b{BpidOqo+A10pudnMb8LvDx4tnLcT>Bw7
z>RbGmlFH4Wj=wZ@Z0_i|XP2*I5r4n>q1rp%3!9kD@kMy!yU_Ld;B|P@g<bSwhp+hi
z$6m+lf^S~nD!X~pdK{;-oU)_I_FJtio&?WgOTKXQfFi#LXcmoZakS?5s%WMB4Dfs;
z%AOhc^oJ*y-IBzaet%|Y!t|5Oq0odG{kk8n4dfq~1DiM7V}(NDdt0mP%AV;L`a#FI
zt;SrFtzn6RxJBE6L(pi6f6;sPr=Qkdw&+N~q)2f}GM-vn`(A6mbcEZysjsg@{of%p
z!(!Q5W3iIfRsl*sFHAeJBQ|XJ_5yueT##Rg-;lSja(k0DW5XKlj>e`P2?fcq%YtOG
zJZV?JeJAc+vHP!s=9=&oZ@es96Ko07Ca0&w2Ddc2GaGha)WxPh`7)LAWD=rd{_yIW
zp0r>{wtWwSE>^`ZTNbF1t_*ApxKB7k@BV8~+v@!>tMi%Bo2jR--BtSkS4tA%eizHr
z{%|_!6k4&X+x)c#%b)v@LXFwVlz8<Kd3bud^05^Xl=Z(Lmi|~ZhCYuj*vq;U&~>k>
zFSTC%_0tcWR2!qs8Fm911@rTHS_9X7FWI+GB&yZ*J!{n!`T5<JWK@H$nj*Kwd(~dH
zrN#comosF>-1RpouYsk3R@oH;#+TA~h2j6#408&*ihkIr;L~0jSSvSNt6A5WA6G0J
zf(8ZP90poNVv%4CY=p%eCnr282cxVNaFNWitQ+AF!qb9Zl%|Y3k#kX7%XtJONI=qr
zxcSf=;SP|}rGAcZF4se|7A0~k$8mES9w<BK0!t3T!K4_|rY%))m@~^=xEyz05#q?e
zt`(|#u|s;CxgkHVs=Mbw`ubIkInwwesw0}Wqc$>bUF<r^{`LBd{G52@A2OJ+-Z!UB
z6-MqgRXevuSaM+oyYCv2@q<Rr%fY<^<|_*YMr5q%|D~z!6&H<sPP|EGW+cjR{Q1^T
z(0!v`;sq9`%~_|v;AVtTvs~I9_K{SSB8D>!L1(beUEWq;+TPxa-4~=;1S1Iz?QyAC
zB(E}wRyR-?H!=E9oN#NWxk%ZkfxJoxHZxRQH_?OW!&-2N3zblwc!b52q?woTY!912
z8gs?)5+3h1TM1s<T5p@8@3DJ&m3Yfjy~JA@?B%?pdyx?dn{(rETNM2DeBdy{eoct7
zaZ2pFX54~+1%hYzAkrtEQ$1f_zij8`58o5Z7VQLpvN2g=(o|BSAaA)@uOD|$SdC{S
zs5V&zMm9$GC0_5%61C!+&|t{wk)q+%u=B+zUyF>$1^fE@*wq$vFJq58tfp%NqAfrU
zkbkAnO>N#>T+9_c@iU@0EzXD#MATHAVoss+%y}$t59gjcJv}pX%&IM3<-RsFM><}2
z4$mPBk=*62`tnT|W*zr%XilLmV1&o&7TD$To;hQ&c(owhn4Hc!w+EdpT23_&7HX_*
z*4u#GV#IJyMP2g_-iOG@+eaP--D9|9m^C;JiQ{eFw$IxZ+Dx0iIE<{O;)@E|?CgF;
z%#AU>4jUI>+rJH>!TF9Q8SRRZWq!j4nn~Vn9-y{Ck6k?NWxXI97oBzIH>W&HQ~B=1
zrgRhYv_e$O8vTBn^d@i`soIx5SK(P6*?2tjP0TynR57%m{G+oI^KAT5JRlNY`>rNf
zp7Bt3<@4RfjU$Y}Fd^Ihd}ViKEFiC@rh`NtVMb?V9cD3$4`)4G+54><t@{sDwt7j=
zV7C<qDWTK-oIS6)r1F<Qi9c*GpATrP;Cef_(;|NP7x~Sap1((v|3!m0_yO;%EY@YE
zi<bm^`lC-4tB~RChJVq^&Me!J-Gi$)w^y^kb(thpkxSL2&S9n)&cJidiDI6ZsRZF8
zCw6Xh+0ni6;enG+>_eYxA-Fvre^{)m?{5IPk~0^1-;DDMp-JD`YJd3Y7oL0W+Ou-s
zp_|}&i-g1TbBl4FgH~Wf6pR5vI|Z8U1ozHTa20D>gVarUowlILH44s>D^_U6DN;qi
zgtwWRUXOzL?yc6SD$!+C2XAQ=U08tiiGXPaGsxPzGb0<3VJ20UDx_*s-QZ$=;vdoJ
zmWLV-X1*m4iIU4QXJ{z<h>0@Q8@Ghdrd4VpCBN?7dz+4IktNC|EzPp9A^@?`SPBIr
z>=<Vl1)^uA{MauV8HLDkoTy+J1d||?s6)xN>jgv^^V9$SXRN|XzFa_uRfAHGbWjCl
z)pC6qI=^0#;`5~_{N>TtgB08GTZ*9T(FOWBaaTco5QHd81${tCG4@sa4Z}#CRG)#t
zMq;;)HQXv#R}}eT=i^S<)Tce9ku@Cj!|0F<WF^!0MYPUUq$Mr%LUYupi}r|iRIVwJ
zT$U_Pvy8tIe@YX$eEA&=`n<53cWTez9SC|kgMlODU%UP#3N&cpYbih*XeEV7khv}p
z_Cjt`VGjL`%8N%v$Ev7GI0sg)H14c3?_3(Fx8OPhOFLmtY`Lk!kYXO|4o_&^SZsW4
z%n|YcbJzC=w3_-WXT<5azn!BvME%G>S6BCx?irj-n{_x`-sPH=neh~4vv7`fzc@uz
za7K{=cq@!R1OVMMA-eQ}0k;nCPc4d0CbHNv9}&r-*M8H^EHD^XeN)T2u+h~exMA>2
z^aRopms;OIr$@x~>zELY9I+G`Qq<_bzDFPRk^;Zf`Q(#}(PKVKs5i9MH|Bp%+1ff*
zIp(mld{)1K_1{e6IlaEU`Pj^)dBMoqt|Ajg2EOsR$1&F$Y@o*i*2e>KjB|_9nBRSs
zOXW)OLTy{TjBIAzZ@lie+Zo~EWud!9GSlC?3#;!g1G{1gr|$QiFe=*zPRq*OU!<9&
zWMd-E4G=aC-oAbHsmlGn^6K_n(mCKEu|xmpqa(v)xX-siAAPU;8Vxz58-HwTR0giu
zfOS`Owo)ahysj<5Rf0qyMwZsG|FIA}0*&QXPHvTpn8U(1_y29$I3+uZL>i1cyk<31
zl+2xsyDx3*V=MQw$t4%#nB?M%@sfFo$g|=v7AG@t7fU4cxndDjM1M-+V0Q<5;=Zl&
zlyf_3P|uF+WoMSr|0;dUh^rPq`S3IrKCJ!-0B$izLAsj8nGD;caT}K8lM0`&uCB7u
zM-N36u$X9{-k;{_RgXNfiiQuv4sXo!1<%LyK6e6dze&xcjM`eh&MZNIBgHEpuMd~m
zR{VVZ$Futfz+|QniF&cH-|9dP&8O6yevbN7gEdunLttd>*v6j1^XBIJ_4H!HUH&7k
z8T<6pg$p)1{hMlC8FW`w7BVSI{3;)=p=iK0kENH!8;VWw>5s+2Swlk8{Ehq<hmOR;
zDOu;U%Cq9cxGX>S{OPlo>~5R;(YknKK{gg4KpdQbhpCDdqeC`g)3Tf)l;i6OUe`p&
zOycQ=>0DZ7!-SXXD!>Js$F{LO(Z328q7vU#2Kou`RKrwm7}fLt*bCb7<TKHIYLB|o
z|0R=zV0dMbE-Ov^yMWKke)3)wN{aY4@2Pl6*FaVes%r13rY$>&)hkRD=|k#*R@R2r
zVE`EafLkIxyzU93C|vT-2G%HOc*HB(m^b_=fQ-j#1qmz>17{2jVxa~D&ar6F8X0h#
z9BFvoTAwzqa|`+9Uw-NJ%kZ!lP7LBq!xD%(?S=Mt;a%4<WVIrOF}fMFm@dc6%Q(gX
zfCVqtYR#GJy_bpTD6lAC;U)@*w_*W9(YTS)zaBpz6xmtStBT9Nv!O$&i3yX56gaP$
z@viPK8>)(}1@l$V{_(@r%I)wot3Fd8BV61&t-t+Y0-VY8&Ea8v)W|SI>z#PVgW&|$
z)&cUb<F_E$_s(u5a9oV_9sW;JwosUT_0cb_I#zmN?Fk?6`y97mQ6Pn7VL=LVn;*_R
zCygLt;K1GnTCVHwc=WNfvtR)}`a*&c*^v4o>O`e{O`Xqodzbhgwx(CF*V=p98A27?
z!dy_xz9{@6Np>DQSYF<@uw_fE@z+paem?bZ-^*YEnn3>Uu{V?3u?NFwl2#5>El(^%
zd5#UF2l<!vUA+E~3tdjT?@x7C*5sPw)49_;)<PxzwF<86GHFnFsxdu#KgdL?iM5nw
zWtcE^UHOx-pgnUWGq@~?Q8*lL26p&hD++umGe2fP{bX$&^*NnBcsR4*^)7@pti;=G
zGfKhAxkQP9BG3%Um@Y<cUh`(bEDnn%$raIIZN|DR%wqO+f^{$Aw>gftvdfQI)bb~f
z+S1<6^Cr6k$YTelhc+o<!`h;Htg$C_v?8mruASW&UXKf{32g`>YqfFt7dObA_9o04
zO-1h1-J3}T#3#(x6xY{@)ICGG-G`mdc_u8a?oDoR+&a!e^gc5~bjhg7Vn3H|q&M9a
zSlWDZv2|VuGNXQEEA_-yWF@@*w&A|sX*OOX3rR|8k8mvT$=Z7TOPyn5U8rv7&N}&`
zK0#RB9i^E<9bR&QjiRC$=5vATHu7MP+|sk(jtnc(6@bCXmYbaRfhzb*8JZ3`<hNGz
z^%@nWq~o_&ad1@Yfdehv-`ZlUB!%>~3rQ|ZFhb>bWoXqCZ<wO8e;*cIQ7pq+s~7Yl
zw@Ua-l1rBHo(~R)NWdZ%X8I8=%%#d;^9|pt+XML>e7f&j`y+qpNYRKLIm^Bc*{mCV
zr8MChSNIl!$Ac$0!uR2er)*QNtWT}BJCsD}6a-7cb5-_z7mhyAV|Q|0L3dR*haiuU
zDTyhO9gYOlrrl&|`Ck#Ajlq>ehhQ@EJPfVb>CqjGoE4J(Z(3_lj>v}QeqX!4-uP&&
zt}^kS)PdB1#vADNn(RBD(OegcCo=!QX+K5U4+{-(2HDGv#p!?hdsi{=qdv2Fo02H^
z$1KDI#Q1jx9#!TT4%V69kZ+&=tMjx$-y@yT+ut7T`YCFhJ7Y4~@t+|BZ|ua*`jK<f
zsDFn(C>=jrQQ>24%on~_0koZU`rW>1mr3EBQYW334w=o2m2uioq5-;SS%RP+q{q^Z
zqV?CfamNeW8G+HCc_BG4`2|y8!uZo_TM3DI_lDG`!Nt$dFHFxKoE4{Pr~FGxogFb9
z9b(=3FX+AiOpzD3MSK|BUMAnHK>kGolg2FhXBC5s{+5B4mzzA|_1FC)GkwdPrZ|m9
zoX%b!Irjc==7Nk556hPYWbKKTjmg4mcHGH;*HPJ5^^8{DKZm9!sXu)FkHIaJ1=yxW
zb_Kt5inm>w0vG&(oj6nOW(ZTwix?)|D-ja;OJ!)BnP50Hu^U2*uF*WB>bZ34)Fme=
zcL8%=Ik`kmny02_9;~Zd<t(c#cn+$%W?JX5G${Mp6>PEDEWsklUS2C*=nb(xWXIlT
z<?cXNO*A(4+g;=kX}{C>?bZ;xy?@jC?8*(Tb@Xh`$<1<?xR8N|Hj1B<#SN=Rk`jlD
zh7*$@^kAyZ!Bx2NA=1GTp7)<R#gdYWA`_{3%3Eb@>#JN}QV#bF3fuL>jQ7GkO8~8s
zC{w60&8*iun>u^NjcCTGl>J6FjBu@;Br8g~oPPX2i!NPkGU@9x8BBfV*QqHg+-fjb
z!>Mssv713mEREh1s~7aTCp-SQIz_t6us(Lr$eMcKR7Jtz6%E33`zF>mYmzV|7eppk
z9E`;b)|{wXQuR#OA!I^_!Y(28`AsGNjsy99Sc>e|N-{H@TbvQxrV017UsRFip^*6R
zOv+XpSv0&Uv#wlO^HDSjGZ_8R>a66i*8yMnNdOYGp7kEBut>*x&5rAu$>$IF{u>{t
z?b3k8fQG<j_V(*(klC-t`__zp^ht{-uAoJmHu{nivL<beA~N!sHO0**f=I<mZ=*4&
zZ{=8JUJ!>DIje?R*QHz2i;Jp9tG~Z!pRq3R`htxngtiex6PqwA`i%qpi;6wDA<^AH
zNaxdqBxS7)sj2TDmhYav(6CXW+^{@j^&JS2o8cS$bjr~7r|P-x*G?<LDihL_8_q>4
z)t|9y>KLX(?YKQ%RpcpB`JHjj^5yVR*fyA*jyarurPbz2hGF>ce5?Ghq$l}L>(VW1
zB4eShD;bVaUa$U4Y7}lMywXC{5wStB5j(y}pGu#^jiA=3b_I?8+14I_3WiZ#=JnO1
z9{;3VUqt>V5pKG%WL|=>0Ho*W<fBdEo2#OGd&^~|s@O{LOS}`?U0Z#YwAdSPfV0={
z47gHR5U(_?G%3TDO*E_Ho5PZLwS$XazQW)GjmvX1$A`uLu2}`YIV@t`AHOS)ysS?g
zE<5snvz=xCuD#9F5SsFOiuyr~e2<|_e6OUAca5jc#dA&sLvzXtQQ6lUuj(G7pcY^|
zZ=62AoqJ>%zZxm8+2E$WUQCnTUVmHP<7I;D`}z=i$9(CKx?%9_NLT5?=Y5Rg^M(G^
z>~bZX4CHcMRlji;yTnnTS`w&3bnA^^M;~mV^}Gz^=?wDJeRUego}S5w;s;Tl<tP{1
zfxJ+cBX6r*yD21<ES7zhJ3IsLQW<0^$48a7rM>)fuJk;5B&17iHYrvAtFz<hH97MA
zF=>w|sO%PfwnY(|ZX&69Vs7K5#ITwT<u~Hv{t}xIelsGmP$gAJlU6F@V-qRguZq;a
zuZoDR`m5)sYHtM^3rN5aqxETjDcuwN7(cRN2iCI81f%U|9L$nG8oa|L;ZA1p$_168
zTQt)=DP*NV&t;N01xPgbH?~}Ro~^&x<8ZV}2UyqnY_=)9dByr~nTV<D{np?0p8Ym6
z-&KKZhbq?;Rj1b4C(K8;n;Ps&63bO3Ke1-xfyq|>ZypI7=^wG-?hL!}%gHyhKWqQ&
zvv@t<(Y4_Fy%tMctV#6ks8SGBSAGKnj_qFfeO7Y!?&gHi=*Ljlm@XswXyWH500+lE
z+S=d8^X26v>ddZIY`JIuN-Qa81;@V=kCjxE!Y#FCM}F(`KdDN7(m(9o!b~bPk&dVo
zWlEGIl9Npp*f-s<S5VI^Un~m>Vv4UJ(Czjk2}p2pjX^ws&1QK9*{s-QbQi@i^``0U
zongk22RX>8wFkjNZTRp+#G`BmU9##Rk?b7%VhZ=IVEs%uDxqDlra^9wmSK#S15b!&
zg~wxMLj5Tkf&(CGxR^bQiC#p3MA7@;1AX4H|8h^Yczz{s?P6HMvdmL1`R2~@;JztK
zzQuL>e^>=F4iKTkQp9dVM)>CM5@`=@&9+KI-hCqphY5=~<eouUNue~xm<~zG!X*)B
zE6+xhU~?cO68iZ<CExATcyInQ`JXqvnw{Ulr>;A27>dO=-!#-qz5X+r^_w>MH*9EV
zj`ZJ^)_(;k49gN$q;T6Y-;1qs)i3;e41^a6T^e-sZ_;LaMad$dTX6Io?YfK-&4r+3
z@!EuX;uuSGuq>FYGq0<&O9adx04^h4g5i`Oc~Rg5m3c?d-YGa??`pRoEd8P=fV6VX
zHM3UsBO@q<-^1Q?gz?(lJv7#};aRsjqZEv{P0<?0UdgOPkX)8<tO?1=W?^*8*f+<y
z$O-VvzWgV6J$42D^QG5s>TONB>6ek=n=LIz-ac~FOZ9u-X(b;H2t*BmM$YHhBDQ>t
zKHlPm){Cy&S^wgT_1u!dp6UEYjC|ooHRQG8uI{cvjm|l@K^-T}mBy(XCSM$o8z49}
zB!Q#jTvz#{sZ{i*CG9Y_s_WKkmPb@}nI)1&#a)FTt%0cVZb0hYsQay`oJ-0pD_>c(
zabwX+z4yF~{H80WwQ$m&pZ~F8okBgMj&}}a4msnYO0jOkKYpg#*Tor3;x1)>tGlt(
z7rWBUGgb}^a#?<7Gg9?VZ9_wXN_SJ2=*~LT?>B9JF6x?rd!+Zj!)tw8d|Ubs<QisY
z1%ddoG9Dhp`NyBiI5mienrc?BU345QxeasqD?lZxMKfWqv4^VO#*$9T>V2aJi(m9@
z2735}Q#%f1edZ1FZfh<2-NBn~8IT*39gwY1NJ*dZyXNoyr8Y5=Z&Izhd!s&+ol|he
zZY>A=^1gK?DrNcH8TpA$iaa-oh@@yIzFlltKT&ihJkZ<M_is}WP`8<NKeZLESv<LO
z;52>1lOtDW*BY9+1H0ik14D?cv5~2V09Gfn=+c`pPOHFyWLVZBT4r1x2DwEZ#yrJ^
z{sRDpS*H@Pi>VCGbtz3&B|ZaoFzw#%;i7<qn?%yMXgO^@0}R(&y<H(P<3S#VWqFr&
ze^fv;ox_}<6FBDvJ4kDNYb((gSS;s$q#h#9XFWuXj>3>}8!_{yV(CDNmlObGv5H4t
z@<iNRym_loy&-u+b(${o*xPrEzl&>#Mp_Sd$UFGjeB=CT_wVv+-$1<ccK+|`wAD(d
z5MTQ(M(~2g=kCK-;T;ZK`{}1%O4asj*Jejep?t;BU04!MCw_}rpMH(Y)T=}Az&?>>
z@wZlvYh&oGo4^TI-xvv}yuVX@UiNRR6tO=4316&Y{Mg&t&V_4-BpF?Vks2T+I0;!u
zsI{9VVzRch_IDRCEMWvBFxM+z9PG2wZsZ1Xo1*$MHfKD;)UopXGTIp9DC076^GQ~|
zq!c=j@Or;f{@*2F@JPzzhyKHX=f|zOyY5GVw^@#f#Hkn>siNqziLCe6R^}M`rBZRu
znt4BKB1@>r$=3xCZ$cumwUtdtnCwj9J>L<~p@}i2<xOk8DG3Tj!9gW^9Mh$8Q;en~
zAS5U_p!4Lb#FS27Y%Crq&iktrlj;l%!Ji=OXUaBhDho}zBZ~0zE>|r{-hEHX#xV3C
zdP<qw>&UuhtvPXtgjDGazKEjIdW&EXKj#qqqFxmPnnBRBAwr|7Enc~m<npnRHvbxb
zgP~SRcor*&0uZ-jjL@5}>Uu7cOs2tzXUf;Kn4}EWx2zfOwklUnPi>X0y4H={T0nJr
zVz2K8Lihch{eL`Drt0>M!G;hxpnPW)2VwhsrjgsX&&XxYZx={E;?N!!AJ(3TaS2J1
zjmnmoa{<c%kxEjEUwpARMYRuEdvo2!v<P+Laq2xA*5Yte-mz<|`uqKKgA}PRD5|d;
ztgj!et1l{8PT+XnF#Gr${&mS)J=LhD@Q`W!*CTPETxi@#Muh25A*!kFc`G<EIN~>2
z=<}02=uWx*&uI<oKfJ)v%S(cnH~(;CeX0IW`tmzL&+S%xETBl<XK(JT_5Low6J;6R
zeL1-$dUjGQ%j)EkA=WcA+CEYrQa`eGL)(T8d-qDxdq;+ie%G`SG!qzDt%7R7#emwQ
zxqvozKr906iaYFjzvzb-yv7vz5g1;*=RFX%alqVh7zGXDW_T0vhZoSWWe&`b#&^;D
zFN;e5BbC+FUcY!}tMXUhf@1e;C#D+Y3C9K$+wtjbxa}n22o?kZ6K^p>+%$=x$U<5o
zY6pz0lX^6r7v+gHl$~M?1bzPlw6LLaW(FYz8dfsrX~D=dBJ;=yG~@a$1C2dIqL;WL
zZ+ZGJ-X^9t7riw;{?B^!bfP)ppOvyGCQ3Ha53LfUsd>gF`7_V3JZCOIW;6fFGaTu7
zF?4%#mW(}?3$&b{lANx|Z-EeFEo;X6ZZ*c_F4c>=MmKW13&W&zmzlgbc-|;fm_0D-
z^|kqmPHRX~D`z8tBuFp~$P}6zoU<Kl$mHar9M&4N<fcuC(v4ro<0<PYR&K={o)+k)
zq?8u0Y(&QKK0y2+J7~0Ff<@bsY)pph8pDdCM&yW*zd;7T-tzbjD3+Z)%TEZ#O*=OA
zN#-TL%Q#VXT&++a*S{MORDL_}+xS3BTnp~D1h5l}Mi;ThZO)we`Jw;4%yzx?Ki-c%
zvTgIOTR-ty3m#&xJv_>1ZIfrx&lEJr*uFZ`*3iuM%#N)gb*9+9R(*4FlNDV1kAi;@
z?(_lrfx1QHLExj}U7Vfk(8qR{Mo-Y@I+ZeaDOV|NZ_mx4B7$Fr40wCzIMdC)53=mG
z*C(&L?=QC@4D@<}iQa5J_0f2Ru7(-sc|A@p82ST%sOTR*WR$ZkGl%9F@XqZd?t50Y
zb=IuqADx=&Rf4Cd<epx`amAD*hH>Dp-t~nC9_$;743T#pr6#F>0BvXn<Csc<XeQxv
zNj?$oU!Y2<8WSapd?0G9e=Bs!WIYu;AeogUEQ`(Ed7r$F9fzM-PTUnoiEb`X+R+n9
zk+IoT`&q3Y8*IhxLKC{%hqgR^L#w-G&9zSO4lsSXx3%T<L<{}WofYy~7mJg5yb~$M
zyvQ=1W8fGsbv`J|%_w+;Dl{8g+WnaC<+1-9)!M>KORfFhZPxvRxay5RZN7yk5JD5!
z7++@w1qfZcvh0&jdU>8@@4p|$s35@7*GeNL2(YIt#!fyRWZ9txfK#eKtqt#Y510Y=
za0$1;Czf?_%xw!h0wX;~%jFEsV7fgGh~x(8e4~c(FaTtuZBPap%|OZL83&KnB5TV^
zxhL0fWs|rRnL)9iu=@m0kgB~Yq|(npm9r9#ki|DS7aW&vOhAPUxgGe8A+=7WAdnU}
z_(y8nvJ!Ay$&mp~hDE&$_w+dv)_bFuX@I@#&VSlvN}>!px$<JH&Zmpr4>zmdCOCFt
zLfpGoG?jbLtgMT-_CvN==VyiT4DXKYx`XA|K8bg?eE9bZEhyM<F3`xkh+H-zyYZT`
zvRb6p<l2obY>6{wa&hL@)me>Lz*e+j$~5+xz@QNgz_VYJ&UGEn0fP(u{kN=EDXA|=
z54@WpXSDWfZe|-;{hEe`HAVIHMfnN>LJut_8gnVJt2jL+ic`~-buGRYkmzy<#yFF`
z{4YEvID(Z_YQm4PC^q+?K8l*uOj0N{>PImG{Y%SRup}U%=@$G9KD38DBL-vo-$iY-
zlB`b^SsQJOByn7Y42|ihU0*0X8)LOFs8V;R$?BL0TG=q?7pK5QkBM^1*w5I3ek0>D
ziUKDv<>j+!wlpaAtKxTjo7bQ4(y=1f&ZM{B)0J#^YfIS#o`5|~THk$pzq*0mnG|o!
zZTj|9e?s%*u}8;tCB1$0%cTwm+~ANq)aP%b5sQa!H_$~4jn#WcJCqaIa5IBG9OrR~
z(}rFc`O(%NBnv;%!{PXG@6MfLUiahJgJm%09iZ0a^777q-*CI6x%ogdIY2IHwi(HD
zFevNa_Ro}=MZrax(YcZ7@r|X)nWs>&ws2p1ipG?f9<ri_nu5k=*+1ZDrIIb|QBp_K
ze@B(H5hmM`o&H*C=Au@g`Rn?dwVb}7rmw3)*<IMAq$bnHsMRC0(?UXQ$>S?}wSk{W
z4h1RC{5~r4QB6^Jc-ZQ*K^pP5Ed@E1<vsFqpA)7C%a~}K$jD83@7(Z+W;`IIRsY+a
z_|f=~f43fXR6qNk#|Hb~znF>#f?#c<(oKy=!pl!pmHNAl@Nn&s(b;>%!26D^t+QEK
zvt#j)DAnkzYpY1?s#Vt#^SHdNKN8)U^}pmbc<1K*vfjY1r3E_UG5xthgsxs;K?HvH
z2LHCD6>AGC*H)C)xmfC`%!X_Nlu?)kC&JhPl*CGFCtdu6%?&M|t6L$sad>7;raUNm
zXLxeNBavhM{m>;7pbn^x`dTVAN1&GN+L`Ap@Vn{gr|a*K^HG8<>IP3`=)Ag&pQ?1}
zJ830R(jod!;~w7_5YR>5C|rqF$JO}EJ8uYCZPXO?H(bz=jW-^hLJpoVpEH5r2D+j3
zSM)^`k{y%L=;jY6<PPLT=M#iL_xPc@YJdr;+p@JTn7=+yUG+n0UC5R#A$8Xe#;zmV
z){)p!O(WTF4*KoqKA8-u-D+DOoN?WCanj4HoPQ_RGlCz<d?a`>3949hk*L%JMx;wZ
zV8!sH;yOV#^gXgFCE(cTw$=rQLQwGaVg`m&3oz$}pb}it6)Y#MZ$ut)_mM;Uan|Q;
z3t938F?I0a47VRQc1Ns5n*jsVO<Om$ZX`ceQI%CZfT*g3DnIu9-li7vucW=c{NZ#a
zb!JOPqa-mXu~E{oWhQk^0GSS%rdQ-Gs}GN0mgTLWkHrSo)0Lw>-N8X%**d8jTL<-v
zivS|WSkXii2lc_8updl2nl_R)ng*-GTE^*3`NMs#wEwmE^Z%6fr;9T>9!c_mCC@Am
zR%}%g<$PM_;~9*r=WZ-Mz$MdCf{3&DfURHD6B8Yg*(XM2pZfn75Hl~|ugtet@^TmM
zzh7N%<rX@%FDKYs<A>N;qXt9OXC}S8E}ylW?rR8Z=;+8H4us3u;lNO8T$b5DqL%hC
z^TY2x$gpiSy6bI))<xYT&e?tN%IZ<ld|*~wi|d9*PLB*fIWTZ?<Z)=eEWCZqfLbWi
zVk?f-g{dj2k5B78C(Z=)hJ`e4$iHjex!wdlA4MfnQS<bxv-Ycs;o#q8umkfuNxj+7
zRkg~3OM?@GOR;5DSCzqBPdf86N;9ebcswa8X&mpTm`w1XkjzRh#}Ta}R<TZzF!9yo
ztw~LoI3byJymaV`*ME)8AboWoU%I-gB0Ppo$-zx>`YO6g$1F%ErAJcIG}W546}Mi0
zoEoDPoN?Ao{G1YUU_3HMXTCV>a;cc8@%PX+apkjMd0Jd}6DN35k@)#3hU(XBcGsp&
zA_(eyEjM*V|8WvRt;$wiGR&$n+E-jIv&hlNeWAA;3Pk<bZkoYP?LGHox&VaN&)Bp4
zkAD>R?ww;<s|fe7SyGo3E_)YHp(|YRUb)iviqvkCpi!&IbhWD+<L<7Tk+x0`-0bc;
z)YW}8c*K0L+C~VSag8~jX&mMVw{G0RMyQ50Iw!O|Z!Ik1Y-l9#oKa;_tNtBj<(p|%
z2ld=9he^qlzLTkPZOR2$^w)(Cesl-iN*z)ye!n{xh7E%o`nRuB9ikR|FZ%)lDk?fd
z>X(m9Ui6KP-vr|jhagjl0e(;u{$2!=rz1!tBH~>f?YQ&rbmD-AZ6fuTe>Q&gx^=#b
z+sm`=$+1(IyS$QFsjlr?U;J@EZU8r-gxJTq@9X<!HLtqew|TQ+_a?*UzMxgH5wWX+
z`j)@nUQ@JjZTn+Mky7C=zlBCy^b@H9t=5pK);Zbu!zwi$Nl}TaM0-(I(cwhZC6%sM
zw<!mki&m`>f2`{6u5`i+Z(m)w>b<#elMh=guf8g0zF+W-JBEqeNcpd)Mmvq=OW*wL
zqLebnS!o^>|H}$2xDK6xj!<u0^yW}4Hk+tTzC)rpm4h=|$7LpfA*FOA(F)Dxe#ws$
zhs1CHuMD<gr1JR~x&O_A!S1xtv@x`Sr4i4W99p!~cgxb7@Rp#}Es<L*DG8=7{k`!7
z8}a_i3no*AU$57$92q;>q<%jl{QZq9H@+`zkKO)kROGYU<APAUfb_y1=*4pNU}TiP
zP%Q3gaiJ2fZ2oW3v5XN&R?ffIgfxer!4UtRh(vI!eMWs-(sa>OlA2<rRKGMO`PD>?
zIzfJfDsJ%Br0LYUw7@jAw2x9Jr@yIY)OEb4@x^JYRkS<Yj04guRL>-(suQ~xrKB;q
zvEb%cNzGN~rUl59lB$y$$CK0FSs$p<HQ?@>CjR^1iIB}@wm7cOG*B8C$Q?}V=KC$m
z<%i3vK#u=EU--K*oB~f}Cjfr*ZiY|!cTfEwvh<*Js#4sXS3u{2>{A~sn$M0R72K0s
zI8=ie-=(pm!l60v`mL)1?}Fk74?P)@_S0yx*Ft1}$PujNPeEhOtqs+|UoAO!paBmz
z*n{$p_B$VZ?F<G00RD8ZIS8(QA6fwZE&(3^AQZd?MIMgzvM(UblVvp({@?uh5W{GU
z3Z}?&>t_}lTexwO1rz%1oDary!i5l`)<XE1jRR@l)S!{SK@Y6Sd0<_NrbEn(UH}jy
za<L)(fpd~2QDC+Wt@1Z`dA0#vnXX>~&<!?VUjl$o;x(Y)3cAC!*I3Fy!66rcA!L3%
zcG#w}qst<QFj6^h9-(G<SAaecmDeQ{{^8OGaD9|Rz&E^>L!`;!B2Zfl!H~At2ul!5
zJtDgq!>XA@S&H=<HRyZ>0GMf|VQoQ~R|2PtL>2&#Y+mF!JmkS7lqZ_pjoAU$dNwWS
zO0&X7VwQs2n$}0Yk_JKk{XF_Lm<GQ(kDDEQv${zhK^sw;UZ$1ohLD<=k+de>2E1g-
z=Y1U)uQPzwSV370dXs0>&JDEr2;vonwvYkBlul3`ii69q0_!e{e-?M>97SlbAw$}h
zFYsJp(r}zPkg5@$##sP=NVtJHxpD=^`y*_VdTY?LV9LcfvSFi9HxV`3U@BCC$RK8d
zW_R;e$^~E#Y`G9^+{!X>+}=dMj*K`=-QmMv8l3MaSe7-8<exPp*klGL&$*rA)UFsv
z29{vRxB$Q4eoYI%QZ5vA7HA`INujHz6{t1<bG!@uyk=@eJ{yP!-MIHOdFRaFvoLqr
z{6;T>&=_qt@VNx&WlZQ90BNV;w2nz>o8@6tD9MJe=-*!~dmG*n_gj{LQXkF8{(2#7
zl`Mu2K0vGu_IMVyTK6nM`|~X7t7%zw{45S^`BM>I`Au`Z^)XaGU3J#Q0JRO!Pk)1<
zse0?JvmQFC3r*Kcd-b95dg!6H1ufiv<8{p2JL+eUybi6-Y;6tLg<Z7;zx{OW_0~%;
zuXZ-D@Y*`JDm>uk^_$$0h1VylXhhE_c(^)D@3!>j9uBbt==Bc(c(rftQ_by<(>>?a
QW8}wPUeo^@jR61v08@RD2LJ#7

literal 0
HcmV?d00001

diff --git a/docs/python_docs/themes/mx-theme/mxtheme/static/material-design-lite-1.3.0/material.blue-deep_orange.min.css b/docs/python_docs/themes/mx-theme/mxtheme/static/material-design-lite-1.3.0/material.blue-deep_orange.min.css
new file mode 100644
index 000000000000..5265c512a3e0
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/mxtheme/static/material-design-lite-1.3.0/material.blue-deep_orange.min.css
@@ -0,0 +1,8 @@
+/**
+ * material-design-lite - Material Design Components in CSS, JS and HTML
+ * @version v1.3.0
+ * @license Apache-2.0
+ * @copyright 2015 Google, Inc.
+ * @link https://github.com/google/material-design-lite
+ */
+@charset "UTF-8";html{color:rgba(0,0,0,.87)}::-moz-selection{background:#b3d4fc;text-shadow:none}::selection{background:#b3d4fc;text-shadow:none}hr{display:block;height:1px;border:0;border-top:1px solid #ccc;margin:1em 0;padding:0}audio,canvas,iframe,img,svg,video{vertical-align:middle}fieldset{border:0;margin:0;padding:0}textarea{resize:vertical}.browserupgrade{margin:.2em 0;background:#ccc;color:#000;padding:.2em 0}.hidden{display:none!important}.visuallyhidden{border:0;clip:rect(0 0 0 0);height:1px;margin:-1px;overflow:hidden;padding:0;position:absolute;width:1px}.visuallyhidden.focusable:active,.visuallyhidden.focusable:focus{clip:auto;height:auto;margin:0;overflow:visible;position:static;width:auto}.invisible{visibility:hidden}.clearfix:before,.clearfix:after{content:" ";display:table}.clearfix:after{clear:both}@media print{*,*:before,*:after,*:first-letter{background:transparent!important;color:#000!important;box-shadow:none!important}a,a:visited{text-decoration:underline}a[href]:after{content:" (" attr(href)")"}abbr[title]:after{content:" (" attr(title)")"}a[href^="#"]:after,a[href^="javascript:"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100%!important}p,h2,h3{orphans:3;widows:3}h2,h3{page-break-after:avoid}}a,.mdl-accordion,.mdl-button,.mdl-card,.mdl-checkbox,.mdl-dropdown-menu,.mdl-icon-toggle,.mdl-item,.mdl-radio,.mdl-slider,.mdl-switch,.mdl-tabs__tab{-webkit-tap-highlight-color:transparent;-webkit-tap-highlight-color:rgba(255,255,255,0)}html{width:100%;height:100%;-ms-touch-action:manipulation;touch-action:manipulation}body{width:100%;min-height:100%}main{display:block}*[hidden]{display:none!important}html,body{font-family:"Helvetica","Arial",sans-serif;font-size:14px;font-weight:400;line-height:20px}h1,h2,h3,h4,h5,h6,p{padding:0}h1 small,h2 small,h3 small,h4 small,h5 small,h6 small{font-family:"Roboto","Helvetica","Arial",sans-serif;font-weight:400;line-height:1.35;letter-spacing:-.02em;opacity:.54;font-size:.6em}h1{font-size:56px;line-height:1.35;letter-spacing:-.02em;margin:24px 0}h1,h2{font-family:"Roboto","Helvetica","Arial",sans-serif;font-weight:400}h2{font-size:45px;line-height:48px}h2,h3{margin:24px 0}h3{font-size:34px;line-height:40px}h3,h4{font-family:"Roboto","Helvetica","Arial",sans-serif;font-weight:400}h4{font-size:24px;line-height:32px;-moz-osx-font-smoothing:grayscale;margin:24px 0 16px}h5{font-size:20px;font-weight:500;line-height:1;letter-spacing:.02em}h5,h6{font-family:"Roboto","Helvetica","Arial",sans-serif;margin:24px 0 16px}h6{font-size:16px;letter-spacing:.04em}h6,p{font-weight:400;line-height:24px}p{font-size:14px;letter-spacing:0;margin:0 0 16px}a{color:rgb(255,110,64);font-weight:500}blockquote{font-family:"Roboto","Helvetica","Arial",sans-serif;position:relative;font-size:24px;font-weight:300;font-style:italic;line-height:1.35;letter-spacing:.08em}blockquote:before{position:absolute;left:-.5em;content:'“'}blockquote:after{content:'”';margin-left:-.05em}mark{background-color:#f4ff81}dt{font-weight:700}address{font-size:12px;line-height:1;font-style:normal}address,ul,ol{font-weight:400;letter-spacing:0}ul,ol{font-size:14px;line-height:24px}.mdl-typography--display-4,.mdl-typography--display-4-color-contrast{font-family:"Roboto","Helvetica","Arial",sans-serif;font-size:112px;font-weight:300;line-height:1;letter-spacing:-.04em}.mdl-typography--display-4-color-contrast{opacity:.54}.mdl-typography--display-3,.mdl-typography--display-3-color-contrast{font-family:"Roboto","Helvetica","Arial",sans-serif;font-size:56px;font-weight:400;line-height:1.35;letter-spacing:-.02em}.mdl-typography--display-3-color-contrast{opacity:.54}.mdl-typography--display-2,.mdl-typography--display-2-color-contrast{font-family:"Roboto","Helvetica","Arial",sans-serif;font-size:45px;font-weight:400;line-height:48px}.mdl-typography--display-2-color-contrast{opacity:.54}.mdl-typography--display-1,.mdl-typography--display-1-color-contrast{font-family:"Roboto","Helvetica","Arial",sans-serif;font-size:34px;font-weight:400;line-height:40px}.mdl-typography--display-1-color-contrast{opacity:.54}.mdl-typography--headline,.mdl-typography--headline-color-contrast{font-family:"Roboto","Helvetica","Arial",sans-serif;font-size:24px;font-weight:400;line-height:32px;-moz-osx-font-smoothing:grayscale}.mdl-typography--headline-color-contrast{opacity:.87}.mdl-typography--title,.mdl-typography--title-color-contrast{font-family:"Roboto","Helvetica","Arial",sans-serif;font-size:20px;font-weight:500;line-height:1;letter-spacing:.02em}.mdl-typography--title-color-contrast{opacity:.87}.mdl-typography--subhead,.mdl-typography--subhead-color-contrast{font-family:"Roboto","Helvetica","Arial",sans-serif;font-size:16px;font-weight:400;line-height:24px;letter-spacing:.04em}.mdl-typography--subhead-color-contrast{opacity:.87}.mdl-typography--body-2,.mdl-typography--body-2-color-contrast{font-size:14px;font-weight:700;line-height:24px;letter-spacing:0}.mdl-typography--body-2-color-contrast{opacity:.87}.mdl-typography--body-1,.mdl-typography--body-1-color-contrast{font-size:14px;font-weight:400;line-height:24px;letter-spacing:0}.mdl-typography--body-1-color-contrast{opacity:.87}.mdl-typography--body-2-force-preferred-font,.mdl-typography--body-2-force-preferred-font-color-contrast{font-family:"Roboto","Helvetica","Arial",sans-serif;font-size:14px;font-weight:500;line-height:24px;letter-spacing:0}.mdl-typography--body-2-force-preferred-font-color-contrast{opacity:.87}.mdl-typography--body-1-force-preferred-font,.mdl-typography--body-1-force-preferred-font-color-contrast{font-family:"Roboto","Helvetica","Arial",sans-serif;font-size:14px;font-weight:400;line-height:24px;letter-spacing:0}.mdl-typography--body-1-force-preferred-font-color-contrast{opacity:.87}.mdl-typography--caption,.mdl-typography--caption-force-preferred-font{font-size:12px;font-weight:400;line-height:1;letter-spacing:0}.mdl-typography--caption-force-preferred-font{font-family:"Roboto","Helvetica","Arial",sans-serif}.mdl-typography--caption-color-contrast,.mdl-typography--caption-force-preferred-font-color-contrast{font-size:12px;font-weight:400;line-height:1;letter-spacing:0;opacity:.54}.mdl-typography--caption-force-preferred-font-color-contrast,.mdl-typography--menu{font-family:"Roboto","Helvetica","Arial",sans-serif}.mdl-typography--menu{font-size:14px;font-weight:500;line-height:1;letter-spacing:0}.mdl-typography--menu-color-contrast{opacity:.87}.mdl-typography--menu-color-contrast,.mdl-typography--button,.mdl-typography--button-color-contrast{font-family:"Roboto","Helvetica","Arial",sans-serif;font-size:14px;font-weight:500;line-height:1;letter-spacing:0}.mdl-typography--button,.mdl-typography--button-color-contrast{text-transform:uppercase}.mdl-typography--button-color-contrast{opacity:.87}.mdl-typography--text-left{text-align:left}.mdl-typography--text-right{text-align:right}.mdl-typography--text-center{text-align:center}.mdl-typography--text-justify{text-align:justify}.mdl-typography--text-nowrap{white-space:nowrap}.mdl-typography--text-lowercase{text-transform:lowercase}.mdl-typography--text-uppercase{text-transform:uppercase}.mdl-typography--text-capitalize{text-transform:capitalize}.mdl-typography--font-thin{font-weight:200!important}.mdl-typography--font-light{font-weight:300!important}.mdl-typography--font-regular{font-weight:400!important}.mdl-typography--font-medium{font-weight:500!important}.mdl-typography--font-bold{font-weight:700!important}.mdl-typography--font-black{font-weight:900!important}.material-icons{font-family:'Material Icons';font-weight:400;font-style:normal;font-size:24px;line-height:1;letter-spacing:normal;text-transform:none;display:inline-block;word-wrap:normal;-moz-font-feature-settings:'liga';font-feature-settings:'liga';-webkit-font-feature-settings:'liga';-webkit-font-smoothing:antialiased}.mdl-color-text--red{color:#f44336 !important}.mdl-color--red{background-color:#f44336 !important}.mdl-color-text--red-50{color:#ffebee !important}.mdl-color--red-50{background-color:#ffebee !important}.mdl-color-text--red-100{color:#ffcdd2 !important}.mdl-color--red-100{background-color:#ffcdd2 !important}.mdl-color-text--red-200{color:#ef9a9a !important}.mdl-color--red-200{background-color:#ef9a9a !important}.mdl-color-text--red-300{color:#e57373 !important}.mdl-color--red-300{background-color:#e57373 !important}.mdl-color-text--red-400{color:#ef5350 !important}.mdl-color--red-400{background-color:#ef5350 !important}.mdl-color-text--red-500{color:#f44336 !important}.mdl-color--red-500{background-color:#f44336 !important}.mdl-color-text--red-600{color:#e53935 !important}.mdl-color--red-600{background-color:#e53935 !important}.mdl-color-text--red-700{color:#d32f2f !important}.mdl-color--red-700{background-color:#d32f2f !important}.mdl-color-text--red-800{color:#c62828 !important}.mdl-color--red-800{background-color:#c62828 !important}.mdl-color-text--red-900{color:#b71c1c !important}.mdl-color--red-900{background-color:#b71c1c !important}.mdl-color-text--red-A100{color:#ff8a80 !important}.mdl-color--red-A100{background-color:#ff8a80 !important}.mdl-color-text--red-A200{color:#ff5252 !important}.mdl-color--red-A200{background-color:#ff5252 !important}.mdl-color-text--red-A400{color:#ff1744 !important}.mdl-color--red-A400{background-color:#ff1744 !important}.mdl-color-text--red-A700{color:#d50000 !important}.mdl-color--red-A700{background-color:#d50000 !important}.mdl-color-text--pink{color:#e91e63 !important}.mdl-color--pink{background-color:#e91e63 !important}.mdl-color-text--pink-50{color:#fce4ec !important}.mdl-color--pink-50{background-color:#fce4ec !important}.mdl-color-text--pink-100{color:#f8bbd0 !important}.mdl-color--pink-100{background-color:#f8bbd0 !important}.mdl-color-text--pink-200{color:#f48fb1 !important}.mdl-color--pink-200{background-color:#f48fb1 !important}.mdl-color-text--pink-300{color:#f06292 !important}.mdl-color--pink-300{background-color:#f06292 !important}.mdl-color-text--pink-400{color:#ec407a !important}.mdl-color--pink-400{background-color:#ec407a !important}.mdl-color-text--pink-500{color:#e91e63 !important}.mdl-color--pink-500{background-color:#e91e63 !important}.mdl-color-text--pink-600{color:#d81b60 !important}.mdl-color--pink-600{background-color:#d81b60 !important}.mdl-color-text--pink-700{color:#c2185b !important}.mdl-color--pink-700{background-color:#c2185b !important}.mdl-color-text--pink-800{color:#ad1457 !important}.mdl-color--pink-800{background-color:#ad1457 !important}.mdl-color-text--pink-900{color:#880e4f !important}.mdl-color--pink-900{background-color:#880e4f !important}.mdl-color-text--pink-A100{color:#ff80ab !important}.mdl-color--pink-A100{background-color:#ff80ab !important}.mdl-color-text--pink-A200{color:#ff4081 !important}.mdl-color--pink-A200{background-color:#ff4081 !important}.mdl-color-text--pink-A400{color:#f50057 !important}.mdl-color--pink-A400{background-color:#f50057 !important}.mdl-color-text--pink-A700{color:#c51162 !important}.mdl-color--pink-A700{background-color:#c51162 !important}.mdl-color-text--purple{color:#9c27b0 !important}.mdl-color--purple{background-color:#9c27b0 !important}.mdl-color-text--purple-50{color:#f3e5f5 !important}.mdl-color--purple-50{background-color:#f3e5f5 !important}.mdl-color-text--purple-100{color:#e1bee7 !important}.mdl-color--purple-100{background-color:#e1bee7 !important}.mdl-color-text--purple-200{color:#ce93d8 !important}.mdl-color--purple-200{background-color:#ce93d8 !important}.mdl-color-text--purple-300{color:#ba68c8 !important}.mdl-color--purple-300{background-color:#ba68c8 !important}.mdl-color-text--purple-400{color:#ab47bc !important}.mdl-color--purple-400{background-color:#ab47bc !important}.mdl-color-text--purple-500{color:#9c27b0 !important}.mdl-color--purple-500{background-color:#9c27b0 !important}.mdl-color-text--purple-600{color:#8e24aa !important}.mdl-color--purple-600{background-color:#8e24aa !important}.mdl-color-text--purple-700{color:#7b1fa2 !important}.mdl-color--purple-700{background-color:#7b1fa2 !important}.mdl-color-text--purple-800{color:#6a1b9a !important}.mdl-color--purple-800{background-color:#6a1b9a !important}.mdl-color-text--purple-900{color:#4a148c !important}.mdl-color--purple-900{background-color:#4a148c !important}.mdl-color-text--purple-A100{color:#ea80fc !important}.mdl-color--purple-A100{background-color:#ea80fc !important}.mdl-color-text--purple-A200{color:#e040fb !important}.mdl-color--purple-A200{background-color:#e040fb !important}.mdl-color-text--purple-A400{color:#d500f9 !important}.mdl-color--purple-A400{background-color:#d500f9 !important}.mdl-color-text--purple-A700{color:#a0f !important}.mdl-color--purple-A700{background-color:#a0f !important}.mdl-color-text--deep-purple{color:#673ab7 !important}.mdl-color--deep-purple{background-color:#673ab7 !important}.mdl-color-text--deep-purple-50{color:#ede7f6 !important}.mdl-color--deep-purple-50{background-color:#ede7f6 !important}.mdl-color-text--deep-purple-100{color:#d1c4e9 !important}.mdl-color--deep-purple-100{background-color:#d1c4e9 !important}.mdl-color-text--deep-purple-200{color:#b39ddb !important}.mdl-color--deep-purple-200{background-color:#b39ddb !important}.mdl-color-text--deep-purple-300{color:#9575cd !important}.mdl-color--deep-purple-300{background-color:#9575cd !important}.mdl-color-text--deep-purple-400{color:#7e57c2 !important}.mdl-color--deep-purple-400{background-color:#7e57c2 !important}.mdl-color-text--deep-purple-500{color:#673ab7 !important}.mdl-color--deep-purple-500{background-color:#673ab7 !important}.mdl-color-text--deep-purple-600{color:#5e35b1 !important}.mdl-color--deep-purple-600{background-color:#5e35b1 !important}.mdl-color-text--deep-purple-700{color:#512da8 !important}.mdl-color--deep-purple-700{background-color:#512da8 !important}.mdl-color-text--deep-purple-800{color:#4527a0 !important}.mdl-color--deep-purple-800{background-color:#4527a0 !important}.mdl-color-text--deep-purple-900{color:#311b92 !important}.mdl-color--deep-purple-900{background-color:#311b92 !important}.mdl-color-text--deep-purple-A100{color:#b388ff !important}.mdl-color--deep-purple-A100{background-color:#b388ff !important}.mdl-color-text--deep-purple-A200{color:#7c4dff !important}.mdl-color--deep-purple-A200{background-color:#7c4dff !important}.mdl-color-text--deep-purple-A400{color:#651fff !important}.mdl-color--deep-purple-A400{background-color:#651fff !important}.mdl-color-text--deep-purple-A700{color:#6200ea !important}.mdl-color--deep-purple-A700{background-color:#6200ea !important}.mdl-color-text--indigo{color:#3f51b5 !important}.mdl-color--indigo{background-color:#3f51b5 !important}.mdl-color-text--indigo-50{color:#e8eaf6 !important}.mdl-color--indigo-50{background-color:#e8eaf6 !important}.mdl-color-text--indigo-100{color:#c5cae9 !important}.mdl-color--indigo-100{background-color:#c5cae9 !important}.mdl-color-text--indigo-200{color:#9fa8da !important}.mdl-color--indigo-200{background-color:#9fa8da !important}.mdl-color-text--indigo-300{color:#7986cb !important}.mdl-color--indigo-300{background-color:#7986cb !important}.mdl-color-text--indigo-400{color:#5c6bc0 !important}.mdl-color--indigo-400{background-color:#5c6bc0 !important}.mdl-color-text--indigo-500{color:#3f51b5 !important}.mdl-color--indigo-500{background-color:#3f51b5 !important}.mdl-color-text--indigo-600{color:#3949ab !important}.mdl-color--indigo-600{background-color:#3949ab !important}.mdl-color-text--indigo-700{color:#303f9f !important}.mdl-color--indigo-700{background-color:#303f9f !important}.mdl-color-text--indigo-800{color:#283593 !important}.mdl-color--indigo-800{background-color:#283593 !important}.mdl-color-text--indigo-900{color:#1a237e !important}.mdl-color--indigo-900{background-color:#1a237e !important}.mdl-color-text--indigo-A100{color:#8c9eff !important}.mdl-color--indigo-A100{background-color:#8c9eff !important}.mdl-color-text--indigo-A200{color:#536dfe !important}.mdl-color--indigo-A200{background-color:#536dfe !important}.mdl-color-text--indigo-A400{color:#3d5afe !important}.mdl-color--indigo-A400{background-color:#3d5afe !important}.mdl-color-text--indigo-A700{color:#304ffe !important}.mdl-color--indigo-A700{background-color:#304ffe !important}.mdl-color-text--blue{color:#2196f3 !important}.mdl-color--blue{background-color:#2196f3 !important}.mdl-color-text--blue-50{color:#e3f2fd !important}.mdl-color--blue-50{background-color:#e3f2fd !important}.mdl-color-text--blue-100{color:#bbdefb !important}.mdl-color--blue-100{background-color:#bbdefb !important}.mdl-color-text--blue-200{color:#90caf9 !important}.mdl-color--blue-200{background-color:#90caf9 !important}.mdl-color-text--blue-300{color:#64b5f6 !important}.mdl-color--blue-300{background-color:#64b5f6 !important}.mdl-color-text--blue-400{color:#42a5f5 !important}.mdl-color--blue-400{background-color:#42a5f5 !important}.mdl-color-text--blue-500{color:#2196f3 !important}.mdl-color--blue-500{background-color:#2196f3 !important}.mdl-color-text--blue-600{color:#1e88e5 !important}.mdl-color--blue-600{background-color:#1e88e5 !important}.mdl-color-text--blue-700{color:#1976d2 !important}.mdl-color--blue-700{background-color:#1976d2 !important}.mdl-color-text--blue-800{color:#1565c0 !important}.mdl-color--blue-800{background-color:#1565c0 !important}.mdl-color-text--blue-900{color:#0d47a1 !important}.mdl-color--blue-900{background-color:#0d47a1 !important}.mdl-color-text--blue-A100{color:#82b1ff !important}.mdl-color--blue-A100{background-color:#82b1ff !important}.mdl-color-text--blue-A200{color:#448aff !important}.mdl-color--blue-A200{background-color:#448aff !important}.mdl-color-text--blue-A400{color:#2979ff !important}.mdl-color--blue-A400{background-color:#2979ff !important}.mdl-color-text--blue-A700{color:#2962ff !important}.mdl-color--blue-A700{background-color:#2962ff !important}.mdl-color-text--light-blue{color:#03a9f4 !important}.mdl-color--light-blue{background-color:#03a9f4 !important}.mdl-color-text--light-blue-50{color:#e1f5fe !important}.mdl-color--light-blue-50{background-color:#e1f5fe !important}.mdl-color-text--light-blue-100{color:#b3e5fc !important}.mdl-color--light-blue-100{background-color:#b3e5fc !important}.mdl-color-text--light-blue-200{color:#81d4fa !important}.mdl-color--light-blue-200{background-color:#81d4fa !important}.mdl-color-text--light-blue-300{color:#4fc3f7 !important}.mdl-color--light-blue-300{background-color:#4fc3f7 !important}.mdl-color-text--light-blue-400{color:#29b6f6 !important}.mdl-color--light-blue-400{background-color:#29b6f6 !important}.mdl-color-text--light-blue-500{color:#03a9f4 !important}.mdl-color--light-blue-500{background-color:#03a9f4 !important}.mdl-color-text--light-blue-600{color:#039be5 !important}.mdl-color--light-blue-600{background-color:#039be5 !important}.mdl-color-text--light-blue-700{color:#0288d1 !important}.mdl-color--light-blue-700{background-color:#0288d1 !important}.mdl-color-text--light-blue-800{color:#0277bd !important}.mdl-color--light-blue-800{background-color:#0277bd !important}.mdl-color-text--light-blue-900{color:#01579b !important}.mdl-color--light-blue-900{background-color:#01579b !important}.mdl-color-text--light-blue-A100{color:#80d8ff !important}.mdl-color--light-blue-A100{background-color:#80d8ff !important}.mdl-color-text--light-blue-A200{color:#40c4ff !important}.mdl-color--light-blue-A200{background-color:#40c4ff !important}.mdl-color-text--light-blue-A400{color:#00b0ff !important}.mdl-color--light-blue-A400{background-color:#00b0ff !important}.mdl-color-text--light-blue-A700{color:#0091ea !important}.mdl-color--light-blue-A700{background-color:#0091ea !important}.mdl-color-text--cyan{color:#00bcd4 !important}.mdl-color--cyan{background-color:#00bcd4 !important}.mdl-color-text--cyan-50{color:#e0f7fa !important}.mdl-color--cyan-50{background-color:#e0f7fa !important}.mdl-color-text--cyan-100{color:#b2ebf2 !important}.mdl-color--cyan-100{background-color:#b2ebf2 !important}.mdl-color-text--cyan-200{color:#80deea !important}.mdl-color--cyan-200{background-color:#80deea !important}.mdl-color-text--cyan-300{color:#4dd0e1 !important}.mdl-color--cyan-300{background-color:#4dd0e1 !important}.mdl-color-text--cyan-400{color:#26c6da !important}.mdl-color--cyan-400{background-color:#26c6da !important}.mdl-color-text--cyan-500{color:#00bcd4 !important}.mdl-color--cyan-500{background-color:#00bcd4 !important}.mdl-color-text--cyan-600{color:#00acc1 !important}.mdl-color--cyan-600{background-color:#00acc1 !important}.mdl-color-text--cyan-700{color:#0097a7 !important}.mdl-color--cyan-700{background-color:#0097a7 !important}.mdl-color-text--cyan-800{color:#00838f !important}.mdl-color--cyan-800{background-color:#00838f !important}.mdl-color-text--cyan-900{color:#006064 !important}.mdl-color--cyan-900{background-color:#006064 !important}.mdl-color-text--cyan-A100{color:#84ffff !important}.mdl-color--cyan-A100{background-color:#84ffff !important}.mdl-color-text--cyan-A200{color:#18ffff !important}.mdl-color--cyan-A200{background-color:#18ffff !important}.mdl-color-text--cyan-A400{color:#00e5ff !important}.mdl-color--cyan-A400{background-color:#00e5ff !important}.mdl-color-text--cyan-A700{color:#00b8d4 !important}.mdl-color--cyan-A700{background-color:#00b8d4 !important}.mdl-color-text--teal{color:#009688 !important}.mdl-color--teal{background-color:#009688 !important}.mdl-color-text--teal-50{color:#e0f2f1 !important}.mdl-color--teal-50{background-color:#e0f2f1 !important}.mdl-color-text--teal-100{color:#b2dfdb !important}.mdl-color--teal-100{background-color:#b2dfdb !important}.mdl-color-text--teal-200{color:#80cbc4 !important}.mdl-color--teal-200{background-color:#80cbc4 !important}.mdl-color-text--teal-300{color:#4db6ac !important}.mdl-color--teal-300{background-color:#4db6ac !important}.mdl-color-text--teal-400{color:#26a69a !important}.mdl-color--teal-400{background-color:#26a69a !important}.mdl-color-text--teal-500{color:#009688 !important}.mdl-color--teal-500{background-color:#009688 !important}.mdl-color-text--teal-600{color:#00897b !important}.mdl-color--teal-600{background-color:#00897b !important}.mdl-color-text--teal-700{color:#00796b !important}.mdl-color--teal-700{background-color:#00796b !important}.mdl-color-text--teal-800{color:#00695c !important}.mdl-color--teal-800{background-color:#00695c !important}.mdl-color-text--teal-900{color:#004d40 !important}.mdl-color--teal-900{background-color:#004d40 !important}.mdl-color-text--teal-A100{color:#a7ffeb !important}.mdl-color--teal-A100{background-color:#a7ffeb !important}.mdl-color-text--teal-A200{color:#64ffda !important}.mdl-color--teal-A200{background-color:#64ffda !important}.mdl-color-text--teal-A400{color:#1de9b6 !important}.mdl-color--teal-A400{background-color:#1de9b6 !important}.mdl-color-text--teal-A700{color:#00bfa5 !important}.mdl-color--teal-A700{background-color:#00bfa5 !important}.mdl-color-text--green{color:#4caf50 !important}.mdl-color--green{background-color:#4caf50 !important}.mdl-color-text--green-50{color:#e8f5e9 !important}.mdl-color--green-50{background-color:#e8f5e9 !important}.mdl-color-text--green-100{color:#c8e6c9 !important}.mdl-color--green-100{background-color:#c8e6c9 !important}.mdl-color-text--green-200{color:#a5d6a7 !important}.mdl-color--green-200{background-color:#a5d6a7 !important}.mdl-color-text--green-300{color:#81c784 !important}.mdl-color--green-300{background-color:#81c784 !important}.mdl-color-text--green-400{color:#66bb6a !important}.mdl-color--green-400{background-color:#66bb6a !important}.mdl-color-text--green-500{color:#4caf50 !important}.mdl-color--green-500{background-color:#4caf50 !important}.mdl-color-text--green-600{color:#43a047 !important}.mdl-color--green-600{background-color:#43a047 !important}.mdl-color-text--green-700{color:#388e3c !important}.mdl-color--green-700{background-color:#388e3c !important}.mdl-color-text--green-800{color:#2e7d32 !important}.mdl-color--green-800{background-color:#2e7d32 !important}.mdl-color-text--green-900{color:#1b5e20 !important}.mdl-color--green-900{background-color:#1b5e20 !important}.mdl-color-text--green-A100{color:#b9f6ca !important}.mdl-color--green-A100{background-color:#b9f6ca !important}.mdl-color-text--green-A200{color:#69f0ae !important}.mdl-color--green-A200{background-color:#69f0ae !important}.mdl-color-text--green-A400{color:#00e676 !important}.mdl-color--green-A400{background-color:#00e676 !important}.mdl-color-text--green-A700{color:#00c853 !important}.mdl-color--green-A700{background-color:#00c853 !important}.mdl-color-text--light-green{color:#8bc34a !important}.mdl-color--light-green{background-color:#8bc34a !important}.mdl-color-text--light-green-50{color:#f1f8e9 !important}.mdl-color--light-green-50{background-color:#f1f8e9 !important}.mdl-color-text--light-green-100{color:#dcedc8 !important}.mdl-color--light-green-100{background-color:#dcedc8 !important}.mdl-color-text--light-green-200{color:#c5e1a5 !important}.mdl-color--light-green-200{background-color:#c5e1a5 !important}.mdl-color-text--light-green-300{color:#aed581 !important}.mdl-color--light-green-300{background-color:#aed581 !important}.mdl-color-text--light-green-400{color:#9ccc65 !important}.mdl-color--light-green-400{background-color:#9ccc65 !important}.mdl-color-text--light-green-500{color:#8bc34a !important}.mdl-color--light-green-500{background-color:#8bc34a !important}.mdl-color-text--light-green-600{color:#7cb342 !important}.mdl-color--light-green-600{background-color:#7cb342 !important}.mdl-color-text--light-green-700{color:#689f38 !important}.mdl-color--light-green-700{background-color:#689f38 !important}.mdl-color-text--light-green-800{color:#558b2f !important}.mdl-color--light-green-800{background-color:#558b2f !important}.mdl-color-text--light-green-900{color:#33691e !important}.mdl-color--light-green-900{background-color:#33691e !important}.mdl-color-text--light-green-A100{color:#ccff90 !important}.mdl-color--light-green-A100{background-color:#ccff90 !important}.mdl-color-text--light-green-A200{color:#b2ff59 !important}.mdl-color--light-green-A200{background-color:#b2ff59 !important}.mdl-color-text--light-green-A400{color:#76ff03 !important}.mdl-color--light-green-A400{background-color:#76ff03 !important}.mdl-color-text--light-green-A700{color:#64dd17 !important}.mdl-color--light-green-A700{background-color:#64dd17 !important}.mdl-color-text--lime{color:#cddc39 !important}.mdl-color--lime{background-color:#cddc39 !important}.mdl-color-text--lime-50{color:#f9fbe7 !important}.mdl-color--lime-50{background-color:#f9fbe7 !important}.mdl-color-text--lime-100{color:#f0f4c3 !important}.mdl-color--lime-100{background-color:#f0f4c3 !important}.mdl-color-text--lime-200{color:#e6ee9c !important}.mdl-color--lime-200{background-color:#e6ee9c !important}.mdl-color-text--lime-300{color:#dce775 !important}.mdl-color--lime-300{background-color:#dce775 !important}.mdl-color-text--lime-400{color:#d4e157 !important}.mdl-color--lime-400{background-color:#d4e157 !important}.mdl-color-text--lime-500{color:#cddc39 !important}.mdl-color--lime-500{background-color:#cddc39 !important}.mdl-color-text--lime-600{color:#c0ca33 !important}.mdl-color--lime-600{background-color:#c0ca33 !important}.mdl-color-text--lime-700{color:#afb42b !important}.mdl-color--lime-700{background-color:#afb42b !important}.mdl-color-text--lime-800{color:#9e9d24 !important}.mdl-color--lime-800{background-color:#9e9d24 !important}.mdl-color-text--lime-900{color:#827717 !important}.mdl-color--lime-900{background-color:#827717 !important}.mdl-color-text--lime-A100{color:#f4ff81 !important}.mdl-color--lime-A100{background-color:#f4ff81 !important}.mdl-color-text--lime-A200{color:#eeff41 !important}.mdl-color--lime-A200{background-color:#eeff41 !important}.mdl-color-text--lime-A400{color:#c6ff00 !important}.mdl-color--lime-A400{background-color:#c6ff00 !important}.mdl-color-text--lime-A700{color:#aeea00 !important}.mdl-color--lime-A700{background-color:#aeea00 !important}.mdl-color-text--yellow{color:#ffeb3b !important}.mdl-color--yellow{background-color:#ffeb3b !important}.mdl-color-text--yellow-50{color:#fffde7 !important}.mdl-color--yellow-50{background-color:#fffde7 !important}.mdl-color-text--yellow-100{color:#fff9c4 !important}.mdl-color--yellow-100{background-color:#fff9c4 !important}.mdl-color-text--yellow-200{color:#fff59d !important}.mdl-color--yellow-200{background-color:#fff59d !important}.mdl-color-text--yellow-300{color:#fff176 !important}.mdl-color--yellow-300{background-color:#fff176 !important}.mdl-color-text--yellow-400{color:#ffee58 !important}.mdl-color--yellow-400{background-color:#ffee58 !important}.mdl-color-text--yellow-500{color:#ffeb3b !important}.mdl-color--yellow-500{background-color:#ffeb3b !important}.mdl-color-text--yellow-600{color:#fdd835 !important}.mdl-color--yellow-600{background-color:#fdd835 !important}.mdl-color-text--yellow-700{color:#fbc02d !important}.mdl-color--yellow-700{background-color:#fbc02d !important}.mdl-color-text--yellow-800{color:#f9a825 !important}.mdl-color--yellow-800{background-color:#f9a825 !important}.mdl-color-text--yellow-900{color:#f57f17 !important}.mdl-color--yellow-900{background-color:#f57f17 !important}.mdl-color-text--yellow-A100{color:#ffff8d !important}.mdl-color--yellow-A100{background-color:#ffff8d !important}.mdl-color-text--yellow-A200{color:#ff0 !important}.mdl-color--yellow-A200{background-color:#ff0 !important}.mdl-color-text--yellow-A400{color:#ffea00 !important}.mdl-color--yellow-A400{background-color:#ffea00 !important}.mdl-color-text--yellow-A700{color:#ffd600 !important}.mdl-color--yellow-A700{background-color:#ffd600 !important}.mdl-color-text--amber{color:#ffc107 !important}.mdl-color--amber{background-color:#ffc107 !important}.mdl-color-text--amber-50{color:#fff8e1 !important}.mdl-color--amber-50{background-color:#fff8e1 !important}.mdl-color-text--amber-100{color:#ffecb3 !important}.mdl-color--amber-100{background-color:#ffecb3 !important}.mdl-color-text--amber-200{color:#ffe082 !important}.mdl-color--amber-200{background-color:#ffe082 !important}.mdl-color-text--amber-300{color:#ffd54f !important}.mdl-color--amber-300{background-color:#ffd54f !important}.mdl-color-text--amber-400{color:#ffca28 !important}.mdl-color--amber-400{background-color:#ffca28 !important}.mdl-color-text--amber-500{color:#ffc107 !important}.mdl-color--amber-500{background-color:#ffc107 !important}.mdl-color-text--amber-600{color:#ffb300 !important}.mdl-color--amber-600{background-color:#ffb300 !important}.mdl-color-text--amber-700{color:#ffa000 !important}.mdl-color--amber-700{background-color:#ffa000 !important}.mdl-color-text--amber-800{color:#ff8f00 !important}.mdl-color--amber-800{background-color:#ff8f00 !important}.mdl-color-text--amber-900{color:#ff6f00 !important}.mdl-color--amber-900{background-color:#ff6f00 !important}.mdl-color-text--amber-A100{color:#ffe57f !important}.mdl-color--amber-A100{background-color:#ffe57f !important}.mdl-color-text--amber-A200{color:#ffd740 !important}.mdl-color--amber-A200{background-color:#ffd740 !important}.mdl-color-text--amber-A400{color:#ffc400 !important}.mdl-color--amber-A400{background-color:#ffc400 !important}.mdl-color-text--amber-A700{color:#ffab00 !important}.mdl-color--amber-A700{background-color:#ffab00 !important}.mdl-color-text--orange{color:#ff9800 !important}.mdl-color--orange{background-color:#ff9800 !important}.mdl-color-text--orange-50{color:#fff3e0 !important}.mdl-color--orange-50{background-color:#fff3e0 !important}.mdl-color-text--orange-100{color:#ffe0b2 !important}.mdl-color--orange-100{background-color:#ffe0b2 !important}.mdl-color-text--orange-200{color:#ffcc80 !important}.mdl-color--orange-200{background-color:#ffcc80 !important}.mdl-color-text--orange-300{color:#ffb74d !important}.mdl-color--orange-300{background-color:#ffb74d !important}.mdl-color-text--orange-400{color:#ffa726 !important}.mdl-color--orange-400{background-color:#ffa726 !important}.mdl-color-text--orange-500{color:#ff9800 !important}.mdl-color--orange-500{background-color:#ff9800 !important}.mdl-color-text--orange-600{color:#fb8c00 !important}.mdl-color--orange-600{background-color:#fb8c00 !important}.mdl-color-text--orange-700{color:#f57c00 !important}.mdl-color--orange-700{background-color:#f57c00 !important}.mdl-color-text--orange-800{color:#ef6c00 !important}.mdl-color--orange-800{background-color:#ef6c00 !important}.mdl-color-text--orange-900{color:#e65100 !important}.mdl-color--orange-900{background-color:#e65100 !important}.mdl-color-text--orange-A100{color:#ffd180 !important}.mdl-color--orange-A100{background-color:#ffd180 !important}.mdl-color-text--orange-A200{color:#ffab40 !important}.mdl-color--orange-A200{background-color:#ffab40 !important}.mdl-color-text--orange-A400{color:#ff9100 !important}.mdl-color--orange-A400{background-color:#ff9100 !important}.mdl-color-text--orange-A700{color:#ff6d00 !important}.mdl-color--orange-A700{background-color:#ff6d00 !important}.mdl-color-text--deep-orange{color:#ff5722 !important}.mdl-color--deep-orange{background-color:#ff5722 !important}.mdl-color-text--deep-orange-50{color:#fbe9e7 !important}.mdl-color--deep-orange-50{background-color:#fbe9e7 !important}.mdl-color-text--deep-orange-100{color:#ffccbc !important}.mdl-color--deep-orange-100{background-color:#ffccbc !important}.mdl-color-text--deep-orange-200{color:#ffab91 !important}.mdl-color--deep-orange-200{background-color:#ffab91 !important}.mdl-color-text--deep-orange-300{color:#ff8a65 !important}.mdl-color--deep-orange-300{background-color:#ff8a65 !important}.mdl-color-text--deep-orange-400{color:#ff7043 !important}.mdl-color--deep-orange-400{background-color:#ff7043 !important}.mdl-color-text--deep-orange-500{color:#ff5722 !important}.mdl-color--deep-orange-500{background-color:#ff5722 !important}.mdl-color-text--deep-orange-600{color:#f4511e !important}.mdl-color--deep-orange-600{background-color:#f4511e !important}.mdl-color-text--deep-orange-700{color:#e64a19 !important}.mdl-color--deep-orange-700{background-color:#e64a19 !important}.mdl-color-text--deep-orange-800{color:#d84315 !important}.mdl-color--deep-orange-800{background-color:#d84315 !important}.mdl-color-text--deep-orange-900{color:#bf360c !important}.mdl-color--deep-orange-900{background-color:#bf360c !important}.mdl-color-text--deep-orange-A100{color:#ff9e80 !important}.mdl-color--deep-orange-A100{background-color:#ff9e80 !important}.mdl-color-text--deep-orange-A200{color:#ff6e40 !important}.mdl-color--deep-orange-A200{background-color:#ff6e40 !important}.mdl-color-text--deep-orange-A400{color:#ff3d00 !important}.mdl-color--deep-orange-A400{background-color:#ff3d00 !important}.mdl-color-text--deep-orange-A700{color:#dd2c00 !important}.mdl-color--deep-orange-A700{background-color:#dd2c00 !important}.mdl-color-text--brown{color:#795548 !important}.mdl-color--brown{background-color:#795548 !important}.mdl-color-text--brown-50{color:#efebe9 !important}.mdl-color--brown-50{background-color:#efebe9 !important}.mdl-color-text--brown-100{color:#d7ccc8 !important}.mdl-color--brown-100{background-color:#d7ccc8 !important}.mdl-color-text--brown-200{color:#bcaaa4 !important}.mdl-color--brown-200{background-color:#bcaaa4 !important}.mdl-color-text--brown-300{color:#a1887f !important}.mdl-color--brown-300{background-color:#a1887f !important}.mdl-color-text--brown-400{color:#8d6e63 !important}.mdl-color--brown-400{background-color:#8d6e63 !important}.mdl-color-text--brown-500{color:#795548 !important}.mdl-color--brown-500{background-color:#795548 !important}.mdl-color-text--brown-600{color:#6d4c41 !important}.mdl-color--brown-600{background-color:#6d4c41 !important}.mdl-color-text--brown-700{color:#5d4037 !important}.mdl-color--brown-700{background-color:#5d4037 !important}.mdl-color-text--brown-800{color:#4e342e !important}.mdl-color--brown-800{background-color:#4e342e !important}.mdl-color-text--brown-900{color:#3e2723 !important}.mdl-color--brown-900{background-color:#3e2723 !important}.mdl-color-text--grey{color:#9e9e9e !important}.mdl-color--grey{background-color:#9e9e9e !important}.mdl-color-text--grey-50{color:#fafafa !important}.mdl-color--grey-50{background-color:#fafafa !important}.mdl-color-text--grey-100{color:#f5f5f5 !important}.mdl-color--grey-100{background-color:#f5f5f5 !important}.mdl-color-text--grey-200{color:#eee !important}.mdl-color--grey-200{background-color:#eee !important}.mdl-color-text--grey-300{color:#e0e0e0 !important}.mdl-color--grey-300{background-color:#e0e0e0 !important}.mdl-color-text--grey-400{color:#bdbdbd !important}.mdl-color--grey-400{background-color:#bdbdbd !important}.mdl-color-text--grey-500{color:#9e9e9e !important}.mdl-color--grey-500{background-color:#9e9e9e !important}.mdl-color-text--grey-600{color:#757575 !important}.mdl-color--grey-600{background-color:#757575 !important}.mdl-color-text--grey-700{color:#616161 !important}.mdl-color--grey-700{background-color:#616161 !important}.mdl-color-text--grey-800{color:#424242 !important}.mdl-color--grey-800{background-color:#424242 !important}.mdl-color-text--grey-900{color:#212121 !important}.mdl-color--grey-900{background-color:#212121 !important}.mdl-color-text--blue-grey{color:#607d8b !important}.mdl-color--blue-grey{background-color:#607d8b !important}.mdl-color-text--blue-grey-50{color:#eceff1 !important}.mdl-color--blue-grey-50{background-color:#eceff1 !important}.mdl-color-text--blue-grey-100{color:#cfd8dc !important}.mdl-color--blue-grey-100{background-color:#cfd8dc !important}.mdl-color-text--blue-grey-200{color:#b0bec5 !important}.mdl-color--blue-grey-200{background-color:#b0bec5 !important}.mdl-color-text--blue-grey-300{color:#90a4ae !important}.mdl-color--blue-grey-300{background-color:#90a4ae !important}.mdl-color-text--blue-grey-400{color:#78909c !important}.mdl-color--blue-grey-400{background-color:#78909c !important}.mdl-color-text--blue-grey-500{color:#607d8b !important}.mdl-color--blue-grey-500{background-color:#607d8b !important}.mdl-color-text--blue-grey-600{color:#546e7a !important}.mdl-color--blue-grey-600{background-color:#546e7a !important}.mdl-color-text--blue-grey-700{color:#455a64 !important}.mdl-color--blue-grey-700{background-color:#455a64 !important}.mdl-color-text--blue-grey-800{color:#37474f !important}.mdl-color--blue-grey-800{background-color:#37474f !important}.mdl-color-text--blue-grey-900{color:#263238 !important}.mdl-color--blue-grey-900{background-color:#263238 !important}.mdl-color--black{background-color:#000 !important}.mdl-color-text--black{color:#000 !important}.mdl-color--white{background-color:#fff !important}.mdl-color-text--white{color:#fff !important}.mdl-color--primary{background-color:rgb(33,150,243)!important}.mdl-color--primary-contrast{background-color:rgb(255,255,255)!important}.mdl-color--primary-dark{background-color:rgb(25,118,210)!important}.mdl-color--accent{background-color:rgb(255,110,64)!important}.mdl-color--accent-contrast{background-color:rgb(66,66,66)!important}.mdl-color-text--primary{color:rgb(33,150,243)!important}.mdl-color-text--primary-contrast{color:rgb(255,255,255)!important}.mdl-color-text--primary-dark{color:rgb(25,118,210)!important}.mdl-color-text--accent{color:rgb(255,110,64)!important}.mdl-color-text--accent-contrast{color:rgb(66,66,66)!important}.mdl-ripple{background:#000;border-radius:50%;height:50px;left:0;opacity:0;pointer-events:none;position:absolute;top:0;-webkit-transform:translate(-50%,-50%);transform:translate(-50%,-50%);width:50px;overflow:hidden}.mdl-ripple.is-animating{transition:transform .3s cubic-bezier(0,0,.2,1),width .3s cubic-bezier(0,0,.2,1),height .3s cubic-bezier(0,0,.2,1),opacity .6s cubic-bezier(0,0,.2,1);transition:transform .3s cubic-bezier(0,0,.2,1),width .3s cubic-bezier(0,0,.2,1),height .3s cubic-bezier(0,0,.2,1),opacity .6s cubic-bezier(0,0,.2,1),-webkit-transform .3s cubic-bezier(0,0,.2,1)}.mdl-ripple.is-visible{opacity:.3}.mdl-animation--default,.mdl-animation--fast-out-slow-in{transition-timing-function:cubic-bezier(.4,0,.2,1)}.mdl-animation--linear-out-slow-in{transition-timing-function:cubic-bezier(0,0,.2,1)}.mdl-animation--fast-out-linear-in{transition-timing-function:cubic-bezier(.4,0,1,1)}.mdl-badge{position:relative;white-space:nowrap;margin-right:24px}.mdl-badge:not([data-badge]){margin-right:auto}.mdl-badge[data-badge]:after{content:attr(data-badge);display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-direction:row;-ms-flex-direction:row;flex-direction:row;-webkit-flex-wrap:wrap;-ms-flex-wrap:wrap;flex-wrap:wrap;-webkit-justify-content:center;-ms-flex-pack:center;justify-content:center;-webkit-align-content:center;-ms-flex-line-pack:center;align-content:center;-webkit-align-items:center;-ms-flex-align:center;align-items:center;position:absolute;top:-11px;right:-24px;font-family:"Roboto","Helvetica","Arial",sans-serif;font-weight:600;font-size:12px;width:22px;height:22px;border-radius:50%;background:rgb(255,110,64);color:rgb(66,66,66)}.mdl-button .mdl-badge[data-badge]:after{top:-10px;right:-5px}.mdl-badge.mdl-badge--no-background[data-badge]:after{color:rgb(255,110,64);background:rgba(66,66,66,.2);box-shadow:0 0 1px gray}.mdl-badge.mdl-badge--overlap{margin-right:10px}.mdl-badge.mdl-badge--overlap:after{right:-10px}.mdl-button{background:0 0;border:none;border-radius:2px;color:#000;position:relative;height:36px;margin:0;min-width:64px;padding:0 16px;display:inline-block;font-family:"Roboto","Helvetica","Arial",sans-serif;font-size:14px;font-weight:500;text-transform:uppercase;letter-spacing:0;overflow:hidden;will-change:box-shadow;transition:box-shadow .2s cubic-bezier(.4,0,1,1),background-color .2s cubic-bezier(.4,0,.2,1),color .2s cubic-bezier(.4,0,.2,1);outline:none;cursor:pointer;text-decoration:none;text-align:center;line-height:36px;vertical-align:middle}.mdl-button::-moz-focus-inner{border:0}.mdl-button:hover{background-color:rgba(158,158,158,.2)}.mdl-button:focus:not(:active){background-color:rgba(0,0,0,.12)}.mdl-button:active{background-color:rgba(158,158,158,.4)}.mdl-button.mdl-button--colored{color:rgb(33,150,243)}.mdl-button.mdl-button--colored:focus:not(:active){background-color:rgba(0,0,0,.12)}input.mdl-button[type="submit"]{-webkit-appearance:none}.mdl-button--raised{background:rgba(158,158,158,.2);box-shadow:0 2px 2px 0 rgba(0,0,0,.14),0 3px 1px -2px rgba(0,0,0,.2),0 1px 5px 0 rgba(0,0,0,.12)}.mdl-button--raised:active{box-shadow:0 4px 5px 0 rgba(0,0,0,.14),0 1px 10px 0 rgba(0,0,0,.12),0 2px 4px -1px rgba(0,0,0,.2);background-color:rgba(158,158,158,.4)}.mdl-button--raised:focus:not(:active){box-shadow:0 0 8px rgba(0,0,0,.18),0 8px 16px rgba(0,0,0,.36);background-color:rgba(158,158,158,.4)}.mdl-button--raised.mdl-button--colored{background:rgb(33,150,243);color:rgb(255,255,255)}.mdl-button--raised.mdl-button--colored:hover{background-color:rgb(33,150,243)}.mdl-button--raised.mdl-button--colored:active{background-color:rgb(33,150,243)}.mdl-button--raised.mdl-button--colored:focus:not(:active){background-color:rgb(33,150,243)}.mdl-button--raised.mdl-button--colored .mdl-ripple{background:rgb(255,255,255)}.mdl-button--fab{border-radius:50%;font-size:24px;height:56px;margin:auto;min-width:56px;width:56px;padding:0;overflow:hidden;background:rgba(158,158,158,.2);box-shadow:0 1px 1.5px 0 rgba(0,0,0,.12),0 1px 1px 0 rgba(0,0,0,.24);position:relative;line-height:normal}.mdl-button--fab .material-icons{position:absolute;top:50%;left:50%;-webkit-transform:translate(-12px,-12px);transform:translate(-12px,-12px);line-height:24px;width:24px}.mdl-button--fab.mdl-button--mini-fab{height:40px;min-width:40px;width:40px}.mdl-button--fab .mdl-button__ripple-container{border-radius:50%;-webkit-mask-image:-webkit-radial-gradient(circle,#fff,#000)}.mdl-button--fab:active{box-shadow:0 4px 5px 0 rgba(0,0,0,.14),0 1px 10px 0 rgba(0,0,0,.12),0 2px 4px -1px rgba(0,0,0,.2);background-color:rgba(158,158,158,.4)}.mdl-button--fab:focus:not(:active){box-shadow:0 0 8px rgba(0,0,0,.18),0 8px 16px rgba(0,0,0,.36);background-color:rgba(158,158,158,.4)}.mdl-button--fab.mdl-button--colored{background:rgb(255,110,64);color:rgb(66,66,66)}.mdl-button--fab.mdl-button--colored:hover{background-color:rgb(255,110,64)}.mdl-button--fab.mdl-button--colored:focus:not(:active){background-color:rgb(255,110,64)}.mdl-button--fab.mdl-button--colored:active{background-color:rgb(255,110,64)}.mdl-button--fab.mdl-button--colored .mdl-ripple{background:rgb(66,66,66)}.mdl-button--icon{border-radius:50%;font-size:24px;height:32px;margin-left:0;margin-right:0;min-width:32px;width:32px;padding:0;overflow:hidden;color:inherit;line-height:normal}.mdl-button--icon .material-icons{position:absolute;top:50%;left:50%;-webkit-transform:translate(-12px,-12px);transform:translate(-12px,-12px);line-height:24px;width:24px}.mdl-button--icon.mdl-button--mini-icon{height:24px;min-width:24px;width:24px}.mdl-button--icon.mdl-button--mini-icon .material-icons{top:0;left:0}.mdl-button--icon .mdl-button__ripple-container{border-radius:50%;-webkit-mask-image:-webkit-radial-gradient(circle,#fff,#000)}.mdl-button__ripple-container{display:block;height:100%;left:0;position:absolute;top:0;width:100%;z-index:0;overflow:hidden}.mdl-button[disabled] .mdl-button__ripple-container .mdl-ripple,.mdl-button.mdl-button--disabled .mdl-button__ripple-container .mdl-ripple{background-color:transparent}.mdl-button--primary.mdl-button--primary{color:rgb(33,150,243)}.mdl-button--primary.mdl-button--primary .mdl-ripple{background:rgb(255,255,255)}.mdl-button--primary.mdl-button--primary.mdl-button--raised,.mdl-button--primary.mdl-button--primary.mdl-button--fab{color:rgb(255,255,255);background-color:rgb(33,150,243)}.mdl-button--accent.mdl-button--accent{color:rgb(255,110,64)}.mdl-button--accent.mdl-button--accent .mdl-ripple{background:rgb(66,66,66)}.mdl-button--accent.mdl-button--accent.mdl-button--raised,.mdl-button--accent.mdl-button--accent.mdl-button--fab{color:rgb(66,66,66);background-color:rgb(255,110,64)}.mdl-button[disabled][disabled],.mdl-button.mdl-button--disabled.mdl-button--disabled{color:rgba(0,0,0,.26);cursor:default;background-color:transparent}.mdl-button--fab[disabled][disabled],.mdl-button--fab.mdl-button--disabled.mdl-button--disabled{background-color:rgba(0,0,0,.12);color:rgba(0,0,0,.26)}.mdl-button--raised[disabled][disabled],.mdl-button--raised.mdl-button--disabled.mdl-button--disabled{background-color:rgba(0,0,0,.12);color:rgba(0,0,0,.26);box-shadow:none}.mdl-button--colored[disabled][disabled],.mdl-button--colored.mdl-button--disabled.mdl-button--disabled{color:rgba(0,0,0,.26)}.mdl-button .material-icons{vertical-align:middle}.mdl-card{display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-direction:column;-ms-flex-direction:column;flex-direction:column;font-size:16px;font-weight:400;min-height:200px;overflow:hidden;width:330px;z-index:1;position:relative;background:#fff;border-radius:2px;box-sizing:border-box}.mdl-card__media{background-color:rgb(255,110,64);background-repeat:repeat;background-position:50% 50%;background-size:cover;background-origin:padding-box;background-attachment:scroll;box-sizing:border-box}.mdl-card__title{-webkit-align-items:center;-ms-flex-align:center;align-items:center;color:#000;display:block;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-justify-content:stretch;-ms-flex-pack:stretch;justify-content:stretch;line-height:normal;padding:16px;-webkit-perspective-origin:165px 56px;perspective-origin:165px 56px;-webkit-transform-origin:165px 56px;transform-origin:165px 56px;box-sizing:border-box}.mdl-card__title.mdl-card--border{border-bottom:1px solid rgba(0,0,0,.1)}.mdl-card__title-text{-webkit-align-self:flex-end;-ms-flex-item-align:end;align-self:flex-end;color:inherit;display:block;display:-webkit-flex;display:-ms-flexbox;display:flex;font-size:24px;font-weight:300;line-height:normal;overflow:hidden;-webkit-transform-origin:149px 48px;transform-origin:149px 48px;margin:0}.mdl-card__subtitle-text{font-size:14px;color:rgba(0,0,0,.54);margin:0}.mdl-card__supporting-text{color:rgba(0,0,0,.54);font-size:1rem;line-height:18px;overflow:hidden;padding:16px;width:90%}.mdl-card__supporting-text.mdl-card--border{border-bottom:1px solid rgba(0,0,0,.1)}.mdl-card__actions{font-size:16px;line-height:normal;width:100%;background-color:transparent;padding:8px;box-sizing:border-box}.mdl-card__actions.mdl-card--border{border-top:1px solid rgba(0,0,0,.1)}.mdl-card--expand{-webkit-flex-grow:1;-ms-flex-positive:1;flex-grow:1}.mdl-card__menu{position:absolute;right:16px;top:16px}.mdl-checkbox{position:relative;z-index:1;vertical-align:middle;display:inline-block;box-sizing:border-box;width:100%;height:24px;margin:0;padding:0}.mdl-checkbox.is-upgraded{padding-left:24px}.mdl-checkbox__input{line-height:24px}.mdl-checkbox.is-upgraded .mdl-checkbox__input{position:absolute;width:0;height:0;margin:0;padding:0;opacity:0;-ms-appearance:none;-moz-appearance:none;-webkit-appearance:none;appearance:none;border:none}.mdl-checkbox__box-outline{position:absolute;top:3px;left:0;display:inline-block;box-sizing:border-box;width:16px;height:16px;margin:0;cursor:pointer;overflow:hidden;border:2px solid rgba(0,0,0,.54);border-radius:2px;z-index:2}.mdl-checkbox.is-checked .mdl-checkbox__box-outline{border:2px solid rgb(33,150,243)}fieldset[disabled] .mdl-checkbox .mdl-checkbox__box-outline,.mdl-checkbox.is-disabled .mdl-checkbox__box-outline{border:2px solid rgba(0,0,0,.26);cursor:auto}.mdl-checkbox__focus-helper{position:absolute;top:3px;left:0;display:inline-block;box-sizing:border-box;width:16px;height:16px;border-radius:50%;background-color:transparent}.mdl-checkbox.is-focused .mdl-checkbox__focus-helper{box-shadow:0 0 0 8px rgba(0,0,0,.1);background-color:rgba(0,0,0,.1)}.mdl-checkbox.is-focused.is-checked .mdl-checkbox__focus-helper{box-shadow:0 0 0 8px rgba(33,150,243,.26);background-color:rgba(33,150,243,.26)}.mdl-checkbox__tick-outline{position:absolute;top:0;left:0;height:100%;width:100%;-webkit-mask:url("data:image/svg+xml;base64,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");mask:url("data:image/svg+xml;base64,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");background:0 0;transition-duration:.28s;transition-timing-function:cubic-bezier(.4,0,.2,1);transition-property:background}.mdl-checkbox.is-checked .mdl-checkbox__tick-outline{background:rgb(33,150,243)url("data:image/svg+xml;base64,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")}fieldset[disabled] .mdl-checkbox.is-checked .mdl-checkbox__tick-outline,.mdl-checkbox.is-checked.is-disabled .mdl-checkbox__tick-outline{background:rgba(0,0,0,.26)url("data:image/svg+xml;base64,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")}.mdl-checkbox__label{position:relative;cursor:pointer;font-size:16px;line-height:24px;margin:0}fieldset[disabled] .mdl-checkbox .mdl-checkbox__label,.mdl-checkbox.is-disabled .mdl-checkbox__label{color:rgba(0,0,0,.26);cursor:auto}.mdl-checkbox__ripple-container{position:absolute;z-index:2;top:-6px;left:-10px;box-sizing:border-box;width:36px;height:36px;border-radius:50%;cursor:pointer;overflow:hidden;-webkit-mask-image:-webkit-radial-gradient(circle,#fff,#000)}.mdl-checkbox__ripple-container .mdl-ripple{background:rgb(33,150,243)}fieldset[disabled] .mdl-checkbox .mdl-checkbox__ripple-container,.mdl-checkbox.is-disabled .mdl-checkbox__ripple-container{cursor:auto}fieldset[disabled] .mdl-checkbox .mdl-checkbox__ripple-container .mdl-ripple,.mdl-checkbox.is-disabled .mdl-checkbox__ripple-container .mdl-ripple{background:0 0}.mdl-chip{height:32px;font-family:"Roboto","Helvetica","Arial",sans-serif;line-height:32px;padding:0 12px;border:0;border-radius:16px;background-color:#dedede;display:inline-block;color:rgba(0,0,0,.87);margin:2px 0;font-size:0;white-space:nowrap}.mdl-chip__text{font-size:13px;vertical-align:middle;display:inline-block}.mdl-chip__action{height:24px;width:24px;background:0 0;opacity:.54;cursor:pointer;padding:0;margin:0 0 0 4px;font-size:13px;text-decoration:none;color:rgba(0,0,0,.87);border:none;outline:none}.mdl-chip__action,.mdl-chip__contact{display:inline-block;vertical-align:middle;overflow:hidden;text-align:center}.mdl-chip__contact{height:32px;width:32px;border-radius:16px;margin-right:8px;font-size:18px;line-height:32px}.mdl-chip:focus{outline:0;box-shadow:0 2px 2px 0 rgba(0,0,0,.14),0 3px 1px -2px rgba(0,0,0,.2),0 1px 5px 0 rgba(0,0,0,.12)}.mdl-chip:active{background-color:#d6d6d6}.mdl-chip--deletable{padding-right:4px}.mdl-chip--contact{padding-left:0}.mdl-data-table{position:relative;border:1px solid rgba(0,0,0,.12);border-collapse:collapse;white-space:nowrap;font-size:13px;background-color:#fff}.mdl-data-table thead{padding-bottom:3px}.mdl-data-table thead .mdl-data-table__select{margin-top:0}.mdl-data-table tbody tr{position:relative;height:48px;transition-duration:.28s;transition-timing-function:cubic-bezier(.4,0,.2,1);transition-property:background-color}.mdl-data-table tbody tr.is-selected{background-color:#e0e0e0}.mdl-data-table tbody tr:hover{background-color:#eee}.mdl-data-table td{text-align:right}.mdl-data-table th{padding:0 18px 12px 18px;text-align:right}.mdl-data-table td:first-of-type,.mdl-data-table th:first-of-type{padding-left:24px}.mdl-data-table td:last-of-type,.mdl-data-table th:last-of-type{padding-right:24px}.mdl-data-table td{position:relative;height:48px;border-top:1px solid rgba(0,0,0,.12);border-bottom:1px solid rgba(0,0,0,.12);padding:12px 18px;box-sizing:border-box}.mdl-data-table td,.mdl-data-table td .mdl-data-table__select{vertical-align:middle}.mdl-data-table th{position:relative;vertical-align:bottom;text-overflow:ellipsis;font-weight:700;line-height:24px;letter-spacing:0;height:48px;font-size:12px;color:rgba(0,0,0,.54);padding-bottom:8px;box-sizing:border-box}.mdl-data-table th.mdl-data-table__header--sorted-ascending,.mdl-data-table th.mdl-data-table__header--sorted-descending{color:rgba(0,0,0,.87)}.mdl-data-table th.mdl-data-table__header--sorted-ascending:before,.mdl-data-table th.mdl-data-table__header--sorted-descending:before{font-family:'Material Icons';font-weight:400;font-style:normal;line-height:1;letter-spacing:normal;text-transform:none;display:inline-block;word-wrap:normal;-moz-font-feature-settings:'liga';font-feature-settings:'liga';-webkit-font-feature-settings:'liga';-webkit-font-smoothing:antialiased;font-size:16px;content:"\e5d8";margin-right:5px;vertical-align:sub}.mdl-data-table th.mdl-data-table__header--sorted-ascending:hover,.mdl-data-table th.mdl-data-table__header--sorted-descending:hover{cursor:pointer}.mdl-data-table th.mdl-data-table__header--sorted-ascending:hover:before,.mdl-data-table th.mdl-data-table__header--sorted-descending:hover:before{color:rgba(0,0,0,.26)}.mdl-data-table th.mdl-data-table__header--sorted-descending:before{content:"\e5db"}.mdl-data-table__select{width:16px}.mdl-data-table__cell--non-numeric.mdl-data-table__cell--non-numeric{text-align:left}.mdl-dialog{border:none;box-shadow:0 9px 46px 8px rgba(0,0,0,.14),0 11px 15px -7px rgba(0,0,0,.12),0 24px 38px 3px rgba(0,0,0,.2);width:280px}.mdl-dialog__title{padding:24px 24px 0;margin:0;font-size:2.5rem}.mdl-dialog__actions{padding:8px 8px 8px 24px;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-direction:row-reverse;-ms-flex-direction:row-reverse;flex-direction:row-reverse;-webkit-flex-wrap:wrap;-ms-flex-wrap:wrap;flex-wrap:wrap}.mdl-dialog__actions>*{margin-right:8px;height:36px}.mdl-dialog__actions>*:first-child{margin-right:0}.mdl-dialog__actions--full-width{padding:0 0 8px}.mdl-dialog__actions--full-width>*{height:48px;-webkit-flex:0 0 100%;-ms-flex:0 0 100%;flex:0 0 100%;padding-right:16px;margin-right:0;text-align:right}.mdl-dialog__content{padding:20px 24px 24px;color:rgba(0,0,0,.54)}.mdl-mega-footer{padding:16px 40px;color:#9e9e9e;background-color:#424242}.mdl-mega-footer--top-section:after,.mdl-mega-footer--middle-section:after,.mdl-mega-footer--bottom-section:after,.mdl-mega-footer__top-section:after,.mdl-mega-footer__middle-section:after,.mdl-mega-footer__bottom-section:after{content:'';display:block;clear:both}.mdl-mega-footer--left-section,.mdl-mega-footer__left-section,.mdl-mega-footer--right-section,.mdl-mega-footer__right-section{margin-bottom:16px}.mdl-mega-footer--right-section a,.mdl-mega-footer__right-section a{display:block;margin-bottom:16px;color:inherit;text-decoration:none}@media screen and (min-width:760px){.mdl-mega-footer--left-section,.mdl-mega-footer__left-section{float:left}.mdl-mega-footer--right-section,.mdl-mega-footer__right-section{float:right}.mdl-mega-footer--right-section a,.mdl-mega-footer__right-section a{display:inline-block;margin-left:16px;line-height:36px;vertical-align:middle}}.mdl-mega-footer--social-btn,.mdl-mega-footer__social-btn{width:36px;height:36px;padding:0;margin:0;background-color:#9e9e9e;border:none}.mdl-mega-footer--drop-down-section,.mdl-mega-footer__drop-down-section{display:block;position:relative}@media screen and (min-width:760px){.mdl-mega-footer--drop-down-section,.mdl-mega-footer__drop-down-section{width:33%}.mdl-mega-footer--drop-down-section:nth-child(1),.mdl-mega-footer--drop-down-section:nth-child(2),.mdl-mega-footer__drop-down-section:nth-child(1),.mdl-mega-footer__drop-down-section:nth-child(2){float:left}.mdl-mega-footer--drop-down-section:nth-child(3),.mdl-mega-footer__drop-down-section:nth-child(3){float:right}.mdl-mega-footer--drop-down-section:nth-child(3):after,.mdl-mega-footer__drop-down-section:nth-child(3):after{clear:right}.mdl-mega-footer--drop-down-section:nth-child(4),.mdl-mega-footer__drop-down-section:nth-child(4){clear:right;float:right}.mdl-mega-footer--middle-section:after,.mdl-mega-footer__middle-section:after{content:'';display:block;clear:both}.mdl-mega-footer--bottom-section,.mdl-mega-footer__bottom-section{padding-top:0}}@media screen and (min-width:1024px){.mdl-mega-footer--drop-down-section,.mdl-mega-footer--drop-down-section:nth-child(3),.mdl-mega-footer--drop-down-section:nth-child(4),.mdl-mega-footer__drop-down-section,.mdl-mega-footer__drop-down-section:nth-child(3),.mdl-mega-footer__drop-down-section:nth-child(4){width:24%;float:left}}.mdl-mega-footer--heading-checkbox,.mdl-mega-footer__heading-checkbox{position:absolute;width:100%;height:55.8px;padding:32px;margin:-16px 0 0;cursor:pointer;z-index:1;opacity:0}.mdl-mega-footer--heading-checkbox+.mdl-mega-footer--heading:after,.mdl-mega-footer--heading-checkbox+.mdl-mega-footer__heading:after,.mdl-mega-footer__heading-checkbox+.mdl-mega-footer--heading:after,.mdl-mega-footer__heading-checkbox+.mdl-mega-footer__heading:after{font-family:'Material Icons';content:'\E5CE'}.mdl-mega-footer--heading-checkbox:checked~.mdl-mega-footer--link-list,.mdl-mega-footer--heading-checkbox:checked~.mdl-mega-footer__link-list,.mdl-mega-footer--heading-checkbox:checked+.mdl-mega-footer--heading+.mdl-mega-footer--link-list,.mdl-mega-footer--heading-checkbox:checked+.mdl-mega-footer__heading+.mdl-mega-footer__link-list,.mdl-mega-footer__heading-checkbox:checked~.mdl-mega-footer--link-list,.mdl-mega-footer__heading-checkbox:checked~.mdl-mega-footer__link-list,.mdl-mega-footer__heading-checkbox:checked+.mdl-mega-footer--heading+.mdl-mega-footer--link-list,.mdl-mega-footer__heading-checkbox:checked+.mdl-mega-footer__heading+.mdl-mega-footer__link-list{display:none}.mdl-mega-footer--heading-checkbox:checked+.mdl-mega-footer--heading:after,.mdl-mega-footer--heading-checkbox:checked+.mdl-mega-footer__heading:after,.mdl-mega-footer__heading-checkbox:checked+.mdl-mega-footer--heading:after,.mdl-mega-footer__heading-checkbox:checked+.mdl-mega-footer__heading:after{font-family:'Material Icons';content:'\E5CF'}.mdl-mega-footer--heading,.mdl-mega-footer__heading{position:relative;width:100%;padding-right:39.8px;margin-bottom:16px;box-sizing:border-box;font-size:14px;line-height:23.8px;font-weight:500;white-space:nowrap;text-overflow:ellipsis;overflow:hidden;color:#e0e0e0}.mdl-mega-footer--heading:after,.mdl-mega-footer__heading:after{content:'';position:absolute;top:0;right:0;display:block;width:23.8px;height:23.8px;background-size:cover}.mdl-mega-footer--link-list,.mdl-mega-footer__link-list{list-style:none;padding:0;margin:0 0 32px}.mdl-mega-footer--link-list:after,.mdl-mega-footer__link-list:after{clear:both;display:block;content:''}.mdl-mega-footer--link-list li,.mdl-mega-footer__link-list li{font-size:14px;font-weight:400;letter-spacing:0;line-height:20px}.mdl-mega-footer--link-list a,.mdl-mega-footer__link-list a{color:inherit;text-decoration:none;white-space:nowrap}@media screen and (min-width:760px){.mdl-mega-footer--heading-checkbox,.mdl-mega-footer__heading-checkbox{display:none}.mdl-mega-footer--heading-checkbox+.mdl-mega-footer--heading:after,.mdl-mega-footer--heading-checkbox+.mdl-mega-footer__heading:after,.mdl-mega-footer__heading-checkbox+.mdl-mega-footer--heading:after,.mdl-mega-footer__heading-checkbox+.mdl-mega-footer__heading:after{content:''}.mdl-mega-footer--heading-checkbox:checked~.mdl-mega-footer--link-list,.mdl-mega-footer--heading-checkbox:checked~.mdl-mega-footer__link-list,.mdl-mega-footer--heading-checkbox:checked+.mdl-mega-footer__heading+.mdl-mega-footer__link-list,.mdl-mega-footer--heading-checkbox:checked+.mdl-mega-footer--heading+.mdl-mega-footer--link-list,.mdl-mega-footer__heading-checkbox:checked~.mdl-mega-footer--link-list,.mdl-mega-footer__heading-checkbox:checked~.mdl-mega-footer__link-list,.mdl-mega-footer__heading-checkbox:checked+.mdl-mega-footer__heading+.mdl-mega-footer__link-list,.mdl-mega-footer__heading-checkbox:checked+.mdl-mega-footer--heading+.mdl-mega-footer--link-list{display:block}.mdl-mega-footer--heading-checkbox:checked+.mdl-mega-footer--heading:after,.mdl-mega-footer--heading-checkbox:checked+.mdl-mega-footer__heading:after,.mdl-mega-footer__heading-checkbox:checked+.mdl-mega-footer--heading:after,.mdl-mega-footer__heading-checkbox:checked+.mdl-mega-footer__heading:after{content:''}}.mdl-mega-footer--bottom-section,.mdl-mega-footer__bottom-section{padding-top:16px;margin-bottom:16px}.mdl-logo{margin-bottom:16px;color:#fff}.mdl-mega-footer--bottom-section .mdl-mega-footer--link-list li,.mdl-mega-footer__bottom-section .mdl-mega-footer__link-list li{float:left;margin-bottom:0;margin-right:16px}@media screen and (min-width:760px){.mdl-logo{float:left;margin-bottom:0;margin-right:16px}}.mdl-mini-footer{display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-flow:row wrap;-ms-flex-flow:row wrap;flex-flow:row wrap;-webkit-justify-content:space-between;-ms-flex-pack:justify;justify-content:space-between;padding:32px 16px;color:#9e9e9e;background-color:#424242}.mdl-mini-footer:after{content:'';display:block}.mdl-mini-footer .mdl-logo{line-height:36px}.mdl-mini-footer--link-list,.mdl-mini-footer__link-list{display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-flow:row nowrap;-ms-flex-flow:row nowrap;flex-flow:row nowrap;list-style:none;margin:0;padding:0}.mdl-mini-footer--link-list li,.mdl-mini-footer__link-list li{margin-bottom:0;margin-right:16px}@media screen and (min-width:760px){.mdl-mini-footer--link-list li,.mdl-mini-footer__link-list li{line-height:36px}}.mdl-mini-footer--link-list a,.mdl-mini-footer__link-list a{color:inherit;text-decoration:none;white-space:nowrap}.mdl-mini-footer--left-section,.mdl-mini-footer__left-section{display:inline-block;-webkit-order:0;-ms-flex-order:0;order:0}.mdl-mini-footer--right-section,.mdl-mini-footer__right-section{display:inline-block;-webkit-order:1;-ms-flex-order:1;order:1}.mdl-mini-footer--social-btn,.mdl-mini-footer__social-btn{width:36px;height:36px;padding:0;margin:0;background-color:#9e9e9e;border:none}.mdl-icon-toggle{position:relative;z-index:1;vertical-align:middle;display:inline-block;height:32px;margin:0;padding:0}.mdl-icon-toggle__input{line-height:32px}.mdl-icon-toggle.is-upgraded .mdl-icon-toggle__input{position:absolute;width:0;height:0;margin:0;padding:0;opacity:0;-ms-appearance:none;-moz-appearance:none;-webkit-appearance:none;appearance:none;border:none}.mdl-icon-toggle__label{display:inline-block;position:relative;cursor:pointer;height:32px;width:32px;min-width:32px;color:#616161;border-radius:50%;padding:0;margin-left:0;margin-right:0;text-align:center;background-color:transparent;will-change:background-color;transition:background-color .2s cubic-bezier(.4,0,.2,1),color .2s cubic-bezier(.4,0,.2,1)}.mdl-icon-toggle__label.material-icons{line-height:32px;font-size:24px}.mdl-icon-toggle.is-checked .mdl-icon-toggle__label{color:rgb(33,150,243)}.mdl-icon-toggle.is-disabled .mdl-icon-toggle__label{color:rgba(0,0,0,.26);cursor:auto;transition:none}.mdl-icon-toggle.is-focused .mdl-icon-toggle__label{background-color:rgba(0,0,0,.12)}.mdl-icon-toggle.is-focused.is-checked .mdl-icon-toggle__label{background-color:rgba(33,150,243,.26)}.mdl-icon-toggle__ripple-container{position:absolute;z-index:2;top:-2px;left:-2px;box-sizing:border-box;width:36px;height:36px;border-radius:50%;cursor:pointer;overflow:hidden;-webkit-mask-image:-webkit-radial-gradient(circle,#fff,#000)}.mdl-icon-toggle__ripple-container .mdl-ripple{background:#616161}.mdl-icon-toggle.is-disabled .mdl-icon-toggle__ripple-container{cursor:auto}.mdl-icon-toggle.is-disabled .mdl-icon-toggle__ripple-container .mdl-ripple{background:0 0}.mdl-list{display:block;padding:8px 0;list-style:none}.mdl-list__item{font-family:"Roboto","Helvetica","Arial",sans-serif;font-size:16px;font-weight:400;letter-spacing:.04em;line-height:1;min-height:48px;-webkit-flex-direction:row;-ms-flex-direction:row;flex-direction:row;-webkit-flex-wrap:nowrap;-ms-flex-wrap:nowrap;flex-wrap:nowrap;padding:16px;cursor:default;color:rgba(0,0,0,.87);overflow:hidden}.mdl-list__item,.mdl-list__item .mdl-list__item-primary-content{box-sizing:border-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-ms-flex-align:center;align-items:center}.mdl-list__item .mdl-list__item-primary-content{-webkit-order:0;-ms-flex-order:0;order:0;-webkit-flex-grow:2;-ms-flex-positive:2;flex-grow:2;text-decoration:none}.mdl-list__item .mdl-list__item-primary-content .mdl-list__item-icon{margin-right:32px}.mdl-list__item .mdl-list__item-primary-content .mdl-list__item-avatar{margin-right:16px}.mdl-list__item .mdl-list__item-secondary-content{display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-flow:column;-ms-flex-flow:column;flex-flow:column;-webkit-align-items:flex-end;-ms-flex-align:end;align-items:flex-end;margin-left:16px}.mdl-list__item .mdl-list__item-secondary-content .mdl-list__item-secondary-action label{display:inline}.mdl-list__item .mdl-list__item-secondary-content .mdl-list__item-secondary-info{font-size:12px;font-weight:400;line-height:1;letter-spacing:0;color:rgba(0,0,0,.54)}.mdl-list__item .mdl-list__item-secondary-content .mdl-list__item-sub-header{padding:0 0 0 16px}.mdl-list__item-icon,.mdl-list__item-icon.material-icons{height:24px;width:24px;font-size:24px;box-sizing:border-box;color:#757575}.mdl-list__item-avatar,.mdl-list__item-avatar.material-icons{height:40px;width:40px;box-sizing:border-box;border-radius:50%;background-color:#757575;font-size:40px;color:#fff}.mdl-list__item--two-line{height:72px}.mdl-list__item--two-line .mdl-list__item-primary-content{height:36px;line-height:20px;display:block}.mdl-list__item--two-line .mdl-list__item-primary-content .mdl-list__item-avatar{float:left}.mdl-list__item--two-line .mdl-list__item-primary-content .mdl-list__item-icon{float:left;margin-top:6px}.mdl-list__item--two-line .mdl-list__item-primary-content .mdl-list__item-secondary-content{height:36px}.mdl-list__item--two-line .mdl-list__item-primary-content .mdl-list__item-sub-title{font-size:14px;font-weight:400;letter-spacing:0;line-height:18px;color:rgba(0,0,0,.54);display:block;padding:0}.mdl-list__item--three-line{height:88px}.mdl-list__item--three-line .mdl-list__item-primary-content{height:52px;line-height:20px;display:block}.mdl-list__item--three-line .mdl-list__item-primary-content .mdl-list__item-avatar,.mdl-list__item--three-line .mdl-list__item-primary-content .mdl-list__item-icon{float:left}.mdl-list__item--three-line .mdl-list__item-secondary-content{height:52px}.mdl-list__item--three-line .mdl-list__item-text-body{font-size:14px;font-weight:400;letter-spacing:0;line-height:18px;height:52px;color:rgba(0,0,0,.54);display:block;padding:0}.mdl-menu__container{display:block;margin:0;padding:0;border:none;position:absolute;overflow:visible;height:0;width:0;visibility:hidden;z-index:-1}.mdl-menu__container.is-visible,.mdl-menu__container.is-animating{z-index:999;visibility:visible}.mdl-menu__outline{display:block;background:#fff;margin:0;padding:0;border:none;border-radius:2px;position:absolute;top:0;left:0;overflow:hidden;opacity:0;-webkit-transform:scale(0);transform:scale(0);-webkit-transform-origin:0 0;transform-origin:0 0;box-shadow:0 2px 2px 0 rgba(0,0,0,.14),0 3px 1px -2px rgba(0,0,0,.2),0 1px 5px 0 rgba(0,0,0,.12);will-change:transform;transition:transform .3s cubic-bezier(.4,0,.2,1),opacity .2s cubic-bezier(.4,0,.2,1);transition:transform .3s cubic-bezier(.4,0,.2,1),opacity .2s cubic-bezier(.4,0,.2,1),-webkit-transform .3s cubic-bezier(.4,0,.2,1);z-index:-1}.mdl-menu__container.is-visible .mdl-menu__outline{opacity:1;-webkit-transform:scale(1);transform:scale(1);z-index:999}.mdl-menu__outline.mdl-menu--bottom-right{-webkit-transform-origin:100% 0;transform-origin:100% 0}.mdl-menu__outline.mdl-menu--top-left{-webkit-transform-origin:0 100%;transform-origin:0 100%}.mdl-menu__outline.mdl-menu--top-right{-webkit-transform-origin:100% 100%;transform-origin:100% 100%}.mdl-menu{position:absolute;list-style:none;top:0;left:0;height:auto;width:auto;min-width:124px;padding:8px 0;margin:0;opacity:0;clip:rect(0 0 0 0);z-index:-1}.mdl-menu__container.is-visible .mdl-menu{opacity:1;z-index:999}.mdl-menu.is-animating{transition:opacity .2s cubic-bezier(.4,0,.2,1),clip .3s cubic-bezier(.4,0,.2,1)}.mdl-menu.mdl-menu--bottom-right{left:auto;right:0}.mdl-menu.mdl-menu--top-left{top:auto;bottom:0}.mdl-menu.mdl-menu--top-right{top:auto;left:auto;bottom:0;right:0}.mdl-menu.mdl-menu--unaligned{top:auto;left:auto}.mdl-menu__item{display:block;border:none;color:rgba(0,0,0,.87);background-color:transparent;text-align:left;margin:0;padding:0 16px;outline-color:#bdbdbd;position:relative;overflow:hidden;font-size:14px;font-weight:400;letter-spacing:0;text-decoration:none;cursor:pointer;height:48px;line-height:48px;white-space:nowrap;opacity:0;transition:opacity .2s cubic-bezier(.4,0,.2,1);-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none}.mdl-menu__container.is-visible .mdl-menu__item{opacity:1}.mdl-menu__item::-moz-focus-inner{border:0}.mdl-menu__item--full-bleed-divider{border-bottom:1px solid rgba(0,0,0,.12)}.mdl-menu__item[disabled],.mdl-menu__item[data-mdl-disabled]{color:#bdbdbd;background-color:transparent;cursor:auto}.mdl-menu__item[disabled]:hover,.mdl-menu__item[data-mdl-disabled]:hover{background-color:transparent}.mdl-menu__item[disabled]:focus,.mdl-menu__item[data-mdl-disabled]:focus{background-color:transparent}.mdl-menu__item[disabled] .mdl-ripple,.mdl-menu__item[data-mdl-disabled] .mdl-ripple{background:0 0}.mdl-menu__item:hover{background-color:#eee}.mdl-menu__item:focus{outline:none;background-color:#eee}.mdl-menu__item:active{background-color:#e0e0e0}.mdl-menu__item--ripple-container{display:block;height:100%;left:0;position:absolute;top:0;width:100%;z-index:0;overflow:hidden}.mdl-progress{display:block;position:relative;height:4px;width:500px;max-width:100%}.mdl-progress>.bar{display:block;position:absolute;top:0;bottom:0;width:0%;transition:width .2s cubic-bezier(.4,0,.2,1)}.mdl-progress>.progressbar{background-color:rgb(33,150,243);z-index:1;left:0}.mdl-progress>.bufferbar{background-image:linear-gradient(to right,rgba(255,255,255,.7),rgba(255,255,255,.7)),linear-gradient(to right,rgb(33,150,243),rgb(33,150,243));z-index:0;left:0}.mdl-progress>.auxbar{right:0}@supports (-webkit-appearance:none){.mdl-progress:not(.mdl-progress--indeterminate):not(.mdl-progress--indeterminate)>.auxbar,.mdl-progress:not(.mdl-progress__indeterminate):not(.mdl-progress__indeterminate)>.auxbar{background-image:linear-gradient(to right,rgba(255,255,255,.7),rgba(255,255,255,.7)),linear-gradient(to right,rgb(33,150,243),rgb(33,150,243));-webkit-mask:url("data:image/svg+xml;base64,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");mask:url("data:image/svg+xml;base64,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")}}.mdl-progress:not(.mdl-progress--indeterminate)>.auxbar,.mdl-progress:not(.mdl-progress__indeterminate)>.auxbar{background-image:linear-gradient(to right,rgba(255,255,255,.9),rgba(255,255,255,.9)),linear-gradient(to right,rgb(33,150,243),rgb(33,150,243))}.mdl-progress.mdl-progress--indeterminate>.bar1,.mdl-progress.mdl-progress__indeterminate>.bar1{-webkit-animation-name:indeterminate1;animation-name:indeterminate1}.mdl-progress.mdl-progress--indeterminate>.bar1,.mdl-progress.mdl-progress__indeterminate>.bar1,.mdl-progress.mdl-progress--indeterminate>.bar3,.mdl-progress.mdl-progress__indeterminate>.bar3{background-color:rgb(33,150,243);-webkit-animation-duration:2s;animation-duration:2s;-webkit-animation-iteration-count:infinite;animation-iteration-count:infinite;-webkit-animation-timing-function:linear;animation-timing-function:linear}.mdl-progress.mdl-progress--indeterminate>.bar3,.mdl-progress.mdl-progress__indeterminate>.bar3{background-image:none;-webkit-animation-name:indeterminate2;animation-name:indeterminate2}@-webkit-keyframes indeterminate1{0%{left:0%;width:0%}50%{left:25%;width:75%}75%{left:100%;width:0%}}@keyframes indeterminate1{0%{left:0%;width:0%}50%{left:25%;width:75%}75%{left:100%;width:0%}}@-webkit-keyframes indeterminate2{0%,50%{left:0%;width:0%}75%{left:0%;width:25%}100%{left:100%;width:0%}}@keyframes indeterminate2{0%,50%{left:0%;width:0%}75%{left:0%;width:25%}100%{left:100%;width:0%}}.mdl-navigation{display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-wrap:nowrap;-ms-flex-wrap:nowrap;flex-wrap:nowrap;box-sizing:border-box}.mdl-navigation__link{color:#424242;text-decoration:none;margin:0;font-size:14px;font-weight:400;line-height:24px;letter-spacing:0;opacity:.87}.mdl-navigation__link .material-icons{vertical-align:middle}.mdl-layout{width:100%;height:100%;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-direction:column;-ms-flex-direction:column;flex-direction:column;overflow-y:auto;overflow-x:hidden;position:relative;-webkit-overflow-scrolling:touch}.mdl-layout.is-small-screen .mdl-layout--large-screen-only{display:none}.mdl-layout:not(.is-small-screen) .mdl-layout--small-screen-only{display:none}.mdl-layout__container{position:absolute;width:100%;height:100%}.mdl-layout__title,.mdl-layout-title{display:block;position:relative;font-family:"Roboto","Helvetica","Arial",sans-serif;font-size:20px;line-height:1;letter-spacing:.02em;font-weight:400;box-sizing:border-box}.mdl-layout-spacer{-webkit-flex-grow:1;-ms-flex-positive:1;flex-grow:1}.mdl-layout__drawer{display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-direction:column;-ms-flex-direction:column;flex-direction:column;-webkit-flex-wrap:nowrap;-ms-flex-wrap:nowrap;flex-wrap:nowrap;width:240px;height:100%;max-height:100%;position:absolute;top:0;left:0;box-shadow:0 2px 2px 0 rgba(0,0,0,.14),0 3px 1px -2px rgba(0,0,0,.2),0 1px 5px 0 rgba(0,0,0,.12);box-sizing:border-box;border-right:1px solid #e0e0e0;background:#fafafa;-webkit-transform:translateX(-250px);transform:translateX(-250px);-webkit-transform-style:preserve-3d;transform-style:preserve-3d;will-change:transform;transition-duration:.2s;transition-timing-function:cubic-bezier(.4,0,.2,1);transition-property:transform;transition-property:transform,-webkit-transform;color:#424242;overflow:visible;overflow-y:auto;z-index:5}.mdl-layout__drawer.is-visible{-webkit-transform:translateX(0);transform:translateX(0)}.mdl-layout__drawer.is-visible~.mdl-layout__content.mdl-layout__content{overflow:hidden}.mdl-layout__drawer>*{-webkit-flex-shrink:0;-ms-flex-negative:0;flex-shrink:0}.mdl-layout__drawer>.mdl-layout__title,.mdl-layout__drawer>.mdl-layout-title{line-height:64px;padding-left:40px}@media screen and (max-width:1024px){.mdl-layout__drawer>.mdl-layout__title,.mdl-layout__drawer>.mdl-layout-title{line-height:56px;padding-left:16px}}.mdl-layout__drawer .mdl-navigation{-webkit-flex-direction:column;-ms-flex-direction:column;flex-direction:column;-webkit-align-items:stretch;-ms-flex-align:stretch;align-items:stretch;padding-top:16px}.mdl-layout__drawer .mdl-navigation .mdl-navigation__link{display:block;-webkit-flex-shrink:0;-ms-flex-negative:0;flex-shrink:0;padding:16px 40px;margin:0;color:#757575}@media screen and (max-width:1024px){.mdl-layout__drawer .mdl-navigation .mdl-navigation__link{padding:16px}}.mdl-layout__drawer .mdl-navigation .mdl-navigation__link:hover{background-color:#e0e0e0}.mdl-layout__drawer .mdl-navigation .mdl-navigation__link--current{background-color:#e0e0e0;color:#000}@media screen and (min-width:1025px){.mdl-layout--fixed-drawer>.mdl-layout__drawer{-webkit-transform:translateX(0);transform:translateX(0)}}.mdl-layout__drawer-button{display:block;position:absolute;height:48px;width:48px;border:0;-webkit-flex-shrink:0;-ms-flex-negative:0;flex-shrink:0;overflow:hidden;text-align:center;cursor:pointer;font-size:26px;line-height:56px;font-family:Helvetica,Arial,sans-serif;margin:8px 12px;top:0;left:0;color:rgb(255,255,255);z-index:4}.mdl-layout__header .mdl-layout__drawer-button{position:absolute;color:rgb(255,255,255);background-color:inherit}@media screen and (max-width:1024px){.mdl-layout__header .mdl-layout__drawer-button{margin:4px}}@media screen and (max-width:1024px){.mdl-layout__drawer-button{margin:4px;color:rgba(0,0,0,.5)}}@media screen and (min-width:1025px){.mdl-layout__drawer-button{line-height:54px}.mdl-layout--no-desktop-drawer-button .mdl-layout__drawer-button,.mdl-layout--fixed-drawer>.mdl-layout__drawer-button,.mdl-layout--no-drawer-button .mdl-layout__drawer-button{display:none}}.mdl-layout__header{display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-direction:column;-ms-flex-direction:column;flex-direction:column;-webkit-flex-wrap:nowrap;-ms-flex-wrap:nowrap;flex-wrap:nowrap;-webkit-justify-content:flex-start;-ms-flex-pack:start;justify-content:flex-start;box-sizing:border-box;-webkit-flex-shrink:0;-ms-flex-negative:0;flex-shrink:0;width:100%;margin:0;padding:0;border:none;min-height:64px;max-height:1000px;z-index:3;background-color:rgb(33,150,243);color:rgb(255,255,255);box-shadow:0 2px 2px 0 rgba(0,0,0,.14),0 3px 1px -2px rgba(0,0,0,.2),0 1px 5px 0 rgba(0,0,0,.12);transition-duration:.2s;transition-timing-function:cubic-bezier(.4,0,.2,1);transition-property:max-height,box-shadow}@media screen and (max-width:1024px){.mdl-layout__header{min-height:56px}}.mdl-layout--fixed-drawer.is-upgraded:not(.is-small-screen)>.mdl-layout__header{margin-left:240px;width:calc(100% - 240px)}@media screen and (min-width:1025px){.mdl-layout--fixed-drawer>.mdl-layout__header .mdl-layout__header-row{padding-left:40px}}.mdl-layout__header>.mdl-layout-icon{position:absolute;left:40px;top:16px;height:32px;width:32px;overflow:hidden;z-index:3;display:block}@media screen and (max-width:1024px){.mdl-layout__header>.mdl-layout-icon{left:16px;top:12px}}.mdl-layout.has-drawer .mdl-layout__header>.mdl-layout-icon{display:none}.mdl-layout__header.is-compact{max-height:64px}@media screen and (max-width:1024px){.mdl-layout__header.is-compact{max-height:56px}}.mdl-layout__header.is-compact.has-tabs{height:112px}@media screen and (max-width:1024px){.mdl-layout__header.is-compact.has-tabs{min-height:104px}}@media screen and (max-width:1024px){.mdl-layout__header{display:none}.mdl-layout--fixed-header>.mdl-layout__header{display:-webkit-flex;display:-ms-flexbox;display:flex}}.mdl-layout__header--transparent.mdl-layout__header--transparent{background-color:transparent;box-shadow:none}.mdl-layout__header--seamed,.mdl-layout__header--scroll{box-shadow:none}.mdl-layout__header--waterfall{box-shadow:none;overflow:hidden}.mdl-layout__header--waterfall.is-casting-shadow{box-shadow:0 2px 2px 0 rgba(0,0,0,.14),0 3px 1px -2px rgba(0,0,0,.2),0 1px 5px 0 rgba(0,0,0,.12)}.mdl-layout__header--waterfall.mdl-layout__header--waterfall-hide-top{-webkit-justify-content:flex-end;-ms-flex-pack:end;justify-content:flex-end}.mdl-layout__header-row{display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-direction:row;-ms-flex-direction:row;flex-direction:row;-webkit-flex-wrap:nowrap;-ms-flex-wrap:nowrap;flex-wrap:nowrap;-webkit-flex-shrink:0;-ms-flex-negative:0;flex-shrink:0;box-sizing:border-box;-webkit-align-self:stretch;-ms-flex-item-align:stretch;align-self:stretch;-webkit-align-items:center;-ms-flex-align:center;align-items:center;height:64px;margin:0;padding:0 40px 0 80px}.mdl-layout--no-drawer-button .mdl-layout__header-row{padding-left:40px}@media screen and (min-width:1025px){.mdl-layout--no-desktop-drawer-button .mdl-layout__header-row{padding-left:40px}}@media screen and (max-width:1024px){.mdl-layout__header-row{height:56px;padding:0 16px 0 72px}.mdl-layout--no-drawer-button .mdl-layout__header-row{padding-left:16px}}.mdl-layout__header-row>*{-webkit-flex-shrink:0;-ms-flex-negative:0;flex-shrink:0}.mdl-layout__header--scroll .mdl-layout__header-row{width:100%}.mdl-layout__header-row .mdl-navigation{margin:0;padding:0;height:64px;-webkit-flex-direction:row;-ms-flex-direction:row;flex-direction:row;-webkit-align-items:center;-ms-flex-align:center;align-items:center}@media screen and (max-width:1024px){.mdl-layout__header-row .mdl-navigation{height:56px}}.mdl-layout__header-row .mdl-navigation__link{display:block;color:rgb(255,255,255);line-height:64px;padding:0 24px}@media screen and (max-width:1024px){.mdl-layout__header-row .mdl-navigation__link{line-height:56px;padding:0 16px}}.mdl-layout__obfuscator{background-color:transparent;position:absolute;top:0;left:0;height:100%;width:100%;z-index:4;visibility:hidden;transition-property:background-color;transition-duration:.2s;transition-timing-function:cubic-bezier(.4,0,.2,1)}.mdl-layout__obfuscator.is-visible{background-color:rgba(0,0,0,.5);visibility:visible}@supports (pointer-events:auto){.mdl-layout__obfuscator{background-color:rgba(0,0,0,.5);opacity:0;transition-property:opacity;visibility:visible;pointer-events:none}.mdl-layout__obfuscator.is-visible{pointer-events:auto;opacity:1}}.mdl-layout__content{-ms-flex:0 1 auto;position:relative;display:inline-block;overflow-y:auto;overflow-x:hidden;-webkit-flex-grow:1;-ms-flex-positive:1;flex-grow:1;z-index:1;-webkit-overflow-scrolling:touch}.mdl-layout--fixed-drawer>.mdl-layout__content{margin-left:240px}.mdl-layout__container.has-scrolling-header .mdl-layout__content{overflow:visible}@media screen and (max-width:1024px){.mdl-layout--fixed-drawer>.mdl-layout__content{margin-left:0}.mdl-layout__container.has-scrolling-header .mdl-layout__content{overflow-y:auto;overflow-x:hidden}}.mdl-layout__tab-bar{height:96px;margin:0;width:calc(100% - 112px);padding:0 0 0 56px;display:-webkit-flex;display:-ms-flexbox;display:flex;background-color:rgb(33,150,243);overflow-y:hidden;overflow-x:scroll}.mdl-layout__tab-bar::-webkit-scrollbar{display:none}.mdl-layout--no-drawer-button .mdl-layout__tab-bar{padding-left:16px;width:calc(100% - 32px)}@media screen and (min-width:1025px){.mdl-layout--no-desktop-drawer-button .mdl-layout__tab-bar{padding-left:16px;width:calc(100% - 32px)}}@media screen and (max-width:1024px){.mdl-layout__tab-bar{width:calc(100% - 60px);padding:0 0 0 60px}.mdl-layout--no-drawer-button .mdl-layout__tab-bar{width:calc(100% - 8px);padding-left:4px}}.mdl-layout--fixed-tabs .mdl-layout__tab-bar{padding:0;overflow:hidden;width:100%}.mdl-layout__tab-bar-container{position:relative;height:48px;width:100%;border:none;margin:0;z-index:2;-webkit-flex-grow:0;-ms-flex-positive:0;flex-grow:0;-webkit-flex-shrink:0;-ms-flex-negative:0;flex-shrink:0;overflow:hidden}.mdl-layout__container>.mdl-layout__tab-bar-container{position:absolute;top:0;left:0}.mdl-layout__tab-bar-button{display:inline-block;position:absolute;top:0;height:48px;width:56px;z-index:4;text-align:center;background-color:rgb(33,150,243);color:transparent;cursor:pointer;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none}.mdl-layout--no-desktop-drawer-button .mdl-layout__tab-bar-button,.mdl-layout--no-drawer-button .mdl-layout__tab-bar-button{width:16px}.mdl-layout--no-desktop-drawer-button .mdl-layout__tab-bar-button .material-icons,.mdl-layout--no-drawer-button .mdl-layout__tab-bar-button .material-icons{position:relative;left:-4px}@media screen and (max-width:1024px){.mdl-layout__tab-bar-button{width:60px}}.mdl-layout--fixed-tabs .mdl-layout__tab-bar-button{display:none}.mdl-layout__tab-bar-button .material-icons{line-height:48px}.mdl-layout__tab-bar-button.is-active{color:rgb(255,255,255)}.mdl-layout__tab-bar-left-button{left:0}.mdl-layout__tab-bar-right-button{right:0}.mdl-layout__tab{margin:0;border:none;padding:0 24px;float:left;position:relative;display:block;-webkit-flex-grow:0;-ms-flex-positive:0;flex-grow:0;-webkit-flex-shrink:0;-ms-flex-negative:0;flex-shrink:0;text-decoration:none;height:48px;line-height:48px;text-align:center;font-weight:500;font-size:14px;text-transform:uppercase;color:rgba(255,255,255,.6);overflow:hidden}@media screen and (max-width:1024px){.mdl-layout__tab{padding:0 12px}}.mdl-layout--fixed-tabs .mdl-layout__tab{float:none;-webkit-flex-grow:1;-ms-flex-positive:1;flex-grow:1;padding:0}.mdl-layout.is-upgraded .mdl-layout__tab.is-active{color:rgb(255,255,255)}.mdl-layout.is-upgraded .mdl-layout__tab.is-active::after{height:2px;width:100%;display:block;content:" ";bottom:0;left:0;position:absolute;background:rgb(255,110,64);-webkit-animation:border-expand .2s cubic-bezier(.4,0,.4,1).01s alternate forwards;animation:border-expand .2s cubic-bezier(.4,0,.4,1).01s alternate forwards;transition:all 1s cubic-bezier(.4,0,1,1)}.mdl-layout__tab .mdl-layout__tab-ripple-container{display:block;position:absolute;height:100%;width:100%;left:0;top:0;z-index:1;overflow:hidden}.mdl-layout__tab .mdl-layout__tab-ripple-container .mdl-ripple{background-color:rgb(255,255,255)}.mdl-layout__tab-panel{display:block}.mdl-layout.is-upgraded .mdl-layout__tab-panel{display:none}.mdl-layout.is-upgraded .mdl-layout__tab-panel.is-active{display:block}.mdl-radio{position:relative;font-size:16px;line-height:24px;display:inline-block;vertical-align:middle;box-sizing:border-box;height:24px;margin:0;padding-left:0}.mdl-radio.is-upgraded{padding-left:24px}.mdl-radio__button{line-height:24px}.mdl-radio.is-upgraded .mdl-radio__button{position:absolute;width:0;height:0;margin:0;padding:0;opacity:0;-ms-appearance:none;-moz-appearance:none;-webkit-appearance:none;appearance:none;border:none}.mdl-radio__outer-circle{position:absolute;top:4px;left:0;display:inline-block;box-sizing:border-box;width:16px;height:16px;margin:0;cursor:pointer;border:2px solid rgba(0,0,0,.54);border-radius:50%;z-index:2}.mdl-radio.is-checked .mdl-radio__outer-circle{border:2px solid rgb(33,150,243)}.mdl-radio__outer-circle fieldset[disabled] .mdl-radio,.mdl-radio.is-disabled .mdl-radio__outer-circle{border:2px solid rgba(0,0,0,.26);cursor:auto}.mdl-radio__inner-circle{position:absolute;z-index:1;margin:0;top:8px;left:4px;box-sizing:border-box;width:8px;height:8px;cursor:pointer;transition-duration:.28s;transition-timing-function:cubic-bezier(.4,0,.2,1);transition-property:transform;transition-property:transform,-webkit-transform;-webkit-transform:scale(0,0);transform:scale(0,0);border-radius:50%;background:rgb(33,150,243)}.mdl-radio.is-checked .mdl-radio__inner-circle{-webkit-transform:scale(1,1);transform:scale(1,1)}fieldset[disabled] .mdl-radio .mdl-radio__inner-circle,.mdl-radio.is-disabled .mdl-radio__inner-circle{background:rgba(0,0,0,.26);cursor:auto}.mdl-radio.is-focused .mdl-radio__inner-circle{box-shadow:0 0 0 10px rgba(0,0,0,.1)}.mdl-radio__label{cursor:pointer}fieldset[disabled] .mdl-radio .mdl-radio__label,.mdl-radio.is-disabled .mdl-radio__label{color:rgba(0,0,0,.26);cursor:auto}.mdl-radio__ripple-container{position:absolute;z-index:2;top:-9px;left:-13px;box-sizing:border-box;width:42px;height:42px;border-radius:50%;cursor:pointer;overflow:hidden;-webkit-mask-image:-webkit-radial-gradient(circle,#fff,#000)}.mdl-radio__ripple-container .mdl-ripple{background:rgb(33,150,243)}fieldset[disabled] .mdl-radio .mdl-radio__ripple-container,.mdl-radio.is-disabled .mdl-radio__ripple-container{cursor:auto}fieldset[disabled] .mdl-radio .mdl-radio__ripple-container .mdl-ripple,.mdl-radio.is-disabled .mdl-radio__ripple-container .mdl-ripple{background:0 0}_:-ms-input-placeholder,:root .mdl-slider.mdl-slider.is-upgraded{-ms-appearance:none;height:32px;margin:0}.mdl-slider{width:calc(100% - 40px);margin:0 20px}.mdl-slider.is-upgraded{-webkit-appearance:none;-moz-appearance:none;appearance:none;height:2px;background:0 0;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;outline:0;padding:0;color:rgb(33,150,243);-webkit-align-self:center;-ms-flex-item-align:center;-ms-grid-row-align:center;align-self:center;z-index:1;cursor:pointer}.mdl-slider.is-upgraded::-moz-focus-outer{border:0}.mdl-slider.is-upgraded::-ms-tooltip{display:none}.mdl-slider.is-upgraded::-webkit-slider-runnable-track{background:0 0}.mdl-slider.is-upgraded::-moz-range-track{background:0 0;border:none}.mdl-slider.is-upgraded::-ms-track{background:0 0;color:transparent;height:2px;width:100%;border:none}.mdl-slider.is-upgraded::-ms-fill-lower{padding:0;background:linear-gradient(to right,transparent,transparent 16px,rgb(33,150,243)16px,rgb(33,150,243)0)}.mdl-slider.is-upgraded::-ms-fill-upper{padding:0;background:linear-gradient(to left,transparent,transparent 16px,rgba(0,0,0,.26)16px,rgba(0,0,0,.26)0)}.mdl-slider.is-upgraded::-webkit-slider-thumb{-webkit-appearance:none;width:12px;height:12px;box-sizing:border-box;border-radius:50%;background:rgb(33,150,243);border:none;transition:transform .18s cubic-bezier(.4,0,.2,1),border .18s cubic-bezier(.4,0,.2,1),box-shadow .18s cubic-bezier(.4,0,.2,1),background .28s cubic-bezier(.4,0,.2,1);transition:transform .18s cubic-bezier(.4,0,.2,1),border .18s cubic-bezier(.4,0,.2,1),box-shadow .18s cubic-bezier(.4,0,.2,1),background .28s cubic-bezier(.4,0,.2,1),-webkit-transform .18s cubic-bezier(.4,0,.2,1)}.mdl-slider.is-upgraded::-moz-range-thumb{-moz-appearance:none;width:12px;height:12px;box-sizing:border-box;border-radius:50%;background-image:none;background:rgb(33,150,243);border:none}.mdl-slider.is-upgraded:focus:not(:active)::-webkit-slider-thumb{box-shadow:0 0 0 10px rgba(33,150,243,.26)}.mdl-slider.is-upgraded:focus:not(:active)::-moz-range-thumb{box-shadow:0 0 0 10px rgba(33,150,243,.26)}.mdl-slider.is-upgraded:active::-webkit-slider-thumb{background-image:none;background:rgb(33,150,243);-webkit-transform:scale(1.5);transform:scale(1.5)}.mdl-slider.is-upgraded:active::-moz-range-thumb{background-image:none;background:rgb(33,150,243);transform:scale(1.5)}.mdl-slider.is-upgraded::-ms-thumb{width:32px;height:32px;border:none;border-radius:50%;background:rgb(33,150,243);transform:scale(.375);transition:transform .18s cubic-bezier(.4,0,.2,1),background .28s cubic-bezier(.4,0,.2,1);transition:transform .18s cubic-bezier(.4,0,.2,1),background .28s cubic-bezier(.4,0,.2,1),-webkit-transform .18s cubic-bezier(.4,0,.2,1)}.mdl-slider.is-upgraded:focus:not(:active)::-ms-thumb{background:radial-gradient(circle closest-side,rgb(33,150,243)0%,rgb(33,150,243)37.5%,rgba(33,150,243,.26)37.5%,rgba(33,150,243,.26)100%);transform:scale(1)}.mdl-slider.is-upgraded:active::-ms-thumb{background:rgb(33,150,243);transform:scale(.5625)}.mdl-slider.is-upgraded.is-lowest-value::-webkit-slider-thumb{border:2px solid rgba(0,0,0,.26);background:0 0}.mdl-slider.is-upgraded.is-lowest-value::-moz-range-thumb{border:2px solid rgba(0,0,0,.26);background:0 0}.mdl-slider.is-upgraded.is-lowest-value+.mdl-slider__background-flex>.mdl-slider__background-upper{left:6px}.mdl-slider.is-upgraded.is-lowest-value:focus:not(:active)::-webkit-slider-thumb{box-shadow:0 0 0 10px rgba(0,0,0,.12);background:rgba(0,0,0,.12)}.mdl-slider.is-upgraded.is-lowest-value:focus:not(:active)::-moz-range-thumb{box-shadow:0 0 0 10px rgba(0,0,0,.12);background:rgba(0,0,0,.12)}.mdl-slider.is-upgraded.is-lowest-value:active::-webkit-slider-thumb{border:1.6px solid rgba(0,0,0,.26);-webkit-transform:scale(1.5);transform:scale(1.5)}.mdl-slider.is-upgraded.is-lowest-value:active+.mdl-slider__background-flex>.mdl-slider__background-upper{left:9px}.mdl-slider.is-upgraded.is-lowest-value:active::-moz-range-thumb{border:1.5px solid rgba(0,0,0,.26);transform:scale(1.5)}.mdl-slider.is-upgraded.is-lowest-value::-ms-thumb{background:radial-gradient(circle closest-side,transparent 0%,transparent 66.67%,rgba(0,0,0,.26)66.67%,rgba(0,0,0,.26)100%)}.mdl-slider.is-upgraded.is-lowest-value:focus:not(:active)::-ms-thumb{background:radial-gradient(circle closest-side,rgba(0,0,0,.12)0%,rgba(0,0,0,.12)25%,rgba(0,0,0,.26)25%,rgba(0,0,0,.26)37.5%,rgba(0,0,0,.12)37.5%,rgba(0,0,0,.12)100%);transform:scale(1)}.mdl-slider.is-upgraded.is-lowest-value:active::-ms-thumb{transform:scale(.5625);background:radial-gradient(circle closest-side,transparent 0%,transparent 77.78%,rgba(0,0,0,.26)77.78%,rgba(0,0,0,.26)100%)}.mdl-slider.is-upgraded.is-lowest-value::-ms-fill-lower{background:0 0}.mdl-slider.is-upgraded.is-lowest-value::-ms-fill-upper{margin-left:6px}.mdl-slider.is-upgraded.is-lowest-value:active::-ms-fill-upper{margin-left:9px}.mdl-slider.is-upgraded:disabled:focus::-webkit-slider-thumb,.mdl-slider.is-upgraded:disabled:active::-webkit-slider-thumb,.mdl-slider.is-upgraded:disabled::-webkit-slider-thumb{-webkit-transform:scale(.667);transform:scale(.667);background:rgba(0,0,0,.26)}.mdl-slider.is-upgraded:disabled:focus::-moz-range-thumb,.mdl-slider.is-upgraded:disabled:active::-moz-range-thumb,.mdl-slider.is-upgraded:disabled::-moz-range-thumb{transform:scale(.667);background:rgba(0,0,0,.26)}.mdl-slider.is-upgraded:disabled+.mdl-slider__background-flex>.mdl-slider__background-lower{background-color:rgba(0,0,0,.26);left:-6px}.mdl-slider.is-upgraded:disabled+.mdl-slider__background-flex>.mdl-slider__background-upper{left:6px}.mdl-slider.is-upgraded.is-lowest-value:disabled:focus::-webkit-slider-thumb,.mdl-slider.is-upgraded.is-lowest-value:disabled:active::-webkit-slider-thumb,.mdl-slider.is-upgraded.is-lowest-value:disabled::-webkit-slider-thumb{border:3px solid rgba(0,0,0,.26);background:0 0;-webkit-transform:scale(.667);transform:scale(.667)}.mdl-slider.is-upgraded.is-lowest-value:disabled:focus::-moz-range-thumb,.mdl-slider.is-upgraded.is-lowest-value:disabled:active::-moz-range-thumb,.mdl-slider.is-upgraded.is-lowest-value:disabled::-moz-range-thumb{border:3px solid rgba(0,0,0,.26);background:0 0;transform:scale(.667)}.mdl-slider.is-upgraded.is-lowest-value:disabled:active+.mdl-slider__background-flex>.mdl-slider__background-upper{left:6px}.mdl-slider.is-upgraded:disabled:focus::-ms-thumb,.mdl-slider.is-upgraded:disabled:active::-ms-thumb,.mdl-slider.is-upgraded:disabled::-ms-thumb{transform:scale(.25);background:rgba(0,0,0,.26)}.mdl-slider.is-upgraded.is-lowest-value:disabled:focus::-ms-thumb,.mdl-slider.is-upgraded.is-lowest-value:disabled:active::-ms-thumb,.mdl-slider.is-upgraded.is-lowest-value:disabled::-ms-thumb{transform:scale(.25);background:radial-gradient(circle closest-side,transparent 0%,transparent 50%,rgba(0,0,0,.26)50%,rgba(0,0,0,.26)100%)}.mdl-slider.is-upgraded:disabled::-ms-fill-lower{margin-right:6px;background:linear-gradient(to right,transparent,transparent 25px,rgba(0,0,0,.26)25px,rgba(0,0,0,.26)0)}.mdl-slider.is-upgraded:disabled::-ms-fill-upper{margin-left:6px}.mdl-slider.is-upgraded.is-lowest-value:disabled:active::-ms-fill-upper{margin-left:6px}.mdl-slider__ie-container{height:18px;overflow:visible;border:none;margin:none;padding:none}.mdl-slider__container{height:18px;position:relative;-webkit-flex-direction:row;-ms-flex-direction:row;flex-direction:row}.mdl-slider__container,.mdl-slider__background-flex{background:0 0;display:-webkit-flex;display:-ms-flexbox;display:flex}.mdl-slider__background-flex{position:absolute;height:2px;width:calc(100% - 52px);top:50%;left:0;margin:0 26px;overflow:hidden;border:0;padding:0;-webkit-transform:translate(0,-1px);transform:translate(0,-1px)}.mdl-slider__background-lower{background:rgb(33,150,243)}.mdl-slider__background-lower,.mdl-slider__background-upper{-webkit-flex:0;-ms-flex:0;flex:0;position:relative;border:0;padding:0}.mdl-slider__background-upper{background:rgba(0,0,0,.26);transition:left .18s cubic-bezier(.4,0,.2,1)}.mdl-snackbar{position:fixed;bottom:0;left:50%;cursor:default;background-color:#323232;z-index:3;display:block;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-justify-content:space-between;-ms-flex-pack:justify;justify-content:space-between;font-family:"Roboto","Helvetica","Arial",sans-serif;will-change:transform;-webkit-transform:translate(0,80px);transform:translate(0,80px);transition:transform .25s cubic-bezier(.4,0,1,1);transition:transform .25s cubic-bezier(.4,0,1,1),-webkit-transform .25s cubic-bezier(.4,0,1,1);pointer-events:none}@media (max-width:479px){.mdl-snackbar{width:100%;left:0;min-height:48px;max-height:80px}}@media (min-width:480px){.mdl-snackbar{min-width:288px;max-width:568px;border-radius:2px;-webkit-transform:translate(-50%,80px);transform:translate(-50%,80px)}}.mdl-snackbar--active{-webkit-transform:translate(0,0);transform:translate(0,0);pointer-events:auto;transition:transform .25s cubic-bezier(0,0,.2,1);transition:transform .25s cubic-bezier(0,0,.2,1),-webkit-transform .25s cubic-bezier(0,0,.2,1)}@media (min-width:480px){.mdl-snackbar--active{-webkit-transform:translate(-50%,0);transform:translate(-50%,0)}}.mdl-snackbar__text{padding:14px 12px 14px 24px;vertical-align:middle;color:#fff;float:left}.mdl-snackbar__action{background:0 0;border:none;color:rgb(255,110,64);float:right;padding:14px 24px 14px 12px;font-family:"Roboto","Helvetica","Arial",sans-serif;font-size:14px;font-weight:500;text-transform:uppercase;line-height:1;letter-spacing:0;overflow:hidden;outline:none;opacity:0;pointer-events:none;cursor:pointer;text-decoration:none;text-align:center;-webkit-align-self:center;-ms-flex-item-align:center;-ms-grid-row-align:center;align-self:center}.mdl-snackbar__action::-moz-focus-inner{border:0}.mdl-snackbar__action:not([aria-hidden]){opacity:1;pointer-events:auto}.mdl-spinner{display:inline-block;position:relative;width:28px;height:28px}.mdl-spinner:not(.is-upgraded).is-active:after{content:"Loading..."}.mdl-spinner.is-upgraded.is-active{-webkit-animation:mdl-spinner__container-rotate 1568.23529412ms linear infinite;animation:mdl-spinner__container-rotate 1568.23529412ms linear infinite}@-webkit-keyframes mdl-spinner__container-rotate{to{-webkit-transform:rotate(360deg);transform:rotate(360deg)}}@keyframes mdl-spinner__container-rotate{to{-webkit-transform:rotate(360deg);transform:rotate(360deg)}}.mdl-spinner__layer{position:absolute;width:100%;height:100%;opacity:0}.mdl-spinner__layer-1{border-color:#42a5f5}.mdl-spinner--single-color .mdl-spinner__layer-1{border-color:rgb(33,150,243)}.mdl-spinner.is-active .mdl-spinner__layer-1{-webkit-animation:mdl-spinner__fill-unfill-rotate 5332ms cubic-bezier(.4,0,.2,1)infinite both,mdl-spinner__layer-1-fade-in-out 5332ms cubic-bezier(.4,0,.2,1)infinite both;animation:mdl-spinner__fill-unfill-rotate 5332ms cubic-bezier(.4,0,.2,1)infinite both,mdl-spinner__layer-1-fade-in-out 5332ms cubic-bezier(.4,0,.2,1)infinite both}.mdl-spinner__layer-2{border-color:#f44336}.mdl-spinner--single-color .mdl-spinner__layer-2{border-color:rgb(33,150,243)}.mdl-spinner.is-active .mdl-spinner__layer-2{-webkit-animation:mdl-spinner__fill-unfill-rotate 5332ms cubic-bezier(.4,0,.2,1)infinite both,mdl-spinner__layer-2-fade-in-out 5332ms cubic-bezier(.4,0,.2,1)infinite both;animation:mdl-spinner__fill-unfill-rotate 5332ms cubic-bezier(.4,0,.2,1)infinite both,mdl-spinner__layer-2-fade-in-out 5332ms cubic-bezier(.4,0,.2,1)infinite both}.mdl-spinner__layer-3{border-color:#fdd835}.mdl-spinner--single-color .mdl-spinner__layer-3{border-color:rgb(33,150,243)}.mdl-spinner.is-active .mdl-spinner__layer-3{-webkit-animation:mdl-spinner__fill-unfill-rotate 5332ms cubic-bezier(.4,0,.2,1)infinite both,mdl-spinner__layer-3-fade-in-out 5332ms cubic-bezier(.4,0,.2,1)infinite both;animation:mdl-spinner__fill-unfill-rotate 5332ms cubic-bezier(.4,0,.2,1)infinite both,mdl-spinner__layer-3-fade-in-out 5332ms cubic-bezier(.4,0,.2,1)infinite both}.mdl-spinner__layer-4{border-color:#4caf50}.mdl-spinner--single-color .mdl-spinner__layer-4{border-color:rgb(33,150,243)}.mdl-spinner.is-active .mdl-spinner__layer-4{-webkit-animation:mdl-spinner__fill-unfill-rotate 5332ms cubic-bezier(.4,0,.2,1)infinite both,mdl-spinner__layer-4-fade-in-out 5332ms cubic-bezier(.4,0,.2,1)infinite both;animation:mdl-spinner__fill-unfill-rotate 5332ms cubic-bezier(.4,0,.2,1)infinite both,mdl-spinner__layer-4-fade-in-out 5332ms cubic-bezier(.4,0,.2,1)infinite both}@-webkit-keyframes mdl-spinner__fill-unfill-rotate{12.5%{-webkit-transform:rotate(135deg);transform:rotate(135deg)}25%{-webkit-transform:rotate(270deg);transform:rotate(270deg)}37.5%{-webkit-transform:rotate(405deg);transform:rotate(405deg)}50%{-webkit-transform:rotate(540deg);transform:rotate(540deg)}62.5%{-webkit-transform:rotate(675deg);transform:rotate(675deg)}75%{-webkit-transform:rotate(810deg);transform:rotate(810deg)}87.5%{-webkit-transform:rotate(945deg);transform:rotate(945deg)}to{-webkit-transform:rotate(1080deg);transform:rotate(1080deg)}}@keyframes mdl-spinner__fill-unfill-rotate{12.5%{-webkit-transform:rotate(135deg);transform:rotate(135deg)}25%{-webkit-transform:rotate(270deg);transform:rotate(270deg)}37.5%{-webkit-transform:rotate(405deg);transform:rotate(405deg)}50%{-webkit-transform:rotate(540deg);transform:rotate(540deg)}62.5%{-webkit-transform:rotate(675deg);transform:rotate(675deg)}75%{-webkit-transform:rotate(810deg);transform:rotate(810deg)}87.5%{-webkit-transform:rotate(945deg);transform:rotate(945deg)}to{-webkit-transform:rotate(1080deg);transform:rotate(1080deg)}}@-webkit-keyframes mdl-spinner__layer-1-fade-in-out{from,25%{opacity:.99}26%,89%{opacity:0}90%,100%{opacity:.99}}@keyframes mdl-spinner__layer-1-fade-in-out{from,25%{opacity:.99}26%,89%{opacity:0}90%,100%{opacity:.99}}@-webkit-keyframes mdl-spinner__layer-2-fade-in-out{from,15%{opacity:0}25%,50%{opacity:.99}51%{opacity:0}}@keyframes mdl-spinner__layer-2-fade-in-out{from,15%{opacity:0}25%,50%{opacity:.99}51%{opacity:0}}@-webkit-keyframes mdl-spinner__layer-3-fade-in-out{from,40%{opacity:0}50%,75%{opacity:.99}76%{opacity:0}}@keyframes mdl-spinner__layer-3-fade-in-out{from,40%{opacity:0}50%,75%{opacity:.99}76%{opacity:0}}@-webkit-keyframes mdl-spinner__layer-4-fade-in-out{from,65%{opacity:0}75%,90%{opacity:.99}100%{opacity:0}}@keyframes mdl-spinner__layer-4-fade-in-out{from,65%{opacity:0}75%,90%{opacity:.99}100%{opacity:0}}.mdl-spinner__gap-patch{position:absolute;box-sizing:border-box;top:0;left:45%;width:10%;height:100%;overflow:hidden;border-color:inherit}.mdl-spinner__gap-patch .mdl-spinner__circle{width:1000%;left:-450%}.mdl-spinner__circle-clipper{display:inline-block;position:relative;width:50%;height:100%;overflow:hidden;border-color:inherit}.mdl-spinner__circle-clipper.mdl-spinner__left{float:left}.mdl-spinner__circle-clipper.mdl-spinner__right{float:right}.mdl-spinner__circle-clipper .mdl-spinner__circle{width:200%}.mdl-spinner__circle{box-sizing:border-box;height:100%;border-width:3px;border-style:solid;border-color:inherit;border-bottom-color:transparent!important;border-radius:50%;-webkit-animation:none;animation:none;position:absolute;top:0;right:0;bottom:0;left:0}.mdl-spinner__left .mdl-spinner__circle{border-right-color:transparent!important;-webkit-transform:rotate(129deg);transform:rotate(129deg)}.mdl-spinner.is-active .mdl-spinner__left .mdl-spinner__circle{-webkit-animation:mdl-spinner__left-spin 1333ms cubic-bezier(.4,0,.2,1)infinite both;animation:mdl-spinner__left-spin 1333ms cubic-bezier(.4,0,.2,1)infinite both}.mdl-spinner__right .mdl-spinner__circle{left:-100%;border-left-color:transparent!important;-webkit-transform:rotate(-129deg);transform:rotate(-129deg)}.mdl-spinner.is-active .mdl-spinner__right .mdl-spinner__circle{-webkit-animation:mdl-spinner__right-spin 1333ms cubic-bezier(.4,0,.2,1)infinite both;animation:mdl-spinner__right-spin 1333ms cubic-bezier(.4,0,.2,1)infinite both}@-webkit-keyframes mdl-spinner__left-spin{from{-webkit-transform:rotate(130deg);transform:rotate(130deg)}50%{-webkit-transform:rotate(-5deg);transform:rotate(-5deg)}to{-webkit-transform:rotate(130deg);transform:rotate(130deg)}}@keyframes mdl-spinner__left-spin{from{-webkit-transform:rotate(130deg);transform:rotate(130deg)}50%{-webkit-transform:rotate(-5deg);transform:rotate(-5deg)}to{-webkit-transform:rotate(130deg);transform:rotate(130deg)}}@-webkit-keyframes mdl-spinner__right-spin{from{-webkit-transform:rotate(-130deg);transform:rotate(-130deg)}50%{-webkit-transform:rotate(5deg);transform:rotate(5deg)}to{-webkit-transform:rotate(-130deg);transform:rotate(-130deg)}}@keyframes mdl-spinner__right-spin{from{-webkit-transform:rotate(-130deg);transform:rotate(-130deg)}50%{-webkit-transform:rotate(5deg);transform:rotate(5deg)}to{-webkit-transform:rotate(-130deg);transform:rotate(-130deg)}}.mdl-switch{position:relative;z-index:1;vertical-align:middle;display:inline-block;box-sizing:border-box;width:100%;height:24px;margin:0;padding:0;overflow:visible;-webkit-touch-callout:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none}.mdl-switch.is-upgraded{padding-left:28px}.mdl-switch__input{line-height:24px}.mdl-switch.is-upgraded .mdl-switch__input{position:absolute;width:0;height:0;margin:0;padding:0;opacity:0;-ms-appearance:none;-moz-appearance:none;-webkit-appearance:none;appearance:none;border:none}.mdl-switch__track{background:rgba(0,0,0,.26);position:absolute;left:0;top:5px;height:14px;width:36px;border-radius:14px;cursor:pointer}.mdl-switch.is-checked .mdl-switch__track{background:rgba(33,150,243,.5)}.mdl-switch__track fieldset[disabled] .mdl-switch,.mdl-switch.is-disabled .mdl-switch__track{background:rgba(0,0,0,.12);cursor:auto}.mdl-switch__thumb{background:#fafafa;position:absolute;left:0;top:2px;height:20px;width:20px;border-radius:50%;cursor:pointer;box-shadow:0 2px 2px 0 rgba(0,0,0,.14),0 3px 1px -2px rgba(0,0,0,.2),0 1px 5px 0 rgba(0,0,0,.12);transition-duration:.28s;transition-timing-function:cubic-bezier(.4,0,.2,1);transition-property:left}.mdl-switch.is-checked .mdl-switch__thumb{background:rgb(33,150,243);left:16px;box-shadow:0 3px 4px 0 rgba(0,0,0,.14),0 3px 3px -2px rgba(0,0,0,.2),0 1px 8px 0 rgba(0,0,0,.12)}.mdl-switch__thumb fieldset[disabled] .mdl-switch,.mdl-switch.is-disabled .mdl-switch__thumb{background:#bdbdbd;cursor:auto}.mdl-switch__focus-helper{position:absolute;top:50%;left:50%;-webkit-transform:translate(-4px,-4px);transform:translate(-4px,-4px);display:inline-block;box-sizing:border-box;width:8px;height:8px;border-radius:50%;background-color:transparent}.mdl-switch.is-focused .mdl-switch__focus-helper{box-shadow:0 0 0 20px rgba(0,0,0,.1);background-color:rgba(0,0,0,.1)}.mdl-switch.is-focused.is-checked .mdl-switch__focus-helper{box-shadow:0 0 0 20px rgba(33,150,243,.26);background-color:rgba(33,150,243,.26)}.mdl-switch__label{position:relative;cursor:pointer;font-size:16px;line-height:24px;margin:0;left:24px}.mdl-switch__label fieldset[disabled] .mdl-switch,.mdl-switch.is-disabled .mdl-switch__label{color:#bdbdbd;cursor:auto}.mdl-switch__ripple-container{position:absolute;z-index:2;top:-12px;left:-14px;box-sizing:border-box;width:48px;height:48px;border-radius:50%;cursor:pointer;overflow:hidden;-webkit-mask-image:-webkit-radial-gradient(circle,#fff,#000);transition-duration:.4s;transition-timing-function:step-end;transition-property:left}.mdl-switch__ripple-container .mdl-ripple{background:rgb(33,150,243)}.mdl-switch__ripple-container fieldset[disabled] .mdl-switch,.mdl-switch.is-disabled .mdl-switch__ripple-container{cursor:auto}fieldset[disabled] .mdl-switch .mdl-switch__ripple-container .mdl-ripple,.mdl-switch.is-disabled .mdl-switch__ripple-container .mdl-ripple{background:0 0}.mdl-switch.is-checked .mdl-switch__ripple-container{left:2px}.mdl-tabs{display:block;width:100%}.mdl-tabs__tab-bar{display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-direction:row;-ms-flex-direction:row;flex-direction:row;-webkit-justify-content:center;-ms-flex-pack:center;justify-content:center;-webkit-align-content:space-between;-ms-flex-line-pack:justify;align-content:space-between;-webkit-align-items:flex-start;-ms-flex-align:start;align-items:flex-start;height:48px;padding:0;margin:0;border-bottom:1px solid #e0e0e0}.mdl-tabs__tab{margin:0;border:none;padding:0 24px;float:left;position:relative;display:block;text-decoration:none;height:48px;line-height:48px;text-align:center;font-weight:500;font-size:14px;text-transform:uppercase;color:rgba(0,0,0,.54);overflow:hidden}.mdl-tabs.is-upgraded .mdl-tabs__tab.is-active{color:rgba(0,0,0,.87)}.mdl-tabs.is-upgraded .mdl-tabs__tab.is-active:after{height:2px;width:100%;display:block;content:" ";bottom:0;left:0;position:absolute;background:rgb(33,150,243);-webkit-animation:border-expand .2s cubic-bezier(.4,0,.4,1).01s alternate forwards;animation:border-expand .2s cubic-bezier(.4,0,.4,1).01s alternate forwards;transition:all 1s cubic-bezier(.4,0,1,1)}.mdl-tabs__tab .mdl-tabs__ripple-container{display:block;position:absolute;height:100%;width:100%;left:0;top:0;z-index:1;overflow:hidden}.mdl-tabs__tab .mdl-tabs__ripple-container .mdl-ripple{background:rgb(33,150,243)}.mdl-tabs__panel{display:block}.mdl-tabs.is-upgraded .mdl-tabs__panel{display:none}.mdl-tabs.is-upgraded .mdl-tabs__panel.is-active{display:block}@-webkit-keyframes border-expand{0%{opacity:0;width:0}100%{opacity:1;width:100%}}@keyframes border-expand{0%{opacity:0;width:0}100%{opacity:1;width:100%}}.mdl-textfield{position:relative;font-size:16px;display:inline-block;box-sizing:border-box;width:300px;max-width:100%;margin:0;padding:20px 0}.mdl-textfield .mdl-button{position:absolute;bottom:20px}.mdl-textfield--align-right{text-align:right}.mdl-textfield--full-width{width:100%}.mdl-textfield--expandable{min-width:32px;width:auto;min-height:32px}.mdl-textfield--expandable .mdl-button--icon{top:16px}.mdl-textfield__input{border:none;border-bottom:1px solid rgba(0,0,0,.12);display:block;font-size:16px;font-family:"Helvetica","Arial",sans-serif;margin:0;padding:4px 0;width:100%;background:0 0;text-align:left;color:inherit}.mdl-textfield__input[type="number"]{-moz-appearance:textfield}.mdl-textfield__input[type="number"]::-webkit-inner-spin-button,.mdl-textfield__input[type="number"]::-webkit-outer-spin-button{-webkit-appearance:none;margin:0}.mdl-textfield.is-focused .mdl-textfield__input{outline:none}.mdl-textfield.is-invalid .mdl-textfield__input{border-color:#d50000;box-shadow:none}fieldset[disabled] .mdl-textfield .mdl-textfield__input,.mdl-textfield.is-disabled .mdl-textfield__input{background-color:transparent;border-bottom:1px dotted rgba(0,0,0,.12);color:rgba(0,0,0,.26)}.mdl-textfield textarea.mdl-textfield__input{display:block}.mdl-textfield__label{bottom:0;color:rgba(0,0,0,.26);font-size:16px;left:0;right:0;pointer-events:none;position:absolute;display:block;top:24px;width:100%;overflow:hidden;white-space:nowrap;text-align:left}.mdl-textfield.is-dirty .mdl-textfield__label,.mdl-textfield.has-placeholder .mdl-textfield__label{visibility:hidden}.mdl-textfield--floating-label .mdl-textfield__label{transition-duration:.2s;transition-timing-function:cubic-bezier(.4,0,.2,1)}.mdl-textfield--floating-label.has-placeholder .mdl-textfield__label{transition:none}fieldset[disabled] .mdl-textfield .mdl-textfield__label,.mdl-textfield.is-disabled.is-disabled .mdl-textfield__label{color:rgba(0,0,0,.26)}.mdl-textfield--floating-label.is-focused .mdl-textfield__label,.mdl-textfield--floating-label.is-dirty .mdl-textfield__label,.mdl-textfield--floating-label.has-placeholder .mdl-textfield__label{color:rgb(33,150,243);font-size:12px;top:4px;visibility:visible}.mdl-textfield--floating-label.is-focused .mdl-textfield__expandable-holder .mdl-textfield__label,.mdl-textfield--floating-label.is-dirty .mdl-textfield__expandable-holder .mdl-textfield__label,.mdl-textfield--floating-label.has-placeholder .mdl-textfield__expandable-holder .mdl-textfield__label{top:-16px}.mdl-textfield--floating-label.is-invalid .mdl-textfield__label{color:#d50000;font-size:12px}.mdl-textfield__label:after{background-color:rgb(33,150,243);bottom:20px;content:'';height:2px;left:45%;position:absolute;transition-duration:.2s;transition-timing-function:cubic-bezier(.4,0,.2,1);visibility:hidden;width:10px}.mdl-textfield.is-focused .mdl-textfield__label:after{left:0;visibility:visible;width:100%}.mdl-textfield.is-invalid .mdl-textfield__label:after{background-color:#d50000}.mdl-textfield__error{color:#d50000;position:absolute;font-size:12px;margin-top:3px;visibility:hidden;display:block}.mdl-textfield.is-invalid .mdl-textfield__error{visibility:visible}.mdl-textfield__expandable-holder{display:inline-block;position:relative;margin-left:32px;transition-duration:.2s;transition-timing-function:cubic-bezier(.4,0,.2,1);display:inline-block;max-width:.1px}.mdl-textfield.is-focused .mdl-textfield__expandable-holder,.mdl-textfield.is-dirty .mdl-textfield__expandable-holder{max-width:600px}.mdl-textfield__expandable-holder .mdl-textfield__label:after{bottom:0}.mdl-tooltip{-webkit-transform:scale(0);transform:scale(0);-webkit-transform-origin:top center;transform-origin:top center;z-index:999;background:rgba(97,97,97,.9);border-radius:2px;color:#fff;display:inline-block;font-size:10px;font-weight:500;line-height:14px;max-width:170px;position:fixed;top:-500px;left:-500px;padding:8px;text-align:center}.mdl-tooltip.is-active{-webkit-animation:pulse 200ms cubic-bezier(0,0,.2,1)forwards;animation:pulse 200ms cubic-bezier(0,0,.2,1)forwards}.mdl-tooltip--large{line-height:14px;font-size:14px;padding:16px}@-webkit-keyframes pulse{0%{-webkit-transform:scale(0);transform:scale(0);opacity:0}50%{-webkit-transform:scale(.99);transform:scale(.99)}100%{-webkit-transform:scale(1);transform:scale(1);opacity:1;visibility:visible}}@keyframes pulse{0%{-webkit-transform:scale(0);transform:scale(0);opacity:0}50%{-webkit-transform:scale(.99);transform:scale(.99)}100%{-webkit-transform:scale(1);transform:scale(1);opacity:1;visibility:visible}}.mdl-shadow--2dp{box-shadow:0 2px 2px 0 rgba(0,0,0,.14),0 3px 1px -2px rgba(0,0,0,.2),0 1px 5px 0 rgba(0,0,0,.12)}.mdl-shadow--3dp{box-shadow:0 3px 4px 0 rgba(0,0,0,.14),0 3px 3px -2px rgba(0,0,0,.2),0 1px 8px 0 rgba(0,0,0,.12)}.mdl-shadow--4dp{box-shadow:0 4px 5px 0 rgba(0,0,0,.14),0 1px 10px 0 rgba(0,0,0,.12),0 2px 4px -1px rgba(0,0,0,.2)}.mdl-shadow--6dp{box-shadow:0 6px 10px 0 rgba(0,0,0,.14),0 1px 18px 0 rgba(0,0,0,.12),0 3px 5px -1px rgba(0,0,0,.2)}.mdl-shadow--8dp{box-shadow:0 8px 10px 1px rgba(0,0,0,.14),0 3px 14px 2px rgba(0,0,0,.12),0 5px 5px -3px rgba(0,0,0,.2)}.mdl-shadow--16dp{box-shadow:0 16px 24px 2px rgba(0,0,0,.14),0 6px 30px 5px rgba(0,0,0,.12),0 8px 10px -5px rgba(0,0,0,.2)}.mdl-shadow--24dp{box-shadow:0 9px 46px 8px rgba(0,0,0,.14),0 11px 15px -7px rgba(0,0,0,.12),0 24px 38px 3px rgba(0,0,0,.2)}.mdl-grid{display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-flow:row wrap;-ms-flex-flow:row wrap;flex-flow:row wrap;margin:0 auto;-webkit-align-items:stretch;-ms-flex-align:stretch;align-items:stretch}.mdl-grid.mdl-grid--no-spacing{padding:0}.mdl-cell{box-sizing:border-box}.mdl-cell--top{-webkit-align-self:flex-start;-ms-flex-item-align:start;align-self:flex-start}.mdl-cell--middle{-webkit-align-self:center;-ms-flex-item-align:center;-ms-grid-row-align:center;align-self:center}.mdl-cell--bottom{-webkit-align-self:flex-end;-ms-flex-item-align:end;align-self:flex-end}.mdl-cell--stretch{-webkit-align-self:stretch;-ms-flex-item-align:stretch;-ms-grid-row-align:stretch;align-self:stretch}.mdl-grid.mdl-grid--no-spacing>.mdl-cell{margin:0}.mdl-cell--order-1{-webkit-order:1;-ms-flex-order:1;order:1}.mdl-cell--order-2{-webkit-order:2;-ms-flex-order:2;order:2}.mdl-cell--order-3{-webkit-order:3;-ms-flex-order:3;order:3}.mdl-cell--order-4{-webkit-order:4;-ms-flex-order:4;order:4}.mdl-cell--order-5{-webkit-order:5;-ms-flex-order:5;order:5}.mdl-cell--order-6{-webkit-order:6;-ms-flex-order:6;order:6}.mdl-cell--order-7{-webkit-order:7;-ms-flex-order:7;order:7}.mdl-cell--order-8{-webkit-order:8;-ms-flex-order:8;order:8}.mdl-cell--order-9{-webkit-order:9;-ms-flex-order:9;order:9}.mdl-cell--order-10{-webkit-order:10;-ms-flex-order:10;order:10}.mdl-cell--order-11{-webkit-order:11;-ms-flex-order:11;order:11}.mdl-cell--order-12{-webkit-order:12;-ms-flex-order:12;order:12}@media (max-width:479px){.mdl-grid{padding:8px}.mdl-cell{margin:8px;width:calc(100% - 16px)}.mdl-grid--no-spacing>.mdl-cell{width:100%}.mdl-cell--hide-phone{display:none!important}.mdl-cell--order-1-phone.mdl-cell--order-1-phone{-webkit-order:1;-ms-flex-order:1;order:1}.mdl-cell--order-2-phone.mdl-cell--order-2-phone{-webkit-order:2;-ms-flex-order:2;order:2}.mdl-cell--order-3-phone.mdl-cell--order-3-phone{-webkit-order:3;-ms-flex-order:3;order:3}.mdl-cell--order-4-phone.mdl-cell--order-4-phone{-webkit-order:4;-ms-flex-order:4;order:4}.mdl-cell--order-5-phone.mdl-cell--order-5-phone{-webkit-order:5;-ms-flex-order:5;order:5}.mdl-cell--order-6-phone.mdl-cell--order-6-phone{-webkit-order:6;-ms-flex-order:6;order:6}.mdl-cell--order-7-phone.mdl-cell--order-7-phone{-webkit-order:7;-ms-flex-order:7;order:7}.mdl-cell--order-8-phone.mdl-cell--order-8-phone{-webkit-order:8;-ms-flex-order:8;order:8}.mdl-cell--order-9-phone.mdl-cell--order-9-phone{-webkit-order:9;-ms-flex-order:9;order:9}.mdl-cell--order-10-phone.mdl-cell--order-10-phone{-webkit-order:10;-ms-flex-order:10;order:10}.mdl-cell--order-11-phone.mdl-cell--order-11-phone{-webkit-order:11;-ms-flex-order:11;order:11}.mdl-cell--order-12-phone.mdl-cell--order-12-phone{-webkit-order:12;-ms-flex-order:12;order:12}.mdl-cell--1-col,.mdl-cell--1-col-phone.mdl-cell--1-col-phone{width:calc(25% - 16px)}.mdl-grid--no-spacing>.mdl-cell--1-col,.mdl-grid--no-spacing>.mdl-cell--1-col-phone.mdl-cell--1-col-phone{width:25%}.mdl-cell--2-col,.mdl-cell--2-col-phone.mdl-cell--2-col-phone{width:calc(50% - 16px)}.mdl-grid--no-spacing>.mdl-cell--2-col,.mdl-grid--no-spacing>.mdl-cell--2-col-phone.mdl-cell--2-col-phone{width:50%}.mdl-cell--3-col,.mdl-cell--3-col-phone.mdl-cell--3-col-phone{width:calc(75% - 16px)}.mdl-grid--no-spacing>.mdl-cell--3-col,.mdl-grid--no-spacing>.mdl-cell--3-col-phone.mdl-cell--3-col-phone{width:75%}.mdl-cell--4-col,.mdl-cell--4-col-phone.mdl-cell--4-col-phone{width:calc(100% - 16px)}.mdl-grid--no-spacing>.mdl-cell--4-col,.mdl-grid--no-spacing>.mdl-cell--4-col-phone.mdl-cell--4-col-phone{width:100%}.mdl-cell--5-col,.mdl-cell--5-col-phone.mdl-cell--5-col-phone{width:calc(100% - 16px)}.mdl-grid--no-spacing>.mdl-cell--5-col,.mdl-grid--no-spacing>.mdl-cell--5-col-phone.mdl-cell--5-col-phone{width:100%}.mdl-cell--6-col,.mdl-cell--6-col-phone.mdl-cell--6-col-phone{width:calc(100% - 16px)}.mdl-grid--no-spacing>.mdl-cell--6-col,.mdl-grid--no-spacing>.mdl-cell--6-col-phone.mdl-cell--6-col-phone{width:100%}.mdl-cell--7-col,.mdl-cell--7-col-phone.mdl-cell--7-col-phone{width:calc(100% - 16px)}.mdl-grid--no-spacing>.mdl-cell--7-col,.mdl-grid--no-spacing>.mdl-cell--7-col-phone.mdl-cell--7-col-phone{width:100%}.mdl-cell--8-col,.mdl-cell--8-col-phone.mdl-cell--8-col-phone{width:calc(100% - 16px)}.mdl-grid--no-spacing>.mdl-cell--8-col,.mdl-grid--no-spacing>.mdl-cell--8-col-phone.mdl-cell--8-col-phone{width:100%}.mdl-cell--9-col,.mdl-cell--9-col-phone.mdl-cell--9-col-phone{width:calc(100% - 16px)}.mdl-grid--no-spacing>.mdl-cell--9-col,.mdl-grid--no-spacing>.mdl-cell--9-col-phone.mdl-cell--9-col-phone{width:100%}.mdl-cell--10-col,.mdl-cell--10-col-phone.mdl-cell--10-col-phone{width:calc(100% - 16px)}.mdl-grid--no-spacing>.mdl-cell--10-col,.mdl-grid--no-spacing>.mdl-cell--10-col-phone.mdl-cell--10-col-phone{width:100%}.mdl-cell--11-col,.mdl-cell--11-col-phone.mdl-cell--11-col-phone{width:calc(100% - 16px)}.mdl-grid--no-spacing>.mdl-cell--11-col,.mdl-grid--no-spacing>.mdl-cell--11-col-phone.mdl-cell--11-col-phone{width:100%}.mdl-cell--12-col,.mdl-cell--12-col-phone.mdl-cell--12-col-phone{width:calc(100% - 16px)}.mdl-grid--no-spacing>.mdl-cell--12-col,.mdl-grid--no-spacing>.mdl-cell--12-col-phone.mdl-cell--12-col-phone{width:100%}.mdl-cell--1-offset,.mdl-cell--1-offset-phone.mdl-cell--1-offset-phone{margin-left:calc(25% + 8px)}.mdl-grid.mdl-grid--no-spacing>.mdl-cell--1-offset,.mdl-grid.mdl-grid--no-spacing>.mdl-cell--1-offset-phone.mdl-cell--1-offset-phone{margin-left:25%}.mdl-cell--2-offset,.mdl-cell--2-offset-phone.mdl-cell--2-offset-phone{margin-left:calc(50% + 8px)}.mdl-grid.mdl-grid--no-spacing>.mdl-cell--2-offset,.mdl-grid.mdl-grid--no-spacing>.mdl-cell--2-offset-phone.mdl-cell--2-offset-phone{margin-left:50%}.mdl-cell--3-offset,.mdl-cell--3-offset-phone.mdl-cell--3-offset-phone{margin-left:calc(75% + 8px)}.mdl-grid.mdl-grid--no-spacing>.mdl-cell--3-offset,.mdl-grid.mdl-grid--no-spacing>.mdl-cell--3-offset-phone.mdl-cell--3-offset-phone{margin-left:75%}}@media (min-width:480px) and (max-width:839px){.mdl-grid{padding:8px}.mdl-cell{margin:8px;width:calc(50% - 16px)}.mdl-grid--no-spacing>.mdl-cell{width:50%}.mdl-cell--hide-tablet{display:none!important}.mdl-cell--order-1-tablet.mdl-cell--order-1-tablet{-webkit-order:1;-ms-flex-order:1;order:1}.mdl-cell--order-2-tablet.mdl-cell--order-2-tablet{-webkit-order:2;-ms-flex-order:2;order:2}.mdl-cell--order-3-tablet.mdl-cell--order-3-tablet{-webkit-order:3;-ms-flex-order:3;order:3}.mdl-cell--order-4-tablet.mdl-cell--order-4-tablet{-webkit-order:4;-ms-flex-order:4;order:4}.mdl-cell--order-5-tablet.mdl-cell--order-5-tablet{-webkit-order:5;-ms-flex-order:5;order:5}.mdl-cell--order-6-tablet.mdl-cell--order-6-tablet{-webkit-order:6;-ms-flex-order:6;order:6}.mdl-cell--order-7-tablet.mdl-cell--order-7-tablet{-webkit-order:7;-ms-flex-order:7;order:7}.mdl-cell--order-8-tablet.mdl-cell--order-8-tablet{-webkit-order:8;-ms-flex-order:8;order:8}.mdl-cell--order-9-tablet.mdl-cell--order-9-tablet{-webkit-order:9;-ms-flex-order:9;order:9}.mdl-cell--order-10-tablet.mdl-cell--order-10-tablet{-webkit-order:10;-ms-flex-order:10;order:10}.mdl-cell--order-11-tablet.mdl-cell--order-11-tablet{-webkit-order:11;-ms-flex-order:11;order:11}.mdl-cell--order-12-tablet.mdl-cell--order-12-tablet{-webkit-order:12;-ms-flex-order:12;order:12}.mdl-cell--1-col,.mdl-cell--1-col-tablet.mdl-cell--1-col-tablet{width:calc(12.5% - 16px)}.mdl-grid--no-spacing>.mdl-cell--1-col,.mdl-grid--no-spacing>.mdl-cell--1-col-tablet.mdl-cell--1-col-tablet{width:12.5%}.mdl-cell--2-col,.mdl-cell--2-col-tablet.mdl-cell--2-col-tablet{width:calc(25% - 16px)}.mdl-grid--no-spacing>.mdl-cell--2-col,.mdl-grid--no-spacing>.mdl-cell--2-col-tablet.mdl-cell--2-col-tablet{width:25%}.mdl-cell--3-col,.mdl-cell--3-col-tablet.mdl-cell--3-col-tablet{width:calc(37.5% - 16px)}.mdl-grid--no-spacing>.mdl-cell--3-col,.mdl-grid--no-spacing>.mdl-cell--3-col-tablet.mdl-cell--3-col-tablet{width:37.5%}.mdl-cell--4-col,.mdl-cell--4-col-tablet.mdl-cell--4-col-tablet{width:calc(50% - 16px)}.mdl-grid--no-spacing>.mdl-cell--4-col,.mdl-grid--no-spacing>.mdl-cell--4-col-tablet.mdl-cell--4-col-tablet{width:50%}.mdl-cell--5-col,.mdl-cell--5-col-tablet.mdl-cell--5-col-tablet{width:calc(62.5% - 16px)}.mdl-grid--no-spacing>.mdl-cell--5-col,.mdl-grid--no-spacing>.mdl-cell--5-col-tablet.mdl-cell--5-col-tablet{width:62.5%}.mdl-cell--6-col,.mdl-cell--6-col-tablet.mdl-cell--6-col-tablet{width:calc(75% - 16px)}.mdl-grid--no-spacing>.mdl-cell--6-col,.mdl-grid--no-spacing>.mdl-cell--6-col-tablet.mdl-cell--6-col-tablet{width:75%}.mdl-cell--7-col,.mdl-cell--7-col-tablet.mdl-cell--7-col-tablet{width:calc(87.5% - 16px)}.mdl-grid--no-spacing>.mdl-cell--7-col,.mdl-grid--no-spacing>.mdl-cell--7-col-tablet.mdl-cell--7-col-tablet{width:87.5%}.mdl-cell--8-col,.mdl-cell--8-col-tablet.mdl-cell--8-col-tablet{width:calc(100% - 16px)}.mdl-grid--no-spacing>.mdl-cell--8-col,.mdl-grid--no-spacing>.mdl-cell--8-col-tablet.mdl-cell--8-col-tablet{width:100%}.mdl-cell--9-col,.mdl-cell--9-col-tablet.mdl-cell--9-col-tablet{width:calc(100% - 16px)}.mdl-grid--no-spacing>.mdl-cell--9-col,.mdl-grid--no-spacing>.mdl-cell--9-col-tablet.mdl-cell--9-col-tablet{width:100%}.mdl-cell--10-col,.mdl-cell--10-col-tablet.mdl-cell--10-col-tablet{width:calc(100% - 16px)}.mdl-grid--no-spacing>.mdl-cell--10-col,.mdl-grid--no-spacing>.mdl-cell--10-col-tablet.mdl-cell--10-col-tablet{width:100%}.mdl-cell--11-col,.mdl-cell--11-col-tablet.mdl-cell--11-col-tablet{width:calc(100% - 16px)}.mdl-grid--no-spacing>.mdl-cell--11-col,.mdl-grid--no-spacing>.mdl-cell--11-col-tablet.mdl-cell--11-col-tablet{width:100%}.mdl-cell--12-col,.mdl-cell--12-col-tablet.mdl-cell--12-col-tablet{width:calc(100% - 16px)}.mdl-grid--no-spacing>.mdl-cell--12-col,.mdl-grid--no-spacing>.mdl-cell--12-col-tablet.mdl-cell--12-col-tablet{width:100%}.mdl-cell--1-offset,.mdl-cell--1-offset-tablet.mdl-cell--1-offset-tablet{margin-left:calc(12.5% + 8px)}.mdl-grid.mdl-grid--no-spacing>.mdl-cell--1-offset,.mdl-grid.mdl-grid--no-spacing>.mdl-cell--1-offset-tablet.mdl-cell--1-offset-tablet{margin-left:12.5%}.mdl-cell--2-offset,.mdl-cell--2-offset-tablet.mdl-cell--2-offset-tablet{margin-left:calc(25% + 8px)}.mdl-grid.mdl-grid--no-spacing>.mdl-cell--2-offset,.mdl-grid.mdl-grid--no-spacing>.mdl-cell--2-offset-tablet.mdl-cell--2-offset-tablet{margin-left:25%}.mdl-cell--3-offset,.mdl-cell--3-offset-tablet.mdl-cell--3-offset-tablet{margin-left:calc(37.5% + 8px)}.mdl-grid.mdl-grid--no-spacing>.mdl-cell--3-offset,.mdl-grid.mdl-grid--no-spacing>.mdl-cell--3-offset-tablet.mdl-cell--3-offset-tablet{margin-left:37.5%}.mdl-cell--4-offset,.mdl-cell--4-offset-tablet.mdl-cell--4-offset-tablet{margin-left:calc(50% + 8px)}.mdl-grid.mdl-grid--no-spacing>.mdl-cell--4-offset,.mdl-grid.mdl-grid--no-spacing>.mdl-cell--4-offset-tablet.mdl-cell--4-offset-tablet{margin-left:50%}.mdl-cell--5-offset,.mdl-cell--5-offset-tablet.mdl-cell--5-offset-tablet{margin-left:calc(62.5% + 8px)}.mdl-grid.mdl-grid--no-spacing>.mdl-cell--5-offset,.mdl-grid.mdl-grid--no-spacing>.mdl-cell--5-offset-tablet.mdl-cell--5-offset-tablet{margin-left:62.5%}.mdl-cell--6-offset,.mdl-cell--6-offset-tablet.mdl-cell--6-offset-tablet{margin-left:calc(75% + 8px)}.mdl-grid.mdl-grid--no-spacing>.mdl-cell--6-offset,.mdl-grid.mdl-grid--no-spacing>.mdl-cell--6-offset-tablet.mdl-cell--6-offset-tablet{margin-left:75%}.mdl-cell--7-offset,.mdl-cell--7-offset-tablet.mdl-cell--7-offset-tablet{margin-left:calc(87.5% + 8px)}.mdl-grid.mdl-grid--no-spacing>.mdl-cell--7-offset,.mdl-grid.mdl-grid--no-spacing>.mdl-cell--7-offset-tablet.mdl-cell--7-offset-tablet{margin-left:87.5%}}@media (min-width:840px){.mdl-grid{padding:8px}.mdl-cell{margin:8px;width:calc(33.3333333333% - 16px)}.mdl-grid--no-spacing>.mdl-cell{width:33.3333333333%}.mdl-cell--hide-desktop{display:none!important}.mdl-cell--order-1-desktop.mdl-cell--order-1-desktop{-webkit-order:1;-ms-flex-order:1;order:1}.mdl-cell--order-2-desktop.mdl-cell--order-2-desktop{-webkit-order:2;-ms-flex-order:2;order:2}.mdl-cell--order-3-desktop.mdl-cell--order-3-desktop{-webkit-order:3;-ms-flex-order:3;order:3}.mdl-cell--order-4-desktop.mdl-cell--order-4-desktop{-webkit-order:4;-ms-flex-order:4;order:4}.mdl-cell--order-5-desktop.mdl-cell--order-5-desktop{-webkit-order:5;-ms-flex-order:5;order:5}.mdl-cell--order-6-desktop.mdl-cell--order-6-desktop{-webkit-order:6;-ms-flex-order:6;order:6}.mdl-cell--order-7-desktop.mdl-cell--order-7-desktop{-webkit-order:7;-ms-flex-order:7;order:7}.mdl-cell--order-8-desktop.mdl-cell--order-8-desktop{-webkit-order:8;-ms-flex-order:8;order:8}.mdl-cell--order-9-desktop.mdl-cell--order-9-desktop{-webkit-order:9;-ms-flex-order:9;order:9}.mdl-cell--order-10-desktop.mdl-cell--order-10-desktop{-webkit-order:10;-ms-flex-order:10;order:10}.mdl-cell--order-11-desktop.mdl-cell--order-11-desktop{-webkit-order:11;-ms-flex-order:11;order:11}.mdl-cell--order-12-desktop.mdl-cell--order-12-desktop{-webkit-order:12;-ms-flex-order:12;order:12}.mdl-cell--1-col,.mdl-cell--1-col-desktop.mdl-cell--1-col-desktop{width:calc(8.3333333333% - 16px)}.mdl-grid--no-spacing>.mdl-cell--1-col,.mdl-grid--no-spacing>.mdl-cell--1-col-desktop.mdl-cell--1-col-desktop{width:8.3333333333%}.mdl-cell--2-col,.mdl-cell--2-col-desktop.mdl-cell--2-col-desktop{width:calc(16.6666666667% - 16px)}.mdl-grid--no-spacing>.mdl-cell--2-col,.mdl-grid--no-spacing>.mdl-cell--2-col-desktop.mdl-cell--2-col-desktop{width:16.6666666667%}.mdl-cell--3-col,.mdl-cell--3-col-desktop.mdl-cell--3-col-desktop{width:calc(25% - 16px)}.mdl-grid--no-spacing>.mdl-cell--3-col,.mdl-grid--no-spacing>.mdl-cell--3-col-desktop.mdl-cell--3-col-desktop{width:25%}.mdl-cell--4-col,.mdl-cell--4-col-desktop.mdl-cell--4-col-desktop{width:calc(33.3333333333% - 16px)}.mdl-grid--no-spacing>.mdl-cell--4-col,.mdl-grid--no-spacing>.mdl-cell--4-col-desktop.mdl-cell--4-col-desktop{width:33.3333333333%}.mdl-cell--5-col,.mdl-cell--5-col-desktop.mdl-cell--5-col-desktop{width:calc(41.6666666667% - 16px)}.mdl-grid--no-spacing>.mdl-cell--5-col,.mdl-grid--no-spacing>.mdl-cell--5-col-desktop.mdl-cell--5-col-desktop{width:41.6666666667%}.mdl-cell--6-col,.mdl-cell--6-col-desktop.mdl-cell--6-col-desktop{width:calc(50% - 16px)}.mdl-grid--no-spacing>.mdl-cell--6-col,.mdl-grid--no-spacing>.mdl-cell--6-col-desktop.mdl-cell--6-col-desktop{width:50%}.mdl-cell--7-col,.mdl-cell--7-col-desktop.mdl-cell--7-col-desktop{width:calc(58.3333333333% - 16px)}.mdl-grid--no-spacing>.mdl-cell--7-col,.mdl-grid--no-spacing>.mdl-cell--7-col-desktop.mdl-cell--7-col-desktop{width:58.3333333333%}.mdl-cell--8-col,.mdl-cell--8-col-desktop.mdl-cell--8-col-desktop{width:calc(66.6666666667% - 16px)}.mdl-grid--no-spacing>.mdl-cell--8-col,.mdl-grid--no-spacing>.mdl-cell--8-col-desktop.mdl-cell--8-col-desktop{width:66.6666666667%}.mdl-cell--9-col,.mdl-cell--9-col-desktop.mdl-cell--9-col-desktop{width:calc(75% - 16px)}.mdl-grid--no-spacing>.mdl-cell--9-col,.mdl-grid--no-spacing>.mdl-cell--9-col-desktop.mdl-cell--9-col-desktop{width:75%}.mdl-cell--10-col,.mdl-cell--10-col-desktop.mdl-cell--10-col-desktop{width:calc(83.3333333333% - 16px)}.mdl-grid--no-spacing>.mdl-cell--10-col,.mdl-grid--no-spacing>.mdl-cell--10-col-desktop.mdl-cell--10-col-desktop{width:83.3333333333%}.mdl-cell--11-col,.mdl-cell--11-col-desktop.mdl-cell--11-col-desktop{width:calc(91.6666666667% - 16px)}.mdl-grid--no-spacing>.mdl-cell--11-col,.mdl-grid--no-spacing>.mdl-cell--11-col-desktop.mdl-cell--11-col-desktop{width:91.6666666667%}.mdl-cell--12-col,.mdl-cell--12-col-desktop.mdl-cell--12-col-desktop{width:calc(100% - 16px)}.mdl-grid--no-spacing>.mdl-cell--12-col,.mdl-grid--no-spacing>.mdl-cell--12-col-desktop.mdl-cell--12-col-desktop{width:100%}.mdl-cell--1-offset,.mdl-cell--1-offset-desktop.mdl-cell--1-offset-desktop{margin-left:calc(8.3333333333% + 8px)}.mdl-grid.mdl-grid--no-spacing>.mdl-cell--1-offset,.mdl-grid.mdl-grid--no-spacing>.mdl-cell--1-offset-desktop.mdl-cell--1-offset-desktop{margin-left:8.3333333333%}.mdl-cell--2-offset,.mdl-cell--2-offset-desktop.mdl-cell--2-offset-desktop{margin-left:calc(16.6666666667% + 8px)}.mdl-grid.mdl-grid--no-spacing>.mdl-cell--2-offset,.mdl-grid.mdl-grid--no-spacing>.mdl-cell--2-offset-desktop.mdl-cell--2-offset-desktop{margin-left:16.6666666667%}.mdl-cell--3-offset,.mdl-cell--3-offset-desktop.mdl-cell--3-offset-desktop{margin-left:calc(25% + 8px)}.mdl-grid.mdl-grid--no-spacing>.mdl-cell--3-offset,.mdl-grid.mdl-grid--no-spacing>.mdl-cell--3-offset-desktop.mdl-cell--3-offset-desktop{margin-left:25%}.mdl-cell--4-offset,.mdl-cell--4-offset-desktop.mdl-cell--4-offset-desktop{margin-left:calc(33.3333333333% + 8px)}.mdl-grid.mdl-grid--no-spacing>.mdl-cell--4-offset,.mdl-grid.mdl-grid--no-spacing>.mdl-cell--4-offset-desktop.mdl-cell--4-offset-desktop{margin-left:33.3333333333%}.mdl-cell--5-offset,.mdl-cell--5-offset-desktop.mdl-cell--5-offset-desktop{margin-left:calc(41.6666666667% + 8px)}.mdl-grid.mdl-grid--no-spacing>.mdl-cell--5-offset,.mdl-grid.mdl-grid--no-spacing>.mdl-cell--5-offset-desktop.mdl-cell--5-offset-desktop{margin-left:41.6666666667%}.mdl-cell--6-offset,.mdl-cell--6-offset-desktop.mdl-cell--6-offset-desktop{margin-left:calc(50% + 8px)}.mdl-grid.mdl-grid--no-spacing>.mdl-cell--6-offset,.mdl-grid.mdl-grid--no-spacing>.mdl-cell--6-offset-desktop.mdl-cell--6-offset-desktop{margin-left:50%}.mdl-cell--7-offset,.mdl-cell--7-offset-desktop.mdl-cell--7-offset-desktop{margin-left:calc(58.3333333333% + 8px)}.mdl-grid.mdl-grid--no-spacing>.mdl-cell--7-offset,.mdl-grid.mdl-grid--no-spacing>.mdl-cell--7-offset-desktop.mdl-cell--7-offset-desktop{margin-left:58.3333333333%}.mdl-cell--8-offset,.mdl-cell--8-offset-desktop.mdl-cell--8-offset-desktop{margin-left:calc(66.6666666667% + 8px)}.mdl-grid.mdl-grid--no-spacing>.mdl-cell--8-offset,.mdl-grid.mdl-grid--no-spacing>.mdl-cell--8-offset-desktop.mdl-cell--8-offset-desktop{margin-left:66.6666666667%}.mdl-cell--9-offset,.mdl-cell--9-offset-desktop.mdl-cell--9-offset-desktop{margin-left:calc(75% + 8px)}.mdl-grid.mdl-grid--no-spacing>.mdl-cell--9-offset,.mdl-grid.mdl-grid--no-spacing>.mdl-cell--9-offset-desktop.mdl-cell--9-offset-desktop{margin-left:75%}.mdl-cell--10-offset,.mdl-cell--10-offset-desktop.mdl-cell--10-offset-desktop{margin-left:calc(83.3333333333% + 8px)}.mdl-grid.mdl-grid--no-spacing>.mdl-cell--10-offset,.mdl-grid.mdl-grid--no-spacing>.mdl-cell--10-offset-desktop.mdl-cell--10-offset-desktop{margin-left:83.3333333333%}.mdl-cell--11-offset,.mdl-cell--11-offset-desktop.mdl-cell--11-offset-desktop{margin-left:calc(91.6666666667% + 8px)}.mdl-grid.mdl-grid--no-spacing>.mdl-cell--11-offset,.mdl-grid.mdl-grid--no-spacing>.mdl-cell--11-offset-desktop.mdl-cell--11-offset-desktop{margin-left:91.6666666667%}}body{margin:0}.styleguide-demo h1{margin:48px 24px 0}.styleguide-demo h1:after{content:'';display:block;width:100%;border-bottom:1px solid rgba(0,0,0,.5);margin-top:24px}.styleguide-demo{opacity:0;transition:opacity .6s ease}.styleguide-masthead{height:256px;background:#212121;padding:115px 16px 0}.styleguide-container{position:relative;max-width:960px;width:100%}.styleguide-title{color:#fff;bottom:auto;position:relative;font-size:56px;font-weight:300;line-height:1;letter-spacing:-.02em}.styleguide-title:after{border-bottom:0}.styleguide-title span{font-weight:300}.mdl-styleguide .mdl-layout__drawer .mdl-navigation__link{padding:10px 24px}.demosLoaded .styleguide-demo{opacity:1}iframe{display:block;width:100%;border:none}iframe.heightSet{overflow:hidden}.demo-wrapper{margin:24px}.demo-wrapper iframe{border:1px solid rgba(0,0,0,.5)}
\ No newline at end of file
diff --git a/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.css b/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.css
new file mode 100644
index 000000000000..5ca19ef5d437
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.css
@@ -0,0 +1,2 @@
+.admonition,.mdl-shadow--2dp,.page-content pre:hover,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list){box-shadow:0 2px 2px 0 rgba(0,0,0,.14),0 3px 1px -2px rgba(0,0,0,.2),0 1px 5px 0 rgba(0,0,0,.12)}.mdl-shadow--3dp{box-shadow:0 3px 4px 0 rgba(0,0,0,.14),0 3px 3px -2px rgba(0,0,0,.2),0 1px 8px 0 rgba(0,0,0,.12)}.mdl-shadow--4dp{box-shadow:0 4px 5px 0 rgba(0,0,0,.14),0 1px 10px 0 rgba(0,0,0,.12),0 2px 4px -1px rgba(0,0,0,.2)}.mdl-shadow--6dp{box-shadow:0 6px 10px 0 rgba(0,0,0,.14),0 1px 18px 0 rgba(0,0,0,.12),0 3px 5px -1px rgba(0,0,0,.2)}.mdl-shadow--8dp{box-shadow:0 8px 10px 1px rgba(0,0,0,.14),0 3px 14px 2px rgba(0,0,0,.12),0 5px 5px -3px rgba(0,0,0,.2)}.mdl-shadow--16dp{box-shadow:0 16px 24px 2px rgba(0,0,0,.14),0 6px 30px 5px rgba(0,0,0,.12),0 8px 10px -5px rgba(0,0,0,.2)}.mdl-shadow--24dp{box-shadow:0 9px 46px 8px rgba(0,0,0,.14),0 11px 15px -7px rgba(0,0,0,.12),0 24px 38px 3px rgba(0,0,0,.2)}.mdl-data-table,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list){position:relative;border:1px solid rgba(0,0,0,.12);border-collapse:collapse;white-space:nowrap;font-size:13px;background-color:#fff}.mdl-data-table thead,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) thead{padding-bottom:3px}.mdl-data-table thead .mdl-data-table__select,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) thead .mdl-data-table__select{margin-top:0}.mdl-data-table tbody tr,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) tbody tr{position:relative;height:48px;transition-duration:.28s;transition-timing-function:cubic-bezier(.4,0,.2,1);transition-property:background-color}.mdl-data-table tbody tr.is-selected,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) tbody tr.is-selected{background-color:#e0e0e0}.mdl-data-table tbody tr:hover,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) tbody tr:hover{background-color:#eee}.mdl-data-table td,.mdl-data-table th,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) td,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th{padding:0 18px 12px;text-align:right}.mdl-data-table td:first-of-type,.mdl-data-table th:first-of-type,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) td:first-of-type,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th:first-of-type{padding-left:24px}.mdl-data-table td:last-of-type,.mdl-data-table th:last-of-type,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) td:last-of-type,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th:last-of-type{padding-right:24px}.mdl-data-table td,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) td{position:relative;vertical-align:middle;height:48px;border-top:1px solid rgba(0,0,0,.12);border-bottom:1px solid rgba(0,0,0,.12);padding-top:12px;box-sizing:border-box}.mdl-data-table td .mdl-data-table__select,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) td .mdl-data-table__select{vertical-align:middle}.mdl-data-table th,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th{position:relative;vertical-align:bottom;text-overflow:ellipsis;font-size:14px;font-weight:700;line-height:24px;letter-spacing:0;height:48px;font-size:12px;color:rgba(0,0,0,.54);padding-bottom:8px;box-sizing:border-box}.mdl-data-table th.mdl-data-table__header--sorted-ascending,.mdl-data-table th.mdl-data-table__header--sorted-descending,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-ascending,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-descending{color:rgba(0,0,0,.87)}.mdl-data-table th.mdl-data-table__header--sorted-ascending:before,.mdl-data-table th.mdl-data-table__header--sorted-descending:before,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-ascending:before,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-descending:before{font-family:Material Icons;font-weight:400;font-style:normal;font-size:24px;line-height:1;letter-spacing:normal;text-transform:none;display:inline-block;word-wrap:normal;font-feature-settings:"liga";-webkit-font-feature-settings:"liga";-webkit-font-smoothing:antialiased;font-size:16px;content:"\e5d8";margin-right:5px;vertical-align:sub}.mdl-data-table th.mdl-data-table__header--sorted-ascending:hover,.mdl-data-table th.mdl-data-table__header--sorted-descending:hover,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-ascending:hover,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-descending:hover{cursor:pointer}.mdl-data-table th.mdl-data-table__header--sorted-ascending:hover:before,.mdl-data-table th.mdl-data-table__header--sorted-descending:hover:before,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-ascending:hover:before,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-descending:hover:before{color:rgba(0,0,0,.26)}.mdl-data-table th.mdl-data-table__header--sorted-descending:before,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-descending:before{content:"\e5db"}.mdl-data-table__select{width:16px}.mdl-data-table__cell--non-numeric.mdl-data-table__cell--non-numeric,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) td,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th{text-align:left}.mdl-mini-footer{display:flex;flex-flow:row wrap;justify-content:space-between;padding:32px 16px;color:#9e9e9e;background-color:#424242}.mdl-mini-footer:after{content:"";display:block}.mdl-mini-footer .mdl-logo{line-height:36px}.mdl-mini-footer--link-list,.mdl-mini-footer__link-list,footer.mdl-mini-footer>div.mdl-mini-footer__left-section ul{display:flex;flex-flow:row nowrap;list-style:none;margin:0;padding:0}.mdl-mini-footer--link-list li,.mdl-mini-footer__link-list li,footer.mdl-mini-footer>div.mdl-mini-footer__left-section ul li{margin-bottom:0;margin-right:16px}@media screen and (min-width:760px){.mdl-mini-footer--link-list li,.mdl-mini-footer__link-list li,footer.mdl-mini-footer>div.mdl-mini-footer__left-section ul li{line-height:36px}}.mdl-mini-footer--link-list a,.mdl-mini-footer__link-list a,footer.mdl-mini-footer>div.mdl-mini-footer__left-section ul a{color:inherit;text-decoration:none;white-space:nowrap}.mdl-mini-footer--left-section,.mdl-mini-footer__left-section{display:inline-block;order:0}.mdl-mini-footer--right-section,.mdl-mini-footer__right-section{display:inline-block;order:1}.mdl-mini-footer--social-btn,.mdl-mini-footer__social-btn{width:36px;height:36px;padding:0;margin:0;background-color:#9e9e9e;border:none}.mdl-card{display:flex;flex-direction:column;font-size:16px;font-weight:400;min-height:200px;overflow:hidden;width:330px;z-index:1;position:relative;background:#fff;border-radius:2px;box-sizing:border-box}.mdl-card__media{background-color:#ff6e40;background-repeat:repeat;background-position:50% 50%;background-size:cover;background-origin:padding-box;background-attachment:scroll;box-sizing:border-box}.mdl-card__title{align-items:center;color:#000;display:block;display:flex;justify-content:stretch;line-height:normal;padding:16px;perspective-origin:165px 56px;transform-origin:165px 56px;box-sizing:border-box}.mdl-card__title.mdl-card--border{border-bottom:1px solid rgba(0,0,0,.1)}.mdl-card__title-text{align-self:flex-end;color:inherit;display:block;display:flex;font-size:24px;font-weight:300;line-height:normal;overflow:hidden;transform-origin:149px 48px;margin:0}.mdl-card__subtitle-text{font-size:14px;color:rgba(0,0,0,.54);margin:0}.mdl-card__supporting-text{color:rgba(0,0,0,.54);font-size:1rem;line-height:18px;overflow:hidden;padding:16px;width:90%}.mdl-card__supporting-text.mdl-card--border{border-bottom:1px solid rgba(0,0,0,.1)}.mdl-card__actions{font-size:16px;line-height:normal;width:100%;background-color:transparent;padding:8px;box-sizing:border-box}.mdl-card__actions.mdl-card--border{border-top:1px solid rgba(0,0,0,.1)}.mdl-card--expand{flex-grow:1}.mdl-card__menu{position:absolute;right:16px;top:16px}.mdl-button{background:transparent;border:none;border-radius:2px;color:#000;position:relative;height:36px;margin:0;min-width:64px;padding:0 16px;display:inline-block;font-family:Roboto,Helvetica,Arial,sans-serif;font-size:14px;font-weight:500;text-transform:uppercase;line-height:1;letter-spacing:0;overflow:hidden;will-change:box-shadow;transition:box-shadow .2s cubic-bezier(.4,0,1,1),background-color .2s cubic-bezier(.4,0,.2,1),color .2s cubic-bezier(.4,0,.2,1);outline:none;cursor:pointer;text-decoration:none;text-align:center;line-height:36px;vertical-align:middle}.mdl-button::-moz-focus-inner{border:0}.mdl-button:hover{background-color:hsla(0,0%,62%,.2)}.mdl-button:focus:not(:active){background-color:rgba(0,0,0,.12)}.mdl-button:active{background-color:hsla(0,0%,62%,.4)}.mdl-button.mdl-button--colored{color:#2196f3}.mdl-button.mdl-button--colored:focus:not(:active){background-color:rgba(0,0,0,.12)}input.mdl-button[type=submit]{-webkit-appearance:none}.mdl-button--raised{background:hsla(0,0%,62%,.2);box-shadow:0 2px 2px 0 rgba(0,0,0,.14),0 3px 1px -2px rgba(0,0,0,.2),0 1px 5px 0 rgba(0,0,0,.12)}.mdl-button--raised:active{box-shadow:0 4px 5px 0 rgba(0,0,0,.14),0 1px 10px 0 rgba(0,0,0,.12),0 2px 4px -1px rgba(0,0,0,.2);background-color:hsla(0,0%,62%,.4)}.mdl-button--raised:focus:not(:active){box-shadow:0 0 8px rgba(0,0,0,.18),0 8px 16px rgba(0,0,0,.36);background-color:hsla(0,0%,62%,.4)}.mdl-button--raised.mdl-button--colored{background:#2196f3;color:#fff}.mdl-button--raised.mdl-button--colored:active,.mdl-button--raised.mdl-button--colored:focus:not(:active),.mdl-button--raised.mdl-button--colored:hover{background-color:#2196f3}.mdl-button--raised.mdl-button--colored .mdl-ripple{background:#fff}.mdl-button--fab{border-radius:50%;font-size:24px;height:56px;margin:auto;min-width:56px;width:56px;padding:0;overflow:hidden;background:hsla(0,0%,62%,.2);box-shadow:0 1px 1.5px 0 rgba(0,0,0,.12),0 1px 1px 0 rgba(0,0,0,.24);position:relative;line-height:normal}.admonition.attention .mdl-button--fab .admonition-title:before,.admonition.caution .mdl-button--fab .admonition-title:before,.admonition.danger .mdl-button--fab .admonition-title:before,.admonition.error .mdl-button--fab .admonition-title:before,.admonition.hint .mdl-button--fab .admonition-title:before,.admonition.important .mdl-button--fab .admonition-title:before,.admonition.note .mdl-button--fab .admonition-title:before,.admonition.seealso .mdl-button--fab .admonition-title:before,.admonition.tip .mdl-button--fab .admonition-title:before,.admonition.warning .mdl-button--fab .admonition-title:before,.mdl-button--fab .admonition.attention .admonition-title:before,.mdl-button--fab .admonition.caution .admonition-title:before,.mdl-button--fab .admonition.danger .admonition-title:before,.mdl-button--fab .admonition.error .admonition-title:before,.mdl-button--fab .admonition.hint .admonition-title:before,.mdl-button--fab .admonition.important .admonition-title:before,.mdl-button--fab .admonition.note .admonition-title:before,.mdl-button--fab .admonition.seealso .admonition-title:before,.mdl-button--fab .admonition.tip .admonition-title:before,.mdl-button--fab .admonition.warning .admonition-title:before,.mdl-button--fab .material-icons,.mdl-button--fab a.download:before{position:absolute;top:50%;left:50%;transform:translate(-12px,-12px);line-height:24px;width:24px}.mdl-button--fab.mdl-button--mini-fab{height:40px;min-width:40px;width:40px}.mdl-button--fab .mdl-button__ripple-container{border-radius:50%;-webkit-mask-image:-webkit-radial-gradient(circle,#fff,#000)}.mdl-button--fab:active{box-shadow:0 4px 5px 0 rgba(0,0,0,.14),0 1px 10px 0 rgba(0,0,0,.12),0 2px 4px -1px rgba(0,0,0,.2);background-color:hsla(0,0%,62%,.4)}.mdl-button--fab:focus:not(:active){box-shadow:0 0 8px rgba(0,0,0,.18),0 8px 16px rgba(0,0,0,.36);background-color:hsla(0,0%,62%,.4)}.mdl-button--fab.mdl-button--colored{background:#ff6e40;color:#fff}.mdl-button--fab.mdl-button--colored:active,.mdl-button--fab.mdl-button--colored:focus:not(:active),.mdl-button--fab.mdl-button--colored:hover{background-color:#ff6e40}.mdl-button--fab.mdl-button--colored .mdl-ripple{background:#fff}.mdl-button--icon{border-radius:50%;font-size:24px;height:32px;margin-left:0;margin-right:0;min-width:32px;width:32px;padding:0;overflow:hidden;color:inherit;line-height:normal}.admonition.attention .mdl-button--icon .admonition-title:before,.admonition.caution .mdl-button--icon .admonition-title:before,.admonition.danger .mdl-button--icon .admonition-title:before,.admonition.error .mdl-button--icon .admonition-title:before,.admonition.hint .mdl-button--icon .admonition-title:before,.admonition.important .mdl-button--icon .admonition-title:before,.admonition.note .mdl-button--icon .admonition-title:before,.admonition.seealso .mdl-button--icon .admonition-title:before,.admonition.tip .mdl-button--icon .admonition-title:before,.admonition.warning .mdl-button--icon .admonition-title:before,.mdl-button--icon .admonition.attention .admonition-title:before,.mdl-button--icon .admonition.caution .admonition-title:before,.mdl-button--icon .admonition.danger .admonition-title:before,.mdl-button--icon .admonition.error .admonition-title:before,.mdl-button--icon .admonition.hint .admonition-title:before,.mdl-button--icon .admonition.important .admonition-title:before,.mdl-button--icon .admonition.note .admonition-title:before,.mdl-button--icon .admonition.seealso .admonition-title:before,.mdl-button--icon .admonition.tip .admonition-title:before,.mdl-button--icon .admonition.warning .admonition-title:before,.mdl-button--icon .material-icons,.mdl-button--icon a.download:before{position:absolute;top:50%;left:50%;transform:translate(-12px,-12px);line-height:24px;width:24px}.mdl-button--icon.mdl-button--mini-icon{height:24px;min-width:24px;width:24px}.admonition.attention .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.caution .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.danger .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.error .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.hint .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.important .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.note .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.seealso .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.tip .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.warning .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.attention .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.caution .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.danger .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.error .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.hint .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.important .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.note .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.seealso .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.tip .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.warning .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .material-icons,.mdl-button--icon.mdl-button--mini-icon a.download:before{top:0;left:0}.mdl-button--icon .mdl-button__ripple-container{border-radius:50%;-webkit-mask-image:-webkit-radial-gradient(circle,#fff,#000)}.mdl-button__ripple-container{display:block;height:100%;left:0;position:absolute;top:0;width:100%;z-index:0;overflow:hidden}.mdl-button.mdl-button--disabled .mdl-button__ripple-container .mdl-ripple,.mdl-button[disabled] .mdl-button__ripple-container .mdl-ripple{background-color:transparent}.mdl-button--primary.mdl-button--primary{color:#2196f3}.mdl-button--primary.mdl-button--primary .mdl-ripple{background:#fff}.mdl-button--primary.mdl-button--primary.mdl-button--fab,.mdl-button--primary.mdl-button--primary.mdl-button--raised{color:#fff;background-color:#2196f3}.mdl-button--accent.mdl-button--accent{color:#ff6e40}.mdl-button--accent.mdl-button--accent .mdl-ripple{background:#fff}.mdl-button--accent.mdl-button--accent.mdl-button--fab,.mdl-button--accent.mdl-button--accent.mdl-button--raised{color:#fff;background-color:#ff6e40}.mdl-button.mdl-button--disabled.mdl-button--disabled,.mdl-button[disabled][disabled]{color:rgba(0,0,0,.26);cursor:default;background-color:transparent}.mdl-button--fab.mdl-button--disabled.mdl-button--disabled,.mdl-button--fab[disabled][disabled]{background-color:rgba(0,0,0,.12);color:rgba(0,0,0,.26)}.mdl-button--raised.mdl-button--disabled.mdl-button--disabled,.mdl-button--raised[disabled][disabled]{background-color:rgba(0,0,0,.12);color:rgba(0,0,0,.26);box-shadow:none}.mdl-button--colored.mdl-button--disabled.mdl-button--disabled,.mdl-button--colored[disabled][disabled]{color:rgba(0,0,0,.26)}.admonition.attention .mdl-button .admonition-title:before,.admonition.caution .mdl-button .admonition-title:before,.admonition.danger .mdl-button .admonition-title:before,.admonition.error .mdl-button .admonition-title:before,.admonition.hint .mdl-button .admonition-title:before,.admonition.important .mdl-button .admonition-title:before,.admonition.note .mdl-button .admonition-title:before,.admonition.seealso .mdl-button .admonition-title:before,.admonition.tip .mdl-button .admonition-title:before,.admonition.warning .mdl-button .admonition-title:before,.mdl-button .admonition.attention .admonition-title:before,.mdl-button .admonition.caution .admonition-title:before,.mdl-button .admonition.danger .admonition-title:before,.mdl-button .admonition.error .admonition-title:before,.mdl-button .admonition.hint .admonition-title:before,.mdl-button .admonition.important .admonition-title:before,.mdl-button .admonition.note .admonition-title:before,.mdl-button .admonition.seealso .admonition-title:before,.mdl-button .admonition.tip .admonition-title:before,.mdl-button .admonition.warning .admonition-title:before,.mdl-button .material-icons,.mdl-button a.download:before{vertical-align:middle}.font-light{font-weight:300}.font-regular{font-weight:400}.font-heavy{font-weight:700}.left{text-align:left}.right{text-align:right}.center{text-align:center;margin-left:auto;margin-right:auto}.justify{text-align:justify}.hidden-sm{display:none}.container{width:100%;margin-left:auto;margin-right:auto}@media only screen and (min-width:33.75em){.container{width:80%}}@media only screen and (min-width:60em){.container{width:75%;max-width:60rem}}.row{position:relative;width:100%}.row [class^=col]{float:left;margin:.5rem 1%;min-height:.125rem}.row:after{content:"";display:table;clear:both}.col-1,.col-2,.col-3,.col-4,.col-5,.col-6,.col-7,.col-8,.col-9,.col-10,.col-11,.col-12{width:98%}.col-1-sm{width:6.33333%}.col-2-sm{width:14.66667%}.col-3-sm{width:23%}.col-4-sm{width:31.33333%}.col-5-sm{width:39.66667%}.col-6-sm{width:48%}.col-7-sm{width:56.33333%}.col-8-sm{width:64.66667%}.col-9-sm{width:73%}.col-10-sm{width:81.33333%}.col-11-sm{width:89.66667%}.col-12-sm{width:98%}@media only screen and (min-width:45em){.col-1{width:6.33333%}.col-2{width:14.66667%}.col-3{width:23%}.col-4{width:31.33333%}.col-5{width:39.66667%}.col-6{width:48%}.col-7{width:56.33333%}.col-8{width:64.66667%}.col-9{width:73%}.col-10{width:81.33333%}.col-11{width:89.66667%}.col-12{width:98%}.hidden-sm{display:block}}.row{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;flex-wrap:wrap}.row>[class*=col-]{display:flex;flex-direction:column}.admonition.attention .admonition-title:before,.admonition.caution .admonition-title:before,.admonition.danger .admonition-title:before,.admonition.error .admonition-title:before,.admonition.hint .admonition-title:before,.admonition.important .admonition-title:before,.admonition.note .admonition-title:before,.admonition.seealso .admonition-title:before,.admonition.tip .admonition-title:before,.admonition.warning .admonition-title:before,.material-icons,a.download:before{font-family:Material Icons;font-weight:400;font-style:normal;font-size:24px;display:inline-block;line-height:1;text-transform:none;letter-spacing:normal;word-wrap:normal;white-space:nowrap;direction:ltr;-webkit-font-smoothing:antialiased;text-rendering:optimizeLegibility;-moz-osx-font-smoothing:grayscale;font-feature-settings:"liga"}html{font-size:16px}body{display:block!important;background-color:#fafafa;font-size:1rem;line-height:1.5rem;font-family:-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol}.mdl-layout__content:focus{outline:none}a.download>code.download,blockquote,h1,h2,h3,h4,h5,h6,span.mdl-layout-title{font-family:-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol}.contents,.contents a,.globaltoc a.current,.toc-backref,.toctree-wrapper,.toctree-wrapper a,h1,h2,h3,h4,h5,h6{color:#048ccc!important}a{text-decoration:none}.page-content,.page-content dd,.page-content dl,.page-content dt,.page-content ol,.page-content p,.page-content table,.page-content td,.page-content th,.page-content ul{font-size:1rem}.brand{color:inherit;text-decoration:none}.section{overflow-x:auto}img{max-width:100%;display:block;margin-left:auto;margin-right:auto}div.figure p.caption{text-align:center;margin-top:.75rem}div.figure p.caption span.caption-number{font-style:normal}div.figure p.caption .caption-number:after{content:"\00a0"}.svg-icon{width:16px;height:16px;display:inline-block;fill:#f5f5f5;padding-right:5px;padding-top:4px;vertical-align:text-top}.admonition.attention a.download>i.admonition-title:before,.admonition.caution a.download>i.admonition-title:before,.admonition.danger a.download>i.admonition-title:before,.admonition.error a.download>i.admonition-title:before,.admonition.hint a.download>i.admonition-title:before,.admonition.important a.download>i.admonition-title:before,.admonition.note a.download>i.admonition-title:before,.admonition.seealso a.download>i.admonition-title:before,.admonition.tip a.download>i.admonition-title:before,.admonition.warning a.download>i.admonition-title:before,a.download>i.material-icons{position:relative;top:5px}a.download{text-decoration:none}.wrapper:after{content:"";display:table;clear:both}.wrapper{max-width:1090px;margin-right:auto;margin-left:auto;padding-right:45px;padding-left:30px}@media screen and (max-width:1024px){.wrapper{max-width:1120px;padding-right:15px;padding-left:15px}}.mdl-layout__container{margin-top:76px;height:calc(100% - 76px)}.document{width:100%;margin:0 auto;display:flex}@media (min-width:1795px){.document{width:1400px}}.document .page-content{width:100%;margin:0 auto;padding:0 12px}@media (min-width:992px){.document .page-content{width:90%;padding:0 5%}}@media (min-width:1200px){.document .page-content{width:calc(90% - 230px);padding:0 5%}}.document .side-doc-outline{width:230px}@media (max-width:1199px){.document .side-doc-outline{display:none}}.document .side-doc-outline--content{position:fixed;overflow-x:auto;overflow-y:auto;width:inherit}.document .side-doc-outline--content::-webkit-scrollbar{width:6px}.document .side-doc-outline--content::-webkit-scrollbar-track{border-radius:6px}.document .side-doc-outline--content::-webkit-scrollbar-thumb{background-color:rgba(0,0,0,.3);border-radius:6px;box-shadow:0 0 0 1px hsla(0,0%,100%,.3)}@keyframes float-in{0%{transform:translateY(.5rem);opacity:0}to{transform:translateY(0);opacity:1}}@keyframes float-out{0%{transform:translateY(0);opacity:1}to{transform:translateY(.5rem);opacity:0}}.page-content .headerlink{display:inline-block;text-decoration:none;margin-left:.8rem;color:inherit;opacity:0}.page-content .headerlink:hover{animation:float-in .2s cubic-bezier(.4,0,.2,1) 0s forwards}.page-content h1 .toc-backref,.page-content h2 .toc-backref,.page-content h3 .toc-backref,.page-content h4 .toc-backref,.page-content h5 .toc-backref,.page-content h6 .toc-backref{text-decoration:none}.page-content h1:hover .headerlink,.page-content h2:hover .headerlink,.page-content h3:hover .headerlink,.page-content h4:hover .headerlink,.page-content h5:hover .headerlink,.page-content h6:hover .headerlink{animation:float-in .2s cubic-bezier(.4,0,.2,1) 0s forwards}.page-content h1{font-size:2rem;line-height:2.25rem}.page-content h2{font-size:1.75rem;line-height:2rem;padding-top:1.5rem;margin-top:0;margin-bottom:1rem}.page-content h3{font-size:1.5rem;line-height:1.75rem;padding-top:1rem;margin-top:0;margin-bottom:.75rem}.page-content h4{font-size:1.25rem;line-height:1.5rem;padding-top:.75rem;margin-top:0;margin-bottom:.5rem}.page-content div.page-content h5{font-size:1.1rem;line-height:1.5rem;padding-top:2rem;margin-top:0;margin-bottom:1rem}.page-content div.page-content h6{font-size:1rem;line-height:1.5rem;padding-top:2rem;margin-top:0;margin-bottom:1rem}.admonition{padding:12px 20px;margin-top:10px;margin-bottom:10px}.admonition p.last{margin:16px}.admonition .admonition-title{font-size:16px;font-weight:700;color:#555;text-transform:uppercase;margin-top:7px}.admonition.note{border-left:4px solid #00bcd4;background-color:rgba(0,188,212,.1)}.admonition.note .admonition-title{font-size:16px;font-weight:700;color:#00bcd4;margin-top:4px;margin-bottom:8px}.admonition.note .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"info_outline";font-size:18px}.admonition.seealso{border-left:4px solid #00bcd4;background-color:rgba(0,188,212,.1)}.admonition.seealso .admonition-title{font-size:16px;font-weight:700;color:#00bcd4;margin-top:4px;margin-bottom:8px}.admonition.seealso .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"search";font-size:18px}.admonition.hint{border-left:4px solid #00bcd4;background-color:rgba(0,188,212,.1)}.admonition.hint .admonition-title{font-size:16px;font-weight:700;color:#00bcd4;margin-top:4px;margin-bottom:8px}.admonition.hint .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"help_outline";font-size:18px}.admonition.warning{border-left:4px solid #ffc107;background-color:rgba(255,193,7,.1)}.admonition.warning .admonition-title{font-size:16px;font-weight:700;color:#ffc107;margin-top:4px;margin-bottom:8px}.admonition.warning .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"warning";font-size:18px}.admonition.attention{border-left:4px solid #ffc107;background-color:rgba(255,193,7,.1)}.admonition.attention .admonition-title{font-size:16px;font-weight:700;color:#ffc107;margin-top:4px;margin-bottom:8px}.admonition.attention .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"warning";font-size:18px}.admonition.tip{border-left:4px solid #8bc34a;background-color:rgba(139,195,74,.1)}.admonition.tip .admonition-title{font-size:16px;font-weight:700;color:#8bc34a;margin-top:4px;margin-bottom:8px}.admonition.tip .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"lightbulb_outline";font-size:18px}.admonition.important{border-left:4px solid #8bc34a;background-color:rgba(139,195,74,.1)}.admonition.important .admonition-title{font-size:16px;font-weight:700;color:#8bc34a;margin-top:4px;margin-bottom:8px}.admonition.important .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"check_circle";font-size:18px}.admonition.error{border-left:4px solid #f44336;background-color:rgba(244,67,54,.1)}.admonition.error .admonition-title{font-size:16px;font-weight:700;color:#f44336;margin-top:4px;margin-bottom:8px}.admonition.error .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"error_outline";font-size:18px}.admonition.caution{border-left:4px solid #f44336;background-color:rgba(244,67,54,.1)}.admonition.caution .admonition-title{font-size:16px;font-weight:700;color:#f44336;margin-top:4px;margin-bottom:8px}.admonition.caution .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"error_outline";font-size:18px}.admonition.danger{border-left:4px solid #f44336;background-color:rgba(244,67,54,.1)}.admonition.danger .admonition-title{font-size:16px;font-weight:700;color:#f44336;margin-top:4px;margin-bottom:8px}.admonition.danger .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"error_outline";font-size:18px}.page-content .highlight{margin:1px 0}.page-content .highlight pre{background:rgba(0,0,0,.05);color:rgba(0,0,0,.87);font-family:Menlo,DejaVu Sans Mono,Liberation Mono,Consolas,Ubuntu Mono,Courier New,andale mono,lucida console,monospace;padding:.75rem;overflow:auto;overflow-y:hidden}.page-content .highlight pre .nd,.page-content .highlight pre .o{color:rgba(0,0,0,.87)}.page-content div.highlight-console div.highlight{background:none}.page-content .output .highlight pre{color:rgba(0,0,0,.87);background:#fafafa;border:1px solid #999;padding:.75rem}.page-content .code,.page-content code:not(.download){margin:0;border-radius:2px}.page-content .code,.page-content .code span.pre,.page-content code:not(.download),.page-content code:not(.download) span.pre{font-family:Menlo,DejaVu Sans Mono,Liberation Mono,Consolas,Ubuntu Mono,Courier New,andale mono,lucida console,monospace}.page-content .viewcode-link{padding-left:2em;font-size:80%}.page-content .class>dt,.page-content .function>dt,.page-content .method>dt,.page-content .rubric{display:table;margin:10px 0;font-size:100%;line-height:normal;background:#e7f2fa;color:#2b98f0;border-top:3px solid #55adf3;padding:10px;position:relative}.page-content .class>dt .descclassname,.page-content .class>dt .descname,.page-content .function>dt .descclassname,.page-content .function>dt .descname,.page-content .method>dt .descclassname,.page-content .method>dt .descname,.page-content .rubric .descclassname,.page-content .rubric .descname{color:rgba(0,0,0,.87);background:#e7f2fa;padding:3px}.page-content .class>dt em,.page-content .function>dt em,.page-content .method>dt em,.page-content .rubric em{padding:0 2px}.page-content .rubric{margin:30px 0 10px}.page-content .field-body{padding-left:40px}.page-content .field-body ul{padding:0 0 0 16px;margin:0}.page-content .seealso .docutils>dt{float:left;clear:left;padding:0 6px}.page-content .seealso .docutils>dd{padding-left:6em}.page-content .nblast{padding-bottom:1em}.page-content pre{font-size:90%;background:#eee;color:#455a64;padding:16px 32px;width:auto;border-radius:4px;word-wrap:break-word}.page-content pre:hover:before{font-family:-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol;padding:0 .5rem;content:attr(click-to-copy);color:rgba(0,0,0,.5);border-radius:4px;position:relative;float:right;top:-.5rem;right:-.5rem;background:#c8c8c8;font-size:.8rem;cursor:pointer}.page-content blockquote{font-size:1rem;padding:0 1rem;border-left:3px solid rgba(0,0,0,.05)}.page-content blockquote:after{content:""!important;margin-left:0}.page-content blockquote:before{content:""!important}.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list){margin:1.5rem 0;table-layout:fixed;max-width:100%;min-width:70%}.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) td,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th{white-space:normal;overflow-wrap:break-word}.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) caption{font-size:16px;margin:1rem 0 .8rem;white-space:normal}.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) caption .caption-number{font-style:normal}.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) caption .caption-number:after{content:"\00a0"}.globaltoc .caption,.globaltoc .toc{display:none}.globaltoc ul{list-style-type:none;padding:0;margin:0}.globaltoc ul li{min-height:36px}.globaltoc ul li .link-wrapper{display:flex;justify-content:space-between}.globaltoc ul li .link-wrapper>a{padding:8px 0;display:block;width:100%;font-size:1rem;text-decoration:none;color:#757575}.globaltoc ul li .link-wrapper>a.current{font-weight:700}.globaltoc .nav-toggle{padding:0;float:right;display:flex;align-items:center;justify-content:center;height:36px}.globaltoc .nav-toggle>a{padding:0;margin-left:0;margin-right:4px;cursor:pointer}.globaltoc .nav-toggle>a>i{font-size:18px}.globaltoc .nav-toggle.show{transform:rotate(180deg)}.globaltoc .nav-toggle.show>a{margin-right:0;margin-left:4px}.globaltoc nav>ul>li>span.link-wrapper{padding-left:8px}.globaltoc nav>ul>li>ul>li>span.link-wrapper{padding-left:16px}.globaltoc nav>ul>li>ul>li>ul>li>span.link-wrapper{padding-left:24px}.globaltoc nav>ul>li>ul>li>ul>li>ul>li>span.link-wrapper{padding-left:32px}.globaltoc nav>ul>li>ul>li>ul>li>ul>li>ul>li>span.link-wrapper{padding-left:40px}.globaltoc nav>ul>li>ul>li>ul>li>ul>li>ul>li>ul>li>span.link-wrapper{padding-left:48px}.localtoc{font-size:.75rem;padding-top:1rem}.localtoc .caption{padding-left:12px}.localtoc .caption-text{font-size:.9rem;font-weight:700}.localtoc>ul>li>a{display:none}.localtoc ul{padding:0;list-style-type:none}.localtoc li{padding-left:6px}.localtoc a{display:block;text-decoration:none;color:inherit;margin-top:8px;padding-left:8px;line-height:1.1rem}.localtoc a.current{padding-left:5px;border-left:3px solid;font-weight:700}.contents.topic,.toctree-wrapper{border-left:5px solid}.contents.topic>p.topic-title,.toctree-wrapper>p.caption{color:#757575;font-size:1rem;padding-left:14px}.contents.topic ul,.toctree-wrapper ul{padding-left:14px;list-style:none;line-height:30px}.contents.topic a,.toctree-wrapper a{font-size:1.2rem;text-decoration:none}.contents.topic a .pre,.toctree-wrapper a .pre{font-size:1rem}.contents.topic>ul>li>a,.toctree-wrapper>ul>li>a{font-size:1.3rem}.contents.topic>ul>li>a .pre,.toctree-wrapper>ul>li>a .pre{font-size:1.1rem}.page-content ul li{margin:.3rem 0}.page-content ul li p{margin:0}.page-content .option-list .option{font-family:Menlo,DejaVu Sans Mono,Liberation Mono,Consolas,Ubuntu Mono,Courier New,andale mono,lucida console,monospace}.page-content .option-list td{padding:.5rem;border:none}.mdl-layout__drawer{background-color:#fff}.mdl-layout__drawer::-webkit-scrollbar{width:6px}.mdl-layout__drawer::-webkit-scrollbar-track{border-radius:6px}.mdl-layout__drawer::-webkit-scrollbar-thumb{background-color:rgba(0,0,0,.3);border-radius:6px;box-shadow:0 0 0 1px hsla(0,0%,100%,.3)}.mdl-layout__drawer>.mdl-layout-title{font-weight:700;text-align:right;margin:0;padding:0;line-height:32px;border-bottom:1px solid rgba(0,0,0,.1);min-height:64px}.mdl-layout__drawer>.mdl-layout-title .title{color:inherit;display:block;height:100%;width:100%;text-decoration:none}.mdl-layout__drawer>.mdl-layout-title .title>img.logo{width:100%;margin:0;padding:0}.mdl-layout__drawer>.mdl-layout-title .title-text{font-weight:700;text-align:right;padding:0 10px;margin:16px 0 8px;line-height:32px;font-family:-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol;color:inherit;display:block}nav.breadcrumb>a.mdl-navigation__link{padding:0 8px;font-size:18px}@media (max-width:1199px){nav.breadcrumb{width:calc(100% - 64px)}nav.breadcrumb a.mdl-navigation__link.is-active{overflow-x:hidden;width:100%;overflow:hidden;white-space:nowrap;text-overflow:ellipsis}.admonition.attention nav.breadcrumb i.admonition-title:before,.admonition.caution nav.breadcrumb i.admonition-title:before,.admonition.danger nav.breadcrumb i.admonition-title:before,.admonition.error nav.breadcrumb i.admonition-title:before,.admonition.hint nav.breadcrumb i.admonition-title:before,.admonition.important nav.breadcrumb i.admonition-title:before,.admonition.note nav.breadcrumb i.admonition-title:before,.admonition.seealso nav.breadcrumb i.admonition-title:before,.admonition.tip nav.breadcrumb i.admonition-title:before,.admonition.warning nav.breadcrumb i.admonition-title:before,nav.breadcrumb .admonition.attention i.admonition-title:before,nav.breadcrumb .admonition.caution i.admonition-title:before,nav.breadcrumb .admonition.danger i.admonition-title:before,nav.breadcrumb .admonition.error i.admonition-title:before,nav.breadcrumb .admonition.hint i.admonition-title:before,nav.breadcrumb .admonition.important i.admonition-title:before,nav.breadcrumb .admonition.note i.admonition-title:before,nav.breadcrumb .admonition.seealso i.admonition-title:before,nav.breadcrumb .admonition.tip i.admonition-title:before,nav.breadcrumb .admonition.warning i.admonition-title:before,nav.breadcrumb a.mdl-navigation__link:not(.is-active),nav.breadcrumb i.material-icons{display:none}}div.mdl-layout__header{margin-top:77px}.mdl-layout__drawer-button{top:13px!important}div.mdl-layout__header-row.header-links{background:hsla(0,0%,100%,.2);width:100%;overflow-x:auto;overflow-y:hidden}div.mdl-layout__header-row.header-links a.mdl-navigation__link{font-size:1rem}div.mdl-layout__header-row.header-links a.mdl-navigation__link i{font-size:1.2rem;margin:0 8px;position:relative;bottom:-.1rem}div.mdl-layout__header-row.header-links a.mdl-navigation__link:hover{background-color:#2196f3;color:#eee}div.mdl-layout__header-row.header-links a.mdl-navigation__link[href="#"]{background-color:#2196f3;opacity:1;color:#fff}.site-title{font-weight:300!important;line-height:57px;letter-spacing:-1px;margin-bottom:0;float:left;color:#fff}.site-title,.site-title:visited{color:#424242}.site-header{position:fixed;top:0;width:100%;min-height:55px;padding-top:10px;padding-bottom:10px;background-color:#048ccc;z-index:10;font-weight:300;font-size:17px;border-bottom:1px solid #fff}.site-header-logo{width:120px;display:initial}.site-nav{float:right;line-height:57px}.site-nav .menu-icon,.site-nav .nav-trigger{display:none}.site-nav .page-link{color:#fff;line-height:1.5;font-weight:300}.site-nav .page-link:not(:last-child){margin-right:40px}.site-nav .page-link:hover{color:#fff;text-shadow:-.06ex 0 #fff,.06ex 0 #fff}.site-nav .page-link.page-current{color:#fff;text-decoration:underline}@media screen and (max-width:1024px){.site-nav{position:absolute;top:9px;right:15px;background-color:#178dc9;border-radius:2px;text-align:right}.site-nav label[for=nav-trigger]{display:block;float:right;width:36px;height:36px;z-index:2;cursor:pointer}.site-nav .menu-icon{display:block;float:right;width:36px;height:26px;line-height:0;padding-top:20px;text-align:center}.site-nav .menu-icon>svg{fill:#fff}.site-nav input~.trigger{clear:both;display:none}.site-nav input:checked~.trigger{display:block;padding-bottom:5px}.site-nav .page-link{padding:5px 10px;display:block;margin-left:20px}.site-nav .page-link:not(:last-child){margin-right:0}}footer.mdl-mini-footer{background-color:#212121}footer.mdl-mini-footer>div.mdl-mini-footer__left-section{margin-bottom:20px;display:flex;flex-direction:column}footer.mdl-mini-footer>div.mdl-mini-footer__left-section .mdl-logo{font-size:1.1rem}footer.mdl-mini-footer>div.mdl-mini-footer__right-section{font-size:.9rem;display:flex;flex-direction:column;justify-content:flex-end}footer.mdl-mini-footer>div.mdl-mini-footer__right-section a{color:inherit;font-weight:700;text-decoration:none}footer.mdl-mini-footer p.caption{display:none}.pagenation{width:100%;margin-top:80px;height:92px;background-color:#424242;display:flex}.pagenation #button-next,.pagenation #button-prev,.pagenation .button-common{text-transform:none;padding:0;height:92px;display:flex;justify-content:center;align-items:center;color:#fff}.pagenation #button-prev{margin-right:auto}.pagenation #button-prev .pagenation-text{text-align:left}.pagenation #button-next{margin-left:auto;flex-direction:row-reverse}.pagenation #button-next .pagenation-text{text-align:right}.pagenation-arrow-L{margin-right:20px}.pagenation-arrow-R{margin-left:20px}.pagenation-text{line-height:30px;font-size:20px}.pagenation-direction{opacity:.7;font-size:18px}@media screen and (max-width:1024px){.pagenation #button-prev{width:20%}.pagenation #button-next{width:80%}.pagenation #button-prev .pagenation-text{display:none}}@media screen and (min-width:1025px){.pagenation #button-next,.pagenation #button-prev{width:50%}.pagenation #button-prev .pagenation-text{display:block}}.site-footer{border-top:1px solid #f5f5f5;padding:30px 0;background-color:#424242}.site-footer .footer-category-title{color:#048ccc}.site-footer a,.site-footer a:visited{color:#f5f5f5!important}.site-footer2{background-color:#424242;padding-top:40px;padding-bottom:10px}.footer-heading{margin-bottom:15px}.contact-list,.social-media-list{list-style:none;margin-left:0}.footer-bottom-warning{font-size:80%;color:#fff;float:left}.footer-logo{width:200px;margin-bottom:30px;margin-top:30px}.footer-col{float:left;margin-bottom:15px;padding-left:15px}.footer-text{color:#f5f5f5}#waterfall-exp::-webkit-input-placeholder{color:#ccc}#waterfall-exp:-ms-input-placeholder{color:#ccc}#waterfall-exp::-moz-placeholder{color:#ccc}ul.search span.highlighted{font-weight:700}ul.search>li{margin-bottom:24px}#search-results ul{list-style:none;padding:0}#search-results ul li>a{text-decoration:none;font-size:1.2rem}a.download:before{content:"file_download";position:relative;top:5px;margin-right:5px}button.download{position:sticky;margin-left:1em}.mdl-card{margin:1em 1.5em 1em 0;display:inline-block;width:250px;min-height:140px;padding:18px}.mdl-card:hover{box-shadow:0 10px 20px rgba(0,0,0,.25),0 6px 6px rgba(0,0,0,.22);color:#000;cursor:pointer}.mdl-card__title{padding:0 0 1em;font-size:18px;color:#444}.mdl-card__supporting-text{line-height:1.5rem;padding:0;width:100%}.head-card.mdl-card{width:auto;display:block;max-width:800px;padding:24px}.head-card>.mdl-card__title{padding-bottom:0;height:60px;font-weight:700;text-transform:uppercase}.head-card>.mdl-card__menu{color:#fff}.head-card>.mdl-card__actions{padding:0}.cards{display:flex;flex-direction:row;flex-wrap:wrap}
+/*# sourceMappingURL=/sphinx_materialdesign_theme.css.map */
\ No newline at end of file
diff --git a/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.css.map b/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.css.map
new file mode 100644
index 000000000000..270e9379f5cc
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.css.map
@@ -0,0 +1 @@
+{"version":3,"sources":["../../node_modules/material-design-lite/src/shadow/_shadow.scss","../../node_modules/material-design-lite/src/_mixins.scss","../../node_modules/material-design-lite/src/data-table/_data-table.scss","../../node_modules/material-design-lite/src/_variables.scss","../../node_modules/material-design-lite/src/footer/_mini_footer.scss","../../node_modules/material-design-lite/src/card/_card.scss","../../node_modules/material-design-lite/src/button/_button.scss","../scss/grid/_simplegrid.scss","../scss/fonts/_material-icons.scss","../scss/_root.scss","../scss/_variables.scss","../scss/layout/_layout.scss","../scss/headerings/_headerings.scss","../scss/admonitions/_admonitions.scss","../scss/code/_code.scss","../scss/blockquote/_blockquote.scss","../scss/tables/_tables.scss","../scss/toc/_globaltoc.scss","../scss/toc/_localtoc.scss","../scss/toc/_toctree.scss","../scss/lists/_lists.scss","../scss/drawer/_drawer.scss","../scss/header/_header.scss","../scss/footer/_footer.scss","../scss/search/_search.scss","../scss/downloadlink/_downloadlink.scss","../scss/card/_card.scss"],"names":[],"mappings":"AAmBA,wJCoNE,gGAEqE,CDlNvE,iBCqNE,gGAEqE,CDnNvE,iBCsNE,iGAEmE,CDpNrE,iBCuNE,kGAEmE,CDrNrE,iBCwNE,sGAEmE,CDtNrE,kBC0NE,wGAEqE,CDxNvE,kBC4NE,yGAEqE,CCtPvE,mHACE,iBAAkB,CAClB,gCCohBkC,CDnhBlC,wBAAyB,CACzB,kBAAmB,CACnB,cC0gByB,CDzgBzB,qBAAiD,CANnD,+HASI,kBAAmB,CATvB,+KAYM,YAAa,CAZnB,qIAkBM,iBAAkB,CAClB,WC0gBsB,CFlR1B,wBCvP6C,CDwP7C,kDEkN6D,CDzczD,oCAAqC,CArB3C,6JAwBQ,wBCigB4B,CDzhBpC,iJA4BQ,qBC4fwB,CDxhBhC,kPAkCI,mBCggBsD,CD/ftD,gBAAiB,CAnCrB,0SAsCM,iBAAkB,CAtCxB,sSA0CM,kBAAmB,CA1CzB,yHA+CI,iBAAkB,CAClB,qBAAsB,CACtB,WC4ewB,CD3exB,oCCoegC,CDnehC,uCCmegC,CDlehC,gBCof8C,CDnf9C,qBAAsB,CArD1B,yKAwDM,qBAAsB,CAxD5B,yHA6DI,iBAAkB,CAClB,qBAAsB,CACtB,sBAAuB,CDsCzB,cAAe,CAIb,eAAiB,CAEnB,gBAAiB,CACjB,gBAAiB,CC3Cf,WC4dwB,CD3dxB,cC8c8B,CD7c9B,qBCgd+B,CD/c/B,kBAAmB,CACnB,qBAAsB,CArE1B,wZAyEM,qBC2coC,CDphB1C,obD8LE,0BAA6B,CAC7B,eAAmB,CACnB,iBAAkB,CAClB,cAAe,CACf,aAAc,CACd,qBAAsB,CACtB,mBAAoB,CACpB,oBAAqB,CACrB,gBAAiB,CACjB,4BAA6B,CAC7B,oCAAqC,CACrC,kCAAmC,CC7H7B,cCqc+B,CDpc/B,eAAgB,CAChB,gBAAiB,CACjB,kBAAmB,CA/E3B,gbAkFQ,cAAe,CAlFvB,4cAoFU,qBCic2C,CDrhBrD,2NAyFM,eAAgB,CAKtB,wBACE,UAAW,CAGb,iRACE,eAAgB,CEpGlB,iBACE,YAAa,CACb,kBAAmB,CACnB,6BAA8B,CAE9B,iBDoZY,CClZZ,aDwRiD,CCvRjD,wBDsRoD,CC9RtD,uBAWI,UAAW,CACX,aAAc,CAZlB,2BAgBI,gBDsYkB,CClYtB,oHAEE,YAAa,CACb,oBAAqB,CAErB,eAAgB,CAEhB,QAAS,CACT,SAAU,CARZ,6HAWI,eAAgB,CAChB,iBDyXU,CCvXV,oCAdJ,6HAeM,gBDmXgB,CCjXnB,CAjBH,0HAoBI,aAAc,CACd,oBAAqB,CACrB,kBAAmB,CAIvB,8DAEE,oBAAqB,CACrB,OAAQ,CAGV,gEAEE,oBAAqB,CACrB,OAAQ,CAGV,0DAEE,UD0VoB,CCzVpB,WDyVoB,CCvVpB,SAAU,CACV,QAAS,CAET,wBD6NiD,CC3NjD,WAAY,CCpEd,UACE,YAAa,CACb,qBAAsB,CACtB,cF2amB,CE1anB,eAAgB,CAChB,gBFwaiB,CEvajB,eAAgB,CAChB,WFqagB,CEpahB,SF2bc,CE1bd,iBAAkB,CAClB,eFiOqD,CEhOrD,iBAAkB,CAClB,qBAAsB,CAGxB,iBACE,wBF6N6D,CE5N7D,wBAAyB,CACzB,2BAA4B,CAC5B,qBAAsB,CACtB,6BAA8B,CAC9B,4BAA6B,CAC7B,qBAAsB,CAGxB,iBACE,kBAAmB,CACnB,UFiN+C,CEhN/C,aAAc,CACd,YAAa,CACb,uBAAwB,CACxB,kBAAmB,CACnB,YFiZ4B,CEhZ5B,6BFoZoC,CEnZpC,2BFsZkC,CErZlC,qBAAsB,CAVxB,kCAaI,sCFyM+B,CErMnC,sBACE,mBAAoB,CACpB,aAAc,CACd,aAAc,CACd,YAAa,CACb,cFgYyB,CE/XzB,eFkZ+B,CEjZ/B,kBAAmB,CACnB,eAAgB,CAChB,2BFwYuC,CEvYvC,QAAS,CAGX,yBACE,cFwX4B,CEvX5B,qBFuL0D,CEtL1D,QAAS,CAGX,2BACE,qBFgLsE,CE/KtE,cF8XmC,CE7XnC,gBF8XqC,CE7XrC,eAAgB,CAChB,YF+W4B,CE9W5B,SAAU,CANZ,4CASI,sCFyK+B,CErKnC,mBACE,cFqX2B,CEpX3B,kBAAmB,CACnB,UAAW,CACX,4BAA+B,CAC/B,WAAY,CACZ,qBAAsB,CANxB,oCASI,mCF4J+B,CExJnC,kBACE,WAAY,CAId,gBACE,iBAAkB,CAClB,UAAW,CACX,QAAS,CC7FX,YACE,sBAAuB,CACvB,WAAY,CACZ,iBH+cwB,CG9cxB,UHgHsD,CG/GtD,iBAAkB,CAClB,WHyckB,CGxclB,QAAS,CACT,cHscqB,CGrcrB,cHucmB,CGtcnB,oBAAqB,CLVnB,6CE8CuD,CFmIzD,cAAe,CACf,eAAgB,CAChB,wBAAyB,CACzB,aAAc,CACd,gBAAiB,CKzKjB,eAAgB,CAChB,sBAAuB,CACvB,+HH+c6D,CG5c7D,YAAa,CACb,cAAe,CACf,oBAAqB,CACrB,iBAAkB,CAClB,gBH0bkB,CGzblB,qBAAsB,CAtBxB,8BAyBI,QAAS,CAzBb,kBA6BI,kCHsF8D,CGnHlE,+BAiCI,gCHsFuD,CGvH3D,mBAqCI,kCHiF6D,CGtHjE,gCAyCI,aHiFwD,CG1H5D,mDA4CM,gCH2EqD,CGtE3D,8BACE,uBAAuB,CAIvB,oBACE,4BH4D8D,CFgGhE,gGAEqE,CK/JrE,2BLuKA,iGAEmE,CKnK/D,kCH0D2D,CGhE/D,uCLyJA,6DAA8D,CK9I1D,kCHqD2D,CGhE/D,wCAeI,kBHqDsD,CGpDtD,UHqDiE,CGrErE,wJA2BM,wBH4CmD,CGvEzD,oDA+BM,eH4C4D,CGrClE,iBACE,iBAAkB,CAClB,cHwXuB,CGvXvB,WHqXkB,CGpXlB,WAAY,CACZ,cHmXkB,CGlXlB,UHkXkB,CGjXlB,SAAU,CACV,eAAgB,CAChB,4BHc8D,CGb9D,oEAAwE,CACxE,iBAAkB,CAClB,kBAAmB,CAZrB,0wCAeI,iBAAkB,CAClB,OAAQ,CACR,QAAS,CACT,gCAA8E,CAC9E,gBHuWqB,CGtWrB,UHsWqB,CG1XzB,sCAwBI,WHiWqB,CGhWrB,cHgWqB,CG/VrB,UH+VqB,CGzXzB,+CA8BI,iBAAkB,CAElB,4DAAiE,CAhCrE,wBLiIA,iGAEmE,CK9F/D,kCHX2D,CG1B/D,oCLmHA,6DAA8D,CKzE1D,kCHhB2D,CG1B/D,qCA8CI,kBHFiD,CGGjD,UHA+D,CG/CnE,+IA0DM,wBHZsD,CG9C5D,iDA8DM,eHd+D,CGqBrE,kBACE,iBAAkB,CAClB,cHmTuB,CGlTvB,WHoTmB,CGnTnB,aAAc,CACd,cAAe,CACf,cHiTmB,CGhTnB,UHgTmB,CG/SnB,SAAU,CACV,eAAgB,CAChB,aAAc,CACd,kBAAmB,CAXrB,gyCAcI,iBAAkB,CAClB,OAAQ,CACR,QAAS,CACT,gCAA8E,CAC9E,gBHmSqB,CGlSrB,UHkSqB,CGrTzB,wCAuBI,WHiSsB,CGhStB,cHgSsB,CG/RtB,UH+RsB,CGxT1B,owDA4BM,KAAyD,CACzD,MAA0D,CA7BhE,gDAkCI,iBAAkB,CAElB,4DAAiE,CAMrE,8BACE,aAAc,CACd,WAAY,CACZ,MAAS,CACT,iBAAkB,CAClB,KAAQ,CACR,UAAW,CACX,SAAU,CACV,eAAgB,CAEhB,2IAEE,4BAA6B,CAMnC,yCACE,aHpG0D,CGmG5D,qDAGI,eHrGmE,CGkGvE,qHAMI,UHxGmE,CGyGnE,wBH1GwD,CG8G5D,uCACE,aHjGqD,CGgGvD,mDAGI,eHhGiE,CG6FrE,iHAMI,UHnGiE,CGoGjE,wBHvGmD,CG6GvD,sFAII,qBHpHoE,CGqHpE,cAAe,CACf,4BAA6B,CAG9B,gGAIG,gCH9HgE,CG+HhE,qBH9HkE,CGkIrE,sGAIG,gCHvIgE,CGwIhE,qBHvIkE,CGwIlE,eAAgB,CAGnB,wGAIG,qBH/IkE,CGqJxE,4pCACE,qBAAsB,CClSxB,YACE,eAVqB,CAavB,cACE,eAbuB,CAgBzB,YACE,eAhBqB,CAqBvB,MACE,eAAgB,CAGlB,OACE,gBAAiB,CAGnB,QACE,iBAAkB,CAClB,gBAAiB,CACjB,iBAAkB,CAGpB,SACE,kBAAmB,CAGrB,WACE,YAAa,CAWf,WACE,UAAW,CACX,gBAAiB,CACjB,iBAAkB,CAElB,2CALF,WAMI,SAAU,CAOb,CAJC,wCATF,WAUI,SAAU,CACV,eAAgB,CAEnB,CAED,KACE,iBAAkB,CAClB,UAAW,CAGb,kBACE,UAAW,CACX,eAAiB,CACjB,kBAAoB,CAGtB,WACE,UAAW,CACX,aAAc,CACd,UAAW,CAGb,uFAYE,SAlDS,CAqDX,UACE,cAA0C,CAG5C,UACE,eAAyC,CAG3C,UACE,SAAwC,CAG1C,UACE,eAAwC,CAG1C,UACE,eAA+C,CAGjD,UACE,SAAwC,CAG1C,UACE,eAA+C,CAGjD,UACE,eAA+C,CAGjD,UACE,SAA+C,CAGjD,WACE,eAAgD,CAGlD,WACE,eAAgD,CAGlD,WACE,SAlGS,CAqGX,wCACE,OACE,cAA0C,CAE5C,OACE,eAAyC,CAE3C,OACE,SAAwC,CAE1C,OACE,eAAwC,CAE1C,OACE,eAA+C,CAEjD,OACE,SAAwC,CAE1C,OACE,eAA+C,CAEjD,OACE,eAA+C,CAEjD,OACE,SAA+C,CAEjD,QACE,eAAgD,CAElD,QACE,eAAgD,CAElD,QACE,SAxIO,CANX,WAkJI,aAAc,CACf,CAxHH,KA4HE,mBAAoB,CACpB,oBAAqB,CACrB,mBAAoB,CACpB,YAAa,CACb,cAAe,CAGjB,mBACE,YAAa,CACb,qBAAsB,CCxMxB,2dACI,0BAA6B,CAC7B,eAAmB,CACnB,iBAAkB,CAClB,cAAe,CACf,oBAAqB,CACrB,aAAc,CACd,mBAAoB,CACpB,qBAAsB,CACtB,gBAAiB,CACjB,kBAAmB,CACnB,aAAc,CAGd,kCAAmC,CAEnC,iCAAkC,CAGlC,iCAAkC,CAGlC,4BAA6B,CC3BjC,KACI,cCEY,CDChB,KACI,uBAAyB,CACzB,wBCDsB,CDEtB,cAAe,CACf,kBAAmB,CACnB,6ICA0J,CDG9J,2BACI,YAAa,CAGjB,4EAEI,6ICT0J,CDY9J,8GACI,uBAA8B,CAGlC,EACI,oBAAqB,CAGzB,yKAGQ,cAAe,CAIvB,OACI,aAAc,CACd,oBAAqB,CAGzB,SACI,eAAgB,CAOnB,IACG,cAAe,CACf,aAAc,CACd,gBAAiB,CACjB,iBAAkB,CAGtB,qBAEQ,iBAAkB,CAClB,iBAAkB,CAH1B,yCAMY,iBAAkB,CAN9B,2CASY,eAAgB,CAK5B,UACE,UAAW,CACX,WAAY,CACZ,oBAAqB,CACrB,YC3C0C,CD4C1C,iBAAkB,CAClB,eAAgB,CAChB,uBAAwB,CAM1B,6kBACI,iBAAkB,CAClB,OAAQ,CAGZ,WACI,oBAAqB,CAGzB,eACE,UAAW,CACX,aAAc,CACd,UAAW,CAGb,SAEE,gBAA2D,CAC3D,iBAAkB,CAClB,gBAAiB,CACjB,kBAA0C,CAC1C,iBC9EqB,CDiFrB,qCATF,SAWI,gBAAuD,CACvD,kBAAgC,CAChC,iBAA+B,CAElC,CEtFD,uBACI,eAAgB,CAChB,wBAAyB,CAI7B,UACI,UAAW,CACX,aAAc,CACd,YAAa,CAEb,0BALJ,UAMQ,YAjCiB,CA8ExB,CAnDD,wBASQ,UAAW,CACX,aAAc,CACd,cAAe,CAEf,yBAbR,wBAcY,SA9BU,CA+BV,YA9Ba,CAqCpB,CAJG,0BAlBR,wBAmBY,uBA/B0B,CAgC1B,YA/Ba,CAiCpB,CAtBL,4BAyBQ,WA7CY,CA+CZ,0BA3BR,4BA4BY,YAAa,CAqBpB,CAjDL,qCA+BY,cAAe,CACf,eAAgB,CAChB,eAAgB,CAChB,aAAc,CAlC1B,wDAoCgB,SAAU,CApC1B,8DAwCgB,iBAAkB,CAxClC,8DA4CgB,+BAAmC,CACnC,iBAAkB,CAClB,uCAA4C,CC/E5D,oBACI,GACI,2BAA6B,CAC7B,SAAU,CAEjB,GACC,uBAAwB,CACxB,SAAU,CAAA,CAIZ,qBACI,GACI,uBAAwB,CACxB,SAAU,CAEjB,GACC,2BAA6B,CAC7B,SAAU,CAAA,CAIZ,0BAEQ,oBAAqB,CACrB,oBAAqB,CACrB,iBAAmB,CACnB,aAAc,CACd,SAAU,CANlB,gCAQY,0DAAsE,CARlF,oLAcY,oBAAqB,CAdjC,kNAkBgB,0DAAsE,CAlBtF,iBAwBQ,cAAe,CACf,mBAAoB,CAzB5B,iBA6BQ,iBAAkB,CAClB,gBAAiB,CACjB,kBAAmB,CACnB,YAAa,CACb,kBAAmB,CAjC3B,iBAqCQ,gBAAiB,CACjB,mBAAoB,CACpB,gBAAiB,CACjB,YAAe,CACf,oBAAqB,CAzC7B,iBA6CQ,iBAAkB,CAClB,kBAAmB,CACnB,kBAAmB,CACnB,YAAe,CACf,mBAAoB,CAjD5B,kCAqDQ,gBAAiB,CACjB,kBAAmB,CACnB,gBAAiB,CACjB,YAAe,CACf,kBAAmB,CAzD3B,kCA6DQ,cAAe,CACf,kBAAmB,CACnB,gBAAiB,CACjB,YAAe,CACf,kBAAmB,CCT3B,YAGI,iBAAkB,CAClB,eAAgB,CAChB,kBAAmB,CALvB,mBAOQ,WAAY,CAPpB,8BAUQ,cAAe,CACf,eAAiB,CACjB,UAAW,CACX,wBAAyB,CACzB,cAAe,CAdvB,iBApBI,6BA/CgC,CAgDhC,mCA/C4C,CAgD5C,mCACI,cAAe,CACf,eAAiB,CACjB,aApD4B,CAsD5B,cAAe,CACf,iBAAkB,CAClB,0CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,sBA3DwB,CA4DxB,cAAe,CAK3B,oBApBI,6BA1CgC,CA2ChC,mCA1C4C,CA2C5C,sCACI,cAAe,CACf,eAAiB,CACjB,aA/C4B,CAiD5B,cAAe,CACf,iBAAkB,CAClB,6CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,gBAtDkB,CAuDlB,cAAe,CAK3B,iBApBI,6BApDgC,CAqDhC,mCApD4C,CAqD5C,mCACI,cAAe,CACf,eAAiB,CACjB,aAzD4B,CA2D5B,cAAe,CACf,iBAAkB,CAClB,0CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,sBAhEwB,CAiExB,cAAe,CAK3B,oBApBI,6BArCgC,CAsChC,mCArC4C,CAsC5C,sCACI,cAAe,CACf,eAAiB,CACjB,aA1C4B,CA4C5B,cAAe,CACf,iBAAkB,CAClB,6CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,iBAjDmB,CAkDnB,cAAe,CAK3B,sBApBI,6BAhCgC,CAiChC,mCAhC4C,CAiC5C,wCACI,cAAe,CACf,eAAiB,CACjB,aArC4B,CAuC5B,cAAe,CACf,iBAAkB,CAClB,+CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,iBA5CmB,CA6CnB,cAAe,CAK3B,gBApBI,6BA3BiC,CA4BjC,oCA3B6C,CA4B7C,kCACI,cAAe,CACf,eAAiB,CACjB,aAhC6B,CAkC7B,cAAe,CACf,iBAAkB,CAClB,yCAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,2BAvC6B,CAwC7B,cAAe,CAK3B,sBApBI,6BAtBiC,CAuBjC,oCAtB6C,CAuB7C,wCACI,cAAe,CACf,eAAiB,CACjB,aA3B6B,CA6B7B,cAAe,CACf,iBAAkB,CAClB,+CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,sBAlCyB,CAmCzB,cAAe,CAK3B,kBApBI,6BAjBgC,CAkBhC,mCAjB4C,CAkB5C,oCACI,cAAe,CACf,eAAiB,CACjB,aAtB4B,CAwB5B,cAAe,CACf,iBAAkB,CAClB,2CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,uBA7ByB,CA8BzB,cAAe,CAK3B,oBApBI,6BAZgC,CAahC,mCAZ4C,CAa5C,sCACI,cAAe,CACf,eAAiB,CACjB,aAjB4B,CAmB5B,cAAe,CACf,iBAAkB,CAClB,6CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,uBAxByB,CAyBzB,cAAe,CAK3B,mBApBI,6BAPgC,CAQhC,mCAP4C,CAQ5C,qCACI,cAAe,CACf,eAAiB,CACjB,aAZ4B,CAc5B,cAAe,CACf,iBAAkB,CAClB,4CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,uBAnByB,CAoBzB,cAAe,CCzE3B,yBAEQ,YAAa,CAFrB,6BAIY,0BJEqB,CIDrB,qBAAsB,CACtB,wHJE2I,CID3I,cAAgB,CAChB,aAAc,CACd,iBAAkB,CAT9B,iEAWgB,qBAAsB,CAXtC,kDAiBQ,eAAgB,CAjBxB,qCAwBgB,qBAAsB,CACtB,kBJpBU,CIuBV,qBAAmB,CACnB,cAAgB,CA7BhC,sDAmCQ,QAAW,CAEX,iBAAkB,CArC1B,8HAoCQ,wHJ5B+I,CIRvJ,6BA4CQ,gBAAiB,CACjB,aAAc,CA7CtB,kGAiDQ,aAAc,CACd,aAAc,CACd,cAAe,CACf,kBAAmB,CACnB,kBAAmB,CACnB,aAAc,CACd,4BAA6B,CAC7B,YAAa,CACb,iBAAkB,CAzD1B,wSA2DY,qBAAsB,CACtB,kBAAmB,CACnB,WAAY,CA7DxB,8GAgEY,aAAc,CAhE1B,sBAsEQ,kBAAqB,CAtE7B,0BA2EQ,iBAAkB,CA3E1B,6BA6EY,kBAAmB,CACnB,QAAS,CA9ErB,oCA6FO,UAAW,CACX,UAAW,CACX,aAAc,CA/FrB,oCAmGO,gBAAiB,CAnGxB,sBAsGQ,kBAAmB,CAtG3B,kBA0GQ,aAAc,CACd,eAAgB,CAChB,aAAc,CACd,iBAAkB,CAClB,UAAW,CACX,iBAAkB,CAClB,oBAAqB,CAhH7B,+BAsHgB,6IJ7G8I,CI8G9I,eAAiB,CACjB,2BAA4B,CAC5B,oBAAyB,CACzB,iBAAkB,CAClB,iBAAkB,CAClB,WAAY,CACZ,UAAY,CACZ,YAAc,CACd,kBAA8B,CAC9B,eAAiB,CACjB,cAAe,CC9H9B,yBAEO,cAAe,CACf,cAAe,CACf,qCLDyB,CKHhC,+BAOW,oBAAsB,CACtB,aAAc,CARzB,gCAWW,oBAAsB,CCdlC,mGAKQ,eAAgB,CAChB,kBAAmB,CACnB,cAAe,CACf,aAAc,CARtB,4MAYY,kBAAmB,CACnB,wBAAyB,CAbrC,2GAiBY,cNdI,CMeJ,mBAAuB,CACvB,kBAAmB,CAnB/B,2HAqBgB,iBAAkB,CArBlC,iIAwBgB,eAAgB,CCxBhC,oCAGQ,YAAa,CAHrB,cAQQ,oBAAqB,CACrB,SAAU,CACV,QAAS,CAVjB,iBAaY,eAAgB,CAb5B,+BAegB,YAAa,CACb,6BAA8B,CAhB9C,iCAkBoB,aAAc,CACd,aAAc,CACd,UAAW,CACX,cAAe,CACf,oBAAqB,CACrB,adyKoB,CchMxC,yCAyBwB,eAAiB,CAzBzC,uBAiCQ,SAAU,CACV,WAAY,CACZ,YAAa,CACb,kBAAmB,CACnB,sBAAuB,CACvB,WAAY,CAtCpB,yBAwCY,SAAU,CACV,aAAc,CACd,gBAAiB,CACjB,cAAe,CA3C3B,2BA6CgB,cAAe,CA7C/B,4BAiDY,wBAA0B,CAjDtC,8BAmDgB,cAAe,CACf,eAAgB,CApDhC,uCA2DY,gBAAiB,CA3D7B,6CA8DY,iBAAkB,CA9D9B,mDAiEY,iBAAkB,CAjE9B,yDAoEY,iBAAkB,CApE9B,+DAuEY,iBAAkB,CAvE9B,qEA0EY,iBAAkB,CC1E9B,UACI,gBAAkB,CAClB,gBAAiB,CAFrB,mBAKQ,iBAAkB,CAL1B,wBAOY,eAAiB,CACjB,eAAgB,CAR5B,kBAaQ,YAAa,CAbrB,aAiBQ,SAAU,CACV,oBAAqB,CAlB7B,aAsBQ,gBAAiB,CAtBzB,YA0BQ,aAAc,CACd,oBAAqB,CACrB,aAAc,CACd,cAAe,CACf,gBAAiB,CACjB,kBAAmB,CA/B3B,oBAkCY,gBAAiB,CACjB,qBAAsB,CACtB,eAAiB,CCjC5B,iCAEI,qBAAsB,CAG1B,yDAEI,aAAyB,CACzB,cAAe,CACf,iBAAkB,CAGtB,uCAEI,iBAAkB,CAClB,eAAgB,CAChB,gBAAiB,CAGrB,qCAEI,gBAAiB,CACjB,oBAAqB,CAHzB,+CAKQ,cAAe,CAIvB,iDAEI,gBAAiB,CAFrB,2DAIQ,gBAAiB,CCnC1B,oBAGY,cAAe,CAH3B,sBAKgB,QAAS,CALzB,mCAWY,wHVH2I,CURvJ,8BAcY,aAAe,CACf,WAAY,CCXpB,oBACI,qBAAsB,CADzB,uCAIO,SAAU,CAJjB,6CAQO,iBAAkB,CARzB,6CAYO,+BAAmC,CACnC,iBAAkB,CAClB,uCAA4C,CAdnD,sCAkBO,eAAiB,CACjB,gBAAiB,CACjB,QAAS,CACT,SAAU,CACV,gBAAiB,CACjB,sCAAuC,CACvC,eAAgB,CAxBvB,6CA0BW,aAAc,CACd,aAAc,CACd,WAAY,CACZ,UAAW,CACX,oBAAqB,CA9BhC,sDAgCe,UAAW,CACX,QAAS,CACT,SAAU,CAlCzB,kDAsCe,eAAiB,CACjB,gBAAiB,CACjB,cAAe,CACf,iBAAoB,CACpB,gBAAiB,CACjB,6IXtC0I,CWuC1I,aAAc,CACd,aAAc,CC7ClC,sCAEQ,aAAc,CACd,cAAe,CAEnB,0BALJ,eAMQ,uBAA0B,CANlC,gDAQY,iBAAkB,CAClB,UAAW,CACX,eAAgB,CAChB,kBAAmB,CACnB,sBAAuB,CAZnC,wwCAgBY,YAAa,CAChB,CAIT,uBACI,eAAgB,CAGpB,2BACI,kBAAoB,CAGxB,wCACI,6BAAiC,CACjC,UAAW,CACX,eAAgB,CAChB,iBAAkB,CAJtB,+DAOQ,cAAe,CAPvB,iEASY,gBAAiB,CACjB,YAAa,CACb,iBAAkB,CAClB,aAAe,CAZ3B,qEAiBQ,wBAAmD,CACnD,UAAc,CAlBtB,yEAqBQ,wBAAmD,CACnD,SAAU,CACV,UAAc,CAOtB,YACE,yBAA2B,CAC3B,gBAAiB,CACjB,mBAAoB,CACpB,eAAgB,CAChB,UAAW,CACX,UAAY,CANd,gCAUI,aZzCuC,CY8C3C,aACE,cAAe,CACf,KAAM,CACN,UAAW,CACX,eAAgB,CAChB,gBAAiB,CACjB,mBAAoB,CACpB,wBZzD0B,CY0D1B,UAAW,CACX,eAAgB,CAChB,cAAe,CACf,4BAA8B,CAGhC,kBACE,WAAY,CACZ,eAAgB,CAGlB,UACE,WAAY,CACZ,gBAAiB,CAFnB,4CASI,YAAa,CATjB,qBAaI,UAAY,CACZ,eAAgB,CAChB,eAAgB,CAfpB,sCAkBM,iBAAkB,CAlBxB,2BAsBM,UAAY,CACZ,sCAA4C,CAvBlD,kCA4BI,UAAY,CACZ,yBAA0B,CAG5B,qCAhCF,UAiCI,iBAAkB,CAClB,OAAQ,CACR,UAAW,CACX,wBAAiC,CACjC,iBAAkB,CAClB,gBAAiB,CAtCrB,iCAyCM,aAAc,CACd,WAAY,CACZ,UAAW,CACX,WAAY,CACZ,SAAU,CACV,cAAe,CA9CrB,qBAkDM,aAAc,CACd,WAAY,CACZ,UAAW,CACX,WAAY,CACZ,aAAc,CACd,gBAAiB,CACjB,iBAAkB,CAxDxB,yBA2DQ,SAAW,CA3DnB,yBAgEM,UAAW,CACX,YAAa,CAjEnB,iCAqEM,aAAc,CACd,kBAAmB,CAtEzB,qBA0EM,gBAAiB,CACjB,aAAc,CAMd,gBAAiB,CAjFvB,sCA8EQ,cAAe,CAChB,CC7KP,uBACI,wBAAyB,CAD7B,yDAGQ,kBAAmB,CACnB,YAAa,CACb,qBAAsB,CAL9B,mEAOY,gBAAiB,CAP7B,0DAcQ,eAAiB,CACjB,YAAa,CACb,qBAAsB,CACtB,wBAAyB,CAjBjC,4DAoBY,aAAc,CACd,eAAiB,CACjB,oBAAqB,CAtBjC,iCA0BQ,YAAa,CAOpB,YACG,UAAW,CACX,eAAgB,CAChB,WAAY,CACZ,wBAAyB,CACzB,YAAa,CALhB,6EAQO,mBAAoB,CACpB,SAAU,CACV,WAAY,CACZ,YAAa,CACb,sBAAuB,CACvB,kBAAmB,CACnB,UAAc,CAdrB,yBAkBO,iBAAkB,CAlBzB,0CAoBW,eAAgB,CApB3B,yBA0BO,gBAAiB,CACjB,0BAA2B,CA3BlC,0CA6BW,gBAAiB,CAKrB,oBACI,iBAAkB,CAEtB,oBACI,gBAAiB,CAIzB,iBACI,gBAAiB,CACjB,cAAe,CAGnB,sBACI,UAAY,CACZ,cAAe,CAEnB,qCAnDH,yBAqDW,SAAU,CArDrB,yBAyDW,SAAU,CAzDrB,0CA6DW,YAAa,CAChB,CAEL,qCAhEH,kDAmEW,SAAU,CAnErB,0CAuEW,aAAc,CACjB,CAST,aACE,4BbvF0C,CawF1C,cAAwB,CACxB,wBAAyB,CAH3B,oCAKI,ab9FwB,CayF5B,sCAWM,uBAAmC,CAMzC,cACE,wBAAyB,CACzB,gBAAiB,CACjB,mBAAoB,CAGtB,gBACE,kBAAgC,CAGlC,iCAEE,eAAgB,CAChB,aAAc,CAIhB,uBACE,aAAc,CACd,UAAY,CACZ,UAAW,CAGb,aACE,WAAY,CACZ,kBAAmB,CACnB,eAAgB,CAGlB,YACE,UAAW,CACX,kBAAgC,CAChC,iBAA+B,CAGjC,aACE,ab3I0C,Cc5B5C,0CACI,UAAW,CAEf,qCACI,UAAW,CAEf,iCACI,UAAW,CAGf,2BACI,eAAiB,CAGrB,aACI,kBAAmB,CAGvB,mBAEQ,eAAgB,CAChB,SAAU,CAHlB,wBAMgB,oBAAqB,CACrB,gBAAiB,CC5BjC,kBAGQ,uBAAwB,CACxB,iBAAkB,CAClB,OAAQ,CACR,gBAAiB,CAIzB,gBACI,eAAgB,CAChB,eAAgB,CpBMpB,UqBjBI,sBAAuB,CACvB,oBAAqB,CACrB,WAAY,CACZ,gBAAiB,CACjB,YAAa,CAEjB,gBACI,gEAAoE,CACpE,UAAW,CACX,cAAe,CrBiCnB,iBqB9BI,eAAkB,CAClB,cAAe,CACf,UAAW,CrBgEf,2BqB5DI,kBAAmB,CACnB,SAAY,CACZ,UAAW,CAGf,oBACI,UAAW,CACX,aAAc,CACd,eAAgB,CAChB,YAAa,CAGjB,4BACI,gBAAmB,CACnB,WAAY,CACZ,eAAgB,CAChB,wBAAyB,CAE7B,2BACI,UAAW,CAEf,8BACI,SAAU,CAEd,OACI,YAAa,CACb,kBAAmB,CACnB,cAAe","file":"sphinx_materialdesign_theme.css","sourceRoot":"../../src/js","sourcesContent":["/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n@import \"../variables\";\n@import \"../mixins\";\n\n.mdl-shadow--2dp {\n  @include shadow-2dp();\n}\n\n.mdl-shadow--3dp {\n  @include shadow-3dp();\n}\n\n.mdl-shadow--4dp {\n  @include shadow-4dp();\n}\n\n.mdl-shadow--6dp {\n  @include shadow-6dp();\n}\n\n.mdl-shadow--8dp {\n  @include shadow-8dp();\n}\n\n.mdl-shadow--16dp {\n  @include shadow-16dp();\n}\n\n.mdl-shadow--24dp {\n  @include shadow-24dp();\n}\n","/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n/* Typography */\n\n@mixin typo-preferred-font($usePreferred: true) {\n  @if $usePreferred {\n    font-family: $preferred_font;\n  }\n}\n\n@mixin typo-display-4($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 112px;\n  font-weight: 300;\n  line-height: 1;\n  letter-spacing: -0.04em;\n\n  @if $colorContrast {\n    opacity: 0.54;\n  }\n}\n\n@mixin typo-display-3($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 56px;\n  font-weight: 400;\n  line-height: 1.35;\n  letter-spacing: -0.02em;\n\n  @if $colorContrast {\n    opacity: 0.54;\n  }\n}\n\n@mixin typo-display-2($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 45px;\n  font-weight: 400;\n  line-height: 48px;\n\n  @if $colorContrast {\n    opacity: 0.54;\n  }\n}\n\n@mixin typo-display-1($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 34px;\n  font-weight: 400;\n  line-height: 40px;\n\n  @if $colorContrast {\n    opacity: 0.54;\n  }\n}\n\n@mixin typo-headline($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 24px;\n  font-weight: 400;\n  line-height: 32px;\n  -moz-osx-font-smoothing: grayscale;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-title($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 20px;\n  font-weight: 500;\n  line-height: 1;\n  letter-spacing: 0.02em;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-subhead($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 16px;\n  font-weight: 400;\n  line-height: 24px;\n  letter-spacing: 0.04em;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-subhead-2($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 16px;\n  font-weight: 400;\n  line-height: 28px;\n  letter-spacing: 0.04em;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-body-2($colorContrast: false, $usePreferred: false) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 14px;\n  @if $usePreferred {\n    font-weight: 500;\n  } @else {\n    font-weight: bold;\n  }\n  line-height: 24px;\n  letter-spacing: 0;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-body-1($colorContrast: false, $usePreferred: false) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 14px;\n  font-weight: 400;\n  line-height: 24px;\n  letter-spacing: 0;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-caption($colorContrast: false, $usePreferred: false) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 12px;\n  font-weight: 400;\n  line-height: 1;\n  letter-spacing: 0;\n\n  @if $colorContrast {\n    opacity: 0.54;\n  }\n}\n\n@mixin typo-blockquote($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  position: relative;\n  font-size: 24px;\n  font-weight: 300;\n  font-style: italic;\n  line-height: 1.35;\n  letter-spacing: 0.08em;\n\n  &:before {\n    position: absolute;\n    left: -0.5em;\n    content: '“';\n  }\n\n  &:after {\n    content: '”';\n    margin-left: -0.05em;\n  }\n\n  @if $colorContrast {\n    opacity: 0.54;\n  }\n}\n\n@mixin typo-menu($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 14px;\n  font-weight: 500;\n  line-height: 1;\n  letter-spacing: 0;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-button($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 14px;\n  font-weight: 500;\n  text-transform: uppercase;\n  line-height: 1;\n  letter-spacing: 0;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-icon() {\n  font-family: 'Material Icons';\n  font-weight: normal;\n  font-style: normal;\n  font-size: 24px;\n  line-height: 1;\n  letter-spacing: normal;\n  text-transform: none;\n  display: inline-block;\n  word-wrap: normal;\n  font-feature-settings: 'liga';\n  -webkit-font-feature-settings: 'liga';\n  -webkit-font-smoothing: antialiased;\n}\n\n/* Shadows */\n\n// Focus shadow mixin.\n@mixin focus-shadow() {\n  box-shadow: 0 0 8px rgba(0,0,0,.18),0 8px 16px rgba(0,0,0,.36);\n}\n\n@mixin shadow-2dp() {\n  box-shadow: 0 2px 2px 0 rgba(0, 0, 0, $shadow-key-penumbra-opacity),\n              0 3px 1px -2px rgba(0, 0, 0, $shadow-key-umbra-opacity),\n              0 1px 5px 0 rgba(0, 0, 0, $shadow-ambient-shadow-opacity);\n}\n@mixin shadow-3dp() {\n  box-shadow: 0 3px 4px 0 rgba(0, 0, 0, $shadow-key-penumbra-opacity),\n              0 3px 3px -2px rgba(0, 0, 0, $shadow-key-umbra-opacity),\n              0 1px 8px 0 rgba(0, 0, 0, $shadow-ambient-shadow-opacity);\n}\n@mixin shadow-4dp() {\n  box-shadow: 0 4px 5px 0 rgba(0, 0, 0, $shadow-key-penumbra-opacity),\n              0 1px 10px 0 rgba(0, 0, 0, $shadow-ambient-shadow-opacity),\n              0 2px 4px -1px rgba(0, 0, 0, $shadow-key-umbra-opacity);\n}\n@mixin shadow-6dp() {\n  box-shadow: 0 6px 10px 0 rgba(0, 0, 0, $shadow-key-penumbra-opacity),\n              0 1px 18px 0 rgba(0, 0, 0, $shadow-ambient-shadow-opacity),\n              0 3px 5px -1px rgba(0, 0, 0, $shadow-key-umbra-opacity);\n}\n@mixin shadow-8dp() {\n  box-shadow: 0 8px 10px 1px rgba(0, 0, 0, $shadow-key-penumbra-opacity),\n              0 3px 14px 2px rgba(0, 0, 0, $shadow-ambient-shadow-opacity),\n              0 5px 5px -3px rgba(0, 0, 0, $shadow-key-umbra-opacity);\n}\n\n@mixin shadow-16dp() {\n  box-shadow: 0 16px 24px 2px rgba(0, 0, 0, $shadow-key-penumbra-opacity),\n              0  6px 30px 5px rgba(0, 0, 0, $shadow-ambient-shadow-opacity),\n              0  8px 10px -5px rgba(0, 0, 0, $shadow-key-umbra-opacity);\n}\n\n@mixin shadow-24dp() {\n  box-shadow: 0  9px 46px  8px rgba(0, 0, 0, $shadow-key-penumbra-opacity),\n              0 11px 15px -7px rgba(0, 0, 0, $shadow-ambient-shadow-opacity),\n              0 24px 38px  3px rgba(0, 0, 0, $shadow-key-umbra-opacity);\n}\n\n/* Animations */\n\n@mixin material-animation-fast-out-slow-in($duration:0.2s) {\n  transition-duration: $duration;\n  transition-timing-function: $animation-curve-fast-out-slow-in;\n}\n\n@mixin material-animation-linear-out-slow-in($duration:0.2s) {\n  transition-duration: $duration;\n  transition-timing-function: $animation-curve-linear-out-slow-in;\n}\n\n@mixin material-animation-fast-out-linear-in($duration:0.2s) {\n  transition-duration: $duration;\n  transition-timing-function: $animation-curve-fast-out-linear-in;\n}\n\n@mixin material-animation-default($duration:0.2s) {\n  transition-duration: $duration;\n  transition-timing-function: $animation-curve-default;\n}\n\n/* Dialog */\n\n@mixin dialog-width($units:5) {\n  @if(type_of($units) != 'number') {\n    @error \"The unit given to dialog-width should be a number.\";\n  }\n  // 56dp is the base unit width for Dialogs.\n  // With 5 units being the number of units for a mobile device.\n  // https://goo.gl/sK2O5o\n  width: $units * 56px;\n}\n","/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n@import \"../variables\";\n@import \"../mixins\";\n\n.mdl-data-table {\n  position: relative;\n  border: $data-table-dividers;\n  border-collapse: collapse;\n  white-space: nowrap;\n  font-size: $data-table-font-size;\n  background-color: unquote(\"rgb(#{$color-white})\");\n\n  thead {\n    padding-bottom: 3px;\n\n    .mdl-data-table__select {\n      margin-top: 0;\n    }\n  }\n\n  tbody {\n    tr {\n      position: relative;\n      height: $data-table-row-height;\n      @include material-animation-default(0.28s);\n      transition-property: background-color;\n\n      &.is-selected {\n        background-color: $data-table-selection-color;\n      }\n\n      &:hover {\n        background-color: $data-table-hover-color;\n      }\n    }\n  }\n\n  td, th {\n    padding: 0 $data-table-column-padding 12px $data-table-column-padding;\n    text-align: right;\n\n    &:first-of-type {\n      padding-left: 24px;\n    }\n\n    &:last-of-type {\n      padding-right: 24px;\n    }\n  }\n\n  td {\n    position: relative;\n    vertical-align: middle;\n    height: $data-table-row-height;\n    border-top: $data-table-dividers;\n    border-bottom: $data-table-dividers;\n    padding-top: $data-table-cell-top;\n    box-sizing: border-box;\n\n    .mdl-data-table__select {\n      vertical-align: middle;\n    }\n  }\n\n  th {\n    position: relative;\n    vertical-align: bottom;\n    text-overflow: ellipsis;\n    @include typo-body-2();\n    height: $data-table-row-height;\n    font-size: $data-table-header-font-size;\n    color: $data-table-header-color;\n    padding-bottom: 8px;\n    box-sizing: border-box;\n\n    &.mdl-data-table__header--sorted-ascending,\n    &.mdl-data-table__header--sorted-descending {\n      color: $data-table-header-sorted-color;\n      &:before {\n        @include typo-icon;\n        font-size: $data-table-header-sort-icon-size;\n        content: \"\\e5d8\";\n        margin-right: 5px;\n        vertical-align: sub;\n      }\n      &:hover {\n        cursor: pointer;\n        &:before {\n          color: $data-table-header-sorted-icon-hover-color;\n        }\n      }\n    }\n    &.mdl-data-table__header--sorted-descending:before {\n      content: \"\\e5db\";\n    }\n  }\n}\n\n.mdl-data-table__select {\n  width: 16px;\n}\n\n.mdl-data-table__cell--non-numeric.mdl-data-table__cell--non-numeric {\n  text-align: left;\n}\n","/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n/*------------------------------------*\\\n    $CONTENTS\n\\*------------------------------------*/\n/**\n * STYLE GUIDE VARIABLES------------------Declarations of Sass variables\n * -----Typography\n * -----Colors\n * -----Textfield\n * -----Switch\n * -----Spinner\n * -----Radio\n * -----Menu\n * -----List\n * -----Layout\n * -----Icon toggles\n * -----Footer\n * -----Column\n * -----Checkbox\n * -----Card\n * -----Button\n * -----Animation\n * -----Progress\n * -----Badge\n * -----Shadows\n * -----Grid\n * -----Data table\n * -----Dialog\n * -----Snackbar\n * -----Tooltip\n * -----Chip\n *\n * Even though all variables have the `!default` directive, most of them\n * should not be changed as they are dependent one another. This can cause\n * visual distortions (like alignment issues) that are hard to track down\n * and fix.\n */\n\n\n/* ==========  TYPOGRAPHY  ========== */\n\n/* We're splitting fonts into \"preferred\" and \"performance\" in order to optimize\n   page loading. For important text, such as the body, we want it to load\n   immediately and not wait for the web font load, whereas for other sections,\n   such as headers and titles, we're OK with things taking a bit longer to load.\n   We do have some optional classes and parameters in the mixins, in case you\n   definitely want to make sure you're using the preferred font and don't mind\n   the performance hit.\n   We should be able to improve on this once CSS Font Loading L3 becomes more\n   widely available.\n*/\n$preferred_font: 'Roboto', 'Helvetica', 'Arial', sans-serif !default;\n$performance_font: 'Helvetica', 'Arial', sans-serif !default;\n\n/* ==========  COLORS  ========== */\n\n/**\n*\n* Material design color palettes.\n* @see http://www.google.com/design/spec/style/color.html\n*\n**/\n\n@import \"color-definitions\";\n@import \"functions\";\n\n/* ==========  IMAGES  ========== */\n$image_path: '/images' !default;\n\n/* ==========  Color & Themes  ========== */\n\n// Define whether individual color palette items should have classes created.\n// Setting this to true will remove individual color classes for each color in the palettes.\n// To improve overall performance (assuming they aren't used) by:\n// * Saving server bandwidth sending the extra classes\n// * Save client computation against the classes\n// it is RECOMMENDED you set this to true.\n$trim-color-classes: false !default;\n\n// Use color primarily for emphasis. Choose colors that fit with\n// your brand and provide good contrast between visual components.\n$color-primary: $palette-indigo-500 !default;\n$color-primary-dark: $palette-indigo-700 !default;\n$color-accent: $palette-pink-A200 !default;\n\n// Our primary is dark, so use $color-dark-contrast for overlaid text.\n$color-primary-contrast: $color-dark-contrast !default;\n// Our accent is dark, so use $color-dark-contrast for overlaid text.\n$color-accent-contrast: $color-dark-contrast !default;\n\n// Replace all colors with placeholders if we're generating a template.\n@if $styleguide-generate-template == true {\n  $color-primary: '$color-primary';\n  $color-primary-dark: '$color-primary-dark';\n  $color-accent: '$color-accent';\n  $color-primary-contrast: '$color-primary-contrast';\n  $color-accent-contrast: '$color-accent-contrast';\n}\n\n/* ==========  Typography  ========== */\n\n// We use the following default color styles: text-color-primary and\n// text-color-secondary. For light themes, use text-color-primary-inverse\n// and text-color-secondary-inverse.\n\n$text-color-primary: unquote(\"rgba(#{$color-black}, 0.87)\") !default;\n$text-link-color: unquote(\"rgb(#{$color-accent})\") !default;\n\n// Define whether to target elements directly for typographic enhancements.\n// Turning this off means you need to use mdl-* classes more often.\n// Other components may also fail to adhere to MD without these rules.\n// It is strongly recommended you leave this as true.\n\n$target-elements-directly: true !default;\n\n/* ==========  Components  ========== */\n\n/* ==========  Standard Buttons  ========== */\n\n// Default button colors.\n$button-primary-color: unquote(\"rgba(#{$palette-grey-500}, 0.20)\") !default;\n$button-secondary-color: unquote(\"rgb(#{$color-black})\") !default;\n$button-hover-color: $button-primary-color !default;\n$button-active-color: unquote(\"rgba(#{$palette-grey-500}, 0.40)\") !default;\n$button-focus-color: unquote(\"rgba(#{$color-black}, 0.12)\") !default;\n\n// Colored button colors.\n$button-primary-color-alt: unquote(\"rgb(#{$color-primary})\") !default;\n$button-secondary-color-alt: unquote(\"rgb(#{$color-primary-contrast})\") !default;\n$button-hover-color-alt: unquote(\"rgb(#{$color-primary})\") !default;\n$button-active-color-alt: unquote(\"rgb(#{$color-primary})\") !default;\n$button-focus-color-alt: $button-focus-color !default;\n\n// Ripple color for colored raised buttons.\n$button-ripple-color-alt: unquote(\"rgb(#{$color-primary-contrast})\") !default;\n\n// Disabled button colors.\n$button-primary-color-disabled: unquote(\"rgba(#{$color-black}, 0.12)\") !default;\n$button-secondary-color-disabled: unquote(\"rgba(#{$color-black}, 0.26)\") !default;\n\n// FAB colors and sizes.\n$button-fab-color-alt: unquote(\"rgb(#{$color-accent})\") !default;\n$button-fab-hover-color-alt: unquote(\"rgb(#{$color-accent})\") !default;\n$button-fab-active-color-alt: unquote(\"rgb(#{$color-accent})\") !default;\n$button-fab-text-color-alt: unquote(\"rgb(#{$color-accent-contrast})\") !default;\n$button-fab-ripple-color-alt: unquote(\"rgb(#{$color-accent-contrast})\") !default;\n\n// Icon button colors and sizes.\n$button-icon-color: unquote(\"rgb(#{$palette-grey-700})\") !default;\n$button-icon-focus-color: $button-focus-color !default;\n\n/* ==========  Icon Toggles  ========== */\n\n$icon-toggle-color: unquote(\"rgb(#{$palette-grey-700})\") !default;\n$icon-toggle-focus-color: $button-focus-color !default;\n$icon-toggle-checked-color: unquote(\"rgb(#{$color-primary})\") !default;\n$icon-toggle-checked-focus-color: unquote(\"rgba(#{$color-primary}, 0.26)\") !default;\n$icon-toggle-disabled-color: unquote(\"rgba(#{$color-black}, 0.26)\") !default;\n\n/* ==========  Radio Buttons  ========== */\n\n$radio-color: unquote(\"rgb(#{$color-primary})\") !default;\n$radio-off-color: unquote(\"rgba(#{$color-black}, 0.54)\") !default;\n$radio-disabled-color: unquote(\"rgba(#{$color-black}, 0.26)\") !default;\n\n/* ==========  Ripple effect  ========== */\n\n$ripple-bg-color: unquote(\"rgb(#{$color-light-contrast})\") !default;\n\n/* ==========  Layout  ========== */\n\n$layout-nav-color: unquote(\"rgb(#{$palette-grey-300})\") !default;\n\n// Drawer\n$layout-drawer-bg-color: unquote(\"rgb(#{$palette-grey-50})\") !default;\n$layout-drawer-border-color: unquote(\"rgb(#{$palette-grey-300})\") !default;\n$layout-text-color: unquote(\"rgb(#{$palette-grey-800})\") !default;\n$layout-drawer-navigation-color: #757575 !default;\n$layout-drawer-navigation-link-active-background: unquote(\"rgb(#{$palette-grey-300})\") !default;\n$layout-drawer-navigation-link-active-color: unquote(\"rgb(#{$color-light-contrast})\") !default;\n\n// Header\n$layout-header-bg-color: unquote(\"rgb(#{$color-primary})\") !default;\n$layout-header-text-color: unquote(\"rgb(#{$color-primary-contrast})\") !default;\n$layout-header-nav-hover-color: unquote(\"rgba(#{$palette-grey-700}, 0.6)\") !default;\n$layout-header-tab-text-color: unquote(\"rgba(#{$color-primary-contrast}, 0.6)\") !default;\n\n// Tabs\n$layout-header-tab-highlight: unquote(\"rgb(#{$color-accent})\") !default;\n\n/* ==========  Content Tabs  ========== */\n\n$tab-highlight-color: unquote(\"rgb(#{$color-primary})\") !default;\n$tab-text-color: unquote(\"rgba(#{$color-black}, 0.54)\") !default;\n$tab-active-text-color: unquote(\"rgba(#{$color-black}, 0.87)\") !default;\n$tab-border-color: unquote(\"rgb(#{$palette-grey-300})\") !default;\n\n/* ==========  Checkboxes  ========== */\n\n$checkbox-color: unquote(\"rgb(#{$color-primary})\") !default;\n$checkbox-off-color: unquote(\"rgba(#{$color-black}, 0.54)\") !default;\n$checkbox-disabled-color: unquote(\"rgba(#{$color-black}, 0.26)\") !default;\n$checkbox-focus-color: unquote(\"rgba(#{$color-primary}, 0.26)\") !default;\n$checkbox-image-path: $image_path;\n\n/* ==========  Switches  ========== */\n\n$switch-color: unquote(\"rgb(#{$color-primary})\") !default;\n$switch-faded-color: unquote(\"rgba(#{$color-primary}, 0.26)\") !default;\n$switch-thumb-color: $switch-color !default;\n$switch-track-color: unquote(\"rgba(#{$color-primary}, 0.5)\") !default;\n\n$switch-off-thumb-color: unquote(\"rgb(#{$palette-grey-50})\") !default;\n$switch-off-track-color: unquote(\"rgba(#{$color-black}, 0.26)\") !default;\n$switch-disabled-thumb-color: unquote(\"rgb(#{$palette-grey-400})\") !default;\n$switch-disabled-track-color: unquote(\"rgba(#{$color-black}, 0.12)\") !default;\n\n/* ==========  Spinner  ========== */\n\n$spinner-color-1: unquote(\"rgb(#{$palette-blue-400})\") !default;\n$spinner-color-2: unquote(\"rgb(#{$palette-red-500})\") !default;\n$spinner-color-3: unquote(\"rgb(#{$palette-yellow-600})\") !default;\n$spinner-color-4: unquote(\"rgb(#{$palette-green-500})\") !default;\n\n$spinner-single-color: unquote(\"rgb(#{$color-primary})\") !default;\n\n/* ==========  Text fields  ========== */\n\n$input-text-background-color: transparent !default;\n$input-text-label-color: unquote(\"rgba(#{$color-black}, 0.26)\") !default;\n$input-text-bottom-border-color: unquote(\"rgba(#{$color-black}, 0.12)\") !default;\n$input-text-highlight-color: unquote(\"rgb(#{$color-primary})\") !default;\n$input-text-disabled-color: $input-text-bottom-border-color !default;\n$input-text-disabled-text-color: $input-text-label-color !default;\n$input-text-error-color: unquote(\"rgb(#{$palette-red-A700})\") !default;\n\n/* ==========  Card  ========== */\n\n$card-background-color: unquote(\"rgb(#{$color-white})\") !default;\n$card-text-color: unquote(\"rgb(#{$color-black})\") !default;\n$card-image-placeholder-color: unquote(\"rgb(#{$color-accent})\") !default;\n$card-supporting-text-text-color: unquote(\"rgba(#{$color-black}, 0.54)\") !default;\n$card-border-color: rgba(0,0,0,0.1) !default;\n$card-subtitle-color: unquote(\"rgba(#{$color-black}, 0.54)\") !default;\n\n/* ==========  Sliders ========== */\n\n$range-bg-color: unquote(\"rgba(#{$color-black}, 0.26)\") !default;\n$range-color: unquote(\"rgb(#{$color-primary})\") !default;\n$range-faded-color: unquote(\"rgba(#{$color-primary}, 0.26)\") !default;\n$range-bg-focus-color: unquote(\"rgba(#{$color-black}, 0.12)\") !default;\n\n/* ========== Progress ========== */\n$progress-main-color: unquote(\"rgb(#{$color-primary})\") !default;\n$progress-secondary-color: unquote(\"rgba(#{$color-primary-contrast}, 0.7)\") !default;\n$progress-fallback-buffer-color: unquote(\"rgba(#{$color-primary-contrast}, 0.9)\") !default;\n$progress-image-path: $image_path;\n\n/* ==========  List ========== */\n\n$list-main-text-text-color: unquote(\"rgba(#{$color-black}, 0.87)\") !default;\n$list-supporting-text-text-color: unquote(\"rgba(#{$color-black}, 0.54)\") !default;\n$list-icon-color: unquote(\"rgb(#{$palette-grey-600})\") !default;\n$list-avatar-color: white !default;\n\n/* ==========  Item ========== */\n\n// Default Item Colors\n$default-item-text-color: unquote(\"rgba(#{$color-black}, 0.87)\") !default;\n$default-item-outline-color: unquote(\"rgb(#{$palette-grey-400})\") !default;\n$default-item-hover-bg-color: unquote(\"rgb(#{$palette-grey-200})\") !default;\n$default-item-focus-bg-color: unquote(\"rgb(#{$palette-grey-200})\") !default;\n$default-item-active-bg-color: unquote(\"rgb(#{$palette-grey-300})\") !default;\n$default-item-divider-color: unquote(\"rgba(#{$color-black}, 0.12)\") !default;\n\n// Disabled Button Colors\n$disabled-item-text-color: unquote(\"rgb(#{$palette-grey-400})\") !default;\n\n/* ==========  Dropdown menu ========== */\n\n$default-dropdown-bg-color: unquote(\"rgb(#{$color-white})\") !default;\n\n/* ==========  Tooltips  ========== */\n\n$tooltip-text-color: unquote(\"rgb(#{$color-white})\") !default;\n$tooltip-background-color: unquote(\"rgba(#{$palette-grey-700}, 0.9)\") !default;\n\n/* ==========  Footer  ========== */\n\n$footer-bg-color: unquote(\"rgb(#{$palette-grey-800})\") !default;\n$footer-color: unquote(\"rgb(#{$palette-grey-500})\") !default;\n$footer-heading-color: unquote(\"rgb(#{$palette-grey-300})\") !default;\n$footer-button-fill-color: $footer-color !default;\n$footer-underline-color: $footer-color !default;\n\n\n/* TEXTFIELD */\n\n$input-text-font-size: 16px !default;\n$input-text-width: 100% !default;\n$input-text-padding: 4px !default;\n$input-text-vertical-spacing: 20px !default;\n\n$input-text-button-size: 32px !default;\n$input-text-floating-label-fontsize: 12px !default;\n$input-text-expandable-icon-top: 16px !default;\n\n\n/* SWITCH */\n\n$switch-label-font-size: 16px !default;\n$switch-label-height: 24px !default;\n$switch-track-height: 14px !default;\n$switch-track-length: 36px !default;\n$switch-thumb-size: 20px !default;\n$switch-track-top: ($switch-label-height - $switch-track-height) / 2 !default;\n$switch-thumb-top: ($switch-label-height - $switch-thumb-size) / 2 !default;\n$switch-ripple-size: $switch-label-height * 2 !default;\n$switch-helper-size: 8px !default;\n\n/* SPINNER */\n\n$spinner-size: 28px !default;\n$spinner-stroke-width: 3px !default;\n\n// Amount of circle the arc takes up.\n$spinner-arc-size: 270deg !default;\n// Time it takes to expand and contract arc.\n$spinner-arc-time: 1333ms !default;\n// How much the start location of the arc should rotate each time.\n$spinner-arc-start-rot: 216deg !default;\n\n$spinner-duration: 360 * $spinner-arc-time / (\n    strip-units($spinner-arc-start-rot + (360deg - $spinner-arc-size)));\n\n\n/* RADIO */\n\n$radio-label-font-size: 16px !default;\n$radio-label-height: 24px !default;\n$radio-button-size: 16px !default;\n$radio-inner-margin: $radio-button-size / 4;\n$radio-padding: 8px !default;\n$radio-top-offset: ($radio-label-height - $radio-button-size) / 2;\n$radio-ripple-size: 42px !default;\n\n\n/* MENU */\n\n$menu-expand-duration: 0.3s !default;\n$menu-fade-duration: 0.2s !default;\n\n/* LIST */\n\n$list-border: 8px !default;\n$list-min-height: 48px !default;\n$list-min-padding: 16px !default;\n$list-bottom-padding: 20px !default;\n$list-avatar-text-left-distance: 72px !default;\n$list-icon-text-left-distance: 72px !default;\n\n$list-avatar-size: 40px !default;\n$list-icon-size: 24px !default;\n\n$list-two-line-height: 72px !default;\n$list-three-line-height: 88px !default;\n\n/* LAYOUT */\n\n$layout-drawer-narrow: 240px !default;\n$layout-drawer-wide: 456px !default;\n$layout-drawer-width: $layout-drawer-narrow !default;\n\n$layout-header-icon-size: 32px !default;\n$layout-screen-size-threshold: 1024px !default;\n$layout-header-icon-margin: 24px !default;\n$layout-drawer-button-mobile-size: 32px !default;\n$layout-drawer-button-desktop-size: 48px !default;\n\n$layout-header-mobile-row-height: 56px !default;\n$layout-mobile-header-height: $layout-header-mobile-row-height;\n$layout-header-desktop-row-height: 64px !default;\n$layout-desktop-header-height: $layout-header-desktop-row-height;\n\n$layout-header-desktop-baseline: 80px !default;\n$layout-header-mobile-baseline: 72px !default;\n$layout-header-mobile-indent: 16px !default;\n$layout-header-desktop-indent: 40px !default;\n\n$layout-tab-font-size: 14px !default;\n$layout-tab-bar-height: 48px !default;\n$layout-tab-mobile-padding: 12px !default;\n$layout-tab-desktop-padding: 24px !default;\n$layout-tab-highlight-thickness: 2px !default;\n\n\n/* ICON TOGGLE */\n\n$icon-toggle-size: 32px !default;\n$icon-toggle-font-size: 24px !default;\n$icon-toggle-ripple-size: 36px !default;\n\n/* FOOTER */\n\n/*mega-footer*/\n$footer-min-padding: 16px !default;\n$footer-padding-sides: 40px !default;\n$footer-heading-font-size: 14px !default;\n$footer-heading-line-height: (1.7 * $footer-heading-font-size) !default;\n$footer-btn-size: 36px  !default;\n\n/*mini-footer*/\n$padding: 16px !default;\n$footer-heading-font-size: 24px !default;\n$footer-heading-line-height: (1.5 * $footer-heading-font-size) !default;\n$footer-btn-size: 36px !default;\n\n/* CHECKBOX */\n\n$checkbox-label-font-size: 16px !default;\n$checkbox-label-height: 24px !default;\n$checkbox-button-size: 16px !default;\n$checkbox-inner-margin: 2px !default;\n$checkbox-padding: 8px !default;\n$checkbox-top-offset:\n($checkbox-label-height - $checkbox-button-size - $checkbox-inner-margin) / 2;\n$checkbox-ripple-size: $checkbox-label-height * 1.5;\n\n/* CARD */\n\n/* Card dimensions */\n$card-width: 330px !default;\n$card-height: 200px !default;\n$card-font-size: 16px !default;\n$card-title-font-size: 24px !default;\n$card-subtitle-font-size: 14px !default;\n$card-horizontal-padding: 16px !default;\n$card-vertical-padding: 16px !default;\n\n$card-title-perspective-origin-x: 165px !default;\n$card-title-perspective-origin-y: 56px !default;\n\n$card-title-transform-origin-x: 165px !default;\n$card-title-transform-origin-y: 56px !default;\n\n$card-title-text-transform-origin-x: 149px !default;\n$card-title-text-transform-origin-y: 48px !default;\n\n$card-supporting-text-font-size: 1rem !default;\n$card-supporting-text-line-height: 18px !default;\n\n$card-actions-font-size: 16px !default;\n\n$card-title-text-font-weight: 300 !default;\n$card-z-index: 1 !default;\n\n/* Cover image */\n$card-cover-image-height: 186px !default;\n$card-background-image-url: '' !default;\n\n\n/* BUTTON */\n/**\n *\n * Dimensions\n *\n */\n$button-min-width: 64px !default;\n$button-height: 36px !default;\n$button-padding: 16px !default;\n$button-margin: 4px !default;\n$button-border-radius: 2px !default;\n\n$button-fab-size: 56px !default;\n$button-fab-size-mini: 40px !default;\n$button-fab-font-size: 24px !default;\n\n$button-icon-size: 32px !default;\n$button-icon-size-mini: 24px !default;\n\n\n/* ANIMATION */\n$animation-curve-fast-out-slow-in: cubic-bezier(0.4, 0, 0.2, 1) !default;\n$animation-curve-linear-out-slow-in: cubic-bezier(0, 0, 0.2, 1) !default;\n$animation-curve-fast-out-linear-in: cubic-bezier(0.4, 0, 1, 1) !default;\n\n$animation-curve-default: $animation-curve-fast-out-slow-in !default;\n\n\n/* PROGRESS */\n$bar-height: 4px !default;\n\n/* BADGE */\n$badge-font-size: 12px !default;\n$badge-color: unquote(\"rgb(#{$color-accent-contrast})\") !default;\n$badge-color-inverse: unquote(\"rgb(#{$color-accent})\") !default;\n$badge-background: unquote(\"rgb(#{$color-accent})\") !default;\n$badge-background-inverse: unquote(\"rgba(#{$color-accent-contrast},0.2)\") !default;\n$badge-size : 22px !default;\n$badge-padding: 2px !default;\n$badge-overlap: 12px !default;\n\n/* SHADOWS */\n\n$shadow-key-umbra-opacity: 0.2 !default;\n$shadow-key-penumbra-opacity: 0.14 !default;\n$shadow-ambient-shadow-opacity: 0.12 !default;\n\n/* GRID */\n\n$grid-desktop-columns: 12 !default;\n$grid-desktop-gutter: 16px !default;\n$grid-desktop-margin: 16px !default;\n\n$grid-desktop-breakpoint: 840px !default;\n\n$grid-tablet-columns: 8 !default;\n$grid-tablet-gutter: $grid-desktop-gutter !default;\n$grid-tablet-margin: $grid-desktop-margin !default;\n\n$grid-tablet-breakpoint: 480px !default;\n\n$grid-phone-columns: 4 !default;\n$grid-phone-gutter: $grid-desktop-gutter !default;\n$grid-phone-margin: $grid-desktop-margin !default;\n\n$grid-cell-default-columns: $grid-phone-columns !default;\n$grid-max-columns: $grid-desktop-columns !default;\n\n/* DATA TABLE */\n\n$data-table-font-size: 13px !default;\n$data-table-header-font-size: 12px !default;\n$data-table-header-sort-icon-size: 16px !default;\n\n$data-table-header-color: rgba(#000, 0.54) !default;\n$data-table-header-sorted-color: rgba(#000, 0.87) !default;\n$data-table-header-sorted-icon-hover-color: rgba(#000, 0.26) !default;\n$data-table-divider-color: rgba(#000, 0.12) !default;\n\n$data-table-hover-color: #eeeeee !default;\n$data-table-selection-color: #e0e0e0 !default;\n\n$data-table-dividers: 1px solid $data-table-divider-color !default;\n\n$data-table-row-height: 48px !default;\n$data-table-last-row-height: 56px !default;\n$data-table-header-height: 56px !default;\n\n$data-table-column-spacing: 36px !default;\n$data-table-column-padding: $data-table-column-spacing / 2;\n\n$data-table-card-header-height: 64px !default;\n$data-table-card-title-top: 20px !default;\n$data-table-card-padding: 24px !default;\n$data-table-button-padding-right: 16px !default;\n$data-table-cell-top: $data-table-card-padding / 2;\n\n/* DIALOG */\n$dialog-content-color: $card-supporting-text-text-color;\n\n/* SNACKBAR */\n\n// Hard coded since the color is not present in any palette.\n$snackbar-background-color: #323232 !default;\n$snackbar-tablet-breakpoint: $grid-tablet-breakpoint;\n$snackbar-action-color: unquote(\"rgb(#{$color-accent})\") !default;\n\n/* TOOLTIP */\n$tooltip-font-size: 10px !default;\n$tooltip-font-size-large: 14px !default;\n\n/* CHIP */\n$chip-bg-color: rgb(222, 222, 222) !default;\n$chip-bg-active-color: rgb(214, 214, 214) !default;\n$chip-height: 32px !default;\n$chip-font-size: 13px !default; \n","/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n@import \"../variables\";\n\n.mdl-mini-footer {\n  display: flex;\n  flex-flow: row wrap;\n  justify-content: space-between;\n\n  padding: ($padding * 2) $padding;\n\n  color: $footer-color;\n  background-color: $footer-bg-color;\n\n  &:after {\n    content: '';\n    display: block;\n  }\n\n  & .mdl-logo {\n    line-height: $footer-btn-size;\n  }\n}\n\n.mdl-mini-footer--link-list,\n.mdl-mini-footer__link-list {\n  display: flex;\n  flex-flow: row nowrap;\n\n  list-style: none;\n\n  margin: 0;\n  padding: 0;\n\n  & li {\n    margin-bottom: 0;\n    margin-right: $padding;\n\n    @media screen and (min-width: 760px) {\n      line-height: $footer-btn-size;\n    }\n  }\n\n  & a {\n    color: inherit;\n    text-decoration: none;\n    white-space: nowrap;\n  }\n}\n\n.mdl-mini-footer--left-section,\n.mdl-mini-footer__left-section {\n  display: inline-block;\n  order: 0;\n}\n\n.mdl-mini-footer--right-section,\n.mdl-mini-footer__right-section {\n  display: inline-block;\n  order: 1;\n}\n\n.mdl-mini-footer--social-btn,\n.mdl-mini-footer__social-btn {\n  width: $footer-btn-size;\n  height: $footer-btn-size;\n\n  padding: 0;\n  margin: 0;\n\n  background-color: $footer-button-fill-color;\n\n  border: none;\n}\n","/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n@import \"../variables\";\n\n.mdl-card {\n  display: flex;\n  flex-direction: column;\n  font-size: $card-font-size;\n  font-weight: 400;\n  min-height: $card-height;\n  overflow: hidden;\n  width: $card-width;\n  z-index: $card-z-index;\n  position: relative;\n  background: $card-background-color;\n  border-radius: 2px;\n  box-sizing: border-box;\n}\n\n.mdl-card__media {\n  background-color: $card-image-placeholder-color;\n  background-repeat: repeat;\n  background-position: 50% 50%;\n  background-size: cover;\n  background-origin: padding-box;\n  background-attachment: scroll;\n  box-sizing: border-box;\n}\n\n.mdl-card__title {\n  align-items: center;\n  color: $card-text-color;\n  display: block;\n  display: flex;\n  justify-content: stretch;\n  line-height: normal;\n  padding: $card-vertical-padding $card-horizontal-padding;\n  perspective-origin: $card-title-perspective-origin-x $card-title-perspective-origin-y;\n  transform-origin: $card-title-transform-origin-x $card-title-transform-origin-y;\n  box-sizing: border-box;\n\n  &.mdl-card--border {\n    border-bottom: 1px solid $card-border-color;\n  }\n}\n\n.mdl-card__title-text {\n  align-self: flex-end;\n  color: inherit;\n  display: block;\n  display: flex;\n  font-size: $card-title-font-size;\n  font-weight: $card-title-text-font-weight;\n  line-height: normal;\n  overflow: hidden;\n  transform-origin: $card-title-text-transform-origin-x $card-title-text-transform-origin-y;\n  margin: 0;\n}\n\n.mdl-card__subtitle-text {\n  font-size: $card-subtitle-font-size;\n  color: $card-subtitle-color;\n  margin: 0;\n}\n\n.mdl-card__supporting-text {\n  color: $card-supporting-text-text-color;\n  font-size: $card-supporting-text-font-size;\n  line-height: $card-supporting-text-line-height;\n  overflow: hidden;\n  padding: $card-vertical-padding $card-horizontal-padding;\n  width: 90%;\n\n  &.mdl-card--border {\n    border-bottom: 1px solid $card-border-color;\n  }\n}\n\n.mdl-card__actions {\n  font-size: $card-actions-font-size;\n  line-height: normal;\n  width: 100%;\n  background-color: rgba(0,0,0,0);\n  padding: 8px;\n  box-sizing: border-box;\n\n  &.mdl-card--border {\n    border-top: 1px solid $card-border-color;\n  }\n}\n\n.mdl-card--expand {\n  flex-grow: 1;\n}\n\n\n.mdl-card__menu {\n  position: absolute;\n  right: 16px;\n  top: 16px;\n}\n","/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n@import \"../variables\";\n@import \"../mixins\";\n\n// The button component. Defaults to a flat button.\n.mdl-button {\n  background: transparent;\n  border: none;\n  border-radius: $button-border-radius;\n  color: $button-secondary-color;\n  position: relative;\n  height: $button-height;\n  margin: 0;\n  min-width: $button-min-width;\n  padding: 0 $button-padding;\n  display: inline-block;\n  @include typo-button();\n  overflow: hidden;\n  will-change: box-shadow;\n  transition: box-shadow 0.2s $animation-curve-fast-out-linear-in,\n              background-color 0.2s $animation-curve-default,\n              color 0.2s $animation-curve-default;\n  outline: none;\n  cursor: pointer;\n  text-decoration: none;\n  text-align: center;\n  line-height: $button-height;\n  vertical-align: middle;\n\n  &::-moz-focus-inner {\n    border: 0;\n  }\n\n  &:hover {\n    background-color: $button-hover-color;\n  }\n\n  &:focus:not(:active) {\n    background-color: $button-focus-color;\n  }\n\n  &:active {\n    background-color: $button-active-color;\n  }\n\n  &.mdl-button--colored {\n    color: $button-primary-color-alt;\n\n    &:focus:not(:active) {\n      background-color: $button-focus-color-alt;\n    }\n  }\n}\n\ninput.mdl-button[type=\"submit\"] {\n  -webkit-appearance:none;\n}\n\n  // Raised buttons\n  .mdl-button--raised {\n    background: $button-primary-color;\n    @include shadow-2dp();\n\n    &:active {\n      @include shadow-4dp();\n      background-color: $button-active-color;\n    }\n\n    &:focus:not(:active) {\n      @include focus-shadow();\n      background-color: $button-active-color;\n    }\n\n    &.mdl-button--colored {\n      background: $button-primary-color-alt;\n      color: $button-secondary-color-alt;\n\n      &:hover {\n        background-color: $button-hover-color-alt;\n      }\n\n      &:active {\n        background-color: $button-active-color-alt;\n      }\n\n      &:focus:not(:active) {\n        background-color: $button-active-color-alt;\n      }\n\n      & .mdl-ripple {\n        background: $button-ripple-color-alt;\n      }\n    }\n  }\n\n\n  // FABs\n  .mdl-button--fab {\n    border-radius: 50%;\n    font-size: $button-fab-font-size;\n    height: $button-fab-size;\n    margin: auto;\n    min-width: $button-fab-size;\n    width: $button-fab-size;\n    padding: 0;\n    overflow: hidden;\n    background: $button-primary-color;\n    box-shadow: 0 1px 1.5px 0 rgba(0,0,0,0.12), 0 1px 1px 0 rgba(0,0,0,0.24);\n    position: relative;\n    line-height: normal;\n\n    & .material-icons {\n      position: absolute;\n      top: 50%;\n      left: 50%;\n      transform: translate(- $button-fab-font-size / 2, - $button-fab-font-size / 2);\n      line-height: $button-fab-font-size;\n      width: $button-fab-font-size;\n    }\n\n    &.mdl-button--mini-fab {\n      height: $button-fab-size-mini;\n      min-width: $button-fab-size-mini;\n      width: $button-fab-size-mini;\n    }\n\n    & .mdl-button__ripple-container {\n      border-radius: 50%;\n      // Fixes clipping bug in Safari.\n      -webkit-mask-image: -webkit-radial-gradient(circle, white, black);\n    }\n\n    &:active {\n      @include shadow-4dp();\n      background-color: $button-active-color;\n    }\n\n    &:focus:not(:active) {\n      @include focus-shadow();\n      background-color: $button-active-color;\n    }\n\n    &.mdl-button--colored {\n      background: $button-fab-color-alt;\n      color: $button-fab-text-color-alt;\n\n      &:hover {\n        background-color: $button-fab-hover-color-alt;\n      }\n\n      &:focus:not(:active) {\n        background-color: $button-fab-active-color-alt;\n      }\n\n      &:active {\n        background-color: $button-fab-active-color-alt;\n      }\n\n      & .mdl-ripple {\n        background: $button-fab-ripple-color-alt;\n      }\n    }\n  }\n\n\n  // Icon buttons\n  .mdl-button--icon {\n    border-radius: 50%;\n    font-size: $button-fab-font-size;\n    height: $button-icon-size;\n    margin-left: 0;\n    margin-right: 0;\n    min-width: $button-icon-size;\n    width: $button-icon-size;\n    padding: 0;\n    overflow: hidden;\n    color: inherit;\n    line-height: normal;\n\n    & .material-icons {\n      position: absolute;\n      top: 50%;\n      left: 50%;\n      transform: translate(- $button-fab-font-size / 2, - $button-fab-font-size / 2);\n      line-height: $button-fab-font-size;\n      width: $button-fab-font-size;\n    }\n\n    &.mdl-button--mini-icon {\n      height: $button-icon-size-mini;\n      min-width: $button-icon-size-mini;\n      width: $button-icon-size-mini;\n\n      & .material-icons {\n        top: ($button-icon-size-mini - $button-fab-font-size) / 2;\n        left: ($button-icon-size-mini - $button-fab-font-size) / 2;\n      }\n    }\n\n    & .mdl-button__ripple-container {\n      border-radius: 50%;\n      // Fixes clipping bug in Safari.\n      -webkit-mask-image: -webkit-radial-gradient(circle, white, black);\n    }\n  }\n\n\n  // Ripples\n  .mdl-button__ripple-container {\n    display: block;\n    height: 100%;\n    left: 0px;\n    position: absolute;\n    top: 0px;\n    width: 100%;\n    z-index: 0;\n    overflow: hidden;\n\n    .mdl-button[disabled] & .mdl-ripple,\n    .mdl-button.mdl-button--disabled & .mdl-ripple {\n      background-color: transparent;\n    }\n  }\n\n// Colorized buttons\n\n.mdl-button--primary.mdl-button--primary {\n  color: $button-primary-color-alt;\n  & .mdl-ripple {\n    background: $button-secondary-color-alt;\n  }\n  &.mdl-button--raised, &.mdl-button--fab {\n    color: $button-secondary-color-alt;\n    background-color: $button-primary-color-alt;\n  }\n}\n\n.mdl-button--accent.mdl-button--accent {\n  color: $button-fab-color-alt;\n  & .mdl-ripple {\n    background: $button-fab-text-color-alt;\n  }\n  &.mdl-button--raised, &.mdl-button--fab {\n    color: $button-fab-text-color-alt;\n    background-color: $button-fab-color-alt;\n  }\n}\n\n// Disabled buttons\n\n.mdl-button {\n  // Bump up specificity by using [disabled] twice.\n  &[disabled][disabled],\n  &.mdl-button--disabled.mdl-button--disabled {\n    color: $button-secondary-color-disabled;\n    cursor: default;\n    background-color: transparent;\n  }\n\n  &--fab {\n    // Bump up specificity by using [disabled] twice.\n    &[disabled][disabled],\n    &.mdl-button--disabled.mdl-button--disabled {\n      background-color: $button-primary-color-disabled;\n      color: $button-secondary-color-disabled;\n    }\n  }\n\n  &--raised {\n    // Bump up specificity by using [disabled] twice.\n    &[disabled][disabled],\n    &.mdl-button--disabled.mdl-button--disabled {\n      background-color: $button-primary-color-disabled;\n      color: $button-secondary-color-disabled;\n      box-shadow: none;\n    }\n  }\n  &--colored {\n    // Bump up specificity by using [disabled] twice.\n    &[disabled][disabled],\n    &.mdl-button--disabled.mdl-button--disabled {\n      color: $button-secondary-color-disabled;\n    }\n  }\n}\n\n// Align icons inside buttons with text\n.mdl-button .material-icons {\n  vertical-align: middle;\n}\n","// SIMPLE GRID - SASS/SCSS\n\n\n// fonts\n$font-weight-light: 300;\n$font-weight-regular: 400;\n$font-weight-heavy: 700;\n\n// colors\n$dark-grey: #333447;\n$dark-gray: #333447; // for the Americans\n\n\n.font-light {\n  font-weight: $font-weight-light;\n}\n\n.font-regular {\n  font-weight: $font-weight-regular;\n}\n\n.font-heavy {\n  font-weight: $font-weight-heavy;\n}\n\n// utility\n\n.left {\n  text-align: left;\n}\n\n.right {\n  text-align: right;\n}\n\n.center {\n  text-align: center;\n  margin-left: auto;\n  margin-right: auto;\n}\n\n.justify {\n  text-align: justify;\n}\n\n.hidden-sm {\n  display: none;\n}\n\n// grid\n\n$width: 98%;\n$gutter: 2%;\n$breakpoint-small: 33.75em; // 540px\n$breakpoint-med: 45em; // 720px\n$breakpoint-large: 60em; // 960px\n\n.container {\n  width: 100%;\n  margin-left: auto;\n  margin-right: auto;\n\n  @media only screen and (min-width: $breakpoint-small) {\n    width: 80%;\n  }\n\n  @media only screen and (min-width: $breakpoint-large) {\n    width: 75%;\n    max-width: 60rem;\n  }\n}\n\n.row {\n  position: relative;\n  width: 100%;\n}\n\n.row [class^=\"col\"] {\n  float: left;\n  margin: 0.5rem 1%;\n  min-height: 0.125rem;\n}\n\n.row::after {\n  content: \"\";\n  display: table;\n  clear: both;\n}\n\n.col-1,\n.col-2,\n.col-3,\n.col-4,\n.col-5,\n.col-6,\n.col-7,\n.col-8,\n.col-9,\n.col-10,\n.col-11,\n.col-12 {\n  width: $width;\n}\n\n.col-1-sm {\n  width: ($width / 12) - ($gutter * 11 / 12);\n}\n\n.col-2-sm {\n  width: ($width / 6) - ($gutter * 10 / 12);\n}\n\n.col-3-sm {\n  width: ($width / 4) - ($gutter * 9 / 12);\n}\n\n.col-4-sm {\n  width: ($width / 3) - ($gutter * 8 / 12);\n}\n\n.col-5-sm {\n  width: ($width / (12 / 5)) - ($gutter * 7 / 12);\n}\n\n.col-6-sm {\n  width: ($width / 2) - ($gutter * 6 / 12);\n}\n\n.col-7-sm {\n  width: ($width / (12 / 7)) - ($gutter * 5 / 12);\n}\n\n.col-8-sm {\n  width: ($width / (12 / 8)) - ($gutter * 4 / 12);\n}\n\n.col-9-sm {\n  width: ($width / (12 / 9)) - ($gutter * 3 / 12);\n}\n\n.col-10-sm {\n  width: ($width / (12 / 10)) - ($gutter * 2 / 12);\n}\n\n.col-11-sm {\n  width: ($width / (12 / 11)) - ($gutter * 1 / 12);\n}\n\n.col-12-sm {\n  width: $width;\n}\n\n@media only screen and (min-width: $breakpoint-med) {\n  .col-1 {\n    width: ($width / 12) - ($gutter * 11 / 12);\n  }\n  .col-2 {\n    width: ($width / 6) - ($gutter * 10 / 12);\n  }\n  .col-3 {\n    width: ($width / 4) - ($gutter * 9 / 12);\n  }\n  .col-4 {\n    width: ($width / 3) - ($gutter * 8 / 12);\n  }\n  .col-5 {\n    width: ($width / (12 / 5)) - ($gutter * 7 / 12);\n  }\n  .col-6 {\n    width: ($width / 2) - ($gutter * 6 / 12);\n  }\n  .col-7 {\n    width: ($width / (12 / 7)) - ($gutter * 5 / 12);\n  }\n  .col-8 {\n    width: ($width / (12 / 8)) - ($gutter * 4 / 12);\n  }\n  .col-9 {\n    width: ($width / (12 / 9)) - ($gutter * 3 / 12);\n  }\n  .col-10 {\n    width: ($width / (12 / 10)) - ($gutter * 2 / 12);\n  }\n  .col-11 {\n    width: ($width / (12 / 11)) - ($gutter * 1 / 12);\n  }\n  .col-12 {\n    width: $width;\n  }\n\n  .hidden-sm {\n    display: block;\n  }\n}\n\n.row {\n  display: -webkit-box;\n  display: -webkit-flex;\n  display: -ms-flexbox;\n  display: flex;\n  flex-wrap: wrap;\n}\n\n.row > [class*='col-'] {\n  display: flex;\n  flex-direction: column;\n}\n","\n/*\nMaterial Icons\n*/\n\n.material-icons {\n    font-family: 'Material Icons';\n    font-weight: normal;\n    font-style: normal;\n    font-size: 24px;  /* Preferred icon size */\n    display: inline-block;\n    line-height: 1;\n    text-transform: none;\n    letter-spacing: normal;\n    word-wrap: normal;\n    white-space: nowrap;\n    direction: ltr;\n  \n    /* Support for all WebKit browsers. */\n    -webkit-font-smoothing: antialiased;\n    /* Support for Safari and Chrome. */\n    text-rendering: optimizeLegibility;\n  \n    /* Support for Firefox. */\n    -moz-osx-font-smoothing: grayscale;\n  \n    /* Support for IE. */\n    font-feature-settings: 'liga';\n  }","html {\n    font-size: $font_size;\n}\n\nbody {\n    display: block !important;\n    background-color: $background_color;\n    font-size: 1rem;\n    line-height: 1.5rem;\n    font-family: $body_font_family;\n}\n\n.mdl-layout__content:focus {\n    outline: none;\n }\n\nh1, h2, h3, h4, h5, h6, blockquote, span.mdl-layout-title,\na.download > code.download {\n    font-family: $body_font_family;\n}\n\nh1, h2, h3, h4, h5, h6, .toc-backref, .contents, .toctree-wrapper, .contents a, .toctree-wrapper a, .globaltoc a.current {\n    color: $color-mxnet !important;\n}\n\na {\n    text-decoration: none;\n}\n\n.page-content {\n    font-size: 1rem;\n    p, ul, ol, dl, dd, dt, table, th, td {\n        font-size: 1rem;\n    }\n}\n\n.brand {\n    color: inherit;\n    text-decoration: none;\n}\n\n.section {\n    overflow-x: auto;\n}\n\n\n/*\n *  Figure Directive Styles\n */\n img {\n    max-width: 100%;\n    display: block;\n    margin-left: auto;\n    margin-right: auto;\n }\n\ndiv.figure {\n    p.caption {\n        text-align: center;\n        margin-top: .75rem;\n\n        span.caption-number {\n            font-style: normal;\n        }\n        .caption-number::after {\n            content: \"\\00a0\";\n        }\n    }\n}\n\n.svg-icon {\n  width: 16px;\n  height: 16px;\n  display: inline-block;\n  fill: $grey-color-light;\n  padding-right: 5px;\n  padding-top: 4px;\n  vertical-align: text-top;\n}\n\n/*\n * Download Link Styles\n */\na.download > i.material-icons {\n    position: relative;\n    top: 5px;\n}\n\na.download {\n    text-decoration: none;\n}\n\n%clearfix:after {\n  content: \"\";\n  display: table;\n  clear: both;\n}\n\n.wrapper {\n  max-width: -webkit-calc(#{$content-width} - (#{$spacing-unit} * 2));\n  max-width: calc(#{$content-width} - (#{$spacing-unit} * 2));\n  margin-right: auto;\n  margin-left: auto;\n  padding-right: calc(#{$spacing-unit}+15px);\n  padding-left: $spacing-unit;\n  @extend %clearfix;\n\n  @media screen and (max-width: $on-laptop) {\n    max-width: -webkit-calc(#{$content-width} - (#{$spacing-unit}));\n    max-width: calc(#{$content-width} - (#{$spacing-unit}));\n    padding-right: $spacing-unit / 2;\n    padding-left: $spacing-unit / 2;\n  }\n}\n\n","/*\nVariables\n*/\n$font_size: 16px;\n\n$background_color: #fafafa;\n$code_background: rgba(0,0,0,.05);\n\n$code_font_family: \"Menlo\", \"DejaVu Sans Mono\", \"Liberation Mono\", \"Consolas\", \"Ubuntu Mono\", \"Courier New\", \"andale mono\", \"lucida console\", monospace !default;\n$body_font_family: -apple-system,BlinkMacSystemFont,\"Segoe UI\",Roboto,\"Helvetica Neue\",Arial,sans-serif,\"Apple Color Emoji\",\"Segoe UI Emoji\",\"Segoe UI Symbol\" !default;\n$base-font-size:   17px !default;\n\n$xl-breakpoint: 1795px;\n$lg-breakpoint: 1200px;\n$md-breakpoint: 992px;\n$sm-breakpoint: 768px;\n$xs-breakpoint: 576px;\n\n$color-primary: $palette-blue-500;\n$color-primary-dark: $palette-blue-700 !default;\n$color-accent: $palette-deep-orange-A200 !default;\n$color-primary-contrast: $color-white !default;\n$color-accent-contrast: $color-white !default;\n\n\n$base-line-height: 1.5 !default;\n$spacing-unit:     30px !default;\n\n$color-mxnet: rgb(4,140,204);\n$color-mxnet-dark: rgb(4,60,110);\n$grey-color:       #828282 !default;\n$grey-color-light: lighten($grey-color, 45%) !default;\n$grey-color-dark:  darken($grey-color, 25%) !default;\n\n$table-text-align: left !default;\n\n// Width of the content area\n$content-width:    1150px !default;\n\n$on-palm:          600px !default;\n$on-palm:          900px !default;\n$on-laptop:        1024px !default;","/**\n * Layout Styles\n */\n $layout: (\n    document: (\n        xl: (\n            width: 1400px,\n        )\n    ),\n    drawer-container: (\n        width: $layout-drawer-width,\n    ),\n    side-doc-outline: (\n        width: 230px,\n    ),\n    page-content: (\n        md: (\n            width: 90%,\n            padding: 0 5%\n        ),\n        lg: (\n            width: calc( 90% - 230px ),\n            padding: 0 5%\n        )\n    )\n);\n\n.mdl-layout__container {\n    margin-top: 76px;\n    height: calc(100% - 76px);   \n}\n\n\n.document {\n    width: 100%;\n    margin: 0 auto;\n    display: flex;\n\n    @media (min-width: $xl-breakpoint) {\n        width: map-get(map-get(map-get($layout, document), xl), width);\n    }\n    .page-content {\n        width: 100%;\n        margin: 0 auto;\n        padding: 0 12px;\n\n        @media (min-width: $md-breakpoint) {\n            width: map-get(map-get(map-get($layout, page-content), md), width);\n            padding: map-get(map-get(map-get($layout, page-content), md), padding);\n        }\n\n        @media (min-width: $lg-breakpoint) {\n            width: map-get(map-get(map-get($layout, page-content), lg), width);\n            padding: map-get(map-get(map-get($layout, page-content), lg), padding);\n        }\n    }\n\n    .side-doc-outline {\n        width: map-get(map-get($layout, side-doc-outline), width);\n\n        @media (max-width: $lg-breakpoint - 1) {\n            display: none;\n        } \n        &--content {\n            position: fixed;\n            overflow-x: auto;\n            overflow-y: auto;\n            width: inherit;\n            &::-webkit-scrollbar {\n                width: 6px;\n            }\n    \n            &::-webkit-scrollbar-track {\n                border-radius: 6px;\n            }\n    \n            &::-webkit-scrollbar-thumb {\n                background-color: rgba(0, 0, 0, .3);\n                border-radius: 6px;\n                box-shadow:0 0 0 1px rgba(255, 255, 255, .3);\n            }\n        }\n    }\n\n}","@keyframes float-in {\n    0% {\n        transform: translateY(0.5rem);\n        opacity: 0;\n    }\n\t100% {\n\t\ttransform: translateY(0);\n\t\topacity: 1;\n\t}\n}\n\n@keyframes float-out {\n    0% {\n        transform: translateY(0);\n        opacity: 1;\n    }\n\t100% {\n\t\ttransform: translateY(0.5rem);\n\t\topacity: 0;\n\t}\n}\n\n.page-content {\n    .headerlink {\n        display: inline-block;\n        text-decoration: none;\n        margin-left: 0.8rem;\n        color: inherit;\n        opacity: 0;\n        &:hover {\n            animation: float-in 0.2s $animation-curve-fast-out-slow-in 0s forwards;\n        }\n    }\n\n    h1, h2, h3, h4, h5, h6 {\n        .toc-backref {\n            text-decoration: none;\n        }\n        &:hover {\n            .headerlink {\n                animation: float-in 0.2s $animation-curve-fast-out-slow-in 0s forwards;\n            }\n        }\n    }\n\n    h1 {\n        font-size: 2rem;\n        line-height: 2.25rem;\n    }\n\n    h2 {\n        font-size: 1.75rem;\n        line-height: 2rem;\n        padding-top: 1.5rem;\n        margin-top: 0;\n        margin-bottom: 1rem;\n    }\n\n    h3 {\n        font-size: 1.5rem;\n        line-height: 1.75rem;\n        padding-top: 1rem;\n        margin-top: 0px;\n        margin-bottom: .75rem;\n    }\n\n    h4 {\n        font-size: 1.25rem;\n        line-height: 1.5rem;\n        padding-top: .75rem;\n        margin-top: 0px;\n        margin-bottom: .5rem;\n    }\n\n    div.page-content h5 {\n        font-size: 1.1rem;\n        line-height: 1.5rem;\n        padding-top: 2rem;\n        margin-top: 0px;\n        margin-bottom: 1rem;\n    }\n\n    div.page-content h6 {\n        font-size: 1rem;\n        line-height: 1.5rem;\n        padding-top: 2rem;\n        margin-top: 0px;\n        margin-bottom: 1rem;\n    }\n\n\n}\n","\n/*\n * Admonition Styles\n */\n $admonitions: (\n    hint: (\n        font-color: rgb(0, 188, 212),\n        background-color: rgba(0, 188, 212, 0.1),\n        icon-content: \"help_outline\"\n    ),\n    note: (\n        font-color: rgb(0, 188, 212),\n        background-color: rgba(0, 188, 212, 0.1),\n        icon-content: \"info_outline\"\n    ),\n    seealso: (\n        font-color: rgb(0, 188, 212),\n        background-color: rgba(0, 188, 212, 0.1),\n        icon-content: \"search\"\n    ),\n    warning: (\n        font-color: rgb(255, 193, 7),\n        background-color: rgba(255, 193, 7, 0.1),\n        icon-content: \"warning\"\n    ),\n    attention: (\n        font-color: rgb(255, 193, 7),\n        background-color: rgba(255, 193, 7, 0.1),\n        icon-content: \"warning\"\n    ),\n    tip: (\n        font-color: rgb(139, 195, 74),\n        background-color: rgba(139, 195, 74, 0.1),\n        icon-content: \"lightbulb_outline\"\n    ),\n    important: (\n        font-color: rgb(139, 195, 74),\n        background-color: rgba(139, 195, 74, 0.1),\n        icon-content:  \"check_circle\"\n    ),\n    error: (\n        font-color: rgb(244, 67, 54),\n        background-color: rgba(244, 67, 54, 0.1),\n        icon-content: \"error_outline\"\n    ),\n    caution: (\n        font-color: rgb(244, 67, 54),\n        background-color: rgba(244, 67, 54, 0.1),\n        icon-content: \"error_outline\"\n    ),\n    danger: (\n        font-color: rgb(244, 67, 54),\n        background-color: rgba(244, 67, 54, 0.1),\n        icon-content: \"error_outline\"\n    )\n);\n\n @mixin admonition-style($type) {\n    border-left: solid 4px map-get(map-get($admonitions, $type), font-color);\n    background-color: map-get(map-get($admonitions, $type), background-color);\n    .admonition-title {\n        font-size: 16px;\n        font-weight: bold;\n        color: map-get(map-get($admonitions, $type), font-color);\n\n        margin-top: 4px;\n        margin-bottom: 8px;\n        &::before {\n            @extend .material-icons;\n            position: relative;\n            margin-right: 5px;\n            top: 3px;\n            content: map-get(map-get($admonitions, $type), icon-content);\n            font-size: 18px;\n        }\n    }\n}\n\n.admonition {\n    @extend .mdl-shadow--2dp;\n\n    padding: 12px 20px;\n    margin-top: 10px;\n    margin-bottom: 10px;\n    p.last {\n        margin: 16px;\n    }\n    .admonition-title {\n        font-size: 16px;\n        font-weight: bold;\n        color: #555;\n        text-transform: uppercase;\n        margin-top: 7px;\n    }\n\n    @each $type in (note, seealso, hint, warning, attention, tip, important, error, caution, danger) {\n        &.#{$type} {\n            @include admonition-style($type);\n        }\n    }\n}\n",".page-content {\n    .highlight {\n        margin: 1px 0;\n        pre {\n            background: $code_background;\n            color: rgba(0,0,0,.87);\n            font-family: $code_font_family;\n            padding: 0.75rem;\n            overflow: auto;\n            overflow-y: hidden;\n            .o, .nd {\n                color: rgba(0,0,0,.87);\n            }\n        }\n    }\n\n    div.highlight-console div.highlight {\n        background: none;\n    }\n\n    // for jupyter notebook output cell\n    .output {\n        .highlight {\n            pre {\n                color: rgba(0,0,0,.87);\n                background: $background_color;\n                border-width: 1px;\n                border-color: #999;\n                border-style: solid;\n                padding: 0.75rem;\n            }\n        }\n     }\n\n    .code, code:not(.download) {\n        margin: 0 0;\n        font-family: $code_font_family;\n        border-radius: 2px;\n        span.pre {\n            font-family: $code_font_family;\n        }\n    }\n\n    .viewcode-link {\n        padding-left: 2em;\n        font-size: 80%;\n    }\n\n    .rubric, .method > dt, .function > dt, .class > dt {\n        display: table;\n        margin: 10px 0;\n        font-size: 100%;\n        line-height: normal;\n        background: #e7f2fa;\n        color: #2B98F0;\n        border-top: solid 3px #55ADF3;\n        padding: 10px;\n        position: relative;\n        .descname, .descclassname {\n            color: rgba(0,0,0,.87);\n            background: #e7f2fa;\n            padding: 3px;\n        }\n        em {\n            padding: 0 2px;\n        }\n    }\n\n\n    .rubric {\n        margin: 30px 0 10px 0;\n     }\n\n\n    .field-body {\n        padding-left: 40px;\n        ul {\n            padding: 0 0 0 16px;\n            margin: 0;\n        }\n    }\n\n     // .docutils > dt {\n    //     padding: 6px;\n    //     display: table;\n    //     margin-bottom: 6px;\n    //     border: none;\n    //     border-left: solid 3px #ccc;\n    //     background: #f0f0f0;\n    //     color: #555;\n    // }\n\n    .seealso .docutils > dt {\n       float: left;\n       clear: left;\n       padding: 0 6px;\n     }\n\n    .seealso .docutils > dd {\n       padding-left: 6em;\n    }\n    .nblast {\n        padding-bottom: 1em;\n    }\n\n    pre {\n        font-size: 90%;\n        background: #eee;\n        color: #455A64;\n        padding: 16px 32px;\n        width: auto;\n        border-radius: 4px;\n        word-wrap: break-word;\n\n        &:hover {\n            @extend .mdl-shadow--2dp;\n\n            &:before {\n                font-family: $body_font_family;\n                padding: 0 0.5rem;\n                content: attr(click-to-copy);\n                color: rgba(0, 0, 0, 0.5);\n                border-radius: 4px;\n                position: relative;\n                float: right;\n                top: -0.5rem;\n                right: -0.5rem;\n                background: rgb(200, 200, 200);\n                font-size: 0.8rem;\n                cursor: pointer;\n            }\n        }\n    }\n}\n","/*\n * Quotation Block Styles\n */\n .page-content {\n    blockquote {\n        font-size: 1rem;\n        padding: 0 1rem;\n        border-left: 3px solid $code_background;\n\n        &:after {\n            content: \"\" !important;\n            margin-left: 0;\n        }\n        &:before {\n            content: \"\" !important;\n        }\n    }\n }\n",".page-content {\n    table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) {\n        @extend .mdl-data-table;\n        @extend .mdl-shadow--2dp;\n\n        margin: 1.5rem 0;\n        table-layout: fixed;\n        max-width: 100%;\n        min-width: 70%;\n\n        th, td {\n            @extend .mdl-data-table__cell--non-numeric;\n            white-space: normal;\n            overflow-wrap: break-word;\n        }\n\n        caption {\n            font-size: $font_size;\n            margin: 1rem 0 0.8rem 0;\n            white-space: normal;\n            .caption-number {\n                font-style: normal;\n            }\n            .caption-number::after {\n                content: \"\\00a0\";\n            }\n        }\n\n    }\n}\n",".globaltoc {\n    \n    .caption, .toc {\n        display: none;\n    }\n\n    ul {\n\n        list-style-type: none;\n        padding: 0;\n        margin: 0;\n\n        li {\n            min-height: 36px;\n            .link-wrapper {\n                display: flex;\n                justify-content: space-between;\n                > a {\n                    padding: 8px 0;\n                    display: block;\n                    width: 100%;\n                    font-size: 1rem;\n                    text-decoration: none;\n                    color: $layout-drawer-navigation-color;\n                    &.current {\n                        font-weight: bold;\n                    }\n                }\n            }\n        }\n    }\n\n    .nav-toggle {\n        padding: 0;\n        float: right;\n        display: flex;\n        align-items: center;\n        justify-content: center;\n        height: 36px;\n        > a {\n            padding: 0;\n            margin-left: 0;\n            margin-right: 4px;\n            cursor: pointer;\n            > i {\n                font-size: 18px;\n            }\n        }\n        &.show {\n            transform: rotateZ(180deg);\n            > a {\n                margin-right: 0;\n                margin-left: 4px;\n            }\n        }\n    }\n\n    nav {\n        > ul > li > span.link-wrapper {\n            padding-left: 8px;\n        }\n        > ul > li > ul > li > span.link-wrapper {\n            padding-left: 16px;\n        }\n        > ul > li > ul > li > ul > li > span.link-wrapper {\n            padding-left: 24px;\n        }\n        > ul > li > ul > li > ul > li > ul> li > span.link-wrapper {\n            padding-left: 32px;\n        }\n        > ul > li > ul > li > ul > li > ul > li > ul> li > span.link-wrapper {\n            padding-left: 40px;\n        }\n        > ul > li > ul > li > ul > li > ul > li > ul > li > ul> li > span.link-wrapper {\n            padding-left: 48px;\n        }\n    }\n}\n",".localtoc {\n    font-size: 0.75rem;\n    padding-top: 1rem;\n\n    .caption {\n        padding-left: 12px;\n        &-text {\n            font-size: 0.9rem;\n            font-weight: 700;\n        }\n    }\n\n    > ul > li > a {\n        display: none;\n    }\n\n    ul {\n        padding: 0;\n        list-style-type: none;\n    }\n\n    li {\n        padding-left: 6px;\n    }\n\n    a {\n        display: block;\n        text-decoration: none;\n        color: inherit;\n        margin-top: 8px;\n        padding-left: 8px;\n        line-height: 1.1rem;\n    \n        &.current {\n            padding-left: 5px;\n            border-left: 3px solid;\n            font-weight: bold;\n        }\n    }\n}","/*\r\n *  Toctree and Contents Directive Styles\r\n */\r\n .toctree-wrapper,\r\n .contents.topic {\r\n     border-left: 5px solid;\r\n }\r\n\r\n .toctree-wrapper > p.caption,\r\n .contents.topic > p.topic-title {\r\n     color: rgb(117, 117, 117);\r\n     font-size: 1rem;\r\n     padding-left: 14px;\r\n }\r\n\r\n .toctree-wrapper ul,\r\n .contents.topic ul{\r\n     padding-left: 14px;\r\n     list-style: none;\r\n     line-height: 30px;\r\n }\r\n\r\n .toctree-wrapper a,\r\n .contents.topic a {\r\n     font-size: 1.2rem;\r\n     text-decoration: none;\r\n     .pre {\r\n         font-size: 1rem;\r\n     }\r\n }\r\n\r\n .toctree-wrapper > ul > li > a,\r\n .contents.topic > ul > li > a {\r\n     font-size: 1.3rem;\r\n     .pre {\r\n         font-size: 1.1rem;\r\n     }\r\n }\r\n",".page-content {\n    ul {\n        li {\n            margin: .3rem 0;\n            p {\n                margin: 0;\n            }\n        }\n    }\n    .option-list {\n        .option {\n            font-family: $code_font_family;\n        }\n        td {\n            padding: 0.5rem;\n            border: none;\n        }\n    }\n}\n","/*\r\n * Drawer Styles\r\n */\r\n.mdl-layout {\r\n    &__drawer {\r\n        background-color: #fff;\r\n\r\n        &::-webkit-scrollbar {\r\n            width: 6px;\r\n        }\r\n\r\n        &::-webkit-scrollbar-track {\r\n            border-radius: 6px;\r\n        }\r\n\r\n        &::-webkit-scrollbar-thumb {\r\n            background-color: rgba(0, 0, 0, .3);\r\n            border-radius: 6px;\r\n            box-shadow:0 0 0 1px rgba(255, 255, 255, .3);\r\n        }\r\n\r\n        > .mdl-layout-title {\r\n            font-weight: bold;\r\n            text-align: right;\r\n            margin: 0;\r\n            padding: 0;\r\n            line-height: 32px;\r\n            border-bottom: 1px solid rgba(0,0,0,.1);\r\n            min-height: 64px;\r\n            .title {\r\n                color: inherit;\r\n                display: block;\r\n                height: 100%;\r\n                width: 100%;\r\n                text-decoration: none;\r\n                > img.logo {\r\n                    width: 100%;\r\n                    margin: 0;\r\n                    padding: 0;\r\n                }\r\n\r\n                &-text {\r\n                    font-weight: bold;\r\n                    text-align: right;\r\n                    padding: 0 10px;\r\n                    margin: 16px 0 8px 0;\r\n                    line-height: 32px;\r\n                    font-family: $body_font_family;\r\n                    color: inherit;\r\n                    display: block;\r\n                }\r\n            }\r\n        }\r\n    }\r\n}\r\n","/*\r\n * Header Styles\r\n */\r\n\r\nnav.breadcrumb {\r\n    > a.mdl-navigation__link {\r\n        padding: 0 8px;\r\n        font-size: 18px;\r\n    }\r\n    @media (max-width: $lg-breakpoint - 1) {\r\n        width: calc( 100% - 64px );\r\n        a.mdl-navigation__link.is-active {\r\n            overflow-x: hidden;\r\n            width: 100%;\r\n            overflow: hidden;\r\n            white-space: nowrap;\r\n            text-overflow: ellipsis;\r\n        }\r\n        a.mdl-navigation__link:not(.is-active),\r\n        i.material-icons {\r\n            display: none;\r\n        }\r\n    }\r\n}\r\n\r\ndiv.mdl-layout__header {\r\n    margin-top: 77px;\r\n}\r\n\r\n.mdl-layout__drawer-button {\r\n    top: 13px !important;\r\n}\r\n\r\ndiv.mdl-layout__header-row.header-links {\r\n    background: rgba(255,255,255,0.2);\r\n    width: 100%;\r\n    overflow-x: auto;\r\n    overflow-y: hidden;\r\n\r\n    a.mdl-navigation__link  {\r\n        font-size: 1rem;\r\n        i {\r\n            font-size: 1.2rem;\r\n            margin: 0 8px;\r\n            position: relative;\r\n            bottom: -0.1rem;\r\n        }\r\n    };\r\n\r\n    a.mdl-navigation__link:hover  {\r\n        background-color: unquote(\"rgb(#{$color-primary})\");\r\n        color: #eeeeee;\r\n    };\r\n    a.mdl-navigation__link[href=\"#\"]  {\r\n        background-color: unquote(\"rgb(#{$color-primary})\");\r\n        opacity: 1;\r\n        color: #ffffff;\r\n    };\r\n}\r\n\r\n/* mxnet-header */\r\n\r\n\r\n.site-title {\r\n  font-weight: 300 !important;\r\n  line-height: 57px;\r\n  letter-spacing: -1px;\r\n  margin-bottom: 0;\r\n  float: left;\r\n  color: white;\r\n\r\n  &,\r\n  &:visited {\r\n    color: $grey-color-dark;\r\n  }\r\n}\r\n\r\n\r\n.site-header {\r\n  position: fixed;\r\n  top: 0;\r\n  width: 100%;\r\n  min-height: 55px;\r\n  padding-top: 10px;\r\n  padding-bottom: 10px;\r\n  background-color: $color-mxnet;\r\n  z-index: 10;\r\n  font-weight: 300;\r\n  font-size: 17px;\r\n  border-bottom: 1px solid white;\r\n}\r\n\r\n.site-header-logo {\r\n  width: 120px;\r\n  display: initial;\r\n}\r\n\r\n.site-nav {\r\n  float: right;\r\n  line-height: 57px;\r\n\r\n  .nav-trigger {\r\n    display: none;\r\n  }\r\n\r\n  .menu-icon {\r\n    display: none;\r\n  }\r\n\r\n  .page-link {\r\n    color: white;\r\n    line-height: 1.5;\r\n    font-weight: 300;\r\n    // Gaps between nav items, but not on the last one\r\n    &:not(:last-child) {\r\n      margin-right: 40px;\r\n    }\r\n\r\n    &:hover {\r\n      color: white;\r\n      text-shadow: -0.06ex 0 white, 0.06ex 0 white;\r\n    }\r\n  }\r\n\r\n  .page-link.page-current {\r\n    color: white;\r\n    text-decoration: underline;\r\n  }\r\n\r\n  @media screen and (max-width: $on-laptop) {\r\n    position: absolute;\r\n    top: 9px;\r\n    right: 15px;\r\n    background-color: rgb(23,141,201);\r\n    border-radius: 2px;\r\n    text-align: right;\r\n\r\n    label[for=\"nav-trigger\"] {\r\n      display: block;\r\n      float: right;\r\n      width: 36px;\r\n      height: 36px;\r\n      z-index: 2;\r\n      cursor: pointer;\r\n    }\r\n\r\n    .menu-icon {\r\n      display: block;\r\n      float: right;\r\n      width: 36px;\r\n      height: 26px;\r\n      line-height: 0;\r\n      padding-top: 20px;\r\n      text-align: center;\r\n\r\n      > svg {\r\n        fill: white;\r\n      }\r\n    }\r\n\r\n    input ~ .trigger {\r\n      clear: both;\r\n      display: none;\r\n    }\r\n\r\n    input:checked ~ .trigger {\r\n      display: block;\r\n      padding-bottom: 5px;\r\n    }\r\n\r\n    .page-link {\r\n      padding: 5px 10px;\r\n      display: block;\r\n\r\n      &:not(:last-child) {\r\n        margin-right: 0;\r\n      }\r\n\r\n      margin-left: 20px;\r\n    }\r\n  }\r\n}","/*\r\n * Footer Styles\r\n */\r\nfooter.mdl-mini-footer {\r\n    background-color: #212121;\r\n    > div.mdl-mini-footer__left-section {\r\n        margin-bottom: 20px;\r\n        display: flex;\r\n        flex-direction: column;\r\n        .mdl-logo {\r\n            font-size: 1.1rem;\r\n        }\r\n        ul {\r\n            @extend .mdl-mini-footer__link-list;\r\n        }\r\n    }\r\n    > div.mdl-mini-footer__right-section {\r\n        font-size: 0.9rem;\r\n        display: flex;\r\n        flex-direction: column;\r\n        justify-content: flex-end;\r\n\r\n        a {\r\n            color: inherit;\r\n            font-weight: bold;\r\n            text-decoration: none;\r\n        }\r\n    }\r\n    p.caption {\r\n        display: none;\r\n    }\r\n}\r\n\r\n/*\r\n * Pagenation Block Styles\r\n */\r\n .pagenation {\r\n    width: 100%;\r\n    margin-top: 80px;\r\n    height: 92px;\r\n    background-color: #424242;\r\n    display: flex;\r\n\r\n    .button-common {\r\n        text-transform: none;\r\n        padding: 0;\r\n        height: 92px;\r\n        display: flex;\r\n        justify-content: center;\r\n        align-items: center;\r\n        color: #ffffff;\r\n    }\r\n    #button-prev {\r\n        @extend .button-common;\r\n        margin-right: auto;\r\n        .pagenation-text {\r\n            text-align: left;\r\n        }\r\n        \r\n    }\r\n    #button-next {\r\n        @extend .button-common;\r\n        margin-left: auto;\r\n        flex-direction: row-reverse;\r\n        .pagenation-text {\r\n            text-align: right;\r\n        }\r\n    }\r\n\r\n    &-arrow {\r\n        &-L {\r\n            margin-right: 20px;\r\n        }\r\n        &-R {\r\n            margin-left: 20px;\r\n        }\r\n    }\r\n\r\n    &-text {\r\n        line-height: 30px;\r\n        font-size: 20px;\r\n    }\r\n\r\n    &-direction {\r\n        opacity: 0.7;\r\n        font-size: 18px;\r\n    }\r\n    @media screen and (max-width: 1024px) {\r\n        #button-prev {\r\n            width: 20%;\r\n        }\r\n        \r\n        #button-next {\r\n            width: 80%;\r\n        }\r\n    \r\n        #button-prev .pagenation-text {\r\n            display: none;\r\n        }\r\n    }\r\n    @media screen and (min-width: 1025px) {\r\n        #button-prev,\r\n        #button-next {\r\n            width: 50%;\r\n        }\r\n    \r\n        #button-prev .pagenation-text {\r\n            display: block;\r\n        }\r\n    }\r\n}\r\n\r\n\r\n\r\n/**\r\n * Site footer\r\n */\r\n.site-footer {\r\n  border-top: 1px solid $grey-color-light;\r\n  padding: $spacing-unit 0;\r\n  background-color: #424242;\r\n  .footer-category-title {\r\n    color: $color-mxnet;\r\n  }\r\n  a {\r\n    color: $grey-color-light !important;\r\n\r\n    &:visited {\r\n      color: $grey-color-light !important;\r\n    }\r\n  }\r\n\r\n}\r\n\r\n.site-footer2 {\r\n  background-color: #424242;\r\n  padding-top: 40px;\r\n  padding-bottom: 10px;\r\n}\r\n\r\n.footer-heading {\r\n  margin-bottom: $spacing-unit / 2;\r\n}\r\n\r\n.contact-list,\r\n.social-media-list {\r\n  list-style: none;\r\n  margin-left: 0;\r\n}\r\n\r\n\r\n.footer-bottom-warning {\r\n  font-size: 80%;\r\n  color: white;\r\n  float: left;\r\n}\r\n\r\n.footer-logo {\r\n  width: 200px;\r\n  margin-bottom: 30px;\r\n  margin-top: 30px;\r\n}\r\n\r\n.footer-col {\r\n  float: left;\r\n  margin-bottom: $spacing-unit / 2;\r\n  padding-left: $spacing-unit / 2;\r\n}\r\n\r\n.footer-text {\r\n  color: $grey-color-light;\r\n}\r\n\r\n"," /*\r\n  * Search Styles\r\n  */\r\n#waterfall-exp::-webkit-input-placeholder {\r\n    color: #ccc;\r\n}\r\n#waterfall-exp:-ms-input-placeholder {\r\n    color: #ccc;\r\n}\r\n#waterfall-exp::-moz-placeholder {\r\n    color: #ccc;\r\n}\r\n\r\nul.search span.highlighted {\r\n    font-weight: bold;\r\n}\r\n\r\nul.search > li {\r\n    margin-bottom: 24px;\r\n}\r\n\r\n#search-results {\r\n    ul {\r\n        list-style: none;\r\n        padding: 0;\r\n        li {\r\n            > a {\r\n                text-decoration: none;\r\n                font-size: 1.2rem;\r\n            }\r\n        }\r\n    }\r\n}\r\n","a.download {\n    &:before {\n        @extend .material-icons;\n        content: \"file_download\";\n        position: relative;\n        top: 5px;\n        margin-right: 5px;\n    }\n}\n\nbutton.download {\n    position: sticky;\n    margin-left: 1em;\n}\n",".mdl-card {\n    margin: 1em 1.5em 1em 0;\n    display: inline-block;\n    width: 250px;\n    min-height: 140px;\n    padding: 18px;\n}\n.mdl-card:hover {\n    box-shadow: 0 10px 20px rgba(0,0,0,0.25), 0 6px 6px rgba(0,0,0,0.22);\n    color: #000;\n    cursor: pointer;\n}\n.mdl-card__title {\n    padding: 0 0 1em 0;\n    font-size: 18px;\n    color: #444;\n}\n\n.mdl-card__supporting-text {\n    line-height: 1.5rem;\n    padding: 0px;\n    width: 100%;\n}\n\n.head-card.mdl-card {\n    width: auto;\n    display: block;\n    max-width: 800px;\n    padding: 24px;\n}\n\n.head-card > .mdl-card__title {\n    padding-bottom: 0px;\n    height: 60px;\n    font-weight: 700;\n    text-transform: uppercase;\n}\n.head-card > .mdl-card__menu {\n    color: #fff;\n}\n.head-card > .mdl-card__actions {\n    padding: 0;\n}\n.cards {\n    display: flex;\n    flex-direction: row;\n    flex-wrap: wrap;\n}\n"]}
\ No newline at end of file
diff --git a/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.js b/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.js
new file mode 100644
index 000000000000..6a18c4558a2b
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.js
@@ -0,0 +1,705 @@
+parcelRequire=function(e,r,t,n){var i,o="function"==typeof parcelRequire&&parcelRequire,u="function"==typeof require&&require;function f(t,n){if(!r[t]){if(!e[t]){var i="function"==typeof parcelRequire&&parcelRequire;if(!n&&i)return i(t,!0);if(o)return o(t,!0);if(u&&"string"==typeof t)return u(t);var c=new Error("Cannot find module '"+t+"'");throw c.code="MODULE_NOT_FOUND",c}p.resolve=function(r){return e[t][1][r]||r},p.cache={};var l=r[t]=new f.Module(t);e[t][0].call(l.exports,p,l,l.exports,this)}return r[t].exports;function p(e){return f(p.resolve(e))}}f.isParcelRequire=!0,f.Module=function(e){this.id=e,this.bundle=f,this.exports={}},f.modules=e,f.cache=r,f.parent=o,f.register=function(r,t){e[r]=[function(e,r){r.exports=t},{}]};for(var c=0;c<t.length;c++)try{f(t[c])}catch(e){i||(i=e)}if(t.length){var l=f(t[t.length-1]);"object"==typeof exports&&"undefined"!=typeof module?module.exports=l:"function"==typeof define&&define.amd?define(function(){return l}):n&&(this[n]=l)}if(parcelRequire=f,i)throw i;return f}({"BS4D":[function(require,module,exports) {
+
+},{}],"vKy7":[function(require,module,exports) {
+function e(t){return(e="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof Symbol&&e.constructor===Symbol&&e!==Symbol.prototype?"symbol":typeof e})(t)}!function(){"use strict";function t(e,t){if(e){if(t.element_.classList.contains(t.CssClasses_.MDL_JS_RIPPLE_EFFECT)){var s=document.createElement("span");s.classList.add(t.CssClasses_.MDL_RIPPLE_CONTAINER),s.classList.add(t.CssClasses_.MDL_JS_RIPPLE_EFFECT);var i=document.createElement("span");i.classList.add(t.CssClasses_.MDL_RIPPLE),s.appendChild(i),e.appendChild(s)}e.addEventListener("click",function(s){if("#"===e.getAttribute("href").charAt(0)){s.preventDefault();var i=e.href.split("#")[1],n=t.element_.querySelector("#"+i);t.resetTabState_(),t.resetPanelState_(),e.classList.add(t.CssClasses_.ACTIVE_CLASS),n.classList.add(t.CssClasses_.ACTIVE_CLASS)}})}}function s(e,t,s,i){function n(){var n=e.href.split("#")[1],a=i.content_.querySelector("#"+n);i.resetTabState_(t),i.resetPanelState_(s),e.classList.add(i.CssClasses_.IS_ACTIVE),a.classList.add(i.CssClasses_.IS_ACTIVE)}if(i.tabBar_.classList.contains(i.CssClasses_.JS_RIPPLE_EFFECT)){var a=document.createElement("span");a.classList.add(i.CssClasses_.RIPPLE_CONTAINER),a.classList.add(i.CssClasses_.JS_RIPPLE_EFFECT);var l=document.createElement("span");l.classList.add(i.CssClasses_.RIPPLE),a.appendChild(l),e.appendChild(a)}i.tabBar_.classList.contains(i.CssClasses_.TAB_MANUAL_SWITCH)||e.addEventListener("click",function(t){"#"===e.getAttribute("href").charAt(0)&&(t.preventDefault(),n())}),e.show=n}var i={upgradeDom:function(e,t){},upgradeElement:function(e,t){},upgradeElements:function(e){},upgradeAllRegistered:function(){},registerUpgradedCallback:function(e,t){},register:function(e){},downgradeElements:function(e){}};(i=function(){function t(e,t){for(var s=0;s<r.length;s++)if(r[s].className===e)return void 0!==t&&(r[s]=t),r[s];return!1}function s(e){var t=e.getAttribute("data-upgraded");return null===t?[""]:t.split(",")}function i(e,t){return-1!==s(e).indexOf(t)}function n(e,t,s){if("CustomEvent"in window&&"function"==typeof window.CustomEvent)return new CustomEvent(e,{bubbles:t,cancelable:s});var i=document.createEvent("Events");return i.initEvent(e,t,s),i}function a(e,s){if(void 0===e&&void 0===s)for(var i=0;i<r.length;i++)a(r[i].className,r[i].cssClass);else{var n=e;if(void 0===s){var o=t(n);o&&(s=o.cssClass)}for(var _=document.querySelectorAll("."+s),d=0;d<_.length;d++)l(_[d],n)}}function l(a,l){if(!("object"==e(a)&&a instanceof Element))throw new Error("Invalid argument provided to upgrade MDL element.");var o=n("mdl-componentupgrading",!0,!0);if(a.dispatchEvent(o),!o.defaultPrevented){var h=s(a),c=[];if(l)i(a,l)||c.push(t(l));else{var p=a.classList;r.forEach(function(e){p.contains(e.cssClass)&&-1===c.indexOf(e)&&!i(a,e.className)&&c.push(e)})}for(var C,u=0,E=c.length;u<E;u++){if(!(C=c[u]))throw new Error("Unable to find a registered component for the given class.");h.push(C.className),a.setAttribute("data-upgraded",h.join(","));var m=new C.classConstructor(a);m[d]=C,_.push(m);for(var L=0,I=C.callbacks.length;L<I;L++)C.callbacks[L](a);C.widget&&(a[C.className]=m);var f=n("mdl-componentupgraded",!0,!1);a.dispatchEvent(f)}}}function o(e){if(e){var t=_.indexOf(e);_.splice(t,1);var s=e.element_.getAttribute("data-upgraded").split(","),i=s.indexOf(e[d].classAsString);s.splice(i,1),e.element_.setAttribute("data-upgraded",s.join(","));var a=n("mdl-componentdowngraded",!0,!1);e.element_.dispatchEvent(a)}}var r=[],_=[],d="mdlComponentConfigInternal_";return{upgradeDom:a,upgradeElement:l,upgradeElements:function e(t){Array.isArray(t)||(t=t instanceof Element?[t]:Array.prototype.slice.call(t));for(var s,i=0,n=t.length;i<n;i++)(s=t[i])instanceof HTMLElement&&(l(s),s.children.length>0&&e(s.children))},upgradeAllRegistered:function(){for(var e=0;e<r.length;e++)a(r[e].className)},registerUpgradedCallback:function(e,s){var i=t(e);i&&i.callbacks.push(s)},register:function(e){var s=!0;void 0===e.widget&&void 0===e.widget||(s=e.widget||e.widget);var i={classConstructor:e.constructor||e.constructor,className:e.classAsString||e.classAsString,cssClass:e.cssClass||e.cssClass,widget:s,callbacks:[]};if(r.forEach(function(e){if(e.cssClass===i.cssClass)throw new Error("The provided cssClass has already been registered: "+e.cssClass);if(e.className===i.className)throw new Error("The provided className has already been registered")}),e.constructor.prototype.hasOwnProperty(d))throw new Error("MDL component classes must not have "+d+" defined as a property.");t(e.classAsString,i)||r.push(i)},downgradeElements:function(e){var t=function(e){_.filter(function(t){return t.element_===e}).forEach(o)};if(e instanceof Array||e instanceof NodeList)for(var s=0;s<e.length;s++)t(e[s]);else{if(!(e instanceof Node))throw new Error("Invalid argument provided to downgrade MDL nodes.");t(e)}}}}()).ComponentConfigPublic,i.ComponentConfig,i.Component,i.upgradeDom=i.upgradeDom,i.upgradeElement=i.upgradeElement,i.upgradeElements=i.upgradeElements,i.upgradeAllRegistered=i.upgradeAllRegistered,i.registerUpgradedCallback=i.registerUpgradedCallback,i.register=i.register,i.downgradeElements=i.downgradeElements,window.componentHandler=i,window.componentHandler=i,window.addEventListener("load",function(){"classList"in document.createElement("div")&&"querySelector"in document&&"addEventListener"in window&&Array.prototype.forEach?(document.documentElement.classList.add("mdl-js"),i.upgradeAllRegistered()):(i.upgradeElement=function(){},i.register=function(){})}),Date.now||(Date.now=function(){return(new Date).getTime()},Date.now=Date.now);for(var n=["webkit","moz"],a=0;a<n.length&&!window.requestAnimationFrame;++a){var l=n[a];window.requestAnimationFrame=window[l+"RequestAnimationFrame"],window.cancelAnimationFrame=window[l+"CancelAnimationFrame"]||window[l+"CancelRequestAnimationFrame"],window.requestAnimationFrame=window.requestAnimationFrame,window.cancelAnimationFrame=window.cancelAnimationFrame}if(/iP(ad|hone|od).*OS 6/.test(window.navigator.userAgent)||!window.requestAnimationFrame||!window.cancelAnimationFrame){var o=0;window.requestAnimationFrame=function(e){var t=Date.now(),s=Math.max(o+16,t);return setTimeout(function(){e(o=s)},s-t)},window.cancelAnimationFrame=clearTimeout,window.requestAnimationFrame=window.requestAnimationFrame,window.cancelAnimationFrame=window.cancelAnimationFrame}var r=function(e){this.element_=e,this.init()};window.MaterialButton=r,r.prototype.Constant_={},r.prototype.CssClasses_={RIPPLE_EFFECT:"mdl-js-ripple-effect",RIPPLE_CONTAINER:"mdl-button__ripple-container",RIPPLE:"mdl-ripple"},r.prototype.blurHandler_=function(e){e&&this.element_.blur()},r.prototype.disable=function(){this.element_.disabled=!0},r.prototype.disable=r.prototype.disable,r.prototype.enable=function(){this.element_.disabled=!1},r.prototype.enable=r.prototype.enable,r.prototype.init=function(){if(this.element_){if(this.element_.classList.contains(this.CssClasses_.RIPPLE_EFFECT)){var e=document.createElement("span");e.classList.add(this.CssClasses_.RIPPLE_CONTAINER),this.rippleElement_=document.createElement("span"),this.rippleElement_.classList.add(this.CssClasses_.RIPPLE),e.appendChild(this.rippleElement_),this.boundRippleBlurHandler=this.blurHandler_.bind(this),this.rippleElement_.addEventListener("mouseup",this.boundRippleBlurHandler),this.element_.appendChild(e)}this.boundButtonBlurHandler=this.blurHandler_.bind(this),this.element_.addEventListener("mouseup",this.boundButtonBlurHandler),this.element_.addEventListener("mouseleave",this.boundButtonBlurHandler)}},i.register({constructor:r,classAsString:"MaterialButton",cssClass:"mdl-js-button",widget:!0});var _=function(e){this.element_=e,this.init()};window.MaterialCheckbox=_,_.prototype.Constant_={TINY_TIMEOUT:.001},_.prototype.CssClasses_={INPUT:"mdl-checkbox__input",BOX_OUTLINE:"mdl-checkbox__box-outline",FOCUS_HELPER:"mdl-checkbox__focus-helper",TICK_OUTLINE:"mdl-checkbox__tick-outline",RIPPLE_EFFECT:"mdl-js-ripple-effect",RIPPLE_IGNORE_EVENTS:"mdl-js-ripple-effect--ignore-events",RIPPLE_CONTAINER:"mdl-checkbox__ripple-container",RIPPLE_CENTER:"mdl-ripple--center",RIPPLE:"mdl-ripple",IS_FOCUSED:"is-focused",IS_DISABLED:"is-disabled",IS_CHECKED:"is-checked",IS_UPGRADED:"is-upgraded"},_.prototype.onChange_=function(e){this.updateClasses_()},_.prototype.onFocus_=function(e){this.element_.classList.add(this.CssClasses_.IS_FOCUSED)},_.prototype.onBlur_=function(e){this.element_.classList.remove(this.CssClasses_.IS_FOCUSED)},_.prototype.onMouseUp_=function(e){this.blur_()},_.prototype.updateClasses_=function(){this.checkDisabled(),this.checkToggleState()},_.prototype.blur_=function(){window.setTimeout(function(){this.inputElement_.blur()}.bind(this),this.Constant_.TINY_TIMEOUT)},_.prototype.checkToggleState=function(){this.inputElement_.checked?this.element_.classList.add(this.CssClasses_.IS_CHECKED):this.element_.classList.remove(this.CssClasses_.IS_CHECKED)},_.prototype.checkToggleState=_.prototype.checkToggleState,_.prototype.checkDisabled=function(){this.inputElement_.disabled?this.element_.classList.add(this.CssClasses_.IS_DISABLED):this.element_.classList.remove(this.CssClasses_.IS_DISABLED)},_.prototype.checkDisabled=_.prototype.checkDisabled,_.prototype.disable=function(){this.inputElement_.disabled=!0,this.updateClasses_()},_.prototype.disable=_.prototype.disable,_.prototype.enable=function(){this.inputElement_.disabled=!1,this.updateClasses_()},_.prototype.enable=_.prototype.enable,_.prototype.check=function(){this.inputElement_.checked=!0,this.updateClasses_()},_.prototype.check=_.prototype.check,_.prototype.uncheck=function(){this.inputElement_.checked=!1,this.updateClasses_()},_.prototype.uncheck=_.prototype.uncheck,_.prototype.init=function(){if(this.element_){this.inputElement_=this.element_.querySelector("."+this.CssClasses_.INPUT);var e=document.createElement("span");e.classList.add(this.CssClasses_.BOX_OUTLINE);var t=document.createElement("span");t.classList.add(this.CssClasses_.FOCUS_HELPER);var s=document.createElement("span");if(s.classList.add(this.CssClasses_.TICK_OUTLINE),e.appendChild(s),this.element_.appendChild(t),this.element_.appendChild(e),this.element_.classList.contains(this.CssClasses_.RIPPLE_EFFECT)){this.element_.classList.add(this.CssClasses_.RIPPLE_IGNORE_EVENTS),this.rippleContainerElement_=document.createElement("span"),this.rippleContainerElement_.classList.add(this.CssClasses_.RIPPLE_CONTAINER),this.rippleContainerElement_.classList.add(this.CssClasses_.RIPPLE_EFFECT),this.rippleContainerElement_.classList.add(this.CssClasses_.RIPPLE_CENTER),this.boundRippleMouseUp=this.onMouseUp_.bind(this),this.rippleContainerElement_.addEventListener("mouseup",this.boundRippleMouseUp);var i=document.createElement("span");i.classList.add(this.CssClasses_.RIPPLE),this.rippleContainerElement_.appendChild(i),this.element_.appendChild(this.rippleContainerElement_)}this.boundInputOnChange=this.onChange_.bind(this),this.boundInputOnFocus=this.onFocus_.bind(this),this.boundInputOnBlur=this.onBlur_.bind(this),this.boundElementMouseUp=this.onMouseUp_.bind(this),this.inputElement_.addEventListener("change",this.boundInputOnChange),this.inputElement_.addEventListener("focus",this.boundInputOnFocus),this.inputElement_.addEventListener("blur",this.boundInputOnBlur),this.element_.addEventListener("mouseup",this.boundElementMouseUp),this.updateClasses_(),this.element_.classList.add(this.CssClasses_.IS_UPGRADED)}},i.register({constructor:_,classAsString:"MaterialCheckbox",cssClass:"mdl-js-checkbox",widget:!0});var d=function(e){this.element_=e,this.init()};window.MaterialIconToggle=d,d.prototype.Constant_={TINY_TIMEOUT:.001},d.prototype.CssClasses_={INPUT:"mdl-icon-toggle__input",JS_RIPPLE_EFFECT:"mdl-js-ripple-effect",RIPPLE_IGNORE_EVENTS:"mdl-js-ripple-effect--ignore-events",RIPPLE_CONTAINER:"mdl-icon-toggle__ripple-container",RIPPLE_CENTER:"mdl-ripple--center",RIPPLE:"mdl-ripple",IS_FOCUSED:"is-focused",IS_DISABLED:"is-disabled",IS_CHECKED:"is-checked"},d.prototype.onChange_=function(e){this.updateClasses_()},d.prototype.onFocus_=function(e){this.element_.classList.add(this.CssClasses_.IS_FOCUSED)},d.prototype.onBlur_=function(e){this.element_.classList.remove(this.CssClasses_.IS_FOCUSED)},d.prototype.onMouseUp_=function(e){this.blur_()},d.prototype.updateClasses_=function(){this.checkDisabled(),this.checkToggleState()},d.prototype.blur_=function(){window.setTimeout(function(){this.inputElement_.blur()}.bind(this),this.Constant_.TINY_TIMEOUT)},d.prototype.checkToggleState=function(){this.inputElement_.checked?this.element_.classList.add(this.CssClasses_.IS_CHECKED):this.element_.classList.remove(this.CssClasses_.IS_CHECKED)},d.prototype.checkToggleState=d.prototype.checkToggleState,d.prototype.checkDisabled=function(){this.inputElement_.disabled?this.element_.classList.add(this.CssClasses_.IS_DISABLED):this.element_.classList.remove(this.CssClasses_.IS_DISABLED)},d.prototype.checkDisabled=d.prototype.checkDisabled,d.prototype.disable=function(){this.inputElement_.disabled=!0,this.updateClasses_()},d.prototype.disable=d.prototype.disable,d.prototype.enable=function(){this.inputElement_.disabled=!1,this.updateClasses_()},d.prototype.enable=d.prototype.enable,d.prototype.check=function(){this.inputElement_.checked=!0,this.updateClasses_()},d.prototype.check=d.prototype.check,d.prototype.uncheck=function(){this.inputElement_.checked=!1,this.updateClasses_()},d.prototype.uncheck=d.prototype.uncheck,d.prototype.init=function(){if(this.element_){if(this.inputElement_=this.element_.querySelector("."+this.CssClasses_.INPUT),this.element_.classList.contains(this.CssClasses_.JS_RIPPLE_EFFECT)){this.element_.classList.add(this.CssClasses_.RIPPLE_IGNORE_EVENTS),this.rippleContainerElement_=document.createElement("span"),this.rippleContainerElement_.classList.add(this.CssClasses_.RIPPLE_CONTAINER),this.rippleContainerElement_.classList.add(this.CssClasses_.JS_RIPPLE_EFFECT),this.rippleContainerElement_.classList.add(this.CssClasses_.RIPPLE_CENTER),this.boundRippleMouseUp=this.onMouseUp_.bind(this),this.rippleContainerElement_.addEventListener("mouseup",this.boundRippleMouseUp);var e=document.createElement("span");e.classList.add(this.CssClasses_.RIPPLE),this.rippleContainerElement_.appendChild(e),this.element_.appendChild(this.rippleContainerElement_)}this.boundInputOnChange=this.onChange_.bind(this),this.boundInputOnFocus=this.onFocus_.bind(this),this.boundInputOnBlur=this.onBlur_.bind(this),this.boundElementOnMouseUp=this.onMouseUp_.bind(this),this.inputElement_.addEventListener("change",this.boundInputOnChange),this.inputElement_.addEventListener("focus",this.boundInputOnFocus),this.inputElement_.addEventListener("blur",this.boundInputOnBlur),this.element_.addEventListener("mouseup",this.boundElementOnMouseUp),this.updateClasses_(),this.element_.classList.add("is-upgraded")}},i.register({constructor:d,classAsString:"MaterialIconToggle",cssClass:"mdl-js-icon-toggle",widget:!0});var h=function(e){this.element_=e,this.init()};window.MaterialMenu=h,h.prototype.Constant_={TRANSITION_DURATION_SECONDS:.3,TRANSITION_DURATION_FRACTION:.8,CLOSE_TIMEOUT:150},h.prototype.Keycodes_={ENTER:13,ESCAPE:27,SPACE:32,UP_ARROW:38,DOWN_ARROW:40},h.prototype.CssClasses_={CONTAINER:"mdl-menu__container",OUTLINE:"mdl-menu__outline",ITEM:"mdl-menu__item",ITEM_RIPPLE_CONTAINER:"mdl-menu__item-ripple-container",RIPPLE_EFFECT:"mdl-js-ripple-effect",RIPPLE_IGNORE_EVENTS:"mdl-js-ripple-effect--ignore-events",RIPPLE:"mdl-ripple",IS_UPGRADED:"is-upgraded",IS_VISIBLE:"is-visible",IS_ANIMATING:"is-animating",BOTTOM_LEFT:"mdl-menu--bottom-left",BOTTOM_RIGHT:"mdl-menu--bottom-right",TOP_LEFT:"mdl-menu--top-left",TOP_RIGHT:"mdl-menu--top-right",UNALIGNED:"mdl-menu--unaligned"},h.prototype.init=function(){if(this.element_){var e=document.createElement("div");e.classList.add(this.CssClasses_.CONTAINER),this.element_.parentElement.insertBefore(e,this.element_),this.element_.parentElement.removeChild(this.element_),e.appendChild(this.element_),this.container_=e;var t=document.createElement("div");t.classList.add(this.CssClasses_.OUTLINE),this.outline_=t,e.insertBefore(t,this.element_);var s=this.element_.getAttribute("for")||this.element_.getAttribute("data-mdl-for"),i=null;s&&((i=document.getElementById(s))&&(this.forElement_=i,i.addEventListener("click",this.handleForClick_.bind(this)),i.addEventListener("keydown",this.handleForKeyboardEvent_.bind(this))));var n=this.element_.querySelectorAll("."+this.CssClasses_.ITEM);this.boundItemKeydown_=this.handleItemKeyboardEvent_.bind(this),this.boundItemClick_=this.handleItemClick_.bind(this);for(var a=0;a<n.length;a++)n[a].addEventListener("click",this.boundItemClick_),n[a].tabIndex="-1",n[a].addEventListener("keydown",this.boundItemKeydown_);if(this.element_.classList.contains(this.CssClasses_.RIPPLE_EFFECT))for(this.element_.classList.add(this.CssClasses_.RIPPLE_IGNORE_EVENTS),a=0;a<n.length;a++){var l=n[a],o=document.createElement("span");o.classList.add(this.CssClasses_.ITEM_RIPPLE_CONTAINER);var r=document.createElement("span");r.classList.add(this.CssClasses_.RIPPLE),o.appendChild(r),l.appendChild(o),l.classList.add(this.CssClasses_.RIPPLE_EFFECT)}this.element_.classList.contains(this.CssClasses_.BOTTOM_LEFT)&&this.outline_.classList.add(this.CssClasses_.BOTTOM_LEFT),this.element_.classList.contains(this.CssClasses_.BOTTOM_RIGHT)&&this.outline_.classList.add(this.CssClasses_.BOTTOM_RIGHT),this.element_.classList.contains(this.CssClasses_.TOP_LEFT)&&this.outline_.classList.add(this.CssClasses_.TOP_LEFT),this.element_.classList.contains(this.CssClasses_.TOP_RIGHT)&&this.outline_.classList.add(this.CssClasses_.TOP_RIGHT),this.element_.classList.contains(this.CssClasses_.UNALIGNED)&&this.outline_.classList.add(this.CssClasses_.UNALIGNED),e.classList.add(this.CssClasses_.IS_UPGRADED)}},h.prototype.handleForClick_=function(e){if(this.element_&&this.forElement_){var t=this.forElement_.getBoundingClientRect(),s=this.forElement_.parentElement.getBoundingClientRect();this.element_.classList.contains(this.CssClasses_.UNALIGNED)||(this.element_.classList.contains(this.CssClasses_.BOTTOM_RIGHT)?(this.container_.style.right=s.right-t.right+"px",this.container_.style.top=this.forElement_.offsetTop+this.forElement_.offsetHeight+"px"):this.element_.classList.contains(this.CssClasses_.TOP_LEFT)?(this.container_.style.left=this.forElement_.offsetLeft+"px",this.container_.style.bottom=s.bottom-t.top+"px"):this.element_.classList.contains(this.CssClasses_.TOP_RIGHT)?(this.container_.style.right=s.right-t.right+"px",this.container_.style.bottom=s.bottom-t.top+"px"):(this.container_.style.left=this.forElement_.offsetLeft+"px",this.container_.style.top=this.forElement_.offsetTop+this.forElement_.offsetHeight+"px"))}this.toggle(e)},h.prototype.handleForKeyboardEvent_=function(e){if(this.element_&&this.container_&&this.forElement_){var t=this.element_.querySelectorAll("."+this.CssClasses_.ITEM+":not([disabled])");t&&t.length>0&&this.container_.classList.contains(this.CssClasses_.IS_VISIBLE)&&(e.keyCode===this.Keycodes_.UP_ARROW?(e.preventDefault(),t[t.length-1].focus()):e.keyCode===this.Keycodes_.DOWN_ARROW&&(e.preventDefault(),t[0].focus()))}},h.prototype.handleItemKeyboardEvent_=function(e){if(this.element_&&this.container_){var t=this.element_.querySelectorAll("."+this.CssClasses_.ITEM+":not([disabled])");if(t&&t.length>0&&this.container_.classList.contains(this.CssClasses_.IS_VISIBLE)){var s=Array.prototype.slice.call(t).indexOf(e.target);if(e.keyCode===this.Keycodes_.UP_ARROW)e.preventDefault(),s>0?t[s-1].focus():t[t.length-1].focus();else if(e.keyCode===this.Keycodes_.DOWN_ARROW)e.preventDefault(),t.length>s+1?t[s+1].focus():t[0].focus();else if(e.keyCode===this.Keycodes_.SPACE||e.keyCode===this.Keycodes_.ENTER){e.preventDefault();var i=new MouseEvent("mousedown");e.target.dispatchEvent(i),i=new MouseEvent("mouseup"),e.target.dispatchEvent(i),e.target.click()}else e.keyCode===this.Keycodes_.ESCAPE&&(e.preventDefault(),this.hide())}}},h.prototype.handleItemClick_=function(e){e.target.hasAttribute("disabled")?e.stopPropagation():(this.closing_=!0,window.setTimeout(function(e){this.hide(),this.closing_=!1}.bind(this),this.Constant_.CLOSE_TIMEOUT))},h.prototype.applyClip_=function(e,t){this.element_.classList.contains(this.CssClasses_.UNALIGNED)?this.element_.style.clip="":this.element_.classList.contains(this.CssClasses_.BOTTOM_RIGHT)?this.element_.style.clip="rect(0 "+t+"px 0 "+t+"px)":this.element_.classList.contains(this.CssClasses_.TOP_LEFT)?this.element_.style.clip="rect("+e+"px 0 "+e+"px 0)":this.element_.classList.contains(this.CssClasses_.TOP_RIGHT)?this.element_.style.clip="rect("+e+"px "+t+"px "+e+"px "+t+"px)":this.element_.style.clip=""},h.prototype.removeAnimationEndListener_=function(e){e.target.classList.remove(h.prototype.CssClasses_.IS_ANIMATING)},h.prototype.addAnimationEndListener_=function(){this.element_.addEventListener("transitionend",this.removeAnimationEndListener_),this.element_.addEventListener("webkitTransitionEnd",this.removeAnimationEndListener_)},h.prototype.show=function(e){if(this.element_&&this.container_&&this.outline_){var t=this.element_.getBoundingClientRect().height,s=this.element_.getBoundingClientRect().width;this.container_.style.width=s+"px",this.container_.style.height=t+"px",this.outline_.style.width=s+"px",this.outline_.style.height=t+"px";for(var i=this.Constant_.TRANSITION_DURATION_SECONDS*this.Constant_.TRANSITION_DURATION_FRACTION,n=this.element_.querySelectorAll("."+this.CssClasses_.ITEM),a=0;a<n.length;a++){var l;l=this.element_.classList.contains(this.CssClasses_.TOP_LEFT)||this.element_.classList.contains(this.CssClasses_.TOP_RIGHT)?(t-n[a].offsetTop-n[a].offsetHeight)/t*i+"s":n[a].offsetTop/t*i+"s",n[a].style.transitionDelay=l}this.applyClip_(t,s),window.requestAnimationFrame(function(){this.element_.classList.add(this.CssClasses_.IS_ANIMATING),this.element_.style.clip="rect(0 "+s+"px "+t+"px 0)",this.container_.classList.add(this.CssClasses_.IS_VISIBLE)}.bind(this)),this.addAnimationEndListener_();var o=function(t){t===e||this.closing_||t.target.parentNode===this.element_||(document.removeEventListener("click",o),this.hide())}.bind(this);document.addEventListener("click",o)}},h.prototype.show=h.prototype.show,h.prototype.hide=function(){if(this.element_&&this.container_&&this.outline_){for(var e=this.element_.querySelectorAll("."+this.CssClasses_.ITEM),t=0;t<e.length;t++)e[t].style.removeProperty("transition-delay");var s=this.element_.getBoundingClientRect(),i=s.height,n=s.width;this.element_.classList.add(this.CssClasses_.IS_ANIMATING),this.applyClip_(i,n),this.container_.classList.remove(this.CssClasses_.IS_VISIBLE),this.addAnimationEndListener_()}},h.prototype.hide=h.prototype.hide,h.prototype.toggle=function(e){this.container_.classList.contains(this.CssClasses_.IS_VISIBLE)?this.hide():this.show(e)},h.prototype.toggle=h.prototype.toggle,i.register({constructor:h,classAsString:"MaterialMenu",cssClass:"mdl-js-menu",widget:!0});var c=function(e){this.element_=e,this.init()};window.MaterialProgress=c,c.prototype.Constant_={},c.prototype.CssClasses_={INDETERMINATE_CLASS:"mdl-progress__indeterminate"},c.prototype.setProgress=function(e){this.element_.classList.contains(this.CssClasses_.INDETERMINATE_CLASS)||(this.progressbar_.style.width=e+"%")},c.prototype.setProgress=c.prototype.setProgress,c.prototype.setBuffer=function(e){this.bufferbar_.style.width=e+"%",this.auxbar_.style.width=100-e+"%"},c.prototype.setBuffer=c.prototype.setBuffer,c.prototype.init=function(){if(this.element_){var e=document.createElement("div");e.className="progressbar bar bar1",this.element_.appendChild(e),this.progressbar_=e,(e=document.createElement("div")).className="bufferbar bar bar2",this.element_.appendChild(e),this.bufferbar_=e,(e=document.createElement("div")).className="auxbar bar bar3",this.element_.appendChild(e),this.auxbar_=e,this.progressbar_.style.width="0%",this.bufferbar_.style.width="100%",this.auxbar_.style.width="0%",this.element_.classList.add("is-upgraded")}},i.register({constructor:c,classAsString:"MaterialProgress",cssClass:"mdl-js-progress",widget:!0});var p=function(e){this.element_=e,this.init()};window.MaterialRadio=p,p.prototype.Constant_={TINY_TIMEOUT:.001},p.prototype.CssClasses_={IS_FOCUSED:"is-focused",IS_DISABLED:"is-disabled",IS_CHECKED:"is-checked",IS_UPGRADED:"is-upgraded",JS_RADIO:"mdl-js-radio",RADIO_BTN:"mdl-radio__button",RADIO_OUTER_CIRCLE:"mdl-radio__outer-circle",RADIO_INNER_CIRCLE:"mdl-radio__inner-circle",RIPPLE_EFFECT:"mdl-js-ripple-effect",RIPPLE_IGNORE_EVENTS:"mdl-js-ripple-effect--ignore-events",RIPPLE_CONTAINER:"mdl-radio__ripple-container",RIPPLE_CENTER:"mdl-ripple--center",RIPPLE:"mdl-ripple"},p.prototype.onChange_=function(e){for(var t=document.getElementsByClassName(this.CssClasses_.JS_RADIO),s=0;s<t.length;s++){t[s].querySelector("."+this.CssClasses_.RADIO_BTN).getAttribute("name")===this.btnElement_.getAttribute("name")&&void 0!==t[s].MaterialRadio&&t[s].MaterialRadio.updateClasses_()}},p.prototype.onFocus_=function(e){this.element_.classList.add(this.CssClasses_.IS_FOCUSED)},p.prototype.onBlur_=function(e){this.element_.classList.remove(this.CssClasses_.IS_FOCUSED)},p.prototype.onMouseup_=function(e){this.blur_()},p.prototype.updateClasses_=function(){this.checkDisabled(),this.checkToggleState()},p.prototype.blur_=function(){window.setTimeout(function(){this.btnElement_.blur()}.bind(this),this.Constant_.TINY_TIMEOUT)},p.prototype.checkDisabled=function(){this.btnElement_.disabled?this.element_.classList.add(this.CssClasses_.IS_DISABLED):this.element_.classList.remove(this.CssClasses_.IS_DISABLED)},p.prototype.checkDisabled=p.prototype.checkDisabled,p.prototype.checkToggleState=function(){this.btnElement_.checked?this.element_.classList.add(this.CssClasses_.IS_CHECKED):this.element_.classList.remove(this.CssClasses_.IS_CHECKED)},p.prototype.checkToggleState=p.prototype.checkToggleState,p.prototype.disable=function(){this.btnElement_.disabled=!0,this.updateClasses_()},p.prototype.disable=p.prototype.disable,p.prototype.enable=function(){this.btnElement_.disabled=!1,this.updateClasses_()},p.prototype.enable=p.prototype.enable,p.prototype.check=function(){this.btnElement_.checked=!0,this.onChange_(null)},p.prototype.check=p.prototype.check,p.prototype.uncheck=function(){this.btnElement_.checked=!1,this.onChange_(null)},p.prototype.uncheck=p.prototype.uncheck,p.prototype.init=function(){if(this.element_){this.btnElement_=this.element_.querySelector("."+this.CssClasses_.RADIO_BTN),this.boundChangeHandler_=this.onChange_.bind(this),this.boundFocusHandler_=this.onChange_.bind(this),this.boundBlurHandler_=this.onBlur_.bind(this),this.boundMouseUpHandler_=this.onMouseup_.bind(this);var e=document.createElement("span");e.classList.add(this.CssClasses_.RADIO_OUTER_CIRCLE);var t,s=document.createElement("span");if(s.classList.add(this.CssClasses_.RADIO_INNER_CIRCLE),this.element_.appendChild(e),this.element_.appendChild(s),this.element_.classList.contains(this.CssClasses_.RIPPLE_EFFECT)){this.element_.classList.add(this.CssClasses_.RIPPLE_IGNORE_EVENTS),(t=document.createElement("span")).classList.add(this.CssClasses_.RIPPLE_CONTAINER),t.classList.add(this.CssClasses_.RIPPLE_EFFECT),t.classList.add(this.CssClasses_.RIPPLE_CENTER),t.addEventListener("mouseup",this.boundMouseUpHandler_);var i=document.createElement("span");i.classList.add(this.CssClasses_.RIPPLE),t.appendChild(i),this.element_.appendChild(t)}this.btnElement_.addEventListener("change",this.boundChangeHandler_),this.btnElement_.addEventListener("focus",this.boundFocusHandler_),this.btnElement_.addEventListener("blur",this.boundBlurHandler_),this.element_.addEventListener("mouseup",this.boundMouseUpHandler_),this.updateClasses_(),this.element_.classList.add(this.CssClasses_.IS_UPGRADED)}},i.register({constructor:p,classAsString:"MaterialRadio",cssClass:"mdl-js-radio",widget:!0});var C=function(e){this.element_=e,this.isIE_=window.navigator.msPointerEnabled,this.init()};window.MaterialSlider=C,C.prototype.Constant_={},C.prototype.CssClasses_={IE_CONTAINER:"mdl-slider__ie-container",SLIDER_CONTAINER:"mdl-slider__container",BACKGROUND_FLEX:"mdl-slider__background-flex",BACKGROUND_LOWER:"mdl-slider__background-lower",BACKGROUND_UPPER:"mdl-slider__background-upper",IS_LOWEST_VALUE:"is-lowest-value",IS_UPGRADED:"is-upgraded"},C.prototype.onInput_=function(e){this.updateValueStyles_()},C.prototype.onChange_=function(e){this.updateValueStyles_()},C.prototype.onMouseUp_=function(e){e.target.blur()},C.prototype.onContainerMouseDown_=function(e){if(e.target===this.element_.parentElement){e.preventDefault();var t=new MouseEvent("mousedown",{target:e.target,buttons:e.buttons,clientX:e.clientX,clientY:this.element_.getBoundingClientRect().y});this.element_.dispatchEvent(t)}},C.prototype.updateValueStyles_=function(){var e=(this.element_.value-this.element_.min)/(this.element_.max-this.element_.min);0===e?this.element_.classList.add(this.CssClasses_.IS_LOWEST_VALUE):this.element_.classList.remove(this.CssClasses_.IS_LOWEST_VALUE),this.isIE_||(this.backgroundLower_.style.flex=e,this.backgroundLower_.style.webkitFlex=e,this.backgroundUpper_.style.flex=1-e,this.backgroundUpper_.style.webkitFlex=1-e)},C.prototype.disable=function(){this.element_.disabled=!0},C.prototype.disable=C.prototype.disable,C.prototype.enable=function(){this.element_.disabled=!1},C.prototype.enable=C.prototype.enable,C.prototype.change=function(e){void 0!==e&&(this.element_.value=e),this.updateValueStyles_()},C.prototype.change=C.prototype.change,C.prototype.init=function(){if(this.element_){if(this.isIE_){var e=document.createElement("div");e.classList.add(this.CssClasses_.IE_CONTAINER),this.element_.parentElement.insertBefore(e,this.element_),this.element_.parentElement.removeChild(this.element_),e.appendChild(this.element_)}else{var t=document.createElement("div");t.classList.add(this.CssClasses_.SLIDER_CONTAINER),this.element_.parentElement.insertBefore(t,this.element_),this.element_.parentElement.removeChild(this.element_),t.appendChild(this.element_);var s=document.createElement("div");s.classList.add(this.CssClasses_.BACKGROUND_FLEX),t.appendChild(s),this.backgroundLower_=document.createElement("div"),this.backgroundLower_.classList.add(this.CssClasses_.BACKGROUND_LOWER),s.appendChild(this.backgroundLower_),this.backgroundUpper_=document.createElement("div"),this.backgroundUpper_.classList.add(this.CssClasses_.BACKGROUND_UPPER),s.appendChild(this.backgroundUpper_)}this.boundInputHandler=this.onInput_.bind(this),this.boundChangeHandler=this.onChange_.bind(this),this.boundMouseUpHandler=this.onMouseUp_.bind(this),this.boundContainerMouseDownHandler=this.onContainerMouseDown_.bind(this),this.element_.addEventListener("input",this.boundInputHandler),this.element_.addEventListener("change",this.boundChangeHandler),this.element_.addEventListener("mouseup",this.boundMouseUpHandler),this.element_.parentElement.addEventListener("mousedown",this.boundContainerMouseDownHandler),this.updateValueStyles_(),this.element_.classList.add(this.CssClasses_.IS_UPGRADED)}},i.register({constructor:C,classAsString:"MaterialSlider",cssClass:"mdl-js-slider",widget:!0});var u=function(e){if(this.element_=e,this.textElement_=this.element_.querySelector("."+this.cssClasses_.MESSAGE),this.actionElement_=this.element_.querySelector("."+this.cssClasses_.ACTION),!this.textElement_)throw new Error("There must be a message element for a snackbar.");if(!this.actionElement_)throw new Error("There must be an action element for a snackbar.");this.active=!1,this.actionHandler_=void 0,this.message_=void 0,this.actionText_=void 0,this.queuedNotifications_=[],this.setActionHidden_(!0)};window.MaterialSnackbar=u,u.prototype.Constant_={ANIMATION_LENGTH:250},u.prototype.cssClasses_={SNACKBAR:"mdl-snackbar",MESSAGE:"mdl-snackbar__text",ACTION:"mdl-snackbar__action",ACTIVE:"mdl-snackbar--active"},u.prototype.displaySnackbar_=function(){this.element_.setAttribute("aria-hidden","true"),this.actionHandler_&&(this.actionElement_.textContent=this.actionText_,this.actionElement_.addEventListener("click",this.actionHandler_),this.setActionHidden_(!1)),this.textElement_.textContent=this.message_,this.element_.classList.add(this.cssClasses_.ACTIVE),this.element_.setAttribute("aria-hidden","false"),setTimeout(this.cleanup_.bind(this),this.timeout_)},u.prototype.showSnackbar=function(e){if(void 0===e)throw new Error("Please provide a data object with at least a message to display.");if(void 0===e.message)throw new Error("Please provide a message to be displayed.");if(e.actionHandler&&!e.actionText)throw new Error("Please provide action text with the handler.");this.active?this.queuedNotifications_.push(e):(this.active=!0,this.message_=e.message,e.timeout?this.timeout_=e.timeout:this.timeout_=2750,e.actionHandler&&(this.actionHandler_=e.actionHandler),e.actionText&&(this.actionText_=e.actionText),this.displaySnackbar_())},u.prototype.showSnackbar=u.prototype.showSnackbar,u.prototype.checkQueue_=function(){this.queuedNotifications_.length>0&&this.showSnackbar(this.queuedNotifications_.shift())},u.prototype.cleanup_=function(){this.element_.classList.remove(this.cssClasses_.ACTIVE),setTimeout(function(){this.element_.setAttribute("aria-hidden","true"),this.textElement_.textContent="",Boolean(this.actionElement_.getAttribute("aria-hidden"))||(this.setActionHidden_(!0),this.actionElement_.textContent="",this.actionElement_.removeEventListener("click",this.actionHandler_)),this.actionHandler_=void 0,this.message_=void 0,this.actionText_=void 0,this.active=!1,this.checkQueue_()}.bind(this),this.Constant_.ANIMATION_LENGTH)},u.prototype.setActionHidden_=function(e){e?this.actionElement_.setAttribute("aria-hidden","true"):this.actionElement_.removeAttribute("aria-hidden")},i.register({constructor:u,classAsString:"MaterialSnackbar",cssClass:"mdl-js-snackbar",widget:!0});var E=function(e){this.element_=e,this.init()};window.MaterialSpinner=E,E.prototype.Constant_={MDL_SPINNER_LAYER_COUNT:4},E.prototype.CssClasses_={MDL_SPINNER_LAYER:"mdl-spinner__layer",MDL_SPINNER_CIRCLE_CLIPPER:"mdl-spinner__circle-clipper",MDL_SPINNER_CIRCLE:"mdl-spinner__circle",MDL_SPINNER_GAP_PATCH:"mdl-spinner__gap-patch",MDL_SPINNER_LEFT:"mdl-spinner__left",MDL_SPINNER_RIGHT:"mdl-spinner__right"},E.prototype.createLayer=function(e){var t=document.createElement("div");t.classList.add(this.CssClasses_.MDL_SPINNER_LAYER),t.classList.add(this.CssClasses_.MDL_SPINNER_LAYER+"-"+e);var s=document.createElement("div");s.classList.add(this.CssClasses_.MDL_SPINNER_CIRCLE_CLIPPER),s.classList.add(this.CssClasses_.MDL_SPINNER_LEFT);var i=document.createElement("div");i.classList.add(this.CssClasses_.MDL_SPINNER_GAP_PATCH);var n=document.createElement("div");n.classList.add(this.CssClasses_.MDL_SPINNER_CIRCLE_CLIPPER),n.classList.add(this.CssClasses_.MDL_SPINNER_RIGHT);for(var a=[s,i,n],l=0;l<a.length;l++){var o=document.createElement("div");o.classList.add(this.CssClasses_.MDL_SPINNER_CIRCLE),a[l].appendChild(o)}t.appendChild(s),t.appendChild(i),t.appendChild(n),this.element_.appendChild(t)},E.prototype.createLayer=E.prototype.createLayer,E.prototype.stop=function(){this.element_.classList.remove("is-active")},E.prototype.stop=E.prototype.stop,E.prototype.start=function(){this.element_.classList.add("is-active")},E.prototype.start=E.prototype.start,E.prototype.init=function(){if(this.element_){for(var e=1;e<=this.Constant_.MDL_SPINNER_LAYER_COUNT;e++)this.createLayer(e);this.element_.classList.add("is-upgraded")}},i.register({constructor:E,classAsString:"MaterialSpinner",cssClass:"mdl-js-spinner",widget:!0});var m=function(e){this.element_=e,this.init()};window.MaterialSwitch=m,m.prototype.Constant_={TINY_TIMEOUT:.001},m.prototype.CssClasses_={INPUT:"mdl-switch__input",TRACK:"mdl-switch__track",THUMB:"mdl-switch__thumb",FOCUS_HELPER:"mdl-switch__focus-helper",RIPPLE_EFFECT:"mdl-js-ripple-effect",RIPPLE_IGNORE_EVENTS:"mdl-js-ripple-effect--ignore-events",RIPPLE_CONTAINER:"mdl-switch__ripple-container",RIPPLE_CENTER:"mdl-ripple--center",RIPPLE:"mdl-ripple",IS_FOCUSED:"is-focused",IS_DISABLED:"is-disabled",IS_CHECKED:"is-checked"},m.prototype.onChange_=function(e){this.updateClasses_()},m.prototype.onFocus_=function(e){this.element_.classList.add(this.CssClasses_.IS_FOCUSED)},m.prototype.onBlur_=function(e){this.element_.classList.remove(this.CssClasses_.IS_FOCUSED)},m.prototype.onMouseUp_=function(e){this.blur_()},m.prototype.updateClasses_=function(){this.checkDisabled(),this.checkToggleState()},m.prototype.blur_=function(){window.setTimeout(function(){this.inputElement_.blur()}.bind(this),this.Constant_.TINY_TIMEOUT)},m.prototype.checkDisabled=function(){this.inputElement_.disabled?this.element_.classList.add(this.CssClasses_.IS_DISABLED):this.element_.classList.remove(this.CssClasses_.IS_DISABLED)},m.prototype.checkDisabled=m.prototype.checkDisabled,m.prototype.checkToggleState=function(){this.inputElement_.checked?this.element_.classList.add(this.CssClasses_.IS_CHECKED):this.element_.classList.remove(this.CssClasses_.IS_CHECKED)},m.prototype.checkToggleState=m.prototype.checkToggleState,m.prototype.disable=function(){this.inputElement_.disabled=!0,this.updateClasses_()},m.prototype.disable=m.prototype.disable,m.prototype.enable=function(){this.inputElement_.disabled=!1,this.updateClasses_()},m.prototype.enable=m.prototype.enable,m.prototype.on=function(){this.inputElement_.checked=!0,this.updateClasses_()},m.prototype.on=m.prototype.on,m.prototype.off=function(){this.inputElement_.checked=!1,this.updateClasses_()},m.prototype.off=m.prototype.off,m.prototype.init=function(){if(this.element_){this.inputElement_=this.element_.querySelector("."+this.CssClasses_.INPUT);var e=document.createElement("div");e.classList.add(this.CssClasses_.TRACK);var t=document.createElement("div");t.classList.add(this.CssClasses_.THUMB);var s=document.createElement("span");if(s.classList.add(this.CssClasses_.FOCUS_HELPER),t.appendChild(s),this.element_.appendChild(e),this.element_.appendChild(t),this.boundMouseUpHandler=this.onMouseUp_.bind(this),this.element_.classList.contains(this.CssClasses_.RIPPLE_EFFECT)){this.element_.classList.add(this.CssClasses_.RIPPLE_IGNORE_EVENTS),this.rippleContainerElement_=document.createElement("span"),this.rippleContainerElement_.classList.add(this.CssClasses_.RIPPLE_CONTAINER),this.rippleContainerElement_.classList.add(this.CssClasses_.RIPPLE_EFFECT),this.rippleContainerElement_.classList.add(this.CssClasses_.RIPPLE_CENTER),this.rippleContainerElement_.addEventListener("mouseup",this.boundMouseUpHandler);var i=document.createElement("span");i.classList.add(this.CssClasses_.RIPPLE),this.rippleContainerElement_.appendChild(i),this.element_.appendChild(this.rippleContainerElement_)}this.boundChangeHandler=this.onChange_.bind(this),this.boundFocusHandler=this.onFocus_.bind(this),this.boundBlurHandler=this.onBlur_.bind(this),this.inputElement_.addEventListener("change",this.boundChangeHandler),this.inputElement_.addEventListener("focus",this.boundFocusHandler),this.inputElement_.addEventListener("blur",this.boundBlurHandler),this.element_.addEventListener("mouseup",this.boundMouseUpHandler),this.updateClasses_(),this.element_.classList.add("is-upgraded")}},i.register({constructor:m,classAsString:"MaterialSwitch",cssClass:"mdl-js-switch",widget:!0});var L=function(e){this.element_=e,this.init()};window.MaterialTabs=L,L.prototype.Constant_={},L.prototype.CssClasses_={TAB_CLASS:"mdl-tabs__tab",PANEL_CLASS:"mdl-tabs__panel",ACTIVE_CLASS:"is-active",UPGRADED_CLASS:"is-upgraded",MDL_JS_RIPPLE_EFFECT:"mdl-js-ripple-effect",MDL_RIPPLE_CONTAINER:"mdl-tabs__ripple-container",MDL_RIPPLE:"mdl-ripple",MDL_JS_RIPPLE_EFFECT_IGNORE_EVENTS:"mdl-js-ripple-effect--ignore-events"},L.prototype.initTabs_=function(){this.element_.classList.contains(this.CssClasses_.MDL_JS_RIPPLE_EFFECT)&&this.element_.classList.add(this.CssClasses_.MDL_JS_RIPPLE_EFFECT_IGNORE_EVENTS),this.tabs_=this.element_.querySelectorAll("."+this.CssClasses_.TAB_CLASS),this.panels_=this.element_.querySelectorAll("."+this.CssClasses_.PANEL_CLASS);for(var e=0;e<this.tabs_.length;e++)new t(this.tabs_[e],this);this.element_.classList.add(this.CssClasses_.UPGRADED_CLASS)},L.prototype.resetTabState_=function(){for(var e=0;e<this.tabs_.length;e++)this.tabs_[e].classList.remove(this.CssClasses_.ACTIVE_CLASS)},L.prototype.resetPanelState_=function(){for(var e=0;e<this.panels_.length;e++)this.panels_[e].classList.remove(this.CssClasses_.ACTIVE_CLASS)},L.prototype.init=function(){this.element_&&this.initTabs_()},i.register({constructor:L,classAsString:"MaterialTabs",cssClass:"mdl-js-tabs"});var I=function(e){this.element_=e,this.maxRows=this.Constant_.NO_MAX_ROWS,this.init()};window.MaterialTextfield=I,I.prototype.Constant_={NO_MAX_ROWS:-1,MAX_ROWS_ATTRIBUTE:"maxrows"},I.prototype.CssClasses_={LABEL:"mdl-textfield__label",INPUT:"mdl-textfield__input",IS_DIRTY:"is-dirty",IS_FOCUSED:"is-focused",IS_DISABLED:"is-disabled",IS_INVALID:"is-invalid",IS_UPGRADED:"is-upgraded",HAS_PLACEHOLDER:"has-placeholder"},I.prototype.onKeyDown_=function(e){var t=e.target.value.split("\n").length;13===e.keyCode&&t>=this.maxRows&&e.preventDefault()},I.prototype.onFocus_=function(e){this.element_.classList.add(this.CssClasses_.IS_FOCUSED)},I.prototype.onBlur_=function(e){this.element_.classList.remove(this.CssClasses_.IS_FOCUSED)},I.prototype.onReset_=function(e){this.updateClasses_()},I.prototype.updateClasses_=function(){this.checkDisabled(),this.checkValidity(),this.checkDirty(),this.checkFocus()},I.prototype.checkDisabled=function(){this.input_.disabled?this.element_.classList.add(this.CssClasses_.IS_DISABLED):this.element_.classList.remove(this.CssClasses_.IS_DISABLED)},I.prototype.checkDisabled=I.prototype.checkDisabled,I.prototype.checkFocus=function(){Boolean(this.element_.querySelector(":focus"))?this.element_.classList.add(this.CssClasses_.IS_FOCUSED):this.element_.classList.remove(this.CssClasses_.IS_FOCUSED)},I.prototype.checkFocus=I.prototype.checkFocus,I.prototype.checkValidity=function(){this.input_.validity&&(this.input_.validity.valid?this.element_.classList.remove(this.CssClasses_.IS_INVALID):this.element_.classList.add(this.CssClasses_.IS_INVALID))},I.prototype.checkValidity=I.prototype.checkValidity,I.prototype.checkDirty=function(){this.input_.value&&this.input_.value.length>0?this.element_.classList.add(this.CssClasses_.IS_DIRTY):this.element_.classList.remove(this.CssClasses_.IS_DIRTY)},I.prototype.checkDirty=I.prototype.checkDirty,I.prototype.disable=function(){this.input_.disabled=!0,this.updateClasses_()},I.prototype.disable=I.prototype.disable,I.prototype.enable=function(){this.input_.disabled=!1,this.updateClasses_()},I.prototype.enable=I.prototype.enable,I.prototype.change=function(e){this.input_.value=e||"",this.updateClasses_()},I.prototype.change=I.prototype.change,I.prototype.init=function(){if(this.element_&&(this.label_=this.element_.querySelector("."+this.CssClasses_.LABEL),this.input_=this.element_.querySelector("."+this.CssClasses_.INPUT),this.input_)){this.input_.hasAttribute(this.Constant_.MAX_ROWS_ATTRIBUTE)&&(this.maxRows=parseInt(this.input_.getAttribute(this.Constant_.MAX_ROWS_ATTRIBUTE),10),isNaN(this.maxRows)&&(this.maxRows=this.Constant_.NO_MAX_ROWS)),this.input_.hasAttribute("placeholder")&&this.element_.classList.add(this.CssClasses_.HAS_PLACEHOLDER),this.boundUpdateClassesHandler=this.updateClasses_.bind(this),this.boundFocusHandler=this.onFocus_.bind(this),this.boundBlurHandler=this.onBlur_.bind(this),this.boundResetHandler=this.onReset_.bind(this),this.input_.addEventListener("input",this.boundUpdateClassesHandler),this.input_.addEventListener("focus",this.boundFocusHandler),this.input_.addEventListener("blur",this.boundBlurHandler),this.input_.addEventListener("reset",this.boundResetHandler),this.maxRows!==this.Constant_.NO_MAX_ROWS&&(this.boundKeyDownHandler=this.onKeyDown_.bind(this),this.input_.addEventListener("keydown",this.boundKeyDownHandler));var e=this.element_.classList.contains(this.CssClasses_.IS_INVALID);this.updateClasses_(),this.element_.classList.add(this.CssClasses_.IS_UPGRADED),e&&this.element_.classList.add(this.CssClasses_.IS_INVALID),this.input_.hasAttribute("autofocus")&&(this.element_.focus(),this.checkFocus())}},i.register({constructor:I,classAsString:"MaterialTextfield",cssClass:"mdl-js-textfield",widget:!0});var f=function(e){this.element_=e,this.init()};window.MaterialTooltip=f,f.prototype.Constant_={},f.prototype.CssClasses_={IS_ACTIVE:"is-active",BOTTOM:"mdl-tooltip--bottom",LEFT:"mdl-tooltip--left",RIGHT:"mdl-tooltip--right",TOP:"mdl-tooltip--top"},f.prototype.handleMouseEnter_=function(e){var t=e.target.getBoundingClientRect(),s=t.left+t.width/2,i=t.top+t.height/2,n=this.element_.offsetWidth/2*-1,a=this.element_.offsetHeight/2*-1;this.element_.classList.contains(this.CssClasses_.LEFT)||this.element_.classList.contains(this.CssClasses_.RIGHT)?(s=t.width/2,i+a<0?(this.element_.style.top="0",this.element_.style.marginTop="0"):(this.element_.style.top=i+"px",this.element_.style.marginTop=a+"px")):s+n<0?(this.element_.style.left="0",this.element_.style.marginLeft="0"):(this.element_.style.left=s+"px",this.element_.style.marginLeft=n+"px"),this.element_.classList.contains(this.CssClasses_.TOP)?this.element_.style.top=t.top-this.element_.offsetHeight-10+"px":this.element_.classList.contains(this.CssClasses_.RIGHT)?this.element_.style.left=t.left+t.width+10+"px":this.element_.classList.contains(this.CssClasses_.LEFT)?this.element_.style.left=t.left-this.element_.offsetWidth-10+"px":this.element_.style.top=t.top+t.height+10+"px",this.element_.classList.add(this.CssClasses_.IS_ACTIVE)},f.prototype.hideTooltip_=function(){this.element_.classList.remove(this.CssClasses_.IS_ACTIVE)},f.prototype.init=function(){if(this.element_){var e=this.element_.getAttribute("for")||this.element_.getAttribute("data-mdl-for");e&&(this.forElement_=document.getElementById(e)),this.forElement_&&(this.forElement_.hasAttribute("tabindex")||this.forElement_.setAttribute("tabindex","0"),this.boundMouseEnterHandler=this.handleMouseEnter_.bind(this),this.boundMouseLeaveAndScrollHandler=this.hideTooltip_.bind(this),this.forElement_.addEventListener("mouseenter",this.boundMouseEnterHandler,!1),this.forElement_.addEventListener("touchend",this.boundMouseEnterHandler,!1),this.forElement_.addEventListener("mouseleave",this.boundMouseLeaveAndScrollHandler,!1),window.addEventListener("scroll",this.boundMouseLeaveAndScrollHandler,!0),window.addEventListener("touchstart",this.boundMouseLeaveAndScrollHandler))}},i.register({constructor:f,classAsString:"MaterialTooltip",cssClass:"mdl-tooltip"});var b=function(e){this.element_=e,this.init()};window.MaterialLayout=b,b.prototype.Constant_={MAX_WIDTH:"(max-width: 1024px)",TAB_SCROLL_PIXELS:100,RESIZE_TIMEOUT:100,MENU_ICON:"&#xE5D2;",CHEVRON_LEFT:"chevron_left",CHEVRON_RIGHT:"chevron_right"},b.prototype.Keycodes_={ENTER:13,ESCAPE:27,SPACE:32},b.prototype.Mode_={STANDARD:0,SEAMED:1,WATERFALL:2,SCROLL:3},b.prototype.CssClasses_={CONTAINER:"mdl-layout__container",HEADER:"mdl-layout__header",DRAWER:"mdl-layout__drawer",CONTENT:"mdl-layout__content",DRAWER_BTN:"mdl-layout__drawer-button",ICON:"material-icons",JS_RIPPLE_EFFECT:"mdl-js-ripple-effect",RIPPLE_CONTAINER:"mdl-layout__tab-ripple-container",RIPPLE:"mdl-ripple",RIPPLE_IGNORE_EVENTS:"mdl-js-ripple-effect--ignore-events",HEADER_SEAMED:"mdl-layout__header--seamed",HEADER_WATERFALL:"mdl-layout__header--waterfall",HEADER_SCROLL:"mdl-layout__header--scroll",FIXED_HEADER:"mdl-layout--fixed-header",OBFUSCATOR:"mdl-layout__obfuscator",TAB_BAR:"mdl-layout__tab-bar",TAB_CONTAINER:"mdl-layout__tab-bar-container",TAB:"mdl-layout__tab",TAB_BAR_BUTTON:"mdl-layout__tab-bar-button",TAB_BAR_LEFT_BUTTON:"mdl-layout__tab-bar-left-button",TAB_BAR_RIGHT_BUTTON:"mdl-layout__tab-bar-right-button",TAB_MANUAL_SWITCH:"mdl-layout__tab-manual-switch",PANEL:"mdl-layout__tab-panel",HAS_DRAWER:"has-drawer",HAS_TABS:"has-tabs",HAS_SCROLLING_HEADER:"has-scrolling-header",CASTING_SHADOW:"is-casting-shadow",IS_COMPACT:"is-compact",IS_SMALL_SCREEN:"is-small-screen",IS_DRAWER_OPEN:"is-visible",IS_ACTIVE:"is-active",IS_UPGRADED:"is-upgraded",IS_ANIMATING:"is-animating",ON_LARGE_SCREEN:"mdl-layout--large-screen-only",ON_SMALL_SCREEN:"mdl-layout--small-screen-only"},b.prototype.contentScrollHandler_=function(){if(!this.header_.classList.contains(this.CssClasses_.IS_ANIMATING)){var e=!this.element_.classList.contains(this.CssClasses_.IS_SMALL_SCREEN)||this.element_.classList.contains(this.CssClasses_.FIXED_HEADER);this.content_.scrollTop>0&&!this.header_.classList.contains(this.CssClasses_.IS_COMPACT)?(this.header_.classList.add(this.CssClasses_.CASTING_SHADOW),this.header_.classList.add(this.CssClasses_.IS_COMPACT),e&&this.header_.classList.add(this.CssClasses_.IS_ANIMATING)):this.content_.scrollTop<=0&&this.header_.classList.contains(this.CssClasses_.IS_COMPACT)&&(this.header_.classList.remove(this.CssClasses_.CASTING_SHADOW),this.header_.classList.remove(this.CssClasses_.IS_COMPACT),e&&this.header_.classList.add(this.CssClasses_.IS_ANIMATING))}},b.prototype.keyboardEventHandler_=function(e){e.keyCode===this.Keycodes_.ESCAPE&&this.drawer_.classList.contains(this.CssClasses_.IS_DRAWER_OPEN)&&this.toggleDrawer()},b.prototype.screenSizeHandler_=function(){this.screenSizeMediaQuery_.matches?this.element_.classList.add(this.CssClasses_.IS_SMALL_SCREEN):(this.element_.classList.remove(this.CssClasses_.IS_SMALL_SCREEN),this.drawer_&&(this.drawer_.classList.remove(this.CssClasses_.IS_DRAWER_OPEN),this.obfuscator_.classList.remove(this.CssClasses_.IS_DRAWER_OPEN)))},b.prototype.drawerToggleHandler_=function(e){if(e&&"keydown"===e.type){if(e.keyCode!==this.Keycodes_.SPACE&&e.keyCode!==this.Keycodes_.ENTER)return;e.preventDefault()}this.toggleDrawer()},b.prototype.headerTransitionEndHandler_=function(){this.header_.classList.remove(this.CssClasses_.IS_ANIMATING)},b.prototype.headerClickHandler_=function(){this.header_.classList.contains(this.CssClasses_.IS_COMPACT)&&(this.header_.classList.remove(this.CssClasses_.IS_COMPACT),this.header_.classList.add(this.CssClasses_.IS_ANIMATING))},b.prototype.resetTabState_=function(e){for(var t=0;t<e.length;t++)e[t].classList.remove(this.CssClasses_.IS_ACTIVE)},b.prototype.resetPanelState_=function(e){for(var t=0;t<e.length;t++)e[t].classList.remove(this.CssClasses_.IS_ACTIVE)},b.prototype.toggleDrawer=function(){var e=this.element_.querySelector("."+this.CssClasses_.DRAWER_BTN);this.drawer_.classList.toggle(this.CssClasses_.IS_DRAWER_OPEN),this.obfuscator_.classList.toggle(this.CssClasses_.IS_DRAWER_OPEN),this.drawer_.classList.contains(this.CssClasses_.IS_DRAWER_OPEN)?(this.drawer_.setAttribute("aria-hidden","false"),e.setAttribute("aria-expanded","true")):(this.drawer_.setAttribute("aria-hidden","true"),e.setAttribute("aria-expanded","false"))},b.prototype.toggleDrawer=b.prototype.toggleDrawer,b.prototype.init=function(){if(this.element_){var e=document.createElement("div");e.classList.add(this.CssClasses_.CONTAINER);var t=this.element_.querySelector(":focus");this.element_.parentElement.insertBefore(e,this.element_),this.element_.parentElement.removeChild(this.element_),e.appendChild(this.element_),t&&t.focus();for(var i=this.element_.childNodes,n=i.length,a=0;a<n;a++){var l=i[a];l.classList&&l.classList.contains(this.CssClasses_.HEADER)&&(this.header_=l),l.classList&&l.classList.contains(this.CssClasses_.DRAWER)&&(this.drawer_=l),l.classList&&l.classList.contains(this.CssClasses_.CONTENT)&&(this.content_=l)}window.addEventListener("pageshow",function(e){e.persisted&&(this.element_.style.overflowY="hidden",requestAnimationFrame(function(){this.element_.style.overflowY=""}.bind(this)))}.bind(this),!1),this.header_&&(this.tabBar_=this.header_.querySelector("."+this.CssClasses_.TAB_BAR));var o=this.Mode_.STANDARD;if(this.header_&&(this.header_.classList.contains(this.CssClasses_.HEADER_SEAMED)?o=this.Mode_.SEAMED:this.header_.classList.contains(this.CssClasses_.HEADER_WATERFALL)?(o=this.Mode_.WATERFALL,this.header_.addEventListener("transitionend",this.headerTransitionEndHandler_.bind(this)),this.header_.addEventListener("click",this.headerClickHandler_.bind(this))):this.header_.classList.contains(this.CssClasses_.HEADER_SCROLL)&&(o=this.Mode_.SCROLL,e.classList.add(this.CssClasses_.HAS_SCROLLING_HEADER)),o===this.Mode_.STANDARD?(this.header_.classList.add(this.CssClasses_.CASTING_SHADOW),this.tabBar_&&this.tabBar_.classList.add(this.CssClasses_.CASTING_SHADOW)):o===this.Mode_.SEAMED||o===this.Mode_.SCROLL?(this.header_.classList.remove(this.CssClasses_.CASTING_SHADOW),this.tabBar_&&this.tabBar_.classList.remove(this.CssClasses_.CASTING_SHADOW)):o===this.Mode_.WATERFALL&&(this.content_.addEventListener("scroll",this.contentScrollHandler_.bind(this)),this.contentScrollHandler_())),this.drawer_){var r=this.element_.querySelector("."+this.CssClasses_.DRAWER_BTN);if(!r){(r=document.createElement("div")).setAttribute("aria-expanded","false"),r.setAttribute("role","button"),r.setAttribute("tabindex","0"),r.classList.add(this.CssClasses_.DRAWER_BTN);var _=document.createElement("i");_.classList.add(this.CssClasses_.ICON),_.innerHTML=this.Constant_.MENU_ICON,r.appendChild(_)}this.drawer_.classList.contains(this.CssClasses_.ON_LARGE_SCREEN)?r.classList.add(this.CssClasses_.ON_LARGE_SCREEN):this.drawer_.classList.contains(this.CssClasses_.ON_SMALL_SCREEN)&&r.classList.add(this.CssClasses_.ON_SMALL_SCREEN),r.addEventListener("click",this.drawerToggleHandler_.bind(this)),r.addEventListener("keydown",this.drawerToggleHandler_.bind(this)),this.element_.classList.add(this.CssClasses_.HAS_DRAWER),this.element_.classList.contains(this.CssClasses_.FIXED_HEADER)?this.header_.insertBefore(r,this.header_.firstChild):this.element_.insertBefore(r,this.content_);var d=document.createElement("div");d.classList.add(this.CssClasses_.OBFUSCATOR),this.element_.appendChild(d),d.addEventListener("click",this.drawerToggleHandler_.bind(this)),this.obfuscator_=d,this.drawer_.addEventListener("keydown",this.keyboardEventHandler_.bind(this)),this.drawer_.setAttribute("aria-hidden","true")}if(this.screenSizeMediaQuery_=window.matchMedia(this.Constant_.MAX_WIDTH),this.screenSizeMediaQuery_.addListener(this.screenSizeHandler_.bind(this)),this.screenSizeHandler_(),this.header_&&this.tabBar_){this.element_.classList.add(this.CssClasses_.HAS_TABS);var h=document.createElement("div");h.classList.add(this.CssClasses_.TAB_CONTAINER),this.header_.insertBefore(h,this.tabBar_),this.header_.removeChild(this.tabBar_);var c=document.createElement("div");c.classList.add(this.CssClasses_.TAB_BAR_BUTTON),c.classList.add(this.CssClasses_.TAB_BAR_LEFT_BUTTON);var p=document.createElement("i");p.classList.add(this.CssClasses_.ICON),p.textContent=this.Constant_.CHEVRON_LEFT,c.appendChild(p),c.addEventListener("click",function(){this.tabBar_.scrollLeft-=this.Constant_.TAB_SCROLL_PIXELS}.bind(this));var C=document.createElement("div");C.classList.add(this.CssClasses_.TAB_BAR_BUTTON),C.classList.add(this.CssClasses_.TAB_BAR_RIGHT_BUTTON);var u=document.createElement("i");u.classList.add(this.CssClasses_.ICON),u.textContent=this.Constant_.CHEVRON_RIGHT,C.appendChild(u),C.addEventListener("click",function(){this.tabBar_.scrollLeft+=this.Constant_.TAB_SCROLL_PIXELS}.bind(this)),h.appendChild(c),h.appendChild(this.tabBar_),h.appendChild(C);var E=function(){this.tabBar_.scrollLeft>0?c.classList.add(this.CssClasses_.IS_ACTIVE):c.classList.remove(this.CssClasses_.IS_ACTIVE),this.tabBar_.scrollLeft<this.tabBar_.scrollWidth-this.tabBar_.offsetWidth?C.classList.add(this.CssClasses_.IS_ACTIVE):C.classList.remove(this.CssClasses_.IS_ACTIVE)}.bind(this);this.tabBar_.addEventListener("scroll",E),E();var m=function(){this.resizeTimeoutId_&&clearTimeout(this.resizeTimeoutId_),this.resizeTimeoutId_=setTimeout(function(){E(),this.resizeTimeoutId_=null}.bind(this),this.Constant_.RESIZE_TIMEOUT)}.bind(this);window.addEventListener("resize",m),this.tabBar_.classList.contains(this.CssClasses_.JS_RIPPLE_EFFECT)&&this.tabBar_.classList.add(this.CssClasses_.RIPPLE_IGNORE_EVENTS);for(var L=this.tabBar_.querySelectorAll("."+this.CssClasses_.TAB),I=this.content_.querySelectorAll("."+this.CssClasses_.PANEL),f=0;f<L.length;f++)new s(L[f],L,I,this)}this.element_.classList.add(this.CssClasses_.IS_UPGRADED)}},window.MaterialLayoutTab=s,i.register({constructor:b,classAsString:"MaterialLayout",cssClass:"mdl-js-layout"});var S=function(e){this.element_=e,this.init()};window.MaterialDataTable=S,S.prototype.Constant_={},S.prototype.CssClasses_={DATA_TABLE:"mdl-data-table",SELECTABLE:"mdl-data-table--selectable",SELECT_ELEMENT:"mdl-data-table__select",IS_SELECTED:"is-selected",IS_UPGRADED:"is-upgraded"},S.prototype.selectRow_=function(e,t,s){return t?function(){e.checked?t.classList.add(this.CssClasses_.IS_SELECTED):t.classList.remove(this.CssClasses_.IS_SELECTED)}.bind(this):s?function(){var t;if(e.checked)for(t=0;t<s.length;t++)s[t].querySelector("td").querySelector(".mdl-checkbox").MaterialCheckbox.check(),s[t].classList.add(this.CssClasses_.IS_SELECTED);else for(t=0;t<s.length;t++)s[t].querySelector("td").querySelector(".mdl-checkbox").MaterialCheckbox.uncheck(),s[t].classList.remove(this.CssClasses_.IS_SELECTED)}.bind(this):void 0},S.prototype.createCheckbox_=function(e,t){var s=document.createElement("label"),n=["mdl-checkbox","mdl-js-checkbox","mdl-js-ripple-effect",this.CssClasses_.SELECT_ELEMENT];s.className=n.join(" ");var a=document.createElement("input");return a.type="checkbox",a.classList.add("mdl-checkbox__input"),e?(a.checked=e.classList.contains(this.CssClasses_.IS_SELECTED),a.addEventListener("change",this.selectRow_(a,e))):t&&a.addEventListener("change",this.selectRow_(a,null,t)),s.appendChild(a),i.upgradeElement(s,"MaterialCheckbox"),s},S.prototype.init=function(){if(this.element_){var e=this.element_.querySelector("th"),t=Array.prototype.slice.call(this.element_.querySelectorAll("tbody tr")),s=Array.prototype.slice.call(this.element_.querySelectorAll("tfoot tr")),i=t.concat(s);if(this.element_.classList.contains(this.CssClasses_.SELECTABLE)){var n=document.createElement("th"),a=this.createCheckbox_(null,i);n.appendChild(a),e.parentElement.insertBefore(n,e);for(var l=0;l<i.length;l++){var o=i[l].querySelector("td");if(o){var r=document.createElement("td");if("TBODY"===i[l].parentNode.nodeName.toUpperCase()){var _=this.createCheckbox_(i[l]);r.appendChild(_)}i[l].insertBefore(r,o)}}this.element_.classList.add(this.CssClasses_.IS_UPGRADED)}}},i.register({constructor:S,classAsString:"MaterialDataTable",cssClass:"mdl-js-data-table"});var y=function(e){this.element_=e,this.init()};window.MaterialRipple=y,y.prototype.Constant_={INITIAL_SCALE:"scale(0.0001, 0.0001)",INITIAL_SIZE:"1px",INITIAL_OPACITY:"0.4",FINAL_OPACITY:"0",FINAL_SCALE:""},y.prototype.CssClasses_={RIPPLE_CENTER:"mdl-ripple--center",RIPPLE_EFFECT_IGNORE_EVENTS:"mdl-js-ripple-effect--ignore-events",RIPPLE:"mdl-ripple",IS_ANIMATING:"is-animating",IS_VISIBLE:"is-visible"},y.prototype.downHandler_=function(e){if(!this.rippleElement_.style.width&&!this.rippleElement_.style.height){var t=this.element_.getBoundingClientRect();this.boundHeight=t.height,this.boundWidth=t.width,this.rippleSize_=2*Math.sqrt(t.width*t.width+t.height*t.height)+2,this.rippleElement_.style.width=this.rippleSize_+"px",this.rippleElement_.style.height=this.rippleSize_+"px"}if(this.rippleElement_.classList.add(this.CssClasses_.IS_VISIBLE),"mousedown"===e.type&&this.ignoringMouseDown_)this.ignoringMouseDown_=!1;else{if("touchstart"===e.type&&(this.ignoringMouseDown_=!0),this.getFrameCount()>0)return;this.setFrameCount(1);var s,i,n=e.currentTarget.getBoundingClientRect();if(0===e.clientX&&0===e.clientY)s=Math.round(n.width/2),i=Math.round(n.height/2);else{var a=void 0!==e.clientX?e.clientX:e.touches[0].clientX,l=void 0!==e.clientY?e.clientY:e.touches[0].clientY;s=Math.round(a-n.left),i=Math.round(l-n.top)}this.setRippleXY(s,i),this.setRippleStyles(!0),window.requestAnimationFrame(this.animFrameHandler.bind(this))}},y.prototype.upHandler_=function(e){e&&2!==e.detail&&window.setTimeout(function(){this.rippleElement_.classList.remove(this.CssClasses_.IS_VISIBLE)}.bind(this),0)},y.prototype.init=function(){if(this.element_){var e=this.element_.classList.contains(this.CssClasses_.RIPPLE_CENTER);this.element_.classList.contains(this.CssClasses_.RIPPLE_EFFECT_IGNORE_EVENTS)||(this.rippleElement_=this.element_.querySelector("."+this.CssClasses_.RIPPLE),this.frameCount_=0,this.rippleSize_=0,this.x_=0,this.y_=0,this.ignoringMouseDown_=!1,this.boundDownHandler=this.downHandler_.bind(this),this.element_.addEventListener("mousedown",this.boundDownHandler),this.element_.addEventListener("touchstart",this.boundDownHandler),this.boundUpHandler=this.upHandler_.bind(this),this.element_.addEventListener("mouseup",this.boundUpHandler),this.element_.addEventListener("mouseleave",this.boundUpHandler),this.element_.addEventListener("touchend",this.boundUpHandler),this.element_.addEventListener("blur",this.boundUpHandler),this.getFrameCount=function(){return this.frameCount_},this.setFrameCount=function(e){this.frameCount_=e},this.getRippleElement=function(){return this.rippleElement_},this.setRippleXY=function(e,t){this.x_=e,this.y_=t},this.setRippleStyles=function(t){if(null!==this.rippleElement_){var s,i,n="translate("+this.x_+"px, "+this.y_+"px)";t?(i=this.Constant_.INITIAL_SCALE,this.Constant_.INITIAL_SIZE):(i=this.Constant_.FINAL_SCALE,this.rippleSize_+"px",e&&(n="translate("+this.boundWidth/2+"px, "+this.boundHeight/2+"px)")),s="translate(-50%, -50%) "+n+i,this.rippleElement_.style.webkitTransform=s,this.rippleElement_.style.msTransform=s,this.rippleElement_.style.transform=s,t?this.rippleElement_.classList.remove(this.CssClasses_.IS_ANIMATING):this.rippleElement_.classList.add(this.CssClasses_.IS_ANIMATING)}},this.animFrameHandler=function(){this.frameCount_-- >0?window.requestAnimationFrame(this.animFrameHandler.bind(this)):this.setRippleStyles(!1)})}},i.register({constructor:y,classAsString:"MaterialRipple",cssClass:"mdl-js-ripple-effect",widget:!1})}();
+},{}],"8QiI":[function(require,module,exports) {
+
+var e=module.exports="undefined"!=typeof window&&window.Math==Math?window:"undefined"!=typeof self&&self.Math==Math?self:Function("return this")();"number"==typeof __g&&(__g=e);
+},{}],"k/OQ":[function(require,module,exports) {
+var r={}.hasOwnProperty;module.exports=function(e,n){return r.call(e,n)};
+},{}],"BI7s":[function(require,module,exports) {
+module.exports=function(r){try{return!!r()}catch(t){return!0}};
+},{}],"jVdc":[function(require,module,exports) {
+module.exports=!require("./_fails")(function(){return 7!=Object.defineProperty({},"a",{get:function(){return 7}}).a});
+},{"./_fails":"BI7s"}],"6DcE":[function(require,module,exports) {
+var e=module.exports={version:"2.6.9"};"number"==typeof __e&&(__e=e);
+},{}],"/2t/":[function(require,module,exports) {
+module.exports=function(o){return"object"==typeof o?null!==o:"function"==typeof o};
+},{}],"1087":[function(require,module,exports) {
+var r=require("./_is-object");module.exports=function(e){if(!r(e))throw TypeError(e+" is not an object!");return e};
+},{"./_is-object":"/2t/"}],"4cz6":[function(require,module,exports) {
+var e=require("./_is-object"),r=require("./_global").document,t=e(r)&&e(r.createElement);module.exports=function(e){return t?r.createElement(e):{}};
+},{"./_is-object":"/2t/","./_global":"8QiI"}],"+9/k":[function(require,module,exports) {
+module.exports=!require("./_descriptors")&&!require("./_fails")(function(){return 7!=Object.defineProperty(require("./_dom-create")("div"),"a",{get:function(){return 7}}).a});
+},{"./_descriptors":"jVdc","./_fails":"BI7s","./_dom-create":"4cz6"}],"S7GM":[function(require,module,exports) {
+var t=require("./_is-object");module.exports=function(r,e){if(!t(r))return r;var o,n;if(e&&"function"==typeof(o=r.toString)&&!t(n=o.call(r)))return n;if("function"==typeof(o=r.valueOf)&&!t(n=o.call(r)))return n;if(!e&&"function"==typeof(o=r.toString)&&!t(n=o.call(r)))return n;throw TypeError("Can't convert object to primitive value")};
+},{"./_is-object":"/2t/"}],"gGgn":[function(require,module,exports) {
+var e=require("./_an-object"),r=require("./_ie8-dom-define"),t=require("./_to-primitive"),i=Object.defineProperty;exports.f=require("./_descriptors")?Object.defineProperty:function(o,n,u){if(e(o),n=t(n,!0),e(u),r)try{return i(o,n,u)}catch(c){}if("get"in u||"set"in u)throw TypeError("Accessors not supported!");return"value"in u&&(o[n]=u.value),o};
+},{"./_an-object":"1087","./_ie8-dom-define":"+9/k","./_to-primitive":"S7GM","./_descriptors":"jVdc"}],"zQQJ":[function(require,module,exports) {
+module.exports=function(e,r){return{enumerable:!(1&e),configurable:!(2&e),writable:!(4&e),value:r}};
+},{}],"nCfi":[function(require,module,exports) {
+var r=require("./_object-dp"),e=require("./_property-desc");module.exports=require("./_descriptors")?function(t,u,o){return r.f(t,u,e(1,o))}:function(r,e,t){return r[e]=t,r};
+},{"./_object-dp":"gGgn","./_property-desc":"zQQJ","./_descriptors":"jVdc"}],"jLFM":[function(require,module,exports) {
+var o=0,t=Math.random();module.exports=function(n){return"Symbol(".concat(void 0===n?"":n,")_",(++o+t).toString(36))};
+},{}],"dG4y":[function(require,module,exports) {
+module.exports=!1;
+},{}],"k492":[function(require,module,exports) {
+
+var r=require("./_core"),e=require("./_global"),o="__core-js_shared__",i=e[o]||(e[o]={});(module.exports=function(r,e){return i[r]||(i[r]=void 0!==e?e:{})})("versions",[]).push({version:r.version,mode:require("./_library")?"pure":"global",copyright:"© 2019 Denis Pushkarev (zloirock.ru)"});
+},{"./_core":"6DcE","./_global":"8QiI","./_library":"dG4y"}],"it4f":[function(require,module,exports) {
+module.exports=require("./_shared")("native-function-to-string",Function.toString);
+},{"./_shared":"k492"}],"jDrK":[function(require,module,exports) {
+
+var e=require("./_global"),r=require("./_hide"),t=require("./_has"),i=require("./_uid")("src"),n=require("./_function-to-string"),o="toString",u=(""+n).split(o);require("./_core").inspectSource=function(e){return n.call(e)},(module.exports=function(n,o,c,l){var s="function"==typeof c;s&&(t(c,"name")||r(c,"name",o)),n[o]!==c&&(s&&(t(c,i)||r(c,i,n[o]?""+n[o]:u.join(String(o)))),n===e?n[o]=c:l?n[o]?n[o]=c:r(n,o,c):(delete n[o],r(n,o,c)))})(Function.prototype,o,function(){return"function"==typeof this&&this[i]||n.call(this)});
+},{"./_global":"8QiI","./_hide":"nCfi","./_has":"k/OQ","./_uid":"jLFM","./_function-to-string":"it4f","./_core":"6DcE"}],"QKlW":[function(require,module,exports) {
+module.exports=function(o){if("function"!=typeof o)throw TypeError(o+" is not a function!");return o};
+},{}],"W8bf":[function(require,module,exports) {
+var r=require("./_a-function");module.exports=function(n,t,u){if(r(n),void 0===t)return n;switch(u){case 1:return function(r){return n.call(t,r)};case 2:return function(r,u){return n.call(t,r,u)};case 3:return function(r,u,e){return n.call(t,r,u,e)}}return function(){return n.apply(t,arguments)}};
+},{"./_a-function":"QKlW"}],"Vobs":[function(require,module,exports) {
+
+var e=require("./_global"),r=require("./_core"),o=require("./_hide"),i=require("./_redefine"),u=require("./_ctx"),n="prototype",t=function(c,f,l){var q,_,a,d,p=c&t.F,v=c&t.G,F=c&t.S,x=c&t.P,y=c&t.B,B=v?e:F?e[f]||(e[f]={}):(e[f]||{})[n],G=v?r:r[f]||(r[f]={}),P=G[n]||(G[n]={});for(q in v&&(l=f),l)a=((_=!p&&B&&void 0!==B[q])?B:l)[q],d=y&&_?u(a,e):x&&"function"==typeof a?u(Function.call,a):a,B&&i(B,q,a,c&t.U),G[q]!=a&&o(G,q,d),x&&P[q]!=a&&(P[q]=a)};e.core=r,t.F=1,t.G=2,t.S=4,t.P=8,t.B=16,t.W=32,t.U=64,t.R=128,module.exports=t;
+},{"./_global":"8QiI","./_core":"6DcE","./_hide":"nCfi","./_redefine":"jDrK","./_ctx":"W8bf"}],"nxhn":[function(require,module,exports) {
+var e=require("./_uid")("meta"),r=require("./_is-object"),t=require("./_has"),n=require("./_object-dp").f,i=0,u=Object.isExtensible||function(){return!0},f=!require("./_fails")(function(){return u(Object.preventExtensions({}))}),o=function(r){n(r,e,{value:{i:"O"+ ++i,w:{}}})},s=function(n,i){if(!r(n))return"symbol"==typeof n?n:("string"==typeof n?"S":"P")+n;if(!t(n,e)){if(!u(n))return"F";if(!i)return"E";o(n)}return n[e].i},c=function(r,n){if(!t(r,e)){if(!u(r))return!0;if(!n)return!1;o(r)}return r[e].w},E=function(r){return f&&a.NEED&&u(r)&&!t(r,e)&&o(r),r},a=module.exports={KEY:e,NEED:!1,fastKey:s,getWeak:c,onFreeze:E};
+},{"./_uid":"jLFM","./_is-object":"/2t/","./_has":"k/OQ","./_object-dp":"gGgn","./_fails":"BI7s"}],"I5XL":[function(require,module,exports) {
+var e=require("./_shared")("wks"),r=require("./_uid"),o=require("./_global").Symbol,u="function"==typeof o,i=module.exports=function(i){return e[i]||(e[i]=u&&o[i]||(u?o:r)("Symbol."+i))};i.store=e;
+},{"./_shared":"k492","./_uid":"jLFM","./_global":"8QiI"}],"+IBD":[function(require,module,exports) {
+var e=require("./_object-dp").f,r=require("./_has"),o=require("./_wks")("toStringTag");module.exports=function(t,u,i){t&&!r(t=i?t:t.prototype,o)&&e(t,o,{configurable:!0,value:u})};
+},{"./_object-dp":"gGgn","./_has":"k/OQ","./_wks":"I5XL"}],"Jnk4":[function(require,module,exports) {
+exports.f=require("./_wks");
+},{"./_wks":"I5XL"}],"ZenZ":[function(require,module,exports) {
+
+var r=require("./_global"),e=require("./_core"),o=require("./_library"),i=require("./_wks-ext"),l=require("./_object-dp").f;module.exports=function(u){var a=e.Symbol||(e.Symbol=o?{}:r.Symbol||{});"_"==u.charAt(0)||u in a||l(a,u,{value:i.f(u)})};
+},{"./_global":"8QiI","./_core":"6DcE","./_library":"dG4y","./_wks-ext":"Jnk4","./_object-dp":"gGgn"}],"3+Dr":[function(require,module,exports) {
+var r={}.toString;module.exports=function(t){return r.call(t).slice(8,-1)};
+},{}],"sUp0":[function(require,module,exports) {
+var e=require("./_cof");module.exports=Object("z").propertyIsEnumerable(0)?Object:function(r){return"String"==e(r)?r.split(""):Object(r)};
+},{"./_cof":"3+Dr"}],"3V0R":[function(require,module,exports) {
+module.exports=function(o){if(null==o)throw TypeError("Can't call method on  "+o);return o};
+},{}],"zakI":[function(require,module,exports) {
+var e=require("./_iobject"),r=require("./_defined");module.exports=function(i){return e(r(i))};
+},{"./_iobject":"sUp0","./_defined":"3V0R"}],"ubM9":[function(require,module,exports) {
+var o=Math.ceil,r=Math.floor;module.exports=function(t){return isNaN(t=+t)?0:(t>0?r:o)(t)};
+},{}],"0KLz":[function(require,module,exports) {
+var e=require("./_to-integer"),r=Math.min;module.exports=function(t){return t>0?r(e(t),9007199254740991):0};
+},{"./_to-integer":"ubM9"}],"tPLG":[function(require,module,exports) {
+var e=require("./_to-integer"),r=Math.max,t=Math.min;module.exports=function(n,a){return(n=e(n))<0?r(n+a,0):t(n,a)};
+},{"./_to-integer":"ubM9"}],"ntLR":[function(require,module,exports) {
+var e=require("./_to-iobject"),r=require("./_to-length"),t=require("./_to-absolute-index");module.exports=function(n){return function(i,o,u){var f,l=e(i),a=r(l.length),c=t(u,a);if(n&&o!=o){for(;a>c;)if((f=l[c++])!=f)return!0}else for(;a>c;c++)if((n||c in l)&&l[c]===o)return n||c||0;return!n&&-1}};
+},{"./_to-iobject":"zakI","./_to-length":"0KLz","./_to-absolute-index":"tPLG"}],"4UE8":[function(require,module,exports) {
+var e=require("./_shared")("keys"),r=require("./_uid");module.exports=function(u){return e[u]||(e[u]=r(u))};
+},{"./_shared":"k492","./_uid":"jLFM"}],"tBLI":[function(require,module,exports) {
+var r=require("./_has"),e=require("./_to-iobject"),u=require("./_array-includes")(!1),i=require("./_shared-key")("IE_PROTO");module.exports=function(o,a){var n,s=e(o),t=0,h=[];for(n in s)n!=i&&r(s,n)&&h.push(n);for(;a.length>t;)r(s,n=a[t++])&&(~u(h,n)||h.push(n));return h};
+},{"./_has":"k/OQ","./_to-iobject":"zakI","./_array-includes":"ntLR","./_shared-key":"4UE8"}],"qGBL":[function(require,module,exports) {
+module.exports="constructor,hasOwnProperty,isPrototypeOf,propertyIsEnumerable,toLocaleString,toString,valueOf".split(",");
+},{}],"+huX":[function(require,module,exports) {
+var e=require("./_object-keys-internal"),r=require("./_enum-bug-keys");module.exports=Object.keys||function(u){return e(u,r)};
+},{"./_object-keys-internal":"tBLI","./_enum-bug-keys":"qGBL"}],"vSss":[function(require,module,exports) {
+exports.f=Object.getOwnPropertySymbols;
+},{}],"NRj4":[function(require,module,exports) {
+exports.f={}.propertyIsEnumerable;
+},{}],"6BDX":[function(require,module,exports) {
+var e=require("./_object-keys"),r=require("./_object-gops"),o=require("./_object-pie");module.exports=function(t){var u=e(t),i=r.f;if(i)for(var c,f=i(t),a=o.f,l=0;f.length>l;)a.call(t,c=f[l++])&&u.push(c);return u};
+},{"./_object-keys":"+huX","./_object-gops":"vSss","./_object-pie":"NRj4"}],"JI5q":[function(require,module,exports) {
+var r=require("./_cof");module.exports=Array.isArray||function(e){return"Array"==r(e)};
+},{"./_cof":"3+Dr"}],"2XMZ":[function(require,module,exports) {
+var e=require("./_defined");module.exports=function(r){return Object(e(r))};
+},{"./_defined":"3V0R"}],"L4n9":[function(require,module,exports) {
+var e=require("./_object-dp"),r=require("./_an-object"),t=require("./_object-keys");module.exports=require("./_descriptors")?Object.defineProperties:function(o,i){r(o);for(var u,c=t(i),n=c.length,s=0;n>s;)e.f(o,u=c[s++],i[u]);return o};
+},{"./_object-dp":"gGgn","./_an-object":"1087","./_object-keys":"+huX","./_descriptors":"jVdc"}],"HDWL":[function(require,module,exports) {
+var e=require("./_global").document;module.exports=e&&e.documentElement;
+},{"./_global":"8QiI"}],"EH/8":[function(require,module,exports) {
+var e=require("./_an-object"),r=require("./_object-dps"),t=require("./_enum-bug-keys"),n=require("./_shared-key")("IE_PROTO"),o=function(){},i="prototype",u=function(){var e,r=require("./_dom-create")("iframe"),n=t.length;for(r.style.display="none",require("./_html").appendChild(r),r.src="javascript:",(e=r.contentWindow.document).open(),e.write("<script>document.F=Object<\/script>"),e.close(),u=e.F;n--;)delete u[i][t[n]];return u()};module.exports=Object.create||function(t,c){var a;return null!==t?(o[i]=e(t),a=new o,o[i]=null,a[n]=t):a=u(),void 0===c?a:r(a,c)};
+},{"./_an-object":"1087","./_object-dps":"L4n9","./_enum-bug-keys":"qGBL","./_shared-key":"4UE8","./_dom-create":"4cz6","./_html":"HDWL"}],"HNVq":[function(require,module,exports) {
+var e=require("./_object-keys-internal"),r=require("./_enum-bug-keys").concat("length","prototype");exports.f=Object.getOwnPropertyNames||function(t){return e(t,r)};
+},{"./_object-keys-internal":"tBLI","./_enum-bug-keys":"qGBL"}],"NpQ8":[function(require,module,exports) {
+var e=require("./_to-iobject"),t=require("./_object-gopn").f,o={}.toString,r="object"==typeof window&&window&&Object.getOwnPropertyNames?Object.getOwnPropertyNames(window):[],n=function(e){try{return t(e)}catch(o){return r.slice()}};module.exports.f=function(c){return r&&"[object Window]"==o.call(c)?n(c):t(e(c))};
+},{"./_to-iobject":"zakI","./_object-gopn":"HNVq"}],"EGJe":[function(require,module,exports) {
+var e=require("./_object-pie"),r=require("./_property-desc"),i=require("./_to-iobject"),t=require("./_to-primitive"),o=require("./_has"),c=require("./_ie8-dom-define"),u=Object.getOwnPropertyDescriptor;exports.f=require("./_descriptors")?u:function(p,q){if(p=i(p),q=t(q,!0),c)try{return u(p,q)}catch(_){}if(o(p,q))return r(!e.f.call(p,q),p[q])};
+},{"./_object-pie":"NRj4","./_property-desc":"zQQJ","./_to-iobject":"zakI","./_to-primitive":"S7GM","./_has":"k/OQ","./_ie8-dom-define":"+9/k","./_descriptors":"jVdc"}],"rGq9":[function(require,module,exports) {
+
+"use strict";var e=require("./_global"),r=require("./_has"),t=require("./_descriptors"),i=require("./_export"),n=require("./_redefine"),o=require("./_meta").KEY,u=require("./_fails"),s=require("./_shared"),f=require("./_set-to-string-tag"),c=require("./_uid"),a=require("./_wks"),l=require("./_wks-ext"),p=require("./_wks-define"),b=require("./_enum-keys"),y=require("./_is-array"),h=require("./_an-object"),_=require("./_is-object"),q=require("./_to-object"),g=require("./_to-iobject"),m=require("./_to-primitive"),v=require("./_property-desc"),d=require("./_object-create"),S=require("./_object-gopn-ext"),j=require("./_object-gopd"),O=require("./_object-gops"),w=require("./_object-dp"),k=require("./_object-keys"),P=j.f,F=w.f,E=S.f,N=e.Symbol,J=e.JSON,x=J&&J.stringify,I="prototype",T=a("_hidden"),C=a("toPrimitive"),M={}.propertyIsEnumerable,D=s("symbol-registry"),G=s("symbols"),K=s("op-symbols"),Q=Object[I],W="function"==typeof N&&!!O.f,Y=e.QObject,z=!Y||!Y[I]||!Y[I].findChild,A=t&&u(function(){return 7!=d(F({},"a",{get:function(){return F(this,"a",{value:7}).a}})).a})?function(e,r,t){var i=P(Q,r);i&&delete Q[r],F(e,r,t),i&&e!==Q&&F(Q,r,i)}:F,B=function(e){var r=G[e]=d(N[I]);return r._k=e,r},H=W&&"symbol"==typeof N.iterator?function(e){return"symbol"==typeof e}:function(e){return e instanceof N},L=function(e,t,i){return e===Q&&L(K,t,i),h(e),t=m(t,!0),h(i),r(G,t)?(i.enumerable?(r(e,T)&&e[T][t]&&(e[T][t]=!1),i=d(i,{enumerable:v(0,!1)})):(r(e,T)||F(e,T,v(1,{})),e[T][t]=!0),A(e,t,i)):F(e,t,i)},R=function(e,r){h(e);for(var t,i=b(r=g(r)),n=0,o=i.length;o>n;)L(e,t=i[n++],r[t]);return e},U=function(e,r){return void 0===r?d(e):R(d(e),r)},V=function(e){var t=M.call(this,e=m(e,!0));return!(this===Q&&r(G,e)&&!r(K,e))&&(!(t||!r(this,e)||!r(G,e)||r(this,T)&&this[T][e])||t)},X=function(e,t){if(e=g(e),t=m(t,!0),e!==Q||!r(G,t)||r(K,t)){var i=P(e,t);return!i||!r(G,t)||r(e,T)&&e[T][t]||(i.enumerable=!0),i}},Z=function(e){for(var t,i=E(g(e)),n=[],u=0;i.length>u;)r(G,t=i[u++])||t==T||t==o||n.push(t);return n},$=function(e){for(var t,i=e===Q,n=E(i?K:g(e)),o=[],u=0;n.length>u;)!r(G,t=n[u++])||i&&!r(Q,t)||o.push(G[t]);return o};W||(n((N=function(){if(this instanceof N)throw TypeError("Symbol is not a constructor!");var e=c(arguments.length>0?arguments[0]:void 0),i=function(t){this===Q&&i.call(K,t),r(this,T)&&r(this[T],e)&&(this[T][e]=!1),A(this,e,v(1,t))};return t&&z&&A(Q,e,{configurable:!0,set:i}),B(e)})[I],"toString",function(){return this._k}),j.f=X,w.f=L,require("./_object-gopn").f=S.f=Z,require("./_object-pie").f=V,O.f=$,t&&!require("./_library")&&n(Q,"propertyIsEnumerable",V,!0),l.f=function(e){return B(a(e))}),i(i.G+i.W+i.F*!W,{Symbol:N});for(var ee="hasInstance,isConcatSpreadable,iterator,match,replace,search,species,split,toPrimitive,toStringTag,unscopables".split(","),re=0;ee.length>re;)a(ee[re++]);for(var te=k(a.store),ie=0;te.length>ie;)p(te[ie++]);i(i.S+i.F*!W,"Symbol",{for:function(e){return r(D,e+="")?D[e]:D[e]=N(e)},keyFor:function(e){if(!H(e))throw TypeError(e+" is not a symbol!");for(var r in D)if(D[r]===e)return r},useSetter:function(){z=!0},useSimple:function(){z=!1}}),i(i.S+i.F*!W,"Object",{create:U,defineProperty:L,defineProperties:R,getOwnPropertyDescriptor:X,getOwnPropertyNames:Z,getOwnPropertySymbols:$});var ne=u(function(){O.f(1)});i(i.S+i.F*ne,"Object",{getOwnPropertySymbols:function(e){return O.f(q(e))}}),J&&i(i.S+i.F*(!W||u(function(){var e=N();return"[null]"!=x([e])||"{}"!=x({a:e})||"{}"!=x(Object(e))})),"JSON",{stringify:function(e){for(var r,t,i=[e],n=1;arguments.length>n;)i.push(arguments[n++]);if(t=r=i[1],(_(r)||void 0!==e)&&!H(e))return y(r)||(r=function(e,r){if("function"==typeof t&&(r=t.call(this,e,r)),!H(r))return r}),i[1]=r,x.apply(J,i)}}),N[I][C]||require("./_hide")(N[I],C,N[I].valueOf),f(N,"Symbol"),f(Math,"Math",!0),f(e.JSON,"JSON",!0);
+},{"./_global":"8QiI","./_has":"k/OQ","./_descriptors":"jVdc","./_export":"Vobs","./_redefine":"jDrK","./_meta":"nxhn","./_fails":"BI7s","./_shared":"k492","./_set-to-string-tag":"+IBD","./_uid":"jLFM","./_wks":"I5XL","./_wks-ext":"Jnk4","./_wks-define":"ZenZ","./_enum-keys":"6BDX","./_is-array":"JI5q","./_an-object":"1087","./_is-object":"/2t/","./_to-object":"2XMZ","./_to-iobject":"zakI","./_to-primitive":"S7GM","./_property-desc":"zQQJ","./_object-create":"EH/8","./_object-gopn-ext":"NpQ8","./_object-gopd":"EGJe","./_object-gops":"vSss","./_object-dp":"gGgn","./_object-keys":"+huX","./_object-gopn":"HNVq","./_object-pie":"NRj4","./_library":"dG4y","./_hide":"nCfi"}],"v5CS":[function(require,module,exports) {
+var e=require("./_export");e(e.S,"Object",{create:require("./_object-create")});
+},{"./_export":"Vobs","./_object-create":"EH/8"}],"pS46":[function(require,module,exports) {
+var e=require("./_export");e(e.S+e.F*!require("./_descriptors"),"Object",{defineProperty:require("./_object-dp").f});
+},{"./_export":"Vobs","./_descriptors":"jVdc","./_object-dp":"gGgn"}],"sbXv":[function(require,module,exports) {
+var e=require("./_export");e(e.S+e.F*!require("./_descriptors"),"Object",{defineProperties:require("./_object-dps")});
+},{"./_export":"Vobs","./_descriptors":"jVdc","./_object-dps":"L4n9"}],"gG9K":[function(require,module,exports) {
+var e=require("./_export"),r=require("./_core"),t=require("./_fails");module.exports=function(c,i){var o=(r.Object||{})[c]||Object[c],u={};u[c]=i(o),e(e.S+e.F*t(function(){o(1)}),"Object",u)};
+},{"./_export":"Vobs","./_core":"6DcE","./_fails":"BI7s"}],"0xCv":[function(require,module,exports) {
+var r=require("./_to-iobject"),e=require("./_object-gopd").f;require("./_object-sap")("getOwnPropertyDescriptor",function(){return function(t,o){return e(r(t),o)}});
+},{"./_to-iobject":"zakI","./_object-gopd":"EGJe","./_object-sap":"gG9K"}],"dlIw":[function(require,module,exports) {
+var t=require("./_has"),e=require("./_to-object"),o=require("./_shared-key")("IE_PROTO"),r=Object.prototype;module.exports=Object.getPrototypeOf||function(c){return c=e(c),t(c,o)?c[o]:"function"==typeof c.constructor&&c instanceof c.constructor?c.constructor.prototype:c instanceof Object?r:null};
+},{"./_has":"k/OQ","./_to-object":"2XMZ","./_shared-key":"4UE8"}],"3Dkc":[function(require,module,exports) {
+var e=require("./_to-object"),r=require("./_object-gpo");require("./_object-sap")("getPrototypeOf",function(){return function(t){return r(e(t))}});
+},{"./_to-object":"2XMZ","./_object-gpo":"dlIw","./_object-sap":"gG9K"}],"RpZ9":[function(require,module,exports) {
+var e=require("./_to-object"),r=require("./_object-keys");require("./_object-sap")("keys",function(){return function(t){return r(e(t))}});
+},{"./_to-object":"2XMZ","./_object-keys":"+huX","./_object-sap":"gG9K"}],"69mV":[function(require,module,exports) {
+require("./_object-sap")("getOwnPropertyNames",function(){return require("./_object-gopn-ext").f});
+},{"./_object-sap":"gG9K","./_object-gopn-ext":"NpQ8"}],"bkZb":[function(require,module,exports) {
+var e=require("./_is-object"),r=require("./_meta").onFreeze;require("./_object-sap")("freeze",function(n){return function(t){return n&&e(t)?n(r(t)):t}});
+},{"./_is-object":"/2t/","./_meta":"nxhn","./_object-sap":"gG9K"}],"LEG2":[function(require,module,exports) {
+var e=require("./_is-object"),r=require("./_meta").onFreeze;require("./_object-sap")("seal",function(n){return function(t){return n&&e(t)?n(r(t)):t}});
+},{"./_is-object":"/2t/","./_meta":"nxhn","./_object-sap":"gG9K"}],"2OeT":[function(require,module,exports) {
+var e=require("./_is-object"),r=require("./_meta").onFreeze;require("./_object-sap")("preventExtensions",function(n){return function(t){return n&&e(t)?n(r(t)):t}});
+},{"./_is-object":"/2t/","./_meta":"nxhn","./_object-sap":"gG9K"}],"Lm2M":[function(require,module,exports) {
+var r=require("./_is-object");require("./_object-sap")("isFrozen",function(e){return function(n){return!r(n)||!!e&&e(n)}});
+},{"./_is-object":"/2t/","./_object-sap":"gG9K"}],"Lrni":[function(require,module,exports) {
+var e=require("./_is-object");require("./_object-sap")("isSealed",function(r){return function(i){return!e(i)||!!r&&r(i)}});
+},{"./_is-object":"/2t/","./_object-sap":"gG9K"}],"8ypI":[function(require,module,exports) {
+var e=require("./_is-object");require("./_object-sap")("isExtensible",function(r){return function(i){return!!e(i)&&(!r||r(i))}});
+},{"./_is-object":"/2t/","./_object-sap":"gG9K"}],"v89L":[function(require,module,exports) {
+"use strict";var e=require("./_descriptors"),r=require("./_object-keys"),t=require("./_object-gops"),o=require("./_object-pie"),i=require("./_to-object"),c=require("./_iobject"),n=Object.assign;module.exports=!n||require("./_fails")(function(){var e={},r={},t=Symbol(),o="abcdefghijklmnopqrst";return e[t]=7,o.split("").forEach(function(e){r[e]=e}),7!=n({},e)[t]||Object.keys(n({},r)).join("")!=o})?function(n,u){for(var s=i(n),a=arguments.length,f=1,b=t.f,j=o.f;a>f;)for(var l,q=c(arguments[f++]),_=b?r(q).concat(b(q)):r(q),p=_.length,g=0;p>g;)l=_[g++],e&&!j.call(q,l)||(s[l]=q[l]);return s}:n;
+},{"./_descriptors":"jVdc","./_object-keys":"+huX","./_object-gops":"vSss","./_object-pie":"NRj4","./_to-object":"2XMZ","./_iobject":"sUp0","./_fails":"BI7s"}],"av62":[function(require,module,exports) {
+var e=require("./_export");e(e.S+e.F,"Object",{assign:require("./_object-assign")});
+},{"./_export":"Vobs","./_object-assign":"v89L"}],"wc3+":[function(require,module,exports) {
+module.exports=Object.is||function(e,t){return e===t?0!==e||1/e==1/t:e!=e&&t!=t};
+},{}],"OI80":[function(require,module,exports) {
+var e=require("./_export");e(e.S,"Object",{is:require("./_same-value")});
+},{"./_export":"Vobs","./_same-value":"wc3+"}],"I+c1":[function(require,module,exports) {
+var t=require("./_is-object"),e=require("./_an-object"),r=function(r,o){if(e(r),!t(o)&&null!==o)throw TypeError(o+": can't set as prototype!")};module.exports={set:Object.setPrototypeOf||("__proto__"in{}?function(t,e,o){try{(o=require("./_ctx")(Function.call,require("./_object-gopd").f(Object.prototype,"__proto__").set,2))(t,[]),e=!(t instanceof Array)}catch(c){e=!0}return function(t,c){return r(t,c),e?t.__proto__=c:o(t,c),t}}({},!1):void 0),check:r};
+},{"./_is-object":"/2t/","./_an-object":"1087","./_ctx":"W8bf","./_object-gopd":"EGJe"}],"xZ9m":[function(require,module,exports) {
+var e=require("./_export");e(e.S,"Object",{setPrototypeOf:require("./_set-proto").set});
+},{"./_export":"Vobs","./_set-proto":"I+c1"}],"pLtw":[function(require,module,exports) {
+var e=require("./_cof"),t=require("./_wks")("toStringTag"),n="Arguments"==e(function(){return arguments}()),r=function(e,t){try{return e[t]}catch(n){}};module.exports=function(u){var o,c,i;return void 0===u?"Undefined":null===u?"Null":"string"==typeof(c=r(o=Object(u),t))?c:n?e(o):"Object"==(i=e(o))&&"function"==typeof o.callee?"Arguments":i};
+},{"./_cof":"3+Dr","./_wks":"I5XL"}],"++zm":[function(require,module,exports) {
+"use strict";var e=require("./_classof"),r={};r[require("./_wks")("toStringTag")]="z",r+""!="[object z]"&&require("./_redefine")(Object.prototype,"toString",function(){return"[object "+e(this)+"]"},!0);
+},{"./_classof":"pLtw","./_wks":"I5XL","./_redefine":"jDrK"}],"Grvq":[function(require,module,exports) {
+module.exports=function(e,r,l){var a=void 0===l;switch(r.length){case 0:return a?e():e.call(l);case 1:return a?e(r[0]):e.call(l,r[0]);case 2:return a?e(r[0],r[1]):e.call(l,r[0],r[1]);case 3:return a?e(r[0],r[1],r[2]):e.call(l,r[0],r[1],r[2]);case 4:return a?e(r[0],r[1],r[2],r[3]):e.call(l,r[0],r[1],r[2],r[3])}return e.apply(l,r)};
+},{}],"s1yo":[function(require,module,exports) {
+"use strict";var n=require("./_a-function"),t=require("./_is-object"),r=require("./_invoke"),e=[].slice,i={},o=function(n,t,r){if(!(t in i)){for(var e=[],o=0;o<t;o++)e[o]="a["+o+"]";i[t]=Function("F,a","return new F("+e.join(",")+")")}return i[t](n,r)};module.exports=Function.bind||function(i){var u=n(this),c=e.call(arguments,1),a=function(){var n=c.concat(e.call(arguments));return this instanceof a?o(u,n.length,n):r(u,n,i)};return t(u.prototype)&&(a.prototype=u.prototype),a};
+},{"./_a-function":"QKlW","./_is-object":"/2t/","./_invoke":"Grvq"}],"qI6I":[function(require,module,exports) {
+var r=require("./_export");r(r.P,"Function",{bind:require("./_bind")});
+},{"./_export":"Vobs","./_bind":"s1yo"}],"z3jV":[function(require,module,exports) {
+var r=require("./_object-dp").f,t=Function.prototype,e=/^\s*function ([^ (]*)/,n="name";n in t||require("./_descriptors")&&r(t,n,{configurable:!0,get:function(){try{return(""+this).match(e)[1]}catch(r){return""}}});
+},{"./_object-dp":"gGgn","./_descriptors":"jVdc"}],"owRX":[function(require,module,exports) {
+"use strict";var t=require("./_is-object"),e=require("./_object-gpo"),r=require("./_wks")("hasInstance"),i=Function.prototype;r in i||require("./_object-dp").f(i,r,{value:function(r){if("function"!=typeof this||!t(r))return!1;if(!t(this.prototype))return r instanceof this;for(;r=e(r);)if(this.prototype===r)return!0;return!1}});
+},{"./_is-object":"/2t/","./_object-gpo":"dlIw","./_wks":"I5XL","./_object-dp":"gGgn"}],"Pm3s":[function(require,module,exports) {
+module.exports="\t\n\v\f\r   ᠎             　\u2028\u2029\ufeff";
+},{}],"JIX2":[function(require,module,exports) {
+var r=require("./_export"),e=require("./_defined"),i=require("./_fails"),n=require("./_string-ws"),t="["+n+"]",u="​…",o=RegExp("^"+t+t+"*"),p=RegExp(t+t+"*$"),a=function(e,t,o){var p={},a=i(function(){return!!n[e]()||u[e]()!=u}),f=p[e]=a?t(c):n[e];o&&(p[o]=f),r(r.P+r.F*a,"String",p)},c=a.trim=function(r,i){return r=String(e(r)),1&i&&(r=r.replace(o,"")),2&i&&(r=r.replace(p,"")),r};module.exports=a;
+},{"./_export":"Vobs","./_defined":"3V0R","./_fails":"BI7s","./_string-ws":"Pm3s"}],"U/D3":[function(require,module,exports) {
+var r=require("./_global").parseInt,e=require("./_string-trim").trim,t=require("./_string-ws"),i=/^[-+]?0[xX]/;module.exports=8!==r(t+"08")||22!==r(t+"0x16")?function(t,n){var s=e(String(t),3);return r(s,n>>>0||(i.test(s)?16:10))}:r;
+},{"./_global":"8QiI","./_string-trim":"JIX2","./_string-ws":"Pm3s"}],"nPGY":[function(require,module,exports) {
+var r=require("./_export"),e=require("./_parse-int");r(r.G+r.F*(parseInt!=e),{parseInt:e});
+},{"./_export":"Vobs","./_parse-int":"U/D3"}],"tlHn":[function(require,module,exports) {
+var r=require("./_global").parseFloat,e=require("./_string-trim").trim;module.exports=1/r(require("./_string-ws")+"-0")!=-1/0?function(t){var i=e(String(t),3),a=r(i);return 0===a&&"-"==i.charAt(0)?-0:a}:r;
+},{"./_global":"8QiI","./_string-trim":"JIX2","./_string-ws":"Pm3s"}],"yexh":[function(require,module,exports) {
+var r=require("./_export"),e=require("./_parse-float");r(r.G+r.F*(parseFloat!=e),{parseFloat:e});
+},{"./_export":"Vobs","./_parse-float":"tlHn"}],"IxAU":[function(require,module,exports) {
+var t=require("./_is-object"),o=require("./_set-proto").set;module.exports=function(r,e,p){var u,n=e.constructor;return n!==p&&"function"==typeof n&&(u=n.prototype)!==p.prototype&&t(u)&&o&&o(r,u),r};
+},{"./_is-object":"/2t/","./_set-proto":"I+c1"}],"57F7":[function(require,module,exports) {
+
+"use strict";var e=require("./_global"),r=require("./_has"),t=require("./_cof"),i=require("./_inherit-if-required"),a=require("./_to-primitive"),n=require("./_fails"),o=require("./_object-gopn").f,u=require("./_object-gopd").f,s=require("./_object-dp").f,c=require("./_string-trim").trim,f="Number",_=e[f],I=_,N=_.prototype,p=t(require("./_object-create")(N))==f,l="trim"in String.prototype,q=function(e){var r=a(e,!1);if("string"==typeof r&&r.length>2){var t,i,n,o=(r=l?r.trim():c(r,3)).charCodeAt(0);if(43===o||45===o){if(88===(t=r.charCodeAt(2))||120===t)return NaN}else if(48===o){switch(r.charCodeAt(1)){case 66:case 98:i=2,n=49;break;case 79:case 111:i=8,n=55;break;default:return+r}for(var u,s=r.slice(2),f=0,_=s.length;f<_;f++)if((u=s.charCodeAt(f))<48||u>n)return NaN;return parseInt(s,i)}}return+r};if(!_(" 0o1")||!_("0b1")||_("+0x1")){_=function(e){var r=arguments.length<1?0:e,a=this;return a instanceof _&&(p?n(function(){N.valueOf.call(a)}):t(a)!=f)?i(new I(q(r)),a,_):q(r)};for(var g,h=require("./_descriptors")?o(I):"MAX_VALUE,MIN_VALUE,NaN,NEGATIVE_INFINITY,POSITIVE_INFINITY,EPSILON,isFinite,isInteger,isNaN,isSafeInteger,MAX_SAFE_INTEGER,MIN_SAFE_INTEGER,parseFloat,parseInt,isInteger".split(","),E=0;h.length>E;E++)r(I,g=h[E])&&!r(_,g)&&s(_,g,u(I,g));_.prototype=N,N.constructor=_,require("./_redefine")(e,f,_)}
+},{"./_global":"8QiI","./_has":"k/OQ","./_cof":"3+Dr","./_inherit-if-required":"IxAU","./_to-primitive":"S7GM","./_fails":"BI7s","./_object-gopn":"HNVq","./_object-gopd":"EGJe","./_object-dp":"gGgn","./_string-trim":"JIX2","./_object-create":"EH/8","./_descriptors":"jVdc","./_redefine":"jDrK"}],"9Kwj":[function(require,module,exports) {
+var r=require("./_cof");module.exports=function(e,o){if("number"!=typeof e&&"Number"!=r(e))throw TypeError(o);return+e};
+},{"./_cof":"3+Dr"}],"0Lz3":[function(require,module,exports) {
+"use strict";var r=require("./_to-integer"),e=require("./_defined");module.exports=function(t){var i=String(e(this)),n="",o=r(t);if(o<0||o==1/0)throw RangeError("Count can't be negative");for(;o>0;(o>>>=1)&&(i+=i))1&o&&(n+=i);return n};
+},{"./_to-integer":"ubM9","./_defined":"3V0R"}],"/qGB":[function(require,module,exports) {
+"use strict";var r=require("./_export"),e=require("./_to-integer"),i=require("./_a-number-value"),t=require("./_string-repeat"),n=1..toFixed,o=Math.floor,u=[0,0,0,0,0,0],f="Number.toFixed: incorrect invocation!",a="0",c=function(r,e){for(var i=-1,t=e;++i<6;)t+=r*u[i],u[i]=t%1e7,t=o(t/1e7)},l=function(r){for(var e=6,i=0;--e>=0;)i+=u[e],u[e]=o(i/r),i=i%r*1e7},v=function(){for(var r=6,e="";--r>=0;)if(""!==e||0===r||0!==u[r]){var i=String(u[r]);e=""===e?i:e+t.call(a,7-i.length)+i}return e},x=function(r,e,i){return 0===e?i:e%2==1?x(r,e-1,i*r):x(r*r,e/2,i)},d=function(r){for(var e=0,i=r;i>=4096;)e+=12,i/=4096;for(;i>=2;)e+=1,i/=2;return e};r(r.P+r.F*(!!n&&("0.000"!==8e-5.toFixed(3)||"1"!==.9.toFixed(0)||"1.25"!==1.255.toFixed(2)||"1000000000000000128"!==(0xde0b6b3a7640080).toFixed(0))||!require("./_fails")(function(){n.call({})})),"Number",{toFixed:function(r){var n,o,u,s,F=i(this,f),g=e(r),b="",h=a;if(g<0||g>20)throw RangeError(f);if(F!=F)return"NaN";if(F<=-1e21||F>=1e21)return String(F);if(F<0&&(b="-",F=-F),F>1e-21)if(o=(n=d(F*x(2,69,1))-69)<0?F*x(2,-n,1):F/x(2,n,1),o*=4503599627370496,(n=52-n)>0){for(c(0,o),u=g;u>=7;)c(1e7,0),u-=7;for(c(x(10,u,1),0),u=n-1;u>=23;)l(1<<23),u-=23;l(1<<u),c(1,1),l(2),h=v()}else c(0,o),c(1<<-n,0),h=v()+t.call(a,g);return h=g>0?b+((s=h.length)<=g?"0."+t.call(a,g-s)+h:h.slice(0,s-g)+"."+h.slice(s-g)):b+h}});
+},{"./_export":"Vobs","./_to-integer":"ubM9","./_a-number-value":"9Kwj","./_string-repeat":"0Lz3","./_fails":"BI7s"}],"5bLB":[function(require,module,exports) {
+"use strict";var r=require("./_export"),i=require("./_fails"),e=require("./_a-number-value"),n=1..toPrecision;r(r.P+r.F*(i(function(){return"1"!==n.call(1,void 0)})||!i(function(){n.call({})})),"Number",{toPrecision:function(r){var i=e(this,"Number#toPrecision: incorrect invocation!");return void 0===r?n.call(i):n.call(i,r)}});
+},{"./_export":"Vobs","./_fails":"BI7s","./_a-number-value":"9Kwj"}],"oSwj":[function(require,module,exports) {
+var r=require("./_export");r(r.S,"Number",{EPSILON:Math.pow(2,-52)});
+},{"./_export":"Vobs"}],"Iwqp":[function(require,module,exports) {
+var e=require("./_export"),r=require("./_global").isFinite;e(e.S,"Number",{isFinite:function(e){return"number"==typeof e&&r(e)}});
+},{"./_export":"Vobs","./_global":"8QiI"}],"tjYZ":[function(require,module,exports) {
+var e=require("./_is-object"),r=Math.floor;module.exports=function(i){return!e(i)&&isFinite(i)&&r(i)===i};
+},{"./_is-object":"/2t/"}],"49XP":[function(require,module,exports) {
+var e=require("./_export");e(e.S,"Number",{isInteger:require("./_is-integer")});
+},{"./_export":"Vobs","./_is-integer":"tjYZ"}],"P/Mg":[function(require,module,exports) {
+var r=require("./_export");r(r.S,"Number",{isNaN:function(r){return r!=r}});
+},{"./_export":"Vobs"}],"EvBV":[function(require,module,exports) {
+var e=require("./_export"),r=require("./_is-integer"),i=Math.abs;e(e.S,"Number",{isSafeInteger:function(e){return r(e)&&i(e)<=9007199254740991}});
+},{"./_export":"Vobs","./_is-integer":"tjYZ"}],"fOC+":[function(require,module,exports) {
+var r=require("./_export");r(r.S,"Number",{MAX_SAFE_INTEGER:9007199254740991});
+},{"./_export":"Vobs"}],"93yv":[function(require,module,exports) {
+var r=require("./_export");r(r.S,"Number",{MIN_SAFE_INTEGER:-9007199254740991});
+},{"./_export":"Vobs"}],"a09l":[function(require,module,exports) {
+var r=require("./_export"),e=require("./_parse-float");r(r.S+r.F*(Number.parseFloat!=e),"Number",{parseFloat:e});
+},{"./_export":"Vobs","./_parse-float":"tlHn"}],"fCj1":[function(require,module,exports) {
+var r=require("./_export"),e=require("./_parse-int");r(r.S+r.F*(Number.parseInt!=e),"Number",{parseInt:e});
+},{"./_export":"Vobs","./_parse-int":"U/D3"}],"rR7R":[function(require,module,exports) {
+module.exports=Math.log1p||function(e){return(e=+e)>-1e-8&&e<1e-8?e-e*e/2:Math.log(1+e)};
+},{}],"+8o7":[function(require,module,exports) {
+var a=require("./_export"),r=require("./_math-log1p"),t=Math.sqrt,h=Math.acosh;a(a.S+a.F*!(h&&710==Math.floor(h(Number.MAX_VALUE))&&h(1/0)==1/0),"Math",{acosh:function(a){return(a=+a)<1?NaN:a>94906265.62425156?Math.log(a)+Math.LN2:r(a-1+t(a-1)*t(a+1))}});
+},{"./_export":"Vobs","./_math-log1p":"rR7R"}],"xkGF":[function(require,module,exports) {
+var t=require("./_export"),a=Math.asinh;function i(t){return isFinite(t=+t)&&0!=t?t<0?-i(-t):Math.log(t+Math.sqrt(t*t+1)):t}t(t.S+t.F*!(a&&1/a(0)>0),"Math",{asinh:i});
+},{"./_export":"Vobs"}],"0Pmr":[function(require,module,exports) {
+var a=require("./_export"),t=Math.atanh;a(a.S+a.F*!(t&&1/t(-0)<0),"Math",{atanh:function(a){return 0==(a=+a)?a:Math.log((1+a)/(1-a))/2}});
+},{"./_export":"Vobs"}],"ZIrZ":[function(require,module,exports) {
+module.exports=Math.sign||function(n){return 0==(n=+n)||n!=n?n:n<0?-1:1};
+},{}],"Giui":[function(require,module,exports) {
+var r=require("./_export"),t=require("./_math-sign");r(r.S,"Math",{cbrt:function(r){return t(r=+r)*Math.pow(Math.abs(r),1/3)}});
+},{"./_export":"Vobs","./_math-sign":"ZIrZ"}],"HsTu":[function(require,module,exports) {
+var r=require("./_export");r(r.S,"Math",{clz32:function(r){return(r>>>=0)?31-Math.floor(Math.log(r+.5)*Math.LOG2E):32}});
+},{"./_export":"Vobs"}],"xEUq":[function(require,module,exports) {
+var r=require("./_export"),e=Math.exp;r(r.S,"Math",{cosh:function(r){return(e(r=+r)+e(-r))/2}});
+},{"./_export":"Vobs"}],"sm22":[function(require,module,exports) {
+var e=Math.expm1;module.exports=!e||e(10)>22025.465794806718||e(10)<22025.465794806718||-2e-17!=e(-2e-17)?function(e){return 0==(e=+e)?e:e>-1e-6&&e<1e-6?e+e*e/2:Math.exp(e)-1}:e;
+},{}],"aBEU":[function(require,module,exports) {
+var e=require("./_export"),r=require("./_math-expm1");e(e.S+e.F*(r!=Math.expm1),"Math",{expm1:r});
+},{"./_export":"Vobs","./_math-expm1":"sm22"}],"6lqk":[function(require,module,exports) {
+var r=require("./_math-sign"),t=Math.pow,n=t(2,-52),a=t(2,-23),u=t(2,127)*(2-a),e=t(2,-126),o=function(r){return r+1/n-1/n};module.exports=Math.fround||function(t){var h,i,f=Math.abs(t),s=r(t);return f<e?s*o(f/e/a)*e*a:(i=(h=(1+a/n)*f)-(h-f))>u||i!=i?s*(1/0):s*i};
+},{"./_math-sign":"ZIrZ"}],"IjCR":[function(require,module,exports) {
+var r=require("./_export");r(r.S,"Math",{fround:require("./_math-fround")});
+},{"./_export":"Vobs","./_math-fround":"6lqk"}],"HXfT":[function(require,module,exports) {
+var r=require("./_export"),t=Math.abs;r(r.S,"Math",{hypot:function(r,a){for(var e,h,n=0,o=0,u=arguments.length,M=0;o<u;)M<(e=t(arguments[o++]))?(n=n*(h=M/e)*h+1,M=e):n+=e>0?(h=e/M)*h:e;return M===1/0?1/0:M*Math.sqrt(n)}});
+},{"./_export":"Vobs"}],"m2OX":[function(require,module,exports) {
+var r=require("./_export"),e=Math.imul;r(r.S+r.F*require("./_fails")(function(){return-5!=e(4294967295,5)||2!=e.length}),"Math",{imul:function(r,e){var t=+r,u=+e,i=65535&t,n=65535&u;return 0|i*n+((65535&t>>>16)*n+i*(65535&u>>>16)<<16>>>0)}});
+},{"./_export":"Vobs","./_fails":"BI7s"}],"E567":[function(require,module,exports) {
+var r=require("./_export");r(r.S,"Math",{log10:function(r){return Math.log(r)*Math.LOG10E}});
+},{"./_export":"Vobs"}],"ymfv":[function(require,module,exports) {
+var r=require("./_export");r(r.S,"Math",{log1p:require("./_math-log1p")});
+},{"./_export":"Vobs","./_math-log1p":"rR7R"}],"hUIM":[function(require,module,exports) {
+var r=require("./_export");r(r.S,"Math",{log2:function(r){return Math.log(r)/Math.LN2}});
+},{"./_export":"Vobs"}],"d1Y4":[function(require,module,exports) {
+var r=require("./_export");r(r.S,"Math",{sign:require("./_math-sign")});
+},{"./_export":"Vobs","./_math-sign":"ZIrZ"}],"20dh":[function(require,module,exports) {
+var e=require("./_export"),r=require("./_math-expm1"),t=Math.exp;e(e.S+e.F*require("./_fails")(function(){return-2e-17!=!Math.sinh(-2e-17)}),"Math",{sinh:function(e){return Math.abs(e=+e)<1?(r(e)-r(-e))/2:(t(e-1)-t(-e-1))*(Math.E/2)}});
+},{"./_export":"Vobs","./_math-expm1":"sm22","./_fails":"BI7s"}],"cxv8":[function(require,module,exports) {
+var r=require("./_export"),e=require("./_math-expm1"),t=Math.exp;r(r.S,"Math",{tanh:function(r){var a=e(r=+r),h=e(-r);return a==1/0?1:h==1/0?-1:(a-h)/(t(r)+t(-r))}});
+},{"./_export":"Vobs","./_math-expm1":"sm22"}],"xO7u":[function(require,module,exports) {
+var r=require("./_export");r(r.S,"Math",{trunc:function(r){return(r>0?Math.floor:Math.ceil)(r)}});
+},{"./_export":"Vobs"}],"DdG0":[function(require,module,exports) {
+var r=require("./_export"),o=require("./_to-absolute-index"),e=String.fromCharCode,n=String.fromCodePoint;r(r.S+r.F*(!!n&&1!=n.length),"String",{fromCodePoint:function(r){for(var n,t=[],i=arguments.length,a=0;i>a;){if(n=+arguments[a++],o(n,1114111)!==n)throw RangeError(n+" is not a valid code point");t.push(n<65536?e(n):e(55296+((n-=65536)>>10),n%1024+56320))}return t.join("")}});
+},{"./_export":"Vobs","./_to-absolute-index":"tPLG"}],"KDc+":[function(require,module,exports) {
+var r=require("./_export"),e=require("./_to-iobject"),t=require("./_to-length");r(r.S,"String",{raw:function(r){for(var n=e(r.raw),i=t(n.length),o=arguments.length,u=[],g=0;i>g;)u.push(String(n[g++])),g<o&&u.push(String(arguments[g]));return u.join("")}});
+},{"./_export":"Vobs","./_to-iobject":"zakI","./_to-length":"0KLz"}],"D/Dr":[function(require,module,exports) {
+"use strict";require("./_string-trim")("trim",function(r){return function(){return r(this,3)}});
+},{"./_string-trim":"JIX2"}],"4j93":[function(require,module,exports) {
+var e=require("./_to-integer"),r=require("./_defined");module.exports=function(t){return function(n,i){var o,u,c=String(r(n)),d=e(i),a=c.length;return d<0||d>=a?t?"":void 0:(o=c.charCodeAt(d))<55296||o>56319||d+1===a||(u=c.charCodeAt(d+1))<56320||u>57343?t?c.charAt(d):o:t?c.slice(d,d+2):u-56320+(o-55296<<10)+65536}};
+},{"./_to-integer":"ubM9","./_defined":"3V0R"}],"H5R/":[function(require,module,exports) {
+module.exports={};
+},{}],"gj4O":[function(require,module,exports) {
+"use strict";var e=require("./_object-create"),r=require("./_property-desc"),t=require("./_set-to-string-tag"),i={};require("./_hide")(i,require("./_wks")("iterator"),function(){return this}),module.exports=function(o,u,s){o.prototype=e(i,{next:r(1,s)}),t(o,u+" Iterator")};
+},{"./_object-create":"EH/8","./_property-desc":"zQQJ","./_set-to-string-tag":"+IBD","./_hide":"nCfi","./_wks":"I5XL"}],"MKcl":[function(require,module,exports) {
+"use strict";var e=require("./_library"),r=require("./_export"),t=require("./_redefine"),i=require("./_hide"),n=require("./_iterators"),u=require("./_iter-create"),o=require("./_set-to-string-tag"),s=require("./_object-gpo"),a=require("./_wks")("iterator"),c=!([].keys&&"next"in[].keys()),f="@@iterator",l="keys",q="values",y=function(){return this};module.exports=function(_,p,h,k,v,w,d){u(h,p,k);var x,b,g,j=function(e){if(!c&&e in I)return I[e];switch(e){case l:case q:return function(){return new h(this,e)}}return function(){return new h(this,e)}},m=p+" Iterator",A=v==q,F=!1,I=_.prototype,O=I[a]||I[f]||v&&I[v],P=O||j(v),z=v?A?j("entries"):P:void 0,B="Array"==p&&I.entries||O;if(B&&(g=s(B.call(new _)))!==Object.prototype&&g.next&&(o(g,m,!0),e||"function"==typeof g[a]||i(g,a,y)),A&&O&&O.name!==q&&(F=!0,P=function(){return O.call(this)}),e&&!d||!c&&!F&&I[a]||i(I,a,P),n[p]=P,n[m]=y,v)if(x={values:A?P:j(q),keys:w?P:j(l),entries:z},d)for(b in x)b in I||t(I,b,x[b]);else r(r.P+r.F*(c||F),p,x);return x};
+},{"./_library":"dG4y","./_export":"Vobs","./_redefine":"jDrK","./_hide":"nCfi","./_iterators":"H5R/","./_iter-create":"gj4O","./_set-to-string-tag":"+IBD","./_object-gpo":"dlIw","./_wks":"I5XL"}],"WN4F":[function(require,module,exports) {
+"use strict";var i=require("./_string-at")(!0);require("./_iter-define")(String,"String",function(i){this._t=String(i),this._i=0},function(){var t,e=this._t,n=this._i;return n>=e.length?{value:void 0,done:!0}:(t=i(e,n),this._i+=t.length,{value:t,done:!1})});
+},{"./_string-at":"4j93","./_iter-define":"MKcl"}],"gGid":[function(require,module,exports) {
+"use strict";var r=require("./_export"),t=require("./_string-at")(!1);r(r.P,"String",{codePointAt:function(r){return t(this,r)}});
+},{"./_export":"Vobs","./_string-at":"4j93"}],"9r5g":[function(require,module,exports) {
+var e=require("./_is-object"),r=require("./_cof"),i=require("./_wks")("match");module.exports=function(o){var u;return e(o)&&(void 0!==(u=o[i])?!!u:"RegExp"==r(o))};
+},{"./_is-object":"/2t/","./_cof":"3+Dr","./_wks":"I5XL"}],"6dpx":[function(require,module,exports) {
+var e=require("./_is-regexp"),r=require("./_defined");module.exports=function(i,t,n){if(e(t))throw TypeError("String#"+n+" doesn't accept regex!");return String(r(i))};
+},{"./_is-regexp":"9r5g","./_defined":"3V0R"}],"Z7lT":[function(require,module,exports) {
+var r=require("./_wks")("match");module.exports=function(t){var c=/./;try{"/./"[t](c)}catch(e){try{return c[r]=!1,!"/./"[t](c)}catch(a){}}return!0};
+},{"./_wks":"I5XL"}],"PmIB":[function(require,module,exports) {
+"use strict";var e=require("./_export"),t=require("./_to-length"),i=require("./_string-context"),r="endsWith",n=""[r];e(e.P+e.F*require("./_fails-is-regexp")(r),"String",{endsWith:function(e){var s=i(this,e,r),g=arguments.length>1?arguments[1]:void 0,h=t(s.length),l=void 0===g?h:Math.min(t(g),h),u=String(e);return n?n.call(s,u,l):s.slice(l-u.length,l)===u}});
+},{"./_export":"Vobs","./_to-length":"0KLz","./_string-context":"6dpx","./_fails-is-regexp":"Z7lT"}],"qgIv":[function(require,module,exports) {
+"use strict";var e=require("./_export"),i=require("./_string-context"),r="includes";e(e.P+e.F*require("./_fails-is-regexp")(r),"String",{includes:function(e){return!!~i(this,e,r).indexOf(e,arguments.length>1?arguments[1]:void 0)}});
+},{"./_export":"Vobs","./_string-context":"6dpx","./_fails-is-regexp":"Z7lT"}],"Z/Ab":[function(require,module,exports) {
+var r=require("./_export");r(r.P,"String",{repeat:require("./_string-repeat")});
+},{"./_export":"Vobs","./_string-repeat":"0Lz3"}],"U3MC":[function(require,module,exports) {
+"use strict";var t=require("./_export"),r=require("./_to-length"),e=require("./_string-context"),i="startsWith",n=""[i];t(t.P+t.F*require("./_fails-is-regexp")(i),"String",{startsWith:function(t){var s=e(this,t,i),g=r(Math.min(arguments.length>1?arguments[1]:void 0,s.length)),h=String(t);return n?n.call(s,h,g):s.slice(g,g+h.length)===h}});
+},{"./_export":"Vobs","./_to-length":"0KLz","./_string-context":"6dpx","./_fails-is-regexp":"Z7lT"}],"OaTR":[function(require,module,exports) {
+var r=require("./_export"),e=require("./_fails"),t=require("./_defined"),n=/"/g,i=function(r,e,i,u){var o=String(t(r)),a="<"+e;return""!==i&&(a+=" "+i+'="'+String(u).replace(n,"&quot;")+'"'),a+">"+o+"</"+e+">"};module.exports=function(t,n){var u={};u[t]=n(i),r(r.P+r.F*e(function(){var r=""[t]('"');return r!==r.toLowerCase()||r.split('"').length>3}),"String",u)};
+},{"./_export":"Vobs","./_fails":"BI7s","./_defined":"3V0R"}],"eRhq":[function(require,module,exports) {
+"use strict";require("./_string-html")("anchor",function(n){return function(r){return n(this,"a","name",r)}});
+},{"./_string-html":"OaTR"}],"HLSM":[function(require,module,exports) {
+"use strict";require("./_string-html")("big",function(t){return function(){return t(this,"big","","")}});
+},{"./_string-html":"OaTR"}],"RtH9":[function(require,module,exports) {
+"use strict";require("./_string-html")("blink",function(n){return function(){return n(this,"blink","","")}});
+},{"./_string-html":"OaTR"}],"efe7":[function(require,module,exports) {
+"use strict";require("./_string-html")("bold",function(t){return function(){return t(this,"b","","")}});
+},{"./_string-html":"OaTR"}],"v3Ez":[function(require,module,exports) {
+"use strict";require("./_string-html")("fixed",function(t){return function(){return t(this,"tt","","")}});
+},{"./_string-html":"OaTR"}],"RECM":[function(require,module,exports) {
+"use strict";require("./_string-html")("fontcolor",function(t){return function(r){return t(this,"font","color",r)}});
+},{"./_string-html":"OaTR"}],"l7+o":[function(require,module,exports) {
+"use strict";require("./_string-html")("fontsize",function(t){return function(n){return t(this,"font","size",n)}});
+},{"./_string-html":"OaTR"}],"uJlj":[function(require,module,exports) {
+"use strict";require("./_string-html")("italics",function(t){return function(){return t(this,"i","","")}});
+},{"./_string-html":"OaTR"}],"vYww":[function(require,module,exports) {
+"use strict";require("./_string-html")("link",function(r){return function(t){return r(this,"a","href",t)}});
+},{"./_string-html":"OaTR"}],"AiXZ":[function(require,module,exports) {
+"use strict";require("./_string-html")("small",function(t){return function(){return t(this,"small","","")}});
+},{"./_string-html":"OaTR"}],"MhVl":[function(require,module,exports) {
+"use strict";require("./_string-html")("strike",function(t){return function(){return t(this,"strike","","")}});
+},{"./_string-html":"OaTR"}],"DF/m":[function(require,module,exports) {
+"use strict";require("./_string-html")("sub",function(t){return function(){return t(this,"sub","","")}});
+},{"./_string-html":"OaTR"}],"X3LC":[function(require,module,exports) {
+"use strict";require("./_string-html")("sup",function(t){return function(){return t(this,"sup","","")}});
+},{"./_string-html":"OaTR"}],"8Syd":[function(require,module,exports) {
+var e=require("./_export");e(e.S,"Date",{now:function(){return(new Date).getTime()}});
+},{"./_export":"Vobs"}],"G+NU":[function(require,module,exports) {
+"use strict";var t=require("./_export"),e=require("./_to-object"),r=require("./_to-primitive");t(t.P+t.F*require("./_fails")(function(){return null!==new Date(NaN).toJSON()||1!==Date.prototype.toJSON.call({toISOString:function(){return 1}})}),"Date",{toJSON:function(t){var i=e(this),n=r(i);return"number"!=typeof n||isFinite(n)?i.toISOString():null}});
+},{"./_export":"Vobs","./_to-object":"2XMZ","./_to-primitive":"S7GM","./_fails":"BI7s"}],"wk7G":[function(require,module,exports) {
+"use strict";var t=require("./_fails"),e=Date.prototype.getTime,i=Date.prototype.toISOString,n=function(t){return t>9?t:"0"+t};module.exports=t(function(){return"0385-07-25T07:06:39.999Z"!=i.call(new Date(-5e13-1))})||!t(function(){i.call(new Date(NaN))})?function(){if(!isFinite(e.call(this)))throw RangeError("Invalid time value");var t=this,i=t.getUTCFullYear(),r=t.getUTCMilliseconds(),a=i<0?"-":i>9999?"+":"";return a+("00000"+Math.abs(i)).slice(a?-6:-4)+"-"+n(t.getUTCMonth()+1)+"-"+n(t.getUTCDate())+"T"+n(t.getUTCHours())+":"+n(t.getUTCMinutes())+":"+n(t.getUTCSeconds())+"."+(r>99?r:"0"+n(r))+"Z"}:i;
+},{"./_fails":"BI7s"}],"83fP":[function(require,module,exports) {
+var t=require("./_export"),r=require("./_date-to-iso-string");t(t.P+t.F*(Date.prototype.toISOString!==r),"Date",{toISOString:r});
+},{"./_export":"Vobs","./_date-to-iso-string":"wk7G"}],"0FKf":[function(require,module,exports) {
+var e=Date.prototype,t="Invalid Date",a="toString",r=e[a],i=e.getTime;new Date(NaN)+""!=t&&require("./_redefine")(e,a,function(){var e=i.call(this);return e==e?r.call(this):t});
+},{"./_redefine":"jDrK"}],"EnIA":[function(require,module,exports) {
+"use strict";var r=require("./_an-object"),e=require("./_to-primitive"),t="number";module.exports=function(i){if("string"!==i&&i!==t&&"default"!==i)throw TypeError("Incorrect hint");return e(r(this),i!=t)};
+},{"./_an-object":"1087","./_to-primitive":"S7GM"}],"nktC":[function(require,module,exports) {
+var e=require("./_wks")("toPrimitive"),i=Date.prototype;e in i||require("./_hide")(i,e,require("./_date-to-primitive"));
+},{"./_wks":"I5XL","./_hide":"nCfi","./_date-to-primitive":"EnIA"}],"Xjk/":[function(require,module,exports) {
+var r=require("./_export");r(r.S,"Array",{isArray:require("./_is-array")});
+},{"./_export":"Vobs","./_is-array":"JI5q"}],"RG8K":[function(require,module,exports) {
+var r=require("./_an-object");module.exports=function(t,e,o,a){try{return a?e(r(o)[0],o[1]):e(o)}catch(n){var c=t.return;throw void 0!==c&&r(c.call(t)),n}};
+},{"./_an-object":"1087"}],"TuHS":[function(require,module,exports) {
+var r=require("./_iterators"),e=require("./_wks")("iterator"),t=Array.prototype;module.exports=function(o){return void 0!==o&&(r.Array===o||t[e]===o)};
+},{"./_iterators":"H5R/","./_wks":"I5XL"}],"g07e":[function(require,module,exports) {
+"use strict";var e=require("./_object-dp"),r=require("./_property-desc");module.exports=function(t,i,o){i in t?e.f(t,i,r(0,o)):t[i]=o};
+},{"./_object-dp":"gGgn","./_property-desc":"zQQJ"}],"um4Z":[function(require,module,exports) {
+var r=require("./_classof"),e=require("./_wks")("iterator"),t=require("./_iterators");module.exports=require("./_core").getIteratorMethod=function(o){if(null!=o)return o[e]||o["@@iterator"]||t[r(o)]};
+},{"./_classof":"pLtw","./_wks":"I5XL","./_iterators":"H5R/","./_core":"6DcE"}],"zP7t":[function(require,module,exports) {
+var r=require("./_wks")("iterator"),t=!1;try{var n=[7][r]();n.return=function(){t=!0},Array.from(n,function(){throw 2})}catch(e){}module.exports=function(n,u){if(!u&&!t)return!1;var o=!1;try{var c=[7],a=c[r]();a.next=function(){return{done:o=!0}},c[r]=function(){return a},n(c)}catch(e){}return o};
+},{"./_wks":"I5XL"}],"WZRw":[function(require,module,exports) {
+"use strict";var e=require("./_ctx"),r=require("./_export"),t=require("./_to-object"),i=require("./_iter-call"),o=require("./_is-array-iter"),u=require("./_to-length"),n=require("./_create-property"),a=require("./core.get-iterator-method");r(r.S+r.F*!require("./_iter-detect")(function(e){Array.from(e)}),"Array",{from:function(r){var l,c,f,q,_=t(r),h="function"==typeof this?this:Array,v=arguments.length,y=v>1?arguments[1]:void 0,d=void 0!==y,s=0,g=a(_);if(d&&(y=e(y,v>2?arguments[2]:void 0,2)),null==g||h==Array&&o(g))for(c=new h(l=u(_.length));l>s;s++)n(c,s,d?y(_[s],s):_[s]);else for(q=g.call(_),c=new h;!(f=q.next()).done;s++)n(c,s,d?i(q,y,[f.value,s],!0):f.value);return c.length=s,c}});
+},{"./_ctx":"W8bf","./_export":"Vobs","./_to-object":"2XMZ","./_iter-call":"RG8K","./_is-array-iter":"TuHS","./_to-length":"0KLz","./_create-property":"g07e","./core.get-iterator-method":"um4Z","./_iter-detect":"zP7t"}],"URTo":[function(require,module,exports) {
+"use strict";var r=require("./_export"),e=require("./_create-property");r(r.S+r.F*require("./_fails")(function(){function r(){}return!(Array.of.call(r)instanceof r)}),"Array",{of:function(){for(var r=0,t=arguments.length,n=new("function"==typeof this?this:Array)(t);t>r;)e(n,r,arguments[r++]);return n.length=t,n}});
+},{"./_export":"Vobs","./_create-property":"g07e","./_fails":"BI7s"}],"TiCE":[function(require,module,exports) {
+"use strict";var l=require("./_fails");module.exports=function(n,u){return!!n&&l(function(){u?n.call(null,function(){},1):n.call(null)})};
+},{"./_fails":"BI7s"}],"BTDR":[function(require,module,exports) {
+"use strict";var r=require("./_export"),e=require("./_to-iobject"),i=[].join;r(r.P+r.F*(require("./_iobject")!=Object||!require("./_strict-method")(i)),"Array",{join:function(r){return i.call(e(this),void 0===r?",":r)}});
+},{"./_export":"Vobs","./_to-iobject":"zakI","./_iobject":"sUp0","./_strict-method":"TiCE"}],"Ui7t":[function(require,module,exports) {
+"use strict";var r=require("./_export"),e=require("./_html"),i=require("./_cof"),t=require("./_to-absolute-index"),u=require("./_to-length"),a=[].slice;r(r.P+r.F*require("./_fails")(function(){e&&a.call(e)}),"Array",{slice:function(r,e){var l=u(this.length),n=i(this);if(e=void 0===e?l:e,"Array"==n)return a.call(this,r,e);for(var s=t(r,l),c=t(e,l),h=u(c-s),o=new Array(h),f=0;f<h;f++)o[f]="String"==n?this.charAt(s+f):this[s+f];return o}});
+},{"./_export":"Vobs","./_html":"HDWL","./_cof":"3+Dr","./_to-absolute-index":"tPLG","./_to-length":"0KLz","./_fails":"BI7s"}],"TqUy":[function(require,module,exports) {
+"use strict";var r=require("./_export"),t=require("./_a-function"),i=require("./_to-object"),e=require("./_fails"),o=[].sort,u=[1,2,3];r(r.P+r.F*(e(function(){u.sort(void 0)})||!e(function(){u.sort(null)})||!require("./_strict-method")(o)),"Array",{sort:function(r){return void 0===r?o.call(i(this)):o.call(i(this),t(r))}});
+},{"./_export":"Vobs","./_a-function":"QKlW","./_to-object":"2XMZ","./_fails":"BI7s","./_strict-method":"TiCE"}],"TVdo":[function(require,module,exports) {
+var r=require("./_is-object"),e=require("./_is-array"),o=require("./_wks")("species");module.exports=function(i){var t;return e(i)&&("function"!=typeof(t=i.constructor)||t!==Array&&!e(t.prototype)||(t=void 0),r(t)&&null===(t=t[o])&&(t=void 0)),void 0===t?Array:t};
+},{"./_is-object":"/2t/","./_is-array":"JI5q","./_wks":"I5XL"}],"M6RC":[function(require,module,exports) {
+var r=require("./_array-species-constructor");module.exports=function(e,n){return new(r(e))(n)};
+},{"./_array-species-constructor":"TVdo"}],"tMyS":[function(require,module,exports) {
+var e=require("./_ctx"),r=require("./_iobject"),t=require("./_to-object"),i=require("./_to-length"),u=require("./_array-species-create");module.exports=function(n,c){var s=1==n,a=2==n,o=3==n,f=4==n,l=6==n,q=5==n||l,_=c||u;return function(u,c,h){for(var v,p,b=t(u),d=r(b),g=e(c,h,3),j=i(d.length),x=0,m=s?_(u,j):a?_(u,0):void 0;j>x;x++)if((q||x in d)&&(p=g(v=d[x],x,b),n))if(s)m[x]=p;else if(p)switch(n){case 3:return!0;case 5:return v;case 6:return x;case 2:m.push(v)}else if(f)return!1;return l?-1:o||f?f:m}};
+},{"./_ctx":"W8bf","./_iobject":"sUp0","./_to-object":"2XMZ","./_to-length":"0KLz","./_array-species-create":"M6RC"}],"vDWP":[function(require,module,exports) {
+"use strict";var r=require("./_export"),e=require("./_array-methods")(0),t=require("./_strict-method")([].forEach,!0);r(r.P+r.F*!t,"Array",{forEach:function(r){return e(this,r,arguments[1])}});
+},{"./_export":"Vobs","./_array-methods":"tMyS","./_strict-method":"TiCE"}],"O0lf":[function(require,module,exports) {
+"use strict";var r=require("./_export"),e=require("./_array-methods")(1);r(r.P+r.F*!require("./_strict-method")([].map,!0),"Array",{map:function(r){return e(this,r,arguments[1])}});
+},{"./_export":"Vobs","./_array-methods":"tMyS","./_strict-method":"TiCE"}],"PXK+":[function(require,module,exports) {
+"use strict";var r=require("./_export"),e=require("./_array-methods")(2);r(r.P+r.F*!require("./_strict-method")([].filter,!0),"Array",{filter:function(r){return e(this,r,arguments[1])}});
+},{"./_export":"Vobs","./_array-methods":"tMyS","./_strict-method":"TiCE"}],"wD6H":[function(require,module,exports) {
+"use strict";var r=require("./_export"),e=require("./_array-methods")(3);r(r.P+r.F*!require("./_strict-method")([].some,!0),"Array",{some:function(r){return e(this,r,arguments[1])}});
+},{"./_export":"Vobs","./_array-methods":"tMyS","./_strict-method":"TiCE"}],"n6bP":[function(require,module,exports) {
+"use strict";var r=require("./_export"),e=require("./_array-methods")(4);r(r.P+r.F*!require("./_strict-method")([].every,!0),"Array",{every:function(r){return e(this,r,arguments[1])}});
+},{"./_export":"Vobs","./_array-methods":"tMyS","./_strict-method":"TiCE"}],"fXgB":[function(require,module,exports) {
+var r=require("./_a-function"),e=require("./_to-object"),i=require("./_iobject"),o=require("./_to-length");module.exports=function(t,n,u,a,f){r(n);var c=e(t),l=i(c),h=o(c.length),q=f?h-1:0,_=f?-1:1;if(u<2)for(;;){if(q in l){a=l[q],q+=_;break}if(q+=_,f?q<0:h<=q)throw TypeError("Reduce of empty array with no initial value")}for(;f?q>=0:h>q;q+=_)q in l&&(a=n(a,l[q],q,c));return a};
+},{"./_a-function":"QKlW","./_to-object":"2XMZ","./_iobject":"sUp0","./_to-length":"0KLz"}],"OWmJ":[function(require,module,exports) {
+"use strict";var r=require("./_export"),e=require("./_array-reduce");r(r.P+r.F*!require("./_strict-method")([].reduce,!0),"Array",{reduce:function(r){return e(this,r,arguments.length,arguments[1],!1)}});
+},{"./_export":"Vobs","./_array-reduce":"fXgB","./_strict-method":"TiCE"}],"k5ri":[function(require,module,exports) {
+"use strict";var r=require("./_export"),e=require("./_array-reduce");r(r.P+r.F*!require("./_strict-method")([].reduceRight,!0),"Array",{reduceRight:function(r){return e(this,r,arguments.length,arguments[1],!0)}});
+},{"./_export":"Vobs","./_array-reduce":"fXgB","./_strict-method":"TiCE"}],"HB9A":[function(require,module,exports) {
+"use strict";var r=require("./_export"),e=require("./_array-includes")(!1),i=[].indexOf,t=!!i&&1/[1].indexOf(1,-0)<0;r(r.P+r.F*(t||!require("./_strict-method")(i)),"Array",{indexOf:function(r){return t?i.apply(this,arguments)||0:e(this,r,arguments[1])}});
+},{"./_export":"Vobs","./_array-includes":"ntLR","./_strict-method":"TiCE"}],"tgt4":[function(require,module,exports) {
+"use strict";var e=require("./_export"),r=require("./_to-iobject"),t=require("./_to-integer"),i=require("./_to-length"),n=[].lastIndexOf,u=!!n&&1/[1].lastIndexOf(1,-0)<0;e(e.P+e.F*(u||!require("./_strict-method")(n)),"Array",{lastIndexOf:function(e){if(u)return n.apply(this,arguments)||0;var a=r(this),o=i(a.length),s=o-1;for(arguments.length>1&&(s=Math.min(s,t(arguments[1]))),s<0&&(s=o+s);s>=0;s--)if(s in a&&a[s]===e)return s||0;return-1}});
+},{"./_export":"Vobs","./_to-iobject":"zakI","./_to-integer":"ubM9","./_to-length":"0KLz","./_strict-method":"TiCE"}],"QXjR":[function(require,module,exports) {
+"use strict";var e=require("./_to-object"),t=require("./_to-absolute-index"),i=require("./_to-length");module.exports=[].copyWithin||function(r,o){var n=e(this),u=i(n.length),h=t(r,u),l=t(o,u),d=arguments.length>2?arguments[2]:void 0,s=Math.min((void 0===d?u:t(d,u))-l,u-h),a=1;for(l<h&&h<l+s&&(a=-1,l+=s-1,h+=s-1);s-- >0;)l in n?n[h]=n[l]:delete n[h],h+=a,l+=a;return n};
+},{"./_to-object":"2XMZ","./_to-absolute-index":"tPLG","./_to-length":"0KLz"}],"ke6T":[function(require,module,exports) {
+var e=require("./_wks")("unscopables"),r=Array.prototype;null==r[e]&&require("./_hide")(r,e,{}),module.exports=function(o){r[e][o]=!0};
+},{"./_wks":"I5XL","./_hide":"nCfi"}],"1c9D":[function(require,module,exports) {
+var r=require("./_export");r(r.P,"Array",{copyWithin:require("./_array-copy-within")}),require("./_add-to-unscopables")("copyWithin");
+},{"./_export":"Vobs","./_array-copy-within":"QXjR","./_add-to-unscopables":"ke6T"}],"hOOH":[function(require,module,exports) {
+"use strict";var e=require("./_to-object"),t=require("./_to-absolute-index"),r=require("./_to-length");module.exports=function(o){for(var i=e(this),u=r(i.length),n=arguments.length,d=t(n>1?arguments[1]:void 0,u),l=n>2?arguments[2]:void 0,s=void 0===l?u:t(l,u);s>d;)i[d++]=o;return i};
+},{"./_to-object":"2XMZ","./_to-absolute-index":"tPLG","./_to-length":"0KLz"}],"ZBH0":[function(require,module,exports) {
+var r=require("./_export");r(r.P,"Array",{fill:require("./_array-fill")}),require("./_add-to-unscopables")("fill");
+},{"./_export":"Vobs","./_array-fill":"hOOH","./_add-to-unscopables":"ke6T"}],"wTIB":[function(require,module,exports) {
+"use strict";var r=require("./_export"),e=require("./_array-methods")(5),i="find",n=!0;i in[]&&Array(1)[i](function(){n=!1}),r(r.P+r.F*n,"Array",{find:function(r){return e(this,r,arguments.length>1?arguments[1]:void 0)}}),require("./_add-to-unscopables")(i);
+},{"./_export":"Vobs","./_array-methods":"tMyS","./_add-to-unscopables":"ke6T"}],"7ksr":[function(require,module,exports) {
+"use strict";var r=require("./_export"),e=require("./_array-methods")(6),n="findIndex",i=!0;n in[]&&Array(1)[n](function(){i=!1}),r(r.P+r.F*i,"Array",{findIndex:function(r){return e(this,r,arguments.length>1?arguments[1]:void 0)}}),require("./_add-to-unscopables")(n);
+},{"./_export":"Vobs","./_array-methods":"tMyS","./_add-to-unscopables":"ke6T"}],"YBdf":[function(require,module,exports) {
+
+"use strict";var e=require("./_global"),r=require("./_object-dp"),i=require("./_descriptors"),t=require("./_wks")("species");module.exports=function(u){var s=e[u];i&&s&&!s[t]&&r.f(s,t,{configurable:!0,get:function(){return this}})};
+},{"./_global":"8QiI","./_object-dp":"gGgn","./_descriptors":"jVdc","./_wks":"I5XL"}],"Adki":[function(require,module,exports) {
+require("./_set-species")("Array");
+},{"./_set-species":"YBdf"}],"PECj":[function(require,module,exports) {
+module.exports=function(e,n){return{value:n,done:!!e}};
+},{}],"Z+ck":[function(require,module,exports) {
+"use strict";var e=require("./_add-to-unscopables"),r=require("./_iter-step"),t=require("./_iterators"),i=require("./_to-iobject");module.exports=require("./_iter-define")(Array,"Array",function(e,r){this._t=i(e),this._i=0,this._k=r},function(){var e=this._t,t=this._k,i=this._i++;return!e||i>=e.length?(this._t=void 0,r(1)):r(0,"keys"==t?i:"values"==t?e[i]:[i,e[i]])},"values"),t.Arguments=t.Array,e("keys"),e("values"),e("entries");
+},{"./_add-to-unscopables":"ke6T","./_iter-step":"PECj","./_iterators":"H5R/","./_to-iobject":"zakI","./_iter-define":"MKcl"}],"3BaN":[function(require,module,exports) {
+"use strict";var e=require("./_an-object");module.exports=function(){var i=e(this),r="";return i.global&&(r+="g"),i.ignoreCase&&(r+="i"),i.multiline&&(r+="m"),i.unicode&&(r+="u"),i.sticky&&(r+="y"),r};
+},{"./_an-object":"1087"}],"lK2M":[function(require,module,exports) {
+
+var e=require("./_global"),r=require("./_inherit-if-required"),i=require("./_object-dp").f,t=require("./_object-gopn").f,n=require("./_is-regexp"),o=require("./_flags"),u=e.RegExp,c=u,s=u.prototype,f=/a/g,a=/a/g,g=new u(f)!==f;if(require("./_descriptors")&&(!g||require("./_fails")(function(){return a[require("./_wks")("match")]=!1,u(f)!=f||u(a)==a||"/a/i"!=u(f,"i")}))){u=function(e,i){var t=this instanceof u,f=n(e),a=void 0===i;return!t&&f&&e.constructor===u&&a?e:r(g?new c(f&&!a?e.source:e,i):c((f=e instanceof u)?e.source:e,f&&a?o.call(e):i),t?this:s,u)};for(var p=function(e){e in u||i(u,e,{configurable:!0,get:function(){return c[e]},set:function(r){c[e]=r}})},q=t(c),_=0;q.length>_;)p(q[_++]);s.constructor=u,u.prototype=s,require("./_redefine")(e,"RegExp",u)}require("./_set-species")("RegExp");
+},{"./_global":"8QiI","./_inherit-if-required":"IxAU","./_object-dp":"gGgn","./_object-gopn":"HNVq","./_is-regexp":"9r5g","./_flags":"3BaN","./_descriptors":"jVdc","./_fails":"BI7s","./_wks":"I5XL","./_redefine":"jDrK","./_set-species":"YBdf"}],"/8N1":[function(require,module,exports) {
+"use strict";var e=require("./_flags"),l=RegExp.prototype.exec,t=String.prototype.replace,r=l,a="lastIndex",n=function(){var e=/a/,t=/b*/g;return l.call(e,"a"),l.call(t,"a"),0!==e[a]||0!==t[a]}(),o=void 0!==/()??/.exec("")[1],c=n||o;c&&(r=function(r){var c,i,g,u,p=this;return o&&(i=new RegExp("^"+p.source+"$(?!\\s)",e.call(p))),n&&(c=p[a]),g=l.call(p,r),n&&g&&(p[a]=p.global?g.index+g[0].length:c),o&&g&&g.length>1&&t.call(g[0],i,function(){for(u=1;u<arguments.length-2;u++)void 0===arguments[u]&&(g[u]=void 0)}),g}),module.exports=r;
+},{"./_flags":"3BaN"}],"f98m":[function(require,module,exports) {
+"use strict";var e=require("./_regexp-exec");require("./_export")({target:"RegExp",proto:!0,forced:e!==/./.exec},{exec:e});
+},{"./_regexp-exec":"/8N1","./_export":"Vobs"}],"S072":[function(require,module,exports) {
+require("./_descriptors")&&"g"!=/./g.flags&&require("./_object-dp").f(RegExp.prototype,"flags",{configurable:!0,get:require("./_flags")});
+},{"./_descriptors":"jVdc","./_object-dp":"gGgn","./_flags":"3BaN"}],"jkaB":[function(require,module,exports) {
+
+"use strict";require("./es6.regexp.flags");var e=require("./_an-object"),r=require("./_flags"),i=require("./_descriptors"),n="toString",t=/./[n],a=function(e){require("./_redefine")(RegExp.prototype,n,e,!0)};require("./_fails")(function(){return"/a/b"!=t.call({source:"a",flags:"b"})})?a(function(){var n=e(this);return"/".concat(n.source,"/","flags"in n?n.flags:!i&&n instanceof RegExp?r.call(n):void 0)}):t.name!=n&&a(function(){return t.call(this)});
+},{"./es6.regexp.flags":"S072","./_an-object":"1087","./_flags":"3BaN","./_descriptors":"jVdc","./_redefine":"jDrK","./_fails":"BI7s"}],"91Js":[function(require,module,exports) {
+"use strict";var r=require("./_string-at")(!0);module.exports=function(t,e,n){return e+(n?r(t,e).length:1)};
+},{"./_string-at":"4j93"}],"1DcM":[function(require,module,exports) {
+"use strict";var e=require("./_classof"),r=RegExp.prototype.exec;module.exports=function(t,o){var c=t.exec;if("function"==typeof c){var n=c.call(t,o);if("object"!=typeof n)throw new TypeError("RegExp exec method returned something other than an Object or null");return n}if("RegExp"!==e(t))throw new TypeError("RegExp#exec called on incompatible receiver");return r.call(t,o)};
+},{"./_classof":"pLtw"}],"0SCK":[function(require,module,exports) {
+"use strict";require("./es6.regexp.exec");var e=require("./_redefine"),r=require("./_hide"),n=require("./_fails"),t=require("./_defined"),u=require("./_wks"),i=require("./_regexp-exec"),c=u("species"),o=!n(function(){var e=/./;return e.exec=function(){var e=[];return e.groups={a:"7"},e},"7"!=="".replace(e,"$<a>")}),a=function(){var e=/(?:)/,r=e.exec;e.exec=function(){return r.apply(this,arguments)};var n="ab".split(e);return 2===n.length&&"a"===n[0]&&"b"===n[1]}();module.exports=function(l,f,p){var s=u(l),v=!n(function(){var e={};return e[s]=function(){return 7},7!=""[l](e)}),x=v?!n(function(){var e=!1,r=/a/;return r.exec=function(){return e=!0,null},"split"===l&&(r.constructor={},r.constructor[c]=function(){return r}),r[s](""),!e}):void 0;if(!v||!x||"replace"===l&&!o||"split"===l&&!a){var d=/./[s],q=p(t,s,""[l],function(e,r,n,t,u){return r.exec===i?v&&!u?{done:!0,value:d.call(r,n,t)}:{done:!0,value:e.call(n,r,t)}:{done:!1}}),g=q[0],_=q[1];e(String.prototype,l,g),r(RegExp.prototype,s,2==f?function(e,r){return _.call(e,this,r)}:function(e){return _.call(e,this)})}};
+},{"./es6.regexp.exec":"f98m","./_redefine":"jDrK","./_hide":"nCfi","./_fails":"BI7s","./_defined":"3V0R","./_wks":"I5XL","./_regexp-exec":"/8N1"}],"Iomp":[function(require,module,exports) {
+"use strict";var r=require("./_an-object"),e=require("./_to-length"),n=require("./_advance-string-index"),t=require("./_regexp-exec-abstract");require("./_fix-re-wks")("match",1,function(i,a,u,l){return[function(r){var e=i(this),n=null==r?void 0:r[a];return void 0!==n?n.call(r,e):new RegExp(r)[a](String(e))},function(i){var a=l(u,i,this);if(a.done)return a.value;var c=r(i),o=String(this);if(!c.global)return t(c,o);var s=c.unicode;c.lastIndex=0;for(var v,d=[],g=0;null!==(v=t(c,o));){var x=String(v[0]);d[g]=x,""===x&&(c.lastIndex=n(o,e(c.lastIndex),s)),g++}return 0===g?null:d}]});
+},{"./_an-object":"1087","./_to-length":"0KLz","./_advance-string-index":"91Js","./_regexp-exec-abstract":"1DcM","./_fix-re-wks":"0SCK"}],"weWA":[function(require,module,exports) {
+var global = arguments[3];
+var r=arguments[3],e=require("./_an-object"),t=require("./_to-object"),n=require("./_to-length"),i=require("./_to-integer"),a=require("./_advance-string-index"),u=require("./_regexp-exec-abstract"),c=Math.max,l=Math.min,o=Math.floor,v=/\$([$&`']|\d\d?|<[^>]*>)/g,s=/\$([$&`']|\d\d?)/g,g=function(r){return void 0===r?r:String(r)};require("./_fix-re-wks")("replace",2,function(r,d,f,h){return[function(e,t){var n=r(this),i=null==e?void 0:e[d];return void 0!==i?i.call(e,n,t):f.call(String(n),e,t)},function(r,t){var o=h(f,r,this,t);if(o.done)return o.value;var v=e(r),s=String(this),d="function"==typeof t;d||(t=String(t));var x=v.global;if(x){var b=v.unicode;v.lastIndex=0}for(var q=[];;){var S=u(v,s);if(null===S)break;if(q.push(S),!x)break;""===String(S[0])&&(v.lastIndex=a(s,n(v.lastIndex),b))}for(var _="",$=0,k=0;k<q.length;k++){S=q[k];for(var m=String(S[0]),A=c(l(i(S.index),s.length),0),I=[],M=1;M<S.length;M++)I.push(g(S[M]));var j=S.groups;if(d){var w=[m].concat(I,A,s);void 0!==j&&w.push(j);var y=String(t.apply(void 0,w))}else y=p(m,s,A,I,j,t);A>=$&&(_+=s.slice($,A)+y,$=A+m.length)}return _+s.slice($)}];function p(r,e,n,i,a,u){var c=n+r.length,l=i.length,g=s;return void 0!==a&&(a=t(a),g=v),f.call(u,g,function(t,u){var v;switch(u.charAt(0)){case"$":return"$";case"&":return r;case"`":return e.slice(0,n);case"'":return e.slice(c);case"<":v=a[u.slice(1,-1)];break;default:var s=+u;if(0===s)return t;if(s>l){var g=o(s/10);return 0===g?t:g<=l?void 0===i[g-1]?u.charAt(1):i[g-1]+u.charAt(1):t}v=i[s-1]}return void 0===v?"":v})}});
+},{"./_an-object":"1087","./_to-object":"2XMZ","./_to-length":"0KLz","./_to-integer":"ubM9","./_advance-string-index":"91Js","./_regexp-exec-abstract":"1DcM","./_fix-re-wks":"0SCK"}],"60EA":[function(require,module,exports) {
+"use strict";var e=require("./_an-object"),r=require("./_same-value"),n=require("./_regexp-exec-abstract");require("./_fix-re-wks")("search",1,function(t,i,a,u){return[function(e){var r=t(this),n=null==e?void 0:e[i];return void 0!==n?n.call(e,r):new RegExp(e)[i](String(r))},function(t){var i=u(a,t,this);if(i.done)return i.value;var s=e(t),l=String(this),c=s.lastIndex;r(c,0)||(s.lastIndex=0);var v=n(s,l);return r(s.lastIndex,c)||(s.lastIndex=c),null===v?-1:v.index}]});
+},{"./_an-object":"1087","./_same-value":"wc3+","./_regexp-exec-abstract":"1DcM","./_fix-re-wks":"0SCK"}],"othv":[function(require,module,exports) {
+var r=require("./_an-object"),e=require("./_a-function"),u=require("./_wks")("species");module.exports=function(n,o){var i,t=r(n).constructor;return void 0===t||null==(i=r(t)[u])?o:e(i)};
+},{"./_an-object":"1087","./_a-function":"QKlW","./_wks":"I5XL"}],"1d28":[function(require,module,exports) {
+"use strict";var e=require("./_is-regexp"),r=require("./_an-object"),i=require("./_species-constructor"),n=require("./_advance-string-index"),t=require("./_to-length"),u=require("./_regexp-exec-abstract"),l=require("./_regexp-exec"),s=require("./_fails"),c=Math.min,a=[].push,o="split",g="length",h="lastIndex",d=4294967295,f=!s(function(){RegExp(d,"y")});require("./_fix-re-wks")("split",2,function(s,v,p,x){var q;return q="c"=="abbc"[o](/(b)*/)[1]||4!="test"[o](/(?:)/,-1)[g]||2!="ab"[o](/(?:ab)*/)[g]||4!="."[o](/(.?)(.?)/)[g]||"."[o](/()()/)[g]>1||""[o](/.?/)[g]?function(r,i){var n=String(this);if(void 0===r&&0===i)return[];if(!e(r))return p.call(n,r,i);for(var t,u,s,c=[],o=(r.ignoreCase?"i":"")+(r.multiline?"m":"")+(r.unicode?"u":"")+(r.sticky?"y":""),f=0,v=void 0===i?d:i>>>0,x=new RegExp(r.source,o+"g");(t=l.call(x,n))&&!((u=x[h])>f&&(c.push(n.slice(f,t.index)),t[g]>1&&t.index<n[g]&&a.apply(c,t.slice(1)),s=t[0][g],f=u,c[g]>=v));)x[h]===t.index&&x[h]++;return f===n[g]?!s&&x.test("")||c.push(""):c.push(n.slice(f)),c[g]>v?c.slice(0,v):c}:"0"[o](void 0,0)[g]?function(e,r){return void 0===e&&0===r?[]:p.call(this,e,r)}:p,[function(e,r){var i=s(this),n=null==e?void 0:e[v];return void 0!==n?n.call(e,i,r):q.call(String(i),e,r)},function(e,l){var s=x(q,e,this,l,q!==p);if(s.done)return s.value;var a=r(e),o=String(this),g=i(a,RegExp),h=a.unicode,v=(a.ignoreCase?"i":"")+(a.multiline?"m":"")+(a.unicode?"u":"")+(f?"y":"g"),_=new g(f?a:"^(?:"+a.source+")",v),b=void 0===l?d:l>>>0;if(0===b)return[];if(0===o.length)return null===u(_,o)?[o]:[];for(var m=0,y=0,w=[];y<o.length;){_.lastIndex=f?y:0;var E,I=u(_,f?o:o.slice(y));if(null===I||(E=c(t(_.lastIndex+(f?0:y)),o.length))===m)y=n(o,y,h);else{if(w.push(o.slice(m,y)),w.length===b)return w;for(var R=1;R<=I.length-1;R++)if(w.push(I[R]),w.length===b)return w;y=m=E}}return w.push(o.slice(m)),w}]});
+},{"./_is-regexp":"9r5g","./_an-object":"1087","./_species-constructor":"othv","./_advance-string-index":"91Js","./_to-length":"0KLz","./_regexp-exec-abstract":"1DcM","./_regexp-exec":"/8N1","./_fails":"BI7s","./_fix-re-wks":"0SCK"}],"Qz2Q":[function(require,module,exports) {
+module.exports=function(o,n,r,i){if(!(o instanceof n)||void 0!==i&&i in o)throw TypeError(r+": incorrect invocation!");return o};
+},{}],"+L3c":[function(require,module,exports) {
+var e=require("./_ctx"),r=require("./_iter-call"),t=require("./_is-array-iter"),i=require("./_an-object"),o=require("./_to-length"),n=require("./core.get-iterator-method"),u={},a={},f=module.exports=function(f,l,c,q,_){var h,s,d,g,p=_?function(){return f}:n(f),v=e(c,q,l?2:1),x=0;if("function"!=typeof p)throw TypeError(f+" is not iterable!");if(t(p)){for(h=o(f.length);h>x;x++)if((g=l?v(i(s=f[x])[0],s[1]):v(f[x]))===u||g===a)return g}else for(d=p.call(f);!(s=d.next()).done;)if((g=r(d,v,s.value,l))===u||g===a)return g};f.BREAK=u,f.RETURN=a;
+},{"./_ctx":"W8bf","./_iter-call":"RG8K","./_is-array-iter":"TuHS","./_an-object":"1087","./_to-length":"0KLz","./core.get-iterator-method":"um4Z"}],"fNEO":[function(require,module,exports) {
+
+
+var e,t,n,i=require("./_ctx"),o=require("./_invoke"),r=require("./_html"),s=require("./_dom-create"),a=require("./_global"),c=a.process,u=a.setImmediate,p=a.clearImmediate,f=a.MessageChannel,l=a.Dispatch,d=0,m={},h="onreadystatechange",g=function(){var e=+this;if(m.hasOwnProperty(e)){var t=m[e];delete m[e],t()}},v=function(e){g.call(e.data)};u&&p||(u=function(t){for(var n=[],i=1;arguments.length>i;)n.push(arguments[i++]);return m[++d]=function(){o("function"==typeof t?t:Function(t),n)},e(d),d},p=function(e){delete m[e]},"process"==require("./_cof")(c)?e=function(e){c.nextTick(i(g,e,1))}:l&&l.now?e=function(e){l.now(i(g,e,1))}:f?(n=(t=new f).port2,t.port1.onmessage=v,e=i(n.postMessage,n,1)):a.addEventListener&&"function"==typeof postMessage&&!a.importScripts?(e=function(e){a.postMessage(e+"","*")},a.addEventListener("message",v,!1)):e=h in s("script")?function(e){r.appendChild(s("script"))[h]=function(){r.removeChild(this),g.call(e)}}:function(e){setTimeout(i(g,e,1),0)}),module.exports={set:u,clear:p};
+},{"./_ctx":"W8bf","./_invoke":"Grvq","./_html":"HDWL","./_dom-create":"4cz6","./_global":"8QiI","./_cof":"3+Dr"}],"m7QH":[function(require,module,exports) {
+
+
+var e=require("./_global"),t=require("./_task").set,r=e.MutationObserver||e.WebKitMutationObserver,n=e.process,o=e.Promise,a="process"==require("./_cof")(n);module.exports=function(){var i,c,s,v=function(){var e,t;for(a&&(e=n.domain)&&e.exit();i;){t=i.fn,i=i.next;try{t()}catch(r){throw i?s():c=void 0,r}}c=void 0,e&&e.enter()};if(a)s=function(){n.nextTick(v)};else if(!r||e.navigator&&e.navigator.standalone)if(o&&o.resolve){var u=o.resolve(void 0);s=function(){u.then(v)}}else s=function(){t.call(e,v)};else{var f=!0,l=document.createTextNode("");new r(v).observe(l,{characterData:!0}),s=function(){l.data=f=!f}}return function(e){var t={fn:e,next:void 0};c&&(c.next=t),i||(i=t,s()),c=t}};
+},{"./_global":"8QiI","./_task":"fNEO","./_cof":"3+Dr"}],"hTzn":[function(require,module,exports) {
+"use strict";var r=require("./_a-function");function e(e){var o,t;this.promise=new e(function(r,e){if(void 0!==o||void 0!==t)throw TypeError("Bad Promise constructor");o=r,t=e}),this.resolve=r(o),this.reject=r(t)}module.exports.f=function(r){return new e(r)};
+},{"./_a-function":"QKlW"}],"X7pO":[function(require,module,exports) {
+module.exports=function(e){try{return{e:!1,v:e()}}catch(r){return{e:!0,v:r}}};
+},{}],"KrKR":[function(require,module,exports) {
+
+var e=require("./_global"),r=e.navigator;module.exports=r&&r.userAgent||"";
+},{"./_global":"8QiI"}],"FQFX":[function(require,module,exports) {
+var r=require("./_an-object"),e=require("./_is-object"),i=require("./_new-promise-capability");module.exports=function(o,t){if(r(o),e(t)&&t.constructor===o)return t;var u=i.f(o);return(0,u.resolve)(t),u.promise};
+},{"./_an-object":"1087","./_is-object":"/2t/","./_new-promise-capability":"hTzn"}],"lGTj":[function(require,module,exports) {
+var r=require("./_redefine");module.exports=function(e,n,i){for(var o in n)r(e,o,n[o],i);return e};
+},{"./_redefine":"jDrK"}],"M+wl":[function(require,module,exports) {
+
+
+"use strict";var e,r,t,i,n=require("./_library"),o=require("./_global"),c=require("./_ctx"),s=require("./_classof"),u=require("./_export"),a=require("./_is-object"),_=require("./_a-function"),h=require("./_an-instance"),f=require("./_for-of"),l=require("./_species-constructor"),v=require("./_task").set,d=require("./_microtask")(),p=require("./_new-promise-capability"),m=require("./_perform"),q=require("./_user-agent"),y=require("./_promise-resolve"),j="Promise",w=o.TypeError,g=o.process,x=g&&g.versions,b=x&&x.v8||"",k=o[j],P="process"==s(g),F=function(){},S=r=p.f,E=!!function(){try{var e=k.resolve(1),r=(e.constructor={})[require("./_wks")("species")]=function(e){e(F,F)};return(P||"function"==typeof PromiseRejectionEvent)&&e.then(F)instanceof r&&0!==b.indexOf("6.6")&&-1===q.indexOf("Chrome/66")}catch(t){}}(),O=function(e){var r;return!(!a(e)||"function"!=typeof(r=e.then))&&r},R=function(e,r){if(!e._n){e._n=!0;var t=e._c;d(function(){for(var i=e._v,n=1==e._s,o=0,c=function(r){var t,o,c,s=n?r.ok:r.fail,u=r.resolve,a=r.reject,_=r.domain;try{s?(n||(2==e._h&&H(e),e._h=1),!0===s?t=i:(_&&_.enter(),t=s(i),_&&(_.exit(),c=!0)),t===r.promise?a(w("Promise-chain cycle")):(o=O(t))?o.call(t,u,a):u(t)):a(i)}catch(h){_&&!c&&_.exit(),a(h)}};t.length>o;)c(t[o++]);e._c=[],e._n=!1,r&&!e._h&&C(e)})}},C=function(e){v.call(o,function(){var r,t,i,n=e._v,c=G(e);if(c&&(r=m(function(){P?g.emit("unhandledRejection",n,e):(t=o.onunhandledrejection)?t({promise:e,reason:n}):(i=o.console)&&i.error&&i.error("Unhandled promise rejection",n)}),e._h=P||G(e)?2:1),e._a=void 0,c&&r.e)throw r.v})},G=function(e){return 1!==e._h&&0===(e._a||e._c).length},H=function(e){v.call(o,function(){var r;P?g.emit("rejectionHandled",e):(r=o.onrejectionhandled)&&r({promise:e,reason:e._v})})},T=function(e){var r=this;r._d||(r._d=!0,(r=r._w||r)._v=e,r._s=2,r._a||(r._a=r._c.slice()),R(r,!0))},U=function(e){var r,t=this;if(!t._d){t._d=!0,t=t._w||t;try{if(t===e)throw w("Promise can't be resolved itself");(r=O(e))?d(function(){var i={_w:t,_d:!1};try{r.call(e,c(U,i,1),c(T,i,1))}catch(n){T.call(i,n)}}):(t._v=e,t._s=1,R(t,!1))}catch(i){T.call({_w:t,_d:!1},i)}}};E||(k=function(r){h(this,k,j,"_h"),_(r),e.call(this);try{r(c(U,this,1),c(T,this,1))}catch(t){T.call(this,t)}},(e=function(e){this._c=[],this._a=void 0,this._s=0,this._d=!1,this._v=void 0,this._h=0,this._n=!1}).prototype=require("./_redefine-all")(k.prototype,{then:function(e,r){var t=S(l(this,k));return t.ok="function"!=typeof e||e,t.fail="function"==typeof r&&r,t.domain=P?g.domain:void 0,this._c.push(t),this._a&&this._a.push(t),this._s&&R(this,!1),t.promise},catch:function(e){return this.then(void 0,e)}}),t=function(){var r=new e;this.promise=r,this.resolve=c(U,r,1),this.reject=c(T,r,1)},p.f=S=function(e){return e===k||e===i?new t(e):r(e)}),u(u.G+u.W+u.F*!E,{Promise:k}),require("./_set-to-string-tag")(k,j),require("./_set-species")(j),i=require("./_core")[j],u(u.S+u.F*!E,j,{reject:function(e){var r=S(this);return(0,r.reject)(e),r.promise}}),u(u.S+u.F*(n||!E),j,{resolve:function(e){return y(n&&this===i?k:this,e)}}),u(u.S+u.F*!(E&&require("./_iter-detect")(function(e){k.all(e).catch(F)})),j,{all:function(e){var r=this,t=S(r),i=t.resolve,n=t.reject,o=m(function(){var t=[],o=0,c=1;f(e,!1,function(e){var s=o++,u=!1;t.push(void 0),c++,r.resolve(e).then(function(e){u||(u=!0,t[s]=e,--c||i(t))},n)}),--c||i(t)});return o.e&&n(o.v),t.promise},race:function(e){var r=this,t=S(r),i=t.reject,n=m(function(){f(e,!1,function(e){r.resolve(e).then(t.resolve,i)})});return n.e&&i(n.v),t.promise}});
+},{"./_library":"dG4y","./_global":"8QiI","./_ctx":"W8bf","./_classof":"pLtw","./_export":"Vobs","./_is-object":"/2t/","./_a-function":"QKlW","./_an-instance":"Qz2Q","./_for-of":"+L3c","./_species-constructor":"othv","./_task":"fNEO","./_microtask":"m7QH","./_new-promise-capability":"hTzn","./_perform":"X7pO","./_user-agent":"KrKR","./_promise-resolve":"FQFX","./_wks":"I5XL","./_redefine-all":"lGTj","./_set-to-string-tag":"+IBD","./_set-species":"YBdf","./_core":"6DcE","./_iter-detect":"zP7t"}],"yRub":[function(require,module,exports) {
+var r=require("./_is-object");module.exports=function(e,i){if(!r(e)||e._t!==i)throw TypeError("Incompatible receiver, "+i+" required!");return e};
+},{"./_is-object":"/2t/"}],"I9w7":[function(require,module,exports) {
+"use strict";var e=require("./_object-dp").f,r=require("./_object-create"),t=require("./_redefine-all"),i=require("./_ctx"),n=require("./_an-instance"),_=require("./_for-of"),o=require("./_iter-define"),u=require("./_iter-step"),f=require("./_set-species"),s=require("./_descriptors"),l=require("./_meta").fastKey,c=require("./_validate-collection"),v=s?"_s":"size",a=function(e,r){var t,i=l(r);if("F"!==i)return e._i[i];for(t=e._f;t;t=t.n)if(t.k==r)return t};module.exports={getConstructor:function(o,u,f,l){var h=o(function(e,t){n(e,h,u,"_i"),e._t=u,e._i=r(null),e._f=void 0,e._l=void 0,e[v]=0,null!=t&&_(t,f,e[l],e)});return t(h.prototype,{clear:function(){for(var e=c(this,u),r=e._i,t=e._f;t;t=t.n)t.r=!0,t.p&&(t.p=t.p.n=void 0),delete r[t.i];e._f=e._l=void 0,e[v]=0},delete:function(e){var r=c(this,u),t=a(r,e);if(t){var i=t.n,n=t.p;delete r._i[t.i],t.r=!0,n&&(n.n=i),i&&(i.p=n),r._f==t&&(r._f=i),r._l==t&&(r._l=n),r[v]--}return!!t},forEach:function(e){c(this,u);for(var r,t=i(e,arguments.length>1?arguments[1]:void 0,3);r=r?r.n:this._f;)for(t(r.v,r.k,this);r&&r.r;)r=r.p},has:function(e){return!!a(c(this,u),e)}}),s&&e(h.prototype,"size",{get:function(){return c(this,u)[v]}}),h},def:function(e,r,t){var i,n,_=a(e,r);return _?_.v=t:(e._l=_={i:n=l(r,!0),k:r,v:t,p:i=e._l,n:void 0,r:!1},e._f||(e._f=_),i&&(i.n=_),e[v]++,"F"!==n&&(e._i[n]=_)),e},getEntry:a,setStrong:function(e,r,t){o(e,r,function(e,t){this._t=c(e,r),this._k=t,this._l=void 0},function(){for(var e=this._k,r=this._l;r&&r.r;)r=r.p;return this._t&&(this._l=r=r?r.n:this._t._f)?u(0,"keys"==e?r.k:"values"==e?r.v:[r.k,r.v]):(this._t=void 0,u(1))},t?"entries":"values",!t,!0),f(r)}};
+},{"./_object-dp":"gGgn","./_object-create":"EH/8","./_redefine-all":"lGTj","./_ctx":"W8bf","./_an-instance":"Qz2Q","./_for-of":"+L3c","./_iter-define":"MKcl","./_iter-step":"PECj","./_set-species":"YBdf","./_descriptors":"jVdc","./_meta":"nxhn","./_validate-collection":"yRub"}],"J5Ss":[function(require,module,exports) {
+
+"use strict";var e=require("./_global"),r=require("./_export"),t=require("./_redefine"),n=require("./_redefine-all"),i=require("./_meta"),u=require("./_for-of"),o=require("./_an-instance"),c=require("./_is-object"),a=require("./_fails"),s=require("./_iter-detect"),l=require("./_set-to-string-tag"),f=require("./_inherit-if-required");module.exports=function(d,h,q,_,p,g){var v=e[d],w=v,y=p?"set":"add",x=w&&w.prototype,E={},b=function(e){var r=x[e];t(x,e,"delete"==e?function(e){return!(g&&!c(e))&&r.call(this,0===e?0:e)}:"has"==e?function(e){return!(g&&!c(e))&&r.call(this,0===e?0:e)}:"get"==e?function(e){return g&&!c(e)?void 0:r.call(this,0===e?0:e)}:"add"==e?function(e){return r.call(this,0===e?0:e),this}:function(e,t){return r.call(this,0===e?0:e,t),this})};if("function"==typeof w&&(g||x.forEach&&!a(function(){(new w).entries().next()}))){var m=new w,j=m[y](g?{}:-0,1)!=m,C=a(function(){m.has(1)}),D=s(function(e){new w(e)}),F=!g&&a(function(){for(var e=new w,r=5;r--;)e[y](r,r);return!e.has(-0)});D||((w=h(function(e,r){o(e,w,d);var t=f(new v,e,w);return null!=r&&u(r,p,t[y],t),t})).prototype=x,x.constructor=w),(C||F)&&(b("delete"),b("has"),p&&b("get")),(F||j)&&b(y),g&&x.clear&&delete x.clear}else w=_.getConstructor(h,d,p,y),n(w.prototype,q),i.NEED=!0;return l(w,d),E[d]=w,r(r.G+r.W+r.F*(w!=v),E),g||_.setStrong(w,d,p),w};
+},{"./_global":"8QiI","./_export":"Vobs","./_redefine":"jDrK","./_redefine-all":"lGTj","./_meta":"nxhn","./_for-of":"+L3c","./_an-instance":"Qz2Q","./_is-object":"/2t/","./_fails":"BI7s","./_iter-detect":"zP7t","./_set-to-string-tag":"+IBD","./_inherit-if-required":"IxAU"}],"ksBa":[function(require,module,exports) {
+"use strict";var t=require("./_collection-strong"),e=require("./_validate-collection"),r="Map";module.exports=require("./_collection")(r,function(t){return function(){return t(this,arguments.length>0?arguments[0]:void 0)}},{get:function(n){var i=t.getEntry(e(this,r),n);return i&&i.v},set:function(n,i){return t.def(e(this,r),0===n?0:n,i)}},t,!0);
+},{"./_collection-strong":"I9w7","./_validate-collection":"yRub","./_collection":"J5Ss"}],"1jP+":[function(require,module,exports) {
+"use strict";var e=require("./_collection-strong"),t=require("./_validate-collection"),r="Set";module.exports=require("./_collection")(r,function(e){return function(){return e(this,arguments.length>0?arguments[0]:void 0)}},{add:function(i){return e.def(t(this,r),i=0===i?0:i,i)}},e);
+},{"./_collection-strong":"I9w7","./_validate-collection":"yRub","./_collection":"J5Ss"}],"3y1p":[function(require,module,exports) {
+"use strict";var e=require("./_redefine-all"),t=require("./_meta").getWeak,r=require("./_an-object"),i=require("./_is-object"),n=require("./_an-instance"),u=require("./_for-of"),o=require("./_array-methods"),s=require("./_has"),a=require("./_validate-collection"),c=o(5),f=o(6),_=0,h=function(e){return e._l||(e._l=new l)},l=function(){this.a=[]},d=function(e,t){return c(e.a,function(e){return e[0]===t})};l.prototype={get:function(e){var t=d(this,e);if(t)return t[1]},has:function(e){return!!d(this,e)},set:function(e,t){var r=d(this,e);r?r[1]=t:this.a.push([e,t])},delete:function(e){var t=f(this.a,function(t){return t[0]===e});return~t&&this.a.splice(t,1),!!~t}},module.exports={getConstructor:function(r,o,c,f){var l=r(function(e,t){n(e,l,o,"_i"),e._t=o,e._i=_++,e._l=void 0,null!=t&&u(t,c,e[f],e)});return e(l.prototype,{delete:function(e){if(!i(e))return!1;var r=t(e);return!0===r?h(a(this,o)).delete(e):r&&s(r,this._i)&&delete r[this._i]},has:function(e){if(!i(e))return!1;var r=t(e);return!0===r?h(a(this,o)).has(e):r&&s(r,this._i)}}),l},def:function(e,i,n){var u=t(r(i),!0);return!0===u?h(e).set(i,n):u[e._i]=n,e},ufstore:h};
+},{"./_redefine-all":"lGTj","./_meta":"nxhn","./_an-object":"1087","./_is-object":"/2t/","./_an-instance":"Qz2Q","./_for-of":"+L3c","./_array-methods":"tMyS","./_has":"k/OQ","./_validate-collection":"yRub"}],"Y0Wb":[function(require,module,exports) {
+
+"use strict";var e,t=require("./_global"),r=require("./_array-methods")(0),i=require("./_redefine"),n=require("./_meta"),o=require("./_object-assign"),u=require("./_collection-weak"),c=require("./_is-object"),s=require("./_validate-collection"),a=require("./_validate-collection"),l=!t.ActiveXObject&&"ActiveXObject"in t,f="WeakMap",_=n.getWeak,h=Object.isExtensible,q=u.ufstore,v=function(e){return function(){return e(this,arguments.length>0?arguments[0]:void 0)}},d={get:function(e){if(c(e)){var t=_(e);return!0===t?q(s(this,f)).get(e):t?t[this._i]:void 0}},set:function(e,t){return u.def(s(this,f),e,t)}},g=module.exports=require("./_collection")(f,v,d,u,!0,!0);a&&l&&(o((e=u.getConstructor(v,f)).prototype,d),n.NEED=!0,r(["delete","has","get","set"],function(t){var r=g.prototype,n=r[t];i(r,t,function(r,i){if(c(r)&&!h(r)){this._f||(this._f=new e);var o=this._f[t](r,i);return"set"==t?this:o}return n.call(this,r,i)})}));
+},{"./_global":"8QiI","./_array-methods":"tMyS","./_redefine":"jDrK","./_meta":"nxhn","./_object-assign":"v89L","./_collection-weak":"3y1p","./_is-object":"/2t/","./_validate-collection":"yRub","./_collection":"J5Ss"}],"oeIc":[function(require,module,exports) {
+"use strict";var e=require("./_collection-weak"),t=require("./_validate-collection"),i="WeakSet";require("./_collection")(i,function(e){return function(){return e(this,arguments.length>0?arguments[0]:void 0)}},{add:function(r){return e.def(t(this,i),r,!0)}},e,!1,!0);
+},{"./_collection-weak":"3y1p","./_validate-collection":"yRub","./_collection":"J5Ss"}],"zl/6":[function(require,module,exports) {
+
+for(var r,a=require("./_global"),t=require("./_hide"),e=require("./_uid"),y=e("typed_array"),i=e("view"),A=!(!a.ArrayBuffer||!a.DataView),o=A,p=0,l=9,n="Int8Array,Uint8Array,Uint8ClampedArray,Int16Array,Uint16Array,Int32Array,Uint32Array,Float32Array,Float64Array".split(",");p<l;)(r=a[n[p++]])?(t(r.prototype,y,!0),t(r.prototype,i,!0)):o=!1;module.exports={ABV:A,CONSTR:o,TYPED:y,VIEW:i};
+},{"./_global":"8QiI","./_hide":"nCfi","./_uid":"jLFM"}],"dyWK":[function(require,module,exports) {
+var r=require("./_to-integer"),e=require("./_to-length");module.exports=function(t){if(void 0===t)return 0;var n=r(t),o=e(n);if(n!==o)throw RangeError("Wrong length!");return o};
+},{"./_to-integer":"ubM9","./_to-length":"0KLz"}],"78hF":[function(require,module,exports) {
+
+"use strict";var t=require("./_global"),n=require("./_descriptors"),r=require("./_library"),e=require("./_typed"),i=require("./_hide"),o=require("./_redefine-all"),u=require("./_fails"),f=require("./_an-instance"),s=require("./_to-integer"),c=require("./_to-length"),a=require("./_to-index"),h=require("./_object-gopn").f,l=require("./_object-dp").f,g=require("./_array-fill"),_=require("./_set-to-string-tag"),q="ArrayBuffer",v="DataView",w="prototype",I="Wrong length!",b="Wrong index!",y=t[q],p=t[v],d=t.Math,U=t.RangeError,N=t.Infinity,x=y,A=d.abs,F=d.pow,W=d.floor,V=d.log,j=d.LN2,B="buffer",E="byteLength",L="byteOffset",m=n?"_b":B,D=n?"_l":E,M=n?"_o":L;function O(t,n,r){var e,i,o,u=new Array(r),f=8*r-n-1,s=(1<<f)-1,c=s>>1,a=23===n?F(2,-24)-F(2,-77):0,h=0,l=t<0||0===t&&1/t<0?1:0;for((t=A(t))!=t||t===N?(i=t!=t?1:0,e=s):(e=W(V(t)/j),t*(o=F(2,-e))<1&&(e--,o*=2),(t+=e+c>=1?a/o:a*F(2,1-c))*o>=2&&(e++,o/=2),e+c>=s?(i=0,e=s):e+c>=1?(i=(t*o-1)*F(2,n),e+=c):(i=t*F(2,c-1)*F(2,n),e=0));n>=8;u[h++]=255&i,i/=256,n-=8);for(e=e<<n|i,f+=n;f>0;u[h++]=255&e,e/=256,f-=8);return u[--h]|=128*l,u}function R(t,n,r){var e,i=8*r-n-1,o=(1<<i)-1,u=o>>1,f=i-7,s=r-1,c=t[s--],a=127&c;for(c>>=7;f>0;a=256*a+t[s],s--,f-=8);for(e=a&(1<<-f)-1,a>>=-f,f+=n;f>0;e=256*e+t[s],s--,f-=8);if(0===a)a=1-u;else{if(a===o)return e?NaN:c?-N:N;e+=F(2,n),a-=u}return(c?-1:1)*e*F(2,a-n)}function k(t){return t[3]<<24|t[2]<<16|t[1]<<8|t[0]}function z(t){return[255&t]}function C(t){return[255&t,t>>8&255]}function G(t){return[255&t,t>>8&255,t>>16&255,t>>24&255]}function H(t){return O(t,52,8)}function J(t){return O(t,23,4)}function K(t,n,r){l(t[w],n,{get:function(){return this[r]}})}function P(t,n,r,e){var i=a(+r);if(i+n>t[D])throw U(b);var o=t[m]._b,u=i+t[M],f=o.slice(u,u+n);return e?f:f.reverse()}function Q(t,n,r,e,i,o){var u=a(+r);if(u+n>t[D])throw U(b);for(var f=t[m]._b,s=u+t[M],c=e(+i),h=0;h<n;h++)f[s+h]=c[o?h:n-h-1]}if(e.ABV){if(!u(function(){y(1)})||!u(function(){new y(-1)})||u(function(){return new y,new y(1.5),new y(NaN),y.name!=q})){for(var S,T=(y=function(t){return f(this,y),new x(a(t))})[w]=x[w],X=h(x),Y=0;X.length>Y;)(S=X[Y++])in y||i(y,S,x[S]);r||(T.constructor=y)}var Z=new p(new y(2)),$=p[w].setInt8;Z.setInt8(0,2147483648),Z.setInt8(1,2147483649),!Z.getInt8(0)&&Z.getInt8(1)||o(p[w],{setInt8:function(t,n){$.call(this,t,n<<24>>24)},setUint8:function(t,n){$.call(this,t,n<<24>>24)}},!0)}else y=function(t){f(this,y,q);var n=a(t);this._b=g.call(new Array(n),0),this[D]=n},p=function(t,n,r){f(this,p,v),f(t,y,v);var e=t[D],i=s(n);if(i<0||i>e)throw U("Wrong offset!");if(i+(r=void 0===r?e-i:c(r))>e)throw U(I);this[m]=t,this[M]=i,this[D]=r},n&&(K(y,E,"_l"),K(p,B,"_b"),K(p,E,"_l"),K(p,L,"_o")),o(p[w],{getInt8:function(t){return P(this,1,t)[0]<<24>>24},getUint8:function(t){return P(this,1,t)[0]},getInt16:function(t){var n=P(this,2,t,arguments[1]);return(n[1]<<8|n[0])<<16>>16},getUint16:function(t){var n=P(this,2,t,arguments[1]);return n[1]<<8|n[0]},getInt32:function(t){return k(P(this,4,t,arguments[1]))},getUint32:function(t){return k(P(this,4,t,arguments[1]))>>>0},getFloat32:function(t){return R(P(this,4,t,arguments[1]),23,4)},getFloat64:function(t){return R(P(this,8,t,arguments[1]),52,8)},setInt8:function(t,n){Q(this,1,t,z,n)},setUint8:function(t,n){Q(this,1,t,z,n)},setInt16:function(t,n){Q(this,2,t,C,n,arguments[2])},setUint16:function(t,n){Q(this,2,t,C,n,arguments[2])},setInt32:function(t,n){Q(this,4,t,G,n,arguments[2])},setUint32:function(t,n){Q(this,4,t,G,n,arguments[2])},setFloat32:function(t,n){Q(this,4,t,J,n,arguments[2])},setFloat64:function(t,n){Q(this,8,t,H,n,arguments[2])}});_(y,q),_(p,v),i(p[w],e.VIEW,!0),exports[q]=y,exports[v]=p;
+},{"./_global":"8QiI","./_descriptors":"jVdc","./_library":"dG4y","./_typed":"zl/6","./_hide":"nCfi","./_redefine-all":"lGTj","./_fails":"BI7s","./_an-instance":"Qz2Q","./_to-integer":"ubM9","./_to-length":"0KLz","./_to-index":"dyWK","./_object-gopn":"HNVq","./_object-dp":"gGgn","./_array-fill":"hOOH","./_set-to-string-tag":"+IBD"}],"VqD6":[function(require,module,exports) {
+"use strict";var e=require("./_export"),r=require("./_typed"),i=require("./_typed-buffer"),t=require("./_an-object"),u=require("./_to-absolute-index"),n=require("./_to-length"),s=require("./_is-object"),o=require("./_global").ArrayBuffer,f=require("./_species-constructor"),c=i.ArrayBuffer,a=i.DataView,q=r.ABV&&o.isView,_=c.prototype.slice,l=r.VIEW,y="ArrayBuffer";e(e.G+e.W+e.F*(o!==c),{ArrayBuffer:c}),e(e.S+e.F*!r.CONSTR,y,{isView:function(e){return q&&q(e)||s(e)&&l in e}}),e(e.P+e.U+e.F*require("./_fails")(function(){return!new c(2).slice(1,void 0).byteLength}),y,{slice:function(e,r){if(void 0!==_&&void 0===r)return _.call(t(this),e);for(var i=t(this).byteLength,s=u(e,i),o=u(void 0===r?i:r,i),q=new(f(this,c))(n(o-s)),l=new a(this),y=new a(q),b=0;s<o;)y.setUint8(b++,l.getUint8(s++));return q}}),require("./_set-species")(y);
+},{"./_export":"Vobs","./_typed":"zl/6","./_typed-buffer":"78hF","./_an-object":"1087","./_to-absolute-index":"tPLG","./_to-length":"0KLz","./_is-object":"/2t/","./_global":"8QiI","./_species-constructor":"othv","./_fails":"BI7s","./_set-species":"YBdf"}],"q3b2":[function(require,module,exports) {
+var e=require("./_export");e(e.G+e.W+e.F*!require("./_typed").ABV,{DataView:require("./_typed-buffer").DataView});
+},{"./_export":"Vobs","./_typed":"zl/6","./_typed-buffer":"78hF"}],"1sXG":[function(require,module,exports) {
+var global = arguments[3];
+var e=arguments[3];if(require("./_descriptors")){var r=require("./_library"),t=(e=require("./_global"),require("./_fails")),n=require("./_export"),i=require("./_typed"),o=require("./_typed-buffer"),u=require("./_ctx"),c=require("./_an-instance"),f=require("./_property-desc"),a=require("./_hide"),l=require("./_redefine-all"),s=require("./_to-integer"),h=require("./_to-length"),d=require("./_to-index"),g=require("./_to-absolute-index"),_=require("./_to-primitive"),v=require("./_has"),p=require("./_classof"),y=require("./_is-object"),q=require("./_to-object"),w=require("./_is-array-iter"),b=require("./_object-create"),S=require("./_object-gpo"),E=require("./_object-gopn").f,m=require("./core.get-iterator-method"),x=require("./_uid"),L=require("./_wks"),P=require("./_array-methods"),j=require("./_array-includes"),T=require("./_species-constructor"),F=require("./es6.array.iterator"),O=require("./_iterators"),A=require("./_iter-detect"),R=require("./_set-species"),B=require("./_array-fill"),I=require("./_array-copy-within"),M=require("./_object-dp"),W=require("./_object-gopd"),N=M.f,Y=W.f,k=e.RangeError,D=e.TypeError,V=e.Uint8Array,C="ArrayBuffer",U="Shared"+C,G="BYTES_PER_ELEMENT",z="prototype",H=Array[z],J=o.ArrayBuffer,K=o.DataView,Q=P(0),X=P(2),Z=P(3),$=P(4),ee=P(5),re=P(6),te=j(!0),ne=j(!1),ie=F.values,oe=F.keys,ue=F.entries,ce=H.lastIndexOf,fe=H.reduce,ae=H.reduceRight,le=H.join,se=H.sort,he=H.slice,de=H.toString,ge=H.toLocaleString,_e=L("iterator"),ve=L("toStringTag"),pe=x("typed_constructor"),ye=x("def_constructor"),qe=i.CONSTR,we=i.TYPED,be=i.VIEW,Se="Wrong length!",Ee=P(1,function(e,r){return je(T(e,e[ye]),r)}),me=t(function(){return 1===new V(new Uint16Array([1]).buffer)[0]}),xe=!!V&&!!V[z].set&&t(function(){new V(1).set({})}),Le=function(e,r){var t=s(e);if(t<0||t%r)throw k("Wrong offset!");return t},Pe=function(e){if(y(e)&&we in e)return e;throw D(e+" is not a typed array!")},je=function(e,r){if(!(y(e)&&pe in e))throw D("It is not a typed array constructor!");return new e(r)},Te=function(e,r){return Fe(T(e,e[ye]),r)},Fe=function(e,r){for(var t=0,n=r.length,i=je(e,n);n>t;)i[t]=r[t++];return i},Oe=function(e,r,t){N(e,r,{get:function(){return this._d[t]}})},Ae=function(e){var r,t,n,i,o,c,f=q(e),a=arguments.length,l=a>1?arguments[1]:void 0,s=void 0!==l,d=m(f);if(null!=d&&!w(d)){for(c=d.call(f),n=[],r=0;!(o=c.next()).done;r++)n.push(o.value);f=n}for(s&&a>2&&(l=u(l,arguments[2],2)),r=0,t=h(f.length),i=je(this,t);t>r;r++)i[r]=s?l(f[r],r):f[r];return i},Re=function(){for(var e=0,r=arguments.length,t=je(this,r);r>e;)t[e]=arguments[e++];return t},Be=!!V&&t(function(){ge.call(new V(1))}),Ie=function(){return ge.apply(Be?he.call(Pe(this)):Pe(this),arguments)},Me={copyWithin:function(e,r){return I.call(Pe(this),e,r,arguments.length>2?arguments[2]:void 0)},every:function(e){return $(Pe(this),e,arguments.length>1?arguments[1]:void 0)},fill:function(e){return B.apply(Pe(this),arguments)},filter:function(e){return Te(this,X(Pe(this),e,arguments.length>1?arguments[1]:void 0))},find:function(e){return ee(Pe(this),e,arguments.length>1?arguments[1]:void 0)},findIndex:function(e){return re(Pe(this),e,arguments.length>1?arguments[1]:void 0)},forEach:function(e){Q(Pe(this),e,arguments.length>1?arguments[1]:void 0)},indexOf:function(e){return ne(Pe(this),e,arguments.length>1?arguments[1]:void 0)},includes:function(e){return te(Pe(this),e,arguments.length>1?arguments[1]:void 0)},join:function(e){return le.apply(Pe(this),arguments)},lastIndexOf:function(e){return ce.apply(Pe(this),arguments)},map:function(e){return Ee(Pe(this),e,arguments.length>1?arguments[1]:void 0)},reduce:function(e){return fe.apply(Pe(this),arguments)},reduceRight:function(e){return ae.apply(Pe(this),arguments)},reverse:function(){for(var e,r=Pe(this).length,t=Math.floor(r/2),n=0;n<t;)e=this[n],this[n++]=this[--r],this[r]=e;return this},some:function(e){return Z(Pe(this),e,arguments.length>1?arguments[1]:void 0)},sort:function(e){return se.call(Pe(this),e)},subarray:function(e,r){var t=Pe(this),n=t.length,i=g(e,n);return new(T(t,t[ye]))(t.buffer,t.byteOffset+i*t.BYTES_PER_ELEMENT,h((void 0===r?n:g(r,n))-i))}},We=function(e,r){return Te(this,he.call(Pe(this),e,r))},Ne=function(e){Pe(this);var r=Le(arguments[1],1),t=this.length,n=q(e),i=h(n.length),o=0;if(i+r>t)throw k(Se);for(;o<i;)this[r+o]=n[o++]},Ye={entries:function(){return ue.call(Pe(this))},keys:function(){return oe.call(Pe(this))},values:function(){return ie.call(Pe(this))}},ke=function(e,r){return y(e)&&e[we]&&"symbol"!=typeof r&&r in e&&String(+r)==String(r)},De=function(e,r){return ke(e,r=_(r,!0))?f(2,e[r]):Y(e,r)},Ve=function(e,r,t){return!(ke(e,r=_(r,!0))&&y(t)&&v(t,"value"))||v(t,"get")||v(t,"set")||t.configurable||v(t,"writable")&&!t.writable||v(t,"enumerable")&&!t.enumerable?N(e,r,t):(e[r]=t.value,e)};qe||(W.f=De,M.f=Ve),n(n.S+n.F*!qe,"Object",{getOwnPropertyDescriptor:De,defineProperty:Ve}),t(function(){de.call({})})&&(de=ge=function(){return le.call(this)});var Ce=l({},Me);l(Ce,Ye),a(Ce,_e,Ye.values),l(Ce,{slice:We,set:Ne,constructor:function(){},toString:de,toLocaleString:Ie}),Oe(Ce,"buffer","b"),Oe(Ce,"byteOffset","o"),Oe(Ce,"byteLength","l"),Oe(Ce,"length","e"),N(Ce,ve,{get:function(){return this[we]}}),module.exports=function(o,u,f,l){var s=o+((l=!!l)?"Clamped":"")+"Array",g="get"+o,_="set"+o,v=e[s],q=v||{},w=v&&S(v),m=!v||!i.ABV,x={},L=v&&v[z],P=function(e,r){N(e,r,{get:function(){return function(e,r){var t=e._d;return t.v[g](r*u+t.o,me)}(this,r)},set:function(e){return function(e,r,t){var n=e._d;l&&(t=(t=Math.round(t))<0?0:t>255?255:255&t),n.v[_](r*u+n.o,t,me)}(this,r,e)},enumerable:!0})};m?(v=f(function(e,r,t,n){c(e,v,s,"_d");var i,o,f,l,g=0,_=0;if(y(r)){if(!(r instanceof J||(l=p(r))==C||l==U))return we in r?Fe(v,r):Ae.call(v,r);i=r,_=Le(t,u);var q=r.byteLength;if(void 0===n){if(q%u)throw k(Se);if((o=q-_)<0)throw k(Se)}else if((o=h(n)*u)+_>q)throw k(Se);f=o/u}else f=d(r),i=new J(o=f*u);for(a(e,"_d",{b:i,o:_,l:o,e:f,v:new K(i)});g<f;)P(e,g++)}),L=v[z]=b(Ce),a(L,"constructor",v)):t(function(){v(1)})&&t(function(){new v(-1)})&&A(function(e){new v,new v(null),new v(1.5),new v(e)},!0)||(v=f(function(e,r,t,n){var i;return c(e,v,s),y(r)?r instanceof J||(i=p(r))==C||i==U?void 0!==n?new q(r,Le(t,u),n):void 0!==t?new q(r,Le(t,u)):new q(r):we in r?Fe(v,r):Ae.call(v,r):new q(d(r))}),Q(w!==Function.prototype?E(q).concat(E(w)):E(q),function(e){e in v||a(v,e,q[e])}),v[z]=L,r||(L.constructor=v));var j=L[_e],T=!!j&&("values"==j.name||null==j.name),F=Ye.values;a(v,pe,!0),a(L,we,s),a(L,be,!0),a(L,ye,v),(l?new v(1)[ve]==s:ve in L)||N(L,ve,{get:function(){return s}}),x[s]=v,n(n.G+n.W+n.F*(v!=q),x),n(n.S,s,{BYTES_PER_ELEMENT:u}),n(n.S+n.F*t(function(){q.of.call(v,1)}),s,{from:Ae,of:Re}),G in L||a(L,G,u),n(n.P,s,Me),R(s),n(n.P+n.F*xe,s,{set:Ne}),n(n.P+n.F*!T,s,Ye),r||L.toString==de||(L.toString=de),n(n.P+n.F*t(function(){new v(1).slice()}),s,{slice:We}),n(n.P+n.F*(t(function(){return[1,2].toLocaleString()!=new v([1,2]).toLocaleString()})||!t(function(){L.toLocaleString.call([1,2])})),s,{toLocaleString:Ie}),O[s]=T?j:F,r||T||a(L,_e,F)}}else module.exports=function(){};
+},{"./_descriptors":"jVdc","./_library":"dG4y","./_global":"8QiI","./_fails":"BI7s","./_export":"Vobs","./_typed":"zl/6","./_typed-buffer":"78hF","./_ctx":"W8bf","./_an-instance":"Qz2Q","./_property-desc":"zQQJ","./_hide":"nCfi","./_redefine-all":"lGTj","./_to-integer":"ubM9","./_to-length":"0KLz","./_to-index":"dyWK","./_to-absolute-index":"tPLG","./_to-primitive":"S7GM","./_has":"k/OQ","./_classof":"pLtw","./_is-object":"/2t/","./_to-object":"2XMZ","./_is-array-iter":"TuHS","./_object-create":"EH/8","./_object-gpo":"dlIw","./_object-gopn":"HNVq","./core.get-iterator-method":"um4Z","./_uid":"jLFM","./_wks":"I5XL","./_array-methods":"tMyS","./_array-includes":"ntLR","./_species-constructor":"othv","./es6.array.iterator":"Z+ck","./_iterators":"H5R/","./_iter-detect":"zP7t","./_set-species":"YBdf","./_array-fill":"hOOH","./_array-copy-within":"QXjR","./_object-dp":"gGgn","./_object-gopd":"EGJe"}],"FrGE":[function(require,module,exports) {
+require("./_typed-array")("Int8",1,function(r){return function(n,t,e){return r(this,n,t,e)}});
+},{"./_typed-array":"1sXG"}],"6jLc":[function(require,module,exports) {
+require("./_typed-array")("Uint8",1,function(r){return function(n,t,e){return r(this,n,t,e)}});
+},{"./_typed-array":"1sXG"}],"dFjM":[function(require,module,exports) {
+require("./_typed-array")("Uint8",1,function(r){return function(n,t,e){return r(this,n,t,e)}},!0);
+},{"./_typed-array":"1sXG"}],"7XA/":[function(require,module,exports) {
+require("./_typed-array")("Int16",2,function(r){return function(n,t,e){return r(this,n,t,e)}});
+},{"./_typed-array":"1sXG"}],"Vod2":[function(require,module,exports) {
+require("./_typed-array")("Uint16",2,function(r){return function(n,t,e){return r(this,n,t,e)}});
+},{"./_typed-array":"1sXG"}],"Mnlj":[function(require,module,exports) {
+require("./_typed-array")("Int32",4,function(r){return function(n,t,e){return r(this,n,t,e)}});
+},{"./_typed-array":"1sXG"}],"7JJC":[function(require,module,exports) {
+require("./_typed-array")("Uint32",4,function(r){return function(n,t,e){return r(this,n,t,e)}});
+},{"./_typed-array":"1sXG"}],"Asas":[function(require,module,exports) {
+require("./_typed-array")("Float32",4,function(r){return function(t,n,e){return r(this,t,n,e)}});
+},{"./_typed-array":"1sXG"}],"ZKGF":[function(require,module,exports) {
+require("./_typed-array")("Float64",8,function(r){return function(t,n,e){return r(this,t,n,e)}});
+},{"./_typed-array":"1sXG"}],"sL26":[function(require,module,exports) {
+var e=require("./_export"),r=require("./_a-function"),n=require("./_an-object"),i=(require("./_global").Reflect||{}).apply,u=Function.apply;e(e.S+e.F*!require("./_fails")(function(){i(function(){})}),"Reflect",{apply:function(e,a,l){var t=r(e),c=n(l);return i?i(t,a,c):u.call(t,a,c)}});
+},{"./_export":"Vobs","./_a-function":"QKlW","./_an-object":"1087","./_global":"8QiI","./_fails":"BI7s"}],"n0sj":[function(require,module,exports) {
+var e=require("./_export"),r=require("./_object-create"),n=require("./_a-function"),t=require("./_an-object"),u=require("./_is-object"),c=require("./_fails"),i=require("./_bind"),o=(require("./_global").Reflect||{}).construct,a=c(function(){function e(){}return!(o(function(){},[],e)instanceof e)}),l=!c(function(){o(function(){})});e(e.S+e.F*(a||l),"Reflect",{construct:function(e,c){n(e),t(c);var f=arguments.length<3?e:n(arguments[2]);if(l&&!a)return o(e,c,f);if(e==f){switch(c.length){case 0:return new e;case 1:return new e(c[0]);case 2:return new e(c[0],c[1]);case 3:return new e(c[0],c[1],c[2]);case 4:return new e(c[0],c[1],c[2],c[3])}var p=[null];return p.push.apply(p,c),new(i.apply(e,p))}var s=f.prototype,q=r(u(s)?s:Object.prototype),_=Function.apply.call(e,q,c);return u(_)?_:q}});
+},{"./_export":"Vobs","./_object-create":"EH/8","./_a-function":"QKlW","./_an-object":"1087","./_is-object":"/2t/","./_fails":"BI7s","./_bind":"s1yo","./_global":"8QiI"}],"4XoP":[function(require,module,exports) {
+var e=require("./_object-dp"),r=require("./_export"),t=require("./_an-object"),i=require("./_to-primitive");r(r.S+r.F*require("./_fails")(function(){Reflect.defineProperty(e.f({},1,{value:1}),1,{value:2})}),"Reflect",{defineProperty:function(r,u,f){t(r),u=i(u,!0),t(f);try{return e.f(r,u,f),!0}catch(n){return!1}}});
+},{"./_object-dp":"gGgn","./_export":"Vobs","./_an-object":"1087","./_to-primitive":"S7GM","./_fails":"BI7s"}],"/Ygq":[function(require,module,exports) {
+var e=require("./_export"),r=require("./_object-gopd").f,t=require("./_an-object");e(e.S,"Reflect",{deleteProperty:function(e,o){var u=r(t(e),o);return!(u&&!u.configurable)&&delete e[o]}});
+},{"./_export":"Vobs","./_object-gopd":"EGJe","./_an-object":"1087"}],"CKoQ":[function(require,module,exports) {
+"use strict";var e=require("./_export"),t=require("./_an-object"),i=function(e){this._t=t(e),this._i=0;var i,r=this._k=[];for(i in e)r.push(i)};require("./_iter-create")(i,"Object",function(){var e,t=this._k;do{if(this._i>=t.length)return{value:void 0,done:!0}}while(!((e=t[this._i++])in this._t));return{value:e,done:!1}}),e(e.S,"Reflect",{enumerate:function(e){return new i(e)}});
+},{"./_export":"Vobs","./_an-object":"1087","./_iter-create":"gj4O"}],"Jr0s":[function(require,module,exports) {
+var e=require("./_object-gopd"),r=require("./_object-gpo"),t=require("./_has"),i=require("./_export"),o=require("./_is-object"),u=require("./_an-object");function a(i,c){var v,g,l=arguments.length<3?i:arguments[2];return u(i)===l?i[c]:(v=e.f(i,c))?t(v,"value")?v.value:void 0!==v.get?v.get.call(l):void 0:o(g=r(i))?a(g,c,l):void 0}i(i.S,"Reflect",{get:a});
+},{"./_object-gopd":"EGJe","./_object-gpo":"dlIw","./_has":"k/OQ","./_export":"Vobs","./_is-object":"/2t/","./_an-object":"1087"}],"rsHl":[function(require,module,exports) {
+var e=require("./_object-gopd"),r=require("./_export"),t=require("./_an-object");r(r.S,"Reflect",{getOwnPropertyDescriptor:function(r,o){return e.f(t(r),o)}});
+},{"./_object-gopd":"EGJe","./_export":"Vobs","./_an-object":"1087"}],"m/tT":[function(require,module,exports) {
+var e=require("./_export"),r=require("./_object-gpo"),t=require("./_an-object");e(e.S,"Reflect",{getPrototypeOf:function(e){return r(t(e))}});
+},{"./_export":"Vobs","./_object-gpo":"dlIw","./_an-object":"1087"}],"VxVc":[function(require,module,exports) {
+var e=require("./_export");e(e.S,"Reflect",{has:function(e,r){return r in e}});
+},{"./_export":"Vobs"}],"lQ3X":[function(require,module,exports) {
+var e=require("./_export"),r=require("./_an-object"),t=Object.isExtensible;e(e.S,"Reflect",{isExtensible:function(e){return r(e),!t||t(e)}});
+},{"./_export":"Vobs","./_an-object":"1087"}],"yE4E":[function(require,module,exports) {
+var e=require("./_object-gopn"),r=require("./_object-gops"),o=require("./_an-object"),t=require("./_global").Reflect;module.exports=t&&t.ownKeys||function(t){var c=e.f(o(t)),n=r.f;return n?c.concat(n(t)):c};
+},{"./_object-gopn":"HNVq","./_object-gops":"vSss","./_an-object":"1087","./_global":"8QiI"}],"vOF/":[function(require,module,exports) {
+var e=require("./_export");e(e.S,"Reflect",{ownKeys:require("./_own-keys")});
+},{"./_export":"Vobs","./_own-keys":"yE4E"}],"1/hW":[function(require,module,exports) {
+var e=require("./_export"),r=require("./_an-object"),t=Object.preventExtensions;e(e.S,"Reflect",{preventExtensions:function(e){r(e);try{return t&&t(e),!0}catch(n){return!1}}});
+},{"./_export":"Vobs","./_an-object":"1087"}],"AiN1":[function(require,module,exports) {
+var e=require("./_object-dp"),r=require("./_object-gopd"),t=require("./_object-gpo"),i=require("./_has"),u=require("./_export"),f=require("./_property-desc"),o=require("./_an-object"),a=require("./_is-object");function c(u,l,n){var q,s,_=arguments.length<4?u:arguments[3],b=r.f(o(u),l);if(!b){if(a(s=t(u)))return c(s,l,n,_);b=f(0)}if(i(b,"value")){if(!1===b.writable||!a(_))return!1;if(q=r.f(_,l)){if(q.get||q.set||!1===q.writable)return!1;q.value=n,e.f(_,l,q)}else e.f(_,l,f(0,n));return!0}return void 0!==b.set&&(b.set.call(_,n),!0)}u(u.S,"Reflect",{set:c});
+},{"./_object-dp":"gGgn","./_object-gopd":"EGJe","./_object-gpo":"dlIw","./_has":"k/OQ","./_export":"Vobs","./_property-desc":"zQQJ","./_an-object":"1087","./_is-object":"/2t/"}],"EPEE":[function(require,module,exports) {
+var e=require("./_export"),r=require("./_set-proto");r&&e(e.S,"Reflect",{setPrototypeOf:function(e,t){r.check(e,t);try{return r.set(e,t),!0}catch(c){return!1}}});
+},{"./_export":"Vobs","./_set-proto":"I+c1"}],"gMo0":[function(require,module,exports) {
+"use strict";var r=require("./_export"),e=require("./_array-includes")(!0);r(r.P,"Array",{includes:function(r){return e(this,r,arguments.length>1?arguments[1]:void 0)}}),require("./_add-to-unscopables")("includes");
+},{"./_export":"Vobs","./_array-includes":"ntLR","./_add-to-unscopables":"ke6T"}],"M1I7":[function(require,module,exports) {
+"use strict";var r=require("./_is-array"),e=require("./_is-object"),i=require("./_to-length"),t=require("./_ctx"),o=require("./_wks")("isConcatSpreadable");function u(s,a,n,c,f,l,q,_){for(var d,h,p=f,v=0,b=!!q&&t(q,_,3);v<c;){if(v in n){if(d=b?b(n[v],v,a):n[v],h=!1,e(d)&&(h=void 0!==(h=d[o])?!!h:r(d)),h&&l>0)p=u(s,a,d,i(d.length),p,l-1)-1;else{if(p>=9007199254740991)throw TypeError();s[p]=d}p++}v++}return p}module.exports=u;
+},{"./_is-array":"JI5q","./_is-object":"/2t/","./_to-length":"0KLz","./_ctx":"W8bf","./_wks":"I5XL"}],"3zKV":[function(require,module,exports) {
+"use strict";var r=require("./_export"),e=require("./_flatten-into-array"),t=require("./_to-object"),a=require("./_to-length"),i=require("./_a-function"),u=require("./_array-species-create");r(r.P,"Array",{flatMap:function(r){var n,o,c=t(this);return i(r),n=a(c.length),o=u(c,0),e(o,c,c,n,0,1,r,arguments[1]),o}}),require("./_add-to-unscopables")("flatMap");
+},{"./_export":"Vobs","./_flatten-into-array":"M1I7","./_to-object":"2XMZ","./_to-length":"0KLz","./_a-function":"QKlW","./_array-species-create":"M6RC","./_add-to-unscopables":"ke6T"}],"G/dM":[function(require,module,exports) {
+"use strict";var e=require("./_export"),r=require("./_flatten-into-array"),t=require("./_to-object"),i=require("./_to-length"),a=require("./_to-integer"),n=require("./_array-species-create");e(e.P,"Array",{flatten:function(){var e=arguments[0],u=t(this),o=i(u.length),q=n(u,0);return r(q,u,u,o,0,void 0===e?1:a(e)),q}}),require("./_add-to-unscopables")("flatten");
+},{"./_export":"Vobs","./_flatten-into-array":"M1I7","./_to-object":"2XMZ","./_to-length":"0KLz","./_to-integer":"ubM9","./_array-species-create":"M6RC","./_add-to-unscopables":"ke6T"}],"K4uP":[function(require,module,exports) {
+"use strict";var r=require("./_export"),t=require("./_string-at")(!0);r(r.P,"String",{at:function(r){return t(this,r)}});
+},{"./_export":"Vobs","./_string-at":"4j93"}],"m0x4":[function(require,module,exports) {
+var e=require("./_to-length"),r=require("./_string-repeat"),t=require("./_defined");module.exports=function(i,n,l,g){var u=String(t(i)),a=u.length,h=void 0===l?" ":String(l),o=e(n);if(o<=a||""==h)return u;var c=o-a,d=r.call(h,Math.ceil(c/h.length));return d.length>c&&(d=d.slice(0,c)),g?d+u:u+d};
+},{"./_to-length":"0KLz","./_string-repeat":"0Lz3","./_defined":"3V0R"}],"hmYY":[function(require,module,exports) {
+"use strict";var r=require("./_export"),e=require("./_string-pad"),t=require("./_user-agent"),i=/Version\/10\.\d+(\.\d+)?( Mobile\/\w+)? Safari\//.test(t);r(r.P+r.F*i,"String",{padStart:function(r){return e(this,r,arguments.length>1?arguments[1]:void 0,!0)}});
+},{"./_export":"Vobs","./_string-pad":"m0x4","./_user-agent":"KrKR"}],"RIKd":[function(require,module,exports) {
+"use strict";var r=require("./_export"),e=require("./_string-pad"),i=require("./_user-agent"),t=/Version\/10\.\d+(\.\d+)?( Mobile\/\w+)? Safari\//.test(i);r(r.P+r.F*t,"String",{padEnd:function(r){return e(this,r,arguments.length>1?arguments[1]:void 0,!1)}});
+},{"./_export":"Vobs","./_string-pad":"m0x4","./_user-agent":"KrKR"}],"hNft":[function(require,module,exports) {
+"use strict";require("./_string-trim")("trimLeft",function(t){return function(){return t(this,1)}},"trimStart");
+},{"./_string-trim":"JIX2"}],"uLyC":[function(require,module,exports) {
+"use strict";require("./_string-trim")("trimRight",function(t){return function(){return t(this,2)}},"trimEnd");
+},{"./_string-trim":"JIX2"}],"dRqM":[function(require,module,exports) {
+"use strict";var e=require("./_export"),r=require("./_defined"),t=require("./_to-length"),i=require("./_is-regexp"),n=require("./_flags"),s=RegExp.prototype,g=function(e,r){this._r=e,this._s=r};require("./_iter-create")(g,"RegExp String",function(){var e=this._r.exec(this._s);return{value:e,done:null===e}}),e(e.P,"String",{matchAll:function(e){if(r(this),!i(e))throw TypeError(e+" is not a regexp!");var u=String(this),a="flags"in s?String(e.flags):n.call(e),l=new RegExp(e.source,~a.indexOf("g")?a:"g"+a);return l.lastIndex=t(e.lastIndex),new g(l,u)}});
+},{"./_export":"Vobs","./_defined":"3V0R","./_to-length":"0KLz","./_is-regexp":"9r5g","./_flags":"3BaN","./_iter-create":"gj4O"}],"182e":[function(require,module,exports) {
+require("./_wks-define")("asyncIterator");
+},{"./_wks-define":"ZenZ"}],"Oxke":[function(require,module,exports) {
+require("./_wks-define")("observable");
+},{"./_wks-define":"ZenZ"}],"ovdg":[function(require,module,exports) {
+var e=require("./_export"),r=require("./_own-keys"),t=require("./_to-iobject"),o=require("./_object-gopd"),i=require("./_create-property");e(e.S,"Object",{getOwnPropertyDescriptors:function(e){for(var u,c,n=t(e),p=o.f,q=r(n),_={},a=0;q.length>a;)void 0!==(c=p(n,u=q[a++]))&&i(_,u,c);return _}});
+},{"./_export":"Vobs","./_own-keys":"yE4E","./_to-iobject":"zakI","./_object-gopd":"EGJe","./_create-property":"g07e"}],"HVWH":[function(require,module,exports) {
+var e=require("./_descriptors"),r=require("./_object-keys"),t=require("./_to-iobject"),o=require("./_object-pie").f;module.exports=function(u){return function(i){for(var c,n=t(i),s=r(n),f=s.length,l=0,p=[];f>l;)c=s[l++],e&&!o.call(n,c)||p.push(u?[c,n[c]]:n[c]);return p}};
+},{"./_descriptors":"jVdc","./_object-keys":"+huX","./_to-iobject":"zakI","./_object-pie":"NRj4"}],"+3ex":[function(require,module,exports) {
+var r=require("./_export"),e=require("./_object-to-array")(!1);r(r.S,"Object",{values:function(r){return e(r)}});
+},{"./_export":"Vobs","./_object-to-array":"HVWH"}],"jLAB":[function(require,module,exports) {
+var r=require("./_export"),e=require("./_object-to-array")(!0);r(r.S,"Object",{entries:function(r){return e(r)}});
+},{"./_export":"Vobs","./_object-to-array":"HVWH"}],"Se8n":[function(require,module,exports) {
+"use strict";module.exports=require("./_library")||!require("./_fails")(function(){var e=Math.random();__defineSetter__.call(null,e,function(){}),delete require("./_global")[e]});
+},{"./_library":"dG4y","./_fails":"BI7s","./_global":"8QiI"}],"y7i0":[function(require,module,exports) {
+"use strict";var e=require("./_export"),r=require("./_to-object"),t=require("./_a-function"),i=require("./_object-dp");require("./_descriptors")&&e(e.P+require("./_object-forced-pam"),"Object",{__defineGetter__:function(e,u){i.f(r(this),e,{get:t(u),enumerable:!0,configurable:!0})}});
+},{"./_export":"Vobs","./_to-object":"2XMZ","./_a-function":"QKlW","./_object-dp":"gGgn","./_descriptors":"jVdc","./_object-forced-pam":"Se8n"}],"vFGQ":[function(require,module,exports) {
+"use strict";var e=require("./_export"),r=require("./_to-object"),t=require("./_a-function"),i=require("./_object-dp");require("./_descriptors")&&e(e.P+require("./_object-forced-pam"),"Object",{__defineSetter__:function(e,u){i.f(r(this),e,{set:t(u),enumerable:!0,configurable:!0})}});
+},{"./_export":"Vobs","./_to-object":"2XMZ","./_a-function":"QKlW","./_object-dp":"gGgn","./_descriptors":"jVdc","./_object-forced-pam":"Se8n"}],"urEd":[function(require,module,exports) {
+"use strict";var e=require("./_export"),r=require("./_to-object"),t=require("./_to-primitive"),i=require("./_object-gpo"),o=require("./_object-gopd").f;require("./_descriptors")&&e(e.P+require("./_object-forced-pam"),"Object",{__lookupGetter__:function(e){var u,_=r(this),c=t(e,!0);do{if(u=o(_,c))return u.get}while(_=i(_))}});
+},{"./_export":"Vobs","./_to-object":"2XMZ","./_to-primitive":"S7GM","./_object-gpo":"dlIw","./_object-gopd":"EGJe","./_descriptors":"jVdc","./_object-forced-pam":"Se8n"}],"qicQ":[function(require,module,exports) {
+"use strict";var e=require("./_export"),r=require("./_to-object"),t=require("./_to-primitive"),i=require("./_object-gpo"),o=require("./_object-gopd").f;require("./_descriptors")&&e(e.P+require("./_object-forced-pam"),"Object",{__lookupSetter__:function(e){var u,_=r(this),c=t(e,!0);do{if(u=o(_,c))return u.set}while(_=i(_))}});
+},{"./_export":"Vobs","./_to-object":"2XMZ","./_to-primitive":"S7GM","./_object-gpo":"dlIw","./_object-gopd":"EGJe","./_descriptors":"jVdc","./_object-forced-pam":"Se8n"}],"VUTp":[function(require,module,exports) {
+var r=require("./_for-of");module.exports=function(e,o){var u=[];return r(e,!1,u.push,u,o),u};
+},{"./_for-of":"+L3c"}],"NEML":[function(require,module,exports) {
+var r=require("./_classof"),e=require("./_array-from-iterable");module.exports=function(t){return function(){if(r(this)!=t)throw TypeError(t+"#toJSON isn't generic");return e(this)}};
+},{"./_classof":"pLtw","./_array-from-iterable":"VUTp"}],"gCox":[function(require,module,exports) {
+var e=require("./_export");e(e.P+e.R,"Map",{toJSON:require("./_collection-to-json")("Map")});
+},{"./_export":"Vobs","./_collection-to-json":"NEML"}],"Cwp/":[function(require,module,exports) {
+var e=require("./_export");e(e.P+e.R,"Set",{toJSON:require("./_collection-to-json")("Set")});
+},{"./_export":"Vobs","./_collection-to-json":"NEML"}],"8rIF":[function(require,module,exports) {
+"use strict";var r=require("./_export");module.exports=function(e){r(r.S,e,{of:function(){for(var r=arguments.length,e=new Array(r);r--;)e[r]=arguments[r];return new this(e)}})};
+},{"./_export":"Vobs"}],"0bPO":[function(require,module,exports) {
+require("./_set-collection-of")("Map");
+},{"./_set-collection-of":"8rIF"}],"swmI":[function(require,module,exports) {
+require("./_set-collection-of")("Set");
+},{"./_set-collection-of":"8rIF"}],"Kb3C":[function(require,module,exports) {
+require("./_set-collection-of")("WeakMap");
+},{"./_set-collection-of":"8rIF"}],"HgXJ":[function(require,module,exports) {
+require("./_set-collection-of")("WeakSet");
+},{"./_set-collection-of":"8rIF"}],"sb9z":[function(require,module,exports) {
+"use strict";var r=require("./_export"),e=require("./_a-function"),u=require("./_ctx"),i=require("./_for-of");module.exports=function(t){r(r.S,t,{from:function(r){var t,n,o,s,f=arguments[1];return e(this),(t=void 0!==f)&&e(f),null==r?new this:(n=[],t?(o=0,s=u(f,arguments[2],2),i(r,!1,function(r){n.push(s(r,o++))})):i(r,!1,n.push,n),new this(n))}})};
+},{"./_export":"Vobs","./_a-function":"QKlW","./_ctx":"W8bf","./_for-of":"+L3c"}],"+mnJ":[function(require,module,exports) {
+require("./_set-collection-from")("Map");
+},{"./_set-collection-from":"sb9z"}],"0+Wc":[function(require,module,exports) {
+require("./_set-collection-from")("Set");
+},{"./_set-collection-from":"sb9z"}],"RABC":[function(require,module,exports) {
+require("./_set-collection-from")("WeakMap");
+},{"./_set-collection-from":"sb9z"}],"3ir/":[function(require,module,exports) {
+require("./_set-collection-from")("WeakSet");
+},{"./_set-collection-from":"sb9z"}],"Djh+":[function(require,module,exports) {
+var r=require("./_export");r(r.G,{global:require("./_global")});
+},{"./_export":"Vobs","./_global":"8QiI"}],"zQT/":[function(require,module,exports) {
+var e=require("./_export");e(e.S,"System",{global:require("./_global")});
+},{"./_export":"Vobs","./_global":"8QiI"}],"sx2w":[function(require,module,exports) {
+var r=require("./_export"),e=require("./_cof");r(r.S,"Error",{isError:function(r){return"Error"===e(r)}});
+},{"./_export":"Vobs","./_cof":"3+Dr"}],"5duU":[function(require,module,exports) {
+var a=require("./_export");a(a.S,"Math",{clamp:function(a,r,t){return Math.min(t,Math.max(r,a))}});
+},{"./_export":"Vobs"}],"Nayo":[function(require,module,exports) {
+var r=require("./_export");r(r.S,"Math",{DEG_PER_RAD:Math.PI/180});
+},{"./_export":"Vobs"}],"pK3L":[function(require,module,exports) {
+var e=require("./_export"),r=180/Math.PI;e(e.S,"Math",{degrees:function(e){return e*r}});
+},{"./_export":"Vobs"}],"3ZVa":[function(require,module,exports) {
+module.exports=Math.scale||function(e,t,n,a,l){return 0===arguments.length||e!=e||t!=t||n!=n||a!=a||l!=l?NaN:e===1/0||e===-1/0?e:(e-t)*(l-a)/(n-t)+a};
+},{}],"cNya":[function(require,module,exports) {
+var r=require("./_export"),e=require("./_math-scale"),a=require("./_math-fround");r(r.S,"Math",{fscale:function(r,t,u,i,n){return a(e(r,t,u,i,n))}});
+},{"./_export":"Vobs","./_math-scale":"3ZVa","./_math-fround":"6lqk"}],"JpQg":[function(require,module,exports) {
+var r=require("./_export");r(r.S,"Math",{iaddh:function(r,a,e,t){var i=r>>>0,n=e>>>0;return(a>>>0)+(t>>>0)+((i&n|(i|n)&~(i+n>>>0))>>>31)|0}});
+},{"./_export":"Vobs"}],"kYRl":[function(require,module,exports) {
+var r=require("./_export");r(r.S,"Math",{isubh:function(r,e,t,u){var a=r>>>0,i=t>>>0;return(e>>>0)-(u>>>0)-((~a&i|~(a^i)&a-i>>>0)>>>31)|0}});
+},{"./_export":"Vobs"}],"iMz3":[function(require,module,exports) {
+var r=require("./_export");r(r.S,"Math",{imulh:function(r,e){var t=+r,u=+e,a=65535&t,i=65535&u,n=t>>16,h=u>>16,o=(n*i>>>0)+(a*i>>>16);return n*h+(o>>16)+((a*h>>>0)+(65535&o)>>16)}});
+},{"./_export":"Vobs"}],"Xbc5":[function(require,module,exports) {
+var r=require("./_export");r(r.S,"Math",{RAD_PER_DEG:180/Math.PI});
+},{"./_export":"Vobs"}],"YSH8":[function(require,module,exports) {
+var r=require("./_export"),a=Math.PI/180;r(r.S,"Math",{radians:function(r){return r*a}});
+},{"./_export":"Vobs"}],"gu1X":[function(require,module,exports) {
+var e=require("./_export");e(e.S,"Math",{scale:require("./_math-scale")});
+},{"./_export":"Vobs","./_math-scale":"3ZVa"}],"1dDq":[function(require,module,exports) {
+var r=require("./_export");r(r.S,"Math",{umulh:function(r,u){var e=+r,t=+u,a=65535&e,n=65535&t,h=e>>>16,i=t>>>16,o=(h*n>>>0)+(a*n>>>16);return h*i+(o>>>16)+((a*i>>>0)+(65535&o)>>>16)}});
+},{"./_export":"Vobs"}],"Q8U8":[function(require,module,exports) {
+var r=require("./_export");r(r.S,"Math",{signbit:function(r){return(r=+r)!=r?r:0==r?1/r==1/0:r>0}});
+},{"./_export":"Vobs"}],"q6pY":[function(require,module,exports) {
+
+"use strict";var r=require("./_export"),e=require("./_core"),t=require("./_global"),n=require("./_species-constructor"),i=require("./_promise-resolve");r(r.P+r.R,"Promise",{finally:function(r){var o=n(this,e.Promise||t.Promise),u="function"==typeof r;return this.then(u?function(e){return i(o,r()).then(function(){return e})}:r,u?function(e){return i(o,r()).then(function(){throw e})}:r)}});
+},{"./_export":"Vobs","./_core":"6DcE","./_global":"8QiI","./_species-constructor":"othv","./_promise-resolve":"FQFX"}],"7aUL":[function(require,module,exports) {
+"use strict";var r=require("./_export"),e=require("./_new-promise-capability"),i=require("./_perform");r(r.S,"Promise",{try:function(r){var t=e.f(this),o=i(r);return(o.e?t.reject:t.resolve)(o.v),t.promise}});
+},{"./_export":"Vobs","./_new-promise-capability":"hTzn","./_perform":"X7pO"}],"Qewb":[function(require,module,exports) {
+var e=require("./es6.map"),r=require("./_export"),t=require("./_shared")("metadata"),n=t.store||(t.store=new(require("./es6.weak-map"))),i=function(r,t,i){var o=n.get(r);if(!o){if(!i)return;n.set(r,o=new e)}var u=o.get(t);if(!u){if(!i)return;o.set(t,u=new e)}return u},o=function(e,r,t){var n=i(r,t,!1);return void 0!==n&&n.has(e)},u=function(e,r,t){var n=i(r,t,!1);return void 0===n?void 0:n.get(e)},a=function(e,r,t,n){i(t,n,!0).set(e,r)},s=function(e,r){var t=i(e,r,!1),n=[];return t&&t.forEach(function(e,r){n.push(r)}),n},f=function(e){return void 0===e||"symbol"==typeof e?e:String(e)},c=function(e){r(r.S,"Reflect",e)};module.exports={store:n,map:i,has:o,get:u,set:a,keys:s,key:f,exp:c};
+},{"./es6.map":"ksBa","./_export":"Vobs","./_shared":"k492","./es6.weak-map":"Y0Wb"}],"zk+d":[function(require,module,exports) {
+var e=require("./_metadata"),a=require("./_an-object"),t=e.key,r=e.set;e.exp({defineMetadata:function(e,i,n,d){r(e,i,a(n),t(d))}});
+},{"./_metadata":"Qewb","./_an-object":"1087"}],"fy5i":[function(require,module,exports) {
+var e=require("./_metadata"),t=require("./_an-object"),r=e.key,a=e.map,i=e.store;e.exp({deleteMetadata:function(e,d){var n=arguments.length<3?void 0:r(arguments[2]),u=a(t(d),n,!1);if(void 0===u||!u.delete(e))return!1;if(u.size)return!0;var l=i.get(d);return l.delete(n),!!l.size||i.delete(d)}});
+},{"./_metadata":"Qewb","./_an-object":"1087"}],"7KBr":[function(require,module,exports) {
+var e=require("./_metadata"),r=require("./_an-object"),t=require("./_object-gpo"),a=e.has,n=e.get,u=e.key,i=function(e,r,u){if(a(e,r,u))return n(e,r,u);var o=t(r);return null!==o?i(e,o,u):void 0};e.exp({getMetadata:function(e,t){return i(e,r(t),arguments.length<3?void 0:u(arguments[2]))}});
+},{"./_metadata":"Qewb","./_an-object":"1087","./_object-gpo":"dlIw"}],"y0Gk":[function(require,module,exports) {
+var e=require("./es6.set"),r=require("./_array-from-iterable"),t=require("./_metadata"),a=require("./_an-object"),n=require("./_object-gpo"),u=t.keys,i=t.key,o=function(t,a){var i=u(t,a),c=n(t);if(null===c)return i;var l=o(c,a);return l.length?i.length?r(new e(i.concat(l))):l:i};t.exp({getMetadataKeys:function(e){return o(a(e),arguments.length<2?void 0:i(arguments[1]))}});
+},{"./es6.set":"1jP+","./_array-from-iterable":"VUTp","./_metadata":"Qewb","./_an-object":"1087","./_object-gpo":"dlIw"}],"sn4U":[function(require,module,exports) {
+var e=require("./_metadata"),t=require("./_an-object"),a=e.get,r=e.key;e.exp({getOwnMetadata:function(e,n){return a(e,t(n),arguments.length<3?void 0:r(arguments[2]))}});
+},{"./_metadata":"Qewb","./_an-object":"1087"}],"bQoJ":[function(require,module,exports) {
+var e=require("./_metadata"),t=require("./_an-object"),a=e.keys,r=e.key;e.exp({getOwnMetadataKeys:function(e){return a(t(e),arguments.length<2?void 0:r(arguments[1]))}});
+},{"./_metadata":"Qewb","./_an-object":"1087"}],"jR0d":[function(require,module,exports) {
+var e=require("./_metadata"),r=require("./_an-object"),t=require("./_object-gpo"),a=e.has,n=e.key,u=function(e,r,n){if(a(e,r,n))return!0;var i=t(r);return null!==i&&u(e,i,n)};e.exp({hasMetadata:function(e,t){return u(e,r(t),arguments.length<3?void 0:n(arguments[2]))}});
+},{"./_metadata":"Qewb","./_an-object":"1087","./_object-gpo":"dlIw"}],"tWeA":[function(require,module,exports) {
+var e=require("./_metadata"),a=require("./_an-object"),t=e.has,r=e.key;e.exp({hasOwnMetadata:function(e,n){return t(e,a(n),arguments.length<3?void 0:r(arguments[2]))}});
+},{"./_metadata":"Qewb","./_an-object":"1087"}],"rYHV":[function(require,module,exports) {
+var e=require("./_metadata"),t=require("./_an-object"),a=require("./_a-function"),r=e.key,n=e.set;e.exp({metadata:function(e,i){return function(u,o){n(e,i,(void 0!==o?t:a)(u),r(o))}}});
+},{"./_metadata":"Qewb","./_an-object":"1087","./_a-function":"QKlW"}],"kvVj":[function(require,module,exports) {
+
+var r=require("./_export"),e=require("./_microtask")(),i=require("./_global").process,o="process"==require("./_cof")(i);r(r.G,{asap:function(r){var a=o&&i.domain;e(a?a.bind(r):r)}});
+},{"./_export":"Vobs","./_microtask":"m7QH","./_global":"8QiI","./_cof":"3+Dr"}],"iOLx":[function(require,module,exports) {
+
+"use strict";var r=require("./_export"),t=require("./_global"),n=require("./_core"),e=require("./_microtask")(),i=require("./_wks")("observable"),o=require("./_a-function"),u=require("./_an-object"),c=require("./_an-instance"),f=require("./_redefine-all"),s=require("./_hide"),a=require("./_for-of"),v=a.RETURN,h=function(r){return null==r?void 0:o(r)},l=function(r){var t=r._c;t&&(r._c=void 0,t())},_=function(r){return void 0===r._o},b=function(r){_(r)||(r._o=void 0,l(r))},y=function(r,t){u(r),this._c=void 0,this._o=r,r=new p(this);try{var n=t(r),e=n;null!=n&&("function"==typeof n.unsubscribe?n=function(){e.unsubscribe()}:o(n),this._c=n)}catch(i){return void r.error(i)}_(this)&&l(this)};y.prototype=f({},{unsubscribe:function(){b(this)}});var p=function(r){this._s=r};p.prototype=f({},{next:function(r){var t=this._s;if(!_(t)){var n=t._o;try{var e=h(n.next);if(e)return e.call(n,r)}catch(i){try{b(t)}finally{throw i}}}},error:function(r){var t=this._s;if(_(t))throw r;var n=t._o;t._o=void 0;try{var e=h(n.error);if(!e)throw r;r=e.call(n,r)}catch(i){try{l(t)}finally{throw i}}return l(t),r},complete:function(r){var t=this._s;if(!_(t)){var n=t._o;t._o=void 0;try{var e=h(n.complete);r=e?e.call(n,r):void 0}catch(i){try{l(t)}finally{throw i}}return l(t),r}}});var w=function(r){c(this,w,"Observable","_f")._f=o(r)};f(w.prototype,{subscribe:function(r){return new y(r,this._f)},forEach:function(r){var e=this;return new(n.Promise||t.Promise)(function(t,n){o(r);var i=e.subscribe({next:function(t){try{return r(t)}catch(e){n(e),i.unsubscribe()}},error:n,complete:t})})}}),f(w,{from:function(r){var t="function"==typeof this?this:w,n=h(u(r)[i]);if(n){var o=u(n.call(r));return o.constructor===t?o:new t(function(r){return o.subscribe(r)})}return new t(function(t){var n=!1;return e(function(){if(!n){try{if(a(r,!1,function(r){if(t.next(r),n)return v})===v)return}catch(e){if(n)throw e;return void t.error(e)}t.complete()}}),function(){n=!0}})},of:function(){for(var r=0,t=arguments.length,n=new Array(t);r<t;)n[r]=arguments[r++];return new("function"==typeof this?this:w)(function(r){var t=!1;return e(function(){if(!t){for(var e=0;e<n.length;++e)if(r.next(n[e]),t)return;r.complete()}}),function(){t=!0}})}}),s(w.prototype,i,function(){return this}),r(r.G,{Observable:w}),require("./_set-species")("Observable");
+},{"./_export":"Vobs","./_global":"8QiI","./_core":"6DcE","./_microtask":"m7QH","./_wks":"I5XL","./_a-function":"QKlW","./_an-object":"1087","./_an-instance":"Qz2Q","./_redefine-all":"lGTj","./_hide":"nCfi","./_for-of":"+L3c","./_set-species":"YBdf"}],"p/UQ":[function(require,module,exports) {
+
+var e=require("./_global"),t=require("./_export"),n=require("./_user-agent"),r=[].slice,u=/MSIE .\./.test(n),i=function(e){return function(t,n){var u=arguments.length>2,i=!!u&&r.call(arguments,2);return e(u?function(){("function"==typeof t?t:Function(t)).apply(this,i)}:t,n)}};t(t.G+t.B+t.F*u,{setTimeout:i(e.setTimeout),setInterval:i(e.setInterval)});
+},{"./_global":"8QiI","./_export":"Vobs","./_user-agent":"KrKR"}],"44uO":[function(require,module,exports) {
+var e=require("./_export"),r=require("./_task");e(e.G+e.B,{setImmediate:r.set,clearImmediate:r.clear});
+},{"./_export":"Vobs","./_task":"fNEO"}],"kCWy":[function(require,module,exports) {
+
+for(var e=require("./es6.array.iterator"),t=require("./_object-keys"),i=require("./_redefine"),r=require("./_global"),s=require("./_hide"),L=require("./_iterators"),a=require("./_wks"),o=a("iterator"),l=a("toStringTag"),S=L.Array,n={CSSRuleList:!0,CSSStyleDeclaration:!1,CSSValueList:!1,ClientRectList:!1,DOMRectList:!1,DOMStringList:!1,DOMTokenList:!0,DataTransferItemList:!1,FileList:!1,HTMLAllCollection:!1,HTMLCollection:!1,HTMLFormElement:!1,HTMLSelectElement:!1,MediaList:!0,MimeTypeArray:!1,NamedNodeMap:!1,NodeList:!0,PaintRequestList:!1,Plugin:!1,PluginArray:!1,SVGLengthList:!1,SVGNumberList:!1,SVGPathSegList:!1,SVGPointList:!1,SVGStringList:!1,SVGTransformList:!1,SourceBufferList:!1,StyleSheetList:!0,TextTrackCueList:!1,TextTrackList:!1,TouchList:!1},u=t(n),T=0;T<u.length;T++){var c,g=u[T],M=n[g],y=r[g],f=y&&y.prototype;if(f&&(f[o]||s(f,o,S),f[l]||s(f,l,g),L[g]=S,M))for(c in e)f[c]||i(f,c,e[c],!0)}
+},{"./es6.array.iterator":"Z+ck","./_object-keys":"+huX","./_redefine":"jDrK","./_global":"8QiI","./_hide":"nCfi","./_iterators":"H5R/","./_wks":"I5XL"}],"y1LN":[function(require,module,exports) {
+require("./modules/es6.symbol"),require("./modules/es6.object.create"),require("./modules/es6.object.define-property"),require("./modules/es6.object.define-properties"),require("./modules/es6.object.get-own-property-descriptor"),require("./modules/es6.object.get-prototype-of"),require("./modules/es6.object.keys"),require("./modules/es6.object.get-own-property-names"),require("./modules/es6.object.freeze"),require("./modules/es6.object.seal"),require("./modules/es6.object.prevent-extensions"),require("./modules/es6.object.is-frozen"),require("./modules/es6.object.is-sealed"),require("./modules/es6.object.is-extensible"),require("./modules/es6.object.assign"),require("./modules/es6.object.is"),require("./modules/es6.object.set-prototype-of"),require("./modules/es6.object.to-string"),require("./modules/es6.function.bind"),require("./modules/es6.function.name"),require("./modules/es6.function.has-instance"),require("./modules/es6.parse-int"),require("./modules/es6.parse-float"),require("./modules/es6.number.constructor"),require("./modules/es6.number.to-fixed"),require("./modules/es6.number.to-precision"),require("./modules/es6.number.epsilon"),require("./modules/es6.number.is-finite"),require("./modules/es6.number.is-integer"),require("./modules/es6.number.is-nan"),require("./modules/es6.number.is-safe-integer"),require("./modules/es6.number.max-safe-integer"),require("./modules/es6.number.min-safe-integer"),require("./modules/es6.number.parse-float"),require("./modules/es6.number.parse-int"),require("./modules/es6.math.acosh"),require("./modules/es6.math.asinh"),require("./modules/es6.math.atanh"),require("./modules/es6.math.cbrt"),require("./modules/es6.math.clz32"),require("./modules/es6.math.cosh"),require("./modules/es6.math.expm1"),require("./modules/es6.math.fround"),require("./modules/es6.math.hypot"),require("./modules/es6.math.imul"),require("./modules/es6.math.log10"),require("./modules/es6.math.log1p"),require("./modules/es6.math.log2"),require("./modules/es6.math.sign"),require("./modules/es6.math.sinh"),require("./modules/es6.math.tanh"),require("./modules/es6.math.trunc"),require("./modules/es6.string.from-code-point"),require("./modules/es6.string.raw"),require("./modules/es6.string.trim"),require("./modules/es6.string.iterator"),require("./modules/es6.string.code-point-at"),require("./modules/es6.string.ends-with"),require("./modules/es6.string.includes"),require("./modules/es6.string.repeat"),require("./modules/es6.string.starts-with"),require("./modules/es6.string.anchor"),require("./modules/es6.string.big"),require("./modules/es6.string.blink"),require("./modules/es6.string.bold"),require("./modules/es6.string.fixed"),require("./modules/es6.string.fontcolor"),require("./modules/es6.string.fontsize"),require("./modules/es6.string.italics"),require("./modules/es6.string.link"),require("./modules/es6.string.small"),require("./modules/es6.string.strike"),require("./modules/es6.string.sub"),require("./modules/es6.string.sup"),require("./modules/es6.date.now"),require("./modules/es6.date.to-json"),require("./modules/es6.date.to-iso-string"),require("./modules/es6.date.to-string"),require("./modules/es6.date.to-primitive"),require("./modules/es6.array.is-array"),require("./modules/es6.array.from"),require("./modules/es6.array.of"),require("./modules/es6.array.join"),require("./modules/es6.array.slice"),require("./modules/es6.array.sort"),require("./modules/es6.array.for-each"),require("./modules/es6.array.map"),require("./modules/es6.array.filter"),require("./modules/es6.array.some"),require("./modules/es6.array.every"),require("./modules/es6.array.reduce"),require("./modules/es6.array.reduce-right"),require("./modules/es6.array.index-of"),require("./modules/es6.array.last-index-of"),require("./modules/es6.array.copy-within"),require("./modules/es6.array.fill"),require("./modules/es6.array.find"),require("./modules/es6.array.find-index"),require("./modules/es6.array.species"),require("./modules/es6.array.iterator"),require("./modules/es6.regexp.constructor"),require("./modules/es6.regexp.exec"),require("./modules/es6.regexp.to-string"),require("./modules/es6.regexp.flags"),require("./modules/es6.regexp.match"),require("./modules/es6.regexp.replace"),require("./modules/es6.regexp.search"),require("./modules/es6.regexp.split"),require("./modules/es6.promise"),require("./modules/es6.map"),require("./modules/es6.set"),require("./modules/es6.weak-map"),require("./modules/es6.weak-set"),require("./modules/es6.typed.array-buffer"),require("./modules/es6.typed.data-view"),require("./modules/es6.typed.int8-array"),require("./modules/es6.typed.uint8-array"),require("./modules/es6.typed.uint8-clamped-array"),require("./modules/es6.typed.int16-array"),require("./modules/es6.typed.uint16-array"),require("./modules/es6.typed.int32-array"),require("./modules/es6.typed.uint32-array"),require("./modules/es6.typed.float32-array"),require("./modules/es6.typed.float64-array"),require("./modules/es6.reflect.apply"),require("./modules/es6.reflect.construct"),require("./modules/es6.reflect.define-property"),require("./modules/es6.reflect.delete-property"),require("./modules/es6.reflect.enumerate"),require("./modules/es6.reflect.get"),require("./modules/es6.reflect.get-own-property-descriptor"),require("./modules/es6.reflect.get-prototype-of"),require("./modules/es6.reflect.has"),require("./modules/es6.reflect.is-extensible"),require("./modules/es6.reflect.own-keys"),require("./modules/es6.reflect.prevent-extensions"),require("./modules/es6.reflect.set"),require("./modules/es6.reflect.set-prototype-of"),require("./modules/es7.array.includes"),require("./modules/es7.array.flat-map"),require("./modules/es7.array.flatten"),require("./modules/es7.string.at"),require("./modules/es7.string.pad-start"),require("./modules/es7.string.pad-end"),require("./modules/es7.string.trim-left"),require("./modules/es7.string.trim-right"),require("./modules/es7.string.match-all"),require("./modules/es7.symbol.async-iterator"),require("./modules/es7.symbol.observable"),require("./modules/es7.object.get-own-property-descriptors"),require("./modules/es7.object.values"),require("./modules/es7.object.entries"),require("./modules/es7.object.define-getter"),require("./modules/es7.object.define-setter"),require("./modules/es7.object.lookup-getter"),require("./modules/es7.object.lookup-setter"),require("./modules/es7.map.to-json"),require("./modules/es7.set.to-json"),require("./modules/es7.map.of"),require("./modules/es7.set.of"),require("./modules/es7.weak-map.of"),require("./modules/es7.weak-set.of"),require("./modules/es7.map.from"),require("./modules/es7.set.from"),require("./modules/es7.weak-map.from"),require("./modules/es7.weak-set.from"),require("./modules/es7.global"),require("./modules/es7.system.global"),require("./modules/es7.error.is-error"),require("./modules/es7.math.clamp"),require("./modules/es7.math.deg-per-rad"),require("./modules/es7.math.degrees"),require("./modules/es7.math.fscale"),require("./modules/es7.math.iaddh"),require("./modules/es7.math.isubh"),require("./modules/es7.math.imulh"),require("./modules/es7.math.rad-per-deg"),require("./modules/es7.math.radians"),require("./modules/es7.math.scale"),require("./modules/es7.math.umulh"),require("./modules/es7.math.signbit"),require("./modules/es7.promise.finally"),require("./modules/es7.promise.try"),require("./modules/es7.reflect.define-metadata"),require("./modules/es7.reflect.delete-metadata"),require("./modules/es7.reflect.get-metadata"),require("./modules/es7.reflect.get-metadata-keys"),require("./modules/es7.reflect.get-own-metadata"),require("./modules/es7.reflect.get-own-metadata-keys"),require("./modules/es7.reflect.has-metadata"),require("./modules/es7.reflect.has-own-metadata"),require("./modules/es7.reflect.metadata"),require("./modules/es7.asap"),require("./modules/es7.observable"),require("./modules/web.timers"),require("./modules/web.immediate"),require("./modules/web.dom.iterable"),module.exports=require("./modules/_core");
+},{"./modules/es6.symbol":"rGq9","./modules/es6.object.create":"v5CS","./modules/es6.object.define-property":"pS46","./modules/es6.object.define-properties":"sbXv","./modules/es6.object.get-own-property-descriptor":"0xCv","./modules/es6.object.get-prototype-of":"3Dkc","./modules/es6.object.keys":"RpZ9","./modules/es6.object.get-own-property-names":"69mV","./modules/es6.object.freeze":"bkZb","./modules/es6.object.seal":"LEG2","./modules/es6.object.prevent-extensions":"2OeT","./modules/es6.object.is-frozen":"Lm2M","./modules/es6.object.is-sealed":"Lrni","./modules/es6.object.is-extensible":"8ypI","./modules/es6.object.assign":"av62","./modules/es6.object.is":"OI80","./modules/es6.object.set-prototype-of":"xZ9m","./modules/es6.object.to-string":"++zm","./modules/es6.function.bind":"qI6I","./modules/es6.function.name":"z3jV","./modules/es6.function.has-instance":"owRX","./modules/es6.parse-int":"nPGY","./modules/es6.parse-float":"yexh","./modules/es6.number.constructor":"57F7","./modules/es6.number.to-fixed":"/qGB","./modules/es6.number.to-precision":"5bLB","./modules/es6.number.epsilon":"oSwj","./modules/es6.number.is-finite":"Iwqp","./modules/es6.number.is-integer":"49XP","./modules/es6.number.is-nan":"P/Mg","./modules/es6.number.is-safe-integer":"EvBV","./modules/es6.number.max-safe-integer":"fOC+","./modules/es6.number.min-safe-integer":"93yv","./modules/es6.number.parse-float":"a09l","./modules/es6.number.parse-int":"fCj1","./modules/es6.math.acosh":"+8o7","./modules/es6.math.asinh":"xkGF","./modules/es6.math.atanh":"0Pmr","./modules/es6.math.cbrt":"Giui","./modules/es6.math.clz32":"HsTu","./modules/es6.math.cosh":"xEUq","./modules/es6.math.expm1":"aBEU","./modules/es6.math.fround":"IjCR","./modules/es6.math.hypot":"HXfT","./modules/es6.math.imul":"m2OX","./modules/es6.math.log10":"E567","./modules/es6.math.log1p":"ymfv","./modules/es6.math.log2":"hUIM","./modules/es6.math.sign":"d1Y4","./modules/es6.math.sinh":"20dh","./modules/es6.math.tanh":"cxv8","./modules/es6.math.trunc":"xO7u","./modules/es6.string.from-code-point":"DdG0","./modules/es6.string.raw":"KDc+","./modules/es6.string.trim":"D/Dr","./modules/es6.string.iterator":"WN4F","./modules/es6.string.code-point-at":"gGid","./modules/es6.string.ends-with":"PmIB","./modules/es6.string.includes":"qgIv","./modules/es6.string.repeat":"Z/Ab","./modules/es6.string.starts-with":"U3MC","./modules/es6.string.anchor":"eRhq","./modules/es6.string.big":"HLSM","./modules/es6.string.blink":"RtH9","./modules/es6.string.bold":"efe7","./modules/es6.string.fixed":"v3Ez","./modules/es6.string.fontcolor":"RECM","./modules/es6.string.fontsize":"l7+o","./modules/es6.string.italics":"uJlj","./modules/es6.string.link":"vYww","./modules/es6.string.small":"AiXZ","./modules/es6.string.strike":"MhVl","./modules/es6.string.sub":"DF/m","./modules/es6.string.sup":"X3LC","./modules/es6.date.now":"8Syd","./modules/es6.date.to-json":"G+NU","./modules/es6.date.to-iso-string":"83fP","./modules/es6.date.to-string":"0FKf","./modules/es6.date.to-primitive":"nktC","./modules/es6.array.is-array":"Xjk/","./modules/es6.array.from":"WZRw","./modules/es6.array.of":"URTo","./modules/es6.array.join":"BTDR","./modules/es6.array.slice":"Ui7t","./modules/es6.array.sort":"TqUy","./modules/es6.array.for-each":"vDWP","./modules/es6.array.map":"O0lf","./modules/es6.array.filter":"PXK+","./modules/es6.array.some":"wD6H","./modules/es6.array.every":"n6bP","./modules/es6.array.reduce":"OWmJ","./modules/es6.array.reduce-right":"k5ri","./modules/es6.array.index-of":"HB9A","./modules/es6.array.last-index-of":"tgt4","./modules/es6.array.copy-within":"1c9D","./modules/es6.array.fill":"ZBH0","./modules/es6.array.find":"wTIB","./modules/es6.array.find-index":"7ksr","./modules/es6.array.species":"Adki","./modules/es6.array.iterator":"Z+ck","./modules/es6.regexp.constructor":"lK2M","./modules/es6.regexp.exec":"f98m","./modules/es6.regexp.to-string":"jkaB","./modules/es6.regexp.flags":"S072","./modules/es6.regexp.match":"Iomp","./modules/es6.regexp.replace":"weWA","./modules/es6.regexp.search":"60EA","./modules/es6.regexp.split":"1d28","./modules/es6.promise":"M+wl","./modules/es6.map":"ksBa","./modules/es6.set":"1jP+","./modules/es6.weak-map":"Y0Wb","./modules/es6.weak-set":"oeIc","./modules/es6.typed.array-buffer":"VqD6","./modules/es6.typed.data-view":"q3b2","./modules/es6.typed.int8-array":"FrGE","./modules/es6.typed.uint8-array":"6jLc","./modules/es6.typed.uint8-clamped-array":"dFjM","./modules/es6.typed.int16-array":"7XA/","./modules/es6.typed.uint16-array":"Vod2","./modules/es6.typed.int32-array":"Mnlj","./modules/es6.typed.uint32-array":"7JJC","./modules/es6.typed.float32-array":"Asas","./modules/es6.typed.float64-array":"ZKGF","./modules/es6.reflect.apply":"sL26","./modules/es6.reflect.construct":"n0sj","./modules/es6.reflect.define-property":"4XoP","./modules/es6.reflect.delete-property":"/Ygq","./modules/es6.reflect.enumerate":"CKoQ","./modules/es6.reflect.get":"Jr0s","./modules/es6.reflect.get-own-property-descriptor":"rsHl","./modules/es6.reflect.get-prototype-of":"m/tT","./modules/es6.reflect.has":"VxVc","./modules/es6.reflect.is-extensible":"lQ3X","./modules/es6.reflect.own-keys":"vOF/","./modules/es6.reflect.prevent-extensions":"1/hW","./modules/es6.reflect.set":"AiN1","./modules/es6.reflect.set-prototype-of":"EPEE","./modules/es7.array.includes":"gMo0","./modules/es7.array.flat-map":"3zKV","./modules/es7.array.flatten":"G/dM","./modules/es7.string.at":"K4uP","./modules/es7.string.pad-start":"hmYY","./modules/es7.string.pad-end":"RIKd","./modules/es7.string.trim-left":"hNft","./modules/es7.string.trim-right":"uLyC","./modules/es7.string.match-all":"dRqM","./modules/es7.symbol.async-iterator":"182e","./modules/es7.symbol.observable":"Oxke","./modules/es7.object.get-own-property-descriptors":"ovdg","./modules/es7.object.values":"+3ex","./modules/es7.object.entries":"jLAB","./modules/es7.object.define-getter":"y7i0","./modules/es7.object.define-setter":"vFGQ","./modules/es7.object.lookup-getter":"urEd","./modules/es7.object.lookup-setter":"qicQ","./modules/es7.map.to-json":"gCox","./modules/es7.set.to-json":"Cwp/","./modules/es7.map.of":"0bPO","./modules/es7.set.of":"swmI","./modules/es7.weak-map.of":"Kb3C","./modules/es7.weak-set.of":"HgXJ","./modules/es7.map.from":"+mnJ","./modules/es7.set.from":"0+Wc","./modules/es7.weak-map.from":"RABC","./modules/es7.weak-set.from":"3ir/","./modules/es7.global":"Djh+","./modules/es7.system.global":"zQT/","./modules/es7.error.is-error":"sx2w","./modules/es7.math.clamp":"5duU","./modules/es7.math.deg-per-rad":"Nayo","./modules/es7.math.degrees":"pK3L","./modules/es7.math.fscale":"cNya","./modules/es7.math.iaddh":"JpQg","./modules/es7.math.isubh":"kYRl","./modules/es7.math.imulh":"iMz3","./modules/es7.math.rad-per-deg":"Xbc5","./modules/es7.math.radians":"YSH8","./modules/es7.math.scale":"gu1X","./modules/es7.math.umulh":"1dDq","./modules/es7.math.signbit":"Q8U8","./modules/es7.promise.finally":"q6pY","./modules/es7.promise.try":"7aUL","./modules/es7.reflect.define-metadata":"zk+d","./modules/es7.reflect.delete-metadata":"fy5i","./modules/es7.reflect.get-metadata":"7KBr","./modules/es7.reflect.get-metadata-keys":"y0Gk","./modules/es7.reflect.get-own-metadata":"sn4U","./modules/es7.reflect.get-own-metadata-keys":"bQoJ","./modules/es7.reflect.has-metadata":"jR0d","./modules/es7.reflect.has-own-metadata":"tWeA","./modules/es7.reflect.metadata":"rYHV","./modules/es7.asap":"kvVj","./modules/es7.observable":"iOLx","./modules/web.timers":"p/UQ","./modules/web.immediate":"44uO","./modules/web.dom.iterable":"kCWy","./modules/_core":"6DcE"}],"VuXv":[function(require,module,exports) {
+var global = arguments[3];
+var t=arguments[3];!function(t){"use strict";var r,e=Object.prototype,n=e.hasOwnProperty,o="function"==typeof Symbol?Symbol:{},i=o.iterator||"@@iterator",a=o.asyncIterator||"@@asyncIterator",c=o.toStringTag||"@@toStringTag",u="object"==typeof module,h=t.regeneratorRuntime;if(h)u&&(module.exports=h);else{(h=t.regeneratorRuntime=u?module.exports:{}).wrap=w;var s="suspendedStart",f="suspendedYield",l="executing",p="completed",y={},v={};v[i]=function(){return this};var d=Object.getPrototypeOf,g=d&&d(d(P([])));g&&g!==e&&n.call(g,i)&&(v=g);var m=E.prototype=x.prototype=Object.create(v);b.prototype=m.constructor=E,E.constructor=b,E[c]=b.displayName="GeneratorFunction",h.isGeneratorFunction=function(t){var r="function"==typeof t&&t.constructor;return!!r&&(r===b||"GeneratorFunction"===(r.displayName||r.name))},h.mark=function(t){return Object.setPrototypeOf?Object.setPrototypeOf(t,E):(t.__proto__=E,c in t||(t[c]="GeneratorFunction")),t.prototype=Object.create(m),t},h.awrap=function(t){return{__await:t}},j(_.prototype),_.prototype[a]=function(){return this},h.AsyncIterator=_,h.async=function(t,r,e,n){var o=new _(w(t,r,e,n));return h.isGeneratorFunction(r)?o:o.next().then(function(t){return t.done?t.value:o.next()})},j(m),m[c]="Generator",m[i]=function(){return this},m.toString=function(){return"[object Generator]"},h.keys=function(t){var r=[];for(var e in t)r.push(e);return r.reverse(),function e(){for(;r.length;){var n=r.pop();if(n in t)return e.value=n,e.done=!1,e}return e.done=!0,e}},h.values=P,N.prototype={constructor:N,reset:function(t){if(this.prev=0,this.next=0,this.sent=this._sent=r,this.done=!1,this.delegate=null,this.method="next",this.arg=r,this.tryEntries.forEach(G),!t)for(var e in this)"t"===e.charAt(0)&&n.call(this,e)&&!isNaN(+e.slice(1))&&(this[e]=r)},stop:function(){this.done=!0;var t=this.tryEntries[0].completion;if("throw"===t.type)throw t.arg;return this.rval},dispatchException:function(t){if(this.done)throw t;var e=this;function o(n,o){return c.type="throw",c.arg=t,e.next=n,o&&(e.method="next",e.arg=r),!!o}for(var i=this.tryEntries.length-1;i>=0;--i){var a=this.tryEntries[i],c=a.completion;if("root"===a.tryLoc)return o("end");if(a.tryLoc<=this.prev){var u=n.call(a,"catchLoc"),h=n.call(a,"finallyLoc");if(u&&h){if(this.prev<a.catchLoc)return o(a.catchLoc,!0);if(this.prev<a.finallyLoc)return o(a.finallyLoc)}else if(u){if(this.prev<a.catchLoc)return o(a.catchLoc,!0)}else{if(!h)throw new Error("try statement without catch or finally");if(this.prev<a.finallyLoc)return o(a.finallyLoc)}}}},abrupt:function(t,r){for(var e=this.tryEntries.length-1;e>=0;--e){var o=this.tryEntries[e];if(o.tryLoc<=this.prev&&n.call(o,"finallyLoc")&&this.prev<o.finallyLoc){var i=o;break}}i&&("break"===t||"continue"===t)&&i.tryLoc<=r&&r<=i.finallyLoc&&(i=null);var a=i?i.completion:{};return a.type=t,a.arg=r,i?(this.method="next",this.next=i.finallyLoc,y):this.complete(a)},complete:function(t,r){if("throw"===t.type)throw t.arg;return"break"===t.type||"continue"===t.type?this.next=t.arg:"return"===t.type?(this.rval=this.arg=t.arg,this.method="return",this.next="end"):"normal"===t.type&&r&&(this.next=r),y},finish:function(t){for(var r=this.tryEntries.length-1;r>=0;--r){var e=this.tryEntries[r];if(e.finallyLoc===t)return this.complete(e.completion,e.afterLoc),G(e),y}},catch:function(t){for(var r=this.tryEntries.length-1;r>=0;--r){var e=this.tryEntries[r];if(e.tryLoc===t){var n=e.completion;if("throw"===n.type){var o=n.arg;G(e)}return o}}throw new Error("illegal catch attempt")},delegateYield:function(t,e,n){return this.delegate={iterator:P(t),resultName:e,nextLoc:n},"next"===this.method&&(this.arg=r),y}}}function w(t,r,e,n){var o=r&&r.prototype instanceof x?r:x,i=Object.create(o.prototype),a=new N(n||[]);return i._invoke=function(t,r,e){var n=s;return function(o,i){if(n===l)throw new Error("Generator is already running");if(n===p){if("throw"===o)throw i;return S()}for(e.method=o,e.arg=i;;){var a=e.delegate;if(a){var c=O(a,e);if(c){if(c===y)continue;return c}}if("next"===e.method)e.sent=e._sent=e.arg;else if("throw"===e.method){if(n===s)throw n=p,e.arg;e.dispatchException(e.arg)}else"return"===e.method&&e.abrupt("return",e.arg);n=l;var u=L(t,r,e);if("normal"===u.type){if(n=e.done?p:f,u.arg===y)continue;return{value:u.arg,done:e.done}}"throw"===u.type&&(n=p,e.method="throw",e.arg=u.arg)}}}(t,e,a),i}function L(t,r,e){try{return{type:"normal",arg:t.call(r,e)}}catch(n){return{type:"throw",arg:n}}}function x(){}function b(){}function E(){}function j(t){["next","throw","return"].forEach(function(r){t[r]=function(t){return this._invoke(r,t)}})}function _(r){function e(t,o,i,a){var c=L(r[t],r,o);if("throw"!==c.type){var u=c.arg,h=u.value;return h&&"object"==typeof h&&n.call(h,"__await")?Promise.resolve(h.__await).then(function(t){e("next",t,i,a)},function(t){e("throw",t,i,a)}):Promise.resolve(h).then(function(t){u.value=t,i(u)},a)}a(c.arg)}var o;"object"==typeof t.process&&t.process.domain&&(e=t.process.domain.bind(e)),this._invoke=function(t,r){function n(){return new Promise(function(n,o){e(t,r,n,o)})}return o=o?o.then(n,n):n()}}function O(t,e){var n=t.iterator[e.method];if(n===r){if(e.delegate=null,"throw"===e.method){if(t.iterator.return&&(e.method="return",e.arg=r,O(t,e),"throw"===e.method))return y;e.method="throw",e.arg=new TypeError("The iterator does not provide a 'throw' method")}return y}var o=L(n,t.iterator,e.arg);if("throw"===o.type)return e.method="throw",e.arg=o.arg,e.delegate=null,y;var i=o.arg;return i?i.done?(e[t.resultName]=i.value,e.next=t.nextLoc,"return"!==e.method&&(e.method="next",e.arg=r),e.delegate=null,y):i:(e.method="throw",e.arg=new TypeError("iterator result is not an object"),e.delegate=null,y)}function k(t){var r={tryLoc:t[0]};1 in t&&(r.catchLoc=t[1]),2 in t&&(r.finallyLoc=t[2],r.afterLoc=t[3]),this.tryEntries.push(r)}function G(t){var r=t.completion||{};r.type="normal",delete r.arg,t.completion=r}function N(t){this.tryEntries=[{tryLoc:"root"}],t.forEach(k,this),this.reset(!0)}function P(t){if(t){var e=t[i];if(e)return e.call(t);if("function"==typeof t.next)return t;if(!isNaN(t.length)){var o=-1,a=function e(){for(;++o<t.length;)if(n.call(t,o))return e.value=t[o],e.done=!1,e;return e.value=r,e.done=!0,e};return a.next=a}}return{next:S}}function S(){return{value:r,done:!0}}}("object"==typeof t?t:"object"==typeof window?window:"object"==typeof self?self:this);
+},{}],"dUxS":[function(require,module,exports) {
+module.exports=function(n,r){var t=r===Object(r)?function(n){return r[n]}:r;return function(r){return String(r).replace(n,t)}};
+},{}],"41Ao":[function(require,module,exports) {
+var e=require("./_export"),r=require("./_replacer")(/[\\^$*+?.()|[\]{}]/g,"\\$&");e(e.S,"RegExp",{escape:function(e){return r(e)}});
+},{"./_export":"Vobs","./_replacer":"dUxS"}],"Rlym":[function(require,module,exports) {
+require("../../modules/core.regexp.escape"),module.exports=require("../../modules/_core").RegExp.escape;
+},{"../../modules/core.regexp.escape":"41Ao","../../modules/_core":"6DcE"}],"0zUF":[function(require,module,exports) {
+var global = arguments[3];
+
+var e=arguments[3];if(require("core-js/shim"),require("regenerator-runtime/runtime"),require("core-js/fn/regexp/escape"),e._babelPolyfill)throw new Error("only one instance of babel-polyfill is allowed");e._babelPolyfill=!0;var r="defineProperty";function i(e,i,n){e[i]||Object[r](e,i,{writable:!0,configurable:!0,value:n})}i(String.prototype,"padLeft","".padStart),i(String.prototype,"padRight","".padEnd),"pop,reverse,shift,keys,values,entries,indexOf,every,some,forEach,map,filter,find,findIndex,includes,join,slice,concat,push,splice,unshift,sort,lastIndexOf,reduce,reduceRight,copyWithin,fill".split(",").forEach(function(e){[][e]&&i(Array,e,Function.call.bind([][e]))});
+},{"core-js/shim":"y1LN","regenerator-runtime/runtime":"VuXv","core-js/fn/regexp/escape":"Rlym"}],"sKvN":[function(require,module,exports) {
+"use strict";function t(t,e){if(!(t instanceof e))throw new TypeError("Cannot call a class as a function")}function e(t,e){for(var s=0;s<e.length;s++){var n=e[s];n.enumerable=n.enumerable||!1,n.configurable=!0,"value"in n&&(n.writable=!0),Object.defineProperty(t,n.key,n)}}function s(t,s,n){return s&&e(t.prototype,s),n&&e(t,n),t}Object.defineProperty(exports,"__esModule",{value:!0}),exports.default=void 0;var n=function(){function e(s){t(this,e),this.doc=document,this.nav=this.doc.querySelectorAll(s.navSelector),0!==!this.nav.length&&(this.win=window,this.winHeight=this.win.innerHeight,this.scrollElement=this.doc.querySelector(s.scrollSelector),this.className=s.className,this.offsetTop=s.offsetTop||0,this.contents=[],this.contents=this.getContents(s.contentSelector),this.attachEvent())}return s(e,[{key:"attachEvent",value:function(){var t,e,s=this;this.scrollElement.addEventListener("scroll",function(){t&&clearTimeout(t),t=setTimeout(function(){s.spy()},1)}),this.scrollElement.addEventListener("resize",function(){e&&clearTimeout(e),e=setTimeout(function(){s.spy()},1)})}},{key:"getContents",value:function(t){for(var e=[],s=0,n=this.nav.length;s<n;s++){var i=this.nav[s].href;e.push(this.doc.getElementById(i.split("#")[1]))}return e}},{key:"spy",value:function(){var t=this.getViewState();this.toggleNavClass(t)}},{key:"getViewState",value:function(){for(var t=[],e=0,s=this.contents.length;e<s;e++){var n=this.contents[e];n&&this.isView(n)&&t.push(n)}return t}},{key:"isView",value:function(t){var e=this.scrollElement.scrollTop,s=e+this.offsetTop,n=t.getBoundingClientRect().top+e,i=n+t.offsetHeight;return n<=s+this.offsetTop&&i>e+this.offsetTop}},{key:"toggleNavClass",value:function(t){for(var e=0,s=$(),n=0,i=t.length;n<i;n++){var o=t[n],r=this.getTagDepth(o);e<r&&(e=r,s=o)}for(var a=0,l=this.nav.length;a<l;a++){var c=this.nav[a];c.href.split("#")[1]===s.id?(c.classList.add(this.className),c.classList.add("mdl-color-text--primary")):(c.classList.remove(this.className),c.classList.remove("mdl-color-text--primary"))}}},{key:"getTagDepth",value:function(t){return parseInt($(t).find("h1,h2,h3,h4,h5,h6").get(0).tagName.split("H")[1])}}]),e}();exports.default=n;
+},{}],"j3Sc":[function(require,module,exports) {
+"use strict";function t(t,e){if(!(t instanceof e))throw new TypeError("Cannot call a class as a function")}function e(t,e){for(var i=0;i<e.length;i++){var n=e[i];n.enumerable=n.enumerable||!1,n.configurable=!0,"value"in n&&(n.writable=!0),Object.defineProperty(t,n.key,n)}}function i(t,i,n){return i&&e(t.prototype,i),n&&e(t,n),t}Object.defineProperty(exports,"__esModule",{value:!0}),exports.default=void 0;var n=function(){function e(){t(this,e),this.header=$("header.mdl-layout__header"),this.pagenation=$("div.pagenation"),this.footer=$("footer.mdl-mini-footer"),this.win=$(window),this.scrollElement=$("main"),this.content=$(".page-content"),this.outline=$(".side-doc-outline--content"),this.attachEvent(),this.adjust()}return i(e,[{key:"adjust",value:function(){this.setPageContentMinHeight(),this.setLocaltocHeight()}},{key:"setPageContentMinHeight",value:function(){this.win.innerHeight();var t=this.header.outerHeight(),e=this.footer.outerHeight(!0)+this.pagenation.outerHeight(!0);this.content.css("min-height",this.win.innerHeight()-t-e)}},{key:"setLocaltocHeight",value:function(){var t=this.pagenation.position().top+parseInt(this.pagenation.css("margin-top"),10),e=this.scrollElement.scrollTop()+this.content.outerHeight(),i=this.win.innerHeight()-this.header.outerHeight(),n=0;n=(n=e>t?t:e)>i?i:n,this.outline.css("height",n)}},{key:"attachEvent",value:function(){var t,e,i=this;this.scrollElement.on("scroll",function(){t&&clearTimeout(t),t=setTimeout(function(){i.adjust()},1)}),this.win.on("resize",function(){e&&clearTimeout(e),e=setTimeout(function(){i.adjust()},1)})}}]),e}();exports.default=n;
+},{}],"brfV":[function(require,module,exports) {
+"use strict";require("../scss/sphinx_materialdesign_theme.scss"),require("material-design-lite"),require("babel-polyfill");var a=e(require("./scrollspy")),t=e(require("./adjust-height"));function e(a){return a&&a.__esModule?a:{default:a}}$(function(){var t,e;t=$(".mdl-layout__drawer nav").find("li"),$.each(t,function(a,t){var e=$(t),n=$('<span class="link-wrapper"></span>'),l=e.children("a");e.append(n.append(l));var i=e.hasClass("current")&&!l.hasClass("current"),o=e.children("ul");if(o.length){var d="globalnav-".concat(a);o.attr("id",d),o.addClass("collapse");var s=$('<span class="nav-toggle"></span>');i?(o.addClass("show"),s.addClass("show")):o.hide(),e.append(n.append(s.append($('<a class="mdl-button mdl-js-button mdl-button--icon" data-toggle="#'.concat(d,'"><span style="color: #888"><i class="material-icons">keyboard_arrow_down</i></span></span>'))))).append(o)}}),$(".mdl-layout__drawer nav .nav-toggle a").click(function(){var a=$(this),t=a.attr("data-toggle");$("ul".concat(t)).toggleClass("show").animate({height:"toggle",opacity:"toggle"}),a.parent().toggleClass("show")}),e=$(".breadcrumb"),$("#waterfall-exp").focus(function(){$(window).width()<=1024&&e.hide()}).blur(function(){$(window).width()<=1024&&e.show()});new a.default({contentSelector:".page-content .section",navSelector:".localtoc a",scrollSelector:"main",className:"current",offsetTop:64});$(".mdl-layout__content").focus(),$(".mx-card").each(function(){$(this).addClass("mdl-card mdl-shadow--2dp")}),$(".mx-card .mx-card-title").each(function(){$(this).addClass("mdl-card__title")}),$(".mx-card .mx-card-text").each(function(){$(this).addClass("mdl-card__supporting-text")}),$(".mx-card-link").each(function(){$(this).hide()}),$(".mdl-card").each(function(){$(this).click(function(){var a=$(this).find(".mx-card-link").text();return a&&(window.location=a),!0})}),$("a.download").each(function(){var a=document.createElement("button");a.className="download mdl-button mdl-js-button mdl-button--fab mdl-js-ripple-effect";var t=document.createElement("i");t.className="material-icons";var e=document.createTextNode("file_download");t.appendChild(e),a.appendChild(t);var n=$(this).attr("href");a.onclick=function(){window.location=n};var l=n.split("/").slice(-1).pop();a.id=l?l.replace(".","-"):"download-button-"+$(this).index();var i=document.createElement("div");i.className="mdl-tooltip",i.setAttribute("data-mdl-for",a.id);var o=$(this).find("span.pre").map(function(){return $(this).text()}).get().join(" ");i.innerHTML=o,componentHandler.upgradeElement(a),$(this).remove();var d=$(".section h1").first();d.append(a),d.append(i)}),$(".mdl-layout__container").css("visibility","visible")});
+},{"../scss/sphinx_materialdesign_theme.scss":"BS4D","material-design-lite":"vKy7","babel-polyfill":"0zUF","./scrollspy":"sKvN","./adjust-height":"j3Sc"}]},{},["brfV"], null)
+//# sourceMappingURL=/sphinx_materialdesign_theme.js.map
\ No newline at end of file
diff --git a/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.js.map b/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.js.map
new file mode 100644
index 000000000000..59d6b8b77d7f
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.js.map
@@ -0,0 +1 @@
+{"version":3,"sources":["ripple.js","tabs.js","layout.js","mdlComponentHandler.js","rAF.js","button.js","checkbox.js","icon-toggle.js","menu.js","progress.js","radio.js","slider.js","snackbar.js","spinner.js","switch.js","textfield.js","tooltip.js","data-table.js","../../node_modules/core-js/modules/_global.js","../../node_modules/core-js/modules/_has.js","../../node_modules/core-js/modules/_fails.js","../../node_modules/core-js/modules/_descriptors.js","../../node_modules/core-js/modules/_core.js","../../node_modules/core-js/modules/_is-object.js","../../node_modules/core-js/modules/_an-object.js","../../node_modules/core-js/modules/_dom-create.js","../../node_modules/core-js/modules/_ie8-dom-define.js","../../node_modules/core-js/modules/_to-primitive.js","../../node_modules/core-js/modules/_object-dp.js","../../node_modules/core-js/modules/_property-desc.js","../../node_modules/core-js/modules/_hide.js","../../node_modules/core-js/modules/_uid.js","../../node_modules/core-js/modules/_library.js","../../node_modules/core-js/modules/_shared.js","../../node_modules/core-js/modules/_function-to-string.js","../../node_modules/core-js/modules/_redefine.js","../../node_modules/core-js/modules/_a-function.js","../../node_modules/core-js/modules/_ctx.js","../../node_modules/core-js/modules/_export.js","../../node_modules/core-js/modules/_meta.js","../../node_modules/core-js/modules/_wks.js","../../node_modules/core-js/modules/_set-to-string-tag.js","../../node_modules/core-js/modules/_wks-ext.js","../../node_modules/core-js/modules/_wks-define.js","../../node_modules/core-js/modules/_cof.js","../../node_modules/core-js/modules/_iobject.js","../../node_modules/core-js/modules/_defined.js","../../node_modules/core-js/modules/_to-iobject.js","../../node_modules/core-js/modules/_to-integer.js","../../node_modules/core-js/modules/_to-length.js","../../node_modules/core-js/modules/_to-absolute-index.js","../../node_modules/core-js/modules/_array-includes.js","../../node_modules/core-js/modules/_shared-key.js","../../node_modules/core-js/modules/_object-keys-internal.js","../../node_modules/core-js/modules/_enum-bug-keys.js","../../node_modules/core-js/modules/_object-keys.js","../../node_modules/core-js/modules/_object-gops.js","../../node_modules/core-js/modules/_object-pie.js","../../node_modules/core-js/modules/_enum-keys.js","../../node_modules/core-js/modules/_is-array.js","../../node_modules/core-js/modules/_to-object.js","../../node_modules/core-js/modules/_object-dps.js","../../node_modules/core-js/modules/_html.js","../../node_modules/core-js/modules/_object-create.js","../../node_modules/core-js/modules/_object-gopn.js","../../node_modules/core-js/modules/_object-gopn-ext.js","../../node_modules/core-js/modules/_object-gopd.js","../../node_modules/core-js/modules/es6.symbol.js","../../node_modules/core-js/modules/es6.object.create.js","../../node_modules/core-js/modules/es6.object.define-property.js","../../node_modules/core-js/modules/es6.object.define-properties.js","../../node_modules/core-js/modules/_object-sap.js","../../node_modules/core-js/modules/es6.object.get-own-property-descriptor.js","../../node_modules/core-js/modules/_object-gpo.js","../../node_modules/core-js/modules/es6.object.get-prototype-of.js","../../node_modules/core-js/modules/es6.object.keys.js","../../node_modules/core-js/modules/es6.object.get-own-property-names.js","../../node_modules/core-js/modules/es6.object.freeze.js","../../node_modules/core-js/modules/es6.object.seal.js","../../node_modules/core-js/modules/es6.object.prevent-extensions.js","../../node_modules/core-js/modules/es6.object.is-frozen.js","../../node_modules/core-js/modules/es6.object.is-sealed.js","../../node_modules/core-js/modules/es6.object.is-extensible.js","../../node_modules/core-js/modules/_object-assign.js","../../node_modules/core-js/modules/es6.object.assign.js","../../node_modules/core-js/modules/_same-value.js","../../node_modules/core-js/modules/es6.object.is.js","../../node_modules/core-js/modules/_set-proto.js","../../node_modules/core-js/modules/es6.object.set-prototype-of.js","../../node_modules/core-js/modules/_classof.js","../../node_modules/core-js/modules/es6.object.to-string.js","../../node_modules/core-js/modules/_invoke.js","../../node_modules/core-js/modules/_bind.js","../../node_modules/core-js/modules/es6.function.bind.js","../../node_modules/core-js/modules/es6.function.name.js","../../node_modules/core-js/modules/es6.function.has-instance.js","../../node_modules/core-js/modules/_string-ws.js","../../node_modules/core-js/modules/_string-trim.js","../../node_modules/core-js/modules/_parse-int.js","../../node_modules/core-js/modules/es6.parse-int.js","../../node_modules/core-js/modules/_parse-float.js","../../node_modules/core-js/modules/es6.parse-float.js","../../node_modules/core-js/modules/_inherit-if-required.js","../../node_modules/core-js/modules/es6.number.constructor.js","../../node_modules/core-js/modules/_a-number-value.js","../../node_modules/core-js/modules/_string-repeat.js","../../node_modules/core-js/modules/es6.number.to-fixed.js","../../node_modules/core-js/modules/es6.number.to-precision.js","../../node_modules/core-js/modules/es6.number.epsilon.js","../../node_modules/core-js/modules/es6.number.is-finite.js","../../node_modules/core-js/modules/_is-integer.js","../../node_modules/core-js/modules/es6.number.is-integer.js","../../node_modules/core-js/modules/es6.number.is-nan.js","../../node_modules/core-js/modules/es6.number.is-safe-integer.js","../../node_modules/core-js/modules/es6.number.max-safe-integer.js","../../node_modules/core-js/modules/es6.number.min-safe-integer.js","../../node_modules/core-js/modules/es6.number.parse-float.js","../../node_modules/core-js/modules/es6.number.parse-int.js","../../node_modules/core-js/modules/_math-log1p.js","../../node_modules/core-js/modules/es6.math.acosh.js","../../node_modules/core-js/modules/es6.math.asinh.js","../../node_modules/core-js/modules/es6.math.atanh.js","../../node_modules/core-js/modules/_math-sign.js","../../node_modules/core-js/modules/es6.math.cbrt.js","../../node_modules/core-js/modules/es6.math.clz32.js","../../node_modules/core-js/modules/es6.math.cosh.js","../../node_modules/core-js/modules/_math-expm1.js","../../node_modules/core-js/modules/es6.math.expm1.js","../../node_modules/core-js/modules/_math-fround.js","../../node_modules/core-js/modules/es6.math.fround.js","../../node_modules/core-js/modules/es6.math.hypot.js","../../node_modules/core-js/modules/es6.math.imul.js","../../node_modules/core-js/modules/es6.math.log10.js","../../node_modules/core-js/modules/es6.math.log1p.js","../../node_modules/core-js/modules/es6.math.log2.js","../../node_modules/core-js/modules/es6.math.sign.js","../../node_modules/core-js/modules/es6.math.sinh.js","../../node_modules/core-js/modules/es6.math.tanh.js","../../node_modules/core-js/modules/es6.math.trunc.js","../../node_modules/core-js/modules/es6.string.from-code-point.js","../../node_modules/core-js/modules/es6.string.raw.js","../../node_modules/core-js/modules/es6.string.trim.js","../../node_modules/core-js/modules/_string-at.js","../../node_modules/core-js/modules/_iterators.js","../../node_modules/core-js/modules/_iter-create.js","../../node_modules/core-js/modules/_iter-define.js","../../node_modules/core-js/modules/es6.string.iterator.js","../../node_modules/core-js/modules/es6.string.code-point-at.js","../../node_modules/core-js/modules/_is-regexp.js","../../node_modules/core-js/modules/_string-context.js","../../node_modules/core-js/modules/_fails-is-regexp.js","../../node_modules/core-js/modules/es6.string.ends-with.js","../../node_modules/core-js/modules/es6.string.includes.js","../../node_modules/core-js/modules/es6.string.repeat.js","../../node_modules/core-js/modules/es6.string.starts-with.js","../../node_modules/core-js/modules/_string-html.js","../../node_modules/core-js/modules/es6.string.anchor.js","../../node_modules/core-js/modules/es6.string.big.js","../../node_modules/core-js/modules/es6.string.blink.js","../../node_modules/core-js/modules/es6.string.bold.js","../../node_modules/core-js/modules/es6.string.fixed.js","../../node_modules/core-js/modules/es6.string.fontcolor.js","../../node_modules/core-js/modules/es6.string.fontsize.js","../../node_modules/core-js/modules/es6.string.italics.js","../../node_modules/core-js/modules/es6.string.link.js","../../node_modules/core-js/modules/es6.string.small.js","../../node_modules/core-js/modules/es6.string.strike.js","../../node_modules/core-js/modules/es6.string.sub.js","../../node_modules/core-js/modules/es6.string.sup.js","../../node_modules/core-js/modules/es6.date.now.js","../../node_modules/core-js/modules/es6.date.to-json.js","../../node_modules/core-js/modules/_date-to-iso-string.js","../../node_modules/core-js/modules/es6.date.to-iso-string.js","../../node_modules/core-js/modules/es6.date.to-string.js","../../node_modules/core-js/modules/_date-to-primitive.js","../../node_modules/core-js/modules/es6.date.to-primitive.js","../../node_modules/core-js/modules/es6.array.is-array.js","../../node_modules/core-js/modules/_iter-call.js","../../node_modules/core-js/modules/_is-array-iter.js","../../node_modules/core-js/modules/_create-property.js","../../node_modules/core-js/modules/core.get-iterator-method.js","../../node_modules/core-js/modules/_iter-detect.js","../../node_modules/core-js/modules/es6.array.from.js","../../node_modules/core-js/modules/es6.array.of.js","../../node_modules/core-js/modules/_strict-method.js","../../node_modules/core-js/modules/es6.array.join.js","../../node_modules/core-js/modules/es6.array.slice.js","../../node_modules/core-js/modules/es6.array.sort.js","../../node_modules/core-js/modules/_array-species-constructor.js","../../node_modules/core-js/modules/_array-species-create.js","../../node_modules/core-js/modules/_array-methods.js","../../node_modules/core-js/modules/es6.array.for-each.js","../../node_modules/core-js/modules/es6.array.map.js","../../node_modules/core-js/modules/es6.array.filter.js","../../node_modules/core-js/modules/es6.array.some.js","../../node_modules/core-js/modules/es6.array.every.js","../../node_modules/core-js/modules/_array-reduce.js","../../node_modules/core-js/modules/es6.array.reduce.js","../../node_modules/core-js/modules/es6.array.reduce-right.js","../../node_modules/core-js/modules/es6.array.index-of.js","../../node_modules/core-js/modules/es6.array.last-index-of.js","../../node_modules/core-js/modules/_array-copy-within.js","../../node_modules/core-js/modules/_add-to-unscopables.js","../../node_modules/core-js/modules/es6.array.copy-within.js","../../node_modules/core-js/modules/_array-fill.js","../../node_modules/core-js/modules/es6.array.fill.js","../../node_modules/core-js/modules/es6.array.find.js","../../node_modules/core-js/modules/es6.array.find-index.js","../../node_modules/core-js/modules/_set-species.js","../../node_modules/core-js/modules/es6.array.species.js","../../node_modules/core-js/modules/_iter-step.js","../../node_modules/core-js/modules/es6.array.iterator.js","../../node_modules/core-js/modules/_flags.js","../../node_modules/core-js/modules/es6.regexp.constructor.js","../../node_modules/core-js/modules/_regexp-exec.js","../../node_modules/core-js/modules/es6.regexp.exec.js","../../node_modules/core-js/modules/es6.regexp.flags.js","../../node_modules/core-js/modules/es6.regexp.to-string.js","../../node_modules/core-js/modules/_advance-string-index.js","../../node_modules/core-js/modules/_regexp-exec-abstract.js","../../node_modules/core-js/modules/_fix-re-wks.js","../../node_modules/core-js/modules/es6.regexp.match.js","../../node_modules/core-js/modules/es6.regexp.replace.js","../../node_modules/core-js/modules/es6.regexp.search.js","../../node_modules/core-js/modules/_species-constructor.js","../../node_modules/core-js/modules/es6.regexp.split.js","../../node_modules/core-js/modules/_an-instance.js","../../node_modules/core-js/modules/_for-of.js","../../node_modules/core-js/modules/_task.js","../../node_modules/core-js/modules/_microtask.js","../../node_modules/core-js/modules/_new-promise-capability.js","../../node_modules/core-js/modules/_perform.js","../../node_modules/core-js/modules/_user-agent.js","../../node_modules/core-js/modules/_promise-resolve.js","../../node_modules/core-js/modules/_redefine-all.js","../../node_modules/core-js/modules/es6.promise.js","../../node_modules/core-js/modules/_validate-collection.js","../../node_modules/core-js/modules/_collection-strong.js","../../node_modules/core-js/modules/_collection.js","../../node_modules/core-js/modules/es6.map.js","../../node_modules/core-js/modules/es6.set.js","../../node_modules/core-js/modules/_collection-weak.js","../../node_modules/core-js/modules/es6.weak-map.js","../../node_modules/core-js/modules/es6.weak-set.js","../../node_modules/core-js/modules/_typed.js","../../node_modules/core-js/modules/_to-index.js","../../node_modules/core-js/modules/_typed-buffer.js","../../node_modules/core-js/modules/es6.typed.array-buffer.js","../../node_modules/core-js/modules/es6.typed.data-view.js","../../node_modules/core-js/modules/_typed-array.js","../../node_modules/core-js/modules/es6.typed.int8-array.js","../../node_modules/core-js/modules/es6.typed.uint8-array.js","../../node_modules/core-js/modules/es6.typed.uint8-clamped-array.js","../../node_modules/core-js/modules/es6.typed.int16-array.js","../../node_modules/core-js/modules/es6.typed.uint16-array.js","../../node_modules/core-js/modules/es6.typed.int32-array.js","../../node_modules/core-js/modules/es6.typed.uint32-array.js","../../node_modules/core-js/modules/es6.typed.float32-array.js","../../node_modules/core-js/modules/es6.typed.float64-array.js","../../node_modules/core-js/modules/es6.reflect.apply.js","../../node_modules/core-js/modules/es6.reflect.construct.js","../../node_modules/core-js/modules/es6.reflect.define-property.js","../../node_modules/core-js/modules/es6.reflect.delete-property.js","../../node_modules/core-js/modules/es6.reflect.enumerate.js","../../node_modules/core-js/modules/es6.reflect.get.js","../../node_modules/core-js/modules/es6.reflect.get-own-property-descriptor.js","../../node_modules/core-js/modules/es6.reflect.get-prototype-of.js","../../node_modules/core-js/modules/es6.reflect.has.js","../../node_modules/core-js/modules/es6.reflect.is-extensible.js","../../node_modules/core-js/modules/_own-keys.js","../../node_modules/core-js/modules/es6.reflect.own-keys.js","../../node_modules/core-js/modules/es6.reflect.prevent-extensions.js","../../node_modules/core-js/modules/es6.reflect.set.js","../../node_modules/core-js/modules/es6.reflect.set-prototype-of.js","../../node_modules/core-js/modules/es7.array.includes.js","../../node_modules/core-js/modules/_flatten-into-array.js","../../node_modules/core-js/modules/es7.array.flat-map.js","../../node_modules/core-js/modules/es7.array.flatten.js","../../node_modules/core-js/modules/es7.string.at.js","../../node_modules/core-js/modules/_string-pad.js","../../node_modules/core-js/modules/es7.string.pad-start.js","../../node_modules/core-js/modules/es7.string.pad-end.js","../../node_modules/core-js/modules/es7.string.trim-left.js","../../node_modules/core-js/modules/es7.string.trim-right.js","../../node_modules/core-js/modules/es7.string.match-all.js","../../node_modules/core-js/modules/es7.symbol.async-iterator.js","../../node_modules/core-js/modules/es7.symbol.observable.js","../../node_modules/core-js/modules/es7.object.get-own-property-descriptors.js","../../node_modules/core-js/modules/_object-to-array.js","../../node_modules/core-js/modules/es7.object.values.js","../../node_modules/core-js/modules/es7.object.entries.js","../../node_modules/core-js/modules/_object-forced-pam.js","../../node_modules/core-js/modules/es7.object.define-getter.js","../../node_modules/core-js/modules/es7.object.define-setter.js","../../node_modules/core-js/modules/es7.object.lookup-getter.js","../../node_modules/core-js/modules/es7.object.lookup-setter.js","../../node_modules/core-js/modules/_array-from-iterable.js","../../node_modules/core-js/modules/_collection-to-json.js","../../node_modules/core-js/modules/es7.map.to-json.js","../../node_modules/core-js/modules/es7.set.to-json.js","../../node_modules/core-js/modules/_set-collection-of.js","../../node_modules/core-js/modules/es7.map.of.js","../../node_modules/core-js/modules/es7.set.of.js","../../node_modules/core-js/modules/es7.weak-map.of.js","../../node_modules/core-js/modules/es7.weak-set.of.js","../../node_modules/core-js/modules/_set-collection-from.js","../../node_modules/core-js/modules/es7.map.from.js","../../node_modules/core-js/modules/es7.set.from.js","../../node_modules/core-js/modules/es7.weak-map.from.js","../../node_modules/core-js/modules/es7.weak-set.from.js","../../node_modules/core-js/modules/es7.global.js","../../node_modules/core-js/modules/es7.system.global.js","../../node_modules/core-js/modules/es7.error.is-error.js","../../node_modules/core-js/modules/es7.math.clamp.js","../../node_modules/core-js/modules/es7.math.deg-per-rad.js","../../node_modules/core-js/modules/es7.math.degrees.js","../../node_modules/core-js/modules/_math-scale.js","../../node_modules/core-js/modules/es7.math.fscale.js","../../node_modules/core-js/modules/es7.math.iaddh.js","../../node_modules/core-js/modules/es7.math.isubh.js","../../node_modules/core-js/modules/es7.math.imulh.js","../../node_modules/core-js/modules/es7.math.rad-per-deg.js","../../node_modules/core-js/modules/es7.math.radians.js","../../node_modules/core-js/modules/es7.math.scale.js","../../node_modules/core-js/modules/es7.math.umulh.js","../../node_modules/core-js/modules/es7.math.signbit.js","../../node_modules/core-js/modules/es7.promise.finally.js","../../node_modules/core-js/modules/es7.promise.try.js","../../node_modules/core-js/modules/_metadata.js","../../node_modules/core-js/modules/es7.reflect.define-metadata.js","../../node_modules/core-js/modules/es7.reflect.delete-metadata.js","../../node_modules/core-js/modules/es7.reflect.get-metadata.js","../../node_modules/core-js/modules/es7.reflect.get-metadata-keys.js","../../node_modules/core-js/modules/es7.reflect.get-own-metadata.js","../../node_modules/core-js/modules/es7.reflect.get-own-metadata-keys.js","../../node_modules/core-js/modules/es7.reflect.has-metadata.js","../../node_modules/core-js/modules/es7.reflect.has-own-metadata.js","../../node_modules/core-js/modules/es7.reflect.metadata.js","../../node_modules/core-js/modules/es7.asap.js","../../node_modules/core-js/modules/es7.observable.js","../../node_modules/core-js/modules/web.timers.js","../../node_modules/core-js/modules/web.immediate.js","../../node_modules/core-js/modules/web.dom.iterable.js","../../node_modules/core-js/shim.js","../../node_modules/regenerator-runtime/runtime.js","../../node_modules/core-js/modules/_replacer.js","../../node_modules/core-js/modules/core.regexp.escape.js","../../node_modules/core-js/fn/regexp/escape.js","../../node_modules/babel-polyfill/lib/index.js","scrollspy.js","adjust-height.js","sphinx_materialdesign_theme.js"],"names":["MaterialTab","tab","ctx","element_","classList","contains","CssClasses_","MDL_JS_RIPPLE_EFFECT","rippleContainer","document","createElement","add","MDL_RIPPLE_CONTAINER","ripple","MDL_RIPPLE","appendChild","addEventListener","e","getAttribute","charAt","preventDefault","href","split","panel","querySelector","resetTabState_","resetPanelState_","ACTIVE_CLASS","MaterialLayoutTab","tabs","panels","layout","selectTab","content_","IS_ACTIVE","tabBar_","JS_RIPPLE_EFFECT","RIPPLE_CONTAINER","RIPPLE","TAB_MANUAL_SWITCH","show","componentHandler","upgradeDom","optJsClass","optCssClass","upgradeElement","element","upgradeElements","elements","upgradeAllRegistered","registerUpgradedCallback","jsClass","callback","register","config","downgradeElements","nodes","findRegisteredClass_","name","optReplace","i","registeredComponents_","length","className","getUpgradedListOfElement_","dataUpgraded","isElementUpgraded_","upgradedList","indexOf","createEvent_","eventType","bubbles","cancelable","window","CustomEvent","ev","createEvent","initEvent","upgradeDomInternal","cssClass","registeredClass","querySelectorAll","n","upgradeElementInternal","Element","Error","upgradingEv","dispatchEvent","defaultPrevented","classesToUpgrade","push","forEach","component","setAttribute","join","instance","classConstructor","componentConfigProperty_","createdComponents_","j","m","callbacks","widget","upgradedEv","deconstructComponentInternal","componentIndex","splice","upgrades","componentPlace","classAsString","upgradeElementsInternal","Array","isArray","prototype","slice","call","HTMLElement","children","upgradeAllRegisteredInternal","registerUpgradedCallbackInternal","regClass","registerInternal","widgetMissing","newConfig","constructor","item","hasOwnProperty","downgradeNodesInternal","downgradeNode","node","filter","NodeList","Node","ComponentConfigPublic","ComponentConfig","Component","documentElement","Date","now","getTime","vendors","requestAnimationFrame","vp","cancelAnimationFrame","test","navigator","userAgent","lastTime","nextTime","Math","max","setTimeout","clearTimeout","MaterialButton","this","init","Constant_","RIPPLE_EFFECT","blurHandler_","event","blur","disable","disabled","enable","rippleElement_","boundRippleBlurHandler","bind","boundButtonBlurHandler","MaterialCheckbox","TINY_TIMEOUT","INPUT","BOX_OUTLINE","FOCUS_HELPER","TICK_OUTLINE","RIPPLE_IGNORE_EVENTS","RIPPLE_CENTER","IS_FOCUSED","IS_DISABLED","IS_CHECKED","IS_UPGRADED","onChange_","updateClasses_","onFocus_","onBlur_","remove","onMouseUp_","blur_","checkDisabled","checkToggleState","inputElement_","checked","check","uncheck","boxOutline","tickContainer","tickOutline","rippleContainerElement_","boundRippleMouseUp","boundInputOnChange","boundInputOnFocus","boundInputOnBlur","boundElementMouseUp","MaterialIconToggle","boundElementOnMouseUp","MaterialMenu","TRANSITION_DURATION_SECONDS","TRANSITION_DURATION_FRACTION","CLOSE_TIMEOUT","Keycodes_","ENTER","ESCAPE","SPACE","UP_ARROW","DOWN_ARROW","CONTAINER","OUTLINE","ITEM","ITEM_RIPPLE_CONTAINER","IS_VISIBLE","IS_ANIMATING","BOTTOM_LEFT","BOTTOM_RIGHT","TOP_LEFT","TOP_RIGHT","UNALIGNED","container","parentElement","insertBefore","removeChild","container_","outline","outline_","forElId","forEl","getElementById","forElement_","handleForClick_","handleForKeyboardEvent_","items","boundItemKeydown_","handleItemKeyboardEvent_","boundItemClick_","handleItemClick_","tabIndex","evt","rect","getBoundingClientRect","forRect","style","right","top","offsetTop","offsetHeight","left","offsetLeft","bottom","toggle","keyCode","focus","currentIndex","target","MouseEvent","click","hide","hasAttribute","stopPropagation","closing_","applyClip_","height","width","clip","removeAnimationEndListener_","addAnimationEndListener_","transitionDuration","itemDelay","transitionDelay","parentNode","removeEventListener","removeProperty","MaterialProgress","INDETERMINATE_CLASS","setProgress","p","progressbar_","setBuffer","bufferbar_","auxbar_","el","MaterialRadio","JS_RADIO","RADIO_BTN","RADIO_OUTER_CIRCLE","RADIO_INNER_CIRCLE","radios","getElementsByClassName","btnElement_","onMouseup_","boundChangeHandler_","boundFocusHandler_","boundBlurHandler_","boundMouseUpHandler_","outerCircle","innerCircle","MaterialSlider","isIE_","msPointerEnabled","IE_CONTAINER","SLIDER_CONTAINER","BACKGROUND_FLEX","BACKGROUND_LOWER","BACKGROUND_UPPER","IS_LOWEST_VALUE","onInput_","updateValueStyles_","onContainerMouseDown_","newEvent","buttons","clientX","clientY","y","fraction","value","min","backgroundLower_","flex","webkitFlex","backgroundUpper_","change","containerIE","backgroundFlex","boundInputHandler","boundChangeHandler","boundMouseUpHandler","boundContainerMouseDownHandler","MaterialSnackbar","textElement_","cssClasses_","MESSAGE","actionElement_","ACTION","active","actionHandler_","undefined","message_","actionText_","queuedNotifications_","setActionHidden_","ANIMATION_LENGTH","SNACKBAR","ACTIVE","displaySnackbar_","textContent","cleanup_","timeout_","showSnackbar","data","checkQueue_","shift","Boolean","removeAttribute","MaterialSpinner","MDL_SPINNER_LAYER_COUNT","MDL_SPINNER_LAYER","MDL_SPINNER_CIRCLE_CLIPPER","MDL_SPINNER_CIRCLE","MDL_SPINNER_GAP_PATCH","MDL_SPINNER_LEFT","MDL_SPINNER_RIGHT","createLayer","index","layer","leftClipper","gapPatch","rightClipper","circleOwners","circle","stop","start","MaterialSwitch","TRACK","THUMB","on","off","track","thumb","focusHelper","boundFocusHandler","boundBlurHandler","MaterialTabs","TAB_CLASS","PANEL_CLASS","UPGRADED_CLASS","MDL_JS_RIPPLE_EFFECT_IGNORE_EVENTS","initTabs_","tabs_","panels_","k","MaterialTextfield","maxRows","NO_MAX_ROWS","MAX_ROWS_ATTRIBUTE","LABEL","IS_DIRTY","IS_INVALID","HAS_PLACEHOLDER","onKeyDown_","currentRowCount","onReset_","checkValidity","checkDirty","checkFocus","input_","validity","valid","label_","parseInt","isNaN","boundUpdateClassesHandler","boundResetHandler","boundKeyDownHandler","invalid","MaterialTooltip","BOTTOM","LEFT","RIGHT","TOP","handleMouseEnter_","props","marginLeft","offsetWidth","marginTop","hideTooltip_","boundMouseEnterHandler","boundMouseLeaveAndScrollHandler","MaterialLayout","MAX_WIDTH","TAB_SCROLL_PIXELS","RESIZE_TIMEOUT","MENU_ICON","CHEVRON_LEFT","CHEVRON_RIGHT","Mode_","STANDARD","SEAMED","WATERFALL","SCROLL","HEADER","DRAWER","CONTENT","DRAWER_BTN","ICON","HEADER_SEAMED","HEADER_WATERFALL","HEADER_SCROLL","FIXED_HEADER","OBFUSCATOR","TAB_BAR","TAB_CONTAINER","TAB","TAB_BAR_BUTTON","TAB_BAR_LEFT_BUTTON","TAB_BAR_RIGHT_BUTTON","PANEL","HAS_DRAWER","HAS_TABS","HAS_SCROLLING_HEADER","CASTING_SHADOW","IS_COMPACT","IS_SMALL_SCREEN","IS_DRAWER_OPEN","ON_LARGE_SCREEN","ON_SMALL_SCREEN","contentScrollHandler_","header_","headerVisible","scrollTop","keyboardEventHandler_","drawer_","toggleDrawer","screenSizeHandler_","screenSizeMediaQuery_","matches","obfuscator_","drawerToggleHandler_","type","headerTransitionEndHandler_","headerClickHandler_","tabBar","drawerButton","focusedElement","directChildren","childNodes","numChildren","c","child","persisted","overflowY","mode","drawerButtonIcon","innerHTML","firstChild","obfuscator","matchMedia","addListener","tabContainer","leftButton","leftButtonIcon","scrollLeft","rightButton","rightButtonIcon","tabUpdateHandler","scrollWidth","windowResizeHandler","resizeTimeoutId_","MaterialDataTable","DATA_TABLE","SELECTABLE","SELECT_ELEMENT","IS_SELECTED","selectRow_","checkbox","row","opt_rows","createCheckbox_","label","labelClasses","firstHeader","bodyRows","footRows","rows","concat","th","headerCheckbox","firstCell","td","nodeName","toUpperCase","rowCheckbox","MaterialRipple","INITIAL_SCALE","INITIAL_SIZE","INITIAL_OPACITY","FINAL_OPACITY","FINAL_SCALE","RIPPLE_EFFECT_IGNORE_EVENTS","downHandler_","boundHeight","boundWidth","rippleSize_","sqrt","ignoringMouseDown_","frameCount","getFrameCount","setFrameCount","x","bound","currentTarget","round","touches","setRippleXY","setRippleStyles","animFrameHandler","upHandler_","detail","recentering","frameCount_","x_","y_","boundDownHandler","boundUpHandler","fC","getRippleElement","newX","newY","transformString","scale","offset","webkitTransform","msTransform","transform","ScrollSpy","args","doc","nav","navSelector","win","winHeight","innerHeight","scrollElement","scrollSelector","contents","getContents","contentSelector","attachEvent","scrollTimer","resizeTimer","spy","targets","getViewState","toggleNavClass","elementListInView","current","isView","calcBotom","elementTop","elementBottom","maxDepth","maxDepthElement","$","tempDepth","getTagDepth","navElement","id","find","get","tagName","AdjustHeight","header","pagenation","footer","content","adjust","setPageContentMinHeight","setLocaltocHeight","winH","headerHeight","outerHeight","footerHeight","css","pagenationPotisionTop","position","outlineBottom","reconstructionDrawerGlobalToc","$lists","$breadcrumb","each","li","$li","$linkWrapper","$link","append","isCurrent","hasClass","$ul","ulId","attr","addClass","$toggleWrapper","$toggle","toggleClass","animate","opacity","parent","url","text","location","button","icon","createTextNode","link","onclick","fileName","pop","replace","hint","hintText","map","first"],"mappings":";;;AAgOA,SAAA,EAAA,GAAA,OAAA,EAAA,mBAAA,QAAA,iBAAA,OAAA,SAAA,SAAA,GAAA,cAAA,GAAA,SAAA,GAAA,OAAA,GAAA,mBAAA,QAAA,EAAA,cAAA,QAAA,IAAA,OAAA,UAAA,gBAAA,IAAA,IAAA,WAAA,aCnHAA,SAAAA,EAAAC,EAAAC,GACAD,GAAAA,EAAA,CACAC,GAAAA,EAAAC,SAAAC,UAAAC,SAAAH,EAAAI,YAAAC,sBAAA,CACAC,IAAAA,EAAAC,SAAAC,cAAA,QACAF,EAAAJ,UAAAO,IAAAT,EAAAI,YAAAM,sBACAJ,EAAAJ,UAAAO,IAAAT,EAAAI,YAAAC,sBACAM,IAAAA,EAAAJ,SAAAC,cAAA,QACAG,EAAAT,UAAAO,IAAAT,EAAAI,YAAAQ,YACAN,EAAAO,YAAAF,GACAZ,EAAAc,YAAAP,GAEAP,EAAAe,iBAAA,QAAA,SAAAC,GACA,GAAA,MAAAhB,EAAAiB,aAAA,QAAAC,OAAA,GAAA,CACAF,EAAAG,iBACAC,IAAAA,EAAApB,EAAAoB,KAAAC,MAAA,KAAA,GACAC,EAAArB,EAAAC,SAAAqB,cAAA,IAAAH,GACAnB,EAAAuB,iBACAvB,EAAAwB,mBACAzB,EAAAG,UAAAO,IAAAT,EAAAI,YAAAqB,cACAJ,EAAAnB,UAAAO,IAAAT,EAAAI,YAAAqB,kBCwTAC,SAAAA,EAAA3B,EAAA4B,EAAAC,EAAAC,GAIAC,SAAAA,IACAX,IAAAA,EAAApB,EAAAoB,KAAAC,MAAA,KAAA,GACAC,EAAAQ,EAAAE,SAAAT,cAAA,IAAAH,GACAU,EAAAN,eAAAI,GACAE,EAAAL,iBAAAI,GACA7B,EAAAG,UAAAO,IAAAoB,EAAAzB,YAAA4B,WACAX,EAAAnB,UAAAO,IAAAoB,EAAAzB,YAAA4B,WAEAH,GAAAA,EAAAI,QAAA/B,UAAAC,SAAA0B,EAAAzB,YAAA8B,kBAAA,CACA5B,IAAAA,EAAAC,SAAAC,cAAA,QACAF,EAAAJ,UAAAO,IAAAoB,EAAAzB,YAAA+B,kBACA7B,EAAAJ,UAAAO,IAAAoB,EAAAzB,YAAA8B,kBACAvB,IAAAA,EAAAJ,SAAAC,cAAA,QACAG,EAAAT,UAAAO,IAAAoB,EAAAzB,YAAAgC,QACA9B,EAAAO,YAAAF,GACAZ,EAAAc,YAAAP,GAEAuB,EAAAI,QAAA/B,UAAAC,SAAA0B,EAAAzB,YAAAiC,oBACAtC,EAAAe,iBAAA,QAAA,SAAAC,GACAhB,MAAAA,EAAAiB,aAAA,QAAAC,OAAA,KACAF,EAAAG,iBACAY,OAIA/B,EAAAuC,KAAAR,ECzbAS,IAAAA,EAAAA,CAUAC,WAAA,SAAAC,EAAAC,KAQAC,eAAA,SAAAC,EAAAH,KAOAI,gBAAA,SAAAC,KAKAC,qBAAA,aAWAC,yBAAA,SAAAC,EAAAC,KAMAC,SAAA,SAAAC,KAMAC,kBAAA,SAAAC,OAGAf,EAAA,WAoBAgB,SAAAA,EAAAC,EAAAC,GACA,IAAA,IAAAC,EAAA,EAAAA,EAAAC,EAAAC,OAAAF,IACA,GAAAC,EAAAD,GAAAG,YAAAL,EAIA,YAHA,IAAAC,IACAE,EAAAD,GAAAD,GAEAE,EAAAD,GAGA,OAAA,EAUAI,SAAAA,EAAAlB,GACAmB,IAAAA,EAAAnB,EAAA5B,aAAA,iBAEA,OAAA,OAAA+C,EAAAA,CAAA,IAAAA,EAAA3C,MAAA,KAYA4C,SAAAA,EAAApB,EAAAK,GAEAgB,OAAA,IADAH,EAAAlB,GACAsB,QAAAjB,GAWAkB,SAAAA,EAAAC,EAAAC,EAAAC,GACA,GAAA,gBAAAC,QAAA,mBAAAA,OAAAC,YACA,OAAA,IAAAA,YAAAJ,EAAAA,CACAC,QAAAA,EACAC,WAAAA,IAGAG,IAAAA,EAAAlE,SAAAmE,YAAA,UACAD,OAAAA,EAAAE,UAAAP,EAAAC,EAAAC,GACAG,EAaAG,SAAAA,EAAAnC,EAAAC,GACA,QAAA,IAAAD,QACA,IAAAC,EACA,IAAA,IAAAgB,EAAA,EAAAA,EAAAC,EAAAC,OAAAF,IACAkB,EAAAjB,EAAAD,GAAAG,UACAF,EAAAD,GAAAmB,cAEA,CACA5B,IAAAA,EAAA,EACA,QAAA,IAAAP,EAAA,CACAoC,IAAAA,EAAAvB,EAAAN,GACA6B,IACApC,EAAAoC,EAAAD,UAKA,IAAA,IADA/B,EAAAvC,SAAAwE,iBAAA,IAAArC,GACAsC,EAAA,EAAAA,EAAAlC,EAAAc,OAAAoB,IACAC,EAAAnC,EAAAkC,GAAA/B,IAYAgC,SAAAA,EAAArC,EAAAH,GAEA,KAAA,UAAAG,EAAAA,IAAAA,aAAAsC,SACA,MAAA,IAAAC,MAAA,qDAGAC,IAAAA,EAAAjB,EAAA,0BAAA,GAAA,GACAvB,GAAAA,EAAAyC,cAAAD,IACAA,EAAAE,iBAAA,CAIArB,IAAAA,EAAAH,EAAAlB,GACA2C,EAAAA,GAGA9C,GAAAA,EAUAuB,EAAApB,EAAAH,IACA8C,EAAAC,KAAAjC,EAAAd,QAXA,CACAvC,IAAAA,EAAA0C,EAAA1C,UACAyD,EAAA8B,QAAA,SAAAC,GAEAxF,EAAAC,SAAAuF,EAAAb,YACA,IAAAU,EAAArB,QAAAwB,KACA1B,EAAApB,EAAA8C,EAAA7B,YACA0B,EAAAC,KAAAE,KAQA,IAAA,IAAAZ,EAAApB,EAAA,EAAAsB,EAAAO,EAAA3B,OAAAF,EAAAsB,EAAAtB,IAAA,CACAoB,KAAAA,EAAAS,EAAA7B,IAkBA,MAAA,IAAAyB,MACA,8DAhBAlB,EAAAuB,KAAAV,EAAAjB,WACAjB,EAAA+C,aAAA,gBAAA1B,EAAA2B,KAAA,MACAC,IAAAA,EAAA,IAAAf,EAAAgB,iBAAAlD,GACAiD,EAAAE,GAAAjB,EACAkB,EAAAR,KAAAK,GAEA,IAAA,IAAAI,EAAA,EAAAC,EAAApB,EAAAqB,UAAAvC,OAAAqC,EAAAC,EAAAD,IACAnB,EAAAqB,UAAAF,GAAArD,GAGAkC,EAAAsB,SAEAxD,EAAAkC,EAAAjB,WAAAgC,GAOAQ,IAAAA,EAAAlC,EAAA,yBAAA,GAAA,GACAvB,EAAAyC,cAAAgB,KAgHAC,SAAAA,EAAAZ,GACAA,GAAAA,EAAA,CACAa,IAAAA,EAAAP,EAAA9B,QAAAwB,GACAM,EAAAQ,OAAAD,EAAA,GAEAE,IAAAA,EAAAf,EAAAzF,SAAAe,aAAA,iBAAAI,MAAA,KACAsF,EAAAD,EAAAvC,QAAAwB,EAAAK,GAAAY,eACAF,EAAAD,OAAAE,EAAA,GACAhB,EAAAzF,SAAA0F,aAAA,gBAAAc,EAAAb,KAAA,MAEAnB,IAAAA,EAAAN,EAAA,2BAAA,GAAA,GACAuB,EAAAzF,SAAAoF,cAAAZ,IArSAd,IAAAA,EAAAA,GAGAqC,EAAAA,GAEAD,EAAA,8BAgUA,MAAA,CACAvD,WAAAoC,EACAjC,eAAAsC,EACApC,gBApJA+D,SAAAA,EAAA9D,GACA+D,MAAAC,QAAAhE,KAEAA,EADAA,aAAAoC,QAAAA,CACApC,GAEA+D,MAAAE,UAAAC,MAAAC,KAAAnE,IAGA,IAAA,IAAAF,EAAAc,EAAA,EAAAsB,EAAAlC,EAAAc,OAAAF,EAAAsB,EAAAtB,KACAd,EAAAE,EAAAY,cACAwD,cACAjC,EAAArC,GACAA,EAAAuE,SAAAvD,OAAA,GACAgD,EAAAhE,EAAAuE,YAwIApE,qBA5DAqE,WACA,IAAA,IAAApC,EAAA,EAAAA,EAAArB,EAAAC,OAAAoB,IACAJ,EAAAjB,EAAAqB,GAAAnB,YA2DAb,yBAxEAqE,SAAApE,EAAAC,GACAoE,IAAAA,EAAA/D,EAAAN,GACAqE,GACAA,EAAAnB,UAAAX,KAAAtC,IAsEAC,SA/HAoE,SAAAnE,GAKAoE,IAEApB,GAAAA,OAFA,IAAAhD,EAAAgD,aACA,IAAAhD,EAAA,SAIAgD,EAAAhD,EAAAgD,QAAAhD,EAAA,QAGAqE,IAAAA,EAAAA,CACA3B,iBAAA1C,EAAAsE,aAAAtE,EAAA,YACAS,UAAAT,EAAAuD,eAAAvD,EAAA,cACAyB,SAAAzB,EAAAyB,UAAAzB,EAAA,SACAgD,OAAAA,EACAD,UAAAA,IAGAxC,GAAAA,EAAA8B,QAAA,SAAAkC,GACAA,GAAAA,EAAA9C,WAAA4C,EAAA5C,SACA,MAAA,IAAAM,MAAA,sDAAAwC,EAAA9C,UAEA8C,GAAAA,EAAA9D,YAAA4D,EAAA5D,UACA,MAAA,IAAAsB,MAAA,wDAIA/B,EAAAsE,YAAAX,UACAa,eAAA7B,GACA,MAAA,IAAAZ,MACA,uCAAAY,EACA,2BAGAxC,EAAAH,EAAAuD,cAAAc,IAGA9D,EAAA6B,KAAAiC,IAwFApE,kBA9BAwE,SAAAvE,GAKAwE,IAAAA,EAAA,SAAAC,GACA/B,EAAAgC,OAAA,SAAAL,GACAA,OAAAA,EAAA1H,WAAA8H,IACAtC,QAAAa,IAEAhD,GAAAA,aAAAuD,OAAAvD,aAAA2E,SACA,IAAA,IAAAjD,EAAA,EAAAA,EAAA1B,EAAAM,OAAAoB,IACA8C,EAAAxE,EAAA0B,QAEA,CAAA,KAAA1B,aAAA4E,MAGA,MAAA,IAAA/C,MAAA,qDAFA2C,EAAAxE,MAjUA,IA+VA6E,sBAcA5F,EAAA6F,gBAcA7F,EAAA8F,UAIA9F,EAAA,WAAAA,EAAAC,WACAD,EAAA,eAAAA,EAAAI,eACAJ,EAAA,gBAAAA,EAAAM,gBACAN,EAAA,qBACAA,EAAAQ,qBACAR,EAAA,yBACAA,EAAAS,yBACAT,EAAA,SAAAA,EAAAY,SACAZ,EAAA,kBAAAA,EAAAc,kBACAkB,OAAAhC,iBAAAA,EACAgC,OAAA,iBAAAhC,EAEAgC,OAAAzD,iBAAA,OAAA,WAQAP,cAAAA,SAAAC,cAAA,QACA,kBAAAD,UACA,qBAAAgE,QAAAsC,MAAAE,UAAAtB,SACAlF,SAAA+H,gBAAApI,UAAAO,IAAA,UACA8B,EAAAQ,yBAKAR,EAAAI,eAAA,aAIAJ,EAAAY,SAAA,gBC7eAoF,KAAAC,MAKAD,KAAAC,IAAA,WACA,OAAA,IAAAD,MAAAE,WAEAF,KAAA,IAAAA,KAAAC,KAMA,IAAA,IAJAE,EAAAA,CACA,SACA,OAEAhF,EAAA,EAAAA,EAAAgF,EAAA9E,SAAAW,OAAAoE,wBAAAjF,EAAA,CACAkF,IAAAA,EAAAF,EAAAhF,GACAa,OAAAoE,sBAAApE,OAAAqE,EAAA,yBACArE,OAAAsE,qBAAAtE,OAAAqE,EAAA,yBAAArE,OAAAqE,EAAA,+BACArE,OAAA,sBAAAA,OAAAoE,sBACApE,OAAA,qBAAAA,OAAAsE,qBAEA,GAAA,uBAAAC,KAAAvE,OAAAwE,UAAAC,aAAAzE,OAAAoE,wBAAApE,OAAAsE,qBAAA,CACAI,IAAAA,EAAA,EAKA1E,OAAAoE,sBAAA,SAAAzF,GACAsF,IAAAA,EAAAD,KAAAC,MACAU,EAAAC,KAAAC,IAAAH,EAAA,GAAAT,GACAa,OAAAA,WAAA,WACAnG,EAAA+F,EAAAC,IACAA,EAAAV,IAEAjE,OAAAsE,qBAAAS,aACA/E,OAAA,sBAAAA,OAAAoE,sBACApE,OAAA,qBAAAA,OAAAsE,qBCpBAU,IAAAA,EAAA,SAAA3G,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,eAAAgF,EAOAA,EAAAxC,UAAA2C,UAAAA,GASAH,EAAAxC,UAAA3G,YAAAA,CACAuJ,cAAA,uBACAxH,iBAAA,+BACAC,OAAA,cAQAmH,EAAAxC,UAAA6C,aAAA,SAAAC,GACAA,GACAL,KAAAvJ,SAAA6J,QASAP,EAAAxC,UAAAgD,QAAA,WACA9J,KAAAA,SAAA+J,UAAAA,GAEAT,EAAAxC,UAAA,QAAAwC,EAAAxC,UAAAgD,QAMAR,EAAAxC,UAAAkD,OAAA,WACAhK,KAAAA,SAAA+J,UAAAA,GAEAT,EAAAxC,UAAA,OAAAwC,EAAAxC,UAAAkD,OAIAV,EAAAxC,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACAuJ,GAAAA,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAuJ,eAAA,CACArJ,IAAAA,EAAAC,SAAAC,cAAA,QACAF,EAAAJ,UAAAO,IAAA+I,KAAApJ,YAAA+B,kBACAqH,KAAAU,eAAA3J,SAAAC,cAAA,QACAgJ,KAAAU,eAAAhK,UAAAO,IAAA+I,KAAApJ,YAAAgC,QACA9B,EAAAO,YAAA2I,KAAAU,gBACAV,KAAAW,uBAAAX,KAAAI,aAAAQ,KAAAZ,MACAA,KAAAU,eAAApJ,iBAAA,UAAA0I,KAAAW,wBACAX,KAAAvJ,SAAAY,YAAAP,GAEA+J,KAAAA,uBAAAb,KAAAI,aAAAQ,KAAAZ,MACAA,KAAAvJ,SAAAa,iBAAA,UAAA0I,KAAAa,wBACAb,KAAAvJ,SAAAa,iBAAA,aAAA0I,KAAAa,0BAKA9H,EAAAY,SAAAA,CACAuE,YAAA6B,EACA5C,cAAA,iBACA9B,SAAA,gBACAuB,QAAAA,ICjFAkE,IAAAA,EAAA,SAAA1H,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,iBAAA+F,EAOAA,EAAAvD,UAAA2C,UAAAA,CAAAa,aAAA,MASAD,EAAAvD,UAAA3G,YAAAA,CACAoK,MAAA,sBACAC,YAAA,4BACAC,aAAA,6BACAC,aAAA,6BACAhB,cAAA,uBACAiB,qBAAA,sCACAzI,iBAAA,iCACA0I,cAAA,qBACAzI,OAAA,aACA0I,WAAA,aACAC,YAAA,cACAC,WAAA,aACAC,YAAA,eAQAX,EAAAvD,UAAAmE,UAAA,SAAArB,GACAsB,KAAAA,kBAQAb,EAAAvD,UAAAqE,SAAA,SAAAvB,GACA5J,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA0K,aAQAR,EAAAvD,UAAAsE,QAAA,SAAAxB,GACA5J,KAAAA,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA0K,aAQAR,EAAAvD,UAAAwE,WAAA,SAAA1B,GACA2B,KAAAA,SAOAlB,EAAAvD,UAAAoE,eAAA,WACAM,KAAAA,gBACAjC,KAAAkC,oBAOApB,EAAAvD,UAAAyE,MAAA,WAGAjH,OAAA8E,WAAA,WACAsC,KAAAA,cAAA7B,QACAM,KAAAZ,MAAAA,KAAAE,UAAAa,eAQAD,EAAAvD,UAAA2E,iBAAA,WACAC,KAAAA,cAAAC,QACApC,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA4K,YAEAxB,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA4K,aAGAV,EAAAvD,UAAA,iBAAAuD,EAAAvD,UAAA2E,iBAMApB,EAAAvD,UAAA0E,cAAA,WACAE,KAAAA,cAAA3B,SACAR,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA2K,aAEAvB,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA2K,cAGAT,EAAAvD,UAAA,cAAAuD,EAAAvD,UAAA0E,cAMAnB,EAAAvD,UAAAgD,QAAA,WACA4B,KAAAA,cAAA3B,UAAAA,EACAR,KAAA2B,kBAEAb,EAAAvD,UAAA,QAAAuD,EAAAvD,UAAAgD,QAMAO,EAAAvD,UAAAkD,OAAA,WACA0B,KAAAA,cAAA3B,UAAAA,EACAR,KAAA2B,kBAEAb,EAAAvD,UAAA,OAAAuD,EAAAvD,UAAAkD,OAMAK,EAAAvD,UAAA8E,MAAA,WACAF,KAAAA,cAAAC,SAAAA,EACApC,KAAA2B,kBAEAb,EAAAvD,UAAA,MAAAuD,EAAAvD,UAAA8E,MAMAvB,EAAAvD,UAAA+E,QAAA,WACAH,KAAAA,cAAAC,SAAAA,EACApC,KAAA2B,kBAEAb,EAAAvD,UAAA,QAAAuD,EAAAvD,UAAA+E,QAIAxB,EAAAvD,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACA0L,KAAAA,cAAAnC,KAAAvJ,SAAAqB,cAAA,IAAAkI,KAAApJ,YAAAoK,OACAuB,IAAAA,EAAAxL,SAAAC,cAAA,QACAuL,EAAA7L,UAAAO,IAAA+I,KAAApJ,YAAAqK,aACAuB,IAAAA,EAAAzL,SAAAC,cAAA,QACAwL,EAAA9L,UAAAO,IAAA+I,KAAApJ,YAAAsK,cACAuB,IAAAA,EAAA1L,SAAAC,cAAA,QACAyL,GAAAA,EAAA/L,UAAAO,IAAA+I,KAAApJ,YAAAuK,cACAoB,EAAAlL,YAAAoL,GACAzC,KAAAvJ,SAAAY,YAAAmL,GACAxC,KAAAvJ,SAAAY,YAAAkL,GACAvC,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAuJ,eAAA,CACA1J,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAAwK,sBACApB,KAAA0C,wBAAA3L,SAAAC,cAAA,QACAgJ,KAAA0C,wBAAAhM,UAAAO,IAAA+I,KAAApJ,YAAA+B,kBACAqH,KAAA0C,wBAAAhM,UAAAO,IAAA+I,KAAApJ,YAAAuJ,eACAH,KAAA0C,wBAAAhM,UAAAO,IAAA+I,KAAApJ,YAAAyK,eACArB,KAAA2C,mBAAA3C,KAAA+B,WAAAnB,KAAAZ,MACAA,KAAA0C,wBAAApL,iBAAA,UAAA0I,KAAA2C,oBACAxL,IAAAA,EAAAJ,SAAAC,cAAA,QACAG,EAAAT,UAAAO,IAAA+I,KAAApJ,YAAAgC,QACAoH,KAAA0C,wBAAArL,YAAAF,GACA6I,KAAAvJ,SAAAY,YAAA2I,KAAA0C,yBAEAE,KAAAA,mBAAA5C,KAAA0B,UAAAd,KAAAZ,MACAA,KAAA6C,kBAAA7C,KAAA4B,SAAAhB,KAAAZ,MACAA,KAAA8C,iBAAA9C,KAAA6B,QAAAjB,KAAAZ,MACAA,KAAA+C,oBAAA/C,KAAA+B,WAAAnB,KAAAZ,MACAA,KAAAmC,cAAA7K,iBAAA,SAAA0I,KAAA4C,oBACA5C,KAAAmC,cAAA7K,iBAAA,QAAA0I,KAAA6C,mBACA7C,KAAAmC,cAAA7K,iBAAA,OAAA0I,KAAA8C,kBACA9C,KAAAvJ,SAAAa,iBAAA,UAAA0I,KAAA+C,qBACA/C,KAAA2B,iBACA3B,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA6K,eAKA1I,EAAAY,SAAAA,CACAuE,YAAA4C,EACA3D,cAAA,mBACA9B,SAAA,kBACAuB,QAAAA,IC9MAoG,IAAAA,EAAA,SAAA5J,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,mBAAAiI,EAOAA,EAAAzF,UAAA2C,UAAAA,CAAAa,aAAA,MASAiC,EAAAzF,UAAA3G,YAAAA,CACAoK,MAAA,yBACAtI,iBAAA,uBACA0I,qBAAA,sCACAzI,iBAAA,oCACA0I,cAAA,qBACAzI,OAAA,aACA0I,WAAA,aACAC,YAAA,cACAC,WAAA,cAQAwB,EAAAzF,UAAAmE,UAAA,SAAArB,GACAsB,KAAAA,kBAQAqB,EAAAzF,UAAAqE,SAAA,SAAAvB,GACA5J,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA0K,aAQA0B,EAAAzF,UAAAsE,QAAA,SAAAxB,GACA5J,KAAAA,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA0K,aAQA0B,EAAAzF,UAAAwE,WAAA,SAAA1B,GACA2B,KAAAA,SAOAgB,EAAAzF,UAAAoE,eAAA,WACAM,KAAAA,gBACAjC,KAAAkC,oBAOAc,EAAAzF,UAAAyE,MAAA,WAGAjH,OAAA8E,WAAA,WACAsC,KAAAA,cAAA7B,QACAM,KAAAZ,MAAAA,KAAAE,UAAAa,eAQAiC,EAAAzF,UAAA2E,iBAAA,WACAC,KAAAA,cAAAC,QACApC,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA4K,YAEAxB,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA4K,aAGAwB,EAAAzF,UAAA,iBAAAyF,EAAAzF,UAAA2E,iBAMAc,EAAAzF,UAAA0E,cAAA,WACAE,KAAAA,cAAA3B,SACAR,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA2K,aAEAvB,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA2K,cAGAyB,EAAAzF,UAAA,cAAAyF,EAAAzF,UAAA0E,cAMAe,EAAAzF,UAAAgD,QAAA,WACA4B,KAAAA,cAAA3B,UAAAA,EACAR,KAAA2B,kBAEAqB,EAAAzF,UAAA,QAAAyF,EAAAzF,UAAAgD,QAMAyC,EAAAzF,UAAAkD,OAAA,WACA0B,KAAAA,cAAA3B,UAAAA,EACAR,KAAA2B,kBAEAqB,EAAAzF,UAAA,OAAAyF,EAAAzF,UAAAkD,OAMAuC,EAAAzF,UAAA8E,MAAA,WACAF,KAAAA,cAAAC,SAAAA,EACApC,KAAA2B,kBAEAqB,EAAAzF,UAAA,MAAAyF,EAAAzF,UAAA8E,MAMAW,EAAAzF,UAAA+E,QAAA,WACAH,KAAAA,cAAAC,SAAAA,EACApC,KAAA2B,kBAEAqB,EAAAzF,UAAA,QAAAyF,EAAAzF,UAAA+E,QAIAU,EAAAzF,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACAuJ,GAAAA,KAAAmC,cAAAnC,KAAAvJ,SAAAqB,cAAA,IAAAkI,KAAApJ,YAAAoK,OACAhB,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAA8B,kBAAA,CACAjC,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAAwK,sBACApB,KAAA0C,wBAAA3L,SAAAC,cAAA,QACAgJ,KAAA0C,wBAAAhM,UAAAO,IAAA+I,KAAApJ,YAAA+B,kBACAqH,KAAA0C,wBAAAhM,UAAAO,IAAA+I,KAAApJ,YAAA8B,kBACAsH,KAAA0C,wBAAAhM,UAAAO,IAAA+I,KAAApJ,YAAAyK,eACArB,KAAA2C,mBAAA3C,KAAA+B,WAAAnB,KAAAZ,MACAA,KAAA0C,wBAAApL,iBAAA,UAAA0I,KAAA2C,oBACAxL,IAAAA,EAAAJ,SAAAC,cAAA,QACAG,EAAAT,UAAAO,IAAA+I,KAAApJ,YAAAgC,QACAoH,KAAA0C,wBAAArL,YAAAF,GACA6I,KAAAvJ,SAAAY,YAAA2I,KAAA0C,yBAEAE,KAAAA,mBAAA5C,KAAA0B,UAAAd,KAAAZ,MACAA,KAAA6C,kBAAA7C,KAAA4B,SAAAhB,KAAAZ,MACAA,KAAA8C,iBAAA9C,KAAA6B,QAAAjB,KAAAZ,MACAA,KAAAiD,sBAAAjD,KAAA+B,WAAAnB,KAAAZ,MACAA,KAAAmC,cAAA7K,iBAAA,SAAA0I,KAAA4C,oBACA5C,KAAAmC,cAAA7K,iBAAA,QAAA0I,KAAA6C,mBACA7C,KAAAmC,cAAA7K,iBAAA,OAAA0I,KAAA8C,kBACA9C,KAAAvJ,SAAAa,iBAAA,UAAA0I,KAAAiD,uBACAjD,KAAA2B,iBACA3B,KAAAvJ,SAAAC,UAAAO,IAAA,iBAKA8B,EAAAY,SAAAA,CACAuE,YAAA8E,EACA7F,cAAA,qBACA9B,SAAA,qBACAuB,QAAAA,ICjMAsG,IAAAA,EAAA,SAAA9J,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,aAAAmI,EAOAA,EAAA3F,UAAA2C,UAAAA,CAEAiD,4BAAA,GAEAC,6BAAA,GAGAC,cAAA,KAQAH,EAAA3F,UAAA+F,UAAAA,CACAC,MAAA,GACAC,OAAA,GACAC,MAAA,GACAC,SAAA,GACAC,WAAA,IAUAT,EAAA3F,UAAA3G,YAAAA,CACAgN,UAAA,sBACAC,QAAA,oBACAC,KAAA,iBACAC,sBAAA,kCACA5D,cAAA,uBACAiB,qBAAA,sCACAxI,OAAA,aAEA6I,YAAA,cACAuC,WAAA,aACAC,aAAA,eAEAC,YAAA,wBAEAC,aAAA,yBACAC,SAAA,qBACAC,UAAA,sBACAC,UAAA,uBAKApB,EAAA3F,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CAEA8N,IAAAA,EAAAxN,SAAAC,cAAA,OACAuN,EAAA7N,UAAAO,IAAA+I,KAAApJ,YAAAgN,WACA5D,KAAAvJ,SAAA+N,cAAAC,aAAAF,EAAAvE,KAAAvJ,UACAuJ,KAAAvJ,SAAA+N,cAAAE,YAAA1E,KAAAvJ,UACA8N,EAAAlN,YAAA2I,KAAAvJ,UACAuJ,KAAA2E,WAAAJ,EAEAK,IAAAA,EAAA7N,SAAAC,cAAA,OACA4N,EAAAlO,UAAAO,IAAA+I,KAAApJ,YAAAiN,SACA7D,KAAA6E,SAAAD,EACAL,EAAAE,aAAAG,EAAA5E,KAAAvJ,UAEAqO,IAAAA,EAAA9E,KAAAvJ,SAAAe,aAAA,QAAAwI,KAAAvJ,SAAAe,aAAA,gBACAuN,EAAA,KACAD,KACAC,EAAAhO,SAAAiO,eAAAF,MAEA9E,KAAAiF,YAAAF,EACAA,EAAAzN,iBAAA,QAAA0I,KAAAkF,gBAAAtE,KAAAZ,OACA+E,EAAAzN,iBAAA,UAAA0I,KAAAmF,wBAAAvE,KAAAZ,SAGAoF,IAAAA,EAAApF,KAAAvJ,SAAA8E,iBAAA,IAAAyE,KAAApJ,YAAAkN,MACAuB,KAAAA,kBAAArF,KAAAsF,yBAAA1E,KAAAZ,MACAA,KAAAuF,gBAAAvF,KAAAwF,iBAAA5E,KAAAZ,MACA,IAAA,IAAA9F,EAAA,EAAAA,EAAAkL,EAAAhL,OAAAF,IAEAkL,EAAAlL,GAAA5C,iBAAA,QAAA0I,KAAAuF,iBAEAH,EAAAlL,GAAAuL,SAAA,KAEAL,EAAAlL,GAAA5C,iBAAA,UAAA0I,KAAAqF,mBAGArF,GAAAA,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAuJ,eAEA,IADAH,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAAwK,sBACAlH,EAAA,EAAAA,EAAAkL,EAAAhL,OAAAF,IAAA,CACAiE,IAAAA,EAAAiH,EAAAlL,GACApD,EAAAC,SAAAC,cAAA,QACAF,EAAAJ,UAAAO,IAAA+I,KAAApJ,YAAAmN,uBACA5M,IAAAA,EAAAJ,SAAAC,cAAA,QACAG,EAAAT,UAAAO,IAAA+I,KAAApJ,YAAAgC,QACA9B,EAAAO,YAAAF,GACAgH,EAAA9G,YAAAP,GACAqH,EAAAzH,UAAAO,IAAA+I,KAAApJ,YAAAuJ,eAIA1J,KAAAA,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAsN,cACAlE,KAAA6E,SAAAnO,UAAAO,IAAA+I,KAAApJ,YAAAsN,aAEAlE,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAuN,eACAnE,KAAA6E,SAAAnO,UAAAO,IAAA+I,KAAApJ,YAAAuN,cAEAnE,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAwN,WACApE,KAAA6E,SAAAnO,UAAAO,IAAA+I,KAAApJ,YAAAwN,UAEApE,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAyN,YACArE,KAAA6E,SAAAnO,UAAAO,IAAA+I,KAAApJ,YAAAyN,WAEArE,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAA0N,YACAtE,KAAA6E,SAAAnO,UAAAO,IAAA+I,KAAApJ,YAAA0N,WAEAC,EAAA7N,UAAAO,IAAA+I,KAAApJ,YAAA6K,eAUAyB,EAAA3F,UAAA2H,gBAAA,SAAAQ,GACA1F,GAAAA,KAAAvJ,UAAAuJ,KAAAiF,YAAA,CACAU,IAAAA,EAAA3F,KAAAiF,YAAAW,wBACAC,EAAA7F,KAAAiF,YAAAT,cAAAoB,wBACAnP,KAAAA,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAA0N,aACAtE,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAuN,eAEAnE,KAAA2E,WAAAmB,MAAAC,MAAAF,EAAAE,MAAAJ,EAAAI,MAAA,KACA/F,KAAA2E,WAAAmB,MAAAE,IAAAhG,KAAAiF,YAAAgB,UAAAjG,KAAAiF,YAAAiB,aAAA,MACAlG,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAwN,WAEApE,KAAA2E,WAAAmB,MAAAK,KAAAnG,KAAAiF,YAAAmB,WAAA,KACApG,KAAA2E,WAAAmB,MAAAO,OAAAR,EAAAQ,OAAAV,EAAAK,IAAA,MACAhG,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAyN,YAEArE,KAAA2E,WAAAmB,MAAAC,MAAAF,EAAAE,MAAAJ,EAAAI,MAAA,KACA/F,KAAA2E,WAAAmB,MAAAO,OAAAR,EAAAQ,OAAAV,EAAAK,IAAA,OAGAhG,KAAA2E,WAAAmB,MAAAK,KAAAnG,KAAAiF,YAAAmB,WAAA,KACApG,KAAA2E,WAAAmB,MAAAE,IAAAhG,KAAAiF,YAAAgB,UAAAjG,KAAAiF,YAAAiB,aAAA,OAGAI,KAAAA,OAAAZ,IAQAxC,EAAA3F,UAAA4H,wBAAA,SAAAO,GACA1F,GAAAA,KAAAvJ,UAAAuJ,KAAA2E,YAAA3E,KAAAiF,YAAA,CACAG,IAAAA,EAAApF,KAAAvJ,SAAA8E,iBAAA,IAAAyE,KAAApJ,YAAAkN,KAAA,oBACAsB,GAAAA,EAAAhL,OAAA,GAAA4F,KAAA2E,WAAAjO,UAAAC,SAAAqJ,KAAApJ,YAAAoN,cACA0B,EAAAa,UAAAvG,KAAAsD,UAAAI,UACAgC,EAAAhO,iBACA0N,EAAAA,EAAAhL,OAAA,GAAAoM,SACAd,EAAAa,UAAAvG,KAAAsD,UAAAK,aACA+B,EAAAhO,iBACA0N,EAAA,GAAAoB,YAWAtD,EAAA3F,UAAA+H,yBAAA,SAAAI,GACA1F,GAAAA,KAAAvJ,UAAAuJ,KAAA2E,WAAA,CACAS,IAAAA,EAAApF,KAAAvJ,SAAA8E,iBAAA,IAAAyE,KAAApJ,YAAAkN,KAAA,oBACAsB,GAAAA,GAAAA,EAAAhL,OAAA,GAAA4F,KAAA2E,WAAAjO,UAAAC,SAAAqJ,KAAApJ,YAAAoN,YAAA,CACAyC,IAAAA,EAAApJ,MAAAE,UAAAC,MAAAC,KAAA2H,GAAA1K,QAAAgL,EAAAgB,QACAhB,GAAAA,EAAAa,UAAAvG,KAAAsD,UAAAI,SACAgC,EAAAhO,iBACA+O,EAAA,EACArB,EAAAqB,EAAA,GAAAD,QAEApB,EAAAA,EAAAhL,OAAA,GAAAoM,aAEA,GAAAd,EAAAa,UAAAvG,KAAAsD,UAAAK,WACA+B,EAAAhO,iBACA0N,EAAAhL,OAAAqM,EAAA,EACArB,EAAAqB,EAAA,GAAAD,QAEApB,EAAA,GAAAoB,aAEA,GAAAd,EAAAa,UAAAvG,KAAAsD,UAAAG,OAAAiC,EAAAa,UAAAvG,KAAAsD,UAAAC,MAAA,CACAmC,EAAAhO,iBAEAH,IAAAA,EAAA,IAAAoP,WAAA,aACAjB,EAAAgB,OAAA7K,cAAAtE,GACAA,EAAA,IAAAoP,WAAA,WACAjB,EAAAgB,OAAA7K,cAAAtE,GAEAmO,EAAAgB,OAAAE,aACAlB,EAAAa,UAAAvG,KAAAsD,UAAAE,SACAkC,EAAAhO,iBACAsI,KAAA6G,WAWA3D,EAAA3F,UAAAiI,iBAAA,SAAAE,GACAA,EAAAgB,OAAAI,aAAA,YACApB,EAAAqB,mBAGA/G,KAAAgH,UAAAA,EACAjM,OAAA8E,WAAA,SAAA6F,GACAmB,KAAAA,OACA7G,KAAAgH,UAAAA,GACApG,KAAAZ,MAAAA,KAAAE,UAAAmD,iBAYAH,EAAA3F,UAAA0J,WAAA,SAAAC,EAAAC,GACA1Q,KAAAA,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAA0N,WAEAtE,KAAAvJ,SAAAqP,MAAAsB,KAAA,GACApH,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAuN,cAEAnE,KAAAvJ,SAAAqP,MAAAsB,KAAA,UAAAD,EAAA,QAAAA,EAAA,MACAnH,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAwN,UAEApE,KAAAvJ,SAAAqP,MAAAsB,KAAA,QAAAF,EAAA,QAAAA,EAAA,QACAlH,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAyN,WAEArE,KAAAvJ,SAAAqP,MAAAsB,KAAA,QAAAF,EAAA,MAAAC,EAAA,MAAAD,EAAA,MAAAC,EAAA,MAGAnH,KAAAvJ,SAAAqP,MAAAsB,KAAA,IASAlE,EAAA3F,UAAA8J,4BAAA,SAAA3B,GACAA,EAAAgB,OAAAhQ,UAAAoL,OAAAoB,EAAA3F,UAAA3G,YAAAqN,eAOAf,EAAA3F,UAAA+J,yBAAA,WACA7Q,KAAAA,SAAAa,iBAAA,gBAAA0I,KAAAqH,6BACArH,KAAAvJ,SAAAa,iBAAA,sBAAA0I,KAAAqH,8BAOAnE,EAAA3F,UAAAzE,KAAA,SAAA4M,GACA1F,GAAAA,KAAAvJ,UAAAuJ,KAAA2E,YAAA3E,KAAA6E,SAAA,CAEAqC,IAAAA,EAAAlH,KAAAvJ,SAAAmP,wBAAAsB,OACAC,EAAAnH,KAAAvJ,SAAAmP,wBAAAuB,MAEAxC,KAAAA,WAAAmB,MAAAqB,MAAAA,EAAA,KACAnH,KAAA2E,WAAAmB,MAAAoB,OAAAA,EAAA,KACAlH,KAAA6E,SAAAiB,MAAAqB,MAAAA,EAAA,KACAnH,KAAA6E,SAAAiB,MAAAoB,OAAAA,EAAA,KAKA,IAAA,IAJAK,EAAAvH,KAAAE,UAAAiD,4BAAAnD,KAAAE,UAAAkD,6BAGAgC,EAAApF,KAAAvJ,SAAA8E,iBAAA,IAAAyE,KAAApJ,YAAAkN,MACA5J,EAAA,EAAAA,EAAAkL,EAAAhL,OAAAF,IAAA,CACAsN,IAAAA,EAEAA,EADAxH,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAwN,WAAApE,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAyN,YACA6C,EAAA9B,EAAAlL,GAAA+L,UAAAb,EAAAlL,GAAAgM,cAAAgB,EAAAK,EAAA,IAEAnC,EAAAlL,GAAA+L,UAAAiB,EAAAK,EAAA,IAEAnC,EAAAlL,GAAA4L,MAAA2B,gBAAAD,EAGAP,KAAAA,WAAAC,EAAAC,GAGApM,OAAAoE,sBAAA,WACA1I,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAAqN,cACAjE,KAAAvJ,SAAAqP,MAAAsB,KAAA,UAAAD,EAAA,MAAAD,EAAA,QACAlH,KAAA2E,WAAAjO,UAAAO,IAAA+I,KAAApJ,YAAAoN,aACApD,KAAAZ,OAEAA,KAAAsH,2BAEA5N,IAAAA,EAAA,SAAAnC,GAOAA,IAAAmO,GAAA1F,KAAAgH,UAAAzP,EAAAmP,OAAAgB,aAAA1H,KAAAvJ,WACAM,SAAA4Q,oBAAA,QAAAjO,GACAsG,KAAA6G,SAEAjG,KAAAZ,MACAjJ,SAAAO,iBAAA,QAAAoC,KAGAwJ,EAAA3F,UAAA,KAAA2F,EAAA3F,UAAAzE,KAMAoK,EAAA3F,UAAAsJ,KAAA,WACA7G,GAAAA,KAAAvJ,UAAAuJ,KAAA2E,YAAA3E,KAAA6E,SAAA,CAGA,IAAA,IAFAO,EAAApF,KAAAvJ,SAAA8E,iBAAA,IAAAyE,KAAApJ,YAAAkN,MAEA5J,EAAA,EAAAA,EAAAkL,EAAAhL,OAAAF,IACAkL,EAAAlL,GAAA4L,MAAA8B,eAAA,oBAGAjC,IAAAA,EAAA3F,KAAAvJ,SAAAmP,wBACAsB,EAAAvB,EAAAuB,OACAC,EAAAxB,EAAAwB,MAGA1Q,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAAqN,cACAjE,KAAAiH,WAAAC,EAAAC,GACAnH,KAAA2E,WAAAjO,UAAAoL,OAAA9B,KAAApJ,YAAAoN,YAEAhE,KAAAsH,6BAGApE,EAAA3F,UAAA,KAAA2F,EAAA3F,UAAAsJ,KAMA3D,EAAA3F,UAAA+I,OAAA,SAAAZ,GACAf,KAAAA,WAAAjO,UAAAC,SAAAqJ,KAAApJ,YAAAoN,YACAhE,KAAA6G,OAEA7G,KAAAlH,KAAA4M,IAGAxC,EAAA3F,UAAA,OAAA2F,EAAA3F,UAAA+I,OAGAvN,EAAAY,SAAAA,CACAuE,YAAAgF,EACA/F,cAAA,eACA9B,SAAA,cACAuB,QAAAA,ICvYAiL,IAAAA,EAAA,SAAAzO,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,iBAAA8M,EAOAA,EAAAtK,UAAA2C,UAAAA,GASA2H,EAAAtK,UAAA3G,YAAAA,CAAAkR,oBAAA,+BAOAD,EAAAtK,UAAAwK,YAAA,SAAAC,GACAvR,KAAAA,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAkR,uBAGA9H,KAAAiI,aAAAnC,MAAAqB,MAAAa,EAAA,MAEAH,EAAAtK,UAAA,YAAAsK,EAAAtK,UAAAwK,YAOAF,EAAAtK,UAAA2K,UAAA,SAAAF,GACAG,KAAAA,WAAArC,MAAAqB,MAAAa,EAAA,IACAhI,KAAAoI,QAAAtC,MAAAqB,MAAA,IAAAa,EAAA,KAEAH,EAAAtK,UAAA,UAAAsK,EAAAtK,UAAA2K,UAIAL,EAAAtK,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACA4R,IAAAA,EAAAtR,SAAAC,cAAA,OACAqR,EAAAhO,UAAA,uBACA2F,KAAAvJ,SAAAY,YAAAgR,GACArI,KAAAiI,aAAAI,GACAA,EAAAtR,SAAAC,cAAA,QACAqD,UAAA,qBACA2F,KAAAvJ,SAAAY,YAAAgR,GACArI,KAAAmI,WAAAE,GACAA,EAAAtR,SAAAC,cAAA,QACAqD,UAAA,kBACA2F,KAAAvJ,SAAAY,YAAAgR,GACArI,KAAAoI,QAAAC,EACArI,KAAAiI,aAAAnC,MAAAqB,MAAA,KACAnH,KAAAmI,WAAArC,MAAAqB,MAAA,OACAnH,KAAAoI,QAAAtC,MAAAqB,MAAA,KACAnH,KAAAvJ,SAAAC,UAAAO,IAAA,iBAKA8B,EAAAY,SAAAA,CACAuE,YAAA2J,EACA1K,cAAA,mBACA9B,SAAA,kBACAuB,QAAAA,IC3EA0L,IAAAA,EAAA,SAAAlP,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,cAAAuN,EAOAA,EAAA/K,UAAA2C,UAAAA,CAAAa,aAAA,MASAuH,EAAA/K,UAAA3G,YAAAA,CACA0K,WAAA,aACAC,YAAA,cACAC,WAAA,aACAC,YAAA,cACA8G,SAAA,eACAC,UAAA,oBACAC,mBAAA,0BACAC,mBAAA,0BACAvI,cAAA,uBACAiB,qBAAA,sCACAzI,iBAAA,8BACA0I,cAAA,qBACAzI,OAAA,cAQA0P,EAAA/K,UAAAmE,UAAA,SAAArB,GAIA,IAAA,IADAsI,EAAA5R,SAAA6R,uBAAA5I,KAAApJ,YAAA2R,UACArO,EAAA,EAAAA,EAAAyO,EAAAvO,OAAAF,IAAA,CACAyO,EAAAzO,GAAApC,cAAA,IAAAkI,KAAApJ,YAAA4R,WAEAhR,aAAA,UAAAwI,KAAA6I,YAAArR,aAAA,cACA,IAAAmR,EAAAzO,GAAA,eACAyO,EAAAzO,GAAA,cAAAyH,mBAWA2G,EAAA/K,UAAAqE,SAAA,SAAAvB,GACA5J,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA0K,aAQAgH,EAAA/K,UAAAsE,QAAA,SAAAxB,GACA5J,KAAAA,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA0K,aAQAgH,EAAA/K,UAAAuL,WAAA,SAAAzI,GACA2B,KAAAA,SAOAsG,EAAA/K,UAAAoE,eAAA,WACAM,KAAAA,gBACAjC,KAAAkC,oBAOAoG,EAAA/K,UAAAyE,MAAA,WAGAjH,OAAA8E,WAAA,WACAgJ,KAAAA,YAAAvI,QACAM,KAAAZ,MAAAA,KAAAE,UAAAa,eAQAuH,EAAA/K,UAAA0E,cAAA,WACA4G,KAAAA,YAAArI,SACAR,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA2K,aAEAvB,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA2K,cAGA+G,EAAA/K,UAAA,cAAA+K,EAAA/K,UAAA0E,cAMAqG,EAAA/K,UAAA2E,iBAAA,WACA2G,KAAAA,YAAAzG,QACApC,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA4K,YAEAxB,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA4K,aAGA8G,EAAA/K,UAAA,iBAAA+K,EAAA/K,UAAA2E,iBAMAoG,EAAA/K,UAAAgD,QAAA,WACAsI,KAAAA,YAAArI,UAAAA,EACAR,KAAA2B,kBAEA2G,EAAA/K,UAAA,QAAA+K,EAAA/K,UAAAgD,QAMA+H,EAAA/K,UAAAkD,OAAA,WACAoI,KAAAA,YAAArI,UAAAA,EACAR,KAAA2B,kBAEA2G,EAAA/K,UAAA,OAAA+K,EAAA/K,UAAAkD,OAMA6H,EAAA/K,UAAA8E,MAAA,WACAwG,KAAAA,YAAAzG,SAAAA,EACApC,KAAA0B,UAAA,OAEA4G,EAAA/K,UAAA,MAAA+K,EAAA/K,UAAA8E,MAMAiG,EAAA/K,UAAA+E,QAAA,WACAuG,KAAAA,YAAAzG,SAAAA,EACApC,KAAA0B,UAAA,OAEA4G,EAAA/K,UAAA,QAAA+K,EAAA/K,UAAA+E,QAIAgG,EAAA/K,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACAoS,KAAAA,YAAA7I,KAAAvJ,SAAAqB,cAAA,IAAAkI,KAAApJ,YAAA4R,WACAxI,KAAA+I,oBAAA/I,KAAA0B,UAAAd,KAAAZ,MACAA,KAAAgJ,mBAAAhJ,KAAA0B,UAAAd,KAAAZ,MACAA,KAAAiJ,kBAAAjJ,KAAA6B,QAAAjB,KAAAZ,MACAA,KAAAkJ,qBAAAlJ,KAAA8I,WAAAlI,KAAAZ,MACAmJ,IAAAA,EAAApS,SAAAC,cAAA,QACAmS,EAAAzS,UAAAO,IAAA+I,KAAApJ,YAAA6R,oBACAW,IAIAtS,EAJAsS,EAAArS,SAAAC,cAAA,QAKAgJ,GAJAoJ,EAAA1S,UAAAO,IAAA+I,KAAApJ,YAAA8R,oBACA1I,KAAAvJ,SAAAY,YAAA8R,GACAnJ,KAAAvJ,SAAAY,YAAA+R,GAEApJ,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAuJ,eAAA,CACA1J,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAAwK,uBACAtK,EAAAC,SAAAC,cAAA,SACAN,UAAAO,IAAA+I,KAAApJ,YAAA+B,kBACA7B,EAAAJ,UAAAO,IAAA+I,KAAApJ,YAAAuJ,eACArJ,EAAAJ,UAAAO,IAAA+I,KAAApJ,YAAAyK,eACAvK,EAAAQ,iBAAA,UAAA0I,KAAAkJ,sBACA/R,IAAAA,EAAAJ,SAAAC,cAAA,QACAG,EAAAT,UAAAO,IAAA+I,KAAApJ,YAAAgC,QACA9B,EAAAO,YAAAF,GACA6I,KAAAvJ,SAAAY,YAAAP,GAEA+R,KAAAA,YAAAvR,iBAAA,SAAA0I,KAAA+I,qBACA/I,KAAA6I,YAAAvR,iBAAA,QAAA0I,KAAAgJ,oBACAhJ,KAAA6I,YAAAvR,iBAAA,OAAA0I,KAAAiJ,mBACAjJ,KAAAvJ,SAAAa,iBAAA,UAAA0I,KAAAkJ,sBACAlJ,KAAA2B,iBACA3B,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA6K,eAKA1I,EAAAY,SAAAA,CACAuE,YAAAoK,EACAnL,cAAA,gBACA9B,SAAA,eACAuB,QAAAA,ICtNAyM,IAAAA,EAAA,SAAAjQ,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAsJ,MAAAvO,OAAAwE,UAAAgK,iBAEAvJ,KAAAC,QAEAlF,OAAA,eAAAsO,EAOAA,EAAA9L,UAAA2C,UAAAA,GASAmJ,EAAA9L,UAAA3G,YAAAA,CACA4S,aAAA,2BACAC,iBAAA,wBACAC,gBAAA,8BACAC,iBAAA,+BACAC,iBAAA,+BACAC,gBAAA,kBACApI,YAAA,eAQA4H,EAAA9L,UAAAuM,SAAA,SAAAzJ,GACA0J,KAAAA,sBAQAV,EAAA9L,UAAAmE,UAAA,SAAArB,GACA0J,KAAAA,sBAQAV,EAAA9L,UAAAwE,WAAA,SAAA1B,GACAA,EAAAqG,OAAApG,QAYA+I,EAAA9L,UAAAyM,sBAAA,SAAA3J,GAGAA,GAAAA,EAAAqG,SAAA1G,KAAAvJ,SAAA+N,cAAA,CAKAnE,EAAA3I,iBACAuS,IAAAA,EAAA,IAAAtD,WAAA,YAAA,CACAD,OAAArG,EAAAqG,OACAwD,QAAA7J,EAAA6J,QACAC,QAAA9J,EAAA8J,QACAC,QAAApK,KAAAvJ,SAAAmP,wBAAAyE,IAEA5T,KAAAA,SAAAoF,cAAAoO,KAOAZ,EAAA9L,UAAAwM,mBAAA,WAEAO,IAAAA,GAAAtK,KAAAvJ,SAAA8T,MAAAvK,KAAAvJ,SAAA+T,MAAAxK,KAAAvJ,SAAAmJ,IAAAI,KAAAvJ,SAAA+T,KACAF,IAAAA,EACAtK,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAAiT,iBAEA7J,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAAiT,iBAEA7J,KAAAsJ,QACAtJ,KAAAyK,iBAAA3E,MAAA4E,KAAAJ,EACAtK,KAAAyK,iBAAA3E,MAAA6E,WAAAL,EACAtK,KAAA4K,iBAAA9E,MAAA4E,KAAA,EAAAJ,EACAtK,KAAA4K,iBAAA9E,MAAA6E,WAAA,EAAAL,IASAjB,EAAA9L,UAAAgD,QAAA,WACA9J,KAAAA,SAAA+J,UAAAA,GAEA6I,EAAA9L,UAAA,QAAA8L,EAAA9L,UAAAgD,QAMA8I,EAAA9L,UAAAkD,OAAA,WACAhK,KAAAA,SAAA+J,UAAAA,GAEA6I,EAAA9L,UAAA,OAAA8L,EAAA9L,UAAAkD,OAOA4I,EAAA9L,UAAAsN,OAAA,SAAAN,QACA,IAAAA,IACAvK,KAAAvJ,SAAA8T,MAAAA,GAEAvK,KAAA+J,sBAEAV,EAAA9L,UAAA,OAAA8L,EAAA9L,UAAAsN,OAIAxB,EAAA9L,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACAuJ,GAAAA,KAAAsJ,MAAA,CAIAwB,IAAAA,EAAA/T,SAAAC,cAAA,OACA8T,EAAApU,UAAAO,IAAA+I,KAAApJ,YAAA4S,cACAxJ,KAAAvJ,SAAA+N,cAAAC,aAAAqG,EAAA9K,KAAAvJ,UACAuJ,KAAAvJ,SAAA+N,cAAAE,YAAA1E,KAAAvJ,UACAqU,EAAAzT,YAAA2I,KAAAvJ,cACA,CAIA8N,IAAAA,EAAAxN,SAAAC,cAAA,OACAuN,EAAA7N,UAAAO,IAAA+I,KAAApJ,YAAA6S,kBACAzJ,KAAAvJ,SAAA+N,cAAAC,aAAAF,EAAAvE,KAAAvJ,UACAuJ,KAAAvJ,SAAA+N,cAAAE,YAAA1E,KAAAvJ,UACA8N,EAAAlN,YAAA2I,KAAAvJ,UACAsU,IAAAA,EAAAhU,SAAAC,cAAA,OACA+T,EAAArU,UAAAO,IAAA+I,KAAApJ,YAAA8S,iBACAnF,EAAAlN,YAAA0T,GACA/K,KAAAyK,iBAAA1T,SAAAC,cAAA,OACAgJ,KAAAyK,iBAAA/T,UAAAO,IAAA+I,KAAApJ,YAAA+S,kBACAoB,EAAA1T,YAAA2I,KAAAyK,kBACAzK,KAAA4K,iBAAA7T,SAAAC,cAAA,OACAgJ,KAAA4K,iBAAAlU,UAAAO,IAAA+I,KAAApJ,YAAAgT,kBACAmB,EAAA1T,YAAA2I,KAAA4K,kBAEAI,KAAAA,kBAAAhL,KAAA8J,SAAAlJ,KAAAZ,MACAA,KAAAiL,mBAAAjL,KAAA0B,UAAAd,KAAAZ,MACAA,KAAAkL,oBAAAlL,KAAA+B,WAAAnB,KAAAZ,MACAA,KAAAmL,+BAAAnL,KAAAgK,sBAAApJ,KAAAZ,MACAA,KAAAvJ,SAAAa,iBAAA,QAAA0I,KAAAgL,mBACAhL,KAAAvJ,SAAAa,iBAAA,SAAA0I,KAAAiL,oBACAjL,KAAAvJ,SAAAa,iBAAA,UAAA0I,KAAAkL,qBACAlL,KAAAvJ,SAAA+N,cAAAlN,iBAAA,YAAA0I,KAAAmL,gCACAnL,KAAA+J,qBACA/J,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA6K,eAKA1I,EAAAY,SAAAA,CACAuE,YAAAmL,EACAlM,cAAA,iBACA9B,SAAA,gBACAuB,QAAAA,IC9LAwO,IAAAA,EAAA,SAAAhS,GACA4G,GAAAA,KAAAvJ,SAAA2C,EACA4G,KAAAqL,aAAArL,KAAAvJ,SAAAqB,cAAA,IAAAkI,KAAAsL,YAAAC,SACAvL,KAAAwL,eAAAxL,KAAAvJ,SAAAqB,cAAA,IAAAkI,KAAAsL,YAAAG,SACAzL,KAAAqL,aACA,MAAA,IAAA1P,MAAA,mDAEA,IAAAqE,KAAAwL,eACA,MAAA,IAAA7P,MAAA,mDAEA+P,KAAAA,QAAAA,EACA1L,KAAA2L,oBAAAC,EACA5L,KAAA6L,cAAAD,EACA5L,KAAA8L,iBAAAF,EACA5L,KAAA+L,qBAAAA,GACA/L,KAAAgM,kBAAAA,IAEAjR,OAAA,iBAAAqQ,EAOAA,EAAA7N,UAAA2C,UAAAA,CAEA+L,iBAAA,KAUAb,EAAA7N,UAAA+N,YAAAA,CACAY,SAAA,eACAX,QAAA,qBACAE,OAAA,uBACAU,OAAA,wBAOAf,EAAA7N,UAAA6O,iBAAA,WACA3V,KAAAA,SAAA0F,aAAA,cAAA,QACA6D,KAAA2L,iBACA3L,KAAAwL,eAAAa,YAAArM,KAAA8L,YACA9L,KAAAwL,eAAAlU,iBAAA,QAAA0I,KAAA2L,gBACA3L,KAAAgM,kBAAAA,IAEAhM,KAAAqL,aAAAgB,YAAArM,KAAA6L,SACA7L,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAAsL,YAAAa,QACAnM,KAAAvJ,SAAA0F,aAAA,cAAA,SACA0D,WAAAG,KAAAsM,SAAA1L,KAAAZ,MAAAA,KAAAuM,WAQAnB,EAAA7N,UAAAiP,aAAA,SAAAC,GACAb,QAAAA,IAAAa,EACA,MAAA,IAAA9Q,MAAA,oEAEAiQ,QAAAA,IAAAa,EAAA,QACA,MAAA,IAAA9Q,MAAA,6CAEA8Q,GAAAA,EAAA,gBAAAA,EAAA,WACA,MAAA,IAAA9Q,MAAA,gDAEA+P,KAAAA,OACA1L,KAAA+L,qBAAA/P,KAAAyQ,IAEAzM,KAAA0L,QAAAA,EACA1L,KAAA6L,SAAAY,EAAA,QACAA,EAAA,QACAzM,KAAAuM,SAAAE,EAAA,QAEAzM,KAAAuM,SAAA,KAEAE,EAAA,gBACAzM,KAAA2L,eAAAc,EAAA,eAEAA,EAAA,aACAzM,KAAA8L,YAAAW,EAAA,YAEAzM,KAAAoM,qBAGAhB,EAAA7N,UAAA,aAAA6N,EAAA7N,UAAAiP,aAOApB,EAAA7N,UAAAmP,YAAA,WACAX,KAAAA,qBAAA3R,OAAA,GACA4F,KAAAwM,aAAAxM,KAAA+L,qBAAAY,UAQAvB,EAAA7N,UAAA+O,SAAA,WACA7V,KAAAA,SAAAC,UAAAoL,OAAA9B,KAAAsL,YAAAa,QACAtM,WAAA,WACApJ,KAAAA,SAAA0F,aAAA,cAAA,QACA6D,KAAAqL,aAAAgB,YAAA,GACAO,QAAA5M,KAAAwL,eAAAhU,aAAA,kBACAwI,KAAAgM,kBAAAA,GACAhM,KAAAwL,eAAAa,YAAA,GACArM,KAAAwL,eAAA7D,oBAAA,QAAA3H,KAAA2L,iBAEA3L,KAAA2L,oBAAAC,EACA5L,KAAA6L,cAAAD,EACA5L,KAAA8L,iBAAAF,EACA5L,KAAA0L,QAAAA,EACA1L,KAAA0M,eACA9L,KAAAZ,MAAAA,KAAAE,UAAA+L,mBAQAb,EAAA7N,UAAAyO,iBAAA,SAAAzB,GACAA,EACAvK,KAAAwL,eAAArP,aAAA,cAAA,QAEA6D,KAAAwL,eAAAqB,gBAAA,gBAKA9T,EAAAY,SAAAA,CACAuE,YAAAkN,EACAjO,cAAA,mBACA9B,SAAA,kBACAuB,QAAAA,IClJAkQ,IAAAA,EAAA,SAAA1T,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,gBAAA+R,EAOAA,EAAAvP,UAAA2C,UAAAA,CAAA6M,wBAAA,GASAD,EAAAvP,UAAA3G,YAAAA,CACAoW,kBAAA,qBACAC,2BAAA,8BACAC,mBAAA,sBACAC,sBAAA,yBACAC,iBAAA,oBACAC,kBAAA,sBAQAP,EAAAvP,UAAA+P,YAAA,SAAAC,GACAC,IAAAA,EAAAzW,SAAAC,cAAA,OACAwW,EAAA9W,UAAAO,IAAA+I,KAAApJ,YAAAoW,mBACAQ,EAAA9W,UAAAO,IAAA+I,KAAApJ,YAAAoW,kBAAA,IAAAO,GACAE,IAAAA,EAAA1W,SAAAC,cAAA,OACAyW,EAAA/W,UAAAO,IAAA+I,KAAApJ,YAAAqW,4BACAQ,EAAA/W,UAAAO,IAAA+I,KAAApJ,YAAAwW,kBACAM,IAAAA,EAAA3W,SAAAC,cAAA,OACA0W,EAAAhX,UAAAO,IAAA+I,KAAApJ,YAAAuW,uBACAQ,IAAAA,EAAA5W,SAAAC,cAAA,OACA2W,EAAAjX,UAAAO,IAAA+I,KAAApJ,YAAAqW,4BACAU,EAAAjX,UAAAO,IAAA+I,KAAApJ,YAAAyW,mBAMA,IAAA,IALAO,EAAAA,CACAH,EACAC,EACAC,GAEAzT,EAAA,EAAAA,EAAA0T,EAAAxT,OAAAF,IAAA,CACA2T,IAAAA,EAAA9W,SAAAC,cAAA,OACA6W,EAAAnX,UAAAO,IAAA+I,KAAApJ,YAAAsW,oBACAU,EAAA1T,GAAA7C,YAAAwW,GAEAL,EAAAnW,YAAAoW,GACAD,EAAAnW,YAAAqW,GACAF,EAAAnW,YAAAsW,GACA3N,KAAAvJ,SAAAY,YAAAmW,IAEAV,EAAAvP,UAAA,YAAAuP,EAAAvP,UAAA+P,YAOAR,EAAAvP,UAAAuQ,KAAA,WACArX,KAAAA,SAAAC,UAAAoL,OAAA,cAEAgL,EAAAvP,UAAA,KAAAuP,EAAAvP,UAAAuQ,KAQAhB,EAAAvP,UAAAwQ,MAAA,WACAtX,KAAAA,SAAAC,UAAAO,IAAA,cAEA6V,EAAAvP,UAAA,MAAAuP,EAAAvP,UAAAwQ,MAIAjB,EAAAvP,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACA,IAAA,IAAAyD,EAAA,EAAAA,GAAA8F,KAAAE,UAAA6M,wBAAA7S,IACA8F,KAAAsN,YAAApT,GAEAzD,KAAAA,SAAAC,UAAAO,IAAA,iBAKA8B,EAAAY,SAAAA,CACAuE,YAAA4O,EACA3P,cAAA,kBACA9B,SAAA,iBACAuB,QAAAA,ICrGAoR,IAAAA,EAAA,SAAA5U,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,eAAAiT,EAOAA,EAAAzQ,UAAA2C,UAAAA,CAAAa,aAAA,MASAiN,EAAAzQ,UAAA3G,YAAAA,CACAoK,MAAA,oBACAiN,MAAA,oBACAC,MAAA,oBACAhN,aAAA,2BACAf,cAAA,uBACAiB,qBAAA,sCACAzI,iBAAA,+BACA0I,cAAA,qBACAzI,OAAA,aACA0I,WAAA,aACAC,YAAA,cACAC,WAAA,cAQAwM,EAAAzQ,UAAAmE,UAAA,SAAArB,GACAsB,KAAAA,kBAQAqM,EAAAzQ,UAAAqE,SAAA,SAAAvB,GACA5J,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA0K,aAQA0M,EAAAzQ,UAAAsE,QAAA,SAAAxB,GACA5J,KAAAA,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA0K,aAQA0M,EAAAzQ,UAAAwE,WAAA,SAAA1B,GACA2B,KAAAA,SAOAgM,EAAAzQ,UAAAoE,eAAA,WACAM,KAAAA,gBACAjC,KAAAkC,oBAOA8L,EAAAzQ,UAAAyE,MAAA,WAGAjH,OAAA8E,WAAA,WACAsC,KAAAA,cAAA7B,QACAM,KAAAZ,MAAAA,KAAAE,UAAAa,eAQAiN,EAAAzQ,UAAA0E,cAAA,WACAE,KAAAA,cAAA3B,SACAR,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA2K,aAEAvB,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA2K,cAGAyM,EAAAzQ,UAAA,cAAAyQ,EAAAzQ,UAAA0E,cAMA+L,EAAAzQ,UAAA2E,iBAAA,WACAC,KAAAA,cAAAC,QACApC,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA4K,YAEAxB,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA4K,aAGAwM,EAAAzQ,UAAA,iBAAAyQ,EAAAzQ,UAAA2E,iBAMA8L,EAAAzQ,UAAAgD,QAAA,WACA4B,KAAAA,cAAA3B,UAAAA,EACAR,KAAA2B,kBAEAqM,EAAAzQ,UAAA,QAAAyQ,EAAAzQ,UAAAgD,QAMAyN,EAAAzQ,UAAAkD,OAAA,WACA0B,KAAAA,cAAA3B,UAAAA,EACAR,KAAA2B,kBAEAqM,EAAAzQ,UAAA,OAAAyQ,EAAAzQ,UAAAkD,OAMAuN,EAAAzQ,UAAA4Q,GAAA,WACAhM,KAAAA,cAAAC,SAAAA,EACApC,KAAA2B,kBAEAqM,EAAAzQ,UAAA,GAAAyQ,EAAAzQ,UAAA4Q,GAMAH,EAAAzQ,UAAA6Q,IAAA,WACAjM,KAAAA,cAAAC,SAAAA,EACApC,KAAA2B,kBAEAqM,EAAAzQ,UAAA,IAAAyQ,EAAAzQ,UAAA6Q,IAIAJ,EAAAzQ,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACA0L,KAAAA,cAAAnC,KAAAvJ,SAAAqB,cAAA,IAAAkI,KAAApJ,YAAAoK,OACAqN,IAAAA,EAAAtX,SAAAC,cAAA,OACAqX,EAAA3X,UAAAO,IAAA+I,KAAApJ,YAAAqX,OACAK,IAAAA,EAAAvX,SAAAC,cAAA,OACAsX,EAAA5X,UAAAO,IAAA+I,KAAApJ,YAAAsX,OACAK,IAAAA,EAAAxX,SAAAC,cAAA,QACAuX,GAAAA,EAAA7X,UAAAO,IAAA+I,KAAApJ,YAAAsK,cACAoN,EAAAjX,YAAAkX,GACAvO,KAAAvJ,SAAAY,YAAAgX,GACArO,KAAAvJ,SAAAY,YAAAiX,GACAtO,KAAAkL,oBAAAlL,KAAA+B,WAAAnB,KAAAZ,MACAA,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAuJ,eAAA,CACA1J,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAAwK,sBACApB,KAAA0C,wBAAA3L,SAAAC,cAAA,QACAgJ,KAAA0C,wBAAAhM,UAAAO,IAAA+I,KAAApJ,YAAA+B,kBACAqH,KAAA0C,wBAAAhM,UAAAO,IAAA+I,KAAApJ,YAAAuJ,eACAH,KAAA0C,wBAAAhM,UAAAO,IAAA+I,KAAApJ,YAAAyK,eACArB,KAAA0C,wBAAApL,iBAAA,UAAA0I,KAAAkL,qBACA/T,IAAAA,EAAAJ,SAAAC,cAAA,QACAG,EAAAT,UAAAO,IAAA+I,KAAApJ,YAAAgC,QACAoH,KAAA0C,wBAAArL,YAAAF,GACA6I,KAAAvJ,SAAAY,YAAA2I,KAAA0C,yBAEAuI,KAAAA,mBAAAjL,KAAA0B,UAAAd,KAAAZ,MACAA,KAAAwO,kBAAAxO,KAAA4B,SAAAhB,KAAAZ,MACAA,KAAAyO,iBAAAzO,KAAA6B,QAAAjB,KAAAZ,MACAA,KAAAmC,cAAA7K,iBAAA,SAAA0I,KAAAiL,oBACAjL,KAAAmC,cAAA7K,iBAAA,QAAA0I,KAAAwO,mBACAxO,KAAAmC,cAAA7K,iBAAA,OAAA0I,KAAAyO,kBACAzO,KAAAvJ,SAAAa,iBAAA,UAAA0I,KAAAkL,qBACAlL,KAAA2B,iBACA3B,KAAAvJ,SAAAC,UAAAO,IAAA,iBAKA8B,EAAAY,SAAAA,CACAuE,YAAA8P,EACA7Q,cAAA,iBACA9B,SAAA,gBACAuB,QAAAA,Ib5MA8R,IAAAA,EAAA,SAAAtV,GAEA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,aAAA2T,EAOAA,EAAAnR,UAAA2C,UAAAA,GASAwO,EAAAnR,UAAA3G,YAAAA,CACA+X,UAAA,gBACAC,YAAA,kBACA3W,aAAA,YACA4W,eAAA,cACAhY,qBAAA,uBACAK,qBAAA,6BACAE,WAAA,aACA0X,mCAAA,uCAOAJ,EAAAnR,UAAAwR,UAAA,WACAtY,KAAAA,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAC,uBACAmJ,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAAkY,oCAGA9O,KAAAgP,MAAAhP,KAAAvJ,SAAA8E,iBAAA,IAAAyE,KAAApJ,YAAA+X,WACA3O,KAAAiP,QAAAjP,KAAAvJ,SAAA8E,iBAAA,IAAAyE,KAAApJ,YAAAgY,aAEA,IAAA,IAAA1U,EAAA,EAAAA,EAAA8F,KAAAgP,MAAA5U,OAAAF,IACA,IAAA5D,EAAA0J,KAAAgP,MAAA9U,GAAA8F,MAEAvJ,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAAiY,iBAOAH,EAAAnR,UAAAxF,eAAA,WACA,IAAA,IAAAmX,EAAA,EAAAA,EAAAlP,KAAAgP,MAAA5U,OAAA8U,IACAlP,KAAAgP,MAAAE,GAAAxY,UAAAoL,OAAA9B,KAAApJ,YAAAqB,eAQAyW,EAAAnR,UAAAvF,iBAAA,WACA,IAAA,IAAAyE,EAAA,EAAAA,EAAAuD,KAAAiP,QAAA7U,OAAAqC,IACAuD,KAAAiP,QAAAxS,GAAA/F,UAAAoL,OAAA9B,KAAApJ,YAAAqB,eAMAyW,EAAAnR,UAAA0C,KAAA,WACAxJ,KAAAA,UACAuJ,KAAA+O,aAoCAhW,EAAAY,SAAAA,CACAuE,YAAAwQ,EACAvR,cAAA,eACA9B,SAAA,gBclHA8T,IAAAA,EAAA,SAAA/V,GACA3C,KAAAA,SAAA2C,EACA4G,KAAAoP,QAAApP,KAAAE,UAAAmP,YAEArP,KAAAC,QAEAlF,OAAA,kBAAAoU,EAOAA,EAAA5R,UAAA2C,UAAAA,CACAmP,aAAA,EACAC,mBAAA,WAUAH,EAAA5R,UAAA3G,YAAAA,CACA2Y,MAAA,uBACAvO,MAAA,uBACAwO,SAAA,WACAlO,WAAA,aACAC,YAAA,cACAkO,WAAA,aACAhO,YAAA,cACAiO,gBAAA,mBAQAP,EAAA5R,UAAAoS,WAAA,SAAAtP,GACAuP,IAAAA,EAAAvP,EAAAqG,OAAA6D,MAAA3S,MAAA,MAAAwC,OACAiG,KAAAA,EAAAkG,SACAqJ,GAAA5P,KAAAoP,SACA/O,EAAA3I,kBAUAyX,EAAA5R,UAAAqE,SAAA,SAAAvB,GACA5J,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA0K,aAQA6N,EAAA5R,UAAAsE,QAAA,SAAAxB,GACA5J,KAAAA,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA0K,aAQA6N,EAAA5R,UAAAsS,SAAA,SAAAxP,GACAsB,KAAAA,kBAOAwN,EAAA5R,UAAAoE,eAAA,WACAM,KAAAA,gBACAjC,KAAA8P,gBACA9P,KAAA+P,aACA/P,KAAAgQ,cAQAb,EAAA5R,UAAA0E,cAAA,WACAgO,KAAAA,OAAAzP,SACAR,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA2K,aAEAvB,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA2K,cAGA4N,EAAA5R,UAAA,cAAA4R,EAAA5R,UAAA0E,cAMAkN,EAAA5R,UAAAyS,WAAA,WACApD,QAAA5M,KAAAvJ,SAAAqB,cAAA,WACAkI,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA0K,YAEAtB,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA0K,aAGA6N,EAAA5R,UAAA,WAAA4R,EAAA5R,UAAAyS,WAMAb,EAAA5R,UAAAuS,cAAA,WACAG,KAAAA,OAAAC,WACAlQ,KAAAiQ,OAAAC,SAAAC,MACAnQ,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA6Y,YAEAzP,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA6Y,cAIAN,EAAA5R,UAAA,cAAA4R,EAAA5R,UAAAuS,cAMAX,EAAA5R,UAAAwS,WAAA,WACAE,KAAAA,OAAA1F,OAAAvK,KAAAiQ,OAAA1F,MAAAnQ,OAAA,EACA4F,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA4Y,UAEAxP,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA4Y,WAGAL,EAAA5R,UAAA,WAAA4R,EAAA5R,UAAAwS,WAMAZ,EAAA5R,UAAAgD,QAAA,WACA0P,KAAAA,OAAAzP,UAAAA,EACAR,KAAA2B,kBAEAwN,EAAA5R,UAAA,QAAA4R,EAAA5R,UAAAgD,QAMA4O,EAAA5R,UAAAkD,OAAA,WACAwP,KAAAA,OAAAzP,UAAAA,EACAR,KAAA2B,kBAEAwN,EAAA5R,UAAA,OAAA4R,EAAA5R,UAAAkD,OAOA0O,EAAA5R,UAAAsN,OAAA,SAAAN,GACA0F,KAAAA,OAAA1F,MAAAA,GAAA,GACAvK,KAAA2B,kBAEAwN,EAAA5R,UAAA,OAAA4R,EAAA5R,UAAAsN,OAIAsE,EAAA5R,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,WACAuJ,KAAAoQ,OAAApQ,KAAAvJ,SAAAqB,cAAA,IAAAkI,KAAApJ,YAAA2Y,OACAvP,KAAAiQ,OAAAjQ,KAAAvJ,SAAAqB,cAAA,IAAAkI,KAAApJ,YAAAoK,OACAhB,KAAAiQ,QAAA,CACAA,KAAAA,OAAAnJ,aAAA9G,KAAAE,UAAAoP,sBACAtP,KAAAoP,QAAAiB,SAAArQ,KAAAiQ,OAAAzY,aAAAwI,KAAAE,UAAAoP,oBAAA,IACAgB,MAAAtQ,KAAAoP,WACApP,KAAAoP,QAAApP,KAAAE,UAAAmP,cAGArP,KAAAiQ,OAAAnJ,aAAA,gBACA9G,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA8Y,iBAEA1P,KAAAuQ,0BAAAvQ,KAAA2B,eAAAf,KAAAZ,MACAA,KAAAwO,kBAAAxO,KAAA4B,SAAAhB,KAAAZ,MACAA,KAAAyO,iBAAAzO,KAAA6B,QAAAjB,KAAAZ,MACAA,KAAAwQ,kBAAAxQ,KAAA6P,SAAAjP,KAAAZ,MACAA,KAAAiQ,OAAA3Y,iBAAA,QAAA0I,KAAAuQ,2BACAvQ,KAAAiQ,OAAA3Y,iBAAA,QAAA0I,KAAAwO,mBACAxO,KAAAiQ,OAAA3Y,iBAAA,OAAA0I,KAAAyO,kBACAzO,KAAAiQ,OAAA3Y,iBAAA,QAAA0I,KAAAwQ,mBACAxQ,KAAAoP,UAAApP,KAAAE,UAAAmP,cAGArP,KAAAyQ,oBAAAzQ,KAAA2P,WAAA/O,KAAAZ,MACAA,KAAAiQ,OAAA3Y,iBAAA,UAAA0I,KAAAyQ,sBAEAC,IAAAA,EAAA1Q,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAA6Y,YACA9N,KAAAA,iBACA3B,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA6K,aACAiP,GACA1Q,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA6Y,YAEAzP,KAAAiQ,OAAAnJ,aAAA,eACA9G,KAAAvJ,SAAA+P,QACAxG,KAAAgQ,gBAOAjX,EAAAY,SAAAA,CACAuE,YAAAiR,EACAhS,cAAA,oBACA9B,SAAA,mBACAuB,QAAAA,IC/NA+T,IAAAA,EAAA,SAAAvX,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,gBAAA4V,EAOAA,EAAApT,UAAA2C,UAAAA,GASAyQ,EAAApT,UAAA3G,YAAAA,CACA4B,UAAA,YACAoY,OAAA,sBACAC,KAAA,oBACAC,MAAA,qBACAC,IAAA,oBAQAJ,EAAApT,UAAAyT,kBAAA,SAAA3Q,GACA4Q,IAAAA,EAAA5Q,EAAAqG,OAAAd,wBACAO,EAAA8K,EAAA9K,KAAA8K,EAAA9J,MAAA,EACAnB,EAAAiL,EAAAjL,IAAAiL,EAAA/J,OAAA,EACAgK,EAAAlR,KAAAvJ,SAAA0a,YAAA,GAAA,EACAC,EAAApR,KAAAvJ,SAAAyP,aAAA,GAAA,EACAzP,KAAAA,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAia,OAAA7Q,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAka,QACA3K,EAAA8K,EAAA9J,MAAA,EACAnB,EAAAoL,EAAA,GACApR,KAAAvJ,SAAAqP,MAAAE,IAAA,IACAhG,KAAAvJ,SAAAqP,MAAAsL,UAAA,MAEApR,KAAAvJ,SAAAqP,MAAAE,IAAAA,EAAA,KACAhG,KAAAvJ,SAAAqP,MAAAsL,UAAAA,EAAA,OAGAjL,EAAA+K,EAAA,GACAlR,KAAAvJ,SAAAqP,MAAAK,KAAA,IACAnG,KAAAvJ,SAAAqP,MAAAoL,WAAA,MAEAlR,KAAAvJ,SAAAqP,MAAAK,KAAAA,EAAA,KACAnG,KAAAvJ,SAAAqP,MAAAoL,WAAAA,EAAA,MAGAlR,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAma,KACA/Q,KAAAvJ,SAAAqP,MAAAE,IAAAiL,EAAAjL,IAAAhG,KAAAvJ,SAAAyP,aAAA,GAAA,KACAlG,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAka,OACA9Q,KAAAvJ,SAAAqP,MAAAK,KAAA8K,EAAA9K,KAAA8K,EAAA9J,MAAA,GAAA,KACAnH,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAia,MACA7Q,KAAAvJ,SAAAqP,MAAAK,KAAA8K,EAAA9K,KAAAnG,KAAAvJ,SAAA0a,YAAA,GAAA,KAEAnR,KAAAvJ,SAAAqP,MAAAE,IAAAiL,EAAAjL,IAAAiL,EAAA/J,OAAA,GAAA,KAEAlH,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA4B,YAOAmY,EAAApT,UAAA8T,aAAA,WACA5a,KAAAA,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA4B,YAKAmY,EAAApT,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACAqO,IAAAA,EAAA9E,KAAAvJ,SAAAe,aAAA,QAAAwI,KAAAvJ,SAAAe,aAAA,gBACAsN,IACA9E,KAAAiF,YAAAlO,SAAAiO,eAAAF,IAEA9E,KAAAiF,cAEAjF,KAAAiF,YAAA6B,aAAA,aACA9G,KAAAiF,YAAA9I,aAAA,WAAA,KAEA6D,KAAAsR,uBAAAtR,KAAAgR,kBAAApQ,KAAAZ,MACAA,KAAAuR,gCAAAvR,KAAAqR,aAAAzQ,KAAAZ,MACAA,KAAAiF,YAAA3N,iBAAA,aAAA0I,KAAAsR,wBAAAA,GACAtR,KAAAiF,YAAA3N,iBAAA,WAAA0I,KAAAsR,wBAAAA,GACAtR,KAAAiF,YAAA3N,iBAAA,aAAA0I,KAAAuR,iCAAAA,GACAxW,OAAAzD,iBAAA,SAAA0I,KAAAuR,iCAAAA,GACAxW,OAAAzD,iBAAA,aAAA0I,KAAAuR,oCAMAxY,EAAAY,SAAAA,CACAuE,YAAAyS,EACAxT,cAAA,kBACA9B,SAAA,gBd1GAmW,IAAAA,EAAA,SAAApY,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,eAAAyW,EAOAA,EAAAjU,UAAA2C,UAAAA,CACAuR,UAAA,sBACAC,kBAAA,IACAC,eAAA,IACAC,UAAA,WACAC,aAAA,eACAC,cAAA,iBAQAN,EAAAjU,UAAA+F,UAAAA,CACAC,MAAA,GACAC,OAAA,GACAC,MAAA,IAQA+N,EAAAjU,UAAAwU,MAAAA,CACAC,SAAA,EACAC,OAAA,EACAC,UAAA,EACAC,OAAA,GAUAX,EAAAjU,UAAA3G,YAAAA,CACAgN,UAAA,wBACAwO,OAAA,qBACAC,OAAA,qBACAC,QAAA,sBACAC,WAAA,4BACAC,KAAA,iBACA9Z,iBAAA,uBACAC,iBAAA,mCACAC,OAAA,aACAwI,qBAAA,sCACAqR,cAAA,6BACAC,iBAAA,gCACAC,cAAA,6BACAC,aAAA,2BACAC,WAAA,yBACAC,QAAA,sBACAC,cAAA,gCACAC,IAAA,kBACAC,eAAA,6BACAC,oBAAA,kCACAC,qBAAA,mCACAta,kBAAA,gCACAua,MAAA,wBACAC,WAAA,aACAC,SAAA,WACAC,qBAAA,uBACAC,eAAA,oBACAC,WAAA,aACAC,gBAAA,kBACAC,eAAA,aACAnb,UAAA,YACAiJ,YAAA,cACAwC,aAAA,eACA2P,gBAAA,gCACAC,gBAAA,iCAOArC,EAAAjU,UAAAuW,sBAAA,WACA,IAAA9T,KAAA+T,QAAArd,UAAAC,SAAAqJ,KAAApJ,YAAAqN,cAAA,CAGA+P,IAAAA,GAAAhU,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAA8c,kBAAA1T,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAgc,cACAra,KAAAA,SAAA0b,UAAA,IAAAjU,KAAA+T,QAAArd,UAAAC,SAAAqJ,KAAApJ,YAAA6c,aACAzT,KAAA+T,QAAArd,UAAAO,IAAA+I,KAAApJ,YAAA4c,gBACAxT,KAAA+T,QAAArd,UAAAO,IAAA+I,KAAApJ,YAAA6c,YACAO,GACAhU,KAAA+T,QAAArd,UAAAO,IAAA+I,KAAApJ,YAAAqN,eAEAjE,KAAAzH,SAAA0b,WAAA,GAAAjU,KAAA+T,QAAArd,UAAAC,SAAAqJ,KAAApJ,YAAA6c,cACAzT,KAAA+T,QAAArd,UAAAoL,OAAA9B,KAAApJ,YAAA4c,gBACAxT,KAAA+T,QAAArd,UAAAoL,OAAA9B,KAAApJ,YAAA6c,YACAO,GACAhU,KAAA+T,QAAArd,UAAAO,IAAA+I,KAAApJ,YAAAqN,iBAUAuN,EAAAjU,UAAA2W,sBAAA,SAAAxO,GAEAA,EAAAa,UAAAvG,KAAAsD,UAAAE,QAAAxD,KAAAmU,QAAAzd,UAAAC,SAAAqJ,KAAApJ,YAAA+c,iBACA3T,KAAAoU,gBAQA5C,EAAAjU,UAAA8W,mBAAA,WACAC,KAAAA,sBAAAC,QACAvU,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA8c,kBAEA1T,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA8c,iBAEA1T,KAAAmU,UACAnU,KAAAmU,QAAAzd,UAAAoL,OAAA9B,KAAApJ,YAAA+c,gBACA3T,KAAAwU,YAAA9d,UAAAoL,OAAA9B,KAAApJ,YAAA+c,mBAUAnC,EAAAjU,UAAAkX,qBAAA,SAAA/O,GACAA,GAAAA,GAAA,YAAAA,EAAAgP,KAAA,CACAhP,GAAAA,EAAAa,UAAAvG,KAAAsD,UAAAG,OAAAiC,EAAAa,UAAAvG,KAAAsD,UAAAC,MAKA,OAHAmC,EAAAhO,iBAMA0c,KAAAA,gBAOA5C,EAAAjU,UAAAoX,4BAAA,WACAZ,KAAAA,QAAArd,UAAAoL,OAAA9B,KAAApJ,YAAAqN,eAOAuN,EAAAjU,UAAAqX,oBAAA,WACAb,KAAAA,QAAArd,UAAAC,SAAAqJ,KAAApJ,YAAA6c,cACAzT,KAAA+T,QAAArd,UAAAoL,OAAA9B,KAAApJ,YAAA6c,YACAzT,KAAA+T,QAAArd,UAAAO,IAAA+I,KAAApJ,YAAAqN,gBAQAuN,EAAAjU,UAAAxF,eAAA,SAAA8c,GACA,IAAA,IAAA3F,EAAA,EAAAA,EAAA2F,EAAAza,OAAA8U,IACA2F,EAAA3F,GAAAxY,UAAAoL,OAAA9B,KAAApJ,YAAA4B,YAQAgZ,EAAAjU,UAAAvF,iBAAA,SAAAI,GACA,IAAA,IAAAqE,EAAA,EAAAA,EAAArE,EAAAgC,OAAAqC,IACArE,EAAAqE,GAAA/F,UAAAoL,OAAA9B,KAAApJ,YAAA4B,YAQAgZ,EAAAjU,UAAA6W,aAAA,WACAU,IAAAA,EAAA9U,KAAAvJ,SAAAqB,cAAA,IAAAkI,KAAApJ,YAAA2b,YACA4B,KAAAA,QAAAzd,UAAA4P,OAAAtG,KAAApJ,YAAA+c,gBACA3T,KAAAwU,YAAA9d,UAAA4P,OAAAtG,KAAApJ,YAAA+c,gBAEA3T,KAAAmU,QAAAzd,UAAAC,SAAAqJ,KAAApJ,YAAA+c,iBACA3T,KAAAmU,QAAAhY,aAAA,cAAA,SACA2Y,EAAA3Y,aAAA,gBAAA,UAEA6D,KAAAmU,QAAAhY,aAAA,cAAA,QACA2Y,EAAA3Y,aAAA,gBAAA,WAGAqV,EAAAjU,UAAA,aAAAiU,EAAAjU,UAAA6W,aAIA5C,EAAAjU,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACA8N,IAAAA,EAAAxN,SAAAC,cAAA,OACAuN,EAAA7N,UAAAO,IAAA+I,KAAApJ,YAAAgN,WACAmR,IAAAA,EAAA/U,KAAAvJ,SAAAqB,cAAA,UACArB,KAAAA,SAAA+N,cAAAC,aAAAF,EAAAvE,KAAAvJ,UACAuJ,KAAAvJ,SAAA+N,cAAAE,YAAA1E,KAAAvJ,UACA8N,EAAAlN,YAAA2I,KAAAvJ,UACAse,GACAA,EAAAvO,QAIA,IAAA,IAFAwO,EAAAhV,KAAAvJ,SAAAwe,WACAC,EAAAF,EAAA5a,OACA+a,EAAA,EAAAA,EAAAD,EAAAC,IAAA,CACAC,IAAAA,EAAAJ,EAAAG,GACAC,EAAA1e,WAAA0e,EAAA1e,UAAAC,SAAAqJ,KAAApJ,YAAAwb,UACApS,KAAA+T,QAAAqB,GAEAA,EAAA1e,WAAA0e,EAAA1e,UAAAC,SAAAqJ,KAAApJ,YAAAyb,UACArS,KAAAmU,QAAAiB,GAEAA,EAAA1e,WAAA0e,EAAA1e,UAAAC,SAAAqJ,KAAApJ,YAAA0b,WACAtS,KAAAzH,SAAA6c,GAGAra,OAAAzD,iBAAA,WAAA,SAAAC,GACAA,EAAA8d,YAGArV,KAAAvJ,SAAAqP,MAAAwP,UAAA,SACAnW,sBAAA,WACA1I,KAAAA,SAAAqP,MAAAwP,UAAA,IACA1U,KAAAZ,SAEAY,KAAAZ,OAAAA,GACAA,KAAA+T,UACA/T,KAAAvH,QAAAuH,KAAA+T,QAAAjc,cAAA,IAAAkI,KAAApJ,YAAAkc,UAEAyC,IAAAA,EAAAvV,KAAA+R,MAAAC,SACAhS,GAAAA,KAAA+T,UACA/T,KAAA+T,QAAArd,UAAAC,SAAAqJ,KAAApJ,YAAA6b,eACA8C,EAAAvV,KAAA+R,MAAAE,OACAjS,KAAA+T,QAAArd,UAAAC,SAAAqJ,KAAApJ,YAAA8b,mBACA6C,EAAAvV,KAAA+R,MAAAG,UACAlS,KAAA+T,QAAAzc,iBAAA,gBAAA0I,KAAA2U,4BAAA/T,KAAAZ,OACAA,KAAA+T,QAAAzc,iBAAA,QAAA0I,KAAA4U,oBAAAhU,KAAAZ,QACAA,KAAA+T,QAAArd,UAAAC,SAAAqJ,KAAApJ,YAAA+b,iBACA4C,EAAAvV,KAAA+R,MAAAI,OACA5N,EAAA7N,UAAAO,IAAA+I,KAAApJ,YAAA2c,uBAEAgC,IAAAvV,KAAA+R,MAAAC,UACAhS,KAAA+T,QAAArd,UAAAO,IAAA+I,KAAApJ,YAAA4c,gBACAxT,KAAAvH,SACAuH,KAAAvH,QAAA/B,UAAAO,IAAA+I,KAAApJ,YAAA4c,iBAEA+B,IAAAvV,KAAA+R,MAAAE,QAAAsD,IAAAvV,KAAA+R,MAAAI,QACAnS,KAAA+T,QAAArd,UAAAoL,OAAA9B,KAAApJ,YAAA4c,gBACAxT,KAAAvH,SACAuH,KAAAvH,QAAA/B,UAAAoL,OAAA9B,KAAApJ,YAAA4c,iBAEA+B,IAAAvV,KAAA+R,MAAAG,YAIAlS,KAAAzH,SAAAjB,iBAAA,SAAA0I,KAAA8T,sBAAAlT,KAAAZ,OACAA,KAAA8T,0BAIA9T,KAAAmU,QAAA,CACAW,IAAAA,EAAA9U,KAAAvJ,SAAAqB,cAAA,IAAAkI,KAAApJ,YAAA2b,YACA,IAAAuC,EAAA,EACAA,EAAA/d,SAAAC,cAAA,QACAmF,aAAA,gBAAA,SACA2Y,EAAA3Y,aAAA,OAAA,UACA2Y,EAAA3Y,aAAA,WAAA,KACA2Y,EAAApe,UAAAO,IAAA+I,KAAApJ,YAAA2b,YACAiD,IAAAA,EAAAze,SAAAC,cAAA,KACAwe,EAAA9e,UAAAO,IAAA+I,KAAApJ,YAAA4b,MACAgD,EAAAC,UAAAzV,KAAAE,UAAA0R,UACAkD,EAAAzd,YAAAme,GAEArB,KAAAA,QAAAzd,UAAAC,SAAAqJ,KAAApJ,YAAAgd,iBAEAkB,EAAApe,UAAAO,IAAA+I,KAAApJ,YAAAgd,iBACA5T,KAAAmU,QAAAzd,UAAAC,SAAAqJ,KAAApJ,YAAAid,kBAEAiB,EAAApe,UAAAO,IAAA+I,KAAApJ,YAAAid,iBAEAiB,EAAAxd,iBAAA,QAAA0I,KAAAyU,qBAAA7T,KAAAZ,OACA8U,EAAAxd,iBAAA,UAAA0I,KAAAyU,qBAAA7T,KAAAZ,OAIAA,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAAyc,YAGArT,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAgc,cACA5S,KAAA+T,QAAAtP,aAAAqQ,EAAA9U,KAAA+T,QAAA2B,YAEA1V,KAAAvJ,SAAAgO,aAAAqQ,EAAA9U,KAAAzH,UAEAod,IAAAA,EAAA5e,SAAAC,cAAA,OACA2e,EAAAjf,UAAAO,IAAA+I,KAAApJ,YAAAic,YACA7S,KAAAvJ,SAAAY,YAAAse,GACAA,EAAAre,iBAAA,QAAA0I,KAAAyU,qBAAA7T,KAAAZ,OACAA,KAAAwU,YAAAmB,EACA3V,KAAAmU,QAAA7c,iBAAA,UAAA0I,KAAAkU,sBAAAtT,KAAAZ,OACAA,KAAAmU,QAAAhY,aAAA,cAAA,QAIA6D,GAAAA,KAAAsU,sBAAAvZ,OAAA6a,WAAA5V,KAAAE,UAAAuR,WACAzR,KAAAsU,sBAAAuB,YAAA7V,KAAAqU,mBAAAzT,KAAAZ,OACAA,KAAAqU,qBAEArU,KAAA+T,SAAA/T,KAAAvH,QAAA,CACAhC,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA0c,UACAwC,IAAAA,EAAA/e,SAAAC,cAAA,OACA8e,EAAApf,UAAAO,IAAA+I,KAAApJ,YAAAmc,eACA/S,KAAA+T,QAAAtP,aAAAqR,EAAA9V,KAAAvH,SACAuH,KAAA+T,QAAArP,YAAA1E,KAAAvH,SACAsd,IAAAA,EAAAhf,SAAAC,cAAA,OACA+e,EAAArf,UAAAO,IAAA+I,KAAApJ,YAAAqc,gBACA8C,EAAArf,UAAAO,IAAA+I,KAAApJ,YAAAsc,qBACA8C,IAAAA,EAAAjf,SAAAC,cAAA,KACAgf,EAAAtf,UAAAO,IAAA+I,KAAApJ,YAAA4b,MACAwD,EAAA3J,YAAArM,KAAAE,UAAA2R,aACAkE,EAAA1e,YAAA2e,GACAD,EAAAze,iBAAA,QAAA,WACAmB,KAAAA,QAAAwd,YAAAjW,KAAAE,UAAAwR,mBACA9Q,KAAAZ,OACAkW,IAAAA,EAAAnf,SAAAC,cAAA,OACAkf,EAAAxf,UAAAO,IAAA+I,KAAApJ,YAAAqc,gBACAiD,EAAAxf,UAAAO,IAAA+I,KAAApJ,YAAAuc,sBACAgD,IAAAA,EAAApf,SAAAC,cAAA,KACAmf,EAAAzf,UAAAO,IAAA+I,KAAApJ,YAAA4b,MACA2D,EAAA9J,YAAArM,KAAAE,UAAA4R,cACAoE,EAAA7e,YAAA8e,GACAD,EAAA5e,iBAAA,QAAA,WACAmB,KAAAA,QAAAwd,YAAAjW,KAAAE,UAAAwR,mBACA9Q,KAAAZ,OACA8V,EAAAze,YAAA0e,GACAD,EAAAze,YAAA2I,KAAAvH,SACAqd,EAAAze,YAAA6e,GAGAE,IAAAA,EAAA,WACA3d,KAAAA,QAAAwd,WAAA,EACAF,EAAArf,UAAAO,IAAA+I,KAAApJ,YAAA4B,WAEAud,EAAArf,UAAAoL,OAAA9B,KAAApJ,YAAA4B,WAEAwH,KAAAvH,QAAAwd,WAAAjW,KAAAvH,QAAA4d,YAAArW,KAAAvH,QAAA0Y,YACA+E,EAAAxf,UAAAO,IAAA+I,KAAApJ,YAAA4B,WAEA0d,EAAAxf,UAAAoL,OAAA9B,KAAApJ,YAAA4B,YAEAoI,KAAAZ,MACAvH,KAAAA,QAAAnB,iBAAA,SAAA8e,GACAA,IAEAE,IAAAA,EAAA,WAEAC,KAAAA,kBACAzW,aAAAE,KAAAuW,kBAEAvW,KAAAuW,iBAAA1W,WAAA,WACAuW,IACApW,KAAAuW,iBAAA,MACA3V,KAAAZ,MAAAA,KAAAE,UAAAyR,iBACA/Q,KAAAZ,MACAjF,OAAAzD,iBAAA,SAAAgf,GACAtW,KAAAvH,QAAA/B,UAAAC,SAAAqJ,KAAApJ,YAAA8B,mBACAsH,KAAAvH,QAAA/B,UAAAO,IAAA+I,KAAApJ,YAAAwK,sBAMA,IAAA,IAHAjJ,EAAA6H,KAAAvH,QAAA8C,iBAAA,IAAAyE,KAAApJ,YAAAoc,KACA5a,EAAA4H,KAAAzH,SAAAgD,iBAAA,IAAAyE,KAAApJ,YAAAwc,OAEAlZ,EAAA,EAAAA,EAAA/B,EAAAiC,OAAAF,IACA,IAAAhC,EAAAC,EAAA+B,GAAA/B,EAAAC,EAAA4H,MAGAvJ,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA6K,eA2CA1G,OAAA,kBAAA7C,EAGAa,EAAAY,SAAAA,CACAuE,YAAAsT,EACArU,cAAA,iBACA9B,SAAA,kBercAmb,IAAAA,EAAA,SAAApd,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,kBAAAyb,EAOAA,EAAAjZ,UAAA2C,UAAAA,GASAsW,EAAAjZ,UAAA3G,YAAAA,CACA6f,WAAA,iBACAC,WAAA,6BACAC,eAAA,yBACAC,YAAA,cACAnV,YAAA,eAWA+U,EAAAjZ,UAAAsZ,WAAA,SAAAC,EAAAC,EAAAC,GACAD,OAAAA,EACA,WACAD,EAAA1U,QACA2U,EAAArgB,UAAAO,IAAA+I,KAAApJ,YAAAggB,aAEAG,EAAArgB,UAAAoL,OAAA9B,KAAApJ,YAAAggB,cAEAhW,KAAAZ,MAEAgX,EACA,WACA9c,IAAAA,EAEA4c,GAAAA,EAAA1U,QACA,IAAAlI,EAAA,EAAAA,EAAA8c,EAAA5c,OAAAF,IACA8c,EAAA9c,GAAApC,cAAA,MAAAA,cAAA,iBACA,iBAAAuK,QACA2U,EAAA9c,GAAAxD,UAAAO,IAAA+I,KAAApJ,YAAAggB,kBAGA,IAAA1c,EAAA,EAAAA,EAAA8c,EAAA5c,OAAAF,IACA8c,EAAA9c,GAAApC,cAAA,MAAAA,cAAA,iBACA,iBAAAwK,UACA0U,EAAA9c,GAAAxD,UAAAoL,OAAA9B,KAAApJ,YAAAggB,cAGAhW,KAAAZ,WAjBA,GA4BAwW,EAAAjZ,UAAA0Z,gBAAA,SAAAF,EAAAC,GACAE,IAAAA,EAAAngB,SAAAC,cAAA,SACAmgB,EAAAA,CACA,eACA,kBACA,uBACAnX,KAAApJ,YAAA+f,gBAEAO,EAAA7c,UAAA8c,EAAA/a,KAAA,KACA0a,IAAAA,EAAA/f,SAAAC,cAAA,SACA8f,OAAAA,EAAApC,KAAA,WACAoC,EAAApgB,UAAAO,IAAA,uBACA8f,GACAD,EAAA1U,QAAA2U,EAAArgB,UAAAC,SAAAqJ,KAAApJ,YAAAggB,aACAE,EAAAxf,iBAAA,SAAA0I,KAAA6W,WAAAC,EAAAC,KACAC,GACAF,EAAAxf,iBAAA,SAAA0I,KAAA6W,WAAAC,EAAA,KAAAE,IAEAE,EAAA7f,YAAAyf,GACA/d,EAAAI,eAAA+d,EAAA,oBACAA,GAKAV,EAAAjZ,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACA2gB,IAAAA,EAAApX,KAAAvJ,SAAAqB,cAAA,MACAuf,EAAAha,MAAAE,UAAAC,MAAAC,KAAAuC,KAAAvJ,SAAA8E,iBAAA,aACA+b,EAAAja,MAAAE,UAAAC,MAAAC,KAAAuC,KAAAvJ,SAAA8E,iBAAA,aACAgc,EAAAF,EAAAG,OAAAF,GACAtX,GAAAA,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAA8f,YAAA,CACAe,IAAAA,EAAA1gB,SAAAC,cAAA,MACA0gB,EAAA1X,KAAAiX,gBAAA,KAAAM,GACAE,EAAApgB,YAAAqgB,GACAN,EAAA5S,cAAAC,aAAAgT,EAAAL,GACA,IAAA,IAAAld,EAAA,EAAAA,EAAAqd,EAAAnd,OAAAF,IAAA,CACAyd,IAAAA,EAAAJ,EAAArd,GAAApC,cAAA,MACA6f,GAAAA,EAAA,CACAC,IAAAA,EAAA7gB,SAAAC,cAAA,MACA,GAAA,UAAAugB,EAAArd,GAAAwN,WAAAmQ,SAAAC,cAAA,CACAC,IAAAA,EAAA/X,KAAAiX,gBAAAM,EAAArd,IACA0d,EAAAvgB,YAAA0gB,GAEAR,EAAArd,GAAAuK,aAAAmT,EAAAD,IAGAlhB,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA6K,gBAMA1I,EAAAY,SAAAA,CACAuE,YAAAsY,EACArZ,cAAA,oBACA9B,SAAA,sBjBnIA2c,IAAAA,EAAA,SAAA5e,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,eAAAid,EAOAA,EAAAza,UAAA2C,UAAAA,CACA+X,cAAA,wBACAC,aAAA,MACAC,gBAAA,MACAC,cAAA,IACAC,YAAA,IAUAL,EAAAza,UAAA3G,YAAAA,CACAyK,cAAA,qBACAiX,4BAAA,sCACA1f,OAAA,aACAqL,aAAA,eACAD,WAAA,cAQAgU,EAAAza,UAAAgb,aAAA,SAAAlY,GACA,IAAAL,KAAAU,eAAAoF,MAAAqB,QAAAnH,KAAAU,eAAAoF,MAAAoB,OAAA,CACAvB,IAAAA,EAAA3F,KAAAvJ,SAAAmP,wBACA4S,KAAAA,YAAA7S,EAAAuB,OACAlH,KAAAyY,WAAA9S,EAAAwB,MACAnH,KAAA0Y,YAAA,EAAA/Y,KAAAgZ,KAAAhT,EAAAwB,MAAAxB,EAAAwB,MAAAxB,EAAAuB,OAAAvB,EAAAuB,QAAA,EACAlH,KAAAU,eAAAoF,MAAAqB,MAAAnH,KAAA0Y,YAAA,KACA1Y,KAAAU,eAAAoF,MAAAoB,OAAAlH,KAAA0Y,YAAA,KAEA1Y,GAAAA,KAAAU,eAAAhK,UAAAO,IAAA+I,KAAApJ,YAAAoN,YACA,cAAA3D,EAAAqU,MAAA1U,KAAA4Y,mBACA5Y,KAAA4Y,oBAAAA,MACA,CAKAC,GAJAxY,eAAAA,EAAAqU,OACA1U,KAAA4Y,oBAAAA,GAEA5Y,KAAA8Y,gBACA,EACA,OAEAC,KAAAA,cAAA,GAEAC,IAAAA,EACA3O,EAFA4O,EAAA5Y,EAAA6Y,cAAAtT,wBAIA,GAAA,IAAAvF,EAAA8J,SAAA,IAAA9J,EAAA+J,QACA4O,EAAArZ,KAAAwZ,MAAAF,EAAA9R,MAAA,GACAkD,EAAA1K,KAAAwZ,MAAAF,EAAA/R,OAAA,OACA,CACAiD,IAAAA,OAAAyB,IAAAvL,EAAA8J,QAAA9J,EAAA8J,QAAA9J,EAAA+Y,QAAA,GAAAjP,QACAC,OAAAwB,IAAAvL,EAAA+J,QAAA/J,EAAA+J,QAAA/J,EAAA+Y,QAAA,GAAAhP,QACA4O,EAAArZ,KAAAwZ,MAAAhP,EAAA8O,EAAA9S,MACAkE,EAAA1K,KAAAwZ,MAAA/O,EAAA6O,EAAAjT,KAEAqT,KAAAA,YAAAL,EAAA3O,GACArK,KAAAsZ,iBAAAA,GACAve,OAAAoE,sBAAAa,KAAAuZ,iBAAA3Y,KAAAZ,SASAgY,EAAAza,UAAAic,WAAA,SAAAnZ,GAEAA,GAAA,IAAAA,EAAAoZ,QAIA1e,OAAA8E,WAAA,WACAa,KAAAA,eAAAhK,UAAAoL,OAAA9B,KAAApJ,YAAAoN,aACApD,KAAAZ,MAAA,IAMAgY,EAAAza,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACAijB,IAAAA,EAAA1Z,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAyK,eACA5K,KAAAA,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAA0hB,+BACAtY,KAAAU,eAAAV,KAAAvJ,SAAAqB,cAAA,IAAAkI,KAAApJ,YAAAgC,QACAoH,KAAA2Z,YAAA,EACA3Z,KAAA0Y,YAAA,EACA1Y,KAAA4Z,GAAA,EACA5Z,KAAA6Z,GAAA,EAIA7Z,KAAA4Y,oBAAAA,EACA5Y,KAAA8Z,iBAAA9Z,KAAAuY,aAAA3X,KAAAZ,MACAA,KAAAvJ,SAAAa,iBAAA,YAAA0I,KAAA8Z,kBACA9Z,KAAAvJ,SAAAa,iBAAA,aAAA0I,KAAA8Z,kBACA9Z,KAAA+Z,eAAA/Z,KAAAwZ,WAAA5Y,KAAAZ,MACAA,KAAAvJ,SAAAa,iBAAA,UAAA0I,KAAA+Z,gBACA/Z,KAAAvJ,SAAAa,iBAAA,aAAA0I,KAAA+Z,gBACA/Z,KAAAvJ,SAAAa,iBAAA,WAAA0I,KAAA+Z,gBACA/Z,KAAAvJ,SAAAa,iBAAA,OAAA0I,KAAA+Z,gBAKA/Z,KAAA8Y,cAAA,WACA9Y,OAAAA,KAAA2Z,aAMA3Z,KAAA+Y,cAAA,SAAAiB,GACAL,KAAAA,YAAAK,GAMAha,KAAAia,iBAAA,WACAja,OAAAA,KAAAU,gBAOAV,KAAAqZ,YAAA,SAAAa,EAAAC,GACAP,KAAAA,GAAAM,EACAla,KAAA6Z,GAAAM,GAMAna,KAAAsZ,gBAAA,SAAAvL,GACA,GAAA,OAAA/N,KAAAU,eAAA,CACA0Z,IAAAA,EACAC,EAEAC,EAAA,aAAAta,KAAA4Z,GAAA,OAAA5Z,KAAA6Z,GAAA,MACA9L,GACAsM,EAAAra,KAAAE,UAAA+X,cACAjY,KAAAE,UAAAgY,eAEAmC,EAAAra,KAAAE,UAAAmY,YACArY,KAAA0Y,YAAA,KACAgB,IACAY,EAAA,aAAAta,KAAAyY,WAAA,EAAA,OAAAzY,KAAAwY,YAAA,EAAA,QAGA4B,EAAA,yBAAAE,EAAAD,EACAra,KAAAU,eAAAoF,MAAAyU,gBAAAH,EACApa,KAAAU,eAAAoF,MAAA0U,YAAAJ,EACApa,KAAAU,eAAAoF,MAAA2U,UAAAL,EACArM,EACA/N,KAAAU,eAAAhK,UAAAoL,OAAA9B,KAAApJ,YAAAqN,cAEAjE,KAAAU,eAAAhK,UAAAO,IAAA+I,KAAApJ,YAAAqN,gBAOAjE,KAAAuZ,iBAAA,WACAI,KAAAA,eAAA,EACA5e,OAAAoE,sBAAAa,KAAAuZ,iBAAA3Y,KAAAZ,OAEAA,KAAAsZ,iBAAAA,OAQAvgB,EAAAY,SAAAA,CACAuE,YAAA8Z,EACA7a,cAAA,iBACA9B,SAAA,uBACAuB,QAAAA,IAAA;;;AkB/NA,IAAA,EAAA,OAAA,QAAA,oBAAA,QAAA,OAAA,MAAA,KACA,OAAA,oBAAA,MAAA,KAAA,MAAA,KAAA,KAEA,SAAA,cAAA,GACA,iBAAA,MAAA,IAAA;;ACLA,IAAA,EAAA,GAAA,eACA,OAAA,QAAA,SAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA;;ACFA,OAAA,QAAA,SAAA,GACA,IACA,QAAA,IACA,MAAA,GACA,OAAA;;ACHA,OAAA,SAAA,QAAA,WAAA,CAAA,WACA,OAAA,GAAA,OAAA,eAAA,GAAA,IAAA,CAAA,IAAA,WAAA,OAAA,KAAA;;ACFA,IAAA,EAAA,OAAA,QAAA,CAAA,QAAA,SACA,iBAAA,MAAA,IAAA;;ACDA,OAAA,QAAA,SAAA,GACA,MAAA,iBAAA,EAAA,OAAA,EAAA,mBAAA;;ACDA,IAAA,EAAA,QAAA,gBACA,OAAA,QAAA,SAAA,GACA,IAAA,EAAA,GAAA,MAAA,UAAA,EAAA,sBACA,OAAA;;ACHA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,aAAA,SAEA,EAAA,EAAA,IAAA,EAAA,EAAA,eACA,OAAA,QAAA,SAAA,GACA,OAAA,EAAA,EAAA,cAAA,GAAA;;ACLA,OAAA,SAAA,QAAA,oBAAA,QAAA,WAAA,CAAA,WACA,OAAA,GAAA,OAAA,eAAA,QAAA,gBAAA,CAAA,OAAA,IAAA,CAAA,IAAA,WAAA,OAAA,KAAA;;ACAA,IAAA,EAAA,QAAA,gBAGA,OAAA,QAAA,SAAA,EAAA,GACA,IAAA,EAAA,GAAA,OAAA,EACA,IAAA,EAAA,EACA,GAAA,GAAA,mBAAA,EAAA,EAAA,YAAA,EAAA,EAAA,EAAA,KAAA,IAAA,OAAA,EACA,GAAA,mBAAA,EAAA,EAAA,WAAA,EAAA,EAAA,EAAA,KAAA,IAAA,OAAA,EACA,IAAA,GAAA,mBAAA,EAAA,EAAA,YAAA,EAAA,EAAA,EAAA,KAAA,IAAA,OAAA,EACA,MAAA,UAAA;;ACVA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,qBACA,EAAA,QAAA,mBACA,EAAA,OAAA,eAEA,QAAA,EAAA,QAAA,kBAAA,OAAA,eAAA,SAAA,EAAA,EAAA,GAIA,GAHA,EAAA,GACA,EAAA,EAAA,GAAA,GACA,EAAA,GACA,EAAA,IACA,OAAA,EAAA,EAAA,EAAA,GACA,MAAA,IACA,GAAA,QAAA,GAAA,QAAA,EAAA,MAAA,UAAA,4BAEA,MADA,UAAA,IAAA,EAAA,GAAA,EAAA,OACA;;ACdA,OAAA,QAAA,SAAA,EAAA,GACA,MAAA,CACA,aAAA,EAAA,GACA,eAAA,EAAA,GACA,WAAA,EAAA,GACA,MAAA;;ACLA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,oBACA,OAAA,QAAA,QAAA,kBAAA,SAAA,EAAA,EAAA,GACA,OAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,KACA,SAAA,EAAA,EAAA,GAEA,OADA,EAAA,GAAA,EACA;;ACNA,IAAA,EAAA,EACA,EAAA,KAAA,SACA,OAAA,QAAA,SAAA,GACA,MAAA,UAAA,YAAA,IAAA,EAAA,GAAA,EAAA,QAAA,EAAA,GAAA,SAAA;;ACHA,OAAA,SAAA;;;ACAA,IAAA,EAAA,QAAA,WACA,EAAA,QAAA,aACA,EAAA,qBACA,EAAA,EAAA,KAAA,EAAA,GAAA,KAEA,OAAA,QAAA,SAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,QAAA,IAAA,EAAA,EAAA,MACA,WAAA,IAAA,KAAA,CACA,QAAA,EAAA,QACA,KAAA,QAAA,cAAA,OAAA,SACA,UAAA;;ACVA,OAAA,QAAA,QAAA,YAAA,CAAA,4BAAA,SAAA;;;ACAA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,WACA,EAAA,QAAA,UACA,EAAA,QAAA,SAAA,CAAA,OACA,EAAA,QAAA,yBACA,EAAA,WACA,GAAA,GAAA,GAAA,MAAA,GAEA,QAAA,WAAA,cAAA,SAAA,GACA,OAAA,EAAA,KAAA,KAGA,OAAA,QAAA,SAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,mBAAA,EACA,IAAA,EAAA,EAAA,SAAA,EAAA,EAAA,OAAA,IACA,EAAA,KAAA,IACA,IAAA,EAAA,EAAA,IAAA,EAAA,EAAA,EAAA,EAAA,GAAA,GAAA,EAAA,GAAA,EAAA,KAAA,OAAA,MACA,IAAA,EACA,EAAA,GAAA,EACA,EAGA,EAAA,GACA,EAAA,GAAA,EAEA,EAAA,EAAA,EAAA,WALA,EAAA,GACA,EAAA,EAAA,EAAA,OAOA,SAAA,UAAA,EAAA,WACA,MAAA,mBAAA,MAAA,KAAA,IAAA,EAAA,KAAA;;AC7BA,OAAA,QAAA,SAAA,GACA,GAAA,mBAAA,EAAA,MAAA,UAAA,EAAA,uBACA,OAAA;;ACDA,IAAA,EAAA,QAAA,iBACA,OAAA,QAAA,SAAA,EAAA,EAAA,GAEA,GADA,EAAA,QACA,IAAA,EAAA,OAAA,EACA,OAAA,GACA,KAAA,EAAA,OAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,IAEA,KAAA,EAAA,OAAA,SAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,EAAA,IAEA,KAAA,EAAA,OAAA,SAAA,EAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,EAAA,EAAA,IAGA,OAAA,WACA,OAAA,EAAA,MAAA,EAAA;;;ACjBA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,WACA,EAAA,QAAA,WACA,EAAA,QAAA,eACA,EAAA,QAAA,UACA,EAAA,YAEA,EAAA,SAAA,EAAA,EAAA,GACA,IAQA,EAAA,EAAA,EAAA,EARA,EAAA,EAAA,EAAA,EACA,EAAA,EAAA,EAAA,EACA,EAAA,EAAA,EAAA,EACA,EAAA,EAAA,EAAA,EACA,EAAA,EAAA,EAAA,EACA,EAAA,EAAA,EAAA,EAAA,EAAA,KAAA,EAAA,GAAA,KAAA,EAAA,IAAA,IAAA,GACA,EAAA,EAAA,EAAA,EAAA,KAAA,EAAA,GAAA,IACA,EAAA,EAAA,KAAA,EAAA,GAAA,IAGA,IAAA,KADA,IAAA,EAAA,GACA,EAIA,IAFA,GAAA,GAAA,QAAA,IAAA,EAAA,IAEA,EAAA,GAAA,GAEA,EAAA,GAAA,EAAA,EAAA,EAAA,GAAA,GAAA,mBAAA,EAAA,EAAA,SAAA,KAAA,GAAA,EAEA,GAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAEA,EAAA,IAAA,GAAA,EAAA,EAAA,EAAA,GACA,GAAA,EAAA,IAAA,IAAA,EAAA,GAAA,IAGA,EAAA,KAAA,EAEA,EAAA,EAAA,EACA,EAAA,EAAA,EACA,EAAA,EAAA,EACA,EAAA,EAAA,EACA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,EAAA,IACA,OAAA,QAAA;;AC1CA,IAAA,EAAA,QAAA,SAAA,CAAA,QACA,EAAA,QAAA,gBACA,EAAA,QAAA,UACA,EAAA,QAAA,gBAAA,EACA,EAAA,EACA,EAAA,OAAA,cAAA,WACA,OAAA,GAEA,GAAA,QAAA,WAAA,CAAA,WACA,OAAA,EAAA,OAAA,kBAAA,OAEA,EAAA,SAAA,GACA,EAAA,EAAA,EAAA,CAAA,MAAA,CACA,EAAA,OAAA,EACA,EAAA,OAGA,EAAA,SAAA,EAAA,GAEA,IAAA,EAAA,GAAA,MAAA,iBAAA,EAAA,GAAA,iBAAA,EAAA,IAAA,KAAA,EACA,IAAA,EAAA,EAAA,GAAA,CAEA,IAAA,EAAA,GAAA,MAAA,IAEA,IAAA,EAAA,MAAA,IAEA,EAAA,GAEA,OAAA,EAAA,GAAA,GAEA,EAAA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,GAAA,CAEA,IAAA,EAAA,GAAA,OAAA,EAEA,IAAA,EAAA,OAAA,EAEA,EAAA,GAEA,OAAA,EAAA,GAAA,GAGA,EAAA,SAAA,GAEA,OADA,GAAA,EAAA,MAAA,EAAA,KAAA,EAAA,EAAA,IAAA,EAAA,GACA,GAEA,EAAA,OAAA,QAAA,CACA,IAAA,EACA,MAAA,EACA,QAAA,EACA,QAAA,EACA,SAAA;;ACnDA,IAAA,EAAA,QAAA,YAAA,CAAA,OACA,EAAA,QAAA,UACA,EAAA,QAAA,aAAA,OACA,EAAA,mBAAA,EAEA,EAAA,OAAA,QAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,GACA,GAAA,EAAA,KAAA,EAAA,EAAA,GAAA,UAAA,KAGA,EAAA,MAAA;;ACVA,IAAA,EAAA,QAAA,gBAAA,EACA,EAAA,QAAA,UACA,EAAA,QAAA,SAAA,CAAA,eAEA,OAAA,QAAA,SAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,EAAA,EAAA,EAAA,UAAA,IAAA,EAAA,EAAA,EAAA,CAAA,cAAA,EAAA,MAAA;;ACLA,QAAA,EAAA,QAAA;;;ACAA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,WACA,EAAA,QAAA,cACA,EAAA,QAAA,cACA,EAAA,QAAA,gBAAA,EACA,OAAA,QAAA,SAAA,GACA,IAAA,EAAA,EAAA,SAAA,EAAA,OAAA,EAAA,GAAA,EAAA,QAAA,IACA,KAAA,EAAA,OAAA,IAAA,KAAA,GAAA,EAAA,EAAA,EAAA,CAAA,MAAA,EAAA,EAAA;;ACPA,IAAA,EAAA,GAAA,SAEA,OAAA,QAAA,SAAA,GACA,OAAA,EAAA,KAAA,GAAA,MAAA,GAAA;;ACFA,IAAA,EAAA,QAAA,UAEA,OAAA,QAAA,OAAA,KAAA,qBAAA,GAAA,OAAA,SAAA,GACA,MAAA,UAAA,EAAA,GAAA,EAAA,MAAA,IAAA,OAAA;;ACHA,OAAA,QAAA,SAAA,GACA,GAAA,MAAA,EAAA,MAAA,UAAA,yBAAA,GACA,OAAA;;ACFA,IAAA,EAAA,QAAA,cACA,EAAA,QAAA,cACA,OAAA,QAAA,SAAA,GACA,OAAA,EAAA,EAAA;;ACHA,IAAA,EAAA,KAAA,KACA,EAAA,KAAA,MACA,OAAA,QAAA,SAAA,GACA,OAAA,MAAA,GAAA,GAAA,GAAA,EAAA,EAAA,EAAA,GAAA;;ACHA,IAAA,EAAA,QAAA,iBACA,EAAA,KAAA,IACA,OAAA,QAAA,SAAA,GACA,OAAA,EAAA,EAAA,EAAA,EAAA,GAAA,kBAAA;;ACJA,IAAA,EAAA,QAAA,iBACA,EAAA,KAAA,IACA,EAAA,KAAA,IACA,OAAA,QAAA,SAAA,EAAA,GAEA,OADA,EAAA,EAAA,IACA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,EAAA;;ACHA,IAAA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,EAAA,QAAA,wBACA,OAAA,QAAA,SAAA,GACA,OAAA,SAAA,EAAA,EAAA,GACA,IAGA,EAHA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,QACA,EAAA,EAAA,EAAA,GAIA,GAAA,GAAA,GAAA,GAAA,KAAA,EAAA,GAGA,IAFA,EAAA,EAAA,OAEA,EAAA,OAAA,OAEA,KAAA,EAAA,EAAA,IAAA,IAAA,GAAA,KAAA,IACA,EAAA,KAAA,EAAA,OAAA,GAAA,GAAA,EACA,OAAA,IAAA;;ACpBA,IAAA,EAAA,QAAA,YAAA,CAAA,QACA,EAAA,QAAA,UACA,OAAA,QAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,GAAA,EAAA;;ACHA,IAAA,EAAA,QAAA,UACA,EAAA,QAAA,iBACA,EAAA,QAAA,oBAAA,EAAA,GACA,EAAA,QAAA,gBAAA,CAAA,YAEA,OAAA,QAAA,SAAA,EAAA,GACA,IAGA,EAHA,EAAA,EAAA,GACA,EAAA,EACA,EAAA,GAEA,IAAA,KAAA,EAAA,GAAA,GAAA,EAAA,EAAA,IAAA,EAAA,KAAA,GAEA,KAAA,EAAA,OAAA,GAAA,EAAA,EAAA,EAAA,EAAA,SACA,EAAA,EAAA,IAAA,EAAA,KAAA,IAEA,OAAA;;ACdA,OAAA,QAAA,gGAEA,MAAA;;ACFA,IAAA,EAAA,QAAA,2BACA,EAAA,QAAA,oBAEA,OAAA,QAAA,OAAA,MAAA,SAAA,GACA,OAAA,EAAA,EAAA;;ACLA,QAAA,EAAA,OAAA;;ACAA,QAAA,EAAA,GAAA;;ACCA,IAAA,EAAA,QAAA,kBACA,EAAA,QAAA,kBACA,EAAA,QAAA,iBACA,OAAA,QAAA,SAAA,GACA,IAAA,EAAA,EAAA,GACA,EAAA,EAAA,EACA,GAAA,EAKA,IAJA,IAGA,EAHA,EAAA,EAAA,GACA,EAAA,EAAA,EACA,EAAA,EAEA,EAAA,OAAA,GAAA,EAAA,KAAA,EAAA,EAAA,EAAA,OAAA,EAAA,KAAA,GACA,OAAA;;ACZA,IAAA,EAAA,QAAA,UACA,OAAA,QAAA,MAAA,SAAA,SAAA,GACA,MAAA,SAAA,EAAA;;ACFA,IAAA,EAAA,QAAA,cACA,OAAA,QAAA,SAAA,GACA,OAAA,OAAA,EAAA;;ACHA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,kBAEA,OAAA,QAAA,QAAA,kBAAA,OAAA,iBAAA,SAAA,EAAA,GACA,EAAA,GAKA,IAJA,IAGA,EAHA,EAAA,EAAA,GACA,EAAA,EAAA,OACA,EAAA,EAEA,EAAA,GAAA,EAAA,EAAA,EAAA,EAAA,EAAA,KAAA,EAAA,IACA,OAAA;;ACXA,IAAA,EAAA,QAAA,aAAA,SACA,OAAA,QAAA,GAAA,EAAA;;ACAA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,QAAA,oBACA,EAAA,QAAA,gBAAA,CAAA,YACA,EAAA,aACA,EAAA,YAGA,EAAA,WAEA,IAIA,EAJA,EAAA,QAAA,gBAAA,CAAA,UACA,EAAA,EAAA,OAcA,IAVA,EAAA,MAAA,QAAA,OACA,QAAA,WAAA,YAAA,GACA,EAAA,IAAA,eAGA,EAAA,EAAA,cAAA,UACA,OACA,EAAA,MAAA,uCACA,EAAA,QACA,EAAA,EAAA,EACA,YAAA,EAAA,GAAA,EAAA,IACA,OAAA,KAGA,OAAA,QAAA,OAAA,QAAA,SAAA,EAAA,GACA,IAAA,EAQA,OAPA,OAAA,GACA,EAAA,GAAA,EAAA,GACA,EAAA,IAAA,EACA,EAAA,GAAA,KAEA,EAAA,GAAA,GACA,EAAA,SACA,IAAA,EAAA,EAAA,EAAA,EAAA;;ACtCA,IAAA,EAAA,QAAA,2BACA,EAAA,QAAA,oBAAA,OAAA,SAAA,aAEA,QAAA,EAAA,OAAA,qBAAA,SAAA,GACA,OAAA,EAAA,EAAA;;ACJA,IAAA,EAAA,QAAA,iBACA,EAAA,QAAA,kBAAA,EACA,EAAA,GAAA,SAEA,EAAA,iBAAA,QAAA,QAAA,OAAA,oBACA,OAAA,oBAAA,QAAA,GAEA,EAAA,SAAA,GACA,IACA,OAAA,EAAA,GACA,MAAA,GACA,OAAA,EAAA,UAIA,OAAA,QAAA,EAAA,SAAA,GACA,OAAA,GAAA,mBAAA,EAAA,KAAA,GAAA,EAAA,GAAA,EAAA,EAAA;;ACjBA,IAAA,EAAA,QAAA,iBACA,EAAA,QAAA,oBACA,EAAA,QAAA,iBACA,EAAA,QAAA,mBACA,EAAA,QAAA,UACA,EAAA,QAAA,qBACA,EAAA,OAAA,yBAEA,QAAA,EAAA,QAAA,kBAAA,EAAA,SAAA,EAAA,GAGA,GAFA,EAAA,EAAA,GACA,EAAA,EAAA,GAAA,GACA,EAAA,IACA,OAAA,EAAA,EAAA,GACA,MAAA,IACA,GAAA,EAAA,EAAA,GAAA,OAAA,GAAA,EAAA,EAAA,KAAA,EAAA,GAAA,EAAA;;;ACdA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,UACA,EAAA,QAAA,kBACA,EAAA,QAAA,aACA,EAAA,QAAA,eACA,EAAA,QAAA,WAAA,IACA,EAAA,QAAA,YACA,EAAA,QAAA,aACA,EAAA,QAAA,wBACA,EAAA,QAAA,UACA,EAAA,QAAA,UACA,EAAA,QAAA,cACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,EAAA,QAAA,eACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,QAAA,mBACA,EAAA,QAAA,oBACA,EAAA,QAAA,oBACA,EAAA,QAAA,sBACA,EAAA,QAAA,kBACA,EAAA,QAAA,kBACA,EAAA,QAAA,gBACA,EAAA,QAAA,kBACA,EAAA,EAAA,EACA,EAAA,EAAA,EACA,EAAA,EAAA,EACA,EAAA,EAAA,OACA,EAAA,EAAA,KACA,EAAA,GAAA,EAAA,UACA,EAAA,YACA,EAAA,EAAA,WACA,EAAA,EAAA,eACA,EAAA,GAAA,qBACA,EAAA,EAAA,mBACA,EAAA,EAAA,WACA,EAAA,EAAA,cACA,EAAA,OAAA,GACA,EAAA,mBAAA,KAAA,EAAA,EACA,EAAA,EAAA,QAEA,GAAA,IAAA,EAAA,KAAA,EAAA,GAAA,UAGA,EAAA,GAAA,EAAA,WACA,OAEA,GAFA,EAAA,EAAA,GAAA,IAAA,CACA,IAAA,WAAA,OAAA,EAAA,KAAA,IAAA,CAAA,MAAA,IAAA,MACA,IACA,SAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,EAAA,GACA,UAAA,EAAA,GACA,EAAA,EAAA,EAAA,GACA,GAAA,IAAA,GAAA,EAAA,EAAA,EAAA,IACA,EAEA,EAAA,SAAA,GACA,IAAA,EAAA,EAAA,GAAA,EAAA,EAAA,IAEA,OADA,EAAA,GAAA,EACA,GAGA,EAAA,GAAA,iBAAA,EAAA,SAAA,SAAA,GACA,MAAA,iBAAA,GACA,SAAA,GACA,OAAA,aAAA,GAGA,EAAA,SAAA,EAAA,EAAA,GAKA,OAJA,IAAA,GAAA,EAAA,EAAA,EAAA,GACA,EAAA,GACA,EAAA,EAAA,GAAA,GACA,EAAA,GACA,EAAA,EAAA,IACA,EAAA,YAIA,EAAA,EAAA,IAAA,EAAA,GAAA,KAAA,EAAA,GAAA,IAAA,GACA,EAAA,EAAA,EAAA,CAAA,WAAA,EAAA,GAAA,OAJA,EAAA,EAAA,IAAA,EAAA,EAAA,EAAA,EAAA,EAAA,KACA,EAAA,GAAA,IAAA,GAIA,EAAA,EAAA,EAAA,IACA,EAAA,EAAA,EAAA,IAEA,EAAA,SAAA,EAAA,GACA,EAAA,GAKA,IAJA,IAGA,EAHA,EAAA,EAAA,EAAA,EAAA,IACA,EAAA,EACA,EAAA,EAAA,OAEA,EAAA,GAAA,EAAA,EAAA,EAAA,EAAA,KAAA,EAAA,IACA,OAAA,GAEA,EAAA,SAAA,EAAA,GACA,YAAA,IAAA,EAAA,EAAA,GAAA,EAAA,EAAA,GAAA,IAEA,EAAA,SAAA,GACA,IAAA,EAAA,EAAA,KAAA,KAAA,EAAA,EAAA,GAAA,IACA,QAAA,OAAA,GAAA,EAAA,EAAA,KAAA,EAAA,EAAA,QACA,IAAA,EAAA,KAAA,KAAA,EAAA,EAAA,IAAA,EAAA,KAAA,IAAA,KAAA,GAAA,KAAA,IAEA,EAAA,SAAA,EAAA,GAGA,GAFA,EAAA,EAAA,GACA,EAAA,EAAA,GAAA,GACA,IAAA,IAAA,EAAA,EAAA,IAAA,EAAA,EAAA,GAAA,CACA,IAAA,EAAA,EAAA,EAAA,GAEA,OADA,IAAA,EAAA,EAAA,IAAA,EAAA,EAAA,IAAA,EAAA,GAAA,KAAA,EAAA,YAAA,GACA,IAEA,EAAA,SAAA,GAKA,IAJA,IAGA,EAHA,EAAA,EAAA,EAAA,IACA,EAAA,GACA,EAAA,EAEA,EAAA,OAAA,GACA,EAAA,EAAA,EAAA,EAAA,OAAA,GAAA,GAAA,GAAA,GAAA,EAAA,KAAA,GACA,OAAA,GAEA,EAAA,SAAA,GAMA,IALA,IAIA,EAJA,EAAA,IAAA,EACA,EAAA,EAAA,EAAA,EAAA,EAAA,IACA,EAAA,GACA,EAAA,EAEA,EAAA,OAAA,IACA,EAAA,EAAA,EAAA,EAAA,OAAA,IAAA,EAAA,EAAA,IAAA,EAAA,KAAA,EAAA,IACA,OAAA,GAIA,IAYA,GAXA,EAAA,WACA,GAAA,gBAAA,EAAA,MAAA,UAAA,gCACA,IAAA,EAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,GACA,EAAA,SAAA,GACA,OAAA,GAAA,EAAA,KAAA,EAAA,GACA,EAAA,KAAA,IAAA,EAAA,KAAA,GAAA,KAAA,KAAA,GAAA,IAAA,GACA,EAAA,KAAA,EAAA,EAAA,EAAA,KAGA,OADA,GAAA,GAAA,EAAA,EAAA,EAAA,CAAA,cAAA,EAAA,IAAA,IACA,EAAA,KAEA,GAAA,WAAA,WACA,OAAA,KAAA,KAGA,EAAA,EAAA,EACA,EAAA,EAAA,EACA,QAAA,kBAAA,EAAA,EAAA,EAAA,EACA,QAAA,iBAAA,EAAA,EACA,EAAA,EAAA,EAEA,IAAA,QAAA,eACA,EAAA,EAAA,uBAAA,GAAA,GAGA,EAAA,EAAA,SAAA,GACA,OAAA,EAAA,EAAA,MAIA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,CAAA,OAAA,IAEA,IAAA,IAAA,GAAA,iHAGA,MAAA,KAAA,GAAA,EAAA,GAAA,OAAA,IAAA,EAAA,GAAA,OAEA,IAAA,IAAA,GAAA,EAAA,EAAA,OAAA,GAAA,EAAA,GAAA,OAAA,IAAA,EAAA,GAAA,OAEA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,SAAA,CAEA,IAAA,SAAA,GACA,OAAA,EAAA,EAAA,GAAA,IACA,EAAA,GACA,EAAA,GAAA,EAAA,IAGA,OAAA,SAAA,GACA,IAAA,EAAA,GAAA,MAAA,UAAA,EAAA,qBACA,IAAA,IAAA,KAAA,EAAA,GAAA,EAAA,KAAA,EAAA,OAAA,GAEA,UAAA,WAAA,GAAA,GACA,UAAA,WAAA,GAAA,KAGA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,SAAA,CAEA,OAAA,EAEA,eAAA,EAEA,iBAAA,EAEA,yBAAA,EAEA,oBAAA,EAEA,sBAAA,IAKA,IAAA,GAAA,EAAA,WAAA,EAAA,EAAA,KAEA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,SAAA,CACA,sBAAA,SAAA,GACA,OAAA,EAAA,EAAA,EAAA,OAKA,GAAA,EAAA,EAAA,EAAA,EAAA,IAAA,GAAA,EAAA,WACA,IAAA,EAAA,IAIA,MAAA,UAAA,EAAA,CAAA,KAAA,MAAA,EAAA,CAAA,EAAA,KAAA,MAAA,EAAA,OAAA,OACA,OAAA,CACA,UAAA,SAAA,GAIA,IAHA,IAEA,EAAA,EAFA,EAAA,CAAA,GACA,EAAA,EAEA,UAAA,OAAA,GAAA,EAAA,KAAA,UAAA,MAEA,GADA,EAAA,EAAA,EAAA,IACA,EAAA,SAAA,IAAA,KAAA,EAAA,GAMA,OALA,EAAA,KAAA,EAAA,SAAA,EAAA,GAEA,GADA,mBAAA,IAAA,EAAA,EAAA,KAAA,KAAA,EAAA,KACA,EAAA,GAAA,OAAA,IAEA,EAAA,GAAA,EACA,EAAA,MAAA,EAAA,MAKA,EAAA,GAAA,IAAA,QAAA,UAAA,CAAA,EAAA,GAAA,EAAA,EAAA,GAAA,SAEA,EAAA,EAAA,UAEA,EAAA,KAAA,QAAA,GAEA,EAAA,EAAA,KAAA,QAAA;;ACrPA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,SAAA,CAAA,OAAA,QAAA;;ACFA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,kBAAA,SAAA,CAAA,eAAA,QAAA,gBAAA;;ACFA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,kBAAA,SAAA,CAAA,iBAAA,QAAA;;ACDA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,WACA,EAAA,QAAA,YACA,OAAA,QAAA,SAAA,EAAA,GACA,IAAA,GAAA,EAAA,QAAA,IAAA,IAAA,OAAA,GACA,EAAA,GACA,EAAA,GAAA,EAAA,GACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,WAAA,EAAA,KAAA,SAAA;;ACPA,IAAA,EAAA,QAAA,iBACA,EAAA,QAAA,kBAAA,EAEA,QAAA,gBAAA,CAAA,2BAAA,WACA,OAAA,SAAA,EAAA,GACA,OAAA,EAAA,EAAA,GAAA;;ACLA,IAAA,EAAA,QAAA,UACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBAAA,CAAA,YACA,EAAA,OAAA,UAEA,OAAA,QAAA,OAAA,gBAAA,SAAA,GAEA,OADA,EAAA,EAAA,GACA,EAAA,EAAA,GAAA,EAAA,GACA,mBAAA,EAAA,aAAA,aAAA,EAAA,YACA,EAAA,YAAA,UACA,aAAA,OAAA,EAAA;;ACVA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,iBAEA,QAAA,gBAAA,CAAA,iBAAA,WACA,OAAA,SAAA,GACA,OAAA,EAAA,EAAA;;ACLA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,kBAEA,QAAA,gBAAA,CAAA,OAAA,WACA,OAAA,SAAA,GACA,OAAA,EAAA,EAAA;;ACLA,QAAA,gBAAA,CAAA,sBAAA,WACA,OAAA,QAAA,sBAAA;;ACDA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,WAAA,SAEA,QAAA,gBAAA,CAAA,SAAA,SAAA,GACA,OAAA,SAAA,GACA,OAAA,GAAA,EAAA,GAAA,EAAA,EAAA,IAAA;;ACLA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,WAAA,SAEA,QAAA,gBAAA,CAAA,OAAA,SAAA,GACA,OAAA,SAAA,GACA,OAAA,GAAA,EAAA,GAAA,EAAA,EAAA,IAAA;;ACLA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,WAAA,SAEA,QAAA,gBAAA,CAAA,oBAAA,SAAA,GACA,OAAA,SAAA,GACA,OAAA,GAAA,EAAA,GAAA,EAAA,EAAA,IAAA;;ACLA,IAAA,EAAA,QAAA,gBAEA,QAAA,gBAAA,CAAA,WAAA,SAAA,GACA,OAAA,SAAA,GACA,OAAA,EAAA,MAAA,GAAA,EAAA;;ACJA,IAAA,EAAA,QAAA,gBAEA,QAAA,gBAAA,CAAA,WAAA,SAAA,GACA,OAAA,SAAA,GACA,OAAA,EAAA,MAAA,GAAA,EAAA;;ACJA,IAAA,EAAA,QAAA,gBAEA,QAAA,gBAAA,CAAA,eAAA,SAAA,GACA,OAAA,SAAA,GACA,QAAA,EAAA,MAAA,GAAA,EAAA;;ACLA,aAEA,IAAA,EAAA,QAAA,kBACA,EAAA,QAAA,kBACA,EAAA,QAAA,kBACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,EAAA,QAAA,cACA,EAAA,OAAA,OAGA,OAAA,SAAA,GAAA,QAAA,WAAA,CAAA,WACA,IAAA,EAAA,GACA,EAAA,GAEA,EAAA,SACA,EAAA,uBAGA,OAFA,EAAA,GAAA,EACA,EAAA,MAAA,IAAA,QAAA,SAAA,GAAA,EAAA,GAAA,IACA,GAAA,EAAA,GAAA,GAAA,IAAA,OAAA,KAAA,EAAA,GAAA,IAAA,KAAA,KAAA,IACA,SAAA,EAAA,GAMA,IALA,IAAA,EAAA,EAAA,GACA,EAAA,UAAA,OACA,EAAA,EACA,EAAA,EAAA,EACA,EAAA,EAAA,EACA,EAAA,GAMA,IALA,IAIA,EAJA,EAAA,EAAA,UAAA,MACA,EAAA,EAAA,EAAA,GAAA,OAAA,EAAA,IAAA,EAAA,GACA,EAAA,EAAA,OACA,EAAA,EAEA,EAAA,GACA,EAAA,EAAA,KACA,IAAA,EAAA,KAAA,EAAA,KAAA,EAAA,GAAA,EAAA,IAEA,OAAA,GACA;;ACpCA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,EAAA,EAAA,SAAA,CAAA,OAAA,QAAA;;ACFA,OAAA,QAAA,OAAA,IAAA,SAAA,EAAA,GAEA,OAAA,IAAA,EAAA,IAAA,GAAA,EAAA,GAAA,EAAA,EAAA,GAAA,GAAA,GAAA;;ACFA,IAAA,EAAA,QAAA,aACA,EAAA,EAAA,EAAA,SAAA,CAAA,GAAA,QAAA;;ACAA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,SAAA,EAAA,GAEA,GADA,EAAA,IACA,EAAA,IAAA,OAAA,EAAA,MAAA,UAAA,EAAA,8BAEA,OAAA,QAAA,CACA,IAAA,OAAA,iBAAA,aAAA,GACA,SAAA,EAAA,EAAA,GACA,KACA,EAAA,QAAA,SAAA,CAAA,SAAA,KAAA,QAAA,kBAAA,EAAA,OAAA,UAAA,aAAA,IAAA,IACA,EAAA,IACA,IAAA,aAAA,OACA,MAAA,GAAA,GAAA,EACA,OAAA,SAAA,EAAA,GAIA,OAHA,EAAA,EAAA,GACA,EAAA,EAAA,UAAA,EACA,EAAA,EAAA,GACA,GAVA,CAYA,IAAA,QAAA,GACA,MAAA;;ACtBA,IAAA,EAAA,QAAA,aACA,EAAA,EAAA,EAAA,SAAA,CAAA,eAAA,QAAA,gBAAA;;ACDA,IAAA,EAAA,QAAA,UACA,EAAA,QAAA,SAAA,CAAA,eAEA,EAAA,aAAA,EAAA,WAAA,OAAA,UAAA,IAGA,EAAA,SAAA,EAAA,GACA,IACA,OAAA,EAAA,GACA,MAAA,MAGA,OAAA,QAAA,SAAA,GACA,IAAA,EAAA,EAAA,EACA,YAAA,IAAA,EAAA,YAAA,OAAA,EAAA,OAEA,iBAAA,EAAA,EAAA,EAAA,OAAA,GAAA,IAAA,EAEA,EAAA,EAAA,GAEA,WAAA,EAAA,EAAA,KAAA,mBAAA,EAAA,OAAA,YAAA;;ACrBA,aAEA,IAAA,EAAA,QAAA,cACA,EAAA,GACA,EAAA,QAAA,SAAA,CAAA,gBAAA,IACA,EAAA,IAAA,cACA,QAAA,cAAA,CAAA,OAAA,UAAA,WAAA,WACA,MAAA,WAAA,EAAA,MAAA,MACA;;ACPA,OAAA,QAAA,SAAA,EAAA,EAAA,GACA,IAAA,OAAA,IAAA,EACA,OAAA,EAAA,QACA,KAAA,EAAA,OAAA,EAAA,IACA,EAAA,KAAA,GACA,KAAA,EAAA,OAAA,EAAA,EAAA,EAAA,IACA,EAAA,KAAA,EAAA,EAAA,IACA,KAAA,EAAA,OAAA,EAAA,EAAA,EAAA,GAAA,EAAA,IACA,EAAA,KAAA,EAAA,EAAA,GAAA,EAAA,IACA,KAAA,EAAA,OAAA,EAAA,EAAA,EAAA,GAAA,EAAA,GAAA,EAAA,IACA,EAAA,KAAA,EAAA,EAAA,GAAA,EAAA,GAAA,EAAA,IACA,KAAA,EAAA,OAAA,EAAA,EAAA,EAAA,GAAA,EAAA,GAAA,EAAA,GAAA,EAAA,IACA,EAAA,KAAA,EAAA,EAAA,GAAA,EAAA,GAAA,EAAA,GAAA,EAAA,IACA,OAAA,EAAA,MAAA,EAAA;;ACdA,aACA,IAAA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,EAAA,QAAA,aACA,EAAA,GAAA,MACA,EAAA,GAEA,EAAA,SAAA,EAAA,EAAA,GACA,KAAA,KAAA,GAAA,CACA,IAAA,IAAA,EAAA,GAAA,EAAA,EAAA,EAAA,EAAA,IAAA,EAAA,GAAA,KAAA,EAAA,IAEA,EAAA,GAAA,SAAA,MAAA,gBAAA,EAAA,KAAA,KAAA,KACA,OAAA,EAAA,GAAA,EAAA,IAGA,OAAA,QAAA,SAAA,MAAA,SAAA,GACA,IAAA,EAAA,EAAA,MACA,EAAA,EAAA,KAAA,UAAA,GACA,EAAA,WACA,IAAA,EAAA,EAAA,OAAA,EAAA,KAAA,YACA,OAAA,gBAAA,EAAA,EAAA,EAAA,EAAA,OAAA,GAAA,EAAA,EAAA,EAAA,IAGA,OADA,EAAA,EAAA,aAAA,EAAA,UAAA,EAAA,WACA;;ACtBA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,WAAA,CAAA,KAAA,QAAA;;ACHA,IAAA,EAAA,QAAA,gBAAA,EACA,EAAA,SAAA,UACA,EAAA,wBACA,EAAA,OAGA,KAAA,GAAA,QAAA,mBAAA,EAAA,EAAA,EAAA,CACA,cAAA,EACA,IAAA,WACA,IACA,OAAA,GAAA,MAAA,MAAA,GAAA,GACA,MAAA,GACA,MAAA;;ACZA,aACA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,QAAA,SAAA,CAAA,eACA,EAAA,SAAA,UAEA,KAAA,GAAA,QAAA,gBAAA,EAAA,EAAA,EAAA,CAAA,MAAA,SAAA,GACA,GAAA,mBAAA,OAAA,EAAA,GAAA,OAAA,EACA,IAAA,EAAA,KAAA,WAAA,OAAA,aAAA,KAEA,KAAA,EAAA,EAAA,IAAA,GAAA,KAAA,YAAA,EAAA,OAAA,EACA,OAAA;;ACXA,OAAA,QAAA;;ACAA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,cACA,EAAA,QAAA,YACA,EAAA,QAAA,gBACA,EAAA,IAAA,EAAA,IACA,EAAA,KACA,EAAA,OAAA,IAAA,EAAA,EAAA,KACA,EAAA,OAAA,EAAA,EAAA,MAEA,EAAA,SAAA,EAAA,EAAA,GACA,IAAA,EAAA,GACA,EAAA,EAAA,WACA,QAAA,EAAA,MAAA,EAAA,MAAA,IAEA,EAAA,EAAA,GAAA,EAAA,EAAA,GAAA,EAAA,GACA,IAAA,EAAA,GAAA,GACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,SAAA,IAMA,EAAA,EAAA,KAAA,SAAA,EAAA,GAIA,OAHA,EAAA,OAAA,EAAA,IACA,EAAA,IAAA,EAAA,EAAA,QAAA,EAAA,KACA,EAAA,IAAA,EAAA,EAAA,QAAA,EAAA,KACA,GAGA,OAAA,QAAA;;AC7BA,IAAA,EAAA,QAAA,aAAA,SACA,EAAA,QAAA,kBAAA,KACA,EAAA,QAAA,gBACA,EAAA,cAEA,OAAA,QAAA,IAAA,EAAA,EAAA,OAAA,KAAA,EAAA,EAAA,QAAA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,OAAA,GAAA,GACA,OAAA,EAAA,EAAA,IAAA,IAAA,EAAA,KAAA,GAAA,GAAA,MACA;;ACRA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBAEA,EAAA,EAAA,EAAA,EAAA,GAAA,UAAA,GAAA,CAAA,SAAA;;ACHA,IAAA,EAAA,QAAA,aAAA,WACA,EAAA,QAAA,kBAAA,KAEA,OAAA,QAAA,EAAA,EAAA,QAAA,gBAAA,QAAA,EAAA,EAAA,SAAA,GACA,IAAA,EAAA,EAAA,OAAA,GAAA,GACA,EAAA,EAAA,GACA,OAAA,IAAA,GAAA,KAAA,EAAA,OAAA,IAAA,EAAA,GACA;;ACPA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,kBAEA,EAAA,EAAA,EAAA,EAAA,GAAA,YAAA,GAAA,CAAA,WAAA;;ACHA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,gBAAA,IACA,OAAA,QAAA,SAAA,EAAA,EAAA,GACA,IACA,EADA,EAAA,EAAA,YAIA,OAFA,IAAA,GAAA,mBAAA,IAAA,EAAA,EAAA,aAAA,EAAA,WAAA,EAAA,IAAA,GACA,EAAA,EAAA,GACA;;;ACPA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,UACA,EAAA,QAAA,UACA,EAAA,QAAA,0BACA,EAAA,QAAA,mBACA,EAAA,QAAA,YACA,EAAA,QAAA,kBAAA,EACA,EAAA,QAAA,kBAAA,EACA,EAAA,QAAA,gBAAA,EACA,EAAA,QAAA,kBAAA,KACA,EAAA,SACA,EAAA,EAAA,GACA,EAAA,EACA,EAAA,EAAA,UAEA,EAAA,EAAA,QAAA,mBAAA,CAAA,KAAA,EACA,EAAA,SAAA,OAAA,UAGA,EAAA,SAAA,GACA,IAAA,EAAA,EAAA,GAAA,GACA,GAAA,iBAAA,GAAA,EAAA,OAAA,EAAA,CAEA,IACA,EAAA,EAAA,EADA,GADA,EAAA,EAAA,EAAA,OAAA,EAAA,EAAA,IACA,WAAA,GAEA,GAAA,KAAA,GAAA,KAAA,GAEA,GAAA,MADA,EAAA,EAAA,WAAA,KACA,MAAA,EAAA,OAAA,SACA,GAAA,KAAA,EAAA,CACA,OAAA,EAAA,WAAA,IACA,KAAA,GAAA,KAAA,GAAA,EAAA,EAAA,EAAA,GAAA,MACA,KAAA,GAAA,KAAA,IAAA,EAAA,EAAA,EAAA,GAAA,MACA,QAAA,OAAA,EAEA,IAAA,IAAA,EAAA,EAAA,EAAA,MAAA,GAAA,EAAA,EAAA,EAAA,EAAA,OAAA,EAAA,EAAA,IAIA,IAHA,EAAA,EAAA,WAAA,IAGA,IAAA,EAAA,EAAA,OAAA,IACA,OAAA,SAAA,EAAA,IAEA,OAAA,GAGA,IAAA,EAAA,UAAA,EAAA,QAAA,EAAA,QAAA,CACA,EAAA,SAAA,GACA,IAAA,EAAA,UAAA,OAAA,EAAA,EAAA,EACA,EAAA,KACA,OAAA,aAAA,IAEA,EAAA,EAAA,WAAA,EAAA,QAAA,KAAA,KAAA,EAAA,IAAA,GACA,EAAA,IAAA,EAAA,EAAA,IAAA,EAAA,GAAA,EAAA,IAEA,IAAA,IAMA,EANA,EAAA,QAAA,kBAAA,EAAA,GAAA,6KAMA,MAAA,KAAA,EAAA,EAAA,EAAA,OAAA,EAAA,IACA,EAAA,EAAA,EAAA,EAAA,MAAA,EAAA,EAAA,IACA,EAAA,EAAA,EAAA,EAAA,EAAA,IAGA,EAAA,UAAA,EACA,EAAA,YAAA,EACA,QAAA,cAAA,CAAA,EAAA,EAAA;;ACnEA,IAAA,EAAA,QAAA,UACA,OAAA,QAAA,SAAA,EAAA,GACA,GAAA,iBAAA,GAAA,UAAA,EAAA,GAAA,MAAA,UAAA,GACA,OAAA;;ACHA,aACA,IAAA,EAAA,QAAA,iBACA,EAAA,QAAA,cAEA,OAAA,QAAA,SAAA,GACA,IAAA,EAAA,OAAA,EAAA,OACA,EAAA,GACA,EAAA,EAAA,GACA,GAAA,EAAA,GAAA,GAAA,EAAA,EAAA,MAAA,WAAA,2BACA,KAAA,EAAA,GAAA,KAAA,KAAA,GAAA,GAAA,EAAA,IAAA,GAAA,GACA,OAAA;;ACVA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,QAAA,qBACA,EAAA,QAAA,oBACA,EAAA,GAAA,QACA,EAAA,KAAA,MACA,EAAA,CAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GACA,EAAA,wCACA,EAAA,IAEA,EAAA,SAAA,EAAA,GAGA,IAFA,IAAA,GAAA,EACA,EAAA,IACA,EAAA,GACA,GAAA,EAAA,EAAA,GACA,EAAA,GAAA,EAAA,IACA,EAAA,EAAA,EAAA,MAGA,EAAA,SAAA,GAGA,IAFA,IAAA,EAAA,EACA,EAAA,IACA,GAAA,GACA,GAAA,EAAA,GACA,EAAA,GAAA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,KAGA,EAAA,WAGA,IAFA,IAAA,EAAA,EACA,EAAA,KACA,GAAA,GACA,GAAA,KAAA,GAAA,IAAA,GAAA,IAAA,EAAA,GAAA,CACA,IAAA,EAAA,OAAA,EAAA,IACA,EAAA,KAAA,EAAA,EAAA,EAAA,EAAA,KAAA,EAAA,EAAA,EAAA,QAAA,EAEA,OAAA,GAEA,EAAA,SAAA,EAAA,EAAA,GACA,OAAA,IAAA,EAAA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,EAAA,EAAA,IAEA,EAAA,SAAA,GAGA,IAFA,IAAA,EAAA,EACA,EAAA,EACA,GAAA,MACA,GAAA,GACA,GAAA,KAEA,KAAA,GAAA,GACA,GAAA,EACA,GAAA,EACA,OAAA,GAGA,EAAA,EAAA,EAAA,EAAA,KAAA,IACA,UAAA,KAAA,QAAA,IACA,MAAA,GAAA,QAAA,IACA,SAAA,MAAA,QAAA,IACA,yBAAA,mBAAA,QAAA,MACA,QAAA,WAAA,CAAA,WAEA,EAAA,KAAA,OACA,SAAA,CACA,QAAA,SAAA,GACA,IAIA,EAAA,EAAA,EAAA,EAJA,EAAA,EAAA,KAAA,GACA,EAAA,EAAA,GACA,EAAA,GACA,EAAA,EAEA,GAAA,EAAA,GAAA,EAAA,GAAA,MAAA,WAAA,GAEA,GAAA,GAAA,EAAA,MAAA,MACA,GAAA,IAAA,MAAA,GAAA,KAAA,OAAA,OAAA,GAKA,GAJA,EAAA,IACA,EAAA,IACA,GAAA,GAEA,EAAA,MAKA,GAHA,GADA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,IAAA,IACA,EAAA,EAAA,EAAA,GAAA,EAAA,GAAA,EAAA,EAAA,EAAA,EAAA,GACA,GAAA,kBACA,EAAA,GAAA,GACA,EAAA,CAGA,IAFA,EAAA,EAAA,GACA,EAAA,EACA,GAAA,GACA,EAAA,IAAA,GACA,GAAA,EAIA,IAFA,EAAA,EAAA,GAAA,EAAA,GAAA,GACA,EAAA,EAAA,EACA,GAAA,IACA,EAAA,GAAA,IACA,GAAA,GAEA,EAAA,GAAA,GACA,EAAA,EAAA,GACA,EAAA,GACA,EAAA,SAEA,EAAA,EAAA,GACA,EAAA,IAAA,EAAA,GACA,EAAA,IAAA,EAAA,KAAA,EAAA,GAQA,OAHA,EAFA,EAAA,EAEA,IADA,EAAA,EAAA,SACA,EAAA,KAAA,EAAA,KAAA,EAAA,EAAA,GAAA,EAAA,EAAA,MAAA,EAAA,EAAA,GAAA,IAAA,EAAA,MAAA,EAAA,IAEA,EAAA;;AC9GA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,YACA,EAAA,QAAA,qBACA,EAAA,GAAA,YAEA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,WAEA,MAAA,MAAA,EAAA,KAAA,OAAA,OACA,EAAA,WAEA,EAAA,KAAA,OACA,SAAA,CACA,YAAA,SAAA,GACA,IAAA,EAAA,EAAA,KAAA,6CACA,YAAA,IAAA,EAAA,EAAA,KAAA,GAAA,EAAA,KAAA,EAAA;;ACdA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,SAAA,CAAA,QAAA,KAAA,IAAA,GAAA;;ACFA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,aAAA,SAEA,EAAA,EAAA,EAAA,SAAA,CACA,SAAA,SAAA,GACA,MAAA,iBAAA,GAAA,EAAA;;ACLA,IAAA,EAAA,QAAA,gBACA,EAAA,KAAA,MACA,OAAA,QAAA,SAAA,GACA,OAAA,EAAA,IAAA,SAAA,IAAA,EAAA,KAAA;;ACHA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,SAAA,CAAA,UAAA,QAAA;;ACFA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,SAAA,CACA,MAAA,SAAA,GAEA,OAAA,GAAA;;ACLA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,KAAA,IAEA,EAAA,EAAA,EAAA,SAAA,CACA,cAAA,SAAA,GACA,OAAA,EAAA,IAAA,EAAA,IAAA;;ACNA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,SAAA,CAAA,iBAAA;;ACFA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,SAAA,CAAA,kBAAA;;ACHA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,kBAEA,EAAA,EAAA,EAAA,EAAA,GAAA,OAAA,YAAA,GAAA,SAAA,CAAA,WAAA;;ACHA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBAEA,EAAA,EAAA,EAAA,EAAA,GAAA,OAAA,UAAA,GAAA,SAAA,CAAA,SAAA;;ACFA,OAAA,QAAA,KAAA,OAAA,SAAA,GACA,OAAA,GAAA,IAAA,MAAA,EAAA,KAAA,EAAA,EAAA,EAAA,EAAA,KAAA,IAAA,EAAA;;ACDA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,KAAA,KACA,EAAA,KAAA,MAEA,EAAA,EAAA,EAAA,EAAA,IAAA,GAEA,KAAA,KAAA,MAAA,EAAA,OAAA,aAEA,EAAA,EAAA,IAAA,EAAA,GACA,OAAA,CACA,MAAA,SAAA,GACA,OAAA,GAAA,GAAA,EAAA,IAAA,EAAA,kBACA,KAAA,IAAA,GAAA,KAAA,IACA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,EAAA;;ACdA,IAAA,EAAA,QAAA,aACA,EAAA,KAAA,MAEA,SAAA,EAAA,GACA,OAAA,SAAA,GAAA,IAAA,GAAA,EAAA,EAAA,GAAA,GAAA,GAAA,KAAA,IAAA,EAAA,KAAA,KAAA,EAAA,EAAA,IAAA,EAIA,EAAA,EAAA,EAAA,EAAA,IAAA,GAAA,EAAA,EAAA,GAAA,GAAA,OAAA,CAAA,MAAA;;ACRA,IAAA,EAAA,QAAA,aACA,EAAA,KAAA,MAGA,EAAA,EAAA,EAAA,EAAA,IAAA,GAAA,EAAA,GAAA,GAAA,GAAA,OAAA,CACA,MAAA,SAAA,GACA,OAAA,IAAA,GAAA,GAAA,EAAA,KAAA,KAAA,EAAA,IAAA,EAAA,IAAA;;ACNA,OAAA,QAAA,KAAA,MAAA,SAAA,GAEA,OAAA,IAAA,GAAA,IAAA,GAAA,EAAA,EAAA,EAAA,GAAA,EAAA;;ACFA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBAEA,EAAA,EAAA,EAAA,OAAA,CACA,KAAA,SAAA,GACA,OAAA,EAAA,GAAA,GAAA,KAAA,IAAA,KAAA,IAAA,GAAA,EAAA;;ACLA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CACA,MAAA,SAAA,GACA,OAAA,KAAA,GAAA,GAAA,KAAA,MAAA,KAAA,IAAA,EAAA,IAAA,KAAA,OAAA;;ACJA,IAAA,EAAA,QAAA,aACA,EAAA,KAAA,IAEA,EAAA,EAAA,EAAA,OAAA,CACA,KAAA,SAAA,GACA,OAAA,EAAA,GAAA,GAAA,GAAA,IAAA;;ACLA,IAAA,EAAA,KAAA,MACA,OAAA,SAAA,GAEA,EAAA,IAAA,oBAAA,EAAA,IAAA,qBAEA,OAAA,GAAA,OACA,SAAA,GACA,OAAA,IAAA,GAAA,GAAA,EAAA,GAAA,MAAA,EAAA,KAAA,EAAA,EAAA,EAAA,EAAA,KAAA,IAAA,GAAA,GACA;;ACRA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBAEA,EAAA,EAAA,EAAA,EAAA,GAAA,GAAA,KAAA,OAAA,OAAA,CAAA,MAAA;;ACHA,IAAA,EAAA,QAAA,gBACA,EAAA,KAAA,IACA,EAAA,EAAA,GAAA,IACA,EAAA,EAAA,GAAA,IACA,EAAA,EAAA,EAAA,MAAA,EAAA,GACA,EAAA,EAAA,GAAA,KAEA,EAAA,SAAA,GACA,OAAA,EAAA,EAAA,EAAA,EAAA,GAGA,OAAA,QAAA,KAAA,QAAA,SAAA,GACA,IAEA,EAAA,EAFA,EAAA,KAAA,IAAA,GACA,EAAA,EAAA,GAEA,OAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,GAEA,GADA,GAAA,EAAA,EAAA,GAAA,IACA,EAAA,IAEA,GAAA,GAAA,EAAA,GAAA,EAAA,GACA,EAAA;;ACpBA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CAAA,OAAA,QAAA;;ACFA,IAAA,EAAA,QAAA,aACA,EAAA,KAAA,IAEA,EAAA,EAAA,EAAA,OAAA,CACA,MAAA,SAAA,EAAA,GAMA,IALA,IAIA,EAAA,EAJA,EAAA,EACA,EAAA,EACA,EAAA,UAAA,OACA,EAAA,EAEA,EAAA,GAEA,GADA,EAAA,EAAA,UAAA,QAGA,EAAA,GADA,EAAA,EAAA,GACA,EAAA,EACA,EAAA,GAGA,GAFA,EAAA,GACA,EAAA,EAAA,GACA,EACA,EAEA,OAAA,IAAA,EAAA,EAAA,EAAA,EAAA,EAAA,KAAA,KAAA;;ACrBA,IAAA,EAAA,QAAA,aACA,EAAA,KAAA,KAGA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,WAAA,CAAA,WACA,OAAA,GAAA,EAAA,WAAA,IAAA,GAAA,EAAA,SACA,OAAA,CACA,KAAA,SAAA,EAAA,GACA,IACA,GAAA,EACA,GAAA,EACA,EAHA,MAGA,EACA,EAJA,MAIA,EACA,OAAA,EAAA,EAAA,IALA,MAKA,IAAA,IAAA,EAAA,GALA,MAKA,IAAA,KAAA,KAAA;;ACbA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CACA,MAAA,SAAA,GACA,OAAA,KAAA,IAAA,GAAA,KAAA;;ACJA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CAAA,MAAA,QAAA;;ACFA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CACA,KAAA,SAAA,GACA,OAAA,KAAA,IAAA,GAAA,KAAA;;ACJA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CAAA,KAAA,QAAA;;ACFA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,KAAA,IAGA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,WAAA,CAAA,WACA,OAAA,QAAA,KAAA,MAAA,SACA,OAAA,CACA,KAAA,SAAA,GACA,OAAA,KAAA,IAAA,GAAA,GAAA,GACA,EAAA,GAAA,GAAA,IAAA,GACA,EAAA,EAAA,GAAA,GAAA,EAAA,KAAA,KAAA,EAAA;;ACXA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,KAAA,IAEA,EAAA,EAAA,EAAA,OAAA,CACA,KAAA,SAAA,GACA,IAAA,EAAA,EAAA,GAAA,GACA,EAAA,GAAA,GACA,OAAA,GAAA,EAAA,EAAA,EAAA,GAAA,EAAA,GAAA,GAAA,EAAA,IAAA,EAAA,GAAA,GAAA;;ACRA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CACA,MAAA,SAAA,GACA,OAAA,EAAA,EAAA,KAAA,MAAA,KAAA,MAAA;;ACLA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,wBACA,EAAA,OAAA,aACA,EAAA,OAAA,cAGA,EAAA,EAAA,EAAA,EAAA,KAAA,GAAA,GAAA,EAAA,QAAA,SAAA,CAEA,cAAA,SAAA,GAKA,IAJA,IAGA,EAHA,EAAA,GACA,EAAA,UAAA,OACA,EAAA,EAEA,EAAA,GAAA,CAEA,GADA,GAAA,UAAA,KACA,EAAA,EAAA,WAAA,EAAA,MAAA,WAAA,EAAA,8BACA,EAAA,KAAA,EAAA,MACA,EAAA,GACA,EAAA,QAAA,GAAA,QAAA,IAAA,EAAA,KAAA,QAEA,OAAA,EAAA,KAAA;;ACpBA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBAEA,EAAA,EAAA,EAAA,SAAA,CAEA,IAAA,SAAA,GAMA,IALA,IAAA,EAAA,EAAA,EAAA,KACA,EAAA,EAAA,EAAA,QACA,EAAA,UAAA,OACA,EAAA,GACA,EAAA,EACA,EAAA,GACA,EAAA,KAAA,OAAA,EAAA,OACA,EAAA,GAAA,EAAA,KAAA,OAAA,UAAA,KACA,OAAA,EAAA,KAAA;;ACfA,aAEA,QAAA,iBAAA,CAAA,OAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA;;ACJA,IAAA,EAAA,QAAA,iBACA,EAAA,QAAA,cAGA,OAAA,QAAA,SAAA,GACA,OAAA,SAAA,EAAA,GACA,IAGA,EAAA,EAHA,EAAA,OAAA,EAAA,IACA,EAAA,EAAA,GACA,EAAA,EAAA,OAEA,OAAA,EAAA,GAAA,GAAA,EAAA,EAAA,QAAA,GACA,EAAA,EAAA,WAAA,IACA,OAAA,EAAA,OAAA,EAAA,IAAA,IAAA,EAAA,EAAA,WAAA,EAAA,IAAA,OAAA,EAAA,MACA,EAAA,EAAA,OAAA,GAAA,EACA,EAAA,EAAA,MAAA,EAAA,EAAA,GAAA,EAAA,OAAA,EAAA,OAAA,IAAA;;ACdA,OAAA,QAAA;;ACAA,aACA,IAAA,EAAA,QAAA,oBACA,EAAA,QAAA,oBACA,EAAA,QAAA,wBACA,EAAA,GAGA,QAAA,UAAA,CAAA,EAAA,QAAA,SAAA,CAAA,YAAA,WAAA,OAAA,OAEA,OAAA,QAAA,SAAA,EAAA,EAAA,GACA,EAAA,UAAA,EAAA,EAAA,CAAA,KAAA,EAAA,EAAA,KACA,EAAA,EAAA,EAAA;;ACXA,aACA,IAAA,EAAA,QAAA,cACA,EAAA,QAAA,aACA,EAAA,QAAA,eACA,EAAA,QAAA,WACA,EAAA,QAAA,gBACA,EAAA,QAAA,kBACA,EAAA,QAAA,wBACA,EAAA,QAAA,iBACA,EAAA,QAAA,SAAA,CAAA,YACA,IAAA,GAAA,MAAA,QAAA,GAAA,QACA,EAAA,aACA,EAAA,OACA,EAAA,SAEA,EAAA,WAAA,OAAA,MAEA,OAAA,QAAA,SAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,GACA,IAeA,EAAA,EAAA,EAfA,EAAA,SAAA,GACA,IAAA,GAAA,KAAA,EAAA,OAAA,EAAA,GACA,OAAA,GACA,KAAA,EACA,KAAA,EAAA,OAAA,WAAA,OAAA,IAAA,EAAA,KAAA,IACA,OAAA,WAAA,OAAA,IAAA,EAAA,KAAA,KAEA,EAAA,EAAA,YACA,EAAA,GAAA,EACA,GAAA,EACA,EAAA,EAAA,UACA,EAAA,EAAA,IAAA,EAAA,IAAA,GAAA,EAAA,GACA,EAAA,GAAA,EAAA,GACA,EAAA,EAAA,EAAA,EAAA,WAAA,OAAA,EACA,EAAA,SAAA,GAAA,EAAA,SAAA,EAwBA,GArBA,IACA,EAAA,EAAA,EAAA,KAAA,IAAA,OACA,OAAA,WAAA,EAAA,OAEA,EAAA,EAAA,GAAA,GAEA,GAAA,mBAAA,EAAA,IAAA,EAAA,EAAA,EAAA,IAIA,GAAA,GAAA,EAAA,OAAA,IACA,GAAA,EACA,EAAA,WAAA,OAAA,EAAA,KAAA,QAGA,IAAA,IAAA,IAAA,GAAA,EAAA,IACA,EAAA,EAAA,EAAA,GAGA,EAAA,GAAA,EACA,EAAA,GAAA,EACA,EAMA,GALA,EAAA,CACA,OAAA,EAAA,EAAA,EAAA,GACA,KAAA,EAAA,EAAA,EAAA,GACA,QAAA,GAEA,EAAA,IAAA,KAAA,EACA,KAAA,GAAA,EAAA,EAAA,EAAA,EAAA,SACA,EAAA,EAAA,EAAA,EAAA,GAAA,GAAA,GAAA,EAAA,GAEA,OAAA;;ACnEA,aACA,IAAA,EAAA,QAAA,eAAA,EAAA,GAGA,QAAA,iBAAA,CAAA,OAAA,SAAA,SAAA,GACA,KAAA,GAAA,OAAA,GACA,KAAA,GAAA,GAEA,WACA,IAEA,EAFA,EAAA,KAAA,GACA,EAAA,KAAA,GAEA,OAAA,GAAA,EAAA,OAAA,CAAA,WAAA,EAAA,MAAA,IACA,EAAA,EAAA,EAAA,GACA,KAAA,IAAA,EAAA,OACA,CAAA,MAAA,EAAA,MAAA;;ACfA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,eAAA,EAAA,GACA,EAAA,EAAA,EAAA,SAAA,CAEA,YAAA,SAAA,GACA,OAAA,EAAA,KAAA;;ACLA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,UACA,EAAA,QAAA,SAAA,CAAA,SACA,OAAA,QAAA,SAAA,GACA,IAAA,EACA,OAAA,EAAA,UAAA,KAAA,EAAA,EAAA,MAAA,EAAA,UAAA,EAAA;;ACLA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,cAEA,OAAA,QAAA,SAAA,EAAA,EAAA,GACA,GAAA,EAAA,GAAA,MAAA,UAAA,UAAA,EAAA,0BACA,OAAA,OAAA,EAAA;;ACNA,IAAA,EAAA,QAAA,SAAA,CAAA,SACA,OAAA,QAAA,SAAA,GACA,IAAA,EAAA,IACA,IACA,MAAA,GAAA,GACA,MAAA,GACA,IAEA,OADA,EAAA,IAAA,GACA,MAAA,GAAA,GACA,MAAA,KACA,OAAA;;ACTA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,qBACA,EAAA,WACA,EAAA,GAAA,GAEA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,qBAAA,CAAA,GAAA,SAAA,CACA,SAAA,SAAA,GACA,IAAA,EAAA,EAAA,KAAA,EAAA,GACA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,EACA,EAAA,EAAA,EAAA,QACA,OAAA,IAAA,EAAA,EAAA,KAAA,IAAA,EAAA,GAAA,GACA,EAAA,OAAA,GACA,OAAA,EACA,EAAA,KAAA,EAAA,EAAA,GACA,EAAA,MAAA,EAAA,EAAA,OAAA,KAAA;;AChBA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,qBACA,EAAA,WAEA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,qBAAA,CAAA,GAAA,SAAA,CACA,SAAA,SAAA,GACA,SAAA,EAAA,KAAA,EAAA,GACA,QAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA;;ACTA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,SAAA,CAEA,OAAA,QAAA;;ACHA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,qBACA,EAAA,aACA,EAAA,GAAA,GAEA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,qBAAA,CAAA,GAAA,SAAA,CACA,WAAA,SAAA,GACA,IAAA,EAAA,EAAA,KAAA,EAAA,GACA,EAAA,EAAA,KAAA,IAAA,UAAA,OAAA,EAAA,UAAA,QAAA,EAAA,EAAA,SACA,EAAA,OAAA,GACA,OAAA,EACA,EAAA,KAAA,EAAA,EAAA,GACA,EAAA,MAAA,EAAA,EAAA,EAAA,UAAA;;ACfA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,YACA,EAAA,QAAA,cACA,EAAA,KAEA,EAAA,SAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,OAAA,EAAA,IACA,EAAA,IAAA,EAEA,MADA,KAAA,IAAA,GAAA,IAAA,EAAA,KAAA,OAAA,GAAA,QAAA,EAAA,UAAA,KACA,EAAA,IAAA,EAAA,KAAA,EAAA,KAEA,OAAA,QAAA,SAAA,EAAA,GACA,IAAA,EAAA,GACA,EAAA,GAAA,EAAA,GACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,WACA,IAAA,EAAA,GAAA,GAAA,KACA,OAAA,IAAA,EAAA,eAAA,EAAA,MAAA,KAAA,OAAA,IACA,SAAA;;ACjBA,aAEA,QAAA,iBAAA,CAAA,SAAA,SAAA,GACA,OAAA,SAAA,GACA,OAAA,EAAA,KAAA,IAAA,OAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,MAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,MAAA,GAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,QAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,QAAA,GAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,OAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,IAAA,GAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,QAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,KAAA,GAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,YAAA,SAAA,GACA,OAAA,SAAA,GACA,OAAA,EAAA,KAAA,OAAA,QAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,WAAA,SAAA,GACA,OAAA,SAAA,GACA,OAAA,EAAA,KAAA,OAAA,OAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,UAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,IAAA,GAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,OAAA,SAAA,GACA,OAAA,SAAA,GACA,OAAA,EAAA,KAAA,IAAA,OAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,QAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,QAAA,GAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,SAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,SAAA,GAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,MAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,MAAA,GAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,MAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,MAAA,GAAA;;ACHA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CAAA,IAAA,WAAA,OAAA,IAAA,MAAA;;ACHA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,mBAEA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,WAAA,CAAA,WACA,OAAA,OAAA,IAAA,KAAA,KAAA,UACA,IAAA,KAAA,UAAA,OAAA,KAAA,CAAA,YAAA,WAAA,OAAA,OACA,OAAA,CAEA,OAAA,SAAA,GACA,IAAA,EAAA,EAAA,MACA,EAAA,EAAA,GACA,MAAA,iBAAA,GAAA,SAAA,GAAA,EAAA,cAAA;;ACbA,aAEA,IAAA,EAAA,QAAA,YACA,EAAA,KAAA,UAAA,QACA,EAAA,KAAA,UAAA,YAEA,EAAA,SAAA,GACA,OAAA,EAAA,EAAA,EAAA,IAAA,GAIA,OAAA,QAAA,EAAA,WACA,MAAA,4BAAA,EAAA,KAAA,IAAA,MAAA,KAAA,QACA,EAAA,WACA,EAAA,KAAA,IAAA,KAAA,QACA,WACA,IAAA,SAAA,EAAA,KAAA,OAAA,MAAA,WAAA,sBACA,IAAA,EAAA,KACA,EAAA,EAAA,iBACA,EAAA,EAAA,qBACA,EAAA,EAAA,EAAA,IAAA,EAAA,KAAA,IAAA,GACA,OAAA,GAAA,QAAA,KAAA,IAAA,IAAA,MAAA,GAAA,GAAA,GACA,IAAA,EAAA,EAAA,cAAA,GAAA,IAAA,EAAA,EAAA,cACA,IAAA,EAAA,EAAA,eAAA,IAAA,EAAA,EAAA,iBACA,IAAA,EAAA,EAAA,iBAAA,KAAA,EAAA,GAAA,EAAA,IAAA,EAAA,IAAA,KACA;;ACxBA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,yBAGA,EAAA,EAAA,EAAA,EAAA,GAAA,KAAA,UAAA,cAAA,GAAA,OAAA,CACA,YAAA;;ACNA,IAAA,EAAA,KAAA,UACA,EAAA,eACA,EAAA,WACA,EAAA,EAAA,GACA,EAAA,EAAA,QACA,IAAA,KAAA,KAAA,IAAA,GACA,QAAA,cAAA,CAAA,EAAA,EAAA,WACA,IAAA,EAAA,EAAA,KAAA,MAEA,OAAA,GAAA,EAAA,EAAA,KAAA,MAAA;;ACTA,aACA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,mBACA,EAAA,SAEA,OAAA,QAAA,SAAA,GACA,GAAA,WAAA,GAAA,IAAA,GAAA,YAAA,EAAA,MAAA,UAAA,kBACA,OAAA,EAAA,EAAA,MAAA,GAAA;;ACPA,IAAA,EAAA,QAAA,SAAA,CAAA,eACA,EAAA,KAAA,UAEA,KAAA,GAAA,QAAA,UAAA,CAAA,EAAA,EAAA,QAAA;;ACFA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,QAAA,CAAA,QAAA,QAAA;;ACFA,IAAA,EAAA,QAAA,gBACA,OAAA,QAAA,SAAA,EAAA,EAAA,EAAA,GACA,IACA,OAAA,EAAA,EAAA,EAAA,GAAA,GAAA,EAAA,IAAA,EAAA,GAEA,MAAA,GACA,IAAA,EAAA,EAAA,OAEA,WADA,IAAA,GAAA,EAAA,EAAA,KAAA,IACA;;ACRA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,SAAA,CAAA,YACA,EAAA,MAAA,UAEA,OAAA,QAAA,SAAA,GACA,YAAA,IAAA,IAAA,EAAA,QAAA,GAAA,EAAA,KAAA;;ACNA,aACA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,oBAEA,OAAA,QAAA,SAAA,EAAA,EAAA,GACA,KAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,IACA,EAAA,GAAA;;ACNA,IAAA,EAAA,QAAA,cACA,EAAA,QAAA,SAAA,CAAA,YACA,EAAA,QAAA,gBACA,OAAA,QAAA,QAAA,WAAA,kBAAA,SAAA,GACA,GAAA,MAAA,EAAA,OAAA,EAAA,IACA,EAAA,eACA,EAAA,EAAA;;ACNA,IAAA,EAAA,QAAA,SAAA,CAAA,YACA,GAAA,EAEA,IACA,IAAA,EAAA,CAAA,GAAA,KACA,EAAA,OAAA,WAAA,GAAA,GAEA,MAAA,KAAA,EAAA,WAAA,MAAA,IACA,MAAA,IAEA,OAAA,QAAA,SAAA,EAAA,GACA,IAAA,IAAA,EAAA,OAAA,EACA,IAAA,GAAA,EACA,IACA,IAAA,EAAA,CAAA,GACA,EAAA,EAAA,KACA,EAAA,KAAA,WAAA,MAAA,CAAA,KAAA,GAAA,IACA,EAAA,GAAA,WAAA,OAAA,GACA,EAAA,GACA,MAAA,IACA,OAAA;;ACpBA,aACA,IAAA,EAAA,QAAA,UACA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,oBACA,EAAA,QAAA,gBACA,EAAA,QAAA,sBACA,EAAA,QAAA,8BAEA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,iBAAA,CAAA,SAAA,GAAA,MAAA,KAAA,KAAA,QAAA,CAEA,KAAA,SAAA,GACA,IAOA,EAAA,EAAA,EAAA,EAPA,EAAA,EAAA,GACA,EAAA,mBAAA,KAAA,KAAA,MACA,EAAA,UAAA,OACA,EAAA,EAAA,EAAA,UAAA,QAAA,EACA,OAAA,IAAA,EACA,EAAA,EACA,EAAA,EAAA,GAIA,GAFA,IAAA,EAAA,EAAA,EAAA,EAAA,EAAA,UAAA,QAAA,EAAA,IAEA,MAAA,GAAA,GAAA,OAAA,EAAA,GAMA,IAAA,EAAA,IAAA,EADA,EAAA,EAAA,EAAA,SACA,EAAA,EAAA,IACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,GAAA,EAAA,SANA,IAAA,EAAA,EAAA,KAAA,GAAA,EAAA,IAAA,IAAA,EAAA,EAAA,QAAA,KAAA,IACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,CAAA,EAAA,MAAA,IAAA,GAAA,EAAA,OASA,OADA,EAAA,OAAA,EACA;;AClCA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,sBAGA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,WAAA,CAAA,WACA,SAAA,KACA,QAAA,MAAA,GAAA,KAAA,aAAA,KACA,QAAA,CAEA,GAAA,WAIA,IAHA,IAAA,EAAA,EACA,EAAA,UAAA,OACA,EAAA,IAAA,mBAAA,KAAA,KAAA,OAAA,GACA,EAAA,GAAA,EAAA,EAAA,EAAA,UAAA,MAEA,OADA,EAAA,OAAA,EACA;;AChBA,aACA,IAAA,EAAA,QAAA,YAEA,OAAA,QAAA,SAAA,EAAA,GACA,QAAA,GAAA,EAAA,WAEA,EAAA,EAAA,KAAA,KAAA,aAAA,GAAA,EAAA,KAAA;;ACNA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,GAAA,KAGA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,eAAA,SAAA,QAAA,mBAAA,CAAA,IAAA,QAAA,CACA,KAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,WAAA,IAAA,EAAA,IAAA;;ACTA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,WACA,EAAA,QAAA,UACA,EAAA,QAAA,wBACA,EAAA,QAAA,gBACA,EAAA,GAAA,MAGA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,WAAA,CAAA,WACA,GAAA,EAAA,KAAA,KACA,QAAA,CACA,MAAA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,KAAA,QACA,EAAA,EAAA,MAEA,GADA,OAAA,IAAA,EAAA,EAAA,EACA,SAAA,EAAA,OAAA,EAAA,KAAA,KAAA,EAAA,GAMA,IALA,IAAA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,GACA,EAAA,IAAA,MAAA,GACA,EAAA,EACA,EAAA,EAAA,IAAA,EAAA,GAAA,UAAA,EACA,KAAA,OAAA,EAAA,GACA,KAAA,EAAA,GACA,OAAA;;ACzBA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,EAAA,QAAA,YACA,EAAA,GAAA,KACA,EAAA,CAAA,EAAA,EAAA,GAEA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,WAEA,EAAA,UAAA,OACA,EAAA,WAEA,EAAA,KAAA,UAEA,QAAA,mBAAA,CAAA,IAAA,QAAA,CAEA,KAAA,SAAA,GACA,YAAA,IAAA,EACA,EAAA,KAAA,EAAA,OACA,EAAA,KAAA,EAAA,MAAA,EAAA;;ACpBA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,eACA,EAAA,QAAA,SAAA,CAAA,WAEA,OAAA,QAAA,SAAA,GACA,IAAA,EASA,OARA,EAAA,KAGA,mBAFA,EAAA,EAAA,cAEA,IAAA,QAAA,EAAA,EAAA,aAAA,OAAA,GACA,EAAA,IAEA,QADA,EAAA,EAAA,MACA,OAAA,SAEA,IAAA,EAAA,MAAA;;ACbA,IAAA,EAAA,QAAA,gCAEA,OAAA,QAAA,SAAA,EAAA,GACA,OAAA,IAAA,EAAA,GAAA,CAAA;;ACGA,IAAA,EAAA,QAAA,UACA,EAAA,QAAA,cACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,2BACA,OAAA,QAAA,SAAA,EAAA,GACA,IAAA,EAAA,GAAA,EACA,EAAA,GAAA,EACA,EAAA,GAAA,EACA,EAAA,GAAA,EACA,EAAA,GAAA,EACA,EAAA,GAAA,GAAA,EACA,EAAA,GAAA,EACA,OAAA,SAAA,EAAA,EAAA,GAQA,IAPA,IAMA,EAAA,EANA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,QACA,EAAA,EACA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,QAAA,EAEA,EAAA,EAAA,IAAA,IAAA,GAAA,KAAA,KAEA,EAAA,EADA,EAAA,EAAA,GACA,EAAA,GACA,GACA,GAAA,EAAA,EAAA,GAAA,OACA,GAAA,EAAA,OAAA,GACA,KAAA,EAAA,OAAA,EACA,KAAA,EAAA,OAAA,EACA,KAAA,EAAA,OAAA,EACA,KAAA,EAAA,EAAA,KAAA,QACA,GAAA,EAAA,OAAA,EAGA,OAAA,GAAA,EAAA,GAAA,EAAA,EAAA;;ACzCA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,mBAAA,CAAA,GACA,EAAA,QAAA,mBAAA,CAAA,GAAA,SAAA,GAEA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,QAAA,CAEA,QAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA;;ACRA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,mBAAA,CAAA,GAEA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,mBAAA,CAAA,GAAA,KAAA,GAAA,QAAA,CAEA,IAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA;;ACPA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,mBAAA,CAAA,GAEA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,mBAAA,CAAA,GAAA,QAAA,GAAA,QAAA,CAEA,OAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA;;ACPA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,mBAAA,CAAA,GAEA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,mBAAA,CAAA,GAAA,MAAA,GAAA,QAAA,CAEA,KAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA;;ACPA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,mBAAA,CAAA,GAEA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,mBAAA,CAAA,GAAA,OAAA,GAAA,QAAA,CAEA,MAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA;;ACPA,IAAA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,EAAA,QAAA,cACA,EAAA,QAAA,gBAEA,OAAA,QAAA,SAAA,EAAA,EAAA,EAAA,EAAA,GACA,EAAA,GACA,IAAA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,QACA,EAAA,EAAA,EAAA,EAAA,EACA,EAAA,GAAA,EAAA,EACA,GAAA,EAAA,EAAA,OAAA,CACA,GAAA,KAAA,EAAA,CACA,EAAA,EAAA,GACA,GAAA,EACA,MAGA,GADA,GAAA,EACA,EAAA,EAAA,EAAA,GAAA,EACA,MAAA,UAAA,+CAGA,KAAA,EAAA,GAAA,EAAA,EAAA,EAAA,GAAA,EAAA,KAAA,IACA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,IAEA,OAAA;;AC1BA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,mBAEA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,mBAAA,CAAA,GAAA,QAAA,GAAA,QAAA,CAEA,OAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA,OAAA,UAAA,IAAA;;ACPA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,mBAEA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,mBAAA,CAAA,GAAA,aAAA,GAAA,QAAA,CAEA,YAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA,OAAA,UAAA,IAAA;;ACPA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,oBAAA,EAAA,GACA,EAAA,GAAA,QACA,IAAA,GAAA,EAAA,CAAA,GAAA,QAAA,GAAA,GAAA,EAEA,EAAA,EAAA,EAAA,EAAA,GAAA,IAAA,QAAA,mBAAA,CAAA,IAAA,QAAA,CAEA,QAAA,SAAA,GACA,OAAA,EAEA,EAAA,MAAA,KAAA,YAAA,EACA,EAAA,KAAA,EAAA,UAAA;;ACZA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,EAAA,GAAA,YACA,IAAA,GAAA,EAAA,CAAA,GAAA,YAAA,GAAA,GAAA,EAEA,EAAA,EAAA,EAAA,EAAA,GAAA,IAAA,QAAA,mBAAA,CAAA,IAAA,QAAA,CAEA,YAAA,SAAA,GAEA,GAAA,EAAA,OAAA,EAAA,MAAA,KAAA,YAAA,EACA,IAAA,EAAA,EAAA,MACA,EAAA,EAAA,EAAA,QACA,EAAA,EAAA,EAGA,IAFA,UAAA,OAAA,IAAA,EAAA,KAAA,IAAA,EAAA,EAAA,UAAA,MACA,EAAA,IAAA,EAAA,EAAA,GACA,GAAA,EAAA,IAAA,GAAA,KAAA,GAAA,EAAA,KAAA,EAAA,OAAA,GAAA,EACA,OAAA;;AClBA,aACA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,wBACA,EAAA,QAAA,gBAEA,OAAA,QAAA,GAAA,YAAA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,MACA,EAAA,EAAA,EAAA,QACA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,GACA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,EACA,EAAA,KAAA,UAAA,IAAA,EAAA,EAAA,EAAA,EAAA,IAAA,EAAA,EAAA,GACA,EAAA,EAMA,IALA,EAAA,GAAA,EAAA,EAAA,IACA,GAAA,EACA,GAAA,EAAA,EACA,GAAA,EAAA,GAEA,KAAA,GACA,KAAA,EAAA,EAAA,GAAA,EAAA,UACA,EAAA,GACA,GAAA,EACA,GAAA,EACA,OAAA;;ACvBA,IAAA,EAAA,QAAA,SAAA,CAAA,eACA,EAAA,MAAA,UACA,MAAA,EAAA,IAAA,QAAA,UAAA,CAAA,EAAA,EAAA,IACA,OAAA,QAAA,SAAA,GACA,EAAA,GAAA,IAAA;;ACJA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,QAAA,CAAA,WAAA,QAAA,0BAEA,QAAA,wBAAA,CAAA;;ACJA,aACA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,wBACA,EAAA,QAAA,gBACA,OAAA,QAAA,SAAA,GAOA,IANA,IAAA,EAAA,EAAA,MACA,EAAA,EAAA,EAAA,QACA,EAAA,UAAA,OACA,EAAA,EAAA,EAAA,EAAA,UAAA,QAAA,EAAA,GACA,EAAA,EAAA,EAAA,UAAA,QAAA,EACA,OAAA,IAAA,EAAA,EAAA,EAAA,EAAA,GACA,EAAA,GAAA,EAAA,KAAA,EACA,OAAA;;ACZA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,QAAA,CAAA,KAAA,QAAA,mBAEA,QAAA,wBAAA,CAAA;;ACLA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,mBAAA,CAAA,GACA,EAAA,OACA,GAAA,EAEA,IAAA,IAAA,MAAA,GAAA,GAAA,WAAA,GAAA,IACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,CACA,KAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,MAGA,QAAA,wBAAA,CAAA;;ACbA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,mBAAA,CAAA,GACA,EAAA,YACA,GAAA,EAEA,IAAA,IAAA,MAAA,GAAA,GAAA,WAAA,GAAA,IACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,CACA,UAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,MAGA,QAAA,wBAAA,CAAA;;;ACbA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,kBACA,EAAA,QAAA,SAAA,CAAA,WAEA,OAAA,QAAA,SAAA,GACA,IAAA,EAAA,EAAA,GACA,GAAA,IAAA,EAAA,IAAA,EAAA,EAAA,EAAA,EAAA,CACA,cAAA,EACA,IAAA,WAAA,OAAA;;ACVA,QAAA,iBAAA,CAAA;;ACAA,OAAA,QAAA,SAAA,EAAA,GACA,MAAA,CAAA,MAAA,EAAA,OAAA;;ACDA,aACA,IAAA,EAAA,QAAA,yBACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBAMA,OAAA,QAAA,QAAA,iBAAA,CAAA,MAAA,QAAA,SAAA,EAAA,GACA,KAAA,GAAA,EAAA,GACA,KAAA,GAAA,EACA,KAAA,GAAA,GAEA,WACA,IAAA,EAAA,KAAA,GACA,EAAA,KAAA,GACA,EAAA,KAAA,KACA,OAAA,GAAA,GAAA,EAAA,QACA,KAAA,QAAA,EACA,EAAA,IAEA,EAAA,EAAA,QAAA,EAAA,EACA,UAAA,EAAA,EAAA,GACA,CAAA,EAAA,EAAA,MACA,UAGA,EAAA,UAAA,EAAA,MAEA,EAAA,QACA,EAAA,UACA,EAAA;;ACjCA,aAEA,IAAA,EAAA,QAAA,gBACA,OAAA,QAAA,WACA,IAAA,EAAA,EAAA,MACA,EAAA,GAMA,OALA,EAAA,SAAA,GAAA,KACA,EAAA,aAAA,GAAA,KACA,EAAA,YAAA,GAAA,KACA,EAAA,UAAA,GAAA,KACA,EAAA,SAAA,GAAA,KACA;;;ACXA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,0BACA,EAAA,QAAA,gBAAA,EACA,EAAA,QAAA,kBAAA,EACA,EAAA,QAAA,gBACA,EAAA,QAAA,YACA,EAAA,EAAA,OACA,EAAA,EACA,EAAA,EAAA,UACA,EAAA,KACA,EAAA,KAEA,EAAA,IAAA,EAAA,KAAA,EAEA,GAAA,QAAA,qBAAA,GAAA,QAAA,WAAA,CAAA,WAGA,OAFA,EAAA,QAAA,SAAA,CAAA,WAAA,EAEA,EAAA,IAAA,GAAA,EAAA,IAAA,GAAA,QAAA,EAAA,EAAA,QACA,CACA,EAAA,SAAA,EAAA,GACA,IAAA,EAAA,gBAAA,EACA,EAAA,EAAA,GACA,OAAA,IAAA,EACA,OAAA,GAAA,GAAA,EAAA,cAAA,GAAA,EAAA,EACA,EAAA,EACA,IAAA,EAAA,IAAA,EAAA,EAAA,OAAA,EAAA,GACA,GAAA,EAAA,aAAA,GAAA,EAAA,OAAA,EAAA,GAAA,EAAA,EAAA,KAAA,GAAA,GACA,EAAA,KAAA,EAAA,IASA,IAPA,IAAA,EAAA,SAAA,GACA,KAAA,GAAA,EAAA,EAAA,EAAA,CACA,cAAA,EACA,IAAA,WAAA,OAAA,EAAA,IACA,IAAA,SAAA,GAAA,EAAA,GAAA,MAGA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,OAAA,GAAA,EAAA,EAAA,MACA,EAAA,YAAA,EACA,EAAA,UAAA,EACA,QAAA,cAAA,CAAA,EAAA,SAAA,GAGA,QAAA,iBAAA,CAAA;;AC1CA,aAEA,IAAA,EAAA,QAAA,YAEA,EAAA,OAAA,UAAA,KAIA,EAAA,OAAA,UAAA,QAEA,EAAA,EAEA,EAAA,YAEA,EAAA,WACA,IAAA,EAAA,IACA,EAAA,MAGA,OAFA,EAAA,KAAA,EAAA,KACA,EAAA,KAAA,EAAA,KACA,IAAA,EAAA,IAAA,IAAA,EAAA,GALA,GASA,OAAA,IAAA,OAAA,KAAA,IAAA,GAEA,EAAA,GAAA,EAEA,IACA,EAAA,SAAA,GACA,IACA,EAAA,EAAA,EAAA,EADA,EAAA,KAwBA,OArBA,IACA,EAAA,IAAA,OAAA,IAAA,EAAA,OAAA,WAAA,EAAA,KAAA,KAEA,IAAA,EAAA,EAAA,IAEA,EAAA,EAAA,KAAA,EAAA,GAEA,GAAA,IACA,EAAA,GAAA,EAAA,OAAA,EAAA,MAAA,EAAA,GAAA,OAAA,GAEA,GAAA,GAAA,EAAA,OAAA,GAIA,EAAA,KAAA,EAAA,GAAA,EAAA,WACA,IAAA,EAAA,EAAA,EAAA,UAAA,OAAA,EAAA,SACA,IAAA,UAAA,KAAA,EAAA,QAAA,KAKA,IAIA,OAAA,QAAA;;ACzDA,aACA,IAAA,EAAA,QAAA,kBACA,QAAA,YAAA,CAAA,CACA,OAAA,SACA,OAAA,EACA,OAAA,IAAA,IAAA,MACA,CACA,KAAA;;ACNA,QAAA,mBAAA,KAAA,KAAA,OAAA,QAAA,gBAAA,EAAA,OAAA,UAAA,QAAA,CACA,cAAA,EACA,IAAA,QAAA;;;ACHA,aACA,QAAA,sBACA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,YACA,EAAA,QAAA,kBACA,EAAA,WACA,EAAA,IAAA,GAEA,EAAA,SAAA,GACA,QAAA,cAAA,CAAA,OAAA,UAAA,EAAA,GAAA,IAIA,QAAA,WAAA,CAAA,WAAA,MAAA,QAAA,EAAA,KAAA,CAAA,OAAA,IAAA,MAAA,QACA,EAAA,WACA,IAAA,EAAA,EAAA,MACA,MAAA,IAAA,OAAA,EAAA,OAAA,IACA,UAAA,EAAA,EAAA,OAAA,GAAA,aAAA,OAAA,EAAA,KAAA,QAAA,KAGA,EAAA,MAAA,GACA,EAAA,WACA,OAAA,EAAA,KAAA;;ACtBA,aACA,IAAA,EAAA,QAAA,eAAA,EAAA,GAIA,OAAA,QAAA,SAAA,EAAA,EAAA,GACA,OAAA,GAAA,EAAA,EAAA,EAAA,GAAA,OAAA;;ACNA,aAEA,IAAA,EAAA,QAAA,cACA,EAAA,OAAA,UAAA,KAIA,OAAA,QAAA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,KACA,GAAA,mBAAA,EAAA,CACA,IAAA,EAAA,EAAA,KAAA,EAAA,GACA,GAAA,iBAAA,EACA,MAAA,IAAA,UAAA,sEAEA,OAAA,EAEA,GAAA,WAAA,EAAA,GACA,MAAA,IAAA,UAAA,+CAEA,OAAA,EAAA,KAAA,EAAA;;ACnBA,aACA,QAAA,qBACA,IAAA,EAAA,QAAA,eACA,EAAA,QAAA,WACA,EAAA,QAAA,YACA,EAAA,QAAA,cACA,EAAA,QAAA,UACA,EAAA,QAAA,kBAEA,EAAA,EAAA,WAEA,GAAA,EAAA,WAIA,IAAA,EAAA,IAMA,OALA,EAAA,KAAA,WACA,IAAA,EAAA,GAEA,OADA,EAAA,OAAA,CAAA,EAAA,KACA,GAEA,MAAA,GAAA,QAAA,EAAA,UAGA,EAAA,WAEA,IAAA,EAAA,OACA,EAAA,EAAA,KACA,EAAA,KAAA,WAAA,OAAA,EAAA,MAAA,KAAA,YACA,IAAA,EAAA,KAAA,MAAA,GACA,OAAA,IAAA,EAAA,QAAA,MAAA,EAAA,IAAA,MAAA,EAAA,GANA,GASA,OAAA,QAAA,SAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,GAEA,GAAA,EAAA,WAEA,IAAA,EAAA,GAEA,OADA,EAAA,GAAA,WAAA,OAAA,GACA,GAAA,GAAA,GAAA,KAGA,EAAA,GAAA,EAAA,WAEA,IAAA,GAAA,EACA,EAAA,IASA,OARA,EAAA,KAAA,WAAA,OAAA,GAAA,EAAA,MACA,UAAA,IAGA,EAAA,YAAA,GACA,EAAA,YAAA,GAAA,WAAA,OAAA,IAEA,EAAA,GAAA,KACA,SACA,EAEA,IACA,IACA,GACA,YAAA,IAAA,GACA,UAAA,IAAA,EACA,CACA,IAAA,EAAA,IAAA,GACA,EAAA,EACA,EACA,EACA,GAAA,GACA,SAAA,EAAA,EAAA,EAAA,EAAA,GACA,OAAA,EAAA,OAAA,EACA,IAAA,EAIA,CAAA,MAAA,EAAA,MAAA,EAAA,KAAA,EAAA,EAAA,IAEA,CAAA,MAAA,EAAA,MAAA,EAAA,KAAA,EAAA,EAAA,IAEA,CAAA,MAAA,KAGA,EAAA,EAAA,GACA,EAAA,EAAA,GAEA,EAAA,OAAA,UAAA,EAAA,GACA,EAAA,OAAA,UAAA,EAAA,GAAA,EAGA,SAAA,EAAA,GAAA,OAAA,EAAA,KAAA,EAAA,KAAA,IAGA,SAAA,GAAA,OAAA,EAAA,KAAA,EAAA;;AC5FA,aAEA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,2BACA,EAAA,QAAA,2BAGA,QAAA,gBAAA,CAAA,QAAA,EAAA,SAAA,EAAA,EAAA,EAAA,GACA,MAAA,CAGA,SAAA,GACA,IAAA,EAAA,EAAA,MACA,EAAA,MAAA,OAAA,EAAA,EAAA,GACA,YAAA,IAAA,EAAA,EAAA,KAAA,EAAA,GAAA,IAAA,OAAA,GAAA,GAAA,OAAA,KAIA,SAAA,GACA,IAAA,EAAA,EAAA,EAAA,EAAA,MACA,GAAA,EAAA,KAAA,OAAA,EAAA,MACA,IAAA,EAAA,EAAA,GACA,EAAA,OAAA,MACA,IAAA,EAAA,OAAA,OAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,QACA,EAAA,UAAA,EAIA,IAHA,IAEA,EAFA,EAAA,GACA,EAAA,EAEA,QAAA,EAAA,EAAA,EAAA,KAAA,CACA,IAAA,EAAA,OAAA,EAAA,IACA,EAAA,GAAA,EACA,KAAA,IAAA,EAAA,UAAA,EAAA,EAAA,EAAA,EAAA,WAAA,IACA,IAEA,OAAA,IAAA,EAAA,KAAA;;;ACkFA,IAAA,EAAA,UAAA,GApHA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,QAAA,2BACA,EAAA,QAAA,2BACA,EAAA,KAAA,IACA,EAAA,KAAA,IACA,EAAA,KAAA,MACA,EAAA,4BACA,EAAA,oBAEA,EAAA,SAAA,GACA,YAAA,IAAA,EAAA,EAAA,OAAA,IAIA,QAAA,gBAAA,CAAA,UAAA,EAAA,SAAA,EAAA,EAAA,EAAA,GACA,MAAA,CAGA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,MACA,EAAA,MAAA,OAAA,EAAA,EAAA,GACA,YAAA,IAAA,EACA,EAAA,KAAA,EAAA,EAAA,GACA,EAAA,KAAA,OAAA,GAAA,EAAA,IAIA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,EAAA,EAAA,KAAA,GACA,GAAA,EAAA,KAAA,OAAA,EAAA,MAEA,IAAA,EAAA,EAAA,GACA,EAAA,OAAA,MACA,EAAA,mBAAA,EACA,IAAA,EAAA,OAAA,IACA,IAAA,EAAA,EAAA,OACA,GAAA,EAAA,CACA,IAAA,EAAA,EAAA,QACA,EAAA,UAAA,EAGA,IADA,IAAA,EAAA,KACA,CACA,IAAA,EAAA,EAAA,EAAA,GACA,GAAA,OAAA,EAAA,MAEA,GADA,EAAA,KAAA,IACA,EAAA,MAEA,KADA,OAAA,EAAA,MACA,EAAA,UAAA,EAAA,EAAA,EAAA,EAAA,WAAA,IAIA,IAFA,IAAA,EAAA,GACA,EAAA,EACA,EAAA,EAAA,EAAA,EAAA,OAAA,IAAA,CACA,EAAA,EAAA,GASA,IARA,IAAA,EAAA,OAAA,EAAA,IACA,EAAA,EAAA,EAAA,EAAA,EAAA,OAAA,EAAA,QAAA,GACA,EAAA,GAMA,EAAA,EAAA,EAAA,EAAA,OAAA,IAAA,EAAA,KAAA,EAAA,EAAA,KACA,IAAA,EAAA,EAAA,OACA,GAAA,EAAA,CACA,IAAA,EAAA,CAAA,GAAA,OAAA,EAAA,EAAA,QACA,IAAA,GAAA,EAAA,KAAA,GACA,IAAA,EAAA,OAAA,EAAA,WAAA,EAAA,SAEA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAEA,GAAA,IACA,GAAA,EAAA,MAAA,EAAA,GAAA,EACA,EAAA,EAAA,EAAA,QAGA,OAAA,EAAA,EAAA,MAAA,KAKA,SAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,EAAA,OACA,EAAA,EAAA,OACA,EAAA,EAKA,YAJA,IAAA,IACA,EAAA,EAAA,GACA,EAAA,GAEA,EAAA,KAAA,EAAA,EAAA,SAAA,EAAA,GACA,IAAA,EACA,OAAA,EAAA,OAAA,IACA,IAAA,IAAA,MAAA,IACA,IAAA,IAAA,OAAA,EACA,IAAA,IAAA,OAAA,EAAA,MAAA,EAAA,GACA,IAAA,IAAA,OAAA,EAAA,MAAA,GACA,IAAA,IACA,EAAA,EAAA,EAAA,MAAA,GAAA,IACA,MACA,QACA,IAAA,GAAA,EACA,GAAA,IAAA,EAAA,OAAA,EACA,GAAA,EAAA,EAAA,CACA,IAAA,EAAA,EAAA,EAAA,IACA,OAAA,IAAA,EAAA,EACA,GAAA,OAAA,IAAA,EAAA,EAAA,GAAA,EAAA,OAAA,GAAA,EAAA,EAAA,GAAA,EAAA,OAAA,GACA,EAEA,EAAA,EAAA,EAAA,GAEA,YAAA,IAAA,EAAA,GAAA;;AClHA,aAEA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,QAAA,2BAGA,QAAA,gBAAA,CAAA,SAAA,EAAA,SAAA,EAAA,EAAA,EAAA,GACA,MAAA,CAGA,SAAA,GACA,IAAA,EAAA,EAAA,MACA,EAAA,MAAA,OAAA,EAAA,EAAA,GACA,YAAA,IAAA,EAAA,EAAA,KAAA,EAAA,GAAA,IAAA,OAAA,GAAA,GAAA,OAAA,KAIA,SAAA,GACA,IAAA,EAAA,EAAA,EAAA,EAAA,MACA,GAAA,EAAA,KAAA,OAAA,EAAA,MACA,IAAA,EAAA,EAAA,GACA,EAAA,OAAA,MACA,EAAA,EAAA,UACA,EAAA,EAAA,KAAA,EAAA,UAAA,GACA,IAAA,EAAA,EAAA,EAAA,GAEA,OADA,EAAA,EAAA,UAAA,KAAA,EAAA,UAAA,GACA,OAAA,GAAA,EAAA,EAAA;;AC1BA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,QAAA,SAAA,CAAA,WACA,OAAA,QAAA,SAAA,EAAA,GACA,IACA,EADA,EAAA,EAAA,GAAA,YAEA,YAAA,IAAA,GAAA,OAAA,EAAA,EAAA,GAAA,IAAA,EAAA,EAAA;;ACPA,aAEA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,0BACA,EAAA,QAAA,2BACA,EAAA,QAAA,gBACA,EAAA,QAAA,2BACA,EAAA,QAAA,kBACA,EAAA,QAAA,YACA,EAAA,KAAA,IACA,EAAA,GAAA,KACA,EAAA,QACA,EAAA,SACA,EAAA,YACA,EAAA,WAGA,GAAA,EAAA,WAAA,OAAA,EAAA,OAGA,QAAA,gBAAA,CAAA,QAAA,EAAA,SAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAkDA,OAxCA,EARA,KAAA,OAAA,GAAA,QAAA,IACA,GAAA,OAAA,GAAA,QAAA,GAAA,IACA,GAAA,KAAA,GAAA,WAAA,IACA,GAAA,IAAA,GAAA,YAAA,IACA,IAAA,GAAA,QAAA,GAAA,GACA,GAAA,GAAA,MAAA,GAGA,SAAA,EAAA,GACA,IAAA,EAAA,OAAA,MACA,QAAA,IAAA,GAAA,IAAA,EAAA,MAAA,GAEA,IAAA,EAAA,GAAA,OAAA,EAAA,KAAA,EAAA,EAAA,GAWA,IAVA,IASA,EAAA,EAAA,EATA,EAAA,GACA,GAAA,EAAA,WAAA,IAAA,KACA,EAAA,UAAA,IAAA,KACA,EAAA,QAAA,IAAA,KACA,EAAA,OAAA,IAAA,IACA,EAAA,EACA,OAAA,IAAA,EAAA,EAAA,IAAA,EAEA,EAAA,IAAA,OAAA,EAAA,OAAA,EAAA,MAEA,EAAA,EAAA,KAAA,EAAA,QACA,EAAA,EAAA,IACA,IACA,EAAA,KAAA,EAAA,MAAA,EAAA,EAAA,QACA,EAAA,GAAA,GAAA,EAAA,MAAA,EAAA,IAAA,EAAA,MAAA,EAAA,EAAA,MAAA,IACA,EAAA,EAAA,GAAA,GACA,EAAA,EACA,EAAA,IAAA,KAEA,EAAA,KAAA,EAAA,OAAA,EAAA,KAKA,OAHA,IAAA,EAAA,IACA,GAAA,EAAA,KAAA,KAAA,EAAA,KAAA,IACA,EAAA,KAAA,EAAA,MAAA,IACA,EAAA,GAAA,EAAA,EAAA,MAAA,EAAA,GAAA,GAGA,IAAA,QAAA,EAAA,GAAA,GACA,SAAA,EAAA,GACA,YAAA,IAAA,GAAA,IAAA,EAAA,GAAA,EAAA,KAAA,KAAA,EAAA,IAGA,EAGA,CAGA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,MACA,EAAA,MAAA,OAAA,EAAA,EAAA,GACA,YAAA,IAAA,EACA,EAAA,KAAA,EAAA,EAAA,GACA,EAAA,KAAA,OAAA,GAAA,EAAA,IAOA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,EAAA,EAAA,KAAA,EAAA,IAAA,GACA,GAAA,EAAA,KAAA,OAAA,EAAA,MAEA,IAAA,EAAA,EAAA,GACA,EAAA,OAAA,MACA,EAAA,EAAA,EAAA,QAEA,EAAA,EAAA,QACA,GAAA,EAAA,WAAA,IAAA,KACA,EAAA,UAAA,IAAA,KACA,EAAA,QAAA,IAAA,KACA,EAAA,IAAA,KAIA,EAAA,IAAA,EAAA,EAAA,EAAA,OAAA,EAAA,OAAA,IAAA,GACA,OAAA,IAAA,EAAA,EAAA,IAAA,EACA,GAAA,IAAA,EAAA,MAAA,GACA,GAAA,IAAA,EAAA,OAAA,OAAA,OAAA,EAAA,EAAA,GAAA,CAAA,GAAA,GAIA,IAHA,IAAA,EAAA,EACA,EAAA,EACA,EAAA,GACA,EAAA,EAAA,QAAA,CACA,EAAA,UAAA,EAAA,EAAA,EACA,IACA,EADA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,MAAA,IAEA,GACA,OAAA,IACA,EAAA,EAAA,EAAA,EAAA,WAAA,EAAA,EAAA,IAAA,EAAA,WAAA,EAEA,EAAA,EAAA,EAAA,EAAA,OACA,CAEA,GADA,EAAA,KAAA,EAAA,MAAA,EAAA,IACA,EAAA,SAAA,EAAA,OAAA,EACA,IAAA,IAAA,EAAA,EAAA,GAAA,EAAA,OAAA,EAAA,IAEA,GADA,EAAA,KAAA,EAAA,IACA,EAAA,SAAA,EAAA,OAAA,EAEA,EAAA,EAAA,GAIA,OADA,EAAA,KAAA,EAAA,MAAA,IACA;;AClIA,OAAA,QAAA,SAAA,EAAA,EAAA,EAAA,GACA,KAAA,aAAA,SAAA,IAAA,GAAA,KAAA,EACA,MAAA,UAAA,EAAA,2BACA,OAAA;;ACHA,IAAA,EAAA,QAAA,UACA,EAAA,QAAA,gBACA,EAAA,QAAA,oBACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,8BACA,EAAA,GACA,EAAA,GACA,EAAA,OAAA,QAAA,SAAA,EAAA,EAAA,EAAA,EAAA,GACA,IAGA,EAAA,EAAA,EAAA,EAHA,EAAA,EAAA,WAAA,OAAA,GAAA,EAAA,GACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GACA,EAAA,EAEA,GAAA,mBAAA,EAAA,MAAA,UAAA,EAAA,qBAEA,GAAA,EAAA,IAAA,IAAA,EAAA,EAAA,EAAA,QAAA,EAAA,EAAA,IAEA,IADA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,IAAA,GAAA,EAAA,IAAA,EAAA,EAAA,OACA,GAAA,IAAA,EAAA,OAAA,OACA,IAAA,EAAA,EAAA,KAAA,KAAA,EAAA,EAAA,QAAA,MAEA,IADA,EAAA,EAAA,EAAA,EAAA,EAAA,MAAA,MACA,GAAA,IAAA,EAAA,OAAA,GAGA,EAAA,MAAA,EACA,EAAA,OAAA;;;;ACxBA,IAaA,EAAA,EAAA,EAbA,EAAA,QAAA,UACA,EAAA,QAAA,aACA,EAAA,QAAA,WACA,EAAA,QAAA,iBACA,EAAA,QAAA,aACA,EAAA,EAAA,QACA,EAAA,EAAA,aACA,EAAA,EAAA,eACA,EAAA,EAAA,eACA,EAAA,EAAA,SACA,EAAA,EACA,EAAA,GACA,EAAA,qBAEA,EAAA,WACA,IAAA,GAAA,KAEA,GAAA,EAAA,eAAA,GAAA,CACA,IAAA,EAAA,EAAA,UACA,EAAA,GACA,MAGA,EAAA,SAAA,GACA,EAAA,KAAA,EAAA,OAGA,GAAA,IACA,EAAA,SAAA,GAGA,IAFA,IAAA,EAAA,GACA,EAAA,EACA,UAAA,OAAA,GAAA,EAAA,KAAA,UAAA,MAMA,OALA,IAAA,GAAA,WAEA,EAAA,mBAAA,EAAA,EAAA,SAAA,GAAA,IAEA,EAAA,GACA,GAEA,EAAA,SAAA,UACA,EAAA,IAGA,WAAA,QAAA,SAAA,CAAA,GACA,EAAA,SAAA,GACA,EAAA,SAAA,EAAA,EAAA,EAAA,KAGA,GAAA,EAAA,IACA,EAAA,SAAA,GACA,EAAA,IAAA,EAAA,EAAA,EAAA,KAGA,GAEA,GADA,EAAA,IAAA,GACA,MACA,EAAA,MAAA,UAAA,EACA,EAAA,EAAA,EAAA,YAAA,EAAA,IAGA,EAAA,kBAAA,mBAAA,cAAA,EAAA,eACA,EAAA,SAAA,GACA,EAAA,YAAA,EAAA,GAAA,MAEA,EAAA,iBAAA,UAAA,GAAA,IAGA,EADA,KAAA,EAAA,UACA,SAAA,GACA,EAAA,YAAA,EAAA,WAAA,GAAA,WACA,EAAA,YAAA,MACA,EAAA,KAAA,KAKA,SAAA,GACA,WAAA,EAAA,EAAA,EAAA,GAAA,KAIA,OAAA,QAAA,CACA,IAAA,EACA,MAAA;;;;AClFA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,WAAA,IACA,EAAA,EAAA,kBAAA,EAAA,uBACA,EAAA,EAAA,QACA,EAAA,EAAA,QACA,EAAA,WAAA,QAAA,SAAA,CAAA,GAEA,OAAA,QAAA,WACA,IAAA,EAAA,EAAA,EAEA,EAAA,WACA,IAAA,EAAA,EAEA,IADA,IAAA,EAAA,EAAA,SAAA,EAAA,OACA,GAAA,CACA,EAAA,EAAA,GACA,EAAA,EAAA,KACA,IACA,IACA,MAAA,GAGA,MAFA,EAAA,IACA,OAAA,EACA,GAEA,OAAA,EACA,GAAA,EAAA,SAIA,GAAA,EACA,EAAA,WACA,EAAA,SAAA,SAGA,IAAA,GAAA,EAAA,WAAA,EAAA,UAAA,WAQA,GAAA,GAAA,EAAA,QAAA,CAEA,IAAA,EAAA,EAAA,aAAA,GACA,EAAA,WACA,EAAA,KAAA,SASA,EAAA,WAEA,EAAA,KAAA,EAAA,QAvBA,CACA,IAAA,GAAA,EACA,EAAA,SAAA,eAAA,IACA,IAAA,EAAA,GAAA,QAAA,EAAA,CAAA,eAAA,IACA,EAAA,WACA,EAAA,KAAA,GAAA,GAsBA,OAAA,SAAA,GACA,IAAA,EAAA,CAAA,GAAA,EAAA,UAAA,GACA,IAAA,EAAA,KAAA,GACA,IACA,EAAA,EACA,KACA,EAAA;;AClEA,aAEA,IAAA,EAAA,QAAA,iBAEA,SAAA,EAAA,GACA,IAAA,EAAA,EACA,KAAA,QAAA,IAAA,EAAA,SAAA,EAAA,GACA,QAAA,IAAA,QAAA,IAAA,EAAA,MAAA,UAAA,2BACA,EAAA,EACA,EAAA,IAEA,KAAA,QAAA,EAAA,GACA,KAAA,OAAA,EAAA,GAGA,OAAA,QAAA,EAAA,SAAA,GACA,OAAA,IAAA,EAAA;;AChBA,OAAA,QAAA,SAAA,GACA,IACA,MAAA,CAAA,GAAA,EAAA,EAAA,KACA,MAAA,GACA,MAAA,CAAA,GAAA,EAAA,EAAA;;;ACJA,IAAA,EAAA,QAAA,aACA,EAAA,EAAA,UAEA,OAAA,QAAA,GAAA,EAAA,WAAA;;ACHA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,6BAEA,OAAA,QAAA,SAAA,EAAA,GAEA,GADA,EAAA,GACA,EAAA,IAAA,EAAA,cAAA,EAAA,OAAA,EACA,IAAA,EAAA,EAAA,EAAA,GAGA,OADA,EADA,EAAA,SACA,GACA,EAAA;;ACVA,IAAA,EAAA,QAAA,eACA,OAAA,QAAA,SAAA,EAAA,EAAA,GACA,IAAA,IAAA,KAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,GACA,OAAA;;;;ACHA,aACA,IAwBA,EAAA,EAAA,EAAA,EAxBA,EAAA,QAAA,cACA,EAAA,QAAA,aACA,EAAA,QAAA,UACA,EAAA,QAAA,cACA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,QAAA,kBACA,EAAA,QAAA,aACA,EAAA,QAAA,0BACA,EAAA,QAAA,WAAA,IACA,EAAA,QAAA,eAAA,GACA,EAAA,QAAA,6BACA,EAAA,QAAA,cACA,EAAA,QAAA,iBACA,EAAA,QAAA,sBACA,EAAA,UACA,EAAA,EAAA,UACA,EAAA,EAAA,QACA,EAAA,GAAA,EAAA,SACA,EAAA,GAAA,EAAA,IAAA,GACA,EAAA,EAAA,GACA,EAAA,WAAA,EAAA,GACA,EAAA,aAEA,EAAA,EAAA,EAAA,EAEA,IAAA,WACA,IAEA,IAAA,EAAA,EAAA,QAAA,GACA,GAAA,EAAA,YAAA,IAAA,QAAA,SAAA,CAAA,YAAA,SAAA,GACA,EAAA,EAAA,IAGA,OAAA,GAAA,mBAAA,wBACA,EAAA,KAAA,aAAA,GAIA,IAAA,EAAA,QAAA,SACA,IAAA,EAAA,QAAA,aACA,MAAA,KAfA,GAmBA,EAAA,SAAA,GACA,IAAA,EACA,SAAA,EAAA,IAAA,mBAAA,EAAA,EAAA,QAAA,GAEA,EAAA,SAAA,EAAA,GACA,IAAA,EAAA,GAAA,CACA,EAAA,IAAA,EACA,IAAA,EAAA,EAAA,GACA,EAAA,WAoCA,IAnCA,IAAA,EAAA,EAAA,GACA,EAAA,GAAA,EAAA,GACA,EAAA,EACA,EAAA,SAAA,GACA,IAIA,EAAA,EAAA,EAJA,EAAA,EAAA,EAAA,GAAA,EAAA,KACA,EAAA,EAAA,QACA,EAAA,EAAA,OACA,EAAA,EAAA,OAEA,IACA,GACA,IACA,GAAA,EAAA,IAAA,EAAA,GACA,EAAA,GAAA,IAEA,IAAA,EAAA,EAAA,GAEA,GAAA,EAAA,QACA,EAAA,EAAA,GACA,IACA,EAAA,OACA,GAAA,IAGA,IAAA,EAAA,QACA,EAAA,EAAA,yBACA,EAAA,EAAA,IACA,EAAA,KAAA,EAAA,EAAA,GACA,EAAA,IACA,EAAA,GACA,MAAA,GACA,IAAA,GAAA,EAAA,OACA,EAAA,KAGA,EAAA,OAAA,GAAA,EAAA,EAAA,MACA,EAAA,GAAA,GACA,EAAA,IAAA,EACA,IAAA,EAAA,IAAA,EAAA,OAGA,EAAA,SAAA,GACA,EAAA,KAAA,EAAA,WACA,IAEA,EAAA,EAAA,EAFA,EAAA,EAAA,GACA,EAAA,EAAA,GAeA,GAbA,IACA,EAAA,EAAA,WACA,EACA,EAAA,KAAA,qBAAA,EAAA,IACA,EAAA,EAAA,sBACA,EAAA,CAAA,QAAA,EAAA,OAAA,KACA,EAAA,EAAA,UAAA,EAAA,OACA,EAAA,MAAA,8BAAA,KAIA,EAAA,GAAA,GAAA,EAAA,GAAA,EAAA,GACA,EAAA,QAAA,EACA,GAAA,EAAA,EAAA,MAAA,EAAA,KAGA,EAAA,SAAA,GACA,OAAA,IAAA,EAAA,IAAA,KAAA,EAAA,IAAA,EAAA,IAAA,QAEA,EAAA,SAAA,GACA,EAAA,KAAA,EAAA,WACA,IAAA,EACA,EACA,EAAA,KAAA,mBAAA,IACA,EAAA,EAAA,qBACA,EAAA,CAAA,QAAA,EAAA,OAAA,EAAA,QAIA,EAAA,SAAA,GACA,IAAA,EAAA,KACA,EAAA,KACA,EAAA,IAAA,GACA,EAAA,EAAA,IAAA,GACA,GAAA,EACA,EAAA,GAAA,EACA,EAAA,KAAA,EAAA,GAAA,EAAA,GAAA,SACA,EAAA,GAAA,KAEA,EAAA,SAAA,GACA,IACA,EADA,EAAA,KAEA,IAAA,EAAA,GAAA,CACA,EAAA,IAAA,EACA,EAAA,EAAA,IAAA,EACA,IACA,GAAA,IAAA,EAAA,MAAA,EAAA,qCACA,EAAA,EAAA,IACA,EAAA,WACA,IAAA,EAAA,CAAA,GAAA,EAAA,IAAA,GACA,IACA,EAAA,KAAA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,IACA,MAAA,GACA,EAAA,KAAA,EAAA,OAIA,EAAA,GAAA,EACA,EAAA,GAAA,EACA,EAAA,GAAA,IAEA,MAAA,GACA,EAAA,KAAA,CAAA,GAAA,EAAA,IAAA,GAAA,MAKA,IAEA,EAAA,SAAA,GACA,EAAA,KAAA,EAAA,EAAA,MACA,EAAA,GACA,EAAA,KAAA,MACA,IACA,EAAA,EAAA,EAAA,KAAA,GAAA,EAAA,EAAA,KAAA,IACA,MAAA,GACA,EAAA,KAAA,KAAA,MAIA,EAAA,SAAA,GACA,KAAA,GAAA,GACA,KAAA,QAAA,EACA,KAAA,GAAA,EACA,KAAA,IAAA,EACA,KAAA,QAAA,EACA,KAAA,GAAA,EACA,KAAA,IAAA,IAEA,UAAA,QAAA,kBAAA,CAAA,EAAA,UAAA,CAEA,KAAA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,EAAA,KAAA,IAOA,OANA,EAAA,GAAA,mBAAA,GAAA,EACA,EAAA,KAAA,mBAAA,GAAA,EACA,EAAA,OAAA,EAAA,EAAA,YAAA,EACA,KAAA,GAAA,KAAA,GACA,KAAA,IAAA,KAAA,GAAA,KAAA,GACA,KAAA,IAAA,EAAA,MAAA,GACA,EAAA,SAGA,MAAA,SAAA,GACA,OAAA,KAAA,UAAA,EAAA,MAGA,EAAA,WACA,IAAA,EAAA,IAAA,EACA,KAAA,QAAA,EACA,KAAA,QAAA,EAAA,EAAA,EAAA,GACA,KAAA,OAAA,EAAA,EAAA,EAAA,IAEA,EAAA,EAAA,EAAA,SAAA,GACA,OAAA,IAAA,GAAA,IAAA,EACA,IAAA,EAAA,GACA,EAAA,KAIA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,CAAA,QAAA,IACA,QAAA,uBAAA,CAAA,EAAA,GACA,QAAA,iBAAA,CAAA,GACA,EAAA,QAAA,WAAA,GAGA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,EAAA,CAEA,OAAA,SAAA,GACA,IAAA,EAAA,EAAA,MAGA,OADA,EADA,EAAA,QACA,GACA,EAAA,WAGA,EAAA,EAAA,EAAA,EAAA,GAAA,IAAA,GAAA,EAAA,CAEA,QAAA,SAAA,GACA,OAAA,EAAA,GAAA,OAAA,EAAA,EAAA,KAAA,MAGA,EAAA,EAAA,EAAA,EAAA,IAAA,GAAA,QAAA,iBAAA,CAAA,SAAA,GACA,EAAA,IAAA,GAAA,MAAA,MACA,EAAA,CAEA,IAAA,SAAA,GACA,IAAA,EAAA,KACA,EAAA,EAAA,GACA,EAAA,EAAA,QACA,EAAA,EAAA,OACA,EAAA,EAAA,WACA,IAAA,EAAA,GACA,EAAA,EACA,EAAA,EACA,EAAA,GAAA,EAAA,SAAA,GACA,IAAA,EAAA,IACA,GAAA,EACA,EAAA,UAAA,GACA,IACA,EAAA,QAAA,GAAA,KAAA,SAAA,GACA,IACA,GAAA,EACA,EAAA,GAAA,IACA,GAAA,EAAA,KACA,OAEA,GAAA,EAAA,KAGA,OADA,EAAA,GAAA,EAAA,EAAA,GACA,EAAA,SAGA,KAAA,SAAA,GACA,IAAA,EAAA,KACA,EAAA,EAAA,GACA,EAAA,EAAA,OACA,EAAA,EAAA,WACA,EAAA,GAAA,EAAA,SAAA,GACA,EAAA,QAAA,GAAA,KAAA,EAAA,QAAA,OAIA,OADA,EAAA,GAAA,EAAA,EAAA,GACA,EAAA;;AC3RA,IAAA,EAAA,QAAA,gBACA,OAAA,QAAA,SAAA,EAAA,GACA,IAAA,EAAA,IAAA,EAAA,KAAA,EAAA,MAAA,UAAA,0BAAA,EAAA,cACA,OAAA;;ACHA,aACA,IAAA,EAAA,QAAA,gBAAA,EACA,EAAA,QAAA,oBACA,EAAA,QAAA,mBACA,EAAA,QAAA,UACA,EAAA,QAAA,kBACA,EAAA,QAAA,aACA,EAAA,QAAA,kBACA,EAAA,QAAA,gBACA,EAAA,QAAA,kBACA,EAAA,QAAA,kBACA,EAAA,QAAA,WAAA,QACA,EAAA,QAAA,0BACA,EAAA,EAAA,KAAA,OAEA,EAAA,SAAA,EAAA,GAEA,IACA,EADA,EAAA,EAAA,GAEA,GAAA,MAAA,EAAA,OAAA,EAAA,GAAA,GAEA,IAAA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,EACA,GAAA,EAAA,GAAA,EAAA,OAAA,GAIA,OAAA,QAAA,CACA,eAAA,SAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,SAAA,EAAA,GACA,EAAA,EAAA,EAAA,EAAA,MACA,EAAA,GAAA,EACA,EAAA,GAAA,EAAA,MACA,EAAA,QAAA,EACA,EAAA,QAAA,EACA,EAAA,GAAA,EACA,MAAA,GAAA,EAAA,EAAA,EAAA,EAAA,GAAA,KAsDA,OApDA,EAAA,EAAA,UAAA,CAGA,MAAA,WACA,IAAA,IAAA,EAAA,EAAA,KAAA,GAAA,EAAA,EAAA,GAAA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,EACA,EAAA,GAAA,EACA,EAAA,IAAA,EAAA,EAAA,EAAA,EAAA,OAAA,UACA,EAAA,EAAA,GAEA,EAAA,GAAA,EAAA,QAAA,EACA,EAAA,GAAA,GAIA,OAAA,SAAA,GACA,IAAA,EAAA,EAAA,KAAA,GACA,EAAA,EAAA,EAAA,GACA,GAAA,EAAA,CACA,IAAA,EAAA,EAAA,EACA,EAAA,EAAA,SACA,EAAA,GAAA,EAAA,GACA,EAAA,GAAA,EACA,IAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,GACA,EAAA,IAAA,IAAA,EAAA,GAAA,GACA,EAAA,IAAA,IAAA,EAAA,GAAA,GACA,EAAA,KACA,QAAA,GAIA,QAAA,SAAA,GACA,EAAA,KAAA,GAGA,IAFA,IACA,EADA,EAAA,EAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,EAAA,GAEA,EAAA,EAAA,EAAA,EAAA,KAAA,IAGA,IAFA,EAAA,EAAA,EAAA,EAAA,EAAA,MAEA,GAAA,EAAA,GAAA,EAAA,EAAA,GAKA,IAAA,SAAA,GACA,QAAA,EAAA,EAAA,KAAA,GAAA,MAGA,GAAA,EAAA,EAAA,UAAA,OAAA,CACA,IAAA,WACA,OAAA,EAAA,KAAA,GAAA,MAGA,GAEA,IAAA,SAAA,EAAA,EAAA,GACA,IACA,EAAA,EADA,EAAA,EAAA,EAAA,GAoBA,OAjBA,EACA,EAAA,EAAA,GAGA,EAAA,GAAA,EAAA,CACA,EAAA,EAAA,EAAA,GAAA,GACA,EAAA,EACA,EAAA,EACA,EAAA,EAAA,EAAA,GACA,OAAA,EACA,GAAA,GAEA,EAAA,KAAA,EAAA,GAAA,GACA,IAAA,EAAA,EAAA,GACA,EAAA,KAEA,MAAA,IAAA,EAAA,GAAA,GAAA,IACA,GAEA,SAAA,EACA,UAAA,SAAA,EAAA,EAAA,GAGA,EAAA,EAAA,EAAA,SAAA,EAAA,GACA,KAAA,GAAA,EAAA,EAAA,GACA,KAAA,GAAA,EACA,KAAA,QAAA,GACA,WAKA,IAJA,IACA,EADA,KACA,GACA,EAFA,KAEA,GAEA,GAAA,EAAA,GAAA,EAAA,EAAA,EAEA,OANA,KAMA,KANA,KAMA,GAAA,EAAA,EAAA,EAAA,EANA,KAMA,GAAA,IAMA,EAAA,EAAA,QAAA,EAAA,EAAA,EACA,UAAA,EAAA,EAAA,EACA,CAAA,EAAA,EAAA,EAAA,KAdA,KAQA,QAAA,EACA,EAAA,KAMA,EAAA,UAAA,UAAA,GAAA,GAGA,EAAA;;;AC7IA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,aACA,EAAA,QAAA,eACA,EAAA,QAAA,mBACA,EAAA,QAAA,WACA,EAAA,QAAA,aACA,EAAA,QAAA,kBACA,EAAA,QAAA,gBACA,EAAA,QAAA,YACA,EAAA,QAAA,kBACA,EAAA,QAAA,wBACA,EAAA,QAAA,0BAEA,OAAA,QAAA,SAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,GACA,EAAA,EACA,EAAA,EAAA,MAAA,MACA,EAAA,GAAA,EAAA,UACA,EAAA,GACA,EAAA,SAAA,GACA,IAAA,EAAA,EAAA,GACA,EAAA,EAAA,EACA,UAAA,EAAA,SAAA,GACA,QAAA,IAAA,EAAA,KAAA,EAAA,KAAA,KAAA,IAAA,EAAA,EAAA,IACA,OAAA,EAAA,SAAA,GACA,QAAA,IAAA,EAAA,KAAA,EAAA,KAAA,KAAA,IAAA,EAAA,EAAA,IACA,OAAA,EAAA,SAAA,GACA,OAAA,IAAA,EAAA,QAAA,EAAA,EAAA,KAAA,KAAA,IAAA,EAAA,EAAA,IACA,OAAA,EAAA,SAAA,GAAA,OAAA,EAAA,KAAA,KAAA,IAAA,EAAA,EAAA,GAAA,MACA,SAAA,EAAA,GAAA,OAAA,EAAA,KAAA,KAAA,IAAA,EAAA,EAAA,EAAA,GAAA,QAGA,GAAA,mBAAA,IAAA,GAAA,EAAA,UAAA,EAAA,YACA,IAAA,GAAA,UAAA,UAMA,CACA,IAAA,EAAA,IAAA,EAEA,EAAA,EAAA,GAAA,EAAA,IAAA,EAAA,IAAA,EAEA,EAAA,EAAA,WAAA,EAAA,IAAA,KAEA,EAAA,EAAA,SAAA,GAAA,IAAA,EAAA,KAEA,GAAA,GAAA,EAAA,WAIA,IAFA,IAAA,EAAA,IAAA,EACA,EAAA,EACA,KAAA,EAAA,GAAA,EAAA,GACA,OAAA,EAAA,KAAA,KAEA,KACA,EAAA,EAAA,SAAA,EAAA,GACA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,IAAA,EAAA,EAAA,GAEA,OADA,MAAA,GAAA,EAAA,EAAA,EAAA,EAAA,GAAA,GACA,KAEA,UAAA,EACA,EAAA,YAAA,IAEA,GAAA,KACA,EAAA,UACA,EAAA,OACA,GAAA,EAAA,SAEA,GAAA,IAAA,EAAA,GAEA,GAAA,EAAA,cAAA,EAAA,WApCA,EAAA,EAAA,eAAA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,UAAA,GACA,EAAA,MAAA,EA4CA,OAPA,EAAA,EAAA,GAEA,EAAA,GAAA,EACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,GAAA,GAAA,GAEA,GAAA,EAAA,UAAA,EAAA,EAAA,GAEA;;ACnFA,aACA,IAAA,EAAA,QAAA,wBACA,EAAA,QAAA,0BACA,EAAA,MAGA,OAAA,QAAA,QAAA,gBAAA,CAAA,EAAA,SAAA,GACA,OAAA,WAAA,OAAA,EAAA,KAAA,UAAA,OAAA,EAAA,UAAA,QAAA,KACA,CAEA,IAAA,SAAA,GACA,IAAA,EAAA,EAAA,SAAA,EAAA,KAAA,GAAA,GACA,OAAA,GAAA,EAAA,GAGA,IAAA,SAAA,EAAA,GACA,OAAA,EAAA,IAAA,EAAA,KAAA,GAAA,IAAA,EAAA,EAAA,EAAA,KAEA,GAAA;;AClBA,aACA,IAAA,EAAA,QAAA,wBACA,EAAA,QAAA,0BACA,EAAA,MAGA,OAAA,QAAA,QAAA,gBAAA,CAAA,EAAA,SAAA,GACA,OAAA,WAAA,OAAA,EAAA,KAAA,UAAA,OAAA,EAAA,UAAA,QAAA,KACA,CAEA,IAAA,SAAA,GACA,OAAA,EAAA,IAAA,EAAA,KAAA,GAAA,EAAA,IAAA,EAAA,EAAA,EAAA,KAEA;;ACbA,aACA,IAAA,EAAA,QAAA,mBACA,EAAA,QAAA,WAAA,QACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,kBACA,EAAA,QAAA,aACA,EAAA,QAAA,oBACA,EAAA,QAAA,UACA,EAAA,QAAA,0BACA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,EAGA,EAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,GAAA,IAAA,IAEA,EAAA,WACA,KAAA,EAAA,IAEA,EAAA,SAAA,EAAA,GACA,OAAA,EAAA,EAAA,EAAA,SAAA,GACA,OAAA,EAAA,KAAA,KAGA,EAAA,UAAA,CACA,IAAA,SAAA,GACA,IAAA,EAAA,EAAA,KAAA,GACA,GAAA,EAAA,OAAA,EAAA,IAEA,IAAA,SAAA,GACA,QAAA,EAAA,KAAA,IAEA,IAAA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,KAAA,GACA,EAAA,EAAA,GAAA,EACA,KAAA,EAAA,KAAA,CAAA,EAAA,KAEA,OAAA,SAAA,GACA,IAAA,EAAA,EAAA,KAAA,EAAA,SAAA,GACA,OAAA,EAAA,KAAA,IAGA,OADA,GAAA,KAAA,EAAA,OAAA,EAAA,MACA,IAIA,OAAA,QAAA,CACA,eAAA,SAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,SAAA,EAAA,GACA,EAAA,EAAA,EAAA,EAAA,MACA,EAAA,GAAA,EACA,EAAA,GAAA,IACA,EAAA,QAAA,EACA,MAAA,GAAA,EAAA,EAAA,EAAA,EAAA,GAAA,KAoBA,OAlBA,EAAA,EAAA,UAAA,CAGA,OAAA,SAAA,GACA,IAAA,EAAA,GAAA,OAAA,EACA,IAAA,EAAA,EAAA,GACA,OAAA,IAAA,EAAA,EAAA,EAAA,KAAA,IAAA,OAAA,GACA,GAAA,EAAA,EAAA,KAAA,YAAA,EAAA,KAAA,KAIA,IAAA,SAAA,GACA,IAAA,EAAA,GAAA,OAAA,EACA,IAAA,EAAA,EAAA,GACA,OAAA,IAAA,EAAA,EAAA,EAAA,KAAA,IAAA,IAAA,GACA,GAAA,EAAA,EAAA,KAAA,OAGA,GAEA,IAAA,SAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,EAAA,IAAA,GAGA,OAFA,IAAA,EAAA,EAAA,GAAA,IAAA,EAAA,GACA,EAAA,EAAA,IAAA,EACA,GAEA,QAAA;;;ACnFA,aACA,IAcA,EAdA,EAAA,QAAA,aACA,EAAA,QAAA,mBAAA,CAAA,GACA,EAAA,QAAA,eACA,EAAA,QAAA,WACA,EAAA,QAAA,oBACA,EAAA,QAAA,sBACA,EAAA,QAAA,gBACA,EAAA,QAAA,0BACA,EAAA,QAAA,0BACA,GAAA,EAAA,eAAA,kBAAA,EACA,EAAA,UACA,EAAA,EAAA,QACA,EAAA,OAAA,aACA,EAAA,EAAA,QAGA,EAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,UAAA,OAAA,EAAA,UAAA,QAAA,KAIA,EAAA,CAEA,IAAA,SAAA,GACA,GAAA,EAAA,GAAA,CACA,IAAA,EAAA,EAAA,GACA,OAAA,IAAA,EAAA,EAAA,EAAA,KAAA,IAAA,IAAA,GACA,EAAA,EAAA,KAAA,SAAA,IAIA,IAAA,SAAA,EAAA,GACA,OAAA,EAAA,IAAA,EAAA,KAAA,GAAA,EAAA,KAKA,EAAA,OAAA,QAAA,QAAA,gBAAA,CAAA,EAAA,EAAA,EAAA,GAAA,GAAA,GAGA,GAAA,IAEA,GADA,EAAA,EAAA,eAAA,EAAA,IACA,UAAA,GACA,EAAA,MAAA,EACA,EAAA,CAAA,SAAA,MAAA,MAAA,OAAA,SAAA,GACA,IAAA,EAAA,EAAA,UACA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,SAAA,EAAA,GAEA,GAAA,EAAA,KAAA,EAAA,GAAA,CACA,KAAA,KAAA,KAAA,GAAA,IAAA,GACA,IAAA,EAAA,KAAA,GAAA,GAAA,EAAA,GACA,MAAA,OAAA,EAAA,KAAA,EAEA,OAAA,EAAA,KAAA,KAAA,EAAA;;ACxDA,aACA,IAAA,EAAA,QAAA,sBACA,EAAA,QAAA,0BACA,EAAA,UAGA,QAAA,gBAAA,CAAA,EAAA,SAAA,GACA,OAAA,WAAA,OAAA,EAAA,KAAA,UAAA,OAAA,EAAA,UAAA,QAAA,KACA,CAEA,IAAA,SAAA,GACA,OAAA,EAAA,IAAA,EAAA,KAAA,GAAA,GAAA,KAEA,GAAA,GAAA;;;ACEA,IAfA,IASA,EATA,EAAA,QAAA,aACA,EAAA,QAAA,WACA,EAAA,QAAA,UACA,EAAA,EAAA,eACA,EAAA,EAAA,QACA,KAAA,EAAA,cAAA,EAAA,UACA,EAAA,EACA,EAAA,EACA,EAAA,EAGA,EAAA,iHAEA,MAAA,KAEA,EAAA,IACA,EAAA,EAAA,EAAA,QACA,EAAA,EAAA,UAAA,GAAA,GACA,EAAA,EAAA,UAAA,GAAA,IACA,GAAA,EAGA,OAAA,QAAA,CACA,IAAA,EACA,OAAA,EACA,MAAA,EACA,KAAA;;ACzBA,IAAA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,OAAA,QAAA,SAAA,GACA,QAAA,IAAA,EAAA,OAAA,EACA,IAAA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,GAAA,IAAA,EAAA,MAAA,WAAA,iBACA,OAAA;;;ACRA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,kBACA,EAAA,QAAA,cACA,EAAA,QAAA,YACA,EAAA,QAAA,WACA,EAAA,QAAA,mBACA,EAAA,QAAA,YACA,EAAA,QAAA,kBACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,EAAA,QAAA,eACA,EAAA,QAAA,kBAAA,EACA,EAAA,QAAA,gBAAA,EACA,EAAA,QAAA,iBACA,EAAA,QAAA,wBACA,EAAA,cACA,EAAA,WACA,EAAA,YACA,EAAA,gBACA,EAAA,eACA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,EAAA,KACA,EAAA,EAAA,WAEA,EAAA,EAAA,SACA,EAAA,EACA,EAAA,EAAA,IACA,EAAA,EAAA,IACA,EAAA,EAAA,MACA,EAAA,EAAA,IACA,EAAA,EAAA,IACA,EAAA,SACA,EAAA,aACA,EAAA,aACA,EAAA,EAAA,KAAA,EACA,EAAA,EAAA,KAAA,EACA,EAAA,EAAA,KAAA,EAGA,SAAA,EAAA,EAAA,EAAA,GACA,IAOA,EAAA,EAAA,EAPA,EAAA,IAAA,MAAA,GACA,EAAA,EAAA,EAAA,EAAA,EACA,GAAA,GAAA,GAAA,EACA,EAAA,GAAA,EACA,EAAA,KAAA,EAAA,EAAA,GAAA,IAAA,EAAA,GAAA,IAAA,EACA,EAAA,EACA,EAAA,EAAA,GAAA,IAAA,GAAA,EAAA,EAAA,EAAA,EAAA,EAkCA,KAhCA,EAAA,EAAA,KAEA,GAAA,IAAA,GAEA,EAAA,GAAA,EAAA,EAAA,EACA,EAAA,IAEA,EAAA,EAAA,EAAA,GAAA,GACA,GAAA,EAAA,EAAA,GAAA,IAAA,IACA,IACA,GAAA,IAGA,GADA,EAAA,GAAA,EACA,EAAA,EAEA,EAAA,EAAA,EAAA,EAAA,IAEA,GAAA,IACA,IACA,GAAA,GAEA,EAAA,GAAA,GACA,EAAA,EACA,EAAA,GACA,EAAA,GAAA,GACA,GAAA,EAAA,EAAA,GAAA,EAAA,EAAA,GACA,GAAA,IAEA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,EAAA,GACA,EAAA,IAGA,GAAA,EAAA,EAAA,KAAA,IAAA,EAAA,GAAA,IAAA,GAAA,GAGA,IAFA,EAAA,GAAA,EAAA,EACA,GAAA,EACA,EAAA,EAAA,EAAA,KAAA,IAAA,EAAA,GAAA,IAAA,GAAA,GAEA,OADA,IAAA,IAAA,IAAA,EACA,EAEA,SAAA,EAAA,EAAA,EAAA,GACA,IAOA,EAPA,EAAA,EAAA,EAAA,EAAA,EACA,GAAA,GAAA,GAAA,EACA,EAAA,GAAA,EACA,EAAA,EAAA,EACA,EAAA,EAAA,EACA,EAAA,EAAA,KACA,EAAA,IAAA,EAGA,IADA,IAAA,EACA,EAAA,EAAA,EAAA,IAAA,EAAA,EAAA,GAAA,IAAA,GAAA,GAIA,IAHA,EAAA,GAAA,IAAA,GAAA,EACA,KAAA,EACA,GAAA,EACA,EAAA,EAAA,EAAA,IAAA,EAAA,EAAA,GAAA,IAAA,GAAA,GACA,GAAA,IAAA,EACA,EAAA,EAAA,MACA,CAAA,GAAA,IAAA,EACA,OAAA,EAAA,IAAA,GAAA,EAAA,EAEA,GAAA,EAAA,EAAA,GACA,GAAA,EACA,OAAA,GAAA,EAAA,GAAA,EAAA,EAAA,EAAA,EAAA,GAGA,SAAA,EAAA,GACA,OAAA,EAAA,IAAA,GAAA,EAAA,IAAA,GAAA,EAAA,IAAA,EAAA,EAAA,GAEA,SAAA,EAAA,GACA,MAAA,CAAA,IAAA,GAEA,SAAA,EAAA,GACA,MAAA,CAAA,IAAA,EAAA,GAAA,EAAA,KAEA,SAAA,EAAA,GACA,MAAA,CAAA,IAAA,EAAA,GAAA,EAAA,IAAA,GAAA,GAAA,IAAA,GAAA,GAAA,KAEA,SAAA,EAAA,GACA,OAAA,EAAA,EAAA,GAAA,GAEA,SAAA,EAAA,GACA,OAAA,EAAA,EAAA,GAAA,GAGA,SAAA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,GAAA,EAAA,CAAA,IAAA,WAAA,OAAA,KAAA,MAGA,SAAA,EAAA,EAAA,EAAA,EAAA,GACA,IACA,EAAA,GADA,GAEA,GAAA,EAAA,EAAA,EAAA,GAAA,MAAA,EAAA,GACA,IAAA,EAAA,EAAA,GAAA,GACA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,MAAA,EAAA,EAAA,GACA,OAAA,EAAA,EAAA,EAAA,UAEA,SAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GACA,IACA,EAAA,GADA,GAEA,GAAA,EAAA,EAAA,EAAA,GAAA,MAAA,EAAA,GAIA,IAHA,IAAA,EAAA,EAAA,GAAA,GACA,EAAA,EAAA,EAAA,GACA,EAAA,GAAA,GACA,EAAA,EAAA,EAAA,EAAA,IAAA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAGA,GAAA,EAAA,IAgFA,CACA,IAAA,EAAA,WACA,EAAA,OACA,EAAA,WACA,IAAA,GAAA,MACA,EAAA,WAIA,OAHA,IAAA,EACA,IAAA,EAAA,KACA,IAAA,EAAA,KACA,EAAA,MAAA,IACA,CAMA,IADA,IACA,EADA,GAJA,EAAA,SAAA,GAEA,OADA,EAAA,KAAA,GACA,IAAA,EAAA,EAAA,MAEA,GAAA,EAAA,GACA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,OAAA,IACA,EAAA,EAAA,QAAA,GAAA,EAAA,EAAA,EAAA,EAAA,IAEA,IAAA,EAAA,YAAA,GAGA,IAAA,EAAA,IAAA,EAAA,IAAA,EAAA,IACA,EAAA,EAAA,GAAA,QACA,EAAA,QAAA,EAAA,YACA,EAAA,QAAA,EAAA,aACA,EAAA,QAAA,IAAA,EAAA,QAAA,IAAA,EAAA,EAAA,GAAA,CACA,QAAA,SAAA,EAAA,GACA,EAAA,KAAA,KAAA,EAAA,GAAA,IAAA,KAEA,SAAA,SAAA,EAAA,GACA,EAAA,KAAA,KAAA,EAAA,GAAA,IAAA,OAEA,QAhHA,EAAA,SAAA,GACA,EAAA,KAAA,EAAA,GACA,IAAA,EAAA,EAAA,GACA,KAAA,GAAA,EAAA,KAAA,IAAA,MAAA,GAAA,GACA,KAAA,GAAA,GAGA,EAAA,SAAA,EAAA,EAAA,GACA,EAAA,KAAA,EAAA,GACA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,GAAA,EAAA,GAAA,EAAA,EAAA,MAAA,EAAA,iBAEA,GAAA,GADA,OAAA,IAAA,EAAA,EAAA,EAAA,EAAA,IACA,EAAA,MAAA,EAAA,GACA,KAAA,GAAA,EACA,KAAA,GAAA,EACA,KAAA,GAAA,GAGA,IACA,EAAA,EAAA,EAAA,MACA,EAAA,EAAA,EAAA,MACA,EAAA,EAAA,EAAA,MACA,EAAA,EAAA,EAAA,OAGA,EAAA,EAAA,GAAA,CACA,QAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,GAAA,IAAA,IAAA,IAEA,SAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,GAAA,IAEA,SAAA,SAAA,GACA,IAAA,EAAA,EAAA,KAAA,EAAA,EAAA,UAAA,IACA,OAAA,EAAA,IAAA,EAAA,EAAA,KAAA,IAAA,IAEA,UAAA,SAAA,GACA,IAAA,EAAA,EAAA,KAAA,EAAA,EAAA,UAAA,IACA,OAAA,EAAA,IAAA,EAAA,EAAA,IAEA,SAAA,SAAA,GACA,OAAA,EAAA,EAAA,KAAA,EAAA,EAAA,UAAA,MAEA,UAAA,SAAA,GACA,OAAA,EAAA,EAAA,KAAA,EAAA,EAAA,UAAA,OAAA,GAEA,WAAA,SAAA,GACA,OAAA,EAAA,EAAA,KAAA,EAAA,EAAA,UAAA,IAAA,GAAA,IAEA,WAAA,SAAA,GACA,OAAA,EAAA,EAAA,KAAA,EAAA,EAAA,UAAA,IAAA,GAAA,IAEA,QAAA,SAAA,EAAA,GACA,EAAA,KAAA,EAAA,EAAA,EAAA,IAEA,SAAA,SAAA,EAAA,GACA,EAAA,KAAA,EAAA,EAAA,EAAA,IAEA,SAAA,SAAA,EAAA,GACA,EAAA,KAAA,EAAA,EAAA,EAAA,EAAA,UAAA,KAEA,UAAA,SAAA,EAAA,GACA,EAAA,KAAA,EAAA,EAAA,EAAA,EAAA,UAAA,KAEA,SAAA,SAAA,EAAA,GACA,EAAA,KAAA,EAAA,EAAA,EAAA,EAAA,UAAA,KAEA,UAAA,SAAA,EAAA,GACA,EAAA,KAAA,EAAA,EAAA,EAAA,EAAA,UAAA,KAEA,WAAA,SAAA,EAAA,GACA,EAAA,KAAA,EAAA,EAAA,EAAA,EAAA,UAAA,KAEA,WAAA,SAAA,EAAA,GACA,EAAA,KAAA,EAAA,EAAA,EAAA,EAAA,UAAA,OAsCA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,EAAA,GAAA,EAAA,MAAA,GACA,QAAA,GAAA,EACA,QAAA,GAAA;;ACnRA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,YACA,EAAA,QAAA,mBACA,EAAA,QAAA,gBACA,EAAA,QAAA,wBACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,aAAA,YACA,EAAA,QAAA,0BACA,EAAA,EAAA,YACA,EAAA,EAAA,SACA,EAAA,EAAA,KAAA,EAAA,OACA,EAAA,EAAA,UAAA,MACA,EAAA,EAAA,KACA,EAAA,cAEA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,IAAA,GAAA,CAAA,YAAA,IAEA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,OAAA,EAAA,CAEA,OAAA,SAAA,GACA,OAAA,GAAA,EAAA,IAAA,EAAA,IAAA,KAAA,KAIA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,WAAA,CAAA,WACA,OAAA,IAAA,EAAA,GAAA,MAAA,OAAA,GAAA,aACA,EAAA,CAEA,MAAA,SAAA,EAAA,GACA,QAAA,IAAA,QAAA,IAAA,EAAA,OAAA,EAAA,KAAA,EAAA,MAAA,GAQA,IAPA,IAAA,EAAA,EAAA,MAAA,WACA,EAAA,EAAA,EAAA,GACA,EAAA,OAAA,IAAA,EAAA,EAAA,EAAA,GACA,EAAA,IAAA,EAAA,KAAA,GAAA,CAAA,EAAA,EAAA,IACA,EAAA,IAAA,EAAA,MACA,EAAA,IAAA,EAAA,GACA,EAAA,EACA,EAAA,GACA,EAAA,SAAA,IAAA,EAAA,SAAA,MACA,OAAA,KAIA,QAAA,iBAAA,CAAA;;AC7CA,IAAA,EAAA,QAAA,aACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,YAAA,IAAA,CACA,SAAA,QAAA,mBAAA;;;AC8dA,IAAA,EAAA,UAAA,GA/dA,GAAA,QAAA,kBAAA,CACA,IAAA,EAAA,QAAA,cAEA,GADA,EAAA,QAAA,aACA,QAAA,aACA,EAAA,QAAA,aACA,EAAA,QAAA,YACA,EAAA,QAAA,mBACA,EAAA,QAAA,UACA,EAAA,QAAA,kBACA,EAAA,QAAA,oBACA,EAAA,QAAA,WACA,EAAA,QAAA,mBACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,EAAA,QAAA,eACA,EAAA,QAAA,wBACA,EAAA,QAAA,mBACA,EAAA,QAAA,UACA,EAAA,QAAA,cACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,oBACA,EAAA,QAAA,oBACA,EAAA,QAAA,iBACA,EAAA,QAAA,kBAAA,EACA,EAAA,QAAA,8BACA,EAAA,QAAA,UACA,EAAA,QAAA,UACA,EAAA,QAAA,oBACA,EAAA,QAAA,qBACA,EAAA,QAAA,0BACA,EAAA,QAAA,wBACA,EAAA,QAAA,gBACA,EAAA,QAAA,kBACA,EAAA,QAAA,kBACA,EAAA,QAAA,iBACA,EAAA,QAAA,wBACA,EAAA,QAAA,gBACA,EAAA,QAAA,kBACA,EAAA,EAAA,EACA,EAAA,EAAA,EACA,EAAA,EAAA,WACA,EAAA,EAAA,UACA,EAAA,EAAA,WACA,EAAA,cACA,EAAA,SAAA,EACA,EAAA,oBACA,EAAA,YACA,EAAA,MAAA,GACA,EAAA,EAAA,YACA,EAAA,EAAA,SACA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,GAAA,EAAA,GACA,GAAA,EAAA,GACA,GAAA,GAAA,GACA,GAAA,GAAA,GACA,GAAA,EAAA,OACA,GAAA,EAAA,KACA,GAAA,EAAA,QACA,GAAA,EAAA,YACA,GAAA,EAAA,OACA,GAAA,EAAA,YACA,GAAA,EAAA,KACA,GAAA,EAAA,KACA,GAAA,EAAA,MACA,GAAA,EAAA,SACA,GAAA,EAAA,eACA,GAAA,EAAA,YACA,GAAA,EAAA,eACA,GAAA,EAAA,qBACA,GAAA,EAAA,mBACA,GAAA,EAAA,OACA,GAAA,EAAA,MACA,GAAA,EAAA,KACA,GAAA,gBAEA,GAAA,EAAA,EAAA,SAAA,EAAA,GACA,OAAA,GAAA,EAAA,EAAA,EAAA,KAAA,KAGA,GAAA,EAAA,WAEA,OAAA,IAAA,IAAA,EAAA,IAAA,YAAA,CAAA,IAAA,QAAA,KAGA,KAAA,KAAA,EAAA,GAAA,KAAA,EAAA,WACA,IAAA,EAAA,GAAA,IAAA,MAGA,GAAA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,GACA,GAAA,EAAA,GAAA,EAAA,EAAA,MAAA,EAAA,iBACA,OAAA,GAGA,GAAA,SAAA,GACA,GAAA,EAAA,IAAA,MAAA,EAAA,OAAA,EACA,MAAA,EAAA,EAAA,2BAGA,GAAA,SAAA,EAAA,GACA,KAAA,EAAA,IAAA,MAAA,GACA,MAAA,EAAA,wCACA,OAAA,IAAA,EAAA,IAGA,GAAA,SAAA,EAAA,GACA,OAAA,GAAA,EAAA,EAAA,EAAA,KAAA,IAGA,GAAA,SAAA,EAAA,GAIA,IAHA,IAAA,EAAA,EACA,EAAA,EAAA,OACA,EAAA,GAAA,EAAA,GACA,EAAA,GAAA,EAAA,GAAA,EAAA,KACA,OAAA,GAGA,GAAA,SAAA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,CAAA,IAAA,WAAA,OAAA,KAAA,GAAA,OAGA,GAAA,SAAA,GACA,IAKA,EAAA,EAAA,EAAA,EAAA,EAAA,EALA,EAAA,EAAA,GACA,EAAA,UAAA,OACA,EAAA,EAAA,EAAA,UAAA,QAAA,EACA,OAAA,IAAA,EACA,EAAA,EAAA,GAEA,GAAA,MAAA,IAAA,EAAA,GAAA,CACA,IAAA,EAAA,EAAA,KAAA,GAAA,EAAA,GAAA,EAAA,IAAA,EAAA,EAAA,QAAA,KAAA,IACA,EAAA,KAAA,EAAA,OACA,EAAA,EAGA,IADA,GAAA,EAAA,IAAA,EAAA,EAAA,EAAA,UAAA,GAAA,IACA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,EAAA,GAAA,KAAA,GAAA,EAAA,EAAA,IACA,EAAA,GAAA,EAAA,EAAA,EAAA,GAAA,GAAA,EAAA,GAEA,OAAA,GAGA,GAAA,WAIA,IAHA,IAAA,EAAA,EACA,EAAA,UAAA,OACA,EAAA,GAAA,KAAA,GACA,EAAA,GAAA,EAAA,GAAA,UAAA,KACA,OAAA,GAIA,KAAA,GAAA,EAAA,WAAA,GAAA,KAAA,IAAA,EAAA,MAEA,GAAA,WACA,OAAA,GAAA,MAAA,GAAA,GAAA,KAAA,GAAA,OAAA,GAAA,MAAA,YAGA,GAAA,CACA,WAAA,SAAA,EAAA,GACA,OAAA,EAAA,KAAA,GAAA,MAAA,EAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,IAEA,MAAA,SAAA,GACA,OAAA,EAAA,GAAA,MAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,IAEA,KAAA,SAAA,GACA,OAAA,EAAA,MAAA,GAAA,MAAA,YAEA,OAAA,SAAA,GACA,OAAA,GAAA,KAAA,EAAA,GAAA,MAAA,EACA,UAAA,OAAA,EAAA,UAAA,QAAA,KAEA,KAAA,SAAA,GACA,OAAA,GAAA,GAAA,MAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,IAEA,UAAA,SAAA,GACA,OAAA,GAAA,GAAA,MAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,IAEA,QAAA,SAAA,GACA,EAAA,GAAA,MAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,IAEA,QAAA,SAAA,GACA,OAAA,GAAA,GAAA,MAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,IAEA,SAAA,SAAA,GACA,OAAA,GAAA,GAAA,MAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,IAEA,KAAA,SAAA,GACA,OAAA,GAAA,MAAA,GAAA,MAAA,YAEA,YAAA,SAAA,GACA,OAAA,GAAA,MAAA,GAAA,MAAA,YAEA,IAAA,SAAA,GACA,OAAA,GAAA,GAAA,MAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,IAEA,OAAA,SAAA,GACA,OAAA,GAAA,MAAA,GAAA,MAAA,YAEA,YAAA,SAAA,GACA,OAAA,GAAA,MAAA,GAAA,MAAA,YAEA,QAAA,WAMA,IALA,IAIA,EAHA,EAAA,GADA,MACA,OACA,EAAA,KAAA,MAAA,EAAA,GACA,EAAA,EAEA,EAAA,GACA,EANA,KAMA,GANA,KAOA,KAPA,OAOA,GAPA,KAQA,GAAA,EACA,OATA,MAWA,KAAA,SAAA,GACA,OAAA,EAAA,GAAA,MAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,IAEA,KAAA,SAAA,GACA,OAAA,GAAA,KAAA,GAAA,MAAA,IAEA,SAAA,SAAA,EAAA,GACA,IAAA,EAAA,GAAA,MACA,EAAA,EAAA,OACA,EAAA,EAAA,EAAA,GACA,OAAA,IAAA,EAAA,EAAA,EAAA,KAAA,CACA,EAAA,OACA,EAAA,WAAA,EAAA,EAAA,kBACA,QAAA,IAAA,EAAA,EAAA,EAAA,EAAA,IAAA,MAKA,GAAA,SAAA,EAAA,GACA,OAAA,GAAA,KAAA,GAAA,KAAA,GAAA,MAAA,EAAA,KAGA,GAAA,SAAA,GACA,GAAA,MACA,IAAA,EAAA,GAAA,UAAA,GAAA,GACA,EAAA,KAAA,OACA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,QACA,EAAA,EACA,GAAA,EAAA,EAAA,EAAA,MAAA,EAAA,IACA,KAAA,EAAA,GAAA,KAAA,EAAA,GAAA,EAAA,MAGA,GAAA,CACA,QAAA,WACA,OAAA,GAAA,KAAA,GAAA,QAEA,KAAA,WACA,OAAA,GAAA,KAAA,GAAA,QAEA,OAAA,WACA,OAAA,GAAA,KAAA,GAAA,SAIA,GAAA,SAAA,EAAA,GACA,OAAA,EAAA,IACA,EAAA,KACA,iBAAA,GACA,KAAA,GACA,QAAA,IAAA,OAAA,IAEA,GAAA,SAAA,EAAA,GACA,OAAA,GAAA,EAAA,EAAA,EAAA,GAAA,IACA,EAAA,EAAA,EAAA,IACA,EAAA,EAAA,IAEA,GAAA,SAAA,EAAA,EAAA,GACA,QAAA,GAAA,EAAA,EAAA,EAAA,GAAA,KACA,EAAA,IACA,EAAA,EAAA,WACA,EAAA,EAAA,QACA,EAAA,EAAA,QAEA,EAAA,cACA,EAAA,EAAA,cAAA,EAAA,UACA,EAAA,EAAA,gBAAA,EAAA,WAIA,EAAA,EAAA,EAAA,IAFA,EAAA,GAAA,EAAA,MACA,IAIA,KACA,EAAA,EAAA,GACA,EAAA,EAAA,IAGA,EAAA,EAAA,EAAA,EAAA,GAAA,GAAA,SAAA,CACA,yBAAA,GACA,eAAA,KAGA,EAAA,WAAA,GAAA,KAAA,QACA,GAAA,GAAA,WACA,OAAA,GAAA,KAAA,QAIA,IAAA,GAAA,EAAA,GAAA,IACA,EAAA,GAAA,IACA,EAAA,GAAA,GAAA,GAAA,QACA,EAAA,GAAA,CACA,MAAA,GACA,IAAA,GACA,YAAA,aACA,SAAA,GACA,eAAA,KAEA,GAAA,GAAA,SAAA,KACA,GAAA,GAAA,aAAA,KACA,GAAA,GAAA,aAAA,KACA,GAAA,GAAA,SAAA,KACA,EAAA,GAAA,GAAA,CACA,IAAA,WAAA,OAAA,KAAA,OAIA,OAAA,QAAA,SAAA,EAAA,EAAA,EAAA,GAEA,IAAA,EAAA,IADA,IAAA,GACA,UAAA,IAAA,QACA,EAAA,MAAA,EACA,EAAA,MAAA,EACA,EAAA,EAAA,GACA,EAAA,GAAA,GACA,EAAA,GAAA,EAAA,GACA,GAAA,IAAA,EAAA,IACA,EAAA,GACA,EAAA,GAAA,EAAA,GAUA,EAAA,SAAA,EAAA,GACA,EAAA,EAAA,EAAA,CACA,IAAA,WACA,OAZA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,GACA,OAAA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,EAAA,IAUA,CAAA,KAAA,IAEA,IAAA,SAAA,GACA,OAXA,SAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,GACA,IAAA,GAAA,EAAA,KAAA,MAAA,IAAA,EAAA,EAAA,EAAA,IAAA,IAAA,IAAA,GACA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,EAAA,EAAA,IAQA,CAAA,KAAA,EAAA,IAEA,YAAA,KAGA,GACA,EAAA,EAAA,SAAA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,EAAA,MACA,IAEA,EAAA,EAAA,EAAA,EAFA,EAAA,EACA,EAAA,EAEA,GAAA,EAAA,GAIA,CAAA,KAAA,aAAA,IAAA,EAAA,EAAA,KAAA,GAAA,GAAA,GAaA,OAAA,MAAA,EACA,GAAA,EAAA,GAEA,GAAA,KAAA,EAAA,GAfA,EAAA,EACA,EAAA,GAAA,EAAA,GACA,IAAA,EAAA,EAAA,WACA,QAAA,IAAA,EAAA,CACA,GAAA,EAAA,EAAA,MAAA,EAAA,IAEA,IADA,EAAA,EAAA,GACA,EAAA,MAAA,EAAA,SAGA,IADA,EAAA,EAAA,GAAA,GACA,EAAA,EAAA,MAAA,EAAA,IAEA,EAAA,EAAA,OAfA,EAAA,EAAA,GAEA,EAAA,IAAA,EADA,EAAA,EAAA,GA2BA,IAPA,EAAA,EAAA,KAAA,CACA,EAAA,EACA,EAAA,EACA,EAAA,EACA,EAAA,EACA,EAAA,IAAA,EAAA,KAEA,EAAA,GAAA,EAAA,EAAA,OAEA,EAAA,EAAA,GAAA,EAAA,IACA,EAAA,EAAA,cAAA,IACA,EAAA,WACA,EAAA,MACA,EAAA,WACA,IAAA,GAAA,MACA,EAAA,SAAA,GACA,IAAA,EACA,IAAA,EAAA,MACA,IAAA,EAAA,KACA,IAAA,EAAA,KACA,KACA,EAAA,EAAA,SAAA,EAAA,EAAA,EAAA,GAEA,IAAA,EAGA,OAJA,EAAA,EAAA,EAAA,GAIA,EAAA,GACA,aAAA,IAAA,EAAA,EAAA,KAAA,GAAA,GAAA,OACA,IAAA,EACA,IAAA,EAAA,EAAA,GAAA,EAAA,GAAA,QACA,IAAA,EACA,IAAA,EAAA,EAAA,GAAA,EAAA,IACA,IAAA,EAAA,GAEA,MAAA,EAAA,GAAA,EAAA,GACA,GAAA,KAAA,EAAA,GATA,IAAA,EAAA,EAAA,MAWA,EAAA,IAAA,SAAA,UAAA,EAAA,GAAA,OAAA,EAAA,IAAA,EAAA,GAAA,SAAA,GACA,KAAA,GAAA,EAAA,EAAA,EAAA,EAAA,MAEA,EAAA,GAAA,EACA,IAAA,EAAA,YAAA,IAEA,IAAA,EAAA,EAAA,IACA,IAAA,IACA,UAAA,EAAA,MAAA,MAAA,EAAA,MACA,EAAA,GAAA,OACA,EAAA,EAAA,IAAA,GACA,EAAA,EAAA,GAAA,GACA,EAAA,EAAA,IAAA,GACA,EAAA,EAAA,GAAA,IAEA,EAAA,IAAA,EAAA,GAAA,KAAA,EAAA,MAAA,IACA,EAAA,EAAA,GAAA,CACA,IAAA,WAAA,OAAA,KAIA,EAAA,GAAA,EAEA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,GAAA,GAAA,GAEA,EAAA,EAAA,EAAA,EAAA,CACA,kBAAA,IAGA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,WAAA,EAAA,GAAA,KAAA,EAAA,KAAA,EAAA,CACA,KAAA,GACA,GAAA,KAGA,KAAA,GAAA,EAAA,EAAA,EAAA,GAEA,EAAA,EAAA,EAAA,EAAA,IAEA,EAAA,GAEA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,CAAA,IAAA,KAEA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,EAAA,IAEA,GAAA,EAAA,UAAA,KAAA,EAAA,SAAA,IAEA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,WACA,IAAA,EAAA,GAAA,UACA,EAAA,CAAA,MAAA,KAEA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,WACA,MAAA,CAAA,EAAA,GAAA,kBAAA,IAAA,EAAA,CAAA,EAAA,IAAA,qBACA,EAAA,WACA,EAAA,eAAA,KAAA,CAAA,EAAA,OACA,EAAA,CAAA,eAAA,KAEA,EAAA,GAAA,EAAA,EAAA,EACA,GAAA,GAAA,EAAA,EAAA,GAAA,SAEA,OAAA,QAAA;;AC/dA,QAAA,iBAAA,CAAA,OAAA,EAAA,SAAA,GACA,OAAA,SAAA,EAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,EAAA;;ACFA,QAAA,iBAAA,CAAA,QAAA,EAAA,SAAA,GACA,OAAA,SAAA,EAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,EAAA;;ACFA,QAAA,iBAAA,CAAA,QAAA,EAAA,SAAA,GACA,OAAA,SAAA,EAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,EAAA,MAEA;;ACJA,QAAA,iBAAA,CAAA,QAAA,EAAA,SAAA,GACA,OAAA,SAAA,EAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,EAAA;;ACFA,QAAA,iBAAA,CAAA,SAAA,EAAA,SAAA,GACA,OAAA,SAAA,EAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,EAAA;;ACFA,QAAA,iBAAA,CAAA,QAAA,EAAA,SAAA,GACA,OAAA,SAAA,EAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,EAAA;;ACFA,QAAA,iBAAA,CAAA,SAAA,EAAA,SAAA,GACA,OAAA,SAAA,EAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,EAAA;;ACFA,QAAA,iBAAA,CAAA,UAAA,EAAA,SAAA,GACA,OAAA,SAAA,EAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,EAAA;;ACFA,QAAA,iBAAA,CAAA,UAAA,EAAA,SAAA,GACA,OAAA,SAAA,EAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,EAAA;;ACDA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,GAAA,QAAA,aAAA,SAAA,IAAA,MACA,EAAA,SAAA,MAEA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,WAAA,CAAA,WACA,EAAA,gBACA,UAAA,CACA,MAAA,SAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,OAAA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,KAAA,EAAA,EAAA;;ACZA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,oBACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,YACA,EAAA,QAAA,WACA,GAAA,QAAA,aAAA,SAAA,IAAA,UAIA,EAAA,EAAA,WACA,SAAA,KACA,QAAA,EAAA,aAAA,GAAA,aAAA,KAEA,GAAA,EAAA,WACA,EAAA,gBAGA,EAAA,EAAA,EAAA,EAAA,GAAA,GAAA,GAAA,UAAA,CACA,UAAA,SAAA,EAAA,GACA,EAAA,GACA,EAAA,GACA,IAAA,EAAA,UAAA,OAAA,EAAA,EAAA,EAAA,UAAA,IACA,GAAA,IAAA,EAAA,OAAA,EAAA,EAAA,EAAA,GACA,GAAA,GAAA,EAAA,CAEA,OAAA,EAAA,QACA,KAAA,EAAA,OAAA,IAAA,EACA,KAAA,EAAA,OAAA,IAAA,EAAA,EAAA,IACA,KAAA,EAAA,OAAA,IAAA,EAAA,EAAA,GAAA,EAAA,IACA,KAAA,EAAA,OAAA,IAAA,EAAA,EAAA,GAAA,EAAA,GAAA,EAAA,IACA,KAAA,EAAA,OAAA,IAAA,EAAA,EAAA,GAAA,EAAA,GAAA,EAAA,GAAA,EAAA,IAGA,IAAA,EAAA,CAAA,MAEA,OADA,EAAA,KAAA,MAAA,EAAA,GACA,IAAA,EAAA,MAAA,EAAA,IAGA,IAAA,EAAA,EAAA,UACA,EAAA,EAAA,EAAA,GAAA,EAAA,OAAA,WACA,EAAA,SAAA,MAAA,KAAA,EAAA,EAAA,GACA,OAAA,EAAA,GAAA,EAAA;;AC3CA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,mBAGA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,WAAA,CAAA,WAEA,QAAA,eAAA,EAAA,EAAA,GAAA,EAAA,CAAA,MAAA,IAAA,EAAA,CAAA,MAAA,MACA,UAAA,CACA,eAAA,SAAA,EAAA,EAAA,GACA,EAAA,GACA,EAAA,EAAA,GAAA,GACA,EAAA,GACA,IAEA,OADA,EAAA,EAAA,EAAA,EAAA,IACA,EACA,MAAA,GACA,OAAA;;AClBA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,kBAAA,EACA,EAAA,QAAA,gBAEA,EAAA,EAAA,EAAA,UAAA,CACA,eAAA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,EAAA,GAAA,GACA,QAAA,IAAA,EAAA,sBAAA,EAAA;;ACRA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,SAAA,GACA,KAAA,GAAA,EAAA,GACA,KAAA,GAAA,EACA,IACA,EADA,EAAA,KAAA,GAAA,GAEA,IAAA,KAAA,EAAA,EAAA,KAAA,IAEA,QAAA,iBAAA,CAAA,EAAA,SAAA,WACA,IAEA,EADA,EADA,KACA,GAEA,GACA,GAJA,KAIA,IAAA,EAAA,OAAA,MAAA,CAAA,WAAA,EAAA,MAAA,YACA,EAAA,EALA,KAKA,SALA,KAKA,KACA,MAAA,CAAA,MAAA,EAAA,MAAA,KAGA,EAAA,EAAA,EAAA,UAAA,CACA,UAAA,SAAA,GACA,OAAA,IAAA,EAAA;;ACtBA,IAAA,EAAA,QAAA,kBACA,EAAA,QAAA,iBACA,EAAA,QAAA,UACA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBAEA,SAAA,EAAA,EAAA,GACA,IACA,EAAA,EADA,EAAA,UAAA,OAAA,EAAA,EAAA,UAAA,GAEA,OAAA,EAAA,KAAA,EAAA,EAAA,IACA,EAAA,EAAA,EAAA,EAAA,IAAA,EAAA,EAAA,SACA,EAAA,WACA,IAAA,EAAA,IACA,EAAA,IAAA,KAAA,QACA,EACA,EAAA,EAAA,EAAA,IAAA,EAAA,EAAA,EAAA,QAAA,EAGA,EAAA,EAAA,EAAA,UAAA,CAAA,IAAA;;ACnBA,IAAA,EAAA,QAAA,kBACA,EAAA,QAAA,aACA,EAAA,QAAA,gBAEA,EAAA,EAAA,EAAA,UAAA,CACA,yBAAA,SAAA,EAAA,GACA,OAAA,EAAA,EAAA,EAAA,GAAA;;ACNA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBAEA,EAAA,EAAA,EAAA,UAAA,CACA,eAAA,SAAA,GACA,OAAA,EAAA,EAAA;;ACNA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,UAAA,CACA,IAAA,SAAA,EAAA,GACA,OAAA,KAAA;;ACJA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,OAAA,aAEA,EAAA,EAAA,EAAA,UAAA,CACA,aAAA,SAAA,GAEA,OADA,EAAA,IACA,GAAA,EAAA;;ACPA,IAAA,EAAA,QAAA,kBACA,EAAA,QAAA,kBACA,EAAA,QAAA,gBACA,EAAA,QAAA,aAAA,QACA,OAAA,QAAA,GAAA,EAAA,SAAA,SAAA,GACA,IAAA,EAAA,EAAA,EAAA,EAAA,IACA,EAAA,EAAA,EACA,OAAA,EAAA,EAAA,OAAA,EAAA,IAAA;;ACPA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,UAAA,CAAA,QAAA,QAAA;;ACFA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,OAAA,kBAEA,EAAA,EAAA,EAAA,UAAA,CACA,kBAAA,SAAA,GACA,EAAA,GACA,IAEA,OADA,GAAA,EAAA,IACA,EACA,MAAA,GACA,OAAA;;ACXA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,kBACA,EAAA,QAAA,iBACA,EAAA,QAAA,UACA,EAAA,QAAA,aACA,EAAA,QAAA,oBACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBAEA,SAAA,EAAA,EAAA,EAAA,GACA,IAEA,EAAA,EAFA,EAAA,UAAA,OAAA,EAAA,EAAA,UAAA,GACA,EAAA,EAAA,EAAA,EAAA,GAAA,GAEA,IAAA,EAAA,CACA,GAAA,EAAA,EAAA,EAAA,IACA,OAAA,EAAA,EAAA,EAAA,EAAA,GAEA,EAAA,EAAA,GAEA,GAAA,EAAA,EAAA,SAAA,CACA,IAAA,IAAA,EAAA,WAAA,EAAA,GAAA,OAAA,EACA,GAAA,EAAA,EAAA,EAAA,EAAA,GAAA,CACA,GAAA,EAAA,KAAA,EAAA,MAAA,IAAA,EAAA,SAAA,OAAA,EACA,EAAA,MAAA,EACA,EAAA,EAAA,EAAA,EAAA,QACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,IACA,OAAA,EAEA,YAAA,IAAA,EAAA,MAAA,EAAA,IAAA,KAAA,EAAA,IAAA,GAGA,EAAA,EAAA,EAAA,UAAA,CAAA,IAAA;;AC/BA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBAEA,GAAA,EAAA,EAAA,EAAA,UAAA,CACA,eAAA,SAAA,EAAA,GACA,EAAA,MAAA,EAAA,GACA,IAEA,OADA,EAAA,IAAA,EAAA,IACA,EACA,MAAA,GACA,OAAA;;ACXA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,oBAAA,EAAA,GAEA,EAAA,EAAA,EAAA,QAAA,CACA,SAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,MAIA,QAAA,wBAAA,CAAA;;ACXA,aAEA,IAAA,EAAA,QAAA,eACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,UACA,EAAA,QAAA,SAAA,CAAA,sBAEA,SAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAMA,IALA,IAGA,EAAA,EAHA,EAAA,EACA,EAAA,EACA,IAAA,GAAA,EAAA,EAAA,EAAA,GAGA,EAAA,GAAA,CACA,GAAA,KAAA,EAAA,CASA,GARA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,GAAA,EAAA,GAEA,GAAA,EACA,EAAA,KAEA,OAAA,KADA,EAAA,EAAA,MACA,EAAA,EAAA,IAGA,GAAA,EAAA,EACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,EAAA,EAAA,GAAA,MACA,CACA,GAAA,GAAA,iBAAA,MAAA,YACA,EAAA,GAAA,EAGA,IAEA,IAEA,OAAA,EAGA,OAAA,QAAA;;ACtCA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,yBACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,QAAA,2BAEA,EAAA,EAAA,EAAA,QAAA,CACA,QAAA,SAAA,GACA,IACA,EAAA,EADA,EAAA,EAAA,MAMA,OAJA,EAAA,GACA,EAAA,EAAA,EAAA,QACA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,UAAA,IACA,KAIA,QAAA,wBAAA,CAAA;;ACrBA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,yBACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,QAAA,2BAEA,EAAA,EAAA,EAAA,QAAA,CACA,QAAA,WACA,IAAA,EAAA,UAAA,GACA,EAAA,EAAA,MACA,EAAA,EAAA,EAAA,QACA,EAAA,EAAA,EAAA,GAEA,OADA,EAAA,EAAA,EAAA,EAAA,EAAA,OAAA,IAAA,EAAA,EAAA,EAAA,IACA,KAIA,QAAA,wBAAA,CAAA;;ACpBA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,eAAA,EAAA,GAEA,EAAA,EAAA,EAAA,SAAA,CACA,GAAA,SAAA,GACA,OAAA,EAAA,KAAA;;ACNA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,oBACA,EAAA,QAAA,cAEA,OAAA,QAAA,SAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,OAAA,EAAA,IACA,EAAA,EAAA,OACA,OAAA,IAAA,EAAA,IAAA,OAAA,GACA,EAAA,EAAA,GACA,GAAA,GAAA,GAAA,IAAA,EAAA,OAAA,EACA,IAAA,EAAA,EAAA,EACA,EAAA,EAAA,KAAA,EAAA,KAAA,KAAA,EAAA,EAAA,SAEA,OADA,EAAA,OAAA,IAAA,EAAA,EAAA,MAAA,EAAA,IACA,EAAA,EAAA,EAAA,EAAA;;ACdA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,QAAA,iBAGA,EAAA,mDAAA,KAAA,GAEA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,SAAA,CACA,SAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,GAAA;;ACXA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,QAAA,iBAGA,EAAA,mDAAA,KAAA,GAEA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,SAAA,CACA,OAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,GAAA;;ACXA,aAEA,QAAA,iBAAA,CAAA,WAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,KAEA;;ACNA,aAEA,QAAA,iBAAA,CAAA,YAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,KAEA;;ACNA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,cACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,YACA,EAAA,OAAA,UAEA,EAAA,SAAA,EAAA,GACA,KAAA,GAAA,EACA,KAAA,GAAA,GAGA,QAAA,iBAAA,CAAA,EAAA,gBAAA,WACA,IAAA,EAAA,KAAA,GAAA,KAAA,KAAA,IACA,MAAA,CAAA,MAAA,EAAA,KAAA,OAAA,KAGA,EAAA,EAAA,EAAA,SAAA,CACA,SAAA,SAAA,GAEA,GADA,EAAA,OACA,EAAA,GAAA,MAAA,UAAA,EAAA,qBACA,IAAA,EAAA,OAAA,MACA,EAAA,UAAA,EAAA,OAAA,EAAA,OAAA,EAAA,KAAA,GACA,EAAA,IAAA,OAAA,EAAA,QAAA,EAAA,QAAA,KAAA,EAAA,IAAA,GAEA,OADA,EAAA,UAAA,EAAA,EAAA,WACA,IAAA,EAAA,EAAA;;AC3BA,QAAA,gBAAA,CAAA;;ACAA,QAAA,gBAAA,CAAA;;ACCA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,eACA,EAAA,QAAA,iBACA,EAAA,QAAA,kBACA,EAAA,QAAA,sBAEA,EAAA,EAAA,EAAA,SAAA,CACA,0BAAA,SAAA,GAOA,IANA,IAKA,EAAA,EALA,EAAA,EAAA,GACA,EAAA,EAAA,EACA,EAAA,EAAA,GACA,EAAA,GACA,EAAA,EAEA,EAAA,OAAA,QAEA,KADA,EAAA,EAAA,EAAA,EAAA,EAAA,QACA,EAAA,EAAA,EAAA,GAEA,OAAA;;ACnBA,IAAA,EAAA,QAAA,kBACA,EAAA,QAAA,kBACA,EAAA,QAAA,iBACA,EAAA,QAAA,iBAAA,EACA,OAAA,QAAA,SAAA,GACA,OAAA,SAAA,GAOA,IANA,IAKA,EALA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,EAAA,OACA,EAAA,EACA,EAAA,GAEA,EAAA,GACA,EAAA,EAAA,KACA,IAAA,EAAA,KAAA,EAAA,IACA,EAAA,KAAA,EAAA,CAAA,EAAA,EAAA,IAAA,EAAA,IAGA,OAAA;;ACjBA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,qBAAA,EAAA,GAEA,EAAA,EAAA,EAAA,SAAA,CACA,OAAA,SAAA,GACA,OAAA,EAAA;;ACLA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,qBAAA,EAAA,GAEA,EAAA,EAAA,EAAA,SAAA,CACA,QAAA,SAAA,GACA,OAAA,EAAA;;ACNA,aAEA,OAAA,QAAA,QAAA,gBAAA,QAAA,WAAA,CAAA,WACA,IAAA,EAAA,KAAA,SAGA,iBAAA,KAAA,KAAA,EAAA,qBACA,QAAA,aAAA;;ACPA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBAGA,QAAA,mBAAA,EAAA,EAAA,EAAA,QAAA,wBAAA,SAAA,CACA,iBAAA,SAAA,EAAA,GACA,EAAA,EAAA,EAAA,MAAA,EAAA,CAAA,IAAA,EAAA,GAAA,YAAA,EAAA,cAAA;;ACTA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBAGA,QAAA,mBAAA,EAAA,EAAA,EAAA,QAAA,wBAAA,SAAA,CACA,iBAAA,SAAA,EAAA,GACA,EAAA,EAAA,EAAA,MAAA,EAAA,CAAA,IAAA,EAAA,GAAA,YAAA,EAAA,cAAA;;ACTA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,mBACA,EAAA,QAAA,iBACA,EAAA,QAAA,kBAAA,EAGA,QAAA,mBAAA,EAAA,EAAA,EAAA,QAAA,wBAAA,SAAA,CACA,iBAAA,SAAA,GACA,IAEA,EAFA,EAAA,EAAA,MACA,EAAA,EAAA,GAAA,GAEA,GACA,GAAA,EAAA,EAAA,EAAA,GAAA,OAAA,EAAA,UACA,EAAA,EAAA;;ACfA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,mBACA,EAAA,QAAA,iBACA,EAAA,QAAA,kBAAA,EAGA,QAAA,mBAAA,EAAA,EAAA,EAAA,QAAA,wBAAA,SAAA,CACA,iBAAA,SAAA,GACA,IAEA,EAFA,EAAA,EAAA,MACA,EAAA,EAAA,GAAA,GAEA,GACA,GAAA,EAAA,EAAA,EAAA,GAAA,OAAA,EAAA,UACA,EAAA,EAAA;;ACfA,IAAA,EAAA,QAAA,aAEA,OAAA,QAAA,SAAA,EAAA,GACA,IAAA,EAAA,GAEA,OADA,EAAA,GAAA,EAAA,EAAA,KAAA,EAAA,GACA;;ACJA,IAAA,EAAA,QAAA,cACA,EAAA,QAAA,0BACA,OAAA,QAAA,SAAA,GACA,OAAA,WACA,GAAA,EAAA,OAAA,EAAA,MAAA,UAAA,EAAA,yBACA,OAAA,EAAA;;ACLA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,EAAA,EAAA,MAAA,CAAA,OAAA,QAAA,wBAAA,CAAA;;ACFA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,EAAA,EAAA,MAAA,CAAA,OAAA,QAAA,wBAAA,CAAA;;ACHA,aAEA,IAAA,EAAA,QAAA,aAEA,OAAA,QAAA,SAAA,GACA,EAAA,EAAA,EAAA,EAAA,CAAA,GAAA,WAGA,IAFA,IAAA,EAAA,UAAA,OACA,EAAA,IAAA,MAAA,GACA,KAAA,EAAA,GAAA,UAAA,GACA,OAAA,IAAA,KAAA;;ACRA,QAAA,uBAAA,CAAA;;ACAA,QAAA,uBAAA,CAAA;;ACAA,QAAA,uBAAA,CAAA;;ACAA,QAAA,uBAAA,CAAA;;ACDA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,QAAA,UACA,EAAA,QAAA,aAEA,OAAA,QAAA,SAAA,GACA,EAAA,EAAA,EAAA,EAAA,CAAA,KAAA,SAAA,GACA,IACA,EAAA,EAAA,EAAA,EADA,EAAA,UAAA,GAKA,OAHA,EAAA,OACA,OAAA,IAAA,IACA,EAAA,GACA,MAAA,EAAA,IAAA,MACA,EAAA,GACA,GACA,EAAA,EACA,EAAA,EAAA,EAAA,UAAA,GAAA,GACA,EAAA,GAAA,EAAA,SAAA,GACA,EAAA,KAAA,EAAA,EAAA,SAGA,EAAA,GAAA,EAAA,EAAA,KAAA,GAEA,IAAA,KAAA;;ACxBA,QAAA,yBAAA,CAAA;;ACAA,QAAA,yBAAA,CAAA;;ACAA,QAAA,yBAAA,CAAA;;ACAA,QAAA,yBAAA,CAAA;;ACAA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,CAAA,OAAA,QAAA;;ACFA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,SAAA,CAAA,OAAA,QAAA;;ACFA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,UAEA,EAAA,EAAA,EAAA,QAAA,CACA,QAAA,SAAA,GACA,MAAA,UAAA,EAAA;;ACLA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CACA,MAAA,SAAA,EAAA,EAAA,GACA,OAAA,KAAA,IAAA,EAAA,KAAA,IAAA,EAAA;;ACJA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CAAA,YAAA,KAAA,GAAA;;ACFA,IAAA,EAAA,QAAA,aACA,EAAA,IAAA,KAAA,GAEA,EAAA,EAAA,EAAA,OAAA,CACA,QAAA,SAAA,GACA,OAAA,EAAA;;ACLA,OAAA,QAAA,KAAA,OAAA,SAAA,EAAA,EAAA,EAAA,EAAA,GACA,OACA,IAAA,UAAA,QAEA,GAAA,GAEA,GAAA,GAEA,GAAA,GAEA,GAAA,GAEA,GAAA,EACA,IACA,IAAA,EAAA,GAAA,KAAA,EAAA,EAAA,GACA,EAAA,IAAA,EAAA,IAAA,EAAA,GAAA;;ACfA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,QAAA,kBAEA,EAAA,EAAA,EAAA,OAAA,CACA,OAAA,SAAA,EAAA,EAAA,EAAA,EAAA,GACA,OAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA;;ACNA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CACA,MAAA,SAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,IAAA,EAEA,EAAA,IAAA,EACA,OAFA,IAAA,IAEA,IAAA,KAAA,EAAA,GAAA,EAAA,KAAA,EAAA,IAAA,MAAA,IAAA;;ACPA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CACA,MAAA,SAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,IAAA,EAEA,EAAA,IAAA,EACA,OAFA,IAAA,IAEA,IAAA,MAAA,EAAA,IAAA,EAAA,GAAA,EAAA,IAAA,KAAA,IAAA;;ACPA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CACA,MAAA,SAAA,EAAA,GACA,IACA,GAAA,EACA,GAAA,EACA,EAHA,MAGA,EACA,EAJA,MAIA,EACA,EAAA,GAAA,GACA,EAAA,GAAA,GACA,GAAA,EAAA,IAAA,IAAA,EAAA,IAAA,IACA,OAAA,EAAA,GAAA,GAAA,MAAA,EAAA,IAAA,IARA,MAQA,IAAA;;ACZA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CAAA,YAAA,IAAA,KAAA;;ACFA,IAAA,EAAA,QAAA,aACA,EAAA,KAAA,GAAA,IAEA,EAAA,EAAA,EAAA,OAAA,CACA,QAAA,SAAA,GACA,OAAA,EAAA;;ACLA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CAAA,MAAA,QAAA;;ACFA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CACA,MAAA,SAAA,EAAA,GACA,IACA,GAAA,EACA,GAAA,EACA,EAHA,MAGA,EACA,EAJA,MAIA,EACA,EAAA,IAAA,GACA,EAAA,IAAA,GACA,GAAA,EAAA,IAAA,IAAA,EAAA,IAAA,IACA,OAAA,EAAA,GAAA,IAAA,MAAA,EAAA,IAAA,IARA,MAQA,KAAA;;ACZA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CAAA,QAAA,SAAA,GAEA,OAAA,GAAA,IAAA,EAAA,EAAA,GAAA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA;;;ACJA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,WACA,EAAA,QAAA,aACA,EAAA,QAAA,0BACA,EAAA,QAAA,sBAEA,EAAA,EAAA,EAAA,EAAA,EAAA,UAAA,CAAA,QAAA,SAAA,GACA,IAAA,EAAA,EAAA,KAAA,EAAA,SAAA,EAAA,SACA,EAAA,mBAAA,EACA,OAAA,KAAA,KACA,EAAA,SAAA,GACA,OAAA,EAAA,EAAA,KAAA,KAAA,WAAA,OAAA,KACA,EACA,EAAA,SAAA,GACA,OAAA,EAAA,EAAA,KAAA,KAAA,WAAA,MAAA,KACA;;ACjBA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,6BACA,EAAA,QAAA,cAEA,EAAA,EAAA,EAAA,UAAA,CAAA,IAAA,SAAA,GACA,IAAA,EAAA,EAAA,EAAA,MACA,EAAA,EAAA,GAEA,OADA,EAAA,EAAA,EAAA,OAAA,EAAA,SAAA,EAAA,GACA,EAAA;;ACVA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,aACA,EAAA,QAAA,YAAA,CAAA,YACA,EAAA,EAAA,QAAA,EAAA,MAAA,IAAA,QAAA,o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iBAAA,EAAA,EAAA,OAAA,IAEA,EAAA,SAAA,GACA,EAAA,EAAA,EAAA,UAAA,IAGA,OAAA,QAAA,CACA,MAAA,EACA,IAAA,EACA,IAAA,EACA,IAAA,EACA,IAAA,EACA,KAAA,EACA,IAAA,EACA,IAAA;;ACjDA,IAAA,EAAA,QAAA,eACA,EAAA,QAAA,gBACA,EAAA,EAAA,IACA,EAAA,EAAA,IAEA,EAAA,IAAA,CAAA,eAAA,SAAA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA;;ACNA,IAAA,EAAA,QAAA,eACA,EAAA,QAAA,gBACA,EAAA,EAAA,IACA,EAAA,EAAA,IACA,EAAA,EAAA,MAEA,EAAA,IAAA,CAAA,eAAA,SAAA,EAAA,GACA,IAAA,EAAA,UAAA,OAAA,OAAA,EAAA,EAAA,UAAA,IACA,EAAA,EAAA,EAAA,GAAA,GAAA,GACA,QAAA,IAAA,IAAA,EAAA,OAAA,GAAA,OAAA,EACA,GAAA,EAAA,KAAA,OAAA,EACA,IAAA,EAAA,EAAA,IAAA,GAEA,OADA,EAAA,OAAA,KACA,EAAA,MAAA,EAAA,OAAA;;ACbA,IAAA,EAAA,QAAA,eACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,EAAA,IACA,EAAA,EAAA,IACA,EAAA,EAAA,IAEA,EAAA,SAAA,EAAA,EAAA,GAEA,GADA,EAAA,EAAA,EAAA,GACA,OAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,GACA,OAAA,OAAA,EAAA,EAAA,EAAA,EAAA,QAAA,GAGA,EAAA,IAAA,CAAA,YAAA,SAAA,EAAA,GACA,OAAA,EAAA,EAAA,EAAA,GAAA,UAAA,OAAA,OAAA,EAAA,EAAA,UAAA;;ACfA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,0BACA,EAAA,QAAA,eACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,EAAA,KACA,EAAA,EAAA,IAEA,EAAA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,GAAA,OAAA,EAAA,OAAA,EACA,IAAA,EAAA,EAAA,EAAA,GACA,OAAA,EAAA,OAAA,EAAA,OAAA,EAAA,IAAA,EAAA,EAAA,OAAA,KAAA,EAAA,GAGA,EAAA,IAAA,CAAA,gBAAA,SAAA,GACA,OAAA,EAAA,EAAA,GAAA,UAAA,OAAA,OAAA,EAAA,EAAA,UAAA;;ACjBA,IAAA,EAAA,QAAA,eACA,EAAA,QAAA,gBACA,EAAA,EAAA,IACA,EAAA,EAAA,IAEA,EAAA,IAAA,CAAA,eAAA,SAAA,EAAA,GACA,OAAA,EAAA,EAAA,EAAA,GACA,UAAA,OAAA,OAAA,EAAA,EAAA,UAAA;;ACPA,IAAA,EAAA,QAAA,eACA,EAAA,QAAA,gBACA,EAAA,EAAA,KACA,EAAA,EAAA,IAEA,EAAA,IAAA,CAAA,mBAAA,SAAA,GACA,OAAA,EAAA,EAAA,GAAA,UAAA,OAAA,OAAA,EAAA,EAAA,UAAA;;ACNA,IAAA,EAAA,QAAA,eACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,EAAA,IACA,EAAA,EAAA,IAEA,EAAA,SAAA,EAAA,EAAA,GAEA,GADA,EAAA,EAAA,EAAA,GACA,OAAA,EACA,IAAA,EAAA,EAAA,GACA,OAAA,OAAA,GAAA,EAAA,EAAA,EAAA,IAGA,EAAA,IAAA,CAAA,YAAA,SAAA,EAAA,GACA,OAAA,EAAA,EAAA,EAAA,GAAA,UAAA,OAAA,OAAA,EAAA,EAAA,UAAA;;ACdA,IAAA,EAAA,QAAA,eACA,EAAA,QAAA,gBACA,EAAA,EAAA,IACA,EAAA,EAAA,IAEA,EAAA,IAAA,CAAA,eAAA,SAAA,EAAA,GACA,OAAA,EAAA,EAAA,EAAA,GACA,UAAA,OAAA,OAAA,EAAA,EAAA,UAAA;;ACPA,IAAA,EAAA,QAAA,eACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,EAAA,IACA,EAAA,EAAA,IAEA,EAAA,IAAA,CAAA,SAAA,SAAA,EAAA,GACA,OAAA,SAAA,EAAA,GACA,EACA,EAAA,QACA,IAAA,EAAA,EAAA,GAAA,GACA,EAAA;;;ACVA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,eAAA,GACA,EAAA,QAAA,aAAA,QACA,EAAA,WAAA,QAAA,SAAA,CAAA,GAEA,EAAA,EAAA,EAAA,CACA,KAAA,SAAA,GACA,IAAA,EAAA,GAAA,EAAA,OACA,EAAA,EAAA,EAAA,KAAA,GAAA;;;ACTA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,aACA,EAAA,QAAA,WACA,EAAA,QAAA,eAAA,GACA,EAAA,QAAA,SAAA,CAAA,cACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,EAAA,QAAA,kBACA,EAAA,QAAA,mBACA,EAAA,QAAA,WACA,EAAA,QAAA,aACA,EAAA,EAAA,OAEA,EAAA,SAAA,GACA,OAAA,MAAA,OAAA,EAAA,EAAA,IAGA,EAAA,SAAA,GACA,IAAA,EAAA,EAAA,GACA,IACA,EAAA,QAAA,EACA,MAIA,EAAA,SAAA,GACA,YAAA,IAAA,EAAA,IAGA,EAAA,SAAA,GACA,EAAA,KACA,EAAA,QAAA,EACA,EAAA,KAIA,EAAA,SAAA,EAAA,GACA,EAAA,GACA,KAAA,QAAA,EACA,KAAA,GAAA,EACA,EAAA,IAAA,EAAA,MACA,IACA,IAAA,EAAA,EAAA,GACA,EAAA,EACA,MAAA,IACA,mBAAA,EAAA,YAAA,EAAA,WAAA,EAAA,eACA,EAAA,GACA,KAAA,GAAA,GAEA,MAAA,GAEA,YADA,EAAA,MAAA,GAEA,EAAA,OAAA,EAAA,OAGA,EAAA,UAAA,EAAA,GAAA,CACA,YAAA,WAAA,EAAA,SAGA,IAAA,EAAA,SAAA,GACA,KAAA,GAAA,GAGA,EAAA,UAAA,EAAA,GAAA,CACA,KAAA,SAAA,GACA,IAAA,EAAA,KAAA,GACA,IAAA,EAAA,GAAA,CACA,IAAA,EAAA,EAAA,GACA,IACA,IAAA,EAAA,EAAA,EAAA,MACA,GAAA,EAAA,OAAA,EAAA,KAAA,EAAA,GACA,MAAA,GACA,IACA,EAAA,GACA,QACA,MAAA,MAKA,MAAA,SAAA,GACA,IAAA,EAAA,KAAA,GACA,GAAA,EAAA,GAAA,MAAA,EACA,IAAA,EAAA,EAAA,GACA,EAAA,QAAA,EACA,IACA,IAAA,EAAA,EAAA,EAAA,OACA,IAAA,EAAA,MAAA,EACA,EAAA,EAAA,KAAA,EAAA,GACA,MAAA,GACA,IACA,EAAA,GACA,QACA,MAAA,GAGA,OADA,EAAA,GACA,GAEA,SAAA,SAAA,GACA,IAAA,EAAA,KAAA,GACA,IAAA,EAAA,GAAA,CACA,IAAA,EAAA,EAAA,GACA,EAAA,QAAA,EACA,IACA,IAAA,EAAA,EAAA,EAAA,UACA,EAAA,EAAA,EAAA,KAAA,EAAA,QAAA,EACA,MAAA,GACA,IACA,EAAA,GACA,QACA,MAAA,GAGA,OADA,EAAA,GACA,MAKA,IAAA,EAAA,SAAA,GACA,EAAA,KAAA,EAAA,aAAA,MAAA,GAAA,EAAA,IAGA,EAAA,EAAA,UAAA,CACA,UAAA,SAAA,GACA,OAAA,IAAA,EAAA,EAAA,KAAA,KAEA,QAAA,SAAA,GACA,IAAA,EAAA,KACA,OAAA,IAAA,EAAA,SAAA,EAAA,SAAA,SAAA,EAAA,GACA,EAAA,GACA,IAAA,EAAA,EAAA,UAAA,CACA,KAAA,SAAA,GACA,IACA,OAAA,EAAA,GACA,MAAA,GACA,EAAA,GACA,EAAA,gBAGA,MAAA,EACA,SAAA,SAMA,EAAA,EAAA,CACA,KAAA,SAAA,GACA,IAAA,EAAA,mBAAA,KAAA,KAAA,EACA,EAAA,EAAA,EAAA,GAAA,IACA,GAAA,EAAA,CACA,IAAA,EAAA,EAAA,EAAA,KAAA,IACA,OAAA,EAAA,cAAA,EAAA,EAAA,IAAA,EAAA,SAAA,GACA,OAAA,EAAA,UAAA,KAGA,OAAA,IAAA,EAAA,SAAA,GACA,IAAA,GAAA,EAeA,OAdA,EAAA,WACA,IAAA,EAAA,CACA,IACA,GAAA,EAAA,GAAA,EAAA,SAAA,GAEA,GADA,EAAA,KAAA,GACA,EAAA,OAAA,MACA,EAAA,OACA,MAAA,GACA,GAAA,EAAA,MAAA,EAEA,YADA,EAAA,MAAA,GAEA,EAAA,cAGA,WAAA,GAAA,MAGA,GAAA,WACA,IAAA,IAAA,EAAA,EAAA,EAAA,UAAA,OAAA,EAAA,IAAA,MAAA,GAAA,EAAA,GAAA,EAAA,GAAA,UAAA,KACA,OAAA,IAAA,mBAAA,KAAA,KAAA,GAAA,SAAA,GACA,IAAA,GAAA,EASA,OARA,EAAA,WACA,IAAA,EAAA,CACA,IAAA,IAAA,EAAA,EAAA,EAAA,EAAA,SAAA,EAEA,GADA,EAAA,KAAA,EAAA,IACA,EAAA,OACA,EAAA,cAGA,WAAA,GAAA,QAKA,EAAA,EAAA,UAAA,EAAA,WAAA,OAAA,OAEA,EAAA,EAAA,EAAA,CAAA,WAAA,IAEA,QAAA,iBAAA,CAAA;;;ACrMA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,GAAA,MACA,EAAA,WAAA,KAAA,GACA,EAAA,SAAA,GACA,OAAA,SAAA,EAAA,GACA,IAAA,EAAA,UAAA,OAAA,EACA,IAAA,GAAA,EAAA,KAAA,UAAA,GACA,OAAA,EAAA,EAAA,YAEA,mBAAA,EAAA,EAAA,SAAA,IAAA,MAAA,KAAA,IACA,EAAA,KAGA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,CACA,WAAA,EAAA,EAAA,YACA,YAAA,EAAA,EAAA;;AClBA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,WACA,EAAA,EAAA,EAAA,EAAA,EAAA,CACA,aAAA,EAAA,IACA,eAAA,EAAA;;;ACyCA,IA7CA,IAAA,EAAA,QAAA,wBACA,EAAA,QAAA,kBACA,EAAA,QAAA,eACA,EAAA,QAAA,aACA,EAAA,QAAA,WACA,EAAA,QAAA,gBACA,EAAA,QAAA,UACA,EAAA,EAAA,YACA,EAAA,EAAA,eACA,EAAA,EAAA,MAEA,EAAA,CACA,aAAA,EACA,qBAAA,EACA,cAAA,EACA,gBAAA,EACA,aAAA,EACA,eAAA,EACA,cAAA,EACA,sBAAA,EACA,UAAA,EACA,mBAAA,EACA,gBAAA,EACA,iBAAA,EACA,mBAAA,EACA,WAAA,EACA,eAAA,EACA,cAAA,EACA,UAAA,EACA,kBAAA,EACA,QAAA,EACA,aAAA,EACA,eAAA,EACA,eAAA,EACA,gBAAA,EACA,cAAA,EACA,eAAA,EACA,kBAAA,EACA,kBAAA,EACA,gBAAA,EACA,kBAAA,EACA,eAAA,EACA,WAAA,GAGA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,EAAA,OAAA,IAAA,CACA,IAIA,EAJA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,GAAA,EAAA,UAEA,GAAA,IACA,EAAA,IAAA,EAAA,EAAA,EAAA,GACA,EAAA,IAAA,EAAA,EAAA,EAAA,GACA,EAAA,GAAA,EACA,GAAA,IAAA,KAAA,EAAA,EAAA,IAAA,EAAA,EAAA,EAAA,EAAA,IAAA;;ACvDA,QAAA,wBACA,QAAA,+BACA,QAAA,wCACA,QAAA,0CACA,QAAA,oDACA,QAAA,yCACA,QAAA,6BACA,QAAA,+CACA,QAAA,+BACA,QAAA,6BACA,QAAA,2CACA,QAAA,kCACA,QAAA,kCACA,QAAA,sCACA,QAAA,+BACA,QAAA,2BACA,QAAA,yCACA,QAAA,kCACA,QAAA,+BACA,QAAA,+BACA,QAAA,uCACA,QAAA,2BACA,QAAA,6BACA,QAAA,oCACA,QAAA,iCACA,QAAA,qCACA,QAAA,gCACA,QAAA,kCACA,QAAA,mCACA,QAAA,+BACA,QAAA,wCACA,QAAA,yCACA,QAAA,yCACA,QAAA,oCACA,QAAA,kCACA,QAAA,4BACA,QAAA,4BACA,QAAA,4BACA,QAAA,2BACA,QAAA,4BACA,QAAA,2BACA,QAAA,4BACA,QAAA,6BACA,QAAA,4BACA,QAAA,2BACA,QAAA,4BACA,QAAA,4BACA,QAAA,2BACA,QAAA,2BACA,QAAA,2BACA,QAAA,2BACA,QAAA,4BACA,QAAA,wCACA,QAAA,4BACA,QAAA,6BACA,QAAA,iCACA,QAAA,sCACA,QAAA,kCACA,QAAA,iCACA,QAAA,+BACA,QAAA,oCACA,QAAA,+BACA,QAAA,4BACA,QAAA,8BACA,QAAA,6BACA,QAAA,8BACA,QAAA,kCACA,QAAA,iCACA,QAAA,gCACA,QAAA,6BACA,QAAA,8BACA,QAAA,+BACA,QAAA,4BACA,QAAA,4BACA,QAAA,0BACA,QAAA,8BACA,QAAA,oCACA,QAAA,gCACA,QAAA,mCACA,QAAA,gCACA,QAAA,4BACA,QAAA,0BACA,QAAA,4BACA,QAAA,6BACA,QAAA,4BACA,QAAA,gCACA,QAAA,2BACA,QAAA,8BACA,QAAA,4BACA,QAAA,6BACA,QAAA,8BACA,QAAA,oCACA,QAAA,gCACA,QAAA,qCACA,QAAA,mCACA,QAAA,4BACA,QAAA,4BACA,QAAA,kCACA,QAAA,+BACA,QAAA,gCACA,QAAA,oCACA,QAAA,6BACA,QAAA,kCACA,QAAA,8BACA,QAAA,8BACA,QAAA,gCACA,QAAA,+BACA,QAAA,8BACA,QAAA,yBACA,QAAA,qBACA,QAAA,qBACA,QAAA,0BACA,QAAA,0BACA,QAAA,oCACA,QAAA,iCACA,QAAA,kCACA,QAAA,mCACA,QAAA,2CACA,QAAA,mCACA,QAAA,oCACA,QAAA,mCACA,QAAA,oCACA,QAAA,qCACA,QAAA,qCACA,QAAA,+BACA,QAAA,mCACA,QAAA,yCACA,QAAA,yCACA,QAAA,mCACA,QAAA,6BACA,QAAA,qDACA,QAAA,0CACA,QAAA,6BACA,QAAA,uCACA,QAAA,kCACA,QAAA,4CACA,QAAA,6BACA,QAAA,0CACA,QAAA,gCACA,QAAA,gCACA,QAAA,+BACA,QAAA,2BACA,QAAA,kCACA,QAAA,gCACA,QAAA,kCACA,QAAA,mCACA,QAAA,kCACA,QAAA,uCACA,QAAA,mCACA,QAAA,qDACA,QAAA,+BACA,QAAA,gCACA,QAAA,sCACA,QAAA,sCACA,QAAA,sCACA,QAAA,sCACA,QAAA,6BACA,QAAA,6BACA,QAAA,wBACA,QAAA,wBACA,QAAA,6BACA,QAAA,6BACA,QAAA,0BACA,QAAA,0BACA,QAAA,+BACA,QAAA,+BACA,QAAA,wBACA,QAAA,+BACA,QAAA,gCACA,QAAA,4BACA,QAAA,kCACA,QAAA,8BACA,QAAA,6BACA,QAAA,4BACA,QAAA,4BACA,QAAA,4BACA,QAAA,kCACA,QAAA,8BACA,QAAA,4BACA,QAAA,4BACA,QAAA,8BACA,QAAA,iCACA,QAAA,6BACA,QAAA,yCACA,QAAA,yCACA,QAAA,sCACA,QAAA,2CACA,QAAA,0CACA,QAAA,+CACA,QAAA,sCACA,QAAA,0CACA,QAAA,kCACA,QAAA,sBACA,QAAA,4BACA,QAAA,wBACA,QAAA,2BACA,QAAA,8BACA,OAAA,QAAA,QAAA;;;AC2hBA,IAAA,EAAA,UAAA,IAttBA,SAAA,GACA,aAEA,IAEA,EAFA,EAAA,OAAA,UACA,EAAA,EAAA,eAEA,EAAA,mBAAA,OAAA,OAAA,GACA,EAAA,EAAA,UAAA,aACA,EAAA,EAAA,eAAA,kBACA,EAAA,EAAA,aAAA,gBAEA,EAAA,iBAAA,OACA,EAAA,EAAA,mBACA,GAAA,EACA,IAGA,OAAA,QAAA,OAJA,EAaA,EAAA,EAAA,mBAAA,EAAA,OAAA,QAAA,IAcA,KAAA,EAoBA,IAAA,EAAA,iBACA,EAAA,iBACA,EAAA,YACA,EAAA,YAIA,EAAA,GAYA,EAAA,GACA,EAAA,GAAA,WACA,OAAA,MAGA,IAAA,EAAA,OAAA,eACA,EAAA,GAAA,EAAA,EAAA,EAAA,MACA,GACA,IAAA,GACA,EAAA,KAAA,EAAA,KAGA,EAAA,GAGA,IAAA,EAAA,EAAA,UACA,EAAA,UAAA,OAAA,OAAA,GACA,EAAA,UAAA,EAAA,YAAA,EACA,EAAA,YAAA,EACA,EAAA,GACA,EAAA,YAAA,oBAYA,EAAA,oBAAA,SAAA,GACA,IAAA,EAAA,mBAAA,GAAA,EAAA,YACA,QAAA,IACA,IAAA,GAGA,uBAAA,EAAA,aAAA,EAAA,QAIA,EAAA,KAAA,SAAA,GAUA,OATA,OAAA,eACA,OAAA,eAAA,EAAA,IAEA,EAAA,UAAA,EACA,KAAA,IACA,EAAA,GAAA,sBAGA,EAAA,UAAA,OAAA,OAAA,GACA,GAOA,EAAA,MAAA,SAAA,GACA,MAAA,CAAA,QAAA,IAkFA,EAAA,EAAA,WACA,EAAA,UAAA,GAAA,WACA,OAAA,MAEA,EAAA,cAAA,EAKA,EAAA,MAAA,SAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,IAAA,EACA,EAAA,EAAA,EAAA,EAAA,IAGA,OAAA,EAAA,oBAAA,GACA,EACA,EAAA,OAAA,KAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,MAAA,EAAA,UAsKA,EAAA,GAEA,EAAA,GAAA,YAOA,EAAA,GAAA,WACA,OAAA,MAGA,EAAA,SAAA,WACA,MAAA,sBAkCA,EAAA,KAAA,SAAA,GACA,IAAA,EAAA,GACA,IAAA,IAAA,KAAA,EACA,EAAA,KAAA,GAMA,OAJA,EAAA,UAIA,SAAA,IACA,KAAA,EAAA,QAAA,CACA,IAAA,EAAA,EAAA,MACA,GAAA,KAAA,EAGA,OAFA,EAAA,MAAA,EACA,EAAA,MAAA,EACA,EAQA,OADA,EAAA,MAAA,EACA,IAsCA,EAAA,OAAA,EAMA,EAAA,UAAA,CACA,YAAA,EAEA,MAAA,SAAA,GAcA,GAbA,KAAA,KAAA,EACA,KAAA,KAAA,EAGA,KAAA,KAAA,KAAA,MAAA,EACA,KAAA,MAAA,EACA,KAAA,SAAA,KAEA,KAAA,OAAA,OACA,KAAA,IAAA,EAEA,KAAA,WAAA,QAAA,IAEA,EACA,IAAA,IAAA,KAAA,KAEA,MAAA,EAAA,OAAA,IACA,EAAA,KAAA,KAAA,KACA,OAAA,EAAA,MAAA,MACA,KAAA,GAAA,IAMA,KAAA,WACA,KAAA,MAAA,EAEA,IACA,EADA,KAAA,WAAA,GACA,WACA,GAAA,UAAA,EAAA,KACA,MAAA,EAAA,IAGA,OAAA,KAAA,MAGA,kBAAA,SAAA,GACA,GAAA,KAAA,KACA,MAAA,EAGA,IAAA,EAAA,KACA,SAAA,EAAA,EAAA,GAYA,OAXA,EAAA,KAAA,QACA,EAAA,IAAA,EACA,EAAA,KAAA,EAEA,IAGA,EAAA,OAAA,OACA,EAAA,IAAA,KAGA,EAGA,IAAA,IAAA,EAAA,KAAA,WAAA,OAAA,EAAA,GAAA,IAAA,EAAA,CACA,IAAA,EAAA,KAAA,WAAA,GACA,EAAA,EAAA,WAEA,GAAA,SAAA,EAAA,OAIA,OAAA,EAAA,OAGA,GAAA,EAAA,QAAA,KAAA,KAAA,CACA,IAAA,EAAA,EAAA,KAAA,EAAA,YACA,EAAA,EAAA,KAAA,EAAA,cAEA,GAAA,GAAA,EAAA,CACA,GAAA,KAAA,KAAA,EAAA,SACA,OAAA,EAAA,EAAA,UAAA,GACA,GAAA,KAAA,KAAA,EAAA,WACA,OAAA,EAAA,EAAA,iBAGA,GAAA,GACA,GAAA,KAAA,KAAA,EAAA,SACA,OAAA,EAAA,EAAA,UAAA,OAGA,CAAA,IAAA,EAMA,MAAA,IAAA,MAAA,0CALA,GAAA,KAAA,KAAA,EAAA,WACA,OAAA,EAAA,EAAA,gBAUA,OAAA,SAAA,EAAA,GACA,IAAA,IAAA,EAAA,KAAA,WAAA,OAAA,EAAA,GAAA,IAAA,EAAA,CACA,IAAA,EAAA,KAAA,WAAA,GACA,GAAA,EAAA,QAAA,KAAA,MACA,EAAA,KAAA,EAAA,eACA,KAAA,KAAA,EAAA,WAAA,CACA,IAAA,EAAA,EACA,OAIA,IACA,UAAA,GACA,aAAA,IACA,EAAA,QAAA,GACA,GAAA,EAAA,aAGA,EAAA,MAGA,IAAA,EAAA,EAAA,EAAA,WAAA,GAIA,OAHA,EAAA,KAAA,EACA,EAAA,IAAA,EAEA,GACA,KAAA,OAAA,OACA,KAAA,KAAA,EAAA,WACA,GAGA,KAAA,SAAA,IAGA,SAAA,SAAA,EAAA,GACA,GAAA,UAAA,EAAA,KACA,MAAA,EAAA,IAcA,MAXA,UAAA,EAAA,MACA,aAAA,EAAA,KACA,KAAA,KAAA,EAAA,IACA,WAAA,EAAA,MACA,KAAA,KAAA,KAAA,IAAA,EAAA,IACA,KAAA,OAAA,SACA,KAAA,KAAA,OACA,WAAA,EAAA,MAAA,IACA,KAAA,KAAA,GAGA,GAGA,OAAA,SAAA,GACA,IAAA,IAAA,EAAA,KAAA,WAAA,OAAA,EAAA,GAAA,IAAA,EAAA,CACA,IAAA,EAAA,KAAA,WAAA,GACA,GAAA,EAAA,aAAA,EAGA,OAFA,KAAA,SAAA,EAAA,WAAA,EAAA,UACA,EAAA,GACA,IAKA,MAAA,SAAA,GACA,IAAA,IAAA,EAAA,KAAA,WAAA,OAAA,EAAA,GAAA,IAAA,EAAA,CACA,IAAA,EAAA,KAAA,WAAA,GACA,GAAA,EAAA,SAAA,EAAA,CACA,IAAA,EAAA,EAAA,WACA,GAAA,UAAA,EAAA,KAAA,CACA,IAAA,EAAA,EAAA,IACA,EAAA,GAEA,OAAA,GAMA,MAAA,IAAA,MAAA,0BAGA,cAAA,SAAA,EAAA,EAAA,GAaA,OAZA,KAAA,SAAA,CACA,SAAA,EAAA,GACA,WAAA,EACA,QAAA,GAGA,SAAA,KAAA,SAGA,KAAA,IAAA,GAGA,IA/qBA,SAAA,EAAA,EAAA,EAAA,EAAA,GAEA,IAAA,EAAA,GAAA,EAAA,qBAAA,EAAA,EAAA,EACA,EAAA,OAAA,OAAA,EAAA,WACA,EAAA,IAAA,EAAA,GAAA,IAMA,OAFA,EAAA,QA8MA,SAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAEA,OAAA,SAAA,EAAA,GACA,GAAA,IAAA,EACA,MAAA,IAAA,MAAA,gCAGA,GAAA,IAAA,EAAA,CACA,GAAA,UAAA,EACA,MAAA,EAKA,OAAA,IAMA,IAHA,EAAA,OAAA,EACA,EAAA,IAAA,IAEA,CACA,IAAA,EAAA,EAAA,SACA,GAAA,EAAA,CACA,IAAA,EAAA,EAAA,EAAA,GACA,GAAA,EAAA,CACA,GAAA,IAAA,EAAA,SACA,OAAA,GAIA,GAAA,SAAA,EAAA,OAGA,EAAA,KAAA,EAAA,MAAA,EAAA,SAEA,GAAA,UAAA,EAAA,OAAA,CACA,GAAA,IAAA,EAEA,MADA,EAAA,EACA,EAAA,IAGA,EAAA,kBAAA,EAAA,SAEA,WAAA,EAAA,QACA,EAAA,OAAA,SAAA,EAAA,KAGA,EAAA,EAEA,IAAA,EAAA,EAAA,EAAA,EAAA,GACA,GAAA,WAAA,EAAA,KAAA,CAOA,GAJA,EAAA,EAAA,KACA,EACA,EAEA,EAAA,MAAA,EACA,SAGA,MAAA,CACA,MAAA,EAAA,IACA,KAAA,EAAA,MAGA,UAAA,EAAA,OACA,EAAA,EAGA,EAAA,OAAA,QACA,EAAA,IAAA,EAAA,OAtRA,CAAA,EAAA,EAAA,GAEA,EAcA,SAAA,EAAA,EAAA,EAAA,GACA,IACA,MAAA,CAAA,KAAA,SAAA,IAAA,EAAA,KAAA,EAAA,IACA,MAAA,GACA,MAAA,CAAA,KAAA,QAAA,IAAA,IAiBA,SAAA,KACA,SAAA,KACA,SAAA,KA4BA,SAAA,EAAA,GACA,CAAA,OAAA,QAAA,UAAA,QAAA,SAAA,GACA,EAAA,GAAA,SAAA,GACA,OAAA,KAAA,QAAA,EAAA,MAoCA,SAAA,EAAA,GACA,SAAA,EAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,EAAA,GAAA,EAAA,GACA,GAAA,UAAA,EAAA,KAEA,CACA,IAAA,EAAA,EAAA,IACA,EAAA,EAAA,MACA,OAAA,GACA,iBAAA,GACA,EAAA,KAAA,EAAA,WACA,QAAA,QAAA,EAAA,SAAA,KAAA,SAAA,GACA,EAAA,OAAA,EAAA,EAAA,IACA,SAAA,GACA,EAAA,QAAA,EAAA,EAAA,KAIA,QAAA,QAAA,GAAA,KAAA,SAAA,GAgBA,EAAA,MAAA,EACA,EAAA,IACA,GAhCA,EAAA,EAAA,KAwCA,IAAA,EAJA,iBAAA,EAAA,SAAA,EAAA,QAAA,SACA,EAAA,EAAA,QAAA,OAAA,KAAA,IAmCA,KAAA,QA9BA,SAAA,EAAA,GACA,SAAA,IACA,OAAA,IAAA,QAAA,SAAA,EAAA,GACA,EAAA,EAAA,EAAA,EAAA,KAIA,OAAA,EAaA,EAAA,EAAA,KACA,EAGA,GACA,KA+GA,SAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,SAAA,EAAA,QACA,GAAA,IAAA,EAAA,CAKA,GAFA,EAAA,SAAA,KAEA,UAAA,EAAA,OAAA,CACA,GAAA,EAAA,SAAA,SAGA,EAAA,OAAA,SACA,EAAA,IAAA,EACA,EAAA,EAAA,GAEA,UAAA,EAAA,QAGA,OAAA,EAIA,EAAA,OAAA,QACA,EAAA,IAAA,IAAA,UACA,kDAGA,OAAA,EAGA,IAAA,EAAA,EAAA,EAAA,EAAA,SAAA,EAAA,KAEA,GAAA,UAAA,EAAA,KAIA,OAHA,EAAA,OAAA,QACA,EAAA,IAAA,EAAA,IACA,EAAA,SAAA,KACA,EAGA,IAAA,EAAA,EAAA,IAEA,OAAA,EAOA,EAAA,MAGA,EAAA,EAAA,YAAA,EAAA,MAGA,EAAA,KAAA,EAAA,QAQA,WAAA,EAAA,SACA,EAAA,OAAA,OACA,EAAA,IAAA,GAUA,EAAA,SAAA,KACA,GANA,GA3BA,EAAA,OAAA,QACA,EAAA,IAAA,IAAA,UAAA,oCACA,EAAA,SAAA,KACA,GAoDA,SAAA,EAAA,GACA,IAAA,EAAA,CAAA,OAAA,EAAA,IAEA,KAAA,IACA,EAAA,SAAA,EAAA,IAGA,KAAA,IACA,EAAA,WAAA,EAAA,GACA,EAAA,SAAA,EAAA,IAGA,KAAA,WAAA,KAAA,GAGA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,YAAA,GACA,EAAA,KAAA,gBACA,EAAA,IACA,EAAA,WAAA,EAGA,SAAA,EAAA,GAIA,KAAA,WAAA,CAAA,CAAA,OAAA,SACA,EAAA,QAAA,EAAA,MACA,KAAA,OAAA,GA8BA,SAAA,EAAA,GACA,GAAA,EAAA,CACA,IAAA,EAAA,EAAA,GACA,GAAA,EACA,OAAA,EAAA,KAAA,GAGA,GAAA,mBAAA,EAAA,KACA,OAAA,EAGA,IAAA,MAAA,EAAA,QAAA,CACA,IAAA,GAAA,EAAA,EAAA,SAAA,IACA,OAAA,EAAA,EAAA,QACA,GAAA,EAAA,KAAA,EAAA,GAGA,OAFA,EAAA,MAAA,EAAA,GACA,EAAA,MAAA,EACA,EAOA,OAHA,EAAA,MAAA,EACA,EAAA,MAAA,EAEA,GAGA,OAAA,EAAA,KAAA,GAKA,MAAA,CAAA,KAAA,GAIA,SAAA,IACA,MAAA,CAAA,MAAA,EAAA,MAAA,IApgBA,CAktBA,iBAAA,EAAA,EACA,iBAAA,OAAA,OACA,iBAAA,KAAA,KAAA;;AC9tBA,OAAA,QAAA,SAAA,EAAA,GACA,IAAA,EAAA,IAAA,OAAA,GAAA,SAAA,GACA,OAAA,EAAA,IACA,EACA,OAAA,SAAA,GACA,OAAA,OAAA,GAAA,QAAA,EAAA;;ACJA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,cAAA,CAAA,sBAAA,QAEA,EAAA,EAAA,EAAA,SAAA,CAAA,OAAA,SAAA,GAAA,OAAA,EAAA;;ACJA,QAAA,oCACA,OAAA,QAAA,QAAA,uBAAA,OAAA;;;;AC0BA,IAAA,EAAA,UAAA,GAnBA,GANA,QAAA,gBAEA,QAAA,+BAEA,QAAA,4BAEA,EAAA,eACA,MAAA,IAAA,MAAA,kDAEA,EAAA,gBAAA,EAEA,IAAA,EAAA,iBACA,SAAA,EAAA,EAAA,EAAA,GACA,EAAA,IAAA,OAAA,GAAA,EAAA,EAAA,CACA,UAAA,EACA,cAAA,EACA,MAAA,IAIA,EAAA,OAAA,UAAA,UAAA,GAAA,UACA,EAAA,OAAA,UAAA,WAAA,GAAA,QAEA,gMAAA,MAAA,KAAA,QAAA,SAAA,GACA,GAAA,IAAA,EAAA,MAAA,EAAA,SAAA,KAAA,KAAA,GAAA;;ACmFK,aAAA,SAAA,EAAA,EAAA,GAAA,KAAA,aAAA,GAAA,MAAA,IAAA,UAAA,qCAAA,SAAA,EAAA,EAAA,GAAA,IAAA,IAAA,EAAA,EAAA,EAAA,EAAA,OAAA,IAAA,CAAA,IAAA,EAAA,EAAA,GAAA,EAAA,WAAA,EAAA,aAAA,EAAA,EAAA,cAAA,EAAA,UAAA,IAAA,EAAA,UAAA,GAAA,OAAA,eAAA,EAAA,EAAA,IAAA,IAAA,SAAA,EAAA,EAAA,EAAA,GAAA,OAAA,GAAA,EAAA,EAAA,UAAA,GAAA,GAAA,EAAA,EAAA,GAAA,EAAA,OAAA,eAAA,QAAA,aAAA,CAAA,OAAA,IAAA,QAAA,aAAA,EA7GgB8d,IAAAA,EA6GhB,WA5GWC,SAAAA,EAAAA,GAAM,EAAA,KAAA,GAETC,KAAAA,IAAM7jB,SACN8jB,KAAAA,IAAM,KAAKD,IAAIrf,iBAAiBof,EAAKG,aAElB,KAApB,KAAKD,IAAIzgB,SAER2gB,KAAAA,IAAMhgB,OACNigB,KAAAA,UAAY,KAAKD,IAAIE,YAErBC,KAAAA,cAAgB,KAAKN,IAAI9iB,cAAc6iB,EAAKQ,gBAC5C9gB,KAAAA,UAAYsgB,EAAKtgB,UACjB4L,KAAAA,UAAY0U,EAAK1U,WAAa,EAE9BmV,KAAAA,SAAW,GACXA,KAAAA,SAAW,KAAKC,YAAYV,EAAKW,iBAEjCC,KAAAA,eA2FR,OAAA,EAAA,EAAA,CAAA,CAAA,IAAA,cAxFa,MAAA,WAAA,IACNC,EAWAC,EAZM,EAAA,KAELP,KAAAA,cAAc5jB,iBAAiB,SAAU,WACtCkkB,GACA1b,aAAa0b,GAGjBA,EAAc3b,WAAW,WACrB,EAAK6b,OACN,KAIFR,KAAAA,cAAc5jB,iBAAiB,SAAU,WACtCmkB,GACA3b,aAAa2b,GAGjBA,EAAc5b,WAAW,WACrB,EAAK6b,OACN,OAoEV,CAAA,IAAA,cA/DWJ,MAAAA,SAAAA,GAEH,IADCK,IAAAA,EAAU,GACPzhB,EAAI,EAAG0F,EAAM,KAAKib,IAAIzgB,OAAQF,EAAI0F,EAAK1F,IAAK,CAC3CvC,IAAAA,EAAO,KAAKkjB,IAAI3gB,GAAGvC,KACzBgkB,EAAQ3f,KAAK,KAAK4e,IAAI5V,eAAerN,EAAKC,MAAM,KAAK,KAElD+jB,OAAAA,IAyDV,CAAA,IAAA,MAtDK,MAAA,WACEriB,IAAAA,EAAW,KAAKsiB,eACfC,KAAAA,eAAeviB,KAoDvB,CAAA,IAAA,eAjDc,MAAA,WAEN,IADCwiB,IAAAA,EAAoB,GACjB5hB,EAAI,EAAG0F,EAAM,KAAKwb,SAAShhB,OAAQF,EAAI0F,EAAK1F,IAAK,CAChD6hB,IAAAA,EAAU,KAAKX,SAASlhB,GAC1B6hB,GAAW,KAAKC,OAAOD,IACvBD,EAAkB9f,KAAK+f,GAIxBD,OAAAA,IAwCV,CAAA,IAAA,SArCM1iB,MAAAA,SAAAA,GACG6a,IAAAA,EAAY,KAAKiH,cAAcjH,UAC/BgI,EAAYhI,EAAY,KAAKhO,UAE7BiW,EADO9iB,EAAQwM,wBACGI,IAAMiO,EACxBkI,EAAgBD,EAAa9iB,EAAQ8M,aAEpCgW,OAAAA,GAAcD,EAAY,KAAKhW,WAAakW,EAAgBlI,EAAY,KAAKhO,YA8BvF,CAAA,IAAA,iBA3Bc3M,MAAAA,SAAAA,GAIN,IAHD8iB,IAAAA,EAAW,EACXC,EAAkBC,IAEbpiB,EAAI,EAAG0F,EAAMtG,EAASc,OAAQF,EAAI0F,EAAK1F,IAAK,CAC3CmO,IAAAA,EAAK/O,EAASY,GACdqiB,EAAY,KAAKC,YAAYnU,GAC/B+T,EAAWG,IACXH,EAAWG,EACXF,EAAkBhU,GAIrB,IAAA,IAAInO,EAAI,EAAG0F,EAAM,KAAKib,IAAIzgB,OAAQF,EAAI0F,EAAK1F,IAAK,CAC3CuiB,IAAAA,EAAa,KAAK5B,IAAI3gB,GACxBuiB,EAAW9kB,KAAKC,MAAM,KAAK,KAAOykB,EAAgBK,IAClDD,EAAW/lB,UAAUO,IAAI,KAAKoD,WAC9BoiB,EAAW/lB,UAAUO,IAAI,6BAEzBwlB,EAAW/lB,UAAUoL,OAAO,KAAKzH,WACjCoiB,EAAW/lB,UAAUoL,OAAO,+BAOvC,CAAA,IAAA,cAFW1I,MAAAA,SAAAA,GACDiX,OAAAA,SAASiM,EAAEljB,GAASujB,KAAK,qBAAqBC,IAAI,GAAGC,QAAQjlB,MAAM,KAAK,QAClF,EAAA,GAAA,QAAA,QAAA;;AChDA,aAAA,SAAA,EAAA,EAAA,GAAA,KAAA,aAAA,GAAA,MAAA,IAAA,UAAA,qCAAA,SAAA,EAAA,EAAA,GAAA,IAAA,IAAA,EAAA,EAAA,EAAA,EAAA,OAAA,IAAA,CAAA,IAAA,EAAA,EAAA,GAAA,EAAA,WAAA,EAAA,aAAA,EAAA,EAAA,cAAA,EAAA,UAAA,IAAA,EAAA,UAAA,GAAA,OAAA,eAAA,EAAA,EAAA,IAAA,IAAA,SAAA,EAAA,EAAA,EAAA,GAAA,OAAA,GAAA,EAAA,EAAA,UAAA,GAAA,GAAA,EAAA,EAAA,GAAA,EAAA,OAAA,eAAA,QAAA,aAAA,CAAA,OAAA,IAAA,QAAA,aAAA,EA7DgBklB,IAAAA,EA6DhB,WA3Da,SAAA,IAAA,EAAA,KAAA,GACLC,KAAAA,OAAST,EAAE,6BACXU,KAAAA,WAAaV,EAAE,kBACfW,KAAAA,OAASX,EAAE,0BACXvB,KAAAA,IAAMuB,EAAEvhB,QACRmgB,KAAAA,cAAgBoB,EAAE,QAClBY,KAAAA,QAAUZ,EAAE,iBACZ1X,KAAAA,QAAU0X,EAAE,8BAEZf,KAAAA,cACA4B,KAAAA,SAiDR,OAAA,EAAA,EAAA,CAAA,CAAA,IAAA,SA9CQ,MAAA,WACAC,KAAAA,0BACAC,KAAAA,sBA4CR,CAAA,IAAA,0BAzCyB,MAAA,WACT,KAAKtC,IAAIE,cAAhBqC,IACAC,EAAe,KAAKR,OAAOS,cAC3BC,EAAe,KAAKR,OAAOO,aAAY,GAAQ,KAAKR,WAAWQ,aAAY,GAE5EN,KAAAA,QAAQQ,IAAI,aAAc,KAAK3C,IAAIE,cAAgBsC,EAAeE,KAoC1E,CAAA,IAAA,oBAjCmB,MAAA,WACVE,IAAAA,EAAwB,KAAKX,WAAWY,WAAW5X,IAAMqK,SAAS,KAAK2M,WAAWU,IAAI,cAAe,IACrGG,EAAgB,KAAK3C,cAAcjH,YAAc,KAAKiJ,QAAQM,cAC9DF,EAAO,KAAKvC,IAAIE,cAAgB,KAAK8B,OAAOS,cAE9ChT,EAAM,EAEVA,GADAA,EAAMqT,EAAgBF,EAAwBA,EAAwBE,GAC1DP,EAAOA,EAAO9S,EACrB5F,KAAAA,QAAQ8Y,IAAI,SAAUlT,KAyB9B,CAAA,IAAA,cAtBa,MAAA,WAAA,IACNgR,EAWAC,EAZM,EAAA,KAELP,KAAAA,cAAc/M,GAAG,SAAU,WACxBqN,GACA1b,aAAa0b,GAGjBA,EAAc3b,WAAW,WACrB,EAAKsd,UACN,KAIFpC,KAAAA,IAAI5M,GAAG,SAAU,WACdsN,GACA3b,aAAa2b,GAGjBA,EAAc5b,WAAW,WACrB,EAAKsd,UACN,SAEV,EAAA,GAAA,QAAA,QAAA;;ACvDL,aANA,QAAA,4CACA,QAAA,wBACA,QAAA,kBACA,IAAA,EAAA,EAAA,QAAA,gBACA,EAAA,EAAA,QAAA,oBAEA,SAAA,EAAA,GAAA,OAAA,GAAA,EAAA,WAAA,EAAA,CAAA,QAAA,GAAAb,EAAE,WAEWwB,IAECC,EAuEAC,EAvEAD,EADazB,EAAE,2BACKK,KAAK,MAC/BL,EAAE2B,KAAKF,EAAQ,SAASxQ,EAAO2Q,GACrBC,IAAAA,EAAM7B,EAAE4B,GACRE,EAAe9B,EAAE,sCACjB+B,EAAQF,EAAIxgB,SAAS,KAC3BwgB,EAAIG,OAAOF,EAAaE,OAAOD,IAEzBE,IAAAA,EAAYJ,EAAIK,SAAS,aAAeH,EAAMG,SAAS,WACvDC,EAAMN,EAAIxgB,SAAS,MACrB8gB,GAAAA,EAAIrkB,OAAQ,CACNskB,IAAAA,EAAoBnR,aAAAA,OAAAA,GAC1BkR,EAAIE,KAAK,KAAMD,GACfD,EAAIG,SAAS,YACPC,IAAAA,EAAiBvC,EAAE,oCACrBiC,GACAE,EAAIG,SAAS,QACbC,EAAeD,SAAS,SAExBH,EAAI5X,OAGRsX,EAAIG,OACAF,EAAaE,OACTO,EAAeP,OACXhC,EAAwEoC,sEAAAA,OAAAA,EAD5E,mGAINJ,OAAOG,MAMjBnC,EAAE,yCAAyC1V,MAAM,WACvCkY,IAAAA,EAAUxC,EAAE,MACZI,EAAKoC,EAAQH,KAAK,eACxBrC,EAAOI,KAAAA,OAAAA,IAAMqC,YAAY,QAAQC,QAAQ,CAAC9X,OAAQ,SAAU+X,QAAS,WACrEH,EAAQI,SAASH,YAAY,UAkC3Bf,EAAc1B,EAAE,eAEtBA,EAAE,kBAAkB9V,MAAM,WAClB8V,EAAEvhB,QAAQoM,SAAW,MACrB6W,EAAYnX,SAEjBvG,KAAK,WACAgc,EAAEvhB,QAAQoM,SAAW,MACrB6W,EAAYllB,SAYZ,IAAI4hB,EAAJ,QAAc,CACtBY,gBAAiB,yBACjBR,YAAa,cACbK,eAAgB,OAChB9gB,UAAW,UACX4L,UAAW,KAEfqW,EAAE,wBAAwB9V,QAE1B8V,EAAE,YAAY2B,KAAK,WACf3B,EAAE,MAAMsC,SAAS,8BAErBtC,EAAE,2BAA2B2B,KAAK,WAC9B3B,EAAE,MAAMsC,SAAS,qBAErBtC,EAAE,0BAA0B2B,KAAK,WAC7B3B,EAAE,MAAMsC,SAAS,+BAErBtC,EAAE,iBAAiB2B,KAAK,WACpB3B,EAAE,MAAMzV,SAEZyV,EAAE,aAAa2B,KAAK,WAChB3B,EAAE,MAAM1V,MAAM,WACNuY,IAAAA,EAAM7C,EAAE,MAAMK,KAAK,iBAAiByC,OAIjC,OAHHD,IACApkB,OAAOskB,SAAWF,IAEf,MAIf7C,EAAE,cAAc2B,KAAK,WAEbqB,IAAAA,EAASvoB,SAASC,cAAc,UACpCsoB,EAAOjlB,UAAY,yEAGfklB,IAAAA,EAAOxoB,SAASC,cAAc,KAClCuoB,EAAKllB,UAAY,iBACb+kB,IAAAA,EAAOroB,SAASyoB,eAAe,iBACnCD,EAAKloB,YAAY+nB,GACjBE,EAAOjoB,YAAYkoB,GAGfE,IAAAA,EAAOnD,EAAE,MAAMqC,KAAK,QACxBW,EAAOI,QAAU,WACb3kB,OAAOskB,SAAWI,GAElBE,IAAAA,EAAWF,EAAK7nB,MAAM,KAAK4F,OAAO,GAAGoiB,MAErCN,EAAO5C,GADPiD,EACYA,EAASE,QAAQ,IAAK,KAEtB,mBAAqBvD,EAAE,MAAM/O,QAIzCuS,IAAAA,EAAO/oB,SAASC,cAAc,OAClC8oB,EAAKzlB,UAAY,cACjBylB,EAAK3jB,aAAa,eAAgBmjB,EAAO5C,IACrCqD,IAAAA,EAAWzD,EAAE,MAAMK,KAAK,YAAYqD,IAAI,WACjC1D,OAAAA,EAAE,MAAM8C,SAChBxC,MAAMxgB,KAAK,KACd0jB,EAAKrK,UAAYsK,EAEjBhnB,iBAAiBI,eAAemmB,GAChChD,EAAE,MAAMxa,SACJib,IAAAA,EAAST,EAAE,eAAe2D,QAC9BlD,EAAOuB,OAAOgB,GACdvC,EAAOuB,OAAOwB,KAGlBxD,EAAE,0BAA0BoB,IAAI,aAAc","file":"sphinx_materialdesign_theme.js","sourceRoot":"../../src/js","sourcesContent":["/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Ripple MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialRipple = function MaterialRipple(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialRipple'] = MaterialRipple;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialRipple.prototype.Constant_ = {\n    INITIAL_SCALE: 'scale(0.0001, 0.0001)',\n    INITIAL_SIZE: '1px',\n    INITIAL_OPACITY: '0.4',\n    FINAL_OPACITY: '0',\n    FINAL_SCALE: ''\n};\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialRipple.prototype.CssClasses_ = {\n    RIPPLE_CENTER: 'mdl-ripple--center',\n    RIPPLE_EFFECT_IGNORE_EVENTS: 'mdl-js-ripple-effect--ignore-events',\n    RIPPLE: 'mdl-ripple',\n    IS_ANIMATING: 'is-animating',\n    IS_VISIBLE: 'is-visible'\n};\n/**\n   * Handle mouse / finger down on element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialRipple.prototype.downHandler_ = function (event) {\n    if (!this.rippleElement_.style.width && !this.rippleElement_.style.height) {\n        var rect = this.element_.getBoundingClientRect();\n        this.boundHeight = rect.height;\n        this.boundWidth = rect.width;\n        this.rippleSize_ = Math.sqrt(rect.width * rect.width + rect.height * rect.height) * 2 + 2;\n        this.rippleElement_.style.width = this.rippleSize_ + 'px';\n        this.rippleElement_.style.height = this.rippleSize_ + 'px';\n    }\n    this.rippleElement_.classList.add(this.CssClasses_.IS_VISIBLE);\n    if (event.type === 'mousedown' && this.ignoringMouseDown_) {\n        this.ignoringMouseDown_ = false;\n    } else {\n        if (event.type === 'touchstart') {\n            this.ignoringMouseDown_ = true;\n        }\n        var frameCount = this.getFrameCount();\n        if (frameCount > 0) {\n            return;\n        }\n        this.setFrameCount(1);\n        var bound = event.currentTarget.getBoundingClientRect();\n        var x;\n        var y;\n        // Check if we are handling a keyboard click.\n        if (event.clientX === 0 && event.clientY === 0) {\n            x = Math.round(bound.width / 2);\n            y = Math.round(bound.height / 2);\n        } else {\n            var clientX = event.clientX !== undefined ? event.clientX : event.touches[0].clientX;\n            var clientY = event.clientY !== undefined ? event.clientY : event.touches[0].clientY;\n            x = Math.round(clientX - bound.left);\n            y = Math.round(clientY - bound.top);\n        }\n        this.setRippleXY(x, y);\n        this.setRippleStyles(true);\n        window.requestAnimationFrame(this.animFrameHandler.bind(this));\n    }\n};\n/**\n   * Handle mouse / finger up on element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialRipple.prototype.upHandler_ = function (event) {\n    // Don't fire for the artificial \"mouseup\" generated by a double-click.\n    if (event && event.detail !== 2) {\n        // Allow a repaint to occur before removing this class, so the animation\n        // shows for tap events, which seem to trigger a mouseup too soon after\n        // mousedown.\n        window.setTimeout(function () {\n            this.rippleElement_.classList.remove(this.CssClasses_.IS_VISIBLE);\n        }.bind(this), 0);\n    }\n};\n/**\n   * Initialize element.\n   */\nMaterialRipple.prototype.init = function () {\n    if (this.element_) {\n        var recentering = this.element_.classList.contains(this.CssClasses_.RIPPLE_CENTER);\n        if (!this.element_.classList.contains(this.CssClasses_.RIPPLE_EFFECT_IGNORE_EVENTS)) {\n            this.rippleElement_ = this.element_.querySelector('.' + this.CssClasses_.RIPPLE);\n            this.frameCount_ = 0;\n            this.rippleSize_ = 0;\n            this.x_ = 0;\n            this.y_ = 0;\n            // Touch start produces a compat mouse down event, which would cause a\n            // second ripples. To avoid that, we use this property to ignore the first\n            // mouse down after a touch start.\n            this.ignoringMouseDown_ = false;\n            this.boundDownHandler = this.downHandler_.bind(this);\n            this.element_.addEventListener('mousedown', this.boundDownHandler);\n            this.element_.addEventListener('touchstart', this.boundDownHandler);\n            this.boundUpHandler = this.upHandler_.bind(this);\n            this.element_.addEventListener('mouseup', this.boundUpHandler);\n            this.element_.addEventListener('mouseleave', this.boundUpHandler);\n            this.element_.addEventListener('touchend', this.boundUpHandler);\n            this.element_.addEventListener('blur', this.boundUpHandler);\n            /**\n         * Getter for frameCount_.\n         * @return {number} the frame count.\n         */\n            this.getFrameCount = function () {\n                return this.frameCount_;\n            };\n            /**\n         * Setter for frameCount_.\n         * @param {number} fC the frame count.\n         */\n            this.setFrameCount = function (fC) {\n                this.frameCount_ = fC;\n            };\n            /**\n         * Getter for rippleElement_.\n         * @return {Element} the ripple element.\n         */\n            this.getRippleElement = function () {\n                return this.rippleElement_;\n            };\n            /**\n         * Sets the ripple X and Y coordinates.\n         * @param  {number} newX the new X coordinate\n         * @param  {number} newY the new Y coordinate\n         */\n            this.setRippleXY = function (newX, newY) {\n                this.x_ = newX;\n                this.y_ = newY;\n            };\n            /**\n         * Sets the ripple styles.\n         * @param  {boolean} start whether or not this is the start frame.\n         */\n            this.setRippleStyles = function (start) {\n                if (this.rippleElement_ !== null) {\n                    var transformString;\n                    var scale;\n                    var size;\n                    var offset = 'translate(' + this.x_ + 'px, ' + this.y_ + 'px)';\n                    if (start) {\n                        scale = this.Constant_.INITIAL_SCALE;\n                        size = this.Constant_.INITIAL_SIZE;\n                    } else {\n                        scale = this.Constant_.FINAL_SCALE;\n                        size = this.rippleSize_ + 'px';\n                        if (recentering) {\n                            offset = 'translate(' + this.boundWidth / 2 + 'px, ' + this.boundHeight / 2 + 'px)';\n                        }\n                    }\n                    transformString = 'translate(-50%, -50%) ' + offset + scale;\n                    this.rippleElement_.style.webkitTransform = transformString;\n                    this.rippleElement_.style.msTransform = transformString;\n                    this.rippleElement_.style.transform = transformString;\n                    if (start) {\n                        this.rippleElement_.classList.remove(this.CssClasses_.IS_ANIMATING);\n                    } else {\n                        this.rippleElement_.classList.add(this.CssClasses_.IS_ANIMATING);\n                    }\n                }\n            };\n            /**\n         * Handles an animation frame.\n         */\n            this.animFrameHandler = function () {\n                if (this.frameCount_-- > 0) {\n                    window.requestAnimationFrame(this.animFrameHandler.bind(this));\n                } else {\n                    this.setRippleStyles(false);\n                }\n            };\n        }\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialRipple,\n    classAsString: 'MaterialRipple',\n    cssClass: 'mdl-js-ripple-effect',\n    widget: false\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Tabs MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {Element} element The element that will be upgraded.\n   */\nvar MaterialTabs = function MaterialTabs(element) {\n    // Stores the HTML element.\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialTabs'] = MaterialTabs;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialTabs.prototype.Constant_ = {};\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialTabs.prototype.CssClasses_ = {\n    TAB_CLASS: 'mdl-tabs__tab',\n    PANEL_CLASS: 'mdl-tabs__panel',\n    ACTIVE_CLASS: 'is-active',\n    UPGRADED_CLASS: 'is-upgraded',\n    MDL_JS_RIPPLE_EFFECT: 'mdl-js-ripple-effect',\n    MDL_RIPPLE_CONTAINER: 'mdl-tabs__ripple-container',\n    MDL_RIPPLE: 'mdl-ripple',\n    MDL_JS_RIPPLE_EFFECT_IGNORE_EVENTS: 'mdl-js-ripple-effect--ignore-events'\n};\n/**\n   * Handle clicks to a tabs component\n   *\n   * @private\n   */\nMaterialTabs.prototype.initTabs_ = function () {\n    if (this.element_.classList.contains(this.CssClasses_.MDL_JS_RIPPLE_EFFECT)) {\n        this.element_.classList.add(this.CssClasses_.MDL_JS_RIPPLE_EFFECT_IGNORE_EVENTS);\n    }\n    // Select element tabs, document panels\n    this.tabs_ = this.element_.querySelectorAll('.' + this.CssClasses_.TAB_CLASS);\n    this.panels_ = this.element_.querySelectorAll('.' + this.CssClasses_.PANEL_CLASS);\n    // Create new tabs for each tab element\n    for (var i = 0; i < this.tabs_.length; i++) {\n        new MaterialTab(this.tabs_[i], this);\n    }\n    this.element_.classList.add(this.CssClasses_.UPGRADED_CLASS);\n};\n/**\n   * Reset tab state, dropping active classes\n   *\n   * @private\n   */\nMaterialTabs.prototype.resetTabState_ = function () {\n    for (var k = 0; k < this.tabs_.length; k++) {\n        this.tabs_[k].classList.remove(this.CssClasses_.ACTIVE_CLASS);\n    }\n};\n/**\n   * Reset panel state, droping active classes\n   *\n   * @private\n   */\nMaterialTabs.prototype.resetPanelState_ = function () {\n    for (var j = 0; j < this.panels_.length; j++) {\n        this.panels_[j].classList.remove(this.CssClasses_.ACTIVE_CLASS);\n    }\n};\n/**\n   * Initialize element.\n   */\nMaterialTabs.prototype.init = function () {\n    if (this.element_) {\n        this.initTabs_();\n    }\n};\n/**\n   * Constructor for an individual tab.\n   *\n   * @constructor\n   * @param {Element} tab The HTML element for the tab.\n   * @param {MaterialTabs} ctx The MaterialTabs object that owns the tab.\n   */\nfunction MaterialTab(tab, ctx) {\n    if (tab) {\n        if (ctx.element_.classList.contains(ctx.CssClasses_.MDL_JS_RIPPLE_EFFECT)) {\n            var rippleContainer = document.createElement('span');\n            rippleContainer.classList.add(ctx.CssClasses_.MDL_RIPPLE_CONTAINER);\n            rippleContainer.classList.add(ctx.CssClasses_.MDL_JS_RIPPLE_EFFECT);\n            var ripple = document.createElement('span');\n            ripple.classList.add(ctx.CssClasses_.MDL_RIPPLE);\n            rippleContainer.appendChild(ripple);\n            tab.appendChild(rippleContainer);\n        }\n        tab.addEventListener('click', function (e) {\n            if (tab.getAttribute('href').charAt(0) === '#') {\n                e.preventDefault();\n                var href = tab.href.split('#')[1];\n                var panel = ctx.element_.querySelector('#' + href);\n                ctx.resetTabState_();\n                ctx.resetPanelState_();\n                tab.classList.add(ctx.CssClasses_.ACTIVE_CLASS);\n                panel.classList.add(ctx.CssClasses_.ACTIVE_CLASS);\n            }\n        });\n    }\n}\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialTabs,\n    classAsString: 'MaterialTabs',\n    cssClass: 'mdl-js-tabs'\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Layout MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialLayout = function MaterialLayout(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialLayout'] = MaterialLayout;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialLayout.prototype.Constant_ = {\n    MAX_WIDTH: '(max-width: 1024px)',\n    TAB_SCROLL_PIXELS: 100,\n    RESIZE_TIMEOUT: 100,\n    MENU_ICON: '&#xE5D2;',\n    CHEVRON_LEFT: 'chevron_left',\n    CHEVRON_RIGHT: 'chevron_right'\n};\n/**\n   * Keycodes, for code readability.\n   *\n   * @enum {number}\n   * @private\n   */\nMaterialLayout.prototype.Keycodes_ = {\n    ENTER: 13,\n    ESCAPE: 27,\n    SPACE: 32\n};\n/**\n   * Modes.\n   *\n   * @enum {number}\n   * @private\n   */\nMaterialLayout.prototype.Mode_ = {\n    STANDARD: 0,\n    SEAMED: 1,\n    WATERFALL: 2,\n    SCROLL: 3\n};\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialLayout.prototype.CssClasses_ = {\n    CONTAINER: 'mdl-layout__container',\n    HEADER: 'mdl-layout__header',\n    DRAWER: 'mdl-layout__drawer',\n    CONTENT: 'mdl-layout__content',\n    DRAWER_BTN: 'mdl-layout__drawer-button',\n    ICON: 'material-icons',\n    JS_RIPPLE_EFFECT: 'mdl-js-ripple-effect',\n    RIPPLE_CONTAINER: 'mdl-layout__tab-ripple-container',\n    RIPPLE: 'mdl-ripple',\n    RIPPLE_IGNORE_EVENTS: 'mdl-js-ripple-effect--ignore-events',\n    HEADER_SEAMED: 'mdl-layout__header--seamed',\n    HEADER_WATERFALL: 'mdl-layout__header--waterfall',\n    HEADER_SCROLL: 'mdl-layout__header--scroll',\n    FIXED_HEADER: 'mdl-layout--fixed-header',\n    OBFUSCATOR: 'mdl-layout__obfuscator',\n    TAB_BAR: 'mdl-layout__tab-bar',\n    TAB_CONTAINER: 'mdl-layout__tab-bar-container',\n    TAB: 'mdl-layout__tab',\n    TAB_BAR_BUTTON: 'mdl-layout__tab-bar-button',\n    TAB_BAR_LEFT_BUTTON: 'mdl-layout__tab-bar-left-button',\n    TAB_BAR_RIGHT_BUTTON: 'mdl-layout__tab-bar-right-button',\n    TAB_MANUAL_SWITCH: 'mdl-layout__tab-manual-switch',\n    PANEL: 'mdl-layout__tab-panel',\n    HAS_DRAWER: 'has-drawer',\n    HAS_TABS: 'has-tabs',\n    HAS_SCROLLING_HEADER: 'has-scrolling-header',\n    CASTING_SHADOW: 'is-casting-shadow',\n    IS_COMPACT: 'is-compact',\n    IS_SMALL_SCREEN: 'is-small-screen',\n    IS_DRAWER_OPEN: 'is-visible',\n    IS_ACTIVE: 'is-active',\n    IS_UPGRADED: 'is-upgraded',\n    IS_ANIMATING: 'is-animating',\n    ON_LARGE_SCREEN: 'mdl-layout--large-screen-only',\n    ON_SMALL_SCREEN: 'mdl-layout--small-screen-only'\n};\n/**\n   * Handles scrolling on the content.\n   *\n   * @private\n   */\nMaterialLayout.prototype.contentScrollHandler_ = function () {\n    if (this.header_.classList.contains(this.CssClasses_.IS_ANIMATING)) {\n        return;\n    }\n    var headerVisible = !this.element_.classList.contains(this.CssClasses_.IS_SMALL_SCREEN) || this.element_.classList.contains(this.CssClasses_.FIXED_HEADER);\n    if (this.content_.scrollTop > 0 && !this.header_.classList.contains(this.CssClasses_.IS_COMPACT)) {\n        this.header_.classList.add(this.CssClasses_.CASTING_SHADOW);\n        this.header_.classList.add(this.CssClasses_.IS_COMPACT);\n        if (headerVisible) {\n            this.header_.classList.add(this.CssClasses_.IS_ANIMATING);\n        }\n    } else if (this.content_.scrollTop <= 0 && this.header_.classList.contains(this.CssClasses_.IS_COMPACT)) {\n        this.header_.classList.remove(this.CssClasses_.CASTING_SHADOW);\n        this.header_.classList.remove(this.CssClasses_.IS_COMPACT);\n        if (headerVisible) {\n            this.header_.classList.add(this.CssClasses_.IS_ANIMATING);\n        }\n    }\n};\n/**\n   * Handles a keyboard event on the drawer.\n   *\n   * @param {Event} evt The event that fired.\n   * @private\n   */\nMaterialLayout.prototype.keyboardEventHandler_ = function (evt) {\n    // Only react when the drawer is open.\n    if (evt.keyCode === this.Keycodes_.ESCAPE && this.drawer_.classList.contains(this.CssClasses_.IS_DRAWER_OPEN)) {\n        this.toggleDrawer();\n    }\n};\n/**\n   * Handles changes in screen size.\n   *\n   * @private\n   */\nMaterialLayout.prototype.screenSizeHandler_ = function () {\n    if (this.screenSizeMediaQuery_.matches) {\n        this.element_.classList.add(this.CssClasses_.IS_SMALL_SCREEN);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_SMALL_SCREEN);\n        // Collapse drawer (if any) when moving to a large screen size.\n        if (this.drawer_) {\n            this.drawer_.classList.remove(this.CssClasses_.IS_DRAWER_OPEN);\n            this.obfuscator_.classList.remove(this.CssClasses_.IS_DRAWER_OPEN);\n        }\n    }\n};\n/**\n   * Handles events of drawer button.\n   *\n   * @param {Event} evt The event that fired.\n   * @private\n   */\nMaterialLayout.prototype.drawerToggleHandler_ = function (evt) {\n    if (evt && evt.type === 'keydown') {\n        if (evt.keyCode === this.Keycodes_.SPACE || evt.keyCode === this.Keycodes_.ENTER) {\n            // prevent scrolling in drawer nav\n            evt.preventDefault();\n        } else {\n            // prevent other keys\n            return;\n        }\n    }\n    this.toggleDrawer();\n};\n/**\n   * Handles (un)setting the `is-animating` class\n   *\n   * @private\n   */\nMaterialLayout.prototype.headerTransitionEndHandler_ = function () {\n    this.header_.classList.remove(this.CssClasses_.IS_ANIMATING);\n};\n/**\n   * Handles expanding the header on click\n   *\n   * @private\n   */\nMaterialLayout.prototype.headerClickHandler_ = function () {\n    if (this.header_.classList.contains(this.CssClasses_.IS_COMPACT)) {\n        this.header_.classList.remove(this.CssClasses_.IS_COMPACT);\n        this.header_.classList.add(this.CssClasses_.IS_ANIMATING);\n    }\n};\n/**\n   * Reset tab state, dropping active classes\n   *\n   * @private\n   */\nMaterialLayout.prototype.resetTabState_ = function (tabBar) {\n    for (var k = 0; k < tabBar.length; k++) {\n        tabBar[k].classList.remove(this.CssClasses_.IS_ACTIVE);\n    }\n};\n/**\n   * Reset panel state, droping active classes\n   *\n   * @private\n   */\nMaterialLayout.prototype.resetPanelState_ = function (panels) {\n    for (var j = 0; j < panels.length; j++) {\n        panels[j].classList.remove(this.CssClasses_.IS_ACTIVE);\n    }\n};\n/**\n  * Toggle drawer state\n  *\n  * @public\n  */\nMaterialLayout.prototype.toggleDrawer = function () {\n    var drawerButton = this.element_.querySelector('.' + this.CssClasses_.DRAWER_BTN);\n    this.drawer_.classList.toggle(this.CssClasses_.IS_DRAWER_OPEN);\n    this.obfuscator_.classList.toggle(this.CssClasses_.IS_DRAWER_OPEN);\n    // Set accessibility properties.\n    if (this.drawer_.classList.contains(this.CssClasses_.IS_DRAWER_OPEN)) {\n        this.drawer_.setAttribute('aria-hidden', 'false');\n        drawerButton.setAttribute('aria-expanded', 'true');\n    } else {\n        this.drawer_.setAttribute('aria-hidden', 'true');\n        drawerButton.setAttribute('aria-expanded', 'false');\n    }\n};\nMaterialLayout.prototype['toggleDrawer'] = MaterialLayout.prototype.toggleDrawer;\n/**\n   * Initialize element.\n   */\nMaterialLayout.prototype.init = function () {\n    if (this.element_) {\n        var container = document.createElement('div');\n        container.classList.add(this.CssClasses_.CONTAINER);\n        var focusedElement = this.element_.querySelector(':focus');\n        this.element_.parentElement.insertBefore(container, this.element_);\n        this.element_.parentElement.removeChild(this.element_);\n        container.appendChild(this.element_);\n        if (focusedElement) {\n            focusedElement.focus();\n        }\n        var directChildren = this.element_.childNodes;\n        var numChildren = directChildren.length;\n        for (var c = 0; c < numChildren; c++) {\n            var child = directChildren[c];\n            if (child.classList && child.classList.contains(this.CssClasses_.HEADER)) {\n                this.header_ = child;\n            }\n            if (child.classList && child.classList.contains(this.CssClasses_.DRAWER)) {\n                this.drawer_ = child;\n            }\n            if (child.classList && child.classList.contains(this.CssClasses_.CONTENT)) {\n                this.content_ = child;\n            }\n        }\n        window.addEventListener('pageshow', function (e) {\n            if (e.persisted) {\n                // when page is loaded from back/forward cache\n                // trigger repaint to let layout scroll in safari\n                this.element_.style.overflowY = 'hidden';\n                requestAnimationFrame(function () {\n                    this.element_.style.overflowY = '';\n                }.bind(this));\n            }\n        }.bind(this), false);\n        if (this.header_) {\n            this.tabBar_ = this.header_.querySelector('.' + this.CssClasses_.TAB_BAR);\n        }\n        var mode = this.Mode_.STANDARD;\n        if (this.header_) {\n            if (this.header_.classList.contains(this.CssClasses_.HEADER_SEAMED)) {\n                mode = this.Mode_.SEAMED;\n            } else if (this.header_.classList.contains(this.CssClasses_.HEADER_WATERFALL)) {\n                mode = this.Mode_.WATERFALL;\n                this.header_.addEventListener('transitionend', this.headerTransitionEndHandler_.bind(this));\n                this.header_.addEventListener('click', this.headerClickHandler_.bind(this));\n            } else if (this.header_.classList.contains(this.CssClasses_.HEADER_SCROLL)) {\n                mode = this.Mode_.SCROLL;\n                container.classList.add(this.CssClasses_.HAS_SCROLLING_HEADER);\n            }\n            if (mode === this.Mode_.STANDARD) {\n                this.header_.classList.add(this.CssClasses_.CASTING_SHADOW);\n                if (this.tabBar_) {\n                    this.tabBar_.classList.add(this.CssClasses_.CASTING_SHADOW);\n                }\n            } else if (mode === this.Mode_.SEAMED || mode === this.Mode_.SCROLL) {\n                this.header_.classList.remove(this.CssClasses_.CASTING_SHADOW);\n                if (this.tabBar_) {\n                    this.tabBar_.classList.remove(this.CssClasses_.CASTING_SHADOW);\n                }\n            } else if (mode === this.Mode_.WATERFALL) {\n                // Add and remove shadows depending on scroll position.\n                // Also add/remove auxiliary class for styling of the compact version of\n                // the header.\n                this.content_.addEventListener('scroll', this.contentScrollHandler_.bind(this));\n                this.contentScrollHandler_();\n            }\n        }\n        // Add drawer toggling button to our layout, if we have an openable drawer.\n        if (this.drawer_) {\n            var drawerButton = this.element_.querySelector('.' + this.CssClasses_.DRAWER_BTN);\n            if (!drawerButton) {\n                drawerButton = document.createElement('div');\n                drawerButton.setAttribute('aria-expanded', 'false');\n                drawerButton.setAttribute('role', 'button');\n                drawerButton.setAttribute('tabindex', '0');\n                drawerButton.classList.add(this.CssClasses_.DRAWER_BTN);\n                var drawerButtonIcon = document.createElement('i');\n                drawerButtonIcon.classList.add(this.CssClasses_.ICON);\n                drawerButtonIcon.innerHTML = this.Constant_.MENU_ICON;\n                drawerButton.appendChild(drawerButtonIcon);\n            }\n            if (this.drawer_.classList.contains(this.CssClasses_.ON_LARGE_SCREEN)) {\n                //If drawer has ON_LARGE_SCREEN class then add it to the drawer toggle button as well.\n                drawerButton.classList.add(this.CssClasses_.ON_LARGE_SCREEN);\n            } else if (this.drawer_.classList.contains(this.CssClasses_.ON_SMALL_SCREEN)) {\n                //If drawer has ON_SMALL_SCREEN class then add it to the drawer toggle button as well.\n                drawerButton.classList.add(this.CssClasses_.ON_SMALL_SCREEN);\n            }\n            drawerButton.addEventListener('click', this.drawerToggleHandler_.bind(this));\n            drawerButton.addEventListener('keydown', this.drawerToggleHandler_.bind(this));\n            // Add a class if the layout has a drawer, for altering the left padding.\n            // Adds the HAS_DRAWER to the elements since this.header_ may or may\n            // not be present.\n            this.element_.classList.add(this.CssClasses_.HAS_DRAWER);\n            // If we have a fixed header, add the button to the header rather than\n            // the layout.\n            if (this.element_.classList.contains(this.CssClasses_.FIXED_HEADER)) {\n                this.header_.insertBefore(drawerButton, this.header_.firstChild);\n            } else {\n                this.element_.insertBefore(drawerButton, this.content_);\n            }\n            var obfuscator = document.createElement('div');\n            obfuscator.classList.add(this.CssClasses_.OBFUSCATOR);\n            this.element_.appendChild(obfuscator);\n            obfuscator.addEventListener('click', this.drawerToggleHandler_.bind(this));\n            this.obfuscator_ = obfuscator;\n            this.drawer_.addEventListener('keydown', this.keyboardEventHandler_.bind(this));\n            this.drawer_.setAttribute('aria-hidden', 'true');\n        }\n        // Keep an eye on screen size, and add/remove auxiliary class for styling\n        // of small screens.\n        this.screenSizeMediaQuery_ = window.matchMedia(this.Constant_.MAX_WIDTH);\n        this.screenSizeMediaQuery_.addListener(this.screenSizeHandler_.bind(this));\n        this.screenSizeHandler_();\n        // Initialize tabs, if any.\n        if (this.header_ && this.tabBar_) {\n            this.element_.classList.add(this.CssClasses_.HAS_TABS);\n            var tabContainer = document.createElement('div');\n            tabContainer.classList.add(this.CssClasses_.TAB_CONTAINER);\n            this.header_.insertBefore(tabContainer, this.tabBar_);\n            this.header_.removeChild(this.tabBar_);\n            var leftButton = document.createElement('div');\n            leftButton.classList.add(this.CssClasses_.TAB_BAR_BUTTON);\n            leftButton.classList.add(this.CssClasses_.TAB_BAR_LEFT_BUTTON);\n            var leftButtonIcon = document.createElement('i');\n            leftButtonIcon.classList.add(this.CssClasses_.ICON);\n            leftButtonIcon.textContent = this.Constant_.CHEVRON_LEFT;\n            leftButton.appendChild(leftButtonIcon);\n            leftButton.addEventListener('click', function () {\n                this.tabBar_.scrollLeft -= this.Constant_.TAB_SCROLL_PIXELS;\n            }.bind(this));\n            var rightButton = document.createElement('div');\n            rightButton.classList.add(this.CssClasses_.TAB_BAR_BUTTON);\n            rightButton.classList.add(this.CssClasses_.TAB_BAR_RIGHT_BUTTON);\n            var rightButtonIcon = document.createElement('i');\n            rightButtonIcon.classList.add(this.CssClasses_.ICON);\n            rightButtonIcon.textContent = this.Constant_.CHEVRON_RIGHT;\n            rightButton.appendChild(rightButtonIcon);\n            rightButton.addEventListener('click', function () {\n                this.tabBar_.scrollLeft += this.Constant_.TAB_SCROLL_PIXELS;\n            }.bind(this));\n            tabContainer.appendChild(leftButton);\n            tabContainer.appendChild(this.tabBar_);\n            tabContainer.appendChild(rightButton);\n            // Add and remove tab buttons depending on scroll position and total\n            // window size.\n            var tabUpdateHandler = function () {\n                if (this.tabBar_.scrollLeft > 0) {\n                    leftButton.classList.add(this.CssClasses_.IS_ACTIVE);\n                } else {\n                    leftButton.classList.remove(this.CssClasses_.IS_ACTIVE);\n                }\n                if (this.tabBar_.scrollLeft < this.tabBar_.scrollWidth - this.tabBar_.offsetWidth) {\n                    rightButton.classList.add(this.CssClasses_.IS_ACTIVE);\n                } else {\n                    rightButton.classList.remove(this.CssClasses_.IS_ACTIVE);\n                }\n            }.bind(this);\n            this.tabBar_.addEventListener('scroll', tabUpdateHandler);\n            tabUpdateHandler();\n            // Update tabs when the window resizes.\n            var windowResizeHandler = function () {\n                // Use timeouts to make sure it doesn't happen too often.\n                if (this.resizeTimeoutId_) {\n                    clearTimeout(this.resizeTimeoutId_);\n                }\n                this.resizeTimeoutId_ = setTimeout(function () {\n                    tabUpdateHandler();\n                    this.resizeTimeoutId_ = null;\n                }.bind(this), this.Constant_.RESIZE_TIMEOUT);\n            }.bind(this);\n            window.addEventListener('resize', windowResizeHandler);\n            if (this.tabBar_.classList.contains(this.CssClasses_.JS_RIPPLE_EFFECT)) {\n                this.tabBar_.classList.add(this.CssClasses_.RIPPLE_IGNORE_EVENTS);\n            }\n            // Select element tabs, document panels\n            var tabs = this.tabBar_.querySelectorAll('.' + this.CssClasses_.TAB);\n            var panels = this.content_.querySelectorAll('.' + this.CssClasses_.PANEL);\n            // Create new tabs for each tab element\n            for (var i = 0; i < tabs.length; i++) {\n                new MaterialLayoutTab(tabs[i], tabs, panels, this);\n            }\n        }\n        this.element_.classList.add(this.CssClasses_.IS_UPGRADED);\n    }\n};\n/**\n   * Constructor for an individual tab.\n   *\n   * @constructor\n   * @param {HTMLElement} tab The HTML element for the tab.\n   * @param {!Array<HTMLElement>} tabs Array with HTML elements for all tabs.\n   * @param {!Array<HTMLElement>} panels Array with HTML elements for all panels.\n   * @param {MaterialLayout} layout The MaterialLayout object that owns the tab.\n   */\nfunction MaterialLayoutTab(tab, tabs, panels, layout) {\n    /**\n     * Auxiliary method to programmatically select a tab in the UI.\n     */\n    function selectTab() {\n        var href = tab.href.split('#')[1];\n        var panel = layout.content_.querySelector('#' + href);\n        layout.resetTabState_(tabs);\n        layout.resetPanelState_(panels);\n        tab.classList.add(layout.CssClasses_.IS_ACTIVE);\n        panel.classList.add(layout.CssClasses_.IS_ACTIVE);\n    }\n    if (layout.tabBar_.classList.contains(layout.CssClasses_.JS_RIPPLE_EFFECT)) {\n        var rippleContainer = document.createElement('span');\n        rippleContainer.classList.add(layout.CssClasses_.RIPPLE_CONTAINER);\n        rippleContainer.classList.add(layout.CssClasses_.JS_RIPPLE_EFFECT);\n        var ripple = document.createElement('span');\n        ripple.classList.add(layout.CssClasses_.RIPPLE);\n        rippleContainer.appendChild(ripple);\n        tab.appendChild(rippleContainer);\n    }\n    if (!layout.tabBar_.classList.contains(layout.CssClasses_.TAB_MANUAL_SWITCH)) {\n        tab.addEventListener('click', function (e) {\n            if (tab.getAttribute('href').charAt(0) === '#') {\n                e.preventDefault();\n                selectTab();\n            }\n        });\n    }\n    tab.show = selectTab;\n}\nwindow['MaterialLayoutTab'] = MaterialLayoutTab;\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialLayout,\n    classAsString: 'MaterialLayout',\n    cssClass: 'mdl-js-layout'\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n/**\n * A component handler interface using the revealing module design pattern.\n * More details on this design pattern here:\n * https://github.com/jasonmayes/mdl-component-design-pattern\n *\n * @author Jason Mayes.\n */\n/* exported componentHandler */\n\n// Pre-defining the componentHandler interface, for closure documentation and\n// static verification.\nvar componentHandler = {\n  /**\n   * Searches existing DOM for elements of our component type and upgrades them\n   * if they have not already been upgraded.\n   *\n   * @param {string=} optJsClass the programatic name of the element class we\n   * need to create a new instance of.\n   * @param {string=} optCssClass the name of the CSS class elements of this\n   * type will have.\n   */\n  upgradeDom: function(optJsClass, optCssClass) {},\n  /**\n   * Upgrades a specific element rather than all in the DOM.\n   *\n   * @param {!Element} element The element we wish to upgrade.\n   * @param {string=} optJsClass Optional name of the class we want to upgrade\n   * the element to.\n   */\n  upgradeElement: function(element, optJsClass) {},\n  /**\n   * Upgrades a specific list of elements rather than all in the DOM.\n   *\n   * @param {!Element|!Array<!Element>|!NodeList|!HTMLCollection} elements\n   * The elements we wish to upgrade.\n   */\n  upgradeElements: function(elements) {},\n  /**\n   * Upgrades all registered components found in the current DOM. This is\n   * automatically called on window load.\n   */\n  upgradeAllRegistered: function() {},\n  /**\n   * Allows user to be alerted to any upgrades that are performed for a given\n   * component type\n   *\n   * @param {string} jsClass The class name of the MDL component we wish\n   * to hook into for any upgrades performed.\n   * @param {function(!HTMLElement)} callback The function to call upon an\n   * upgrade. This function should expect 1 parameter - the HTMLElement which\n   * got upgraded.\n   */\n  registerUpgradedCallback: function(jsClass, callback) {},\n  /**\n   * Registers a class for future use and attempts to upgrade existing DOM.\n   *\n   * @param {componentHandler.ComponentConfigPublic} config the registration configuration\n   */\n  register: function(config) {},\n  /**\n   * Downgrade either a given node, an array of nodes, or a NodeList.\n   *\n   * @param {!Node|!Array<!Node>|!NodeList} nodes\n   */\n  downgradeElements: function(nodes) {}\n};\n\ncomponentHandler = (function() {\n  'use strict';\n\n  /** @type {!Array<componentHandler.ComponentConfig>} */\n  var registeredComponents_ = [];\n\n  /** @type {!Array<componentHandler.Component>} */\n  var createdComponents_ = [];\n\n  var componentConfigProperty_ = 'mdlComponentConfigInternal_';\n\n  /**\n   * Searches registered components for a class we are interested in using.\n   * Optionally replaces a match with passed object if specified.\n   *\n   * @param {string} name The name of a class we want to use.\n   * @param {componentHandler.ComponentConfig=} optReplace Optional object to replace match with.\n   * @return {!Object|boolean}\n   * @private\n   */\n  function findRegisteredClass_(name, optReplace) {\n    for (var i = 0; i < registeredComponents_.length; i++) {\n      if (registeredComponents_[i].className === name) {\n        if (typeof optReplace !== 'undefined') {\n          registeredComponents_[i] = optReplace;\n        }\n        return registeredComponents_[i];\n      }\n    }\n    return false;\n  }\n\n  /**\n   * Returns an array of the classNames of the upgraded classes on the element.\n   *\n   * @param {!Element} element The element to fetch data from.\n   * @return {!Array<string>}\n   * @private\n   */\n  function getUpgradedListOfElement_(element) {\n    var dataUpgraded = element.getAttribute('data-upgraded');\n    // Use `['']` as default value to conform the `,name,name...` style.\n    return dataUpgraded === null ? [''] : dataUpgraded.split(',');\n  }\n\n  /**\n   * Returns true if the given element has already been upgraded for the given\n   * class.\n   *\n   * @param {!Element} element The element we want to check.\n   * @param {string} jsClass The class to check for.\n   * @returns {boolean}\n   * @private\n   */\n  function isElementUpgraded_(element, jsClass) {\n    var upgradedList = getUpgradedListOfElement_(element);\n    return upgradedList.indexOf(jsClass) !== -1;\n  }\n\n  /**\n   * Create an event object.\n   *\n   * @param {string} eventType The type name of the event.\n   * @param {boolean} bubbles Whether the event should bubble up the DOM.\n   * @param {boolean} cancelable Whether the event can be canceled.\n   * @returns {!Event}\n   */\n  function createEvent_(eventType, bubbles, cancelable) {\n    if ('CustomEvent' in window && typeof window.CustomEvent === 'function') {\n      return new CustomEvent(eventType, {\n        bubbles: bubbles,\n        cancelable: cancelable\n      });\n    } else {\n      var ev = document.createEvent('Events');\n      ev.initEvent(eventType, bubbles, cancelable);\n      return ev;\n    }\n  }\n\n  /**\n   * Searches existing DOM for elements of our component type and upgrades them\n   * if they have not already been upgraded.\n   *\n   * @param {string=} optJsClass the programatic name of the element class we\n   * need to create a new instance of.\n   * @param {string=} optCssClass the name of the CSS class elements of this\n   * type will have.\n   */\n  function upgradeDomInternal(optJsClass, optCssClass) {\n    if (typeof optJsClass === 'undefined' &&\n        typeof optCssClass === 'undefined') {\n      for (var i = 0; i < registeredComponents_.length; i++) {\n        upgradeDomInternal(registeredComponents_[i].className,\n            registeredComponents_[i].cssClass);\n      }\n    } else {\n      var jsClass = /** @type {string} */ (optJsClass);\n      if (typeof optCssClass === 'undefined') {\n        var registeredClass = findRegisteredClass_(jsClass);\n        if (registeredClass) {\n          optCssClass = registeredClass.cssClass;\n        }\n      }\n\n      var elements = document.querySelectorAll('.' + optCssClass);\n      for (var n = 0; n < elements.length; n++) {\n        upgradeElementInternal(elements[n], jsClass);\n      }\n    }\n  }\n\n  /**\n   * Upgrades a specific element rather than all in the DOM.\n   *\n   * @param {!Element} element The element we wish to upgrade.\n   * @param {string=} optJsClass Optional name of the class we want to upgrade\n   * the element to.\n   */\n  function upgradeElementInternal(element, optJsClass) {\n    // Verify argument type.\n    if (!(typeof element === 'object' && element instanceof Element)) {\n      throw new Error('Invalid argument provided to upgrade MDL element.');\n    }\n    // Allow upgrade to be canceled by canceling emitted event.\n    var upgradingEv = createEvent_('mdl-componentupgrading', true, true);\n    element.dispatchEvent(upgradingEv);\n    if (upgradingEv.defaultPrevented) {\n      return;\n    }\n\n    var upgradedList = getUpgradedListOfElement_(element);\n    var classesToUpgrade = [];\n    // If jsClass is not provided scan the registered components to find the\n    // ones matching the element's CSS classList.\n    if (!optJsClass) {\n      var classList = element.classList;\n      registeredComponents_.forEach(function(component) {\n        // Match CSS & Not to be upgraded & Not upgraded.\n        if (classList.contains(component.cssClass) &&\n            classesToUpgrade.indexOf(component) === -1 &&\n            !isElementUpgraded_(element, component.className)) {\n          classesToUpgrade.push(component);\n        }\n      });\n    } else if (!isElementUpgraded_(element, optJsClass)) {\n      classesToUpgrade.push(findRegisteredClass_(optJsClass));\n    }\n\n    // Upgrade the element for each classes.\n    for (var i = 0, n = classesToUpgrade.length, registeredClass; i < n; i++) {\n      registeredClass = classesToUpgrade[i];\n      if (registeredClass) {\n        // Mark element as upgraded.\n        upgradedList.push(registeredClass.className);\n        element.setAttribute('data-upgraded', upgradedList.join(','));\n        var instance = new registeredClass.classConstructor(element);\n        instance[componentConfigProperty_] = registeredClass;\n        createdComponents_.push(instance);\n        // Call any callbacks the user has registered with this component type.\n        for (var j = 0, m = registeredClass.callbacks.length; j < m; j++) {\n          registeredClass.callbacks[j](element);\n        }\n\n        if (registeredClass.widget) {\n          // Assign per element instance for control over API\n          element[registeredClass.className] = instance;\n        }\n      } else {\n        throw new Error(\n          'Unable to find a registered component for the given class.');\n      }\n\n      var upgradedEv = createEvent_('mdl-componentupgraded', true, false);\n      element.dispatchEvent(upgradedEv);\n    }\n  }\n\n  /**\n   * Upgrades a specific list of elements rather than all in the DOM.\n   *\n   * @param {!Element|!Array<!Element>|!NodeList|!HTMLCollection} elements\n   * The elements we wish to upgrade.\n   */\n  function upgradeElementsInternal(elements) {\n    if (!Array.isArray(elements)) {\n      if (elements instanceof Element) {\n        elements = [elements];\n      } else {\n        elements = Array.prototype.slice.call(elements);\n      }\n    }\n    for (var i = 0, n = elements.length, element; i < n; i++) {\n      element = elements[i];\n      if (element instanceof HTMLElement) {\n        upgradeElementInternal(element);\n        if (element.children.length > 0) {\n          upgradeElementsInternal(element.children);\n        }\n      }\n    }\n  }\n\n  /**\n   * Registers a class for future use and attempts to upgrade existing DOM.\n   *\n   * @param {componentHandler.ComponentConfigPublic} config\n   */\n  function registerInternal(config) {\n    // In order to support both Closure-compiled and uncompiled code accessing\n    // this method, we need to allow for both the dot and array syntax for\n    // property access. You'll therefore see the `foo.bar || foo['bar']`\n    // pattern repeated across this method.\n    var widgetMissing = (typeof config.widget === 'undefined' &&\n        typeof config['widget'] === 'undefined');\n    var widget = true;\n\n    if (!widgetMissing) {\n      widget = config.widget || config['widget'];\n    }\n\n    var newConfig = /** @type {componentHandler.ComponentConfig} */ ({\n      classConstructor: config.constructor || config['constructor'],\n      className: config.classAsString || config['classAsString'],\n      cssClass: config.cssClass || config['cssClass'],\n      widget: widget,\n      callbacks: []\n    });\n\n    registeredComponents_.forEach(function(item) {\n      if (item.cssClass === newConfig.cssClass) {\n        throw new Error('The provided cssClass has already been registered: ' + item.cssClass);\n      }\n      if (item.className === newConfig.className) {\n        throw new Error('The provided className has already been registered');\n      }\n    });\n\n    if (config.constructor.prototype\n        .hasOwnProperty(componentConfigProperty_)) {\n      throw new Error(\n          'MDL component classes must not have ' + componentConfigProperty_ +\n          ' defined as a property.');\n    }\n\n    var found = findRegisteredClass_(config.classAsString, newConfig);\n\n    if (!found) {\n      registeredComponents_.push(newConfig);\n    }\n  }\n\n  /**\n   * Allows user to be alerted to any upgrades that are performed for a given\n   * component type\n   *\n   * @param {string} jsClass The class name of the MDL component we wish\n   * to hook into for any upgrades performed.\n   * @param {function(!HTMLElement)} callback The function to call upon an\n   * upgrade. This function should expect 1 parameter - the HTMLElement which\n   * got upgraded.\n   */\n  function registerUpgradedCallbackInternal(jsClass, callback) {\n    var regClass = findRegisteredClass_(jsClass);\n    if (regClass) {\n      regClass.callbacks.push(callback);\n    }\n  }\n\n  /**\n   * Upgrades all registered components found in the current DOM. This is\n   * automatically called on window load.\n   */\n  function upgradeAllRegisteredInternal() {\n    for (var n = 0; n < registeredComponents_.length; n++) {\n      upgradeDomInternal(registeredComponents_[n].className);\n    }\n  }\n\n  /**\n   * Check the component for the downgrade method.\n   * Execute if found.\n   * Remove component from createdComponents list.\n   *\n   * @param {?componentHandler.Component} component\n   */\n  function deconstructComponentInternal(component) {\n    if (component) {\n      var componentIndex = createdComponents_.indexOf(component);\n      createdComponents_.splice(componentIndex, 1);\n\n      var upgrades = component.element_.getAttribute('data-upgraded').split(',');\n      var componentPlace = upgrades.indexOf(component[componentConfigProperty_].classAsString);\n      upgrades.splice(componentPlace, 1);\n      component.element_.setAttribute('data-upgraded', upgrades.join(','));\n\n      var ev = createEvent_('mdl-componentdowngraded', true, false);\n      component.element_.dispatchEvent(ev);\n    }\n  }\n\n  /**\n   * Downgrade either a given node, an array of nodes, or a NodeList.\n   *\n   * @param {!Node|!Array<!Node>|!NodeList} nodes\n   */\n  function downgradeNodesInternal(nodes) {\n    /**\n     * Auxiliary function to downgrade a single node.\n     * @param  {!Node} node the node to be downgraded\n     */\n    var downgradeNode = function(node) {\n      createdComponents_.filter(function(item) {\n        return item.element_ === node;\n      }).forEach(deconstructComponentInternal);\n    };\n    if (nodes instanceof Array || nodes instanceof NodeList) {\n      for (var n = 0; n < nodes.length; n++) {\n        downgradeNode(nodes[n]);\n      }\n    } else if (nodes instanceof Node) {\n      downgradeNode(nodes);\n    } else {\n      throw new Error('Invalid argument provided to downgrade MDL nodes.');\n    }\n  }\n\n  // Now return the functions that should be made public with their publicly\n  // facing names...\n  return {\n    upgradeDom: upgradeDomInternal,\n    upgradeElement: upgradeElementInternal,\n    upgradeElements: upgradeElementsInternal,\n    upgradeAllRegistered: upgradeAllRegisteredInternal,\n    registerUpgradedCallback: registerUpgradedCallbackInternal,\n    register: registerInternal,\n    downgradeElements: downgradeNodesInternal\n  };\n})();\n\n/**\n * Describes the type of a registered component type managed by\n * componentHandler. Provided for benefit of the Closure compiler.\n *\n * @typedef {{\n *   constructor: Function,\n *   classAsString: string,\n *   cssClass: string,\n *   widget: (string|boolean|undefined)\n * }}\n */\ncomponentHandler.ComponentConfigPublic;  // jshint ignore:line\n\n/**\n * Describes the type of a registered component type managed by\n * componentHandler. Provided for benefit of the Closure compiler.\n *\n * @typedef {{\n *   constructor: !Function,\n *   className: string,\n *   cssClass: string,\n *   widget: (string|boolean),\n *   callbacks: !Array<function(!HTMLElement)>\n * }}\n */\ncomponentHandler.ComponentConfig;  // jshint ignore:line\n\n/**\n * Created component (i.e., upgraded element) type as managed by\n * componentHandler. Provided for benefit of the Closure compiler.\n *\n * @typedef {{\n *   element_: !HTMLElement,\n *   className: string,\n *   classAsString: string,\n *   cssClass: string,\n *   widget: string\n * }}\n */\ncomponentHandler.Component;  // jshint ignore:line\n\n// Export all symbols, for the benefit of Closure compiler.\n// No effect on uncompiled code.\ncomponentHandler['upgradeDom'] = componentHandler.upgradeDom;\ncomponentHandler['upgradeElement'] = componentHandler.upgradeElement;\ncomponentHandler['upgradeElements'] = componentHandler.upgradeElements;\ncomponentHandler['upgradeAllRegistered'] =\n    componentHandler.upgradeAllRegistered;\ncomponentHandler['registerUpgradedCallback'] =\n    componentHandler.registerUpgradedCallback;\ncomponentHandler['register'] = componentHandler.register;\ncomponentHandler['downgradeElements'] = componentHandler.downgradeElements;\nwindow.componentHandler = componentHandler;\nwindow['componentHandler'] = componentHandler;\n\nwindow.addEventListener('load', function() {\n  'use strict';\n\n  /**\n   * Performs a \"Cutting the mustard\" test. If the browser supports the features\n   * tested, adds a mdl-js class to the <html> element. It then upgrades all MDL\n   * components requiring JavaScript.\n   */\n  if ('classList' in document.createElement('div') &&\n      'querySelector' in document &&\n      'addEventListener' in window && Array.prototype.forEach) {\n    document.documentElement.classList.add('mdl-js');\n    componentHandler.upgradeAllRegistered();\n  } else {\n    /**\n     * Dummy function to avoid JS errors.\n     */\n    componentHandler.upgradeElement = function() {};\n    /**\n     * Dummy function to avoid JS errors.\n     */\n    componentHandler.register = function() {};\n  }\n});\n","// Source: https://github.com/darius/requestAnimationFrame/blob/master/requestAnimationFrame.js\n// Adapted from https://gist.github.com/paulirish/1579671 which derived from\n// http://paulirish.com/2011/requestanimationframe-for-smart-animating/\n// http://my.opera.com/emoller/blog/2011/12/20/requestanimationframe-for-smart-er-animating\n// requestAnimationFrame polyfill by Erik Möller.\n// Fixes from Paul Irish, Tino Zijdel, Andrew Mao, Klemen Slavič, Darius Bacon\n// MIT license\nif (!Date.now) {\n    /**\n     * Date.now polyfill.\n     * @return {number} the current Date\n     */\n    Date.now = function () {\n        return new Date().getTime();\n    };\n    Date['now'] = Date.now;\n}\nvar vendors = [\n    'webkit',\n    'moz'\n];\nfor (var i = 0; i < vendors.length && !window.requestAnimationFrame; ++i) {\n    var vp = vendors[i];\n    window.requestAnimationFrame = window[vp + 'RequestAnimationFrame'];\n    window.cancelAnimationFrame = window[vp + 'CancelAnimationFrame'] || window[vp + 'CancelRequestAnimationFrame'];\n    window['requestAnimationFrame'] = window.requestAnimationFrame;\n    window['cancelAnimationFrame'] = window.cancelAnimationFrame;\n}\nif (/iP(ad|hone|od).*OS 6/.test(window.navigator.userAgent) || !window.requestAnimationFrame || !window.cancelAnimationFrame) {\n    var lastTime = 0;\n    /**\n     * requestAnimationFrame polyfill.\n     * @param  {!Function} callback the callback function.\n     */\n    window.requestAnimationFrame = function (callback) {\n        var now = Date.now();\n        var nextTime = Math.max(lastTime + 16, now);\n        return setTimeout(function () {\n            callback(lastTime = nextTime);\n        }, nextTime - now);\n    };\n    window.cancelAnimationFrame = clearTimeout;\n    window['requestAnimationFrame'] = window.requestAnimationFrame;\n    window['cancelAnimationFrame'] = window.cancelAnimationFrame;\n}","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Button MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialButton = function MaterialButton(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialButton'] = MaterialButton;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialButton.prototype.Constant_ = {};\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialButton.prototype.CssClasses_ = {\n    RIPPLE_EFFECT: 'mdl-js-ripple-effect',\n    RIPPLE_CONTAINER: 'mdl-button__ripple-container',\n    RIPPLE: 'mdl-ripple'\n};\n/**\n   * Handle blur of element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialButton.prototype.blurHandler_ = function (event) {\n    if (event) {\n        this.element_.blur();\n    }\n};\n// Public methods.\n/**\n   * Disable button.\n   *\n   * @public\n   */\nMaterialButton.prototype.disable = function () {\n    this.element_.disabled = true;\n};\nMaterialButton.prototype['disable'] = MaterialButton.prototype.disable;\n/**\n   * Enable button.\n   *\n   * @public\n   */\nMaterialButton.prototype.enable = function () {\n    this.element_.disabled = false;\n};\nMaterialButton.prototype['enable'] = MaterialButton.prototype.enable;\n/**\n   * Initialize element.\n   */\nMaterialButton.prototype.init = function () {\n    if (this.element_) {\n        if (this.element_.classList.contains(this.CssClasses_.RIPPLE_EFFECT)) {\n            var rippleContainer = document.createElement('span');\n            rippleContainer.classList.add(this.CssClasses_.RIPPLE_CONTAINER);\n            this.rippleElement_ = document.createElement('span');\n            this.rippleElement_.classList.add(this.CssClasses_.RIPPLE);\n            rippleContainer.appendChild(this.rippleElement_);\n            this.boundRippleBlurHandler = this.blurHandler_.bind(this);\n            this.rippleElement_.addEventListener('mouseup', this.boundRippleBlurHandler);\n            this.element_.appendChild(rippleContainer);\n        }\n        this.boundButtonBlurHandler = this.blurHandler_.bind(this);\n        this.element_.addEventListener('mouseup', this.boundButtonBlurHandler);\n        this.element_.addEventListener('mouseleave', this.boundButtonBlurHandler);\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialButton,\n    classAsString: 'MaterialButton',\n    cssClass: 'mdl-js-button',\n    widget: true\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Checkbox MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialCheckbox = function MaterialCheckbox(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialCheckbox'] = MaterialCheckbox;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialCheckbox.prototype.Constant_ = { TINY_TIMEOUT: 0.001 };\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialCheckbox.prototype.CssClasses_ = {\n    INPUT: 'mdl-checkbox__input',\n    BOX_OUTLINE: 'mdl-checkbox__box-outline',\n    FOCUS_HELPER: 'mdl-checkbox__focus-helper',\n    TICK_OUTLINE: 'mdl-checkbox__tick-outline',\n    RIPPLE_EFFECT: 'mdl-js-ripple-effect',\n    RIPPLE_IGNORE_EVENTS: 'mdl-js-ripple-effect--ignore-events',\n    RIPPLE_CONTAINER: 'mdl-checkbox__ripple-container',\n    RIPPLE_CENTER: 'mdl-ripple--center',\n    RIPPLE: 'mdl-ripple',\n    IS_FOCUSED: 'is-focused',\n    IS_DISABLED: 'is-disabled',\n    IS_CHECKED: 'is-checked',\n    IS_UPGRADED: 'is-upgraded'\n};\n/**\n   * Handle change of state.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialCheckbox.prototype.onChange_ = function (event) {\n    this.updateClasses_();\n};\n/**\n   * Handle focus of element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialCheckbox.prototype.onFocus_ = function (event) {\n    this.element_.classList.add(this.CssClasses_.IS_FOCUSED);\n};\n/**\n   * Handle lost focus of element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialCheckbox.prototype.onBlur_ = function (event) {\n    this.element_.classList.remove(this.CssClasses_.IS_FOCUSED);\n};\n/**\n   * Handle mouseup.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialCheckbox.prototype.onMouseUp_ = function (event) {\n    this.blur_();\n};\n/**\n   * Handle class updates.\n   *\n   * @private\n   */\nMaterialCheckbox.prototype.updateClasses_ = function () {\n    this.checkDisabled();\n    this.checkToggleState();\n};\n/**\n   * Add blur.\n   *\n   * @private\n   */\nMaterialCheckbox.prototype.blur_ = function () {\n    // TODO: figure out why there's a focus event being fired after our blur,\n    // so that we can avoid this hack.\n    window.setTimeout(function () {\n        this.inputElement_.blur();\n    }.bind(this), this.Constant_.TINY_TIMEOUT);\n};\n// Public methods.\n/**\n   * Check the inputs toggle state and update display.\n   *\n   * @public\n   */\nMaterialCheckbox.prototype.checkToggleState = function () {\n    if (this.inputElement_.checked) {\n        this.element_.classList.add(this.CssClasses_.IS_CHECKED);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_CHECKED);\n    }\n};\nMaterialCheckbox.prototype['checkToggleState'] = MaterialCheckbox.prototype.checkToggleState;\n/**\n   * Check the inputs disabled state and update display.\n   *\n   * @public\n   */\nMaterialCheckbox.prototype.checkDisabled = function () {\n    if (this.inputElement_.disabled) {\n        this.element_.classList.add(this.CssClasses_.IS_DISABLED);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_DISABLED);\n    }\n};\nMaterialCheckbox.prototype['checkDisabled'] = MaterialCheckbox.prototype.checkDisabled;\n/**\n   * Disable checkbox.\n   *\n   * @public\n   */\nMaterialCheckbox.prototype.disable = function () {\n    this.inputElement_.disabled = true;\n    this.updateClasses_();\n};\nMaterialCheckbox.prototype['disable'] = MaterialCheckbox.prototype.disable;\n/**\n   * Enable checkbox.\n   *\n   * @public\n   */\nMaterialCheckbox.prototype.enable = function () {\n    this.inputElement_.disabled = false;\n    this.updateClasses_();\n};\nMaterialCheckbox.prototype['enable'] = MaterialCheckbox.prototype.enable;\n/**\n   * Check checkbox.\n   *\n   * @public\n   */\nMaterialCheckbox.prototype.check = function () {\n    this.inputElement_.checked = true;\n    this.updateClasses_();\n};\nMaterialCheckbox.prototype['check'] = MaterialCheckbox.prototype.check;\n/**\n   * Uncheck checkbox.\n   *\n   * @public\n   */\nMaterialCheckbox.prototype.uncheck = function () {\n    this.inputElement_.checked = false;\n    this.updateClasses_();\n};\nMaterialCheckbox.prototype['uncheck'] = MaterialCheckbox.prototype.uncheck;\n/**\n   * Initialize element.\n   */\nMaterialCheckbox.prototype.init = function () {\n    if (this.element_) {\n        this.inputElement_ = this.element_.querySelector('.' + this.CssClasses_.INPUT);\n        var boxOutline = document.createElement('span');\n        boxOutline.classList.add(this.CssClasses_.BOX_OUTLINE);\n        var tickContainer = document.createElement('span');\n        tickContainer.classList.add(this.CssClasses_.FOCUS_HELPER);\n        var tickOutline = document.createElement('span');\n        tickOutline.classList.add(this.CssClasses_.TICK_OUTLINE);\n        boxOutline.appendChild(tickOutline);\n        this.element_.appendChild(tickContainer);\n        this.element_.appendChild(boxOutline);\n        if (this.element_.classList.contains(this.CssClasses_.RIPPLE_EFFECT)) {\n            this.element_.classList.add(this.CssClasses_.RIPPLE_IGNORE_EVENTS);\n            this.rippleContainerElement_ = document.createElement('span');\n            this.rippleContainerElement_.classList.add(this.CssClasses_.RIPPLE_CONTAINER);\n            this.rippleContainerElement_.classList.add(this.CssClasses_.RIPPLE_EFFECT);\n            this.rippleContainerElement_.classList.add(this.CssClasses_.RIPPLE_CENTER);\n            this.boundRippleMouseUp = this.onMouseUp_.bind(this);\n            this.rippleContainerElement_.addEventListener('mouseup', this.boundRippleMouseUp);\n            var ripple = document.createElement('span');\n            ripple.classList.add(this.CssClasses_.RIPPLE);\n            this.rippleContainerElement_.appendChild(ripple);\n            this.element_.appendChild(this.rippleContainerElement_);\n        }\n        this.boundInputOnChange = this.onChange_.bind(this);\n        this.boundInputOnFocus = this.onFocus_.bind(this);\n        this.boundInputOnBlur = this.onBlur_.bind(this);\n        this.boundElementMouseUp = this.onMouseUp_.bind(this);\n        this.inputElement_.addEventListener('change', this.boundInputOnChange);\n        this.inputElement_.addEventListener('focus', this.boundInputOnFocus);\n        this.inputElement_.addEventListener('blur', this.boundInputOnBlur);\n        this.element_.addEventListener('mouseup', this.boundElementMouseUp);\n        this.updateClasses_();\n        this.element_.classList.add(this.CssClasses_.IS_UPGRADED);\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialCheckbox,\n    classAsString: 'MaterialCheckbox',\n    cssClass: 'mdl-js-checkbox',\n    widget: true\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for icon toggle MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialIconToggle = function MaterialIconToggle(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialIconToggle'] = MaterialIconToggle;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialIconToggle.prototype.Constant_ = { TINY_TIMEOUT: 0.001 };\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialIconToggle.prototype.CssClasses_ = {\n    INPUT: 'mdl-icon-toggle__input',\n    JS_RIPPLE_EFFECT: 'mdl-js-ripple-effect',\n    RIPPLE_IGNORE_EVENTS: 'mdl-js-ripple-effect--ignore-events',\n    RIPPLE_CONTAINER: 'mdl-icon-toggle__ripple-container',\n    RIPPLE_CENTER: 'mdl-ripple--center',\n    RIPPLE: 'mdl-ripple',\n    IS_FOCUSED: 'is-focused',\n    IS_DISABLED: 'is-disabled',\n    IS_CHECKED: 'is-checked'\n};\n/**\n   * Handle change of state.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialIconToggle.prototype.onChange_ = function (event) {\n    this.updateClasses_();\n};\n/**\n   * Handle focus of element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialIconToggle.prototype.onFocus_ = function (event) {\n    this.element_.classList.add(this.CssClasses_.IS_FOCUSED);\n};\n/**\n   * Handle lost focus of element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialIconToggle.prototype.onBlur_ = function (event) {\n    this.element_.classList.remove(this.CssClasses_.IS_FOCUSED);\n};\n/**\n   * Handle mouseup.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialIconToggle.prototype.onMouseUp_ = function (event) {\n    this.blur_();\n};\n/**\n   * Handle class updates.\n   *\n   * @private\n   */\nMaterialIconToggle.prototype.updateClasses_ = function () {\n    this.checkDisabled();\n    this.checkToggleState();\n};\n/**\n   * Add blur.\n   *\n   * @private\n   */\nMaterialIconToggle.prototype.blur_ = function () {\n    // TODO: figure out why there's a focus event being fired after our blur,\n    // so that we can avoid this hack.\n    window.setTimeout(function () {\n        this.inputElement_.blur();\n    }.bind(this), this.Constant_.TINY_TIMEOUT);\n};\n// Public methods.\n/**\n   * Check the inputs toggle state and update display.\n   *\n   * @public\n   */\nMaterialIconToggle.prototype.checkToggleState = function () {\n    if (this.inputElement_.checked) {\n        this.element_.classList.add(this.CssClasses_.IS_CHECKED);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_CHECKED);\n    }\n};\nMaterialIconToggle.prototype['checkToggleState'] = MaterialIconToggle.prototype.checkToggleState;\n/**\n   * Check the inputs disabled state and update display.\n   *\n   * @public\n   */\nMaterialIconToggle.prototype.checkDisabled = function () {\n    if (this.inputElement_.disabled) {\n        this.element_.classList.add(this.CssClasses_.IS_DISABLED);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_DISABLED);\n    }\n};\nMaterialIconToggle.prototype['checkDisabled'] = MaterialIconToggle.prototype.checkDisabled;\n/**\n   * Disable icon toggle.\n   *\n   * @public\n   */\nMaterialIconToggle.prototype.disable = function () {\n    this.inputElement_.disabled = true;\n    this.updateClasses_();\n};\nMaterialIconToggle.prototype['disable'] = MaterialIconToggle.prototype.disable;\n/**\n   * Enable icon toggle.\n   *\n   * @public\n   */\nMaterialIconToggle.prototype.enable = function () {\n    this.inputElement_.disabled = false;\n    this.updateClasses_();\n};\nMaterialIconToggle.prototype['enable'] = MaterialIconToggle.prototype.enable;\n/**\n   * Check icon toggle.\n   *\n   * @public\n   */\nMaterialIconToggle.prototype.check = function () {\n    this.inputElement_.checked = true;\n    this.updateClasses_();\n};\nMaterialIconToggle.prototype['check'] = MaterialIconToggle.prototype.check;\n/**\n   * Uncheck icon toggle.\n   *\n   * @public\n   */\nMaterialIconToggle.prototype.uncheck = function () {\n    this.inputElement_.checked = false;\n    this.updateClasses_();\n};\nMaterialIconToggle.prototype['uncheck'] = MaterialIconToggle.prototype.uncheck;\n/**\n   * Initialize element.\n   */\nMaterialIconToggle.prototype.init = function () {\n    if (this.element_) {\n        this.inputElement_ = this.element_.querySelector('.' + this.CssClasses_.INPUT);\n        if (this.element_.classList.contains(this.CssClasses_.JS_RIPPLE_EFFECT)) {\n            this.element_.classList.add(this.CssClasses_.RIPPLE_IGNORE_EVENTS);\n            this.rippleContainerElement_ = document.createElement('span');\n            this.rippleContainerElement_.classList.add(this.CssClasses_.RIPPLE_CONTAINER);\n            this.rippleContainerElement_.classList.add(this.CssClasses_.JS_RIPPLE_EFFECT);\n            this.rippleContainerElement_.classList.add(this.CssClasses_.RIPPLE_CENTER);\n            this.boundRippleMouseUp = this.onMouseUp_.bind(this);\n            this.rippleContainerElement_.addEventListener('mouseup', this.boundRippleMouseUp);\n            var ripple = document.createElement('span');\n            ripple.classList.add(this.CssClasses_.RIPPLE);\n            this.rippleContainerElement_.appendChild(ripple);\n            this.element_.appendChild(this.rippleContainerElement_);\n        }\n        this.boundInputOnChange = this.onChange_.bind(this);\n        this.boundInputOnFocus = this.onFocus_.bind(this);\n        this.boundInputOnBlur = this.onBlur_.bind(this);\n        this.boundElementOnMouseUp = this.onMouseUp_.bind(this);\n        this.inputElement_.addEventListener('change', this.boundInputOnChange);\n        this.inputElement_.addEventListener('focus', this.boundInputOnFocus);\n        this.inputElement_.addEventListener('blur', this.boundInputOnBlur);\n        this.element_.addEventListener('mouseup', this.boundElementOnMouseUp);\n        this.updateClasses_();\n        this.element_.classList.add('is-upgraded');\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialIconToggle,\n    classAsString: 'MaterialIconToggle',\n    cssClass: 'mdl-js-icon-toggle',\n    widget: true\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for dropdown MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialMenu = function MaterialMenu(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialMenu'] = MaterialMenu;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialMenu.prototype.Constant_ = {\n    // Total duration of the menu animation.\n    TRANSITION_DURATION_SECONDS: 0.3,\n    // The fraction of the total duration we want to use for menu item animations.\n    TRANSITION_DURATION_FRACTION: 0.8,\n    // How long the menu stays open after choosing an option (so the user can see\n    // the ripple).\n    CLOSE_TIMEOUT: 150\n};\n/**\n   * Keycodes, for code readability.\n   *\n   * @enum {number}\n   * @private\n   */\nMaterialMenu.prototype.Keycodes_ = {\n    ENTER: 13,\n    ESCAPE: 27,\n    SPACE: 32,\n    UP_ARROW: 38,\n    DOWN_ARROW: 40\n};\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialMenu.prototype.CssClasses_ = {\n    CONTAINER: 'mdl-menu__container',\n    OUTLINE: 'mdl-menu__outline',\n    ITEM: 'mdl-menu__item',\n    ITEM_RIPPLE_CONTAINER: 'mdl-menu__item-ripple-container',\n    RIPPLE_EFFECT: 'mdl-js-ripple-effect',\n    RIPPLE_IGNORE_EVENTS: 'mdl-js-ripple-effect--ignore-events',\n    RIPPLE: 'mdl-ripple',\n    // Statuses\n    IS_UPGRADED: 'is-upgraded',\n    IS_VISIBLE: 'is-visible',\n    IS_ANIMATING: 'is-animating',\n    // Alignment options\n    BOTTOM_LEFT: 'mdl-menu--bottom-left',\n    // This is the default.\n    BOTTOM_RIGHT: 'mdl-menu--bottom-right',\n    TOP_LEFT: 'mdl-menu--top-left',\n    TOP_RIGHT: 'mdl-menu--top-right',\n    UNALIGNED: 'mdl-menu--unaligned'\n};\n/**\n   * Initialize element.\n   */\nMaterialMenu.prototype.init = function () {\n    if (this.element_) {\n        // Create container for the menu.\n        var container = document.createElement('div');\n        container.classList.add(this.CssClasses_.CONTAINER);\n        this.element_.parentElement.insertBefore(container, this.element_);\n        this.element_.parentElement.removeChild(this.element_);\n        container.appendChild(this.element_);\n        this.container_ = container;\n        // Create outline for the menu (shadow and background).\n        var outline = document.createElement('div');\n        outline.classList.add(this.CssClasses_.OUTLINE);\n        this.outline_ = outline;\n        container.insertBefore(outline, this.element_);\n        // Find the \"for\" element and bind events to it.\n        var forElId = this.element_.getAttribute('for') || this.element_.getAttribute('data-mdl-for');\n        var forEl = null;\n        if (forElId) {\n            forEl = document.getElementById(forElId);\n            if (forEl) {\n                this.forElement_ = forEl;\n                forEl.addEventListener('click', this.handleForClick_.bind(this));\n                forEl.addEventListener('keydown', this.handleForKeyboardEvent_.bind(this));\n            }\n        }\n        var items = this.element_.querySelectorAll('.' + this.CssClasses_.ITEM);\n        this.boundItemKeydown_ = this.handleItemKeyboardEvent_.bind(this);\n        this.boundItemClick_ = this.handleItemClick_.bind(this);\n        for (var i = 0; i < items.length; i++) {\n            // Add a listener to each menu item.\n            items[i].addEventListener('click', this.boundItemClick_);\n            // Add a tab index to each menu item.\n            items[i].tabIndex = '-1';\n            // Add a keyboard listener to each menu item.\n            items[i].addEventListener('keydown', this.boundItemKeydown_);\n        }\n        // Add ripple classes to each item, if the user has enabled ripples.\n        if (this.element_.classList.contains(this.CssClasses_.RIPPLE_EFFECT)) {\n            this.element_.classList.add(this.CssClasses_.RIPPLE_IGNORE_EVENTS);\n            for (i = 0; i < items.length; i++) {\n                var item = items[i];\n                var rippleContainer = document.createElement('span');\n                rippleContainer.classList.add(this.CssClasses_.ITEM_RIPPLE_CONTAINER);\n                var ripple = document.createElement('span');\n                ripple.classList.add(this.CssClasses_.RIPPLE);\n                rippleContainer.appendChild(ripple);\n                item.appendChild(rippleContainer);\n                item.classList.add(this.CssClasses_.RIPPLE_EFFECT);\n            }\n        }\n        // Copy alignment classes to the container, so the outline can use them.\n        if (this.element_.classList.contains(this.CssClasses_.BOTTOM_LEFT)) {\n            this.outline_.classList.add(this.CssClasses_.BOTTOM_LEFT);\n        }\n        if (this.element_.classList.contains(this.CssClasses_.BOTTOM_RIGHT)) {\n            this.outline_.classList.add(this.CssClasses_.BOTTOM_RIGHT);\n        }\n        if (this.element_.classList.contains(this.CssClasses_.TOP_LEFT)) {\n            this.outline_.classList.add(this.CssClasses_.TOP_LEFT);\n        }\n        if (this.element_.classList.contains(this.CssClasses_.TOP_RIGHT)) {\n            this.outline_.classList.add(this.CssClasses_.TOP_RIGHT);\n        }\n        if (this.element_.classList.contains(this.CssClasses_.UNALIGNED)) {\n            this.outline_.classList.add(this.CssClasses_.UNALIGNED);\n        }\n        container.classList.add(this.CssClasses_.IS_UPGRADED);\n    }\n};\n/**\n   * Handles a click on the \"for\" element, by positioning the menu and then\n   * toggling it.\n   *\n   * @param {Event} evt The event that fired.\n   * @private\n   */\nMaterialMenu.prototype.handleForClick_ = function (evt) {\n    if (this.element_ && this.forElement_) {\n        var rect = this.forElement_.getBoundingClientRect();\n        var forRect = this.forElement_.parentElement.getBoundingClientRect();\n        if (this.element_.classList.contains(this.CssClasses_.UNALIGNED)) {\n        } else if (this.element_.classList.contains(this.CssClasses_.BOTTOM_RIGHT)) {\n            // Position below the \"for\" element, aligned to its right.\n            this.container_.style.right = forRect.right - rect.right + 'px';\n            this.container_.style.top = this.forElement_.offsetTop + this.forElement_.offsetHeight + 'px';\n        } else if (this.element_.classList.contains(this.CssClasses_.TOP_LEFT)) {\n            // Position above the \"for\" element, aligned to its left.\n            this.container_.style.left = this.forElement_.offsetLeft + 'px';\n            this.container_.style.bottom = forRect.bottom - rect.top + 'px';\n        } else if (this.element_.classList.contains(this.CssClasses_.TOP_RIGHT)) {\n            // Position above the \"for\" element, aligned to its right.\n            this.container_.style.right = forRect.right - rect.right + 'px';\n            this.container_.style.bottom = forRect.bottom - rect.top + 'px';\n        } else {\n            // Default: position below the \"for\" element, aligned to its left.\n            this.container_.style.left = this.forElement_.offsetLeft + 'px';\n            this.container_.style.top = this.forElement_.offsetTop + this.forElement_.offsetHeight + 'px';\n        }\n    }\n    this.toggle(evt);\n};\n/**\n   * Handles a keyboard event on the \"for\" element.\n   *\n   * @param {Event} evt The event that fired.\n   * @private\n   */\nMaterialMenu.prototype.handleForKeyboardEvent_ = function (evt) {\n    if (this.element_ && this.container_ && this.forElement_) {\n        var items = this.element_.querySelectorAll('.' + this.CssClasses_.ITEM + ':not([disabled])');\n        if (items && items.length > 0 && this.container_.classList.contains(this.CssClasses_.IS_VISIBLE)) {\n            if (evt.keyCode === this.Keycodes_.UP_ARROW) {\n                evt.preventDefault();\n                items[items.length - 1].focus();\n            } else if (evt.keyCode === this.Keycodes_.DOWN_ARROW) {\n                evt.preventDefault();\n                items[0].focus();\n            }\n        }\n    }\n};\n/**\n   * Handles a keyboard event on an item.\n   *\n   * @param {Event} evt The event that fired.\n   * @private\n   */\nMaterialMenu.prototype.handleItemKeyboardEvent_ = function (evt) {\n    if (this.element_ && this.container_) {\n        var items = this.element_.querySelectorAll('.' + this.CssClasses_.ITEM + ':not([disabled])');\n        if (items && items.length > 0 && this.container_.classList.contains(this.CssClasses_.IS_VISIBLE)) {\n            var currentIndex = Array.prototype.slice.call(items).indexOf(evt.target);\n            if (evt.keyCode === this.Keycodes_.UP_ARROW) {\n                evt.preventDefault();\n                if (currentIndex > 0) {\n                    items[currentIndex - 1].focus();\n                } else {\n                    items[items.length - 1].focus();\n                }\n            } else if (evt.keyCode === this.Keycodes_.DOWN_ARROW) {\n                evt.preventDefault();\n                if (items.length > currentIndex + 1) {\n                    items[currentIndex + 1].focus();\n                } else {\n                    items[0].focus();\n                }\n            } else if (evt.keyCode === this.Keycodes_.SPACE || evt.keyCode === this.Keycodes_.ENTER) {\n                evt.preventDefault();\n                // Send mousedown and mouseup to trigger ripple.\n                var e = new MouseEvent('mousedown');\n                evt.target.dispatchEvent(e);\n                e = new MouseEvent('mouseup');\n                evt.target.dispatchEvent(e);\n                // Send click.\n                evt.target.click();\n            } else if (evt.keyCode === this.Keycodes_.ESCAPE) {\n                evt.preventDefault();\n                this.hide();\n            }\n        }\n    }\n};\n/**\n   * Handles a click event on an item.\n   *\n   * @param {Event} evt The event that fired.\n   * @private\n   */\nMaterialMenu.prototype.handleItemClick_ = function (evt) {\n    if (evt.target.hasAttribute('disabled')) {\n        evt.stopPropagation();\n    } else {\n        // Wait some time before closing menu, so the user can see the ripple.\n        this.closing_ = true;\n        window.setTimeout(function (evt) {\n            this.hide();\n            this.closing_ = false;\n        }.bind(this), this.Constant_.CLOSE_TIMEOUT);\n    }\n};\n/**\n   * Calculates the initial clip (for opening the menu) or final clip (for closing\n   * it), and applies it. This allows us to animate from or to the correct point,\n   * that is, the point it's aligned to in the \"for\" element.\n   *\n   * @param {number} height Height of the clip rectangle\n   * @param {number} width Width of the clip rectangle\n   * @private\n   */\nMaterialMenu.prototype.applyClip_ = function (height, width) {\n    if (this.element_.classList.contains(this.CssClasses_.UNALIGNED)) {\n        // Do not clip.\n        this.element_.style.clip = '';\n    } else if (this.element_.classList.contains(this.CssClasses_.BOTTOM_RIGHT)) {\n        // Clip to the top right corner of the menu.\n        this.element_.style.clip = 'rect(0 ' + width + 'px ' + '0 ' + width + 'px)';\n    } else if (this.element_.classList.contains(this.CssClasses_.TOP_LEFT)) {\n        // Clip to the bottom left corner of the menu.\n        this.element_.style.clip = 'rect(' + height + 'px 0 ' + height + 'px 0)';\n    } else if (this.element_.classList.contains(this.CssClasses_.TOP_RIGHT)) {\n        // Clip to the bottom right corner of the menu.\n        this.element_.style.clip = 'rect(' + height + 'px ' + width + 'px ' + height + 'px ' + width + 'px)';\n    } else {\n        // Default: do not clip (same as clipping to the top left corner).\n        this.element_.style.clip = '';\n    }\n};\n/**\n   * Cleanup function to remove animation listeners.\n   *\n   * @param {Event} evt\n   * @private\n   */\nMaterialMenu.prototype.removeAnimationEndListener_ = function (evt) {\n    evt.target.classList.remove(MaterialMenu.prototype.CssClasses_.IS_ANIMATING);\n};\n/**\n   * Adds an event listener to clean up after the animation ends.\n   *\n   * @private\n   */\nMaterialMenu.prototype.addAnimationEndListener_ = function () {\n    this.element_.addEventListener('transitionend', this.removeAnimationEndListener_);\n    this.element_.addEventListener('webkitTransitionEnd', this.removeAnimationEndListener_);\n};\n/**\n   * Displays the menu.\n   *\n   * @public\n   */\nMaterialMenu.prototype.show = function (evt) {\n    if (this.element_ && this.container_ && this.outline_) {\n        // Measure the inner element.\n        var height = this.element_.getBoundingClientRect().height;\n        var width = this.element_.getBoundingClientRect().width;\n        // Apply the inner element's size to the container and outline.\n        this.container_.style.width = width + 'px';\n        this.container_.style.height = height + 'px';\n        this.outline_.style.width = width + 'px';\n        this.outline_.style.height = height + 'px';\n        var transitionDuration = this.Constant_.TRANSITION_DURATION_SECONDS * this.Constant_.TRANSITION_DURATION_FRACTION;\n        // Calculate transition delays for individual menu items, so that they fade\n        // in one at a time.\n        var items = this.element_.querySelectorAll('.' + this.CssClasses_.ITEM);\n        for (var i = 0; i < items.length; i++) {\n            var itemDelay = null;\n            if (this.element_.classList.contains(this.CssClasses_.TOP_LEFT) || this.element_.classList.contains(this.CssClasses_.TOP_RIGHT)) {\n                itemDelay = (height - items[i].offsetTop - items[i].offsetHeight) / height * transitionDuration + 's';\n            } else {\n                itemDelay = items[i].offsetTop / height * transitionDuration + 's';\n            }\n            items[i].style.transitionDelay = itemDelay;\n        }\n        // Apply the initial clip to the text before we start animating.\n        this.applyClip_(height, width);\n        // Wait for the next frame, turn on animation, and apply the final clip.\n        // Also make it visible. This triggers the transitions.\n        window.requestAnimationFrame(function () {\n            this.element_.classList.add(this.CssClasses_.IS_ANIMATING);\n            this.element_.style.clip = 'rect(0 ' + width + 'px ' + height + 'px 0)';\n            this.container_.classList.add(this.CssClasses_.IS_VISIBLE);\n        }.bind(this));\n        // Clean up after the animation is complete.\n        this.addAnimationEndListener_();\n        // Add a click listener to the document, to close the menu.\n        var callback = function (e) {\n            // Check to see if the document is processing the same event that\n            // displayed the menu in the first place. If so, do nothing.\n            // Also check to see if the menu is in the process of closing itself, and\n            // do nothing in that case.\n            // Also check if the clicked element is a menu item\n            // if so, do nothing.\n            if (e !== evt && !this.closing_ && e.target.parentNode !== this.element_) {\n                document.removeEventListener('click', callback);\n                this.hide();\n            }\n        }.bind(this);\n        document.addEventListener('click', callback);\n    }\n};\nMaterialMenu.prototype['show'] = MaterialMenu.prototype.show;\n/**\n   * Hides the menu.\n   *\n   * @public\n   */\nMaterialMenu.prototype.hide = function () {\n    if (this.element_ && this.container_ && this.outline_) {\n        var items = this.element_.querySelectorAll('.' + this.CssClasses_.ITEM);\n        // Remove all transition delays; menu items fade out concurrently.\n        for (var i = 0; i < items.length; i++) {\n            items[i].style.removeProperty('transition-delay');\n        }\n        // Measure the inner element.\n        var rect = this.element_.getBoundingClientRect();\n        var height = rect.height;\n        var width = rect.width;\n        // Turn on animation, and apply the final clip. Also make invisible.\n        // This triggers the transitions.\n        this.element_.classList.add(this.CssClasses_.IS_ANIMATING);\n        this.applyClip_(height, width);\n        this.container_.classList.remove(this.CssClasses_.IS_VISIBLE);\n        // Clean up after the animation is complete.\n        this.addAnimationEndListener_();\n    }\n};\nMaterialMenu.prototype['hide'] = MaterialMenu.prototype.hide;\n/**\n   * Displays or hides the menu, depending on current state.\n   *\n   * @public\n   */\nMaterialMenu.prototype.toggle = function (evt) {\n    if (this.container_.classList.contains(this.CssClasses_.IS_VISIBLE)) {\n        this.hide();\n    } else {\n        this.show(evt);\n    }\n};\nMaterialMenu.prototype['toggle'] = MaterialMenu.prototype.toggle;\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialMenu,\n    classAsString: 'MaterialMenu',\n    cssClass: 'mdl-js-menu',\n    widget: true\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Progress MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialProgress = function MaterialProgress(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialProgress'] = MaterialProgress;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialProgress.prototype.Constant_ = {};\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialProgress.prototype.CssClasses_ = { INDETERMINATE_CLASS: 'mdl-progress__indeterminate' };\n/**\n   * Set the current progress of the progressbar.\n   *\n   * @param {number} p Percentage of the progress (0-100)\n   * @public\n   */\nMaterialProgress.prototype.setProgress = function (p) {\n    if (this.element_.classList.contains(this.CssClasses_.INDETERMINATE_CLASS)) {\n        return;\n    }\n    this.progressbar_.style.width = p + '%';\n};\nMaterialProgress.prototype['setProgress'] = MaterialProgress.prototype.setProgress;\n/**\n   * Set the current progress of the buffer.\n   *\n   * @param {number} p Percentage of the buffer (0-100)\n   * @public\n   */\nMaterialProgress.prototype.setBuffer = function (p) {\n    this.bufferbar_.style.width = p + '%';\n    this.auxbar_.style.width = 100 - p + '%';\n};\nMaterialProgress.prototype['setBuffer'] = MaterialProgress.prototype.setBuffer;\n/**\n   * Initialize element.\n   */\nMaterialProgress.prototype.init = function () {\n    if (this.element_) {\n        var el = document.createElement('div');\n        el.className = 'progressbar bar bar1';\n        this.element_.appendChild(el);\n        this.progressbar_ = el;\n        el = document.createElement('div');\n        el.className = 'bufferbar bar bar2';\n        this.element_.appendChild(el);\n        this.bufferbar_ = el;\n        el = document.createElement('div');\n        el.className = 'auxbar bar bar3';\n        this.element_.appendChild(el);\n        this.auxbar_ = el;\n        this.progressbar_.style.width = '0%';\n        this.bufferbar_.style.width = '100%';\n        this.auxbar_.style.width = '0%';\n        this.element_.classList.add('is-upgraded');\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialProgress,\n    classAsString: 'MaterialProgress',\n    cssClass: 'mdl-js-progress',\n    widget: true\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Radio MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialRadio = function MaterialRadio(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialRadio'] = MaterialRadio;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialRadio.prototype.Constant_ = { TINY_TIMEOUT: 0.001 };\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialRadio.prototype.CssClasses_ = {\n    IS_FOCUSED: 'is-focused',\n    IS_DISABLED: 'is-disabled',\n    IS_CHECKED: 'is-checked',\n    IS_UPGRADED: 'is-upgraded',\n    JS_RADIO: 'mdl-js-radio',\n    RADIO_BTN: 'mdl-radio__button',\n    RADIO_OUTER_CIRCLE: 'mdl-radio__outer-circle',\n    RADIO_INNER_CIRCLE: 'mdl-radio__inner-circle',\n    RIPPLE_EFFECT: 'mdl-js-ripple-effect',\n    RIPPLE_IGNORE_EVENTS: 'mdl-js-ripple-effect--ignore-events',\n    RIPPLE_CONTAINER: 'mdl-radio__ripple-container',\n    RIPPLE_CENTER: 'mdl-ripple--center',\n    RIPPLE: 'mdl-ripple'\n};\n/**\n   * Handle change of state.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialRadio.prototype.onChange_ = function (event) {\n    // Since other radio buttons don't get change events, we need to look for\n    // them to update their classes.\n    var radios = document.getElementsByClassName(this.CssClasses_.JS_RADIO);\n    for (var i = 0; i < radios.length; i++) {\n        var button = radios[i].querySelector('.' + this.CssClasses_.RADIO_BTN);\n        // Different name == different group, so no point updating those.\n        if (button.getAttribute('name') === this.btnElement_.getAttribute('name')) {\n            if (typeof radios[i]['MaterialRadio'] !== 'undefined') {\n                radios[i]['MaterialRadio'].updateClasses_();\n            }\n        }\n    }\n};\n/**\n   * Handle focus.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialRadio.prototype.onFocus_ = function (event) {\n    this.element_.classList.add(this.CssClasses_.IS_FOCUSED);\n};\n/**\n   * Handle lost focus.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialRadio.prototype.onBlur_ = function (event) {\n    this.element_.classList.remove(this.CssClasses_.IS_FOCUSED);\n};\n/**\n   * Handle mouseup.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialRadio.prototype.onMouseup_ = function (event) {\n    this.blur_();\n};\n/**\n   * Update classes.\n   *\n   * @private\n   */\nMaterialRadio.prototype.updateClasses_ = function () {\n    this.checkDisabled();\n    this.checkToggleState();\n};\n/**\n   * Add blur.\n   *\n   * @private\n   */\nMaterialRadio.prototype.blur_ = function () {\n    // TODO: figure out why there's a focus event being fired after our blur,\n    // so that we can avoid this hack.\n    window.setTimeout(function () {\n        this.btnElement_.blur();\n    }.bind(this), this.Constant_.TINY_TIMEOUT);\n};\n// Public methods.\n/**\n   * Check the components disabled state.\n   *\n   * @public\n   */\nMaterialRadio.prototype.checkDisabled = function () {\n    if (this.btnElement_.disabled) {\n        this.element_.classList.add(this.CssClasses_.IS_DISABLED);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_DISABLED);\n    }\n};\nMaterialRadio.prototype['checkDisabled'] = MaterialRadio.prototype.checkDisabled;\n/**\n   * Check the components toggled state.\n   *\n   * @public\n   */\nMaterialRadio.prototype.checkToggleState = function () {\n    if (this.btnElement_.checked) {\n        this.element_.classList.add(this.CssClasses_.IS_CHECKED);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_CHECKED);\n    }\n};\nMaterialRadio.prototype['checkToggleState'] = MaterialRadio.prototype.checkToggleState;\n/**\n   * Disable radio.\n   *\n   * @public\n   */\nMaterialRadio.prototype.disable = function () {\n    this.btnElement_.disabled = true;\n    this.updateClasses_();\n};\nMaterialRadio.prototype['disable'] = MaterialRadio.prototype.disable;\n/**\n   * Enable radio.\n   *\n   * @public\n   */\nMaterialRadio.prototype.enable = function () {\n    this.btnElement_.disabled = false;\n    this.updateClasses_();\n};\nMaterialRadio.prototype['enable'] = MaterialRadio.prototype.enable;\n/**\n   * Check radio.\n   *\n   * @public\n   */\nMaterialRadio.prototype.check = function () {\n    this.btnElement_.checked = true;\n    this.onChange_(null);\n};\nMaterialRadio.prototype['check'] = MaterialRadio.prototype.check;\n/**\n   * Uncheck radio.\n   *\n   * @public\n   */\nMaterialRadio.prototype.uncheck = function () {\n    this.btnElement_.checked = false;\n    this.onChange_(null);\n};\nMaterialRadio.prototype['uncheck'] = MaterialRadio.prototype.uncheck;\n/**\n   * Initialize element.\n   */\nMaterialRadio.prototype.init = function () {\n    if (this.element_) {\n        this.btnElement_ = this.element_.querySelector('.' + this.CssClasses_.RADIO_BTN);\n        this.boundChangeHandler_ = this.onChange_.bind(this);\n        this.boundFocusHandler_ = this.onChange_.bind(this);\n        this.boundBlurHandler_ = this.onBlur_.bind(this);\n        this.boundMouseUpHandler_ = this.onMouseup_.bind(this);\n        var outerCircle = document.createElement('span');\n        outerCircle.classList.add(this.CssClasses_.RADIO_OUTER_CIRCLE);\n        var innerCircle = document.createElement('span');\n        innerCircle.classList.add(this.CssClasses_.RADIO_INNER_CIRCLE);\n        this.element_.appendChild(outerCircle);\n        this.element_.appendChild(innerCircle);\n        var rippleContainer;\n        if (this.element_.classList.contains(this.CssClasses_.RIPPLE_EFFECT)) {\n            this.element_.classList.add(this.CssClasses_.RIPPLE_IGNORE_EVENTS);\n            rippleContainer = document.createElement('span');\n            rippleContainer.classList.add(this.CssClasses_.RIPPLE_CONTAINER);\n            rippleContainer.classList.add(this.CssClasses_.RIPPLE_EFFECT);\n            rippleContainer.classList.add(this.CssClasses_.RIPPLE_CENTER);\n            rippleContainer.addEventListener('mouseup', this.boundMouseUpHandler_);\n            var ripple = document.createElement('span');\n            ripple.classList.add(this.CssClasses_.RIPPLE);\n            rippleContainer.appendChild(ripple);\n            this.element_.appendChild(rippleContainer);\n        }\n        this.btnElement_.addEventListener('change', this.boundChangeHandler_);\n        this.btnElement_.addEventListener('focus', this.boundFocusHandler_);\n        this.btnElement_.addEventListener('blur', this.boundBlurHandler_);\n        this.element_.addEventListener('mouseup', this.boundMouseUpHandler_);\n        this.updateClasses_();\n        this.element_.classList.add(this.CssClasses_.IS_UPGRADED);\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialRadio,\n    classAsString: 'MaterialRadio',\n    cssClass: 'mdl-js-radio',\n    widget: true\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Slider MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialSlider = function MaterialSlider(element) {\n    this.element_ = element;\n    // Browser feature detection.\n    this.isIE_ = window.navigator.msPointerEnabled;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialSlider'] = MaterialSlider;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialSlider.prototype.Constant_ = {};\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialSlider.prototype.CssClasses_ = {\n    IE_CONTAINER: 'mdl-slider__ie-container',\n    SLIDER_CONTAINER: 'mdl-slider__container',\n    BACKGROUND_FLEX: 'mdl-slider__background-flex',\n    BACKGROUND_LOWER: 'mdl-slider__background-lower',\n    BACKGROUND_UPPER: 'mdl-slider__background-upper',\n    IS_LOWEST_VALUE: 'is-lowest-value',\n    IS_UPGRADED: 'is-upgraded'\n};\n/**\n   * Handle input on element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialSlider.prototype.onInput_ = function (event) {\n    this.updateValueStyles_();\n};\n/**\n   * Handle change on element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialSlider.prototype.onChange_ = function (event) {\n    this.updateValueStyles_();\n};\n/**\n   * Handle mouseup on element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialSlider.prototype.onMouseUp_ = function (event) {\n    event.target.blur();\n};\n/**\n   * Handle mousedown on container element.\n   * This handler is purpose is to not require the use to click\n   * exactly on the 2px slider element, as FireFox seems to be very\n   * strict about this.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   * @suppress {missingProperties}\n   */\nMaterialSlider.prototype.onContainerMouseDown_ = function (event) {\n    // If this click is not on the parent element (but rather some child)\n    // ignore. It may still bubble up.\n    if (event.target !== this.element_.parentElement) {\n        return;\n    }\n    // Discard the original event and create a new event that\n    // is on the slider element.\n    event.preventDefault();\n    var newEvent = new MouseEvent('mousedown', {\n        target: event.target,\n        buttons: event.buttons,\n        clientX: event.clientX,\n        clientY: this.element_.getBoundingClientRect().y\n    });\n    this.element_.dispatchEvent(newEvent);\n};\n/**\n   * Handle updating of values.\n   *\n   * @private\n   */\nMaterialSlider.prototype.updateValueStyles_ = function () {\n    // Calculate and apply percentages to div structure behind slider.\n    var fraction = (this.element_.value - this.element_.min) / (this.element_.max - this.element_.min);\n    if (fraction === 0) {\n        this.element_.classList.add(this.CssClasses_.IS_LOWEST_VALUE);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_LOWEST_VALUE);\n    }\n    if (!this.isIE_) {\n        this.backgroundLower_.style.flex = fraction;\n        this.backgroundLower_.style.webkitFlex = fraction;\n        this.backgroundUpper_.style.flex = 1 - fraction;\n        this.backgroundUpper_.style.webkitFlex = 1 - fraction;\n    }\n};\n// Public methods.\n/**\n   * Disable slider.\n   *\n   * @public\n   */\nMaterialSlider.prototype.disable = function () {\n    this.element_.disabled = true;\n};\nMaterialSlider.prototype['disable'] = MaterialSlider.prototype.disable;\n/**\n   * Enable slider.\n   *\n   * @public\n   */\nMaterialSlider.prototype.enable = function () {\n    this.element_.disabled = false;\n};\nMaterialSlider.prototype['enable'] = MaterialSlider.prototype.enable;\n/**\n   * Update slider value.\n   *\n   * @param {number} value The value to which to set the control (optional).\n   * @public\n   */\nMaterialSlider.prototype.change = function (value) {\n    if (typeof value !== 'undefined') {\n        this.element_.value = value;\n    }\n    this.updateValueStyles_();\n};\nMaterialSlider.prototype['change'] = MaterialSlider.prototype.change;\n/**\n   * Initialize element.\n   */\nMaterialSlider.prototype.init = function () {\n    if (this.element_) {\n        if (this.isIE_) {\n            // Since we need to specify a very large height in IE due to\n            // implementation limitations, we add a parent here that trims it down to\n            // a reasonable size.\n            var containerIE = document.createElement('div');\n            containerIE.classList.add(this.CssClasses_.IE_CONTAINER);\n            this.element_.parentElement.insertBefore(containerIE, this.element_);\n            this.element_.parentElement.removeChild(this.element_);\n            containerIE.appendChild(this.element_);\n        } else {\n            // For non-IE browsers, we need a div structure that sits behind the\n            // slider and allows us to style the left and right sides of it with\n            // different colors.\n            var container = document.createElement('div');\n            container.classList.add(this.CssClasses_.SLIDER_CONTAINER);\n            this.element_.parentElement.insertBefore(container, this.element_);\n            this.element_.parentElement.removeChild(this.element_);\n            container.appendChild(this.element_);\n            var backgroundFlex = document.createElement('div');\n            backgroundFlex.classList.add(this.CssClasses_.BACKGROUND_FLEX);\n            container.appendChild(backgroundFlex);\n            this.backgroundLower_ = document.createElement('div');\n            this.backgroundLower_.classList.add(this.CssClasses_.BACKGROUND_LOWER);\n            backgroundFlex.appendChild(this.backgroundLower_);\n            this.backgroundUpper_ = document.createElement('div');\n            this.backgroundUpper_.classList.add(this.CssClasses_.BACKGROUND_UPPER);\n            backgroundFlex.appendChild(this.backgroundUpper_);\n        }\n        this.boundInputHandler = this.onInput_.bind(this);\n        this.boundChangeHandler = this.onChange_.bind(this);\n        this.boundMouseUpHandler = this.onMouseUp_.bind(this);\n        this.boundContainerMouseDownHandler = this.onContainerMouseDown_.bind(this);\n        this.element_.addEventListener('input', this.boundInputHandler);\n        this.element_.addEventListener('change', this.boundChangeHandler);\n        this.element_.addEventListener('mouseup', this.boundMouseUpHandler);\n        this.element_.parentElement.addEventListener('mousedown', this.boundContainerMouseDownHandler);\n        this.updateValueStyles_();\n        this.element_.classList.add(this.CssClasses_.IS_UPGRADED);\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialSlider,\n    classAsString: 'MaterialSlider',\n    cssClass: 'mdl-js-slider',\n    widget: true\n});","/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Snackbar MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialSnackbar = function MaterialSnackbar(element) {\n    this.element_ = element;\n    this.textElement_ = this.element_.querySelector('.' + this.cssClasses_.MESSAGE);\n    this.actionElement_ = this.element_.querySelector('.' + this.cssClasses_.ACTION);\n    if (!this.textElement_) {\n        throw new Error('There must be a message element for a snackbar.');\n    }\n    if (!this.actionElement_) {\n        throw new Error('There must be an action element for a snackbar.');\n    }\n    this.active = false;\n    this.actionHandler_ = undefined;\n    this.message_ = undefined;\n    this.actionText_ = undefined;\n    this.queuedNotifications_ = [];\n    this.setActionHidden_(true);\n};\nwindow['MaterialSnackbar'] = MaterialSnackbar;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialSnackbar.prototype.Constant_ = {\n    // The duration of the snackbar show/hide animation, in ms.\n    ANIMATION_LENGTH: 250\n};\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialSnackbar.prototype.cssClasses_ = {\n    SNACKBAR: 'mdl-snackbar',\n    MESSAGE: 'mdl-snackbar__text',\n    ACTION: 'mdl-snackbar__action',\n    ACTIVE: 'mdl-snackbar--active'\n};\n/**\n   * Display the snackbar.\n   *\n   * @private\n   */\nMaterialSnackbar.prototype.displaySnackbar_ = function () {\n    this.element_.setAttribute('aria-hidden', 'true');\n    if (this.actionHandler_) {\n        this.actionElement_.textContent = this.actionText_;\n        this.actionElement_.addEventListener('click', this.actionHandler_);\n        this.setActionHidden_(false);\n    }\n    this.textElement_.textContent = this.message_;\n    this.element_.classList.add(this.cssClasses_.ACTIVE);\n    this.element_.setAttribute('aria-hidden', 'false');\n    setTimeout(this.cleanup_.bind(this), this.timeout_);\n};\n/**\n   * Show the snackbar.\n   *\n   * @param {Object} data The data for the notification.\n   * @public\n   */\nMaterialSnackbar.prototype.showSnackbar = function (data) {\n    if (data === undefined) {\n        throw new Error('Please provide a data object with at least a message to display.');\n    }\n    if (data['message'] === undefined) {\n        throw new Error('Please provide a message to be displayed.');\n    }\n    if (data['actionHandler'] && !data['actionText']) {\n        throw new Error('Please provide action text with the handler.');\n    }\n    if (this.active) {\n        this.queuedNotifications_.push(data);\n    } else {\n        this.active = true;\n        this.message_ = data['message'];\n        if (data['timeout']) {\n            this.timeout_ = data['timeout'];\n        } else {\n            this.timeout_ = 2750;\n        }\n        if (data['actionHandler']) {\n            this.actionHandler_ = data['actionHandler'];\n        }\n        if (data['actionText']) {\n            this.actionText_ = data['actionText'];\n        }\n        this.displaySnackbar_();\n    }\n};\nMaterialSnackbar.prototype['showSnackbar'] = MaterialSnackbar.prototype.showSnackbar;\n/**\n   * Check if the queue has items within it.\n   * If it does, display the next entry.\n   *\n   * @private\n   */\nMaterialSnackbar.prototype.checkQueue_ = function () {\n    if (this.queuedNotifications_.length > 0) {\n        this.showSnackbar(this.queuedNotifications_.shift());\n    }\n};\n/**\n   * Cleanup the snackbar event listeners and accessiblity attributes.\n   *\n   * @private\n   */\nMaterialSnackbar.prototype.cleanup_ = function () {\n    this.element_.classList.remove(this.cssClasses_.ACTIVE);\n    setTimeout(function () {\n        this.element_.setAttribute('aria-hidden', 'true');\n        this.textElement_.textContent = '';\n        if (!Boolean(this.actionElement_.getAttribute('aria-hidden'))) {\n            this.setActionHidden_(true);\n            this.actionElement_.textContent = '';\n            this.actionElement_.removeEventListener('click', this.actionHandler_);\n        }\n        this.actionHandler_ = undefined;\n        this.message_ = undefined;\n        this.actionText_ = undefined;\n        this.active = false;\n        this.checkQueue_();\n    }.bind(this), this.Constant_.ANIMATION_LENGTH);\n};\n/**\n   * Set the action handler hidden state.\n   *\n   * @param {boolean} value\n   * @private\n   */\nMaterialSnackbar.prototype.setActionHidden_ = function (value) {\n    if (value) {\n        this.actionElement_.setAttribute('aria-hidden', 'true');\n    } else {\n        this.actionElement_.removeAttribute('aria-hidden');\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialSnackbar,\n    classAsString: 'MaterialSnackbar',\n    cssClass: 'mdl-js-snackbar',\n    widget: true\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Spinner MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @param {HTMLElement} element The element that will be upgraded.\n   * @constructor\n   */\nvar MaterialSpinner = function MaterialSpinner(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialSpinner'] = MaterialSpinner;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialSpinner.prototype.Constant_ = { MDL_SPINNER_LAYER_COUNT: 4 };\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialSpinner.prototype.CssClasses_ = {\n    MDL_SPINNER_LAYER: 'mdl-spinner__layer',\n    MDL_SPINNER_CIRCLE_CLIPPER: 'mdl-spinner__circle-clipper',\n    MDL_SPINNER_CIRCLE: 'mdl-spinner__circle',\n    MDL_SPINNER_GAP_PATCH: 'mdl-spinner__gap-patch',\n    MDL_SPINNER_LEFT: 'mdl-spinner__left',\n    MDL_SPINNER_RIGHT: 'mdl-spinner__right'\n};\n/**\n   * Auxiliary method to create a spinner layer.\n   *\n   * @param {number} index Index of the layer to be created.\n   * @public\n   */\nMaterialSpinner.prototype.createLayer = function (index) {\n    var layer = document.createElement('div');\n    layer.classList.add(this.CssClasses_.MDL_SPINNER_LAYER);\n    layer.classList.add(this.CssClasses_.MDL_SPINNER_LAYER + '-' + index);\n    var leftClipper = document.createElement('div');\n    leftClipper.classList.add(this.CssClasses_.MDL_SPINNER_CIRCLE_CLIPPER);\n    leftClipper.classList.add(this.CssClasses_.MDL_SPINNER_LEFT);\n    var gapPatch = document.createElement('div');\n    gapPatch.classList.add(this.CssClasses_.MDL_SPINNER_GAP_PATCH);\n    var rightClipper = document.createElement('div');\n    rightClipper.classList.add(this.CssClasses_.MDL_SPINNER_CIRCLE_CLIPPER);\n    rightClipper.classList.add(this.CssClasses_.MDL_SPINNER_RIGHT);\n    var circleOwners = [\n        leftClipper,\n        gapPatch,\n        rightClipper\n    ];\n    for (var i = 0; i < circleOwners.length; i++) {\n        var circle = document.createElement('div');\n        circle.classList.add(this.CssClasses_.MDL_SPINNER_CIRCLE);\n        circleOwners[i].appendChild(circle);\n    }\n    layer.appendChild(leftClipper);\n    layer.appendChild(gapPatch);\n    layer.appendChild(rightClipper);\n    this.element_.appendChild(layer);\n};\nMaterialSpinner.prototype['createLayer'] = MaterialSpinner.prototype.createLayer;\n/**\n   * Stops the spinner animation.\n   * Public method for users who need to stop the spinner for any reason.\n   *\n   * @public\n   */\nMaterialSpinner.prototype.stop = function () {\n    this.element_.classList.remove('is-active');\n};\nMaterialSpinner.prototype['stop'] = MaterialSpinner.prototype.stop;\n/**\n   * Starts the spinner animation.\n   * Public method for users who need to manually start the spinner for any reason\n   * (instead of just adding the 'is-active' class to their markup).\n   *\n   * @public\n   */\nMaterialSpinner.prototype.start = function () {\n    this.element_.classList.add('is-active');\n};\nMaterialSpinner.prototype['start'] = MaterialSpinner.prototype.start;\n/**\n   * Initialize element.\n   */\nMaterialSpinner.prototype.init = function () {\n    if (this.element_) {\n        for (var i = 1; i <= this.Constant_.MDL_SPINNER_LAYER_COUNT; i++) {\n            this.createLayer(i);\n        }\n        this.element_.classList.add('is-upgraded');\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialSpinner,\n    classAsString: 'MaterialSpinner',\n    cssClass: 'mdl-js-spinner',\n    widget: true\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Checkbox MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialSwitch = function MaterialSwitch(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialSwitch'] = MaterialSwitch;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialSwitch.prototype.Constant_ = { TINY_TIMEOUT: 0.001 };\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialSwitch.prototype.CssClasses_ = {\n    INPUT: 'mdl-switch__input',\n    TRACK: 'mdl-switch__track',\n    THUMB: 'mdl-switch__thumb',\n    FOCUS_HELPER: 'mdl-switch__focus-helper',\n    RIPPLE_EFFECT: 'mdl-js-ripple-effect',\n    RIPPLE_IGNORE_EVENTS: 'mdl-js-ripple-effect--ignore-events',\n    RIPPLE_CONTAINER: 'mdl-switch__ripple-container',\n    RIPPLE_CENTER: 'mdl-ripple--center',\n    RIPPLE: 'mdl-ripple',\n    IS_FOCUSED: 'is-focused',\n    IS_DISABLED: 'is-disabled',\n    IS_CHECKED: 'is-checked'\n};\n/**\n   * Handle change of state.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialSwitch.prototype.onChange_ = function (event) {\n    this.updateClasses_();\n};\n/**\n   * Handle focus of element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialSwitch.prototype.onFocus_ = function (event) {\n    this.element_.classList.add(this.CssClasses_.IS_FOCUSED);\n};\n/**\n   * Handle lost focus of element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialSwitch.prototype.onBlur_ = function (event) {\n    this.element_.classList.remove(this.CssClasses_.IS_FOCUSED);\n};\n/**\n   * Handle mouseup.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialSwitch.prototype.onMouseUp_ = function (event) {\n    this.blur_();\n};\n/**\n   * Handle class updates.\n   *\n   * @private\n   */\nMaterialSwitch.prototype.updateClasses_ = function () {\n    this.checkDisabled();\n    this.checkToggleState();\n};\n/**\n   * Add blur.\n   *\n   * @private\n   */\nMaterialSwitch.prototype.blur_ = function () {\n    // TODO: figure out why there's a focus event being fired after our blur,\n    // so that we can avoid this hack.\n    window.setTimeout(function () {\n        this.inputElement_.blur();\n    }.bind(this), this.Constant_.TINY_TIMEOUT);\n};\n// Public methods.\n/**\n   * Check the components disabled state.\n   *\n   * @public\n   */\nMaterialSwitch.prototype.checkDisabled = function () {\n    if (this.inputElement_.disabled) {\n        this.element_.classList.add(this.CssClasses_.IS_DISABLED);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_DISABLED);\n    }\n};\nMaterialSwitch.prototype['checkDisabled'] = MaterialSwitch.prototype.checkDisabled;\n/**\n   * Check the components toggled state.\n   *\n   * @public\n   */\nMaterialSwitch.prototype.checkToggleState = function () {\n    if (this.inputElement_.checked) {\n        this.element_.classList.add(this.CssClasses_.IS_CHECKED);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_CHECKED);\n    }\n};\nMaterialSwitch.prototype['checkToggleState'] = MaterialSwitch.prototype.checkToggleState;\n/**\n   * Disable switch.\n   *\n   * @public\n   */\nMaterialSwitch.prototype.disable = function () {\n    this.inputElement_.disabled = true;\n    this.updateClasses_();\n};\nMaterialSwitch.prototype['disable'] = MaterialSwitch.prototype.disable;\n/**\n   * Enable switch.\n   *\n   * @public\n   */\nMaterialSwitch.prototype.enable = function () {\n    this.inputElement_.disabled = false;\n    this.updateClasses_();\n};\nMaterialSwitch.prototype['enable'] = MaterialSwitch.prototype.enable;\n/**\n   * Activate switch.\n   *\n   * @public\n   */\nMaterialSwitch.prototype.on = function () {\n    this.inputElement_.checked = true;\n    this.updateClasses_();\n};\nMaterialSwitch.prototype['on'] = MaterialSwitch.prototype.on;\n/**\n   * Deactivate switch.\n   *\n   * @public\n   */\nMaterialSwitch.prototype.off = function () {\n    this.inputElement_.checked = false;\n    this.updateClasses_();\n};\nMaterialSwitch.prototype['off'] = MaterialSwitch.prototype.off;\n/**\n   * Initialize element.\n   */\nMaterialSwitch.prototype.init = function () {\n    if (this.element_) {\n        this.inputElement_ = this.element_.querySelector('.' + this.CssClasses_.INPUT);\n        var track = document.createElement('div');\n        track.classList.add(this.CssClasses_.TRACK);\n        var thumb = document.createElement('div');\n        thumb.classList.add(this.CssClasses_.THUMB);\n        var focusHelper = document.createElement('span');\n        focusHelper.classList.add(this.CssClasses_.FOCUS_HELPER);\n        thumb.appendChild(focusHelper);\n        this.element_.appendChild(track);\n        this.element_.appendChild(thumb);\n        this.boundMouseUpHandler = this.onMouseUp_.bind(this);\n        if (this.element_.classList.contains(this.CssClasses_.RIPPLE_EFFECT)) {\n            this.element_.classList.add(this.CssClasses_.RIPPLE_IGNORE_EVENTS);\n            this.rippleContainerElement_ = document.createElement('span');\n            this.rippleContainerElement_.classList.add(this.CssClasses_.RIPPLE_CONTAINER);\n            this.rippleContainerElement_.classList.add(this.CssClasses_.RIPPLE_EFFECT);\n            this.rippleContainerElement_.classList.add(this.CssClasses_.RIPPLE_CENTER);\n            this.rippleContainerElement_.addEventListener('mouseup', this.boundMouseUpHandler);\n            var ripple = document.createElement('span');\n            ripple.classList.add(this.CssClasses_.RIPPLE);\n            this.rippleContainerElement_.appendChild(ripple);\n            this.element_.appendChild(this.rippleContainerElement_);\n        }\n        this.boundChangeHandler = this.onChange_.bind(this);\n        this.boundFocusHandler = this.onFocus_.bind(this);\n        this.boundBlurHandler = this.onBlur_.bind(this);\n        this.inputElement_.addEventListener('change', this.boundChangeHandler);\n        this.inputElement_.addEventListener('focus', this.boundFocusHandler);\n        this.inputElement_.addEventListener('blur', this.boundBlurHandler);\n        this.element_.addEventListener('mouseup', this.boundMouseUpHandler);\n        this.updateClasses_();\n        this.element_.classList.add('is-upgraded');\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialSwitch,\n    classAsString: 'MaterialSwitch',\n    cssClass: 'mdl-js-switch',\n    widget: true\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Textfield MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialTextfield = function MaterialTextfield(element) {\n    this.element_ = element;\n    this.maxRows = this.Constant_.NO_MAX_ROWS;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialTextfield'] = MaterialTextfield;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialTextfield.prototype.Constant_ = {\n    NO_MAX_ROWS: -1,\n    MAX_ROWS_ATTRIBUTE: 'maxrows'\n};\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialTextfield.prototype.CssClasses_ = {\n    LABEL: 'mdl-textfield__label',\n    INPUT: 'mdl-textfield__input',\n    IS_DIRTY: 'is-dirty',\n    IS_FOCUSED: 'is-focused',\n    IS_DISABLED: 'is-disabled',\n    IS_INVALID: 'is-invalid',\n    IS_UPGRADED: 'is-upgraded',\n    HAS_PLACEHOLDER: 'has-placeholder'\n};\n/**\n   * Handle input being entered.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialTextfield.prototype.onKeyDown_ = function (event) {\n    var currentRowCount = event.target.value.split('\\n').length;\n    if (event.keyCode === 13) {\n        if (currentRowCount >= this.maxRows) {\n            event.preventDefault();\n        }\n    }\n};\n/**\n   * Handle focus.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialTextfield.prototype.onFocus_ = function (event) {\n    this.element_.classList.add(this.CssClasses_.IS_FOCUSED);\n};\n/**\n   * Handle lost focus.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialTextfield.prototype.onBlur_ = function (event) {\n    this.element_.classList.remove(this.CssClasses_.IS_FOCUSED);\n};\n/**\n   * Handle reset event from out side.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialTextfield.prototype.onReset_ = function (event) {\n    this.updateClasses_();\n};\n/**\n   * Handle class updates.\n   *\n   * @private\n   */\nMaterialTextfield.prototype.updateClasses_ = function () {\n    this.checkDisabled();\n    this.checkValidity();\n    this.checkDirty();\n    this.checkFocus();\n};\n// Public methods.\n/**\n   * Check the disabled state and update field accordingly.\n   *\n   * @public\n   */\nMaterialTextfield.prototype.checkDisabled = function () {\n    if (this.input_.disabled) {\n        this.element_.classList.add(this.CssClasses_.IS_DISABLED);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_DISABLED);\n    }\n};\nMaterialTextfield.prototype['checkDisabled'] = MaterialTextfield.prototype.checkDisabled;\n/**\n  * Check the focus state and update field accordingly.\n  *\n  * @public\n  */\nMaterialTextfield.prototype.checkFocus = function () {\n    if (Boolean(this.element_.querySelector(':focus'))) {\n        this.element_.classList.add(this.CssClasses_.IS_FOCUSED);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_FOCUSED);\n    }\n};\nMaterialTextfield.prototype['checkFocus'] = MaterialTextfield.prototype.checkFocus;\n/**\n   * Check the validity state and update field accordingly.\n   *\n   * @public\n   */\nMaterialTextfield.prototype.checkValidity = function () {\n    if (this.input_.validity) {\n        if (this.input_.validity.valid) {\n            this.element_.classList.remove(this.CssClasses_.IS_INVALID);\n        } else {\n            this.element_.classList.add(this.CssClasses_.IS_INVALID);\n        }\n    }\n};\nMaterialTextfield.prototype['checkValidity'] = MaterialTextfield.prototype.checkValidity;\n/**\n   * Check the dirty state and update field accordingly.\n   *\n   * @public\n   */\nMaterialTextfield.prototype.checkDirty = function () {\n    if (this.input_.value && this.input_.value.length > 0) {\n        this.element_.classList.add(this.CssClasses_.IS_DIRTY);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_DIRTY);\n    }\n};\nMaterialTextfield.prototype['checkDirty'] = MaterialTextfield.prototype.checkDirty;\n/**\n   * Disable text field.\n   *\n   * @public\n   */\nMaterialTextfield.prototype.disable = function () {\n    this.input_.disabled = true;\n    this.updateClasses_();\n};\nMaterialTextfield.prototype['disable'] = MaterialTextfield.prototype.disable;\n/**\n   * Enable text field.\n   *\n   * @public\n   */\nMaterialTextfield.prototype.enable = function () {\n    this.input_.disabled = false;\n    this.updateClasses_();\n};\nMaterialTextfield.prototype['enable'] = MaterialTextfield.prototype.enable;\n/**\n   * Update text field value.\n   *\n   * @param {string} value The value to which to set the control (optional).\n   * @public\n   */\nMaterialTextfield.prototype.change = function (value) {\n    this.input_.value = value || '';\n    this.updateClasses_();\n};\nMaterialTextfield.prototype['change'] = MaterialTextfield.prototype.change;\n/**\n   * Initialize element.\n   */\nMaterialTextfield.prototype.init = function () {\n    if (this.element_) {\n        this.label_ = this.element_.querySelector('.' + this.CssClasses_.LABEL);\n        this.input_ = this.element_.querySelector('.' + this.CssClasses_.INPUT);\n        if (this.input_) {\n            if (this.input_.hasAttribute(this.Constant_.MAX_ROWS_ATTRIBUTE)) {\n                this.maxRows = parseInt(this.input_.getAttribute(this.Constant_.MAX_ROWS_ATTRIBUTE), 10);\n                if (isNaN(this.maxRows)) {\n                    this.maxRows = this.Constant_.NO_MAX_ROWS;\n                }\n            }\n            if (this.input_.hasAttribute('placeholder')) {\n                this.element_.classList.add(this.CssClasses_.HAS_PLACEHOLDER);\n            }\n            this.boundUpdateClassesHandler = this.updateClasses_.bind(this);\n            this.boundFocusHandler = this.onFocus_.bind(this);\n            this.boundBlurHandler = this.onBlur_.bind(this);\n            this.boundResetHandler = this.onReset_.bind(this);\n            this.input_.addEventListener('input', this.boundUpdateClassesHandler);\n            this.input_.addEventListener('focus', this.boundFocusHandler);\n            this.input_.addEventListener('blur', this.boundBlurHandler);\n            this.input_.addEventListener('reset', this.boundResetHandler);\n            if (this.maxRows !== this.Constant_.NO_MAX_ROWS) {\n                // TODO: This should handle pasting multi line text.\n                // Currently doesn't.\n                this.boundKeyDownHandler = this.onKeyDown_.bind(this);\n                this.input_.addEventListener('keydown', this.boundKeyDownHandler);\n            }\n            var invalid = this.element_.classList.contains(this.CssClasses_.IS_INVALID);\n            this.updateClasses_();\n            this.element_.classList.add(this.CssClasses_.IS_UPGRADED);\n            if (invalid) {\n                this.element_.classList.add(this.CssClasses_.IS_INVALID);\n            }\n            if (this.input_.hasAttribute('autofocus')) {\n                this.element_.focus();\n                this.checkFocus();\n            }\n        }\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialTextfield,\n    classAsString: 'MaterialTextfield',\n    cssClass: 'mdl-js-textfield',\n    widget: true\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Tooltip MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialTooltip = function MaterialTooltip(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialTooltip'] = MaterialTooltip;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialTooltip.prototype.Constant_ = {};\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialTooltip.prototype.CssClasses_ = {\n    IS_ACTIVE: 'is-active',\n    BOTTOM: 'mdl-tooltip--bottom',\n    LEFT: 'mdl-tooltip--left',\n    RIGHT: 'mdl-tooltip--right',\n    TOP: 'mdl-tooltip--top'\n};\n/**\n   * Handle mouseenter for tooltip.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialTooltip.prototype.handleMouseEnter_ = function (event) {\n    var props = event.target.getBoundingClientRect();\n    var left = props.left + props.width / 2;\n    var top = props.top + props.height / 2;\n    var marginLeft = -1 * (this.element_.offsetWidth / 2);\n    var marginTop = -1 * (this.element_.offsetHeight / 2);\n    if (this.element_.classList.contains(this.CssClasses_.LEFT) || this.element_.classList.contains(this.CssClasses_.RIGHT)) {\n        left = props.width / 2;\n        if (top + marginTop < 0) {\n            this.element_.style.top = '0';\n            this.element_.style.marginTop = '0';\n        } else {\n            this.element_.style.top = top + 'px';\n            this.element_.style.marginTop = marginTop + 'px';\n        }\n    } else {\n        if (left + marginLeft < 0) {\n            this.element_.style.left = '0';\n            this.element_.style.marginLeft = '0';\n        } else {\n            this.element_.style.left = left + 'px';\n            this.element_.style.marginLeft = marginLeft + 'px';\n        }\n    }\n    if (this.element_.classList.contains(this.CssClasses_.TOP)) {\n        this.element_.style.top = props.top - this.element_.offsetHeight - 10 + 'px';\n    } else if (this.element_.classList.contains(this.CssClasses_.RIGHT)) {\n        this.element_.style.left = props.left + props.width + 10 + 'px';\n    } else if (this.element_.classList.contains(this.CssClasses_.LEFT)) {\n        this.element_.style.left = props.left - this.element_.offsetWidth - 10 + 'px';\n    } else {\n        this.element_.style.top = props.top + props.height + 10 + 'px';\n    }\n    this.element_.classList.add(this.CssClasses_.IS_ACTIVE);\n};\n/**\n   * Hide tooltip on mouseleave or scroll\n   *\n   * @private\n   */\nMaterialTooltip.prototype.hideTooltip_ = function () {\n    this.element_.classList.remove(this.CssClasses_.IS_ACTIVE);\n};\n/**\n   * Initialize element.\n   */\nMaterialTooltip.prototype.init = function () {\n    if (this.element_) {\n        var forElId = this.element_.getAttribute('for') || this.element_.getAttribute('data-mdl-for');\n        if (forElId) {\n            this.forElement_ = document.getElementById(forElId);\n        }\n        if (this.forElement_) {\n            // It's left here because it prevents accidental text selection on Android\n            if (!this.forElement_.hasAttribute('tabindex')) {\n                this.forElement_.setAttribute('tabindex', '0');\n            }\n            this.boundMouseEnterHandler = this.handleMouseEnter_.bind(this);\n            this.boundMouseLeaveAndScrollHandler = this.hideTooltip_.bind(this);\n            this.forElement_.addEventListener('mouseenter', this.boundMouseEnterHandler, false);\n            this.forElement_.addEventListener('touchend', this.boundMouseEnterHandler, false);\n            this.forElement_.addEventListener('mouseleave', this.boundMouseLeaveAndScrollHandler, false);\n            window.addEventListener('scroll', this.boundMouseLeaveAndScrollHandler, true);\n            window.addEventListener('touchstart', this.boundMouseLeaveAndScrollHandler);\n        }\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialTooltip,\n    classAsString: 'MaterialTooltip',\n    cssClass: 'mdl-tooltip'\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Data Table Card MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {Element} element The element that will be upgraded.\n   */\nvar MaterialDataTable = function MaterialDataTable(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialDataTable'] = MaterialDataTable;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialDataTable.prototype.Constant_ = {};\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialDataTable.prototype.CssClasses_ = {\n    DATA_TABLE: 'mdl-data-table',\n    SELECTABLE: 'mdl-data-table--selectable',\n    SELECT_ELEMENT: 'mdl-data-table__select',\n    IS_SELECTED: 'is-selected',\n    IS_UPGRADED: 'is-upgraded'\n};\n/**\n   * Generates and returns a function that toggles the selection state of a\n   * single row (or multiple rows).\n   *\n   * @param {Element} checkbox Checkbox that toggles the selection state.\n   * @param {Element} row Row to toggle when checkbox changes.\n   * @param {(Array<Object>|NodeList)=} opt_rows Rows to toggle when checkbox changes.\n   * @private\n   */\nMaterialDataTable.prototype.selectRow_ = function (checkbox, row, opt_rows) {\n    if (row) {\n        return function () {\n            if (checkbox.checked) {\n                row.classList.add(this.CssClasses_.IS_SELECTED);\n            } else {\n                row.classList.remove(this.CssClasses_.IS_SELECTED);\n            }\n        }.bind(this);\n    }\n    if (opt_rows) {\n        return function () {\n            var i;\n            var el;\n            if (checkbox.checked) {\n                for (i = 0; i < opt_rows.length; i++) {\n                    el = opt_rows[i].querySelector('td').querySelector('.mdl-checkbox');\n                    el['MaterialCheckbox'].check();\n                    opt_rows[i].classList.add(this.CssClasses_.IS_SELECTED);\n                }\n            } else {\n                for (i = 0; i < opt_rows.length; i++) {\n                    el = opt_rows[i].querySelector('td').querySelector('.mdl-checkbox');\n                    el['MaterialCheckbox'].uncheck();\n                    opt_rows[i].classList.remove(this.CssClasses_.IS_SELECTED);\n                }\n            }\n        }.bind(this);\n    }\n};\n/**\n   * Creates a checkbox for a single or or multiple rows and hooks up the\n   * event handling.\n   *\n   * @param {Element} row Row to toggle when checkbox changes.\n   * @param {(Array<Object>|NodeList)=} opt_rows Rows to toggle when checkbox changes.\n   * @private\n   */\nMaterialDataTable.prototype.createCheckbox_ = function (row, opt_rows) {\n    var label = document.createElement('label');\n    var labelClasses = [\n        'mdl-checkbox',\n        'mdl-js-checkbox',\n        'mdl-js-ripple-effect',\n        this.CssClasses_.SELECT_ELEMENT\n    ];\n    label.className = labelClasses.join(' ');\n    var checkbox = document.createElement('input');\n    checkbox.type = 'checkbox';\n    checkbox.classList.add('mdl-checkbox__input');\n    if (row) {\n        checkbox.checked = row.classList.contains(this.CssClasses_.IS_SELECTED);\n        checkbox.addEventListener('change', this.selectRow_(checkbox, row));\n    } else if (opt_rows) {\n        checkbox.addEventListener('change', this.selectRow_(checkbox, null, opt_rows));\n    }\n    label.appendChild(checkbox);\n    componentHandler.upgradeElement(label, 'MaterialCheckbox');\n    return label;\n};\n/**\n   * Initialize element.\n   */\nMaterialDataTable.prototype.init = function () {\n    if (this.element_) {\n        var firstHeader = this.element_.querySelector('th');\n        var bodyRows = Array.prototype.slice.call(this.element_.querySelectorAll('tbody tr'));\n        var footRows = Array.prototype.slice.call(this.element_.querySelectorAll('tfoot tr'));\n        var rows = bodyRows.concat(footRows);\n        if (this.element_.classList.contains(this.CssClasses_.SELECTABLE)) {\n            var th = document.createElement('th');\n            var headerCheckbox = this.createCheckbox_(null, rows);\n            th.appendChild(headerCheckbox);\n            firstHeader.parentElement.insertBefore(th, firstHeader);\n            for (var i = 0; i < rows.length; i++) {\n                var firstCell = rows[i].querySelector('td');\n                if (firstCell) {\n                    var td = document.createElement('td');\n                    if (rows[i].parentNode.nodeName.toUpperCase() === 'TBODY') {\n                        var rowCheckbox = this.createCheckbox_(rows[i]);\n                        td.appendChild(rowCheckbox);\n                    }\n                    rows[i].insertBefore(td, firstCell);\n                }\n            }\n            this.element_.classList.add(this.CssClasses_.IS_UPGRADED);\n        }\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialDataTable,\n    classAsString: 'MaterialDataTable',\n    cssClass: 'mdl-js-data-table'\n});","// https://github.com/zloirock/core-js/issues/86#issuecomment-115759028\nvar global = module.exports = typeof window != 'undefined' && window.Math == Math\n  ? window : typeof self != 'undefined' && self.Math == Math ? self\n  // eslint-disable-next-line no-new-func\n  : Function('return this')();\nif (typeof __g == 'number') __g = global; // eslint-disable-line no-undef\n","var hasOwnProperty = {}.hasOwnProperty;\nmodule.exports = function (it, key) {\n  return hasOwnProperty.call(it, key);\n};\n","module.exports = function (exec) {\n  try {\n    return !!exec();\n  } catch (e) {\n    return true;\n  }\n};\n","// Thank's IE8 for his funny defineProperty\nmodule.exports = !require('./_fails')(function () {\n  return Object.defineProperty({}, 'a', { get: function () { return 7; } }).a != 7;\n});\n","var core = module.exports = { version: '2.6.9' };\nif (typeof __e == 'number') __e = core; // eslint-disable-line no-undef\n","module.exports = function (it) {\n  return typeof it === 'object' ? it !== null : typeof it === 'function';\n};\n","var isObject = require('./_is-object');\nmodule.exports = function (it) {\n  if (!isObject(it)) throw TypeError(it + ' is not an object!');\n  return it;\n};\n","var isObject = require('./_is-object');\nvar document = require('./_global').document;\n// typeof document.createElement is 'object' in old IE\nvar is = isObject(document) && isObject(document.createElement);\nmodule.exports = function (it) {\n  return is ? document.createElement(it) : {};\n};\n","module.exports = !require('./_descriptors') && !require('./_fails')(function () {\n  return Object.defineProperty(require('./_dom-create')('div'), 'a', { get: function () { return 7; } }).a != 7;\n});\n","// 7.1.1 ToPrimitive(input [, PreferredType])\nvar isObject = require('./_is-object');\n// instead of the ES6 spec version, we didn't implement @@toPrimitive case\n// and the second argument - flag - preferred type is a string\nmodule.exports = function (it, S) {\n  if (!isObject(it)) return it;\n  var fn, val;\n  if (S && typeof (fn = it.toString) == 'function' && !isObject(val = fn.call(it))) return val;\n  if (typeof (fn = it.valueOf) == 'function' && !isObject(val = fn.call(it))) return val;\n  if (!S && typeof (fn = it.toString) == 'function' && !isObject(val = fn.call(it))) return val;\n  throw TypeError(\"Can't convert object to primitive value\");\n};\n","var anObject = require('./_an-object');\nvar IE8_DOM_DEFINE = require('./_ie8-dom-define');\nvar toPrimitive = require('./_to-primitive');\nvar dP = Object.defineProperty;\n\nexports.f = require('./_descriptors') ? Object.defineProperty : function defineProperty(O, P, Attributes) {\n  anObject(O);\n  P = toPrimitive(P, true);\n  anObject(Attributes);\n  if (IE8_DOM_DEFINE) try {\n    return dP(O, P, Attributes);\n  } catch (e) { /* empty */ }\n  if ('get' in Attributes || 'set' in Attributes) throw TypeError('Accessors not supported!');\n  if ('value' in Attributes) O[P] = Attributes.value;\n  return O;\n};\n","module.exports = function (bitmap, value) {\n  return {\n    enumerable: !(bitmap & 1),\n    configurable: !(bitmap & 2),\n    writable: !(bitmap & 4),\n    value: value\n  };\n};\n","var dP = require('./_object-dp');\nvar createDesc = require('./_property-desc');\nmodule.exports = require('./_descriptors') ? function (object, key, value) {\n  return dP.f(object, key, createDesc(1, value));\n} : function (object, key, value) {\n  object[key] = value;\n  return object;\n};\n","var id = 0;\nvar px = Math.random();\nmodule.exports = function (key) {\n  return 'Symbol('.concat(key === undefined ? '' : key, ')_', (++id + px).toString(36));\n};\n","module.exports = false;\n","var core = require('./_core');\nvar global = require('./_global');\nvar SHARED = '__core-js_shared__';\nvar store = global[SHARED] || (global[SHARED] = {});\n\n(module.exports = function (key, value) {\n  return store[key] || (store[key] = value !== undefined ? value : {});\n})('versions', []).push({\n  version: core.version,\n  mode: require('./_library') ? 'pure' : 'global',\n  copyright: '© 2019 Denis Pushkarev (zloirock.ru)'\n});\n","module.exports = require('./_shared')('native-function-to-string', Function.toString);\n","var global = require('./_global');\nvar hide = require('./_hide');\nvar has = require('./_has');\nvar SRC = require('./_uid')('src');\nvar $toString = require('./_function-to-string');\nvar TO_STRING = 'toString';\nvar TPL = ('' + $toString).split(TO_STRING);\n\nrequire('./_core').inspectSource = function (it) {\n  return $toString.call(it);\n};\n\n(module.exports = function (O, key, val, safe) {\n  var isFunction = typeof val == 'function';\n  if (isFunction) has(val, 'name') || hide(val, 'name', key);\n  if (O[key] === val) return;\n  if (isFunction) has(val, SRC) || hide(val, SRC, O[key] ? '' + O[key] : TPL.join(String(key)));\n  if (O === global) {\n    O[key] = val;\n  } else if (!safe) {\n    delete O[key];\n    hide(O, key, val);\n  } else if (O[key]) {\n    O[key] = val;\n  } else {\n    hide(O, key, val);\n  }\n// add fake Function#toString for correct work wrapped methods / constructors with methods like LoDash isNative\n})(Function.prototype, TO_STRING, function toString() {\n  return typeof this == 'function' && this[SRC] || $toString.call(this);\n});\n","module.exports = function (it) {\n  if (typeof it != 'function') throw TypeError(it + ' is not a function!');\n  return it;\n};\n","// optional / simple context binding\nvar aFunction = require('./_a-function');\nmodule.exports = function (fn, that, length) {\n  aFunction(fn);\n  if (that === undefined) return fn;\n  switch (length) {\n    case 1: return function (a) {\n      return fn.call(that, a);\n    };\n    case 2: return function (a, b) {\n      return fn.call(that, a, b);\n    };\n    case 3: return function (a, b, c) {\n      return fn.call(that, a, b, c);\n    };\n  }\n  return function (/* ...args */) {\n    return fn.apply(that, arguments);\n  };\n};\n","var global = require('./_global');\nvar core = require('./_core');\nvar hide = require('./_hide');\nvar redefine = require('./_redefine');\nvar ctx = require('./_ctx');\nvar PROTOTYPE = 'prototype';\n\nvar $export = function (type, name, source) {\n  var IS_FORCED = type & $export.F;\n  var IS_GLOBAL = type & $export.G;\n  var IS_STATIC = type & $export.S;\n  var IS_PROTO = type & $export.P;\n  var IS_BIND = type & $export.B;\n  var target = IS_GLOBAL ? global : IS_STATIC ? global[name] || (global[name] = {}) : (global[name] || {})[PROTOTYPE];\n  var exports = IS_GLOBAL ? core : core[name] || (core[name] = {});\n  var expProto = exports[PROTOTYPE] || (exports[PROTOTYPE] = {});\n  var key, own, out, exp;\n  if (IS_GLOBAL) source = name;\n  for (key in source) {\n    // contains in native\n    own = !IS_FORCED && target && target[key] !== undefined;\n    // export native or passed\n    out = (own ? target : source)[key];\n    // bind timers to global for call from export context\n    exp = IS_BIND && own ? ctx(out, global) : IS_PROTO && typeof out == 'function' ? ctx(Function.call, out) : out;\n    // extend global\n    if (target) redefine(target, key, out, type & $export.U);\n    // export\n    if (exports[key] != out) hide(exports, key, exp);\n    if (IS_PROTO && expProto[key] != out) expProto[key] = out;\n  }\n};\nglobal.core = core;\n// type bitmap\n$export.F = 1;   // forced\n$export.G = 2;   // global\n$export.S = 4;   // static\n$export.P = 8;   // proto\n$export.B = 16;  // bind\n$export.W = 32;  // wrap\n$export.U = 64;  // safe\n$export.R = 128; // real proto method for `library`\nmodule.exports = $export;\n","var META = require('./_uid')('meta');\nvar isObject = require('./_is-object');\nvar has = require('./_has');\nvar setDesc = require('./_object-dp').f;\nvar id = 0;\nvar isExtensible = Object.isExtensible || function () {\n  return true;\n};\nvar FREEZE = !require('./_fails')(function () {\n  return isExtensible(Object.preventExtensions({}));\n});\nvar setMeta = function (it) {\n  setDesc(it, META, { value: {\n    i: 'O' + ++id, // object ID\n    w: {}          // weak collections IDs\n  } });\n};\nvar fastKey = function (it, create) {\n  // return primitive with prefix\n  if (!isObject(it)) return typeof it == 'symbol' ? it : (typeof it == 'string' ? 'S' : 'P') + it;\n  if (!has(it, META)) {\n    // can't set metadata to uncaught frozen object\n    if (!isExtensible(it)) return 'F';\n    // not necessary to add metadata\n    if (!create) return 'E';\n    // add missing metadata\n    setMeta(it);\n  // return object ID\n  } return it[META].i;\n};\nvar getWeak = function (it, create) {\n  if (!has(it, META)) {\n    // can't set metadata to uncaught frozen object\n    if (!isExtensible(it)) return true;\n    // not necessary to add metadata\n    if (!create) return false;\n    // add missing metadata\n    setMeta(it);\n  // return hash weak collections IDs\n  } return it[META].w;\n};\n// add metadata on freeze-family methods calling\nvar onFreeze = function (it) {\n  if (FREEZE && meta.NEED && isExtensible(it) && !has(it, META)) setMeta(it);\n  return it;\n};\nvar meta = module.exports = {\n  KEY: META,\n  NEED: false,\n  fastKey: fastKey,\n  getWeak: getWeak,\n  onFreeze: onFreeze\n};\n","var store = require('./_shared')('wks');\nvar uid = require('./_uid');\nvar Symbol = require('./_global').Symbol;\nvar USE_SYMBOL = typeof Symbol == 'function';\n\nvar $exports = module.exports = function (name) {\n  return store[name] || (store[name] =\n    USE_SYMBOL && Symbol[name] || (USE_SYMBOL ? Symbol : uid)('Symbol.' + name));\n};\n\n$exports.store = store;\n","var def = require('./_object-dp').f;\nvar has = require('./_has');\nvar TAG = require('./_wks')('toStringTag');\n\nmodule.exports = function (it, tag, stat) {\n  if (it && !has(it = stat ? it : it.prototype, TAG)) def(it, TAG, { configurable: true, value: tag });\n};\n","exports.f = require('./_wks');\n","var global = require('./_global');\nvar core = require('./_core');\nvar LIBRARY = require('./_library');\nvar wksExt = require('./_wks-ext');\nvar defineProperty = require('./_object-dp').f;\nmodule.exports = function (name) {\n  var $Symbol = core.Symbol || (core.Symbol = LIBRARY ? {} : global.Symbol || {});\n  if (name.charAt(0) != '_' && !(name in $Symbol)) defineProperty($Symbol, name, { value: wksExt.f(name) });\n};\n","var toString = {}.toString;\n\nmodule.exports = function (it) {\n  return toString.call(it).slice(8, -1);\n};\n","// fallback for non-array-like ES3 and non-enumerable old V8 strings\nvar cof = require('./_cof');\n// eslint-disable-next-line no-prototype-builtins\nmodule.exports = Object('z').propertyIsEnumerable(0) ? Object : function (it) {\n  return cof(it) == 'String' ? it.split('') : Object(it);\n};\n","// 7.2.1 RequireObjectCoercible(argument)\nmodule.exports = function (it) {\n  if (it == undefined) throw TypeError(\"Can't call method on  \" + it);\n  return it;\n};\n","// to indexed object, toObject with fallback for non-array-like ES3 strings\nvar IObject = require('./_iobject');\nvar defined = require('./_defined');\nmodule.exports = function (it) {\n  return IObject(defined(it));\n};\n","// 7.1.4 ToInteger\nvar ceil = Math.ceil;\nvar floor = Math.floor;\nmodule.exports = function (it) {\n  return isNaN(it = +it) ? 0 : (it > 0 ? floor : ceil)(it);\n};\n","// 7.1.15 ToLength\nvar toInteger = require('./_to-integer');\nvar min = Math.min;\nmodule.exports = function (it) {\n  return it > 0 ? min(toInteger(it), 0x1fffffffffffff) : 0; // pow(2, 53) - 1 == 9007199254740991\n};\n","var toInteger = require('./_to-integer');\nvar max = Math.max;\nvar min = Math.min;\nmodule.exports = function (index, length) {\n  index = toInteger(index);\n  return index < 0 ? max(index + length, 0) : min(index, length);\n};\n","// false -> Array#indexOf\n// true  -> Array#includes\nvar toIObject = require('./_to-iobject');\nvar toLength = require('./_to-length');\nvar toAbsoluteIndex = require('./_to-absolute-index');\nmodule.exports = function (IS_INCLUDES) {\n  return function ($this, el, fromIndex) {\n    var O = toIObject($this);\n    var length = toLength(O.length);\n    var index = toAbsoluteIndex(fromIndex, length);\n    var value;\n    // Array#includes uses SameValueZero equality algorithm\n    // eslint-disable-next-line no-self-compare\n    if (IS_INCLUDES && el != el) while (length > index) {\n      value = O[index++];\n      // eslint-disable-next-line no-self-compare\n      if (value != value) return true;\n    // Array#indexOf ignores holes, Array#includes - not\n    } else for (;length > index; index++) if (IS_INCLUDES || index in O) {\n      if (O[index] === el) return IS_INCLUDES || index || 0;\n    } return !IS_INCLUDES && -1;\n  };\n};\n","var shared = require('./_shared')('keys');\nvar uid = require('./_uid');\nmodule.exports = function (key) {\n  return shared[key] || (shared[key] = uid(key));\n};\n","var has = require('./_has');\nvar toIObject = require('./_to-iobject');\nvar arrayIndexOf = require('./_array-includes')(false);\nvar IE_PROTO = require('./_shared-key')('IE_PROTO');\n\nmodule.exports = function (object, names) {\n  var O = toIObject(object);\n  var i = 0;\n  var result = [];\n  var key;\n  for (key in O) if (key != IE_PROTO) has(O, key) && result.push(key);\n  // Don't enum bug & hidden keys\n  while (names.length > i) if (has(O, key = names[i++])) {\n    ~arrayIndexOf(result, key) || result.push(key);\n  }\n  return result;\n};\n","// IE 8- don't enum bug keys\nmodule.exports = (\n  'constructor,hasOwnProperty,isPrototypeOf,propertyIsEnumerable,toLocaleString,toString,valueOf'\n).split(',');\n","// 19.1.2.14 / 15.2.3.14 Object.keys(O)\nvar $keys = require('./_object-keys-internal');\nvar enumBugKeys = require('./_enum-bug-keys');\n\nmodule.exports = Object.keys || function keys(O) {\n  return $keys(O, enumBugKeys);\n};\n","exports.f = Object.getOwnPropertySymbols;\n","exports.f = {}.propertyIsEnumerable;\n","// all enumerable object keys, includes symbols\nvar getKeys = require('./_object-keys');\nvar gOPS = require('./_object-gops');\nvar pIE = require('./_object-pie');\nmodule.exports = function (it) {\n  var result = getKeys(it);\n  var getSymbols = gOPS.f;\n  if (getSymbols) {\n    var symbols = getSymbols(it);\n    var isEnum = pIE.f;\n    var i = 0;\n    var key;\n    while (symbols.length > i) if (isEnum.call(it, key = symbols[i++])) result.push(key);\n  } return result;\n};\n","// 7.2.2 IsArray(argument)\nvar cof = require('./_cof');\nmodule.exports = Array.isArray || function isArray(arg) {\n  return cof(arg) == 'Array';\n};\n","// 7.1.13 ToObject(argument)\nvar defined = require('./_defined');\nmodule.exports = function (it) {\n  return Object(defined(it));\n};\n","var dP = require('./_object-dp');\nvar anObject = require('./_an-object');\nvar getKeys = require('./_object-keys');\n\nmodule.exports = require('./_descriptors') ? Object.defineProperties : function defineProperties(O, Properties) {\n  anObject(O);\n  var keys = getKeys(Properties);\n  var length = keys.length;\n  var i = 0;\n  var P;\n  while (length > i) dP.f(O, P = keys[i++], Properties[P]);\n  return O;\n};\n","var document = require('./_global').document;\nmodule.exports = document && document.documentElement;\n","// 19.1.2.2 / 15.2.3.5 Object.create(O [, Properties])\nvar anObject = require('./_an-object');\nvar dPs = require('./_object-dps');\nvar enumBugKeys = require('./_enum-bug-keys');\nvar IE_PROTO = require('./_shared-key')('IE_PROTO');\nvar Empty = function () { /* empty */ };\nvar PROTOTYPE = 'prototype';\n\n// Create object with fake `null` prototype: use iframe Object with cleared prototype\nvar createDict = function () {\n  // Thrash, waste and sodomy: IE GC bug\n  var iframe = require('./_dom-create')('iframe');\n  var i = enumBugKeys.length;\n  var lt = '<';\n  var gt = '>';\n  var iframeDocument;\n  iframe.style.display = 'none';\n  require('./_html').appendChild(iframe);\n  iframe.src = 'javascript:'; // eslint-disable-line no-script-url\n  // createDict = iframe.contentWindow.Object;\n  // html.removeChild(iframe);\n  iframeDocument = iframe.contentWindow.document;\n  iframeDocument.open();\n  iframeDocument.write(lt + 'script' + gt + 'document.F=Object' + lt + '/script' + gt);\n  iframeDocument.close();\n  createDict = iframeDocument.F;\n  while (i--) delete createDict[PROTOTYPE][enumBugKeys[i]];\n  return createDict();\n};\n\nmodule.exports = Object.create || function create(O, Properties) {\n  var result;\n  if (O !== null) {\n    Empty[PROTOTYPE] = anObject(O);\n    result = new Empty();\n    Empty[PROTOTYPE] = null;\n    // add \"__proto__\" for Object.getPrototypeOf polyfill\n    result[IE_PROTO] = O;\n  } else result = createDict();\n  return Properties === undefined ? result : dPs(result, Properties);\n};\n","// 19.1.2.7 / 15.2.3.4 Object.getOwnPropertyNames(O)\nvar $keys = require('./_object-keys-internal');\nvar hiddenKeys = require('./_enum-bug-keys').concat('length', 'prototype');\n\nexports.f = Object.getOwnPropertyNames || function getOwnPropertyNames(O) {\n  return $keys(O, hiddenKeys);\n};\n","// fallback for IE11 buggy Object.getOwnPropertyNames with iframe and window\nvar toIObject = require('./_to-iobject');\nvar gOPN = require('./_object-gopn').f;\nvar toString = {}.toString;\n\nvar windowNames = typeof window == 'object' && window && Object.getOwnPropertyNames\n  ? Object.getOwnPropertyNames(window) : [];\n\nvar getWindowNames = function (it) {\n  try {\n    return gOPN(it);\n  } catch (e) {\n    return windowNames.slice();\n  }\n};\n\nmodule.exports.f = function getOwnPropertyNames(it) {\n  return windowNames && toString.call(it) == '[object Window]' ? getWindowNames(it) : gOPN(toIObject(it));\n};\n","var pIE = require('./_object-pie');\nvar createDesc = require('./_property-desc');\nvar toIObject = require('./_to-iobject');\nvar toPrimitive = require('./_to-primitive');\nvar has = require('./_has');\nvar IE8_DOM_DEFINE = require('./_ie8-dom-define');\nvar gOPD = Object.getOwnPropertyDescriptor;\n\nexports.f = require('./_descriptors') ? gOPD : function getOwnPropertyDescriptor(O, P) {\n  O = toIObject(O);\n  P = toPrimitive(P, true);\n  if (IE8_DOM_DEFINE) try {\n    return gOPD(O, P);\n  } catch (e) { /* empty */ }\n  if (has(O, P)) return createDesc(!pIE.f.call(O, P), O[P]);\n};\n","'use strict';\n// ECMAScript 6 symbols shim\nvar global = require('./_global');\nvar has = require('./_has');\nvar DESCRIPTORS = require('./_descriptors');\nvar $export = require('./_export');\nvar redefine = require('./_redefine');\nvar META = require('./_meta').KEY;\nvar $fails = require('./_fails');\nvar shared = require('./_shared');\nvar setToStringTag = require('./_set-to-string-tag');\nvar uid = require('./_uid');\nvar wks = require('./_wks');\nvar wksExt = require('./_wks-ext');\nvar wksDefine = require('./_wks-define');\nvar enumKeys = require('./_enum-keys');\nvar isArray = require('./_is-array');\nvar anObject = require('./_an-object');\nvar isObject = require('./_is-object');\nvar toObject = require('./_to-object');\nvar toIObject = require('./_to-iobject');\nvar toPrimitive = require('./_to-primitive');\nvar createDesc = require('./_property-desc');\nvar _create = require('./_object-create');\nvar gOPNExt = require('./_object-gopn-ext');\nvar $GOPD = require('./_object-gopd');\nvar $GOPS = require('./_object-gops');\nvar $DP = require('./_object-dp');\nvar $keys = require('./_object-keys');\nvar gOPD = $GOPD.f;\nvar dP = $DP.f;\nvar gOPN = gOPNExt.f;\nvar $Symbol = global.Symbol;\nvar $JSON = global.JSON;\nvar _stringify = $JSON && $JSON.stringify;\nvar PROTOTYPE = 'prototype';\nvar HIDDEN = wks('_hidden');\nvar TO_PRIMITIVE = wks('toPrimitive');\nvar isEnum = {}.propertyIsEnumerable;\nvar SymbolRegistry = shared('symbol-registry');\nvar AllSymbols = shared('symbols');\nvar OPSymbols = shared('op-symbols');\nvar ObjectProto = Object[PROTOTYPE];\nvar USE_NATIVE = typeof $Symbol == 'function' && !!$GOPS.f;\nvar QObject = global.QObject;\n// Don't use setters in Qt Script, https://github.com/zloirock/core-js/issues/173\nvar setter = !QObject || !QObject[PROTOTYPE] || !QObject[PROTOTYPE].findChild;\n\n// fallback for old Android, https://code.google.com/p/v8/issues/detail?id=687\nvar setSymbolDesc = DESCRIPTORS && $fails(function () {\n  return _create(dP({}, 'a', {\n    get: function () { return dP(this, 'a', { value: 7 }).a; }\n  })).a != 7;\n}) ? function (it, key, D) {\n  var protoDesc = gOPD(ObjectProto, key);\n  if (protoDesc) delete ObjectProto[key];\n  dP(it, key, D);\n  if (protoDesc && it !== ObjectProto) dP(ObjectProto, key, protoDesc);\n} : dP;\n\nvar wrap = function (tag) {\n  var sym = AllSymbols[tag] = _create($Symbol[PROTOTYPE]);\n  sym._k = tag;\n  return sym;\n};\n\nvar isSymbol = USE_NATIVE && typeof $Symbol.iterator == 'symbol' ? function (it) {\n  return typeof it == 'symbol';\n} : function (it) {\n  return it instanceof $Symbol;\n};\n\nvar $defineProperty = function defineProperty(it, key, D) {\n  if (it === ObjectProto) $defineProperty(OPSymbols, key, D);\n  anObject(it);\n  key = toPrimitive(key, true);\n  anObject(D);\n  if (has(AllSymbols, key)) {\n    if (!D.enumerable) {\n      if (!has(it, HIDDEN)) dP(it, HIDDEN, createDesc(1, {}));\n      it[HIDDEN][key] = true;\n    } else {\n      if (has(it, HIDDEN) && it[HIDDEN][key]) it[HIDDEN][key] = false;\n      D = _create(D, { enumerable: createDesc(0, false) });\n    } return setSymbolDesc(it, key, D);\n  } return dP(it, key, D);\n};\nvar $defineProperties = function defineProperties(it, P) {\n  anObject(it);\n  var keys = enumKeys(P = toIObject(P));\n  var i = 0;\n  var l = keys.length;\n  var key;\n  while (l > i) $defineProperty(it, key = keys[i++], P[key]);\n  return it;\n};\nvar $create = function create(it, P) {\n  return P === undefined ? _create(it) : $defineProperties(_create(it), P);\n};\nvar $propertyIsEnumerable = function propertyIsEnumerable(key) {\n  var E = isEnum.call(this, key = toPrimitive(key, true));\n  if (this === ObjectProto && has(AllSymbols, key) && !has(OPSymbols, key)) return false;\n  return E || !has(this, key) || !has(AllSymbols, key) || has(this, HIDDEN) && this[HIDDEN][key] ? E : true;\n};\nvar $getOwnPropertyDescriptor = function getOwnPropertyDescriptor(it, key) {\n  it = toIObject(it);\n  key = toPrimitive(key, true);\n  if (it === ObjectProto && has(AllSymbols, key) && !has(OPSymbols, key)) return;\n  var D = gOPD(it, key);\n  if (D && has(AllSymbols, key) && !(has(it, HIDDEN) && it[HIDDEN][key])) D.enumerable = true;\n  return D;\n};\nvar $getOwnPropertyNames = function getOwnPropertyNames(it) {\n  var names = gOPN(toIObject(it));\n  var result = [];\n  var i = 0;\n  var key;\n  while (names.length > i) {\n    if (!has(AllSymbols, key = names[i++]) && key != HIDDEN && key != META) result.push(key);\n  } return result;\n};\nvar $getOwnPropertySymbols = function getOwnPropertySymbols(it) {\n  var IS_OP = it === ObjectProto;\n  var names = gOPN(IS_OP ? OPSymbols : toIObject(it));\n  var result = [];\n  var i = 0;\n  var key;\n  while (names.length > i) {\n    if (has(AllSymbols, key = names[i++]) && (IS_OP ? has(ObjectProto, key) : true)) result.push(AllSymbols[key]);\n  } return result;\n};\n\n// 19.4.1.1 Symbol([description])\nif (!USE_NATIVE) {\n  $Symbol = function Symbol() {\n    if (this instanceof $Symbol) throw TypeError('Symbol is not a constructor!');\n    var tag = uid(arguments.length > 0 ? arguments[0] : undefined);\n    var $set = function (value) {\n      if (this === ObjectProto) $set.call(OPSymbols, value);\n      if (has(this, HIDDEN) && has(this[HIDDEN], tag)) this[HIDDEN][tag] = false;\n      setSymbolDesc(this, tag, createDesc(1, value));\n    };\n    if (DESCRIPTORS && setter) setSymbolDesc(ObjectProto, tag, { configurable: true, set: $set });\n    return wrap(tag);\n  };\n  redefine($Symbol[PROTOTYPE], 'toString', function toString() {\n    return this._k;\n  });\n\n  $GOPD.f = $getOwnPropertyDescriptor;\n  $DP.f = $defineProperty;\n  require('./_object-gopn').f = gOPNExt.f = $getOwnPropertyNames;\n  require('./_object-pie').f = $propertyIsEnumerable;\n  $GOPS.f = $getOwnPropertySymbols;\n\n  if (DESCRIPTORS && !require('./_library')) {\n    redefine(ObjectProto, 'propertyIsEnumerable', $propertyIsEnumerable, true);\n  }\n\n  wksExt.f = function (name) {\n    return wrap(wks(name));\n  };\n}\n\n$export($export.G + $export.W + $export.F * !USE_NATIVE, { Symbol: $Symbol });\n\nfor (var es6Symbols = (\n  // 19.4.2.2, 19.4.2.3, 19.4.2.4, 19.4.2.6, 19.4.2.8, 19.4.2.9, 19.4.2.10, 19.4.2.11, 19.4.2.12, 19.4.2.13, 19.4.2.14\n  'hasInstance,isConcatSpreadable,iterator,match,replace,search,species,split,toPrimitive,toStringTag,unscopables'\n).split(','), j = 0; es6Symbols.length > j;)wks(es6Symbols[j++]);\n\nfor (var wellKnownSymbols = $keys(wks.store), k = 0; wellKnownSymbols.length > k;) wksDefine(wellKnownSymbols[k++]);\n\n$export($export.S + $export.F * !USE_NATIVE, 'Symbol', {\n  // 19.4.2.1 Symbol.for(key)\n  'for': function (key) {\n    return has(SymbolRegistry, key += '')\n      ? SymbolRegistry[key]\n      : SymbolRegistry[key] = $Symbol(key);\n  },\n  // 19.4.2.5 Symbol.keyFor(sym)\n  keyFor: function keyFor(sym) {\n    if (!isSymbol(sym)) throw TypeError(sym + ' is not a symbol!');\n    for (var key in SymbolRegistry) if (SymbolRegistry[key] === sym) return key;\n  },\n  useSetter: function () { setter = true; },\n  useSimple: function () { setter = false; }\n});\n\n$export($export.S + $export.F * !USE_NATIVE, 'Object', {\n  // 19.1.2.2 Object.create(O [, Properties])\n  create: $create,\n  // 19.1.2.4 Object.defineProperty(O, P, Attributes)\n  defineProperty: $defineProperty,\n  // 19.1.2.3 Object.defineProperties(O, Properties)\n  defineProperties: $defineProperties,\n  // 19.1.2.6 Object.getOwnPropertyDescriptor(O, P)\n  getOwnPropertyDescriptor: $getOwnPropertyDescriptor,\n  // 19.1.2.7 Object.getOwnPropertyNames(O)\n  getOwnPropertyNames: $getOwnPropertyNames,\n  // 19.1.2.8 Object.getOwnPropertySymbols(O)\n  getOwnPropertySymbols: $getOwnPropertySymbols\n});\n\n// Chrome 38 and 39 `Object.getOwnPropertySymbols` fails on primitives\n// https://bugs.chromium.org/p/v8/issues/detail?id=3443\nvar FAILS_ON_PRIMITIVES = $fails(function () { $GOPS.f(1); });\n\n$export($export.S + $export.F * FAILS_ON_PRIMITIVES, 'Object', {\n  getOwnPropertySymbols: function getOwnPropertySymbols(it) {\n    return $GOPS.f(toObject(it));\n  }\n});\n\n// 24.3.2 JSON.stringify(value [, replacer [, space]])\n$JSON && $export($export.S + $export.F * (!USE_NATIVE || $fails(function () {\n  var S = $Symbol();\n  // MS Edge converts symbol values to JSON as {}\n  // WebKit converts symbol values to JSON as null\n  // V8 throws on boxed symbols\n  return _stringify([S]) != '[null]' || _stringify({ a: S }) != '{}' || _stringify(Object(S)) != '{}';\n})), 'JSON', {\n  stringify: function stringify(it) {\n    var args = [it];\n    var i = 1;\n    var replacer, $replacer;\n    while (arguments.length > i) args.push(arguments[i++]);\n    $replacer = replacer = args[1];\n    if (!isObject(replacer) && it === undefined || isSymbol(it)) return; // IE8 returns string on undefined\n    if (!isArray(replacer)) replacer = function (key, value) {\n      if (typeof $replacer == 'function') value = $replacer.call(this, key, value);\n      if (!isSymbol(value)) return value;\n    };\n    args[1] = replacer;\n    return _stringify.apply($JSON, args);\n  }\n});\n\n// 19.4.3.4 Symbol.prototype[@@toPrimitive](hint)\n$Symbol[PROTOTYPE][TO_PRIMITIVE] || require('./_hide')($Symbol[PROTOTYPE], TO_PRIMITIVE, $Symbol[PROTOTYPE].valueOf);\n// 19.4.3.5 Symbol.prototype[@@toStringTag]\nsetToStringTag($Symbol, 'Symbol');\n// 20.2.1.9 Math[@@toStringTag]\nsetToStringTag(Math, 'Math', true);\n// 24.3.3 JSON[@@toStringTag]\nsetToStringTag(global.JSON, 'JSON', true);\n","var $export = require('./_export');\n// 19.1.2.2 / 15.2.3.5 Object.create(O [, Properties])\n$export($export.S, 'Object', { create: require('./_object-create') });\n","var $export = require('./_export');\n// 19.1.2.4 / 15.2.3.6 Object.defineProperty(O, P, Attributes)\n$export($export.S + $export.F * !require('./_descriptors'), 'Object', { defineProperty: require('./_object-dp').f });\n","var $export = require('./_export');\n// 19.1.2.3 / 15.2.3.7 Object.defineProperties(O, Properties)\n$export($export.S + $export.F * !require('./_descriptors'), 'Object', { defineProperties: require('./_object-dps') });\n","// most Object methods by ES6 should accept primitives\nvar $export = require('./_export');\nvar core = require('./_core');\nvar fails = require('./_fails');\nmodule.exports = function (KEY, exec) {\n  var fn = (core.Object || {})[KEY] || Object[KEY];\n  var exp = {};\n  exp[KEY] = exec(fn);\n  $export($export.S + $export.F * fails(function () { fn(1); }), 'Object', exp);\n};\n","// 19.1.2.6 Object.getOwnPropertyDescriptor(O, P)\nvar toIObject = require('./_to-iobject');\nvar $getOwnPropertyDescriptor = require('./_object-gopd').f;\n\nrequire('./_object-sap')('getOwnPropertyDescriptor', function () {\n  return function getOwnPropertyDescriptor(it, key) {\n    return $getOwnPropertyDescriptor(toIObject(it), key);\n  };\n});\n","// 19.1.2.9 / 15.2.3.2 Object.getPrototypeOf(O)\nvar has = require('./_has');\nvar toObject = require('./_to-object');\nvar IE_PROTO = require('./_shared-key')('IE_PROTO');\nvar ObjectProto = Object.prototype;\n\nmodule.exports = Object.getPrototypeOf || function (O) {\n  O = toObject(O);\n  if (has(O, IE_PROTO)) return O[IE_PROTO];\n  if (typeof O.constructor == 'function' && O instanceof O.constructor) {\n    return O.constructor.prototype;\n  } return O instanceof Object ? ObjectProto : null;\n};\n","// 19.1.2.9 Object.getPrototypeOf(O)\nvar toObject = require('./_to-object');\nvar $getPrototypeOf = require('./_object-gpo');\n\nrequire('./_object-sap')('getPrototypeOf', function () {\n  return function getPrototypeOf(it) {\n    return $getPrototypeOf(toObject(it));\n  };\n});\n","// 19.1.2.14 Object.keys(O)\nvar toObject = require('./_to-object');\nvar $keys = require('./_object-keys');\n\nrequire('./_object-sap')('keys', function () {\n  return function keys(it) {\n    return $keys(toObject(it));\n  };\n});\n","// 19.1.2.7 Object.getOwnPropertyNames(O)\nrequire('./_object-sap')('getOwnPropertyNames', function () {\n  return require('./_object-gopn-ext').f;\n});\n","// 19.1.2.5 Object.freeze(O)\nvar isObject = require('./_is-object');\nvar meta = require('./_meta').onFreeze;\n\nrequire('./_object-sap')('freeze', function ($freeze) {\n  return function freeze(it) {\n    return $freeze && isObject(it) ? $freeze(meta(it)) : it;\n  };\n});\n","// 19.1.2.17 Object.seal(O)\nvar isObject = require('./_is-object');\nvar meta = require('./_meta').onFreeze;\n\nrequire('./_object-sap')('seal', function ($seal) {\n  return function seal(it) {\n    return $seal && isObject(it) ? $seal(meta(it)) : it;\n  };\n});\n","// 19.1.2.15 Object.preventExtensions(O)\nvar isObject = require('./_is-object');\nvar meta = require('./_meta').onFreeze;\n\nrequire('./_object-sap')('preventExtensions', function ($preventExtensions) {\n  return function preventExtensions(it) {\n    return $preventExtensions && isObject(it) ? $preventExtensions(meta(it)) : it;\n  };\n});\n","// 19.1.2.12 Object.isFrozen(O)\nvar isObject = require('./_is-object');\n\nrequire('./_object-sap')('isFrozen', function ($isFrozen) {\n  return function isFrozen(it) {\n    return isObject(it) ? $isFrozen ? $isFrozen(it) : false : true;\n  };\n});\n","// 19.1.2.13 Object.isSealed(O)\nvar isObject = require('./_is-object');\n\nrequire('./_object-sap')('isSealed', function ($isSealed) {\n  return function isSealed(it) {\n    return isObject(it) ? $isSealed ? $isSealed(it) : false : true;\n  };\n});\n","// 19.1.2.11 Object.isExtensible(O)\nvar isObject = require('./_is-object');\n\nrequire('./_object-sap')('isExtensible', function ($isExtensible) {\n  return function isExtensible(it) {\n    return isObject(it) ? $isExtensible ? $isExtensible(it) : true : false;\n  };\n});\n","'use strict';\n// 19.1.2.1 Object.assign(target, source, ...)\nvar DESCRIPTORS = require('./_descriptors');\nvar getKeys = require('./_object-keys');\nvar gOPS = require('./_object-gops');\nvar pIE = require('./_object-pie');\nvar toObject = require('./_to-object');\nvar IObject = require('./_iobject');\nvar $assign = Object.assign;\n\n// should work with symbols and should have deterministic property order (V8 bug)\nmodule.exports = !$assign || require('./_fails')(function () {\n  var A = {};\n  var B = {};\n  // eslint-disable-next-line no-undef\n  var S = Symbol();\n  var K = 'abcdefghijklmnopqrst';\n  A[S] = 7;\n  K.split('').forEach(function (k) { B[k] = k; });\n  return $assign({}, A)[S] != 7 || Object.keys($assign({}, B)).join('') != K;\n}) ? function assign(target, source) { // eslint-disable-line no-unused-vars\n  var T = toObject(target);\n  var aLen = arguments.length;\n  var index = 1;\n  var getSymbols = gOPS.f;\n  var isEnum = pIE.f;\n  while (aLen > index) {\n    var S = IObject(arguments[index++]);\n    var keys = getSymbols ? getKeys(S).concat(getSymbols(S)) : getKeys(S);\n    var length = keys.length;\n    var j = 0;\n    var key;\n    while (length > j) {\n      key = keys[j++];\n      if (!DESCRIPTORS || isEnum.call(S, key)) T[key] = S[key];\n    }\n  } return T;\n} : $assign;\n","// 19.1.3.1 Object.assign(target, source)\nvar $export = require('./_export');\n\n$export($export.S + $export.F, 'Object', { assign: require('./_object-assign') });\n","// 7.2.9 SameValue(x, y)\nmodule.exports = Object.is || function is(x, y) {\n  // eslint-disable-next-line no-self-compare\n  return x === y ? x !== 0 || 1 / x === 1 / y : x != x && y != y;\n};\n","// 19.1.3.10 Object.is(value1, value2)\nvar $export = require('./_export');\n$export($export.S, 'Object', { is: require('./_same-value') });\n","// Works with __proto__ only. Old v8 can't work with null proto objects.\n/* eslint-disable no-proto */\nvar isObject = require('./_is-object');\nvar anObject = require('./_an-object');\nvar check = function (O, proto) {\n  anObject(O);\n  if (!isObject(proto) && proto !== null) throw TypeError(proto + \": can't set as prototype!\");\n};\nmodule.exports = {\n  set: Object.setPrototypeOf || ('__proto__' in {} ? // eslint-disable-line\n    function (test, buggy, set) {\n      try {\n        set = require('./_ctx')(Function.call, require('./_object-gopd').f(Object.prototype, '__proto__').set, 2);\n        set(test, []);\n        buggy = !(test instanceof Array);\n      } catch (e) { buggy = true; }\n      return function setPrototypeOf(O, proto) {\n        check(O, proto);\n        if (buggy) O.__proto__ = proto;\n        else set(O, proto);\n        return O;\n      };\n    }({}, false) : undefined),\n  check: check\n};\n","// 19.1.3.19 Object.setPrototypeOf(O, proto)\nvar $export = require('./_export');\n$export($export.S, 'Object', { setPrototypeOf: require('./_set-proto').set });\n","// getting tag from 19.1.3.6 Object.prototype.toString()\nvar cof = require('./_cof');\nvar TAG = require('./_wks')('toStringTag');\n// ES3 wrong here\nvar ARG = cof(function () { return arguments; }()) == 'Arguments';\n\n// fallback for IE11 Script Access Denied error\nvar tryGet = function (it, key) {\n  try {\n    return it[key];\n  } catch (e) { /* empty */ }\n};\n\nmodule.exports = function (it) {\n  var O, T, B;\n  return it === undefined ? 'Undefined' : it === null ? 'Null'\n    // @@toStringTag case\n    : typeof (T = tryGet(O = Object(it), TAG)) == 'string' ? T\n    // builtinTag case\n    : ARG ? cof(O)\n    // ES3 arguments fallback\n    : (B = cof(O)) == 'Object' && typeof O.callee == 'function' ? 'Arguments' : B;\n};\n","'use strict';\n// 19.1.3.6 Object.prototype.toString()\nvar classof = require('./_classof');\nvar test = {};\ntest[require('./_wks')('toStringTag')] = 'z';\nif (test + '' != '[object z]') {\n  require('./_redefine')(Object.prototype, 'toString', function toString() {\n    return '[object ' + classof(this) + ']';\n  }, true);\n}\n","// fast apply, http://jsperf.lnkit.com/fast-apply/5\nmodule.exports = function (fn, args, that) {\n  var un = that === undefined;\n  switch (args.length) {\n    case 0: return un ? fn()\n                      : fn.call(that);\n    case 1: return un ? fn(args[0])\n                      : fn.call(that, args[0]);\n    case 2: return un ? fn(args[0], args[1])\n                      : fn.call(that, args[0], args[1]);\n    case 3: return un ? fn(args[0], args[1], args[2])\n                      : fn.call(that, args[0], args[1], args[2]);\n    case 4: return un ? fn(args[0], args[1], args[2], args[3])\n                      : fn.call(that, args[0], args[1], args[2], args[3]);\n  } return fn.apply(that, args);\n};\n","'use strict';\nvar aFunction = require('./_a-function');\nvar isObject = require('./_is-object');\nvar invoke = require('./_invoke');\nvar arraySlice = [].slice;\nvar factories = {};\n\nvar construct = function (F, len, args) {\n  if (!(len in factories)) {\n    for (var n = [], i = 0; i < len; i++) n[i] = 'a[' + i + ']';\n    // eslint-disable-next-line no-new-func\n    factories[len] = Function('F,a', 'return new F(' + n.join(',') + ')');\n  } return factories[len](F, args);\n};\n\nmodule.exports = Function.bind || function bind(that /* , ...args */) {\n  var fn = aFunction(this);\n  var partArgs = arraySlice.call(arguments, 1);\n  var bound = function (/* args... */) {\n    var args = partArgs.concat(arraySlice.call(arguments));\n    return this instanceof bound ? construct(fn, args.length, args) : invoke(fn, args, that);\n  };\n  if (isObject(fn.prototype)) bound.prototype = fn.prototype;\n  return bound;\n};\n","// 19.2.3.2 / 15.3.4.5 Function.prototype.bind(thisArg, args...)\nvar $export = require('./_export');\n\n$export($export.P, 'Function', { bind: require('./_bind') });\n","var dP = require('./_object-dp').f;\nvar FProto = Function.prototype;\nvar nameRE = /^\\s*function ([^ (]*)/;\nvar NAME = 'name';\n\n// 19.2.4.2 name\nNAME in FProto || require('./_descriptors') && dP(FProto, NAME, {\n  configurable: true,\n  get: function () {\n    try {\n      return ('' + this).match(nameRE)[1];\n    } catch (e) {\n      return '';\n    }\n  }\n});\n","'use strict';\nvar isObject = require('./_is-object');\nvar getPrototypeOf = require('./_object-gpo');\nvar HAS_INSTANCE = require('./_wks')('hasInstance');\nvar FunctionProto = Function.prototype;\n// 19.2.3.6 Function.prototype[@@hasInstance](V)\nif (!(HAS_INSTANCE in FunctionProto)) require('./_object-dp').f(FunctionProto, HAS_INSTANCE, { value: function (O) {\n  if (typeof this != 'function' || !isObject(O)) return false;\n  if (!isObject(this.prototype)) return O instanceof this;\n  // for environment w/o native `@@hasInstance` logic enough `instanceof`, but add this:\n  while (O = getPrototypeOf(O)) if (this.prototype === O) return true;\n  return false;\n} });\n","module.exports = '\\x09\\x0A\\x0B\\x0C\\x0D\\x20\\xA0\\u1680\\u180E\\u2000\\u2001\\u2002\\u2003' +\n  '\\u2004\\u2005\\u2006\\u2007\\u2008\\u2009\\u200A\\u202F\\u205F\\u3000\\u2028\\u2029\\uFEFF';\n","var $export = require('./_export');\nvar defined = require('./_defined');\nvar fails = require('./_fails');\nvar spaces = require('./_string-ws');\nvar space = '[' + spaces + ']';\nvar non = '\\u200b\\u0085';\nvar ltrim = RegExp('^' + space + space + '*');\nvar rtrim = RegExp(space + space + '*$');\n\nvar exporter = function (KEY, exec, ALIAS) {\n  var exp = {};\n  var FORCE = fails(function () {\n    return !!spaces[KEY]() || non[KEY]() != non;\n  });\n  var fn = exp[KEY] = FORCE ? exec(trim) : spaces[KEY];\n  if (ALIAS) exp[ALIAS] = fn;\n  $export($export.P + $export.F * FORCE, 'String', exp);\n};\n\n// 1 -> String#trimLeft\n// 2 -> String#trimRight\n// 3 -> String#trim\nvar trim = exporter.trim = function (string, TYPE) {\n  string = String(defined(string));\n  if (TYPE & 1) string = string.replace(ltrim, '');\n  if (TYPE & 2) string = string.replace(rtrim, '');\n  return string;\n};\n\nmodule.exports = exporter;\n","var $parseInt = require('./_global').parseInt;\nvar $trim = require('./_string-trim').trim;\nvar ws = require('./_string-ws');\nvar hex = /^[-+]?0[xX]/;\n\nmodule.exports = $parseInt(ws + '08') !== 8 || $parseInt(ws + '0x16') !== 22 ? function parseInt(str, radix) {\n  var string = $trim(String(str), 3);\n  return $parseInt(string, (radix >>> 0) || (hex.test(string) ? 16 : 10));\n} : $parseInt;\n","var $export = require('./_export');\nvar $parseInt = require('./_parse-int');\n// 18.2.5 parseInt(string, radix)\n$export($export.G + $export.F * (parseInt != $parseInt), { parseInt: $parseInt });\n","var $parseFloat = require('./_global').parseFloat;\nvar $trim = require('./_string-trim').trim;\n\nmodule.exports = 1 / $parseFloat(require('./_string-ws') + '-0') !== -Infinity ? function parseFloat(str) {\n  var string = $trim(String(str), 3);\n  var result = $parseFloat(string);\n  return result === 0 && string.charAt(0) == '-' ? -0 : result;\n} : $parseFloat;\n","var $export = require('./_export');\nvar $parseFloat = require('./_parse-float');\n// 18.2.4 parseFloat(string)\n$export($export.G + $export.F * (parseFloat != $parseFloat), { parseFloat: $parseFloat });\n","var isObject = require('./_is-object');\nvar setPrototypeOf = require('./_set-proto').set;\nmodule.exports = function (that, target, C) {\n  var S = target.constructor;\n  var P;\n  if (S !== C && typeof S == 'function' && (P = S.prototype) !== C.prototype && isObject(P) && setPrototypeOf) {\n    setPrototypeOf(that, P);\n  } return that;\n};\n","'use strict';\nvar global = require('./_global');\nvar has = require('./_has');\nvar cof = require('./_cof');\nvar inheritIfRequired = require('./_inherit-if-required');\nvar toPrimitive = require('./_to-primitive');\nvar fails = require('./_fails');\nvar gOPN = require('./_object-gopn').f;\nvar gOPD = require('./_object-gopd').f;\nvar dP = require('./_object-dp').f;\nvar $trim = require('./_string-trim').trim;\nvar NUMBER = 'Number';\nvar $Number = global[NUMBER];\nvar Base = $Number;\nvar proto = $Number.prototype;\n// Opera ~12 has broken Object#toString\nvar BROKEN_COF = cof(require('./_object-create')(proto)) == NUMBER;\nvar TRIM = 'trim' in String.prototype;\n\n// 7.1.3 ToNumber(argument)\nvar toNumber = function (argument) {\n  var it = toPrimitive(argument, false);\n  if (typeof it == 'string' && it.length > 2) {\n    it = TRIM ? it.trim() : $trim(it, 3);\n    var first = it.charCodeAt(0);\n    var third, radix, maxCode;\n    if (first === 43 || first === 45) {\n      third = it.charCodeAt(2);\n      if (third === 88 || third === 120) return NaN; // Number('+0x1') should be NaN, old V8 fix\n    } else if (first === 48) {\n      switch (it.charCodeAt(1)) {\n        case 66: case 98: radix = 2; maxCode = 49; break; // fast equal /^0b[01]+$/i\n        case 79: case 111: radix = 8; maxCode = 55; break; // fast equal /^0o[0-7]+$/i\n        default: return +it;\n      }\n      for (var digits = it.slice(2), i = 0, l = digits.length, code; i < l; i++) {\n        code = digits.charCodeAt(i);\n        // parseInt parses a string to a first unavailable symbol\n        // but ToNumber should return NaN if a string contains unavailable symbols\n        if (code < 48 || code > maxCode) return NaN;\n      } return parseInt(digits, radix);\n    }\n  } return +it;\n};\n\nif (!$Number(' 0o1') || !$Number('0b1') || $Number('+0x1')) {\n  $Number = function Number(value) {\n    var it = arguments.length < 1 ? 0 : value;\n    var that = this;\n    return that instanceof $Number\n      // check on 1..constructor(foo) case\n      && (BROKEN_COF ? fails(function () { proto.valueOf.call(that); }) : cof(that) != NUMBER)\n        ? inheritIfRequired(new Base(toNumber(it)), that, $Number) : toNumber(it);\n  };\n  for (var keys = require('./_descriptors') ? gOPN(Base) : (\n    // ES3:\n    'MAX_VALUE,MIN_VALUE,NaN,NEGATIVE_INFINITY,POSITIVE_INFINITY,' +\n    // ES6 (in case, if modules with ES6 Number statics required before):\n    'EPSILON,isFinite,isInteger,isNaN,isSafeInteger,MAX_SAFE_INTEGER,' +\n    'MIN_SAFE_INTEGER,parseFloat,parseInt,isInteger'\n  ).split(','), j = 0, key; keys.length > j; j++) {\n    if (has(Base, key = keys[j]) && !has($Number, key)) {\n      dP($Number, key, gOPD(Base, key));\n    }\n  }\n  $Number.prototype = proto;\n  proto.constructor = $Number;\n  require('./_redefine')(global, NUMBER, $Number);\n}\n","var cof = require('./_cof');\nmodule.exports = function (it, msg) {\n  if (typeof it != 'number' && cof(it) != 'Number') throw TypeError(msg);\n  return +it;\n};\n","'use strict';\nvar toInteger = require('./_to-integer');\nvar defined = require('./_defined');\n\nmodule.exports = function repeat(count) {\n  var str = String(defined(this));\n  var res = '';\n  var n = toInteger(count);\n  if (n < 0 || n == Infinity) throw RangeError(\"Count can't be negative\");\n  for (;n > 0; (n >>>= 1) && (str += str)) if (n & 1) res += str;\n  return res;\n};\n","'use strict';\nvar $export = require('./_export');\nvar toInteger = require('./_to-integer');\nvar aNumberValue = require('./_a-number-value');\nvar repeat = require('./_string-repeat');\nvar $toFixed = 1.0.toFixed;\nvar floor = Math.floor;\nvar data = [0, 0, 0, 0, 0, 0];\nvar ERROR = 'Number.toFixed: incorrect invocation!';\nvar ZERO = '0';\n\nvar multiply = function (n, c) {\n  var i = -1;\n  var c2 = c;\n  while (++i < 6) {\n    c2 += n * data[i];\n    data[i] = c2 % 1e7;\n    c2 = floor(c2 / 1e7);\n  }\n};\nvar divide = function (n) {\n  var i = 6;\n  var c = 0;\n  while (--i >= 0) {\n    c += data[i];\n    data[i] = floor(c / n);\n    c = (c % n) * 1e7;\n  }\n};\nvar numToString = function () {\n  var i = 6;\n  var s = '';\n  while (--i >= 0) {\n    if (s !== '' || i === 0 || data[i] !== 0) {\n      var t = String(data[i]);\n      s = s === '' ? t : s + repeat.call(ZERO, 7 - t.length) + t;\n    }\n  } return s;\n};\nvar pow = function (x, n, acc) {\n  return n === 0 ? acc : n % 2 === 1 ? pow(x, n - 1, acc * x) : pow(x * x, n / 2, acc);\n};\nvar log = function (x) {\n  var n = 0;\n  var x2 = x;\n  while (x2 >= 4096) {\n    n += 12;\n    x2 /= 4096;\n  }\n  while (x2 >= 2) {\n    n += 1;\n    x2 /= 2;\n  } return n;\n};\n\n$export($export.P + $export.F * (!!$toFixed && (\n  0.00008.toFixed(3) !== '0.000' ||\n  0.9.toFixed(0) !== '1' ||\n  1.255.toFixed(2) !== '1.25' ||\n  1000000000000000128.0.toFixed(0) !== '1000000000000000128'\n) || !require('./_fails')(function () {\n  // V8 ~ Android 4.3-\n  $toFixed.call({});\n})), 'Number', {\n  toFixed: function toFixed(fractionDigits) {\n    var x = aNumberValue(this, ERROR);\n    var f = toInteger(fractionDigits);\n    var s = '';\n    var m = ZERO;\n    var e, z, j, k;\n    if (f < 0 || f > 20) throw RangeError(ERROR);\n    // eslint-disable-next-line no-self-compare\n    if (x != x) return 'NaN';\n    if (x <= -1e21 || x >= 1e21) return String(x);\n    if (x < 0) {\n      s = '-';\n      x = -x;\n    }\n    if (x > 1e-21) {\n      e = log(x * pow(2, 69, 1)) - 69;\n      z = e < 0 ? x * pow(2, -e, 1) : x / pow(2, e, 1);\n      z *= 0x10000000000000;\n      e = 52 - e;\n      if (e > 0) {\n        multiply(0, z);\n        j = f;\n        while (j >= 7) {\n          multiply(1e7, 0);\n          j -= 7;\n        }\n        multiply(pow(10, j, 1), 0);\n        j = e - 1;\n        while (j >= 23) {\n          divide(1 << 23);\n          j -= 23;\n        }\n        divide(1 << j);\n        multiply(1, 1);\n        divide(2);\n        m = numToString();\n      } else {\n        multiply(0, z);\n        multiply(1 << -e, 0);\n        m = numToString() + repeat.call(ZERO, f);\n      }\n    }\n    if (f > 0) {\n      k = m.length;\n      m = s + (k <= f ? '0.' + repeat.call(ZERO, f - k) + m : m.slice(0, k - f) + '.' + m.slice(k - f));\n    } else {\n      m = s + m;\n    } return m;\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar $fails = require('./_fails');\nvar aNumberValue = require('./_a-number-value');\nvar $toPrecision = 1.0.toPrecision;\n\n$export($export.P + $export.F * ($fails(function () {\n  // IE7-\n  return $toPrecision.call(1, undefined) !== '1';\n}) || !$fails(function () {\n  // V8 ~ Android 4.3-\n  $toPrecision.call({});\n})), 'Number', {\n  toPrecision: function toPrecision(precision) {\n    var that = aNumberValue(this, 'Number#toPrecision: incorrect invocation!');\n    return precision === undefined ? $toPrecision.call(that) : $toPrecision.call(that, precision);\n  }\n});\n","// 20.1.2.1 Number.EPSILON\nvar $export = require('./_export');\n\n$export($export.S, 'Number', { EPSILON: Math.pow(2, -52) });\n","// 20.1.2.2 Number.isFinite(number)\nvar $export = require('./_export');\nvar _isFinite = require('./_global').isFinite;\n\n$export($export.S, 'Number', {\n  isFinite: function isFinite(it) {\n    return typeof it == 'number' && _isFinite(it);\n  }\n});\n","// 20.1.2.3 Number.isInteger(number)\nvar isObject = require('./_is-object');\nvar floor = Math.floor;\nmodule.exports = function isInteger(it) {\n  return !isObject(it) && isFinite(it) && floor(it) === it;\n};\n","// 20.1.2.3 Number.isInteger(number)\nvar $export = require('./_export');\n\n$export($export.S, 'Number', { isInteger: require('./_is-integer') });\n","// 20.1.2.4 Number.isNaN(number)\nvar $export = require('./_export');\n\n$export($export.S, 'Number', {\n  isNaN: function isNaN(number) {\n    // eslint-disable-next-line no-self-compare\n    return number != number;\n  }\n});\n","// 20.1.2.5 Number.isSafeInteger(number)\nvar $export = require('./_export');\nvar isInteger = require('./_is-integer');\nvar abs = Math.abs;\n\n$export($export.S, 'Number', {\n  isSafeInteger: function isSafeInteger(number) {\n    return isInteger(number) && abs(number) <= 0x1fffffffffffff;\n  }\n});\n","// 20.1.2.6 Number.MAX_SAFE_INTEGER\nvar $export = require('./_export');\n\n$export($export.S, 'Number', { MAX_SAFE_INTEGER: 0x1fffffffffffff });\n","// 20.1.2.10 Number.MIN_SAFE_INTEGER\nvar $export = require('./_export');\n\n$export($export.S, 'Number', { MIN_SAFE_INTEGER: -0x1fffffffffffff });\n","var $export = require('./_export');\nvar $parseFloat = require('./_parse-float');\n// 20.1.2.12 Number.parseFloat(string)\n$export($export.S + $export.F * (Number.parseFloat != $parseFloat), 'Number', { parseFloat: $parseFloat });\n","var $export = require('./_export');\nvar $parseInt = require('./_parse-int');\n// 20.1.2.13 Number.parseInt(string, radix)\n$export($export.S + $export.F * (Number.parseInt != $parseInt), 'Number', { parseInt: $parseInt });\n","// 20.2.2.20 Math.log1p(x)\nmodule.exports = Math.log1p || function log1p(x) {\n  return (x = +x) > -1e-8 && x < 1e-8 ? x - x * x / 2 : Math.log(1 + x);\n};\n","// 20.2.2.3 Math.acosh(x)\nvar $export = require('./_export');\nvar log1p = require('./_math-log1p');\nvar sqrt = Math.sqrt;\nvar $acosh = Math.acosh;\n\n$export($export.S + $export.F * !($acosh\n  // V8 bug: https://code.google.com/p/v8/issues/detail?id=3509\n  && Math.floor($acosh(Number.MAX_VALUE)) == 710\n  // Tor Browser bug: Math.acosh(Infinity) -> NaN\n  && $acosh(Infinity) == Infinity\n), 'Math', {\n  acosh: function acosh(x) {\n    return (x = +x) < 1 ? NaN : x > 94906265.62425156\n      ? Math.log(x) + Math.LN2\n      : log1p(x - 1 + sqrt(x - 1) * sqrt(x + 1));\n  }\n});\n","// 20.2.2.5 Math.asinh(x)\nvar $export = require('./_export');\nvar $asinh = Math.asinh;\n\nfunction asinh(x) {\n  return !isFinite(x = +x) || x == 0 ? x : x < 0 ? -asinh(-x) : Math.log(x + Math.sqrt(x * x + 1));\n}\n\n// Tor Browser bug: Math.asinh(0) -> -0\n$export($export.S + $export.F * !($asinh && 1 / $asinh(0) > 0), 'Math', { asinh: asinh });\n","// 20.2.2.7 Math.atanh(x)\nvar $export = require('./_export');\nvar $atanh = Math.atanh;\n\n// Tor Browser bug: Math.atanh(-0) -> 0\n$export($export.S + $export.F * !($atanh && 1 / $atanh(-0) < 0), 'Math', {\n  atanh: function atanh(x) {\n    return (x = +x) == 0 ? x : Math.log((1 + x) / (1 - x)) / 2;\n  }\n});\n","// 20.2.2.28 Math.sign(x)\nmodule.exports = Math.sign || function sign(x) {\n  // eslint-disable-next-line no-self-compare\n  return (x = +x) == 0 || x != x ? x : x < 0 ? -1 : 1;\n};\n","// 20.2.2.9 Math.cbrt(x)\nvar $export = require('./_export');\nvar sign = require('./_math-sign');\n\n$export($export.S, 'Math', {\n  cbrt: function cbrt(x) {\n    return sign(x = +x) * Math.pow(Math.abs(x), 1 / 3);\n  }\n});\n","// 20.2.2.11 Math.clz32(x)\nvar $export = require('./_export');\n\n$export($export.S, 'Math', {\n  clz32: function clz32(x) {\n    return (x >>>= 0) ? 31 - Math.floor(Math.log(x + 0.5) * Math.LOG2E) : 32;\n  }\n});\n","// 20.2.2.12 Math.cosh(x)\nvar $export = require('./_export');\nvar exp = Math.exp;\n\n$export($export.S, 'Math', {\n  cosh: function cosh(x) {\n    return (exp(x = +x) + exp(-x)) / 2;\n  }\n});\n","// 20.2.2.14 Math.expm1(x)\nvar $expm1 = Math.expm1;\nmodule.exports = (!$expm1\n  // Old FF bug\n  || $expm1(10) > 22025.465794806719 || $expm1(10) < 22025.4657948067165168\n  // Tor Browser bug\n  || $expm1(-2e-17) != -2e-17\n) ? function expm1(x) {\n  return (x = +x) == 0 ? x : x > -1e-6 && x < 1e-6 ? x + x * x / 2 : Math.exp(x) - 1;\n} : $expm1;\n","// 20.2.2.14 Math.expm1(x)\nvar $export = require('./_export');\nvar $expm1 = require('./_math-expm1');\n\n$export($export.S + $export.F * ($expm1 != Math.expm1), 'Math', { expm1: $expm1 });\n","// 20.2.2.16 Math.fround(x)\nvar sign = require('./_math-sign');\nvar pow = Math.pow;\nvar EPSILON = pow(2, -52);\nvar EPSILON32 = pow(2, -23);\nvar MAX32 = pow(2, 127) * (2 - EPSILON32);\nvar MIN32 = pow(2, -126);\n\nvar roundTiesToEven = function (n) {\n  return n + 1 / EPSILON - 1 / EPSILON;\n};\n\nmodule.exports = Math.fround || function fround(x) {\n  var $abs = Math.abs(x);\n  var $sign = sign(x);\n  var a, result;\n  if ($abs < MIN32) return $sign * roundTiesToEven($abs / MIN32 / EPSILON32) * MIN32 * EPSILON32;\n  a = (1 + EPSILON32 / EPSILON) * $abs;\n  result = a - (a - $abs);\n  // eslint-disable-next-line no-self-compare\n  if (result > MAX32 || result != result) return $sign * Infinity;\n  return $sign * result;\n};\n","// 20.2.2.16 Math.fround(x)\nvar $export = require('./_export');\n\n$export($export.S, 'Math', { fround: require('./_math-fround') });\n","// 20.2.2.17 Math.hypot([value1[, value2[, … ]]])\nvar $export = require('./_export');\nvar abs = Math.abs;\n\n$export($export.S, 'Math', {\n  hypot: function hypot(value1, value2) { // eslint-disable-line no-unused-vars\n    var sum = 0;\n    var i = 0;\n    var aLen = arguments.length;\n    var larg = 0;\n    var arg, div;\n    while (i < aLen) {\n      arg = abs(arguments[i++]);\n      if (larg < arg) {\n        div = larg / arg;\n        sum = sum * div * div + 1;\n        larg = arg;\n      } else if (arg > 0) {\n        div = arg / larg;\n        sum += div * div;\n      } else sum += arg;\n    }\n    return larg === Infinity ? Infinity : larg * Math.sqrt(sum);\n  }\n});\n","// 20.2.2.18 Math.imul(x, y)\nvar $export = require('./_export');\nvar $imul = Math.imul;\n\n// some WebKit versions fails with big numbers, some has wrong arity\n$export($export.S + $export.F * require('./_fails')(function () {\n  return $imul(0xffffffff, 5) != -5 || $imul.length != 2;\n}), 'Math', {\n  imul: function imul(x, y) {\n    var UINT16 = 0xffff;\n    var xn = +x;\n    var yn = +y;\n    var xl = UINT16 & xn;\n    var yl = UINT16 & yn;\n    return 0 | xl * yl + ((UINT16 & xn >>> 16) * yl + xl * (UINT16 & yn >>> 16) << 16 >>> 0);\n  }\n});\n","// 20.2.2.21 Math.log10(x)\nvar $export = require('./_export');\n\n$export($export.S, 'Math', {\n  log10: function log10(x) {\n    return Math.log(x) * Math.LOG10E;\n  }\n});\n","// 20.2.2.20 Math.log1p(x)\nvar $export = require('./_export');\n\n$export($export.S, 'Math', { log1p: require('./_math-log1p') });\n","// 20.2.2.22 Math.log2(x)\nvar $export = require('./_export');\n\n$export($export.S, 'Math', {\n  log2: function log2(x) {\n    return Math.log(x) / Math.LN2;\n  }\n});\n","// 20.2.2.28 Math.sign(x)\nvar $export = require('./_export');\n\n$export($export.S, 'Math', { sign: require('./_math-sign') });\n","// 20.2.2.30 Math.sinh(x)\nvar $export = require('./_export');\nvar expm1 = require('./_math-expm1');\nvar exp = Math.exp;\n\n// V8 near Chromium 38 has a problem with very small numbers\n$export($export.S + $export.F * require('./_fails')(function () {\n  return !Math.sinh(-2e-17) != -2e-17;\n}), 'Math', {\n  sinh: function sinh(x) {\n    return Math.abs(x = +x) < 1\n      ? (expm1(x) - expm1(-x)) / 2\n      : (exp(x - 1) - exp(-x - 1)) * (Math.E / 2);\n  }\n});\n","// 20.2.2.33 Math.tanh(x)\nvar $export = require('./_export');\nvar expm1 = require('./_math-expm1');\nvar exp = Math.exp;\n\n$export($export.S, 'Math', {\n  tanh: function tanh(x) {\n    var a = expm1(x = +x);\n    var b = expm1(-x);\n    return a == Infinity ? 1 : b == Infinity ? -1 : (a - b) / (exp(x) + exp(-x));\n  }\n});\n","// 20.2.2.34 Math.trunc(x)\nvar $export = require('./_export');\n\n$export($export.S, 'Math', {\n  trunc: function trunc(it) {\n    return (it > 0 ? Math.floor : Math.ceil)(it);\n  }\n});\n","var $export = require('./_export');\nvar toAbsoluteIndex = require('./_to-absolute-index');\nvar fromCharCode = String.fromCharCode;\nvar $fromCodePoint = String.fromCodePoint;\n\n// length should be 1, old FF problem\n$export($export.S + $export.F * (!!$fromCodePoint && $fromCodePoint.length != 1), 'String', {\n  // 21.1.2.2 String.fromCodePoint(...codePoints)\n  fromCodePoint: function fromCodePoint(x) { // eslint-disable-line no-unused-vars\n    var res = [];\n    var aLen = arguments.length;\n    var i = 0;\n    var code;\n    while (aLen > i) {\n      code = +arguments[i++];\n      if (toAbsoluteIndex(code, 0x10ffff) !== code) throw RangeError(code + ' is not a valid code point');\n      res.push(code < 0x10000\n        ? fromCharCode(code)\n        : fromCharCode(((code -= 0x10000) >> 10) + 0xd800, code % 0x400 + 0xdc00)\n      );\n    } return res.join('');\n  }\n});\n","var $export = require('./_export');\nvar toIObject = require('./_to-iobject');\nvar toLength = require('./_to-length');\n\n$export($export.S, 'String', {\n  // 21.1.2.4 String.raw(callSite, ...substitutions)\n  raw: function raw(callSite) {\n    var tpl = toIObject(callSite.raw);\n    var len = toLength(tpl.length);\n    var aLen = arguments.length;\n    var res = [];\n    var i = 0;\n    while (len > i) {\n      res.push(String(tpl[i++]));\n      if (i < aLen) res.push(String(arguments[i]));\n    } return res.join('');\n  }\n});\n","'use strict';\n// 21.1.3.25 String.prototype.trim()\nrequire('./_string-trim')('trim', function ($trim) {\n  return function trim() {\n    return $trim(this, 3);\n  };\n});\n","var toInteger = require('./_to-integer');\nvar defined = require('./_defined');\n// true  -> String#at\n// false -> String#codePointAt\nmodule.exports = function (TO_STRING) {\n  return function (that, pos) {\n    var s = String(defined(that));\n    var i = toInteger(pos);\n    var l = s.length;\n    var a, b;\n    if (i < 0 || i >= l) return TO_STRING ? '' : undefined;\n    a = s.charCodeAt(i);\n    return a < 0xd800 || a > 0xdbff || i + 1 === l || (b = s.charCodeAt(i + 1)) < 0xdc00 || b > 0xdfff\n      ? TO_STRING ? s.charAt(i) : a\n      : TO_STRING ? s.slice(i, i + 2) : (a - 0xd800 << 10) + (b - 0xdc00) + 0x10000;\n  };\n};\n","module.exports = {};\n","'use strict';\nvar create = require('./_object-create');\nvar descriptor = require('./_property-desc');\nvar setToStringTag = require('./_set-to-string-tag');\nvar IteratorPrototype = {};\n\n// 25.1.2.1.1 %IteratorPrototype%[@@iterator]()\nrequire('./_hide')(IteratorPrototype, require('./_wks')('iterator'), function () { return this; });\n\nmodule.exports = function (Constructor, NAME, next) {\n  Constructor.prototype = create(IteratorPrototype, { next: descriptor(1, next) });\n  setToStringTag(Constructor, NAME + ' Iterator');\n};\n","'use strict';\nvar LIBRARY = require('./_library');\nvar $export = require('./_export');\nvar redefine = require('./_redefine');\nvar hide = require('./_hide');\nvar Iterators = require('./_iterators');\nvar $iterCreate = require('./_iter-create');\nvar setToStringTag = require('./_set-to-string-tag');\nvar getPrototypeOf = require('./_object-gpo');\nvar ITERATOR = require('./_wks')('iterator');\nvar BUGGY = !([].keys && 'next' in [].keys()); // Safari has buggy iterators w/o `next`\nvar FF_ITERATOR = '@@iterator';\nvar KEYS = 'keys';\nvar VALUES = 'values';\n\nvar returnThis = function () { return this; };\n\nmodule.exports = function (Base, NAME, Constructor, next, DEFAULT, IS_SET, FORCED) {\n  $iterCreate(Constructor, NAME, next);\n  var getMethod = function (kind) {\n    if (!BUGGY && kind in proto) return proto[kind];\n    switch (kind) {\n      case KEYS: return function keys() { return new Constructor(this, kind); };\n      case VALUES: return function values() { return new Constructor(this, kind); };\n    } return function entries() { return new Constructor(this, kind); };\n  };\n  var TAG = NAME + ' Iterator';\n  var DEF_VALUES = DEFAULT == VALUES;\n  var VALUES_BUG = false;\n  var proto = Base.prototype;\n  var $native = proto[ITERATOR] || proto[FF_ITERATOR] || DEFAULT && proto[DEFAULT];\n  var $default = $native || getMethod(DEFAULT);\n  var $entries = DEFAULT ? !DEF_VALUES ? $default : getMethod('entries') : undefined;\n  var $anyNative = NAME == 'Array' ? proto.entries || $native : $native;\n  var methods, key, IteratorPrototype;\n  // Fix native\n  if ($anyNative) {\n    IteratorPrototype = getPrototypeOf($anyNative.call(new Base()));\n    if (IteratorPrototype !== Object.prototype && IteratorPrototype.next) {\n      // Set @@toStringTag to native iterators\n      setToStringTag(IteratorPrototype, TAG, true);\n      // fix for some old engines\n      if (!LIBRARY && typeof IteratorPrototype[ITERATOR] != 'function') hide(IteratorPrototype, ITERATOR, returnThis);\n    }\n  }\n  // fix Array#{values, @@iterator}.name in V8 / FF\n  if (DEF_VALUES && $native && $native.name !== VALUES) {\n    VALUES_BUG = true;\n    $default = function values() { return $native.call(this); };\n  }\n  // Define iterator\n  if ((!LIBRARY || FORCED) && (BUGGY || VALUES_BUG || !proto[ITERATOR])) {\n    hide(proto, ITERATOR, $default);\n  }\n  // Plug for library\n  Iterators[NAME] = $default;\n  Iterators[TAG] = returnThis;\n  if (DEFAULT) {\n    methods = {\n      values: DEF_VALUES ? $default : getMethod(VALUES),\n      keys: IS_SET ? $default : getMethod(KEYS),\n      entries: $entries\n    };\n    if (FORCED) for (key in methods) {\n      if (!(key in proto)) redefine(proto, key, methods[key]);\n    } else $export($export.P + $export.F * (BUGGY || VALUES_BUG), NAME, methods);\n  }\n  return methods;\n};\n","'use strict';\nvar $at = require('./_string-at')(true);\n\n// 21.1.3.27 String.prototype[@@iterator]()\nrequire('./_iter-define')(String, 'String', function (iterated) {\n  this._t = String(iterated); // target\n  this._i = 0;                // next index\n// 21.1.5.2.1 %StringIteratorPrototype%.next()\n}, function () {\n  var O = this._t;\n  var index = this._i;\n  var point;\n  if (index >= O.length) return { value: undefined, done: true };\n  point = $at(O, index);\n  this._i += point.length;\n  return { value: point, done: false };\n});\n","'use strict';\nvar $export = require('./_export');\nvar $at = require('./_string-at')(false);\n$export($export.P, 'String', {\n  // 21.1.3.3 String.prototype.codePointAt(pos)\n  codePointAt: function codePointAt(pos) {\n    return $at(this, pos);\n  }\n});\n","// 7.2.8 IsRegExp(argument)\nvar isObject = require('./_is-object');\nvar cof = require('./_cof');\nvar MATCH = require('./_wks')('match');\nmodule.exports = function (it) {\n  var isRegExp;\n  return isObject(it) && ((isRegExp = it[MATCH]) !== undefined ? !!isRegExp : cof(it) == 'RegExp');\n};\n","// helper for String#{startsWith, endsWith, includes}\nvar isRegExp = require('./_is-regexp');\nvar defined = require('./_defined');\n\nmodule.exports = function (that, searchString, NAME) {\n  if (isRegExp(searchString)) throw TypeError('String#' + NAME + \" doesn't accept regex!\");\n  return String(defined(that));\n};\n","var MATCH = require('./_wks')('match');\nmodule.exports = function (KEY) {\n  var re = /./;\n  try {\n    '/./'[KEY](re);\n  } catch (e) {\n    try {\n      re[MATCH] = false;\n      return !'/./'[KEY](re);\n    } catch (f) { /* empty */ }\n  } return true;\n};\n","// 21.1.3.6 String.prototype.endsWith(searchString [, endPosition])\n'use strict';\nvar $export = require('./_export');\nvar toLength = require('./_to-length');\nvar context = require('./_string-context');\nvar ENDS_WITH = 'endsWith';\nvar $endsWith = ''[ENDS_WITH];\n\n$export($export.P + $export.F * require('./_fails-is-regexp')(ENDS_WITH), 'String', {\n  endsWith: function endsWith(searchString /* , endPosition = @length */) {\n    var that = context(this, searchString, ENDS_WITH);\n    var endPosition = arguments.length > 1 ? arguments[1] : undefined;\n    var len = toLength(that.length);\n    var end = endPosition === undefined ? len : Math.min(toLength(endPosition), len);\n    var search = String(searchString);\n    return $endsWith\n      ? $endsWith.call(that, search, end)\n      : that.slice(end - search.length, end) === search;\n  }\n});\n","// 21.1.3.7 String.prototype.includes(searchString, position = 0)\n'use strict';\nvar $export = require('./_export');\nvar context = require('./_string-context');\nvar INCLUDES = 'includes';\n\n$export($export.P + $export.F * require('./_fails-is-regexp')(INCLUDES), 'String', {\n  includes: function includes(searchString /* , position = 0 */) {\n    return !!~context(this, searchString, INCLUDES)\n      .indexOf(searchString, arguments.length > 1 ? arguments[1] : undefined);\n  }\n});\n","var $export = require('./_export');\n\n$export($export.P, 'String', {\n  // 21.1.3.13 String.prototype.repeat(count)\n  repeat: require('./_string-repeat')\n});\n","// 21.1.3.18 String.prototype.startsWith(searchString [, position ])\n'use strict';\nvar $export = require('./_export');\nvar toLength = require('./_to-length');\nvar context = require('./_string-context');\nvar STARTS_WITH = 'startsWith';\nvar $startsWith = ''[STARTS_WITH];\n\n$export($export.P + $export.F * require('./_fails-is-regexp')(STARTS_WITH), 'String', {\n  startsWith: function startsWith(searchString /* , position = 0 */) {\n    var that = context(this, searchString, STARTS_WITH);\n    var index = toLength(Math.min(arguments.length > 1 ? arguments[1] : undefined, that.length));\n    var search = String(searchString);\n    return $startsWith\n      ? $startsWith.call(that, search, index)\n      : that.slice(index, index + search.length) === search;\n  }\n});\n","var $export = require('./_export');\nvar fails = require('./_fails');\nvar defined = require('./_defined');\nvar quot = /\"/g;\n// B.2.3.2.1 CreateHTML(string, tag, attribute, value)\nvar createHTML = function (string, tag, attribute, value) {\n  var S = String(defined(string));\n  var p1 = '<' + tag;\n  if (attribute !== '') p1 += ' ' + attribute + '=\"' + String(value).replace(quot, '&quot;') + '\"';\n  return p1 + '>' + S + '</' + tag + '>';\n};\nmodule.exports = function (NAME, exec) {\n  var O = {};\n  O[NAME] = exec(createHTML);\n  $export($export.P + $export.F * fails(function () {\n    var test = ''[NAME]('\"');\n    return test !== test.toLowerCase() || test.split('\"').length > 3;\n  }), 'String', O);\n};\n","'use strict';\n// B.2.3.2 String.prototype.anchor(name)\nrequire('./_string-html')('anchor', function (createHTML) {\n  return function anchor(name) {\n    return createHTML(this, 'a', 'name', name);\n  };\n});\n","'use strict';\n// B.2.3.3 String.prototype.big()\nrequire('./_string-html')('big', function (createHTML) {\n  return function big() {\n    return createHTML(this, 'big', '', '');\n  };\n});\n","'use strict';\n// B.2.3.4 String.prototype.blink()\nrequire('./_string-html')('blink', function (createHTML) {\n  return function blink() {\n    return createHTML(this, 'blink', '', '');\n  };\n});\n","'use strict';\n// B.2.3.5 String.prototype.bold()\nrequire('./_string-html')('bold', function (createHTML) {\n  return function bold() {\n    return createHTML(this, 'b', '', '');\n  };\n});\n","'use strict';\n// B.2.3.6 String.prototype.fixed()\nrequire('./_string-html')('fixed', function (createHTML) {\n  return function fixed() {\n    return createHTML(this, 'tt', '', '');\n  };\n});\n","'use strict';\n// B.2.3.7 String.prototype.fontcolor(color)\nrequire('./_string-html')('fontcolor', function (createHTML) {\n  return function fontcolor(color) {\n    return createHTML(this, 'font', 'color', color);\n  };\n});\n","'use strict';\n// B.2.3.8 String.prototype.fontsize(size)\nrequire('./_string-html')('fontsize', function (createHTML) {\n  return function fontsize(size) {\n    return createHTML(this, 'font', 'size', size);\n  };\n});\n","'use strict';\n// B.2.3.9 String.prototype.italics()\nrequire('./_string-html')('italics', function (createHTML) {\n  return function italics() {\n    return createHTML(this, 'i', '', '');\n  };\n});\n","'use strict';\n// B.2.3.10 String.prototype.link(url)\nrequire('./_string-html')('link', function (createHTML) {\n  return function link(url) {\n    return createHTML(this, 'a', 'href', url);\n  };\n});\n","'use strict';\n// B.2.3.11 String.prototype.small()\nrequire('./_string-html')('small', function (createHTML) {\n  return function small() {\n    return createHTML(this, 'small', '', '');\n  };\n});\n","'use strict';\n// B.2.3.12 String.prototype.strike()\nrequire('./_string-html')('strike', function (createHTML) {\n  return function strike() {\n    return createHTML(this, 'strike', '', '');\n  };\n});\n","'use strict';\n// B.2.3.13 String.prototype.sub()\nrequire('./_string-html')('sub', function (createHTML) {\n  return function sub() {\n    return createHTML(this, 'sub', '', '');\n  };\n});\n","'use strict';\n// B.2.3.14 String.prototype.sup()\nrequire('./_string-html')('sup', function (createHTML) {\n  return function sup() {\n    return createHTML(this, 'sup', '', '');\n  };\n});\n","// 20.3.3.1 / 15.9.4.4 Date.now()\nvar $export = require('./_export');\n\n$export($export.S, 'Date', { now: function () { return new Date().getTime(); } });\n","'use strict';\nvar $export = require('./_export');\nvar toObject = require('./_to-object');\nvar toPrimitive = require('./_to-primitive');\n\n$export($export.P + $export.F * require('./_fails')(function () {\n  return new Date(NaN).toJSON() !== null\n    || Date.prototype.toJSON.call({ toISOString: function () { return 1; } }) !== 1;\n}), 'Date', {\n  // eslint-disable-next-line no-unused-vars\n  toJSON: function toJSON(key) {\n    var O = toObject(this);\n    var pv = toPrimitive(O);\n    return typeof pv == 'number' && !isFinite(pv) ? null : O.toISOString();\n  }\n});\n","'use strict';\n// 20.3.4.36 / 15.9.5.43 Date.prototype.toISOString()\nvar fails = require('./_fails');\nvar getTime = Date.prototype.getTime;\nvar $toISOString = Date.prototype.toISOString;\n\nvar lz = function (num) {\n  return num > 9 ? num : '0' + num;\n};\n\n// PhantomJS / old WebKit has a broken implementations\nmodule.exports = (fails(function () {\n  return $toISOString.call(new Date(-5e13 - 1)) != '0385-07-25T07:06:39.999Z';\n}) || !fails(function () {\n  $toISOString.call(new Date(NaN));\n})) ? function toISOString() {\n  if (!isFinite(getTime.call(this))) throw RangeError('Invalid time value');\n  var d = this;\n  var y = d.getUTCFullYear();\n  var m = d.getUTCMilliseconds();\n  var s = y < 0 ? '-' : y > 9999 ? '+' : '';\n  return s + ('00000' + Math.abs(y)).slice(s ? -6 : -4) +\n    '-' + lz(d.getUTCMonth() + 1) + '-' + lz(d.getUTCDate()) +\n    'T' + lz(d.getUTCHours()) + ':' + lz(d.getUTCMinutes()) +\n    ':' + lz(d.getUTCSeconds()) + '.' + (m > 99 ? m : '0' + lz(m)) + 'Z';\n} : $toISOString;\n","// 20.3.4.36 / 15.9.5.43 Date.prototype.toISOString()\nvar $export = require('./_export');\nvar toISOString = require('./_date-to-iso-string');\n\n// PhantomJS / old WebKit has a broken implementations\n$export($export.P + $export.F * (Date.prototype.toISOString !== toISOString), 'Date', {\n  toISOString: toISOString\n});\n","var DateProto = Date.prototype;\nvar INVALID_DATE = 'Invalid Date';\nvar TO_STRING = 'toString';\nvar $toString = DateProto[TO_STRING];\nvar getTime = DateProto.getTime;\nif (new Date(NaN) + '' != INVALID_DATE) {\n  require('./_redefine')(DateProto, TO_STRING, function toString() {\n    var value = getTime.call(this);\n    // eslint-disable-next-line no-self-compare\n    return value === value ? $toString.call(this) : INVALID_DATE;\n  });\n}\n","'use strict';\nvar anObject = require('./_an-object');\nvar toPrimitive = require('./_to-primitive');\nvar NUMBER = 'number';\n\nmodule.exports = function (hint) {\n  if (hint !== 'string' && hint !== NUMBER && hint !== 'default') throw TypeError('Incorrect hint');\n  return toPrimitive(anObject(this), hint != NUMBER);\n};\n","var TO_PRIMITIVE = require('./_wks')('toPrimitive');\nvar proto = Date.prototype;\n\nif (!(TO_PRIMITIVE in proto)) require('./_hide')(proto, TO_PRIMITIVE, require('./_date-to-primitive'));\n","// 22.1.2.2 / 15.4.3.2 Array.isArray(arg)\nvar $export = require('./_export');\n\n$export($export.S, 'Array', { isArray: require('./_is-array') });\n","// call something on iterator step with safe closing on error\nvar anObject = require('./_an-object');\nmodule.exports = function (iterator, fn, value, entries) {\n  try {\n    return entries ? fn(anObject(value)[0], value[1]) : fn(value);\n  // 7.4.6 IteratorClose(iterator, completion)\n  } catch (e) {\n    var ret = iterator['return'];\n    if (ret !== undefined) anObject(ret.call(iterator));\n    throw e;\n  }\n};\n","// check on default Array iterator\nvar Iterators = require('./_iterators');\nvar ITERATOR = require('./_wks')('iterator');\nvar ArrayProto = Array.prototype;\n\nmodule.exports = function (it) {\n  return it !== undefined && (Iterators.Array === it || ArrayProto[ITERATOR] === it);\n};\n","'use strict';\nvar $defineProperty = require('./_object-dp');\nvar createDesc = require('./_property-desc');\n\nmodule.exports = function (object, index, value) {\n  if (index in object) $defineProperty.f(object, index, createDesc(0, value));\n  else object[index] = value;\n};\n","var classof = require('./_classof');\nvar ITERATOR = require('./_wks')('iterator');\nvar Iterators = require('./_iterators');\nmodule.exports = require('./_core').getIteratorMethod = function (it) {\n  if (it != undefined) return it[ITERATOR]\n    || it['@@iterator']\n    || Iterators[classof(it)];\n};\n","var ITERATOR = require('./_wks')('iterator');\nvar SAFE_CLOSING = false;\n\ntry {\n  var riter = [7][ITERATOR]();\n  riter['return'] = function () { SAFE_CLOSING = true; };\n  // eslint-disable-next-line no-throw-literal\n  Array.from(riter, function () { throw 2; });\n} catch (e) { /* empty */ }\n\nmodule.exports = function (exec, skipClosing) {\n  if (!skipClosing && !SAFE_CLOSING) return false;\n  var safe = false;\n  try {\n    var arr = [7];\n    var iter = arr[ITERATOR]();\n    iter.next = function () { return { done: safe = true }; };\n    arr[ITERATOR] = function () { return iter; };\n    exec(arr);\n  } catch (e) { /* empty */ }\n  return safe;\n};\n","'use strict';\nvar ctx = require('./_ctx');\nvar $export = require('./_export');\nvar toObject = require('./_to-object');\nvar call = require('./_iter-call');\nvar isArrayIter = require('./_is-array-iter');\nvar toLength = require('./_to-length');\nvar createProperty = require('./_create-property');\nvar getIterFn = require('./core.get-iterator-method');\n\n$export($export.S + $export.F * !require('./_iter-detect')(function (iter) { Array.from(iter); }), 'Array', {\n  // 22.1.2.1 Array.from(arrayLike, mapfn = undefined, thisArg = undefined)\n  from: function from(arrayLike /* , mapfn = undefined, thisArg = undefined */) {\n    var O = toObject(arrayLike);\n    var C = typeof this == 'function' ? this : Array;\n    var aLen = arguments.length;\n    var mapfn = aLen > 1 ? arguments[1] : undefined;\n    var mapping = mapfn !== undefined;\n    var index = 0;\n    var iterFn = getIterFn(O);\n    var length, result, step, iterator;\n    if (mapping) mapfn = ctx(mapfn, aLen > 2 ? arguments[2] : undefined, 2);\n    // if object isn't iterable or it's array with default iterator - use simple case\n    if (iterFn != undefined && !(C == Array && isArrayIter(iterFn))) {\n      for (iterator = iterFn.call(O), result = new C(); !(step = iterator.next()).done; index++) {\n        createProperty(result, index, mapping ? call(iterator, mapfn, [step.value, index], true) : step.value);\n      }\n    } else {\n      length = toLength(O.length);\n      for (result = new C(length); length > index; index++) {\n        createProperty(result, index, mapping ? mapfn(O[index], index) : O[index]);\n      }\n    }\n    result.length = index;\n    return result;\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar createProperty = require('./_create-property');\n\n// WebKit Array.of isn't generic\n$export($export.S + $export.F * require('./_fails')(function () {\n  function F() { /* empty */ }\n  return !(Array.of.call(F) instanceof F);\n}), 'Array', {\n  // 22.1.2.3 Array.of( ...items)\n  of: function of(/* ...args */) {\n    var index = 0;\n    var aLen = arguments.length;\n    var result = new (typeof this == 'function' ? this : Array)(aLen);\n    while (aLen > index) createProperty(result, index, arguments[index++]);\n    result.length = aLen;\n    return result;\n  }\n});\n","'use strict';\nvar fails = require('./_fails');\n\nmodule.exports = function (method, arg) {\n  return !!method && fails(function () {\n    // eslint-disable-next-line no-useless-call\n    arg ? method.call(null, function () { /* empty */ }, 1) : method.call(null);\n  });\n};\n","'use strict';\n// 22.1.3.13 Array.prototype.join(separator)\nvar $export = require('./_export');\nvar toIObject = require('./_to-iobject');\nvar arrayJoin = [].join;\n\n// fallback for not array-like strings\n$export($export.P + $export.F * (require('./_iobject') != Object || !require('./_strict-method')(arrayJoin)), 'Array', {\n  join: function join(separator) {\n    return arrayJoin.call(toIObject(this), separator === undefined ? ',' : separator);\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar html = require('./_html');\nvar cof = require('./_cof');\nvar toAbsoluteIndex = require('./_to-absolute-index');\nvar toLength = require('./_to-length');\nvar arraySlice = [].slice;\n\n// fallback for not array-like ES3 strings and DOM objects\n$export($export.P + $export.F * require('./_fails')(function () {\n  if (html) arraySlice.call(html);\n}), 'Array', {\n  slice: function slice(begin, end) {\n    var len = toLength(this.length);\n    var klass = cof(this);\n    end = end === undefined ? len : end;\n    if (klass == 'Array') return arraySlice.call(this, begin, end);\n    var start = toAbsoluteIndex(begin, len);\n    var upTo = toAbsoluteIndex(end, len);\n    var size = toLength(upTo - start);\n    var cloned = new Array(size);\n    var i = 0;\n    for (; i < size; i++) cloned[i] = klass == 'String'\n      ? this.charAt(start + i)\n      : this[start + i];\n    return cloned;\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar aFunction = require('./_a-function');\nvar toObject = require('./_to-object');\nvar fails = require('./_fails');\nvar $sort = [].sort;\nvar test = [1, 2, 3];\n\n$export($export.P + $export.F * (fails(function () {\n  // IE8-\n  test.sort(undefined);\n}) || !fails(function () {\n  // V8 bug\n  test.sort(null);\n  // Old WebKit\n}) || !require('./_strict-method')($sort)), 'Array', {\n  // 22.1.3.25 Array.prototype.sort(comparefn)\n  sort: function sort(comparefn) {\n    return comparefn === undefined\n      ? $sort.call(toObject(this))\n      : $sort.call(toObject(this), aFunction(comparefn));\n  }\n});\n","var isObject = require('./_is-object');\nvar isArray = require('./_is-array');\nvar SPECIES = require('./_wks')('species');\n\nmodule.exports = function (original) {\n  var C;\n  if (isArray(original)) {\n    C = original.constructor;\n    // cross-realm fallback\n    if (typeof C == 'function' && (C === Array || isArray(C.prototype))) C = undefined;\n    if (isObject(C)) {\n      C = C[SPECIES];\n      if (C === null) C = undefined;\n    }\n  } return C === undefined ? Array : C;\n};\n","// 9.4.2.3 ArraySpeciesCreate(originalArray, length)\nvar speciesConstructor = require('./_array-species-constructor');\n\nmodule.exports = function (original, length) {\n  return new (speciesConstructor(original))(length);\n};\n","// 0 -> Array#forEach\n// 1 -> Array#map\n// 2 -> Array#filter\n// 3 -> Array#some\n// 4 -> Array#every\n// 5 -> Array#find\n// 6 -> Array#findIndex\nvar ctx = require('./_ctx');\nvar IObject = require('./_iobject');\nvar toObject = require('./_to-object');\nvar toLength = require('./_to-length');\nvar asc = require('./_array-species-create');\nmodule.exports = function (TYPE, $create) {\n  var IS_MAP = TYPE == 1;\n  var IS_FILTER = TYPE == 2;\n  var IS_SOME = TYPE == 3;\n  var IS_EVERY = TYPE == 4;\n  var IS_FIND_INDEX = TYPE == 6;\n  var NO_HOLES = TYPE == 5 || IS_FIND_INDEX;\n  var create = $create || asc;\n  return function ($this, callbackfn, that) {\n    var O = toObject($this);\n    var self = IObject(O);\n    var f = ctx(callbackfn, that, 3);\n    var length = toLength(self.length);\n    var index = 0;\n    var result = IS_MAP ? create($this, length) : IS_FILTER ? create($this, 0) : undefined;\n    var val, res;\n    for (;length > index; index++) if (NO_HOLES || index in self) {\n      val = self[index];\n      res = f(val, index, O);\n      if (TYPE) {\n        if (IS_MAP) result[index] = res;   // map\n        else if (res) switch (TYPE) {\n          case 3: return true;             // some\n          case 5: return val;              // find\n          case 6: return index;            // findIndex\n          case 2: result.push(val);        // filter\n        } else if (IS_EVERY) return false; // every\n      }\n    }\n    return IS_FIND_INDEX ? -1 : IS_SOME || IS_EVERY ? IS_EVERY : result;\n  };\n};\n","'use strict';\nvar $export = require('./_export');\nvar $forEach = require('./_array-methods')(0);\nvar STRICT = require('./_strict-method')([].forEach, true);\n\n$export($export.P + $export.F * !STRICT, 'Array', {\n  // 22.1.3.10 / 15.4.4.18 Array.prototype.forEach(callbackfn [, thisArg])\n  forEach: function forEach(callbackfn /* , thisArg */) {\n    return $forEach(this, callbackfn, arguments[1]);\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar $map = require('./_array-methods')(1);\n\n$export($export.P + $export.F * !require('./_strict-method')([].map, true), 'Array', {\n  // 22.1.3.15 / 15.4.4.19 Array.prototype.map(callbackfn [, thisArg])\n  map: function map(callbackfn /* , thisArg */) {\n    return $map(this, callbackfn, arguments[1]);\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar $filter = require('./_array-methods')(2);\n\n$export($export.P + $export.F * !require('./_strict-method')([].filter, true), 'Array', {\n  // 22.1.3.7 / 15.4.4.20 Array.prototype.filter(callbackfn [, thisArg])\n  filter: function filter(callbackfn /* , thisArg */) {\n    return $filter(this, callbackfn, arguments[1]);\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar $some = require('./_array-methods')(3);\n\n$export($export.P + $export.F * !require('./_strict-method')([].some, true), 'Array', {\n  // 22.1.3.23 / 15.4.4.17 Array.prototype.some(callbackfn [, thisArg])\n  some: function some(callbackfn /* , thisArg */) {\n    return $some(this, callbackfn, arguments[1]);\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar $every = require('./_array-methods')(4);\n\n$export($export.P + $export.F * !require('./_strict-method')([].every, true), 'Array', {\n  // 22.1.3.5 / 15.4.4.16 Array.prototype.every(callbackfn [, thisArg])\n  every: function every(callbackfn /* , thisArg */) {\n    return $every(this, callbackfn, arguments[1]);\n  }\n});\n","var aFunction = require('./_a-function');\nvar toObject = require('./_to-object');\nvar IObject = require('./_iobject');\nvar toLength = require('./_to-length');\n\nmodule.exports = function (that, callbackfn, aLen, memo, isRight) {\n  aFunction(callbackfn);\n  var O = toObject(that);\n  var self = IObject(O);\n  var length = toLength(O.length);\n  var index = isRight ? length - 1 : 0;\n  var i = isRight ? -1 : 1;\n  if (aLen < 2) for (;;) {\n    if (index in self) {\n      memo = self[index];\n      index += i;\n      break;\n    }\n    index += i;\n    if (isRight ? index < 0 : length <= index) {\n      throw TypeError('Reduce of empty array with no initial value');\n    }\n  }\n  for (;isRight ? index >= 0 : length > index; index += i) if (index in self) {\n    memo = callbackfn(memo, self[index], index, O);\n  }\n  return memo;\n};\n","'use strict';\nvar $export = require('./_export');\nvar $reduce = require('./_array-reduce');\n\n$export($export.P + $export.F * !require('./_strict-method')([].reduce, true), 'Array', {\n  // 22.1.3.18 / 15.4.4.21 Array.prototype.reduce(callbackfn [, initialValue])\n  reduce: function reduce(callbackfn /* , initialValue */) {\n    return $reduce(this, callbackfn, arguments.length, arguments[1], false);\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar $reduce = require('./_array-reduce');\n\n$export($export.P + $export.F * !require('./_strict-method')([].reduceRight, true), 'Array', {\n  // 22.1.3.19 / 15.4.4.22 Array.prototype.reduceRight(callbackfn [, initialValue])\n  reduceRight: function reduceRight(callbackfn /* , initialValue */) {\n    return $reduce(this, callbackfn, arguments.length, arguments[1], true);\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar $indexOf = require('./_array-includes')(false);\nvar $native = [].indexOf;\nvar NEGATIVE_ZERO = !!$native && 1 / [1].indexOf(1, -0) < 0;\n\n$export($export.P + $export.F * (NEGATIVE_ZERO || !require('./_strict-method')($native)), 'Array', {\n  // 22.1.3.11 / 15.4.4.14 Array.prototype.indexOf(searchElement [, fromIndex])\n  indexOf: function indexOf(searchElement /* , fromIndex = 0 */) {\n    return NEGATIVE_ZERO\n      // convert -0 to +0\n      ? $native.apply(this, arguments) || 0\n      : $indexOf(this, searchElement, arguments[1]);\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar toIObject = require('./_to-iobject');\nvar toInteger = require('./_to-integer');\nvar toLength = require('./_to-length');\nvar $native = [].lastIndexOf;\nvar NEGATIVE_ZERO = !!$native && 1 / [1].lastIndexOf(1, -0) < 0;\n\n$export($export.P + $export.F * (NEGATIVE_ZERO || !require('./_strict-method')($native)), 'Array', {\n  // 22.1.3.14 / 15.4.4.15 Array.prototype.lastIndexOf(searchElement [, fromIndex])\n  lastIndexOf: function lastIndexOf(searchElement /* , fromIndex = @[*-1] */) {\n    // convert -0 to +0\n    if (NEGATIVE_ZERO) return $native.apply(this, arguments) || 0;\n    var O = toIObject(this);\n    var length = toLength(O.length);\n    var index = length - 1;\n    if (arguments.length > 1) index = Math.min(index, toInteger(arguments[1]));\n    if (index < 0) index = length + index;\n    for (;index >= 0; index--) if (index in O) if (O[index] === searchElement) return index || 0;\n    return -1;\n  }\n});\n","// 22.1.3.3 Array.prototype.copyWithin(target, start, end = this.length)\n'use strict';\nvar toObject = require('./_to-object');\nvar toAbsoluteIndex = require('./_to-absolute-index');\nvar toLength = require('./_to-length');\n\nmodule.exports = [].copyWithin || function copyWithin(target /* = 0 */, start /* = 0, end = @length */) {\n  var O = toObject(this);\n  var len = toLength(O.length);\n  var to = toAbsoluteIndex(target, len);\n  var from = toAbsoluteIndex(start, len);\n  var end = arguments.length > 2 ? arguments[2] : undefined;\n  var count = Math.min((end === undefined ? len : toAbsoluteIndex(end, len)) - from, len - to);\n  var inc = 1;\n  if (from < to && to < from + count) {\n    inc = -1;\n    from += count - 1;\n    to += count - 1;\n  }\n  while (count-- > 0) {\n    if (from in O) O[to] = O[from];\n    else delete O[to];\n    to += inc;\n    from += inc;\n  } return O;\n};\n","// 22.1.3.31 Array.prototype[@@unscopables]\nvar UNSCOPABLES = require('./_wks')('unscopables');\nvar ArrayProto = Array.prototype;\nif (ArrayProto[UNSCOPABLES] == undefined) require('./_hide')(ArrayProto, UNSCOPABLES, {});\nmodule.exports = function (key) {\n  ArrayProto[UNSCOPABLES][key] = true;\n};\n","// 22.1.3.3 Array.prototype.copyWithin(target, start, end = this.length)\nvar $export = require('./_export');\n\n$export($export.P, 'Array', { copyWithin: require('./_array-copy-within') });\n\nrequire('./_add-to-unscopables')('copyWithin');\n","// 22.1.3.6 Array.prototype.fill(value, start = 0, end = this.length)\n'use strict';\nvar toObject = require('./_to-object');\nvar toAbsoluteIndex = require('./_to-absolute-index');\nvar toLength = require('./_to-length');\nmodule.exports = function fill(value /* , start = 0, end = @length */) {\n  var O = toObject(this);\n  var length = toLength(O.length);\n  var aLen = arguments.length;\n  var index = toAbsoluteIndex(aLen > 1 ? arguments[1] : undefined, length);\n  var end = aLen > 2 ? arguments[2] : undefined;\n  var endPos = end === undefined ? length : toAbsoluteIndex(end, length);\n  while (endPos > index) O[index++] = value;\n  return O;\n};\n","// 22.1.3.6 Array.prototype.fill(value, start = 0, end = this.length)\nvar $export = require('./_export');\n\n$export($export.P, 'Array', { fill: require('./_array-fill') });\n\nrequire('./_add-to-unscopables')('fill');\n","'use strict';\n// 22.1.3.8 Array.prototype.find(predicate, thisArg = undefined)\nvar $export = require('./_export');\nvar $find = require('./_array-methods')(5);\nvar KEY = 'find';\nvar forced = true;\n// Shouldn't skip holes\nif (KEY in []) Array(1)[KEY](function () { forced = false; });\n$export($export.P + $export.F * forced, 'Array', {\n  find: function find(callbackfn /* , that = undefined */) {\n    return $find(this, callbackfn, arguments.length > 1 ? arguments[1] : undefined);\n  }\n});\nrequire('./_add-to-unscopables')(KEY);\n","'use strict';\n// 22.1.3.9 Array.prototype.findIndex(predicate, thisArg = undefined)\nvar $export = require('./_export');\nvar $find = require('./_array-methods')(6);\nvar KEY = 'findIndex';\nvar forced = true;\n// Shouldn't skip holes\nif (KEY in []) Array(1)[KEY](function () { forced = false; });\n$export($export.P + $export.F * forced, 'Array', {\n  findIndex: function findIndex(callbackfn /* , that = undefined */) {\n    return $find(this, callbackfn, arguments.length > 1 ? arguments[1] : undefined);\n  }\n});\nrequire('./_add-to-unscopables')(KEY);\n","'use strict';\nvar global = require('./_global');\nvar dP = require('./_object-dp');\nvar DESCRIPTORS = require('./_descriptors');\nvar SPECIES = require('./_wks')('species');\n\nmodule.exports = function (KEY) {\n  var C = global[KEY];\n  if (DESCRIPTORS && C && !C[SPECIES]) dP.f(C, SPECIES, {\n    configurable: true,\n    get: function () { return this; }\n  });\n};\n","require('./_set-species')('Array');\n","module.exports = function (done, value) {\n  return { value: value, done: !!done };\n};\n","'use strict';\nvar addToUnscopables = require('./_add-to-unscopables');\nvar step = require('./_iter-step');\nvar Iterators = require('./_iterators');\nvar toIObject = require('./_to-iobject');\n\n// 22.1.3.4 Array.prototype.entries()\n// 22.1.3.13 Array.prototype.keys()\n// 22.1.3.29 Array.prototype.values()\n// 22.1.3.30 Array.prototype[@@iterator]()\nmodule.exports = require('./_iter-define')(Array, 'Array', function (iterated, kind) {\n  this._t = toIObject(iterated); // target\n  this._i = 0;                   // next index\n  this._k = kind;                // kind\n// 22.1.5.2.1 %ArrayIteratorPrototype%.next()\n}, function () {\n  var O = this._t;\n  var kind = this._k;\n  var index = this._i++;\n  if (!O || index >= O.length) {\n    this._t = undefined;\n    return step(1);\n  }\n  if (kind == 'keys') return step(0, index);\n  if (kind == 'values') return step(0, O[index]);\n  return step(0, [index, O[index]]);\n}, 'values');\n\n// argumentsList[@@iterator] is %ArrayProto_values% (9.4.4.6, 9.4.4.7)\nIterators.Arguments = Iterators.Array;\n\naddToUnscopables('keys');\naddToUnscopables('values');\naddToUnscopables('entries');\n","'use strict';\n// 21.2.5.3 get RegExp.prototype.flags\nvar anObject = require('./_an-object');\nmodule.exports = function () {\n  var that = anObject(this);\n  var result = '';\n  if (that.global) result += 'g';\n  if (that.ignoreCase) result += 'i';\n  if (that.multiline) result += 'm';\n  if (that.unicode) result += 'u';\n  if (that.sticky) result += 'y';\n  return result;\n};\n","var global = require('./_global');\nvar inheritIfRequired = require('./_inherit-if-required');\nvar dP = require('./_object-dp').f;\nvar gOPN = require('./_object-gopn').f;\nvar isRegExp = require('./_is-regexp');\nvar $flags = require('./_flags');\nvar $RegExp = global.RegExp;\nvar Base = $RegExp;\nvar proto = $RegExp.prototype;\nvar re1 = /a/g;\nvar re2 = /a/g;\n// \"new\" creates a new object, old webkit buggy here\nvar CORRECT_NEW = new $RegExp(re1) !== re1;\n\nif (require('./_descriptors') && (!CORRECT_NEW || require('./_fails')(function () {\n  re2[require('./_wks')('match')] = false;\n  // RegExp constructor can alter flags and IsRegExp works correct with @@match\n  return $RegExp(re1) != re1 || $RegExp(re2) == re2 || $RegExp(re1, 'i') != '/a/i';\n}))) {\n  $RegExp = function RegExp(p, f) {\n    var tiRE = this instanceof $RegExp;\n    var piRE = isRegExp(p);\n    var fiU = f === undefined;\n    return !tiRE && piRE && p.constructor === $RegExp && fiU ? p\n      : inheritIfRequired(CORRECT_NEW\n        ? new Base(piRE && !fiU ? p.source : p, f)\n        : Base((piRE = p instanceof $RegExp) ? p.source : p, piRE && fiU ? $flags.call(p) : f)\n      , tiRE ? this : proto, $RegExp);\n  };\n  var proxy = function (key) {\n    key in $RegExp || dP($RegExp, key, {\n      configurable: true,\n      get: function () { return Base[key]; },\n      set: function (it) { Base[key] = it; }\n    });\n  };\n  for (var keys = gOPN(Base), i = 0; keys.length > i;) proxy(keys[i++]);\n  proto.constructor = $RegExp;\n  $RegExp.prototype = proto;\n  require('./_redefine')(global, 'RegExp', $RegExp);\n}\n\nrequire('./_set-species')('RegExp');\n","'use strict';\n\nvar regexpFlags = require('./_flags');\n\nvar nativeExec = RegExp.prototype.exec;\n// This always refers to the native implementation, because the\n// String#replace polyfill uses ./fix-regexp-well-known-symbol-logic.js,\n// which loads this file before patching the method.\nvar nativeReplace = String.prototype.replace;\n\nvar patchedExec = nativeExec;\n\nvar LAST_INDEX = 'lastIndex';\n\nvar UPDATES_LAST_INDEX_WRONG = (function () {\n  var re1 = /a/,\n      re2 = /b*/g;\n  nativeExec.call(re1, 'a');\n  nativeExec.call(re2, 'a');\n  return re1[LAST_INDEX] !== 0 || re2[LAST_INDEX] !== 0;\n})();\n\n// nonparticipating capturing group, copied from es5-shim's String#split patch.\nvar NPCG_INCLUDED = /()??/.exec('')[1] !== undefined;\n\nvar PATCH = UPDATES_LAST_INDEX_WRONG || NPCG_INCLUDED;\n\nif (PATCH) {\n  patchedExec = function exec(str) {\n    var re = this;\n    var lastIndex, reCopy, match, i;\n\n    if (NPCG_INCLUDED) {\n      reCopy = new RegExp('^' + re.source + '$(?!\\\\s)', regexpFlags.call(re));\n    }\n    if (UPDATES_LAST_INDEX_WRONG) lastIndex = re[LAST_INDEX];\n\n    match = nativeExec.call(re, str);\n\n    if (UPDATES_LAST_INDEX_WRONG && match) {\n      re[LAST_INDEX] = re.global ? match.index + match[0].length : lastIndex;\n    }\n    if (NPCG_INCLUDED && match && match.length > 1) {\n      // Fix browsers whose `exec` methods don't consistently return `undefined`\n      // for NPCG, like IE8. NOTE: This doesn' work for /(.?)?/\n      // eslint-disable-next-line no-loop-func\n      nativeReplace.call(match[0], reCopy, function () {\n        for (i = 1; i < arguments.length - 2; i++) {\n          if (arguments[i] === undefined) match[i] = undefined;\n        }\n      });\n    }\n\n    return match;\n  };\n}\n\nmodule.exports = patchedExec;\n","'use strict';\nvar regexpExec = require('./_regexp-exec');\nrequire('./_export')({\n  target: 'RegExp',\n  proto: true,\n  forced: regexpExec !== /./.exec\n}, {\n  exec: regexpExec\n});\n","// 21.2.5.3 get RegExp.prototype.flags()\nif (require('./_descriptors') && /./g.flags != 'g') require('./_object-dp').f(RegExp.prototype, 'flags', {\n  configurable: true,\n  get: require('./_flags')\n});\n","'use strict';\nrequire('./es6.regexp.flags');\nvar anObject = require('./_an-object');\nvar $flags = require('./_flags');\nvar DESCRIPTORS = require('./_descriptors');\nvar TO_STRING = 'toString';\nvar $toString = /./[TO_STRING];\n\nvar define = function (fn) {\n  require('./_redefine')(RegExp.prototype, TO_STRING, fn, true);\n};\n\n// 21.2.5.14 RegExp.prototype.toString()\nif (require('./_fails')(function () { return $toString.call({ source: 'a', flags: 'b' }) != '/a/b'; })) {\n  define(function toString() {\n    var R = anObject(this);\n    return '/'.concat(R.source, '/',\n      'flags' in R ? R.flags : !DESCRIPTORS && R instanceof RegExp ? $flags.call(R) : undefined);\n  });\n// FF44- RegExp#toString has a wrong name\n} else if ($toString.name != TO_STRING) {\n  define(function toString() {\n    return $toString.call(this);\n  });\n}\n","'use strict';\nvar at = require('./_string-at')(true);\n\n // `AdvanceStringIndex` abstract operation\n// https://tc39.github.io/ecma262/#sec-advancestringindex\nmodule.exports = function (S, index, unicode) {\n  return index + (unicode ? at(S, index).length : 1);\n};\n","'use strict';\n\nvar classof = require('./_classof');\nvar builtinExec = RegExp.prototype.exec;\n\n // `RegExpExec` abstract operation\n// https://tc39.github.io/ecma262/#sec-regexpexec\nmodule.exports = function (R, S) {\n  var exec = R.exec;\n  if (typeof exec === 'function') {\n    var result = exec.call(R, S);\n    if (typeof result !== 'object') {\n      throw new TypeError('RegExp exec method returned something other than an Object or null');\n    }\n    return result;\n  }\n  if (classof(R) !== 'RegExp') {\n    throw new TypeError('RegExp#exec called on incompatible receiver');\n  }\n  return builtinExec.call(R, S);\n};\n","'use strict';\nrequire('./es6.regexp.exec');\nvar redefine = require('./_redefine');\nvar hide = require('./_hide');\nvar fails = require('./_fails');\nvar defined = require('./_defined');\nvar wks = require('./_wks');\nvar regexpExec = require('./_regexp-exec');\n\nvar SPECIES = wks('species');\n\nvar REPLACE_SUPPORTS_NAMED_GROUPS = !fails(function () {\n  // #replace needs built-in support for named groups.\n  // #match works fine because it just return the exec results, even if it has\n  // a \"grops\" property.\n  var re = /./;\n  re.exec = function () {\n    var result = [];\n    result.groups = { a: '7' };\n    return result;\n  };\n  return ''.replace(re, '$<a>') !== '7';\n});\n\nvar SPLIT_WORKS_WITH_OVERWRITTEN_EXEC = (function () {\n  // Chrome 51 has a buggy \"split\" implementation when RegExp#exec !== nativeExec\n  var re = /(?:)/;\n  var originalExec = re.exec;\n  re.exec = function () { return originalExec.apply(this, arguments); };\n  var result = 'ab'.split(re);\n  return result.length === 2 && result[0] === 'a' && result[1] === 'b';\n})();\n\nmodule.exports = function (KEY, length, exec) {\n  var SYMBOL = wks(KEY);\n\n  var DELEGATES_TO_SYMBOL = !fails(function () {\n    // String methods call symbol-named RegEp methods\n    var O = {};\n    O[SYMBOL] = function () { return 7; };\n    return ''[KEY](O) != 7;\n  });\n\n  var DELEGATES_TO_EXEC = DELEGATES_TO_SYMBOL ? !fails(function () {\n    // Symbol-named RegExp methods call .exec\n    var execCalled = false;\n    var re = /a/;\n    re.exec = function () { execCalled = true; return null; };\n    if (KEY === 'split') {\n      // RegExp[@@split] doesn't call the regex's exec method, but first creates\n      // a new one. We need to return the patched regex when creating the new one.\n      re.constructor = {};\n      re.constructor[SPECIES] = function () { return re; };\n    }\n    re[SYMBOL]('');\n    return !execCalled;\n  }) : undefined;\n\n  if (\n    !DELEGATES_TO_SYMBOL ||\n    !DELEGATES_TO_EXEC ||\n    (KEY === 'replace' && !REPLACE_SUPPORTS_NAMED_GROUPS) ||\n    (KEY === 'split' && !SPLIT_WORKS_WITH_OVERWRITTEN_EXEC)\n  ) {\n    var nativeRegExpMethod = /./[SYMBOL];\n    var fns = exec(\n      defined,\n      SYMBOL,\n      ''[KEY],\n      function maybeCallNative(nativeMethod, regexp, str, arg2, forceStringMethod) {\n        if (regexp.exec === regexpExec) {\n          if (DELEGATES_TO_SYMBOL && !forceStringMethod) {\n            // The native String method already delegates to @@method (this\n            // polyfilled function), leasing to infinite recursion.\n            // We avoid it by directly calling the native @@method method.\n            return { done: true, value: nativeRegExpMethod.call(regexp, str, arg2) };\n          }\n          return { done: true, value: nativeMethod.call(str, regexp, arg2) };\n        }\n        return { done: false };\n      }\n    );\n    var strfn = fns[0];\n    var rxfn = fns[1];\n\n    redefine(String.prototype, KEY, strfn);\n    hide(RegExp.prototype, SYMBOL, length == 2\n      // 21.2.5.8 RegExp.prototype[@@replace](string, replaceValue)\n      // 21.2.5.11 RegExp.prototype[@@split](string, limit)\n      ? function (string, arg) { return rxfn.call(string, this, arg); }\n      // 21.2.5.6 RegExp.prototype[@@match](string)\n      // 21.2.5.9 RegExp.prototype[@@search](string)\n      : function (string) { return rxfn.call(string, this); }\n    );\n  }\n};\n","'use strict';\n\nvar anObject = require('./_an-object');\nvar toLength = require('./_to-length');\nvar advanceStringIndex = require('./_advance-string-index');\nvar regExpExec = require('./_regexp-exec-abstract');\n\n// @@match logic\nrequire('./_fix-re-wks')('match', 1, function (defined, MATCH, $match, maybeCallNative) {\n  return [\n    // `String.prototype.match` method\n    // https://tc39.github.io/ecma262/#sec-string.prototype.match\n    function match(regexp) {\n      var O = defined(this);\n      var fn = regexp == undefined ? undefined : regexp[MATCH];\n      return fn !== undefined ? fn.call(regexp, O) : new RegExp(regexp)[MATCH](String(O));\n    },\n    // `RegExp.prototype[@@match]` method\n    // https://tc39.github.io/ecma262/#sec-regexp.prototype-@@match\n    function (regexp) {\n      var res = maybeCallNative($match, regexp, this);\n      if (res.done) return res.value;\n      var rx = anObject(regexp);\n      var S = String(this);\n      if (!rx.global) return regExpExec(rx, S);\n      var fullUnicode = rx.unicode;\n      rx.lastIndex = 0;\n      var A = [];\n      var n = 0;\n      var result;\n      while ((result = regExpExec(rx, S)) !== null) {\n        var matchStr = String(result[0]);\n        A[n] = matchStr;\n        if (matchStr === '') rx.lastIndex = advanceStringIndex(S, toLength(rx.lastIndex), fullUnicode);\n        n++;\n      }\n      return n === 0 ? null : A;\n    }\n  ];\n});\n","'use strict';\n\nvar anObject = require('./_an-object');\nvar toObject = require('./_to-object');\nvar toLength = require('./_to-length');\nvar toInteger = require('./_to-integer');\nvar advanceStringIndex = require('./_advance-string-index');\nvar regExpExec = require('./_regexp-exec-abstract');\nvar max = Math.max;\nvar min = Math.min;\nvar floor = Math.floor;\nvar SUBSTITUTION_SYMBOLS = /\\$([$&`']|\\d\\d?|<[^>]*>)/g;\nvar SUBSTITUTION_SYMBOLS_NO_NAMED = /\\$([$&`']|\\d\\d?)/g;\n\nvar maybeToString = function (it) {\n  return it === undefined ? it : String(it);\n};\n\n// @@replace logic\nrequire('./_fix-re-wks')('replace', 2, function (defined, REPLACE, $replace, maybeCallNative) {\n  return [\n    // `String.prototype.replace` method\n    // https://tc39.github.io/ecma262/#sec-string.prototype.replace\n    function replace(searchValue, replaceValue) {\n      var O = defined(this);\n      var fn = searchValue == undefined ? undefined : searchValue[REPLACE];\n      return fn !== undefined\n        ? fn.call(searchValue, O, replaceValue)\n        : $replace.call(String(O), searchValue, replaceValue);\n    },\n    // `RegExp.prototype[@@replace]` method\n    // https://tc39.github.io/ecma262/#sec-regexp.prototype-@@replace\n    function (regexp, replaceValue) {\n      var res = maybeCallNative($replace, regexp, this, replaceValue);\n      if (res.done) return res.value;\n\n      var rx = anObject(regexp);\n      var S = String(this);\n      var functionalReplace = typeof replaceValue === 'function';\n      if (!functionalReplace) replaceValue = String(replaceValue);\n      var global = rx.global;\n      if (global) {\n        var fullUnicode = rx.unicode;\n        rx.lastIndex = 0;\n      }\n      var results = [];\n      while (true) {\n        var result = regExpExec(rx, S);\n        if (result === null) break;\n        results.push(result);\n        if (!global) break;\n        var matchStr = String(result[0]);\n        if (matchStr === '') rx.lastIndex = advanceStringIndex(S, toLength(rx.lastIndex), fullUnicode);\n      }\n      var accumulatedResult = '';\n      var nextSourcePosition = 0;\n      for (var i = 0; i < results.length; i++) {\n        result = results[i];\n        var matched = String(result[0]);\n        var position = max(min(toInteger(result.index), S.length), 0);\n        var captures = [];\n        // NOTE: This is equivalent to\n        //   captures = result.slice(1).map(maybeToString)\n        // but for some reason `nativeSlice.call(result, 1, result.length)` (called in\n        // the slice polyfill when slicing native arrays) \"doesn't work\" in safari 9 and\n        // causes a crash (https://pastebin.com/N21QzeQA) when trying to debug it.\n        for (var j = 1; j < result.length; j++) captures.push(maybeToString(result[j]));\n        var namedCaptures = result.groups;\n        if (functionalReplace) {\n          var replacerArgs = [matched].concat(captures, position, S);\n          if (namedCaptures !== undefined) replacerArgs.push(namedCaptures);\n          var replacement = String(replaceValue.apply(undefined, replacerArgs));\n        } else {\n          replacement = getSubstitution(matched, S, position, captures, namedCaptures, replaceValue);\n        }\n        if (position >= nextSourcePosition) {\n          accumulatedResult += S.slice(nextSourcePosition, position) + replacement;\n          nextSourcePosition = position + matched.length;\n        }\n      }\n      return accumulatedResult + S.slice(nextSourcePosition);\n    }\n  ];\n\n    // https://tc39.github.io/ecma262/#sec-getsubstitution\n  function getSubstitution(matched, str, position, captures, namedCaptures, replacement) {\n    var tailPos = position + matched.length;\n    var m = captures.length;\n    var symbols = SUBSTITUTION_SYMBOLS_NO_NAMED;\n    if (namedCaptures !== undefined) {\n      namedCaptures = toObject(namedCaptures);\n      symbols = SUBSTITUTION_SYMBOLS;\n    }\n    return $replace.call(replacement, symbols, function (match, ch) {\n      var capture;\n      switch (ch.charAt(0)) {\n        case '$': return '$';\n        case '&': return matched;\n        case '`': return str.slice(0, position);\n        case \"'\": return str.slice(tailPos);\n        case '<':\n          capture = namedCaptures[ch.slice(1, -1)];\n          break;\n        default: // \\d\\d?\n          var n = +ch;\n          if (n === 0) return match;\n          if (n > m) {\n            var f = floor(n / 10);\n            if (f === 0) return match;\n            if (f <= m) return captures[f - 1] === undefined ? ch.charAt(1) : captures[f - 1] + ch.charAt(1);\n            return match;\n          }\n          capture = captures[n - 1];\n      }\n      return capture === undefined ? '' : capture;\n    });\n  }\n});\n","'use strict';\n\nvar anObject = require('./_an-object');\nvar sameValue = require('./_same-value');\nvar regExpExec = require('./_regexp-exec-abstract');\n\n// @@search logic\nrequire('./_fix-re-wks')('search', 1, function (defined, SEARCH, $search, maybeCallNative) {\n  return [\n    // `String.prototype.search` method\n    // https://tc39.github.io/ecma262/#sec-string.prototype.search\n    function search(regexp) {\n      var O = defined(this);\n      var fn = regexp == undefined ? undefined : regexp[SEARCH];\n      return fn !== undefined ? fn.call(regexp, O) : new RegExp(regexp)[SEARCH](String(O));\n    },\n    // `RegExp.prototype[@@search]` method\n    // https://tc39.github.io/ecma262/#sec-regexp.prototype-@@search\n    function (regexp) {\n      var res = maybeCallNative($search, regexp, this);\n      if (res.done) return res.value;\n      var rx = anObject(regexp);\n      var S = String(this);\n      var previousLastIndex = rx.lastIndex;\n      if (!sameValue(previousLastIndex, 0)) rx.lastIndex = 0;\n      var result = regExpExec(rx, S);\n      if (!sameValue(rx.lastIndex, previousLastIndex)) rx.lastIndex = previousLastIndex;\n      return result === null ? -1 : result.index;\n    }\n  ];\n});\n","// 7.3.20 SpeciesConstructor(O, defaultConstructor)\nvar anObject = require('./_an-object');\nvar aFunction = require('./_a-function');\nvar SPECIES = require('./_wks')('species');\nmodule.exports = function (O, D) {\n  var C = anObject(O).constructor;\n  var S;\n  return C === undefined || (S = anObject(C)[SPECIES]) == undefined ? D : aFunction(S);\n};\n","'use strict';\n\nvar isRegExp = require('./_is-regexp');\nvar anObject = require('./_an-object');\nvar speciesConstructor = require('./_species-constructor');\nvar advanceStringIndex = require('./_advance-string-index');\nvar toLength = require('./_to-length');\nvar callRegExpExec = require('./_regexp-exec-abstract');\nvar regexpExec = require('./_regexp-exec');\nvar fails = require('./_fails');\nvar $min = Math.min;\nvar $push = [].push;\nvar $SPLIT = 'split';\nvar LENGTH = 'length';\nvar LAST_INDEX = 'lastIndex';\nvar MAX_UINT32 = 0xffffffff;\n\n// babel-minify transpiles RegExp('x', 'y') -> /x/y and it causes SyntaxError\nvar SUPPORTS_Y = !fails(function () { RegExp(MAX_UINT32, 'y'); });\n\n// @@split logic\nrequire('./_fix-re-wks')('split', 2, function (defined, SPLIT, $split, maybeCallNative) {\n  var internalSplit;\n  if (\n    'abbc'[$SPLIT](/(b)*/)[1] == 'c' ||\n    'test'[$SPLIT](/(?:)/, -1)[LENGTH] != 4 ||\n    'ab'[$SPLIT](/(?:ab)*/)[LENGTH] != 2 ||\n    '.'[$SPLIT](/(.?)(.?)/)[LENGTH] != 4 ||\n    '.'[$SPLIT](/()()/)[LENGTH] > 1 ||\n    ''[$SPLIT](/.?/)[LENGTH]\n  ) {\n    // based on es5-shim implementation, need to rework it\n    internalSplit = function (separator, limit) {\n      var string = String(this);\n      if (separator === undefined && limit === 0) return [];\n      // If `separator` is not a regex, use native split\n      if (!isRegExp(separator)) return $split.call(string, separator, limit);\n      var output = [];\n      var flags = (separator.ignoreCase ? 'i' : '') +\n                  (separator.multiline ? 'm' : '') +\n                  (separator.unicode ? 'u' : '') +\n                  (separator.sticky ? 'y' : '');\n      var lastLastIndex = 0;\n      var splitLimit = limit === undefined ? MAX_UINT32 : limit >>> 0;\n      // Make `global` and avoid `lastIndex` issues by working with a copy\n      var separatorCopy = new RegExp(separator.source, flags + 'g');\n      var match, lastIndex, lastLength;\n      while (match = regexpExec.call(separatorCopy, string)) {\n        lastIndex = separatorCopy[LAST_INDEX];\n        if (lastIndex > lastLastIndex) {\n          output.push(string.slice(lastLastIndex, match.index));\n          if (match[LENGTH] > 1 && match.index < string[LENGTH]) $push.apply(output, match.slice(1));\n          lastLength = match[0][LENGTH];\n          lastLastIndex = lastIndex;\n          if (output[LENGTH] >= splitLimit) break;\n        }\n        if (separatorCopy[LAST_INDEX] === match.index) separatorCopy[LAST_INDEX]++; // Avoid an infinite loop\n      }\n      if (lastLastIndex === string[LENGTH]) {\n        if (lastLength || !separatorCopy.test('')) output.push('');\n      } else output.push(string.slice(lastLastIndex));\n      return output[LENGTH] > splitLimit ? output.slice(0, splitLimit) : output;\n    };\n  // Chakra, V8\n  } else if ('0'[$SPLIT](undefined, 0)[LENGTH]) {\n    internalSplit = function (separator, limit) {\n      return separator === undefined && limit === 0 ? [] : $split.call(this, separator, limit);\n    };\n  } else {\n    internalSplit = $split;\n  }\n\n  return [\n    // `String.prototype.split` method\n    // https://tc39.github.io/ecma262/#sec-string.prototype.split\n    function split(separator, limit) {\n      var O = defined(this);\n      var splitter = separator == undefined ? undefined : separator[SPLIT];\n      return splitter !== undefined\n        ? splitter.call(separator, O, limit)\n        : internalSplit.call(String(O), separator, limit);\n    },\n    // `RegExp.prototype[@@split]` method\n    // https://tc39.github.io/ecma262/#sec-regexp.prototype-@@split\n    //\n    // NOTE: This cannot be properly polyfilled in engines that don't support\n    // the 'y' flag.\n    function (regexp, limit) {\n      var res = maybeCallNative(internalSplit, regexp, this, limit, internalSplit !== $split);\n      if (res.done) return res.value;\n\n      var rx = anObject(regexp);\n      var S = String(this);\n      var C = speciesConstructor(rx, RegExp);\n\n      var unicodeMatching = rx.unicode;\n      var flags = (rx.ignoreCase ? 'i' : '') +\n                  (rx.multiline ? 'm' : '') +\n                  (rx.unicode ? 'u' : '') +\n                  (SUPPORTS_Y ? 'y' : 'g');\n\n      // ^(? + rx + ) is needed, in combination with some S slicing, to\n      // simulate the 'y' flag.\n      var splitter = new C(SUPPORTS_Y ? rx : '^(?:' + rx.source + ')', flags);\n      var lim = limit === undefined ? MAX_UINT32 : limit >>> 0;\n      if (lim === 0) return [];\n      if (S.length === 0) return callRegExpExec(splitter, S) === null ? [S] : [];\n      var p = 0;\n      var q = 0;\n      var A = [];\n      while (q < S.length) {\n        splitter.lastIndex = SUPPORTS_Y ? q : 0;\n        var z = callRegExpExec(splitter, SUPPORTS_Y ? S : S.slice(q));\n        var e;\n        if (\n          z === null ||\n          (e = $min(toLength(splitter.lastIndex + (SUPPORTS_Y ? 0 : q)), S.length)) === p\n        ) {\n          q = advanceStringIndex(S, q, unicodeMatching);\n        } else {\n          A.push(S.slice(p, q));\n          if (A.length === lim) return A;\n          for (var i = 1; i <= z.length - 1; i++) {\n            A.push(z[i]);\n            if (A.length === lim) return A;\n          }\n          q = p = e;\n        }\n      }\n      A.push(S.slice(p));\n      return A;\n    }\n  ];\n});\n","module.exports = function (it, Constructor, name, forbiddenField) {\n  if (!(it instanceof Constructor) || (forbiddenField !== undefined && forbiddenField in it)) {\n    throw TypeError(name + ': incorrect invocation!');\n  } return it;\n};\n","var ctx = require('./_ctx');\nvar call = require('./_iter-call');\nvar isArrayIter = require('./_is-array-iter');\nvar anObject = require('./_an-object');\nvar toLength = require('./_to-length');\nvar getIterFn = require('./core.get-iterator-method');\nvar BREAK = {};\nvar RETURN = {};\nvar exports = module.exports = function (iterable, entries, fn, that, ITERATOR) {\n  var iterFn = ITERATOR ? function () { return iterable; } : getIterFn(iterable);\n  var f = ctx(fn, that, entries ? 2 : 1);\n  var index = 0;\n  var length, step, iterator, result;\n  if (typeof iterFn != 'function') throw TypeError(iterable + ' is not iterable!');\n  // fast case for arrays with default iterator\n  if (isArrayIter(iterFn)) for (length = toLength(iterable.length); length > index; index++) {\n    result = entries ? f(anObject(step = iterable[index])[0], step[1]) : f(iterable[index]);\n    if (result === BREAK || result === RETURN) return result;\n  } else for (iterator = iterFn.call(iterable); !(step = iterator.next()).done;) {\n    result = call(iterator, f, step.value, entries);\n    if (result === BREAK || result === RETURN) return result;\n  }\n};\nexports.BREAK = BREAK;\nexports.RETURN = RETURN;\n","var ctx = require('./_ctx');\nvar invoke = require('./_invoke');\nvar html = require('./_html');\nvar cel = require('./_dom-create');\nvar global = require('./_global');\nvar process = global.process;\nvar setTask = global.setImmediate;\nvar clearTask = global.clearImmediate;\nvar MessageChannel = global.MessageChannel;\nvar Dispatch = global.Dispatch;\nvar counter = 0;\nvar queue = {};\nvar ONREADYSTATECHANGE = 'onreadystatechange';\nvar defer, channel, port;\nvar run = function () {\n  var id = +this;\n  // eslint-disable-next-line no-prototype-builtins\n  if (queue.hasOwnProperty(id)) {\n    var fn = queue[id];\n    delete queue[id];\n    fn();\n  }\n};\nvar listener = function (event) {\n  run.call(event.data);\n};\n// Node.js 0.9+ & IE10+ has setImmediate, otherwise:\nif (!setTask || !clearTask) {\n  setTask = function setImmediate(fn) {\n    var args = [];\n    var i = 1;\n    while (arguments.length > i) args.push(arguments[i++]);\n    queue[++counter] = function () {\n      // eslint-disable-next-line no-new-func\n      invoke(typeof fn == 'function' ? fn : Function(fn), args);\n    };\n    defer(counter);\n    return counter;\n  };\n  clearTask = function clearImmediate(id) {\n    delete queue[id];\n  };\n  // Node.js 0.8-\n  if (require('./_cof')(process) == 'process') {\n    defer = function (id) {\n      process.nextTick(ctx(run, id, 1));\n    };\n  // Sphere (JS game engine) Dispatch API\n  } else if (Dispatch && Dispatch.now) {\n    defer = function (id) {\n      Dispatch.now(ctx(run, id, 1));\n    };\n  // Browsers with MessageChannel, includes WebWorkers\n  } else if (MessageChannel) {\n    channel = new MessageChannel();\n    port = channel.port2;\n    channel.port1.onmessage = listener;\n    defer = ctx(port.postMessage, port, 1);\n  // Browsers with postMessage, skip WebWorkers\n  // IE8 has postMessage, but it's sync & typeof its postMessage is 'object'\n  } else if (global.addEventListener && typeof postMessage == 'function' && !global.importScripts) {\n    defer = function (id) {\n      global.postMessage(id + '', '*');\n    };\n    global.addEventListener('message', listener, false);\n  // IE8-\n  } else if (ONREADYSTATECHANGE in cel('script')) {\n    defer = function (id) {\n      html.appendChild(cel('script'))[ONREADYSTATECHANGE] = function () {\n        html.removeChild(this);\n        run.call(id);\n      };\n    };\n  // Rest old browsers\n  } else {\n    defer = function (id) {\n      setTimeout(ctx(run, id, 1), 0);\n    };\n  }\n}\nmodule.exports = {\n  set: setTask,\n  clear: clearTask\n};\n","var global = require('./_global');\nvar macrotask = require('./_task').set;\nvar Observer = global.MutationObserver || global.WebKitMutationObserver;\nvar process = global.process;\nvar Promise = global.Promise;\nvar isNode = require('./_cof')(process) == 'process';\n\nmodule.exports = function () {\n  var head, last, notify;\n\n  var flush = function () {\n    var parent, fn;\n    if (isNode && (parent = process.domain)) parent.exit();\n    while (head) {\n      fn = head.fn;\n      head = head.next;\n      try {\n        fn();\n      } catch (e) {\n        if (head) notify();\n        else last = undefined;\n        throw e;\n      }\n    } last = undefined;\n    if (parent) parent.enter();\n  };\n\n  // Node.js\n  if (isNode) {\n    notify = function () {\n      process.nextTick(flush);\n    };\n  // browsers with MutationObserver, except iOS Safari - https://github.com/zloirock/core-js/issues/339\n  } else if (Observer && !(global.navigator && global.navigator.standalone)) {\n    var toggle = true;\n    var node = document.createTextNode('');\n    new Observer(flush).observe(node, { characterData: true }); // eslint-disable-line no-new\n    notify = function () {\n      node.data = toggle = !toggle;\n    };\n  // environments with maybe non-completely correct, but existent Promise\n  } else if (Promise && Promise.resolve) {\n    // Promise.resolve without an argument throws an error in LG WebOS 2\n    var promise = Promise.resolve(undefined);\n    notify = function () {\n      promise.then(flush);\n    };\n  // for other environments - macrotask based on:\n  // - setImmediate\n  // - MessageChannel\n  // - window.postMessag\n  // - onreadystatechange\n  // - setTimeout\n  } else {\n    notify = function () {\n      // strange IE + webpack dev server bug - use .call(global)\n      macrotask.call(global, flush);\n    };\n  }\n\n  return function (fn) {\n    var task = { fn: fn, next: undefined };\n    if (last) last.next = task;\n    if (!head) {\n      head = task;\n      notify();\n    } last = task;\n  };\n};\n","'use strict';\n// 25.4.1.5 NewPromiseCapability(C)\nvar aFunction = require('./_a-function');\n\nfunction PromiseCapability(C) {\n  var resolve, reject;\n  this.promise = new C(function ($$resolve, $$reject) {\n    if (resolve !== undefined || reject !== undefined) throw TypeError('Bad Promise constructor');\n    resolve = $$resolve;\n    reject = $$reject;\n  });\n  this.resolve = aFunction(resolve);\n  this.reject = aFunction(reject);\n}\n\nmodule.exports.f = function (C) {\n  return new PromiseCapability(C);\n};\n","module.exports = function (exec) {\n  try {\n    return { e: false, v: exec() };\n  } catch (e) {\n    return { e: true, v: e };\n  }\n};\n","var global = require('./_global');\nvar navigator = global.navigator;\n\nmodule.exports = navigator && navigator.userAgent || '';\n","var anObject = require('./_an-object');\nvar isObject = require('./_is-object');\nvar newPromiseCapability = require('./_new-promise-capability');\n\nmodule.exports = function (C, x) {\n  anObject(C);\n  if (isObject(x) && x.constructor === C) return x;\n  var promiseCapability = newPromiseCapability.f(C);\n  var resolve = promiseCapability.resolve;\n  resolve(x);\n  return promiseCapability.promise;\n};\n","var redefine = require('./_redefine');\nmodule.exports = function (target, src, safe) {\n  for (var key in src) redefine(target, key, src[key], safe);\n  return target;\n};\n","'use strict';\nvar LIBRARY = require('./_library');\nvar global = require('./_global');\nvar ctx = require('./_ctx');\nvar classof = require('./_classof');\nvar $export = require('./_export');\nvar isObject = require('./_is-object');\nvar aFunction = require('./_a-function');\nvar anInstance = require('./_an-instance');\nvar forOf = require('./_for-of');\nvar speciesConstructor = require('./_species-constructor');\nvar task = require('./_task').set;\nvar microtask = require('./_microtask')();\nvar newPromiseCapabilityModule = require('./_new-promise-capability');\nvar perform = require('./_perform');\nvar userAgent = require('./_user-agent');\nvar promiseResolve = require('./_promise-resolve');\nvar PROMISE = 'Promise';\nvar TypeError = global.TypeError;\nvar process = global.process;\nvar versions = process && process.versions;\nvar v8 = versions && versions.v8 || '';\nvar $Promise = global[PROMISE];\nvar isNode = classof(process) == 'process';\nvar empty = function () { /* empty */ };\nvar Internal, newGenericPromiseCapability, OwnPromiseCapability, Wrapper;\nvar newPromiseCapability = newGenericPromiseCapability = newPromiseCapabilityModule.f;\n\nvar USE_NATIVE = !!function () {\n  try {\n    // correct subclassing with @@species support\n    var promise = $Promise.resolve(1);\n    var FakePromise = (promise.constructor = {})[require('./_wks')('species')] = function (exec) {\n      exec(empty, empty);\n    };\n    // unhandled rejections tracking support, NodeJS Promise without it fails @@species test\n    return (isNode || typeof PromiseRejectionEvent == 'function')\n      && promise.then(empty) instanceof FakePromise\n      // v8 6.6 (Node 10 and Chrome 66) have a bug with resolving custom thenables\n      // https://bugs.chromium.org/p/chromium/issues/detail?id=830565\n      // we can't detect it synchronously, so just check versions\n      && v8.indexOf('6.6') !== 0\n      && userAgent.indexOf('Chrome/66') === -1;\n  } catch (e) { /* empty */ }\n}();\n\n// helpers\nvar isThenable = function (it) {\n  var then;\n  return isObject(it) && typeof (then = it.then) == 'function' ? then : false;\n};\nvar notify = function (promise, isReject) {\n  if (promise._n) return;\n  promise._n = true;\n  var chain = promise._c;\n  microtask(function () {\n    var value = promise._v;\n    var ok = promise._s == 1;\n    var i = 0;\n    var run = function (reaction) {\n      var handler = ok ? reaction.ok : reaction.fail;\n      var resolve = reaction.resolve;\n      var reject = reaction.reject;\n      var domain = reaction.domain;\n      var result, then, exited;\n      try {\n        if (handler) {\n          if (!ok) {\n            if (promise._h == 2) onHandleUnhandled(promise);\n            promise._h = 1;\n          }\n          if (handler === true) result = value;\n          else {\n            if (domain) domain.enter();\n            result = handler(value); // may throw\n            if (domain) {\n              domain.exit();\n              exited = true;\n            }\n          }\n          if (result === reaction.promise) {\n            reject(TypeError('Promise-chain cycle'));\n          } else if (then = isThenable(result)) {\n            then.call(result, resolve, reject);\n          } else resolve(result);\n        } else reject(value);\n      } catch (e) {\n        if (domain && !exited) domain.exit();\n        reject(e);\n      }\n    };\n    while (chain.length > i) run(chain[i++]); // variable length - can't use forEach\n    promise._c = [];\n    promise._n = false;\n    if (isReject && !promise._h) onUnhandled(promise);\n  });\n};\nvar onUnhandled = function (promise) {\n  task.call(global, function () {\n    var value = promise._v;\n    var unhandled = isUnhandled(promise);\n    var result, handler, console;\n    if (unhandled) {\n      result = perform(function () {\n        if (isNode) {\n          process.emit('unhandledRejection', value, promise);\n        } else if (handler = global.onunhandledrejection) {\n          handler({ promise: promise, reason: value });\n        } else if ((console = global.console) && console.error) {\n          console.error('Unhandled promise rejection', value);\n        }\n      });\n      // Browsers should not trigger `rejectionHandled` event if it was handled here, NodeJS - should\n      promise._h = isNode || isUnhandled(promise) ? 2 : 1;\n    } promise._a = undefined;\n    if (unhandled && result.e) throw result.v;\n  });\n};\nvar isUnhandled = function (promise) {\n  return promise._h !== 1 && (promise._a || promise._c).length === 0;\n};\nvar onHandleUnhandled = function (promise) {\n  task.call(global, function () {\n    var handler;\n    if (isNode) {\n      process.emit('rejectionHandled', promise);\n    } else if (handler = global.onrejectionhandled) {\n      handler({ promise: promise, reason: promise._v });\n    }\n  });\n};\nvar $reject = function (value) {\n  var promise = this;\n  if (promise._d) return;\n  promise._d = true;\n  promise = promise._w || promise; // unwrap\n  promise._v = value;\n  promise._s = 2;\n  if (!promise._a) promise._a = promise._c.slice();\n  notify(promise, true);\n};\nvar $resolve = function (value) {\n  var promise = this;\n  var then;\n  if (promise._d) return;\n  promise._d = true;\n  promise = promise._w || promise; // unwrap\n  try {\n    if (promise === value) throw TypeError(\"Promise can't be resolved itself\");\n    if (then = isThenable(value)) {\n      microtask(function () {\n        var wrapper = { _w: promise, _d: false }; // wrap\n        try {\n          then.call(value, ctx($resolve, wrapper, 1), ctx($reject, wrapper, 1));\n        } catch (e) {\n          $reject.call(wrapper, e);\n        }\n      });\n    } else {\n      promise._v = value;\n      promise._s = 1;\n      notify(promise, false);\n    }\n  } catch (e) {\n    $reject.call({ _w: promise, _d: false }, e); // wrap\n  }\n};\n\n// constructor polyfill\nif (!USE_NATIVE) {\n  // 25.4.3.1 Promise(executor)\n  $Promise = function Promise(executor) {\n    anInstance(this, $Promise, PROMISE, '_h');\n    aFunction(executor);\n    Internal.call(this);\n    try {\n      executor(ctx($resolve, this, 1), ctx($reject, this, 1));\n    } catch (err) {\n      $reject.call(this, err);\n    }\n  };\n  // eslint-disable-next-line no-unused-vars\n  Internal = function Promise(executor) {\n    this._c = [];             // <- awaiting reactions\n    this._a = undefined;      // <- checked in isUnhandled reactions\n    this._s = 0;              // <- state\n    this._d = false;          // <- done\n    this._v = undefined;      // <- value\n    this._h = 0;              // <- rejection state, 0 - default, 1 - handled, 2 - unhandled\n    this._n = false;          // <- notify\n  };\n  Internal.prototype = require('./_redefine-all')($Promise.prototype, {\n    // 25.4.5.3 Promise.prototype.then(onFulfilled, onRejected)\n    then: function then(onFulfilled, onRejected) {\n      var reaction = newPromiseCapability(speciesConstructor(this, $Promise));\n      reaction.ok = typeof onFulfilled == 'function' ? onFulfilled : true;\n      reaction.fail = typeof onRejected == 'function' && onRejected;\n      reaction.domain = isNode ? process.domain : undefined;\n      this._c.push(reaction);\n      if (this._a) this._a.push(reaction);\n      if (this._s) notify(this, false);\n      return reaction.promise;\n    },\n    // 25.4.5.1 Promise.prototype.catch(onRejected)\n    'catch': function (onRejected) {\n      return this.then(undefined, onRejected);\n    }\n  });\n  OwnPromiseCapability = function () {\n    var promise = new Internal();\n    this.promise = promise;\n    this.resolve = ctx($resolve, promise, 1);\n    this.reject = ctx($reject, promise, 1);\n  };\n  newPromiseCapabilityModule.f = newPromiseCapability = function (C) {\n    return C === $Promise || C === Wrapper\n      ? new OwnPromiseCapability(C)\n      : newGenericPromiseCapability(C);\n  };\n}\n\n$export($export.G + $export.W + $export.F * !USE_NATIVE, { Promise: $Promise });\nrequire('./_set-to-string-tag')($Promise, PROMISE);\nrequire('./_set-species')(PROMISE);\nWrapper = require('./_core')[PROMISE];\n\n// statics\n$export($export.S + $export.F * !USE_NATIVE, PROMISE, {\n  // 25.4.4.5 Promise.reject(r)\n  reject: function reject(r) {\n    var capability = newPromiseCapability(this);\n    var $$reject = capability.reject;\n    $$reject(r);\n    return capability.promise;\n  }\n});\n$export($export.S + $export.F * (LIBRARY || !USE_NATIVE), PROMISE, {\n  // 25.4.4.6 Promise.resolve(x)\n  resolve: function resolve(x) {\n    return promiseResolve(LIBRARY && this === Wrapper ? $Promise : this, x);\n  }\n});\n$export($export.S + $export.F * !(USE_NATIVE && require('./_iter-detect')(function (iter) {\n  $Promise.all(iter)['catch'](empty);\n})), PROMISE, {\n  // 25.4.4.1 Promise.all(iterable)\n  all: function all(iterable) {\n    var C = this;\n    var capability = newPromiseCapability(C);\n    var resolve = capability.resolve;\n    var reject = capability.reject;\n    var result = perform(function () {\n      var values = [];\n      var index = 0;\n      var remaining = 1;\n      forOf(iterable, false, function (promise) {\n        var $index = index++;\n        var alreadyCalled = false;\n        values.push(undefined);\n        remaining++;\n        C.resolve(promise).then(function (value) {\n          if (alreadyCalled) return;\n          alreadyCalled = true;\n          values[$index] = value;\n          --remaining || resolve(values);\n        }, reject);\n      });\n      --remaining || resolve(values);\n    });\n    if (result.e) reject(result.v);\n    return capability.promise;\n  },\n  // 25.4.4.4 Promise.race(iterable)\n  race: function race(iterable) {\n    var C = this;\n    var capability = newPromiseCapability(C);\n    var reject = capability.reject;\n    var result = perform(function () {\n      forOf(iterable, false, function (promise) {\n        C.resolve(promise).then(capability.resolve, reject);\n      });\n    });\n    if (result.e) reject(result.v);\n    return capability.promise;\n  }\n});\n","var isObject = require('./_is-object');\nmodule.exports = function (it, TYPE) {\n  if (!isObject(it) || it._t !== TYPE) throw TypeError('Incompatible receiver, ' + TYPE + ' required!');\n  return it;\n};\n","'use strict';\nvar dP = require('./_object-dp').f;\nvar create = require('./_object-create');\nvar redefineAll = require('./_redefine-all');\nvar ctx = require('./_ctx');\nvar anInstance = require('./_an-instance');\nvar forOf = require('./_for-of');\nvar $iterDefine = require('./_iter-define');\nvar step = require('./_iter-step');\nvar setSpecies = require('./_set-species');\nvar DESCRIPTORS = require('./_descriptors');\nvar fastKey = require('./_meta').fastKey;\nvar validate = require('./_validate-collection');\nvar SIZE = DESCRIPTORS ? '_s' : 'size';\n\nvar getEntry = function (that, key) {\n  // fast case\n  var index = fastKey(key);\n  var entry;\n  if (index !== 'F') return that._i[index];\n  // frozen object case\n  for (entry = that._f; entry; entry = entry.n) {\n    if (entry.k == key) return entry;\n  }\n};\n\nmodule.exports = {\n  getConstructor: function (wrapper, NAME, IS_MAP, ADDER) {\n    var C = wrapper(function (that, iterable) {\n      anInstance(that, C, NAME, '_i');\n      that._t = NAME;         // collection type\n      that._i = create(null); // index\n      that._f = undefined;    // first entry\n      that._l = undefined;    // last entry\n      that[SIZE] = 0;         // size\n      if (iterable != undefined) forOf(iterable, IS_MAP, that[ADDER], that);\n    });\n    redefineAll(C.prototype, {\n      // 23.1.3.1 Map.prototype.clear()\n      // 23.2.3.2 Set.prototype.clear()\n      clear: function clear() {\n        for (var that = validate(this, NAME), data = that._i, entry = that._f; entry; entry = entry.n) {\n          entry.r = true;\n          if (entry.p) entry.p = entry.p.n = undefined;\n          delete data[entry.i];\n        }\n        that._f = that._l = undefined;\n        that[SIZE] = 0;\n      },\n      // 23.1.3.3 Map.prototype.delete(key)\n      // 23.2.3.4 Set.prototype.delete(value)\n      'delete': function (key) {\n        var that = validate(this, NAME);\n        var entry = getEntry(that, key);\n        if (entry) {\n          var next = entry.n;\n          var prev = entry.p;\n          delete that._i[entry.i];\n          entry.r = true;\n          if (prev) prev.n = next;\n          if (next) next.p = prev;\n          if (that._f == entry) that._f = next;\n          if (that._l == entry) that._l = prev;\n          that[SIZE]--;\n        } return !!entry;\n      },\n      // 23.2.3.6 Set.prototype.forEach(callbackfn, thisArg = undefined)\n      // 23.1.3.5 Map.prototype.forEach(callbackfn, thisArg = undefined)\n      forEach: function forEach(callbackfn /* , that = undefined */) {\n        validate(this, NAME);\n        var f = ctx(callbackfn, arguments.length > 1 ? arguments[1] : undefined, 3);\n        var entry;\n        while (entry = entry ? entry.n : this._f) {\n          f(entry.v, entry.k, this);\n          // revert to the last existing entry\n          while (entry && entry.r) entry = entry.p;\n        }\n      },\n      // 23.1.3.7 Map.prototype.has(key)\n      // 23.2.3.7 Set.prototype.has(value)\n      has: function has(key) {\n        return !!getEntry(validate(this, NAME), key);\n      }\n    });\n    if (DESCRIPTORS) dP(C.prototype, 'size', {\n      get: function () {\n        return validate(this, NAME)[SIZE];\n      }\n    });\n    return C;\n  },\n  def: function (that, key, value) {\n    var entry = getEntry(that, key);\n    var prev, index;\n    // change existing entry\n    if (entry) {\n      entry.v = value;\n    // create new entry\n    } else {\n      that._l = entry = {\n        i: index = fastKey(key, true), // <- index\n        k: key,                        // <- key\n        v: value,                      // <- value\n        p: prev = that._l,             // <- previous entry\n        n: undefined,                  // <- next entry\n        r: false                       // <- removed\n      };\n      if (!that._f) that._f = entry;\n      if (prev) prev.n = entry;\n      that[SIZE]++;\n      // add to index\n      if (index !== 'F') that._i[index] = entry;\n    } return that;\n  },\n  getEntry: getEntry,\n  setStrong: function (C, NAME, IS_MAP) {\n    // add .keys, .values, .entries, [@@iterator]\n    // 23.1.3.4, 23.1.3.8, 23.1.3.11, 23.1.3.12, 23.2.3.5, 23.2.3.8, 23.2.3.10, 23.2.3.11\n    $iterDefine(C, NAME, function (iterated, kind) {\n      this._t = validate(iterated, NAME); // target\n      this._k = kind;                     // kind\n      this._l = undefined;                // previous\n    }, function () {\n      var that = this;\n      var kind = that._k;\n      var entry = that._l;\n      // revert to the last existing entry\n      while (entry && entry.r) entry = entry.p;\n      // get next entry\n      if (!that._t || !(that._l = entry = entry ? entry.n : that._t._f)) {\n        // or finish the iteration\n        that._t = undefined;\n        return step(1);\n      }\n      // return step by kind\n      if (kind == 'keys') return step(0, entry.k);\n      if (kind == 'values') return step(0, entry.v);\n      return step(0, [entry.k, entry.v]);\n    }, IS_MAP ? 'entries' : 'values', !IS_MAP, true);\n\n    // add [@@species], 23.1.2.2, 23.2.2.2\n    setSpecies(NAME);\n  }\n};\n","'use strict';\nvar global = require('./_global');\nvar $export = require('./_export');\nvar redefine = require('./_redefine');\nvar redefineAll = require('./_redefine-all');\nvar meta = require('./_meta');\nvar forOf = require('./_for-of');\nvar anInstance = require('./_an-instance');\nvar isObject = require('./_is-object');\nvar fails = require('./_fails');\nvar $iterDetect = require('./_iter-detect');\nvar setToStringTag = require('./_set-to-string-tag');\nvar inheritIfRequired = require('./_inherit-if-required');\n\nmodule.exports = function (NAME, wrapper, methods, common, IS_MAP, IS_WEAK) {\n  var Base = global[NAME];\n  var C = Base;\n  var ADDER = IS_MAP ? 'set' : 'add';\n  var proto = C && C.prototype;\n  var O = {};\n  var fixMethod = function (KEY) {\n    var fn = proto[KEY];\n    redefine(proto, KEY,\n      KEY == 'delete' ? function (a) {\n        return IS_WEAK && !isObject(a) ? false : fn.call(this, a === 0 ? 0 : a);\n      } : KEY == 'has' ? function has(a) {\n        return IS_WEAK && !isObject(a) ? false : fn.call(this, a === 0 ? 0 : a);\n      } : KEY == 'get' ? function get(a) {\n        return IS_WEAK && !isObject(a) ? undefined : fn.call(this, a === 0 ? 0 : a);\n      } : KEY == 'add' ? function add(a) { fn.call(this, a === 0 ? 0 : a); return this; }\n        : function set(a, b) { fn.call(this, a === 0 ? 0 : a, b); return this; }\n    );\n  };\n  if (typeof C != 'function' || !(IS_WEAK || proto.forEach && !fails(function () {\n    new C().entries().next();\n  }))) {\n    // create collection constructor\n    C = common.getConstructor(wrapper, NAME, IS_MAP, ADDER);\n    redefineAll(C.prototype, methods);\n    meta.NEED = true;\n  } else {\n    var instance = new C();\n    // early implementations not supports chaining\n    var HASNT_CHAINING = instance[ADDER](IS_WEAK ? {} : -0, 1) != instance;\n    // V8 ~  Chromium 40- weak-collections throws on primitives, but should return false\n    var THROWS_ON_PRIMITIVES = fails(function () { instance.has(1); });\n    // most early implementations doesn't supports iterables, most modern - not close it correctly\n    var ACCEPT_ITERABLES = $iterDetect(function (iter) { new C(iter); }); // eslint-disable-line no-new\n    // for early implementations -0 and +0 not the same\n    var BUGGY_ZERO = !IS_WEAK && fails(function () {\n      // V8 ~ Chromium 42- fails only with 5+ elements\n      var $instance = new C();\n      var index = 5;\n      while (index--) $instance[ADDER](index, index);\n      return !$instance.has(-0);\n    });\n    if (!ACCEPT_ITERABLES) {\n      C = wrapper(function (target, iterable) {\n        anInstance(target, C, NAME);\n        var that = inheritIfRequired(new Base(), target, C);\n        if (iterable != undefined) forOf(iterable, IS_MAP, that[ADDER], that);\n        return that;\n      });\n      C.prototype = proto;\n      proto.constructor = C;\n    }\n    if (THROWS_ON_PRIMITIVES || BUGGY_ZERO) {\n      fixMethod('delete');\n      fixMethod('has');\n      IS_MAP && fixMethod('get');\n    }\n    if (BUGGY_ZERO || HASNT_CHAINING) fixMethod(ADDER);\n    // weak collections should not contains .clear method\n    if (IS_WEAK && proto.clear) delete proto.clear;\n  }\n\n  setToStringTag(C, NAME);\n\n  O[NAME] = C;\n  $export($export.G + $export.W + $export.F * (C != Base), O);\n\n  if (!IS_WEAK) common.setStrong(C, NAME, IS_MAP);\n\n  return C;\n};\n","'use strict';\nvar strong = require('./_collection-strong');\nvar validate = require('./_validate-collection');\nvar MAP = 'Map';\n\n// 23.1 Map Objects\nmodule.exports = require('./_collection')(MAP, function (get) {\n  return function Map() { return get(this, arguments.length > 0 ? arguments[0] : undefined); };\n}, {\n  // 23.1.3.6 Map.prototype.get(key)\n  get: function get(key) {\n    var entry = strong.getEntry(validate(this, MAP), key);\n    return entry && entry.v;\n  },\n  // 23.1.3.9 Map.prototype.set(key, value)\n  set: function set(key, value) {\n    return strong.def(validate(this, MAP), key === 0 ? 0 : key, value);\n  }\n}, strong, true);\n","'use strict';\nvar strong = require('./_collection-strong');\nvar validate = require('./_validate-collection');\nvar SET = 'Set';\n\n// 23.2 Set Objects\nmodule.exports = require('./_collection')(SET, function (get) {\n  return function Set() { return get(this, arguments.length > 0 ? arguments[0] : undefined); };\n}, {\n  // 23.2.3.1 Set.prototype.add(value)\n  add: function add(value) {\n    return strong.def(validate(this, SET), value = value === 0 ? 0 : value, value);\n  }\n}, strong);\n","'use strict';\nvar redefineAll = require('./_redefine-all');\nvar getWeak = require('./_meta').getWeak;\nvar anObject = require('./_an-object');\nvar isObject = require('./_is-object');\nvar anInstance = require('./_an-instance');\nvar forOf = require('./_for-of');\nvar createArrayMethod = require('./_array-methods');\nvar $has = require('./_has');\nvar validate = require('./_validate-collection');\nvar arrayFind = createArrayMethod(5);\nvar arrayFindIndex = createArrayMethod(6);\nvar id = 0;\n\n// fallback for uncaught frozen keys\nvar uncaughtFrozenStore = function (that) {\n  return that._l || (that._l = new UncaughtFrozenStore());\n};\nvar UncaughtFrozenStore = function () {\n  this.a = [];\n};\nvar findUncaughtFrozen = function (store, key) {\n  return arrayFind(store.a, function (it) {\n    return it[0] === key;\n  });\n};\nUncaughtFrozenStore.prototype = {\n  get: function (key) {\n    var entry = findUncaughtFrozen(this, key);\n    if (entry) return entry[1];\n  },\n  has: function (key) {\n    return !!findUncaughtFrozen(this, key);\n  },\n  set: function (key, value) {\n    var entry = findUncaughtFrozen(this, key);\n    if (entry) entry[1] = value;\n    else this.a.push([key, value]);\n  },\n  'delete': function (key) {\n    var index = arrayFindIndex(this.a, function (it) {\n      return it[0] === key;\n    });\n    if (~index) this.a.splice(index, 1);\n    return !!~index;\n  }\n};\n\nmodule.exports = {\n  getConstructor: function (wrapper, NAME, IS_MAP, ADDER) {\n    var C = wrapper(function (that, iterable) {\n      anInstance(that, C, NAME, '_i');\n      that._t = NAME;      // collection type\n      that._i = id++;      // collection id\n      that._l = undefined; // leak store for uncaught frozen objects\n      if (iterable != undefined) forOf(iterable, IS_MAP, that[ADDER], that);\n    });\n    redefineAll(C.prototype, {\n      // 23.3.3.2 WeakMap.prototype.delete(key)\n      // 23.4.3.3 WeakSet.prototype.delete(value)\n      'delete': function (key) {\n        if (!isObject(key)) return false;\n        var data = getWeak(key);\n        if (data === true) return uncaughtFrozenStore(validate(this, NAME))['delete'](key);\n        return data && $has(data, this._i) && delete data[this._i];\n      },\n      // 23.3.3.4 WeakMap.prototype.has(key)\n      // 23.4.3.4 WeakSet.prototype.has(value)\n      has: function has(key) {\n        if (!isObject(key)) return false;\n        var data = getWeak(key);\n        if (data === true) return uncaughtFrozenStore(validate(this, NAME)).has(key);\n        return data && $has(data, this._i);\n      }\n    });\n    return C;\n  },\n  def: function (that, key, value) {\n    var data = getWeak(anObject(key), true);\n    if (data === true) uncaughtFrozenStore(that).set(key, value);\n    else data[that._i] = value;\n    return that;\n  },\n  ufstore: uncaughtFrozenStore\n};\n","'use strict';\nvar global = require('./_global');\nvar each = require('./_array-methods')(0);\nvar redefine = require('./_redefine');\nvar meta = require('./_meta');\nvar assign = require('./_object-assign');\nvar weak = require('./_collection-weak');\nvar isObject = require('./_is-object');\nvar validate = require('./_validate-collection');\nvar NATIVE_WEAK_MAP = require('./_validate-collection');\nvar IS_IE11 = !global.ActiveXObject && 'ActiveXObject' in global;\nvar WEAK_MAP = 'WeakMap';\nvar getWeak = meta.getWeak;\nvar isExtensible = Object.isExtensible;\nvar uncaughtFrozenStore = weak.ufstore;\nvar InternalMap;\n\nvar wrapper = function (get) {\n  return function WeakMap() {\n    return get(this, arguments.length > 0 ? arguments[0] : undefined);\n  };\n};\n\nvar methods = {\n  // 23.3.3.3 WeakMap.prototype.get(key)\n  get: function get(key) {\n    if (isObject(key)) {\n      var data = getWeak(key);\n      if (data === true) return uncaughtFrozenStore(validate(this, WEAK_MAP)).get(key);\n      return data ? data[this._i] : undefined;\n    }\n  },\n  // 23.3.3.5 WeakMap.prototype.set(key, value)\n  set: function set(key, value) {\n    return weak.def(validate(this, WEAK_MAP), key, value);\n  }\n};\n\n// 23.3 WeakMap Objects\nvar $WeakMap = module.exports = require('./_collection')(WEAK_MAP, wrapper, methods, weak, true, true);\n\n// IE11 WeakMap frozen keys fix\nif (NATIVE_WEAK_MAP && IS_IE11) {\n  InternalMap = weak.getConstructor(wrapper, WEAK_MAP);\n  assign(InternalMap.prototype, methods);\n  meta.NEED = true;\n  each(['delete', 'has', 'get', 'set'], function (key) {\n    var proto = $WeakMap.prototype;\n    var method = proto[key];\n    redefine(proto, key, function (a, b) {\n      // store frozen objects on internal weakmap shim\n      if (isObject(a) && !isExtensible(a)) {\n        if (!this._f) this._f = new InternalMap();\n        var result = this._f[key](a, b);\n        return key == 'set' ? this : result;\n      // store all the rest on native weakmap\n      } return method.call(this, a, b);\n    });\n  });\n}\n","'use strict';\nvar weak = require('./_collection-weak');\nvar validate = require('./_validate-collection');\nvar WEAK_SET = 'WeakSet';\n\n// 23.4 WeakSet Objects\nrequire('./_collection')(WEAK_SET, function (get) {\n  return function WeakSet() { return get(this, arguments.length > 0 ? arguments[0] : undefined); };\n}, {\n  // 23.4.3.1 WeakSet.prototype.add(value)\n  add: function add(value) {\n    return weak.def(validate(this, WEAK_SET), value, true);\n  }\n}, weak, false, true);\n","var global = require('./_global');\nvar hide = require('./_hide');\nvar uid = require('./_uid');\nvar TYPED = uid('typed_array');\nvar VIEW = uid('view');\nvar ABV = !!(global.ArrayBuffer && global.DataView);\nvar CONSTR = ABV;\nvar i = 0;\nvar l = 9;\nvar Typed;\n\nvar TypedArrayConstructors = (\n  'Int8Array,Uint8Array,Uint8ClampedArray,Int16Array,Uint16Array,Int32Array,Uint32Array,Float32Array,Float64Array'\n).split(',');\n\nwhile (i < l) {\n  if (Typed = global[TypedArrayConstructors[i++]]) {\n    hide(Typed.prototype, TYPED, true);\n    hide(Typed.prototype, VIEW, true);\n  } else CONSTR = false;\n}\n\nmodule.exports = {\n  ABV: ABV,\n  CONSTR: CONSTR,\n  TYPED: TYPED,\n  VIEW: VIEW\n};\n","// https://tc39.github.io/ecma262/#sec-toindex\nvar toInteger = require('./_to-integer');\nvar toLength = require('./_to-length');\nmodule.exports = function (it) {\n  if (it === undefined) return 0;\n  var number = toInteger(it);\n  var length = toLength(number);\n  if (number !== length) throw RangeError('Wrong length!');\n  return length;\n};\n","'use strict';\nvar global = require('./_global');\nvar DESCRIPTORS = require('./_descriptors');\nvar LIBRARY = require('./_library');\nvar $typed = require('./_typed');\nvar hide = require('./_hide');\nvar redefineAll = require('./_redefine-all');\nvar fails = require('./_fails');\nvar anInstance = require('./_an-instance');\nvar toInteger = require('./_to-integer');\nvar toLength = require('./_to-length');\nvar toIndex = require('./_to-index');\nvar gOPN = require('./_object-gopn').f;\nvar dP = require('./_object-dp').f;\nvar arrayFill = require('./_array-fill');\nvar setToStringTag = require('./_set-to-string-tag');\nvar ARRAY_BUFFER = 'ArrayBuffer';\nvar DATA_VIEW = 'DataView';\nvar PROTOTYPE = 'prototype';\nvar WRONG_LENGTH = 'Wrong length!';\nvar WRONG_INDEX = 'Wrong index!';\nvar $ArrayBuffer = global[ARRAY_BUFFER];\nvar $DataView = global[DATA_VIEW];\nvar Math = global.Math;\nvar RangeError = global.RangeError;\n// eslint-disable-next-line no-shadow-restricted-names\nvar Infinity = global.Infinity;\nvar BaseBuffer = $ArrayBuffer;\nvar abs = Math.abs;\nvar pow = Math.pow;\nvar floor = Math.floor;\nvar log = Math.log;\nvar LN2 = Math.LN2;\nvar BUFFER = 'buffer';\nvar BYTE_LENGTH = 'byteLength';\nvar BYTE_OFFSET = 'byteOffset';\nvar $BUFFER = DESCRIPTORS ? '_b' : BUFFER;\nvar $LENGTH = DESCRIPTORS ? '_l' : BYTE_LENGTH;\nvar $OFFSET = DESCRIPTORS ? '_o' : BYTE_OFFSET;\n\n// IEEE754 conversions based on https://github.com/feross/ieee754\nfunction packIEEE754(value, mLen, nBytes) {\n  var buffer = new Array(nBytes);\n  var eLen = nBytes * 8 - mLen - 1;\n  var eMax = (1 << eLen) - 1;\n  var eBias = eMax >> 1;\n  var rt = mLen === 23 ? pow(2, -24) - pow(2, -77) : 0;\n  var i = 0;\n  var s = value < 0 || value === 0 && 1 / value < 0 ? 1 : 0;\n  var e, m, c;\n  value = abs(value);\n  // eslint-disable-next-line no-self-compare\n  if (value != value || value === Infinity) {\n    // eslint-disable-next-line no-self-compare\n    m = value != value ? 1 : 0;\n    e = eMax;\n  } else {\n    e = floor(log(value) / LN2);\n    if (value * (c = pow(2, -e)) < 1) {\n      e--;\n      c *= 2;\n    }\n    if (e + eBias >= 1) {\n      value += rt / c;\n    } else {\n      value += rt * pow(2, 1 - eBias);\n    }\n    if (value * c >= 2) {\n      e++;\n      c /= 2;\n    }\n    if (e + eBias >= eMax) {\n      m = 0;\n      e = eMax;\n    } else if (e + eBias >= 1) {\n      m = (value * c - 1) * pow(2, mLen);\n      e = e + eBias;\n    } else {\n      m = value * pow(2, eBias - 1) * pow(2, mLen);\n      e = 0;\n    }\n  }\n  for (; mLen >= 8; buffer[i++] = m & 255, m /= 256, mLen -= 8);\n  e = e << mLen | m;\n  eLen += mLen;\n  for (; eLen > 0; buffer[i++] = e & 255, e /= 256, eLen -= 8);\n  buffer[--i] |= s * 128;\n  return buffer;\n}\nfunction unpackIEEE754(buffer, mLen, nBytes) {\n  var eLen = nBytes * 8 - mLen - 1;\n  var eMax = (1 << eLen) - 1;\n  var eBias = eMax >> 1;\n  var nBits = eLen - 7;\n  var i = nBytes - 1;\n  var s = buffer[i--];\n  var e = s & 127;\n  var m;\n  s >>= 7;\n  for (; nBits > 0; e = e * 256 + buffer[i], i--, nBits -= 8);\n  m = e & (1 << -nBits) - 1;\n  e >>= -nBits;\n  nBits += mLen;\n  for (; nBits > 0; m = m * 256 + buffer[i], i--, nBits -= 8);\n  if (e === 0) {\n    e = 1 - eBias;\n  } else if (e === eMax) {\n    return m ? NaN : s ? -Infinity : Infinity;\n  } else {\n    m = m + pow(2, mLen);\n    e = e - eBias;\n  } return (s ? -1 : 1) * m * pow(2, e - mLen);\n}\n\nfunction unpackI32(bytes) {\n  return bytes[3] << 24 | bytes[2] << 16 | bytes[1] << 8 | bytes[0];\n}\nfunction packI8(it) {\n  return [it & 0xff];\n}\nfunction packI16(it) {\n  return [it & 0xff, it >> 8 & 0xff];\n}\nfunction packI32(it) {\n  return [it & 0xff, it >> 8 & 0xff, it >> 16 & 0xff, it >> 24 & 0xff];\n}\nfunction packF64(it) {\n  return packIEEE754(it, 52, 8);\n}\nfunction packF32(it) {\n  return packIEEE754(it, 23, 4);\n}\n\nfunction addGetter(C, key, internal) {\n  dP(C[PROTOTYPE], key, { get: function () { return this[internal]; } });\n}\n\nfunction get(view, bytes, index, isLittleEndian) {\n  var numIndex = +index;\n  var intIndex = toIndex(numIndex);\n  if (intIndex + bytes > view[$LENGTH]) throw RangeError(WRONG_INDEX);\n  var store = view[$BUFFER]._b;\n  var start = intIndex + view[$OFFSET];\n  var pack = store.slice(start, start + bytes);\n  return isLittleEndian ? pack : pack.reverse();\n}\nfunction set(view, bytes, index, conversion, value, isLittleEndian) {\n  var numIndex = +index;\n  var intIndex = toIndex(numIndex);\n  if (intIndex + bytes > view[$LENGTH]) throw RangeError(WRONG_INDEX);\n  var store = view[$BUFFER]._b;\n  var start = intIndex + view[$OFFSET];\n  var pack = conversion(+value);\n  for (var i = 0; i < bytes; i++) store[start + i] = pack[isLittleEndian ? i : bytes - i - 1];\n}\n\nif (!$typed.ABV) {\n  $ArrayBuffer = function ArrayBuffer(length) {\n    anInstance(this, $ArrayBuffer, ARRAY_BUFFER);\n    var byteLength = toIndex(length);\n    this._b = arrayFill.call(new Array(byteLength), 0);\n    this[$LENGTH] = byteLength;\n  };\n\n  $DataView = function DataView(buffer, byteOffset, byteLength) {\n    anInstance(this, $DataView, DATA_VIEW);\n    anInstance(buffer, $ArrayBuffer, DATA_VIEW);\n    var bufferLength = buffer[$LENGTH];\n    var offset = toInteger(byteOffset);\n    if (offset < 0 || offset > bufferLength) throw RangeError('Wrong offset!');\n    byteLength = byteLength === undefined ? bufferLength - offset : toLength(byteLength);\n    if (offset + byteLength > bufferLength) throw RangeError(WRONG_LENGTH);\n    this[$BUFFER] = buffer;\n    this[$OFFSET] = offset;\n    this[$LENGTH] = byteLength;\n  };\n\n  if (DESCRIPTORS) {\n    addGetter($ArrayBuffer, BYTE_LENGTH, '_l');\n    addGetter($DataView, BUFFER, '_b');\n    addGetter($DataView, BYTE_LENGTH, '_l');\n    addGetter($DataView, BYTE_OFFSET, '_o');\n  }\n\n  redefineAll($DataView[PROTOTYPE], {\n    getInt8: function getInt8(byteOffset) {\n      return get(this, 1, byteOffset)[0] << 24 >> 24;\n    },\n    getUint8: function getUint8(byteOffset) {\n      return get(this, 1, byteOffset)[0];\n    },\n    getInt16: function getInt16(byteOffset /* , littleEndian */) {\n      var bytes = get(this, 2, byteOffset, arguments[1]);\n      return (bytes[1] << 8 | bytes[0]) << 16 >> 16;\n    },\n    getUint16: function getUint16(byteOffset /* , littleEndian */) {\n      var bytes = get(this, 2, byteOffset, arguments[1]);\n      return bytes[1] << 8 | bytes[0];\n    },\n    getInt32: function getInt32(byteOffset /* , littleEndian */) {\n      return unpackI32(get(this, 4, byteOffset, arguments[1]));\n    },\n    getUint32: function getUint32(byteOffset /* , littleEndian */) {\n      return unpackI32(get(this, 4, byteOffset, arguments[1])) >>> 0;\n    },\n    getFloat32: function getFloat32(byteOffset /* , littleEndian */) {\n      return unpackIEEE754(get(this, 4, byteOffset, arguments[1]), 23, 4);\n    },\n    getFloat64: function getFloat64(byteOffset /* , littleEndian */) {\n      return unpackIEEE754(get(this, 8, byteOffset, arguments[1]), 52, 8);\n    },\n    setInt8: function setInt8(byteOffset, value) {\n      set(this, 1, byteOffset, packI8, value);\n    },\n    setUint8: function setUint8(byteOffset, value) {\n      set(this, 1, byteOffset, packI8, value);\n    },\n    setInt16: function setInt16(byteOffset, value /* , littleEndian */) {\n      set(this, 2, byteOffset, packI16, value, arguments[2]);\n    },\n    setUint16: function setUint16(byteOffset, value /* , littleEndian */) {\n      set(this, 2, byteOffset, packI16, value, arguments[2]);\n    },\n    setInt32: function setInt32(byteOffset, value /* , littleEndian */) {\n      set(this, 4, byteOffset, packI32, value, arguments[2]);\n    },\n    setUint32: function setUint32(byteOffset, value /* , littleEndian */) {\n      set(this, 4, byteOffset, packI32, value, arguments[2]);\n    },\n    setFloat32: function setFloat32(byteOffset, value /* , littleEndian */) {\n      set(this, 4, byteOffset, packF32, value, arguments[2]);\n    },\n    setFloat64: function setFloat64(byteOffset, value /* , littleEndian */) {\n      set(this, 8, byteOffset, packF64, value, arguments[2]);\n    }\n  });\n} else {\n  if (!fails(function () {\n    $ArrayBuffer(1);\n  }) || !fails(function () {\n    new $ArrayBuffer(-1); // eslint-disable-line no-new\n  }) || fails(function () {\n    new $ArrayBuffer(); // eslint-disable-line no-new\n    new $ArrayBuffer(1.5); // eslint-disable-line no-new\n    new $ArrayBuffer(NaN); // eslint-disable-line no-new\n    return $ArrayBuffer.name != ARRAY_BUFFER;\n  })) {\n    $ArrayBuffer = function ArrayBuffer(length) {\n      anInstance(this, $ArrayBuffer);\n      return new BaseBuffer(toIndex(length));\n    };\n    var ArrayBufferProto = $ArrayBuffer[PROTOTYPE] = BaseBuffer[PROTOTYPE];\n    for (var keys = gOPN(BaseBuffer), j = 0, key; keys.length > j;) {\n      if (!((key = keys[j++]) in $ArrayBuffer)) hide($ArrayBuffer, key, BaseBuffer[key]);\n    }\n    if (!LIBRARY) ArrayBufferProto.constructor = $ArrayBuffer;\n  }\n  // iOS Safari 7.x bug\n  var view = new $DataView(new $ArrayBuffer(2));\n  var $setInt8 = $DataView[PROTOTYPE].setInt8;\n  view.setInt8(0, 2147483648);\n  view.setInt8(1, 2147483649);\n  if (view.getInt8(0) || !view.getInt8(1)) redefineAll($DataView[PROTOTYPE], {\n    setInt8: function setInt8(byteOffset, value) {\n      $setInt8.call(this, byteOffset, value << 24 >> 24);\n    },\n    setUint8: function setUint8(byteOffset, value) {\n      $setInt8.call(this, byteOffset, value << 24 >> 24);\n    }\n  }, true);\n}\nsetToStringTag($ArrayBuffer, ARRAY_BUFFER);\nsetToStringTag($DataView, DATA_VIEW);\nhide($DataView[PROTOTYPE], $typed.VIEW, true);\nexports[ARRAY_BUFFER] = $ArrayBuffer;\nexports[DATA_VIEW] = $DataView;\n","'use strict';\nvar $export = require('./_export');\nvar $typed = require('./_typed');\nvar buffer = require('./_typed-buffer');\nvar anObject = require('./_an-object');\nvar toAbsoluteIndex = require('./_to-absolute-index');\nvar toLength = require('./_to-length');\nvar isObject = require('./_is-object');\nvar ArrayBuffer = require('./_global').ArrayBuffer;\nvar speciesConstructor = require('./_species-constructor');\nvar $ArrayBuffer = buffer.ArrayBuffer;\nvar $DataView = buffer.DataView;\nvar $isView = $typed.ABV && ArrayBuffer.isView;\nvar $slice = $ArrayBuffer.prototype.slice;\nvar VIEW = $typed.VIEW;\nvar ARRAY_BUFFER = 'ArrayBuffer';\n\n$export($export.G + $export.W + $export.F * (ArrayBuffer !== $ArrayBuffer), { ArrayBuffer: $ArrayBuffer });\n\n$export($export.S + $export.F * !$typed.CONSTR, ARRAY_BUFFER, {\n  // 24.1.3.1 ArrayBuffer.isView(arg)\n  isView: function isView(it) {\n    return $isView && $isView(it) || isObject(it) && VIEW in it;\n  }\n});\n\n$export($export.P + $export.U + $export.F * require('./_fails')(function () {\n  return !new $ArrayBuffer(2).slice(1, undefined).byteLength;\n}), ARRAY_BUFFER, {\n  // 24.1.4.3 ArrayBuffer.prototype.slice(start, end)\n  slice: function slice(start, end) {\n    if ($slice !== undefined && end === undefined) return $slice.call(anObject(this), start); // FF fix\n    var len = anObject(this).byteLength;\n    var first = toAbsoluteIndex(start, len);\n    var fin = toAbsoluteIndex(end === undefined ? len : end, len);\n    var result = new (speciesConstructor(this, $ArrayBuffer))(toLength(fin - first));\n    var viewS = new $DataView(this);\n    var viewT = new $DataView(result);\n    var index = 0;\n    while (first < fin) {\n      viewT.setUint8(index++, viewS.getUint8(first++));\n    } return result;\n  }\n});\n\nrequire('./_set-species')(ARRAY_BUFFER);\n","var $export = require('./_export');\n$export($export.G + $export.W + $export.F * !require('./_typed').ABV, {\n  DataView: require('./_typed-buffer').DataView\n});\n","'use strict';\nif (require('./_descriptors')) {\n  var LIBRARY = require('./_library');\n  var global = require('./_global');\n  var fails = require('./_fails');\n  var $export = require('./_export');\n  var $typed = require('./_typed');\n  var $buffer = require('./_typed-buffer');\n  var ctx = require('./_ctx');\n  var anInstance = require('./_an-instance');\n  var propertyDesc = require('./_property-desc');\n  var hide = require('./_hide');\n  var redefineAll = require('./_redefine-all');\n  var toInteger = require('./_to-integer');\n  var toLength = require('./_to-length');\n  var toIndex = require('./_to-index');\n  var toAbsoluteIndex = require('./_to-absolute-index');\n  var toPrimitive = require('./_to-primitive');\n  var has = require('./_has');\n  var classof = require('./_classof');\n  var isObject = require('./_is-object');\n  var toObject = require('./_to-object');\n  var isArrayIter = require('./_is-array-iter');\n  var create = require('./_object-create');\n  var getPrototypeOf = require('./_object-gpo');\n  var gOPN = require('./_object-gopn').f;\n  var getIterFn = require('./core.get-iterator-method');\n  var uid = require('./_uid');\n  var wks = require('./_wks');\n  var createArrayMethod = require('./_array-methods');\n  var createArrayIncludes = require('./_array-includes');\n  var speciesConstructor = require('./_species-constructor');\n  var ArrayIterators = require('./es6.array.iterator');\n  var Iterators = require('./_iterators');\n  var $iterDetect = require('./_iter-detect');\n  var setSpecies = require('./_set-species');\n  var arrayFill = require('./_array-fill');\n  var arrayCopyWithin = require('./_array-copy-within');\n  var $DP = require('./_object-dp');\n  var $GOPD = require('./_object-gopd');\n  var dP = $DP.f;\n  var gOPD = $GOPD.f;\n  var RangeError = global.RangeError;\n  var TypeError = global.TypeError;\n  var Uint8Array = global.Uint8Array;\n  var ARRAY_BUFFER = 'ArrayBuffer';\n  var SHARED_BUFFER = 'Shared' + ARRAY_BUFFER;\n  var BYTES_PER_ELEMENT = 'BYTES_PER_ELEMENT';\n  var PROTOTYPE = 'prototype';\n  var ArrayProto = Array[PROTOTYPE];\n  var $ArrayBuffer = $buffer.ArrayBuffer;\n  var $DataView = $buffer.DataView;\n  var arrayForEach = createArrayMethod(0);\n  var arrayFilter = createArrayMethod(2);\n  var arraySome = createArrayMethod(3);\n  var arrayEvery = createArrayMethod(4);\n  var arrayFind = createArrayMethod(5);\n  var arrayFindIndex = createArrayMethod(6);\n  var arrayIncludes = createArrayIncludes(true);\n  var arrayIndexOf = createArrayIncludes(false);\n  var arrayValues = ArrayIterators.values;\n  var arrayKeys = ArrayIterators.keys;\n  var arrayEntries = ArrayIterators.entries;\n  var arrayLastIndexOf = ArrayProto.lastIndexOf;\n  var arrayReduce = ArrayProto.reduce;\n  var arrayReduceRight = ArrayProto.reduceRight;\n  var arrayJoin = ArrayProto.join;\n  var arraySort = ArrayProto.sort;\n  var arraySlice = ArrayProto.slice;\n  var arrayToString = ArrayProto.toString;\n  var arrayToLocaleString = ArrayProto.toLocaleString;\n  var ITERATOR = wks('iterator');\n  var TAG = wks('toStringTag');\n  var TYPED_CONSTRUCTOR = uid('typed_constructor');\n  var DEF_CONSTRUCTOR = uid('def_constructor');\n  var ALL_CONSTRUCTORS = $typed.CONSTR;\n  var TYPED_ARRAY = $typed.TYPED;\n  var VIEW = $typed.VIEW;\n  var WRONG_LENGTH = 'Wrong length!';\n\n  var $map = createArrayMethod(1, function (O, length) {\n    return allocate(speciesConstructor(O, O[DEF_CONSTRUCTOR]), length);\n  });\n\n  var LITTLE_ENDIAN = fails(function () {\n    // eslint-disable-next-line no-undef\n    return new Uint8Array(new Uint16Array([1]).buffer)[0] === 1;\n  });\n\n  var FORCED_SET = !!Uint8Array && !!Uint8Array[PROTOTYPE].set && fails(function () {\n    new Uint8Array(1).set({});\n  });\n\n  var toOffset = function (it, BYTES) {\n    var offset = toInteger(it);\n    if (offset < 0 || offset % BYTES) throw RangeError('Wrong offset!');\n    return offset;\n  };\n\n  var validate = function (it) {\n    if (isObject(it) && TYPED_ARRAY in it) return it;\n    throw TypeError(it + ' is not a typed array!');\n  };\n\n  var allocate = function (C, length) {\n    if (!(isObject(C) && TYPED_CONSTRUCTOR in C)) {\n      throw TypeError('It is not a typed array constructor!');\n    } return new C(length);\n  };\n\n  var speciesFromList = function (O, list) {\n    return fromList(speciesConstructor(O, O[DEF_CONSTRUCTOR]), list);\n  };\n\n  var fromList = function (C, list) {\n    var index = 0;\n    var length = list.length;\n    var result = allocate(C, length);\n    while (length > index) result[index] = list[index++];\n    return result;\n  };\n\n  var addGetter = function (it, key, internal) {\n    dP(it, key, { get: function () { return this._d[internal]; } });\n  };\n\n  var $from = function from(source /* , mapfn, thisArg */) {\n    var O = toObject(source);\n    var aLen = arguments.length;\n    var mapfn = aLen > 1 ? arguments[1] : undefined;\n    var mapping = mapfn !== undefined;\n    var iterFn = getIterFn(O);\n    var i, length, values, result, step, iterator;\n    if (iterFn != undefined && !isArrayIter(iterFn)) {\n      for (iterator = iterFn.call(O), values = [], i = 0; !(step = iterator.next()).done; i++) {\n        values.push(step.value);\n      } O = values;\n    }\n    if (mapping && aLen > 2) mapfn = ctx(mapfn, arguments[2], 2);\n    for (i = 0, length = toLength(O.length), result = allocate(this, length); length > i; i++) {\n      result[i] = mapping ? mapfn(O[i], i) : O[i];\n    }\n    return result;\n  };\n\n  var $of = function of(/* ...items */) {\n    var index = 0;\n    var length = arguments.length;\n    var result = allocate(this, length);\n    while (length > index) result[index] = arguments[index++];\n    return result;\n  };\n\n  // iOS Safari 6.x fails here\n  var TO_LOCALE_BUG = !!Uint8Array && fails(function () { arrayToLocaleString.call(new Uint8Array(1)); });\n\n  var $toLocaleString = function toLocaleString() {\n    return arrayToLocaleString.apply(TO_LOCALE_BUG ? arraySlice.call(validate(this)) : validate(this), arguments);\n  };\n\n  var proto = {\n    copyWithin: function copyWithin(target, start /* , end */) {\n      return arrayCopyWithin.call(validate(this), target, start, arguments.length > 2 ? arguments[2] : undefined);\n    },\n    every: function every(callbackfn /* , thisArg */) {\n      return arrayEvery(validate(this), callbackfn, arguments.length > 1 ? arguments[1] : undefined);\n    },\n    fill: function fill(value /* , start, end */) { // eslint-disable-line no-unused-vars\n      return arrayFill.apply(validate(this), arguments);\n    },\n    filter: function filter(callbackfn /* , thisArg */) {\n      return speciesFromList(this, arrayFilter(validate(this), callbackfn,\n        arguments.length > 1 ? arguments[1] : undefined));\n    },\n    find: function find(predicate /* , thisArg */) {\n      return arrayFind(validate(this), predicate, arguments.length > 1 ? arguments[1] : undefined);\n    },\n    findIndex: function findIndex(predicate /* , thisArg */) {\n      return arrayFindIndex(validate(this), predicate, arguments.length > 1 ? arguments[1] : undefined);\n    },\n    forEach: function forEach(callbackfn /* , thisArg */) {\n      arrayForEach(validate(this), callbackfn, arguments.length > 1 ? arguments[1] : undefined);\n    },\n    indexOf: function indexOf(searchElement /* , fromIndex */) {\n      return arrayIndexOf(validate(this), searchElement, arguments.length > 1 ? arguments[1] : undefined);\n    },\n    includes: function includes(searchElement /* , fromIndex */) {\n      return arrayIncludes(validate(this), searchElement, arguments.length > 1 ? arguments[1] : undefined);\n    },\n    join: function join(separator) { // eslint-disable-line no-unused-vars\n      return arrayJoin.apply(validate(this), arguments);\n    },\n    lastIndexOf: function lastIndexOf(searchElement /* , fromIndex */) { // eslint-disable-line no-unused-vars\n      return arrayLastIndexOf.apply(validate(this), arguments);\n    },\n    map: function map(mapfn /* , thisArg */) {\n      return $map(validate(this), mapfn, arguments.length > 1 ? arguments[1] : undefined);\n    },\n    reduce: function reduce(callbackfn /* , initialValue */) { // eslint-disable-line no-unused-vars\n      return arrayReduce.apply(validate(this), arguments);\n    },\n    reduceRight: function reduceRight(callbackfn /* , initialValue */) { // eslint-disable-line no-unused-vars\n      return arrayReduceRight.apply(validate(this), arguments);\n    },\n    reverse: function reverse() {\n      var that = this;\n      var length = validate(that).length;\n      var middle = Math.floor(length / 2);\n      var index = 0;\n      var value;\n      while (index < middle) {\n        value = that[index];\n        that[index++] = that[--length];\n        that[length] = value;\n      } return that;\n    },\n    some: function some(callbackfn /* , thisArg */) {\n      return arraySome(validate(this), callbackfn, arguments.length > 1 ? arguments[1] : undefined);\n    },\n    sort: function sort(comparefn) {\n      return arraySort.call(validate(this), comparefn);\n    },\n    subarray: function subarray(begin, end) {\n      var O = validate(this);\n      var length = O.length;\n      var $begin = toAbsoluteIndex(begin, length);\n      return new (speciesConstructor(O, O[DEF_CONSTRUCTOR]))(\n        O.buffer,\n        O.byteOffset + $begin * O.BYTES_PER_ELEMENT,\n        toLength((end === undefined ? length : toAbsoluteIndex(end, length)) - $begin)\n      );\n    }\n  };\n\n  var $slice = function slice(start, end) {\n    return speciesFromList(this, arraySlice.call(validate(this), start, end));\n  };\n\n  var $set = function set(arrayLike /* , offset */) {\n    validate(this);\n    var offset = toOffset(arguments[1], 1);\n    var length = this.length;\n    var src = toObject(arrayLike);\n    var len = toLength(src.length);\n    var index = 0;\n    if (len + offset > length) throw RangeError(WRONG_LENGTH);\n    while (index < len) this[offset + index] = src[index++];\n  };\n\n  var $iterators = {\n    entries: function entries() {\n      return arrayEntries.call(validate(this));\n    },\n    keys: function keys() {\n      return arrayKeys.call(validate(this));\n    },\n    values: function values() {\n      return arrayValues.call(validate(this));\n    }\n  };\n\n  var isTAIndex = function (target, key) {\n    return isObject(target)\n      && target[TYPED_ARRAY]\n      && typeof key != 'symbol'\n      && key in target\n      && String(+key) == String(key);\n  };\n  var $getDesc = function getOwnPropertyDescriptor(target, key) {\n    return isTAIndex(target, key = toPrimitive(key, true))\n      ? propertyDesc(2, target[key])\n      : gOPD(target, key);\n  };\n  var $setDesc = function defineProperty(target, key, desc) {\n    if (isTAIndex(target, key = toPrimitive(key, true))\n      && isObject(desc)\n      && has(desc, 'value')\n      && !has(desc, 'get')\n      && !has(desc, 'set')\n      // TODO: add validation descriptor w/o calling accessors\n      && !desc.configurable\n      && (!has(desc, 'writable') || desc.writable)\n      && (!has(desc, 'enumerable') || desc.enumerable)\n    ) {\n      target[key] = desc.value;\n      return target;\n    } return dP(target, key, desc);\n  };\n\n  if (!ALL_CONSTRUCTORS) {\n    $GOPD.f = $getDesc;\n    $DP.f = $setDesc;\n  }\n\n  $export($export.S + $export.F * !ALL_CONSTRUCTORS, 'Object', {\n    getOwnPropertyDescriptor: $getDesc,\n    defineProperty: $setDesc\n  });\n\n  if (fails(function () { arrayToString.call({}); })) {\n    arrayToString = arrayToLocaleString = function toString() {\n      return arrayJoin.call(this);\n    };\n  }\n\n  var $TypedArrayPrototype$ = redefineAll({}, proto);\n  redefineAll($TypedArrayPrototype$, $iterators);\n  hide($TypedArrayPrototype$, ITERATOR, $iterators.values);\n  redefineAll($TypedArrayPrototype$, {\n    slice: $slice,\n    set: $set,\n    constructor: function () { /* noop */ },\n    toString: arrayToString,\n    toLocaleString: $toLocaleString\n  });\n  addGetter($TypedArrayPrototype$, 'buffer', 'b');\n  addGetter($TypedArrayPrototype$, 'byteOffset', 'o');\n  addGetter($TypedArrayPrototype$, 'byteLength', 'l');\n  addGetter($TypedArrayPrototype$, 'length', 'e');\n  dP($TypedArrayPrototype$, TAG, {\n    get: function () { return this[TYPED_ARRAY]; }\n  });\n\n  // eslint-disable-next-line max-statements\n  module.exports = function (KEY, BYTES, wrapper, CLAMPED) {\n    CLAMPED = !!CLAMPED;\n    var NAME = KEY + (CLAMPED ? 'Clamped' : '') + 'Array';\n    var GETTER = 'get' + KEY;\n    var SETTER = 'set' + KEY;\n    var TypedArray = global[NAME];\n    var Base = TypedArray || {};\n    var TAC = TypedArray && getPrototypeOf(TypedArray);\n    var FORCED = !TypedArray || !$typed.ABV;\n    var O = {};\n    var TypedArrayPrototype = TypedArray && TypedArray[PROTOTYPE];\n    var getter = function (that, index) {\n      var data = that._d;\n      return data.v[GETTER](index * BYTES + data.o, LITTLE_ENDIAN);\n    };\n    var setter = function (that, index, value) {\n      var data = that._d;\n      if (CLAMPED) value = (value = Math.round(value)) < 0 ? 0 : value > 0xff ? 0xff : value & 0xff;\n      data.v[SETTER](index * BYTES + data.o, value, LITTLE_ENDIAN);\n    };\n    var addElement = function (that, index) {\n      dP(that, index, {\n        get: function () {\n          return getter(this, index);\n        },\n        set: function (value) {\n          return setter(this, index, value);\n        },\n        enumerable: true\n      });\n    };\n    if (FORCED) {\n      TypedArray = wrapper(function (that, data, $offset, $length) {\n        anInstance(that, TypedArray, NAME, '_d');\n        var index = 0;\n        var offset = 0;\n        var buffer, byteLength, length, klass;\n        if (!isObject(data)) {\n          length = toIndex(data);\n          byteLength = length * BYTES;\n          buffer = new $ArrayBuffer(byteLength);\n        } else if (data instanceof $ArrayBuffer || (klass = classof(data)) == ARRAY_BUFFER || klass == SHARED_BUFFER) {\n          buffer = data;\n          offset = toOffset($offset, BYTES);\n          var $len = data.byteLength;\n          if ($length === undefined) {\n            if ($len % BYTES) throw RangeError(WRONG_LENGTH);\n            byteLength = $len - offset;\n            if (byteLength < 0) throw RangeError(WRONG_LENGTH);\n          } else {\n            byteLength = toLength($length) * BYTES;\n            if (byteLength + offset > $len) throw RangeError(WRONG_LENGTH);\n          }\n          length = byteLength / BYTES;\n        } else if (TYPED_ARRAY in data) {\n          return fromList(TypedArray, data);\n        } else {\n          return $from.call(TypedArray, data);\n        }\n        hide(that, '_d', {\n          b: buffer,\n          o: offset,\n          l: byteLength,\n          e: length,\n          v: new $DataView(buffer)\n        });\n        while (index < length) addElement(that, index++);\n      });\n      TypedArrayPrototype = TypedArray[PROTOTYPE] = create($TypedArrayPrototype$);\n      hide(TypedArrayPrototype, 'constructor', TypedArray);\n    } else if (!fails(function () {\n      TypedArray(1);\n    }) || !fails(function () {\n      new TypedArray(-1); // eslint-disable-line no-new\n    }) || !$iterDetect(function (iter) {\n      new TypedArray(); // eslint-disable-line no-new\n      new TypedArray(null); // eslint-disable-line no-new\n      new TypedArray(1.5); // eslint-disable-line no-new\n      new TypedArray(iter); // eslint-disable-line no-new\n    }, true)) {\n      TypedArray = wrapper(function (that, data, $offset, $length) {\n        anInstance(that, TypedArray, NAME);\n        var klass;\n        // `ws` module bug, temporarily remove validation length for Uint8Array\n        // https://github.com/websockets/ws/pull/645\n        if (!isObject(data)) return new Base(toIndex(data));\n        if (data instanceof $ArrayBuffer || (klass = classof(data)) == ARRAY_BUFFER || klass == SHARED_BUFFER) {\n          return $length !== undefined\n            ? new Base(data, toOffset($offset, BYTES), $length)\n            : $offset !== undefined\n              ? new Base(data, toOffset($offset, BYTES))\n              : new Base(data);\n        }\n        if (TYPED_ARRAY in data) return fromList(TypedArray, data);\n        return $from.call(TypedArray, data);\n      });\n      arrayForEach(TAC !== Function.prototype ? gOPN(Base).concat(gOPN(TAC)) : gOPN(Base), function (key) {\n        if (!(key in TypedArray)) hide(TypedArray, key, Base[key]);\n      });\n      TypedArray[PROTOTYPE] = TypedArrayPrototype;\n      if (!LIBRARY) TypedArrayPrototype.constructor = TypedArray;\n    }\n    var $nativeIterator = TypedArrayPrototype[ITERATOR];\n    var CORRECT_ITER_NAME = !!$nativeIterator\n      && ($nativeIterator.name == 'values' || $nativeIterator.name == undefined);\n    var $iterator = $iterators.values;\n    hide(TypedArray, TYPED_CONSTRUCTOR, true);\n    hide(TypedArrayPrototype, TYPED_ARRAY, NAME);\n    hide(TypedArrayPrototype, VIEW, true);\n    hide(TypedArrayPrototype, DEF_CONSTRUCTOR, TypedArray);\n\n    if (CLAMPED ? new TypedArray(1)[TAG] != NAME : !(TAG in TypedArrayPrototype)) {\n      dP(TypedArrayPrototype, TAG, {\n        get: function () { return NAME; }\n      });\n    }\n\n    O[NAME] = TypedArray;\n\n    $export($export.G + $export.W + $export.F * (TypedArray != Base), O);\n\n    $export($export.S, NAME, {\n      BYTES_PER_ELEMENT: BYTES\n    });\n\n    $export($export.S + $export.F * fails(function () { Base.of.call(TypedArray, 1); }), NAME, {\n      from: $from,\n      of: $of\n    });\n\n    if (!(BYTES_PER_ELEMENT in TypedArrayPrototype)) hide(TypedArrayPrototype, BYTES_PER_ELEMENT, BYTES);\n\n    $export($export.P, NAME, proto);\n\n    setSpecies(NAME);\n\n    $export($export.P + $export.F * FORCED_SET, NAME, { set: $set });\n\n    $export($export.P + $export.F * !CORRECT_ITER_NAME, NAME, $iterators);\n\n    if (!LIBRARY && TypedArrayPrototype.toString != arrayToString) TypedArrayPrototype.toString = arrayToString;\n\n    $export($export.P + $export.F * fails(function () {\n      new TypedArray(1).slice();\n    }), NAME, { slice: $slice });\n\n    $export($export.P + $export.F * (fails(function () {\n      return [1, 2].toLocaleString() != new TypedArray([1, 2]).toLocaleString();\n    }) || !fails(function () {\n      TypedArrayPrototype.toLocaleString.call([1, 2]);\n    })), NAME, { toLocaleString: $toLocaleString });\n\n    Iterators[NAME] = CORRECT_ITER_NAME ? $nativeIterator : $iterator;\n    if (!LIBRARY && !CORRECT_ITER_NAME) hide(TypedArrayPrototype, ITERATOR, $iterator);\n  };\n} else module.exports = function () { /* empty */ };\n","require('./_typed-array')('Int8', 1, function (init) {\n  return function Int8Array(data, byteOffset, length) {\n    return init(this, data, byteOffset, length);\n  };\n});\n","require('./_typed-array')('Uint8', 1, function (init) {\n  return function Uint8Array(data, byteOffset, length) {\n    return init(this, data, byteOffset, length);\n  };\n});\n","require('./_typed-array')('Uint8', 1, function (init) {\n  return function Uint8ClampedArray(data, byteOffset, length) {\n    return init(this, data, byteOffset, length);\n  };\n}, true);\n","require('./_typed-array')('Int16', 2, function (init) {\n  return function Int16Array(data, byteOffset, length) {\n    return init(this, data, byteOffset, length);\n  };\n});\n","require('./_typed-array')('Uint16', 2, function (init) {\n  return function Uint16Array(data, byteOffset, length) {\n    return init(this, data, byteOffset, length);\n  };\n});\n","require('./_typed-array')('Int32', 4, function (init) {\n  return function Int32Array(data, byteOffset, length) {\n    return init(this, data, byteOffset, length);\n  };\n});\n","require('./_typed-array')('Uint32', 4, function (init) {\n  return function Uint32Array(data, byteOffset, length) {\n    return init(this, data, byteOffset, length);\n  };\n});\n","require('./_typed-array')('Float32', 4, function (init) {\n  return function Float32Array(data, byteOffset, length) {\n    return init(this, data, byteOffset, length);\n  };\n});\n","require('./_typed-array')('Float64', 8, function (init) {\n  return function Float64Array(data, byteOffset, length) {\n    return init(this, data, byteOffset, length);\n  };\n});\n","// 26.1.1 Reflect.apply(target, thisArgument, argumentsList)\nvar $export = require('./_export');\nvar aFunction = require('./_a-function');\nvar anObject = require('./_an-object');\nvar rApply = (require('./_global').Reflect || {}).apply;\nvar fApply = Function.apply;\n// MS Edge argumentsList argument is optional\n$export($export.S + $export.F * !require('./_fails')(function () {\n  rApply(function () { /* empty */ });\n}), 'Reflect', {\n  apply: function apply(target, thisArgument, argumentsList) {\n    var T = aFunction(target);\n    var L = anObject(argumentsList);\n    return rApply ? rApply(T, thisArgument, L) : fApply.call(T, thisArgument, L);\n  }\n});\n","// 26.1.2 Reflect.construct(target, argumentsList [, newTarget])\nvar $export = require('./_export');\nvar create = require('./_object-create');\nvar aFunction = require('./_a-function');\nvar anObject = require('./_an-object');\nvar isObject = require('./_is-object');\nvar fails = require('./_fails');\nvar bind = require('./_bind');\nvar rConstruct = (require('./_global').Reflect || {}).construct;\n\n// MS Edge supports only 2 arguments and argumentsList argument is optional\n// FF Nightly sets third argument as `new.target`, but does not create `this` from it\nvar NEW_TARGET_BUG = fails(function () {\n  function F() { /* empty */ }\n  return !(rConstruct(function () { /* empty */ }, [], F) instanceof F);\n});\nvar ARGS_BUG = !fails(function () {\n  rConstruct(function () { /* empty */ });\n});\n\n$export($export.S + $export.F * (NEW_TARGET_BUG || ARGS_BUG), 'Reflect', {\n  construct: function construct(Target, args /* , newTarget */) {\n    aFunction(Target);\n    anObject(args);\n    var newTarget = arguments.length < 3 ? Target : aFunction(arguments[2]);\n    if (ARGS_BUG && !NEW_TARGET_BUG) return rConstruct(Target, args, newTarget);\n    if (Target == newTarget) {\n      // w/o altered newTarget, optimization for 0-4 arguments\n      switch (args.length) {\n        case 0: return new Target();\n        case 1: return new Target(args[0]);\n        case 2: return new Target(args[0], args[1]);\n        case 3: return new Target(args[0], args[1], args[2]);\n        case 4: return new Target(args[0], args[1], args[2], args[3]);\n      }\n      // w/o altered newTarget, lot of arguments case\n      var $args = [null];\n      $args.push.apply($args, args);\n      return new (bind.apply(Target, $args))();\n    }\n    // with altered newTarget, not support built-in constructors\n    var proto = newTarget.prototype;\n    var instance = create(isObject(proto) ? proto : Object.prototype);\n    var result = Function.apply.call(Target, instance, args);\n    return isObject(result) ? result : instance;\n  }\n});\n","// 26.1.3 Reflect.defineProperty(target, propertyKey, attributes)\nvar dP = require('./_object-dp');\nvar $export = require('./_export');\nvar anObject = require('./_an-object');\nvar toPrimitive = require('./_to-primitive');\n\n// MS Edge has broken Reflect.defineProperty - throwing instead of returning false\n$export($export.S + $export.F * require('./_fails')(function () {\n  // eslint-disable-next-line no-undef\n  Reflect.defineProperty(dP.f({}, 1, { value: 1 }), 1, { value: 2 });\n}), 'Reflect', {\n  defineProperty: function defineProperty(target, propertyKey, attributes) {\n    anObject(target);\n    propertyKey = toPrimitive(propertyKey, true);\n    anObject(attributes);\n    try {\n      dP.f(target, propertyKey, attributes);\n      return true;\n    } catch (e) {\n      return false;\n    }\n  }\n});\n","// 26.1.4 Reflect.deleteProperty(target, propertyKey)\nvar $export = require('./_export');\nvar gOPD = require('./_object-gopd').f;\nvar anObject = require('./_an-object');\n\n$export($export.S, 'Reflect', {\n  deleteProperty: function deleteProperty(target, propertyKey) {\n    var desc = gOPD(anObject(target), propertyKey);\n    return desc && !desc.configurable ? false : delete target[propertyKey];\n  }\n});\n","'use strict';\n// 26.1.5 Reflect.enumerate(target)\nvar $export = require('./_export');\nvar anObject = require('./_an-object');\nvar Enumerate = function (iterated) {\n  this._t = anObject(iterated); // target\n  this._i = 0;                  // next index\n  var keys = this._k = [];      // keys\n  var key;\n  for (key in iterated) keys.push(key);\n};\nrequire('./_iter-create')(Enumerate, 'Object', function () {\n  var that = this;\n  var keys = that._k;\n  var key;\n  do {\n    if (that._i >= keys.length) return { value: undefined, done: true };\n  } while (!((key = keys[that._i++]) in that._t));\n  return { value: key, done: false };\n});\n\n$export($export.S, 'Reflect', {\n  enumerate: function enumerate(target) {\n    return new Enumerate(target);\n  }\n});\n","// 26.1.6 Reflect.get(target, propertyKey [, receiver])\nvar gOPD = require('./_object-gopd');\nvar getPrototypeOf = require('./_object-gpo');\nvar has = require('./_has');\nvar $export = require('./_export');\nvar isObject = require('./_is-object');\nvar anObject = require('./_an-object');\n\nfunction get(target, propertyKey /* , receiver */) {\n  var receiver = arguments.length < 3 ? target : arguments[2];\n  var desc, proto;\n  if (anObject(target) === receiver) return target[propertyKey];\n  if (desc = gOPD.f(target, propertyKey)) return has(desc, 'value')\n    ? desc.value\n    : desc.get !== undefined\n      ? desc.get.call(receiver)\n      : undefined;\n  if (isObject(proto = getPrototypeOf(target))) return get(proto, propertyKey, receiver);\n}\n\n$export($export.S, 'Reflect', { get: get });\n","// 26.1.7 Reflect.getOwnPropertyDescriptor(target, propertyKey)\nvar gOPD = require('./_object-gopd');\nvar $export = require('./_export');\nvar anObject = require('./_an-object');\n\n$export($export.S, 'Reflect', {\n  getOwnPropertyDescriptor: function getOwnPropertyDescriptor(target, propertyKey) {\n    return gOPD.f(anObject(target), propertyKey);\n  }\n});\n","// 26.1.8 Reflect.getPrototypeOf(target)\nvar $export = require('./_export');\nvar getProto = require('./_object-gpo');\nvar anObject = require('./_an-object');\n\n$export($export.S, 'Reflect', {\n  getPrototypeOf: function getPrototypeOf(target) {\n    return getProto(anObject(target));\n  }\n});\n","// 26.1.9 Reflect.has(target, propertyKey)\nvar $export = require('./_export');\n\n$export($export.S, 'Reflect', {\n  has: function has(target, propertyKey) {\n    return propertyKey in target;\n  }\n});\n","// 26.1.10 Reflect.isExtensible(target)\nvar $export = require('./_export');\nvar anObject = require('./_an-object');\nvar $isExtensible = Object.isExtensible;\n\n$export($export.S, 'Reflect', {\n  isExtensible: function isExtensible(target) {\n    anObject(target);\n    return $isExtensible ? $isExtensible(target) : true;\n  }\n});\n","// all object keys, includes non-enumerable and symbols\nvar gOPN = require('./_object-gopn');\nvar gOPS = require('./_object-gops');\nvar anObject = require('./_an-object');\nvar Reflect = require('./_global').Reflect;\nmodule.exports = Reflect && Reflect.ownKeys || function ownKeys(it) {\n  var keys = gOPN.f(anObject(it));\n  var getSymbols = gOPS.f;\n  return getSymbols ? keys.concat(getSymbols(it)) : keys;\n};\n","// 26.1.11 Reflect.ownKeys(target)\nvar $export = require('./_export');\n\n$export($export.S, 'Reflect', { ownKeys: require('./_own-keys') });\n","// 26.1.12 Reflect.preventExtensions(target)\nvar $export = require('./_export');\nvar anObject = require('./_an-object');\nvar $preventExtensions = Object.preventExtensions;\n\n$export($export.S, 'Reflect', {\n  preventExtensions: function preventExtensions(target) {\n    anObject(target);\n    try {\n      if ($preventExtensions) $preventExtensions(target);\n      return true;\n    } catch (e) {\n      return false;\n    }\n  }\n});\n","// 26.1.13 Reflect.set(target, propertyKey, V [, receiver])\nvar dP = require('./_object-dp');\nvar gOPD = require('./_object-gopd');\nvar getPrototypeOf = require('./_object-gpo');\nvar has = require('./_has');\nvar $export = require('./_export');\nvar createDesc = require('./_property-desc');\nvar anObject = require('./_an-object');\nvar isObject = require('./_is-object');\n\nfunction set(target, propertyKey, V /* , receiver */) {\n  var receiver = arguments.length < 4 ? target : arguments[3];\n  var ownDesc = gOPD.f(anObject(target), propertyKey);\n  var existingDescriptor, proto;\n  if (!ownDesc) {\n    if (isObject(proto = getPrototypeOf(target))) {\n      return set(proto, propertyKey, V, receiver);\n    }\n    ownDesc = createDesc(0);\n  }\n  if (has(ownDesc, 'value')) {\n    if (ownDesc.writable === false || !isObject(receiver)) return false;\n    if (existingDescriptor = gOPD.f(receiver, propertyKey)) {\n      if (existingDescriptor.get || existingDescriptor.set || existingDescriptor.writable === false) return false;\n      existingDescriptor.value = V;\n      dP.f(receiver, propertyKey, existingDescriptor);\n    } else dP.f(receiver, propertyKey, createDesc(0, V));\n    return true;\n  }\n  return ownDesc.set === undefined ? false : (ownDesc.set.call(receiver, V), true);\n}\n\n$export($export.S, 'Reflect', { set: set });\n","// 26.1.14 Reflect.setPrototypeOf(target, proto)\nvar $export = require('./_export');\nvar setProto = require('./_set-proto');\n\nif (setProto) $export($export.S, 'Reflect', {\n  setPrototypeOf: function setPrototypeOf(target, proto) {\n    setProto.check(target, proto);\n    try {\n      setProto.set(target, proto);\n      return true;\n    } catch (e) {\n      return false;\n    }\n  }\n});\n","'use strict';\n// https://github.com/tc39/Array.prototype.includes\nvar $export = require('./_export');\nvar $includes = require('./_array-includes')(true);\n\n$export($export.P, 'Array', {\n  includes: function includes(el /* , fromIndex = 0 */) {\n    return $includes(this, el, arguments.length > 1 ? arguments[1] : undefined);\n  }\n});\n\nrequire('./_add-to-unscopables')('includes');\n","'use strict';\n// https://tc39.github.io/proposal-flatMap/#sec-FlattenIntoArray\nvar isArray = require('./_is-array');\nvar isObject = require('./_is-object');\nvar toLength = require('./_to-length');\nvar ctx = require('./_ctx');\nvar IS_CONCAT_SPREADABLE = require('./_wks')('isConcatSpreadable');\n\nfunction flattenIntoArray(target, original, source, sourceLen, start, depth, mapper, thisArg) {\n  var targetIndex = start;\n  var sourceIndex = 0;\n  var mapFn = mapper ? ctx(mapper, thisArg, 3) : false;\n  var element, spreadable;\n\n  while (sourceIndex < sourceLen) {\n    if (sourceIndex in source) {\n      element = mapFn ? mapFn(source[sourceIndex], sourceIndex, original) : source[sourceIndex];\n\n      spreadable = false;\n      if (isObject(element)) {\n        spreadable = element[IS_CONCAT_SPREADABLE];\n        spreadable = spreadable !== undefined ? !!spreadable : isArray(element);\n      }\n\n      if (spreadable && depth > 0) {\n        targetIndex = flattenIntoArray(target, original, element, toLength(element.length), targetIndex, depth - 1) - 1;\n      } else {\n        if (targetIndex >= 0x1fffffffffffff) throw TypeError();\n        target[targetIndex] = element;\n      }\n\n      targetIndex++;\n    }\n    sourceIndex++;\n  }\n  return targetIndex;\n}\n\nmodule.exports = flattenIntoArray;\n","'use strict';\n// https://tc39.github.io/proposal-flatMap/#sec-Array.prototype.flatMap\nvar $export = require('./_export');\nvar flattenIntoArray = require('./_flatten-into-array');\nvar toObject = require('./_to-object');\nvar toLength = require('./_to-length');\nvar aFunction = require('./_a-function');\nvar arraySpeciesCreate = require('./_array-species-create');\n\n$export($export.P, 'Array', {\n  flatMap: function flatMap(callbackfn /* , thisArg */) {\n    var O = toObject(this);\n    var sourceLen, A;\n    aFunction(callbackfn);\n    sourceLen = toLength(O.length);\n    A = arraySpeciesCreate(O, 0);\n    flattenIntoArray(A, O, O, sourceLen, 0, 1, callbackfn, arguments[1]);\n    return A;\n  }\n});\n\nrequire('./_add-to-unscopables')('flatMap');\n","'use strict';\n// https://tc39.github.io/proposal-flatMap/#sec-Array.prototype.flatten\nvar $export = require('./_export');\nvar flattenIntoArray = require('./_flatten-into-array');\nvar toObject = require('./_to-object');\nvar toLength = require('./_to-length');\nvar toInteger = require('./_to-integer');\nvar arraySpeciesCreate = require('./_array-species-create');\n\n$export($export.P, 'Array', {\n  flatten: function flatten(/* depthArg = 1 */) {\n    var depthArg = arguments[0];\n    var O = toObject(this);\n    var sourceLen = toLength(O.length);\n    var A = arraySpeciesCreate(O, 0);\n    flattenIntoArray(A, O, O, sourceLen, 0, depthArg === undefined ? 1 : toInteger(depthArg));\n    return A;\n  }\n});\n\nrequire('./_add-to-unscopables')('flatten');\n","'use strict';\n// https://github.com/mathiasbynens/String.prototype.at\nvar $export = require('./_export');\nvar $at = require('./_string-at')(true);\n\n$export($export.P, 'String', {\n  at: function at(pos) {\n    return $at(this, pos);\n  }\n});\n","// https://github.com/tc39/proposal-string-pad-start-end\nvar toLength = require('./_to-length');\nvar repeat = require('./_string-repeat');\nvar defined = require('./_defined');\n\nmodule.exports = function (that, maxLength, fillString, left) {\n  var S = String(defined(that));\n  var stringLength = S.length;\n  var fillStr = fillString === undefined ? ' ' : String(fillString);\n  var intMaxLength = toLength(maxLength);\n  if (intMaxLength <= stringLength || fillStr == '') return S;\n  var fillLen = intMaxLength - stringLength;\n  var stringFiller = repeat.call(fillStr, Math.ceil(fillLen / fillStr.length));\n  if (stringFiller.length > fillLen) stringFiller = stringFiller.slice(0, fillLen);\n  return left ? stringFiller + S : S + stringFiller;\n};\n","'use strict';\n// https://github.com/tc39/proposal-string-pad-start-end\nvar $export = require('./_export');\nvar $pad = require('./_string-pad');\nvar userAgent = require('./_user-agent');\n\n// https://github.com/zloirock/core-js/issues/280\nvar WEBKIT_BUG = /Version\\/10\\.\\d+(\\.\\d+)?( Mobile\\/\\w+)? Safari\\//.test(userAgent);\n\n$export($export.P + $export.F * WEBKIT_BUG, 'String', {\n  padStart: function padStart(maxLength /* , fillString = ' ' */) {\n    return $pad(this, maxLength, arguments.length > 1 ? arguments[1] : undefined, true);\n  }\n});\n","'use strict';\n// https://github.com/tc39/proposal-string-pad-start-end\nvar $export = require('./_export');\nvar $pad = require('./_string-pad');\nvar userAgent = require('./_user-agent');\n\n// https://github.com/zloirock/core-js/issues/280\nvar WEBKIT_BUG = /Version\\/10\\.\\d+(\\.\\d+)?( Mobile\\/\\w+)? Safari\\//.test(userAgent);\n\n$export($export.P + $export.F * WEBKIT_BUG, 'String', {\n  padEnd: function padEnd(maxLength /* , fillString = ' ' */) {\n    return $pad(this, maxLength, arguments.length > 1 ? arguments[1] : undefined, false);\n  }\n});\n","'use strict';\n// https://github.com/sebmarkbage/ecmascript-string-left-right-trim\nrequire('./_string-trim')('trimLeft', function ($trim) {\n  return function trimLeft() {\n    return $trim(this, 1);\n  };\n}, 'trimStart');\n","'use strict';\n// https://github.com/sebmarkbage/ecmascript-string-left-right-trim\nrequire('./_string-trim')('trimRight', function ($trim) {\n  return function trimRight() {\n    return $trim(this, 2);\n  };\n}, 'trimEnd');\n","'use strict';\n// https://tc39.github.io/String.prototype.matchAll/\nvar $export = require('./_export');\nvar defined = require('./_defined');\nvar toLength = require('./_to-length');\nvar isRegExp = require('./_is-regexp');\nvar getFlags = require('./_flags');\nvar RegExpProto = RegExp.prototype;\n\nvar $RegExpStringIterator = function (regexp, string) {\n  this._r = regexp;\n  this._s = string;\n};\n\nrequire('./_iter-create')($RegExpStringIterator, 'RegExp String', function next() {\n  var match = this._r.exec(this._s);\n  return { value: match, done: match === null };\n});\n\n$export($export.P, 'String', {\n  matchAll: function matchAll(regexp) {\n    defined(this);\n    if (!isRegExp(regexp)) throw TypeError(regexp + ' is not a regexp!');\n    var S = String(this);\n    var flags = 'flags' in RegExpProto ? String(regexp.flags) : getFlags.call(regexp);\n    var rx = new RegExp(regexp.source, ~flags.indexOf('g') ? flags : 'g' + flags);\n    rx.lastIndex = toLength(regexp.lastIndex);\n    return new $RegExpStringIterator(rx, S);\n  }\n});\n","require('./_wks-define')('asyncIterator');\n","require('./_wks-define')('observable');\n","// https://github.com/tc39/proposal-object-getownpropertydescriptors\nvar $export = require('./_export');\nvar ownKeys = require('./_own-keys');\nvar toIObject = require('./_to-iobject');\nvar gOPD = require('./_object-gopd');\nvar createProperty = require('./_create-property');\n\n$export($export.S, 'Object', {\n  getOwnPropertyDescriptors: function getOwnPropertyDescriptors(object) {\n    var O = toIObject(object);\n    var getDesc = gOPD.f;\n    var keys = ownKeys(O);\n    var result = {};\n    var i = 0;\n    var key, desc;\n    while (keys.length > i) {\n      desc = getDesc(O, key = keys[i++]);\n      if (desc !== undefined) createProperty(result, key, desc);\n    }\n    return result;\n  }\n});\n","var DESCRIPTORS = require('./_descriptors');\nvar getKeys = require('./_object-keys');\nvar toIObject = require('./_to-iobject');\nvar isEnum = require('./_object-pie').f;\nmodule.exports = function (isEntries) {\n  return function (it) {\n    var O = toIObject(it);\n    var keys = getKeys(O);\n    var length = keys.length;\n    var i = 0;\n    var result = [];\n    var key;\n    while (length > i) {\n      key = keys[i++];\n      if (!DESCRIPTORS || isEnum.call(O, key)) {\n        result.push(isEntries ? [key, O[key]] : O[key]);\n      }\n    }\n    return result;\n  };\n};\n","// https://github.com/tc39/proposal-object-values-entries\nvar $export = require('./_export');\nvar $values = require('./_object-to-array')(false);\n\n$export($export.S, 'Object', {\n  values: function values(it) {\n    return $values(it);\n  }\n});\n","// https://github.com/tc39/proposal-object-values-entries\nvar $export = require('./_export');\nvar $entries = require('./_object-to-array')(true);\n\n$export($export.S, 'Object', {\n  entries: function entries(it) {\n    return $entries(it);\n  }\n});\n","'use strict';\n// Forced replacement prototype accessors methods\nmodule.exports = require('./_library') || !require('./_fails')(function () {\n  var K = Math.random();\n  // In FF throws only define methods\n  // eslint-disable-next-line no-undef, no-useless-call\n  __defineSetter__.call(null, K, function () { /* empty */ });\n  delete require('./_global')[K];\n});\n","'use strict';\nvar $export = require('./_export');\nvar toObject = require('./_to-object');\nvar aFunction = require('./_a-function');\nvar $defineProperty = require('./_object-dp');\n\n// B.2.2.2 Object.prototype.__defineGetter__(P, getter)\nrequire('./_descriptors') && $export($export.P + require('./_object-forced-pam'), 'Object', {\n  __defineGetter__: function __defineGetter__(P, getter) {\n    $defineProperty.f(toObject(this), P, { get: aFunction(getter), enumerable: true, configurable: true });\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar toObject = require('./_to-object');\nvar aFunction = require('./_a-function');\nvar $defineProperty = require('./_object-dp');\n\n// B.2.2.3 Object.prototype.__defineSetter__(P, setter)\nrequire('./_descriptors') && $export($export.P + require('./_object-forced-pam'), 'Object', {\n  __defineSetter__: function __defineSetter__(P, setter) {\n    $defineProperty.f(toObject(this), P, { set: aFunction(setter), enumerable: true, configurable: true });\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar toObject = require('./_to-object');\nvar toPrimitive = require('./_to-primitive');\nvar getPrototypeOf = require('./_object-gpo');\nvar getOwnPropertyDescriptor = require('./_object-gopd').f;\n\n// B.2.2.4 Object.prototype.__lookupGetter__(P)\nrequire('./_descriptors') && $export($export.P + require('./_object-forced-pam'), 'Object', {\n  __lookupGetter__: function __lookupGetter__(P) {\n    var O = toObject(this);\n    var K = toPrimitive(P, true);\n    var D;\n    do {\n      if (D = getOwnPropertyDescriptor(O, K)) return D.get;\n    } while (O = getPrototypeOf(O));\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar toObject = require('./_to-object');\nvar toPrimitive = require('./_to-primitive');\nvar getPrototypeOf = require('./_object-gpo');\nvar getOwnPropertyDescriptor = require('./_object-gopd').f;\n\n// B.2.2.5 Object.prototype.__lookupSetter__(P)\nrequire('./_descriptors') && $export($export.P + require('./_object-forced-pam'), 'Object', {\n  __lookupSetter__: function __lookupSetter__(P) {\n    var O = toObject(this);\n    var K = toPrimitive(P, true);\n    var D;\n    do {\n      if (D = getOwnPropertyDescriptor(O, K)) return D.set;\n    } while (O = getPrototypeOf(O));\n  }\n});\n","var forOf = require('./_for-of');\n\nmodule.exports = function (iter, ITERATOR) {\n  var result = [];\n  forOf(iter, false, result.push, result, ITERATOR);\n  return result;\n};\n","// https://github.com/DavidBruant/Map-Set.prototype.toJSON\nvar classof = require('./_classof');\nvar from = require('./_array-from-iterable');\nmodule.exports = function (NAME) {\n  return function toJSON() {\n    if (classof(this) != NAME) throw TypeError(NAME + \"#toJSON isn't generic\");\n    return from(this);\n  };\n};\n","// https://github.com/DavidBruant/Map-Set.prototype.toJSON\nvar $export = require('./_export');\n\n$export($export.P + $export.R, 'Map', { toJSON: require('./_collection-to-json')('Map') });\n","// https://github.com/DavidBruant/Map-Set.prototype.toJSON\nvar $export = require('./_export');\n\n$export($export.P + $export.R, 'Set', { toJSON: require('./_collection-to-json')('Set') });\n","'use strict';\n// https://tc39.github.io/proposal-setmap-offrom/\nvar $export = require('./_export');\n\nmodule.exports = function (COLLECTION) {\n  $export($export.S, COLLECTION, { of: function of() {\n    var length = arguments.length;\n    var A = new Array(length);\n    while (length--) A[length] = arguments[length];\n    return new this(A);\n  } });\n};\n","// https://tc39.github.io/proposal-setmap-offrom/#sec-map.of\nrequire('./_set-collection-of')('Map');\n","// https://tc39.github.io/proposal-setmap-offrom/#sec-set.of\nrequire('./_set-collection-of')('Set');\n","// https://tc39.github.io/proposal-setmap-offrom/#sec-weakmap.of\nrequire('./_set-collection-of')('WeakMap');\n","// https://tc39.github.io/proposal-setmap-offrom/#sec-weakset.of\nrequire('./_set-collection-of')('WeakSet');\n","'use strict';\n// https://tc39.github.io/proposal-setmap-offrom/\nvar $export = require('./_export');\nvar aFunction = require('./_a-function');\nvar ctx = require('./_ctx');\nvar forOf = require('./_for-of');\n\nmodule.exports = function (COLLECTION) {\n  $export($export.S, COLLECTION, { from: function from(source /* , mapFn, thisArg */) {\n    var mapFn = arguments[1];\n    var mapping, A, n, cb;\n    aFunction(this);\n    mapping = mapFn !== undefined;\n    if (mapping) aFunction(mapFn);\n    if (source == undefined) return new this();\n    A = [];\n    if (mapping) {\n      n = 0;\n      cb = ctx(mapFn, arguments[2], 2);\n      forOf(source, false, function (nextItem) {\n        A.push(cb(nextItem, n++));\n      });\n    } else {\n      forOf(source, false, A.push, A);\n    }\n    return new this(A);\n  } });\n};\n","// https://tc39.github.io/proposal-setmap-offrom/#sec-map.from\nrequire('./_set-collection-from')('Map');\n","// https://tc39.github.io/proposal-setmap-offrom/#sec-set.from\nrequire('./_set-collection-from')('Set');\n","// https://tc39.github.io/proposal-setmap-offrom/#sec-weakmap.from\nrequire('./_set-collection-from')('WeakMap');\n","// https://tc39.github.io/proposal-setmap-offrom/#sec-weakset.from\nrequire('./_set-collection-from')('WeakSet');\n","// https://github.com/tc39/proposal-global\nvar $export = require('./_export');\n\n$export($export.G, { global: require('./_global') });\n","// https://github.com/tc39/proposal-global\nvar $export = require('./_export');\n\n$export($export.S, 'System', { global: require('./_global') });\n","// https://github.com/ljharb/proposal-is-error\nvar $export = require('./_export');\nvar cof = require('./_cof');\n\n$export($export.S, 'Error', {\n  isError: function isError(it) {\n    return cof(it) === 'Error';\n  }\n});\n","// https://rwaldron.github.io/proposal-math-extensions/\nvar $export = require('./_export');\n\n$export($export.S, 'Math', {\n  clamp: function clamp(x, lower, upper) {\n    return Math.min(upper, Math.max(lower, x));\n  }\n});\n","// https://rwaldron.github.io/proposal-math-extensions/\nvar $export = require('./_export');\n\n$export($export.S, 'Math', { DEG_PER_RAD: Math.PI / 180 });\n","// https://rwaldron.github.io/proposal-math-extensions/\nvar $export = require('./_export');\nvar RAD_PER_DEG = 180 / Math.PI;\n\n$export($export.S, 'Math', {\n  degrees: function degrees(radians) {\n    return radians * RAD_PER_DEG;\n  }\n});\n","// https://rwaldron.github.io/proposal-math-extensions/\nmodule.exports = Math.scale || function scale(x, inLow, inHigh, outLow, outHigh) {\n  if (\n    arguments.length === 0\n      // eslint-disable-next-line no-self-compare\n      || x != x\n      // eslint-disable-next-line no-self-compare\n      || inLow != inLow\n      // eslint-disable-next-line no-self-compare\n      || inHigh != inHigh\n      // eslint-disable-next-line no-self-compare\n      || outLow != outLow\n      // eslint-disable-next-line no-self-compare\n      || outHigh != outHigh\n  ) return NaN;\n  if (x === Infinity || x === -Infinity) return x;\n  return (x - inLow) * (outHigh - outLow) / (inHigh - inLow) + outLow;\n};\n","// https://rwaldron.github.io/proposal-math-extensions/\nvar $export = require('./_export');\nvar scale = require('./_math-scale');\nvar fround = require('./_math-fround');\n\n$export($export.S, 'Math', {\n  fscale: function fscale(x, inLow, inHigh, outLow, outHigh) {\n    return fround(scale(x, inLow, inHigh, outLow, outHigh));\n  }\n});\n","// https://gist.github.com/BrendanEich/4294d5c212a6d2254703\nvar $export = require('./_export');\n\n$export($export.S, 'Math', {\n  iaddh: function iaddh(x0, x1, y0, y1) {\n    var $x0 = x0 >>> 0;\n    var $x1 = x1 >>> 0;\n    var $y0 = y0 >>> 0;\n    return $x1 + (y1 >>> 0) + (($x0 & $y0 | ($x0 | $y0) & ~($x0 + $y0 >>> 0)) >>> 31) | 0;\n  }\n});\n","// https://gist.github.com/BrendanEich/4294d5c212a6d2254703\nvar $export = require('./_export');\n\n$export($export.S, 'Math', {\n  isubh: function isubh(x0, x1, y0, y1) {\n    var $x0 = x0 >>> 0;\n    var $x1 = x1 >>> 0;\n    var $y0 = y0 >>> 0;\n    return $x1 - (y1 >>> 0) - ((~$x0 & $y0 | ~($x0 ^ $y0) & $x0 - $y0 >>> 0) >>> 31) | 0;\n  }\n});\n","// https://gist.github.com/BrendanEich/4294d5c212a6d2254703\nvar $export = require('./_export');\n\n$export($export.S, 'Math', {\n  imulh: function imulh(u, v) {\n    var UINT16 = 0xffff;\n    var $u = +u;\n    var $v = +v;\n    var u0 = $u & UINT16;\n    var v0 = $v & UINT16;\n    var u1 = $u >> 16;\n    var v1 = $v >> 16;\n    var t = (u1 * v0 >>> 0) + (u0 * v0 >>> 16);\n    return u1 * v1 + (t >> 16) + ((u0 * v1 >>> 0) + (t & UINT16) >> 16);\n  }\n});\n","// https://rwaldron.github.io/proposal-math-extensions/\nvar $export = require('./_export');\n\n$export($export.S, 'Math', { RAD_PER_DEG: 180 / Math.PI });\n","// https://rwaldron.github.io/proposal-math-extensions/\nvar $export = require('./_export');\nvar DEG_PER_RAD = Math.PI / 180;\n\n$export($export.S, 'Math', {\n  radians: function radians(degrees) {\n    return degrees * DEG_PER_RAD;\n  }\n});\n","// https://rwaldron.github.io/proposal-math-extensions/\nvar $export = require('./_export');\n\n$export($export.S, 'Math', { scale: require('./_math-scale') });\n","// https://gist.github.com/BrendanEich/4294d5c212a6d2254703\nvar $export = require('./_export');\n\n$export($export.S, 'Math', {\n  umulh: function umulh(u, v) {\n    var UINT16 = 0xffff;\n    var $u = +u;\n    var $v = +v;\n    var u0 = $u & UINT16;\n    var v0 = $v & UINT16;\n    var u1 = $u >>> 16;\n    var v1 = $v >>> 16;\n    var t = (u1 * v0 >>> 0) + (u0 * v0 >>> 16);\n    return u1 * v1 + (t >>> 16) + ((u0 * v1 >>> 0) + (t & UINT16) >>> 16);\n  }\n});\n","// http://jfbastien.github.io/papers/Math.signbit.html\nvar $export = require('./_export');\n\n$export($export.S, 'Math', { signbit: function signbit(x) {\n  // eslint-disable-next-line no-self-compare\n  return (x = +x) != x ? x : x == 0 ? 1 / x == Infinity : x > 0;\n} });\n","// https://github.com/tc39/proposal-promise-finally\n'use strict';\nvar $export = require('./_export');\nvar core = require('./_core');\nvar global = require('./_global');\nvar speciesConstructor = require('./_species-constructor');\nvar promiseResolve = require('./_promise-resolve');\n\n$export($export.P + $export.R, 'Promise', { 'finally': function (onFinally) {\n  var C = speciesConstructor(this, core.Promise || global.Promise);\n  var isFunction = typeof onFinally == 'function';\n  return this.then(\n    isFunction ? function (x) {\n      return promiseResolve(C, onFinally()).then(function () { return x; });\n    } : onFinally,\n    isFunction ? function (e) {\n      return promiseResolve(C, onFinally()).then(function () { throw e; });\n    } : onFinally\n  );\n} });\n","'use strict';\n// https://github.com/tc39/proposal-promise-try\nvar $export = require('./_export');\nvar newPromiseCapability = require('./_new-promise-capability');\nvar perform = require('./_perform');\n\n$export($export.S, 'Promise', { 'try': function (callbackfn) {\n  var promiseCapability = newPromiseCapability.f(this);\n  var result = perform(callbackfn);\n  (result.e ? promiseCapability.reject : promiseCapability.resolve)(result.v);\n  return promiseCapability.promise;\n} });\n","var Map = require('./es6.map');\nvar $export = require('./_export');\nvar shared = require('./_shared')('metadata');\nvar store = shared.store || (shared.store = new (require('./es6.weak-map'))());\n\nvar getOrCreateMetadataMap = function (target, targetKey, create) {\n  var targetMetadata = store.get(target);\n  if (!targetMetadata) {\n    if (!create) return undefined;\n    store.set(target, targetMetadata = new Map());\n  }\n  var keyMetadata = targetMetadata.get(targetKey);\n  if (!keyMetadata) {\n    if (!create) return undefined;\n    targetMetadata.set(targetKey, keyMetadata = new Map());\n  } return keyMetadata;\n};\nvar ordinaryHasOwnMetadata = function (MetadataKey, O, P) {\n  var metadataMap = getOrCreateMetadataMap(O, P, false);\n  return metadataMap === undefined ? false : metadataMap.has(MetadataKey);\n};\nvar ordinaryGetOwnMetadata = function (MetadataKey, O, P) {\n  var metadataMap = getOrCreateMetadataMap(O, P, false);\n  return metadataMap === undefined ? undefined : metadataMap.get(MetadataKey);\n};\nvar ordinaryDefineOwnMetadata = function (MetadataKey, MetadataValue, O, P) {\n  getOrCreateMetadataMap(O, P, true).set(MetadataKey, MetadataValue);\n};\nvar ordinaryOwnMetadataKeys = function (target, targetKey) {\n  var metadataMap = getOrCreateMetadataMap(target, targetKey, false);\n  var keys = [];\n  if (metadataMap) metadataMap.forEach(function (_, key) { keys.push(key); });\n  return keys;\n};\nvar toMetaKey = function (it) {\n  return it === undefined || typeof it == 'symbol' ? it : String(it);\n};\nvar exp = function (O) {\n  $export($export.S, 'Reflect', O);\n};\n\nmodule.exports = {\n  store: store,\n  map: getOrCreateMetadataMap,\n  has: ordinaryHasOwnMetadata,\n  get: ordinaryGetOwnMetadata,\n  set: ordinaryDefineOwnMetadata,\n  keys: ordinaryOwnMetadataKeys,\n  key: toMetaKey,\n  exp: exp\n};\n","var metadata = require('./_metadata');\nvar anObject = require('./_an-object');\nvar toMetaKey = metadata.key;\nvar ordinaryDefineOwnMetadata = metadata.set;\n\nmetadata.exp({ defineMetadata: function defineMetadata(metadataKey, metadataValue, target, targetKey) {\n  ordinaryDefineOwnMetadata(metadataKey, metadataValue, anObject(target), toMetaKey(targetKey));\n} });\n","var metadata = require('./_metadata');\nvar anObject = require('./_an-object');\nvar toMetaKey = metadata.key;\nvar getOrCreateMetadataMap = metadata.map;\nvar store = metadata.store;\n\nmetadata.exp({ deleteMetadata: function deleteMetadata(metadataKey, target /* , targetKey */) {\n  var targetKey = arguments.length < 3 ? undefined : toMetaKey(arguments[2]);\n  var metadataMap = getOrCreateMetadataMap(anObject(target), targetKey, false);\n  if (metadataMap === undefined || !metadataMap['delete'](metadataKey)) return false;\n  if (metadataMap.size) return true;\n  var targetMetadata = store.get(target);\n  targetMetadata['delete'](targetKey);\n  return !!targetMetadata.size || store['delete'](target);\n} });\n","var metadata = require('./_metadata');\nvar anObject = require('./_an-object');\nvar getPrototypeOf = require('./_object-gpo');\nvar ordinaryHasOwnMetadata = metadata.has;\nvar ordinaryGetOwnMetadata = metadata.get;\nvar toMetaKey = metadata.key;\n\nvar ordinaryGetMetadata = function (MetadataKey, O, P) {\n  var hasOwn = ordinaryHasOwnMetadata(MetadataKey, O, P);\n  if (hasOwn) return ordinaryGetOwnMetadata(MetadataKey, O, P);\n  var parent = getPrototypeOf(O);\n  return parent !== null ? ordinaryGetMetadata(MetadataKey, parent, P) : undefined;\n};\n\nmetadata.exp({ getMetadata: function getMetadata(metadataKey, target /* , targetKey */) {\n  return ordinaryGetMetadata(metadataKey, anObject(target), arguments.length < 3 ? undefined : toMetaKey(arguments[2]));\n} });\n","var Set = require('./es6.set');\nvar from = require('./_array-from-iterable');\nvar metadata = require('./_metadata');\nvar anObject = require('./_an-object');\nvar getPrototypeOf = require('./_object-gpo');\nvar ordinaryOwnMetadataKeys = metadata.keys;\nvar toMetaKey = metadata.key;\n\nvar ordinaryMetadataKeys = function (O, P) {\n  var oKeys = ordinaryOwnMetadataKeys(O, P);\n  var parent = getPrototypeOf(O);\n  if (parent === null) return oKeys;\n  var pKeys = ordinaryMetadataKeys(parent, P);\n  return pKeys.length ? oKeys.length ? from(new Set(oKeys.concat(pKeys))) : pKeys : oKeys;\n};\n\nmetadata.exp({ getMetadataKeys: function getMetadataKeys(target /* , targetKey */) {\n  return ordinaryMetadataKeys(anObject(target), arguments.length < 2 ? undefined : toMetaKey(arguments[1]));\n} });\n","var metadata = require('./_metadata');\nvar anObject = require('./_an-object');\nvar ordinaryGetOwnMetadata = metadata.get;\nvar toMetaKey = metadata.key;\n\nmetadata.exp({ getOwnMetadata: function getOwnMetadata(metadataKey, target /* , targetKey */) {\n  return ordinaryGetOwnMetadata(metadataKey, anObject(target)\n    , arguments.length < 3 ? undefined : toMetaKey(arguments[2]));\n} });\n","var metadata = require('./_metadata');\nvar anObject = require('./_an-object');\nvar ordinaryOwnMetadataKeys = metadata.keys;\nvar toMetaKey = metadata.key;\n\nmetadata.exp({ getOwnMetadataKeys: function getOwnMetadataKeys(target /* , targetKey */) {\n  return ordinaryOwnMetadataKeys(anObject(target), arguments.length < 2 ? undefined : toMetaKey(arguments[1]));\n} });\n","var metadata = require('./_metadata');\nvar anObject = require('./_an-object');\nvar getPrototypeOf = require('./_object-gpo');\nvar ordinaryHasOwnMetadata = metadata.has;\nvar toMetaKey = metadata.key;\n\nvar ordinaryHasMetadata = function (MetadataKey, O, P) {\n  var hasOwn = ordinaryHasOwnMetadata(MetadataKey, O, P);\n  if (hasOwn) return true;\n  var parent = getPrototypeOf(O);\n  return parent !== null ? ordinaryHasMetadata(MetadataKey, parent, P) : false;\n};\n\nmetadata.exp({ hasMetadata: function hasMetadata(metadataKey, target /* , targetKey */) {\n  return ordinaryHasMetadata(metadataKey, anObject(target), arguments.length < 3 ? undefined : toMetaKey(arguments[2]));\n} });\n","var metadata = require('./_metadata');\nvar anObject = require('./_an-object');\nvar ordinaryHasOwnMetadata = metadata.has;\nvar toMetaKey = metadata.key;\n\nmetadata.exp({ hasOwnMetadata: function hasOwnMetadata(metadataKey, target /* , targetKey */) {\n  return ordinaryHasOwnMetadata(metadataKey, anObject(target)\n    , arguments.length < 3 ? undefined : toMetaKey(arguments[2]));\n} });\n","var $metadata = require('./_metadata');\nvar anObject = require('./_an-object');\nvar aFunction = require('./_a-function');\nvar toMetaKey = $metadata.key;\nvar ordinaryDefineOwnMetadata = $metadata.set;\n\n$metadata.exp({ metadata: function metadata(metadataKey, metadataValue) {\n  return function decorator(target, targetKey) {\n    ordinaryDefineOwnMetadata(\n      metadataKey, metadataValue,\n      (targetKey !== undefined ? anObject : aFunction)(target),\n      toMetaKey(targetKey)\n    );\n  };\n} });\n","// https://github.com/rwaldron/tc39-notes/blob/master/es6/2014-09/sept-25.md#510-globalasap-for-enqueuing-a-microtask\nvar $export = require('./_export');\nvar microtask = require('./_microtask')();\nvar process = require('./_global').process;\nvar isNode = require('./_cof')(process) == 'process';\n\n$export($export.G, {\n  asap: function asap(fn) {\n    var domain = isNode && process.domain;\n    microtask(domain ? domain.bind(fn) : fn);\n  }\n});\n","'use strict';\n// https://github.com/zenparsing/es-observable\nvar $export = require('./_export');\nvar global = require('./_global');\nvar core = require('./_core');\nvar microtask = require('./_microtask')();\nvar OBSERVABLE = require('./_wks')('observable');\nvar aFunction = require('./_a-function');\nvar anObject = require('./_an-object');\nvar anInstance = require('./_an-instance');\nvar redefineAll = require('./_redefine-all');\nvar hide = require('./_hide');\nvar forOf = require('./_for-of');\nvar RETURN = forOf.RETURN;\n\nvar getMethod = function (fn) {\n  return fn == null ? undefined : aFunction(fn);\n};\n\nvar cleanupSubscription = function (subscription) {\n  var cleanup = subscription._c;\n  if (cleanup) {\n    subscription._c = undefined;\n    cleanup();\n  }\n};\n\nvar subscriptionClosed = function (subscription) {\n  return subscription._o === undefined;\n};\n\nvar closeSubscription = function (subscription) {\n  if (!subscriptionClosed(subscription)) {\n    subscription._o = undefined;\n    cleanupSubscription(subscription);\n  }\n};\n\nvar Subscription = function (observer, subscriber) {\n  anObject(observer);\n  this._c = undefined;\n  this._o = observer;\n  observer = new SubscriptionObserver(this);\n  try {\n    var cleanup = subscriber(observer);\n    var subscription = cleanup;\n    if (cleanup != null) {\n      if (typeof cleanup.unsubscribe === 'function') cleanup = function () { subscription.unsubscribe(); };\n      else aFunction(cleanup);\n      this._c = cleanup;\n    }\n  } catch (e) {\n    observer.error(e);\n    return;\n  } if (subscriptionClosed(this)) cleanupSubscription(this);\n};\n\nSubscription.prototype = redefineAll({}, {\n  unsubscribe: function unsubscribe() { closeSubscription(this); }\n});\n\nvar SubscriptionObserver = function (subscription) {\n  this._s = subscription;\n};\n\nSubscriptionObserver.prototype = redefineAll({}, {\n  next: function next(value) {\n    var subscription = this._s;\n    if (!subscriptionClosed(subscription)) {\n      var observer = subscription._o;\n      try {\n        var m = getMethod(observer.next);\n        if (m) return m.call(observer, value);\n      } catch (e) {\n        try {\n          closeSubscription(subscription);\n        } finally {\n          throw e;\n        }\n      }\n    }\n  },\n  error: function error(value) {\n    var subscription = this._s;\n    if (subscriptionClosed(subscription)) throw value;\n    var observer = subscription._o;\n    subscription._o = undefined;\n    try {\n      var m = getMethod(observer.error);\n      if (!m) throw value;\n      value = m.call(observer, value);\n    } catch (e) {\n      try {\n        cleanupSubscription(subscription);\n      } finally {\n        throw e;\n      }\n    } cleanupSubscription(subscription);\n    return value;\n  },\n  complete: function complete(value) {\n    var subscription = this._s;\n    if (!subscriptionClosed(subscription)) {\n      var observer = subscription._o;\n      subscription._o = undefined;\n      try {\n        var m = getMethod(observer.complete);\n        value = m ? m.call(observer, value) : undefined;\n      } catch (e) {\n        try {\n          cleanupSubscription(subscription);\n        } finally {\n          throw e;\n        }\n      } cleanupSubscription(subscription);\n      return value;\n    }\n  }\n});\n\nvar $Observable = function Observable(subscriber) {\n  anInstance(this, $Observable, 'Observable', '_f')._f = aFunction(subscriber);\n};\n\nredefineAll($Observable.prototype, {\n  subscribe: function subscribe(observer) {\n    return new Subscription(observer, this._f);\n  },\n  forEach: function forEach(fn) {\n    var that = this;\n    return new (core.Promise || global.Promise)(function (resolve, reject) {\n      aFunction(fn);\n      var subscription = that.subscribe({\n        next: function (value) {\n          try {\n            return fn(value);\n          } catch (e) {\n            reject(e);\n            subscription.unsubscribe();\n          }\n        },\n        error: reject,\n        complete: resolve\n      });\n    });\n  }\n});\n\nredefineAll($Observable, {\n  from: function from(x) {\n    var C = typeof this === 'function' ? this : $Observable;\n    var method = getMethod(anObject(x)[OBSERVABLE]);\n    if (method) {\n      var observable = anObject(method.call(x));\n      return observable.constructor === C ? observable : new C(function (observer) {\n        return observable.subscribe(observer);\n      });\n    }\n    return new C(function (observer) {\n      var done = false;\n      microtask(function () {\n        if (!done) {\n          try {\n            if (forOf(x, false, function (it) {\n              observer.next(it);\n              if (done) return RETURN;\n            }) === RETURN) return;\n          } catch (e) {\n            if (done) throw e;\n            observer.error(e);\n            return;\n          } observer.complete();\n        }\n      });\n      return function () { done = true; };\n    });\n  },\n  of: function of() {\n    for (var i = 0, l = arguments.length, items = new Array(l); i < l;) items[i] = arguments[i++];\n    return new (typeof this === 'function' ? this : $Observable)(function (observer) {\n      var done = false;\n      microtask(function () {\n        if (!done) {\n          for (var j = 0; j < items.length; ++j) {\n            observer.next(items[j]);\n            if (done) return;\n          } observer.complete();\n        }\n      });\n      return function () { done = true; };\n    });\n  }\n});\n\nhide($Observable.prototype, OBSERVABLE, function () { return this; });\n\n$export($export.G, { Observable: $Observable });\n\nrequire('./_set-species')('Observable');\n","// ie9- setTimeout & setInterval additional parameters fix\nvar global = require('./_global');\nvar $export = require('./_export');\nvar userAgent = require('./_user-agent');\nvar slice = [].slice;\nvar MSIE = /MSIE .\\./.test(userAgent); // <- dirty ie9- check\nvar wrap = function (set) {\n  return function (fn, time /* , ...args */) {\n    var boundArgs = arguments.length > 2;\n    var args = boundArgs ? slice.call(arguments, 2) : false;\n    return set(boundArgs ? function () {\n      // eslint-disable-next-line no-new-func\n      (typeof fn == 'function' ? fn : Function(fn)).apply(this, args);\n    } : fn, time);\n  };\n};\n$export($export.G + $export.B + $export.F * MSIE, {\n  setTimeout: wrap(global.setTimeout),\n  setInterval: wrap(global.setInterval)\n});\n","var $export = require('./_export');\nvar $task = require('./_task');\n$export($export.G + $export.B, {\n  setImmediate: $task.set,\n  clearImmediate: $task.clear\n});\n","var $iterators = require('./es6.array.iterator');\nvar getKeys = require('./_object-keys');\nvar redefine = require('./_redefine');\nvar global = require('./_global');\nvar hide = require('./_hide');\nvar Iterators = require('./_iterators');\nvar wks = require('./_wks');\nvar ITERATOR = wks('iterator');\nvar TO_STRING_TAG = wks('toStringTag');\nvar ArrayValues = Iterators.Array;\n\nvar DOMIterables = {\n  CSSRuleList: true, // TODO: Not spec compliant, should be false.\n  CSSStyleDeclaration: false,\n  CSSValueList: false,\n  ClientRectList: false,\n  DOMRectList: false,\n  DOMStringList: false,\n  DOMTokenList: true,\n  DataTransferItemList: false,\n  FileList: false,\n  HTMLAllCollection: false,\n  HTMLCollection: false,\n  HTMLFormElement: false,\n  HTMLSelectElement: false,\n  MediaList: true, // TODO: Not spec compliant, should be false.\n  MimeTypeArray: false,\n  NamedNodeMap: false,\n  NodeList: true,\n  PaintRequestList: false,\n  Plugin: false,\n  PluginArray: false,\n  SVGLengthList: false,\n  SVGNumberList: false,\n  SVGPathSegList: false,\n  SVGPointList: false,\n  SVGStringList: false,\n  SVGTransformList: false,\n  SourceBufferList: false,\n  StyleSheetList: true, // TODO: Not spec compliant, should be false.\n  TextTrackCueList: false,\n  TextTrackList: false,\n  TouchList: false\n};\n\nfor (var collections = getKeys(DOMIterables), i = 0; i < collections.length; i++) {\n  var NAME = collections[i];\n  var explicit = DOMIterables[NAME];\n  var Collection = global[NAME];\n  var proto = Collection && Collection.prototype;\n  var key;\n  if (proto) {\n    if (!proto[ITERATOR]) hide(proto, ITERATOR, ArrayValues);\n    if (!proto[TO_STRING_TAG]) hide(proto, TO_STRING_TAG, NAME);\n    Iterators[NAME] = ArrayValues;\n    if (explicit) for (key in $iterators) if (!proto[key]) redefine(proto, key, $iterators[key], true);\n  }\n}\n","require('./modules/es6.symbol');\nrequire('./modules/es6.object.create');\nrequire('./modules/es6.object.define-property');\nrequire('./modules/es6.object.define-properties');\nrequire('./modules/es6.object.get-own-property-descriptor');\nrequire('./modules/es6.object.get-prototype-of');\nrequire('./modules/es6.object.keys');\nrequire('./modules/es6.object.get-own-property-names');\nrequire('./modules/es6.object.freeze');\nrequire('./modules/es6.object.seal');\nrequire('./modules/es6.object.prevent-extensions');\nrequire('./modules/es6.object.is-frozen');\nrequire('./modules/es6.object.is-sealed');\nrequire('./modules/es6.object.is-extensible');\nrequire('./modules/es6.object.assign');\nrequire('./modules/es6.object.is');\nrequire('./modules/es6.object.set-prototype-of');\nrequire('./modules/es6.object.to-string');\nrequire('./modules/es6.function.bind');\nrequire('./modules/es6.function.name');\nrequire('./modules/es6.function.has-instance');\nrequire('./modules/es6.parse-int');\nrequire('./modules/es6.parse-float');\nrequire('./modules/es6.number.constructor');\nrequire('./modules/es6.number.to-fixed');\nrequire('./modules/es6.number.to-precision');\nrequire('./modules/es6.number.epsilon');\nrequire('./modules/es6.number.is-finite');\nrequire('./modules/es6.number.is-integer');\nrequire('./modules/es6.number.is-nan');\nrequire('./modules/es6.number.is-safe-integer');\nrequire('./modules/es6.number.max-safe-integer');\nrequire('./modules/es6.number.min-safe-integer');\nrequire('./modules/es6.number.parse-float');\nrequire('./modules/es6.number.parse-int');\nrequire('./modules/es6.math.acosh');\nrequire('./modules/es6.math.asinh');\nrequire('./modules/es6.math.atanh');\nrequire('./modules/es6.math.cbrt');\nrequire('./modules/es6.math.clz32');\nrequire('./modules/es6.math.cosh');\nrequire('./modules/es6.math.expm1');\nrequire('./modules/es6.math.fround');\nrequire('./modules/es6.math.hypot');\nrequire('./modules/es6.math.imul');\nrequire('./modules/es6.math.log10');\nrequire('./modules/es6.math.log1p');\nrequire('./modules/es6.math.log2');\nrequire('./modules/es6.math.sign');\nrequire('./modules/es6.math.sinh');\nrequire('./modules/es6.math.tanh');\nrequire('./modules/es6.math.trunc');\nrequire('./modules/es6.string.from-code-point');\nrequire('./modules/es6.string.raw');\nrequire('./modules/es6.string.trim');\nrequire('./modules/es6.string.iterator');\nrequire('./modules/es6.string.code-point-at');\nrequire('./modules/es6.string.ends-with');\nrequire('./modules/es6.string.includes');\nrequire('./modules/es6.string.repeat');\nrequire('./modules/es6.string.starts-with');\nrequire('./modules/es6.string.anchor');\nrequire('./modules/es6.string.big');\nrequire('./modules/es6.string.blink');\nrequire('./modules/es6.string.bold');\nrequire('./modules/es6.string.fixed');\nrequire('./modules/es6.string.fontcolor');\nrequire('./modules/es6.string.fontsize');\nrequire('./modules/es6.string.italics');\nrequire('./modules/es6.string.link');\nrequire('./modules/es6.string.small');\nrequire('./modules/es6.string.strike');\nrequire('./modules/es6.string.sub');\nrequire('./modules/es6.string.sup');\nrequire('./modules/es6.date.now');\nrequire('./modules/es6.date.to-json');\nrequire('./modules/es6.date.to-iso-string');\nrequire('./modules/es6.date.to-string');\nrequire('./modules/es6.date.to-primitive');\nrequire('./modules/es6.array.is-array');\nrequire('./modules/es6.array.from');\nrequire('./modules/es6.array.of');\nrequire('./modules/es6.array.join');\nrequire('./modules/es6.array.slice');\nrequire('./modules/es6.array.sort');\nrequire('./modules/es6.array.for-each');\nrequire('./modules/es6.array.map');\nrequire('./modules/es6.array.filter');\nrequire('./modules/es6.array.some');\nrequire('./modules/es6.array.every');\nrequire('./modules/es6.array.reduce');\nrequire('./modules/es6.array.reduce-right');\nrequire('./modules/es6.array.index-of');\nrequire('./modules/es6.array.last-index-of');\nrequire('./modules/es6.array.copy-within');\nrequire('./modules/es6.array.fill');\nrequire('./modules/es6.array.find');\nrequire('./modules/es6.array.find-index');\nrequire('./modules/es6.array.species');\nrequire('./modules/es6.array.iterator');\nrequire('./modules/es6.regexp.constructor');\nrequire('./modules/es6.regexp.exec');\nrequire('./modules/es6.regexp.to-string');\nrequire('./modules/es6.regexp.flags');\nrequire('./modules/es6.regexp.match');\nrequire('./modules/es6.regexp.replace');\nrequire('./modules/es6.regexp.search');\nrequire('./modules/es6.regexp.split');\nrequire('./modules/es6.promise');\nrequire('./modules/es6.map');\nrequire('./modules/es6.set');\nrequire('./modules/es6.weak-map');\nrequire('./modules/es6.weak-set');\nrequire('./modules/es6.typed.array-buffer');\nrequire('./modules/es6.typed.data-view');\nrequire('./modules/es6.typed.int8-array');\nrequire('./modules/es6.typed.uint8-array');\nrequire('./modules/es6.typed.uint8-clamped-array');\nrequire('./modules/es6.typed.int16-array');\nrequire('./modules/es6.typed.uint16-array');\nrequire('./modules/es6.typed.int32-array');\nrequire('./modules/es6.typed.uint32-array');\nrequire('./modules/es6.typed.float32-array');\nrequire('./modules/es6.typed.float64-array');\nrequire('./modules/es6.reflect.apply');\nrequire('./modules/es6.reflect.construct');\nrequire('./modules/es6.reflect.define-property');\nrequire('./modules/es6.reflect.delete-property');\nrequire('./modules/es6.reflect.enumerate');\nrequire('./modules/es6.reflect.get');\nrequire('./modules/es6.reflect.get-own-property-descriptor');\nrequire('./modules/es6.reflect.get-prototype-of');\nrequire('./modules/es6.reflect.has');\nrequire('./modules/es6.reflect.is-extensible');\nrequire('./modules/es6.reflect.own-keys');\nrequire('./modules/es6.reflect.prevent-extensions');\nrequire('./modules/es6.reflect.set');\nrequire('./modules/es6.reflect.set-prototype-of');\nrequire('./modules/es7.array.includes');\nrequire('./modules/es7.array.flat-map');\nrequire('./modules/es7.array.flatten');\nrequire('./modules/es7.string.at');\nrequire('./modules/es7.string.pad-start');\nrequire('./modules/es7.string.pad-end');\nrequire('./modules/es7.string.trim-left');\nrequire('./modules/es7.string.trim-right');\nrequire('./modules/es7.string.match-all');\nrequire('./modules/es7.symbol.async-iterator');\nrequire('./modules/es7.symbol.observable');\nrequire('./modules/es7.object.get-own-property-descriptors');\nrequire('./modules/es7.object.values');\nrequire('./modules/es7.object.entries');\nrequire('./modules/es7.object.define-getter');\nrequire('./modules/es7.object.define-setter');\nrequire('./modules/es7.object.lookup-getter');\nrequire('./modules/es7.object.lookup-setter');\nrequire('./modules/es7.map.to-json');\nrequire('./modules/es7.set.to-json');\nrequire('./modules/es7.map.of');\nrequire('./modules/es7.set.of');\nrequire('./modules/es7.weak-map.of');\nrequire('./modules/es7.weak-set.of');\nrequire('./modules/es7.map.from');\nrequire('./modules/es7.set.from');\nrequire('./modules/es7.weak-map.from');\nrequire('./modules/es7.weak-set.from');\nrequire('./modules/es7.global');\nrequire('./modules/es7.system.global');\nrequire('./modules/es7.error.is-error');\nrequire('./modules/es7.math.clamp');\nrequire('./modules/es7.math.deg-per-rad');\nrequire('./modules/es7.math.degrees');\nrequire('./modules/es7.math.fscale');\nrequire('./modules/es7.math.iaddh');\nrequire('./modules/es7.math.isubh');\nrequire('./modules/es7.math.imulh');\nrequire('./modules/es7.math.rad-per-deg');\nrequire('./modules/es7.math.radians');\nrequire('./modules/es7.math.scale');\nrequire('./modules/es7.math.umulh');\nrequire('./modules/es7.math.signbit');\nrequire('./modules/es7.promise.finally');\nrequire('./modules/es7.promise.try');\nrequire('./modules/es7.reflect.define-metadata');\nrequire('./modules/es7.reflect.delete-metadata');\nrequire('./modules/es7.reflect.get-metadata');\nrequire('./modules/es7.reflect.get-metadata-keys');\nrequire('./modules/es7.reflect.get-own-metadata');\nrequire('./modules/es7.reflect.get-own-metadata-keys');\nrequire('./modules/es7.reflect.has-metadata');\nrequire('./modules/es7.reflect.has-own-metadata');\nrequire('./modules/es7.reflect.metadata');\nrequire('./modules/es7.asap');\nrequire('./modules/es7.observable');\nrequire('./modules/web.timers');\nrequire('./modules/web.immediate');\nrequire('./modules/web.dom.iterable');\nmodule.exports = require('./modules/_core');\n","/**\n * Copyright (c) 2014, Facebook, Inc.\n * All rights reserved.\n *\n * This source code is licensed under the BSD-style license found in the\n * https://raw.github.com/facebook/regenerator/master/LICENSE file. An\n * additional grant of patent rights can be found in the PATENTS file in\n * the same directory.\n */\n\n!(function(global) {\n  \"use strict\";\n\n  var Op = Object.prototype;\n  var hasOwn = Op.hasOwnProperty;\n  var undefined; // More compressible than void 0.\n  var $Symbol = typeof Symbol === \"function\" ? Symbol : {};\n  var iteratorSymbol = $Symbol.iterator || \"@@iterator\";\n  var asyncIteratorSymbol = $Symbol.asyncIterator || \"@@asyncIterator\";\n  var toStringTagSymbol = $Symbol.toStringTag || \"@@toStringTag\";\n\n  var inModule = typeof module === \"object\";\n  var runtime = global.regeneratorRuntime;\n  if (runtime) {\n    if (inModule) {\n      // If regeneratorRuntime is defined globally and we're in a module,\n      // make the exports object identical to regeneratorRuntime.\n      module.exports = runtime;\n    }\n    // Don't bother evaluating the rest of this file if the runtime was\n    // already defined globally.\n    return;\n  }\n\n  // Define the runtime globally (as expected by generated code) as either\n  // module.exports (if we're in a module) or a new, empty object.\n  runtime = global.regeneratorRuntime = inModule ? module.exports : {};\n\n  function wrap(innerFn, outerFn, self, tryLocsList) {\n    // If outerFn provided and outerFn.prototype is a Generator, then outerFn.prototype instanceof Generator.\n    var protoGenerator = outerFn && outerFn.prototype instanceof Generator ? outerFn : Generator;\n    var generator = Object.create(protoGenerator.prototype);\n    var context = new Context(tryLocsList || []);\n\n    // The ._invoke method unifies the implementations of the .next,\n    // .throw, and .return methods.\n    generator._invoke = makeInvokeMethod(innerFn, self, context);\n\n    return generator;\n  }\n  runtime.wrap = wrap;\n\n  // Try/catch helper to minimize deoptimizations. Returns a completion\n  // record like context.tryEntries[i].completion. This interface could\n  // have been (and was previously) designed to take a closure to be\n  // invoked without arguments, but in all the cases we care about we\n  // already have an existing method we want to call, so there's no need\n  // to create a new function object. We can even get away with assuming\n  // the method takes exactly one argument, since that happens to be true\n  // in every case, so we don't have to touch the arguments object. The\n  // only additional allocation required is the completion record, which\n  // has a stable shape and so hopefully should be cheap to allocate.\n  function tryCatch(fn, obj, arg) {\n    try {\n      return { type: \"normal\", arg: fn.call(obj, arg) };\n    } catch (err) {\n      return { type: \"throw\", arg: err };\n    }\n  }\n\n  var GenStateSuspendedStart = \"suspendedStart\";\n  var GenStateSuspendedYield = \"suspendedYield\";\n  var GenStateExecuting = \"executing\";\n  var GenStateCompleted = \"completed\";\n\n  // Returning this object from the innerFn has the same effect as\n  // breaking out of the dispatch switch statement.\n  var ContinueSentinel = {};\n\n  // Dummy constructor functions that we use as the .constructor and\n  // .constructor.prototype properties for functions that return Generator\n  // objects. For full spec compliance, you may wish to configure your\n  // minifier not to mangle the names of these two functions.\n  function Generator() {}\n  function GeneratorFunction() {}\n  function GeneratorFunctionPrototype() {}\n\n  // This is a polyfill for %IteratorPrototype% for environments that\n  // don't natively support it.\n  var IteratorPrototype = {};\n  IteratorPrototype[iteratorSymbol] = function () {\n    return this;\n  };\n\n  var getProto = Object.getPrototypeOf;\n  var NativeIteratorPrototype = getProto && getProto(getProto(values([])));\n  if (NativeIteratorPrototype &&\n      NativeIteratorPrototype !== Op &&\n      hasOwn.call(NativeIteratorPrototype, iteratorSymbol)) {\n    // This environment has a native %IteratorPrototype%; use it instead\n    // of the polyfill.\n    IteratorPrototype = NativeIteratorPrototype;\n  }\n\n  var Gp = GeneratorFunctionPrototype.prototype =\n    Generator.prototype = Object.create(IteratorPrototype);\n  GeneratorFunction.prototype = Gp.constructor = GeneratorFunctionPrototype;\n  GeneratorFunctionPrototype.constructor = GeneratorFunction;\n  GeneratorFunctionPrototype[toStringTagSymbol] =\n    GeneratorFunction.displayName = \"GeneratorFunction\";\n\n  // Helper for defining the .next, .throw, and .return methods of the\n  // Iterator interface in terms of a single ._invoke method.\n  function defineIteratorMethods(prototype) {\n    [\"next\", \"throw\", \"return\"].forEach(function(method) {\n      prototype[method] = function(arg) {\n        return this._invoke(method, arg);\n      };\n    });\n  }\n\n  runtime.isGeneratorFunction = function(genFun) {\n    var ctor = typeof genFun === \"function\" && genFun.constructor;\n    return ctor\n      ? ctor === GeneratorFunction ||\n        // For the native GeneratorFunction constructor, the best we can\n        // do is to check its .name property.\n        (ctor.displayName || ctor.name) === \"GeneratorFunction\"\n      : false;\n  };\n\n  runtime.mark = function(genFun) {\n    if (Object.setPrototypeOf) {\n      Object.setPrototypeOf(genFun, GeneratorFunctionPrototype);\n    } else {\n      genFun.__proto__ = GeneratorFunctionPrototype;\n      if (!(toStringTagSymbol in genFun)) {\n        genFun[toStringTagSymbol] = \"GeneratorFunction\";\n      }\n    }\n    genFun.prototype = Object.create(Gp);\n    return genFun;\n  };\n\n  // Within the body of any async function, `await x` is transformed to\n  // `yield regeneratorRuntime.awrap(x)`, so that the runtime can test\n  // `hasOwn.call(value, \"__await\")` to determine if the yielded value is\n  // meant to be awaited.\n  runtime.awrap = function(arg) {\n    return { __await: arg };\n  };\n\n  function AsyncIterator(generator) {\n    function invoke(method, arg, resolve, reject) {\n      var record = tryCatch(generator[method], generator, arg);\n      if (record.type === \"throw\") {\n        reject(record.arg);\n      } else {\n        var result = record.arg;\n        var value = result.value;\n        if (value &&\n            typeof value === \"object\" &&\n            hasOwn.call(value, \"__await\")) {\n          return Promise.resolve(value.__await).then(function(value) {\n            invoke(\"next\", value, resolve, reject);\n          }, function(err) {\n            invoke(\"throw\", err, resolve, reject);\n          });\n        }\n\n        return Promise.resolve(value).then(function(unwrapped) {\n          // When a yielded Promise is resolved, its final value becomes\n          // the .value of the Promise<{value,done}> result for the\n          // current iteration. If the Promise is rejected, however, the\n          // result for this iteration will be rejected with the same\n          // reason. Note that rejections of yielded Promises are not\n          // thrown back into the generator function, as is the case\n          // when an awaited Promise is rejected. This difference in\n          // behavior between yield and await is important, because it\n          // allows the consumer to decide what to do with the yielded\n          // rejection (swallow it and continue, manually .throw it back\n          // into the generator, abandon iteration, whatever). With\n          // await, by contrast, there is no opportunity to examine the\n          // rejection reason outside the generator function, so the\n          // only option is to throw it from the await expression, and\n          // let the generator function handle the exception.\n          result.value = unwrapped;\n          resolve(result);\n        }, reject);\n      }\n    }\n\n    if (typeof global.process === \"object\" && global.process.domain) {\n      invoke = global.process.domain.bind(invoke);\n    }\n\n    var previousPromise;\n\n    function enqueue(method, arg) {\n      function callInvokeWithMethodAndArg() {\n        return new Promise(function(resolve, reject) {\n          invoke(method, arg, resolve, reject);\n        });\n      }\n\n      return previousPromise =\n        // If enqueue has been called before, then we want to wait until\n        // all previous Promises have been resolved before calling invoke,\n        // so that results are always delivered in the correct order. If\n        // enqueue has not been called before, then it is important to\n        // call invoke immediately, without waiting on a callback to fire,\n        // so that the async generator function has the opportunity to do\n        // any necessary setup in a predictable way. This predictability\n        // is why the Promise constructor synchronously invokes its\n        // executor callback, and why async functions synchronously\n        // execute code before the first await. Since we implement simple\n        // async functions in terms of async generators, it is especially\n        // important to get this right, even though it requires care.\n        previousPromise ? previousPromise.then(\n          callInvokeWithMethodAndArg,\n          // Avoid propagating failures to Promises returned by later\n          // invocations of the iterator.\n          callInvokeWithMethodAndArg\n        ) : callInvokeWithMethodAndArg();\n    }\n\n    // Define the unified helper method that is used to implement .next,\n    // .throw, and .return (see defineIteratorMethods).\n    this._invoke = enqueue;\n  }\n\n  defineIteratorMethods(AsyncIterator.prototype);\n  AsyncIterator.prototype[asyncIteratorSymbol] = function () {\n    return this;\n  };\n  runtime.AsyncIterator = AsyncIterator;\n\n  // Note that simple async functions are implemented on top of\n  // AsyncIterator objects; they just return a Promise for the value of\n  // the final result produced by the iterator.\n  runtime.async = function(innerFn, outerFn, self, tryLocsList) {\n    var iter = new AsyncIterator(\n      wrap(innerFn, outerFn, self, tryLocsList)\n    );\n\n    return runtime.isGeneratorFunction(outerFn)\n      ? iter // If outerFn is a generator, return the full iterator.\n      : iter.next().then(function(result) {\n          return result.done ? result.value : iter.next();\n        });\n  };\n\n  function makeInvokeMethod(innerFn, self, context) {\n    var state = GenStateSuspendedStart;\n\n    return function invoke(method, arg) {\n      if (state === GenStateExecuting) {\n        throw new Error(\"Generator is already running\");\n      }\n\n      if (state === GenStateCompleted) {\n        if (method === \"throw\") {\n          throw arg;\n        }\n\n        // Be forgiving, per 25.3.3.3.3 of the spec:\n        // https://people.mozilla.org/~jorendorff/es6-draft.html#sec-generatorresume\n        return doneResult();\n      }\n\n      context.method = method;\n      context.arg = arg;\n\n      while (true) {\n        var delegate = context.delegate;\n        if (delegate) {\n          var delegateResult = maybeInvokeDelegate(delegate, context);\n          if (delegateResult) {\n            if (delegateResult === ContinueSentinel) continue;\n            return delegateResult;\n          }\n        }\n\n        if (context.method === \"next\") {\n          // Setting context._sent for legacy support of Babel's\n          // function.sent implementation.\n          context.sent = context._sent = context.arg;\n\n        } else if (context.method === \"throw\") {\n          if (state === GenStateSuspendedStart) {\n            state = GenStateCompleted;\n            throw context.arg;\n          }\n\n          context.dispatchException(context.arg);\n\n        } else if (context.method === \"return\") {\n          context.abrupt(\"return\", context.arg);\n        }\n\n        state = GenStateExecuting;\n\n        var record = tryCatch(innerFn, self, context);\n        if (record.type === \"normal\") {\n          // If an exception is thrown from innerFn, we leave state ===\n          // GenStateExecuting and loop back for another invocation.\n          state = context.done\n            ? GenStateCompleted\n            : GenStateSuspendedYield;\n\n          if (record.arg === ContinueSentinel) {\n            continue;\n          }\n\n          return {\n            value: record.arg,\n            done: context.done\n          };\n\n        } else if (record.type === \"throw\") {\n          state = GenStateCompleted;\n          // Dispatch the exception by looping back around to the\n          // context.dispatchException(context.arg) call above.\n          context.method = \"throw\";\n          context.arg = record.arg;\n        }\n      }\n    };\n  }\n\n  // Call delegate.iterator[context.method](context.arg) and handle the\n  // result, either by returning a { value, done } result from the\n  // delegate iterator, or by modifying context.method and context.arg,\n  // setting context.delegate to null, and returning the ContinueSentinel.\n  function maybeInvokeDelegate(delegate, context) {\n    var method = delegate.iterator[context.method];\n    if (method === undefined) {\n      // A .throw or .return when the delegate iterator has no .throw\n      // method always terminates the yield* loop.\n      context.delegate = null;\n\n      if (context.method === \"throw\") {\n        if (delegate.iterator.return) {\n          // If the delegate iterator has a return method, give it a\n          // chance to clean up.\n          context.method = \"return\";\n          context.arg = undefined;\n          maybeInvokeDelegate(delegate, context);\n\n          if (context.method === \"throw\") {\n            // If maybeInvokeDelegate(context) changed context.method from\n            // \"return\" to \"throw\", let that override the TypeError below.\n            return ContinueSentinel;\n          }\n        }\n\n        context.method = \"throw\";\n        context.arg = new TypeError(\n          \"The iterator does not provide a 'throw' method\");\n      }\n\n      return ContinueSentinel;\n    }\n\n    var record = tryCatch(method, delegate.iterator, context.arg);\n\n    if (record.type === \"throw\") {\n      context.method = \"throw\";\n      context.arg = record.arg;\n      context.delegate = null;\n      return ContinueSentinel;\n    }\n\n    var info = record.arg;\n\n    if (! info) {\n      context.method = \"throw\";\n      context.arg = new TypeError(\"iterator result is not an object\");\n      context.delegate = null;\n      return ContinueSentinel;\n    }\n\n    if (info.done) {\n      // Assign the result of the finished delegate to the temporary\n      // variable specified by delegate.resultName (see delegateYield).\n      context[delegate.resultName] = info.value;\n\n      // Resume execution at the desired location (see delegateYield).\n      context.next = delegate.nextLoc;\n\n      // If context.method was \"throw\" but the delegate handled the\n      // exception, let the outer generator proceed normally. If\n      // context.method was \"next\", forget context.arg since it has been\n      // \"consumed\" by the delegate iterator. If context.method was\n      // \"return\", allow the original .return call to continue in the\n      // outer generator.\n      if (context.method !== \"return\") {\n        context.method = \"next\";\n        context.arg = undefined;\n      }\n\n    } else {\n      // Re-yield the result returned by the delegate method.\n      return info;\n    }\n\n    // The delegate iterator is finished, so forget it and continue with\n    // the outer generator.\n    context.delegate = null;\n    return ContinueSentinel;\n  }\n\n  // Define Generator.prototype.{next,throw,return} in terms of the\n  // unified ._invoke helper method.\n  defineIteratorMethods(Gp);\n\n  Gp[toStringTagSymbol] = \"Generator\";\n\n  // A Generator should always return itself as the iterator object when the\n  // @@iterator function is called on it. Some browsers' implementations of the\n  // iterator prototype chain incorrectly implement this, causing the Generator\n  // object to not be returned from this call. This ensures that doesn't happen.\n  // See https://github.com/facebook/regenerator/issues/274 for more details.\n  Gp[iteratorSymbol] = function() {\n    return this;\n  };\n\n  Gp.toString = function() {\n    return \"[object Generator]\";\n  };\n\n  function pushTryEntry(locs) {\n    var entry = { tryLoc: locs[0] };\n\n    if (1 in locs) {\n      entry.catchLoc = locs[1];\n    }\n\n    if (2 in locs) {\n      entry.finallyLoc = locs[2];\n      entry.afterLoc = locs[3];\n    }\n\n    this.tryEntries.push(entry);\n  }\n\n  function resetTryEntry(entry) {\n    var record = entry.completion || {};\n    record.type = \"normal\";\n    delete record.arg;\n    entry.completion = record;\n  }\n\n  function Context(tryLocsList) {\n    // The root entry object (effectively a try statement without a catch\n    // or a finally block) gives us a place to store values thrown from\n    // locations where there is no enclosing try statement.\n    this.tryEntries = [{ tryLoc: \"root\" }];\n    tryLocsList.forEach(pushTryEntry, this);\n    this.reset(true);\n  }\n\n  runtime.keys = function(object) {\n    var keys = [];\n    for (var key in object) {\n      keys.push(key);\n    }\n    keys.reverse();\n\n    // Rather than returning an object with a next method, we keep\n    // things simple and return the next function itself.\n    return function next() {\n      while (keys.length) {\n        var key = keys.pop();\n        if (key in object) {\n          next.value = key;\n          next.done = false;\n          return next;\n        }\n      }\n\n      // To avoid creating an additional object, we just hang the .value\n      // and .done properties off the next function object itself. This\n      // also ensures that the minifier will not anonymize the function.\n      next.done = true;\n      return next;\n    };\n  };\n\n  function values(iterable) {\n    if (iterable) {\n      var iteratorMethod = iterable[iteratorSymbol];\n      if (iteratorMethod) {\n        return iteratorMethod.call(iterable);\n      }\n\n      if (typeof iterable.next === \"function\") {\n        return iterable;\n      }\n\n      if (!isNaN(iterable.length)) {\n        var i = -1, next = function next() {\n          while (++i < iterable.length) {\n            if (hasOwn.call(iterable, i)) {\n              next.value = iterable[i];\n              next.done = false;\n              return next;\n            }\n          }\n\n          next.value = undefined;\n          next.done = true;\n\n          return next;\n        };\n\n        return next.next = next;\n      }\n    }\n\n    // Return an iterator with no values.\n    return { next: doneResult };\n  }\n  runtime.values = values;\n\n  function doneResult() {\n    return { value: undefined, done: true };\n  }\n\n  Context.prototype = {\n    constructor: Context,\n\n    reset: function(skipTempReset) {\n      this.prev = 0;\n      this.next = 0;\n      // Resetting context._sent for legacy support of Babel's\n      // function.sent implementation.\n      this.sent = this._sent = undefined;\n      this.done = false;\n      this.delegate = null;\n\n      this.method = \"next\";\n      this.arg = undefined;\n\n      this.tryEntries.forEach(resetTryEntry);\n\n      if (!skipTempReset) {\n        for (var name in this) {\n          // Not sure about the optimal order of these conditions:\n          if (name.charAt(0) === \"t\" &&\n              hasOwn.call(this, name) &&\n              !isNaN(+name.slice(1))) {\n            this[name] = undefined;\n          }\n        }\n      }\n    },\n\n    stop: function() {\n      this.done = true;\n\n      var rootEntry = this.tryEntries[0];\n      var rootRecord = rootEntry.completion;\n      if (rootRecord.type === \"throw\") {\n        throw rootRecord.arg;\n      }\n\n      return this.rval;\n    },\n\n    dispatchException: function(exception) {\n      if (this.done) {\n        throw exception;\n      }\n\n      var context = this;\n      function handle(loc, caught) {\n        record.type = \"throw\";\n        record.arg = exception;\n        context.next = loc;\n\n        if (caught) {\n          // If the dispatched exception was caught by a catch block,\n          // then let that catch block handle the exception normally.\n          context.method = \"next\";\n          context.arg = undefined;\n        }\n\n        return !! caught;\n      }\n\n      for (var i = this.tryEntries.length - 1; i >= 0; --i) {\n        var entry = this.tryEntries[i];\n        var record = entry.completion;\n\n        if (entry.tryLoc === \"root\") {\n          // Exception thrown outside of any try block that could handle\n          // it, so set the completion value of the entire function to\n          // throw the exception.\n          return handle(\"end\");\n        }\n\n        if (entry.tryLoc <= this.prev) {\n          var hasCatch = hasOwn.call(entry, \"catchLoc\");\n          var hasFinally = hasOwn.call(entry, \"finallyLoc\");\n\n          if (hasCatch && hasFinally) {\n            if (this.prev < entry.catchLoc) {\n              return handle(entry.catchLoc, true);\n            } else if (this.prev < entry.finallyLoc) {\n              return handle(entry.finallyLoc);\n            }\n\n          } else if (hasCatch) {\n            if (this.prev < entry.catchLoc) {\n              return handle(entry.catchLoc, true);\n            }\n\n          } else if (hasFinally) {\n            if (this.prev < entry.finallyLoc) {\n              return handle(entry.finallyLoc);\n            }\n\n          } else {\n            throw new Error(\"try statement without catch or finally\");\n          }\n        }\n      }\n    },\n\n    abrupt: function(type, arg) {\n      for (var i = this.tryEntries.length - 1; i >= 0; --i) {\n        var entry = this.tryEntries[i];\n        if (entry.tryLoc <= this.prev &&\n            hasOwn.call(entry, \"finallyLoc\") &&\n            this.prev < entry.finallyLoc) {\n          var finallyEntry = entry;\n          break;\n        }\n      }\n\n      if (finallyEntry &&\n          (type === \"break\" ||\n           type === \"continue\") &&\n          finallyEntry.tryLoc <= arg &&\n          arg <= finallyEntry.finallyLoc) {\n        // Ignore the finally entry if control is not jumping to a\n        // location outside the try/catch block.\n        finallyEntry = null;\n      }\n\n      var record = finallyEntry ? finallyEntry.completion : {};\n      record.type = type;\n      record.arg = arg;\n\n      if (finallyEntry) {\n        this.method = \"next\";\n        this.next = finallyEntry.finallyLoc;\n        return ContinueSentinel;\n      }\n\n      return this.complete(record);\n    },\n\n    complete: function(record, afterLoc) {\n      if (record.type === \"throw\") {\n        throw record.arg;\n      }\n\n      if (record.type === \"break\" ||\n          record.type === \"continue\") {\n        this.next = record.arg;\n      } else if (record.type === \"return\") {\n        this.rval = this.arg = record.arg;\n        this.method = \"return\";\n        this.next = \"end\";\n      } else if (record.type === \"normal\" && afterLoc) {\n        this.next = afterLoc;\n      }\n\n      return ContinueSentinel;\n    },\n\n    finish: function(finallyLoc) {\n      for (var i = this.tryEntries.length - 1; i >= 0; --i) {\n        var entry = this.tryEntries[i];\n        if (entry.finallyLoc === finallyLoc) {\n          this.complete(entry.completion, entry.afterLoc);\n          resetTryEntry(entry);\n          return ContinueSentinel;\n        }\n      }\n    },\n\n    \"catch\": function(tryLoc) {\n      for (var i = this.tryEntries.length - 1; i >= 0; --i) {\n        var entry = this.tryEntries[i];\n        if (entry.tryLoc === tryLoc) {\n          var record = entry.completion;\n          if (record.type === \"throw\") {\n            var thrown = record.arg;\n            resetTryEntry(entry);\n          }\n          return thrown;\n        }\n      }\n\n      // The context.catch method must only be called with a location\n      // argument that corresponds to a known catch block.\n      throw new Error(\"illegal catch attempt\");\n    },\n\n    delegateYield: function(iterable, resultName, nextLoc) {\n      this.delegate = {\n        iterator: values(iterable),\n        resultName: resultName,\n        nextLoc: nextLoc\n      };\n\n      if (this.method === \"next\") {\n        // Deliberately forget the last sent value so that we don't\n        // accidentally pass it on to the delegate.\n        this.arg = undefined;\n      }\n\n      return ContinueSentinel;\n    }\n  };\n})(\n  // Among the various tricks for obtaining a reference to the global\n  // object, this seems to be the most reliable technique that does not\n  // use indirect eval (which violates Content Security Policy).\n  typeof global === \"object\" ? global :\n  typeof window === \"object\" ? window :\n  typeof self === \"object\" ? self : this\n);\n","module.exports = function (regExp, replace) {\n  var replacer = replace === Object(replace) ? function (part) {\n    return replace[part];\n  } : replace;\n  return function (it) {\n    return String(it).replace(regExp, replacer);\n  };\n};\n","// https://github.com/benjamingr/RexExp.escape\nvar $export = require('./_export');\nvar $re = require('./_replacer')(/[\\\\^$*+?.()|[\\]{}]/g, '\\\\$&');\n\n$export($export.S, 'RegExp', { escape: function escape(it) { return $re(it); } });\n","require('../../modules/core.regexp.escape');\nmodule.exports = require('../../modules/_core').RegExp.escape;\n","\"use strict\";\n\nrequire(\"core-js/shim\");\n\nrequire(\"regenerator-runtime/runtime\");\n\nrequire(\"core-js/fn/regexp/escape\");\n\nif (global._babelPolyfill) {\n  throw new Error(\"only one instance of babel-polyfill is allowed\");\n}\nglobal._babelPolyfill = true;\n\nvar DEFINE_PROPERTY = \"defineProperty\";\nfunction define(O, key, value) {\n  O[key] || Object[DEFINE_PROPERTY](O, key, {\n    writable: true,\n    configurable: true,\n    value: value\n  });\n}\n\ndefine(String.prototype, \"padLeft\", \"\".padStart);\ndefine(String.prototype, \"padRight\", \"\".padEnd);\n\n\"pop,reverse,shift,keys,values,entries,indexOf,every,some,forEach,map,filter,find,findIndex,includes,join,slice,concat,push,splice,unshift,sort,lastIndexOf,reduce,reduceRight,copyWithin,fill\".split(\",\").forEach(function (key) {\n  [][key] && define(Array, key, Function.call.bind([][key]));\n});","export default class ScrollSpy {\n    constructor(args) {\n\n        this.doc = document;\n        this.nav = this.doc.querySelectorAll(args.navSelector);\n\n        if(!this.nav.length === 0) { return }\n\n        this.win = window;\n        this.winHeight = this.win.innerHeight;\n\n        this.scrollElement = this.doc.querySelector(args.scrollSelector);\n        this.className = args.className;\n        this.offsetTop = args.offsetTop || 0;\n\n        this.contents = [];\n        this.contents = this.getContents(args.contentSelector);\n\n        this.attachEvent();\n    }\n\n    attachEvent() {\n        let scrollTimer;\n        this.scrollElement.addEventListener('scroll', () => {\n            if (scrollTimer) {\n                clearTimeout(scrollTimer);\n            }\n\n            scrollTimer = setTimeout(() => {\n                this.spy();\n            }, 1);\n        });\n\n        let resizeTimer;\n        this.scrollElement.addEventListener('resize', () => {\n            if (resizeTimer) {\n                clearTimeout(resizeTimer);\n            }\n\n            resizeTimer = setTimeout(() => {\n                this.spy();\n            }, 1);\n        });\n\n    }\n\n    getContents(contentSelector) {\n        const targets = [];\n        for (let i = 0, max = this.nav.length; i < max; i++) {\n            const href = this.nav[i].href;\n            targets.push(this.doc.getElementById(href.split('#')[1]));\n        }\n        return targets;\n    }\n\n    spy() {\n        let elements = this.getViewState();\n        this.toggleNavClass(elements);\n    }\n\n    getViewState() {\n        const elementListInView = [];\n        for (let i = 0, max = this.contents.length; i < max; i++) {\n            const current = this.contents[i];\n            if (current && this.isView(current)) {\n                elementListInView.push(current);\n            }\n        }\n\n        return elementListInView;\n    }\n\n    isView(element) {\n        const scrollTop = this.scrollElement.scrollTop;\n        const calcBotom = scrollTop + this.offsetTop;\n        const rect = element.getBoundingClientRect();\n        const elementTop = rect.top + scrollTop;\n        const elementBottom = elementTop + element.offsetHeight;\n\n        return elementTop <= calcBotom + this.offsetTop && elementBottom > scrollTop + this.offsetTop;\n    }\n\n    toggleNavClass(elements) {\n        let maxDepth = 0;\n        let maxDepthElement = $();\n\n        for (let i = 0, max = elements.length; i < max; i++) {\n            const el = elements[i];\n            const tempDepth = this.getTagDepth(el);\n            if (maxDepth < tempDepth) {\n                maxDepth = tempDepth;\n                maxDepthElement = el;\n            }\n        }\n\n        for (let i = 0, max = this.nav.length; i < max; i++) {\n            const navElement = this.nav[i];\n            if (navElement.href.split('#')[1] === maxDepthElement.id) {\n                navElement.classList.add(this.className);\n                navElement.classList.add('mdl-color-text--primary');\n            } else {\n                navElement.classList.remove(this.className);\n                navElement.classList.remove('mdl-color-text--primary');\n            }\n        }\n    }\n\n    getTagDepth(element) {\n        return parseInt($(element).find('h1,h2,h3,h4,h5,h6').get(0).tagName.split('H')[1]);\n    }\n}\n","export default class AdjustHeight {\n    \n    constructor() {\n        this.header = $('header.mdl-layout__header');\n        this.pagenation = $('div.pagenation');\n        this.footer = $('footer.mdl-mini-footer');\n        this.win = $(window);\n        this.scrollElement = $('main');\n        this.content = $('.page-content');\n        this.outline = $('.side-doc-outline--content');\n\n        this.attachEvent();\n        this.adjust();\n    }\n\n    adjust() {\n        this.setPageContentMinHeight();\n        this.setLocaltocHeight();\n    }\n\n    setPageContentMinHeight() {\n        const winH = this.win.innerHeight();\n        const headerHeight = this.header.outerHeight();\n        const footerHeight = this.footer.outerHeight(true) + this.pagenation.outerHeight(true);\n\n        this.content.css('min-height', this.win.innerHeight() - headerHeight - footerHeight);\n    }\n\n    setLocaltocHeight() {\n        const pagenationPotisionTop = this.pagenation.position().top + parseInt(this.pagenation.css('margin-top'), 10);\n        const outlineBottom = this.scrollElement.scrollTop() + this.content.outerHeight();\n        const winH = this.win.innerHeight() - this.header.outerHeight();\n\n        let min = 0;\n        min = outlineBottom > pagenationPotisionTop ? pagenationPotisionTop : outlineBottom;\n        min = min > winH ? winH : min;\n        this.outline.css('height', min);\n    }\n\n    attachEvent() {\n        let scrollTimer;\n        this.scrollElement.on('scroll', () => {\n            if (scrollTimer) {\n                clearTimeout(scrollTimer);\n            }\n\n            scrollTimer = setTimeout(() => {\n                this.adjust();\n            }, 1);\n        });\n\n        let resizeTimer;\n        this.win.on('resize', () => {\n            if (resizeTimer) {\n                clearTimeout(resizeTimer);\n            }\n\n            resizeTimer = setTimeout(() => {\n                this.adjust();\n            }, 1);\n        });\n    }\n}","import \"../scss/sphinx_materialdesign_theme.scss\";\r\nimport \"material-design-lite\";\r\nimport \"babel-polyfill\";\r\nimport ScrollSpy from \"./scrollspy\";\r\nimport AdjustHeight from \"./adjust-height\";\r\n\r\n$(function() {\r\n\r\n    function reconstructionDrawerGlobalToc() {\r\n        const $globaltoc = $('.mdl-layout__drawer nav');\r\n        const $lists = $globaltoc.find('li');\r\n        $.each($lists, function(index, li) {\r\n            const $li = $(li);\r\n            const $linkWrapper = $('<span class=\"link-wrapper\"></span>');\r\n            const $link = $li.children('a');\r\n            $li.append($linkWrapper.append($link));\r\n\r\n            const isCurrent = $li.hasClass('current') && !$link.hasClass('current');\r\n            const $ul = $li.children('ul');\r\n            if ($ul.length) {\r\n                const ulId = `globalnav-${index}`;\r\n                $ul.attr('id', ulId);\r\n                $ul.addClass('collapse');\r\n                const $toggleWrapper = $('<span class=\"nav-toggle\"></span>');\r\n                if (isCurrent) {\r\n                    $ul.addClass('show');\r\n                    $toggleWrapper.addClass('show');\r\n                } else {\r\n                    $ul.hide();\r\n                }\r\n\r\n                $li.append(\r\n                    $linkWrapper.append(\r\n                        $toggleWrapper.append(\r\n                            $(`<a class=\"mdl-button mdl-js-button mdl-button--icon\" data-toggle=\"#${ulId}\"><span style=\"color: #888\"><i class=\"material-icons\">keyboard_arrow_down</i></span></span>`)\r\n                        )\r\n                    )\r\n                ).append($ul);\r\n            }\r\n        });\r\n    }\r\n\r\n    function collapse() {\r\n        $('.mdl-layout__drawer nav .nav-toggle a').click(function() {\r\n            const $toggle = $(this);\r\n            const id = $toggle.attr('data-toggle');\r\n            $(`ul${id}`).toggleClass('show').animate({height: \"toggle\", opacity: \"toggle\"});\r\n            $toggle.parent().toggleClass('show');\r\n        });\r\n    }\r\n\r\n    function styleMdlCodeBlock() {\r\n        $('pre').hover(function() {\r\n            $(this).attr('click-to-copy', 'click to copy...');\r\n        });\r\n        $('pre').click(function(){\r\n            var result = copyClipboard(this);\r\n            if (result) {\r\n                $(this).attr('click-to-copy', 'copied!');\r\n            }\r\n        });\r\n    }\r\n\r\n    function copyClipboard(selector) {\r\n        var body = document.body;\r\n        if(!body) return false;\r\n\r\n        var $target = $(selector);\r\n        if ($target.length === 0) { return false; }\r\n\r\n        var text = $target.text();\r\n        var textarea = document.createElement('textarea');\r\n        textarea.value = text;\r\n        document.body.appendChild(textarea);\r\n        textarea.select();\r\n        var result = document.execCommand('copy');\r\n        document.body.removeChild(textarea);\r\n        return result;\r\n    }\r\n\r\n    function quickSearchClickEvent() {\r\n        const $breadcrumb = $('.breadcrumb');\r\n\r\n        $('#waterfall-exp').focus(() => {\r\n            if ($(window).width() <= 1024) {\r\n                $breadcrumb.hide();\r\n            }\r\n        }).blur(() => {\r\n            if ($(window).width() <= 1024) {\r\n                $breadcrumb.show();\r\n            }\r\n        });\r\n    }\r\n\r\n    // styleMdlCodeBlock();\r\n\r\n    reconstructionDrawerGlobalToc();\r\n    collapse();\r\n    quickSearchClickEvent();\r\n\r\n\r\n    const spy = new ScrollSpy({\r\n        contentSelector: '.page-content .section',\r\n        navSelector: '.localtoc a',\r\n        scrollSelector: 'main' ,\r\n        className: 'current',\r\n        offsetTop: 64});\r\n\r\n    $('.mdl-layout__content').focus();\r\n\r\n    $('.mx-card').each(function(){\r\n        $(this).addClass('mdl-card mdl-shadow--2dp');\r\n    });\r\n    $('.mx-card .mx-card-title').each(function(){\r\n        $(this).addClass('mdl-card__title');\r\n    });\r\n    $('.mx-card .mx-card-text').each(function(){\r\n        $(this).addClass('mdl-card__supporting-text');\r\n    });\r\n    $('.mx-card-link').each(function(){\r\n        $(this).hide();\r\n    });\r\n    $('.mdl-card').each(function(){\r\n        $(this).click(function() {\r\n            var url = $(this).find('.mx-card-link').text();\r\n            if (url) {\r\n                window.location = url;\r\n            }\r\n            return true;\r\n        });\r\n    });\r\n\r\n    $('a.download').each(function() {\r\n        // button\r\n        var button = document.createElement('button');\r\n        button.className = 'download mdl-button mdl-js-button mdl-button--fab mdl-js-ripple-effect';\r\n\r\n        // icon\r\n        var icon = document.createElement('i');\r\n        icon.className = 'material-icons';\r\n        var text = document.createTextNode('file_download');\r\n        icon.appendChild(text);\r\n        button.appendChild(icon);\r\n\r\n        // link\r\n        var link = $(this).attr('href');\r\n        button.onclick = function() {\r\n            window.location = link;\r\n        };\r\n        var fileName = link.split(\"/\").slice(-1).pop();\r\n        if (fileName) {\r\n            button.id = fileName.replace('.', '-');\r\n        } else {\r\n            button.id = 'download-button-' + $(this).index();\r\n        }\r\n\r\n        // hint\r\n        var hint = document.createElement('div');\r\n        hint.className = 'mdl-tooltip';\r\n        hint.setAttribute('data-mdl-for', button.id);\r\n        var hintText = $(this).find('span.pre').map(function() {\r\n            return $(this).text();\r\n        }).get().join(' ');\r\n        hint.innerHTML = hintText;\r\n\r\n        componentHandler.upgradeElement(button);\r\n        $(this).remove();\r\n        var header = $('.section h1').first();\r\n        header.append(button);\r\n        header.append(hint);\r\n    });\r\n\r\n    $('.mdl-layout__container').css('visibility', 'visible');\r\n});\r\n"]}
\ No newline at end of file
diff --git a/docs/python_docs/themes/mx-theme/mxtheme/theme.conf b/docs/python_docs/themes/mx-theme/mxtheme/theme.conf
new file mode 100644
index 000000000000..c1f2dd4016b9
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/mxtheme/theme.conf
@@ -0,0 +1,20 @@
+[theme]
+inherit = basic
+html5_doctype = true
+pygments_style = friendly
+# stylesheet = material-icons.css
+
+[options]
+header_links =
+relative_url = /
+primary_color = blue
+accent_color = deep_orange
+
+fixed_drawer = True
+fixed_header = True
+header_waterfall = True
+header_scroll = False
+
+show_header_title = False
+show_drawer_title = True
+show_footer = True
diff --git a/docs/python_docs/themes/mx-theme/setup.py b/docs/python_docs/themes/mx-theme/setup.py
new file mode 100644
index 000000000000..24e29e8e560e
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/setup.py
@@ -0,0 +1,19 @@
+from setuptools import setup
+from mxtheme import __version__
+
+setup(
+    name = 'mxtheme',
+    version = __version__,
+    author = 'Mu Li',
+    author_email= '',
+    url="https://github.com/mli/mx-theme",
+    description='A Sphinx theme based on Material Design, adapted from sphinx_materialdesign_theme',
+    packages = ['mxtheme'],
+    include_package_data=True,
+    license= 'MIT License',
+    entry_points = {
+        'sphinx.html_themes': [
+            'mxtheme = mxtheme',
+        ]
+    },
+)
diff --git a/docs/python_docs/themes/mx-theme/src/js/adjust-height.js b/docs/python_docs/themes/mx-theme/src/js/adjust-height.js
new file mode 100644
index 000000000000..6b4e5acd0a17
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/src/js/adjust-height.js
@@ -0,0 +1,63 @@
+export default class AdjustHeight {
+    
+    constructor() {
+        this.header = $('header.mdl-layout__header');
+        this.pagenation = $('div.pagenation');
+        this.footer = $('footer.mdl-mini-footer');
+        this.win = $(window);
+        this.scrollElement = $('main');
+        this.content = $('.page-content');
+        this.outline = $('.side-doc-outline--content');
+
+        this.attachEvent();
+        this.adjust();
+    }
+
+    adjust() {
+        this.setPageContentMinHeight();
+        this.setLocaltocHeight();
+    }
+
+    setPageContentMinHeight() {
+        const winH = this.win.innerHeight();
+        const headerHeight = this.header.outerHeight();
+        const footerHeight = this.footer.outerHeight(true) + this.pagenation.outerHeight(true);
+
+        this.content.css('min-height', this.win.innerHeight() - headerHeight - footerHeight);
+    }
+
+    setLocaltocHeight() {
+        const pagenationPotisionTop = this.pagenation.position().top + parseInt(this.pagenation.css('margin-top'), 10);
+        const outlineBottom = this.scrollElement.scrollTop() + this.content.outerHeight();
+        const winH = this.win.innerHeight() - this.header.outerHeight();
+
+        let min = 0;
+        min = outlineBottom > pagenationPotisionTop ? pagenationPotisionTop : outlineBottom;
+        min = min > winH ? winH : min;
+        this.outline.css('height', min);
+    }
+
+    attachEvent() {
+        let scrollTimer;
+        this.scrollElement.on('scroll', () => {
+            if (scrollTimer) {
+                clearTimeout(scrollTimer);
+            }
+
+            scrollTimer = setTimeout(() => {
+                this.adjust();
+            }, 1);
+        });
+
+        let resizeTimer;
+        this.win.on('resize', () => {
+            if (resizeTimer) {
+                clearTimeout(resizeTimer);
+            }
+
+            resizeTimer = setTimeout(() => {
+                this.adjust();
+            }, 1);
+        });
+    }
+}
\ No newline at end of file
diff --git a/docs/python_docs/themes/mx-theme/src/js/scrollspy.js b/docs/python_docs/themes/mx-theme/src/js/scrollspy.js
new file mode 100644
index 000000000000..cd4e798c6353
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/src/js/scrollspy.js
@@ -0,0 +1,111 @@
+export default class ScrollSpy {
+    constructor(args) {
+
+        this.doc = document;
+        this.nav = this.doc.querySelectorAll(args.navSelector);
+
+        if(!this.nav.length === 0) { return }
+
+        this.win = window;
+        this.winHeight = this.win.innerHeight;
+
+        this.scrollElement = this.doc.querySelector(args.scrollSelector);
+        this.className = args.className;
+        this.offsetTop = args.offsetTop || 0;
+
+        this.contents = [];
+        this.contents = this.getContents(args.contentSelector);
+
+        this.attachEvent();
+    }
+
+    attachEvent() {
+        let scrollTimer;
+        this.scrollElement.addEventListener('scroll', () => {
+            if (scrollTimer) {
+                clearTimeout(scrollTimer);
+            }
+
+            scrollTimer = setTimeout(() => {
+                this.spy();
+            }, 1);
+        });
+
+        let resizeTimer;
+        this.scrollElement.addEventListener('resize', () => {
+            if (resizeTimer) {
+                clearTimeout(resizeTimer);
+            }
+
+            resizeTimer = setTimeout(() => {
+                this.spy();
+            }, 1);
+        });
+
+    }
+
+    getContents(contentSelector) {
+        const targets = [];
+        for (let i = 0, max = this.nav.length; i < max; i++) {
+            const href = this.nav[i].href;
+            targets.push(this.doc.getElementById(href.split('#')[1]));
+        }
+        return targets;
+    }
+
+    spy() {
+        let elements = this.getViewState();
+        this.toggleNavClass(elements);
+    }
+
+    getViewState() {
+        const elementListInView = [];
+        for (let i = 0, max = this.contents.length; i < max; i++) {
+            const current = this.contents[i];
+            if (current && this.isView(current)) {
+                elementListInView.push(current);
+            }
+        }
+
+        return elementListInView;
+    }
+
+    isView(element) {
+        const scrollTop = this.scrollElement.scrollTop;
+        const calcBotom = scrollTop + this.offsetTop;
+        const rect = element.getBoundingClientRect();
+        const elementTop = rect.top + scrollTop;
+        const elementBottom = elementTop + element.offsetHeight;
+
+        return elementTop <= calcBotom + this.offsetTop && elementBottom > scrollTop + this.offsetTop;
+    }
+
+    toggleNavClass(elements) {
+        let maxDepth = 0;
+        let maxDepthElement = $();
+
+        for (let i = 0, max = elements.length; i < max; i++) {
+            const el = elements[i];
+            const tempDepth = this.getTagDepth(el);
+            if (maxDepth < tempDepth) {
+                maxDepth = tempDepth;
+                maxDepthElement = el;
+            }
+        }
+
+        for (let i = 0, max = this.nav.length; i < max; i++) {
+            const navElement = this.nav[i];
+            if (navElement.href.split('#')[1] === maxDepthElement.id) {
+                navElement.classList.add(this.className);
+                navElement.classList.add('mdl-color-text--primary');
+            } else {
+                navElement.classList.remove(this.className);
+                navElement.classList.remove('mdl-color-text--primary');
+            }
+        }
+    }
+
+    getTagDepth(element) {
+        return parseInt($(element).find('h1,h2,h3,h4,h5,h6').get(0).tagName.split('H')[1]);
+    }
+}
diff --git a/docs/python_docs/themes/mx-theme/src/js/sphinx_materialdesign_theme.js b/docs/python_docs/themes/mx-theme/src/js/sphinx_materialdesign_theme.js
new file mode 100644
index 000000000000..15e62e905267
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/src/js/sphinx_materialdesign_theme.js
@@ -0,0 +1,174 @@
+import "../scss/sphinx_materialdesign_theme.scss";
+import "material-design-lite";
+import "babel-polyfill";
+import ScrollSpy from "./scrollspy";
+import AdjustHeight from "./adjust-height";
+
+$(function() {
+
+    function reconstructionDrawerGlobalToc() {
+        const $globaltoc = $('.mdl-layout__drawer nav');
+        const $lists = $globaltoc.find('li');
+        $.each($lists, function(index, li) {
+            const $li = $(li);
+            const $linkWrapper = $('<span class="link-wrapper"></span>');
+            const $link = $li.children('a');
+            $li.append($linkWrapper.append($link));
+
+            const isCurrent = $li.hasClass('current') && !$link.hasClass('current');
+            const $ul = $li.children('ul');
+            if ($ul.length) {
+                const ulId = `globalnav-${index}`;
+                $ul.attr('id', ulId);
+                $ul.addClass('collapse');
+                const $toggleWrapper = $('<span class="nav-toggle"></span>');
+                if (isCurrent) {
+                    $ul.addClass('show');
+                    $toggleWrapper.addClass('show');
+                } else {
+                    $ul.hide();
+                }
+
+                $li.append(
+                    $linkWrapper.append(
+                        $toggleWrapper.append(
+                            $(`<a class="mdl-button mdl-js-button mdl-button--icon" data-toggle="#${ulId}"><span style="color: #888"><i class="material-icons">keyboard_arrow_down</i></span></span>`)
+                        )
+                    )
+                ).append($ul);
+            }
+        });
+    }
+
+    function collapse() {
+        $('.mdl-layout__drawer nav .nav-toggle a').click(function() {
+            const $toggle = $(this);
+            const id = $toggle.attr('data-toggle');
+            $(`ul${id}`).toggleClass('show').animate({height: "toggle", opacity: "toggle"});
+            $toggle.parent().toggleClass('show');
+        });
+    }
+
+    function styleMdlCodeBlock() {
+        $('pre').hover(function() {
+            $(this).attr('click-to-copy', 'click to copy...');
+        });
+        $('pre').click(function(){
+            var result = copyClipboard(this);
+            if (result) {
+                $(this).attr('click-to-copy', 'copied!');
+            }
+        });
+    }
+
+    function copyClipboard(selector) {
+        var body = document.body;
+        if(!body) return false;
+
+        var $target = $(selector);
+        if ($target.length === 0) { return false; }
+
+        var text = $target.text();
+        var textarea = document.createElement('textarea');
+        textarea.value = text;
+        document.body.appendChild(textarea);
+        textarea.select();
+        var result = document.execCommand('copy');
+        document.body.removeChild(textarea);
+        return result;
+    }
+
+    function quickSearchClickEvent() {
+        const $breadcrumb = $('.breadcrumb');
+
+        $('#waterfall-exp').focus(() => {
+            if ($(window).width() <= 1024) {
+                $breadcrumb.hide();
+            }
+        }).blur(() => {
+            if ($(window).width() <= 1024) {
+                $breadcrumb.show();
+            }
+        });
+    }
+
+    // styleMdlCodeBlock();
+
+    reconstructionDrawerGlobalToc();
+    collapse();
+    quickSearchClickEvent();
+
+
+    const spy = new ScrollSpy({
+        contentSelector: '.page-content .section',
+        navSelector: '.localtoc a',
+        scrollSelector: 'main' ,
+        className: 'current',
+        offsetTop: 64});
+
+    $('.mdl-layout__content').focus();
+
+    $('.mx-card').each(function(){
+        $(this).addClass('mdl-card mdl-shadow--2dp');
+    });
+    $('.mx-card .mx-card-title').each(function(){
+        $(this).addClass('mdl-card__title');
+    });
+    $('.mx-card .mx-card-text').each(function(){
+        $(this).addClass('mdl-card__supporting-text');
+    });
+    $('.mx-card-link').each(function(){
+        $(this).hide();
+    });
+    $('.mdl-card').each(function(){
+        $(this).click(function() {
+            var url = $(this).find('.mx-card-link').text();
+            if (url) {
+                window.location = url;
+            }
+            return true;
+        });
+    });
+
+    $('a.download').each(function() {
+        // button
+        var button = document.createElement('button');
+        button.className = 'download mdl-button mdl-js-button mdl-button--fab mdl-js-ripple-effect';
+
+        // icon
+        var icon = document.createElement('i');
+        icon.className = 'material-icons';
+        var text = document.createTextNode('file_download');
+        icon.appendChild(text);
+        button.appendChild(icon);
+
+        // link
+        var link = $(this).attr('href');
+        button.onclick = function() {
+            window.location = link;
+        };
+        var fileName = link.split("/").slice(-1).pop();
+        if (fileName) {
+            button.id = fileName.replace('.', '-');
+        } else {
+            button.id = 'download-button-' + $(this).index();
+        }
+
+        // hint
+        var hint = document.createElement('div');
+        hint.className = 'mdl-tooltip';
+        hint.setAttribute('data-mdl-for', button.id);
+        var hintText = $(this).find('span.pre').map(function() {
+            return $(this).text();
+        }).get().join(' ');
+        hint.innerHTML = hintText;
+
+        componentHandler.upgradeElement(button);
+        $(this).remove();
+        var header = $('.section h1').first();
+        header.append(button);
+        header.append(hint);
+    });
+
+    $('.mdl-layout__container').css('visibility', 'visible');
+});
diff --git a/docs/python_docs/themes/mx-theme/src/scss/_root.scss b/docs/python_docs/themes/mx-theme/src/scss/_root.scss
new file mode 100644
index 000000000000..f805a8fb8f0e
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/src/scss/_root.scss
@@ -0,0 +1,115 @@
+html {
+    font-size: $font_size;
+}
+
+body {
+    display: block !important;
+    background-color: $background_color;
+    font-size: 1rem;
+    line-height: 1.5rem;
+    font-family: $body_font_family;
+}
+
+.mdl-layout__content:focus {
+    outline: none;
+ }
+
+h1, h2, h3, h4, h5, h6, blockquote, span.mdl-layout-title,
+a.download > code.download {
+    font-family: $body_font_family;
+}
+
+h1, h2, h3, h4, h5, h6, .toc-backref, .contents, .toctree-wrapper, .contents a, .toctree-wrapper a, .globaltoc a.current {
+    color: $color-mxnet !important;
+}
+
+a {
+    text-decoration: none;
+}
+
+.page-content {
+    font-size: 1rem;
+    p, ul, ol, dl, dd, dt, table, th, td {
+        font-size: 1rem;
+    }
+}
+
+.brand {
+    color: inherit;
+    text-decoration: none;
+}
+
+.section {
+    overflow-x: auto;
+}
+
+
+/*
+ *  Figure Directive Styles
+ */
+ img {
+    max-width: 100%;
+    display: block;
+    margin-left: auto;
+    margin-right: auto;
+ }
+
+div.figure {
+    p.caption {
+        text-align: center;
+        margin-top: .75rem;
+
+        span.caption-number {
+            font-style: normal;
+        }
+        .caption-number::after {
+            content: "\00a0";
+        }
+    }
+}
+
+.svg-icon {
+  width: 16px;
+  height: 16px;
+  display: inline-block;
+  fill: $grey-color-light;
+  padding-right: 5px;
+  padding-top: 4px;
+  vertical-align: text-top;
+}
+
+/*
+ * Download Link Styles
+ */
+a.download > i.material-icons {
+    position: relative;
+    top: 5px;
+}
+
+a.download {
+    text-decoration: none;
+}
+
+%clearfix:after {
+  content: "";
+  display: table;
+  clear: both;
+}
+
+.wrapper {
+  max-width: -webkit-calc(#{$content-width} - (#{$spacing-unit} * 2));
+  max-width: calc(#{$content-width} - (#{$spacing-unit} * 2));
+  margin-right: auto;
+  margin-left: auto;
+  padding-right: calc(#{$spacing-unit}+15px);
+  padding-left: $spacing-unit;
+  @extend %clearfix;
+
+  @media screen and (max-width: $on-laptop) {
+    max-width: -webkit-calc(#{$content-width} - (#{$spacing-unit}));
+    max-width: calc(#{$content-width} - (#{$spacing-unit}));
+    padding-right: $spacing-unit / 2;
+    padding-left: $spacing-unit / 2;
+  }
+}
+
diff --git a/docs/python_docs/themes/mx-theme/src/scss/_variables.scss b/docs/python_docs/themes/mx-theme/src/scss/_variables.scss
new file mode 100644
index 000000000000..7611d4386f5b
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/src/scss/_variables.scss
@@ -0,0 +1,42 @@
+/*
+Variables
+*/
+$font_size: 16px;
+
+$background_color: #fafafa;
+$code_background: rgba(0,0,0,.05);
+
+$code_font_family: "Menlo", "DejaVu Sans Mono", "Liberation Mono", "Consolas", "Ubuntu Mono", "Courier New", "andale mono", "lucida console", monospace !default;
+$body_font_family: -apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol" !default;
+$base-font-size:   17px !default;
+
+$xl-breakpoint: 1795px;
+$lg-breakpoint: 1200px;
+$md-breakpoint: 992px;
+$sm-breakpoint: 768px;
+$xs-breakpoint: 576px;
+
+$color-primary: $palette-blue-500;
+$color-primary-dark: $palette-blue-700 !default;
+$color-accent: $palette-deep-orange-A200 !default;
+$color-primary-contrast: $color-white !default;
+$color-accent-contrast: $color-white !default;
+
+
+$base-line-height: 1.5 !default;
+$spacing-unit:     30px !default;
+
+$color-mxnet: rgb(4,140,204);
+$color-mxnet-dark: rgb(4,60,110);
+$grey-color:       #828282 !default;
+$grey-color-light: lighten($grey-color, 45%) !default;
+$grey-color-dark:  darken($grey-color, 25%) !default;
+
+$table-text-align: left !default;
+
+// Width of the content area
+$content-width:    1150px !default;
+
+$on-palm:          600px !default;
+$on-palm:          900px !default;
+$on-laptop:        1024px !default;
\ No newline at end of file
diff --git a/docs/python_docs/themes/mx-theme/src/scss/admonitions/_admonitions.scss b/docs/python_docs/themes/mx-theme/src/scss/admonitions/_admonitions.scss
new file mode 100644
index 000000000000..a571427da24b
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/src/scss/admonitions/_admonitions.scss
@@ -0,0 +1,101 @@
+
+/*
+ * Admonition Styles
+ */
+ $admonitions: (
+    hint: (
+        font-color: rgb(0, 188, 212),
+        background-color: rgba(0, 188, 212, 0.1),
+        icon-content: "help_outline"
+    ),
+    note: (
+        font-color: rgb(0, 188, 212),
+        background-color: rgba(0, 188, 212, 0.1),
+        icon-content: "info_outline"
+    ),
+    seealso: (
+        font-color: rgb(0, 188, 212),
+        background-color: rgba(0, 188, 212, 0.1),
+        icon-content: "search"
+    ),
+    warning: (
+        font-color: rgb(255, 193, 7),
+        background-color: rgba(255, 193, 7, 0.1),
+        icon-content: "warning"
+    ),
+    attention: (
+        font-color: rgb(255, 193, 7),
+        background-color: rgba(255, 193, 7, 0.1),
+        icon-content: "warning"
+    ),
+    tip: (
+        font-color: rgb(139, 195, 74),
+        background-color: rgba(139, 195, 74, 0.1),
+        icon-content: "lightbulb_outline"
+    ),
+    important: (
+        font-color: rgb(139, 195, 74),
+        background-color: rgba(139, 195, 74, 0.1),
+        icon-content:  "check_circle"
+    ),
+    error: (
+        font-color: rgb(244, 67, 54),
+        background-color: rgba(244, 67, 54, 0.1),
+        icon-content: "error_outline"
+    ),
+    caution: (
+        font-color: rgb(244, 67, 54),
+        background-color: rgba(244, 67, 54, 0.1),
+        icon-content: "error_outline"
+    ),
+    danger: (
+        font-color: rgb(244, 67, 54),
+        background-color: rgba(244, 67, 54, 0.1),
+        icon-content: "error_outline"
+    )
+);
+
+ @mixin admonition-style($type) {
+    border-left: solid 4px map-get(map-get($admonitions, $type), font-color);
+    background-color: map-get(map-get($admonitions, $type), background-color);
+    .admonition-title {
+        font-size: 16px;
+        font-weight: bold;
+        color: map-get(map-get($admonitions, $type), font-color);
+
+        margin-top: 4px;
+        margin-bottom: 8px;
+        &::before {
+            @extend .material-icons;
+            position: relative;
+            margin-right: 5px;
+            top: 3px;
+            content: map-get(map-get($admonitions, $type), icon-content);
+            font-size: 18px;
+        }
+    }
+}
+
+.admonition {
+    @extend .mdl-shadow--2dp;
+
+    padding: 12px 20px;
+    margin-top: 10px;
+    margin-bottom: 10px;
+    p.last {
+        margin: 16px;
+    }
+    .admonition-title {
+        font-size: 16px;
+        font-weight: bold;
+        color: #555;
+        text-transform: uppercase;
+        margin-top: 7px;
+    }
+
+    @each $type in (note, seealso, hint, warning, attention, tip, important, error, caution, danger) {
+        &.#{$type} {
+            @include admonition-style($type);
+        }
+    }
+}
diff --git a/docs/python_docs/themes/mx-theme/src/scss/blockquote/_blockquote.scss b/docs/python_docs/themes/mx-theme/src/scss/blockquote/_blockquote.scss
new file mode 100644
index 000000000000..f62ff71e8340
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/src/scss/blockquote/_blockquote.scss
@@ -0,0 +1,18 @@
+/*
+ * Quotation Block Styles
+ */
+ .page-content {
+    blockquote {
+        font-size: 1rem;
+        padding: 0 1rem;
+        border-left: 3px solid $code_background;
+
+        &:after {
+            content: "" !important;
+            margin-left: 0;
+        }
+        &:before {
+            content: "" !important;
+        }
+    }
+ }
diff --git a/docs/python_docs/themes/mx-theme/src/scss/card/_card.scss b/docs/python_docs/themes/mx-theme/src/scss/card/_card.scss
new file mode 100644
index 000000000000..31e4cd118383
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/src/scss/card/_card.scss
@@ -0,0 +1,48 @@
+.mdl-card {
+    margin: 1em 1.5em 1em 0;
+    display: inline-block;
+    width: 250px;
+    min-height: 140px;
+    padding: 18px;
+}
+.mdl-card:hover {
+    box-shadow: 0 10px 20px rgba(0,0,0,0.25), 0 6px 6px rgba(0,0,0,0.22);
+    color: #000;
+    cursor: pointer;
+}
+.mdl-card__title {
+    padding: 0 0 1em 0;
+    font-size: 18px;
+    color: #444;
+}
+
+.mdl-card__supporting-text {
+    line-height: 1.5rem;
+    padding: 0px;
+    width: 100%;
+}
+
+.head-card.mdl-card {
+    width: auto;
+    display: block;
+    max-width: 800px;
+    padding: 24px;
+}
+
+.head-card > .mdl-card__title {
+    padding-bottom: 0px;
+    height: 60px;
+    font-weight: 700;
+    text-transform: uppercase;
+}
+.head-card > .mdl-card__menu {
+    color: #fff;
+}
+.head-card > .mdl-card__actions {
+    padding: 0;
+}
+.cards {
+    display: flex;
+    flex-direction: row;
+    flex-wrap: wrap;
+}
diff --git a/docs/python_docs/themes/mx-theme/src/scss/code/_code.scss b/docs/python_docs/themes/mx-theme/src/scss/code/_code.scss
new file mode 100644
index 000000000000..f4020d9e9e50
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/src/scss/code/_code.scss
@@ -0,0 +1,134 @@
+.page-content {
+    .highlight {
+        margin: 1px 0;
+        pre {
+            background: $code_background;
+            color: rgba(0,0,0,.87);
+            font-family: $code_font_family;
+            padding: 0.75rem;
+            overflow: auto;
+            overflow-y: hidden;
+            .o, .nd {
+                color: rgba(0,0,0,.87);
+            }
+        }
+    }
+
+    div.highlight-console div.highlight {
+        background: none;
+    }
+
+    // for jupyter notebook output cell
+    .output {
+        .highlight {
+            pre {
+                color: rgba(0,0,0,.87);
+                background: $background_color;
+                border-width: 1px;
+                border-color: #999;
+                border-style: solid;
+                padding: 0.75rem;
+            }
+        }
+     }
+
+    .code, code:not(.download) {
+        margin: 0 0;
+        font-family: $code_font_family;
+        border-radius: 2px;
+        span.pre {
+            font-family: $code_font_family;
+        }
+    }
+
+    .viewcode-link {
+        padding-left: 2em;
+        font-size: 80%;
+    }
+
+    .rubric, .method > dt, .function > dt, .class > dt {
+        display: table;
+        margin: 10px 0;
+        font-size: 100%;
+        line-height: normal;
+        background: #e7f2fa;
+        color: #2B98F0;
+        border-top: solid 3px #55ADF3;
+        padding: 10px;
+        position: relative;
+        .descname, .descclassname {
+            color: rgba(0,0,0,.87);
+            background: #e7f2fa;
+            padding: 3px;
+        }
+        em {
+            padding: 0 2px;
+        }
+    }
+
+
+    .rubric {
+        margin: 30px 0 10px 0;
+     }
+
+
+    .field-body {
+        padding-left: 40px;
+        ul {
+            padding: 0 0 0 16px;
+            margin: 0;
+        }
+    }
+
+     // .docutils > dt {
+    //     padding: 6px;
+    //     display: table;
+    //     margin-bottom: 6px;
+    //     border: none;
+    //     border-left: solid 3px #ccc;
+    //     background: #f0f0f0;
+    //     color: #555;
+    // }
+
+    .seealso .docutils > dt {
+       float: left;
+       clear: left;
+       padding: 0 6px;
+     }
+
+    .seealso .docutils > dd {
+       padding-left: 6em;
+    }
+    .nblast {
+        padding-bottom: 1em;
+    }
+
+    pre {
+        font-size: 90%;
+        background: #eee;
+        color: #455A64;
+        padding: 16px 32px;
+        width: auto;
+        border-radius: 4px;
+        word-wrap: break-word;
+
+        &:hover {
+            @extend .mdl-shadow--2dp;
+
+            &:before {
+                font-family: $body_font_family;
+                padding: 0 0.5rem;
+                content: attr(click-to-copy);
+                color: rgba(0, 0, 0, 0.5);
+                border-radius: 4px;
+                position: relative;
+                float: right;
+                top: -0.5rem;
+                right: -0.5rem;
+                background: rgb(200, 200, 200);
+                font-size: 0.8rem;
+                cursor: pointer;
+            }
+        }
+    }
+}
diff --git a/docs/python_docs/themes/mx-theme/src/scss/downloadlink/_downloadlink.scss b/docs/python_docs/themes/mx-theme/src/scss/downloadlink/_downloadlink.scss
new file mode 100644
index 000000000000..353df2c1824e
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/src/scss/downloadlink/_downloadlink.scss
@@ -0,0 +1,14 @@
+a.download {
+    &:before {
+        @extend .material-icons;
+        content: "file_download";
+        position: relative;
+        top: 5px;
+        margin-right: 5px;
+    }
+}
+
+button.download {
+    position: sticky;
+    margin-left: 1em;
+}
diff --git a/docs/python_docs/themes/mx-theme/src/scss/drawer/_drawer.scss b/docs/python_docs/themes/mx-theme/src/scss/drawer/_drawer.scss
new file mode 100644
index 000000000000..0eebb95e35da
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/src/scss/drawer/_drawer.scss
@@ -0,0 +1,55 @@
+/*
+ * Drawer Styles
+ */
+.mdl-layout {
+    &__drawer {
+        background-color: #fff;
+
+        &::-webkit-scrollbar {
+            width: 6px;
+        }
+
+        &::-webkit-scrollbar-track {
+            border-radius: 6px;
+        }
+
+        &::-webkit-scrollbar-thumb {
+            background-color: rgba(0, 0, 0, .3);
+            border-radius: 6px;
+            box-shadow:0 0 0 1px rgba(255, 255, 255, .3);
+        }
+
+        > .mdl-layout-title {
+            font-weight: bold;
+            text-align: right;
+            margin: 0;
+            padding: 0;
+            line-height: 32px;
+            border-bottom: 1px solid rgba(0,0,0,.1);
+            min-height: 64px;
+            .title {
+                color: inherit;
+                display: block;
+                height: 100%;
+                width: 100%;
+                text-decoration: none;
+                > img.logo {
+                    width: 100%;
+                    margin: 0;
+                    padding: 0;
+                }
+
+                &-text {
+                    font-weight: bold;
+                    text-align: right;
+                    padding: 0 10px;
+                    margin: 16px 0 8px 0;
+                    line-height: 32px;
+                    font-family: $body_font_family;
+                    color: inherit;
+                    display: block;
+                }
+            }
+        }
+    }
+}
diff --git a/docs/python_docs/themes/mx-theme/src/scss/fonts/_material-icons.scss b/docs/python_docs/themes/mx-theme/src/scss/fonts/_material-icons.scss
new file mode 100644
index 000000000000..51a2d73764be
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/src/scss/fonts/_material-icons.scss
@@ -0,0 +1,29 @@
+
+/*
+Material Icons
+*/
+
+.material-icons {
+    font-family: 'Material Icons';
+    font-weight: normal;
+    font-style: normal;
+    font-size: 24px;  /* Preferred icon size */
+    display: inline-block;
+    line-height: 1;
+    text-transform: none;
+    letter-spacing: normal;
+    word-wrap: normal;
+    white-space: nowrap;
+    direction: ltr;
+  
+    /* Support for all WebKit browsers. */
+    -webkit-font-smoothing: antialiased;
+    /* Support for Safari and Chrome. */
+    text-rendering: optimizeLegibility;
+  
+    /* Support for Firefox. */
+    -moz-osx-font-smoothing: grayscale;
+  
+    /* Support for IE. */
+    font-feature-settings: 'liga';
+  }
\ No newline at end of file
diff --git a/docs/python_docs/themes/mx-theme/src/scss/footer/_footer.scss b/docs/python_docs/themes/mx-theme/src/scss/footer/_footer.scss
new file mode 100644
index 000000000000..eee96cb39528
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/src/scss/footer/_footer.scss
@@ -0,0 +1,173 @@
+/*
+ * Footer Styles
+ */
+footer.mdl-mini-footer {
+    background-color: #212121;
+    > div.mdl-mini-footer__left-section {
+        margin-bottom: 20px;
+        display: flex;
+        flex-direction: column;
+        .mdl-logo {
+            font-size: 1.1rem;
+        }
+        ul {
+            @extend .mdl-mini-footer__link-list;
+        }
+    }
+    > div.mdl-mini-footer__right-section {
+        font-size: 0.9rem;
+        display: flex;
+        flex-direction: column;
+        justify-content: flex-end;
+
+        a {
+            color: inherit;
+            font-weight: bold;
+            text-decoration: none;
+        }
+    }
+    p.caption {
+        display: none;
+    }
+}
+
+/*
+ * Pagenation Block Styles
+ */
+ .pagenation {
+    width: 100%;
+    margin-top: 80px;
+    height: 92px;
+    background-color: #424242;
+    display: flex;
+
+    .button-common {
+        text-transform: none;
+        padding: 0;
+        height: 92px;
+        display: flex;
+        justify-content: center;
+        align-items: center;
+        color: #ffffff;
+    }
+    #button-prev {
+        @extend .button-common;
+        margin-right: auto;
+        .pagenation-text {
+            text-align: left;
+        }
+        
+    }
+    #button-next {
+        @extend .button-common;
+        margin-left: auto;
+        flex-direction: row-reverse;
+        .pagenation-text {
+            text-align: right;
+        }
+    }
+
+    &-arrow {
+        &-L {
+            margin-right: 20px;
+        }
+        &-R {
+            margin-left: 20px;
+        }
+    }
+
+    &-text {
+        line-height: 30px;
+        font-size: 20px;
+    }
+
+    &-direction {
+        opacity: 0.7;
+        font-size: 18px;
+    }
+    @media screen and (max-width: 1024px) {
+        #button-prev {
+            width: 20%;
+        }
+        
+        #button-next {
+            width: 80%;
+        }
+    
+        #button-prev .pagenation-text {
+            display: none;
+        }
+    }
+    @media screen and (min-width: 1025px) {
+        #button-prev,
+        #button-next {
+            width: 50%;
+        }
+    
+        #button-prev .pagenation-text {
+            display: block;
+        }
+    }
+}
+
+
+
+/**
+ * Site footer
+ */
+.site-footer {
+  border-top: 1px solid $grey-color-light;
+  padding: $spacing-unit 0;
+  background-color: #424242;
+  .footer-category-title {
+    color: $color-mxnet;
+  }
+  a {
+    color: $grey-color-light !important;
+
+    &:visited {
+      color: $grey-color-light !important;
+    }
+  }
+
+}
+
+.site-footer2 {
+  background-color: #424242;
+  padding-top: 40px;
+  padding-bottom: 10px;
+}
+
+.footer-heading {
+  margin-bottom: $spacing-unit / 2;
+}
+
+.contact-list,
+.social-media-list {
+  list-style: none;
+  margin-left: 0;
+}
+
+
+.footer-bottom-warning {
+  font-size: 80%;
+  color: white;
+  float: left;
+}
+
+.footer-logo {
+  width: 200px;
+  margin-bottom: 30px;
+  margin-top: 30px;
+}
+
+.footer-col {
+  float: left;
+  margin-bottom: $spacing-unit / 2;
+  padding-left: $spacing-unit / 2;
+}
+
+.footer-text {
+  color: $grey-color-light;
+}
+
diff --git a/docs/python_docs/themes/mx-theme/src/scss/grid/_simplegrid.scss b/docs/python_docs/themes/mx-theme/src/scss/grid/_simplegrid.scss
new file mode 100644
index 000000000000..c4155950c031
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/src/scss/grid/_simplegrid.scss
@@ -0,0 +1,207 @@
+// SIMPLE GRID - SASS/SCSS
+
+
+// fonts
+$font-weight-light: 300;
+$font-weight-regular: 400;
+$font-weight-heavy: 700;
+
+// colors
+$dark-grey: #333447;
+$dark-gray: #333447; // for the Americans
+
+
+.font-light {
+  font-weight: $font-weight-light;
+}
+
+.font-regular {
+  font-weight: $font-weight-regular;
+}
+
+.font-heavy {
+  font-weight: $font-weight-heavy;
+}
+
+// utility
+
+.left {
+  text-align: left;
+}
+
+.right {
+  text-align: right;
+}
+
+.center {
+  text-align: center;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+.justify {
+  text-align: justify;
+}
+
+.hidden-sm {
+  display: none;
+}
+
+// grid
+
+$width: 98%;
+$gutter: 2%;
+$breakpoint-small: 33.75em; // 540px
+$breakpoint-med: 45em; // 720px
+$breakpoint-large: 60em; // 960px
+
+.container {
+  width: 100%;
+  margin-left: auto;
+  margin-right: auto;
+
+  @media only screen and (min-width: $breakpoint-small) {
+    width: 80%;
+  }
+
+  @media only screen and (min-width: $breakpoint-large) {
+    width: 75%;
+    max-width: 60rem;
+  }
+}
+
+.row {
+  position: relative;
+  width: 100%;
+}
+
+.row [class^="col"] {
+  float: left;
+  margin: 0.5rem 1%;
+  min-height: 0.125rem;
+}
+
+.row::after {
+  content: "";
+  display: table;
+  clear: both;
+}
+
+.col-1,
+.col-2,
+.col-3,
+.col-4,
+.col-5,
+.col-6,
+.col-7,
+.col-8,
+.col-9,
+.col-10,
+.col-11,
+.col-12 {
+  width: $width;
+}
+
+.col-1-sm {
+  width: ($width / 12) - ($gutter * 11 / 12);
+}
+
+.col-2-sm {
+  width: ($width / 6) - ($gutter * 10 / 12);
+}
+
+.col-3-sm {
+  width: ($width / 4) - ($gutter * 9 / 12);
+}
+
+.col-4-sm {
+  width: ($width / 3) - ($gutter * 8 / 12);
+}
+
+.col-5-sm {
+  width: ($width / (12 / 5)) - ($gutter * 7 / 12);
+}
+
+.col-6-sm {
+  width: ($width / 2) - ($gutter * 6 / 12);
+}
+
+.col-7-sm {
+  width: ($width / (12 / 7)) - ($gutter * 5 / 12);
+}
+
+.col-8-sm {
+  width: ($width / (12 / 8)) - ($gutter * 4 / 12);
+}
+
+.col-9-sm {
+  width: ($width / (12 / 9)) - ($gutter * 3 / 12);
+}
+
+.col-10-sm {
+  width: ($width / (12 / 10)) - ($gutter * 2 / 12);
+}
+
+.col-11-sm {
+  width: ($width / (12 / 11)) - ($gutter * 1 / 12);
+}
+
+.col-12-sm {
+  width: $width;
+}
+
+@media only screen and (min-width: $breakpoint-med) {
+  .col-1 {
+    width: ($width / 12) - ($gutter * 11 / 12);
+  }
+  .col-2 {
+    width: ($width / 6) - ($gutter * 10 / 12);
+  }
+  .col-3 {
+    width: ($width / 4) - ($gutter * 9 / 12);
+  }
+  .col-4 {
+    width: ($width / 3) - ($gutter * 8 / 12);
+  }
+  .col-5 {
+    width: ($width / (12 / 5)) - ($gutter * 7 / 12);
+  }
+  .col-6 {
+    width: ($width / 2) - ($gutter * 6 / 12);
+  }
+  .col-7 {
+    width: ($width / (12 / 7)) - ($gutter * 5 / 12);
+  }
+  .col-8 {
+    width: ($width / (12 / 8)) - ($gutter * 4 / 12);
+  }
+  .col-9 {
+    width: ($width / (12 / 9)) - ($gutter * 3 / 12);
+  }
+  .col-10 {
+    width: ($width / (12 / 10)) - ($gutter * 2 / 12);
+  }
+  .col-11 {
+    width: ($width / (12 / 11)) - ($gutter * 1 / 12);
+  }
+  .col-12 {
+    width: $width;
+  }
+
+  .hidden-sm {
+    display: block;
+  }
+}
+
+.row {
+  display: -webkit-box;
+  display: -webkit-flex;
+  display: -ms-flexbox;
+  display: flex;
+  flex-wrap: wrap;
+}
+
+.row > [class*='col-'] {
+  display: flex;
+  flex-direction: column;
+}
diff --git a/docs/python_docs/themes/mx-theme/src/scss/header/_header.scss b/docs/python_docs/themes/mx-theme/src/scss/header/_header.scss
new file mode 100644
index 000000000000..edfbda8e93c6
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/src/scss/header/_header.scss
@@ -0,0 +1,182 @@
+/*
+ * Header Styles
+ */
+
+nav.breadcrumb {
+    > a.mdl-navigation__link {
+        padding: 0 8px;
+        font-size: 18px;
+    }
+    @media (max-width: $lg-breakpoint - 1) {
+        width: calc( 100% - 64px );
+        a.mdl-navigation__link.is-active {
+            overflow-x: hidden;
+            width: 100%;
+            overflow: hidden;
+            white-space: nowrap;
+            text-overflow: ellipsis;
+        }
+        a.mdl-navigation__link:not(.is-active),
+        i.material-icons {
+            display: none;
+        }
+    }
+}
+
+div.mdl-layout__header {
+    margin-top: 77px;
+}
+
+.mdl-layout__drawer-button {
+    top: 13px !important;
+}
+
+div.mdl-layout__header-row.header-links {
+    background: rgba(255,255,255,0.2);
+    width: 100%;
+    overflow-x: auto;
+    overflow-y: hidden;
+
+    a.mdl-navigation__link  {
+        font-size: 1rem;
+        i {
+            font-size: 1.2rem;
+            margin: 0 8px;
+            position: relative;
+            bottom: -0.1rem;
+        }
+    };
+
+    a.mdl-navigation__link:hover  {
+        background-color: unquote("rgb(#{$color-primary})");
+        color: #eeeeee;
+    };
+    a.mdl-navigation__link[href="#"]  {
+        background-color: unquote("rgb(#{$color-primary})");
+        opacity: 1;
+        color: #ffffff;
+    };
+}
+
+/* mxnet-header */
+
+
+.site-title {
+  font-weight: 300 !important;
+  line-height: 57px;
+  letter-spacing: -1px;
+  margin-bottom: 0;
+  float: left;
+  color: white;
+
+  &,
+  &:visited {
+    color: $grey-color-dark;
+  }
+}
+
+
+.site-header {
+  position: fixed;
+  top: 0;
+  width: 100%;
+  min-height: 55px;
+  padding-top: 10px;
+  padding-bottom: 10px;
+  background-color: $color-mxnet;
+  z-index: 10;
+  font-weight: 300;
+  font-size: 17px;
+  border-bottom: 1px solid white;
+}
+
+.site-header-logo {
+  width: 120px;
+  display: initial;
+}
+
+.site-nav {
+  float: right;
+  line-height: 57px;
+
+  .nav-trigger {
+    display: none;
+  }
+
+  .menu-icon {
+    display: none;
+  }
+
+  .page-link {
+    color: white;
+    line-height: 1.5;
+    font-weight: 300;
+    // Gaps between nav items, but not on the last one
+    &:not(:last-child) {
+      margin-right: 40px;
+    }
+
+    &:hover {
+      color: white;
+      text-shadow: -0.06ex 0 white, 0.06ex 0 white;
+    }
+  }
+
+  .page-link.page-current {
+    color: white;
+    text-decoration: underline;
+  }
+
+  @media screen and (max-width: $on-laptop) {
+    position: absolute;
+    top: 9px;
+    right: 15px;
+    background-color: rgb(23,141,201);
+    border-radius: 2px;
+    text-align: right;
+
+    label[for="nav-trigger"] {
+      display: block;
+      float: right;
+      width: 36px;
+      height: 36px;
+      z-index: 2;
+      cursor: pointer;
+    }
+
+    .menu-icon {
+      display: block;
+      float: right;
+      width: 36px;
+      height: 26px;
+      line-height: 0;
+      padding-top: 20px;
+      text-align: center;
+
+      > svg {
+        fill: white;
+      }
+    }
+
+    input ~ .trigger {
+      clear: both;
+      display: none;
+    }
+
+    input:checked ~ .trigger {
+      display: block;
+      padding-bottom: 5px;
+    }
+
+    .page-link {
+      padding: 5px 10px;
+      display: block;
+
+      &:not(:last-child) {
+        margin-right: 0;
+      }
+
+      margin-left: 20px;
+    }
+  }
+}
\ No newline at end of file
diff --git a/docs/python_docs/themes/mx-theme/src/scss/headerings/_headerings.scss b/docs/python_docs/themes/mx-theme/src/scss/headerings/_headerings.scss
new file mode 100644
index 000000000000..a14f7ef56b56
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/src/scss/headerings/_headerings.scss
@@ -0,0 +1,92 @@
+@keyframes float-in {
+    0% {
+        transform: translateY(0.5rem);
+        opacity: 0;
+    }
+	100% {
+		transform: translateY(0);
+		opacity: 1;
+	}
+}
+
+@keyframes float-out {
+    0% {
+        transform: translateY(0);
+        opacity: 1;
+    }
+	100% {
+		transform: translateY(0.5rem);
+		opacity: 0;
+	}
+}
+
+.page-content {
+    .headerlink {
+        display: inline-block;
+        text-decoration: none;
+        margin-left: 0.8rem;
+        color: inherit;
+        opacity: 0;
+        &:hover {
+            animation: float-in 0.2s $animation-curve-fast-out-slow-in 0s forwards;
+        }
+    }
+
+    h1, h2, h3, h4, h5, h6 {
+        .toc-backref {
+            text-decoration: none;
+        }
+        &:hover {
+            .headerlink {
+                animation: float-in 0.2s $animation-curve-fast-out-slow-in 0s forwards;
+            }
+        }
+    }
+
+    h1 {
+        font-size: 2rem;
+        line-height: 2.25rem;
+    }
+
+    h2 {
+        font-size: 1.75rem;
+        line-height: 2rem;
+        padding-top: 1.5rem;
+        margin-top: 0;
+        margin-bottom: 1rem;
+    }
+
+    h3 {
+        font-size: 1.5rem;
+        line-height: 1.75rem;
+        padding-top: 1rem;
+        margin-top: 0px;
+        margin-bottom: .75rem;
+    }
+
+    h4 {
+        font-size: 1.25rem;
+        line-height: 1.5rem;
+        padding-top: .75rem;
+        margin-top: 0px;
+        margin-bottom: .5rem;
+    }
+
+    div.page-content h5 {
+        font-size: 1.1rem;
+        line-height: 1.5rem;
+        padding-top: 2rem;
+        margin-top: 0px;
+        margin-bottom: 1rem;
+    }
+
+    div.page-content h6 {
+        font-size: 1rem;
+        line-height: 1.5rem;
+        padding-top: 2rem;
+        margin-top: 0px;
+        margin-bottom: 1rem;
+    }
+
+
+}
diff --git a/docs/python_docs/themes/mx-theme/src/scss/layout/_layout.scss b/docs/python_docs/themes/mx-theme/src/scss/layout/_layout.scss
new file mode 100644
index 000000000000..fbc02229d3e1
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/src/scss/layout/_layout.scss
@@ -0,0 +1,85 @@
+/**
+ * Layout Styles
+ */
+ $layout: (
+    document: (
+        xl: (
+            width: 1400px,
+        )
+    ),
+    drawer-container: (
+        width: $layout-drawer-width,
+    ),
+    side-doc-outline: (
+        width: 230px,
+    ),
+    page-content: (
+        md: (
+            width: 90%,
+            padding: 0 5%
+        ),
+        lg: (
+            width: calc( 90% - 230px ),
+            padding: 0 5%
+        )
+    )
+);
+
+.mdl-layout__container {
+    margin-top: 76px;
+    height: calc(100% - 76px);   
+}
+
+
+.document {
+    width: 100%;
+    margin: 0 auto;
+    display: flex;
+
+    @media (min-width: $xl-breakpoint) {
+        width: map-get(map-get(map-get($layout, document), xl), width);
+    }
+    .page-content {
+        width: 100%;
+        margin: 0 auto;
+        padding: 0 12px;
+
+        @media (min-width: $md-breakpoint) {
+            width: map-get(map-get(map-get($layout, page-content), md), width);
+            padding: map-get(map-get(map-get($layout, page-content), md), padding);
+        }
+
+        @media (min-width: $lg-breakpoint) {
+            width: map-get(map-get(map-get($layout, page-content), lg), width);
+            padding: map-get(map-get(map-get($layout, page-content), lg), padding);
+        }
+    }
+
+    .side-doc-outline {
+        width: map-get(map-get($layout, side-doc-outline), width);
+
+        @media (max-width: $lg-breakpoint - 1) {
+            display: none;
+        } 
+        &--content {
+            position: fixed;
+            overflow-x: auto;
+            overflow-y: auto;
+            width: inherit;
+            &::-webkit-scrollbar {
+                width: 6px;
+            }
+    
+            &::-webkit-scrollbar-track {
+                border-radius: 6px;
+            }
+    
+            &::-webkit-scrollbar-thumb {
+                background-color: rgba(0, 0, 0, .3);
+                border-radius: 6px;
+                box-shadow:0 0 0 1px rgba(255, 255, 255, .3);
+            }
+        }
+    }
+
+}
\ No newline at end of file
diff --git a/docs/python_docs/themes/mx-theme/src/scss/lists/_lists.scss b/docs/python_docs/themes/mx-theme/src/scss/lists/_lists.scss
new file mode 100644
index 000000000000..e654c6bc9201
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/src/scss/lists/_lists.scss
@@ -0,0 +1,19 @@
+.page-content {
+    ul {
+        li {
+            margin: .3rem 0;
+            p {
+                margin: 0;
+            }
+        }
+    }
+    .option-list {
+        .option {
+            font-family: $code_font_family;
+        }
+        td {
+            padding: 0.5rem;
+            border: none;
+        }
+    }
+}
diff --git a/docs/python_docs/themes/mx-theme/src/scss/search/_search.scss b/docs/python_docs/themes/mx-theme/src/scss/search/_search.scss
new file mode 100644
index 000000000000..e9df00240ecb
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/src/scss/search/_search.scss
@@ -0,0 +1,33 @@
+ /*
+  * Search Styles
+  */
+#waterfall-exp::-webkit-input-placeholder {
+    color: #ccc;
+}
+#waterfall-exp:-ms-input-placeholder {
+    color: #ccc;
+}
+#waterfall-exp::-moz-placeholder {
+    color: #ccc;
+}
+
+ul.search span.highlighted {
+    font-weight: bold;
+}
+
+ul.search > li {
+    margin-bottom: 24px;
+}
+
+#search-results {
+    ul {
+        list-style: none;
+        padding: 0;
+        li {
+            > a {
+                text-decoration: none;
+                font-size: 1.2rem;
+            }
+        }
+    }
+}
diff --git a/docs/python_docs/themes/mx-theme/src/scss/sphinx_materialdesign_theme.scss b/docs/python_docs/themes/mx-theme/src/scss/sphinx_materialdesign_theme.scss
new file mode 100644
index 000000000000..e00dd37f4663
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/src/scss/sphinx_materialdesign_theme.scss
@@ -0,0 +1,28 @@
+@import "./material-design-lite/src/color-definitions";
+@import "./variables";
+
+@import "./material-design-lite/src/shadow/shadow";
+@import "./material-design-lite/src/data-table/data-table";
+@import "./material-design-lite/src/footer/mini_footer";
+@import "./material-design-lite/src/card/card";
+@import "./material-design-lite/src/button/button";
+
+@import "./grid/simplegrid";
+@import "./fonts/material-icons";
+@import "./root";
+@import "./layout/layout";
+@import "./headerings/headerings";
+@import "./admonitions/admonitions";
+@import "./code/code";
+@import "./blockquote/blockquote";
+@import "./tables/tables";
+@import "./toc/globaltoc";
+@import "./toc/localtoc";
+@import "./toc/toctree";
+@import "./lists/lists";
+@import "./drawer/drawer";
+@import "./header/header";
+@import "./footer/footer";
+@import "./search/search";
+@import "./downloadlink/downloadlink";
+@import "./card/card";
\ No newline at end of file
diff --git a/docs/python_docs/themes/mx-theme/src/scss/tables/_tables.scss b/docs/python_docs/themes/mx-theme/src/scss/tables/_tables.scss
new file mode 100644
index 000000000000..90325b7e840e
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/src/scss/tables/_tables.scss
@@ -0,0 +1,30 @@
+.page-content {
+    table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) {
+        @extend .mdl-data-table;
+        @extend .mdl-shadow--2dp;
+
+        margin: 1.5rem 0;
+        table-layout: fixed;
+        max-width: 100%;
+        min-width: 70%;
+
+        th, td {
+            @extend .mdl-data-table__cell--non-numeric;
+            white-space: normal;
+            overflow-wrap: break-word;
+        }
+
+        caption {
+            font-size: $font_size;
+            margin: 1rem 0 0.8rem 0;
+            white-space: normal;
+            .caption-number {
+                font-style: normal;
+            }
+            .caption-number::after {
+                content: "\00a0";
+            }
+        }
+
+    }
+}
diff --git a/docs/python_docs/themes/mx-theme/src/scss/toc/_globaltoc.scss b/docs/python_docs/themes/mx-theme/src/scss/toc/_globaltoc.scss
new file mode 100644
index 000000000000..368d2a58e715
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/src/scss/toc/_globaltoc.scss
@@ -0,0 +1,78 @@
+.globaltoc {
+    
+    .caption, .toc {
+        display: none;
+    }
+
+    ul {
+
+        list-style-type: none;
+        padding: 0;
+        margin: 0;
+
+        li {
+            min-height: 36px;
+            .link-wrapper {
+                display: flex;
+                justify-content: space-between;
+                > a {
+                    padding: 8px 0;
+                    display: block;
+                    width: 100%;
+                    font-size: 1rem;
+                    text-decoration: none;
+                    color: $layout-drawer-navigation-color;
+                    &.current {
+                        font-weight: bold;
+                    }
+                }
+            }
+        }
+    }
+
+    .nav-toggle {
+        padding: 0;
+        float: right;
+        display: flex;
+        align-items: center;
+        justify-content: center;
+        height: 36px;
+        > a {
+            padding: 0;
+            margin-left: 0;
+            margin-right: 4px;
+            cursor: pointer;
+            > i {
+                font-size: 18px;
+            }
+        }
+        &.show {
+            transform: rotateZ(180deg);
+            > a {
+                margin-right: 0;
+                margin-left: 4px;
+            }
+        }
+    }
+
+    nav {
+        > ul > li > span.link-wrapper {
+            padding-left: 8px;
+        }
+        > ul > li > ul > li > span.link-wrapper {
+            padding-left: 16px;
+        }
+        > ul > li > ul > li > ul > li > span.link-wrapper {
+            padding-left: 24px;
+        }
+        > ul > li > ul > li > ul > li > ul> li > span.link-wrapper {
+            padding-left: 32px;
+        }
+        > ul > li > ul > li > ul > li > ul > li > ul> li > span.link-wrapper {
+            padding-left: 40px;
+        }
+        > ul > li > ul > li > ul > li > ul > li > ul > li > ul> li > span.link-wrapper {
+            padding-left: 48px;
+        }
+    }
+}
diff --git a/docs/python_docs/themes/mx-theme/src/scss/toc/_localtoc.scss b/docs/python_docs/themes/mx-theme/src/scss/toc/_localtoc.scss
new file mode 100644
index 000000000000..cc3401e7aeb9
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/src/scss/toc/_localtoc.scss
@@ -0,0 +1,40 @@
+.localtoc {
+    font-size: 0.75rem;
+    padding-top: 1rem;
+
+    .caption {
+        padding-left: 12px;
+        &-text {
+            font-size: 0.9rem;
+            font-weight: 700;
+        }
+    }
+
+    > ul > li > a {
+        display: none;
+    }
+
+    ul {
+        padding: 0;
+        list-style-type: none;
+    }
+
+    li {
+        padding-left: 6px;
+    }
+
+    a {
+        display: block;
+        text-decoration: none;
+        color: inherit;
+        margin-top: 8px;
+        padding-left: 8px;
+        line-height: 1.1rem;
+    
+        &.current {
+            padding-left: 5px;
+            border-left: 3px solid;
+            font-weight: bold;
+        }
+    }
+}
\ No newline at end of file
diff --git a/docs/python_docs/themes/mx-theme/src/scss/toc/_toctree.scss b/docs/python_docs/themes/mx-theme/src/scss/toc/_toctree.scss
new file mode 100644
index 000000000000..8e20b32f8a4b
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/src/scss/toc/_toctree.scss
@@ -0,0 +1,38 @@
+/*
+ *  Toctree and Contents Directive Styles
+ */
+ .toctree-wrapper,
+ .contents.topic {
+     border-left: 5px solid;
+ }
+
+ .toctree-wrapper > p.caption,
+ .contents.topic > p.topic-title {
+     color: rgb(117, 117, 117);
+     font-size: 1rem;
+     padding-left: 14px;
+ }
+
+ .toctree-wrapper ul,
+ .contents.topic ul{
+     padding-left: 14px;
+     list-style: none;
+     line-height: 30px;
+ }
+
+ .toctree-wrapper a,
+ .contents.topic a {
+     font-size: 1.2rem;
+     text-decoration: none;
+     .pre {
+         font-size: 1rem;
+     }
+ }
+
+ .toctree-wrapper > ul > li > a,
+ .contents.topic > ul > li > a {
+     font-size: 1.3rem;
+     .pre {
+         font-size: 1.1rem;
+     }
+ }
diff --git a/docs/static_site/.gitignore b/docs/static_site/.gitignore
new file mode 100644
index 000000000000..45c150536e5f
--- /dev/null
+++ b/docs/static_site/.gitignore
@@ -0,0 +1,3 @@
+_site
+.sass-cache
+.jekyll-metadata
diff --git a/docs/static_site/.nojekyll b/docs/static_site/.nojekyll
new file mode 100644
index 000000000000..8b137891791f
--- /dev/null
+++ b/docs/static_site/.nojekyll
@@ -0,0 +1 @@
+
diff --git a/docs/static_site/Makefile b/docs/static_site/Makefile
new file mode 100644
index 000000000000..761f96630b2f
--- /dev/null
+++ b/docs/static_site/Makefile
@@ -0,0 +1,26 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+all: html
+
+html:
+	mkdir -p build
+	cd src && bundle install && JEKYLL_ENV=production bundle exec jekyll build --config _config_prod.yml -d ../build/html && cd ..
+
+
+clean:
+	rm -rf build
diff --git a/docs/static_site/README.md b/docs/static_site/README.md
new file mode 100644
index 000000000000..b0e58483330e
--- /dev/null
+++ b/docs/static_site/README.md
@@ -0,0 +1,42 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# MXNet.io v2
+
+1. Install Jekyll https://jekyllrb.com/docs/installation/
+
+This is for hosting the mxnet.io beta website
+
+serve for test:
+```
+cd src && JEKYLL_ENV=development bundle exec jekyll serve
+```
+
+build for beta github pages:
+```
+cd src && JEKYLL_ENV=production bundle exec jekyll build --config _config_beta.yml -d ../docs && cd ..
+```
+
+
+build for release:
+```
+cd src && JEKYLL_ENV=production bundle exec jekyll build --config _config_prod.yml -d ../release && cd ..
+```
+
+test:
+
+https://thomasdelteil.github.io/mxnet.io-v2/
diff --git a/docs/static_site/src/.gitignore b/docs/static_site/src/.gitignore
new file mode 100644
index 000000000000..f53a1d42a454
--- /dev/null
+++ b/docs/static_site/src/.gitignore
@@ -0,0 +1 @@
+static_websites
diff --git a/docs/static_site/src/.htaccess b/docs/static_site/src/.htaccess
new file mode 100644
index 000000000000..16420306f1e1
--- /dev/null
+++ b/docs/static_site/src/.htaccess
@@ -0,0 +1,24 @@
+Options -Indexes
+DirectorySlash off
+RewriteEngine on
+RewriteOptions AllowNoSlash
+
+# Show file instead of folder for example /api/docs/tutorials.html
+# instead of /api/docs/tutorials/
+RewriteCond %{REQUEST_FILENAME} -d
+RewriteCond %{REQUEST_FILENAME}\.html -f
+RewriteRule ^(.*) $1.html [NC,L]
+
+# Prettify some files like tutorials/io to tutorials/io.html
+RewriteCond %{REQUEST_FILENAME}\.html -f
+RewriteRule ^(.*) $1.html [NC,L]
+
+#API docs needs specific root
+RewriteCond %{REQUEST_URI} \/docs\/api$|\/docs\/tutorials$
+RewriteRule ^(.*) %{REQUEST_URI}/ [R,NC,L]
+
+# Prettify folders like /get_started
+RewriteCond %{REQUEST_URI} !\/$
+RewriteCond %{REQUEST_FILENAME} -d
+RewriteCond %{REQUEST_FILENAME}/index.html -f
+RewriteRule ^(.*) $1/index.html [NC,L]
diff --git a/docs/static_site/src/.nojekyll b/docs/static_site/src/.nojekyll
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/docs/static_site/src/404.html b/docs/static_site/src/404.html
new file mode 100644
index 000000000000..c472b4ea0a78
--- /dev/null
+++ b/docs/static_site/src/404.html
@@ -0,0 +1,24 @@
+---
+layout: default
+---
+
+<style type="text/css" media="screen">
+  .container {
+    margin: 10px auto;
+    max-width: 600px;
+    text-align: center;
+  }
+  h1 {
+    margin: 30px 0;
+    font-size: 4em;
+    line-height: 1;
+    letter-spacing: -1px;
+  }
+</style>
+
+<div class="container">
+  <h1>404</h1>
+
+  <p><strong>Page not found :(</strong></p>
+  <p>The requested page could not be found.</p>
+</div>
diff --git a/docs/static_site/src/Gemfile b/docs/static_site/src/Gemfile
new file mode 100644
index 000000000000..754372c2f24f
--- /dev/null
+++ b/docs/static_site/src/Gemfile
@@ -0,0 +1,52 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+source "https://rubygems.org"
+
+# Hello! This is where you manage which Jekyll version is used to run.
+# When you want to use a different version, change it below, save the
+# file and run `bundle install`. Run Jekyll with `bundle exec`, like so:
+#
+#     bundle exec jekyll serve
+#
+# This will help ensure the proper Jekyll version is running.
+# Happy Jekylling!
+gem "jekyll", "~> 3.8.6"
+
+# This is the default theme for new Jekyll sites. You may change this to anything you like.
+gem "minima", "~> 2.0"
+
+gem "redcarpet"
+
+# If you want to use GitHub Pages, remove the "gem "jekyll"" above and
+# uncomment the line below. To upgrade, run `bundle update github-pages`.
+# gem "github-pages", group: :jekyll_plugins
+
+# If you have any plugins, put them here!
+group :jekyll_plugins do
+  gem "jekyll-feed", "~> 0.6"
+end
+
+# Windows does not include zoneinfo files, so bundle the tzinfo-data gem
+# and associated library.
+install_if -> { RUBY_PLATFORM =~ %r!mingw|mswin|java! } do
+  gem "tzinfo", "~> 1.2"
+  gem "tzinfo-data"
+end
+
+# Performance-booster for watching directories on Windows
+gem "wdm", "~> 0.1.0", :install_if => Gem.win_platform?
diff --git a/docs/static_site/src/Gemfile.lock b/docs/static_site/src/Gemfile.lock
new file mode 100644
index 000000000000..2eab21c74003
--- /dev/null
+++ b/docs/static_site/src/Gemfile.lock
@@ -0,0 +1,84 @@
+GEM
+  remote: https://rubygems.org/
+  specs:
+    addressable (2.6.0)
+      public_suffix (>= 2.0.2, < 4.0)
+    colorator (1.1.0)
+    concurrent-ruby (1.1.5)
+    em-websocket (0.5.1)
+      eventmachine (>= 0.12.9)
+      http_parser.rb (~> 0.6.0)
+    eventmachine (1.2.7)
+    ffi (1.11.1)
+    forwardable-extended (2.6.0)
+    http_parser.rb (0.6.0)
+    i18n (0.9.5)
+      concurrent-ruby (~> 1.0)
+    jekyll (3.8.6)
+      addressable (~> 2.4)
+      colorator (~> 1.0)
+      em-websocket (~> 0.5)
+      i18n (~> 0.7)
+      jekyll-sass-converter (~> 1.0)
+      jekyll-watch (~> 2.0)
+      kramdown (~> 1.14)
+      liquid (~> 4.0)
+      mercenary (~> 0.3.3)
+      pathutil (~> 0.9)
+      rouge (>= 1.7, < 4)
+      safe_yaml (~> 1.0)
+    jekyll-feed (0.12.1)
+      jekyll (>= 3.7, < 5.0)
+    jekyll-sass-converter (1.5.2)
+      sass (~> 3.4)
+    jekyll-seo-tag (2.6.1)
+      jekyll (>= 3.3, < 5.0)
+    jekyll-watch (2.2.1)
+      listen (~> 3.0)
+    kramdown (1.17.0)
+    liquid (4.0.3)
+    listen (3.1.5)
+      rb-fsevent (~> 0.9, >= 0.9.4)
+      rb-inotify (~> 0.9, >= 0.9.7)
+      ruby_dep (~> 1.2)
+    mercenary (0.3.6)
+    minima (2.5.0)
+      jekyll (~> 3.5)
+      jekyll-feed (~> 0.9)
+      jekyll-seo-tag (~> 2.1)
+    pathutil (0.16.2)
+      forwardable-extended (~> 2.6)
+    public_suffix (3.1.1)
+    rb-fsevent (0.10.3)
+    rb-inotify (0.10.0)
+      ffi (~> 1.0)
+    redcarpet (3.5.0)
+    rouge (3.7.0)
+    ruby_dep (1.5.0)
+    safe_yaml (1.0.5)
+    sass (3.7.4)
+      sass-listen (~> 4.0.0)
+    sass-listen (4.0.0)
+      rb-fsevent (~> 0.9, >= 0.9.4)
+      rb-inotify (~> 0.9, >= 0.9.7)
+    thread_safe (0.3.6)
+    tzinfo (1.2.5)
+      thread_safe (~> 0.1)
+    tzinfo-data (1.2019.2)
+      tzinfo (>= 1.0.0)
+    wdm (0.1.1)
+
+PLATFORMS
+  ruby
+
+DEPENDENCIES
+  jekyll (~> 3.8.6)
+  jekyll-feed (~> 0.6)
+  minima (~> 2.0)
+  redcarpet
+  tzinfo (~> 1.2)
+  tzinfo-data
+  wdm (~> 0.1.0)
+
+BUNDLED WITH
+   2.0.2
diff --git a/docs/static_site/src/_config.yml b/docs/static_site/src/_config.yml
new file mode 100644
index 000000000000..6dcc9c2aa405
--- /dev/null
+++ b/docs/static_site/src/_config.yml
@@ -0,0 +1,62 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+# Welcome to Jekyll!
+#
+# This config file is meant for settings that affect your whole blog, values
+# which you are expected to set up once and rarely edit after that. If you find
+# yourself editing this file very often, consider using Jekyll's data files
+# feature for the data you need to update frequently.
+#
+# For technical reasons, this file is *NOT* reloaded automatically when you use
+# 'bundle exec jekyll serve'. If you change this file, please restart the server process.
+
+# Site settings
+# These are used to personalize your new site. If you look in the HTML files,
+# you will see them accessed via {{ site.title }}, {{ site.email }}, and so on.
+# You can create any custom variable you would like, and they will be accessible
+# in the templates via {{ site.myvariable }}.
+title: Apache MXNet
+email: dev@mxnet.apache.org
+description: >- # this means to ignore newlines until "baseurl:"
+  A flexible and efficient library for deep learning.
+twitter_username: apachemxnet
+github_username:  apache/incubator-mxnet
+youtube_username: apachemxnet
+
+# Build settings
+markdown: kramdown
+
+#redcarpet:
+#  extensions: ["no_intra_emphasis", "fenced_code_blocks", "autolink", "tables", "with_toc_data"]
+plugins:
+  - jekyll-feed
+  - jekyll-seo-tag
+
+permalink: pretty
+
+# Exclude from processing.
+# The following items will not be processed, by default. Create a custom list
+# to override the default setting.
+# exclude:
+#   - Gemfile
+#   - Gemfile.lock
+#   - node_modules
+#   - vendor/bundle/
+#   - vendor/cache/
+#   - vendor/gems/
+#   - vendor/ruby/
diff --git a/docs/static_site/src/_config_beta.yml b/docs/static_site/src/_config_beta.yml
new file mode 100644
index 000000000000..dc4ecdd25f65
--- /dev/null
+++ b/docs/static_site/src/_config_beta.yml
@@ -0,0 +1,60 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+# Welcome to Jekyll!
+#
+# This config file is meant for settings that affect your whole blog, values
+# which you are expected to set up once and rarely edit after that. If you find
+# yourself editing this file very often, consider using Jekyll's data files
+# feature for the data you need to update frequently.
+#
+# For technical reasons, this file is *NOT* reloaded automatically when you use
+# 'bundle exec jekyll serve'. If you change this file, please restart the server process.
+
+# Site settings
+# These are used to personalize your new site. If you look in the HTML files,
+# you will see them accessed via {{ site.title }}, {{ site.email }}, and so on.
+# You can create any custom variable you would like, and they will be accessible
+# in the templates via {{ site.myvariable }}.
+title: Apache MXNet
+email: dev@mxnet.apache.org
+description: >- # this means to ignore newlines until "baseurl:"
+  A flexible and efficient library for deep learning.
+baseurl: /mxnet.io-v2 # the subpath of your site, e.g. /blog
+url: https://thomasdelteil.github.io
+twitter_username: apachemxnet
+github_username:  apache/incubator-mxnet
+youtube_username: apachemxnet
+
+# Build settings
+markdown: redcarpet
+redcarpet:
+  extensions: ["no_intra_emphasis", "fenced_code_blocks", "autolink", "tables", "with_toc_data"]
+plugins:
+  - jekyll-feed
+  - jekyll-seo-tag
+# Exclude from processing.
+# The following items will not be processed, by default. Create a custom list
+# to override the default setting.
+# exclude:
+#   - Gemfile
+#   - Gemfile.lock
+#   - node_modules
+#   - vendor/bundle/
+#   - vendor/cache/
+#   - vendor/gems/
+#   - vendor/ruby/
diff --git a/docs/static_site/src/_config_prod.yml b/docs/static_site/src/_config_prod.yml
new file mode 100644
index 000000000000..2135f9c1e9a6
--- /dev/null
+++ b/docs/static_site/src/_config_prod.yml
@@ -0,0 +1,59 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+# Welcome to Jekyll!
+#
+# This config file is meant for settings that affect your whole blog, values
+# which you are expected to set up once and rarely edit after that. If you find
+# yourself editing this file very often, consider using Jekyll's data files
+# feature for the data you need to update frequently.
+#
+# For technical reasons, this file is *NOT* reloaded automatically when you use
+# 'bundle exec jekyll serve'. If you change this file, please restart the server process.
+
+# Site settings
+# These are used to personalize your new site. If you look in the HTML files,
+# you will see them accessed via {{ site.title }}, {{ site.email }}, and so on.
+# You can create any custom variable you would like, and they will be accessible
+# in the templates via {{ site.myvariable }}.
+title: Apache MXNet
+email: dev@mxnet.apache.org
+description: >- # this means to ignore newlines until "baseurl:"
+  A flexible and efficient library for deep learning.
+url: https://mxnet.incubator.apache.org
+twitter_username: apachemxnet
+github_username:  apache/incubator-mxnet
+youtube_username: apachemxnet
+
+# Build settings
+markdown: redcarpet
+redcarpet:
+  extensions: ["no_intra_emphasis", "fenced_code_blocks", "autolink", "tables", "with_toc_data"]
+plugins:
+  - jekyll-feed
+  - jekyll-seo-tag
+# Exclude from processing.
+# The following items will not be processed, by default. Create a custom list
+# to override the default setting.
+# exclude:
+#   - Gemfile
+#   - Gemfile.lock
+#   - node_modules
+#   - vendor/bundle/
+#   - vendor/cache/
+#   - vendor/gems/
+#   - vendor/ruby/
diff --git a/docs/static_site/src/_includes/disqus_comments.html b/docs/static_site/src/_includes/disqus_comments.html
new file mode 100644
index 000000000000..d9400f2de194
--- /dev/null
+++ b/docs/static_site/src/_includes/disqus_comments.html
@@ -0,0 +1,20 @@
+{%- if page.comments != false and jekyll.environment == "production" -%}
+
+  <div id="disqus_thread"></div>
+  <script>
+    var disqus_config = function () {
+      this.page.url = '{{ page.url | absolute_url }}';
+      this.page.identifier = '{{ page.url | absolute_url }}';
+    };
+
+    (function() {
+      var d = document, s = d.createElement('script');
+
+      s.src = 'https://{{ site.disqus.shortname }}.disqus.com/embed.js';
+
+      s.setAttribute('data-timestamp', +new Date());
+      (d.head || d.body).appendChild(s);
+    })();
+  </script>
+  <noscript>Please enable JavaScript to view the <a href="https://disqus.com/?ref_noscript" rel="nofollow">comments powered by Disqus.</a></noscript>
+{%- endif -%}
diff --git a/docs/static_site/src/_includes/footer.html b/docs/static_site/src/_includes/footer.html
new file mode 100644
index 000000000000..f6c63a8d52d2
--- /dev/null
+++ b/docs/static_site/src/_includes/footer.html
@@ -0,0 +1,51 @@
+<footer class="site-footer h-card">
+    <div class="wrapper">
+        <div class="row">
+            <div class="col-4">
+                <h4 class="footer-category-title">Resources</h4>
+                <ul class="contact-list">
+                    <li><a class="u-email" href="mailto:dev@mxnet.apache.org">Dev list</a></li>
+                    <li><a class="u-email" href="mailto:user@mxnet.apache.org">User mailing list</a></li>
+                    <li><a href="https://cwiki.apache.org/confluence/display/MXNET/Apache+MXNet+Home">Developer Wiki</a></li>
+                    <li><a href="https://issues.apache.org/jira/projects/MXNET/issues">Jira Tracker</a></li>
+                    <li><a href="https://github.com/apache/incubator-mxnet/labels/Roadmap">Github Roadmap</a></li>
+                    <li><a href="https://discuss.mxnet.io">MXNet Discuss forum</a></li>
+                    <li><a href="{{'community/contribute'|relative_url}}">Contribute To MXNet</a></li>
+
+                </ul>
+            </div>
+
+            <div class="col-4">
+                {%- include social.html -%}
+            </div>
+
+            <div class="col-4 footer-text">
+                <p>{{- site.description | escape -}}</p>
+            </div>
+        </div>
+    </div>
+</footer>
+<footer class="site-footer2">
+    <div class="wrapper">
+        <div class="row">
+            <div class="col-3">
+                <img src="{{'/assets/img/apache_incubator_logo.png' | relative_url}}" class="footer-logo col-2">
+            </div>
+            <div class="footer-bottom-warning col-9">
+                <p>Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), <span
+                        style="font-weight:bold">sponsored by the <i>Apache Incubator</i></span>. Incubation is required
+                    of all newly accepted projects until a further review indicates that the infrastructure,
+                    communications, and decision making process have stabilized in a manner consistent with other
+                    successful ASF projects. While incubation status is not necessarily a reflection of the completeness
+                    or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF.
+                </p><p>"Copyright © 2017-2018, The Apache Software Foundation Apache MXNet, MXNet, Apache, the Apache
+                    feather, and the Apache MXNet project logo are either registered trademarks or trademarks of the
+                    Apache Software Foundation."</p>
+            </div>
+        </div>
+    </div>
+</footer>
+
+
+
+
diff --git a/docs/static_site/src/_includes/get_started/cloud/cpu.md b/docs/static_site/src/_includes/get_started/cloud/cpu.md
new file mode 100644
index 000000000000..5415e96b6534
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/cloud/cpu.md
@@ -0,0 +1,8 @@
+MXNet should work on any cloud provider's CPU-only instances. Follow the Python pip install
+instructions,
+Docker instructions, or try the following preinstalled option.
+
+* **Amazon Web Services**
+- [AWS Deep Learning AMI](https://aws.amazon.com/machine-learning/amis/) - Preinstalled
+Conda environments
+for Python 2 or 3 with MXNet and MKL-DNN.
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/cloud/gpu.md b/docs/static_site/src/_includes/get_started/cloud/gpu.md
new file mode 100644
index 000000000000..725d96581776
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/cloud/gpu.md
@@ -0,0 +1,32 @@
+MXNet is available on several cloud providers with GPU support. You can also find
+GPU/CPU-hybrid support for
+use cases like scalable inference, or even fractional GPU support with AWS Elastic
+Inference.
+
+* **Alibaba**
+- [NVIDIA
+VM](https://docs.nvidia.com/ngc/ngc-alibaba-setup-guide/launching-nv-cloud-vm-console.html#launching-nv-cloud-vm-console)
+* **Amazon Web Services**
+- [Amazon SageMaker](https://aws.amazon.com/sagemaker/) - Managed training and deployment of
+MXNet models
+- [AWS Deep Learning AMI](https://aws.amazon.com/machine-learning/amis/) - Preinstalled
+Conda environments
+for Python 2 or 3 with MXNet, CUDA, cuDNN, MKL-DNN, and AWS Elastic Inference
+- [Dynamic Training on
+AWS](https://github.com/awslabs/dynamic-training-with-apache-mxnet-on-aws) -
+experimental manual EC2 setup or semi-automated CloudFormation setup
+- [NVIDIA VM](https://aws.amazon.com/marketplace/pp/B076K31M1S)
+* **Google Cloud Platform**
+- [NVIDIA
+VM](https://console.cloud.google.com/marketplace/details/nvidia-ngc-public/nvidia_gpu_cloud_image)
+* **Microsoft Azure**
+- [NVIDIA
+VM](https://azuremarketplace.microsoft.com/en-us/marketplace/apps/nvidia.ngc_azure_17_11?tab=Overview)
+* **Oracle Cloud**
+- [NVIDIA VM](https://docs.cloud.oracle.com/iaas/Content/Compute/References/ngcimage.htm)
+
+All NVIDIA VMs use the [NVIDIA MXNet Docker
+container](https://ngc.nvidia.com/catalog/containers/nvidia:mxnet).
+Follow the [container usage
+instructions](https://ngc.nvidia.com/catalog/containers/nvidia:mxnet) found in
+[NVIDIA's container repository](https://ngc.nvidia.com/).
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/devices/nvidia-jetson.md b/docs/static_site/src/_includes/get_started/devices/nvidia-jetson.md
new file mode 100644
index 000000000000..39ff95191886
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/devices/nvidia-jetson.md
@@ -0,0 +1,4 @@
+# NVIDIA Jetson Devices
+
+To install MXNet on a Jetson TX or Nano, please refer to the [Jetson installation
+guide](install-jetson.html).
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/devices/raspberry_pi.md b/docs/static_site/src/_includes/get_started/devices/raspberry_pi.md
new file mode 100644
index 000000000000..1fb68f10abcc
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/devices/raspberry_pi.md
@@ -0,0 +1,254 @@
+
+MXNet supports the Debian based Raspbian ARM based operating system so you can run MXNet on
+Raspberry Pi 3B
+devices.
+
+These instructions will walk through how to build MXNet for the Raspberry Pi and install the
+Python bindings
+for the library.
+
+You can do a dockerized cross compilation build on your local machine or a native build
+on-device.
+
+The complete MXNet library and its requirements can take almost 200MB of RAM, and loading
+large models with
+the library can take over 1GB of RAM. Because of this, we recommend running MXNet on the
+Raspberry Pi 3 or
+an equivalent device that has more than 1 GB of RAM and a Secure Digital (SD) card that has
+at least 4 GB of
+free memory.
+
+## Quick installation
+You can use this [pre-built Python
+wheel](https://mxnet-public.s3.amazonaws.com/install/raspbian/mxnet-1.5.0-py2.py3-none-any.whl)
+on a
+Raspberry Pi 3B with Stretch. You will likely need to install several dependencies to get
+MXNet to work.
+Refer to the following **Build** section for details.
+
+## Docker installation
+**Step 1** Install Docker on your machine by following the [docker installation
+instructions](https://docs.docker.com/engine/installation/linux/ubuntu/#install-using-the-repository).
+
+*Note* - You can install Community Edition (CE)
+
+**Step 2** [Optional] Post installation steps to manage Docker as a non-root user.
+
+Follow the four steps in this [docker
+documentation](https://docs.docker.com/engine/installation/linux/linux-postinstall/#manage-docker-as-a-non-root-user)
+to allow managing docker containers without *sudo*.
+
+## Build
+
+**This cross compilation build is experimental.**
+
+**Please use a Native build with gcc 4 as explained below, higher compiler versions
+currently cause test
+failures on ARM.**
+
+The following command will build a container with dependencies and tools,
+and then compile MXNet for ARMv7.
+You will want to run this on a fast cloud instance or locally on a fast PC to save time.
+The resulting artifact will be located in `build/mxnet-x.x.x-py2.py3-none-any.whl`.
+Copy this file to your Raspberry Pi.
+The previously mentioned pre-built wheel was created using this method.
+
+{% highlight bash %}
+ci/build.py -p armv7
+            {% endhighlight %}
+
+## Install using a pip wheel
+
+Your Pi will need several dependencies.
+
+Install MXNet dependencies with the following:
+
+{% highlight bash %}
+sudo apt-get update
+sudo apt-get install -y \
+apt-transport-https \
+build-essential \
+ca-certificates \
+cmake \
+curl \
+git \
+libatlas-base-dev \
+libcurl4-openssl-dev \
+libjemalloc-dev \
+liblapack-dev \
+libopenblas-dev \
+libopencv-dev \
+libzmq3-dev \
+ninja-build \
+python-dev \
+python-pip \
+software-properties-common \
+sudo \
+unzip \
+virtualenv \
+wget
+{% endhighlight %}
+
+Install virtualenv with:
+
+{% highlight bash %}
+sudo pip install virtualenv
+{% endhighlight %}
+
+Create a Python 2.7 environment for MXNet with:
+
+{% highlight bash %}
+virtualenv -p `which python` mxnet_py27
+{% endhighlight %}
+
+You may use Python 3, however the [wine bottle detection
+example](https://mxnet.incubator.apache.org/versions/master/tutorials/embedded/wine_detector.html)
+for the
+Pi with camera requires Python 2.7.
+
+Activate the environment, then install the wheel we created previously, or install this
+[prebuilt
+wheel](https://mxnet-public.s3.amazonaws.com/install/raspbian/mxnet-1.5.0-py2.py3-none-any.whl).
+
+{% highlight bash %}
+source mxnet_py27/bin/activate
+pip install mxnet-x.x.x-py2.py3-none-any.whl
+{% endhighlight %}
+
+Test MXNet with the Python interpreter:
+
+{% highlight python %}
+$ python
+
+>>> import mxnet
+{% endhighlight %}
+
+If there are no errors then you're ready to start using MXNet on your Pi!
+
+## Native Build
+
+Installing MXNet from source is a two-step process:
+
+1. Build the shared library from the MXNet C++ source code.
+2. Install the supported language-specific packages for MXNet.
+
+**Step 1** Build the Shared Library
+
+On Raspbian versions Wheezy and later, you need the following dependencies:
+
+- Git (to pull code from GitHub)
+
+- libblas (for linear algebraic operations)
+
+- libopencv (for computer vision operations. This is optional if you want to save RAM and
+Disk Space)
+
+- A C++ compiler that supports C++ 11. The C++ compiler compiles and builds MXNet source
+code. Supported
+compilers include the following:
+
+- [G++ (4.8 or later)](https://gcc.gnu.org/gcc-4.8/). Make sure to use gcc 4 and not 5 or 6
+as there are
+known bugs with these compilers.
+- [Clang (3.9 - 6)](https://clang.llvm.org/)
+
+Install these dependencies using the following commands in any directory:
+
+{% highlight bash %}
+sudo apt-get update
+sudo apt-get -y install git cmake ninja-build build-essential g++-4.9 c++-4.9 liblapack*
+libblas* libopencv*
+libopenblas* python3-dev python-dev virtualenv
+{% endhighlight %}
+
+Clone the MXNet source code repository using the following `git` command in your home
+directory:
+
+{% highlight bash %}
+git clone https://github.com/apache/incubator-mxnet.git --recursive
+cd incubator-mxnet
+{% endhighlight %}
+
+Build:
+
+{% highlight bash %}
+mkdir -p build && cd build
+cmake \
+-DUSE_SSE=OFF \
+-DUSE_CUDA=OFF \
+-DUSE_OPENCV=ON \
+-DUSE_OPENMP=ON \
+-DUSE_MKL_IF_AVAILABLE=OFF \
+-DUSE_SIGNAL_HANDLER=ON \
+-DCMAKE_BUILD_TYPE=Release \
+-GNinja ..
+ninja -j$(nproc)
+{% endhighlight %}
+Some compilation units require memory close to 1GB, so it's recommended that you enable swap
+as
+explained below and be cautious about increasing the number of jobs when building (-j)
+
+Executing these commands start the build process, which can take up to a couple hours, and
+creates a file
+called `libmxnet.so` in the build directory.
+
+If you are getting build errors in which the compiler is being killed, it is likely that the
+compiler is running out of memory (especially if you are on Raspberry Pi 1, 2 or Zero, which
+have
+less than 1GB of RAM), this can often be rectified by increasing the swapfile size on the Pi
+by
+editing the file /etc/dphys-swapfile and changing the line CONF_SWAPSIZE=100 to
+CONF_SWAPSIZE=1024,
+then running:
+{% highlight bash %}sudo /etc/init.d/dphys-swapfile stop
+sudo /etc/init.d/dphys-swapfile start
+free -m # to verify the swapfile size has been increased
+{% endhighlight %}
+
+**Step 2** Build cython modules (optional)
+
+{% highlight bash %}
+$ pip install Cython
+$ make cython # You can set the python executable with `PYTHON` flag, e.g., make cython
+PYTHON=python3
+{% endhighlight %}
+
+*MXNet* tries to use the cython modules unless the environment variable
+`MXNET_ENABLE_CYTHON` is set to `0`.
+If loading the cython modules fails, the default behavior is falling back to ctypes without
+any warning. To
+raise an exception at the failure, set the environment variable `MXNET_ENFORCE_CYTHON` to
+`1`. See
+[here](/faq/env_var.html) for more details.
+
+
+**Step 3** Install MXNet Python Bindings
+
+To install Python bindings run the following commands in the MXNet directory:
+
+{% highlight bash %}
+cd python
+pip install --upgrade pip
+pip install -e .
+{% endhighlight %}
+
+Note that the `-e` flag is optional. It is equivalent to `--editable` and means that if you
+edit the source
+files, these changes will be reflected in the package installed.
+
+Alternatively you can create a whl package installable with pip with the following command:
+
+{% highlight bash %}ci/docker/runtime_functions.sh build_wheel python/ $(realpath build)
+{% endhighlight %}
+
+
+You are now ready to run MXNet on your Raspberry Pi device. You can get started by following
+the tutorial on
+[Real-time Object Detection with MXNet On The Raspberry
+Pi](http://mxnet.io/tutorials/embedded/wine_detector.html).
+
+*Note - Because the complete MXNet library takes up a significant amount of the Raspberry
+Pi's limited RAM,
+when loading training data or large models into memory, you might have to turn off the GUI
+and terminate
+running processes to free RAM.*
diff --git a/docs/static_site/src/_includes/get_started/get_started.html b/docs/static_site/src/_includes/get_started/get_started.html
new file mode 100644
index 000000000000..4999518431fd
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/get_started.html
@@ -0,0 +1,474 @@
+<script>
+    /** Defaults **/
+    /** See options.js for the full ugly script **/
+    var versionSelect = defaultVersion = 'v1.5.0';
+    var platformSelect = 'linux';
+    var languageSelect = 'python';
+    var processorSelect = 'cpu';
+    var environSelect = 'pip';
+</script>
+<script src="{{'/assets/js/options.js'|relative_url}}"></script>
+
+<div class="install-selector">
+    <h2>Installing MXNet</h2>
+    <p>Indicate your preferred configuration. Then, follow the customized commands to install MXNet.</p>
+    <br>
+    <br>
+    <div class="install-widget">
+        <div class="row">
+            <div class="col-3 install-left">
+                <span>MXNet Version</span>
+            </div>
+            <div class="col-9 install-right">
+                <div class="dropdown">
+                    <button class="current-version dropbtn btn" type="button" data-toggle="dropdown">v1.5.0</button>
+                    <ul class="opt-group dropdown-content">
+                        <li class="opt versions"><a href="#">master</a></li>
+                        <li class="opt active versions"><a href="#">v1.5.0</a></li>
+                        <li class="opt versions"><a href="#">v1.4.1</a></li>
+                        <li class="opt versions"><a href="#">v1.3.1</a></li>
+                        <li class="opt versions"><a href="#">v1.2.1</a></li>
+                        <li class="opt versions"><a href="#">v1.1.0</a></li>
+                        <li class="opt versions"><a href="#">v1.0.0</a></li>
+                        <li class="opt versions"><a href="#">v0.12.1</a></li>
+                        <li class="opt versions"><a href="#">v0.11.0</a></li>
+                    </ul>
+                </div>
+            </div>
+        </div>
+
+        <!-- START - OS Menu -->
+        <div class="row">
+            <div class="col-3 install-left">
+                <span>OS / Platform</span>
+            </div>
+            <div class="col-9 install-right">
+                <div class="btn-group opt-group" role="group">
+                    <button type="button" class="btn btn-default opt active platforms">Linux</button>
+                    <button type="button" class="btn btn-default opt platforms">MacOS</button>
+                    <button type="button" class="btn btn-default opt platforms">Windows</button>
+                    <button type="button" class="btn btn-default opt platforms">Cloud</button>
+                    <button type="button" class="btn btn-default opt platforms">Devices</button>
+                </div>
+            </div>
+        </div>
+
+        <!-- START - Language Menu -->
+        <div class="linux macos windows">
+            <div class="row">
+                <div class="col-3 install-left">
+                    <span>Language</span>
+                </div>
+                <div class="col-9 install-right">
+                    <div class="btn-group opt-group" role="group">
+                        <button type="button" class="btn btn-default opt active languages">Python</button>
+                        <button type="button" class="btn btn-default opt languages">Scala</button>
+                        <button type="button" class="btn btn-default opt languages">Java</button>
+                        <button type="button" class="btn btn-default opt languages">Clojure</button>
+                        <button type="button" class="btn btn-default opt languages">R</button>
+                        <button type="button" class="btn btn-default opt languages">Julia</button>
+                        <button type="button" class="btn btn-default opt languages">Perl</button>
+                        <button type="button" class="btn btn-default opt languages">Cpp</button>
+                    </div>
+                </div>
+            </div>
+        </div>
+
+
+
+        <!-- No CPU GPU for other Devices -->
+        <div class="linux macos windows cloud">
+            <div class="row">
+                <div class="col-3 install-left">
+                    <span>GPU / CPU</span>
+                </div>
+                <div class="col-9 install-right">
+                    <div class="btn-group opt-group" role="group">
+                        <button type="button" class="btn btn-default processors opt active">GPU</button>
+                        <button type="button" class="btn btn-default processors opt">CPU</button>
+                    </div>
+                </div>
+            </div>
+        </div>
+
+        <!-- other devices -->
+        <div class="devices">
+            <div class="row">
+                <div class="col-3 install-left">
+                    <span>Device</span>
+                </div>
+                <div class="col-9 install-right">
+                    <div class="btn-group opt-group" role="group">
+                        <button type="button" class="btn btn-default iots opt active">Raspberry Pi</button>
+                        <button type="button" class="btn btn-default iots opt">NVIDIA Jetson</button>
+                    </div>
+                </div>
+            </div>
+        </div>
+
+        <!-- Python Distribution -->
+        <div class="linux macos windows">
+            <div class="python">
+                <div class="cpu gpu">
+                    <div class="row">
+                        <div class="col-3 install-left">
+                            <span>Distribution</span>
+                        </div>
+                        <div class="col-9 install-right">
+                            <div class="btn-group opt-group" role="group">
+                                <button type="button" class="btn btn-default environs opt active">Pip</button>
+                                <button type="button" class="btn btn-default environs opt">Docker</button>
+                                <button type="button" class="btn btn-default environs opt">Build from Source</button>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+
+        <!-- Julia Options -->
+        <div class="linux macos windows">
+            <div class="julia">
+                <div class="cpu gpu">
+                    <div class="row">
+                        <div class="col-3 install-left">
+                            <span>Distribution</span>
+                        </div>
+                        <div class="col-9 install-right">
+                            <div class="btn-group opt-group" role="group">
+                                <button type="button" class="btn btn-default environs opt active">Pkg</button>
+                                <button type="button" class="btn btn-default environs opt">Build from Source</button>
+                                </button>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+
+
+<!-- END - Main Menu -->
+    <br>
+    <br>
+    <br>
+    <div class="install-content">
+        <div class="linux">
+            <div class="python">
+                 <!-- START - Linux Python CPU Installation Instructions -->
+                <div class="cpu">
+                    <div class="pip">
+                        {% markdown %}{% include /get_started/linux/python/cpu/pip.md %}{% endmarkdown %}
+                    </div> <!-- End of pip -->
+
+                    <div class="docker">
+                        {% markdown %}{% include /get_started/linux/python/cpu/docker.md %}{% endmarkdown %}
+                    </div> <!-- END of docker -->
+
+                    <div class="build-from-source">
+                        {% markdown %}{% include /get_started/linux/python/cpu/build-from-source.md %}{% endmarkdown %}
+                    </div><!-- END of build from source -->
+
+                </div><!-- END of CPU -->
+                <!-- END - Linux Python CPU Installation Instructions -->
+
+                <!-- START - Linux Python GPU Installation Instructions -->
+                <div class="gpu">
+                    <div class="pip">
+                        {% markdown %}{% include /get_started/linux/python/gpu/pip.md %}{% endmarkdown %}
+                    </div> <!-- END of pip -->
+
+                    <div class="docker">
+                        {% markdown %}{% include /get_started/linux/python/gpu/docker.md %}{% endmarkdown %}
+                    </div> <!-- END of docker -->
+
+                    <div class="build-from-source">
+                         {% markdown %}{% include /get_started/linux/python/gpu/build-from-source.md %}{% endmarkdown %}
+                    </div> <!-- END of build from source -->
+                </div> <!-- END of GPU -->
+            </div> <!-- END of Python -->
+            <!-- END - Linux Python Installation Instructions -->
+
+
+            <div class="r">
+                <div class="cpu">
+                    {% markdown %}{% include /get_started/linux/r/cpu.md %}{% endmarkdown %}
+                </div> <!-- END of CPU -->
+
+                <div class="gpu">
+                    {% markdown %}{% include /get_started/linux/r/gpu.md %}{% endmarkdown %}
+                </div> <!-- END of GPU -->
+            </div> <!-- END of R -->
+
+
+            <div class="scala">
+                <div class="cpu">
+                    {% markdown %}{% include /get_started/linux/scala/cpu.md %}{% endmarkdown %}
+                </div> <!-- End of cpu -->
+                <div class="gpu">
+                    {% markdown %}{% include /get_started/linux/scala/gpu.md %}{% endmarkdown %}
+                </div> <!-- End of gpu -->
+            </div> <!-- End of scala -->
+
+
+            <div class="clojure">
+                <div class="cpu">
+                    {% markdown %}{% include /get_started/linux/clojure/cpu.md %}{% endmarkdown %}
+                </div> <!-- End of cpu -->
+
+                <div class="gpu">
+                    {% markdown %}{% include /get_started/linux/clojure/gpu.md %}{% endmarkdown %}
+                </div> <!-- End of gpu -->
+            </div> <!-- End of clojure -->
+
+
+            <div class="java">
+                <div class="cpu">
+                    {% markdown %}{% include /get_started/linux/java/cpu.md %}{% endmarkdown %}
+                </div> <!-- End of cpu -->
+                <div class="gpu">
+                    {% markdown %}{% include /get_started/linux/java/gpu.md %}{% endmarkdown %}
+                </div> <!-- End of gpu -->
+            </div> <!-- End of java -->
+
+            <div class="julia">
+                <div class="cpu gpu">
+                    <div class="pkg">
+                        {% markdown %}{% include /get_started/linux/julia/pkg.md %}{% endmarkdown %}
+                    </div>
+                    <div class="build-from-source">
+                        {% markdown %}{% include /get_started/linux/julia/build-from-source.md %}{% endmarkdown %}
+                    </div>
+                </div> <!-- End of cpu gpu -->
+            </div> <!-- End of julia -->
+
+            <div class="perl">
+                <div class="cpu gpu">
+                    {% markdown %}{% include /get_started/linux/perl/perl.md %}{% endmarkdown %}
+                </div> <!-- End of cpu gpu -->
+            </div> <!-- End of perl -->
+
+
+            <div class="cpp">
+                <div class="cpu gpu">
+                    {% markdown %}{% include /get_started/linux/cpp/cpp.md %}{% endmarkdown %}
+                </div> <!-- End of cpu gpu -->
+            </div> <!-- END - C++-->
+
+            <br>
+            For more installation options, refer to the <a href="ubuntu_setup.html">Ubuntu installation guide</a> and
+            <a href="centos_setup.html">CentOS installation guide</a>.
+        </div> <!-- END - Linux -->
+
+
+        <!-- START - MacOS Python CPU Installation Instructions -->
+
+        <div class="macos">
+            <div class="python">
+                <div class="cpu">
+                    <div class="pip">
+                        {% markdown %}{% include /get_started/macos/python/cpu/pip.md %}{% endmarkdown %}
+                    </div> <!-- END of pip -->
+                    <div class="docker">
+                        {% markdown %}{% include /get_started/macos/python/cpu/docker.md %}{% endmarkdown %}
+                    </div> <!-- END of docker -->
+                    <div class="build-from-source">
+                        {% markdown %}{% include /get_started/macos/python/cpu/build-from-source.md %}{% endmarkdown %}
+                    </div> <!-- END of build from source -->
+                </div> <!-- END of CPU -->
+
+
+                <!-- START - Mac OS Python GPU Installation Instructions -->
+                <div class="gpu">
+                    <div class="pip docker">
+                        {% markdown %}{% include /get_started/macos/python/gpu/pip_docker.md %}{% endmarkdown %}
+                    </div>
+
+                    <div class="build-from-source">
+                        {% markdown %}{% include /get_started/macos/python/gpu/build-from-source.md %}{% endmarkdown %}
+                    </div> <!-- END of build from source -->
+                </div> <!-- END of GPU -->
+            </div> <!-- END of Python -->
+
+
+            <!-- START - MacOS R CPU Installation Instructions -->
+            <div class="r">
+                <div class="cpu">
+                    {% markdown %}{% include /get_started/macos/r/cpu.md %}{% endmarkdown %}
+                </div> <!-- END of CPU -->
+                <div class="gpu">
+                    {% markdown %}{% include /get_started/macos/r/gpu.md %}{% endmarkdown %}
+                </div> <!-- END of GPU -->
+            </div> <!-- END of R -->
+
+            <div class="scala">
+                <div class="cpu">
+                    {% markdown %}{% include /get_started/macos/scala/cpu.md %}{% endmarkdown %}
+                </div> <!-- End of cpu  -->
+                <div class="gpu">
+                    {% markdown %}{% include /get_started/macos/scala/gpu.md %}{% endmarkdown %}
+                </div>
+            </div> <!-- End of scala -->
+
+            <div class="clojure">
+                <div class="cpu">
+                    {% markdown %}{% include /get_started/macos/clojure/cpu.md %}{% endmarkdown %}
+                </div> <!-- End of cpu  -->
+                <div class="gpu">
+                    {% markdown %}{% include /get_started/macos/clojure/gpu.md %}{% endmarkdown %}
+                </div> <!-- End of gpu -->
+            </div> <!-- End of clojure -->
+
+
+            <div class="java">
+                <div class="cpu">
+                    {% markdown %}{% include /get_started/macos/java/cpu.md %}{% endmarkdown %}
+                </div> <!-- End of cpu  -->
+                <div class="gpu">
+                    {% markdown %}{% include /get_started/macos/java/gpu.md %}{% endmarkdown %}
+                </div>
+            </div> <!-- End of java -->
+
+
+            <div class="julia">
+                <div class="cpu gpu">
+                    <div class="pkg">
+                        {% markdown %}{% include /get_started/macos/julia/pkg.md %}{% endmarkdown %}
+                    </div>
+
+                    <div class="build-from-source">
+                        {% markdown %}{% include /get_started/macos/julia/build-from-source.md %}{% endmarkdown %}
+                    </div>
+                </div> <!-- End of cpu gpu -->
+            </div> <!-- End of julia -->
+
+            <div class="perl">
+                <div class="cpu gpu">
+                    {% markdown %}{% include /get_started/macos/perl/perl.md %}{% endmarkdown %}
+                </div> <!-- End of cpu gpu -->
+            </div> <!-- End of julia -->
+
+            <div class="cpp">
+                <div class="cpu gpu">
+                    {% markdown %}{% include /get_started/macos/cpp/cpp.md %}{% endmarkdown %}
+                </div> <!-- End of cpu gpu -->
+            </div>
+            <br>
+            For more installation options, refer to the <a href="osx_setup.html">MXNet macOS installation guide</a>.
+        </div> <!-- END - Mac OS -->
+
+
+        <div class="windows">
+            <div class="python">
+                <div class="cpu">
+                    <div class="pip">
+                        {% markdown %}{% include /get_started/windows/python/cpu/pip.md %}{% endmarkdown %}
+                    </div> <!-- End of pip -->
+
+                    <div class="docker">
+                        {% markdown %}{% include /get_started/windows/python/cpu/docker.md %}{% endmarkdown %}
+                    </div> <!-- End of docker -->
+
+                    <div class="build-from-source">
+                        {% markdown %}{% include /get_started/windows/python/cpu/build-from-source.md %}{% endmarkdown %}
+                    </div> <!-- End of Build from source -->
+                </div> <!-- End of CPU -->
+
+
+                <div class="gpu">
+                    <div class="pip">
+                        {% markdown %}{% include /get_started/windows/python/gpu/pip.md %}{% endmarkdown %}
+                    </div> <!-- End of pip -->
+
+                    <div class="build-from-source">
+                        {% markdown %}{% include /get_started/windows/python/gpu/build-from-source.md %}{% endmarkdown %}
+                    </div> <!-- End of build from source -->
+                </div> <!-- End of GPU -->
+            </div> <!-- End of Python -->
+
+
+            <!-- START - Windows R CPU Installation Instructions -->
+
+            <div class="r">
+                <div class="cpu">
+                    {% markdown %}{% include /get_started/windows/r/cpu.md %}{% endmarkdown %}
+                </div> <!-- END - Windows R CPU -->
+
+                <div class="gpu">
+                    {% markdown %}{% include /get_started/windows/r/gpu.md %}{% endmarkdown %}
+                </div> <!-- END of GPU -->
+            </div> <!-- END - Windows R -->
+
+            <div class="scala">
+                <div class="cpu gpu">
+                    {% markdown %}{% include /get_started/windows/scala/scala.md %}{% endmarkdown %}
+                </div> <!-- End of cpu gpu -->
+            </div> <!-- End of scala -->
+
+            <div class="clojure">
+                <div class="cpu gpu">
+                    {% markdown %}{% include /get_started/windows/clojure/clojure.md %}{% endmarkdown %}
+                </div> <!-- End of cpu gpu -->
+            </div> <!-- End of clojure -->
+
+            <div class="java">
+                <div class="cpu gpu">
+                    {% markdown %}{% include /get_started/windows/java/java.md %}{% endmarkdown %}
+                </div> <!-- End of cpu gpu -->
+            </div> <!-- End of java -->
+
+            <div class="julia">
+                <div class="cpu gpu">
+                    <div class="pkg">
+                        {% markdown %}{% include /get_started/windows/julia/pkg.md %}{% endmarkdown %}
+                    </div>
+                    <div class="build-from-source">
+                        {% markdown %}{% include /get_started/windows/julia/build-from-source.md %}{% endmarkdown %}
+                    </div>
+                </div> <!-- End of cpu gpu -->
+            </div> <!-- End of julia -->
+
+            <div class="perl">
+                <div class="cpu gpu">
+                    {% markdown %}{% include /get_started/windows/perl/perl.md %}{% endmarkdown %}
+                </div> <!-- End of cpu gpu -->
+            </div> <!-- End of perl -->
+
+            <div class="cpp">
+                <div class="cpu gpu">
+                    {% markdown %}{% include /get_started/windows/cpp/cpp.md %}{% endmarkdown %}
+                </div> <!-- End of cpu gpu -->
+            </div> <!-- End of C++ -->
+
+            For more installation options, refer to the <a href="windows_setup.html">MXNet Windows installation guide</a>.
+        </div> <!-- End of Windows -->
+
+
+        <!-- START - Cloud Python Installation Instructions -->
+
+        <div class="cloud">
+            <div class="gpu">
+                {% markdown %}{% include /get_started/cloud/gpu.md %}{% endmarkdown %}
+            </div> <!-- END gpu -->
+
+            <div class="cpu">
+                {% markdown %}{% include /get_started/cloud/cpu.md %}{% endmarkdown %}
+
+            </div> <!-- end cpu -->
+        </div> <!-- END - Cloud Python Installation Instructions -->
+
+
+        <!-- DEVICES -->
+        <div class="devices">
+            <div class="raspberry-pi">
+                {% markdown %}{% include /get_started/devices/raspberry_pi.md %}{% endmarkdown %}
+            </div> <!-- End of raspberry pi -->
+
+
+            <div class="nvidia-jetson">
+                {% markdown %}{% include /get_started/devices/nvidia-jetson.md %}{% endmarkdown %}
+            </div> <!-- End of jetson -->
+        </div> <!-- End of devices -->
+    </div>
+</div>
+
diff --git a/docs/static_site/src/_includes/get_started/gpu_snippet.md b/docs/static_site/src/_includes/get_started/gpu_snippet.md
new file mode 100644
index 000000000000..1154507db2e8
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/gpu_snippet.md
@@ -0,0 +1,11 @@
+CUDA should be installed first.
+
+**Important:** Make sure your installed CUDA version matches the CUDA version in the pip package.
+
+Check your CUDA version with the following command:
+
+{% highlight bash %}
+nvcc --version
+{% endhighlight %}
+
+You can either upgrade your CUDA install or install the MXNet package that supports your CUDA version.
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/linux/clojure/cpu.md b/docs/static_site/src/_includes/get_started/linux/clojure/cpu.md
new file mode 100644
index 000000000000..c16eb9ada34e
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/linux/clojure/cpu.md
@@ -0,0 +1,15 @@
+You can use the Maven packages defined in the following dependency to include MXNet in your Clojure
+project. To maximize leverage, the Clojure package has been built on the existing Scala package. Please
+refer to the [MXNet-Scala setup guide]({{'/get_started/scala_setup'|relative_url}}) for a detailed set of instructions
+to help you with the setup process that is required to use the Clojure dependency.
+
+<a href="https://mvnrepository.com/artifact/org.apache.mxnet.contrib.clojure/clojure-mxnet-linux-cpu"><img
+        src="https://img.shields.io/badge/org.apache.mxnet-linux cpu-green.svg"
+        alt="maven badge"/></a>
+
+{% highlight html %}
+<dependency>
+<groupId>org.apache.mxnet.contrib.clojure</groupId>
+<artifactId>clojure-mxnet-linux-cpu</artifactId>
+</dependency>
+{% endhighlight %}
diff --git a/docs/static_site/src/_includes/get_started/linux/clojure/gpu.md b/docs/static_site/src/_includes/get_started/linux/clojure/gpu.md
new file mode 100644
index 000000000000..26293f6e077f
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/linux/clojure/gpu.md
@@ -0,0 +1,15 @@
+You can use the Maven packages defined in the following dependency to include MXNet in your Clojure
+project. To maximize leverage, the Clojure package has been built on the existing Scala package. Please
+refer to the [MXNet-Scala setup guide]({{'/get_started/scala_setup'|relative_url}}) for a detailed set of instructions
+to help you with the setup process that is required to use the Clojure dependency.
+
+<a href="https://mvnrepository.com/artifact/org.apache.mxnet.contrib.clojure/clojure-mxnet-linux-gpu"><img
+        src="https://img.shields.io/badge/org.apache.mxnet-linux gpu-green.svg"
+        alt="maven badge"/></a>
+
+{% highlight html %}
+<dependency>
+    <groupId>org.apache.mxnet.contrib.clojure</groupId>
+    <artifactId>clojure-mxnet-linux-gpu</artifactId>
+</dependency>
+{% endhighlight %}
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/linux/cpp/cpp.md b/docs/static_site/src/_includes/get_started/linux/cpp/cpp.md
new file mode 100644
index 000000000000..ac664185a61a
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/linux/cpp/cpp.md
@@ -0,0 +1,4 @@
+To enable the C++ package, build from source using `make USE_CPP_PACKAGE=1`.
+Refer to the [MXNet C++ setup guide](c_plus_plus)
+for full instructions.
+                
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/linux/java/cpu.md b/docs/static_site/src/_includes/get_started/linux/java/cpu.md
new file mode 100644
index 000000000000..2140a34af7ad
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/linux/java/cpu.md
@@ -0,0 +1,17 @@
+You can use the Maven packages defined in the following dependency to include MXNet in your Java
+project. The Java API is provided as a subset of the Scala API and is intended for inference only.
+Please refer to the <a href="java_setup.html">MXNet-Java setup guide</a> for a detailed set of
+instructions to help you with the setup process.
+
+<a href="https://repository.apache.org/#nexus-search;gav~org.apache.mxnet~~1.5.0~~">
+    <img src="https://img.shields.io/badge/org.apache.mxnet-linux cpu-green.svg"
+     alt="maven badge"/>
+</a>
+
+{% highlight html %}
+<dependency>
+    <groupId>org.apache.mxnet</groupId>
+    <artifactId>mxnet-full_2.11-linux-x86_64-cpu</artifactId>
+    <version>[1.5.0, )</version>
+</dependency>
+{% endhighlight %}
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/linux/java/gpu.md b/docs/static_site/src/_includes/get_started/linux/java/gpu.md
new file mode 100644
index 000000000000..cbab5e60ca47
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/linux/java/gpu.md
@@ -0,0 +1,17 @@
+You can use the Maven packages defined in the following dependency to include MXNet in your Java
+project. The Java API is provided as a subset of the Scala API and is intended for inference only.
+Please refer to the <a href="java_setup.html">MXNet-Java setup guide</a> for a detailed set of
+instructions to help you with the setup process.
+
+<a href="https://repository.apache.org/#nexus-search;gav~org.apache.mxnet~~1.5.0~~">
+    <img src="https://img.shields.io/badge/org.apache.mxnet-linux gpu-green.svg"
+    alt="maven badge"/>
+</a>
+
+{% highlight html %}
+<dependency>
+    <groupId>org.apache.mxnet</groupId>
+    <artifactId>mxnet-full_2.11-linux-x86_64-gpu</artifactId>
+    <version>[1.5.0, )</version>
+</dependency>
+{% endhighlight %}
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/linux/julia/build-from-source.md b/docs/static_site/src/_includes/get_started/linux/julia/build-from-source.md
new file mode 100644
index 000000000000..4b39ac633e0d
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/linux/julia/build-from-source.md
@@ -0,0 +1,2 @@
+Refer to the [Julia section of the MXNet Ubuntu installation guide](ubuntu_setup#install-the-mxnet-package-for-julia).
+
diff --git a/docs/static_site/src/_includes/get_started/linux/julia/pkg.md b/docs/static_site/src/_includes/get_started/linux/julia/pkg.md
new file mode 100644
index 000000000000..35971305ee2d
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/linux/julia/pkg.md
@@ -0,0 +1,10 @@
+Install a pinned version of MXNet like this:
+
+{% highlight julia %}
+]add MXNet#v1.5.0
+{% endhighlight %}
+
+Or directly install the latest release:
+{% highlight julia %}
+]add MXNet
+{% endhighlight %}
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/linux/perl/perl.md b/docs/static_site/src/_includes/get_started/linux/perl/perl.md
new file mode 100644
index 000000000000..d9fc8d5328bc
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/linux/perl/perl.md
@@ -0,0 +1 @@
+Refer to the [Perl section of the MXNet Ubuntu installation guide](ubuntu_setup.html#install-the-mxnet-package-for-perl).
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/linux/python/cpu/build-from-source.md b/docs/static_site/src/_includes/get_started/linux/python/cpu/build-from-source.md
new file mode 100644
index 000000000000..4adf039739d5
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/linux/python/cpu/build-from-source.md
@@ -0,0 +1 @@
+To build from source, refer to the [MXNet Ubuntu installation guide]({{'/get_started/ubuntu_setup' | relative_url}}).
diff --git a/docs/static_site/src/_includes/get_started/linux/python/cpu/docker.md b/docs/static_site/src/_includes/get_started/linux/python/cpu/docker.md
new file mode 100644
index 000000000000..1980a0999f92
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/linux/python/cpu/docker.md
@@ -0,0 +1,43 @@
+Docker images with *MXNet* are available at [DockerHub](https://hub.docker.com/r/mxnet/).
+
+**Step 1** Install Docker on your machine by following the [docker installation
+instructions](https://docs.docker.com/engine/installation/linux/ubuntu/#install-using-the-repository).
+
+*Note* - You can install Community Edition (CE) to get started with *MXNet*.
+
+**Step 2** [Optional] Post installation steps to manage Docker as a non-root user.
+
+Follow the four steps in this [docker
+documentation](https://docs.docker.com/engine/installation/linux/linux-postinstall/#manage-docker-as-a-non-root-user)
+to allow managing docker containers without *sudo*.
+
+If you skip this step, you need to use *sudo* each time you invoke Docker.
+
+**Step 3** Pull the MXNet docker image.
+
+{% highlight bash %}
+$ docker pull mxnet/python # Use sudo if you skip Step 2
+{% endhighlight %}
+
+You can list docker images to see if mxnet/python docker image pull was successful.
+
+{% highlight bash %}
+$ docker images # Use sudo if you skip Step 2
+
+REPOSITORY TAG IMAGE ID CREATED SIZE
+mxnet/python latest 00d026968b3c 3 weeks ago 1.41 GB
+{% endhighlight %}
+
+Using the latest MXNet with [Intel MKL-DNN](https://github.com/intel/mkl-dnn) is
+recommended for the
+fastest inference speeds with MXNet.
+
+{% highlight bash %}
+$ docker pull mxnet/python:1.3.0_cpu_mkl # Use sudo if you skip Step 2
+$ docker images # Use sudo if you skip Step 2
+
+REPOSITORY TAG IMAGE ID CREATED SIZE
+mxnet/python 1.3.0_cpu_mkl deaf9bf61d29 4 days ago 678 MB
+{% endhighlight %}
+
+**Step 4** <a href="validate_mxnet.html">Validate the installation</a>.
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/linux/python/cpu/pip.md b/docs/static_site/src/_includes/get_started/linux/python/cpu/pip.md
new file mode 100644
index 000000000000..b05480e1c967
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/linux/python/cpu/pip.md
@@ -0,0 +1,121 @@
+Run the following command:
+
+<div class="v1-5-0">
+{% highlight bash %}
+$ pip install mxnet
+{% endhighlight %}
+
+MKL-DNN enabled pip packages are optimized for Intel hardware. You can find
+performance numbers
+in the <a href="http://mxnet.io/faq/perf.html#intel-cpu">MXNet tuning guide</a>.
+
+{% highlight bash %}
+$ pip install mxnet-mkl
+{% endhighlight %}
+
+</div> <!-- End of v1-5-0 -->
+
+<div class="v1-4-1">
+
+{% highlight bash %}
+$ pip install mxnet==1.4.1
+{% endhighlight %}
+
+MKL-DNN enabled pip packages are optimized for Intel hardware. You can find
+performance numbers
+in the <a href="http://mxnet.io/faq/perf.html#intel-cpu">MXNet tuning guide</a>.
+
+{% highlight bash %}
+$ pip install mxnet-mkl==1.4.1
+{% endhighlight %}
+
+</div> <!-- End of v1-4-1 -->
+<div class="v1-3-1">
+
+{% highlight bash %}
+$ pip install mxnet==1.3.1
+{% endhighlight %}
+
+MKL-DNN enabled pip packages are optimized for Intel hardware. You can find
+performance numbers
+in the <a href="http://mxnet.io/faq/perf.html#intel-cpu">MXNet tuning guide</a>.
+
+{% highlight bash %}
+$ pip install mxnet-mkl==1.3.1
+{% endhighlight %}
+
+</div> <!-- End of v1-3-1 -->
+<div class="v1-2-1">
+
+{% highlight bash %}
+$ pip install mxnet==1.2.1
+{% endhighlight %}
+
+MKL-DNN enabled pip packages are optimized for Intel hardware. You can find
+performance numbers
+in the <a href="http://mxnet.io/faq/perf.html#intel-cpu">MXNet tuning guide</a>.
+
+{% highlight bash %}
+$ pip install mxnet-mkl==1.2.1
+{% endhighlight %}
+
+</div> <!-- End of v1-2-1 -->
+
+<div class="v1-1-0">
+
+{% highlight bash %}
+$ pip install mxnet==1.1.0
+{% endhighlight %}
+
+</div> <!-- End of v1-1-0-->
+
+<div class="v1-0-0">
+
+{% highlight bash %}
+$ pip install mxnet==1.0.0
+{% endhighlight %}
+
+</div> <!-- End of v1-0-0-->
+
+
+<div class="v0-12-1">
+
+{% highlight bash %}
+$ pip install mxnet==0.12.1
+{% endhighlight %}
+
+For MXNet 0.12.0:
+
+{% highlight bash %}
+$ pip install mxnet==0.12.0
+{% endhighlight %}
+
+</div> <!-- End of v0-12-1-->
+
+<div class="v0-11-0">
+
+{% highlight bash %}
+$ pip install mxnet==0.11.0
+{% endhighlight %}
+
+</div> <!-- End of v0-11-0-->
+
+<div class="master">
+
+{% highlight bash %}
+$ pip install mxnet --pre
+{% endhighlight %}
+
+MKL-DNN enabled pip packages are optimized for Intel hardware. You can find
+performance numbers
+in the <a href="http://mxnet.io/faq/perf.html#intel-cpu">MXNet tuning guide</a>.
+
+{% highlight bash %}
+$ pip install mxnet-mkl --pre
+{% endhighlight %}
+
+</div> <!-- End of master-->
+<br>
+
+
+{% include /get_started/pip_snippet.md %}
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/linux/python/gpu/build-from-source.md b/docs/static_site/src/_includes/get_started/linux/python/gpu/build-from-source.md
new file mode 100644
index 000000000000..4adf039739d5
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/linux/python/gpu/build-from-source.md
@@ -0,0 +1 @@
+To build from source, refer to the [MXNet Ubuntu installation guide]({{'/get_started/ubuntu_setup' | relative_url}}).
diff --git a/docs/static_site/src/_includes/get_started/linux/python/gpu/docker.md b/docs/static_site/src/_includes/get_started/linux/python/gpu/docker.md
new file mode 100644
index 000000000000..d4d8cf295458
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/linux/python/gpu/docker.md
@@ -0,0 +1,48 @@
+Docker images with *MXNet* are available at [DockerHub](https://hub.docker.com/r/mxnet/).
+
+**Step 1** Install Docker on your machine by following the [docker installation
+instructions](https://docs.docker.com/engine/installation/linux/ubuntu/#install-using-the-repository).
+
+*Note* - You can install Community Edition (CE) to get started with *MXNet*.
+
+**Step 2** [Optional] Post installation steps to manage Docker as a non-root user.
+
+Follow the four steps in this [docker
+documentation](https://docs.docker.com/engine/installation/linux/linux-postinstall/#manage-docker-as-a-non-root-user)
+to allow managing docker containers without *sudo*.
+
+If you skip this step, you need to use *sudo* each time you invoke Docker.
+
+**Step 3** Install *nvidia-docker-plugin* following the [installation
+instructions](https://github.com/NVIDIA/nvidia-docker/wiki). *nvidia-docker-plugin*
+is required to
+enable the usage of GPUs from the docker containers.
+
+**Step 4** Pull the MXNet docker image.
+
+{% highlight bash %}
+$ docker pull mxnet/python:gpu # Use sudo if you skip Step 2
+{% endhighlight %}
+
+You can list docker images to see if mxnet/python docker image pull was successful.
+
+{% highlight bash %}
+$ docker images # Use sudo if you skip Step 2
+
+REPOSITORY TAG IMAGE ID CREATED SIZE
+mxnet/python gpu 493b2683c269 3 weeks ago 4.77 GB
+{% endhighlight %}
+
+Using the latest MXNet with [Intel MKL-DNN](https://github.com/intel/mkl-dnn) is
+recommended for the
+fastest inference speeds with MXNet.
+
+{% highlight bash %}
+$ docker pull mxnet/python:1.3.0_cpu_mkl # Use sudo if you skip Step 2
+$ docker images # Use sudo if you skip Step 2
+
+REPOSITORY TAG IMAGE ID CREATED SIZE
+mxnet/python 1.3.0_gpu_cu92_mkl adcb3ab19f50 4 days ago 4.23 GB
+{% endhighlight %}
+
+**Step 5** <a href="validate_mxnet.html">Validate the installation</a>.
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/linux/python/gpu/pip.md b/docs/static_site/src/_includes/get_started/linux/python/gpu/pip.md
new file mode 100644
index 000000000000..249cd5b54052
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/linux/python/gpu/pip.md
@@ -0,0 +1,74 @@
+Run the following command:
+
+<div class="v1-5-0">
+{% highlight bash %}
+$ pip install mxnet-cu101
+{% endhighlight %}
+
+</div> <!-- End of v1-5-0 -->
+<div class="v1-4-1">
+
+{% highlight bash %}
+$ pip install mxnet-cu101==1.4.1
+{% endhighlight %}
+
+</div> <!-- End of v1-4-1 -->
+<div class="v1-3-1">
+
+{% highlight bash %}
+$ pip install mxnet-cu92==1.3.1
+{% endhighlight %}
+
+</div> <!-- End of v1-3-1-->
+<div class="v1-2-1">
+
+{% highlight bash %}
+$ pip install mxnet-cu92==1.2.1
+{% endhighlight %}
+
+</div> <!-- End of v1-2-1-->
+
+<div class="v1-1-0">
+
+{% highlight bash %}
+$ pip install mxnet-cu91==1.1.0
+{% endhighlight %}
+
+</div> <!-- End of v1-1-0-->
+
+<div class="v1-0-0">
+
+{% highlight bash %}
+$ pip install mxnet-cu90==1.0.0
+{% endhighlight %}
+
+</div> <!-- End of v1-0-0-->
+
+<div class="v0-12-1">
+
+{% highlight bash %}
+$ pip install mxnet-cu90==0.12.1
+{% endhighlight %}
+
+</div> <!-- End of v0-12-1-->
+
+<div class="v0-11-0">
+
+{% highlight bash %}
+$ pip install mxnet-cu80==0.11.0
+{% endhighlight %}
+
+</div> <!-- End of v0-11-0-->
+
+<div class="master">
+
+{% highlight bash %}
+$ pip install mxnet-cu101 --pre
+{% endhighlight %}
+
+</div> <!-- End of master-->
+
+<br>
+
+{% include /get_started/pip_snippet.md %}
+{% include /get_started/gpu_snippet.md %}
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/linux/r/cpu.md b/docs/static_site/src/_includes/get_started/linux/r/cpu.md
new file mode 100644
index 000000000000..0ccbd9f66efa
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/linux/r/cpu.md
@@ -0,0 +1,9 @@
+The default version of R that is installed with `apt-get` is insufficient. You will need
+to first [install R v3.4.4+ and build MXNet from source](ubuntu_setup.html#install-the-mxnet-package-for-r).
+
+After you have setup R v3.4.4+ and MXNet, you can build and install the MXNet R bindings with the following, assuming that `incubator-mxnet` is the source directory you used to build MXNet as follows:
+
+{% highlight bash %}
+$ cd incubator-mxnet
+$ make rpkg
+{% endhighlight %}
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/linux/r/gpu.md b/docs/static_site/src/_includes/get_started/linux/r/gpu.md
new file mode 100644
index 000000000000..3e26d319e4f0
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/linux/r/gpu.md
@@ -0,0 +1,16 @@
+The default version of R that is installed with `apt-get` is insufficient. You will need
+to first
+[install R v3.4.4+ and build MXNet from
+source](ubuntu_setup.html#install-the-mxnet-package-for-r).
+
+After you have setup R v3.4.4+ and MXNet, you can build and install the MXNet R bindings
+with the
+following, assuming that `incubator-mxnet` is the source directory you used to build
+MXNet as follows:
+
+{% highlight bash %}
+$ cd incubator-mxnet
+$ make rpkg
+{% endhighlight %}
+
+{% include /get_started/gpu_snippet.md %}
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/linux/scala/cpu.md b/docs/static_site/src/_includes/get_started/linux/scala/cpu.md
new file mode 100644
index 000000000000..3cc96bade7df
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/linux/scala/cpu.md
@@ -0,0 +1,14 @@
+You can use the Maven packages defined in the following dependency to include MXNet in your Scala
+project. Please refer to the [MXNet-Scala setup guide]({{'/get_started/scala_setup'|relative_url}}) for
+a detailed set of instructions to help you with the setup process.
+
+<a href="https://mvnrepository.com/artifact/org.apache.mxnet/mxnet-full_2.11-linux-x86_64-cpu"><img
+        src="https://img.shields.io/badge/org.apache.mxnet-linux cpu-green.svg"
+        alt="maven badge"/></a>
+
+{% highlight html %}
+<dependency>
+<groupId>org.apache.mxnet</groupId>
+<artifactId>mxnet-full_2.11-linux-x86_64-cpu</artifactId>
+</dependency>
+{% endhighlight %}
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/linux/scala/gpu.md b/docs/static_site/src/_includes/get_started/linux/scala/gpu.md
new file mode 100644
index 000000000000..9f458112a0ae
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/linux/scala/gpu.md
@@ -0,0 +1,16 @@
+You can use the Maven packages defined in the following dependency to include MXNet in
+your Scala
+project. Please refer to the <a href="scala_setup.html">MXNet-Scala setup guide</a> for
+a detailed set
+of instructions to help you with the setup process.
+
+<a href="https://mvnrepository.com/artifact/org.apache.mxnet/mxnet-full_2.11-linux-x86_64-gpu"><img
+        src="https://img.shields.io/badge/org.apache.mxnet-linux gpu-green.svg"
+        alt="maven badge"/></a>
+
+```html
+<dependency>
+<groupId>org.apache.mxnet</groupId>
+<artifactId>mxnet-full_2.11-linux-x86_64-gpu</artifactId>
+</dependency>
+```
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/macos/clojure/cpu.md b/docs/static_site/src/_includes/get_started/macos/clojure/cpu.md
new file mode 100644
index 000000000000..1e27af5e5dd7
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/macos/clojure/cpu.md
@@ -0,0 +1,17 @@
+
+You can use the Maven packages defined in the following dependency to include MXNet in
+your Clojure project. To maximize leverage, the Clojure package has been built on the existing Scala
+package. Please refer to the [MXNet-Scala setup guide](scala_setup.html) for a detailed set
+of instructions to help you with the setup process that is required to use the Clojure dependency.
+
+<a href="https://mvnrepository.com/artifact/org.apache.mxnet.contrib.clojure/clojure-mxnet-osx-cpu"><img
+        src="https://img.shields.io/badge/org.apache.mxnet-mac cpu-green.svg"
+        alt="maven badge"/></a>
+
+{% highlight html %}
+<dependency>
+    <groupId>org.apache.mxnet.contrib.clojure</groupId>
+    <artifactId>clojure-mxnet-osx-cpu</artifactId>
+</dependency>
+{% endhighlight %}
+
diff --git a/docs/static_site/src/_includes/get_started/macos/clojure/gpu.md b/docs/static_site/src/_includes/get_started/macos/clojure/gpu.md
new file mode 100644
index 000000000000..ccbc24db96e7
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/macos/clojure/gpu.md
@@ -0,0 +1 @@
+Not available at this time.
diff --git a/docs/static_site/src/_includes/get_started/macos/cpp/cpp.md b/docs/static_site/src/_includes/get_started/macos/cpp/cpp.md
new file mode 100644
index 000000000000..b2e632b0f117
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/macos/cpp/cpp.md
@@ -0,0 +1,3 @@
+To enable the C++ package, build from source using `make USE_CPP_PACKAGE=1`.
+Refer to the [MXNet C++ setup guide](c_plus_plus) for full instructions.
+                
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/macos/java/cpu.md b/docs/static_site/src/_includes/get_started/macos/java/cpu.md
new file mode 100644
index 000000000000..e8c320e9462d
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/macos/java/cpu.md
@@ -0,0 +1,16 @@
+You can use the Maven packages defined in the following dependency to include MXNet in
+your Java project. The Java API is provided as a subset of the Scala API and is intended for
+inference only.
+Please refer to the [MXNet-Java setup guide](java_setup.html) for a detailed set of instructions to help you with the setup process.
+
+<a href="https://repository.apache.org/#nexus-search;gav~org.apache.mxnet~~1.5.0~~"><img
+src="https://img.shields.io/badge/org.apache.mxnet-mac cpu-green.svg"
+alt="maven badge"/></a>
+
+{% highlight html %}
+<dependency>
+    <groupId>org.apache.mxnet</groupId>
+    <artifactId>mxnet-full_2.11-linux-x86_64-cpu</artifactId>
+    <version>[1.5.0, )</version>
+</dependency>
+{% endhighlight %}
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/macos/java/gpu.md b/docs/static_site/src/_includes/get_started/macos/java/gpu.md
new file mode 100644
index 000000000000..b17ef33478ea
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/macos/java/gpu.md
@@ -0,0 +1 @@
+Not available at this time.
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/macos/julia/build-from-source.md b/docs/static_site/src/_includes/get_started/macos/julia/build-from-source.md
new file mode 100644
index 000000000000..85045cacf8f6
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/macos/julia/build-from-source.md
@@ -0,0 +1 @@
+Refer to the [Julia section of the MXNet macOS installation guide](osx_setup.html#install-the-mxnet-package-for-julia).
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/macos/julia/pkg.md b/docs/static_site/src/_includes/get_started/macos/julia/pkg.md
new file mode 100644
index 000000000000..35971305ee2d
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/macos/julia/pkg.md
@@ -0,0 +1,10 @@
+Install a pinned version of MXNet like this:
+
+{% highlight julia %}
+]add MXNet#v1.5.0
+{% endhighlight %}
+
+Or directly install the latest release:
+{% highlight julia %}
+]add MXNet
+{% endhighlight %}
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/macos/perl/perl.md b/docs/static_site/src/_includes/get_started/macos/perl/perl.md
new file mode 100644
index 000000000000..c9b04e328edc
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/macos/perl/perl.md
@@ -0,0 +1 @@
+Refer to the [Perl section of installation guide](osx_setup.html#install-the-mxnet-package-for-perl).
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/macos/python/cpu/build-from-source.md b/docs/static_site/src/_includes/get_started/macos/python/cpu/build-from-source.md
new file mode 100644
index 000000000000..b8b5d2119bd0
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/macos/python/cpu/build-from-source.md
@@ -0,0 +1,2 @@
+To build from source, refer to the [MXNet macOS installation guide](osx_setup.html).
+MXNet developers should refer to the MXNet wiki's [Developer Setup on Mac](https://cwiki.apache.org/confluence/display/MXNET/MXNet+Developer+Setup+on+Mac).
diff --git a/docs/static_site/src/_includes/get_started/macos/python/cpu/docker.md b/docs/static_site/src/_includes/get_started/macos/python/cpu/docker.md
new file mode 100644
index 000000000000..2f0f04ae432c
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/macos/python/cpu/docker.md
@@ -0,0 +1,35 @@
+Docker images with *MXNet* are available at [DockerHub](https://hub.docker.com/r/mxnet/).
+
+**Step 1** Install Docker on your machine by following the [docker installation
+instructions](https://docs.docker.com/docker-for-mac/install/#install-and-run-docker-for-mac).
+
+*Note* - You can install Community Edition (CE) to get started with *MXNet*.
+
+**Step 2** Pull the MXNet docker image.
+
+{% highlight bash %}
+$ docker pull mxnet/python
+{% endhighlight %}
+
+You can list docker images to see if mxnet/python docker image pull was successful.
+
+{% highlight bash %}
+$ docker images
+
+REPOSITORY TAG IMAGE ID CREATED SIZE
+mxnet/python latest 00d026968b3c 3 weeks ago 1.41 GB
+{% endhighlight %}
+
+Using the latest MXNet with [Intel MKL-DNN](https://github.com/intel/mkl-dnn) is
+recommended for the
+fastest inference speeds with MXNet.
+
+{% highlight bash %}
+$ docker pull mxnet/python:1.3.0_cpu_mkl # Use sudo if you skip Step 2
+$ docker images # Use sudo if you skip Step 2
+
+REPOSITORY TAG IMAGE ID CREATED SIZE
+mxnet/python 1.3.0_cpu_mkl deaf9bf61d29 4 days ago 678 MB
+{% endhighlight %}
+
+**Step 4** <a href="validate_mxnet.html">Validate the installation</a>.
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/macos/python/cpu/pip.md b/docs/static_site/src/_includes/get_started/macos/python/cpu/pip.md
new file mode 100644
index 000000000000..beb5eb4fb797
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/macos/python/cpu/pip.md
@@ -0,0 +1,73 @@
+Run the following command:
+
+<div class="v1-5-0">
+
+{% highlight bash %}
+$ pip install mxnet
+{% endhighlight %}
+</div> <!-- End of v1-5-0 -->
+<div class="v1-4-1">
+
+{% highlight bash %}
+$ pip install mxnet==1.4.1
+{% endhighlight %}
+</div> <!-- End of v1-4-1 -->
+<div class="v1-3-1">
+
+{% highlight bash %}
+$ pip install mxnet==1.3.1
+{% endhighlight %}
+
+</div> <!-- End of v1-3-1 -->
+<div class="v1-2-1">
+
+{% highlight bash %}
+$ pip install mxnet==1.2.1
+{% endhighlight %}
+
+</div> <!-- End of v1-2-1 -->
+
+
+<div class="v1-1-0">
+
+{% highlight bash %}
+$ pip install mxnet==1.1.0
+{% endhighlight %}
+
+</div> <!-- End of v1-1-0-->
+
+
+<div class="v1-0-0">
+
+{% highlight bash %}
+$ pip install mxnet==1.0.0
+{% endhighlight %}
+
+</div> <!-- End of v1-0-0-->
+
+<div class="v0-12-1">
+
+{% highlight bash %}
+$ pip install mxnet=0.12.1
+{% endhighlight %}
+
+</div> <!-- End of v0-12-1-->
+
+
+<div class="v0-11-0">
+
+{% highlight bash %}
+$ pip install mxnet==0.11.0
+{% endhighlight %}
+
+</div> <!-- End of v0-11-0-->
+
+<div class="master">
+
+{% highlight bash %}
+$ pip install mxnet --pre
+{% endhighlight %}
+
+</div> <!-- End of master-->
+
+{% include /get_started/pip_snippet.md %}
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/macos/python/gpu/build-from-source.md b/docs/static_site/src/_includes/get_started/macos/python/gpu/build-from-source.md
new file mode 100644
index 000000000000..7cb61a59c8bf
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/macos/python/gpu/build-from-source.md
@@ -0,0 +1,2 @@
+Refer to the [MXNet macOS installation guide](osx_setup.html).
+MXNet developers should refer to the MXNet wiki's [Developer Setup on Mac](https://cwiki.apache.org/confluence/display/MXNET/MXNet+Developer+Setup+on+Mac).
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/macos/python/gpu/pip_docker.md b/docs/static_site/src/_includes/get_started/macos/python/gpu/pip_docker.md
new file mode 100644
index 000000000000..b4585922e8e0
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/macos/python/gpu/pip_docker.md
@@ -0,0 +1 @@
+This option is only available by building from source. Refer to the [MXNet macOS installation guide](osx_setup.html).
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/macos/r/cpu.md b/docs/static_site/src/_includes/get_started/macos/r/cpu.md
new file mode 100644
index 000000000000..beca0d414730
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/macos/r/cpu.md
@@ -0,0 +1,28 @@
+To run MXNet you also should have OpenCV and OpenBLAS installed. You may install them with `brew` as follows:
+
+{% highlight bash %}
+brew install opencv
+brew install openblas
+{% endhighlight %}
+
+To ensure MXNet R package runs with the version of OpenBLAS installed, create a symbolic link as follows:
+
+{% highlight bash %}
+ln -sf /usr/local/opt/openblas/lib/libopenblas.dylib
+/usr/local/opt/openblas/lib/libopenblasp-r0.3.1.dylib
+{% endhighlight %}
+
+Note: packages for 3.6.x are not yet available.
+
+Install 3.5.x of R from [CRAN](https://cran.r-project.org/bin/macosx/). The latest is
+[v3.5.3](https://cran.r-project.org/bin/macosx/R-3.5.3.pkg).
+
+You can [build MXNet-R from source](osx_setup.html#install-the-mxnet-package-for-r), or
+you can use a pre-built binary:
+
+{% highlight r %}
+cran <- getOption("repos")
+cran["dmlc"] <- "https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/R/CRAN/"
+options(repos = cran)
+install.packages("mxnet")
+{% endhighlight %}
diff --git a/docs/static_site/src/_includes/get_started/macos/r/gpu.md b/docs/static_site/src/_includes/get_started/macos/r/gpu.md
new file mode 100644
index 000000000000..3fc556a8dd91
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/macos/r/gpu.md
@@ -0,0 +1 @@
+Be the first one to contribute this guide!  
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/macos/scala/cpu.md b/docs/static_site/src/_includes/get_started/macos/scala/cpu.md
new file mode 100644
index 000000000000..623a8a56fb4d
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/macos/scala/cpu.md
@@ -0,0 +1,14 @@
+You can use the Maven packages defined in the following dependency to include MXNet in your Scala
+project. Please refer to the [MXNet-Scala setup guide](scala_setup.html) for a detailed set
+of instructions to help you with the setup process.
+
+<a href="https://mvnrepository.com/artifact/org.apache.mxnet/mxnet-full_2.11-osx-x86_64-cpu"><img
+        src="https://img.shields.io/badge/org.apache.mxnet-mac cpu-green.svg"
+        alt="maven badge"/></a>
+
+{% highlight html %}
+    <dependency>
+    <groupId>org.apache.mxnet</groupId>
+    <artifactId>mxnet-full_2.11-osx-x86_64-cpu</artifactId>
+</dependency>
+{% endhighlight %}
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/macos/scala/gpu.md b/docs/static_site/src/_includes/get_started/macos/scala/gpu.md
new file mode 100644
index 000000000000..b17ef33478ea
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/macos/scala/gpu.md
@@ -0,0 +1 @@
+Not available at this time.
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/pip_snippet.md b/docs/static_site/src/_includes/get_started/pip_snippet.md
new file mode 100644
index 000000000000..e67c3331c91c
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/pip_snippet.md
@@ -0,0 +1,14 @@
+MXNet offers MKL pip packages that will be much faster when running on Intel hardware.
+Check the chart below for other options, refer to <a href="https://pypi.org/project/mxnet/">PyPI for
+other MXNet pip packages</a>, or <a href="validate_mxnet.html">validate your MXNet installation</a>.
+
+<div style="text-align: center">
+    <img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/install/pip-packages.png"
+    alt="pip packages"/>
+</div>
+
+**NOTES:**
+
+*mxnet-cu101mkl* means the package is built with CUDA/cuDNN and MKL-DNN enabled and the CUDA version is 10.1.
+
+All MKL pip packages are experimental prior to version 1.3.0.
diff --git a/docs/static_site/src/_includes/get_started/windows/clojure/clojure.md b/docs/static_site/src/_includes/get_started/windows/clojure/clojure.md
new file mode 100644
index 000000000000..0b25ab9018d3
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/windows/clojure/clojure.md
@@ -0,0 +1 @@
+MXNet-Clojure for Windows is not yet available.
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/windows/cpp/cpp.md b/docs/static_site/src/_includes/get_started/windows/cpp/cpp.md
new file mode 100644
index 000000000000..b2e632b0f117
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/windows/cpp/cpp.md
@@ -0,0 +1,3 @@
+To enable the C++ package, build from source using `make USE_CPP_PACKAGE=1`.
+Refer to the [MXNet C++ setup guide](c_plus_plus) for full instructions.
+                
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/windows/java/java.md b/docs/static_site/src/_includes/get_started/windows/java/java.md
new file mode 100644
index 000000000000..0db1f50590a2
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/windows/java/java.md
@@ -0,0 +1 @@
+MXNet-Java for Windows is not yet available.
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/windows/julia/build-from-source.md b/docs/static_site/src/_includes/get_started/windows/julia/build-from-source.md
new file mode 100644
index 000000000000..cdc185416329
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/windows/julia/build-from-source.md
@@ -0,0 +1 @@
+Refer to the [Julia section of the MXNet Windows installation guide](windows_setup.html#install-the-mxnet-package-for-julia).
diff --git a/docs/static_site/src/_includes/get_started/windows/julia/pkg.md b/docs/static_site/src/_includes/get_started/windows/julia/pkg.md
new file mode 100644
index 000000000000..cb79177e5bbe
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/windows/julia/pkg.md
@@ -0,0 +1,10 @@
+Install a pinned version of MXNet like this:
+
+{% highlight julia %}
+]add MXNet#v1.5.0
+{% endhighlight %}
+
+Or directly install the latest release:
+{% highlight julia %}
+]add MXNet
+{% endhighlight %}
diff --git a/docs/static_site/src/_includes/get_started/windows/perl/perl.md b/docs/static_site/src/_includes/get_started/windows/perl/perl.md
new file mode 100644
index 000000000000..2655f7281486
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/windows/perl/perl.md
@@ -0,0 +1 @@
+Refer to the [Perl section of the MXNet Windows installation guide](windows_setup.html#install-the-mxnet-package-for-perl).
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/windows/python/cpu/build-from-source.md b/docs/static_site/src/_includes/get_started/windows/python/cpu/build-from-source.md
new file mode 100644
index 000000000000..a527f51d919d
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/windows/python/cpu/build-from-source.md
@@ -0,0 +1 @@
+Refer to the [MXNet Windows installation guide](windows_setup.html)
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/windows/python/cpu/docker.md b/docs/static_site/src/_includes/get_started/windows/python/cpu/docker.md
new file mode 100644
index 000000000000..a3df5a92aa99
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/windows/python/cpu/docker.md
@@ -0,0 +1,34 @@
+Docker images with *MXNet* are available at [Docker Hub](https://hub.docker.com/r/mxnet/).
+
+**Step 1** Install Docker on your machine by following the docker installation instructions
+
+*Note* - You can install Community Edition (CE) to get started with *MXNet*.
+
+**Step 2** Pull the MXNet docker image.
+
+{% highlight bash %}
+$ docker pull mxnet/python # Use sudo if you skip Step 2
+{% endhighlight %}
+
+You can list docker images to see if mxnet/python docker image pull was successful.
+
+{% highlight bash %}
+$ docker images # Use sudo if you skip Step 2
+
+REPOSITORY TAG IMAGE ID CREATED SIZE
+mxnet/python latest 00d026968b3c 3 weeks ago 1.41 GB
+{% endhighlight %}
+
+Using the latest MXNet with [Intel MKL-DNN](https://github.com/intel/mkl-dnn) is
+recommended for the
+fastest inference speeds with MXNet.
+
+{% highlight bash %}
+$ docker pull mxnet/python:1.3.0_cpu_mkl # Use sudo if you skip Step 2
+$ docker images # Use sudo if you skip Step 2
+
+REPOSITORY TAG IMAGE ID CREATED SIZE
+mxnet/python 1.3.0_cpu_mkl deaf9bf61d29 4 days ago 678 MB
+{% endhighlight %}
+
+**Step 4** <a href="validate_mxnet.html">Validate the installation</a>.
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/windows/python/cpu/pip.md b/docs/static_site/src/_includes/get_started/windows/python/cpu/pip.md
new file mode 100644
index 000000000000..d5c7f1fd08f0
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/windows/python/cpu/pip.md
@@ -0,0 +1,73 @@
+Run the following command:
+
+<div class="v1-5-0">
+
+{% highlight bash %}
+$ pip install mxnet
+{% endhighlight %}
+
+</div> <!-- End of v1-5-0 -->
+<div class="v1-4-1">
+
+{% highlight bash %}
+$ pip install mxnet==1.4.1
+{% endhighlight %}
+
+</div> <!-- End of v1-4-1 -->
+<div class="v1-3-1">
+
+{% highlight bash %}
+$ pip install mxnet==1.3.1
+{% endhighlight %}
+
+</div> <!-- End of v1-3-1 -->
+<div class="v1-2-1">
+
+{% highlight bash %}
+$ pip install mxnet==1.2.1
+{% endhighlight %}
+
+</div> <!-- End of v1-2-1 -->
+
+<div class="v1-1-0">
+
+{% highlight bash %}
+$ pip install mxnet==1.1.0
+{% endhighlight %}
+
+</div> <!-- End of v1-1-0-->
+
+<div class="v1-0-0">
+
+{% highlight bash %}
+$ pip install mxnet==1.0.0
+{% endhighlight %}
+
+</div> <!-- End of v1-0-0-->
+
+<div class="v0-12-1">
+
+{% highlight bash %}
+$ pip install mxnet==0.12.1
+{% endhighlight %}
+
+</div> <!-- End of v0-12-1-->
+
+<div class="v0-11-0">
+
+{% highlight bash %}
+$ pip install mxnet==0.11.0
+{% endhighlight %}
+
+</div> <!-- End of v0-11-0-->
+
+<div class="master">
+
+{% highlight bash %}
+$ pip install mxnet --pre
+{% endhighlight %}
+
+</div> <!-- End of master-->
+
+{% include /get_started/pip_snippet.md %}
+{% include /get_started/gpu_snippet.md %}
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/windows/python/gpu/build-from-source.md b/docs/static_site/src/_includes/get_started/windows/python/gpu/build-from-source.md
new file mode 100644
index 000000000000..27e90fad4f5e
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/windows/python/gpu/build-from-source.md
@@ -0,0 +1 @@
+To build from source, refer to the [MXNet Windows installation guide](windows_setup.html).
diff --git a/docs/static_site/src/_includes/get_started/windows/python/gpu/pip.md b/docs/static_site/src/_includes/get_started/windows/python/gpu/pip.md
new file mode 100644
index 000000000000..cbcd9d44d6af
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/windows/python/gpu/pip.md
@@ -0,0 +1,74 @@
+Run the following command:
+
+<div class="v1-5-0">
+
+{% highlight bash %}
+$ pip install mxnet-cu101
+{% endhighlight %}
+
+</div> <!-- End of v1-5-0 -->
+<div class="v1-4-1">
+
+{% highlight bash %}
+$ pip install mxnet-cu101==1.4.1
+{% endhighlight %}
+
+</div> <!-- End of v1-4-1 -->
+<div class="v1-3-1">
+
+{% highlight bash %}
+$ pip install mxnet-cu92==1.3.1
+{% endhighlight %}
+
+</div> <!-- End of v1-3-1 -->
+<div class="v1-2-1">
+
+{% highlight bash %}
+$ pip install mxnet-cu92==1.2.1
+{% endhighlight %}
+
+</div> <!-- End of v1-2-1 -->
+
+<div class="v1-1-0">
+
+{% highlight bash %}
+$ pip install mxnet-cu91==1.1.0
+{% endhighlight %}
+
+</div> <!-- End of v1-1-0-->
+
+<div class="v1-0-0">
+
+{% highlight bash %}
+$ pip install mxnet-cu90==1.0.0
+{% endhighlight %}
+
+</div> <!-- End of v1-0-0-->
+
+<div class="v0-12-1">
+
+{% highlight bash %}
+$ pip install mxnet-cu90==0.12.1
+{% endhighlight %}
+
+</div> <!-- End of v0-12-1-->
+
+<div class="v0-11-0">
+
+{% highlight bash %}
+$ pip install mxnet-cu80==0.11.0
+{% endhighlight %}
+
+</div> <!-- End of v0-11-0-->
+
+<div class="master">
+
+{% highlight bash %}
+$ pip install mxnet-cu101 --pre
+{% endhighlight %}
+
+</div> <!-- End of master-->
+
+
+{% include /get_started/pip_snippet.md %}
+{% include /get_started/gpu_snippet.md %}
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/windows/r/cpu.md b/docs/static_site/src/_includes/get_started/windows/r/cpu.md
new file mode 100644
index 000000000000..8b7151f9b94a
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/windows/r/cpu.md
@@ -0,0 +1,15 @@
+Note: packages for 3.6.x are not yet available.
+Install 3.5.x of R from [CRAN](https://cran.r-project.org/bin/windows/base/old/).
+
+You can [build MXNet-R from source](windows_setup.html#install-mxnet-package-for-r), or
+you can use a
+pre-built binary:
+
+{% highlight r %}
+cran <- getOption("repos")
+cran["dmlc"] <- "https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/R/CRAN/"
+options(repos = cran)
+install.packages("mxnet")
+{% endhighlight %}
+
+To run MXNet you also should have OpenCV and OpenBLAS installed.
diff --git a/docs/static_site/src/_includes/get_started/windows/r/gpu.md b/docs/static_site/src/_includes/get_started/windows/r/gpu.md
new file mode 100644
index 000000000000..b08a56986cca
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/windows/r/gpu.md
@@ -0,0 +1,16 @@
+You can [build MXNet-R from source](windows_setup.html#install-mxnet-package-for-r), or
+you can use a
+pre-built binary:
+
+{% highlight r %}
+cran <- getOption("repos")
+cran["dmlc"] <-
+"https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/R/CRAN/GPU/cu92"
+options(repos = cran)
+install.packages("mxnet")
+{% endhighlight %}
+
+Change cu92 to cu90, cu91 or cuda100 based on your CUDA toolkit version. Currently, MXNet supports these versions of CUDA.
+Note : You also need to have cuDNN installed on Windows. Check out this
+[guide](https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installwindows)
+on the steps for installation.
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/windows/scala/scala.md b/docs/static_site/src/_includes/get_started/windows/scala/scala.md
new file mode 100644
index 000000000000..74b7d45c6d79
--- /dev/null
+++ b/docs/static_site/src/_includes/get_started/windows/scala/scala.md
@@ -0,0 +1 @@
+MXNet-Scala for Windows is not yet available.
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/google-analytics.html b/docs/static_site/src/_includes/google-analytics.html
new file mode 100644
index 000000000000..e9afb2093b55
--- /dev/null
+++ b/docs/static_site/src/_includes/google-analytics.html
@@ -0,0 +1,12 @@
+<script>
+if(!(window.doNotTrack === "1" || navigator.doNotTrack === "1" || navigator.doNotTrack === "yes" || navigator.msDoNotTrack === "1")) {
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
+  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+  })(window,document,'script','https://www.google-analytics.com/analytics.js','ga');
+
+  ga('create', '{{ site.google_analytics }}', 'auto');
+  ga('send', 'pageview');
+}
+</script>
+  
diff --git a/docs/static_site/src/_includes/head.html b/docs/static_site/src/_includes/head.html
new file mode 100644
index 000000000000..9a565c756c07
--- /dev/null
+++ b/docs/static_site/src/_includes/head.html
@@ -0,0 +1,18 @@
+<head>
+  <meta charset="utf-8">
+  <meta http-equiv="X-UA-Compatible" content="IE=edge">
+  <meta name="viewport" content="width=device-width, initial-scale=1">
+  <link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
+  {%- seo -%}
+  <script src="https://medium-widget.pixelpoint.io/widget.js"></script>
+  <link rel="stylesheet" href="{{ "/assets/main.css" | relative_url }}">
+  {%- feed_meta -%}
+  {%- if jekyll.environment == 'production' and site.google_analytics -%}
+    {%- include google-analytics.html -%}
+  {%- endif -%}
+
+  <script src="https://ajax.googleapis.com/ajax/libs/jquery/3.3.1/jquery.min.js"></script>
+
+  <script src="{{'/assets/js/clipboard.js'|relative_url}}"></script>
+  <script src="{{'/assets/js/copycode.js'|relative_url}}"></script>
+</head>
diff --git a/docs/static_site/src/_includes/header.html b/docs/static_site/src/_includes/header.html
new file mode 100644
index 000000000000..314506476985
--- /dev/null
+++ b/docs/static_site/src/_includes/header.html
@@ -0,0 +1,49 @@
+<header class="site-header" role="banner">
+
+  <script>
+    $(document).ready(function () {
+
+      // HEADER OPACITY LOGIC
+
+      function opacity_header() {
+        var value = "rgba(4,140,204," + ($(window).scrollTop() / 300 + 0.4) + ")"
+        $('.site-header').css("background-color", value)
+      }
+
+      $(window).scroll(function () {
+        opacity_header()
+      })
+      opacity_header();
+
+      // MENU SELECTOR LOGIC
+      $('.page-link').each( function () {
+        if (window.location.href.includes(this.href)) {
+          $(this).addClass("page-current");
+        }
+      });
+    })
+  </script>
+  <div class="wrapper">
+    <a class="site-title" rel="author" href="{{ '/' | relative_url }}"><img
+            src="{{'/assets/img/mxnet_logo.png' | relative_url }}" class="site-header-logo"></a>
+    <nav class="site-nav">
+      <input type="checkbox" id="nav-trigger" class="nav-trigger"/>
+      <label for="nav-trigger">
+          <span class="menu-icon">
+            <svg viewBox="0 0 18 15" width="18px" height="15px">
+              <path d="M18,1.484c0,0.82-0.665,1.484-1.484,1.484H1.484C0.665,2.969,0,2.304,0,1.484l0,0C0,0.665,0.665,0,1.484,0 h15.032C17.335,0,18,0.665,18,1.484L18,1.484z M18,7.516C18,8.335,17.335,9,16.516,9H1.484C0.665,9,0,8.335,0,7.516l0,0 c0-0.82,0.665-1.484,1.484-1.484h15.032C17.335,6.031,18,6.696,18,7.516L18,7.516z M18,13.516C18,14.335,17.335,15,16.516,15H1.484 C0.665,15,0,14.335,0,13.516l0,0c0-0.82,0.665-1.483,1.484-1.483h15.032C17.335,12.031,18,12.695,18,13.516L18,13.516z"/>
+            </svg>
+          </span>
+      </label>
+
+      <div class="trigger">
+        <a class="page-link" href="{{'/get_started' | relative_url }}">Get Started</a>
+        <a class="page-link" href="{{'/blog' | relative_url }}">Blog</a>
+        <a class="page-link" href="{{'/features' | relative_url }}">Features</a>
+        <a class="page-link" href="{{'/ecosystem' | relative_url }}">Ecosystem</a>
+        <a class="page-link" href="{{'/api' | relative_url }}">Docs & Tutorials</a>
+        <a class="page-link" href="https://github.com/apache/incubator-mxnet">GitHub</a>
+      </div>
+    </nav>
+  </div>
+</header>
diff --git a/docs/static_site/src/_includes/icon-github.html b/docs/static_site/src/_includes/icon-github.html
new file mode 100644
index 000000000000..e501a16b1878
--- /dev/null
+++ b/docs/static_site/src/_includes/icon-github.html
@@ -0,0 +1 @@
+<a href="https://github.com/{{ include.username }}"><span class="icon icon--github">{% include icon-github.svg %}</span><span class="username">{{ include.username }}</span></a>
diff --git a/docs/static_site/src/_includes/icon-github.svg b/docs/static_site/src/_includes/icon-github.svg
new file mode 100644
index 000000000000..e6c5f6dfd6c4
--- /dev/null
+++ b/docs/static_site/src/_includes/icon-github.svg
@@ -0,0 +1 @@
+<svg viewBox="0 0 16 16" width="16px" height="16px"><path fill="#828282" d="M7.999,0.431c-4.285,0-7.76,3.474-7.76,7.761 c0,3.428,2.223,6.337,5.307,7.363c0.388,0.071,0.53-0.168,0.53-0.374c0-0.184-0.007-0.672-0.01-1.32 c-2.159,0.469-2.614-1.04-2.614-1.04c-0.353-0.896-0.862-1.135-0.862-1.135c-0.705-0.481,0.053-0.472,0.053-0.472 c0.779,0.055,1.189,0.8,1.189,0.8c0.692,1.186,1.816,0.843,2.258,0.645c0.071-0.502,0.271-0.843,0.493-1.037 C4.86,11.425,3.049,10.76,3.049,7.786c0-0.847,0.302-1.54,0.799-2.082C3.768,5.507,3.501,4.718,3.924,3.65 c0,0,0.652-0.209,2.134,0.796C6.677,4.273,7.34,4.187,8,4.184c0.659,0.003,1.323,0.089,1.943,0.261 c1.482-1.004,2.132-0.796,2.132-0.796c0.423,1.068,0.157,1.857,0.077,2.054c0.497,0.542,0.798,1.235,0.798,2.082 c0,2.981-1.814,3.637-3.543,3.829c0.279,0.24,0.527,0.713,0.527,1.437c0,1.037-0.01,1.874-0.01,2.129 c0,0.208,0.14,0.449,0.534,0.373c3.081-1.028,5.302-3.935,5.302-7.362C15.76,3.906,12.285,0.431,7.999,0.431z"/></svg>
diff --git a/docs/static_site/src/_includes/icon-twitter.html b/docs/static_site/src/_includes/icon-twitter.html
new file mode 100644
index 000000000000..e623dbd6efc5
--- /dev/null
+++ b/docs/static_site/src/_includes/icon-twitter.html
@@ -0,0 +1 @@
+<a href="https://twitter.com/{{ include.username }}"><span class="icon icon--twitter">{% include icon-twitter.svg %}</span><span class="username">{{ include.username }}</span></a>
diff --git a/docs/static_site/src/_includes/icon-twitter.svg b/docs/static_site/src/_includes/icon-twitter.svg
new file mode 100644
index 000000000000..efc0ecf6781d
--- /dev/null
+++ b/docs/static_site/src/_includes/icon-twitter.svg
@@ -0,0 +1 @@
+<svg viewBox="0 0 16 16" width="16px" height="16px"><path fill="#828282" d="M15.969,3.058c-0.586,0.26-1.217,0.436-1.878,0.515c0.675-0.405,1.194-1.045,1.438-1.809c-0.632,0.375-1.332,0.647-2.076,0.793c-0.596-0.636-1.446-1.033-2.387-1.033c-1.806,0-3.27,1.464-3.27,3.27 c0,0.256,0.029,0.506,0.085,0.745C5.163,5.404,2.753,4.102,1.14,2.124C0.859,2.607,0.698,3.168,0.698,3.767 c0,1.134,0.577,2.135,1.455,2.722C1.616,6.472,1.112,6.325,0.671,6.08c0,0.014,0,0.027,0,0.041c0,1.584,1.127,2.906,2.623,3.206 C3.02,9.402,2.731,9.442,2.433,9.442c-0.211,0-0.416-0.021-0.615-0.059c0.416,1.299,1.624,2.245,3.055,2.271 c-1.119,0.877-2.529,1.4-4.061,1.4c-0.264,0-0.524-0.015-0.78-0.046c1.447,0.928,3.166,1.469,5.013,1.469 c6.015,0,9.304-4.983,9.304-9.304c0-0.142-0.003-0.283-0.009-0.423C14.976,4.29,15.531,3.714,15.969,3.058z"/></svg>
diff --git a/docs/static_site/src/_includes/social.html b/docs/static_site/src/_includes/social.html
new file mode 100644
index 000000000000..474404ec8cdd
--- /dev/null
+++ b/docs/static_site/src/_includes/social.html
@@ -0,0 +1,14 @@
+<ul class="social-media-list">
+  {%- if site.dribbble_username -%}<li><a href="https://dribbble.com/{{ site.dribbble_username| cgi_escape | escape }}"><svg class="svg-icon"><use xlink:href="{{ '/assets/minima-social-icons.svg#dribbble' | relative_url }}"></use></svg> <span class="username">{{ site.dribbble_username| escape }}</span></a></li>{%- endif -%}
+  {%- if site.facebook_username -%}<li><a href="https://www.facebook.com/{{ site.facebook_username| cgi_escape | escape }}"><svg class="svg-icon"><use xlink:href="{{ '/assets/minima-social-icons.svg#facebook' | relative_url }}"></use></svg> <span class="username">{{ site.facebook_username| escape }}</span></a></li>{%- endif -%}
+  {%- if site.flickr_username -%}<li><a href="https://www.flickr.com/photos/{{ site.flickr_username| cgi_escape | escape }}"><svg class="svg-icon"><use xlink:href="{{ '/assets/minima-social-icons.svg#flickr' | relative_url }}"></use></svg> <span class="username">{{ site.flickr_username| escape }}</span></a></li>{%- endif -%}
+  {%- if site.github_username -%}<li><a href="https://github.com/{{ site.github_username }}"><svg class="svg-icon"><use xlink:href="{{ '/assets/minima-social-icons.svg#github' | relative_url }}"></use></svg> <span class="username">{{ site.github_username| escape }}</span></a></li>{%- endif -%}
+  {%- if site.instagram_username -%}<li><a href="https://instagram.com/{{ site.instagram_username| cgi_escape | escape }}"><svg class="svg-icon"><use xlink:href="{{ '/assets/minima-social-icons.svg#instagram' | relative_url }}"></use></svg> <span class="username">{{ site.instagram_username| escape }}</span></a></li>{%- endif -%}
+  {%- if site.linkedin_username -%}<li><a href="https://www.linkedin.com/in/{{ site.linkedin_username| cgi_escape | escape }}"><svg class="svg-icon"><use xlink:href="{{ '/assets/minima-social-icons.svg#linkedin' | relative_url }}"></use></svg> <span class="username">{{ site.linkedin_username| escape }}</span></a></li>{%- endif -%}
+  {%- if site.pinterest_username -%}<li><a href="https://www.pinterest.com/{{ site.pinterest_username| cgi_escape | escape }}"><svg class="svg-icon"><use xlink:href="{{ '/assets/minima-social-icons.svg#pinterest' | relative_url }}"></use></svg> <span class="username">{{ site.pinterest_username| escape }}</span></a></li>{%- endif -%}
+  {%- for mst in site.mastodon -%}{%- if mst.username and mst.instance -%}<li><a href="https://{{ mst.instance| cgi_escape | escape}}/@{{mst.username}}"><svg class="svg-icon"><use xlink:href="{{ '/assets/minima-social-icons.svg#mastodon' | relative_url }}"></use></svg> <span class="username">{{ mst.username|escape }}</span></a></li>{%- endif -%}{%- endfor -%}
+  {%- if site.twitter_username -%}<li><a href="https://www.twitter.com/{{ site.twitter_username| cgi_escape | escape }}"><svg class="svg-icon"><use xlink:href="{{ '/assets/minima-social-icons.svg#twitter' | relative_url }}"></use></svg> <span class="username">{{ site.twitter_username| escape }}</span></a></li>{%- endif -%}
+  {%- if site.youtube_username -%}<li><a href="https://youtube.com/{{ site.youtube_username| cgi_escape | escape }}"><svg class="svg-icon"><use xlink:href="{{ '/assets/minima-social-icons.svg#youtube' | relative_url }}"></use></svg> <span class="username">{{ site.youtube_username| escape }}</span></a></li>{%- endif -%}
+  {%- if site.googleplus_username -%}<li><a href="https://plus.google.com/{{ site.googleplus_username| escape }}"><svg class="svg-icon"><use xlink:href="{{ '/assets/minima-social-icons.svg#googleplus' | relative_url }}"></use></svg> <span class="username">{{ site.googleplus_username| escape }}</span></a></li>{%- endif -%}
+  {%- if site.rss -%}<li><a href="{{ 'feed.xml' | relative_url }}"><svg class="svg-icon"><use xlink:href="{{ '/assets/minima-social-icons.svg#rss' | relative_url }}"></use></svg> <span>{{ site.rss | escape }}</span></a></li>{%- endif -%}
+</ul>
diff --git a/docs/static_site/src/_layouts/default.html b/docs/static_site/src/_layouts/default.html
new file mode 100644
index 000000000000..2b6894ef28c6
--- /dev/null
+++ b/docs/static_site/src/_layouts/default.html
@@ -0,0 +1,18 @@
+<!DOCTYPE html>
+<html lang="{{ page.lang | default: site.lang | default: " en" }}">
+
+{%- include head.html -%}
+
+<body>
+
+{%- include header.html -%}
+
+<main class="page-content" aria-label="Content">
+    {{ content }}
+</main>
+
+{%- include footer.html -%}
+
+</body>
+
+</html>
diff --git a/docs/static_site/src/_layouts/home.html b/docs/static_site/src/_layouts/home.html
new file mode 100644
index 000000000000..9fffdd9e5fb3
--- /dev/null
+++ b/docs/static_site/src/_layouts/home.html
@@ -0,0 +1,177 @@
+<!DOCTYPE html>
+<html lang="{{ page.lang | default: site.lang | default: " en" }}">
+
+{%- include head.html -%}
+
+<body>
+
+{%- include header.html -%}
+
+<main class="page-content-home" aria-label="Content">
+    <div class="wrapper">
+        <div class="home">
+            <h1 style="margin-bottom:40px;"> A FLEXIBLE AND EFFICIENT<br> <span
+                    style="margin-left:100px"> LIBRARY FOR DEEP LEARNING</span>
+            </h1>
+
+            <p style="margin-bottom:40px;">A truly open source deep learning framework suited<br>for flexible research
+                prototyping and
+                production.</p>
+            <div>
+                <a href="{{'/get_started' | relative_url }}" class="btn" style="float:left; margin-bottom:20px; margin-top:0px">Get Started
+                    <span class="span-accented">›</span></a>
+            </div>
+
+        </div>
+
+
+    </div>
+    </div>
+    <div class="key-features-section section">
+        <div class="wrapper">
+            <div class="row">
+                <div class="col-8">
+                    <h2>Key Features &amp;<br>Capabilities</h2>
+                </div>
+                <div class="col-4">
+                    <div>
+                        <a href="{{'/features' | relative_url}}" class="btn btn-action">All Features <span class="span-accented">›</span></a>
+                    </div>
+                </div>
+            </div>
+
+            <div class="row key-features">
+                {{ layout.landing }}
+                {%- for feature in page.key_features -%}
+                <div class="col-3">
+                    <div class="card">
+                        <div class="card-text">
+                            <h3>{{feature.title}}</h3>
+                            <p>{{feature.text}}</p>
+                        </div>
+                        <div class="key-feature-image">
+                            <img width=50px src="{{feature.icon | relative_url}}">
+                        </div>
+                    </div>
+                </div>
+                {%- endfor -%}
+            </div>
+
+        </div>
+    </div>
+    <div class="ecosystem-section section">
+        <div class="wrapper">
+            <div class="row">
+                <div class="col-8">
+                    <h2>Ecosystem </h2>
+                </div>
+                <div class="col-4">
+                    <div>
+                        <a href="{{'/ecosystem' | relative_url}}" class="btn btn-action">All Projects <span
+                                class="span-accented">›</span></a>
+                    </div>
+                </div>
+                <div class="col-8">
+                    <p>Explore a rich ecosystem of libraries, tools, and more to support development.</p>
+                </div>
+            </div>
+
+            <div class="row">
+                {%- for feature in page.ecosystem -%}
+                <div class="col-3">
+                    <div class="card">
+                        <a href="{{feature.link}}">
+                            <div class="card-text">
+                                <div class="card-header-title">
+                                    <h3>{{feature.title}}</h3>
+                                    <img src="{{feature.icon | relative_url}}">
+                                </div>
+                                <p class="card-summary">{{feature.text}}</p>
+                            </div>
+                        </a>
+                    </div>
+                </div>
+                {%- endfor -%}
+            </div>
+        </div>
+    </div>
+
+    <div class="news-section section ">
+        <div class="wrapper">
+            <div class="row">
+                <div class="col-8">
+                    <h2>Latest News</h2>
+                </div>
+                <div class="col-4">
+                    <div>
+                        <a href="{{'/blog' | relative_url}}" class="btn btn-action">More Blog Posts <span
+                                class="span-accented">›</span></a>
+                    </div>
+                </div>
+                <div class="col-8">
+                    <p>Follow our blog post publications to get the latest news on MXNet and its ecosystem</p>
+                </div>
+            </div>
+
+            <div id="medium-widget"></div>
+            <script>
+                MediumWidget.Init({renderTo: '#medium-widget',
+                    params: {
+                        "resource": "https://medium.com/apache-mxnet",
+                        "postsPerLine": 3,
+                        "limit": 3,
+                        "picture": "big",
+                        "fields": ["description", "claps", "publishAt"],
+                        "ratio": "landscape"
+                    }
+                })
+            </script>
+        </div>
+    </div>
+
+    <div class="community-section section ">
+        <div class="wrapper">
+            <div class="row">
+                <div class="col-6">
+                    <h2>Community</h2>
+                </div>
+                <div class="col-8">
+                    <p>Join the MXNet scientific community to contribute, learn, and get
+                        answers to your questions.</p>
+                </div>
+            </div>
+
+            <div class="row">
+                {%- for feature in page.community -%}
+                <div class="col-4">
+                    <div class="card">
+                        <a href="{{feature.link}}">
+                            <div>
+                                <div class="card-header-title">
+                                    <h3>{{feature.title}}</h3>
+                                    <img src="{{feature.icon | relative_url}}">
+                                </div>
+                                <p class="card-summary">{{feature.text}}</p>
+                            </div>
+                        </a>
+                    </div>
+                </div>
+                {%- endfor -%}
+            </div>
+        </div>
+    </div>
+    <div class="trusted-by-section section ">
+        <div class="wrapper">
+            <div class="trusted-by">
+                <h2>Already trusted by</h2>
+                <img src="{{'/assets/img/logos.png' | relative_url}}">
+            </div>
+        </div>
+    </div>
+</main>
+
+{%- include footer.html -%}
+
+</body>
+
+</html>
diff --git a/docs/static_site/src/_layouts/page.html b/docs/static_site/src/_layouts/page.html
new file mode 100644
index 000000000000..b6f0b9ac104d
--- /dev/null
+++ b/docs/static_site/src/_layouts/page.html
@@ -0,0 +1,25 @@
+---
+layout: default
+---
+<script>
+
+</script>
+<article class="post">
+
+    <header class="post-header wrapper">
+        <h1 class="post-title">{{ page.title | escape }}</h1>
+        <h3>{{page.subtitle}}</h3>
+
+        {%- if page.action -%}
+        <a style="float:left; margin-top:20px" href="{{page.action_url | relative_url}}" class="btn btn-action">{{page.action}}
+            <span class="span-accented">›</span></a>
+        {%- endif -%}
+    </header>
+
+    <div class="post-content">
+        <div class="wrapper">
+            {{ content }}
+        </div>
+    </div>
+
+</article>
diff --git a/docs/static_site/src/_layouts/page_api.html b/docs/static_site/src/_layouts/page_api.html
new file mode 100644
index 000000000000..bd9d5534c68b
--- /dev/null
+++ b/docs/static_site/src/_layouts/page_api.html
@@ -0,0 +1,52 @@
+---
+layout: page
+---
+<div class="row">
+    <div class="col-3 docs-side-bar">
+
+        {% for p in site.pages %}
+        {% if p.tag == 'main_docs' %}
+        {% for doc in p.docs %}
+        {% if doc.tag == page.tag %}
+        <div class="docs-card docs-side">
+            <ul>
+                <div class="docs-action-btn">
+                    <a href="{{doc.guide_link | relative_url}}"> <img src="{{'assets/img/compass.svg' | relative_url}}"
+                                                                      class="docs-logo-docs">{{doc.title}} Guide <span
+                            class="span-accented">›</span></a>
+                </div>
+                <div class="docs-action-btn">
+                    <a href="{{doc.tutorial_link | relative_url}}"> <img
+                            src="{{'assets/img/video-tutorial.svg' | relative_url}}" class="docs-logo-docs">{{doc.title}}
+                        Tutorials <span class="span-accented">›</span></a>
+                </div>
+                <div class="docs-action-btn">
+                    <a href="{{doc.api_link | relative_url}}"> <img src="{{'assets/img/api.svg' | relative_url}}"
+                                                                    class="docs-logo-docs">{{doc.title}} API Reference
+                        <span class="span-accented">›</span></a>
+                </div>
+
+                <!-- Let's show the list of tutorials -->
+                <br>
+                {% if page.is_tutorial == true %}
+                <h3>Tutorials</h3>
+                {% for p in site.pages %}
+                {% if p.is_tutorial == true %}
+                {% if page.tag == p.tag %}
+                <li><a href="{{ p.url  | relative_url }}">{{ p.title }}</a></li>
+                {% endif %}  <!-- page-category -->
+                {% endif %}   <!-- resource-p -->
+                {% endfor %}  <!-- page -->
+                {% endif %}
+            </ul>
+        </div>
+        {% endif %}
+        {% endfor %}
+        {% endif %}   <!-- resource-p -->
+        {% endfor %}  <!-- page -->
+        </ul>
+    </div>
+    <div class="col-9">
+        {{ content }}
+    </div>
+</div>
diff --git a/docs/static_site/src/_layouts/page_category.html b/docs/static_site/src/_layouts/page_category.html
new file mode 100644
index 000000000000..9033d9a21399
--- /dev/null
+++ b/docs/static_site/src/_layouts/page_category.html
@@ -0,0 +1,18 @@
+---
+layout: page
+---
+<div class="row">
+    <div class="col-3 docs-side-bar">
+        <h3 style="text-transform: capitalize; padding-left:10px">{{page.category}}</h3>
+        <ul>
+            {% for p in site.pages %}
+            {% if p.category == page.category %}
+            <li><a href="{{ p.url  | relative_url }}">{{ p.title }}</a></li>
+            {% endif %}  <!-- page-category -->
+            {% endfor %}   <!-- resource-p -->
+        </ul>
+    </div>
+    <div class="col-9">
+        {{ content }}
+    </div>
+</div>
diff --git a/docs/static_site/src/_layouts/page_landing_tutorials.html b/docs/static_site/src/_layouts/page_landing_tutorials.html
new file mode 100644
index 000000000000..53c3dc399e07
--- /dev/null
+++ b/docs/static_site/src/_layouts/page_landing_tutorials.html
@@ -0,0 +1,13 @@
+---
+layout: page_api
+---
+<h2>List of available tutorials</h2>
+<ul>
+    {% for p in site.pages %}
+    {% if p.is_tutorial == true %}
+    {% if page.tag == p.tag %}
+    <li><a href="{{ p.url | relative_url}}">{{ p.title }}</a></li>
+    {% endif %}  <!-- page-category -->
+    {% endif %}   <!-- resource-p -->
+    {% endfor %}  <!-- page -->
+</ul>
diff --git a/docs/static_site/src/_layouts/post.html b/docs/static_site/src/_layouts/post.html
new file mode 100644
index 000000000000..b36f5af590b0
--- /dev/null
+++ b/docs/static_site/src/_layouts/post.html
@@ -0,0 +1,29 @@
+---
+layout: default
+---
+<article class="post h-entry" itemscope itemtype="http://schema.org/BlogPosting">
+
+    <header class="post-header">
+        <h1 class="post-title p-name" itemprop="name headline">{{ page.title | escape }}</h1>
+        <p class="post-meta">
+            <time class="dt-published" datetime="{{ page.date | date_to_xmlschema }}" itemprop="datePublished">
+                {%- assign date_format = site.minima.date_format | default: "%b %-d, %Y" -%}
+                {{ page.date | date: date_format }}
+            </time>
+            {%- if page.author -%}
+            • <span itemprop="author" itemscope itemtype="http://schema.org/Person"><span class="p-author h-card"
+                                                                                          itemprop="name">{{ page.author }}</span></span>
+            {%- endif -%}
+        </p>
+    </header>
+
+    <div class="post-content e-content" itemprop="articleBody">
+        {{ content }}
+    </div>
+
+    {%- if site.disqus.shortname -%}
+    {%- include disqus_comments.html -%}
+    {%- endif -%}
+
+    <a class="u-url" href="{{ page.url | relative_url }}" hidden></a>
+</article>
diff --git a/docs/static_site/src/_plugins/markdowner.rb b/docs/static_site/src/_plugins/markdowner.rb
new file mode 100644
index 000000000000..ef4ccb0a701b
--- /dev/null
+++ b/docs/static_site/src/_plugins/markdowner.rb
@@ -0,0 +1,30 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+module Jekyll
+  class MarkdownBlock < Liquid::Block
+    def initialize(tag_name, text, tokens)
+      super
+    end
+    require "kramdown"
+    def render(context)
+      content = super
+      "#{Kramdown::Document.new(content).to_html}"
+    end
+  end
+end
+Liquid::Template.register_tag('markdown', Jekyll::MarkdownBlock)
diff --git a/docs/static_site/src/_sass/minima.scss b/docs/static_site/src/_sass/minima.scss
new file mode 100644
index 000000000000..b8c46d0aecaa
--- /dev/null
+++ b/docs/static_site/src/_sass/minima.scss
@@ -0,0 +1,64 @@
+@charset "utf-8";
+
+// import grid system
+@import  "minima/simple-grid";
+
+// Define defaults for each variable.
+
+$base-font-family: -apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol" !default;
+$base-font-size:   17px !default;
+$base-font-weight: 300 !default;
+$small-font-size:  $base-font-size * 0.875 !default;
+$base-line-height: 1.5 !default;
+
+$spacing-unit:     30px !default;
+
+$text-color:       white !default;
+$background-color: #fdfdfd !default;
+$brand-color:      #2a7ae2 !default;
+
+$color-mxnet: rgb(4,140,204);
+$color-mxnet-dark: rgb(4,60,110);
+$grey-color:       #828282 !default;
+$grey-color-light: lighten($grey-color, 45%) !default;
+$grey-color-dark:  darken($grey-color, 25%) !default;
+
+$table-text-align: left !default;
+
+// Width of the content area
+$content-width:    1150px !default;
+
+$on-palm:          600px !default;
+$on-palm:          900px !default;
+$on-laptop:        1024px !default;
+
+// Use media queries like this:
+// @include media-query($on-palm) {
+//   .wrapper {
+//     padding-right: $spacing-unit / 2;
+//     padding-left: $spacing-unit / 2;
+//   }
+// }
+@mixin media-query($device) {
+  @media screen and (max-width: $device) {
+    @content;
+  }
+}
+
+@mixin relative-font-size($ratio) {
+  font-size: $base-font-size * $ratio;
+}
+
+// Import partials.
+@import
+  "minima/base",
+  "minima/layout",
+  "minima/syntax-highlighting",
+  "minima/home",
+  "minima/blog",
+  "minima/features",
+  "minima/ecosystem",
+  "minima/docs",
+  "minima/getting_started",
+  "minima/colorful"
+;
diff --git a/docs/static_site/src/_sass/minima/_base.scss b/docs/static_site/src/_sass/minima/_base.scss
new file mode 100644
index 000000000000..79f5959663e1
--- /dev/null
+++ b/docs/static_site/src/_sass/minima/_base.scss
@@ -0,0 +1,278 @@
+/**
+* Reset some basic elements
+*/
+body, h1, h2, h3, h4, h5, h6,
+p, blockquote, pre, hr,
+dl, dd, ol, ul, figure {
+  margin: 0;
+  padding: 0;
+}
+
+body {
+  background-repeat: no-repeat;
+  background-size: contain;
+  min-height: 100%;
+  background-color: $grey-color-light;
+  @media screen and (min-width: $on-palm) and (max-width: $on-laptop) {
+    background-size: inherit;
+  }
+  @media screen and (max-width: $on-palm) {
+    background-size: cover;
+  }
+}
+
+table {
+  color: $grey-color-dark !important;
+}
+
+/**
+ * Basic styling
+ */
+body {
+  font-family: $base-font-family;
+  font-weight: $base-font-weight;
+  font-size: $base-font-size;
+  line-height: $base-line-height;
+  color: $text-color;
+  background-color: $color-mxnet;
+
+  display: flex;
+  min-height: 100vh;
+  flex-direction: column;
+}
+
+
+/**
+ * Set `margin-bottom` to maintain vertical rhythm
+ */
+h1, h2, h3, h4, h5, h6,
+p, blockquote, pre,
+ul, ol, dl, figure,
+%vertical-rhythm {
+  margin-bottom: $spacing-unit / 2;
+}
+
+
+/**
+ * `main` element
+ */
+main {
+  display: block; /* Default value of `display` of `main` element is 'inline' in IE 11. */
+}
+
+header {
+  z-index: 10;
+}
+
+/**
+ * Images
+ */
+img {
+  max-width: 100%;
+  vertical-align: middle;
+}
+
+
+/**
+ * Figures
+ */
+figure > img {
+  display: block;
+}
+
+figcaption {
+  font-size: $small-font-size;
+}
+
+
+/**
+ * Lists
+ */
+ul, ol {
+  margin-left: $spacing-unit;
+}
+
+li {
+  > ul,
+  > ol {
+    margin-bottom: 0;
+  }
+}
+
+
+/**
+ * Headings
+ */
+h1, h2, h3, h4, h5, h6 {
+  font-weight: $base-font-weight;
+}
+
+
+h1 {
+  font-weight: 200;
+  @include relative-font-size(3.5);
+  line-height: 120%;
+}
+
+
+/**
+ * Links
+ */
+a {
+  color: $color-mxnet;
+  text-decoration: none;
+  font-weight: 300;
+
+  &:visited {
+    color: $color-mxnet;
+  }
+
+  &:hover {
+    color: $color-mxnet-dark;
+    text-decoration: none;
+  }
+
+  .social-media-list &:hover {
+
+  }
+}
+
+.clickable {
+  &:hover {
+    cursor: pointer;
+  }
+}
+
+/**
+ * Blockquotes
+ */
+blockquote {
+  color: $grey-color;
+  border-left: 4px solid $grey-color-light;
+  padding-left: $spacing-unit / 2;
+  @include relative-font-size(1.125);
+  letter-spacing: -1px;
+  font-style: italic;
+
+  > :last-child {
+    margin-bottom: 0;
+  }
+}
+
+
+/**
+ * Code formatting
+ */
+pre,
+code {
+  @include relative-font-size(0.9375);
+  border: 1px solid $grey-color-light;
+  border-radius: 3px;
+  background-color: #eef;
+}
+
+code {
+  padding: 1px 5px;
+}
+
+pre {
+  padding: 8px 12px;
+  overflow-x: auto;
+
+  > code {
+    border: 0;
+    padding-right: 0;
+    padding-left: 0;
+  }
+}
+
+.span-accented {
+  color: orangered;
+  float: none !important;
+  margin: 0 !important;
+  font-size: 120%;
+}
+
+/**
+ * Wrapper
+ */
+.wrapper {
+  max-width: -webkit-calc(#{$content-width} - (#{$spacing-unit} * 2));
+  max-width: calc(#{$content-width} - (#{$spacing-unit} * 2));
+  margin-right: auto;
+  margin-left: auto;
+  padding-right: $spacing-unit;
+  padding-left: $spacing-unit;
+  @extend %clearfix;
+
+  @include media-query($on-laptop) {
+    max-width: -webkit-calc(#{$content-width} - (#{$spacing-unit}));
+    max-width: calc(#{$content-width} - (#{$spacing-unit}));
+    padding-right: $spacing-unit / 2;
+    padding-left: $spacing-unit / 2;
+  }
+}
+
+
+/**
+ * Clearfix
+ */
+%clearfix:after {
+  content: "";
+  display: table;
+  clear: both;
+}
+
+
+/**
+ * Icons
+ */
+
+.svg-icon {
+  width: 16px;
+  height: 16px;
+  display: inline-block;
+  fill: $grey-color-light;
+  padding-right: 5px;
+  padding-top: 4px;
+  vertical-align: text-top;
+}
+
+.social-media-list {
+  li + li {
+    padding-top: 5px;
+  }
+}
+
+
+/**
+ * Tables
+ */
+table {
+  margin-bottom: $spacing-unit;
+  width: 100%;
+  text-align: $table-text-align;
+  color: lighten($text-color, 18%);
+  border-collapse: collapse;
+  border: 1px solid $grey-color-light;
+
+  tr {
+    &:nth-child(even) {
+      background-color: lighten($grey-color-light, 6%);
+    }
+  }
+
+  th, td {
+    padding: ($spacing-unit / 3) ($spacing-unit / 2);
+  }
+
+  th {
+    background-color: lighten($grey-color-light, 3%);
+    border: 1px solid darken($grey-color-light, 4%);
+    border-bottom-color: darken($grey-color-light, 12%);
+  }
+
+  td {
+    border: 1px solid $grey-color-light;
+  }
+}
diff --git a/docs/static_site/src/_sass/minima/_blog.scss b/docs/static_site/src/_sass/minima/_blog.scss
new file mode 100644
index 000000000000..745f70408ee9
--- /dev/null
+++ b/docs/static_site/src/_sass/minima/_blog.scss
@@ -0,0 +1,41 @@
+.medium-widget-article__item {
+  transition: box-shadow 0.3s linear;
+
+  &:hover {
+    box-shadow: inset 0 -2px 0 0 $color-mxnet;
+    filter: none;
+    border-bottom: 1px white solid;
+
+    a.medium-widget-article__title {
+      color: orangered;
+    }
+  }
+
+  margin: 20px;
+  background-color: $grey-color-light;
+  padding: 0px;
+  border: 1px solid $grey-color-light
+}
+
+.medium-widget-article__title {
+  font-weight: 300;
+}
+
+.medium-widget-article__content {
+  padding: 15px;
+}
+
+@include media-query($on-palm) {
+  .medium-widget-article__row {
+    flex-direction: column;
+  }
+}
+
+.blog-more {
+  margin-top: 40px;
+  margin-bottom: 40px;
+  height: 60px;
+  .btn {
+    background-color: $grey-color-light;
+  }
+}
\ No newline at end of file
diff --git a/docs/static_site/src/_sass/minima/_docs.scss b/docs/static_site/src/_sass/minima/_docs.scss
new file mode 100644
index 000000000000..09924f3166e7
--- /dev/null
+++ b/docs/static_site/src/_sass/minima/_docs.scss
@@ -0,0 +1,79 @@
+.docs-logo-docs {
+  width: 25px;
+  padding-bottom: 4px;
+  margin-right: 10px;
+}
+
+.docs-logo-container {
+  margin: auto;
+  text-align: center;
+  margin-bottom: 20px;
+
+  img.docs-logo-image {
+    height: 75px;
+    margin: auto;
+  }
+}
+
+.docs-side-bar {
+  margin-left: 0px !important;
+  padding-right: 10px;
+  background-color: $grey-color-light;
+}
+
+.docs-hero {
+  margin-bottom: 20px;
+}
+
+.docs-hero-left {
+
+}
+
+.docs-hero-right {
+
+}
+
+.docs-card {
+  background-color: $grey-color-light;
+  padding: 20px;
+  height: 100%;
+}
+
+.docs-card.docs-side {
+  padding: 5px;
+
+  ul {
+    margin-left: 0;
+  }
+
+  li {
+    margin-left: 20px;
+  }
+}
+
+.docs-action-btn {
+  a {
+    color: $grey-color-dark;
+
+    &:visited {
+      color: $color-mxnet-dark;
+    }
+
+    &:hover {
+      color: orangered;
+    }
+
+  }
+}
+
+.docs-faq {
+  background-color: white;
+}
+
+.docs-architecture {
+  background-color: $grey-color-light;
+  margin-top: 20px;
+  margin-bottom: 20px;
+  padding-top: 20px;
+  padding-bottom: 20px;
+}
\ No newline at end of file
diff --git a/docs/static_site/src/_sass/minima/_ecosystem.scss b/docs/static_site/src/_sass/minima/_ecosystem.scss
new file mode 100644
index 000000000000..91d545bf8598
--- /dev/null
+++ b/docs/static_site/src/_sass/minima/_ecosystem.scss
@@ -0,0 +1,9 @@
+.ecosystem-page {
+  .card {
+    background-color: $grey-color-light;
+  }
+
+  h4 {
+    float: left;
+  }
+}
\ No newline at end of file
diff --git a/docs/static_site/src/_sass/minima/_features.scss b/docs/static_site/src/_sass/minima/_features.scss
new file mode 100644
index 000000000000..dd04a39f7953
--- /dev/null
+++ b/docs/static_site/src/_sass/minima/_features.scss
@@ -0,0 +1,23 @@
+.feature-image {
+  max-width: 100px;
+  filter: grayscale(1);
+  margin: auto;
+}
+
+.feature-paragraph {
+  margin: auto;
+}
+
+.feature-title {
+  float: left;
+  margin-bottom: 0px;
+}
+
+.highlight pre {
+  box-shadow: inset 0 -2px 0 0 $color-mxnet;
+  margin-bottom: 0 !important;
+}
+
+figure {
+  margin-bottom: 0 !important;
+}
\ No newline at end of file
diff --git a/docs/static_site/src/_sass/minima/_getting_started.scss b/docs/static_site/src/_sass/minima/_getting_started.scss
new file mode 100644
index 000000000000..550ddaaf30db
--- /dev/null
+++ b/docs/static_site/src/_sass/minima/_getting_started.scss
@@ -0,0 +1,178 @@
+.install-selector {
+  max-width: 800px;
+  margin: auto;
+  margin-bottom: 40px;
+
+  .highlight {
+    margin-left: 20px;
+    margin-top: 20px;
+    margin-bottom: 20px !important;
+  }
+}
+
+
+.install-content, .install-widget {
+  visibility: hidden;
+}
+
+.get-started-from-source {
+  background-color: $grey-color-light;
+  padding-top: 50px;
+  padding-bottom: 50px;
+}
+
+#lang-demo ul {
+  margin-top: 20px;
+  margin-bottom: 15px;
+}
+
+.option-title {
+  width: 100px;
+  float: left;
+  clear: none;
+  text-align: right;
+  font-size: 15px;
+  padding-top: 7px;
+  padding-bottom: 8px;
+  padding-right: 10px;
+  font-weight: bold;
+}
+
+.option-row {
+  padding-bottom: 8px;
+}
+
+.install-inst {
+}
+
+#setup-options {
+  margin-top: 15px;
+  margin-bottom: 15px;
+  margin-left: 30px;
+}
+
+/*
+ * Drop down
+ */
+
+.dropbtn {
+  background-color: $color-mxnet;
+  color: white;
+  font-size: $base-font-size;
+  margin: 0px;
+  border: none;
+  min-width: 100%;
+  padding: 10px;
+}
+
+li.opt.versions {
+  padding: 10px;
+}
+
+
+.dropdown {
+  position: relative;
+  display: inline-block;
+  margin: 5px;
+  font-size: $base-font-size;
+
+}
+
+ul.dropdown-content {
+  display: none;
+  position: absolute;
+  left: 40%;
+  text-align: center;
+  background-color: whitesmoke;
+  box-shadow: none;
+  z-index: 1;
+  margin: 0px;
+  padding: 0px;
+  list-style-type: none;
+}
+
+.dropdown-content a {
+  color: $grey-color-dark;
+  text-decoration: none;
+  display: block;
+  padding-left: 5px;
+  padding-right: 5px;
+  font-size: $base-font-size;
+
+  &:hover {
+    color: orangered;
+  }
+}
+
+.dropdown-content .active a {
+  color: $grey-color-light;
+}
+
+.dropdown:hover .dropdown-content {
+  display: block;
+}
+
+.dropdown:hover .dropbtn {
+  background-color: $color-mxnet
+}
+
+/*
+ * selector
+ */
+
+.col-3.install-left {
+  margin: auto;
+
+  span {
+    padding-left: 20px;
+    border-left: 2px $color-mxnet dashed;
+    @media screen and (max-width: $on-palm) {
+      border: none;
+    }
+  }
+
+}
+
+.col-9.install-right {
+  margin: auto;
+}
+
+.install-selector {
+  .active {
+    background-color: $color-mxnet !important;
+    color: $grey-color-light !important;
+  }
+}
+
+.btn-group.opt-group {
+  margin-top: 5px;
+  margin-bottom: 5px;
+  display: flex;
+  justify-content: space-between;
+
+
+  .opt {
+    padding-bottom: 10px;
+    padding-top: 10px;
+    font-size: $base-font-size;
+    width: 100%;
+    border: none;
+    color: $dark-gray;
+    margin-left: 5px;
+    margin-right: 5px;
+    background-color: $grey-color-light;
+
+    @media screen and (max-width: $on-palm) {
+      font-size: 11px;
+      margin-left: 2px;
+      margin-right: 2px;
+    }
+
+    &:hover {
+      background-color: $color-mxnet;
+      color: $grey-color-light;
+    }
+  }
+
+
+}
diff --git a/docs/static_site/src/_sass/minima/_home.scss b/docs/static_site/src/_sass/minima/_home.scss
new file mode 100644
index 000000000000..4bcb7fa8ff9c
--- /dev/null
+++ b/docs/static_site/src/_sass/minima/_home.scss
@@ -0,0 +1,181 @@
+.home {
+  margin-bottom: 40px;
+}
+
+
+.home .btn {
+  border: 0;
+}
+
+a.btn {
+  padding: 10px;
+  padding-left: 20px;
+  padding-right: 20px;
+  margin-top: 20px;
+  background-color: white;
+  transition: box-shadow 0.3s linear, border 0.3s linear;
+
+  color: $grey-color-dark;
+
+  &:hover {
+    box-shadow: inset 0 -2px 0 0 $color-mxnet;
+    color: black;
+  }
+}
+
+.btn-action {
+  float: right;
+}
+
+.section {
+  padding-top: 20px;
+  padding-bottom: 30px;
+
+  h2 {
+    text-transform: uppercase;
+  }
+}
+
+// =====
+// Cards
+// =====
+
+
+.card {
+  color: $grey-color-dark;
+  padding: 10px;
+  background-color: white;
+  transition: box-shadow 0.3s linear;
+  height: 100%;
+
+
+  &:hover {
+    box-shadow: inset 0 -2px 0 0 $color-mxnet;
+    filter: none;
+
+    a h3, h4 {
+      color: orangered;
+    }
+  }
+
+  h3 {
+    color: black;
+  }
+
+  p {
+    font-size: 90%;
+  }
+
+  a {
+    color: $grey-color-dark;
+
+    &:hover {
+      color: black;
+    }
+  }
+}
+
+
+.card-header-title {
+  height: 50px;
+}
+
+.card-header-title h3 {
+  float: left
+}
+
+.card-header-title img {
+  float: right;
+  width: 30px;
+}
+
+
+// ==============
+// Key features
+// ==============
+
+.key-features-section {
+  background-color: white;
+  color: $grey-color-dark;
+
+  h3 {
+    color: orangered;
+  }
+
+  .btn {
+    background-color: $grey-color-light;
+  }
+
+  .card-text {
+    @media screen and (min-width: $on-laptop) {
+      min-height: 180px !important;
+    }
+  }
+
+  .card {
+    &:hover {
+      box-shadow: 0 0 0 0 !important;
+    }
+  }
+
+}
+
+.key-feature-image {
+  width: 100%;
+  text-align: center;
+  margin-top: 30px;
+
+  img {
+    width: 70px;
+    filter: grayscale(1);
+  }
+}
+
+// ====
+// ecosystem
+// ====
+
+.ecosystem-section {
+  background-color: $grey-color-light;
+  color: black;
+}
+
+// ===
+// community
+// ===
+
+.community-section {
+  background-color: $grey-color-light;
+  color: $grey-color-dark;
+}
+
+.community-section .card {
+  background-color: white;
+}
+
+// ===
+// trusted-by
+// ===
+
+.trusted-by-section {
+  background-color: white;
+  color: $grey-color-dark;
+}
+
+.trusted-by {
+  margin-top: 30px;
+
+}
+
+// ===
+// news
+// ===
+.news-section {
+  color: $grey-color-dark;
+  min-height: 500px;
+  background-color: white;
+
+  .btn {
+    background-color: $grey-color-light;
+  }
+}
\ No newline at end of file
diff --git a/docs/static_site/src/_sass/minima/_layout.scss b/docs/static_site/src/_sass/minima/_layout.scss
new file mode 100644
index 000000000000..2097210d0dea
--- /dev/null
+++ b/docs/static_site/src/_sass/minima/_layout.scss
@@ -0,0 +1,350 @@
+/**
+ * Site header
+ */
+.site-header {
+  min-height: $spacing-unit * 1.865;
+
+  position: fixed;
+  top: 0;
+  width: 100%;
+  padding-top: 10px;
+  padding-bottom: 10px;
+}
+
+.site-header-logo {
+  width: 120px;
+}
+
+.site-title {
+  @include relative-font-size(1.625);
+  font-weight: 300;
+  line-height: $base-line-height * $base-font-size * 2.25;
+  letter-spacing: -1px;
+  margin-bottom: 0;
+  float: left;
+  color: white;
+
+  &,
+  &:visited {
+    color: $grey-color-dark;
+  }
+}
+
+.site-nav {
+  float: right;
+  line-height: $base-line-height * $base-font-size * 2.25;
+
+  .nav-trigger {
+    display: none;
+  }
+
+  .menu-icon {
+    display: none;
+  }
+
+  .page-link {
+    color: $text-color;
+    line-height: $base-line-height;
+    //text-transform: uppercase;
+    //text-shadow: 1px 1px rgba(50,50,50,0.2);
+
+    // Gaps between nav items, but not on the last one
+    &:not(:last-child) {
+      margin-right: 40px;
+    }
+
+    &:hover {
+      color: white;
+      text-shadow: -0.06ex 0 white, 0.06ex 0 white;
+    }
+  }
+
+  .page-link.page-current {
+    color: white;
+    text-decoration: underline;
+  }
+
+  @media screen and (max-width: $on-laptop) {
+    position: absolute;
+    top: 9px;
+    right: $spacing-unit / 2;
+    background-color: $color-mxnet;
+    border-radius: 2px;
+    text-align: right;
+
+    label[for="nav-trigger"] {
+      display: block;
+      float: right;
+      width: 36px;
+      height: 36px;
+      z-index: 2;
+      cursor: pointer;
+    }
+
+    .menu-icon {
+      display: block;
+      float: right;
+      width: 36px;
+      height: 26px;
+      line-height: 0;
+      padding-top: 10px;
+      text-align: center;
+
+      > svg {
+        fill: $grey-color-light;
+      }
+    }
+
+    input ~ .trigger {
+      clear: both;
+      display: none;
+    }
+
+    input:checked ~ .trigger {
+      display: block;
+      padding-bottom: 5px;
+    }
+
+    .page-link {
+      padding: 5px 10px;
+      display: block;
+
+      &:not(:last-child) {
+        margin-right: 0;
+      }
+
+      margin-left: 20px;
+    }
+  }
+}
+
+
+/**
+ * Site footer
+ */
+.site-footer {
+  border-top: 1px solid $grey-color-light;
+  padding: $spacing-unit 0;
+  background-color: #424242;
+  .footer-category-title {
+    color: $color-mxnet;
+  }
+  a {
+    color: $grey-color-light !important;
+
+    &:visited {
+      color: $grey-color-light !important;
+    }
+  }
+
+}
+
+.site-footer2 {
+  background-color: #424242;
+  padding-top: 40px;
+  padding-bottom: 10px;
+}
+
+.footer-heading {
+  @include relative-font-size(1.125);
+  margin-bottom: $spacing-unit / 2;
+}
+
+.contact-list,
+.social-media-list {
+  list-style: none;
+  margin-left: 0;
+}
+
+.footer-col-wrapper {
+  @include relative-font-size(0.9375);
+
+  margin-left: -$spacing-unit / 2;
+  @extend %clearfix;
+}
+
+.footer-bottom-warning {
+  font-size: 80%;
+  color: white;
+  float: left;
+}
+
+.footer-logo {
+  width: 200px;
+  margin-bottom: 30px;
+  margin-top: 30px;
+}
+
+.footer-col {
+  float: left;
+  margin-bottom: $spacing-unit / 2;
+  padding-left: $spacing-unit / 2;
+}
+
+.footer-text {
+  color: $grey-color-light;
+}
+
+.footer-col-1 {
+  width: -webkit-calc(25% - (#{$spacing-unit} / 2));
+  width: calc(25% - (#{$spacing-unit} / 2));
+}
+
+.footer-col-2 {
+  width: -webkit-calc(30% - (#{$spacing-unit} / 2));
+  width: calc(30% - (#{$spacing-unit} / 2));
+}
+
+.footer-col-3 {
+  width: -webkit-calc(45% - (#{$spacing-unit} / 2));
+  width: calc(45% - (#{$spacing-unit} / 2));
+}
+
+@include media-query($on-laptop) {
+  .footer-col-1,
+  .footer-col-2 {
+    width: -webkit-calc(50% - (#{$spacing-unit} / 2));
+    width: calc(50% - (#{$spacing-unit} / 2));
+  }
+
+  .footer-col-3 {
+    width: -webkit-calc(100% - (#{$spacing-unit} / 2));
+    width: calc(100% - (#{$spacing-unit} / 2));
+  }
+}
+
+@include media-query($on-palm) {
+  .footer-col {
+    float: none;
+    width: -webkit-calc(100% - (#{$spacing-unit} / 2));
+    width: calc(100% - (#{$spacing-unit} / 2));
+  }
+}
+
+
+/**
+ * Page content
+ */
+
+.header-section {
+  .btn {
+    background-color: $grey-color-light;
+  }
+}
+
+.page-content {
+  height: 100%;
+  padding: $spacing-unit 0, 0;
+  flex: 1;
+  margin-top: 100px;
+}
+
+.page-content-home {
+  height: 100%;
+  padding: $spacing-unit 0, 0;
+  margin-top: 100px;
+}
+
+.page-heading {
+  @include relative-font-size(2);
+}
+
+.post-list-heading {
+  @include relative-font-size(1.75);
+}
+
+.post-list {
+  margin-left: 0;
+  list-style: none;
+
+  > li {
+    margin-bottom: $spacing-unit;
+  }
+}
+
+.post-meta {
+  font-size: $small-font-size;
+  color: $grey-color;
+}
+
+.post-link {
+  display: block;
+  @include relative-font-size(1.5);
+}
+
+
+/**
+ * Posts
+ */
+.post-header {
+  margin-bottom: 20px;
+  margin-top: 50px;
+}
+
+.post-title {
+  @include relative-font-size(2.625);
+  letter-spacing: -1px;
+  line-height: 1;
+
+  @include media-query($on-laptop) {
+    @include relative-font-size(2.25);
+  }
+}
+
+.post-content {
+  min-height: 700px;
+  background-color: white;
+  padding-top: 30px;
+  color: $grey-color-dark;
+
+  h2 {
+    @include relative-font-size(2);
+
+    @include media-query($on-laptop) {
+      @include relative-font-size(1.75);
+    }
+  }
+
+  h3 {
+    @include relative-font-size(1.625);
+
+    @include media-query($on-laptop) {
+      @include relative-font-size(1.375);
+    }
+  }
+
+  h4 {
+    @include relative-font-size(1.25);
+
+    @include media-query($on-laptop) {
+      @include relative-font-size(1.125);
+    }
+  }
+}
+
+.copy-btn {
+  display: none;
+  position: absolute;
+  right: 0;
+  width: 60px;
+  text-align: center;
+  padding: 2px;
+  padding-bottom: 5px;
+  font-family: monospace;
+  font-size: 15px;
+  background-color: $color-mxnet;
+  color: white;
+  border: none;
+
+  &:hover {
+    background-color: #eef;
+    color: $color-mxnet;
+    cursor: pointer;
+    border: 1px solid $color-mxnet;
+  }
+
+  &:active {
+    background-color: $color-mxnet;
+    color: $grey-color-light;
+  }
+}
diff --git a/docs/static_site/src/_sass/minima/_syntax-highlighting.scss b/docs/static_site/src/_sass/minima/_syntax-highlighting.scss
new file mode 100644
index 000000000000..e1ebf1087113
--- /dev/null
+++ b/docs/static_site/src/_sass/minima/_syntax-highlighting.scss
@@ -0,0 +1,322 @@
+/**
+ * Syntax highlighting styles
+ */
+.highlight {
+  background: #fff;
+  @extend %vertical-rhythm;
+
+  .highlighter-rouge & {
+    background: #eef;
+  }
+
+  .c {
+    color: #998;
+    font-style: italic
+  }
+
+  // Comment
+  .err {
+    color: #a61717;
+    background-color: #e3d2d2
+  }
+
+  // Error
+  .k {
+    font-weight: bold
+  }
+
+  // Keyword
+  .o {
+    font-weight: bold
+  }
+
+  // Operator
+  .cm {
+    color: #998;
+    font-style: italic
+  }
+
+  // Comment.Multiline
+  .cp {
+    color: #999;
+    font-weight: bold
+  }
+
+  // Comment.Preproc
+  .c1 {
+    color: #998;
+    font-style: italic
+  }
+
+  // Comment.Single
+  .cs {
+    color: #999;
+    font-weight: bold;
+    font-style: italic
+  }
+
+  // Comment.Special
+  .gd {
+    color: #000;
+    background-color: #fdd
+  }
+
+  // Generic.Deleted
+  .gd .x {
+    color: #000;
+    background-color: #faa
+  }
+
+  // Generic.Deleted.Specific
+  .ge {
+    font-style: italic
+  }
+
+  // Generic.Emph
+  .gr {
+    color: #a00
+  }
+
+  // Generic.Error
+  .gh {
+    color: #999
+  }
+
+  // Generic.Heading
+  .gi {
+    color: #000;
+    background-color: #dfd
+  }
+
+  // Generic.Inserted
+  .gi .x {
+    color: #000;
+    background-color: #afa
+  }
+
+  // Generic.Inserted.Specific
+  .go {
+    color: #888
+  }
+
+  // Generic.Output
+  .gp {
+    color: #555
+  }
+
+  // Generic.Prompt
+  .gs {
+    font-weight: bold
+  }
+
+  // Generic.Strong
+  .gu {
+    color: #aaa
+  }
+
+  // Generic.Subheading
+  .gt {
+    color: #a00
+  }
+
+  // Generic.Traceback
+  .kc {
+    font-weight: bold
+  }
+
+  // Keyword.Constant
+  .kd {
+    font-weight: bold
+  }
+
+  // Keyword.Declaration
+  .kp {
+    font-weight: bold
+  }
+
+  // Keyword.Pseudo
+  .kr {
+    font-weight: bold
+  }
+
+  // Keyword.Reserved
+  .kt {
+    color: #458;
+    font-weight: bold
+  }
+
+  // Keyword.Type
+  .m {
+    color: #099
+  }
+
+  // Literal.Number
+  .s {
+    color: #d14
+  }
+
+  // Literal.String
+  .na {
+    color: #008080
+  }
+
+  // Name.Attribute
+  .nb {
+    color: #0086B3
+  }
+
+  // Name.Builtin
+  .nc {
+    color: #458;
+    font-weight: bold
+  }
+
+  // Name.Class
+  .no {
+    color: #008080
+  }
+
+  // Name.Constant
+  .ni {
+    color: #800080
+  }
+
+  // Name.Entity
+  .ne {
+    color: #900;
+    font-weight: bold
+  }
+
+  // Name.Exception
+  .nf {
+    color: #900;
+    font-weight: bold
+  }
+
+  // Name.Function
+  .nn {
+    color: #555
+  }
+
+  // Name.Namespace
+  .nt {
+    color: #000080
+  }
+
+  // Name.Tag
+  .nv {
+    color: #008080
+  }
+
+  // Name.Variable
+  .ow {
+    font-weight: bold
+  }
+
+  // Operator.Word
+  .w {
+    color: #bbb
+  }
+
+  // Text.Whitespace
+  .mf {
+    color: #099
+  }
+
+  // Literal.Number.Float
+  .mh {
+    color: #099
+  }
+
+  // Literal.Number.Hex
+  .mi {
+    color: #099
+  }
+
+  // Literal.Number.Integer
+  .mo {
+    color: #099
+  }
+
+  // Literal.Number.Oct
+  .sb {
+    color: #d14
+  }
+
+  // Literal.String.Backtick
+  .sc {
+    color: #d14
+  }
+
+  // Literal.String.Char
+  .sd {
+    color: #d14
+  }
+
+  // Literal.String.Doc
+  .s2 {
+    color: #d14
+  }
+
+  // Literal.String.Double
+  .se {
+    color: #d14
+  }
+
+  // Literal.String.Escape
+  .sh {
+    color: #d14
+  }
+
+  // Literal.String.Heredoc
+  .si {
+    color: #d14
+  }
+
+  // Literal.String.Interpol
+  .sx {
+    color: #d14
+  }
+
+  // Literal.String.Other
+  .sr {
+    color: #009926
+  }
+
+  // Literal.String.Regex
+  .s1 {
+    color: #d14
+  }
+
+  // Literal.String.Single
+  .ss {
+    color: #990073
+  }
+
+  // Literal.String.Symbol
+  .bp {
+    color: #999
+  }
+
+  // Name.Builtin.Pseudo
+  .vc {
+    color: #008080
+  }
+
+  // Name.Variable.Class
+  .vg {
+    color: #008080
+  }
+
+  // Name.Variable.Global
+  .vi {
+    color: #008080
+  }
+
+  // Name.Variable.Instance
+  .il {
+    color: #099
+  }
+
+  // Literal.Number.Integer.Long
+}
diff --git a/docs/static_site/src/_sass/minima/colorful.scss b/docs/static_site/src/_sass/minima/colorful.scss
new file mode 100644
index 000000000000..fce0dfcb1783
--- /dev/null
+++ b/docs/static_site/src/_sass/minima/colorful.scss
@@ -0,0 +1,370 @@
+.highlight .hll {
+  background-color: #ffffcc
+}
+
+.highlight {
+  background: #f0f3f3;
+}
+
+.highlight .c {
+  color: #0099FF;
+  font-style: italic
+}
+
+/* Comment */
+.highlight .err {
+  color: #AA0000;
+  background-color: #FFAAAA
+}
+
+/* Error */
+.highlight .k {
+  color: #006699;
+  font-weight: bold
+}
+
+/* Keyword */
+.highlight .o {
+  color: #555555
+}
+
+/* Operator */
+.highlight .ch {
+  color: #0099FF;
+  font-style: italic
+}
+
+/* Comment.Hashbang */
+.highlight .cm {
+  color: #0099FF;
+  font-style: italic
+}
+
+/* Comment.Multiline */
+.highlight .cp {
+  color: #009999
+}
+
+/* Comment.Preproc */
+.highlight .cpf {
+  color: #0099FF;
+  font-style: italic
+}
+
+/* Comment.PreprocFile */
+.highlight .c1 {
+  color: #0099FF;
+  font-style: italic
+}
+
+/* Comment.Single */
+.highlight .cs {
+  color: #0099FF;
+  font-weight: bold;
+  font-style: italic
+}
+
+/* Comment.Special */
+.highlight .gd {
+  background-color: #FFCCCC;
+  border: 1px solid #CC0000
+}
+
+/* Generic.Deleted */
+.highlight .ge {
+  font-style: italic
+}
+
+/* Generic.Emph */
+.highlight .gr {
+  color: #FF0000
+}
+
+/* Generic.Error */
+.highlight .gh {
+  color: #003300;
+  font-weight: bold
+}
+
+/* Generic.Heading */
+.highlight .gi {
+  background-color: #CCFFCC;
+  border: 1px solid #00CC00
+}
+
+/* Generic.Inserted */
+.highlight .go {
+  color: #AAAAAA
+}
+
+/* Generic.Output */
+.highlight .gp {
+  color: #000099;
+  font-weight: bold
+}
+
+/* Generic.Prompt */
+.highlight .gs {
+  font-weight: bold
+}
+
+/* Generic.Strong */
+.highlight .gu {
+  color: #003300;
+  font-weight: bold
+}
+
+/* Generic.Subheading */
+.highlight .gt {
+  color: #99CC66
+}
+
+/* Generic.Traceback */
+.highlight .kc {
+  color: #006699;
+  font-weight: bold
+}
+
+/* Keyword.Constant */
+.highlight .kd {
+  color: #006699;
+  font-weight: bold
+}
+
+/* Keyword.Declaration */
+.highlight .kn {
+  color: #006699;
+  font-weight: bold
+}
+
+/* Keyword.Namespace */
+.highlight .kp {
+  color: #006699
+}
+
+/* Keyword.Pseudo */
+.highlight .kr {
+  color: #006699;
+  font-weight: bold
+}
+
+/* Keyword.Reserved */
+.highlight .kt {
+  color: #007788;
+  font-weight: bold
+}
+
+/* Keyword.Type */
+.highlight .m {
+  color: #FF6600
+}
+
+/* Literal.Number */
+.highlight .s {
+  color: #CC3300
+}
+
+/* Literal.String */
+.highlight .na {
+  color: #330099
+}
+
+/* Name.Attribute */
+.highlight .nb {
+  color: #336666
+}
+
+/* Name.Builtin */
+.highlight .nc {
+  color: #00AA88;
+  font-weight: bold
+}
+
+/* Name.Class */
+.highlight .no {
+  color: #336600
+}
+
+/* Name.Constant */
+.highlight .nd {
+  color: #9999FF
+}
+
+/* Name.Decorator */
+.highlight .ni {
+  color: #999999;
+  font-weight: bold
+}
+
+/* Name.Entity */
+.highlight .ne {
+  color: #CC0000;
+  font-weight: bold
+}
+
+/* Name.Exception */
+.highlight .nf {
+  color: #CC00FF
+}
+
+/* Name.Function */
+.highlight .nl {
+  color: #9999FF
+}
+
+/* Name.Label */
+.highlight .nn {
+  color: #00CCFF;
+  font-weight: bold
+}
+
+/* Name.Namespace */
+.highlight .nt {
+  color: #330099;
+  font-weight: bold
+}
+
+/* Name.Tag */
+.highlight .nv {
+  color: #003333
+}
+
+/* Name.Variable */
+.highlight .ow {
+  color: #000000;
+  font-weight: bold
+}
+
+/* Operator.Word */
+.highlight .w {
+  color: #bbbbbb
+}
+
+/* Text.Whitespace */
+.highlight .mb {
+  color: #FF6600
+}
+
+/* Literal.Number.Bin */
+.highlight .mf {
+  color: #FF6600
+}
+
+/* Literal.Number.Float */
+.highlight .mh {
+  color: #FF6600
+}
+
+/* Literal.Number.Hex */
+.highlight .mi {
+  color: #FF6600
+}
+
+/* Literal.Number.Integer */
+.highlight .mo {
+  color: #FF6600
+}
+
+/* Literal.Number.Oct */
+.highlight .sa {
+  color: #CC3300
+}
+
+/* Literal.String.Affix */
+.highlight .sb {
+  color: #CC3300
+}
+
+/* Literal.String.Backtick */
+.highlight .sc {
+  color: #CC3300
+}
+
+/* Literal.String.Char */
+.highlight .dl {
+  color: #CC3300
+}
+
+/* Literal.String.Delimiter */
+.highlight .sd {
+  color: #CC3300;
+  font-style: italic
+}
+
+/* Literal.String.Doc */
+.highlight .s2 {
+  color: #CC3300
+}
+
+/* Literal.String.Double */
+.highlight .se {
+  color: #CC3300;
+  font-weight: bold
+}
+
+/* Literal.String.Escape */
+.highlight .sh {
+  color: #CC3300
+}
+
+/* Literal.String.Heredoc */
+.highlight .si {
+  color: #AA0000
+}
+
+/* Literal.String.Interpol */
+.highlight .sx {
+  color: #CC3300
+}
+
+/* Literal.String.Other */
+.highlight .sr {
+  color: #33AAAA
+}
+
+/* Literal.String.Regex */
+.highlight .s1 {
+  color: #CC3300
+}
+
+/* Literal.String.Single */
+.highlight .ss {
+  color: #FFCC33
+}
+
+/* Literal.String.Symbol */
+.highlight .bp {
+  color: #336666
+}
+
+/* Name.Builtin.Pseudo */
+.highlight .fm {
+  color: #CC00FF
+}
+
+/* Name.Function.Magic */
+.highlight .vc {
+  color: #003333
+}
+
+/* Name.Variable.Class */
+.highlight .vg {
+  color: #003333
+}
+
+/* Name.Variable.Global */
+.highlight .vi {
+  color: #003333
+}
+
+/* Name.Variable.Instance */
+.highlight .vm {
+  color: #003333
+}
+
+/* Name.Variable.Magic */
+.highlight .il {
+  color: #FF6600
+}
+
+/* Literal.Number.Integer.Long */
\ No newline at end of file
diff --git a/docs/static_site/src/_sass/minima/simple-grid.scss b/docs/static_site/src/_sass/minima/simple-grid.scss
new file mode 100644
index 000000000000..c4155950c031
--- /dev/null
+++ b/docs/static_site/src/_sass/minima/simple-grid.scss
@@ -0,0 +1,207 @@
+// SIMPLE GRID - SASS/SCSS
+
+
+// fonts
+$font-weight-light: 300;
+$font-weight-regular: 400;
+$font-weight-heavy: 700;
+
+// colors
+$dark-grey: #333447;
+$dark-gray: #333447; // for the Americans
+
+
+.font-light {
+  font-weight: $font-weight-light;
+}
+
+.font-regular {
+  font-weight: $font-weight-regular;
+}
+
+.font-heavy {
+  font-weight: $font-weight-heavy;
+}
+
+// utility
+
+.left {
+  text-align: left;
+}
+
+.right {
+  text-align: right;
+}
+
+.center {
+  text-align: center;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+.justify {
+  text-align: justify;
+}
+
+.hidden-sm {
+  display: none;
+}
+
+// grid
+
+$width: 98%;
+$gutter: 2%;
+$breakpoint-small: 33.75em; // 540px
+$breakpoint-med: 45em; // 720px
+$breakpoint-large: 60em; // 960px
+
+.container {
+  width: 100%;
+  margin-left: auto;
+  margin-right: auto;
+
+  @media only screen and (min-width: $breakpoint-small) {
+    width: 80%;
+  }
+
+  @media only screen and (min-width: $breakpoint-large) {
+    width: 75%;
+    max-width: 60rem;
+  }
+}
+
+.row {
+  position: relative;
+  width: 100%;
+}
+
+.row [class^="col"] {
+  float: left;
+  margin: 0.5rem 1%;
+  min-height: 0.125rem;
+}
+
+.row::after {
+  content: "";
+  display: table;
+  clear: both;
+}
+
+.col-1,
+.col-2,
+.col-3,
+.col-4,
+.col-5,
+.col-6,
+.col-7,
+.col-8,
+.col-9,
+.col-10,
+.col-11,
+.col-12 {
+  width: $width;
+}
+
+.col-1-sm {
+  width: ($width / 12) - ($gutter * 11 / 12);
+}
+
+.col-2-sm {
+  width: ($width / 6) - ($gutter * 10 / 12);
+}
+
+.col-3-sm {
+  width: ($width / 4) - ($gutter * 9 / 12);
+}
+
+.col-4-sm {
+  width: ($width / 3) - ($gutter * 8 / 12);
+}
+
+.col-5-sm {
+  width: ($width / (12 / 5)) - ($gutter * 7 / 12);
+}
+
+.col-6-sm {
+  width: ($width / 2) - ($gutter * 6 / 12);
+}
+
+.col-7-sm {
+  width: ($width / (12 / 7)) - ($gutter * 5 / 12);
+}
+
+.col-8-sm {
+  width: ($width / (12 / 8)) - ($gutter * 4 / 12);
+}
+
+.col-9-sm {
+  width: ($width / (12 / 9)) - ($gutter * 3 / 12);
+}
+
+.col-10-sm {
+  width: ($width / (12 / 10)) - ($gutter * 2 / 12);
+}
+
+.col-11-sm {
+  width: ($width / (12 / 11)) - ($gutter * 1 / 12);
+}
+
+.col-12-sm {
+  width: $width;
+}
+
+@media only screen and (min-width: $breakpoint-med) {
+  .col-1 {
+    width: ($width / 12) - ($gutter * 11 / 12);
+  }
+  .col-2 {
+    width: ($width / 6) - ($gutter * 10 / 12);
+  }
+  .col-3 {
+    width: ($width / 4) - ($gutter * 9 / 12);
+  }
+  .col-4 {
+    width: ($width / 3) - ($gutter * 8 / 12);
+  }
+  .col-5 {
+    width: ($width / (12 / 5)) - ($gutter * 7 / 12);
+  }
+  .col-6 {
+    width: ($width / 2) - ($gutter * 6 / 12);
+  }
+  .col-7 {
+    width: ($width / (12 / 7)) - ($gutter * 5 / 12);
+  }
+  .col-8 {
+    width: ($width / (12 / 8)) - ($gutter * 4 / 12);
+  }
+  .col-9 {
+    width: ($width / (12 / 9)) - ($gutter * 3 / 12);
+  }
+  .col-10 {
+    width: ($width / (12 / 10)) - ($gutter * 2 / 12);
+  }
+  .col-11 {
+    width: ($width / (12 / 11)) - ($gutter * 1 / 12);
+  }
+  .col-12 {
+    width: $width;
+  }
+
+  .hidden-sm {
+    display: block;
+  }
+}
+
+.row {
+  display: -webkit-box;
+  display: -webkit-flex;
+  display: -ms-flexbox;
+  display: flex;
+  flex-wrap: wrap;
+}
+
+.row > [class*='col-'] {
+  display: flex;
+  flex-direction: column;
+}
diff --git a/docs/static_site/src/assets/img/Github.svg b/docs/static_site/src/assets/img/Github.svg
new file mode 100644
index 000000000000..17414ee24685
--- /dev/null
+++ b/docs/static_site/src/assets/img/Github.svg
@@ -0,0 +1 @@
+<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd"><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" version="1.1" id="Layer_1" x="0px" y="0px" width="24px" height="24px" viewBox="0 0 24 24" xml:space="preserve"><path d="M15.5 22.7h-.1l-.1-.1V22v-2.5c0-.7-.1-1.3-.4-1.8 2.3-.4 4.8-1.6 4.8-6.1 0-1.2-.4-2.3-1.1-3.2.2-.6.3-1.7-.2-3.1l-.3-.3s-.2-.1-.4-.1c-.6 0-1.5.2-3 1.2-.8-.1-1.7-.3-2.7-.3-1 0-1.9.1-2.8.3C7.8 5.2 6.8 5 6.2 5c-.2 0-.3.1-.4.1-.1 0-.3.2-.3.3-.5 1.4-.4 2.5-.2 3.1-.7.9-1.1 2-1.1 3.2 0 4.4 2.6 5.6 4.8 6.1-.1.2-.2.5-.3.8-.2.1-.5.2-.9.2s-.8-.1-1.1-.4l-.1-.1c-.1-.1-.1-.2-.2-.2l-.1-.1-.1-.1c0-.1-.8-1.3-2.2-1.4-.5 0-.9.2-1 .5-.2.5.4.9.7 1.1 0 0 .6.3 1 1.4.2.7 1.1 2 3.2 2h.7v1.4l-.1.1s-.1 0 0 0C4 21.2 1 17 1 12.3c0-6.1 4.9-11 11-11s11 4.9 11 11c0 4.7-3 8.9-7.5 10.4z"/><metadata><rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns:dc="http://purl.org/dc/elements/1.1/"><rdf:Description about="https://iconscout.com/legal#licenses" dc:title="github" dc:description="github" dc:publisher="Iconscout" dc:date="2018-01-30" dc:format="image/svg+xml" dc:language="en"><dc:creator><rdf:Bag><rdf:li>Roundicons.com</rdf:li></rdf:Bag></dc:creator></rdf:Description></rdf:RDF></metadata></svg>
\ No newline at end of file
diff --git a/docs/static_site/src/assets/img/R_logo.svg b/docs/static_site/src/assets/img/R_logo.svg
new file mode 100644
index 000000000000..78281f78ff48
--- /dev/null
+++ b/docs/static_site/src/assets/img/R_logo.svg
@@ -0,0 +1,14 @@
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" preserveAspectRatio="xMidYMid" width="724" height="561" viewBox="0 0 724 561">
+  <defs>
+    <linearGradient id="gradientFill-1" x1="0" x2="1" y1="0" y2="1" gradientUnits="objectBoundingBox" spreadMethod="pad">
+      <stop offset="0" stop-color="rgb(203,206,208)" stop-opacity="1"/>
+      <stop offset="1" stop-color="rgb(132,131,139)" stop-opacity="1"/>
+    </linearGradient>
+    <linearGradient id="gradientFill-2" x1="0" x2="1" y1="0" y2="1" gradientUnits="objectBoundingBox" spreadMethod="pad">
+      <stop offset="0" stop-color="rgb(39,109,195)" stop-opacity="1"/>
+      <stop offset="1" stop-color="rgb(22,92,170)" stop-opacity="1"/>
+    </linearGradient>
+  </defs>
+  <path d="M361.453,485.937 C162.329,485.937 0.906,377.828 0.906,244.469 C0.906,111.109 162.329,3.000 361.453,3.000 C560.578,3.000 722.000,111.109 722.000,244.469 C722.000,377.828 560.578,485.937 361.453,485.937 ZM416.641,97.406 C265.289,97.406 142.594,171.314 142.594,262.484 C142.594,353.654 265.289,427.562 416.641,427.562 C567.992,427.562 679.687,377.033 679.687,262.484 C679.687,147.971 567.992,97.406 416.641,97.406 Z" fill="url(#gradientFill-1)" fill-rule="evenodd"/>
+  <path d="M550.000,377.000 C550.000,377.000 571.822,383.585 584.500,390.000 C588.899,392.226 596.510,396.668 602.000,402.500 C607.378,408.212 610.000,414.000 610.000,414.000 L696.000,559.000 L557.000,559.062 L492.000,437.000 C492.000,437.000 478.690,414.131 470.500,407.500 C463.668,401.969 460.755,400.000 454.000,400.000 C449.298,400.000 420.974,400.000 420.974,400.000 L421.000,558.974 L298.000,559.026 L298.000,152.938 L545.000,152.938 C545.000,152.938 657.500,154.967 657.500,262.000 C657.500,369.033 550.000,377.000 550.000,377.000 ZM496.500,241.024 L422.037,240.976 L422.000,310.026 L496.500,310.002 C496.500,310.002 531.000,309.895 531.000,274.877 C531.000,239.155 496.500,241.024 496.500,241.024 Z" fill="url(#gradientFill-2)" fill-rule="evenodd"/>
+</svg>
diff --git a/docs/static_site/src/assets/img/algorithm.svg b/docs/static_site/src/assets/img/algorithm.svg
new file mode 100644
index 000000000000..1647cfa53ef3
--- /dev/null
+++ b/docs/static_site/src/assets/img/algorithm.svg
@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" enable-background="new 0 0 64 64" height="64" width="64" style=""><rect id="backgroundrect" width="100%" height="100%" x="0" y="0" fill="none" stroke="none"/><g class="currentLayer" style=""><title>Layer 1</title><g fill="#ffeaa7" id="svg_1"><path d="m20 56v6h-18v-12h18z" id="svg_2"/><path d="m62 2v12h-18v-6-6z" id="svg_3"/><path d="m11 3 8 6-8 6-8-6z" id="svg_4"/><path d="m62 26v12h-10v-6-6z" id="svg_5"/><circle cx="8" cy="32" r="6" id="svg_6"/><circle cx="56" cy="56" r="6" id="svg_7"/></g><path d="m41.41 35.59c.37.36.59.86.59 1.41 0 1.1-.9 2-2 2h-16c-.55 0-1.05-.22-1.41-.59-.37-.36-.59-.86-.59-1.41 0-1.1.9-2 2-2h16c.55 0 1.05.22 1.41.59z" fill="#5cd6b3" id="svg_8"/><path d="m41.41 25.59c.37.36.59.86.59 1.41 0 1.1-.9 2-2 2h-16c-.55 0-1.05-.22-1.41-.59-.37-.36-.59-.86-.59-1.41 0-1.1.9-2 2-2h16c.55 0 1.05.22 1.41.59z" fill="#5cd6b3" id="svg_9"/><path d="m45 41v2c0 1.1-.9 2-2 2h-8-6-8c-1.1 0-2-.9-2-2v-2c0-1.1.9-2 2-2h3 16 3c1.1 0 2 .9 2 2z" fill="#fcd770" id="svg_10"/><path d="m45 32v1c0 1.1-.9 2-2 2h-3-16-3c-1.1 0-2-.9-2-2v-1-1c0-1.1.9-2 2-2h3 16 3c1.1 0 2 .9 2 2z" fill="#fcd770" id="svg_11"/><path d="m45 21v2c0 1.1-.9 2-2 2h-3-16-3c-1.1 0-2-.9-2-2v-2c0-1.1.9-2 2-2h8 6 8c1.1 0 2 .9 2 2z" fill="#fcd770" id="svg_12"/><path d="m54 57h4v2h-4z" id="svg_13" class="" fill="#b6b6b6" fill-opacity="1" transform="translate(-0.37593984603881836,0) "/><path d="m54 53h4v2h-4z" id="svg_14" class="" fill="#b6b6b6" fill-opacity="1" transform="translate(-0.37593984603881836,0) "/><path d="m5.624060153961182,30.879699230194092 h4 v2 h-4 z" id="svg_16" class="" fill="#b6b6b6" fill-opacity="1"/><path d="m62.62406015396118,15 v-14 h-20 v6 h-4 c-2.757,0 -5,2.243 -5,5 v6 h-4 v-5 c0,-2.757 -2.243,-5 -5,-5 h-5.666 l-8.334,-6.25 l-9.667,7.25 l9.667,7.25 l8.334,-6.25 h5.666 c1.6540000000000001,0 3,1.346 3,3 v5 h-7 c-1.6540000000000001,0 -3,1.346 -3,3 v2 c0,1.6540000000000001 1.346,3 3,3 h0.18400000000000002 c-0.11200000000000002,0.31400000000000006 -0.18400000000000002,0.6480000000000001 -0.18400000000000002,1 s0.07200000000000001,0.6860000000000002 0.18400000000000002,1 h-0.18400000000000002 c-1.6540000000000001,0 -3,1.346 -3,3 h-3.08 c-0.48800000000000004,-3.387 -3.401,-6 -6.92,-6 c-3.859,0 -7,3.14 -7,7 s3.141,7 7,7 c3.519,0 6.432,-2.613 6.92,-6 h3.08 c0,1.6540000000000001 1.346,3 3,3 h0.18400000000000002 c-0.11200000000000002,0.31400000000000006 -0.18400000000000002,0.6480000000000001 -0.18400000000000002,1 s0.07200000000000001,0.6860000000000002 0.18400000000000002,1 h-0.18400000000000002 c-1.6540000000000001,0 -3,1.346 -3,3 v2 c0,1.6540000000000001 1.346,3 3,3 h7 v6 c0,1.6540000000000001 -1.346,3 -3,3 h-4 v-6 h-20 v14 h20 v-6 h4 c2.757,0 5,-2.243 5,-5 v-6 h4 v6 c0,2.757 2.243,5 5,5 h10.08 c0.48800000000000004,3.387 3.401,6 6.92,6 c3.859,0 7,-3.14 7,-7 s-3.141,-7 -7,-7 c-3.519,0 -6.432,2.613 -6.92,6 h-10.08 c-1.6540000000000001,0 -3,-1.346 -3,-3 v-6 h7 c1.6540000000000001,0 3,-1.346 3,-3 v-2 c0,-1.6540000000000001 -1.346,-3 -3,-3 h-0.18400000000000002 c0.11200000000000002,-0.31400000000000006 0.18400000000000002,-0.6480000000000001 0.18400000000000002,-1 s-0.07200000000000001,-0.6860000000000002 -0.18400000000000002,-1 h0.18400000000000002 c1.6540000000000001,0 3,-1.346 3,-3 h5 v6 h12 v-14 h-12 v6 h-5 c0,-1.6540000000000001 -1.346,-3 -3,-3 h-0.18400000000000002 c0.11200000000000002,-0.31400000000000006 0.18400000000000002,-0.6480000000000001 0.18400000000000002,-1 s-0.07200000000000001,-0.6860000000000002 -0.18400000000000002,-1 h0.18400000000000002 c1.6540000000000001,0 3,-1.346 3,-3 v-2 c0,-1.6540000000000001 -1.346,-3 -3,-3 h-7 v-6 c0,-1.6540000000000001 1.346,-3 3,-3 h4 v6 zm-52,-1.25 l-6.333,-4.75 l6.333,-4.75 l6.333,4.75 zm-3,23.25 c-2.757,0 -5,-2.243 -5,-5 s2.243,-5 5,-5 s5,2.243 5,5 s-2.243,5 -5,5 zm11,24 h-16 v-10 h16 zm37,-10 c2.757,0 5,2.243 5,5 s-2.243,5 -5,5 s-5,-2.243 -5,-5 s2.243,-5 5,-5 zm-3,-24 h8 v10 h-8 zm-9,14 v2 c0,0.551 -0.44800000000000006,1 -1,1 h-22 c-0.552,0 -1,-0.44900000000000007 -1,-1 v-2 c0,-0.551 0.44800000000000006,-1 1,-1 h3 h16 h3 c0.552,0 1,0.44900000000000007 1,1 zm-3,-4 c0,0.551 -0.44800000000000006,1 -1,1 h-16 c-0.552,0 -1,-0.44900000000000007 -1,-1 s0.44800000000000006,-1 1,-1 h16 c0.552,0 1,0.44900000000000007 1,1 zm3,-6 v2 c0,0.551 -0.44800000000000006,1 -1,1 h-3 h-16 h-3 c-0.552,0 -1,-0.44900000000000007 -1,-1 v-2 c0,-0.551 0.44800000000000006,-1 1,-1 h3 h16 h3 c0.552,0 1,0.44900000000000007 1,1 zm-3,-4 c0,0.551 -0.44800000000000006,1 -1,1 h-16 c-0.552,0 -1,-0.44900000000000007 -1,-1 s0.44800000000000006,-1 1,-1 h16 c0.552,0 1,0.44900000000000007 1,1 zm3,-6 v2 c0,0.551 -0.44800000000000006,1 -1,1 h-3 h-16 h-3 c-0.552,0 -1,-0.44900000000000007 -1,-1 v-2 c0,-0.551 0.44800000000000006,-1 1,-1 h22 c0.552,0 1,0.44900000000000007 1,1 zm1,-18 h16 v10 h-16 z" id="svg_17" class="" stroke="#ffffff" stroke-opacity="1" fill="#6d6d6d" fill-opacity="1" stroke-width="0" opacity="1"/><path d="m5 53h2v2h-2z" id="svg_18" class="selected" fill="#b6b6b6" fill-opacity="1"/><path d="m9 53h2v2h-2z" id="svg_19" class="" fill="#b6b6b6" fill-opacity="1"/><path d="m13 53h4v2h-4z" id="svg_20" class="" fill="#b6b6b6" fill-opacity="1"/><path d="m5 57h12v2h-12z" id="svg_21" class="" fill="#b6b6b6" fill-opacity="1"/><path d="m47.37593984603882,5 h12 v2 h-12 z" id="svg_22" class="" fill="#b6b6b6" fill-opacity="1" transform="translate(-0.7518796920776367,0.37593984603881836) "/><path d="m47 9h12v2h-12z" id="svg_23" class="" fill="#b6b6b6" fill-opacity="1" transform="translate(-0.37593984603881836,0) "/><path d="m8 8h6v2h-6z" id="svg_24" class="" fill="#b6b6b6" fill-opacity="1"/><path d="m55 29h4v2h-4z" id="svg_25" class="" fill="#b6b6b6" fill-opacity="1" transform="translate(-0.37593984603881836,0.37593984603881836) "/><path d="m55 33h4v2h-4z" id="svg_26" class="" fill="#b6b6b6" fill-opacity="1" transform="translate(-0.37593984603881836,0) "/></g></svg>
\ No newline at end of file
diff --git a/docs/static_site/src/assets/img/algorithmv1.svg b/docs/static_site/src/assets/img/algorithmv1.svg
new file mode 100644
index 000000000000..4c92d39c868f
--- /dev/null
+++ b/docs/static_site/src/assets/img/algorithmv1.svg
@@ -0,0 +1 @@
+<svg id="Layer_35" enable-background="new 0 0 64 64" height="512" viewBox="0 0 64 64" width="512" xmlns="http://www.w3.org/2000/svg"><g fill="#ffeaa7"><path d="m20 56v6h-18v-12h18z"/><path d="m62 2v12h-18v-6-6z"/><path d="m11 3 8 6-8 6-8-6z"/><path d="m62 26v12h-10v-6-6z"/><circle cx="8" cy="32" r="6"/><circle cx="56" cy="56" r="6"/></g><path d="m41.41 35.59c.37.36.59.86.59 1.41 0 1.1-.9 2-2 2h-16c-.55 0-1.05-.22-1.41-.59-.37-.36-.59-.86-.59-1.41 0-1.1.9-2 2-2h16c.55 0 1.05.22 1.41.59z" fill="#5cd6b3"/><path d="m41.41 25.59c.37.36.59.86.59 1.41 0 1.1-.9 2-2 2h-16c-.55 0-1.05-.22-1.41-.59-.37-.36-.59-.86-.59-1.41 0-1.1.9-2 2-2h16c.55 0 1.05.22 1.41.59z" fill="#5cd6b3"/><path d="m45 41v2c0 1.1-.9 2-2 2h-8-6-8c-1.1 0-2-.9-2-2v-2c0-1.1.9-2 2-2h3 16 3c1.1 0 2 .9 2 2z" fill="#fcd770"/><path d="m45 32v1c0 1.1-.9 2-2 2h-3-16-3c-1.1 0-2-.9-2-2v-1-1c0-1.1.9-2 2-2h3 16 3c1.1 0 2 .9 2 2z" fill="#fcd770"/><path d="m45 21v2c0 1.1-.9 2-2 2h-3-16-3c-1.1 0-2-.9-2-2v-2c0-1.1.9-2 2-2h8 6 8c1.1 0 2 .9 2 2z" fill="#fcd770"/><path d="m54 57h4v2h-4z"/><path d="m54 53h4v2h-4z"/><path d="m6 33h4v2h-4z"/><path d="m6 29h4v2h-4z"/><path d="m63 15v-14h-20v6h-4c-2.757 0-5 2.243-5 5v6h-4v-5c0-2.757-2.243-5-5-5h-5.666l-8.334-6.25-9.667 7.25 9.667 7.25 8.334-6.25h5.666c1.654 0 3 1.346 3 3v5h-7c-1.654 0-3 1.346-3 3v2c0 1.654 1.346 3 3 3h.184c-.112.314-.184.648-.184 1s.072.686.184 1h-.184c-1.654 0-3 1.346-3 3h-3.08c-.488-3.387-3.401-6-6.92-6-3.859 0-7 3.14-7 7s3.141 7 7 7c3.519 0 6.432-2.613 6.92-6h3.08c0 1.654 1.346 3 3 3h.184c-.112.314-.184.648-.184 1s.072.686.184 1h-.184c-1.654 0-3 1.346-3 3v2c0 1.654 1.346 3 3 3h7v6c0 1.654-1.346 3-3 3h-4v-6h-20v14h20v-6h4c2.757 0 5-2.243 5-5v-6h4v6c0 2.757 2.243 5 5 5h10.08c.488 3.387 3.401 6 6.92 6 3.859 0 7-3.14 7-7s-3.141-7-7-7c-3.519 0-6.432 2.613-6.92 6h-10.08c-1.654 0-3-1.346-3-3v-6h7c1.654 0 3-1.346 3-3v-2c0-1.654-1.346-3-3-3h-.184c.112-.314.184-.648.184-1s-.072-.686-.184-1h.184c1.654 0 3-1.346 3-3h5v6h12v-14h-12v6h-5c0-1.654-1.346-3-3-3h-.184c.112-.314.184-.648.184-1s-.072-.686-.184-1h.184c1.654 0 3-1.346 3-3v-2c0-1.654-1.346-3-3-3h-7v-6c0-1.654 1.346-3 3-3h4v6zm-52-1.25-6.333-4.75 6.333-4.75 6.333 4.75zm-3 23.25c-2.757 0-5-2.243-5-5s2.243-5 5-5 5 2.243 5 5-2.243 5-5 5zm11 24h-16v-10h16zm37-10c2.757 0 5 2.243 5 5s-2.243 5-5 5-5-2.243-5-5 2.243-5 5-5zm-3-24h8v10h-8zm-9 14v2c0 .551-.448 1-1 1h-22c-.552 0-1-.449-1-1v-2c0-.551.448-1 1-1h3 16 3c.552 0 1 .449 1 1zm-3-4c0 .551-.448 1-1 1h-16c-.552 0-1-.449-1-1s.448-1 1-1h16c.552 0 1 .449 1 1zm3-6v2c0 .551-.448 1-1 1h-3-16-3c-.552 0-1-.449-1-1v-2c0-.551.448-1 1-1h3 16 3c.552 0 1 .449 1 1zm-3-4c0 .551-.448 1-1 1h-16c-.552 0-1-.449-1-1s.448-1 1-1h16c.552 0 1 .449 1 1zm3-6v2c0 .551-.448 1-1 1h-3-16-3c-.552 0-1-.449-1-1v-2c0-.551.448-1 1-1h22c.552 0 1 .449 1 1zm1-18h16v10h-16z"/><path d="m5 53h2v2h-2z"/><path d="m9 53h2v2h-2z"/><path d="m13 53h4v2h-4z"/><path d="m5 57h12v2h-12z"/><path d="m47 5h12v2h-12z"/><path d="m47 9h12v2h-12z"/><path d="m8 8h6v2h-6z"/><path d="m55 29h4v2h-4z"/><path d="m55 33h4v2h-4z"/></svg>
\ No newline at end of file
diff --git a/docs/static_site/src/assets/img/apache_incubator_logo.png b/docs/static_site/src/assets/img/apache_incubator_logo.png
new file mode 100644
index 0000000000000000000000000000000000000000..96804c4a55a72bec853b3ae85bca0635207cb79f
GIT binary patch
literal 16552
zcmbt+^;?u*@b*KO$gY%#w1`WIq;xIP-7V-6Qqmm?$RdqPcPvOO9nvZwT>?uip>zmH
zH+<L6_kG`g;QeKvy{^6XoHJ+UoSC`jo+naELy?^H0Vx0g<jP7=9RR>N!hR1TCd7XJ
z{J0zf0L*|gR94R?b0^C?bFS<7>tbtyS7i%rRZPsc>6g(s;~ASmNB6{}RYGL!NZy5w
zvg8rTk$~PXA1#RA=M$bRqNjb^{Us+b5Sexw6#Qq)c`v}i-WxgJ_NqN6PO3QRg@DWH
zqc%t-aQ_Zr=(m@A|38nsI#3WnY7@w9<%8GK#U;^jJ^|%PSo0>EjF5p$xhBua?A!GF
zH9cI<4|@Hhc=G?7&59XnlQhpxErvR?x)bO=buM^l*<;Jy3w*gri|cz<y!hn8Spq4u
zME7rbAhCIsJl&l^^5X%2jg_v<g|aLvB>?#PD7SVGc5L5p8t8937Cf$5kUaiRk`Gmz
zG^AOEL&lbEf!`0!F28NWu6LP4v^m6F9>TQRh8^Q!JimnX4iEN(CoQiJU0RQZcnr7}
zUuC4B&st)suX`SJJO3MoSANQ1qa1m0R+_QV!ZmE~ZFb`LP1|-pv_P|&+Tp{H$@1#)
zVZ>Nn#BeU%X;v3c?PXz3r}p|HS7Xz`L#reag-31w$(lC?!^)X1DJR*nL-m%{u%5B*
zLU5U|Sn{RQ>8U_1N>j7x-0gZtef`~`yZW|lT~grJ6w{CG^nrA-6aOg(DxadaaS=sd
z`BV2c!1dx;({738fcf5))3%8(FJIop(H8wi#=uQ#(0!5S4Go8uj+_Mdw*UR7T8i$*
z)^WjNE<xPRjNey_vW11q?zZj_p2y*ngq5l7)t4u(BeL_h+x|Uozq&jgxXxyHI_lN=
zrssb*a)X~Vi?YnGEQKN<$6;}CTKw^p<gtAdW<El3vMAkSf9EI=bJX_vVeMD3zbKSK
zcSyK)z5PF9lIQgx2_M#YRew3{M`k)TK#*8}INr1DM<jjE7ZWADI2;ITIelkF1^`uq
z^|6^PcMXm#sXQ_NEgB!WLM=8q!c>$^cgpY?Nv#|iU*2gG^PO>fIJ)k);1^%x;M0;J
zn~>UEA^^xmmE(2H;$AwQ>F2%v&x~0pgGd?39w`=U?Ua*`CpCSZJF~s)&6$D}@)H4|
z)xeZ#MRlg=K(Cy1s=@jRcY5{J?i?3GCDH#5LT^%j%xDt~#&o(*zV_b4galyb6u#<~
zS~>*3)E9j5^QFvOvG+x+VbB*a@Thn(E>#q_jBtdfNR2Qwug+>MVQ`KTcn}V94+D{R
zC-;qBKN*5}H&)O3zMfY=jsH0b`7r&gmPiJI@5=&f(sU9C-R>}={cjKA(xr_Mr{+4Z
z`&dKaMXXCjC2{SiP{W8c@x#{}60i9HypYa87q%swF%^TKd;lQez!IkKXr&U8s`Dli
zW-N);3rUNFJAcy2HdXqTF9SJdW3A{O94|yHfB*a`%^}rS`^~K}WVPCA!c+;NQ&#hY
zQ7Mi8&;!%ZxckR!6XK!MaCtT@I(KwE3w+JD?o>DdTb465@UJIH$52T|XpZby%bhlv
zcdStF#o+)U)4%kq**cg=tG66)e9a?cZ&!9JCJx(6>e0bTIu`UF_>gzOL-JiL3%00Z
zh))0IM~H5<mP+7DV|46{MbF@)uz2lWJ?7^-E>BeJ2PKS%pD_U;@N`f$f3zQ&+6J42
zjSUO5xWly%mDf_qbZDZIhSx<nWlW?#Es>buYYlT@hOK*`WNPmE`DcE9D9!``nFNv(
zWfAT4MLtMeu=<Wqbeag)2X1o`$f{6$Kx$bOkLpAr*Wtvv|3`M!YUcc%v@~%}cUCv^
zscTt?XAb%z0Cn79HkDOcuThwy+Lmh!nRK$fI4C?*mRG??c@7T@6ENhpP!9XY+swJW
zpDGQUjvkwK=sL|40`bq)z0SQn`*ohJN`F`Eioqd=!~1E28OyRdlGBAVDb0|C#gE3Y
zTuJ$M*x2(TwMl^(y!ZACX4noiMhJf{SuafljzDU7V)o|1$O-*qnEBlAx4-r@p?6su
zT1HZv;y~64PjG2Id{*3ufg7!QtCK9XslFg#amxS?zfYvg8=pHpnOt1-!*#$n0IIP^
zp$3s83WR*#=;#%*KJl~oVtjF4GJkZWsKL`<W+e;F@wprf5D;(x0G6%?Qv|^~#4P<l
z?uWE-#h3G59={>(|9(hbkP=vDeBVU&yLnfAv)>HjDgJsUO}6X4K%9#7<1ix)Xhy`N
zqP$){cV&+kr1_&IH|w7Fp6;BJQsRCBn$A6^;5Qioj7|m4(J6r`rxVW%$kT0i{{G!u
zu)Y3W?LB_5*Ry8o0ttbeB6lpm4o3Loq&0nBAS7U&y6mtZ*8VYwN*<io{$`J=RK^6W
z5LZX*>Uzl$e;W*p;xIn~jcGJE-9FnnygyJg+>fl7*ig4aWsKi7T;VGC7Ox>o<+rpM
z%27r0^${>7_J_fLn~awe+OoTS*+EcvC=C6Uptu;yHtainS#CMMXMQ(tfmL^RhwtzL
z0N`iVvt*c7#{iI=Uhxi@%>t+Hc?BV>#m0^*Bo44MC&M~to16UmZq%gE0O_3qnJ$+@
zl&*?#9-@>z-xdD++2X~W4<4+B8$2#o=Rs{tEuO|?FWH%cMsH6do7Dl}CC&S~iN!_l
z*kG&R^8Ln#6b2nH06=4K6SXMz8_BoD!;1O<mxD941==`g#W3d+gwvFk$wHqZcXABW
zjK7D$W2s9B#96l{?5M$0kaE57PS1H07fZ;oU#h2R@+3XDILx^C8vv@7n<A6Ke7oMN
zpnQj4)sT0!G17=%M~WDVCZHq}I<L$4UOFbVQj0F}O26%uIvQ8)ATQgM`#y*YDuhn(
zMlrD7a5x2Pv8--=EW9jYvrcJc5|bz1k8fNY%TjW<4r|ne){l}`JGF2C0N8$@hE#y~
z+5Lk@{Nclee~J_-0x9bNH15h{te?c|fwnj=SOAY!RnWk0Mn^DzG^fJQDAVefVDqIw
zgFVK-&&A2j$DHhI8bje2ZwY1S=JtnmYL(B<GL6PXO7vNkH2$(GzJ-l(@cChSIfwbg
z6W4Je8o~Q2(Y;iD1N4ShQS%pWk4f_VI-6Gg&2_c5-#$((H?B#}4Zd1r`6C>DuTI9<
z>tr&2qynXEdUR<kommo4@@=qF!Vu+AP~idEU#q(grcr6Y&R6B7De{+6?$5k29qPDv
zK?=;n6J{1Jr!upZeE&yt(>JXrtlArI7S_cdOSLSY781nMXa$W1lS)(f&00715#IY+
zsvxGhb6Ik$@U6=0cJJMS)KK6r8Oh6IdH$rQ?5V!#jXBN}lVDbp?1o#r2{%uo)F!TT
zYHw13%H^10U2vJyZh0o6Mo5sI)l1dE8#zIFgS_+MC(FI?g`>eH3O|FL{h^@X#)oyA
z95;sHWLzA>oK^`hnD6B#Ys|t^)R`z>%;)?4H51)UR!zf6#n~vR-6JhnkPCss004^6
zJ}>Mj1n+<Tyt-8i(socxNV_$~T2x4y=_~ni$W>tky1AZPz?%$Dbjs7T;}gtS(T3B3
z5Ah(6>uBxNxdY0R^X#D#V21;1q5YwNVyj!hs2k4!v$$Hsxde5sL1_6KLqfcs%pknC
z^?3L)>H?QdOcsBcZp%KZ5;?4f;vqDdka2O)&%Ae8ozof?SC=#V3EyU4T0bsC6to4h
zKpT6N?2OU9)&qaJSbtj<I%sju9Dg55?+P2*TXdCjz+ncJ#`>OVX)hE!8ISkdEu2q<
zau(?xIBY3=5}p)Td~Oswz1<YBO>5BFE%hrjE7P_o@D8qwX3M&dKZa`rr>uPDc55jF
zp1mi^wnP-P8V)kW`&vFxPx;G`YFB`Guyv5EyLT`aWSu_3&aTdRRn!=qkGRRB(HTF$
z1!NqqJ1SD2aWQllli(4vHh)UHTrmX;^zfAVKiVBP>9_;%v=dCpY|)vpAm<=VqH-~-
z7Pq~|nvdH34v0a3(O9s;o@)CaiYJ~@xN9n%!9Q+g28v>-Z~^F+I5E(45^JgV@N|pj
zcis2=)!$AwuJg2=IKbOq?Myusfv%XhTX$(${Yb$Ss)2XPQ`2VDRa0inshVknMc3~b
zF$B?u?CxU<rT@$XFzub1y-NPf3$5so_2$qC9jRa(kc9FtxD!OJRv6y!7s9p#6#0~2
z^5m|C-R@d)!-fmld9M8)#pBzMsL@n4gwz9VFPENQA5J)Zw^x4qM<i|s&jg4EOwMM7
z>m)KgFRGMv*NhXgTf3r;7h-xZ!iNTrfv@xGX_RAfE|w10R>D(75uxn)*WZ(eUY;ez
zd#^JKVompF%v2TOOWdld`BglJ>9tQ+l^+Q$t~wtV5`fqPe_^jjLLm9;qrCGb0Y<zv
zEsO8d4rh1F_Eu~qmc_llcQ=M>yQG4~4(%^f)=lC-xM{v^T49_xI=@Xa0)#nMe{{#|
ztuQEp%`MXK0Olo=g`kG<pv_YRD8EcmOYrpv7T=MGdQY;{CZd&F(-l;ly!M9Q@1tkx
zIg&xq#S&bnqPH$q6B%5)?<*VM=&zUnlt8iBh7y@peQm_se3rUs>l5MNW<)(fxucf5
z<-^K#%~|&H=1u@ip@!*Tjo>t4lCTL9r^-{oXMr}2*hFcpHKqFEdlb+nMxe#EW#n}F
zxyBVYf2-<07h8KK0wofl;BB~LmEgxjm8C`HcRkQ(kuX^J0c7gZ=)LoM`D41r*Yk^R
zwokgZHTr3di)J$tzmo?#-%wmepDjG_>}M+n8)1H_weFWzZ4J}1UbxKOIGm$Xk|{0?
z@i>%0V`?@F4-)+x460X26VN46kX0U0bhWty%>4P`LxJ0;P90non-7AzSDdI6hmIv_
zvPArlQ&0E2`!KJ{>qVZJc(5C2l%FKBkbve+c65TnmHbDDKUkzZt^RJZUVLFmD6-K0
zTLNSf0(_RZj>wySHQLIzHT-Eb#u#qrOH1)Mu~FO1Th<}41i_zTOfTJhpp=CJz<U+C
zjH<&S;D2!*o~#WccUUUL=yA*v+>Q(yEphpB<%Mg#ch<3>mfOz5GD2S7B5Z(e=eOQ4
z00->qwVMERpK;jAW7(l|Zo$Pj<rWS&Co@a_8Yqm2bkHEZ)uj0wX6prBE_OLX1F^gi
zc#5KSCAxXw63Lc7G}so!6Y&j%Xr&V4NH4u79Z-YEg^iEeZjYcAQXW?EXG={5X^Glf
z&nL(JMjtDYF(=I#ktnJ8Kh=wxJ9w2dn^vFnhnxG-3SR`ZL2Gj@Iw~fnN$1V}Q#c|`
zKiPDfiU=Gy>HFzJ1+IWgcKg~8%dbgx<e!>^WwR;u#<Py%)SCNuX5v%iTzpO=$360q
z(R4B=j8Z*}Uv{Ib@COLI(H!DsFllnt3fL<*a&rzIjA@cYu`ur2cE8_$`7k{nEe5I<
z(U$DCcn@a|zU|OP7zYdU9m=@mzzs-PmgL|ybv)VpXZ<z~S=leT)IS~$-C;@0zSbgU
z;alX9d|+j$j(UUfa+rNNP;>WLfrq>yi*5KY^vgjtmrEF8%y@OUXMwKhrLnvTdckv!
zOGY{8?FOG5QOu6;W1Ld2VUUX8hh}F=%*jMXtNm$L&#hQSR#ARA*)#XDF@sbH@A!36
zTGOM@jr;EtvcuTR#m3!>y;mnnH=oVpF#mCM69y-R>DT4?C)CQhq={Jtd+D-S;eKA&
zLu_|aaXa|7`>iv-v9EZUm2N!++4kD1s&B4lhk3ULuFKrchEg*5|Lx@V7n0-r8hdyg
zrHB<=>!-C_S^TyWLmM&6M;eE(@k|WKni_X%omopyPwLSMS@i?dm<Gk-rX?$*UB^KQ
zeBdGUPhv+@UR|GY-c;L7Z<-oV4!k+J2-J4B(e3P)s`t)05ahxseDo-^wJ>_oq<C?h
zD$wLB-TEbk*wASv#j3dF+wduSCAFZxgH_w7p>PAg%QT(BH5DS5)<kS3i^$KZVI(5Y
zCX!OlG1I`?kTDYNBd*hbf1M7Z4e<;I<xd`VyJjY8ZoJ+wSt%U#d}TheRzi8gOfG}3
zRekNkNMc&$Xym)s6z5x__7102&82*ns<C(_5+s8Mu#42c_S$%wIla-A4NJOXy?iC%
zN!b>#@V5%ni_dxj*pW}ByMGT6N@!2!4SO#A=Yo7Jzdk$X<e7(RFiTe}U-5pF0%vHl
z(q%O8)nq&PwP^~vd8VdZwQZzgdx1KtTZ3FtCzM55qr}-j(sv4})_3`k%fCFDe9=zj
zvkpvl**`9O>}sgR-{VNsg}i8e6{dW+$fn6eLReZ!K2M-}LU&QHQTtoXLf8qPPiEX<
z%G@yU4@2IAUnmk*lQr0&^_qJD>0Hvm?;jINl@9?G;epINNuB;)HV@zar^jPtw6prk
z{+|FP$Zh{%FiSpt7)X<O>CK+B&F;|Pr30$6!O8z^#GvQ*hMm}y`^|iVSEj79QiWR&
zK>Iglyn~Rc!Pdn+NqL?tSA5`W2$}Px)9Q)8beih&c|N`2v5CiUa+I++N>fq%n*9mS
z=R4&zc7%Wo`i>F*uMZ=HVLTb)w*6jQE|)`!0AN&mk}s7K)F4c(4cAtctx*7vt-^ii
zWKsreCbMcnVFX~F7hN|1V6si1)6W7Hp7MM82KDrljs=~SX+lALyM~JIxAb8F_a9%G
zDz=BKocvz_Zwum|TC2TG8;;z{TG))C@_R|AQm6O)F!;eYe4vI~yM6Pn0!i-o{>WRm
z8|T_y*By^#kptlmXdw@~#^hL3(54zNp=Y_gXmVl{`a%<@#a5Cs#G?LfZt1nJ9o0^u
zJoHu-v&gGE6QE3?^mB6A`!@YALKKkc>VL?*gQU_2GUQm1tsnqOM0RF?KDz$|xyJ9#
zggqH@Q+jLRUt8tnm>t3`F8}Tr%Dpi+IoW6=<|;E#U`b`-+HE?LuB&PAZmy;W)uH0N
zwGL3cj`#q=%~F>bVHc^$l2f(JQ!{cQV9YUfNS|}rqpn@|=`JfHSxGk`H1naSKd{43
zkVKo?Rt1VbXyI*OCa__q5+2ug@Sopo#Hv)Vusua*%N*s22e!9)%=*)0`vr1B(SmNE
z<^u_f0hQraD_kB<_e~-UM3%b~sK060j<0-c{(Zsir>gO-d$P3qZ4*>kbw4}e_9orz
z!BEPD2;;eYXIOEprnj`}i!xOw2E`=?ZcN)mX&0`M;;D7n+-2*)Co3BqllFk$AOmOb
zSTZN{sA`VFd~4hLSH@^(o&Gn_x-if^Fb@Ql1Tz+=GHn1oquUVU=wD<nZm!kY*j=*x
zP-7j2aQjoe_uG5@r=3*Qc2~;0e>YDd&gV&TRA-S+4>fMf^ohu-z~Y#pB<IpAFJF>e
ziQi2>H`!|yU!YU};eJ7ruaTpsL1#9q2Fyly2&bwU4sDY97x0xUfs^#UbasF6Vx67-
zaCTxbLiGWtaBe!0*Qq>rUTMM9G%j(vH*3*K!|UO_Ma@~BpFjR#Q-90Yxttmf&Wkm}
zCq6e=|3U95uur6a4n7TEp6@36;IqA=yy!lbE{hWSQ%2*|KTqFRrg!eap7WqjQ8}Q1
zVG&4jthHa#<xs?=|F=98c6r;<Szm~da>WKQi_4pz!?y0|w*-7$%6Qt^5hyk>Sv-RI
z-fHd7<*h=Hu8pgNL`dKO$qLO<REw|IQ&JNJaEGt<gl&qETy$~&aN4{mkWrpUpb#uV
zm`&gtj7?E7dr&fK^>pFw@|NpNwR)3(@*jqzfbCg7lZJW&5n&uw3;n%Czg?s24}%|S
zEVv|9FOyxQ<~aIK{u6im(HFTKepstWmU-J@I{`!1_Jw|%VcYRs^$rRxNn6)of8w#a
zaN|QX)iCknkqqM0B(3TAs$}k@Dx$UABhp2BmTl|*jKo$Y=W=zijgnNDeRbNF4cGY`
zdlfK7_Y}#sb;4_>%=TP`DZG)s-aFAry)kC3I$v?+CU4br?=o!tMM|oGljFqBKly%7
zDgH9r<ryz_`ThXFmkF5u%fyN(VOSB{U$b9y>@z7e+ZBh-Qd%F!SG@nUe`eCzUer_+
zd9;1M9B2FLe$pStn$xcY<pg-SZ`X!q`TpP9uu^useFEST17){g&u<LFI)92gH@~}A
zF|Pr}^GFO}H;*ia_yU0AHvZdLS18VEL1Q*3X2q#DJCGJk0{WV}ZFo|+_Tde8oY&66
zKiIZ|7gxPWMC01ce-!u%+^@7l=qGuC2M7RQV#28HDl%f^jqQwv)AmMugKLk_QQh&u
zk$Wx%J*AAN=h$|rsvS&WH4{7UGOgL?Q2`9;ay;mK{;xz3rotR<A?<pQZGqpw*s`;)
zHX-LL7<98)QD$7(IN+MyP*UecI$K%LXsYQ_rrK4uRW2164C(SNnRCAFktDk;g8cZ|
z#??7^o#tz9!}tP<^#CJqJ5F$$3>5S1aCUHx2xxZyY#GYJZ9Qm~q#}yRfDdb_%%c#^
zmJm-I0SdLOGTcKo*rQQ||8<w9mu)!Et0fs!Y?x2e|7s?f>CD}2zP<B-&#Z~>wv`&E
z|DHr6qb3&{a35Mj-wEhrcF(Qr7IR|`T*cdJmLf4Xguv+0>WVqp*kkbg$Y5t~;Ak);
ztO_Uh9$4hA(TF}!#Z))sU-*iI<%CiW%FrlYgzyt%50!@wjh?u?VCm9qaIysJFGG)n
zS$7FX7akYLe?Y0@{0fPkYOc6MI$<(a$69xu597g&V&S1cF72Z|YkVLoO*&qOF-7s(
zAq>7Ie<1n$8oim9x&?Brvt$^QK&0gm0J6{lxE%@pfLQX&H5=dwr5u8U<sYC7a^+_Y
zLhN`)-a}3t+A~F_Wu(qr#?ChENMo@;jn~Nf1!IUOARxeMz+%nRdv)`I1uCKcc<>CJ
z3|f+a4q}{)A;)mU!IOl-<1Zjw5GF>bQuX%hV0nG^OJjrAd)lncS!p8u#dmEg6uZI!
zK60=NxpH0f*}6Q(y~RIYP9@e;6ha9ANB-5$1%tTs+0O*yF)@>idtdY!WO^R1+qXcv
z=Z1}VC2xKGeD|Kpm8m=kI3DUi&A=+(ue#mti_l{sYlGu*hLhfv8#xtf*H1xBexXtu
zvTipo(%Yiah)7w`&odJH6&OnIK#vSrx+bk)q_1q$p(4niZ*AP?o8{m+0Us6iFHZ?W
zR?i#C>f4o6&QIS$x9WnB^*1auQBHRilr&2be*HD&o%~6z4{a5=X|_K(&__a-dVD0L
zB&N1Ip$?MDDougBQ}@QkfWOSFvsS%{{cheU#K98ZmMUAR#5xM3ty(J3ZaC_2FdQ*#
zV(R>zXPi0Y&lk3`Qqr2B_N7RU5yuoERP7_~W}ZxkX43~+J)0+jOh<b64PJ&E#E4|+
z!S|L}mV`XZ391UL|A-|NI>q4H>^|q*D_851^#~-{!FiO=h7$tXXR<1~rjk>FtP()F
zZyY`<g6w<rRqVU1t7CA!YOAo<p*tdL+B9aRCo0vja%5@J+ZSBxFgswgEeS9L8t{l4
zb{7kn>!_SJP6*Vy7#-8#Lb*Tb?2-U#P#&m<&VuI!^-Cnrl<N{HzKo}57HI6$47>kr
zB38Ezg<Pp~Vybbarq_Lj6^x_}LN)VmG9-P@)LIrp2*S^J8S`6hdB7oFb(8ZtC8R9z
za7M0%hv)hASi9(9{!T~4g3r2?5xYG2u{1_Uj`%l*<qp|EjkQjHirLhVub((Qd?P+r
zKt6VP<yEC_{HZh56Qm9E5<V7#s!#AMvd~uX>}x%Q-&gFqe19jKAH8fC5?Pp`<w`(o
zroBL%U%=)gD5#ku>Q|p2S{yT2qCPeiy?7`(zk=WxFR;D@2Z(nsoOq)SpGp;g7TFlI
zEzEmN0CqEa1Su-Tgp+wG0eUoLJ&-`ejV$NXdsS`ZD9Os1TEj)tecybx!CW4iF!F$e
z1y^w>y4xD>(4I5SA&SH_StwsC#yEMBtoiLa$E4_M)(0sA$FfCp$mP;hLL4)TF@!4H
z%+jDGpV=h(^e+|P6Q%T}&nKj_0%IS^>BOQ;?(H|2Z8#P9jHYJ3(bQdIH5k*YR<<g}
z-~fCU#yN;^E%C@2xkgK3tk%1eSxn&@YEOQu0^l~HFE5|A$+3m~HI0bFheDBhg?r_W
zJHiipd?k8{n)P~~BI7@n7^T7QLaZ-+-@$VP+||)j=hUM-k#6L~q@d@FE|4zbz?tQd
zn+hS_bZ6DVpXpXZx^ws-`nut`;SsV3zZ?vDLbu?oE}^w}WdKcoER>u?UFe>8_Xiif
ziTQ3ak8#WB4sAT|xcuY%TJZjf^}sv0z(*c=JwErb_fL$I^(*S7_&zo#=@?@uv8K7W
zHf@$dmq$6~1HEdUc4J-=fEYIND)U-^+npVFqEhbaFeiHJ`odoEzK0F`OtBC*Dc-gG
zmU)qJ@rOaPsffJo_eDW5lh+CdFp7@Yv7?ULiKA%RFN&rq(Pr^GHdf<=gthqW|Eg)t
zWL2SOeFN?4$7+&<RSW>%CJoe^Y?}Z+BmfO5ivg~T0Dz#>rU0h#!KZ`hGeyXxqp<;c
z(=#niPWUfH#^*LsepX}vqnrVJur(HDJSl1+4kb>|uTy8~Qe%CPb|D?HGnzmx2e)?+
z>lqY+KWE9fs6&(3wBv#o6}mLVxa6T3lw%fhaMeP@yBshJR1!K)cW_n4s^D;4f^aL#
zT@9pe#*7**^FXa&3M-OMD$ij&18**Ci@)ghC`1IlB&3V07Yq=F5uv9a>{A0F1jums
zs<1*FK>bSOTu?aoc0@ZkZa9Ab;DttHQAWC(ADss^sQNv8SD{OnbmBFdf|j*CUfmZP
zF|#VNN9g@uF2LP;audokO@W$3x5ip3lDtTX?|bBGP(&2Jrg_=aBh758F86Pq^Yl)b
z8+YL;I~0YM%C!JvTqSK`tIk#>wzBjoBP^>SxE4kbn>l~iCYvp+cZ-Q8uE#LS1ym=R
zbJ!#y;!wdIrHw>48><+DR~8hyAn@k|Kn0G{aO~@`JZqZ@8aYAyBpyK2w}bESCF+=7
zr=L9ZsJ{?l`hgSStgL4zGa3TGU_&!{*<{TnCH<3+sEM_hWr`bCf-r8)gg9SLa(z{^
z1NUze*Ky#VOx>Cn$c{gj2$m|BodxwU_^VDVTB*a0j0FnwC$-&G6{=UBP{Dop1uFF&
zMa4qX@Sc)^w8;Qs2&^8&T4rxBP!p@>3q1T(VkSlN_1xqK4ZY27#*UCqKkf4V*#*FC
zHg$ZRL(C#aF812kV0gP04ma60qxR_gKKRbo8g54qfer7;4Ai_~jv6KgQ+LM}u*ty*
zl3_6#O`U{^mJSTl{=J5Il+{|evSj$`57RYw(?`{Z^gqg!PBgb=T$CAJa=rXz&tb9H
z>T^Du{oZf~5&Y&Az>3oa)Yx{gx1C^$?JD%kNoJzNJ4|N3>_&pDt*T)gaP&oUu5R*R
zD<kWgpqmB}n27+$tJm=O`C=qs`&Q@`#*3okZaRWruoCO&9fDw2D`O^$^vM!PKp?Kq
z8qLyPRC4?fB-=_?h<Jva5ND~Ud))qGOb-54wX2=f1kr8UI##p&C4orZ)o}7ur)%2P
zBYUoD9=#V5$mqJY<?r_~NC>uIMelbg)boNT`0;0jZj<F+(*|8NV^Xy~4~x_$6%}9E
zuQigmP`d$A2rO;8d9kowJ55tbOa30~Y=@T9xx$H?c_SG3V8cln)qN4psh0qj3)nGy
zJlO*VKwB*oU=*{8WWQFmZ#;8;v~w2xzK>InVIBq>gJ#P)RXA)=*DaaZr%Jknp7ggm
z>1D6A5$-3jwU^zlUGh8}Dj5s#n>iZuI}n(dC@-Dy8t{IzcwLpNUHDU*YuoSB3xEgU
zBK=fdp~sxhlwU5r0XKyRN%|zss8<qoH|(fmOSs5-DPv-A7bf3`MvHwMe8^Vc{y=ol
zY_;*k9^;h+vTk22)yRHlqe6%}CX($}u<xVQ+ia>Z_1M0QWwy4H{SiRzXWY?FCb_!x
zc&<0Fcsn><oIg-+AqWwJGF>HPYd1doQ^w)(u)AgF#eoysWrFv-+vvLZp0qf+gKuV1
zOn$Or3lCw2TXlJ~M?)bXQzc{ueW(mgp_R#uo^IIL=2e0w*ml-B`G+A6ZbG`FkfTv=
zva;F3Ni9BAe=4qbz8-qmla^WH_}>yCtFb{ao!^LpAE%<#PW}%)mZP0+*pB)S2sK3?
zn`FAZj6GWYS<~tjqM|#uqlj@b9ocCNu>GB}$CN20w)gya*U8Ly=I><5dYe(h@6VN-
zA`5(b+_Fvi+w`Tz?13AocX%~!rVv=_`33VTXusw&tS?N5bD2z7--d>@-GbP+89-7=
z&&9omTHN`CdK=rw6xY*uSf&Y~wmUD@W6AG0PjWRs<$wm{gATB6{xXRrzm1GV_1Rxb
z`Nl$PjF%wuqjbuIOKOoyqe;yu1V1fcQb^xi54qUVW@~pWF`q21tv@)r`;HL5xEk4S
zZ&zw@n}2w7zy2ys>v(8V4fp<|zJu>X4UzFU0BGVZ_muryYjH3VVS1Sad7awN{luQ*
zP$wi!fS1T-d9v#96@oyZ)(LZ~A)T!Bh@bFI4fl4l+IkDnzJS-@MC8VE2XCMzK;Y*y
z_lmI#6~^H3*w3OgnomISc6Jpuu_vobg6y1Bz!MxN-E|0;KRYvW1?NwD8*Opicg%}b
zr|mzRJ^nj2%Vn$bM6^P5A55xCX&rFnrYqtN3?E$M=Mms?da`n=tnhV=C83+`z(aS=
zS;EeQ$9+-9J?9Np#^@@w^(39L^CnEgjzeuhy%Z-!^~aAnEihXP2JHfb-_!APN8E7_
zp#W;J*<`N%r6E#$!AX%5xL?1yz$<@;lgX(c15@U<MYRF2;GL#%lCcKQRKF=^89f8x
z=$6x*O9hBG=w7{Npdj%}j2Bd5LC4Wou8aP<4wT?B$_OoI&G%<bhBy==jC(YAK{jF8
zI8&a6=bmWLex};`wU$cTw&9P4oynpcuxrtFGoARy!7CZ5nk0Um{`c$UGF^w){UGhe
zC_DfJ%~LIb`ucuq)Mz`y(22s{k&v1uZ_P1RsOO!|c?_;x5OkDd#<J8yi+{BR;;0bc
zcO5QKFmE9Ml&)&*;BND&gn-iw?~gz4PtDj0CqcwuqRS?$!v3u9g9!_&n@^Ay+<K-T
z%KQ49MqD#jfv*itwtwL4_aZAn`?Z;mRI&TWE9KOh_ItKk%CpCPr~+n`#mq>t*>Jb}
z1?1Sw$diDSO2DpVXmLNkTHSxYt)TNwt3WxUq`@P+ZXezC&3;MW@~I;FU>)(DVs$Z2
z4_c6!l6qrt(I5X<dU5-<PjOea2!0}~O$QgI-5DH&i{KWQ%%7iQZ9g|t83jJme*opb
z<b5Oum*mgVR0tKIdS}G#b(aW86p%%kV#D+u!$$OMWY2QO8e5LemT(9EzE40*`=%Ux
z1_yTtVLkdO`U&!}jhH4>;V|*<p{nhgc6eep1s0_5_mHB$jFTY^?6MG-*oKU_SO##s
zA|42cJl<{63k_RA`+iN$E<j)Ub(hZ+coHe(%>BGZ6FB-FQnOb}9UtmG&HCdh=C@bd
zquZU#p~}CYB?liC<B%<mBwN~!hSP<zg2xi&daX7dvV`p=^TaNf$Lp*y+)-8WwJADO
zfh^7lv-YRLh8lJlXZrPVqx(D(;RZA+@v(NObsMp#VNW8>9zZ?3LinRX(X>i=6igkH
zWeZQf<TbRr9QHR$$<p^GryqY%+uQI$)9}4`k4v&I)kZTloGo3$)9&AM(C5_1u{$Sg
zRI|}u*W8564?@prTNJ<k`kQFM&@rus#SqVmRZQ|_Q}he&?E9i53Ey2}^ogrkb1uk{
z1YjqIsBSr5&GUKjWY~EHr79%nfZiT!54o~&D!44r7t8!M6+8%T2HCC@{w!KKDC6-2
z1#2^(QiCD{_AB82yH~ZR3okdk7dE`RTP*|3@!XJ87oF_`RPFkqAvG3~$*9cJfxm?k
zHJ)toDA<cEw}}|lj<ojfxRG|(0KFPQ_G+I^=5A&|EPi+K6nZ%YKA{HhJ!Z8$m4MEr
z!7JVat}$$n4!Yz&e9x^nP$|($CcG|+Nz!?j1M6{Z$7+=^&IKSiD5eP2d!L|2H6&z6
zobYT7`s{Gz4XOhGUUWHJ_uI5Mf{pSjYbU2klnwbp>??z*jHUKJ-UJ4Z!B-kg8XB7l
zd^7a57vk+)2gDgQorwb*?~9K)x%P8d243<M#EPHAetu2fpGd*6he`{_4cQtJ?!yEY
zae)On#8@rvC{Hz7kHIVoKMX2CcwEdETr)F2<?<cAm2Tv=X@6!Hh&54x4t|sE=<O2r
z4~yl-W7!ZJ#y{>-l^2np)8Hn!j?SEkHf1{z2)9G-bTMA~CS0zggchgkM3IXupWDNA
z*tnALG&PJoEZNy*enIM)s<nP#rBLh=!9l)@jpP2#t;KWqVtQV&Z@e;hy`_t8FY}^W
zXJUd{c2ZqAKZ`HX9<jCA#jYQGNDCw{{|I+A9l&A1@3OA`l%yjXLzSd+d&o7OR%Y_}
zUL^DdnIklSKjY$~vf`_0jVI~!PXzY1bstqCjiB7}T|Oa0{hq=y5FvVSE$Mqo?2dLU
ze-SZz_lq<v?6E&uQJ3F@d|ky@-4m6ZY)}?6s!K|rkZy=RJ|1ov{brjb`{<rC9ik`W
z!>!F!z9PQK`TWrkeKPv49xENQZ7<8El#yc^X$;7kUBFf_cc1ugVlPR@u__A`V%xz_
zDoMdTz&XNh+v<eD*of^lS5(UCa(HH^Z?=MP5jyQb6WG}8X}<>}m`!u7{&5(Hu0W38
z{Z-!UJ_4IHX6-$(ME#({tRU}=<81x&LPg8Vz=jwCS*3~4h6ji5QlgQsLel#49Bz#|
z-u8)Q6&pHXPTCK*LJOU!15tkHPfv4}c!=&?4DBlHy`&;z>1s4_Sj-iWBUW{|-qX-n
z6_T4W5qT~46v<6eHYHRgINk?1q=|kTq}HCmfO}lm#A6Rzm!Cf+oVgd-<sZAO=8i>9
z;_K3YJ|SI{=dT36_;Ryk7|dzzBizysCE#U}c7EWv%|i%Jedfr+O0#mac?Safft2bP
zR(i(5$}wdt_BLA|2yu#?UElTI4cs01%G@5fci78X=@h*7%y8)H?N&B0SJCqh&=T+&
z)jU>pn9>Ph4v{TvTL<G41NWOdzciv}tmaI$`%=iI+y0(BQ#QtVkr^<d-kn^Wsstet
zsJ$om+AIa3bCV6fiGkB~`r3qomZX8d#Z_BekAi5<f;wy~_}p)CXYRIP!&8mHV%#3r
z?AUJW2pdi|BsWL?P$)j5GHkVfVj<dyJ9%8M9l=jXC-%9)Nikc1?G9+)8TjI$z8(eh
z`7wxho&KUL@#vBx-yigBnOoy=%@)BC9sggBvYHx;(2%gQS0m<uBRoyQb95b7f5(l0
z5Ej$uY&(7ZiTYD%&DaU9i_>$Yv;EH3qh42rz>(9Aoy5*g1ealZ+xotAqo5$#$KkR~
zgweQ8%?0@$#Hz({58OdgYYsu~2^OKn>h3~WBDcEx2DdLVpQZ(DRvZ1{!}?!@_G9Q#
zAYo`2K}@$by-b9Rh_N%>J{c`WpZFfXh-2!E$Uqk2U{0?09u?~NHSlCWF%9o^$tVt>
z(Q^7TP?G)fH>FGVR>Si=f@aIB&P?@%rsISG9Dp!}{#yG2(cEY`);N{_gW59?>u2@|
zaz$694H-d|6@w7O>(qEO+R$6_f;K79;WC3)lqT#+8tuzx`?^&2tGMdLAHorC9xNEL
zC}L{AAA{~D+lf&X5%BgsL1{cCXaI{uq+4ghPO0?(2){!k%d&Uw2}sVP{;Rp1N)rRH
z!f;o@A-sLz3%T@`sNGjHa^fML(yM~v1qpUb771ZVyQzrv;M?W35i^hNHcXaVueviQ
zO$&|Fu6@$AACGgX%T~hf@EXhzvjT71qp#i;X17w1x*VdQa5mFlGz|sHY)4i*DW<Q-
ztOxWsiCNY_WAZrtdCSlDA|{<doljWBAmt~`Vk#xk3*08#%HfIZ^jI|Oe%@eZfez>M
zSly%8a3+<Ky{mrE*xm4W_DM8d(DsN*N%5hRqJz06G)Bra_im*QXB~I0C{OEN;E&(I
zr!R)$%zYP8<XqeGQ4hW6oM;(Nj>U5xo=t~0N*FB^um3dY&Z@cB$ThH?L7(wP&%vvH
zMc8ic#ix_oo~Arc-UC^V%o>s5<^yFHFRPU?q6*in%_&mW?5~6El0S{O8$N9}+X6BR
z=slC|7Q9~lMD44qJ0)gQD6*EJnwpwu-||=0(|g~Y1@5GZE)%OewEbAub};8hk5n6#
zuL!)K!6qVY@8L`|ay0+wo;yJ>(3nU>3zJU&18DidOn+$M>jrKIN!I=iZ7?E}>LOP0
zkL$?S1o@8r_Z<?R^YT*>EO&sAI-d2!NQ%2}N?j_9>su=<dji(IU<&fzwfE6=rMOmI
zjF|SnEga6XQrvV0tDi78-X$3yF^o*3-oFikB{r9^s`yCEf~)YM*6H#t3v^-NNir-8
zTxb^W=o<b3&i{d+-l-)$4KD(IRDeWwPZ7}rDsEX=4?5POK>Vz{c6m_3OvPAm;Lq>|
zY17Xnopvj%(?~E);zGW;PR>b9>lAD_X0ubVf!={NaM^mNym~5qtmZ=Q|9yNf>!`DM
zF@{2(82|<vvfOi;?70$kyw!%S^$|i}x_;89?F9`#OJN9JXJ@B0x9_8nb~@NMkGr1y
z<Wf8^ELAqmpTsX4I=r%^I7e5pt#QnS<p{)Qdsf(?wrho+eA2ln0=&#mW9E==G`VRG
z$+I}ajA#t}t>mEe=Tw_VjfN_cl<%JVvV{2*PV=9=gA-jORdYLp=x|n2HKdK2ELs~I
zXm+@1H~HASPrD@IPH7y{8n$#^p`j1@vdL&Fy@#?|^c$9zk9jNj^6b}UaNzOD7%QWL
zGE*MeyDoTF@@n?}nWy;fendgVo!a>T-`zp398a7OAb0O^fs|?R2tzhcgBmg#>&x(o
zGw&t;PI1D#`_8KZDa+T|g03h*%ECa#o;p~>Sn6Z9^{YCxufLC=h{y=EE4rg``MKpf
zMnlgixxPR%VNYD#4&!BEZ%_b$!_i_t@1&f+hf`t|5oyFdFe?Y4_9Ie&cvd2Qigfr^
z+bo2ERO`_LcnEpcX6c-UgDq61@Aynl7bn-25b<YogQ?qe$_moSQ~$4jzl~R39{q0j
z($~+j?b8^<MXoygAGLdvGc{`e2xtvo>y}>pJ6h8eg&q%)srFg=OH^U}D)zO!e;wML
zbdm<_L|KSsQj&fr7af34KUg#&W~tHM!iKrSWeEPt*!tz?w8<$cm3xM~G*K}_#aW+U
zu6ZI6bmnYGMA?wkmx@c}kSzr&dN8b&2;Mt&QZQ|lO#Q^82Fr=$LP`;rnIk7&57cPf
z)u92uvZ~ME(dloOwZSIOP@7@V*UlECk`|0E&3|vAPWBNyDWBSB0+$9T<;@Qx4G}^X
zmt%pU!V6AiDIT2BcYf@2@p3SnjqqXmQf59C?Ft_^dB+`IEmNGWTgF1Rwe{<c-kFm_
zR-41!bmhA=>(Db_;8i-DwjF1Brdd*ha>R?Y%@Hu;V2bHSlw$_6tA}>>_Gye!-2nnJ
z|KN<qQ6X#s3X!RvL<cxwhR^&VNI4O)Bblz~hDYY?qj$hN4-3uS5VG&Zx^YUp$sKM&
zH*d(cUHu#?N*Xvn@AsFqnpi84nmtTy+f`~#BKfk~iebFA>i4khxp9PJg^8Yb$grgX
z>I6ABr)FPHiv$uK=TO?vpI5y`x5n&B-?+Fm7CFz`MieXVw8{6OdXmJjM<(nYg1<};
zl?-#b17x}xivFx@-EOAv|0qFC=ZGad6Q@2N(^ioLLoJ|JP{`!HuFw6hnY3_te}BI)
zu}-5CrrjaQIMI*@3vxQ>W<|@W$Pth6-r*CVIBNM(Q_d**krsc-<;b8v>j?!kV9|5_
zY3-M=dNc_u>FaV*KnAMlfXcuL39a}JL9P*!aO(s2Ot#BHsRlipMowu--n6)%1g!@P
zxcqn@g9A8X+;(#Urc5R!O9N0$p$9YcpD0?cDMTbae+m`n)Z9#yOEC&sGg#jC$o*<~
z)PLK)Qyq1WTwMqk@T-{gz9C?`^3JjzNcz5)L%R{l%$fv=c?6#S05|3+-MC}3n_yx%
z5^jc`ac6|^3Gmfsq#J+Lvq5Hx^_e)ZBkv%b6Vp5!cD_`YYC?0^mQDxf@PHr65XCcf
zO>ZccVgxfzv2~9@`$<Dq2OPvkAT^(Lh*{if%9!)Bz}4oCcs509p7top884ugJwd7U
zL0r%2K7^+Gn!!(tb;@%c(~$Kn(B9S#MW1%A45U5{`-}%DsY_GfhIG1Z>VXq7mi7E!
zcbLZiox)Ow>lj!Y2DW#-2O@-7n|Djf8YKh9KW&8=$Cte-j+S;94(3=Csw$UY)JXJ)
zqGV)r%~jP)p142$QPz_qSXq+SM5yDsGU9WCj((O)o(7h~0ul41)DCHB5m+a@?&hRY
zG97QbV2eb&GB%Ki@?w8I1I7}|9{^s;PNKh`4?o2QJqKo$mXxdt)5l(6a1TXT_Q##w
zsgrQ<M{GG8o4i>5J<s7pIyZ20)l5uUSz%5<$ccwoW{HivIcJ`V#k1>iZIwyWWc@m-
z3I|ppO1jsQf%gD}EDd-?PT=>`$|nv!E6Xi}y$3aq+2gnADJOgO-mZ<P((sbO>OSgl
zk^}Vv2Im@a)bD@jAGbYS-0V8{<l4GSl(DfdAzN&5@zLL7q!V~WcO~29&Crvk3vc*G
zVu5cen@^#JUZnZ+xf?tk2+&p$>anh_C!4XE#?qbe?z8&wj0+9k(diFX;LxX!U2e_V
zY=s%KQwK{>Cd0^eRnBe7qe!PbgE<voyRy*siXdR7*q3~gqf&7%9oZ=5Kbd?#uKo*9
zRZ~&2uIztfe=%oFc6B9pE&GUoSe2HCDY2vQyn1P!UA}VmFnV)<cB%rUBRV_cvyz^5
zd54j&WxB?b*1CGS*<_n}yWfh1bO*ikd{__DfM-nP4JCd?K{^4(7ErNf#!mf~-88jn
z{IfSU2RH)TtWoAC*m+*LA@P7GNHJ9&vqY>XU<Y4YV$od{b1dNZ<4Hs}A=pw+W$1{D
zBP`xz+y345kY@QLUGvSay*<Cp^y=()M(%P+S*;`Ci-FRfFD&wQMiVOtaiZiO?63DH
zHjz!;Vk(Mn>)t5HTdEkJ=A5pOW9~9F{#NWpl!@Q-P^V-5f>L}8de%@x8;swDE$}ht
z_%LBH%S;$b<E{DWA0uuW6T5X*7O~nOZ}U^d4l?kV$hwRXF^kP5U57G6lM5kX6#~B{
z01}Xf${K~=*|4=sT)J#(`hSl+6^J4MN+9vGXlMj^=J`>fI2w1rt1X1jmeF;!FypEJ
zuVJ(8Xzbnh2E^x&9yyOYabH;#&u%`;mmE(2eJ_{R@8qNRu37NLV>Z^PkRyi(IPUxr
zD6zw5tNUpknyy&doqbh`4Pbud^V6}4yG#e-`6hK!jiJZ*1(bfeA<)#wljcE>D`V)L
z+WQSo6eAvOI;Ah4^-5kz5bJzXBu)PFh`SI0gVwoI(OtlmD8~|l79O#yXuTSc8S#2;
z_1EuoyJ;f{;(hwCeXQzf+t|fnT)Y28AJNs$K+ZbPupbo_SCj|@RP{#RJy$EwO2^W4
z>XfO+LhN^mY4#VDYS06a9|gnNvU-lhXvd-$UZDTEc@{vZnlP^je=dN%M(A?wK97&+
z^h+|AOiK?MYTrvRP?03-6O2swGGdhMiKS;_o2iO*IE#mI!LIJ^hXFPgTr^R~^3X-d
zH=XxsGoJH=J+8YrKmsHHrL*PRae)mIAbL1>`SmAJsvr}>?{e?qd#aED9Oeo{KL)J?
z(H|Hr0FB9Y$?RsbE3x*r5o53Lp&*!?yXim|a{cCVztQsitdVuHPX;&}sP7QB&k6PN
zA!a8eeO&W)Vd?nBrSZ__8PLji+|Ed?znVbc_xGjEez$+lUhtv)RPoRI{MdZ6_IkRJ
zH{frgWP!7^B|;|$%R`BehGOorggF@-FyqOij>qHcVt4=8#LK}0#hVSoVwRuB@gLea
zJdcrsr(tg=5$%3#4<f?k3G!iT$*0c2+<$d)dGND|jmVkY`UPEwYs-O}3-3Y7Y`3OY
zQ=S?1=ucdnoL+G%cWo>NTr<5pOErn}(wel1tFeRzX$TB`UYVrh!hdMDu`;7nzh43~
zj#%SyylaQ@)>E0M=qn#Zv9ee{Ow3m13Vkh#2WY7bS=o>PU4wUlcn~X=Ob@bt2Q$ul
zecn%mI*tL}im@70Y*t&_Fz4IY1WnYG_tw7QL`q>J9v0E;>YuM(^SQrjpt%aNLrDWb
z$IN<aPM)PKl^F1qHs`A9kSVrh&D@5@VzBezz^Ai==@N66P!OTM-_l;4*JN`$q+v_|
z{QZj&iMcQ6@zPH8wfL*e@33t%j}3(!lu1KPu+GBD-rHSNu!MC+V8hS_f%QhRhj2IT
ztezj#$BTqLMSJ+AQm21|%C#@&lb}yXf&mEu=WT6Di)@eW*=4W$hsi09$h?^nvER7^
z?ayz14YZ$~3>eeNpXnPUP1$Qa$U~WFFFxfI4bHwEAPkl~sEI6&x$T^z9xymQO(y%`
z+`2&P9q@Ka>_r;=&$nVTDh#yI{2~Y`^HpC?2&u4~=6bg+$IW6#h5yyXEcNmA&swVJ
z8ILwijpu>4!O@FOwY1_byYTsuocg!BOUJy;yAzbBlCOJ+IuZ8$ii>~t{{&WM-cCkX
z?rvT`!38)=`?q7S#}k4vd(&;s6t$B7HC5Mqa|yGc3g30z(T|(E__Iu@;mJUKiZ1>n
z_bl`367MExFw%eC%-Gz**7zp@@MX7#XfNPq%v*EOIYr&FX;V6Q(4h?>*tb@cTsoC=
z^Q)`nc7Jrp0z=%#)Lm29vfPf5IA6~gmJrVI`p1}iNgSGJumJ!=>XH2sj6R307#H8&
z(xZfGfH3lXY1x3Gh1X6W$6n3l+DP#JX5D@<gZmCX4nWz_RLp8Yb69_|-J742LLT@h
z+mJW)?^chZAX@175sIf$;CFrSM&#vO!1tL53k%{-@&AN-uXR8=U(nv()Z<VY;I|Tb
z+O}Wp>rNA=M4GHuw|!?@`f#<eV*cfMO*4+abMUSl>kkHR#=|Yt>6L4Tn&)s%tzB2N
z{h{#XMHC(8_N=qDTA+Anv9P+L+R$$v=wr%T46z`KlYn(>doKoKdi+jGjPLz66#Cbw
z&f?G<EvIR_HNn)_wkKU_adw<i+i20?I4OezxO4<xkJh7i1@?Yj^WL2F(2*(P0Y`hL
zwHs9hUQK01im;uJurk8IoJODPrrKaSQUA3OdVA!~k#?gkGCQc;dv6O#AI&IsyrFy2
zWHqq!1A0%X>%V4bzKuju$t>7_0r{65G;M{E|H~ZT;;iaL>GX6H&c=uSHh41ruq>%r
zXK}RGLjpe1U;U*A&3Jcb_P;G_VosZ-0{wY8SG=G7CxBsdtK4369LH_dc4W_DYO=*A
z92UCcM}he5zh8uQJZ$VHWO8n~-e~+l&h)&^uyPA2B+2POZYbJ|eLaBTzxM)=J+1z)
zxBPsJhq}4PdvTcVbl_?Ic3s``)t_>c1w}OtE{j_VcvD*f9Ysafu<BKT|K{bsDtJr(
mpRN4YD*fLO|5?tvTcXBS(BBL1l%26t0Lls)&<eSi@Baq}Cr0f6

literal 0
HcmV?d00001

diff --git a/docs/static_site/src/assets/img/api.svg b/docs/static_site/src/assets/img/api.svg
new file mode 100644
index 000000000000..169a11bdc18c
--- /dev/null
+++ b/docs/static_site/src/assets/img/api.svg
@@ -0,0 +1 @@
+<svg id="Layers_1_" enable-background="new 0 0 64 64" height="512" viewBox="0 0 64 64" width="512" xmlns="http://www.w3.org/2000/svg"><path d="m46 25v14c0 .552.447 1 1 1s1-.448 1-1v-14c0-.552-.447-1-1-1s-1 .448-1 1z"/><path d="m55 36c-.553 0-1 .448-1 1v11h-18c-.334 0-.647.167-.832.445l-3.168 4.752-3.168-4.752c-.186-.278-.498-.445-.832-.445h-18v-11c0-.552-.447-1-1-1s-1 .448-1 1v11c0 1.103.897 2 2 2h17.465l3.703 5.555c.185.278.498.445.832.445s.646-.167.832-.445l3.703-5.555h17.465c1.102 0 2-.897 2-2v-11c0-.552-.447-1-1-1z"/><path d="m54 14h-17.465l-3.703-5.555c-.371-.557-1.293-.557-1.664 0l-3.703 5.555h-17.465c-1.103 0-2 .897-2 2v11c0 .552.447 1 1 1s1-.448 1-1v-11h18c.334 0 .646-.167.832-.445l3.168-4.752 3.168 4.752c.185.278.498.445.832.445h18v11c0 .552.447 1 1 1s1-.448 1-1v-11c0-1.103-.898-2-2-2z"/><path d="m25 40c.553 0 1-.448 1-1v-13c0-1.103-.898-2-2-2h-8c-1.103 0-2 .897-2 2v13c0 .552.447 1 1 1s1-.448 1-1v-5h8v5c0 .552.447 1 1 1zm-9-8v-6h8v6z"/><path d="m32 24c-1.103 0-2 .897-2 2v13c0 .552.447 1 1 1s1-.448 1-1v-5h8c1.102 0 2-.897 2-2v-6c0-1.103-.898-2-2-2zm8 8h-8v-6h8z"/><path d="m52 19c0-.552-.447-1-1-1h-38c-.553 0-1 .448-1 1s.447 1 1 1h38c.553 0 1-.448 1-1z"/><path d="m13 46h38c.553 0 1-.448 1-1s-.447-1-1-1h-38c-.553 0-1 .448-1 1s.447 1 1 1z"/><circle cx="55" cy="32" r="1"/><circle cx="9" cy="32" r="1"/></svg>
\ No newline at end of file
diff --git a/docs/static_site/src/assets/img/artificial-intelligence.svg b/docs/static_site/src/assets/img/artificial-intelligence.svg
new file mode 100644
index 000000000000..819c98b6feb8
--- /dev/null
+++ b/docs/static_site/src/assets/img/artificial-intelligence.svg
@@ -0,0 +1,20 @@
+<?xml version="1.0" encoding="iso-8859-1"?>
+<!-- Generator: Adobe Illustrator 22.1.0, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" version="1.1" id="Layer_1_1_" x="0px" y="0px" viewBox="0 0 64 64" style="enable-background:new 0 0 64 64;" xml:space="preserve" width="512" height="512">
+<g>
+	<g>
+		<path style="fill:#F0BC5E;" d="M43,17.279l-2.456-0.818l1.158-2.315l-3.848-3.848l-2.315,1.158L34.721,9h-5.441l-0.818,2.456    l-2.315-1.158l-3.848,3.848l1.158,2.315L21,17.279v5.441l2.456,0.818l-1.158,2.315l3.848,3.848l2.315-1.158L29.279,31h5.441    l0.818-2.456l2.315,1.158l3.848-3.848l-1.158-2.315L43,22.721V17.279z M32,24c-2.209,0-4-1.791-4-4c0-2.209,1.791-4,4-4    s4,1.791,4,4C36,22.209,34.209,24,32,24z"/>
+	</g>
+	<g>
+		<path d="M32,15c-2.757,0-5,2.243-5,5s2.243,5,5,5s5-2.243,5-5S34.757,15,32,15z M32,23c-1.654,0-3-1.346-3-3s1.346-3,3-3    s3,1.346,3,3S33.654,23,32,23z"/>
+		<path d="M17,35h5v4h2v-4h4v4h2v-4h4v4h2v-4h4v4h2v-4h5v-5h4v-2h-4v-4h4v-2h-4v-4h4v-2h-4v-4h4v-2h-4V5h-5V1h-2v4h-4V1h-2v4h-4V1    h-2v4h-4V1h-2v4h-0.414L17,9.586V10h-4v2h4v4h-4v2h4v4h-4v2h4v4h-4v2h4V35z M19,10.414L22.414,7H45v26H19V10.414z"/>
+		<path d="M59,18v-6h-6v2h4v39H33V41h-2v12H7V14h4v-2H5v6H1v41h26.101c0.464,2.279,2.485,4,4.899,4s4.435-1.721,4.899-4H63V18H59z     M61,57H35v1c0,1.654-1.346,3-3,3s-3-1.346-3-3v-1H3V20h2v35h54V20h2V57z"/>
+		<rect x="9" y="41" width="20" height="2"/>
+		<rect x="9" y="45" width="20" height="2"/>
+		<rect x="9" y="49" width="20" height="2"/>
+		<rect x="35" y="41" width="20" height="2"/>
+		<rect x="35" y="45" width="20" height="2"/>
+		<rect x="35" y="49" width="20" height="2"/>
+	</g>
+</g>
+</svg>
diff --git a/docs/static_site/src/assets/img/book.svg b/docs/static_site/src/assets/img/book.svg
new file mode 100644
index 000000000000..15969fee7d52
--- /dev/null
+++ b/docs/static_site/src/assets/img/book.svg
@@ -0,0 +1 @@
+<svg height="504pt" viewBox="-4 0 504 504" width="504pt" xmlns="http://www.w3.org/2000/svg"><path d="m488 232v224h-200c0 22.078125-17.921875 40-40 40s-40-17.921875-40-40h-200v-224h32v176h116.800781c33.121094 0 65.277344 11.28125 91.199219 32 12.960938-10.398438 27.519531-18.398438 42.878906-23.761719 15.441406-5.4375 31.761719-8.238281 48.320313-8.238281h116.800781v-176zm0 0" fill="#fcd770"/><path d="m456 232v176h-116.800781c-16.558594 0-32.878907 2.800781-48.320313 8.238281-15.359375 5.363281-29.917968 13.363281-42.878906 23.761719v-184c25.601562-25.601562 60.320312-40 96.558594-40h31.441406v56h-72v64h86.398438l9.601562 16 24-40v-96h32zm0 0" fill="#e6e9ed"/><path d="m176 218.238281c13.601562 2.480469 26.640625 6.960938 38.71875 13.363281 12.160156 6.320313 23.359375 14.476563 33.28125 24.398438v184c-25.921875-20.71875-58.078125-32-91.199219-32h-116.800781v-192h32.160156c-.082031 1.28125-.160156 2.640625-.160156 3.921875v60.078125h104v-60.078125c0-.5625 0-1.121094-.078125-1.683594zm0 0" fill="#e6e9ed"/><g fill="#ef7385"><path d="m318.640625 62.480469c-2.480469 5.597656-8.160156 9.519531-14.640625 9.519531-2.398438 0-4.640625-.558594-6.71875-1.519531-5.441406-2.480469-9.28125-8.082031-9.28125-14.480469 0-8.800781 7.199219-16 16-16s16 7.199219 16 16c0 1.921875-.320312 3.839844-1.039062 5.519531-.082032.320313-.160157.640625-.320313.960938zm0 0"/><path d="m262.71875 145.519531c5.441406 2.480469 9.28125 8.082031 9.28125 14.480469 0 8.800781-7.199219 16-16 16s-16-7.199219-16-16c0-4.398438 1.761719-8.398438 4.71875-11.28125 2.882812-2.957031 6.882812-4.71875 11.28125-4.71875 2.398438 0 4.640625.558594 6.71875 1.519531zm0 0"/><path d="m203.28125 107.28125c-2.882812 2.957031-6.882812 4.71875-11.28125 4.71875-8.800781 0-16-7.199219-16-16 0-2.71875.71875-5.359375 1.921875-7.601562 2.71875-4.957032 8-8.398438 14.078125-8.398438 8.800781 0 16 7.199219 16 16 0 4.398438-1.761719 8.398438-4.71875 11.28125zm0 0"/><path d="m176 56v20.078125c0 4.082031-.480469 8.082031-1.601562 11.921875h-100.796876c-1.121093-3.839844-1.601562-7.839844-1.601562-11.921875v-28.078125h104zm0 0"/><path d="m175.839844 216c.082031.800781.082031 1.519531.082031 2.238281.078125.5625.078125 1.121094.078125 1.683594v4.078125h-104v-4.078125c0-1.28125.078125-2.640625.160156-3.921875.878906-11.359375 5.839844-22 13.917969-30.078125l37.921875-37.921875 37.921875 37.921875c8.078125 8.078125 13.039063 18.71875 13.917969 30.078125zm0 0"/><path d="m72 248h104v32h-104zm0 0"/><path d="m376 312 14.398438 24h-86.398438v-64h72zm0 0"/></g><path d="m376 312h48l-24 40-9.601562-16zm0 0" fill="#969faa"/><path d="m424 216v96h-48v-131.359375c7.601562 2.238281 15.679688 3.359375 24 3.359375s16.398438-1.121094 24-3.359375zm0 0" fill="#fcd770"/><path d="m424 180.640625c-7.601562 2.238281-15.679688 3.359375-24 3.359375s-16.398438-1.121094-24-3.359375c-36.960938-10.402344-64-44.320313-64-84.640625 0-11.921875 2.320312-23.199219 6.640625-33.519531.160156-.320313.238281-.640625.320313-.960938 13.4375-31.519531 44.640624-53.519531 81.039062-53.519531 48.640625 0 88 39.359375 88 88 0 40.320312-27.039062 74.238281-64 84.640625zm0 0" fill="#e6e9ed"/><path d="m456 96c0 12.558594-4.160156 24.160156-11.199219 33.519531l-11.28125 11.28125c-9.359375 7.039063-20.960937 11.199219-33.519531 11.199219-30.960938 0-56-25.039062-56-56 0-12.558594 4.160156-24.160156 11.199219-33.519531l11.28125-11.28125c9.359375-7.039063 20.960937-11.199219 33.519531-11.199219 30.960938 0 56 25.039062 56 56zm0 0" fill="#69d6f4"/><path d="m464 208h-32v-21.566406c37.960938-13.394532 64-49.699219 64-90.433594 0-52.9375-43.0625-96-96-96-31.472656 0-60.351562 15.257812-78.136719 40.128906-4.398437-4.945312-10.734375-8.128906-17.863281-8.128906-13.230469 0-24 10.769531-24 24 0 6.566406 2.742188 12.695312 7.296875 17.128906l-29.082031 63.054688c-4.351563-.398438-8.679688.390625-12.511719 2.207031l-32.101563-32.101563c1.519532-3.167968 2.398438-6.640624 2.398438-10.289062 0-13.230469-10.769531-24-24-24-2.792969 0-5.449219.640625-8 1.558594v-73.558594h-16v16h-88v-16h-16v76.078125c0 5.050781.648438 9.816406 1.824219 13.792969 2.335937 9.703125 7.382812 18.648437 14.601562 25.863281l32.261719 32.265625-32.261719 32.265625c-7.632812 7.628906-12.722656 17.285156-14.992187 27.734375h-33.433594v16h-32v240h200.664062c3.824219 22.671875 23.59375 40 47.335938 40s43.511719-17.328125 47.335938-40h200.664062v-240h-32zm-16 16v176h-108.800781c-17.472657 0-34.613281 2.929688-50.953125 8.6875-11.390625 3.976562-22.167969 9.289062-32.246094 15.777344v-164.9375c24.007812-22.871094 55.289062-35.527344 88.558594-35.527344h23.441406v40h-72v80h89.871094l14.128906 23.542969 32-53.328125v-90.214844zm-280.03125 48h-87.96875v-16h87.953125zm15.222656-60.359375c-1.742187-11.785156-7.070312-22.839844-15.605468-31.375l-32.273438-32.265625 32.261719-32.265625c1.808593-1.804687 3.480469-3.726563 5.011719-5.734375 4.367187 6.03125 11.414062 10 19.414062 10 3.664062 0 7.144531-.886719 10.296875-2.390625l32.101563 32.101563c-1.519532 3.167968-2.398438 6.640624-2.398438 10.289062 0 13.230469 10.769531 24 24 24s24-10.769531 24-24c0-6.566406-2.742188-12.695312-7.296875-17.128906l29.105469-63.109375c.734375.0625 1.457031.238281 2.191406.238281.480469 0 .953125-.078125 1.433594-.105469-.890625 5.273438-1.433594 10.632813-1.433594 16.105469 0 40.761719 26.078125 77.089844 64 90.441406v21.558594h-23.441406c-36.007813 0-70 13.070312-96.558594 36.984375-8.976562-8.046875-18.886719-14.921875-29.527344-20.449219-11.046875-5.855468-22.914062-10.109375-35.28125-12.894531zm193.082032 116.359375h-64.273438v-48h56v34.214844zm13.855468-8h19.742188l-9.871094 16.457031zm25.871094-16h-32v-113.375c5.199219.878906 10.542969 1.375 16 1.375 5.464844 0 10.808594-.496094 16-1.375zm-16-288c44.113281 0 80 35.886719 80 80 0 35.664062-23.921875 67.304688-58.257812 76.96875-13.671876 4.03125-29.71875 4.054688-43.574219-.023438-34.246094-9.640624-58.167969-41.28125-58.167969-76.945312 0-10.648438 2.023438-20.886719 6.023438-30.433594l-.03125-.015625c.152343-.359375.28125-.695312.382812-1.015625 12.632812-29.488281 41.511719-48.535156 73.625-48.535156zm-96 32c4.414062 0 8 3.59375 8 8 0 1.246094-.246094 2.007812-.382812 2.359375 0 .007813-.007813.015625-.007813.023437l-.28125.847657c-1.726563 3.882812-6.433594 5.96875-10.6875 3.984375l-.039063-.015625c-2.753906-1.253907-4.601562-4.152344-4.601562-7.199219 0-4.40625 3.585938-8 8-8zm-48 104c1.128906 0 2.222656.257812 3.359375.785156l.039063.015625c2.753906 1.253907 4.601562 4.152344 4.601562 7.199219 0 4.40625-3.585938 8-8 8s-8-3.59375-8-8c0-2.136719.824219-4.105469 2.449219-5.695312 1.445312-1.488282 3.414062-2.304688 5.550781-2.304688zm-64-64c4.414062 0 8 3.59375 8 8 0 2.136719-.824219 4.105469-2.449219 5.695312-1.445312 1.488282-3.414062 2.304688-5.550781 2.304688-4.414062 0-8-3.59375-8-8 0-1.28125.335938-2.625.953125-3.785156 1.4375-2.597656 4.144531-4.214844 7.046875-4.214844zm-24-56v16h-32v16h32v12.078125c0 1.355469-.128906 2.632813-.257812 3.921875h-87.496094c-.125-1.28125-.246094-2.558594-.246094-3.921875v-12.078125h40v-16h-40v-16zm-76.265625 72.425781c-2.527344-2.53125-4.671875-5.355469-6.40625-8.425781h77.367187c-1.734374 3.039062-3.886718 5.878906-6.429687 8.425781l-32.265625 32.261719zm32.265625 54.886719 32.265625 32.261719c4.613281 4.617187 7.886719 10.28125 9.804687 16.425781h-30.070312v16h31.929688l.015624 16h-87.945312v-16h40v-16h-38.089844c1.914063-6.113281 5.195313-11.792969 9.824219-16.425781zm-76 64.6875h16v88h16v-24h87.976562l.023438 24h16v-83.800781c9.433594 2.464843 18.488281 5.96875 27.03125 10.496093 10.59375 5.511719 20.296875 12.535157 28.96875 20.785157v165.039062c-24.734375-15.847656-53.71875-24.519531-83.199219-24.519531h-108.800781zm432 16v208h-200v8c0 17.648438-14.351562 32-32 32s-32-14.351562-32-32v-8h-200v-208h16v176h124.800781c31.183594 0 61.808594 10.742188 86.207031 30.246094l5 4 5-4.007813c12.160157-9.757812 25.792969-17.308593 40.535157-22.453125 14.648437-5.167968 30.007812-7.785156 45.65625-7.785156h124.800781v-176zm0 0"/><path d="m64 328h48v16h-48zm0 0"/><path d="m128 328h56v16h-56zm0 0"/><path d="m64 360h120v16h-120zm0 0"/><path d="m296 360h80v16h-80zm0 0"/><path d="m400 160c13.929688 0 27.175781-4.425781 38.328125-12.808594l-9.609375-12.792968c-8.351562 6.28125-18.277344 9.601562-28.71875 9.601562-26.472656 0-48-21.527344-48-48 0-10.449219 3.320312-20.367188 9.59375-28.703125l-12.785156-9.625c-8.375 11.128906-12.808594 24.382813-12.808594 38.328125 0 35.289062 28.710938 64 64 64zm0 0"/><path d="m400 48c26.472656 0 48 21.527344 48 48 0 10.449219-3.320312 20.367188-9.59375 28.703125l12.785156 9.625c8.375-11.128906 12.808594-24.382813 12.808594-38.328125 0-35.289062-28.710938-64-64-64-13.929688 0-27.175781 4.425781-38.328125 12.808594l9.609375 12.792968c8.351562-6.28125 18.277344-9.601562 28.71875-9.601562zm0 0"/></svg>
\ No newline at end of file
diff --git a/docs/static_site/src/assets/img/bottom-min.jpg b/docs/static_site/src/assets/img/bottom-min.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..25ce6dcd37c741e4d3f3ef902efa0e216d2951b7
GIT binary patch
literal 952968
zcmbrkWm6ms6D*1Zcemi~?h@SHStK~@;_d`@cMpr(;<muz?(PzTd+-2Bp8MW=>->V#
zJ@aX*rsmU3SJ(Wv`QHyXTvbICML2kPI5_zK5$?YoxF|T({|{*B=xCT&|06yw4h}9p
z1p(3jNWn@*MfSg7W#C|-{a?!Pb8_;_sC|+6qGqb2W9sSbnVmfa|9{)yzhO8$RFqYe
zJ4ARoI0QU+L_GNaM&Ulg!66_Z{=cLCA0Qzkeu6_lfk*vs8xHmVFBo;<NoTpQQ6mzW
zckiFg1qxcrh)$n$VJL?hzD($y8Og1^*!(l8Am=>nzz+QFyXfj;U;LAlXCYB<_37!S
znAP^5Eepq#gvnE&DopOclj%0x%;O)8``>=A>Mk^tec1N>3^;T-<A$^&eCMPt%NohG
zLB25aN{;sTm0tAtr9%9Wi9P;kyU24X7k1r@*p`olK`kEO&bd9awje5&#~bd8$({B-
zu_dqVSj^(uJ^maxc3ds6rC_Ix`;(<^;(o!taAwy&`30U<z&2KF*k$i#J#+LEtun9w
zRr)S*<I(8dmeBH`i&BLkTOP7h=z>^$9GQYAuc4SRVPZQhB$@LDQ7Gp}Ct%2TLt?LE
zxg7!dTv`!sW$S4(EEQ*9w-sKsn{3kXK&4%PYz`mGJvpVhxno155xNSE&064agV{4K
z1n(Z0z)ju$SqeFBb{twm@TS1##{1<_j>a_KTE}uLThvu|;ipp_Lin{7<s^{!!fae4
zrm*x1_ng3V=$XsMR(dyMb`{iDZ4%+4!C~WXRVB-V_4$QrkXz+4baN3w>^?CVlxn4A
zxfl=r9OkutZmewkz5@;zB8~F)YaTbi(RdOLNLj_#)N?1V*^R83E>OKt{%yFmOq{8j
z=-Mr4ROwY5GEn-GfY7--L%^|FM*9O~*ruiDMMyUZv$-T_e3Th)mi)~JN!TbP9yh1H
z?hLu!oyQ)S`jmZM6k0`!*Me>*KvI}>$tH<SG0g^Q=$0mSmc||S=1{Q@Hx3L0*+s}#
zH^8oOfWkLhjTGQ1pf1d!J@PEw1Y;R`ZZz+z?cj8lJHoA&)>|dBsEz$wjJQ&rMct$L
zxz@T^5=GrG{ZeL%$S>ku+ZUhKE#`tmbekZV$VD@{fW3U0s9u)Z&kERpfBm{#kGI`C
zJcrJF%ZmrQMr0!{sU|Pg_-n_%AAZ^X!$Q}4HxF1yT$={$2>MjB>QzG;s)6=y-Dm(c
zc)PX~6ujUfM(MB2eHlNb^}I1Yg??@KyyVUaXwxV_rzkPsZ7%lxrdFy{+2rk<o0VAQ
z`hmmJ#$BFZ8MLORdkIfaL3!3SS7(nW)OK7gOFh8)&&Y4TccK%LG$Segy_IS{z$cnD
zR%fu)UXYcN4F!C`ysvJ?&lT0k^`q5#));?TBmmcY=0$%5|K7{oj*Cq_1^oNUQ93lQ
zoC=U1^S8dUVeWH_(W6-#0;FTQi|Y5A2>SAcU6YdGDcW(w&ThcNeN}1c6q1ixlP61D
zMxz<57FYi5wCi@UI>WrYx|3GyJw#ez30=>aL#SdM$;#Sh)Xkb;HKE}-B`7*D7v4|z
zyhe80z?X$tuN`%t;j-!xut1(uUW@SAHHa<_y93XC4{n+zk4raW&%AunITz6vQZ8$M
zVI=bGOO-v+)x&dBwWjhz2oh4{QlWQv{(Dg>KO5LuFDjVWn^)C7*yO9>^962s_zxXv
z8*rcLk5P8eDi#wG@A9ozAf5EKlzezbp#2(Sgz<B_2l*dyU*`xhYgTKiXrTOT{dykz
z{w6g^Vx_5W!*D+3B%1_y!WC-Iigkyd#$vf6D+meXUN#mNWcj`1kk2IA5qZ{|4J$h-
zEIR{XL4-s;$rvwf6EfHCiFkl;{*=>mFwK30!wTqAlqHKDU+JbLDaNS?RXMK=GKOAk
zFW~3oqo*T|qwiIbWj3T2)UOeDi=ZSh(qZ&#+}5Zo?|>6sVxuA)R79DKQaZQb?uWx5
zdv=`&)EUIm^(s%$>)>A5G+{W+QDBVc)_2kBrl^;bRE=5vHCBLVWy7k7cAFWhn!;S2
zSOXsV#41x}fJ9?^MTLI%4vkp7$cX6*X3ph+DN%G*JHcl}^_6nUIt1Ly53K3OmbJtn
zgsV-;>Xf^@jC2y{4(t8i6ki-taGJ>&zafdOS^w3d%cFHV?)d|AeXAmz&wDicn63tI
z^hnNMq#3bPVyTJ;=K%~FE-f3W^FD+^zGR5=N}`&bQ7RQOvSD^NBhzTdYNUw-MeYGL
zd;$!Wlc;cZ1k%OTXqdY+#u+VuJsP$4IIb`blojRzBzu|-Bx6A6;MUF;Ik=uGR5@=>
z0%$*OHjY1G7`NwZljMPR$o;Pi3Mo}b7QS<~&=LPx7kqab7L0~GSXI}d4e0v_EK=Z=
zB#I~ba>=6V7PJ3LUcmfO93ntJ$Ejx*%*M)tg=6>Hjd@bx%fNOSB;C?Qkt3f8W+2E2
zJ)uNGs(laD=Q`T9D2d@``r-9eEVyU^>X|Y!9uMy);uY;e<f{lmBNpzT%+YqC@M**?
z6&ea(A9KEi@+9-uD%j-~v)#Ivd8sh`yyzi!SNC{Eo@l3Y)7m$d6wwhfK}QkKvZJt{
zQ-8TJL`RN<z?!`@#@mP6ZNNOk3BLeef=zXZSt=6y52fO`i|0NUWH-^g@Mo?FD%yYH
zn{7c-D)|UtJT+h4>`KxmUq0wDtT-I=PfUVm`AT{FnMMp<o44^SC6?SkKNkxpVys(S
z7jPEFT(bMS&(NuBrNMBrd8V58V)w_@h!QEp4n|?R)lC;6^t7-j@gH1?ccp9Oq+0@X
z`T8;HHrk8G=!rpWXL%Q1P+ZNbi#BJCZxy)7XC9j3%|szYw6c-d#gfX^u;nN?M|Oz~
zum3)N&7J|MF+UMN9;YGSZ8Zseo18@5(P+GM_r0D<Q?6Uo!2=if_`i@h961p|V{-F!
zUf5>q0!$aYOhXkQCBrmUV&&5raQYJ!LJgRvLTv;agptWTj<1>05E;ct>#9`gCJe|0
zR&MKDk+*93;#@k`T#d7C)W3@^3*`Rqouf{KY#mBU6bN#J6kckOzL|<#b&<zG`utgK
zP>FYnpM==)UcQdP?=R{{KN59sMj~7#k;jnJw58phJEIO<OwgE?OtU-&Sn!E_b_%vZ
z7c=`cU-jt%<bzbpu}n!aKXAAtYDNMazbQ%QI0oX9I~BA+oY%XjTaX^<kpXNNm0rlp
zawdA<qR?W!tS7ms_J^#`#e3z$0%=MVXR}7D$(t4QjEj5Z3N<8@(n)Dfm35rf@6ph%
z?|}~=H=+8(2XZBW@ie897Gh)_Hz;yoUeA5-X=~?Ey5JfQ(QDj4w3r!_F)iZGl#^Y$
zrp`Oy1{XqQf0c;=`c6A_?L9!8S}Yy8+Kt;Nhg+;$K?qri%erXlYqP$L1{6!Jear3A
zq0rgbmO+KX#U8x4yaBD8CP9lUy<c)<4dV^b5A)1fF{#0@B%XsDXTs$Jw~bp&=0JT%
z9#CP|>^bAyN;(%epy%n&ZI$@`PV}LAjb{F7V)ScSQOM9jiHI%gF~QKgJ(?U)3Ib0H
zmH%GmT)b!(SKSH>SB#{M%zbTw5CSQl8mkp#O;Ue?_k{ZB#Qc1gJL;A<0^O(++1ojA
z!P*u2oiNKiv}|AT6ZC4s&ioGl<!f@=iiRX8WANqqbRl&=jsN3_$}Ozq1t}`=uA!We
zD!Ztry3)KJ%-g6|e(<)o=N$&og}ag7gndFm4sZitV!*Vw=7osj&MyyHy#=~KvY^Tf
z3(+kelt^Gp%qOt=k)!N*@13$HA3w-q*`u|Y&)1EUA{kf4s#y9BzHS;)lah=PMpB3@
zS_~D#5&4d$L&MeAkDJB&s|sBvq&*qVS}Dk}j`u2poSG48X8i^VdSqI}@QeNO158*Z
zXa*({D{%=9=}#M{Me^Hu5}rvntN!GNcBfj-6DW1zdEU(-j0GXxY_pdZC_pl<-(C@I
zUlFKV*`3sK08Tg2LBGTNZ{w`9#e2@Rvz%<;32!tgmABV8(Xi|&;RlRLXL48wsIgXv
z-z)p2tNot`ZRVadt#E6;BMqUbm?4W>&*1D==w+yv)Ru~gCRH}3k&-NlrYvu}rVPlS
z7IdnA{F}P8QJYHY;c4_b<GKB$W||(9w<v<XhvS82VaiQ#W2`9GEK9S+YECcDxWn!6
z1U=t3!>_NQuU$Xm@7@k2${1cFK#<(v*uVOTA78GNTXR>Xshf>KMaHWYc!~>|7ke5h
z(5xN;bR>J9_pRXE_BeHW$&;0dS~)IGN@RZJ^2vUm&7F);z?18$;?$P35S1Tmp~Juv
zXs}E85N8LfkH4PSuLKWR%NjoYNM(5t8Kft#Wna$ti-L+WW0BfGTd+#f`izE_$Rv=Y
zQM(l#OIF5?1@|q^PuT)9bEOdJQRXE!J0h|9rv~jcBs{lrtIKUHyZxdr;4DWuic=RV
z(y^Ep>10a4B?LS05qVO?07E|$el<enm>q0Y3KB#zX2&f&>Uy1cBoS@S>EP^o=-hWX
zl*j@;{HjpaGhIxs0Nj9XRH{Jng~~nO+0yg;dy64ob3b?$kIkD6T_F8&`!!*lCkLAD
zJ=^nTe1&`sMnBdG2onsb8O5_P41uimQo5Nka?Mbumy^hF^EN~R{D_YX=n3!<UAO0E
zL2I#*q=JjXRb7Ao?nH=jJ|Z4z?rw&NLLbz_Py4hKZ!SB?@<vem`*!UGB%+OFu%q7-
zrB;lv?jWvE&~F<fLycA4c~Z77WXHONIYH{87VhKP#kRmY>6(v2)Eqh>jmcHbnv%14
zlla8WqONQq#OMMdZQJAg3MtnX=ZWYs`jp*a7E>2(s#}#NPhoP|=MOu~A}g4F02P?|
zo%sXy9877CT~8A?Czh+VG0&8#ey%wO33x6_AuY*L{*eqxiW!EX;vlyql7t3!-1@Oe
za64p=V6npIMawWsq7xH{2fj+5*=F<zOVs9Gy{kMH$!qh2vU@{OGp@v>?)k|p7st#p
zu)P+EWtVDwTi^=>k-S?Lj*~CsjZ}mMdZT2_Hg;`I*@RBm*W%P}*Fj007PN$$@o<vM
zE(~6O>r~g1@dg#i6;=^bFbsU?CI7|H7ajqM9lS%XS9kcH6QmBx!81<lz?}6(0I@?K
zY)$ty>s!Cts(-+`{N=>c^{$JX4c8>ff;?aAvn(YLUMn=}4WWteBJ5JYi`u6p*tWUZ
zpdM_E!aS|E&9!BRV^;c=Bfwv&#Lwdo2%noJcR!%eI$Ft!Cb1A-bgqn)_6$Zz-r0+~
z@O9CJA3*>`^}5uuXRu9xF*)uU!9liRh?%QHZJ3yo>u@i|TP5?N?eXF1-EgB5%GV@X
zo5Vh*XdJbu_=ATM-%Af6rzqmQ+legDm`c<!Dt7(s!DG}H5VJ(|7xr3vpim>`$P=F}
za&7ojWMDbso+|E<P0lnZKv#L!)}91BQ$YK)Vy&q{tWKwBy;CRhn;MX{in8vnwXyTp
zfY1e8JgDDC(eYR&xGwo9XJyml{^rJsgpMnY8gYmoIA3VpKnkjX&G@4IM!qR?<Ew1O
z#%CN;?+#5VmTMtPL8p(Y4`avaJnJ|CZhXZ@#Y#{UAam4fyXt-gOXN=<(-O^nI`CSl
zn+Zs#oZJfi79#Sa3Na`vIwu4VzY(=dGqBRt#B_5`g<ec*P|tuahdiX@mEyAk6urVB
zI$cOX36L9kcy@@&^V_V{WBirC4<OV8w|Sy*<#KZ78ff_7`ohz#%*|^74+P9POZb0j
z+-;?&5HS5}BO@Xh>d*P1dr%xLidpl$Tn;$-AuPTv|HXGRhAN1wNktkdnQ?qfymfI~
z*F83pLT-g?m>%-Wm@H?Knjm!g^cvl3@%-pYvLJDiy>_srnPbMZJ(>jZKe#WiJm5-%
z*uiArc^lVbr<NfWp1cG~$O4|`>xBNgk7mrqj0|a5kSS2pVspdBcrn9E<*MYfypN|C
zHlLMyO!kQn6BE<$Zabc#l$~p3fh{SB(}do|Jbg-?<v%$0F?M7ic9*3nc-3dFox2c?
zbt$_p1tV&jYlh|H`pK{Z+IdS?X5<&IlZMeHuX-k^&PVrF^}=Qz*;EBtIp|BZsCp>s
zkXEWD+phch9Cd%1>#A_#0LLf^U}aFuZu)LStB645JFlg$j-*~g=ym3MqbTt5ibXDZ
zURMP?ll)rV*J2@T?pThmUhn}mIbXG*cBsFazvH_4#7sD+z1+(w^Jr+()N{M%{8e(<
zvFbvsPTQgCRYFcXCr(Myn(j8<x?VJ$T(`Q{$E87}HA2zRRPAN*p@%Qg>;NmnQU$LX
zdW4F@#KpR&ZZa4;7Z`4uxc{{n>Wul~`Xf#?9*S6I{|tL!ZUK*z*qM0O`0h~|RZjH#
z5&O$V{d+z^LceZwR(7o*6g^};z%nw`H)<Y2h1{XXfX3+SVLkGUrK5F*?cP|ayc??|
z`l=V8XsNb2KWcaI8ssn6Jg;t-F8Et|+IDtWp5LGn6Z~m^F7_@|{g4Uyz4p&P2iyn~
z+2uqbKgGQM-V=WsXYgW9+O@WmbzPm*oJEZhFbup@v^OeQ!CBSG2-f@p*r;wD{p%>?
z)l+&>0YJ_k3LV`JL4dqBTTzbBoorpgysmK5Yh{Dl*>75^qC<}G0$o@F8kfmU+rI9B
zJ>%6pQ)v@wCpSmNnJZV`QhE+)*ZZz!f?rlB)W{?I5RrIrKKEglW1+bkp%9=RW&}>C
zGfnWg?pRPdailhRK+Cp&vYc<lVqW+}AaUw$5$Vh})P*rzu_37FVYLh`Qj?P9`R_^2
zblhD4%*6uI>OeUQVO8q`0QLAoLo|#N?O9*%Xg6^415B-}{<5AqXRqLOB~ynpkW3~3
zRJ5a^uuZ0@L}(Wn9Vc5?npNKJixW<H6<*>&-T%2#rq<MXMv!+CGxBc^%T~npGQml}
zLD%@0n;GNjsbfP>djLOW)Qgbhu$eA5v(Yh~^V6-49y4o1(=%MrYApe?Tk)o9N?h8!
zJMmd$ZXFji+_w%tERNHqZ5<DfwtC|T1Sg-eN2^wRZL0t-Hir8Sw9gGO{$9I$vi=@H
zbDEr0#v!!iCQFUaEVNiz@B~tKf|MsANQ={;YoNDI6m&%wL^U8F@y(Zv4+!?x<<UH_
z2l@u82gDM)vXY6`T$@|$v5yTClpDVBq-42yvvRWE|K_5=(7YJ?&HbF>-E{^%*%>vg
zpChWv3B(U`vs;#<kG<<pUa7vaZ}9!ussmberfZVX3wZ}Lu#>oY3#>)<@?mMl)=j2I
zjO`1eC>qH3FK>p1Jn0@|Ul`I@5{WM2XW?khk{#l4IJIt8OfrE%6#M12a@L8|^3{{6
zku5RXLui2ryhVhFEF$gTE>3Jg+GBjeL0-7?xQ6ihKv^e5`Uhm)<f7Zss;3bd2^I=1
zuk~~TpXWZ=9Wq<%_CJyP?P|rGLhLe8F@U;ni>8@PXndyZqoWKzN16C)0cpMisGif-
zag9#kz2SX7OsEn+|G0LQcZJwm&$zeAW;*T9vlPJDL?)i@4M*F>Nr2t2Mb7%S)QQE7
zMkk~0S{4$NfSIDr3)GwwgM`-@v4{#RCZ{CZ7=;qPU?Ig(n6=zdlz266k^50N7G@5J
z1035D*&($bBY>2?+eA=O`QG_ncP8bHARS#pGRFH9>aw^BPPiaj`AKTzR7PqjL0vV$
zcEP?}FQ>%4=e`TKn_H1((LXtlPa#Jo*Hkh#m$%-Pt0X+{a9tOeL1N8{g|JyEmQ$y7
zJIZZ<1x_^Zk_}<85$lnie=xZ3Br8b24u|BI%v&P&?{!L>e=#?DDmRp13?(IX%0PsL
zy&rJ27=BT?{ld;iPV$NNX-dpLo6@&wU6dijiiV(~9`As2<tqL&aoL^A!=<dIeyMt#
z@$H?haMP4nIxYa7Z|2hphi-GPW0p0BDpJ;l(ofx$^1xWFwpr82^$u9eQNcF!i~VuZ
z<%@=PHHWtevb+_DEnSi;o@ucEYXt+Hratg@O%khNXGTG+d#nQQgldp#5MRStZjHFN
zT_sT;Oi{8=gaAXk-m~z8qI^Bo5c3=2_ul$A=4V35pOG43lG6WJ$?&M`ZDel?Y)3zR
zc&Ga;Ew2x6N>R=MzRY*E=pF%Vh1vb}mY8t3;$7j)-6Vx9hC6y}dhd`DA=Nr4R-J><
z^T#C#vM0y$3G3!sg@AQZ0XsC3YNV7EFdZ&Q%kxKBgZ&mOy~;@?R)!OmU!!I2#nMV^
zG*QS$;U<^nw9iqRut(WDVhEaj5$#qOp$L0G9rmwLC%Zrb5?ZXoI;<g?j06z5mFl=*
za%x%!YZI&LETr<Cimy4RFjK*`s_<el0|xt~e^X=@ULh&oi=PnXuTv{?T+LoC8Tj}D
zZ%KGXikE*^Sb)^u$&pgRTA~CY-3=~#+Avo?_%d(H!KRz|BGI8d5yi>Aejjg@{B8G(
z%SgUCSZTmhrr5MNyrt@y^lpHS*`(xsDYP>98~tJsmW+a@(`JV!-_J@S-e);5hjFDB
zfN@jr*3JZI*Eph<s&-}hyIqKA3~dUKSSmXHG%Ue4w&c2c()UTneUwxI9cE7~P$0f@
z9U%%!$J-UvcKK1I0K5QH8QKM?3t>BWgCPReL?_Eea)P|~4>p}E!-lg{VdK2@wsvg#
zVlJeOBR2rAGe|LaYGg@nZs28EO&=Tk?z(GUXUEAl70|Xgdz`U}Yb<c0z24ADR9Ipg
z*e60Bm7%WvZksNLnBc{?{y0B-Hy(!-+4eEB^Y$NH&sJrl+QuSrQY*Tl?*+lwFg;9m
zWljE?!JHvqW{Z%#)dap$gXw^Klds9s{pAx#sl4@<M#GV`(-Z1$Idv&VMx|bzu40p;
zUe86u%v!>7F$^fx5ZzG?F!=z`t$O=BM`Q^he62k_1rnOx4~*e^zklOe`mU|LM`gcC
z(@T2m2II&azrR0j04wN(Wz&O|V>47ON-jt0+c_V4dbE1|wE-uWf%u#j3iUTC1-Pw3
zI3DqSyBka~hh?5l_(G#L`@h7q%fv2bkEXZ|7#fX9TJM%9f8Qo%_7v=eFuDBL{+!wR
zz}D9GUa+-<HZ*>@1F2R3pun3B#~xnD8nwWj@&rnT<DL3&@s_d-l$9OqPAq+K_@{|l
zv&a;~8PsWgx}K+>7DjpVPI$u;SvSwwE<>B)w@(l-=gbb2xe><0kU6m^Niqm()Ui$_
z39B;8z+2Usx-p3MBcGi4UCcp3KDDO*+q_EFH0Ary!Q)u{E>(wWqQ}<>&u}Tci$UEw
zhbr6cyt36R+f!C^N;T%05iEnSXf^dqLgSNd@+_94I0VFn^|ipyFMsRRl77_Vk*ybs
zco;w+&mGxed{fA37MfS%$h-W4Hc9FhUPK4T=1$Mn6vrf7>pd<ObO4>DQuP3MwqBt2
z^3z?s#jQ~@+e3G7=YqDx>cLu61{>THLsp=toeKl$x~!mS1jt(;K^Qo3xF~6xQDccx
zW-%dO?|hl|0S#NnN$rr2f}iZ5RaCs;T|eZ%eB*QaOIw~r-7=qWg|!8%vu_XL)U<$B
zQV>p+T+bZC6CNinH~TPnD|5Adm096U_gi3oz`9MxK}i;Y<hxccLiJJ?nX#Ift2D!=
z=T*gv0FRQLYZ@9^l%7w4*DQGm?9|e=<y=~Vp9_Pp%_eCzFttgRC{KT8r!3SB@mr%0
zcW4dEQ7`}G;5J1u3~o>bc^o9f#^&C<1(m1$3|zRf9(}cWF%;-&7?J4?9OiHgf-9lO
zA-tTyku|1JGh2du>)DXigdAmr7UKaT889qd&Ad@=I0$PU#S^L&EK5UN_fmc?QI^J5
zHGB4kL+&sloJSoe%$6`~YJk2sh%wl^Ia6!cdLb?KB`z$@3#x)4tdab4@#q<#PV^gG
zYxfh!kGX9U(M4R}#vsK=X+^=wYhn!3Ehf8U`CsOfM+GfmG}W2CZew!0DWoUMXF$y}
zB}bm|&gW$xC7FtIbQoQU|7ti0?fSx1T>58L)J*49H`}b1bO8m1)Q9j>C=rn-mddP=
z-bsjhMu+{<D7!(1+7YSCU0|6*bC=va<BGYl!{s!FHFBWAko_8td`H#IMN2d$R}#A(
zduR0xb0xx|l|33<o!-s@t~JX8GI7&awC3yN>>vt$fW=;0JGySi(_&G4qLG1JdawWd
z02Kv_pp)({PNv*h1q6O-J{l6zA%5P^O3DhnbWrOWU9CnGJIk@{d`SEBqiiMqf3CYS
zb;*mOyr!*^N6*|R*ii{2C&$`s@;^B94!wlHA1scMMo~OL<S(ybX?-U4d@^~wUh9_l
zyWLb!TK=(Q7$>cKlzKgTG6EHxrL~1HH^-AdZV!|6F$Wpc|KDc^+G)WJLGSdt{MZ*%
z?p+*#H{0!vYz>pIohlxAl;W6{>;PeD;ns8Iy7oEZrpGz4gh8wme9t`>+24x4nX)gF
zMtMZ8_@nv<Z|!99HlWYJFH7)%@pbyWR~#^}ufS|{w?bIAnb1G;i{Qb_=N_tamB*5+
zOl*hpYkf5GXqjyHJuNXeF%W<RrC6{@AKmHKRl|etzL;Q?iVZvS)~5BD$_-=YL(mn3
z?VaZa301O7QZ;CI(eA^VBZE@bn$B;ZIOeFx6Vf^Q1#5J$NTVO?LZ4yIuu0;aJ`g7(
zv3`MlQ);*Yirm)Gg`$S&1nM5eM)0Zo+Rp5d<*(Y>s9^=1)gh83Wlxv^;Blb@?wO`J
zK})iyZZ4YAg>lU31KfrqFNy34XtU{P?=)|JR!*DCg~duZU}<@g5WPHv2+|_bRRtWu
zp+OSHrPHPdMWWHEE=1{YhdW200wJ*Bu~MvH$xGO0=@*3#Cxf2kFiM0j+NNV8ftw;O
zU)!t3xbfmU+oBvjX_VYVJvd7!h38xlv7?)Pae+Y>AMtF<GL&WRZ_-kK(;!`Oy!c42
zERQsHn`k<5!D_yYL6_D3;vgvd)808^ZQatq5(S~o_*_+#57?koPz@Uc1u-?!_tFM!
zQJ}+LV7;0GjHSM%^$OlS%|2nIWmPz6!i`C{;l?A=T%#y49pGyjTzvAhz&pmeFnE4l
zhfB|p%H>k>r7*C1s&TX9AYo!5u*owszae~ixiF}XPJFfuZ(%<O=5sNb(sfUw6wXud
zOwuM~I)|!Nf=XkR=l=#JVF3|MD<0d#+F+jI7uBD3x|t_F<=b?CT23x=Jljw|1g<8>
z)XmDdM-2U2iY7G996NDpejoNT%y1$regZF8o91#s$n3qM{BRusD-9fBX<N)=cF!%L
zdu6sJm>v3l#jM1Om;E*mQ748w*^>!2h@qy^?Yy|Twnk|)LX6=<05HI)hR4ao|H450
zG2lc<ENm)Z8BgFmX;<SQ2UxQE0v3Bw+Su$qPmY}Jbz^YS!jsav&7rZv^bp?hi*{bG
zgu?8Es{4T$8AI^xR=xidNc~)y#KVEnQcfF4t=2y`=c{K)@@4sWs|w=#VyT&}k<^3~
zzqdl$O-axvI}ITi!YXjqI-C6%Qr((SUYFX*jcumsTwQg5tKfF5xXO$x(AQLfQgfH(
zqJ~+M370tV4%94yB9Lvm!yNv~ntzCEF*9&ZCy{2L%4ty>&6T9w>%d1?mF?wP0h|c$
zR73;e1c#{DDQa94J%_|8BqCtTa$V;tGq-dyxG2>MF%lf51EX0<AVgZ2`rs%fv&vlB
zbyFKu%Uz}~W|rpXiO+UqT#_`a=@GYs3g=@Q(SnKY?Kfh|?Pv4n@4s+rG-l(yjmO@-
z+>7>Cq>6411Eu#Uzvo3>fEV-It8GKDE{S*9NzpE-smTIrlGa-E7qV}VHc%5B&K(>o
zzAQ$uv8+!f7!avI3@(V$2o&sf8Y!0O=e*7D%#?rm2}>btw!|8|bm-5@*|7{qid#1S
za!8$wZZJBRC}b?Dcn7)yTqD+n#9acND<);()87Gh?3h^$hOlg{F+gSjmycs|3`!Ek
zxWKP?nWNJpD>m|(&<@Ezo23C|f;S=#74k-{>7Rc#`snSUwI`aqhBhRLMdgIh%EPk$
zgR^n381~B3hk339rAi4qhk8NYley$l*qnpvCDA~WStc5u54dZ7MmV6&WqOSS0e({x
zk|<D=BZkCLQ({}X?gb@sytFKK9B+nw3(!fZHP?>ep)zqcNh-PRa`ewiFHPb_rG9{#
z^_=NAJ3`hk765rbAAs+60NJKhM<+@Q!Lj?&won0DKq;JctRXDD_<~Wwr`lrtw=|Ej
ziYyu!NLzvBX6{@%_09nS1sApVs3MHMSSD2Mn^p=Y6ztS8SB2sB5X(ji&|4svF0Yka
zCwf*+hdyPcz5MJ5jWt7AaEo4oDV!vJN9hBgbV3E>3Wq)KOmk(AD>Q0}Hf?@DL=zFu
z=(d@^c$=lF#<GoT6F+W*$7<N&IqlJwZdOf_%<R=Q30#rJ)=3)nSXkVD2UIrQ1-TIE
z<4lLR|LA0IeN-wa+UZ*V@p^{{aZf_{RfUf#j|07G4W_aw(6P#R>h}q4KCqp9$*ewO
z?ufqB6yLm2V9iAI8|x8kPPJ_kfo|-`pobW=sG>}pOOhevM%P||f5=Cx5t~5nS`OLB
ztyq{SKonU*G+&IOLBe(vM{HBDvO*0BMl@VWMh)ZTQ(+d85JiaDP_Cziyx5}mN7BUa
zXr`xSH}*6A_hdTdTnf>SH5+WR*_z;_HDFx;$C8{Yt-~3<7LuV2nfyhJ*KSti)I87!
zW7z|-jkCqHqmfs*u4BJs&RBx-xFPg{Xt1iry?awJKx<svJw4=9>MK#nradXao<-U;
z)<H8vOeX;cunO|_t>oiSZkkEPBIR!gZ_?#Ma!*ngDS`!nens>b*%WyV+?Lgz3_Qiu
z%?L^rC?QwiVNVjavX_K09$m2G(osOVNZu=(+Dk%!cK<$9xubu`MAq%%DILtma0wl$
z+rg6B++*=bG<49YU(N@5El*XOza5@%wE$(u4ExoNsETp&-$L}rj8Y<6vj13OV2?Vr
ze5&>q_rDCG@P&hy3tHh(qT1@U+uJoGCPb^2X!lBxw9oB&E)43m+k|b;!i!8);?yN?
zu9tMO#B-`v_h;4Gx|N7A5tM%e56yLU(}lG9t@4MlSqZg!9@Xt`Oy^GBo;Q>q1zW0W
zqP;Qo5on9eUyUa7lR(Fu9aW1tfM{A~`{q_V=6}}5yX9>~FUYR+yL3jiF$@3a`Ln_m
zsBnkRJQ4#+{*tR-K@v!s_RYipjWl}54RMSM8wvHx{?W^5(=1;VEkF83ey)PU%Z17D
zlq8m((1pgew5<x@#W&57`LwBzu_00z)MG~lB5eJi`~i{-+u0Ck@9Yro^5(AdgpoPM
zHMjm`@Pqj8PEhA~C}nCm7xX)%AQ=q}>X*7a+32k%5f$O1${1B<pVFtGO`X5kVaU00
z$z7pW77H-Zn@q-v(QK&+E<cBjyceAOX_Tz0JhU}Evp=HwRjHh0W*U3o{1xN6z@@1&
z`i<)~_$kyFMp>Y6nY169UQnh?jGtvuQQk`D#4~E7rtk9|?4==2G<NytB-w10u#4?}
zDo!d>zpiqbc;+dEekq`~^H<~z{H}(Jn<VbpQ`250=YjNv<*%~VnjF5&rLPT-0>mB3
zJAqL})H_4Qjc8Oav~v61nBEC4aFYEV+&1>k`Qp~fX>W5=5X31;lFED=xyZVJ6gaC_
zH4b#4zqw?bWk#$dvF~%7zBr0b9aag&rgG!5Vv2%fGu;jP7O%DCn>aUv%i@izb?5fq
zWz<h9f7G(lE-6+0E2fmQ2A}>a=4_05m7FSXw{k8SK4YME!-yR44giJ)SO&5Ei7`6e
zge~rCZHb)jRdq415}=w2PuQJCndXc={S63-KzJVzaXO17*K>dF`SVXv^{1(Ix=f|}
zqV*7m!Z*l$h9qO?y-n!U_+VPOLd)O8L{9$bykDKl@GV&9Mfx+<65nKEW(L{pY9+l&
zx~%t$4vpg?)U;yX4CQ=7&M!XowlkeUuM(~cvA0+58<h-Si}Cm<uZs2iXDVV!i{<5m
z#)phW(K$#qeh7-)%#9YB)B6P8ps>E2TP@Ycmd7ftZ+ufBGYF6P9_i_5m(kS!cHHeR
zX%GKwDX#8&f@IgD7?vf5fH-q6#1@cCZuqxjQ$DjjiMVt*vHyTndhVIs9iM2GlRjVR
zsxNBu4zO6o)wW{Pu3s=SJSIsKF`Ik)ltL8zR_(+$A`n@nWN1Mfn9nr{ncZ4t`S9)&
zL$|#wnp|B6mH+k^mJ_OzpUfw{{+&4M+GR&W0TN>ULtA+mxah6E;I~vG|HL*|PqFMe
zgEKwyv*XY%J~6>biCr=+*RCKEKO}j(mN2H7H(4iYJ3fSVKi59qxsaM;tZ(F~W-8gK
z)hzE*K<9?yM{uZ1<z!WhA)<vI1Z;)xa*Q0s)3`lgQe@zfG_`eCOorKDE`JRSp1QH9
zMIUwJG(BgVj(XRnM3JvXK6%+^Rx7<GWzJLz3HnCI=={;auZ=Za$1&r@b0@O38c5pc
z`lyv1I4;yN-zk^SQtK$#fWd^^wDH#B-eeoV850k8DPbyit`tNyPDwZE6F22%5lMCB
zNrrTMt-50m`bpOy=#a4?cb2@kWWvC;MTho!w6^el6BZf{3O^OXaLkV!eM(cu%PuLN
zE9XRNRhJ}o`~LaB0NOp<%azqKr3=|&H#{(V8FL;`+IxalT2pNZ@woM?4kL_e?-9>;
zYxj+TZqRW5O&Km`SoLZqvdG12$qurQpc7pMkJ<K;Cr7fz2w!lhPUG-AcKkHTrI1Zv
zUrO2}iDImf@P41O@r1+A?a}nUbw({z;^-$B7o;kEh}QZ}%8rpYl%{+2C#;#@ld@<+
zw1*ABrWiH1pKi_lJgasi5CA6A-g1nKYg8AX>ujBng>P1Ar~H<V7j#;|!q?a`S?@`Y
z&u+$fUPgbiK3Vu~TJ#|5HErL{7t1uhO|1H?R&-lv(MSJ{RNBq|eGJ&#oF|;Pr#jS-
zC7Cs;;rT>PMXk%t0MnhDZnA08Ub2tZ7VRZp_t%(yq^Y9aZBy+Xol?enfbX$G8e2~U
zF>Vr17+pL687k_$tS#eM<~XvPlZoVF!dTav5gEkUQqHqTKF=IL_A$Kb3lHKs9ecNk
zyTV0n^|!YH%S+5SYlI^;u1LB_t;G-@q5G*`O|Lg4+aPgbcz21AkI@hIy|~o&aNRgc
zw2be=-|&!;dL-PUpsIm7ekB(RuVRmmk&-(8g0tl$UXlZ|wcn($G;yw7coHxF5#E(l
zDalw0k1nlMQlEph=-OQ6Tw@$KPLAl5Qnf$syrk@viV(16zlVOec#)+OdbJsOmy4z{
z<Mx;Mybk;l;JS9cdySE?=aeH+t#poI*5F0Z+SRaayNai<9B1%SaoxC$u+Z+4@VFsp
z6AHwbiLZPuk>cJlJf+-8U@fmZnUUGc&38sZP}sMsA9@$VA{Mbso(nG`B}PM`3uJfm
zE9I7yqEl6$!8eT%HHEv#zM2zPEBj%4rSoxQ9#)E)iI<*(>Wm@#NB6J|@3vau!Y*Qs
zgs^rMwYTFyG>tvd>F42NIV7nFxcH<7>|7tS`?baSHWE+;XjDX>R~5yOeA{pW#th%`
z{l&Djn0XC9UgRHH$|w1q&PVT8YE$K9nM;P$5<{r$-pTDqPQB{JOkZ$?=7E}bDXes)
zi@)4od8xrrsv5TM&pA+vdv${Uc9#{n!PRtQedtvu7^!G6r8dYe`!^n5%a1*A@nFdl
zW_ZpCT*7!Tn3xb#jsKM-fLCd;6{oa`zYfdx&0*dot`%b)!<cLiVb41TOzy#J1}dKG
zK4GFe5-%V>Z~rPNyJxG`bCpI*pTfi-QS^v79So?grgAQ>7rVh&<Hp!_3r^jW<oP1F
z|3vpPL!<%{{h6)|x5U<`1v2vC8W5CB<+f|B-@iG!e09A4R-`Qkqatlzngd*Mc-tAX
zX5j933dS_)^Yulmb<@($Akuj)HZzW@U`T^joDS_EsqyJ!pYA+T^O&-c)}fo<g(Pqc
zC-@ze13>_E($yc|a)`lpF5T@%YfWgRi(hJXPqaT<S=P=I{i?}$U1!_|FQ%xH2Nfzl
z`Q=@^i@dzf11K*uzUK(KB^6Xw&Prfb19OLJ1P$)^-wUhG;4OZoM^8AP6|;{2y>o}l
z!{S7wf|<_P$~bj)0=II_ljG|1Ev6Ede}6j4*2rLWD=b2B2pPY;Ew?b%3Y$U6>B|+1
zmo0-H@t`A?a$90BXC0egI1wre)d)jI=i64dY9H*SV&|9D<G3wGxP|!vUD4dg+t5VI
zkb*v}8JOstO@eo+mAN*}EHor!`b_0)_aQ_puD(}+jdS&KeLy`w5r7QXbp9*)JvCwN
zZ;54J@Clo+Yjlok1A-dd<J{l1v`eWc<AYoqRMZ)C;*X1f4CWGJ=A9=@$6L(W)*mE=
z*_pM_2E&=1xo;zBl;Yx4&%-pz3x4SU637u^Wuim^d6s+JUh+yp2}pU$l-^+8$l^7G
z`RR8EUs3Xr5!1MxSze7qTB@>RY{?`++5J>)TMTm&BGK#s?HcA{^PDnkK?v&T?#X>&
z4*M85Y*b8(z2y9YYcb~6)p4#1KAHTAoAT(|<xwtG)`e|9py9S{Cri}6(3k~hKd(fz
zBr6n^)r;NC&}Y3^v(YGa$b2ZMC1^w~{_{8$h97&7qun>33<+RZS+zXx>~M>Iz0R7g
zuS?r-m-Jcr9~hn!Be9~2uaPhMte~PN`nIv~w<Qf4YGJM+Neh;GYQ=Li`D{K}0i>=B
zd}=C?ze|W9Q>f%~c<?d2<Q~M8G(NRJagF_c6$tO+Mz+z>5lq<mbW<2_ut<Q-bZ!hR
z@PK*6e;~lgoM0mnt8Q9kF}4*vJ^X?C+g2%(+PfbT|A@|&_62s-Wl+#ARfyRbH=8|)
zN^|$uCh~EezgDDdS1**VMQNx6qRY|UCK3Il0~N#X>|?1em~l?om4>)A`3n~(_jX6<
zq%<o3;Dr{xwbl3$E6f{l#hl-YZpdG^P<USHI#ZEgeha2+yKOPLWQ%#nPV7|Jtwyqo
zEGBI3jXwF`Wp;dA)lOk%Fk<7L-F^~u=4~`+-^gIq%UM4T%fJhMHkjdCgORBofpdNB
zjGf;cT`MXn7+?M+_0Eh4H|#oAarm_qvucO!7o=rUKKxrGvW|Sf0mxkaqpev__zSvW
zUKtx9_)?Ko+me*(=GZCZ?QvujK!(2pOXgvpR+gtE>T&lbnvr+Ze=LWCDCST1*fwUf
z8JK@jcd+DWDC%^~zlqh%JVw%}Fssx2i|&5qpbXeH39$U_XoV#V;eF=FwC_<elntC_
znFJJjNXX$fI1%D!hMiQQE#P;#lFe?fdJrA|^%Yw%ptKaE;MZQQj$&Ee1pDiIGw61h
zgGSsJ9jP)k;bO$a3gcYNgv_PHUBb@Y1i-g%d#5KVo0}!N8;YlglLzfm#GUlDX4`zs
zHE`q2#cBoKwvAwi)W+?HHrW}=#+W3OAe{i0d;ssYiIP!D2DSU=EU2Po2LWpP2jJyv
zAc^{CM0iYVfp!L%qeOuQz?bEdhc?28s5c|8-%R{~Qz;|6ovynZ(eNe7#SM1rvszz4
z4kgMVwRHmhRbfy|*^D~6@Mj%k&rS2I<?IU1A01efy4}K_%})xqZ8ks@j^wt$+Y`Ed
zm*`m35@B`?=^YWfIi`|XL(m;V`DAix!^5r=1^X&5?iD`Yx^ar3K^`Q)TcyeZTuoxu
zKiWKArtoHrEu?Mcd0`0M{``-;hTF7jZ!Fu?xi6+g>zlD`N5fZ%Fn?NVClWkYk@%o9
zEn9fw1&XC1S*9$sA3PL6v0`<_o=U>Tb=?XUu-3X_;X+eE+is+buF{Ehhr=6bq)zst
z9_Gq>Gfr^ySI%^cj9EO5HLx_^>1$b)bg|XF5a)V=&0>@@k^W`+IFdgN8}SMt9naUP
zNTggPdJ3s+Kig|pRk<hfPNV-!n$}}N;zfsL`c!?eg>bEYO@Mfn{H$BuZo(o2(t5Uf
zH5V+wTl7mmu(K|}VaA5+1N;70a7(u*eMm4>6$qJzB=B#)dUZy-seH=rjaN6PcIYYF
zgN&ueTO;@6BsR5mJv$t#mE~%aaYc9kZYn6D{Vw?78SPK3gJ#yiLav~5^W8Uva3Kg@
zTHt1mnn6wd9(ZZL`gpZw3#i}jj>jv9jLU>Ga%h?Al=Uv-s^xAUbl5^rjHPCSM0_<K
zXb#510K_kNH?#TwaZs@(>WX2TVRkw87VO=;IS!t}i>7<K&Csz)!{x_teHSp>@8VTk
zaMgQtYG;hxebu$zJZXX}d`!sN5}c({2{!`3OtWqrtbIQ4us}3)rY!x6$uJB2JXtrN
z-IjR<4B6cLw|QrMu-}+LNmc56{Wjz%t%^w9#p2T?Ois>*W^Fx_qKV@?IB>){L6)oA
ztBkOni(9Ptp{T`kwEWR%SUs^VlSi>q<s!heD1~fe2KPu#BifM)r>?raiQ(L`^~6{3
zyU&iF?=6p!Oi)0~`K4hEjDa%*#PlPQOFugtbkM?^0Cvw#dx!<46f==0V->yIahH2)
zh$e)WBK2L|*K^9i`EQ1`w)0y|z7@#G0CrkgvZ(Z5$Hfgd*k*!tXtq**lBi_ZNGCcR
z1G;<!#uhzOIf19gN#AvmB&o&zgJW;wr(ck`{DYm-S=i(Som;W4Uem67a+gSpYlzP1
za4JXJsgHOZnR(P)Z(Ab>m-1vU7J$=6DGFHiG!i11Z@O5`0<F`|e#=MH@V+G?L88tN
z%RxJZ7;QmGG9Bj-%Ju7C$2O3MSP1G(FD^9eXCikyEK+ROE%~@of@t3isSJQFgfZ~9
zs^Nh4K6~Q<e|T!njreEs1K`QX$41Z4sAqp>!VQ&2%llbDS0TWZ2UwXG#g4AFcC}Sm
zOR=pbLd<w4uuOo!KeDe~@$OW-`S~RobHC>aGuk0b@S4A>yNq^_!Z!y=d0)nD`HhjX
zad2vWDu5ut$eMaSaP$w=SAoT@GSgu^^d%3W%@>yquvO35!qkce(_5D$br9I|=2oT_
zFGQW5Zdyu>q~GUB+vo4YeY%etb(MQ~-EfNBEQw0dde2INpTesShVD+|XyHK5OS43e
zn)eKm8RU@7#S$gxF0&;wo=ftG7m%Y>dD*GR$K|Tc%h#e?8LNWB>GP{9!hbCsJ1Fdm
z_0Iyoi<*R2@dZD^VlRnO$6W`JQUwOTVr{svEdz1(bX1HL1tGiOqV4y>TzBcTzCD((
ze&xq`OCRGKMz=bTy795x;4{fD*`l5Ib<1jkFWMN<l%jX4++%@3vBVTr&dyR&oUuaO
zRnj7=GoU_6_?7I-%P6AkxK#eC>8Uy2?^B#_>rje`z6FNV2?|49wxE)$C0>F`+WtY#
z?s(qd8Yu_@gj<UKebdy{NpNG#?yFa&mRFh!sIx7tmCD~(MsIdDEs~GA_we~|2&}53
zi<3sf73e9RF{riDx7q71Vv<l1#nQX4WO0JAb(Z{lKv36xy&lp{Zn`<;^mXW2rhfd>
zjhf}pm1RViL2chxKZYSX%@3`BXlMXJq-u!LGz^qp3;iO(!DrFokcE1^8UW*Vp84jE
zZwk^5@lfmc%0<knjD!~uSdRB1%_gIO?jkFCQ<!;dOG`6d^+8}H)S|CljOEbAy+N!m
z_aS}vnWouF7Vsq(Yq1sH^KL6E5Qz79vMzkRPN3fZ9uF5B-xaRW^<&1+H;?#m)J}G^
za7s=`Q=5cn0#5gXZdTC$&4Jf$T31VS#+MNMmRA)UEO4lP-?8wxi*jX~_O-207(u_3
zn1(1*1g8n%r2{Jq2vqPI<8+ZelvDx?zUA$c81YXis7|opQ4<IrMe5Y>@eV|tWX<w=
z^>TB~?yIR4dGQH&)qIyzy-hHC@2vWiOVg2Kk-29W_%K_ewtlf=1JA)R99-w&JD)Vn
zwtg7_osc_iblN~|Bp92|o&Ec(8&cSFhyT6eUW4A6!gl%h%3|CU-S<|O%~i6F&<S5y
z3)&<lE{n2pEmv1^hhlnqB!~CB(V5&?zH3wdtqoQcw%r}H<d6@vzW_N53i8^dxqs6f
zXwD=I4#J=Rx||6NSSJjWU3`7sV109p+dI;Hm9uHCDYjyib;^=#gUJdbC*{@^4~U(K
z12%T;Og5Vzo*apSh|SI9P!y>U3sq(E>1OIj^Z{PYai7N3)}QLE;;9Suw@01nk=*!y
z>53d)PBJ=g{sKIXE{~E-Epu($x7Jk$%V>Jbi8km?$h4?jFy+P|2CeOiO!lu#A!iM8
zROpx8M+X{riA;&pfk&_QIE=)D1$k&?vk|>NsY~g}yqjx@{V2g8&$gX>+?{P-9h<RU
z@S0_r%&vr?qR;lGuUju_W#at$5n?i)z{a|3(aOte(cZi{zF#{@p;D#2!;>~H)4!I;
zaX|vTIlso)S?)9tV|&$ssf*Rchlx{u)^_0x9N>CKOqy9^^GwY946~woiV;@rG{DzR
zh-=w9Y|~FjW!Wa%gv6Iu%@LXh1npCz&G{|Wx|ww4_bHE;+I*TcNx@E^%iJcI16tq<
z_}PghuqL6w7yA!LY2EGG*B&kr<{Pry!^B)H-T}Q1*yknt+;i*lPr~?oWvmdvS{ueZ
zN>Sk23rVO_XHtFU#_NLDT(Z9F9&zW}KnASjSjdr(BHy#A-@AgmP?$~2Q7g8io1<Ds
zUB3WG!S1z@5=~mjT67kC@Kt$y9-`*b*<{c8xWHwU^4DKOm#;rq4bjU?W@U}(sg!nm
zp!W5j^EGU$Op5rZOJYST?8G5wO13wfF67&pjI&R8aG#G&pyes!mIc1WvTAECbdqg1
zOX%2oz-K30jENtf71LEqqF4}Y5+>M#S^77&vZ4OFxpU>_#whruf-N3OztwO4sYR^z
zPOGZFvDqW0vzJ&Zl#bbSfm_THgY_s@+n!e0F|>Y;<?TqdujSO<o{)H~a8#;6MAx-`
zVV#`<txyg7H6@bdCWDBXviDBnBAP^v=3+76c{s^vev5i~q^P|GCvDzhdPycwt>mpr
z<J!(q4M2d9FOmJ#Y$@zxiL!=8YmR2^Rx=xy3&8Fv7jiK(jsJSffpb7UW>nqeeB7>u
ze39CBS0w+lR9}S~`AOf8@zOk>y1bMLH%T+iuC7t+^O^9)jihE1(%O#Mk4Z8s5K_8e
z4svy8N{D!&;6r0C%IDeVo`=u*jL;fGde5Ediqz<udl%$W2fdX0ExVVp4g&A=1iZ74
zfFtsgx~=?94F|?7j-0b?uvLRMO>N|B!Qy~vHhl|LK~h-V=3{8=nk+lpAQ0r~nJ1-V
zlY{x|4N1!`Z&2edF2yXxc|$#`^jtD&QZr0S9xCuEk=L>x{>>7E7J`+Ok;+#TN;J+Q
zo!<B{8&ZGR^C`T*{w#KIZfVc<rxCIa1Jf0H<R!C$CTug{W3n;8%$ck!ZQlIhKRE7g
zu5>c$x2%U}-t+no|JAPk<n3@9<&En0hptA4t%(O!lPF5)M5_(kT82$u-RiHFB^M?%
z^!i(uCAbxq1yN*f$^8hs5~ci-?}&Y&w)u_XWft~&vHZLlo2a$hyOnseSA0-LydjI<
z%B@T2$?h$Bwfc)<Wjk~Fz-(VS-9!NMg2Mn2maPzlz|MmA97OJzY$eM`toafx=pz3l
z@u!9lJ40WG;pDAq8)zVfJ&hN=(fDVyi=efsn@*@=R~jHN{|1XGn!dCdDtF}^$4SX*
zw!nX0lOXPP21R?{LoQ$p&-m){{{U4$s=rLDWz99&B91dFUfGO~Jh0}x^KA);fq8kv
zOS$J<<+4gR&}`U{t9x`MF4sv3IlUG=d~Dj~Aa076TN<wo(~$|+UAAphgWcqVH<7I}
z<5EW6=a_MUZiQkaO$PMfpIuQc%S$}Rv_clIjPTyzTp*z8rf6?KsmoTil2$@2X=+D;
zYc>5SYqoDL*2l$e4?c_F;X2jSl@0XIJ>K~1F41RA@^wuBDORg$*qI!X42WhMEouz_
zP0@Sjb)wt0)aH!u+)^rrLuq8gI?nY8yi(Z-jJGq&@e!kDN*7*hs8ZR|!abcI`Q7Nr
z24hXXG4g8w^e55iA@$^AWsyQ`J6^HOwA7L6AR{ZKY<f7v860o|;q%LmI;qbQfSZzk
zH|Hr>&vucaQaOExGI&~+Bxz9BTf2H%&{{FiT<}i|R+No6_S0A<o@hefAx7Q6?lc?L
z4^pMoC~yxolTR?GNw>|yNe;{aBKAn#DJ)us<ifkxGFogpj2CY;_Xv-|#sz_X>R$6w
z+>O~oe=duetxC-{W!P@qXiB_si<EoqzTBLFA4xOKfs_y7i!Ypzt-5_#OlrpLO<b?c
zo(r^cW-@>UY27(UBfz>EH-q+My+}k(5~fql+`5gG%BvG%Jomk9=3eF=U%Fziq3-L{
zoKlu*FBbvlnbL&T15wmDEnw?ZIY?~HJ!A(QUS<&1q0mwu4?MzS^rJ9U>>;jF!dBeF
zZ?zu)5Ag6)pa^XViGw`X2F!|TPQr)EH<m9m$>eP4+`)1WHDX?uHTox<^PT)Z-8h$0
zsm-j&qYSb6n_8)iB?Yf_*4(1G$w;#*QZ6eUQVs-uRTnp`P;U3lgk;S}fGA6y^({97
z%3nu*V|<$b09HsmlT4u_2=VK!s@rcTMh3B8=#C#s8|=zBKQPZgs*K_xS`U13>VjCN
z;<B6N{^K`G;rsio=|tKgd_@Kx#brQkb<*jc!HwrAju=NMK?hG~LRAU0emN^w$t9ky
z`;enCD1t-pn_hoS4Zh5(8{rzx3I(>UFS_bS!r&kmjO#M>d6B9HH78r<^DmCthqow2
zv7XJ(5WMzN!C<)m0L-zGXQ6a*g1EKel7*Kw$bB*s8gv@P(zF6~cavSmG}(kp(RsTf
z{{U4*3FoH#9oG3i-pdmZ<*#?@54>Wy`a~yn=7fmOPIanU6$D|GUs%r$UH92sQ*HLX
zHAsGRWZIRy_Sa43P7KMwDCzG*d*ofF;vjpRyJ;D!FKsrEu+XLD=9L%Di+~nFjK5GG
z-g({mQ$og!>y<5?=xgc9^s-lEOS?f{Y=+_GPgt)f`{%e>&24)169BVgk=t9wKoOh;
z>k{#5jBrH4v=6<g^0uvQvYY2hdEpWM=4NA`Lgs4zctY5Mc}h1mA`fipJolh;V*vAQ
zb((E;fyfknuxe^syWa`9MVPHL`yC&c9Tn8n)aKgt`p^8AUE^cg+KyOmaxKnRhY`+&
z%1FEO$Bh6>L_GJJbw!rl7Ztj&^e{HMzYrJZy_1&JgFVx=a><y+)1hAHVMq=^^*h5W
z$<Vcl9S^xHO0Jo<jXe#E)x_|)w&0^aq}%9F3tGopPaG$>4@%#kO<mz1Gixx#>5@vB
ze7eqNdv%h+HM&ko*<#C5y)0>0$(X{5DH!7Nh3G@HbOx<B<NZ8eB^90-51H;~g}mtt
zsElJRSFH~4@8&d)GNaV_GcXu;)@Zc71~h|b=sd+wJnP^SXkN5%6$ce-Q#9V?5?i!c
zwKZeR*2R-T1<Nz{MNQ@N%Qs@-XKYTq^qyU8XSur(rC|G)h~*X<@jCH|YWdTUYeSyt
z(2R3#iOrfITI~Gz9A<h5#Ucytt8<1E?$TL0shOTQ^*ei3<n3ZB)(a)t);d2ibx{!d
zycf=#T>|<ATxwO?UEuS{C$uNhrBycf&n&s8)1D_pk8b+Fw_6$2u3sK(ae8Af`#Um=
z^2;Qu)u{I{JZ_$9Y%Y75?$YTC(e8LM&HB`H&8<`O!*<&_V~W*Db=O~Khj)Z7_N0(2
zG25kZx_&i{6u~?zJq?WpxFg^hdM^?_7f*IAQkfWs^fsnGVyUCc=LfvnN11gVSnQ-L
z%(pWG7cG|Fkw9CmEXB{DzjclUN}g?U!v!}Udu(TBNKzT;Zj)^qzzPv`(PASDq(;JP
zwwUeIq{`gOpd^m2-^%P<C)Xk6bA+DpFs9r2qY^DS7+1X0ZF1x?Wn43HZ_hpS`!_uI
zT^tlL+1kL&17M~&sC(La=&VKxEv-DvsZ<6f>z(O0k^AR`g=Tx1E=8Eh(0O3nrIz!3
zX4WDavlHj7fktf>XO?T+H!nx#1NS1b<I|A2*RnJzHb5GeS*oxOPKnNlH*+I{EAOGI
zlyc3tNY1ly>BM$EB+NIXb1sw0`a+tn5*KE-OT_Aad8Rw`E;t)q+J_Qar=GE`HJrt%
znIz@qDClRW+DO@2h5ot7=rC$O3av+;>)B7uq=616F516&O}-@~<_Qhu<6puvIr9SX
zC}CAEbKf`DTQ&##yE>n_leXxi=91f|r0k-HFod=zV&p?p(6t<H4&o0$WNudv7k@+L
zp8Ms>nVx2Ew0ZA`pZQJN1cUFiWT+4v>jgXD1#PdsctVQ}SDMNC4)Ei1hMaglogp=x
zHe+DAwQAC3w!8$GyLGU&2tCb9boDNfsJth!@KQt@(;Rt!ROiDiQOPdSWq#&De4eLV
zvB<Ms4A*N;sh$!V)XR(6)sngV5EFi7xzBDXF1Ze;6<;;5$GEBNm*_yuZ)Ht{2+M@`
zP3Q@w)qz`MWYXs`Yi|v`)WxVk`?i^8x4RY%hTnN-n#<!XihgQXd|uAZ3~_wCyO?G%
zursbQUar;23PtV9VVydMH|E~?m#F8yd*qp?nT!ECmp7qyalUJMnOJX!euW+{bj<LL
z2T*-emO?O)%xZF2e&aM9g0`oHAnI)#1Z6X$a!qfKnmf>Y2D^FZVI3pKA)Br80h$^i
z91SSKD95(O0_o5X^LjOj_I@c-*ym{m-jzo@X;){b*8`YFj6Ky82EFP$A^2MB(x0o|
z$D8QRukXE~L^B(KWJ>kq9z24p6>Yi&O6vm~4zdL>L!5dhyehkC?)v$*U)NfX3e+=y
z4@4#mG8f#PtPM-!qia{g6$fs1n9cgC!f~LNSn3AqjlBN=nCF;8c4xkAp6i)sSP8xJ
zSeOBM<%;FK$_`9?i!;p3zCW+??zh9F&8ckFX(P7;<?X5c!;_EI!PkpV49yO_XKOpt
z^telvvwt!0y1Sm@pZOxQV%JQtpt5Oy&7+NgP3tzi4NUJ~`SacM)Qrvul(OW**jBpR
zKOUxeG@nT2FDRYwgmh7f54I9%W}8VFkeKt+__Q>-W0G>XyE6u7S)7qVNxdmrzRU(G
zo3=lW9IuM?_V+c(V`11#4Jq@_Q$`@3L*Ze0t(HKrX0qO)?V9y0npWO^grp|*A6WcU
z?*@6FJoDcShRn?0Uv=I{ZB*FfXE)W2cHLb{!+Z<BL*lhz_sqf-JGi9<tQt$&Ep&id
z>cfzR_Re4uCUOd1R@b2s<(}$XRuvnxS?*y+wS7Bqi`>;X#o|qAi>$o&%e5KTYn$2~
z8x*>fiAkC5+f*HrYkPX0=7Dy>e3w?mpBlzFr1gc0@h0@XVAz=^z7x{?PEO-EdG+_M
zo9`xHqvK#Q&|L)WaZD*d2h345(lHw?D<)=37rt1o%CPRH(3>>XwQB(cTNwwow(i&+
z>m330EZ8JCL1A#etXu_ZrkB}~{)n5MbB(m{knGIQcJpl0$zk42-ucy{BkN!HDDTl2
zx$4YW?qdz!p+kx2R;dyyuT@oLquS9nG95Q=j-D2F3%w7MfXpI0%h_8@&9RZ+nb&^m
z8P5{Vr}QR;CDx8?P92Y{(J39#j0_K`=~h=V{2p|UXQY=0+^^TIuXA0Ah%r-FzH`+I
z^U~@rinPuv<kK^{TCT~-RraELqPBB>e+IoQarH_@7TVJbU^YrelX?e3$})4b9G6`)
zE}9x8%{|E|g&m`39YPyZj?6kX*P@BjBsaqm)U9a9^5Cn_sOhdUDE$YgIY8Gk-8|2H
zmPmB>&wb3XSgC~*bgC1SB(QeH)XN}qGYLI8b5ze|(?V3Al|@N2CqrLXst+DgYHUNU
zsHcz2CNkHt#hIZvwCY@<J^q9tA~KhPI;z-XeCD0qHXs{jmFkkVb)>FiF@;-N&(e8L
z6(2Oc4xP<8cE*FWtQlqJ{WSjoFesMMZBkRpXPX;&(${M-^QD+!BiBwN^7a_SYIg3P
z)4sjs-EI%RO^NE{_=(Llv0pPTo~9Cb8J0?k(^r6k;3b26W3dHmC+JkF!RQK>YTT6g
z7<PoZ>_<re?v**BZG5)|o@V~*o_X({=epU0#^CgfuNBxV`0a54oj)@@zt>^oTI9M-
z2`8kwG{~Pa^T!2RwXit%Wb52aeSE`!NYT1K-PnQF*`|n1doy;`R{6E}8Y`qw^TUI0
z)EdgWJ>svUktoE-t?aLS<J)jueS@BKTb#RNp<2?JniWnm^CDG*v?yZ^@2lMZ09G!@
zKZDO7nc7P%A{9*AbGG5JaA5j&E$TYUDNr3M{HvUbg@`<xH)$0RjhRTKW=_;RBs2M4
zj@rM3p{PNr48WpQw7a_+4e{oN)@@zUj()vO84!QAs%^iT`I~v?zGiQ>x@Vmj5L<Z&
z{q2xAm3Gqp*`D|GE_d&;X7D+%bBi=(G&{E~@;go_xd*NNKZ53&;aOoDYPY-l7n<F7
z=a}Q!Gl%tCAjc^<)>H>n$AafH5b!Np$q8-bw|#j-uKUj<$lj@}N78raRf`Q_USoRY
z57beaZ>Tw=XT8Ukujj43We1#RnWtO251!X>0Yc>m;8FB$RV4PJ8(GqltJ9&(Lg#+f
z+gaf`asYSCK(n5hnw2;=nUEwgFo=$(?3MRhH(YB83DTJA#ZDQ?_-%>kS0czpUlr_G
znVV~wndIBo%=UhQluFuvyP04B2&S43_d4?WkCZcb<s%1A<Q57FVY1N5uBmA{TKLP*
z_{Mpj`0Bbc)o*;#?Oe{C%l9+e=HML)+rp<a>kA92g*>fK$sA0qSz)uydIF-(9~617
zmG6yi{Bt*avbeHPQ!p}JSua*zQdqKMLcVlRO1|d(6Qc4(TKVq1U5}n5$r<5fM6pmY
zM(XPrNGZtTt5LKcdC2(U&fa9u%IY8_R*J4|tFor`;>U$bOhX_ksGCIZQ(V7zIeG4^
zlnB_hR`jxT2P;clk13TnqgUMbZ1X(#GbGnvefL9C0DMRDN68gw*3pPy?!3juM3jtW
z`c~`TX+R{@5s8PI&)3#f)253YO><1l%yA4J)Eefj%YN<BdBjG05e2?>KeAo3LYjh6
zuzpu1><qz2z2af3K)@aaeYplsi9uS9T4QiU0eXeP^g4Xwr&vdKOkh^E)j1YtzIIdG
zj0Jr0pjS^#QP&8Mza{BM*sBfPy1iSWpj{>n&!)(O%{<Nh(??Y}SXWqAv&V$&);F~c
z^t3lpf`}3dN?--`)+027oeevp9dguqlgEji{A?c0a_vz1=GuFi?s!0cW@nyt1hVzj
zyy(#`QKOnalCWcw3dyceD`|eybU=ej=FiC^bm^mbb*MGyN`iAUJ)NCC%pPMHDdMww
z^3fi+VN0B!A#{^qMSu!^7l?g)Drg@?Nt~)Hl7#d~v@dk!;HxqDD~kx#t(QIM%GWx+
z>tj}$dCNL)OGy`*?!C+2X1A`ls%qz|et2L_%2&>+G1mf0NT;%CKSB`?=`WCR3Oa4B
zcwj%IC>#Us{S%)1;qPvy`cjr(gtxT)z9J~#Hhv1xji`H3G?{1Xk|H97(!?HRlbh{8
zR32x(;Q`r~jm@<eC1J6Sb)5#rn@8AV<NYh0$FH-H8KVf7Gz{K1GThU9lXk>(8gq2c
zW3Qg)nTEp62L6*5?%wF^iSKzM%Rrhqw^N|GF7m)r`?YUx+zdycb2^Vnvo;9?Wst%$
z^tNh8BW>svN?96xTGwZ?9cl}K2aM}{L*F*?$7QX~0Ojv#>CqR~^6gEgta8j1q@})1
zNwd`td}A8=<v%$>t)6C%sbs4_a1P$Rpq*h<7RCcERLfScfvWmzm&>t37StS7cL94{
zo3P8|-0c)rlf~zr=bDitKKW05m;lX`fomqF`mYRYuc1{~qc_w;k4SXqzU{79i%U)7
zR`$(}vUBO~ROzog)1BDqyr<mE%)+%b*F3dXUAFbzI&Qj8irmLUx{>!=eB+-|_DDC{
z-4Y#RjYQy^XrcP&UpNJLwje)Q819dhd}UUe;p*}!&7y5)(`l(0?qN=}Kbi}k=(}1u
zFNNKuQ+eySR#zb&mnXCW%VHMAnaA$b(l?OMhcga}iz7G-ZytP!W}Zr5J&H#)yX_i8
z;Vv&P24Y*@#eipl`cj3S;cJ_y1<Q23&wTeRSMxL6@P<&#^CXPFQB+FmiGHutYwG;%
zG7gTsub%s?@>Sfuu+c?yu^0qVm#nY!4q}tk`8T=mnY^b*4)bq3*E4S%RUqwGc+t?E
zGo6g9-wNzAi0Mn*^srm~(}q#$YG4fS)kkg2MeXAv=e{sZwbGYVt{iT@C8M3ZRbR*)
zILDcpt4CQ<=Maz?zNMJVCJMX`ZQwU~X2YVXK{q1^(jNV3w_q35D{|G0_ZWg#-_2MT
zS(=M+yAoE`R+ZeQW`j+{S$u?@;HJSfER`HxaiBd~*F5co`Er#FN{QPuGi{>xGtA5)
zJ2Tz1!(Bj+E!(Yennijdf+l$8&&oCPGsZBmAMh8JZ&-*U5NNu;COg*GfTCo+d9-`7
zfUTL0D!%8w+F}XO%kvPI`U0x;E}r%>xz2gcvhI_MV(#BG%->-@e$QPU<ClhpL#w%L
z>9yw(3RNNLQK~utlJV*#k`w&TaFLFztIG~Ad!E@Z(m1cIYn#ajuPD-6trJ~E!rQW^
zkkE?l{-Sr7qvm+xo4$Fg1<x<B@*)HQ?V+opN9xesiwcBAT(H2iMVx7sW)Y+y0llsI
z+aD!XgIm;n^E2CUN15h(nSaV2=Gu7n>aQHFfKwfoQl*bN3x6XWBz{<)gv^2lZ@Ft@
zMH(3c{{TO-TeWhfSTxMZBV`9)2-dDxU!HrvMOApbg1KX+E3DP%M69K>bP$}_ezj?`
zcJH1jI;8da6Yv*WN>8Vjt_s!Bi<ouAlo>JHDK`OY))*U8d9)ekV_K(grOpJ$=6hhv
z-yz%E6|7dBjb>A}REk|0WO3k?{AB>*Rn%t}t?oCc@2QE%t<rWS&Gukk$#Br<9B71R
z0BvrSndh{;%R!h`GGx-Ut9MT6cZ{!Tmg)QEd$o$Wo_pqdmZzbnZ=omQ%bf-T{PUu7
z<h=6iPjur?(>*XMJ=>P)$)S=}_i*yHPI_K0%SVcPnYX%n!W&&;&u&%QefEAq>6cN!
z*6W<{epjm`0VKZknTIO?NonjHjXlc4pElIeXH_}QRN~&~`|i7Qd!2)l4e_#>PLMq^
zCQUhZzUF4Et39&s4k9y8GVRWV$~@56*>s7`bZl+vd1!>dwr&Y~(G{-sV|?K>bnF1{
zT?bT_v{;<EP8JjH(^;QNB?Fv_3B^a!`PJ!U9Aa=X%_zFwP225q0d)PjPczSQ-9LQy
zJj~AwRHL&2*1|TeW>>a^iWf6xa_dwaP~`S{@ov}>gxcLCKd~~}9Ebr{k9VrC&ht+r
z_a-7EGi`3t%*^PDFR&ueJouKaW`3_*n;8OUqIhQV0QR7Y{Y(!cDdD;$i#l=u7A?Uk
z+X{O-bN%&Z7{v1cun*iiznPwUt9qAjzsJCx1;0Q=vC+9TsAsD-y4h|?&6ovOYF(i_
z2ubKP(gJ_1)Z}(rsw<4iW1g?1x^F41a-Wv<x1p+3#}_mtHtnZW3r0A1v#}$V@^BvK
za<#^X!Jf|sXPaH2n`fDt`{C10Y#QP<%Bh6k3!;I5EnLldcRs3mAe&F5GxUCMbZv`;
zZ@uk9oUxzgF2>UOS1wie%+GXZTF-MV*39?D);L81sy(@rm~O<+Tb#Hk?A>4^QP#*d
zkDjW7Kc&|6NgU+nMD40b`x4~j)uTObp`%Tx1%~r{&pppi39r~s{Q64Rlglh2I&Unn
zST47y%j+Vx)0v<&t3rH4?t_fvWv6OBm(5FVyXHt#WH~EK7cM#%Y})MxYtzAJO$XoH
zK)AFlVt{+JvZEs8Gxphe5U4X#A@eiBWBv17_svc{IA}J4vuc{{EGf}gc^bjO$+?sC
zZf&N$1D|Zh4_5L><$-x^sXyq_adE~~{{R@5S0L;BGPY)UjEkA>d47i4?}xh5%E+<W
zTYMfBWs#4bS}X{GRikt}^jA56k)bTzZF1oR4=;4ot>{v66<8@-qMbR*R7E;k1Mdl=
z=4NI!DiP}pzdC4agCb5fGIB_xWDUI>I5wtPBdsjC;Yw4jW$O&}8CwAIvth;Nc+9sZ
zl94`~b$?8pkY^>?X+mzw>%0pSQrlLb#B7cgOv#a_*e~*7+U5}-ufA=3c5S9?*6w;r
zT*j%9H|whHvylG7$QE=eMQ3A-2{2(Zz(-xs!%s7@2J^Mw?0QBllF(O5e2S6doR{3p
zn4nzuJiANW_QYL~*BGr{Td&-RxRU)dlhrDGcuV!-`WlQhx!TO~Q|mkPP0lRVb46kX
z;RQv%UzYT`9iE?Gtg*^--!nYf<qx3~c;NM?9OnZFcGShqBWhKPW>sr3VG-wrNl55c
z^%CUP8Fn&w`T@Q_rewsz`;(qq@qh=CZ)j_Z`7jbNNkElavw$I3h2cktt_CR(rq<HD
zf}TmGieBbwSYN*Q<<M>`dAJ48<rvjVF8eFOUs;QkR`C5%O%(=tUuR_aNpD%<0(~?f
za39dNY4fH<CH|$zml;<+hvFf#GWNsv*FEsSeBRYAjnVil;~^!~xXHQ`^&UaFTXhK7
zBBS0y^3I}?piU}%L~2thBpogeBrN_wwPC0&2DJBoUX1VxS>}7@#h8H8=}sJAc(d%Y
zrc&=!ct@7>z8hv$X+6zN&|zP(C7Y@jXTqcy<86p<8jDXZ`K?TunMY<(5Ll*0o04`)
zPsZhO%tL}{1>1wI?tNByVyI)IUgJJhj$70u_Oz4fUGbt-YD0=E&M(;?sHAO=<&@uQ
zCad}BSXve#-i?~?sB`PPvL1~}hx&Ule|7gG^nU^vadp|#i0zqf$iH(lEa?VM6OT&c
zx`*=5V&n71JGXL*8x|J{0=Bwx0s$`L$}7q}kT|Su=kr=)RneniBO!6cZQ9oxos02>
zjL&?`Wgc7?M|R-T35~J9QeRG@3ZlM`DiL~hdu?pAt+Uap71El&mTF(R#v85Kqr`{h
zh&?F|w#zBaae*qt1dRHnbiFIokEauY;bCLRg)v0n_3LakAnmbm`04GjF&^t`daUhD
zbr67r$qS9GnDG*Rk5x8O?HrS&@|kORZTFeL#Y{|h*%d05HmPHviRzw+Z=2lnUu*MD
zQfwA>zI3(kmuDH7neVtpoKiLPj#n!0v%4$hDY{P{DngA4{<1%=?awvquF71<LvVKF
zqiUNMmSw}IRfgk5#i8%29k$xCL;!O$F{{wF-&uLWd^+uGq=dd5X5f!mJpl`V8gW^%
zp(*fr0St<F!xt0LkzXI;<6va-Sz`7>1rw#&jdnO$YFb^M-HF!R?2K8w!m469_upEU
zV>3mS>Yognp1D1I^H&EG;!#I}^KGq^10SCBG>pTdjSB4iY=1~W5r$eRCK?eQS!Dy3
zuCM#q8COHG8ZDdVc(=v?91AHfsl>C(+SAW{)QIewYGeuZzDYW+9xI8>id}gi3qZJs
zO6k%#>q<<lCK_kuLIw4nZorKttHk4|)T+OXJrj{uw!_q}^7N+U?Q=1nMUvWd99>4Y
z?$w)LjdM>&DQ$A#ok{yzeS<N2Rtl2}o`<{bM8IPGOwwm6vs>9U^66Y&&sr8G@YOhC
zsc4O`ZYl)oi<;>SUOVNuDGn-2rW{@4R)_29A&{}Z5~hp>wd#Ivu9j^HjFU<(?JH5X
zRO@`vGoX9Ved(#rN?<RI&vEED@=J5qBJ7`2-PyWJOqjZZ&8}CPk6T$w^tYk&J=Dl=
zaKK_G`p*lTRAm|^P9`U%a(3~%QyHgpRib;A9W7KBNqObG@1#pj0&UE$_61w%RW)U8
zR92ApXqSQtyjgQH$57<g;$|s#ZeOO-X*@1ilHQ@U<~2A=YA}`xbP;1M0BKg9hSlW8
zFdbQ|-bSOhy8E@#m4izMMd#aF+j<(`uErF5LaC$cmGs`s@M|pjrE~4Airk95@tzuF
zIL0|$Q+USV0h?a(Mx~u8F{?JO*$UC4tsZN4vvLZ~0+t(VjT@Qcod{{s-Yo09t{-&9
z0neQYJ!u~J+mKxBTr=C(f#!QA@Mo88iFN7Rq!`&;TI+MjwCS9f{?^6^a_b6;c2WK8
zT&=A&_5C?@u8%GDZLAn1%8aQiUAoEE8@gow06T4kL~Xl`0P*WHc}u*a4BW8)032zl
zHY)3W_`K1vJe;G*4tqm!Qs|3xF0E;<bfxxYQdIbz4?WD4lm%^ld`huO_Q$BhY&li7
z^!#NA<4<VCTNeueiQ;26>b=Y*W;$EJ`*V;4p(Hb34+5U9Gy}X6M*&M1$O(e6#||oY
zIwBN!zv*_adA9!DXBHFMn`_Z|{(0sxfNdgyEOu(|B|eQ8J~5!I%Rbh(rjQgF)1Em+
zK?DT%eK@bNKCW)6cq;>2k0@wfQ1hjxwJN+OyuA#e!{piq#lER^F(O7bh2;x9UARvQ
z%sZ$y;Tck)3oRupflkXFT%dHSQSdm-uf4esE=O_-ww)5rf_YXrJ#u|7UM?`xX`1{d
zu4^{sg5*2&MCy={DbRV&4;Iq2bUoTEK$t}sQ6*Ap{Wq8%EWqbPL2WP+6zdXYok)k%
zE!!O#u|Ba49KaS0WGZnk{{U;`JA-CuLNJ@2l0KoHS|r`gty0e)P~_8Z8&vuj1+9rg
zS}muy+rE7*AU)?cv+au3vgyY%oN~E^-0W=p1KSr{`OI@M8=&XQx{o$nBzld?-4mTH
z^>Qqnb!SMoVsj+D%_L_p5>b~I)lNO;W6-TU4HlD~)xKm3e(76#B}*j`ic_HAqk7<C
zqKS+o$U6g+)+nmilW00B8RA_waU3=rp1U&Md*hS}xOM$;EwMUBq(f`WG=;L#HD0}X
z1n4^^PSR!0wNN3@?2lnz+imNxFHIceA<&xr(dPv*t=x-Qn-Fe39OnZGbmff@GdyOf
zA8X@!pDo{AJbFgoOx=}hqodP`<*{1Y({6`JZv{sk%6L#@wke<Mfw_EwcHOU}CZUkd
zht8&LpdLQ5jdd=<?cT%7S^6I$3T#hrm|`+*N%{k#8mO~k9;*y^a7X2y`QuH&TZXUU
ziyIwn<t7(v>#34O7H~h7*;DYOq34z<q_SKVl0i+Hj1{!;sWrq0L2|dxIi5}Jv72YF
z5T|&@&^bi)WV9T~1wuqY7N$ns(8I}#YgQ;t(8yj-zSL_lj>x7W(4Hz<jf*jFc7d3!
z(lDuWE5zC!Q}0}2UYE;za{mA*_bfov3M`AEEH7>4*Y0PwW;3gF#9^{}Y;8Sfh{t6@
zN~M@YZuh8rE~om!rz~O?Tq_xU6m_}RdEjAgiRU`k9@?ZMcWR>9(mr{keh#!{S@x=G
z4SwCo$c>S2q1vE*sY8@|o)?(F)y>Kcc%~z$@%W9<@?)y~ADqZPEj6a;c2WQ&gz(A^
z0HV2esGGyLo+Dz5z(vuP&jcSC8nAn<vP@{+^>n~2L4CF!{q=Ip%2P+J?=&BFCp3ma
z{T6~}KA!MD7?-z=$UQG<?XgisU@j)5OFFTuTaG=<RNsER$A<KEZF(;mm)Nscz%Yt<
z*#~Sc&VeqTdfuPcxqKeLW*n!cZu-^4Gu=v1*3O>Rk(03?^I}77lP)dCl@RHm(6rtC
z6vtfQy1~&o$#--He!cU&CEz&WKRukcNElrP0E;^_HIwDtqn~r1a{YlFIRtg+A?j$m
z^pPv2IsTrP9Quu9W>{ig(%mzaDpeu4mW7)wH4CZ5Q^Hg_*F1pqjHd{!au|;U+CA+_
z(s^;tkHyW{A6M9oUk&c?Nia707DCV}QlZ9n(7fO+a+Jj%UfaW<$-$)2k-+ydm5Q?5
zF1MG5N$h5Z^lo-SdBJs_db=KbmbynR)#-4G+-&=-`yOL(UX988J^<*5E=O8??_5B3
zO+75VTQt=F0B+`d`uSDZ`j<HB%QLj3s(5C|7YF_ZP-8aheK`c*Y2<5k>6Yq15~m4!
zp*`(#lJFX4N^)N9&5@wdnR7=0VxyPdxUr4!CoB>HGZ4;=%4e;TpIDk*EH|UuqDJfi
z{VLKeS+#QQu}*3C#+PCw!FP-RJPX+D#}{zpu9TCRC~7Bck;!K5O64jJJ6eJ8#&)UF
zO<h7<PA_SAiN&h&nhktcwx>F^n^>iDT83>c?Ra&Xo0F-~QI9H|4b;o=2s7V2>5Hn(
z$}TCpHp06HzWCfB+nM<^!~x50hTCZP(Z(e6eKolbn(u$;9v-zJpxV5#@5~B2rLE3L
z>PshA`SwYTQGxwB-(TX>RtoFV`SQg|SmS-w7pwvVBcntukqtJDm_r(j?ZxYGxq<=G
z7`VV`83ITjr*nIeR+Wuf`1vM0wPRT;91#g(eyZx{L<Ws2r7q}l%f*0d2^!5MUGB8M
z^B{i7B!MLJEgPs7EH)!Y^b$G{8`0inq=1_p!TwiZ2N_HL)TQQ4Q(cJCt9~js%>dqN
zCPv5YzKhNJKR@=DK%r#^T!VHvOAH5U*hbrFk!EA!e?{ezZ$GtlzYf&=Wa#|tE>!ag
zYX_MZ(L8-yb|ptfg`+?gUIr<dvEWw6-W!rM3LV%Is<+YR>^If>?=8i8`nad|lq=1r
z2MAOx)A@}Ot}1A(0jM;oX)C1xjLj*C1nI#wha?=iO7x=%kM?=!XkcbprRhoRlco`f
zSSGr4U>@ppxl2iW{8>S`M{b!_2AK&?idM<>h2eg!J($91?iGBD%$+)Y7rE$*y9D-=
zbW|*_TBJfN)&|2==*rD1`Qx&F@fV>>+3$6(BiIubBy&;d+}fT@TwTUWs})+*pR`k_
znd7Jqg~|5Z)p{^{m&aMRL%H#>dux{9i+!~EBaKs_M74c%e^N;4V;1X3bZOU~Id?)E
zfqtPNW@I*Wx%!H+U;${j1MB?#L#NQ{Jn1i`xAKG%bd0&!<@#NkBNm0l>*a1*NPfN^
zK8dWFump^_S%?bT%eYTXriAikUA^}0suNxdx19u;+trtO%meR1(Omc~-z}N&NIhrr
z&rT4hOuoyc?$z+0LbhWu4uW|QH_eM6u>(r2pEQhw@K(EIe0GLrk1Nz`-72}Z=6W|e
z={(>>ZsBpanrSmCp*KJRq9u)c^DT2cavIyr{lcWxvuw3Yg)cgkT)O373kuBIf1!CY
zy+(@S*u{0ge6971lj}zP#*+MxZ%e^7xkuOReAbLlf(BnscUiU{tS<BR_ZF*4QZw_e
zdPFpx6?KXZbaJ_fAo53>9C4a}pG-3(LB5P3AT}x^fhC7+4}s4uxFqQ;d8rPFlynpx
zooZwEeFkzWG8pU0i-F4yH`VJ=%C-;1TFjb5LY)<MlF1(RJ@cD|qh(aMoGDVd;dSGh
z(N)iUXjJxpBK9XR!Zo|$As0_yIcJRDpzH*Dum$!ZH0JD_X~{kp7d4a*eD@j-(o54e
zXaiH+@WB;4pSg3TdXkn0x0yG8Oj4Mj1=iL6=R=1$Iw=efWQp=Tw@EEfYXI0bk9Ffg
zWazUj7U!%Agy{BF1-hNkip|p3g(<78kUEL!r|_9Vj2T<6+TlLks{sa8npvX=s<TT<
z%UW(`tgC+cVVz6KGfj7Ek=~kEN0#E59WpB%wx3$J%XJQN%gz{*VbJEVsA(e&gA&|^
zAw=gfUz3ihflmjH^(!Sb8>P$RWT|f`*|H}z@x&!sx1qFLMqcdboYLJ#guAi!1f;2I
z-Riu98x_uUltljkJ<XXq8aiy7!g8;aHJ%1_ZKN;Q_~=<K4EH^kyK=S3DzdG&C-)0{
zO~J#Hl~yfbqUSiT85$)6mu`op@;V{9BbPHIu5JM$`ob5v+!XocE{yOkeTcmX*67p_
zLFxp6HMD$j;{rXkR{8p?JI2T&uBErAR-k?KWL(cBWLVZ&s$_?~cxY7T56j(hOkeQV
zDv-rBXsA8Y=?PZr3|+YhzM!hRbXjhuqlzcb=xxd_7r1hjnUW^FA%rEmS!*}*Pb94&
zKw0LEz%2~5M&(xZuR8TkO}$r?t-Ng`&+JvO+@l8W1056>eA5Y+>D*pAmaAKi!0&wZ
zfEC$>ys&bcJB!T19k+{7@*@=3)cP}r7I$vp#ZM!hcbl12z2#zoAEH?7X+akHyO#>F
zIF@AzdfIedMOuoMp7#X~co?GuK3>?1d2G)YQtdnO@Sfg^o31ANqY#*4)cq0did%YU
zCT(GrLu1g1k}Pux)i<kemN4`y(DJA2SC*1$*wQNfmyGY_)gn8}C#>@LufE*m&P&8V
z5Eb-<d`Z!BMY8Lbw}qW&-L8!)A&Ry2k78n7%{F;mp}mp5>1mJ$LnNG=eX$!~GpV3e
z(gtXkydd7{kb~^H-)QMPCb4X*Or<C^lT+(?W*vKSI_AQ?$(g#33Wr;TM_tAjz9#JJ
z$@H?f?_1xTM{G{^ja*r<<BC|u>&g}xXdefsSE+M2n>GuRsR`x9YcnQqnWg=^hTLtu
zt2F8e%UT%_^Jks=a^-4};ZfLXsLUuYHrU<5PrSujUGye1yHKpauSuN5z?AwYv{gkQ
zl(l=P^J~jCgnC;7zvvd=&FrigfF+8bM?=e!NfKRWkR^KTYq}Jnx!WuBSOWW5m>C$g
zYDctSEj{Ig+3V-|pQ4{oBG!3(Cny^U!m5yY*Q|4>g84taa)Of0y53#9A{im{xO|r0
zg}WCGB)z5*A=fx3o|Lo@){q$VoyFxFXU25Z<WDS8Xp}P=jNK$sRYN8P3}+o!9(N)>
z@z5Uh1si1CINiOOQ}^>-?EBg5@K$jqy37=gjtZz=UQvgKPev_F3(p-=Fz_t*!Y2BG
z#BhA~Fu+G1m@fXZ20l(P)+VdX0)i{!v#%Lr7iE04aadQepoAixU?ZwQAwGA>d3xco
zL<UuKOx58`jXO7BUbF1QypoMkx^CjDt3W}1dnx+1I^Pep5&b@<+gjVT@(?V>k>K;C
zo#sWAp|?lB^6Xd3<MnA@ia$MM0X=B9)2Wf>H7mw&`>znm%GJXa12hw@8-F8!u~KlS
zMppRxyT74qVbmIxH!uR;S7oJNtsmxSkz@L4aotCg*4dqIw7TnZZo^eVlA(^+mCc|u
zc<ZmB0qC(pnA>~Q%wt-^;B!1Pzicn_=UQIn+ME{n>EN9zmN{w-eBP=zJ+Y?Ul73@2
zJ&cZ_dq*nV>CB8<2J$e3taU5A%s#?c)%7YR-G0qlTi7-SOBY1bIdlwJt9oWLY_qQ3
zwX8?kxY5<lvCK4=O>?Oe<1DURt5a7V6RCQ%M%Q5Cep{^;zOV<S<h6>8^D}!ydY2{-
zcEGNP!OTQ&E%Oo4py8xd^B3bJT#^y9=oTO))%dAT8<{yxha^fYq34=zoO_*fUvRDb
zUGDr+gzQT)N11HTOjo{`vRAgNa-5q<n7TJ8GUsac)hYxDKw%|pWL@_$q%oV-cYo(f
zgxboHvT}ORPrbYNIt4CFLX$64<pYkV7Oev%9}lk?T`0D2l#|q?8>6)G3Ww}1X|0A_
zP~6DPii@x76cr+22ru34olo?H)<EVP7ItP7$Y1%*Ir0fzf%GIEm&J5`TIjraq|$)S
zx<{0XEd_XXVjB*%v??bikJ5Iw=^RdCp?fVZT5L>gF%B#(-vJw6)CFd1_M;mF9*HH|
zxCt1}w5No80YIvo06wVU&Q9_lJl*6d9`D3yvM*_^m^HZa<IiuF<xJgF7a#L|JZ|Vb
zL)Qf3ls%owHX=4~SF0k#(0@iOC(Rf`nwz26mS`pO#H;gHW!B)1Cv2}nKp0-|;j!3s
z)0^}p%h6N!QpQr@(9`+;U=fVhE6;Ux4kvG8vckE)mPE7G8C!j&-HA6Y(`8%M^cRHh
z^cNMQ)Op2iHLr%Wb+lWIz~|u}d3xi|KJCZ~G!Bl+yB%3=UYePpd1p4YgF0~KQAT>g
zT1{d{f*KJZr+Ts-M+-bmMA>;Ii%ko;N%NfB(Z&!M;3-ALb+tpcP(@sE=N|agi@mRj
z=+LD;o>|jgpwG$cxga(%%g1wUje*aW<2t#W1#0T6WN9m>%}7pi(BxI>!)oZMCZ1cP
zEhqEeTBfQtZl`me)OOc}ld8o9Vk`-AIjIR@z-~%b-Fe>n&(YXcG)F!Z7nh+jx4hU`
zRSRC}A)&UR7nxz9GcHs0nP$~9gS75~IUiC+tEio<*pz>3r_6%3nqOJB69-X4S~jI-
z10Z)gAAKh;t!p8mx#eB8?%aGEzf9wc<g3WRNJ)G;pVddJsZD9>Awm4Cd_d{?vZ}o<
zq_na`@Nh3GO?!1dBeLH}P8ElyAKZ97HuWg>^9a)F%{*9FSmXVSO`chsYq8FW#_^?K
zwHp33Dw7^UtR@ET-jZdMJ75t0_7t!dm~I!Vzb+Z0HseL!>cyeS5xI5$01iSMrz|JU
z)VZ{mx1{Wzd)k_Ban9c%apoi%)halr6o_duLa&0`Z7`^8w_e^+bV;?9u=WGkfZscM
z*1=jizk~8^t%`Q*9B4@kLqXdq=V(e*m|Ys=_aHk;k#8GXgA(@lcaEK>XYLEpUHM9m
z+uJB{74Ct=et<eO7D;fR5t&BMMdz_MB(To-h^u9=KsRM~HsvHjA&9+mT)ObD+xP?y
zDUth`Z_beO$x^!~Dd-S+&vSijp6J{@*#Lz_$r}pm0{KCBOmwbk7w*+QxfJZ=L#b+e
zqycD1O15X(fl2kU1Smz$l&Wq>iSByLyK#oKUG50FIM}Fyp1;u|<BSomv@5f)0@rX6
z>x4(wvmK^jS9BjOoHB?)hG(v6Q?M-g*!ka6Q!UDhg!V~^17__Y`S2zMdKd5YaEr!J
zdaom?ql%5wLxk($fhAzmYIbxj2Ts53u0tx$*if8$nJv!x)wO`>o7(8=m8y=o%D)4*
zIk{2J$e3Ce7T*B%Qd|}r;wL6?EW2RNvBV(09=ExcyzPYfa(7%(8!2=>GcFc&<jyJ@
zodt$`7I+-{P$V{1g<Ay%Fez>3buMmDD;>UtOs^akZMi0lJ!!XpL~)H{wNc?`eZ4j~
z72#IyldIsJ3<jgy{O79L8Z4yjqa{mI5A>xHBXqdPr%qo>j)TTkf&=~D#xV^a?LMc<
zds63%>z?fux~$cEEW`Lqy1i@7rWTSMOAHhcolW3l6f<a!2!#0T*W0ve5bhQQ@Vf<V
zR3t_ZBVz#C)Sim9Z)cDr2V37p<z9)*tk!9`iKAs2jJ7&jPcXUQ`(7H-iIW`<kIt!f
zhTdhVyAAZ-d(@LxO0XbF9;7$wJZYt03Jt$rEikxtMWSP_lp8T6vcY}N9+U3gvc%ct
z>gb{yxy1;&m=2$DSCQ}R9U)K^a`k#57Nu6p3NTbu73yz4d`?3NlOL>g9`#8Hiy`Qf
znq0v4hHa(l^}T6WBqJoAqu5YB6X|@Xxd&LFpFV9RHf`OQ28lw75>E;Aft6ukPF^QN
z$ljMk+Z=E&j5%C2{pimrKGe0yB!<j>PW~5TL|`Vikz_^ZWSNI=XGb({xySc`iG?pl
zFqO~rh>({xt&o-x*3ck&=sXPC!9ZJiw)XltK?@H&!jTtFWv)p+c5W8pr;GrOFI=gb
zoPxIARMzwoct{r6j3G+&BS73M$x<4dqa+pWJS(d*S>bksbtGNHQNGHw$)r^R#L8Jx
zEq9Wq&<Z@tx0`#_mJ`hZCvZm7cw8(6Ddl|d;As6~rwGiK*v>q1*!}Aqzq)u44Qlq4
zWQ?~&UnF%*f)&n%&OEIa6JWspu4%7O=8X!z6&^!zCn(KFU47rA8{)pod?EGY?^JXv
zw=C(rl*Ywb(fnM?Fq*jO$hMrjDg!{;j_#zCR97^oCZ72>PC#zye?BlR+7<5J&1uEJ
zN;gU3cjs>By6G*deaDy$8R6h;s}g3uS}IzizD_`9v$BdAW+oLg{bDsI6^&05gnE6e
zEWD(rOE$g7#`GnMv`Uo;LghCtHV)Asao4w88}t&#D%Z~$S;nH097yW4C8w_(MC#Ow
zY3HLA-pS7v>F+Ia1Bvu5X{SK1(33hZ6kpybriae#pa-+_bk~wLxU(wN+VHw7b#7)a
zJnQ_FxOj?<@rX18uy(nj7QV5}k$th_a?^#nX>JWXb^QxS?evjAG+ywIkoJC4<kvbk
zIlirHqBB`s4uR;;GAmU&m6%@nJPK?}UhBRRqt=ddrcP^Ydzvg;`EG}3SwPofqL~&q
z_cKpTQl;geCZ<Z&rH?Sc)5PbC7MSUqbf;xulFEiW5v#;O8M7dYuZbR8qRcX_r!|}f
zy<^0>a<?XuIhukW30TdQ@;=_6I-i!NADb8#1bqv$@{VPuwyPTlg_?p_c_+zxvadF+
zSKAO<>JloyE%N7Hr^-`WRc}WVf>qw7$o%u6bMH?tu!WS!ivEy{Z2Rtet8c*Iw`irY
zmnI4`t#h81fh7ukB9}7V*c9TX=_xBWZFMd|*Lf#Wf2*pYUTZ)cbJo2?&q6p;k=Fn~
zH@oIqfw!q48RM;suL!Vh<*I#NpE#FG!y6R#%Mryr-v>#&1#6gGGy`n#pJ?K(NS2x6
zSATDYX)gGdx7O`j<7rO%1wLP%Ld#cMON6fzT=uO`GUsqe=NV<=o?ktliISate7|ye
zEdU#YbntflgQarrN41Ln-KCyB8bT;U2T`rZpv<*a*GOcQQchIl%}7;6E4o25I;obf
zc?(j<vSBdyZ;Q_%VHpi+oGnD{$SXIAnv*b)Y?i6F4@SRRuqSif&4M5r!x5f(Lf1g&
z-Di;W&V`<5G}#~wR;il|!Xvy`=8A)j1M@b^HSG2ZcX*RP7lc~=@4P(`U=;dET&RzB
z^J%V>p-PVVKC{r6n4FA{Z&X!U@VOOEp3g05;Fp|dPrGSbkHE}r4b)x?kSh|&yDgmM
zflgV7H$9;9)22WOIlQ#$6}L-xVxt81iqorZgqa@hx^tYGJ+4yM&AP>2TK13F;N=kJ
zZ=oK5{22|uk#yt=8g$T$Lv_=mqUkqE<z@lqYHVy<nG+uJU)XfLC!W#j;VDx9%m{-o
zH((?qq>Ms~J3^m4mfg@qsWU{YV1<r?OOW-KD%Az^RW~Iuwri9YUQW7UE0p1JF?;S4
z9UoX_3&$E&IwCB3d1kLp5R+=ggN}yxHQcaMQ4WjWFVHM6R>geq*Htc~%I6~~3%w5@
zUbspcEHc_&<2A@odAbF1?OBYg-x=6BdorUM)k58sId~;Tk>MKdCT>!lSV-Hif^J7N
zUb8Dhe`?R1^J!q-)oA_CdzUX~=ABlzH;+7?3;j;g^5;$GofnPPB-)zc=RDTaYCpDm
zS|DAsbE09gP%IfPj4M}rPcK@z+EkT?vaEC^<(s_?8=!M0LE`&re>UFav~Pr;qw|f9
zQ0_H7=G|zQy!V+^CPhkJFI^<6`O!u3Z43h6Oz@z)P5mTS5j{_!a~Yu4<xZy$G4QUI
z*id%Nw@=mx1m>QX7EE+owJ3U9uvs(56Hk|0dnYxd;texFW?7)&cIwm4m3bT{UWQv5
z6jbQ^?%=ulTaPzOfQGUPGEgmDZRV20F&HA>ad0sKueO-_hia~4BJa88U+b~)LhYRQ
zGs{{*-8p;wwGLGSOcOd)LY+Y!F|}^C#vS=znrYOz=eqlBWu59mYHrocL7Io8@xYBy
zcy$9`pgA+QI6g8i+s~0R%B~8QBX3UBmCnnr5x(x_TB=elC$ujw$7f1xotCc>p>mXd
zdEp9Mu=Rjkb&-3cb8V)IdoHo0!om_WpsVSzVIH1@K1%_?6YWY%C6^>U*BVl|nZ`PH
zQcv_X%~g8$wq=SAOJb*k-|62_FRIghWriRnDq69f1kWr{fb#8{?3;%kTz+qsDO{Ls
zxcn^`Ut{xk{(EM&*?Oj#th%uE&NvadLp{lI4)@Bs(B(&{5Jz2PSk!QKN1k?V6V^*N
z(6-mI@(kX)O1YY`dn>cZE_4L5TSp^+_;<c-zkTGTFlD9w_0JH<#5lZEtFn+%-PfZI
z;fLM%M_zg0<0?3T6MxDDg)w%7z=^Ti878}Lu7x`gPG4wGSLHhGz2XK1EZy>YH_U~>
z*T?!^F?pL=vr1ZXKyPu7v9@$>8qAw=@M7*F`S#D-wh;okzc0RSi^{dCG)S+$2LSO)
zMab!v?v*+E45q_JSoPtfOa+I=K(LVVjQ-RkM)srpk=Yhl^F7w}9M5&=JEwGxd0H&g
z)$XB8w0E|wu#mRyf<$G?=Uar)Q)q&j^ujI3uv)HL(b{RRy<Fn=FwlVB)}<AWf6<U{
zbtLPm10EFnKm;9qpnY_Z!(B6sN!@No!>m9p)1z_1EB-$uLt-iDJ#_`O)N!W6=dwq)
zp7v|}kUpJDmG2%i2PbV!m<5X1R9qg|*U}ME(oIPYwWV!!###ffdGsy7oCXUd$d2gX
z5eq*wWE{hzaPw9|IHfk}IL*Yi)fstYvfQn`Y84JoKDWkt)V=4ERMQDTEgT^@k1yBW
ztd}J!x8xqn9;HR+zGc49l=Pi^$-M;35y2#(vFig}&eP_Bf$sr;uMhlHm2R1PMpuSK
zys#P%JzFhq^k||=CL=w+kZh{hy<?%GbUsAHXkSJN&f&ee73?S@v~qW?v~J~rO^$Vz
z(o)5oJg7nq>-2ZvJkZh`Le5R6o@JKqp~#gy>fEO`*zg8TmYLRUNAB>!?xzM583(x`
zwd+=)rRHO*sRIt1mgj2sXfaT@*RL~q=)!JtzPt0W%Ho&=kptG(ONdS){x@++xzwGb
zQRQCY5qmIDt9Q{GWxa2R+9m)VS>I|t$i-NFZ8h7?ZqzJHxmFwZy-rLC0o5*@MB3Y9
z8fsk%CwhqpQI&(07mOV)9%LIDZYGnVta}k3qYNTDnu_J+TGuZ0R!%BB<RN<>C2<_S
zOjk<toWTXdD&I+QknDXUnh7h{xuTs@vcHO6MzC0_UCrQ$yR!r1PyD5+U)d$EymMLZ
zeB}>br^~#<@P;^HMEdOWjW{DuCEf#oz4IisSlG>Kd**G_X`=P0nY83iG~Y*Z7CTGs
zZa@G;?Z(8~pyR3S2!NcLMexK{NgufGcABRuk0-6^`cw!*PtE1uYP&*9!M8IiWya7`
z)V+MYqPO9xL{hvZ`Xd7G8p+Bm^o9<0H0NLB$z=uKbw_b-Dw3oJ+&kG)d8CA%Ii>O2
z<R#-MzofnMmZ$XYZIz7}>)aq=Pi%A^JJmUUjtRX)j~=;Ag7UeZo~q@~0NjVVkK<6@
zDD}xYuONiIY!p_UT??f}#F$fyE$xzd=D6bEGnvQNc^1}81&rA=)&cogxB@#;vjhw)
zp69#VfQO-TGsc_SBlYO|MI9!$&v}Hzm=ulm#68A2@k;JTlAhG*;2xffCR&|3e>Uu_
zbXEB_-VqtS`GsE6v_9pj_p><|?BCMm;+kE-mw~S6GQ|Kg={y+WOSX&RFv)N)SGmps
z^zM<uK@c0SP(!Z(lt630tNC_B6gvAgxaU2%s^+x}dE>wVabrJY;|tC!<4cOP_Q}K#
z?SA4HOXt{R8^1x~6mFAUIzf-3LC#Rl4(>L2l7wxo+?S;D;e<vT5^jLLp7uRSF|Zo8
zS&L}%UZ{B$QkPEVnlK2hjYUyffJip=17YLz*dxGp%#fJrXM(4abh;yVPb(*TwQ3$p
zj?}a;uTNO%{K~Zb4FXu9l8SZw*3nhFO**I)3kPyH5Pnt{OO*+gEl`cCyPmHMHRwsY
z-D~yY5mEDYJ}a%7zHfHWeex^RT@!oM{KZc6z<a8%In|4|aUhaY-SH;3t{#r++Vb$U
zzJlis!MT0MbPWm?95NeKdLK^V)oq$x4VMXY<v{X8VHM3X_k&*ghayq+*%p4Mf0p#+
zpwq8EIjV?PBG{&W^2)D=LTzS`Na={TIL}!|U-#Vd#c6v<PErVeR^`?wr4}-q=Z6B@
zs#IP28tpu{fbW~{ey)|-7D%;K9C^3O*4wPf2N(jvL^vX?PCx{aX5@nQ&|`|;@Jm6-
zPImCB)@?=FO=zHaJT`BItu+g4B4E;5I%rCZLbjKo_O;6`8=!hbZciV(M<#}VwW;*p
zgs;X(+6mt|vaBsVL!PMG7-agDDGW0&F1Pfh#2(F8iS?Z3R;)pNM8!IpzguzRGYn`z
zi+k`{?R<-SkDQ~T^D2ddbUvNJz*u7i*1>aoPKX^@X>+V3zVcFg*_C6p^ECeeZIA?D
zH$mzA_ugXE%glV5FD%!;+<oLCXmzeoL|pLUC*FT-E$L|J(x)bCc<SD|Y(~CEvj$KP
zY=5pvr|xw|jYK0{3m&6!<%vqXO<Z(t`b&CR;?D#xwqFrar=>f6s(dr7a_rfHnz_KP
z3!>(Pu(@m9*z&fPai3+W+Z2Xns+oF@_A1GQVl@T6%qM`AvU{iUq@3y!&{T+1a9Ap6
z=mhW)H@dkymiXq%Z`&T`9@nhcrJi@2+<{}xgo_T>>S@srElAbLdq)$3%@*h0Sq&bR
zN1M{QE~TkHb64edFd?kXbVo_&z$<#-o^BSdlgSNpOMM|pb3FHL+(-2ON*>Qm<)uN`
zt?d23is@3>Ae!9Pwt0=JJ7)9V9bETc=1Jr#SghKl#MRQSZyH4_jX(y>K$sq0SE=!U
zdRc?lrLwThIl`R2EQP{-*U&smq0m*TUrM*97{IX<WQY0TDCPC4FKVe=w#tVdU!m;v
z_bxg({4`L1onZd}0@&8C{GyG1%8R;#65E1t;W{4!x%!~9p>+x&IKPEXyh5Bi(PTl#
z^oZ_qhqDx`TC#dKPS_b;{SA+rP$Czvflg!S0Qp7R9vu+g44qrbonT@OTc#J<>?Z`m
z!WwtC()e@FH^)uU^D}$)LH2|dQg7*CJ)S9|>s-o?U)XMV#=ov?#I>2U&I&GVEnh#`
zBqr11!eb@gx<;LesYgCkuT+g*X|GV{NSR_QrG*()A>%MjSb~g+&#2Rji?c+KLdg4)
zQl>&d(3GbhjR&r%F3AZBI6<uo+$<=F{bz$MRq>f;s-m^@&T<-)8D3``UH<@w8&hIk
zXH+gfVnC$E5LMZ_&2xNvC@!>BW-EPYD#1UZ)j8UCvUF~GM06yIY1euCVnH5#XWu(&
zs|_woKeaU2^=OF=dy|wtbS?(Veza7&LTZnvmdd^Wdg4z2!xDJrUhS`s33~Kf+^COQ
zK~u@&*KX&>Mf-lpZ+vB0+ud2#Gks*}H_bBD`Qg>$lyyl^(t0B+AsIE7LE$pLx9;T~
z^OuMfJf!d`urt{4v?P=%*mdY)phWa&;VhKtF^{W80^!b?_5m<3CR(qPMd24ddmdSW
zT=IIpZFa=q99J4TNL8f{<IhoHZ|c83UONKQO4ae3u&!mAt)uJoE`H_GIeG_&LG=E0
zVTpn}Zv5mx!8cwxj70R?QU*E)I`C~G61BYgfy{l~&Z7Lxn*-ME4(Y&5bQCSh&VQG;
z(n-momEbQ`;!=}a)2Z{SUh6Aj?FY_@z_jZeuTL%3@cVLrXs-K1eHzsASeX|xLcJQN
zKH0QxD|*V>>Boe>w3f9DmSl_qzWd5<*j?=LZ*r`KrwuUa;Dyq03-)E~w-nCvSr?dv
z41z-*;0vZmiKZqyQ{P^>Qt!03xYn5=D}buqAGE$}YL3LLHK}x#j!Y}LacV|-LiaY2
z)Karrmeyc2v+nsXNan4Zcx8-7E3@rRkIfF#xJ$LDuk(l}X@moUoig_5eF4abbLh_X
zvkPHR#b(gDu6cOy+trr4-xJ>P&}ye>vA8LvTJo%}c+by4Iwg8^!d7@(Me3|wAD4*i
zd|Tb6uZP)@=}38ZX<%wya_u}cf(3^H>a;LZh#{|lK`{0unz1mm!cJ%gjOK}(@736?
zt-yDd)VDIV+sno3P%Tuk1?`8v^q}F3u7F&5Ox75gLb(YHAn0=9uEl3jc50as@2vW(
z=4w)e@M*aLSLxxJT~PvFq?hMG<2Cv%7<s-j97b&vCbZ7zeoLbuH;pr&&fsB(myM);
zU@Wu@b?!_tq=MQfMCS*=Vd}ZwArZ%{cD2jb`DJ1BFwoaAr-vP&J#(sp=dZO2^HN&0
z8Pk`Vp-GmpW~9&$yw`1dhd-q$<*995>b&B&QsI8shDeSxqSx@~4dvSGySQ_fWzMvW
zs$0tRnRT#4ct+;NAuPFWO?<64ffgwtbS7JB3^MS$zK-<erT+jgf+0llMTV6Q+D$=4
z-#MXbQ3tVnv`a;S=vJ8laG#-8X|Dp?cv`7~^ESIjc_Y`tV>44Bx-RRT9VZ((bJGYg
z$$59CB3M>36xrnHcCuU_Pgn>q$JIvunaY!)eoL<8WyL3)W`>{G`SP&;09*jm`WK6L
zZdmus1HU`*rF(i23oYUc&GED)+UY5aB`9=GO*Nx+O`zPN6MZjTc{3}DI4y7Vj!yQ6
zw*H)c2u+WI?P4uFVClSmyetdyX@7g-sqfN8t(TxS-D)SglDfs-q*CrhVj1mg0FG8)
zrtJ_8E!rm*Q*J&W(&-5*GJ!G(_|qPzG`Kver^J4e&?3YF7$Oc4wyN5kb?O=usa=pg
zeLUA36(RS1CL>a;u<LdyFo-7O;+zqBA3THB1N@@3xE@HN4YN5)0kS7h_k}!jtkMpb
z%^Et_G)c##W3R-OQV8$io|c_v1QX%>={%3S9^*-jN4nPyQz-!<8dzgbZJmG$XIti-
z84?be%JwD!<f4TE)>vv&={$Lb;aN+4yBJj!+}p+{TXqR>2`*5{?DyUbE@pL1+SL{y
zWG^th@JnX58KpKAV{Tn@$yTh8v4TtZKZcKg=~8dVf%RL|eA9;2WM5)lwpnQScQwn@
zFA42slGAIl1)X80>O7aL$R-2()uW}GRHfGJLVfpNIszA->ypEMmiqajz?j~)U@vYJ
z8-FdKO_9@tXQWw~xtPmUPuCHS{;bq%s3L}Ver1;)nwF8(IQ-qZzbFZ|UM$^F9DhV}
zNV#Fm)qT}a`V&tB1t*(T@>T`LD{ma>dAt>_jm@9=%@!q6y>%TM&pMi19dm|T>O(pF
zI&?0X^UZ&Jm7#)_g8Y|F;q;)NrTF5EMuP8ZmOHip#5+}Q8vt&ABEk$7n*}mS(@cq`
zdlUH`D%}IB*b(hJYI>IC0tm(70s7J-?Z9UQG{@R1^UoqXQUEnDM)swha}5mg_@aTY
z?0U*>zIo)m2b|}*?%Le1=0&*?lUlVxw@Y%G=gti>9wEs5&l?!IymG<onp+_y3i;NM
zh7$$s?Fq#7>=cWDROs^Zfs{Py+{ulN1_@>Ew*jq?tjKQcHTVGd$WzkEk0f4Z={%%>
z7lc!cCi#x`mZ$1e`IozOK5r*m;(GL=3Coc(bcJLc^fcCZeiq|ZZB~?s-=z3*)?eP1
zWDTSdS-^XDO$pnwl*qbVcBa5K0M5L{cHX?Vp4Y2w>#LyCIDI`%1#-cQ)qU5S<?3gG
zldNc`R)cE5Y}TRfmD{<Ox^5sbvAX+sEst5&SylXxJ+sg`#MS)se8Pe^n#COUOK?J8
zvTq*+vf91TxkYI#v|2{j*s}9r#q5@LK>#cgqfFhgV<qWr#HdfEgQ<iga`8S4Jhw=F
z+vcbmMpIyA=`$(v&?G3$V1OoNRwvX5L460sY&sr56JciC1+=_jx~f;PaHyJkuQ6Aw
z&cwGh3)>LC&ig%XRyN&ovW5B!fJK<t40B24D{_ya7T4;Kkdo!v^VFz9tD<Fjdb{Pw
zxu-qe4Ylojdedh?70V2PT((eAxP#RwDXmel!uvGB_-XNlYT4XBzexPpW^+0(%qP{u
zVW-me%n922=eLZi$?RU{`b#Z+iH+BI4Ghxz`+Z&`S<Gwd*lGh?rcBycGoylDt+Z#i
zD4Uo@VI3(7ju~U?G{&oQ4+xs}<CAP+N>YID&5*TG32v8v@Jp(nJ^@b)3}#j`#IcqB
zgW&aN>d8Q731`PHeU9}Nn)+7{gKpN5L0QSWnv$R|&V@R2<1a2*lUNB=?OZc$r)9UW
z0EYvy$`*#VV6b4yPMB4VPI`_&*6i5rOuH-ciU$)}`X?x5cRr#CEyDF}+f^=696;6v
zGPMs_-9wKx11)iQ+TGXndtrhYx)_Al_Eob%nRNQrnYWBpLhU8UG>_aoz3$q1c1l}r
zO||TQAB~Jt0HtXQF@po8$(|BvL6RQ^S%q*fnVYkW6X5~9h02n8j)RA5CrjzHu5ut<
z9gmi!n9w!M>*;9ax=WP=bsE+ppklcYvv+<-5-z2CjoQ8{4x!0?dS4Xq<HoCc$JsfM
zro;iB-MTBGK)CL_#n0g#v3ngih}^dS05bZWPd6!Ioypf@xpN(o1XzgV+Ues675l7x
z(x?^SyQ4b21Ld8x&nc%xd5#7>!kF1;VF>MxG$*kd<Vg-|<!V*XsJ0D)>N;j1;xL@i
zdv;()7Ps@vY+27D!b9Wm$o(~r-AlK|R<k!*mR=gkEyxdZKXIc)=wCcZ$)&Mca>|d1
zWCUh!>so)eDLgf{bG~P`OBMdWaZjF5yVz(8PhOstsrvr_9iwJ?P7duUpcF-u9@4JS
z)cI<m9;VckkGLp$hbhjf%Qoq4TuQ!;J%D0`&T~&Z#;%FZZ5&nVi#+Bp4_SUyAu6wt
zR2LT4tl;|xCQ`P_BI3uhmA_VNie&?8Yo#~Z8^bG~wU?E8D_0BCNr;VS=h$_VoRu$9
zjKo6r77kj7+PH446dBgZ8v|MyrLq<>@`{O$wOBH?p5tPlxt{NTN*Aj~?zZguJKT+}
z`b+!_%OZ&<j!Br)Mu$*PP?s8&boXXJop*}NWHfr?l|_pXFuFT%+W@1KpH^((%e!5N
z=%mq>IeZ2v7=PvzCyQ+fZqE6D7t%UcFgMh>+QF9RzB>H4n{T3WY2Uf*eUXooPkNK7
z0Io?DSEAM~thH&WeUEkYw;RNgP>8JCD}i^Ty{j|Psc!qGi}Xw-=eFGfWUvM&EgQX_
z+Q%$)Q1-`CJf?GiMnE^6DGY!%G5HN!<ImQk^bH<luGK&-y0G+w23>_{9EP_;`)63H
z7Lq#WsSLA*l(pMz+c$z(GJP`Dz}Q`a!GX_Qd7_GGhg3k@u+D8Jda%U6xi7vkfbJY*
zZKyL(9K>PeIvr&93G}Y#dg%ImC)_tj=uU8p^e4|W2v#xCc~u#!-L6Kl9mv7cu6xLr
zy5bV;rmm&HwCx;-cUt4^Qz_?iBgaP#)yt^zzQC7rjC-2*RO8MGOuWji<s$uE8Ft9J
zCS(hIY-F0zuESit*W`7UTLA~g$P=O|1%fNCh69K2oi)unJA8qwB8wUkwv%T)qEZWj
z4T8rt(!H!V8^c(vRBhJEAqv1QoTQ^2e_FHCiLrhgl&9N91ut{Vv7tJ4D9u;g(UERw
z+eOPiS>w#0CR4uBmmgZc#w8#pW-65HM#MCCOXt37L|?e<2qfFo?OyRuSvt(zxP-jt
zF^4;kC`9ahrj|Yqdgei{(jh}v2vttOF#fW`V-S<WIQ32(J(2U4blx-6`JpDBk|TuN
ztKkluddgu8Sh2fsn(v$~rC<}8K6T$Yisj)3ZB-8u0n@XD8*W%-=-`oB0F)bGu>PQq
z7KCJux2RDL!w4uGdT+FK;yT-DTN~EUg58LS6CzEqDl&BuylP8e%K9eTT~$^HsnLl1
zbl~2x&^PzbL+>8vtpv^G@<Ewu__}$M!+xwDI}On1RO0PuwpXt{^XBRoly`~}qX|&n
zyE^7EE6WtKL2;B780Le%Aft2#<-Sc>jQ;>V%)dWk=IQQdIe(38Q*-NrtJ<@32hnp&
zVqShsKh*PhFHDexUduV|a@$r-+N$%J=~<b`!m^n)ll<V}k{8_hUDY9ec12h2K~8br
zG2CCZCBVf&SKRFy&lma2Vqti%jvv-Jr987(+d7pJbHh+{=7yQ+TszCBM-6naq7?oM
zW>IHbGTG2+@n~;?(cNkj9sx9QJ}X#I7TRHJ!lJiremvZqz`U9)+j9eBo{s+ET2N?2
z`R;1(*SO1adS|CfwDK1S#tXMj#e22yRXny+1Mj927wc78`UyAO;|7bOtWq=Ll$zgU
zk|fsF{zWTQ)7*`ynt?Dk>_JJz9fT7g4IOISs4>5&sFt3wdLcROj)yqTB2JcT(mE;v
zc+*R*-lz1S{Y@ypD-G%A<J?QiSH~P8JWP8s7hK-GX)8aE8d*Ad{i*LjL8)eFH*JUj
z<_Dw?EX~k)_gLUm=P7tI_da3@NGUyN$}WDK>E{;qWt|&B=wO<`Vyjg**EJ>+LV|rX
zZ65SnST-D9TdSV=Zk}#KLGwX!osNRI&CR;&z!ek^DRd?NhJ{*c;b*9DZb5MR>~5-~
z@@R0tgXJt4$ALAuIF=>-c5T&4NaS*=SD!T%s26rKJj}rI%Q0H=&0}jU%T24b9x2Qn
zw>p;v)1hBBUz_sAM!N(!tlR4w$5KH@tlEpGO-*-R>jKorBjH9qiOgvQ_G4>~i2Ke?
zdV)&ctyuPomY9ND3Sz5*i8aWbL7RXj3EYx3#t>HfuMrzHPYXXHi(_o&PiSQJ3nyb?
zqr#d5%4pJ^{>&SR)HgK&c5RF<FG6Cqw{$^)<A^q-!<MS;?uA6}u1&s>&v;WtjQ|Ps
z$Pqc6rqKb&KP^$JBGfi3%d8BG*!GR&TWjX+JuLM5Z-^fApoIl_eTQ`SZjsKqK8oC(
z!$uW4T?cM;Bv$8Kv#x}t3ed35uQycY^&mZ`Ye+$RuC?6paX{KHN8}aSg;P7&)VWE0
zy<Izb;-lK<9qC-Q?(_74E6aLUZcuwstGgXZM${)zscBOuv>yF-b=S4Q2S*SF-nBT7
z8x{SuNpgPW$yZ906H&2$dhGQcYr#!+_ffJXD)68l^k<p31<nyQ=sfeo^aotR%TrER
zPMcPa>#7W1TB|RZNKW47`Y_iU1=+*&#HNFe+*XbCf`baq+?S~Aze{;5=zIjO3GuTX
zI$zp5XsDq5)A%XQd$B>7Y?N)iW6axbSg1%<K8CSXtB(|va}and`cJ~kQ5_d;g+Aq0
zRZiT|&F!V%>wF)v5YvQo{yl7%og2EaE?ev+)tuJ*3Qts6;@urewzm4$2y>{nA+_1x
z5HxID+Qq8Z2Cz)H#3&uwc=}Byi0zo_GcAN<0N59L&ph%T(!0(r=9{xBLcl<J_gYhw
zH9w(ZTIB32^zQu|T%UmXowQfPDpuzhM`3b#snNFrd+B#hXSTPgZx+VGN9OoLaaM2p
zwFcz(s=Z5{s(~smHo4b1tU#tF8pNIy4W-_Kz{nv{WqzlRIl5nIa^U4vgkPn<oh@so
zyyCMooT*f@+vo!$9e6=HnqQJt?+HL{XB(85<p|c2!W*$O@wW0~n|SrUgj>Y!KW5gW
z*}HuX6JE&fhAdN+$3<>RZuX0;JtK@o)K`H-Cvu}LR<A2i+9#=7nP}krueLT)St2gh
z<J`n9p8)&-7B#8oS~bKkUaIu425s&PDpefNtKH@l?q@k#jJ-VPxvwojVzQx1NwPa4
z?od1<(gE`cbS_u5n%eIqep<CP3%1t%%?;74*s}gv%Y}rR#SOKz&dQd%F<_I+Tc8*>
z54^i^6!UvDU8@5((b~9;)q-JZ&j(5tFX+Ufod#+=%WLU*(gxe(l$i#@AqtrVds#*O
zAEKs%XQjIT0AY^*06vK8mn${Ua-#rr>cfE(?f!9aGDJYa<1>qDKPdC5r|BPG;I@cJ
z9ou`$3-*TQZ+?r2tJ;BNSlNYJX1q@-H!zW5fxJ9$RUa<_-IuBG+GWRG7k-uVShjcv
zzMkzv5E5!Q=;rW8&4==#W3*^a^9GP10ZG%Qu|_PG`-%ias9A<fm8xsD@R+SnT%PF3
zURo(Say5GM*3G?da_8$;2>>$wTuNBsjUG`KVy0#C^=-Yz*~vFdW>@!oaB1XS!r2oS
zlawxo=Y14cVq4=8l!_RqF1L0sTUUw?YEW}pUg~`3uuZ2i-IUiF^SO_!h)*sS^hk8q
ziI25rxTD3LT2l}gy7D-~y-R`TtjZ?AT<8>+gvafcdP`4rq6c)MkzjVsF(hvUI}zw=
zAT%j$PjWr4jf^ANdCK%-4Gl0WTXB0WdV2LY>Ig}LUE=Ha>nfOF=De=a9q!ypti=sV
z4B}!cz2w?mZbjDJDLjtteJ98=Llk|VG!|c|o98_<lqY4H**$kNRJrjMCirRdvg?I~
zn`KO;wDtXOVKGXB(C#YMj#4rH?!s9Vmti`0U{w%T!?hy)IFH6H%N`LJWES&LXw)fI
zE_tcSF3|K!$mSCn;OBSxQgaa=ZEwp4Tp8l$Xuf%1LmLDA?YNTfNtH;$mlP%`{UTaA
zy{x(e16Je`_;M>NkBK(O1>LcymXihK7FZ(%RHFiR_6{xSztCX8^3Cqe0Z@DUWcsx`
zomG;u2mtM>9b`K)fH|p;*1MXwjkOy&*!^!;p6Dr~!$;A5vs%Vkhucxm*F3dXZ7$M0
zWs$vMpP3=~a;90R%=MDdQCy7S6@0ukna`Yy_rvtqI%7B<9oZ^y=Cw;iL+j&3^sgTJ
zGlpqXg3<88m(z-zMq**6jD&#al9Pdj&DuGB_IA;WsUnMG0{zQZUUwTeMdi!IHQs)^
za-!n$xylkv)b2;lRovHzLiNQbgqtdbZJY)!l4_$vQBvI|f1I1V612K8xbqpeEUjJk
zx}0_@J|tzjl0ng1IOyvTwJ#YN?9|Jy`g5nvUztItnMkctOkjj%2)YUnr5Y`VUH978
ztS-x^taOCr&{>8^FX>Y<M|6!gj>8Ttz4ZEoc`8J@X_ia%s>p?qeWN5D6P=SCVTk%9
zb-e8^Tu10_Q#toV2gch|?^zSJjAW9tuJp@0#~&C*yi+@f-=AuD$2be&<eyTj{aqn{
zuyP`_s=7WW%}hJ9$Ig<u1uKkVlg6_FH137fPDH<6g%G_e%)CAPha<Mpc@7zBbz>^4
zIyQ4(WMOlo5vUTfmy9v0PZ`$UXl-_O^#=U_4xdIfJUTB=4E9i5=p*Mpv}I_Yrx$5w
zMd)C)NdsqLuvsZmKn$X(O5md#D$_{j)Y^5=!yfd!Xitkb^W87i)5}!}M0rbp4K*QG
z*pG40pETz8Xyac-3N&hiC6#*HYWRz8l;+N$+N+G+`-}H&bOdsrGh0u)cs`TP*q`rb
ze3RI=5UPwjp}O$m=o0O%+wlVQ#t7}4o8FbrgG)=XuyP~fXt*|TY2TDOJXHCM+I$Rb
zXtoOUfOmtm-&kr3KLarMzN*M!3dOpgM0?G7$F7K*V?x74D^8rwXncEAkYpZogtMxC
z8KT!kUTEsETun3*Z+7esTsSd79+eH5(7BJkc7vRP#Z}|7Q6CpkRTXel+j>H*R>K*g
zT)`S;5`6RBdiNsD?N-Y<1<ywp6Rq+1%0(k=@=m^Zs%IP|S@;@Ir$E5Uzw>;neWgBX
zdz3Ptwzg`!&(fMz4_~44QPtX;5FM7fJQOZ;JEnZkY4OoLtX}|qK+KX;G{P5{7e+bD
zn2m%^IBp)f$BEFL%ojX5L1VY4dYA{GrNP&EU$%NM1*_h)I1m#r&!wLILNwZ>Cb9nj
zpf#wY#~gY5w(G!J+;$)zye+BoH=}#hm1+e)p)g$|j$ZQcb9aVF8)IFlr_VVcf<8_s
zIu|$Cmt)t5l#0zFl|{tzC2;7twlfvW3ecGyTIS83qWhZf)OmHOD)|liJ{KJv_5Iv~
z63(=9-+VvXt*4`Os=R`Yy3XK&m2*lLIaU<Osq9^m3tQD=(ddUo6nN^d&rE_xcwj%5
zI-?*6xesTxS`LfND}(J^HD|^(J*@koFbpGVHMR41o5&7<6Z(Hh;Y>&l9Z&}raT*)w
z1;v%otz;4%mTs+@cSC0ZOz&0{Pr-qZn2hw#nvQ|B*B$EeM61i2vMfn$paks;0f--5
zp)2Z#R8gi8g1E*f{%RJz%5@l4%hbH})F9!O*X#&^m;n=UpEgB%nnFiWEl`1ll7Oa_
z+XKkdYgUUwm^Q;Iyw7Rk4QTt!A<IxrwMpQy1oixEfok56=4M*$Cj+kYv4%v+6WXZa
z6S{LP&9Wi;9odfv33`pWnG+fqpP4!L9l1&wC8g!g$g$FST5n|Iy)TvB9oY%Be9v5w
z#UgbrRbdwn^;pB+ej*hNRyKAn9MYawuyEta@mBg2S|Y0vUVGDlDVnBJsG}~zV%z0g
zyxGa0OIbI~*7eTJ>95bwTwbiixgk3xM;98u?b%?8CTqSe491|8J6|yoCGe$BBxZM+
z**5v&Me;N)O+|!c9g9+7c}-)Zpg&SOEK30`#Y)A|O?Cwfnue@F>*{=2<+iL(_iLm8
z8l?<-N3_k0;=P^>%<|t)=yZ8%ZC!~_ht|+T>{)6}ti;LAF4ZNj%}MZ2Ru;9Z?LAb_
zHRV?*MZL&rnq)BD#jjLf=^XObonxRy^Heu=A~u|sGQg`ku`N4vgw=JaVClUdl3=@C
z8;v{j>vVpEuggJQ^fR%Q1-~x#o_st#zMeI>e;S?TG}H05WNV_kVYq&3iv3F0Avm^J
zz4nAr;EHP~&vE|hfQeaDC+f({5=>Eg%VL{nMA?%#<TdJzheCvvS~^7V^Xp*dTrN7?
z)R>ENlHwus=+jj~GmrDrYt(eROT)bZ1U8=UIncv%B#EjeqBJtyPcV;0C=uc=Kqi-_
z1xJlRDA*u1;RNjKTWZp)d!o}%6Pq;wGP(4${9R(`FFnzW11ncE=v?W{Kwgc^?q|?f
zhw$OnC^;&@9nxSnvsl<ZuG7cKr+W`vcjhU~bzM2VnxZ`V4n8;&8kPD&UqVb-leKc~
zBcs$Qx9P{)i1WIqAK_{Dm-IJL9FZl8knC^}2I-0V2>w{a6E`0WkE~qYglv|AbZ}hu
z)?T3#cR;^;Lc4s`0m>N4uy~R$M`*OO$oJQ!E;XPRt!Q79U=MsCO11}OTi;T`XRkGU
z^SGxbQ<8lqqn`PFokp~#!D~J?wN^$4?{TBda9@`vp%Z{Qa4;{r*9SnzxSNzox^K@b
zVN0T!6F!-rjRYX4=JB+P=={F1Q)JbHbS`~zPoU_pbJf3OWQl81R|M&inqsEa$=cRn
z^Qv?%f66)@g_OXWTF%+O9;KVOO15&51)hY1JtBo{^w1n}{{VCvt)v#LxHg1h3_MbJ
zViio+7iPhxW_~s>`4!EfsQA^+7wubTP3Q|-ElJSTYB{{*Omj=~pazw#ndsDX2hp!$
zmoDA|fs8aHxFlU7EXv21$}ce16j(lZN`>kcD{E`0qlPa~IgvW&hwUr-lavnF&rMZ(
zZ!<c*PIoj_?Bz|_qXnr@nbkS%0z+++ULn#n<zCrf!DJSGlp*rvzJ<(vk}`e8J!M}$
zgyJv>>O8{Bmt08%1T85G9!B)dDnuz=c;@WBuOF-yM~<+o#JV{~9yU3ha<@@q;<dfg
zrw>KZk6TO}+R1SZ&n75xl9v&xH`xX__H=-VMHRXrFfmGbUvpwv`XW*uG-BPTpToBD
zsEW8IKPCyDq<hwIQHpM*(kuMkZ1{|{w{`djR+<$aS9?Y^m|7f1FSIMn$r7c?i$~xZ
zb#pMZRx+(W8Hxy6zFLL#owjmWqprl7S_LZvskfP)=TZQ(1UnO}PsS0=sWxb5P#BGx
z9iTMh{Jf(rxvN(tozm~iC+7!RXPpl#E6y|Mf1V1B>-@r(QZ^k-Zk0DBOuCUcDerN(
zBK_;ZOl|>_-jGA*{<`~9j$T|Mzt9+X#(Tfe9Jw<vs|C7r0L<aiJcEHWwR4YMsV<B@
zyicgy=gMYcbW9H10Irtfe=o<z+GALuZP+hF<T6Cku+$=@p>bN8`02pj?#y2{+MZ|#
zfX9g5Y|~Un4Z9N2Dy~!%nIqmPcLrQBpzT2D*nyO79rcHIfEIvW4po^9qX>cpr4=&W
z^~;p~+g!}|tAb32XzyiB7{R)w*dIFsZz{D>hOly-2~k(w`q~h~7P~~>FT2%Ud4Wch
z=a$IQ>RhC_hb;kh)C;KcH>O8)d!R_8t&EC>1(~?7azj?OUh6!Apmh-Z6~POjqV);*
z_StK<@TmRs9m(^{i~|kyX+`n<+S9AIRsmg&V*UAg0Ob%eJEP57)Ymm-Y~&Y)6qsc*
z?a?IrR_UKZ7EL9jQJUz2xz(l~m&Wv}@~`6kzNl=5E(Yg?tglzMS)TBw1am7U#(R3L
z(QLHGQ1WBp>u>FTqMdJh(AzFlC~(wAY3^#qy*wG2o@RBW^<mk)%VKe%Puc8JWL)UH
z?^6M;dsn2QQi|76<`&A|>pSrlm};VINH#a8+tZ_R;x*r>seKdkt5UR0$$ML*c6c=m
z)Uh4fjsrm6v*R6}c->*W%ohUBT)@BY%q!*Y%fsW%tu#lf&1RmPCD1(3b0>;#BEb52
zQt23HI>IW@LhEbnAaS0HzbIwWsHR;sd4W8ATZZdb_N{^Qje*5#oU-;#PHr$}jIKf1
z31xKfKrPBL+#{fNPV|m~jjvCTHHtI_I-Y98E~K$(-WdR{)3UoD`IC0fB-1rwUzRNQ
zUvoU|3Ci_jnlk})a8uNkp)4y8A=htJ+`J83_jy<wx$$dmjn%~`1yT17(jL^Y5pIXc
zh{)`lW4iq6%(-0XXl1RlWb3wA(d(2N*te|r>beI~v(p&Opz?om`~3-!EYfT2Up{M_
zvJnml+#ov{`G)Z7#Gm<hLI@I>iW)HsIIV4@yTu*(Qr;6dnD1q4W59ql(s2ZTpAx@t
zHQm#V?$C{)+llbqVzO75ZG8D+Ez|8qMKCS$B4wyda~KPRmm)L^YgQ=|fL;1o;gK4V
zp}%lJuAhu@EbQVSCVB2=90fc%@0ppMy;Tmj($+K|Rwe!AGQP~PQRkk+70acFpzUj>
za?5un?i17HsEms~`c}3O2dUnP%I{h*knPe^`Hb@HU2aPHz9Mw;YDyE_4bV_GKNUy8
zX_0n9r#KKjvtG0w1aA)XwqKuV<k{FzmSx6w+g82#{{TV{sZ64Uz{Dhbf=S>W7QO3R
zVGe1}jNb}#p>j!D6(~9^G>C8KGMQ&vs4Vpk?OToR7K1?g(_W{2c3JoA$8*S*QLU{_
zM)i^N76eYUD(4nGvksQB39D>yzOxB3W+V%O8rEw0?eg^kX4-8nq5J8c`JQ{XeCEm!
z<n_!4@u^=nI}mF&n8N_e*UA+@^Q6ViNwTuO*I7bPXju0$^>)>oCwh-3+Zfim5x|_P
zds5t9(>-BdYwEmKjWc0cMo=dx-nfvXr*6DQgAL5a_q6m~Pr5tsYCiiHVn|xs&3jG4
z0}@08I?@(^I!$4qW=$+IJJUsv=gFk6Z(kbH1~w}Y(#r~C>a8CgDGCu=tICrG;J3Nl
zdEzYf%x19!?dSZtQ9qq&P0dmbOKEDL=9Pahi1n#XS?U)JFGwh^tUuQ#MG7Q+RLsn?
zS&mhSV$GSBb<Fo)b3E_-)hbQl@wHbD0QVSXT{;FAr2ha5(YEc&85zZ-m418PkbY1k
z_$>Fs!Pr7VORRBIH$4H}Q&MO|NNXCexnPi@$11=s&~wyk%Xl}?97IK)#&<q{gUreM
ziW(V8Ej??AhKfkLUpEs5w|dCOP`oh1w>avn^cSk%ArVC)qNcZ{Q-)Sw+Ho9Q&Ai}I
zVUb=wI%(_aO__SkAsZ%9Fw4*6khN-4SE^B^KHqKfc!qP(L*_0QdZ#Jn@Gh&u*g#sZ
z5n%X#859Oso|lb{{Lzp=9M5xabn&t!oXj>C%=6sL%RSSYs@IkbEGWH7n)+JsCiKgt
z#Ed>QwUd}tW2hasuc5OoR_9gB3D0nd=LMO`x}}v-=*j*2kSSDx#dh?$Jd<*;A4+GG
zO?GwdsN37Cah-~~zEa^8eaz0GK@w{2eIy4T)=0WawPD&D>ARGv#x*R~eHIlfg{~|$
zCKIScOx|wDs>m^|fi#O$_To(mFOx{6)DV3vH^}TRI4sy?RzK7EIlv3%2HV+~M{i!P
zT8}BR7{P@NA-{6Gb{kUYu?nT3ZnTTFC5Zj=c|EtC9Z)3*b2Hhi?U+HopKsw|v1V=N
zc_|Lf%+w~{zD+%`k>`zT`m!h%6#k8BlVe{eyjqBk+!cMCNN^R%3E#{pCDACB{SjvX
zJMGi>9WjziRqYCBu4=`g!M^k*p#8IjnWR*qo@2h<amZH|74MnrOA@-)?42|M;%t|1
z!WzwET`XHRygDLUcaCzp3(E}aZfk7mQjYfs6BIxV=8EdiLoAPBGFwEEtB;7R6G9>U
zN=rM5k-V-6N-p%&3M|6ydD0p{iaNBF9b0-|gdMxuR*dCAw}S8IPv>+PYX{rgI!|OP
znf_gRyk7b4O%7&du7S;R)>O}PGdFW~=BswK@-GSBUf)fdly)#W;D=O`L0Yb+5V3cD
zlgAkA^bS}kr-Ap=b3q$a2^uqTR_{S8C(rTwwS5|E3$8#@Vg740O<M2Lc?~M(-vM*o
z?{;FAI9M+{_rv`ckogw<4Lz_@scHw6<<*7kV=UZcX=r*qC8eomHn8fLd^tdwH=eZv
zA1n+d>9&SWr<-*FQAueFR_f%12{=cqA<xYRRCr9-!y@K_40el3S71?6EHh!<QDb$c
ze2^Ax?WJv-15k@*HJ!Z6CIUDxbKzE?(>>25k3G!HbyLuYjLo(44fYj&casd$H?+W~
zmLPLZ$5di8NSYcud8*pp>-v&gC@%HMS4jr39D5!B)}~zDycpK~bC@HF!Z1rj$Zvem
z;e?wL8CrPhTq?9pJp~|80v+p=c1{ihJ<m>ct}hLJS*VHYJlK-+x-Lp*r>Px{3`L79
zuZXB#dTIjdyB(oqR=#fHvt9|V=<I!c6zBq(X<7Q!?<&w;Xi>Sj+$tprx+=mog^WQ>
z`(~;}my_#8!naP@l@@@eYWsk(=>r)9Q_R0`WE+|C%KrdS<z8Hd9L&iy^F8z2V}!zd
zUTv;>m;1UeGumr3bk7-pWD;}^e-ny%fm(DWK@t(7<gt6OaGI@(KSXnMvS7Wvm5PUU
zM>1m7Mm8n|9(_WF#@F?gfoE+zYFN!!X9CCAIYQRvwp^-1e$(^kbLH#Vp5cz0#w|@b
zaaCBdM6RvMcHH-2=EgNo5iE`m-2VWdZR0Hi3X!rewkS#Ld$NJ6HrDHO+uX-i)-+S#
zb|j-8Q;}1vB7_>-429L;EWFdn{hrjFF_&dVbXAG1fWW0@gJT2C+7l3qQceJXyU}XS
ztFc=(MRZjleqO5{W@h^Np5|eYjN7Yiwwa!i?wr9QXtM26wOAB4(DbW#Y$-ZXK7?Gx
znW|X<+qf1tDg6?Q%Z!KiXiMK%Iyl9aN^Za(I7}6bjjaZjn3Lv&-hu8ybHP`hRsh=`
z*ryKf?;k(+WTXuDFwaoq)$<Ba*@SyiTaP~XcHWo4I{j4eL}Kd##li)2yhT_2GfF}J
z$F4M56}V_Nap}Pj&yus^t!DKR2f9BbKLnxKhGfD<l2$IjBhM{$?5wuu%mDx{Twq(Z
z?UxcP;Uyb1h@X`(yd!hQF>YcM$R9n=AeS|dGbM{NJ@eeZLd1rZ<kQ@|TdneC$sx{}
z)H$~*WeHo>dLd>cF3+?ZYp-7}8!s_E@GN8(-xGN&C{HxWj*b)3z)JXKF0WPj`lTXt
zwbm<J5}^YE)|RZ0kT;Futmz3PnD*=jcn{DNIDXmXoBdaq0ncx1NVta^tQ_-gSnP9-
zyPW2$K?6JU5>_Xt7R!>F2F5scyX<sqbH_&*-R4<8%P{5@bLQah^iF9|=0Hte#S$#b
z1{u-!Aya<Y2=PsPz%V`N^fcpDXxG0;{6i`E(ezP0;^>WVqpftEN<4FFnQENTdzmCz
z@0p(XbQIARzPqMmj~yF_WDwf=BBurz$P36Ac3rh#cG<0_rD{zJ7w<4UaI7;+ysYvT
zwMp*NhsPSN$b)<2yq&qr=N-#WC~DCW6z;gln;oxWK4<_wlR}I!6-C=Vh_Ul;(VqP0
zue)cR%aa7#%TiD&Q0m815R6~5F7wCfIdMtb`NwGH0qB>1T&fDt&opVO2i;gg*R^_=
zr5cH@vGRFHh@GBcLb{JKu2O}c1KpkjmV@-=RSF#qnTowA4|73FnNh7+&C&IU1#_;W
zW+Na;T#<Ifmf6BCW_J#FtsYyaCcb8Gu6gcx?imX#QfTa-4rfew;KYYO@a}Ww^7aD5
zJCa_L*%(}3yT5b2*%k^DIN!rc=~tna=iUO|_|5d4zeSpz%L5c)`M(TT%c8WFv&F7?
zY8kbKvUJ~iW-HS5^ODP_U1H^3o8O-(&!l>HPE;N|x9ua;NC0nHH+_)|v+X)082KWP
zap#~b>203c-3sw#xhUkevW`uETa7|=UFh*8gYx65Io_Mc;k|Gaw0y#(x+2M8;M?YF
z)Lb;qa(vpHP!siB#fl@EhlgZ)MP8O-%Q5WP7_k}9x<>+n9KAwKJ@ei4^WQTw7}n&s
zS4~^S1S8Y)ljpi$>+PQ~vF}V70*hARD^{MCYqu{~Vpl4_{<F+kNBGQJes5dZZPL@%
zdWshNJ!<4j{Ocjr+l))TMRDt&SDw9_T6BJPIFlzt<JK4Yx^lL?(RX)C994c=ndfum
zBP->JsFT8~$03%#L`?<LgRh%oq0GB7deH2+a@ZE6Xnn?oZ1+~>YZVI%Inh=F(46!4
z8lfx7o<1!sLF0&#^}6>mldbGuK+rMAG*IkQnlOegQ<H@t8gt;Q8zwEKT-qhVkkcYn
zEX1JLcj<hTba`UF+U923`<b3+tqQUg4e!2QVj0yIxvh%(pY8K-uB|T{6ve96NX)Yy
z*69*?=WGh)-|wg;G!O96=H3Y;UNx)x<5L$m)3sjYy|h;knSoAQ;;idssT(GXS^ewW
zWU!|0mCVOea?yM*T19zpsn0p?XS((NLo``-I?@njMRf`q$5BqS&;SPJHwf~O4^1xJ
zDhA!*!EpEto;Jn0lI`hEfSx9cTAYk5t4mG62;EN43Wb!3=mC%KjXdX>oVZDeh)&*d
zo{!H3lf$)@EW$`zSdz*fyQ7h}Mg~PZzh<eWj-|}?hb%$9nVw4?W@dZiO18#Ady+|A
zqptH*n0G$n+{p!=bIGJ8O*1Dpy^Kqi3TW%+H+twvYYS$^7uTR#;}gT-uzTa2$`N+J
zLHg$>O{z>)n5+6kkZWRy($-T3R|jZx(WDbPQ<?&Hj<8k<%688@>^ypHJiAj}TP+H_
zEM}iTa1A?XwHREG8k#vYF+pyV;%238>anqa`|D|yJA3fV;H$7lWZWf7a;-xano_UQ
zIdNjOk~8$Tt<|`lHde)Mv*rlpt?4x{2cLH!wTc06(62D{sCd5`6qr|{JMGm>=-l%(
z+E{bU%-dYd%*-W5CIDXfu6yTb5zi@Q&a}{SFkDOx!XtO5ZundA!4=v)5u1#6h4qmj
zNNh&C#U6XKUY7|rIp8?DpFQjo{O=x4NOi#ktZA>iN*4+^t_rE?hZQ|es+U%8(D{0&
zlsUh?XPt|V<%mfP_YPSw7ShkXzk_Yr6~w=WRdRCR3`_NG({unR;9KVuT@-YZCbg~l
zasEtwFWT;jo)i_e(#Gi+ORh|UZ(2YOO{M6G{(h6P{{Thj-9>{$C8jk&*2ZsT{{UdY
zcP17V78Eg-iqI)oHmn3veAlOQO{1O-v&_x4^E2NwGkGVpA2U7AIutvA0i9(*w84%-
zuQ_0w%@G*JWZv7R=KP`35s<VIx!XWG6)t%^SYR}{(lt<Ez`f05iffzdY*yw#V-n@d
zp~^)3niE1M9ytl;?o4HSZk9@t-(*VUe(RfdPBW9lOisi|76ccdkJF=aE1eY;X^HQh
zU+DZmmek>@-~?IVJI>&vy<MY`nO(29B`G9tjB4H+3n#YWdz+ie$=JCW5z{l(Amw+3
z&bHH=^b4?aO)Du&o6b4WW}Sk2dII$A>UDOvlxei?9A3V4?n5+Is{5X1Z>DBuXSw0M
zfo5iV<~-I;E@4woF2+P&%zCVN*I1Ha5x7e$vY+TNBccq~a;&L+DRnz<H6$};!qk<S
zMT`0|JC5y{v<28vx6zq%#<dw<y;H4UckR#$G@(gu9)n_{2WSkOrJ0>`k$aOB2ud1n
z>tPy?gKXro_fBjMF0!CIqH)=5RkAe2VY8MMnAV(53Ol)_`P)h)bA0uTOiasqS+P~n
zhc>KPr2B9}LRXqvXW%Q>uiW;2p%*cD<YOheJKY>rw2bT}RZG?=Anq%%Na#<D&O1Eq
z+(t<4R@+?6_sq=CHqSErgEKQTGodR7&s#%?l;B96+xg1;xcm)YY2zbK#13zIDXIpB
zQV!@{KatqdA4-pGP+_xF>g_+E(zyJ($1V27A>Qb4c9yPZP~}u+qce2?J3FOH*r;*Y
z0-HhQv@Fj%7b1@>7St}e)&wQIP`Bbn-NTOr*^j0m^bRjgt#Y>QRY3V>l+oH`Q?YD!
zMGT$ke$mE55R^}cz?>BimwFNdlIMrIG1!FCY~Mb726Tn+mAiy|@b@l*FNKyOkXRSc
zl?RurLh#ubVj$tp9>ok#@?M|HrH<u4e9wH6Jk0kz%*hG|%*@QrrOvBI?h-f%%AdH)
z01UCwIq4vnMzJu~U3!8rU_mwT+rKjC9OEz0#wh@1tc*!EN<NO`^7-lOD?|nFwYs^E
z1OP1B^GxbW$~tt;bzz|$#no4NnSpa%GhV;St6^qjDX-~$qnmZ4BUQ$U?;NllYme;S
z@sZb#Dq}oL*=DZWM#11c4*dnh*m;@0lgkOJGOYM`Y<_KCSCnE`z;$A6)BBri@61fK
z$HU~kvu=J}AXQcxBZ*yEEHUXq7C}QxkH&pUsaf)!V_M#{hcs4ed*)_su6v$lW@hC>
z`<dst=VIhqX1?alhgO3RZHkb6*q2EOm&@8!kFS+g0Z=5g*_pZ1?>&-g^0^~!PJ#E%
zMJwe-4n;f;K}AbqmzQ#$#s-%rs79qjJ)fQ-ZPK~lm%_DIeqGz6po&Y(>s+HkS0Wjv
zAJYc8CA5=4ZR-4Kd)S*DSa@)}c1ZP-fZaSSGD|cal6L4_RWx@p-=}lql<NeH#Y%bX
z`Zh?9Vh^B+6sPTs+fE5(e!;~bdu{d6eDa<W8o(jZB-lo^JqzHY;q<xEc^9Mzi^@Jf
zidWh@759(a&HUFhGczQOnP=S3GcEQGGaQj-ZL^{&k}P2rBH4^>144YCY8&<T5u9+U
z-O6o8dF%RoM8oLF3MrXQOUtnNW-d=NctIIsYMPxHRJj4R2`|Mhu|Gs|<j9}ZLl=PJ
zmqBDm=;~n<w)3=d8zk4>kzvUlR=NV<CX&3-<@1$HrRn1xOMq70ol^N)gKt}%bKN{X
zI+tr~zsvbe&XLQn;b8*{j`LG{GPfpx21B(n)3ZwDuYCETls%#1i*#0nwUnCv8V*=Z
z`b}!?NwAi-F7+5*7J_X#E#McYLs%IW@aMW_*}Q&cZdOZi$0n5rY36O~`QAlh2_esQ
za!&VFO4hB3GN0wjmajx5!S%AA&W}iUDY6;@ML#w%xa-tvvGyJ`42@PUlgE!IS*hV|
zYR$b8V(uSQYE-jwoi6mB&aAj0W$TfN_q(TD9H(u6&a%2Cd(NZGcABq1M0_rg@TjxV
zo4FJreYn@txq4lLAh5kEx_HHXZVQwcT6)g9ZnCs<h}$M_WM>DyW81gh*4+N`hd^MX
zEx9R4wx2a&E3|}d*v~Jcr%MrlmnO=#<ZtJj869YCjBrbb+01D|tX&tM?F{vH_g{SS
zLs!Oo6$mp3Nn|YKiDrSGtn(P}51K63r1NilX&xgiw$rE#*$Xq$>!2+a;9W16=}Ek#
zsFibeP}1%)!<SU~%f^Maj=jTyTdn7ouLLnv>du7BDm1KmS3w<ghax`;zF9ObhIHHO
z<vE{M<ZK;vADwNlQ|5YkGqNO|i=6Z>I?&wR$d<|m&Y<d{%&?$X%GagtbZ)6Igw4pf
z-Oy1n(}Uw8NkG&c(!~85joifqmA>X+aY{C-S<-KwhypDjv7KR(w=7n-P}n<Kl^s;$
z=P-z-!yv}B*uD0<WhxQX?EG3>lDD$M;k(g=shfG5XS+bgy_ueR$I_L#=JMJXVjX&q
zFw3OCYrcoq<Y8zAoq6evA#3QgI2TgA=u9*@RfzDM1jRyKm!SOpWuzBVFTmloUs-b9
zGBFSVFG>KMO17~w0)A%ZG6$=ZGOfXcdj9|_d*uBZiuceQx1ML6Fan`^UX)&T(W{f5
zeP`oq#sueDXXEc2n%2}V<Pa2o>9WHvZ?3g0`8KMYHd3M4Rn5;YN^X;EUrl6c$Sgx%
z5_@R3q*-I#ty!tg(rH-Ul<X0JoSBuV$!fb|2DN?ku}$@SDO-IHjLqcre7CjH^(Tij
zHqCQ=b$3b3%=X_xUs2mGRMXu$UpdhAQGVgVsoCQ-0PJroD^_~b5bkP?zfO2b<-SQd
zrIUj!Y1zVanh#L;^Tuk;9jjSseTQ&zXvI3T)aI>hk@mhe-k~0O&=5V(JC`!(+<mRL
znD>+pW44T~>7k~lX086OQ<l3LKg?9{m4}ATF519`BxyWocW&PyqkA$*{qtd5mUIa=
zu4GAC+COzHyOmNNciJgyOG;bNqe#5Q9VTh|F@ZpNcZKM042%G%w~QD}OmIlbuPk<}
zJBAI8Oq_z=yHzQI^EdNfe9X+xe8yF*%+G9e{&hwfV<STe8&Jb>h~HbyDLUhth1TIS
zu4&aTHmGrOeN68e1)1YA)O{HW_f-MTS(*8phg;!cy|G{4Jt0kzVqF=l!B|w0+@8;9
z;jR4t08I0tbL{tCxhdW%VYN%4i{##T-u1{UTdQ*m_2piA{xHB`6b781vhmsyb^tUR
z8TZ?5e7BV(n^-lMP9%}hImPKl90z)py6*iMrj+#W`|KJ?HAt+<#F~;XK%JQ3((_OX
z{-&RyO_@&_FD%PfNu0exu@{nCIIpvberLQ4aD&@S$u#pbGcz~S35?9oe7(~-l`Xu<
z;MCQ{ylW4&7(8-L_SsmDE@2Qj=EQd5HAYtM8oTGZx}|LFl2@#cT}W$BeCoeC9$DXg
zCftygg_&%zC)PXze3;lr;AVOLndd|2p8MhcxFel}In?*@IG9rfxrIBywk|uYfujJ&
ze>p+HH|h7@R=zE}xLDgXl1OLMeAW%--9M8I9NwJA2tuN)!FoN$x^-jzc~*VvX_ZQQ
z?*zE9WPmz$oY<P8ilCG#BkYkGO^OP3?-(o0ylGeSVpL75?)zh&-FiTyl1G^&d6}7>
z`L?-wj%H>W=v<oRT{BzMjWD75?3FpC%ydwQ`DQL+#I?|rMfF@4(-Gcj+2=gdZ#QCF
zvTCT_`XiQDY38k6JiT``o9`Px-bIVjnk`Ce*Gh|oCg{8NRx3!*q6CT25+mqRd(|Ex
zYL7^)h#5MRT8R;2Q&ovoS}{7_?|#0%zu!6E`>*Fb=RVIl&pG#X-PiTHuIu%3ic?ph
za`L~nyemRVt^&^*+GHg5xLz8!tYW1NYa`~vV~U@KHF|htXG%ohtS)|&<sgUiDZb8Q
zQX}!jqtFDpy)l&U->N%f%5J$2`19#)Z=`rq!@KA!dMt&PgRZK7mOiBt$KKxCx&oT<
zXH&giKYbQG8^(*Y1LL@w0v}9bA^vM=FTF%%L6M&07v#JJR9ihhrf`Z1!gKoqiu?*s
za;je^@+t+ULA^e`V6>z}St;G%+rEOmVkf{>{mNt(W5c+E@S4Z}bN=tr^{AyHRl}GT
z#pg31U0#RqE}!HBh%!0qp^7gP^K~Aj<KOx%N1v?<QLw&?B)Hv#+<01nwj5n4)pWHo
z6P@Ui0C4Qt%0Bo6!oN{Gq|G+T-&}~)I(13eoaNzS@H#R(3;>^-SD$uW(U7)uc>9$+
zTx;jH)o0vr7TM8lm`RIWC7+z)(fwQUtHP(}YJV1TeR!%HRPa{Zqk)pzbMyEVE9*kr
zE8eo}ehfL=WPSwvY_%{~d(kE{pyB_<;;i2Lsyd?`-=i`<<!!NwTBrh~%f7cRWf|xF
zK^FgAv_kD{E~(rex^z(gEcl$ZO2(T;<Gu82adZ3yFKpfz$}Oxt@!(%D4|)!tF!;4<
zP!_jk>2dc6pIjF&f9c|`**A%sK9Vz~XxnQVkn)^QPk!CU8Q<zWRsH_ci4Pw;^!GQZ
zI?I|0u%S_0ijV7bvgdDKrYirtVlt+%wb=9r_B2hkW`5fvCl_^WC+axW<+L(%u)>n~
zIM#eG8+dkG>Dt#+>JPLI>&1+?)S|{G-N|K?U$f4*eTJIFiTkiYbbB;CU=^OMwr<q=
z@4Kx0-@-z?IybcazpsmATva$}VhZ1&^!es`CoZ#~;-Qt5CL6k}Up1DA|2t*##p&t*
z`mg-6Ll+m=&X=rC%Mv$<O&9FtRy9vkn(y`JT=dtyHcXy^550D8^;it#G4YOZ+JE4v
z^Z<J2)AD|yT`ghWNkM!oJFp<AKz8Xoofk9gHHf_1b`Ik`6ZmjcG^*h@W8HQ8zT5_7
zWU@R}-T#NSQ}J>*Y<<#_3T(X9M*j6O{nSLJyR-X^f0M>^vk<v_DdQjaLrTuiPD&o#
znAeobNm;S!nK&!EQkYWBE`(eHSgf3=jgDsf27hcNsV{tQ9g=PaiOSrv{SRXu(bGMV
z-<&3^d%`OX-vXY|Do;G#w9ICL-8BQhoOQUbclpMK>;F#G=*yHgv1vXT%Vpt{2Z<y$
zQEIUR=ALhHIZRq75OV8@z{Q)7xEp=F*6)??SzOC4zT>DXCE-?3q3m^vYlL|CP&l2m
z{L+)m1#ktw@3#22BEtL_cePJ;@ZWA-MEF`sbA#B{H=Us7hx^1gVOI@&*<&VwCceFa
zcf~&I&kU^G8Ldtdeq)s|Ii?@tlxmt>-{rPkdHw@`T3z%*XV%M$?V<m*N}2{K>CK${
z=e+4tHmX&3?9v?~-lung(KFk)TG?6ZhhpYZdt^_`<fNbXiPx^s?^}Z3_*EX^rau8>
zl+AX`&f}c&50`!^x5I_mI2`#!n*4WF1jg&Z?Ni5EGea_)`>BIT?4`~AG@ihoYYK8R
zlv7N;YrEgTScfNNA>{%dU&c;zprJob?X_H?2#cGAtu&S$rKrXK`XW7Z?y0B?D{`6n
z-<yI9CF)C}4@*hkc1yhY$Fzz*Jy5ofa$!mzttRx9$n}6;v9Pf8xyzwLXsxE3BXW+K
zBhy;4Vgm`jHe3HToWDC40vt`q4taWVTaHYHtjNW$TTwKBwiKPznK>P_?<AD?J5|)j
zi|e>udgE(Ub*?n{zLJUZ(v=D9>aV7`>1Hh2q7`yU!R1qAz~(8Q@cHMU#YXvwh`-g7
zCIt~*u9$4UEXM9Rj4wAYGGB3(TkFj*Df++0(1+%t)<4r82$g%lwgoh!ca8}ER27(s
zbFpQ1YtJm@RWNbZmP|_gO2o}PtAF0SXZ}5sw0LOT|C;9r@f<`&9~I|QMo0IR+U|O{
zMOYq$os^f$>nr?JT_z_wpXOHue7=!lwr^n{{PHOE=TKz6%-i{*r&LWg{2$2REuGPY
z2+~r}M4kj;esd#5lo=I^mi&5oQ^VZ>KPA_hJFjbdoI-DHqnm!dt%-#sm58bg{eINi
zAT<6yu>W!69kSErYjDkN#!jZA*L86v-A)sq&UI{qg6oSXn_=Ft-dhXk-6GH=IPouD
z?%JJ3ovChVW%EW?skhewI*$}=zTV{uyqo&D_HM=b60Jkn@n__NL+ca$A1<!OYk9xV
z-^w1z*S~#4np+Pbxsl(D(#LeNgPuKTdPj(&!rlQw;Tw;D*rkqvkx5wK3CeThLYE}R
z{tMtl8Ya8qN#O44?p$?uq}Y?_X?FxhzOazk)l21Acv_J|@H>&*ea!P0C>7^@`u;4S
z7&=o%`7x1pBUFv4GIq&f!!<Q5MKmoV?5=Lg4-HBzt1|ZKKc)BI=mP$k-&F>QiCm03
z)_ToDniibD_oJ2?DK!4a^Qmt+bt0C%OS;5c6=aTi+H5Sc{HkSYktt0-<b&Vpqx~C)
z?P~jU%*&8W)ME$mM!#8!G?iVt{r)SCGMC76GTXX!!4AL<Vs-@qyLszCK_xxD=WTii
z9Q$<J_r)C72h}+xlMBb+k5VPS&lM@DaPfV3J9##0Qg^}aaOUZw6~)K>X#tEGjZw1G
z2sG*aA}07vF9M;~Hv_IzI^z9J3VM2tzejNathAhJunex~a-2T|qz!-Nt7xl={Lyej
zXlq-+&`GcPjS0+QwXG~&5uPg*CUSO6_svW}rs>K~T{I1lO*_0TG<#iaTMd+9u_67_
zU*Swp!&tHw?SNm;pRMZkTz6<B2@%2Mzh>0uCDa|9U}e7E$tr?nm!YUK2c2>-U>5zg
z(LXu{u$xe9>yf{=lnyz)%FgxO&KVhP-ZOgb-(1IU?*%$w8-ti1?zf#+i~MaDMbW*2
zIocO>EJ*p*UV_=3|09w}@tR%Qz%ZlMv2($h<xAlyfM10LCPdFKhUJJZ*V})ZK~-zu
zxaMkjmbvmZo|2%0dw>0lCu<aY*IiiOcwfGq5YCUzZf~yr5fL!`S^`>+o+>TOlQ`e-
z{*kEoX?e!xwFNs#Tg_{@4<Ea3wA|=mJQNOmnX)}Fciq86D;Vnl;de;e2CD4~1TMaP
zbVcv5#Fldc)7BR5VbfWiiF;>fxb0L{b|fenRXN8*n8euDYDqt=v2;6BKjJy<ZUkcF
z{Mvz2bySxG)6zE8Y+atYqL?c0B{tiyUpeWXOt+k&lBg|>g`*c-!0!{HPN+-fC_W1k
z5l=4;C3onH&2~<=FC`j%K6eXij3q6uI*KwxuT;{_fZOqczvvLgf6C>FpZ`jp-QMUg
zCa&D)`t=tqU!PxerKHoTV-@m~8XQS$VK~=M{%UKdRKFAP92eO6K;&L^IxLD>8z#DK
z@cHrdh$miOdh^bF=co`2Vnjv%oAy1B%n`rv5FvYeey=+!VMJyxiX3djerpu;6(f)6
zG$t=?D5!eY14fua+=gC$X3`su9UEibx11UXY`Yz@%4u2W8?|Pgb*Ho@xh^5O-QRYx
zS^<b(#vR@L+c)-|$;T>F#~9W|LPua*^VI9R)#x0_x}i-gKvGRN-oCWw)Ixdm*;8ET
zJQc*&(Bg2IMb8cVbMcJ*zjv*(o$e-0J}iRV2NzjsZQvE`cyk>RP_6$iy*4=g?%m%w
zN@q{OQz@?Ao*s|ItOoZ-Us`X-bbRD+qvwupoUgp8YBhcjKLd1&Ol$h&ilLmGsa3(D
zKigs1-<AV*V_z%J{WkvytvoXS^R4%1R&|PZ(xax}WTr8ytc&g>I!iqD^~e01rmBd{
z?X+)>#al7L{8~BOd0M*T^K02p!x&cyzcd|%L!9F<>XE|<`CERe%D8-M7b&Kpl<1~G
zRLc1s<F8Y$cMoib?|NhQ*Lf<4(<_!?n~5XJxC=gRwBT2#$L$Xs%<l$Fr-J_Nqj%;P
z`DrJ~Wu1;QXpbHER&S~`jsiXG{8a`QDMzFpE&RUK+Oe9$^Vbe|odL4`UDY*a_*Jc~
zDlG2a+QZy94$Ws?f27e{(T)D>rM0;Usa4Me+_`pTwvOiAhZV$a&teTkb5LMcjmm~j
z)p22WnndbLEefgPJ73<zA{XZIrGvW<%<M+9)Lpp#$xv-Be0WW#XwNBoG{(x<B52-Q
zhkkSZ6{cs=H`hAgoTTKXxeWygSJ#F6I<m`+H*lU(h<6r7qTtqEL`lIVq{or+UMe(l
zemoSA0s3hdqZQW|F!c+8$Y1UXqINA*wZUlA!MHz<ZglosoR~JKy`^A$P;qYAFPO8*
z3SeJS&t5TQ9R|`{)eV}Cghg`}9DV&7t&HR=#s!tQ)E%M(LN9JFW=D{8+2VO2$}FQ(
z<Pwi3JqDGF^V;C{jI-@3ij(`l{Zl#&$In=FudpLImqt%tRdZ@#&x!a9e#B*xwMV(f
z?%_wBw2QP_?h!spC)wG|bqh%{q$-<&dIuLZ1@*{}c^BWyR5K&=V`owUa3LEHqGrXD
zyuhdJg_YGWEX@g_X}@mYrQ51UE=iUzZ(J5XtKhTtot40SbeEg+Dd@+`!i3ZSR{rMw
zR<R`lZL#`Iy1sr8(9lnuV~H=g7snc#!D2SQbG-T;`u)q$xg&1iWMFvuLtY=rP|HQ_
z-=D^+CKfOMqsgWo`iPx$Pydp!3jWIf^s39#c52I2lkwjA!QbYW(sg#owXa%}68o=y
zf^8f1v$IVU*5Y@J?A#lM14klr1?(c<sSUj@eJ|`)tP*tTR#EcD?@=eIm!lI)&U2E?
zj)g_ou{xRXnDsC>hht4?yC1KfTLv0tkIsmSP&|d}AkaM*Pj4H{SDcMzX`aW;D`S<<
zf=8FnKGgq_;{t=`_Q;g|_>cYJQINxoMb5%i%qYB-qoOyBXB2avau(XlVy|1*4OM#A
za=S=mo-j)7YvEDKq$BjLtL;W-2GyI78l3hj#}s}s=l`sE)^swdWyYw^jIiE}?jmN<
zCR|xe<wtW~Ifa(eeD%xSwoeAg>7FgZv&YApIZ#bKfy@R?Qm40A4B#^7j?k@;yn@5u
zXbH%rEVHU>Y9&!UZYti^sFD51bxXjZ@mPsFJ$g)KIVG4!e4e4L4hbpZO$tUakv3j4
z$b255zq8p5m6}dQ|CpH(fdwQt7L%HC?)YvzOXr9h2089)E&Bu1tk36B@$%Iq2g3*>
zpXa#jl+<chKQeP4)Uo5L4}Fn33SJU^Ygp|klC_Gaq79`lyE_cIE88zHR)(HZBNgtB
z9*;fVxE?*`oGj~88UJS@iu1>>ayO9+1%#?6x~j$z`<$<NUg88trbyOj8~BbRHbeg1
zwi7OW>w1%^1W^7E)LIUXe}LAOrd0+U`hCqml_lxoy0C;^2#C0m*`MIx?Ys&2S8Npe
zl&zATaaN2vr}S><8AnuG?4O1rSYCcUGKZhyWANZxy+uIrVWK|ii`(~aV>(Xqd2LTG
zkOsrS7xZP`*!)LblXD0BwSrL-3O_stT!^9AMNk=FMfnoHgG*HG9NM_}OUc5)3}4{!
zhe-QND%G%`&JD@!!$}M!uGSMiq^t-?IAFnb#Z~F9%7U?SvWxKtzPoQH{~HwfX|()`
z)?Fm_{31P|I6pQh;`bl*=dbIxKcUnPwx=0JCtb6rE)0j7D2iyP6J`FmU2<`@XnFu1
z*LwfX>PJJtkZ@r%Rq2n<l49J$wdFUQ{S$W@+ge`*sJ-z<c#QYkCVnuu%sjVwK-UFa
zLOq@<@H%wX%i8&XDRVlVY_YQnlpP4k_UT%xIk@rnYQ&^MTU~FtPq*)ojmpGg<%ZE2
zkvPwUW)o^V{cUg&FXe6TW4*R6?(@049OTI+m+Mj+7q5L5<oQ<;#GWqt>^}|~zq8l7
zH{0>4dz<3Lbtc{P$Ev?;Gc3iVq3=0o@VS_v#qQeB%<GW%l}lV=`EjOGfcCf}H?4as
zj&d)5PHWil$h5_5<RIJl!!o&wGtF$Sy$fa+9ESEJA51bR^V(`BeJHy0(WJBKLgE#$
zhN$Z;QIcKF0uSCqaH#Rz6#4Pl<A?;0xOncMN5GxX|LpOAcEqmpg#@?Nzx?}2M;Efn
z*#>8R*F8f`baGf-zl@`UMrUhYX~-pw^doBwtYDoP$anU--5HN=^W_emHm4_NmPn)>
zzq@W4z1*mak_`z1Sw9MkpDEo~ta*su`8WP;kxyLg2;bj`S?+kZ$@cbVS>#4J<}<O-
z^^e^{y_}6-8@D{Z<HF9Eh08aQ-FpHTqM*-gIl>*no~(;bY&mX`=`r~icbbd~5J^)i
z8Cj9M|GAcqcJ4F>P@1<3SxK&UKmYI4b$9cz+qGi>XrRTt9uVeXUk(BV<Jp=1nmYTC
zd&Om8PIc3?F{EyY5p>jh-8Lr~CHx~Kv8w8=D|Smx)kGspBXaZ!VxM~7;<od4@>zD|
z0jmoj?SI(iBr9ON@EUY!{ckR{|55n@0oWK2<XPPEZTD5P*K)8IApbmCGQ1Ec8W5#}
zl_sP7+K`cH*JP~4pM>_N8RVAV^Qd?;t1!;b_}=FOo{tjKbf~?nk8jgiY0p|HtR6pH
zKV7Q)VQ@jHt02QmOZb$$RFfFa3s+!y^FIkJpxdnXH>!j=*WfpHXYTZ`$#XaNG+WZ=
zSqAHkzPq3}o^E$MX<l+}7lj-gm3l1nBPwbZaFp~@D40T}-hzbuP3lVdlGVl^A@*6k
zz@qF28q26sMwYPqH&4UE{sL}}pG?PH5#Dzk4i$c8-RGBiL-xFiuyIm{xR<+bh^o-P
zkJCdr#msdM;@qCr%tp}W;%9A6U}F1Kj}@wO^y#mE9La9kwb$=4n>q%oT%NtP$J*U_
zBB+*C)unHBISKxE{J8!>uy^vrf$*a<2mbs7?E}6bSdUtVV#Z}SuUuH>w0x0-Ss`*P
zoc>&QMgt@`F!<7S^Bz<~{pr@Pxrk484&^o+F6@+p($ulg?=SWqw>qqjX!@V;oWJxh
zn_%hWWCZ5i0Clc7IT^@L#{HxBb~t?gfFERJo;ddipkYBiVXwR7`la{dkWvt_$VodA
z!aGTb?k!QXnLcziQqW9y_UP|)y@1c{Hpa_=d21~uPRe+CuwC@CpuM8)Z3v^V=uSNE
zUiNHbZ_nn#9KnYD3&IkO>%qED?qk2DiC$R!s=6aNZMi$Mg|CXQ8lU_xd3!RDbMD=j
z^b%W8&RMlI!0A@$J@h@N%_WhiUl&H%E1~Ql9lA(rEalY*Nj)lNh)jJ{@-w)sG}YJt
zrAN9|`S)@E={b$15H8r2y->ye)2)}d#xeB{>)%4|_B||ypJt`%YNSNP4=2Z0X-iH|
zyby`?V~t+}h6F!vA3UFMyZKoS&6GV*(|fCth?=_opJnCLG%YB1bJ)FL-1kU)qvv|O
zfon$vVd%wwE0BD}V~xd5<A{*(ICIJEwNqcJB7Ni@ZWR;1Y3t37_?Zkn1T%j=cMCs7
zyIwA3?{R+qR{BaIT`JUIm}5yqTD<dFGM7B6)NM*hvhj9GU&B#-_}(5tz=9w!RHZC{
zk?d5V98YoR%q+G(_@nyS>~eEMMjYvlx@y1<cKcD}Gq=0lE*gFM!2E_bwsk0@vsfiH
zrb|o0!!A;h8XLfb@4flQ*e~fkAJ@LR!Q*i&){8gxUvI0adVN*48EKOZZ&SRkT{HIA
zlGf??zf*pzC0Bfn<m|g@&TQy7S?BEN)ZAvZ^{CmVsf_cqMmpW0FH(1Hj>piOW@rB$
zQwWpLA1wOsQ)<`#h%|eWGZ#RT`|~aP09fVef;P@Su9t9~DV`9X$Y=~eY<_*uH~>#z
zth-xy$}*!`TN2nCG8z~WpU#}8Z-LOT#MIvtYbg@^{3s1wh1rgLGgiZe66`7S3iI{v
zg^j&`&|!}Zm6s77?|QTRwh}3g#QiH>kf5nftHXqme}7*45zszCQTlc8<XKisWp96+
z;zPO#PvQeNUR}F$FO5Uav>vpszti1|F_H018`1qIeGomH@!`*s5TLOSc`$&F|M~6m
z#BDxrQ+YR+<)YEUV_g-QIjNNgeM9ws3hO8Bp_j#6d&V)|$r44lX1DeAxlGW=&SSyL
z4W6^}v3+4jr)q`tcNdAhv&<g&V=bkg^b5iNcZ`HAnNLOjcjkQ?Y7e`vD3|`%qM`Ht
z!|=fzaPl$|{u7n$;)t>A^1nq6&;TTi4t<f;(D5P&27%sQyep<b*Jm5|!)2<ThOORD
ztuI-I?r-cwKwSe}`QvXgj^$MZwb&1^)WJ)axbLaH8VR{fbX>uFJ%|n8z4=xnc}aT5
zJm=wl7FxIOPTzj-x|>gDKcDin@0t1BZ{44ND+!6_mnIB-KBf(QIv5Mq?6<#qn<yH2
zOS5L}xhX4j`f7!!y|})cnx!z-u~vv)N%FZS2T2)!@riz2kM<TZZETTq<>IBg?J8Oq
z_9n5%>>yw|T+pH<N~>j?i2}U(>%Sen9y!-66sF|%U;!zGDqm|6ozT$GT<l0>hkWt<
zR#L=8U__eFJXlu%)4s4PbiO-D`++3EN55H(BIB1%r-Z4&jRe)1Cyc_^APJXRo4-zd
z!#Vf$X;sW`|M*+7IhsxObe%fLX~jt3wR-Sx)sZf#Z=Eieb6@2CaC%52;`{31ySdl_
zpFH=W?&~5m0SB|=NkcD=jpcX4?N20E4@*NK1rJd!Tr%b_#RM}@&)9KuC&2T~@@~gD
z;XID|!^mOhOpgAk;4AQ<3SIl>PglmmGt?`LMtZ$5PnW9$6$*fCu2#2Wp2<B|wXK+A
zV<y%{!L;irifQd|r2~<4`NyC5b5SHs#iwWD*&h-w&`Q1yEWec6^$oSsJ^ag3?{I)V
zDy8%Y<q#Qp0*$Y)1sT;J-*nXJ?!Gh8r2}Fsn0WnlBLx%u?_PG>=rgHa+}-?lro4eK
zB4W)j&UP}z^sI-UUrEQ$qM{uBwjjyNTRKsT3kE{Kk-k=nZ5iL|-tEG*Ew9$c8$++t
zUYU!0T2n9rz2y_yLrMYttb*;&RyiH)Sy)%TExWzGmwuyjsYpYo{LGgY-UkiQsqZeL
z<R~>hLYTFD?Gt9p1A+u8Gv=|l`MZUwv^ZiRcBw^Sfx%J4;iF7a{u8;p`nynD=itJ(
zlEqpTt1*R_WkF_*zy+38Eme?vcUiFNq<;D6`lLn3t+Vf}V@J(?3sgvoZ@Ye+v4F2t
z-U4J3=;}WA56zs9y$o0Y1^X53&(Qh<BbTH4cJQ(8fJUNd!b_j*U!upmx`18}h^@6L
zV0KdPZgo^4>$O>!@0v|ij#-S1L+PCdzXROBF=g#3i3>4WcMzY2wFu7ff5&Y#+~b}E
z)`zCUg<bjnX$hevjBo!qR8d>r{B!XLUuh~+G=8i*j<co?24vu#U%euA%iBqJ^bXIG
zD_!&Ubq&Ydp2YcH$S3!j!C2Z-g@MWS9AWtGX0r(Ygo&cn$vCGQTfahn#$wG3E&NBh
zLbn5=aO!}Zprb%f;S6$J2U)3#3HmjgG^gpG`n++zBy*=2vsn{+Tim+#qckVzpQoeB
z6PWAWE;0Ye(Ro7~F3Dr!K1S(g{$6XA3}HqweQ*+I7he!zWuAh*x@qVwB05jdgk>7%
zrKZBT<Z}Uy80flk3p{4_F8HNa+e?;XUDn`*(8^2<Hv@jFJwWwkpB}9pvu@C{A0j1r
z74w<&!5P}Xz`MP?+urMZQ}EJ)!-tsvsJrP!5tebJ#o>RCyLphfz}!KXy!SK{y}JXy
zt2LP@Wk{<1HgBW*jEkGC-~Ud%GQan%YuEGlOm`&m+3>ZVyV|dmM7jLl$?OcGF3rOY
z8yD}aJ$L%GxTgQ&k;k?M9uc+LkohTO0m^nQ;p8j)*fypX7Yt)vXW>lku5=c%IyTC7
zr;q2dYNLDX-nN8&$<bG^@v6J6CO4F&=fxTMCRJIYOuj|b`-of)&q@Erkqwm(3u0$p
z+REkkb2zqNdY+Y?<d#tS<2OvEq{oc&g)QQ|#OcV`aZ{rxT|cMfH9PVxbM9mL4y~Zu
z8QX4dWk!0P7}o@&wXsCb7i+#^^XN)$ZRf!U!rM`tnR!*!@!i~Kg9LR1e8U*;?-epx
zA3~V~b^)LDA<>6cF}Cs{!ENiN_2T4%`pc4?>>ob6{!&EGK6C3XR`*efOndB23qBV-
zZEbY(4~wuh`7HurHkIo@($LV^F#Yl8Y*V8N?9$_mf9@ZNzKe6IF-sq8BFz?=$&us^
zu{eu2%N(bwL))5~Rvsc9A59)m{dE01`B%Vm{3U--i^UfCrmL9|b``eYrwlA2cpews
zgJqjDio>pP1b^jlBm>14NxGLbg3DWX<cFRFW)8L9nHgTe{P$x)coEREk~%$_JY%^Y
zj=rHe`?IoSCUSiMOnk0zbn)%Y=wtfG$8NJAk@xKDqOY7cDX+?7yWY@$X?)TAPvwJ5
zkb)ZBR(LMYOq(s9^WR8MW6Za_t#8*!dgI~F=PA$H@Bw8?YGVxW#x?fS5VewwMi4-U
zcHlcC=iG(z&fN{`vzVLe(wF${rA|a8%#`Uqy{FBK)WVBB=&Ak0vGCGb%-cLr^Lb9g
zhJx+~&5BDTc7n(&-Rxka^Aj(s)SDL0c|50%yo7L~f_Y_S5acE4JCeE~!Jh0I;Pq1t
znT@Yacew5tdQcTvChM5iuAoV91GcEj#W(04ip0J;>AeC|TK~Dp^vKj)_)U=dxWDCp
zsv-2QJN=%Xv!h#<DBz%#hqC|RLreVNEyrii^y7n~y8b;Z1Y#Oo>n0JF=Xbl+IMX}D
z;G&zZgy)?lQH9ZyQyM=Va@~Icay@rz6zf8?%#(Ud3VPrVu3B5{`biDH2y8g#_r2Qa
z+D7erMw+Pn1*sGj-<cV=$V)dC`J(FPt~5>OFYCU|8qwhS^;x;jL6&sqOipWRY4x<=
zlvKmVZr@TXd3gnD*uZ76j)O;QmxaTx)6d4Z&Md5-p79P`$Fz4q$&#({r~JJ{;h9gb
z56_%kb**r5l-prz0j`p+I49RWuuzRWih2uru-v@hi{nDRLK9!|8;7bX#pb@Lt)*se
z`%t3)lxWokRnPfU7k@b<=|Dt2|H(Z%A;ur_TybS<s5GnU{qw(%r$kTx|LfHMTc=N*
zIeU)dfAgm<oIZ2;<n3?Kc8X>Tt43!>JVF`Hf~U59Z!#X{NTTP2;9`@lq`j7#STMh9
zzQin_c2)G41bVG*1)@5P=$ql9r_+TOUX`wBwu%p|Y3{4?NpKLoGN~xwBs{L)m$qmx
zMx_AFW?A@ky7&jCs>5DUsa6SPt3Qku>@tvS=(;c@K7u;aSEHDlTw8Bukk;l+V?%~6
zBJrOBpWfME7}ei<+0I?WbWG|u{?7Oj%6tWmM+|(SEz)xseo57y!}H8(mv>a->h}x9
z><w6Eo9Ciqq^K$Si<0b8Vb>$b<85JcDQJyCpdq!eP6qKEwg?76m>@c@e2cG@`-*n8
zUwJMjJ;8O$QwDwEV8GcmZ?ThM1O%>gmOp~x#1fMxtdc;SxDY4tSJfEoMB#L1Yjzq7
zp~MgvbHd%;lNKu<?ldQ^g+YF7W?{vZ3tJN%$weUHDr(r*AkGGgi)$Jf_s9^`^JHQr
z-OZb-3oe6?&OZSa%rcY2J<4Brx_uS1YoWp_u@kti{xJ+3!f8%V(37bLbhP~-%#)K-
z9Q@RXFs&F5B`es3o_RKxML}L)6Zu3D2KEHquZr$ulf_B!)SczWTYV@YKW;{N1Dr|{
zo4tXO9w^?+4N_}Dyfn7ZzSCXr2y~&YTs;wbfMRyG6H2^8;EI=BbTu-o6h|n$wkS|3
z!;b;%U<m5nj2VUU>LXR8Q7$xb4PV#%2aaT#|A@e6oR1@7^x&htptYYU@hY}>Bd3Y%
zTlUJzn_plVsjLqpYdWc~wo@e#;_!EHQ1N{23ebBrFSV$Wc5rIl1youv4m3qh+2e%z
z<0HM2fbhX}hD||xK~bZI=J6PaGCgxDB~CVDsTXcZFLKksrU}ET!l7TOeCc82!jCLB
z3{FGPp_8^_Rv9y$L(XqMuL2BG&(5g<kbJGarZ`W}6rcyt<4KO_Y$-QzUh-(yisBP^
zoP`D=r#9QQVYWTZ%h1XwzudD1(6rQ<ombozW0nLeGIogozVgBFy_669cQ7)z%ADab
zQfdma^<92K8J{L-g76s(AqU#NeXk3RoVFUT04kl(cZ^vm$dFvzA4&4eht#W}z%^l3
zh-Kb&0iuTjhUgLOix{d=UnhFginkR}!h~tf@TBRIe2E&@kR8tu3U5bQ1|!V-nqs%2
z5hYVP({jy4#d1T8fO1B`RJYGK65zuC^qmRSt8Q+X!n^@?kcMyUjIN%K!?q^D9*ie)
zSY&k-PZ7T|ehuYJi}%;4gU4m(haC`%VtU|!>qhW4ghoznTaG%H4e_l_v-SaQ15ni=
zimLIgZW!Czq|N?Wdk*<ZLVD&vXKgb9*^=I}zTpA#agTqcN0Bd1j87(GCA<pbsj5vJ
zB$t?R2Q#xKe+QCK#vB@fa|~ED1o>2Rkbo@2FCf%78RT77$q;W;fDO|NtyVwQT?8-L
z5}?hG=b1DsDG&i=Ej2zDJAvbGsgbrBAgNL4dFA;SpkN^b=|Aq9iH%F$t(=Hm)@W|o
zlR|$uWEnRlu8wyYvw~_MC=IBIrC3WWsG7F3Fi>l!3<-gXE2wu4wUNC;l6{l}%i9E)
zpezsp9_$0&BUqjE_TpC{L~j!S0nn6LqfJ~>#irPNaI)tEBFDWqhc#hj#H9TGm+fo-
z!rb&dddeX)=nSOKx2(e2u^s|S360I7XG&>hE@hIsrgMjUY~fU-xGn~=qO*}&J@h1X
zJCY&I@CvanD;G)&;;<%($=+Lrj(87@xT9RRaK3$VA+tz^xgQ9-JseNi8ubjV;tmmc
z1-<ApI8Eq-yg)KKJDewYONxJY5Me`QKGB7JC}B0f4}JOWV60Na!Xyh&p@C9IvrwS2
zVZV_gFUQhJcEUZZR<c%d>*uMC#V?L53NsWZ0fv*)*iP!2SIJMb;g&Qr-+Guaze1RQ
z1;9C=paM2!p>u!&)nMJ!@}Fv?$GciNKA<d)YHQ?~rx}xI8>r}we9m}@v-UXlBr{@@
z4)ePzXD|O{;6uQjS2m~`OB4Ath6JqM5!Eh+Pg>-#%5tcHi6kR<JFa<b8}EPc!%WTJ
z2!UE>^8>8sOWgA^LI~LuW;j(O#4%BVFSx>CIwu5FNlwOGG8@1Okkdq(@fYm%&;u`m
z=Vl&IemruXrf7fd4|1)E$SQ7@A(R1g9bTQTkJ!*T^odY)$dm?nm-&CBfoAerK+d|5
z^&%TH3cMfiLwrTrvsM))rVe*De(`O%-Z9*B5?oxQ^fJ<G#Kh7x)7MRu+YiK#3`X`7
zsP%#r>@bs;xtUxh3m0@<`cjXX<!`Esp=UMB4H`6=h8B?h-^<B=0uB`C5**0|;WUGL
z=6h$pnq>up+X?;X0fI>8tB5oxl)pS*S6yIRFvQuG*m*)Hu|p+L<pW;|Lq*Dpp{AWL
zwy}P$M(9tLoG)V;E|?FUs_RO)`0trMmMG`4+NlDDUv1YKL@BP;kTVmXQp$mfCZ5&B
zd|@Ce?rqE2!$%z!)KJEX2~X0#G9XO%5G$0(zAql{zY$8SQ{~s7Ac%KbSC^%dk|zkP
zI#1#%!#q3W<y>sOHsK@DmP4Z#g)OtuN--utrR|}F6PCKs&du2NQ9B>;R*w`L+cH&H
z>uf!2Az!Z6Rf9vG4IgOvIE7S|SIY7<W+-is`e<9CS;UY6WE|sME=!Z7D!h;vnh#?H
zz_9!wLfb0v5ggM*ljL}u3U$b#)6?9*ZIP?X>Zv>i)>a?w=0wRNq2MvgAWag=m1tWY
zye<I?-s3y@fLQU8`WJjPVNZKSXH?p;l#CT3ifQ<gh+cEa_VD*aA9ZyBrn7<jTmxls
zM>)c`N)5^2uB&ZBxqgaJWSB3@mf-^mvlQ;l%@tJ*F?JpDb{v18l+gV?u)2wog7z`1
zoM~)vl>^kmrE_C*_(YZ!1)R2}Z1W+qh=tWF8qGLV4!V=}%Y}FTqhl(a;9FVT!W3*%
zbI66F#4U#`Gt~v;<S0Qr#hbhW>k`ggcb1>Hf$1k67kkz?wW{9rAP8XWZc&w3S&Ib`
z5}p|$Wr`+~v#OdYZK2+?j{81{X5G%&iUzJ_S<#oi4CLkzj!lboovsI2+Je+vK5CyY
zK`*I6p&(ASYEBn>A}yzTz#Jl4nMFpGBp~$1%jyN?E^H0i*%M^9<>cVXB~F$ghiT*Q
zdBxVhAT&}CU+OfvlR1?`)$dD*SQZIEiF5M4vxD>#Hk4smH83?SxdWM5+~B$e%E2Q+
z3Dw*ZvsC)URGNoxel&JP)yDqjMN3s|PTJzcL`gBBh<>gQsQ~a|xOsWG9<YLL!$V_|
z@Znc2FmWrInWND1X?J*1y*SA5Vt+Y<_lA|@jvEH0Dp(;$*wGFqr+K+VvtVUn3_C54
ze9g4acu}IqXdl#}=)=jyOrYB2Lg}*WwTuADB4ZK#k!e0|;hjw8a+srICq>Dyn!sS`
zT<=11{HS$P<p(Gfeyws;gs?dz)+Sn`REYZ`R*&#;Fw`s(q8BbJ04wFpf-VZ9c;V;p
zcyf*>5kd5b3r0SH^S;NSK)3xy2WHP&7OHwP4AAS;@>XvzG42X@KM5oY3X;u)eB8TH
zHa??^s0bw$2Z4N398tE!JG8!<+mLhE5MlYm3L|ZntwD{TO-@qahl_qx`DDHhhCd71
ztt;u!FK4blPa_10?2CDxH;Sv{=PYvepMsFgcysY{1+#oVtgKNry57N7V%z-?_uxt7
zDE-75vE<^73}P(M*F+_kA_as39W+~XF--KG@M5~PEyJZo2}xj_7;{YtIWIJgrbS?u
zsV|HQ;|*ZuNIamRWu7ak`1J!Q-w!ksX-Ou~_3)2i<+LPu%)CwVrW@D{S+9Frgj5+q
zNU{QWXpKcU0W(H*@60Esoz=j$zg&9hC!j^!OiUtsgS$857Gijy1UI!b18!#0@JO#s
zf~CE%Ll#O}wXm#;3TA{7>}zmzFgXQt6?!gYl6wVWjMt*owVs=ujp=&NbM{86uQAo-
z^mHb<5R^&oRqu(Q7Qu>}kkf?^!tVhnRKrA6#0XT{F&vA7vp7Gb!8|RD2$O)$dFg_d
z!R|FY<A=2byTZtkFabmNsPSU`^8*p(s$x8ET$_n}A<APBg*OrW(sq$g3VcM-pX`eO
zEdW(S@U(Ta-FrV;bGcVaqu8=t3IcP0U&4JR8YCI$fdH->t=Tkt7-`H+dMT-B;ytFT
z58+jix))7Hq<Kjea<@}y1u4*?dz$IRwOc;w@DY(@Ei8ZV)DYonxN-3bxe^E^n1>Hd
zq)t}abAv>Rq2-J#AUS&q2O2y%;gID|BZ(8Jpvy>!pbRQKC(Bo=Iv74gl<Bp!Np}m%
zMV_2)0VA<icnCr%zQDSW_uP9tjfhan9R*Pm$1_1v$aV7cz1v0o?Km|l$FNr#L7C%&
z$rnY+!!yiAZkrE~#DrR^Nir8Nsxa&CFpg+XZl=2xl)8ydtustiQPJrg$#@cht|1hg
zWr<W8CL(a38GZsuzST6XX~IRvCR(Bj1kYm&6=1ciil7bj%!5F-P0IRK=O)+Xn#;)r
znHl0iDjIDQTw;CgV4*U+d~NQSwF?z0)|9QyBG9=F=Z9)VX%)^z(Z}%&-@0NI@MsY6
z6;z{ujwnY#60yoI!9r$+5UNWj+2vyv<S|48Bqk7^0xGpot^s-vX}9@!3*w5J>}1z{
z@ktYMsLMH7j>}NTodJB8Cn%G!yP_I51C}nRv!ij732se+#p62+4zBV_ykg`dMLD5L
zl-TlVYzPh{Cg><|lIpd7*?9z<;vO|B*1fjQ_}*C$!IF9->-5d>^{`?(Pa)pwOW6pr
zUy;s-UJXI%stYfG6)zK0u=S$Lgu{VfcEI@xRgq*_eBEW=U^3L0FmtbIQfR}<({p?5
z-u*xiGpGdxDE0+y#S|kK!N@uJTou3}sL6vsue`);Kzni~)n0%Fo6D0d#=)vc1|kJz
z)5SX=_svIknqeZv+?sYt@i*N(c~$hGfftbSN|j_pt#wuIjM}o2@0^2FdCS(o&IZT`
zoD(;iBcmVWdJ+}jL<s(}=0a7Y>$ZKAVZ>5{XR70g&v8yKOq#%*b7{E)@y+rdGBCPt
zk)Bi=U+M8Ao-TKLj@%mx^&j2(xTP{bp{2{h47AFJ5}@`5scr#2dWP9T*=n3g%0@m$
zQpp>o>kKcV@sEa(dZo~Hmk%~u6S2tPPOmG#?JJPw3YZu<J{BL|U<1FRp;uNfADEqg
zFN^OxU5FX*$a&7r{Og1msDL)aAJQ!!D&IoKnkW0ycKU1a1n`pCRUDWu<xu@Sry<1e
zW_i71YGASU5EN=$1fCRoJS8;cU^U5d7>Ef*gliNF(ly^}p2Gq;DCmJAL4xZNqf(Z5
zd7al99X@a&q?)s(^aE^aennIfXloocO^73u_tnclJ}iC{=k@K6>z@R*D9P7qq!S+$
z|F~JGj+KGo<4Vzv@MRNNeaXCg5kyYVIW#lvi)+^jY*6g6<g)xYc3yhGdJxt8egvf<
zU`sK(Sfyro5_j}Nef8pp`yF3amQDt~OBR>gOf6H!4nFGfsooV9?;}jh3Bkqc^&CTS
zMszV6EEbRCmCY$$+SalTBcU}4iG>V%m-n}~FRZf|stc@S5bx!F$7DQ_1%gw{tv<Kf
zBn~9nRTzv^5p=;CxLGH8cDX8=$y`Pz;=5j46e|~C{D`kEOD{Ny!|V$Xb+qKmT5^QF
zti3DNp;m=@^+6#0@x5!*x$#sDZ#^nBNbpSJ>u3C?IfJVO6}G<oLe<*??KB1)=iZL3
z%QnktcL&}sdZbPBDxK0K33_LhP|C5fQ+NDi>G4W+HGs|rlK<#yYs{39mF8$`kcd@3
zq`z!gz0>>^)X1ouOn}-N7qm>8^?Y~s-@?<jHOnEMT{_gyo>&}jn%Jlq!wRBq$15yM
zamaO;CuN%fU1-fg0*>v)siyQa3GlY&j=IPqx=Gm55$9<vV+}$#s6u58n;bHx(fwoQ
zinb>ySKjteEw-}e<~&t;D0fH=IL}a@AB;l>6puI)&mye8nw8hi1j>t}Gv$9El|T)l
zwQ|(50XCE}>9`y@@d9fc>D9EMq%je&FE<)ro+e}lm7{Z>4~~QISf6*M$0CbVVNSfj
zY!qLMP_{kA*8esG^2u}%sbr29z~j9%0QIWkqiA)xo?uVU2?tAidqNN(6qaGEvYCXZ
zinUcM%3zW!@#d98uY##u3Cut;$o32)6pBESam-mbb5EGpRz?sdV!A7yij}g-@Wj_8
zG0an}ZRe8Bttohr8LuWHj`orf@FheRsp95~56J>d*PJ~8G-S(_TJ2|Yxx}Pfzs}h)
z<?(43VX$jt^g!f(m#F;5LgF4E1U}?cI==YLTfayZT3D^LlqryfUzSgRF?jVgcoEns
zL2tK{%{=liaNFW)xzzjR=joqv97*=pX5|Gnm~+0qWhZ<XOcbO>oGYG}mk#C+D3JIv
zfUMgj47%e>u+)j^F)$s6sE@dc&%z<flF7N2eJ3daR!poMIgRrQ5NGOwjj~kz%&uHT
zMw+!_XY2Lk6O-}yO`2yWJ{aWf>~}2)q}i-^qWEzFTMQBR9~BI=gOwrN3ayMzq_VPJ
z&>dCZO8mD$e4QBy>eA8~&ztMW&I_rB@$%t8r$feE<iDO(HF8^L&Ey*6ll|Fgf%v*_
z12Xz*W_E5Ds~$a(yPd-z5v+n-X~hR$CBq+B<Ag$Lk=<tKuXg>suI4!ZN-{kY>QD$b
z5B1RtS>Tc_?XMfG9(BV6V%j8Pt6aAX>}LgGBbv>W3qEqzMMjP!0~xTqC#XX8tfj-W
zJ}P?1da<Q*Gaq;x!q9&YLRmOMM8n4_sMq4Tk+>6cy_gHNGO;+LhMTq-3=$>*1D7Q{
zf-@Z)*<vi5aA;f=Ls@Jr+Pwi2fJnk2T^+{L4D*Pd@^va(3A$>&ZS2}eJ@6BxKoY}i
z9ZKAjo>wH@nT`X`KjyvSCJ!{T<V#LMd--vakYdTWFXia>guNnRn;~OmSY$ey<-8p$
z;kBacJsgrzy<BdXrWq<MfPyJ07Eh(dS(6B~0FfUvd}3595GSB(ARN3@u$?tJ@O_?U
zbyd1>Gw58uxvgbT4SzMQG^-}!W_PfQvsRJu2=@iqje1;f0jsMuu0{^$t6v-Q(Q^0O
zaOs4VEvWt^Iph_x0*Iu|P7vm)6lz{FzCDYuU9Dl>uh~hbxM+X_Bi{=`8OfP=eH1ji
zTKigAGB$V`d2L%c0fP<+Ppl?>Rd%w*<SZj_A>z?Xu*A+OhrHjXriP%of{`p7D@;HH
ze-(-I0A<diz@a$?MrJ6!?;0iP@(fVyG!BJz(jg1w{Rt_+;_>*<;+=tbd3<q|?;?M>
zrcZT<oFU_T3J!(y8`zn~KlY=QKDska;4^!AZA%efUWfZqtX3{x4@;_!4b8?te1N(3
zC}EVgh9il{w(_ab74lU|CVE<?x1JeDHkYqsbY_As=GsBk#Pw*4VAnwq#g0|YjSZ#}
zeEri2(}k%xB}s~3abS!mvbB(5J^=-usIz0>@(={$W>&RQfM<m<IeE;a<Wuyg8bSDA
zjA<^tgq*rl>swck8KE%k(hyT(A-v;FXjX5O(Dq;otT5R=dJFjc{{zQQojrla{~t7d
z@^Je9f#YZYFF3y1erL(h@3oVH8yjWFK4=cPZ(|XjXFHV*=QOx)DAb%A5>ijwQqs>0
z)$l__taVoU@mkR`KscZK)^`cb4fyr#<T1WX_#o9qvlvvw{5GZw#IA2UtA=dk#elom
zZG&wjBzsBEI5^ES+1;q`36i^Tf;;>}<as61oVSgy?9bLs+La(JYjMkYd8O<zgY)4D
zPLgxqw9l)G!%nmpA2Y@qn(HM+bPhd@AEN-{Tydw8%!?@QQ&>U(Es~F2xh@~{o<26k
zu;jcE7hY+_WPaah@kXGVbsz!*k=E8ps@!uR0Y=vBclc>!OZ8M+F<IEc(h7X!Se+XF
zm@ePa3HSR%8jHp*LlsiI%%Bn5Fl9nZk9k}2n6>lP<IeU4@cg5D)(VzwXDVc%)JY||
zsqq93j}SsnaFGPd{<Y~V-c!kG*UZ&cYKp2nTDZy_Lvm60K3AFY1ipd05tF-0nD$A>
zPCV&m=Zq@SB+WWrJWc+6;=LdIcu&9E`E>w$zmQPEmok(qeSzRzrD$)8Azql_8FLdN
zmp84sz2q?+X!Z6H1BIwj?ESnOgxj?Y?N2AE%ByPeW0tHu5Bnl*JvgIOI)85$9(-J#
zR+cy(E!Ws6=%$H%st#SBA8j28t0=0|Hg;V9dSEz>AF(_roEQRS6LyPd)gOV|rUEOK
zvPL@=HOB2>)2lboKKIq%s64cO(K2n}=fI?@Z%Cs*g<z@jSkqjkBN{j5Ahd|G!9}UT
zI1X5Q)}iTnM#LK_j9?ztTvD>YNO|N%q6#58y&L(+a?P($sVv2BbkY*y>c9%SN%5)I
zy`ouUt>@4>;;6E#F{2yc1NbBb6wa>V&{cz|D|Vz+=lQP_>jAGI5sq%Re+2>3^OXY3
z%+Cy%&tMPgLil2U#o@vif^>xxs>N;Z?4?@o>k3q^nTk1;XlGkuW4=t*Epb@}Z__!`
zmBlnB>r<WvvsQc~FK5g7m8n%f884fCgpAGY2+~GG8n!g|#O2|Y0A(;FV%-5!+(uc&
z-_jN!@n*D&1YEMS9KiLtnD><i$5nz^JxhiIZTE85DD#H&6giTr<dp_kH~ESHx#s3o
zu%)*p^rrkiL(!oVr}VJ}KW6L*RYSKr`)f)S_-@LL5$Dp~L<TGG2DpT(dBK3#4qJl3
zys`u%`j4C3y$fQTSJ5BKCuJ%N$(p=RLLJ5MwFc5?N=2BXzEVlJ&FCQ*lACFkqMB&U
zTUoAXG{q6(IJ_+Op4u~`xt8~}lHED&QG0^1Fqfz7;LYBzEfN_?*leZrxT0>6unKb{
z<L!LRNHn_By*L*Fh(4TDaRU3_2h0zd6>e*!A&vU+g2@JxSG0@^V|Ze=&qA$=b;aa6
zf5!>qlxQqd0=3#Dly`{7wWQf^@(hsn%?m7H91pKG?$kXUd}y@(#@I<N(^ARSGv5|z
z4i(+{fGmmbfq2`s845!XTXqQ!6O~g;v330k_D&s8=RgeR%aTkeiM45oB<vaS6AA}@
zZE{d82yI+Vux&Aei!a8i%;M+wH|w-;uZAFEn69yCKWhDr+%lQ*A)=sI%4y?lOuuUC
z@L)5`!YFT!s2&Oa<d|EC9UfQEZJyDsH0M@gpx#(4DdIdw(Y6?MHXV>y)XH4<aVTG6
zPOJOMIZp}O{csZmin@8fu@>@8tcw0mL$#xp>}8)H*Iy5Xs;PJuEGH*pshjDA)-&=?
z=<zYw6G@a)xc+|NY<axsbk+@Qt4agr_>~Q2)~74E?%Zr0r{T7Ba4gdIYv(Op_w&Z^
zbv%ohJsdW?2eGXKG&7Lggw`TXZk226O%{R)0RsC2-<IJH3_p^pFSn_cPQ7IiOnj&S
zFWgmyu!yKCXpLS1(~~wnQh=qO!gC#3zi*t$DzzK1+z<>d_A5#J<S!G&72>Cm(vv8?
zW~ff$>T84qv&#cSx2;ujjf}2-yVH<V%$`x|aW<dyB{bB?JOQd@dfr#n4-il<9ZqbU
z)~T=%g5ZTTbyi&;WT(^H&Pv(rip-=0)~G5LxB=XF2uH)iw5drr)ynr#z8-iR_yGXD
z8mQR1>Cl<`+WMCBh_kmjddqUP%V0*y9-s5g;hv9!x7*pw$>g#}4*L=;aqG+w|KX{)
z029rq!fvc@*2~onCN#OaS|sKZ#({|*V&UME@11?@wp8!(b*J3AIbvnkS4(IJEvPqk
z(xUAdyuox#fPkhf6ynYtvN|gyKtsY-sMgByNdP#%(v@_(ZM{XmvdTjBRo1c91A+aX
zi(wU}I1bHGC6?~5-##jk#lA2H*CC>Vp^{>Mw!M3vi@8|f%_n2T`P`DU5rMHt@F7<d
zl1<_@$#e4%7OjA4)E*>D4dhh|E2UivK5WWk6SiSJ214KGGezc89!pPk^?3mGDu!xt
zJVh4Qv?cV%^FU&&?MhS5o8-<577}z9^RXvD+t}UgTDnR&M;UN^yiM&Rl3qS;ROwR&
zQMiK3z3NX~dxDvVE2ehM$3d^)j6QCsck$?uYXkhis;+k7xtqdQ&i`ZoN;3Uf;Q)R$
zyEHkw^gWBeJ2`C9p+BuF_ldm+HQ#FS4k7QO>ws1d2=`$^1?b{k?UHh5v98Y?+`Ktc
zWRWH=jcWjqD~D%vG`ljD&q4@C71Ef<o%=cm<EKADW7KDKeQqw@O)MS+NxfExtTpFN
z%0tChndJm|I^0?Ru=ms>MkX|FqbjpGU!hb1U}2vU9^82C_uRP&&ii&%3#PWvbeH*g
zmh>$No3C*ahIb;m+q8sco9~Co+NR5H6ZHujb#+q;yy%PJdcqtv+|`Nnq6$650BLB3
zE?n-*!TNI63~Xqv)mH0t(SkO(Ee)y^T(5eQxuVKrCGzA^Lm?V_(+PZFb$aZLdk9*7
zqNJtvmLifg8RK$F4^y9q2fq8%hG0)t=Gr<GXJXu@Xo1^u#M3;!UpH~Ct+@XKctD50
zoS5|LbCt7~&(h|BPC8T14w`bt#Mv3^n!O2-q|lo%-HBmaXOB%}s&11>!=i<sK9#T;
z;nP`OcVmEzoo>@ar<CgLL}RIMQY=PGp&a`>^o3;!tThvzM5dZ)%}%k)(?T;cttffe
z3QMJvt4VU{p`#9K*E?4kZk6U__j5v1eD>{`+{SmcEVc=P#g^pS%H0V}Q%%qcUBf7<
z!ZjMMeR-cl2y;|rs;PO~NR1htwz1zURxBOqId3Hb2|2E`Z!zas^jpV9h|cyo^$U<-
znH4oW-K_$e6$5x(0y9~3#;HZ&wHa(myxS6s(23Ek?le>xO}uVxw{sRoIu50{r7KM)
zff8BGt;Nj=$|(xgTeJzl#x>2Wwk-MjuPdoflYI!(mPswFwKPgt^4ZH|K|0<U>!{_k
z9JIK*mzJ@W>PE;y&tokfc7mdbi>WIPlmcdSHca8ZfU!CZC!#Uv&9a(JtkGA^X0(o8
zn#Wd$HO|)Nc^w54H_I=fq?69-begbEaNYCtDpN*nm7b0o&!GSTH5N<DR?P6;nGv@)
zMtVMOmXj7QNu1vGG_zGyDAH;q)kZg?qAMxWldjSdD<!n(!5P!6H=0SIHl()^Jp&K|
zWYoTHiw<3MN;Jzl-wurO=@2!Y#i`3p6+G(>h8XNl8kaB6@3^&%W77Hb(PZdXnvLuX
zb(Y>*E6vIthIY}MR_Fi#Q%K_MrgC+N$5WqbFE>t81Q2PO(NmIVWC4SEI8n7{raDet
z51nr0jV5a=j)APz=e*;*+))OykXnrEX|O<sD7eF=xzAvR87*z|?g^$WwZWRj8Z2$$
zeG=zAa*l#IwB6?E<973nh$4qsUS`ZW`ZLlUNfwB<lB;nBqW}{UhdFd!c<o{`)J7dI
zEdT^8^ya!I?YM%`d97Yp^tk7)hcl^(uyb^gQ#XwoK5mCbXxE!_Jz+-CQ990vR8s(A
z7%4J0#<1&RnYhnb%c^t{BNS=PtmztQ4JA)JuV9sy>zQn)dO5FkJl%?0iO*M43u=&?
z#_8uo+9A<OvRW_`00JICt>dUE9MhMb*d~~g%Ut!C*5xrGXLLq$)i}oZzL4bT(JpSZ
zoXurxOrJkhs#!dGrQ=H5Nm(}X%b|@XFas?z&wDufEFh(NMrmjQV3sR<w~Y7CJ+Bs;
zjV#KeWVtt80Qt1gT>T`lQd*Uj@0BvSdXTZ&@wshUV&~~#O9rAHJZnoa)N31YzL7DO
zwg7<Vdd*flC{wO_7;lT;CWk!RZ(wq3qnOnyM49Uru%4x&i^RLiHmD}tQBgVcFcTvz
z`!dnSTG}SiW2qb2FsDQSnu*d@m~R%MH69z0b5)M38rg|+R`#Z(V5K8T#x=bNyf>0#
zWuBQ^nkpP?NDT7Q=SX*&ortC?=H1;fPEA@&1re*uT=a`F)+~ocS36_0;iilu2faLW
z1mu|=wHa?@&o5+L<<pv?;-Qk`aX5#gm2*i(k*7YIiDA8S^s=WlIs*h?Y_iuq6|C)N
zdgo4JIyrimKSXLM%6ic@fC!0uCb_oQQI7F&RA|t|l)2o>Zjnl9X$}dp(=w}gYVxZ>
zRsbxs>7G99^o!1~Jau(Nb6&iCQ=aoeBM!nc)q)3{TG6aysgXM7%(SG*qy)K(x<x4%
z4lkcsy%J`6JnVvii)<3+y<aa;BCK~<u8g&#2<gGKv6a!D9W-L~Oc4`G=}c$QaT>sq
zdOWF&g__qy)Y7F%25&8esKu7VQ3~dojX?)Ct;M*xm!T1~W32?&9_K+3vd<ohdB)8q
zflW~=alM&YnWw0eCqZV4vXlywq$XpcE{Un7HfL0taZG5&s<9raW6<GexfV`ZX@gr2
zI(+5qOwcr&foToplHJ>!b%S7wkZ(M-E-A}SDv*aWMC>G9dGr|7p@w>)JWW(BD2dum
zZL0CIv2m_?P^%iwS7Lcv32s`8W^{8cuS`~$YgA)HM9HfdsvOa3X&K|xOSGHDdQ>eY
zfLU)eur`WS8s!}cXRbOySB{v|)|3#h9Q#8ZlR(p$w)0iV(JyXoo;pp+RV}h^+L@(O
zQJ^9Wx+RX2GJ2UBA-6D9EGtmMLgyWrlxc?P<)(H<rA)17kF<Dh)0X0;SSfft`E{~V
zt3ee*#oMJcqSMc56{+XlXyj_4(UmqZ40WZ+)Z9bQrE_#NOlg+WQJ$mY?i!eKwksWp
zqyVz%QBf{6#a+f56b@;#3ecp~f(uPjl*Y{T$7r$h>q4Lmo~x6s2r|~W_o<R?QJZ>9
zEm{^&Om#$P6sSXrq9xS2Ax1q6Y6m8`YG`Gw!wA*Rh*D-<R&|!RbwlUflTD>FqT}u}
zX{&hX+`QG&Fy7V=W`&Ze){cIY4WueWod$&SJB6P0HB%v;)05~WT1=Npgwim+e#VK@
zEmRdsXlV{FpQO&^Jtj(W(G^f>p1I}F27JAXpQ6rgwU(k(r#$qb&XJamE;SI~s8?8R
zLTSlKrE^5lj2$AZY1FkFB-(N#7rK{~EsUyw&Z;XdsZMxyHnZaHm0CP?acC-3RXoVr
zuy>lON`h}Iqn~Sr(Bk6eo_fniI$c0>-l(~oCV+6v-m*+Y_0Bp_ITbOEXPdE7jxM6-
zRfj{1nvfE*Xq$PF&DH{>rPV$z#}lHtkyL4}7M$gkEwv{jY_}0N6apC;?t(Mbu0HZr
z3N~T`iu#jNfw4N7Y9=N%F{2DED@t^sCm|Pe!|2P+trHYoX^jEQx994jdFxo!q&T)n
z<-?mnntEZ<iKQ2&vyFx;bO_+>m9o!SbRsO3W2lSENl{eTpr<amI>(b%(?Zb7=tfn~
z*3rwO)eMa$g)|k*zGji6O_^%t&0(!=FsO{Tphp)|R%58F2(lw-s(C;(;_X9IO^Ys=
zr7h>6s%(mA7e1O%IK5b$l)()u=Dl*!UP`K45_xLMQ$%B~qDLQ1&XX1cXlX``O<ZLm
zk6c)qS1z;X>{XM|OL^#-G{9MtYGBgF#}wkz5zDTo8eA%BlQR_Rm9!brszwid^nj$s
z0#l%~T?81ovqub<Iy|xK9&VhmQmp2Qb42CST)NJ!#2Tp8RJ0u1Tgu$F^R^PEbjwF@
z)>LmP<ZYISK|oP5v=mj^$}p`n=#*4xmdh#9(Qxv+%SgCdRIpRY*?e7bRGNm|+h|KG
zEoGwzH0BK=X_2x=JJQ&5S4KM%H#9LgRD(8kE@@fx6gekJW^(GP(o6t~ZdehP&xgJh
zw2O&aw8V4t#gmqqU8_+^6E|tpm_72?ob&4E=u16gKof^H5jV3&aPrh~O0v~s8tj3&
zq?jhq1B}(-*Or4Ohl!;U$iu2MPYR}`=#q+gT{*dO-g^>dzB?l=rkEjTqe`N*9CAH@
zG}58-tuEzSOsQ2iAYqOw>W4!ZMds`bXP{IreaJK%=a)dT*i_U~sL))yy#qZ2#->Kq
zjnG2E?g3MagA<{GYKdxy&=3wVt4l*Ikxb6THqK+IWvf^_Y)&6O=$p8vV|aDrS}t|2
zpqgN!pdD%qS?Y1@(h8MlHkj>VbdyS(%RmMwo=(fhbv8|>D&^&(=IWv<2rFq!n!{PB
zdP}1nxq4zZT$iqaG>Vc^n+jE_k~E3Sbv7m$IC?p>nF~f|sDK3U)QYs(3>qg)Om(!h
z&a{|XHiI1p3u#nizb4iy<GnT%+=Gyb(TZ*BIMs7%wIryb2`FVDA=5)l0R|hnY+@6a
zNl}b+2Q^%D3TX<QJoV??YK~s4^V32&^w(D@6w*ZHxeBX3ij2(ljq{{UylV2fwneJW
zohCzI0n5>zvu;N$R5eO5+|B5+(#WQSqUT&oq~8})!E36|8raV0qG6LoXE&yEwqdq$
zs!Ec+eH)y2ZYdU-HYC6pPd`eb60Hiv7_Ai*F{2`wns5ndnI*n-UFDYh=Z8Rq#~J7n
zuFUQ#kesWRYZ{@&p{fYc#ee{eb;3Ig=wO{oCUwDVDoS(|SS4a6V_2)lN?AP!nA2(H
z>H?NFWN6H{Rn5_!gie=@su`AzX!FXP(}wCa6F`|2%Y9OTg9H&4o{BWl5f43BrGsZa
zfaTiGH*=HJF}f&Oi5pF0du1BuptIQwLNVOZmML7*lE|?zLrBft2pSTKre|eL-l2@m
zTL~&DRw}aJEofP4Nryu@`g65SV)4^t;ndEstj|Sr7FK-SZ&1o+(8kt!pkSJK=5+u=
zlBo%-^l8o29Q8Kfxe-lcZZ*czOJJDMR$E29s<ScK#*<32vq5KVXQaC4%dVP{#lxjT
zi$ei0!##Eg&FHN~IxC+>5>|vr$pOtKvDXcv)v3nyOAXaV%7~>3FG#HFmRmV)DWG+q
zHk#Vb+RteQ+G11y#T?piAl1XEWfo3!lwPb@*(@I}l}2}#*tfBUm}{K9($8~K8(E}V
zNvWM&=!G>ljymbNyzNT1N~bmhLnVu!KBmDrU@|nk(+*v9E*laVimMTps!fQji;Q%t
z(UkDAG99#wob^<!*OtNYM_sw0xfywAo*RjSK!=mDh<Iw&T2xgal3faIs!?fS(DHg_
z^vKpDQw_77Xu;{L$QF2Os;L~tteq|8C1uRJ!bNo{uo;HnFp^a~HfpAkD$ag{Fa?&V
zxQ!E{D4EdBYYocMDa76nQj*TJn^T?|>lU478q}J*=L+YHy#`1#JqG1b8VQi%=`#&n
z(y6GXa&=*)%^}66o_01!oYE$=c*d8>)bXq4THR-FnB6(7u<2B%MLI<-6=6#Ao_@M*
z$x@b#L6webfiO8bYH2y7nvFU2WL1Ef4wWQLlHNMw)j8+6&(J|w$3SPZ%Ai+K&$Dr!
z@XA_E!J_7&ZBhdmF$)XaNq`BQJ(LpA4gz(GV5akR29aYza?Cn8dTDd?9KA%+jQ49j
zNVgeb)=bus49{z<ba6nTtD6K=*rm6I%m4rb%=DURsiDFuDQJN-rkZRMQo<{reO4U{
zld7S-@ac1&^5~Ltnu^j{1?chAnnY&nOm(#uR9w)E^k4u0M;~z06t+xRp7B65vz>u}
z0-BpS`WcJYnzaWU9lK(;Lj^T4&#mTF;k&X`F=SNevAskT(+ur^13=d~xJ#&Qq@`(5
zO*AaI`-yBC&6gTPJxtY$4c&5Q)uu-3ED#foIM+8Cl=F=)XD6$5om8~S6Ef5Q00DCA
ztvDJ(PaQJoT>Svj%Pn+hrh;5+7lyH^5wajO!fi$B)d&#bVW}Hoti3aKPI7dl#vu;G
zMX&$>1;^D|C;~0ZA3s5rQevKNn?U7&Lo~|cP{VOeOibBxtd5Y;GZ^&TtU6q3r9xR<
z+nr{lX=nx;fB+0nJ|6nCMATIY8E$7ACY~AfQkE(unbD*;)H2l*NWpJ8og|HbjQRFv
zrWdT#mCr=Rwg{-ul7Z)-FhBqtole@!dDs!1>ad=cXL(M{O(tclglh?E5p!8FnQ182
zrK$v)=jaC!Wwf;BvofxpbS9HwdDt)z02t{_lO$?DW6&7SKqEQbh$753tZsBsRLt~3
z!)$fSeAM&<1en~DPcWrvX)`MwHU!l}RGV=?3<Lu))rfLssGMQbQ=Xz~9BSK2SuV5E
z9pzK07mlYkqsyUlZ*5Wp6$z9jmc?3pwNr+>8M*|R(-klP5@2)m0$^ugVOq5on6T)F
zZb1s6#*=1kYG<TT5vu3uc<Vq3gS;};g+y#v%qF6Cq=yV`Ii$1!FbwLHbM-Mch_R@X
zV>(Dtv{MNvnwJinGgVmcG)Fe~%h>ssOG&ntf`|&X;+PzhG^=q5I}%}@qd>$7h9<z6
z6KKmx$EFGi&eO0EL#LeGwDNGxqBSNb4)uiTrOi~+1(LX{qG$!eX{J0KyRxNN1XeqP
zXkdY^eW(H{%|lfsCIQRQQ$Uq0?#W9)BRz1f9BQH=bh*Qz(t(08&p>XQOcqgfjybNa
zTAG>(y!05r5X5tIF)B^V&Y+g=gw_{Lf?}I!nYszbNz0=e)~7Wa)N^);+W}^yNv1{H
z5MoW|&DA-Z+9k0(?wAY^(D?emLkNqwiqTCpNK~gp-WxOvEyF5KC!?9I;~FmGpRy6s
z#LP*io|01l!W_<Bvo$7&Ln|F^lQak#IXb{`LRoAq?&VXQjS?P-kDq3N^6he`Oz#`f
z#YPn4T%9_tt0i@j8Zz2o0fb4fFIcFEtpTDm)is#V&@L|E9&T*UK$7z+j!Ni(mZp^G
z0+`a1CoMA4+^n(cu3m^9%5|)5%8AiESeTe$0<M0lPdw;t%crn3LI#?iF7|~qsZN0)
zXH;agg{szN)UZ<%xRuTV>1u19wR}C;IqHR@h9V0M9I#|AdZ@hXXnDM~=jl>b)&NQq
z0&(0B!4slz!dA15Yco!ga;%n2>l90zH_;jC!_!YKveI-)fr85DcEAaUX*-O1Ry$)l
z=e~L)YCr=}fLZ7<GF@k2AXBG2?@2kULXw!E(2AFhJ8l*llF8Vbr9hdTGIgX4WfY~z
zWu+~1zFjq?S~oGMfO7=IZo+61n{!Ev3(oYSD2p6Z0X%k=EVAfxbh!&O%UrsYRYigf
z?W0!0VkYESYnr98p1G}3y+~C|5M!Gu&<TlB1yezeRi%zgmgKYoQL1Tk>c>ODC9G@7
z-4j-rOCp+jGAPbtS(?vWZ!IX#M~6;j)K;RQ0h(rZ)0#*@WY8y$dR-Fekyw}s6o#<U
zF5^efW<5Mx$3X_OLK@2HJsy1BH=L`^Y|{&hrzK^y3rdjDfCA0ovlM6~MyME>&7Wdw
zX6_=iLjhr}3XWZcrW?F8S>8<DBS^3XmA0+t$Jc31=E>4ldaGGlskGRwtpEiNmv%A3
z1i)Y{jS8Hi_9nyvYBop=4WnGC<*a3?mF+HcN{|@p!CZ56wv)?6EHLOB=IA-@iY4k;
z00QUHiB#5f!B7Okby!z5c9PX51jd7!T;|#B4HY=-VO<LyY)u2hS|le9nkH`>XiF`v
z7;d6<Y+74G5CrpXIZXs;#J~y5K8nLgTc9gDav9sGsS%e%UOLjk-WqIUsMQk8^+L~B
z%c7E-#x$a3&6kt4mqan_05Jf#`h_ft8zzv%X0qQ*D^$r~oKsnB#NxT^)^8eb3eyL<
zC6j5(<m&~VxvhgWrWZpoFD*3Ab%?u?>d~I9P=&?RB;|obSOGH_?^3m?tgX4JWaG17
zJc5eNJIh6utYs5O4qmhw>6KXIaW<(&T`)?aQl_aCWvxwEv(HwLB-Q6>w;(KLI+L^}
zJnRJ&#+p#-jHa$N=Z=Wc#-=+VtTz+SUTv%(saG`-8z~hCHI`hBx`Z-$`h*~gpmRW&
zj(F2)G*3GmP!kr$F=Y|hv9!zK=+m2eS{UqxvD`v@y-XC`<EcvnqbaPmL~C%J=Tyk1
z0>h$W-etN`LjXg;ZgcCRBSe!;Ii$2qT-6;l$JaNVWNpLIs<n>bPcK*?;_hHgfB(b)
zI}iW@0s#a81ONmD1pxp70SN*F0R#XA5fT$25ELRY6(cf11|TFeQ6)4&VR1D>Fj5vo
zCPh;=Vq^c>00;pA009UA9R=IZBG8M341NG#bNEk9U&}oT(-|@)qUt}V14g(k4$o>-
z?<(GV2C>XDzgSpfAi6erfhz5DsJKQIHw11~3e_h=MuOVyMb?=<R#@9M>otl82tbhH
zVhun;5j$nrjt3K|Zw3LMSTWbRPKK}Q=15t_n`USGgfOqwIU9N@M`56_m=ubZI)w7L
z>57VBRxO?6%a@EVKS#)9CoCHE7+WCDC<j{fvf?)wY?z(5HcXV{d70gXzX-|BN7Oi|
z`HW;n@I!#6I(7<LO2hLl?5Ow#(4`#4pi{yyp6Qq=RcBe5l*(EJLSD+p7?~HYpY~sP
zEP{I?#DVTD)ITv~Hf%l2R0k3uDZnWj?Ti`1jrWn%{{UTF*cGS2MQU!dKN_?bP$wsp
zl*o~a&#{_?r;|3ENk}zi1TzIAgkS0k;pc>elng)?exryP-R^;lY%rn$Klhd3Ws}c;
zy#D~X_0@Q)$%xSLPPAp+iq}-=tQD-$Jv(Z!_@C5KDH)0{oYTxb@a?YdE3bp%eLwF3
za!NCa2&5dcNAlpYoH(qb3V)|i;YS~iM9g^4gyhE4lsveiCVwU;9}{mzDHY5TQH7NP
z30J2ys%FGJPwZa@5~y1aaDvq<I?kkGo3*<i4Si*U?^TJhYin#dvlCWSYnh$rdcn<-
zQfDSbGavSV1{p?Cu~Z)mwPvd-H;W8MdE1xxXOfxCJBo$PG~Z$v$%Zl2k70Dl3W$Oj
z&&vwBHz0=Dv8{^#04kBS?LPCfz%26%XUgYqr0gLXc@r9boa_seyh|y7JiWj+dm>*a
zB!7L!V@~PCfAfk#{f*hAPBze}d0e7Fqnf31b1TeV1Xcj7Dzc&YjPa9^4HP6R1Wl^r
zYV+Z9P}4KsY#H2Eu<dHs8i;h+=*I6A>gJC>(3s(fmeG`Ir8gzH*+{G~7~)NC;1&c~
z(!I*-Ojvr^nx%P{MWO!ytVLC_pAfLKQ#ucAqH}Ce@?>rjZBv#hj!s-*Pc}57OL&OQ
zGG!-dr3E|j#*YJ-$%u~_s2JqJ($8m<9mPT{up{elO)f?i0CV<Ojy<Evyt`WX%{F0d
zbLLuVbeiCj(kX42j-^OCorrH7W}Re~qhLSVLpqi2QU=*#6*ZO2lH@UhLo#XM4OaSE
zUxpkcVt(=n$9=Sjn5_jP=QSXPQ)JBv{;?USSrBv7dZh+FMe5Vi#4w4Y`h1^*!Sfhn
z+^HPZLy|=E_P{Q)-zX1TRtzO|F8M@Y{l+kS3Hp6TL~k*+oM0D}XALGZypqW8W?l{t
z?8u$50nWX$%UpQZ;*sp+Fq>Nkw|l4%fiZDE9kcIkm8Vyx0R2M#rt3w*3L;@!OPV8)
z^&gNdl8Et;TW`IJ33k#YQIfu_uTs+C`@^jZU>m0~mTXIyc1=yNIG#`)!rSe6ZrO7S
z6=8tM-nbHKC1x=ma<C=#4kqlE0wJJ$siu7`2K-|$A3G<59H(rUlbDFo$F9AHN$>2S
z^ZxQT=LhNLc8so%YNH$t%FT7-CP<9%f)S=vWc|)<`((s%$4tvgWpg#9Y?7HNnUs;P
zsBkt3Lj=kuFd`)43TJXDkNeLYXBGy+jurISYiG6u5j;dg-HBtSodTJcTYJNZ-D@03
zh0c`3^~;~Chrx9tR@8p6_S<byEa5Sme%T&pY=P}?jvn2Gu$hiEN7kvIxXH^ZYo34z
z)A&@br!17A;5^syms`~qp%7M~BulE?)L9H-$Z!TqM`@mSH^i4aDx8@>l+4Fg#fx%R
z_byiBBdY8(U2#^MZOriIia^>+=kna*26ozi@Aky6#Ju%Q{r!lV@%=qmi|&BdD(*2g
z9Sn#lXG;aqDYI*fCSsB!DDO5=5v&Y4tmkDIE#s8}-+1=Caqe2{rcwImd5;`~q`x6?
zk(?}9r|vWFKlXAb&e5i_M(XBmd}&yS(OL$BOjeDM1Cu}9jHnPGF_fOtZTXtC?A$NP
zT`Kf;le=%ARB>ZcHLWB%b85c&34N7&7+E7Mugvz}dCHJcUi)AMOX^aZB%*Db=I!7@
zB2FVKHGGI3`o@k7nE4lgRaB2F7aqdXD|5>sC0d)YT1~5G1Yu)M(n>*hhHf7;Pmi$k
z2N>w*T@Hwz1{er7`CQ6tahOvk2%&{pir9`CRE&5TcMs*na`BKLHHB~zCW}cL<@%L1
z8(_{W#27<KA#7B3f-scQGA!#kJT;p*GIYT(N_jV8)Qg+!xo$O?0&XbS6=jNgTN=7s
zI*aEZhRg~JO-!P*ptT?P*UE3{)nr1gXkMU+!zp77r$;d_by1b)i70~nGuul>5W5hN
zQ&g(->@PWY)riQS6B!W_TTamn-zqc|=#zbNt!aT9$N`K_`BgH->J61|$aXm7P*MtG
zPk=s0!osw`*jD<W7r2S_ug!KS0N);~)?)%@4mQ*dX30i!^Sq4oX@{1+E+~wpTr01>
z+1z^>@wa1?EDyDj5jnXWk8OzTMLCMkAhfZ>^j>!fvs$%MuMD+78!&117j~H$-+iW>
zZB#=NT%*LmwhePhbNGsb64DDC@k|S7j<vLw@)q|;VpkVuf?<PQz`%wK?mIFsAXo*(
zwapHYGV?PyCByLMPN_SgvmTQX)jQ$D#~s7(1B_Q>%uL2i{QNJ4nKU_qOEL+oGx_lo
zJ2$L{lox~jrz^dvFM^VtQ^Jg^Xko=nVZEJ=XZIPLpk^{)V~*bwln6fi*7=c}V6+3K
zrI4`(b2m`2*hf`FlnHzA21=oC><Ku6iQHn%B(^rms)zy+{1*Igi_u^I06EdF#O<3*
z0>=mbQ<Uk-ZyVy?{Zk@=;DnBE`{O6bWA|l-Y(v0jD@03i)9rM^h_pR4geD^@gxF{z
z3M6JmboyjQGmc_L&p;x7`Y+GhCOg+FpAo)z&)y>iEPGi1=1YuY(Tj9I#y@E?^caaD
ziJ#svNo%LzI%LM&f&!+(si;<YNjWvD4<cr<aHEI}y3^wmQc{Lnb*tv6U$!DMo=5)x
zlf0Jl6T1j7qClibAliIKk}5|S30R2~^>~>W3Hrz_i<TDC<|t`Rx$`j<qILu1L@|tT
zSKLANTE-mY{@D!WM{KqME%=bg%#HwYmTs@Mo>uW;Q=56uCp!*Sh&w0}we08fjn`~8
zIN+45#NqN1(jm_{97w#c^PYgq)4j_%Tl*CATRAJ|6L9|kJH8Raia>8DhFn<x0RI38
z#*6}big!~%@3xp~<qlv@PFmg<PBZq+_MDjIUP~Z+Z;7^bV1OM`ICmpyiP*Lr+Dpt+
zC>x05s+<O#{Lp7+y90gCCwOZyemG(@)hKRG9<I?;Ap}N$caNP7J1s~0Wde-~yM4^q
z#sUB!qO=iNSmP;zC7kYD0YWQQ_m(y&jA(GKoEg}LaJY=HF>aOoNu|N32$>KWh_kn|
zrImF1H$1Pdrk7j+nwF(yv%q2_&gx{OreG`t2|>=BU=m?8?go@#YgIbC&8d4NI_Jj@
zIo47_F{hA}K9R((d6BBr)$*Tk7pyD0R`GOzsW>2=yv*|0E2h4dK-bWhyHI}}RwYWX
zcs5$9U0?~{O>McUVTxo4N93)9b}ZK=3D_(3q@npbr5?SZE~NG_OHpg<4M-Lkzzbz7
z;*;3yAL1@_{OiA%Rw!y}b(l)eiD~i4=bP~3h_bV)rWd*z25DJ}_!MN;T8&b<9gK!!
zeabNW_|5hijI_xn*F2saM>fqf<TukXJ4b;)n&YsXy8LFO%(_USMF8cNUlvM0+r(#n
zJx9nx8t$vS!u+G$b86zQ@xAY7-#x3*!Qt39wEF|npk+n&K|Lc|V3oGS_7^BMDnWyF
zViuXPPb1$9phJN11EF6kRx2&dO3y)^O7{nIuAFx1e85-KT%F+8W|fVFt6vJ`w6-fk
zLpydToHa0YnI%^+(;6)*Uy%5L)REj0;O`(+CuXGKMVJXe`$jgop^(K1M%?)-b!G5R
zW^oY>%E-*ssJztTRyEb1`#6{aT;z`5ZJ3#iiJvnK9t%!R-?Z-6FJos)l{tDlI_yP4
zn6j=mV`sK9cVJ|#h9DzTNMn9)HNHeE)4xQn7?A!5_%A<#{G*g;i;vW;Ca_NOC(87*
zDk3afO6O9LV~38A5?0PIT?19Pt=MV-d+fsxD$5GGmJPbrZ8%!2t^xuza6gMH*-Mos
zwNCMy?VtPPW^rTIGoA~O=H4Pb9&kQOsE6~g%W}$N<}*``lTBHv@c{+KM935nwt&A=
zEsEa`Yp!AodF}n3J0^TaI$ORnAWC93@xBav;}<+pV8;eIm<~2D{zOxgG5aQ~+))8K
zU?>7j{jxsWHn{NsnRN<#V|Azb>3iK|xwd0$(`@?+?y->e+#619pSAjgN&GW;8Ht7_
zq>pK_!5j2rcH*!dqr_kwgv`W5oWu<+FJXgVb*2I`%T-!C9d6kkn=Vo!I#}|;nA$Nc
zkOPR+xlE>UQbWAtkn!GNe=15e{g!WOe%s`DpA+{vrhuqT=Xm!%KBqC(y{G)=?UGv$
z7^SzptCmllhn^x}Ytxz4zu&tidlFHaEtxQ2mae_hd`vqUaymlB3fqF6v1R2M>ov|S
ziNJ{wK*w2pq^%NJSsP}<KV`tHOayH|b06eP)jNRJrE;7wn_0;6TlmU*d&=3Z@9YCK
zWxQR_KXS>K`<a;OlFH3&Zu4Rk4PsbTSOF&}T-noEx(Q#0?)^39%Jxo7<%UHy`NLs?
za#lY)lfp}Z@t#~~<6D$vgO{x>^l-q)#6)lOdrefB*RJbKg{gW~P%hgR^}iie8a%_E
zA15QSV|e<{A9$X@@*{ZJLc(%CSDE|Cz%8KXDKaZPFk&3`-J~SXiGENJ6ZqPomt|@E
z-R7c76)uS=-RUZwrmFS>R8-gsxyzgREEo&}16^)!ObG{iigM2vJoZ|+97cOq?op8{
zUm=@hUC5=4S^Q>nyCph%W>aAMxk|6uZ^4U%Qc%F!sH?*zfby*?lw1z_skxdq&#Pet
zWJWF6f<gZPLa>U~dS4G-qX?UgU`E3bP*cB|P;%oeysoJ!RPR18eq{riQy?`5tlp~n
zV^3wvY#TQTos2O@)^J#m0C2$HR23#8(!YmtEVd@#tn9B*wp%=A@}cC>P0JUpR#e!v
z4P%NMyF^R|N=$O<f-VpNOi9KQVqlW`^xU|YAGEh6$1Bp0YEx>>waSH6uWn3H<-O&L
zA!p2cy)^KXv=I*d5-=K_aTtRM?f_`I$;b?+?<!g8I1>{2PWG!BYxHKXvzQ!4eZ?`H
z(cKIS5tFfItdo6MArR~^3^D9|SUZwTVS0GVXS6UP#7MScOKvMHRC6H=Y;JsVH0!22
z!nB*;0+7;AtiZ!etJlL9r_o-=lUAur%)lgaBzVNhHVR{uG<V}1=l2(L`A0I|W(m;;
z9EF?hk^{UR3i{kl7gW><j4@e-I>c=#qDnHw1$k^5s-dl6IBNqKr$!VI5rJHeyCeft
zc^MSdXd;cd%I|$}lM0pz$PzfS9mc5m%x{Ipa%+tJb(f5RE&SQqR8uxt@*s)vnldHL
znahC(YD+J$oZ$~jbxq|ZmK)y$r8&xkzy$b26xo4+tgKk>z=<81Vdp!{GH1~{$@m$K
ziYF-qHqypkGqmzmLpYWcSEUp1U9%|#BiWF3cWMFb)*K&k&e{m3N_U^yeOd7lPFC6~
zTbX|tkcjc0wnR@7C`bu2o)L30FHP~|vrlN9qkO5Sw!}$gYH6#-eY{DPold$$x-Pi&
z3L^YKOEiO4FO2E$T*hti8N}@>v$+vkqdSitzA-=k%$ZL@g8=^kqIi0)x!U2wb^DMM
z>Ji4<Jr@v0F_XqnWeWx}c!KEgf^C^9A>$}LWp=oVz@FFvvc-cdCpALldDE2C=XS!W
zbs^eeVJui_k}=tg9HlUg9@}wc60wQYf-X=?huku_a9I9f#7#|}>vZsdmSzR<f!``G
zxE`pF6EU0r0F<FX5n;A$ou+e?>J|hEL>2V=F|;d3m6#mcF&fZiS5tkPDqMg0iZXo0
zOpGdbGuuxH%}?_ziv%N}cP%Nv^;}LcR5cbDxZy_TFK?+?d@Su+dN{EBiEWe5oR&<<
zOw55#=*BFUGC5`jLb2p;I{@jrYZcAbRdoQttxHj~=A5QPI0P_@#?yf_DQHA^8No{r
z=z3Gjken2hu^t-(lf+ADkr_GKV{P)hM4azkZNA%50VDqaD&n$c<+k!$q~Qm?DzG3u
z!8Y5CVvJCPYHARt!`$3B)erh-L>f&cF`Q4tW}e1Pt)o$B)}^*>GrZvb`!dT~5fdI}
zBQ-I4wFko+V6QKn=>xqsZhBRbcFC`A+C;5p(rp$rwA-IJ5wjdEx)rBdQv0jg(&?2;
zPB}UWZO8U-Umc;c3uM3{wcH&nxQ3+uOX22B%$fJ2jc&(x>Qw!sz6wW>CLwjK+KGam
zMpKJ90_7X*Q!(Q-?Uc!Jg4}FlFa=weBm^2?GuV}K1iZh~>YP<<EUXqoWMX0|epFns
z8%OOkJQawLCImR5N;%@I8yN#LW}QMVmff_(Lk)Iq&nU$I08$ne6FVr=j~(mOENS%H
zZYB$vM1B=$c9!XtdLm8;rc&vT^O_^3d8S&fk+hhP^XP(>)mCp7tA^UhZ7Ob8v9kd2
zBotX<L}n+>e0;%QqDipA#cvGLPHyh2_9AEFGZHDDJlK@pIf$&?G4>CeSE5yc{KV6w
zjg5Sw3zSJ5&OprVkbB95!GvxG#msNC8ux-VL>DGUeNx=#-BBT#k9SAoBX-jSSt0J3
zyktv_t;X1V%pNMkNXA*_Jm<i?XGjmzS0@?Jk{ykDePKB@09lPjvd-<+rLwx}iL(Cy
z%z4SUizyI;A#7!7Spbw284PE+BuH@XZ*OF2*2>ak%70Eq+_uf<H{W>6LU08!C-NyG
zk=&*<K<TXa&0a?YMCb?pWK^X#ntX|c(!o>MvhcI`gba%NY^enHZsROe&uZo>b#z=j
zDfvwLC+WBmGd?R&b(7l}rj;`msA|iIVm*X;-IWkC2LAxXwEE*BmfSmHXEN(s#K}i3
z$}{(!x5XjkJ-NZ(EJoxoytgCTF`N1-rG~Cf7()Xo2*~^N_RVaJg-t0T@^DB1j0}Vi
zBW|audzqB0{<BGVNWPn?sH&G)-b|(}`b_+G!T3=*%`fD}FD-;raWB!c`eJJYO?!nu
z902A~v29&ZRW!tc#|gh1TzpEax&Hv7wwf~{Qx@tZOjcIjTgD%e7@hK%7&%Ry1w5>q
zX4T*7rgKn|_fsSzZoLvHyZLs$G2(Wcwb;8(=U@ml`e_wb6*rBoB75t$ae*pngrX)~
zy`z|v7~K5VcaIXBP>{~~*V_<M6jrsv*liOjGVLByInK<)M8`mH&V{=NWoyk(pEas9
z_ek1K<r^OG50Au(z7X?C47a8zRNdz%POApBLb16ORsw3-mFZH;B>OC}x3#)i5h4>g
zr?M4hH*ABMJb6b)hM#W|jrW|LqipWvz7tnWwPiUE()696ZeOu>NQ+DcG3NNNeC#qt
z%*31wO!>;HYk6?@GgIMNmYrZWKN+{eXVU7bnyQkVmn{DPF#a<miC|ZuByoCDaW&aV
zXH;!f=deI!o;~Hl2bS}Rw;u(WR@x2FkZWBXVSRMvHNsQdM8aGxHyIk%*<dC#wZH*0
zH6yFQ2FYoBw`EgQdFz4P3kD>NA~mU!)ojexI14H)uTE*zAepu7xEx%93oR&<kcn0(
zW2#JbFbI?w0LgRPNMX;oOx~(y$gsFm6sDUJpyDw+<xJZIS*QVY+Nud!!ypwgfmVI9
z7&Zn|rW7znWcMvTu{<sYahwWew&xX;WFvrzy~B@ub9N@g4M*`_?^AltcCB1s5i$~H
z(GS8xL}O~kUX#UoNDrdwy{g^<gT@S+;HVL_KFcl&lPy^W+mhs<2MB^bAOhtg+0Awb
z=ED(Hmk|J~wd)1Lmooc0;Yj1-iIXCRo)d^ZRZ1ihZ8?B80Jb4ya_-hHJv34h>{Qe_
zQJs0#S0_#x8C-u8s~}bE3qoc$-z=-#RyE>L9aS>Un)^-dHEDH5cOltZY*rF;<Oomn
zbFC%D4r{1j&APsxpY0yL-~Rv+YZuBV6Mo2Z-&w5#Az)S;P-*22G*}+$)w|_#VJHaw
zaVs6+vpJ}-u)5_65ic_GWW#bNDkMlG@m7-lP#!jn@#(&7Gm@>N#fnO7$rCD_G`oDR
z9kxR*m%4<QkHDjgjgY6APu4?`Mx``}Z!s~9Z%YSwD46m4;|f5$I+aVYJG(`C%wQa-
zO%9N<a+)#&r_}rGy3yhy7OYE)VgCRaA}09Ww(?diWc~TyjKsw;7$=@wM2qGD8fBXs
z@vWt)VshjJRWjX5bp6Qv1{I3c;&*nkxCxa$)2z?tt!CNSPGjOR^@?#Od0~#<EUu?m
zaIH|9)OpJpt6DjZzr^iq$dtAiD_$}6*4qP<a|+AOHpsJVmf*<qF|kJ(^AJbiXz4wh
zljUoH022d^=56Xyu_Yo}(%*Rd&#Br(xEPdA%$CfwzVMO4jwDf$xepOFZPc^VtMplk
zgW_l;lG4`~05pnBrPl0wx~d?qp2?Xf+BR$v8^pxr0iSa-EhR&Y@r`Nzs=EmjX9x90
za`{Z*#!|m7O<Fmmd^F1^jh)zY7yOz^Ygn)`HNv_ewGjf-nszJ8PSL0su6m93qT2*L
z=gKE2-hi{gn!w5|iHfYYTDZ>B-ZR`7R)k=5%+D^Wu(n9H(X~BeVV$e1oD0ToxX3-C
zMo30uangn-QB;lc{{T~?7^2{DIpFvf8#?vDJTH%K(upcniyK`^(S}4M$P0Zln>4LX
zCrbYS=BF(9rrE+iTkbO{k>N9n{aa`M0E$N-hA|p#n|vlFzL}&9cG}l1H(^LDEtloK
zXOs--zm;9)V;@B=8ScfTo@65CgfFREenez_?UUm;NiH_x6x3VJ`_AjVX1QXHmaOnm
z>ISZGwMX1WEX^$D0(7+oeshr2G8SdVBa9(t)^P#2Y3hhiuxB<Ux_i|AiJ{pwBn>)f
zz{g_04;B(uuA?F0stj3^OeM@j@>Qt^2w4Fi)hj~H&05D&Xgd2y5-620VZ~=o(wE3@
zm7x6Hmic3k@(j;(k`!SwiLT0R?d<Co!ob%-3w){F)KbiNl2I)IaRS0}+lI+W<tq}4
zkYdYi25`u11kA)9`g2DwY7*J0!*s!K8e^85gmDd23`7;FhD^k}whZ9_a>c_M$!V0#
z?Z$Ue!kQ(Q&1!cAGidlgdD13w>yjZ-Bwti#o?BwV&m4_Ifo=-aZuPx^ZqwL`@aAv7
zkPcJG#AT)<4YEh^g(wO~h)p{Kd9t^n9vv0eV&R-vWYkHtB3wLXA$(<vg1=q&Sz5Y@
z$X&OIvh#XdSU#gESy@z9Wu*Ii4P0iG0u6%3r}MYL;D3_K^vaXSG9UPDdg2S24#~w4
zn8e`y^3Mgl3&_?dFt<5_I7+a4#zhuylclO>$RQP0)(0-it%?5tyxY>bNg3s0?WWrD
z6_ZO&4|{Io?#Po$L}y@z5SJ-3j?$Uq<9NL@D5WxvD+M<!M2{T%n808Pb`3TB`&wxu
zWRrn6ra48Bx(wPtSp~j-;ZAbNfGy)JX;qubV06N+i%=2G9>^xTB-taDqI-JrBMxfS
z8p!dtRZ2%>!sW%rIYCt}0oGslmLua6f^sv;0%NjvvK-qRu`ot5Pb$d>+@@}C9+CGO
zgvcr9AKRrd39i?d5MGZZ5i%7kS4;>(mU27n)8n0R9JAdhN9N6B5Lm57#fJ0CQ@hI|
zc8@En$MWFPP>)h}`pERH*9vi&-*^82ihNFOpIC*LI<t(KHW6gb{%8E+cG<p_bk@8(
z3awP2caC%SHgzi1!^Qhg2Jy!ia|xcG`M{*6AeK=+e!?<b181sV5H?+vg3iR%dvBjR
z3ifI>b$hzp<nUVei#l-$=u2hB^Cj)*iq5orS4W^sk_{fW6zBcqM%`(Gi4rK7*jF>3
zwg(^6G-gw5W>7G_7)ECsr@G}}a0dK`U@n?=o0?=m(5D#igKZhhWiEE}DVZKa*3PA#
zY5+GVgH#l)gn{BxuyPK~_&(t>#7h*OOJpcqJG@4{bPP#v=O!b68TFKr0am4f)i#T|
zY~;rVlw~sBnkO|;>eO8^C)km8SS{#LjMJuHR{dEY&43Ki8xmK*wBP<^SjJBzv?df?
zu&k1ga}o}T2QX%5#?fC*w`4v6S4yK0Z+^FBjGmd?_cB<^b_jPE_=4ZqhXseO7o=6m
zMX3RtR-bXxz7u~Y6uH73t1mdtEnUtPfjMfkJzRi{rho2EW_aZigoU@_xsV(Ttv?3A
z42<!Sp1vkEioDlQI?WSHy2H2IDYeuAERGPu=24ro;#s0Ou2J^N-xuMFl<{bxOOS#M
ztvijvd^K=P0#YoMQY?7QDGN2U<R8B$A#`mpc?~c|o@8aKIYmuThN=d#kXVdRBoW5h
z<Mu;}s^ElrwQE2Fs1zN??V~sP8<1Y}wM~eZzIM&l=JT3jMQ50o%*eCOY5RLSWk2_Y
zAG}85Btw(5Z<Hyv>v*nWDy8Q+_lO8%(i35v?b&?siH{XxN|nHkRKTuGE{c2Z*IqKr
zGS!yh2=e&X8DSc+?iW}tERHPPys$}!*d%coUYi~xyO;ybNYl}>-ZMe)rozU!SogMf
zeJgzBnK`9t<=D49uVd{TD0uNAmdKB8-}zIC(6XG=Bw7{U8C-rqb864GD&#=}<l%@_
zxLh(co0lHOI=v(zLK1uM+c`#fpA#_$@ok`OZH^0AcuvC2O8caz4X<y5+(%3p%HnLU
zz#rSzsIiVnAp=KDv&6N1$w?^x02$(EKCry;oQV6`(<fO3QOBh*mJ62%k&l#R@c`r3
zTTZ!IXO*d2S}d++j-owrGeXOPJ6EUMTHL$}H`n?i4m!l5XYObG<VpQR%zYCQrtr>=
zr?G&_&j@Kqtx-y;n(oSlmy8+6d6AFS?n$Y{;U6O#lyeyzg;^^f5LBpVIlz}ZCLy1x
z+G1b6<D%@?nJp3w=ZD}ckH5m|hS7&wu*Zn_L<q=7DVbq$fo!qW)}djRu#o);)VtgB
zW7xwYk$Zq9BDk%~He=(d<)5@<Jl3N~`mas0#mjPLg>`_*g#!WU_KrCyOs%mzW^o6y
zV4^Z7&iOfAAJD+fO)YC3&xo&&7wQrPyDn-^wej?P5ml(kA0R=Iy!~}fbK+6Mb8~_U
zxkQ(XE|IiBJwE2yg6vw8-6+k;a;eKJ6}aGQu&d0B*;+})NUspe_a<;FfCpuQyTvku
zX+g=2n0gxe#6knSOw2frz&20h!<?Co_Vn?T`8DD>sLUfyteo?<G#-sUvudmfj1Y@#
z{?a2#bWLOiPQwQ{Yhe8Fl2Nt_%ZmdvVHVf4i2#5&iD)cmYzu%(=zq5DAL*@uAZsA!
z>uk02>ug4OjOG4x%HwMCmQWxe0y^V`Ctfnr!d^zL9QoAan(S^$JgyV%DumecLWWS<
zt(6JBJ+!qkq&J47jp>X<Rm6XCCLyo}vb;M?%tTw$U15Oy1nD|&$&A)mX*Ma~H`^_(
zzT4z~jDtGTaXZXL*;FC6Qn|d!r0`Q+s@h{WSvt!l{Jd?pPFRoVs%*4)=EgQQfmYa=
z-Gdn+_c<lV1aSWVDzcX#awUmJHV9-)FeFQa&ltxSE;}OQscvODeQRYiUTZxd#LU!q
z$H>wl8%~lvKywy9EM_4op0$n?L~amtx!39xhf@+0E0|=7N$o+kr%t%nS*R1Vxje|l
zU}qVtd|ZLa`i#^XV9Eps3AxZm8g-u7RvzQ10-}r8QJwj%&<{3MPp9AeL3#^Ikui7=
z#KI?m#Kle0EewzQ@wVAoV07Ejk&zQQ^Y;0~vYj?uJ@TCNIM%&Lre6V@PEOgTh?V7U
zYyoV9uX&C0N0{ehcKKlxX|vYXzas@wTz5+lg6m3bLAG(lMN5%z*hfHwgXsuK<E@qU
zhiF#>{EmYxyvSezzM5ihqp3Z_-b~aj3oV9w=YdG&3k{mZa?YCWiM4F3f|BmlFjjR7
zgl3XM4q%h%{{W+<lfR6G^=XTI2z$B<?AszZ{TH7(1^#b0sROgG&np2t@!^F##t9Tb
zWz8j4qE5N3sF{K>+Hi(Fkd`7kDna_vqWkRNqq>baCcO(0vpI>+a8*R{;$%6n@17?k
z$WDHkoZ!N%C5VY4R6%dek&FHE$j;-r+pH_?#_E;2pu{!R{6xPMs%m2)dbE-n=^bg(
zehXDl@x<LjHW)o)gLcL1HB5wa+y_0d8mD<&9A@FR8WPcnE81w-1i(x^9kGgRxdL-A
zBoi=#A_&OZC&?Lawn;}+%-A+{NG0{3`7+-6YCYz@rOoO9hCQqy5gsEMF}TdkK)}Sw
zY`BsuI=T@uDqXv`LDE)cWycaAMqEJK>}$nvk>Er#F*#VqwDFq)BFV_DPO8POn{hm`
zt(S4#Nt+X}Zi>KQNVdXf0f^rsBLR$5a#-888_1o@1`-Ehbjqbz$J8`zSRzL|1uY;3
zIoOEmD9&Z|I)$;VUPC5(f+y2&sKDIlG+`wW!JGg&<RihHzCA4BQ#F8!%EU^2xW^`b
zcrmHA;!yaH6XtjQQgIvjcKbtdFh$$NEJDpGA+7$V#m9dS2_hyI<(ek(WTz#qt$2qo
zs8rpBNn;uy6H396l<b(FoE;e7J-~Q?sbQ_Oq0Mt{r3{ov_?lN@rFziBpcSr>4X~Ni
zju~3V_3cQMa_o$s)?4k1F1G<@jAE<I3DsA^`M_BI`*Uf}Bech2bva#Um=f4)D4)rY
z$ousA#W{#*HF|yDb4OzSApYfSvP7$|AkmoyZOvvT7`d3(fn}DWK|&Y>GSFn|Asxc#
zr7;%JSaA{JB7BDs(_PNU$hSz-1~K=~QPVA!Epxm~rJFiqHXte6YL`(kDhUS<V^6WB
zS^H%*v0v6pj$8A}vLM(Zs6ETNFd)($#wxaEIaFOL)+)?j?3=cGGjAK<@8shY64Fxg
z+ab?6jB{cFyaDed^TqNA=aj?_7)>rGyy5|)BvIJST8lWu5pNN=FKHb(zoOKRON5uo
zwWd2!W@;k#CVEwR-AU?xq&>`2!om!iQx#?bzS=a(=C11uk}qg##fJPdUGu1(%?S=8
zI-6F&)A;qZwMC9a=k_iJ&<R*mM4-cPDmEEztaIYZS0jCHKdh0(oCNtQOZbp`)qX0Q
z8EGV|4H<};_{Nx+89L;^fpbSIz{$^Li&FX85UFABDCpBG^DlQSIc{nt59)CJZ|S6o
zLD_svk&ztnncr;|e>T<ugp_mL1EpA%19(S>nci|(aB6l`NJZ)AaQofelM-bE1Rwqo
zn5Kc)`5T+@0y-_LL=1K_jG!eH>>eMtu4ckh%q+ep*7dZ06ZNCzJ|>$BBL0obn6i$k
z6BGc*5o5?w>dHHnj-7)^dtU816~d);)kuU#-gunfY-Bfh;^`J!1ZOxQd}D6P7eM7r
z6Nr$Pf}62O^4dkqO|j!<?vhp2C5x)bsuMRpNx6}gG}qccH+2B(_LN34;7ogH$vGKJ
z4WO<i8*Sr!`@xUZ89sdFZRVY@iqjj+WPKL&V{>lKXJa?ZJiUb|$3Q}B!2a1*vn;UI
zC%9A*%A*BkQ8o{{!OkgCZv|z6gOhTV112zVvP)cWgM^1UMDtJ-M1RXUWF8A<b7-Ft
z(or)o0|DYc=2+p4hH&R-+ArC&2gWQ(KF<-F4(%bzbX15F#xi~~99#P_!Zn*XTB*(x
z*<j<nD(q(2RLS;#Db<PB31UoDQ8l5XJ^guRf2np<Uv;F0T!Iqv*K9=w@7II>04(1T
z@J)lwzsN}mL5-Dhaq#0iZ;|ymKlHHeXaruK+Lbz~>CTbd)mpV^nIF_6wYw0FhAnC&
z<JdaP<CH;hE#Tre-ZM`j4|C3L2MJoGrye7b&M26}g+hr``j)GaxuHcs**2zQR8a#t
zXWk4<oD3J-<7Nx001)-nH{z%hHr>;@mT~d<ZMbc=bCKFdML8x}YcB6Ln=(dLmrY?N
z8fZwZhUkb{?HT?y@tStt{;q!zALiE#4sI9%X!O$nDpxEimHDyhv8%Sys&FUPh;t$<
z_G35;-RZ_6Cv;e3S*HBgHixClVC+%8PCg_Gp^PK(6*=IM<t8(pIPK{<q%UggoY#33
zlrJqzm8Z<<ErEKs*9brtx~2&$nAE_OpCRX$6))WBOo2rUO#$H{00S~?;37e;y@dEq
z5X3^u6+8mi<a`s)A~)L_P+Si@0FFR$zmFM%bVMn3tZ2f%5+2`6s=jR1o6AnG5x;?q
zkNM`BdtFxI+Y`J&$(-(0#y!2G%Z4|V?xQ2mjODd?A4*fw4YP(b1->dLmn@Q*nVjnq
zQsiAfq%*Uu60m4+oGsFc4AQMdD?HB3N}9ll%{2td7Uvpp1UuEO&~~FWD|{cpMQ+Dj
zPEjguTp*q`sSt#@5)$LYSdh}5CP&B=wo)4GF1Xhivb_Wm*wdaRS$^89O67rnSjYDF
z+DvWH5DS7~(FQO{(x|jpuYS@T)yTi6wgcAYM=Qha#Y=^d4D44_+c0c_h||>!of+J1
z>~0<oY+=tT;fhf>_>SwY3qq|swYy7%)M64Gep&uwK08kJA{V7<4D_7P1L13V@iV))
zu7weai2c5UIu1c|oskP<!g|YbBMCsp<37|mLTK2qBydizReTW*leU7(aWQG}kS9|~
zY+{oH2x$VM#;2O`?z)4PcN!E!IR2b4*a~Kk3nFzHrdEk$^VJ3(ouyU%&1&O!fX>rD
z^^<LW^V;Qynl~&()Y;?23w^M|YeLAlnXX0|Ze+CWI%ccdhK~ztiz$<eS4YB6ai0a`
zH;9yhe3TTdScj?yOs7OU?B3L!ci|gEPH!8;V^~B8=4~}tA`5FSv_4D6)c*Jz#FU8b
zCGyf4yIWkV904|EvYD9Rf)M1~aYVI}H{pGy0eCuz1U$=z*p`~ej1CUfYMd&i<9In*
zqhS(dl%gSvbcLd<PfUWTxpA=pL_lJ6wf1bs@(3fwU%CcUE7Y8_E>-npaVcuGDsDal
z2NRH4*9GKf!A|EfJaG0cv9)cy*iwb1R|w~sV|zFV$(?FaJgnSd%<p846aje{Kl8&a
zfEOA#5!htLs9bC?b8o1?LoBkxaH$Ya%o7S?%xtZ2-#kz;GaWO;_K%{)syIoTo!C*v
z7|@-DW_HHyvSuU35B&F;jMC^Ct&JuiD}vMtvB>sYuGd=mWa`cck(1<T?KzFU<jaU|
z{-^$6L~<Aj{{T4H=|BV|H|&_7@sqrLqI&r|DX+5HB5t~VC4;qkQc|gl6`=<)Be;<G
zE#WoPrHzXgZZcW%?tf8Y;!U9$OXptNiZXm=&_fX#I)K`H*(qTYa;%+7u+UudIp%!1
zPb~Yvf&T!NExqC<FUZZJ<i`}p5t2V<n3hh~7E0f$mPX1RLqGh{oOxN~q8MSvoF*5Y
z`zE?20FsM(V*#X!Y~#f0Fn5@kDElhRn$_Dq(zAj^d&a&Ig<NfNDugDvm@VRBcbNF^
z8bobYyk-7Q)sPTn%*>{l^}ZuDz~xN`l_&Zd+iEqU)p_EL7443BR*iZf*o#{C*jD)+
z6wKD0>cQr?JCYd!TX@8TpK!x??-2v_GnED&UfAQ2$N-N`2nJYv%T~*ipgEX`e-ZEQ
z6EXh)peyM)l#b^a9u+>7jA3@hYrau4mgus$i+v4p69GJD#AW5pD(a^t@<>dXa@r#)
z$Vj*iQOU}JY4$aR3Jzm3JB$xGoy+c4HmV7S?UW=^ij~U8knpJDvYFIJi-qGHp!o)x
zzIP%92rX4=V^6XyGf5RGjX0#=Kc*pjt)N-0)8(_l&R+1eT4@<9CPHB3a&Q=Rt14G!
zN*dv9hO%i=PI6!sYoVxcv!<CUx*U9&i7>yi35X>ef$27iwZUFp<?k%=SquS2G&nFs
zW{Z<wchE&;CC)ZH*i3By0Oi<+R;gwy@ciCKu$Iw)MH+pmh{=qp{WCPk%Suq)XlACh
z#eq=*>dxHBl@a<Z*lVPDaOGE&Nb%d6bl-Vg9fe~_qsA2bcI^ifl{d!wHe2wuRjG-%
zZCQ?xCn_ekingyvfzbKB5q7hLZ=UZE$@+}>wxJY{G2&xGbFT~`+Dc0zJY?UfFBITv
zh4{9|Y|UDenUiux($MQjn(mR!8VjPqWXHG(fGCT}l{VUCT5Vic7-IL?vQ_}1=}3$<
z7Q=*bOGy6!PSL*jbJ#Kmt5CLb2cP;A5xilDjQ9_e6%y}*SUD_%)1h;>DV?%E<+N`T
zl*q!+a8l)QY6}It6AOazjnl6QP91h!r(D3aYoNYGS4ch*%N3PfJbK;7pJ6fZ)DVSx
z2u#Sa)a%=9%~_}wMV+X&06ZOQQ9BLdyRdOiRe<M4!YR_*V55}Nu8sRzkv>KmIk>rj
z0dKxRXM+1V`DKrirX^nGL)R6|m;~3iQl_{$is$Y&bZQJ^EEhGU4d^q}Ec?qdGD9Oi
zRxq&bnJ!Z$Ivfy_&RJhiH?ZEX!+A)FSY(e##0T#)8EQEKoi*vU&hs4<YyGX2W*-Z7
z>^<Ytd5z-_kG8Kdh)b*=717gGS7x-`#J4+*iQZXa^q;{$`?eWORY+uK{9-aBhw2&4
zgE-J1vAjdu03!p#KX}^@cm;-2+$D~iMLVi;-s990%a2J5D#PnZ;wx?)&2!D?xjH^W
zIUZD7K=cOWh=kSm5K4$)jtm6;sAmypUWG9R`JZIlcOS)!Be5rzk?p@PBY|u)@QL%-
zQ8Tb8k8;W!X4=ITiVn-BC-=p~4J1D+b9K1mV}sei1WseG7`U2ZxLkvfU^~wpvu>%0
zl>D6==(7vDX)6H$_5^LVb2#x9+I$j^N!w_`k^|%;tGrUEJ+!NGM?Z!zz`8V`Vo)G&
z_-dD|<YxY3VpDHvol=B!B}&Ix3W%3Y`<G_hDg|R8YwHF#lIt!qv5@HZIa0C_ral{D
zrrWl?S=qrj(&oY`IOFimK8xPiIJni-`Bs>ngrs6lc8WnUcDN0|?pC9?ZOCAcjFjzK
zHzs7yC?7<~2r_*u>K~9*U(jf8%vQ@LMHS|nxJCypPqUWWETVlj>)3E3vHO4rJ-3@&
z2t`aGpD_?c`fIeSI7=+l2EkdZ$I0K*iWZn>2`3j2d1Q56h9V|Regu+jSqmA>Yq(Oj
zb+1yXVP5U;v0<{#31E`x1m3u^v1jELmRfu!Ei14K+$G_hrUq6$XC(s`RE8qGG}}mA
zCRncuWx6hQBIguMd8EKC(J}J|CS(eqYy>%P@b!p+3&UK13-iDhQbQOby0pF8e~1oc
zh~3BeYE%sIoHfYKFt!*>CxjOxV4!!{NpM@?Px@B*xn#){?KhvpRY1k1Lp5VwUvxh0
zgT;PG_Dg18vCND}FJwW)H8~M3a@U%bfMq@@jlX#xj`*(0V!{obJiu*-eH5&2Oh$=N
zGJ&78n2$0ejd>#V35{aBs@XC=$9zbcQ?bU5l1$j#05K7UV6_<?eNJC)Ak{HCnr0t6
zS#-v!@~{leMqr71dxhCd15wJZk2zM?Uk-K?+C9Q+f~N{ISt#U8Eb)kjD^Is&#n2RK
z=^@)~w#*Dgt07=`jlQrqU-s>3)vj;B^lVivn0|QWaO1>@vH7gT?Zt&ZyvBV;pW1Rh
zGrm%9P1LelQ^N0}_gbZcKL(JHYG{$2lWKRBXX3`5K`zwV-MHDgX>$vX_j@_zF(Q#E
zt6{&IxdJelAPZw+A$A;xq=*i%j0-tMH{CW)PW4R1L%bgNHyonjAet+V_NK&et{A!h
z01V_JQYmLJg2w)-oUl;3#(3>b=4C1}Hb)}=06X(4+r%(;w7HRNdsedoB)8dZ>Y3#V
z7m#t|Q%)!;t*JcisB-7`xWX08SjYgIBq9ydVG$B%r_|*Trot9G<18Cvgn!mn<=1TR
zW=HG)06jc(ZIx-V$;KfmroIYgkV0XG{XpeR2)br>iFqJEQxl)#A3xg+`^ox`RnML{
z?xShUxL^oZ7{7#eVKB^yq_{@PGq2s!WOk{Gh~>AHFtS)&G8U9yk9$ooM&2=j<xw2q
zQpj1)Dc)Pi5P-`u%c0T=HqOgNoRH>L>2zB1yJlkZ0j?w8FjMnaIf0pt^Orf?zeM>a
z8F-xxcdK}^KJt8I#EzC5(T=s+enf9Lm%`>d3U(tLT*Yl(Afk52o&Nw-#@SiDS1Po1
zgmhkRc-tw~CySYdGQwf{&(O|o<3E2cR3WThX|ltpqb*h(_H-dy1E++#14h$4l5P#P
zwA6kFEeCd)YBN%4MW#$rDGO+kg_N=tnt_NzqbfF8M^g!_Gwm-#BFn4K<)LV>)irZe
zQ6z1PNC=)2Y0mdBaWF{XCS2e%ei7qL-|-sTB;syeL5P;bUX-9vt4%za`o}M=k`d?Y
z1W5c113~`)M|Q_>G|)<jfN{tX0YM7_Z67p-mwf!-=?snK-pZFb=DL+Z(72k(<UMgt
zBYquEmM$94b6OAEN4XgW%u9?XlyEO5AD|~4{fkqaW0aIun!1w&ocGPEJkIQJw$3!?
zm0I%_tJbepZPraVc$I_qvD|=OuwT_xh}yNA&aWh<Q#)1J%iF|6SCIk}K3ym|B57B2
zF&b8&vNoyrw9muV*8-dD^)&I-NwxI`Y^y!wzQRCzm{_{FM)`A~%S~xE&3WE(s)4mz
z1*$OHXPo+0{bo}hXKel8bNhmGBfN5$wYtR#o<CdCj(2NBT4XZQ&Nu%6S2vdkSngB8
zC(KXelwrWr5gdmySuZ4}4lP+4r|@b>$AOUGPTv_p!URBMW+%@siu!xARBSSpK00U2
zY=n=V1C1D+VLPS&0EktZjUh0Z3DdOVLS)<GGRwL^a^=Pyjo{)2K-Pohs35VUvt1J6
zhSIKBe>hBmi$`fKNPniHjEOXk*+%50oDRikwOEC7r8TRMc9_#^iIQ#VT7%FB6@XvC
zQudXL)(omlO;DLF%XtBJpOnCt-28`&Ea|-WI^$f_W4mMeh6sx1^G6G5<2gU1bLt}}
z{Ni~-*T&A{1T(hyup>bhIJYhEH;I`VB{5`2CBST%X(VNX0NFmnDIYZ$QRmM3azNPV
zW>1`X@>OWcC;dZA3*1|^BbHW6IEk%Qu2(RRXI$n@T~@6wFy9e+LRLex9)FF{G64gO
z{iihv+KrjXI<1I|fc&QI{v!*jQUt|EBj=Y+0RoYUnEFy2_CnUg4Izm>CR3EuXKf-k
z2o3F+ugbECq;5FppMPRLH^#J=#AEUvKN*d@O1XP`K9T<bQ!_hYi5%=Tbf4+vhYmH2
z_1B)+4fO_-ERf)FmM@mAzrMP82QeFF!8_ujA<NywCgu?5?HT)LoSBc>*XA*-&L?Pt
z8C7-mmYJK66Qx=%CfWyG7A!I^unqmwpYfQ=Kn$0OA&yMUsnr;5Ifm7EEGG$M3G+F3
z^&&AK3`Bc_zUdhi(@(gv13Ecz24#;AbY(&PvH$|1mQR_@;eVrPC@Ie$wv+MEu1v-V
zk&uAd`BaR6_&syWXpxGm?zn}LS>8^Z)ED{sbD*~7cpyi<!@wuzrzLQ+jaU+VO~>rx
z^djSHnaDP=3SrCjoK1JxE-D=%&tl5WbR`=tceQD%=~ArRvuLJSx87(?D03B>fk4!3
z<u9EC^3}#-?~y1I62hc^jFN*`KCxlt36Eu|4A$_Ag^EEpELc@kMSA}L@!ujoQG`*>
z%;NbD=bH~^Pu^+s!u4!2mD(bWR=>Bpdo3`b7r59>WSJl8nDlXSb0&9>tTq*O!eA8V
zL_+S<Yg)FwdSwt!<dv#7fa<bqEtOml99tt$ckKK_Pv^Az<xPo!ol#=G;QVmlb{W>r
zA~71On4WK)LV(0z+N@#74{i{6GqgrpEvZ<*h?tm_5!Pbd7gNFf;_ULY$;}Ml9DBVm
zinhXUJDrY+Q0~FaafjG|mic2E17-@ywcB%Nkd+glPTwzb&Ci6_T*U;;vb#dPrVFQ?
z{+V$wH6?q>HJ%VDOaVt@U|A_Cw;(Iaj519iC<&N}p2fzN{+ruAjR)r0<7k1W9&F$N
z&ykfhW_bqjK5H+z>s+~#_Oa7GXU?1d0P&L_@SLzvuqNX8?=o1OgrxE8?qL}fP)b1D
z$3>;}gJ-o$Y+5)skqjy-aW;gJnU-(kDQ@}}M4Q6JIyD(xB%AP}PTWYc)Oon<=-8JC
z6fd$KQhUijm9G|Z)Gf=eVt(^ar+>~XLg;w5Swl)EwT&8DJTHbFZ6xDj9Y03iAYQ#T
zER4EOLl`VQN`hnZD(ap5Wtz<=0ehHCtgF276U{A5=~Kq~G8H0CGN{4)KeB4FuLUnE
z-=4`?!AR!&=HI>?EdKzzsA-P|8^R`IoKD=6B@zBGzVq%ee7i%AryOoLVq<8^tRYG4
zUj`QA9u9F|-B#9CrMSis0p0>7JW57um?pCmFshG|30S!ZlRSvu<C9%0;VxFGT}Xk7
zTN6ex)@>+c;x!9GQpshcS)JQfy*<sMh+*80N^eTON|+(rH<^rQ=o!sLgf-w{)tI0b
z<$2hd7_fsO*~~Hlj7s)GjvF54$-qsH5dgYxKXE*yh~`RQ$b80xh7*|bV|4Vq`yR%a
z=OX)^<L?M13$HnAZqFNJ7TZC5W4;xiSc*-t<Z4k;H;J)zO9@z5)C|HvbnU89n?(Nr
zFK+2EQ`ExOCu?np&ySq1JX|v^>gHX4_n+rHdMzZ+ITv_IOh+X!O94jaKn}^$fS}!|
zTd~echUM>$EpUWa&#bP4aTGvB1sIIB!|Qeex!Gsg6zr~lYmz$CR4fd|n=$SPah&Z5
zlM<#l;1>*CGcXF=6{anMqvpahRigJi<|y6hImtbPgndqOn4RJkE}zMS`)wP6MpTG`
zV{vPkqA!_0>Ae2{-f6MI(3I&d*>ESQ$HuPg_%*OoGFx*0063VFhAJ5+b4KSm{{UH9
z$ySifO+br<DqU+%*ut)pGL9~g(d&7ZYo{E6Nz@HgEWMgPFJyu%@N<3VoBC+ewxX=K
zpK(;f5fT+^jOto@EZrgi(-u8_<ht{y?K+~+_Cn5`2|Cw?Yw3*WZZ=U$2p9X|#}dWw
zB^Vofdl@D9JTfDC<X)Ok!Rj^QPvk{1`$QUjR6T}mDKpLZ?J>6^h!NASI(NkBsO|-}
zYD8Q(2Bzygi%_XVOJ>yyjM(E=4{phi#2aPE3GHiJ%&nQRq&X^>D2%>QS*Dd%IO}4}
zgo0#B--1+2f?;)259@*u0*M|GkDjw20Y1nA%|y8A&(HeLzq$t(D4i1M1WEga+hWub
z6>rVW>%UW|Rcsc(2FQ|&+mux{UHDjXR_UH%V&uYyuo4`|qa)|A%2(N5(9V`Zl-YS#
z;1(9h_b6>gtm!mkO%jJu<*AYY8C6SYMU<R}bczP%oI9DzC(fF%DV0lkhvZM7e`(B~
zY}j^&x;(&JcGD+NYeHOe(gYlXq1<<Y9;ysv40z^NTiK<o=l&5Gx~@I8c#1=%brS|=
z(_zaDj#hna5{B%TZwCUgo5~_HG9wB7ewtG!w3V9R@EUtlYOYF>?jp5_<CV+=a=m4%
z%v!V+)I~LCrjL2YDAL@_QVsMLiJeB4pFC*3=A~kJHJd7DmoP2ht0)iL;ce{~nn-fI
zvc?=Uv4o?D_?U|S01tlTFgkJzG~H4jKH9@7g)(G^S<|*KK)DMiA#mbIU;rd^md;RM
zNeCN6l<A43Z{7!<TX?}g+GoxQn*RVrnpEyY#8}g8)a3{N0DhytG22rP7T>&1(L2_T
zN_<Nv<$?vp(m$kzV#IT_d&z9kA(Ty<@}=7)vczyG_Y>otl_pHNQ@W^vuTAjKY&p$0
zn<T6_Rxm|8H1Q#)Y+=%?7b~4d5=a!*_*_tOs+_yAfgh3#aD!pAFYZFKiQi+%{TZsq
z<iz5wxdKbX^v#IkkdbWe_!;vXk`jhb$YOb73)|Oe`c|#ja7?3YovEe+R9MZjJYW-S
zFc&c~GrS2&K;r^s&@ggtVQ<8?naSHJyb)kYmy#i5`sq-X%t?MqQX!V?4PfUy$;M_&
z-IEH-%-u^uoD<kt0!Bh3tfzq1pGf`jGXf4Nj~OG8Gc({sigtvgHxg~9rQKk-4pTTK
z8<53I6H}A$$eqciODGtOsU(2Z240f<HmfC6>^E#{1XDH8s_rd_NEO)5a4Ssi<~&}K
zDL0YVNapi9#y(11?5gH1(Y3#WUC|M|#9#oHnbr$Jf+O4&g|l(3?=6x^xfeP3e{C5V
z!LwX8<2seI*aJ}?=^7d4H;FOmrU8$L4FyAvnBahb9z`3|dnMM-D!rT1fnbATGWB!h
zWf`{r0821j64ycobeJ3pc9>-gR)=W(v+%7wUKrg<ijXQxbv;0O_G_NQBe2JAFtCps
z<)4UF2-1Jp)y-r^Ew?_m!4hF{3N!+lt+tPT8Nbd$HPrT3OW=0{0>e}{_?k0WA!oH@
zQCYH`_#Tu7n^z`z6wX9>^SqWQxRGUbc2}{JGH^vHD1w-i9tK02H~#=l8BuW(%Pf1F
zag1@QrKfGcbnp_6^s0)s<SMrR0PMz<!VIl;>D0bscT|L#Ehnixp~7m+N}mkhl+5LG
zm9uz4Qc}Ai#E#n{Y)PrD64R^6FkZ_A2KKTgatnehjqQA%G-Z}VB>6JQ9y4NVIFHGr
z80qBZU_=VmIjQc%0AplLtRemKkq}+4BdINJcI4{>Y@E{g{v}(I+g9?}mov5|R(ik9
z^^=Vm7aSDtj6~T@R0vVB26Dfc7TALKFZnnZS;($6fpSv?u!>7<5Ex>>nL^z&!=X!&
z7;R|W$Y~S{<9n%!*4(($1`)7g>;$#Dvv!{$>j<6<aB!}$&O7ZH`_6GWn2d(VzPd72
zLSz!LupUD6l~mQOl5Ml*Ev`pR(%`#bW(Xq7B4TiRSbRd%dn6$O3-4g81a@Z=R7*sc
zG?x~1>!^-9ZP*Hl77+}jevxpd!s9jh^H6g{4qA%m4n(<f+Dc+E%@i!nmz@WuDDvNH
zlS;DFSyU2$vb&_ww#GvWIY`zH>mkgFOo*o`p9e%^26`D0LB8I#=3z$}k(iD0jhR{S
zn_<^luw;;?A{VCJWt5n|QKAg15WD*>E5skk5tm&fr|^I&0usw1+Y;cc`K;x)BFMj+
zV4S`2RssJ2MDC)PpUZ4S=Hvk(HguF&iJGsj>gB}$0BGO-UL7!WR?LsM!Dz)|RL0Xj
zMV|&ljQrzvqibx)5*EPCF*MhGuozay+Y%8G#IB3zdvjfJZ|JFL)VHNx_>0TmQni>%
zRLYK032_x{&O(i~p*Gm?2qjdk(q&s!%M$AXrygv%tt~w!{>9MWxR}eK$we4L0(@qy
zj6HC5hB_?4rChC7R$sO#vH4}Fb~BSf+EDIOc+e&z6|Ga30Lz>fWV1$&7i7xT)&QC@
zG0J8{&2!=y$2W4admDLVZ}%Jj0FVM??Fgr^Bej;Rg33X+fIB*C>Jl5N?MyFn3frt$
z{3AFe#1Z~s6juKHumJt$nIgzceB&W=F&qhI3xSyqJ|-jVu}N?m*j5-E<W??~^MIC5
z7>s~2f}A8)s?mm9wH6LaBsd7pDd-S}nx9^2-I!Z>X~5BKmbiGD>`(TWzfux1*|5;s
z7d5y*nGv;Ii+{<1#D`-<af9=jFUS}!6dToC&B>}xwaT=um8%M|hRY4!Q4tDwK{0j3
zRc2msyl1#<M#2p86P5Tr(h6;mKE;s1U%D{l)^mF-%mmrcmH^fDpB(+X(~rxzd8SVp
zapsR=2?$KtHdoXikLmbG3YyL^6|_JI-qK321dVH2&4o!T4^g_$w#k{7Ewf0qvtp|Z
zq?}}XuLq2fZEOd*n`ygy8kwvES*RZ$U}F$Z8;h?TDLDG4j(m7|TScmt#E4s<$egB$
zJJjuE&Hb%-oi*Z$cPAMwjViw7w=J?FI85NrA3lhQ{66Vm?X>UvZHF+x5{)*M!+M&x
zX`%%yZ7OS6Nz19)AP&SHnNT)|YfBc-OEqf{(c@v(T2U<VmxF;+!`t%W74Zvfz%8Ki
zx@G{*6C%0iH%_yg(~e6P=?68kONlUv0p%zE030}t{781{0Rz}OnLzF518!lxYK*sH
zcr?~0wm4*~vxs<~J|{HTOcR`QYv%rTDkSTLl+vt06`E)yi3#M~n>)R?>q^y1aomYj
z%<|YgP!?7MLPMOrz!xYpf`gB2o6B6NXO)`~fds-4S|U;l34q`;6SncZNPmeAS!$TM
z%6Ye)Kw-&O1%!}65R?>E2Us}>V-yUS0h*=9nK}$X8C#)l12#MiTeYzl0<IMoEL@-+
zPRvc)J4@Tgj9%dyRZaf@ri?ez6>wK!56D--COOXQFAmrmbDf93EYqy?-Uj8>9cwO3
zk1na+NDes+r>%=Dw5hj23@s4`3?`9JDc>zz3Zpx@&3Q>Oy=L4-p8+>&>8d!cn+5Kb
zB#38CjiV_p46M#apcexZgPtDlWWW`rC(vi_DG&valfIwn!Gn~e{W{#vo2zn&U7Pef
z>By?65p!8;ggt8r@#6!;49gWOJ+g(RtyU7>&N|_}7!eG=CkgI~wj{y~prFDEHi;Z(
z28RdP(C2A7t67bOfwY%2tH8u>^vQ`>dO$}}m!-27I8GE}+p#<hLtvT6Ss&F-EANRn
z{#RH9s2>vKnxZ5Q3x#fDZqgt5MC*=|$@sQr8l>B2U<G(Zr2}r#I=0ObY(Q8j8+%7U
z;!<usfi31RFu~k>+))FY5)g15xO|u9bmYT1*()c_$2d{3PV@f&S6;U=H(bCmWtP<)
zzYQeMa|m6wSB*n9U5VXOPSH7n0y_(LvR$fMCpV0vOWd5x6H>ZL4=&%C4y4N_W7eaj
z3!-jQ>ww^N?D00i{-VMOuSg0s^EqLzB+GdjSt1O0oP-q1WaE)y63ZvREm;C6Qlo60
zziRPWNtU_y7~(n0lliN}z;C-CJKZoATMRgX3E`(dDXaQE1$0O(v}T&&am=J8kK7ET
z=4MCr<a6Lm$?}C@{{TRp<;s;5X45kf22!FBimdl&G);-&A;wTZpxlIX-cd0n6ElpG
zn1Y`fGq!1qBh(QYGGS*_Xw+%(Vda7n5kcs@77~(wF47X8J~Fo`t#)zy*F`WIkP~`?
z!(6Ql0kage$5{!Ped?N2LK9}|t`y63W8!BSs&6+AXC3|1^saqYP<dnh#fgls5#AFm
znCv;7Heje$nAR;=3v5`<L``U!&)zaTOhlz{xRmCW*d*;Y(e16hc|&QZR$}-_QS(};
zvaMxBw-uFL<Da*g{kCS@=Les$b_FX25_w-us$Z_XK(FO#gYZ)~>D4{W{{X}R!(`sF
z=X-Wc&-s|<*~8j)oJo+**l@OPLlqyWQBj8m?<D~e0TGo}wQlFJqT<|^xib(QK>6Eb
z<(9w^sh4`z-Jn@8U`0EDWI-^-1PC<3HWN_=R9>)2x<&y9R*CXD*AHf0ux?>$utgh~
zj`o+q%!mm+_%Be}s0A<<afyjX<uVzP#x2fbySb4#YXZhw4b&%%Ju+}VsTHacDc(Cz
zQ5yRw9-)bYRbMv%CE*Ke7YAL*uSw#!Y4$9EHj4UXoYqkj#v*w{V2{fS?<eYK88agT
z7m{Yw0L4TxkhYByHnK9!rOHCRfBMh7n8NV_ycqncoCWWv-c;$sKIun|KIbu+w9LT9
zr6Vb}0qHs8_OD@K9Z$2+)%<d_as1+YLj1`!=yi1nn3Rm~0X@D30AWI`SnV6esdJ%S
zU?Ax@WY*I{#1)rnH|ME!t21yy*Nh-xJ%?E!+D~_-8R8d@Blpkx&Ii0>x8U9NIqO9l
z)3@Gj@!tpTXB72CSy6Sfd5EnG1X)S%<21cWeko(?i;KB>iOj#Qa<Ar>UQgzX@|@F}
zMYv+XS0*sN4GB>Z9V6??L_5zOd@?pDjlQI39Gb84>-s}bFiM@{c#_-DW+6=95I}5D
zm>%Y52mPUbagvU4K0c_qXtt9(6~#_quj(?JttLhnsIcPf%s84YnU_R0OL0DwsyUFu
zKqgjp0K6YBcTIK`>}iohPVGQ?*yBY=8Cn|?u?U1m$86S7!NSaR7h2XjlpIRNOp3OS
zpw2Q|&JqvX`<z@!uml`FN@h0RG2R+cI!ERE!^Skqyur_pp?j84OLm~!D-d`lELE6P
z!3^y+Xm@et3`G>oMU6n5WsH9~5&lX%LEXgmli4gP*KAWS%4M2YNan_)xRM5Ilqt$&
z*;^*z0Lp!^a)UG91_j(KInG&;b-?dFJo&)_E%q|rV<2Rwh#gmxrUjs|+)uEWuBGxQ
zE#%Hdk|QYx!8vGB7aDl?MRf>anMwwIJYoT_StU$K(psd9&4nh<CX4{<Pkiq)I4mm6
z9y*hUyr2Psg@KtAoXM6UMB?Ly6~bmSx9_V#<aS>&$9`6+Fwf~LE03Q!r#zg`I<h!l
ze6ODp<rPQVrK^h%C%lg|bU37BfPXBbEZDSm(_S8O_8YVQUTA-G^IA}g66hb+Gle^x
z&!GOW%OCC?=pPlh;gh^CG{63JY}fYpz5qCkILdjHmMA+K0}U;hI~<Ve^`dH*UZZ(=
z{6wvqhUE~I*Q_&OJ8D{qHk#-LB6h26GxpE8+7YJQvR@F=t1INdR3&i=6sio4(OuK4
zZ?m=DQ82o<8py8ZYJ(xDT8Uxuo$z2^rVzwr?b;YY@;Jfp44jzTD%vzrX^h&kOMsWy
zu(?!Y<}Zx6Vkhe=$SUflUIW`@nQkPFWKS5grlG+NWK2vGpV=oGV|y*hk#6Ull~VlY
z6gI)LZIZcbmJxZp-75>cqT^!~`U8DjI_#EdqaNe88fI-HWR%QpHGd?9$8q9URtxhv
zT1cLNn+S`VcQuEN%MxMBcGxxcjASk)OyS6cpz^SZUs0Fh2HOl{fNCE6MI|#H*?$gM
z<BSo>a4oWKME1@Vs>&s`ts@p<n6ucj!ifG+X)|QxhuXVtQ|C92s9yZJ=KlbArfic8
ztW7dv#XL>+Od}}|JGF-oX->82`+BTo*aH?{{b8EJjrOfkY||`&LeH<3cMzlnwkVo|
zv^3cHl;CYJC7b-9a^s2DjY*p5)8o5PeYaZ^mue&=RfKH-``J?Le4Jtxn&ee5&Lx0Q
zimqD@Ye(pooR1tSFR4m}=SXu?cYdK{^M5d8OXnP;Xy}r}c&!`Udq=1-W<ZD#Fpx5g
zUoOz%i;%<Y8`^wa{M0c_Uxp@QwR@N3**%*dE%K|TyTssbET%c7>4!+pu%0i5<uid6
zjMljpE65TxHdmf4uVq$3NG3dZ8DhMt^(fsexuw~yGc)Es%FEce6XuiKn#FR+>z-)s
zQ8?p*#ydEk<75%=YH`s9if)bRPspovHacPTx!dCRrfQnxnqg%D!<q2wm?QmB+dFyE
ztCKrh78h}4*EA4TWMdId&;guMHuWq>OYeK>?OetR9!Cr>Z`$A!)*B>nyjso1D_a*U
zIaw%p@-IQEQmJlP27FmMWQf*u%ep7KfBpTo!Ja$LQXXov8C5R1z>aP%uRg0RgMomV
z^z*)W$-@qckOjQ?pGL>#9>vPVSZ*y+$?{ExY3(#2QqXfIe|#qY06;6x{Qm$LwVP*P
zma@M!hdJA9uEw>364jWQ!S%9y<SgcPhAhZAWB7E`9QQ_wDwT^xY~L7ttura)V;mxj
z`k|6(p;l%Iwoj%FGcyZZ#C-nil1I6D*hTu5KpYIVPu^=XmFf)~mGVCwE$f-hpnlBc
zr^Li<1sneWjUl62o%iKbchiiv_<N>fC=|uD3~N!v;bmQwo&DWDseT$|kUDiwAe-gx
zRFcw}&YGt+Q4}uCh+f>dkqm^$&0W39<27($Q&~#4lG;A^^!?*4arXzdPU4}ApX^3y
zG5()hHBse!jg4VA`A$((3+e6;hIEuz{{Z-R#fa$Evr<UdP(~(Ze#p68wO1{>v9LK#
zrKUq9Ri`Q^^Xt?~zqQb1%aqusSHT{+RfQurP}ADL>@yf)Jb*`+6R%P=iQiMTw50e9
zlHcuE3{Cp79ae|Tuyg)f;Y=KInTd$T4;pp$tRlt)OAcCO!b5r-wq<}GQ1uK+_#1l!
zg2nUMRYp~8l~)TQ?P^Atxm@Res#61Yp3ae$SUewvW6iUFYJrgj{%l05yxP>7Ilq|A
zc~DCOfFEn7^O!k^+ZF-kjd7KN*la<2uwlGKW@DVDJjRhdH0Dm_W32O8Z~;u5nVHOy
zt_^kJ(Pfn8&g-U;2cczs5*I8EFa+&*3n{E|3bN;bcBCwm^q%;BuM$GEUAk_3>{W@0
z?h0;})#p8k2{~Nz;63jKS9=!7w&w({SJ%@6EUrH}UmmIlzwMvCB2TW{1rk9*;YaP7
zVDrCTB9QZ5p%^jadHdddXwL@|BmVNARdvKm)d_KgV9ySyXvtH8kgTLcla({XJiwfs
zmRr2KGS$<WBbg%Xm?4~jSy~&DT)^%T&uPaks03L_0~CpcdzKpsg~+z_;!_!<DA?sr
zPwFfQpCg;A`gO9xqBnsPitVgrf1e=ahjB}STm&%?VKM<F2-+$&IbPMYR+9wZr>QHY
zw=_vy^Q_5|VaWk=2?C!UFotHjpj*Q2l<=?-ZnVOy{*s7OiBNkV&M|Fj3zF+yi;0;p
zm)>*d98L|29y4P~t$XKOYec0{EU3kgET)duNS#opZC}(9dr;KZkT~%p2R<TIF*{9m
zCxKQg6r!`CMkv|g0c^>EUB-v2+RE7q9s6rDsJD@62~18QQuc^t`5M7r+54+o4B%R{
zZeG&+=}ONus}6ezoE{*fIP(u3d}VQp_yepn+BZ=5HfFUBVSBKq0(jzQh(2Z`e;8mx
zRYp>5gfD7y1E+5WU6q(~ANT(NQIt}DqGH##TLc`=(v6~c=U9^9#K6@*FTYA_hS^Zf
z#)E7!Lk1(1a-KYkYUdF6jIjQ0C?p)MykgI^Q2tj=TNwtxOjl_SJV@6_lNmM{Fvtjx
zF~~>jJVeHFBa)fhITzMJnD4dV;%SVqvX~0P?XB`HjdjxnEN#AWxepV*O>hN?5CY1v
z?=|4IiD7y8s%8#2fb!oAxle@;dlH<~e$g}I0OUe;$dB_A@gekyl<1xkW{oMck)Aj6
zM8S4}rWDG~oE_yFbpkFrorX;+M+!4Yos*yh#}ST<$pu*xzK?fPXoNauV+>5<3k0W_
z&NupvA*G9r$|KkJ%OepL*gtsL<svy7beQIu@u6BXULWlie%O_FG(#w}o}n*|G(>dj
z?U*h}DHp_kC8spJ5wLMl;$s(Bz0q65)vPBx9HVmhG%jhhM4}t~K$!mD;%~Io^SuPS
zj@;l1c?Ow!Y8)-Pjc;#c<}1J1s#}RwM|6@dko(&<F!>OH7ufBU+YO}Z%}BsD402<@
z#H>k$3yYm5nUq!zA!zajeB?$@vk8W+khWEFw=w*&!iWW~303{WHeKir)vaX_Gq}=;
zJAAUViv{aC!Zv!)LZ9ji7}j<^Pa|(|rN)py?rh514P&4zDQ{T8>Y>`80g8sW=GMAe
zSP{=)i#J*3u30WXn4S^CmT7C5)2t2|#oawtg{9Pr<t5PXA<$7(NRqSy&c$i$HR^Q?
z^&1B2tb<s7OW84%I;Aa9=&=k<giZcwvt%b0!dY%V+SAQAw@6iIHCkkRBE?g)((n;i
zREe;;9PJEj>=@F6sa`XP9~j6HfZhURDk*U>3#dR@rv6RHWKv-PJ|mXmF`j)@GAVu~
zea%wx*EzcAh76o`oEXO39&$wyit#0tA|yg$(^Xj{*89F-ix_gYRwB>+L?T(c^@5Jn
zh$Oi7XYG{SYoTZa+D(Ow#7R!W0>3g`=l(nI_ohsum6v0OYc7HTp2g!NA%uBf_L;V>
zm(>}*eU@-%jKt-wtg0Op;bm+UHmpa#9Af4jXmU4+-Zb4_M~a_sO|N)66=JKYvx^-d
z&5Tk|b%T0PGu^_yzlbV2;VfvxV9tGQiRS`y3DiYH3wG*kavzfC&j3HBJDp^XLk>K`
z@3>e6l-e@plrh%oH++8fA}pp@vYn<zycQ?g697%j<jwNCO-z%5VZs!qR2|sK2qQ~(
ziK|}7_cd<P#P`9!O@nDf-(l^S3qV49MT)3lbupFz&8%8$*OiEE^?^U(!{;tF@mQrg
zi=~vSHhUS_+m{<++%D62{!c6rh+LeJ%L+Fw=CsZWIBB~>1C4baf9CQ(T3rEgb~jP8
z*Cq=ok}p1<cf>!mp+;$l<HC21<2vG=pA65e-_L>lW0M~_#76I2Wc|)><}v&mR;gei
zpippEVp-2Sw`VdLU(RuCU(uP#7Uju};;>GcWGg-Hit7+A$PF`T=4C^U%Vnj?VPStz
zw5yFA)=#bNA%bzv&`4;;<?W^rEN1>T9FOf3V?G=<lQHmAkQQGNCWLr}g+!Lw6~?~M
zo9ZK#%KpCh_{?VU=Y9VG&zSR!-eit03B`ITi}E2!b%r-7h=J1#N0o&-wF*yleLCFS
zHYwKXhrhC{xB2vGZhVP6roZ&{$l*NUu}}g<%Xlox(WV~*Ven8`=Q!pF!V>x}TBSsW
z5Vkh`{;(Eonc8hfNL3)I4mkGY2h5^trcNvlY30h8@z>^K<Da%Ckk_!x(>^QIPZZ9r
z8*I~G1cha~hUQU~2uIm(v=-AL9!Z+y#LNKPb=n${9E=e1jiMqkdy7ItQHVEo%oGH~
zi$`<cCRt-i#z8)J4KyKFMA>L21o!0<h>Y9gGn#K0xDaXIpNQJTK;ahWz9wK3Y~rzT
znBLIJVbKNVe0@gPr4vk4GjUSTL#?T0Y3oISB9+V;3f0szyID$S^aKEDn>Fr(fh1am
zlLzEym&zYby^yYz!3!F`iM_#Eou00`sM2Nu77k<P8%C)DHTh?{e14GUVSu=3RZ5(s
z&3PeT1i?gmcRzl7Ge<q?GIK|I*mn{|r`ZBXlQfqp-fXhnbCsKHcGif>+~J{XFB;2y
zWI!tVVPVY1A}3}{1tALF)H7VI+OV)cUUIN^SxWO%xVn~;8EiwCoTV{g!mw-LYK*Ru
zP}=NiwpG|rE8;UHJb%;Fb1waPTmg?#@r1H}TwYwqL_h~NE0qWU{7CYuiU=koc8&9n
zb(Tn$8pLZVBTa>S>e+NjZ)Ai_l8Aj7G0t*@CfqoHi2<G~*UQQR7T!8B?2-~0S=y_>
zA-F%9VmYXT4|d@H0Jn7J@wQj-P0N_cEw_Q&@6vGM154X3&-F(+U-K-)OiY+@&Qa1h
z{sk*s++PbDOz`uSypqh2F;Z?jCx5w)ObYCo@I@Fgun}cjZK}=2yXtCc=_`6uYe^F8
z34UN^nC};30(L0NLoo_0kKfb&qMrSbIKt+_IiS)(F!H9NyC>FWxS5=v(U3g8sqhBL
zvapO-vSiX=%mIMy5X3#zuUGAzr;HXLIgw0a1i6%DvWNs619-3tEGwPIZ26Y9W+Dgo
zuOdD3H(k!95P*-%BE+kmDYD@FJVq8NdGi=3o%WpHX^c370>_9|W7tgQUzm9eX{c*K
z!f7Cvhqwm}+r|f)?2ObuE@5#jKA{OOA64rUyyq~j8_l^WgWuPGL9kF9WCz@~B5xNk
z2*xdon1jIn;Cd(f!RWOcXXB==e>EMGHd$a#{pQj8&mOqX+k8@`#kLLaX9N`c-1#VP
z)L`u%W=>}Sd6)!E2HUztH~A{lSDns-B7-w#(qhT#F_IV(D2#!aNggwO<9fB3ZLqPh
z_19A>C&!Et=D-OqJ*6Xf+?NCgyK!yGZbwN*MYVC;A<_d89_ij<2HlQ5Snb)V^2Snh
zRs*ujNv-jwZ6ouO3>7U$NzjWcjJHd>wc)A3A!$r+u&;VamqznN{dY;3{5tOsUOiJv
zY|OPiTNDZL@suEeb|MM&EljP}boDFdx>YE-ur4bF)6%?LK&7XRHR=lq$y=*TAoR~P
zk)Om9y`jn5_LJC2c|x7Sr)XfPBq`QIYpE(2g}dv>%tS_Qylst%Xt=#J*oIZrCSqh}
zCJ*%Ti1^5qSt&HEnX(Ix!^JWZdSjFB4GgfhR&lOrcqh(LvNAr=_A3=;8j1&SDa!u<
z2Mo$L=d>%qa+zl><j0TU=Z0LSHq}aH;21tP!5yQNiS_PSbH5z8KsF(uCkhzlD~TD9
zTyS(j1QDE&@{=**<{LqFp-bJeQimHM>Gadbsn(8WWTr^8(k<)+X{RlulFeX7YWh*#
zefDGZmgUQJM$41t93(=@&Q<(9JF38CQ96pe+$)a};zeo~q>;rprt}3XFp7vN2LNtK
zs^@NM)8{R-uKW2|tlEM+req>wJ;utSbF4`&shF+6@(?XHfB^-p*C4+TY{|KerClXd
zD^Ogr+-YHAB5Nril*Ai7uX86X6Dr8Sz0GYZ{Qm&&DSk?9Tae|-IO;PqOTwm4epI&|
zFH&YvXA4#4NN2Rh3I;qekzC|tW$=P7r!kU=F=z<DfYu`|(+5?H8Julqq=ZcxaIAgR
z`u81i+(2R=_swkJtps8O%8i9e>RB_08_-TqR#o0@BVcH`tx=qCUkqN}SXJKP6Olcj
zyhKm!ws_AUwXcPBqb|tFCyF46%UGDm#^S=_93rPCS%M8vVjeqUPFGnm8kXv)EA*{9
z@f>)}JmVS?qu^KuYd9d&FOvcxucD^&F+g<KCCfJFJ0wM%#HD7lC6?ZmreYl<kVFDe
z8)EO-5s1r*Ao=F`{{Z8e#rRlE+7cTs5<}ZroYn5-#8O1+E3j~N)RslQvf&;E86&)l
zD*pgaR!?N|-~=R;#7vo-`NZ+|cQ<y>ircId!x5X!1B3t}Vk={gUt!z>KR^Ud{zh|d
zVYF)7_KZ%P+Nm~6g0RG_pElE7GZn|lZc{#UY@;0h8GM3VVjeo?a;;*rSk5$3GIe>n
zN$h;g&!+zX%UmT&iO%AKlFxXSh=W+5F^CUp$UU-RWA3+$D#)rKKh!n+gg`X*aAKk_
zM+<B@@sSc!w0lSJ-b>h4f!X%mWW5zJ4GEdrH;L_-4nC9?18F4Vuy3#?%~OALbBD7&
zdHYTV{Y@;}(s$_`cg@AYttwT<uzB-=110iY5cAnT7cYHGXWhW$j|q_=^V{ni%<U9(
zc8M+TzjSFY^6go(X$xyI1!EFYZ6@u%)WpSB`>LSK*3l;9aIqoT#JkBla41!{CCtq&
zq?`5j*SCcZDWnl2g<5Ci8h^zlmdk<IfvV2YVy3MbJ39nU!b9l6n1w1BAgJM$Z`k;x
z>PV1)=BG;4yL-TCHNr{`y+o#GuVOg)xyeuovAUv^#jhJ5w9ZG~N6mVaM8x@tsKXg_
zLm~x?h!43OD3P6{Bwhs>zVQ)(yM8#C<7qRO-WKjy6GU*QaPmXM6hZzfg`|lrOqel6
zTC9w+%~(t-&3gbM*?`LN%BAc8lQ0O>8z*&>kX|Nbf0;aG(aK1oW5n+fHnf?Hv|@6Z
zh?yCJi=USx5x@LGB@$6NSrGd6VjI9zy6dE37wh4~3l1SuJ+|g!#%Ynt!=p5liCdsG
z5SG+*L3k<1_}4!`i8JGpUmh_B(E%f+EfH6_I-C-cdvPp<Lo^~hue>lAI)_W_i&J;S
z(FtjLx~O2ND>xR%Sh&`@TN5H!ub!WBAPz2R(c3yj{{R`LEYnOx^##LjuqCxZx=7-@
zLjfRTX?3*;1ajX{3Z&>tnIyY+n)z^(pnat%$V``<vM?f~Mx9t$WiyDDx@@XFqZcg#
zSVU-cvb2QRt|N1lTCzDK5%=RGZSsXsaT8qYeUD=xT&NL@SrM=Trvy+jm%8k}*E<v>
z4j6c|iRC$V@!5IIRJ!@JxNBgchX<#I@YBlCvyG$UY0G;uYC6lG4V{-&WY!+mfD}sO
z=WN<OvuK##1`BbRz8#LVc>e&<g39cbHD)9oJ&Kt5L<H$`32{W~f=jtDd?bznD)42H
z9Mt*qPVx0PGq(9>j98cC&ncNMJ8jFK>?7IAN-1jGJ`FHU$jh2@>l_YK=C@fX)kpJE
zb2~)sKlO{_18a^R-%;cDkBsKV%{zSJSwB&SJJDQYIQ$NOXv9HsmJi(TD>)co#{U2q
z42Es-0~4~yiHE9ym?=Nhg_$zjobiwQ3F@NZoAQvgBZ#fo4J<#-txzDK#Y>E;)=GLo
z)3QvamB6yO2p#s?ESf}QnEhJYcOjle$;02590eJS%uEo>#O9QiHp0g)Ce4d8BC0bL
z%$@)X04UU~m4`^oPx@BU&$x{L0MB0^x>qk~ENztc?d$bBz&X3gNxUA(+5{?tW=_0G
zj|ox4$jZuBu|}r~TZeMDS|eo+p=t}@!*h&@hx)xfvaM-(#_T#uX6Y-NnMf|<)_lUP
z^}WIF(^i`=C=;nrIkUJ9oo+bHTGJm?d7BkNQxLJHY%VJ*mVnF&bZp43SqrOL0fQ)W
z4rU}^W_f+&ev-;u%Hkj`5ru{f%yIe{^BF!SVr%KFWC^?8Q`4k^t$Q{t-$^%6XL(L>
z{{Tz^&Hn&YdVmuQRy4ogngdp`!q|)&#M)d<oc5VNjZ|!@;Pp&vYvoLjAkK0pj!^*$
z<-*#;-@*i0CGke03W6MEde*!|(_$SE-b^=4fN5{@E*og7$moKea~ZtE&O1_|iSzj4
z%%l}|?W5CcGH;a5lcdfI_g70$$#PK1Q$?J(#@SbPp0unxWXOneC2VX$?A9dlWI)Mp
zv_1e=BTma3%Dld@*Da-98XK>zwiOv|Bv$O~o@J-o42{V+8;~B#wv9~@PQ|K@rosl)
ze4)WPsMjPrn8|tbM8Iuz%^-rhJWhEq=L#dNCJG^3M1I6D+`{5=hc=o>;2T%3VqHbE
z<pv$Pcs#ro0P+Hv(<hCBGIr5roIvGfB^S~|Vdsp919V2)%tTf$t2NR(Rft9B*Utnq
z7}I5Ev>@GC=>d|eVG$z-#Ds<cW4ll8CUZw<r%ZX7z8<78WcL?5qSc*dp|%prBe;}-
z)JYDbyY2+$o)8X#D+6H`%;l*vk#Ku%6CH}@6_~p96SZEMuImXslSWCk7GmxXu=f=P
zvDNM>Cfh;;A$p~i1dLgWmQ<@CgR+_ImMJZg>b+b5>!t)j3zp#7;pa5Q8L_U4S4l+4
zn}^`=c8}rm-nqwZd^VZ6r-V{dGF#;>BXAoxA8CykOyycuF0w^OZ8~roDOHw3O`<(I
zn*uVrK|#VBerA=eqdDwwLvMo*xI#`Kh9&~DPl~D~OBdO+^R#6p5x<B;nQW+Lz{wn$
zBJ(@s37*?mSjFW8^Ot**%YnsJwlDg0!7)}NhbO?oiWzRiMybf9L{z7NHS?xU$7dft
z>WZ0>k@pdn*CkTB20U$__daLD8HlbDIbGzgs1C?DPi3t-Ml71GpLk3}&ihP{jmr@{
zR_t6v?=$=QlNgHbQ22<=zx|eMG|?F|B(1Y)@wYw;kQ}R2X~>(Nb$+w)a8U$Swxh83
zu@EyQ1C*nw#6@KK+~66V@Oc{P0NAoKZI6jFV>cw)iqy;?E=7tk2TsLfx~+-`xq&H_
zXp7}bnA?2RhJCSYHjM$WHOp1Ga9TE|!ypdDk%1hWc4zOj%)@;npG}9(tPkP@6so|<
zc3ie*(SkDOWLdeAYo-tN&fcGXeLwc7m0HDTH{7EO?7c!Hxd^Syr_5r0=XjY3iHNds
z^-V!-y;$nP#*oV?Gzhqm7;VVDJMB3*r6hkb_U^jjL=nJ@2<F){9wIdT`C2H^<NBUi
zt}m1=xFloxK)BDDSng91<Llko;lnJtWfe~(<b+0i@8FIP<1-Tzw8U?erXuZWi}T9s
z^LUJ<MYmcoE+kNUpsd@mZez1Ffce;Li+?zbra#grc=YkUPyyz~9+30*<C?l=H6;|p
zk_K2Ozvg4aL!c+bZ7}u2S7BsB!Gu1PV~t5HdOM!7Cz6b2E9YT{9fI)!;nKS+vG85m
zH!H+7(>vu>cUMbuUoVw)!>9BF(GE;>&ZIqR(}OI;Gen|enl+#8CQ<?ovBW%-#%ar#
zW}zfpl;a6VoqI}ysb)Z-EV7cS<5Ynz0f8%$uJx3^uD#1Oo7Bp$T$+P>fDv>3-JD5H
zT{s(-61WI*6kH3Gv^vWGsX$i0jID#JX{LrECP?Q6&{}g+Q!@}ave~LBrZB4wNe`X2
z2>i2*`^n|BY#|cOq|cut#r(m6ggg-&(Z4mM)>riUq}EMUNr4b$n1lzm5fQmec0fdt
z9P&FYGdQ0Q#grg9<#X$>qsTc;m@yLH%xeh|GAO~4iQFq1oPAE8n9LnzE>2CWF+DEI
z#)&a)5sMK5<AJC%-N!Wrt=ecz^$^zWhKX4AoGP!4)Kxlgd`2wZrqrmqmfkCwn+%21
zQiDTb1h6I4!j0)<QHYu~V;unwYz;eOTnaN$T(05HPFP{rp`}D^Hd?-#W)-fjd2k`u
z*dcMNK9#E|7)3ow2{1Ba%E-YcCN|sndo;SseboV#O6JEv*H)IVotqYsJ4$R?vtmcN
z+cE{QUrd<}`*w8>YKqmi#dr%dnq6RBWxG2;V$(7nXh25OA9#)~T-TKpgTotKt}|({
zU6&h?I04UysM<0jB3!M>6bj_v$~TC}-cQX_;qKXsffWhO?ftcV_TTE9!NU;22@4`6
zU68vxvdWIdkV4u^iG(jsDEFIVV~Sn@5i`6-@G@Ho@}|g=9uu~H`4b5ek4eudU2a$2
zK~95HLsJ_N09U9jz-(5<!bVHS#{x=t1b*I=_cIw{qMkcJRj@b;XMu@DmMKC$(&q`z
z6I7t5VmTtjg3Y26V)top1U!69{@4s)>LUo2HnYkEnsQKCZ2MW;l<AeT$7&sN3Qnk@
zU701+AoppOT5LV!QC3PUszXW-^(4f$*ATVEZh@-#%^~b<coWn>x-3L=?@g@6#LNNX
z=bYEZ{{V7!-|jGD^33Lvp;W*!;c_L`iyJ^j&1Y*&{S-1w_5T1F`)%n1%-}-XIPgeE
ziNgEzNC6yncW@IF#LRia_?+^Y8Q&JRxVBXgPM*kz68QnaesKAT=-YE;MKC3bI#L14
z779kkJ28x%_vCU}GrY{?c=PCq;Y#f0jS=qWeM99FGZ_IT^;}y3(UvM#F%z^dV6jq9
z2bZA6{%3*2a>cR?I+|Y@<C=6t*q!X8*vC35X~Vc3r|R59Ra~}erIQnrAL6lf(6up+
zDEu<4N9`V)a`BTenyrY@{#ueVQ&iYOvC=GP2qc18YkM=h&T@XS1^E@rH6;>lG~sP2
zYOtrO8pb%~>Ip9+g#vpJZT3>`Tfm;#OHy@Dfweki#_v{@cGXhU%}rgmf^n5?M&~u)
zLZ#+Tr#sCoPs>8GG7C_U_fxSkdbZ6N_cMwA069AeJ1)1f$;?)HtimLqJjO1{%|lw@
z(K6&!<9UcyYCv&pq>*EYggBdBcj2R|(CeRcolC_^1Br1bd}`LjcO`=?0A%UXRjt=K
z4yC=Aw57G(!V7L#KoRF0h9tqIA9$vU8nDQZmJjVPvf_&~gFm#^juscm{#rwm6PHRR
z;|WyjHq7=*A-s&4QJf92gFLD+amGPB@IoYJWT%wxkvvFeEqQd#Q$9dhs|PwIG5-L0
zu<elqGXyg$)VLm8AyU?WpZ;L%i9SgrvY1^t>gI84Xw^PidUjJ99Ap}4ieUO+x#15s
z*lWMJQtN55t$e;aYAjLeC{k{{aaF$F%uHX~mf2cGD9PStX9xxqCzoDU4MbBuHpM_&
z(!dL;brlhcA8m5KEl#ut%?;CTkIGFA7~Gaby)p5GL)NhqcVf6<{{a3xoYJ|CsAdf-
zg)y}0c8B}E(zGr){{W@|e5+YPL7P~z`;C)k2=XY50{n{;h~6sK%j6?pgxa1h5ESry
zpE8KvcG^a9uqPd&laUlO<WueWX7&F7b{rr$FZ^bo6E_wxN6X`%=Rf0|VRSiR&-Cex
zvAod<j056uSkN1_QEn@s-pPPFDN2ngSZs1LP~a^nu}BluI%eE)u+>IWftrrwy70tI
z;JHbH>-(9KoDz!uVH-6zWt+jKy>o?it^BmTP=KLrwWp=0HiV;Tn8ThHVhEyQcA1|M
z9<dWMF_LYo$|SWGR_Wn$T5)ysD#G;uA+YK;5>d3R(qg!B$4*yNmh(PjvLKA0Me3TR
zw3j9aY~WRM_r%O1%Q~ianVGc7ZI&5S4eh6#@coj*?^NXz>b5MGiiHe_k}jcC>DJ5`
zwT+UJ+Gn7k?ed-a!*+4(jlCHgNG}c1QFzc4$?oIh9e$tZ7t5f^Kaf_&evLa#8r!k<
z=q^u{a#@KKOo8&_3#wa5xTJi<f5U7Tlbxbg$vpN#mcZG4z+dH3k|Lf&Q!o%Q{(T-K
zvW4KV$9$Vy{{UaOT#0No7<&-=apePqw#4r{;?%}eN`p)43k^s$-Hm?H=(yxtV)i)e
zExhh^nxqVrkc5DmvN!(#k1{We!7ZP!OziI|0aZ;oOq&d_$3zJNK$9$$Yz!HaNr@2W
z?~N7vCYtc}jV`oi^N}<1&Hn)7F7UNRXaO^9&j2KodwTKt!<8aGs4O4M!igE!sXMhH
z8DhHZt&5tKO}AE=)wp#*%unS^XM$W#1bpLokaJ{;XL*^Q^XLJ{;B!eX%{^RioLIAQ
zxgI>ioj%sgP1>ePL)59?NVl79wBmZJ((Q{rC3B1M6B%Gg#gHyr64Dcoj>VTo2Gep5
zVm81w*O+QhYQ4(VYH=Nj-1zqGEhs)x50@&s3ooFVTAM=v=DEQ@#9UpN3cWLE0o(~5
z<s}~kGGmuG*or1kY#W7Lg&7s-Kl7C2GPAcf1Vc`9_uC+5K;{#%G&f=L=6L=c#Un;Q
zUCpz2nI|f@ZIF(r3(pdgBqA&{4Jt#u+tU_eA)E!V3#g%t-x<lqxEd>JnAm$22U^5u
z?=h3(6sb9l<+BTgk6>PbRfg^q)U4vVO90AT({MJcYRMS#t#6#TY+Ohxcvc$L4w{t~
zLcqi!p8&>wwey%8Rr%vhg4MQJA1FAqUAl6ZI%ykIX(YPf$u;Gz<cBS_EnPVd1-wA6
z3Wa$}*|;qXVqBJqOlm~cMScZf+V0#dI*NFd&AUTcMpJ#U6AYbF;){%3u;|j1=&x?`
zk$J>Ub3ax@M8QrB1x<;S*(GsC$0(NL3CQsikU!Jm?5(%&8yDmz<#JKWB9g_HU`?BL
zp58Kck1=oIQlMn8gQO#?wV@fP#@5d_#lfmYxdL158%%w$WO&A19-yYIe)9^PWDw8+
zDD=|Joj1ZQ3?iDLI0w3!pQmGuGQilpZN_gcXD=l&7`>ClIEDy!W5a`vFd&SeMED@K
zjHplxu`=YH0jgzVjHsEOca5pPs8X}kbl{dP>a)MOcJ?FWGoQDhk0RS3<UW;Zy{cUg
z+`yF#Wc!&>X5C`w%Cb#t*ER9d3FVfU7sy)xD=QEIJrO0hIgitRI9f5lE(1H(rjMK2
zY&W}jY!UKoM+lh5mhC*OB{mlW7&Ea9<ybKDh+7M13-4(LPzKJ0+!Ya@WK81(Vq9d9
z?1p5_5ls3nK3n3!wq78%ouFwol3c4xOMX)`Ikp7;Nk&BN=&Fxs^NyOdwwL?zT)HdS
zYtj&@z_Rd-hO<iQ2^dDh{dp#c5Rt8D)ZvgfIlrrZUwe4Z-Zrs6B=h^7uy*AWlRM1j
z`}$9w1CUZbUg5L1B-XIaq>boGWodhW7ha)Jte|5WitA#1;hufva2Y+w+9ezVXlPF_
z?dkjA%z;F~^)a(CJbs7v%9I;#=OWsCCWST$LrhB}c%9&gi^&ml1+yx}X0Al-d8(0u
z04`D%cN!Xe<nPNqv2n|mStvK0#(^7Gd1OZ*XyJr=r<m3Q8W!D=o5bxCpfmcWDs?ku
za<28U0XFJfUaorrm7liQBe4$hJP1N#;xjoh6A>}@=rb`QQKSb7d}Wj3eHXt6=B&a_
zrrwnQ08>KmTz%!qfkKoIbO2f6shJ*AXJbP*i&9^d`C9mI4K)i?$YEAw#}bklo*HQ>
zb?brJnKaoMl#m24;s|-3TLFlx67w&^sGJj5Vt1Y#cZpFN&KMQ}4vtZ(Lj=Ku$%##P
z$qD%G1HFLWLV94N_K*xq!-qrdrcl!uiSy>WVz@N$0VD=PKYLA}0O;VdpJ!qrTSMf^
z3v|4S2VvxNjH2Sm0_)Y~b)`4KoZUj$iqnTeQ^UWVMbG7)p>9+%8ehuXX5S66)j?Od
z*;Ndqb7g>7no+c2@5OVAk9$o+CCUh(l)?riq?8?Pae>6gV`*A!ljk)SO^fgV$zU|%
zR<O7ym+K0~sby;sUdT5u>Cs*!*Um@;a=Ck*Ai`?Dg|zLcsB7J5g4_EawN0f5M?)$U
zC3cb2$$&Bibkwy`8e<Qga&pdLS*X>5Ogt)9GdPCo%06q109i~%9@6f~fIGTpSXz2I
zn2P!4XK0=@f+Ba`c}PzK0?TqoX~j4xI|adX>GCzi>P7t<%2f~)Y%*(Fl))(2qORE6
zQk9*a)p|HTDuI!d8wz5PY~MH2*!bB+SuBZ$w3=j71X1-Lae^e~h~}bDS6#D9W=zL?
zxpT?~#`#*Q+9|<sMJwQdh-!Ed41q}G_*6zNA+rONhAF6;Z5Vx`AplB-4QrW@c}eZq
zF&9QX%!~ob_Z5)*r_!J>KdH@UoW)Z);Vcx)!36D&o^(J1P1V2rFVytD*qbPt@_Ju9
z7>N_e+@>IF(uj@PQMOESfEc9yzS_rZ1E@_40Dx&4)NEh`USvxk1x3)fGfL2#6T}fS
zGlZ8$`DRXCJ)9?T-k5Tys7@z%=6OSlCCEDIH#30!&Q~rD5|6pa88d+zuLNIfS?#J>
z<q4;xqAWJMhfGZ=Hmy>oE=_Nb%6=<x3tVH+=dDn<(P{f-cb=-aizkIM)VTGZ^NZwl
zrC>A)CMCqmDw*qj*Vz#n+bRz3hGUvfX|TOHkTwijlrUD|_QXfVCry6e7z0b98R8~(
z#h48I&3_(abK@$Encin^HPpT&u%{=)&1!<!1$C8PO|cbXcbWF&8zeOA?Fj*~HWoyd
z36jWUZ#q5GH>?b0a-0G*iIjHJ9pUe)J9gHCDBVMw_}d<ybSsPk>wyn*P!}y9vWixB
zD9F<SuNGXnsy5f<D@xpL7R}mNdwQeH%J4#0S!I^FuBBj^oV7dY`fSYYD;bZxN5T!6
zKlh^yu90qn`J7_}mk!Qcbd9Ds?-4Q^Z5d8vE?42Iha@{REzLHBwKL^t_xj255udXr
zCVsLfY~ji|21lM_6{aSga*MIX!3<(Ik|oBa0$PQhbq|dj^0?GmOP+UB7m5WdN%J6;
zj?0WViCM-K8n+<Mt%}wwcy}Ikc}65$$$(XrksjG)J*l^CX(NR*X2$%dEs@C7neyEr
zM+=`Xlt>2Bd^TCkAbX@>srB8g+N>}&afVO%%CQ3tWXKm3$rt>lc9<y2_qJme_M@WA
z2=&XE#~Z`Qi84rf$j6;v9Qvaeak9m51Rx9<E=4&-Mel2ep-c=64YRu1wB>zfOo$^;
zEWSfOkj^@0;`n5$b5@9i9N{#~1WRvE{{R5aL%p*!ypkPB=euCjGm|1}OicNn0j250
z7XTRKOfaAy&RSASOeVD0B)v9JN^OeHuf`8bXa{*by`{4b8c@g`>cj>E#v`Vs%2oRt
zrh7A=c>An~gr=&bp|(#?8EuG(v1IJBO!P=%?GZ)c6j_E%+om?oxXsH4`6H;fM*PSZ
zBQ+U1cWWmo_8y6BLurawB1|C=+*Py;{EV`EXYZPr2wrRkY3la6d>kY?yw0vd289)v
zUdXL7zyAPcn8Adh)>E}*%w*)KKMks0iytw)gBuHC$Rc)R=lyyQjpsDa7n3tHEM(3<
ze$t83L$KO8R*P+!<;zQuinT&`&~#-hQX2v49dh9zgYy$KmCoYh8)+6UL}Z-F$$Bj3
zi=za0Z8D3D=V|kwzWCT3goSE0&w(g>FmU9Fb$M?)^3E`8V~b9iIV8$s^Ou@-piu8@
zZa}bDG3^*`u?wqJJU$J%TThwj`r2_}2W)YUf|m8mj6g;lspwmkrbqRvBP}t}Xp@(c
z<QS;>*6(gkY1T9R&b_u8x04|KjK_@3VjU=C95yPTTCrO}Z&3xl6<uZng#Q4=I}dEn
z?Uh%#31Q6CwgBJlnIsG$@}$e*sH^j=u^*VUe^xLJ0Njr|Ml3|mB4}2d_V*}nKRK?S
z^)ncLAYymL)^k5f>s3eQ*<-t_iLAavUbBTAI&Rr9BRfuijBocRBuk`AHpz&OrJH2r
zu>GZ#M6HQxydU|`tnzkHx7szd=4$7R+Z#WmGi<3^6J8D}`^ir3)HLY75h4{xEkKom
zlXDJg5ei>}Xw)Vd+BQZ8Af~bIj<mnCpR!nO*b38YxOrJOHA2Ea&W=zJWMc~yqfbz|
z?PDHtW})QB%_IH8Cnxn!@+?fI1zL#7scy|$XOctG-@=xg7(XrIJZ+5`U1SV`_Yz?j
z^t5a4i#uvT5vnVp#u)zq8eJ&J`9|5~XBNW-10!K_b5E-^>po{xh$jo<#t-Ny#QBmR
zQur6fMkYA|qjv;mpFin~jMRjeec8yoZ9-??)XVeE?Wv7CAn59ml_ChLjMzChu<>j-
z{3`1}I)j9N`+Hc^>@3-dw)FvqRdTKoS2wXbbD;%aQ0=m%rq3vIVs!Vh3^0Jw&dS^E
zikj2TUw2P~fhx4*ik6}nqZ1AdAmg-5c<?CW%=h9;DBYl~QnDL1E4r!hV#|V^Fyo(T
zNCj|5v`K!cEH=b7SPB9QAh~AI#twd?(x$}d0^z!=ry6~(lZ~QblZc+BlX9uFZGo&K
zee-yT%|PUdQJZ+lnBQ!Vh&ClHly&by^jb2_Nqnh4X#F$k6R_<wGb)YjC?r!XVecFl
zS^PxOdV--4fq`o3*Nz%xlCODez#b+_$Pi4VS!mklOtHI33`JMP&>BuTDu{Ji{X1<{
zt^7zDE~`qrn>K7*_Vp>XT8w*>hvm9_teB0cG_->$iSJ@##@V5a%b%B-S_C2bt$ATZ
zm7oq@q8AqKX=QT!$O3S{Frai&k7IK>4Aj69U@iy}6UTYa`pxIYPHR8z!xWNh6u6Os
z*fZl0Q!SswH7Gf08%|nz_2&Uz<uI`N@iT^t45dvVeN}_5WsPA5^^dG1#oz)qy8S#v
zc%-<IwArjYoDl|G%!?@2nU%GgvsvFY;ASIz_{Xu{QT_FAe7Cf5=}BaYzVcw^CQ2u7
zvHHP?ft*pq2w@DtOithArX)7TS%)lAvFqT)!?%q(p4)lF91)aFV<f+5iQ{H5v-zf3
zr`>ad^DL%papQ8!9UzcRL7OGmO%q%y0W9+hPMdMF0h3>O^5#O6uB%RDu;G^4;IZX;
zq_tf(*?Ksdms-lz8!&>og9t6&a=KKy+PzL6)s3AV$;cL^r*yDMa8MnQmRdHK1)sER
z1YK|RR^INQ4x%ivsC59?)%@4W)mvEEEsM*hz8L#3qD%>IA)0`5u2G+~XA%X7M{#M1
zl`5#p)QHVWP5Oi44ueX^13zg~kYjEeA_QlDxv&1r6@)vK?dXbz;~1NyB=X}kC7-{c
z{ot*WNryOps&@&PQztgg*7(I07Lj+FDyY)PwmoLWWO(zCYHaNCtl=3fKyv2H-^`Tx
z-ZM$GE};^2pCU32)h#-NrRvkHR=SRHuf3hoUn@jKM$QNm3t>5ha)^)VVmnQWaZF=X
z{{ZA%MC~}(71dA|w|3#51faSc&eQM;CU3O+VGa?c{{Vht9|IO2$HZqM`VtNk^Y<*<
zh*pl&SErXOk0Aq?`kcS|DBu|^#NdKtqm9qauqp!Bo~q&eLd~UXfqM~fCpGUDw#z$3
zEkA9tCw~$j37C}3btq=R5T;K*T_$pmJK_ciY;HS(;NnMVnDpO$rebC%ct6bJBB&6j
z5IW}$wPyLH1hZ1B0{%?2?X<9-I)>`62z21|dE~aI@OtWee1m+CywucPX2Bwh9A-Jl
zkY$1%@~v!Z8jt78yM&yac;g5oLB2Hl@nYNw#g{G2w3=-9>bf|=HO#EoP{k%=ZGvYa
zcrB5Rm_8*ZyCwui?8?2hhbB^xjJY6nsGNBKoE^5A!e8d`g;*NbJ4VGo9&JYWW5i=O
z^AnfO0$9k8p_q{ogB*Qc6;#n#b{BMh(H@rK1lT@>^g9myXBi>|1II>!2AoL`YFZg6
zXq#7(3}2F&lF3W}uQhF;5jd2!^CGWfutLd+p|GuzrhUu@$W#QR)3i)&mm6K(66Pd$
z<5sQ^#e@_vWOWMDYC^(7iWU&tX}#SksGV(6H`rJLO{ln=Hzwo27Q1qG4q{jD6Fh3~
zM)IMtrS)6gQCb4XlwYp9Oo1u~%9{6Jgjrn24LIcDLcKv?$Q8g2V;WKS*3=^}z($o2
z5w>sfjGrYwn`P<jRTgHKzv3y{3O=(|x74Xn+AgF-Nd9GM9Pyv$Dz%}{h0QH4Vw|V8
zqAX9d_idXSlU}<XTomj^?NX0}B4ykeMTH)dIEbHNa$!<XEGR{Ef~L)?E;|+q!!8Ru
zxt>gaKr@PLg9Ndo@{`COL?f2S6F@7kBgF57F+U-R{FBIzn&lby=~_SiqKM>MRUSyQ
zFz*^#XE)qn4`|=Vj%slnalI2YX+^s%-J}(hAvG^4e+Ft*rA!wfc<wS|#be57-DN)R
zzj$-O*mh6c%hYnuWGrsBfK{k4Z*28A?Sev9N{EQ_oAU5qzFSE}TC6?8Z#-bjVfUw3
z@>B2WA1F7`g?@62O?N+m>DN%Q<y%i&h_v}pk+Bgvh}K%=cP-g<diEgN*7;><iL#yL
zhV5iHjKt2<a6^NJRJdzIGMow8H_ub`JAVs2=t6KVEFJN6r!<S#{<679y>ln>DLCF|
z3Swu)ZIp*Pe{V7e0m^YnYv*8&h>x6FV<D;`{X-c)bCV^~IU9OUoT@=431#?^afJT>
z)_veHi{FCnb}5yZ<eFI^%vF{lPgEcA6{9t6CM>bbC!(^5*Q^YFAl$AK?VzKV4pEhZ
zc9BV$Fv!Y0Ol+Gm0n~1;K@lX%yS7#ndb*b>6~YI0C5V8SB9h;Dj~S;)EG#k%xy+Qn
znMBM0w2NhpS03{|*&NmpP00)PU^N8rWHe_kmgQO|CL=1(>UI%|B2E<?oPiJGAd#@6
z$R2mm8LY?kJLR7Xq^X#g^xtf=Vp5dM^M%X|-qVF)XvRiHb|n7*x3@WOZP2)%_|Kx}
z>@)nvfBO@gY={}vodERh7As8r^s}rNYLqseXEoBTI-}O+0kKSrnvBjFf2JlPjQ;>e
zn4jMoxM7${?w6R!nhkR~);fh1@vi#`sbVr85T!v(ZGi9Sx^l4!YTRV0KbB@yAV*Ks
z7#`}as*8(pK@Sp&-2}7Q(Z#Hh!BnA+QTZ9$EC(A5z_v+<jO9(*ZB><pGP^p;1V1if
zqBjRROODYHhkID6?6wuwa^+^+r)a!z#8H^>@t6Vl8)Zi<t7USU%L?t6L|2Ym#I7bX
z-2VVmXFj}V{{V_IBX}$?GdZP6AvbL0Rtr$09avhMCg5GG%q{~rdSX{H^zse$@&R>V
zhFp5WRx_872(3;bPjYu8)Pbsx#B3@l-7n3vRki1>YEx!rB~)(|NM&t0+s=CHG!(K$
zYZMkpn+Vx(Gc#_~&XY{rM|<_GDp&@F5E_n`woYCp%2m`1Q`!>Hjsp>v7ra5`I{xO>
zhZ@v1+Cndl#>8Z%N0#8Th6a9XnqAs6fb6kg)IsUyq3UF9IA63*c+dSc`;8*0j)NOm
zab(Zj;q8A?<CdK^EHRl}$XNm{Ut5ePD)A>k72U=Xya?RmAfjIdHZ~)Y8t!s=%7)mS
z96lDez^MgWgzQi#j#Guh_T&8u7ab8+xtrS4dC9Vwm{<-Pi2N#)d7Yyd9hJJoLx7Q-
zZ;J_YKqFVKDY5_#Nub~hjN+7JaRN$3N#sy{vt)`r0%9<><vCN7b`}`SX-eODoLHI8
zz!b$n`Qn8+^V4TgntKSDn@o)O=972atm`{eqcs^P4FHCmLf0g=#4=-dZ#(12WGbo<
z$gq;G?Lpk#0w=N3u6g&9l~FdV0)(7SLaSuNgpf@jTB+EyN`c^WpLm&9YicnRET+Jz
z`i`aD$!cs!QyC?|OmNGsgCGI8JX23^+Y)>Qg&KG<cTJN}xA@CLpBaVd;|9}n?P*n$
z(4f5QmTN3E*`B}{8U1BGs2$E{1*fs*`<%@Aj~V{}S*WZ)DuTgF046Z*8SshktM>zO
z@=y^wc#Pj^-VH5Lc24_!Pxga0+wODzy&q%pQ`-|jzf_{JDor1MTsNJuP&R|4ynScG
zJ~)w!1PPGUx+`?cn#)f!kr0ZWAR&wq2E;C87qrGk@ss{CxSIgO25U1)C1a3j+jS2m
zzHv?4h_Us_JWNIi=V1^!=;>o7n!{y&C2&<L1mCoc_Ga@t<p_we&S^}Id)B2p+S1&Z
z%~oUFEIkr2DdR6OW7#uS2(2TIsXWpE!Xpa;QLiRt(!sss+2G8`kYxDrn|($*lkQYZ
zyx^<L!mX8GsWY0Eo=KSCim}izRu8f6I4_{v{_}Z{IMJ9W-~Rx_Gn@YaLJWpG-FPw?
z0@$=|zZ0*Pepa8FJ7G7M$?t7@+OAFKwm7m7R<xbfqOwY~fkgQnGUDKj*DLUFIyZ@l
z%_;m(jO!EB0h7i!araPAFwqLIo<<2vRBHk$`QXU@5hI1S0h|-zu>)?iq5y`fELAf_
zxN^dyj>pKr#J*lJ@Lc%f)6S!@W$wXsicrezo%tB@82cTd!aANg943ipjIHq^ZVW`}
z$2mSHH`#2(K!qDeBF&J|^jN{QagX_0x#|A^3Bdhv89!x^?iw*JG)bRkQ8!aIYceCB
zP{;re)Xw78w|!*xWj4>5oSd-MJQW2(zq_&uomKH}+^kQJ(AW2K#A|ikNR-)^>51IQ
z8jG(oDX!1Br#(Q6FG~te)6&oZd(}nSS~+RTrlDuH)<nvhcdk5~5d!%Hn&XnIu`?~5
zr=CnfOzxottYB??8BSvN#-*u)e{E=(6B(FuY`1$&mT4`5)MizClGCuVnRgJOr(tKa
z34l}bj)}>xG7v+h!6$XPt$3`i5q;4e34|1;wU^9db{8&Y%)wY}=kMYMOvFb20Ok>t
zD|mw_!t#4aN0!Ea<wkW_Q!`OiF40U(ZQ~ZBdj)H@ait0?iOCm|$~#Te=CB<=A&|35
zd!=sa6gM?BNH8NOG@8-w0gSl*T7fckPtD-?-+lJpam|<%sf7}<+B{_b`CD&rz06~g
z%qk!&xT00dN(jxWFAn2mc&fE#TX-leaMS9t0h1=fDVT`Q)Lv>Fp%z;jdxH8P8Dns3
zMrs@fEL~SwT*4PoqCLzZ@@J5I&0CQv%wk*8nz3b8wyhs?I#&qVlN6TNI_DuGo-8?M
z%34Ad7hZOrnLkM2r59zF9c?zcv4`E(VPt{|PC9Ex6Apl2?-wf?kcc=0Z5x|`ECErt
z#b?h*sMRRgV=zKP8kX>a!47kpIsw*A*{Zj)<908I@%}}ll|7+_un1&fc@dnYRGrDQ
zcHDC6RQ~{+nA`9F075+Phrcf)9E?W^hs^lP&UkLC3%ru7$%*C1I38_2ckwf}9E1rL
zZy#8fvb!ORa^Qp9E6_z3R6DUwPwS1h-c+VoXeoAL!H)(r^mZ-;Cbe)VYU;9jXQpx$
zyo>?N$B9zA%Nj$|veno+V_k``P_f{|$9%%JpRA)|D;WbzP-;qgOrmRYKTp4{^ZQha
zawo4wnT;8oH3bZvb5*W`)aQLd&HzhT3x~9Mhy(WRJ+x}wNSjlvTazU!96-Ue*|%mU
zz;o0>C4shnyxMn&#*`~jJ@ko(>1<YM@0t^Qat%yan6jeSsT8`K)LTzeI`__p$DEKc
z$r(DZShL+iscdkhL4yN3%)xT_mW;9sjmRn9N>a!M10wkLUcT9foD2wo!09KNO_+Ir
z%vuS$`uVDJ9Y3@KhO4I$XSjjeapUF4)Scf#f#45yd5HbIL;-2#vdp`_Gqi_Uu|_qV
zdDh4cut17(BD&^z!3wrD+ay0pQ=nJLTY!FLmF(}$be2;tPIAcxC<Yz{hpsmg{=CP9
zSf1_VBn)j93GKoR$As;MG8+L=Fymso7_wguQ5=zye+P)1Pq$oV#W`9EY@=uY0K2p4
zAEIXm!Nlaw^El$#jbP0TkKCRtd5H#EOrhoz9(hctILB#gMc8!?Jq(N4Byyx3DECIz
zGSeimXr`P4bpzGx?JGk9A^_umJI0y$R}lOQHSa@0%;@(cav?fsk?*p~)@VKj6rWDr
zVm2H=*iE9-D%lY_jiTz&JfURT<Rq7zAS9z<+F<_xOwJJz$i(VfF0F~mZ5=s)#_F}=
zy{Y2uq=s^XWj4@Rt+S{?AsZO;mbGtBDJ3k5I*3RoX>j<gBC1dg9Cr{_S+4dHoSSUm
zP=V|Z-F*h%nRQ8&mlbW|m~nhF7EBIY%Wzx{ePRc?Qr18~n@2Z{Spcvk&IaC`CpBs^
z+0GTDt7_CW66JNc&oVoI)&%ij&R&?bl(#h>fL2C3Z);Zxb81zxdJ{a|R?BSL?mJ#=
zzc|d$GxP<_#FW~d%r~z;HLQ$de~9uah>oTgz$GeP+|<$FLbg~IX#7le^D-iAC=fqy
zqt@%fSlXqyGr^YG6R?)YxYdEkvUS?BSj9VbElY-KRgw%lCW30~gH6>t9MG^8_hm}O
z+u1EX))CDH`=Od|wN5LX2|zD@lA#MYJk%(Pm>m?WceZ)Ab2FS!6Dq%D3>3`BgDkaS
zHhK4Tof8n2$skT4lw9K3zZjSTn8TBrL<Xr|@<cp|v6bi{3zyrwsCCm8j*7!&V<Nt;
z#jr9L!WdJb#KQF!<j-?=&G(tx(j#))A?+{H!sN-?W+QEol@Lo=cGQwMCu!S!+lFzO
zpF3v|4lykL;qO)ennN+-4l{g`E%(C~+XJy4n|yiW>&1BDUW%QLB418Jk)t;8iqu}D
zk`A?cwq=Ins@gA8=rvXiUolE_?dHwK3%-88USWOii3q!7WCaTU063F?(A^k@a6Zgi
z)jboI0g`*<0j&gO%a}w>n1xj^pI`j9Fd~#g!WE*`z=9ViuiP&D(`nrKU9*+h?cs&#
z#4I!owQUWGT*CQw5EfbZtRpS&H1zG-!xB{Cac3FGQ$)7PmA%chs#|6n%|gy->lmOj
znx`dlk<za<X2@>*STT$g+6f0gLWv2Q&#Pj&Y{)6|y+UCTrYo?CMHz6oq4SPVM2glx
zL-(UFh3|~wCjwYe5&B&)CuOo|F&Hs{nbC@KNj@5iGQzgjrO?q9R<H$HZ_xbYYA98O
z&D1R94h(Fyt*uPWb{v*NM%M{vD5yw^?S@E@kwV2pa+t;`)2A3PcY>S}Gu|d+{y2%A
z{{R9+%2s0$$tEns!u{Jz4Mwjq6EZ`M+p<Ro%49R-6B7(6=n};2D6=_d`j#}oaV{W?
zCYhAXgNR~dE?5={7;{^XCM|7<1729%R)O@Xzq>|6$8k<Z1pHYUAHKz%P}-HHnsdRH
zNC93poY#>a8w+2@b78gZT$ZA}sA=(?aUXm@ze+eSPVSMVYSLhu%}%`_l<Bi3!;&Zg
z0g@>)a_5$I7<*ouXst!on8rDlPD%M#A~YN4l!ntyQyTA3jcJ<C2RSNUrQL8wF%044
z>RK5h68DeoTtrU(aJMpyy#jU2aHNQeORVi_eYrEf*&nELUk=m*BTX_}E90hXS#S?@
zcY{)asd%<IqF(m7n8(k|+wTof=S^`bOnlm0LG}mzV9bdLW9PPc$M2Pc`fIMmmkGwa
z*)SLqpVY~k#F-d1tWG#_FRa+UrkX~TavK&NZJ8L-mD_z0gkIvwoR{h?ww#Fbg96qf
zCfu3XY|Gcitx1|9)(jkVwVaPMrFIh4mIssGLp;5b(~!8|X^}SZ2`J`fa*FL~1dJ_f
zHY+*tUjQg$CSai)jsg|96Hy-B{Y?xi?H#vD<4XxPIfm95vaN}VIe8M<J1XA-KFdE@
zC@|-FnVV>uX7j<`VkT#EIoQC0qOv){d~<QIcQ4V(G_EU`*kwy;D3sc}r=4hq4m#7I
ztB@W40C8GkCzkOOU8fy^8=Rp)5c^7zn6cd1_a<N17|l4zlk&Pm97a?fjaX-YC%Rs$
z&vC*Q6=-RZ1XirKQ?vmZo8hh%j*=3(Yl&n*0ZwWG(tR)pidrp<{fjour#8fGxkhio
zHlL2zq?{^j%L)jGuE|75E+>%v;Eb7Z5v*$D)<ns-ksQepOsvkL9TPFO+xW<h=WMFt
zuJI_e6_oaX_R)dcDaCSr?A0Qa%x+Y-h?Eo%PQ@TW8xmU7A2kH2%>3(06ui{~UP@zi
zzAGGvveS*6#>`tiE~92{wCnUT!l&3Lt~}=W2x-J@a^A2N3YuKF!dzY>rJl-wMErgd
z2x-rU%mIo??UIkquB3NRHS2LC_Dm*)%OBdEKH!l={vU5_MOz)=hKns}PFzvGwRHk(
zSO=~qf*h;r7Bgj8uxB$paTx>Sc{j|2MrnVQrgA+(vDSWEuOLIcu^+=U!pnpZNZ=M4
z1rjov_9>HdO^<5mGdSzhQyE4$(XE>34Am2W0_j?zObaH|iY%1ewwe2Zob2p^1F@vh
zbZ#Gp+Mwk>1s!c*P0muOfM$@<9$_%_PZ`nA^X(ZWM9JL)r=$iB?Wu{<dj(p}w1)^;
zrp}yjL>gwFVnQx6rr%uE{MGH9HmbF<iWftl3l^&B$$2cb1WM^p;7VwIrgjjf2@345
zZzF&B(k{o-f|kM2donn{#~~H%@}C>-v*XW*1P-5XKU_rOwmm+{Vp>Wq@qXBxCzD*K
zkp#@~fx+xP3Xrls;!Kp0Xeh!RBE}XtmF<bEk3KM`>pIPpC6Ps(PpU|I$$(Z>3a?PI
zelwIm-K!Vnk)C&)Sj?2hb+eB76-6*7X70mUh@OzOHpy_0+b=F2n2gjO;de+w8dDLL
z=1)jL5|hp(i{)j4iqN%GtRU?|^DjHj*gO5q`JLroB7mMNQA{1jSZfXm0bXu^*<gEl
zAa7!K9yRBNi)X6U_#O(2*GAQJsw+F&J#EcPWt#6G)Qfcj#8j%(_dM;Kn``o2LWF)u
zga+F|Z9H=AM^<|#bi<jK)XQpwzC$DRMqN*3BM4*oHo0b{B5xN6M<JNaC}v5606!lW
zbtTq<0ILLAb+{85Qrw8E5Z=WR41o~NDg=%oww`-J*0QC;I?PT+Q^&34gk;&?RWZep
zAUuG#TG-PStR)&`@06a)R^}UQB`+}zHfAQ{F-IU=qjLJrMYh?z?J<=GcdpD~^D`{5
z%iGkMt1_jznLa+~k5wxVh#p(cOz-u!c0r~DrGO)_#F4}f5bQ-56D9G-E2W%GF|kRF
za@xR27oT+{r&Wjpw%1&Hnn#_}=aNk&SBckW<X>1qcl{kE3dsY9eqU2$xaFcGz=6K4
zcE>v1rN@R&=D5Qzg2Ez5W(qguMTZEIDz{gkwo|vTl~&k8D>7DhMncJi{b{)eF&koL
z5x_>lEYVow%nZ&;8VFctx0iA2hxCtFJ_5;_jyxE|qfBj^W}toQT5|d;y36et%PKId
z_SnP-w!rh#jz}9aGqmNpqIT=d>Yx1kMTS%0W087({t0qBFAwC+pb!<N4$L&%&G9QQ
zCxlz>Su-W(W~d=xZ<`b56*DC>GagG8k>GAG6Fxvmu`%mieXbbCNRyc`1I(ahggit6
z9Lx$bVnT3QVY3+62E?^_w9nJzyE_}am!#$5C4wst?S}QGH70CUrXkvp=;6UuUdSG(
z3l<l=09&%JA@1#BUJ$(=SzrlIXcX@_&n9IE5V2T?DAt%o<I4;_N=(@k1BD#KjOYF3
zMztt=a4lLhSQeyZ@8e=75pOhL&idIm$K7<>2axw3-e)?721uUKMvX{DWAQB}4sZNr
zAvcpBfz_O~$X_sc10+x)mmu5Lc2msOwC~sL)0Y)CsS@Ip!?gin)hBYII7o)F9|m*t
z<ad-LV!+fZx9B*SM2vuu@|{{?G<n2o*PujAdYZmnnsZXvw;|rUbY)dfvy}2vx6sZG
zXpDrlTN&u*Ib;6-!f~#V1k<z?ng`ZN)z$6p5rH;X0S!-HqeWu<Y&cb?5LRC!!e3;C
zD^-d!Td}JZ)JsW%up})urGFp8;$};p*vKAdo6PMIXcm!Dpf~n+X9VSQ$b5>BIkXAn
zgmjV2`1-LbAVf(4hWw+5#^lrMT^j)9Mer*LNr*lMWm_DMagF?Gz)Y`xu5Gf!qfl=n
z6N_dfin|#UDNKrHBvx61L<TdQiY9h~E^(8_u#QgVTh6im>0ugWSf^XhFFR-bVHo`s
z)lnZHH3X_bgQnMEZsA9@i5hcGx+_@&jU)`CshUNQXAt}!Q^o05+EWoa4Yj0<{{RZq
z8v!wGHi3oOa!l%s)D~{9R@sxcjv=R5n%ZE_JtUT}3Yy}@Yw?H7!Zz3)q=@F0F6wmR
zA{>@geO)>TN*t(C0ultlUgUktAl@QW`0HcpJ50EiuT~C=ry0g4^hEE~z3`B*aWs$@
zsgM)&=hxbENw2b{gt?D9G^CiqnuXV<J(<h$Pj$GKTZP0N*Hhy>&w-7CVlbnLP85)&
z90Ge)Zv0@U!smfbQ=wwnd@nsZbbCs8vg&HjE)cr6V1RY$_D>=v7E}C{U_+#{Zd|4;
zag@`-Byd<3+@xnBdUV?fX7f9GEo9l+9$S|fz}{z!9^HqLGCxZ5Gm{-)$R0y6<ty$z
zi%`Grc2fN+s$4u`n4F#X1_#bIosGf}TkLZi&J~x|Yf?itlQAi{l4|+K7#fM=?HXQW
zD<WfaF=w<2$mFd$zR<xCJ%+Vb+dc~*6#hl4b%kfh(vNdR(5!+fV$T>bqvM1_Kctf-
zDVJSu&O$0LwaXlFb7_`Vp*O@BT85+T!fGRFk{o8-fHKhaiqn<W5mn5cB*;d%O4-lp
z!^%Q)Q~v<woJ@@J^R#E&?FvPX!JPJPZzjZ&QY0ZHN4$_m!p(*tDiL}tO+zh`o^vu;
z1+YlvDKByZ77k6Nn{9=WzH<7BT&ik6BOtaC+A_G8wsDF~I;38}nfZ!7O&G?hP((}T
zJ8cqX`m0=L{bIwLRvCQY?mBWugl<=OdqmDxZ(Q-0A}K3`Z@=E&ol6z&XVdsyaf>pW
zRRQ7tGJkv%C6?lYU7DA=uQp-gD;S$GF)=e6#%&YkTg(=<%~0oM0~(a=c6ME6*g1sP
zA@|hQ8h*{Nb+djsGwG|B`z6H--qWxo^zpX$&O4dt_7#samhU{i@iW#ebkO72^O_~g
zQ5>RR_%=*!^*+K@H{ex5J=nfl_DRb#@oopg&7Cr})0U#eJ9G|U(RJ$EYInxeo@>Xo
zMCbj^X}rCLsj^yi*bNJyyI@yP1;a-v$R-5A3fv6RSvCr;^^OBmj(Qyu%@6wrn&B}P
z66mmnG$zGu8Q&JxooHaR@tI0Hm;o1fHP9=fJ6u>C4^grui0-CruWU_7sH6QGOf<-&
ze5^p9%#(&p$q;jmJ+qP{<Biown!q7Ue1rwm0=lb2q*6SIq{1nJW8^ri4OJw011EG>
znX2-x*mCbFdyDHayR{}I1rz5)n2kwMjuK3@Q*nG;xyy4b4p^U`z^jKE*UnVVvl*Ay
zkUxoSTfn?z8OCw+4$i5@4r6}|Fjta^{{T17sU*nL?oE)}9%-(lV}n(w%onJjNF=*<
z-T`<kQORx6zcY;bfse}{85?aI&i?@L00oxtQY;c#7Ku!xv+$qj#=Ke6bq>Q}=#|Y^
z91U<qTa8{sz<Z7Z16oueQp%mb8Tgl&oyd5;S0nOVEETo_i)L&#by+2<b;{(xOM*8U
z%Ej4MvF5UmoVGNE$XtNQ8RfJ{Kq>|qaZ0MzV%ID&0kGJeFhD2$xJGS&MIb|!bjH~k
zY}<8V*;QljKY7hRW<#ox&*>cc$~1t7+}hOd^(`Wj;!<Q=Z}qnS09tca17I6@iaM$3
zKLDuDIpZfP%O;_;<oAe-vVWITZe<>Fq3~=$8MCMXp3ol~fTL-S(vgAqZRj3n!y}25
zM~scb=N)~@=ZZ-_z$h-jRnw$X@F?;_BcHbUrAT*2DslkrPjD6VldubI$|h!1F`j2=
z%JI$6QYK&qL8s~j448F;7-aRZ$k<ZeLFZ)26<bWl)pKiCW-hMb$(`h?@{pex-PVfo
zJUEKmRgk;?0Ght0tw$_u%VrKV>YnA(C2XOV=Vnay9LkDok36qRE9~no)*Z;YbrkbO
zYj8d|&Qz{Xbexo%TeP-A<q=IwReEOfz5`~OfuEz!yv?d8G9pXFdth90bXx@cQSYud
zepV-iI?0?L@rVOwYz=~sfJzg~^G(l}x19d~ocf<QzlzD8ix?&ZOXhiSkMq7OeVxW5
zAZBx5{p*0uSMQZUtY;ONAV-QmTlpq(fr+DL5cw(mqM6%-&zPnW?w81oV$+dq@|ii*
z2dzp8uM&b2ka#k9T6IRMd<bv%3=k>dJN@Fy1=Peqq$hYd>mf0pxxfQt@eMkp)gl(z
zMTIcP_uI}%k$Ih@$|D?eEtonB<!UywxjnmSqQk6b7ZX;!>IzB@SJ-iQB1kNTPHT`O
zs1U|gwkXuf3cHGR3p9@EVOE$CJ~8pbg0oB==FH`IkgI9gr=nDWiNXk(8N-pE(|1jC
zS-5@~JwX!s@+zvtqSm562DXNoWTPumRWhC$%EgHeTq~D|mBvz84wG<)+gS682UVHw
zDosw6ZBy+n?TXVavrQW0nWLu{Iar1LAbDxEPLWvx)yKM;*$H{2oZ^hgT!yPrj?E?5
zxY%Z5$-^u(;23K)nA)xmD)$wrBV^KA-NBL3a8cI&4E(uaMmK;@5#RhRo9XrfQZADL
z#}g%wEK$WZ@*Rk|GZr9a127$JWa40DzdyaoZCSC^RYW#AQ@$%vVuZp5tUkLOp5A+Q
zQ;+Lv@xC%QEpA<nnvg_gAi<xkXn^U`(EIV6#*uYnfB@&Mlk&=GTUq&=$nu#tEi0Ba
zZ)TyWr<XnFd}@2wz^&?GL_Mt*6gNc%HOM7EmO&6s<7b-|nLRP2yApW4QqV3#7pvXn
z^88-VAD?<H9!VQa+i3T0I?SA@f4E=@A0#qSHb8fh>=OYj+m>+^?n=L;&8L%>W|L|x
z1-fe9h6i05HY+rYtXL4ZfGb*<0m#>>sHWhbGI#}F6&(#yN@6&$<hcQXL~2z4N;+tl
zWi{rYl6_N*s~^?XA>qf7<<gO<WS|Sq$0AkJ&MbJ->V`5TK~bOjqBQL2iDlxFY>ve#
zJ+>)=oFB@399>rfhKN%Nsv8$9T6Ln1%}@Q=#QJ#gkVGXD7SS9OnRIbv#QM<rQ?L`3
zBzRVcz%B+QfO5r7H*smifD1!}Jz>8!dM+kTBN-bRMr||VCuqibIEbZLT5_P6^A~Ba
z+xEaI9~rPt`%wP&*(V(~H%W*HtfnMl7cyK7#{eg@jtKBnoN@f6?)DGmiq7+c**sYK
z)&XN-+B4PwaW&404Uwc+XJeNp<2jKmoNENM%Ap*I5RwYZRkb;U<}rl<GgZQaV;OEn
zva2G!PkFH*g_6Pvtgu46T>G!F5;L^VjEBPPBC)!>t&<p?=0rta4UNQ%F`zQzms+r6
zT|o{*Y);!0X0F=*0Ft)*g5w|)=4MMOhVvV{e&%zD81oVJIYeSRi$7Q0rwoW8c`*eX
zvuygw+oW^Geb1SX5MH$Qn;-;2ODPicu2BngN|o4KOJ}ZSK^y(VmoJ`Aj87qxY-CQ;
zm0~|UrZ(C#e9Yyt?IS#UX2J8%7>BZ{@{4u)Bql%dcg|nbQIX2hwGTTCeK<ZzE7s?Z
zY3?Atrb%14mM}2w19$F;rzN&3stv!A+6TN$X~04jvN#m9&qmcMs;njRZEn<i1_xvj
zWhnO`RHyvJ{_&dU{{T|NXZXwE*KLCpXWZP=ti_}5vAi$kzaoypB8oFBMxc2k!%b4j
z1Hmc`j~G+DPW<@BMS{-xEw+mXVK-w6{h=8g{9Cdh3rxD#aY?-~XW<rWGOI`rl=;g2
zky)cV&Q>1R$6#5v8gW>{)~}Fp$az@NSl1d9&APPV{{TycWk+1(;I+jqhoGJsO;$kT
zEQqPKR!7fIYh`+*r{fH%?x);Pmp%%2uG3`7nN?)nNjUlD<`z?L<vzB4o%3#AD43t#
zv_aIm7!M&hkPb#`h8ufbEW=TyoN(b~1373q%V`r=S!YqnEpcRm&7Ie0b7ah87*yL?
zsJgItMZ~M;D~PaMsns)uk8L9XK5JmiOb+?T%SeIG_OTdI^4zIU113egcM@t;(GJhe
zGAWtOQtVo973q3)rwKR)%|)<KvGeCO%EAOK4WR!3hcdm6t-#!fi0n<B<hIQ}{{Z7K
zA_`(AybRe?O{7Y#F<BmK60$eiVg#5o;<|b$-mxoesCNtP?W;|>t1?kdBbD`@xZ>67
zwTVnTLwnRMfszs12%9kx5nWPzn#%PZ@gk*8e*XXttR7byJvam9H3!^QDB?3dL?9+4
zNl;TWD?|hsi6lrTdX*Ji%5bN6oVz<vwKB1uJ(DLhzV?$W03}(U>_#T59};;nGm-nm
z-2h@x#l*|@i5;P_$uTWkge$34YO=90jCskhB6|WN>2_IRe<A`IVIeQI{-pDph!~#G
z{{Y<mvZn><FP}%o%dHvX1UXD)AqgvTy=7c&2@IEAXc1YFDpoTbNQPt2+IE(X>>eyV
zqm)MY9!zsdtKDP0w=aWr32aqt%Kf%|eg6R0rB*;5^KzJ~omOEKk?)qc!$8CLoJ_|*
zJAPLwAzD=&tVBZuGvmR7E;ukoX?lUDbg|WI>LDyeO72MCl8@UTw{h)_R3}bc5y9CY
z1ggqutByVzAPowo&riIv4->vq7P81fSZSmX<8!6!VNNc1v2zxg9_ZT!Xyw-X3M!xm
zA4$c?RIM|EVEb&Oi9GL0F(oH$^E%aO6PCtK3dx)C9fQU+N_eoQCv2Y)8zNy;p3E5O
zt}XQ2#&Vh3CVdV#M;Xuakv=4SU}k1EvtjbchH@X@eajG4C02K5F_?+}0LaLJ{rN^G
z<SrjxYGqpoGg+{)WF&dHQI}7WIme;x%Mi$F3ZbwGM<9Rrn4fi9fFO^FOJ*dd7d!Ec
z-z!WFh0a?7D=eHKP<_uG=ZNxRER>9p`5Kl9G}y^&({gt){@JXM5fd0+*;Ha$b=tOu
zsG$ozt3(M{Oct-p@k^YiH6!f2;>`u0)pX6ck+4xSv>*pI+bc)(SFn`@c?po-cPZuf
zId$zs0R`J6)wOCxi}j7ip8Hf}9`vt}lhzDjzhWtf^O|h`09c62rb$sadn;;UHSAxZ
zG*4puuDdxLd`>v#wLwHbr8E?y6LAL|<WZq~g!c&o&?kDN5|Qh%YG#h0GkbPfC04q`
zy|v}`A@!$bY0rTbOnEpap@L#J>b-m}GNWt3n(LTshD6Y^<{wbdv|GTtO?d<`2uwaD
z$iA~qBHGHJVtS5olFG^DAp`o!hG!8aRw8Vxb{vxovvHpVrGWk?TxC2zFv<#kvv80o
z!Xj#froEnmo$J*ilRDEQI(qj^$QIJd%&lXNO#R{@{e+|jpXML9w6lWcnk;Qce79e@
zi=2)KSOhe!+COZL8fKLdEx)Q-v>+49@Zgn&Huj-M7t;=`$i_z67YNPfKCCR*B*3Ri
z(uT`OP%ys3Nhxnx#f>e@a*c<f*MR|3G^DA#B<iqUa-_l5(=PV^0M=Akv^3zIZe!aF
z;K)q#o~{5UOERvib&dDeRm8j<MI|APybCexZJLW@!y@V@ZN2qq+8`MQJB*)*IF@bZ
zVB&MZPng;^jNfc6rXwf!%}>Uj?EQW)Lz4n9<%%*TxmA(ZX7!6rv*LVboUXv~IK>Aa
zg?f#;W<YJPLry<uP{|T@vypAKQZMS(M0?BwCOv3G&BW~`G617MT)!+Sm?KcmOl>~2
zeAf`2w%;soDy@(b!VnuJ0Uq6YRFp19HB}2MOECWcwy4CP26?i$fm?zwHi^d0?m5Zx
z0~uKiu-+i!U38~S7KgX(Nl;>yYO$TK_b#7JvXODI7}1w$ufY7m7?nq2>~VdTS?T0E
z#7xAi=L?%9$)`Bq?o7m&)Z~M#I%Ikllpf2m?JGnoxkRNMw{+lH#H9ZKRPV%#^w|ls
zVzOm6K^K)FWQg4|bgU>vm8>})-hn`nNVTg9^~X)9PAe7}YiY<WlgA`LM|p{X4gEZK
zhkl%;HX9bL*r#5Yh_*!9QY9aidS^=ln>89#Qfj|s=TT2$TN%cD#1sPO49&b_9X#c&
zk&Z~2^q=vMR07hIVM|AN-XrRF{qth@X94pVdrt`T@wR`AFTD8g7+Bcbgy07M0Ax`f
z0=x`?!9n<#pRZNcGoPIyRN71xs`FXQ4El}blOEk80ne0m*N_wG%d6>9LVXWtDcU@F
z$ki+op-%TT<_A63x45=t<SS@;Gl_A?>)Yh1-NDtV{{X#a-jGC)Q3n8N#^(e0TDFj%
zyagZT4pwVHb|V}canIWqKIcv}%Krem8Lar$EpH>S@yjL1#&b$<P4t#)3jY97Acx4y
zi;T~YIw*ci=1`|tvJo*bUsE&YMaFh4H1f;dT6Hbqyry{=hqN~hCh+asnTbYEE?W%)
z=dH$Svk_I;k^w1`t*8a%1zW73$WEyOLnHRq7id9VX|jb0EYlRLdNq}>qE|di_<gev
za<T$K&Z>Bi?xeF^;`+$52<CwIIilEHjey2dIg))_76?dXlIq33DkMG0g$32J(g17&
zIW@Q-BCvw$`FI;{SB4d6bP=M9y{-DDcdIEE6UD}NY&DpBNk<Tfm|)^&19|n*#j$k~
z4Abn*ZR6V68QVIa1rrd9A-eK>OvWO~RAfj^IGrhWRk0r&8?)Sfj8Gu^26jf6B^b38
zhp9sVv?x}wj(g17eOja1>w3MbBfv~x*0w}XwvTepnaO6AJJJcyv79mCQpiWv022%V
zZXZ*d!mA1$AuSHMfXHB3ii;HLF0-h{KT)o$DpT#HPfMZ(OPoX=q-Mp$3pDE_CzPso
z+?!*m&6i$m!m7DuAmvw1n;ovZV#!^GONBk{oVx(US7>jHg48Lkxj}%VY@DZw6d1<g
zF*ePq(&z+KZHCdjIaToSjiLw6-xAw4`T5_#?q6}5+c;BJ+jxS+{{VC+I2W-tW~ue3
z&+>lwngReXxo-Rl7B%G)!OO-R90U$hQ8A2>9X-;Z<~75SLB(J#_<L#nwEFxLdc>cA
zSm1NsA|zr`$jCz_a(HlIycG@MArN14kC@_&PliO#-f4CNsF5(<aba<3CW8SyQFB=V
z7?^G{J4YO5(9_AvGzPU$-rkzd=_t2OY=Ayul)|7U-j-%dC7z#UbK}`t+jvSGHWdc3
zXSukn4}h6XcfvT7G5`#O<RhjgE=2o@gCDt>-Xyn`Le{`G+^}!7QXnM4m=|$%q@*0e
zarvudU+wI=n3RmRl@%b{F0;fX?haCBQ;|HpdQxM%l%P7)sKZz*vR7MZO%hPhk#it~
zN-HRATUeEb!(3Az^_ResXC#Za$bmvupsh(9uDi}6y|Lhe%G1-Pi!(<)+S`*yoMY9%
z&waM>kul09Nqa0gIEG7L5$8Eq&7`O`qvv$622Olu`J9N7&U;3~Sps$KvVA{joQ=21
z<H)wi+`K{cf~y`($;`~+_xp%gK=MC!JQonllFzuj(ftf-9Xyz?6D5kW(SsRnSYl?y
za!YI)gn2H8@D#|V*7(N=@U3aI7r6Uksbpu(kRWu-&mY@9@sY*j%}fNhd~lhsv0r|a
zyxc@(vN*Q=_sPbrh{18nHtL6Xs*s#f5S&P^N)p|gf0}!31wS1+)Kj$vS5Xz5#b&BR
zcV(q*P?j!XgUA$PO=UJBGgnUFyOGAYOu<W6$Ky;0Xjcr_A#ej{P+fL)<+}th>>+{}
zn10cxrBekyVinBv%OMp6h;7LK0C8Sx4=V~B(|qb`oYqWsh0fbdjM4FOwrTgw?AY27
zLsQ*>saJAl%MVcEG<4t`{vVEB;c~}~e#*_$3O&t9$MFHMa0gv+Gm8jziyT(Y?bh8N
zaKFbbs(ZT<`;?s=Y@<#^(JyOKvC}e^g!^|^GQNXbdCO)dd~N8JJcuL;l}AVjBC8--
zO53?1hNkYhj{+!n5yDB&e6A{pPR|zCNY7fY**7U-$+Wk<0q(6#ZIWg|ysG7e98MKF
z?H_RFR;>XfwKi%1n(t5rA$IF2eT6Y9A-2%2T8J)h`(d9GF`7L#dx5h*7|*br*N6&H
z5#u!VkGFf7ByD7r^WrlBro7e*j2YZ~agR9_$V{O7KsN<l8QgPV+B2?hfWehD)o!1$
zpI)HOHqdpf7#HKVty7xNI%Tz1Ygk;M=LgA7X#vwohl|zhx*8E%SF{&Eoq8Qs%dO%_
zg!jcHfM&yXYy&3+4t#QfaOu_vM*TEW5Mqq`LOCOx@`$_zDyKQ2p3z_?G^k5n_El*d
z!vbS%rVIApZ;Vb)*v@|tK@?YrS#jkOwaU%ByN~ehtg+3MXcl!dZSdCM@<^`xEa0J4
z9W1vwVO+BXYUHD`LO7qZ;e;|1F*v6(vfQ{HGNws-)$^s=7-G0MprO1&VHX%g#0<pF
z#s2`~=QKD>@WL4aFqY2ZoKD|7F1qm|H2zMA$t~cBN`YJ$6Jm`eB75Y7^ohx?0D@xA
z_lq=Ye(KL*8zN76mJH7vFzn+6#x^3Cbdp!j0*k&2DvSF_i<cay+8j?PB$G#uGOuA5
z3l%E}4)LbfJ-E5cva5XBs|uJ|11XCLpKSZA#A8flr!t`e;~7|MDcx{BCU;zZX7gNZ
z?ZjZj)hY`MY7%=XTh?<I`g~;l_|p+Is-1~#wi5)RppWNS#Yr1hIM_b5OzG#>ab-XE
zfwwq`dXRqL3bBHVKTU0<43<$NXNV|5ZAT3)YYLM!CO}L3bC*-RbBURfnVJxN{Kd{G
zk{kf#=48r8#R=d}el|#jXFhg_j~<b>`5#jmRb<*%AY!AxH<o{lLLVS@qCC!V82gi%
z`>VyGbxDY7N<^1ktlJ1wo^Y?c{{XLF{qfg1x9ydUpz!Lx$s2YeGLZ7JF_(AuB@@RM
z_o9C;+55e$;Kup5#?uosv}e_z<CUJ;tcI!OQ2Ne4r{62Z7??b;2(sp!F-$>hY7W+b
zVY6<*jpjUM{{a605a+}98QFq(un<+q&ok#Z7v6D$-#JB!@j0wU&Z#*qVJT$>Y?Y4s
zd{4R@h#3yE)N6ZNY+h;fn|<&w!5$#6cj#e_NDCHwj9ns=VfD<6o<za2*5W19ZOWEL
zt9ccsML}g+@eA9qR}dyl%_a-+8vB0{%t@j#)6N00r$_Z%^G8>8mIft^sQj;}EYLAy
zQqyMOL8pgA?#hpMyivxt=TKqbwov-Tn&S<0#ze(-s+)&F{MFg6QYjfIEj9lD)}u9I
zkw(IbjV1DHnbf#aD%(2s;$5lLtm9Co@|ZexLp||e#+(A-2%#`mov7Ni@XG7#a>puZ
zmu;|I7favP8xdDUH|fW7AZLzA%v6;s9mXay<@H=mRp2gQ`7P%v)M2vl>S>M&8zH$&
zsDD9Oms6FsV4J!+M-OmJs}$><oGTuZw}$Tg5s7+@Go@HNk8UGNMOcXlOPIkXM-1_?
z2<rYU&Ioo-DSeHet}cNVZX4wl0u}D;P`B?EM?N-@%}qQkocdbbzy?qu^b$esXDPc*
ziZ%iA>g5H>{n`%b%DRr$fK+!bc9b8wrD-SJ-8su(nk>(^(~baSekGTy?I6&1^?E9p
z0&HpiW!M`!LVCfSOP(@DQ!^oG;v^28s@3W(R<#P@PSB7lKPtqAt6jU(fvA~N0OgbJ
zLBWD;-4(-_loV$B@yPm&FFCHVeag0rjo>3c8wx$vnPEj}bdR!P19LKrr>0zihgdB^
zX241r`sZO7<~yA7GAxEnaSR5@&j=d(nVdmyFDU}p7&-SjxcK42Xqh*&{W%aGXYbd{
zd5W7tA(?ZG+p)xyXVC&J6)44X9Hus0OqUo@@OW%R3U@4e6J$4I>yl1#JaL>+0deHu
z?6g1)dUT3$UKwiIB|Kkp&6(vijf^J@)-v8zj)N)&QY26wJ^i`k5>{=pNM<#r8sSmy
zV!JxnI?Wl+#KpYjO4SwL)Wwv0m`p{B6Fercc34=!!F!1~n(vvMrU`*^rkV+NpkyUc
zGbT+A79dtqs4^7n8h<QE8x;`Sb>ILjq^-l^A=M~=$GBi>B>R0*=v{X(b4`+iuvp$m
za4;Jml9|U1L{Y3k*|c~P30guu%h>BJ82u+W@z^WQK<~WexHi79=BRRuRiG+9$&m_I
z&uxvdKeyq?MDn=XlVT-Gm<624k#e2$_S+z#6el*BL{1m)x9yvZWbZqkAtCv0VH(Oy
zfi<K&CL&5;L=#;QM`gxOB4Zen2p$`G^igqW9C_b(BPM>e&jR1lCu!a%$Ej@)I145h
z5ULOtYpfp0bIp^q=9m&z2(rTDtV}~zN;v|}HK|>whuW%jTtho=o_?}fF#6{+_c<~%
z5tAF%S$a|94Q~E1R@{-0%3(~q9gVEzL>#GojaX&*5xbKMiJYE4w(}L<dAU#C+5Z5F
zX9hver64Y?I3!E%RGbj0ftqB0T&SP<L}sH*ZzOiDT~!kb3H^EaxO>73a;BOs7}_YG
zXrnLGJSHWUo*-i&Q5gy&87R3j&X;8G;&Ea<PASmBXHXf<es_BDe=#*>GL^3B#a>)&
z@wX#nYY?@kBCyBUnszI$8|-yd@XNX_ikq7I160!=it}+$ddNdG`N@)+vpJ!*6MiOj
zG<$2$4AUkfP_H?uSOORkb%$A)me-jEZ3!cd-aDHQeA8?(g4DhbPd7xR`l~#yzxkY5
za4DRx;#gUyYj(yJT4_2C;dY5z2`YemK#Oigylz7yJ7hPC7_3=!rP4MmKeh~%#Jc4&
zol{H;0B0Xt#wU;FjL_9=QA<!Lpcl6$B`T^A3fuRb9rw>U%V3$qEx3+zfq$KLxiQta
zA09JVWKu;XHZ~W!C3WJmV$Fv+mPT03C8oP!5x>iP)hh#bVaXw7IPc7?p2mX_JyYni
z<sunveVaQFu!{<Sv0bo6?2YHdD9*DIp}t_ct(j11u0MkYf>SBVy0v=j1x?ywOE)62
z)Xg#RBH-o4kO=w4QLkB#_%T*4Sw6K^t5rQ0y~5BUCI0~L2*mmPdYs6P1JuQKs@?e0
z*%+NgZ{Z^)8${2?X!O*o9i5IErX-{olrq3^QuOP=U9&3E(4;?~;s~a=3iCE@%#kmR
zV}grdCi^6{?+|AkylpNH2N<9;FORx`g9wNY4cT;}D|>?`3<QAp7akEgsWWPHmHeNl
zH=LlwtbZ}14s+W#8d4R)6db|XA6sO2<WRZde6dsB6A(we7M2u#NSxQN0jJ-2r-ZRl
zI%Qx~I`g1?`z*PZ;QObTm-wXxHiQI6rre2-DP5o-oDU6*`7%awzxQHs;k_q6BPh=s
zO30H%&SP9U?nJsGAS9-YGHd|`#G7jzza{P&5;h4iP^5NQyp}<a6OqP5$&^O`1xF<W
z9-64jEn+Kx7MjY>2UR*+VJgo%7j1i<N=0!Yf1i%!i2+8G_hkkH$Jn{U*8l|KC<Ur#
zBVaM^cmOiQEC-MPW41GX+mY-L6{mh@brdFGg4V8GT`JLRIpY4yP<w8$uPlpFcY4yA
z>tCT|&AL!Hv7c`F&%FNtj@T<Q<Fr<-SO#A9_L*_0$w`^RS6t)VVjnRx6?cK1;(k+~
zzWKlTnH{GQ9$6!kfghCJ{U`6yVaf>P0Ge259t0_Wx)PEZg(UNt$I>C_TaIt|XktPg
zJvc)PDH)lJOV6O{%#48$68QFp7sP{h#>_pkpmqz8Tt4^HAYsQd@!tSb<fO=bsTKv>
zKoA0*RrZMrWye<Eh)d}hmgVjE_<U`oRXqOy-$}-<ZE2Mg12V`JOXSKOC#KIEshl&I
zxV2~2_*3R1HJeivl>rkGwnvznjIkyyWXh%?irQPe+sJSZ#2}ct60j#Nbb^zmpSC#W
zQmBm@pGg_cwY1sZAh?tkc#173PMKPkn%22hp_Xf}7$U-$&1Iqo7?;Wec%!Q@Mpku-
zkr6dNgp##B*t;V%E%F#2WK#<%T!F{&5~F&qjXzJCu(!8XX)jRRY_0_R2AB+)kySEX
zYo`P-07R`V@RPJSuLZt#6psSq%XtX#_u+M!8D6cjNs`f{EDQR+im6uMf)?*N*=7gg
zOw<XH!XK@1Il$C40${Hgb__-hr$fF$)vU|bL^d?-GEWynOhIpu75@NI`&!r;D3A!`
zN2t&l%GT3ol&Zd<Z`G7J`{nnIdi%{=F0pD1!O=IY25WQu7_dfS2WUzeVUV?!p;&g{
z*FN^<$W(FIh3KvQggk5^ZiX%zk`l2~gIOo2lpc{pp>`vLDaM%nNXk^%BEy$&&x|gJ
z0u#zCHpf~UVN#-)%D?Wb@2pHk7fjQn-PZCts+66G_W|=Y)@i1SNsO*O-|~8hPH@4D
znBk|buI8j!KWdUjoN*&gEI8jKr=aG<_HlU^$fs)G(F`2bwA(f2Ty?22;?xZ8v6<iW
z@5^Z3PG>9@%Ok_qF9Jh_=D0Nap60v3zUw)T-X?i{@#(InWtsfW^_P(N;g5YPoGlPl
zL{kh85O^E>6*K#KDh6DrJ!rXJIQ84c`TqcZm6yFXN$Oks#>BtJPT}L0`5&OLB1J~b
zrT*nZC%9FztgPu15oM=)*%pFy;X8ZAr+zF<B})F_;|!R%A|UH1+z*44*whvwX9OeK
zS%P1Qna$&@c!&2q2Jo$+p};Ou$i>eIlE!XOrbSpVu69Ve59BG44K=)J&uy;8F#Aeo
zLaU4gm}?2DT_Z5#*`{PBQ4R?SjIrZOsySq&lTs#{3d7<e_3zG<4Z6i#_Zl)Fr%95x
z5;e*4(n2yOVq!(<hX=oT2@Lt1X~$Xv4kL?qsI!+UL;>ALUTEp5t!X0RbTg1Z0I#0R
znN5?;C;Dn~++=(wWhCBiQCxp}y=#mekcqbL_sqg}<uNivJ8uKaA7k1YM;v{pCFp_X
zd~;w0PD#nnpBeS_>WJOIF)<@E1)O-KrgojT^dXqqW+Qos!V%-P8#mlT{yAU3SuCBV
zlJ^!0{{T%V5u3T(o!>bhc@wsLbAvwM#hQ?J2#kDpc`Kgc(%j~AnPowPw5asc$MWnP
z023#O%GmG9u3B!Q(O5$C3+EfkbVgP#))t+ooXL<>(8hj7ui@dCjA!8yNCG5u8JW*%
zqc?e9;Bs*isnG9lJ4s%p?Hf0e8!p-uqO(Eeiz!Ic+CV{_vzMPMr(ojI$FkFdE3cD+
zGqauMJbA9*98Fj-wYDpa1;B{g_pBi{BYpRonP`4W*);>Y$|EZAgMb}MQ7-^KwU+!I
z8)=lb20fZ|mDasQ;dK-~`m{#96WpS}5^>1GTBImdTBAu9D}lw6{7uzx#25tz%_g+2
zdfBIZc8g-a!^rZNKWkKvRFCTqcsY&u#1FSQTtur&-e8M<S)?{S^0s_R9Ns@$<$6}E
z`BJoG2b(-lzA%`1Z#<^D;jr0ds_<KvxT^ua6VfZ>VRa2?sWGZoH*kQ~7Y1T~jne~|
zz}BmCJhiCTRKVfdscOe8JM@%5IF_c=l%oC8sD;AP##}aocHcjT4ttszRpwg9Mseja
znxwYqoGVC4#q`0Ty}h|M-wAC;s1;TATf|Pqdg&xp7A#|I@1A_}#ZFab-EZ1+M|720
zT!)bf&2GN?9tY;>CLWclF~6HHyp8_=CawFkU`8~Mrq;6MjY7?PTE%SC;no`4ur@~6
zLZu00UPjGdby9<Kj^XV!Ygw*Sv0U=as)xJNZNX^|f4D-&QMPLARHChEs0=ed%M(Sg
zggRhFu;mSFKr)BGxJ^O?w$EBefilte^1#0wCB#u$(|~asb#>wY0CqC*nEOx#!#>W&
z%t=7|B&J5zYBsD}HOpwg`;q3=5>Etq&e>%k-5$H~0}e|UhI<rn+nmnHX;rT#zx(t`
z$;SXMj;r$+UJ89HM3sd^Tsb>#ne?0ylAVTlCvG%hj0;mF#rF_R(HL}WBQRS}?>IB}
zhq%BFZ?we6<=I>mL~lHCQhde%tu*e88HwMLBaA*|3vJ^W(%QlSu|$j(rTxmMn-4d{
z2uU9pndIPs0{OFgGqsq?PA6Ys7C`=7oYq{ualNu>Rjx%^IlM+~1O&pxM<Pc93R2g}
zta~g(c2niOnHZljmZV|3iwyYy!&}G!a}~nfQ6#2Urqr1;DKnB{t?~#%>`@Pl0jA9(
z86m80(?Eh+nkPLAH(b+flZ5B$U<Oz?XC&#I_BL#E?!m36?IwDyv-mGclM4>Abp{5q
zryxX5SvuVbGxsB?TpB4=O4J)J)6h~IC$XNNdHd#_J$~W&8=TGwaWE{3t|^kwcTrGM
z4r5-^Hti+8NC0iWY400dq*WmpBd`?D#|n8DFHii+CJMHu8Ms>}Mo2gqINWBppG7T>
zJ|Q49m^+b4Po(*rT<uoIPn9fVg#G0(9ttV95$+B$IPodX{`TH-Ow09E?f#|~wlkXc
zYoW8dY>jUC-Bnv-evLa%eL^?BBS@|h3{~AVV#BUl(4uGCt69_>!5EwOk9;OH_uo%}
zCm5He-yQMe0jHupHuMKNuO6jp*Hc7afvweW6h637Bp<f?C3AUh#+LIXHXRh|6K2Cq
zsw<VjkX0C!59PIz0Wn#pHQCb0IZ{_4a=_7u_F94Db%@6GT-t04VoYPeteVJ4yo?nz
zYPhTa01oz*iw!MTZ+u(K$JR%SmFnf|Ct{$Sb#n3vs?oZPx(=~*p%yyo@~#dD4gnmt
zAvh42*DsuhpSZs2_T_mRs-$;doogV`$XEab1^p$L9VVw~H<1%F!P{*uaTQdiL^n~+
zY0E!)na1g_vSw3XOs7n_u}(V?uod-z%9#C5pnj9qRWTy2=iEw7#_KaO7)>+#A=-Au
zGE&hoU<fd*ghXwV8%|VAzDBSX+|N0bc#W206PkILU#%mmrc#<=m0bz5T=LSry@sze
zE=p6Il`4?xwya6E5T-DOOzPR3&=vhhVaBA+K{8VS)$Mt*HY^}Ek6IS}u}aR(F|BE!
z%{tQJ(4|P3>lmAhh9I(_L^WHLP_9iU+O*3OB2kGsQS*c@n}+i&CI-*tlcv~$(CReV
zddkuWt~40wG+dZ1Gzql`EHE(_8Bx2#suF-2x(4wn*acc|Imm^x=QbrT5$#w}soR#>
zCde#ZODvI+-ioUd+htXH<50%(FqayT*hD>uN?7=rZB+X;#8upYB%Au9H5jsXX^nLS
z`56~S1%&MdA-v|PT%e`ki7w#d<*JY=C>}x;PrkcrWoJ!yLFJ_NHf8{1%myr!6^2ZI
zjELTMnq?BxnCy~1MB)N3A0IiThby5uB_u>sn@8StPwFym;{ZN0P7ToQ?rj#E18R1D
zJepf_iH{rY=5{_)JCsKv+bR<zrD%AuO>bp-QAW3XpopT=PZu&OIl>X2AO*+uJKNX}
zK=10RBP*|xUfCW12I2wb1vrnBB&MVf8xA^!BV|(`q_@fNp`4`!62pac*sjIMeorA-
z99sfeRFYc^&+i9rLzUSHd5%wlotppyROTd!k+abizh08A6YkFQ<n13lw~gYGVfH{J
zw%NB(s=^%wR?EX}JAiGzSC~=}UJjdy(kiW_%9PTww`nw{NodsBa?%va%P5=m+iOf2
zu6Nf>5?WFNB6r&w5@2Bq>6FOMsa(C}=8VZLJ|ivoOQSxrd}r-AI|iD0TC;4Nf{7?x
z$1B&|WIG6v?-A6t(vpr2G4SeDt8wN9&Kd~_BSXhO;ryOsAY_gP8}QwJi)pHN27+3>
z*8)>KW2{zF+3wgRU0YAyK*asTFMm76*~&(s>}-}LaXaml_K$8j-Qk8f*a3js)Arjc
z@b>`*jXvdx-7}|DQGZr?hMQc&s?D83(32OaE-S*(5d$7EApug%WMYeex?(o{_KC_f
zAggSMk3rsGf-G$-<>~(bcq7b&afQt0WB&l?^MpI705p+0)mH{Xv~kIf#t6=@c&xXm
zF2Iabfxo`>Vqfr8b>aL_5i>qAlX`2ia;kt7=nkM&u@`C%O^ZCyMnneDoiR;@M4r}l
zD;wsX)I(Z?YVkh(xUlVN!YNN?yAzBel`kblw=t~;7Zb-)D3`>=!82Md<+<I_fF^S=
zGdu3s9~)+%saCQxha%?L8PZ|svSB}^n`WV5^<4xF)GjK?21SFMm+Vkw?%_+>e&MIF
zMXu7W=@PfJPUUt(i9(omEeX5xUJm1gW(gI-rp2`zYZ92h)xXMj#jp!bgcEDEGqIwR
zpD~2*ZzUCqB1Q;eCybY%oNw70^vR-wC-%#O+KTpKg<hHo+;--BYYvp@#kEL@RfBGr
z`8`9ja$*g23N;D-zTSV<Z;*o9c}jr$cskTx0q#V=SxI3OL<GcaaWO2F*EEi6G1sUZ
z=l%L5IT3AiMkO<ofb*kT0m{2=Zigz>*XnZ9CJPMorPkF$YtQAdOmBg0wYtS_<9I6B
z2UAj<cCE8hV~LS7{XpR<i8+HlH#F+eJIwv^9i|`}gh>WM)P_SQ$;+%sw4oBSV69bk
z1+}wK6r_N!c=+Xq63NAl)dCq<LJC4yxnIsXM<F#e26SSxR6+`RTPnf|0Wu_#s<{~h
zRbg2e1gs!JKwxdh54qpcXDgqy1Cw+IGLU}T#@R=3rKq+e1W3vRb_5raVX}UEMnDwr
zBSd(jXD$!tl+D=M$;J$UaEx+ICFTogn8@GnCSqa)boGSKIBjaqHCcUqOWfOh9i;LL
z2E0Ftl^Op49N(106d5n-uuY9UnFi6S0;{3Jh-BiXMQxnnD$@B)dE(UVXn5IlV?1$;
zqaGbOJ9wC3Nz?-$AH8T8pzhi0@we}sNwe`{*&JqKyDW`Gj+<O-6t$_TXHhQZ>NbQ6
zpdK?Yw#-3Kff}tvR!qj9biR|NT8Qp2ttqBi3!^b4(_ll8xVLWbFw=`OMwn7@GstHv
zFp+DudUlrEBM&xc)hF~+t2Kf`fA1&8ER2VF)*u2aIH;~vZX9HD9kHl|*d>{L?HH?+
zDp9H}JzC6>^g`vOCe}Gz6*<{6F})5THk@sInU@TyATrr1h9K$WK~QIo2e6OV-KTLe
z$y%p=9UrF_AzyZ9$mVA@+aX3#%M06z*BND`$OmUR+B1~;jL%aeCyv<eFpANIACjqw
z8IQE8WNQGje=y=ChonaaB&TRjjeXu^X=zB>Y>uT>Vqs0Xo#z}8+mFnX$N37|)nTT>
z$WMl-a!Vj8)AJ5q-Ln(6*k(|bFE;e68&tGjwLIBWV`e>qBh)-@Q~oeK=<bYA2rg~6
zNS89SJ^TE#QZh|DCOLEa`(~iO>*XV0<&Kz(5+w4B;zK@SO<`M#I6M~Q>LIhaNpbTq
z?}E}1XJwDHQ=ce_Qa^`w?X?rBPS)&3QsEXri%2c?HBLAEtz7dHlt`OwWc3)Cx1RB!
zTMe4(v{Gm`5_ILND{?J5w&mfgh}MczsM37ccWcbcPHFqaMi6}b>aaz;MQd{8bd=xU
zmoI*<wE@2EdQ&4mh_V~Z66m)<(*SZ4IUzAIx<UkOvk=)ucku{YoH2})%#_7>f3mb{
zi?%rrHrRZy%MRoO#AMiL+3U$;R(oUY+B74#Y=YyA4zXTwnfjHCCzRO!uSKlv>@G1e
zQ2Pk$optrsLg&P0xe{xr87x^7pfun2sf=I77|Ei2x^*ba{{Zi7<RrF<E^A)=IefR8
zGdtM~42}5?$6;WU#H{`^5v&aL?;5ZlI^~^A{p1<85?m4URdu;935r7qH$ZWd+`0X1
zh$y3Rn7FN`tDNen?vZ-BI?Hk(>6MgJ<65EFDQb0qU1DyRaebUZ$)H9FEm87R@%GMC
zo#dL_M~dOkoUfOeiVa<KJggXLjDnCQQ3?x*s8v^L0c!wOz&<#}GTgZDK|TOmwk^af
zsbz5@e26+`4reZ*?_pX)uG%Mq3B*>&oj;~xa>)MxPJ_>^p(OtRa5E7&wa4l`JbNxI
zS8<E6VaynjA@bM}73cZG*fdqf0=JGvMn9!~f&|GergzOQ8SNZdt}1=_Oj@7nTb~Gz
z64<w1A%Z9m?#w|?iHRMvPN-}Ek<{;%mURtc6}TkO;ZgLPR!d}#HhxhV8~yrn`iata
zKX{?5Jb6wv9_FK4IH5D119bN4L`2BQlwSPHJh8qSSx=Oq;}crMZ@n6KVj8I(Cr~Mv
zhG};kim1uZ?3l+f(ddLsHZ<)tW_Jq}5nMXKwVGp(Dy!R_t!ZJnq&0p&VXWANG}Ics
z_MZ@qQ{6`lvufaxs!?p&M#>~(x~!0`EqftBeX@&7Tv@8UKtShFX_){R6oCOnnrBI1
z!_dxsghcp^Uq6%=q<$eTD9vxYpXW`m4^RyM0QmD$m{U1I$kcLT3p)-_X0k{tyhdLU
z5dcLeGbIzr0lL<1J|I>KD*I^v0N?S0zbK3Vo#teNkBQnccz)h9knOf3m$JFEgz<@)
z>5aJaB7q!d{{W6LHn=R1)2}himA|i;PTrPhc}!=LtLR@ArZIXw_k4;g=@i0c)2%Qf
z0<)a!U##o6KUSF=Tpn1`pR9Ngs;Nl9e5N2l0}PRN<JRUtrhst;5zIt4=Go1MH;y}E
z_^yGJc+VK01VxMi9Xuv}{XgS5{&J89;Fn12sk)u98Q5cp1bJvi6_EG#DYsFeR3FDK
zZSI=`s80<}$zmhP7DVx$SVg>)&E7Hb5xP~FwFS~fp;x$6sd4d#rnQ?v?V7~HAaZ=~
zIs7^iC1|rzo_8P0uf<l`m6_9FEXxUyti0fJ^`!5@*ux%1T>US7QZO-nl=n`VJ(_J;
z#(%hs)`ofz_`6?|g^Cp^W~}7}u|~4-1$9Ch($upGsrS4FMkD}_uki{UvG<w-WNpAk
zsEMa}ym`q-5;%m=h|m833}k#zd;P{~1=w$;FM#Bn^Uhs>T%Efhd7mJ){mxl#1}W*=
zWO<m~MHaV4PV+eVpNjKKF>arBobNwVQjLa(xCr_U=YHvo*DZ@52^8(jqcp$w%AA><
zpmRp$G-4*pgWXslnUk54k2ADwwrLT*qNvTf*)V%=<ZgVajOLoyWhKm6pXs?qsM0Fc
z8y$?Co&Kj{oT*&h&jFv5@Q9B^$ND?*Ez$YFz4vtMi6o^WvfVPCfd;kqJ;Q^`SsG+Y
zQm464QWJfN1i_}l(r3G<8<6fDQ93{YvZ)Uc&tcYrvu}C1V2Va~%n;ZFw}c4R;b1nb
z*l2Gy2c26ODT$=RHhXtMO9AU{pJgi3*FM1<ci(8iiBsltSFH+vV1e<1jF^ubF=f*y
zF|=hDO>1f))zlTPaa`{)8-Wl(lyKQQ&KXN9jN}OvOva%RQB)O^By;kN-_Up7cKx!0
z0aV&$tOp{~nT{>q*~F;ZGZ32~rh?Mgf;LQLEg`l(Zm(vqKW>ZY_O~3bMf{YdMbevH
z7BhYKochTRk1UxO@H!KqEl5d-L)-&R(=9b+`q5U&j92`c&a<EQ==d0AT$$cVa;y)`
zh%c<CjC+me#2V+sy0aQ3E3r<}r;cB*BC5=ZkJ~C3F3O10Ok-AoUb6X@b>wfn%<r2_
zFMJK^AX}U7wi71M$0}zFvbiN#JwC~rt&MWARH|4REk(mM?g-h4ElF{xNH8T&Nko&9
z!>MBB?Ku-(s>XbC`9ws0dS28*hZx1#UrATDc&i}L$ga-78O=cef2!zLQYxA$Iwsv^
zg`do}>5jAe&L8DaGhWVDaMcszC%H*aIgh9^h?tI3$Y&3lqGEpM0S{ow30GuCDTqXV
zsOl*ZgTB<D>VoQ(h$&R1finm3o%UgFQ|JwHB)X4K?+z9TbY=&Ku;sjkx{a=+AEkwp
z?XWA+)2`MQ1HGP(k`k8cj|qI&eV17>HfG<nZN6_K^{2?2=d*-^cpJFEvkptDz6^B%
z8GNVA&e>ss_&qe@JQSWsoZ2QgiQgR|8hufZLGYGNJI0uSoxLU}h(-W&EwZ3T;7*L8
zRw`DA$lt)kTUX*^UNx^8rHrd>dZl8iYQ;%Ts+ASflVgfwkfw4XJkBKoCbrEG78C5f
zs+BTEbhekx5q%L_J`^HAT=9Grm75oz!<+vAS(eMqz4w8on}*qNAoTig4AG~9Y_ec}
zEXY*O(>F?(g>XqZ#9XIAD4HzB7v{ufq5j;13D7QT*+PVKH;ioq6FS&I*hUaXj1aBu
z!*b(0Zmp}-U8S~bS~)P&KPu>%YE$oY<XD*VmVT0~YNcB*8ONN&4)H!IPhI+@4PyQM
z$7C?uGX@{obk9p7V;T$ykv&JO+>rwU8IEz+w8*QRuF=I}g|ZnukCc>Xhr;5DZY!eG
z*P1XF;l6h9wrZCqka7u`pE)`ckypDkwL;DITDu)Fvh){8&A2!J0J(H$jPa2X^^PUM
z#3JFHuwALiSr&~3I#rldAu}H|oMXZnR>I;VeB-y=&)!xdxa3zV;SB8@7~WztoWx}`
z6^d}Evk~~Ek;7$gY_Jwo8>X9Ru*%UQAvV9MTpQFK?i^~VDFK|~&dn;$u4<T(V-Y9O
z8i1#zOu22M-Z}1q=s69V_7gsI<cZncgW0^WeD=XEip*u(kj$mSfe?QA00p{<Q(3(Z
z-fviC0YKM7RP~F5W_gbG7%WwUw{50lDJ1OfIQJW8j(_Di<!-!YrhU4Fna|q`lq?2+
z{1F)fM>`C08ll*2H%}4Oyj?Ji`}Bb#ouYhrj}w&Oi99BwedR-{!hsn%Gno+Uo%YJe
zwP=K5wFOfh<5JY8X-afXsAtPoE_#)j!5%~-jASbA;=!4!#mApW{{R!T#O;)s(`^i$
zU`2O^LbG5kt}^OVWlS08Afr@z5f2o|@4ncGT-JW|)o#R`AZ$d^YBG)2w$hegi-cG{
zL#x(;;$8JD4%qDp4f3lR4W*alrjvgVWsKBx%f|l5Aa#B~$;wqZrDjea&M}V}yvLZ4
zhlv>5A#Vc)fkjGhivIweaNbn{ZZv^<iij7dP6hSDb};H)ex|QgufzsMy@NjIZ@;5v
zL1i&)qCyDh8Y<m$?Aq0pdc}Ckj}u*K%fY&vF>c8b2xgthVQu;CZN%Wyvd!^YUAE3D
zjMi(C-(eXPs49P`1Zh*eOh%O(iC1NAUzZnxBseotxXt%vr?<iQ&ltdp(nC2rU|Sa?
zYuRGD83Q(U?{B2eZ;GdF!^vZ#Tk+U{p+-s#b~JU^07Lc5o0u$}FFsG9>|)CKvx=&4
zpHIIYn+S|@+BnJ0W-i;24z{)}=LbI#4#OFggEBtw0Nij3bad0IENw>sNh|)c1b6aW
zXMQ@Dg{CLY-jm@Y8D@5^Tq7qBXxnN2oe>$N7&d?gZ!^5lHGrE*1x=cOy?UKFZld$S
zhYWvAfGgb(vhNZhjUED+&FAYh`L3{WSh8}$@dWnOt~fSNVnQici*?NoY9f>@F=lEm
zz$vY5d&Y{WLYN1rc(G`5*Tq#8n`WIn;k9RC5}2BQ6kH5hGy739GxuR7?ge=(TKqFW
zm?hnawsRVlA&pSwK~34H!t333X=5=_b2GNln)LqwP^<*<d@mfyb7h#w`R53oFi!}7
z{hB;t%H)+^Hn~;+BwNIfb2d6F?)iD2LBwy8rcUs6QU_pr79u8dFwz3#5AE0kCM0HJ
zMLmn;Hu~&)U{~e_<C81c=4vz`Z6aK$NCXxK<<jhpT_twaV?veEl_odMYA4!<4Kqad
zsP8*AudT5_hjnR9pG^Z{x?*;Zvcpb&Abt{9e1GC|F<B!UL2s3(eH!>+KY`cXJ^*7S
zVK&&3<v+KgId`j#2pGPIiTy>B{%81nra;FvU;d4(f6cH$EM}Mzh=NeAY)VxD4U*ge
zHj4nGnBfAZPhmvT*`})y%WLY?xA$b|O~pU`!ExWfwMRj0e6oZvu?wWhl|kEDV}ffR
zVuh3EiAE{D*7Q1UE8E*Gh&Zq|hc4wFE~j<09Wf|qFnIxRm?0}EG_at;Njgg~_?m>{
zG?g1~FN-0Ja|<9!ON>~8hCrC0ML>25{{RSu1j5%0ieo2n1ui~rVbA%=&GHIH3vW#|
zT=^X7=f?Wvbt;`Kn;?`r(eYhfB}<T&H&LTooa633+X$wAe4{cl2bzcb>pRsRRuzn%
zOftZ-)u<R2(n)OkNBEBzTR_BSr^J&^z^J!<eB~tP%=ve5$L;7b6J3*l;#f3dxlB`h
zV=V|T+f>nz#n^LOSbsd*9}V-F5Wq^df+)n_vZ#On*Vnv3qS)F*w*ywGCnKEUgU$_A
zTO3Z+Gh)OMVQfoE8avMM8%%wu<wgc8T-xn~MJM}6Q+lcGb`-aDy<9b`A!e;3j<rsg
z=So}(D)GAFn=4_e$Ga`?NIz_G!c=U_dY#9a#1|tell?no5uW-%jgD)az8oz#9X{7i
zQt+LrJVHp(w_x0hNJ#!#4VD;iduG|Td5PZ^eM@2Fcr1<nqtB%jrdio#geJo&^S0ip
zSvpk%_(l5$OfO+cJNt=^2sW7fN~*bgX6pKzw!>=~KbcMjnT4mQqHjYpZ$)nm85^c6
zScY*)M}t|fZhv20MK$}zB1%Rvlk($;jqIi)QL@F_Gsd{v#z-uh>Oxp+t;d>s$^(d7
z>-;9P2{Fhq6?RS9?CkW^i(^zou*+@cVsU}}k;jbtYV+tLBtSSk8uKG5D^K{Mn(5UL
zkG-NT^?PH6R=ZhGTC3`|8DCa>hvGJV9CXb>-0Dh$)$0QYZ6(LfSBg<Iwu`2oHI$G!
z5h%wrM`R6TciCDpr*Pa{D(nNHkp~hx1j4*V)B}n~HDHp|h(_eWTWk*_pBW#Pn#>JE
zh3Q4LA(0iWMl8u^1hpxSup8U5_RBMt-U~$lO{P}M*Wk4|m1?sGrt{52P7NefgRZ4m
z!k>v|u^jBd=k}K@owNMLQ}U~+kqw7)(}`1IU{Q2xJE+*+NwCO+IQr%Jd}Ng|5iuh_
zy;4O)o7paqft-Um_~#;F?pyfUD6DH&D~)8Iag^L6CkdbYcEl{0&y9jhquftGJAQ^O
z^Ia&-EiMXB4vE5@qH_6i)9vLQUf!Mr3+*@Ch8^vJHPs8{*d#hq**7KSXHQwWNRFpj
z6imcMS^`Rzj0Lf>D;sALV5}jCjpImI9b4P9ene3~`w?8Ftl_Oz#)k*Bezd|@XN*=k
zRfL>o{v>DkdPlgU3%2E>;qao9lyZ=UZtCq+`P0H-4Rn<Cq(XU9+C?Evuuwt=4rdZ=
z4`zal8jgKVxhfqW-8G|n$%`Y#6orT$*AF5@_vGgT)-qIQ`V<x}W@CK63=2d#5c@i8
zs}oi1f!d8``{yLBilb0yl1V_7s8-a!pHt8_p!sSXfKZI9)=cU((tv_xq14`(kgQye
z3D|&qaj;EliHwyWkzCLHWW;+}%_zG2o_2aj<kRc_00_?|J||(#7p%SF&rTTUYD$fR
zeIA2FW!FrA*wnwKK8f=?%*4cPla$6-K^bUdEVyQ{`C;bU?UZ;}?529O{7@dAa5m+!
zPULeqTuVnzCSY8nB>a%`pZ@?6Z@-PRY1<SMRIE>>O@U@jE7AaJtgT7PFqqz|s$Y;5
zNk;fwmpfoj=Wit(CVd?+tu~5c&Ytgb(M3HX=reYX!|)UNgv3UwlaG{F(=CrUJsPoc
zgocuezGSrxLJ&lzR67ZLg?e{o<2fm4xxAUGI@0D0W%j6|Cxnkr1))rRf-<5T_!x-s
zAHHf5LrL=wBpDAF5dFH2eyZ~gWlPBO8D~#B56cy6wjy%P+R2k7xvYB$jf|nk-lLY&
z1;%{_InLQ&$B?WhW+bvAXX$8TciGxWRzSRsy#6DKQ{OyPIx_+wu1oZqvoNUD+UV*R
zm5DirL~r#26Sm=2%LkhJEgWE?y>uHG!#1BILM%B=YPO#^6>PI^DXQA9gRyNg;x?uu
zeefJ(BcA1+c=IUvK(l4e`~i~?nhD{4u(_bi31z7$*cFpAJ8q-vWK7spDYFA;7YeAn
zVZj+l$BEw=1GF$yJer})h04MDP9Mg=!Mpn@jO0bO;lp{7A%L#6=7UpdD@I;cbB}m{
zKn<dnTOq(H0|Je7XYn@Tp;A-Xrx69}vN}VQ@O>BDpuu8NTW)bpR#+{0kjD@SoNtoy
zH9FL=BoNjM61EW61QuGTw>qQ>1Z*}25h84H1<o_6qED`N<WmZW-aAKSOEl9|&AQD2
zty-a+MfeTiCC>3NVQ{_^0V4)8IvULV^WaQ@oT2+3g3X1_DrY&ztw9nWBQ43CEnF$G
z@U-G&@78k}xfxE>&ud{ALZwNJgpjZ%HA5}ZaY5phA#c5>vZam^dru<5^E<{hs9LL_
z&?CsYKMj{pxLJ!*Tux_vx5YK8qaP94a{|rwhP7h7;8t8z$QYf9CJ^1$1C(!<I%7Pd
zW_BssH^VO^IM)9FOQ?oM3_|P4&a<%Gg+*HYMh0r9+x%kHl`Y&Pj+w}IH%OBSnP{NL
z`kHG}EXknLw3N+S{^G;}s?=>>)x$Jvi`q)4N<lG}DY2S`XN~%gUAsHt%Y^o23I3~j
zui=nhx(XdGhs~>;I}u*5D(znJ9A42rX8|D(XB1UH&hCY8JOgk6TLyN5mJb7+O6!E`
zFe&e5!FDrEkaWXR$Yu64l-3W#P~jhTwsEJ&d^PQq<83mwB7P+~s2dKL*hZgu>$bi{
zzbW3!J34HRqO!0Fy%q05uQ+jc>xIcRK$Gma%UDM1TSB(**k)O<cAQKwQyKbJNUC5{
zb&z#OZ?8DdlmU8p_FLeAhLUW>09Yjiv2wE^oc{nD9DMdEE&N7lk^N0<O$ZaS%-E`l
z=6AJ6+uS0fI2jDML>at)pGCy*nafaK;hcw%)-uIL=e|{RqQcpljKc;beLv%y>@$MF
zjTDC^D+I)Zk1J>qw6E?uKWzQC%HKLSM*#x^&C~!HY6NW7Dz1<r4#^Q8!`1Afq}tQ2
zp__qMYRF;ZS#=f+maJO&EDH6EL~`5ER>rN3MC?;^#vZ9;!;VGkW9B~;F3_<$m2RD_
zRR_8Yvn%QsAR5e#cG}Bn4Sh@wEw8E6f5BsYUN9YA#`6<jZz`;PLn??F<>&tM#`|T-
zcqU0Ad%(%r?zJ6L_#!7GK~J0w4PsDGnw?fVCKtTMIZ_2P4@JY7UOP&*xZWnci2YLp
zGchQhHWN8)WNkJlZ$(I;d6yc0zjb(m=_@RqrVY(F*p%L*iNtA^ZG4l{q|yHS^;=vd
zRU~she)0$Uc9v3N-a%RB>g$<-_ig_Gj9j)M!U?TsgYmS+3gf{trGrIV#JRBZ-elU-
z{aO-MUB+!=2EmjiwJvPC4?}9Sl-ZrQPHiMxLq-6&;PDTX>BdadYfCAF1Dnovl`CxV
z`}Q#F)CSz*B@qNNvXPuay}MxXI(QjF)opHT%;0Od{{T!@Y%~P8Zqt&mJ#kxyiOTEe
z%4Nn@>`aI*E1^k={?TtI)>f{X{{XlcVY5kjt`|EfnaUf@#O$h~1KRVIYPxyFdhr}=
zNi2CuWK?lPn-0>at`q&Wg|~QKn==hKhQf-pH5L5Utgx_eg#;{MV(E3h+jDYZd7rt?
zGqR@^Yp<uAN8XbYlm7sFc;733TU*;g(<~_&(0byVh;1&qT=m6Wk4R`nnyz+564p;3
zj92%Db5oWLrl&sqVe#iPjGj}l?=iO55f2f#p^2U63;Ml3l;16!0UA~IIe(q1*XX+3
zji<z~XmaB&>{hJKAbFIA1i2!^6<jeboTN~$9{vrCwj@U1?JGX=u!AZ#Si&e<5W$ex
zq!etPd8MzDD8@&DoNEi+BB=3)9FQ8nKI9sg*nDspHZRwV)fd~VSlD4o4%Z^-H&GLf
za6hhWhS^@r>YpXY@ddx#dYF+k@--UJA~7eUxmi@`i^GVjzrQJf9tYv722Ya)K@qRo
z*l31Z(<j8@>sJVtDk)gvax(=JwAMv+$uicLX|}aw%-fjv_GV1Yg(g-f&2z;Bcl?a|
z7)LPAv4!=_7V9|TTQ`ivOg7s;461OJGuj=d>NbQXxleT>1CoHGEnguGC9^Y@DKS1^
zpvS>eEwt)!i1`6T)MWSAs?$%lwZx;0?=r24PaIPTx=RBh>&C!0H7VNTBq`auC(9FM
zH$=v5(Pd8g2~;e8Y_LbL3<l>Iiq0~QB~H*Bgs~E+7;Jl3<BV#`xoxTxDzys06Cpdm
zk(|cFvKjLdBO|o=nUeaz0%7rrlzLcYH!np+a);S<qvYKC#+ewx<Lk$wJV9l_F?{oH
zz!o?W@F~ccjA-x$hDLwZeb0&8h{g`{rx?$2=AChbJl9}bk12V`#?!vo6_{fumC6@N
z-wsnht4k?enUSwCa=)}SRY2Et%Pu_TI3nehy=N_g2OMGf#st8u=S2kewX8qtbqv&8
zOtRFIP{i?)%{~CEirGZeYIdDv$~w(7obei9F*6*ug5n}EGz>w;Sefd!5DO}`>>*jF
zBJGKLR~#q7aUuHW9+sKaT+b^RzPUPGbOFY)PN1?0B2~`rTmht9undin858FjKP-ui
z<+Nuh##gBC?iP_sc9q)EiHs)$w7Y4*ta4G!lP=qDfJ|&f`BFZWC|SH)D~6G#@4K}}
zT=?1FhWX^T$Wv#`dsoMqHJ1Pbg;$7)%MD9gEjVRp&dRf$wD7D{ftX)vx$Z?N_A$r$
zZy0?)@JiK5MP3L5*i#<WrN18YDzjGOtJzSfq$VpwOiXqBihTX)krSxYi7Av#rAPcD
z71n10*sUbtT9s1OmjyQJ^D$tTkX|;Q7*fCZ>GEZ~Pa=b!JAL9N1?wzSe{&xY9@EqG
zP|XgUlP1v?e#IH86cw`p{{Xmr?P^XnV3xTYIH-Q~`yIB_shQqmDqC$P!xw!aYP?=W
zez6iUV+rh&HNwe}$zrR3+U$IA_DE?@BALTuXsrt*c}N`o<(kvDfF=i){{Rx=J03G`
zg-%{m3Kx?z_+SP=pBbFpalB<limq2=U52v7s2vJ_)1MiAb^YAhW{lH%ZAs`Oy@%zC
zV6M)}utn5ms{3iU?s7o`GN}ImmK4tW;LObJlnp4ju1+}S;8u^tg5W!AVA^aQnL5K=
zLQGbnN_k}}&9`m^TkkV7SK~6tc*wv6@PHXCq+tf?&RDo4<UhEf=fO(-M$#p&Bb2Vv
zUe;A<j@7J3w1t4_De2R~a&Zv`^R+Nyvx36nPXdpMkU%s-V^FIFccd&fPSw+SL=8qC
zyAsWKNo-k+{@KQ02=mX>AV8C0aUXbJd(BhfVOgnjt$4^Q^MDyWU8Px-85cDftkupV
zTB6C0$hSv~6cuXw7Q?2xzviXEbU030luLLsMNEdKxr^Rz*_l__9EaysW<UB$<P$q>
zg{4tOo(yOdjKsr27TAgE_`}Cdsy7P7VrfKZD#QU<q~IpWd>0~cJP4i=ASIts6AOE;
zL@AK4ge7qiurhMYvJPhj@qG|R>VX@Y(K|eOXrU5z)>ej6_bV<3Z9P52$ZcBS@!$%k
z55>$rg5b<}nL%)wkrC!vdHzgYxJ5ar6NMDn43n9OFY&U%SStwCS=n&?JdDKT&p7ZS
z=5xj<I~fs_>Ojvjy{Rg=umRFu&c6PhJkBCuzbxG_M<*Zk028*wcZ9h)Zb!409LL0S
z<32KrYeb)8*TBnRo;%N>c*lT_pI47^#;FVj`}j>(xq>EkI*LV2Ge46QQ}}+N>8HV!
zm;lZ|F~0y$N<4r#M$+4l2nV!AWA>K78bP7^;mPn^O?8TNx4Kqxw=J2dj~kA`DIK$6
z50rKsNX%SD_9WrUZl^*lV9c<iBh2rLq1=OH#-fN+p%r2bgJ^;ii)Ey!kKWaa!694$
zoMt~vpPi0lZzHR_@<d`7Xj0&H0p+M_ON{>jsJ3UPX{cD}ghyPA_v5)?ml5+Copm}?
zZ8HGvTJ=hY`h?))aqQd9c1HV56n@U5R>V&$dL5CDNcI=E!P2if77!X$Dl;W|<+eT{
zb1T!sJ1N9?n*$&w9`ea&bGGyQ<)PxFj(EfV+Dkloc+I><c=NNTYSlwF{{R(28DVie
zn<F*^w%&HgG70fpP<T%Ry1F5K@sn7H{{SSA;GZ=A0Nzx~Xkz`-i;v_%=HDJ(nqv(&
z>d5L_`|BKrH|6U<528;$dGub;Ex?b1al6P>$;&7*Wh$VW>#C7ZZ}pQqZ?&<IwDFj#
zek9t(t???n#yFN`l_UC$iNfRm02<e0PIA&(rBkBsjO~=rO#H|&?XV&e<~4VNDU(|D
zDk~~=4Me6|e>lYcuq@TO{nqAfBaS%%J_ihV2!yrf=1eyID6geejGjXrGxm&8^cXWQ
z$Mv>%(_To@bt9g(ThkFCMIwZ(R+v=^?rL9dV#>QiesjLOJ71Z=<1k|+gIG+I%n`=j
z#3>~Q*3%k@uee;QZj{!sIbsOYR>491Be5^`k6Qx9o(~APpL5b&X~KfU_~~*Xie;D?
z;z9w>c!GuRB|3}0FbF4dL>3Q^G0X077BgO3ER(NdcvDzi)M2H{VPnnjQfsxW(^|^{
z=ar*FZ^E!_pqUbj2AX=wF`8#5-fKryA#H=$f>%Ck{{XmFYlRb2O^n<(otMq)V&{=@
z+Y-~R(O|e>Sui{J+Xi~f3pI7~eHjjWEF`t0%V^Bzr~0a9*|t`F_^#H%HI^yd&bGBu
z#{GnJSYwcZ8&Ke|vTc`v*wSaOlC<qKm)Pr5Q7(tQ!KK%gK*{NGu$D7TO!D5vC%uy(
z0`TOr)56_$LzAk91;usf$1^TO>ebjTH)Kq4n8)$zt(<&v%^g3|5oX>aK9+fT4nfG3
zaIsOK$$UPeWClmQJc~+*Rl5dv4ZivF6#$yEszej`aSn1>)`6Ec5p+>HiUuYe3;clR
zpSD!z?m`9dUf9=%xpm;q+_uvqJ;X_o8_kR@;y+G7OmDX#=Y%sGMCp2bz{5_}<)EY?
zp|s9gDE^#d(tql55jI`csrY<wB&25_{_3*}`;a@#$xP0D51Ir3MI`$2==`T9Qhahe
zPlEfI#?I8Jjx|hthW*u4y@thxIwEtxM$sE`jnpzaRhCcfnsG>2<&5h203~qW%FRH3
zsglmQ0+`C@2tUkZ5(|bL8TABeY~l&4lb75!8n(c$8R1Zd@n-du3^<lwh1#EVs+S+9
zx(phi<jF>z?a^fY-zC*gAR7?t26<6i11C9>+@mU1o1|DZwPZ8;eK<$$uEPU|CHS$&
zHK+-2*$k>YtOjcK3C4)Zi5jU|g#My3_Uv556p)nfQuC>^kUQI5*u`3Ar_XgQW<k?!
zw(2RE8r4bO4K7>OCb*ySVhxTm%EH9T;^vECT$s6zGqFdU+ZX+d86UP!iJTwR;?Fp5
z6Z09ydWU@R`+81U+xmEo@|xIyn+7j<+@2*Btd_$xEkj^4Vu_KTjd+;Ll5Dlff@TxA
zgJfrUs?FS^i?8jp`mGRD*DNaKzQ96b;Pn3h_w_Se;u@Qvhuh5jx~Ghg{@JIl8=^2W
zJs^K1%0q64%&<;EndJ)cYT8vRRC}k}E%zTYKfJ-jZ_8Tzn^4FplJh6P+!2lvJvy^<
z_R62!JOGlNpg@6v5oqO>88fEY3+OFA+pD!Nn#FUjg@0zcI}7B=44RxBl0MoT<!Nd5
z^moRwP{IsM`4XK_x43z^WY65m&h?Q6s7q0(n<H3!mY+`oa!2<O5_5z`?#;T{iP?H5
z!${mmgpB;TMx=37PyyB16c0A}&THzD3eClU7VuwtVFaclc-xPq6-J|4YZbK}MFn%H
zF=C0-!>mK$hi#obAsUe<+Ns)&Fsi_n9P%z%r`WT_{NR?<6h~&~5^WwVG0RaWR-152
zMNxwrpB&$R0=n{JH9_@Q$FP;4*5fa9+G>;#Ow=_N*%^pX#Z)X{O$6d&>ORKPRCVNT
zV{9~H1VUnOe|F?Zw8zFO7s?EEPa17l^rmpTNICJyy&wI$yUy{x9M8rvn;k7J`KUkJ
ziZb8lY3|!p@$7I!j&_LP9%R~2gEP&&G$1o^&K1&K>tICc(YEc?X-Cras$6_Vv8T={
z(P$u(V1O1qoJ6;YVx~V<&JI}$`Fv(L%)wlw7Z(GM(ru<Da>nrJ-e;EOIzJhpysGZ>
z_TlZ)-PZ82x~#cGmP{O2ZJK#Dn$Oo)rIgQoP)$xzPXbJdldO~J1=Ue)T5}J3bn5oi
zB(%w;q>F4WMn&mV{j*?+11v3x>XfoqJZ49HRKlxSIFB>u)(T^IigZQgBv&8pC=rJu
z<<Az*elq6L_DpB(>!<m&sLNb^%Xp{$agCW3gCzqd2LZPdo$yXa%qiu@B6>RG^LUUH
z#!gyTG4)Xv+uGvWY%<sv1Ltgw;7Fl|5v3$E_QXs>BXiu!PX;+iz}|70va1*%m;o7<
z6#ToS)+;J0HeF9UZAFW$hTBTiLn?TWv!yY%4xwtgx(%9x40@17C`!ayupS2#cv-A;
z%@s8WkA;85Em{7zRBLKh=Dpu?IpsIeNVU%imfHzr0EfhPo%8<yGhBZw6_+)FKN3)4
z_*nNekMO&{wOW=ds==L#E++>qyhcy|0JWAZpe9ca?KTt;9-(1J@hw|WFBr!?{#v7z
zFitgCh2G|(Z#ewQnq8Yg3?tO^`aDvK)@s`slt~n17u-v$SG5~5)`sPg%1C5qVyqnN
z-NE#)1uhtilM?y#CUIKTFrj4ZvrCuy{{Y5spL3eDaK6xuKTig*3f8G<wivbGmu1da
z>E`2^uG<P=^qt2>`<qx(GFKfAVw)Mh`M(l^8%{^M7TK{_P?9nK0LZswGQFgCb{@8a
zv0}RgiE3wN2^3D^MFqUcIkmKeFBrYJ7o_j)*BP`zCvo0arsjf<CuNgAwiNBW;V?x?
zX&swQObpuFRntMEDv|w9>Wg_R-$YJ3_;goUhZLYzR$%)!o$|c#co}`p^S>G16EUh)
z2g6|G$jx8dws=Zu*G6@P&blgLuDVIM2J!a|Z~SH=$-^wT^T(l-l2aLF25y!<;=8n3
z%mETVsVEOCpu!1llZGE#5tc)zOrjXRRNYzG6CYXpLlfGqkv&%8kQs1ON_BzfrgFrD
za)KsGYpq=cvCk~q;Xay7#NPmycB#E`76@zuGgBTm$l=>h5{pa%si&xFFgpFUy8`M5
z!V}7BTy(2od<!0+TZjRkPnl6ZVPc%}9-$m3Ph=RN$tTY=y}7$lYej^KJko0wY6HHo
z@v%DvCChIrBGc#dq9ZHEJ1U8<!M3l|bucCB;0I=Tw^8&(H`+tU5Bi-JCqE?P2naTu
zd_*|nHs!QKmbRfrq1z|UX@8%lfR?U<vPgiBg(=@#6{;#k6BuwYIlOgDXZ~mHnil(6
zjK0LwI}tKQ_VYOZ02%)PSwHolF$1Q~z^gL!Sc{L;*5|axRIoWiP<6)=rnSDl#IP-`
zyoNg?1F!y9YNptblQiS6+b`1F)1^JRqt5hZC6N*vV^^l8WF}_6P_9A-g@KW>nzif~
zI<*x2J-i|)4StC_%OEyJl@}@ehgQ?gte4jkyIy&zwp~36mMu~f#Uf;@7Bh06upoh3
z^85)fo}(h*5te>5Ys}}JxAmh~?O6be1_`aQtXpM}5S*<XtST`uEtZ^^Z!%H$zIgMW
zc%tCIEVIY9c2|ih%Oep&kzjmt@74>9j>u@a+Y^uxGdTk?Q4nAwseEl0Ww%amJb1x<
z26@7Wg@#|5-3s&{yl-R#jP0;vM|oE7<ZL;fW#T8ydCf9SF#8yDCPYLL+=3k59j5Wm
z7<JrA_Yoo<)LRxtWoa`OL8*(u^D3GAS$EkfW2MV+gOD459M?s4uVq+|Z+VHDwVP(r
zxWxG{NR6W&d=kRVRfOv+DW|(NTG9_bI-4dh)iuv{EGB56a^H(i*iIUi95jXmvDS^T
zw8_l;W7cxk_ZLuBnpJd|hu_q78y5?V?V5BH+*E@#3o2X?R@Lj+FU<!fu`&WhjzS&3
zV{}@j1}i-Z*l)%3E>_#NwD7XI#&YI-k^$wXKPj2?i*|;5ej>yDUh`<%E4*E1vhww@
zoA!<guX}{1(*@RTl+_N$h~dX$3yGe?D--j?y*i`t@prH9v}!wYtT|fADHanji8wP<
zM9Fc4rCMEid!C=f=yJu}dj2QIorw58v#S>tYEk_fq7vRQD>f=aX@~6{BMu$_h{`qQ
z)f#(<Eo0WWj#%jl30`X)*G{T(Gf1@vwZQHKSJb!@QrUkwuhx~;s&m7u<!{>CIhu6z
z%NH!dGUaL0>^&2ZCT1WuY%-0diXM>Es}-t*nXOfl&Ij%%mW+*-+hwiVnUJh5K6mtp
z&Qr8)aJ1rcV>oG&C%0ag)-y6{N;|1Ij_qF3(#lScwZfAQN}5!T<93TKTP6WuTu6HL
zU6)Hq2~^2CPbs`DrEkq_A(YfvUz}PS6R1xBFd`Uso?(YO_{K)kBcBeb$n(5L{{Z5b
z#?5leE`yeqe0IqXcc($(;CYEl?4i$J4u*4x#Mn)hg<E&)ejiR#4rc5Ei?V(55&*-W
z%ccIEVGS!aCdy9Y$kxc;4EG)}P^a*%Mlzirl}XKQvaLQ~X#^ElK~+)GqQ}g=3=@&c
zQ9kCm&ZlRhz_?n#8&&U;OMh{^Vwq=VAuvK(3!o$S&-`hFwkd#~LlOo{I54ucaa&V6
zPu79Iwr*>#F7Z}TAZjh5bt#r<x?4R<ZG%6?f8@?-6#b&(yIBJZ?vmaiTSiacoULlb
zGm}NVUe)+qK5X8x`8dR}B_Pc#;B^E}Yat78NOu|`d1cd4>YPQmVU}?gc$dq;9YBRa
z*032;u_ZinP-!B+IKBW~Pgd9u9+^u_4%H|OVem~vtWhF%fc$MaT!y}vFUXnu&h<W`
z3dOY9SSZrArpx%vi1zf_nCmDAG8S-+vKK8qft6byDAuE_>Y_3>$^QU2AVzQlPMA<c
z23ql_+KG=u{kC#9jkf0wm;+c-vY}Kp29lUBN5nv;a3gt`%cCEK>63-wBz~XgH<|QP
zI#~q?w7$tFH7eoAIeZ?%B9k(l{{T2&@${y?`-u#;T&+5NBgEroWc-8)6!LR>*UW*$
zBr`Su3T4UP$BQteR#=7!63dX(7&e;Y_Q@vuK^Uf$I9c9R+BK|$6kIH>R@s?h$pT5F
zA}?A=90dt`>qytHW2wuBH%OUhI#A7hNhL)qe(JiIeAgRtBI0}_Q8@~%h#2M-2&u@c
zRId?l{oReU`%1VGM$;-jyy?p-)qU-$8JLLr)Ltt=NAi8f**Qv$i|pdI+O4Kfx2<AZ
zE<Wkl)FtUm;<8@c+Ik{zV7Gz>w{?p%P1UUS3R;Jz%uK8I-#46snEGv6&)!YC4PxF#
z-jMSU1h$35&c{3c>!%%E&zF2e1MBZi+utXbl7lR#v{?z384BlpAzR)=lgfFR$Nobi
z6DllB$=9&IRj^Q|MqHRB)GQgF&CTRyBxT;+Dr7cFWdM3Bq&>vt-7DS6*{d-zGZS9J
zAb}^UL|C!GE)EmrPbF?L`oQ~;y4PVo?Xt=NDykDP^YAXW371AzGb@QQ{vkfM*4dj8
z$wd9-*2RSB)+V<zYSjuC1=8ljimp^h%J!KX^rpo37ssR1PLJw4am^>WjVcyjK*iCj
zRg3kJYPmx!zF}IGkfB?oJLJ?WYw0<YTE%@xx+xsRfFN<f=N;pC{R;`m9;fV6CKQ!1
zWfX6yZoygu>Z+mJ>R!?6P%B=TB<Z;vgh*pVKk3a`s>fv)j(FQHrPrNG0-UN4j)AU6
zbd!fXjHlCiiQhbU%Bq-`%$&@Y)5CJwG^ZqHYG=0UzbHNT<A`IGUe^*bAOa>eY^^A<
zTBeA|<Cb)X%$DBy=&$XQ$cTvNOJ_}m5t8LBXcvso*SfCrlJ<zO{6)BVbz5r2D$d&3
zg>)$cUN)<CbN0kKh-tO}wMB2H)N?f9RjMsUDkLmG`ck@@NV6HI$PSBoN<9iEsb`Ba
z5*?&OigK$WyEb*oqg>)wr>mxz2-sw>1D259O@f=sfzkL!p>Mpz<l+<>YR1)#A?<#q
zZDy>+I^m&@N!Db9=lpi`kNWzUoMk;t{51&F`u&|_V18M_POHiE5aI0Rwn^Gl`vh>H
z&?R}Y)<&oKboCTjfx;0PD1j-MiJMA`0$@NxU5u{RAkjM%DquODnSYDHPqZam>p>^w
zd-E2iHIT5l9JJMiRYaV~M`_9-F;n5CYI|w->)7CzYBgLy&NI$D7ZQ~7&v4~hlN~L}
zl>lbAeDx=PCW}jiMW!cF_d1xUFN_AAXK3^v32f)>=nZ1OO6?fHi8K=SM@feW#_uUn
zN@J5oBG)!jhReP%rT+jV`b0_i%A|6@47y*?$Ej`c*naoTFMoLjljNp-O=q~yKx~pc
z<sC2{fN~ScE5cm(gVYWt(Z+w*q8QL>`q+|N3<)m<L9DJRN^C+?VZ@dstq12<;3^wz
z*kodu7_n9mClOi!dVpA!Fjxn{i>mNaEDLojYoypd?&o#bdP`)*dYY?ia9i%`v|JFe
z;7X%pF*CKA^O~rN^(!$vw%ciyn+ma2q`E68hMyJB9YFjkU&+2x(<6_$y=zt7b6%y7
z{{RkI<k4M>R?u6?QzS=ZpShImzzxTSPc8V*?HG<wU63YW@&)S`5ylC6<x|@eVK0tj
z<Mx@`Ic@j(a}yIaQyaus)9YH8GiHTYw(GH@*hKtm*071OEE(BMiK1L8+S$;QOb%&;
zMSM>mJG6^Fqn3Z{e~GMI9mZxPcNqYVJ&YojOq93H8ncl;8Hh~SXeuroy3YZ<Fm7Sy
zk}}D-q0#Nkga&Ihm1e@awDE|(m}aN`^_Z<dzfF&8UB(4W2(-vIoiuy1i&mL3S*Ttb
zDV2fPiKwht{{T++X1=0X)7@nR9dp0lQtB-t<Q|PxlJK!3?zfm((Lo%Yh5RA_CO7-n
zsiLqf!ZN!hD0yH9Ir2x?aS=g>S(cTFrj_3JtGw@VX~*fO5A-yOHRIr(ytDn;^o}rJ
zE%_`052_+S4B+&^<Qoo<;=qmP!op0%VSxad8P?8!pc0lq1Vm^{ksFM`Kk5i`Sg+Cf
z>9yXiCbG$773x-r)W0WFbb|syV;=-8kStk9vL+^EXfWhpe#%RD%84+Tuy#B#7BOUe
z#w5-D*hs4fxgl0QS?r9a8Tn8dO7(#H8Lg><J;jYa%~_sIBNbNPQ|mcJk*u{D!&{>t
zf4p?-AN=Bo9rqZBWLjl)Gf_)#+HodS>eXHjj8JoE4r;3qLe$mo*VsCUD#uo%Sfhy)
zWKPggnpAS7d`dkOe0i>4RrR=$?HS5r^->aZ!74;?*l_k+%x1hrYcmkw?na>*A6fqZ
zezg#lFWy?zUh91p8=?OIy_n5p`RXs6OM)Y<*XVm*wl6l#=O@l+T7_`|Lv>A6Wb^1c
zuTs{=>46qB^IV9M+Dj^AvY@EQFJ$MUZHcgCL8$~ZgsPuqWR^#a;S-niwls{?5DO8l
zaq!OKhR$k>?nO&59H)U~*??536&j(_I#*3$HoF-tUlV{sU?Gc_f@(HFE7nj*GmD5t
z+P?WJ@ddeVs~hH;W!$wzneTg>8T(F9r#BlBI=${c(j`nZuoxPP2=xA09+MvciTm5r
z#__%hO6SLFiE)!oUsJabqlOeq*S3{28_xUB^2BfVULfBYWTt4YJx(^`8T(FA*cENr
z9;Rm^1A{-1F_G6b%XrTjrh<ST_FNe3gN!KSIroRjgWEg~V|+{~($z5sNemf>0v8c6
zxi5Bs*d{6^wcxs2A|boMAR2{vK8piR!o}*#m71cg7%&4`)SQcXQd|zT)h`Gc<m!)`
zq3qHMmZW++Q>*;mmtc&UfQ^67*DMwh{gzs2{(_m3DJk?tciKJ);X_7{?6Ypg)^m5(
zK4xJXg7~4SGBPN}gFgzYa8GcpLmcOjux3fVNsBP!$)X|oOPIpnVc&isz(9^8VMeEJ
zzsD1*D#|408nY;ne6&Vz^)}Mr4_>uQ_lXO%ne<T~Q{q0ITCIqV$LYKhDXjXIYMjF;
z@6~Yezpp<1cbJ&XL#pL<T^2L4W71+vObNus^4z1auG=!N`+y?fpK)e}ayQW#qYMS>
zF^=P^(*i{jUZr4KR(#lJr8@H#6gfwpqGm3ixEQkd5g-8|aF{o9TpEhJ5Bhi)@hAI~
zXZ_^<Z<wRP%de&@Gus7a2}Dnc;T)j7ESV)H+0`E<R;#8;PDrGJCcSuxDE&Ae+#TTl
z8tclY)hpFbYZ`Y+MU;q3CupJ(6zc?Qd$nSC{{SvOUJQz`a8th1lm7tDjJTkuQo3+w
z<r9(eEz6X#Zj#l9dKzhz7+5|`(q9RoeX~;8jkC6U3)iuvTuHPJwttdQ8kliv851+Y
z23p~BWSj8c0tDiCYG*Z0D5&ilJTPpCt&7ibW#t^I%9t{<y%kktpiMw@0nt_jA<fB-
zI$X>s!a%^}2^B=(W>^~*ovJk@^VKVLJdP8|)w(g3cGy#+FRM)g&3U^bsNh5p8Rm$P
zFVAN)?32Y`bkf@0ifljwb4y&6p4_IvV|5XV-zG|@PP|l&kvf%WQ#*t9%`{`%SRDQK
z^=n$%m7ueDoY5CP>z)URoW0ZT><q&DDn|vxMM9D=;L{oL#&^n9RU%exTC&S+lRFU+
zR*q;<y_aRr_f6mb0BBtH#Vc+4p3cOt)+dhped5&C9~IX{8!E0RrQ8JBG|sb9dt%7b
zKB?)O2_6cuFF$ispk}yDlrAA~z5p{jN04s*Vkh^@l#y^^CjOf|JObF$6$Q8ot)!-V
zV?>&y(;Ta0w}aUsXeJpcMyM)N!n)!&n2Zzw!P$}Qvj%wQ9Px79KH@t9XLB=+BxY_A
zVnRq{m$c%SE+H6mSEW=12i)Z5rD$DELNWewapS?9JwM0tZR06f;?+h7$*u3szIqY=
z05nIXJ-erSNBtnH)x38OCF5wsm*iOwUo(#4$b$w4QbQ+hk@Wrc#w7rOswX@eP$ZI$
zQ=A4OCU=ag*bc#D#Kg#CN3#|!$g?%)H6sgYRi*E+aFT`j5fYH#Iaq;r6mwvS)>=hE
z=*;_DBdA$VX((cbsti0$W~Rv=ZeV-njS6Hjg{Vjoa*&x+NvVic{VgQDCZvt+-Bl8=
za^hi(a^Azq)?<-J5-9XWk0LPp_H|f=O=>%N^S0SIL@Me$TSy!Ym#?kHIcUf$)~$`1
zlFz-P(J}=RrWnW+#DPde&L+brm=Qqzg#1Dx1`X^Og61nE%80@Yg;t+3AuqZvb9wa#
z>kXq8)+D(pcI6Z+y3#JwaKQ}D@>$|zd_6r&?Fh3)@@FcTk(q+u*H4%>*{?C^nf{8{
z;-_gmOz_j~%%j3=mPB~pDYg@4u|jvQ-DQ!WSb%)0QAeuop(AB+d1qx&0bC&GNU82k
z&S$|2;FrEBR+maxI)_W7#z3S>P7FkHz@KGs9c+ojsVQ8KiBQ0oInrhoPg8bjlLV#j
zF}`jwk~`+5b}LyBUZbm6xm1@kwlrXCR`eK&hHj+6*ox5S#D=Qb(d{_URbGuP6{O|^
zfGC7+^Dug*OPTXI{qjF~$dQGKV@H8TQb)K!Z)}|67oL4Y>>o{tuxjnol=Bb5;L_^h
zAAnydO#lT$Z0Xh}<!NRu%XsWMkqmjuyOmuwGxU9y^YWk7=PC@bsDXRE!<y3De>p2C
z1(peMV9ca}cUW*p21J>|bgr8N5lMC`53;lpy)+<~yq3aTWe6DG><4mG5W;MG%5zZ7
z1gb0%oIrKZrX&1BQSQ_baVtp1Rdm<TZA@$vj~E>?jN(74QJ{&v6^gLwt?7A8S&&_;
z6OEtEBZ`g(S6xdsYqG0JY|<l?nzGb!X5O`jxEz_u%@haP!D7xk+BvI-4MF)`y(CyD
znWZY5Ft!jJn#glbpn8M&W}w3E>h!P^_-zBt0za#1W2bhU=H3f7-z!X660Xr5{9unF
z;}f(`iH<u?I-U9#1T#*HeT7=Zoi4iU%msysQm{EqqUWk+^1+F=nAi%cvZ6t~X9fQN
z9ql#}xn#FdBO#opbyf(6CV0+4@3i6RRhVR9cT8iT;}YfBH>GI~Fz{!`Y^`xst5<WU
zk<6KFF(bRn*xNA*_4?N?U6TtJB|_@2rNeguCc9p}J|#3YD_A?k?lqXRIfhfJzRu~?
zZN<w3ag+6q84Wd4=n?ZBj(HiEr{AakdLkg4IEGeS3g1jt9`32I32ZQ*U-OgcN5!>c
zRL8*p7!uAv@>Wt{IC12+9fq5VH;muzW07Tj$l+4+32|}iZw%XS6Q$Kvm&~`EKFhc7
zGWe1WK+n#!k8a(EJZ+X%Ai<F$mQN#r-xeb}OSe(Uiv;(cT(O&SaSU%W5tgZ{)wzn)
z9nel$(%9E^bG!{olLYU5Qn0$n-A6S)NM~KKfz*VFYT081rpalI?x%XGXtMiAKE}ZO
za{Le|<R|u=Mv-#dlT55>dT8xd#hVt+P2RIJmcG;IpqnIuBX4bvdDG5Ha%Yhg=D|5J
z^}4vSMMgof@Smyi5Ig57^PI|LIdXwE16L`Z)ht|;g<?s8aUl>A-vB!&;qEHe4Uwv_
zi`lC@$DG!l)y;4Ux}~bnA(kwnxlcIbWCZeRsHb^ulSsGO`XkrnCZ?~!>GR6^raW&u
zW}6$i%9^9i&@sb(9!d^))<|UU^`BF|Rd7m9^rf<fgG5GVH^??qdDsQj{7%&LLYA;v
z5XYvhb~^3WU2K%;vB|L+Hifcixa4*GhO*|ysD(zL?%PM3&)!b(Iiw?&ooAX`T5iZ0
ze0SyzhM9<Y+s3LKDV31BYg1(&7NiJwg1NWkgkl#iWCQ?pG13*OEde2qwOXV{VPCE`
zp#`*vvv$vF;4plFVT?m227ktR=$r(@sU?nmfo74#T6;nn(Ca8|SDILIKRyEzF(!JX
zOJ_|SU9~(xHpZD!(=Uvfo0=Wp!>!rRtXdnQ9@^8YBi&WmddkH$G~fam0+F909B0W+
zR<vR6T_AiT$6@cC@8cO3SRzr6p8o*qXG>>uLngg(_xWWquWn0oY$BW#1g`wR3qn+Y
z68g=DY+1?yPmMcNaYJi2Qqsn#CRZcHqPW*bmZdR=KRMXEXe*CPCH*ZB#T?cS(R!gw
zj9GACW$_4b$R6^g7H%_E2P!dl)fC^Q*E-h(8EfWJ$iUKX9e;C+J_aW?(+e?ReZ*#t
z61scOI2)XEUk$FoVE(RIPDzPDm6edHB5EPlTftW?tw1#Dti<9#;)4xvV9z=z322c&
z*UT||Sje6e=@TI$L#ot3!mU^P&(S_+JWQ6D-nOXkKBQe@99nJp3x`RrK){@0TEwt;
z&uxl?YNVb41aP>H&Oi5;wGFN-F4v0gjlM%W#a&&J5H4E;gOqPE=L~?CxjLU4JgRIC
zfio!MGa0F`qQ0Do$<0<`Z9vA7{wySw#F9;R1c<IC38XtLODz$qqH4(a`DU3|n%;AC
z!i_Qr`^oe7IOvhnm&<AT!Q13t&$jue2_3QG(~xN52@Md-wUuz%^|35g-1GkcC;p%e
zzPQdVIJhxSU1yADt41s=oG@lFF%#g6d^j<vujB&aB{2mt9yuOwJ7sjPr5JQ7MRHPG
z$DH_-Vu?(w6=ag(<+z7KQck@QHryk;4o=v;#)YWDuA0DN{YFzTt{m8s<vMd}ru3N&
zx^K9ruU4c>eS*ro>-&ZkugQ8AtwMHsz*l^|k%Q<4mtr80Yh)<`(2c9RG$0{ujqAVy
z_>Gg=F>|RI{!f0MgOwh=?)O~Ic+ICc7W-z?kOWh-S`MQKBq5e&c74FJ#*$UIOE7;{
ze*2Ex=V*~3kCqff#7yCgL3~O#h?o)m=Y7kRfw4f&A@M(#$jbq1S$|N+6K511jaWvg
zaChIeFzvR1om-qS5Ou?{!UlC`3EE%{YqqNI30AvkC-ZA-6yM;ae@^7E$kVICB<9A2
zw)0)oEu(&VXZ!STN=15#^0Dot-d<Y%=mgK1ne?vMUXuYf>YZyHaz!17kJIM%ru-32
zb@Za;psbc-7;P=fG}+q3&N4qR@fJ-JwrOijYUpAPHetl%&yBLO;Yh5Eu`4}P0n@CV
z18rsgGpO$cjs79L#PG|P?zc~_46WK_APjRZSSGN`nQxvyeFnE_y~K8tSv=c!TXVh$
z(~<6N$2V$nY%>ZE?yd&M(jaYy1C}eZU~s4OPo*^~VB2EVwp{ZtU+ME0!UOqdokE`}
z>@U-X!e<#hclu^mSV8cwBbs|y-7e%C(JK&~((K4yt&p*;{6ce@TbRnsBh-=KPk_t$
z{{VFV0L)d84#t%HC=aIp0OV0WqGS*!wjsR0(=e7K$f}9cIUc7M^$d|RlhQ=-y-_17
zwPUuWICQS336Z86P&j?fpJ&hG?hI4;T3gs;AafB8<T|N?wJUywALyy{vM?UXWap7i
zIJ**p$eoHOHB&Y{CQ<T4#w*CWF?@N4Jr>EnWDiiJ%G;K01@#)W@uxL%Gf!$<6GqAD
z))Q@LC;?P;1T+rSM(6Po=lW(~#0^#!Y17J;S<97;x2&Od+!U8I>v}<$ewsF7G^?_7
zY*(<G#-_yfpQIP;TV|Nm*i_MUsDQ|GU;&xQ^XDj`_H@NW#j47y*v&WDCKoc>Kukcx
zQ@zVDI$JnS$H(QqC?aEQiQaaHOp{neZIaNdGwBu?@XDH`K}b&gqbKFI64gFrw2CJF
zd1FiqttYhf8+_-gzMlBFpmo;wTYE83GGh|Eg(TX<uyrd1RidwTWg(W^+%VZV+F)}}
zPqeAp%QjBSjfLxQgW3doc!mD}JmpdaJ|v6vPSus?nUTd~@D@|m!LACNc3E%7VdmYk
zN@vM!lp$qx5XLr2*76Q#yMFq$MXbfjY)I`B`(?u!T~)`VM9<Vm<rL@bn|O)JYVNY3
z%~gcUGh8dXX)!qTf#j&z<)*kCM5-(*O3NbMmbX5zK$>Cz+d6Uq)ho<?shU+z`w6pf
zbs9UJZ5nN;Ut+cN(y>M_OaXkkR<&g$Qm4<jF?j`ssm56=uIglJmAg);sft#`l=iaC
zDw~M-Egdi_R8Co9Qyq=Fs5a?wFN*i;r>T`<>TSxE;yua=QU3tr<ICz)dFS_;*>E_O
zCA<yR?kA_)RUaG97)+zRaXWf#BRS0H#!l1s$|rsPvVYEYGhvLDk?qKb#7<isx`qNL
zfz0-gfmYK4>8HSZ)X7P6A|la)o!Fe!*)6tHI&gKS&B-a`a#NdnBUQa!Jxf-;=!l1-
zQLA7#i5^0E5fVNorEb`EH$ov~Ho4UyuWOv2F2bL@naJTaDTHbgZPY;nKO!=NIFF~d
zrA#uk!fYd#r753qJ5Joo07<6;8d_IW%1+gF+IwLW;&zY;GQey&me_oDTe+Y6=*}mi
zAhMj2tSOkWAScE~+hpyO;dC^mOD$Ptw-XC};sz}6s;I7|E3=%=is^s8lkk5W)N1}>
z&OiKd83QIzBdC}vxKdF46lWjw*__*dIZf=W?%=c>AIMLA{OkNm#VE@JEnzS;X&I@h
ziB_!VC6kp(RdF(1Q}~s3PLFbX7`9CDZ;ek+uOHM1*B(5wQ5`kW7nifbzDj_1-;__p
zB4RhszZoqu5mpZI^`f+*#K|>e)_!GC7ii5?L~@9MF6UR7mcT?VMq{mbd|Mpy3QWU&
zDL>R1VBf1IWj%UthLx>37LT%}`Zg1$(g2g+c$@PbmG;hG4jz{#A4;R4GZQR{t^MeX
zRPinILEqltTZ*<`i#K%t0By7D`sq%xZG>P9`<cZ$CWEtGj><d33-T+DX><OYUo~4Z
zRZwB~=lL|11(n{dIz;fHt4@CNoVWY<;E!p*;@2yDPCCNv81p2M4=BpEtkFK?+_in?
zGiYq2QDGycI=j-j%Nkv!n^_A0Ujvao#X(??0gMhmnZnh6vLeRs6)W%idTrt|Cyd5v
zE;}_WjIr!fsxT*1u9vZ969*i}c$vuKz8C4nRzqS${{RCB`2#dN1i?JHU5>C#F-t`k
zWG%==fe6y8OP}33mY_UJuzw|<&t#}6oMirh{#e>4&eJ$BF+0ixjOTzd6~7rWZ}+Y4
zYOcwM<vt*!euC2jghX2GXv>^+PGhU1<H~fXiMbenmOe7o{{Wv{I_cguFC{|m)*95J
ziRRD(Zw1JZ&eElmOJQjQd=ma5pcwJzHq96$N%k>~CN+%6`y8;TL)fg5+w7h20RbWc
z6B~e;iq{hL^|^sKJMkiko(zJ9agrmQEUqe!>79sVGOB8zNh;U40zr92m8<GO3=mA}
zjSvpQscr^sG-syORMKPBBd#co^6=9s_ft7#%fIQ}wW!(mBL|#!jfYdGYRJGP)UDT_
z%<~_4LitiMiL*u}Q)-l%Pv6wU#2J2^lA8_V8i>{#3k4i2m&QY3n1n%uKp6oj$P4`8
z7wL=$i7cPIladjF4ja!elzek-qd)VH<@1x`<O9g0n-MWFJILprBN^r=Y^m^F)11(T
zI9fT?S3VAG$G6dGFVJEnq9@1g=`r_>^D(gAw_H=!jdjC6onGZ@F*2wx8|Nt>r)l&o
zL~i{7vHsoFtRT3yqW=Kf6e^?}4TWH)4sy-Tp<^-5ZG)3H{{Z-XKkq2*?$<c9b~L&6
zQm?XOPGGZ9V`kitU3}!ZSG+#bnN&qnquCjv0h7d-`GVrW=NshU@|`flJ0uBvG+<eF
zbM}nWE5)nmT<g~bhA1Dbt+imAM-ekSVHVLL8XC!UtTzO{ON?+q$j(@=DnJ}k;FUVz
z3G+p%F#S=31v-0jXMN`<a7+eQ62GUKGE0`({6+reyyX2x+H$xOtHl;|zsbQW&30t|
zv=&)adJ^IiIEKOHD+<3&k(TuHTfzrmwWxp#j2h=2KorbGe1c0Lx8IacA2B&UndcTp
ziVm35n-;39*rjxl25JcQms$do5~*-WiXkP8naR&7M_+oO$yXMfWp)*9DfVzzvxrD-
zSP&?|vHhr<)+U;(7<+1Zz;?#0s72#cXYJ^iHfdHO6EO~6{{Y#swlH`Z#%NwGj%$JW
z;K8YJl+SUqQ5UDTPaz;F63r&4EdyseI?g95l#}c$6(ws+w+3);a4ajLX;n%}^>Ddb
zAnaCsQju@1OC?Uq+%YRoov4`SjN`v8r){!`!)fX(&SfzXD{B|N!wNO?0#?gWNt8Bb
zS;S^`ojN+~oIvVH_T^4$s0Wu~fOZkdFFq?*Wo<xZpOLD`t2HXt7SRd&&pA79IsX7r
zphqQL92bEm#d*}mebE+MxoW(Hcvhr`FP07E!-+>;@*6)0lApQGTW=Z54nPOxXGsUo
z<XmFuSLs&sGrn)@AF49sa9EBEav_X4i87e=+wNz5w}_qR@Alg?bA^S-&-0PD9rDKC
zg$!iuF|hvtE|)tUqRL4Q5OZ)iSzzM4LmB9u`-2JWlQPOd?j0QLFgZvkERVSfB*2vI
zhNB4Fgr=U669W;p*<D~(0cT;oumwN&n|RL|S@5n)W~)?`_+)Y0D~QY)6fBv)T>cwt
zPTW<iTtXo=*{?O79@$!|?CL{d*QB*8+|;NPgI8v<mulJCMY1I(unoHHRmq%6&$ve`
z)32w1*}DBe3r|_UN=H39$GD>6wzFgzOc6$ET8nAj;tKvEXPIPb`elfSan{itvEly!
z4y6315vXYv7{xU;HI3O&gqBhzJ1#bb8j)O!5-@Uz$^CPM{&4q*7|D`)JIAtk?K_6s
z=jF`!k&WkWCC7XoaSYma<-BjaZJYl15WSde7V#<E2j&qzVs?^<j138X)E?i6Y1TM>
zMpNFNkIeO;-UNqS_#HI*2HWqE{{UZJbN=(s-ZnTN7_=R8UNdadzupF3v^49i@NBLV
zT#SZeIT!5)mi!s*RKEa48D*;%Kr>WBE4BiBW1?X8oSf6i736s}o>(BSEnaC`!z~tA
zg+ayu%S;pvv}v%=8WMsFu!&0(*$h<dai8@#kl#$YS^HrRcp!`R%?(l!YS}R5j5C_a
zh8%OU{_>57lK%iRD%G0>JC(5ru7z8*(WukNAt;H9l;9wPFK~t|7O0QT9FIBA`ZtVc
z@4isDRIM%1Z%>Z+Vn2xGe(^F@6ut#*+H1nzr!U02tmLxNp`ir}C~}0x_h0&_Dm%S(
z98{X><8Xqbqfk$F5+a40TgH84&i?>=Mi<mhea22-k`9{FQq0#WjQl}P5RBAmIm;#z
z{{XRL2#6}c7bE({?<Y7R{VX0|nNEj6GICI<lK%klL}KH63mMk=4Q)J1yNxXg4Nkwk
zyy07Fxm)i)x2Buged_eGT$#%(&m$Af8e%P~Ch>Ra<+edcz{rh?m&6wPv>8rfXGNZB
zD8OVj<OQb`4fST0?jlN(8v0Bfg4Aq#dh*;z`~5m`T8mUCUd@^!DT`J8Cjz3+L|kDc
zhIYZObG*pad5<Wc+j>qF1>RBP%<nV40ZPb7U(K7^&AzcpChv@B_*%af_I031HXi$E
zgYo+m%t+>)FZ@|#>G2jBmG3V4IxQSX6p8rT)BgZ;R>JPU&Y@W}t-(gE3kB0^^(Mi&
z0xM~vMUdMb=_1l+Vq{F0deD4<8rZjJXgzJ;jD2sGL$V+<fK&;U*|6DU9!WuY&HW?J
z1a4yyWPj4eWq<RZQHeR)HxZ8>XYV6#Gm-F#<)(0z*b=}W1X76iT5h8!>*v;@9(|J+
z6BN@@Ls5Z?jN()IKpvIjk86Deoi%4ZvuT{u$~(_F9QY}K@e!=-sIG|RGmjyXUdfNv
zGsYu+UL$ypUs=qjI}-ginoNX(NG7-$Lk24%%GxD}sv@U>m01=L8iEkjoKdbpatb1u
zrI(8VEdz<2GP%`@N1CpiV5Lk_so%IN9H~llwVZ%gw)Zw&b}2!jr%zFJtBvJcAyv{4
z$da`In$g2GH|F!U-iNdn&<|>-^4F|cg_7Kx6dd7n+N`T%2W8q@Yk{~wdEZT+>em??
z%$82u;r^-0Zuai^TdOsWHva&QK+OFVPbu7da*Tr^`j!O!g2AP*$bkr~XPnOECp*mK
z{J;+*!EY)MCoEXD)M8i)wEzI14AfvlPGI7oY}2H3uAe<Xvv0hQqW($F8<4=<WU|Tn
zSLN<g!(C@%Qjoi`F6!e<)l3*EZ;9WGedPGwX3dhDXs#OO!Vt`>X?mLGwac{5o48Gh
zf_o-Dv2nv=nsd(L=6}rKM4JF}O<xakIX`ohWEEshlRTzMbiIZywozunme>(>R&54V
z$|Sh)E>IaoCu+6StgBfM)mOZdvR#^t51d3reF8-I3L<5MWL|J~<s127=9i|ZYbl3n
zqSbX%nv3Gi#u(7Q?WrrW%01n4R!`)~)@Z?tJQmU74tVKmjN`-a&H-%`IWJ+hrCI(V
zQL^1o3yL!R8d-wxrUKjo>@-tByNfHvo$*IrIy-e#t2FLa=@6OMa!8$&tR*|CRvj1g
zseBMX7Y523A5pg-IKqfhmz<_?A15SA+r7o7H_<GWn#xPQV2mtQ8v^16Fiq1q5y-@0
zzuY{jRLOKtKZxdL&_+pi3`R^&$=b9T*p0u4f44><0W7eU<<lY5ERdqfs7x_mhuDpp
z+RaeG&XF-b<#UDdt_7BQ<ZDQ1UUdXURt`aKwCWmb^wR-sdLrAa?m4CPm|6DFw^!gy
z0CzsXsaht~yzKVsJe!}ybNz_T8amLa&22qb&uaErUz9`C0|LcIrP1S~ATT08dEuEd
zc^5W#ocCl);WXb*qTCT_hDz>1sf+zl^6`jp^Oc{rFe&dR8HZdTQg2*7e-49O*Py!g
z-uq+At#WeK_E(m#MJYW^d1N4t^vIV95{uvOTg%q7I;<@7UsU%g`B)<UluN=M>wE$8
zE;$&g{*#!;$b*Sba!Rq`acV|#gt1NBD_>K*jKs{w4<Z@ONLScp9-D8TKVW2oK;qWk
z$&nr*ew=)AnJwdtddBg@vS)djn#2*<2jFl{c}&igJf_uR{pZ#Y&JblP0sDr46mX?t
zWZT(frd0=z-L|81n3fjV&S5+<21;OJ{0I`!9~i=!ne!E`q-JM_V&t-t;kOVGsM{u7
zWZ(?gU590}{OEvR2O+R$S=^U76{qfaB0pC;gFYp@APhartS@K-d8PfEZ<~*`+aUJp
zTVyMF<4>$fg(tW5af4F9Z?=<Mi?U$ljn+yRdr=C<DnQbGo1`Gc!h7bnqo-bb1BRDI
z!tZX`{^aDysjjkQM8h*I@iT|yi`ULqxcalYsOq&{@H_M?+5#w=c4@Un$RdI%h!{4G
z)8wZM<a3o+HWd`uOCT}{&l_jg)Xq@RA8}I2u&WwpCFaF5lRpuhnVjVw-UfZK`+nTv
zIi$l%s6^`2%DZDLe|=M$nH(Wo=xmat(9KBUm)|jw`b#`|dGxOYZdV=mnVHQ$j9pQY
z%*~{mEAD>lvowb~J2x2p=l9BDUy54aUy0}m^r+C(6f~aLC~F2pKBSmHMew8I$m}B}
zIR~*v3I=gn<8U~{?n(Jeren{Hp%W<7l_R%VdQ6#eaE*>ZJ^Er)NYr}B?FQVcHJmJ{
zf2lg@>egHfCGwe*nIik~pS(m2K};+Ysju5Gv3h~(R@!9Zg0R^i{{Rd|XxE^F+p|*B
z!qmgEt9TbdFbPE>Q$OkI9G(92_R1)S*+7snM*52lc)Fz)SGSCyQx~i3{vPhyr?e~B
z{Hl!HrZZ5qNaV58<c_uA&Rd*LK4wqIT5Sc@2-K?9Z7^bfL%d{Ued9}+$xc`OM$cJ~
zj7;ga`r1lkD%wo7<#;-5?JDEit<U~*l@S42g7U-J7Hu*HAm$=0L@*O6S<hh1{{Rro
zb?N+TFum1z%bspopGcYd$`&AZTvsSFE*EsB(}|p~5Mt$*-C@t+<;2{U%@%K}5^~F4
z*H3hP=lx(Xm&<JprVk*7vLt{Hs8TSnvd05Os;Z8aTZq=!qPzlHb-p!iF!jaD0;Qbj
zZ1}_(Amejg2YFHHvjh+q#?&UvOnu~j-nwj;b5uKi*cKYO-zIeTjBjS2wVZ1X=!D0{
zav%LM*UIuxdu3%<@N&=&zB43MN>Exa<?2v5YvHL_j3sQs39^Pm6Tkg>c*%^2Jx@&-
z8jf`rSgnsAXLU}7>=qAV^D{ZRt)W4QcrHyQnt{J4ZD)i{M4)M=Mpy?0zRv~;5w7Gj
zoNu&p+a!N{`<Q{9zMyA`MCGm8hCG=cF%ga<8_${h^@``$4P(!>l0yT(ndU2FV1H~-
zb7TxtQ)0z)$0}Cl1}$L&4QSa)qu@qtDI(iEWiJjX$O&eeQ5*ys(#X#x;vAln;7Efe
z3@*?2+3W9a&zv1n@Xq<B+G_^G<&94BHY+zr7S1}YUe*ddwU64xE1unPp4!O9^Y!<W
z#x%jfDG7oWOd5&7fKlz3GH`PWnUGjaMAe8iG9lJG62e(Kf7hYIKUC44t2&L=Y1Q3s
zSe&_JLYR_nhgG)>YFu2pZVfVB_>u!Y`DV3eg<(`=;C`^%_n%Gke1)*QWT1w2Iy#iq
zfQA8s)D1?XrkV|{Qiqp37~g#Tyl=Gc21lp_On=r6+`N>;<PPRB2)Gs|6<1wlLHvNv
z9CLs1r=%<}0kctXk8C=In(C%y8c%G|uO$6mmou$ysi)mJ;e3|)SZ19BvAht@L}qjU
z0K5D){#DGZ34{B3X~Hwx&iqPQa&n}=m1B_tGJqX5E82GDyp-qt<gPjM58^NO)#pEK
zsWBFoSd|W{1aR|wp9bYWi%&P-KX}<e$4ou(9~!pIzOzA8OIl}fKHbk>pnc3?&d@8$
z#R#US<(4+3w<{_-zr94}pHyo@Vs4d(xZKYko+2lG1*~4602kM!KFj3k#8qyg#|Oa-
z;SqL8#Y<6b0Nm6x;Zx<{-T>C1+6J1nb8WYb2;GZ_Qwa4c#fjur>hJtZKkZ^Cc-kh3
z%I+ZJQH~ewZeu*BeJ~sVvazVC&DAU}G=i_q)r2<z^E1q+F7fvy1E*j1j@h^9rXmdl
zvs}oW&zSXzm`Qqg!kzd=NzVZc(qG*Yg4U;-6nS1EUcJC&tr3SG7-{99ky2+Ra<L7_
zhzxkEq4Is2=P6d5Gf&ORv6nyITJ_r~nN7OGI*rRTE<uc@n}{rnmC-L&QcTyqrY<P8
zbuXVcM8lcO*F=xcIm1_EDj}C^2{a*UI33(!5#}`w-Ksc3z|CztiT3;52>$?+M?#x@
z9onX#h0ryGWX64X&ly#at4Tb(FgDw_jN&om!q7^IP6#RjyDNIO{NT+kAo~O)A9I`T
zIjJbxuEo(;W%#ukqN~Nd+iHZZLuGtU6-w3%SiSZs0{KU#jc20QUNVyt8l$Ckz4SF%
z?5lL~o{GSHNw@fkR$GLc3~BROaX>wreo%XI;7rPA5ofDESB$+x;gWD^g%5NQ5l-_y
zJxt2dGEepK^np2vnClVSoUvmZR7OX{a$}F{-dw@lL%MCqh$uFed#MfKLe4ufO-FXY
zkg&O*@_l7b4~?|QC)KfDEI|)&=Z(CN$igwZlbJb1eKp^2KKI`vCcLIxE##4j+S#!>
zVirqh4-+fr%2RBa0;qUKoLh5i62z~nE!|Rqk@%g$iG%z=<~Ba+Gh7U%Iy<9qr>Dn7
zp~=$i3>3gHliZUT-h7dP@x?%X4%43ktT7`HBGcA2sjqG!aT_((6b1^S=r+<u&*Z@d
zl^6!B)><SHYAcu`;0HMB?bU9=)OsaI-x`fMd+Y^MoJKxtgG?iu{A4$8(pT+}@y^Ei
z&%ZgaZ<KEx2XYaW*br)8N-9`>+x&R?gAFDoHbHiOIV+Ej`8+>hBjg2|Y@Nm26>Tt6
zg^{FcM6qD_+cYY0c9|@j&<m);cT|A`R48m$I{J;<7aI`mH{mh9qQPyWDAT87eo3F0
z&gOXKJa~-H<nI|F{{ZaFNd3fen1Ng<E4_2(GI0PAOeZxL69qfghBI}I8mBjzTwnnJ
zDN`mkCE;IE+14P*Err_oe?*wi^N89z%4)N%n@O-WB8s+b)qN&P-BM?F&VJZ4u|Z6L
zs+!@xHk^^;q>SeABj-j}6sYr3Gxtvk@sK4mjNHC>=@HXugkJpMuWKsA6{_(%iF8tV
zh+MS4(-x*?Wt-AD$*e4&$x3W4%C^^T)V$>y6u4HKWu+M(afjfFGeKtco*DUVf)mwh
z6?abdOZsg+XK8(AGl4S&6BP%Q+Rzk)itR$5;yC`qL_w5gIT*3WH6eAaP{azs=|Xi?
zLhXyDaQ$Y>>)$S|D2aNCH*CV2VMkG7+X{(<>DEZg8(FIJnq}W^=vy^vpDx@KAu9f?
zU8is6EK%n&S1!~bReTB+?3Bf5;5@lYCUAI6#Kn6otoUf%9@uz-md)VfPF$X$RB)%b
zEOFRdVtb9nEn|Hn^+e{jlQh+KY5RNnyV#xVI)9S8AyyK}){!nA)q{z9NR61XGcVJ^
zvsgAF+D$sBa=2XWzH(w?)BNUxKMN5DJV1mgs?=QOnzvP`C$&ajC5AYw`{^jw>sm5V
z&YJPG4?^QV@C0UeEj5tcO0Oc2oi=CFY-b*25x1rP0M}cHNTj(RDI%FK7=!8ZV8Dd2
zVDz1KO3zG8tzdYu;1QAWlFK9kRq}c;jGHGlsow18xwBdzS6=%2qulBh`X9K&`NQJ{
z;v<nj<oGS&0Lef$zXZqA6E@v?EKI2+4^_vP;x<+z2w_;X5iO;tS1R*Ww$gfeN@A^!
zn4W8)s9YhOu5{~McJcdWr(mmxioFa;DBP*8!x{95f%0AatLhBMjqp>y-xLP>lMKGH
z0(?$y1K@JJ?9Nz(E;P#zpGvAz&0yOR2&z*bxAM`0n`?#XEE>T6dZ6*J>BtT6Hj3>y
zlbyE0vjad7K9RQmY;ErQC3Mbi0<%?Qw$Vm^)<H~fwcsH|Bgr2=W5mScZ2Oe0CH`V|
znA>`_je*pE2-|AW28&}UFQ|m-mn)Gb#GaJ-^Ez3uNb7ZVXeK8%RIpeKK{0?hBM{MC
zYJ7hWSSKNzbb*X;DV+G}D=4_U(rTSsb^z=jD2U59-k!?n`}^A`##({*2gmuR>lJmh
zE7K@Nin@y9H9a)x9GJ)7rukEsY!KQ>NR5#giSspgH_I}W_t0Y)lOtmdq$Mjgo@zR$
zxvVG(=_fJ-X)+4W2c`N|I$@fQq!fm$aaywLX{9N%Y4;DY^gvd)CKP<mGfo~>CMWlw
zNtog>llPu7H~srLc*$If#IU}9ES#_r3968@$+mjJe<a`*v;|UR#g}xXN-bNCeBp*o
zIWp3<V@Zl1sMhyYNm=#d0wbZPn_!Q;qvE!pi)tF0jh#Nl0AXj|9LE?K)ITPw<I(IW
zsaZ0$6HGvKiT;GP)`N}Cet=RiQvoDjm6xJc(M;11aYE{IOCEO@6RnAR9r8Y>(suOI
z+~v!*KW%gu3b*-n&b<0Ec`%DK2Ebc2%vqIcJtcCYt*_;*gbK9z#F(%>yGRXOgtYy=
z{VmZ17g4^x8!TIbQakNKm%Cotjo-u1uB%rg^UXV|waUtkd<&gcWkTb92b#!2^%ulb
zKEHBV`(QBCT!t)f!?#Mc$W2bvD{MkjF(W%l)=gjl+Gll^o(zzbOj=zBir6o%^uS5@
z>&cz+vh06FmR0zD`wPt$O}d(hs|<C_<03lZVoFMPoc-f10JBPbWgqvoCmp|YclDgD
zJ8AW$w`==Xm_-?rKCI9`-4tVPOPnZm-NwYQ!>HirBir%wT?c2d7hNf!m{eW9L2Hgh
z;$tM`GRkz?`n0Yt;C^PhB2BDusOnbh>TK)NttQdE_*x{G%PPv41Ykr_J49(=*1MMF
zleHT0hH`Vir}&>jQNi)cEZa|@aWNcXb7}qZNV9#WCKT^_VhygYJ!6ySA|_)ks##jf
zvrW}lzN)R%2u|xa)KeAum!2@<TcoJwA}1s#fSxQQ=PIoyXh$@=ZuiX-)u~N+WagD%
zz}Z_E2U&!pIE}et)J+1I)qSr@rpqnXsI8QXF1>9~@lhqi4GD>eh`A6_07q1LL?>Ra
zv4gN;N_L6j9x{B-l8}lPhCgpzrhGua4DW<w$&xJp0639aluat{XW@3!pSFJ3nfZ=t
zT%s!s{{SccR+9Hg#O)oBdYh1HU!m+MSmfXe#<L1z(PC0tLHz2d^Gb<lCzPqtxtC#Y
z5>i1s%(7v+Ix-jgXG!Fxqyl)pwxDZS_dk^h$SD$ni^!VxOmR`gO)0``*qCl=3Fx-a
z5t0eyDYQqX&u><lVN0B$NwIt<H3_y0FhGMVX^=LZL7d`MWkv{EGQT1q46mj&B@TQ9
z47kAKlonp!-7|k%vF5pdH;kVdZgvW*#xT?E`sr%OwjDP09Td~a)$ul~E|V38M78wg
zH}&#MEGU!51H#!kwDIMKyiUcD{{Za7f1HupBQ(2|>h^n!*V}cGzNXqf(u!&<I~V~P
zn;e08M%kr6_P$Aj#it&Vw!@!)TL5p%$$6Q^Knjr|B?mJk#Yk<BnGi(haHV`zqS5Rx
zbjGw`yxO)*Z#ny)T_W+?R}<GM08Cm@{A#K_D>`eu8JFvOXMASk!%VKY=@SWhR1){B
zGg@T!$Yn%J-Xj;97Ht%T70i<-7X?O*?X_xh*jNVB6ST}sK;=P`xmqIeORm}}(3#hM
zGL0{7s9a`EiEX=SE<8>|{=IkNAsJ95mees8NUGR}N)~&~eekDzuFk6oW@~?!cD0yW
zA+gAHs?z0zgV)w^x9b4sSDJ3s%YHRy?WiapQQ~mbVcLb#Qq`y@`p^j)(YJA>yfpx1
z>!L+XCp8O!)m-cG{Y`nr7YcTAHk|RFe`?#Xb5FYo{+ai*=>Xg+K_!iEr1{4QdkSSK
zuvN-pEZtN)Vw<T`WtDXMXKT4E)Y9Utiv|2Y{^|Ls*!J3SNbW<zaz+nw9Gt5J>b-eS
ze|K9O;jG{JEs#{bNJ;eT8k~}-&YMap(v`zev6co!?B46nvxRuN=&>YKx{RCDHP9Me
zUL~qqhcF>#Tg-{V87U^>Sj1_>W2VOG2IbL~!%;faZK%66cCZG_59cQ9BDmd6q&wZ6
zN{>CN0$*QFyUqK1T2bl$0Kc~QA<ucqY)8a7c4*NP@$t8u{{XLD6MuAhTxX1w<W7E;
zl;brAcQc)Z(B`Hcu%hDTY}53Z{RpIBOptmy?3$%=HReiP`9IWGqRS3=Dm3JAdY<i0
zx4Ths(-QA|o;E0rxh6Zll|~#X_TKXR#Zk5{MV4Nr!R+g;E{?`2k=(g->28<R^JL(5
zlC&S2$PNjWf=!1XL7(FYwyk4qh?$s(k>N4Vu#4h9^%3b2AE=^!44Fxnwy+r<l!u*x
ziPV?6s@a_T{zMlC0c9W;8=BXjX-Q|^G`Syw-ezV%`zW5#>0UoG0%B0%&e5x;tRZbR
zmzmlpXq@<d5%IuOZOM^kqM{UcJHcZZHsBkMXKyX+gdG}8!LAcgj2S~BH_CMhu3tMg
znr|jx#Y;=!6_>+Jx-BZ!n4~}?heoCn>w1Ohe1fUE!_#bAPfvqX!FyUN=Qkr#>y8v#
zKI7XnJoucq@F|$IaCTZzpMI90eZ3-&S@iz^84)r<Fmv4OVRAAo+cd3M&~TUD(UkEg
zR#*z5hR-E4GE=~0AuSdzP}>V65UjTSy<&KfmKMZsFrPWLPyJu`xD`4&yKe;MuOHCw
zo-<2W5-pKf{_*dht8Cx=dVic}{{WBD3S;A!q-0G&=j#->sa;9St^oG($E4)tgX{c1
z{_VKc4TL!pVk8I`IXPUW?MBu%#6>!r37X0XC=6KCD($PYvGI$t2&*erXzQ5{Qv#c6
zvL$ZQ;Fq(P?%<xe{j*33W~WvJh=|C`NeP?bBYe11dFR&C8j}A2Fdj4KZL@DXVNUau
zhGV(e^-?1vahRCxQ@l9Pz*v{1viEv5c4b$<>4|Q9869P*(}c6SWOtKLsOBX+m0G<E
zFvmV-YP{j<9suRYN!h9x_E%lOjOjD29PRHGo0_Q0^pzYDNwAD{)qY)9!SV_z-XqiR
z0c`9Xcz%01Y5Jj579^cgyD8W;&f`3020wkmX~bZGwUft*7?k8r*--lIM%JNGLeFl6
zUyAIub@JoES@x*U`@mG#E-OtFF4KK$tt%Bk(M4mah^ov?S$t2YjI5}Ag)c9+zS}1&
zSdj|$4_ToUEqrvoCfcrzKzQ0X%S!%^t45@lvUngNONX}hs3dU#X+n};4Dqz?;DDu8
zaV9FKN@l=vyr?aBr0(yUIDA~ev~sIXk*qLpSj&flOmVgzjV_o=hJ}Tu2~^)z>V9hX
zSAN%G!Fz-g4EaY9kKZUH?8}iKb2~<T%+IcngXJv7Ph3WC`^<ghly=4#TS|zbUzJ=}
z8kCTNAO}jY@Fq-A)T$RXtS42b#IS}yq)G_-j3TdEr@5#L4YV!A!y}0urL^m^5S22U
zw>?IY4=0UE-r&kk)9l{cYRh{FI+bHk0)2b~V!4y(Iu&e|yMrfPyi-~jj@g>ms#b=d
zPT)j18%f4A;Ar&@(I2aS+CIOLU@|hpVap4Ymfay39u(hC8A*~7?9@qz(=+^&nr#`n
z(%%07^ybv2XW}?MK3P6;a7Y4-j&0}BUN@N23<~)2)S206h9)+^P`ZN(Fb91+h78aX
z9TPmJc~p*ZVR;zT8s}y~M0{mtO6L%2rp-87GUTttdJbzXY-xRl1)OEeCmd#zz4dvg
zVO65G&YMql*U7LOcu8zU3pTFJitYT~M>%<Ym63BI3(Eybu{;AdEr)$df4#rgr}*?~
z?^3AG^A6DxSQD{w&#nC@Y@w<;k#Wm3>FS^csZmuXLvp&iOt&*#w0h5($o=O4`sWM@
zQycx|P$RIYhi*<YW|K0FotK#pq)P)CSrAPq;X#F$LLucbw%=#8mycP-PmqsF!3?s2
z%}1Lo@trCr#pRnEzzvC)kG8rG5nfshEEd&dadYS*RxPALVLKjy6YooH{{Vc~YVw%2
z8T;bcNnn`jO5mg#qcky@)BvLFO@<pl;stAuO%vr)D>H2$QEok9?2Mwjj=My}P);}y
z(Po`{CIM|zUdxv{T+hB#Mz-yOP&mVr(~%exxX5}&c*#DheOP;YWJjlrQ;ieICM(#9
zk<$x}dvscpkkQR#SD{*(P*zqg8Hs9Uy{K857M8WPTJ|-}HtH;&!_Gt{kh5MLt~zRF
zH5lXUrYRggpcY@;Z5WVD{s#qyf&}(Xp)+SnUfQGx1r(0^c!{6i1;&*W4DU1LvLw@(
zz`i<b#+kfQzbt)8jm>;ykiCK#1q3Gqhm>TDOmmNjaV&Ekb55$Ts;E_TCN(1_+i^f~
znVwO{_b#!RmFgA^)M!Z@!f&UQrS$8O8DryR@gB4oC{4F|KZz?#R$B>bEg`xCPnjw;
z0hnWV1{qc=nWIpSzjDoD?27t*H-=Uodk^g}QR6LsXSG_%dnT9@mn^kenX4#*%)#jd
z#7%(9zu$?(pHr53s0Ry8%D3oJrlD=qmM{ud=O=!@dQz|ITNP~-5c?;2;8d-vX`LvF
zwxGB+k6Tz7zIe!-oxg1<5}JU*CMRw1c+T??AHGP&E_+vUuqkKk)&;04!y2Pkx76BR
zXGYX{^TcWTg5IVcr>2d$mn~W?!DMzLM?xc$ZECBp`Dpx7@iZcAYHp!xrGuzfvKva~
zsmy>`(?zK2MVi(t{0(K+&?)>e?P=Ig3gThzs$M{|t~Tr$iLkn3&N97Qca$FD#+%e+
zNqtgVVj&$$7Ma}N*@vuuoS!qi<PP(;Z|KP5F&PY3v?cmR4~fREEK%-{<m-`%lF|oj
zNZ(WDR<0g+;~t_iWFr<gf`FOy82qE0(gX92^L0)KoSZD@eA7)qEn5glQM^RUD45UQ
zaV)GNhJzXXB|E@)`4>Fr4P?EdXYUa|(cBCXM&BhgQrI}GE+Z>}YKencIikXhEQnv#
zU+#FO>BDQqs`VP_dPg*tpW;fKbm_k7*nTNAQ!X&A(TNvXa+3lx5CfWw><pjfXOt(?
z{{VeFWM=;W9kM(O`p$mYQO#JYR>7^TLsO1X*J&w&P!(ik43xn!cGd-!2R`E`#?e1{
z-tZL(5pv4D355m(6S04`Wd&txp!)~zHERCa%nm97DAOFWW5}X1e*XZK<F`qAX(8FD
z*NnY9FFd0~2?~}SI*T|nsf0uo6O~*2TIH+`wirl}0dfe`tZqHJL+Kr!DQ|(Dz6}G0
zU#B$Rx1{BhfnJ`oY8HmD8KVg8WWp{zY-}Kojf2?BO)vid4wPkDg_FvYKY`u|th*$O
z^`qKVuLbU`+V?^tC}h?&J}g+EPqqi4COylW7Te0z&G8euEiciVPRHr@F_-s;@RRWh
zk6-xuQ?zw#EaAwBOQJuBM}_g2p!l&#Cpyh8a<;^rUO*8YnYA*ulD4J7T(VOL3H+rL
z*P6Hg0MqkCJ?WTcuLbVrW|O7*O{5W?;GxYKEeb9&H%YynybF8@QjdK5-gCxG@3iGX
ziFYC+lVLJwUz%qsYpE?pr`NY#m1IW`ZTu8ZiN$VQmy?=A9MKHFsSQ{)hU`(!K@RiF
zXqcIajl4<7vrkT4DJx8zzw>sNYApW%x__sg{TK17;?1XQ)#3oE1XpmfqFz-idreG~
z8p!dpGa5}cT5z267$t;SnpW)1XW6Ryhryxx8yOi_ahYuCS81Ka3An+{I1j!VX5!0H
zu&2}rL0cTum%*VinuL|4>BkzRuicO;-|Aj*gltAcY?(8elG}LY=ViI-{?he3kkm=r
zu_JBkZ+$Y`oKBU?eCpAsY(|%-S)a)vK0QDhWtF4?7ud0wKb}V{wj$O}CT+1Iv(#xm
z*A6Q!E~fBbi|Mf&Vy;uM`Yl~WBH69iUPL`sUF}Dl&H8r?d8UnWROYWl;f$~q49vK|
zNNMiwU2Rd5SCJ!7t!y}%>2x`i$6cP5;AyV+3Wgi%Y0njh{CwH1@D~b+ya0`cYuw{g
zN!j+orJ_BlgxH;`Bqu-XCu#ch+96(%8_w8s=5f_c6w<=T+@>Nul!o1>UbRH~OemF<
ziA2ZmykO55lF0KUWklnh0L@B%cIPHOBc44e$L989PFlk`$=mmt{D_PWr0FFv$GTa(
zPWx|%`BkW}ZTMigH1JKCdSsNqWT>{8+9DEVv}4lqST=PIM)+)uuw!W7Y2PRbbYEot
zT#oP|TAg5Pbi3?_uqRbpBF|J_usUr8ATbSM*5MFu<}YQRRj@hKRo7{=fIMp{!R{o#
zyP&6bHTfT^GW@iYWQ#xV)>Zu|H~D<}6#3gH>LdA5&uv-p3u_W^zz&Uezvf!!4>2zp
zU9g71OY4f{3^)G(892y@6vw9s8ZcQ^b7IPaH|(`+u`>|S-2ob{8HRKc-lZEqxOw~J
z{r><b?T_Dd^34K2t|HAyx!hr8RrKlA7dng1#iQSOI>}s+$j=hB&7Ds#O$s;Em71mB
zY^aE<slINj8C+S;wW}4`RpypXYkvc(oUhklO)aAZTTXEj$FH0H5@eVr2wb&SOG`se
z)L3YlwQ}*dyhWQ##N<yI8H=0>MXKuOLK_-j&22E5BwUV4fA+Z$oWSILZ8=wuc*!}k
z=9?w4u|`MzXZiG>IY5TZL~XB%rPXDcFXRoO!Tjh61_68K#6nGs;-CTR1>*~SLFhXW
zMI_+3T*j2<lVp<lQEQ<JlpJX4Ba5+%(^fAaFli;=40Q=xTf?yCcr=Db(mvxq<A8!1
z4Y7k0=5l}UO}NNh47GCD?h6%qwvmmth?(((0K`jjs=cn9rOl?x?75Q@AMzkEftk+3
z`kfdPV$r3S(n+-Fl88M!MVz(%?#Ha<u!K;ito^4oI36?zC|jCs^D-=Io0lpD)>jgF
zuAM^LP_|KOvm;snmJ4F7_ytNVofUP%YKX}c1Y0Org;3LBe~Bh$g}D4pxib(AESC|J
zSvobDYRv_$2*Z`nx|qhpLF;tV-u*I7*$K4@t0FNoyzeAj=YLV(^+(?Ms>B<?qv#+;
zTMn5{Dxx>nUp>%)S;)~M9VW!N2VP~fJSG6Qjq_Kxc9Zs2i>O^YTn?l48ybfI)DcOx
zr2=|Gs@_WS+4M{#HA{-QTBdKwAXBR}rr{J;sEc0_Pi=ds0cHL+T9!J#opAU|s6?qN
zNQ4E(sk1f5lod-~(LQk+oo347J7al+bvm{TPd1<5^+b*}Rz%n#_`xuhmaIzbF3ndT
zRrE4-UB}xFf7XB3tbdvN^En@Jg*(j7I$O|=XsX)>A|0K4gd=%4%|)}YGz`I+F_9dm
zBMXl&jLyRYl%hsb*uGFlJ4_c5ne>1a*C~%p@_$IiYP>*_h_HiLtg%Pba8qoST^(Ha
zTm}U3F_>XXVVt>+a-XOlG`Ps|I>(!2$ZfPyRJ>RjCslj($w`!v0m@hm6|DX7+Ge!x
zb8mv=41Vh-IUJal(-VeYGUWx^rb$I-xOfIig@FXU6gW`L^HKi*Yan09a(>wZ{NKJ$
z+~midy<>UWA|@5S1pfece4}NREQ=N%-&+Dnv1XGcp4n8vf>oMhD>4$D@*+xLVV+Mn
z{j$_>O;E~vk9%UNNA*%+JSUcD22*XLH<QD)(LM@y&)?JU)#1QvdTDjUH1S~6Nj~^4
zZMFay!feSIOrHG-&HH>z$r2(bKJ!J1{7E^l9`@H6KlMXUR-nEGt5Y|KObGKK9M=ja
z`A4BFpQWl|-^q|8#sqhhDcU0)v}u}POGl2GArtziBtIls6Oda;L1};7D#xiGRTAUq
zgAjU1tfY};s+^hLa;OhYrj|Z7X-B8lM%(9%+A~AiBE;wM819;r8Y@k2cG^{dIby?D
zp*DlLS~&9~b`INSryW-;%&g@_<*Q7MHnfexJaFMQs#j&*HLkNV$kc7IE_zi@{oQ=l
z)eF>Joz94n&ellro=Ywq=c|{(cA1DBNcfs-Kthk=aFDLBYiN<O=)_jK)b|_~*G36f
zE6N#?P(!ro)lS=?IdHdIiadFo-Zsc#L~WaR-_Tbep($g`8r95#<aD{4U+$mlT~OBS
zR%{ii=3ns3b0tepBF#46xl-4SFi&C4yX~@U$5@vk0XL7GWN(SE`b^zzLRncBae!(v
z25E6ITT`%MGXy(S<Y}H{TTH;BKMJk9>(M>c_subd8g8KjO_OS@)5}moV+K5T`EL{H
z{{UIMz)WNe&ih-2$kj6R(o^v^%x}LO)ks-=(m($IPumdAhY96VDOZu(XwTvW%%vxq
zXiCNJ%2pp1(ow3}Mcb0()YR>)g*kS!D%driidCH1t7Yiun;|clKDlLDuVlm1RqCd}
zmg=<@Y?g}bsSSx*nsSqMnbhc+$&tpL4KDiDs?rujF8x8UDkoZ~hR8SZIw~%*1BS;S
ziSw4iCU=XKx@=5kT2jLRJwgq)-!ZL}&6MxA2!i#2lKS7&Ct``oo#tm2fN<3Vw{d(u
ztxD<^NpkQm+HV*Y3fURY{LBa|mPBtkGv_OAb<PRfb{5C~01sD;0#p2lJBr6OR)Z`O
z@<aMSJpT6>6`vR-$*qYlCY6zS0kY|`&eK7se!-T1!d2F<%o&1gh|ECD#FFG9WUCtC
z7C~|7m9fnLUa4pys;L)E>bGJshSJ&@MP2tRs~o<kHF(K_E%Qis2e2i<!v6pW`ueFI
zbm^*N$?uxC$y?|Cc=Y|T^Zg|_{!bo}zurgfljdf9IL!IY_Kk=9;(xj6ypF|%W*ca1
zX9erEq=2B7L?w|CaY4p>?};7QxPqQVGoRxwPVSwjcu;`T2#~lrw%MS@_Ly03uXz)<
zT<W}*K_58C`}F?+IsX8Yje_W^a<v<nD+Py2Qmm+fORd<AX6G`B;Hxq*#fH7oq8W{+
zX-TeLxOKX-QqiX;Z>-SwJxFIYjsE};cHlBoz9q(n!_`*$wH3zYL$>Ojmyfpd${;&f
z02dxLE15>85*DSS1s`QI9&y5K9WuCTPGqzdGRSQHWJJQqbH@HBG+L(<?vGVu9Y;h*
zt=9LNo1QX$vBoi4x`bj#pkb4{4t;;`C(@G)EDR2`Mk}UyRHn3ABc;+Vhk%q!K@8S~
zFqi>6<BaP=F>1SD=dF`7Xyg}Cxf&Ox3^H`~{vgJ8*AowcYjuq746ce#-y?tGApGj*
zpSJ#h?kgai(kYdj5_u&oGhyU@W}Z=+Y4Ekn3N}w6qhs3ITQTYPNSA{Z3W#KA(&HCB
znv<DTHApV$zm|5ZdVc;AuJ&y$WLmTkFaG<pi4{F8jq}RGr`MdRJYr|z5s)N&0B^sr
zYo-JBZJJ)66c!}iY~~PAkrVeBW=HVaQvy8p<FPTDX0&@fbw!2mp*$Ks&p{!<xe18t
z)0+<fJg>ctMVz&xAazXsA)Vh_5|ec78e!H1N>|2K(u_VA)GNt}iIJG|8)w!|6bV4Y
zN97==3Cle#YRdY3tCPCZpe%pBs`b*i5PPL4u-W+;Wc;ex8S$D!Aigp)8%EYtFRakS
zhh0nM6*&8aNl*RGYcD%C64|ZsZz0t96_acw5pB*aRk=5u;sPBaywq1UExs96k|}bt
zmoN74fOw1?%unY~3C};WX3fW_hgo2TrbahJPQ7b$qut{Yqltq0D1wcrR;Jd<q;#FD
z%mIZ(uIaU!=y10a>EqIO7~}kl`ef&s`n-iAwI~dxp#)1NN)6Id<wxL^ndk$|pmE0r
zaku<^G_@0$12#wv?2s=I9)l1>F_+Ji_McDR!~XzYN~`damB2Ew?JtiTLl8<KBV{*x
zZf57C;fa>XS=>yS%(v|)@Cj;o>~UQ}d`aeMS@xiajHJqe^;%ZuJ!~+Xqys{mY#ARq
zBN%b+4FfC_6Cq+qlxER`+9osWDKb}WB@dphW+x^yXd7H&JVBId(Gyu^A9!0qPTrO`
ziBM1TM~(1eOT>I*2&BIJdIaooGZ{a&L~Oa7OPg7u)!qTsDn<0hEu@2k3yR?|;7rJr
zvLpWLgtF2`01Dlyh_c8w&lvGJ;hW6pYamlymPiaAxg1@g1B;pE<M~J+w3Rb7K<9U!
z7w?vwHR_o}l(6K>Ke<&fFzx&tCX0(<c-V>q4fFf|01rmijI!*_p7oKGOh5{Xdh7;$
zd5RXj)m5D8bcIfb2ABDIu;!-4`yHpUU#FTG5x!2@TanyVoUqAdxiZBZt$<l^NDC4r
zLrjNqaW&mFc}tE;q)htA%+mt&oUk2j;g!7EP89MYFXWFtAOwzJMy+5AVl2FKZ%39)
zIg?nICQ&O`4uf_+RT&eVd(6yhi#bAyA#7t%L36WCO+D2vY0AZNjWE|&5*!ND&c40M
zp=A^7xm7`|Pi+;pAf&e(W}fwba%#*EDTJUECTYCdNK;1AVHOQh;}j+BZ5C8#O@KU;
zAxn1-kNN42gtg@gO0Dh~rHy*VBm6-n(MB*HRV_DbG{dXiRrFcFA=r(f$((<eH#Uxq
zJ_M(e3Vi@2rXd#i#g;(+(HT6^F_GkW#+#<a756naA=k|#um1pQz6{KLZ_D)=5m8J|
zQUZ1A`&n;&hPDzM;j^0QajmDOKh9`mzL<!nu9;+Ju*qe~-&Y>yQpVWiFr~pRt2A}v
z0KwNHOwD#6!P{y>EeW0W$n)bAJedPOCO$+ai*G(9b#%Iwqb%w*Ymb^&3gZm0yymfL
zNwR^j8yuD_rc+`vGfUgwEDD!3CPd@(mR%9AUhG09Vau5R06vFGxzn%iLW&K7u>2%C
z%{h6?n<jv!4Ld3&*rV;%#;JB%sV;q_tqqWUp&_x5rHL11X%K${ARr<P(}vPJEH21*
z+cOjhYE)$;x&2w9+cM;%Is3?%Fxa;1XzoGDi=5YnWx#%`h$D!SDKe6uQSgJDn%>rj
zblKmkuPMvlkxr_hV|j?n9D_$lmIvjfhmjdRW_>j9dz_R8ptx1Xgckb_`CcHL5jOnU
zZ}K9c-Fb0D4|ECDpM{XaSUFJDOnAneK}3Su#m|RWQ$rkLZefUWACy^KF10+1tZMpN
zlPCu+1~&6Gj6%}NUNvl9Y~<AHGUt8EH7j9cl31zbJ!?3ZL&_zDrlwpc7ze<&kWoWW
zOS>D}rg*62@Eo1tuE{<LGtEPhS1bsLIEnuNIsX8$9Hd7qOcRT5<$;oq10VxAGczB_
z`{aGbe*0wM5lYq>`(-Pxp!j0-TB#1Zb-wbWISExW9==K`{{R_E51SYjCRDb&BYzni
z#!e&wRjI2rNAObwYqQ(hLni8dt+xz+D*V*38KEvg4`xR5w))kWV<9o0w%G&l<sYhd
z&ACn_**vQZ&hfP9fCA&(7{R<ugNOz&a6TqHghgzbEg_Q!Y~_*+2so!oYTIigSUD&0
z!C)%odB+5)rnyK99-y1&(`<yf^Kl+VL~c<SPB*1Vn$?9htD-gbZfXo}(u72Mfk{$w
z{SdC#EHU%R%$!$)-y$dW+H-02P!fb}%O2;?lrWOuIMU5_G}%T#{14DhR$yb<Bmm3_
z_{K0+(2-*+7N1q-jbvq)R0~6srlqE#A__)i;uX?$jMWS->4<Xlx;B?-E;ZB%qV+qx
z=ZFs8a9|{I0gQ00tZmwHmxpPpc@Z-i6Z>YIh#Mf-Dd!?nw#6|qCDhHd*1{zrlko|H
z)xW5q-Y*yv7>d^2Q27R+nv%6!tA(^cjw}1C$&KtBMG5Y#GZXr!(w_e1TQYw&lYk}z
z56g|0*fX6rgi#T&{a*C%?T15zaC0M64%%&;k44Ay4V;ns-%A+Y>mo;ajyh_^mXs6x
zLo+6v7~W$viq1k<);eW!+<N)A-g@D0Wk5Sj?HQ~bPNK*iT0b}o9sTBIXPrl_!G5xg
zBIR7Fu$8cjP0}J)+}l>GQytFMuKPvBeSbxj6e?GUoysuEOvs8pHOm(~N_{FH8<mP~
zIchb=f5LNI_|^;p;A1#cC+64t#nzwb=4GWM&7uqA?uxu5(&nkommuu1rvL|~-0J0!
z-fHM*)`w~F0qtK0*E~U7;!fjQK;gLZdBbB)b3o-;0{Ye#GhdQ!iJR*R8rUr$Ihh_V
z$7^H-MdBw8AjfI*<28ZXtjMAu$|E8o6h-`>cu~P268Fhd5G(QKv<PtK9G>a0<7o_i
z?&+Vw(YOIL!~*WX!AyACXV!yey_y^9es9>BFZg;>o;DU!a!9Nk&;I2KQ2pHNH@TNu
zQ+mQkdlA>&i%l>%A#4$pNWO=6S%K-@alD0SIq@kAYbbjKqOK4?qW~6k)tT5qRZMvs
zQiU9Z(^V!tBQ~qo_biOwtB3I1a%+abXe1%vbH<Ujz~@a5tVvOHsV_CxSrC^R`KNV-
z>bond>Z=ZWaSMELpLodpsm(;YLGyt=Otb$0F&j(QhFr{x`8dveB79D7@!LNC0GfX1
z(UJ1o)Ar3O%A!u4o7CAx!6=Zv)(mBsiy%#0dBn`cdy@|sO=O3s>=+@~&<MKzXUug_
zj)FM>>{~DnE>MLt%udt%irc2tWiSQNug3G{tyy5nNtBUi?U4~8S-#$KcH_u*k(iM>
zuHdG|oEAGR*<{qQAuJagI}e=B{{VKpoE~Z43YX?|eMTqp8$M)XHApDB*eCgr%Tp%7
zW@%OZ)}dKhas1(vf&Tz6L7j+aG=Sx)tADfX%}Dv(b~L7VNAhX$2G=JeD{TxT&l8D{
zi_6!UW}jNU(_)RSHq3I{KYY=fD%4UlsV}DWpwGOY+bA?oOHAPSfx_?wi)b48MyE;4
z9_rWBDr6!r1P9^E#{%O|q_@y;`&)>srn#)uYOwTp>(pG9m`d1TFgx$LPbuF8u@$7)
zv`vRX)nd#ysU&D`Uop)o{T8Aoca4TieYxd9hKrGu{{X1e>t?L@Q7XKqCKqbFlB&pv
zOKUYT=DmZg$S^UlsyFfvedY*1xEr(J$8G#MJGQ`1c;5&09OuM-nDVkngVX6ncxIty
z?b3nmpCu$krG8ve9DI>GQ$|FG)XYcxYwh@T$(SJPkS#R8XzHP!V>G!jundpl0gft|
zsnuCt?V5F)FR<r2@uVhA_H328=Q_ck;w_xgLK&%5g0jeUhqfIZ+HFR`{<56$Fj=Nv
z8f}qlbk-XpYNJn8c<y!GYkq65Bt<)y*O=0PO6Wlk$mc9NQC`aj{_yvWhGWO1PQ??J
zdfc9Pxst9kn!=5=;Gi8^s0^vh9b6#uX&Ksle(*f!CK2qsX}W;v=f-tZ0<3dYOBTN{
z3=u-wn901ktz8I!U^NmBRJCL~Lkqu<gPk%<7TUKhX7BxYHksE7JzPMnsd)x9Y5X+f
zTCb;Rm{Il2XZ>cNxPjj+D3pIJG2;OqynJsmUvMobE)fk`x|L(iMbPFZaFHsgES&M@
zEbhvhdV@@)U}oc)<a0AWeu;>eFMDEYMS0CzZ+duqW&_b9x$TGNY0dJTMb6K)H$BtV
zFj2H%Z6)P>K-FxZmNjZ7@a#SnnQO=-7#9f`z$KXh)@-Jw%md-SCf5N!$`m>zOcYA&
zF8ivGB$Vljmfc2KA8BgMiQjqUFINsMiNi5BS28t9{`T>THCdz7<3m=-Spgk(f#rk<
zU9=&<ZC#9Kf}D8>t8#jh`t~JkORI@N`YJ?Zrg9^EfgQIlU$lMw`cD2g^!xPj=zpuk
z$C%isl+4GZ<z|MbLTep``xL?=vt>?+8<qNGO?AVaR^^s%_?(#AB6p5CQVZNfi-3ix
z39?(F$P!Pe;$_p+DS$McgcYU#03gJXL_xyU9doLbH3ut?PcwrYxyAtUm>gh`mia&C
zG}cV=)8zm`qqA{eaC}t-XJnTH2Jt12Y)Fz?-^1?~X`$JEu;F-_%}R`4S1>vGM&=xA
zB=baCpvSSXJ%zZ&@Xh@+N&f&)#~dc9o=o%PWK+nu_3@Mn0{uzNNgmeymBOR0vyOjk
z9ic3o-#JD``(n;Tt2`*!r`0X#w5CV#5botO(tmubV3nlVf%=`Wr)4)OT$)BRC~T3u
zqSadWmQZZ10BdW%h}*?!UTEjS`l4-CO?jYV^_6{7vUP9;>Q**0IR&m_CT0gM%7Ot)
zk@nTbbAKK{%_Z2T6<diS#GIzW%5_vq-Y$j6Z6I+n0$HK_uf*r^6vw!!Gp0YP%9hP$
zhq)4X2^enzRwbM}4$j26P4b{?Oqis^LD<QOGMN4v{{VLE`kBZn8QvtMx8F2+etqk8
z4M?pQ3^M02pS0x>mOD;}b6|C)?_qMMurvO#M?THroHvgeX%r2>h?UO#wC@1&%e)^-
zO^tCpyy<E*8r0o<8WsX%A_%~9R^d0@8K5uyObTQ<O!2GPG|sgD0LpfW+H;JYhzh`I
zRamzEq}#K{KD{D0n2gbuMPQkTjg~p6He*e|{ZYKm^E2x?QITStglT=EmMFEG1c$9z
zD~*VPB!*yw8of+9TH36v<6ViJ_KXpaZ0XIn`dkK~OXM~d;o|=Ql!$aW2Wq6&Zz&ig
z6p=4wwe`R_uxm)M${e_$?{O@FlQA(d$Ie)lS=1H2qi2wXaf#zGBdFf7Yof+byf)Ps
zUNFX45izQ9HB3r^7}bQlxwz-k$M5%x+6oIN&Qxf~k`3SvYQr@X5+y_m_a0KYShQd@
zhZ$Si6%v|EERP<W=9~g{l@npbVQpqwo>H>H-Z<}(J5KOt1rRW*sip$^maQ_|Ac;S!
zVh&`u<x2ha8XyDLZU+)lw?yaE;Q0Ashlt4)`rQsZunJb6Q*2V9^xZMd&aVNRZ;3y0
zAyL$<b9|{+$}CX{9zHosjg`=d29m6Rb#d*b*@jd1lg4)10J@nO7E@#ec3i7S3GMu{
zEjig*fvZy{<mIiQUn4)ryRxl0N`WrARzI}lIR(XwPa3Zx_z%}IpC8Hp0DhDAo;^R$
z@=xtK2O=DOH_h@fa@4jBcOW%76LbYk6>k(`P7b?aY^|psWO<)E^7|jv8AvCt+2a)+
z>ra@ne1KwLc~2Z<OL+*@vf78rEhZArkEBNPwrMm812-M0+K0LqHSDoHg9fpM!$Z(M
z^Y)zFgTyC+9{8|6e8R)!+61~|?|hB-f*Th-(uc7})WJApO7yvm(GbbF4$HT}a~#w8
zfHJ$<XkO;zQ>zyE_SLXful7XF<uf7rTkE7Q8+0Qza)_$vnEwF3U9)|slxor6UXAQ$
zA*z9#@zOEr^`Cx^jxF^qx5sa5Lj#!4^OeZGwbf#al2CHpp>r}7*!QggHizHUq_%2(
z9HwS`Z92Z6H&+I&Jtgl#3il3%8SAPuH5ls9C3p)b%*?{zRG!(WfVE`_D=e0{xAEyE
z1v}35x>aWFyZ6CrFkYzWA>!Ru<GDk$TRoECjd4qO9OY*1V!0DvF1Gc>+LIf!a&u5*
znN^8XSef{-^c?B$(Z+;ZlCAstM)R~rnWoLwOwHSgntfIM1bKMP49Ko{)x@Cd*d0_h
zc(yXO^WPB}r57vf6Jm%O#m=8G7B(RgToM~8UYzIj=wsPz+FIKCY6jKgo6tF{ah*oi
z+F?ancI*=4k&I@Y^E)z8x9>5UB@e5Is^0fbkPld%F*%|{#dl*!Qll)@5Bh2$gSf2A
z`6o2$=DLa}A>ER|oqm6;i1g(?muwxwOvt3qz^ob4(a4b7X{liWuoTu|xuUwo6+0M^
zzQes13s#VQrjRPb6XO>Xa+{VFkFLm(GB-+(BRQ9i?@Ft(c5Fd7xbZFKA{Y~-a5`3I
zrIoiPM5A~a+Y2=m`%8x*{GK2-^jSzS3K*AiB4y4YaL!uhI(IOQ$|Ee9wd^ApKWEN2
z-`1JRP~(9=$zbD8nvoel@$~aE5erguk%qp-hFfH@krO{iKrRT_6U)FQjY+|&0H;iD
z6%Gud7zVlGWM7mD%UwamU@qp`(9>MaA4;v1xJxY5I^sNeL2u0<ldoruE(lZYWI9Bx
zgvTF`TRFKi2l4{kLRWU3j3VT*^h4gFhz8kt%!?}Du~)=E(7#X(NgpJ6KUu4n6l6>i
zjB~8nypIqW`7cm%VgMeLm1O`q4y=4z>*pY3zM?*|W6!6I@%wH&=Kla$zn$~%j!AjV
z=Q&RCIn1+}{b!ks)2@3f6D6tAijRp{gqHJ+<zq?^$TEn@F(}F7`%+LvFxn)UZ0t>=
zl3jC=h?W$Xl*c*rpwt&C_H9-=jgTzUG_b0!>n?Zn&t_ea?)dMq48mLqaGbRf<*CZ)
zYnU=6ycCSII;D6VWaFAVA>}Y}_!y>l%Jpp6IO=AfNekWX4K;i9+~N3ElJKhQZ?Eo|
zVWn==Oikvh^w?E*rt*fu^#z;3DGfL{RMg#(gSQrBdT2lIvs`?20>7SsP0AN*Wz&Xx
zZLt3ULPjEX^PW8?EIPR*YtI?9PI2Ef+Yoh45Me4|gyktN@(7~N$~8+LzfXbdso4C}
z86NUE*Orm{X>Vy?nFiPtG&f>|=1vSd0m>nWVv?!W)A?1KmDaOx!Q7H`F2UJ!y}Yo*
z%~1SW*y4a$(Xgo=(JF}ZkGW#c_gQLUYecnRm#~7Y0X!h|C6GGjEm8AZSrf}h=&|v<
z{(VP)#96E}mk_<>sLTHVuD*Yawf^PV{5j)Moc;0yj(#*Wj~$k^8^i<cYpzYB3eWrW
zO#J3^L@5irvhw1>EUHRnx?oK5iP|!z-KCsoXq>ENUc#L^y7*l)8mTv#9K!{yT7i`n
zA!Y+hrgXuQdW*)rimW8*(!FN5LylP19ExM7Pp^ueJm)mxkzJ9<#Dg5cQuSB+Dk?(b
z`6o1mb<_>-f3dTgrqS&e2l0RpPac)JsDxQ~+GN{{42f83OCZ;O`UUGa=-pM%(I&y{
zfeUU}b>zKwUXFL#ea4}vKh!ERpu*i_otS7a9V~e`;MxJfF0hnYfd1^wE1ahdW)M5h
zY6#ep=s(psxw0`Nx&aOcu6tSaG&LTW0X|VN6CZK5U8vSB&V71}av_N=q`ZITRU#7Z
zCY-s1bDCdJJf6~-A|jv48leU0_lewe%|z$NUQXNx3vGrYe5Ft-rYyOuXL-R&l%m+p
z(>p+YQX<IS2Pc2tsQ{jT-a(U^uxwMFc4r#dV|yjx%}H)oWPe}3$84=u9oos8wq)5j
zM_JJuxelu_z1Ur9%d8b=xz)gfke={k&a}EBA~%CmjSTEfipjX8mNiU62~Ep$&!yP4
z8`ug`2C5CT)IXv!ezRLWy2k{8YM8^4XUO(meqJ@C$&O3D@CXU(r)GI7X<U))86*8A
zzJ26>H~!){@65lSN>SLT>b9-cbvJuyCX@}cjY$zD?o*SCm7=0%1j00?&RY+X*OC3E
z6p!3z?~|38TIQZeeGM8?V(U7lrwaH34P?5D%W=-Z$oegYnfSs8*DOG=#&?E3WsHXf
zc)Ez|iQC3Zud88IW?HLEQ1Pi%?3T9ew;&Zee)vK0=MI^gb5aN*zwq>6Opfyf_c=lS
z&clvWNwlFLvaZ@TfHFU{-(TV3p4XmVXLYvDf7kOR$6bQO>dFCKBMfY8$&B;qz9zUO
zac!n%<{Jr4>Ye6fOt->82tmacbh2Qzg>k*`>Sx^70!^CCIziR$5sr@o8D;Ut;Nn_e
zoWawG*>O!hAkFrTnRLzdrWapQ13I8v3GFy5B{3PRGpeGvv!<POcNF#(FboTcQjn3d
zf(!0Ygei?<k9O^omVPhVBoPT(ncdQJ)uOeHI|h1{l5N(MRK7eWwX4<Ix&bp1F&v?5
zoAugo(1DrS$w4wR_sem;$HZt~3r;zwLHC&=u|*v%el?5b{gFULoi5JdyiX{Y+t7SL
z=8`kS^3~y~e4ykiEsU(sVzw65!kx;*-cg_D9815YS80=5X(yUnrB{>6076Q0ZG&vp
zP|Gzw8YwUW!C14VNJttkIx1^aph{vETTov1QW-HLB@?07>RH??)(+K^GS9DB$_2G`
zthF=aL9v&{9jOpu7a}6{0OCEzGbb}MIa0ep)xve@-t<qd+wBnd4A-w(HuHo<#Q5b&
z+TD4bS{U1Jd~Y!^IZ)RQ1vbj~41nTJ5<xhhn?ZT7{$R*Vhxa{)d$y~6t#y{p19*WB
zb{<oB*0L;{U?F6nH_bBdZxqP*@s>c1QvqtZR%XR9w%(HyAIKxdvr2RBc8&qXo8qID
zL*?i{tZ{_D!taQ(HZC*2<;-J0<CLmF18qCGVsK2fmp-_>bN1UBTUA2vrenqw^PS?b
z7dgU2sEQr=W_|Ty)tXKgM@sE;VyZR=^4smFtk+sPYZT{eIc`gd-U=DFIm(dY37Hp#
z%EWFGTy;9Wp%qg0fY)&J6hj<NA!UXVyu!)pc~=7)HLa=_ZfZY+3<r+`i9^2(T$E^r
zMR0~k^hRyVXw7zGz+n6<TABKY^j6)<!u>_*wkfVjZmHDefgxf8Nql+yE3<!YdTsUn
zwN6az&rSaTkwd*}GwHs6;_BIV=p7==M(-79=$Ip&PP%9MHfmai#RXV{Sf#;~*9@bn
z%+$9DGK0BZE3!BMOBewc8^MIR56GsdGdW?&5y=EVWbiK{$nYnN195`FmYAv=8R{3p
zw%eJL3QV^#v0ZFwa%q-t^}p?#Vp`hlh9^)e#`rqyoyv9-HJF$+ad}5&Yf)J0Qgsem
za@3TJ`P{dWpN{b{5L--ni(9htg&Li?UY%C1N9~3C<4<@Y%6A<}kU+PMg~OGr(1Yb=
z1Gl$7NDh0>X)WVT?vkIGPJRCXJKGGSL^JPb#>94>*%2l6yhQlir%u~A&+W(WA{P_q
zc;Y*AIG-A)=Tf6;GH2@pj6xd>oy4asOPQxCa0SCnqB6Eu(+_<J3=X#UqDdxs?-t6+
z2U4<LX9^*a5LPQg8&)$^Y}mGM&mXzW$rj3Vsj6l;1Xxj=XMB(GB~8J!y3q?QGEyfa
zFj&#-+2Q;=6DhP({{ZexWaTFm=-0yss(8Vvj;hFFmo)C0n>vLVtX-;3-OAp;K-#!d
zqS7N%+Q;e@sUxi|)EGugk2B{KLJWx`RH^1TWhPvUY#4$ejM5GY)P`%{2ZZA1zJeMy
z<7)?$EZVLvHJwnrw%M${n(O9zRe*b5ho0#sE)dPF#GFSaL1w`jWrxuI0cObP4r4SH
zHSX({T9#`LtuLax5i?hL4M}h{s<PdvOj)qH$mSn=%>Mu)m)zxqRU&CM*{YW)sGPCl
z&Eua$pr6dJ>=SrHCN`bDD8!DUp)FjwUO!Ndp@x)g`~Iz?Ji?|YG{wU;1V(DH(|W`N
z$}T{_u1Ibtls&@RP%Icfn*I~*;{f1Y1q6eiakLJ>zEPe^ZF#vXM(keWG3TV_Cu7`B
zGU?T43CgtFZ+Hr0Ngc6LKWvZOb6e{Al8Kcp@;_;v=1NN^Z`=3u)KnF!*G727@luHA
z@8c7<?2?XyjE_O_JL4rOq|wQp{^?S+a!6F_`hK1wEPcmV7=pmF15KzYji&%$j7JW6
z$PO0!;KUhiow8i=(ZgTKmE`KzVar)AVI291@nvv|$T!%v3_({8vyZ;L#{neS_<#=}
z1G17B1&a)+-$>_st2nq{S~V2cw2_uoUeff>Nw9N}3@GBjz`c<OwECsVNV8dKU8V!;
z$voGlvWI;2kJi~cM%!!A67+7!<3TZzzTS%4uy>i<rhk+7&-?Tj*;vE?YPW+2ShoSf
ze;Is?ME)UI;ya4P0-pliEQja5kG5&$raoU*BF>mG5s`OI57QdCEcqe&rTZhm=aGDi
zA~SFErabH4u44MBQZ*S|Wg@F#KGoqx>C_io+5Y8R^*G}bFp!gEMiFx<DasVxb-iI$
zoqaw(FI2hE9a4)mbs!Ykvsu*{!L|xut)54uZ4>8hr_;$UM<@35Lz!8?(7Nsr15Wnv
zny_B6;&ROLv&eO7{N%d1Z#mM^oOl^nWDFtm;xuoKERDC@H61goO{tx{?AaLN0>BD-
zFvs%$0N3&u%+N{VA_>di;$~An2>Ru}-^wrrhNf-SQMBhA+A<)1rXgGTufh6Ngp3tY
z$*_q16z82f<t>St%*PpfI>U+rWK~8r$8+4AXx!weVcL4@#K2qL3K+-NIp!izWCaDW
zL`FAD=r%4+xuyM0iOw^;4jgVpK9EHd@gg=B^8P~SMDH77SV-A{p$^qZ@c#h1d*+HJ
z)>}aT0P347`>C>S2}L74nn^kWyH%K=qb`%Vc`b)?jIvwVbm#nheSJ(u+v8i=!I}F<
zr|p_Ai{YBl4BlwYnbS8o7NVvrj2&oW0?7(2%{B?4i48_cl?F{LiH%?aTNV|jFSgN~
zuZS;bnA{5~$0oQlT1>{A0H5K=h(l%=>0YS!453c6uB$Js$%K`9zR)L@v1^wK!~C_s
zEFS!#9aR+*s1U`v6E=@?W_@ppQ8@U;mjP^)_{FV`_=qUUiws8RsuacV?gZzIfFhBI
z)z=l<Sp)Zj_nX8fH~#?51UX;dHJRS~V?TJ=(b)pg9%_}cAfa>SD}iy4B?2O1Mn(y}
zl0y-+2(*zWnuYb;q=}4~nTUz4LgmQab*7%14V<&9714-#pEM|oMuoOK7X_F4IvFgL
z5vEEDZvsBQzG^G#a>iHJ<e4QiGE=|##qJeKJ$K2cZt@n%glYBGQc1od$m)Lc!IS%X
zL40I##dzpj;6CRLAZNs7X_DtN00X}#3YgC9hVp+Ql%Xu^u_Xpv7i<I&m?U3%L>G-s
z4|3RRV@;3kxrz`_*Pt``_C>as$PvbJw!Jha)N!?9CbJKNkqJL?9^KH#R+13pJo#p!
z2J#zt&B#FIV;&6Ba>F2JYwH;ZY16g(vxzQAg3sSI>Dlm8b8s4}^{zbJBmCYo?+Ce<
z^LX>=QS(;HT^AJzvcQi`{b&4oP>xqqnp@5O<TF;G5gz3sJYbJB$A#*qwzi{c-AY-j
zZ4MEI&k<z$RnpCbxpKi&)3Dmq;7)po<>w^HAMlf}tEZ^$OGX<7>-%~qj5&Oq0;Whr
zM)S0Bh@X}A#IQg$KZ}9~v&78&3L+v$kKJ{NG$5pVH6h;J(ng@xDBO$D%MCj`Vr0|E
zxQH-@NVLP}My@kgfWBC_BFOVy{WGE6!ljd%06O&TRZ*TllYdAVFA*s}Ga@_b1vr)h
z%W&oAH^?IWxfa_@6zox(dj8Ro5t>6{H3g0}a;GtKIY;A8y7K98qT<I0!WA)SeU()P
zQ<Lm44IA?Al5*5>tzSNqIXDyfbhG?q7m10tMON3wY}9Q=NSZ@%V<tRpIkx^{Fa}J(
zM^KnMub9poz~upesaIkse<>{f9w1unH#JJW*4b^EX+T_5nvL~rlCqUTK=(gTFN_rF
zY-4L%Y`A-QH#g2R_mMw&Q}e0YO#ay;wnT3+5v1-n)0(jH)GXx2&?OHUu<kZUpeb8a
zVu9Wm1!|Rm{-dKybt;XPf+;3QbnQUw8-bP%77jVlx0`$A+h|pgQ)&Wh>QV@{wA3nx
znk9bH78ag~v#34oeU6ZQwn<5m5oX@AjSjlhQ9DF-2<jgvRm&3-W!SZwm@8Y@0Uq5u
zN5?k^`XeLzrg1K=1`Rp62^~s`TAJ;HZA!+`tou)~w`G{;UZcGS^qlAa0E#^**&5c?
z+|~nYl3B)f@`iLY4=q#~t6AAGQF0rMRk*1ISucuLi#?J<hLwoKu4Z@hL!?Yp=2c)>
zD$qXKp7Kw!>s$c4Kt#V_=pRejA~vox`U>sMJ(gt*l8O&WswXU>ETG36Lb~48Hva%V
zx?<Nh0FBL;IgKog`}D6MRo@eow5xaEh&`^GrO5Yc%`wx;bMF=8;BfcqSEwHY6U5m^
zw}mu<Rvug8A~Vi=`w*_6nf!>1)3>s_DTPo{#Eh6GEI2i84piZe)8>Bhan8A%TS|{<
zQh6N0qT71^0H;uRE*Yq#mKAtClIC{Kk|I~r$#M~E#6XC%k;f7-$`<<SU#!3m%7w9(
z#k5ZQ?~g8KER;^pUQ;-goMdLZJnabOqU6EP-^L(F0gwJ)@#sIOcb&O-@%wsy^PV&O
zdhsWTPx6o6{<PF(%;QOj_RKMt4_~;AQjpe=VsuUY`0UM$gMCYsT40(G+dNdh)KT~%
zl0CX%jnp*`<0OGb#WCss0GfAMYADTaw{3p$<G<F^>Hd8uE{G6SehG1z*oI>-K)U|H
zsO|1&H}SSka@aWHVmlMHNQ2bFd%F|tZ~ik)c_z-|+2d?8HcsVTy&5t~B6ls#7F$wQ
z)ls;c>F1w@g>6kvUb@MaDSn^%!hG+rZ=N%p#BCG4Z4n#ID3)pZm|~;g`Sknr{{YGh
zO@!L3o@02QPxGE<7JX)u&2fA$bBan9zvrc#wylO{rT)Q@I%Pq*WMJIlu;wtd_}=Pc
zG;)&EtQKnvgq1*!4J1iK{{WmjM4B2?V}4757clm<0Yg$%$&t$nRi%=hsOe7?E)nt_
z^UQg`2!ye@SN+1L9x?AM49sma8D$|FeHTXv1BHo1fy`w5<CbYV`%OS%wWaDvRTr8)
zyXAH{KG_Nsn$b`YPxoNRwIMfV+U8?Qf%bEan6IcfO|dFr25wm-CHJQIv%@50NQQAF
z0M$-W=31mcYidt_GF@t+)J)aUCe<EURC`mIH7!FEhGu+VvY@VXWy#&PsOnP?4OPfc
zYUW9%g=UJfn{hL%4|c3WnoAA3j>W71u?=i#11he`#n*%^QJTe$Y!yfH(y#9Ar5R+w
zx{Gms0wk?Wow_lA2G$4#i5gKgll+My2QEgr54w%o(XFN0^#1_7tX}4+QIGo1_~zf0
z6X_drjrF+QwVfNs?K1_Q<=sG{Y>62Z49dt#H7EzMY*i%@LtwFI%y>+JoQsU>P0GIY
z2U&r<;_4)P+Mbw+m`@;CT*3j`W;$%wC7f2CNelwpY0Xx*NfeC#03{{x)3qBP$^LyS
z*KLJy30*qj8{EF$pr{&scyl1nSpNX$#zs|KI?97DtXClc>9_c=Bn()cuCj$h+1s;d
z+t75@n5qqQ#R_8UU(=s8>n%^GWr5CQr+xC;I-Db$e%PikA)pLla#KQx%BwQeSY)`n
zC+cEzQL)@=%A8qcVtRZ}x@O_WusJo*i<UsHDAeeKq94-g4$8X%*%KauzDi+0$0s0T
zPvtW6A^lrsqqFYZ#?I`Wziqt)x2(YabeIc-ib{7cO}uaX={5k#1J~eO9<lYmq~IbK
zr_U!ebiFPEGz&0N+DvmssRcWlT{%VaC~G>%iej>@aZSsXp7ZwOrD;%z_cnD6Prs0k
zc=VjplwXMBAH}evw_NGiG?$F?o-%x5l`doqSvX<*KC^j{A(#$!^y3@Ty{1l1L~p+F
zJYsjJ8``WnqzoPH=44n_y(kFD*qM!)5fEGNJITyw<LG4_X^hjaO&0Gw#lZv`)JMJf
z@>u?vnV(Pb>-v1gX?{xJVE9STr~N>oaT~p+B|i@%F)-PE_M4-`efsU^Y}E_i>6Pr~
z#Ts{w8aLBVHHqV=&Qp?DtN_P>km6^6wV9$0&oa$zpVmZVF`G*AV`#;Gc7Wl{HKtlM
zymR_qP8U{zcA&-Sy2zYDZAAX#Z%(Ou%-GWIRFVw11yoXrrazxXa`%hY^UE^F{{Syc
zjNf;&d!xebk;vPWOzB<|a$+Qwd&BFaS*WK1f}WJ4mFjm6)q15GEKK|Oz+|8+y;^(w
zDX8;Xu0rnle<F}%{@MHD4}lEkpdELtMt{_OZInQw+K)9>0$@&Mrza*=tp=f6hFquZ
z^xeHa@S-E>uISNf1RDk_SLdg(u6XaNkt(v}bYt!2S!b?wH<ai+zZ2y)Sf6l&Fc;SY
zwae)Vl!I7LX&nCmE|vLy@$<@Jcgm;RR8B6&*DA~)#;W<l7D_HB{EFUKxD^+-zxkSw
z@X{tdMq~;ITl3kGKeS~30Ayp+ZUf{<U8l%TDcfuo8vKgx09A6%?wHC!JO2RfvlAtl
zRC^s^0LvgNdmulzMmVVDCkK@7O5L>BP^GdWD<l07JIq9CACO6{97~S!ty0qoytL<=
zgrvu9qifYh%1b(mIcNFbKKx@pCq+>DZQ>_B{+C6E8=RF|yLFY@jS<);7tHcwY*N$}
zR>kVABh#yG`)2bK6BoG}tFZkBJnx$kn8qdC&IF$_Y$+h+=3!;0B6rIWF0g&P903Ov
z0oD>*1Fu#+foZR)DnK1BH*EOCGo`@H2sW7|b~&MARELY}zp_SrW7jq<2Q&~Nq;W{C
zSt+uc=ez+$uWaT^1UhWj2!4?u&v#2ZXS^m}FfKi^vmtHVqBt-}D})NmBgZMgk~@5+
zXLC99{{Y4E>3-3l=Ofqed`{5K$~=GiC@B+|gjgg)RWlq5Z(lrSbH-Cwb(O@&_14y`
z{vkO#2#C|Wg5`4;Y0S8ewrN$_S&WRavXjU}oa?>hRh0}IZ@;RWM1g;K{(S*+V7RG4
z&FfiFZy$YbH52OW3@$pAYVm|7vmh@`<Zwr-sW@Zw7og9|GfqG1(44iuxMui2aV6x7
z7c=i;Z7<+9or+6Kvfe96u=#97Eew>;&bRzNz2n;#@bip$9m?*lr$8V0h&tyGj(v+y
zmC*uK3h-KmIJr`CJWhY#tZrT-#Cquy5vZebk`=(@40-1V!}NX6pZfH!_EsV#@4R{A
z8P`5I!WvOKbrhe<g8TH_RffZRN+UEq{gLLb*np~;22hJ6#T8fim?_y{Iii2JT;{Jc
zI+d;Re8%~sA2i#nT1ZII)y7&d%{5GoJi)eDg5eNEEKao`BL~VR1%nNd$5p9o!N!=%
zcU0ijwDT6W(t=7SaVXxNo><m#V`nw6+UXynrauQ@WgQWZIVah>Q%-<d^+PXs$jPe5
z)E6&9APH{rMbIOPA*W7)jCOF87N@734WxLEOk&1Nwu=yFAmZ9S^1W{9i6mI%swGO-
zBqTgyiUtcKptQyl8jhic2d1ez2A;gsU_r`A=)h+7K)s{w*(X6YmAu#0#08E#(r2L>
zjhyK-)|NA_+9il6H}SN8Bz^i6bZJ59r%&`_wWLA7I#_bX5lTzk2v#!*TMP@*JkHR+
z%V=XJJ=~n?Ef;BQ;wQ&=!ifTyob7<3D#H1k@74Kx;zcsZ?r90I<aC?KREj3*V!fsg
zZa7oEG340=7TkA<jrQ`jlr>X2fEcrRKA&q`no|s&^JV>=ew|*z!J?6ol1()9yR_@a
zX2dTeI}j$YV+f#BmZ_YwwUBg9!?)Dj()MYdT0<+hiTK6GGl<Lo0GC#hF(@Y-1avG5
z^M>Lp1jTbBWJfH5HuWYf^>g5?reHzTc9G7pR1ZTg%A+Oe8|{hkd*xX9u%vnpyv*3D
z+Us1J#G1$$vCV<bc$R6dHCTX)rQ6ors;P5uEJcZcv$pd&w(}7O*Q6u>NM=m=s!D3u
z81`;Z3mtrp7$Gc!F1W)mtrhwyPrX~H<6xwKSzC`Y42dMdZZA_@MX6{X$N;nV&9Q&1
zGKtC3QDuqaUDJ#Fqc+3B7UKxdyhK5E#Qy--^2Q`)dCYqL{;}&hbYe|y!P$!;o$^1<
zSD|t6d{_KEW`Db1NUd5tf-8q9i1xwqz_3UCzTUHlCUc3x<UkV}oQ~yVSFG8To$7~C
zv7fiBNesk|D@dD5J5_>z4FJtSz4oM`4LYs{j;5vFU%#&(emD^Q#E9{n6EaIf2gskG
z#^f=Sf@bFllNH!#nHQfd6-18#{c$r)f2fHnF%eGb&0A$w405KSru3(fHlx3{D*f@y
z0Evd{M98cHR5aII{@uW0T`4hH5v*eA!c~;54U~+Iv!=|?xldegF+FkTBqAawIex?H
zqTtn(ToOg527URT<WNBi%74eFjN2zPH$u7ZsH5B5aAt~8?AZ~o%!5F%0%E<1z>|si
z9gr7sZ5LMJ9XjB^YPNR8e!e+b^I80|f{0pUAc2h@VT*DrxeZpfu=jBFd<((S)SW=3
zW@XN}IU{U06`EXgw$axmJKoTD=E~mK!CF9`sU^p(KZz6(2sopo*|)KjEj2_{8m6F0
zt}>`Ez3(X0;N%RHg=U2a1_N0yrogy0OZb5Q0Mrg5oul`$USNR7f!nV%EE?p;X_*yy
znT%x1DO8MwEl~vJzD=-ar7nLKjMh)f11iKzq9zr!d1)!cS~0Fcj1Ayqg`3U*b3L<G
zh&@z-bs^z?#AopQOuh4mvjL-&*VZsOs<OXa3)X^-HIfI#UG_=tC$x;ce0u)?=CeCJ
zHT@7@k~23y**<VZcNgR2GnXbFA#N~Y2*M{(sif7b6I!~Z&IQGy4$=Uz(0Wz5TxT8E
z&jvXh4oP!8{Wko(pQwzP-ywvvQI5SFY$Ml<%tp&VJu>;+M0U<>BOGJB{6DqDGPDHb
zf7Xh8bp*y(Sf<05hs0~Cc@S%!QEuq5uyCetE;LCTZxg1UTcD+EiTk_;Kx8FS#`A%u
zSwFg(Res2W)Z1BBBmk_pZaFh0K6c2R<7_CAouo=v@{dY#LimxdCi8~l)=P5)vOlr6
zs6?NPj}XnPa)ET>qp88Ygh!Fed=@OQb70Q5QDhqeYgoavUc)Rg$ds+8z-Aza3Ipsk
zYOvOko!gWiKbjVS1q?^yjwm2uQTH|E==Hh(0F==KXE+upl%$zdBRh3LdW6NYwGss)
zLQrk#H!2e$&J$&|+gsdLXQ$TLrwK1lKx6jlJd)g9IU5w7{yidOW(pk6)9jr80N+ph
z!2J)H^!uFK?>3+1oY<X3Ks7X5@?gmg5`Y%v3qpi?<>xcBN156z>p%=3Q;$(3B=MSv
zR7!Hy>sG!*PkCw8Tslp{xmdu+<~Q3`d96XkuU4Xv=9lcUrDf=CW|G<C`7Sb4s*8gm
zB`$OPWd|5D+vU}|n~uEtD0vi&2h%+O2#jEBfu)uoEZ$giHdiTj#e=sJnW{ctiiiy<
zr<!h`Lfuvy(V$dME^N`MALwms)d7-;)US=8dEK^TVK&G3ilA43Q{bhtZPRqiPP1Oo
z>jkw|MpNWeY(!JE7i)=TtwY(m2x2$M{#ngn`!31<04MF~5IrrQ<c~^TAxVvC9f+*_
z&`0vD=kWpZzqTWsRkUIQ9N@?AC^7_Kr&Ocv?{E@=0+2O<wSWt-RVy&zX|~UMrwh4t
z>DsEjE6=^t&ZNx!WER`yDT?<YGpPv-Jf^k1hymrOwy}gbbwx_a!`KYt1XY%92IB$?
zk7FO2-J@F#0PmeD?9bbLeW+;G0%{N0poMY1l>Ml!mMtd3^+Oh2Y>6^!TdL^P+{#L&
z7-Bk=AcYtbcZo$`gt(OMbl+V&@8>*i8Ksz(rgIw1`7Tt)^Y~5SfG|#JatH1fN3_gN
zaArc}5RuJc!EW0Pb)vP7OS7jzRlLOIcZ6ydDzHal&3Zqz7=*BEAY)v$8wvtHl6`c<
zcV0$4$7RfEW}b4&%VgOO+Li}CvRUpR1gXsSm04nmO=8F>8|XvRi~gEKV`wIAo*he2
zTI2j={10i<#-Ui=#?m|Ij96|2Wj=i5&hZh2<F9R4iL65>(L*IO147VM<PET>37Jm*
zcZ9M=(6%r8TTUI{Qq6Df%#Q`_r^F4q<(CKC>aG}TC5$792#B5N!i84|>8xeL0JsvE
z2Dyvn5TtJ#ZyQgE)oig4p^1BW?+UkVmVl#FpY<zJU+2^dfyCe=yor*+=6s{apcyRS
zf|aR8`(Hp`OuRP4tDQ%d2$cOu@yRL8{t!5y?J{Icgt<kg{{Uh!lyt?1$_My(2Wj@&
z6MskHDB;UJwI%eFS<=WLUt8ED486s2saPEJ+jEwQ-60WN(VLxll$a;5CRRMx#DFY1
ztHgrkO6_ZyFe7rR0$7_gm<20)BFmo-6?=p0X7gPKfP8!rtfDK}RLE~Jn@%ZY_X6UB
zdm;G$0PHd`oMB^#>+KnDt*C)rNYC5(cyU1#z|V}B<2y`#+j@WJrF}Zxt+*pXuC1lx
z5SWNZCVY20b<;aIbt<o8AQ`CGwlWBj8X9y>nk_+DQmbEl*tuZwhEfU%++m&&26V1O
zY0|o_3Q*XIjq_AzB3NYLur0(b<;vJaSM4z~6X=ladV@8xDJd+E&|aVSgCC0N{C#Ot
z?2;xckI#LCN0tJA3_|TS9h0QY=W%>_-g;h3oRAFKA~uz62(vgv!%0YzB;o3jtM4Q^
z+1q@b{<B&LJxOqXg(BYOT8@)SqXtTJc3WH;G}<nvqlfAya27dfWgh52kml*u<YwX%
ze!Rf7Os>W$pUXL`?H7n+({24$JJ+!>pXbx-X~7<5BZzcHq$iCJai{G%K#-eaHTj9u
znX-xog{Z|JTukFQa@}=*xFwqCOs0j92B%e5O_F<RjKcBkmEEtJkb7{qzWi|{+UB<f
zy5rU_LgSf;;$x87JZmAUl}iFwJaMd%gaN6nsOTli{{T8?6v;DaVf?~IHD)z#sX4C3
z`lI41!LS`#I23u7CPji6`LH0QPM9+~b%~xm6&b{ePDrNF$@^)|VWeX~Cac|Duu7&?
zE^_UZScjCINvexAST5?Va=?tfb4qct$}?Ht-0wP69NXAUq-I5$aZkKdOZvN;5)k8#
z0YbfY+H_~>o#a@D-mJ*3W%#h;7y+(3n*Os^3A28!eBL%P8%>ryg+I?;dNiwT^r=|x
zSOkDQY+<QE_s0r0BJ|VNXK6(jPfKge!gU0OneA$guv8C2**@#PG`$$E2f1V=RgtKn
zb)$O;jCAsACnkJtlx-z@F45O99%C{wSU_WK<8u3*sUdc5C{3=Gw5L9^d@13^BTz16
zf|N6oy{jEu(B1~(-f^=msDjA7XjRoV;VW8L6&YAusVk{+T=tjN-hr?v=P0rhWk)vQ
zXm7=4D`JI}Te&h}b2A=wN^@i&@uUQmjA;ut+N?2rxm~lrq~zn%*G?lpY~S{u7z^Za
z7`R__Mp@N%<O=L4nA(xdrr=2LKA+<YZH6O}m14-9rY9w%Vgg`R-jXbOSvyFG0GH<P
z8~I`aYeB7zYV^A5z-fW=iX9}YESL)<Wc0LNQ@bn_uvSwO*Ybx60|-gGa^4wX$O={<
zz-&P{1($T_;POpxbsZB_r%s2&f3`nCnK~u3zL9nNhOVa`YNb=#3vNz~4h6UwWVlyp
zW25tUWJktlKK74E$8Kn>bN)8;^P5kcA|Gl4k?9+L`Qrpg=ZK?4w#b4sY4t1Z5GAnj
z7>_w-*jE`QUc7pV046ln1VLG<T6(MiR|nd@<s&t_oz`Ulf|{CZk0U20Hy!gzh$?!H
z`gNK7QBFPL3#7G?kh?MJr%CZy*-^e2bN+oawf0_CYD_6Yl?mIJ#OK~ov~;Yk!>Umc
zFnwZ$*Nw7F<e;UZ5Xs|IMC_nW^P=U}6eyt@O>&*T!p2NYA~5!nEu;Op<^KSMDuI+P
z=>GtfFM|V!A(@ZfWMdyiHnt`T30p$-wWz8WU}^e>oE^0*+cQq>zUl^^x5y@*tN@))
z{{W+BmWzL^;<GiqKe2OUKb2eduWdp^nzD~iuNn2|vfH*N{=I+3CUzn`T=8Je4Y7?-
z5GVm#xQ?boT7{V}{{VsC3}yjCHKqmIGHR0U8b?$fS~9lFMcdHNOViHlW;KZstG;&c
zKN+3=>ouzuaIK}X{2hgHG`1rY<{{9*u%HoHi~j((R&jiqYEDe<=1xa3lblV(vcppv
ztzmiu=U)E+>e<DS0f__tMcE(jEUpOHL}Xc{c+)qXL8a_jD|$m{FtDTQt2wJLa->-;
z0T~4>rDY6=fxl97{KjgaBYw{b8Q?P-ADd7!tej1mh|@QMwsD*7JLTDe060hcwdH%K
zTI^aKSuhsmq1hlvWGjs8DP(G+A*#P*#Ed}9fR*JpFKSU+WsdvI&-2Xq+b^aAOQbQC
z_|dNcnp*g(W@Gz<mt$+TA_a3ZwjF{cLIY{}Vs6^@A*O|`67^7%Ef_SmR5G_yQ(l{O
z{YXn#n+?TLeKBj>2xib_!am67^u}oO#joGb1K$cZ%`lcMwIz|eHW>Z#W=>{s3LFrl
z-*||`x-2Sdwab+@S2g#TFvE?D&u}wNg-U{0a5<}3KK_E3UfaUXl2`evt6l4sflQZ~
zuWBZt0Rs5AnU$8vw_0+YuI}8K5{VLanu23SDIn?;h8L4P{Xz@L!MaMf59C}9{{SsG
zIW)^Y=6y8a=EmfGO!@#z3L+c&$YXf+`TJ&`JVUZZ-H<4dw_?q}7D(~mVp6+W3&pGr
z(N(im>P@6rE}Fo_N*(OInC7CE)3>}Zr-(R{c73z!pT__fV`0WGj6_R-_T0z;v_CGj
zvQNatRbR=KQ!LZ;b{Rk>$Tn7LV&`L^L(0PE^;KRF$j;Mb`gH2Z%>$o9j&4T6$jO*1
zL`5H~X%dm0<B%8M5uc!0kepAh{*VDO#rpXDgiVD42+<om6VY0|PB54W+>Nf*Yoz7{
zy2}Tz3g)4=DZ1J~;^bO|ouos_Ow7<e7pqKZs`l=ao(#2QpfxuOp8&0=?>znBPQt;O
zsbaKSMlgMAn2lAEpau@0q13eCxDx^@A6j=h84%l5O0>a7Y4HJG`^wXK#d4DgQz9{|
z>S=`Fi5WnROAAQ*EP6`2NoTa>a0Ww0XgE=935hT2fUwv>rl&Y<130tdeB*?P7IpaE
zc7mu7I=2{}F<2wGC8>!u4Cfh)a)`oW7opixfuicuE>R}@UITIvl<FY=02ZxE(o7`X
zv?SIqHAtUuv&h_LCMrB_S6io0s8p<wPx4jU+zjJm@+%jojs17@T{b$KtdHNPjq)do
zBy-G6PJG#{LBhB1Da*1`a>UH<G|)KG<iniv=M>_AbV#q3mi3%s5f7_p6cH{=OCBAq
zAk^0drxYwAN@5})M`<eO%xnxSy0nWMBGtO_x`y2c^Y;~FN_0dr{*}!tbvi;ps8oZW
z)XneNiu21ROR4WxopLT9WVPDI7bV3rSZCs5f|4Be@<jKnDiQMEO<b;y!E_kf<_9(C
zs(xzJlTj7=we*`Jtih(bN98;i9EVS6F4$vN=agy<{{T?1K|diGjTV2dUTb@M_QX-1
zHSbKvpBX<5;}&AgJ+M`RWNbSWB3$=D9sM-wVSNtzcjtRki_;OVnU<VJ%A(Q;{H9FV
zKFcgA{%HRIIj5`}ZGE*Y#m{|M>DTz3rZCv^9$I$-iJXrom-zde1htzzwVks|s=Ieo
zwriKcw-q&yT)S;U*JTy~+?*lY_y|ql7&R8B)oO&WM~=q@5z1mAqbqE|DblwslVBi7
z<}+>PGMFjK)FNXef4q<1H=G&bLpgO|79^JdL-UPCcKdf_fA<U7;$U1RyTNePVv_Z@
zpzkZS1!4#6cYFvkBzbK_Llb7umdj1$$02E?EZ6q|ObgsrW>&5Uvi>pWX`28O5?H>@
zu42Tr8bV!c&NCl;XX_Y60TuCWD2RwOnC-v*{rv~unxO+-r|YHv0DXW~7uNF_fbr*)
zYy%APQ#ri!6k-&17$7W}jtNw+jB<s{euA))K@1j~3-2f^%UBr{Rx28<FkGsqex)cY
z&YKT5V_Dy)CSFy)AfOM5%yE&o8J`o6{{VsHwpb^;pAXQHS8@rG!gbDJWbEoXa5`(j
zPh<-15RY3uy!blmj!?y7<FuB%)ixVlW1|lZICr}f!Lt%k&H_zk4=|4#_{oUkGnFIe
z$<9$fV+)=4>N)*5KOz{;(g*fqV`i3lnekUeYeo&-B$JV*PWEUi&&G_TL`@g&{$u|D
z(`(>rEqE~t8J9hMmkX7bpQqmVv*2J^X_8u}UTN{|sN1>gzxdPi&L7lVx2c@zpwud@
zYoJ6IXuj11tTejTsll$st4WmGxAIoC(*~wJ9dITd+E=I5R$CccrBbk$M1)xTS^9r!
zJVa04QWa`wu#%V|nHg5wo+S7=MLuE2wY}9#u4Bl^=ikxrDjm7LEpC+{3-Hx4{Hfq|
z95M_0_1ia`A}p8!+jdt&!HYXJW~IKfINy;)$9L4CS+;1Gx*O1Xy*RXk+xZpt$;U_Z
z^$Ypb=Odq7j`1WBpW_HiemM~v#$O_f7Q}S+%Tf)SNo3k|!FWY+nA^}cfMn$^#)>8!
zlCJhDYV8E#nzOT@r!CJ_0ux9;G$dNNQwSw~STPnOgH1TGmUdxn1v^vU+=xTf(%Nd8
zEwyIQEVT;*T@Pk!=RE3;NBvf|%8cX*a&OcEJwr*cQe^U-I}LKoo$Qq)xht!P**C4(
z8dN#akbQ<jEhZ4{qvJGVHL3oWO!NcbTfr-fqB5LK)iE;p<mQ$^OmW*rh&#hMtS)Bp
z>^u~k3!cf_Va*x5Ti2HqRH=J;U+Hw^YYeLOxq7ma<*NN^Pb&5V$5TUAw>;=a^y*wZ
z#Wl>z^;w<lhW`N0lJ?qb*%WI!%i<TPv?q;9=$VGgGe?i<f+Rn=mp(UaZfg|PjrWY4
zEh<Ay3;CgGis*~3*6sWI0IKqr?!b*a>4?myP6{cw*tu3H+tnQ}uk4n$*bgN$Go3+W
zMINP$@it=ooM5n)$T8FcxpmmD`(*t@dII^0ocVnQDWavy;IgAgm%P}n3*%z7s3vkb
z`0<=UMbQebT~k|qo5*EzFjGHMCH<x5^Ao%~;LY-tkW8={q^eI<7*HJ7y*xw64&Ejt
zONSqhT$7tE<)3&GL;0D!=P8Nt7Tp}Tky!*zbVuAk$fV-|P!t`TN1guwCO)gbso+7a
zaR#f8-bd=+CvE39{Dx&Qg#)lz5(|v_^MvRzfjIuAj?yV|7$-J>2b7h2tg0moTR^Ye
zdduct7cBj)bHx`0w}Nl&uvnY4_5hZqyI|W^w*q7XB}_|gq~>R=CThX9v)2{YOmg5^
zU}!~HstW8THGlP$_xB3oav^9tDxkj+P)q?`xKRla6@ZPMr<4_;;^WQda6d`^0H~i5
zIDsLnX}J-Tx2H43@i{5Uh0o~?G4JbR5EW{KMQ~iWcbgBdSC2coooC2I7NFlSk|HwX
zTVND{L5KuP!PzIe&(~}qJ6DK?(-x>jDIx2b8Q(O@34~4Sc5m6W6JeHr_>?0SvT1+`
zq%CQiN)^eW8CKZqXmv+1WR?=mFcQ({KYlY$ZwJC<$r-h#f~@ZkX6s?8f?#O(iiaW~
zD}If~?sB8%t(7)4ir+tYrI*FK`SF(HjfQWipA^<!aq|*R+%_1gp*yRY($O6EOfua`
zvbYRgXOJz9i`44an&r+U*byFolYIXGymLh>HkPG(OVleK(K~s?10PkO89Aiems5$&
zT9>r4Qa-4W7<|j-eZ5i7{qdfCVkD*_C(O>#Br~|SP~npW#1VvK%#_JWhDpna)^{7c
z^G^=CsSMQy1->lIhMiL&hj@l{!GCXr4NiEb_0!X{Q-e;UZZfMq!A*7`i8At?l?pbp
z!(rMuoWRWQITm1+D{UH(#uKvNcVi@^<`)Z$`kmuck~yf!(h~}#FU@WI%~gZ=nl`sz
z*GQ9HG6Mz<(Y~og_5*BfV_G1~X)U(aII8%jDNKdb?R_C)lvsd|=^vV?pBhCu5x&uj
zK58I6J<(j;$+DSxhRXbAmmA(z+QR!SvpTk{!}Za)&E1PxLzgNe4;5eOqO>MmMb1Ih
zc`2y~_R3k88mf*u^@0Q{yhW**^QcsctP)r9?Ikf0W~&a;sIKFNx+0}vA%u8bifb^-
zMv^N0i4GSC7~LcV?L_crnHMd#pEyyrLtHBB%_i=jPfl+?#N4OfoY9*S;peLUWta1?
z3tKi{A8rg=Jv4_(FE4Fa(KlEqANfzFlNFoaRp%T?>k|f6TxKJA+Xe}x0v_UJ=4AAA
zyN*>Z34CQ!Z?s@cRBFFjR1k^EY1+Mu5)&vfGP&P%F==C6=N}t=um~3C6S9Il{j)<b
zIe?oGL1CNe`iaW8Gsf6azG?t)_A}5%PG%sAOK(Al-X}coo?{`Ot9cawu5(i@mcBVM
z8^$UkT)53&%a%|Bw&x@J`XodK!!w^6Wt4}UPozK&(n_*G$ed+hlb(xVFqQMl);H5F
zE^61NwK8SI##G~x<7tC`Z<Fq=aWlzGG@meeldA-S8Vp!AVszMC;Gr``%~D$rVslgg
zTa+26v8c2prrB8^V_~!bsA{z(uG4#~)>Y83M;cvd1ojZe7>LWea4DA*8IfX#u|_e*
zc@#&-_uIzsX3nu0{lAi~jdjt=Yw6V~p7>!b-<_NFDZqq3S9^C11p9<M^N!KyA~*4q
z=guSoS}=8)*cOvB9AaX9KE$Uf6fofO%X^+@kpj{4Ub)zKsx>R(@m+LT*cOCk>5s!`
zMp5wAoN3XuN_@83-LT5fWmf}(R-_jIFIP~)>#>FZ0DhGg@650j>>BOXt(w(HMUKV9
zNqCB2BV}M`5+*}Fi)RkZ(wYWGQLaJNH>H%(?rkYqVWVp4O7$JTCUcoLh@a)idD3;7
z?LbzsYU(K~n?qV_A|xv7JrB3;q*1H_EFlI#EQgecM>6}3<I~5cul~WY7ExF$10yLJ
zENyH<!e%fBf4Ua^rNCxtb&kzB75>|Lf9j+1Y?eG^C1XCLw*GNJ`44V=c=TH(M+Xzc
zB#3gX7i19HAIUi?oAjf7Ou(K|nt~V!r|Zalns;}NHXwEA{<vKm@!DeN3kWNiL`O3v
zP6P0f=lFbB=QQ{<Po^zLwy*3#?l_QmULa(lqttvkOigf)Fp$*didSWeQINp!vt<ug
z@g#C8Tz#CZVr<Q|*Z9`a8br&obnbR(hbLOv+5-~$i?Xu}qeRuLR!em`<*3`7TR1gm
zmBhW?r$p7vRe3qAmn2a154g(G5a{ek2-Ls6m11RsSnt&iOf_j!#^UES=$JlOnOV}c
z6ZPq*d|3xKU~P9*nB@vM^dc}Mlw5e~B^DyE{{T;3C6UM?Gx&kL?!Z778JWtvvaD1x
z8Cj5*@@UHJzO$Rxe=^9SWidETug(~YBQwK=f|@!^3iMf@X_~Jwb?J<mgD9C8uLbh{
zo@Q{0PG)CBnm+Dg*1PRdQ!1{ER6`f4J@vI0bydHodp~UZdR@Bdm%lc|A55*WsE*fN
zIT1WceJK9`zN@vk{<hvJ=Akjm3538(>Q3F|=^+iT$~6A~*3p2dw$+S_QPKJAt6Q=n
z8mX+IjC8A{AW7lu>n0i8qLt4+eNff{lI8|ge?b<-6A=-bd8{oA9Q?{=E4%~4DOvX*
zQ<L>EnwSz-9I{J@b;qQ9W^;YB$L@tev?4)W*7)Y8UzoAT``<r&-a=z-KntZNalaYb
zB%yeqml%XLK;6*09AXx@6JkS*PSCKC{{Y2^>||BWSkPLZW=L3*=d?(j8LnDQ#kAAI
z3JAb19mNWt@Wn$3kqfh0YqBC5(yA4Oa+TavG$sgs6Q?}Z=?JR?l9yvF1-D2&C~aG-
z0^=rz3<$x<h~C(p#wf$OUSnOdP>qIsMql}%`#Z}#Y5aZaM4BB=U}od{?5T{c0_8O`
z#EZ;%-xH-<Y{x3Kt5Y9@g~!bMhuo`1BqVi9uW+sv6WnBM+D>OFPS!+l;FN4$a~b_h
z5GDj?S;s7y`0btssO7c%E59N)iJad8v)lFpRVluG1f&iM8OEwe@i85P$jVHlivIu^
z)3nKo#VXV7o+px(Oit>cb(e-rd7RPpPKgT5*66Vl2E6|O5ta}rO?qQjcWP*n{7DAu
zRjl8mVwU&^D={&;Bo^Nj>%5G_#;ZQ^r8~HZlu&B|q42V~jYYCk0g;~`xX=6+8_Y$k
zs`OXwWsb68Y?~G<a_Ms(?grLdvgHC#P1apXI#``*gNMMFn8C;2q+V3q_GhnJ-N#t`
zOhm<D=J*_5)9{xBq`LhyD^kRDX17id_;6<XVs`d-W^a!hJ4dhW>1hnCs-Rw>wASgI
z9xHNSq9E*DRtC`%z3Js`*>vVj6B95~g%cAoD)Uv*C`da<Tr*Owy&XDXRanP$ou-Gg
zN>a8#2T4+&^!w3+1-|iDQsFb%d2FgjT<nDVDs0GS^l}q1KXW;yRrYBUJ!->-$*gHm
zD%O%#HO6dozzhs$=UmkEnh#8_@m=<2k-HkyU=fS?7M-bbGDH3bV9eC?mWJ;PChzR7
z4U`D#ju6z#eKSsvNZ;i!s2P*oC$~;;=a};qZ5yhK$u+v|Ey~wNc0|fUnruw+<@%;J
zpERIGYwxH#+G=&I=Br!Ux8svLz-E_asI~whx5{JgM=r7-ZKoKf@QI1kRveV@^-5c1
zqNP|$w2dW;7>wLSd_$*i?o_hTwh0Yr%dJs5H^#d&TUdoSD39~${{UG5$WCL%(K**0
ze9B~t^=R?s`|IPi*qr*y+J)OSD~ob+C;D01`TLDNo3MXD`cV+~wV>^+(D#EU5<qG-
zHoqRLBw0ef+<@z{Vtu?*4eMT#21AfTDG6;7DO=bhBaNn@_R>b42wQT|_ljGSAC@zR
zBRqD`xg3HZI4J61Qx`yW?>(Aw_#kHnH;wUt4P30Q(TZD?=F>U<0A9aa?ugz<vZP7L
zGc^K&v9@oMJ~M1kc4@EXK47r9K!ixlssvyQg{hpq=Go!?xr_!9{Ko{E;j;rX8x1PF
zC1sYxfyT0dBHw3LQuKs0BW$gFkOY%7CA7dIC^-2@LrS#IP(EM?3DvAEqzt=Qs=^~L
zFOut6jXQsOouYVlV@-CFjzIR$w^^#$)q5iV5RPxlAV<ZMSY<jr!_6rB6JBtCMqQpL
zoNt-s5K;ytH)bZ=$~<~jsd`OpQ0U7~o2d4HQ5A`N;E4V06vUoi);SCc9F_1}Mv|ni
zSAm{<9?3YEov~-uz&y7yY?jZA3EFqTh*G&szj2r+KtL!kDVMP7cPeWL9E*$}?Hdh|
zxdCmWGB99{U_cdLP5^Vx$+_g#f~9rZ{Qa`${{TNt%Td)vnzP>Trd6>T$bVH{+VZlA
zwiF{-@q8>Hw(6a04PGKSyfzm5%}K3|9JpUmNAEdDQ<Oy4v#4pOR(Y<*=DN(E<f6e+
zg>Tf=HP|SPG$gDMMWUH>;Eb7w!Oq7!X7U9DHXL^<Q?ZZ!`*M8w!u_Muf35V?LzsKb
zixQz;X?mNQFH3G}D5oDidGM*FWfnE5xm4Ew0IBg?!?*P3%Xlc-OUf*=yJd^8wdz`S
z>)K;@naKYDYRuIC0Nx++PwlXb6c$SjyF(@>J~QLfw1vwkI7_UNtc#fuW589a?7g^x
z$2Ab^za&oDYt<~<1_^}$6=|6B2~xWB;aKWQnbMDL_cpTvT1G3bIa_11TuVy)$6s7d
z@$sECd$8M{^-@MnXG5vM%-0`m(;ee%(sb_fi!NN5xu$k+fZaO9rXi82Gq?DU{n{f0
z$b4rIFVmy;S*(549nj^nFQ_@Cs<XeiK3W@|&ghgqZdUPkLhh1faZ^{PJCqze^w;L9
zt{P_<9NXi*EB^q=m7CJhnY&&qSRfr8;;Ry~VLMK>5t`M0<V3{}l`TkpH}SO9U(Gw}
z+mi`dbF#*pS;dFoRhk`XWuEy&^8+Yha8ph_KYj8~n)pZl{{Wl-HyErPX?k)dtxZqE
zQCk)e!r&y$%48L{aPqIbz6&+^m^$H_Ebmm}ZiutnR5vWr(^2|u@tK;H!pxU4KFQk*
zV0Yh0WQ@qhxf2(B&+m&Ec9Qs!j$R^XeBb&)94n+1S6N(DBfb{jxSvx5$5WqmTCLoV
zF*u5L%((|zl!JytVSl2vk8NXend@{I>G2LNNfJK^EUO%ASR87NWiU=eJ4LIoAr^G#
z<5ZSBk!5K1?0I3q370?LeBWup%`((Bi|b%jt37PQxLEF$gMRQ!c;75kMX{JWHIBK=
z7o<k{zImM9c*sQoiCm6ZK5t=Q{mkV3=Z+bqaxyjH#6w#V5dl2s%*>RadD{qw^7oD3
zOsm>$=j@cXDo>%7ck4(hm=j#9CZ+_AuOHTP$)6DzlZI%v*XhQ0h|hNuX3!T~6wR`=
zhh7;;d#JTO+}n1rmj>Puoj!q#*daobG#~|6T{ZUPQIRq2$a7Af==SAm7|V<ty8tYN
zi&gR-LqF+~6QQ(;WEuYe35>n~>c<o9cZiG)GV4D?=!`aUcxc*o2v<P<TUx_0EMuss
zK+T$_nhWGMwL5Q#3EOhZkq~N#50Y5C(szsLm~6<z1`+ujmPEwFrPW@@hp}BJAi(Kd
zNld|>(>w^wMi}b!oYEPx<1l&OUqk1nC3E-JBr(FA)eSWL;p#*%L02d163|d7EozA+
z#G12hJuq`pF)LCbqqR&IP;{by41~9Break+2%aSJ_bMv3R;@y;+)WioMW+%>qP4k=
zHewueK9z4WuOY?hqi;@Y6&{d0yhq0#Mb%m*{g>&kBTATqv5x-n;(Q-3<gd4|qlG(>
zPDkn;r_S>q`}<~};df6b$BfW}mD4QO9#AVrOqmGsM_e>Hm=cOdD(~4lZ?SpXvSwrT
z#0Gzy&}|patm{<cd9oSD{X2ZWyj*zCr;LCn9~Ck>_f}8+%nYawdW-%$5Esku6v9ZD
z;S7ODm8J5$v&K(|%XVH2e2ja02(GDpXIFHj&9PN|Dh5b2OlBr0Y_z`G7T1+Ln-`Cm
zGgirwhQ!91Do&#*%fHdvdUDfx63U?wGxQv$ixq%Uvfmd>WKP-J)TMBWmHBklvdPwe
z-7hp?KWf)byj-;tk53+R_;ltCgN-N$uuKDb3?RNdY-jM;Zo9KJtP&yfI)14oW7;-)
znI<y2ppqz!RC_en?cyWBe$j!q%a&9X+^E{d1=h%!wGm+z**DxvipP~yNp7N_NQjac
znnqsrN(;gLd+`hIsA`<p&#YlGHTI^p4_F9Bq+MEdsgJK(QmywpXyE|Zz=_ikE-DF^
z+gw9nh?v$)$g6Ke?Zu}w_d78;u37y0`)Pz_R7!QQ<w~n!(qhdO-ycDUIDx__<s3Ty
z03uow;&On&6z01mPOZu#d5FLw3fE^N#OK!WI%x9?IlRx`pAmqj&&LM1_QK9d1xcbE
z<r5MIENRCir@Z~VO5r46$&7d?XY%y%V?Q?*5=KEib_T22EfSFiOLYmI9RZ13ml9)s
zYh0$gnW@<9eRizMOytNH1ikRk#j+QP8vTX7n)Z#RO5T~JavIvtH7Vzm&7!L=%2qp<
zI6ew8<xsl08F~#2AuYbQpYa&6Bb}mu<&<NSRk8VL?K{VhLXERQ=O+D$M0T12`59!u
z#O>lM$MuWbj7_JUrc*@$2Ux4LCcM<HUgu_6ke?YjsTVk|L>oy)^}qS|@Q_i)BdF^v
zTPqVH<p$BZa}Hs=9lW{7)P@?z!(dn5uIm(8Sik+4#V`iFfW=1rxTW?3ix$}jDB=(w
zj4sfy$?h}DkmQKhC-6{d>OB*OModJ;*xJBwa7)s(;EYgKK}e{;Tw4<un(-W&tI|@A
zLHez?_{+j;sXz*2{C&jWez4li@_FxEpdJ%7Ak*H=VRAW~{i8OWu*5n7Mpy=b<@@9m
zZ!<O9?D;o2&&o&w7)x9!=uxt>F4pNGU^|aLjDPWGb#P(8WUgR{U`OU6PM%FUfSI>k
z!0UXf(=uUpFwZF%rqbCeAs`K|(;?IiLuv{vvO|{vkqsi_D7$ji>r7YKeg=??(h**p
zZPOpWr`A-u`Y!BM`YM{kexyf@=1OP8Pl)4|JQ$8D(Nn$I4yrWV^B=jvKCvjxw(&ld
zWCKnKFY|#L@1OUcCpOJ6AnRU<Ew?Do;n3Xeo@(dL@y2bDwC}ttzP~Z5eQo838%%FI
z&hwKVAfx-S;+2x3@N_f++DJrzmShFK)E614mpw+@#zb=8AXYQ^g{Yj;6fe`1%7lFc
z$OAWqNQ7MJ=K{OXyNE@!pU&*gTP_udu!yWOV<-KnWh*?ImAjA1cahs{bAA+<61Xrh
z!AT9UOxxStIwGvXmiJDL8|q1%zt%d8SvJIeR{Vd$WRl3<tr(GG?JrSz_gKp8rKvo-
z?|9OkwyiNbgW@V|wV)?bf_Zz`5F`5TtO|e)73J@zHH39tGKz&ErCT^-<daxir=U}&
zY$r<HapizyrUA#g8*;a+PSqE1coRTgqYQqM>dy!^ezS}T_@4RtCMF)AcUo?)g!`>F
zAnqz1Mj)sJyWBc*Cyr@ALv&9K#^P%-w+OzITbI)2E@%;@p6+x(mMY^}9(F9(OZo`N
z+sCf+6TUP%)lra&LZ`BAHd%JNY?X3iYFKbCIdd~2lPM|BrA|};U8D|b@y7E8Dnz6-
z$el%UT3Y~Uz81+-EFh}5lxVJRFAjhLm50}y6Ofshdc{qU9W#G&Gn&k<SzkPXM9Gm#
z5||p!+j$k$AMVg~aV98i0aa<FiOwROr+NE<+69<`n5T9<D@@O3e1NycnOZ4sc=L`h
zF-ZoI4cYu)+%as;%_|A48OxG3nrSwu+m@U%E8DnT@Np!1XLjwfW+&)!HxTE8;8;-`
z?-L0o>KaRsEs}_Z%q+)B9kiPSxnyK*K68ILc9>iVoIT-_%Rg=KOK&+CUzClb{{S*P
zea>k%T9)($-;L&mPW+1_Dn}bVhebs#?mbrAjg|n60Za_oWWE78F)&jSwUbyO7Zp_M
zfUYJW<15U2EBnOA%oJ*20`;;LBhXv@=SO`2bX8<5$Q@}+O(aY{{gGm1H5&a=g9TMG
zs>?8_51#5VRW`vZV_-now{zK6STEqYWZ8g&h#Phq8j@><m%qN$11G^`wgbUfYE{RS
z>a{@UbG>Jwt^+e^$uUgu)nJT~Kt`fp*2qrVPS_uNU_|!BLv|w~$c|ICOJ&CgI6tK0
zCUIr)HRF)zAXLRi&za`6cw5+R4{}@o0F`-iqc<F;!Y);_x0L+d*E(HMjs^BF+ITt?
z!V;3t$SvkmB&I`FvKY~AMn)&>$23Pz0*Ta+9f0h%^%l!Yf)00;DYjW(disjpdfhhb
z6?)o*=Ay8#rBP7F@=~=GCR1YN7S-Z;9wU5M^D!K+5bLYl=W?0Ka(47-R(bsx$=v)*
z?>qW__~kymAX&yo(R%MO8)XkQuEu<L`2HW5>$o{`d0TwMY+3&Rwc9B&#O?h@BAv=-
zClezQLQy?i#7OfVW1zlGY((qTVhk<-E?HiXW~eYamAHlQ66C`vGMV&F-<mV%WD`jv
zALk4HF^Tx{Wd7Kh=~^rcQys){tQOS-V3c5dftz%kWK6{Jf{7BCn23m(mQ0S1Uu`Y@
z^$>V~2WqcFI5x}6jMp%#Sw<S0)#o*ldupj*^jKlZi0O97h6<LSUzCJiHnWDFo2*=I
zS$af&UYxu{G)hLxYuvu*JMA${?ON%mdJg)=o1IxXSd1GI8i!qq6UrQQg)7SFnNt^x
z7zDtEuzP9@fcGu&Csy5H_Z#fAm>R3Iv0!k^5P}}ZX>_a?FtwpQBghF&4gD0uNpc-J
z!UMu*<yqTloL6#H4opkj0UxR$oz4{w#h(T&<`|VV3|_Rjm#Oh!O1_^_ay!w`!f}7u
zPJa*-bW|VZ)2(g2Q+b0|6xUILxwNdg5D24KS+MM}&+!=bMNv#<n;h*1`j+q1-x2O%
zp4g`37qq-EnnuVIB@(QMx61=y8Nubpl<gnCpaI#dAQdZFLBk7HO5t_hK;#S;2VJsL
zwoJ)=;!CfI&bJdc`#n@~?JR`U32LnbZ~g*y21Z8~1meh)&GEniIURo5CMZ1DaYdTA
zg6C+FE|oVcNbJEX%!rl_H^gwCl*V5<QC7(I$6R@wQjY@F@Z*wSl>vdboYTs}>FOq#
z&QKLJvzqNNuqO-DBy19y6FcNiYdUbi_p3he_L=b<WTG*rGqC!9-hJfHjM8M1{f}4<
z8jR<O-f}$RcXM_ISc(H)v^VErysD;IGI-VrpCN<n8C1Hpie<6R+`cB!va!k{Cqq<;
z$ZRn}mX%ipRTg|kCUFL3R>gc%_-)O0D-06LQ6FyQl2h32KiW0o#Zp3w6di1wY>3pM
z7@kDY7*77%ODVSy4q`<^41Sxp_jQiR)0HsWE|MwBO<iTpqib5zhd9;~KLdtBx^hdH
z2zpglZOYS*<mWW<6U=0!m9X;|+==Eiw7XY)#5+yXCP6RZ0TYrVyzK)p2409TqEQ0F
zHn5EL-f;fsIS;k#&ACp(6|4*sPp+cJ^D8|HWI!8SmKa6zlchpuCT0VM4+vW)IM402
zAkH-A<5rMi0asMaJcC8su!e6hyf{LWEIu<~fiuBMA8n&@#6g{fh%=*6?^x?mABUzR
z_Vn6ke3=m3M|ty_bC$qm3l?g+lgTbmgOb1~TF!7oagv(Nnz}f&ZY!;x<~?f0_E8zH
z6v4!LPt<=UKlV30G*N(e46?b0yKM11V+;s>(4b{z9J;&F<(W9oCy_D*$}79uxGb#J
z7OQZYX@R+>)+<CuYQZ1ht$rV$zo$RwpfV%OVb3DN@esTi-VZ8AZA(F)-8d=Ni~CXN
z4PjF~_fdgPT&T^#qEp#3j);vKtjF;Efd(~GY+yC%nX+6PVM}PpqO?w(ESr_0E!!H+
zK1Hm!!)ZN1(sD!G;0^+^FJgCMRDau@F>@BT8=h5MA{ajA<&1Ubc#K7LTs|>$9J@>x
zV8s0AHDhPINMnz)^QT61yxS%*&*PRRO4H_ld^w&nQNWMo8dZRDg)qJBrL~C~la)|=
zJ7s)1H8n17CjS88-`=%~HfqfF)@&xi&nWU@*tS9SycaskAvb$hSRw5$?M;Gi01P4g
z!$uG??vpCVbGL32I`cS%Wnjb#!*AjZp(x+^7e3h%$Xl5z8nLD=1$7;kN(;5~kR)XS
zB`dEIwXz^gqBI))tTM}RNM+_TeX3WP6E`jbNGIEYV?S2;E}MuUVAHfwC7o5r`<=BH
zzXe@l=vp|2l$1$oo^+++L!Qw$02gV(9)LTk)0%^qciIjRlFCG~el@}}rM6RVFm(}{
zp79eg#s>mQ1(P0f)uvrgOBem7?V7oAYKIqML`hPgi$fuB3Ue}#TdZ+*n!d|nhGfGd
zDxfFdpL3D=N-?W3I0ED1XA%?auW}T@%uG5%s*=G7lXE1&A=qU)OS|h#=Poma0>zNc
z+UH%wGaLTdRdQ^Si7rf>{r1oL^itsY5dn)e1<N(m@pLm3GQ#7BG5P|XA8I^})+X)l
z+*gvFs{<B75KF8RF`wc=Hh(e{$1A0hF{gwbpo!A=pL!mVO!z@dm{PS?!5Jz1s_g)=
z1a=3Z#1JRTa4Rg5txyf$rnrmLa-Qg1M+VM2ZmNd~+8r~A{{Z2X<${wg!qc}mQJA0r
z*J>@qgaAZA7T#w60QD+l3xI&+X1ib{pxmfIx*|9F&%B~>!NZ>kh|Rt^ypNctHPnN%
zHZR$;JaHceNoA9U7?w}%o9wy3`{$J>^U0(DOlJ`GMyTfC8e>15Eu$Tfvb_HQ{{R@w
zl(36zRPkzy97Jp*EBkvbJ2w5Xe>QQ+{@6wb-uWjb;!1b}69Qb~6zeLKObF4lHQgJq
zgn;q9i*dtIgv3sxXLMN!(gr=~FsNH%?*Y!PJ=EaCAb)w2-Z;mMkEzMTjOz6ijHyyh
zjfUE>?%UafQhRT3I`mpGl-KU6x`F*??dhkFEA?YF^5)Q($=WAx<fS}x%hMt<Qv(`C
z+^#YmBo-ORcs;k;2YAWxoMb5T&v5Y%h(c@-fi+A+32fM;MshwA<O_*%ofhZkm2v>g
zKr_F)#S#}IedcHR0Uy&jvjMzJ&hdlgEgV+gIh90m<um8ZkLn!Kc>9A~jBhcT{QMP>
zG5gloyMs556N1A)?F&t7YiA8XWm`+r(nt}cYm?Q}A*d<XQ_5y?)NH^U(deCSX3ckQ
z^ztI##DC}vu%K{+8Q&twCeLYUU}t4~Z5fBPk?3>|`P9+zJH!rHV)ZRC1)p2Q)vj*k
zn%<o5{`IJ%oQ7z!L2T8DD27H@-`qEUxRS&qNgb|rrdZ7~)BqZ#?)Li94=u{H_BzHY
z^_1A|cBE#TaP(Byn+F4D+$n%K%`Z)NoKiZ-WNm4U_npGpoTw6|>h_v-c#&rx2PqOL
zg)!WmN}W4`-r1E>RRgr<KI<*qmP~%49R4EZ*oe>D)}?8*=nT5xd*(tWMqr|=0kHe*
zw#jR^^xL2}#E&MfFpH5C=kVNkerhaXTWA~q0QuDOPL1o;Jbsqr7a?|)R-KM7r)@-w
zeFSF7-2FHg;lg;n9fQu#BhyT9nZ0lbXD1{X>aHB8p0IFtSYZ+~@nnb84UW(#S<IA-
zZ>O|5`)6K6R<_l|NXiIv{AFi*djMqE2rDGH%~6nUUf3tVc*>JIm?TAXZf}}UBI8@m
zYP7i$lQKQI07dn*?-7?b_XWx$l#C6EHeIJAGSPJ{bz%H^t8q<sCgj4|7C9bhgb;&P
z5kZ$IT>WOHSGab<-Y0KL{{WX+?UDP8+9G`hIZoKagCGd(<7NysWtEBxY{|!50O_<2
z>!7B?xhmXXOz*r@Sxk|@CK_--Vo$u|3+FH~)v*9)AQ|ukmT)xb;?X<ON@5T&Sd%fI
za#uGtCnu74H!O6q!a;Tj-w~6>2PBbqPcoJeDH{%&<xe^-YL35pS<B(aD2&Ona=0;+
zFT_@8sNi3vpzCUov(nQGLFdrrrmaD{1f1V-#ZCx_5}M`m<L-_tGO>!H#7vJ8kGcaq
zE}nho`N{E?H(hGVoxI|HFxEo=elZ+ZAE~m4kbsvT4lo8wqbHUQQVNKe{jmTd@PlI;
zFmZxLJH&2ZnYQy7W7orbqsGMTd^pt8qXYGFz~v-7Eyo*75})yaBP0=xn3m4oL&QU(
z&0#@`3PZM0JoL5^S=^f>T3E|()(o`%^Lq-YiuC(!&bHSuGoo;9zJ_Wvy@U3GDLv%B
zlDm$(fq|*6Ez%oH`}EW!=8DpbrkkN_HG1n)#i{$B8KXz~l6TZhjdkFLIS`H?>n*)g
z$M9<zNMp2)baN5YN^qx{l=^8V3)B%NT)7tQo%G7Fiz)dAOl`bL43rBd3t=&gEyrQS
zvQ*JHQL5c_ncS~6Y_(pnP%+}Xc05LZ>(Lu+Vkg8eH{%f*<Mzsv{asUljf!(u;%na{
zDxV8UEEof2g}Qf{o><1E>aWTgWJQ)!V%D6E>*=&cU~NUcKC>Uyr^<|y!Pe$V>;#Z3
za(be6i0iLPOn69XBO)q%#|Y86FP8Ur_T2I?h@KHMglw&+)7i=!f_E1uBylmTJgVIm
zW2?sw<mpG&^0I0DQ5a6CcFCsY+H9oB8T(m|f1IH)7g4^a!D>yZbIVcD38d)!iklht
zPzanwgG9_lE9ToOf#v=Q{ZcYee7xgVT0@HuN7GpZYcJvd03vmtre0&nj;h`HgnE(5
zsOsenrsa5$^Bc~-+4n#b#7bj?#Ej1PQSB(iPo<_6K%-kE1m&Te2tHFYyv)k~0OvTg
zJ>S~WD!!_JI@L`)EWE58oi!EJD$^dvpp5l3MVh8jb@16zZESU1BsNs11lq5Fwl;QJ
z5oML1T4J<D(r*wwI>%9e#xXnmf+Np~{WF2~d3>Rj=pUluC4sp(ic5XMV{Ak(yg6%v
zk{XX%&#bv)R6Jp-pEEnvFqxFOnqx#cuJLlTQ$9D1%k@qH$O4=r72b<>gG@dZwJC+M
zs-8<$q)+6ld$yxyL2fISkrr;9ak5KNoM|?3(C#~%(m+UY&$w+jl2yT^&v3~l*Rca4
z8c-L=B`->6rGq<o$%tUgW~Eq??peU(pfddR^w)rnMG+;pFA)+&0w!Dss6+J*)4wdH
zu__x#S7Qu>Ewr@Ci>3+zuWbsQrDjop&u>B}Dz<pAajY4!JNVD}!9<Ul-X<e*9TC#Y
z^A3GqZ)J*V+Q+#n7rYwNlL-kS4BooQi5m<{Srd@~^)!95+TO<?{{UWaR{6kfY4CYk
z$sxd4W$mlFyy-qH^^)jcX2}{Wu&)+sqp5Mp^ZVwiQ#6!$NrC`#GaF+@I?*E&7AzcR
z$V&*AVc8T2%C4o71OEUI^N>7<Aay1~EI`0*F)=F->DY7s0JHmN0%1nbrAdrUhErb}
z>W;2|GvG)0N)qKGmv@^II3Rqs$Wz5^iBe7rbL(vHmO>mUY+oYwv;t$mmM`zcmUr-W
z5BQZ#2{7h7@0=KW%qCS`t0J{0+{l!1ZjCVyOCw{=2!E+-vzO%Umc|oay5(YX9H&ih
zJ9bqT6unZb6$G<h{JlTVq~P8<)u>2buU(tkr4h)YBUh6fPT%jwBbC|>tug7NN9R~M
z)%P0$ykCOl{Io=7jjl_cx=x&-$}~F@kssp#%uQr2nU}Yc3Vo%3o%Y)d`zB<G1r-wP
zkJ|)f$_6GUdEB_|6N$6z@4}~Asy1tL2>$>X;xatW*##pp#(er$QP^;(CM1pnCV5QH
zrrXBxM{jptC+n2u&y|Zi<UtXP4d{S-Ry*?A2%~Qq)cXOa2!kk4k{a5L-pwS9bz_x<
znV2jTLON}@xqLdcm?_j<olM(&Lc3fdOI+$Vg<$40Q5$fWVZ@G9cNwS|cWBbA<YY{Y
z?+k_PBpQg24FSlOp$jF$&Pq95OD<BckgIhjXD_&UEeWXkI)d4-u6J$aYwSw;-)gTw
zgF`FVUY*eUcj7&xxlvaT)T=>|GO}Y4KinLTO$2wp2Q+TWbtdn8dDP7&`x59$!NiOd
zKAFQN%_k4NCCxC^bxKVzv0Bf%R62X9w{n%!(n)~i2yu<0Q_}8TuuW4mZApJ*i`Dj%
zrlqu)=79~XkZzgYXEfCNi>VoW*}rAZgw(dtP&-hlg0Uvg^u^$60Jxb+>iV0$G7Or`
zsG`@?5cIws{>G+JY{V%Nd<>vxRh{(<7$q?i7>2nl-BzwHK^!LKpP@s~nL*_y6zSH3
zKFfTTfEbckxdMrt4<{SiAQ}}DIa~^5ry2JcspHsDb)wpuc*p*woo9{O89OK>#kd@x
z$A$KrF4)h6j@h*N%CRg5t2DRtL(KHunKwpENpr-bZ#&SU4@8xR)An{oBmV%|ERR2W
zFu__dDH(~F!7>_<LQg9-L$ntOkZ9?vbVCg^&y~^}9t)JfPT8r}_bHgoKD-1Sk|L8i
zB9GrBe=JWTNinnzMEs)&I^ktHYnY2s7?mUqq_@A_YMjiPO|Zt}hkh)JEs_fB%8T8q
z8MOuP8UvBWH^ckopYfQ4JKNbyBU^&}NwYK=0LJ47gA8XPWM&F41VOE^8*?4XHYtp>
z^12ljgfbH%Lc^$PFqCx>Nn4)8t@%}*HjvEJ^sf6Ifn6Y#j6r?n#&s#jfn&c)uo4W|
zkh6g0W1}7<s|Kt@?PYU}F486+keP|DMOwMDCy9|xO#c9yDkfNokHaoGoTu=}Sx0MG
z`J94l@c9%uYLS(_jFOmTR58t&S1E0dML#AM(mO`*u46UFC5`0nK4yHNdyPxW6CK9`
z{{X7uYHH_Gi0Yw}yd`+Qikw+ffte`yA1)d3LW?_N_T*>d83ZsfHh(c{egeTdZCH-u
zL7W94h@o-gR=GK;9J1JR4a}Y7Ad5orVh(BQ9@*}k*J^dNZL71y!B1a}xEu^mrqXgF
z4s2G`d><_~-{nO`o$5_atVCCvym1(r%9bXjmAW+)1$$G~nr9$Q_QtjO%s~f{3D5if
zO*@2C<BpkuQnPLv1P(@h;Cz@7mSbx!j8^as+iqW8Ff4FMrcHkyz#oG9Jtr^3vr{n5
zI`CYVA)2KhYRE86b9VEMHBSLlN0`s^=}Z0EW_bzsHLwexuCE)%Q$shHk(i!cak=bz
zgEu&lG8z2?1%@8wsF~k1%{bi7*6NR4Pie<e_e?k@3jYB9U!HWt-)CZMlMIVLhq*(a
zIjzAKr0uy7?-AKG>d0j&;agBK)1E8sRJ2JfU}a$KJu)LyDIF@uuPwulTuWZ%gjp7V
zR^NG_89)^JP^R9gb5EUzQ38iloYG0XPeI+P)f?RzQIK`4)-?k3#f674g!48r{2yF~
zbc-O1(b;Z^dxoeh%nR4KM-wRY(q<H`LdFS56cj#KT2PcEd9LNG(sdrF=xZ+(J3L(4
z$h1KCHG9A$T-I&PFt1z<p4R<|9X_-L_C`eSn#Ic%Z~jPY_5^-v{v_ICu$^Ak^~L&i
z)bcPZhY`k8W%MXbfh}KCKfiGIFlDukK%#ugd2b;zwRjw+xow4vOCg%1GH%S+(yBgJ
zvn$nBRxum>F!|Zy8LigauHU2v)n(8{$QK@fd<|z7hC%Ha(*;mW-h7njou)Epnqz#Z
zuIou$-Y$0^=(dnmurans7amxECZAZ`+AOeRC-=nzETDh}c=Cx{a$@2vmn#m>9&#o^
zcZ#gy)#q1Lcb+zbMM;cPsp-immTI#zx~*KbXt#Er>cv!%X5BArTZnDGXw8tj<;iUB
z_^vR@)<RlC6|01P)0M3%ka2Xn!8RVc?<*ihfj&Gmdjo9a6q5byr>}{R?SdU^7aYuy
zOvwG^UJ&6UcOo!3r(QZMZ>wq^+$OS+V+n6D9xNfsPzSh>z&-6@7fKX>oAb}x*o>TJ
z+*dgqU}rYkV|`@#jd_HqI+A&2V5~A@0DA~V9auuRu=Yodn+xUfxeKIpEu!k7+K0i1
zEIFLBGAmMvfP7U{H0>3~AouW*DeL<2llt=chg(4Or3)=sURe2<9#$S&viD&_^$dt5
zyhWT=w>A&&=qcMOsZ17*^~`p+Pq?V_Ta`rFX-tvM<yr@}B&IyhOAZ!;h8#C(o7>B`
zl{o6tE-Qe79#g)>9*To2Y84cZ$|Dv^MKET6?~I@Gn!uTbH%a)|b7ATo;$UVr;8p;d
zEcmTq={e?Am7V}BOKc#OrYtgi?~fC&p}=G}+3EiPmLUw7h<5z==7kV!Jb0LyZ>78O
zI!&a<q|{{-x7I|=eRfXYDoY+-<jh*hHp?bmS!F-N=3e##H4x&(nHZGMEc^OYd&(?K
z<yCOetQlKbHl;@6#z3Gyl5qpZww`gQ2MdnNa=*e+1sS#!$1TR`dzO2-uMx%NiI5W9
zvT_PXeXVir_~Hkf)H!x{y=rl6G_V*30jbg6EWO9YCxf>BeHT1nrzU&|`gr~RLG|Z&
zjC7Y+=BhhbgU2(m+nZu%^C4yZAf6(c>%X?u>z156k5V6%D3-hWD!qDY_tE4g#_6K?
z^!^?FH#1wVXt1}6_qNaiarQKe64*<UQ*8l5R2U6LHn`c%C;j?DH8%B9>LO68c4xS)
z1`;N9jLCzk!M4vlrzYjHf&Tz%WOG3|nYgIbbpvXVmTm&or|nIl??Y{V8i!Y4O9j!i
z#72w|Mnp$4M3ohX0*6qxby-ZI)R4OdOw_=OR9h=Hg`b>FnAw}gDQ=A@#5xqYj?*A^
zn&}3KqK!4@#%C+RyG^mnOVmwUp@T6an#BTl^#N$0qM^o}#htGO?Y@5yh{c7>Wk?uo
zG}zN)36oCEgpGm<_NnUs01H~KnA;GkLsHauHT{(3TMR^y#g5{EB6A^vuFWT4vPYHv
zdX<8Z<a$fS2gOtvwB~QYLw;837Ha}SLLj%9$oWz@$@-5;%Y!DwxU|tzueet+u}pe?
zou?=Mw77_S8z<$3CCB5wI=idYOrAvT1UXJNVIEBHGcy_g0C^8!4;A+r6p>McGU_=s
zRJ#jcuc!Fu?X=CX6-pWr7CPzB4i0my%$s)m&!Nemy^H%{mE0=aSlZ*lEF+T;QbG!I
z0tI!qn+460^DIb-G(?IwUiqXoAz1q>ZxX;CucglMkFJYDvmHTTGMM(JCS1t7pvrMk
z5F}&~CVX({lbE=Ym_>FTFpZ2UD+AZXSf8?)iT26^iefP&F=|XCiyBZ=X~quRqj{X4
zmmDLBnV71ESZIpJN@FzGQ%6a(xL)q3Z?K9<CARUUDkkW5NR3vpWFs<gIRYwtauqeY
zQJk(wlE%##0A$!@D%Ld{CUUAsuJg3U+Bhh|l9=3!KXT1r`rwK*v4raH%i+~!>(0ac
zK-j4M=Oz>2<UzKVrG=T_@dHmWt#z{IY2z(i14u{Y9$|)U(tw*7aRUPw&G|&g@8ohL
zl;;LncG+dX=8qBJ(vr?Qz(yjw8)t?Y`*R^j2xM}V^+EifJC|RiWaakp4LoMri4Kv5
z&gBv4mN#$!whDPL)9D{m8A`a6`-kcF&rzvxBv2smR%=DdrwqQ)bGRrzA3THoi$abz
zm_w<u8F8Fq*R;J4MzoN!rCm?|0FGzTONO*UT=cos>!f#y@l@lw0Q8kYwLSos2=7|U
z2F9NG7=uiY8KBvvaV(8im$A5E2g)?4qnCuL)mr0zMKWK=azmw8oGy1;PA6R+y7gBc
zpI9w0hYTq^7j_r>Po#Ls+X@81ayd`^<!mXLGLqYT&>zemoYv`b{-87F3*$bEjiG~|
zb2IA`wgiMZQxE;~RgQ42ntuRX3CK?4MGLZJC1B0vHcYuQlTTNG_nZkd%EfyGs#|+#
z2-TIoo<)(<5ILgTBeN-;VQ~7$R7d2cktzd`md5+<yIO4u@^`nMgUu>q@hsP*hP8Q&
z@r4l^W%-G-yj6Lo(ityQ#n*;uubNi?&rlc@))9WwazttW0K2W4wPU+mfDx-q*ize%
zX~ftVrhK>3bm>@?oW6rbpHdu>_o-+xyjZ<HA#Gy+08PhPNF}THHwt2M%vk6^A5p<T
z<!CR%UK4OX)M_KrjmW0OK|YGt?pZrRgj5_Lqvvl#OULrG%9iPaRMDNM&+#M=G)`Ac
z*?QF%nu#b)Yzc_$jMLQ>CJcvL)FOAm)#=@dUC+U$Al!+GiP~1~VlY=1)cF|FV7au0
zp%?Y1i7q;AxWxYewuztBs6pdV#<MtTkp)5Ci+odDpzR=2##ftvmKz&E5jq&iTAke-
zT-fyeqkO3n6z?|~zVW_x6xWJoKuew{l*wXo&&*831VoU>r|lk<geJu?kMofpir_Od
zH%t90t_*mbjEK*#89s=#>c<il#d?)%eMzs>?k}}PtKn>JB7NNKs_PjUZ&hAJwwKC8
z^0mloNqM)+A~?<$?>RhY!Cj~B(rbmNg@%#<=e}~DzdB`7vh0Ze0G?_WOQOsTGlrjQ
zfdK8x6{g=@cE#>c$P&^U#!50}C;GaPF}882J~CBJqat&q>Bk-iNuaqB!Ru5{S7y_|
zre-E%0M9!Qd@ct(q|sdFC6T7khCzdLitXjZbdya>2n$r@D^+k-k-E|^v2}H8Nsr<;
zY=M|juVI%$TpVI#X9x9`YSIRlo&yFGE{#FG@~atH-_?=M@jLFhfj-P4mK>23e)!}6
z0BJb!*|RYP`^~V0O(d68ffP)Z=e|tu_YopPIWsK$q}nA6Cc3!D%#6hS#w>YG()XI7
z%2>yOoEYAA#SbZ&!-AF;aVebJ_Q}iB$(nFtN*Wzy`if?M^0wW>GM5#1i$&;5jG0sx
zZ2GP%_bD0FW+B}NqB>^$iq4N6L|>YjipdNpnTh2NfY~w`q6aCnIcUN5SveqEaV-tN
z$!!-psn&Ib%x^Oh7654{+sb3f&!YF|$~1uN8#kt8xg2FiMx$HUx2|`X$r*{-YxC7t
z(8}^JzkIV-IU_&ioU&6IWdP6N^+b~$I^h>;hdg-OZ}S-iv2x8Yw)cz1rcuDMcHhUD
z^PgXFyiV}eP%&Q}F#^lsE8lsUoLg9f=Dj-9->dV~DFQ5uX3J&nBb->l97Lr50HeFV
z@+)r8l9oe3a~8DN+kEKL))CvjkhB3xU8SAMolk@9w3E$lKdzI6J(Zj-1$4>Ul;4he
zU8wZXrXv3NsswPL>C_fC;kT;|qphbEcvyExfSgaWMC2&jr%9HNJ6yBB@d<5SsANZv
zL;X{aEzCVNASUV-h&pF&=B<!Mog3;X>AfYCs@S&pgti7pE@{T1Y=c|7IY6RjIk!z@
zPL*n5i|f!4IZg35)5^O*TdK|!?h&+X-7-Ba&y@}YZetmy`3o^Oj$L}u<rRUxB>BNR
znNj@e>D{)tN0ZcQ^O$1wDyQ|&i#TTRUv}zMw>(-}41k}uW`n3N^tBH`ab*QW*n?}E
zKZZHJo-&K9C4@?g8dj?j4{nI%Pc&UY*<SOf3Hy7qlxOlUMgrM7dyguX!?f9%?q<|n
zw$=m2#2s5hf#c<q$%Q2~!dVPf4^#nfzW!#v%v`+D%vRwnwlX%I6O;5lggtsv_jfKq
z$|HG*#?L06&^@b?^eLX98Pt~+5nk&50B__FS|+2T{N#EsFD?^Gpp<RrJmG)FGCy2r
z?=$E>sxwTrHI;vkoCjuNa1qWJ2X2J1YwNYAm1Oc|b0C?K&|$##WmsS4aO3BE{{XC*
z<?+sNy@aB6`nf<!gJE{#t;|dp=>g3)tky>+YL@1$SdcO(prMo9HC8^9*x`qD@YF33
zlM$z;AZG-1V~FB4+sSfA(i6PPSGb5yr9eo8r&gsRMFFVNIPu;2jDgfxOsu;}PNi(z
zAq~|ncAmklS^of+;|l`W@vKx~Km%N0sW`6g(1VF3$1}q&&p1^pHA1FFl}s510a*zK
zLK9j5c8Q1?jPo3})TgK=I2>b1ZavV-=@Hof0663J&4<`aGI-2H#`EVaOzy4c6u#Ce
zd8+>azJ3>HNC*;4PDdsOXqifxXt)Y21!C(=_$|rj`Osn<%it{OSpwqJfe`WIJSJgX
zeXGGE6*F#Ayv}%t^}WU=Zp!}vlnr({d`b#2Tf{nGU<ZCx?9E~#21H-jqP&P%M%E{|
zj>PHggk4hxV}CiTmgfqHiJzk#kg4?1{{SsE-;?uUaa%ZT24UAC)kuz~O)m&ibdZ-B
z5gAFCPQdK_v-F^+KZgzurIk$nqR1iw6COW{M9vEccZt(yd+6(|wfmcrt!H7h!2VH=
zMG=;ar%tCCq^G8YX1H709kXrs!17*|H;al0Lxm?I+fcZ9_3xf>o9_cTrB4~XKA-0!
z>y*{AW{LL!0&Z1<n^pR38V+J(c%7yeN1SJ<hxub@S>XQwVIw!@MAg5a(||BbZbZy&
zF{yvPmqA9;F`Ck$F045@P*|InQ!e@G_ONWcMg}x9lCt70P`A>Zm7ijn_Agbf0N-?x
zf+H9kEhukZR*-r1PLCheTIa3-Fww9qcVra!@e}7RM;j~{4lq$AdBodKEZY`SG@ckW
z!q%HW-CE2&>8`Sg6{;s%RHFQpJ?oh*ihO*ugcsrWPF@gNWq{2kEU?HZ(@^2I_^`3B
zT#js4u~Jya&5a?T&&#|l?;;@n;AWFM#jI~3uJe-RT7brBRf5f^6Y!wgH7g2HBbKZZ
zyRD23rkgj?^%$@KwvXJ#U3k}sY^z+R$%rt$NWVa|TfWSzRiCYt={JCx=$dmUaBuMP
zc+Xg9R+cT|dJdu!@kL#Fx5x#JFE%}@3>YK$P1Ggfg3=8-!X~_aOBt7T_biyvS~FRT
z%DzZKZzd}`YsAj@B_L2x!JaVK+biy;DU(BGU0jZ^z88*tKhLG<5v`}H9TBp29|aTX
zMZk+dN2pWwR%KYi<+NoB!(W<RewosDA4uEx#Q+sv%pc!xLHf;8ml~0gpXWT65f-J@
z=Vtm3>KxNprQL{a>P^$E{3+__u>cI`K1MBvU=SBgl%CP2Nv&?2scn^PM)NVsHvO}0
zw#`Z;R8AU?3krh(pZLzRUyYt|Q%?7d1I=;9wXFfKpZi7QR&o^AHV6r`1qBiHT{eXV
z)|RIs`Be}F06mefk;z2c)DoB&9bLqQf+bfP6}LIKWE@IO5kw+WHTj;=1i~`SD&<F}
zYBUpJR@kX6%;$JarZ<v_-*Td&E0y9^UDg?7j^3>bA8ko&mFTu77Eg$d7cCWgYy{ub
z>{WaKRMsrmJ{UT^S2|apV;Dbqc)8hbZcC*I$Y<nA@LByKKv$+unB}bs>xEj%g5%~1
zW!DUg(II1oPHAB!bPEWj&Lb|u`SATiz5o&U6CzMbqM!*pIUo(i35Eys$Ye}(4%1a#
z))X0Vfb6nBhDTw=?k!IL03oJ%5#yE?+s=5&pRCnCv!})pL{?hyK+7qW3~?Nim`LOS
z6zv5f1rZ#lmnfstaq%SOO1TM?{6Ggyv)@TmI;|;srowmFcwo$u%smqzy(%Ji{e<}a
z2<DM$Q({9k`nxo31bK`f?6W`do+bxBhuI-|SC8QiYzO_|8K}jJxS+nDeP2r<Rf2hc
z4;-|u$!cU{ah(1hH{&~R>s|yw?IsLOO)s5o)28+l7<~7c063Bl)8`39rGb=qn8cJr
zQ84|xnYEAq00Cs=`hV6?@$aKJ7mfY64;f=zp^=17GxAzqt?l^Mez8JqrW&qdLQrP2
z0#Zzw%xstPePYH<h*lKH)l|*dnReMT%W07<rei0@JM${kJYX6AWNJ|^O$NX~k%Sdy
zoa<Iwfd}ktEQQR77;cW?ZRSi^4*FKrf#a6-ZOVpJz^H&CwNvlR>E{cz6Pi$$ZGt^_
zhe+axK*nZ#Zy1^6)dX2!J?&u3#4C<iT$O6}BPmhptzow;Z>bnO4S?$d5Z83m4gUa$
ze7>y1ktJ-1KIhJg@|vA8nCH!TUOBa!>1^x2Uo~{~T{(ru>2_a{ZOX-v!{2-cad4dJ
zM;*=0O%v~;xSCH<xy5v=Dx&zV{;;(atyT;1T{M}N%1*r#l<n;9#OdB{qA+G;igWmd
zh5=V&TI@kFIas=8TbfL|-l~pAtXlUDAq{5wODA5sUm^bh_lDo@ui`)d0J@z*tGMx&
z#CV=#Bfc!ud&qUA!Sg*Lza9$Iber9bc4(kVixNrR9Idq_u~(lA^hrb)S-l5Y#-qgw
zMfHj`>He}kBgdSW%~#=O6O50`Oyqw4lbVACp3Y*&2zJ3SjG5GGiTcL-_(bng-urm;
z{qcNe3NcadDU!?g&7`McrEcz;a>ZS^`_>`O*mv4l3mMo4X!9i#zFcE<4Lvqzg-A+G
zP_R%*XBiUNem0$qKc|r!LeL-<Cj7gojAv+!oG^zh*E7#dXH*4*v^j&)Hb><Ew5}Gd
zNfBld6Ri|YxBvuMvhKm1Bcx=lgV4DlrE#n(!`nzF-c^GiII7K18(8`6Sk75)%z4VB
z?a^o-?Y1}|0XtR^CKL#?1c_r1ar28cJ?MMHL`t{b1AjSNxXu=ZQ%;{fw{F|%>>NFY
zNJit-#44fGUmIi2y5b?1Ugt6A$onM~p<(oKuzrBS{{WU&ruW_9m$b!?Q`kqNEC?a`
zmnc-^x$OvCk^7xH&LM=O@np#Cw5B@ek1E_zPV0xt*NGlX;fWF#VOlbil|v3^c^fCd
z$b|MNnXU>HklC7WWjRbHs^4jxqm1PE9NK>H!bMIT^FA_m^x{(+#r1*H(+XbjCj5I?
z2P6&;n2<0!#Y=Ag0ON0t`5u0f&WEX(`0uj%$0)E4>6&B<vUL-v+A6{_H=#E~UXk%R
z(h7(swhekP!7s@~Wc|+hKj$>NE5BKkpK6<`I#$Sy(8+~w%a(uK=(Dz_iOpKPSGapj
zPo+yur08nhNZ6u%JvdWc1zmcKF6zna&-^0&rg-h)@M%)iLXN~~p_gPANxQmWg)@Wq
z@j!)(BMxsnZ6=>i#83i!3`lQ+*KM#ZKF+OxWrYJCVrDX9Z@Ah#<qh~=G4$VVC7*tp
zU6{2*Vl|3=cX`AxnnXtd#*=6H6djEK%{~R!`(?k|jo&dYJ-r?0s$n79rd-GvI)yo7
zaH~dQr1mg-hTyI2T58yj#je(t!z;O=nnMH}h?)jAxeiU+3X)U3)lWOAYAX7VOnLKx
zGY174ShUn`&1F!<{LZrmzbs-`2Z=<bg282Hr~8~DYQg1B!)#T~DAHKY%`o4n*|iSm
zH6{UVWoIZ&x^C9=>i}A-XW3A&ABk!q)?*j@%lf@7;4iYVvtd<XyQaI57FuyTOiXGm
z?wZ(yxZ&bH!r8Q@e-F2`&U+`v-3skkG|u%_zRDf}R6+q^s=M0J@#b?$a@A*MTHJK>
zE^F_?&QT%<!A6$<0C1+xe-K2?def2P%rAwa;!P>%wCO*TjZV%)Z<G3`BkFQL?*n-=
zoUY^TjzU}2QDt#jdtwJuxVRw78oLZkS{n6nVFLlxto(zdAfjVX(aP(|z}*{BR`Zeu
z6R}M1k6^hq**~`Uh#)O8b6-qHLt-)`D-Sz0!gVv2Y8Iyqf2XfG5%DSYutUbUMDfXu
zrgKd>3t~BSLaQeMk+<$KF-52Rf&1j2P0=W`ro621MF7>b))}aMwaVLBzeA;k(d1{&
z(VymWe)76|lKGJ-0um6}mFl)`A|-Ie)?vJW+R1{E%ODP(w!W&`9MfH}TmoRDC&!%B
zD21Ts8e*(hd!WPWV?MHYnSkfF;bO&`R`oVQ1!$aG31N=ny4APQz?p7Yh5rCw?<~P8
zJ*-+#mB4JoyC{UOV=@$|%u3HpNOe`Da;dUPeMC%k8zkfNG;3cOuR7gnh#NsqOR;c!
zTHC0qGL1PkiOGf@rqSgogdJI~78zxP=djMOnJJB%mPNx|K@le+Fz@cpPAIorP8aCg
z6w7e7Sji$UGTq0XYF<5}@;Cd<ERu%zT5gprGMWIUSG1UON(|XCKc+FW!j)7>mm`_a
z-w7>V>&XKR#JXL!I<3><5euuQ&hu(Wo^o*!yhP2CIssja8I$9NQzbb$Y9hK+7yxXU
zfnJ|!vk`?=s@@CR%#QNj0ZxI;0T<|0EDU>)17aIqALX+ipIHUJj4_cZiMPAdn^=pc
zH5SL|DsrCwck2UbS}d#E0opefwagmjlE?rpczQ($75=YK+vUA^V(czdQ(IZB@oE`f
z4M(=h-_uQ<t)g>Y+AeJc`UBE(y`N>KRGFbu*&Dd3&$ZM@^8GodVpjv`krQZ%6OK&F
zG}S{Y>CK&W<<I~q0d)pM>@&W~lLiM99wt!{xXefz<)PXdkqaISf}ibV{m$dJnV&?L
z8h0{1K1VUMR&2+JSV3&c_JKB639un?1gW&MM-&^Qg-VHG1c6AGqCP8Z<vrPGBrKO$
z8A%s2DZ%5)H!;|zGG-e{A~gR1oLoe#_;QT6JZjL|kpBQzKw_+yyoilO%&)5xmtid}
zM58pL8#<H}>5S>N=w7i=h_XjGiw&ySV&7SI6*nqVt?pSeR38?UqK$mFd=72L#zxW-
zGcd5Nwm}p`#A)>GTU~*M+8Qw_rEvS%56u&r#{H2<6Wap^T-IbQ{v0k!cx3HoM{wW=
zI<1(G)0%sQRuV<7u$f_5PRXXVsEK$?ZJL#xf`|n>Oe-NRau@*C7EEoLTyUpeG6qUG
zVV|}`Y!%1Yu$t_44Dp=4#oE#;wkl7}qZ)KaH%VyrNE`{xZ~p+DKA)j`V6$a#BaV#?
zSU+)_$#1{?dPmD>&oB&(tQ_7EkJ&WCYR&E$oH39x6493qv|%=S&f_@fll^3l2L2-~
zSPIODCF?Yc2GSYg9j4NL<&4m(6if@hhwUyVL!iRVJlAJ>%7M=rrU!1LUNY40YQ({Z
z^lCiXcn_`q6K#+gF+2I+%uHWoU80Kv=tBdhE`O3f;~(<sET?wR#3f&gDG48QxLrzJ
zkJn5tN`qfXiJ^?%Cv5Yd`A{K?wiB9BJW6IDA2B3Mm&t)Khb09tvIq&Bf?S*q9Zi`f
z3Gp6&@*;Qg+Vj7_1_CjhL^(*ZWM*>6b*e)(0v~lCJ8zB&T7!F&`GXUphSk_vyh+UD
z3oBcWH+}+>huH;KY>G{GYeI0AC%mo`TvN&B$6Yd$SAvPi2|dbE*E+>+m#tgRWsk(|
zOp&g?OT>u|bG22A9X!})jIY$nYnJ(IClFGdI1z#dXCOW)&i<2>{8I_d^HC|Q#Q_sw
z$337*>yer!6Uq^%&s;s$tb3Mki#9CxT`gYNIcaDA0FGk>WrP44og3mxjP_S!#H|WR
zstB#z?49FKzcZDu-}%mJQ)FH|_-*D-hsPBO^M&(zS1olZd*TH;NlRdYg?*)peUX*}
zF_*0{q^4wx@BaWg%W0go#B`c_cAxx9fka~j*2*@9=*MqxdPn`o(QjTQ+I6Jmrptt4
zG~4Z{*bLMT6H3v_r<sV6u#GWW(^Q#l8V$v6B4?Xzn{xUopVNyzpe_|{GO3Ayf^}NJ
zS*a+|YF1fAbK9rRYw-neCmG^)^!=wb2Z{!gGe<EEIenUi7gBV=qJI#i%H;>(x0wF`
z1Gp)No-(q!DOFZ#wngp%4KTc05TB36b{*qqK^YExM~EUB-X1^+py_CdIG_$bXPa%d
ze*SmL?p#uQSdw2UnC7Vuk0P1Pu#4(5Y}2&}jgb9W3wi{O^{BaGSrAZh!G<!j<2hz}
zQnY4@JDzF`;J7T`d71Q|m~zS=B6_ludM%*l^>1fssLNlOrqDc~TV4W%7XyjSaeQvZ
zTQK|^PMrQ8kM$j<vHM+4#`>Ma$;~rj$tNQ@M%>e|Cl|^qcGE_K&JEjZ(=%>jN_HsN
zz9&3wDd%e<vMB@|$J%Aqhc5OM7??mat$9Ipm8R+p!6t78nYChLa?>n-Ua7*bTwQfB
zS50EBnho{eS|A7oU{Ng955Ao%ScXW{xj0rDd|<U3t@ia8q+e$^jEe#7Bn{O;KnIoU
z4VS4wT2J1*G9qD{X1>SvfJu4V*Gl5#*hJOv@!pvzD&2#kV!LQE*2aB?AOw$*nR6m6
zXWT|=JD@bxXC7O~WbNW((tq2IQ{^_5$5Z&}wjwm+<D~)SxS^6wwdoVI+sN?~Ii{Bj
z_2(nR#AopweqBSrfi2>EjN)$FPW@xA+40W4zOyJ33kYmR&FCoD+okEQ9G~IE&iho+
ztX8rOo0?_VdyhI_6dAkZI9Gtg44c$BYyH#mcJ-;T(z+QQmJ9tpi2nd7M4b^VTr#WB
zQ3XZGTpO49Q^d?uVX|t|=w47cj?t^J66)kU3Py8Govp?tu#?_s>6aSb#`mHzj@yh8
zt<7ts;H|=#0Zo>GnJuh0cb;(poBr7%XB)!FlfFzuP5{A1(qqI+9Jnn)F@?#7QlKB!
z#*sCV%FOQL+L|K0MFus>uN$4Yhn;IUUkz1pjAS2t3PwhGO{1<2;M*yP9yOJ>Rf3xF
z$t>*alNjfva5-tI>|Ut5JAG%rvW?+XH5R(T%j-^$YSP57pifdchG|z~%n52`5NS}I
z=6hwXKPLjR&-Ds)@bcX}(AqNznBI2ATwJuL4^X!}#ugov9%P~IkW^|+AZNB8+iyyo
zec~p5r+B6vLOO%XP`pM*k*aDNZ6UB(-!|rDF~y<0%DDnqS#n-p*c}R6W_B<k7{n~M
z?1dqP-)NRCCcwti`=@@8P+a*sLkU}Nw*LUMb3ZX0NXTO^Odg$9L(+w!TArqRV<mM*
z?Gfp>?~ySAQJQRQt1*HT5zaVCOmISq#TL<Ntkl8-uK>{J6aF&dfV71S9?$|g8|}6$
zcxLfCN$(uKP*8v7G=>amtt?{EuT<s+2D2bKrN;dL%~@T&hlWx`zHK>1jNE4&)|e-#
z*pXaxX)qZ>Q3zDjxni^Q$EnNIR5RCUUS}*$(3Qms-r?iD)=FWK7Dt12%FdKMXO@B#
z^MWKuq8$t=_(+QkB1a}Sh@5=M>AHLa6+Ix#J-K)t=jh9`WAtPYQbvEAj}zzqy*|Fs
zFvkxy9Gu#;R4fhpWhZ`_(X%To0)vQK5WWO#GMOooEgm@x&R=qtJQ~Wo!L=C^#&Rd6
zqET@|rt)5?Am^!!5r@-2E|m3^zExHx;eyvzOu1icvA&I_G&Cds08km5=s3A%V^?ST
zrG_o(n-s#-N<+-9P~IH!==HqEG)X^k!ZqpsqilB`$>U#6ZH0F+*U43YsB$h+QUFF<
zuVaGS4yBdxoO*gRN23B}3MK%-gDYx3BFfErmeHtK=P8HHT}lS!pr4G*TRUe_Gek9H
zAM|7yzTZwh%_gK}N!rpU9mr}|8A~Oqnz^PhYgl8COiZOavcsW41v>CauB*m9Xt$~H
zGh(N+)B#bjt4!Yq!A4jZ_C_*)matr847Jb!t(+F5=os-HJQjUUeP`YrY=q{L1j{1>
ztN#G=tZk*TOHZ@{DLZ;&!IT1hwf1^Y^Ln;#^PJaX`ZSgf<X(Xh#hJ(=avitQ>~I=q
zI*FLlCn;Kmk=}4Mw?S^oTBKb*wVtm~OveVzXQoUz*C5{}wwCGIV@IdhEmJfkax5(h
z*%5JOk9>ZaLh6r!Y2e^%qdvVZud1e>6CZxR_nLCX!fh=3OeLEwlVeWy<EQGgr$ZFf
zTGL_fCs5U_)R{;lRHVf4o7!~MPi*fvwsj%J3(`<(OM*1j$;6eKGbw;lYB#J!>M&Ce
znKPUV;H|bt&pDqZ+-&>h#$y1x3It?+Tjvd@d_DQ&1DMX&SF$WOIK*!xp@NAyU&+xq
zR;j+<V+wxDFT9&P1QJZB^2&`C5nW<rAmg6=_hhe|=4TCzhHn*pn`+o)psr!nChF(<
zt<`!_qga?#fC5-WirYAxOE8m(DyPzmHX>k&yJh|bW_LZR0xTonzQWfBHdPX6X*WzI
z?Ns#pPLbf+mXjN^L}tryLka?UO`zc7;$_crll+=0ROx{LyB3R-nHdy%2PvK5T6*t7
zj=lCN$Hh4iQ@+Vmu|yfw12YT~fD*+-PWaZF%!sFHpC=#S8D_m<<JLj5kANZ55)jfJ
zP<Dk@YYtoAxw0`cA6v;!k%{tHf}Uj3UlY<cKS11SyD3~{Ho-;u;07;DM*cE?#z69z
zj3$X%uPcT%MKB>?;t6@DJ(Tanvt6spXN^*XG7fB4fY{Wz;j7q8*^R6=Gso}dB4;3^
zR#EBWjOK<=J>pdDGf%ZM8pSEO3%Hhibv^S^*i!SzGTvrC_d`r??lR2x*i$o9YJaL9
zN*a$q&3CaeP)s3fl-QY>nVFfLBCh~RljpLuJgyL(lipx9@p@#9M=4=SnO_fa7y|P-
z%=kz}PoAH2a;r&YAv^$DYI74IJ48m@x8M9eN@~)H&-%jKH^>uY{AE&vPd2oMn$a3v
z-)Cdat41u=+%QL+?Sx&51Z2d-%uQABu8umF35N4i7?b9`>)pqpmDwQA)-W+X*p<gF
z!&zR7h<QnEm5zO_`60Ai!gE(=Sl2wD18+pIoYL9@cslhJleMbq%Rx6aD^V%3CfQ82
zQlpWawnb?^@U%t>Z4W!hWAB=Pt5eeFdoR9M!-MHNpv?z<i*)z(pBGXS<;=_PPZgcS
z!!886jkpmqkD_evJfS{22UysdS&^h!;io=I_zcW9It;%?fJByA1NSwo0gDcwi6k%K
zqdFufiHzmxRHm%L+Qgh1=~G)bv})dWDc9U_VJnKAI+aIJf*Dy5m!Nnt%L|r;8&Q1{
zfvyAt%)oKy6IG{{e7K4uG<9}rNZp4mYZzXU#O4NndEX4<eX~GR?)PS^=7o&X41h1@
zm1LE|X5vD%Tc8U9VL*<UxSNs1#R-P8b<a9@mPge35AVOCqnqlZHBbIa;ypBDlugSS
z6ET%edgO8ZfuuP$Y){-@x-qtUm)FxP+6sEzwpdcz_|;`Gi(qQC4`YJ6PidJtbQ(V*
z(X_(fjg2W*ML~?vZA9*)&~*{|Ju^@bxvgR+Y0|5WXfkVqgIy6x`w7;@GnV6lQKUSj
zWTg~Lk1Js#4Wi)kF{0Wk@KCJ^@x*p6zfg>4HICa!%z20+KdwQ_IYigocQH=bVFMl;
ztbTt5KG#ewlVaxzLtquH%Oi$XV*9KFL#2hzV6xc3EX15L1?6y3nGva1g^-D@S}Tmq
z>Ng6*<zZB}d>NIBwS*Z?jhS#+>@8B_&4AlP*%iwbC=~grQEehN)u^S`BfVz&=DSft
zy!MQ>6g|2r8}#dnXA%=?>!!@vO2Pfg<NYWE1#1p_I>|yo&Rfbfx?>$8#8o&lDPv6b
zPVq6aXBDeotvd#lZT5wv(}Df$QP>DW*jxC<S)k|IjqfF1(3Mg>KK%gwhM0RbV7jW9
z+#)<dm&+G3Cy~sz&Vyv`yDME<;a*@=TSP}4^MSHwjhLOnN@w<qWTI72&KxR?#PYE3
z1^TBs_}WXRezsQ*IU~S0h(OMUex5MAA!F&M^}HWZn{kqf`o-=mHe&YW5Kmbp?B$!}
zK-j9NJL@D_G?jLBHFKOIbxY?C4(o2&f*>InLnO!yY>c1BBR3`T9Ml9x<GAU1rwLM~
z?wS6lU8>aQdaoQc$Z(m;&nlW>%urEHoj9h`g^AQoGO)qe4e4B(&6z7nQ}pSY^sCLg
zNlsf}R#8rAZaXNkRII}N%W_0^@M@M<P6y?>1eOvOB}QCIW<!s{W_0FoC#<m1?<md4
z6t+u_F%MLbk!`+Mi1w7AW+Z<8hkO-G#^($Gnc?2q@dhfJD_Be6hVsr)w%Ma<CUPid
zyF;v^&<f>aBPLwRN>CpC$yC5d*{-qGpRveffEL|T8+eu5a0gu0U1pK8vsC5RR84mT
zQeSPxZ`Kj9rlFhw!5ZQ}hzt6PgBlf~uWCflo~=^U<#d&YX}1qjDfiZ+H`-!w<e<K0
zxgR;d`W!}R`faNYOQ!AMX<1lIPo77-5ELk#5^x^v_B6;K8@2^rQ(YGMaD8{xota|$
z!%L(=G0lYwc*wM%io++wT@hsp4`p_Wv|F)=;PygT-8#bLv-or-Ex$Df!x}&Y2APaW
z$!P47?yJAgP_0H+R$^&8umn~d1VS%e9XwH*{0!Pj1shDKX>AQ^qTB(f*7dAh!eN@M
z^S{PcCENpz4qAKSXO9f}7>v^PTCcuiCU+^{_s<y{Fm}bn_6FzXWV1vbHP0@OWlKYe
z4iubWM4*V0SB$MtiH10M^{Yieb7ON|EvgP`(z4RIWPMMdZOhO8W1dOB4qmLuJvI*`
zJYYvokmAqVEB=M$*pG6;k4c~Z0GGoPFt#a^XX9o2U_^UIOw+w?zaaqrAb-)=)w)SJ
z8qH7`=B-MLw&6F{vp6pH2<~00J@ERr<&wHSOFy9wGY|YABj^5+Ttw!*`j06Z?67ND
zvng10%7o$*lAVT0g9Q>CIPk%dow)9E9!zpRA?D_y6}N>p#hYh5kR&rXsF?dSM41d}
z6-IQq1-{D#Mzz}w({-A2jaVnlM6+%}QJ1NMxfXP6m4d3$3SqulYy@wUK4x>k)x(XJ
ztke7|!*fv*Ck)Lq>#(3ES5=ELw5+OWuTZ)6ebuhRt7F(+2EsITP0w+bB5gXuN4cDO
z@ee~O&2|A)t=yBg0tYY&<)&3z#^7vFVQx09NN}9GF+3Y*+_$mYQ7D~-2=OzpNOBz`
zm$h~>cH3^ctnF&(>aTT{K?bXOBWVk|e<Zx_S6w?(lL_8<z;8YgQLbr3TYtED9(ii(
z)s8Yb`FnUDg2>>UM;ql;^_*-R+21oC^cw`u>WJ*$Am_&@4)<|g$men67G`@)2920a
z+U9Rsy~D>OW+r0<W@ig>FApw75r`2j4$S&aK0bVXBXOO^^n0Z)=m^Oa3_5;X2QA0-
zJ2GQ$GC~_*M0*rQYe`e8_DUn0k}`G{y<iZTSM^SuxyN<wF+ZBmRg4kF1j{1PO|O#b
zvleoriH$n&T}f}KhU;f0&ni`>XEg{EqOD=lN3kH;WXw@^_#22Y@oScv!VdEhaAYY`
zLXDP@s@?AI7_ZsFBrdPtATNi*HV<urY+lkGhishr-#3{rG(S4@L2+amfbuX=1QdzK
zDF|#t!sd)VsJRyDycMV7QqbfkHN_QP#}KTdOEy2*J0zHLlj~cVAv+6WBZ!Xjwi;M0
z5GwkWc$~C`)-tYHpoIJLkwI=woYZ`{xBmdf*d~MnNhJyv#a8rLQOTg>8&5RxV?x?N
z@?79bMt=ucWh7c#l<e13)3a6t7WELab%Oqa8b8DPH4F*WqqbLaJZAfh#Me!mf0CV(
zQ%o=&9>ebHz}H`%Kt+{`T?UzBkAn8>V`GHr*1t2P?^Y^y7>Sq|5y-P(LxyR_rfBot
z-8~it8A`}8Y>t2b0JJ53>p<|L73pkQ%Q)}X8@XQ{F`X2(M#ZT3gya!KOhrN_=(>Wj
z<M2V|pagneoa~`nmZnl4{{Yah*^5$4%VjUAO|$<1noW(@HQjod(XdLNO*y<Qt9@W4
zT7$D`n@cDm<d+PDerX05iQ5D6w6u;cIS|LE->OJvcAt`qDV!pbPF)|9VkHIrEs&KT
zQHkUC{1xTtr-!zv1v>QeRawis9*VvFKWX%zC7n@f`37IFAB2}&dTJk+TTVx!VkJX|
z`q)0Mjv7Ts^s}_e&M;g38LB@>L}caJ<?V33aT5st0C1eVl&?{FWSJDq&0XW%4rxs6
zt92%OC43kMHt1H*YPA`%SoyJ6iD{NfwTPI2QIAZ}c+q{TZ@x<tDaw}O0bw7B(GN1M
zMxBQ2GVlQBlWk^w428C2a^Vkg?*lJrty>!AM;|<@Mp+RZAvsDvBH;=VF9&Sz>T9gw
zKb2enf@6GYI}4Nm3ck1TnydH;N2B-L2RMX)a;zo;ULyE$cG3xJ8wy*l*R9P!=tWv)
zvHh^OF^r)xTTE<V92Jl=1Opuc7v!U5!lG8>$tA>=6JgU9^sdEC3vfdRIz5IgXDs#C
z?DuNFO>q!D=D01qJ<E0;A^3x&rgZjOWo_&>b`qR(+g{X~q>k-3TaPikVm^G4g>=VF
zuLesNOiHC?2g574YiZAsSrklhmzQiz+gOc>oR7Z-iw7|z4@Fb{l`NRJwi3hwZ-hjA
zcg^>iobd{Y&AuDt9|Ho$HVO7h3U{67ZTH8Nu!WNwj#uPN@QL}!=PC1)0uM{nVsRtQ
zz%cpU?4Mmd%MxN8HO`Zp42`$>6ps51;^%3TF*|+Cc-|ei;j=lV4Bw$<w9(UvcFxs}
zC0^$5akjq`d1b2|uH-J<cyaNc<2*vKQ?R=OW>a8#XeYLz0gVti*zZ$iD&V$KTr$$4
zbvfka<p*_ByEtY3nz)Q8ky7%f<O8i{nMQBg5X*K!3gw!<nay!!6^H_@*&P}37P@Br
zL!)JjnB&k>krS01HJ%Kcl|8+(NI|NCn5?RDcWUo&Ky6rvAszriTzB}9p3oQuYMw!=
zT4|MCw!mvFQkt3LB4mF{8eAb{$X^wX0LINI`=1D|Z)XV8pEa50<u%+_19Dkigr;aI
z;;Rpo#0pO?2fJxNdI`vV7gM*D*=Fd|-Wo}S>KuW`*w!8!U#6`Tu?(_S>8Q0?Hfl~n
zg40*d7^y4TF8Ta74EK(V_&4!dH~?l)05s89K<=KgY64YSnJm$Har`;)w)x9#ADBgQ
zVqPRK`wjlQ;|p@^8^QRR0`+d`=QEt+?=FLCX|&Zp_mOUTf-Ej{c`1{n5Zx}q>RBGD
z6b5lkB(e?@uxNJhPn{OT?J=4x#~nl`hc!nRZhA!ojS8lKDLrc8IHd`h4yD8_ps2ZC
zh_F>jY+KrTs6w;MPScg4ozd5=0lvL!P_9ydW*qYa9P$k!hepRZ*_7PVQy*k=K7O<7
z)6M}DLnj7#1Oy#+O*#r0^Bi#372T+ge~h_1=*P8W`-iPjsn~&XYcF5@X&7ri(NhEF
zGkn@O#K(xmKuNDD#5Yy)_J2cieGsCs^(b{0cU)}xC;LS){{VR(KN&mYf<FwXafbkU
zVo%bn2@^!JezMR10K?92G3GgSvJ5qL8-k6QpBYkPyNuP}uF|om7B`$xm3XA0akA4*
zm?PH!us~10F)~PB9${M3LKO$B0*du%n_)KD9VPeXVmwUPbx7b9rKL1$yA4a7aQ8Fl
zo^f`cASWmdiC~yXlWY;2Mpg_7Z#L<HBT=1oFXH)qh%iKDBLhfa4MQ6i+2Ug+JnfVF
zW}@Y6YmiJ6Vld~Cl;bv@8(A@5Le%j0=Ub-GE`>jAfKwa#DmEbwqh!KR!&%Z3Ht}29
zHpRjVRck9xO6$dUM4tM5oRi=*w~rMvvuh)E(mm*E3Cdiizz$v(Q!rxSqVcXC)L0JA
zIK4UM0%!P8c;%-lDy{if=0cT;f`TcGyyhv@(xjz#mdx@KkN%@g7ta1>aGUJa3RzpL
zLb~a&8KgRXLsFTX^fsFcu@-lbj0h7N@{Z7CsP_pTZsQ8&HjW}d!dbk2*mK9OAmfY<
zRZQ4$##m(qEQvniuMQ+dqp)LXtx859CZl}unokY)jXv)UqI8W%ZStC)jXzQ4`(=6H
zeDm*rU9+Zb*XZ<AZq>x=F}9~?(tKmuF{1LFHpJDqxy@YVT6=Rul7!hTu>KTYc$M}w
zVV6uC`RmAzQ?wlKJ3_vlIZ6k*6`o^wofdZ0D&+azCf!)Nj)T%GAGvfEpkj90BXR<^
znMpy)W~7g$iTutvZ}OK~tWd2QZ7q*jXziyfH0@$gJr*?zD}+6?s1%Lb(;CWx%6jo%
zpt5}Z<LtduX$EhJXBMdAQUFcH7FsA`suV~0^|?EmJt;rhT1%$L_1SP>40enxn=W*j
zOz%6ArbLU6IX?v}t|Ql$CDvvRG;99lQikd&O`87zpIW!JsI+GC6qs2y71=!O)dvT-
ztLgW==grk4Z5*I&PX5vtYR@YY!n0hCh{5V<w0epm?tRobhh?&5{6D-=JkysIieT44
zgQ*?IUA~ZGVu#<EA3h`(9dOyw?akY^jGk4{Wr#FG#evZdBRu$c*$v|G+ex|X?|yUt
z0H(6_5|l1vXYnELVGE2Bh(yPxjKk#g3obczXqg_T%yz)kctplN^5;ub8CT&+h9fci
z{{Rm0n{OG@`vTU{Q344>WxEklvmGjQtmNePi5Qx2Mr~N({{Y4^rMA~E7ORWs4Y-V;
z-lyP{K9i9Y#w1XT8K2CY)D<YQ!OG$8nOzyFSd-M=*l(GWpGeKWd>!R7IFXVti~^1*
z&HhoA`y$=-;6NF!GgY1%bj0Y(fcF;35ALzK>jw<GI{|;8vp0zW&-(SP8`(r<W0dL)
zJSV~Fcjhxi*=>E2-+3=Y7aDJmUZn+aH$LBL)Tn~n6Kyi0Ls+>{@su(a@j0@YuFf8o
zC;h*m%xy6)DQ=NpJj_I5d^vebcq<FRm{9V@u!^Wzk53+xz5N|!i*c%8hTA>mf-wEO
z>8epllP)`&(lf9#t<rXQ*!J=<5F)a?qFtuMdGVG6gR69^M?<lb5E^*S3~^DOM9fIW
zCYe{S+Cm`3L9A!aGB=I#%OOc4n@22W^0|=)oQO+sI@&7;gt@Qk6E$gQVi2IND~6Y6
zY<^tX7YL1o)<u?(0al$Y21Qv@(PUwXvrNQ~sbHp?0DwqlTLTL~%{#u^nwpApCC;~W
zr?n}({z9ml@)6xKmAW?}9ZP_c<lIvnGsbDk>86OwvmV`|RqFI8?lxqYaz*AR#Mm1E
zaOz@8hgNxs0T9X%mQt$68#~`7Ljwf;*S9g;SXEwp9-gpUV1DBOV9p|Ib$gV=8O*Tf
z9{HE{1Qa!~yC;(hVN~rrq6Tdus{N)mPi2JU#`sgQ@0KfqEbta_l7Iq@r)lJK<22$T
z3*kF_V+MF2_SkdA4-b^ed>?Kq!K%Q&HA-7;bu=l>mU5O@Hay)W0CC79ON<s;5uFiH
z8OPq}g}#y!G@Uc7#4TzF)5Zg}@&&}8vV2Yt=_1eD7sw%#fKsy)9b6R_p{mmSYBh0;
zf@@U`9>tV}H>T?S%pa4S*4RCyj9f`od}212FM6u?7Pp-x1$N=xcBN`r4Mgkg{=cp=
zmD1DOSc_Alxs}_cdh-{m6GrMpCLX8~pQ?bCK07SC+&zrQR|y$$vP<;qK5w%(=SPkY
z?}i4y2RVK?TH%}R==8D3Rh6!I0ZhPrCyWOofhYi5HY7At2ZS*{rea2XqT5jLbSlEG
zSM6E-<<gj-bDPZXGNv^r1hNQuK^da0DK%L4Xx?FQsapligf^9~vsS31is+oau?x40
zG~;EPRig>Un=E?uDsV1YBSPiui*SX*NV8G2%EdBvt3VIAMgsW{GASuS^F&<jsSw<O
zVuhnxOgR|uJo!X*H-3KA&~1z{VL*)Bsb>1l`~k;$AU}y!{fCD!o;!;JfrLj<$#T*r
zR^m9-)$N>!kEzMavF3uZn@s~`#Fh++Vt<FEBT|k_Oop!^fwICPb?1Ldki#B4GR!Y@
zkW-QyE8<bH6hpYl{8SRo&8uCIZC+ZHP(xvfsSr)`r#3k8J{>ye9GLvG&0yf}PH02@
zd^Tr!GMSXJYm0q0@xFY2Z>-_*TtXIeW&mUUE1q1p%|(1|*f^=Q*-TEczp-XH{3uqY
zDVXjY)j(HppinXRCBngv6Ocm#F`Ivkss8C%T%v65MOA*z;I9^a8dG-v0KZ5G_GEcK
zc~Ktf%GEADO)1IqxksgP69PocZ8H+p8>l7mmVT@4kcLWoF;=T35`vShGrTEP+QE#j
zbt~8@Dcf?0S$|07%zq|>66U_VoJvf?7^e5G1d_Xvx~PB<$hx>%X~0AW#6}=Rk|i^~
z+e;&`WG2R)J(3{$wug-80{kvVEdx|F(q}OwI3>WKqf|2dI2jc6TM%i6@5^a{{{R`P
za_Ira&m$?uYyhCeA_#jJzCz{5DpRFU?#LyLH^hwY1(P$r_}Dir4=UF?b{3WD8f@9%
z6<y6}S_w~V$3(m(l}<1QOoSrKBuFATPVtqw6@>I$0IvIUk9;*}I2PX=L<xl1E^`ty
zwno$Om7v(whHb-WjbDD4%W!IiCVdJd%N1fwrn7YrkhEEG2U|=^vLk$Glt__|!ecV+
zrKho<aH6>a<%JQ3DG`xfK!~#&a5x?D+j|<>TC5aHoj1V?>#3zNhj;|M_rT53{OBq8
zL{8k~xWO20@{PVqBaORxNlcW?&iSH$yEv8)8QUGOS4jHru#{o^6eE+G8IuXsnW_Vo
zViw8&0H~Q6-VOz*?FYwZoEtHGH{VP6QmvA>uH_;%t;K^G&2{v`<CFSFCT2zhBZ&Et
zk|A`?Pv>c1S*3(VqfTK)3%zb4tn9E75E@pi2scYx9;=k@vR`h~BB=8lA&G(Nabo0`
zQL~m?I!2zlk*7PJsDgH;a(a!qoU#4Z#AvpdrZh8C(uT-Qy7fR_#!{1l0w5%W&1P-L
z1PEh?jqrz#YU<hrvsi*#34((OATIU_rE!<fnWjA_0^NXJYUn1V$I{!h*+rr=m4X>z
zLnqI$;vf!61|@~GiIsw#w$rbbOB<AJJ~LH!O-LLP@wOa3JmSm#1o0$2>c9d0Wjpou
z!AT3iGNhfF?V`)`*y2QKCLf6ettoZoOoBa~dkd>NWy`m0xZXB-UXhJwlqusiEV8LV
zWw{yT#G6>nxvm7%1Da5n)YVHM<W|U@wyL~u)<km|0+5;Pb0%U8Q*FyKDbcOGZu?gd
zby}6?uf+7_#C$fAgzdcKgpB5$1IF(9t*%JZnJ@@o{6@36gl4fkZa{@Sr@V1BY`BUO
zRBVQzfM`_yxwi80Ni(E%sWJA6n}6Mj&L7e9#QAFRk+Y66e9X>i@da!~M2HFqLd|_y
z0acMzi7QHNP4;v}t)#STGEul~my4CAPEg%iZm9f$iIwh&nW=a(J!W^q`xu(X0?6Kx
z!B`<0#&FlIny{QTYz;U8_f&bRL>d+aWxVV>;=D|~duk*{X|pt-Pf4&dv`m*DO}F|)
z@oy=ww|H(NCZ;KWS*meT>Q#-hVsMmx!7-c)XU3eV_YzaE?VY;>z<B-k^f`-2<&_*!
zx+cmByGe5S-V=hpJWC2>r+LOJWU0CF<S`$m(J<lj$`Tyv+T-c>=}=g@muyDk#v*H6
z_U2ubbb<tn@@cA0dl7d{OI;ZcH%9m?ow8$``7;2C-biI5mPo>*Vi7UzsljTBB|J`d
z{{S>m7HutaVIyfqtZK<EAS79t3bB>XpPYL8Bh7@6Ss<Q>^I`32IJr$aiqqwAcbX5m
zBb7`H?2N8Q^FX{{^M&t~plQ=5%rAtD&%U2-zUpYGRu*epl}YS_>GsPv<2Lwhw9Z#a
zf)z2UEZr)Al{G13F>UCWB^0pvGZfN|*M$JT11v6LSu+!{MymrT88#U<Ns~GM0C^rJ
zCK<mnnf{vP3YKhYhEV~(iOUl{&X#z`44YLMu(siw7;wTVf#U#s)&e<f#Lor9XHycG
zhLZ?{q=<O<$~*hSaS1N@oUlY}o<%c*x1Gi}`>=7;u?XG(r#6iJqBBV?uEay*nXQp;
z!y+Tl2bA%{nUmgl%*;kwa4BI`3;zIF(<CH>mnH>~BpdOky^ScqA+8AFl_!k_V;Al~
zW;F_a^@)hp0SRa=SePC6Ihi>omi~9h->m&9JW^&Sp-?1PW6sPH1;P_@164|gRTAWa
zjWxCts6Hb8o{JUc?HWx5Sy7u()$ps7sw=nLo$E(x!I^2_{R(+(x!baMf#%?h8Ry+I
zrWmgJB#z6pjY7Z;y2!K<a)r)z*1MBn_n}N@l<l{!uK-nRMil&}a`Y{%t#8tD=l6yu
z;k4x-OKmI}=CkZcj7K4fRo*quvq&}_#CQp;#fQy?c?QV(_{@3XGxQ>Gz|9fl&}vUt
zs@pY4m$Z-E5f9W5%#SwHHrQ~1#75Dd(|dXlTZEL6(`=5XT!U59(&H{VTB;>>+##wF
z-+*h>Q2Nad9?G_$?5|;?ZETHhB37}+pJc`^<0-GGKq%Vr3De;d6E)FnYHBcL7tT0M
z=FGCcJ+|{ZXCdo|!45_vmHGS7nAK`XIIS*{iJCk?{qH!vxsLskArTs2k=zx9>V^0<
zImM{iza1vEkAA3|4hBBd%7dAdB9^3@(;;Js%gLz$<03e35}iQvQ-ZRJ_o47oy6ljG
zD(~1oQ0Dwy%M0~REahwif*vGMf^ox)-AgS}NtJzpcHn^h&YM_qenvOA94xqTzm=ia
z3{-N44Ab9}hCMdO{ZWiAM>CY>J-|fij_JDK#hfz6*{TQSS7vDc0R0qY`Qu-l%+K#k
zVC|%4^CoUtoYOd!NE|WiWRIx-0Lu}_cJCVu#N`Ry%8^}hX06qiU+!%+o~ObnFD5q1
z0})>PvNAt>{kQc~nO%^;uvJ*-If}KJr>*U*V8WE=(C3U=iM+O1?^(!&e{?c@6yrZ!
z=9^$C+#-&tx@N@nHaFdMn9-8KB#$UI5~E7soYK%1Z>3Bt(OI6|C2G_Gcj?qn{jD1;
zNVkrDGLhIz@e8(9VYRAMZ8nExisIJ5N!LEP*@*-p17iHmmM@9>Sh|*~m2N6^i2K`?
zqNr7ph(o_FmRBANjDp9Ze94Dx#`ezhx9qJ<`b_L5k+!{;sWJIyoMdJuSio24)0OXR
zZ({4=?<o<Oo)AU;-bW9{+cg1ps%%NC+k%%isr4=l(x`l}rBb6L30|>mJ3tYtyW2P{
zjg2?xpfpBuh%G2i0%+Jpl+N$X_W;{d9(4%tf5)ui6&iskGu<NMTc)Je8ie%3#Ek4u
zor)qRJY-9(y<J+B0$qa&<}6pl;(vUr*(<RM*l8Bzwy??OQq$@IA&}qaP7+tqF$KMb
z?81&`g(E(4BnKipoe(y+=K+Pk5ggiby>>i{h-mcoO`5DpsL5pc<k{amN8ET>PJh+}
zM@hTVrMA+1HFs&RRA0ES5fZW-<1!qhL~`L~ywqrb*jrU-!Du**2FVnfq$VWBb5M`#
zuI>ZkN__Cbo?Azxe{8)cG`4S0BQ*`xeFvW=>S#G;$G!x@%U5FQUVR5c6{=}K_=}VQ
z17gZ7Mxx-UN314!Ow8!R1*z7F`<cXe?JQr}Mt|n0Ugb2eLY~w!ur$_Iz6}hzldSSd
zdQAS=ck4{vLt*8WBwPnnxXeeKxf2ihedPZD)}T{nVfY^iDWAU>r_EmxD+qY;{UtFz
zHjJ2<+k8P6GbzxfAl;kw9^<X0wTK*^=N{YsVHPqN*@xhTnee*xn*B6^p5Cuy3h64f
z2Phk?+?xGToiWR$rM2e8$4SO%HfF$kW7Qe9lKI8hcO`PY%FdBm&M{%`WO1?B_Hq4t
z0FxojmX%4A>DGyG`>ih@jNcNRa4PJ}D#S+uEI%A0E2Ee(hweEKR4wnX$U=#U)}gb_
zO0v+CojPX@G`_!vmio#o5M?J<Ovi*d6K0-*8#2SE+_Xf;Db+j2AiDJqp_*(dLXbWY
zJ8d|fE*nt9PPmSe^u*5e1A1ac7Qz+%)#Epo9LavEpGOxaNqT73c+@aE^<kS>R*Jvr
zEM!l>XEh+KtJY1rs|><~5YG-J6bM4(%*ToKAGqEJefF)qc1Wn;83idNT38e{eTg-e
zkJfsO$m@H1HO_L21(XZ=p~pm%7<r<^NIQ|n%su|R;YQOr8-D5iFP}{#=M~F16&ci@
zG3F=I2b9ERj|og<Pt;9c_BxFEeNOp5zE0n9<M+v*5thh7b<i%4?8JI=m<e7hZo%DA
z0{khGnVI7+>wr@toq?0o#pyT+k6Nw;i;`vrqS)v%-7!0Axvjp~0>DzgYhmfrbp$Mi
zEql>@{{V!9_DDs8eSY;WyZ-<W@;Ys^b*CEyx)5tUm0>Z@Wu;;uw7ATq1rZ4qXI%K%
z`ara$tcrS7bq`{=afyM)`B`O^bQx{qEFsikFiZ}rOKP?UuYroCZsKqR!vPz8G_=7$
z1RVVzIGvBdrG*J!Yd|I#HZ?)vX(cQ+VxV~(!8yfU^354l{h2y$IO6=U0V-X9SWgbg
z$sSukb+h(fp{ayS3N=eDcrE8JQp~ttkXz^P9*a{R!CLJ~=vOLhq}dC#k2M;Gn}p=K
zX4b<B$LsQ)DV$-pn8I3;+Rm3(wC$_vR!~zWS3%9>3DubpYX(=VV4`b6+>S!VPUW_5
z^_7Q{H<gJ53E^>>EaSh_gzdcXgsjH^OPmfA?o;WYSB5;>k-v@gA&|6lC*_4XzoS0H
zJu4JQI-z<|w#rrkgJB9`owNcsS&maVR5o)_ku=4m>Z|SpQqaPwXbMztU1mC{1O<bc
z9moWne2yYLHvFCQ{AGCj_2=;M%+`MTp@ncBdMcWC6jP@%TC+U8Fp8F^(@e5!t8fKh
zoRzdSmNrdGW@x|t6_!Q>M?*^!I~eEGamo`GXj`Q?hqAMUs<#z&n!s@|KvE@>nz4Vf
z82kJB+F;@>!Mr!i{g!N#vV3?H{AOE}*(eh;V1Nwuj?ofRGZF(hr){#{^uV~nJnyuJ
z#&<5rBAFqTLfx7-AAS**-lT49azfohRy0_aF=qqLb`wD?L$aS~H-cJr8-4{%YP6vw
zR-nmU#Fjf}Xw_Mk1E)&`6>*;KmZD@_xr@%P(MW8*lqIW(q9bETU2J<@A`CrWf$EQd
zvH?^9K#r~qhAV%<4j@aWt&*IHkeHF=ySi~4GVGD^U%7jX*68RUh{h^qVtE#NYH2|_
zvifxw`cB;|oDu&3PjTfG&7CKRq3x>3HT!ZMA>ar7po#t*4eD9h3x#qiwvjQC5hH++
z!PBgSRO^jgCC#jQo}5$zeA7ZsZen~iCkl5WoNRoD(dEdOF^ga1NoJpHY#HrVu~aw2
zGj6=sWBo+)Se>qQV@HAQlob|if@jh_Cjuq{OCm-m&XmIJEDel?b54L>t9Bz={{Ug>
zMUs4or;r)OXz((IV*`UsJvZ?Y=mQeBPo{?@!Z!XPvA;?nW+Hr;Y+DR|p~lYWNct?u
zbZ5qX-k{DK#t`lGik;1Imw>|CpxXyMC6N;|5gt@gIoMptr*HD32o`Rd#Pi@V@kYv9
z3z?B%L0hRI>H9`U+<GMYjVX1SA!?i|yOMRA#(TQ-@rC)sw9?>UU9yK=KXEj}kT^MU
zjiX9m<v|wAXC)HUzP1i@te|)K8F2_6JIwAg^?6Eh$PreA)@Z~;0P%s}JZ-jWi->ka
z#x~k@Qw2UL*-EX}V>|D*3L?dLt03BgPo^Hm{{VF2XX_zcXrP><RIb*^)QLY-j5};G
zN^=lg!rZUVQ`R<VuF|ydCTZ_nU73q6<4oaq6(WC>*TfFfK6fwMk$vT2+QbnbjyvjS
z0J6=!5Qf=ksY58EQp6+v=CprOjMU?ECe2Hh+i7`YQ?xDov=?=dEI!Z)PCQF-BR#+3
zAM?Q9m~pzfTgYa!>~n=-UqO!skm;~X7LJ>1+cc_)@{SFe-ez;m4lD!p&4$gFMiB(L
zg&8yZMwB+}S4Ed^6SU^6e>Rcl{wQ~nDf2o10C`i4T2Aw~BF`VR#O9db0FB*C+OlLH
z46S6OlJNx?PP#@GDx4cvZZ>TV;-aH9kM`%Xw}R*EKjS8UA#vygA`Z{t^8TL8W~o=*
z)rf=$A#2-0X!}PiHSR>A^wVfV+F;FRP%6^2qIE?!eYT}zN8~Wt8br$NyOtL4g+?pl
z(VYJPIHQ!b85)g95sOO!mJ|#flJ?ZV7`5wl%EIOeWI$PZehL}LkKX{zWynvA$0)xa
zny*5bC4faIbxnojc;m5-`^ZF$1jK#JWhka{%GM8>%+l8-s6GtI{@CV#u6JbfRrePk
zAiXKgt6@u>LZDblHyLfyybA$?0n13Qz*}_K-nEiZW)|H(o3f~cvU5&tFU+Brh5-W-
zr^=29Rk1U=Z!pVE(;Jkpr8HibX~Wl1QaZ#WSVfK!uecO9+9;ChqeRDNAxm8;Tw360
zv^W9<w(^~e#*)&lcya)iWizLY&DBK6$jpqSVBVpdCnrG%m(%X_>bEnD`HT(W1~{ef
zO4MiY>nEwpDM$L3MrxlOGS(Y58ByqJVgr0VrZ9hL&oIsK!U{HxYfhC%x)ZpaRdy?8
zo|gOmj92$VC+=~9vQU1OBo3`2jH+fJXNY92;CQ|M;G^JCI*Os1;=*oEZR-<-=V{F^
ze|(%Tx&BGp&Uu6s<i{POE3lN{A*e?S=anmT-MGWM#%NoEnB(_GHeId6xP(Dyk@0ln
zw(n=A^PV=G64)8GXv~o$?W#Mv`^z@K<@zyy^4$D_^Xnl00Mp5Svl2URk@d$VMl9gT
zvbFd{+fx3UqiOFTK$M#n9dn$EIQ(W|dv&*c_ymqFVpeciv+HP<UQY%=j^7RPKjpSf
zW&=TIreAH!qb$~_GECi)9olTn@6Ka#nVvD1qm+YVNj^UACVu>B(vh!SVAA2d!2W1*
z$w?SM#Bo%7q(IJa`4|w3H0x8!RZ~xQ&Pji#Y>eB)W~sKyX|wE#AuJ)@De5e$29_}j
z)sf1gl1y+T1+d2&*50i}WO-hat8HTQ?mFFwaYF%Oz=Hj@5%%@wXZFN&bvLrLh0<ps
ztp>eH+9$b?(m2zgSoRd)`6!4Kxc-8mNklaoW}hZJPIFWZl5++18COkxi0nIO6_qHU
zpY}Glh>4xC6~aoyHHFHsfay>6r(xMG3jxAY#a5Ft-;IN)-@|(<tkoK0XT+A6;E#;y
z_v?Oa+}c>Pl@!M^g$FZ%%Pj42o)cJ<q8Ip)u!1cyy>kSnTVu<2GC2f7fLxO0kcYr+
zE36YgnPD$n?sV?hqRweA;#bL!Ub<&KOZ<8lfDy=#@>9xkpZlcYc`}&v8IUL_p{TD>
zk8J%;p|J%qzl_%BnCa!4?Hgv0!et2Mtk2{g(;EbTB>e}ZDm|I9e~02dy^Z(%u>J_t
zH_$w~$Y!k*6a<DwYfqoy_3%ZDCuU03<Kbkx9@50U^^OfywKQBy=Go|S(CwpjWSK(9
zKUnAQla*RJj-M1UGb<)eNpUHRsc7lES`JDz)Du!dYAF)GJGGnt043?=6z3Hlc~1WT
zEyyMz!855>_LmruSh{zRjo_nimpH<SHkK0rfE4SH)wgDUO@>PzF*+y1EgKhY@jcCn
z63rg^nkI(o)MQNDrXkk}7A)%)fsP95=_62*>LOjHT3Ic;1@Mui)ok3?OdD2<qE1T-
z`ie0S(f<HJs(Hx0jGHu$9>G0TtSeEoIdUY-tPRN7fr*XNluMOJoV4f)_6q>h$|UF6
zmmP*HC}Fv9q{rOXMT1k8%((!!4hyzmP9VF+(gH0RXy%C`E&1X+_C{2gVs0-e*@W3&
zOs!?AJkL`edVPVKv@BX~(4$AY8cXy0l(VpSosSHKI{4N}S$3nXI`W}nqb0!**dEDE
z<%bcna~aEY@$?xIreWv>#+&g}x7h4ZeR~!E03^Rg(Pkhu?&ApGnNR#CGfX}glQKWJ
z`uY0FiOqy5iDvR&ala_qqv7;+9y>A)bzXmmKOm+fAor%;8KQRanG)hedrW_f{{Xz&
zC41z?*`Y*;c9y+ZQEC`CmP*{S&E$Q?=kh}4F-mi2SeWs)eyyVl1}9;`4rU@Y?>5(o
z$o$$oVZuy!3^2JFPp6MhFtU-#vHQ%%-&s;->I$iBKvEJ>13O3FX|$&G#is`JgBxXu
zIAj(&saThxB(nxe%j3y=F?`L4%3m7y=5(8ao=Ki{TVz5E1%x<1uV|L>CT!I=fVNTj
zdyYo`0I-b8m^oNP2O|f3-^6C7xn+1LX5i9{m7E-jLpa0&J6tCPh`UbN@wY~?Q1@eE
zl|!3mabOs?n6jwPBV31rS(L%4rx9R>*?`FLjq!@<LW)c4e5X_tbfv3ZQlHE?RT7Y|
zuyzkSDqR)|&`mw(XpQI12rqQKyo$$n*RyC?p_!pZVj=dQXt!uV5yS&q2}w{FfYjr{
zW=eSwJYv099idFqh(tsX@yQtT$RaJqHVOe_6T3E?+r;A9)h0+B-*#w;eOonRVTcdO
zwBJJ>6Fh_dQ<7VuV=Dc8u+Ou`S!>Zot$Z?){abj%*m}2sHYL7ih(QM(e12*_O|Yls
zzogIBRQ~{Uj6OYw!`29vVnap6dYCnur7vjh!&_JMT61GGL)wW%>!tc53S^7Uf1gAD
z0FR@Y@dU<*9CYcN)Ne&a;NNDUPSW#5Z&sq1!v3k6jX4BGv6_WvriPe7l$+IUY{5&N
zJFX})7YufubONR52I>~uBQlLxOd%SToe&x_{XE2grVA4DuHb6#tz>a^UWj{ZG8mxt
z+W!Di7Ng?Z$3F9K@wDL%*=1ww^ybM72&{4^&n)C$rdmY$Yi0?bob!?5V<J3BWDhCY
zD!ETVRIxE5GZOip;(?$O3fgsrbzf$9j74YJEtOG_1U0196)t<Xvy~#`k8<PX*`iEK
ztXCz-iY#TzKy@wkTKQ=jT;O!X?p4VGdzHGf8W@0F+-lHXe-A{>Gs<uFw-W4RB)!c;
zzrm$k#a|vvnh^(AElRqK7(pwNiC9Az07lp`1!AYJPPw+b6yi}^Q3NZVb0ru^J{4}g
zs|pd{#77`#W}3Jw!mOkd?~)S0iP*DH<jS(Js<5FIs})x{n!e#l_FD(vxD3QWEplAX
zR_L2GR@bG8k_nBvPbgUFIay!vsW{>=KUUd0$BC#*7mPO51Fda{BP!J@k3kVLksg3^
z&CACZ1ewO95$&uzW+D(Xk>pQpDSf(yanC#N1+@8(SL`XQ9*ZrxZ=uAIz9MImfU{KF
z@aucwL@ti_f1I&A(pW2}6FjGslx?`lN1;dLFE`CSoJN{6_>c6|pH>`f$<DZi0Ud@G
z^?-EOlaZ<{<M|X^0i_<Acr}q|Lds&l9N&MrjlQ#Q884Zm2#NX3@J_)iEdKz8*}UMH
z0l|v^Z&Y>9T5p=R(!td3>Af6bH|i~7mD^YvOM8h-vKv&tR`ICv3s^0M)v!34R&1BT
zqI8*PfYgg-*7ma6tFV@K+`5Z4kX^NB;Dov2!VCyMJIwejfCcy%o%bA#_rm(aj?}O+
z<Z-fbjH`3tlZZ<B_KQvncyV)KOz8Wvw$Id>`JZbx>op0fGQM!Zl89}FaaDmJ@jEo&
zAcDC9+-y2FWlNC-)~_p;Y2;<9S1f#Rb4L>)_#AU*(#8S3z$r2kSxAzQ`vn4;Izd$_
zI}$OzUu?dab2Y;J7sqTwmpFyPCbsaczo6gH1dmRBJ9X*RhX-L#Ioei2QW3i&Vqi43
zC;>KgvC1T$TTLQ0;KAbpSuU`c0T8ma)?7ETl>EfXlea8YIba_HrDzw!4jj0VD2?_Q
zHmUAvrLa(QKATK^=kJ>4^1+`)Zv`bDo-%=rV123Ef0k}p6Cb|Y=Na27T5UH8uKFVN
z7$w_GyGa#i{>7sd0*6;`ZDbt(03(07wlD9OrkovmU3}CX?yUHuMRjAGCWtRo3kfSd
zI+IxwSw8JHg8e^zF&XXjZ9l`N{*$)=Kfxv^jVhJ>jp44{Hu`tu;bicWp!?OCKG30h
zihr)Vp&eL;0d)TW#*Bwh)_X!LnW>OrowK&lrW<EbtH)8MH{xy8>}37!Q}^iFpjs6o
zGC6Zd=c{ipuZx#6Uw+OQ`twbVL&Rt96J3vRE-e&xb%+#%mmRTY7P=5?s@3X1hJ?gP
zXYmjFrA8u<!u##Gr9hZq9HK%~ZnEv8e3clVwDi-?2I{6OuY;R7FaH2fu)HPA{{RjI
z#AM~*1yW+N+U8e8qd{j1XP2w{dv0DWCvup*8i0WNISNneV+iQ&6%)r&4prlB<B01u
zgmxxk%s@SX1sh@lHAoo75y_t;F_MWM$=htvi@V&_p1Br4#1`?yl+&eU4jUk9y)sKa
zNQ`S?3YMEMIhRJs>MG%V-FyZ59Ah0?w@631hyvNVa<v-($Ll1raVb5g*SSo=PFv`=
zQ7jF-dT{O%F$Hci!*!<G7=|`DjD(E%n9VS~y`{{M!6f2L#3lsqE8Dx<;BCdb&?yVG
zrWqU>!NH%X&-%~vn`2~VR~g24+G2P_%xP}ueBj6BK<2Oi0M9(yb5SlaSs6d1b4M@f
z9TUI#@wRg1`iNw3i*+b+nq}{drEA>S?VWwG?vkkzuh$}F3H`M=uLDjNQ6?$L-@oUk
zc+@(vs+73ZGq(Pl7Fa@4Ua1eJocXP6Gf<hEuJr!^&Qm)_qGCHCcps`a0@&9$d-gkg
zh>y8br6zk?pg=J6;QsNNbE=_*4^^u2XH65dM%k%c_Q)VX$Ncib*=E4x-KR@=QcBLY
z&u&pdDj!Q_%-%3m7ZnT}quwwxYM64In)a3E635dxGFgMvERVl1A9?=(a$a#v8J=0J
zE2`@w6ME0KEtIV>!5qs^dWIYctm|!oO~^XkN?+oMV2Z6cB<esn@|M0!rE8O-E1sg}
z$T_Zj+F<6gaLV=MZ?qyhO+S^zxuO&vq9j*_O8tn77FX=SAc}VgWJ#1NRVj-xa+=~g
zTc}!aL<MRKtf7gC%i$2a(UD=p9AlK?9!aZkpkimn4D#K1xA~(o#9pnMmo-IN)Ad|7
z`<!0W!dJp(4>`vkGbU0TG8a2RWA-;hV}H0=XOPGSRXGB5$HHgjD%!U^9r{Zq!>4G<
z#J9V4iZ(K3c{$+$JAF^Aez?tYe@uFB#d}77-hI!D0Se%#3Hu-9=6i9?=j$~6MU>`3
zV~MULbs!bnI2jV#dDQm=)Cu*7<U<hO?wsWOL}Sp(Yw1J2omOo_=Om+y=AUq0R&!EQ
zmn?AU^FH1SFI<YMmKVLQwRXhno5s++X6bASqo`eN9YL-}x2UISI~t~<Ed#^VbrX%H
zgmbi(ovYcPal2?~ny&UMYEt34T<rkr6{5&`X#2*eTP%@RR-VAUO?J`m>VMay=CeNE
zOI2I|<;rqdGF#+F6M~PtOgB)d#Y}RI_S`FiID3{9?#<D@`3e&A0a(bUB8a)oL@?l`
zWy4W~*6F(E+iTk|IhiSvD#g+)eCb-ZPa5vArIV})g_thMIazJKPSH*!Q4EEcN*Vr@
z?dC9Jk&I@m`L&<MwU}O?Wo1Z-iL_Cxn(Z;9F=N`*sR_8DjXI$UA_$oSOK-!|nyh|E
zNN!Uxy%Z^y70%w#ZotpLx8A?yR=UhNi@!OIxEN3Enk#y>1f1)&=C$77+z<84M-drP
z%IH$CNKC9Zn}IJw0~itpLn&lAw9MbCR$Ug15q23Ce-6=wtKLnW$R;tC`Dz{PvZ=1l
zq8RW+nrB04-L7|Ooi;>IDgDrDTLk994Q?>Vjpi|@3{Ls_X979is&HS~nayfUS4u-#
zTs#qY5InoF2MT&juSXlUeLQ{S?e~-WMm9K{#&Vu8Is0W(h4MT>%Mu4V$0^f%RS0c`
zEU$d+I%0E3F%369_w@SB<a}eII%~+YYP7m%MIY|W>k+lm-x98T%$`zGIH8_ZwruXh
znPKrHy?T=6$*p?^Du{%!j$9t~=19EB&vI7-pT1B&7m+c3cfDtP{{XK+jmjew&JIb<
zu;X!%1@{=~m}JaO9C6}4m9I9iIXL<TqFjk2frPx@ltAX0eCRcii`Eed>kDXytfF70
z*oSb7bY7~FF(R-)jH0b$2_akz$YU!p?PRY_ht-xhDqp7EgD+LBJfsF(mhQH)&o9l>
zZ41&$Dc&s3%K8z){W#K<BHJ@XcH&B%e!nFt@gH%s(r7Q1jXZ73A)2Mr{T5*C%~-S=
zV+;t@K%J+9%FWvIRyc;9Qj0yA8g-bj$%*+`R~Fc&hw~ukFKQ$fP7qk>nq8{)A+NSH
zD%oiroknS#*QzsW>mW+Y{l$j62#R;jF(B)z11bHd&fYa7p4O&1k0EmsJEV~Hg()?{
zIYfgx{Dx`KzrD-P?L{u>guoiJ@^;NSz#`aT3<C~5P}ogP@F$zO82rp)ZESCtgold}
zkOpS~jGvE&OhiKGjQ!<(p;n4Qc*K+&kCx1DNd!v%zVTz&&=}UvCRHAVKPdE{yxYe4
zGcystnDdrmA=4xLx+TBN_RQB2*2TC{+hxF<{vZM#re(oYFePzWr^ka3^zAG`L_JbV
zQMjTZv@bQYFufB}>>gT$W?WG!DCw)Dqa5f_j6p7~bwR`!q~qt}Vj>!qv1GD-j}0CH
zV@9;1_?ldwy3@XceDVr-$W7RgXoT%Eg%Pkp@(B8((yT6QCsC=YtJ~j-Y|<|v-K)^{
zua++~c>blV%Pod!x_o6jhe^Dm<rFa<_Ky4I6iu6z)f$7M?x%He&UkQ)67j&l>nPdw
zc2?Q8uW4!Mowu0VZKgM$PugcR8q82Jr>GHyW_6i+tpLj;q@#KJ+A_gL%2Z~LZ@#Du
z+)b2N_ULiYqp(h?a=?XQ3EqC_#1!cRASj>Kp{LqA+hXHMu5)=5(?@jZ3GWNb!7fjH
z$$6Zpn|EkJh7~JpgLG!g^zE}%WG9CH*(G4WWi3#cgIadBW^##$-4MTHJI~xq1dWbD
z(h?ouw8>mt32vuf-08<61JA@Pi(?3(NNS`<68L#O27Zez2G$u7LP}-nhHlE>MVeiU
z3}tG$M(eJ?j%p~H)|k1};AAMaK9jyjz(mGuBUG;=DMv9j6Pj)=MAIV$Hw3&H6oASK
zLw-BN=_&1nmN)&jYYP&k&UCmNbS&1yiA26VFAT$grz7tsKfp&L7|EUHcP+ea@#B#`
zA|jjN+l*=-{JUADud?d*h8JX>7T<ZJ2O7k4KdN#*UOgui3x@+7Z1*NcdVe*%$~BUb
zv@<!UtGC>%8Mgc6mcog~#>_k)KO9@f*;qkLd<K0~*-7#f0$E7UTVOZJA=!4cO^D@c
z#!L)n!y*EHpmvzV?+osa!zM|~A!k1qMC`JW``;#cOqBX**1=Lpv3jqyc?@lUdva-7
z{@~>!VN$M4kph{Cz5~<88!$|rqC8F|okp{VN4%cEKv)_LHARasrJgZL)RMO;^q67Q
zV8zv$AazPXT;YTpM<LtB#WTnv7y@wM9U%JdOcmw#Q!$bbBN;7e)kH-?0Hvs~mSeWk
zb|usl+2<fdD~n9Cq`SXQ)Sdp-vG3^}h-}|^ozn`4?QSlW>nzw2PuMM|Xzm%*V`|Z9
zp?0iBx3TJ+V$AZg>6O9Nrsn!YNBp!U^SDKu?nRsV%|-~(Y~nF{b`A+hG6;UvV0}LG
zO(9o4PcCH>g%dH^hA=Y)6-S+g#zgs@^Y6weWWmFM;E1NI4>=>BK4w4VmF$u^;i5X0
zz%RVtXqd>}DxTBf5u0wIFG!J@`Ne*5GP+47-9qzNpU)VTe!b^?puJE2F(0=6mSiMr
zasEy6vpTG(^3kHrhUw*{Q*2(7JxaT-Ab?{*ws5&%e-BoI{Ke@slg4wQ8{u16g9J}k
zs;!{WsG`yEP0d8nZY!*2LP}3l)SXO}wNbZb-5F__OtGUHp~~pEhefvQQtHhuaqDpo
zRMl(Qj9`O5z7^Dz8k4AK=7N?Y$(*!fIh_5^9<cON3KY%u%{x|<kG8cmmP>b2(8{hg
zGe=WSZjeNBtv-})vWwv3QKc1(TCFq&tvff4oXQ-A$*xw@0*nvUIH~&ykn=GYi?S-V
z1WS0?rO5Zq;}c!hPl9)+rboj{Cp9Mb>oYM|G$<F<vs~IWmSnjEt|PT_20n9q)9y_x
zAk4D`J%SV{n=X($RG#a_9+enS-799S7UnM@9}+p$2ct!zFsL7G-8w2Jv6~0UAH-{L
z-PTia*GB0uY!#!W?sS-iSkx<Kws0%+ZGh_pVhlBe@I}c6Nllt?t%)`!D^aafyzG5`
zcoE31E)LQ8wb>IjZ45;Lo0X=8)+n@{CRhDkhic;6VVVQEMTjE!kw$9E>v+yq5|eC-
z9I0u88K=H21$Dh)r(dFoIQ%|=n%`V6t4p;G;(A{!BHToApV_Y&Rjx&aF*>xDV94SF
zv2PsuPDcLRqa?~>Y0;01io9F@0BJEAxPPa<BTRb~(h${=73?vAnxH`Y8IM4p1sFf~
z6FU@V)_KXEObxWl0ybbI<DFLyQ-ZHb)vS--(x|~#5D(we?*IvgD;-KiTqqwIrbi;M
zIjV&B0Z#rWZ5RFYMjS*LMjXVUdr0!@;^^Q~yi|TwiOw_CCF%bFlbVLz<C1KTj%mET
z^?jYRy|O-@HjEP6d=^M)Z5WTdcoNPccZUG>xg}a<XZ(~{yNQ}^Ff=iVWlHY1Jt?wO
z>5&LVsi?d*s-1XEomQ?W@2hn`MH;5KX;$g7X>OX=17-r;?P;<vaWNpNuS)A6@|Bs>
z-f4Kt#gch-H7*xSKy|Z((Pv`#&;D9`R~9mB8tkcV8!DQ2qGOCQ2y=Xs1*u~O@_Ob9
zB670G;q{?m6BCt!WmckmZHTVTM@R)4G{@KH?pJscb%L$CGfnoDSb1#Bqsfm1N43(B
z5SHDiS+=>2eG)CS&ow<V`cIie?UOstpW*coro%8+&E|9W%OS*X#K!yJ{-MJ1Q<7cf
zCF%QO!-dqVEW}3H6X4=Xdnzt6oP>LhQIY4yN8DnkV>z9s7G(=;CX)g-jyV(YJpTYW
zJO-TY>EokpbU<~E90&kJ`V@K1ALXbbOH(N;jNCu8Qw6lotj_bF=hDW|akN1c?E|OZ
zJ+>VaQImlv&<a0VbUHFciqYsUW?;^;PqDJyz<9Ui5ruYtIx(8oqr8NjyJSVpa#AlT
z)4nwIMoee$%zkB}#I29l3QJdcjDi()YP89<no0I)(HN-R?VQ(N0<N6WozbY)Ikp@v
z*$j^pz9QSJLkTOn{+V9Qg<Of+GR*{7m|)w|%|t}Qs=-M}{@KD|ezdhaG=4DTQP&X+
zR*y}z)o>cMlvcazr&{yvU8??Asc>f|Tg}KKbRKWE;{&kIn2#}=$jr#N=21{^e3WMY
z0Ja4Vu!mfA$uR1!TfhWnnkF>XN30*lVrg57mT8`Ab$YCJi#Y7Cfsz^ci+E1uFDp$*
zMGGX`x2mzD#HG=6;%rQb{D-p@#eapI+H5CLxj8a!WN)m;xT5F;mi(8W!`;2@5N-8V
zGa6p%&n@J)_k=lLX*gWzB*WGm#%tSkPAtHZN;Z!UvGl6mhph8iE_#bk(yJ-jthFau
zr4;TLTw83e6u=Gi^w?FT)!lY2>Ke1I7sQ}{tSr#m3$jp54$fJuhvkgrbB@Mf>~)zK
z5^mFeG-4fMyBT})6?r-QJzz~IQf9vmD^TC-#xgdA;^0w8or42wNtpCOz%+MwN=jET
z{kingIi8|8-_sF}4*JhtQJEVfi~j&o(`+m=RW4q`*J0tlOb=X4NAH8b%g}zZsSwx#
zg)^-yX=(){rq>AcqAZHgc^i4((wb%o4?RJ>*Bw5ZcQ14?8rYCnPHqLZ`dI$}s4I3y
z793%JXfYwUl+I8AYgnn=zeM^hIj{m5{nmPEl3w{+e*XZ;AGS=#wur^~A&k3Oic%PA
zT)SW!Gs*1R`66UW2tusuDGLpwsK{6f%7$*}HffrTU;|SuP-dlMY*(vLk@1%uKDG^u
z<3!Fqr7bg4pnQYyH%+J`r>3g6Da+czD?ub4C1H#_)9(=v2I>YXJ?F7jWJb*z9UPiD
za#mnVEe-2Z+Nsuqi3H_I*b@_S7QtPRnD`FW_T90Zr);ZCiIv*tejilKW!I}Fs-;?S
zKG8u&v1OrAT{Lw*-|6n^iHh4LH}t8n32YTNtfhHge+^-qiVfBPOb4apMH4uicG0}7
z^~XDpbFNkBTdQgICG`7cWy?S1nvjnRQ=h0e&+(tWEVgH){rL~~)ypjYxSajx5x-lq
zyuiyhAJrM-KDf><VkTn?!bEWXJ#mG%jlbhF>1Sw>2u}kRDs#-J0xVDG9yWX|eOLzy
zOEQ{z?di$QPyPL!+O8Fl5ju7F9q@ErN_fsjgKuG(kKxt+<Dk?Z)f!%z5uJ2}al|z$
zNuDHHr9++Y_1eMuXI^_Zg*#Ka;^l{W-ubHh?I<#>h!gfiU#BLx5xQmkVeO1>AejkQ
z`+Im3E799ApTpd^-ojYcZOf%vW^K#O1uTw<q}En%wl#!{pPKV|+!e=-<1Y^zwj<KC
zj2j@eafRucQsE}`$Z&43^jonJPghI##-^pv<X3cN@#eP!RxSQbKEYP85tOc`;AP4d
zE5@P2=y7W&Fcu~dObUr<RlfF7CaluJS-fZ4R6PFxjt0_UxIpjn+c|Tk8CI=}XT>nu
z+IdG)TF0=M-OKL?j(VbN{{XU1YNyDucKwvaT+<ATXf4WQqC5^&5lhzF2mJ=1t7FYi
zSQu&*CY7UB3325Z(c-#eI*>~B3voI^;kuP*n4PCz3=)x;DAOmU%ot?B9!KgNpk12I
z^Bcy~noGuYHt(&3O?jLc<uEXEJlO^H_odYvm(+<}q0~Pj*{)P(2c)s9)GPJ>01+h0
zoRrqk3DmpGP1EL$S%cMYP2(sCIB?7p01m1qD@3K$=6Ct!>a%GE15>ge3CgZGXAIM>
zdv|d()tc8nHY0oT8A{aBwhqljViYwagCcMQ%cpi0Ul3olP}X@V%ih|0T|0l}o<<<q
z8IGfjjq^y|R&E@#!lb2AGsLn{({<~e5z;fX+CkTQ0JSb((C_K~bA7yFgoN!ezSEv|
z+dpj55++1ttI01-6^5-55mJ<tNX(4RX;TC>^Y@=bq^vkp78)gGAeKbQvS<E>O$L>y
z73&~!)sfC=oZ{MME%~`;I8Q_r$(J}dWnemEa3*rl>|r<wZGe(r9j0K>VCuRUrcBA4
z&S^jIL-}LG`3Vnh!6dF)d@=t3?Z9(RJ~oFxdHz(UwMYirp{m%6@MQthV^QA&NYk|>
zL#l;lQflkc0FW5vGNw-~Wm;{In1{BAj0OpY;cEM=+V){?y^7Buu?EC7bB1arDC*T>
zta3r#qS}Y&NjZ;>55{6L=?J-TG!})44m<k#s(`L@1008WuE@zD-Xk>4F4?tqC6p$V
z!)h2_ZPm*xrM~)HqjFNlHM*l*$G{b*2~?oG>yh7jm-}c;<L1qu&uZ;kUt+==YWvc&
zpg`S1VfGcpnwffhU?Vt5d5MS#n4K;^((7P&ow8;l%<2*(t2q<4Ol_a{l+7!&vUmPR
z>Kx?(Y$q-Z3ri#S%F;30y<NS^qYgN*<I{f`zn={*auJS#Hk^c0y&GrYnvU5TKBQc5
z^gT?@W@AFsMn-aU8*?)<IxDtN$Y>H#GVj1E&(vrXw~UP+PT$1ox@=5FDO8}1isX?k
z^YNrZIAe7RY4X?V^`v>P@#aMMoo@3YU_TbrJnv@xr8uE<>=4`Lag#GVd2Qo-Oz~jm
z+fLYDcwr8TPdauDx~iOWhC;Dnlt(v7OQea#BoX~P=Pe1PRZ}^lAkk98PWjID(l(kg
z`3uTsjX}wA1n<IgR-N@5GZFG3jJy8r{*#}?hryK*i(xVKw|vCCvx;P)%VayX0q^M2
z*Am%)Vb-qkF`K$6N6AE%1nr(OCy5N^mvvRlfvnVDrs^auy`v)J8!7aiMe;2!Ax8T}
zlfI`@c!HU%L5p0oeDNM|qhgrJ{j$9?twHX%cN)l*FYIGHX4$4v8<wOQKbDExQQ-&i
z{{S<Za6~<ITXMA2e5zyoK=7yJ*u!zjvKcE5ve9=4o$t{bl}-kHc$0`~=r1)!#fwtx
z(Fhe~uR0H5r&(m>f6LS@PE2m0m{i0cDbnU<`u78n@tR<aYUzO0DwW#&Km->vC!LT%
z!IU6ET<OZ<t5GUFJ?~YNRx^E@`lKXHq_{5CTL#x_!&9aq#{FOIGBMG{ueZgxmgK~d
zh`d!Md<|2Kg$|=BeT2TN=bez^-SRG3R-T6C>44TWzBWgx?hHru`U^rR2$i6N7&kHo
zFBvxFi7E4zOjv}>tII=rOi{4w*lLQjxh$Mm^w3Ko;zv%v#V_{CiPe$)cV<v8&XAo@
zJ=HbY&11!tE<F_kB{D^u@`#Dgyq~DeAUL8XEQBy=OzZPAXfe?nth2^x9(s(+Ze_MC
zy`5obFZ;p2ri(H<g7(&&J%OJI-!!Gfy-da$*=4LVX7%PH<A)4dL1ng5s~2R|lkxb+
zqEYUU&9hDA?h0f1C+jSJQV^V8@KK*|*vgY3X*=0bA?;au02>TIE9nSE-R;oaWy%Xg
zz9$@AOH)r{59{uErU$B^n79p4Oximj27JcFk{%~7Sg}#DD0gES*iG!!YpZFl$SyMk
zYc1gu1aNS+qWYYyK=ry5VLQOiYo)=8h^#&On8s+MJ?_nfODpJ(7&5X;(sbB~%No_8
zm@KcVSsmWm){f4hURPw;?Mc<j2W|O0jmI-Df$V{TGP7ba#q91(CjS8Cr9!lcO9le4
z{{U1InmDf8j9o0M>iUZ^2EXs@)X0x)W>YdVwC%oW(f*rL7yfcp%Y0=AnKNc-nu4Zc
zN(g=@iTzVKu};_&ZjnSm>6(eyEVriLwCRuy3sL)@{{VMe0~3hQRk%$41xvPMqe{~%
z@7px$C}L9}h3_U>q80GT)@$#dj#0?hM7eA_^<w=`zOQP=jO(^`C;-<r0La%Ym`o6B
zFBmAv)-IHB#@e;pg=WcqqtrNQ3C#rgF4rqX(_PHLGZ~p5Gd@J75YhFG=WU*HO(>dV
zxVVWeyv|PX1DpId^kh!J??%rZ7NHDGjEM~=`-+NWups#fSfv%?l$%Y4MrmJo!iQ5n
z%kRm`fBcI1sMtEu5~&Fv1)4uE#F5AJnC7k=H5X!hy)l-5-9I+K9&G@R;!pSSIGCRG
zMt$#_OvY&U1ti+;v!#NGFI4JIRf#mw)KhjNYg&9dNY>h$nL)O@k^1K)3cXlBsjlSe
zj@#d9TF2zrx1=_m28w^{5c8__v@F+M!F|edb0Gog2z8<!uG*9bWX*zgM+tz=#98(9
z&Z3>yG5cnM5^T0o&+TTSLT}$IuvI*=g}Gj&r?>cpQWpgC{g;na#I%%|C&Jp+hM%a{
zzdQ?AS6hYnRzNIKkk#Q*RhWnqt))wIL`1f(tBG8>0G2>$zsgotlPya42!`T&n-Il{
z;-cr?Bq#Utp+_xIbC!NpUzaS+ii&wee%K;JLJ$hA_*Kps@s&<jrW%!c$7*lVuO8Z!
z%a$!LrMg%O=vz)`x4~p}Eb3Z$(QgbL*>GA1xy-vY*`9Q+0GGL}JoQB?)lj%3wcOL0
z!JNvn&TN+SH2l=qF?&k2n!@_^D^Z`r&H!uGF-((ZQEchT7}fwLN=S&ODt}NeNd)aX
z;7kFG%SJ{^dD|?RLUd(J%x$(l+bile05x7d*k2JcGZPgYn{`T|-ia`+Vm&`?qJHPl
z_<VNGL{R~i71LTH3L~g<TRvBcGxb%EO$~kqXZM(tB3|%TTz`Fnm|Xn#_0fxlqKwyq
z230<mHcC{e$(ZWnksHiLT|QP$BNNyVZ3!K{BNWm@MS|r@_!Ll?iL<n4@97b|&fq-A
z2QNU4qX0-r!AviVnL$S4QJS2s#HWr{sOx7iu(GW>C3NULpNRptzD`S;HJ+{_Ux*+>
zvky_A-;Odg$MF&hbn2Giv*e+S;}IKx7@W@*oFsXcs!=XDg?+~*Ft`p`6|4cn?Q53g
z&_p$xDj>|wxn6tcnTGr(Gf=Bq)Pyl0RIe&oLZxtPnFF-=%|-puSeq4+;DS`LDeeno
ziz=!hW=3F!<aeohqAmDl__dU{IR}@-l|{p@EHR%pGDJVC(xGKve%RR`BYDn>z(s5A
z#ZL`3#`y!+v14qLnb>6wltJY${(V`&@ew&Q5xmWPSw=<5#kSNV-I~o~h?cU=1()W=
zv8SY<L7KwA1-M_t10O@Lgq%iR)()#g0Dp*PW6E`*vd~sr)gH~l9jM)~A}pH^CHY;e
z3qD}jX<3MCXPXSO{yDZ&I9uazjo1-}$c;I{gFA2O^;UD}c*<jTMt=AT-Z`5}4!X)T
z9p#ooHG3SAA?Y7@9DTE+)O^V8w9nK}TLiJ6CY;BgtmP6j6XWj`zS_1`AN7fuW|?FM
z_RRy7uE^nr{zmwAAhwKLkXIuc{{S|{nDh0DUkyDWnus-yfySR<%+6?HMX4%c>$<C1
zW@~9aRqW+&Hc^(uM%9R&=6>|WlXV(`I=sb;mf&l7a<Vs>@jv4zE?8D}t#UNdinetM
zKFr{k`{gZV^`nCn>Uxs=w@rEj@~u{Y`Kpx9wN%cegESef;45i}{v$eFeK}@zw9RrP
z>)WMfiO4%ke?`@As%g`wx;8~H;oWVPBFz&8W}a&cYwa*4o0f6u%f{27t~zZvTgXG=
z>Ni}|WVXFJp$c?5kZ%HPJDNzc2+Wv7UNT2)r^sC=2e%XsDW_JzJX#CXX+*)Hs?^e#
zNx^BU3DzbR?CHnw=s?gZ$RBHGvnZ1W+uD1}9z}x@vE@UOvYE*H&*Auhyg11buE{=8
zC@S8I-Zv<z{{YI>aY}Ww1umACUrhqBV^Y=AXe1Ofx1160FdV4Q^^*u8ks=w>oEoiY
zs`*1IBFmX!Vh{KeRHRV{71vVK4dF|Ln7;!C+IB^;VtJLVp94BgIm?{C2-C|o`4hCJ
zie^DX(X?W8`*TGudcuo7rzgf&o-bU@s@~qi*Ap9k+c;-d-+6v|wQ9@Bh>p0+#SFDs
z<8X<ZVTO3yC|E8q7{-wATqZb_#Nkfm<396wFCLW@-(UtneghFR@iCoRW8NMlzAjwm
ziT#kni4!Q54-_%Jf&TzG^{Er?QMd5|<`$C5mCDtHsx=??&yPs?B=VTVz6wfZre~W^
z??4dxx0HFj`mVt(WT~c<oqMbS>Hh$i<^J9>IBZ+gfcFWRe%|5Sl1mcv1`yaRY{zWO
zgrJPj#p%<r9m^MFf>MbY<I5_ut-P~rj|CZH#Wu{w+s5tlNqJP;X(3xGHb?T0j8B-6
z@s=tTu;VkNnjiT_mitMpUfMu+l2JOdB&XyX%%~*y2clCcjHAD{jsF1SJ8J<aOc@Kl
zDn(F#0V_V{lDPb~y6IsrWzPc=$^CO9GCLGQN3Y|Q?8;=4dVp<jghL5@%tk&@lx9_G
zUj%YRI}#sVYjnG*Z0}!$P|Aps)2)#_r=bg-`o=by-_b8Lr1nsCp+ONICmc{JY08V?
zU@?alYOXT9b~UCr>qd47lGm-nxhvStm<Ica5tLg_+_{!&Ma`A5P;$W)*h(mBbXGUj
zDZ5}NBW?#7_kd<=4Zg-F3-rgOae{i{RT1ujBCvN;z7i3<7I?{>mII4ZtWL^Ci>dA>
zg{QR<9Sv2j_h%sXJaa*wF?jOX@-c}20IZxH;cfK&v*Igc&KLQ{`H?R{87Y{F7$3Ti
z_STl%WKDLOk4&B7^5AL;W1P^w)LvI~d=D+Gy4WH6&hZoM4=3}q^#%n`!zDehC|EG1
z_au;Ik&~JVxTTKGal9&mqT5ICss;cYSeK3X<8|@rp$__l9wp=NJ52-qM+uLP{+c{-
z_l(D|TIR5+TNUq4<;qT^&IgsOGNWOEQ&&Uw<#M9!toS52C`L%4Hi>N#Jy=m1&9&OI
zJR(RY2y5%-E0o=4ms0NR(`Er?RjpfXmsmNoS%9}h8`4$UYaxjv5e;Ii5p9gk{Xg?c
z9y)D2xLs&D#Q-zD%VuQCNVE8S!<ChpDAq<L@y;dp6;Jc0lRsRHj7-k~(%ExPkIr=_
zJo#lL)tb>&y0vw><GJV|wFPDAVcM}W;ZptNU_f=j%=JY0uPPVJ$b?R4qD$1N&T6(r
zDXtl5txlqCi()gv$P9800jFt?8kQAc?@-ecYOikPn?XImr5$r9j*El=kx{jn*=;G6
z-l)m`AV8-4$mr9;_iXBDjZ%v2qE^n6Bh=(aNvhaZ&#Udr_<W3F!slLJU8U_M%qo@(
zQS2ora!NGKFf&q^Z4E8iSOvB2taR<H=%f*%8keajvcU1O;q;R}JaXxSY1eoWk$$U4
zP=ZZV749!qUMhXwu`a^$#BFDGGgC}P*3{C-#I{n=I7jFHjbU9{i~T>(r}@qPu%dS7
znp9UpnQ8ZRGqc1(pU7)TYEwS-*as^yF|<5fduYCE#Aedx>^gymd?{E1`D~w07^(c_
z&Ra}d)9gkGNo)Yp>_0gl+dM=Psn~pPGxvyep^#Yg^fQG203|Yo>9@-^pHKVG+ZJ7H
zS>~X=yN$(d$~T|9O-J7O#a$+yZZR6v9NFhMh~*3nj{<XrU4WH7M{YxxIh6&nM<f92
z$;a^fP{3_dTydT;5wvE}k_K6KN`_1~ByDjVRgN(|#2tg>l%fE`AsrtAm4O$K9yl=^
zXC4yN+evCC{X)kp*>!u7A{<#cs00bDGG~!zj#vZdl*Eh_?tgnlgVVaJuEnTON<b)6
zr)p1o4@Y0Otyf96$Qn;mY&sxl=|WZ5yE=u+N`FXE)WXm7b}vmT&xMr5C-WqD&ILb4
zpo*)UDEz{VDe>apDXm~)CP#@ycMwsaz|?;ywTplj>>zwTgq$&^f^wahK}a#c0TU|J
zi+fNxq$pXb)`D$=vvA)wrn7YmAm=5O*h@J58TIE8Aqlg*ZM5WVwCC1Rove)IN!rLx
zR+n(K#S|{u{>*4SQOex6L#@9F{vfa;BVKhKM!<=)n<8gD-L0o3?qx&eq!!h+Bf~CC
zDfLK+D$ah{9%eC{?-4jL6FzKR_d+nqU&oMskj=7k#2t`Tn5=Q8*-S<H-qp=nK)P%D
zhtRhd2YfC)aWj6j)v#lLP&HS|i+zn-7h_8!v#Em}sK2RGw_DvD1A@Tf4{?t%r38+c
zsm*NWZOBTtW{CKKG7a|C<NZR!`e~akzNGAr+h~Ago@<f`o>R!DeW}L^dV{h`N=K25
z`4UE@9T8s5+A~@f;PIYg%xPMS$k$qb#7h=&@_}CpW_dF*Q@&H8`^HeSyIz3Gs4rGQ
z)~a@_RMT})m#LZs=niRcn^pkCkWc}3DaMt~)+!9v3uu^+xZCP63+t<0S#UeWfs&qH
z@hkMwSS!4}^-<}(QYHGPV|kA;O1os2YE4>fPhtC>sVJ>cM`FOlBJFoGiq438>N3CL
z3I44GnpcfBuN_{hWrJ}V!?;$t5{1F?fsx*0ddit}=a|hBnXjSl)l6A(MBd)$MCPBS
z+P0J3HRx#RCu6R+P@!d&vI~mGmuE$u#oAudEQ^oaRT)V{%ay}5z>7MMSXMxdV>jtq
zn*zdAdWAI7vQs*#f2HYBl^+05BwbalAl3XpzE#r9tkkVG5c`Ofm}aN&YEy+G%>uqr
zUuV0|;kL-Wz-78?PN!7kPz|*UaUPK*b$Y!t;rAnAsc7E^y2R;HF)<QM>IIosu`00*
zIvheRDoQpfk4-i38y0^M?eAB&@(jdp7=*IPjd8r-DFmzQ&S~e)wNj023NM_@<4zic
z&S>TN6Ej?9x@O$^ZTUOodGVX9WVVgBjjb|IMV2bGM)Fcq%1V78fQ7sXds9*R&Kj0_
zU^_O=r_uzbVj&=Lr{0P6naTYTwdH%76|RaF*Dyg*rq`t%nuQxPssyeqSm;X~GV(b-
zROmYM;Yz!uyG}5Zba1Y?$~tA6`^Z_)s-+NNsmg^*TK@pX5kaWpG_K{8HCwnQPX)qW
zC!7AnNY4rg#$rqW$~N~uF1tMw_O9@c24pikhQ%B00?C<~h@Gc-joBHg*X^>yQdD-#
zQWaMq8)fh-ONXvD!QN&ulkr|i?sK&|@nO-=Q&@YYz&mGHX2|DXPO#Zu9sWZSxK3M;
zCvgoCu$Dd#RSp-MF{gWPal8H1ZVDR~sMk>Tq-4?%SWli%K-#d{)L_DSL>4f({ITnT
z+zGVa4m9O{(bKFgb>J`2B<B8ifhe#*Ex0nVt`MVuIh>aN07kUU;2?6OmCRt;BO92+
zT>k)c+=fkr3Ma=1k5B&qgx@FYd>M$CoQRT`7^$Y+Hi5|Lx>^op@a;7;n_`=_eM#}L
zc_Q+Aa-T&O%aPBrnaTeErO))1BPzs(hh(BXX^;s!oN@i?Biz|qVEh?GV&Z3<Mn>`G
z4UBy5UWn}q8i1Db_nOpujW(UEzW)GmV|@M}FU&B-%ueGviFI0)y>z|8(wx*@7EDIA
zjbG2Wo9^StazlCK=wiq8OB%_6#RpQWD=)Xc?Ssu0MBLh@U`c5N>V`_xtiTr%@+0|r
zDa^+4^nmVDV>obU9BYZCyoWKMaT)$F`0|~o+4vPFHAIsjImO7-py`_qigm&X8jW_J
z2{4ip2)kHEe%ktK)DdS;yQr0z>Q+~QJugs+({GgvB6+9-bh{yHlxu8Cwz$d~P4vxx
zfAIB~7@EJ;Q>IHfE$4iszj0|}J4Br2w{$McvY+MYx0{jZ1;K(@uEaShcmi11DIe%l
zw;5v_0|zk?h^1n{^B&NI#X6KHYpCRzmZ?n5ej;pD7;}XN8hB}jN-L>uxS%H}B1N^D
zJf8qXy4CShixLdD;S5tdF4QQsN!H5LC3I?1QhRRe71EgNeV5ukZKZ44JCxk6C^#i>
ziAYWq^NsLllyN;oN3My6lLlIqP8x-ecX7_mMUiEZ6I8#fr_F9v^R+>`-9q*?q+{xH
zPO@iPqAUbjpy7y^k>fDZA*d6pY1akD{L|tcLs3NO-<PQ>3r}&PUSVy}`t<BZx*d9>
zW;uT4A`>wHsA7|Ie5+<^SWCwBVbiJKfJt;N(dMTU6kW$Oncht2QXUtbk@u9&1-wg9
zh<=o#pHKVsSo<n*aEy2s+52spBo^(A)>GeGzA~#pE{Q!1I>577fK9J#9+L&Yci;B(
z9r;EwRrpkb!*NLM=#Mb{-#^jPkL0Y)wA;h$y!rug(jAfM8~yqmiE-Obrr@aJvsV37
z3sq9Kkx8mml2vuo<<qH%HmSx&+s<gWLX+61YmQJ-fSYr;A|pb!4y6_IR$;>7W3Fut
zK*m(lElQ$I)hSVm-94N59ijp<KgLgzjNAMWbB&10hvmPt3w6Z&H{8lNjL5lh$7nzB
zGn_|#@)cZ4WPw$=@)k3J1V)rm*Jv`$HY@W$e{;u2k;vWB0UX<5YpYvm_XbWD689*#
zWBPjaawGLl5b{cPwyuW2#U?`Ss!LF*%TH6_I0lr`lyYS?%u~2ExvbbHQQHaFuE_a&
zXunpBL<p~71{XPC$$nydjoDn18%SAF%TRRL?X^YidcU=@VV*L}EA-aEVs}vEWq?w-
zwDs6nDd$zn00z|`>O|p_B+{AQcZq@zak&QgH7(id1x||PE)LfX2EX6p@}Kr?xo*z#
z<u7KK&X^t4^(Fc%^~Qer`(${H^Zx)bQ5_az3`u$&c*zovRc$g`#Bbv_+9Qt8b2FbG
zs8NVAB#3y-$ENe+ZmD#0P>@wE#;aPwdM9a*6J4*dR5bCi%iJ;xLn(*kGmGLgUR)A+
z=Arpj6sV{~4+0e@+f*o<bJ`B#OCX<?`Jz-S7R9+?-C*ZV`^?T%f3~uw=A^InKN`o4
zPRFI+g$*@R5zE9^XIE@wedPZD)PbIyqlokvUjya$l-C-P=CstQ&5kEI=XU35YOm~f
zl~267GRoSs-FiX_7G@;PQiY;yv7cTi{AB}yD?@@LWdb^Btd{*T))Lz9Vx3asX)}-&
zlcUo5RQ1WTCYvC?;p_ecbQ7y)v8pCS=4V#73m%MQ0<_e@E`VAo8qr+m@7b;cnNXV6
zt68N7B_>?9$O87J_(IExK5W}{sg8AdcgF;Bb68`d-uCPyovk@`-BFp@*xgy@r4P6^
z25PU1I)ly?V~oJLl;@}1SFcWAK+Y3Uiql$!1yBnBdCGb49a+29IL{g51W5OR((NF3
zqjOSPDqDR>0}x~o?6;Q{^fB~Yto|Js{7hu1k8QY|kz*9%ogdDnRwQgPFLtP+EJ6=A
zue8Bwzub66i_I#Pvegf5f39gt;>~a!Lshh$L7v{V%{uWA*sET>f?u<=qOESya=bOV
zA2`<MwkbaZxX+o0-Z+$yd}Db1*#p2NZxI4?wG+tWO{_C=!jV`c+P`kcEU7!G;hMk@
zS`sLoq;hjgD3A_zlkKcAUZ3O9U`=3zYIb79j+kf3VKe^#S)rHpyGgBbw<O3U42E+?
zZ&gbtCS=niFGl|W_{My1=!7rWy2P!ljT*p7ud2E3s2N~EWpHCmmb(+Ygho&89+XZ!
zjrC8Yt&#VHGa5?fW6-{!a+M1gk6_`aXtB>5Z{1X^QXi7n4p+T$rtp#0wRluFJ^iL~
zd`(){f+m|pg!quFnE>T$RjX=Uj?p1DE?%+^t}c<8)t6wemt(a1s9$5hpi31Lmr+b}
zEiqm|aszmrnV9i|*%=(O&Pv3iX)38!{kCn?QNrdJpP*%Fnekjq2aJ!5IPt!9$#Rp4
zo&NwVq!}J7kSUWhUg_3pNZdz3M#08p4Y6dRc&ex8HL~hAElgO9n{df8W}-&d3uQsb
zSvaGCu(HNPlNr3kjE?f#9bGf40_s$yQ#9stC$9`YF_DnqlZYv#;}UIp3!3%PCCWGR
z4~W&cVtxeZW~_o&1DQFwOkEa>k&ClYXx*z_;;R1LefFH=vd(26CJtDcmz?9^&fioA
zTU+g{y1CI$2NCo0DyL=f5;IP%eWlZKx&RKPol^aREb{6{G7%DSy#3=(oCIV~_a`V3
zbFe;(+lvd)3Wk@-<|cavMj&Ly%toa=Xi^O3!H9_NWY3><QcH7NbkZ={k<?98WpRRb
z(;l4sagqWF+HfR+ESSyqo#ND+q-;)_-izpFYFcc!m6Xsq3t^UBAYgH9M8tR2ckEsa
z5`s3f98zL7lyH&C>Af?ue+SfC8|6h6SWR)(OKOlfBVJ}ALF-@DcYD1uM>T$<wx$%I
z_TDw<fU<=MbNdk6i*c3~)vRPzB?mC_A=7Gv`Va3IQO0C(1mJSzI*N)bdo?Cb9H|{r
zWo$UjGJ{=O)3KV0Y6+)K(VlKY)08zOTiqw9=`lN+3<-0z*8cd!2$EM0z%o&T2T@RJ
zbmLJ+?TfIq@d>c0s{MtqkIZc8Y_%4}Q8TdRntFBVJ@p+b<g6tp1`{B7Dq;Sb3b$G7
zZ0Zz^Z*k{X$KIY*LY?Vj-gXK%$}#!JwvJF2)w$P|nuu0g8p}1B2UU{L{u|g^XyuWy
z&TTWaWd~S8&)ZX|3r`4^Ej^i~-aGGkW~+K@8<e`!CYx%=9rNSoB&TjZimt6ma5(zz
ztvnf2dA)H1W(K8^%>$WgN+PDuZl}z&Oja_zAivZ^@;RV{^0e_<q_qTld#_$;lg9mg
zNu3L}BQ;2yd~YX68TVn6=XuH#A$GLF$(78AHSpU10Cs5+Gp6+(sIyfKEK6)h9&^d_
z`N!z6maqUddr|LfhB<w=X;+<gH`CWSVV>r?&Lj0kYQLPcIx=#Vj=3e~s)?1M{{YB<
z081Rt^EeaZd??^k8Kj>ZqI0Rv^>&Nl@=IU`mnq7~_WDy}A}7)!N?>8Jvrau+h+dsd
zx!LY?qFR^{3kmd}xs20(CiY}!-_s^}M0#l)BGiK#xLM2(@an+tl$4h(;I+!Ct8en{
zY0{k<yRvxk9g?DNJ~T9mcl8$Ctf?$osp?Ie7Rbqr+O07eJ&BCMjkcVk-V|*KQ)CBb
za+R~1(rDEPRdw{roxZs_lAZHPyX`K7%da3}KyOskubL(<DaBlsW~P^6LEV5agGi5D
zOvy(!2GfFD`Oi5sk!VT1V|(8RA8lgxtg<yYyicAZvCaeDy`U)H3$d|A7lp<(%!2So
z4k3ihPFISFWWW`=u9%a(?SP>2hN62-Ms@rpkxox`Oi~geyVb+tzF2Q<I+2AOD=e}K
zVq$XKDlC-b2>eW$6YNCuTQ2jhWNJirM)uNck!BB_^Z0xZo6Zly@;^4r76qC?cya8X
zF&0bQ>CJ-EtJqtV;2>nQ(;8YrR`e6@+<xHIMP^Jv5*QODE}hvk4kE2+-ZF!G$`Nv^
zxGF5hqge~#QMT;bFU-aHNDdE~#76Y(;Qo`Gt|^m_>q_ac#l-C*>53(_weKI}H~hKV
zX`Q18MJtWG%<nN1KVS8lAi?Sa6RI)|(fqdCHXSd`%67!IQpjXXZRRpR?QjkbpGSbG
z;0HhUw2M&s<=xg23)18pK@wKg2-MmwYo@s{aRh9uU$)Bs060p0phId^JBC~e1nitB
z;bUt1W&kvmoqC6I#)~aw>jrw%$;wk7UwNjqmQ5JcQQc0nL0sj60j)596n617&21P6
zm<La%FUsSZrFGP!Q4{Dtr1Qj%QkbTbx=MRc;c!u?w_m4VEubR9EMC@a!2&A|0kH=L
zy+snl*S@VYTAvEFe@$J`_#&g%sUbOPA}q9HG}}d1&~K~I_7-L#o+dL)=(gomwQ0WP
z3ub6*p60U(c8J4F6Ddiap!(Jip+CfY<AvV69^g#Zv8C#&9&4U(b0w=NT}c+>84{{Q
zsr1uBo7yo%q-=7Ek7yh&20jxoriFx(&rj1PlP)g^PmGMzsOx3HRJ8i^1|l^bTvp|w
zI>eYJzy^_6D^wk|ss8{}v6b%-GxJ=i6D~hqGB=spE3q{Qjh~p##$q*Vjjq)dd*&7v
z*2q!R3P!e$-|J8jsFiZQig^0KD(4z0h+bn^bIpgG*ds=cgUw-aZmn%aP{~_F#_s;_
zdD7kATGa`vsiRTPmJbss#!pn&IJLA|wY`A%dL>&Br@&RxSRcf5;;=x@*-QZC$1WM2
zo#~_pJIgr%_czo9RN(GgwVLUW)K=6j49hFE$RxCb1wGn((~;|^wR(p&KGk!nVTnJy
zT8XCt8jX-|QdOQfL}ct>Ij2YUs)=^i5o89!x7|^vAXsK?W2PurWcn-ntHPbgr#MdB
zr~d#PWjb|1og{zLH95yd+#@6IGwTs7t_KsJQ@(C9CCN&bmf29bT}<V%>A7SIh34sx
zlNWyyoX6xj&9K5_UK2V+BZ}NT!g-Q3`YvUH)thXY5YhV_vLq+VE(u3Q^YT;{MD3Jh
zy6wbJ)9u>)(7fACZ}k>+Q@LuPoHQop?izMXjs;)CmWVl?ou;<Ej^_xp)=*Wc2o#*~
z{{Z%dF%u=6V?O;PpnlEJxf#faIW44r<ChUxgB_!wSK+%|qt`+h)z+bA+3^aRS!w&d
z;Uhjl#r|WP{{Sx(m`Hql%wkBF$)VjO&SXc*fJCQ+xAQ<eNy=QugZR>3!j_`wby;X-
zR`fPxwIua1GC!wjmmz`+aT!F+=iZZtN&f(YsLpo-0?1dntz{F%Wf8+jsqJZu$$=a|
z#aec&-+LWrxjKw88(*FGP9)O3Md*tUw6~Z|3mojJc3Wxa)Xnp%E<)>b$JSPBRC_O@
z-dG?_)ue0`Soiz|V_C;0(kZeDF!vS4e&^7q`dGy6%4aLLg==FGw+x)L&(9X*L3FZZ
zazMbCe&E9y6p;w;1b`8lFC{aZL`RsJftmEtJYIv%N`I*_^`ArsZt9td9|b!^?Sjb&
zA<jH@ncp^?f(@a$6YEc5?`g(pGx2;)`mDdnss>e9sV>u4PpKz5t?BnYYS2+E)B%~S
zS&HhOGyctaqt+_0R@<o<uSYJw;oyi>v(0><t^`K={m+T>UQA@<bkdt4Wpi0Ftyv1q
zO`qzPOVT%bTv3$M*=b`s!$!c=u?8a^M<aRPE1>N-6dH|%wpNrWO|ZdQwBoC#JS!8b
zoAtJ&uGpJ)n(4{PTABi=Qf<18rs-2O-D9c&HW-E2m9{Qb39#@p15wDHQ*dfEJ%io^
zrxM6WfP}}7ym11w4Ww8ZeVujPIbvmMuE^hcna{sUnuWSE`9F<mw<BwlTBE743;Z7S
zD+y^d<ku2jng{;?O?gT;V=*%@)yz1$64J^#i)z1=tc2nxKL~sECqcEew3_j?&1v>X
zdhZ7-jFszlB((YjG?fUYU3sp5^(^MoKTeIh&pRh*!LY(pHu0RNgpO4iD<UyWp-Hgm
zgE%g6P)xO&$B)^1%XzUtaHwRWYgTC;^+BquLi`If$H3@z!}BP=QlwNYlJne6ixedE
z`S$vnH`cxyaqPkTl(QfDR~e{5cLC0b*fM=1RLSXXR%>(JQiYr%{E!@v%cVICE**0|
zJZ6+TBT{Z@`vWal%ItumahlA;>B=mRjKO8GLz~%JlV)pJJilBP;3Z5<edT7tnex@r
zHz=ymMpuZTRkn-LKBouH@tS<{y<TN*_O=gub$7btXG9FZ3%Cs{yZR@=PDJs^nVH^a
z3K{rNPH9#6X|fq)I5?vMH!+V(J;7u|^Y@7KgX^wA6o;L}MC#oA^GL>E!>)2;^u}pF
z)h^W*sL`f)8;@<b6U;5QPzXi4I(utzWHNt2MS~YYxbGbk9*9}ZN0w-}5LA}D{vm1&
zs}<H)yf$`3YB%P*jb!Sx@_OqErf{TJ6F5c3<C}kuMTWU-Y(g+G5s{PV)mSMrpTzCg
zYs_@bP401aLHYSd*7+-g_?Dt^SsN@G)|kPk$c|bWmA7hXff!*)@IECYGdO9I9*|)M
z{1WhAvI4~n8&orRhHWwejexGkb{U*Sc!s#_y2w}s6fz?&k)>&=Wwn|DCk=Y%OhSoz
zd3E-=)`n<x*3%ftA7*pfq9y0LVv2>FEjp~*0z}W=+LY}@rJ4axb&c;f)U=8%ID*Y)
z<<b>eTk}$Ylar1+tDb5>vy@SIVCm4ZDyH@iJWA>zo>8m4jrm>t*15%0CREmz{hQ3z
zZ&ApU#~3KewfoO<xf~hC7a!cpQyq*9m?+p}V0nxc(UER_SB&0cX~VFj%G)Q|Vet~!
z;!-*F9I`&Q&HlYKasL2x+-XU$h`k`rB1h3h0MRV2)k_u@Y@wCjOF!ESeznB!{y%&{
z3fc3NpKn-*2u#ZkP=BUaD$~I}npZ8_F;?i+)Ja8iM>W`gT`fi~-FnAdR}0d4qO4P=
zDq~zZo3aw6N>G>D)g0(=N$SNRrqEt!qaTMvWs;X!al|Z(eCV)az6T{UW6;aZow&h&
z5~?#%`D!+}^`6|NP#_MR>RaYXb-C9&%qf^XEHF!qOk~7=$Ikfioi30nD^X)rE@L$g
z0X699{{ZjmVY}VU+Zasqos+yyb1kMaapm}yl)UkpcA`|vfw?uWuYumI)DJ^*d5MXQ
zTp{VFs1M#%X#)2hJ|po94PK=ZYwOKSe=PRuTz;sXEOwlAjXj^B6{fmIB0q=in2Co<
z!7EvVQKi1*DVohY0HD6j4xPa4)2&dsN@KtHcW=bQbgDx8QyNfqYBeJ@mUdON0Hm9z
zh*7H4qy>c#VZ?Q6cj95MfRf*9tmj^nRx0;;evlX*0CQX1byw%er))|gpy8eg`z6Xo
zn?on2H;Co;vRZ3m+cyPBRMm~9H{QQAY3;d}S^laPCi1x~CNDf0rPSn8#HVdNz4b!8
ztspZdNt)WVR#VG7kh-g$L>Ku+y}z)Gt_1DXgnfyezY@ySABkZp%V0C+Z?ug-XYd-)
zB!TL$C254|T+YLius9f6zb8eqP#>41He+atAv3H8`c-_4)93rEjTS#S;zwJZdn=eJ
z&BAh5J8n9AK|qzmv>=;-A*opGbfkR;?>|iCAmyo6<9;Spbk(a!B;xi{*y@bq{{YzA
zk4O@jnS~bwzGKl#masxBzFKf2S$r0u%h9#BMn~RH7%zkE9lI)iZy&sU;={3k8E0<x
zM14$Vpi6m!)VLF936O(pk5-vU_MW*Kkc&4any=|J$vmQWiQ6jg+%Xib+*dS%c(~JG
zEl)c(v$OIz7XgvpJLGh(tdAJrAILG#DyiVfn2s?JJ4A0Yw(**cp{)TWCK4UV2Slvq
zJkOci#!aPxK;%wL<DJw#Eb3O8$()rg70)yqh)T^9-x2~-96xC-qt3C-lLr*3*?GX2
z3<l8~c;!3_C&a?ydEoMo1V;pIl@!T&&xNf!6i~AQ%d789TOKka=GW%hkqt+L+UBDo
zIpsI(3zbM=M(@(T*|l8N54qmONhIr-uy6?d%1*Yhxf_z%iRVm*H2vnX&UfE<%}j<W
zSP9Z%XWiq?hc<^FBl$5pkNa8^58(Q+>`hxQkIJz^Ij_DMMkzTpUuE`7cBkUEOrv6`
zArB}!O&pJPUa_kJCwY%b^orDT%XYwkYgW4}mwZMFB*zALOcWZ(nqw0QP=o{H=FQ3?
zYo=noc3iLtUB0=wVl(na89tN#GH3Snqxo7}rziDKr}^8^Vo1Fh(r_wE2kXp?jQR9=
zN}(J*FZ4}XXj+m@nZ<QaGJ|AHmeo(((yY=&FKwuuh}7#tj3!j5N*1BC<fl=z_bl0V
z(hcd_-l!9r-zzr|u&yIsPPT9N=^MwGTg&6iLuV_}GMRz?AbAs>!m#p*$dAcxZN5@2
zaQf1)-kq;W%|D`|=KZ5wAidtDX4~!@69fLV6!&V0-G?<*Su(&G5g_$VvaRgey+W{o
z1J$17>R7IL3`NB8ZJ*)ejpn_IR_gx%dXklMPqvI|jSMxNcsXzYX{%C!f)9{8XbIE{
zljnT#=~AmMJXE{)d61zRjiFr%eR|}th&^^>h{Jf98Qf@p5V;Z8X0o*By>{!qor-$8
z+dspI<7&eWofp0HVrtT_Pkse6yky2nZ}BTSB}-r1v8LUxT+upW=kU5=IjniqtYGqq
z;sDOM+8VJ-cuzPd@`4^aOn;5WC8y(7OVfOz(Izvn>YtQ#>qV&t&0{yNY*AMeLyC~$
z%n{7yiosoxiPQv!^_ZHkI-OR6%4_QNebOvMLuwprM>D4?a+vHyQ}iS<e?1v9kvnZT
zyAP=D)SSiL6{Qy;lUAVQ&hzVTs{a5DQX=ti3YFo$2CAo2^P{)X4m$0I29_cu+_n8K
ztlV<?nnQv-x5>>g_Zr2XJcc}ETF9UCn`YTF%wsfgiFYio`Tqcj8M4IcR4hhl$2~<E
zs<4OH21vk}M9FXU{{Z6k>guZuEvJ33A_r}(DOWAph$H95Jt<En^-umr+ZH2ea7lVl
zp*QLg7Nb*4Z5S%cA7NXCnieF^`3G4<3CUWvP#|37CSxk?$+L~Ds$*Q!252Fex}Hp%
zL1S|xf&Ty*V`)%G;5TZ-Gj<}d)k0%g)T4|Q&1&s{>YCb}>Yf3PD-d#ZrnPM(TNUhq
zijr9Li!lgj5dq}pq83af*J)j44VKKxmx5-a7A~Hz5g`r(u5kn#7uGej>(9z;8#miO
z;l5mdw=pt1QZlnXJhP2gym5;puz>115YiK|RM15k@gA|xhAzsHD3*4cLLA~@w+cpn
zlV6&pxXSLY6U$D9i%8?+$9$+W?rMzKK5t=XxF=}?y(d?DAx-cb408EqqZXP))uq$6
z3$!QrRsR5V(wFItX>*sNq$OhI8sHfSGS%GP8LHMQ_gfjIY*(Rq)xol-cpB}9tilP9
z6Ie4)bQ1dRU0K-QiB82bdz7d0=-6gf4sQs<o^CCnt5S$ERB<C2WZ()EynII}m^hzq
zfyj5^YvQ{H1JvA!<a~-FKD}Z*`FZr|6@owS({CP|N9`Pkd5lW?8P^;pIdhVd`-0qf
z&It1{7`|fLZXv}FCInA^8&d4OJR2wlN5Po`Bv3J-ItdO;l*C3zOAK+pl$Opg$);vN
zeMqvn&;y>Nxxm@F(Y(-I`mU+UV~oiE049U~0M$k>Wt=}58!?+k-@Jcmo{_lD@UZ+v
z4j`D3nTgW$^1kmjZz(M)L6J6O&(rvNsi^yzsIZFVsFb*nP(xz89`8q_mg)h@Ogj{9
zRP=oERawf`n^fR}uBTSePb&-5G|A^qtJ3gfj;*E@qwiHZj;}6x>6?{lJUX^Jn8C59
zYI5TuDtANm7#L3z7$cYwEjHy1DELRj<CW={tEVbmSghz;dm+XwVG#cS=!YVR%I=h~
zOCrZHQGiuVW>_G#tJbV$vORNg+#=C?O*U|KwydRay;UbL4H8-T9g0WH3`Bfcy43S4
zPxlSw8^m~xykjDIR@@D_#__a$l1z5oRl2K4+Hf|#1I;W~?pX6<4TMB?#z8cgG(Y*s
znN^D^VM3CMH>cqSa`9|`5Y{2E7DZ7bB?Y70I(G&qSfdDH9<5#=w9JL@uRN`P_=+n`
zvrfFPMoiC{(JhHpnuV+sCbDYBpotj>jd#nxiAFNRJ;j!ox94kU)dzaD!aQ$2nn8(x
z?X54es-G8E3{spEw%ZPACUI^6bqik3V6x3c+z#rT_l>4uGxY*Qd_fd-1Lwm{(}5N?
zvfH2qWelTQu(+R6y*Wb}wxX`|j0@&_+rBf53bw_p+Q3*}@olTX{@`Fw@0zOpy|*eo
zDV#3da+}HMxnX$&<9rkOiAw;0wk)lry!K=7sx;mWr!qw{MNqo*-9~X-h=q9FrKcjm
zh9(;d@m2GPn3*YKfIWgIG~?iKg8V;&FUS}WapbX<)G&e#qmb#qw%F@shD)Bql(T_l
z!(VJ+j^9<={+{fsCTs;rxuQVz=~}$rDO+x)T(1H$B0GJ?OvGlRsLP!QP_hD8vuKdO
z`d2ao#8^1gYbkb0K}Rv8JY05I_Ug4ohNsnSC;tGp5cutyjRLS64RDBqLxLO;12a2E
z98G53laMX>PUS5X2^ek1G=zid+MKQI##yJTKX{)T?~h!pq9>WeZ2~MRd!FBy6ARr|
zsm$Hu?(A(t<wH|cX&PhY>w9Fko%3?$Q5+9%J*oN;)%QK1ua9pWv#f<cPRjlwhcXt+
zTEwk_A^5GMLu6^4EA9x5Q%?&u_7B6Hz}-sHuMF&VDqHuKcA&3r&^ua%PiFXxk2}UU
z^5|9^2#8Bd5VB)N1@Jwm#;)KlY+(=9G|7@NDUn(f*%&h-W@<YUslEm_^OHVy^^ZAT
zIao->rsaWg$>HY-CmU@)a~`|++b1K(%MFdW*hiD+>S9cJ#b7xjY;8MlK0j~xmN15P
z5qu96GXs}XaydZq%{B0gTgmOWnx`#7N_AP7)2z%ZZeff)-07d;(f+ZFmHWrAg<5Ef
z3sGh`BphStAV>88rOfu|?Ss{8Y@LA`IcJ}dMD0Ic{xbEd(oKb&R;4<bHp{N+GVqG^
z;6raB$qC#$WV{><&&F%oVcz%ItB)N{+<GadlH5z%J_C#Ey0P(Q`KG*UU0VxOrS07&
z4&&|WPoNnrw+$~=l4_D?TsH_o^6O_8J^@4S0v0RycB&wleabMBNotA?Jm>g{1HMMI
z1G@ow(iyygYIIMDJ?hf!%9Pa55UA?Qu~pR>Al>q~N$n9+Lm2s4P%Qu;qwxe#!iE_Z
zZI<AdM@yWtW;UH8E-CXYwspD*HGhaAHW!0L`!*uWdM#_S%q{7b=@jybj~j%W$d{1=
z{{SX7T5?$84w}L@B2wO>3Gp$76HiTnpZd;ndSEJamlc|DsV~aEVq;JL0C6GdZBu@n
zzz(5sA|fo?w9pwnBL4uoS<|&@y#5^I&A^yeZQDjOP4hP+(Sx@c%^8Inm3-C$ech9v
zO(X>GrcRW|s{P|%>WPX0*>y3O;wG@S7%(h2jM6NHXPFhHi;`)|i~tB0xl)FhPH7Xh
zshs{KT#Jx-rN5=c8ES^b+)#RYF*mJqpKoJTaWAAaWr=RgLCdM?GHG*A!D6XdUZ9aV
zxdb-rPFxuqmSS<=t|KBOrXmHgQ)x}bALZJZkBpX8Y(*5bn#pSn=hOGiUt=n^2WSfA
zz9US#c6m<vevi22i&if3lC7x9k?mb}i#$DB?Wgg@$qd27#{0|zNqfXJxTRr>kBes*
zz-2^)qPJwoMFRYtN=_`dudKhPB!mY;N6P86Rdpve*VARAk@3q@BG?&M$f7Y6&d!$k
zTDKT!<WAeheqtLF0k5T${{S|$99B#-fAC*8kj+}Z2G?X=4i7z)Xqv{hwxg2nsX15&
zR8?n1l(sb+_5G|$xdiF+gaYb`ZfN7cXK>#ew2@`a>RkiCvt90vdZl8gb<rWV`$~;p
zq4lBA22kOEalq*>B-ta)Gl#ztVY8dqaf~@SwTIEpQm7?s-#x{KSWv|2V{xrx>1ks@
zy_h4$WT$_v-0r1XJZ-y8-VaDVvT;a9V3u6`ziZ0O$xU#EDzyP*MWYMqO|wA+&A5#{
z-8NAr<9`p2%h+pHh1II*MW%b3ubnFMn_kGyq~5klTt9@qKuYympu$EvoqzxVNpPyr
z3_<Xvb2h=>o=nG_D3M$aFi3wQHE<M#R76kSZR333(sH2lw_Y<;I4cuAgEAAi&(3{+
z#y}Bowk?vT3kA#SbdGQPWO5nt*b_{aB4-~{^7@ka1bSTQ{Dp_@5tHU}lDn*=bKQH}
zZH3kN)`*Q_WANKJnc%@cO*DlRuDF+E>{&?0hY<;8T?#{K<nDBK*=-jWpG^uk)YX&)
zZMg`@$@xxwsx$upV{({?r=%{lMU6)0qT7Z-Y$eH;TxQ?nEG{e6tQd3ErzA65T*dzB
z^xk85#XeO(7nNuHfQCoJi5<Nv(j@nqsV`kSj9#xLr)o#dknjC4E(AF%_UJ`uzI}Af
zbM>3xQ?|{xmYJwRU?=cvQaiwCZt=`?{x7J*nAi_#C78gnblPQ!u8Po7r`<yM<qL6a
z-^5Fqu~=c&aydDwl<!GXct?^N89R~((@yXsmn_9v?tItRD6&5^8YVD9Z$_zVc?F#+
z-k#O4Z3F|RM1Oh3$0h`2#*-ZRkX*HZvDOR?l?XQ9Y?6}GpmRd8BWHx&_}#UXCx)R{
zEsQGVfrZZ<w$$r;ORiQH<eoW3IkAD7b(z0X0QCi)@H3~+=Mtw(K8;CHEKx1332$7f
zGBnu=w1b^+SvG8X=*W@_&c~52Nvyn&sm&G!ZASMeeKz4^HvF=T8Q9`sR@W?8=Aloj
z%<y*CDFeMUMN4d!YSIj(-rsj48zDEzl9ay9Sy4$u%uj*a#%N~&6TNNG?}Qt{;|?7u
zd<3{KLhKgW%J|#oiy1QjhD6JbGG<3{j6q4Qh@bcX<72N!WK4Q}Mj|;qQHZR|mCOV*
zi*DV`_f&M^;F~&)%TqFY`QCKd4|V`;4z`$1mgn;->srx$AQ{$G+$(g%#Fo>v{W#>o
zBatx+n9sDAD$Cs#Nc72BX}X5#1~ZUBvwso3(j-3Qjc^Thns*eib7{J}1`gyL5X(}s
zmx}Q3Gfrhh#pFtdup|8BPl=N?LBz;u_vcYo^S>|C8LSLVVwrzY1#=>r+jZs11=Okw
ziOo8LEwh}SpF~z%7>A*Tf~Qi=a*%RU37-+;Y<r1TOO~{em<I}^cG!nhwB40vwxv;~
z3(v)=1duYmr=~BT1}{pap2sY-B4P1u9TT`WWq;w-ey3K_l_N71D6n8bG^jvKN`pnP
z$q}7LNkK@By;*c5mZxg>za+=;3up*73G<kokG$1PS3&VHzwM`NHSHDw%u41iK|(7|
zSdm!Mb|j?xK&UVP-&utd1;Pv`q8XZP^}eB2YiPxdMo~?+kKUjqaoC5<2^x+FPCIT-
zZk(bU`=4X1yUT7cER>41My$6c#*#(yn%(qf5h^tljICP%l$aR(xF(tw5Ysa`<#7GY
zWS&OR_31l!+cc1pxdoTo40QT=pAi8Y{M^+zM{cnV`v~XkcATHTmc!#~BMegId+(pL
z#sP3RoS5S?8MZQJVSKA3nJi{W69}gyzewOJSk1D$j6B#nIQ%(6ek&o(Vn5Sc5VR{}
z?L}#`VQC9;pToZ+Oh<j6H7^<7bAwt(MfPpB2^!0B8C9+2x&Z?2**wxG{Y4WoBk3_7
z*h?x?v3Z?pCQ#9#fR#0mwt{nlR_28I2r&NuMSV5)59Ko3EsWGl^UZN!l-jy`82XZs
z+<w24k#XaiRj#|?%D{Cl6+V@w2fXJgTi;5DN?H~awk`hvC(Om_1!JUE%Csv-=KdqV
zBoNECoIFeR&9EXlL{EIx)6neO?lE!3JP7#ZI<~Fue<2j0oM0oOE)-gcV%fxm%UrF{
z)lL8tOcoleN!pF6k}i6Bx51s#*viWnJOs2C8*9$`<%26EjN2*ReG}xQYxNlrFH=(C
zl2w{@?D%u^TO=O=s`}PfEVb}4h^a7;viY{DOjU^!;w6g{{4w*DhF5B?YXV`YSmh_V
zCSvOa)(N7c?FF|r>EBU2U-zIfKb0`t(SkrWEk?h%u4Fa7785gL<7k_jufqL~09&Df
ziZ~Z2v-pJi=f+M~k_{{rI)U=COCO-k8({_>S_zFM`|rAT^!o7>&yA7ImtkbKE|Zg}
zIe696q{abgZ*wm)CghY^upXO!_?rf=88w#p<yYzmOG8`~Mph!5&t6vpOJ3O0c(Pt<
z8%C{F%wD})nwM%T2JQ({L#P4^(@$A8>Hts>%|Zq>nd{R|BNVtHVa<A$ubFjL&}*(n
zY|Zu7O`xq_%Wm0frJ=|HvApdYOnBcGeQXko^1pP#jWR#2Sw)Z~u{gV;$l-1D*DCiE
zxNqh;Pu6yp{API?$a7}G83UwsRBu7UIZdNnf6(v5hc_XZ*>T%gw`tg|G7E6~m9J{B
zRr}zzRue6G_i04?J6eTQqpg+r_=&)d4VcX#Vs;=K#s}jJFQLmcj?l=M=ru$2>j$NP
z%98ymx^&Qdh%iICG>Z-RrDo-Z@s?}>$P(6n)Co%Dd_)I{jMQ(l-p1>Zn+&eX5|myg
zzD_<8I+b}#)X;rbuqMW7I&;R)H)<%XY$IwFL3UL*L8p~YG7zM>BsMiGY-I75Sbu}k
z`Nie%yAZ3b1iaO89^!5DWr8fMJ*ulMvcwzY;)6S>);te0n%PXY26C8;X|~*J-WItO
zuev3Uw!*VL*$bFoV8c2MWH)Ac^T9KKWCaOdJ@82M>c$aQ<2G{H2O&zJqDDlT8>|@=
z*(Vm3RXzgeXWZ{Ij^0&V%AaKSEKzByjk031M|eUR7LmK`f2b2pj$@CJBX&8p;wS4q
z=Qoer$L;aWFW?s&gi9^UIWLac22tR1?l6zwC~0$zxAfECO7bE|q*%!xjK&mkou>|Y
z0G@y2C#}0gUb4M{N+LIr&!m=ZAy<5+5Q+;t(#h>pn%!zR>5SX<!4suUAB26neLA6F
z(?*$9R*J=@%b3k@d#cp6>-*&io)_6VafsYj@UxiYJLN4Xn22r5nzVmsPl$n^p9!DR
z@#50O)@yvh8c`A}D-D>8i*_NwVrc;TmRZS}Dc)dae}`seEi@M)>+Dey{XFEx-(fNg
z35`~J7g5pZ9uVpDrMmRHnfy63?B5wBA5*LQ=#FcTSp5a*QoP%;q>Ct~%d6qBasA7>
z((C*YXHvavc55OfYGi5e=(Q7?Qp776=BCrHLJW0bi~3z_YtYvX)oMhvq}<Y!Z6s@%
zjx#YTnEcq9(GV6}Kf~}o$|fzdw%gCrKd@O>uXjPMjY&bSzP)u@LlvvkV>;Qw8y;-v
z;O{HBr`QB0xZIW-r<eS2C;B!Fu%b?6b;m`E-GaTl1CrJUqq<pF<5sk0y+7(F88ae7
zBGK(z96DFbA+a7g<8-Q*gq>E(9U{&351nl}_#PC=#c$fZzlgRmsWIp~BE?BBx!z0m
zjM8F#wqO4M<~(sb%#!2u`cu<x+bA_51gL?Vl_t;-wbZY<8KPIPhc%CQG8%jNkG<w|
ze2I~mm_@X|^6qKKO>!AgmrkoQH@_iv0yG%<$DLae*($@m6spBtCTm-Gw9b1<N_xv~
zBr>Adw5{Roc4<g#pKNN1vu2{fN&X*ke{oCE)5&E0MPRYyi-%)VtYp%j?Nld92rWqw
zg0YJ(K@dn01jOw)QMAt1K9f0Gdh1*`veK5B7s6s@Q7=h%AzF}k!)zUGKHQYmXd2i;
zX%cL}0gzNofF-J_Y+M?;RbIr3rG!xtJ48faoDA`m!Ngcr6spFQxrx~5l$~P>d=&13
z8<J09!<fbuW+0YzT*R8NqBq|Fr)ey&s~E*dY<ZqpGDXKMFa#r&)~T#-31ydpQI(Mj
zS&Ov0b}I)=T5mQI!D(^FJhMX5zdC6*u?v}x8)l$#%LO^wZa{`UVd@6w;#MP<U2FE=
zx(;n$Lre@xDAzF`z2-m&js$fG>XA-+@x5JDn;4q!F_uLuFu%#oM5o`fX0ebwBrGNJ
z+Z)&ktEZ(>ak>&&rU9<1w~rokRNB(z;pc1#<D*V5u%#dtnZYwyY)ZHUXJGqS7@s~)
z&;e9ILZW`4V|Q0r>zq!+I1vCfo@DjF!rC9F202w`E!{%Aup+1JJbpXIB4$)!I&(I@
zf+H9LFv;_pWVhWbYX*aC@=`lkFg&GpB1y}TZcO>~{r1V;JW4(qRfCURrZPW#{jg0$
zh*|YYk9mp8-zTppiA?AI{rJx~ACyrng{{^jsK^_3WXR;q%NATG$$<wQ=k!Kbp!@KE
z2R%zvS6x9cvdEoHeUvQ@0LhqyTZyrF){<G)stZ+~xv+aIMOuBPO5!f)Vl(7sVto_&
zT0Nr56Zyp%T5NX=Bci^XF>g@RZr!>fZO!+Mrg?<tA_6-(CTBA<pL2yjwj}+n3Ejif
zGOVhvar`|AkE{bV7a%jN^mEUzG3VBJGeZ(Er%H6TS)7^tksn6d>gI+201r;ptYl0R
z7ZEnzhr6i4z0Hk8B(jk#S%@CxT7{d1vUKe<C~`a=9bX&viPoL!kd_066C9kVfLtMB
zxYx^S3LO3&U^Fe#Jpz%Y^`bTp2C!!=$1zoiQFXaxHdZz2ATcK?{u$>Mpatc_Bl~Q@
z{ZWh_voXoXM`~95r_Ym0C~H<gy4zDwuWWfL!zs1h7N4M{v-wk}+d{a^tmE%ctfo^2
zjD5yim=Q`NLoRfdC9ICxjfsh!@n1SF4A9<PZBAQksT{=?>}mE`Oa7Z&h8j!=^kW7G
z30pFsSG-KlzYsNQ?HHBIoTWyn(Gb1FcHh%WU-Zk2Xv=G`26TNzM+Y;bbnI4v`h5W9
zT=lz0K6L9-Wm6@6j!kB<I%co#zb?dbfjBECs-`;u8kNfcn?Pj0bZ<_6J9lV|{A%x<
z{9eq?`5KwVY0N7LQ;$*90TxY1cI81`S+Q5!7>ucu>5)lD0B{)2&l8rk`gY^6#X1nq
z#a4w8o@sr`x^xcW{^)`YYV+!~0+73Jz8^NgHXER3Y%()f!N@$fFgLGo(tK()GQ>x?
zbV1KBhI((ej40eDacYCAayJ$P#xX@R(mA~T)3=e8Di*R}(1lqN2i>bugv5yj2GbU0
zR8V1T)_bxeW^&tj!rE|0c^JhuR_WtGs;wH3onjJOB<QS^(P>w6P}_}$4Hxm?D1zrX
zQoz(X7FndZj@p%A*v^=i@l9Q((DcQiS%X3dHD?Nu#w;j(&<t7jVA3kiw9aHXQRxQE
zvbVTzWQ7zGkg*~dI3Swp>E1Dg#U^;u-b`gI>tL1;%G%X>)vmu{7B4-W&ytU4DPKDi
zJ~qpP&Xot^53jAcaa|T#5JfN$BpdIS3K?y)ksOL54SX%zDPuRS8;tT-RxKSX;fyV&
zcPWanm@|S&$iYCfNvZ-sx=JCH7Tu99nCMX8{{U$7mj3{(p{$@0AKPCH2vX}=d`o2X
zw2oK~npKw~!wSO(!gk!N$jOl9ek^*#?Q+~710l#~A!NhZd$lVy<z1rHdB*bYcKO<M
zy5cV&S7JC<TLhr6nLLY{y`+N1!_4^c&v5S;7cH(fAAfUViMP1vlwh`>P3IuWGAEAt
zAtGFGr|L0f$H9GIZS$W|0x!HMk^8S;>oN{3pj5OpJo;8}{{Ut6G_^y*ZN<~o@1X_~
zzjJEy^y~8dKm#w74GS|mZAMsdo>7{j_Nt}U6>#vhIkOxsbPGi}q1arb+fJeoyU!T=
zoS1?8x?54x6`DO$d(5$x>uCeiO5L~JY_Y>86lQWEEW!>GwD{U%FF~ml5?Qp*jEUe;
zImS(#M^PB}V_bO&#Pic86Q@*?x4^8tmxyF7pD)l;lLO*o(JB(BeM-Acb(%tpbx?%V
zH7iDkX(f9rd{4H2?sNd!m6Mjai4r0G{S{yf;bpY;+|fdXhwU)5z(9r_g}SAts+0I=
z()0WCKALL#t_t#)5!qA-&mkGl;v?}<c}~pJvbSV)lL70w{_@YFNXQPnLRl^qPN;Cp
zhsRZqWu#U_9CAXBKgI{>Zhzi8aul4LjG1w%B1C975?Y%xP)sY?Y&1U)iCo+db5@n8
zR;zlq5HxBiZGUl`E8GbwGy$oJ2k6&p?K0799Lqn4eAJoCq(6z=6CvPaGOyA*(2r=C
zDUe&jB35zkc@UdxJ`57-nwZ#3*sAI*H8?)xvs&85nO)JEBD-}3=Cr{pFp!K9<YGBg
zI?y5|`Dl_RT`l0k!Sj`}KK%M=R)(|x05JYffRU`k!_g@o0kr$~Rcs5rrE^{O*7~10
zzowPEX{e0WYgw^(b$%IP^y@9P)d9vLwd%)Yke8f|N0g?^;Ws!})a)mLGghJz0d}&b
z#~`598P3^+QVzOliT)uln<@-YrFG5I+{#VfdLg`tI~SOj__X+)CSot6cK-l%n^S*F
zHX|7WAe=<RL37hO<u=3zXqlPaKk?(d7ZUr)<xWF}pJC-}chyEcxSX=M4w4PXf(Zl5
z{OI2!uDwKB;P-aG5n=1k$t}3Z9C^!<xvg4ZT)B)5B?KD89k^B~lb%AT$mBeS(LAGa
z6;oCC_b9ndHR@w^4|!XuJvy~75$<hnK=X2g+uN2O5bXvq8hoT-JN&d!1Jh<81FAO9
zzc^#FWTM+eb$O6k(&v<8*Qb5MeVM>)P(Ea6RYH?DtupVuCQI#GgbYnya_MZB@3api
zroqMeOQS84Sh~hDntCDc4pX-Cw0@}DQ;Ai2!6EXf?9tLMx-eX~nVfvYA(NI0Not8?
z!>g?D0OsDDjIxh+V&SQKb_Vw2ukx}cM0SOWxfHNoZqvsq63XyB((xc@MJN`c-FVf{
zov4g#lrFqZ#D6jtkT41iNLgbFWa{k;QMZ=Nc&&XXuswVg5j=L~U^yFFA-65$r+xEN
zXJ18lm@cH^*VtkaX|mSLV%pUCT(S@Qkb!R9O|4-c+|*7zy+y~bk4^U&&7wTY#(K`i
z19k#gnq^hhCmWRSACw%UKW{s4;wC<_a^Nj>Y6N7D3HeO#yyWs#IyNIr5-auKZ|V)Z
zW1P@|Jx;8zQBQSN23x*v2txF9fT6D~M80VWtt<-9v{_ZfUcy@%{eGL~dB36QfcU7}
zHT5((!%nRJsl|tBz-5<qg2pp`KUB)SZf|SeWY}S<0Q^jp%<t)@giV-ik(=Ju`Yuj1
z_45PU?nGj9VTg=SgMif$GZW+X-XeA=oYEQEcaxAc)l;x`jj@ryj3a(4;~Awdr%w&-
zME+4nrk|%B)8#d(SFJqJYVCStJ-79C+2jGDY;|=t5D}|G)N3+6vu{l#;d+9Kbk;&8
zuf@bf08jA=Z)F~z!?tC}Vh+d}thi4+jKsvXT8Q_R0S4Zrwrjm@=|LmsUczSwXs+3u
zpPgZ!TNT99S($CYT}VY-uPJI~xmjeB2paE&3WJDys<IfJD*dbQebH<p(<?!yqbKvL
z)~<mlt*B^rTJ||sXxjpbl9s^071%20r8g@ScFtP}t9jv&Fl#JR1#Z)=#L6!ntv1z!
zI)S|ma!uL-f<Nm&hSo6_UQTSetYp@qYTx$Lrq)NzoBRiR#m7y#K9MmgnDtXWP&lLf
z`vL~AVGClxnvo_8RkM<;+O(}GxaIvQZO35#5m<JEnz|ZBWWo9vev0=L-NZG!_9~ji
zG)(M-l%`-}chjOu`eHwbP5_XAtXpaNfaJ``jAoC2pysQKN%ptI<K-vvT+EJZk+!1J
zmj3{4F^$}7BcI2|A9~-2KIB)ME|RK<c>t}jLdFUH*x@q)v7dNB!Uq;!+f0@Po(Hpn
zKKyQSekZJe&Q2%cy?G^>Og-G`tDV~YbVqIS!9&qXNszvA8DN%nTh3Py$q_*pDU6pY
z(hs#&fsFjRYk@;sj&&Jnk-7fn1~OQ2F}%<CsJ_D@oh^!0xrloO+G7tbzO{s_YL)=j
zRjf?#F1IC1wLyyts&OI9lYtVE!p##<ijI{s)zpDPMCJ8{3E=Q!AOiXANU-L4PH!Xi
zxss!0R|-ViCu-ZSOcNqx3JNSK72qvJx<yjN#6-u|B!mr%J{(eWg$w*N3(~VVrQ?tv
zTj{D7UqEHcD8Jmfs~m>V7x46dR_V{*Y_k#8d1@vlaWr6N$ien-v0=;uV$KW)s_A}Z
z<&rGybN*pIXJbp13fvd$*QU6Qd^d<KB6{x(;fxKtkmp2nSKMcm@Gi2GfXn3o<nW^e
z#*&4uk^w-(L6C-8KGzXUOu%_w?$g~EN4yMWN{4C547j)VG}t90SV~s}il+Siv+6hW
z@$$+Vg}Q7JPNpWyrJmKEaY{*)cAvFhg`4WIV*zxIXK}3`t|eqAwlKDt5K4w5SowOb
zwZ!(kn-D+A&vD~2CBGbGh}fOxI_GK0%}9N%I#ni=99Ft(3Ts?)xu@J2xmhT(9f)A(
z;Zjk$mq**s;^*yd=F8M90rl5TRUTIBjuA6ip!`A~X@aE8($UZ3tXp-fTbJ<sMB1?v
z2`*F3#0UBd`%BHY`ds^1hRX_AWMoI$3Ko8n-XurNOn=x%x6+N65kEba64rv_QD+Oz
z1?2gUKrRddm#?j!B4lUBR5W5$pw8E}%gFZj2r)6qs~i|6vLhcOEGdP=%K{=dgbc>4
zQ`!TFL+l>})6kE7-43V@QbSSJB9kR*bDL$7#_hIJ{{UmD;n??jJ*O0A-iZ%`-IN2e
z+7~kD{A#i@U70I`Gnxl4eLH^<R*CDhK7vvaQA?x)X3Fk|@eFR^oT+1>!6kBuA&r%h
z%q$n^0%jx&iiA5$%EDA*?4gZqxl?WUw9(JoG$4<8FOn%TlAFQCwwp7ye-5-p%T{(B
zHY_>WS6Q`Rh@D%AxU*`Ps3D|74%F9J{{S@ZY4&4gnyTAR5*0vrUazFuw)uJ@jrErJ
zH0}F4DlJ)i1sh_fr&*v2vs_J8K_{zGpLGPPwHmD;(E+2fyzZva)?E@TMvy5O3Frwe
z=FMb9B3Z=^A9BXf(klW@k@yI2ou9+-F_rFV5_#^H7gJnfXX`bauB0{kt?E~Z65!J{
zNwDQE$GEjd$(pQtJxv7VNXX99rzl-xVnx1si*sH$=+nnE!Ud<R56UdeXh9QpN_FMT
zxowCE_EOJXI@DWCgD%rz?g%oV^5MRs>3zo*JO2R17^3VBiI|a@iHVW=yh2vFmWn%S
zh{7gIU5dDA+(mbM8tgqLpzW+I224Y)1gf1N$dc=j$oq-G<-X%4lTu*Vxs-v7&oJ8~
za?+w84MV0T9MS4C9HTwd91$f2ypxik`R7cUp@A$Dr#)3;pJMU`V`9pY2dCKMV1&(w
z8~h_D%x?=$;o8D3bK^=O4Mwz;WjVg}02NsQBvJx!!i~!1Y2QhRWr9W^-&EWgH6OxS
z5L<o6TNt>?lpm=zz?K{Qp!AX{wGoy_wwuQf^=FUWQbh!@gIPJxn>1Zp>!9K{I3o5c
zhrZ2QB9+y!kXIH4{{X&SC}FI|uUiTbZAl@dv!p8;k0X*I`=ZdU`jZ>UK@`z$%<4bk
z;MlE}M%?$yW5^gec6nJXi--&^F7km+{{Z9C+x&sxC|@(ye*xlEZTonXn^v(B65IF1
zk4+~sO>=6{y=?{w>}5N~(gX1(m;V664CbH498V(>0+^8av%$|ZDZ}$47>PkmRkQjU
zCd}QhHOW}{5lo*jrUN&+dl6V9W(r1qBlpcXIo+=rsg#w*MEVxoMtx^&5+I$$b`~%W
zmp!&yENC38gtM!%2-luG=a1Y#jJS&AlE67kZ19sJgEK!$K@7QAXHr)?&6grtT4{wc
zgPD>&2_G}EU-9y~BO0NGJ>J`8Wwm}DnGlanIw#x{BT1V{mRKEL3Z-x8vsdaJN&D%n
z;8#p;b2AoB*i4xK&FA-S+kocMQ?xc?sbd0sd|@NAb|~0gV7%3$6y;SA``a|wpTwy~
zY9IX@o{{J8(hs>XqR**`jXuoH#7ydte)`7gV&SG@AnIk!k(zKx+8v00Xqw?A%&ltm
zsYI<-^}7}Tf;26L@jh6>b4`bZA1tCm8g8pIQ~v;ys`^j)^pb(9AsPZ<nt+s|(=6+e
zWHr7qW_>ggnAFrc&)-emJ)3Pa619_<AR}N6+Kcn}bf?}JX0~58X4&r8iLGr+sb5qE
zzK>Wh`>3_T9SWI}nIfw(4CN|_iFJsX?_3s41Vn8};=Yr4-j=34o}4*9bCk9UdV@2R
zO2#vZaOahs74$n(8h15YM+z6vMImbLoe(|f(}{y|L4|(I9gqS0jhn44FJg^)jaw76
z8mk)m%qmll?ZAfHOIk=J@Yb(7n(Oe5V(#-LLe<;16r%<K+7KH)E4F7-o*F)Fo69Qk
z!yStRW@2L^KWrjC-5t66=kX`iY>DkmAZeThy2e_F^y(H?Lc?1i;S8&0c*Q!R9`0dL
z`HRhCW7BFLX-|@1fltc4h>bF=*NY`KVOMBwC%Ea!^xwuI@S4*HRm;>-)OF%jJ7k19
zWR~xw&PMyj+$F&$8E|GAoYCcDs#nXy(RRQ|aGx=@0h-DPBG<oSRO=^6?JD@MWFr$j
zRT@%jI(8QvnaVyGlbIGKC56j!)A%-<;zZ7L!P`=>4S39HPePp$4hROcWu+`iifOdd
z4<Vw^7LYJEMV}3c9wC(ggG{~=5zeg6fB0}h7BFm=$sfonSz^hdNIBd@U>F{yGON<=
zlv|5z-LpBlrGii`rVI-zofa(<9!<2$RtRfOt7P`={7SLL&bn)=z-2;qoZ53@y&J77
zKpC`3=N}s~V<Q(b1-=!;p3#V!$G98mSwS2SDP>g~Oiz0@4L~fi6=dn0k@UM}X*CKN
zBLE;M1X)S)vuEMZ6)XFPZ8hhel9=D)5s2{g=7t+Z!pQq5$9dzms?7Hc-lae*HN!1W
zyJfP7pBs7z6X0=5i4toe@L%4orWMfTTIFxbgEj>~J%-WMSSdJyZOt*Eg4mQo+$oZg
z$F?V62;17u<|HXl;g@G#>#9p!xW~&k&-<E+Bkefxk@|-c3S*dVkDNL4{&#O4DwtZD
z>7Kp<JHfA_N@Fx-_g;yMv(D3})Q9?Y7JmMpdq#iO2#Kb4^cgjj5XG(hIg3~7MgVh}
z-vVHSf`9fGGBR19RReiGFWNtN-J9HW7X|=EVzIBXE+T^bNoPQA;>|`$M+^nJMCMrF
z04>#sroBG7KK;wCy~CD^cpqEa9;rc;+a;Qx`@+f_3N;fe5d~6_=iVG_q|c?0=4xa_
z?GiD*>DxTc(GJ`u7aC~An~OPXO^#zqW)E!s9baauvdfdo<(bIzpOA9&^Y@04Y9Z^*
zGP9pxBsE5wb!M1*7PU{Bw<+s-uSd97@XSZ|>rtgle<t3bnvoK1)Hl=DXDl3d92Qh4
z3ObbM*!oZHIJ1D!o)bu-G@V;`$cUJnk24X4JI?v039h^6HBCM=+X5=p5>KO2=zomR
zzxuWE;ro3{+^<`#0^qia@Jg#<#zcr|nV>J`*JtqwK#;~)0%>v2X9)ONO~%%Exf-Q)
zM@6(R^-e|v$b)hWmc5~7J-}Gu5HF3W5!3YWkF3;1U=$!SV}^(`k;9^M%=p7p4u?pw
zR;sSSnKpD^!o#f^g__aDHEq+mb#7~rNxxlysqz`BME)acGugMKU5a;O)$QoHj`wEO
z*-KFm9w2cW>$Pk#!13S2@1*#&O`Q*R`?k)zVii~u8o>zZLm3z_Q9PrMI30wzNkHwM
zM@*vVkGap{FQgW8>I!U&Y85!3br0+5BKS#3ZNJx;(>k2Fb8Xf3%o!a={{W8>nkeq`
zn20D-B1S}O-Hgy5ygt5afWK=^s`O}|0ozVEs;9JTI&3SGbuB3A7a<di6=V#DkI>03
zK*abMbIZ{Y?WAO8Gf`UJh3W{5f+70sgW3+T$i7~r$5O=0s!&2j!{xSDy>7q3Y;|T5
z`<+v%vF*)4&*P>P^N@jT`x^vbW8flC<jHD#F3gK-n$%OyDoF;(Vw#eXJAXK@XDsi`
z8JcTOc|laziHVxjzb;By*(@&2=01_ETCGc<DVI+>6H|c=bIng>2v-|h$*%;=W_Z25
z<prMV&3f{jEvIA8hHV?fK?u7|Sz!dn)jh@Fc)!{v=f=Utb{_LMfhMvSn{X+E`<xe=
z)<D9rhEuG?zlXbS`~&_lyXjoo1;VvWG?cZh`#46BzB9nyvP+l-sp+KijXiKvWt0B^
z7{yErR#~H~jWQ{Z)r^klV9CS_iYG;n>X^)_$!6{1t=kZ}0MIoiex>O*(hMB2R`7@f
z>6&C(GVQNbH9$o=szKEcDc0|}rDdk~*lROQI9rVvLo-yDfxSf*6IxNZGncha_lkQ~
zx#a{bf>9-EUu2A{vveTrG5~BeAcC?R#u{;uF(y1Miowz$Yn@buT#|-)Dv47QVBE_D
zVbmLCVdUO9qDZK2x>@(?Mg2O5)5cHS=ZvV}OAnM}@9G6Am?$v`lO_Ex(PBk0B4Yv_
z!c!&YUQ)9GU5g03u4iYb@cPcmd}%1Jo`K1>oN_0*oMS&+jB2D2%h4-qnF>=1u9NUq
z(O|1f7+lD>&>1^0xa|CIP9^5L5Uoe!T7T2^ED@Gz?Ajxj(Le|*B;)pU&dXHz>zTpc
zP;rSU27wVy<Wq}3Z>Bq8J)>!n{m1xZ;B_se=aUbY0`nuk++<GEpHt`{XFCO59_^jt
zX=$NIOfXiEIb;(;W-=sZAhLFh-{M)Q)1vlrex$1Y#+t#^m$@8FZ8I4Gww`Rr^h%1s
zEg~E&O|`F1RJ%;o;6M$90289ImxicW(@C@wP-pmZ{{UwkUYLA3+*(Bhshn*|FGr-P
zjL<*&sLQ|DZu(q?qo+SkJHL}!rD?Go6I3%uUriHa>(B!9v4?4lj<H<8C?5i_8!)o3
zZ>?uhzSWglz-CPBfgC?45sZzD;}a0=lRAT8jsS?<<VQ6S*w@RKjj^RagjWoS_?8cW
znHwOL=oR4VkQL+;9<G<Qd9-bmJ~43erG~%~VzW7=Wjftg!+pYnP0D;0gB-vlZ?r3_
z#S}k~Y1E@CA9Z@i3T!eYg65T4<Vi=dZ5p<q^ab&#!kf*TSIRS6m>oGWFe*ROB0=_#
zV>*1pmr_nAsL^2sFldfd-6B7yeAU8Ui3sHqjye7PI(O4=`v#XSMvPW5VtnGutD|QS
zTUhl13A$y4o|39+Ifa*iIKtX%UCMR}t(3SmXi~<QVTmlvCbv&)n<b>sfFTitsLGQ+
zqj~0^sx6?zTe(JPS&0+mgD{d9pCkVOd1vzlER4~Zw!>*U!WmWOquV(6z}*^aamO%)
zF94<07!&49MoKu5M8PSw_9Esb4H4CEZ80=XmpCkKaVm`(=BHLD=V%}sm!B49xyJML
zhRB!^BPQ<?G!CokzO9q^d}e}7+f##OHD<&lz7^3i7As$kt}2AS3RH?JtzctQwbR~W
zjUi%jv1&Y~>!65NX=;Fgkto3e>|R-F4)eR3sX^_EOCJXcv0<cyS0p#vY0RAYOv%JJ
zxrCArm;vUlxC_qOJa4rE2(+nZ;zMF2FS@L%J67MNgqQScu7X95woh>i_w1^pGhxY5
zw!y1x@~!I7Cl9N3tK<bv(r>=QN>4?sJ9d5_NkHKVqe3Gp=?Y^56uza1#~q`1bJtcR
z$o~L{IL!3vnzY%o2rBx;(~BxO)%7wcDJ?@%Ac4&}Hs4)Dz)%M39k%^DWz}}1gDTN|
z2)yzU@y;Nm2pJtBV>TtmOlg$Nd^uvqA8v>pv+cQLuqH=&$Ea7=7@#{X6~)^K5E}JI
zXd&Iv`K6zfxem1~n)X@8wrtpz8ApLwwZ$L_i7ou(##R3C=9&;XZL`L2&Uf@@=}`I)
z>dmun@mpw{5?S^2;(nw4<7k+Y7%3=$dux&KKW$s8XBj8@NN0&e<fKWUD}2FCgY=UY
zX_S54%ZG8XIs7}nx_rVe2jb3FIzP9y^6c4}R+BA!=Ec>h3cVPO<|8g`79=Am7aP@Q
z5ZR%q@*m<Vg%0;Hz5R`>trID^w9IW06F;YVYKFcb*nNyM!v5Oh0zV=6zXlUKNtVN@
z$4!W@6N_P_xektN{9>W4pWS50^WShCGg7e;IKSb?Pxk}(dX#>(=^A2(3ig*&c*N`U
zJZ7bQs+U@CGu`Pj_1SM#P;OZ7T!0_&@U*7GkNz_mtIU<wOt_?Uy(QkBF|)N=%k-Sn
z$OWwDQBu7&{j8vTXzIEAH&uh#YJbkzzJK0N-f9yFtwWdLhhti8Skcfb)}8}*uloGa
ziKBf`y;_ws<#n2sh(+Ft+L<_5ip8%4%42*$Lo3=GaS5iYV#I9Y%1-w?RkYfTP)l+G
zC94_nC%l~Q!knrSTsb;mq@9{K=W1vYYbCB@CS%6<$%)fz;1bs!PUVgSxYk`i(^P>=
zO>AUYgKdrAmmxbIozv}gIG@Zs(@18b<Y3E;?Z-{Mxw5rO8g(nAondG1o_P3{qesfa
z?T{miy<)RW*H++PM{31@;n=!5(;%my)Yu&UA|Z;Z42a(W>;WLb3eD=^^fI~dWHEyq
zMxINs`Bn(b4$*Q9WKRmY1%@vX3R8B*K-6IbR=C><Z6+`cY$cpFy?GO`7P=kSjXFiO
z<a0uw>f5<_63g3$pKzdJJnv0$s_Ao997OaE{d{YxV#M&`k#PC0xbINWv{4yK?+|5o
z>Xmv;O&>ldCPet|@-dWv?TTkpL6Q7I>g;Md_|F(DFFAf<u-2;pdv63WFkEA12?1b4
zfDl;fUtSkjt~fDK%tdVE1-5hC99TBqKdQr%sPyx^gFsgU@gzBDxjJVCcJYEFMG!Tr
z)OAF-?AT~run9qnra+t}3ru<pOq6U4n%va#tXyQ%rf*Pyk5J?ntY_DlwGwpczDM<p
z=A*ns?)H}o<29|igZt41FR>Esa<xj!L??`Y!ecbj<xzRAtH?J$d`ejMiq|PH<pvFv
zFjzJQVX|29H!&=TouINjdBL^dM%v3y7-vf>Rp{qif339>ZH+duCUl!n0LxY)w<{+E
z;<6AXkAvd};xIYSZRT}Dk8E*_uq=U$(qUz)E2#(9YzUYf7-5NrG}NHjRv0Gs7#wH*
z-Zb?{vrBm+Q0v6|3+M4|SB<$uk*p{kZ0h4IEHdTiA3RvOi_VKSC9$@{8ERwla+L95
zo==~!_?%3c%SuRqNQ^`-%qM!FW<N0>h)~{rB4mE45KW-vv{lk3GJfYL{{H99o)GDP
z@sTqTKC5>{J;UyI^_kz!ePw0Ln6hj<TxHj`c6>U@Y5}jBH2(lM5;FCzAh0oUE_Yo+
zvu91a5&3M7AH#q{sIn%7X`NN3cH@$9)l>!!zk5(+-XR!~9>oCt?>>VvCNmd2+OdM=
z*`-3cv0XGcl~{vau=Fp|7?Ks$uv3Oxz(y`NTk#PD(TURnCfKmd$);v|_=v>Tv1GPZ
zpP)7x4SMv=Kbf0A7NM9+HA<l-B>ixPW39Tqn-D<420+O~ZIc-0NnDK5Y!9{6s}U*D
zbPA!w`Q=3gQi@SOWtxWVy2Z}4h^tplL>dB6FHGg0U63W?CMG!M@)H|5DE1bfx|737
z)aG<cY;_gOH^}ppZZ_j=gE?(JezW+O`)i2Q+~MOR{5c#7<%y&dbgV3@E;L%E)6*}x
zS6~RJ5NlHDLjCQlHfLDd+Punq_$@r(E-DUIK*pe*zdtl5V}A5)^vtQQte`hB0k<g6
zxy4Uo5Uf{Z9F$WDuc#w7+GZyz?f6}}&-<MoyxG(fn$Mk9)a1Km5{16bbm|f&e9X%!
zE<l?NH*XvlqwVYhbDshY>{2hri2-iST6N893y6A=+$*8(YRE83YP1LNT|k)u14CBQ
zncfZ~B#YGt!Xk0ikz-;n;s9x(TyVIMK}CrP3fxTOzy@<i{{Vih{i1Tn_9?N9nPNc5
z_Me_IWy*%npS<T9&0*%-)rw(FZ{f4QR)A9zJ1s&QlTCZ>6TYrobko7uR8yKon5(ED
z<<T{@YiT`EV_en{3+%GB<SK4zWs_VJr(B7P42`%6xaWx1V&tpPwG}E~mdrOzw7tk#
z1`?=b#C^iI?Oz>5{r4L=$B4wuP!?HC8(G}eGH_-$`E!`!vAW`6LT0;q91}JcD2<}3
zrYwA6vtnLyj>VJrVGBN3N@wA)M5}hHz{1#JYs^2)EbuG%gKJ(tr5Ygia|~>gF5SxP
zVw?uK>|vN3^>4fobihb&&b;&atiCYGc9Fd5h0f~>(I?8GR-df4O0X!CV1vYZ{{VcS
z`h9UAsXm+LiMvs>UZXb=3(1Y9a<kb{qSbb^>w@scnHS}&NRMakY=!f&VZ4lB4nWB2
zaQwF^z_Dv+Pr~qE@C9?q)=c+Y30$S8Spp&=nId#T>Q@XOAW@aqBrZJq5r{wfzhY2z
z9S#l+IE5ci0}Wj;i|&ZB6Zvn6d}#HdA*Vf-DJ_ik-z}GBu~ter`_@Tm+M!pOhlPGY
zbk#rYPBH=t1~SQVdF6wT%7{5BnCCG)GZ-u5QeBaO$DzCZ_`sQ`F|&8<E!n)B#$Nca
z75tSW`i14`h0+1Y^EvlEEA1~<^W>+}f5r%NFc912wC%{az(QgRvRE8ZCN^pg^B&=z
zVkg@Qqo1~D!yaGb)5oPJFl;DB!1cX>leTDfI<CT{%9J^aFX7GmEY1|8@g*rQ@Pe7t
z44q<GI6ohjBkiY=>HEf5C}Z4Wd(MwZ<E=qi)2iDV@xq1IdK^q~+{cETsmT4Yz8Z0f
z{T4S4a#6yrn`|pIWk18Hfr)pp(<;X(ooh}{AsV{1dzWh|SFTt)FC1C(qH@P0uVNoT
zk_q^T3kG=3T#=1cu^Jcu09LkHgBwnn((A>`<H#&a^@gluxPyx{$ll3YJE#7UP8g;#
zp~F<#>2qr}7ChN}#t)A<`(~WEv7^{j%J_c{flilr>DpAt6c~ZWb5{LTYiIEk2Sx)#
z=W(C<m3AD=YKan{9Q2Umqg|M*NOY>Q($t#^QYl7;9k<m4qV7u-nmuv~Vg;_3`g3uG
z*tHf+@0y@My$*{}Jl0r(+F6L$R`bdYN_oqySDLQlkku(|5lMegz1iW6h_3DizE;47
z!N~~8PT5~%Xts0mz+-(BroF}1olNDrjk3?b5UH>Dbi+N|2VmFJ$#8Ch%_d+p&F5CK
zRczMFW(b4SMdKl+CSjkMq}MFc7;Pj3>jcQC0=U&R0Nyb={gwD3walt@-E>x0GNY-g
z<2vBoD=e|6RlB^w=n=g2+0rgIsuWsAs1O-qyK1w8G?U|fIWyxU&Inc~H96O~M2bpe
z_+%`ly?B-O*!-UKYOO-uX0vKI)ZGkXY&K+{BdNNi$LY;?m$D7NXpH^5CbZwXr`^q5
z`}GDMX~F|<k<sSsaC2%RG{=m<PTS`(4+>1g7i4OK!sM8PSgrU!SFpH5L|)O|w6#=p
z#<?VmbYD`}VYK^<nKkE#*gV_RON_+Dn1eE8ME=qQzPL<#D{5ab1W&gv`td{xah1#m
zlr5X+tQm!|n~nk*BiT1t6{TXCVI1yqG5csnQFDX=^;^F<8*+@W{#FH%!{q}L&1Er=
zMLWcUj%l@XrWpx!Wr*7Am!JE;pAF+?v_B+>D{1bVXoc8`O}YHtnf(U>z^88AbwX#D
z%!z@7Lm;%Xb#}zT4`Nt3VRE)iPC9JxTV_H6Bb9p&1C=btfso~y=PPDT(h-nmp<GQ*
zZ6Nu=qb^yTz=6yFJ@yWOtkN+j9G>3hn=w3j>}wf{b(At3eu%hlIeCARYk`!o1gHFF
zwfVY)z}22?)7P5wD?6A4aUPYHf0*?1KUSR;Qcc3LsMUKbm$9y}OWLK1$ZP4ZI@ebt
zYfn|5l1VJ8_=nL)xwe&Iup4EpBAFtYlov0`%PVi?)B@A)fPt5=Xv9}1igcNkVa$=q
z5sb^EVF&$K92v`4Y%fUo{IZ-x*z>CFHIo?|aP#9Tr@gZo#y?<K%*0HHK@?;*L;Gtu
zgs{xl)+^hAZN7fdgA!BX3y0~-jxg<9OvB`23M6JmeH9Ze4X*s%E{t0xE(Q(Ss4H`A
zx;gJzjE3D^E0bLg-Yc9&rvpDM2mp?3nUbF|J0$&LB&1^*`pz*C*`eEvt7K86>GJ(f
zt0*>6n`8d~{8=srY2cW|(zS5+RA>oO!<_0=s0^re9aV^wbu(Ag8YVL3%p$qZjL`}M
zRjVbdsuyp3uFb06UICC!@IWOTmFe|6!iU^gNaIWWM$wf!Gf>6jslis&8hNLzboy?#
zdJs(e%NcB@C|a1&!@ZVQ8phBR0wcyyA&|5a=^j4HA}2YWEiD<Unu|iKA(~#H?h#tD
zbwYhrhgJFr)E2R2bp>>S$L=##sus}MZQ9H*1Py{M7fVvZNNX&2rMu55nb5`oH(xr1
z?#3*URJ)R!8Ln+KJgDGj<iOxkIc2kB+D3R~Y+B@`xX*;fa*t?Hnt`*YPEmN{IbJzE
zIMwhn9GKZrIc7c1olessoy`S?sQ{mCR4DeHk&)5J?P|d6mCH@7W|O5;)KLBHqAIAA
z8;r#7TSZXQMaZzZ3oHXd;3?N$U#=E7?`1J=hd@3{@bsvYyL5|#pTpmx#%l&bu`Juh
z`LxW$LZoX^tup;cX<q1ZH1BDs3)8JS6l045TLjd-aMVz%sj*tbK_|>@yVMrdD{3@(
zuVrpj@`0V!USoYoWmlUp@6;@Ws4Z~-eDyDEqSH&JKw>1uFhuVIMsz6MwnqlMPPEGt
zn#?e2px1p%_a-5{j%cLM!ymH_Q(A+e41isuoe%(=={CU~Bi)4An1fVuV8$iAOoPj5
zms1A>$BpvzL`6-qvw9w;V(*=6EK_c25MwW*YEV`{$*9-&5x>+ABHI=MW&sr(8#M!A
zr|r@u%luV*9Z@5;sXfVcmLqYXb*&v!z!Gg2Kn$x+N+g-fpUT3R-JKuxDe_!%OlJ*7
zyp2T_lhpZ70bC4N=0K)S^TWjNKQa@5nTVv|tl@drw_et!OkS}+>LyU-l(J_b4nG4>
z8#7|AFbwpB9!$x7&z-lYP1o!~RZyQ>g%{r8?wJ`d8+>BrjgphD$Tfjxsv8tWR;6O&
zNoi|4yvAwjhTTZXoJ$O{vZB>ytRtG@V-LZ3AAqBD%&pQml7vfSp9B@^fqsPT={Z;b
z0HIYAzlg(*Qz4v<Zem_gAumwQT>)AtS{)kXSAPS6RW57J_4f+$y~Nlu5ni&US`@Fc
z<mzY!64v0DEB9&t09Gm@gAv}xz}FE$i0*-AswTU0k*Yiu3bR!U>>E^mYUTxq-YJ<y
zt4@1muvQ*%u}}HStP78>MD+V|>ts*oZWPCYEk7bR@#zEOl$7r~<yhXpZK;GE!Au|O
zn9n$afQI^S=X{;NY-huO#QBZqeZIL7@y>G*#ytS~%u6mdBFkkFVV!+OxT!zfHBue6
zKri#p1{6y*`XacHcRAGs)RfzG%#N}Mt%le2EybPmR?O9YCC5Je`XBbxKB|w+Txa>p
zbN-jBCJ3kS)KMsy)U<9H_2U=@!VVR5J60odUYn-Sh?^F*y0!i_6=~X*m#D$kS98?7
zymM~lvW&KJQjql1Kl&AO_=tA0iN*f_YQ*|P$dpL!4BL;Ukx_<CxjH>~zj@Z%sp<7A
zP`JbmaCL1q%QcGw3Q{7ZK%rwA#_>R+Dm<%MONU<&8wu;mk1D4p7E=gmFV}dH*tJPL
z>IAsqpTm_i?<ret8vy0K=XGYu?$f1--)+9LP=h-BA;&yx$!l}Xiwx4trtqBHi0+mI
zmq+mUWJH${fl$^`&^q}*Ra8!L<`5FO@V(G{88IMiu16xy7C7Yp0AeFGFaH36$QhU{
zU-t(e@UYkd`^ygqucoIc)!z6FS#-VZWY;mTM#*M{`6|C-B&5r7`pYtC3d)blF!GIF
zYiZ1;%Oe(7rd!C3r6HHB2+1H3Y~fm5X10y%6Gu)SV|+9Ca(4H5ueZt*pIKEz*7&^M
z%QR^Qw0!j!PG~kxVU^2V4j^pA@&S_sop_y{a-3EuViT)d4j^mRpIzn-Mx#dCAXYQ~
z032Wr`{uJrq!8f`%&cRTqB)x;zQxHziI59eGsa?ajfJaFhMtZd-q{Jy!TaRT&Twbt
zwCd?4-Erg|(L3l32w<)?T|D}$0*Po<K@-wUW^8f1Ty&zDlOm5I<K)Aco%2Zks2yF7
zGcztvE>MqKvZB&y?M>Da@P)q4opG*9>}l5O#_If&-;|7xf<%huSt*%@jKN97vvOQW
z@(KkW+T=~9%38TwNrQArP#<#5A;RQcrHt(WSd_vUSDf6K8r72pJJ07TycC4SoV9)`
z-atTY)pqu>={aAry)}CB7*QlA#zT0C9zS5hB=lmMU((KNLeGBEAZdtj$&cPC$#-*_
zQoGhBw6$cDASvMi#%ox8Cxet%o2c4M*(P!`DdQ9LxE|%fophu`)o!Rh!g-?1XxH8?
ziIMXYx9qmD9u(>p(f!Kkb6XI*s^IAEGmb~ICVK0}aO|)#7UE#$3f+F|)$3!YwWNNP
z)8An#jV3|9>G37!9k=U}WmP)VI>l0{XhuLrW_dBH{1FNv^9305n$?v{R;^B@H$z)G
z_MDW!E-z3*U+k&Pn|75Fjt6Dsiz)WjOsj;O)-ck8YYRTMnACS~TcoG7`Cv7uWE$r3
z+`|ipq%^pdSxReDD!4%q93VF38|~t7q9sWz<<Iqb7B`vk4LMQh8*+)6pFWyy-tl8P
zvmk6TW6*f@GJOl{c!`*dR4J}pV<i$_2<YL*E2So6XJXMjqi#h%evuo@Mm%6%VmTf@
zUY<g@`cS3sPI=AB`h4wQMzqU7^}mNw#d&I&w%ZQ`I8x)9;YVOctN#GIG3gke3>oy(
z4V~5iMNRkmNtTtQvZMQq^qK(1Ij_~K)GigQ>bi;Z1A;Q~>883>YzHcU*SB!-WyH70
zSxZHEVPh_LR%pNdG)`KO)byi#y+oxiZ(6losk5v-m56*K?OLB<UaIs;@{Kup;&&3i
zP~4d4h(CApqVCXkYySZJ-22-#L+2DD(4%CpE=Vvcuw&1UPu??Dm@9kCM=#;R$n??d
z+N_(V6|ba)Cu4p5$DARF+jCTzZp~MDvgw^W&7K&uC)FzXsh0{`Wq2PA4W0e%vt37Z
z4p}`vv_)Boz(QpZYjW5JJkN6#6cJ3kj17I2ltHL+7N{qGz2ZMHLN5tbmWjkn>@eB}
z$%s8Vq>Xzmh|<i9h$l<B?lOMal<H~;ov=fZ2*rkz+(tsTSS~OV6FV}AFeYnD+`8yN
zma=}^SW|M^{jt=QslqR?hr&M()Qvf&Rrb`NXICLTLEC=Wq}JsHGL{Q<_?*_`YsH!l
z{`)LgLP*BeqeIy;BR4Q8oJ-_4mAEAKa2IP93=KXAE~Z%fAzdKDQ^v}mg^<G&6h&Q0
zjQsa#_p7AFBC{jRwy<<}&2g*QGn9%OsHC!+rTV8~St*1J=F`3zNJGvL<ld+?rsWsX
zWP>!0BdM5UwK8q9WZBbU%#f|5DZb&cnz{8*$&4S+%#SmK)+KU?c*XrbE=yt{Q;`KR
zGlRF8lu}KKP+6x0`;%nlYb>sQIcfTY?AK}cD+e&)UA#hT*V*1;c=kl4oH=huAk2iV
zRwlQ_7*iJzrU{D*4^4Nszf`kY{{VJ-C+0A?XDDF~0{O=0@`>yXGn4LaW?)CDL?UFN
zgD5jvA0s-A$qurFD#GQo&L(#$-}lWYsj~!n+?-k|!{hMd<7`unYjg;aEQE(qYM8Xx
z&A_wIh@FiJDUj<K0w<c7;w18&rZY-;F<dhEc2PK%2}DOJ+mT|wSnfmlk}+D@EvpPJ
z!G;Ds#<ZefEl7bzSvfw5nNY{HDUg=g;TKxLW4XahdC4vQ^wBQXG<5p&R*1xz@fpjO
zp)eB{R=5x_2eHWZvuq&+?c&5lBBoS6wh=_kxVI{mWsMxQ0p4*4tw{2%QKXv(6)U|C
zV6nT6bSoAzFl;FL<CEtd5ZVB!{{W0+{Jv90vn{)<8)!7JnzZR#ElD<z`)ajR(&%t4
zc6ML4a^sww6f9z9J|j56A}Nf7F=XgC$=WuLT_1$%2<3WaZaOE@B07$JBW=bCW@jQ~
zLUYDf5$qmpVV}UgL2PX78huKOD-Ll?8BE95-t)(gqkKq=CV-=h@7>i7(#E=CH;m8@
zA+HQ<_*`UU?b3O#e|=ty+puzHkr8^TMkM)F(`;EG7^y{cR&W<?uD32$^(Q&f2qK+R
zMkAY1Fvy9rU5ti$ZM;cm{pIoY=)1N-Z)6T*efN$${Em`R4YpCDlp0*3Q(-J2$1kO%
z9>2o~7_9eY%IkU<2H_~NQAyecAV)QTv~-&TrO;De#Xh|#ptBIyNrOTLb|H=DQAZkj
zbG;uC1h~cSblyIn_n&#Gara~+rVo7$O&J+Er&c2xHE~E)ao`@GwrnRG7x4{XON7F?
zak7%d$tzO)tzdZE>&+g3+-b3g<&ap%FWf4tEF`PgYNydw+?Bve6HW4b8n+^N8vg($
zvfYyt5d}ZA&Q~~F)MUmrvQ0W6A{K4O-t(1|5u?`29Bj<KHu1g^A7?=foxU>_B0s5Y
z)(*!2=PBN2KMs8o7<~$=t|J-zMDA9Uxc(s+F{NV+iq@2-L?3zDoLPmCquIEsITH`D
zrg;fJveUZA<{74+`kG30=%0DZG!HjtHQ^s=6O^tgshu|cs?VbNQ@%6xQa#ms71$C(
z6A`Avon|vwj^!e%s=c)hY{XqnlZ~XrVq~GJGLVZ}JqsJiMvOC4+sJ<p2M#$Ja%mg;
zD|-=QdNo~#Pj)D|Ldz~mDwEnXZ+<yS(seZoZ3QIPoAv=PBx(|EllNxz*0g@^El#@0
zn)I;xrGE(9u5oe3doZSEW@eR0xu-4{Uq-4GqrXs%6$&6rEZ1r86l}_w1C-t+UeKSQ
zObJBHGS-)1Wq#vGzLPhmofA2lQh`?Gg`-mI12dsovs2f69{VqBDmOZ!Zm{X>Q%SYC
zfvZ-KR;9@7BC5=6R|w`?X985XPk<Nbrh8r<R$x)7Okgn6Gr)ih4p$Sj#N;%=u+`ZE
zw<g$u6Z+DaDwnEtZYFUsYMDTk4E^T=_Wa;t3O2|{vrl0}e~(H;QZEdM0`ZbN7T7)~
zG}m<58K3S6jujg3Oe9_q^o^K^HcSX6is{z(Z}LUHK$DE-Tmae#Y)&_5QcDPiYO6Hl
zOp`B7rpxTLlj))=B}X%s9Ho$n8<6SgCpk{$l+PNo;gw90Si*lcpG4N5neH<Xvy86E
zLGsN;w^4Rt(-xi!%X|ZMhF@TI@WLJ_3D5B{9zIzvU2|DOLxTu#d`|O91#7i$1`2nW
z(_{zSK&tbQy@s=wd2p0jr*T}(yf;)~M%zXb`symlCjGz`Y|EBsZqI(xh=b)+hvm`*
zBLSjSVQLU~*_mr5eKz<+=l)LF_v0*qD8W;-1o`DKP*8KZOyH!-Af|rsYea5Om&k;I
zw59}mmCk+r&S|yTX5wI9Yfhl%5*+7PVKuoEwB>i5D=@$^gGe$XX(-9?lFexxf*YTB
zn2nrM8TaBVX!hT+67=dc)A^Ql<gOqpX_l;AzJ1qEn{`8wDFYJ9ZY?uVj#}+fuXKwh
zKPU^*CuzyeSSDhW9LC>!h|KZ8orRgE9EsW((PkiDIlRvMn>$1%a&nE<>1+dnu&Jvy
zmd3zcplqMtKYWtfCp79YTd}9HM%dS>5-?i*imc(Yw>>!3G3H`2$-&rc#-2TvdT_*+
zWCe<t7c8dM{p{aB4pte->XbdID(O2fQiIu*twpT}4Wc*C-#>WC{{Xx7hTT>K>3TgM
zYLk7M!B<;Sm2!K|!DfV+RTlD?o?bPpJ-U-5EwN^$YLVooXCzk}aOEtBT!!n2ZhL}E
zurl~aR-+MEV#P};&Hg-aym8q`b@)n=XgWWnSJ`bHT*MNwXScG=?9(+8;5AnjN(dEd
z6qi(>)y_I#F%!XrQ3ED$0jFuoqd%Uy^0On&Z~p+M{J+WC9R_BQyH1u0lU&92k(GWL
zkf5#zI2y8#m%==_!9rF2j*jfo&I0{;ok<<wLz+F;+;RZ*@9d)txseuBww*;$O7*I)
zq~c3~tWyNnxIQ7(v{4S*2R7`J?X|-a7bA{oqt_C~T9$h^F=2nkoMgR4O}fgk&0e^_
zgsRHj5wtLdW;5`#4dq!`z#7{JU;8NAQ=PQCK8pOR&17g+O2D?G8BA(eBO>YjwBDjQ
zRO`hYY6dH-m*yW-0LUPT;Wcuqd+ntqT>uIY2G(7O*_zg!MyiCByhA($?A7rD5!E-(
z)xL2rsE|0{dosY&=<@>LaXF^-6BxOzGa8U_D>8vjY>S4Cio*r%HTU+&L79g$=D%;q
zXEk8=brW@KXxBi&O%QnYQGnQ8*tRWmGcy}(z|{(3%@+lh%vod=H+L!Vn}HXdmOc){
zTUNphEUT(T;idF8N0zr=rqrGrXP!c&pqQnwr*%nuW@2mFil9Xkrp3zhW}ldyvY3|(
zWs&|eca7pcGZBF>1|^z&%OjpIZ`#$~K)71$xqP^?(IEnuLi-`H0kkEYtI4*Nl>l&D
zOv~aRqBEA&$TGmiZ5pzFlTBX=Ny(ldWtOeM+7gp|IqH!@z_kfm3zU?}QkFAxMwTR6
zipF}D7Z|*P+r(y|n7n{$KQ85p^^<Uxc3CRZlnuBX$-LTTPud+Y0SPmn)Q4%z=Acos
zM=lp(12V00vU3rAsUliy@3e7>L;$;!BO}uzDq$(0!ByLIPDFjZYsxvSnyj;BNp7xd
zdXUQd@BF$C6;XRL?|Jmvc<(Rx6fOhxMs54zN1H?f9K|wcB%q6Vgn*(P&ve1zAu}aA
z%`Je<(nL%mBFeBH<xqZzq4|E|<Fy`j-W9!kWce7)4$pOOZgKnXG4UwPN9RGi<V3|^
z74(ZOfjLb|dSc+)Z(lf`6|CBQs1kO64@df<rx;Rdwo{gOAg;-{QF}=kfN6sG#}GZ_
za;QTsBnwe&x`kIt#GGKUv&IjEe~cf}d8U+xp;K)x85ac@TzQusTTDz(iN%C*mKjXU
z?=)^z4KD3=-B$H9w??0<*VG~hKjnaBY>slem0UX2jRi=Yuz@CD86G-arcHiboQF3c
zAt<#UnFs0qGRFOrzKX6X=bywCV`W?q;nCjXOBHV7wWkeY)vKz@5X#r;+69n)`Qmeb
zOAx+>b}RFB*fY_0W~jbGJ4D86SH^;1@Ij6#+e%n6ZE3`CKtu#Pe*XY?d+`m<H%iYI
zKI()kDVJDVKT36?%L|k56Dy5%=87qM#f_qTwLMQRK*hJf=_TCYu7AWc5UOFCfA#8}
zIH$zzG$}smkfqM8rYMtBL?fL;mRAT8Jl2vyb*#j0-{I>cnpUl5k2Nlw`@4*6r4#t7
zUq8eX@I+z9R7&$o>023L+O3v3DCJjgbz5r*6Di+80~T4!&IR0THtjEM!K%u(QK=wl
zTXl9;c_pJVCNdRkFzLQRc7l*{RRqn>#k08G&q=cm2t%~h$#+*OmJJuy<JCc7sJvxO
zKWP#bb(K#De;{DT85l7zl%i0G8M^|_I@C^C*dZ$);l3jmT0v*Bu`%}QomO$3DMtH6
z!&av2Nlk@Nojl~zV!(ANOPZ6ls`Hv*2<r-ykG-;Y!cvT&i&Leil8MJ!Kd2JQ;;tH{
zhG${<#4o(0Cz%{Tp4m;%4H%arrS*?BKo#9!4wlkWiepi&Dz@l~{{RPvCrA{;V1xrd
zFbzzhbxGzd@@a;Nb3k18s+7Hh0CN~ove!9eea@2{brO&1+nRj9d#(2k<B)02Fq4t%
zIzJ?}dXFm1K@zzxbvlf@Oj#I*8YV~&XB3dyQ>0g5%kRSNvbia!)um-;wR(L>yJi$x
zk}FrUE3Lf5U~ueTW#v(az);icg6bwP204^rq`ki}rR{r1-D(ieb0oz{iv~fV`{h@i
zM{|Y(&rlG9E=E(4{khNLBZxGpe|X9L@Dg7Lg|1M%u{==0limt<o$I6~Y{9M2+_1C~
zf$FTzwx)5-Wi3$K4<S3mV$TdX&)W@xI%pK>pObPJe{(aGrU7>uDcV>W5xLGBhtSZ7
z8DnMbJFNMpyYIpf8kLIhE2~l?VcsVSI_NKN9e#}7MMsGS7`j3=8A4%JB>?Iz8lTDa
zIY)Zhf1TrPvN}d5nx?clQcRfaIf#<^ZGcdiExj&~_QJrUZX4$NE03tm!3@HxShn*7
zOwiKiAS^w$KQYtst^@p^_328<RApA<hh~;f_{-zHHw0ADUuW*VnNp|;ll97Lm;kZc
z-pF<{><82Mem}1x$@4{r={$;n@1`6Et^ikS&08&Br|OBEpe4!fhQ|K@!?mXs{pD<I
zp4M~m7!e$&G{_0RUQ0!v%)Z<q;AAuPzAQV2cOvJvq~)1!u%*2nQ53Z3TJ7GiRF-T8
z*50{POC;^t4ExP$V08B6ZMT%4^csl5z9_JH<~(Qbje#mP&N6bxk{OLN{kq~V4`nMV
z%@H2nvCdg~ba7}TicF-7PN(cqJ*BNVm94dUIhB=m#Qy*iE)2n_aN0uLy?*>MVxrj)
zR&_Q9@a%7VuzvGZwhy)vOmiFJQ`{%XTE7;t7ejyZ`nHUd{KMaJ5H!dUh>VP++feL=
zZ_}s7%N^3$!HAvr(93N=GK7pW(<0u}X}O?`6`KtmYMU%d+M$!ENim%?4fAs4om9<4
zn!YyA;nc2S?ZN$V8K@r}Lg3Ds@}a1!7xy`*Y`sBqBO4a+dLXN{(ehycl0a?0ZI>b#
z`t_$%K32Hmyg7TeB6pn9hE{iTUVs(`)QU`^p_&*EBxRij)mpvgme$r12y+lspDJ#w
z(?V*dBKaP}ZtE&7X^M~>zeb;9rR$tPN`Hv3>Kxl_OaA~1wE&WZGfsEsHD_CrOGZD>
zfFb>3mF@DI0WxvW!XY#;V;`ky)(U1>r_C)g@F>{7X-!#1!fq-lzem&ATHE2X003EK
zf7CVv1ob2>IB9i92Rp!ZuQzQgQul^?&+moEqhZ%+ba7@R*rl-6pZ0h6&A-i95TSLx
zlRN#4c!SFl78s??(TIPIIEEduJE@l$UGdMb)@=P}mFyjOEYrz;@1=Ip?W{3b5$1On
zID8FuBJFconN*UPkt71EE;SS_bg6P#>GGM3lC2+FezMviNrZ8<w(<{<5ihkiIK!A-
zDC~>rK=iyM)Y$T$Yuow!H?BkzS=;2qu682`Z6GnjxYMi^*&`cFLUijk6l@I&s>DsK
zh}-PO2;yf?u5<I7wvrWMnx@$?Fs7;)l*6OX>d(1|$45o_`^ZM+8CgnV*(}r;r|Q!d
zK(sMDoi>GlS)f5HG@=?&*BPp_jcCojGUF+d@=S;-Rm%W7DZ7k+oV~)@!qT3Sj;29D
zNiuVP6BA^1)97bD6u4x@*(((35MsTyIgpRbV8#F!wqa(`#&=s{H1S9j{6JznZ|P!Z
zs<6$Tx$Bkq&BjwB33O0V05H1<ic;gQLe<|IPUtpSrd9Sa-z*;r=u9ah>xo?1pPLt)
zwuuq+fb0S|kJC6a3yxR>FsASS0IFFvf&)M28TLMtBi6W&O8Qw&$8Of4*sp6}Y<#bf
z^)tb3{J(i86I??mKiyNVQqLS_PEaIndv(~7>aA+SthBl1!|pXZA~%?fIo*XmpTpiP
z*({l#WFzRlC0fKuxY2A_8prjuLdfUWoEadgGB(#0^t3A@Y2&f|l;-i{2w)(mCSWPV
zW|HOB8&5l_=(O03kM^hN31P<ge%W!d^+S(qr;G^lAsp4L_ZpQj&+D}G<U6NP6y$vl
ze-a!;RpR#eZjaG+B;$(H6B9dafFfoQiupr_Bhm5Yag;c!v>UYRg=bQ9-~=i>Ys`<t
z6^>U262ZER&?;1wHXcyG&*9^Ur*~ud8BRl<l`a?LvS+hJBh52T3M1w`v3T$9%z9HU
zHbW?ny{TrNarngIeXi5ZrlX(Bm%E4*cD4g-(`@@zfSc&LtIck*#kNKQxCeS7T)9&U
zGethT4mqgahC!CATvKL9G*N}n-!Gt<#zbz`Mat?8QKbDs$xr_PiZFH<=Q<p--Lhgf
z<`bIF{{T+be+~#2xe^ArLfv@NgSHd6d_s5pLDOzrdVOF`ms!QM`=yxDmbi$bb|{?v
zLm5nC^XE11_Y1+S@~&24_GPnMQH@(yvt=N>86o+KEY;QI2G+bs?!XA=pBc*!G(J|C
zwR6OZ7<ORr8@y@fH`YGslRAiUcex7JnOopaNM5ftD(FK6a!xBU9IjYF!Ph1kvCy@>
zOUF*Akt12E-Xny`hH;LS5yYGnhv|+S`C}|Vguyhoam*7<O;Kf(^*d<i+yb`fu?0mN
z`N^4$_PuqjuyDdscvoCoqT1EDWH-bJ?Hl}xza1~Rdw7gm`&a-rwgzy@hRM8{n2NSd
zy`@r9Y)3Uz%oS@(Obw5TG)Np!a!_HKal&sRNtUIJF?dAFhy{Vu$RSQO#O>OhOh8Wf
zOjD_vmp0I>!a#mi$dP2kah@SNaacKYA2Cu!Wxzsz=6+gHZKy{D(^)Ks;|9*Mh!LvA
z3R;<hHI(riLR$@*ix#fHSCN>VrwTUPZ5v_JQL@y8I$}S3+ibSylek8z9d?TXITl1m
zw0X$K<(%bE?RZ96-@IZSA1(7s`JY_s2Pb;e&0sEEphIbh#Vh)UI!R*4+b>XcrF<=m
zft*_3_{IPS;VdkzW7^o|HWSkAhO^aP9<#41ZN&Fk_KJB31uI-vCw439Nv-YfbF+7V
ztM%`I$%K-N`zwjanU6402NGhEk8IJLqMH(RnkS-N<Wq;HTL6;5tznVyY464h{Q6Kp
z7+G^WIT_5%Z8H&;UE4~OVqR!1;FChvn(PKFq*<kW?h9q-C+|<z1detd46b5ERl*lG
z7c;!?ma9!+N(fd1KbzSxF~GLob}zB?i19e2KEZ6MI$B=Bq9-s}X}*dcM-iLv{Hm_&
z6Zv#>1rEn*1QZY}e8-LT3tr}o?2inB^!tLG+)9qpztz94j#34Dp#K2bqtQ_^&N+9*
zmPTo;OI>24TC%S8D5oAWu&RL77&BO+G10Na=J6RjHo63DY!V5DNkSbMRXxU*i)jA<
zaXUnEmB7KmRxuD}XSX@cS~wkLVE(TTT%Co0GVFdgCng`&Lo<{_i0AP+M_pLN$H{xu
zgJ9YSaTK#@pYe<Ez?`me-ZEBK()I_W0N7<!#rBiQR4YuQK^3G>z&lT`nw$N__nuXl
zb%nlbJdXK3e{9lct4q`woMwEJI1dar9K;M$LAOg4N7T&ZPZJ5pN9N9X^N0fN-YVES
zmK5;R&?>VmTK@pF`N;jZ@yc-grCBlgDjc$GmI~R4+8Q%4Gc)5({@v`2zNHHH)CGB3
ztUxR>&8XDqS%{&@r_=^y!y_o4%g-60fC`x081otYKpbzu=kW7*UV)3N>Aie&ND^t9
z^HyOhgLY@<0?bYnHI9@dA&yuT#06K9#3PdeB0LOYJ|M!)YnfZ$ImDnA6x;0^#nzD2
zn^8VE#%L?u$1<*+bxc_*ywbAYb2B7UGnJduU=I>J;;6|uU8}whuqqnbmh9GbM@pMQ
z;Zm~<8p}$q9i`~6mZ|VXw7BCe{B-NM^WIh&LchAZT{)>tt5`Q<pJ##_m9?97Vj;C}
z*SZrUBw@y4=9^mF@*~nU2DtdMNW5uOmCYv2MZ+?eigf)LP#{x{+1BPF$@VzU9M&Qd
zsOXj!>+B(k9H`(^II_xxEhq}8D)q|aJf7t=t$RxfI7bECIGxY4W?rlBsmEP5!8lVt
zki^JgM1;uAV^6xRGjc=S*qGu{Np=x_XDES&dBN;cHy5%Qqt%z+RJTU1+^j8>*)bI~
zyZ}QWr=0<Yh5jo(CmVoCiRDrg(u1>Di1&QrVhd=2f+HD*2zW8nD_5;#eZ0og=DWSS
z*2uOixgF5uN=u1;6}FT%W|tb&5+zU=Z)KNr3Em^Yc*S0?WvM57ist4e%&4TvphAyw
ze=VqOUTbw|<a<e!gC<7BnX^?p=<y*ItYj_CnJA&;#W_E`;KcQWLknRt!qx1l{{RPS
zp=dfUxk8>Kh6EOrrUV2^%%;lv>(!QW;;rUG+uKyC;>A{97%*&9nFSkco;cN}P0p@s
zVvm6wKv`JBKV156wkTB#E=a8VN5L+$Vw}xn-RClv9PRs6Uqa60vi+w-bN47A*z1i0
zDerRDx(SN)!Z~u2851a&+j!n!WfESSVy?u9D#|KXT_ZZM(^{R7a~aEQk}1W69M0_`
zl_x1o0$+8uExOZXr|NOU<~=J>>lm|k&Z8s}n!i}iV_hqsMSNkUn{Dc<k9X;*{5v0S
zE813@J!@z2B!9x4k9ARQ%gmJ3V`5}~3B`C#g7v07#LDruz+3uhf7{fj83ProZHb+p
znaxLey#2__@`^Qd8`W0$h{=$Mvv%qe!W=g2Ss9PKpS=CEOqc+$*qP-sGE*Y4<Dj_V
z3=y}dEugD49$jE64?&H}B6XjZpmB<kj@il!64a{lgPCj`lyWiOc;9^1Tu-=SYXSVc
z(|L<57zBR#_mTG-W5CXmEX?h<jUFT3LrJWi9SmVf5u6mzvkljT#hb$pC6G0AG>%aj
zA9phxA~{NTjR-c3+i(JzB#cZAS&~8~NWL=}aQIVk<+YK%2zY@0rL>hB;fxIUNPFEV
z(Sl!;!NHG^$Dw%v-2k$l$@9Mcf+NyQpBY*<t96;T?HWx;sNx2_{Z*P-rPvhP*!1t|
zH8#O*^W&2{?H-jSb+r1MhcCJQBaiA4IZ3dKh-B;E9ZC73O#Ap8{vnx)s~pqfCdl$-
zgHEJwJE9WVYP0w7lHR(@H{nr}I*ImKoxK$ic9Q^$ZyQvD)9Bf0&}-FkGM!fCbG7Ym
zS<@bs_p5xJhbT9aYo0&~t&&>N>{mDj%fJ%TT#U%hnPak*p6w|oS<J(ou9RVy8deKC
zYrWi`q97*uv3k%zjfZ14>aT?|Q|C&8E9r>UEge`#8v3^_*4oxlWPdI7l;9zjvc{EQ
zz-qEJI?l-Y%fBQrGw{dI<z(UtVrMlO7<#7^QuUg0SVgoHb#fi*Elo|p3od83QqE_8
zxE<1Sf=raI4A|B&E{ex12!eeO8<AidJ50!*wYVd@>A5k=Z7G1`76HsBgwESWef@Cr
zws1FSIMo?w0ufwdD%v?N9RC0)TGh_+LcojFIOT<VR>u`9VMI+{B}b}8v|FJH!WjaQ
zB(tdCrN15HAMT31X)&4FN+v^WrY8M8sTvwwR|uEbTdtQ}l~OhEl`WeHFm@kA2u|{9
zY@y)MzL+EL8zQf9)&SX52)!D0dpwwk)9)A5={LQw#t6l4;pUbk3T8-j&Sp$&BW_tg
zsxwe$3a1(S&TqcaKj$dEy=^LH&e~<mJV#K|N-5PW8qKywXg4Oh1Q*Et;|5wHhAyg!
zOA;<z`VhE*Y}snk^@*z6SYLbkgd0K^2)uM-g?sQF{eaT!r(ZVZF%hV+!dFuxB0gJY
z!~3M9tG!rWOtv<ej6vjhTOxfYER0vJ)&SJrs}pU=m3fR2CKL#^V1F!6ehZ9z2oSD6
zk!XFVb*uV4Am?{Ewirxq2+IscH93!U!aY8ff}>=_1x*>21F}a=p-g!^wfgG#qSm8w
z+|OisXxx7cM;&3jX_!RwT}5Qa^s4~#>0ISj5^-RNUf9po_4S%>#+Itc;oa?3428oA
z7rz<bHC{gNampH(Ns3-IJ)WpwRryKpkWwcF76XNo>EKf_6aHf~*#W1iZ?Ns={{Z`w
zjL|2Bc1}!oAqNg;bDG)osG>HmXH{gj^QQ;<4xFjvtr1{MNFs1P;wJs3>cSl=fUqz#
zq%K^?Ab0rj^dHp|8Kzm{e$|!`b*>skH0MdH4ac#KX`4sP?G;3V-BUZY<~2uFoprD`
z5nd7z!tBI>L}7D{D^Cc8ob9x(17I|zPI8p77!|}vX6I@nS+OgjShG_Q77&2O^ON$=
z9DLV#(mg)jUZoW-wOBK>#QIFgO2a04Xz;-Z-Y~#`xXF0<=iiKvjZ#EFCq@V}(`-Gu
zxUjWL0<@e=MrlxXE(FPZcArrXaj2>XZHhDal0Po2`fIyzpi}%hsVgmJYvsC8hgvIX
z(Ak-TTyI=`<yI%sck!BAInh?uzBB`SKkggpI0j2FU}~K&ir1B_vUaYxgDRu&PNNn_
zqWBf9Ey6%4rK!*4*-Ps>LDgFd(v8zrMqj%H+d2sBCQ!)<QTwF(Y16q)T|{F<i!w$y
zYZ+*kUyt;wc$E`NY|xt`_pCpNPi~cAewAc#kG&QKZcJzK0mQzz<#u$1t*R1H`QVLY
zPIxu7!up6NhYAgfP5ef(Sj4u*wMW9{&a3U4JfBTEj7utChIWXY6p29%p@?~LSn`?8
zE?E~>#=`iq!I2((thmMin?_OrWGgOJmEJPg8CgjO6KD)sXiUuc#9R>>Tdo1z5nv3!
zE11hpBu#B2Z{`<_q;ncYnawrhW}yVzQz}H)Kl70=&7i6T_j1i&uFNLTB6eq#MDp7-
zo>5_&J;P}`$Lhl0fbUOU5UL4zm1&1KMn%wFnmxd$P3Cp(R=gbY&ZkzW#N{1PSGkCM
ziat<KQxTsL@g^^=Tds3W2ynbkPje%Jf}Nsdy|6EfZ5`E&Gh<T3F|F#*=r0CqHYPH<
z`qVLk5gXzCMCPsj(<5#C$P&xWY5<j4B`iitU}Bx;&^|$SeccgdFaq9Jm4+i!vg~6C
ztTUSdDP*rL#E`LtRwK8RrITWTqb4L~MsXG8{O2r!gJ4$PGT+XYo_Sa1n<~$UZMLJi
zS(BZzb7{(uL~U4~gPc;A)h1><42DDdEy$n0L@HTfOU+#5P}#*4jTCH`u?I2)x|XAK
zaVlZ*#Vn4+amk)!Mf{cY_Bnm*cAy{xs$Qu$Ta;y~?eCwkft<t&G)s+{=B*;KrVXd6
zP)9OLbsKg9LVo_^#P9r{!;>VNBbuW&tZ|Ml<}v()#Ao}yh>lhh5c!#~?ep&`_|tVv
zn^nDmt07y5I4J^V;eEZX0`&7cMo0D9Lo5p7ArmrRbCwI>fsLa8CELuQ^Ul{)e~hps
zPI+dExb|~0Pzt4SlOBs-Y?kVnWx8lyvP+wj0knP}%K)G>`&sw4EMyT}Bs|X98~NLA
zU=bQQ%Wgh1_w*muJAdsu?y~TOjYI?c<62}uCUSZ&B1}6^#>wLjUnxMH_1Vo)z50ct
z#s2_m&iP4YVRRK6XD%v;oe44SCD|NSu0^ES_h+eR@eyw7i4g`dZbOZO0wBM?GaGR{
ze2=xD5{_D>0<V$imBGQ-Ws^2Hnc&Y~#H4nJoK<HtuZINbRGe#>l9?i*BArsUYRul_
zRfGOBNHSgZY{8C$T;lhZ4#Y%)sX=o;IL&xD>D;|FG!rz+7IQ!zH7p)oqN_F2mn;+6
z#};r563sY6x$DNPlG9w~f_v7m8E#@!oP6?oXZbY03o6dk!0YNgrbJ9BGR->8bsq~?
zD$34GQ+P@NS8h;$un4KP#j!ApZW#nN>UGV2QDHknpendxf<3Z?V5i7TY7n`hW0+QP
zrh2N>yo$F^HPJUBmAP#ERUVokZ*ogD{{Z(XoU9CGTM-R0C;$y4;2!3k3&y@`n&vmx
zD|On=dGkBf*t<u*t-$3bpkb<a0=lM%Cgq(in|7h)zfKRtCU9&pe?`~_){Akr!4r}4
z2#AwlnDP!iWfGGgZm2!ytV(rlJYkUK>|5=V&6ApYnzJ+&gsK7OF~)X`QLll+BoIC)
zO9it^xH+Y`t4xM#OA@6Af(oCxgvZruD?|v%Lab7}pa&$l1g=&bxap<CQ5YdJuEqvg
zs_Oe3@;`{l&-rOd?KuUs#N}eDbS-vpowxJ}FflNz{6We<7(vW_BJ%n&lNjPcr4f!<
zyVoleUQB`Zjde|%L!Iz<^ZenT#X&mC_^(e`A$_`l41dQzZLt1Z<>pACikyfMk?xR5
zOz_rXd+U)ZVj4T`=V7Q!)9neJojMJwbyKZrVW^0wNNJ24TcS)HKP^{xeTgbsS=v#U
z27ocPYPv$!sIVhLuD`o$b!d@+O_s;zPo(+M!zdpMi{;lnvb@yH3BfD%rQH@F)Jo(_
zoND=)4mP78#0RWJXTQVt`1s8kaC(BKb%qo~FCsRtr^bJrtPv+BQ+#big}YSBv<Z!8
zabXb0potkk1Xr!4#Fe`ipKYAm<7o7VncrkneUAc-36lpeI+}MeH%_-v4W5T;x`n2A
zcH+haZT!Dk{&)Tv{WmjM{{ZkzBv#W{%Ov|wOaAkH@`S+LNll*?U+RhDrrq7#d9n{r
z1-DANkTsf`OFKYd*n;$>ZZx`@d}gakDFFsYk2(JU2*UDHIDs$2u9j4p7kJB)F=F2x
zE8|Ubnv?)(&Wwe}9q01n1<XB_nnGc=8DljS$l7i<Xs1|m)N#`^?9vb}xAU3gQ8*q8
z&(Tg&9hG78ro&(_v^YVA;Sn?RD`c=lLrtYsUAmXz=@a0cFI78scsp2k=E^Y+Wmf}2
z-(0+jbCloC%)w&5Nwc@toYB|NrCn{s^;&Sj{?Qr&ifcM^T6`*#6&ej&C>v(YO#3xP
zaajUxDY19~cV7_+_{o%&a0!5&MUqKoSGL-=)cDnE0?sU=Jov`Rdvr!Px%Ey&Moj(U
z1*XaBd#g|ag>&0T<Aobe1em=|jaGNmt<DGQ9IYV`=i_1u+Xp<1%ubWISvKaI%EqD$
zHtGPaN-LhAJ-U6M^lN2$B19?H8ue7HzUV?cP}A3OulE|Nbjcq2HCw@}nbgpwF=hq?
zIs8BOW;0OSQZpW)JeVVZ3l^*us@h9ArGB-n^fTt#YcoWnR<P8SSxjB2f8%=In_*B+
z1s4;7hI~j2ZbJu8o=uRgUS}dBg0{90+LuOu&gf|<30R^VKf`Sdc|l6{O=sCHm8MrJ
z*9a$Ck&BDYXVEh|WdSLR+;XX~96fdK0qqz`uWA9wgc%K$nF$2!S6U2;oGRO~Uw)t(
z8lMO|=rmhs9s~obu>{x%73@})lq3>f;eVK=-ekMD<mE`My=m;PwloFR$W5tP#4#{3
zJf>?GD<K9fO>=cuKD-9Tx!cBeM1(}>r!W>5l&Zs-YznN7p{9c5l4)UyN4FuFmM>+4
zGec|*g9(u6WQo{+9YscQvUMOR)D<ea@svD?eGGTV=3vHnEbtg2lTiZKVqgZcEC+J3
ztxFcHbt>gktbJv^DWs<@fu}y%p=DsU!c~Tp%tb{4<lJDpqcd6TwMi&cQ)>H9n9V&j
zIg4#J$WLXL6z~Yu`MKJ9qM}4Q1b+Uw{@8!MY5^>&*$@iv1(cYO9El_aj#wSH&6b)~
z6C9#S0Akn-n_-qEhpB3SWCKzW4zWTSBP38U_;r?V#_U&sNH3Es(X30ce&LL!Yu&Ua
z0hiBX=m?F<BQ1SK`2aIPlY=}+W-Nx*Ut<|Ez=O}Z+9gIlPB{Mc(9Uo6$({J}Tykas
z%u6N&h5+JxWW;ar&{z4-Jk;Dyw0nsE0P`YYQf=!SW~kzX;w~Ci#K~0>uP>vOY1z6&
zC>F&b<44$DWWh{mm&@ohO`~QSH0?jHwYtd=h%5g9-V1OL0Nd8zzdc`%;tmko5w~m<
z{rY~>{Ij3KqqTb{n!xN8@qRg;MEKfx&fCT>aq5_`Ck<&#M3nf!4fL-MF2^vk_N=QE
zD4|IaHUQ1P50Uf$HV`OvmJHpM2B$vvNU3WgcJqO;L64=Tbb@QHoN~0jG5-KpcK2`F
zNhq43nzg@h=mGQ}`Ak*brktbAMUpq4#>sN&jJ@%vrYdwe-q}V+`OaKcbCdTuX$-BV
z(ycW(s-4~7h9o+_3pihW_|<CC<TNZ37*N8+nWTk*tPa=OQ8=xLer*^7m>+X6{bFE7
zXX==l%a%T4#$RbKP19=Tr!e%;wSidO$&;ik4CSG$S#jSm9X`p>(`?#;l4;8ck;#vo
z0tHuAp^a-Ea>j4*h|SgQWO8z|z-b8;s<B;zE49P=aTpmo#jx$R_yH^~ICC@*AQ<9^
zr(IDVM{KCB$lo<i2dZ3w{jpva>~zU=95EjDpSleuViBq4fFn0Io9vcevU#W4E7c<n
z61xswR_|_=Q>jesA2;5*Vc(6k*IJt;d-#F3YRV!eMq*;YCiO`XQ`nwdbYV#ihA&t6
zr_6fSiSJ&?;)L3M4F0|RX5N#UQOz}#gV&YKi6B%DKPk0llV(&t!%^cH`j#scq2ta|
zr$Jf~YK#m11DB7D3xyRE-pGut`S#)`K$$4KjBJE(*vR*ooep?4#hTxX=MqJ-6B#C4
zxn`HlnR7P##g|DB0iF<{#I?06OTw-6T-l`L8=$@0DUyy_vt%)nShL)Q3sA0iE2_f9
zJkN$ip2fxcbJ{MC3kDfxNG7=nIZskR?gw33BRW%M$dwb>AzIYpS1kDFJ)$Z-=#@5N
zE^ZC1+4rtnc+G&F`(kJG#A#i!#)w49FTqBV-X(Ihi;lBgqO(pGv3f#I7cmt=CCfBg
zl5<qSzLq}mW%Y<RY@M|_Q7{v+$0<~`J))@thywyT?Byj9{l(GLnN_l9Q&7|EA&)D1
z)|&C07Ki-RQf!#QZEBa+T?bC@;w$V6G|e3NRFW`dX$p;&Z8p>Rrw*F6x_c~GyasVy
zvbf|>Ic%WpvSLTPX%;5B&@v<XJuWnt6FDyDSjfd4l29_ixmjtIrD&rj)jm=BI@u+*
zGD*kIO?)RVKsL9#RcHgXr3BEnoj%hf{{R{$JFdJz9J)>a0G9g7IgO`j(&h&8W?IhI
zF!6mev=yI_gveGV&dW;O0ckR!884luw9<r+vSR8rr{4?Pre<f%;F}fX4}ocLaDtV$
z;~(?E--9wf0W;~MM1?1|9I@=MO@c%^j?kf4606RIV74Hg8puw>I$Ji{YSJo?=3j@<
zPXfypstX}G_^c?l2Woa$Q^2oXIo@Q5%aQH3-=yZP{{Yc{pwAIG{{ZxKE>ox4&0F&T
zPdEOWeScSo?BMmzU-(>n1k6s^12ewDB@`+|Wa9q-T;-pU0nB-v()Np<<%UGlBP}9*
z)hE&;9tL5N<IW@(Kh(B-%+6LaU4)o*%-E2ImoqcU_1jxchdt0OZy_a^ed1LuVwak;
zdIpdMqb6B-PM{nuG9y=wj*MsU`uDQdWJWt-tm&OLsob-+YKr&zmmD1=>#gj$I*koJ
zHlWuv=O)OG-w{vq=h>9RjF8Uz4904^m2D{V_QWz4$lU%CYA2b1>(ibxu<~I>8Ovui
z%4hCrqyW;Y!km+3mX9qyu+|6|V+*|5nuz;-KTWMF{_^4v#_^Sp{S#0Ok4PVxfaacY
z50#ZRK!eczPPEgyY09kVThBhCFf03NQDS7s<cy;a!jYf(p-4ueB6p*PrtOdQmVHx<
zbVQ?kt!c@(28uHv{a<|oKR32!c@a$LXEAq%YYXYL_70E}_-JP?t@hR%{+Br(x=TZO
z1HWEs+G6uTh>EW)*N=ihWJ(|&ezlfHd&Q93N@xg9)BK1@*l46IeD8{5SR)-mV>hdC
zKRv2qOJmBqn-}>`I_hjymA7)SS#YuB9(6w&ZOmu%^%faaq%5XmOLWuJpfgykDZL95
z{Y4p}hsmWn(R0*YI8agx*rk67%_?^H^XT#GWDK)mN>44st}CYrL>h9LG_{5&imb6}
zWRvvJsi)tViFfe&w)~EbOGRulAzEIgzg^ml)AtDwmY57`Xh612Pnq0ect=zoTU#v3
zIaReaKTkPmxdS|wTT0`w`pLp-HlUnb=-0{Ig?m+HRLm{*J+*r>3#j)k^D8I8O>&ef
ztXemxR!>!_oUD%Eu^0w^Ljh6l26DL<vx@<A_teT_`k4=bG;F$exT4=q*<1&5W)ciU
zxPutF#xca>mmTte&AP0rb}P1{WlhD;6B)^ImhjCoxYtT-N4j?@EU3#Mle3mJX0{pu
z4l&q;_Xg-CY_;yGI6Ib!MY`k=nVVvqnc&C>+9NmKc~3ayJL@(Wjsg*4%PaPmxmk%r
zYib6Pfh|~=EfC83gKDZlS2ZWy>Lyn9j9mMhDJt(`$#Vntktn^UaD0i`X$t3^faNLd
z)QM8Y_7$h($rsLa*%iOAzVUGqdr1_W<bC3{qrW)@+GwptNv&IgDn1hUld_4d-Z>vB
z+avGH`JM5OU*U*A)|rNHALkbwxkeO6DU6@F$^kX>`muy?0~0T|T{E*IU#u}O0sKNc
z{O4b*=NYKp)J{}_ZCy8y3fi-U)9$X(0Yq7?{Dt@9*H!a<-kW}0Ppoa{`Lvbr(z8|)
z4Tl{wvX?vW5fh8v3zkI3j#`vNUUWyM;<~zY%<al}<uT`C2NWLWu?B6xH=X9TleT?E
zX>DCQROPy@Vy$(#GvYAW`D;!{$V_6VX0Bp+{v_S$E1M;x>vk?^%{OFBj!Z<yDAU$a
zcBEs<s<k3}$k@k`1<@Dk#2Kl$1?im8(qUJykK)MQ!4X#>T}q)smj=IH@?PD}vdt@#
zb8P<rxThl9wYQlUByv3Xi^tDS(y(XZQIm`Udr>*7qpG#D^sZ3`Q#Awg>yiHe6cqck
zuO>KAR%uIyYE`2h;;osLE4sp#I`jK=N9|wDlwrif7&A!LmGX1dHQvQwZO%7eV3<f`
z6oHA0)61_E@5C4E4AsFfGggx>SOf{RaGQ;ezX2IL$BeW*VzgvWA2Fjw`hwXHY_`K0
zjGj}za{ibXiu}s<*OYwTtBUaLaxgj7(inInCG?g}cP)diM|}$K9VR7I)45C#c2Dse
z##L2O>ruTg63tGz+N`HWhTj&EKg5OoLc<nj9wUm|KLQdDy&-bOmOJ|nh#r~nTmyZC
zkV8H_vwDfMp&r_j;|jQ*zbl<z&ZyWW@nd2oH~NUtVD$MQmsDQd!<29Mogt23#vBsF
zfOea~+fLZyT5oAEzkG4X+9nRHk9ey3i2nJb&gz)WS~%Z5*k!PWePuTF`;1s0_b`rF
z$ug+`=u(d4lWjca?6N3OhymU_i#@4}5Lc>N=PgdE5U<Sw<~xBRs|sM4g*{S++tZ&x
zyz}+5dFE5T@wc#?vm)BMV~57mm0b)46E!+O%SkY~!Oxzh-7c_QfU92BN6IQV0cO1M
znu$X?8=7wkwE{w(b-*U`7&g_K&k3ZiNMxmucxtBEmMB76fpqCmCN~k9ob7UAHD(l=
zCn<tmPWS{w#O*a%)h?{s)vmwbqtI?~!&A`djAU~y#>;wZrmk0FbtU_I1QMni%N532
zGde3KEzUlgJ@G3oR#LRqsUDRP?kp{GdWf6AmWCuGSqDl(Q}-}Y$+IwDc_BPzCP#CO
z)o5C@@s%B0Kyow2VCE*ErMA@;H3(`g%#*r`0M-*6ka9t>7{%k}Nh~<y0usV6!znlx
zzRx(9pK3uvyY(w22OMBRN10CnQ1Gd6rv`R7nPJCtOh(n4B`j&SW=pVwi0xK48w2P^
zEkH!X{{V^4`p18A%t+4Dzok;*=$sh&3}+r};1{e#B<?S`@PQ}ZF3m#e&!Q-PU%0gW
zNAi8OOx{0zF38rcc24y_Y(i`PZ{8y{IfB0VjATS`i2i>wJ7$>@I&cK)s}F?3r*{{p
z-fbed$;<2Ov$T3yjtWFXWW;#cEa`9_R#E%I74BbItMr4Egv?G`o#hdZ2-P%nzAJ<i
zH<K=AWgqp3<?a*4!I)|`N-hVnsDwa>Pw?5~2~V8c^<*WIYY0}Hii>o*T3q>xW=hhC
z^wAT$URlvE{4z7*96(qb$jM%CRjbb~*fY{=69;JDcge<+OiMgw)8{nv>bqF-MAFuA
zkM4N3lM1xTV}CnkL-LuQ;ve=EvyXn<DvDr_vY|j%d)cqYW~Xlpc!7!j9S!a<^H6Lf
zPqJB}>0Ef;w8JK_%#|??R@RnlY?RE;P%1T8=QS+H)D{8AFbhA<PHG@APfoD7-X<n1
z8ntLC&;DSA8XAg)Yh6UaPavF;7D1F==L|@eXiR`?rb;}{6i3`!m~7S<)Ar|TCWrg#
znOr57b&$<h@)1U{Ny)@S&tB2BK=)cl3`xjVnZW+W*{B?y*YE2&roJCiK$gC}S$X_K
zP*=TT(LF(3nspMVUU`qoIN=2Lx;;E(5dg;!G6so-m6&Rpdoxs+n|6*v;$t{}DJ)xB
zgWYiIxBmdV0)L5tm&uQ2RVkHxtgc&Dz$}E8QYDsVCC9da*mCUBZQ7?!%M@B6Zpj?g
z{OT^tFuwDM*%1RdPE|GaGZYgJt%S!T5!*DrAzkj0o#t`vZ?mcsx1n$v)tVb0N!dj=
zKyM*Bo7J#54}P+aWw#mC^wCJgiuj@BM#SP}lcl;b>L4@CJ)Z|IK*f|A%-wRLBSP!>
zW0d~4S%t>ayC9-BDH@QAl*uPokGcrTK*_CXP>X@p)ndYZ_M6)RLb41n68_$nU@j%L
zs0iAQy+}W(Hch$gO)Q?oml!A2yr0amPT&$dDIn@qN+#vNs2Opj`yhErsaU(T**@nR
zbA7zSkKWZM5Ky+ndw^fVwn~{uSQ#Bk=eV@VT}!T{&O+*N{%)o~Yn_R3TQK6PVXX2N
z*ryXf9t~{3z{ksIk%QVN&)*-G`E$BkBC)W<Ewd`rssx*0&?i5Ys6~}Krot>fzbR5(
zOrm1Vl&vW#Q(F(3u@_H`x5OqvI%Icn=}`&kvs7)@=FTHx+QQSjoytdWi7|LUX>~UA
z`8fB2DLLL}8Tu4BiP@oqG6f?mE+yw}ykyUa%36K9TQPrx--2NnXw%1bvqS*;DiSfY
zFH46zoF_#Z%!VGS@CXbZO|Y)U6caTQg(;6QTDa8G{{UOw>qjhiuObS(b5FXZL07eU
zgDIR(tk2h~xl308aLtySN9POmddK~LApWt%#?f3eQfKu+o)3Kboc{pEefa+X8Gayb
z6ng420_!6lo-&-A96mJl@^rF%oXqD5m5I%-%6|~huCBYyPQH(3=OTR&TpokUOPhDY
zm9S;Iq;2Y${vJFQZThEqj~YcpDQ&cOoKZq~Mj|%`PH96P%T!@$b@xpx8MhxeWw;4v
z@e4Ro0L@9UuvO37H3=B4r!0Lu#!fY4g9(z3bI5xnv>@hT!a0U%k{;|p^<|Uj#^k^g
zA8B0UwA(Hy8kLnAum1pX<1d*(8l(7h3Ka6qZ-i;n68q;mOJ*WITjD&tGm%(I6`RT}
ze2IeH7zTXp5rZ=Y6O)2Gh({Iq^>3~hJW2~V%?cFk3BH;?HXrWk+?i|OVduxsEu!l^
z!#U>D^*UkC;xywfY!_{akLrla4drr60D2o{BaFg@u&w>A_C~T?j;4Q)O&63?SkC+r
zYmyl!TY0&>*?CX$Xtp}6$jcWV{S%+WB?WROJvjRup!P*^m{GxSF|oK<c7EIE9sY~<
z^shax(iu16AF8x7jDtAI6vHI2zWG3YvE2q`s&!&rRn}M#0|Cx5^9>bjv=^vSy1u*j
zbcfd6i?ij$wu;eJ-HZ5yCI+)}Lz$O4Ggad{k>jJ4hV3!8-4p1iYG#y|d@d|}PE{2q
zLrA<?_Z#DPnuyJ}^h|a^s{ojU9`X>~rAf5<`-oY3p>>U_Xt8*<obCm4MC_S5f~F>w
zrJCREXsB_g#OIw+>3ra{{f)pGRKgNWGm`tNA=1{EN5JP2khB~{BGlN0kPi!}#j~$f
z;g(g>_AfMsOAu(lLY>NF(!kuJTk_>JWLMHbZJn1GvwBEIuuO#Zkj%%KjM8e&?JR|l
z)U`S+;M0AbQPZ`VH8Dcfgi7sL$etsN5c?XoAu`dL_G--)5NeicNJywT0gg04_h2p~
zee$olPh^9b^v)}Tm1~3KP_aN|R2;ZnCUYJkK4xbMa12k*T)7Lo35LQYEHObrgrt=#
zh7WF{DmPJ?u*iLeoPdwRk+6)&u!U@-3|^#4D~**}nFS)gTZ8JSG6%rcaALo5W-<g$
zF~i(GGX6teuoesvkcgUsqPFx{OH2T45Sz|MCya?@{{Z(K{Wc?+nA7xhz=Lhs){#zn
zP&%F4)&ybJth;z;qE-&4-I^-*Lpf@V?z+f|awU0XH0n9ETv+SMLgu{><!ngt3CefL
zBkkR(>1@-_kl}s!^nxImA-lDjsJVmppXb;1nf_njZ1N?>buYHF)AjF)@LaMof6i)y
z1~n{3(|{7g03A>L(|?mrD35Y76VaqQ26%~1QdpW=Qbi_HC6`EV>B+MYmdtOG0Uvr`
z$=*PXdnSg12U3sf$l_;j;h##Y8rxZZk+WTIeQ}{JLPIOW$}dhPqi0P`F;FIwQCI`W
zw{;X9XgJj&Wou3Cg6^A)EW17`hw|HwRW$a>j#R)gKRQvAS73qc#s^afa**t2Voulf
zI%3%6%3RgXp1#(zd!f77<m+X=sb*yMpM>d&_WF7Te~C_Em4XDL@b_e{0wb~}IYv_<
z6LSenkSN4_w7ozl(s6(CEYr64KqSB=8LC*`$_iZT-F>x0YT8w##VfH}rKd{Jpw)3o
zZKLIS(NFC$Iy`dJ>gDhEb`KF8o}tS>?i-cmmL<HG9GDR%LKT>2e#BX_M;MUpU=C?*
zh9I_lM_l4Ru*rs(Hk_C8D5MxOoT0Fm$MFC_JwBDEr+X6<ByvTek;Tyn2;_XTQ%NG@
zhGUo1XYJ@52k?2#ULrLs8!|YM6c_F}AL9_Cn@UtB3-R|v)DP;dGbjFA>VN#JUX?Fc
zq<ob6bu~|&QwAjDPg5^<q9Vp<J4J)vPysDi)<+!L$q%m>EZ_;8<HpUV0l+$}HaCdS
zrade`t<nQAwr?e{;q|AFT%5mhXX)}hlVK;@YE;cXO_EbtROhKyhE%q-b39`%Q(dI8
z3dWgWD6L|RGg0HT`!`T<##Uy_w0QEc)TB-n*(i)@6joT2d_=aG;W07pTgVBA0Vam8
zQrs?{+Mp4&b|Xo$s+K3WQD3yZCbzJPeZpjc-F=l8)~Rf2FmY|1mGh?Ylj^cEz=57y
zPLZjV7RXMosKtvL0@zA0U`v}Wd9ggJ>E$Oew}M>$p~*|t0?8|Gio^+;mdPaCS&&Rl
z9Xm>y-{C8gh@HIUb^?!QMQEbgkvMHuF(nJ8(C~rbRYNmBJ)qmvmK=)&(6bs}4~s9_
zA+qp?hXt-cKUnTOf#ErDT6XtuT!S_<pC{?3JXG!na(P>^FhjjTW?m?Ps)!PhC@8cd
zqX(+A!l0O5O!HG;Q&1QpNAyH6V+tvgpPcQK<kCfF)q++NRN^REaP}-;r_fxCSh-@+
z@;^U#pYJPXPj@4jjpKZXl9-4~eXgHqLrj=Pl5E2$`A$@(6{cNs<=5O0<sZut5>Xga
z$clY7%|BF{(8SnfL13M_JI?$603}$K*0`)6sh{Niw*Ff21z|8wnI^V#RPZX>cxUR3
z#&UcP-lpFA$_)Pi%{+Nus}W%PTt-`Z(%hKPjZOtcSrEwLD~!@V+v+(3`^a?3#738E
zs>G!3R%JUTmSsYk_EuQ2j)$DyuiHaUvKItZqC%ix6|oT!wD&4cD%yaGuatZ19DEi~
zQ8P`{Vgy3E_sPF3eOo!A7{5;Q{66_&Us$!NpLshdxbpP592_8CpSDNd5dQ!~qsadN
zxcKNB<cPP*!O%g{+gntuH3l{-nhUd8+HT8ZPa&<P+p#P-?K2H_vZi7AMv=czN*Y@V
zrX%=_=iXO{@O9n0CzJuqexb-I6p&;L%uL3Yvwss-Mnp{SwnWS<qXaflGc^eQpc^&g
zn93tvjjg0JY(b61jaSrfHzvK7M8(F#nez?vMFs;bSrBsNO&3+<vtveM;w3e_MVzA?
z?jtp#>!9$>*BijWoX)~6l*&&uDcx_z$0*w?HVIA`aBzDDf0svm9`(=oh6}Q(;*@VO
zSUkk006cKpKZm#XrHG&J?X1@u6~wYP`t%>(Gi~QB6Dq$?u3C@#_Q|Q+sH@2giIf&;
z-|s6<D{sfH;=%)@uu2JedIa?6#zkVDJXu-G{ZEG`YA`zkQ%HrIRiJd2p;|FdcO1IN
zon?iezlkG_;M44$)h<8`+o~)AWO?lG{vBUSTS*4Oek4lGW>O&u$cP~<@NtAjE#6`b
zv~vj=7~htxz-5JSMawn6w^vhZc@9~7W{+yaQokE@#<Fwqu@73!!Gg-i1GS!qb*;8&
zE#V6{=%4=pW^{YvTyXSVNnsN_N(~*loUAV#9H=bmu?r-zQ}c3y{+Yg3v9Q9j(gc2C
zOyH6$%}Pt&9FhJ+rbf+isTJ6(AP-1AQ8OD%E$c;<+jC3}tRQ0wM!C(1pTd#?&nsEx
zoqDZQSO!>OJ51bk&-Y$*?N8fUe_fV#+BrY`BNs1hm9Tc)2u3AiUfqg?`FtqqB4(7(
zb1e0l{{V=$W)Tn|?q7zS5y!W#g~#_FWMbJMoXNxM=t|FS9J5SG*t58~R%M9-bZ*0#
zDck}*Du|Qdr%_j3oKMYNc`FHVauw4j0L9xVLnRYn%=qMOykq%3s#?ZG0qjvbPVxT$
zl&QAsID@O{)+JSP<$W1H5VKE9N>boEDs2N5b74l_lN(OngF6%y9LTt)2uRO}_6i0;
zP(RSJa_~42UY&AfT|45DYY*MH=lfy&zA=BQXZdPdbr(ZbVuG)ruF%i@$}mS~tbhD_
z{{STZ-^e4(wu(#VaY2?Rgf?(?eM^z)yb$rn7Uj39&#dBdz$_a*T71Y_gM-_iZMJVa
zZJTC`zr2#m4J$Sp+&#DXQ*TubNTVx22PZOo%+6k9Ueaq>!I+OTUOO!+GJi<smoQBB
z<*1w~d(6y~$xUd8VI1jjKH_XP`F2}lbN>J|dcNM6^44!bA!<@t3{^$v@Vh_UD>N07
zuiP2c9?y7li-B`X2}}*QzFe-q(te{q&PR>&?@t{4vdn<!T5!hud51`!9#<2_;}kEJ
zCsfg^W~ixBP!p(rMLN}SqZ46Knz8m)c~9`_nKmVoUQI!orHHkC#|w?QsIWh{ICDP`
zoYLq0UazM6a`a$%Sd93ASuj@ZIT9L1#i$1Ntb4m|c6i%3ki;HWV)a%arF>nzckR|>
z1ca)lkoXCWu#c*kq6>!s9HSe1(VYPumpe1~la}>;Ah`Vme5W*Y@IW4Y@|>uiuXVlu
z0Qc5^m+O4fCI-z?&3onvh<c)pa9rg6(V9J=t8?c_w>zPhZj9VCI^_Wk_+U=OzEj~h
zEAqa>cBJj~0+X_3ICIV-eKh(0s=Q)0{BGvbMaXeq*vrEwr&^3CjfP_z%4^pwtoI?n
zOH_kJz&aYjY)l=m{*9hn=HI?2Xk--CISP8Cg>K+{iN>MPDCcDY+9j2#K9Si&-!7QP
znYm~w$o=#6W8V+rQ>OJX-Ar5}3yU>4UwE;5Wq<C@)UU*G2H(>~Kb>E>xu@CYW6tQZ
zt%>7qJ>rtgQ*|1D+F$Vo`*x1AOP<FMq|M1LNLddT(ubHyQIDO9a}9EN3~1M+S=BQN
z^MJ6iE1U|ALt=AKmAUJTw*(}(EfY_vX0f%#gx9py0|SYW<x#B_3LjTMsR6soMbnS%
z)KyoqGvXZ5#w5Z7q;1(1N-<vwwvIOC;y;IMFPkg5^(@t&&GW}9HqS+cE*{nl1VaOu
z%b9<XZmkv@?4o9HxWU+D!#&+aa?}i=QgCgCL|#$A6|-v`MP^~L{{SY*g+pn=>`gnY
ztihbLm8F{lL}e;ANEB_3{HW-Q%$5aR>%@CoN0!9a^4_o$tfV7YiTp<9^!RRRYrB+E
zv!dR=aTzl+JIL+7m=ZJMD|JmN5DRPC*D(DlpJHE7tAB3V=h6yC>yl+#O2SOy0jprV
zqdQc!N`vbu(rc@zi(9Zt2vFesYqtyZgnuy3z|ZDIht)VVoqF2RVaS+}DH+617p7vu
zx*o;<03`naAjFYGMv#&V`%a_%rcP0UmDmx*PZ^eG`vhmevu{NHa?LYBA{TM9KXZfk
zwC%Up#LtMOJvwHh*yTKS#!L}!i`r;1GCG5a9nyke$Yzf)@VQ2QyiT3Jy+;}Q%_S@+
z$;Iff>Xl<SPWxmI$RWn7+b$GWQe?UXl=O#KIOVEbid9Mm!OsLQDu77AW`zEYMLDFd
zBUIKm)JuD9-ad;dHRAOM7S`TG8T&`4DacuV7HrA-%R>>PX>%X8S5!XA0&ASy)>LC*
zhJVB<nXT1tSG`@AyKd?Y;wg{BXbcr7wZ;yOVW^1^Te<3FoH2oO2O>D}EOc>k;y<`0
zGDK%2QMV|GuGLi31@<kjy=zCY5J>WlQ}vb%n&X-O0NoxtT>7)2rYoC?zr5^lQRgOS
zXo=C%1n0Qm$5hx{N*f6!wXkf`AZD_=d4ms|nKPRaPl^Tp_9sD(G)iM$XLY`j-#463
z9=kI-SDJNk1<#g8*=?#w&DQby{#qP~s)(?%21|*JH!zsNkGb@Z)H`VpkhaC<p*!8P
znq{$UEe_PQ>8&#5j^20E`1`ey!zAC?I<Rnhy&`trGDnQePLVrmHMga=;##TZrhOt1
zAv5jQmYdX-av<POL151+EF7sD*-n2CFv*sqH1ROi+&N}B>KCRmMqpxO*%GyY^8Wxw
zn2fcz;S~Kew{+fA%R*Gs38<fRx1@axxlTmK)QJ*K3@*Sg`kYa6LzOe;260mY(3zi!
zPExl4JX+e7-y5p48Cqd8tE3Flc`O*w&SheJ84$RRW4TW+mM`MKoVQ^?Q6Uj{6p)7p
z;qG`fH*~f!veoI7inIB$+KCoivWCDCCKkY(RLxq%%v*NtY(ApN6m@sd6M<cbE3oHl
z1*fpobw^dG8b`Ij`5{Liji0*fhw2NqvxVFboE`@@1g<CV6tvAzs>vA%;(N>#PFA%4
z0EiL0SKEq*Q`D4xUDI#okIX6FNs6)|EUX<BQW+7r<&<9Y2(3*iU`%TTwL*x|$_*E*
z%OHuIF<b+)k`-ddC8_K_&MYX8+U<%^1G^rVR|&<hr}F{;tU3lRrYV9}dUv<>cROtR
zH`4=7pTsQ-O6e?(a3Z%><0jTj#KS$fCr|eipZ7X!!Wvd4+oIKpFfF%@vL-;FpdxG>
zwSUNzo7n&s?R<@B5tbAR%~ka8otjoh+N|tcw99Pf%%2DhpP~M5_-*|0Br_5<r}38s
zceM;o(n<2=ZKxbF^@)#J@>4&_rh%Qs@UDni^370+DxA$<4e&M^uZ}t^mePp*<39a2
z{{Sd&OM#hGc+|BS{&FHpc8(+UB_%#22W=y-0C}87Ovdw?kBvJ_h|h^rna!su*($cu
z?AFum^%h9!^Zj5HgL?OvQL~($H~p<#66dfIk>6!unuDSC+EJ(xbx>UB)nRqTv#i<e
zy9&ac<fjv{QMg%LxIHH*4!uh8=CrP>?9VWq)8qP{c^k)=E(hdiZXxOWd9OR^*W!2F
zVl#o3gUT|zY>RBo4kPheTT(9VaLn}9!lzKQ6ar6nl~qjJsCc;eGk@A68*XP%)!K_n
z4zxLYc!|lLQ4xYu?S?a;hB+GBDF<+5Y*CiJY7&T_gpMz6W1lUj(@R{pP(TZ>lQpjY
z07z+w$is?!WkcwvsNhEP5s0uCTBO;!E%xWa!=aNfA-q9OM9h6@^<47_npSJB&}reQ
z-)QPqWm8;kLAI_`j&2aj;s~zH(={1QqJ|84%~Bw5yKm%(nfjo~hJfX<<xWOoqlkBc
zQ$T)G1V4GD6vm*4jX=eh?GTLZF*!!y+=EMOquVr~Xct|3=PQmzF*@W%^*H4D!`sBh
znf~oFi;vB|z`mwixgr-!s)Bgy#RE#P#-TGdq1edUd@6nq45R(iW`7UQ8!em^0`D7g
zPmR4`A|`z&Hrq~##G{+5#I}FYljsE?VsWYT&8|wM&YN}5H5%nw>z9S%IOP4r#?wDu
z2f<PQ08vEJHGV~y7D)d9!#12ajrYWGW5O{~<*>R`L%7TB)SI={!s-k(%Id<10vXDe
z1ZCqbQ6#t(3ddodEfme?<-wn0K)nTw{5duPj-@s%3fp1zOx&_M(b33jgT2Lm-EVeW
zh#lO!`zGFvY~~G5Rf_J}%-~9roik4@NyMl_Z=rW7=7U<Tw!gm8Yiag|BISL7ik~An
zUhSGp<CjDYOKt-fa9|$fvbpdxJ>mi)ND2sUJS}l!jSfTuaLqS_n9Vw@w&G!jKX@Au
zZHFT@Z6(zQje7Aq_r|#9kTOlMF=nf4f?hB^^?V}Y^vDX~s4Jq;#8nj?l#6T^V@xI_
zSEU-H4S_s?SDYi<!+g4Ak51~BH5p2&FzzmCEV{hzOw^_#e2((7B^aDG5q^?_w%bnA
zA9%*s8Y@m#Uvq#b)~$l9@MzwxHSRDohSTQ&B<E-*LMni!DU)1L1#LA%mq}}uutF|M
z^!lW={{W9iM2Mtt{^R+IHrglo^xfAs-;}R=eoAOjS{#c)!|ZDhKhLCn{{SsRHP~5#
z%*{k|tQ0~ZGAC(?g7Q<1+mu9CY5=3ShCsHTUCgxEEaC%%e7>?EnJ_VCWj8GbM%-pH
zb5L{@YHIY_2T$Jz&i<F<-02i7<x$_<GI9z=3n6e+<nUs6Nnj>vsWLv%l43c2doN1z
zyQA7D@rNvH4sw4dOgRjw*>yxVe~5?rpe$raz=l#T3cg;wo2g>RM>qiKKo-9uUc@sZ
zRL7WI8IP&WJ!NM})fEx8RTE;ZhG+vbsid<_+kIH@0#l+ju|WcK7qa3&cY&DO#;tl_
zijpbvs&-b&$0RdSOl3qsLJTjMp@~qXnKDLXMqVN#CO<GE6V^wmDVzKtaCh@L9+k`3
zRre4c7&-b63&iE0mkR<alhVpTKz}{7&Fe*rQl`TdBr_nkifujB%;%z!?P*xMMRQcH
zQ>KA_HW4=_O5K@|Gg(2cYWH3jMn9<un|}>5)cR<TH&hN<s7PLS^J)9drHpE`F;ydx
zovx3J1(z>u_Xsc7PuNYwaAjzdITls0sN{9d6{NhOFI&<>V~0kN{ErhEuB=X#SNi_|
z!!*^f+qg>ll_EFn&0>`<*6h=2PJa;p0A{8;=ciXW50Gt$kI#Y1`|-1y2yWAyLy=_s
zH4clKiTX#Oar_;5ScGKGVN&fSa0CVu#PZVm=DbOP1o4}8{lyBsx#Xj4oulgj<R)Zn
zr`%Fi*lzR0{Um=ZPXHvoQSTlQAfsuK#Ga7Vx*G$k61}KHOevFQ08_=Hy!;^F5?M}&
zQQmXecN<SEQRk;10JM8ciRDNe{Y^5Zrd;M0V(c^BKe0AlUSo|{L&jScD#ex|u^Mim
zHoeG2q9}=-PK}!_h6+OLd@Oz+W?RWLXj!P!Yiwxi=M7Kbi;|LiDv5ngksvb#_;MY_
zVKr2>I1AZn^Eq2i5g3DskExKDjqs<C4CQNPjzNuGc#ccaVUt!3g`56UGQ0x<$mCA*
z8*7u<@y#rEJ8_zQ?`h0hb?*1X2Aq<yZ9y7=bF#^tAXr%1VX`%uIH(se_jH|>h=eU8
z2T)aciE@f;yb(H>fLAx7s}wDzRo$av#dmP#LW%IC@>PyPU^d{|x~oc%c3xA=<g%&4
zjfXK4Gqg$|V|H>_{+Vky$mPwRng^etrU({nWCGkgAZsh36^L1C^{3f)<}Gr@0^>&0
zgp8~nq{3ykZ~p+9XP0joFTdscov^=D{{Z?G><Z~#O#a!X-eprU%M_Go_f-8q&+^k;
zvd9c=#RNI5m+16g>Y1PY+x!DyfKtIz4q^hPooef4YI0_yn5In3X=wxFBQi4)y!vTD
z4Ad*`Og2T^Ubi=xuRe{L8o;=Zh)m?~`{`fQY5I*>HHvhl7pI@7_Ka0H@7|(1WHl=!
z_KM=NmKv2*;PbF>19f5)C2^CNyCYAb2LsA>#BugjiZ!8!7Jd1}@){Or)=Tn)a%1<v
zdfGfJ(5%9?peX(GPjo8B34L}}aiN-jlern7mmNt_mXtMUYEzR<p3h9Kb(0DK91DKo
z9I!?p!ZT1|KT@|dHkeHkf_M>bZ8WiCZESH_UfJP>u^9gVjA6`46vPkpDjcWL_}<Qu
zkSQ66{Ie|o02xlWSb0g=u{R#W&+(QbLc=2?VvX>ke+d%EuxOyL1dCnyL1xTw_UX{<
z$tCE@&QSv?+p}4<S-|Kmgz0D~2%&`tnEY`hx0gE(DC0Oh;$Xu`+RfI!Zy)-5XVXaz
zx;67%R?iduU%wepcDY<LRyYcaWDvl#qxmS$8;F=a#fO6DhWthF6i1vLA_&~-?Ov)e
z)n1A7dC<)wxcbM`=8rQBEWGb&U5&z-DUzDw57X=K{6QO{zEpy38=;Gs84{5m77?*-
zv`FTcSK1_Z)2EK5Xd#zdUr=>~lQi3gL2BNaYMepNIo#@0Rxo&1R&V}~kWnQajDUik
z7tfed-n!<?l!CDWHqAu9m6VLc%mHop9+R498y%F5Rb=~V*!(}n_?<q(6w{ME;6#6X
zKO|F{BOn~u!afTrn{+5QVmo?tR4|FtA?tHMtZKFEHbWPwQ8<Z~Ri|ME!J2j2aS{l9
zX>du3ELYU|2BB@qziKnwltdI#xxX=yiI7R;-EyR(Dr#N)lw%sIeaR>_d#;!j+SIMX
z&Lc^)E(=EF>+fFhajiM7TrSn{H%bo5`hF&r&3BZXm6%zca>|P~KZ@*W)MWUl@XNu+
zI8(37vv-prDSSfY<dmpFRv1{>>#FI>e-a77@j0tKzV6UQl0_FbgDv)q_fA4xUUY#$
zTuDZxh6T4wta-2kUIGtjLtwe>HNm2L#b(lT)7xNOR^iDJ5q;l~XSQhDtI<&;a6F0z
z3Pg%y(zGkSJ8FV$Sy&@fB)y=T)4Wd73xTLiGPH6qEA{x6lsPls$11PeC-P<JqNxh`
zXKvpbI=ML8kJQO9#HHKIPkdZ*U`8~#u*(bee<=Q_-~Ry6fRAX#dN07Xa<!J!Y&A}<
zn?Wz9pS1q~C1DeAgwR<pP!*ew7KUc7Q8xHS@KK+odAxdPntdw?Hn^p$5rpgOilz@4
z-|lYD8k%x(rPzIu>8H#pwxay`ei`ZGhl?^~ilk8<H^e}&`pUrYAz!A5=~d_)zH&f?
z%ns$WNsVKUh~4X^Pi>QJf_7E&VhptDmg-2E(ra}DT%AW@qV4@PNiOxKSMfuRQyb8#
z8LLFXNVE3F$6l0(l|p7_7{!vjOzk5Z4<h(PB=_SwZO>-)!*vS&*5j2E_Zw@7??rpz
zUa28An9VU#okF37<nvu+K}R#qX4#+t3)7X}^shJG-q_v-Pn<{(bOAueEwY&iS>SA0
zA%p}mxx~aoaXFvc1|^LM#JX2$Zk<^^-%_?2J4Hp}l-HQ8SEefuuu|uV$D<4mMr8o4
zjGGcH(pV8~5*75}mby5~l$eNuWd8sU?#t#=Y5`a7uQce3qIDeeZk^VEG%Bgx2-Bn{
z<yPTI85vG-DBCouq>AQpanDR_dQypsbt5%ocGhgi@<-+Q%{+u|rb|<S{3^yeKP$ia
zD^K-R_p|=x{*qhKf%MkSJ<3%YUZ!LHJ1+|>QFiM+Jioo|Ych6G>9>^=IiRPTnVRdo
z0%QDUm|xP!X09EHUQuTUOiKf)=DY|KQVi?Smug$U>LA@&YZ_oHzU*_FHr~v(wK)p3
z8hdpCC|!3Vqu4<<62Pxov1+g(a#RY~w5u`b0nCOaw?YVdNX*VoT0^NaC2T|25Vo8U
z{Ab2~T*hpz^G;e|-DJkgrp(wUx*z678&|EtXQo@tdj|MicFHT2ItpKEPfmR0#g(lt
zW+}GRuchs*2%^TMo6bs3c<~s@@RP4(LawKBoJ3(*)3pt|YXtQu3sFmE<&(sii$+SS
z#QA4ohKC#^=Zr0+vJryHms<zcY)Mx9_1oac3j<Z6oi@bCfR-u^XAH^Qre;>yA%!%n
zdtmM-T6105G*S-c@Y1bVv8y%hAeK|?ns(h(xZzZ<W(Abi<_MS`oQQRxUL5Z;IpZ!*
z8f?m({{SuveV}sqOE2Dx(MOh|sK#-U5gQ-eocf*PRA4f-Gib#=alWY)V!;Y+kFz*f
z*V&8^I~zfg#!gvKVUUJ#IhwKEOn21fgyq=Cc#(v##5ddsCvQ4cI_Xm#YBfa8qL53g
zS_c}ST~D9&Bot<sT(XJBl}M2N=ZvrAHiI;3eLz>6YgDHSlvg$^=fs#yW0&7yD@~C`
zj|fmGr;+FHGw7cuCFozP`9?ex{{Z?T09tEgazD0ym2Dk3VXuJdazwqPV|n#ia9|c(
zksVI9%?@27jeI`IvGyvl5C?~}mkg2NI~8;`<H0JHBTr3q4g2w@re_R_<0OotVrC~*
z9=m;0v`Y8S6CRA+dS!9PF+)PNc-uLY2H5H3luw}kt@3A-$$BBZ78Kmp>%d)>4?m26
z<40{SsmJY$n2Po%G{63ZXvG(Pr%t+cUo>pwX4}j}YDd|A%;QR6K{zGXPPFE{G0?NY
zi82u#XJN7l92Q6ngxFAkAcWe(o<%X|H=VY@G}r{ww;FFljBfV>e}`=F^E!DH*KF5%
z+X^XddZa@;i5%Y!`?E2lCL>X`Ad>#L@TZX!;I>DO_E0fpynNB86Ou8gR+dFSm6XO5
z`P)9e>O@U}iOpakflBNe=6b>o*lD0itRHiqQyAfhk(k(e1hBtM7E<75%B8It*0Pm}
z8c70SCQ?vY{67IRc%u4DwP}cI{hpb`64(mqO{=wRDgq9dTxnLDgCsvw=5izcQknjp
zQ;QLiC1l6SMg@I+K@1*3(ZY@<L+cUy<jj=sF+55qFL~4L<68$#y1h2^um0sZYyHdp
zIhV8`%JIs=V>O-s028qunkB}+dhE4Ay}nP=<YZ=Gse9r%inQA44lL9t)w+KRdTq*6
z`MPB8iQe(?w?Ssr9d{5N;er%d8ldKYEOjkLDU6{NThnO_!5E9|+gT31PSi&!_S`UJ
zhGIwFNyNp97u3m85oVy=a}K3Y{)bTvM+rTRd|?(@gY~qJ-hbX!)E_$~x}k|*cO3D&
z=GaPr(?P2ZxHHlu)yE*ksF5zAnA{~T2!drc5w<a|A*FC(cMgV@mX$$dm$|bxQq@^j
zF-%EuV`E~0ojfI$c*GR#wiKv`aMJ7*dmysZMC$5p>U~wTMYU^}24a^HRe~-fFasqt
z(p-m%oc>^|CKEiMz_|`tC;(C;7UBSE@wDtE33(s|jQOKcC36!kA<YSOYBvu~w+_lT
zBIUAp7z(WF3z2tUm5E5q5Te|irB`Yyu$0Q@?>F(9hl+z3ndQ`3J8cn{Shioc6QS^n
zvSkCviecZ}^wniYW+ij?mwr|XWFo(}U%YYfv&c>f<p&mKQTaLMHki^#-Eij{yy-P3
zJmY7P@171~2F<1#M!s!;##VH2N|kZ2F{sBY23fWXSR3)}8K0i?C+|+ug{R!x*X-JS
z=iK@9(<#MXvu38*ecKZO`lHoeA+s1!^8Q$zhGYK#(M(UVw=tjbk4cTEjzo~m5VF!W
zRV*CUGP?<E^xMYyX;SZE?Bp?JafcFZJ}7TO*t0&)zEKD9<2n_J^orf4`;Ta^duA44
zPQy>?a3|h1Dm_&Dbt^@KKNd%!PdMe4w~JL#gk6S3G4;m}{&NoAWCzj1(gL{2jxihY
zh#BQ-J^q@RTx7@I=O5_GpH2hppz}uC@f!0w0yni_=Tu+~I~(O6H|k)Yb&_$66Wk-3
z<Bq6Tr58)yXsK}NKxclw+Mk=P&l$hQHW}=#EA;;Wj!rU0MO#>o-iaYCzlV-63!33M
zJH$>2%xU$CH4`+{ff^{1knPbxp^lk|l*QKP+|1{U18tT>OKf*Dc}es*Shb8+)Dw{s
zMKO{m&ukMUJW3B}BHCtM{5mlfX1xn;oZd=iNh8Ut4BC9)ub7<R?p&fde~lGgw^cK*
zvwcDbB4#I+OoYT{hyp5@Eqo_>O$k{xli~$OI&&G6uEIA{hKvTEXcZX|`^?VyrOO>a
z0x33GV^Ex{P|H=7;Y`b>bN+i}q1wx1&_j6SL|o^`pzP{l8DstHBPJzfF+ca|U;WB*
z)bC~9m4q2q$YoFWb+c9f0O?xK+caa4YvU02Rz7P8_v|v8Oq#1I>HZ&{LVN0e@9buq
zebrNAdQS^L1sa;^lMqd{r7FR}O7vw-Z*FR=s*trSSK%*-jG?inxn`_T{+k%Bx~vWo
z$RK%d<7}wOO_Us60Cl2jYToxsuomA*)uWh4il>4SUbW5_VMR9AE41M+8RPdpt>cAK
zS1DVZqOL}gfs*<nlbz)zK**7@n669)O}jm7jHE(N%C$z<yo|aM`<qLgdvGWz?Sjg>
ziggK^@3o>S`;}Cw^zN)!U#PM?<iB<cBU%2bHSUZ!xxqq>HqIe{t`XN3IE9yl{{Tri
zi(yUN(<{^M%*wjR7%Mjzna*wZw0-8%GqDsx>I6oerk<q245piuTkOu|CDpJ^eu2gy
z=N?nrP0{#M+{v6ykgy%4dNollMgj}^EIia_WGrxh{8{^B9UqBUBhr+~ZHUVHUEpAz
zS<{>im`yqiv)eM;5_Sy0O0j`4@4soY#7Kzz&2T$QG{%x&)}$rMBe23LDBgC__JX=(
z3no$Jh1a<8t){zB*p*I}pGwRGU(;HbOpgtq_F(=}*_^hjD-L553TcNK^bj@_2nuUy
z!Oiq-HQJ&W+X5@MWKTEEDwtLe#BIyCiCH3<`QU!9UOQFg=wGUTmV88i`Zac)KT?lL
z`WVW|_ZAiyQ^?9=(HD~R#cS3mHnetnR~R^SVgq$7X4$cBe8Cqv+b80&j9ZOdq6}bs
zjc_&aw>o-k*xyopss5e2O!=JCt1@oUlCxIb%l7)NQM0EsRNSVn>u6qDGn*Hi=(zGz
zKkzKK2l*_Kj=6>r6q!j*4?iapy!o9N{{XC59jfP2!xClY263|=g(xF8{)24O)eCl*
zSxsX?y@#`1X4TNIO&|XNrC$JKX}y_7H@jCqh|iX#F_f9vp;>~`D|FK*%e=jr{&Ih8
zQwc1B8z||vY0uJoXYV`D>5Lg4!^P+IplzRfV<V8kz@i#7Gr!(cCrmrri<o*893|#=
zPllgxvOOuluDzQRg018^qT~vANt;e&qzZg&MrAW3XDnLev~mYleK$SKu+n2c?-MdE
zT*vJ=qU(`lawM~8n63+sU&E0<npt(zV$++SzE-HqEf~Z}qgz_a>O1JTa+A)h0r2<l
zPF4)s4t7<a!#@wNI-Z((d$WH&nj*N-MCOlbKM=GNS9ELELTB}k6ztXJvc4DFoIVm#
z=1I&)@=%hRRkKkT3$2)M+#jkWd(|axZ*={#lVLVMqzPeo2sNmf)a*~Dn*>&d2DL(S
z2y(;6X+?BBp}59^L&D6)sD{Opbn@5;b3~uY7|-G__ehLk%%-bzOmT(9hHCf9rTUv&
zQf~8PZ81D^Mc>;7@=7{)A`)a}*XoV?CSfr)sWWPD$M!TUTd_sEfzq#jueqx*#WUyW
zw9ff;ZF&)J`E9Z-RYj1E=3@#(d`$VAw9fHEb9vir(|G%}@1@L2uAUnny|^&zv41Xu
zk(e4OpLHC=TR!ear&QDz12>WFt++B~Aa)e1xtw!OwOO!K9b%JHVTsC>$n5}4ON62r
zJ!LSvWKj{yW&{4cH2GN~$-IJ-ck5zUsq@Nw<B*MCO<jfAIW_>|>@gOFfH&(ciHX#1
ze{m5_j<T^gge7o18>}IIF<QlT7T7L>-(=Yqfe-=n^PFN%25gXy+l-GP#&G~C6zX!_
zLIH}o1y0k#o~bT|c8;@KY?suwiQd+oVv$22nLNUB`lSRSP!ln}NpKB@lQ~&9V|lOg
zieg}3vZ-~T<fSp~Ur;pKtF-?B&A+V22u(`YO1-IeYbP?ri!x{O1~SsvwADNo?6r|5
zA(Pc8(=I)P5jQL8>m}0v0KC)V`s%r<{{X+S>893OpV<9NQakJ%)X%bs@VT@wVh8?H
z>QnhkA4&dMpRfM_M^666rRZOINR4FH41XniPOBxrYSX{DCuZrkU|5vS%F9P5H%#Ir
zrUnc3@R^T3VD^qVcC)J1rY)?+^i}q)<uJ*L8eN;`uvO6zkWXOg6l<vBUj}R^WWdLa
z(f${Ev7$eP*DT-0e*5Iaz|L77Vt~UgQybuk83+qGFOt0yvNXAv)lRLYsp}6QK`tGU
zchF#_iQraaHHCt;t366cMz~wLzWJhm^%0Kv(Wwznh}x$)t#K`8#4}E*46A7ecVJ1t
zN0RbW$jd+PCk{f*5q1aa9GI9QiJvhUR#<vvg+{Ai+UWHYijGDxlRKA<K;o4QT0N$p
z0Vz{01(wZHA7p|ZV8Aq|;GyUGCbRL?8(T{r;^Y4S0G+p-f5gxLQ#10x;$`KKTggpT
zgIoEldaU<#NbuV>-xC|PJ52JHPDDm?f?NqYK3m3|YoSG3?ooh9QbdT%kL<iZPPGlU
z6-J>Jsak0<z!j!nT&qIsl-gQPT%J!9<8>JeKcA>j_Jj;llx~uN{vEN&Cals$zlo+Y
z!NqM*pE5DdCoGp4uQB{W9Bi$b_vU?PDRT`lP9|ihd5<7b>smLkxX1qheZGM*Br_Hk
z908%Nz_uw{9MP*6xkHZW8thm`M1J_G+n#8P>85Qe`k99zd{?1&cuta%K(r6|hyBuV
z6WK<}XHA|~kQuMUej_=hn!5g12<up2FH7x#V{=T{-o#40IeVOOGmk}Y3$Rh99}CEd
z>N*#itgv$CmpZe*eR);I+7PdH<rY+!FIrCA9Z=_Z@i_x08^_e<l)-O0E$izhLojEF
z4B-6~U?r^O#*A+72`Eil^N%T9K?_I;YZyO~cv%<WHVR?@=LdAOXBl|X@`+BPquGu&
zFV_KqD#=;3f-AKpsZ|R>SjZP;xV}pWfNNx#X;uk)M=0zqwwW1^G02rpHv7P17dwHu
zZjIYQSfFo3)B!YXp;KGAp5Q-N?NY^?O_rWIEw4;uu?dHqayHPVGYGA5(^lh8MBJ*}
zj762q0Iz7}yj`7@z9uKZO{3vmn^m3eDq+*nquPv!>Y`inE~1PnkfmH^wxH^#BqthO
z*yS3WPvsD8GxEdY!WfC=2N__^O(>3K=PDp(mve3um7{SPf;8wP{{W1cpRCva0NpD*
zV982+p`_%MLm1r-&JZ!k?OeGO>sCFZ(y5Z)4IASzXy$8IWw(NY<J2P+5g+F(wp*nU
z8J9BO2W||XRz}h>>A&Cdm+IU90MXM%8DCNih~8o{cHVXlN}$vJO{4x?!XSiadGjjN
zMU~>U%8&w-z-j<Ml7SzN=Nb2!MR2vKp-22o3+}7Jf|*W*aFT<N;|T>-;aPjSC&Av~
zr@57q&ZFfng~JbO<tU?Uvx2Q@%36I7c=4ADga}WiQ&mKh=K>_=1|oXt*@2;Tlq?HH
z)Y5k8->GdKbTt^A<9`||h03u6WE4}zHTTRytLCtBSI>OYN<#f&#unn3%Bqvpt1C5G
ziPUZFr4@s$*><A%u7M(r_lbsY27a{*bX-rQ{{Ywga)V_w8gu+a$t!CZuM^0M0yZ_y
zY6$PUP7bc7R<%^9Myo@jgfbv2z{b6B;?SitZsgCI-Y3m=?v^2jvr&*A!YhnmdG!AP
zj`)wwYL_>LA_5QvB8O~nXH8R)AHG%zHqOHzsxyB+Bsi~pQbp8)A=W!@TiUwCyBmnP
z9F|Yk`DDT<&|GSdmQm98w%WIaD3tzMu5Y76vfAc##`bD0+U##$i!z1KP@QWxYdV8C
zW{Cd)Z1%Qo{{W|J(qsHZG7GO6QqQ}pEX`Sjs%m6w-=9vK$N6a&9UNB}1;n?>ji+x)
z(YwP6c^OZyAU!5xXz{s1#W~X`mFq$i4j|P;*{Md?tY?S`oZcaOV_<!?juSHx!y+Tj
z`f+liiJelS?b`v6tm+q_Pfo8fsQ{j`k$a?KjB}d{!>byF0C;KZn5Pg_n<%)0%R$#+
zu??O_%hSYHpGz$#tQ=0;xtl?7n4Gdin+7==j}k1gL`gGhrbNih6kX#MNXG~DL{IsN
zf`pJUSAcAwZYwyK9$5_R8CdVfvucyY!?vS5J0b~($K=#K3hX>sf1ti_d`|KOB2ZCm
z<&P<u5ZmISl`<z7XoR9qPhDU{8ERC{ih6}5uol{k2X%6RGgpxzTjB^7WTrtzqD9PB
zR<zpbq^l_uxWJ5m>o<{9j8g6cVXbry*AH!{P!R=tjkol(0VCHo7~nHhz<ysmg-vQK
zm+108hgsf+q*at*h-Ye+c<qd2XZ&WSVia35r*Vt*q;(?xjuSQkn~Guppcw@m4vfU_
zCBAZwClHG+tXqIqr>RF`N}M2yY%Zn8ev7(g;4zqrwx&uV&1SDmrjl(A^;Mj+qck+=
z1o57HOOIn9Lr@;;;+P1co_p{)BParqXpk_{wmoAjKG|IBF}9-M&)d;JN~HH?@ZR45
zbgF`;x_@s<6DOs#F#iBl<b6-1eZ*sDb{5~hPu%|iEYBjD{{ZxA84C;qVtK@RTb>o_
zBR269o;RQ7(47ybE6b_whbe622^ANjp6b$aohesds^@0y<y0lEezu4STZRrTtifdw
zV#qvPO~!30Ov67eg;B#O=eWG#v)LbWirNYcwJ4Fg(VgcMD-F%iVsGUzvf<A+$q)GF
z-e?b$`<~54xhGAw7y~9kF{ab@DJ<0YE49Zp;_TGO2UOqj3mf8Q7b-lk8(8a|EM>Z-
zeAM`R`#dyBpK9TqaOC<!BWUA?{lnXDN-O4X-zUrtN0BV2{{YFg5vvFEx=S*XC4JGF
zcuOzUjMptqbajOQI=xqjR55FmUw1_|w+M}<W?492+FW6{u8P*e`km<*gZ3=wX_6EV
zm8d<ks=KJBB-t>Xu|%Z39k)<~zJ0xA{4SYB<Awfl%Ho8xE7mmWu4ZYdW2WQ-Tghrz
zN1dZO&P-Pqz`f}rHKa_ox&r$EuI{mcVlI*B3g>DkE*Un%nKSvCFwdn|P0F)K#vnBN
zbDE$X+B7c?tN}D@9i0{8)Y(*Os^_JgM*idYdSfH`Cn&FdDH9%rw1|{tQ0FcFL~cot
zik0CvEB5NPTUP69*IuC3@l}mNo6GvJ{{ZofV2ruI5p|t(Z4mltgZ(0N_=b15n96#k
zq-+>f!PY|1Yi3BdQoz|5t9VU%cNCq}$!ryyFzDju#ypi`RoKOtL~_H8mYDPyh@zIw
z-J;WW%Vcj_9#Q`Qb?iEuxvIr<*;L!7mtG7gM3hJTa(45+Xz&bqp@s&dAR=Qrg>(Ej
z$F2KY(U9bE1gOT`0bB_!JG7v;A<jlhk)PWm{{V+EAMwchjJQSakJEDc%MF&0L0Cbm
zojs|wmMAfKDy$F_6Iz>54wFfxgt(P_V8IbK8jbH!zs;^MudN#4*re(?XHL0ac{$p4
z+MzIe_-UHAuty12trom37AzAa8I`pv_Q>EbylpSFN^sEyy2D)ilO||_tz2u8tgRw(
z8da}xTg>Enn9X(DbgF6zHr-fWtphex5>SvpF+J}s=4(u}n4*&@h=~#86FD{FHs4I5
zg)nU{#;sLf0B*l*r}E<U#x!J$Q@cM5)?9MrWK0z%FDqHFgXE`OO~Qe*Q%gce-7U7;
zJ)wj<K;jbOE<zS!3^LIKyL`GLrgM!(KyF#hgN6`%q9W+eWqL`}95rq*gUtD#N+w^2
zj!g2IoQ=4bqN8>Xq<zLu-0$i>N+KujfBhJqBr_g)o+7!}deS%9CvCs}hw$qp9b|J@
znJBNc-cf%=e&(9$zc}=w=hmer>q?+QT5Fb|wd)`)UahB6O4be3G`FHbx{Cd{B>{qa
zEY~`O{%RM6`ln@=6UHRc*->pAB4bifD(l3QNK8QyOp$V*{1!-zh7S@j8Cv^VaI*OB
zB86<a)aH}s{Zm_X{V4wc6Fzf3a^&x;(u|+a)bmj|ZR;f&KiiDI9B;FI3;zJ7oVCBX
ze)<%DPVCj<BXiH&5A}Md;Zo7gPw9xkJ{#o<5B3F?lTs*Yz0!1J<r-H{eVb7?F4*aq
zvvjXHU4tHGb6+jA=)uVJDf-EbvejAZ*<Khp3Of&l1<M_6Vv+hrFl3fcacNzC)WGIQ
z$cZuq1e4kmNxRD#KWH(8*qNk{aPf@PEQ)K_t$_DYhM+c%C6OEDE+clV$u2`&;9AKK
zRJR%Wq@Y>;oc+Ith+%JTN9IZIOu3@kGBbG@f`)cp(Uwq#^wvUav9W(bbjF6x7oaht
z&as5)9Zs!smP~OejGwvnjkohw7&>CdKYu`<GZQhi%w=hjhnJ^~@inBvT6L|048ki`
zgEDbnV;OeAGACk*%a$Kb#a36ois4-r27_U?(h;bVmr3MsT6xULds@$ps%c_zTtKzi
z-%(^@<U$DZOtQst4AqzJ^!SyY@6%RzEZwv$6a3b(82OTEOq6#}35B>Et`;3ts@i4c
z$l@a_Fbhez6L0&w`c_LVR<Hq|wju4T!D4|3)MMNxTQbIxOhJs4c$mW-w#}m=Nkj%Z
zkayXx!TT>dPN-BFz}Dz^!42&uS!1~_?2RDOV-d!81L3|HZRRR4XHLAC-#^CMjEvdd
zF>|v6f7Z#y@|yLzBEu^*Sh$0*vg)89>N<*yOU!bUA^jR{i!8c;%U0sDPjhr!AOO^~
ze`X6bjB1*5LJv`>=n!jYXrb-TxG2z6nof+bnk5h=&v~5F>?B+&0kXNY>gK?z3@lTr
zJV6!0(k|*o$4!cb3s#<(YWM?Z_l)27&1{F>M9!VN^)v`-F6^^oHjf)(_TEZB1AWb{
zi3kPeoJ5*-jx5gh4<a|)8c{O?8gYtj#e>xgow7Mv#x(P8MD6D+M#dAA-c=tAi`BHY
zbi7?nsR=Tc8z59Y(Hm^O6@XhRU4~dfTWq;mc}{5+4#SCqHdjg|b#L$Q9m~>ky1lNB
zv-FSBev1D9^3SAw#!uh!58wX)qY*Qcwp-hjur%`OYagCxd7tOe4ni;Qc;#$Z`d7Vb
zr}l=T^&xT9Fg28k?VDuG5+X5c>}rBFB7?mCFl^!1i&I@YFYvP(a^`AW4&BC%ZH^yt
zStd2AIGtQd?Un-_K7(YvxmTY`!VF;ZCkam&O8Jz`p@uEE_vnWaQ&MJEY`(pA$(O~Y
zhEjU1cV?gd{;bUMlbkF+(zwgJ#iHN%a0+d(;>1i?bWVgZ?GAHTd>O*y_M9yH^zrO{
zKKx+UMFfNF=%j76XSVT=;o4F$s+B2mE+1*6rFc`uktQpQ9fXX>>WI(c0p7A>BnURc
znJM2EFl+&m*qJ<6w3Kf%yv)ri?6~+`azz&`D#UM-=<@@mRbls$=3+Dd0HH{|Hkb%3
z(%0A2MX8eN+?GYT>^O8xepflpvw-J~D7C8TjT@F0ZoAEk;KK~25b0t|NoTjIb-DVa
zh#`^;28HVLR?`=WM_@IjX4<OM($j-fm8ad|ps*a9RPwPJYz&5(Z{8%inm-TJyTezn
z3AYT7<fSEJL}>p2AxDTfsWAz)ynmb!1$>;>wfl13e)6~*skBN0Z7k<D@xEFvbhP4&
zd}U3i8ZiF=UGmkN&rT_f81p%$j@1#qs%b4s&ogSi9YSqOfs>|Mp7X3$ku;fcc(K?T
zsfgu5)Z=`fS8OG_lNZodSBN$X07AnTW$ZkWk^87S*Sk4xHf(w+rh|hA*4>IA*hJ<E
zNTPVc+{Cb0w}SOGO_YgkkCYbOQnnI6{WkHw6S!2<tmMYiF`rR6qUuV0XhEw}OWrFD
zQ@5eBJ~+hZ@3wLw`qSH8UCgk^t2=dd1TY45WmjE+So9ej04^m@9>`e15wqUJ00tF-
znTd@vQF|s)oW-cSAvJ6W$zZ|lB^rIdXKY<Mp08T+xQ3Ucr`smWETUYgfz=#Rp%6y)
zDuBMurWBLIE9<r8Oba?nm=)${Y>d(3CsR)eV>`RD{{RVmm?C|)one`!X@_Ea+iOsJ
z{{W^=n8>enXqm{3x5Ze4)Se8}XvG<mi&A{%PwN1qm&rvcNzP2kPV?h$N^2KsuMBV+
z%%H<<_nG^gtcxd7+Xqnp0Cst%A7H+w8p&orTw7!~;y?|M8DL7aYU?0-lgn!8r+?$0
zhT2B*Od~TXJu&4`(`I!GUi!cgRnlp#5$`Qi<QrE2p6~*>{_XSpnxP7`)&@pI_?YyM
z2`@<fQJ?4eKhOUFr?~rGGCQuB{{Z?rObAl2MW`0`-RU?aODG`xL@#_b8+6-KjQR(e
zn4KkX-A1$%4p`P4=kWEvYk!Gbyccx@(Z~oomm8571;FBQ(X$RSPgxkzkh3-JZfno4
zp(ve&kP>AA1VR<Mz&LO6VKFP{!-)k1W|qiKm#duVyw|4JgS=7{A2}!14{7PK6MnYS
z6U_Jf-f@$>##`PEbDzVX{{R(^87plWul*G(1Cu<bl%tPqK`~^qkuOD0^j5J-YuE}>
z`ZSi$A|f&)JucMOyqTSDW-SVHs%&1YxzwKranFiV&Tc`_4$HNNKm4(u#8JIe&P2>%
zZ6)XH&}VJ(ohRRK1wQR+m0w1Uec@J$1f+b##6<YXlZx)(&$wv+0LPqDvQ+G?(W+GT
zSDL5_{k2k2*ZHe_-gMfq#$e2vWHXGhhOq%7C=qb=nJ#pw?Z2mHp&}B{#~CtU<umhv
z#79<zdS<KUyI`{1A|7y?K&usW=d#VIe2fmWyiT4Op_EynDLp5#dYWQ21@vEd=74(}
z<3ETef@{t+J?4s*kF4f{8`O}){^9zGNd&7ZR&}5-0h-ljaQR70mI#rUfYE3qx+2{5
zvTQBphR-$W)k)Pj7Pi>!01YFJgS6(C7wKZCl=ZZh<;a9dPMs0$)0(E=h*{b`;|?|=
zj75VFYhx@RH7ZlXPf<A9rWoy|yL$u0ksg&p-k_C=-Xx3Dd5oe$RdoTIa3I>!T(Tx(
z>IzLV6O-mQ&%8s~S)$cmt`1aRuRnOFH9w%`ZrHM5hwn?$BIQ19w-v!ouQ8K5&S~xG
zxRb|wOKOE>>W50frz7txC>dcQ+u;ki<;G5GBu2zZeR+l4v0r{nl3_{DqC6h>v+l2G
zp*HLz^l7@vD_3ePbW>o-53sUaU=odEONnk*wKg}E!1>;IL{8azPYZ()m2`@zg%rTV
zeR^pSMSWmAS|mdN)>=}QcPsl>EfTa`u`HW2O)A^96Id+P>MYyLW}fh#=v7vdG>1Hz
zX7T|xA*vnDy=U5RsalGTwV!TV@A`>n5Hphz6ShHct=EBMqvFe9Tn$j7iQab7Vts{}
zJHsB}=MeL(@NjC|d-oJphiu~>QX|A{?*UXNXz>^y4VxC6&c)lVI*y>mVzq0lI6^GR
z(^9r8Drz4t(`?V-@xm|DynS}8X@qQS)0mN&62**@R}rfhkxa%9=|4mM%6&h7<%#$g
z^8Wzt{{YHgLm6zp+Yj^q0FO;QU(wRzVrSeka(11k*KDn9>&!&{9-E&!0)t(VQ}mzJ
zc(Oe<%`QL=(+aV2scBQ_$>lIvL7jRfqZl$vpvO>9uEyc$4JzR=gd_qvMmK+96p@^f
zJh>3%Gz%!!Ow4{v=DOltU{-3or3qB&Kkw6<$xO_^%$yD%>MgJD{WfSL15VR(iyFht
zM`dYMh9Yut+n6n}=|;0HBG4_5T^-X?ZV?BE`mjIiU7lL4oYMn)EU_luWZm@H9@d2E
zD&fxAg~9J~r}&a~N(a#Y09H)?`QwD()R$~oYfui4XG~-A&T%EirMS-<WWe~r2rm*#
zfC-5WrX+KmL3|5o5!+dX2~!i(?ed3p>9!r*;lQ1Xd=JVv-6hm;$u>-s@nLT~r$nMv
z*A|(puVNX&^Ai~!Cojm3k<3>TL;)Oon&X{UsB7usncMc&OlJHn^!^I6V7*ZVLsJ%q
z@e2O{ss~1a5yrtzx*eK$?V@eKs~N16!UFZs3yp2@{CnHS(vpdm7nYcX>0uSHN+W15
zZ3c$DbvK(;`06g7#H=tv$GJ_pFWWzdJd5IOY|=B$3MA}V3~Im8YO)w%Z9_6szY&Tf
zwQBL2si?2mg6SYJoFO?PDXq$6)tW)bhs>(Syj{gui2Od_Wtigs0G9KWA2qD0v8k>r
zu=@Qd2P(RT`ix9_EdBizw`6ah@yU)pJv8gHQQS{dsueC8!hdf~Hm|+3DxR_GkX>G6
z`$`Qy%p6Xgk|75pHT|3zOi~|}jI`I-v}cbx#ZnX;r+xCwd%ELlJufmFHH#JT-GpXs
zDJcmUpOlP!h6dc@eKtIr;Ko*RBo$|%ES{(MXC-?DFp$7tSgWbtR9I$@*%a7<4qw0W
z6vTb+6Xp`;*J!m?Qm)Cg^f~Q@R85Dr!?@xG4UOCY7BFp_j*sltZ)IxbESVl#Q%S0=
zgUv`LDAho<b(B*LC;WH^Bb~QL^-E$~?Wd$%q9Xdr^*dTX%c%CMQf6_SxYjtCn4G0x
z>{d-Ec~7}<K<rz@yqDEX$<LlLvGEfbeVz76xH}m{n*^qJn$()gZ49qd->0QVZl;hj
zs#WdCS%$ju2BeCIPPEF6IfX{h7}?=rk{^k+(Zb)fs`!bor_->TpiI7SdYMO{{U4)z
zb&sd-{J-(^^PWHb0{fi0Ffxe$0Q4s-M5%BxUP@+vee!&NByt`l3Dut73bRaf_A2WU
z_)E@W68g4k&d$mc0ZUOb(<?UYzOwCIn@Os#Li4s#Y{fgno0ptvm9P@@9x?JGDKe6l
z$j0DYr7DoX{ZWPYIi{zmBEivlSyhKltOaWAN};W~8+nl$R^IexS+_FTJIh9WH7&NG
zF{dA1iFC1CyF-q;1fi}IgZ}{R$VqiDG6aeyV{i?)=r3o#n4i<UVE(P5!e)1PC`i}>
zI<#}uY(VvUO+WKP<Wsr>y24jF?^+vDuo#l{)`0&25X}2pzLPV+W73A45|X=e?bhC8
z5ogk~#8u6`D#S~}bJ&;8XA9~JH1xx)@+%CNxs8@gSD*>a0D0aa!U;nRO-bUovfDg}
z`>>~+?;RpE&y`62g1&1Xpm+M85#fU~Yz5UQ(t(46;4EjrR5s#aG_(G&;vcJ*G+&z6
znDDs+sOivAsULouMY*Fi6ISa@qZTS<-{=)g$6Daht!O<bG(eNnEk+b^GLK5NPSNd_
z?5<#I);a!)w$eLKo&NyDOo?w5+1+-$4x}A7Yy{LYlla+N7SEoF-iv)WQ!07phd=63
zA*w6eX_V6ngWs6Xx3g({kpjK|I;Cc=<H|$gB{|r8Z5)awCn*nQ&L^f^p66C8ersFH
zv&zxTWagy{oi|D_^wZ3tSH~?Bm{r`TmecEHJRv6^wih}1OH>P5406Pc`Xc%C{vWan
zC!n~nx9p7~X7_k$&WbkexLYDkVMTG|(Hud+w(nYQS*br5vqI59ShYb(PinN;0(79v
z31=fezCJfid4`Ox0CWEUGAC)?4ErZ?kBM!RIV$Rbnz$^@gs=RcmmHj;`8_tX2}$`w
zCke3=7`8<GeL0L|M=#aX13n$rh5R)>QSBLscLsNrgs?0LVs!fyXWTpTxb9Q2Pr{g(
zRRA{s0G!lFl|&;oVm92ZRv#1CWj=`UXXhKl_!~nBI5H_H%~X|b)reTLRP8Pw^y+7Y
zyw3Tc4*G!39@4^`ng0MB<;Z}AC|OF;;14;7#vyBaAQ&(^333tJu9!L2R<-KRn$-yi
ziQ6VNpC(fHL>UCssK6zj{V^>a>^nc}Hq8v&WWbxSKZwl1M-m~({{WVK_{PrcpFy3A
z`F~;m0Q7=4D9O!ASH`Rl^Vd|^crFiSp7_nVlnxH%@XxPb+As@&fn{xjOx1#MT7}fe
z$VROBnU^(`zYIV^sn`f>XfbR_ac8EMzQeH-W@DFJ36jbP2|;C2^|nKJ5K!}A4kSC9
zGHfPThQ4wlSa30_7*x~7tWsA+ZW5Io>g=+1PqNF4G{}y}pQ84FZ2DXXjW<dxDe3+k
zTw=tDfFrA+&UJAUsB7Pv(kz+X6>G3tI7~i0c(s#G1azqZ)e*+bakBBne|31946fNQ
zNLGFNjjbr`*PY8;u^p;JN8WEEF)^!4Ci}Yu$N0iqKg3_{s(OB*0T`)UO%P@KcU^Pj
z+1n$*Mo;!uFjFMBi1Fx48tGISl$eL2YBbbMn)c9y+fY(Wy}~o7*b5eSUWgp7OC;S5
zV@$}-LIk%jZxaY@V65mN@Lo~3LLxPBArCVjmJ^(&cHdSL60Bir&uO0wZ1bsGYJ<+*
zagWmNQNEf*u0IUCh5i)L3SM;GRx?R=xYGlDet+8zRfxAim4B1?e~+{AWBcvOwNBCP
z7fmC=vI+)2OSW(D{dv#S9-vYT5*>4~uCCdt@I|yYH9b6RfI7W0CTf-P>}`6~XYly{
z08;bM;t**9N*OV{L}>x}ZDvw;x#>Kz^IfY34>`4`gS|G&?175S#Yv}Fo!|YI#5`mi
zaX2x&=l3*B{{Y3*vhUGJ1wQ66p&s26oI{M@DyNw_{4PGWTdKdKc-l7eF}6f+^_qFa
zG|+E}f}UGROblrHWKjuo<agv{6Xqu!HZ<EdXD7ts4}r|ZCIZuSuzSgr<#-P81|rL9
z9K<9Nck3N{gkI&*I5D|Z*^KOA9&R(XK$OnVT4nG|J1bD{OIliPkJ=K;7qs^}!tFx)
zS*SXsCrh;JjHo2Df-%ZS_m)(d)9X!0HH`SlE=3U&8^;_Z%-{!)F|=eBcp<{KWz#W@
z{M{#DOU}CGnS0zZ2(r|?%3UoG@e)X6tLsjaWmaVQ%ggRMi_J{S@mp=p`N_okEn_w9
zxV&u`L$Q|+y`mo<)IKr_M;zqct6H^q@LxSSv)1Vn;rn7aG(jSR_SK&=%|j6p*EUd0
z!B@x3%<NorarVfyc74knt1x*#d*H{!hb@bfW+9c*Hf+d?!=1YW6Tha18>05tnFT9>
zclTL?zsqHRa3>~Yre<<Kl)i`gU`)Q3r`>E!is<$X7Dk_54gltrP$PpBR*9K}(Ls>o
ztIOWbxT9#KxX6@D#QFYCC7LCDn+%%<L0Z<)ir9~dOiUDxL`nFM<Wy~=U1a*+<Q4Sf
z$w61Fi4|(LM`3#trP&BHyHB){=bChYQPi0;`is)JOf!srnV66t%4>0>?W+RuZk5w`
zdT6!#Eklpk&5NGx!X6nV5d#LvpEEO>QnyX*^T?t-E=>C^Feg@ysv1(GN?f;4tr@Pm
zTd1=>y&`002woD@4V-5LRS+86!@^@xkek*dR$<CHM0lCaA~(L7#Vh*DE}O33Qgbk)
zfy`wTdz+|US!}+;TOw;KbWC-G3$(+Tt3}qgC>xciZWIiR%!tqBJIALuP*p57aTy>v
zQE=L_!HynoVOcjbC*{CLui2O98Jj1OSB_+Hisx5LH)L%d3r;o!T`><lunHkWB~)d-
zsDh3PftkuJ?WJ6^_=q+nvm$;wVi(--2-mr)z$erTdh86-M2h`xJinu4`fnpKF{ZR^
zQV1ncI)|#Fe}|j-Z%qFH5qN4NC>#aE%<8p*t*L;IO#{VdPJR?_y1|=v$A`78>0CKh
z)T<OsM2zIX@8AJt9(-lP256NgIDC0dn{SmfuPCe1?9|6?O3yFh;2o__H&H(Ok&RTO
zxXlP4)4Jl4#<;0*st*hHa+`R=(q4%<*^7z)0Cz#Svywf_T&>k+9Uplk!EwDQldGsE
z1%^MSWRLEI?KZHBddP{68+qGp{WP^%u+vI)itGzKA$6=6$&ST85w}&;oJnzfOD@Qi
zs?cb9bQ@;T?dK;ad5Gd%3-vchqVB>vx)ecAexj78PhbSb;l2%ab_V9EqHLQK^*R*J
zI&})mxpm;SsM9=H#0Nzf$<Qct^~HTOn|>>pR|?9QNU~&h&V~8S_xs!LWskDfAls>p
zboM#U)zq^FP)ch7Q{@q#Gn@tZo>3E<?SOoxo6MAL$CS=Q9Db3Wq2DY20CR>}fm^su
zA_Ga+dQcMAfB;OFlP&X3;=MYYPn>VhQK&hq7b`Z*W5=90j|CfjVm9{I1Jil@yzA~h
zYqKHlTO$K9UllW<+~VnWK3#!6-%GC}12eqDgv9x-$E{q$;@8IatPPuNV2tsREOf!L
zP7~V06fatpq-#@9OmSpq{p9}u(vDK)vUNlXE*OnTBE3_MpEhhx518W<8)l3X6)l!G
zXk^4U_2ho|um1on`p55^X8UjJw8=yY^vjH&B}-0NOq8~aeu%rlZ~#}X6z_({IJair
zyAW55uf;YHbEt>~*T#K*k#mM-MS6{;&K>#=i0p-FtobY0Uq2~ot|E7Ch)rR=&2hk`
zg`a!o-{1i3#PE#$S^Mojy!u%kzlUjh&T8?-&?heK4uOwEJ=KmFrMYKEdofOhi2j|L
zR-m|vpPJ4w)U6wiS5r-X@Ygp=c{8@l6{FA<Lq8Iz2fccd*scccM^rlR8p&3n4Z7;M
z==`>ZMN|Cz(v&Q${oI~5O+fhxY{K(V9u+j8&*Bm5nhMPVxE!^`VEb$|{XEZ%_{M2b
z4>wmUUK=3^RC{X<E8!t*<2at%<cznj144-Rvb^7ko;rCsrG-1d&S^qH?kqz%kuz!|
z3}pq(q-`wH%eIZh2#Ndf6EpLepF6}vZy7spj4i<!W<^OM>97SHR^kmf(`TLNd6}C`
z*m+Gv=5>y+dps(SjG)D;rlY)mlAN8Vj71>X>uFp9_YOZz4tJzcnz|wMc;#x&w%AhF
zD(^)!Yey%kKh=I_M>M_RuVgl26oB>6ZBO<mu47lQywfk(W%Pr;i1y=O0HtOJ8jywU
zKe%yJXKu?ncrGgkPPh3NIsX74OpduRCb?S(COr6!ww<MFmP}=PH42f`l8z=rifJU^
z#=BO=A_G_=Jxa{ZlOnw{vkrL_<aAo7rM9g6fmpbnV;QT7mZfI`4hRT!3aqDS!1H~k
zClMW%U<rGaIv^Js#*{Gvlb3Xn&toSvvJt+RGYMM=Cn`w)08zZe`(gf1;mG{QPIV7t
zo^grz&Du+|Tc~6Bd*V{++7eV!%Wcx);!i5C+tyNYbL%*m-I9p$_tJ6PP`>`5mTC&x
zql!ks6!l?myzg=%EEI?#j^0C6OQF@BmKw`DOEs%ZI&}Hh{vb@MJ9JoKKIxy<N_Rcu
z_RkshnueZs`Eak+>aRm-+p|IQC`E7{M>r$C(;xZslinliYz`wPG2$Ztd`|JVoUoCr
zY$b`K(`haXM-P7`C0t$#UY}W_4|YnPk(HFdPW(uvRdo1|9H(d>>Q-5*%=?B%qTpm%
z%kO!>-E?eEr2Juc2h>nQaUvI81jcyaBlj6uExiDMu$3~Qg*Z6+4kf=BH*G)=G9^+r
z$mx*D@jFic0JdnL1#FmaKI;uQpD_?S#CVb!3G+U4TY~rJGo{D+g?vjkGZKXqnFJQw
z_>L>nL#y|kFZkeX^1<9tezCN2+t4)oM;wR;ORdTp%|OC-L+n1u^LO)j&Gz~!pAk8!
z`FJ1mM)5nsb&W|MRjobstrN9_fKt^F<LmEv#W>Q)U|0Z{7X}PTki<`>q(VA1B**yl
zsxWq|i9NpOQ)3K@Oo*pH_Hyqu3hR0Sn&*%wSk#bPj;ntA8&VUioR7Ax=u#sA6Qa>$
z$}GLtX5H|nsc>D^p};cRHe+QUYV%d2)Voa$9KWP)ScM|#vUBe(t|s1ysn;uesWwG8
z_F5C$=^JnJpZdxZBYvjOEv&zd2<?4U#gpx_<AaT$V1VHZZ0GRc7r7(LrwgxPgS>w0
z+F-*p?}b7v_OG>dszrLJ(!sewv-ngyCO$BJlkr&R)cSwNd@1l5=i5gvUk*8A!m9>E
ze@1had8(zh#@l^>6HW&-V+11WV^}4<s(52!02Bkf9@!g~_*1^y9iyI><2)He=nRj|
zUTRB{Dh$hZLR#-_r4IJfmK!d689s_G4Fbxm>t3qJX>&OBrqWG-+Te43ooEM@eMDzJ
ziAUpHy)@fVu_|6E(wJj}RxdO=0t5s^e~9+?2%lNaOu#SDXlkkvvQs`HekKvLIqY*E
zJ7hDdZC+pNFskKdk#B>53tn8tsLZkxQl6b#ASQg_rVQzhX^ofrxy2XHeAJ1G)NO6K
zZMJuNm8mZ<m6VL|pN{HO1PijSk|~WY+iKZ>8)=K6SF1?v<Wh1rQpmOD)t^Ldv*vvJ
zXwBnor+if_ZwWQ&8dFnay1*Ye+<vfFGdo1bA0@Y#na%c`Bc47MP1-wr4)c+}q?X^S
zRIm|U(Vq-w8IEwShb>apw3{)Sfp=5Wn~Q>zjy672>y%S&m^AE#jW<<ho-GK|bu!ds
z$J5WP^J<h`bgU;&<yzf+KDe#Z7+xdF_UAit5%Gj)_Z-<XzDaOkPJa7NZ4o%YQztqQ
zF)^Ofl<yhssd9<Q1w?Rx7475tKhrVN5gT&D@Xhk#7;Pzr*C090dq~VWC&9-KHTRhD
z^1xX;h=P5sM-k<TJx7(i*xbqEw1kjCY_gOZO0QijRc0EViv*dDK;h(+i|rsH)uq_f
zTItBxfTDkFPWAM+9z=a_KmN>oe`xs(!Om+;ha*mQX}|9mlm0OS?u-|e=#wqQcIsOq
zw^H(dwiq$LDZ-7CuT=@3F<7WT;vP1_xfix`-(q)eAM2my-}aA4+j!5p^w#)uPY>p<
zuJ@@}h99?P+R;H;9kY3fiOs)!Lt%V`0JlI$zoaB*Y0bZXOVJQIs7Ksy>84CY<rJzp
zL!8WyzfCf@TE2xIzsK<xanC-Tr_ojOejsfa#!{kp>}?jc6_TR0T{QZt>XzoYa2K9w
z<tnodjTE_Qi@<Ek*D^GeiE*P_;{>scAJx7!M>@WcoH*penAgrhDNm@BZ80!Br-bR8
zS7oj^i7D|w#TNsajD*ZcN5{3Bk;}-ml|pKub6mG66GT|(C|Mw8Vt0y(3DOE&{Y+3k
z+fGRu^y}{$F}8%=i;q&`KJsUgZ-e@yBD+g+$o9$oT<DkELbDPDTkkbi!P$TkuPLEj
zkKeVCx>jsv(9?n`ky|AiTw(*iPJGW_G7c752Vmv~Hz>z(<vcN7L4%AXoir5ao5?&!
zT3`BXZM4j98c)Fal<lzX>Pq3I+iri~tmSftSM#W-ld80HUsr84ltK&Tou^Yw0u4bC
zsOhSD#LZR6aczcz&OeBkGH5*eoZtLU?U%W7r8BA~9QYiXvrtx3EF(EK1g;jzjrn<%
z#~?VFjx*+`9_WeG(pI9ABT#Sptq><k@+HkCs%%N-og-IBCz7NY)N9vcZAsa`qMgzc
zh!j||h`<;NPC)7V{A8xPJIV$3=T3yKvnf~AQoCq<2c-BFzm3Zti3pnZPEx1X4y6z9
z{kf2WtD#h{FlWXxe8$oG0w85gJ)&5XR@0HjJm)|0x#t$na2U@KpBVezGS$>n9oeXA
z#cbeL)Wb={{YDJO)Rc9^gyhdE0TMfX;F$$Mobt`!d8^KU4!JPrrc6RNBTO%c!#{no
z@w$%(O$S--ba;q?<+e<7`{f{eMDO>L<ot;3zA#2(jwytD#Qy;7ltji@yo7BMn>?(k
zB`mq_NE^v;lWZQKCRF7mXjo)Vo9hqkuUWLc_KiVMimqwwP&1otmuicIw>1*WX&K})
z8GB(cTtc|*E{2@1fVMb@Mkw1=fatZxNkLS=P|g5*a=`1Gh*NC`^ynhktDH>s$`Q^c
zB#&XAB|NYI+;AbOlq{7{Cnsp*lZx}%856{}%kvkVS15@CA2K5ZGlZ55?K)j&gv%#b
zjgY@iKbJEgdvn>l$5dzU;7}pCJ}j_6g+6C_pFxgA5rz7T{{Ymm&iV;C{$$S?BfuGz
z)04EpJ4`6s<Gf)10FO%q4dBt=_ME7`cMv2DCsx)Qk^A~n<O;gjc$#aVCnxLDvpu`i
z{ML+lDhKb?Vz0v%qcWBm8C9z5uN5m%xT@+J6*)wv#jGjo&_Xg}jd8$|0BJ|?`+uOb
z5;nT%6SIB?<5|;v-xegGf@TT=LY$UKO#c8C?TC&bW_p#Avt;*=S;0pQDD3CoqgUH?
zSZtHy8CPhAOz7-3RwFOsRg5`DoV*S$h#(@(@8TzHHR}l%KbAa2T2i1vFsh=6jjfK@
z5xmZAx9acX$E=K*JDS6k?IphB62H_rS{OuUgyNGjjX~z>8ecGo`ESgm4si<Za@Us?
zJvw>;(#nw(!F+W4>Na2a-L-`?GrX6Q-g3VV@Pibb@<w~Pt?;Wx+HW8TZHKnGU<QzY
zQ?&fs;WO(v(c}C-TO-zUMn2lxW}tyBMw@2gOSH%FIrHt)s~mQ%1N=Q_beam~Cm~Io
z(uz)kGhd161Bme%zox2O#*1R1d#cE=WM|rQ=zx}#giLKS5`*G<O2CQ2tdg%%8P#4z
z$pOt_M;9vEiz}ag-~u((B0{xCaHyJrq&Jli&3a`zj2sK8IHTM=CZk9r{{VE~7JFw-
zfAyS>yhxnyJc2B?UP9Fb#T%}s&y9UfwTXqEsDnw(q%NJERbei#qf@Vf0HNX$8)n(I
zPZ;qrw3KbPAE=p)_mK#k=O07o_uf9TOK+$T^8*;eT1H6qo8INTBkFNBy4LniR!@6`
zG5-LVY+&F_Mx5Bs+aNctQyIK7VUmLa&&F@6Ru*Y$T78g?J9zN7133;yFR2Ae&UV{4
z@8c&6gn1xjmcx^_@ji~)Hk?p>oIwoz{9-&fAMg0%U_I-sVy&B4!p$k1>*^{xGs=r&
zH{W^B-zOW%^mN6-9JhmDC-2d<QhSWLiPM#_S2kPPVn#<YM_Ri5qnzt3QyV6z@Dq=<
zciAR0<CBjWjYot$;&f_plA$RWU9vJb<jB~gCu6{fYzo7&G(qAYm8}HM6cCh&Ha0!8
z&e1vIBMK*N@Yy{`jMGSswl{%2prT}td6bc&lJi`jg5qX(nb>3C%#G%Dk7#C6Z*7z>
z<4~qbcQ4w1kUW^@?>q1R0O)_m)FXOs-}!<k+qALo6fEZj1i+IV`}Ci@S?$O&Vq$ij
zqdZN=l{tG2PX>11(wuLm-^*_9cK8)}teb_^`+8a0&Ye}m=zxYja7Ax>4|o+hb*{SC
z9GyJ}My;^?;25-AIM<7t#T_wn&SNmL@d(>A$tcqGej!u+DfQz{v+pXclXWv{sel(=
zvqa6VuPV2r)0Avq#8*oLVJ)Xut?e(gWBCBYv<HSpOz-!2z8Ym>A&@BvlYpiGI#hAf
z*P8Op<4i-HZ5DXL7~2h#J#<^|%+4Z6pkq=9l<8=LY#nF0NNJ&<(bU!|cI(KADcT68
zXT*M?-Lb``5`pI6$Z&F+(A7$dY1TYWpAen-&T$wJ<4rARY_Cyrlr`&s?lZVdPWwyv
zzLFUkK1V+B31yZ*FHwlD_8=o;4YT-g_B#QZ;7DptoTxGQCCNSb!9@DF;J=5f1b-%<
zZIzw2Jws-+OmLsTjAnp-XSgCVWSa`3{hfu#um;_EoYTS+P#2nteZ@&$7LEo3#X1FE
z6WD*l*Li$yX_lW>qM@{%HEP!jwCpOuM2$OVJ7v^OC|WWi8NL!tf2`V~Z*my98pu4-
zf3$h7JlA5ptQQt_mdvPII-+Jpr8n9KO1jf8Sbq^GlGgZ$-g3FuVvP@CjRrt!-E|&W
zUSwXm6-<=xnt;sQu_BQ;<hk24nu@Y>EaMU5JkQ>JVGx9FL%DgGn262clbm?{am}L(
zj(lf0`ScLO;t{!A)>B?5g^fwpnmnzK3r>l^SM-+Va)^*3+wz)|*48#gMs}DHjXaAt
zowkL`1WbU8jHHgWN82rwVALF`%wt#p8UFy4^?UT|h-RA5Z6u%aG8r@AAmTn#8~vW%
z3MGJXo$xX8GAB;K;x-tO<%1I*F)o*Pyr@|sr)4;=GI*1P$q4BT4|6DEgQS?L%IG^!
z1cvJNwU(V9j~g3E49-eteaDBL<%#4nI8?y=Wt1|pA6XpxQdWP%Y(T*|AtK@TE?zZ7
zUF=-WJipXtWdSX(XdnQ2j1#5_f({xWPMK>cY2Mj{nze^a9#1}OG9ri|j>seYS#i6D
zMUxW|@tMF`HG%<jT7m=@8Jgr-r~d$E?<n3U#FWq49Wi#QGg<jtO7XwkJgZIj8No{$
zdV7Hivh#aLi_$+qe<1#-{{Zx-_K#G-NZ5h?Wv`i1Olr&Q3D^k0po4IK+ZJjv1N-w6
z_c=Mp7#kGCjO{4cDO_f0p|+W*OytJckb%*p*)cIYO4>*<FuVyhrqaDMz0DRw7#(ad
z^sXe78z!Ae)B}QSU!h5HO+7?&J$4`kwVRM8)(FM5z}iG(SI)V*OMbA;8E_hFq}uk2
z?22XOq(Wd8)iq$ywMrL#gPQ?0XNalOV4H*|*}i`e*04`o2YmkkxbdH=a;)v9>T#b~
zFd!0Tsm9*Qb5Neg!*YBqa3T{?Ti$yIN$#%G3yTuXAYyeSa@EQ#>D8>@5eW>ArgJxx
zvC;l|ZFrkO3$<VeQ0i?BF~OS<N2mDC1m~9RZvOz52k>l`6z(!vtN3L=%F=zq#eb_S
zGuUaS>>=1YCpkez#iekIkcV5W%9a?GV%AMyfHk5a!80;jZ<;YkxaU8J0tpox9#i}~
zgfg;`k6T|9P)G7aOz)fRJLN9PrW^GwpyWsl)2nJu1_V!e{{T6oGo3xM20}(M#O$tk
z?ONQKKv&nJgbk3kgX1BvGB%q-s#V<8YW?z5P8;6O*2(_dNI*i*3w`|f_NxIa2MH?E
zr3Wg~Q>*Xp<|eT69YOh_?;53e%O;|EZjyov1&<jtuwasj@G=D(<u$VObQ`t=Ay|o@
zaJ<tDh#FI?grV(>iIJF?d`9BI?3608EnmbBgMxJ(RaZ-Z>N<^zAUj#rZuKOhuC&;I
zQ-(?3D4+40_|3n0+x%wHx5@}-CU^SZY`bzRsHZIIRxD1?!I3XBIjNsJTjz|U6VYgW
z9L~CFuNgBEBRMON!$p&U6E)v4rJ)vYDQ_z^DMT(cYiYG=O<!I|;K}mEHBs~a9c&a>
zuZv+9g7otrTkq>JAM)5Hay(~^<I;AF>y>GJxuo$s;3VUi30SB@p@)J*1SUGN!d>na
ziIS!)GOlCom>FJEr&g>iWcZwE6eWZSv+e+h0D;~x)i}U|jwOFi*ksbg%Ek&ShT`UC
z(tFNYF7+zMqx|ZJ7>0^0!IX%y%&09FWtNm2Y*P`Y5cK<&S_^%yd*q>>Fu6y@K;=>f
z4^e6{7AwjUmO|Uhe;GxQaAJ*w@aejUs-pZj_Ai&qD~~q>We!i(6FIC7?IV;`D^7q0
zT~Z}gDASmSnU}BpC;12O{{Ypdd@Qd&eY4DeC;epw_Ud9e!U;zMF~p`PIYo;5Okw$+
zUZ3X{=z;5FhF7Ok+LI5bOvkIG>|dqE@Y3|^*w~1LPom462ZyRmtB-hHAI>I4K}pyS
z^y%)@TQ+TKwj!3D)Q|m+aAU#ps|^}0rUt!As1n%+s9jzhM@5*D--_@|Ri}b-tE{TC
zE@KJ~lnXSQ=T*IOYKBJ{gC4RWTy5q{^^E4FdfF~9>y_#^xqG(h&44DO(@cPMJyjC0
z<-a32VQ)7?<y4uC`PZ&dPe*%=Ou76)W<VEI6~y%8P74S)?WUlv_S_CqU&Fq1_4eIF
zVI0`9#%6{7!o1_x{-<oR?FbDSWHGng?=w644#OO>Vn#B2`Nr+}MI585Lp8X7)SM$p
zaMu|ImqXsa=lnp}n+r(#u#05n{qE4Gejx<O{Hbq9@Kj91Nar8R-ZM+v+!Rirp6wv>
zQV3pyP;%h#aBZJ|bC!H+3Q_vcjiW16t)_sw_LF^=;?H2t(vr{OB=wSJH-f7HIiR!q
ztH$rid46Ct_e{s8lpLvy&CNsDD!&@{S7f3FJ*}k>%XBBhwoQ#1;uKBvnfUF83Qc*R
zO%T7L&*B9{F)WQqWTj9j(}-1Q9#Ei;%PrDgc74Ri{lsO_8M59R?Y7Y^BM6J@7om2-
zv~seTiXtGJ5IXv%W}(B-+ULQcF)JXK{jNO})>mM$cNh4cnx>1O`fG&fRf=tPw{53B
zjyLg>;%7emPVpON{{ZJaePr%C##n8n?&(&mrJT>Wi@FhfXRr8mo-w*^mttVE@CuU?
zAdN4e8v*|S?&bdg8(Y&acb5aLWquDyt4Orb=FKV@5QSXOO)Km|B7K$LO&;QEU4}lw
z7pdw86)e-SO_B{M-)M@~+gB(#HMImaR&G8T5P>dH6Ovm*&JhvD2|+|=@t+*{#O*&>
zxa6Ei>gB(Th|NRb_0^3gWAPMBpY6G>QX>*R3MOfXxS?_}PUxOXBWsUyqW~te$4hM6
z_l#u04=4*AlX*E#+bKJ=E==1}Zd0eMD$dZt;l9L6k=(JB7U(F((>j3AIr2DRAf1aZ
zLf5REMOw3V#Dq4o>GHK-9wcs!uQ+PfMxGXI@<Nm&;(YJ9M=Km-hYBYlZCg1ehZ>G{
z-xVd;o~{HWW0c%=slBo7*Km2(LBiqFIy8T%s&eIbT{#%O>dIJEsyhjc%Hxl8Mt|R?
z-yP@I_x}L&W7Ed|iaA@BabP4YHYfQj#7l#ggYr9NkzmmlWDKOt&T=Pd-W?I)gTLHQ
zr_@eeky@y$C^z6Kon$3<*^~bOTBPjoPCIUU4p#XY2d5l}{G;9ycWA4vI(<DMUgFZP
z_pZg(>Na7kvL%TlTOh_{W^$Y6-MX;a&6QhI3s4RPb}+xRxy^(hWlgnYpQU(B1G!>B
zZns$+l|w|-${01;YlZXLC&q2L4o!uYuW@bTh_L#{%^`N3qW-=#<Ue_<OGwm^PMQ1%
zP?h|fr&LDW@_KJiQd6qzw=S-Q%&IZ&vhsbVx@cG;zJ?prNOKl?Ww%zXd#cGS^f_Qt
z9=ca-N+M;vVv0SzYRZwX9hTtrgN11lIA1_H85?irt$DFvRfRl?W;WPLa$|KbYKZq%
zs#T3@chxOsoJ6s-;n3`F5{cAZFS_FXOM7mZ%^`*o(Kc9!f<k6yW_e|B><a_wNVr;D
z;$kIRPP#wo=?K*VM^TZS`<&C&4!d1)!sV&SOCyUJ9)LT{l%~9C%8Qm<=GbAix)Y^$
zpfKhl@}o8I=SWL8E(17wZ{tOd5xF0kR%)dKr8j)r_suM3DTGF;W3ur~Xs!iZ`O>hq
zcnde~X_iPbm8IHPxHU~8sHLx6N;a+^6<h;Vh5_c69dy(auguMAC7PS%D$`a2&c>2b
z&CBFdqNaHh<bPgV1}4dtHUnTVsrhD3ny-bM0kx9|X%R#PQE`r@-*Km@p#F&vAqeEa
zh_f(mCPq$Z?S<AtitXj;!M-a|wPM|QF_@T%jknJ``TNfi=2qmjgd(Q{n&2P%$@7!@
zPBL}|Oxc0&X4^(gZ8@~!WVy#4$sHm14|q=CjcZU&2k{eNW3tArr;5^Rhw0P6;V_49
z(ETh9EDBwrUr@<rpQIC=F#eT8L7}SCi*?Fk_Mb!8CCIH#t1DYKX_8C2vxF2B7W>3~
z_$c2xPka%9@f&aH{{Wn3azuB?NS;UKPaNJSEHn=7lFCb2G4YJtkB%`@1Tb7yJUCLY
zYjun@N{FD=zJFEWRD|B{=UrhNhG40xaeJ=w3(*gCc@kZXn<VAX)F9Uhcxo0XjKQJx
zu7paPTuXR6^h`NQNiy18=69X4&y87|iCA!6lCjQ}B-h3=sV#6M397_d!ZKrI&Sqey
zVXP)>Oj>!w%ol3UX-#i*a|+Znmz9VinvR0o15F_1ke4Bo+M4iscjm2B6wx8QY)JnA
zCrB%8wr#V1-2Pw(Y{>APr^Mu!_NFK9eLs2Le@spmn{a8xzgYeM0R26tBhqrgav+~i
zzf{Ea*gVnpRaTx^KC0FxAPA#)^jvnOf&7y>sKkwdT-PXvBY)x#_uFQi63g{QEPigF
z>B;g_K9#@Rr)CTb3}bxL^vBz%&FOmp=US$(?mKsFf-yi-8?zqWev$R*N5{Et3@+M_
zEkyLzmQ)VwHPtO_J$l<>rDM6J3YVx{TnDkI8B=d?I2*M);{1e859=Is?)J_f$%Q%K
zwv3qD?@pudSW?5XF0EiHOrG<)77cQSX(}6IP#DuHvdiad%o-X|3g^jAR<*<sv)7f?
zslPO-DQnUkztA9Y^U<QzPH8C<do38w`%W|?xTimf3-YP2(j&(#lKa{wR>+x;Nu84_
z9USF)omq`mchs%sk>)LiX%Q03Z@~oJt2<=*^XSm`4p1%5fDde({ZX6v+bPUNh#A^r
zl86dHf>}hlRSC^h35%^csI*zA8P3~Jrk`Dmi!YC^GnduTalweL+b6IxX=(Bjb}TZ}
ziL9os>1<_I^jSl5;7-~uu54AbxMtiHo9>8=OFFGoRv!K{TJxsH3~!Mfjw398LO?2#
zk|`ps9IU1?*?Vj5;dhOnZpbK=oQ^KtwQnIp=C5i`rtYC)yP;Os8a0;0XAV1Ns|Lyl
z2)$lgsMk<kU9J`Vt-UViykLTnD6X)`+mucwy^B)XY_nO^+bV5=1SLw3Gvg?LlOZ#x
zt>sogAqF~hs!FYu%<`@i?iZ0++fO103CMyu+6;^@QjpYWej_#KJ7{SC02S%FhrdA{
zePm8aZMjaVtL^YyInaHBqv{1#=jMrW-bX*oVZ_7^+Vucm8w=pcUuG85Gco&Or)GrC
z;#xJ?$n<xb)k`+Y0B*FT#-XOOPKdIKM<&5Lci|PfOmxOeq2=k~GW`XcBqElFTuZTu
ztg8F<5lGBr#Lh%*;~=Bc&QG5hUL$CUfH9fnC4&c(J8hCfgnu?hX%+jMV|x}Nxpkqj
zCBTUI#?UL|lLwXMDAv0qc8Y&PD}rh)Sxqoz9^Ly%c%CtLa@%fO%<qD7KU<2*7%nh=
znJvgBB<E^+?EC`C$xZpGsE*&2>Lpdvmc)M^h|Lq1zIbM=!EMtxqoqFEA`e?y(G^gk
z+hQyp6S&O8g!P>Hn2eS(JZCSLBBWoFZb>TV`H>DWbT%0+R?l7P@T!V5=HxsgG>dla
zvdKSEpPBC;=NNsei7PY3sLHw<ts!ra({%MdERL^2;aWQq&*8fxw;H;9SD`g$S)*2z
zpi#a@@BaYk?dNQQfSu#eWMOT?h$SvZ`8@P-%{_mUd}mWV#IiSu-aaMww%=)Np`yro
z;cSu&jkY`ylaUzIPEP%)n!(P}12w~oVg@=Sh~E;QKX`HS-_S}xgrJ1%*)E!zMOs_7
z{-LTi^ukr7AGHfs=KO__qVcD*4kVn}b4c@T@V@;OFdAKqiIJF?(^)sFlATtEv$ZVB
zl0M4xl{+eKk!dk^Pjw!tIhdL7Co5o<2GJXj)+_EO2)U6KGxd1NsAcmNW2_~i(v1=Q
zjI^>X#L6d6R6<_Xag<bShKimvB6IkH#^jXbj7xTj`^{ke<yuxCWPqT>6$a%Oglnso
z+ND_!ZwmG)hLLVwh*`EiV>z!@d*3V&<0LRg`sY68Fu%Oq{{Y0VpJ8G?Opz@ZNVQ74
zYCru8nWPC#hfXCiPe-y-R(7(9#{U3ynd3I|0|wZx5|zf^xkuR!OiXc^`k0t>%(tAB
zNA0CLN`iO1^GVeacgDdUSrLXwhE=O<blY$>v^%88mK7?Z-dEYGLg15Cwi94{%H{`@
zP;WpvlZdpnY1M3-8P#D^I_-Xo0kseoIE00~?m@}<^P0iLX`&MJwzCOrtEWd=-dC>8
zz8;FN!9|Oy-3(S4oYqN;53}T3O#NxXD+@Nwda~_6uKguxQe!CQGI9_}S@7NZooZGj
zCDz6lT;Sism`pkia#*9^AE**vVR<Z7w^jEOZ%H=%lzK|Y+O)ruCIpOPLNHy*IS!$y
zTB6nPMyu{&@<*t3B_}=H=kwP6J7M8#Q>Rpkacg;|=?N}d$!G)p1ZLiHd}V=?UmVpC
zlv|Kr?>j`u7?f#x$f?hZACIHmt}?Eqt3|<AoJmO#>YBAXG`iQ!_`qR#oZn#~%37RO
zrs~CHK<QZp#!ek+3cOD=?{gM}t0}G2<R#B&tt3T5P*rQvZLc6<*dB(Zvu<1MpL2x~
z#$={hK4aujzCc80BMwuX`NrZiv2h~!#uv*LJa@t0>paKV2QQ+vEFLYPAXV+ePhy~e
z$p{+I=!qiQ7h{0Tk%1MelCz+mRuz_8UK++0mn`db$J4Ry>vl^DJ<wx#_cLsWSot_U
z6WKJFs2eC_6k^`Il)z8s1){EekxyKL;=4^ynC><ZC#+Uv=(UcxwC2n7?K$iu3tams
zC)she8z1~<JbB4vA75(IErv>BVmI_ab<?u^^T&KRhbkH|VZbaAP1B{NtT5FC9_<^<
z6f-oX@T%((e5PzLeLti8ot37(?ay*Nd_+XV9Bn^eVzxztVTeEv9DP(-2vghqJ8}e#
zar?*r0HG1eVld;6#(j49%;jc^jNtu0$D&7@>8~`>GNp9$Q`#c9N3PL|m?;%C=xt__
znIFDX{{VMe9$f4=vrQg)g;>T|kg+9*4`x=`5ufmnN;*Zm(FYH<jTRv^+UC6E*&FX0
zSTjg_L7=|f5aN5M+7hBNqPwiDGpv6wpYX=W1sdUya5LjcXJwN~(+;Y-na<G~Q;cW~
z8!T3u&W5Ymy^A!kmVbGpScg$eV?RtzDq=Z$81ODRw3Ng~qaMrsH5Uj3RE5@ICF@|l
z0$QzrHAtA5LkpIhywV?Qr>YB5VZDm8D;ez=k&Z>aX(R76_bIPbq0C-s5x$$_QfSl?
zwTjwS`JEcTgFwlV6T>Y==+tM?h0Bb>_T|W=h>3(*%2!l}B4h|kG>z4)r+q{H{k+pv
zA$w@m_^VHVjxrle<mZ|}xqbcQPail@AF2oUjGwHSnFyTUY@i%J4m{TyBK<ox3NLl{
zbq<qOxBLG9#B|ED{T`#J*)1}IA$II3!sxi-eWo@`<?{62d}WCif>DfFu6UFTJ3`oA
zJ<Bl7bv^Wj&?hO{VX@Zj;Vg$F8amjI;p>d-D+*dk%!<%zUI@y<9iMZFX`!OPzY1z1
z!n1h^-Yr2dOrRO@iQ6;}9`xq5uJN$Qw8LFPWLqI+x6V>DdlH#TO4s5~W}fm@mBv*z
z_kzz~R0GuLV+mSie0YAAi~j(ps<cjs*@qglTWO?eFf7;_%C94>a3D)oqKFF;FKH13
zVKm;N-%gG1R*Y<Wok+(j99bKyofA!qE;cz}0USgAv;P1u*&3)KmY-_~^LUxwYtyx6
z2+{a;y7DbzVPRoCqzaOg7O_=CjNU?SVMb5hN7UlOl<k!m4ws%^)1N?sOn^2Sg}y>V
zcPNu1zL#^@S?|hP&3DPDwLMI25kfTT0Ybb9Z4u%#?<dpcA4NV32O=gs#{A<r$KRoE
z`*Sjn93LN~1mbrhh?vO#02w>P_>MS>9V>onNid3sF~YeAVQe$BSXqsd7@{DlkR}0A
zRhgW8G&H)khfYkZPgtFa5w@Moc(H>M`{V?`0Zhbw%1dO~h`AlbU9ekB%z9XJnkt5A
z7ar9@reJ1gEq1U-25H5KT$Pd|DLK=l-8k!)%04p@zl_>=?}GLvQVSLkZ^U_TCpE*m
zy|K^QF%0T(Ovr~RfGFjqDsr91(lRhV1Y79w5vCmD3!4nRG^%U1E(}5!SwNuvPa}j}
zcPZ619Z8^Tf%-5106{4x1DWd)=hK(v!1_r6COtoGvLlqlM=6hX*wYolgR@!w@K3JO
z<J1amr>Ns7M)woxqsID*F`!1~Z0YTze7!5BwnUVq=+$V4ZOu<sZL;9IGrekAK1Y;S
zC|e=3xwz&sv&2!U08jR(I;J8Bz_!>cJNR<i>-OYE(>$)9Cq~&!`tDae);g-Blmoxv
zZOtYm^_0#YjqS>P*ZL%<rILg(pOL1~by{6yQ^^O2k0lc!JIrUWzhL(WuNo{vtxHd4
zIiIwSe$$Y+XfEy+)pU0<D%TTcpIol87f#_z_hyYoa5jK$1acCL{vU47U}m?$dXev<
zp88`q)-6Sg5Fi`E8Hs43;tpB*hb;d9x4WBdGZP{(rR4ehi1-xXFP8HLa>9}!9%T`f
z9Duz`uTYqq_U1G<E?z)-{Op>!K79IAfK>aIu*s%IeyWk>u^dDhL`R+RV<*gVB1&Q*
z6-(M_;!Is>w64o~#kM(}V###OG=@jS>bs0kPEU+@@odC?{*^#Uyo~3O$zq?k!GZ}3
z=T8F9UbA5&ASW)YPgy0#vpA9fETE!AWh}plISplOgpEYSw%I9@BNd^o-a_{PU?e>#
z5Vo|^V-RU+sP-Nk=OeYJ{N`sg)hKl9MH+GhB4Z<Ft>Y#4IeA{sOscF|5LSL|v|=aD
zocz{R-BA%1ShpaR3W;GNu064C!JO#>DZQHPe@u*C)vFNPaKY*2FqwtZHe_TJgzp+s
z;O~zeG&$c!`U59t?^p#o#ket^QMmel5H=&MGBGBvBFZ(&r8K0!#OA4{d*&`vUa@I`
z#p%BnHE8DTyP3D&pvL>+{_=!~)|V4(z5F{rPE2^kwVIMWx$XH|;29}`V`V@R!;LkM
zwN*~Bn`FnY%07$aI3*kGLq90~ZT6g*$=@am_K5Mcj?uj2fpkGm?6Ib-2Hx^v%_qp#
zs3yo8Bon&clAUVLTavt8+g2X1%XjP>i>it9s+gNw_Kxh{7<+_sFdTHmvSKF@`d<>c
z=T)JFF|d|<TM`g*KFI=d+IDVU)GL9Hj%Ayy_ur^_a~5f}xpM|l+Iu5Xr6N-Ji6F)B
zlMYWl=P{8HzD&dn4p8OpU?cvrJwN`JIrJVxSF-`36Guu;K}YV2hi1SfdRClTd^!u(
zV<Z0jPH)F7*j;p)m6yerxv*586vo+qF4@S2BxBR}^lbiBc=QkX$EtVjf`)u+AabZm
zDI8|6JKNjSf8_JN@yr}Ey<hInriee(wV6&N>~ocTD$oYh{vT}5J9ae+v`|&*(R|yX
zsHK)5Nmbhh&TB|aO#aP+2n9uP;0=7~(fu{tbSOwgdHgvE{-M)$_)(hXcHLgarpAK3
zor{bsI*yw(dQ3)mkj!gMlhofhC%%}_%{`xoR6D1s&pXVS#K+DtI*Wsx1v_JDXM;Rv
zEek7ZBRH)}{{Xw3)(gJs*@k?|XVP;|DLppwvsr8`Dpid!d!cVFRqzy?8B6mj-$n%N
z49}2H9MODR-!<|6sV9(3(=>@3<3FlKe=15EP(|Vj4i`B-M=og~-*ouiPGfvmGl@t#
zMO&l+vmS7YOt;M*xmvif9YciQv&M@Bc9ci2GkXBM`kbz)bA(FM``a@g%FU;VNB(Kr
zO^b8SCAw~z5$*L-FX;BTgFBb(&PU&p<=`h={{ZSfGIroImLg&+9OwAPd?SQqQ0=sU
z%|VhnvraWak=J*Vjh=?*@d`Lx?TRP`u5CW67~lq;qOW-?ubPb1gM`RAKHaF!*6{1i
zx7u=JZL`K!18Q9ZsRUY~PWZ<n+9ORkhO(|~LVRVyDy3CX1r=&Jv7<2G3^H9~ZJv?U
z49Wc%=-QoLI}w}=S$`Xd>$*`obK8}+DtDr4A5b{t!a{bP8^hx@h&EQK5q8N~Ac@n?
zY<1Pk3o%C6HQr&)IG5IGg~6^o(`RY7Ii;|I{e0C_1FKUq8Q=8h@0!bdEfjig940eK
z)xe=>beL_t+qw&?Zd>fS(aAGO{{ZMFCHqB}ZMURM_>AK!B8hxWeHuMK#_@<K3kf{D
zi9^aG6+P4*ywgBJg=im5vRatIE`Oq7uv=%(mKg0G0o;Z+^xrqmStS{~&({Oa8-4MN
zKaA~`2+Bj4JEKgEfC-Z`i(5Rj9l?^f%4L4!+*b}YF*s1v1i><YFKnnlH~40v6V3`P
z0UMaf`ufNi<2$8HG`ym!(-s6>?m=_1m~3Hg%IfZL2543IgRJCQOtcpf2Q1$us|Nz6
zCUbb7z8}0;p;piLCqYR0#%UHtV?Bd_OR_6G=45oRA=Fd5OOseoGZD+urp=>uIsX6{
z%`&lAvXVVkf1`BdM3jh*kEWdgWVgnD->&)kr)>WKsK-K&<BF$RxX3QeF}(#1)O5B@
zxr&ag3a*BKCc^`hll+ChLg&-!cZLM1gcFMW4#&r3pj26jfC0^7HoG}He&5YohxI2p
zr7T8<pwiQ{$F{54F0o5TNJ1T`U9_@^t;QR%Xv7`uOnjypUl`6si<ys>Ns({K(I6^T
zRg#T9_(b_HO#;Gez0l20fm^6i6$U!4GEictDTRMjUD}|Lq#W&_a@qD;uz5`$?IBt<
zYe4skm8q|2q|Eb?J93=;GrWHf$&#$({Z4D23w1Jft@x3}fP>QMwm)x4pBbe)+uN-f
zsS6#d*bG9U6_lve+b3~>8a#~Z3j=|eh~Lcl%_29owCbq$m6k*NM=}hknBVe-Gi|7E
zpQy5bQEj%w8F%BS+boYo%vVX2tT|7EjrWnw0)N#L8DoEP{pNSgQ<3{uPSZ0H7Jw^&
zBl$M?MEcgFV$hL^E|!<)4^VMK)K$a!(L~%HZL6BW%xUe6ndRwB1@~$OYA4&MTbZBn
zkux-av+|B6VM^-`_W7BaoY8}w;hDX(>Q*<_w|w_t7iDE0YL)%c6@+JTp<t_ohXv2k
zMqHkDo>&FI#G=ZfFENCSw7RqF(y{PlWd*gq_JJQd{M9OnBM2S;02I_oqi}h-BbP=$
z4<OIs=I(MiNCA4y#pTlHZPSY_)@q$5R~OOSOa*{PK0=GJQmY8N0QrGZ6AElX9FnAR
zt7-|<IH)L@J8s5pr0w5cb5&fau^yjaT6u7rMU~<rmG<R!biGW?Ymgxhip^7#1gvvm
z3L~2=?Q;*){OA5g@+E-|VF)Y|!+54spt7yC5o=UYQNt+xr`DIs!oxLt2gNnLCU^9K
zlI61dF4UFa&isco%{|t{h*Y6S01u|2ZH8TGUZ1LRh7+A(8%9@ru_MY4e-j1cHp!gj
zoKA8QQ}@!p-*tvE@&HP6-o(UAuENOr<&I$#^)z^whyI|5deSRGVB6IlLV_;bL&!Ai
zi_Q`3l>?_aY*)4y&rr>5o;$|fyK4h6b_;~@vplRabH)lxf(k*2Ptg;ZTw^&x_Tc$~
zVH|(nR6}~^M%gZvL9GPAuV%Bv+zTP)tprU-JkvElbh#EEo$zG2?Gjr|Y&M`~Adcdf
zF%6kk5|qL66~GF3yGm0(Lo~Gjagfs5E#6T@X|ybd>5!JLWEWbpqAThQfu!B^tsz4d
z?K3=1+2=R){kF)T8+uGc=hS(D7}|1NgfOJFg95cM5x1g6Qw^3xPv7%${EfX%NP$H-
zpNI^~Bbz52C(%=$rcp0~pXZ|kHSfVu(`?^4h0#TNVeQ8Ls@o*M4PX890zdUzbKZ1w
zg=$rbXqhuo<rsSm3i7_t;VEfJFwSYJKMAnu@#vc~R?s*LRjyP9%-Z&<U68G?`f02D
z{?bJ&cBk@Q4bbR72H$zAd8efMe%oZ`i)bT-eEo|*h!AsT8JFS$$GDv=hQQmVa|8u2
zTW>!Z-ZzQU=lw#a9Z)Sq?C0+}sZ28axQUWj1~%9ox1?jn*+y0?nVmJArFC=sOFP_D
zWW>)AhIkkltiIbQUPSIpoc-f9g5jk)Fcia$<8V*un4N|s`2LZJ+v|@n)1dG8nzmtc
zV+$uN{{Zo+?@wd_TVW9yNf)d{bsi-Su{>2<PODfY*R5-9aLQ0nT&I;VS-XMRnO);(
zG=<*X!>^o*X<<x@CUCa<kKww?HZ|n$FK$fp`=hfZ)ygApB&N-k^cK?-wC0pz2L4)x
z!Kt=p&c$a|tvYje161DS>>U}V&@qrU32hBLy2w)IW@n!{^ga%tu0CRSk0rThj~Efn
zgBhsU_R}!0bY`GaMG&g3vzi9|u8d^zZfdURuC_*=5Qm(u<PtTDZ^jIe!|^^Isl>2G
z+SpJ7MDDV5R{NUV3bN<kEHdqRd`;w1Pkn(_Y31%UK4q!FO>i+PqwV5OoJg1!YJnt$
zvSWWre-&Gtn2^y%Jq{+;lO$RN&Z|D&S*p@Q31z5i6>co{+IG*|*Xt2s)m#rw?Xco>
zqxgsOj9dIi51xa~whiafkH*T&HFM_F&!>_fNHQJx)MrhV9%=OzZu4B*r|s*6E+@{>
zjg8dEiWNc^B_f~?wxKK?q!Yv<AlJW`fhnI5z2nRR<2H=mF^+-Dc1c@tJ!R``7}aX^
zfU_;rUvAXR8P+RZjh#Ad*!(qE$y}%-E20u{ZqA~)Rk?|g<~|d*Xy%t(?HkpWifl84
zRX7`IEYwCM#=y)>N6b!G@YtkYs4=xj-DIU@HBk}5WZF@`7=g}m)R1IH!vbZC>CLi*
zKADD2scXH)BVGR9W)9fE)vVaIablD6M5xi{VD#Nf-TqURWreCbu~=k_b`}c@N@pcr
za0INhD+Y4pO>IGuEI1a4h@3?K0FH7w&L=poa#S1d5T#Dr>Q%e?)Cnm5z<<<w@BTCP
zoSz=08~k&4ABfzJaKW9q7Q$6)%vKaCt%uC?zN_jKgMVrC@%)hy&~xhry>kyqm>N-<
zD0W%nj@ni`>I5>${%^k!s&-t_P*b3tHmm#9sr$`A2(G7*H&=Pmh?})yZXbQ?1?WHX
z$~$}7yV)42s_qi5r@AI)W@cs=Juq0$n>HJf?CPCQS|vs-S!JzPPCy|yDcOh$w8IjY
zSUw7hD@4b=-nul_KC|!KvJefsv{2KAKjW61EUQIvyn03n$S_V4;CKP!);7V_i&*BU
ztc(IBn3!tA+G)BO&SPw%Fx^I<5B~ru&UhFi#up~A;QY+aS^ofKM6hF&#ASJoa}%V;
z3$_O}(&ehz#mw^Og}kqpS74&b8#-Hx%`U_L0FY+9Kk57Ay@e44x9>dp!oZ9rW<Xi>
zw8ofy+LBwAL$1Sv1bLheYHNuKG=orb*!cwdtxa_CTC{NZgIRb5K$?xP>l}Ln_l#$e
z1Ipw8;F7lz75SEpW4@<Be-IS*)?B~!jO0wjPH1!0OHgio`X!Pl-Ik;Cq_cU6f`=?z
zk$6OH8_Fg|wX%&)KQgi9qv5Xam6<8lSduv1WH<%$ILZFb)2o)$C7E4<rvVd!7Sj>a
z$F|6)2qX-ElM}PEd|0grmm8P<p0kk{`z5+gm88akzwR>zHik}X(>~U`W}QFjr8B7x
z^xSzu1+A1Ak4sqx5i>Cns?S{tO3&fBkDlM5*BP374h_f(eSp>!Xx)D0xva2(Xk1Br
zAYz<M!*U%L>9kw_09mxpjI9$2w=rY#^!-0(fR7R#6rt2Qa?1K%z{vvI3SjmL4BPn?
z3zsNeWKtFyDAAV&*YCC)-p~b{%)pFb#`78ea-T0T8i(sgpzXh(`i1B=nNQqiky$n@
zv%gTiUbQe%8>`#60+Ohfp+E<d^@Fy5?)koNx18U6-z2v4oZ@_p&T;1qlDk&zxDmu?
z4`D}eXmmghCXIHg^9f&xU)130D@B$?ldOM-O6(Dxx66ZgzFezQlZhyvP*kQC4Uls^
zlNB0q!}m8VM6X8jwAyX-CTEo86q^j>X;)(F3eQ-sbF(w@$9&#=`UG#!C2$}Nz+|n0
zaaNojrH!@=t7Y;t5i%(un3;^Ik-I@jBq0iO=E&+VZm5|nV>RrO#*MMQVa={4>S-xq
z_Expb$BGj(puQ7(X}B9*u>|XCp{J=rolO0_Fw=cT^RFj62Kh34XJILUnUS&|xdgEP
z04)PGnbbZacJ-XwZ>_WIBh$_@2W`qYpA(6WM+}R)ctzdlou0*=T9m|V)0$&%O|Nj|
zop2JnP6X%g{DI|ck5S)!y)SIItcd;}AMc)AV1kvFn+|!*dV<XUg%#lX@p@{O9k<)8
zF0>M;zQY}*jJHIIAAa1O)22pf(GhZ5Lpw}P6h|+J@_;$b6=2OlT(2KHZ!FYz?Gw$v
z%iFmChM%*qR)+ro5cs>dqKBJ6eJWnm-%e<$g{M_``-^&;AIWLQwqs1DsLQ_^v;#lm
z7+*`s^z%NNU5H|qIc`Z7hU9+{4<oAK@sxk5MT$AL+@fM5X(|34tsIKX%xyE5C$6Q9
zghZln<7vmto|#H;e^g+UfpU!kWbL@;T#xlz6B8U%IXrrTGIL1|8eLexC^(7DML_CB
z&zWtue%N26F{Vx7%hIc5E&`9@6-eIg%``hNAaeDQ_G4~6Jj}#mG=^>^ZTf=H61tTv
zHnpX;3OE@KPu>sejPuBYCMFAxJM(nF+d-YL97er6-<ZSl3YpC}hv9R`e9qZ8T$Y#_
zi0n+}+vc1@*h`lO2QVTh`csU(u~yTW8jt&<nAFc}%CV8Lufi3vGI6}VGZ)953oD#w
z%}YG2eRfcA$Z@rdl@d}d-3X`bj>$T~*#PCIA2$1DnD8<Qt25gR%^DaU;RQe3<C^^b
zRd~g-hLkwxMCnFT9EW0yc6)j`X^EEGf{jIrWW{XI7R6Y1ElhYzb_pezoL#Qk-Gw?B
zbGApH#40K3Y(#I0IObVZy)O~XV@?3&YQ87?7IOap#dKE@KN%d*TO(+fn&P_ePF<q<
zFC;hl4SYCnr`SbnV9Np$*6~$Ql})6adQaYPE<CgKZ<Jupb{%XWy`$qJe=`)_(QOUQ
zR_s%)972DidE|fIoaR=xB)_j;`;R%c*%DDZdT*XL+Zn$p&);$U(~YC1W6K?B71l;B
z{v+6aHuakf7>Lpsd~MDu%c?$IUWJo&*>k8lN84I0PUIn$OWkS6bVtj?xvT?NOXfrY
zQm~q}8gjE&<tde;+T5(<Gf%Biv+mn*e%e-@E;{O>G06w4l6I;xsYflmXN+9V<&=0b
z8(ozu6{{eeu-{==MRvY=8X$hQm{X0dNEYQz!n07^H8SOKFo^e4Ze`0;^F)a&>zvgQ
zD1}%ZNQ=aar}}!iQIe}kIMn7Ds5fn9f}*G~Oh*Brh<Wxotjc<;>UfmuRo}p7XKquB
zh@LT=eY&@fQ_fak5BD6;*Xz@}lQ@R?69wix@93rUK}K4*x`7jxjOh<CReq_L0LT9T
zvh9p^JGxbS-|ZTrLrrJ<{{SLOiO;Cd0usR<*NElTcH4PogfIbTH8#SP8~*b?kdoud
ze<~z+uTHe|!P*T|P1E8=or^BEeArRqCoGWr4LmZJHR(E;#p&ZiOH|fAG_SwYZT5)Y
zfksi@FIADSt*%)dusxM&yp=K8$fkFhoci;ckoVN9Xn+lc0u}W5s9o3Q#-5+-)U@8^
zLkOhgjI0a;iUxB~juTm4m*;<H5r^lDX8TM|9Q@0Vrjm2+AR|#%bu!8kI);&Eu|_F`
zmaU<vR~A}RQu?V{=amrYs-e%`b695@OpikSTVnqJGfA*H9&`RV{5`jIjQ%4a?yeBo
z=jSG4h-`2X5whc<71cE-!^bWGGLt|*>osjHNTXK5e)hs)bKeRh#EwSXXAHj)(D`PV
zTrQ-uR@o@0T0DN$Xg0)ip89lGF<p#fj!5!<<2h(L>@a3yz|Wz1-f#(1P^^^>XBHM;
z!<+l#oYo+RP;^g@ze=0MDvQYf02#+}^44~omMNW0f}0EKY{1bTc9$7z<!ReM{{Tuw
zk@>n&MqJ&tq6em5Ub0R<VTq1DB7RaiTEf`If$D&S++x;;C>l9D>RkzDqrbhezFGl7
zX^sSZ<Y7lEV3u9wHVKoJHe*TA4&|webNGX9D8U}hF}o#FdOeWEjG2hTxbrEBb2PNp
zzb!IOl_@PYK&zmKUgb6mE2vBeiXLiMEc8{UgW8*Rq;*$`+5kzCVNwv~PFOCR?Al6c
zI;@_fQYu*D3frQ_UMcNZPEcTl#g=~&hl<CWudGKl$o2mKK9wypm6ccsvfV=Mn-d@6
zKUBtk_zyLusmcEUtt*FXN_D>#DMm+}pT9=7(^8(uXbzcF*49f{OqE3Nal*@xE`26=
z<eBe~a8b0!FW-DNL`cSOwh41NjOm&C;3Jbee2Y_SVRBHKPP;+Y39B{8LE0DsR7DjK
z>Gq$M7^T@!H}F&^at%1;R>0!a!7^qv*zHx+<z&eJ08q`l><LIh-EapQw_Oj&D)ejl
zcAsK<*nqGlVF1~DZPm46-rGYgE=UG)%HvZ>7%(M9Kv=H@I3F6saYCZ2tPE~A?4itw
zpX+L)1JA<%H<{h-u<a!Tdbq}+*JiP|k^5NN`evZ?tvu?zX4{E%#qdq2A*Q9ZN;^cg
z0Z6T}D7g^k$6FqAp{=>k;ZU-*X@2rn8%_p7zMm29dk&4)FkUUwlQJUG`<##&A(@{!
z<M;mnypQ{mh{-sBl9Y@<&Q8KP5&Ms$CO}GJy=fVjSd^FC?9lq|7Q}%*je8=HCF$c;
z{Oj7%sodLh{u=q5#r-r;pml#p14DuwQMAl@(BOBKCz?|Drp{|6Jp~caQOBPpE`KFD
zR-t=#oHt6*#(M2pec#P*r;j;rkMzMFV?l_`VML}^+`lhQK+MFC*!Z3OH1)%7pfgf0
z>g;1Ug#?C2#Z#Mdn20~swo%iaYOfm&)T`AKPMmwlLCo>#3|{{LDU4)%v0>QNhF(6)
zZk!n8V;N<V$xie8V}ya6OAF4^=`)moXe2E)3=34?q9!Shsn@G1Yq!l=r4TZ!J4Vwl
z-$>-lM>4^lTNG>T+o@Qo7+~`M0KBGqR@4L2oJSF#;qicFj!tpgveyvC;(0S0L}JIA
zKx0?b`s{V9NMxAa<;ox;W`BHEp+s~qY{=d``hL?Mf&Tz{1B7ic2SR`&Sg~BK_Fl3~
z1^ydCB2MM^Ts@>ioEF*p=k1qH8EvH>whVG8jNi`kpZ;CYp4fs^W`!Fz(dNJZ0MceM
zHkcfoZ{(vzA4w<#bkF>KBlByJ;4`SifryJoC0IYPVP#CW3u&7p#^qSeIwY-HEFTet
z<~PFqv35PSsM0^;6ht|184fe@k!6(=a={Y?2OtE~01Z*H%Z4~5!(t!QT=L98RS6&7
zGM>_!`yv}UuKBOWAYA0nRgtZg$^aH6VRKdO2wY2uVqQYUdtqemD#d<NQk>O`o%h#e
z025P!Z|pCS9RtEoHG<~kxGxuTMgE$E!CR8eM}gH;E|C5yRBGnZv3k!@jjlQ~e?Ek>
zx1gqGJwNN#B0Tzj*`vS`?~G=NrY}%8N!mxZveo)dX#8b{Gp+fdUnBVtz#7C1;}BcL
zeacu6f#8*gq=awde;8?!@&n3ZGl5UpR}u+`kB{q?m;6MTIkx`*W!F9@P=Si<+1y65
zrE^`n?GSm}16z2KAMG?#iTnDisb8=_lhdCIu``yEb!;$Vz1LT;E{kGz?Gz|d2@>xM
zG|)mxD+If9+tn~z6mo_~14Pui?q?8Ml429Q8wzG)?nLI5`E+VjR^?5!f1T?OH$di@
zSKQAM)h&BvZ;eR<0XXyQ)r%TD9Kng0U~i3`kOP5Xw5&m;_UWCWvp(deSnBobpLK(q
zkq1m;5b&*L6u`6rH~0f22xc{YVQxb-c9ZJcqadp46YhP2eC-}Gsc`oj=oI0gW_~BY
z`b@?dz;TiuQM{3yE;3%2h>}HtY)13&JpTZ^`e00LG3m5N%3>$U5J|^_V%Wgj05&c~
z5zM#9jl5wAPu%62<8d;QMwO@1`OVMb0Z+n#IhHJTy`#mfiS(=CR+)+p#(-v*+3Z7}
zs!$~}Dbud%GuxarjVw9kolVx+2GS`qk|3R@%*Pp+oc;3X$XQ^@_!Uhg4&{Qh*PdSZ
zbR#2~@m>(f&k4$6MQJ>45Bh`P^+?*+hJVCzx|qY{HEbren2^*0$WThXC-jbg$0O=}
zKgLmkn;gr1=4TPVc<pOwB}b!JPGKD6{SE*V4gf1wSlz6n)NT8#hQZR8!laH~RDy4_
z83SuZUe(Z6S}TF;t7QOsT_#t*W1Yf<QF-Q(XWY^VpW+|)iO->l$@`tE9?QBp^@kr*
znnaLkcUzlG2Khf*Wd8uiCmg<_iH!l{2Gq+pn$FnAjO{*l+aUfubLu-tM)l#6(d%qT
z3biw3$1pnM(JdsjSr1=RNMkCD9h3oF@p9IGoR7WzC<uGMYQ`**!&zt%`7b=zBmGso
z$#N+o%SHM6JYyFrVTtcEJIIvA*nd7n0A!<si)sa&64K*Qd@Y*E>cyz*Tuc_C8*5P!
zM!dxQj>xX?fkb`AZM?)~lkBkL%A0E1XSOH(L`qvKDVS{jP^uPkhOLo!7#7{xkvwK6
z5V0ZvmC`?Z70lpQTEs;T!ny{};vM=CjF|jNB(JhUnqL~?;4bqJ7#gYx2g0l!n7F7A
zL5d(*Ds*ep6R4irctKexi_;}w%0NA)iqp$``z<>Y667%vmq(Ab%{FD7mFG^9Mx|dh
zfnVZ$a^C*w^Eo-BHFjwGSTNvZIDAOwhp+z5e$$#1?S3)i*7k-a&OuD6hsfqjKgMZ6
z;>^yiYF}f7e%`Z|ek!cIf|!WgK&-i2ElAG(XVYoHnV%3t=XmoTVrG0{k0_i2`-y-l
z^93H7gO}9U$LPp3aR7aqVLeag$HX1V{?kuLOOlnTCKStsft;p`4o!}m$)+G3!0HJl
z?Wyc`idO#s5Vhrb0Pp0Hn-yXqiZYOw46JyPp{LJbtaYrAiJ>lER&PEs#eazy={9B{
zl{%K25HV`CG39L$lmG=IIjG-*waGEU$m#4=$^NOQ7en)Tz^nRVv6~FSw!>fgdRsM{
z!*h@@3Z^jb0Odd$zrwX*A(GBad2y4b$Kb?~3}&jM2C0Rnu+@I7hN;GWrE|9-^Y@4r
zFuH)OEeiJNjic-L^pX)7GfRNr?$~?cxQSyoIfmiHPWf7=TYIKsk+L(H@`1&ipFRHo
z?<XL4E>e5&ECK2Wg8f;vPD~f=9E<OSPWi-h%;u@88e&ycfrC4jj~nVL6kKRV{+kGC
zr6h@1y>hVe1D9t`jmpK%WtIXIzrQoLua_n^#fNKFyuae@sLRGnO%=jcO=Ku+ySm$1
zpIK;RO=US)W4fxRP??+7w^YNC)_)(?#=O{-uPchQkT*9^GqF!omQU@U@r?bm_K!)Q
zj7A_h)Uw&dnJmP{{B3QKh*K>!X$Ju%%u!-0%M}f6iz%KVR;T2^6aY&PfN6FGmDV|}
z7Y#mQG(!orXk!vol(devU)J@){{TL^pE}ce^zJeIJ15%s&2NRJ*RZnVA~7Cz%3P&a
zqA;hx=9sOt@I>wTg0eXI7W2IAksPN1s(3|rP(x|zG|8bKahimFdp5>?tlQ(itqOB%
zw{$k5J6BB4o`lC7e<AZUt&<poka~_Rl%1E9{@L}4fR-q*m|e2S*B1e(8IFbi$g^8G
zWzv|*%~g=SKm>IrJ-3}N!jA(u5<JJ5#}b&uFbcWXH9>Ndh)|jh@?3z3GUZrpmApbY
zMr-0rxVA!^HWa6fms!l?Vj9Uc(XbURDDzpZ!byY1XU<l3%5_E<kP!*mGji&*v+u?$
zi>gRqLCIys$!XUkG`DkFNVT=|rn!m`FVu5ch>bSB&RpNqXu3dHZLs{A<vZs6T|_bR
zCCKX{5vlSmHe-&U$TdJYYPGA!9WZjoE;XQpiAE;7OpC%S3q=O1vl?lTT~x*X3%Si&
zqzT_!3x6{?1^Z8o{vR+KWD}_?uKj@Z{{XL9@jjYIXxWy9=7o!HXh+_D`5)ucONi>T
z1jvYCMn}~8sd92h26DpQXX_~O_Ek8>Z=QW5d3}7cB7bu|ykMM6&RbV4m=R=0`;yp<
zzo`O)fVNG9N0l|BuwV>(A=24~gtpucYqPLd3=!r~F!6+ONm+ZHTDLp$zC!{l<>x3*
zxe2aHr)A`1F~3_P7Bf*q50-v>797YyrxW_Jv2bBC6ZE6c*CU58n^O$ao5J4sw;df@
zjx$SNdhJ)#-P28j5g!@KJl4r@->w`NX+2%U0|PL1SgcesQ6U5*GqG3cv89b!guWO`
z%4`Fq?XS2_#5HVnf-3v|D{L7GdytW|AcLi9!hy}v=rPvR>9De&#S0Z1e8xayavGFG
zbhq4^a<phf)hUqVsj&8nR!lf|<xQ8OX43}CF40YOT5Xya4(~NR4Q*tEG-3(q%1v#X
z7R<KTrgFtIZH~pR1VhJc^YarW=O#R4N8WMB^Eh(=GdaHeXC@+i=b7=6ZRG))yIz16
zitdW%n2xYNXF9S=;g>8?mTqFr9smh}8cXCsR&=G@F+8WE)ulX%+jMQ3nQvj6dTsmq
zr=M(bDB5za^KBb6<<4Q|wXoU*j*;<gmikO>mVDHrcsyjIe$t1-b5IccLe$MBfW9Lj
zNQwk^R*Mn4H<XE%mopUXdT;QEkJz*RF&>kac#fS)Bhu(hu%5s(foBFxp3Ru474|z;
z9?KqOOYLL~DWuDl%Mih<S*^_-upJxgQjtUp^wfekxtfB)dTq;j+;YnwXenK(T)&9C
zF0f{ROG0cs^ouGREK{v26KU#!E<27>9${>9V~p)H_u`0b6}M-W9N~TKCmwdmo&zK4
zMZ`8M1dsePR|xKOMmuiL3jJJX>&8^0B|VROfM{MyD9o*njMUBpLazbow$tdjiK$>P
ze)6CDv|~TGd&i(mg4sE?Stx#!Nts$@8Y>PBlGBno@jGPs%|VMYu^9d3kVTU}9la_e
z+^m71eMB=4Ik1lKt@)JgL>dp=)aA$KXEm)wr`hBDLOjz|*`PO_HZEp2-xgv7V}%48
zhAhhdhH)5Cw;UoJOOcTb&X%(D5R&tqfUoU?CdgKNz`=<ukv`XS5%94|o6{KwDaWl6
z{eVPh+qX};nPeSmBN$QRlsY_yX^-!!t@Zx^_puqG#7S6;b$(ifX`O8Ps#aX;g8^%V
zeNT-f{{Yle&9Nc`5|ip^9BK(qQfw%rvq_-rk6JH^w$j<x1BL!sKXbRI{r><UN4C<8
z@;xWeqiM>nlvZJl<h3?R8y=tWh{|Qwi>2tkuH^P5sBmtNUGsd2&QUw(-<Xe#&cISp
z;KgSfO`=AhT0mc5ocUz^@4BW$iXeW<BY*1`Ij)&@Bqu&oikXwI5Z~5v(V2sTg?~>5
z5VB&oFhl9=X8!<r$&A~dLCKPMG|Q6WK2b3fvpCW0ScP^hXL!pf=ds~T4UN}qJ1U{$
zK#8^a7OePVHi_ws(#9HBMk`n}@>IEUgA$5qRe~3jTFWq(820v*;JCo~#k7W~Q*|0m
zBdX_vZB^%G(2(p4h~0-RX_+nX3n824XB6T@jYKR9=1?qnC6$;xR%A^DngB<ziOMz*
z<y=f?jA2p(ZS7$@o>aRUg`QfH^^2>p*TZZ-P_(JC8ec`!F-=@BdgK-<9uWZ$FeAcn
zfMPb5*9T(T3XxkQe2){65xnOgIV0n2AQ|zC+CMIL+cw`MMYF-W=A#Cwr;tD7xJ3L?
z66dXO&+Zm=K4Kx%J+jKvvDTjA6D>XDY^;fN?!=BcgvOMV#EkjR`pNb3YvY=K7`lm?
zD(<&jGV{Bxy^&0XbafN-`;0Q4gpPHT404$W{Wi#r=0yu{<SZdWq*d>qB|GE1aSX1|
zt)-tV-gxKyeIv$x*>8B2FE-!mVgh1j?DDkf!P}{`nqxZb#_nq#!)gB088gHnVGHiC
zm1W3*wB^EzVIw)LtK0+xYLUm;R#`k~BBE?o(|whhIo283eDhwJa%ev}`+W>>W)vKw
zR1b2Bx`0eb{j2+@5xnuuF&^UbLm`_(iX-HHKjrg1#k^Jj0J%R~<x>8VsphZ~@7WWL
z<t&+;qg71hV%@vUv+#mGHEs@4E*6uQn<t{_aXy0_{CXmGWJK+;aheO})*qz)oSY#3
zoopDKs55I%eNokdE9upcIr^h2j5f`1o0GkU#60<~v3h+>M1q!`%EY;n*lgc`H^Wwj
zO3TGS1z9veRbjfAPO)+;)|EvvntDs$nuvC2`TRW1t(x(ZAcmZ8yv)q;HO6RHJ-f|E
zZG0S9+260wyA`9RSVsl=WXz%lb4H$OPDIY-F{w-rrzMJC<uN3~laO8lBL0=abYR-e
zgl43`H*f)&{W)Sd)T<e%L@i5BYd<YQ&S^gjRWohubDd-*_bDSSH@d@5A|sq{Pls&0
z%*p+Q=)ytJiOnmF=}w9MO>79&RrCJ<Ua{#B=WH(?GRdE_rn2?hCTGS~j#Zu!tO=M@
zd1u~E1no`nksnc$A9J6>_n~JskJ4k(N(Lrzn%Zi!jE1&}=L1^TVC|HxCHu)LNcWtX
z<Mpc6f~vzP@ji(D_(n(T9IC#0fMu%FsOpA{AvGL~=9ys#n&5hX$lPof-qDiS77PyZ
zTSirK({V=@$BmanT6DRanvNFD@OPc!E3|2?hm@vCfWw0MMn|6+s8b{fWhVnGJy``5
zC~(A03lYWVrrUynDrXhg?{9Pl!eZiV9LMVK4LP3I9;L6YL4+g{UYco&pAcs!R?$G}
zFhL;fy3SU+aRQk?&!uLqcFea>uUXt$vN(nx3jLP!W}s>!9ZK}zk%Wm29aXijcQUQL
zE`uFlJ?gaz)&Y9F&6{J^6+|`?0D)flUY(mQ=a2;gF)<Mt9-DsIcJeBQPwkD|rF=Kr
z$$8&=^ONImLh*(ixESz^-zEV3738+X_w`w!a;Nv8Cx0}U2w}GZ!7ua_kxwa^>e0Y`
zG<U)3Z{Fh}e=Kg&!<bnCM^l`WLGe8Oy-?1kiAt1p>}Hkmwc8~d5kybr>GcQE36~!j
z++l-^GHJ;cO;_Btu(sM(_XU40(d2L<2%;vfVzk~dZbaJ)OvG$Czval_#7x9`Pwfza
zhFDOQrc10e=a-G3e^AC+{{V9Br^H5A8H(23J5JF!5&r-w`^pk2BeG^vWhO`&V0Ff#
zd{k^20q(BL3to3+rN$=o3iQ;wRH*8<+0NfH(JM^qfzVXZg)&n!IwsZv<s*OhjxxWC
zNP_bmqckx;c1(>ilHk;U<I-k8{v=(yOytMLCn?7&H1^h&^A}|8yy5N0vrnlOOxW9(
zX4Hs|bNq&mZ?@AKpmpimWJj$1M8^AVmIgu4MOs8^-7in5^#1_x%z9{WQ)Qcf5KQz@
zoM`Z24t-{sFssX9J=E-xoTp8jRg9~$ldG222$lprOgUl6l;-mxwojPlnq!sO(A2De
zzIvm5#e-yO-wK*iBCN%#cNLTlQFggx$A(5Qc(lg`c+5=1^jH$jVz_r!V6=ESgO!0~
zeA>G#x9*ebF}Y@hM+(QZYt*pP)t5aweXLpXq;B+$=S>ogIDTrdR+ABN;1WUuz!$r#
z@0xMTNTzk#qb3fDA+_ouAM##bc`I3|IX`oiQSMnGE-#dTCS$&@0`Nr{q=0hV(H`GS
z)Pgg7<{V-&c+Su|ig(fzBcJu_KYlR2^675Si4QyEe~(Y8^sU5}3XN;yXcy@pzon#t
z>ClmnIXR(eoon|_^(lz)nyZZ!{2cQsnW_UOe1c&VEIU2Pb#cutt!+j2aoaVHaERmS
z9yXk0NSwsf9vZ6h`4QT8EZ=OOf~&GMP=@r|8leQ(lG9SQvF9Nls|denPgSr@jMNkg
zV90HbVa;@&0DnejG>FWFrZc{6^^Fl=Pc%>z+wTjnA98Dx4hBOyQAtwNLTM#!iv*;0
za+Hf(4Op`^D1x&O%9}QU#IjZa*Nu@}Ktygu5t^VtUwlQd+N$Pt1;@Ft$%2@LV+Vnd
z;M{B}2bY$v3ABwaSen=DWk;P?d9_&TwnArzw~3Z`Jgh_&u5s20r|lXFh=oL*Ek0X4
zu84t+qb5Lil4bg<-z(}vce7?VxbTa~a5(c$ut!Zm&7oty56P0u@w~=PN7Q-}gyK1V
zU`&jL+sQ|b^KJ430C<e17_v*FMbig;?daNi64N-(y`nWE{M9A`c$7|P{{a0I=CFKg
zw<FRVU42Bw^_J0-JiSIrP1u3z(ylgtY}6tI>ZdG>CFKH{I5Q~v9ut)`^LUGx+wEn?
z5svXb5L{yL#d_>)>;&rdYIPz?NTjFsgD8leTWnFZ=iJQ48UFy4IR(i#*0gf6h{n%0
zvT{J&?gIM|U<p)5owM~w2;;<&N~&f)_~Jm-Xh5|;G#i>SgemrAk}G$nF{KtR+g25Y
zYLtq)!nqe!Y`yum?lN;lXI7_La>t7=!YPPRMxdfa$BfcnMwt6Ep%*<)(B%H9jQ;@H
z{ZpS<$?O<zf5$5LsZ%vyHPtv#13O}<w$+A4YHb9ibrb2tx`_QXgM+K3@%#0OpD~mE
zGUv{YoY~uj81(-D89%lk_7mx$f4ZFhA)oevg|y5}WeDL?`~51*IWSg@RwXlD>1hpe
z;nb!GekBtz^#vdD9xzd~#EI<^bL|>zvk*SfC#|+l+9otIh*U(|4x5e)uE{i{rs(jw
zV0}CqXCS@)l&Fd4V5G5!1;hjE%v=Th9+<_fjKm{NV&Py4{va|0A$i$0e}~E^NnqHn
z@RkIu%rMj;qcbW=h|rAH6iK}JoUTz6Y{qFkW*@e{AJU-mm_zQ%<l=VmuL(u5Y+pbn
zqHCu;8g`)EXIb?~C`)?yQ9ES@!1G++mBycRhteWvZ2bijm4B<zm=o#u=@H^f(zg$G
zj#{yUH3vus{Jl2##QNyN3^(QG%dNDh26p?}ezCJNJZ3h@{qs-OJ(XSsS@B^DZ>yLo
zks{RfBC#^D%A`P3wZM?lk33J-pRKZ4QB9xUEvLB>IMC~152yIX&m!M6@i)317LFM9
zm#5Y<Xj;Q6JM*Hmh)F)?4^E!CcTP^b1|YBjLCQ}}Yog4;wK1P?5Jnqs!+|$Uji-4=
z*+^vDF)NRb(fe<o=LkZyF!JdUG#M_Q!tBi@z^i;2L}40(K<n7x#L3%0SCH8s)>fKx
zrb!c7US0q!IKc)PK!{avb79`o-F{}}Xs&kj)GYO6ngY0$kN*G&;dz`w#FWD{+lX}b
zApusk?6u^Dn5RqYaC;C+XIYtYBAltiuVd3!-s@os;{^=K)@d$cCeFQB2XxujZ2?3_
zxwmL6ndz*Lab6Ed{Hs<FKz)i5ffJwAw$U>g{{WIdI4Jwb&)>vh$1lBy@7HYcld$i;
zxcGf21Z6Pf_$f2&VV|aqAL=8T>mbOsk&)pun@q&c42bW@mS7@N6Sn@T<ZHTdsceMR
zrImzTX$s9q<(h@Ln;ctKyP}@mrI7?h#smU^Zs*x`hI1?5AWN}v_E;N*I|VButEEW|
zg~C#h^-|@Xn?&t~Tgb+3f-p2%e!6^ezg8h}Cg|eA3hU@rPt;Bi>m08u_Yf@93wve}
zg|vbnjr_z$Bzn`d)rbou_Crn^+l0o|c(6Dexq67qkrb5WDBksGi=o2Pu)j!SNUl>B
z%V%SuQ$L4YKZ<7w<YH11CB{p^6na$<HCqYW;l63Nhw$U(=fiZhzAEYMxXCPkx1yK<
zb4tU>legYZY6*(^dKWo()^uoqOn(0W$X|F(nV_|0o+A}tXYP0Lykz9ez^*c5G;o+=
z+nVb+T!F*mD_q8Q!t~f&6mi5KTb5W}CU9>WeWbQ^Z8iS@2e(M6V7H@ZPP<cX+@QF1
zv<5MKtNC1>5bgDOhH4fIG{-_|8)sZ!9Ym)zqZU!EwyFO9$D;S;ylGZWt-(%ONol0;
z!X1moY1V?XWF2K<n+;f_z`2>G7gt95`Nt~DF`kVbxuVC-81)ysVxXi?OMFE#+^Gcr
z01_`|S8gq2<KiH+#|32zJ(`OnHGh(+3F%?*bFzO>@2zbMZt=NRD8uF&dPaEfUzT|g
zjiIcEwj#U%xN}jEogxZ89y5ROGc&gT0KRAkB~xs8b-khdpSPtI%WW<L_nZztE%W{}
zlarcU^>(){?e3emq9P8$BOkYWc*ovt=WIe7Y2t+?eJWc0CV0kbmy6{Q{{VTuA%7Jd
zYWu|%4ou_zAc&1F+})%Mnf>!t2vw=u#(2Z}4AWlP^@xE?r7C}SrH;5-I7sds=FE22
ztkc|IqL>Q#9?QkmC?V#85wfvU_H7lE=f|vO3I6G8hbwhjfyI7-+xCo~sf^XzAyDHC
z3-Y4|e^h!Lkz42KWB&l1M+?alMyV574_VjKVL<y#wam5+%u-Q-CKCo4C5OFMJ6B)d
zSHA~mPq$p7a$80@6?GBRu`ol&?u%gbo9L9kZGnhZ^|b(iDQdQyL_~#6w6)qMT4+RX
zfwYeqL617BokaQGeB}Xy3*$6RVQF&7s?*h(HL6vL%7CAYefANVZwl)v<MQ7KPj4P_
zJWkm&5%=dK&)#^SUlX5MXkDa>?N%?<zAs@*C)YuaS7h^6--vJgoXo0qOh$Y^b??jJ
zphP@x_FsHhw8M=wXWSEFWTs@MC;mBBo0(z*CfaCoT_~B~mTisQweq@+^9c)%S&*$d
znC<Q9FH$%=jDgSi#zDxa#xTD9F7jL<6St)Z-tBy@b6ShsG53)XlRK99AKVP9JZ{&V
zw=h=h&TTtyf<Mvnm?&z5;@rrj$~76<6fxrubF8w;oI2?Ny+vK@T;`JydX;Wp#52C6
zQHAF;bqj8c9e?Vdb=jxED^;#AO6y}Fo+Jeum+GHH9~Nz$a!{p=XwOY}Wy%L#wCz_-
zm$6!{YRc4gBe(f*9!MiU&M9){t#Phd?XuOz^OiSNO)gF(N01p00^8Ph;}}!ALoGlH
zq(WvqZJTmjai7}(4rbhF)FnkwEk>oAI*p#V)9eTkF?wHXL1T8P#llD`n4B0BDs+r<
zbvcejmuZj^Kz)U-Twv+iS!`8xE7WI<Ui=*IJ#VK#0?OQvJ$b93;=~kcMb7KzIgsfk
z8LNI}Z87noNAwYzobtTsn1TJ~d`v}AHa$CPHY-O8&9_;VPptBBs`t8L9tP$6a8S}8
z%||cd3FPYetN<b@Rti?8o0PeF3LU6e%EiM*u`!}DTrKU_AWDc44<(N*)5~iC%S(>S
zMp>zd9SD|C9uyOd8QL>lX4{5W-dZCTb~+Vg7cy}x$cd0<9?)RP5|AjIcPY^iBl$E3
zlsJ=`s`zRaBl#!x-zZFt)J#|&FLXqGPp6Hu_VlX;Q85;TLczf2)>Dt_YvX*io`wCe
zJP2g4VR}xg!`ij2@Yp}1H7X5f$tJQ{a4C-%f%tElxyaW@NcotIe)7`FT8)YXT|@jv
zBr}?g*4{2f9(^fSU8@L5zOAh^8Xlim5UGOZ;n`L2$I8_Ra^Q6(6)$KE0L2U}*(xw_
zFp6o{i#B**ls28w9it*6sPq2-!Gkd^4eS2^X>eQk@%wL@(eEM|W5F&mcHhBmK5&Dh
zu;)F(&rg(Mxe$`$OQ>~%)t2rh5!%!mThxDKYmyoB5v3XtToW3oN&%|`#l@mMx4hQt
zFhzxT$tlmN8xyOf4RxORh>3#DDZDL?Rw!JVmqQ3h!m+?1Gl_kM+>M+aZD3*qA@p}j
zdv#8^u&!3D$!SU%yeiGYB4ZKltB=jPjU<;D3sGbw#s%kMB|I76rb=hbf9z!Eh$xx!
zJ~BhbfA6R7zo-3XxXWJ7CpZ591{2ZFH_zKN34w<eYUSC`c@dGoe^++V@F%?f*2fuf
z^{@TOjL`s-Ls5UGnjAFERnj}m?F-XFuLj<^ahJtgPYyO$5sHz63CcwshPipFib|?t
zH|39SQxf6-0J+Kk06F_${l}sL7~zS!__Jw@jQYx@KJvW9t<;*REI9uFj9~A~cBwzs
z*POfl_SVefun4Q=O2Fk^x(s2<?X1Yh9I?hzF&rXsN3gYdX4kfj7d48pXd?PDvjsL(
z!u}zFD~Mp`kyL`NqT<m)Wd%7{rgbzHSXcF}oU)$2LF-bhZn_k))F^3CV@z*4`Yt8<
z0YhufYFkFAlbjj(j!)d?m&PswP3H|^w<k3gbE~GtGwvrM8tsHbOuAtOCsR%d9mRTg
z{D5?^9nR!dMcWX5>V^gMDtRh3Hbe=kbt`<&kK#8DL`tyy#&cHR@f$dSgNN^<1WoW{
z+F?27(%XEGm&Q_|g~fwo4aP=!7Hr1R3BP2RkT&WGJ1!>GZj~(w37GL2Z9U5X8@_H>
z%};ezq>LZxKZnXQ_<nz+EY&4(sW);u^ApE#fsE3jg?u>bBn=K@U5nIf4R*CggO!+%
zB8bK{W1}r#2nM1_bkDK8CtQt=u;!`9-!w(@*MDHTwz8FTh^^P8F*&87Xi4#&8jhc2
zK;-x0bffc%VvldS7+$F}I()`$!E!My<;n;6h$m{z#gQbrLVi?jMAPb<04(4^?iVec
z(_a8(h9Q;}!%1qgKa);;R<b8Q#38k?KVS2OGdpkp09gegB15Qg8#2(_;lH7M<a$(G
zuy>78Bj36s&<leE;LEg$o{ZdJMqs202wl)5rq`>jA>lDIWe_DK{YJH-ZE1RX9BR*>
z9pbz|m2LIn-RQ)5Y-G$#l;W8wXfSGPnt2AtZ(XXzF+z5nO6bTEGZ7b2vsp(Df{@ak
zyn27>f~+cDq)dB}Jmo2@&8vpw6B9GM>?b@S^2Q1nPYpU^Lio-oB*D1?FoWJ_nJzG3
zW(R2c+c%um`AgzS#mtA+3T)}Un2Kc7lgMqYHP73f$IOAk*wj09+LN8b4)`=hHjb3K
zd8Pu1@fcl-kzyw=#4&6^rEv+kvaZn+l{p}if?3#5CUaP~GWo0@(eVkfR=2hOvb;qq
z@dVo!>{$q0rg_n9M*`{=#gQm+Nf(G*!}%v!_+OcukKV0`t!WJ%MzZSKH7?ItRjSY(
zoK=rBXZ%S9;_Eq))2%UrVEz0|WPjJD*}UX%k25?=KK}rcV|dFI0?|OX@Jr<-wyR*P
zC^Xemn>uq*dpMWgXp%nN(VCnZ;VUoOBYg4box7D1<;vK|&F43mn3%QKTSPt>pfJz^
zBi$yD?M=`^kmXB&QMSxVEVx|76vXYZ1feqrry%TGP>IZ$kzn%LL~im28nbUXwC%oq
z#Al8(86JxGrN{i5aPNMm*In){y}1#v^YvNqFes`1^1k+BJ>A0euMsU=6+hK@F@Ja}
zlQS7=9GQ_chhtU|tuhYmglN~rMpd0|w9~lMl;gO&MMEZ-j2Sq|r$b7n+|md^c2hM=
zYgNi`#QUk3vZ9v^9R1E!sLO1Dhh0T_?yE}{a-$QE;r7a|nln+3I+q_njm%>Y`-h0+
zAY5J@O_h?=$Cnko0yVVod&-SyPfy}^Jix@Ao={jDZ|D5w=5{z|BlMS`&seH*c8TyG
z+zhMm6^!aL%Ke$jn|qbS(%!u-N!cV*0~0-aM#-uyNxgv1-7?TdZRnB^dW^2|_qKCQ
z7@MoY`YD}0?68t7wuAS~2-Xfx`HpD+0GU@)Mt=`Pdt;om$!(UIdTAuVv{DutBxM6y
z!P9U!HAH**%etyaf~@JwK`L)f2}n#3>7~P<=TJzuR;9>)I>2s_7wWubgMH;@qDFjq
zwIx?iuw(o>ds<-TtyPJ<`N|~=C!QeWErjQXfyCS?4LQdeJNp#XPOiB+3)p6S<WCe9
z0PoInVmXmXPb$mG=_W%?8qf4tDY^g;p1)n>WM?(#=9*3ZP5eZ9ed5d?rZsF2@%)AN
zl=*v6h}7qm=j8s`A1U-(I?EkjuYm(UT;h21k{~V5&*Ak|)=AWLSqXAX<_z~2>GtZZ
zSl)&YNn#{E3Y}=VUYNzi@H6!P069{d?zgEnslJnX+?iB8QrEJ&Zj&hnsY3m8?YgA}
z85P8DJ7&|HWmu1MW*VoQq0pd!W}RSWn07uR2+02G$o9lBb*22&I%qJh5@Hf$d_<oB
zo~4ZbS&kpjY@Pa-gFZ?zvpzE8UNA8Q?JZ9neRo$rEy+Yy(hDH@!zG*l07<qCjEI3U
zJTfJOOyc~r{`jW>#HuC$5?zBh#Txv}A0>S(Wy%ECLqf$?qMtS`Qij!LK4v74+fA2U
zV=|%4MU)-F3iXt<*$zun0(g{18io;8*bo7>zW~&c!f2GZCyL7@Mtm;|hule;^0t{)
z(5ls&x_hc2fg#J33h8Mt_fq!4N0gBD&f8=RN7s%20GsbN^PXeYeQas9Y+47LC;^R_
zjT=uH<pZYGSQIZ<jWkqI8*TUL7^8)`Ov{r5^DxSR1kxgF80{4SH1tiloxkEg6G$OJ
z5>qBykU`B~{(hmFe|g#HPO+rMn<?XQjC13iZRm%$$euI4RtZN7Y$kIyG>$eePxGJf
zEc)9gA02gTFZndCRU2N>0P>tE<+t)vSvJ}PZ9<LDn1B!7L?C=sFkq7*f3GapmTI<w
z37i;xdLm)gxz44tExEbXK4fp9x`v-t_~j_?rF9untDDBAe-OM1QjtN)p0><XOtdyR
z?AYeaiP>Jstqn3WyLKB8lz+y3bjM2X)jZbKv4h~hRtC^xyFXdxo+Rsa7Zy=owT8Ca
zbrUF%OLqJ|t~BTJx0sQ|oS~w5Wn#CmN9uBOQ0qTxVd&}D6ugfkwhxmW{@GT7x<bpA
zNMNK&3N6oF5nOsQc0w1LcHFGQn7~ZLdPm%5p|sz%wGK6q%Xv}$;<z&<2CYo1jRIY{
zu5$8|b4qk8BVObT>A@A%ZCKClo==?Pnm7BCm~O=_<C-1011j@z9?wvx3A6s?`8i02
zeX^$e$l0onREV2N-32ODn-QU(8%Lj{^ItBsRSeKWmD6Txt1gAU0c$$eS`R+Pc1D;`
zb;2^}^3&}EXki^z4rh!c`!1ksy^9+gajp;nGtVml+E^$Dex`(`Wm<iy2b@lT8%FkP
z2#=t^@A5hG;{O0EIvN@LzA|HN@w<Qm{R`-QOYas0q$zoK{oRcF&oRr^i@6k4WrUSi
zuqKM0t*>T}SZ``Z%&~1%g@js-&vOcl66tE#Hno&pq`?$^yrnS~x0~}barj>vp&q(k
zqdId=XI0d~`9}$fj}wcD=dj&$KuQDE5))y{mxoeRnA69T%qAz>BGPljWg4-@UfD68
z!i_e-HNtCi-`LVF=ETPS@<?68y10(@hib`z+F<v`p2rwq3=fs+m4J#?yS*|nYMmy<
zgw9_QU*cTX=k9(rgl8m<L2oMWN=KyFq5yFd!>)xAfsV<DmHd+2K~{QKTE${{fp%&Y
zc_>F3Vov5(#v<pNtB=HYSb-gg7}cmbm*fCkz_C2Zd|B31hOrH~CqR~+P=H$76k#~V
z$;3?6dDmMxa)D$LU&X`NH0+t_^=BfO8E(N!<oV{_y#E0GjlBBI%+5!rIQ`A$6OhO)
zvu-{!eBNeqclyXDP7h-vV}%4qonKM*st83JO|z!3*?vA@zu%mnAIStgCIl33S4yws
z*R=3s-*V9DHS40QEd^@GBmN{k?4>yW0E7aff3O**@6md3XT%O8+`iMHudiZjy;gF^
z{{T|Gko%l>Kf`Np=`oQ5Gm-T<z9=GD5LO%B;y+sGD>fFG_cHxTv@E|aC11-+VVAai
zar7wI12nQVG%Cn^nT(zm^>%t_04oCAXP;<dlukW_b(=|G^d6n~swU8BNPTPKEFp++
zL|JdV)FxnA1^DUBMe}6NF~pm$98v|mY-QHV8^SUp@6~LT8cH>2<xRMLc=}eRZ~W#B
zw8aa-The2FGA9mq<AeS(f9$_DuotA^NY0wQ^XejT)4OK}?lOYOmX<N9JB_V4A>+ow
zhD*eF)b|pmKAU4EdF)iS?Ks2S*9@PpO7SIO5sX>B({jvlkydCg+FM^W2o!2Htj#6G
z8unh-E)vc%g6XiM$=YSC$?{V^pF_$trb?G|fPcHR%x#s5LdvEdxVDIpGnO#QVk7Ld
z+5Wn=@&)+iv|^$`*vXNk37U^2JW38fOvmv8%#m4{p!l%ao1xvO+8kWWR(wQpVCI=7
zENo+cw9Kq9l>kQ`a6>wZ;?mnH!P~94f{=Ph6D4ZNa<YA6?Zl`31aVC?oETgPbo+%w
zNlZpG<AT*0O#Ad(i=Dn3{{SeAobd|`=I!G=c=U{%P!r089ms*XVn3B<DPS`-y6{q~
zfkRQVO>M6fnE1E^tJd9Vz*6zvWTQ;4TGgmq>7g~%HNof*)K~#kYaOO6mt}^QTizrW
zKD3Uq?19<T?Y`*OY5v(;{laqI^LT^#B4B?_?q~Vgr}GQSfMoD}fihHyo&yK649X@}
zE}b&O)niXAC_uhDMCD|Yq|*nd+gwcLGMK22QTLYyHszGv+A$NUM+=3!;bs89+kCAL
zy`!;HB5?x*$q`!l0_z;d3(+;jRWosW6%fVUZFv)aWL7{hI^txd97)6&wXocLS8YlB
z)gSdsC<~t(69oSN%y`LfWbR6&7E<ui(BhkIYOxn5up-8yvrUtnsS&lEz+yD^l#^Es
zLk4O{T^hLKqyq_)6nK>^8b!n@REo9KyxKm!cZkp4bL%$E{LT?TjFt&I^%RG1%Rc`2
z;|h9Ca!0lSnZUP+!;1kUE172TBmV%r<Mw@+LXCv|Uj4$5L$v)sK1`y9r%U49K#m$)
z{{SRJl<f$l`w`C<b5A7mYn;n0k~^IY)q|azY_nvZTJg&DRu)f@-wS93<wNVIesVsy
z#l&<FME+x9BAfO-Jf(nDT+qh^{vTAb1gGoMe2DOQ^%%12LudIb?Ouay=9e68q|40t
zpX7OnR!w;S0KA+Y8=_gHTbb1Pw`qpvzO;&AumjAXaS|gJh(x%|OeVh&vZ(B#LU&bL
zjtgkGXzfO*y6{IGmyDH08v@F27OXWXl}`{j%|S>D=Ia!#_pSS19xNY3j4^^3Q6N$z
zB21aWiHYAi4a%pq)L5R+SK&eCHTOsjYPcg2RmJJ&Y%SuN;}{<mPp6sli$1d7COFR7
zw9M@XDq0k|G7m5^<=JAPj7rYbttV2-wvy14e~1|!xRccM?6>~%##UK9fQY<()+`jv
z#wJ1>GF$I6GLjG;c>uW!;R0W@$#0rufa*16l0DY!!A8<s&W`X+&o|KytoV&?WmPCv
z#=zL3w!jyW5`dXggA6*^BR}V3{{W0-7yzMg=k-89utp3>s=ilb33@5Z5G9QXTUZj{
z>OQ#9)hxQX%U_rLMmqvT^;<Cw+|v!5gOIEwT8VQiwP)M~JE%|ufdgV>Br5MH4#t4S
zo(-E1GPv(=Rjitf>{8Dc`(v*aT}kX#r&KD~j<Aj3uzyhHK3?~DI0m`7JflP(L|I}A
zGU8Gr?rZ1mnh(yc<>74wF(1f=4PiO-{qW`_=WPA6?qV}dDK&|kr<>SbpX1UpeEGn$
z1EO${;mkW1{Rth~#UjR~rTfgzE1dF?rdzgT>ID0H43&>#gmTGqu-WvbrfIfx+U1&t
zn%zA7=$dVND{J4ZX`rg>HDwSJep`W!PQDQFB9Bx+lT#RMkeaH=Z%xaNh=uA%mu2QF
zip6n?6C^ZZ+Qlia06YplTXL9)sK3u3FEKf%Hd##*CH0pC`q=?d)@#4#c**PnB(A|u
z>+4?$%|G(TPRsm#(abfeAWHD+8Wg9^Ybi?(9z!1J2x&6JS=w81(!))Fr3_Vrt;hqc
z;^7~09zCorhIH0BZgpJM6%p83YJ69S6vcK>Gku?FeeyMomCcbLJfNW$4LpWWq&{qd
zZc5kCj5%})B2jHGAhPSL;bito8D*lSP}9{#FcJIa-{aSRLNE^{?vvwYGxG61RypII
z*)TDm`5r#tSN9*j**-I{H`+S_Gn~)X#gM(eob*-+Hmb?oOP+e4_h5gK6y0Oxi?E6e
z1FZWGvpckx(^N`y#Cx0dR~n%-E!jl<=DQHhLF6l*Cli)FaFfQ5R-Gu%Z$HzHS#K%g
z0vib#HIM^b2E~5c?I+u{?n(rIkZ1KqZ6z@gW;*(n>p1XaE~Rs>(2IakA9><;^{g;Y
z8J3*2gma%+Y$XDPW|i`}Mlzj{wvN!&##=-<)e%`6PNy-_brxj=)+UMBZ*JsF&Sk@|
z7`dHKOnzA@ReO_>9%pZTWNVQ|oc_R5End4<-q{yb)U@Rm<EmDvAP$lZC3{Ronjan)
zH556Mlc;UsUx`|Qm=EE%nR}(4w63eAkbP^IjeVl~HP}BNU|y%@igSO652R<-7nOz-
zO)wmfzd5}4n3?D1N5Mw=0nEkemZS>(EVvQxEX3%c`LAw5C~%$FD;fCpsC4pVXVNjN
zl-aVZjknF_cbL=H+jS9-otn{r6EfHXtux4we&GvdYb};pJ@n%e?5a?rRhU^v#$z=#
zaJ4fSLS82mylD;E7|NXBW8@PVrNI^SanTL1Z;Zba7c65Zl<%Lz@&4$uS!C9~EC)|C
zzfl31AnVT=K6KKYVuh!sxwbh?he_$Cc4=BfyrwA8+K$0xd80J_$;9hJB|>=BjgXkd
zJPqtvUmiEDMpfTYtrX-UK*&!dX1JTN<He^LG5(A*=>yAo&f^)|9bFwF<Y(~d>~@13
zJRHIPN`ubIw`umZhv_)YxklOd86SD4rag#Z#}*uC)cQ_;oSePZ6{~`>Y*-r8S1bgq
z6TJMq<vYI8EJ0VA;&b0vWVgcEdSeeO{<%E+^Xa4NKFQr|)%qrc#OZp!ume%*8mZl#
zqco!7at|&<jq=C&aZLJDN~?2o_;$RxGm94Hg;9$1*2r$3y8CdHh@GQ}*BEfAvK}VL
zt_gi*8^<W3(p`a^H70B@CraFFNa1~qW1@H$7_n&F&FR~>VtCC94|`aXw2yA%UM0m|
zwvxEjgcWyScP<JdsR_MFwQ^%=<0)d>QOO;vI;!PbnwLB*tRi8782FJwf@!DTa#@im
z9px=SzQYrCcakyyignRpsO)9M%-(4Q>k*vAb(6IzuXHR|X;qnDYHNn|?3P_9J8qG#
z(|bxAO~Do%rxZcJRJEi~3Q8%@jOQtg<rC{W{b2dud}=}5c|xK*VK0<zlOhAOb{8I4
zErZ@CK1Gq^4u8aMF_RmQ$~GMFIlM+bSt>p+dmO@(tGsEch}6ac&fbWw>nFiq(b4;{
zZq2?u-&wZv5I334uo<C`_26>jmNkVkV7mQ6Dd%KhnIX)$sD?eUPjZ%vp*6wVNrd!=
z7!irB9LjT7V?fp)%i{)SC&rpF?#5+@&4gZ|$Ct|SO1~!&AKd;;;{kjq8>7-V?K`l^
zps2)*D!&NQH6o~e1i+XmjK2b!La0jH8Uv#op0X7S42b~~>7o~Yz;;Kncw-}jSUFmo
zEb0FMmX;0uUXqi3d0w|U@#)HC@2E{|`k9H36B8bhx19bSXA6WG{7WCy*U9lCIjIau
z*I-BKA;|NbKKToHX>rB2T<H0aw>X}1k`;l>#6)>lv#O9YAuZ&lJZ~Pnr|$_<?fawn
zdOmh1Bg;l}R9r@-VluN~dVZxisp4TcaS&w?6Ee31V@HPp%Ru7Pc$fL~)2EHDP^A-O
zu-&jq*W-P&On-G%&R(R4H+cgkn3#$}<Ckn$doXibSgzpA&j{a%na<q5PLfe4Jd4bI
z#`<RAcuF*l{p1X49&T9OpX{sW@aO5bpTtxBVzCj=Nwr@Gc%AYik&I*ajWzL>zB()c
z__91pL{XC(qUY_5K&k}(dFGEh+AMd;TTO?1YUFXI5ed8tSzlhF{L_!F{W8mP1$Ok#
z%puFCJu8fF{AVI#@Kf72+9D$&c9@*@>##i0pp-7z1nNV^^@#pToVyr9>$c$@k&6jo
zh{QleWTpx}MH7>sTW|jWkOXG{H%Xv<B)E=YlgWh0%Z!$$tj~za^rb(R=*>joT68M|
zaY@d3^#1@k=WVlZT`$DA^2x)lbCySq{XjOX!^zxwyJ5T$?csph&O{ZSUgYBroBHO#
zT(ve#bB_B)7zEV`jzTl>@FC1~IR31sGzpm-JNTb4--t=%&cxZxJ>r`%M2AghgQHUX
z&YKwv=c#GKI;@%8V+zX+Ql;c^S_R>0v43ZUW_4Ju$pBo<)It+CC^L2;s{mo^1q-@h
z5JuL9TjJ-m;Q0lC<85IrT$ZvNY${@-kelG<8wsu^Nq~3tLi-_-F%u&&QSWV#HPdoG
zv1S9seWOz07I*=;$dnAx%HO&*XZ=Rg`)@zIZTors^J&f#oHEtQ^%h2SwZOJy42G_D
zf{wk1Z;#Y})Nh2yi6MwCQRnM7owv_FdA9y?Hp`V=X+)sZ^i<HQEP?n7S2}@nvBm<O
zba}gDQ6pbUJ!wQkNu?pp!XOk;LZJfSA{`_|?K0Eq***C=Q56ky%CYNo^!m$J6{1QD
zotJYI1Q*<8qFF*_03^7KYOn9Of6Sxc&@5cSYP93k0}AVLwELWFnqly|Qy^itFH#eV
z3Yo|q_s_D7q)HU-K*DyS$c&)EeI;_$cCBs>T|en&P_4wop*y*3A6*J1b)L*%?8D2e
zgz9XJWd0kZ%H#NQcw4Z{lH^iTJ_aK+yHQfuoY@Y$E*y{F)OV7IjVi2Hvt5k-B#)g^
z&%9>bQ#n$|@FW|hkirx3KYo7;n9m$zA~z_E@WVL8#>3l=GH(`4OvLXH<M~N%AGoiu
zrUQ77uHW?jtk8l2qnm@_gBh*Q`jhaY14s;snA&2|a&(;O<S}AGdQ42BOr?b-Cgv5^
zJJV^OIYlK0U<smM+6nOy=}G?C6pJ;9<6RFM$_2Y{&|An@tZ(-wb6;OHA=w@<vO-y>
zR*WSuCt~9yP%(67iF;ww;#Xy7n!3N#h(^hb^?>LDGf|ssDudM36G4IQ!MU`=Z<Pm~
zqz)MNfsE^?wMwuj#J?~!cEcGCEo?YnorZYTYsXAlU9*<WxHo-4=WS|!Xr(-wGpv8~
z8I%T2?NN(!wX0$)GXsJP(vqx+B13(+Hw1RmpNr~Daw#Gy+`S@XCM0yrCe1)4y6|zm
z%ho*QZQ(YGGwbU-M8<u3SD1&VB6&#i`<czZlTL^`$%1nYt}`(4GdTmi#6hxG!eUBF
zcPxCT^5J!Zj~%ti{Dk8oK}#QB`=8{~Hug5QWcd@x{{ZvHt2g$@65=q!=VgM~REaV|
z>K8YpqU(wmX@Ep9+QnVuvXYXSoJ{fNC!5dKYDL;<ZfU6Y`fy<X0B;*RsIkIBCB9Yv
z0B%Q>5Eh!H8aKVH#~a9Lv8)@j21zk*$6?CN=y;rkHxhY|ND4`jNi7(6Q#z}#$1nBW
z4|<@F`C<J|BWo^DK4olD7#hh==v--O_cr1Vs->1zF=d^PGsQQ))ndo5rOaL^KcZ_Q
zn-OgTO3PR68e7vq1zQ)LGx=N1D@Yp10^y!B#%wc;vsn>5*{)I#q~AM8mWHDH;4n))
zLd?!3cJ(L1qfV=DPEo4_cuBHDv6~b?Z9K9qcd!X2U<AF`8#)dk_;{ViC*$U3ey~@t
z{Jj4F$56Xkq=22BX_qVeY=umv(pM~!oWV}n9=>yK6nDkQ@Z%}8>)K~wT@zaC4so75
zv9l<tqrNOGy~ET7!-;G<nQv)kZVtB?<uC$tF@wb9=PIwh(j|5`?`HYqeA+#0psc(W
zH(A*3G3i>l+IXmLI2v#Yji<&Sf1$rQyzP*}-ezV}Lz5BvYAOorsF?JsDkn#EXQ>tl
z@`Qh=SKUD@V1x~@pj9j8uUEZ$j-*|BQ#i^KTThu~Mv6$vNMsC#KnbS;H|A;S^inUd
zD%Bth*mKp+$?I=mT_}wn1`6}TCw+z{a?1PaCft9=(P1n&2=0Uy)%1ZTT!TO2If;6s
zIL!S&szn`t@yPi`_(DVX{{ZxS{jg>tch8Rq$H!Wb5ECS_Txad+F%k4bP}T_}jul`g
zgmKeq4l>j)Qwrs*ME)e`Em2<GiL59Xmj)S0L`<PE?Vn)5#sXsqau@yEfRR~StIlVC
zLj1bH8TzAtQ$Oe{Mkj^MJe4*<p3p$_0Q|?d40oS8gmPIU1PG#bF;e_x@+e(ZPEzCU
z(<Z~06ApBv5^6?u!6<T$NPYrYq0jU#jMm~{0ogm$rX?}sH0Y0ft|V}zu=7`)^y&qX
zzwM+P1U#qsg&c;xky`%%q!VVCVlS1f{z<X8x0+(KX|07>*!7^}Wk(uGZK+n?%xz`y
zlo1vkBR1RKMKC6Q*`p3=PL{~@{r>=yNSGVynf@Uk&Ou~)h{xfrJpTX@pT2+IJN0jc
zw~5c&Dy|L%n3Zx14J@D9f0B^$wwQzR#!Sc5;)jvrh9;e3^!>7Y_{tzfVG%m3JKJ8g
zK6h-#^UhL9j-O@GKcI4B^R}J5&!bxc;DRzK?JR(cDGi!rN67Uk4&_S5BjdPQF+_}m
z7uaONNx*kUJ?3Pm&U=4(I%q+COhiwKiIR?!oMMT9*z~OU6tE{w2t|J4qJcvg(bNpY
zl`xa8%3D3#gUGU#RT7zM&Zfd@kj$p<ZDbED66?7Z2UAp{+_DhxuKmjD{jBte!y3t0
zF$4<^dSur3D?n=kM#5%j1t$9ejzcuUbkF|KGF63`-Wd0mc&-9;<JzL@uCcVvO=f5-
zO|N>QY0@gvi((FxHK}c>{-iES7QJ2I4dM9iqn7Slmy~A2WLuvv%9+t_Ld)Beo)zqM
zvsg#Aw>xM$X;)vlXB=Ztfs#E_TVwwKDK6A5*xQ&{u!{Qy2j97BphF`diNnq(c>CUP
zRWm!x#~9v9C*(^FHmzH7`)zKVb|rzPz1BgpcH{HU)5nbX=KlcW%=pL7@}!Iy$1Kz1
zeKjkF2G!D8Uaz>XXyQkm9XGSyo}Z};BJG*GX|JCFT5=-2ohoK~4Ax~U;~b|0m1+{$
zwGnc#-TrE&(1*7_&#bN}=}=SXnV(~CBRAjIR9{OgMsFFoNny&m`~X(8u>cxYD%`2f
zbT7kVM&jJZb8=)`Tz0me`lhVZ<7=i6BhDEyZ_SqF1aP?He!ViwYlyZq>lJ3&5{9^y
z8mt-IGCe3fUOa7cT9)B3G1^Nsm<FlpBN+oQoZf4rkEihba<e(E9G%u=9D!~p{k4{L
zaz`-|ktiYPBd$*ojr3E<i0j`6f0k{$#@6<RK;%q}?*=~+W=9wdpD`1NF;^!}^E19$
zA!#hdGuKZ{-%>t$)W~ZUC-Gp6)LYwm^9%9HVM{j`SlMWw1gN&<<0;rmR=VxEMbw+X
z!DPrOhPGOAQxml0#@>e$H0$l7a~iuHieT3_dI_3z9S1QbJLaePAgATflv#4JKxe0p
zpj*4DaVy%Lr$~RwGO6|0QD(YuMOSHw>j27#U3$Qw)mC;?B089o>;k7WIFE93I=RYd
z9$x&-7NKB|ou;!mJAl4d_+z=;FO5-+GC!qTGVyg%uyF;(24Nv4V`nv=olQ8hs2SNr
z0sgb!OOS>h;V@BRF@K@4hY}U7gnumoBDSr+h!y=R3;7_A+sNR@DTv9BGajF)GCan9
z*-m~Z7)ya&fQa<*w0cHRk_$b9GsvcMZx}uWlZ+|$s2rajk(zSmSradh^&_0~Gducz
z+4bu=r&$lfEUG7IQ0(3L4mWOl##lKkEg|K~BRCj?Mj<5D@D>bT;HAYA5Hoie{5*#i
z0#I0%3R*ljLHNQ!$JUQKq9$W*C6oRkuayhemOm{!4Zsh|XldvxRzWdh;$;`Q5DaPp
zV_I(~b>&mtc2S(yekulX5~pIGEG3$3M5${Al40=$dKTK25<@jD*g>uRd8jL`RK#n^
zL-@tG=4$}2T(r3hPOzc4jil1uI_PlzAXn!ov*DHbkpQUPbIWBp$^<MP9{^gZg6&sq
zpZ;oZZI5P6s%-<A5GPG@CN*Y{$~ntxkTuNJ^J#ea;L=`E=1;C6c~N8-6R649Omh5h
zQkaO6)%7skWi4Z+t!M9>{@BPE1?@{yex42mFITt@TM1f~5;0hcLQV@i0vaG%mDr-0
zn9}SPq88?LyR%lmOb7I-8&0G}6t!2*nG!T=#N6N99{Cfr#QG&f_bo!|ho|1@IpxTv
zbZa$%Z>;s%gVgT|_LovVBF?$|zDB(@GnP!;ebb~>P4*pPY|NJ#N%5^~C;LJ<J48ff
zmKQ}t<rRzcyDFBb(p(FFRjirydO{^=4^1|wxv)FxHa*8|`iJ``e;|Ib%S@I!JqBP)
zd4?1WjLdvuaSUS}P9{9g_@I|O25C%tSmIkx636ppmdPiU);6h8&(veK#4fapb5^hx
zZM1V%ljM$Cc|%)GnPSYnjF!<<qes5^pi<FwIXESm4EIcgiD{=HX<T(B^<I2rea7Z;
z!;+ctpTocJW-{Y@tu*}=#z+E64zpx%#CC=SXZvQs<buaawbwjMcgXrIM;Lm!Z@V{{
zjx#ZeDE`seQ6a>s5R!r^Dc*e$qIq?Vgus@Fh(JY+esbc(`kkBaGdLT`aT7khl0QW=
z6PsfSSaMTZ%%VmcaH}X>gm4HQ^NRRP$aL+o%!bpkv5}_L+fKMjFPDiKjIcie0G}IW
zwZqWW&EefuU7A=y+6kK_B==1H_{bt=n!thmQG)^QMkeCn#G}Lv3=M~9<-HRkYx?y%
zYAs{^-u{{Z46(C1t-p6wB@PLl4+ryNV1{^@%&0xqp5<kbFLG<hYb!#k#DKI~l6Z8R
z6^K4Mc4~Y|fQ$$Far1_hxvXUAtmu5n*NK-v;KcKR!U-A{{{Wj#I9;{xt$eiTpXAuR
zzW5~|P*7e9eK*_8Z4ombns)Nw7#0wFwoHBh04f7*KFhr}jkn34%j1k8=s@S+uAdXi
zGVj1`vm^fi(9?N)cFcH?!zm~Ul7Z_2jPWvji1!f~tb-+!9S&Ln{b`wohQ}oYP*o@7
zK{NY-eN1G>oKskmf?ZJ|s*ZnAjUfT$H%7dt-DQWiD5HWc&$l%i`qHSaD5~0u)B?tl
z+RJg<sLyqXWLNmSVr$GI{cI3g^{ucuJ#IrFx=)bW{-%(=Eqnm6Ku*87Fj6OM>6Uv-
zAr=EVLao${%=NgO(i))15!ktmO|l(Fa8;L+%S|@5Tpqy6WH6g?g@~W)5hM&KK6s!h
zu#t<<p>&8^K^rTB{k3R#))c=?8)})=0llKb{j%R8T_c+N!|TjS+ilE{(5lLqZ)Mv<
zQn)%$%GTA`H>kFgNM*Cx*S3VItPL}Mouh$~9m^?p%dcZ-WO___+aq!F6!izwaJMa5
zOj50C)~R{7tWPZ?SNuPkn+5O5w{BQ@*=aELl<2cKf#Fk50<v73Q|ewOb48i2?(b%n
z+ctHDK3P(g?{KpA<x^8+yGrsJn{!P+>Sc1M5&4|P`bRld%6Aj?-5o7O%I>RWsdR7n
zT*e<B@Pa}j#lTcqIF~mR*DVJi@{6h$<PP(jdCj8|Ltc;>nafU2aVaMj+1OPnafpxU
zihX`jLL>PXFg%MdNX@rQWas>SD;MV0&)#r2Y(<(J%;|@^51159jV2D;Pa9|}359Jm
zWa7<(vuK^ER#sp0dfr-bLlwT3Vj6XJV}=&yi~%420K3h|G9D8biBELCHrqFmo#Rj;
zu-dY1u4&p2h;hP+j~SpMMU6SKu9nF9PtfB|Sqt4K3onxf0Hz~LS=rO|1i&I&AChAu
z&wBB@)rH6ihG)+Hh8&Br>}O$kiTFt8-_qu3nx+`N^*W=PbaB?(8L2WAr&*kcnV5*I
z-)haX$d)oV{B!)q6iE1-M*ZsBRP(4<&Aj>Xo8(777}+Ay>*5copi;Y_t1y;G{Ni(b
zpE-RW8=)FleqH8zq(|_3XCgDP6ij?XvGw0-rE*QyLA!`kD6gegDb!_=Zkf)c!DqEN
zoYNFa(=47jtKbL@e6)9tMi-rmG}{m+A(K>p5V~x6L$!7Fv4W6uWL|S61hyu0^s6t(
z!YmZ(D(ie4G9#CTOjrfB6w^}#qvbaTYf>{t)=2I3w~3$R8Tk&^!e>f+Pptm{vkyt$
zU}k*^H|pCov7Tix`S!}IEU+RL9|Mu+c=glvI``KxpYe=u>ciqyS2_MOYysqP8Kp-P
zbmpdC1dPY8`2JlVe`j_wMn)PlK0t6cC?WK#Cw&fEebsbC#d>TuTJvB?Y_u|p)XS;^
zh>y7l=5dzQT3TkA8HmnP`{`Ap)zE_H&PenAIjBz6%5u4=DiWV+zfBm;Eh8<Lw^8Ts
zUnNM=-8RCk>8DXf)#j)1v*FwJ$15l`*yK0=04m=F+LtW*;qYw|XuL+cQl?h`vDX1r
zp&hI%bxx7N)%WK$qQKi0T6DK8!0@Wzi{b1SXBTDV+SMk)ZzT$|sVI|d;%S!N<38pi
zBp42b&mJenbci-v4cX2f*G$RGm6AbCL`(fHwpm9{pF@#uomg_5r7os(YWs%6K(zNA
ztyw2J_R*(|LWsXMvR6{Ys=&KRrCPXf4kNx^HL9cgI=EN>o8SwyVjiXYMjPxkYf;(X
z+&U!)D@}llFBbK!bC(2$o|j)N&c!e+{_8zzR%PoeLd&r-<i$--@amE%PP-C&Dc3#1
zE!jFJ*Qe@Jly{4jq-^cp)#Ok5QwIlsJZj1mv_W5@%{Bc^fa6B6oX+sl&V!*^UpVfM
z{{RA63S>@NNY`;Faw15eV&QQL_2Uq(<AKOAncQt0k@-Y{X;#L48aq&_>;C|FJ8==q
zo^9CA8*l1f3S$!K8vG(kNTjDU4W@y_Y4iCd81Uu?5mJ+0;@<Zf+H)av=1%o)SEFmT
zqaqh+)V9$|4^C(s3$dnAsE@YOD%Qb`=Mtto#Zi+x{k|ltRXNn8X5%*W)0zDzW?1IN
z$xgxN3y#B)=3*llT)|uDqr*(Rc^h$@hq6LIpaA&w+o;OmmXW2ww<Jn|iiu%!VV9tf
zE#nnEL@b)&+j6`~Z8w#beme8E*P0-dCS{}ap_z!8^f}~wr3EWzWE9D6Q~v;2NB#9o
zzH)c17xG5lbMHyt(d?G>vozZt?wZbj?>}hEs)@}p5ER}gF(OkFo31pKg!5{?Ar7%*
zg>$5{eL15$b3cu;3NxoGEh*2wrN*P}*VR^CUC3`!H8Uh>qOsdF{fdiwcKg~SqIlq=
z+jkVSh^YfIk#W^iC9b%k5SR(rqH?g$W;J5uvSsR)>8f@g-f5Ar*<myw42A%Is;xPE
z1~j55jN=lbk@c$^y?fdDP_vQhd-NyMd647lA3{@_D&*B*qg3to`gc7{U~fJn24*+(
z{j*1UL8g|Ja3UO!J4O`mGmed`)|@}R{TClRqCb%7%}l?k+I)KN@#FluYp3%oO~3`&
zvkUXrfwws2F|^EOMs<@$TL!X~7MZ%(Zp}eK?gfKfdD>hkmIFA;1YrslHWwul!0SD;
zedpz)ssfXp0TTnX#P6D^ZCocIy;iV6eTPzYPISdofp#lu8!Euok)ZS)heKXPmTAq(
zi8z|B=<LorJr)|t?IyI=&gurZs{2-_r!1t#E3k#n_X#MwP=Tyc7D6?bN$e=A?v(=b
z-HB~=D%Dokl<^vM;~n`z)yxjzC7OYEie5B$&$@}GzS~I{ljo?6<Co0bV^X_jlh`t5
z_QaH}wwkbuoTbteJ8j~vOD?7hgQ{schRMO!TGhVt8K?*KjLi#yTrAeMibE4yP=23c
z%%-NS`zmq0I{F^S0ey=xAC;@vw)QA2Bl8U^%k%wPHuR4(8T;iS-tB113oBZSfh}i1
zm1X|`-~_P12J;dcx<+onu$vgz9_DT7v~7zXMYd1;%NC;rjK-{pM?%yQ{+ivYvxC-H
z@{)5)1ZVK)mJAx~t`_zr?OA#11O;=P*%l$=$wD2-6V(`9{TUlf7V$C0V<W6Uq_6zm
zqnU{xx*-wUWVe!;nNrEj5E~M)o|O0wBZOScix<37v+4KaEmrEKr$O*m)4t37=Wp3{
z;g;5@VfHl6XV!k_DUTvNowuF8Z2lfFm04C+AeXz0{Z7jbiefeP$f+vLIwExnkvf3!
zziu%HvMfeco}C)`<r>9doU>eSCM<G4Q0K}gcwu{;iZWm)OcKj8-)oT&D{X*CyEn>h
zXlS=we{;P0L2P1Ew<+-(a50+G{WZ$*1Mr#8sm-^XnIfQJh_c%A@%Zx@gNTNlMppjt
z6tm=IF{KzQP)wXh_500Tqg-x(5Hbq)4BB(d4hPi67xuz(WOzhJ{rXk-+pjrfpsc*3
z`2tFG_YQvB;`SvlCq{XwjZ-^**?b79zh=auuw|e3F3qH-A~i3ALbOEh9A>1*+eX?-
z_hrDEK__uT8skPaK+ehcZG2vtmM$kwdtGL#4_D?lShC7oe+;u8l?G~(>>Xybu6DA#
zsfd-voTB_yytU~lvICn4?Ko%i9jP2RVUh_68_1TZlzd8@7}2+hP+6`+m8N0U<{WT(
z0zE5F=JECM``6eE4oQ2d@t?f<ZH9dK$E13yW1kt#5p0ljMye9j(NOAe*u37{7~j%<
z{Ao#(u?)*I{Y$3?J`*#HBb0;}{C4!DTXx0-$SMXcA3H6X^z;1HCL+qFZ$U20CnNMs
zdLQjL*x08ms6-NYkdMM`9z=FfX-G_jZXyvSwi<>AVhji8pXVnL9SEY34TxfJh6jDM
zZwcE6%pNEI0G7*mmX*CS&^zjLP9nWgkoJrNOk#|e;dToyj5|$Dvfp>q+`PlHPo3hz
zvu?WWFwz)Iha}geQbcW(Ba0fGnUmD~>kA)n)5i==fyzT3^$mgXO3Y>Xo@$Jlo}mm-
z!D%m<ryQO!I$ywSx(8u(eAB=QuPHWudTQnBXik?^>(czS`8i1|YYUyrwiHndz7YL2
zHp(nDdS=63IlV(VywANj%^$-q6ly+4P2F?-Y|d$gF&ASYg$p2!7WIzx*PB(>Wo!mk
z@u*kLR3I-pB6IxYefnvhFDh}(G>c>-(@hvV^`b(1^>y}KY4N^Zlj$B;7@yZUS#Z{X
z5(YL-R<LZSD<D{VH)C|lM|QP*)`+T9T_r50boYh~%FmP;&z!aWn#Y$~a#OiwxP`DF
zR~wvQez=UEytZU=jproET3G<$$4wMSEMg^-o6iaKP{-kxr_+?Iel})imY*56eeH{g
zmIP%}nA8$k^i%2k=ijE69NNXn_+i9mgEJTQ%Ht7dc#rvJ+c2j;h-3Y+W|{v0(WjcM
zZK&JD{)fgya{d~bogiW<yt7p)CZ~ViGJf;SOio!Cfl<XJgDYF)=9`%$Q}vp6=JurQ
z{C|h`i1eA?C+>6ijHU9qUM9SA{CZ|1`e~fsHi_PG9%^bcIWhH?wjxKOyc08oU`&aG
z#^0F*mAwA|5aytB_=U{oW0B_?p<E_LIB_Ope^KFhne@^^8K@!(W=M)>);{qbX<o?v
zvXA?r%^V=!Y@gh){7xU~EWQ5UEq}N?rx7+)mM41;oc*wV9ZVX6A#9>40EW9E8kEUj
zv$+oR;kkGF^O_(307x@aP?Z{%r+m@AP(d_S5;hwtDJv?H(}D%<oYEZ6d_#}|8imcj
zh`chPVgydDt;%=Wa-7I%`@~F+rcsv4PAg2(o$20;iI|a?*lbnzN4)+A5s~|3d{`3=
zjx9KW`}F?+`#HS(bDaJCKC<lu*QZ&*zm2j!@^ki_-_AaMd7$~NV>bM)@vS!;k>~9h
z`_HDEGZuUgpN;^>OJ~sLdRLM9v+tDVoe?I@>&EGbll^C(zD#HD={`2k^O5L}@2fA&
zn?__KaX(3q${El9!~jhY009F60|f;I1_c8G1_1yI0RRFK10fPIK@cKQVQ~Z#GJ%mo
zQlYWIAR{wka?wLmf|B7B@bRLu1tc&uRKi4KbCW=W(p96g!{Yzi00;pA00}<<tiUu?
z+hy8Y=$s8)K$rgjy%kD3#&as1-$w{k*%NV$2?~`y=NEylofE}2)P6GAyiDYuy3iUd
ziyv_WAKhV^yUvWW$RgfXLBI_75Q!m^gq~{rRbu9sw29z!x`AsaNYL!~slpln*`Ai-
zg}_bxN4qUdu3#^8>?HzR<!n2E@&5pn>A3kKFuD03$`my4&t^#6G!Q}|wZmed_oGmq
z_lGxdK*^^VvqQtD9;ydKWWx4a7uk>`dZ995W)lhM7xsSYqgp0N8UFynB*RdjyZEP*
z2FjU@CyxM`1RJK|laZUCd(y}!JE~(CoKF@m;Dd1wX-|M+C+Mn>d%X-IP8AUM4}uFX
zijV!JFOsvLNY6d=+iFk_dCkW}CyJM4Wg)yc?9s?xVOTJ*4<tv5LOFwpPHWn50egJ7
z9gQWRUNqV^o$}U$%XcNK6QdVqf5azm_r~5!+Mq|6S2DAGRxo)<TfOx>w8XN$*h4Eq
zeAaVq9>O}oTDJQI!`&Wg<Yq$jQaWUyjv;4&$IWH0MBV20l0<d?04w=na4KS^A?Ooa
z+Ep-?on&gi70xDgQVuy|xE^YrB3RRIh{L;vk~q8!k#mkY117XK@UJhp<fLP6Ci0cy
z=7hGGFL#o*BP48LeXN5C!@E*v2`UIV5s}(-R{OFUYjr?HPZ^EqJQM6jAbRSZE^ssI
znp{c9Dx#Bh8m0^lZV>Z(1yGj)E>O=4$DPpeLHKMk;yn@m8Ec1~lLPIBGO}R%nJ#b1
zdsPgDkjNn>l~C#Yq(ZZ))dqNyjLo_p34`u&HN%KUb4@csUgicTV|zSgWjq&U0z_tH
z`KoMg5q2E@C`LEPU7@Y1CAbZ-Ex-@=bW9CuH+z8fvUVX!p@AJ7K=aLORa3+Yd2`}0
zjDTH~JAB3y2jHpM#KrE_4eoF@IwykQ++7Gw7NQHf<Te)fwHz@xm<{Bt8ccKNw1Iod
z$UD9CUhrCyhFK{Q*(xC+)+%!$aLpStE@egn(}*2WkPWtiahi}hKvup;nAR3u?&;^V
z_%vMM`i+8l2Z|U8$hR*<K@1R>Ni7byF)E*Q8&2+W4s}|#4~;n?7~K;4D&5^mfRn(F
zf;j0thWc_;_o+*y@Zg`mL!^FOP>-?+^SmQ;;uOQ)eFo2F;d{!b@eTrhC`r~8t#36W
zs(8xVJaqEr5Ez5JTt>_0ArTH^+vS=h5L(~vIea#8SDC)eM##e1C&(+7ycc<*O))Mv
z=c@43-UzJP0{%;9suQ~*ejM=dqlv#BtBRRVf^OOa#Co79(QSn=jDRoZpJVK&w2mmk
zge;u)+SqKX)@k>N#C{67eXz20Mb`I`7-?J9$-~6!z$&YoR}IF6HLix?;qp#SkXl-N
z(FMV`D0NQ{bvMWyf|)`HBpsTNiLu#yHk4*5ImgjP67u$wr!Ah-Ps?eFg|kuK==XY{
zVXek_MbXX09bIm_P3I*8ERzN!yxI68B$*`>cNwy$y?7|Brs!(8>;`8<_`io#@W%U%
z7xh#_1}w)U-Qo-jUzuB=w|D_`V{ii&RcQvf-u8-?5l+m1))iuWf^@^82RxK|?KzMo
zWC@OoE=Y*Ze^$`vxzs5@{*bO)atE1B9D*%tV<VKTE+^0gZ1$KO6y1~SvsXYUj}`v_
zrDk3cd!35>6*5fZs0VVDM+un+64I3VsDY{jy?^mDGn!=QI$aYzHb6$rZ~%MGUh>j|
zK1w41Yo~*x6lL9-yg!>~HIsz5p;n5n0QsZid)|Dkv+r6${t?Fti`~e&0;g5HfuEWW
znaNV_gXdW`)jN3VP|xPHarD^CLW7~l?2K8DROpIj3owJ$R)xdjv_nstdADb%??l^b
z?0`kJYUjzSrV#odj-fH7wJ72<=&2S|4RrN|*-cjl$tCatc{Ljtb%vwNtc4)Y?<umJ
z+Rx7ebn1Yn$<c9FV}wt;%q!D>g~%OKvAarbKQf|f)NyD7Cbvz7aR)Gt7!dB6h6Lve
zey6tV)A2Su6LI7ajg24=x;ovrT1WgUZ;yLK(Ne5tOn|Q1-Ydqrrn>>j@ng`Z5OaHU
z=!63NSTnk}8zY3(-f?#YI9Sl(&NV`D<;d|z4l)~;1%Bvn9%zv2bp!LwNtY5`39y_b
zZk|xQO%HhJj1K!ewLz5{(nCYrHbJzXOn3*hsp7(M4=yRD3_3vK78+%O7=DP{zo@pW
zdaXbRC^rX$@DMIm^Il7t)0#IVnNEh*q)qs?>SPyUDWs6tAaDt@S=<w*rZxh3@Cffw
z%b0Q{7z<ZG@>76<9p<JQY<Y27oB3@20OJpj{4Ocr5^jNU<f98U;&`DW5Ris!#cq2}
zbj0*~rGGWZ$250fN0^i)XWD9k{WSGiXm$-In>FfF-b{N&eGx9hNC?mvPcs&c0(mWH
z1=rDQEFP)H2&gPdr-kl$aW{xnOPJ8;0@fv08}|)$tU4+#aQwGGj!4UJ*l?6GmF+Zw
ztrb2?GD>t}nB;gUk!!4-h_?B{9uaZL5rmbPUK))t$gKpJzpCP_;?pOT!U7L!^@i<f
zoabk|D}wPk&^(b+Bo0Zy%YN<L)@`!ZkqOoS4}v5VAi_OCR9#N1Maa8+&=q@Qv#4@s
zMDpi2-Ek<+<<YTCEqk|!PLLu(aK!9`zNp*)or`;#4;P6S3T${@RawkkxhA)m%2#78
zKJ#Cas##IZiGQ@3&bWdYxiqE#+~!xZ#*ai3gQ=FfI-M4C0ehl$Xcx5q0Gh)xLYP_A
z-1@FUIiRBMwEU2Q4Z>`^<-voAM8JGF;>__zcME6roq@rOrbq;8R(o%R5>Fta8o<%z
zux{XQ$$O(_rEx>dm{{sL*#>=8ZRp=<zsUd#@A^~SmPaC-DUZoziS9Ga64RC*JyUQ3
z4d6;~B<fLGrrGub<b)AonoEvJo#j}E&Q0kFhW2a}_MbQ?jpGY65y47x)bv6FMhQ8e
zG+=6}p7h@3Jc&2ie(t0}%M1Si;W{kPIiad_V3_KRLl4HvZ0WZxnY!CNJ>Z^+_>7R&
ze(A-mfFNYE1QnG{=;0@5hK$HlhZ{f(`L6k`Xj7j>?1)hnM({>j7}!2Y`0Bmna1(Eu
z_Gt<8y3$X<9EJnuWt&MGO}m^?)RC}Sa!2^0-Bb@W>{P0a_VrH3OXA+D(+SDBW`=G9
z)q8LUs`i=nUin(I6~#XjpH1zKw@nn-036e$dp+RwGQTr^GMTwej=AFsp$~p+A|g|p
zf}spI(N}1~>^Mh>QZ(4j=VHyy;#0r}H~?(S$t#FQ+Riv2T}$041~kkbEz>n}b1B!~
zw+ShmlLgu*2Vr(Bq~bclmQr(M4M^s=74DFO95C2I4tre7DsVs4HRFkq3My2?y1;Vk
zow_m*vz*Ym$xGT8OL;kCE+)#D4nEVjy%Q-iTroYnzN%W*joyj4u~B<daNi)FT~)yO
zCSh%AedW%G#Yf?2{Ud^{y#D|QZiDw-vEAb;hPIriNPdR->W$e%{Fi6@#|r_OjZX#H
znn-RJH!&Vzb`gtRK1!c*93vBcsnZQ`$DScJU7RuH_A{CiAHLp(=!;Br$qbIG@r62=
zc}MP=T1H`byNbAZoFpvuS8I+Jd7*yrijNjBGzq2DDjU7GBGS7~COi;M8;>Q$W>FB^
zEmi*j@(pwTN@g|-{{W@J7HQ&}00#M$izsu*&ys6F0!&$42Li1w06^ucaPva$B0SW?
z2o^dv%9%amaZ67XiMX=W2jVRkZ$((sY4D1I>KxiMpzmZ!B~}d>1b}3d3Wu~I#NKmH
zCUSoOb426C!q|AAB0&SH3tPCW5!wq$>WE_+Mkl)_riL-GFZP|TXy;9_)Ta<y1;<3^
zJ-@0GTn?7EssK#bU3iEVTIi`c^Lu_^nch?ch@T2{(67A1z@~4uL*WSfvcgL6IvW0o
z@G(mYHjLIz$nYpb=eu{!9pjMw){Sq)Wrq>#w3!koORpvF4Tr@iWy_c&tSmj{D#Byx
zqwgk11*wl)FL<VsTc`?)q-SJsQU>QT?oClfh;`${?qvgrZrErwDb0>1$>XQ&s!i7Q
z_`+cj4WlS(m?mF5u2V~RbXf{dzKg|-G-(LJ!g$#@2N3FxaT5Wl_-~tJLVhb;k^81F
z^OQ}j`OG2kR$i-B=(yZ12E%5Bl;`zFif(H?M^%e5b{HVYJrUe?`Q@s^gxI+IM~b{O
z`kNto?z!QB?0K9U&EstLLX~1jZ?3{Kg!YS*B(578XH65weKK0(^Ff0sb8-s}I#@t1
zal<L6I|z?78qqf5o=dg>OPzoXQ~V*YG!jwvHudybgDbTj@%zP?BItK*{{X1$;eMxP
zLql{5i9Qffm;eT+5k3$(schGY))BaCnmWe8rfA%`zF}RscUD6%h06Aw9*ZVYG1RY3
zrLy`}1tHTk+h}=h7Y=pSyEVCA&S%YgRUCdQKlbc_<hgv8vKsGA(v8|i9*9ev;pkPF
zio~bhohuzKm?^dFARawaPWyf#G?TzB+&Ru^j+%Rz`E%xqk>scDpPANC*y0Z)?&q`-
zCP{;Ml-X|_#?30X=D(PjaA6`!<h2BPAe?30t$?3dPQvE6fc`htU4z<f(fkUa`8vt6
z-uc)<018x04TJFa{{XcRPEO<1K{YaW30ca!wX$d!TiuX3UUp<!9qO}Lw-aCVDUyC&
zQ^T80m!Aq?V+XbSCl@+SIbXs7zDt6XRZ`Q2W0EF8W3u)_<FxNUgDcu3yz-6GoB#tg
zmO()#Jd>uaD5UoOT?+SIK8lef_+0Fp%~x$#I-cQao4Di?h<yoT$j=GNeAZaE&*cgR
z_Ud*3U5WtVYbWBD4yeF+!|I)BwD1;BVGjg347DX3V6xGplV`i68V47fqD!#vH&Bn^
zv*fBv-;=m1RO>VxIbYzU9HtT94a{nrf#tnS$^#K4m1Euuh$_H6%72LD#=1OXl5xQO
z;6R+wyxU}oIL0IUD$T1%(+yz;?Qb=uma3<Yo2kxMP8F)?(k#xr(U%sIWN=!Kl5CAf
zR58^q-5Lb&YB9mznciD2DKxv&V@U>>4sgoWR)!ZIKrYHE5C8@GKt^V;I9rqpl_R`y
zB>g%pGS)$F)jQEyqCd(cw!_H-;@vv6Qgp}Nw-1P_6>)x&s&`bH{6t3gK&g@0>78?P
zj;>k0lSy7WG-=`-AWW#t0y*px7xY=iiKidzn>5_q)5AnKJb7&J%!$$5QN+i#4>f!>
zBy>}Vj9#npdo#Q|Sgac91CiJ+JVWZdMb{-652oq)*<vM(Z>H8c*o0e7YDXfC$T9~c
zKn`#P2S{!a)lhtNUv^q7O_f##k_j^^qux3n@TpY9E(x;i@%>7bAOfbqGyrCgAr=Fo
zlfqP+ofSFf5`-NweG`Lc;;T=ZZN|KjZQ5i)4R~=T8tGJ9POJAw8NxNpzk;aohVbEZ
z;u>EAN$Rl6n}MKc6^J(om}@{Dh*_;!)FFT;kuHehwRRC2ltFWJK}vV~@=?hIQQZm+
zltT^C6HS4))0*~oR%#)+;`yslVGVgY^HJTfmhmn;Q>PKjqMgcFb9K<y)jM!M6ixQ~
zj`=G$KTVpFtptD(YZ^G1LZg7q-?XY{bYyt(Mr%}l5J{Cx&%eXfMY1}lNnCQ<>|FIK
z{tJ1b>;yJ(gu~s8)}-vsaYH)<aJS~OjLI;<nO}QQ>T(H~dzVAFZy-Mj)j8S7ei3Wu
z^E_*_m_S*8gBe{3brbL_#BNFYBLE97W<PcBwCx;^mZ-Vxr%beY*q@5aO@eMLghgDx
zSNtZEDdL!JTFPVUb0-Syw7|t_nOAe&WpMy`Hppt4B($`XmDi2<v42s*5Xh|@R&RC{
zn;(dDDB`Hm>%~?$y`nxr8@bu9<CBE&b0m*cA<Zy+k-1(cVU03e_7jH|>oqW-41gL1
zzQW8fsydB8-)!oh9@(AJu|q(AJktYMU4AQoS+b|VtfZ?KcBh{}7xAgKqOL|SWw)NH
zfMX3PC90V%qyfS#N}|eB*_=pkL?mjDus`kyODah8)n<<Q)fI4bK=ViV09%McTzDYX
z(kv}=9+<#i?F(34KknFa8uL8XvxQnh_JBT!T%iJDPDnPRvI6iu7rPB!*unkDq&cP|
z_eR)|SwWS%6ddm%q6lub<y(UD971>@>>+`_m_^ZsH$lZacl|>^JxXrx`h!oAi>MyJ
zD=;rMAyeq7j6`}3R|p@~we|zoVc!q8(SADs8@|xuqAE8BrmKD~5AK-P8<&&88=lYr
z95-gjp8<2g%XBSnINVK@=+^Ncbn#C6w!mwLwJM>KOV@>sEdKx~WO~YDX3*@TiW{fG
z?jcSApwy_127w=D067(UZo5i&V4gQXifkC(=Q`+<eASVFa$Hc-#QfDvPjQg!fK73U
z)k9kMHMpL9$X%VaNAn%30|b!ns<brGGDZB9fvN{`gag3Tw5*e|1$aPJ8cj^^k|BfN
zIVwiAU*^2pc?DhA%dB#M7ig#&d`A&P=tcA9Q@lC#m6>TCg+>xPzbJ)I6xuUg-6ox0
z@DrO;5f^S&;HGe#)F%khTse-aPYZ2%oH(RfFxnP&k0fsEdM_BU{NWJ}H1N;3>#j?}
z;RQ2yx^eg+BfE4*B<mMv$lqkOpA*q`YK&}W5j@E%ttw|wU5Nhx(yKkk^4NS;A&6??
zN-b&R(LWUGhx(7vH>IU?;Hx#35FBAtXB@?sYNa2nF95vgyG=hgD(rx}MJw|_;P4VS
zCpdH|+o6U@$XOiP21(nZ(V`Y81ml_vcOn;OY;qV=h{PbZ*M!fyVK8*+oEljt7Zp(6
z?tW?01_9#;j_$CB^5B~0&E)W$(cNxHV(vyO@j8_Aj}*-6fW!<rXRgDrVj;OE1AC#+
zETTt}9w%$BH8_K><*IfT3zz5<u+awZcZuB=EHfr`S)*py$Eq@9i|DR#1U~du^#@Oe
z(`*PEX9$K4C06i>1l!R+TgA~N&xyuWX9j9COsHcf<P-a{*L~QWl~<l*%KoljVwo)(
z{>hHwY@fR4WeWIM2G$V4?HdGTfXwkIqp%Xv(Y>Hs#xniT5Iq8T8p+5ady%ibu<liE
z@9IYV+d|5-@&5pB&LwA2@#%EpF}Kzb1Rn|HrD)I*f!&W%ZLk}JW~ElmLzHp~#E9s<
z<lAv8wbPcHhM6jfek}3vPQpJela4EsD5f|tm=29{Ld<24GKHAjA$W^gNb*ekj?^<6
z8gBWbVWGG^C1waIgbYq1$)_Dn_!_6+1Y8Td;FtyyTGi~5z3`+NkVh290atqZJ5&*Z
z0gkAKg4@8#6+wg984|HLpG8f$h>K*mL`28c6HxNY)jM?B0(XnEKHsM3+q@t3S#)00
zJ~^zei6qpjOnEC|ntaoP7&CE9`>HV?ns}&)Z8oiFxOxSxf@gTgG>5l~RJM~Wz;0v9
zRQP@?jz~d>S+5XNsyJE<!9%>P-0<=0orac-n^}7U{{7YO7*@bA`YsAGEk0_dyU&4h
z%5+sE;v|TVk}HgCsttEBrl$1o5jv*a-cgXLwZ^qe1Hq}C({%v&xfM`wsYsEDr|O5b
zE=J103#N|lN0e|*`@7(Fa#xQ(sv*ET6<6_b;H~Y1;z`ZP@*GEmqd1*`yxI{Vg}QN7
zg=R$B#B~VB*NXN`t4RPZBQA;9MlmOncyze99=R$cxz!+!OT$o-4<d;zjJOozj(F$E
zH2viuN#a&uO_bs8OWHp$QVa<LqHuJuw#&1*&nC%{8MQdWe}g)42%X!!#iWUaQaXiI
zu-6IdiE(xuGspdf#^q1o5+jIFnySV&Cz{4|$~p5)8=~!rlB~&w4L4JFq(Z>V6w90s
zwZ;*W=RTqNDuIpC_eID505Pq!N5t^oOYCqCtydBKEX!pl*x17Rp~b-(j(<oBlWCce
z!V?@gl89?sz<0iiaP!q}ys@qw30DP*_-bBwIae4X!p{~+cQm<XdXyo?;LjD}6hH&>
zI_l2uCDY6%k^#Alxs)NSF*=@TlGk=OG}|Z{ZT&}>L|UOO9FVYm`A*1fT10QhiL0gg
zZ1Dz|<~SfpqzrkK_EH$Vyzj^%TDjuyyKnA=2H`phKiuV09jqr3eLi;!v@y=$915rd
z1*Hd7K7-V*%}Jx4@%k#{bOAlYk&BPbI};uS&bCL#R5SZTr}urCy6W;YYHxQzz7~Gy
zUM<yoyBt8PMYw>-tXbF&D29Ugz&Rr;OD757t+IABb512UyL?6=kxVBag~H-CWDL8I
z3pY9K1Cv$~-8_^I`NJ>Dy;q8(MMLMR;F@sSPhYW51y6auRN+yj+GaeKZX@8GykK)G
ztV~&Mc6?U@+96t)@U(0;0GD|$VWv91+YU+d_pkugWi{K#WuGKNouJ|BsN!k#^Ih8;
z!J%I}bWdwY&N%Q?i;U*%z6s>Q9eFQgM5$15S;L6no4Rs9mm4a~H9*~H7~rU+H4L=%
zMWi{gmP3-Y^DDDcd+``2CE2?;{)9wufSz2!H-Xp_Wb#HhH8wB-1CnsV22lca*Q%)F
zIo)4HkXji-#?YM(4LPnT5@kFv0j=RU5?c-PPCe@?96Wj^5>uK?cpJx>U}tnN)M(3*
zR%1k!45l(@MmeWJV{<<>H6BSZqA>!|Z<5NKACAE=tt%}M@YM1x5?8)s(P~z2p-hwH
zs>M3a0l?gRQI^wmxbk8?GWb;Shmg=9YJj%rF{dR)l+5H)JGxa3qneHO=823+(-|Wz
zd$G8E5p#G&;Z<|o8!?6~ni{}n0`_TO^92Oc)l6_+YVZlaMXe&=?*uHt1T3>(%>ew6
zm1g6qei<vC`do8F;Aoyx!F!hUL*G9XBo-MSNSK_EA<y%+!61k#EaVe#H!MzD^|KsT
z-M3w5ssWDGvu`BW!T^O~Q^U`@xmjXI#8#Ja&;$>6GmGAYJ-qgrIJ`YkW2?T{bHV;r
zdZNmL2Z%^roY8p)dYQ56oC_)Ljh@L^=~W^c;c!niNVVKJ^iw*n7vvCYsj!g;+TyB;
zRi4rVU0@{%akv9j?6(T91}+BylBZGb!@TQcn+<K7b>agM@=n6`{8L5kP;>tPDqP$q
zM+FjG0P{p)1OP^`m}*vgurP4uoE=jh9xx#?o69^*@RQ6G3u#brIC|)u;0d**R&}@!
z-Bibfhc&v!N}B+|K2oE<tl7de3nMs?#c&4;@lKN3vxkV_sR~h;nI%ZwA_p;&afa@j
z{Llt;D1Z44`6rXzQD`&oMbohjMZ(sNNabm|r+rRxw}FiloX0c_ARPG0tQ_|V4F@Rq
zmlWH|5&^j4I3`~J0!BdI0P{v;UT8J_-D`uOE3!0AAo2ygOS8+k9}1u`QKR7*f4i>D
zMtI>xf(V;sJQdzSeDW)RD4ZiyVWh>=T3L_~o4?|8_*yxtm0yXs5fB@3s_bFhoxeUw
z;+-s4V@4TFABy3OiMIsS$;`r-2K^Han=U6MQxhFi0fCeVO=}tXY)@UD5PPTeI;?|?
zblCRIKv{=XnHO8kBY1sRhuJ)ocdr=V(CC=~+tlBs(9--CVMAPf3J^mDl_$x_;#yrf
z*g&<uNRaIVigP85EyNC_CJROvYae0WDr6FlPL^@RRxC!QF-RMmL>>viF`^kwwhbJD
z$a`9%bH6k&&jpZKcUJU-e~tm@j%2!aOI?VbP*WHMCftn7r`R^&<T)YTof94Cq(*WU
zdkvPS)iVw3S^E_1RVp`kMNQb)_edk&;F@ii8X{a7U{MWghy<aCvWr^YaG)5v5);Nb
zxZpG!ZV|J7Yaj$C0sitspq%=m&{^PTEm20&kBEj(#Yyj9f9Xv*T&~c}r09jojA%v{
z*i0vo2a@1V6(Ef5msLUU<B8Gep`aeVC6HMi7k~*bIRzwpPU8!P*xZ89c=TU${{ZUk
z>mOhfH~!MKDmH4%eD)kI_>{(j8K+xPQN;3$g4R8~2$f(&?GmiLkT1#*mmiL(OgNwe
zBx2Am(AG(~oJXqdPN@8HHz0Yg&AwlY9xtK|rqCtCSee)XYnTaOH_pl1q}jXx*xa)^
z1rIPB_wkJfBw|U~A<lT8qRbVUu;sGzuL6gKum1qFrv`_$#!j06`<fKN8oPE;gNP?@
zR)&tcuWEj1!EJp0Dx8u{#$^IP%>!eGIVczemhi|bb{CxfNPWR}^c@u8aF$v(W(L7`
zx(tgzObrfhO*Xp2-Z)~P%27DJ!v;P6@b$~&vTK_^1*3|-=gE7)!{)MeEK5UI=CU;V
z*77YX%wQ$RZoZBv90ix={{Xn5;@$OGhg};7i1J!I&dOL@X>*lqPa00W5Djh<98-yL
zH&zjKkUlPGW5UT{IfIhjbAiGM_wH2};299p3YlYRvlb_eBZ_1Rmpk5E5zXgd>QKTm
zoa368J43MhAse=y7WI9V$TQ9tf^fOIgBqjF9pG%Kky@cRY8&gCILXjvI;<tmA9+*g
z5Y<Eh0X*1P&Pt(h#R5&!aWrm_hUAylsC}+V16y^9%^ql3=PrB`1F5*f*m);?#cwgt
zs$X;iv&f&ysoSUupnY6AK><GQsfZ3hF3{$`-FtKM39j$!9ButA$q3>(>}~2C&j5f7
zVCilNxIj2#^w~xlaP$Ma%1(j8*{7Y*07eb18KL-L&pd-xrgnb{9w&~?R}3fmwp|Ra
zTyhH49W4{jf{mFu?FY=PyTgS40KQu@d#OjN@Kb1Gf29cp;zuExk}5dG;?t6KlcBB>
z<E4-|OK>nD<@7{`%mrpNSRyIX@fwe?2)ny3%;x0wgI1Yt6v;IqldP$Y`5@YIg+=C>
zCBilcX)qddDyfdl>2n9P0@c|lID>DpaZVz1fS@{~Oo91!JPqKy4g(R^$Yz9=8E@=}
zq|$SCXUPLs4p{zyWjSq+%KjZwONSz3(J{VZS8yB!l~=owDBbmmvEYt|M;;qtQ^!*l
zTvNO5)J@16wA7g{b}m0-gdLfIgmehsyeP?NiCU($pbRE6DXo|3JQs?oX<?m0stIqL
zF4_r;3^r98_kdg+p?24NQIZ>T%?BYUlmSP1q!Ecz!cB>rbySH5Gxbcf-%-Xn7zvHW
z@+r_90L-I`M2?#XmzLr^Ewzr0iX`U<?*O$Kr*9u6R@&a`z83RoU96r#h`S7eWagSf
zec45=9KL93v^jKDGvHHPPdg$ocWyBp5l`Jd&S3*cV{FCRh<81Y-FTOE`wYU!kV>T5
zj)^yn`X|+Etu1~1AvM)N9w5@GxE_cv;WXYo<-tZ<8NE`h^MnJOc<6x107tUX^+ydp
z8yroYPo@)KhcUAK6i62*iZ>($;U&Rj-wHOoo=bL+Xl_tn%<KZtLd${y&3FakCndd2
ztp|6SB-FqlepZ<~99j-d9wM9F(>a;ma&_>)w-nG}(mE`Lt1#x<qSeJ@Ht5I4_M!YB
zyn^i~?<q7?0H8~WfPnj!mUSg(NV<Jx0bojd1e+Q7D(pYhs<GIBJ}p8a)Bgb3RC7$_
z3Z%gzAp+`6@O#bmPi;+b#5f{aLoqx0my#C1*^Lg?gb8l(B6EQ$gAWAOmg*rsL3xp=
zMoV1gp?ry1_bms}d)2;SXH*tAaFw1j=navxiEF>9WDBkRMK}XC>eNVgh3(+}Jqi+d
zC}(?3q|d#otSZ#t*m7<IP3^I~f`mwyFz9(91q<ka4tpcjaZ?X)Y4cOu_F<-{f<4;-
z-D5zq?GGWW-V+n2)lSEI;oW5%44CSq2K!kPv7pHTcyv~VlrHn$d1MhS2RK<*X~It>
zXE0~*x|^Uw8)lM&Uh0kZu3>hD3}9My=&3kWarFa&ZW~M+D$Oq7KXl<}C;E{BNtxln
zNZq`wwMI)^d$Y@dL{EN5;v@H5N|S=VF~Z^$G*O^HXz7}KeeMH1?<G`_%%bwN?CjV}
zOFJDXPVVYdr)zGK3?^Kd2Swpyg)z`-q?}k_XgIB1zTUd0x4Q0~O~>CPT~%p)+8dp?
zl<Yy{(ClF~t5S17;MkQrGjpjldd|%gOg^OL?*9PF3e?Am-?C|@+z}jH5%BDkBsO3D
zqEIl*=ZV>H+C|`JajLjR4H^${>UT%aX5(L}<*H^gQrMrb1=*;F>19REs^VTxGP@`-
zc`iAw!zUpxCBA3|G&c)c!C;&VgKzl?re1KndNp5pTnP*rH>xdTFz6(>o@G>s1&qQY
z+U3c1Is~$wCvMDhMqs)u1%OvA03LYChtsKWK8OPH>{H8z$cHfItolUH=_h1Kt|!Yx
z2_g16s{{5kbATRbS(UJ~jUveAFMXTCR-+F05Qr$|C)EbedFF#bEOst2rqN;Iaml?@
zIp)<X>UlL-IiUK4VZGgv$dIL4#!nTc)?_IyYrPhI;9m0b3U1j>I5f6tt~zj33~J!z
zh~bUX0IE#F!T@f!R9Od2RP$-kR&FDZKtMMk2MN{LC^ja=L!&jCf<04h{_;og><1*m
zMi^-Q(7sDWoRyryD5jil)GES^FkG9;t6ICYz7SOkZEM|!-g0OFYrz>REP%vRG)`ou
zimr>Cnh^nmCV`(JD4QDt!3ifIu$R?rz5D^tEeQo?hT)}fa&Z~vnsC1Hs8tO@;Vo-y
zyEPF!l_oYl#>DkT;AE(WxzV0sGFoXlxM!CWwyT;<g9biIyZXu@!0`Z-4JTG)@NDx%
zucmT)E)bM&qZHr6&zV_#kp`>84ok$*rlVm9TAfz7pVehL<;^lqsfKQ(0cR_j(HJ?W
zZ`06>+rd+1I<-7lcx(l53;2WL7I;3WZesh#asL1c0|N|^!Oi~wDWRZb;y00#u;kTU
ztz%8WHgKL=r(t8E(bZg&oaBo$_p|7+9+rhg?%{um?DX>Xu8Ud;iw-K+5PXn@h14Ho
z#inIa9o<?kdle{tHtsiv6d2ZbrX%O@Uez=<Ukjy9`(dbO5Qnx<B^W8VYj-W~>v1?{
z>%`X(e3oUy;D*;>b4`dw9tmOQz0_gCtCR;gNgPn!lz2y&R}_0qZ~my04i0ffTUE0^
zMU!d*PbpbX%?aN!6PjpLaU^TYqHwEHJ)Pt*8ZTyE>#YkVl}47IG+>0z>$Ni=GO7Mn
z=>%EUSx#$;Zsj)!ok`%E0F&m0fGfq_V*b|xRW}jF78VULm{t4dHm>)$B^!#4fz;`-
z!D+Ks#Pwbgy554@1U`@wdFHK(kawvIJc962-iYFflwdTs5gIMp-J5!Nw(3^%qg@t%
zck@HS0VMQSqUK2DqRw>92SGM2n{-ZVaDlzeUlBTLt=~5T%eb71@b+fZZ|Y6{E;;gr
z?K}umkPAFFS+}w<IXHJz<+R0w&jj+;wXx6y`#j;Ex~UwOv<)(Nn9X>p)CFGHkDA_Y
ztyLu9V~b<Mi%zM<%*fj*5QIaDxjF^nZu7Ly_D3fWj8nEgh+t!U4G8YHKiO0+s{6Hz
z#QK&QWV6E#9ToO}2eZB32u4Xc>}-$pwIvH`m`8nBFv*_|@}}A2s&;y&#q0)KsF4nq
zj=WK`{t;v~geQ6@j3Qp(a&Ogmp6dbfRR%v5XOA-rM->snSx4_`Zwp8OrnYPLS2EM>
z3opvhJ|$-E*YFKmVR-o;ntZj_D9aus6izHn4f9s)^;l`YktiD*a~xDQLtaVxqmNSJ
zv)SgnGjw3EhmLTJ7e5!GX5~s(9SU|Pkz_y+6QR*)Lks@^m_Dd$VlE*z*yIgvpkNGW
zAJwz&>4Aywb%R=gXJ>A1_NsF`0lNoO(~giXa(kTe**3M%*Jrx8CY=d7SqDhCE#tB$
zx-hZ=8__z$6_#bP?_HkZC&?cjLSvv=OrIsIp-+k|jI$OTp&Tuo)6Nmu-46X$PHT*1
zD<?D*;{ixgiSt4c>$fvy<eug;KrE%kLr2Lp$TwqdLZ=6du2H%8Cm00g5eEPh)faMu
z3B<RCOTsSW$x8!ikkH)|iGp!G+_g@|=Vwwr0UV*SkF;`8;`t}IxZBHuWYnD6Y!jqu
ze3uP_HsKz70o@>>4YP-u8+9lgBNG7gRWP-}?H^R!#m;_+!1uLKTu+JG4rs$;f-?f^
zMUlAK2A_3sbu%GQWjtQisnOEO$JIQL@&VwlTC4E6`M{kMzlVv%I?d0R$uwbgf+%LV
zuW%-~2SZv_T5X;t=*vLB@-@JRMBC1CgGU0LFwE9u&#D-zw-X?Hx+h|;@pGd;%6U4(
z?9Bz2covl90!Om=$pI31-;#6BcEV{kGaM8C?X>Yrzf2-Bym1J{j$E#rV~Y%+kCF#)
zhC_cU%LR%i;2&MCzi`(Bz_v%xAHHSk1MPDPd<6PmVb^cA`Sf1z%&44hT07vGA?H32
zj&#8vH6MA}s=KCEM<(Qix`j+$PE_0{GT=wWKOh0}3J9Kw#4)Xy(F+lE?NJ$ipi_<_
zNF5PI=f~AsP*Yt|uWMb1J9_<BYGxM+-?3Hu=Z|5g#Qw>+F}OTa;2}+}hKblg1*G)~
zqoZ4=7jr4z0Mg#Fgj5hJkEq&2m*1w#ns#9P&RX3@=+zU=c%*{8fJaqn-hAshA`F#h
zIJXex3-I2;$0NClT6m2P$=<6>4z0RqgN9L%Y8Qeu3GQo~t4u25s|}pfc=c4w8lN>y
z#5hQQ1mfm~$av<C?Mm%u!b$<VzA>D0S+=NqPObw)ZW<;ysV;JRK!OSL(MSxi=#BV=
zN*_dNtm=e6xfr<SQ_D-cQNIlo{{X0^p9g`#X6E5=<~XL{pLdFxc*OHFF<_f5G3CuS
z7j*+0ZZM<&0GW8)9^g8jYbv%R`mG*R_^x=bQil7k=Nls-qX>m;BFN6^zMCd=MB{L4
zf2S7Qo-q0><v4==_3rh<^w=(bN@6w1y-@K}B-A;$iwI94u`|G<dX169BfjR?d!Xu@
z3>PnTf``@CsTp)fcsiIo_^;*^;3_<~5a8D*06V7qkTB7vYx5Gd?;Q(Lgl`XuHzO4z
z!N?hFg}N(|5C;^(>W%Qmct0{|wSe|Q(M}>hF)6mfeBo3LcC0*kAioil2aMSZIrxF%
z7ykgH9)%n5PlDUHWQ;pYeD*`wb74cnd>Vw?J>l4$r&Umv1E0P5S`hB&jH$QkiUI;`
z!=xEa4c)lT$cT7><h9s6lCo2Rqa6+ArZC^m$*!Ge{2?R=1DXOca706QM=_!neFnK?
zb5Fu>8RsYt_8f5PMChm+%z`4^RX2BYVx-}A7Z21RCWkT4-W?TTO~9EBPGi_cnQ}Hm
zb*|6e<P}D>mT1B}D3Z*9<d1p!ki*jQsurCwB0W|}`5n0|Zlc#qTg#H9;N05n0v9JS
zntTIj)k6b~kir3ooq4b0xVhg=p6Y29zcQu+KB~@}$31{yWRohjAWt>gDadr@Eefjx
zvv54sd3Q+75a`qtCfT9}R!zd#5S(IBQ+9l;od|n6>^LN09LBJliHsRLj}*a%E@uA#
z6yW#d)=h_14z;*-P9&O<vg7^PGYG>FIEA2*`)tmj#P6aa%vV8sEr#_g^`9%K#Oe(e
z(WETi=#Pq*RWfNo$p|Afa+0puvpA-PZUJrztMJT3*<MIq5lpy#t(mL5lVnMT+;vf&
z1H~V{s`zL;73Sou^V&{XAs9|BJ{zYP&}xYdjF1Eum_|i71C6+>>p%XMtizanM5Fu{
zU&}OX{>%^k7gtzZPHBwqu#)%c2L&Y1Er`wuUA7EZJ*VcMP4F*;$QAf~*%EZ#Drv_w
z&~wK=*g7Vd_NgCdrz8j?iYUq+_r+!l2i1E-lx*uy#UBMi-|o6U!Eebocsa4?pADGH
zc&eAp=U7zYJ+u2P-ARP?AqyIgE(e0}mcTq=WK*tKeBn)k&JlqB014pa**ZeQSF|;t
zQ>U13RQy(m{{V%Xhy%@Kf;`5<y*5_eaR;E3b2~k%R_Y1?%y5S^<WQP!$nZM_#%O|6
z66W`>KbWF7On=I=bQ>Zt$UN1?Aw3pF#YuR$vJ+6eqKDqn7+uV1utV!L)jfaVYfnCf
zdri7<A>THrxVSvjusN<CV}kr@E+w8?Dsn2b3_C1Kam=nrX|#d{N%G7BPd!wPLdaR@
z7BGMh6V(74O}H8Yyl1e(>Qp9qHqf`Khmc=97lD+^<C~sS4i@1KJV?!Z&3N-fhEr^~
z^5mLvVK@&yXb|t3KX+BZt_YGvM+@>;u@WI~;%G6Q%3-D|I$PzPl}k2SFAC+t59J#j
zCl7Z1P6^SuWNor=nv~n~Cty=GJ)TeEoLW2Q18lCVVeV+tV77Ljs(Fvfs1s9*&R$8@
zj|j*u3=|E*J=vY-8`vrXv^p<Pn7XUGG8hvND<I-Q(H4orglD}7&wE%n{7}Dy5Ix>0
z+s<j8O7(j?>^~~XohC4O1(LX9niY)LqvCl!Xh4kB)3H6y07gsjq|<@(UMAI$pwg((
zAlfBmq9z0Z)e+uC=j?=3rFlAWU(J46t~^s6yvg%uO>5&(;vQ<Jc-YU{9YT@@u?s#a
z&~$KH)Y{l=Ae|G*TuL)yk0k5Us~~bJ1Pzmx#+N$a0n9?S9n;o!Q(=~waL(%@s638B
zP~N5YJysFQZk>U-@hYApd@d$^5V%k3H2A6w09@{pRbh?9y9=z2DuTd!Bbp}O>q2x=
zv<$sdd@w-=FrMSUCo+*EBbuo9F|HmQitRK>CUE$DB?4O_{5Fn=42H@WqToz~z{ulZ
zJyl#>0~Fy6u+<!_r|#I+xtv_BNIMHk^-ynStjSH>fqsg;Kwoe&LZNn!Hx@*)o$bbW
zt_UYfp55;y0i!n@mueLpBF$-310k~M8_~+;I~Bb)$24!g53KBFBMK@%hJg}XPFf>?
zSU_8?8w%CM-%XiGMievUE6=LRn*%3|DaARXroe4{xTuT{0(G*sI&Gc^<jM6`F?i~#
z7gcGS=Y)+>!A5iA(H?;rmNk39Iv(+BWH|Y${BX~NqIJwCz>X5EIG++ASZ0RiGrypO
z8bvS~*!-xC^NY)YP9`#dm~At4Zb5iPZd9_($3AG-yzGy=tbEGN#FO(VU55x30Hz#R
z56)2j7#{==+|eNPEk?+1W)u8YB=bUXg45)z1CP&Rig_)h$USyu5g@=K3c*u&;tms%
zsZxu}vdky4s0PqNVFVOT0HHu$zZw+3>LUmnG|4pM-6Cz0bl_-dLd|>_PVU=^0Bx*z
zsBnD;wA(Tpi($K1!RXX31Hk^JOjKnzjnl#QvIeCNIpO5xtD-ug3r#}Z6k+7L+N>C=
z<Ciw^t&^^9iX$E|eF09y{>|EEc|Ud*QmiFuMgIUL!(qFaTN@?RosrZgh;91xKn+RA
z1rMn+h|DK__?He~=%z!m+Tl2HN6~*R&*=hDhL=R43}#&Ul-Rj2fmZ;GP2<TAy3Os?
zoRbK^%1w?yuLui96Yxh3S34U<hS>!mwB&R~43H6aJW~pk(|edm9`>Odmb~8ZI4mSt
z!(Sq-!fj}n&?4A0iD>7dr#Nvr;op=>7RQaFgsb8=Liz;XyrOsCpqrUQynhne6eZML
z(0MWyEORk^R3Ezy5z^>I`~t{~Tx5gfoZ-RBs6E@oh=PU70e4cZDfsG*(tXl;ti?G*
z!$h8?TrF}=I7adPNZ4o-YG=rP>X{>fD%ks6fzhbuiAQ&{cT~asD#E8|&-IjKV=>TJ
zA_I)!M|%$OD9JW~#dw?!=|*lC3O{xU7Pee=WSnl`ykx_lKLDh;lp9oZ$gIst^pq^h
z;m4#Sa);u}UFKnUD{lkyP7)4Fi*YGyPplvy2n}vfh{BxK)Av{Z03p2fT2*(=e-fKq
zyjTR4IL1aLS=DRnsvyn!5MfR%F!0}PtHa+oG5k3p1S-BQcojv(vSHrLd8hZaO@kP)
zQ)z69V>suw)1k=@?zg+4RLTjU2TUHLRqqwZ5wZ_0zV-anL#q6a!yCb&ak|7R4KoCJ
zCX5BvEz!xi_)2yFY+ItOyB5MQr#fasr!m1F5x0_gEp-RlXCWw%Mxl77)qnnFO~0x$
z=!llMS}v@?02gNnvA2%$L3g5g&LkC3mg{LMse5#$EKgNUlyQj+Zd|9H%?$qljkz}H
zxq#<%;%{|y@qp99-npj;A*N8darlcXrnTL4RWO=|+W!CuiuHI8Wk6wh7=Ka(mNdv&
zG(qEZ+$38U?yJSXJHIX~w+GwYh+lXBhem}u8N>J4{HQa{AF-Hzeo8<<4|&qPH6=9f
zxEbh-9_w7L)u!0oZkq<v5>PXdo;*>s3E30uh(yUm$f9CB<8>Gq#)n=q3WIAC%z!yU
z>@<T!i{9#;FeGS<ief4=R)39}(+HmvWyq3Fr!|rL&s%*FwMJ*nwkeK(MP028_Gapy
z3T-90e6uQ}gGNwzp}T;S=_vSJ1Ey;(D|y;;L8p`>VUW>hgp6KWPl~o7=?h9ltm<}2
zdmal<4j`@*=z+mfK^$=iL0-@a(R!&8=Fin+<A{0eZYI!fCkcYJB%eLLdrmKO{AD<k
z2jF2-bFYr35wA5-pd7<wlvrQ=?m)yq;~{&CLyOYs&}!1AUHziS#`IsEklw|I&m|a+
z>jrXA2MYu(TF;tsgPU|cLfj$J_GmgPjDP99)d_Lc!{rLI@QPjrlM|B7t|T9#88#EV
zLq+_+ood+I{c~beaM)Ro1_rUjUUMj0W`JK{E<6BLH%*!OUBXuG7ZHO%qr-%MQZPnE
zQ*((D@C3nB2p^kw=DS52aS{oWev7nKseIVj`L54Z!J+{!iykn$6DOH-p>}@GU(k@L
z(HL5HumU|4HIBYQ$C9XC>^|#4nb(@b`9x&3U(0vN6FNqTgw`31rZ}q%Q3D`21#F|y
z7M9VhYQKnqlBh_m;K({_^jnnY+ORznTGp}R2ndW70L?qQEX7TZ;xdJtMXx*)e)BST
za!xm#wC0|B&e4$I&~qu}veeNvbsFgfIJi03N8|^Jf&gQ0%r6OcCafj%0ABr5NfW;x
zAc13z@bE&;qYf_D3SchDxJ80+410t6VMfKEx1rib?KeBAg|5eD+jR?v%@Z`)a7IPf
zUWk7Ysn&I85$c(`%4Vz{0k%$uX4a;c4l7cf_x5o7vYosm%|H!kYd=$rTcA#W;uec1
zYNH)PE_SVN1)76|Ij`n8CXg{XIzH}4s<G4|yGDj1@%_*p^9S6#*z`cm<eEFz_(Apu
z61R*j$cBwcNAT=Clt;**2j4d=vsyT>6C7FOgxVvRO)cRzPnR`>pXyDSHkh)s_&6!?
zV*qhaZ73%Bl*d;z+DUWI4oTvjCmc9g^<FOYLF%3-5xM1}8MafcsZq`ll)+J)4v5X5
zh`3Lfw-AT6U?><HC2-hwUD$<_nn`hUfabU&?aPI&$?CXWJ_CUt1Pg~B*&90|fH%Bk
zfy3TN)CF><7j*0+nHyg)#Y|MhA+0)+QI|NI17%FNb+$brG{aqaO?78`xilWCnE}rO
zKZ0O!Wr5)cn45)>7eNHk;S=hq_fFNo;tKdkGs|WkUPyabR7NaBDY{`U;g(%c9ppq#
zym}^^tnq{CbTfC!2@en(`=-Ho5JsCOOMnQ1*_<R<5l!(V9|`D$BVmF^pV$Pe)t19F
z^Hf`m`9gLsB69RjkkbJT*%FVs-&JwJ5)hGyNY2V55s$O#o(T?%eAkaDHkDPb{Eaev
z(ETkMbWJQ~cg+&_GLxJna6=O+G&l*3EwJTuZQcAqT?qq1m>J1HbX-n|X8ihW(t`m`
zb6HH{9LE5Fq|eH-&D{bwlLN^do<jDR>_e0%HU9v3%MCNl1b3r83g=(nW~K4{yEAbB
zd95oJG;(N#A9N3?D~^2>?^F`4g0ty*A(eaw)=<sfi-MBa$SQ7@J_NdXr#YnM86i=`
zamcGR>@0hDw5!o^^gIsTHdAxD1X{q#ZDtnl6NEwzAZp^NZv(tssu=fl8!|1$=)J;)
zstEKDj&#}bS`d%%apK?(@;k)Ce9_LERQLe(J01(qjo?R;nRC7iiaF)|%fc*t)>qkm
z@I7p^ZBl&-{5qd_!^AmQPUAIq_)&6>E*&->Yh!S`#PhF3N$z$da3wvIT>4Ee$DHe6
zyF;p55Cd|2mtv&XS}||=-FCAi=G=$D5MFK^Ns^r2CQba+cE>`A@Zy-zbzPOj7Z&0!
z;G6yx5KeMR#>?O&U8}W-=v8(Wjz8r@Mr(ri6}S}k62rv^rBLE&>W{nfLe8<^n=Nfz
zbmJf#QQW35A^{ibq8iBu^&m@e&LrcCqj5_|!8n;WKULt6B^mz!dLg_C#EiUG@bVW^
zQMC`=F#Ad5oOTfq(4)N!(}x6HYBk%#Mf^tMCx;{f_hjG611(|CnN|emRPMEztlGm#
z-_erzAaQDjU7hKq`6_OZB(4P=((CjKH#;qY<FdSi!EsLBcm5qovHGl0$Xl7%L^Ms&
z-Z9ZW98Z8|m>4NtfFL``-hrHIRz+bi(w`61jBb}GSyRI1{ZVUORhXLdq$80ak;zT$
ztn4)D51CTx&V9U-7;44e(Rh&j5(g<Rgl$NYeDc}ZNuyFR<J}!X?G1)JI0z>t+d(gJ
zaBCn$s^T-=dsl8Dqbx84uG1$?;8$%S)W#SAI;~trV*dc)b{4t@ZU{Aj&Mg2Zmoq5$
zRlep399QdaP`Uh*txevp;Isu29PuJ}uNiy;*NVE;NIX!7A8370#KUk}rzIP%`JjyK
zbR0tJAVSqMc6Z6)!EwnQ5R-@^dMcKEr-I{ftoq<Nhdi$cx`l2IyJb^wdmJ#kk@{?D
zlb2M-oyP7rrmBHyXd$Ii6wAThOkoW$Oi!9{o}UFn3(jt5?5j=|SZb#ei6u-IUfFeC
zOE7LWK`wQ{O+DNEC3{qz@VdVdkydRyf+rP6W%XVWso*s!tby%DYFrClBcPN?D3mvF
zz~*@-J@?8oD<IkOS3x#J&r(xx*lfFJ{lal9EC&5X+&CvQZ1hfP<HA=@L<<JI(ch@;
z-zD!9*$sFkbVI~ieFnf$fG$eKfaQxlyih~|#HvjMh$JD2xr8Oo$zQb@%J>yS1d9x;
zzxc18HF>1p+hhS3bFykCtFZ}pvK34XC&J&|RB`I$(%{xaj(k&LguxtBNojBg7|L~2
zD9cZZFKZ*<q+oqemleKzE_B<VK;nDecS5Gu3C8JDgo4~7fwG|N7=f7Jn+9%n6-~tZ
zqUz4>++F=!KZMLf^=OTSbL$HI<K}v77i&#J(Ss84c89*BPZ-T)>ftn8oc4lCq9AGz
zy%U4`cDUd!`^IM@%>mt10rdJgc2Yss3@D6pTHD8(q{(ITFDi>L>4;_MpEYYgyQdmq
z8T?a;F2|o#&kPgjhO;{|A=BumOuD9qoH=k#Xqt0=R$ZVpzNQe-b=qR6a{*_)g;v1$
z#3v<HjXQwJ$yT9;q<dG8R6exb+Bc%&xGaYBbX-^hpB$lZKs`2bC(ugFl|iPXPAX;D
zhNC3x%>rjdPXH%3m|dqEi-zqWU2dv{GkM2UmswRIu^C|aC*p8!7QM(l&S--LG($`R
zV3M9#!sxdjB<)v)rzAzp2)fS*Yu&^Fl6k=W2ff7t2+S=gv~)vww;BGVAT(c@;<!($
zc_R`YCWGo0W*fK0(=?}=4v~{Msw@PZ(Az=cgG;c(w8hh!B(d2Q?1?1gtj_A4_2&vj
zAyPp4uL}SG^iJJ*-P5X%1CO9tk|AeK90$!04iFu~6_)dR%#M9k2KRvCrsnfEMnxLp
ze@l@VaZK<EpoZtHr_Ztn1b+62aq2TxjD~?WSo%S(9K!Y<?^CtJWY7n-DbXAiLv-48
zKUh>6!tTuDBV6n!qO8x-OchL##9!p6?_An{Q}S79%%*_jP%_$zWBj3VRf)(OO5bt(
zM|0p8f{4cMd;-nJV`bQ>w1OJiVBZ6{!X&txqZ_MKCABP7u(Y*Wqd~y&?R8uwdrx&o
zeG`1q@Qi{ujt5jfI7H&B+M3hkB^&|F5>FM`+V|vZRLxa;Tm7Lq#l{?e%4ANW(1su>
z96eTe%%&F#VALRT$q|mD$@;0o@=#n2ip<t=`5}r#OlQ?HC{%1ZeVJTy(1WUsEOUa4
z8gn|7=$07uO}lQ>^jU(B-n3tsLOPY6%W_dhsq`fR1W%$jiQAlnW?=e+G)4i#>bU})
zYflo1kWq<teNX_jkAn7{{$)RLjA#yo^5@~r!ZAk#>Y`l(7?6EXWe(8M7=VU9uQc3r
z*zqrI#gQ^fEOsWG+9W6I9Kw$3t8klTITU6Mj~P_l{{Ye@Qou{ug~v`RsH*tgB;7Ue
z_gtSsvmOV2=tmugBDd){cpY$o7^FewAzKQmh?K((rbB$n2YX-z)f<JUfY)G(V>N0`
zYk+9V07PMNPRWoitcA*SGMzB2e`GS)%yhDN;tCs;H|hS8hwP!O>H8~^KJS8c;F6<#
z!^t{BOh-fvO1p`G5yiWNyNn3fEeqYkJob#xx+ez)UEXP!7{n|B%J77~FtUDX=U+54
zztLs>o)1z}eS$he{{YISTHNutLr0q4VIaNn;vq1=Il?%Hv_SBn9^CD!7#WaY=%;X$
zb13HWN&(onOC74+VL1<y;)Yn^OKHX8C&_kJur}fT(DPNFiH-xwP-RYeJK(!3!>8X}
zjkEcVB;ifDLj)t0J#|(?ezUQW$XG1LX@l13;DJtj$ysWnd$QFBq*)u%tplUg+8zjn
zy@bJ!uG21cH)DU4z(Y5_eoBko2i;F)F+6b}f~;&O5#3B-do06-K=X2D8OZp_=(|hU
z#}f?({(%fE_@1kQPmn20a4xxA9rG%25<W|cX>*Q11ynYv@X{Iit4S?cQvtqf*f<ZG
z(1MMz2l_HrtZ+ZlRmp2D=CpY)JYwYx%F|%YgnF#Q*?t+Xd*+RtAfu6AoBq<g6imkw
zatl6MDlapvS$4W@;v2q4qM-V&CfMD8b+UM)0t*@RRql9|R~zFF?=-=S2>Z#-M5nyD
z!#RQIq?SFkF#efY+>{z%5<`T7ofv8fr@*UWaq_y%o0Wtyjs{$FglzY?k5y~fCV?$%
z`XinJK1iRusfDC_qSCU(W$y*=6{8wH3tG*Uk#y|Oy>G;<v0ItdsklQ#HM|wZHCyjp
zP~g@NB`qP*f+0X-X*I1QE^9D$2&+w`N1;WIbvqa{<P^2j(Gni~;N~?>e|1@b#ilcL
zlZ#75LVSYWn7;-1E;k%VRJG5KRZJPyB@@g-Xn-KEW!6rbxN$p|BnfM##V`nf8W6zd
z*q&+4GUDrcY&juwk{gtoDL?f-#bzc^B5k_w**T=PmM4hRnN%5un@NBmAprNJ`liGP
z2kM=OjGd@hiFS$d=K%V{pSrUnRAMGn+Wv@r;~pvj@J|Po%CO5NNYr=cu=pUxFgRI)
z7qY^AR!wW1eHFEo(`<wy(E?&o-mO+o^yY>m+=4*jQ12@jm$s0|9wi^bq<p1Y=AFgF
z^tv}Db_+Xnj;q4Tu^bb(iv>21)vuD$%Qbd7%f;SQk;NH)ui?=QO#(3=s_|2>P+C|V
zV^?a0>AZkHR!f6gPdwI!SBEZY@N(jsTGo+t?8T226Olm=CHIu#?JIHr0Lv+)ESDQ2
zF7FFTCh?}h2OcR|BX<$-UaLx(PK68N$ryX3tXQ!OuAb~yi^L%JI-8}3@~Hwhi8(5S
z2$r66a;f)>GX`(*O~Wh}kcG*{LZMUpN~*x-AG;GZIwIjtZXMz!JK66WCoCE4tl?k<
z^Cak+ahi|_F+9=C?i3K4ETst;gmNjmhLVP9_=c?+S$1kJ=q|e}i@nlq@LiV$gNtOw
zOR`e|){~+*2{xRFMgu9h{SdP&nHs^m7dZstohIl2YyjaK3iUxZ@P)O`_fOuK{{V%G
zU7jS-Qi%{-lyeA|(l!qI9%yNV6mOrZH<Oq|5lWeS#|1M*);(0n$`P5g4AcpA*OE6H
zro1uvIE3kJ3-jcO0oFX$iy@insu$Q208AKi1Bap`cxAyfJ_G>&0HUFS?e?VaWSd~q
zZi(j%<teZr^+yw(CKeiWwa9w$LC>fOi|(uMK#dXh$ysWkVsYlGyB8SwCK|NeXaY)U
z49sHDJO|Ww9QdsHWFZ-Ihz$cWfG$6ZM;;00#>%at6Dg3+C^4kfXu#2AaXO!8bX!?l
zm25JzxvvF1)&r>sOPg86-18{=Ek2@9&1pc7Rhg&V)BgbVj>#SJ>VQuXb?+G*LTpKx
z3X$P$Q`yW)Ex^MTGaBlt?+<97qORf6-y}@U<jN>C%=a#Lu;crosg%;a?23DBXz028
z(;VwJA<0&!PSXVMdMa-395CZPFC=l*nS0K2!3?*SpqF(E+NX$h7uf)RrDeaRe-5oy
zeO7Qnj4pjhT3Kprrgjq;sgAkr@e4cmRE#y#Ccv)Misr9$!I>+wv<H9yam4|4v4Orp
ztF$(CEe7g{nen>iRDsE`s=duQ9iHE1rhX8PO&_||z1OYh;I(SI@@sYKRYQtq0JXTL
zcU2rry~e~0l*rFjJI2F<M+F<%H0rNp>B$(xM~3jM*3&I#R8~?D7%@pFf;ku-NL*5M
zbs+^P2&TXR;7+NS>Am>49;&_}8^PJgLMe83Ip>R<<xS+U7r6YFW^DIw40yixG~#hl
zlhoaKh&SXGY3{*QjPDOP$r!qR{W&-Gf;SlzEUv&DB>0=69xQVSyU4c{HsLsV@J`g<
zVTEEi1la?|24x-h0s#uOoY@i)f;CQtdu}-nsA6Mm(&gR@UK7}9&74N?RXi_|Yg?i*
ziRP$3ML&6Gfy63zE*CetV-4`h94wwgV<1$^8pi(sc$JCUy4g@A-ussCy9h`k{F8|j
zVh}fIE%Ul2R!^3^bzD)gfBmEUt<nY1!q;R~-q7SA7M6vQKX^r&o=HF+=)AglyHx)7
zwK(t9i+L>n0MlZ>7hN6Nl-y4|I}Bj#?{N8THwIA5i36G=6<US<Lea2IkdY|J4ymS7
zeVQMbSa=8OR%~`Aj;cX)A275xJqF3{$ljHa$gG~j2b!kX;vo&}W>>TIQGAa?WqPJ}
zjpL|J#y#Ca1*aI9UMz3N)eNpyWRg%u;DH-8V{vsrIELrtqJZ0LnuG_cyUiC{PACB;
z@Tj&3hY_3}3B}Z3k@_BK*j?Sm5uKCY4R?o%?GFGS1Dz1kos+W(;+*GgQ@-u?abd_h
z+Jz)y+&o5#B8@%bJ?zjU4F>v~t464k1YQ<X1vkN;?y_UR9aQ0Q55vV};cueL6}GVA
zYXeE<HD1fYw;l=Oz~~9mVT2g5(5ClwCUDJ<MMdr+=L)lc8TDQ)BhQ}M6xt-8qJYS{
z%U6Ad<EK=?Z$2ZMal#3+t#wSffzVvrphqO@)i;Ce@L$DY7s_D^<WQ-L6|Skcx=e6P
zzEFT@;&KaljBXHTK6?sgCt}<anrm1dAcln&qQDOuF9a($GI%ur7{Lutd&<lo+9e#=
z(*r<=oJeNqCzmBsA*Tr=!U80(C2j1}cd<DSzNj)fHF#Vlr;-;Xe-1(QRNEo=f#5X@
z7v0hze^O{`+~C%@J2kyk&E5Q*(~P5w7|iB`pb(gJuMlCsS1j7tX&K^jjBWO(ROaIE
z4X2V9Zt3!}86#{B5$BqGI`vF(ITRtad)FRLi>&MnCiisx=-xdQAoqcCc&FP&w<L3O
zcX-W^%xvSc*{aj%Q2rynXrpGCK37fgU)69w0;(3Xk}`Ee?;JNdbJ~5Y^9IT$o1DiE
zi$~j{{z&*EKl!qKTBjXq`}#vhXt%qgS-2xCHycL4VAf>t$fn?AX7dZQ<38;$Z{}4R
zY>iIyzA5wOoJ~xyfs&+gaVn#?srng%5R9N3Hwfu}CE?T@ls4CD@zFRV@_?9kb97iH
zyA4L3grT}TE!33UHuiX+4n5Y-G-e=rEJwTn&itp6z8Y-|>CJCOK%vbI=Nkon&`p{$
zXy&R13`ZLxFcb7z>TDAQdV?YC1%KgDW<pb}+W2@26{XO&B0Uh^aR5er7qcDYYW-6~
zf%QPXmLN=L=ADI=TZ%@Ss*P*FIHtkj62?@eE$X~o(}npXyv({Al<*kcK)FrtXt}op
zN1`&vn{gb9uye4Ld@mt*=~E4Ja*;BcY1<3P$naFl>AuoADtLGRvxVfWqGA?BP8hzq
zm|fr*F*q@d@j2Qc=F=IIo6i&W1Yw!#g^7d=u(9o4&hj27mwK&Dg36V8W6_cJS&or9
zW;!OB0ARE_Iqdv4IA^Kg6QSg8x>GHT-gY`fTpWNFk<DB*YK^oU6Sv$w0<agC<e%Mk
zeDQJQB?C*d;q!l0?S0|%LgJePKrwi_(wm|>Y!?3jOk4fL4S^3d=F91{k;W{5kwiH8
z=#ENeQOQr<QzTy_l4&uVCtbiqTw3X)swA1+DX?`2{uub2_GDUpQ;s2$(LW(^%Zd(+
zGbAjqmXf51xaBG2)5D9;fkhNcUBo5~U(IjTOWB>WR+R0WqhnfdFp=aY>KnmKIph4G
zCsD~6K;aXM%tC%3GU!qAK;V5rVk8oX+;{!Tp~b`_4}u(HIJBp>@zUw+c$>6e;8h|W
z?G#C?+4o({DkBkS#Rqm%%N^bZGJH5yKp;#v+Cthu$pj{Be3LM>&cp{ik?^(w;*3AL
zC;N&cDJu>PVVT}yP~I*Oh{cS^S!#iUr;_$)k`s>czs+UERd791TKp%+5bspuAlan|
zDc3iS04BM>Ivv7Lwu3;L1bu-bn3(2?#N%}PP1<ryH0pco%h6@s0y&TN7D2$OcXOl;
zIIf)D@OYr*DbsnGLv|s@DIp9Y$H@>Dh!O^H7X%3>L<z9+wz8^8XaTrxh6_m^@&nNU
zdX7Gd;Pz%utf7h6vds^@aJlzKBnDqyc4Vo!<ho!4(q1uGi!_u;bG28%eg$WW8a}Gz
zmE&uqpQ3DH!t3*Dhdq<sv#zBXB;R3ytGFtTe=s3YYkBAMP9_=iD4qJuXg*}3F%c$s
zxM;ONO($b$<)}>#8QS@T1gtom;ciP@x+WYAE-gJp)D-*y=1d!g4r@2Ok``PRW}J^B
zC<yNG$1tyT#O&f1zD~{ogZh`cJ`3}I+`Z6%k7RqtXAr&O5#4<^Ss&uK{8VqU5A>|V
zVHgyvHU3#AyA^7V{?TY!h8X=PW^$<z-+9}iRdr+T)5?0T%EmHrUGsh=JQqIaAJJ58
zv~H@99Nl$N-R1{~7bhkB!kRK43opf^TNHjrTVeN2)?nnbu#J@q9MOP&Y)YZS!BiM%
z;leq!AHE(ce7J-)Q4M&RO|Ys!;VNa++OGOvcT<RH$vWxNx+IXW_kx?dyK0CM<0Pj2
z*rwv~$wOP9Xoy*t1W$EKaf70C!_0cD&THDqHboYfe^!ao>{mSY5*i#W??#ER){HQD
z{{YG;;$|^p!CmwVq;i=~f-+ZZ6B$pUqlLH{AelT<_gfF%wfG~wGMaGs$WMGb*=bmV
zj$IKAVK_*cMAdwF)}>{kRGkgWnyB7PZPDB@l8jiai5V(`D+%~@O@X010*%Y7k`!hL
zQ8g(RIPw~yyH1PnbQ?dK-<tk=NdEr-vL;Hf4zTE{hP}q`90w#1bin|e&k|EXkqBd?
z`ON%L$4Y!bA7sdhw-sq>;HWmE=K!`Mk9}PDC<9^olj0k=!DfLv1?={EVD(oGEnxDp
z%1Q?XX1mG8$)|!22C;9WN)P_8@Sq8sBPdQS*MD;N;D1e%WwM457@7|Wg`PN2cS(Ys
zgceB+PPQ%ep;_V)<cv|A&WM73X@n49tg!@42NkW*2SN_2M4L7xI|z30F0#qtVuI37
zl6*a*%!I<`X?TR`zFamPZrut!)m{f8sleG#kPW3V!P=IXPlE@<ggC%k&7?@=viYZO
zKQ{ZPhRA^22dhx2JO*7=XE5T#*~=2D1G}huhDFK2)8;I<UrZg&BZyC&B3r`h-4j8+
zXjH-AhHL&8d4y!pu5sKGqU`lM%uXE}$uZ4#Tfzz0&`7vb14G9o*=cYio)v5oAvBjb
zzzA<O#jcJ^#L+f6Z1Nnqp=Y*dG+^S6w#ht?%Np$e0A)8wM1zJ$k{F0KCxQ)h3%un#
z08b=m=B;nD;&Y_^gT2|nVdzbffyENnQVeN16ft3sQ?dTmTS9%JBo)S2XnRJAt9P7S
zLTYK1qd;&@5rM>*)RmbS0Xjkf)dsG~9}$!Zwd{jKPOd7ieqz`%Kv8Kv!GqAkbEM9y
zZK@4^I!5GVMeL05l}n<=Zkj!e?`$fdbon7*?H8hkh{!E->#$s43pe($i=$g8ZVa{(
zF}O6IM5jl3($fdrvTt_bt1>GK3*KSww(LZv4$%3{_GdsfSP3kth&I8SvjI9?CVf|q
zvs+Ab)ffiLG+;R4{_7_G8=(2{Q7ewjmR3HaXSKcQW$%@#cg=nn3n6BX1BT4%)@8pW
zrx>f-YWhMO)T$3hC^+g#AFAplm4TjI7ZsIW&g*jmLh#dQU>y|)7P^saozd}C?lWaE
zi2CHaBY#uwekuFB5oj&ZQ>llDPVZRpIEIIs&)}PP0d8p7l|d(q%HlpqVQAE+5=Rl3
zO&!f~h(?D{oHA|E^I4xQp3|I9%jmKj&OCzDm<u3p(S93@^gI(~&V7;H!X5obH5l6L
z9YilN#3zO|&VxkfJdlAIAZNX7`ldj2Lx+{o@xDK|{&o^7)dqP5mM|7Vquk@j6=W_a
z(KmX$pCMqfOf748YKub#Lcuo(+;Kf_px(!lD)^s%jtFaRVC0SQz~H2ls%XRC0p<k4
z?6!3<aAMWhPs31efNv9`ZV+mc(p6y4;4h%Esa3!k#o<o^N8VxAfl=NtiBzr`7@emZ
zDul2^X{KtYdG8HE0t0kdWC8J2197?-F=Ep@FB3{=JGrI}{N1b~NCz^rsaOvtZ5C}T
zKipNZ7BnId_UF>-{{V1Z2a%aseE!AoQhE4nC%x?(qC8A#D9jy=J=O@Gw^b>=liV>f
zWmpq%Mf{ht6}g<1GAcZrb=~4TaHmN+l?mT-P7INmZh=d@PM2}b7?P(2Ie9#QK=)Xq
zJGk;+4L-%#(Bsi@D<~=Zt*3b6<0u@ldMoa44ynM}-84`^95Pls5YKcJ2sImwPbB@;
zx(Gi-7|6R%(5DgxZ8?QwE6DKKc8kx^HvkRv!lqhrN0J}GsXT%L<30(*z|A~@3V`<K
z`>QJ;A4}YK(HsX>6;up*=ph!H#f{T*ru;o&J<Y7hXo2D!)x*r(Z%9ou!(-X~%PPLy
z_*|>Bn3IO^r0$mk#|_axdJK+=mwAQL(Y%o)H#VHmEO!kPLr4%u)nrJM!<uv3P0aBJ
zW~)9MZ1}Fw=99Q0&^(uEs3#S%Xe_F^@LL#)n-vqHY=Canx&*dPhl+FwwY+XQ0G?@A
z?ClN>Hk;i7(k$uDRz;EKywUte96cI|020`gH}SjVyj|$|Bj&HeV}oSLkeP;)eY2S;
zLqM<_9e-&9%Whbt`60Sa$yp|}wZjPqC{Nu21_5s+C8fi6L)BBl>Xhc$6(F3Ydn{=9
zN^CVnR}7h?rZu7Yz*Y$FX=^IG;^d7N3+!=3!2~Q(+_Oz#G1h1#b0adT4E2jbdmmEJ
z)?|lv){G+wznXKEX3K<ue~Pb6I}x-MS(c|EG&C&Wp<$U>41yp_fS#(ScwmfNVCS*K
zUnQ#5bsG)qR`@G-l_H=20AsS(SyPvJjF;V(8Q=F^(KPmot;oN_=#CdTpvF*)y~B4E
z^VK_ewxIQFN%dA`EBWe$z?St?-)>2c#L*774OU=;NpOdHS!#a9PNXl)TVRX~4Oxc*
zK1-SSt|V-*Ox>eJ?}PanWgC@V{+cK!42ctU?XiQ;C96u}6axBKVCZs{*&S8$<8j?i
zwWI@=$vb=0pEXJDJj^GCx4v%ZviHgLSs!Pb{1=<1{oBm2jG&?oSBxwk=;Eq8RuvhE
zkNJg>dBM!5Ov(zO0fba$vBf#B4WIWLBx(_~--lH=JJ7*DlxT>C(k-5DQEiP5rBFfS
zbSP!uIyzCXbDJnw-gQLb0$fgk3>4c;7S3jKCz_W57|`%F&*_9Ca>;K{i(2MXo7)~Q
zs%dCpmk*-%k^Uf)&Q+$s_01XX;(C-MVvi*dG8se+lSl&mPyh^qZRa)kVT}{5nu}c&
z#1`quqtZrw6ykGadRa6Y^ad9uwZi0rIO?L2;WH~Kbz*#=1i%Sk1M<L5#%dq??3gjP
zwK<c?9|#Tecl>MyX6KR;!tv&WIrRxWHW*p|0H^OHaVE%5l5iKq4-S%xo!$-NZ*j!{
z(Khl~Y0*8z8$z>1N5ZknP%bg(gF)>T3Gx<Bzxs^7K>q+cGi3+A{(`-_4{2I`g8+aQ
zZ+LI2nQcM@ep@3FW2$Ce_0x=D7U5(8wtNsGMoY<~GF0I1og+rLA*s36b~uI$z!!4x
z3P6F7x*oyU1io*|F1!@P0rfzq9)UCjNluA-THP+KyjHFWyQG*2;|sxHo@nBRwtZHO
z8$3hB=7#GVAJwTxafC#0UfT=DJIXg#_guGXPGaEoO@a@qu|6P#U~!VO?uiGjm5X2E
z;@)c`*<Buc4#IS@Bt5yJ95{t=rKLrl>nmMDoCgb_#!g(7NH&6C$UM`rPvTMFdT>~p
zIcT2ZIwm>_(!zNwkjH#N0JFr?sFZSUM^!B$*8<*1xvn4|E_o&r+4h4<pa6J~X#gzS
zp<Sif7n^(#nsu3m+;dq9jm+>(#KucRUIr;f=B9kW(`?B$a8GN^HwkO6@Kw9I?QjmF
z-OKy%oJ;XylY8ekOl9{mG)5xcJkzFM5z!wmX~YS)DOZZVB-_S(r4tWvF{cfZve0UI
zpw{k-f$;k=fV7M8^G=dUkb*yZUoXnv^S6rPg{5V%l3M6J2)e@!xRpwoJYfmsgMc{z
zsp1j4ctm9o6G`CKi=o{&3=CP4O6?5>Vg~ogR;?gtEP5tYCVi;-F3!=<ROUb<A?A*4
z0QD))B&u#`;zTXRNrRax+;$Fg!5r8W5gPD7wSlJ*;bq#GMx@AHt*sZ#f0a-N$S-rq
z5EBewf*LkuD=r&G?4V-|$1<SzHA`H7G7@wOt7zIod`~_>Q6b&vrXKPM<DwRD@kx`_
zOF$CQo=ezm&lZK2I0@VDaBOe{BJaEqM>i*nVrU@hyG=uQc*JBCQQ6LDhcubW$+$dh
z!^O@20F=_w21G28Lcm<;69cKW#NyWc6T8hzW)H;;>Aa!<?&O~}kg(>ps!q;K9%z5~
zj&JuBA9!jHMWb-6EEGvM>WsNSf}oL${*=bYs(@H2VFQ`Ebh<Z!9%y1RAR-%yOzKgY
z5)-Gmt$W@aASUS81EO`9*FGD3f(HzWl#k_d%spk28?Ix)=vr3?e3M6ctEa@R5Ef(#
zZT`RbSrP<jh8EPUWBNbk1q)Lorq5;(gn@C+>Zp4sypfk5st_Hz*7QJ3?>KysY?@eI
z<Vd;_U2=#9tuss>ZdNR-2gy6yvIiL$DxP2`ZiC?-BB&GhnYr0>M+m2PVLF`?vvYn3
zqpT;u$PCBD2A4VmPX$@#pz+uSo-mx!aOK4?hP>{(fmt_nDBUOE)BqJhC}Lq5i`(eG
z4s#q8h8P1#O{XJ}nB=K_-8ZunxGmPXz2kM47bfKDZXQU)ocJq?j;IWEoJl<b8^Gay
z%i?=NV9$JUV#VD*z+03J5sA#6S1WamaSR^(YF0iFGEIPwvT*f68rsh4>4eY$_#ulC
zf)s9j+kI6#VN~}CgboqeYBYaUk!;k&*gq8Ry9mluEX=vfAW3m`g@mVl+uq{bCdlML
z+iGpzsPmA7AR7X6fVdK~@5?8V2--JFJE9vWQ^{&TL>95ZmeFeFjev(d7Y=}Ius>jv
zybmCRW$;>DESV%PdO9Ou#=3uWaEu_2MB|mLon`=V1PnE>!g#FJ_ply?aZ#QW?yGpj
z;WqSA>VFWedH5p-{X37I%s9gf9(wJPvc%GSuDzm4ae&OY@>_)EITe`Eak>PQ?*mDW
zD^|l+BMlaH;XU2sE<n(6QA@Qm#h|D^b&*7j3@-Fprjxj4I4nL4xUC91vxuIkk{7`b
z;J6PvCSk)${{STTh>ohD@ZlJ~h`f?>1PSD*MLbu2aJ5%fRf`^Ip6$)cbYz`~lX6U*
zhjYBgADGz?>h2Kl%Azq&$?-J(6k3ZL=$*O=vMb(zIFv>p+>x`LBZ)kAl{*C9LEuw^
ztvnI-ygd*jhR!R^MtbZSW^@RLhPV(0sNw=Z=rq_=%8MfdQ^T6H=JF(!RnCxnMtSV>
zN7;q#I0-~Do2y{N@23<o9`NDC$vCygINi&ls9n^i%V2AlHF&UDYPiCSGzFHY-3h+d
z{Ymj`Ip(}L?5enZ(@l!P?yU-q8^6PcAiE_lp;M<bGDNICDakltF9e7eLTS)rt@(o8
z?`s#467~0XhdVc)Jr%8@FvB$?sD+UbPgLly-Ij7Tg<#rkYlj_Eq+G{U;qNg#@C+a2
z34u}WI@zSzW*i<o>{A1&xxz>6x0vjT@SNB4-GurfX!8rShcl(Z?V-1@0Lk+x22#(N
z>#}CeZ_Pt{Q*3k<M;@t|F;h6FS-4rn{a3n-qdS8xGb)rewh=D2S46)Ao!e%p;2x_j
zoRI;v-`WAbcAcNeYe$|)+#zQixvN1<CE?edWj)O#+(nZ?Aox$sQrztj)5z$F2URwr
z@7Z`9cU|tzh+jf=9Fg)w4byYcW?Ju(m}ry%CspD-p`i4FvI5DlDbi>IsFlGElDtk~
z^fp=uO^S7>h#gx9VkhCzIzY^1LU!#YNgXc=V^hf(!qQp>kR>;IaJ}45H5gmGcyw7+
z-+`l>b;jsJsZw)*=Ce0n$|uo8-DQhTF|gi?H+sIv_>IcVuD3#+lqStlcXUVEAN2&?
z_PkPI=c*^Xo4Qelz8E0!K>q;xH|h#CL+%I>#a3_nN=@$_7B8ECP`Q0pjWx7-p=Yo(
z`L#wGt;Ev2&(TcMsM$Z2!~893Hh5~NQiGm{;Dj7ot>6O6(de5e5x_vd1WA2Xxq*$o
z5veM*H#BvSoq>&I{{S~c*Smf|1_JD@qiNOI>vZB~txu-Rz`;-TJ!ValS};_N)aeYS
z=#}{!@m}}MGZZ-G$yn&Bh_gt`%?b_{&6258U^}J<%aWu+u!7^sGrVaY6QXN?#e@T&
z{3w!%WgQvqyzopEdp$AWZ`Ekk?9&pMW?J^DMtPzdX>VcVznIiRTUKv2oaK12lnxgE
z0I-NR#Ne`5*ib&G<w34JTvzfModwPUMv}72surAnp%7XrG!d7wxLj;7>a}zHqRS^%
zaS9XcTr7p<W;65HNFyjx4k(@|2FSW>yT#TtTIjJlm=_wCo(ZQH4X1cAeGbh<_E*Z$
zn*1kZM<rm6WgWv%0ueGCod!DFh1D83i5WKPiE~3>v-2l8fV2qDJLZ8#EePQlONr}}
zW2zI90ksQa*O1;R#b%;@@8+|^k>IM}97-p;Yc~hMFuXK?KXm6G%Dt9=R}WR0P9#QC
zlI*UoTEm7i^G?A@!hhjWhd<kKlOMjruzNs*%}<DzbwSfP=&a3o@*L2@=Li4{fli7u
z-Twe^M34+N`VY1Tc~ovs1%K2fFtm?%Bv|8^!=FSiz%NAKXjxTlF*j(tz6fDp9=%b_
z`XDe8KqFu!0MI?-3l&<!`w<OpL9N}k%%vSxOEt&As(-XRljnGzl7{!Suv)@?n{p2L
zp{zpPsNpqEy%wmzLl)`sO_7LOiq8XVv*epQo|{1=q8iX=25vn<AAL~G_$Qwmned;k
z$4TI{+3mE0@>eCX93smmumOc`x})6h>E^#c^UZs13_Qb@MfXD!4sAqW;!H&GPi;qc
zgqZSQ&$p6^BQ0FF;+vfu3~IGIFKLct5s%tDIU(P&ATjrjik1L0P5ZG8#R{cYkTh(3
z*JTs$KIVMU)>Fe;M(S&&#9ZaGhQWRBS2bv$wba3I{VO2j2=1Mjqq)RS2QVy#NZ&;4
zsx*`4gg894j^y1Cz9uvksFeb!@VxR0nFk@&4*A-%LF=l1Do_6ar;>KzxJ70I90#6j
z+8`*gf#tIn5G59o05%R$uoC{uQh%ZA`9gZi(4z*uHr0PA(H+ry>Wi4%2T8hl#?T%c
z$+^n$7<2mYR~WGR*<>7H9O+cqttZR0Z!(+rOnf(*%-ne(kBP(1HhW?ZUP~nBV?HjN
z6G$(0<ji4Ba1XBMo~1PUD8){^j*4A`gV4%%;~b8fD!2nEQw}`>IFDzMZlKN7g4XYT
zWnmav-5LgnL6pL9_i%jEaLZ$j<269u_=22R+?~BJ(VlA-HW4v9xQ2^;wL47TGsO?N
z#em`Fo4Q@QjWBnNb;>QmU29z%5cBbc;h2?n(`m#;t|;Zb9TN!^CO86%0m@0Y6zey&
z35{YbsmI>8JL-v!_1`pSW=O>fS9#;fFg32QZvdHaZ_Fxh4{@{<{{Zz}uw=}`dZ~TC
zw4iZnVF7H)!XnKuo?O=qIC`RS0#jr#A>d=((#th(xe_ApAv3$DG8sP-tqmld0=X>2
zeAMmsoeMF95Wklah{O%2otE?Dh||e&vNtcmA5{k0b3)|V32ib+=)ae5U5BAw93YOc
zj(M+wU~f#*uF*3n6DKr4cM;7tTI0AvgWXT5vY5aDJ3Udh3m-k21)AWAA(fBG4S-sv
zXvuSL<!ztC7?x#wO^>R}UTM1j0I;4Iz+huB2nvrjHF<Cd@3FYp2UJNL8~BwQ9!Z#k
zX|zQ6O7S(eXT8xj9>+a|^}MGzQ*k6)FcAP6g8G&0i=uHbTcY5pwb(A)NCq=4(2SYS
zifP>u39j^%2t4*nLk>3;+1=zUjuGB6!sZhpj5t^|o+W!0^VF=n=?jL_v}RF&!2|q#
zgNuynsE}Qp8I)RTObd)aCl?2gj|3ovm@=<UpcR0gE3q-WahyoCryxMca8E8HRm*IO
zVDC9JzUD*nU7C<=WJIFSK;gwUT-iPATYorJjaE#VY<VXzNuA;X0NyQhTL*z%wAr|e
zaGcZH?}yD(bh9)KIz-}<t2U))lZ3m%&T1+XCP4^^jW%m!>*CcrJk&X*I>yBethXuu
z0MvQ}(DM<GS>}Ia=MjoxY!NOr-2|R!{{Z<;9<2*qxss3F(x4mEjLOVX4L)~P8K7*)
z9!b}8oZ>zR<nXktQ8pScHSHHW$XPfCMD?GloI~KMI8}2)O-%v;wO#GE!s3{Fu<#t=
zk_5fe5H5(OCtTGq4lE>&3RAFwa(OD4%=-d<DxdJ&>30^1L<3#*Ck?FC{{ZPnp+Z2r
z95ZSCkpz|kQP@o4x0I?(ser*Z4)`XTa>oZw@Q6>xN&XsxU-G=wHj{@5E)s)cqg{}V
zIU)$+5r{IU5v&JMD7JgoT44({%s(^?;<BCH;RuR#Ti~goDX?@-+@0~HYMj$bt=L<Y
zv{$v)g0p+Ke^R(<Bu^!)ip-NKz>*}M3pp}VgpOPj_v>2^E+uw$$v8=yD#8bdYY0o5
zTLJwsWn@n4SK+psvGWGdfWuL;+2BNkZr1`3hB~1j^-l270^UkY=!W($yuM`q%fV6P
z4<fbsAlMEzB635ydr3Q^bjpg2;>)jO*%p??o5Y{l8=Vl{ZCRg1QTPjsNESY7q0dw^
zzW|;v)fOXyjC9Ed=PASQDAPamr?t&53@jR#Al=hu44@{HPNQVNkT|Cd@(GrYygy5)
z%PLZ_98=s2jzd&#W<lz!1`hPV&D@d3aJK<><y6?mGSe3xsg$mBzz}I+q$JxmsTbdq
zDdf6xA_}a{+CUx?nfa>a*TTzuZiAot&660`(QKIKq9Y>U35%dXs^H_pDO&cwq5@l0
zx7RfiTuE0u4hkel1i^9woi-l>c=OoYyiU=*QN>x-soFk_UMg*KujrZGREw6}IZc~?
zBzK)ztn$9e7nN}FCNwF;2WGC^7ZVwyEGKfCb9xuVZS+}{9o+AcuT%ttc0kiOPqPj9
zZi!9+Iry*Ug^Ol-tuj*%Xp{MdYSQornr)={LJ;VK<h_QNC*CLmf77}YV__|KLATkK
z6?3yS9A0>xj<IL^3NXEc*EDSK40Ki$K=WJ|lJ=YOSpc)z-N#ieo|&ys<c-{2WB|Z(
z>#}9uKP`Y4x}Ap!L8p5BN}|RLoh=HSv_3G5$28JU1Pg5ehchXGpqU((s%+sddBv7$
zn_W6MS*h1Lpm9~zFc<c7Fg>Om{TF5cmq00{?I!m`)Tv81X>$n8vstyuFuC}jNy8H@
z)lzFix0>H{IaPcZBKfNB%5&nXPf!ZOJYf+JF>XqupxCq(BcC2hT3RIX<c4KL9q&0P
z$opBm;BQVS<*FN7B!t%DHn^DRjpY;uK^Sp1F8HdhARG;o!EeK&+4W3wdWEjjVf-yz
ztj5`l88r><cKbb=qR=ew=$uDH+)dOcOK{6dFsi!@%qQBLTGuz6X0inQR%kXyv;-~-
zBr<qj@`Gi%*}Kk2ca_1j$oe8J{_nHE%2)GI_k`l}N5k7c`u%|-n@X5_To287YT+RK
z6|oW<Z3bzr#KDEpWOAk_&g+T-9X}LuBy1;kZ}C+dO|EZ^*2xP~&Bw&~*JHBDMj~*C
z6W29Wv>k#;fZ07&+T&s_vs1lJ=c18mEBI~0@stEJvsH2a*!rU*{UCq$l2QCBk753!
zI|sI6>GL{s8$G9wsYFA(UTA9vTRB0YQTy#VUsX6}-<W7r%cAj=D?tXy#M5!lfj5)^
zZXpiLIC4fl_yOvV@n7>od&=ZTywDSl2Okct>atntkKguoV`<{hvug=NODc!6&crLo
zQ)dj2Gm8!{(Q=|k#ay-0KV>T^i)y9@eHN=bIC&u1bG+0#lj1|K9Mx8X4I-Br_fsXr
z2Ej%SZ|1)nvyWDPgf}USMt(~Bq@IXpNxv|sy*?kxIHMcF>DO&Zm65n!)90e#j1@_x
z$H4Jz?<8T&;)A=Uk5wU#?Eq7sQnhd~?*(Sfkj<WxsvtU<0i}ERehHiJW*8dbvOBG)
zWRHFoKqv>;zca`uIn-TyXsNWc9L{Om)s8p)0x~m>VP1|BT6hvQQ~v-c?uVG@n9|^J
zw3K92`@zxo*>>+d`I%2Hbw?8Ba3}+b*dBjXtc|f7@<Z<FvIi5yotUK3Ze3A^&C0wu
z&zje@&%snJwjf)?F4oY{?B8NlcT^fNgEiS1PDX#SsOJ{s4vErWW9q7Zr=!H?@6r=H
zp3XGY=Znt5e+K?;0XKcqbYjXeIn^o|&}Fp2oIvx8lvu^`KvQ#^&;;cOZc>m30Lu-Y
zH;eLw&@k)8FiTr5j!MmLsClU{u6v><n~D?nkVlfxxvPS8i)DlJti$0L7x<t&K3BYz
zyxMx@joPf9$Bvd*UegyGxS+4kf)<JRqBb=5h9K}8K$g41$f7~tzXdqJzckq~k{$xI
z%>m#d{PtvpwsyKgq?3n?s$}%KX_K0vQNgp5S7)dJI<$mAc+-JIOrvSdIsh00cm<i}
zf_<Jqw*sEvLx@i#jaRe<ype_v5lt8=d&9*ExLHxHejj%bm{Ers01T|A?zrTl#GvE7
zkhCT?Tv*?#oy!hjsfP<J+flQ`2woFm<cIK@7MnjvS>d=*h+W5kUct|r)Xu<6H2sb!
ztzkQLtsfnSVIh~8%>WzkM)J)7>)kdLGEXEytoHu^Y^O;WqB!$TBZ&*O;x1*z9FyCE
zGq||wtI{TXAl+}j6b5A-P$o0v5gc}Fv)U|Z`LA%oyfOgmv*K5Q%;GK0Ii}hb1w^)l
z+mYGA(1Cz+S$i(ktJMqPw^>j|G7gtV3nLaCW14FQ180GkNlyFRTin6bTyRE2EX$fU
zW@|jZsJGa$E(|cT4R^_Ssn;x?M&z#u-D*A{V6Hauw#?r`nLdbaxY*~K3>(e%dFqXW
zub@B%0|4x?Ik%eljaGx_uy?c%ij(d<l~Y8!*r*|+&Q;4nxXSz$T41$<b>=cu>`chI
zFz90%ILW`sY1ruxs?)Rs@=iS)bimd&q(J7f4J92=!@ZwHmPxpQHXP6($>@Z!r*f#x
zzu`PqW^&y8!fo;S^ipIt9mjMghf)W2*YZtq4Xhqa@G1S}8ir?4)qf$Rd8@U<dAw6F
z!|=RJ`K&Dv8=i2m;i>m-zJ&tjj9;>G#t3u~rwL8cFQR-qgd$x|pv8_gb7x_&@g5}X
zC&gg1mXB4aTv5AS17<=1<pLEKm$r#^CwoqtCb~&7NY#H5i<*Vn>~1`=UfsNckRh_X
zmV_;Cy_aCO2CUlY>#^QR&_U0FWLCI-A8VQ>!+jtB03^@$r@8NMvl#_Ycdc&oV%FfM
z!v~N<fiSWLNaNKsQ+ujlCxxVh`~j!On~Rg&<N1%tIAD8PUyDphz%W&T83vTtYieFJ
z4p~kU(60?Z5#6NH?y$I{cN-3iN-S@n*!Vr1xBxS8c1yGd*L(21k728{Hnldqakn%9
z6`ck?ijgDz@J)B2L-A^X>2x@g)it6-k_K?U`B(HQj&=%eKL}l(t^WWi2-2nc-WPAB
zW9)+L)2mkKFooG!M-@Cw#BmZwRO#S24qH6ewS;_Se=U*duID(y{u={)^C^u0Fp{zh
z)o1MvX~%ODq~DN1!PO@7YZXoN$;e@Iu1>MTVHc2kD>NGor+Tee1M;;`iNvg%QvFWN
zE`}Ge*k6T3gpyG%d&k~Y;yuyHTXAKI5V5B0USUoeZY}iKZb4}DlybS`nJ&<}=;xlu
zbK>8XKYB3_C30~%Mw7)zdm$SW;#N3G!x^21%z@~pRzYyS#({f1HSKZMZRDVXOgX^n
zO5EK9@s(1bF*x*Z_+NU*WabH!CCAwc4R?U!s2sL%2uUOlH2(m-1bAl1%cdM><FU9{
zT_Br!BJSpcPAlf<S;{vt@=Xv<NF45rZv}BqPtiAiql?TT0~DKTuEw~0N*>8Tz+v?l
zPPC8*1Z)~Fwk@AjCH8GG#T-=#mz?jxNHhN9W~uQO`z<G+PZ*rchp;%$724?4lkWgW
zs=gaq;WU$?nsmnzbI}M3Q}RPJ(|1}`cDIfbiAw;LKx)4!Mt;+|qdia|t{a0>wZw*t
zUybaYo^qKdMNBf$>zct4o?$!^cz=}BmcjUyiJlllT?Re@IQnp3$slvvZPPxBEe`~6
zfudvZx|v};ZnFb?eR^yqTuj2<F=B>HyR;l7KfJ8*;2B*w;TZ%(%m#u1M#jW-LU5fn
zG={v$p0Tsrs`KV8vMz^SX!xb)%?mb}_^h9dk42%MMA;2tGr?ws)^w>;Z;@&n(Yw`q
z_vjJ1BmtzU@dPg06<{waV}j0hobu|tO)hC!G=?|*`6C4}ue)yS#PCNMr`f58H&%$v
zALFp>yV~pXF41ZTTqOxEX@t@bHKUM?=|;yy@;UtqVQqSC-i)2iKkm&iIzj{q7UY|S
zvYbXRZ8?Af8PI6LHV{MtTRHVZenm#N=7-(5!R5_p><6es&NiqYnQ>ovMSwK1#@4d!
zEu?3_{R+6p;?SoEx%JpG%?oSfhltr58=xIZki>srsZ*q6p1<XGi_>Ff_j9Edypeok
z5@|c*6F7f_FuJ6)aLpSw(ZLTH#7|8=J4Pp90s?bd9A^O_yU;8B*^ROr#UBgHq#^g%
zWsh&QY>LmlaU)&{;8v>L^X>zp)E_WH7{wfc8F`?4vgtR179!e!5IwIoY;~OA(`kVV
z7k0s%YObW&x}!B|c!AZNQDMZ!kOjmGLT(WIfx%`3by0dyJk>zb*@N#Py4r1Q`7GH$
zG2@sj33fmuO!lg$6Qqy21For<v0^tlreJsMqA7v9%~$Sf`?rNws0S;mguU|siJPW&
zbIZGGZ8qT^<5QcDy=eptrB^V@W7MjSK6?%A0WdzP7Gd^zyY@#BbG~*%CmU>w*eu;t
z4~O|jc9_qgM0Y^r5BC5F<EK!|YIR;1GaQyucDuyV<L8o%!DITap`p)-&>;;mho;8a
zro{0=Pb3PpmXvXxuQHloQvqam$5XZoJA&)+M8mviB2PV;n-?Q+Rle&8;~dexBPd+x
zj_`X+&YahTpMv8d2`40RIBTHU?KcmqY*jMoJT#PVQS<1tPJ<EVm`KO>jpVcM9FK{B
zown+Vb{ecdcJi7&>CT^=CgLX8Mra8J?Kyd>g`%;s8aX+&92*Th^+S93mdZ6-%{kA_
zV>hzhgCS@K$qm=os-e^=@XYdrPJ4rbI^n7^x{{(fBXg12#R0r+#>=^?0KGZMQXBr$
zeV(b5gKR_Sh>`iB8`{Wj_eMgU(T+X~j=>;Yrs8{zfIte=h7jK<l{k$|{>HmnV?!CC
z-aY}qm??+w&*rK)x#C`D;j!iu%X>v86HJe6d%aWKV~Igh>IswsL|k!Capbc{VZ<Pa
z8wIB5`R%>FD3F~XaSIhHdbz^Ng*v84#_az9%BJB=(hqrOic0MbdoV@!NR-GSw(GRJ
z3w&j|1i%^%j=pG~^kw;izj)~ALd^k9he<$z%@hl1d(rYuH+xI!aR8&d96{Jj@m{NR
zjl&$ZMZ&C{2TQNRX&@2mj7DIelK%kovHi<YD6Gshd8~vqd0A@k50GBz_@D!6vo4|+
zhC?<TI;VK)!0IwU%{Ol-G+f*AF=Rh7Jkbzkbt~FLa~pk<5dJHn8@iz+Z1)HK!8o!Q
z(QA7uoH}W_@p)crA_LrpF5&HWl|d^|C3}bAbTP1N?GH6mT#jmLOGYW;sk)z@DM#+-
z{mR`hYrO8AdR4v!Wuy>i(OU5d_WuA*2-iET+VVXUMJk+HEPBc}Ud7REI>zynTsa^<
z`Hguah!;^i(xWaaVn@M0@d<Gsr&ItuLIu?HBR2G23A4DX!e%1&;Q-~uItdT~D?w#;
znnLR%QlHf{7BETS$sOl7D+7??fovTU+#igY%~NlE5aY&ur9!4A7fqyHR;N!8n9Qn9
zsCc~F&?kyO*ou3qXBY0cAYuL!(?u<BJfRmFZq9y)#kE>DG(YoA4s#4$!KfTS-gA|5
z(>_5susWrf6R`%_txX%k&x^FDWrdADRN_c+6VW;=%z>lF1VoBW4<Do^$i;EV67~}<
zce90i60@Y>?<|oqjAH1#G6?g!m^#XK^I35ho@iq24+7)Zl`agkpZSy9-L{O9K|99w
zgmD%QdfH&2k9z!5q~gO0d#v0iHBq=lEMdOrIwwaI4h(wgtPf}ccr*!=*9t5k1Mi(&
z?UG4Np3ralKg!JJuLKpViP+mUp$*m&<IJO-hZ2SAIkeh-B@9%b;%U>EMn-KTP^`+0
z4jWz_A`c`rL=epnH4Pa{tSp%7u&pLx5)nOB%iIFU93#rg+n{_D0viM5v_}gYcf91#
zgi;Qt1m<{>oeXg#4=C6!cb0kt_RD}5;1S((rei5phj~8G`6F^c`JynA3a||eH)idb
zp=KLSM5hAxG|V6fZ9F#jqDrv}T^r=1e469+Ku&LO)dPx^SSv_ENmz^h#^ySr3WZIs
zX0AL?7-ZZ})lJw#IhC0y_=5yb6U9kwP+M*qsVBQGW-CmKLa9x^tiov^dqX7{u(o5S
z%fne@2IGIi{xf%fSjh{<x@^)<!(hyyd#rd1fRG^zR0M~Kgyy)0;uu!@f0q3Gip3bA
z%sI56FHETXJG;zn_JDJ~YSk(WaSdDovt-BlM`laJUBg81#@h0!nw%!yXn@9xHMZC{
zDV#=DWbLzqq$<Ik=4~S)Gs#t?dE!r`{x}^ekh0TbF|>$|9TnLp%c{@28!8-C2J{KS
zs=NsC;nh}=#E4kj_sFIOLNzJ%JcsV6InZRjbW{TY-V!d0gk~o+ayDKjvA9U$5#2Q}
z#HU*nL$137{${+4)A7-F_cxJ83vWE*XR3xk!~r>{pUeQ21*Ax6-M<9L!gr*qjKQ7&
zNIrH$yQAR^8n1W%0I*qNJ7r(IxAka_67uJd=t40|y6hj!_!aD!^4MY<A!fCxPrxgi
zqj_jqRP7SIR33#J_pL&0G0ht^wm+#(djNoQ>a>8g7Fn-0@YpjG=)aWH=0ekBB-Cyn
zjQtbh51vSw29XENP@z*o;@g?%pjKruG`PYTd++=?n5W-pgl5;-Tn7x;`zmB}Kz-Yw
z`RoP6=?bG6fHk?_cY<~{yL69qgQ|keO^|u4u^Ai|VuMGJS<>wW+=!G2k%>2P{?4n2
z^g`6Zo|&h8;{kVP)2K~88w6zJh=#O?*7lV>)EqAJTysO9RK-el9dYi?p>H(1bMpnd
z8)BN^0fv<p$nZeoWy&5Z`Tmyw0F`$-<Z?}GvOZ|c0DvUijfU}a)fW-m07y4aZ1E*J
z-<IeN*8;M|9%o=OGV|3qz|j;ew0TAYS>8DQqjVWf*EpIidZYbA<fIG9;&`SBFb5S?
zvRfsA!1||RA={)TkX&Xur$fn#W9F>kaPBF#BXsCiGe(Uv>^ey}P!uY~h8sBO6w9eO
zvH6q(H*yhBumJ7~plb$LE?v<u&|`FOi1jJ<rZGmYIwI^1#Zm3kcIp?01<=jbg&1rt
zqI00`!T|SeYcKgkClU!4;JxyR1+UMlAI8tgHc3<7gLi1&iu1E6Lw3>Wuy4E<e#^w+
zBrH%yBkG^swwnu#i8A9la!+fo=7eMLIOswk;BjG(RCZ?%K649-kPckae({^b4oKwP
zxx&&<!Di=t(A5#)m^Zji3}ZZX9gtsu&2le1N||+Ld;wRzj}A$&3pDGpB4qLk><&6;
zHC%QyK~1*nOp8pTY-ixKN5wWm!!yA=hBdp5(#mj#yw5S3&1qn!9@h_5?}S?;CviL#
zvE2D_TeQY*c0gvIgh|HpnH&cM*3^09kPC`{F=9ST5M!M&DCzT9ySF+B;NYGcpNr8E
zA=c)pmIjDu<IPSQRD($w65SOxfa7uro9eq>E$cSzOgM`~miy5t&o!1unBboGm*Kox
zGf}$WZS|BK1w&)OUQ=c%nAmf04Ic3(7Dlmi+CMU;U^dE+b=pgDnu6VKiK&Jsitk$I
zc-V64wQ2aK9s5rqbXHOFUxwWLvIzTleHXYTXg+t?$-GK<sRke&-}0J680+D*kFsx4
zrNYnbWsHrSlgYvhjFda7!!bLr=$o*h%rM+!#m7~Ftza#PgVcS4y20@|?4;3D(i&SY
znyHP`Wom;x5GAdC@rlc#2JR`)F#J?KuNMUX$Hn*<PIDx61z@9G<{f6=P`hmi{D%^(
zYDc1Rg7X7JH-H$qpmCg`I~{Yp)@A}SO3AlAOG-rgU2*#X#qtP_N-`9)$=Q@F#$g^?
zBpeTStCMa<n=D_w9JEpz1&<TK82h%D^>Vm-N1@4oF~{~tH@wF~2wZBckAc{fl3EBV
z#-(3_5IAZNi*wn;EkP&xZ?yjavb`qj+A-^8oP3;Cra|h7ME)~AYg(@Pqm?F@=w%<{
z7oX`z+9f*AW2m3$aat4eos1+*+V)WPM}_wRF_e48M7F?@$#y0<FlOQ7=BP4*OQhk<
z!fqFrpDEyg-hp)N>Wocrj4|iUcBX@h9L5k`@G0$kaEu~%pbb`SY=zOU;niosOp=Zr
z!W^ZfTyahmt9T^j_)X|^Jr-^OpHwIAs!#V7;qe|z$RTF751Ofn_I>(imb*B7b{G!@
zr1eg|C%wam`-Q2p&;<neKu{kiHNgw3HMH0O(;>3Jk44HByUC@vM-}|Pk`~zG(MOBT
z5lyGDvI*lW!Sm*$yMBL{m|8b9*{@Rgi10T^>a~`Vd8|0h$&90dV0BX%24CEaoVf%5
zhJcJ-SpNWY@I!s|20o0*W*6Rf#!M=w#&A)m;*MuUNq{&^8<+(L7S;gFWyu(T$)000
zbdMlciL@Uhh$1#knJ}!(Q#$Uba~*t538p*21#0qD+V=@rNrXHuX<2vJq}Tz(qG0Cz
zPfkc_^VoKOC5YampnQ<CVIK|2<c1n_sp2#4w(Hw**lvtA8-LHXiSTbY^D>!=wA`GV
z(KwPkBZ51?hSYAPa!nW-Tb_M36mv!eIs}8v*dfo->rzT_V+*jhGkoymofMgq1%^w>
z9P0fS8IFhu76V}zJJ>Dhb(x=v2d533LNZwS0cWwd&&e|bMuPI1>CS!FUe+*wyJ3J@
zKnTRzR+WCM-X&NIW8$4GI!%*s(-vz7)etFD-6h5(!eAGX)l)BTc5xPo%~37adv!b)
zP1@c_cXXfZ#;PPjamLoR^T|y{jGbafs%PBa2s$b)b4j@(Q^;^K0UB~oA}F>`8!pNo
z{{VOZXVrEr6O)6mqUW*H>~o0dgd8#PPIfWuM=OwuHo`u84~5P3lqADIRe56>`mWM<
z*jncYTu)WoSyK58;!{C28!Z}%S&Y#G14-C7s`{f3Cq5=W$~SqQxV2Y3;b;Suk!uDK
zd6?vYbNSwXWY(|_8vw-TWyEHpMw>`zC(qFx+eg^VG~mWC_19rTI2#<|29snS$r+1;
z=TxHNdfP>2wbn)ou_N&Z)ijoS!z9@h+a&=e`znl7<*)U!YnoVMV%<?;2M}=LsI)s2
zJVvRlZ6;ly1bkB1=?dtKPRxxuB1@)P&K%ITWS<%i?&mYI3k}2gNR-nK@Vg*X8l-*W
zfxCZ71T~@n;Ibak$z#Pgf6xScLl2UO4|HtTf;Zrf0T;t$4kG^kdVhoin-|%qW@^k7
z3^~?Y-P;>XbHcm=pdC*X?xAlu1~=r4;u9YQ?$rMPyJlJ_-f0=H<+fJ!z9DBys3ZFG
z9}R;lbMZvnkr9|$9fKhX#W#31UHh+bsk_=9TR)kL`);+U=u!Uwv~qNob~8}&#NYC<
zo{*ec4OiiC6NHPR41sa6<*siHP>P=1Yt4HKp){SnPOhz=q67fP9AhKRI!FYQ(?r-j
zWj=R+JZ2Q&XfA(7r$p2#t!-DcJc4NiXn@4oY38#I2wtQ|s=SNxAv^}Lwz}V@4V|l0
z5cj#j@(Izn=0^x9_>sJ#B5YAOF&smSPKoWQOE{ahd2H2L2sPR;eu(<7Ac-!JArfNG
z90^b`(ta+n0PXfm0P-6zAw&zONyCv*1-J4?#KP3q968(Oskm^F8wf5lAQ^;z{CYrq
zOsZ~i<g3xAed#%_)=m)&c8R2ngR={IqmCam`fh)AH)L7bqVB2)eej(W;U*6vu0gT|
z&Tt*$1<QH#U8SrU_e|b-{g7{Fr=#<CnN736KLtJ8D(@aeO*$Y3wYos!u?D+I%~1RH
z)zJGsXoHC0s4tVPbOXRUlb4z<W1nM#Ra}e=^y<7U-0RgaF|CqI1Ogj%SksFK0XUP-
zXuQDO_!ZdcLHEIN39V$e*%m^=p!4gq-k-4-pc(y!5S$b8S)=2TZV8||vl~2vR&8|o
zZ6IWC$yKFJlbbG=WO*PQz}q?EZ^S2WwVoj&@v3eby0~hg!LyOr`IqA7_BC}dePu_6
z=dG86Ds0xF{gi#s7-J@d6I5fjJ)h1t@?OetIIapgoYha`97D@*n$^usijy<IBe3o{
z;f@O_vS}_DVmYan_J=iE<dd*&&Wm(+fX`I#yJL*I-r${0rJ@=*VqK+<b~v_4fxh_&
zmpQ<>%Q7skT+T5Hxt6ziq|R&Z-8RFbd3SkS8=1&L@Kt6Yy|(B2ZS+L_<8eXJc?5P1
z;WW>dNGa&I=+n-1E3r;L7WQ2gd`yto_OEDB-IHfJ$&e4xRG7JLQPp6PO~tcwJy&HI
z%RFZ!>=TKn4V6bb^%+@X5}r&stsX+?M5)NGH$q!aBsXW!D#F6%GSbTtaet2_>>_d&
z+H9sMvDaCWXO_VUyToB(`IV~0faG*NHZjMdlI;M=cplp{WC&bW72z%?>=S|28>~Uf
zR$=GPcD#w;Q-Bo+c0LE1&yO9DSy`YsAIp&)6B~drt$Pje#%Z`%YDjMYN@&3tt*Une
z>Lo$(d~B@gLPa*06l9f}7SyfK>wv2L%W42>9GZilM&M72^(ye}uv8ziAOm=5M21a<
z!Qlv8lnLUgyA*JefN0dX!tDX>E&zZy^HhXTpy_sG;9rMU?2S2W5gnSC_)5(HMJ&P$
zE~zke0eFkY&3GQk9Z~T*m&62hf2Ja=JDgwKPTnYQz0_OkB?$%r9Z}qH1a+O0;b`@8
zY<F~89r}&R;Icw-*=UZRBz=~AlUh%Aa6;^?i1ueRY6_j?XhrP1V*xHZuD8tr6<Fqa
zs%H=SJPqL?g4+lG0FeIxD+EEp$Ua*Ih*m9e$SOszk#KG1ntRGj!aR{M!XObbDXnmq
zO|HC^hkre6yiaF#Jk#3HMW?C<K_`L-8(ctpMc{E=tUKDmT4gYSsx4@n9tdw?mUUig
z2$}UkuL7Jyvbx=Wdhj_-*Ox^nh(gV5H>pDUo+{zkn^18QfGnwY4jgA-xkLCkeA$}!
zZm$RDs=2jKqU{WBI(7o&UegMziHkt}(@9?U%VGA6FHM7Ww4i>Xjf+RBsNs#5jv~sl
z+)jkz`KnZ^zTwX!;30N=APaz1Kj8vDWLnaEL4bN}j83PI)jYIC#)*yZ9cnFzkIYqb
zN})Q54)b4mO~-ZZx}{LG4d4Q#LtaJPJ}bi61@pUZYNj_ezk6HZ4CI}cikZwTQ=(vV
zp3@HCL0h=v02UTPFxaOPc>p&gH!<QRym%ptK<#!+u!CebBwLK;oN&Fu8^IQa_b1fa
zR%1jA=VeS50%mgnaD`6;4H6@dHFJ!(CXqU>A7Fk1-q-ove<+Vqx+8_Sqo8O;#M`0+
zdw10lV{#E$IKk0)aAJ<730W7(L>MZj9owpHad)>H!Bt_z%!td8RjKEpRbFE#5V3iq
z_|;3lw#lYmKw~mC;+-e`<b;=Q#~Dn*Fc$fOxGA}HlWpM$sA(oHhHM4V?-ia9eRg^9
zLDer|lTh(D7T?(&SB0Z`>n{1ke#oYhc?94mKyDQ`JW~L<^)1N;n#Skd3DV+Zoq}+;
zGr@iv&g^`Ub3o9pEm3F!HaEdeeB7=9#K+CYAc1Qbz}B@y(|eeB?_6y+Gk$_orqVxD
zti_L#BNgeic_Re`>=dez4)<I#D;T03n3C0IhfJp&Rb7k95fbMCJOVbR+$ioEQ_6MO
z5^Ol2F*g)xB&UK}B!FcJoYhhR(YeA8d~nqBDww9wWJY!zRGq*|;cu`&02`;+Bbwu~
zNWp%gIi!JRPN#}%a5pBd$S3s@orSZLL?C1g){2?JWVGjPp7B7T(G84kKp4@$<vFI-
zY2esDHQqe8#14T41FCdXZ-A5VS*h4VY(ri2U5Xz`p@$Q!N*m8{FcA%wl1$#(Bbm&q
z(`a^+vsqkJ_drQHG^+uT2gL4b?K-Hl0UoFqQ{ls+-lFdV{+&=Hod8WObwM(8YICYO
z(;@<?g`5CYT@Da@(}@QnR#e#Ml<ns*unuQb34I#9;OKS_!=X*5owj6MiRY7aSsaig
z_l-8>ftQE(e2CEk3n{XHv;-{8*DwAv3LN&EP@zb;bA)UfPxhwB8?R|sVadze?QCiN
zAS{c_bX*~NFIDX{JJk*$W+=4$5#Jd*k5yv7G5-Lhp&xkY9&0CV`lAL39-<Q&SA@3z
z00>mt($3I#Mz-d&61>r~+ixO;o-foR?EI02&U&Hv#!vMB0Lp6sG>?k+3X@&{x3WTg
zSTD0jB_-H#iR7S3(PwXqkTE1r7(|1`FgQ(%uVmxivw6%Z_?=OK)6-8R1U;bJj|F1_
z+*W{m6JhOeb@*ig54;YBKbJJuK#?ONoLbjrC1;ePKJCIfWRCc)mncRQZZwb&6gTNt
z5`Qbl_)*DTUf-%BUPD?sX1%iC`gUsI5cZyb>e<B{?K?k-+HcSaUimHbUMBrif^75j
z2wGRWyX3rQvp3g@&tYUzqyU8I8I>(%GFsw)D)5dur`2(v3uLeSPL&QUoKF7GyGLm4
zRX?$+n<%m38U}=3N+S`mfgKl(;xTF!E4OsLg61_(bvjJ59hvi0`{&2$LLA4#<5G1_
zEmYd~9~`Q4y(fuKZ*Mg>LIPy(nP1Cq#HhzR{7(eq(r$VyND+riAq~LdRlfEu&ott}
zB)~q1NgP6?+`XG3_WV^=Yng<}rP_BaWOcIQK_gjO)dhH}ebapWTL_FoSG;y+Ob;{<
z-8%tV4EpRbjic3Q)3RZ&1r5jVx1n4UNprFYHPzb5FZXe9PLgg;%M5)N^GP0IT6{8q
znJ8K<vARnE`KyM%;M9Ce(`VuLO(Hb1G969<<+2b2P1lDJgzpuAMnCg6Cc`A-P|pYp
z?wP^Ay3FQyb62<*)SJr8xXPJFMQ0dmPn?tkj9ed@nI~0|VZ;-07Lr^wLjytftV!Sz
zzI_n_95-X)oXEwv<ED_QxEGDbfy09D9n4uXwBpw7SgqNg1@SV3I5y{v^FU3c@j&9m
zjPSlnBNQE!@pqAg@aU_O>YsTZl<`d=gyD7M`JnewhPI!&aOkPC9s=+L=-E}<h@T0Z
z(}c3#ycK^&pa^M@U3f*<pY3r>!;B0ASYFEGag~vaql8ey9(G2;8|b5-*c-*tfEtpx
zY&clZcaBN;*uyM*VK+urdm=_lHK~PC&2ACQ3n0^nl4B{mIz@wC9%vACoY0P_qEmyc
z8;8j^T~na^Iu+q}jicB<w%3Yv*x0}EIC-jMY;3vN0IcdS6vmivi=q#*^Y!yYHxo&i
z=8t&N$T)%y2Zo8@lb(>Y(E=LLFFX}SieuQ+^C}||4do;%c&`o_%4udQM{}PIfIgFj
z+HNOBLX{K5Hva$>RnK$#Jlx2+`xz|jK0KO-j;0w$b@IdJ5QhqwPpZE9&Bg*BcFT2*
zN*S9DOUF<avykGyhGtid)TqGg2k4IFW;EX~2v)+t19=V`GXl#rBYPY9GLvkL!<;|3
zAvhWj_L%ynhtYVLi%&E#?@k_klXYJFCKL_PyB&v|6fNF|ACjHgfA)x0ghldpprLm5
zcaXD`*e!4<SoU_GfI+Rb$QM(K{ZNDbDbrI1`S+zbO*afY_^%Z@m-Rpwmc(#)U6zq8
zJP*ZKgL-G05jgGeQeQ}uAC#y=d{=0m4TLONgvSH`e1g;TDDDQ+VI27(AYau4OSf_v
z1m`uuFozD%SbtFxP{t-e_M6pv!3*Ld0t4@8xr33+Dent#v(GZB5*jV$nAX1zUiU-{
zQ<onlV&>b{Q*<}|N-|A?Gg3R|i2Xw(2qaDZh}_fy5HVm6026(pN$P@WJMH<So8PxX
zI~qpw+)oq<0-5qh#Iz`l)}sOpTQY{{`ep&cp4M9X-gzrUfYrx>ZQO1eGbzwb$7dcg
zIq*^0sGnkxzpiqw*CFJax@v}xJlEZvTaU{LB<}MF(ql8m_kaqnYd=um5BXJbF^_Hd
zD}Cq#&0=*dcP-9mMP4r$UzlB?rmjMsW)(6XFLZZlyg)o1r@<3=jf}RZqB6(2n|%zb
zZ6W!z_=8d0c*jrllzoQJqSe?>M_Fgy2YeHFkUA_dU4BrGnK@>K`?2vu@Y)(W@me8v
zu*SKpBvJ*l#oCogzZ45vQFYFjRd2wAT6?|!07WEK2Nx7h^~l+W3tNEBt0;HFV{8iX
z_E^QuxhDp(w|z%s(3x`R@4csVPH}=8Cq3INgGSCP@iB%G<^C3JWS=$pzG=k0?acB(
zxy{L%N-^E|K+5)o1MeMV+56BZhRZZ-&w`J`3&P=%M%>nAGF=xcteslh{8cM4n5FlX
zQfhbe3qYDnj|4;eQ^x-QBxXo=pvetN3ssSL^D%Yo)5F0Rw|KZYxyMCMycWBMAF6R<
zae4ZokGie8e^69#way#XRNsMv9ma5h6QXg!7@5REaRA-As-B9p-yy0c%zFcx-9)1d
zrZGRFE^yG5>=5YdWsp{v01=+-U>yQ6O~VhFK;d@tBKI1OMI5MLA0-?m5=|P`o(@7H
z;ary@C8ofv>YdNi#ZThIt^}!ulF{akn@9YqzJF%T0uhlmQ<c;ALx3&S95g@;>#_;p
zh#qLlGcaAtlAP3dU49l(!(q!JuI5L1Z~C_REX_7M*K3Gtj5;_cchq={$6c{fk@j7u
zMcP@}nhv|!@B`+mV-XE~jm?S#IMi`*Co+h8oX^70`KE?_+y?0bhRz7_M|51E(Aby2
z%<P}Ms~(p_z~5Cp<ul}ODw!Ch^+E+fgmZ!rCE6ZHWZG@@5`M+k@SA_s>y@2HmnU4f
zVJVT?8C`+KEcVL&Yrbe4a76If>@4ovjoe@+#sFCz6nJk4_=A28NBD040J;{8w~;8^
zgFRCSJky&b<*k+Reb5}A{{Rcy#83?t0MjxGaXp{g<Xmh~wiySiU~u9yRRgsHRL29%
zCy=6C32gBRtt8iA%ijM0ggug~HPAP&s%ixP0OhJIqVbqcmdX;xb_#y35T(@4P6Oap
zov;4@sy@^&4|#Z57C6e2P1(i4jwlcb`^HCvqXoAK<hp8K%|CfJ2qC5iF)CoxY}=|O
z%^HQBN8tq@4Rh!Qs%8z-rxz45B^cw-T}}~MkB+hypCsPxz1!V7LxcnIPX(vjZ^=nI
zA!gZo@;sD>1n7NRze(Zhv)yR(KjCEC0mvf+XXKqXl-At%qW(w<ptQ4u7NR?ok({Lv
z8t7Vr^X62_C&m@Tot!qi1v+8aj~UP)Y0_&B7V+kGwBZ}h^3gE0lZBvU7qi|4QL)PD
z#Q7!%0QeKcqRKQ&$uD^ck;c2O;)yPCnK_h3Eg1{g$rLd%fDdtxYch)}RL6~s2P{{(
zRSUOvW@@y05V4jDp~UE#=)P8z!%4?<9PiCD6>iUom1E#lav*MtlramDPmPlV2*fj)
zTvkHJSZ@xiJxY@9&io(Hz0emBuYmjtHgP_gBMogs0SqpTqSC0ko3l849Z((HTgH4V
zR5xibF@yCdLl_{1;C05$2u%QT*nBN(BlVx~j$$l;aA>ry&uP%Ys$&$18FE!7X`Ay}
zrkT+gHQBt)VK)UKlN*<x6+5oS{kKE$DiZ^lK%ikvDrK+(xk8^29*a=rGew6F%>!)8
zZufhLpUM#t-a0SM5GCdLqSJmhUD%+<<_r&1<_u0JIr$(5Igd10=W(BsZ7}D$Z*U=Y
zh90F=r|OGwK3maX@eM+6##3gg6CWUqxyDKmY3g!KI5p+7*)oC7Pnyh>H)@##X{;mS
zsj3qsqh}Ix*P?N|@<h1P&hm!qiSkZ{v7V&pgwys<x{f+7JFnR1Ft&>u{irgXV(5E?
zu{Jgs#L|DZ=v>V4kc42qU%Fw3=0LQsX~g{ylSpp6gxVB?saC|`C#o2Ofd)Bhn=_e>
zIFu#h+d26JA+ro`0K62Ngb$kL?^G_)AsbFFHzRtiX?L0jmeQi)0+1XKD7BzjlkId|
zE}x4Fcdcne*lZF?dl=?HYe|xCz%DXT%%-uzyD;aH>$jtT)D#_Of3UkF$&Ab4QgNi;
z7bN!wV6kWgu&TDLq=90DDBd24_*}P3kBogn_Dpr=p2mo~iA<+evvBr<=u^A65CaSV
zIV@$8{7%nUL5~B%=XjgYp(DC!cg<(9uEVRM!>Xe2&N1RDn~ogdWT`%SC;lVqk~l`{
z7n`P>^T}2&Er3IEvSE7x!p8#<$r9<T-tZj7(6MyekW^eobCCF|R~*7%zC#eYqGTC^
zY?UWsE;=a0#(^0d4r-oGo=TaFK%b#rJa2)|ias-l?9(V-7gc%kfQIYDj*6oI?%GE<
z5VI_(Jsg|ira7!R%te=I7}@N)#ByFIHP}v&1d@Q7wVd#IGj)5Q`k}0&y<VX7777~L
zHCs)D^2{J%uQ0giqM+dGl{8)b%|KLWbW1Oq9v;z8tX+FxgV3jrkKHR1en?DqSLNiL
zy(ZjMZt2GO8W6Ss-$y3WEdnmoY0&RGO|!!yI;YgBRk03@*(#ezs@!w>R!$B8ryas%
zgTs<wEUhhZ>#FZ)9S@169Bd7F$Crqv5oyar2;G26{t#=vh{qH+Y0JK-elKeDw689S
z@N`0FB8A-CGu2KZQl2l05f)R5&GHBWHEGELz}@E$**HI3?y{8Qhc!v2oMFOv52|qd
zd$kBInLy&Gz#MpgxY@xoc9lz?)PQqqZV+^wiWdew73Q7xX(!N1<FFqIO)zp!0DF^j
z{u9pgOUMHta!q&vxs$V}L|n>L#umk<H7U*}Y=zrgx2%ay3}DQkqNHwM4`}s8uKxf|
z*}^nDN_OD0M#B-|TB8Blpj_Z?-4-UbvALi{j}?0pZ@|QQ*->*}&dQM%Al_v|zW3-8
zfeobAz|h6eBrr>z%`nST&L^9yET&1qOL+6-sYDDk$GqdgJTq_ynac|8edx?(s<Z(-
zHZ<YHVCbC#v@$=dL?QqlU@j~9E%gWkf|CIq6Ct%29E^U+@GY7G>h*Bv!ClpAv9Z$W
z?1lgx&Do!lp9w8lf{HLX?5aE3l`#f>n_UdEQ;nbycx)Od-Dxo>65?zEJ@0B7dkjBR
z`E^ZtBJ2}nd_9@)dFZ2d_e7#mD1-M=<`$%sZGgwDFKLB?XN$UYABN1hD<<v_nqy?@
zox6y9R)ttvPt^%&yoG!%C(R#f=mzfmW><lYCA`Xegq)Ka?xXfa!ab3yal1$PRaw$w
z=A#pc$jy^Uz`#91VV-J`dk%B^phO8QI`mc=r%wDrgA#0s9MzrQx8%2u7Qg#V_8+*b
zYx@U?S&ei&!uDh2fW32mfdEG|(CUwAF6C^3BN@er0<tLX$#5M|h+vsKSG#e=+)Aqs
zVIif~Tve&D<Qj)42s)#+zs#(r%>fT*3r{pRW=}w}#4!2nWwG3$?K9@G0TZ;g&NorP
z3ALHTpvwadizwZcJW`C5{pTOT8OJoiOmfW`JW!S4Y0F!yCp33#kv}cI5xnj|B1>@?
zJduuTA+Hq>(0|xoMF!OOL++b~4*NKB9JaPiM#wyLpMuM4-+)qUoXNH`%V3uO0H$kR
z-ZHyE!7-Lcf}~$lS34&3RbLzr%~p5>-s-6rlB++CY7OeoR~t&qILf-!#aZpllkr|A
zLuEL-8OxG+7;=<+_C3L|fwAuu+A2mN?G)&iShU2-bEb18KPsY1%CW#l2L+I@*-&L*
zE>sfoPsGYF`VJ$>8Hz20KMs>m_mvU=%VwnlODWW3jYNFYUkzw%9xl4gtZpb#A;9@7
zGM4#`12RwEtzGq9rLvUBB-~|*3=JA45L*X!>A}57_@dGZXp$hQ566UGMDX?xVa*V`
zFoW*@0F=hVXtz&-A(g3sU=26~;$M*_>hSCz8BgyS7HGS?)=ceRHp2F)xBi<aZn~Zr
z_@lW3KV)19CDJVk#&D9u%$-%tS=xj!2A8%5N~_CwN*&;H8u7$P%{lmLP;hoHRa+E!
z7)&M^hIJEUGq3UsK&}K`bcE@6B)U21fr9#fR7g%O6&&e~87&=>H+4uJS6Qavcwo5t
zrZ^L3+eLUH08Ihq&vEC3rh!uwKA8E0BX<q>^IB%7tRZ)>JI$!vJvFWGX$Ko|Q@YX=
z7Mn*-DXn2|W_c0B+v=^6MG}0~>RiFesL3ZZEZQw-UOJa|?X)oU5{)21{!>#J_RoZF
zkAk4qvLIl2G3vWNcZ+wuH+NN9r{5jtyNF%7i@3-wPN!G&xGEp@1w`A)Mj`BoU7TE8
z$oi%`#dk%N?za!s1K<oZowf5!YX>a-P-zbB;f8-o&80zqbSe&wH%36TFT|#A%T&x%
z9k}jJ;-2lz=D&szrwE842o1MhY0%iRH{_ZKaEArF+*q6Cp<B#&#wVK37E4yb9?@PZ
zM{tg4Sequ^O3*CJiXIos59T5j%bp6ev?~kR=I_6s1F##Q3FnYs1n_@`?zyP@$ARf|
zZOs_AYjK$uoREh{-c|OD>9BZQ4tx_@NqD}wFKVl4{Z_CL+BdM7<|9Rt_YciU01`;z
zQ+Z5TQzPyH8jJH<Z1qtM;j;G&iA5aS9Mg>A0y!al2t)2#?dVOxMTX7V>E6i^_)h7X
z_JAtjTH2w7rO>526*u^s*MdFeCkubRDVxCGmm-<>&Z_I{Z4@#y#$k4lIws>7<hi3&
zhNJ3|%8nW|XcMGyL#a6hTAfCNXfqvC-Gs(hpE8|xRm2&@LX=?Sen?7Y6ayL>1aX8Q
zTFc%f`6HPrz0hl7efU;^4k+BCX8hB)MdO;P+HN*(JlqEbl}eQz{h=8WVQ9VKDkSU~
zst>W8xF)unKRJu2d(Nj$h?gE{-wJN?%mc`509$t!y%D+Du4u+97~ry-blI-JGtD+C
z2=qH5`z;eEs*Ewa#BxMAza=53#UAdd&NTF!9*ZQ>dyGWKy6wKxfb~K*u>SyUmU;!5
zqfIb9uNX^yy;bTzSkpT!=I3ov*E`w6Dcj8Z%UH%DKBzmsrf}7p$T<~#hy0<Kr)_gi
z{4oK+IAa+}@odk65pwwJ3*H|E?1p@hE+LrR(~6udcqVl@l7YciOGNly-B7%*O_?*Z
zGva`CH^6Y@orE;NI!^_6Q)4pxr~D&)Ex(eC^Muis7WFB<3J)W$OBNQqyFLnW7a3U`
z(T6?lV>)J0`>G6U8Tp_i4HsRV_#N%8(pcv<VCtGj2WWZ~QP2#Vro)<BI?S(sX9fJR
z@R>(>rc?|4^*vg6^g_nDZ^1l+#1v27cBn3B{IimKYKEL_EzUI%vtVb*8HdlPS7YhH
z2O?U$1IEo;{uF17dMw3J9%<rA{uu6n<AYUfLCyZ*2=6vxID(tK+PB=^7!BbFb{9DL
zm72)Bl)(4<6UAy*h1J+S0Ysl9(nUH#dYh)*?!pH&L8{EiHaL^o=NyAi%~cX~3Z^<k
zr249cK>RmYs!zhuM4M)1HZTqDb7bJ=mdm2$hngS}O}{O6ds5YtD-r0tFQ&&gkSaH8
zJ*$C>tlGx5KU0vABFlNMJES8uBY69~f-|zrV;Lm!3oF7fNw8cHk`o*pGb2>*2^Qvb
zM*jetbOiSDXt_R_D0lnjsgFd!fdnD0XcrVR$_lqtPM$6}0$?pUfS*;Cu<-z^%YW2&
zu69~Nq~~HJ;?LxSV(jD*)P(0Xp~o!MQs$adCsb-Vl>l_NGi%uvhnk)P!4LW5nq~p=
zLCzSikBC4$QHK^`2SrE#(gAiH(|jWfxh^Z*`JtH~Bw@8dY5FYmm8N{tw~VsdlCvtz
z@2$=TYu_hh-GE%SY@8`l8M*dMhBr6+PUq8MM1AXI9N@KkBV(|3ag^rsz#te6IcORL
z;^I&Hlt)dGhYb253@>pu)eR<g4UU%65C*v{fKh5+RP7O#LAO-t-Hb8joEq&~rK6(D
z6~4r5FhX-f$ve89i!x&*AOZ*K=20N8U57LDOauybJ_Q+g_)4(@KB#I;?8&1M@>Lu+
z#;Bg{!+8BwHipmQe#!E9ruw1)5xCwwP;5c7IHw9@At}*`((RvxTAoKNO|egOlZc7S
z3c}j{-8~$5gu(_ZPZZx~{F6y#F^SR3Md6l!h2*BrHV~;2BNiP1=B(U+U&Lq*{aXg(
zP1VUT7qS2zO0Rc?gtT=sh8j<q<9iL6m)!{^R%pCn22a6IXD7Hzx~la}v@n6kiOzKF
z;DzuZW;QLw54&j+x&h1VNHc_NA);<dcyk%yF}FVN>$S+(5PbGdDDv|}x}fnoen?y<
zv_~w}Fk0poIlPYqTn7XRRt+V`mK&}6$Z@haak$`x+ev`+gao)42)O&M7jrpZMBHtO
z#MD3<l)+<S<#m(0J`y^~USo>z7$f#vQwb+P6XLaI;cEf79Mw0#wkWpC=_Oo~9%Ww+
z@1>EJv>87Icyah9v%NG$5cu|CTBBR{j^kwPBsRyKpm5d&-5sb$V6#}k@YrIL-G56I
z+dmV0p&tkzEuPC{^(yg7W0oN+SZMW<fV?<)>^7UCU}Q8^CfQ1<P>|f4#Wlc+cpWAT
z@m-nDrdykb%{RJxz2EYK_nn#KiQGL^Qa@8u!9+{`O;pKL{_d-AxKGE)3`EppserpZ
zSp6k41CMIQeFhj+om#U=7+K-?tf~z(Ag2Nj5(@qmUx8ZAA;c8j$2twXIFPt4a72e`
zcv%;cy_ZXTkpZ2cfbs6WE)YAo+BusljTKoKD*oxsfpSH{e6;(a5=3#;1VlEg94*E|
zaRJ5$0*Mmqq4)0+-Ea;m(|erF(a$7*4)U@RPnN*Y8$=K-dDT&)HFS*u;Hgr==D`?Z
zdan?5>F^2+WuiClTk})+&pxFeXA62t8Pt>4Wc}?{2Y*PNhaMn1&)6qoX%;GS2$%#|
zAqC$gVWa)o8ybS#8a8BeOv4^0oXRF&TR&7mW3q5gAn^(2f6`*VRO1YzQ2zkZh$3eC
zCyXRSEDnQvDGm0ok~E%20Dg(Uyi~IWjT)kHhDp7kYEWrt)eJ2QCuO2}7s?ej++4-V
zs?c2$*D7ZeTY@jd{?7(QnJ&>bobz|YLVH>rPZ4vzLz-lU=HqNih-N_RDzzrJ5iJJq
zvEYOkY3B=3@<v9Z=uj<@yv`7YOo-+dVo#!OPH8<tvz&bb%rL?jnkRNtedr)@0XHKv
z`mVKX`s~vw)l_JLF5>`n3O^&i-iu9<6ry8Q_;p4mp1Li36m@@IN!eMTSTu50!Wsbb
z`zLv{+hOeSC24O@ux)b#rp+mQ%%HR?38hmIG3?}uH*L$Ob{6$7vBd>@E&v67(N(UG
z$`Fv<mjq<LwOPb~56wG$^#_;=BBbKw!3@l-yel;uz=E=Ll-wb#f_?5~7Ry*$hnV7+
zDL%6?hm3{bN{i+=D%9~mED76=VJE27LEhRf%8$k7tm529*2v&{TsNbRMFCoF4p$si
zF6VwRdodX&*b?WsTM<qj>jv{3tZ+>@%dQYRq6hfqow<rY&1a&QQZ!n}Gn<!(ip1PU
zbd6&)d>07Vo0KK2?D!Zmi$~R0V;f2U8j>`Ho(WEIGl+)f6g9Y~pQ<<6;Vyy{gC$IZ
zn$JaW%5qA~yEPr%F_`w9$6$^q=&rMpc8vDrQ*i$PRgi)XTXHS<q8bRyr|!U<CdCyK
zuEA!s->8kg%@;?Vwq~+s6>DtDE;xi{okrqe{r3Be8yIR*n=)cglI*$Y_hM>(95!wc
z1awX$$+<k0V`pn|@l+aJ?=y7&0F~Z`ofT&^vwQCw@m1;7bMFi{>QI4Ue>LOwTcUOc
zGIP{shuySPB6B;dSApoFTye1Qa0@+}5%SrlACe%R<?X4}tt-U?<eMRacr9J?Ln;`5
zz0iagHbEMfZy-kC&ud^pr;V}f{Z%qJ$S}Wz+(Xc(y{$QkOlwS8{Lqu#m(QLQVZi0Z
zG=x#N`bRttu0=a>yYD=LaK`$Mdv)E)$>7cMQse%jI7(xih+0j#=8TxAByo~}J=;d|
z6Ct?S=9^K(dHTxi#)+i<_r$U^!=ZM1C6lPS0EphFX<_qEA=-?S$z@TD#h|xTe(8nI
zk0=Fd;!{W7fd_$27AwsNQ;I3Pc^LYmhX61+#4ES)9n5C8IC2>*^OWJ<Y6hzAcJ+;r
zhY<$I1C86Usx~$N+5k=6Yys0|>${ed=fy@Bc-lReDFvjGMs~cd_dLEy&c+dzNF5C7
zjmeopPKx9GSN{N1KJ~-<NA9^3#_O|pwEQsPJK8SK+1V|#LjlcFqyrA_7U7p8%$4{|
z%bzqbgSs5=iB&t=(|2jGbwk2re{vY9JH+4F66!}@9Z|D8D+f_4{{V?m80NCZ7rk#Z
z{5T23J>{IE8fK34HG)4ex_g^aa|SG?YBYn@ww2=IIS5pPhJ;wEkpjTxnqY+Ud#KMi
zOb;aAlA_Y!cV=zLc0rbSh%3Af>nJ_$!<lp^x+&6YBc~K+$w2Z^JW&@;lfg3gE!q6C
zyde6ljo}<&qY3=5gci{Ng=%BO^I(XzvrW$=;Os5JV>r;2;u`T;2VFN6TCZpTXBm}I
zukClJ7&owykWVf=)_8<!ffE`1jGXq5ikt!1Na6*dJ8<}KbM_)$??~DFx%E3NBuU`1
zi*waBF}Eb|8@If)PA3t7I4us$p=F4|=(vR#8z%f!>9K`_nrw=Uehc!Sur3XXboY;O
zK6@@2*6f3AQTCt(cQ|g3cbXo+2dZhjVdPP}S%jy3(FBRmg(1=EnnRmM1b(fVO}2R6
z@bS_U3&>!|a8&aU;msF!Rc0d5$rBC2=JG2`pmE+H#)wm?TMggDru$j&J2MUiJ9t!)
z!pK8wG*oFG#w`k%!4s=w=BIhr@<FbtTF`V02y3v29aDT=t3F8kL^N_&76k1U8J!cn
zH}hY_leae<1DYcfoxA0*^hVlkgCfzhG+-UN<h!?JkW_-E3~6=Qz;wVfAV^M;kRx5P
zh2~_ZK<?xYhIZ3!GVBc9$i>t+5irw=I>Hk!3dFw--T@2FDo2C3bSb5zW;RpBx;{vT
zu$;+8SlpIC{MJi~jXp}G-yoo=bAV{_nffOXc_&Za3?Sbm1t$v(BX{P5TpDCJ9F;sJ
zot~qMrbDMC#mDHpOmu`k?z=@+e^zTFQK0&$@q82^6CWj$n(vYjKyS%FFW&U9S**`=
z!RF~k8`<d(K8hn_1Ay_41ELoWW1IPb3XAu&&>u7~j62365Cxsf$&qsnP8JccL2qUa
zGq1F>Vqm9S3n{)zT*h`<P3wy2N!C*Ub!e#aJ>43j{g6lMH(jC#$+McD{ox+0S!j3_
zn5upuAK{W4`bwwxuZiHAJG^!zNoeG&vaoTIHS-F84lv~qn&{HmqFVgH3WU4)tbkLo
z(tB{R;DUsf(rXH2?{Nd>uvp$4EJsB4Jo`Wt2w8AVlUj?T`fp`nA-=ajHT|NP1Bhq&
zULFbL<QIfH5$G<hCoWtN^H&XVKiVWOWu^x-P0D8X72WPM0mU~P93k!EoiyJ~@-iaX
zEZbet(13-UK0Hvg#PQc-3#%Hz;BXO{RanhTxJg77kRmuH{{R%<(RhP?s6ZNXRHd#5
zy{K{%z@j@>XdcKrp+gxHfr|Csz-kff4g>0ii1{EgQ6Yoic4V->?i#|@=F}S24$k~i
zZ*Zyr(;!C_=Ku&MLU6@ZnEZv49dmq_X$;q*ruvKyI1X|q$T_|1*wzEY89NAbv6lhC
zvy#J!ubyNT5#5oxBJ0}8x|G?h=1FUGZi$AYf$0gxnId7$kBL*i;f$4R56%cx+Bat+
zyHzZ&D7P!YO*(>jERfiBTvl_Q%}5^<@dF@K5L}%EJd=NWM0?lNWVVwnJS45!VD8f%
z7VR)z($kABsPC{GQOP%TzB7;trYgpuW9r6IJ(PSF6YV*|5mCAveFUR-$~yuQW_<*q
zCC|-H5tDlTf+6lXt|<Qi59)>H0`M^JA9>3E0Co?myP9L7;7W05Iha>#r*3jl%QeF^
z?f3Yd!g4|#E%+i@#*3dMGkGzzPM)Zb{Jm$ibXt6Z66nv84ISMfOh*RsS!F5#4l+vP
zk`DZ;%A67trmO~&Ab~v<M+cYQ)>T;Aju_4?$V7}&4L?K-@F}GnI4L!xaKoye>>M%4
z2;>*wJm>B^ihEoQe-PEWA(?;?`_~LE<<Sc^$oQOWoxM3i@eyuAW=y2pc_&n8E)dS_
zn|Ug9>eHMdg3j_lhUf{DZ;78Yb7P&BvlmVd56Feoc_4^?MG>GGD{nvx*f#TAb~v;#
znBCRMu*L`rJ+0}@88M>KyEE7V$z`JUm=my$C|i!GiJV@GEk42S!EA!<Q;pvS$l_~X
z0oyag;^a;v!3K)(cjJ;WZfCB{*8seeSy{8~i>}7pK<G?*Dj}fLyIUR-s^g^p09Kv{
zW~xsJOaRYcn#$4XwbwFXLs=M{;EP)|51Nlu!cGf%qHy;H-AsK|+886w`#e+~jlZPm
zjBg9BBjgtpN!=1xG3pS^H{`Mw9!h00K`n6V)MQg(HDAqHfaR~69c5H#xgtt<0!Wdr
z8(mha?qLTxhM-_`jqyaLwtJ^taL^|@30ZG9`&zP_ELj+iDYGUUQ<{Qb-Qj_dY6;{u
zy`G*gCdgMMuanW+9T+MX^tF&#V8!Tv%KlU{q)A8H&!YFDR#I8!LT+2Y9YM`i=iOrY
zDiq0#h{<+;?J%EJgueQS%~fN8rD*r2A$F#;xx^3&^i@F2!l=pWjT+34359ct>;oy_
zqYEwM?EY!^yFEwuhlEYnhd<KP*k}t;h<PL8YllI#r`n5hRF8Q2l0eV}My0<hqV3z8
zDlP^4FP<Tx1v3$sW50&^txA{Dd$*c?oYws#_+;z<01Bb+yGbrTrG_>LbD_WGcvv8Q
z>7v@AU87hvEAFXi)+WX9xb#upzwnvE^A#?0^ZBhU=(SO5ZrO58mTsoa_LTS)fu`g7
zwJ2{75lmovNyc#aj+b4WIqYerg{PisSmN0rZ(%wDfy6}PA|X{EPale4uSt!Fs3p$g
zWp->B44QcSLzJT21>2)m=?SL?cYt(MssT`Lz)@GU;sTuza^{8#fq}6z5~R1f-9MTW
zu{D&LO!;hsR*}Hq4a-2XoITNL^DvGW5~Ua}#OHCjvLxYaNcyVSw{9oqqE>UWh}dpw
z{D#dpy)K9GfG5d+@TvWmnl#9>2aWv`TTX^3z%zF;p4<%q<nTfo(H(j0_GrBPAylJH
zx6Iz^yBYw`Ka%Y<vi7B_wJ+{Y7^%{8TO7jSKD!(XgXKB6TN^OqaX2T6;=h)(ljyTn
zsWMFXlYtmKIgcgoNZ9;2nO#)e-8T;{fcMozo?H-q@y|4Hyqp9^ctWng>{b`mK9pe%
zB$$)N>kGND(x^C&S5%(e;hRcn7^u5<iBU0z7PCDS@8W4j_|VJfg#6JcI|gu`>?Sae
zC7K(I4PlLVl<PD@XMkI+(c@057XJW-u*jzuun~j{Ng9+JH<C2Pq7&0ao|_tK2Rrce
zC=yAG^60OQi?9v_w*V}`P}dS>7lqe8LRW`K;joNOV?pv7F4<HIGz?`)rANaTlf`$K
zj*j{-Zn-0(w|Ci}2b+zTiH<ElH5m|4z9@~EN_UFj0Lef$zi^#O8=!x(xP%4j33jVM
zJnV*t><H$a9oP}hZ}i%BWjlAbNaZt@-0q#wDip(;tZ1pSI12)qO}!eNN*)m-s@`M#
zZFl?4UYPMWR;EN#V7P?f`tb$YH=?;Mf5oLTSppn0Z!o)6I=F;^la(iVU*v#pRtGPz
zL}7{OAu}6iBdT$AFLrz!qiKnF9;!q<*sF9!0lLBaqZH<5HBR1XHvL;22PAZYe8S5w
zL@snqnr;P;AC#jdrpMlCxF~)lI&bx0Mq2u8c=JHwadi1B5OQ)m!I%%4d)?4S%~2Bu
z@=AbkVkGG|Lk%|ixTa!xiQdT)JGxNu`JN#b<&r)ad~3ZccGp=_tjeCdsgeZ)KA{f=
zsM``Uai5AfTUF=E7K{0<`KP(VdH3L*f#%M#r{jjSjDQ?H7L?D$6=u|FX<eLEbvKY4
z<C3D*3XXV@H-NJ=io{(G8F8|SV8;bK?->M~ipvXu@<g@#N7Xc%lXB6zs~lo1{H~k4
z*Os*DeNe*;kVdyRQHH6;!}^gX!g}ptyPzIxwlTFZLxw3Bkh9=Xshkwcg9pmWLC|QA
zcJ3tEQN+izHHEhV>ZoTd%A<})j;HJuK+sMR4FEQ5t2+}IeB$<1sz>Ticdlxqh#C_i
zR_+6{7-syJXmiFO0W87~#a45ux%f!yos;FH+zkpKHKJU21-jQe4KK+h2NqDEDTvO(
zWpPX_Yblr~S41nWAsyk0M$DQ)Ykl(vC3h5Vb3a7=*F3fkmRT&EAfna)Ovysdv2pPc
z`ypmhU__?2o0(3}pAb(j#KBUeag|WCcZYqZBN8$}v$*qA39S)~UVN1!FA*F$scD~0
zfa-k$c@E54a#c<YP~dU_0u54omvTCxigQJwrdZ5kM5i>%u2UuqNKGU}K6U7ZCfBDC
zoNByHw~_?5K_O4z+xv<mdy1)rjko}z-ZlBhvk35=x*_2XB}@_i%F>Y|&0)je&oW^G
zaEgs5kSV8<V}Roch0fr=^BtwW$ndbL#2q<uL%`4hM-aejM-AxPIG)ibfJDrQ`vZB^
z8YNf^r&vM`rA6BrCy+EK%ZZ3i&PVB$n@L(T>Y0hz0Jo|f&%4Mj21+9kcIJoCI}Lhl
z9PtBinn4-P!yv1$#4N)6I$OuD1WyG>=9ptJIi|Q<GQ0uVf<CIDY+n&wq9cKo+MMQ`
zf~w&rY;;tqo4Q<DJy&C-00R2_7iOqjMqtE3?}qK<kJ%7jTsyKxS`|TzEr_ZGlN|R>
zO@QA(h%L4l&~QvWIwAolGmup~*S)Y@2I%OnZjFWA(<I{hIpLdnAYKo>p<WkMxIB<{
zH*UbZ4ArBO@=W{3Q**DF15Y&Q7)_@>tN3)u!Z@2DX%=c3g0v3#Y{?maXQR9MkN8cO
z7CKygnO@uf0PR*;I_(EeXkMlP<lR-oZ*ZO~lJQ+}O$Q52&OtjAnJ(g;0a>9N)23sq
zthEOH7Z0*0dY|mKJHHkyXS6pBJ|$+1@(Y5Wyl^Z$96xm2j9TG03w`5Ys^hiHe7@FY
z*>W2^M8t8O3OB_T&<!>T-FL}EG(hNzs^;H9gF}s;(Zp(uE1qj9Qcft1h6wbI1yOTL
z!7jpi=$!cdAylo58w@M8HxV`jTh#@rg`1yua^bK7Sxy|MByw<+PUS{{>;5(nIm$cS
z2B<i`fR3#H00<54$w;6kk%^8uC%Qx2JyAkf<_QXL<~kv`JZ|6@g{#EYk^(*r_XONW
zv{X2O$bh^k&;C;0Kq5WfX_(mNmOZ>$Q8+3Fx#iiJxC*mFvn-F2h-+s)HeU7p7IRRe
zcL;Y@rq6cAypDkqCWFNAn<C~o7$sjFudHej64vgwmn7MCjs4X~ejx`$+&F_z!t3~0
zPt_R2Kup0>nHcywR|w<cPIQh5gC_~&BUEnbo{AYtEMIK>)MB2l)#ZKYC&1x0RhY_w
zpm!&o^h_<ONZ>QeV@Ywy1r;j)03;m0QA9SK@UgZ7(M%VCAs052sz`f}1U~wi!&raz
zRY0?dAEie6d0=C=-S19nn;dY)A{8F&(PkV%@|}wf?a4LGoGu{eye_mh=(iYd3#_+{
zW^(dIT*r(ad{AtP8faP;Lah7|dB$-DjwNnXBX~0`N}xDi3~OZU4XKO8oE1s6Dtq+}
zy%lg;2Zf>n3xurYNMMXcGFz;q1`+$LjrvZ>z{hS=_uR0DPla%@8tmO^!->(WLYExL
zRd@TrxgN;X7q_$*OI!rV;HcvxCr%%d?9LF+N#K|m)4RZ)u&cc2ZmEX8n|+&NM;k0b
zZw_c*yZRzzW~8#9ndXDB(E{1!WaAq0U&4dKJ1E6BoBY$w)h1S(3}Q&>3Y=mtft&;Z
zr12u_!s0Li;xogNY2ECU!Q@khV4>n+Y30DFY(D3W*Bcc%Y|RrGQ@3X*M|_aLelWaF
zf20KB=FzMOcNwvSTwS89`V^ad?w;}hJ>#_?1?EcL6Qb10aRUq<hVYmi4S-RCkr@8~
zV1{i!5{7YofaRG}Z6z=@q~ab{`!WzNsZr6;4N!sJ6hDO|@V=|WMc=QI;I*+?Oe%zz
zFkJ|d7E)U_J3aX*T)H6;>iBOQPNi3TmIr6WRWZXQP~MBLIU-0SMEQpYbkRFojTKlE
zzz>p(XJ8cuhq%TtvA`SG;*D0uT+kv8PSp$QMp6)LV$7=U!#NXVCwl%#h6~jq+v=N%
z^%;bi@T}W6<7J4Hz$_!xI9<4N;-~Rl@(7GAyPAE{o%dzfd9;Ma&%Oro#279QA;mbF
zHAi>O_LcXArpHAXMIz^dCPb~;6^yeF4hY!d`Js-rUE=dLjT0H@sj#~olf@9jg7)%U
zht+$R>nk874-Fb&y+~2_bQ;CknnYZYArUAO`Biqd{s!Ep22pNI)S<oZ{`*12T>k*t
zy_cVg%U~!(xj%4JZXwA_Xm1#%AloT~@<BL{GJz!M72O-On(v$_V6-rO6N7;o3+*5l
zoI}Z4_D?54C<9}j%|6A9E33Fv1e42x0$fQzq2OUZMeQCdF|s?FjVyfdDW?SPJBZkI
zP;NeG4AWv3l^oxZ*_U>Hdk#8&<E|A!P3>%kyG~<@7?|vyGCrss=Z5;DXaVWOjVyu<
z-W1+E?2gRVSv+wCct6p!H|Vme2EKB%drzoBbNM5c)vC9`c#a5|w4Nmx!<zgj3^UJ)
z{xwVklXEKNw1GTss$7^1jFAD(Yno*=K;d<w2YQ=9cNaF>4~iS-n)X=oeIXxZXBeB*
zFXUkU@&cI@rrJY-y>91!pZP$H0tiER(rR-<+U(7WJ>wgEK~kFZLr5rlZE5nd_8bbc
zd*2TmCjc;!)qB!B(Y7Sncx>+t6+-CCl7bFtf~Fa8X}E43N+h|*;+@|yt(O930Oq?c
zFUa>tnx#sEa@-ro&u6+;l%M|qP{Z|I)&?-|5NsMbCtI`!G3WLF0F_nX7eQb)C`@j&
z6l5Dl$}BSAao-Sx{pUkGwOi9oo*|qCO{LBt7W+H`kx+5gHW&_P`BcNPn@_SzW0)H2
zxw*M8FhozZeAJ&koGyysrb^)p+p>5pqzm4C=LMXhGU*4ZWY6I+`$vi7oqSLRfDQ<V
zcIorjVWem;DU*cdH$78BY1Mfl{^}9`0NZFdi-ZuI47&^kIDwn-LfDq1Sypf_a1Opf
zK^@r$yi|FHGePF6)PD9rz)WLlM~G?FIA9`Q$-F`&G=k>;07ghlaF7l~cYuSHihV-@
z<hLXZP3uMAD(rnnLBTN)izx$U0M*%DZ_V_C9?(ViL0#LMAQ%yv>~x$Dv;YaLzKOdo
zhV<ct*da`p%q%Uc@&+<u8W(BC;Mu`V!W9fJExk-YxjqEu=PJ0yARZgTs?wva%29|B
z>VP2w1OhrMVYxo)Yg`MC_3pJ60H?zo(g5OAZyWMJ5Q-2*E4S<o5k&8JVLBO%p1gtt
zGrAVyv~9L&s?th<g`TS>uz}9Vhf#f?Xq$$c*c(ir5i>SfYF14Iwti}+6EX)fftViw
z3MLWp$U`DQ9|R%2+`!7)8!>q3t=<O{<&udRb<rK}`@8z=A~9d06PXjhru#2<!4oGS
zy`0YgoguFY<|aT!$C3q&Ef}5TxT7EhZ=ebTvABWnCV=|4e?gLd6Irkta>c|Zw9;XA
zxsaU{v3PZIVclsi<P_f|H|rb&q9+U8EF(vMln8rEgM#)s``6eG64v;5a#efAK!@=L
z_fs#a^8WyoY#k%ywIP2G16Y0N5K|3WjnFz9=77Wn+%g>#%dp|URbvX#d~P`*?8!Qr
z`xY!1NKtGvvx*0ME^4WBvn|nCgv@*sptXe(*0{l785KdSCAWFGS1Ud$$_f`Sh>=Oz
z?QVh(4ZJoH4GWFRvs>}zSFwrb`b4cuuhBgEpyA#$G;_S8>;&K(PV0&ID0FZ@XDOw^
zp%@1PD==WJu`9yM5&7(aiI|vAywv9U{>Xz2IV12z#jVpXB-_<|4p<dPX!c=HWk)pb
z9}U8w+*#tEg6!@GP2}g|>16K~?0%OYvOfKcFC0r`*?&)(JH@P?qRZZa=VT0)NM37%
zuj0-5H3WCrOIzRkCrpep5tWcK928zptCK+D7Ue)O25A0@k?&e@G|z;)il)y*7z2qg
z(HU7>xZqp>KQ$}cuF)L<DyH>H7xTO)S*#L%Hbi%mrmMy&hJ)8N1kOi9!&4Slxa64D
zlV`MgE$TS&+ey(}cUf~sd%Y4k2w8`GmGLqUl5Ev+7#Z#ZsadcHO*kGgM+{e97BG^g
z?Q1`~R4*4Av=j`~?A010j7rg^Tr9XQ2x9Lr0PY%)6(^qac4E=hSB?UinO%$<W<-<f
zsM2k%G}&}pmQe`C?b>hiPp8STdGuYBx%h9OU7nGhAG{a-9f9oCj>zeftx=$eEpWuU
zNn?(fbM+~;pZc8x{*~G32UyF5pNcm0L^ZLQU8TPiU4^DG6wumui(|`OpY9W`Uzb&z
zU;Y+irVemcZqK4@op}x`u`tzW1H5s?;k3f2T=wK6PbE?>lqO<uAeX!75P<Cvn^`*p
zbmDp|HcgXp)6n}(=$>9UL=m!$M>`+~Jj&5l1GLtD<8gQzHF6+HxyvPM<iDEVnjY2_
zy2g;}+IlC7l-m1KX~=$ML5zEdk7_z6wwdn^JbXAK_b(*w>HZLqHU9vWgg4&d)S(nG
zVRPNDG&fr4sQljI6*6iUVQFyqr+xK0=LhMJ-47W!fXa3`{o}#IOnl1h;}dOh;tH#2
zJUj5;#UBo3aM%K&P2_QCDYR)xAYg6mu@_Wvd#}Ma2mV~Z2ZCUpIij<5LSvIwPrsh1
z5hoLdz+F+Ubuu-*z))W^UEEI(Et4>gD5Oh7*b{Z0yF4Z0R*f5cwiLnlZx4l#HsrsO
z*hV*Yk$u&Xa1qr;>j)|2QE}gaqQl)%-K9%I9Ftra0CR!oqLEFxjR^}aQIJe=h*=wi
z+&EkclOPg!%5)a9k2EIB@hTD>(E>2AsNZlNisg(IC!V`B1`sg?x8$BMHP|5Ml53!N
znzM7S$`<|xImCXDh4e!WGGiICs9oH?@vQ2ee^F2_vWW}day!9>eNZsbh{q*28}rRc
zV}yc_jgGdkGe;9a%i0}QZLC|@RhTwZupBKIc%CH#%V3xnKmuwA3T?0KyB;T1cH`pV
zI8?0cnB0e&5rM&^43lNT;!|}t>VZA=U_6Ga$wm@JUaA8?fwV}OK1(5BvU^DLM<qF`
zBNZlLo=8V78ylF9@|`Xf1+-Qi2iYmOv2MrevIG!FBWJY`g|=(6#1E>ocx=d0kM1ei
zR(ua2skHv9sBs`0@e3%~eT{ci#`m<uRf}TaRw<EaOh43`0ngPrQ#B_x>=Ifsn6zx9
z_qC_l6DNx7?hRvoqZc{myS8<A?JKo0p}MpZzwt(2KnL!-eNQ3(04ld>bmm5%1p3C1
zs!Y!GpCta$2Vd~Jd)No_8V~HdZ)bB(QcF-{<J@@q!tBq*M86SpOdMR$!%nVk_Z`6k
zUERp)*9)RC8olInFc9Cvg}j2$CyY;qj)z1p8&Te_L|Do4MnJ^>0D=Xsn_Xrl97nnv
z-5iV}=nZ>;ZhNgHBQvTS&Y6x}dY~||gPrqGaJmQ=jaD(Mh`Oz&P8d^jbwd8LWKVZY
z2GSn#micGYr-uwpKU6J+Hc3;AekEW|@!&Zx=C*v+O~g;p2?SkI2G%YmP%_hZ0!q1~
zyv<XKocBp5n$*hIMC{-_r|6<x+A`=7zZ3+xIj)^-wUB)FQzTA&6+>CklWl&bQKHTc
zj8W>I`jcb+!gB?+RYUD;%5Yd()2yg@ERbe$Q;!q^OlGdrk&A@KIh=J(vAxkQ#v1Er
z78;Jjx+5iAMxY78s<qa?ABrMeT_^OXV{@q-Bbec{!Jn;@DF7<GNNAne)p13ol%E-<
zk#c<kwQkV!yB-#kuQWHjUW+E%6RZFV<-#gf)!c_ANE5`L*$B8W5t>8{V^*Dqz)X4U
z8C%IRT+F&>vEAJuZ&!y!LA$mOaNa!1bDmycHg!&>+O2$dX4rYBGrMcr9OIUHrZuz$
zUh14=aPah7t^&u2SX}13IKiI^&|^MIrs(jsQ*-FPF+!zEUF~7GPu`^d_WcvwX#jDz
zMcLZu$@pf<pLR1pnLU_Va$(6-Fn6poB~%S)IGAwHswcWK1Z3`rI>znbsfRqlZ084w
zULZsjiwkV~OmtXFuy=#=f-+hRmQcJ;tQM+G<x?*#6Cb*@juz#GP7(`Gm{}1mCsDDm
zSz*&9;ui?}AT0BRP-CiK4|EMw?A1k_Hs^>hdOa<-$u>$jpT|I?;t+4~O>4$1{{YIL
z19L;J&INFvU5^vmDBN&(3&|6@VZLfHxtNS`3)N{%iRys5Zu>}So+WY33mQ$&lJ^1V
z<(f8N7wEHdUGYE=(U(Pj@wFs*Ol&^Z7*#`CPmeUf%uaEJ*dD7RAf}eL#5~ZPOfB_B
zTt@2$%%@Hk!+*m60OP(;yr3|-poB;Pq5?L=PencLCjH|$w>b&FeDgWpG*U~X&Zv^f
z-FB`2013mxVgw<FI5~xf@tj?FPM7y^TsW$jjv)%;f;*(HKP1c;$tCU(1gDq1tpXci
zAyhP(@%31YP=9vkd+2XDvi|_oQM&MZQKvL~4e{%|jJh-93r4v54Top&>HNygUJR!D
zRvFJlOcw$RcR&I-qjwo7CrQ*R2IQ&-cdf;36{K6$6e4ukHbxd4MKWCHNpjw48IP(v
z!Pe!qj&bA>ZAZrN9?vC|<+s`_A5;&C#c9odIHQ*Z^GzEbNZ?8%@D3^96Rr)D2lUxF
z!KRBa^@v@btwEH+eHtym=G@*&uIk5w7#bI0F_W<wnd(<!pvP)s_D*AXm|do=_&PpH
zqnsyFgo49ux@N3#gX$;jRdZZKm?_P9?~4v0%0^4EMK@XaZV65yo~gsRXa+fj7nn^1
zoTrK-?<zUoLctOE6YAnu3@sg393NENLJagqLn$~2<})Z?3tBwTCEp~*+CxLKGi3JE
z!g9lsr-(S^G4f59yLel|q=>mb@*^hvlzJlU7+AtNy7Igtv)d67!8kjXMN=bq5GR6y
z$47|dRo|6Y^WYX?yWV$GX!sz12uGYPLMG3HIUq+#`YCf<5wl_feL3Jtr7aqZ{F7V-
zwBVWHbBytoIf$|b#(3nLZ+P412>0*9;D~MhYjKURV{%W!2K>`r_&8)_sNj(poMsbS
zxNuP*LtgD>Q#Y?WqeSmZBcMN2NP>nMPGgHY<r`^DN{$m#gB6AU0Axu0*6W6R(Sm9Q
z&s!o)8p8hot$14TJvCVeTc(I-R+yfbK*l<e=1`EpznH>)DzGOB4Q^`SRK;}bIJe@r
zS>HZ+-&Ak3VxdrhQNL76Sl3&NhLEYnH`{wYva`fO&?j!ZIizI0m*H(NZhyi7tZ#*b
zA+piIc7oc?Z+T1elcFta{g)Ky3RKSM(>Ye0&jG~^=29nzqK9|PdtE%t1tHCS&VVPH
zf*{{Dze=UsA5_M*k0q{ig|f+2Z{B_w7uPa?AOUm5KR*rpD!F*oiO%kv*`+*_Y(=p-
zcCPWwHfSCb$fx)77=}>pY0k#yaZY0?;+BV-zo1nq;3nbDU-*R~R*SQpJe5||bY0^j
zxTcckOTXNsOi#M6tdubX4|R(S=nnvm$zQ=a#)V{c;)}c(nS6-R2^kT|cpY}0sN9e}
zPy!5Fmdg$(94u*g9XP9!=_gdoKOLDcIp>rFZb>I$^jo9-<c1df`s_Y2hnP*m0kG(g
zu-}q)=x*;Yc5u-d*ju^2F(JI;^VI|1mRNH}U4ZA#+X#YSgbnoIff7dYt-J`95uTEV
z;XTcfs5+?LW*PXY#z7232;x>vq+RHzalaQXC`px>aZLmPdFGyb%t6hQnO-P|JAvSy
z1Dkqe{E)ReWcu4@6&NZt)X6*?vYR0z3AdWfF~s{?LExJVk{Sm!y%GRkYr~m66ZZkn
zj3x?rqYK-i;;I*h*5Q&F3!)~d?@3ggMA-FJLpcrT>X`bE%3l`fn~kK#5o@X~4=?W;
z9dKBSY0`6~Q`HeZHva&WG)}@^B2%4U8f1K7I}vb@y_%)JRqZjtczug^wFjU;o_Qux
z9oL5Ad-Wn(V@?GLB_F+^7`d?i$+nTA?O2YjYi@)HnK|@R_i+CJp+{e{nJ|Q5H|wzH
zosnpJuY-&%<`KJnlsmjbAT-$UIil|%hs6=SRN6QN>^O<!PzzZno#8YP4+WWQ*!_p#
zrfl=cQF&z9=EKyl{6UQ%-L%Ft>&131_8KAYGxJrPXgTqO9}iHf7d|ETm?}%>Bu*^J
z<~AMT+#wQ*%u|8;>~hxrh~K;*ewK~8mF4nZ{LlV~oQudPUbD-a#uu9Eo%dADqf^CY
z{+Yg{%8&i6HSXg-)SNTN9}Swj2yY%L;-y;-S}y<~yO>SGPY8xYVdM}Fq$%BpQgTee
zN`p(^e9^GiMg@9wsY{*jJmyy=?KlrqeYgA?Q6zVdzZrxNP6^Ot6`WtH2k#pW{Y|&U
z45_ex(i2J4!p-#9XO_Vs)mrb0I3L~<ejF9)J-Pgj!)#abLRwv68g*2dNEs?)D7VQ7
zgOL9KC{zvNY@B`IQ-h3e2G7K*VtU)qLUt)T+Spw;a61|0J1QJ5-!0G{JyiGjd%Boy
zEjry(aOOf9Y|p$TepJKA=!OO#ZP_l+=yPGjZmi-QKC3B>k}9;~91)UQ>-uaU?uu{y
zO$hkhY7y{-#b)=0ryl44OMT}`x81tKA#I1pOnevfpYz+g-XyDvbj#f6;lWz1Hin02
zTg^CG?J|vv$yqo;WH%Yb<*G9v5wr}lD>Fs7x{GXZd2?|xi34_ZckHSbyjynOR#byK
zsu>T8y+K8W+IvaY#HBjI<a1LU<3(z6o%c*JN|4upL9A{GXc}@v3CdG+L7~lH7tC|>
zPcmi%<P$HUM;B+R&EPI=s%L#f-ZX@FL`EG40J$`1jst{a<r=BN7OKImVE*E&OJ08P
zo*69ZpMtceNFyn?ROuj%%-uh-?Bq7Eh<2eG;lkP*r#?XkFM_S&A*G~Ux~jsZIo@IV
zDIv1qJg&H_>};2aEj$g;-Nb;y11**~8p-`e`vj6yIpa*gP9<4J9HmG3Q;Qb!U7FxN
zNRmVdB?AW{wasOzLv(nKr8u<1zu`k!OnRV8OQn&Z3Y&)!c!XAFNm+xi^;ayZmjFT_
zv;rO@UMLZO0qc3$s|!#cdsE=NUEp5%QDzGxph5<y0TnR6tXQrH!)iD*d0ig1M@d5c
zQwRiGRZa77Tt&Q;VjdYzh8Je#F-LTM<vS60Gs+OZB|={I`GC7MPel5Af*_eAg6&kL
zwWZnc%K5653ufD~B~oi~GYC1wtg2vZZKG4|SniH6ypWQ><M37L0j15y1ubv8nChK4
zTyP#oZ9D})b83AM-KcT3L^xd>EgI~w;=4Gg^1}O$c0sgQ?`45FF}#1mr7nyzc_>re
z24Ns;h~%Y=(X+EY?BVL4fsbpN;y4hhy#8R{-FL?N02K%CYn*gde4HTsB~+z<&6ibB
zIn5RV_c<vhc4fsEVgoo-0BOkc=Nk@NSO~G62tiYmkE$7$D=A}(1^E=~O3XO?P$Zod
zKIy*oP1W16pLUq~uENID9tXxF@l}bu#-4~r9!vRO{fap{NSKh@-9sx!gr65ensM+W
zB;!8N<<DTlo4U@^n1QrNo2>CVeRlh&W}T_0{*@dlGu|U!iSsT!-pg1w*Jg}h{{W?E
zDX@A3Y$2n{%fj8(`7IMmOC{yssSg%l{{XzuH#;*e#!neT?!;quq^A~07@+F1?xlE%
z&ypq>3+?%Cwa5PeX<Fj=BXmoHK|k=DVkBYIB27Pa`^ng9VQagmYa(}3Lrq$bFXBUD
zC1>2AEX(<?1Te?xC_{-kDvc~4gQDVvv8J)rTHV7wCh)R2VYp$OI7)j+#4!f8PsIXc
zZh?C&U)FIi5vJfk=(WvQ9EnF33Pevg?x$VwGEq&lehO~le3P&WPp2_sc?smfu}^Ej
zb?h#?2WJl)T7FWn3_vvDZyeK%w!&{7s=M6B2N<4lv(0-$7naMCZ3qV%GSU?`G;xpJ
zNfn0gC+JpfW%fG@OPopIoA+Xg4e~jamHtR+gmox;!hF~BT(=v<rpcQ*-?G0{=nm}l
zBy<w5A!jRfrm1Z3fR06GOu}SM7W@+rqG5#tEXb7hwe28`S`MncHx_##8^Jf?hFY)4
z+3Ic;M)#fD1fH5GbO>{XN#pu;l-w|LHwxr5mNe&_Nm!d+&K9w<qlvt^Dh=;uH-?Bi
ze<7kR!VU)suvt9f2TQD)2`epA0i#q|oJcw<wK)w!sZe96NY+q!Wk!}9M?6V@02dOa
zw5z$GJKe*Q1Vcc#5ouEA)nl7bILzSI>YzNqSrkY#qTjNrOPS&1hVk((D^hj<NbK)3
zt+qThqtD=k0e2I?EJct2JrVHxAya@wuYHE~J4E>apMjino`_*@9ZuZ_Bhh8S7y#K1
z#bZy=8QxRPHqnaNlawOw(g)2vMu7u#>&|}aKXXpQpE8U$UWHBypoSdf4v6?$8*=!o
z<E3D+XBI@uO^j=>Uk}d8KIyP}>a;dOF@R&L(#mHWOpi55-ixxo1y<)AGE^PZ*;8XX
zuyHuMk4091Ewzg6?8nPR{{V=L?Fzd~Te~AWuG@-Dm<kcpreY$&(*Q2TLHE3gpH<oW
zGccCSH;Si9Nt-o&-ELVGUD!s53_>0{HFu6Q44QfjTJ%C)kh*lXlUO|yK&z$m<Wq3E
zn8SxpC85bfyZSOY6Pk8TG0|C0c05MhA)HQ&6Pg<X%F><ptkdeI$PA$lASvH)Y_APA
z1^_uKZ3JpR$~QvHsC^uoN#GN3jCh^Q?FBTzSY&ff86!MUvTgXL+G*c~)5kz=fi}Fa
zWLbNGHysxn@dt=ls?r|V)34ac&Yw+(DlGX*%9Tw20AKuUl3PNU!Z^aE-LqFd-wf4L
z+8^!>(fg`Y=r{7W=2vOPcoQErOs@_XgZ;Y=j~}WT()zD-&qQ%QM(~~}xU<<hwK>0e
znbh4D`lkpM9$OoJDaKyxCz04xZm^q#E^1itT5Q*5UFge{BLebtYexlUG(g!7V}RW{
z$dx6I@H>jj6eE*=`Y&O)hgElTO&RE(NCYzp(+G+4MBt`=I+dGTJIF*1s<?F~wwrGt
z?%6vNcbxOYoe*B;A5?NN=!C^AAKzqhcq{qO%bIFt@n(96y38<v=d=F+ip%pBM85{%
z<Pn&}50PHr2v2hV0Gb<>SR5~@((1M+;u6-6uED(HB{<pFjteSb&Of9p3>E`2zZ<gv
z!Z^20nv3&8b;lDr6zdh8TE7Ml^0g=6h2CItgf4SJ-Kv=#9MK^H*G3wHffhjF4$WRS
ziRh{fM9do*UeLc){I}2dY~qA9pt-L1Lqfgm23|-Jfw3IWGU;=Chxl4A3tgWuz0kM?
zaYzt)ujRkww8Za0D<Sn(BC&#bpbXR`IIET9ywqGgU-T3Vw3~Aaiqf_H7GrezAti|9
zOJ|~}?^JTxEp8Gg=ztOd^gDl?6tVRDQb}no;H$S2nE*ruOc&0!DZsXgfk%szjw;nW
zIRP03P&>3lRjI@BHB@s=ig>9}>;&R$`7X-9*}Iu@>YOU$ekO8Lj-5gWQkztgGHY{G
zolcN2)(qZwCWf#uklh*rB65XXHT0O)VItPlLhUr%{9+<=l;MaRF6t428)MZ}Z{o8r
zL|=%z3*2pz@lm@uw<5D3&diBk7vG^?6PQ}4zzzY`3!|CB;mD_Z0(mbabi?Ys;eDEJ
z{1C9rrw6&Ti}`P-U!K9Rthl53eybwjC*r;Gpa{7tn0rR1*pC&=>^<E-iBW;0dOvh#
zKDXH6jkLSKdff{Fh2xu0*_6T_XbE_aL`8}CBQ15Icr$5NbGIAIp3^^a!tP6o0D%@d
zs@+0+k-s9UyQWA!biI=VntRd5V3uOu2Z7B-7FO`PL7EmB6@8gF$5>4c?^<s;a#k~R
zW~)RFWA{)SFlU{W<hXBpO#c8W#g6SbOlaG}%=U**s%b52Np=(NreP-*8TPQIIiwp%
z;EP{UyIb836O<q)43x;=XFst`&vUW?I127|BG$JZyq9J)-7p`QkqB6YBsSy=6-3e%
zSmNu=w9PTEhj`))DInD`>Vzi%I;R-{q<Y8IH+l9C%gGI7%7o|#KO{*qpm&f+*-Vy<
z&Wi|dCj;_Nas4*<rs0BR#L9agP2&5Bz>(}~m|JPRm6r(FQS9Rdc6v`SXMu(P00mvZ
z>xh2KwNNeV3C*cR!*%S+bsHLL0Sy=g*a%Fb+BHD$d()ud3cKrXU98K^Qe`Fs8>8l`
z5>ClM7X?tx64p<=>njlGDaLUW<Kb}luNf8`I<0T9o8BOK;w#t^f;#1}K`tci8nk^-
z-tSCwjlEY=c`TgTgn1@??LaZRmkpW-Cz|5lkWPw#ecQRMT}u3Nl8277ugSJX&vbez
zwqX$F4ddma!f7-dK<Zt=W$e4&98k;?@YQ0(e-P<?0`atAXICb)26);&eGyq;38NQ%
zpH$z(tN#FC{{Sc%PIbl?4sAxuCQ&OW;lo9`+e?ZC?rQ`)NL3vu?+VA_0{10hm*fu-
zbtaRIz|*2}bt#4&jsW+1r?V{=X+87I5zB#9G8_>-;O!WB5Cq+Q%Z_b68RU$2ZF9)u
z10aMwjx9cW0h)A&skvS_5;~z_ZI1)*ne#!U3a)cO)XkHvoF@370)f#!oCx4kzzm~I
z0oLi@jw0%rFfK_zz@{gK+yeG=;)#;7bx_mjRN3xX8qSYRo;R}#d2>eRG~nkJNgY$6
zlxfKV<m0eRZX@luNkqBO$BE=&4{g>(N-Gd+awjr)sGkG>08rOwvs-x)6)L#=f*#DT
z;ctVqCJZ;2*30>1k1L~cfCq{v?|V}jl=-Z?+JR9bxOv4=eG~97x9<<Q=7ii$rbuIT
zbG_##mo}B$1^2kE{p`hzrpxmq75vbS?YKxB0xk{jH@wwt%<qy7ZAXl6ohL6f+b}Q*
zn9Wu>2Qa?SMYK-?b}n>s@KXin$vA^KgBhaj(h1~HKaA%eHEq-f;ey}jDhYWJ$#_VI
zbWZz{aSn#?rW{a_DTD(3*G9p)PkqNPC1!(W42I2Wx8}Y0{@ZNG-FxT~cmyU*gLAhO
z16wv=M|PW9#`FX11XRkqT~t{-_q3mikVKc_%lANHH+Su^n)r$hFZQY&heSwhMCwuA
zd$5J?txPN6EfL2Jl{kV+h`x7?m*9=HMLGbeB=XfZ1OlCp5%eVu=7|Oo&AZ>@ZefsC
zip~yg{)-ic@t6zo=+nj4Gej1Xssj!1mZv$Vp;cH$Rw}6mdW1z*@yMb4F@kNpL={UA
zBiT$XBx*KAoLoHr0O1|b@&Y?Q5Jb0+FuWmwr>IWL6D^?!7u6AIsQFF%#}e_v*M*Lm
zYY24v(!08jaKKIMi40|2ZKdMGI0L~lqiPuem`;2F3GI{Y@Eiuwc(7F4-ZWxw1yUT_
zf$tKI3QcsJS6{>~Bgn!7BnB1?j}w~qX<;9!*(MRLX`kFc`L6A^iFHQis!}D?4)Av9
zmD{^Cezn^j(T$&q?KMyIhW_imCqp7WOR|hjX?8a_>BV+hTerQ+tH&!ROMTq7i=4V`
z@Q(#gX`kU-W*zSYBYW(2daiE}(ov;E9J|+myK|}swjRoD5%FD%8_R=b=0ucPOk&NV
z5p$0eYC8TB+Y{=J`;GRKI&8i2{m_Xp0{m8+JKv0fOml90N_-o(`K(4=1nJQP(&rrl
z_A3dC@iFmKT<UeM!xOqT8!6~iPMreFXSC@?*SDT~TNYkAqyCN_Yxquljx%ww)?;L+
zRK#lH!bWncGeSnZ6%f!(s!s8as*z=S6VdifJFSC0tND1nN-5#3cIHw~5EDZg7HHwk
zJ))aj=HeqQb3v_dcIkJt?PgQFEVk^qJd#1EL`IfQAcWCNv<(M_r};u2L3!oCA^bp5
zn5B#R@3X^Qjt%`|B7_iOM(_fDDbO6#7<EQalYx;Rr99&IT7(H_)gKiYC|RALN@#rC
za1x#3k~v*ySMt>J@)Dw4R<}lQcA^O6=8OCg-8!!f?{rdWR%vw?a3OBN-bQz<jJP*5
z0bn9?#1nvaf<kj}fJwgvy@Nkg15qcuil5xPK&!eg5t^)^#<{VX0Z2R(2-4Xv1!{JS
z?>6m19P7rQbJ=Hi{{SGE3u{ph2cD}YGB!3zb_3Hi_!yu7s}R<Z$Vc((HId1e*gcPt
z$yk?@2<(-To4L;VeHLLHuzb)PgXXg=y3Va906xJW;ui|?=ss%K*m8LJFXj|K5LgdI
z2CK$8gGPWd>Y`nfd8QMK3jm#5Wi;c)*e6U^UTDD=H}NmSt6&?S6-Y6yaGh)psq@~r
zxO7vG1Ox2#>bM~1KOc0?L8^sM!oCunRB#3^sA15OP>eu0`K@JS*W7Y8Qd$Uf@dae@
zZ|XuHKw<q{727cOmEj^nZh2Wqb|w63zz^xoKp=*g*-sV;cc4u&TxY7xm{BckdM6gJ
zlY|cuOu#ido~y-*2LvEGqRq}H*X$haw1{c>36<e#aW3V}09bP5c&3*fPwuyzJX{m;
zID0MmB3j>dx($aaq5}S5Uh>t%q9NO-7N~BMW5?`fZdK2Az)vuTwkOp%k%9C2VK;_Z
z0kyXd0#jS}f(xSlNP)iDDUZR5N!7(R8rn{oO_oxZpQgh+Cr*maz-$hv>uxHEZxfx=
z4E_qQoxmxqs51ALWu<;g2#qpLAcAvIbPCR8QgIbMk$f%+r7CpCXgH>1IuqXKwVL>1
zR&DZ59pkZLXgNZlTV0i>92>BO9pP#52~WlTp0r?RNtAo0`<kApt06FmXvOA<bBYMr
zos%D{j?BNp@mKr9(?s`S861~sBLhQ*uQQ-jW#ii?UWw9hIQ%}uA$o%dx7jXjCX7Pa
zo(gq=`=}dQ_nc9MqHL*(O>vp3;30qFERcLOT3<e95$$ykyI{ZY`b^+o_k?<`(B^^e
z?y8P)=4CU$;8{%D5&e?`K_%b~24iku$V_1`7vt4DgTSVi^&36>4*kM6C23@1eYf*Y
zBoSty(p5IPi>en!Mq(uwo-H~p!556&vkn-CP@eWf8!+h7RZqXAyvepE>NeTH86%o>
z*$7RE1LA;^UmizhrA`24-?DD=-2frKJN;Lx?IM#O8WL^6Fz-Aw8?};yW*eBFWGoDg
zTGdq44>(F7vZy=SNYQCbH_>IO&?by6rzHEt=1OZ!=zwJxQ)zh5dpS-65Ifprj{Yc|
z)J)+Af)NVm#$!OdRVkMrNngalA0!|&;Q)SHFNzz%?w-0S4XL|XqeqY3T3`++!G%Pe
zPw=!N*0&x)#sz}jE5>ODKbJ3xXof>aJSf``aOzVCJ<DgPK+kp$t=5-UYjCmsb51?w
zS%JW9$<#4<)3{Cnr%AfP0d~6bOeA6@ysntsUBjFVa!w&klPJhLBBS057|l77nNJDM
z1apaAfp1}Lf#kf_yOf6#j`kvFnmf;=!hIJNrtv%gMW++DnLFayr{(Or&P;K6sOOf!
zwV*0+X_6Z}4#2EHZT!{;Fqwzm5tljOlyZGnc@qQVvk-DbUB-OXOb$6)H8ZnK=Z<AE
zTu$nUqQJ{>8I^GsSNoiogp&*0Xg)}OU+$2-myzgpW`w`Gc^rsoD=_i1%aZXXmp-G(
zj;f<yU4&wIz8Uvc^XfOw=vZnpSv1Q}7c^KbA+Fwe&l1Imh~EMbZ9pw7GX*`Oqy2@w
zR;<Bo;h{1rHPYNxw+pV-Kpp_%KLszHMW#xA?I)gj>aT=B^+p^rJrwt%#QWV=SO>vz
zR9N}S$*#po`1-Rif-u&cO~qwVmmAo%IoT2$jLBcfYrKkhb{zQ>+qc5#LOZ74s>$}0
z{_dM}_MgZg+AN&}&r(sm?|$nJ2A$IL^5UjlCSCMSCD)nRrA6VU*&R?Jx`KYk9hWP`
zQj40zH(S}?I&xGuat_iI0f%|Xqd3L(AB18uWj_=+-DJi}*Ci4G?%mN3!<Nqzws4D;
z*U=esUT2nzCYdqGPAZIa57dXYHk&o=w!(X}Yi3F=Z6^zZ5}4A^Fvk#u>OGBTCY|Kr
zVgB<?ZN;H+DtS)N8-Q^fl^Ty^+63oxUq!!^+@OD}G;T7D6ETm@z^IWv?u)flY2fgv
zp3vYlHuO~KQfnO@*lbw|HJZwdZ#_cn&u%I>A0aW)0rFjvk9Z<0HMm?MS9UBg@<ho{
zJF+Y_Oa~V62Sdp_2-A;5HfRO>(_c7u$@jS@-}6~#!DZdy9Wd;4G`M0Xf@4Mj0G%2W
zi|RSn&^0)hJ7!p;)o6afIBB8|?x;!@0_4~#^xh<6Yc53Ow`qeRyD7QngG8$Apbky(
zKhbtmS-|+KAD~6OmAZFB4f&@yyO}`{6$-9zyc$SU-2=&0W9wT5Fn(#$Q!#gA3!^x6
zi9t|#2O}0#n+z9da5?lqIjyk`%y}S;f$X~D;;^@%P9W;3HPC!_o1#fNJ2fpm;J3+C
z#fGjI4L~^+7X+emfTs>lk96jz0lNv+ry{k2V~|42s@E~VqvCK*CN(=|8-x`3062rx
zryyeq$vPcS<2^J(!f>;%6~b5JR4~T%)i}5`U<C5uvjiVSt6E2*d1%671l=5}wHT*X
z?EJ1!+JTXb<oGGurQ64e37|G$4v2!|mn0)}N1|Xgt6i<tfPU%wth0<?q7zQ6zJ8uc
zM2ya?&Q$&X0BDH(jJnB_Oj#cTF(2p>p9H58ebN;&`3EBdf-W7M^vNXO0U0?FR}+9F
zjELmBB?!>r-c?TN#3Im2HmgamA76^dT^;V_%^gkFYWP6-IaMYe0c>&Ny_Pk$TGLDp
zU=r;GtN`Y*3Dbup0n0Q5%%f+L!~Ir3m$k%aYb?WrCgJZWj9qmJ)87)%KP(}*<GAJT
z0MXHFvWOruUUGOYThKvhc=@KafNwgK-^-F?*{mCE#458B-@;t4Fi)DqL_j<f!-0Yn
zONGxa8!2{B>)ePoRS$Pky<y}cHf5sWC3}|Xv&T^3XF-`w1G}cy!1Ybvf50zmRN*ak
z=TznbZ^1l4)Cpak<~^{VCk$ae&s5ieImMGP4RIY7_BOzG(O}Qs3)yJ00vhp({1YAH
zE}k>fWO7H?sXyAXY@71hBAvvaX!2Br7R)^RPV-SUH)Db~-URbcaB0o5vSUl#)!a}G
zXc$NNPZ##%<c;$X<nSHGS>lOqF3t>dl;RE&0gUB%rgMNo7#l^7aAyo607l0`jAM!N
z%{|T|g_XLY?9asy3aCLFuEL(@2mRR&KJqt|Vfhd=Ekf0X_a_S`InC~x)-}JCc6TuQ
zVC%#w6#K$j7Fj!}K#@;<-b=`EO)4rMYk&y14kI46*fJX?d+(w)e`#xd0V()SOF~A{
zaD*TYhrs+rIF?m84R0%PgCYAZ+FUsGMg!X(Y7ZaP!)VFDM3W{tr+AzMEidS_Doa~z
ze0H0aX$aiakE)jv%tYzYNWo3B2PNS(2#>DLDBjMFiTFl?Qn$teV1z*tEq0C^SBSU-
zW-4MY%Xv}8%XneybG*xvcX~&P4(-#6nnv&KWh|+PZl7s3FvLB+%;>6AzD9H6jC568
zAz^(p0qq&Ir~S=SriAE;BqNM5u_r?Bnq@af9Mu@o#$`(jT4H{SwA(`kT;z0=(G#E&
zsk-hYdFZ^BYGn=i5TT|np|st~Y^zp}{{UD1S7@pK0POz&%I#GJ{{WZ%RaaFy64-#8
zfrwP<78}Z@Na=LL!(hqnviG*N*w2KH8s@6iJsbX4XOkm&_gENOY#7w*B1=ezw}{}F
zd#Uld!Bjo2q<AUWo~s9D6Zmw5A5`PA!axCHAIp1l9J(qFbEf90`XI(0Eo?5)P&Q@(
zAyjj^l_<ke!{7tVs;(sGby3HuLQR?zpo0=Dn%Yl-1xAPjpLqyy{{V6j(Mcs(hVOMk
zU8gQ-#P8-)3#qn~@pAwvn}aLSaI%9<wHz8x3w#s^#Y`Opl@c~FuNfAdK~4{&cplI^
z`YKMp0I8q%)d-gb+9^%*pP)|;if4Fbp3ehg8J!R^=R-%ydkurtf4Ea6#+@!ASG*RF
zg`aTQ^(ZOLRTRj{x|Cgwhb?%ZMP@Yr0KTXb0&Jp_c)ruoFk`0NA@985`l_yVYQeyM
zP%KKBt6n;_p=0h^1h#UvVL7j1{3E6QNq`>c00uzo3xmpRVWKI|HwC;(2!^)~XhZu;
zu;M=JQ2zk(jsF0rY<KN(H2JnXAwvT$_Z<*8d-5;C2u1jtP=Uh8Te+z3G~im|9P#>4
z@e)))unF6t^Bqt}6^2L#h{+p+A#XM9`@lViESs7aqWu2=DOu8{8e`t^ADRKpVYJ>*
zhB?@q918N}fIH!TIq~X&yLv)#Y@7uS^nPofB;xkY?J_cdG+g?Cmro^|fS)abTTSi5
zcqbN!v6&FYi>i29(RUMc3!PD=jbFkQp8o*qv@U3W54?Y@CvQX+paTzLbae`2GI$Ot
zzji7yjRW14kb->_4bYfywU7o@;+tR_i_c^7(?v>`p<SFdH_hfwOR|Xersv63s>l1J
zW_*@6I}nJl2ua1Bh{|(^L`fmA4?(IdeL{DG>4|SOOb|0P{{Us$NPYvj_8mlKnj9kV
zWdaJm3z}cLI9a`ggCHX$-{QUkH(Jveh}FY14h9@g{W6D$lOIK9of8Q$#W8@yN^m4J
z2!!E^okH32wrTFfYP4F`3l;}5&T8*Vs=VGW922UB%#eS=uJ$;;H;W=3(~$)NI;fH%
zdH(=PXWdkDqms54SoBjU?*WW`Q9p6o8vg(%SyXe29U@9`z~Ua>awR$$JH{-qSZ_B@
zp?)1f0PDsRhw0UBuxJ&+q)qZvA3YPWk|z$SwI9aezq+6r3(gyxdZ7&zyT=dA<Y73v
z@@Hhur4mlfS3wp{mjeJNe~z14*n#V}P*;l!Pr)50*K5ubPZV$`PqB3Xch>6{Op8O#
zd#%sa6K6@je3SuH*=U3`YN-I;<U!QRxL)DSIAd$3iRGwO8tQ<8B1AN2a;iKA<Ywx-
z_#4fRX~cl+&Z|UZI4|J<;E&=~fO?ch7Sy*S-P5j{JQaTI`Q`CW!b|r_$T<jmT?HQ<
zTt~<#O*oCp@b^xgQ;q`RJi$#aacje!z^b@f!@Yda4Y0pdWeC}rPr@-_X+F_U!GK)B
zH}X~54C}h4LC-&uCdt+U;c;02r4&^blf_F+`6{d~CSddGsk+I{YjY%#WZl-bCq!{0
zY;zkKyQ|JYJN;dD{{UpFj_$N&FaiUrl2tbm(Kb4&t!r~=U8rJ_lS(xn=MDk6S$28;
zubS-sgs#kO`HlLj1Nx;<Y;cmPxMHuyd?Wm>_nt}4=t6(+yT=|1t1fLQvC+Z7QhCAn
zH9(TX!=hubI?OzO%0GH^_kYR~^N>3-?^>K=77vQzQ}8$6iS<rxSvG^8s%`=uh;VC0
zYR$w<d=Q&kl9fJb(8+PaIHwVwTLi&cFsqK>A5wNmHL7}sA-c;qnOsoO9l`TjA)AB!
zZin!H`xNX)-5<J&bVzi8Z)Bp6QN;s=@M^WcHGDIS^3gu$ikvCb=sKHmQInbANka|*
zNxbHHElQ5)(++(znI!_!;4RgAr9`~qK2}2`nb81Anc%f1{Sbjiv(vHWGg1ElxhLR`
z=sZxxz~q4#YByEkZFlmyIiZzeXZlbs527B{XkC@ot#?e2zjgIO?KoQAJkvLhV}iyX
zk4-{rvy4S>Tmoox=$$QlusynAa{MmvkU40bZQF4G$zCG!@lC+y0!z5KpnLjs$(?Pm
zNdUh-3o~-EVU+-xc7F1xBoF`yfXc!-2k2CXGH2w2U~Y$eQw!&vlnfs}Djd>xkIhY5
zIPDzxs)M}%&L3rXs_#!FmVS%HQMgCwl>FUjM-vMi4?8Ddt5e1Z_IP=w$KEwKjr}!V
z0P7Fkc!^FRSEdWVHYK|ox_3e5vq9#wvJs<hQCh6Ai3pap(BTDU=7NdN{z~E0!{hZP
z;Bgn5g<792;VRggL4#}Rs|LIX1Oz+c&uQXeQ#1#N#`L)Aj^GZzBqD0N=8u3+nj6fh
z9XyI`R_y>{;>Kt^_I8$vuch8x-uXb-EMSz_rooGvm;)F+E%BAB<Kk0{Qr~J3fFGQx
zbQ^gu3+3^`R;hvQ0B=RY$TWNr_E9l8pfOSd7w(LMkP9PY=DavWgUFx&4vdi>huxp)
z$y!+vm<2h)v+8HSCtj^Pbw=}ba_d5?_qbu^#12Zc$%)NYd1YE6Z|w}zoJ?j)&k2?M
zN`NNe!MPu&VO>umj!m7S(`KWJu2%L@@xAW)D{LJT;km~Y00f(C-QEFD;j|OSnik8^
z3jng>rOtC>DZdqq4CX{FL~IGujm~lEntRu7;{2#jCHHO230MSsLtj+w?Hd{|d7U%a
zn}Pk-76y&y6EcA<4+cEdfO!LDn!|z)XIa>C9*RTiZa3zp&kpon)3FaVaqg%p*i3|4
z+-+@0F873{{pV*G9`AgAg_SmZkhXB1@cG?fVKda|yCY~;?p=(QhiJ40Q=*KP3HFR=
zpZ9NUhXd^dL*rC!!35<qit4^BH9>1;V*Hhc9DZ;P(-o={i1f`F-+;Iy=ACbNCW@@(
z!9M|*gQ6kBi*7y$fqj-FaZD0pcxIeyH-@2hYQAk$VbLU#7ipyT58(sz)pp7hXnQQB
zC)O8esZ;baK5MfV7|gr&U6zY*n46oGQ27N_@z-awBkQs?9-m{5!4`ARIWe=tGI|69
zyT;UO`3<!Q0Yyf+;O}U8Ct-N0Bg{%Y;rON=adrIWeA8Wp-J#5DxWO`=?U5czHfCxD
z{{VHDzE*;Eu!f4hJu=I0yKhzQkC<8aC*b3<WKnIn7w;92*NNk|N05tYwCdyZTljE8
zxWM4DE#28Mf~WnZ1PD@D-G;n#H(!HPmo_=*vQB7&0~a)Be-17uii~tju*94*1O(^~
ze3-UZM%atV&cf>`F7gX9pW$Q%g#ojYAOopcVmc#=qb<$Ns33&x@cr?`xf@Qv3xUXq
z3tFx|rDY&D^*YM+DM=j0p$Nn=9;F;_eOw#E-Ows@>M`vGTrwpOL@ee4tv70cJm=(_
zpb&##nJ$g)ld+&vtibR%9YQb&k2fX~(c*s|s7QRCVQ&HDGoovmM37$7?9T+}GX6Qc
zlfx>_+Up7AnFY{|yg%D%UZp-v<OXO4ij-MV@u?_(5Xs=8$SB=&U~|<}A;jW-6KmZu
zrLGTqnVv&c0EP^ZFf9XF(R)w8!J}F@Dx!@#ExV4SeOGvBz`*h&6SWGDdrhvK2U!W2
zDM@|~6Oal+n^DcaRzRVsTppWlY5oz7qnJj>kag&U191SBKxx0`ypYI)mju!UN=yLx
ztjmK+_8*CV@hL~9+VM`97I2?L!0`}r9*BxYa&oEJ-u*5K_@L0!0u>Tk=U9l*A8Ih_
zRwgtE4fmB!_OqS1K2oVW5dv~s*kUoNXA$=-Jrz74Gyv1cAnYE{R7;w7AIu<d3Yh&u
z7I3=oZnPdYLds{mzo}U-Zl?_ZS}j%whdXf0Lc0uOgQ9!GW=4}JLxxsD-NrQ7adFc3
zM}Nme9y*0Mi>43B9p;eMQyP><cT53G@|8ROHt;FMwO!%lIP3yUk5G(?HJ#)N(Tjw7
zqq#_To{QTglswME)p%n>7G&=J=<mmnMo{q45;>dwo9;+W-UVDtlAWC<JsjLgzMDO*
zQ=>uVo?bKS%@E<lC_6Hmg!-&4V_G+jh3<(bP|ex%D#i0)R2LtDI^7BHTs{Xqj*DiA
zb8%u2i(7nG&!QWy0uPdIBZoxHbkB*sQyau;nQ|@8RPL}5viPXC5X*RGyj&fhlBV!j
zH;iNoBvS_EI%meyq<s^CX^d>q!-AgbvqyK=I4QEw-wtx_3YG>$lfiaP;@9FK*~;B7
zI2*+nIIVA@qzHUSn}y-)vg*nq+JxWZH|DO|%_GgW*{1jQ-G45BxeYJWt$v8}9!Qy2
z9_h(3%&OyX1(i3q^LQ6*qFmF9#V!CUm1}OyQvww_tvU?T>`(|#<s~?<aAG~<b>f6=
zKJU#GR(dSqLJorK9S*~=?9z*WeU#62QLbZM79|<oj0Y%;kQ6FTr*x`Le>PkoW?~SU
zzxJra<~7IrQ=Hy~UpczoswxZ=ik8#=08;0LS|5yVmi<$j;0A3vrjgZuIj7L3NU^n$
zs0Sk`hc}7{ZyK&G=WV0f02D{E5V7p<1NuvshGpFa*N&tHepappf4E0CK<cT9sZYd4
zsFGKPDTC-1O{)55<`)J0nx7>dRuXXzd84d)C+GMt=7#WeCu6Gkb!X^5C|<)$y%pWw
zWyP_7Sy@#db^2UxqZL!|ULqs<Maqa5P)q<-iZYGyIu8NCZjkNM@J){P9(;n=G-M%n
zIs)*QMDW98e;2jiqe3UVJI!b18mmr8qWI19#8ZRVDvD+&s^-1iB@^K$+?N$+IAzIa
zJ02M7681dsIi?9N$A}rkG6~z_9~@Bv<*kliL<4EX#~x~5LH(R}gRktIT}k3tR;sOq
zGl#sr{L>G3(p@@2xSW=SUMcrylpK+<ecQzh@=Z4p)UU(^rl*>0FaQLe7ETl3kBWE<
z#d3sEdEFYA)i>~|V)KQG*s&4?w~DJ2su>{VYpjXD=;9IHs%O&6PSo#=qDbK=mbe>E
zUa6Pw9C2_QHe?<pWv~F_PUp@B%|5F59zHfi2{^CGX8Lj?)qn8mp_<-sJ{=a200IE}
z4md$<j;QXj#{4e3MKOuEgqTfrUba-~FzPJ|xo*A^rOlkjE~pVv*$yga<gU!jgj<BR
z(P&2eNwH04B7KnsIn8#5M9}8WZ8E)@ZS{qjM<=)ytK71(anEw6yVsH?c#TIS^NxfN
zKPtj)Ye&gcEOs94&fz9PJI<sr5vdkireqCOFys72X9ZrXow_1$jw#aTIFRnTCYWHm
zxh+iW8fU>hq}OA;R$vKQm7W1ij=y6?suuZjMjhZ{am6{NjDGhn0UV4UD}9$1yA_sL
ziuY5)u~IBx_dqbSEr==%X|fedTtYHkI9RhAEJGU7eyPN?Sb1!=?at1NGgO|tE)XC*
zm_iKZnRdVK&Z2m#gw*zo*}Rq_y^jI<AaIM%MY`NsjO>moRRpyC7Jqp@yA3+cBPU)J
zCQ=S;JT<wej7D*{riD*-_Q?8$XS;rhJa{h0P`R&=>@yIm)~85#q+uS2c5=)^am|@j
zm^Fqs01?q|*ODpHi5*d#RYa17g5k7|=nzr<F!`r=p6Ags?My<XJwj)Vo!Gecl}aE6
zw0Os&B1B}4V#rmf;5-=x{{V*rQ}aBVsy&*C{EP~@9RLce-yZX)=CmloUdBC<z=S0G
z+NX+iTkSvSgQ|caxD@$}#|^acTg`7bK4?Kk;*kFUxE-S9XBXJw=6H*E9!b~WY^k_)
zA7dtlf&HO71u|d^ob$jcebXsgn|f-Z?p#$zG@{o$o4mCVCo>3L)EXVtqYlzR2Q^2$
zZ)l-S<}Buzi3@&}Nbair4?UGjr!edM*JWrUBTmXC^aKKKVKL3>j2Kgi7$#rpY@7&l
zW_jv1drxutEX_>1f68f$Ph{zHf11!>9;<@2klnyuw5n<cO8i<rFo+zH-DzNsfq(M8
zimvz)FJ#}~JzsVL7H;>Z!vq^3-}Q8Pc%d|jc?b0)8?pVxHUOBDoTG$MH*w*7NJSb9
z5ICIz#47fjET__;l2%xuWL{3uVJYIB7X}^Ue6$K&Db#oNlnF$M7f!V%+Hbz>kR{I{
z;E$0)4ApUP((s&I`i+svu0xv1%}=m%VDr^x0ba|ReN%%QIigG6B)r-*fDt5-c}HBf
zcxneR!-ydqVu)^fYabPD`GIAV;lTnV3F^FTV~daKHc%-a2|2L-$*mkJM~O?Fx+ke#
z8HaREC7{@lqkAOWec*GM`kEyRhZ#)`u;z(@nB=S&Ft?kEd2@Tn0S)K78Reiv7Pa_V
zMp<25w2YITnvIPLra4A?HFyOL+U*Y<ovbfp$D;mzFNqr?1qR}ICfsAHiK7SO3vl|%
zGTj^&QzOpnHJvtLsLe!c(O+k=THiD*&}GLUoHrOGx0Y(3@a;Siu*hsTF3YFbu$p)C
zRNGRuTE+X>Gz^g>lO(K;+MCUJH3tP<&TFe3+LA+Nz^Y}foJL!aCN)L`-4F>9*!ZiE
z`#4^CDal+An|%l<wUu;!K=o1@8gQ2TPSFW0s_6cp7M0!d3)-}KC)RVdEr?a|FuK;e
z6?{!TAsLS}*OrrrVaf_{rrNbNy_=%#A@8Q*L&&p#!tHE(u~=MBs-`M7t&3zVFA)w6
zix7#V%pzvOLf&mkc-~^~l``0g`6FszWyQ#wr|*XXSUD$H!El>`QRbp+Jiq&Gux%EZ
zao{+pzybI#3k{CL?R=BB^NuOFU;;<%j3cv0CgBK9>H=Ml;84l8`x-6)DvfO4{2`fy
z;ewob0EBOqVXrmAW@brQGK+1tAQ+8T8zTPo;5!@6YL*ua+6J6akTOTi72#_L9dlWe
zD4bNcnmK9^7^qaA>3X_n-J42r#wy_>%QPX+5}T7jXHbp?x7zrpyR)>&r%{B6Kbb$h
zEp8cc#ptRS?`{dVRQ6*T=`+_v`Z#Lc*mr|ZB}vAcT+(2DhLZy&ga<Gdfx#bXzXV1h
zTX%(ysk^l#^k2p8R{1R+N22~H203b?z}-4{X=Gu%KFGyctZOig$d95eeMsD-F1IOW
z)_rzP>BM>kR1j(#aKg77ssbmb%1u84p893pW1@98h~o;06X)_)hb-lI8gk-zY_hK7
z988aj?A@K`oi*7zH})zN+6`vO0`N+t++E=S<}GQ2^5@8d`zIST$0e-%lq{z{C<uT(
zHfG+?S?VPnzRzZ0DT1XJv^mX~h&n5Qr?i1CIwHr{ci}TQBT|glU=QmfCHIZ@DqTCd
zf4b|i`KA%T=z1o*A9GH60h+xx<~VZ*;%Ujt`9S*J4BL;@Wly!gtUxyOO@_DU#T?Us
zbJ#<Atjk;L8!b+)kI@a*xDcrrF5zV0-Q0H*njPa+DX1wtI?)n_3t}Yb0&sBxBy-p<
z;vjWfj-tTB`H;PhdVIoc8m|}!CZl>(r~5`qaXpxRq^|>ak2frvMuIlPerk<cZ*5Wz
z2q52@uUd2kxvKTCPV-?k;`m(Wc$22B6TCRI>c5Wa)Wqm;f{74FEAYYJet;$xQD#Fj
zPZFmSfnX<r5SndB;`56r0izeDiUDrv3pPC8$zg`Syms*7oB?}Dy5E{M^2uZ}2t}0Q
z#3QEC2i}YD12jJxseJN99qJ^{Fq<qS2V%_hOu_)b4tCPW5(}dc$g2VPRXx<;;P(e&
zh?N#&K1ud1X&2?1Yn(=aN{3b^1uUX%b>a_qe@J^5%`zX8VORrxYbLsp^|B<sr?D9V
zb1B|8hWP{qjwZTn2nfTgZVp2^POFK%k?nRUjO`l)G*)f=*WykI;LwZ%P9OS)h~3oB
z@36iq_?X{n5P;Yd#`^3kL$HpPvX~?+!Zve8Kg#VwvKGfF`=PfS(A^aGd4kpMIlh}`
zEB%kftV4x90C8T?k<n)3euL~(EHIq`m)x|FtJkTB_%Rnxl3`<pghFs_&HR-xF$n}J
zo9D)OS#KWcvBAp}5nx6{W)$>6q#t?+_@EhzsPN=qc_{Hu{H((8)NWiIsMx^a>^oMU
z*$=$z*`NOaMnVS*={WTz#3sT208c)Uhnfex@5@+vwGx`)+410Z4`s@;YEh#Rh9m^S
z*4@B?a(JT?Uw9xxPN;5@u{p<1T+|lM8BzB=ovl4}+EAAh0u*=8689{{OL^_mnf<ms
zqYG^4;G5mm(bpm9ZOpY8v0wg|=A(Vq>9FmBsg3t%!~7-;R*QU;ec1q8RqZ&RR96j$
zBX29OeC^IXRzOPeG=_jO4d98ksD0V?ebdiihrA3|(F9QZN^S$^vla%`B=RLx3=gm;
z-x{lTFKHfp6axTXDIsS!L&dI%;MJqAzSH|A-isyNcep40)8o9hlgU+QXl{9l6T~3#
zO^r7#$aPN`oo_LscoJY9B?<Z`9pG}{uEswDkDQ_i6~LaQ8T;2?%?!j-f6-lyt(fa6
z_jO(FnUcIMXlr@|H=SY2?F$bRjzMo%fX7!p%5ihShZA&nn_@X^@x~G6oor^yF|P&q
z#rJbh1vNvBj$|X2VMfUclO*d9RcE)#Ra#|?ZJRJj;;28;P=!=dVekyx<H#-6#rc#L
zLQkNT=2ULUza&X>jmogzj&?%}ab>ZB?!+Ic*){^81BfxUpWZk?C)o%0U&)W{viCn^
zNIDfwzv?RjOOAt*fAEPXo35P<V?(_Ynq*=uoB1JdhkH%L4(y+jslQFQLk*@h35A9(
z(iZhM9p;@>nHO$lu)GxEt*5DkFutU_4p>J8l5m#NBqv-{4kYrrBNIt~%l1M8uB@zk
zr&4+SDbK?9i2&xH;scyFigl_Isnfa44A*JyFfsvbnH<z&H7~{h+59G#`YhZ@UI$o@
zcrOEUAC&5(ny!dmEEpI$YFCFAAnix^Th!S}jSdF;t2_W(iZXL2t@e4T-Y%Yr#0L!d
zIUfgUb`!$ERqoI6$37tp9h^IRi@B6Y2R&0rV_a_U5h&+OpOU^YA64SUT&7zN5!Ye2
z`c{|J&~Z{}L4tF#i4{!w6{-6X_jF7guKOckASVu};%#H%;88LkW6^Px?a>xI&WOp9
zrvpIB<*=OcU0_^d>R;Wq8-!g=94-6Z9Oue&i}RgliX^wR<EH86jo3?b*JETBvvi9S
z#dzAw9%jLJl^F6a)4U@_&lJoB{iMTYNE{02JcJ?aG*45qo4?gL;{%3)AHCgdd1|RZ
za{(_d&DmHXc`c8El}Ns|YDJTl3E*`@NBm$7oDtNw(R<k~*KpBOnlH24%ZN|MMdDcg
zBlkml44*VXb~oK&-e_l~m+*T-y(H|{>@Yg3gwUja?s9Q+oW~MWFt9W+=jx!^$B5a-
z>@-rAG_$%915A)}duB#V%x2_bE{Jnk;v=3&VsQg`Hy3wFrg$gn90pK$4yo8gG@ID&
z%7(vI8+t6HEYZ(ZdM?kA;ufOEk3=EAH1D}AH2jM87$c<0BsRD={b{7Qn*`i+X}@{@
z094s77EU;O%vgEEN+T4~`KoPefQFH(ZY}wdq-dodY9S5dXzHLBRj}x^f$;ko`xBt|
zkR8z``j`t+-1_bIh#yru4q`edVqiHG0Ji=A0CYhJ9v+Cqrx+E>2cpnS_4`J1Pb~!I
ziGx?#&uruti$=M`E<0L8wHuc}98`FxYj;7Cc=cHT07qfCeUSqR)nauzgM4!ugzQv{
zL1Vb4m$wY`1L&-9kB1cRyg8R-Rc7Lk00mHN@hQfSX~=->*YZs*aM0+US&hU4ltQhI
z&ZrqWKpHAdb{6{<g;2gmAO|H+5hr}R#1ow^kY%|mw;Ns@6hM1^1!Xplh(|O-;iDK%
z-f_?JqY~2vc$wnr@3KuE&eN}n{S!Ni9J44#+=cB7F8xii8=CY|=DELi=7|TYYwqC!
zI4Lf1vqBLXY;kN47YPZ)r;*KJd<{^0U95!W!XWKskLE<iV-rCF6*+uCEHJ87r}~B@
z=DbdMeAO3^xNyr-7bsM5zN4wJrZUwY@}SS%lzc6D_$^GMu-O88#+FCwvKI@%HXioP
zA5eUX;InbfHq86Bfyo~WuzCnW@9fWooiU!UpjUl7QSbi%l-~=k4um2nGaOYh+8YtQ
znCMkP>)w@1I7iJkJ4I;?X&wQoKSfAL@Y*rPr)T3-Z}!fpZ^;fS*J}qkxnIZ(Jz9jB
zmj2rs3#spq>)k_eNF2&zDT(pKlj^FMxQ$gfXv<}bo^8oykBzuOFb1k}!oZ!o+1X8z
zrSnnU_j1rZmpe3VJG}<ab@TnpSA3Va&yd&*Cd;J!Qvoq|6YvPj$7(*9R&VVsVdw;)
zN`WQ>q6?W=gXjMM2m%LYY4udFy!%{7Ab}BqtJ%Ux<eJe=reY^~Sm1&Mx=J}PEgl{`
z?y_)@vuFcg1~~FS;;&oa&-q8oV;m_1;!GE8=W~eWoJIJj%W6H={U`wjN#wn!2Y-d5
zrg$y(uxfAQsjyTnhQBH5n<I*F#M#0Ifil&ZE{}gunG}YDH=Y3q1?>Zn8Y6d99$+DP
zO}o2DW+B4gy5BU;3<EnVFKKyXNR5Ui!s@0b;O`t;nm45IeOHCVca8U47Zn#A7q!{k
zM+<E*%7PCqQ(=$rJKh=FV3ENe6TBxYE*~XuDkqHy;JvCWsqmk(c2C{X)}j58Zu?9%
zq60%rLjFtsNJd`(h9(;VeU6FZr3a1IjylR44$7}%nn?tm*x;ez)O(^{GM)*At*QxO
zI>J670B|ism!g=0#}2D`j)K##B}Bc>!oYY%{wl57ZG=pzhY0lAF=9DzP+T3lICU#A
zZ}`|p{@$2y;mvy-(2A9R7N)R$fyq1<if#mPI<4Atx7<2tsl>SYa0qTxj8UD6B)z0~
zwbsgx2Qm&B@h!&>6*b|=s4!3yIJv8W_Z_|fcZxf$5M$(wIi^B&o_3t%{ecUDmbguV
zsMdj#CQ1-P3Fy2M#}2DMwK2t5LHR6&K^~iD!Vyj&ww4k7e__%%A>gY%MGzm}d?RaG
z>^1<R4k0I&&Kn&hoF~Z(DvcO7nD2pI>#SP&AP9@XemX#I;oUdlfNiX)vBbFVYw%6G
z_eUjcg#A+nAR(1r*mtUX9Y(3MvB9U$B*#6CeB%wwc`wFs^b<0-U4&sH^Ey{#r26=X
zYr}pe0^?A-A5`p3t-SVdgyL8omkHv(joH}q)*)QDfIQJK+(XjKA(up85i*VEZ8GO_
z`k~+Upe8+*l=65D2ThUqK^^xDZDyhtLqLqQJ^o6ggak`nhGun61e4+>=s+UL;o!rG
z8yZ9iIw5nJ`X>}agyiKk@DY#Cf_O8MJK9R6h`G3=7_m~x;w5&fdGUlA`RJNk#40Rt
zK1DsvA=setDwzxUSbw;zfFEGoBHr>aS8wLfp$%xT*iPqZEd%v7Q2rB^ZKZEDlTIW3
zH&*te&1CFf29zus=KNH)wG)Y)_<)8U(M)gfdvczrM)BBUvA7=QvQ<9!v(w1F#Cj->
zh~ayh>?HEdIx1q_f6%V6&1C-3iNlnDjqn?PR?igg52_JK-IzC_Y(Y(yVhrci6AMj+
zrLAnl;hu}LRc%X<PlQR-BXVS+cNa-VbS$KN)=oGZ)L0>Y>%4sPP5YPL+7<r*1DZRq
zKXs#z#Yoy&nV)zAWxy}~NB;m+W~E#;pUTz?iak}#hZFmtNfbw+35HtVX4xs0I6KVb
zvXdkAXb_riALdowJ5h0-1X>WfKZ)7ceh`d+Y&xcL-B_V8;v_Yqc&`N}G#S<2DNSqd
zoI&_5%}!2E)v5TcU~*KXv<x9pbYW?XTtuSbhO3!}JjADg6v!Q2Wj-X`*qbAIt}sWs
zs(u@HQ{3u81@4C9$t*fT$W;#~Kq4fq)-*sm^+(3{yZ->HHzCK>H^M$?(%KF$^TM2Q
z%+EAUdM~Sl;ycZY$SUl4I1aJJ5E)A3p*`(4;6?>Og#c;4#?#4Og<G-1<*~#60BY&^
z{eHvcw2PY2!f&(~4bQ6Nfor?dagv9*l_ZgzEok8<%2lA1oa`~#>FKN8?u7!-){L4s
z1*;xU_Z6iFBP5-J9S8W>eqkADyALC@1J`9YJ>KXNRYLJahMDggSQ>N8>KEZT@(xf}
zh5R%jZ!yg?%2NwiP5?OJR{PKTc$9ZuTGB_wG8o$h)f;s|Zn}I*?_lx!;R$gz2+KR3
z=BXT0TP&kc2La-pQISwKPdtc51d(nDmhDa$M-UEvWo1T~VLV-#D~cv4<~4L`sR(C=
z21PfzbA6DR8q@=i6gOpi6rS_s6StZ!)2a)KA9;p+&ZlcVkW6Sc=FkE3K<zt7_e|Ur
z;WZjZ#a+z8BhTy&nGK%lfyE8xK1kp;`=JQ3ZeSVd_838p{t~~4CJ^?H-q1VT%!r*~
zL1_>|8=&T$BP7(_%7YI=bytH5oI0Zf9PPmX;W7c@wT8LBnHd1W#7yLb^5F=A!u#?C
zW?!m&vLTf{(w-j5L}Q!JHTN|fZUM}oU)=)@*OHLnekkI7Ij9+=@lr`~^i=?n;diyw
zF|DZ9j5G30fk%7JU?aU=qx~~k3lO8kEVL(M#Tq%swo^Nr9DS#C`vZ7V7zSvPL!|Ru
zY=mK#PrZp50>N<EmTh;sQOP>`Crex}D43mdM?BLer_DDEI!_r~Gf&;bCT_QzJExLx
zAUnc}sOGzf%{oiVql_x8xrjC>>t(t~gQ!3N@*HsGWk`??O~1hc2;=q#<I#IZ0O4?U
zooTpyl--9UEcda4%IiD<zNck~sdLDvaT4Ji<r}zy-z?FA>>t;>n-!V(>>u}q2CKXS
z-R60x7PJgAsV~hiHKQhl1?R#M?&`F?m&wxKqOk};8lWY#*wG%S0Q^O^;i8XJ?+B8P
zO=^sN(_B8gGv%_7sQYVmMYNb)*%K~1Gj=1=@w45xs$mrA*A*&H9TA<=NFKI@MbD!U
z3|pdZAnO3^ywJ(0j4z;Sj;ttn+a7xkN@p3KVGYtHy%GDmE_GLqFa?uLI>TJF+p9iC
zSK(Cb6q@c|nKV7sGQYE-2>7Zq;6kbgWn)E19w)r$skN9~=0kWzf^_0+gU6DQyCe2P
z_$CkaFBXej)CeA_uo!N6oqh_%#FP)B1|2@AWJQzkQ=1;TDgpbPZ*35$%uUOTCl|jE
zH5g68Yr(~EZFMA&Q}2oP;Gp8wIB$4@6Hs24?1QJg9q%aF{Dza&*Ivyay}PRy_Kxg%
zfSn#l-Os9HrI97<Y|;9)MkhL6@1l#cKC*+ciqg&;Rk2_Dkph#!41c)bgc0%P5xq{%
z=6azVLK__$rR@DfZe_$DC75eZs^dPZC$|Y#iho4n9QMhPZVKWv2*Vq9yOL%KbnM3c
zc4aji@m|dr6{$yEo5V2pCs1@95djb<+9=O8naKxtxv;YMx+jkAto_|v%a7F(aKCx2
z_D2%0Y*3mUFo`jd3JZYmK;VP1cSH`dhV>U>CyCh7L_`j0zyXkMiNhXXaoJ^xo5}J=
zef2}J`!EC`@>xp_GI<D1IH`<H916`*#Zj$zpy@|4QMRak-V88x1S*cfiy|y<2w0pW
zOw(M~V!!(d_wO(k5fDw~dxDF9tlbkTaX9DIG<Q_4Igo@6t}t+dK064^Bb&b5{{X^e
zxcjO5l}N(nVhkU)J)n%e?!z2#p#&R^oC0uO(}R5zb~vh-TH|#uW=)<6u8GTIw;6t=
zM)kcB-Hekl(>5bsp&V4qIgX6M*_y~Pq`RtdF4UW=4k~wRf)6uf>p7zQ2+3zCMZdz;
z5(}imwLFy;R22k27qT0yr*Arxw0qc9o!&f~XdowGuTrMt#-MRQ<(RW@vFJ)ZI!^|A
zN~R+QD-9R20SHJGT~Vi)MnEtL;Dlk26EWIRQA(b8-(=C+EB^p7{t?kUxb2;v$!5td
zr$>AGG*%KYgP;nl4zVe*WJ(C@x_Byr0XJDESx2IAfOA{d&Tb(Jv8ZnZFf@;DB&Zy2
zpOS60DTlq*qqOL~j?UTY>z$7v8d6ckPI5O@gEHf<L|afCoZQwIpLVNpDkZ0j5AL2S
z4jB~34<*c>O`?0s6&gCkty=#8`&ON<{{Y&vaW6k1v;i!@@Uv^Bc%TxF0t^Af@aUP{
zQ}(|qU<_tl`KQ}K?<+Ui90EQXl<ImI`m4dULE>ThgmKd*&*HE5jR&K1UJ+*WS*=hp
zRp5VWcx$_`DgMZ>V?Q<MzUs3h3x;>GR4F}<j5VT7j~3YoAjBi#ram{=b8t?(>lga1
z>2@O`ZmPEz3A(5YqWqK@i20&1(W+Q_R0SUm<El0$-44NQV(Hi^Qgejr>Cvf}8s5`{
zMr22{sut3zY2bLHaC71~rtTd;)l85>K1fV<7C6TQ-@2X0j}<@goZ5i2J&}|JRmNgT
z(P}XcqRT?(Gvq>M{xJyJoVOOitf6w6(}lDBPOwzkdfhrTwXUmF&n!g-IGElok`<)E
zfoTC^ZtQ-es>S#r%=HLZ(rgx;P0Xhf>tmj1HjQSq;Ud`21ju<Lnaoa`9S8}68S*6z
zZK@6R1gDo57<BPo2H|M_kqc1YlDL~Kdtsj@?9q=tu1<=C!01ukaKt(kbyL4hu02t;
z+vtJBD$MdBIvzg;q4^Zj9@0w6c2fJiIJuG00$_<oX;t2D?d7wHH}X~_HEO`Uo2=hW
zr1^#XDzBz~Xpw1o&<-UWG@Ija&BZ&sW$=<=&Hn(D_8-eD#Y?piIRX}c>0APMmgb1{
zK^P%Vby>5}>=#b*POk!={#abjklv`huq0j_Ix0*d#P76RpUjP%3t>3w2>2-P@aL%l
z@R|d&E#WD;&6Y(2-BUMj6?Rl$dpbEA@lC}Z+A`8sTvU;&B!fuE)F&SHaJ(%F&9qC;
z^_kiu1vZbWe(Ty`9x2$Hi#`M^0|avDy`p{!&;#B-Gw**Tq89M_qrCRv9TD2;cX%zp
zM$CYL)c*kJW*Xzx%f#H~_e3*zV$xw&ZZCI=W1G}$lq?fUgl6I}t>-$n89D?9e$3<A
z3DIao$Yc$Nan2+TV&7qd!xOQ^a8ZrTlQ||5Mpr;9IY#p(t~xnWHY5`Xo3aibN@1oi
z$zNoKxUddpWmR{564o-{AI!TG=)d1D#f#yZ6OTJ-!BD97M&m$|7i()A(cPMF7))oX
zcn1I=aRo5hm|)oNqb0P!%U3T276yp5^GCgDbTy>hd8RqFn!+Hs>Vt&hq({m*FL|v(
zTZSCu(9tFUM?@*QjKRi*EK?+4)l|pXoF$Q$pV<f}2OjKsFAID}K#z4np#K0{m5W$q
z3FWX|G>&@#7SD>hGJ%-hK*HlH&8jfna41WU#ZE_bCWejydrkw@WFUWNg%OHRL3Cx^
z-hE0E9QMfbS7<U%5~@HtM4;NA1@|t{I^hYNih(MVPJR=F{1s{7*Vw8qjw0jWrsR)N
z2-rM;df5SLgFb)ot%93dwWZzb$c{*7cJwPq3D{`1B#xN`z}CWa)OJA|>^cvNfJS$U
z80nL}P1zsvrwH#cFy_tDxSgUf(*TZVG=At+YhTiCo;yW0kGp*3`>Mki7!pupUHrz2
zs(`Yr>3!3xs6ha^qnN#s#PTHh1b9Zn3}ayPl;8pW<IxW3oPDCM8!vg}7iXl?ib1@f
zCjgPvUmY<An_Vivwnv`wj}F3nh7llpPS7pZGr>*17KNDY0Q!X?$1$RG*6hFzwEqCY
zCJ~$Q(}E;_<wHNDD3|;ISu&fN)8#5+6GYA#8Jt!?h7k9U>?L@bP9AMHLQOG<4rpIO
z00lZ^uVsf1>LSUeYSt!rhHMJ0Ce7rPSh#W@x`uXg;9hftT~IaquWF{!DzgLH-xQTC
zhZr0NNJw>`{U|tVbj|Z6&(y_&s^CV1N~-FOE%sVK1wNyi(G3w6L%C*=o6327-VTYT
zbxsh`Zmk<N?N1}n4S>h`cAqmRW^EV`mHw-W08JL<>a%O7$e}y3yfQH>mXQZoD)h^@
zTryLVtM_pn<Am%XtaGtbsqR1aD0rMOyF<(<P=4&O{G#LJxDc~@gFJ=TxIgJk{71FJ
zk~jV}Pk8X!;yM(@)HLeTbUP!8%d(0t6kx`(ue^48>$Ad1ES`!bvwzt;8_rRX*wI>(
zYnR~;-)I6MIn8J|S`5ta775M&09Sp}IHkyLmw2>1g7Auty7tARZebZE*Fei@2KsIG
zP$iBYZ9>Kkxud=^IUhAgcxFYHdIZi4!Brd#SVF?_)l(t3#v(<mvM)G#43Kw{WVX2i
z53+ctST)6ykujq1QKgQl<StR)t5MI6Sp|Rcjj)$O)S<L*KBSZSTOVDSlk)!n(0Ky^
znCPWQXm7=PRP)UoXG57Ds2pHUX6lPT)aHpF1mboL+**7Uz^qNWp)CMmR&7sbpCzaV
zXRyzn&{w<$$Xr*3n1BYTVr$qzouc^cjA5}2IrBnUjk7!v4FuX(1TIx0RMc!2xbB1b
zQM~G%G@KictSG^P`=I>Q3QvKB*#3%@N>yE;$Pl-kQkEcdi>KV@tf0UtkY%Gd;!|Wi
zoJ~0yOn9kIrtq1wl|ah9p||&%(Wjasq*cNsO58b=2XqISJl2ZvGF5|VO_~DjC&&=K
z`?H+beu<d-E6<6h>^us_-nUgpos=GiRHJf9Ayf=wS}iwnY*U#hDOw9BID!Leayxxf
ziPkx$!Bfj%M<i{@KF8pVn@O^W4a!f^4)2SR2N8+sv%<rSpVB2<lPlP1KSia2{uB<l
zxGjFgUEL-ObW~YV+2mDPW0Q^&5a^Ggl}WhU?KaA^!>0+&FtpUJUPtBNjofygB#slR
z0o~plC(7tpn{!(5QaEWV`F~xymj3|U7Zfg2+uu6I`2-7VgL-cEx*-H+vKgKWC%8g4
z#`yL74c@2PoX=5-2^g^d0Ckn2q!e~QCB5PEM%oSofI{l7C%W~V4Zlio4QphT#kqPf
zBq#p>FHxr6=(6Hal1+kbsJwTN;_?>{^1t~%{Z>mRjp4)_LJ-=6yk|#jb-RHll%~UN
z1ErL}qFwMS!SzDzstMy|{{Y9pL~0~T!Uh-~xh&QuvBcxUd?j{@SV@DN`YP1E^~R<r
zS~Lo)KIWKY={l;-Bxu{FMC++TO@s!I_jKYB++z!gOb@6_rGRgBR=nSeyQK?IEgdmi
z=(S*;tHk^C3pTN$INLRFAxt&8^G`F2OxI=}@RMX(F$>e2k0rclmzPI7rU~MV$7ZE&
z65WQ_rZx`<6wi_ua|j(*v<USIED!9sA&;}Zq21wp)5Tf@*o`{<hnG_=ocu7ZASuU7
zwKe{zcq<9$xH$Ti`Tqd!DWFuSFk{v@eV0Lf98La%E8JlDjk29{U`oV8F7nk+yE#ZU
zj$t{j6Q;-EBu7<M*qAp)QREd-_tZn4_cWLHgV_Zz;Ow-6E^c2?nIQ>pcwnl8Mi*d1
zqRgiPVn~(YB*N6Vjj@)<$4|rNjw)4vk@E`_wBepTQI-`b6f9L5WO0Z-N}<Km_Ej0;
zyiJuSyGAC>PSJvpjR5f|!9}fP@r2QVr^-%>9cU*@eUL3x!=76RhL4^8(6<!fZ8)l7
z#59r$4a9XiCuPet&;bqr^x2qE4Ko~PiZa=_RLHhm)7m*r)!~kgR$lUPfKDe2%40)X
zZN*j=FwibaejV!=%^aFNcp$vWoFg}6TvK2?_mS5vg?|l}>W$n<^;TZXp%9ydHhkxb
z%oNZYQ}GxwYulfy(Cjf5^<K#c9r<&8Q$`n60mn1sq=-a`GFog(Bv@f)iZEmgvImJk
zcX*sz(Q<2i(>uV^8Qgh=+Pkrt6*+TH6)6xrH^QZqyUbX)fx%h5=xpl&G_+w(gaM52
z3X8{<*5jo6RAJRTx`gjHR(04^5K3T|xB#*gz&LoPpD_yzlgA{}g0;DFLg1i{u!QU=
z`K*RSEQ?GO>n(LdXNj)B@$YadH+_^fSxOgC_&7Y@uzQXAru$fD;<K+&)lZeS`F)79
zN6A(rcdn&UYun3og-{!1lZM(j4;pq=1OEUkw_K@~kX68J7W6^^@g6^va$FG`q%;C1
zPbA_=NBAxB+6fariP&2=DvR#z_ngn^LI7)@3C2ZJ2kN1fznU^XsW#sD$f3PPwtSqq
zk?MpN___1jm-?p1n@Y-)zB4kS3qb?*kalG<f2Qju=lg6M*;5?OrDjUXZ_KZ4%>&6q
zmIAMdrLN`*AhgtX{4D8^+C%dcW$trb@w(Q?S+$ydM#z^OMmTdtxce;BX+GwmXdo%S
zW@__ZD!rPeNB;m@Y!jV_3#2z#5{;bT{694y;~@Oj^UrA?(9<Ox>35!i0v(ZR{5QG*
z;HSIB*8c!YQSm&Ggw9IGB7rh9Q8Tgu;l&@ts@C(}x{mUj%NqXxERcrdoHDYUO^ycX
z?Q5zUT@I)i47&1Lw9SwCP6r9ixg&SG3whobuuS<UyAK0#K4^J_OT6<6Db@b~d4+Js
zv9r;m>@;q_{I7f?;D5<zR}?o~6gh{)!;x9QFQt?qK^knWyz)i?^;rdRRgbqZysx<<
z$V?`ZLt4P!B?ZiBpED`pq&soPnqe36`x+bb8io8`(i%TVxY}$6YTOYZiejMtgmU75
zA;RHN4t?=AheYAOFwkz;I)zc?+)dM<Cp7)hIP=0&Y8T=05vfj(X8!;s;Sju)Kt*P}
zv?mI6vF-zJG{D>}-D`4$5ZE$DJUw+@#ZK~(>7w@T6Yz>mr*3#|nnTBpqv)v?T5)Ia
zRbYb{N*`bV(XL9PcZiWF?=`w)-oIyxl6%(~(Q#Qxgnq<l!5xjesgXZToI=i@YjZ~+
zM58C#KWj1Bo<~&e=bSC{Tz+do1NycMP~c%eB<b{6i5$=&pFNEqbn+m3r3BUEuH)o^
zP7vWA1^9wm@->W)*b_#iV;waJ;$=q2DZxI#f*bEDPcS6Fm|t;Uv#?0dyqj48O@X5o
zrg_~`yBRenSQk$28rBcYESw(6-3~EtHP{2+JFj&@+&{W=2i?0BDY!9)*2y|Ii=*UI
zd~g2%yG2hCxWk%${%oK#^HlF&9tJYd(PRkh_=B6z4se@>mN42wD+QX1F|#_VHjLU(
zAO8RnkSI|z?i?r3D&?U2*I84{<QxwWhjrS3m(n>qqG=8pgh3=<p-^i$?*2i=XWrZ{
zD~dS@PW#GD!tum$KoF_x#S#ZJ;Cig30}*xThDeKOh5eSMOG{vdY1DIB$||$GIEY}A
z8S1Vo_Lj6_&KM`_uwBpZBayPm!BZ6bD+bO7?C=WTa1X7}Mn1fdoKLXihytkHD_v1k
z+NKGZStA(WH1jG;J)f@6b-yjM-nhm}aFV_7wCwNsqDl6U8^#nml-`@J5iN76cpahS
zQ%K#<PAKLe`Rx`JX?=DNA7m_O{M5D$#!+);nOc-^(;1)HG&PM7*6biOW=d;IMiFDg
z$Eve`)3+b>D~lEQSj(JGx_EO(?gK^x$B8T4KLlf%HZ@v(cH9d*ac-dz{+g}W{RHTn
zKXGfq08n9!I=$ZLRKq2>aD&M=rzCf#-8m<2o#EwZLP+jvFm<$|AW(BpT#>L~^_vwe
zso{SDc%vbdoq&!nx7T4=>QO-nje<LxW7YR4?zms~NACu)XF{I#oCxK~N3wn>yAz0T
zAD3ZPIN)(o9Ot>q#Z=FbS(g+hB1c7@7b1K?5#Kb8)2)*X4BRdd(_^BacJlsK+3hu_
z;EdvEC*+5{@?XKMFV|zC!-i@a@*^IyIrBt1o3ePxdm%W%X^)Xnn}W&Ab@}ACtJR+A
zyg9GLz{Y@hxo1+GkJJ-8=4B4dCzsGB4;CJOVc36(JW7Ij9F{>g(J1vt#o3ETnHzHk
zQ$P=NTaL}v0ledoQrdI<+{)~fp5i5yKYobW)q!_8H!J;xg-e7uH0P#^#f$)WP0{=2
zH|}cce<?!Ze3zEr;tVO^@8T?sg1#o$^jJ&BW~Lr^x+O9U$6qCf2e%ot=_#fOwv*9R
zBopA7Obs~`DxffP*iFsVJcdC##dy<#5P5xpCUQ)IJ6RXcC8KuvtDY$A(M*t?A9aWx
zn>;5}e+|^$J|pdnEiH#Q*j)^&6Micze2~`>*&B`cy4wczNfXOpSviCwkc9m63ASbV
zWVK$)8a&Vt_HNG<>+n)jLNR4fV<qh$mxQ5kZmBrBBpAvQ`-!o0j4snaV~m~-O1nnn
zlcMbuOdO>C%ekm8EDr`j;Gw&&!Z8UFyR40!QL%qC>P~lc4Z`qlsp4;T1De~ARXTy}
zwkKN2hNX81#UCa9o=$kkbB9PxbDK@Oq9U9x38LV}do6?mZHSI;T#C?!`UK+~He?}H
zdwk)2^IfB}Rh`*h%!6B>ozol+8&{`s{YmVrObZdSkI`obX9NLj+kyQYjMH$IJBVC%
z4R#wH6*kOXET<)axE_e)a#vfhNS!#W!hAHm2RoM)R2o!<0_?;>bc}TzIvWp`$K8!%
z2lUwtTa9Vz(+fs4Y%y=Mo1#3>j61QyHfP=lR{)smQ>1odL^IO~Mp)tpMBreNw?U2n
z0J6Ce9$`Kon;(!UhXZ!FYNjOxf2UM9`~u>&a9M~3IqV)5mk%K;R3taE{KyKSj5!;O
z`Ko=}rtfY32#SDvw<r*06F5V|bTCyKgUK=PbsDEhv%;kC(>b*aCF9JJSK@nz>ax%-
zEB+Ai?*Z2(;Vhe*Sm=d-0DnkVZ~B7Xf)l-)ABt?WJIr)m+Z;Vno@?5!;(US^;jyfC
zo5PBJI#j9v19CUbFp_CU#WpBV`?-;FGaxB&BMDZfT;}w;c*6wFsO5+cnlRqc*$W-<
zS<|fGRYovkFmYy_7*D-9gc{dKZC+4N!hP|<3_y<%UYjCD@jY}_QHQ(|y{B8In=;Dk
zrs#Hg?+sH!J4;#j)cB0?1SO%c0a=n46gMAJ3H4VEY;;mwAP>@pOvHUv&hhvuzBB5q
zO-8q*l;a|^;HSWK_@e3$;x59Qxa^!R3h<glldyPPTR#-Y9nc5$19kbo?O()j0yH4z
zqkXan+Hq4Sd4}Mj_=4kB9Xyi`CW?Y%(J<~hWi*Olfl0qrn=5yH0b0rQvM2UgJYpVd
zOCBNY(a4ic-}IgbYoN8Qaq|$J6KDqI6!UDA8h6aa%Af{BoX#AeDvW(O%5FNL@!4uG
zu^&||V|C;DRjGq!r<T&4=K(OE-_z{-?!*X@J`pO67$XG1WwTl^sNEbiUhBR|yX>m7
z1Rq7m=k_?E<7dPnzuBKN`ChGxplJCZCYUmE#m#sgA>i;rX?qR>!O=QKZF_ZICg;vp
z^j-V_=ApN^UQ3L6Dn+LklzohrZ6JkIzyAO<9h7F&b2r&$uMgMk3oOu){+~g7)ym+q
ztwfMNGcFO^y24^rLC`8=`jhNwn;es|U?T@r?O~rql%1IuPolX-)SDB`tM%DI;}FjZ
zb=%@@y-=O;=z!fZd*p)wq`_b_zG>EL8rcY(Y#5k_;<pf;-<qpJ+G3xQtWW75x}fI{
zZjX|sNt@(MPF-a@imkZA?W(UWZ?aCi8NJ@!-dQFJjqc<fKq|ARgPWQ+Xb?rdXtd(O
zG0!<v&9hJhfM<wA;x(}PAVYiw?Z`QHlZlQPi;0k$*JCHVt2dHz0TA{!n?nX56C(wU
z`0O@iF?Dj{N!!QlJ~<nLn92|0+cc~=^A#KQOx?xeZO6eJ*R)p^7hHbr+zRmmvqc>?
z5Wsvhsy;1NWk`IRg#|Q#Uotr$>;&1wD85RQTF&L8^;s~60Tx1G(-FezPkiim<h)d>
zPm_oEUi5lfW_luiz{pRcCQf?<hMY&q5^$4M+=gk#?<hIlOHBPX6NK70lr-7-m*g8f
zf5Csyqa-+mmZ`AbsNW0Y`&X(NBXjfwtrzl&D8%-BU)W`P!)2g=aM@Dt20_^npE#)C
zV@`5!^iHW7Osa%qrz5Kjs`U$9P&MZfS93(LaVJuYldjgx_>UCUebmUs7+Ny2XogYU
zAJi6D&Pe-a$;T6X6T^aT^uwbvKXe7mV)nWYyFZ-wd;n9h*whx{HB`H{_s!oqo9dkp
znw0%@*e{MfRW}lQL!u07nm}u_c6`@|9__R%cB$RRsAu_4BXb|3k{hD2AM%6;w7#G>
z#vEek0fJ)=P=3JAXRyM4gKrBX_f>3p9x3O(Z-(+7+?78Q1OEWLO1AL2eBaGm02L2Y
zbOV_Bj`3bphZAlH?=qeTfyKNNj(M!GIz9*jMMkgZKj*Nju3>rpk1(9tHC%8#u`G8*
zQICwk%rAEw{*~<1^pt!82Uj2qACLD{9P-WEoS_AxjppIFSzskW#tvzqQq_>B0DA$E
zq8ZkZfr?asBZ*cNSz*oA3)-Xs>zWyJSW6uknr;fO-8i2M#l8!*c6x=x2y4@3n`u&~
zi<>X@Sthjyv*9zc_J}CDgU)uArw6c&*N<zWre=*T=)UOHN^1yn!?9{1WOE%jp}yi1
zMf^#!%7BVzgr;wA1b1HNd{f#X(JGsz)U0#G7L+pk7pY9)!mz!7@8RMtn!s4|iKN_r
zu%LkJC{25dSUfsAf~H+Ua&}=kqCt!9j;M?PW2*dt>Ub{FO~EZ-%{J1Xa>61kP>2YQ
ztHZ@uFI9ND-7l=&&H@S(x;|2_@0Z!#r^k;(+%16vqA({kAPQJy=54d&w+X5kC-Yem
zXB+e=j5h#rRI%$X8lxoVv`7qh$pM5skVh5l(9fA}z@2c+x$stWqc^$duHMyJ-<+!6
zpcO)xAX&k<ek-!kaGRDs_u&ekXbpQXnRo1~&|zhdd-PxUk7Vd7LOtdO9E|3uRUgt&
z*ms@UO}aH0BP4uZipa?FMK)pWBkF`jIG#!8m<h8L1MYJJM9OWr#f29aMU3RUIEkMm
zEl55m-f^<U-MZ!40x#;EFxGrM(FWX5AP1P~qB4Kev_Hzyo!6#&Gpq28v6|=h7*P%C
zs^V?!nFxxs&yAn&RlP?P+u5Eg*<p08Hdutg-mq^TC@vDAaZqraH?bZ__qDN0>Tu*X
zamiZA`;BpN9)UC(Ug=cGr-;I-Cl2W3G*s#V$1s_NjShzXs{!#<LohTza5|#X_ZWd`
z;82Mq?8rLbW!Lcf?6NS|VPy3)^i!oa`P_spFQk0O`9POVf*X`TX?MK+Rf5S2yo^Vp
z%N_Px3CI1nP7Uph$Yj@#-AFC8Ww3t<%PCw{h;>8qZppqDjh7CjTO5f(!mjU4p3sCN
z49`{n0L>H8twLHI;C;fTT1)V_3yte1wcsAGWiillwWDdd772}adJ9bO%~q+|s?1%M
z>t#`|4eaiR42{B+HC`-4pf|G|3KLHdCz24%M6mn6;eQak`B-c@xFh^#d0i2RR}J)X
z{O8q9OWAavCA#-jG0JZ%IrU72*fw)1guA>T4oiEyq-FU}AQ!jt3)(5t7#hp|Q44;H
z#utyOy(i>GgrS6kb*5Wg;&c(yX~-ZqGLCC_V8t4!-3E_LAM%Z+>o+(LRqU{*ab7p%
zI0a(MNJO@zS~7CC+(a+uGv>dX{{WxZKg9^mH<DxKpM}oJjm*jhBpLW!8T+pF+Fj!>
zM8~=Ka4-!XY_r63`xyrO?6U<`Ca58_n%`-~M3}I;XoH%fYB-n;&SRorW9wF&90LOJ
z&?D?ecZ*$0dmi@~-7*Hk6l`&554lZ-mrHsbc7JY3xS1QDl&Y;I+peb|oF|MS_fB<;
zWO!_xtcF2ohz5zoIw!PP+w&VL#g21vZiN2!le2dYs<5cWgG@$k<WrT7qi$6Q6U{ey
z%>kU{+r>CEvwfJZZo5kbQ2g|TQSZJ@9xjTDX|OunLFWkl;UHmcfE-hZr|mrIs=3tu
zYH$uZ%8vwr<gO9i9&tZJ(D6P%>>2H3Ij>`h(gSlxGj~SNV~Tcs6K-`OIFQl0jGhZA
z$RN@*oYTCTw;RV)aS&}PnE}r9S!6!TL#%D&h#z=KJyu|VN+x(H0}xl<H(twOoln>;
zX=sSo$uzjeXZBDHNIpOzW3Higl3N$VRSI*5#M>JuS83M3s+9#=W-n|(c8Zc*;d4JU
zTKV^y7iaA5;!8he*e#5-;c<o9s?l`#Uff?L+GshC0Adcx6)yA*SV12JPrAiO@;r4%
zMNTaS3b8e?&&2~J+4A6;7|<iCCB?wGQ~Rgyx&9m+%C1<-cbD4{`lg-g^(~~pS@;iD
zCe7gVcllap(G2odV;c^Bfp*{{Igi~{s_i3nQFDPl@}c2z9wVZpIo(rcb8#xB?;rxG
z?^IKaDJ(uV@lLbHsNht0RcSakkvcCufg6Nx5=Kba$V1GaaY*jrJ(M2=zO-)<!<RKO
z4$jU?OK}Z?)LI|vnoDbt0d7K6U?{o1Akr=bTI&qcoSTMF6IAZ`Ait30<A;KS)CAvX
z*dq4%N^m37V^gtIs9T2(;yIzQ(Tbys{{W*Td_%lGb_m=M_K(dlopFo2s@*vR*kf{B
zH;)CasgsfFsTxl82hD#s>9a~F<7DC*>l4G8!p_nx%KrZVBA7$IYxyT;gezXB#_D-5
zYP7Ex6ns*SRZJ|`R4h@F>XjaOdnnI%%rE^+viI7oTtckq=YN`|P4k(EKLp3p=Qx7x
z4X9%%!kGL);v<qSCl2`{JeP{D7Bk4n*~J;U_FX=!A|Y{70HpE{R~3`k9X(Zuy8dGp
zPOc->4L%4GA$v_NOvO?C+yVMREOks%QSmTFfmALbgo9xT;f<|Hrg(yexKxnB<~Nno
zw?V}1H(PSMW0?0z0qxg`Awe8lbq0&b4i3C?OfF-@hc`p}CpvwGu!2JDj>8`LmDr`c
zJHi)Y4W=5`3?}0cm0wFV_>V*hBShBuA(hBR=z<?L*m`CV9FYDqWOOT_^GELK0$m)H
zobL}Q+Ba#@V+#Irerx%T`TdNV@Jg9uWJX1(kl^VaN}g<j_U2(#d!LTjjMyP(Rt}yr
zWenbS@L9;0e39Rb@>GpSP~RhyN|6qSH0BkxU8DUsYo_Cc@gdTf=K)WB-<wjXwv=`v
z+ZmT}J4`fZ{tyD2CL5rEDWDg2v+0K~j@*VzJ&r&7YH{eH&K}1Os^qB&nCUyj)xFtj
z<R6_kAq0F-@d=o}lFa6^LGT-*gT0>sv|vxd*R*cMilI!?j61D}=AhGjxsk(WOH!=`
zQw8p)JQG<+f_;JWE3>k&ECe^*Aww8)XNo;j=r<1#1COG^Pa8`^$37{W+SJZ^D>RJ2
zzmj8vJ?Gc}jwgqfw2urePy{`{B-1qx>EKG42H(Ef<T#;X%2-B&5ZO=!;`5-<5*x#1
zrE$+)p2#H<YR#H798TBZkur&wy9mDzwG(*7(I4WmzcrDmWdoY0X#)|>SRryrf75N#
z=e&o4e>W7_1;GCRO7PRL*mD<II+c+gsHP6_r^WAom1scUByh3k5Wb*j6qC(+q27tc
zoBfxA9;kPs;Ia2Us+CU4?jwDwwL0w_JYI-ld8sgZs#vMvK0RD@M9Cm5nNJVFKXI-g
z-#j}%`I0=EyGuhJO+eE*r!mcSjr{UL?GO)|aA?&$v6Fbrqqidt^%9TU5**j!Vavpa
z^qj)I5^Q)Owb(;slhWzGcT?bX3m~$l((fR!;(IXMPc-R!i2%+(F~4(;S2tIOP41}x
z8(5K5q=5M3u+DqN1($27MwL&+b7t|Sm1kF)#Ic0V95Mlo6)y3wIJ}c!iqC5Y>$oZS
zoe?kx$u#1lMnAqvDYLkK(XtpD`B4WHn`sNh?G2L#jX58g`KdSnrr+sCUpH|11>&gk
z=N`HuJIUQ|6ix3@yjEp6x{<Ix>hUz=Zo+0-8_$l<#q8hpF#gKz{vB;tzVego9x_?1
z^+3;cPo&1d5iaX1GL6{x532qnLx-lr1c+ChhcdIIz|++o=3%VC)5M90E7_|HO@n1a
z0+BFsl)+21i(wXP{{YG#gWZAE*E>W^O8z4w(&)E-5Z-P2p#;)(yx0E#!+*MXbV4*=
z&S%X|7T0K=8_Hz255*kBA;;v0-oBG@);Y%|C9cSz4}C&vbvJ}8X<+`+h&Kn!g_Djy
z?h>;908a3Kt9N07sWre(kv@seG~2w^PD%Kv)ss%3(C{lXB)G47JuHk6Y<Mb;Wm>Jp
zy^*|KHqm0UC*`u{o6L?3b&AkC2cAgD0py?QK#8hd@I~04E^~bT$G9FmivDvxYR4J-
zH*a=vbSTSaT6q=JEa4XSY^&owA<9)9*NJA6%~Es=k`7C}pCl#63<&3%aG!0&=1_(-
z>Fp}-r1-)f+gpK!RGOXm7@~J$nqlvtT@2-is4AxD4czu}RJ*#EFuM*=RYull4MJ%I
zW`%<O6(}8CfRj<~`XJW+ksn}6jAIiqM-36V%?&nWG78XasWtA49E!Doj+<urd0jD`
z*f!mV1$_~c+4>@%h_U%{$$V7vD!^;OVsl5$9oM+|Ba0EoJc@SZW;h<PnzG4L&FzDt
zp7b`shyB@7WlF9yFW(Ixx_NLH!=;4ep`f}Qd&7~)W|~WNE3_EIBxQM8whqD^?N0<z
z-oZxSU?GT%+oCtx?fZta>Qh^SLB**jZQ$gh)bbqu(Y`R9ZuGmEgx&KFZzNN^QsOpK
zhxcFX?Y)+4I1kNdMxGX<x?Ls=`Pm#@Q%v%*cj477^-gc9!g>&yY`qX@st4S=Tj*6b
zj5!h{gl-JV7CX)8P&XFY9qRy}&k4J^k2K;4)ib<n4e@(el~?v-Hfy(=<WSwD@Jofl
zp;7LsY)xr4R4M_BcG*e*vHlrw*Q&v|F}rw7l+x#7()QFRx3jUX1Y@(0l530IuS7EY
zIl6;M`F)H1k7ktr0CmyA=F`k)d#pRcFOgf+vyDd1%GcL@v5_#Zy@>pl^(vW;9MJo>
z3{2G+IW91>*?Bfx7NK@wRJe$7$)jE>)Z1!o88jxCXfAWei6v%S0>cj7Jqjx!LEjrJ
zr0n2@)!oBuu0fH)=v?ZEaBz@9aF(zc;T8z*xNpDc5Zz<9Ik`CGUFfVwfuMbhzKIv&
z)FH2QJaBUM++FEylM}o+w^e#|20CZ*l=4HI`5%?KD1qZ<t^WWB-R}+cFumv=R#6$5
z$rxw=H_3jH=B7z&eN$$m8Dqo^5F#^K6mDv>=AwJU@Ur%p;DGMK31<j#m_*?16$t2N
z%FswZU}j{i9CPV(&h@?XV9r)Lr{W&tyjwUAg7#hsLeSgvD<gkYJQA`k1ah4e;K&{h
zOeFX0Bc0d(04d-1A>n2w`32e8mluBOl{!zWiyNDnY~?44aE8#NC!<q7;zgz<Sa;Xy
zDEKSApWSeqolyDsLK*gE{K{{Q%w+KPmA2(K(QkHlyLfQs6NOrS!-t4<R_>U6O}1Df
z*u?NS=7U_=8;%&nuE|KZ3vI$gz+IWGx3zRjyV3osC5`Utbq6*70LMQtyieWfiF>wC
zKQ7kKd)W(G4-jR4IiEG}pRzGAlo}j3&6AzqGM+YTATN3kn#?@>SG-hIqun%Mb;k=Q
zM(ybrZv)GUX)eV8BfRU>DsYCOaL^iOC|(x33Up%a5$Mzey*>!-O>m#i5RVVZHxo!0
zqvo9ExKwmPq$(B$$!@B>&#>_dKy#YN=Q6$5pZdPVfYuXtVjOt$U%{_#hwU~-11Eyk
z+kDk3RX}lq@p$A{WO(mj_l<ENN~vAa_cRuPqW=KIi%eu&i{zfp(MTAm)|<lchY|)>
z^F_EaESyrm2D=D@q0}nGOGq3tRRuP8klk^f0XuoRCKC?7MD|ne$!_lTxx%7q^Q(|O
z6+%Z1Jpy|jPXs)<&;wJ&R<vXCD|9i2PFbgK%s3+?96m!O8Y;{fy^vS(;EfKFh8F(-
zy!P)56xdzR?K)_HXy87(2F#XjwbVp|v=|0m&}O`KaZmJ{eWMU_PY~VcHjsv7#>{|$
z&?9nIC~E+5;!xSaaclmcRcf_~C!!`YBdW_~+$-<>4#x(!hNq%$cFDgK@=HvKy6(ks
z;{<r)RoU3X#iA3jVQFj2Z-(}jRCwYYWrXr+#qwLN#jd>*i<^+d6GaN+UuEC2WNvQG
zqzx4ct^ubeol0rO^Ef|sQOyS9yOA=O`s|AXJIk|FqCuK{<EML!F5QXY$8wM)lhm%x
z+8DDljpxj#7ZPx~PO`I*uO`7|h{xFX2tG&ca9mN%z)sR#N!I9~7?{xXMjinT9M)wF
z{6$BE_h*4k7H`jHe0F9CfP^w~;r0mos!d@dkXcg&{)<|pVE$P6$x(ak&@WUrh};_u
zrc(f_`mxob_LMH8ahK|IK!%X(wOy__hZsL&`40Jo2OBLu_Jslq8-(?l+C<S|sY%+~
z*jdFwPxV4DLI69w#NaaB-a-zcS?9)2$ZUwkATMUFV~9^90Ky=kBn{vf1*7qQZPsMT
zSvE=ZR^0>CqjWcWLj^&|oWdGL!nh*>oil!AdsUCXf0Rf(P{Lx*Z`DJaChfk@9TF#@
z-9o{jAL+8m%mf~&OK0yJ^+sCj(~c?7F^n#s4-_$0t6C`2_dz~4EZa=y#0B7ei1%H=
z3kr1oBM~XM>%EN<?*Oz}JlK<mg7=i4!gs<Ig8+_#%3?4dn#!eTxPMFyqDl~Cc#J2u
z$BJirMBWaG(8`2VXI6}sh{dINg>xMULP*dd;qe_%M_Uv_8&F5}tjQF|%%jhwA~#9+
z?EFu6iVmetn`P1<o<dQ=-s~}hgL0MHS@&I|C+?loqR+f^`t2%Ulx;Xil9^-hbz!kb
z?&@zbPA9S)d9v3>BL!R>K%RvX9TtSpD&XF6<;8C?%_08)<C-$pQ;>S>luWtR97mv5
zE+X7;h*7*gNwEw9c%tZIW&AoJ4tBgH2?feT6vo$c*)E&IN(Zymb7W736%k&OUpt2o
zG0{-&fDsy${4Bh4=&Zq_3Zw$eWJik*n{(Td%bLhQ+rcuE=(@A;2VV|}*zh!OdnL()
z9Bw<P1`;F#X-y!Hbc7?dvK=HrAqXQdoN!Jk`l4aJ7L4A1Wk{SSai1a45W^R|-8%_(
zZf%M<Z#&7mt}|^Bid4oYpQ2;?rtYzW!UAUfA_MLOgzGVwut0RVb}UVC-y|0LA@@%*
zvkkfUr?sTb+;fFhwJ2fmbxsB|ZlhsYmp32H8}d>1d=1ly;G>WWiIg*qg%d8MN7XxX
z4(Q?b4##0*2Ju*+c6*m|S|BaO9fkxM97)SzTopEe>9@yJJR7StH|V@y@;haVs{&@*
ztqG)`eaMr=Ye>7zx7g0^!pAh?T69$e9}{m5M74qU8Id|B8J`=o@=tq-WANWtl~wrg
z;uqnXto%GOMccj#p6KSgL(3T4Ef)i+M7vM`(ROz*Ij|px9hG-BrU_}v2<VB#(<k>u
zzj!dZExejwd<tWL#0Vs6v(-8pOnM0WBH+A(`F`GDS$iUO9h>yEZ|t3hrW+BjR3YuK
z2W6VUiN~U3Ru`G^53!QSh<8K?ks^2<kz<R}>e!AXBvq^^M^p%XI?++QB36i>ysjB<
z$|V~ZS@1&IeVrIKUTQQI0nBP{F73Fdrc#dVq9eSiZW&os!QHlFl4%?&G+)dr;;Vh8
zk};1Y(hoKKm+TgRAJ>lD!BN~fjl;_o_InI@_(#<;>?a#@h&imz_pZT}Mf~DDbyF>@
z$>oWs_C`3aCg#B$budSGa_AP1E7~x!7k0+}9ajnKbr5gou<U4>ggSXF@jRb^*i*WW
zJycSEx_l)we*>Q0c3F<n>VE|dAL;40`l435r9&EBEq4xSzjV_Sm@VFL@>Y3p<XU+w
z+!`(UkpYjno?H`t_M=S#@5tT@{{V?edsw9lfeZOU{{YmTIGth@+`&oi^n`56Hyqq#
zly*8E<9>+ASsjfJq{d-?GHz8Gb$cagH0rQJnK~!qZ^Z&hd!ZOJnleH4Pz2<iJdiHC
zrrz#I3o`u4)m#UamJ*4C_ekXn#QbIF-YqymDDJbmo`3{@{DwRGjHCQVeD;O-t!NDX
zt#$|)E<aO~i>h`&9ZQ=WZ|H@A#D&F7_{xGEzTT^efzcbkJ%;p-7JxK$)4fnAhs8ME
z;+wq6W7S&#l`a(;FrEc?TnvselGTm*FJ-`~-X-3urtqTO*pg&Q1PlWYMOEydbjQh1
zr%)Yoapt>6cW7%IGV>WC$H%(hRTmlIf0AL}1%yYuy_XTUBv84!7A6y?mkqs_9YEuI
zWaQCI({0<)S8GV!gUvsCO%-}gcN|1~muX{z4dK$_Jk&O%Tya#Tr-8S@MR#x+1keuw
zx~k`-MrtbI039@xSoZ23+>mGy?2uE#Zzgq~iH>L=6{D42=saR=&MWj?rnONwXv@}B
zCN!Hf2)~+$IjXMQm}wdGRZA$0m=HL9p{X2@AU^HKXzI7mf(BdM&<$oL!1DVUorlk9
z2Fa%%cpW^rZQz~wL^BWBw<DT6jwEs<1>tLhPwa*E=1K%%8+d@SVAqLF@fihhM3GUs
z1(f)>a#!x22tTV}RayE$lkiP>4LGpCIq+4UZ(~zGHLgcJhcit26;$+u36CX9aqYlF
zYpzN=uyag^EHZAN6zLd^9o1C8$3;4{z1M!qtKb_9T5wk{CTE&`S~o3yq#lX5sfx{T
zo7xS^w7(hoLVKjRM4Xm^3wMP1{hcIzldvsq_FudzMi|T$yV|K_c%VUPxKlMB@Ce<d
zVFVrK{dQ&%o|_0Q!(eU@0{SfQ7LKm65JCcJG}m-$d|yOA6z@1$d$T-HIGs_?`Yh+g
zu>Fx3sRJJkoPI%Q^9%U38qwsOLtM!QGAW=Q@&5osOM|x^JRN15Ttazi8iNZBU<5<|
z0K)9%i`>Ev_94er(No0PBBNF5y6krmxA3a=W&=4Ygho&&H8|r8@^Xd=GC18$6OeOv
zo93(1tHTMo@=PSMqpjq%tjP2K0EI=*$AshHSbnO%-c6Xx`lf~ki#1$|lOLx<9O_jf
zr6;q*#}V>GIjn30`jfNE)f7FD@9jG^7wfh+8USAJ&unZq7QX_&i&)egu@z~e!}MO@
zYr2am!qE-`G{`gzHJ<Zy9|hUNsuKatv!wuGjUHiWNc2&=MbG}{75@Ohq>0?CK1F+9
zx)5nlbV5E5ZOoKE5N-pk1V;NnD=i7MpepY91NJ;L!FlpolPS2URPncj>Z!{bF5WY!
zM3!8%^;#^7js85iY|9hjek@^cDH)<c)idL;LUAsqQNlO}Uduat0*}38FZr5R^Bwd1
z9@B$I&tdTPR%C&D>9@M7J?duAo%du4jh$MucK22IRI~k%fhNfJbpYI}ryHj9Iv$IF
zI{?7XdnwU&d@jQbc7O1liv$O9S|-gB;L-VGR7^04IqRB;Ew@S7*>jnLsmT&sO|7SY
z${`v1wt>kBc442oB+jc$kt2$#_WqJWtX?DoEfdTx%}KyG=>{{+c3Ax`{nL9yaI)<c
z@5?WO?T&xt!*l`AT|QhZcc7HZlgU_YWmZ(3+N=lYor(?KbMsCV>CW`>)pokoU#S@&
z_X+UF`Au-Pc|z|wMyZ$^i^Ih?A-pFgLYX{I_|0H;_8{<soYxt5B@8WL-^<lJ)GRTU
z=_Dc@QIEgrJQ1hNIZ+vSN!<!Nz|1Yf!7;_S#vzrpU6eKNi_DM@GPMLIj`E$`WL)V1
z&MNIkynEdNv41scz2V*0bv{UYpmN)kZ5!@BgJ&I)_pXt~G}~2;Bw47*sdt`nWFUf?
zf{D>y2gP`YYaWIcy`+yt!)r+lye!<}c8_Z{IO<e`^iIRNGPa24ngV6#(4)IqxV-fX
zb@ds!?#Q}qI2eQ$s|+!rneak8#x$O=pM-1;!`znNiM^Afh$(^f-@L}0nmCkay~)1L
zE{digT_L<NAr)#<6CXX0TH$a+<@HQpAd9O-OQE`5AoWflhPYJ2W1W*j{(h)#jlt)$
z!d@`??DX1yh+0S6(QEc(Rdjg-;v6+Y4``$_9?(rK!@3%D8yktH&TkN$O&8>2E=3)&
zI3)S(@jk7(G%h#ty1wN|KLjn3{CWl&?jwRCSO9=m6ZX&Wn;SivPCrE#y757%e9<t%
zS~^P1z1R;0Vi5dRd|SG9@OR06Ekh%aPG}AxysyPH1~^L{?z@r~2iKZ~l`0@7oKg2q
zI!1^PT;~z}D1bwutvftKLr$R)iltXejnRPWmV5??1Cpr>HFsJqyUh<|EiQypb>oAz
z3p#a6Pe)uKyx{7evefK@-Zji4uFoU0Cf$2Q8QgEn?K{9B!ANVVMmU(lo*dW@4jEfC
zb8NLhCS|0=6O>A4-*_t#r!m!JAbKDlPrQ5srl<@Nf;a(dFa9$}p&<>&J9zM3CrF-)
zRt9m+ee<+q5B<ora^=Aqb+EXM6TM`^gjymnpkqQtdvtPm7@vYQF?%gGNx&jGE4RY>
zXwA2Vtw%h+D$y8tHynB|5T`zFy4B@>!WreWcyKD1?%zuMqavm;7$;D)uIB#$)W4eV
zpV;v_Y8{4Jdn*L=LRjGr38UR>hq`o5j2N+TiA@b{F*riAO(r}Jxqyc2sWu9w-qF*G
zdMc&#oWqXSY2by)fSoO;cOEY}p}S6wB6>1q9m?-^e9e&X1Mprq{c~BfcsrI*Q@x7;
z)pl7rInF;Is5U7%&nHGgk}7Ys@tdJFqV72a1m`P;TYZu?Pu|gM{u@M<<Hauhk+W`m
z)7}m^I%od?3C9yoAZJ8+jg?`JzCfzw%{mnF);-Q~#dhH0h^JQ=-pCN-JLVWTg^?S+
zH*t6PNAH_Bhb<BCx;<03YB8>FMN!z|NRK4^L%jnJnrVRe(jh(6o%skYB~mVJH<uHh
zB+@&^kq);sH=1B+lHW9(4fqu22f5LP6c`$(3{x;l8#_apgCRO7rA{!^d2H`;0X*)y
zTJ9^`V_g2O&1K!ssrx4^zQ+{j5qvYC<cEZBJ3xRF?JUVeej$_1GfpU5V6yW@Fw~My
z0dz+7N5A`u$}P68@o3AQpJH!*sK8@KPN~?~MVvC|nAeS3REl_HG?~CjxI-dLsj(R*
zlTws}9FqhhW2%c*nJ1F*Q*{3TNw&d$DbUfp?uwanv~f9n5xH5l!1^LEwXMQq-WEW+
zx)^Q6G>)NEh@$e#_?;On!Lnu{GtTLO&ZPV@Z@N<&M{?*GcuXK?+HdT=x4GX<gw8U3
zKF&nA=bziup_b8duKxg%xpP0x%jicH6#H8|4)?hf*mF!Bkq+=UoQlxG%S1ECEPw;k
zXSt4lT5)NoPRFf~{{Zpb>Q&tqM8oOduM~9NS<l<{sE2$`%RZ}JKDI*?KK%ml;DA00
zIY!dtNq&clC+^>u3|nn2f5@#Vjgl&ciJf_cdbHbyJ>at>1afHDL4R`6dB{V_I`iS#
zlZ*cV?pAYYV!o2NuX_pE;oyelX3wUo!*!qyz81Zt^prd=+mL1-GqB7+^cw`BI||MM
z_Hle+3_#L0<8Twq2*TH9Ar0}LH37k*n};52-cdDtD&Iu>AX2rg>*S&)ZK+t;DK<#<
zoT)aI0O(^zsnLnoJh&l0c+v4f1L6LvqoR1HNEu3}gbd4bKB=ybWib&H84xnWb4Lbe
z6ixh;InMAmPNhFXa5x0%E^Pk*QV~_YHjij=Of75f0n7#B(aOQ687Sm!<P(q+#Tgt5
zK3fR_vBUi!%&qnZ5E|q1RU1j6_?R8e3jS-pdt@{M=N>_5U@j;R3x|*~uwI*E&1BIo
zR%@(BAcg6Oo=Zw>0PJ)fMqu?#Wh(XjBPP!jA;8=fr)I!B!~|f@Yugv}S`<I?HOpIl
zChJ-|86-FU>nh**np@NzKlB90HQ}4sxT0|KLvOTxON5jgh15V{Z{_;!9D=b&Soke#
z1MoX8Bsxxe+Xo~-Eu!ZSlBoF^!S+SS2sxmEID}nxhj(Vz-px*SomCb*JP_9ra8bQF
zp`+r7-21a1l|Q>|7>w<~3U6^UF)nXZ>wgoGRJ+m);&C_fP43_e3umBIACHT#mo)zX
z9{4Q2en_^bcx@TSISaA(PjQBj(A@eX6$$yN^vPw_daTI2Bf4Fht_>#}G-gq3QXm2P
zbO>cZi0>SR(J(Hu+okqaYZ(R3bB~7kAY0kNx%R3aB^8i&$#{#2=r;JDkl+y5C5`#1
z<tXIcpv%B@`CIG^mCZ03*A053;i_976y5#<B7iM-9S;->OiF2chLEMkuIt89fd_&Z
z1GP>Uc0huMj=*Vkc(MQ(5DB^m86e{PnvJc*@@x~Kt#ffaa!+$+qn%^LJ;pedtMg6=
z1IuTnmjNrn_GQKwgfxcwBD_wgKUJdC{gLk<kJ>tH*Je_M#=9L7)1OV8ZRDHXyACH|
z_lBW0t?jgh^u`b%g9L1j_LDF9gH~zK{9-vHGEU5y432>r#d0A$2lqA{V96UaXVo3o
zW;(AU#C@}DE@=Lf{!o`V*+<>e1I;u^I2q=$W@)0MA|U2E6m@e$b>gg1I3^z9s8<uZ
zc(;lt3iloISz0<mm>H=Br8qLXf82xvDmJrw#(UTCgmZuRO}mT$q8j$J4kIW)hE@UW
zJx~YDH3D7TPxqNt#O9JUwUh%SmjI-L1Ri5-;Q+RuIYYwn_9k6PxQW!ac6O7IbNtqG
zWHmCc_D_#>NCB>4^Gyfthbfa5g;+Gxb}SCGjsT-)1l`wofJe;>cVpmCYn(=-j;I$m
zxdin$vWnGQcN%;x=1{~1A|tMf4iYl(kIFhGc&P%KcWN}1?iP9<^R=LYEy)zqf|E=7
zk&ss<(t4eO^lF1YmFqCaEQ$D)l)>mzg;uf06)<iZm|Vk=Idm|Z1%MXFX9#aQv|{Qb
zC3Z~Ir|nYz03TrC0AhQ%0jd~j;tXKm7ry0~?E$U*C2IG4_Sw}l>99#A$P6?X89$p;
zF-Wjitn2=j?bI7bh`ZpvYd1L=XJ)1;j%QU*?}K62&NWU4y{HA3XQVlih^Nb<{Z$%4
zv>x~44Zk3c@ED0aR$ailAPC-ZupL$V>V}-3YY4gcqXmTJ$wBRH0OI~+jJlz4ZjJW9
zhFxb<baWZ7#HCn~`dq1;KKraaO6?QMnElgE7V*_ghLSq0$%KCi?T?nvWINE^6?1^c
zP>Gh)Yd^Tk%27oK4xqvjxEO|u+Qab@Q6h^@H=TL}FoyPbwu=UqUNcZZl=<xaq};|@
z&ysko#yiCjLFl`|d``x5Iw1hu1PstOJe{y&eIPv=Q4Vm6a4?SbR+h&$T2p`;O4KhR
zm`nmKb=m3)uDPgr9G8vL94@tB`xc!3$Gfw{_F{iMfPNW#{SdK!ND2P{Q0=)jxI*Kz
z@WZhmB-m`&F1sxyHZX_-m)OyvSKl0zxy?A9!;#WLqQ<yubwu}?Ij88F>V=dBF)@<D
z%3i?$SSLxx;^LfSIGpuBPA?L34Er;x!9k4}gM{LDM8~*$)icH9jFN6vPdG^Gv~eDr
z2j0}Ad%jBUZE|Omf{yUgZWG6<1UMU`9oVtvY_gsUMeh%aY=TF`BD^f3&=gE8gO9~}
ztfy(PV2?ky!30B;M-8KA1@9+AfwFJjHr3ibdk*p+bync;JQuPG$qC;d1$=KLpQ>yC
zF#0PVDDQmNwCp3vW*Tq7em7<x+{61W1!eX~ntiFvkJQ=vA>d(m(_tAH=HzZkB<vgP
zd%twh{{VsYU8K|h0QHTx!4^5R>fonQre{(oXSoaUXjM1{rH4-?;U5#l8##dg0BJ>j
zH4_fzntQ6J`&^DJIR60cQ;E*Y_d;q=V_!&&@R-?j0Bs6X*#yP#<g&~T8x4bk-45Sl
z#pZb;DpF{+9ZDEOT3~#YNiM0EPqCMJMEOqBFws7X`HlIZVrOTj$wK8-4se$S)4jl)
z(6|kJaJ|#FTj2^UFV#OYG`jHF9T4!if|_4d*kE%Bt^WW~-MP5DSVYg&15wp&9M<{7
z4R`3%!5jCoj7Tj$N_)0H7g8@J9|dUjOqq<cMU1q=oF+Oh(L*nNR+Bbh8LT*-VXV+F
zXX>3G8y>gVZ7h|1?bJd8?_76dEGSg+Dy|0Lz9BPsx_8~Z*rBl!K1)Uw`L3r6rH>x-
zT#0oVxYeC@i`0gL(&c|KtKUB~NdvXh@>;m;&Bhi@Fg&(yyAE323*r!)in+*pid-5A
zFO}~<;EWaOrWsMddF&E<&`9ZWhl%9+lw%8R>Z~wKc@3T>xXL~!mpN!57+~zJCENnt
zq%_ss%J&P&{{Z#YO`8&Y&S=A%h6v=C8`>NAgWl$bLKbU^)<=?Ir9q<rbGaspkDnDm
z@(QPT{QxqVhMOQ~GPth`PsMu;E?R(oJ2HH<`YFXEA0%SS+H(M2@Y`9mDzpss9T#f_
zlXB7rnu7lK6Hd5!rr@N<84cCSjK*3|n(efMftONgf)4<OJ9}Dl2)MefUEArO(Nh?`
z{Zk1I+&6-F6e68gLGwV^<e2Hhg`CMD9oN*3il}XF&xo*bWFrs}n@B+MLPE<zn=o(D
zWE+QR0Dj2*jZPFsI=i573DSGLmc-5Bv)ZqVrIak)x;h<x!;4z?T>j|d0p)wP*{Otb
zFg(7+m@{^ZE6lBF$Umm*!~|4l{{Ud6TkeJ~Xxe89xIRTtZ92_<Y!C=pIM1pb3h_gn
zM_p7ouy9YYE7}4)iXzE{@cgy^0F+G`e}(@5x&|rlK5}rIM^t2+o&ZKj01o)-7m0^I
zh(mu6m;UHM!#zalv7y6IP{k(`Mz&tiX!A!pgXXnxk07)Xg|UP1Sxxrwg^-5&TkQ>V
zK$m67W%k0zN%Po$Q*>}&fnMNyr-siBQ@i<H#dw3vo|ahzVA7=LeLkqf3?r%8$7hHz
zPxW00CHP#OyDcv0a|^;jb0dSk){my|nEJ16zz;2+!8-urw0771-B&aoyind2{{WOo
zb7{<VE63iE{nkSwLIMcafkYe*7UHiIu^&|2On;0gx4UpGZyT)%S$Av>o^6Hfk`c3>
z&`%<Zu=m*92L+Ywvx*XFP98VWc;;W#b3aAT>_jo*pgnhXdrn^W$wjt}j%bGr4L;N(
zZVj;bEWybUj8mw{>V`yY&89(K(^`FYHxptI`_q%6U}^%F6HT$^x9bsuz0e=ZuisbK
z@{6B}J|@ptvbk+YJf%{?+vj7Ftk;@NHDQN_mr%y?`l5#ZoDca^iOvG(*_*BML&w>v
zpGz!)E)6E*qNvi*ml=$abh+kA(Hj!kP2a0fU-Fg6BltC1<Iven>9CpR5x2C{k?NcS
zDx1>Pj{UZE^<MBt+AvWPDu+i5C2`nd{{RK=6fKK(btuU&Jp?ArXMP<&cl@Il`_ev0
zedC>%2Avh%N3=Nk9ly*+G*8|3N`xG*k~pn<A)Cj+WpoTGZfPW7n<E#^2&5hcel9^|
zC^Fm-%^7VxEN_rr+Zkc1_lnJ@*OCXie~f#M*>{mN#KgvF&JQ^-aOQ}_EE<n=$h>N|
zhAV)87_S=)l1<l%?vEgjcGz;L-<tN#)}eb(`7H{n+5%4FynouMmY?Rn?*9M^p>vL8
z=keI{E*syn?D(%PapI~uu9ywAx+Xg;Po65HT_1+~%TErAv;!49Cfm`HXa|`JaRLY<
zC4rA;y1lS5b>M@IH?+u2zAQnr#RAF2Jg4ZYZh#>1o7pxPH$pQOCvJf{#n3q`iP(3h
zKF^4pUP-<thcNJqAChc0xNJ)B$d&CC!5r%t=n$6@AxGUb0~}VlZ6NPNaL20m%J)!S
z@I%@=Nfa1w#m9g^Q=-g+OiHFI00`a}^-u1xOi08oyLbc}ecO|$)-ij6UF5e5#`8$R
z0u`MC_*$7!yc}E*`^qE4)0pO)MU2q6=DSM+n>P~cF4RfW?$>DU%WObw&ox!{{J-T^
ztO;-faA)ePyt}-DJHPU(76AR)hVt%fv`J`y8WmRnG{cxx3mZGIyKAFgyH(ym-D`_-
zpEUZOBL@zgb5&eAv;3;^aS%66q+8;vMOt-jI?Ad66-ADFN!)Wn&}||Ev*@)a>{xM9
zfAsWd+J54-ze~8O<amXUyN>{%;PGJ^aXTh3w13o&^S7KnM`lx+_M8W*dBVqZj!D3i
zhCG&R7_!qMi0^6s5&WYu_Kom1Ke#N7JV&VrLC7nUx0SXVoW}%W4T_hFx{L2>)6Fs*
z){1e(GH-Oi(oO7&9x;shruh3ggV99$2ahFSE{dPSZ^;mQ#x(iFFM3b(-4Qivxx{MT
zOWiv(DhwANK#o$hb{|EPS^(M;aT>H<){o3CHcsOUm#X$@e9UYplwQZ+J>BeG!5;{L
zw<kLcbxp$DayFZ`T6~uUq9C<FbD9|D^5m`Ev=7vEAsj6oj8wlDNhslGa!bk5ZlpP^
zFKgq+3HaPi=a_`u*EP*|otABJ2gzhLR<aW8SQ_cN%<<8F9Ts3|*Q(y7%jz}&L&0uZ
z#gvHAWZ?v&0elwE<xvfIfx)aqHyd$E0mrn^nnZh06U`dUv$2iRe2@jaTX3+RA;LlA
zRW|l|vEcSy(GBUk%Mf1q3;A;!Nft4pxlkI=8UsQC8VRP;^F(7NSUD+2@t*fP`c?5$
z#ypd6$fxI_5WVKvHuE9MWz<_A@6Ttj@0uE*9^mMr!<3I`@(JX%&>h!HAuVXLH6;+z
z3<eCNk~e{uL|_Nv{@vx1>N&&A%A8Dg8{KhJ$u|`Wo-q^Vn);M#9n%>OYY*KBX^0m3
zp>KcoMo&GQCKr%dwYKZ9cy0CD#R1_29t(g&@SlE#HSX;}-X{TtvBQc0cD9Y-r9sE4
z5JrmVfz>=0GdpVKze=a#2+2DYI*r8A6=z^9#eIEMTooI-7~Ow$I0-cgI=dwEEC;)H
z(O5e}kyP0;cC=YFpuNHDw}gdV=Lv&+(Yyt`2ZxB{o*RaXV%C#%_cR9h+%+n>czJ_p
zxmx*3%Vu&nxGVD;J=yizU?~8t8wdz!Ef9uOnhLR~>vS7fWHrass~Te0m_}sW?aL5Z
zvH6e9b-+qGY%9#{I4DhqujCU0Xj93#d6lixu#HY=2ZC3J7r<t$+(tSowE@gDI?AbK
zNey#J%?pEYkB<XAQ$v9lpQ5VQw+xLeQx6bbZ<+*$12LyGWIm9GM~XE-!yX>vz%>gH
zO%uh>SCX1=K5&#%Kx3NG>0}R!J(UM~$9>s3f%_r6ggK556UjVT&8WgSI{yGwJ@<cc
zqlE0rZo5MSk!Ks{@J%2)T@Rkv@EGcb$07DK`*v5bTK$7PqLt}Rqk=zsQK8j;DECmd
zP8>p$MfYj?Eo##rHIqgg-y}LLrceZlH{_izbBD!V`-g;Ryd)6e$gHmJvQtj!ii7L+
zT1h%_P}YWw?Eoj@V=9ck$Ga)OPZgBFIpn2PhE@^?(x!h!XSf#`(1$M=%yC{ksh^=u
zg51I6jmGz0OB8+E8^~~9%m>j6Li}n>{^tJbasc@+Y0mf{L97U4vde<vv~SmMw8twz
zCg|@I_D#FyPpTO_$3t+vt-yj8kOC5#6ac-K1L(Nzz6yZ)6f9d4$81`=f9fCMa0}c&
zB_)*x@bigL+zdD!#E^h(NVCFVd+E?+I$+Ea-ApLnGJm8m3xk{L5W|PHF}C+pn#R6h
zyHhEqBopQk01JS#B?~uMMk-bwjQ;>AbScO-k7;g#&8R<gF2{p@NN!ecG3JYac3g@C
zIorNV*reitj_UrXJ3qTTC-+g_GsXFWaU4}Py9kV;eHAv+z+^?t^C_m4ZqY<kFBgv_
zF;fl`1!P=T@=D^fTx9GYcS|YakKKc&r~zz|$S4MwbowLW1RpdAhaHecCYV|9Q6`Ww
zc{u|-LiR{1aI`hitB}^fX~-zV{r1!0&B&713nxU8eh9&6Xr3?>ecK#Qq#`r0)c_&Q
zGsvB-mlJ5!SHdZ?f2q6$yGgzl{{H}D!q%BRR%`f`NcgU*e!%u{{17nGKqX0~hk3kZ
zI2Qa-AdOIKdrz@fEPRUGE%@|Va6;kDjexWLD+ij#10v8qDv1O;4k@`Q)Q@VW++YmA
zO>1v*P!4UN>*Q5k$D46cEI8+kUK6A_)BVeO0Px*NGK4fz-sJ&g(?8)G=Bal~9vCxq
z0-&>p*JfhjFyk3HPV=s^m>mb$_HK4MF&)#uqFUCC;^A~4HMSg;nt&~lCF#}-00!k8
z6N^nz01jHL_RokTXH+K-1P|diK0*+Up-i6zA)ivR7HHsxZ6RpbSE~1c901h`!ua$;
zVpY_L;DEjpi8;>Q4kYtU7~d{&<FN10IDq2UW}vo_w;S-->oQF@aO8O)DbsUK?s9sj
zk=$19$j>pFA|*H)W^Cag!Bhil0&xZl5yfXlrW5*Hr+5h*N|0eNm~b)zkl|rlKUCaw
z-IMbjL0h<~;5M{0;gXyP6&&k+ZJ9xplitO?+%tjP6O9+ohN0Z`RLJ4aqOl|}1J+TM
z0CfKV)!-GeeVGqwkbo7K0Ic15p?BDHQ{BHQ_qisJf7Dw{n-aZO3vAJWvHFkrP4O8g
ziq+=+XoLscx8S@6&KeNB4j7sC1XPX2`=LDH#V%0hyl(=5z-)ghQ?pQZ9%;B=4kLz0
zk$@NC;6>Mo`SlLI!wwg>(KGLxV^iL5HKAS?_Ng4e35UC8DLQ1YlYsV$bX1ISJ{i#r
zJ)xt1j~-!5X!dqE)jo3wVW&!sOdw>|;e=PZHqC_kE&-$M8_kw=Y2bhAVi6_BuRVh0
z=1)(ua{$j#KVWY#2>zRGVI|CC`e1T7(_#25h$`fmY9IM}PQq{EF`B1|n9Er3>6l)N
z@fqy$O*oilIiVqr4<4w@G`h*wsY$LU;Dx5kH%M>zRf7Y)qmb2mATgtpZMQxulZ|3`
zK(}}}xnS<yOgj!609R-1_`R8I!fgt;1aB~W7iXf(G^Y~<&#FIpLYoY6Yl=2w+vzBQ
z4lQUpp$2~Oyblq}XpB9QlSiW0HlJyBYLufs>A?KeYPgLzMLL5nvGxp7XN})Li+j8;
z0y5m#7J4sq${rU-ng%K|b{PAYA4}{ucyn*eEZX8d7ETyXnqyx`j2sCF3WQQF6%X+L
z0LpR3FY9C_^v*BoC`{C7k2GzKIx&MuonluMZ{3oNbJ@g-L+0i9ZMR>s7YKNs1o<qO
z)9_yAFIBGZ*#V)>uy-_CFgRFqS~VS|pb9_)0l7^%PsK3LRQEBM!8V!dhU}6wvaVlM
zjS>K|@i>5CG9KR;`m49yDVU2LEE<k0HiuY_P`Qnl5`>quWy6x5H%&f^F#XO;AZ|G$
zBTax7xLgpd({bnab0=Vu423(*#zRg+QdR;4VQJRM(-|SIN+w|WSw;qe2-fR{(S_9M
zmo)ql%z~m?(iaVa=R^bhPq9lz^+#idPQ)jY09##C%Um@IvxQf<gJn{iy_39}LEwa6
z5h<ob7LlKteD|H1%zKaODhZ)jwBOl3=V7zAUHVEmsmzCffu1S1mhT+`66$c;bCD%9
zV6WN3;x!GMCX(#a&MIzn;zr{l5yM<XNAP%Vy-)%P*;Kbg7)ZB)vYm>awB2@<O9Pnf
zz203Gpbdt^f$ELT+7K`yy%e57M|VC4Zz_zY_m1}q&W_Sk3s2gFWgUR}p|Au6{>zW5
zC}O7e;HW<-L^_30j*%P=iy*vgsg|ygqWqHpJVGYfC(EnVzbrgczV3^#w%N)TgMTz~
z5}ZW0)m?rY><_wmFX?v?vA=xbA&1!<*SQVzQ+D6YXhvz6sK3U=E~I8Sx30z#F${tb
zMvK~}3YgUiZtF<v{{RY3Dy}S!SJ_OP=clUeF;Z?B^a`z7rY&e66+~ffCXjN){=#)J
zM+1%kH5Lc}OaP2u;gYpRk4{BRF#59WHV+3<joVB!`;GQ+A3d>VITA~bFSd87#d<@h
ziF-Yrjr7^>ne!-pj>=ow7|_=^+r!9gDgffIy5{*ER$;}yn?H+5^!-BqSa0^(XDOS`
zVE{KsLUT9uK)JOZ0dGEB)5Zn{u!+2sZ1)y}%?65@VqVoE)98+LFwg0-8GX_6$w_8z
zjtPOZXAL@hKuy9R0MVir#ekk^g~xzcSv*`2HJ=unGE;&6kRe{1J2f`Abqm59iJogB
zhw$+FslwJUJDX8G+qanL%sV7x5Dp+OBE>lW0H~)nIlN~Hj%x*+$7aL(Y;^ws<*9jF
zWO#}n!FIr$8BQd(+9|!)XCwQ<(n#~#O`2|j!vr5SXVEy9@ah((gk$cXKiZ<{wqwC4
zZq?r&Y1mr9E_qck3;>65Sbq7IjUf%^F9X=>Z|G6`vhMMY<Sw&w$@NS)>QS#$kJJtm
z@I%=nq@XvbxX(eB$(LxXTrdtz8T$RLW#{^r^2+vmJ3cExC(rCKKB*sbNdExd?n%<_
z^ARZh-C96fqW4?&2)ju1I~;DysB=Bn2~>c7Ak!HXOB&g35!DgBAcM^|d$u^oFmzQh
zI-tfJug1`-xtK_6V8Opd*{Riw-o*MZ{I(Ig*~Au3%bvh{rwIdqYSnjvyrvK?b_0dH
z_@n;9IGd3i7DQ|kX}A{jS?$Q1m6n@pcv#cr!D?(cBAf$hFt`cv9ROL9u}gtAu;KO&
zVvruF=27xoFS0w={u4dl&QX&$B>YuOU!e>w8F5*<rXNeKN;xLU5@(=U$}V`A5t?J1
z+CY)?lx||^h&IUyYpA={Y@OttyGUXqQ>v*L^cg1iZ}*$wM$c|-$c2cPJHC_5LN|vq
z$cuc|ZNH*$m%9s_@lGQ7DFkYwhiRz6QxCB5l=4k2b8{O2`^H@J1`|nhi~yUzPLOp+
zgEHZmTo)C>QzY1zA5f~v;dJBZnokR+w{^Hic`P*;GHpSKD*eNw!2G9?P48R9hY}{*
z6n1d`0MNsQr-pO?0HH+pS<`tQ6Uli-s{P%1G36lv-Mcu@?WgyQr9DkuO1qt}QiuhV
z{Z?dv57=~7rQmj{(B|E&{m`0*@>lT~Dts0C4TT#Kal|PwH^#01014z7;+WdJ@;<7k
z-_vXwXW9eZLsf`Nud03*HxZn(IoUb29$G{<CcBAF`?d%WIBX&{<bffyDRze6Bkg?G
z^4k9ZH<WLd&%<p7;sa7ZA5{cd%{&TZJ|*A`-N$DZjicBNo~1hwm~3z#Y}_oLO#J8T
zu;d5WGRt5CS94jol2aK_L__Wn44pJpsXLH${R+w7G~W{ewyC8<S}HK-q@&=j>?88J
z;c-CjQnBh27{JPU4ki^%$~2u-{GvksCuwOv5~CYCU15Zr<AVMzEYG|bkx(ML{T`hJ
z)o~xvRUB2T3~tgJqB>emT#&kx-csBjow+8lgbc$(o~h)Wzsq1h+7U9@#0SkVH-UBB
zor&Gm%5?91mh~bbt|Co7oRb>nHT#bSWEA2Z(SAE3N4^$Pv|~r0+5E!`Mh2fZ1RxI+
z&Ou^x$rn6%Ctc0n<h)&(&2vvD=#Qq}2!12LAJcVA7P=buYZ<I;Ysqs6GSUr!U8#>e
z9k^F#cj3BDn{hODV}bnbqA=QcKw#EUu-)jR5jn0Yh3;)z0l=(6od!ko6Pl<hQmF0T
zB6zEcG?`Dz7qwI1KjjF;*~z~(p@pa{VB^H6PJ@v917Z8j0q*{+m$X9_fDICOHNRuS
zJEPtxl6HGg?wYM>H|#R>u@^g=mRwNXFx<9(F|q1~=yY3>&qYi;REC3-h9y%QDY)r&
zos$l3@CXbpHkG@*oJtO<LSz0Bbyn&~j6G#l!%mRFfrl%8>YGn8GEP29U@r@y!}^5m
z&kc`3vDi-?n&q&4ffj2=o%Uw407l5&1h;P$AB@GIg~bnLT8$z7?$9YXgy3$73^;GM
ziE&QLf)MROnH1c9i*&Xnx|!gq8f<NdoJ3Bk?#44~?;!C{YhN;H8-HSJoWc#iW%q)q
zoPU&Pcqpp>0QyJnooboImGGH_*yNr?CSS~GyEuEP37oAfvD3gEa8B?llw#hVx&+W3
z1}2>D)X+ipq6zd^!%lLUP?k^(XaqMza8+Q2R?o@GDlwiXGT<Z3AP;G(-Ov93M$@lV
zd~9_5*SoV&5tw^eIoOT4VhfJfxu=rs^s3v%la*?nnDcLV52Edzo}l(dC~@*#oyU;g
z`6_N<{K63XL|YHf>`{BPuurE!yk6kDOZ!LUV+i<_*eN&jRNr##FPD51adwW*Jlxgn
z_h;f$L8DW~)Mk^$CLT*S5YgtHxWgEKPC*<b6Ds7VNDyIIro{aampH%(ht-Rn8__ot
zo+J;cS-C*&yE|L(TvKw`a!!Ij-P3Tj>}!j34vT=e1mYYFf}_1{>(l=L?h^~1NuM=X
z$PL~Wn_XT_(S>N?vj{Kj_-~ckQ*)zF8T}(?6f<}~+94ZiIHTeN$c`zH;rOfr{-B5P
z8-HDf0M1YWq4ooi{{Sl2NA2fK)d$vgWr*zIb#<5ei6hBJsv_DVO_edN(T<4b+z#^C
zQIk@WadWb~G{R}d=kJg%V#$vC%77btw^ZCJ)^KV4Ey*<RRjKd+fAgz+VFx+PdBWjh
z0aXL&xWiAh7GQ!oCe9wx^Bj%ID9apo4<VwMl`-@}?QlMc+~cmmOy$)M?VL&j16m34
z3pq>yw!{XIy{j!YU%7J}-cnE_^91wA@d}XdF^=`1+Bn@DbR2hHK1F4WDc!_JR}|P|
zfCAaUlmKvdfDL)4OPn(!!_gb+uG!ukl!yd{uFR(Ak{hujlqm4sekWyq6Rfq@3e$g8
zQ+1680k|?_C1*DED8^jd$wf-8@Uu3L)Sn&uFtiT}u@y4?DWLD&Ktm+*92iSVsU`J8
z88@;4%%<5E@ZD0T@lv2<ktLvTMlc@%cqTaVR`4^_LfzipKIqh;45mA}L#Dy@J))xv
zgf=)f$bKbow0oF3lN(5FHVJ!<i@?U=1BTPT+~G6E7C=DgHjAGJ%21u2{=i^A6m+_A
z!r~nrZmHm}84nA;${0&(HpfU`#3p`dZrXVgng#F842fPA6;O0tq0Pm<$o9D=kms@w
zRUpTzf;z1LMaB4R=A6ecnGxcs;|*xyQ17ZT1M4_(_Yu=);{%r^p)Fc0&)7eCIGwY`
z?bePQ)wT(MN&f&FbWL_Dp|T<(HZ~eY^UgsNR=iK;9Ferl47N;?9NmX9vKt^GK*In8
z@@A7So=rkAhL-bMtn^2`bA~VQyD+ETQ<_d@MKp~sWaff2ltqQrX9~Fj+p5yqWMa}@
zb67<uZjq3OqXn)Y^G*!52Q}WzCc$N3=;I+lrQS<pK<$nt;DUG~tdBJEIcR!?*(nm*
z<%O-ah?!l0S8JWTNRlS`XqYM0c{p30mP26z*_3ZmtDWg>rUc5I_Kl@hs?ah4#45O;
z5iS6mp-toSHdP7+2fDxT>P6Es!+~}P<=z$9>afqS>G`4Bzv*3><kY51TyxzM3$Pb+
zH3(n$Z1t)B7i8@8?V~U1t8UE0cYaDu^Vl|f@Ol){hO-N!p2uKzDW?7$kcS6M@dEOh
zxN6TLB^=&tw7;U)XNTKy$*5zheYXqwC^a73z0dN4TK3pFV#&ADnz`J$+AXwrFVHR<
z4)ag1BAeVYr4ISwruWiOk}3ZHvGy;?OCP!eBa|aO#_k`>8K9U%o{IP4skvtA$KbQQ
z(`C4&?BUU6Hdz6G)Vxoo3F_<r0O;f^J*TV!kF<$snX<Y0DJiOaK}Gqe1myS}RYU!#
z;_kll{uMX7tD$yA57g&5M}kCpBO8voae(JYPIFs&l@6Frspcu~Fl7yt>wV;>c)LYX
zX8bs+6r9}7?0l6cQX?SKWNhkSg~rHD8mDyoj^cztTo$Ei{{R~tZI#g^@g-Q789^Aa
zYWAxebPGI3XYqS2K^*khC5Ob)jI=e*>M-P+kF%5av=7WFG+yxFGjJUi<vem2b08oZ
zzyuCU-z%ys%`}b74m?bKjt==N;VI>fdq|RE{M9JJ;?Q(fh705c(KyCuL&`WR5d7av
ze8Q0SIC&7i4!l3kE`^}^N;ws(`_LVG<^B^Qn;(dQqRrJtE`uaUA<x6in<o(3l>>Sq
zgS2*c!wh)9SFc&vpU5u@O!d?3=8f9iEd5Wn$r;=q^<5A{F*$2l?&~`;&BuV^q)}#H
znES_LWP~8NfG3(4Xm`TMf=dP{<FIFva6Q!qi)AMJ0{6BNy2lluX!vXyjq~}C&0L=l
z(N=3g%N>!%ONEu1`cgt3WqSlc$y17xy#m`Z3Yo*J&710~f2pbf+-%_W)lno(gI$Hb
zYLLg;YC0x-4gUZtF4@b5z#+}C*B2Fkyjcx-{g>i*a?EqZWHM;iW_vA&=gaIkF_E1^
z9*Nl98PENymhC!cGn@@mi;KY|^V`K=5ECC%G7H?gE3>us!zAHQ!dxv9LZ|mmFa}v@
zsY7cw01h_bi;4V)B)|bMEp=7StHVIl@4Fg8eMXNE!w^Belj}O%5p!7Kzs7hb0_sC>
zbK;zEw;|0&@xwhVw5so#xT@T_w(_fC0_-P4&0he*@NU;M>Zu6DCAxG}iz!rNaO&9>
zfI|H~i^4uDD%e^F3)ru_Adq2yF{|D?FZWUE3dm6h)p~5!eqZ_=e$Ja`b=T>t@XWLH
zoRRxFZ1<$5%NkAzP2I`kfk$`|1FCo4wWWdDhPerdjt8PX&D7nnHaIb^IeSDV2DTBz
z`SIuxyN$}Y;+clEJGM0$C}}T;2>V_fLV70?1dO9$h2f`7tm_HV-tDLURT^X-NI{*B
z&B0s$05H+<R&z<sKA&f*Gr};3O0SKMaNlaCP4guj6vz93o0FX1ioHBgh;SqQgxpkS
zT+x_WxzHA`k59G~4mSwdy{u36!lj6`Ot|EL*y1HPz~+sQ*eIb4eLM~vY_n^K^x2zR
zz2|sd=gku?ClF5i>hy*$%FYN+c*4{BuVO&-TE45kAbl2Yc6~Na;D|m;inyDFIUFw4
zs9Qpw8;8O;o?l}!L@#`gO_>UKjQta0X&Qunr2?jr?QHQdwofA~!Bc&m4#*9-CD{oO
zi=>E#iN)@(#f-FSmtSykLI@53bkQB`WIyP30}E6HQyuU+%tCgEjFsmlo;$8H@I?p}
zFh%lnxF%p7>m!Nlu$D))lA`|iT8%S2?+Ud>(x&3Ui;28hAG&V$>MApl10;MDOm#L-
z&p(<c!98U^ar9)ieWiDnI;(->qI3pSBz1{Lc9XFm%<Tfq*?}9J{?nk<cYN{stkf(U
zgbs)d+R(l839{mB5#09O@1NN?Aw5x3hY|RuFdR`JM4^TpSyk=4Uo}s>;Z~*n0;o)R
ze3!adf1vE(qVCuuFnrhX8XfdR2~t2i!|19o3WeBs4yzOW=qjIeRkvuF#F>T}aT>}4
z!0A6E()U2c!g(y!CXrc|I-!Zc*<i0%Am3nh;;OZ*88NnNe375Jbal}<JG^vn0jliB
zx}rO_y@ujBDy^$d3`THxt`ddC+to>Ve7N)AKFz?Fo>+LS-s10Fnr&_;MW<lG!R<RQ
zC5p9QDD+17z}R!L(qm|tDwEw0Bw@&_Vs>1XsH`k(-w3+QzEH?xXy%?;_O}xai+D*x
zcpI;H73r}3<q{9{rkr>IaE}o8mGO}sR8wg2-0vP}2T6!oHMSay${>;kF0j8h$qmBt
z*yL=Tivz_M@WAbSh*}{u-$=M?4$4=FwMM!SvT5B$=*ynjJ#t#TxphY;0M<Y$zsz!Q
zori+<SOUvUmWIPmKev>l#^?V4Me<n51Bm|s&~37w2taj6AJU=Bc_N_F$l{|`OHa{z
zj-NyZCJPwJTK49;l5!nZ8nv=Dec#fL-Z)FkxTk>GiP0I2cF6mykafbmO^jog6m<JX
zdaE~vUK=Kkh<U7^-N*F+rV+Nwf^tIUu=p$e={}c7!x7J6iDTqu4U=1zadcK<omlI2
zH#c6<HscAfMXEDPq4zLPM=XG!U8D~*>v9_a2(0)ocALEl(Z4gdEsSq|=HjIOZT3PC
zMw@e77qUn0g{rZyUxvV5{(%hZ6Y8}Aej83F^hQgH_mP{MttbzgVZg_4JFd)uEqaKv
zf#j_AZw0wj46>eyx%*rY#Y&hxWa?1zSs`x&dX?`1>Z9==e?5faem0-!D<)743ouq=
zK4^{Z?0nE2;I%ru5F-h^YM5eoX}2U;>a3?2qrwHCC*pfMGpa^{G*q!W3>E>G<_O{s
zE==4mA;XiGvQbS9=)5)9`d*=CEwcO9U(7SnJ;1nVB~*4P1k)lUCkC`r5I91sO}L!T
zfK^*j$42GmjfH^OaNf}85P0EK<&`N12LqI-E_UeVjo#25&JZyP!!|eIqZ3%*Ba-pJ
zra!p@2QH|IoK+r=gnaMzIP9=<uh3kqI6Z<i4asWUMD<zWFFuw+T<1Uu^710H$Nhts
z+0JZ<gPDuUpXHA|t=}~bRvuxwZTGfiH$>s7NMEMR(Zt3-5C=s!x-dZm6nCD<BP2H*
z2%ckO85H-6qUav)@?JSAu<>fm$mX!P4B&#hOISVD?>pd}!Uu(dbK+ASh;Fp#2}Ij4
zRWp{N*G@mS$8O@p2M@Aib+p_4S^cI>%$%3rS0^EmPdI7Jo+VXlu*UDSO8#Kj{4(s+
z-c9x!9ggrj5D<y+A408!M$FYpbgh%^o!4oi=Q}&xsf?V-nK`b{P*tSSj@ED*f{G-|
zL6fllBQT$u#5ZuyF`5GzkkWKsBVs<g8d_OQ1v|zMngBbwsf5S00?QDTCY?(>d}qmK
zT0dmmMYMw>^_={WmsX})&AG4wdStk?d{llW5#_L*#)#{Zm=?a!Y~bnpZLrZy6PMb)
z(mw3sjx%pW-@B+!k<@*-mqUEYBN%QL^(yVC(}&ShJ>DCZ{2UXp((asDP-O4&qU-`W
zr^R`e4N9khjW8)RFBlRUeV(Y3dvF70dwq_~KkNv7$!-#QtuiN3$7CC7t+UMV98o>n
zP8M`UX>gn46zBRgLNBvGg~+!hrT(aHhCH4^7l`;rAa;8_nxP$H7mMe!!-Fnm+cOhB
zs_b}&1b18{B1Y)RG@sS`D{>!aJ)YlWr~u}*a35WQRYKeN0J;5>a5F(FLD5r}K*xvY
zA7`oq6CZ%alDvJP*ZnP8)juRNaD;yHyYKoMjzo_{V1ODwsW(R7s8K9zDu)ZbnYPQC
zPmqo2HSy^yBiN}w%Mx&kFhqWGj2PX2?xDdnG5kVdeKK<kM&o}sIc9rHeqnz-{{Z(d
z7tMS1eruA_tCYs83}R$sw-j#Enm>PsK4UpRm*L)d*P<{S>YKB|GeXYnl^f=)_BqWw
z^;Fu-Pl#=XXl}pYLK(DQn6}?#!Q;tgj~Dk{Ww05j>FCu>uYB5aS0_~M<1Kr2AdJFz
zaUG&^<r_FBNKrLSLVaN}PV;1pfZ(Cxo<xg)n$kqw!eV-+G!W*@Ct<@-xK2hEjCprO
zIBGk*p0)@y0gyzUYB{_(1)2L<Q|!)%aC58?SzrwwL3>n4^ty2GY6x%S2UIT-KYz2H
zAqY+G7qwb;k_gD5o_%@j_m0mIMlh1_CpbZ>0&uA6quuZ)3V?hkReLE7!WuuNIG8ph
z>A@Y`$C}Qo&LjGC*$iz<JKgk8#N8dmHO}Z3WgW_F6Z(#2WKMi~CpQH;CL0#Dm0EWe
zYhV!^hQ^FTRiTft@5b#8clO(1%?qcpkpBSmo2o0A@f!p~7)D?rIyCd+vN4y2xzm!n
zOUWmuTMfbx@*s|zc&y1zh&!hWH%1k6Ua9uTu;{x@nPEBEWO6c|aKQu<FyIsFG>>Ob
zMA1vOi^ff>)0!kaZkf|&jtRJfow@?euP;4Kp>CEIoIrUkQ`=3i0t>Vdm}&;h9K6$M
zHQkxFv}2wtvr=m+To&geFsc@pG#D+k^-q^E*h7KPrpJ22fS(n#Uc-b8paqe*^VubY
z5b?;_IzV{B<r%p8os9fSq!p~~%G36<S_A(8q<muu^G(G~?JX@ja~%H0Q?n@;x-HgI
z-tr5>-CNA2yT_bpY^?sy;u%v%v0^mvoKDDM<bikvQ+$sIjZgvYY?H-qwdVaeA+@{y
z@6;g^P&E3&8;`31awq(taXX49{Zl^Zeqo{z;bITi{vb5Z&1EBD{{Urs52`^wrE%CS
z7Nu#B9D*=ILu2(I-tLpbUIwAoK(LPpc!>$e3)v0=$;YCuz~o~Ifq4!HE#2KNb4(M(
z_bAJ-&#9Er-%=98#A+6wV>O=BYmb7ME+^=xrmDw}*fA_E^-<M0WQ$CWC<vI$Co#Oq
zP7ZN4@?I7+F}_G!6okO+XLxd5>mj5GxE6^`#y#fUaV{%U@>*1nO`Q|GZGq;rq~ZSn
zNniX7Kide~wRTSIey~5nC8ToIFm6Dyg2xLKY?uK}$4R&7R`VS4Pa;u*GAI(!S@?&T
zT+s2gq<U=e)UOTlg)Cq>drnchd=0vEw!{q~!cYec%LmOHyL5SkEsUQyP8hHnq;Lm{
zij<z)gk1U#$nYuRIKnG9C<cm+xAbnJor0X*$LNL@v|-BmDYZ!WO3h*w0R@C<##3=L
z(#CknTlAfi_;NdPZ)9V?xf$-LwWqFS9oDdz;&KX(CgB*H@UnE%c-J{VOPm1tlzbF0
z4zDS)+B93!FY<{6!+WRLKV!w(YLopG{{ZJ@qQQR+uC_$#hmcv%-Mii8(Pq*?^4ca-
zfOp9dS{yF4sx9QT5O(;VRJioY=(JA+=EU(&A)1kzoHB6EaHvb4BNsBK4r8H1V~K5E
z&rBYz*bE@x(&2R2jny(qj5>rswn@bPlm#pd_B8`buGSi6Fu33_s?jYCb@79ESSvl(
za~vjAGzUXQLAT(VOO9X(S+L(<B<<$rKC2>$iO@;{Vr{U&E7%V|+*P@iRz!OT_f+@D
z@w+gdTt=G=36E7c7$SI{J2Ec;dkybF<87}c!l4)Y0$>t{hM974HSzoq5c`BKcmx~b
zGsaU&xTqk6+$J!%b84xB2*h2%LyU-du^mtu@j%Svp}nE(6UOP+z4)Qxa}zMI_+`k0
zg@XKM=+59ybQ)pRh&j))h2BkCKdcPA(7;;UG0`?l+YO#PQ8<N9`kK5I$9muD&1t*0
zEJ8xm8D8N=d$BRhq97eMllW!;)U5U&v7-*d{{W+vT4BaG>W=e#AGW}|E8)V&A@NmN
zPvWX#G5+#WWJXJ~w%l7PgRqTA!W7=>?KvY5oSJYG1>Cr!j+=zjf4n}yf-)g6mp3Pl
zJkY&oTh{*oBxfP%Wt&Rj_s!t5<eL^(KLl+d{{Th6Y}GRD6$vf;*1B#c;vj>`1nJ!q
z)m{<yTu-6^m;fk!Dxx1u>;6}3hu{;Bh+U<RJe->cLa{n*8U@N>?yUzS9MgNMT0DgO
zSS*3wFcYwb{SZ9CKz?!cRU2aps}sSTg0TA<I9(BE809G6?8fUY=6=>z_@HAEj)fbC
zWVNM)d824=%@PUVs8gsKFr6+sDPeQG92XFMMC^^>2u~Eqr(Y4ixRp-@C*|@@bDd4$
z#}-!M3-G%`1y=c}7=Xf7{S%viWa*@3*z%64;~pGD6lOwg&S;J}6K*>}4ji2AQQogr
zw7N+`_h+IV-7k3k5Zt8S5&5SHsgc$>FKds@I67%CwZl;mw_Mk{{B=$g%{r9beQ(J$
zj8)DV)HuK^N^Nh|9mmpPAK5AkA7X(tUzaeh-3k{Bef*cQ`i&nobEPr$L;%scG#wF~
zIEg90$2UT3PoR`8;|)c-!*FYwb*Amms$d>VA)NI`0|$27<kr9oEK)q~vJ~Y-#B8S}
z)g6WN7-1Yut=e0pZZdAIEsrE*pZx*&9i5ClVs;U{21vnU3AaTJ?G66``-AsZAo?ud
zXOGRX&<PUm3$+K;Bzr;A>5|RIHQ;H0i-?vSPSnoBaA1$BIg~5~hb^2`Y%oC48T*m6
z&Wf;WnnN(h0jk9~ufxd`ot3JwmONn*x(}4>*F<9$@2a?C+}}hu+;N|vMhi%~YhJ=6
zVr|-W3+~;9B6#pbj!C429E79nVJeII!U3Wt=R{pl=W7U5eUTisUM)&D*bqm1E}MK>
zSu?=Q4k!`4c072bwOeLWn#+p!Jyqi{K1!iE5x@eBVY7LYqCOXbKV(p4yggaPH^w|U
z`SngNX{KWk*EB66QIWwC+%3ez(GLFr4ZFsIJy&J}^=~Q^okhY6MURnBHCc*XP2Y&c
zOyi=<NR82<P}XMRc_}Qa(&pcRgc`%%whVmJro9TT{-%ZOnLzh7H2Q=|rH~-_Cs4Ej
z?cifku<Rbp{{VH?SZ=M2{z%>oZ8sMP09~AM<Mwn$H%G+nD^IrEP8FR1T*N4&21MuR
zgWh;dHBh9^5Y=?~OvR=n^gvT3xSsP34NrA<xxO=Is+Jap*e4L`6(3BQygK4g-ly_W
z)jZ3c@>{*Bf+tFY_g(^bPx9P^IDfhOJFaCBQg}#1coQd%sHdLpu?=t>(cK2z#@}@Q
z>)qLRNP*1~O(qIwnI`IFnMO}`!Vei6Gd7j%(SPi`PJDST)G5Cl0V5>d_z`UuI%~Jo
z-4X+}RY|JBY+TUqX<HQ_aPJo8s6CErU;}WiKN(+w4kq~{y^H5$VAI|mLIZ=xG(PG1
ze&1;900&cH9*BLDd~Jd1qwJmcI}~*Y99S@)CB>n8G}?%>smMCaqmV8<p=Pax`Jx+L
z)LQlpAEb3nA;8=m(3cZnm}=GzyPe?&kX3PF?BmtMlzbJSdAng9^%r-Pc#-WiY!hg{
zh>Db)L<x~7UDZS%%L&fFGIp*101FL97K3gYT?b$e0N*K12Q0=&_)OtEQvty2K^VAz
zj8Bi6&5kT5;x-ISZ4lmvl4)}TpS(Plj%@m)kGi1^Jo);qEPAiy(m(XV_OTboBtzdO
z+Wc2X1yD7R=;CaIVEfSTY20>dP_xJRL}E2$gwwpz2bu&mjYAYL;GF!E!L`#}=%c->
zSQwo&O^h3k;$zxQb1FdY>U|1}j0Fc?qp;2TA*zhrkAjdHdsPl1j+gzv^gJzhc>Yag
z)yvH6YyoEZYP0T%^jXTn9tq##Ii`rO48D#z6`LGE^0K7(VRw4>5eJ4HpNjtg5%&iU
zsK74If_^Z{Y#{OmPQjIkQQd|(fjON9-vdp@9hwE4CXfvlsM(xYDsxVu7`&9afp+7_
ziP?~FB{Wf}a&XDq6C44^=(D`1fiH*|-pU5`{{W>BB~))j7-F5Lia4=ScaelCxs5k;
zTm(pAJF+4Nl8J??JEWXk)kkM4tBd8JM?Dty^}LZAc&kwandQke4qI~tI&pp@Ty5lt
z52i1qfP$tnvx(q>%^NN%VP=?G(9OfC3;zHSHugg=e>D2fop>(WR+-@NzwsJwq%Ujz
zS8m<IZP=9NI-8BRvXxr^Z-{1R$x}6-P9fgN#cY)K)#Dsm261HQt69y7%GxH8n0rc`
zZOy#6gk0BNNGr4#IGyZSd8(b>5iev<D{@_-pwYz8)2x|5w1X{oMdP0HtmpGi1&4TY
zo5K9f^i9OxAF`=XvHO=YS{rXgaXIR;;<Isul{$u>lS0o4M|%^=^0J&d$JiI{r1_t<
zd{GKLSHRBGu<WKoc$)=NygUZzRE04f$WeTyc80g1#s2_mE;v|REoVj7XCw0r!g-R%
z3uavP*%%DotwWB&^T|ID?+kQKkinN*tF-2)b=2J+WGlb9$5G##JS!ryVRpxcy}lZs
zLao|pkGg-n-*pHhO}^8>Oc)~w=7!f#Y3%#g%(sTuhJD+xG?9tqh_FZ5Dbh}y5C*+Z
z!lrcQQ^|E|?{Vgjcn7lVkP(5t9Ry^m4@BgB&^*etkC~JjTpS<~YjH)Aa%)6k*l`YG
zP-__t1<IqGBthb<J(SCR+*eiGT9~s))72MD*l0Q-XGRovY6k*5kst@*c;cKlgt5u6
z;bZplYg#@ZYAuKC1*wG4Kzsqx!DocI4c9`^tse!O-ofavHY1Xp2O+_DL&T$SFi7<Z
z-L1X{<&Tk6n?dkc`0;v~jevdAD33r27p&T6%p0uGg41ONoAVyw)GbZZX4dNBkKRyg
zSkdnfYhlGr)CZD0jO5|Kp%HIH(l8v(sNY2)vy03kEC<O64ry?XBI&ttP&XQGV=*X*
zqzqyF*HrNW___hOX_M#{WXWI1X!Nr7dn*r_LQf7T{o#3ZINiLuA|1Nxy#DFs&2#Y7
zkpKr+szLZFxHlG13a3sdC{1V?TcYAy=!BdG{1csY>lp=hVFQ0Sghe`UHUz?UDzt=B
zZ|GhiZmD1$?I{!V;HP`Fi67LYed~mxt&0!Dp(7oFIiI(6;;&J(Tf_mpc4~9r7lM}K
zK;YGp1TH6F-KOyOJKX_3yt&d$2q~57F{gdxWDV5-*Mm6RaTRXx_n+N#;MNjw>m+hc
z1PTr^Ixl=9n~uhZ(R|E_E#?r!&LLcvVkH|akUicUPw3FRB-y$S8v<-P#%GEbG+R8%
zbTQ75coiK+O4b57A_;4YPaaA5nK?S^v$G9GT;DX@1RV1cff&e#bA8AgXA{H@5TAvh
z-87cGZkF&Vn46p%tGM_^t36ZW04gyuMTTfuwS`9>O<&D_&+L+3#{ljEbVqA|2Qr;4
zoZ&DH%ro;=D&x8*(P@3WF0h7ymnEVEwYC9Mmb)VyI6|WE%pwdMsg46RICMmu0vvJ|
z;D+r=uc!}8BXrY&(T$^s;G(^RM~Sh?0uA%BlHlAgcgZ_@k}|crbXD$%%fu&C!Af2m
z6T2~@r8Jy!b;prM2z>(VIl*wrH^Ef3(Zib2<I84rYT}+xwY?W-X~j$&Y_Xke6>ZY*
z0Y$rvJ=mXVHvQ+!EXGKm!~Xy~1@;tVfLZ8m_KoqA>-K>p<N}Iq=a^Kx$H$4uw;Yg?
zv*0sSNNaKe<n>i4&~N6s^a-!6QQNrR;S<R`hLZuU-4W!iPKm=D!aLoh;AcUZ+oh4o
zwB7xtSG4jE^@v%KWZ2yw^R>`H+<&@&?`bp|k?pkmTXJN)o)~V8yRc7O(SU4x_LF10
zIJZ!Q(XDYB9ts~*UJXRg72$IvBf4rdA5{Ef78-g8LwMFM0UsnT0e7Z*4YXRkd=vMY
z(Id%GeLAhNaNcR=uOi1|nmXAQo)L91!7=hODfp}nN=67h6)x<BRlCG#Br3u5+f}>6
zC^@0+uLF+fCE}-N^KaS`aSJ1NQfWPM`&I>!@t?EoxBM<E@Xuywj(#KzVxKaHwzaSH
zO^X!3TJ6`!$Q?p5)|^xMc&p&3&y;M<)WC7(+idsjnjHnX<#kDe{qxxb3%DGYcGg~5
zHw&{>6x%i<#OSdr#16kzl}6%k)Gy^nql!7a0m@c{qvCauIH{i^h1*fT69q<N<edZ?
zAaK6^daDD0r^rHaSz~(Wp2;Fy&~oUhklyMiIk_u!zv)>%Nw76>@f0Qs{9IN8k2U6n
zfgdC@T1$e_#d|%O<ZUfxCc}^F$sY3$y`&+0WXDuK>agz^daS@70-8e~RgY}`WuJHt
zsyDQt-O%YM$s14FH=|MQsyI2<*b6H?xSsz2c5KF{5At>o;<^-bIt6NA$^6Hf(>2+E
z@118G7fDT!GlceIYseUnvl<|3c6SrIG3J_D+U$Rnrp6j)1@$&qKsN-Cs<a<fpv@M6
z+&r{9B0~3t_T)P={+k4yHkiU3K|dv@$wd1O;IpzK;@(>h^*Woq9D(MW_e=r;b@&jd
z_s(_faDWyv86Yv)@mUQ9ikn;mzy$Cq<=9_<Pc-dj-C<fCmsJE5*9VM-iZOR{Vz{5M
zEX{OUzZpD!$D$?x_1G7Kx^^Y$v(;o4X4@w}Syf!v&?>GijpogP2$<rW1|kA}1{*cp
z9mJ*)-8ea}=1h7fV|#~-EGkAfW2-z3ksuqj%~JU%c$vR6ClSWez_kSYH6qjPEXHA2
zQw?a5%OzLv;l`Y5Y*zgvn;miFp{{EW-5(UjVOjO+3?T3vRI%}hv)S@YE$S0-cb%Q>
z9Y*l^D6`9FgxzsbiOt4OBXn)vSp#D+@mUv{*_v(>#3y>JL7-y$O*qS2gQ}DeO)e)O
z0;t+UhqS4IKGE?N;o~W|uk8Novlohu1-DeGc4nVyu4%O^)dDVIzG=dfcr$ewD1Q(S
zTPOroz~3y>CC_=VL++?Ix^I@dC1gd@4l3p+gBz;xcc9PdNBDuj{9z5?$@I)A#CD8S
z%WxhzN;s)I!10&-?EX#x)##i-fDy<55gHJ*I|a&iF;KyFL8S0mgR(IO7c}(Ic6QeG
za5~p1pnc|C6D{34)y6_|Z}3l&IcB0lb)^U{uOIK#8@3vJl^40ED7Do*9F!_dCN;?&
z;OX7b8q@^u;c<mtyXZeWf4NSo+sV03kk-s9<Wwia5*_NZL-8one_JfV0}V#Q$2^M2
z?=<p1WaTD$1(^={{kH?%I0rXa<F|^oGw>T42X#rNspS_dEfLP4N5@&vJ_*|FGVbgE
zXCXN&mv($WbP_fZj+PgJ#A=RiYG;YW?`hE=03QW>?5}G9^-W;=l8x%uV^8S-l~9Z#
z^9MewvADd8IgW`v1TXb+!HO(E8mHr?8)nS9>7*Y1osU#=pNh)>fU4iV6L>nJ3x%o=
zs+<mwlBDx*Z2rhN9oM%GfafXAG}!0_^Hr#@4NLRs2%3W)a6mJ{Q>oJd;lq}iO6_I$
z%9}o=QZHn=t`FsXI-%`U8R?>}-a0a7L(NlY!=|u|H;E`%ofjNFf)l@|hRmg9-AArl
zE@RPWhYaw;aE9$Vck1gSlL_-j?<hOHWc!hlI(jCYeVU-3%+hDzybOCD?tlbt^5=Y3
zcuLjC{jC?_vwi&<fcy74s|N>gOqw)Enf8q2iFHbj#C-BucbHqzaYPAy!Ob`ZoFItL
zWl+ZFePckK27rLzyE|r-KB<`w@>Dyr^NS8y@I*Hg;#_Pb#nu8TSQ=*#A{AD|NE}vi
zWioU?Vj6T(V2K>KDuC0ezTfx9VMI2j31sjF<7SAIR#J!EIvhOH+hc3p3HzWQ)mrJp
z44AT}AKri_Vi3cP!_jYHsern47D7VF%?<;Ep`CJP$vb*D0;o5enl7KX*PL_WRoj3z
z%+$)O-aY3i%c=v(Sn)z?7LW>InYv;0K!ut%X_L&Ze-zRQCvO6tC%SRzwb3-qxvSi$
zXqCo&Hga10IiN*0TK2?Ko6>HKaO6;e=HiYCxST{c*FLDu{{V#WPrVv|6Ur?p`$rZ1
z)s};je)9}8``_@K+O4ypj@}(YruLDTcl!yj93*^@GM$P=`h*Ly98To12^W-i3%9Mq
zMfYXZJ)IMA8V)Ua8TEzyvn%f@a6d4gj2Vx3AaOaS+}m_+=Kv2|EUC4u9!uJJDC7zj
z?>D*KW(nJ@$|PZ;^KOa0VK|NwKV=T+c4}5((!boS^itc-4M<d3PS9U6?;xJ+SKf8Z
zsu+yxa#OpifBuro7imA((7xzknqXcdK%D2|c#=$&tIpjV527}wP0{AbM&+iHI-RnE
zTTc$_NzF662C1Y3Sg-}!8Aho76U;R9D+`>)vn^q;s?%*cf;+P5GFO97urbkpiN8_J
zI~6BneuWr+YpmZOjQ4xKi^K%CpCsb!bipT>v}`-XUy`>q_~DqTok<GBScUEta2^Wc
zbM#tMXlm*fXpa4^hjVUQ7+S~Hkn-M8rl;NGoUVgUcJU*p*(j93=$Im)^iC}ptl*pv
z6fVZPnevT}0-MdJM<KsOxSPyy`#;Nf?6^F7AGY!_HXJyEp+Mo+md#M`g`DKA-Zh7-
zS~5m#pzlf!+{RsQIU}=>2jYaZ;tuqMa>o!;qYW2%&6J!n?y;}{O*}Kq-FAv|Ip0;7
zR>VL~J>5Hz#DrsZ<f9^NQI7X;{{UT;Qs24pWpd)a^b5(;4+69n=2mC3w4H@FapTaT
zik$9{fMq5|H@x9)A2m{|nAaREXaaeXyCo)bJMQ%<^5?bBaLcAhVh^HxqU$J-joVz?
z^$+m1q4XO({LnZ~MLD*IIL3i$;%>B1BI?rPns#n(T$P5%g7%k4n*<=gv=?=2a$e`v
zc$$2VBAXPdS>xS$F`8p>cAAaK@X>W>Z^1emFoK5ZCl@Y%V10<k+8x~vA)@Ei95Hu4
zMmLF5{+fsVXWlE=X?4)e<b}mvD!b;Lzfr-(oEkW$jh&{R_p`4~s`7URf;A`?_j-;h
zllZ>b0mTZKar<H>QDX}Mb6Wh2tX;%C;|+%G@5xsps~&r*ZXVASnY`>6s?vGy2B2|G
z1+3<@y383~8iROyr<&uo&1m&J5&Oyj7IJCPR&F8==LHU6q<h94K-EumiuklSqBz16
z%~WU&ZK@IA<c{O_m|A2=$f0#~2omT<<5ULoL%PbI=AgI;;-b*wJfT&9ABDsm=S7x_
zC8q>$h@-QQ$7{}68><~ks?Sszy&g*xxuay1!N7xj)tp%_j(ap4H6fuL?(2BYSIHj}
zoZ@y;Wxb#vt>;p9>HMdPEd{O+F^N8i-o%1<t<&B$;Eq}zAr0Q)?jB(Rybw{}BQe2K
zZDw=v4uy7k!{9F&1JguE!X&eo5R5XViQ&yg?K3>}2!aKGO>PiFgN|)K<rsD=lai_&
z_T%q;(>fIerLM%r`!YCi*yttTPg^K!nN!3y>V!o}{@br^r@~i+Gy&v>>1dQ9`5<m1
z%v)wfag<`ggPiB~9U?XV04abLw3!7t)66fsX#z<Jo#kC#Tat0bt<eBRT9r)HXq@*`
zTSccvgyK%Jf;S1Jw61*GGEzx+KVvs`Tz;*EYGJbS2BcW+A9UNbykSFH0cAFhd9UWb
z%H<5qEX5Oqjx9zHh@Jo_6=M}LRf|j&;2G^`eLBQ3?L5kMDr~EvnAI6Fj5(&o8-jbv
zWWZ$VCq(wXsSzW>-}0_c9hwwA>a;lWB?ETs_P4!mqV$@62-$c`L%n#afHI>pR$~vS
zM$9tS<q^R=j?lxp;hb<PjaJor_l#P2qjtoOxBGSt7gamD!tIumi<0z0a31rdXSB=c
zm?MjMuflo6&p)yJq6e9TErrL_{j2OLz1zlY7Y^nYTB9<xa9#+)BbAE0GGpS6<jQk~
zA>ll(nIdy-igfZhh2k}%(0`TT6~;eVMBb@)t<n5ZggP%eD?O}%JY)nws(cKL{{S-0
zzzOFIMMbU+8?F{`RhSt@97*G#O@iyY&183vJ%;)(Z?iO}jCCeliI8zSsmXsJ4?=dx
zjqaCMX;q27At*zcP_e_}3e4IS+eVc-et{U5;h)uI;%S07LK#%1erqn{3rFeMnJL~G
zE#e5^vJ|r8CKW%`8#tqUc`N<nN6%@7xrBPBVr|+MuVDF|gLkwyc64+h1&+Fh({(U%
zLxV#0%%3E2opEGGL~&Ah#MAWqLP^O1_e^U|_hjA0eiY40pwD{m+mduVY3~Hqxz!E;
zIBO_aPr(@<RLOXb>>mu95>t%3U8PodrS3_OMNoI{CPZJ7acBC9U;+mXeHF+p0W%AO
zw_QHS$U>=&xLRkZl9|5d2T!^Pk?T8st<P0UyK781Xk0Z`Fm^co>|p&!frg>KJdoKZ
zf+XS?Ok0vN4Z%#tUe`0Gt9psI>2b)av{+&x<#wthjRBP5hA_txn&&q`F`A^&NvsXF
zVihNwXcaQdErB-Kod${hI|vm6#2z6VhZHd!LJtA6f^Cbl(`JH7$O{dbMoW&tGFEdV
zn#T@tH#-|{s$&>U!V3q%XcoI8?!hemNXx4}{{YgR1CGNa=&a3HG9eLAI_>4vG~*kt
zcN5QG-e3W)<nwT=GNvY8@dyTrM(p5?^i)_~#^dCGWp+}-$>0llqXkgIO{aQ2;;Nqa
z1_w6}4r-lpV>6-nDvxu6n~AXyIjCr+_Cp%n`I3#rJ>$&XIAV`pOR`dMsc{6hJrgRl
zvZF}`93U#x+#d%4A+8qYsFHiPJC0%#3u-;ZzDqzBLYb6KdZ8|14iUptCO9LaAwP0)
zM>IaeJEomoWP8^O5?e9R65%#){3eG#-3WJW{HmrmQ^+11Q%_&97+bvlh+3uD`m}{I
z36b^)X#W5~fX^cfEQdj4cYs4PenW~FXww;SC~q)Jk2l>9c#o=`$~6p)Et(n9`Js5K
z2bIwvg>c{t<AN799$QWkO{QJfK%u3jx8_DgGzxAx4G2Q$h^TNmqw^sUNR6y(u`ss!
zb6HklV;p^wRh|rWLdDnUGfmt(tOMNw6K#M2<eiYRCm8gF41G4hamav-k7c9Uo(r<I
zvdltF;X1@(XpMLxTIq-09MtG`JPa-vOMullG;u29?1GEiRGYEGPCE|iqCp&H%Ozo{
z<h%sKhvh4O2Kzc8w??<(<WJ$W=kE#o8f?-U-luM#Rc5$u?9UKr{;Mf3Z)kH#NB;oF
zzoPL6A4PTw0RI4W_JTlru`dt$nQ7&PWnPOad?8x$3KytZi<5?Mi6snRGZb`H*!Jgu
zoG$b!hZ~?oU!r$__m!Q++y_MLCZkOAO^kGgD?r37Rfjt!8jj~?p&ipedEf|?1{ze$
zz#o(;?+s92O#H_rB#`0FR20F<xxdj+4L{7OccMuhna;W=9VFq3F5OqOJka*!9ymm+
z{oz@)!1`TWRF{Lp5R}{D1pfd-l;R9G()mWoMjAl`^$2ec?hl%%LrIo?t_P7++``vy
zu>8W~9;-75fl!u5J+T6#5IFn3@x4OJ81>j;&1fKwN}I9Z0<Q@gq3i@T5VB)_C}>d~
z-9lW~d#(#GVEvJ~NE(jiha~UDe7*d6AtO+l=0Dwa+vKU1vDY4I(+%|(S~!98Kwx!B
zAoasL9TS>!+&?IU_ko~^_1Ms4MLEb6(cd`kUP@~k3qg^^IE+k>P`eb>xW>(8YXEOd
zMpLvLOsLm-U1axS9(P)7{%d|}3{`pfpw~6SCkgaVE~QXNKSG^`DLVTN=`;u*n^hBl
z#O3CmG1GAp(_^SqnN|3s6NSKCrvPw~jlZ&_U4u9K26!hD>AwJrhWnjSIl?j$o^awa
zgPZ}jiujsa9A!oBvNi{SQf`LkgbSe;w?%s@Vf4t$FZOnSSM9fk+>n64ehAnz=eN~V
z#N&RFr<(Si@<QaH4HC{iY2F6hb4~*k72<T_*m4;rHKfda6GkcH?G05X_5T2bMLDQ9
z&%sq=hZd`j@G_v|2z}dxvtT9KCkHjc$1nC%jN!)i`zCm-6zWbK83%Mgf(}W*3cIIX
ziO@rc1LNqdDn=TlU8W<^2zNb4B~1O>@e(fpsl-SD2o%~WkLguhH!wBQZK+0D>TS<7
z0@c4H20=_c*bnvDdoL}&B;Q0`Dlzz}o_d7TE`X;ZMkPVDD^H50{S|#=HG&~L;x{3`
zQmU4*?!yfOUnO$DNuEnvtey)G^$1SzxVO<TPZ4DDFL}`k0Om4Y(i_I4&qY6a-Q)R&
zdtst8e&|nY&IaKvyJHo`%*IH?dd(K#*JiTS87Eaf7o+1XrabdP3u#knM$RYxwyr1)
z1laXog72QwE_;D^=n$E%)8#6dbu#i8M_5cf!Fj;z8S@J^xDn*D#$El_`x+Sv?!ktn
za!?%i;4aiQFA8jPK>OZhLN_kH!upWhEEDhup7Nt24sDW+<{?#c3$u$Vu6fY7%B)<*
z<O<)V&)H8P&O|{(7Lg$Brgy9-RZbYj{n_WL^F}4E9|e)f@F?#^&s~RpiMQw~Fje89
z+y4OQ!1YEzrpW%^w0{<tef`;efN?nEXUaAbA;7!lox6<G9z}mF5BNjdDE|NjqtOfe
z7HcNBA2VR8mNa&p;$q3zoX}~{9t&^_xSwQkRLG<%yy7{$jZ+5m#6M7vggv963r$l6
zO2LsvtXw)EE7Y@|>CSGWBHxN1#oRnNslLnAs@9Xp0bqxXfu2YARhv!uigPaWPYiws
z97jNZ;d=2+9pp_n^WcFE{{T%;U!pds!<y01Q`*wpoNbcBh-B}A6FH5JA>G&xKJjIW
z7VUkV#l1+MqPP=%nl*cd5#MD-y#9b86@~k_DvzokbRIDcTECVGZ-JnL!tlLz8$PHc
zatp)V#l9%bD!+Kc*JgoTwOknX@dY1fkh+zTcFDEcIj4sTt&<ubu)^oNAfGj>kka9-
zXB#IQyhrv&2=zXR#L}?CQ!6GwH4J$V^0jxO&DB|d>H7p)EwRu^-5hzR69^I-0Y-5!
z9hDksk*0FnUtvQ1=(-w^c_YJPpm&2Wf|Bfc@!3?hgMFeWdxAg1%b7bK*1hesb1}_f
zZaD@0P_r68yG4W?O8wi-lbl#h+3b|)p;?&5kWQMXejzY91TTjJy0vCWQ;?p!F0&4@
za15^FXv+$igAv6@lOk8iWbIb*GaSCq(HMa!P2f3uEdyXOb2|n%;lkF%^IR5DZ#3*K
zd}4P2!EbM-=5kzAd4+B4Bn~Kt5+PQKjys@zQ!uz0bn1bDnsy@k+uoTWB``jVvl@<y
zpl5fzFXy6lCo#7#4zNVE(RXDbZ?VH8q2d!vh?ArpWmXj!=3)i8bA@o~O%6YpD`)aX
zSuYJw4XDP7e25ySHR6?69B<Ep{5KeTIe&y)3rI8p_K!4L3$F_|mdD9X?K0Ekv;}+6
zzu_1lf(q5-?d<gq^^O}Z)Y^ZMw{q;|raW%Z69Ybqr1TRhg_y68h{;!;@@z&?qHsI&
zZdX^v`1s0{JT3>(04Fduxxw!60rM)84EtFXA^PyT+{I8inx17b?>f%tOs7wyZ}Cr)
zD_+$f2~BZ4Gexy)pA8>(n~)C0pZH!AxYNdVaI60SauDZDL+6sst_*)ryf0>bHhaV1
zg`8c3oNz#xzmgdlJpz!{F~_B$XdOP0v+sK|?BI0hzVoxb?0DhLYRo^`Z?f5CXBhb>
zO(DF)$;v+No1Jvx=(W~vAO)qZu-qFI-RHl0IasfFuQj09R!snvRE99va8(JYTHf!5
z??s4-!yqR`PZaA5vQl+Ofm0$jd#2u74!f>K2B^X0vvYAXs6#E9)n~vIG;ho7oSP}m
zaR4=vd?0DjX|#>V!g?m)r7}kasrjH`?SbYYX>WpN<VfdboIR(={{WqUlgLR`DGu?!
zhT|^kV<g<4bM;5?>b3s1Mnhu$)NtsDfWk+aULN(c=23uN>?8H;4T??0F3G&rKK;)-
z#o7)@&tpYW)`p1#)pmwFG13|{jHU|mR-(gi&UhgJWI7|=IJO69_h#eC3)7jNC4ceT
z^D6D&Q=srRMsc;&$D;Ng3H>NE3WU!hxThQxsTBpL#^~JpDW7@SyFhWDb=oN0c?5EJ
zUdL&lP_$Z<J%XnOPXku!5yZ^cc!In&M0uiLLaE5F6*dn<E#0-=n=)6M1^f^Xt%@V=
z-f71peV*sl4a#3Nk#JeIlb!Y$(f;$=(p=*JDDPa~zpc;RX-DuVwTAY^nOSD%bWqil
z&!Rlhk$}I|4N7ohDlp-3_uG`A4q=10j3;4hT2D7dfdhxx90Jcphwlh!@?Xnt&mU)~
z2<YTE?EYv!0=zhW^4XG~7s#hr@C!D$8>>2|>2F1ziix%NtUf63_nyfG0w*)7Wr!iy
zyg6w38swdTR%dxJpt_tu+sS)DuLyvJ*d~0_;=L!G)2yIyJ0YuG#ZxbHP8vfwcNO9W
z{{Ui+H&Uiv(Zp)1ID?xFujQgRSxu)An*>Y_A}3%o1_(_cPubOFnzkx~{**OY4TkcK
zm<cDU7LyAmv|;8^)gNs{<0TC($yFwcuZu4v#)kVNq5;)CmVHq$ABr?y+i`sn$qUk`
z*AMDDGe8P;h6Bn30;D`DRG-=zdS&bq;4^7ml7%N;>|_xOu$OKD_>Kirb6PQoH_c&l
zFqs{Gt2WcZ&aXrGZjPv582KZ7LDc$;!W-tK01^BqhIx$9vuVjfF*WWS@ZQTNg1D-I
znL193w03{*Ol!J5s=F!6Wa?LX{;s#e7iXpj;L-D4q0M0Bn!;N#d*7m{9O$qCI5o4A
zY>sG0-8NR6?LRa!#XgCmE?{$5vJ-UKKsOyc1>6Y8py1)=jF_+?JRdYOO%<z6i-zkz
z%BWoM{ZYk6Wrs0wDX@5t**G3rs%|VDry*iYDovowqz&RZoxTUjc<Yfv{ui`f(dOmh
zrUfzmE5lcLhl7HG_ArZbkQQt4s>U86s}Szm13b8^)b8-r0Shvb0P}Km1BcBEV2_OL
z0nBi_3UDtD#@3BJ=KYT{v|&D_0BUwxAf9b2S_FEKxUXOreTRP%qq;&feAgAnErnz$
z{MQxiQZ7Q%kIguk5ZTdq3EuNj`-Pqp9aBNN03Hj){_V5%vynjD?}*Eu$o~M`$NekW
zei6~O+md6CB?cXGHaZ+mA0UWzv{oYfA}a_uhMZ6!jKVs$7a5gMmg9mA!Nq)v`-gXr
z-#!b+qHgrsFEfz{{o(vajQ;>iv=CK^;H~#W{{U^!p7NP!>zGEulSK3hrO?txRPsyc
zv>4Ii<SG=16$ujE3Z@z1?;`SZLQu-EapV;998f>$!LaxFBEWZXJY>>+kexe@Aoa-|
z<3gS;u#MhBLH!FxG<-JYe~NAwHN~X2sv@Cd#D0^>P1rO-#5KP)laKvKhhGaSaUn#T
zJD*er195DZ<v$c|xO~*vza5T>%s8k`r;AjA*JPV%o@kE&ZzS;_>0Egnj>}29B(%H)
zCABHwo(kA$KBreS>=I3yI-ox4iFIdwa=0wuH`nYq%Pu*E;c+9=V?O?~P7lc5Jy4i9
zk4D{Y<0aAUI(~~cW;toGvKrr-001Pw&>+ba8#bKofZur>GFdh59;!>h46Zqic90`g
zFgz<T?fy#P7$NN;3-<sYU5<1;&1YN6F|wW_#$)LVIYBVN<8PX-#@^*)swq^Fip}0l
zV>$`>rxz2Knt<|mN#nr{@Z#9uoU00e^&d2Fxs(ki^E}f|A|uUvP7DK+5e7sc(DxiR
z_gJ@T4d4xo1%{n){1q5A=8S#IO}MXXHC3ROMiR3;5HAg3B+LsZF|K<E8VvM7#*70o
zt`yr;<L^#EWE&oGh1(w;o-vU_C70OIIPRQ9UR-{~F@*bE5cbTk2)4^_Cdo71_2Qil
zIq@m(VW{lR@CWFhgt5>EHm}44;v+49hl))fsBuMQ_g^&+#3W%wxSt?2_=hyvgT+6(
zsT-%*Cz?2FFy<HB_Llgc8?WoYuWFtqISbNgsLzb7m$c@57r01Mg{{O-IbO?w9!Z~d
z-1qOCnqdpQ<ocBMy9+~)?6EuUO`EPX(hegsrAvdv5TqVvRXCsbsxYgvm?Ms8KCR<`
zMXhhT(7ne$`<6&#z2UG($~xIVEf@ek^N@|RAn$pqb63J0F=pg|FcU{b_^xRU966vI
zNs>yjE{{}Zh&zrq!hdB3l|d~dE{h=0zxj3^Nq}RwnjpdvBudI)c_Y6?n3#Vk%poF+
z>N?!6wQ(yZ$@7HZL|E}d#_1gj$$&5)C`UZXp5gni;D+gTJ8H9aXHWEno1K7vZ`eIX
zg&(?c-)FexlFMhZz!5Ohg~N+-U;BF0v9a7aY;TU<Utzz+ccg646hhBofegQ@Gj*dH
zJyG!6Z;qFUacR{woGm#l$dl%?Qy*3Mk?Nr6M<hXQdPffebVA_4U;LLC=+KYxPRx_$
z*);)23%!9D$1}%hwl^8s1Wu}$4kzZE?ZJZMg7JVEUAp4py@CsVXyn;KJHcVZqJI_E
z@>om^G@R?;IwuL{oDW1~3d7{22e7x*SoeXyL?8#2fzb&exLd<}R86rspCy$Z%-0{x
z?1Fath9{HxuB$iVtk;Yr@+rlgnwzH&ym%o=y_uvkoj12plqybTa&ZS!vu(*qoGe*4
z3rF;bTcNFAhf(FD`fP?-o(hI1%$klrMPOP<4as<FZ~CKrU|T=;biq_yMoyln-l+th
z6J<y|3E=CVi^RtQCz8#rbE>v-!iE~7PkCDqK$>KY;D@wfaM<nOR7f#hy#4Nldk!p~
zf>DPuVDJdD{{U?2R?n`&hk=;y%qKU!Vbg-i^zJkh;$F*$P28dkbt1uE6NIfoIoA1o
zgOYjf+{_1ncw4ln-2QF=C+}0Malx<2JhDip-lso;k{Tqfb{yZAMRG3w7t_g1G&Fhz
z?w3#MGLPW)j7OpW0F`<*cWMu>lG9!ziY;jO-)4mDKxXbIX1RpyZGc+tGO`5|;U~zf
z>{Cu<=bYILBlGU6#PVSM(Wo0$*hnWrskAsAsnPmu5m^v@R=wZPV3HZT%S9l9gw&1r
z)-~ABA(#l}HC`YAV-K{8aW)0oCP`k!4F>|ET5M5kg$IIIe?F*^@tgAWL})|CbHJ=(
zvk11B4bX{a)o@NScS6d+XDan6R0cUC;K(C-y`yw-i!0nV6b*y#0QFpU@X%~*19Ek8
zvMb1J0OOA?XhtfMPY}4EfGuWm(KE!+g%C&pPY#Mf;x4aK13)>FtU<ce=BlyvX|xL*
z;WsS8%4wS;1T3pjWlvS_h&t?m>O{2PF_=@_))-!V^BomX;Yzcb$2V5G@}F!iFfu%f
z{3?{?hCT>@8tA${!BpHuQ=G?zFatMD092#{jAs^wQ0|?;IH8EV0v=48qYY!Jx8#Y!
zAN4y0k!wq?6P!kvQ`puQ5O|J2r<WcjFcDB#a3dmx$%Qy6v@i}9MPzJNVEmR_Ae=a!
zIFK{h!((R@M^wiLwXqh>HXAo|%4=MG_Yabd>SCGd5Ct>{ztsgLuONXr5vpi)`YqY2
z<8)>Yi1@|&jL>WFx7xbI)5_rx=yqC>(Fi^M@0@HI(|5{Im^mZ7#@O;$i+!gXVkqv6
z(@3Q1m`5%u4I=Hu?6JUey7sr6d7rWis8sQ@>bI!8p6%lDwOW-hYF%+s=ZBUzqT;l$
z^7{rbG`c@m*@#&X+42PwDDOK5!F%Oqvspc*j_Bnx07&5|blKr}iTbY>y~$=gK^w4#
zv!<w8t)I!rPCnA{9dIN3S0>_U(sNPJY}T3hUueI3QcsZCp!u(4ZuMMBaq;1J;ui>3
z#EVV{yGYSAkGv0T`>qJw8z8)q&Z$OR-Pa&7GNngRI+6^4h9^11qQj1`7H~NP2>ZzD
zsJZgscSPheU7oH9!rO4sG%y`W{gp>laBu<Q&1or)Bvw9Y(@V3q0Up!NWpnu>2fk!_
zsu*2O<YR83zVdk?%<&AmB3;?scbWSqoH7f(gJgmV8}1)<#R4Qdya!O^&%JxUv84+%
zwmg%p$O1I7Eq^DBN{gCm)9&{#pH<p7fz)AyW?Lo`Mh9eJO*fxKlBVGUhRzU25OgTG
zHO}6qZv^T&FJj~;ZdPXD%QZNf<GOX|ywiy9q(|ioCb$x=#tZZ-!C8QV&@bY>^+@;y
zXFP-IxGd$z?9(+V)wFxP6M$tSINh6yZYI8i$C$V%3__oH?zJ>2-FoeKHDeJ2*+kvq
zRrll3cySg}&<3{3(@C!M%{h#uT^P%Pb9?5$?w<UJ;DKv~nS7R7DA>a1QN4ujxiB<w
za+~g!w%qewm`HAJIF(4<19V0~5_;?zRBG_lV@pq#-Af@foy>OprC1{qWg+9xggS*z
zqj3yP4%8h}0eEqfIVZ&K&s0go>7I(fhf^n}iGc2sCyFFB%)4eJgiMRi9fR+Ck=`CM
zs=K_zKmZFPRGx4Ga5I!}Sxl}9%`E`CW|bfFLlMO>#YYf&^+(UH!LIn*z#?#yb&$*K
zkPTK?9tsZZk3>qzYfLp>tH1#8(o_ATb>VHyXXJ(usp4@vulBstO9^g#b~Y<8K_lBA
zx+lX-k0Y_(9|OJKN0J+^B*;cw4T9nxJhM&j8Sa%vp`!CKi!NbEYp6E-QzlM6&iTpQ
zJktwHp67K{1}rlHHc1s{&B(xmmqkDOob-u88%^#VZi`8Cnh5CRLBjk_{)8=A!l3@E
zu6IKe7-J8;#S-jgI$G?UZ5)b?&Ffz~FJ~p1_P6c%Q4m~jDzzchr?v5%Xn~9*`j|rn
zKYiIML$ub_$LO2B<LI0eMq4R^+G96dGoVIX=Ul5@c@>^Ke?$r3ng9Yft(vO$JMu&i
zRYaGYHRQ*N*(@z1cFhYof5T)z-kz#KFcs~x2yJHPadaHc4$MasL$I*?RPQY)nJK}b
zc${=XDYq}&toyuUk#alkcmVS&$JyV6akBPa2g706bF05)s(u<(pt0WL(_zEnIEAB0
zQ;F6Gmlfe36nDGQev0g?zSF>+Jifs8#g(Ol?GHaKg8j}T{X`XT({eU?sP$EFyf`xn
zup^qJ=7Mw4aM*p8{E@mAY2O`D0+nV+JSO1?eH!&1{{Sof7l_qyKEGr6E{?l@oJsWC
zrhQg%A$vQ)4-*3FdF*~H5xVQyHvyk#FF8&XSZN7+G8jY|S~Nn|v6Os65bD%m1KxT!
z60<wQ@KJE*`k4A6=oJDWG&2<b$)Us?-KV0$4<GqYA+t73`@-M!<c%lWLkVfVGYGZi
zygH#c`%66q$~(B79PyRixr9y_vaL>okFx&&50OK7TgY15G#B+p!A6*GqHAWJ;owuZ
zgFRNE8=8*UmNAFsfy1kgNE<dKM|jpX-epq8`gQxG#YiVk2n<@5SgOC$<w$_C1$%Vj
zc`J$)WO0F*M|P(BPc$87-1S}>Yz-R#z$sw*%5Zf~%~VG=<gmJvYWHo8AY_K=ef+Gr
zNb_0!l6%UM8_Bwq){;$+T^3PLRh~mMH>%Y8u1p&Ql5C;%4U7143q|ZXgmp|I?|E%9
zMquVq{xhx0%0Bb5K0i=G-Q$PABbWtZb!uQ1xD4CCszf)7w|;8BlLjO7R1HJMq51?`
z!%^*HFbIuGCl-ZoedBI4vT$o$_O;GBB1=Pz5+X+5WDlAoJ)N1jiONXH3yq%Bv3(W6
z5aGlsfy~DbWI<vG#h~mv^hO(dyjKA`J<0V~q~n(Z#4a&(8gghk!Wfx^*$XrmA0%Xx
za-!6U9HAAe!VVCAsEpAd3<s%|A+vX!f|r2jM2MLw_)R@I@=hh}7_PiQ@vulD>n#e*
z?1*LW_DnIk{=s;0k07;dBh>H+?u@zfPPlEz;G{8`at&IQQH_U|&pe?y&pfP}7%#2L
znv_kj8^rLG;NhVKWrg1)cTZ)0!@N%Gc&fmC*wz7yLeGY(OHG~7rbKbPr$b$mJM^)%
zjblV*;s`tk1m`-Kse(?9S-_7q;lqcG!MeyX<bP>K^y=aqj*G<a7c<3MyQt|rvspZr
zkL#2y8V8_SCkr{4dFZ{J-$XG}BIAcJu|ui13F9zTh}q&4-dV=t6v`P4(S$5FF!4?-
zrKL8V(53(@-3k_^MYKlE1d@5Hl2h2x!epmC-~O&XtFdCj&lBE$N+}Qj07fAPKXl2@
zVaefUOs!h`dF<7s1<ayQ{nL&UNu~+bR$j~f-|(ja0uQ~cuL&YntrwjLPHS)B$u~Tf
z;2ql<(b7s1+D&ys{UIO3slU!j@MD+k$w+Sx-|STX0L;*w7`>*Q5y4KM;`wN)hB2)T
zYexXQmad;!C?55{drPkmvHZ}TYl!KyhuDr@VP;Gy!@EhJ&=a7^$e<cyKiysYvW<p&
z+PHtS{!_OsAc+BCq~nvllhrl|S!A0f9JJ%fU6lcF9_dZrq6m&4tjYToTGs*qrK^zu
zAb_!JzMoa2Y`iVL&Wbmi1IciO#DaqMsX98eN|}&}#7^+i6Z@+3y24?u9EsI733J}%
zi@znLGu<Bm$Ury0o0^UK50m6V)TZT0gU%KZ{1b=T6U7sU4xEsNjW&AxRYBa+>~cDf
z3NwhAJk;6klgTz=l~15RLFN+(mQGqBekq6jOAZ0~pdooqYZz=?{<#GXYeNA8Ho`lP
z5Ky@n^4V?kn(|M>L^~~BM+DZ2ZOMZmvz%@Etg6w&>Qiyls`j~n<lAPPULNuveE7n7
z0;6#?!qzG<``}$Vol0)=^;EEPVNQ?$RPBmS0;d%+T+claIIBMycP#=K+5YlR#fZRe
zw_YhMpENFGq${u-Z9Q{V4loBLmJtgqGxiP^fr;+-S~WxNm;)KmBL_sm7(1W<%$)qu
z5rkm_LBnA^>B>Ejj9)dWm8oi|=L&Bv7>vXqJarHy%?|W;6j<73u&CTelp7{KtD4qs
z_y$UYDAZ*mf(^(y-!$@i?Hi-C5kJ!?n47GHOnzA_Ly0`f&6}sDn@PhLjd>vrsZO4n
zKt%F)1n}a2-l{iR01&cM-Blwk3Uss^9!aMbFzMy6OMRgeE!9tS;|;etjg1W*!!5&I
zmubv?%er{0MWnUH`AV9Q(qJB1riyqN;&7NvH5v)KZyjM6mO<m1Y3F5{&cO8n10_=X
z@>`BAFf~@fCZ{*AbyRDd03;iDuAIiU4BOzG9ru!r&dwyt(`O7IaVPfK<+CMb48L?Q
zi&f)jX)(<UX5-CTn@qU<6Po}VG=fSbSvJwGQiHI$rSLRx(MTpFD|x33MBNF8c@)7<
zv<G-Smkvstf90Po%TxtNaJa&}Eu1|_(P0O>9EI+;YO&&fbx#O!n;hpw;=^^~;+h9)
zr5KOA(W;K~0B=U>kKOi&82#7iP=__(AMwdgic<+^bnH>gSshy9I%cVdQL5ZBZv&F|
z9N=^!GYi^zGsSAMBHdF1nkg4fgALW#@^OwF!f<pLftr(MxrbK8;Z$#X!`?ul;iL8Y
zAFPhKZ1!W$RYt}cpQ5S#qxVg8011@n90E6eY&ZO&IASd&&l<|RkD5B&2o(&(W{h!9
zIcbquqjDsU2w1hGd4-pC^ST)+#Wsat)0V@T60*p7BbY;ekR^`gs3;!qh)(t}`e=aO
z0HR~O#T_n~($4(cSfT@}5lNHkRWc#KgeIA(msN2#MoQ1@7$F<Z`2JuS9;oam4JL07
z(%D&u$sD*Z!mhzlyr)gNVm|`2nx8ePTz<x8w0d0$?%3!0A(cnZpj^((q_@;U5N7L2
z)hFPcy6o0tG)@?aH@HpCJfmf`^+zK%TkU%{hgFa^JhqGRN5?Imw%e^Qkp*T-o9DMT
zI|=!0HxC;RwkM%mq`X6#EC`jJ#=Y;SK-OvYY%U5C_nDZy=B-!)I9bcJedC=Cr%wL>
z71)j@m}};4n&A4Kna+HflQAToh>ga9>vdEfffpi8O7hN)8LU%Qt@*XKgvUX?ToC|N
zcxCv3E7_w|PxS00y@TPiH&=aTo)};zn;*Vl%pN0=Kn$P+%tEJS3^$3K(ZxvSHDu^j
zsj-lHofbtN?ZNn2SP%P^**mWpIeR2W*JefW>X=;mK;YrjqC46ZA>6^q4Dla|@lkw#
zOSS+(%paPpOJ2tZG2Y1mJ0o=H1WcgUG^zp2`5@pT_-_lfvp;IefoYk>+>cby(@ekv
zP*J<6InCx;W)P8#S%0vA!YL1mK2EY50Pky=JVOT4;9#pzWN|+5e3gusQ?`7N_M#6Z
znMNU{QG4Uv5}Saz&D}ZKcH@Y>&NSG|-<s@Csp7zEJ6<po7~TPb$ZygZ`NHuwiz2LV
zTuu2R(}aftE(#Fe;#2nz?Xiw`K`Dd=zZ{zY;bq!rw)}B?mi0d9I8KW}{!qCG<+C*&
z5`=x$5AC+hrt5eU?I{;S8wK1tEUGm85Rni^8B*$Tf68_|ick0+Dy<sL5%_MCen=5!
zkA}>KOFC|Y{#7y`fWvrVv0wahJyYD%#ikYqOK`TK0$QI3M-4)8BNSKxpG8DGG{JMp
zCCAACBpYy53_6h_PDCQ{C&bj;Z3N*hxiY62Cp)LGn7Fa|rVk6TUsO&v5?N`#0SK=(
zlm2L5C{YYxkUipzS!9Hgoo{M2E8bbkb}6J<c_AnaZfw2XQ2?4>c|P-&VnVrl?c|=|
zT;OCWcoqg%6~@ZfLP*r7mv@w%PWMj27<!VK_jKULA*oF_JERmzY0%mgH@_oHlN-s8
zX(M{`bbR4dnWRHvVDdsKk%&83Pat$sYEdo(@nl=kWCRd7smE+j8WM6s-J+A2Jk>b`
z+(v4QJ);P8X~IJE<FMJa3r|oizmD$g`dwslE5{d)f{yX_Zi3<=Sc0B^IQpy2Hp+GV
z)3Y}ih5R<*QDJ0EO@wC~L4W`yRgzo*04~g2MdZ8MC_Xu6rZ{SwcMLq%O#!2zOeFAB
zoxKW~fuRYdIiPnTrg*hDAG(+y1+A8C-a_#Xw*``BEG_>46S1eNmmXPNS{z_xh4MeT
z1<b=|Ldu;N6|3cNnC5d&!A@>f+ubq2(Vd29{o^MA=G_pB!TTIGQpbXf)EtRlgYCCg
zAK7YA@jUuvvw8iWexFT7$W{%Y>-nsM1T6tSW__sZg-71JFmx9xJWl#;JI!z*W@;HJ
zwj03n*_lRK=efNP`Pp(xF@7o1dL#Evr%k`~i=aEocb+ZOY&S+FI^gCP9g&Il5`;yH
zJ3SZkTk~G&DEmJxnaG3;11G6osLBL44g&uG*JVR}qV;N{onXvE)fWbbVlL@*ST(*k
zV)46x3ZHdKpJl;=JIWbH$uZH!6o$_E;1!vt<zrKWY0_}+m@T>_CpyU6{Yi9sLeLoV
zw#yVv^9j@c0F+~g^%7M_G}JDfCM+zV^Ki*MnCUpvzqbm7Kr(%$u1vv2fv*FJc$=)l
zeoH2oxih`&=h!FYjw&@q>EZcLJ)er_15S>y?>Swz#nCuAoleMVvBO*$D>{%l`%-0O
zSbiIFr%ld3Bu*Nq<fcWSYMc!+37!xoU8Oq>QS4||Djl;FRbV)5fp}+#I^Pt+(H_u6
zuI_VpyTv{nWQ;CF7LWiDFuOC4P4AMURiiEDrWVtr+(!;mHVNPYWX}Pj;-d(5H&43T
zs!t`(m}n*jii??n+oi(+zcRa98D@vT#RelpE7o`RH2qipAK5?|7XtDRnO&gY-2uNO
zOP*Q_$L3$gr+%SvdM&jfB}6%}dK4lZ1E3MGknFnkU6ZqkrU~5FBB~Y+4Bh!BIoRpB
zw>)+hkGkCW%FBubGyn|e&1B}{9&0_3k9eL}**lcUpN8LXkM6SZ3TEFArpvV1!-!S}
z;b_@JgEk>tP8;48j=K(Qk^MJcgw_&A7ZCs=8rRTuxE&B6g~G^|w7U&6F?CcH<{mE}
z<s95^upRmj7SU*`J)~7FvE-Oe1BbjB^j)Qd9tQVSOI&OOXX=^vt;p@;ic_)R7H+EK
zotoKM&Kd9<TyeVoO6{w<MrcDxlhra>9hWAr5hR7}i9J&=;AgDrnP<EM(5B&mgbb_#
zD?JK1-Fv}*4dc-ZI5!VSK)AY|5IC&~8#F1nTTz&T%60<MX@CIAR$$+fb@0g@bXLLv
z9OXC{Rd)W6=6}k`wHj4GOn~vlXe>Bv_GuCjm(q4WQ*;1me7nz8d8q@<X3>OtqwR$0
zCq61*=h03V)oAJh-MYUu>@>g8FjEe`CzxHPiDdr(3E{<6X=59N46V@}(k9u$WRa`?
z0K$neFqw<H7|)V7bL8wG;?ZK>s0k_OJC!`shz0lL6P#KJ0!-}B#Lq1d{{ZpY50ccB
zZ)T4*l|ciOv_pDf87|#-_)OPS^BM`VAlYg#JpN{<+NCLR{Yl0rE-9h7`!li$xP;<8
z0yicY!OMzt+8Vqr?8f~HABkH0jH?QqG%A}(<B>$HxgDCD1a}*ShxEn5b|w%ApJ}eV
z6h7v)Mvyp0VPz&dO3dZ^uVmkt)fuHwW2e#EA{$5x^C%b;8hux_^!q+4hM@Q_U>O|A
zQE@*R+wTSZ$1LG_>~1P<GY3ayAsKGGLU#Eu-`{DC_O^IHgy81_WAiZ&x~bvhcrDfe
zcbf#vA)`H9dHsVJU5oo}fyC!#IF(TPC$Zjtx@q!RghY@sPQXr%)d-CghaU<*jYcPn
znl>};nlWEg0f~DWJTjad%B;lR?;?14C?M642PEdQl{#3R761^U=4`SJZ_NT=1W4q7
zF6{#mC*9FIf4U6=kvqz9RROI$5y+63*)t>yoG#T55nw*MdWD@H%S=3*0RxI;H@mS0
zabJ=WQ!xoRx~lK)jN#rR)FNE^otl%p@>0i^m;`Z64|P^}Mq>HLmcwj4>ksB`o@fVl
zmkFgv`yZLf<kWGwI;^TuE*_Gd48mLLqZK-GN5;$EznajUhrvS#{=fKHg*f*~^FgeJ
zU})zTM{;*LAnxhsb=(vRgTqm&kx*H+Mydg`jBptR1&%UD7QX6QC=%d!hYKzjRZDC-
zDok<XlAwH?p4?6W*jzmFRQoMa8FxS*g6z}-xc5R<7FF#Jh7i%<Ze@0-`ELA`dXJ0U
z#HlQKA?=C4e`Uta<-{R#P@h1!**HZuPo?2$TC0D!%J!?7K+kuCPbFgNa?rD>0FHxU
zxv7KUD?ze1NGZ5)msCh5gr0)yBvF#wx@^hM6VYTIS~?AxJ<JE@iWZoa?VQ$3+#k}8
z#!02WJh&+mF12u9%sUJf9y^{zUTSw@JyS5d2{j9h4q2}2wAFdI@inl!Eeb>#1T*(l
zT<%(WBZa-@>xO&QDyXYRN3#B^%fe}pdM5qRm^vV3tq&enTFaccrnr}Y;RevVm*Qq3
z!<=<Y4U0GqJ7Qm8R2Gq?;PBZ8+;7yZ&dh|E=%($(C)ESqXVC$P!+5I~S&olXeUpDB
zp$-U=0kj1@<_qED#INGhGsatOxsa}YTN`05o{9eec<T5-<;7)-W^Ql^?tT~G-Ego^
zc6Sh+DaFlmN25Rp+O#+r(5vVa;$Yt&Bnfn5j&2hR?$``~b1MjGH@(T@vp|W5j208o
zIvVOP6Ue9l5l%lqR{QKn%IYLmBKq$jT5(@QT>}sie^d&m_>aa90IT+TXY;*N#NI79
zlU&zf!pXL2<envXLgJ3|WJ+v5m4QG5g}A!HZTm!wT!w07xIq2Uw8qQC3v$f}GqIm~
z;NTCR*w%w*saz5EjM)slkT}}ecRpb_A8Oq8=zeHTI)nRNp$aCQEtWmVpXw?@`$oKi
z@fGZdQ!QZwp+e4agm>5_g%1hBb{IfAs7?%P958Dy&>&7H5=t`adx!Msq_P}iNdq;%
zMy1(OBUvio>Nb8aX9lA`Mx$UcTBvCLm6(<vqR@Q)$o~NH43+I1U)^=BCSmFZe_^o4
zz&Lbp86hpfe=>xKHrjI#SBC(@(2bhZX!xwV=ls!!G{`_h#y{#?qMxuV0sA8`S~{q(
z+U+)bt5Nb&>V6>_0*#Nul9FiG2g%3|?X55F=KQ5Q18Uv0g`jaL$No~9aRrX3&m(p1
zL&uw;HZi@R={k*{lj@x4s@oo8H5iHUa^{7y1CYrHYg{o7BT&BxJ&$brrs&L^Ua7{+
z4vwKW8La#X*Y{iX;JXtbW((Y9QZ%i)(Y!Hpl;)7?bjTC=cqS5FF;bsp!us)2DoGAJ
z7rTj{B~J{r$Uj9(??+}E+!LH#QP^buWJ`B2g3}3~dBkd&J+<R>9H|3o>WsLXm79zp
zJDeq@gDA+V#+4Ix$TID13aI<`HVnp)sNsD*edGwlB!V(KDU$BdTn{EH!e_$G?^F$%
zB7Z6|-#PUhttA|7XNQk9_&`W^x}fk<U~dKdrnL2HvtHIRIkR9gTYZh1bnfoDBL&2j
zRDvmpzOoenDuHJ=R7M&!gF_P`3EV0RU7JerF{2?HzDO3o4~vT4Ef!6)L){%0@cT<%
zf2cy4ZC6Sg#|NnaR1d?6xy<lSoHg>{Ar(87fjl>aeLle$Yg28<Aq7u%XqJiLlH;>o
zhC7-ss63ImGMqXnimxPIa{mCGnxQ@09Ozk{uX9GA6@|o%$`}SA=2vQTNzIG5IzRni
z`CYuv{{X6T?QY|b1-%o+QvT82GN%j)<;Q0ooJwgl-Ixgd*(S!tt8n4@p!3EuLqf+f
zyzK}?7>){KRh-6X0C8F9j58#{w#NSeP)?Rr%aKeWwESw(kl;9M2LyapWm%$_3U@@p
zlHymgFV$RzCXn(|q*UG!^-aD9G>#LjrwzjS9ZEKxnL_eSB&Okbs$2`XrUQc!1-G9?
zLZFjA5}HTlgg06VGh{7Nk!j>paT&{sZY(p1D%-ScS)Z#^{{X|MfO*+ASxWKGMNe_$
z<9QYxB}3|HA_HTB@gE3s&t{3+!mDlMyf_?rK|}bYj)gN5fVZY-oac7vVrX!O`#>s{
zoJquO0{2Z6>CLr26z5)xCO<K{bbxH65UNXsVi_joyF5na4B8=DPR%oSE@_{4Nti(5
z0^NX0BIKsz>3=9yE^b^tN~=NERFRd8D>Z<?`vOP$cF5uiHgFJ7@POY<gw88@p$4Cg
zzfrWu_(Sd(cfU6vtl&-_kI`?qn~eq4i|REWyQba6&>@H;E$;mlnhsx8;j!v9Lv`)C
z(Zl1iqgV;?IRYq9oF`Wt%ZhY!O%82M!{@5Mbw&94#n5x8IsiW5;uE0gbZ{w{9q)-w
zX<>F3ewT4x2X8Z>O*q&aR^OQFpy3xs?3+WQsd4%sP9PX#kzN+L%^%ZmutVDRjAQH6
zCwGkQLxYE0k3Eo}@<J8JX~a5g{%d|~GED3_98DclZyWKjejo<<#@vfQ#Bv8evoVNB
zUiFr99;+tn#OyuYHv^1*jq56SX}In;-h|;Y1SjF{=&`v@K|DFsPAnW+bsZC94`!w7
zn)Yh5_Dy?^x&+#7Ynw3E#!JLsxpf%caFMG(RNUc9#$v;StyGaso!C5qpQ>gMX%9YV
zNbW;@8i^3&mcqCpA9qXsz@A1Ik3?o3<0S$b;6djof;YY;WXNFtfeZP~`JgeftdBJ2
z)a^diOxn&(=9=1uJ<t!q8kC)Q_L`i>ytIliZPg9tz2Z8lFJnuCxkRSPqFf#i0lXDQ
zw~iAFwHGkcY)#6nMYx8lj&^P!x<&F?oV?WJN5u;=kRM%y+Rkp5P2724Xdjs5m`ngr
zEu9m?2*@0hH%Y|OHfCCg7>;L7YI}-y2Une3c52u{O*&?+PfG<?SyUR(;o}!i6{#9-
zj5*wDfdywVi-1O3?4C%$!voDv;5zV+ilsj|l`tz_Ix7J7IFq2}yiBYgZ*|-dm@Pj|
zj1Co`)#DlfstS~y;XgyqU|4Wcdzv`!U3i^J%SW5+;j-JUra9uxM}&N~MV*a@nHeDm
zhQ|ON%8YS5$JmXA2;OtLU+iZVO&59cL{AT}b~?cLO~L>@@%LIZ@>NYfOgCIXB5&0T
zREH1hy6sG+IRfMGRVq|(<!pS_IcPJ*Q4k%w15LW12+>*DgT^?ja!B-94At9sSv1Kg
zSY#31mea-gvnrK5G@!ZnlfzZo7!T*Nr@Iy)%+Ev!hKnr4!ed+xkt?%OfsoO5Ix##y
zb=YV}<q`5!oX$Yc%`lQZ@2aNObO_j^bm5;3fKL@pnT(~OQ-UroiKh1q#q(8zjaa?L
zN)53%`_ABq6q$0tn~xGzFc{Z&Y-4pPfu@^xu$>BXi-3>;pOQD(#b@3^qyZEf1F>E~
zi=jB#2ayO^P{?b#ttT|l>@38M$(8DdhLHFb5bfa{)xtIu!EpUX=o1F~w(!J-j`8wD
z3S>*2?{!vLLO4dJf{ZQzmul=+S`C6@L_Wko?9|V~SFr40^3*Ab*lZ-9C86~QoFvW&
zyH^ztmv2r<*lAG$7(JuKGYvlM$j>cGi~vk8W?U9wWaeehU4+?!!&|6nC&-{di3pDF
zt9NJKK=@>9ma6=dM}GJ<54tpJ(wo0+p3e880&6lLX&Ixf)OV~C+260|S%VmLLJDy2
zDS?6KhXr=AtYR@5ai6h$n&WrGtm!~+^&?>4n$KO9*ynd|qH-4}MBwH-B0gPKNNn{~
zYBABj2Fd3O8fqO3G%a>KwDLd)i#wlG&$w~Cd_ry=1AWjf8w?Fsv{~yvXCOf++3f^*
zh1%&fhT9Rk?G;|^$fnzRxl^rC&Ud|pwi7#6BTzF7i*SJJD$a^7A+6JN#?Nk>9q?I?
zk0P55G#Y(Gg`JP1wjbfSObXW{L7&lGtuaxtarwFw!8PBH7O>kyAOoQP08(%99w&}T
z`BeZ(@>&81s^Uppc3Ejnf_%0xz=`x#7&%I&%Afh0;u=JwEUbIRM+YI{GF)<RRN^F?
zr?Q}7-VM#YLXQafCt+_q&qd;3sg{EZ66TiFztvKt*1UTHJV;2yV0Dn9IBJ2!*Wn!n
z{<}SzO&?XnE8QQ%r0DrC1u$Ozjas@&G62t|mafC*p5~BqA$Eat0#LdLTniQ2Ztc^H
zhXlsdx3E<=H1i6~r@Z2XI=^VZKSOV|(SfDs$u|X9@noLbtoWwG=~K!x3&Pq?^@Lz|
z3&1P0)(R5N@JLQDZiHfH2Y*TudzVGHU3)4?Ij6Kp#3zytF7|i@n!~)w6UDq0%Yko1
zBYfNJXI8mz=+Ov4z&3HRCRv|NgCb+i27u-Uzx5BY`Nc?*Ig5RWSg{!(X$WixjPv^w
zh|#z1ySz;?FTN>H%)^X)kT}_4%qKLubh>a5(nvb51_>joiN$|Ev7gIf-kYxr9#BLL
z*>cVHX<pT5Kj@F+98MgDTPiuEHgknR)jEiuNKADX;kX5MP}ZMm#vL6MMgRrd;*}&O
zNO6JHc7P8EaaA1y@~Y>d5USIe@RB|XWE?LXIjiEdADXCaVFtgdpyn997fNke(m+i!
z5_v0v771Y&jF1nVL*Ob;YiAX%C#tVjnc&}&eiziu&m`NDts-`31!v+YT-Ocypm4RY
zXNnR<CfsNUjez1Vzz-Ng2p8F<7RJMOJP%c*4}xjlZP0=^l<7PIfE#j7O$m*8e(Rof
z2SBNZHe)=aL?T-#Nqd4gfc!!-Cz5>FX%PY;J6MDX-nypPCTCkCpV(|-?6kvpNef75
z2U$aL`_1XHd@<^aazi|dcK8~M(8{Z1o~hnCj<R)uze28r$7e1)ud`P>U^2iVYD^)D
znEwEFPPSu+PW$#Y2^}s9aCd4ygHPQ2&Q2t{<gHphdp)xQphv>#{@}T%5J8Yeh>!Jc
zY>}6HA5;P4;Dj7T+GL%&_lX=1p&Q%ekJ%P3c?X6Q5WG*9pQ1JmjhZI`V||aqn`Kk)
zDHFWY^BmJ~vTQkK5!s;G{{X_PwB?XM;nW{#Fkr0T_vVNWZb#li7aS;%0|UtBe$KE5
z%{2H2$vxK}1u>i~+5z~&HwZ&(v9$;FjfQ{n%-BTt0Kn4L3;6`62#m0V8)^hupliuS
z>2J*))8ZZ`xNs^DXRUS-kmS5Xh(tM*Of*HU(E{1Yt;%_!zl4+OhUsO>s00k+R7THc
z2cU!{povd7Ug;e)*x3Qhbnwwt?%7Sx1=^9psQ&<`?6`~#Uhm9T743{*s&w(d-{PGv
zJXi5X5#+KIPyH#9+9BT@f~TKnCOE+e7t~|mQpgz8o(Zm^JSDBWZl1@qo!i5`3Z(W6
z0f=y3@LD#@H`Qxz>VOaJ27jO+OIjun0yIW9!~GYB#19!m!&#B$459apuz$Q3ieL+-
zxt#M|uAiBYnsXjJoTxWFET|k5#^?trRVuVk1=`7WlGR#~bD4);yb~J#0MhT>2LY(W
z55abEF&Kw5;==Y8`telphm6{ID7|*uT&{<O?f9&n4)qd&mb>6kkZtQ_A9ikdld~{`
zLtagXEs=ycN#uya+L`iR-uJKAGFwEvpVnnZ;fdNNncaNwHcpntCZ0uUOe%Qj)XZ=|
z;C0w|je}*dHlRGoTsE7?fV>^my3O{9Dw%1GcI7HNx{}9FH2H6dj^u}TSN@WSQ-*vX
zNhMPx$LdWz<5{ktr~T0(t_2(IF#_nx*OJJrh!f<1Z7>9_);~Wj5f-H|j%~LoDb6?s
z0pyquRQi3AjEk0U9)Slr?lIp!?mY&J#e_`pi>}Snc-b($#0<{u#%bVGAQ!Qj1>$JL
z%V(sjY&XsmP3p-2`6^}7!-PAJ6*iX=nS{7O{M^-G4J=Sybr_`<_nZk9M(0%11>rb~
zHBKKS%q(rO>Qlb<rBO4S6;s6T2sc$~G&cy{bPH!?lne1x*)ihK4l6KWjymmk6h9VU
zeIWtj#16d^h^XfVk?%GvoGGw69{1>m<6DRxGJ*pEG0^izz{UaTun49LjPhR7q?F@?
zGvKrOCX$JVMkP3A<z!ts@LXKO<#qU<C6RERi^DKHmU^#zAqN?0SxWTT!GH0ZehA#S
zyz<dBms6LHiscBzPnrvV2(;y};j9iO2;DG&?*o=mi_LhNaJf%-8-eWuF)6v`vhfbP
z9UbLDN#UXy>?p%rGUl$zqtNW))vESHEUMCTPtAC7nAmW+ra?@H5B<Xihl(aCQ!clp
zES&bAGby9bnvkwI?e<the^acy5m2~t54sU7#76*B9!kgD{gGBLm;`CZ&tSV=4_mKz
zp#;$r^4Y^?s7e0-O7R|uMz^x<3<L7-6-YDzXB)WlMD;54atGa%aXEs2Q%afkN(YIK
zYua;Trw_6{pnth$+GbJ61;Pr{M#&|(sUIUF=9>c3)dv@Urww=Xt2DSuC-I9sjgTeX
z%|YofrPT9+KB<9>?rg!ye<-+rMbRU4seK0>_I*b++L-1tlbjgy_khO@fD?q_o+{46
z2hC@NvE-S$MoLpa4s%1bo2z1dkiID@AnrSnZp>_@ggd(Sp9LSfWsM)@fy1g8DdK=Q
zb54@SwWN=l)__!M8Zgtk(sV@&HN>>2z*uA^%rL;{vM$5@)<#80c>b%bfoWMfi(XTY
zsu-_%OadElr+xFv>vokeDS~tjQTd&#iePyL<e<S)j`x<U`DMS_VGrez>b>!>`$VCI
z!tST_8zy2I1~-*Qc(J?sZlAd4m>dCB1IGshDYuA>)hd4#Li|IONGB$g?<rYqqQVZb
z`znEwXL|C3UfwKBAe<uT>aKCmuE;<T@fY?<)n?z1J(SEi2N5X$01dxm_%xW$gsug9
zBWa)fvj?S-mWF^2mctc$Jv=0Pz<rE{A5wPRW=3h_9V5CqY!aJeX9o?^u*_sK1A;sg
z@pFhnP`uL`(Lnb``Pf}laj%R^2tqO@P;dL{so^@Kg~V9%ObvT=jMZW)5&;~L&}{aC
zX*=>=(Nx9r3obgQ!&*Pwttx!g%UBwy7XT~;*K4Ij=Yz)o06?f;KKi!t9R`W`-ui@-
z(GO@4*!k>k5rnm~sVX-Z8a$Nujv^f6b-d<35P7)*GDQ&%XmZ6p)8nWTow<fOlnst*
zpzkWthmIx_H}stpsxs$k8HG?ftM^$=x+#WH!u!N`oDr7Ij3(fy%p1|3?y5khOWhxm
z=vb40A8E(-MoK%?uJf#D6|RS{7rIq5oS7Of)LPjMxB|+X-ebkhc4`f96JSKDhYl+a
zWgY&??^Zuyu_8utz2oprAZkK5gNJlH)Nkp@KMo*nfH(}hpiONi=o%3&i}6GYSOE($
zuBy4D9C0zRK75yLpieg&ru!v~>V?jz$=NP42|N}^Yz8=~lEBMwOc+_Z)2szi!bV`l
zxFKqSN9>&uCf8O7<zyGK;7S3m?@6RigE&;&9_`pb;AK7Y`L!tXLk&?+6WPQKM#Gqp
z!J3b}bb%QR(Oh=;oGgB*dGSLhx8p0=7`--rZ<5K|-l!g1Gt?ZMI275V+K+}jH_1J(
zEWJWK<bLvzhqv<a+XWLm3J_$xqavWg<X%Z$90Yw6Vp)E0oe`9w8>E~*)GE|LaMZ$h
zc>~#5gR07YXhs@fdMwVuGSNzTX&s#3%x#H`w!SJd8ho-t4LYP7Xr5%)IQ)>#vsw=r
z2H#^-bRoONqoA-~%gJ%us|ES5;MJz_{adR#btXAERL!r;P2|q-J<X;skvghVhPF4<
z)z?p%!s=l3l8=S%Ve(95)diw74H@~b&x`u9Os7<_$MFQh>_SL`4=589m_+!1#eJGx
zdr&3Zm3nmHw#At$ts>C<9XYPoNX_H}`zo|*MC9+Bs$^R|7GkCo<}878YLP7;G0kDG
z+M8hCVVuu2>}CCx&Ha;zsvW%TEvH3E%&Umvb24%yhUw7CXb%$EiG|u;+gJ-W$ADBB
zP)_rJ!v6q*rW(U`H$R$iYo6F`_@?f6E&<~0B-=2vPHBoEvOWof?r_fNFow{JxLbAx
zX@Um0ny?P9Z|<U8&LGdCsK!Mi!c6qCoGi$3I=HVB&H?-E8?SB?=fwfJ+A6djIUzrD
z^$Se;YYQem1m_w`89B(U$NS?iG$1fxY%H1C@OjiQHt`7Ou)B4H%BW#m7HTlus*)#F
zn_D39(KYRZ5}wEOPSsX!BcW7eep{xHdTeg<8{NB}9FagcJ%zFTNM~cWBUPi~znDj*
zk<7VI0kn8&4~@|~GH@V;A+u@Q?N)1j0Xt?_1Vcr?WF1;th>#a$7@Q+~6X>aT6=!=8
zi7R=7+R+V`*;5N?HxS__$lM|eUcgUY3B{EdZ5bX*bnsdfH%7XJOakAEoI!w*w8N7(
zBgt{uChDUY*1r}8famhQh&qsaWY38Ctka+nZSr4;T3sD=DtAHnqN^Mp91-E_I41zc
zo#JvSzjXSJ!hF?*Lxr>LCb`h#tSQ1+4epK#Ow{{b7iY&(Z2G!+Dvl?Hhz84Xvxy@`
zU~KWtKf0jbZQn!#ZM|9ECv;PYyvG-0orvQudH4XRO;!;}quGBltV@lNhP-z;v*w27
z7DVypgq{mX9+ud0VEH1P=UKt1-7rJIGx1Ii!`YjXIVKZH%y1mlM)rzahz=b>W0}6+
z6}4FxxQ{)PbKbxDvQg7xDq|qywAnGhoeHG*=({tT&}<b(*6je;dksE$qr7ujAWz;C
zP24$jLihM1p3FhcV_fA(xbg|HY|3i~>Tq46i~;X2yaJn#OQ}Ca;l;e&Q4MolG`eaL
z@Y;H8K1jh?tw(hK0Mx<tS1oo5E{G$I_#ut8892sE7AkB6c_(4+P9WpYMfi2Mns{<i
zV`n4xY-@jI!0q6!@asbHjHO6-cr0@{tz90Q4t1%w)US5_sO>V`SS%;SE>G_$*296{
zIgL;ld&f1yk+L$^3`{&)RP))C-5(PE8!E?A<mhXkmlaISC*s635R45aXlB{2D4aur
zWq{ut5BXl*8f5i_qjqVh9izr+vV8G<Hr*Cy?*&rY;24%BQ^X{k&s7&~ox_1l!sbpc
zWb4rq3xTLP2V{gDrVEAubc3467PNHYqRh+UhG!*`@?I)WclNmx_nn>4IJuj_0ld=H
zmv(+=T-Iy8E82GSa7GJzveW$`IDPo<CM0iNGz<XXfdCGPz`?&jR!%cLb~+Eta6FT2
z58qPv5A2Q_Oc<&=fd2q0-QMmIkQyx*)H7_X#m7EKee0k907`MX0kqr(`4*5+b52jL
zL_GB15I=a)Kd1;OkGkyV)a0bNlpM@H@Xw+n#6Bt4quTxlByPt7dK)cDJ{N)HQ(y{k
zJy957Zk)4Snvgt3FWoYjAL%KX!U<I!0jAk#m?7>E;A9PU=4qdb9Q@J9SQS!n7m@25
zH3&ng)$Y%t_UO5&z-=N?5a%fTaT`2N&!TL!nVmxa0P=f5bdPnGg1D^7+nFnfZ1i1G
z-D70*QG&A^)}9M6OstJFKB_Qd_#@(^;js9dDy|d2g<g}-2?~Q+POViO!_icx@Dq1f
z(f<Hy1~F|hzdze`tyg@}{v*CCQg--6G<gU|Ws1#*1x>lXv=ijf_Gaz*DvoRNVIL(@
zYc;#zH&p0|du4WdbwM!E8I;O&+*x+FiniCs-D2y9V+(*AvoI5y9WojR?t?qF8wQJ_
z0FM%fPnz+2soTeh;EC8k3EQZI0RdZgBuRC8(hBg>0fWnBi?STJqD~5JYCb3Ykg&^|
zb{93`78kkC0nIv)yCn}1jLufdQ;S^37m)LqKIu+#%-%#otkCASa**Dhq$1GTbc_LD
zEs#ScmlaETnHT{2qe$LEUUz4jV`|ZDuu*uAg6#ESxiE~%wi(?TSx)oHeB;9MHEuXr
zVk*zB;`XMJ?g)cea55YIcIDkRJr|m=Ad-1XYo5`arBxJh1D^Iv4W4cYL<&(oP=*UI
za0%{XxPu6`vrB__v%fS)izFnvz;#q*z1U#x_q;2!RUKO&G~rUKkA=B6s;>DK%~ZT*
zTGiSN29pQqobldLW1@JCjy@HLyFi=a!pI#}2bUU$d_EO=)cw;E7i?`@0~Q=GOlYZt
zgnLs8sUN1v#O#YLe{!4}I5x`8*P37q!q8kiB)2u-tGqs=>|_G=D$}UnJ<}+R6owc^
zX*|JdxP3ey-DKxsBMIc7ww5O}>(gP7X@Go!{Ugt2d=^0yjoZJ{jz)jNj8=P3K5Id4
z7rxJLw9obsf$)hdOt$|3`0d8=rZP7|CLH&9AuV;-{T#m<fX77YMy*2mPRnB4A5;*y
zY&v<|rtYy{6FetW+o#<I&IvAVs%KJ)pw|adBbMu-KxsR@3L`(T$5aNhOCsCQX6lqS
zY$jn*XmsXG{MDru{{TA%_!<2Wy$XU|#+lJDHGl@Oyc!YxF5%a2iY5MfEacyklfMq~
zS?o6%^hP_%E(55zQD;HuY>eSK@;N77T--euve%L1!nvzW7F&g(PkP|ywEj?Fgg=Jm
z5xfto$TCTV{^w-iJFgsCa|z#cYImkMIxvqVk!{eA3ejk6n&&#JTJ}K9I7mYsM|fxh
z9&g}=77I0u$)l2aa3*iXIF0MtVpCmGotjCuA<1s<TL*|LuW=5^`@`az)|VE8hRg25
zh$9wL$D*r1;{}gyE5W`=j+{=sTgf{~hTSk~*Cw{*nsHc27l<E3DTY<0ebJa*o`Z=o
zI3WBL1A)&XLCEr5?iY;VRPi_rk#BWO;b@zVJ3k9C%%d>TwXz>Y#819BE(hd=-AdhZ
zSqDnF?^=EZPj<_>YJC=s3&P&Y*TLfRC3%h;2V3pc*{tPg&<Coy5}GhoZR6eu$J`sP
zi{Fo1D3zY@MhtGO)ng5AM&VOo;}(@c!gUUm=DQN3P2EDH-5xDntNxX|8#xt3!FhM8
zX6tJYl8aNnU7EZPivDB1z%C;Tfcov@RQ!I)lG?G+4f_}uUC=MPOROE}n9^|C6I@&l
z;cKvAJX3o_#1m2S?^RHdFdG|v$ItevOsT*-C0No85->PVlw_+(7UqL+xba67Cc4dW
z`7X;B+$7mhsOLqsR7h_a+n~)f5EZP9j(aT1490wxrA$qSyt*zy3*nb_D?SKDF@NY+
zYhKq!#du#+5S8Ma6-?X=PNia=D$*AvqWAZaID<rbufoh8bE1EDM(}g=;<qVM7bfKO
zEAg25rTLk`W~;sOY)!|-Ql$7}iGBba031~sluMnKE!kb7lU~;i$L{!fsxF5=F>xMC
zCR6_a34x^eo-=n;9u-$p5g@s%w7tWF8ih;@-QUlS$}OnrI5A|L7#wca02XX`gZi^Z
z?sy)k?oy=Ib=PTuj1*C9K@1wW@m;2<B+Z;xX?bq5a;sQh7gXV&6_i0aqZq8A?qIq6
zx&-l0l|qPc;xbfTaEVRADUx9O$VuQ3DDTvD`6^+}ZtUffo{*ah&@9|wF9ROtH1X%O
z7xrS^_VDK3Ykh=cp#K1v+m{utOT}85Wi@a-Kp;%;RROHpVn@1j5`;OX3U2ivO;60z
z`)nd<Qehx|Wy5)7kAxCXfKE#^EKxYAJfDKyuFSt=2yie$$%TW>*ZQw!(zF{M)_~@N
zJoqe$A0^^#2RqE&H@)o6^<m-@u~R$Ls*Icv`?}*19_zImJ(gdoUKY4d?7U8F4pI@F
z(eb*_-8Vb=Bf95dQU(Z72Z&4{YUO-A@IGc&v%2x9;8zQ+E`vuH`Jl3WSGajV*d%Ql
zF9Ua-&>=4Ne^atRd$kAE5Jqhyf!J;AyNCEu6mJ|(h}@|JHwUSd!seOC+$Rc@#msn`
zIgN%8$b!5N7@C~sz1^OXJGzjJ)S_HS<ZhdB+1@%6WMB*RS9AM2XRX#$-%+QjD)^d0
z0u5~VC&2}vAL)czT+#R1PXennj&?s3*>4eb{HkLA0HiFPnj-G_1Y|%@Z5j>EqyC&$
zAjS^l)_55B`YV&h=n_VP<uu6A55%_bG<+@3%{vL4)rowE@K0;QY6#}44+C%vgg`<a
zPMLK`IpG#npByGa0*y{S!TpezQ8>W{K@z{4KLwip6<4(R-Fx9%extrY6NKHPJEobV
zNyHD$loUSpp~q0`b;Nn8?(pZZ?3l{j?2Lluka;;B>d*8IMlQdW6v^Iy&}<iTfCH_O
z-C%16EFpTV(XtB2aVpSpV&Mz&{MYb?Fo?I_)5FMQi~&avuFvKKG+xbE^$3JQ;3w5G
zeA8M2ZSN=gm5XxoRh>D(xIU_wcj?_!+}Cq5p6B5vU~o<jr>Qamqe!=o0J|kQ{H{Wy
z9Mdpv6=NqUQ5@AgdTy@IQhG`RO$X<<#8~HfINNF1Yz99>cbp>^0l@=~oAC~XQtxTM
zfxoJuQw!O;Z1ThAzV?YE2EO0$v36HqF(OX_6>4w9_!;7khhg7-=a#Ce-T>i4(FV-r
zs=gB<RwFP+k{~&xgmO8AUmPW2^g`)@zG!tpKwL6L=9^jZUR=?z(^bY$urlCKzXS=y
z>7FQ;8ZHngAag1E^jLAX-Vp%_X|X`IlRP7z1rB~7Zk>d-!v(z)Tpfw6#?E)R@>J?S
z5SmO4`Ks(=qww=^<r*tJ?==1j8@vubbupl|q_}8#g<u?97!w=3Ljd58@p5yY4Trm!
zQytV|pA)iFJ3V6ecX+Bddi=yL!pNd@JP_`xNWE6ip%San?GB8YP!sW8o})(+)uY9{
zmYJ^!GTw?M5%<`hLHIA>h#gUqb=pL-;XjpjpJSgz_%vx@X3Y~f@d%vLu$~Z%&WWH?
z1AWIq`=`$dQn)9%q>$h~3EP!7US@q^ImXf*;gQ970nFrsa8oTfqU36v3k%5v9w?a`
zG}TEAX)1Rsx<9vWPIE`1-em7HL;hFbR=a;M<=LjdVYpfWP9V*|Rn0nvH-WdoPX6gU
zXH+f>Kleo8ErZbLosOB;_d|F6IAgg(Mb?fZ(`U4@bbK~wY_g~WBZ|=Aa6lJ~IL97o
zz+vqm0m-HU5&@EoN+hh>;{(h5ui}7X&kD>_%dNIEz3i{?FzL)Ho%dm_;du5BK57qj
zy*hF$GOX6`o@C^|nNS}y0SF=mxhEe*o2fLSFuo1+!lqmD*yDtogeMLc4?NImWlho@
z^INeZMuk0&wuKEg8$<s9?%HJRYl-9$0My890by*8@%&8WzY3=7$JI0MY1~J&HgG@(
z10eC>5Q3U<)WLt!6hNe7s(EX<F7AbIN^a}Gp?`tsryhtu13!rxqccR)AJ9<13quJ(
zuZFM;T-=#V2_6=Jm@z(tUz!A(RXfNDgipL>8Dq-`8bXG}M@irszXjCICfnoHUzpg?
z33F-NkV7{Bnq2H;0Eq1!qxntnx2ao^1(-7QSWPa_FgsY_;n5IeVrd$L$1(3{!tO;m
z$IUt$H$4`8(I9+wLxkO`?VmJc8Vw$Z{t5UL$Pd-IEH3C%KZ0EZa9y30fpp`h+?94#
z6A&!_0PQGW{X~40g+5)sBZcga7Y$TAxvX?VZw0Pt)(piQ%6?1_rNkXjf(#zJOk5%@
zQM8X4%^m1A^OdWBQ5XZg5s>}wR2EDn=lTS!+~VWswj0lF+k`Bm3gz=&1KFKDbWRvC
z$yTO!g!5UIyji_H5r}Ba@n6ksdTsYk&@yMlO3<@xecxtuexs5p?gJl|@^#vC$y9Z<
z)pm-J`ju0S^$0U{gyVP<krQQ)b;L|A(cO+=RiNqVE3#9A?K0ma9y%3N4(*snMOlP(
zRIv_3F3VTZQFuKQ#wYF;@C7i&BL|WUExWms!c!n<I4EOyzRA7k`_U57irgU#ZJffV
zFE@?@f{J7FIXpvK7UY>@X(}U<5}rB$RXGWwNwEFUXbsKhBwF^2+*|TeTN>E{=9g(}
zWPynQ5#lndh@@b(#Y7ySY{-NTdGta_Hi14!VsPFgg7LiHRBQ|<K&Ht0A%dY0d&7!H
z$#*^G9pAeGV)`agyn;KiZech+nHz*ySuo;~Nd_TR9m58BPb337*9Qh=g{{e1%ov?h
z+9OygwEqCKNk_V@7}7`^p~QPhpqK_h1S|JX?(n--ls@6@CA^GeQ^xzTVJWv`al4YP
zB+QO{(}@pr$!O$NK~2=#hy#1)3DDx>h;fwcBZNwM7vQ@=EfxUX26YXE#deAhb04}f
z!HySHiIZr*$y4qqv^A3c0T5eAL%=uce#{Eprrg65j8yrI(+^TqTtq@A)k%d0ObdWh
z{_wjMc+-GY519Ry4xCe&(qrbPFabQm$rFy90Vnk=Txv#KlT5oH6_Wwra#+FOs&h6~
z1`3y-(uucepN8Dav3VVig8k}W{;+=P#AtSO3c^N6UQBF1;bi)4zfi0jlAJifb9kU#
zxqh)fMFL4HIX6~b5pAdNsfT%&58W9KIKllwP%v%goA*^}f_$!2To-?B*R<K#FImn5
zm7z32Yw+thNm{vreD(`W`X-09-XQL42YHA+?P>Nxk{s*w)mfCl5Qvw%ylFo0(H9Hl
z3I0%vcSd-JrPQ<?*Lk2!64wFASZFm_&P&4jE}t!S7KhP>RUBg{DrR^aCY-vVg&8pO
z>QhRD;KuVz#m&js0Xa6xT+sniY&pJ>YbG}=(cWm>Jh&h*)bRnu48V0m!Mrwj<_Y3C
zp)F-M--HAurYH7TrY_U;K*~7jdyY=->eFkSDb%<lkxg_wmw<tLXm*1viQT9z@oJ1P
zU19GaFw}iMc>-3hxJE;JGh@{gh43Sq-DMF`+uSlAHC3h^fX6J5!}qS9cUmp!0OqC+
zM@9bt&L{qbxk;pNJKE@+XjGpbQyNN~RzKxmbQMTEIi!Y>G0ZBC@8}qL)UfL*{orvj
z+MB}NRZ&W&jd`)?LSu#V(OVgIGx~Hw#nuSSLO5BYMo2i4(hOQ4*$*9dw0zNXaXXIa
zCs;wQXTAu8W+}z4F0il>Xx#)(s@w?rCYRvtZE>CiF32<XUP7qqE`2OR9FRwn!u(kl
z5aif49|d`J{;Ma@X+X$1aU2wPbt4$ye!(O;?JbXdEgI0X#D*Wa$z~n!D~diE7zbDE
z%|_1<*OKB8u_zdji0~8WoMphZ!$?yP8W_syq!t`6{Gl@o27F!Ap=^T9&lc#j`?Aib
zXe2A5y^JNkirDM7<vVk)S*-G9&_n+K$Z8v|zpmW>0M>mLO?V`9O&%j3J&nFLfOuxA
z?gp7@-U@{?(#fJ9Nw4)zdHo{a6<NXa;e#u*0R{!tNVa+|?f1CNP@->nXkC_&JU|2F
znN5M(q1oL$E>#*0Z|N>_V7KG}RNZJUwUf_}4tXk)c^jffR5A&}qA)olVPc>Gkxj-;
ziFQFf#}<hB0-bRpIVTUs%ak0^1+?1dk3{CVl{42qD}J6VkTBp4_7K-J`h?d>ekg@{
zr(OzeB%X_knS1{L?OKHG{l+l<q~`cw`6sp1%dQRE@l@Q)Lx8*)Llc{D!F(T?-<eJv
zTyF>x)||g7P7dsUio!Mgso;|L5Zz%omiwmRZVY}`PpMIlF}nZ`eN&6r-P}Y%W+&9U
z=iDi!w4ZkQt3zG!DVX0%h|eialLGCseO8$EhwQscdO=;0<(}{<m`8dcX1gOR)@92w
zk<l@<=+dJ17Boa*RdKga`-RxcORV=(FejpGjVB3XxfJI|FrMF%ZJH(I9D)|fqmWI^
zYoW~^p%Qr_1e<YQBb;il+6!?oXx^#B%W)loL_*28m3GXKvvaHv${ErT-N<PmD2ALf
z$k|mOhnSJiio9^ya#l=eXhiPziTDAx*=QRkMbOYPx9b}Dtx=Y@W3C#|Na?Dm45EDz
zh@`{^c+vN6<H=-R0uT$&dCa+F3#^B-h8qhW2W{Fe?ugv7f9eZbSbwtH1Ua!I>JS9?
zPme{Id)wu0ku0Z){YriNGn@eA^_#m+6a6D$(+{f6&V+X;{n&0`hS@ZZ`Jw^#XEf^?
zyQ82-31f?~@PLN*hPdG2s*;8Ar<)l~nyjP=BV7xM&T~gR)Ni%J;gT1dr`hfW!hO`|
zW7c&|ag^Qmn5fR0rjw3@kM1QnF|TR4A|+CjjjUs@26Kg4PXZShkbo{=-Rf6}j4kub
zsJlS;tCNuZ)?mtR>+E?TLGcss0hZ5Vn~%|G*p-=kk+@h~L___d)m@~K)2hfjd!eyT
zCZ!kRrzgbN_I_wiIx`&FR%~c*qRahThy5XTXStx-L<1_BpY+WT1nN^9&>SvA1RILC
zb{=|f1sj!`@#epaA>)JDZIQ__&%DP4-S(H#?IG{?SV)gF;_NJO<OCsd{{TRkDiciL
zCon*ieVEBOk~}B~s@~}7VzLK!qRi&1xU=0i9)&f%+Z^EO^_5wm;gdzq-?s$UG~sWE
zMu>zrsblyjc>7FPPC;6A)Tfzicbmj=N7!py)W}WGveUnsHYg3k!|`6#JNXz|3>o?>
zGJ*Bc7;A1yCVRpF;MH=#X*<g;o86wRkL4>bWbqt!j7@9tLiUa!=7tdWf;l2DN!L9U
z@d6fzUcG9LAEko|<FiezxDpQpeE6Yim2jb*7a{D(<g`<~7;nt%o#ui(*8)m#-4x@W
zFR_~9j0{md5wmc+(%XHS82<qMp`C<Jd_GEZJAJy(f2Fp2RK)BCd(iTHp=NL8u>Sz~
z><cg;`6C<cL+;pdLH3Cjg{pbpph%C)bWZbkPXenu6!>~3WJoHjX<_v#()v!2c(kG~
zV<R;VYnv8C;gVU^H}4;pB~S-8XS{Wz%|Cj5o2NJ!)I_c^WCL93h;{A6CkHSaDLdqm
zqF@-jEeH2orM4&xQZLNtgiSzx3;7U#smO_Aadk&}n_<YDZD5g}daV(|Ij!eMi0l$#
zJ$8F61I-*Z>Z1$llRXqzK?%5?O`%o{?KN5jYOU}Gf4x;RR;&C~<8VRtCuGR7s5{&y
zrURtf5V0b3RVMix6VW#a8^m)=YuZ{L<N_@>yAQOP5mq8qGYI=e*?<%3J@0PoJU(TA
z;n<op#{5(8U-)$)izDL!c7DS-1A(%;cx^`9Fdx|yIO6=4O$4fh9KjsbEFGcr`=Ulw
z2BdRdVS9caP8lj}c*fxlLkyn*GYPxg6Hnf5d7e8Y_f7<HwutV$V;|I|G_|3*zjn(J
zbVq#epiV!EB>dIj5IRbpyE7#l1?-Si;eA|R8COOujN)kZ8yyhSbObOGwMX39#e2tT
zJ<V~2+>V8NAj7o2MHuLXr3lMDhyst2xNQOQRW8Rh)aOLN##0V?2a20J9H8iS{0=&%
zz<?v71)<DGW^O+KwWsw9#ZB19Lu0V`*hkf5;9EQ<$X~vB9Uvkg97cFH=#Rn(iM6_|
zYq*}EY(6P;d>61VGxa%m1>SipHybIp%>e_((f<H&K#<n93H;VW6Dw}GDT&-XmUy;0
zTQK&GSaVpM(|Tv#X~_mutnm;0qk<OiTLXlY?5a6Uh3AhYJd>>4lZZTuacKVlO3aTP
zhwzViOmX5iILc#ZGVM&MX=`sfsztyzJmZ=^sKi~)&<-h;2W5Sj;yx?H2IKf9mbhq%
z-rOHFJ*%L5#<ZB?Hk(Yr$u|qLb99bKfbhc89BG?M5ZB)i0y9D}IJ?zG^9@ICf^j%l
zd=+Yj9@zxm_fIW$7T|*yj|$(L7F4=yx|LDRKnuZw{{The@7+?5R%SgDOPq3O5>;3^
z_YLup&w^_^0NX$$zdaK>j2ZPjR&^=v($YojzxeJ(Zmi%AoI;Nb?6I7=qxY?D8qC+K
zV={;#Qfjw&=ROg-XMay134%P+J5z0ta6UrIGS>hm=n!#M#5fygv(t(mI_wJM5eJtI
zl3IH!iEl+E!Pe-TCGT^;vd9khtxQhjoJ~4+d}@a9Cl@uFUMdfk!@dcg1S&XPogQ4L
zf!ZX6hY0(=YCi0ie{$7?Ea}}6X8yNLj_RE-N9KLc%~=nzd2RgF$s*i3CrR&#C|V&l
z!K4m{XY#2pY0JY;=8o@l)X4zc^gAJ2&t%D{Ey*^<qwJfzf+McX+~5{K;crIUW`}!b
zdo-x_pFN%?RxI+*VK8Z!98TZljhZ?~%|`t{iq&N1khlVQVL79|B0n-?q+_T+&iN)p
zl4F8<M=aN9r}}Rwwog3aZnf+Vc?rBjd7%VnE6p_9+E5$_1M@|bq9Nm~Avd#AVXnCx
z5r?$MTka>~o;)Gw62?@`8@Y{abtp}Wg4e+%SHv9FfF@!%u0LZDad7g4ZJW9UsazAu
z?%Ogro2~lQ8xCl-_^P4xiTn37-}Hpcf+mw{L2JnHk@Qe`gCO$??mrG`$GfD#6Q1hD
z{-hhR#>bdls*rOXp~aUNRqNGp)vdS1cAC^)8v)fL3FN4_VmpuaN;$Q!kJFmOTOgm^
zJ;2S3-99Q40;Ffv3~ZkhE;c%yCJ|#%(7SCQ6<1Viog|xGXaZ?6Y@LVwJvOuv?-3<b
zj@L2)$>aAxwZrD46KfW`NhD^nSSt#woJ3>}flw6=38+6Yx}#Qs;@4sewvBA?N2yh)
z)Ng0@nCUQdP~0bl)mJ(_<0;?edrr5S-7X|YRM^8~vP3uLqTvnMmvmj96uLLvs_bo<
zxeh;6%Jf|qjo`oW2F&|REl;NKHEX3e;;7CoaRo#Epqqyi8yhr7Xp(eC{{S&SOnbwC
z*x)6?{1#L6+wIV6eAUCZNb0k34Em_T+sWvT@n{3hdrkol14Df_aajulCv)nYKJ|`i
z^A-xwzc<LOOe<Av_fGmI5azJ7&XTgoc_0N!t;JA{LAX&3t3TW}WX6VtqeI{N-j4hC
zpUaXX3H#R$nIVpjoLa9=s19=nEn1K9Tk%Klsk0a#lq*K6`v?!h_&(5KHdmcfK*08&
ziXqM~Ck$CIH}1=Q57?jmy)XNA2>{%3A_~%>2>Fo;2Tn^<nyDM7SWN`{RZ_zv7&r2=
z1h5ET7s5Cr_lzE)2qMC+t>HY81dkJjq^$83O~R{PYOOc6Rw>xW@foX67~%FC_)aC!
z!P=ER107f4&=~Kpx;)d%k9Nw0{{YsG22`!P#GXof+7)-%oZ-|b0{3wFBb{fDfJAqg
zjxsg6sM-{VXvEh)+LNNE-AOp=&hxr(8xKV|L&!0jrpkZ$Q#g9&nNiQ2@&lqZi5bn+
zX69|}dMPM0UJ*Dd%TMl@*8uV^7V9yA>!R?}ryC>3ZG?kXlfdL*GM#K;<ch7uc9qyW
z5a~&FF2-k#H9?IXp~t~&A%#_I!F1c|gs*Ocnv6j{slEc^>W%Q*saM1t2%O~)WGI(X
zZ~*K%G%evC6ZGbW`(QpbZxhd81V3<&sM=iNJXw1z>OZ|cR$NxMS(><Kla;X5f&CUj
zF`3!JWFkCSA^yS{Qy!~C8sOn2NRxD6ZU|OwCT{gj5?_hWCm^y60lta|0wbOvvliKr
z*nf!Bd?u%w&Cp}1*eybfi0W;%?FHlr*4tzfNdRWxeoC8I%45PcJW6OPlUm37P0SuQ
zL>>fci~v!w7@Bz<f<EMI{{UqZ_caSWZnRucJJicp;!U}8>Yi~H_fF(T*@x7piu#Dr
zID<MUCkWCpERfQ~i%JG5&InZ6Ug~3b^wCq^izW{=)8a6JM7X+-%?$qlb>gX7-5dV^
zDCTg5)FRQ2rYFJhTUAxH19L6fbX`i!?)sw7P!x)|p}K>?Fq)2ZVNb={h>EpGv-w)8
zJf2pFR2m*HGoO-SfO8qD;jC>uSl{Uh&F^a*G|Nqcbt<DxO0fccQwvBk3W8n%W*n}B
z@j@KnUQb+;JI>8h#>4Y(H1773@isl7BPRp{Ftp=#t}DhG&nqm=G8#FQ;=va)34!e+
zdlmf<0lWqNdo^Hp_g!S+3BwBvQU^CXMI@4BqXiGW-3)$Ssj%9_Go!RCFiPERCp?~F
zSG4bf6Adt?Ch@^KKqaU1H-SxtR+7$9n$;K@l|C_Ys$}UL)8`NpG|4rf6O(L#&m~Fl
zPH<r~I0+AXZHiZfP7%U(SZIK>Ct_p3b5G=Q2FpbECm;Q*&zM@H;*AkI213CP!yu!)
z9Vky{t-Wt#oeHSRZD#f->l_dg<8h)Fjo=nXR0xK&k1dCLxx_lLAH=92euPhe2<V$8
zn+t%2p+Ojk`0URD&7=|JtlIRORybl`%tq`0^ps)17(CF!RwB2O)Y&)i`>+oNftSTZ
z2Q;H>LRHw=sPp+oX|l!{^Ux{A`bGUP-G2&;8Ss=8xt-j&XBdhH3!iolIpQ3buwprC
zzXrIL^8BTFr$r`=RSzJh9OHy=92V$_jaKVt!n9~!DphCtUWCeF9Y(+*wAcssg&Az5
za0Uu*BKyqZkB@d$%bf$sIjuSr=DdDhYMFbF;gh6lpNN~4AfqL`eo6?*%{mpqer<$@
zCW?LI;jhR8%}q*}vSwbZu~7hzZd|F*YaeV4q#%w7gR|Gm0PvL39sF*-$wzgKV{Of$
zL{N4nj!QO^BZ%g|3beVLj;%q~s_Vxfo4jTxPKXM$o?x-vG%48oJx^Ajb=x;sHRI7_
z9^gI+-WoW4R%3O^&>BHm&6A5>O^>tYnLXH-LZ<9Z95ht0L<)@7!!>4yw?CO#8EI6<
zm6aCTcoK3_-Ws4B7&DlOLMh;W5zO6Wc4Xen2_0@w4j2TEQmw^=+;rr#z(abk=RMv+
zQMw){%=AY#r%ozOo<v#}WXG+Rgrg%1en^Q7F7!iknTIwr1sS1NdYSqZU}15dfdE<F
ziSs~VaNXgAi$da$g`g~`vClq`j;2Zy)|@2lDt0(YC&?H$x`Vm}ml6=1U7Xv6m%b3r
zqgJi&9(vvuM8Xle74F}d*xT{j?;v5s<;@d?^l<=m!F_<^uU85EEgN(1A$lVijnBm#
zj1GYdZL^$r9rRg*>|N3B-^6h8+5C~PML&3|Mz$wnH^B#zq~?!wIaW09?F^BrRLKp(
zAzt<<Zh{^<n|+tz5292NF)6rQ=7QMf6ANC^zr(8SE?{Z1GtD-eXnuCx{Lr&PyRHJ1
z){t|=6W-?MoP5-f*7bqpj&!o~T2ql9p6h_J((4d4Fo(^tTQZA#w!=o~A0-zPuzOe8
zsKisSGa;!fdDwvg99^cix{gR<s9%5GX>bul64u{}&Ba%y^Ff$9yi({1j%g2Zcsx<Y
z$3TOpL@(J$X+M?AQmjJ*e1^uHedAyR8c%z6pPFM$+q@J=7POU9KTV!W*oYFeXda2f
zKafu**vR1LZ5qOSQzHmG95O05h5DzG?0DQg&~|?i2TW*IK#5Zms{S1^8~UbE0BTCE
z4;v7nz%|5q1k)UJO}HgL1vcxc=&9kH>O00&S8s;{eo77(#G(Ljloy4(5tf})$94^o
z)>fr(Lv+!0E<-dgCdiA)BkVTkdU<RZyF1yxIX9Z7)&@5V19HN$M(9BVV{V;L#NO;m
z4j$=O7*&I-xcNIplTWI4{dY$|HV#s<23C4=Tr>08cXoQDe?z~@CkvfuPsB@{=bjjJ
zQ#Ao~_>}-*sgIqki*O2{)nWD{=&vSxm-0)$w(4<iwVhmm#o8R!H$wwI*%9A5?mbhE
z@htw~gfHf}e{HrIhP9vD3Fo`+-0Ui3pS)?{bqcQGbB#aNGD8Ul2t*8Z8p%0YN^H7(
zX~UsT2ckYUvId!Gnj)h+vQHDQs*qY>j%mj{{+k;Zdp#ll08Z0}@?+X_xS+~lIt4~I
z;zD?P2*pk9$-|byJV8=$c7xqbGPj-iuL_+e^k`S6`#dk{JVF`b1S+I_lZhNbly@9N
z{^*i1k$qI)8KW4SL=RNp(@q;a5&QaX4rw<SR_lBEk9jxqO@=m`{Oy@R*C#tEuUXsQ
zk*|b)?u#P;v=<aaHL{2Q0F($lTA|FH>SrepHASL}pCQqd`zg~I1o$GMAZe1cwW3Fw
zWX@@>-^mf2T*7fNqc!tZ#?T$)p5uwQNt^j67qYk@$>I?3)x!gkR|VnXIwra@%@KBy
z1qCzyn85Q*Ys1>&Wj_PBAKf>_Z&my%J=eqhtjjIxskphB#)_0Zjt;oGAmbL~i5RK6
z4+Ekja)|ZSKpopi^jV70>KD8hzE>5Kn)dp%gkXbZ4XII*%_5-v(@S2)Pol%%#ePLw
zcLDyLhHkSgWg-1$>DD><ETN9{KW023Wt8wl;JkSza3NXU*Q_W7yUuS#llwz)bV3J(
zv8IYM)eDGU%W=0Qmkpxo&=1LRUMF^UG;EK$rN8=}nj>Hrt0U86fDESR6XgpzLkKzR
zu<UQ&Q_eLURZKLhkRLxZLEuKAe=I0;T(|c}Ve!a3p<e6WvWRv1B1?d2j$dX*;9xyX
z)RjrKgPVtYG0>{-W8MRUM+MpG<B6P!PAnkC^qJ`h0KyT`5@U*z;h+~r?lzzDoB%E1
znzY0Mf!z4)AOdcAEk@}fc$#4lWw=an3yR9%`K~c25X(a~cdD@VK*$bD5BkcWkvTAO
zP95705Js`8kGv4sKwYLfsg&{|5#HArDRC2GRbU-Cw9m;F_J)%7xHwCrs;kT~+0Rw?
z!lO$o4!jVSJ-BI^_m{XfZd9#T3thpyQ(XF^$Az*?T1shQ#M?MQ_@YDjpgdc~H7TL(
zc9HT=1C6enZIS4v3jy_6HPi6P9qdwm#&eppsfaGi5;9-Ei-<-RU{v|+IGpyk)aZ!7
z#}e*?M9>8IMv1auVMZ{~bCZb<8EC1Bh9ooH>ZVB%!(jl<s9clrT3gW)rzGeSB{<*~
z4kI<KzNy+Gy{d0-!Fyjtlma~Vo_?D(PdHj-?)TmP5gq*;xS<3ajf9C8RwbprU?&*&
zPx(S2h;Oih-z77S4?=+uFMp21y4f)(LuY3ZI|=Tpw6XsHy6}zKN8bFwel331@ZZwB
z4W=lbsk%GVh(MOp6+6bwpe&<(g6qSP^Rj8hh`aSon+W~KW^Z>&*_P(z#yo=ldp>Jg
zuRj%($@03+B}N&8Jj5>ncY{b)bj#^#)5Ip<!BCHQYI>^z_Z%aYzQ?mlmLD*!dn37B
zo6)1$W4B5){{Za^6zH9VIKU?<=g9ycOt=6t#gfWXJIW=}-YoqkZ&N1Di;k%7F~I(A
zsGE|W;E9!V!f!DMnPfSrV!&p|6i6wuF<vZTRBYohjKhuc2$5@!Ya!mb@x>Ptih8LB
z7zXp2u0yQiz{28XG4FjUWRUS1PF&Sij?VTB5nv=)FdCF<2RMw&RQ>IHatPlS!05Me
zx;+JuhO|IIuVcn1HR42-{{WV78k|i(vaQ+OQ${$E@HwXMIjz7MmAFD>PEr$G3m7p-
zi-a@FX09{zM>Co>q;CBKMyc57S9yD0-e)E4;w<GB*_7QI9MSwnRA>DtUVu2itg3Ex
zy^$%7ZLt}(kTB<x4zVbii%p8`-P4X1nzY63#!&BZHapK3$sVXnXtXqQC~lSp9z;s^
znn(88cv@QH@<&ypkcF5li9+DZT)z6Y7-dEe8+r3YwfHcB(8)r3zSP+qiW!BYivBlg
zbx-XOj!iBdSG7W?z+(FsL85pBGa*4~etZzLI7%R3E?=AEh{WFyRCT(@xFhWr`3otN
zRIvX5P((;Ocon512#Y9;KLrK7`SfkpMB8cJbwh3uvo_JeAFpPi4W<wK%~Lem>QfAi
z)l$&HM>ldz!VL$CbEC78R=W*=l1_>VoJ~J9#c@K!CSz-;I>)9jqG%4eEEXO~g*lD=
zMio4B?E?fV@;mHLVQd@m*xXGsh-HwK*(+5s6wYe32h?jx?wNcifrTe~@XrvU%_T75
z;xkcBl_Dn6>;C}4d3R~6a%NyTRNoiQ5g1$|b5)y3%pkSLUWk{lz_q3qWN4tl7z`O4
zN{;fO!rdW%E)P`co1qvhVJG6b>}BB@10k`Et(!yJXEsyBQ??&y$``zHge;f=I_6QD
z6u~b*1`99@fD0gaEX7$qYfVBerB*Pya(rs4(tBJ0eL0JrRR#u)(Dpxd-bfr2*@%H;
z_MAbbxJ-t?uqv@@n%q#&7dxho(#t@qWQpdx1AyY%rbnu;+UZWO0FQ$0bh|-5#hCv9
zLRBh_Gn<5L{!0S(K>d-^>@xvi`^uVO9C@bhhW*R9mfZTM;j2lo#^_^Cs9YSDO|Tz5
zjGC6qh}8#F9w*_x5_Ga*Y5c+t#yGjn4X~WfOM0jS06xz$oJ;T<%7+7Zas*pKoI^2U
zzva5HbBtL*-c5uKc_3UTE=$=NX1e<9VRLD>8{o#^&0-sq4%Sti&K`*W0I|0-U_QGE
z#L^q;HXfshMZM@`mhnwEnrAPX9aQ2a3p|4M@K7R|Av^N~FDPn2Sp}1{=&~7X_lDDY
z4#0er;HK}4%7}edK_BllJS7{PH{poM2>zz%dY~M~i7anDLJ{3CQ;hC4P|@LN*P7I!
zHz&|7R+OFbx7BLXvo|xlsruTT4%Jd_GT*#t_PMEscxQ&gumV5ROmql~<Q8u;NbrC8
zLN5nnVE0t5{R3#2Yg<@>?eX(X2FNSo{8s2k1^877!O;w)Y}aGX?Kga(I^+Qwt8rCc
zDAgGQ03Hi^IZ7dfN99i#opNMmslDwwMkPnP!&7#ngg`ecv+I+SSjaQxncjA{yC7$}
zAG&wmRi;6BHw_0w*~eQCk|Joj+}PxI*>)Mzg7)0O@l_mJ#{vN|EQ!S0sNrEV&>OD^
zHavpxjeey86BtJTo<z7{U8b~Qojgh5%%2OI3T799dO!aFDs{mS7rH(-QWyn~6UhL|
zXEDc_RSar9lRT3k8gK_OV!I_XhNFgV<H)8~VbnB-4=G>jiRXn)Cq0;H*3Y{?<zcAi
zHcZ?;6Wkg6S|Q(te`H`bi?5(5;tP$4l*l=k08SN7pJ`*RUIe|fZB|p40IdtOGYwjW
z@8btWy$-}YmX(ZpN^}n3yZdghybyzk?P(GwI_!J{S97kY0^u#L25^0(FN!@Dkco4#
zZqgZLnJ5YW08Sqbu-{qAj4!15<t_U}P8%Rf@c=T-3|Hcrga$Z)oFQ>oe2@X>(54UC
z6~-fU*|?6VjLOqz1@3)z5o&OT_kx`MbFiqoj}yL&DN~0FFX#4ew%Ax4{{YnMujP%V
z8<`wRHk+)3{5CPpEo2c3c(MNg43Pqn642a~?eNxU6T&r3g-jL@Y2rRc%8;Z0-i=hi
zZoJWRSkQbTWTO;IvgOSH=&S%7c`P*h+&%3!X~}kwU%{=#^F)&Qf>3jtq-az)erur6
zFy|U37O)!=s7E47=DSmA$^QU+7iuquOf-!AkhYr}C+MSi*z7R(HxU~7rxD)2cnu45
zN4o7z<6!a6njjIyZ#m}4z`gD`$ke6+gb)#oI7v<o1l*I7JB_nHOGb!-!sFq*by8x@
zwy2qPMz@EBPBBoABySg;Y^n#f)mY{pniZ6pC|h2srB-`~=HL0MRBK{z8}!79=ztd%
z@?PPw<$ti!=QzL#;niG6eBs1s7xP>5PiZEmAkcS*l3{76Nh6vBOuH^9_cTsZ;&&hu
z%_=6Q7-`;~YNNxk9qGBAEAQEnWnJ%@>>Zd}D6;ImovnD@z%UX$Rd5Lxr&7jxrahRO
z0zxwY4U+&HqCs(1GLA|ks>B=w&Qm-Q@dRZRQIgU4MBV=Y;Rxug9O0h@#SNJNHUTwb
z3%EFKHk*!0MK^=qGgg!IPU8d&)1l3COec==lY~X=Q;s|hu+<1GeZt7xbSQ5ZXTT>7
z?10gDS}={dW4_FRlCSr&e8Ljxc@ve>KI;)T%2Va9tndmD>eZwB1Z=5=;|)rEMq|x2
zt$jvfBbiJs!#;Df*=x`JCuGSXqW)?;&>7~4$0Dr}#EkYBAfbb`VcgI^k^~TLIBhY9
zvNnAbqGdGW4JNSv0PH@y02ry#cl`l6ZFL)*bxiMjHBH*B;mtTXq|Z1UR(b{ElPY_&
zptN&O1ll9oo(s`P95||Nzmg8#fZv+cr9TLq(}o&?&dv%a5F|MQPf0@z&s6s{*|syX
zCabB89A`u1jy}-gzhB)4Mt<m*S<myrP`w(@r0#gPf^a9j1bK-<F}J%G<C1n0i#0vP
zY-Po|AA91vMK@EZC6zB8e9;H-06Jm@LJjX|RPNu-GL%LVTO2L|A<lboJ^uhf$rGDm
z6#gh&EI4c=5s_V<pMTk^G`<9Pj6D?!6!>9a`lpAE?bhZ5tWAF$!hJVk-7r*RZAUce
zky^VD^Vl*ZIIU~2d<uD&vEn(WLxsP>W*ltC9LdR8w3!;+Tkl87X5(^D`@xY-1b_(Y
zpWQix;(SOtpwj{aqr_8#nNjjNa!!mrj;TD>jthxeux$QQegZaP#frf<?-`i)nUap~
zqsxZRb-yK*8dT|zAh-l`4*7k9uFi-DBayRu@J8X+x{G5C^;%UM8YP3qq7R}s6(te6
z;y%V=BV=a3TfFj92*@{X&w1C_Eb7f~*X%W<9MQ4MU=xgHvy|HkK?DXN1Rxw-49G<=
z98K{Y!h2+q#U0kv7@j?~sVU^XjlkmHvhB=b`hc2gww&PyUTVmTxYK?vJa$>Z(y0~}
zx+aVawINpTsX5u}=8cMMj7B*5qoKbP+@Tu)pA8zSfUYW}aXb^+=Q+)YGh}iW*>utY
z7TEDk!pyB1{<H{nA}cZ4aL<~jd@$punhotPq<3b-e}&n5r@h!{_ilD49w8Din7XJs
zCgblaebZ(PDmZ1f_Oe}`h8TwoIfTp-*kehUCqSL$q3WMv`ExiPNXy!A)@MI^%{o5#
z7Y`(DU}Gx;XYU*7s&VlV)kv~&^-n11?~?ahK8T5eBlRPu#><Krt26;tw2Kp?Rn&Na
z^hRc@ppFV(p>HlJ;sA-l56x7<`J7I;l}~is9(+^f&KF)4?x80TfQX(0PD$9?89v}H
z!$rg&6L(LXDpjdVWWku8GM>uxo$h$8$fFKsQ*5+5Cg^!7HUKQWnAv3&8%J`8uq_!J
zwp>$b1Nye)*3X{K2wNCe5{|<-cWN~1*-n~g!^@FF#yvq!-6Gskhj#+uTCf|h_qh}Z
zbukC3I!QusiTelJao7x&hL2V6wRo6C`5|gFo#$i>wyrPk5P^x1N5^A`eipnib<CrH
z#K_^I?BT!&?*?I29io>3;vPs41qzt?TV}3vhsg|xw;i6#f%IR6Rvh0v!px%vjwer7
zA&0fo8}0YdAcF)QHcmFvYaBR0+UP3AZq@$)$D{uMU`1vn&Qld=J)^8<rXzWHen@ZP
z_-x(8e(txp?tGL;F+Z|w9h$hWVYBL+_jE)XfF)I9n^eXC>|Z6>*!Q<A{xG7D_gFBd
z5pS9yjblq#_j||Yh-D%-oMRjMqrTJJy4*5Od5TZ)x0EJSAL>A9CBe|)ltN|OUF;|H
z02MZvG69<qsM4Yf3+D0hL54fPs0x787zYUkQEP{M;?g)OQ{J}^^$k@Z+Ky<*$pEg;
zh`0g+y^v9;99?1sg5DUVTL{ag)j%(u)k<far!+OqEp^4h9s}`O%=#g4Rz<)S;-%Uu
zG@iTChu=8icQs--**UOa^iLAnl;BQzts%|&p&$8-KlLpGK=lY>-DwWgT2T(AQuFXY
zz|jI0x+h^Nz6>~s>Yx(or0l$r!s6PDKiXFvhr|uILD?9g?01p3&tPe524qSkUqcA}
z+wLau(GCkIK9sF%JLbA09xML<Nk`9UQ!|o?E&&Y0tmGp&ijC)P{L`Rr$6bJa2_vdF
znthd&gmZDe+ZC)0n$*Ha7+&-ZgE3{CDc7PtNdEvFIy|=UospfBOGwUasWd?c8-%*Z
zhSYGt@Hm9>e(V$goAcOKLp8#~suy-==#7Q^_HnwVTFQVC!szRgBQ1_&97ZW8B-}3#
z44x|CHVqn)<WbTTX_Cpp3F1O<!`&Ad<hU=zbmJToM26Tb+}nVUhRZT86vtH!wrEHh
zlm1gj48j{*laRJ6rWixNqHTt9=$a{2VEh^J1-j=vG|q_bc3OC8Kb}~M7<*ehU}aEo
z>Z1S>DSJ;n5bzO&qb804U9~bAtdYH<21jFUVLTID;O)~k_#rKQ7dQc|U8NW#-7pWj
zHq&IL7+s;PoUB)C#_j=RE%Z_19MBmkqeoh=u=3)IT}i`wt4zJzj|5|SoOR%jqBvaI
zE-h~p38D)FyxV0KqCn<Ohz1$OBUEfEHhoizY2)2K?B(RDQ!`b<Ny8(}SM2RP3)Ozi
z`L(JKtSTpR@*&?OF7^U?t3zYf$_F)Z$EB0YP26ypU7nRv+}=j{^Ie{pYp&B6zG<QQ
zMp4oegGja)7;y@bn}H)fhz8?6)v{fplLsRywulZFX$jq1qq?dsYjp(A?1UFPPD*wN
z)U1&?FMJ`XM`mFNsq8pvyi~4dQLty(+OkBLaUO`=fFVq{{65pK-iCqXz47wcZrUyb
z%q?iop<lyr1fjj`^y0I7pcDCSRptFr_KK+HJCf|<6DdQgw=xZtVOg9G>Hh#Jk7;co
zIEEa|*%-LnYOK9JaI*|>^|#dkMjR;RpE1Ehds9E_9MCOzHwcnT)xO9702!mukg{N#
zt~X7VhXO|t!FVi3ISO*R%|zlxm?Zfq+>jp2pZHZA=DY$7Xp7-dJEPv?d!c|leO3bO
zW=Uv2H4PxhEodJlQ|~eEPN4@|@mt53ObrAEigf208W`Y!6lgaH1CFIIx&g%+A_fNv
zqnG;%Lwl{`7dJvMQ15Y0d=wK`eEEx@(a<j)9LMD=Hujf!N@#OkUME??tW8H_991yd
zti<-}9~pU|OCE0H$5lUrPyW^X_Izbq?+R!#aGPtzP{cR~Lho2+^A3QR>XB3c9bn)Q
z!$t3m9^<1V;&8YXFj<PmGcu}Fw;Q7(qj3vF7UY4NHJW^i0M8JByKECgcR2q5u^N}C
zEOi=%N|)Wd7P}_{12wK6nx^bHpq&=uJ2-B>B7(TpMlcgEW-*-gOz!FGIHD%qP?&Q|
z%YLgNjy7oN9(xA!Y6p&n$7i(Wx9X~j6!@PJ=oTyt-QUj3iimG#HDjYnJ`wR;LdxUR
zA$>l?E4kGOM&pXj5-L;pqv9e&th>g~O_n91Z?Z6l868lzDMm)yOS98sZH^iBSydRe
z9GnwJK1)fFunZKcHSXhkEl~pe6Q`cc%EJRP1Y(SdToJuGn26=YH`r(c$z`U)upASg
z(_=9sZI3wE(rNRJ5WK+%&pnfeH<6=4#Qx4Izy~m6G}lz2P{;%pmZpYd-T~G^9Wflx
z-ULrH?cnDY^W=rQtS6~dX-lpu7Relwn%cODHARO+&Czmr!m!|nvu>%-Pp~hVaVSEv
z0~RAE>}h3gNM)M#v^l2`=AqqIHg9NUulbWk7ZB0G{S(bLQ)xZqBZ<?W9%x1;=gkTG
zz)+U6Ly{qy;m%YUZ=%i9(H&Q4Cbw`{85szlg0AX!_c0^h3Y1H`)2v<scHtnq4wIZA
zh}2*q?#>*cw!&c*J}mm8CyJ&+aoO)US_e5p{Z(O^{un&Cs)UUOgP>WHWCd;fZ+}0^
zi7zm5d%Y1Pol{08J=Dn@BZr!&gohIDeAkL>oxHyin+)><C5VOz=t{fwK~-~p@K_WZ
zPNqfP$>vou0X{7ZFJ+*Ab+#ShXjx$><VqeK)}9_K6JlIQ)=F_OHQ7=G9n)}pl`IE~
zGW1m@P0VDDksC8Rcp>~iPe8K7W2e}35M&UbzU_2yUxxdG^jS8OS@78(jE^9WR{I|4
zjh+^K^()${Ro?8nCbt`DW$yv|ET6c1)@Q@iCkA$kE{b#QH;0lUDbm0>xya;I7rIZf
z2~`<LaB<%BOnah^^+%6D2L)0Cd?CA_4<$>yV~2}XP*kVxAX<2AvzTnI%B=<M9?Q6G
zd1(j=eWi{a=jUn>6<_H#*_sK>^+es38~v_7x)+Q^G+72>sp=Eu)E(W<xDH5|!x6WP
z1F*-nMxpi{!=YL&H+?L$_}DLM96a_kn9S(jrC0B3(`DbJ`>D<}s^ms(E*kVjrIjyE
zp%Qv3u5ciWlx<dtYR%YMJ!j~nag^!e9aY0$S*O-kGsr5>Y=}Fv^-csbra9sYXal=*
z{NC^Izn*?UOfmP48ic|sQ!jNQ-)b@m!PD94&k{wDa3$>`M+72j8z%rBP0oi!>Z&z>
z`$a=(4+;9Gx4W!cE}Wp|qCt$*YSGYabPxi7$I)qUh=;c@opYf;SlUtDs@Jgjg&^Yr
zHsnK()p(vj9Fxa;QcqG7F}Rm*<twv3mJey#sF!64pt~v65-8*Np{|!g_?z`k`?{Va
z$OV{yJlnHdZ2UKFzoA4mqV03CEzy1DM}HKH4uLtv+)dH6Of10(zj@r?o=#c0=is>@
zGba3mtk(~*peNU5_e?Dri511M>#$w#iR9E9h*;?{susp&SgRS!ZmxF05HT72*I%D(
zZ{b-PfHOgjEnqg{Q}^FT6!iNh<+w+l(IdLQs(jTqvB)n~aDplzRK|Wtc_T7Ng_t>P
zI9v^(5ZcTm7#j22QwYQ)^5&~C<hi07pk1-m%x>=ZM>tHINzj!=!kG7ty9{o$gm>Lc
z7q^}zOyT#)hN*pE$UfwSNP#fXkO5R~ArS-Sn{r?(PCuG1jGu}kWKGt(p?zGqZTD=9
z<?>yYt~r29Pol(X3p5KvaYhMrk~3NB`=DIL)E(5Qr`|B&b4GuZ^-XhXLzv)s6i6WJ
z$y0H5a0H;>>X&djMv9xeu$o#x0kWaZiIPB$S5EI=o(2&fs`B&J>qnFYnNtbjGO`0c
zI;VhH_-{ILC_x2Md)>$?%?3OTGaXY?_o>yw(a4OJCZ1x*`6_ba(?9P-;fRgreF6vn
z0CAu6>=vh0j-S*a0g2b0VIAV@c`mkGZ3;&MPD#O}<o!I;VEe-ItFgl!+mpU2*rWr|
zS9lmfi7|EA8u00o31M_}U7o8s2T1iO)??2{HCLcUvPk)=b9_?MDUN77L`v0Nw(ide
zSrcQ&?3)X0Y>*F^G<LyrHyG2!$XOKG*b|O-8Wpba3Ncfr*y_=;*i50k<BQ?;bO3rN
zI7IAjZ7%rACOPP?=!t~T7+Z0{#ZD*GA-4ByRXfzMIwz1P$yRko{zN-tS;3?=f2;?I
zz(VaptEvXJ?JR}a-vd!`@K84Z9!Qt_&1*}Cn(V#Pfs9@g<euuZ=J;E1n*RVYhUIfD
z^vzP<2QpO_pE9Y_9#ivHJ>ZzNl=q@#zW8a0YI#KM_Ax)n#Y1s?6M+EPUh4=IcL{xf
zok|<_hYX{L<gd{*-0;Y8LBW*95D(322=&>8g{B)*k<rV>Dhbz`$N)W7l)kCPogD|a
zQ}mCjaO~6^akh&$pVi6pj;<j5+(olY1Q90rbY9cO9!|?=w!$6BL#fd(KdTLtUZY7v
zF4K?cws5nv$D(<4H_;7~p$6d#x3o*T5V8(2JQGKK*)3D^4GPKKoF9oy?+14T9-|~|
zT1Z>O)zdY0i$?_DW#B@c(}&S;OeeHaY`o9nFnv|8a8)*eY=UDi$qZfPGXtW}6hiVm
z5y~Cqs@-_4aXfAzX0L}Ji6yTD4nswskA`AwjYhhWxh5skbykt97#l8&D)j1A$CC`e
zO*jC{DU{OJ_GXBWcFPNaCo#b^4v6nIoyqxaj7{-3UZ}$dspzUW;j;m_jtekFxGh(;
zWvNm3#52`@4#(D4yKhze){k2)97m!sKEtx6KjUMrWDt*tK~@oE{MP)C&0D*qC#h1S
z^7R+ln*QbLw4aI_jNX6g39xb4X$hc)GJm;Jb}xK8=$z^3hWGk*l2I-skiFmvJGC)W
zJr}a*+7spV8eHyNHsXhFxD90pgC3h>V}|=LZ<fs+D_Pl;qHw%nc;eL3XcmW03E7d&
zb}#h>@ELf8*iAZsUc(4NL}-}M4ZJW`s}JEclcaGfgKE_|za|68R>Ib_yD!-f7EjC3
zRB-^>>5lIK0l}hYpCnEO)dBH!+gJ~|h%lo_RV=kR#^~c^P2l3W$PvL<b6&ensP4`c
z)pob{taQW7%Xv!eH7Hcq6EU=&P>U<m7bcmXRKn>Z;ADwoDYP^79bAEZJAz|)5vVF_
z_GraRv@<q=qnBx_SvK8)JQXHWb{TtwjJP7g*8`B$AVHg5M~i!mfTV%h7zAki))>43
zoqLdubzSpECgJOOvZ6&Ja%ye?&PwfP5cU|^ckH_dd58Fw-HP2ZU9Y>)Ql>BqriVY;
zanEP7S)_TVoIDN3#bLSNvwj+2fyX_Lb{xRwtcVSDEOL0Tgd@A8C#gf)J0#&4oZb<t
z;fY*n$r%Iv*?2+D6G(M|2u0yImWBe^<`jMrQrY0IiQfQ?v0N&ya2aC1%~K5wVI<QL
zmlWYuuzy#WLnA+9iULl%Gj@GG!MGdUR!8hO#{~*qN!hpdPL?=GC|NYe6`7{jf6_k3
z6~wP<`!jE5VDrL`SOJ~~{uB!@1uQq}v!mKir9msgM1WN;^hgmfF2C^F1U<$H-ubS<
z*oXfB!UYN}d_xOZ0gei>#Y+wYmlaUeVbRL7gs?ff*sjv}&er)S;<6?5wP`;j{4XY4
z23}z|1`9A{4H-f|ddB_ZIc?&8NyLWtfDVqIs*nEwO;6oFVDE;qN9Aw%M!;QZV6rm^
zr%U@SJap}H%|!CiPD*>{Z@JtCfGDZ@mL~ESRhT(C#b9>n_JU}|L=laqWN<wY-X|f+
zP!*m4WMbW%<u#$+&agR3GEQqjg`cRSxNHvu=8Oju2NA+kK*mXci3c&&UG)YB;p;1O
zT0ydGceE<Gj^|0(wrWC)(fKB|-+02w893+iPdU<Yn`*4;F2Y8!#Z+)EbDvZ+#NGf-
zE^arL@@Y9LjbyO+*Rx0Hi31k*ROY;MRocQT3t!-wKYHuxjBp9i>?~trQd|$wJMZH2
z?Z?#^GwaJ?<?5{3!fnS;w@<yI(b>+30`^tzYV!i%qvW%2S})=5zw|Qy03`O-sa561
znFbq)2qcpM884Q%$zsAM6d`9>!}7#0XX>;cG~n09CK=C?$-+TURv~)<lD`dydCwmJ
zv@6>zaaz2u5OF^gH(uC(RC1(B$**gVmDlrK8#LP%10|B;n<NqFjh+7h2!9^_izhe_
zRqVSDrM6s}uDA58_GGRR_AWKgHTbnoCk_qJDBs;2h+GEA!Awh|(GO%tM00(-mSx3Q
z#F){+0r#RDIfaeWo*OE7IqHn_O}!vw^4L^e;Ck7AHQNr&P>|OPd8~`<h(zxIb_g+j
zhG|r3X&gC(_L@~(11b{&{84EmR=7BNg~d`2Ys5Gk8*@!_DhjhtQ3pb)g7{4tsKMO;
zOs>#G?;3bDUA?V%Y>--89&5I>^<+hknjq3~8E7{`A*U4CVh^duB2@<CjKuoFbDH_+
zo?Ygjy`WD9mLCIcf8AyV%<$qJQRarYT#eN{A?*Ys4P_@e{KBm2P0_UX2f<S~Khopx
zMspK%_SRqyfWy5ca8#e%IAJUzB>AI@v(_Q>-k9eGwbBLivY`I$guc-+3BK5Xn&S4<
zF(Y(y3ZZ>Ep~Zyjtfslole*C-(E&^b7?LLI9a8UL2p7I;jVeK&)MIqT?o~&=H;kNu
z9x{!!UK^i4vyFn%!hDt=?g`ED4P|}b-WLkF%d)zM@N-R&&9HUZ{{X~#r!Ma9?PEmu
zTX-1GRy9Z#yK&R`wNhFMzSm2n!(&wInIx;rf+89NN05X>;Pq20-XKRLa?FL4=Do=E
zD3IY139YCt%i<G=-j3+pk3G4$z%6ceX&VRFG8_Yko&Lr46Al}_QRJ#R9_!sSIkS;X
zz#U1hj$|w@C)or_r1o&cAs7L|P|U8$QVD?#DvSW(?F9BSE0g;wxXXwqcY?(4B*H_^
z_2cB3E#aYsAaF*+`$EeG4B7$6s&2!_G{+8Uq`B3Aaam0CUd<YR_R0u>glX!T>Q{Iw
z7VZ(1vy=lW{{RlCSD;)5KV=D^`>>5CO`a-Uo}=5=A$t$5)8<3Is0TENdk#(tO)uh8
z)|uJuJWowjk=+_>kN*Hiz4S&K^E(Bz*>RJH5NuE`YfkO-HwAAQUY}v4V(?C(IDi^V
ztk~d;>9Qi2^>NQ--A{EizMp9PHk<3sc4k%WO!1jL)UUY?W+K{qxrA<_<FpS|QQ-1y
z{Z(3+Mti^I5*p|NGtN_-Dn7GGr2N2xqR6$sHSJ=z9$P4qDLj_HA%)qsHNb|t+~Uai
zcRdqvQ#B#&@>RtqgS+16@J$cm_HPStAvf<B*m)Ypr7#|2G~3jw;2i9R5&lrZh;2yS
zeo}Z9X9I9kWxN8vbaAtc4EB3M!<y7f`m4h8SJi(KlcT6c?A^;w8!J1yYs-KdYMRhD
zyT>^Ds`vO667I~<?yb&L-QOf(*!>V9vnkJ+%&)^>zdlQZ05l2P!*KDosgt`C)jD^E
zd7-AO*?$Ga4*<`q@nN#GQ*`V%eHw1{Z`d0JmU6x6Dj|;3f*d|;CQHxsqjE{v?6Jl^
ztBTKSw<%I<qlwX}-*`@XFJXkji_&QuHKa-Yo9xicE-NBX)H#meWpmrY2txMws&0d<
zD{qg2a36Z>`Jzear9S+|#+NdPl4C9l#9bvjdU9Hb{?s!206VkP@dKwYuFqxMRjjGv
zF|J9laEk@v?!>0F%G_&#m>Kd}Y|K0vKnj|)9QlOfdlACuf+@UQi^}Z|rud9sx*vMe
z_jSFOawrhsawq^;`7Tgg03d-9*Dq9J>I14$D0rGdKNXyV@lJ1%PLje~8TleH7Pgol
zsb0_oYMqwlW6?Rn@*>KkoLN@H-!<BcDZE2oZ-G=a%c?Xv`Vb`9R(<X{iHm}U2h?k@
zung^@p;VrkF4h2Oy$Wsx*ZC%n&4tkX2YJ(#*7JrI-}4{vv<wK&`MTXT#s+c2uE~Xy
zSXO0lMtmdFY)CSUL&Jl98(a9Gfo%Y~X#ya5E9h27CC`?NUAc>Zg`oUX99q_sBTfrc
zG(b77X#^g86Ta_Ib7|&^)j79tk*d|pFk{PSs8twwIN?7PEjYeQ#TP)FH}qcCyG@`U
zRmWqb_ac99wWGrEcVLHeO@PTvGD__fm|7!=5z%&<j~|h-^Iz&50kCuJ@LiUVTrzys
zR(;!L*ka1QK9W%<cu9p$P9Qx|unI9bf#i|s5p0U!2oQ&{^F)UL&<~odNyCYRnKn#m
zU@;oonyhHMR=n9#Wk7we_(TBmOh**Sr|ulv{uMCdDKK&NQ;6iVk`BzfhXLm8gS_nS
z<Lsy7rB<9Xmqa(4{^bn2Gn?k@l<q(N@A%nv8$F)$QN`Kn?^AI3Cqu9~lzN>}8_tXH
zHYH})5O#1V?!E-8#{|>8L9Ha}=5C#Y8wF&-^ULa(J4b0A+59Fu>QmhMk0$QZq78{H
zUKgCalPb$h6NrAwyKSR{5Kc&2h}cdUhmIjT1FAA9KY=JgrFR3wXu3D9%SCMZxjgQ@
z!!89?V;VBWJJZ1vXMH7amQ=v@{#Rt2*U+j25M;MdAI(W5*)ZX3O)+A59!jZZ9?E;F
zY_moi*%48g#WzTU9Z-xFcU$>WL%#aJpa5`fxLG~GmuO=o8;FR;dGu9VQHNIOjx2RM
z_r{VS1nFl2shDxjEUGltXx;qd_dv13jh1you{V(l_+23J1q)#%jMu83;*(tG#-=Q!
zxV@!nwHc=nSxL^3$rj-44Vj6;;+u`n0}Lv3+ucwkyk_@1Ir<<m8}EPmQKA6dg0DU*
z?q_XtN9lN<qBaYIiS=GKn+`ma11VGjpks2xbJ>_~G333cpMnC;7u8>?$kA*3>{q-8
zsy-LD;baL98^CY~Zgz@N*wD_w;_Ot3`E4tmmb*a6d8-KZUezb9e(AqmrOgJFPfR;=
zLwUqBd7%Q6TGD;!8I&Pk?o20VO|UowG)|06aO-7wBp`D)S1!>+G_Q1g)WrRVN$#0F
zVN`SuYu|#h#$jc{V7db&@5HVoau-)Z4iKur7HN-~0}<32x7nz16S*SF#26`s#%^>A
z6>9P&?(m4t7h`O*IJgV5;C#inr~XpYqU|u(m@W;F*C(`LkVrBul4)~ecxXA51Af?a
zg#0fJ54N+#c6x=b6OwDN6PE&?-q5L-D3(@hHz(Q&(U>cF)N<xFUIvOG&Gbh%0zA=)
z0R>pK;9@1b^iG#ZGXNigl^g616UDbxK(eR<a1)+nTrWiO91PMf<)Sp`ij2X%zD)1|
zG;YHxS9@=OeGz|n=RX}s8{Y8LY(?csWmg8g4hpG^W!@<1ATHlc#}|d)xRu%%SP!s)
zt4w6(+%$C4Xz>uy<fCJ(=CS)Yg~bcDj2?JxvQf$hnOWm}`mcB7qwdL{HT;tYWwr^i
z$f9Ew6b?Ghx%{nMcAe)I$mz6SAJGqB6Xu(8CTDL2aMGs8W<zCYKG7eB&yoc6U6Qx<
z6-Y95aks&C%0Ti2s?};?=+E7DX}pYYHt7kpnC!8@pNcjj+ZlRp014x&a39haXnn1k
zd#uhpNC;cFYd^f(W<jk&a61w5QHrgd$D(^#M19%fOA(9}Pr)3le39OH2gzlQ5A|7O
z+OHmkdMd7HBXnjn)>Fe=>{@4dN7p6yRcAjFRW~>jIH{*K?ZuGKVThxM`MYVFb0vj4
z1!jo<00j&$bATM<P_k)YKC!fe*{T?DKFsQl;k!rVXn?$j1?|&gz86N$&0fc3gzE;o
z$d@Cq_f*K;Z?^t{Q|@T+Zf^ZYR5c|;pZ=)x_@Uux4*scL3YSMAy=_r5$uW;S94+Rk
zP+{)`VQhM%h<C0m@-VzuV4f)6QP0KIAL9~yo~g0_0Dn?3X>?`IDn8-uca}@|mOMEo
zOJjWS0d{uNB%i!>-F_SuSG$NNLF$?t6r3=PHy4JX0P^O%UDKF)vIo%-_>zU@Sb6X$
z+&2Du4TFUA;F*LF*y$SuBTu^$!I(`VnY(=v$4q1$r$T`p*J{9js00L2C(nRG4Ipyk
z)d&s%x1*gfrX1Mwm6{$C^G$&2FK_)QU9JwEM#~v>7Z*<eZ3?>{=Q{zXc>rXhf77+?
z@+j|OE^0StY-s-5eRg}Z>Z81<><#2KC=*hF&ym@Y-8~JKhRE7>O`Cp#%l*Qca}UDk
zPGE#Q9uJ%insypPY2KWZx3wEMY|&^}g|ysUhU@CN?di7Xx_OW3)eX5C&1*XGKn?Oe
zHWYt?GD115uW`&m(ZwB2l*|)Pnc4hGP7VE8qiM@%CR_vsHgV5pclmvgyj|(}A*?Lj
zAl`C9Npi|yd3}>{apb8ASZrS0>c;@7wUl>+dq&xkt|sAEWjHd!#4H`r_RYbLN~On(
z4TF<1Hyl&kccTd(6!yhNL%Juiz?rh%d$Vyx&U8eKf{^Us-eC+vnD`)HNbl2l&4>0-
z+L3Cy9;&M<;%p2#Y(5}D@s~~Vh3$;_ttm8o6b|&i8wavM{g7JHbRtv#0MpYX29ul5
zAiEt(kx+?q@a4i6VWiUHcU^mNE3$S*QD=$W*t)5JIrhP8C(BQQsm+e-!8OuAzIfIO
zZ#2d*=S9F&Di;>sH@WDky`}VsVD^OSjuKoXojH_u*!rIUiB+C2CJOA`nxuQoU6O-F
zD0@xFckGzf%gu)+!(`r&T4%G0$k^>N9aXXFr#D=#2*546)|-F|+jVJCCB@%FGH1<s
zu2$G08Yik7#7hq`r=HDTw6~>{KpR>ENFWie6izaG$Bj6Ez4C!CB4Y`(uFF<9;a#5y
zKJ$Q8XbwBm9*egO^r!w+O3(CX?z<r|sd@Oq?ERJnfgm56txbSs-GTI2&o^7<n)e<W
zDnYiNMO2T|W!h>xEmQvhgG%R8bwu}!U@(4*MVpkyBD0W8tf9X({9e)+dNfYVp`Nmz
zy-}DLJeAu^dBpq^$ZNa;ZHZVK+rv;=wQz;}Hb%qkoq!PPPZ#475dI!7G9;ovakm14
zT)|Pm4vL_r?hZgtzQjY?IA`X!nnPJ4-XE~&YphYYLmeK_1H*?9>4n93p5O<XgD(vy
z-f_30d1rEPP;tw4$U~Z7r)D15;SO39V<|@0jhWMV8EF3i_)M4=Iu57`)q%Z3;BV;~
zr<Xa#<f5ENivomwz<hNIBa#U3c6xIiGEcU=e3N6D&tPz492`LL9zj1H8jmkyo2b++
z`ZaYLBO}Dq$qNw%QHhf$E4hC;$JENmI~Vrd83KtT%q+o1F?JJK{K>!7MnPWaSiY^i
zisG4ip!qmvxXi4{g>pAqtg>)-z)CkK?rINU@H~*-ZIs>Nr=ci_r&32lXjL%PaF(#D
zp~dE02ZTZdGKv2HWTp&2PA-a|#Nt84yWrE}rVUX(OHm;W@+5T#;dGqFu?UPW7+{NX
zUI*FUc2Z)f!~3E>`><4FdgihQ{S<xUslU{#xI;40X<_7nZXV9uWAmGo@)&nr(a3Ha
zw_{!IIp3HrA$V7^$ev`a0yaNYn=uj26T@2Cf_cgw(T<D%0EbWPWOi`c%vZN1{7S*T
zM(Qm|xKHL5S+>VuZRt6uiP@6&iU2Z{22cCd7Tw2PnZfi<Z-WSqTQ#T~3;D14p}HTM
zIU6UmZ(*p;4&||-q0DIsl4-znbI79+qBjW6BQwWf_FeM(LEa!khaPG#sZYckP{Hf}
z01I0mv<u#D+9Uq}x-!a+!dal62n#XM#7@HIA8DDvU-*XoLUB^@L=4lhTI8XAE*dH=
z>f0;R97<^B6U7<Gl+qvb=74mG!kRG|#>871HQFZb6Jm(~k#LOMB6r>d@Kr`EP8ZS>
z_t^Z8BrX~!svZ{u>bxNVPM9(a_3~YaS0K(eoKgKE6KekesC&WRY=5w*ikJnaBN5c1
z+Wn#9t5{Y|q|MP_H*b=;rX7fWrD6_Bn;&1Z$)*nlP`$XX$#%Cjp{6{zD!t=}hZm}E
z;*#Eks)g7HZ1X!EeNeX);Aeu*f9JPKB7JSMErW1O`fTEdhM<wqr)O2>I9+f@*sxh*
zg`<wavhdOIO>rF;X05$pxNf^8U+rC$_f&xD@S4e=d`f*5Um{P?724?l!bpJZ&ywvV
z660pG673a#v@i81&8xA<=Jzhok)IJ%hkNfF)fUdK)jFxt#a7;r4~VKI?QNaZsw@M7
z?uR{9V|7sIWXA<A1V0etZH^`eTozKbxA$3R!(iO*Fqr=U^j1$Kx~^xQ%!HJEuTs2I
zbbY1|nffDROTGU6g4HlU=!Cl!f;fgOPk+4aklxb3L&x(i6V+zr?|l~)@13H|Wc?Az
z;y7l#_k7e6*9UqcMF)-MW*F{hcWyplrU{$4aZ%)k%?zOpbEg*qxBE3)w%Q&DfmCNt
ziVkgOS%X%=%}3l)C9{?{S*tZnck}?5&Fx;zKz(dgfEPRz?LFNJxJD0c<XlGt1u9<S
zKBrVSfcPScb{5m6jQ;??H5;tL7N$vAWU&U<X8!<~kd8=70rm80$(+wL;#_qo2UB(W
z9$eLScXd69-Uo~)wbhzsym}#ZIPj6=TvYwzLG-uL2^45K>gD~O{$@5i8))`&&mi2E
zgv$0oKeEli;Q6$xwB`zZoZ0g%lt6eh2ijP%x7m$(>>s7t{{X_Q>SmP}I2PwQ51z+G
zWB6|-*7i#6J;fnk8Jw|m^i>6J&MUR&2;701dmy-xHo`a~A)iD(<^KSP*65}_V5(p}
zhrBU=5+t>eZn>ksgAg2+Q*<!YZE0mrI^8g}*ragsUeFI8)pSNK`Wk;IM(5<~A(Nxg
zIGVKc@-h%M1>f2w33J_-GPA}<_S<Dc-VEA80P;W$zv2}4RZf2E^l}3eT^am#2tA{;
zGtdR%Eg`;(#@4}B#o65JatkVh3Ii1%yQ%b16#G3U1IF835q*(iZ?Tcjs`h(Zep0k#
zFKU=Rdk#R!;2~uvWB&TEBcl}fw(xRh^G>J`c`B^}kuP^3q_x!?=o-mV8{XNokVX*9
z<JjUJ87$##7Jk+CpS`7c;@|PJN>vZT?D;8@JI8f)$W!83hiz$djzB8$+B5)2YVEXM
zPOdOeW4X1%>Z!Wy!sBCaBnqnGj?Oa+v<B4<5o4-$Cr1p?Q3%N3i`q+cDxq;OOV1Vf
z)P#PZnoFbEx+FL+7|pzTDLc3S00<aq5DMV4@gZuA#NpMck{haOtxkhpF5Jp{YgCaN
z(;kYABO=ea1vr+2gofF6nuirha~)_;b`w#}iS$Dx1mOi43}|WcN5<e2r@OTA>X{Vl
z4)l5Gj`sxp;OX-Tn8<OcDohYlD*h-W&UwPB{{Zd(04leB^XzyC_02a39?HpZKQ+ZU
zJ%m1%1=(tUNX7C%gTU;|1HMQV*{c5lX<d=xUD0-U0G!-4x??i;musgz56yPTc5uf1
zO6?EBsL0P%H6+-&ZRooV5`QqCn(VY124lNwdt*QXs&H|HYo4pJJm754d7`CJrgYv^
z@~F@QwN%(Al)`fl^aR1owF!}`ub!AimuaaZTt!x>dbp>3^+uNY0*uBJerr4_(0MDB
z@3Y}MbI}m!)j$&mh3&9Kk-jXp3p{*A$6piLe9FulA|#K9Lv_uMRWc5omB}}iZt}f0
z-<*UXG2Pf411+=NpNe+#+k+Pf;DjBTlj#dZ?L)m+zCLUDj`{4J=K<=B{n#BLd*N|e
z1Tj&l(o26xl>B{>=1a9;om1U}69L9#bCrd)BJGt7t(<66ODf{>OtyzjgCGqja4M;0
z0$*9@AkoEcI1`1&-xTv#6U4w7X|Uad6&NZ~CbJLl+&8{3vKuLvZVa@JDl2x6=8?F=
z`9V)}pWm_aOz1)ujP?TFAneFe4E6s2g{cY!%z&62*4*M6t?lEB&dVn-A@}9EpJINy
zO`P!@rppZX(9!u?nA&y6hnk?rV{AAX5bidN)f_$BSurH<UN6-*?%hh>sKM<uF#iBU
zP@nv~0nb08F^%EArVyU-mB-au2$b9ya{S+^Tki(VDHHI7ZW)LkmrY@){{Xvtz=JI_
ze=V7k08(o(v{Z57ir98|u>`|Uxfmx}jTt5|RC$hGXH`;T(HvC|XYWY;K_9uTYv@$D
zq|G;R35NqWOmJ<MwG562H0YXmZN;eXnjy~RB3ALH@Y<adiLFAbj1xb0oK6=SchgkB
z0Pix^>%$Do@|i>1pYFOhsfIkI2ml5%`pkS4!(RUY`awoXGdmlI;Kb9YA#f;66zMpw
zc6hAsQjV%HF|BdvU@W_*Gy9c@$w2Q|ApT_C*E0n3Wwz_L-4oYm74GJ}ij649054(j
z<n5U`j<VuW&CpLRO7OH{VBprS2X3Z4R%P#<o+8{&^V49yDU@RA(i^%wkWO<1VJ?n3
zC_Ogj5e1JD2>CDmGycZ{wK3|QEo3wvfqO8u1!f9#x%AuKTiOF)u)CaW){Z4Nyr|;_
zw0s(-_=yLiuGWd*WlPztw+g8ZKF+JPi00qjGKu$2^L|N~N&M0lDGNRb-%z>*QLi8m
z1>sU|?XFZ_;h|bx26*yBkJ63qLI#~ii^io>-UiQH6P(A!>$3^A{{T<jwc{dXSMMs~
zG6qeaD@!Ua#OeY}#nosrc0Oi#Sr$|eX7t_)dq8e&)=|P*XBRYJhXGV@c-4OqjeqwA
z{IGnKUD+*PHspbfM5Dgfj_3=M$ykX#h~-V^%-iaqJ*^PphvGPVHWD41VB~{)$hqUy
zP7GzdrGGDGmr@&y37#lB14PJFd&bWIeg$?KaN#3#N}?(M078<&Z5pa>b@;(lNS<ng
zOtd=@Cg85iF8x-r?D$;c6N*A(SV6tGD#ja>K=~@~JJ*t`xE?B@c_eRVDqy(n$b!@6
zz2JzyXb?Fr8#So4yzMa2>Y4;paF0Z7-GY5l&G~+vpyI~UKD%Z{yFHf!9Tr8zqEkkl
zQH2h32&&lOleRPP+zC9Lf2P4~$Ci5>0D@e6wiqp}!Vju!Gqen2u1@&unG_o%nYv+y
z$uK81eKrd;IG#cmvfFu;cD|6mHT=f>)RT<Ip3M8!pY6Frz#8u%nu{6q*y|xK9i7B{
zlS}EopWZiyBymuGf#{*$vrJizhy!bhxL<ilA2{ZLz;yR|tnh_m`0qgWfYliROUa>)
z=U92LPwu-%0wdHW5y9FXg?Jw94;mJ_$l|2?vEM}FN{n0Isrr9(!9}!PC4l(DxbqtT
zG~X<LN7)<GmiidlBq6%`z%+7g)!ldso)(vQoN$HOUgylcw3;pES7&h^^NgQ@amU%+
zp0-9@P083n=(A-vyV0uKw>?S&#c$aVSy8$0C<$xnHrjy!3wN&=xH~cPQTG&Y`k!*W
zx{momn5A0W@RV3u+7EUh*gRFc**>d+@dF-Fu=x8k{(s>O(8E56VUN5LE?<|+WZPbA
zjSu^iF*`5$PivmteU^We_s=|(;?VF`aUgo54XhnY$crIxV5#q@?=jq#;j3G^XyutJ
z!s~@eub}3?9=LGo6N{<THlJyk1k>EtZXg|@Ifa-r8Xii_sKHsSLgDWp6l4cEe-?ut
zhz_SwgdbETj}3W3F&dv1ousv;z&b5uF2W_f3bKn+Y@Ts_f_sW|nbrjCFe=RoopQQw
z;TxWYHZn~kji>0lGao~@I%)B1&gt|*<7W+{IE9)pCgmJ}3rIej4V=umi|iM?6`?9*
z?lbGNM(uPG@pUJC!D?=J4l74M^4LX|Oa<r349Pyb1G~XbykVPeY&%-lF~;-sPN^-T
zRzD;hr<#@g*4Yc=DWvk4Ot5ssrV)85uqv<`W^5hBI8dcbaNd7KiJhK&JbI#pj4q!|
zoL98=47OYEPsu&g-r^pKrxcly!4!PeLc2{7q&ViNvF^o-ykUhvwli{|?7<J{r2EAJ
z-_oBXwsCM=Nk>HZEF<<!#f)N(TvOe=e)gI9sf_N4!YQ{1;EtgXvn~kr3LQeM2ekb9
zg^&OV;1Lj8fFIjqcPR%K9=1{^PN?A_z_cck%FJ|4pqmvK>zN;<AaJ?t3_qAG+6}su
z7>(zK!i=%U3z%*$GwO$ilYUDjqe}Lg2=Xb-H7ig64rQsIgbUyOIv@`tkQ@_EIMTr}
z;wqK`%=oIE;~qfZWN}u7{{Y3MK8wWTgn8KrEEId|`)1{P<ofL5Hb>lt5zwwE<zhBv
zH(7Ux1KpnR6&Y;i;;B9V0HjK%-H&<1e2`^)O}Q=bo>Hm2e@rhIfAm>4kd>hLou`Gi
z_=&<c<$=E_*}*t@T@BfT(B_yN8tp_Q1^u1aTg73n=-gv3=H=k<7I;7hlF4-&HSG=g
z6iKU3v=)Z)s3+){*Y?V;9n&)u#}myuz-Cm*IAmqF+QiuCv#9(|=nyU#OCAp^6zQbe
zjq2X3-r4Li$LNOZoa{rB?2o)Y3p}<R=FxyUph$>m;zgx56&Gd5olvn7*9?f+rik}d
zJ_;$-)7^R(0D0LEnnP;U7=J<43j<@wB0<oqLcKF?YqSD$?hT0ZDdWDYP}QW<yvk(p
zP9~tS<qPo0Eq6k*1qpE=0nImZshb%A$1%5x9{Kes#5AJkwv<5Q)n-iWIkI5o!Dh!6
zxlEzxn~M`3BZ(r*+1>O&x^!Kg=7mrCQ3kCl-Q^;Ere}xAIO6S9Ed3g3<sD^qhEl0)
zA@gwv+0`6W+N->-fNOu#yAkRWi7a@x<tq>}j_w!yliQLOcz>OWH+^v+YRy-ORK(tz
zAq}f0I6F|9OUv>clW+>=ou_&jWd`ElFDIE#J}WeMQ@-`D7UJGZ1qTMeammrgf)HCr
z8%Wm{=vr1_zH9i5#(I=UWjarL`a(VtiV>zu2ms>~@K$r7w%|mR$<5TB1;OA@$cuBy
zWNCd$2f}F|_vV9viDCTGkMmE(4Cvth0QOc`XA52nt>HiAT(ypA39}~%<l=-yM-Pe#
z#yBEF?8oyUE2je2i9eT;5)DB;1<E_Ds&B^jS;=0_V_0&+VEogfdpUU;V%g?6jtlrt
zWojVltSp@Df8F+T3nwG5b_c~vSloj5V65>Kn@B<fw}+ZB2wm^69M^)2a1Sib!@zC#
zf;!^_p%HT)*HsvY4R&xBPJ&3J-<eK3CI}$(DCj=Ru5h+K&P{MDCmdt?uC23mjTflv
z3oYugXPd{8B##X98xCS)qM6Ip54(K9>BnKtuly{NaKF@9Fye8<9zIJeprDli=?U;g
zh=N974Em}<X3jGniN)L@NNba1Xzf)nTryT~4UP6?s1}-3+RyDqSXw*I1D?ScL1|VB
z=u2i0kewrnru=Q9!HtAYN~F`liemf9tJ{L)^5UO{jNH6k=Za_D3*rt1UC<;Ke(CL;
zVZp3$E*yPVWDoxUPCK>|ZRn}7wBO*uAOp>R;o;*3Va2M2R*`bj>1aWt{qsyGrPPN$
zv9}<(Y_yEm1jA9xsP;yB%9a|=IQa2}ixgax)8>WEb5eOkCS#)S6MmzbadkI8be>#M
zkjhlrKeQ)6Yw`a8X7agxTv7B#2yxthx^llHFZ1RWWb7nMMje^P&TEQ0y~)SZCE4EY
zar>^>N{EcJE3?$D_L~<N3WXXi<Sx5G?(xCF0Q?i(GdZr&Q~Qd1fl;F4ssq3+>bUL9
zFcIst5wdW^l|q|vj4dsZ1JxNC@dz1qytaZ%{_Xz&t-rd$`mS&lKPWm5Xb|w?TJ0Pa
zLxy6TTxSv17Hio*1$L5fls7-rN%9GU8GBhn5STZTu|3Z&-aJ5*!0XHu(*t<TzG|em
zTo+BB)Swr=&L1W0f$~|>ttX*JJl0Y^TM3rYz(1`CD{x>g=y2$yx^A-I5jcYxOn+Ir
z$O!14;TGl<F8Y|ui&L@odcFt!4?Uw6Bd&;Bg1FQyPOCwc1t(5j<(7LOHKJbkS*#q<
zm!HJ#1YGiOBnRGNX|#amD!2%<oCJ?BySnZF0Q6_~RLwroMX+%Q#@#d35bo0N0MEMo
z$DajuzRqK=3Tf_(u(`S5M%y!GCXYB)u(uTUhEb;k@#C}|;9KgBci@CdFtAdl$71gv
zX~TG3x}|A5&8JQetCX?4Pmk3daXrUIjLB0taWv&RAX`SBB3r@0&&7WszcRQJ=P3Q%
zO1LTyb?6QHt)@O{<9uO)oDl>Dy8c@ZOm}w96YVQ^OdvZk%22y$h9Buo!9<zw9ibcs
zs%AeC{x9hX#g3+Ct92|qHZ&IFHcY}KK+PF#L5s}jj_>+v6x=#D6r&*25S_ltLdMgq
z1pGAICBxcu7!C6w54w265SU#?wK8c&g=)ur6-Uzr!+ED94hpOr-uE@3Ap^gK)ZRUp
z1?{&NE0z2zEeEb^_<g0!_1;=`XP-oOO)2=o+-vKPg1_Fl{B~V5?l``AY{=4c+4{yx
ze&UOr=aOOes}sqYDcob;Rlz&+s5z{gcK(-ShRe7dBynj&_$~W0aG*O8H(!THKs-+!
zhULT_s75dd+2dyE>7phY*I0B~%PDhrA1%7S<**YifhZJAIFEwJNA9&dD1mM{CmD4|
zTc*Eb@f%kOGolAzO)^tRhcaic+3HiS);5(v(^S&u9Th=~N(?ejRYj}}HjIR5yg|Pe
zaBe~)DRZ=MR72QxY7vR<IUpLqZuexK^5&6?ngjxIMp+1*kk-uM3eN8^lcW^po<#x=
z!8L=)W;9UWG<&+b9UQ6eWr{3Qqfx<U$u#+d2dX@yCBb>E<1*l?ma-<I$XFpQ?gNeT
zI;{BY7tEogqZG%8{Sj%FwWI^8Z-*|8RyB?h&!QGp3tLgs0OTeR@C+ScX-^;aX1p)o
zY2@YM4JodFXK-|T&FH&MIxg@M-F?!7Dl&362zO>OW=pqGZXoCH+cZIunAIO?5HSlT
z96;Grdnz!OJ~iaAchv3&7(zaZrUDxQ@#!is;L_lpApk}aFU%&0#$Suzy{a_N{V<&b
z3^G48zDtUn&?W6Vj^*yG@3S)h096;*B=A%VSl0<J+!5x#@dmqG6#oEq*>6d}ediu4
zw73op-T5MOTu#H{c=#?mJ?4E^wN#IQ`v4GVy2vA(`~;&nH`(qTmLXh$w!n8r4tTL?
zut~I@p;?UpF}womvef|UFKme!JIQVwZ*$yoV|l7){XGU@ZN4QD6a%1sX9boKCO!(L
z5OfzRlTSC@6P<$gX|boO%NdS=cq_8~TX2Qi==4sskCG9|3)JlpIlx8}OPs(+zZ6>g
zjIu$kwoMScH6MTI*k%?<;j}9eC*3@jV`t;`h-)-0W@P!RxCV$F6N4&yrC6Cw?&{?_
z6yr{pvrpTp(~@U%*~V0x{{Y*G*iYTQlh`_v3cC%SIVyxvt#<Ifh&Hp`1TX;&9M<M6
z-qWuzRc6<3_LYv26~V%7ibdvg$pGGqDU;AHDks_bBlw5A{{ViwbRes=IzPMq6hm_)
z!Y-`tkksNs8vt+!%5EBCI*zkKxv6@UO|eFUk~U>GN0UiM5OXu=pnc!f5xLomOZY=`
zOl#?iMx(*}nGmZ0;tR`NVLg14OI#p{SvujpQ7v%72bz<8=S0BbRfD@Lp}dNc=hYsA
znhblXwA|&1*`nH&4VpAyRSURIoe(9DX=O%@SnR@fIxiDV-%lm{!VIk1Bgi6f4}$QT
zIA@7rg0t@jrl?rH-3Cd}`d~1PIib4Rjmur>S&Ghe2XKXz2?zS21#?Z<_q1nK6WvJi
zLi1$a`Hm18bRfl*Fj)h^?7&XEHlupCH89}jnbum2I8u@1;F&$^>A>LOb!=ce;Do9E
zr04{9eNMq8r!RtVE$tPU8PmlIK?0v<94~i#l*njruE1Vc+%cqRhO_D?VTQiM@J%MI
zI+4&E{?iY8Q*xZ>BR#IONO0=2r1CtD#$sU@Tsa;H%)34yuLU9Tb@t?L7F%Gmx)J6U
zXBqfL(*wFIPV?e^!zT3nwiHgocXogeke{+NL^HD0ZQAV50u=+g$2h-?dM7>Zc*I>c
zOoq70L}J4*j0I*@jqV&1nq1>;Q>+=0&g##jHsTJes`e=Xv5jkcMi_L7gbptE#8YG|
z&e%?nc`f1Wy*|L|R1TV_A0+XY6_j<p%@C8uRR#`dTU2iW&~lAX4x~+-+l7#cN7)dZ
z8Ut=mE-I6aCOS+DxyM&8yEMg`c!YP4)i>~)B%Ut3As{an^^nc2b@LL2?BOGmtiOd-
z%>JDfJ?GWH*L+HerBTi39uqsx&r`<3^BmKr($mBh*xJ{7)ZAO}GMN}jZMZ8nqQM^s
z1`)6s@=$2K*hoIdG0__|!R8#A0Pir7$fF}M(FIAx{m2{Eh+UnnbdB=kuCSlsv;$<u
zwzVcPtf<_gJ*4)vj&XPS3&O{PB~8g$q4$_UE#u5=p!W!|h!^U+O&SfHiIeb8ui8%`
zg{!ktp`?L{$IPzNP|iVU({*v(f(J3PLA3488h$87JWoEUrZ)SI*<v+T;)j}Tpo?=6
zw!|ir?*KAbYE+EQOg~bg$Fztu#9S3(5e^j_h)=RwX5NSZLCc~okB!!)3)whGrqNIh
zPc<?QLsP@eI2U-H;i)RCLB;JD%b7#ZWCl`jC3^sov)Ogg-K^%hr}Zu)<+D5vc^|CV
zX1%Iqr0v2ROC0fbGaE?<HvZck6`0wg<FDjEigX%XSN{Oz^G&<lH%Fu(d#Z1|TIj%Y
z@jT@X7b=;1YC6nrZIsr$u-NAXRy8<0{E)uE_??!cN6iT)z$$|o{5R;=a$Yd&^X9J8
zXRzeqz^ab!UM|O6HQwn=Va4GQY2bsqHBxe)_7lU{*W!6LK8u25U++x)6AN2a_>LZD
z_FbipA+rw!r8=8#Mcu)6${2VwlJ7*~Nf_IVa#k~t5wb9sie!9cKw~)Scm!ds8?0@b
z-hmz9*OBDeMSzM91*YD_7*G4RkNT7X%8kZ2vov^tt=w!ExYi%gY6)3lf#jRtQvxuP
zmqLlDN8?YCUL)FA-j+A`pnueMhhLKu?M{fK#*PDu76&C&uEE+cK;|v9PZY+!gWVwe
zjy%wRcGrR+@f_7TjT~p>zU-X|-A-x$07~YT$L7pRnPVJB$qmL$M-D+V5p_&O%Ht8Y
zlJR%Mh~`k1Ne^s$!8O6sd_Pp?{qJXc2bwK*7ii$`B<6r#3x7#OoIR@DA7xq^-Pp`q
zHQ2){79IL}z?J_1iPd%pI3Iu65n@LUYd+0fxt$>!zq2s_c@$(P7|t|8IwLE4x}n~=
z5jE@7tbDgZZYI`nX<35<qC2F3ZlivaUG!Xb07Y%rj#*DtlTD8i$7Y)|aZcQ2hgITf
z!hEvX%@Kk7t1w7DTLkxQ#pn?w&N5EefYI@~J1b#+KJ9};Ii{W6(C$S#nK0JwnT=Lb
zCu3#rX#?h+h&6y^mcg(O2yoiR4=tMZlN|v20va=U$RXa=i%xz>hdKSQ4q1PdF%Dt>
z04lqhCoK?RWyInAl|o!RO#c9SB1L%KqT}*FhYZ0^yNC*xc}csGpCsHd;cq7+8BaJ&
zGWZMtOz?$4%xuQ>kuG@+afetUaAAr<a@nD>nN-a2(KP@QWGZ&jsK2VL9wNoL1==Z8
zC&DBrZyyt3Ha2RUaL$^q3KC~jNS0)VDsS<y<X3^@kT@Xj>yJT_rq;Bd3}T+LoN&^n
zUI4HJCzs%$UNP@PDow{Yn(!hB`X*uT7!ScZ0EBGZyp>6J60^}a6MI1!onGJF8?$-+
zN@(9`Ba|Uv<8@AHaM#GHZ>dInQI}X*wW!_=kq~I#sJd<jmmD*y0tty$tw3OWYS1iJ
zZUfZ~=I*>I4#G#>gKR}a#6Z)MF_2#J!f~;!2jZ$gXu!w&gnbag_Zh?Dk)}&ENa6R5
zhsAdG;u*4-GwQn=1DxzV<M++E6<xq^Ztuxm)anb)hiq6vKf-AFCq^nSzfE=nAV7|L
z9Sm=Xrhw65wm%9_kqa=!{H0Ve$e`DCXkCS-+B~&(chWu|9x!ItlrnS^(_r=;{{U{n
zufvwWj0eMhyE7ReKJz%@7LNJZI2jWhQ0lX7K_O-PA!6CL6bN%3C-plok^%?OHcOu~
z^h~LKR`{;UI)^lUQ(1+qnvVxJ6_cu5`6m&?sw_OpV|&idWKRt2gPY#aYs_&gcyR_+
zRQod;7I)QObLzW08s^&@Z7JKW5`ScD;z%6~Uv66o?=f!26#R9^-}aC-avRvh4sws;
zJ<jqe?WV}?@w~u;nr;x`4zBMTx^hl-HZZw?TBfXay%QzA(={D79~8Iygd+vkXVk9~
zNFjLIBC~ck6(`dj<9?wN9ws}8(5afHX#>FORYMxInF<ZEPc*6w$jL=&blMs)_^qra
zmXV<mvYU@UnSrj{CHAtZPVbO0LAtEZDJVJg!x@RD(&B9hHLV^NHo?0FU7oQ&*iexV
zk^LnZrue|`-<mpIPjOVwM{`5NOEr<WE6EIQEP^uy0@k_R<H7g%H<Ghy)5K81#`sY^
z<DLDXMc5uEix2Fr)frM7!)0UsyYqWZ10_Z5ezHDFJV2g-1B$g!Ok^<KTn0Pjm?pEk
zvB(R!CxQ7%`60a4Jb*PYj66C(;S2bU6a7eJ`*c8fwN2dy?d2$kGLu@<%UhwFOdb(*
zZcWt2Am8q?bDH)X$5cTTsj=hI5VcWI1PF#a9<#Beh{<u-H=N)Hni9~#NOFW>)Tptq
z^19Nu(KmUPsD4FpQMvbL>9^V&r-4labTRSAkJxF-Dd~PY2#0RF_gWNh^HaZn?O9c(
z2|t@uM(brWdHtQl`XRGY7KS}G1n<RtPlc9L!vo51iO!CLZgl%Q$m<(z4Fh1cYEEZK
z)fsMuHx>#maO)|d!%$QJ;V`>Mb6i94aK*s`I^AZ*mw5%bLNQ-ACO5hU;v>;F4()`_
zO2ikt;*Fki9MG}J2VivFx>-6Ux8^t^=E0VoP%N%|6+nsVs<s+z^g&Q5zjgf;Kx$~-
zGMn8BejD6#U-+YHxxNN5lP8%8qDyiLllzw4*w2XaMBOq_%@rzL%{=tA)mJwai%!gh
zDy|m?G~6{nykvQ+-E~cA0D3I=>~H$IxC~oxbzO;|{{Z?khv1n}_{6lh+u*3N^eRa$
z$~q%&i0YgqGhPLMCBw+40m(ja2Ld>7UenLPXk`P0-BgYOI3wYz0P<cMw1>eR(${>K
zhM<s8(LKNb1`&~z2gw$UG@Xb{=9>3cd@vF$j_`8{==&y&bYBKK!ld`H(g_wGa)En;
zh_JnrKl4CKTTo@r4+UA|dy{K&Pjw^5#NY6mO9P`A64TJ5DWmS%);4(IO*JO=99L-#
zsY;j--QxjvFvtG@HRl_ZLPrd?3jklsf-5;;k5$6Rkv0cUG*0P&g__p19*ZYPK4WI5
z<*>Aw(~isR$;@~@h}pIc<@Q+t{{S#9ypRUOdWcsXc4V9Pg6wx5TQ3a~d67G9{8k<X
zqW=H{1@nHt@V|shPpqSE@@K8n@nZn-_@TdoNKTwCJ=lqtXgrggYPzX?P2rj15EF?w
z;qR<fTCO?<=2cgb2MaN1HBSfbi8q+Nkl&K-s3Nj0Qs**Se@<Z%3&;p#fB-`UVjxCG
zlF$Hsb`Nf4UbR0N{dd|lS%f$I6hBt3@=o6dQ61BcH~vSBJyW59<Wr(NZxe2TQtrcP
z0$3~>=ilYA(BY_AJ%bq<IBK4MEB^p>0;u?=knvT&1Da?7uPp#_*zdZiLuvz&X<vr@
z=6=X~E%_`(vn(LSUe7|98tO;7a2zvg@U^gf*AZ@su<Dgw=lZITXb#G4W!Zzr7LVO_
z&-Cus+4-uby*@OZDsfHt$~i+!>pMJO>R^x1KhD{c+3P^!a22I0zxEVmu5gx{ggPeT
zZ-wppg#1*iur-e5&3LK&r8Y@1Iy7-Bex|PGh$K9BhleD~*L5mmo@Ef{IFd-qg5uwj
zYg`D!F>pC2;@%?p?PfZlZZ_BlGM%iRs;Mtyaka+w8G@)W?QJsH3>v03hiQ+)BXphv
znrLYcO&BT3A-?mF05)?!0Epb8_j(~1-;KQ;bZ*AepENv8ijkpFEpTv66JN2qdc?2d
zGFjAK5V&C-39|A~2m!%O+<qk)cV{M>`c0RI7k1&zXbE2wEl!>&Lw2K`>NW{&AZi~a
zz1~%BGuGvP9y6Z@6_zAGWngdligR5CH&EXWYlo-U$?U*?P{^#>f7#iYM0a$A2-)ux
zH?jwc&lp}hFD`1Fv9NW_FT!Bo=cou@xy!EJGPNqhhN@7rUs4ZPE8O`7?hUm602Y&o
z;~8z9OD2hN<ei3U4<(Utzg<%Ufe=B|F3uQulPSf{am@n_bLY&eW}`d7EE%|P3BAi|
zHw$SynhwG2a3g1lqj(_LG@3+nP$CCZB^}pMbPi!r#KEc{8XyJ;f#|5Rk8yIE>>Z#J
zzS!|<6$iTxcyv`Mw7?;`t@z4NkX&w6%-C>QtoZEHYfKWF1=GRg6*szVx}8#ZH89nd
z*g4Qzc-b~MlVGa|h%u7L<hm!cT!$V@T-=N&kVUP{5KTIGLH*WkbOL>BiNgmgXr}-^
ztF!iaWisQt7V=NuH3lkk&*Zds%`+1s9yt_do2dfAnmp5*ML{AyONs~o0C6+r35M0B
zpQZl*<uTYAL~ybsyR*ya-`Fc2E)axNe(_vWHw_~Od4$pfYHlAj;B^+s<`Ygjz&XU^
z2ok$ZLxtAfD!F5EOzQK&$AXaYQhdMRL_2s#0;8&_hDhj|OU+F(h1zoV8Zu1o$At4J
z&CrIMRA=(t`)#q9`lz!?@i;fp4RhEx@QhQ&7DdC=Oos?S3(6sl0FvUVec70Iv{3R{
zqp<iG<M4zswDJ2fk#IaPi9!y`wZh}-t~A>i*=#&dz4iv<X}BJ*u-I)Q<WLTC1Q_7H
z_YF)%&C`|iQ;faf_0a&%=wQd~6b=eNlhlYu?<!l%iq@;X04Qj4aI_u=R0Bi6h~P@(
z)$Gy}>$Hw&jn{}P+3igq^)Gn*whi`yF{tCQr*s#Q&`QbeKlGz5(`N;nj(j=!A@>v|
ztUPBjm|o{(0;%5Dsa36h4+|Bsc844LlRqWxx_q?TN@<YneM!wWI36KgqQU$+r+`4U
zLG@d;T=M3-LWB{s@tgkuw|V~nwN55(ndFQ(83&?h{{YR?#yLHbNJ99h(NE!<jV8$W
zC<E#iyJ`?7?a;2tbF2gLRdBYahIG|Z?`bllNp(Hz4oY#FuW`2++wMR9)%=RT_7pFO
zC>`A=6W2uISnm)=b>JWJtxP&e<ai;4xd1#*L%U$piff3e_l@ZN<Zo0-Bam8C9~Jq3
z?b)kz;u8n%sL}udBsLFSnMgqIZrg*bsfS_08z}DsGtGYzsH@~|iTqa|ROXC8L0^ec
z-PmU!WS@e29fz~iYrrx=99CW12#!h(8oYR`wk3y%Oxy%BsmWR@$I*LLgrl6a`7J57
z>-dyuztyNqJ4G@F>H!npP_5nps2MEY%@(dMPTs8ekIK|{Cc}Z`v|4t2AKsitbw%Z_
z{LbAUG)DDUK8quQJr@;=!HL6xOa#$nnarXv)fj-_glzEZ$!|KC0FmzOg8HKaBcIsn
zyj|c|4z2<It1HwF#Y=%{z0`L=3XcQd)GWKBw>&4}hVnBy>~81!gMTIbRyoJbaa!yK
zTS2V=@WJ*os&CLE8{+6jC@5TbPt#;weiyQMA&<3(c(jkoga>88RVA&vU#lSzQj@ON
zwE_*~uM=s2e3Z^B>}&>meQg0$40S2S4_wg&<HSNRDN%EY8p3OyXn-0|B~qJRh6H}W
zP0Z$x?u_w3^a+Q}TcR>P#_(wvS;bYekB2askD`oR6OdJaJO*mBu$M`_wgWl0s-E%Q
zG~g#BLIVXS15x>zDsbCBXoT(Kr!|pq37A?9f@yOfOtI0;Zk9GjnUzhL+}u>O=m8v6
zCkGn_yID*U#>*bu+dnnGOyu$PKw==Cm`bSHOw(5%B-c}<+a50BoIAeM$6=pj+ym;I
zK^vq>HjFMBki&me9ve^x+rqL7L-R)6&R$_ucXx{k6JUtmqj9Ce5**fKRgNzYBXn{w
zQ=EIs41BvhkoG&b9&JD|p7X2~PWGcrfZqz-Y*<qfym+f`V2tFoG`gJ<*B-7gvzcB8
zf*iCeMJADrz+?*di-%1Yy0fWBw0Wy6rs(*tz4UU)Q57pXm^;}IB<Us*@YzI_8-1F1
zK;~9+wAko&WvUXu?-s590179TMtE%Ec7cKI!g>@7j*+gY_)md1x`D*HQ#Y!=y)NJz
zx(2~$9;w5-cmV#Qr@Mmo^inB`L%RmX*{rHGIbXeTdHEw|0Umcu9ng6+>5~qqOnea-
zn>euXb>U~-wtW_^pHvOrR1T&SaRF%r6dd0qb8~I#hx~?06Zg*fs1wB*g4~b6VQu^l
zc6}3J?5E|tjp_E12S+x3iz20FpjYiInx|H?-A`>E&o_IHaiAKM-s+rP-^71Xzn;&U
z{6SB<XZ0qxEIDvZWrw=0Jhpy6XA%b%WwK2td^w@bt{I8E^hXvO(RRxn7x9^fr|WLO
zG0>{o&G`_V9NNt9e@k9+UfF2Bs&hr!&<Oz9S8w!!<j&Jj>LsiP=D+asPxRI&<`sIh
zPNty5--4|{^v|{&jI;pL9@UfUp5~pH^JydtLhN!J*`6I2X&<4+!BH)oj$vxYkAf0@
z`Ih}8AwPN#rPH^An&RKcfiKai#>4%9orHm7q9MQtk&s7pX6*to_dU^!_Yg4Jl|#=d
zL)jKzm*lRU4qy%(I2Bl*(m!=kq1Bwy2?xU7h&jiM!V$qZkk<`Up~dE@pM!QC{B5I1
zog(En1y(Yobr7<{n!RCF?e7MyV%@e%KJt^9j9xmb>}X@|4~xht@2Lr*_hv$oQjv7>
zx-fg(L(N1QqvWOr{{W&9h1NW>h2iddzw9u)On7kW6I^L_nr>_2{o9WeF2^(F3DCy0
zO|!+l0TzLVzz+{3@SIEF$o-LrRqYL?dNfLP>voXBIWSMvW@{K6p$`jLv9BfUfrSYK
zU|);bo?w1ZjE)Fl>HbrLhIUsEqSwdGAN;%T(~|ruZXu(l+=|R2n}gGvXE2{4u1y~8
zoQc&(`CrODyBc6-jm{SO-rH@&@I?1)vj+f=@PPO&9kTb$I`5~2*04MR8$F@|$)^bN
z*w|~EgwGI+G`IqIb5vemPJ7CYoFr&de(jH!IOMAubV<bL`*jFZ@V%`tbtm^)6eexz
zpDh5KCzk}tV_ZANa8N$*{0fI^IE3#akMUr1iP_<MVEHP8-8A@)B?ugss>ulfz`Sx*
zF|>@rX||<XV{A-L-xi9a+ZL9XPsJqiO#A7ON4Losn_OkpM680KVAvig7E>3vM<52w
ziu=dKreG}4A-H(vIjq&Hc5^n>r(J_awdanKhl&;M8sFJSz#0hmOF}>499Ssa{S{X>
zbj1t06`0-nk(+n|sEiC~ZW%XSoVC5taRJlwM7*$q#zpsTm|80|R0C0qiq4zy59+ed
z0Bm&FV)j5C!T$hq5%-vjctWx6S|`a*;i$ywU=#U*rbw0AsncsIG;ucMyT`hb6=nLz
z1yG-o_aGo~{{X9Do=}W5z1eX@HNB<@WL)9Na5J*20|(+CbOD+eXgAg8iB|!DFLn{Q
zHb9d(@<$y~Zcnq(XNgCMf`#0D6I}uLKnI1B+#fj)v^T20CzeZ&!?%Gu4e7TBmYXs^
zV3xem7>wTfZ4U2fQJSCEz3^6O@jT?zaI%wxaVer^aU9X!4f$H@@ZFazNE2x2vH?>M
zs0>o3a^M{0RK2WWsQy8>g_HY4--N8r<{kw&ecxwk9~5=iVLZ!xF8$ZHuf-1l`M^*M
z>Nwk(+4**V8M~+RGT<!PQkg#yv}1Nu8N~5KzP(N6Q2M`s<c*pkjcuC2z#|UPHd@Jf
zo{D5*s`1U%A0?bzeSW~bIRt2LlKd*e5ar8bw;e*2hv|eHQHLY@a4N8I_Z?&EsJ)|!
z7WGGQ{k7qThM@>^Odlm;^MlFOLd|)nCxAo{F;tG3E+^;q2UG5iNlq>7^gv@qSfko7
zi8$)a@f4A_YYlc@=(>pR#u30~0x)=sAZoWms9Ub!4Udv(?w{JM{Wprvki-7{7va?J
zT3e}Wth`74#?d2zx;@LeWPFnYqOW+;JqjN2^*cC(Zsg;H$aHHeOc5!d2+K^CM<tRM
z&;gm?u$MSA?&Xp$8bC+zD2BLAv742P7y3_JGMjt>Fp);RK=Ym%g^^Kr2txhg_@{#A
zKLKwdhWG?IoYqdIPvN`tSvnDh!Dshu{ugL-Jw&Y$E%_d)fMTRw9NK@vvl&1<{-JNN
z_|&NX0I4u6LCt>-kaNFPP34eoFYa04WO~lRM;?g%;C1?OLvyrKD%?6P1f7S%_fvOA
zToiAj&k_3>wPx2h(^L;~?e4Cbguv55;q4!ktiGXmUs_$x5r7E_!+=`5<+nPvXyi57
zpaDfUFqao-LI6P1XN2B4jjo8k*VAn-&gmj|%-uGasUrYpXOiuQ1Dp%HH9XFjD3;Ns
zLZPNT*~u80!{n<xfp7_wEPa$cg8_Dy)vH>b^)~x57E^L9Jd}5J988R}P;jt%Y<M7M
z2R)5zhCO-kM-4#9c?K@z@eJdvCbpRTs;jV^!BO{=9CP7yR<=5|aJit^6!7Pk1G1qL
zp~*C1nZ5SJHshZ~aPsR)%H#y%*9?d-gUFGLo8OA4#|L<SWe}QN;iE;63}F@s<eq(1
zLR#Yl1s&7u97ac)aVI!!^XQ-4hYaI)7X&WT1$Zhq1Nln3L|>6o=DE2}yi<G5?&J?t
z%uZl0>CHQR!sP6#)<dw~(y2eJSvPkHX_O3}X}Pq(IT@8qQ@@00x1ZU&K=BENJab)|
zjr9P7D?zf8-HLJ=F3V73!4e}PsqQ?VYei5~0e~0<*&$k{4+v(z1^)nrxrhG%(g!^X
z;AIVI03!FYL_PPLF9lHMA3T$=VVxGPZ>DI=P7+7vQX1DdpCtb7lks1K1;rUC!E=KL
zHP_Ki$_Ei9T!hY1sv`(!1dnRk>@F>E5L||CbbOS5sd(Q$<4?2IQg3?tA?Jy*yp-Ru
zs4%mS^Zx*#K`&_ouLKCfZZ+tD<;iK`+3gS(Qv=G|2m)^^`{zC!6TaoA6G!U03sihA
z#5~qgxMq)qg~aq(xROv9%@&VQb?i0H`Y!`2U>!zFF7F4T(`jFu{?e*7@9kZ*G=X?*
z&T8N|-DJJy{dO24^1t^1fvz6>9QvR^1_(p$nRlH!FTAWQDti>i3Sl|Us7_|{w|Be{
z#+`Yu=O0E`EfaSRn{*_s+TQY?mU{>Ai1cwzwxJ03gSn@=r)N3RcK#3WsfK^%oY`(p
za2cV$?em1!H@jiNV^gXkY8m)KV_mz2pgDFrvRqE<XXLvrVbz5+gTRPDl5znuYSxbQ
z4A|ZYrpF{M+D>=(y36=3em~OUtTF?Tw#m&nz$gl|YP4?W1vHaY*oY?5Q>3)U24t|e
z@{uJMO<J#;#es!K72smJrda4jVRKI#p@uCVveAU87=WK3kDkNg1&8`pyCV#ElduOD
zNhjS^F;!z+$MOv_-<rF7JvUYAMo7tXi;3!|4{;OmD1Q%CScg=p1-c%n_b+QpoX1_R
zi<l^YaodNIABZp2woE(i!aA=5&Ig0@M{v}DJ>6CCxLhnio$uKh$3dj^)q5qCK=>V*
zz+NGUiQ$?8WD<7xi2x^V3{>NjTap%Y&JoK%xU_m%NDp{tM0S2PHlONZ@ltodq3nqf
zrxn8cGJau4iaing(9BO@Iv@`;;@{POl9!zzF3k8N2`&&mDzh<>_)Ri=*JM4-EZt8q
zyGMCUu(qR3oFzDhlZan1Q%QX?9>TN6c6+jR33G@5b1N!GyKhF>aSJ2$P9{vdZbG1*
ziy-qIG*U*ZAVLId>#&Z`M^gJwK)e-OyH_**5hRnDM(^&}Jyt~IRe;DZ7%=d-fTZB2
zNckZL8S|A<z13}aDxkKL4_V-vMBFIA*9U@rAzXZygtYuFwJRf<-eg%j5aO5*blfxg
z)6aK{5gJ)p%yh~jwX}?!vj$VL8#2`p1VWq#=fxRi@RjJD44wWSYQjLmGLB0%Rf7w+
zX4$GG?2`^}?2GJjIwFXclwBYbO`2j-*|;M=g1ChC;%F9eySgC<f|s%Mu*uRiP+mj<
z!5_YG?)iYJp8Bg?$JRVXgO=BV*EbtuntOoZE5guAIPkiIbPHnvzf>T1g$?I1j{d@!
zSxu&Is%mKCC0B6$7EUg3F>{EjF2XZ@ihLNlpyxU44~_t;SQ>qye}b*Lb1!MPqNmxa
zunU1A2QXJ<G#od8YxhawfDdW$Yv!s_sU&3R@Y`5joOs)E=Gk^yu4~-kG4ZnO&%na$
zSgG+A%~ZzMuvBZZknMo|<DBw(S#oGyN#Ly6wbcF7Ey%jtH5xBvumS<jqznW3wO4~C
zR&S$(`D}Hk>}v2mQyN9=d0NX5_^5R1+pRW~QGyC^H76J9{{W4xumVv4Qf@OpBsR7H
z`1MW_>SEgN?yQU}a9KDOlrs`XRM@!~dM|V4P@UmqbDX|N_$d;xAH2k4DdwKXI;B&V
zjsE~;1LD}<H0k`TKUBuB0Q(QpP!Rsq^8Wx(0?0WC<g<~r(Q!Q!d#XT^=ZhxyU740^
z2er+~*W`pAC^q`S!G*Uj91}(gPirvpb%GK1r{lDDZ!X?5x|<v|2WN)h`JwkuyH8b|
zK7YGmA!=Z9lmH*<yVr@<As99&OFihA>Wgp@AdHomGb*bdGqGK)Hrh!)1p1C0C3Z?8
z3}XxpefflNJn2x#K5B<&ewd^_6&x-tmKK<|C0*DbIqAtTjgveyQG15!niv4ldxzkr
zN!cLbdH(>Z9N&l@tHsA(H0Z@et}u1V>Y4p*{gb93`s^l~^^e&wxuAI3Bq3$(4c7F?
zFGR+P&;@cD>GLQ6>QordGjsDmPaccM+Q@hR0K;$ZvBuTOVB#TI(Rgq4lmm@7K0KGV
zN~1ryyC)V9`h8VXZXh3;tZ=+!EQoV(?vk^cbVf`G<c;XK+mn#-c1nY0tp#f>@`U4O
z2DEu?@UmV{=@O0b7BK#LycU;PW0JC_WUQpd!<!@GhvGIo_^2%p?%Y3Ct3z#uH-4(@
za6YIUZV<va@H~^*Nsc)j{S_zIbRQYO-$8UJ_Hl*_n$RuO$yFZa`_ImKCqu<i2yfow
zRPxsiQ$t=3{4aT+d7=DQYy?9<S*LyQH-3Ynb}?K@bbES*%i^78(QKZ98@QA91**_&
zpUg8}Cl=e5&D^h0-89$jF`j`ETG23=Do`%-Z|PM#`gJZG(SI<7-P$kxBmAt_nl?G0
z*mnGnL||bEcnP|Aj8a^jW4J!LE%tDA;Z$%laQu-E{RVyshd3=9?6RAAtwJ+}fP5B5
zH38V{J>bQlkM3HjYk`nfAzus_7AwbqV%d?+HJ~-XTdL|4-8i>K83<hXPacTLnRArG
z9wdcOI~DDCP95j^jJ1eDT+&+ok)Fa%PP#b36TLR*nlQYVGlUf=IJ1j$9;#p2gQ5I~
zMGy&yP5@vsN^pkaH`Jj4&Lj@I7|<Dyds8I^Qe5_L#+R7cOjm6%*lBOoQGEjENW$P%
zcQ}!RZpLW5n2W9|Hz~S0s?Ln!Y>k6*;*IK6j6DAU*+NHjTjrP!IuyX`k|w8UY&5ud
zSmVK1um)C-n#t5iXz2te960aE8%AoNGf~cscp<-AwY7uvjta4c2jG*b$yTdYyo?lV
zW_qs8MyaI6-@-sEvi6EjagD=@eOGOGbsg5V?U8%t5N$zs62mIhNjQeMc6&yc`s^!J
zCldpm%IUW+^;xhGP?;M5*en<G8jkE+(_tX19eMqYHf9X2zDDRH3eN8_znD%bvC#pq
z$74%PIw7z9S)ZD<?zG7m7{scM!;3zN)4uQBYFIufOkZWE0|!1KMLJ#RA+5+q6b34#
z&S3n4%B!Yq+IjKSHQ0!|t6$O(mdCIy@YphI&;2UcT-UR!GaD?u?Se_kTil_mk2L=7
zi15;4X=_i|jBl5}riw9Es%n$KH*|ef*;$QnK2i1>*NE^uVhO$7O<U}sqZelY{{T*F
z`HvqXw<fz0{+p-F-+Nw5C_>m&{vF-XI>f_%XzZa<4zXoO0gSuE6-p<gl}2Aim44!u
zU!>V*`zGS*B~u=HCI<dvpT$?E0rM%?n!+IUUegS15#9D$VmYj;(P=y+Sb^!Y*m=L|
z*{@~n=uY+!IFgB^?sp#qaWst7QhvB{^iffu;tw3Sqq^<k=XlsATr9JFWB&k6fLXYJ
zrw9600nwy|KjmM$t?3~fRL{#*@0D-9XUzx=s^}hS))2Cp9SzoW@8slsRGbH05Z`NX
z&lo||FiIm0Rynu$EnnS{*2^|HgXLv2aOIGw6KvAU`E9Um62aBW!&bB2-i@}vv})CZ
z;cc%MZxjoKX4~~I#iO|XZP4r<5t@5XciOi@bH0k3X$~Up^+AQ0u1_`ibDss_*$`dm
zh_f8;G{%UvMz`jiG?n9|@Olu3jiC9jX}~L-MQfhPhi^0wcqqH1kAkrjw&t~PO)mcc
zPgI!vuFMZb_iQJ_?LzFh-aWUOAo?nXI0b&I!1-&u){Y@Lt?vPXM8V{$VC^`KkA>2j
z<`8c-SB%!EMg~IF-z3s<gTWBoEu4DK1xykfesZ#jcSHmQE#?&#K0k;o?=lr9HRMAp
zgTsn#iLn_UG|(L3n7B^y7?8XyY`zDISK!upbAN4{*T|wIHcNgfrJ;?7^D8bp4oRfq
zZQ$n?RNN1|PD-<0TZ?;9%VEPwxs^}Z;+jD^MsS2OrK9Sb92XSBhDpg0kWDZSV5W9=
zYRqg0$#xFUjDvE(1n^(@O%OpK;g8vN8jO(+!t7Qnv(sogM3S3c<H7_17f-0p@tE1Y
zyB=z_Y2Xa}lY4ACFAD4*_<`nNO6=Gk+niNHk6R{FHGR>F@W1fTVBZ)YSyUT~gj?X8
zDYfk+VQl4XS7~$J{{Ra#1BqE0VZ+a4O-I1>oq}D7fN|9sBB3yew1S-36q|0BPVwB6
z#4iO#89c?pxSK6qg~NeZsR}KeRP}B~W>rq{mLb_&G#d8yMwkt--PKeD&pr}0M77N}
zBE%}i*oTeBITltFCGX{hI$cnSL7=yA5y?|y=Q2RW>Zw+u;?UuFX`83fb_$q|8y`7Q
zi8}TI3I;SOr@XICzEGY@Ggh6d5ziui!8<*Mx!*;m%cns2reS+TPY^isS=t&CaEM+=
z%<81gxGHDvSk}GS%>LUo*X&`C!31m7SW<2>V}i}gAe<UCanK?IXtB;@jYowx_gXyy
zOPs(t-FB8R=q~>Nl6W|dd<JBoa5uH#YmTtgVS$b>JfRKNQVd`NCSwX=+uI!kE!Tl^
z+hYF!D=K}N=KP_4Z;?$Mc4yuZ@J*g4M)gO&?UDGgW*$nbo%e#|&Zyn4E%dh5IP?j^
zpBz%Rp5hug=n>tf?(Mx9WC&rRX%n6j%!;i}r8&)pNa{YxF~N9hj_KUxX5P_Mf7`c8
zE|2Qn`s^f{5*@_wMq1!3vcVJkEJwxUyHlz^-KH}0U7CvrzlL6_yAJ_mPRB<y;%at+
z){I``GuYzetBN)mxT1Thx%ZR<OvyZ#^RWK_Y8tGahz@}XbDRJi%F>~4_Sq5lRf|C2
z9w%qzK9>5U4b8`pxmUvAJyW+B@?)OITUMNkr0PeDZq-??9F;RZ`)sg0W+DTXGB7+i
z&m?c%g5%BCLn?R~ru+DG3FXt-+U}U7z+NM-1mTCiZM7-*aXGSh0#%M7=gm0c8J{(i
z;Q6ecz<omZ8nURs&sCc$w%-+)dxS3=eyE1wcsJyYolv|-RCiuO$2>w)T43Uo1)d53
z;}}nt&@uL!&mi(>giZ=ycHil;)UOoTA7y(l`RzW)4E0c3nB;h>aYev~la>R`GNo#P
z!~pHq@;1#ewA+Kg*5ML~j`aLC?=brgxK1VB)iaprw_{Ff^5jGeK=WP-2Ya310*p1;
zwEHLaRYfvt5q}9B(8|w`nsCGcb@VL~hV#ID@;Ys?E>VO&EHn$+bZqdje0U~L6Oam_
zs7_61hI;C#HG+%oI+$IckosGb?{Zq%+=yCi_DCyu8^~7-1;^xpVZ%Hk>YscjaZETW
zhNVAkhs4gPOlH-Tpuyyei?fn`O6_ri2N9}%{Z`A{paS<>jd1YwOuQh3C}^ip7a!MU
zQynMoVQ`c~T-Ke_VV;N<)Bs^0G-q+Jnq5pZG?AiyDo@v#(Gaz19;%^h4s8a+pa2;u
z6RN7<6=y&^7iB%~zcC82E-Vgg<$VHsKp9;$#1i8Rq^w2s-FHTb+UM01qVz$ff&@YX
z1z!Q-H&Na1HPgvA2^#ZFa243vt>j&zqf{CD%uUql6IvJq#DhA8{{V=LbFTCcCEDLs
zT@c!MbLyUPQLRt`QQjalU7nj7S&AGdJ!G!X;N2j?tx>`?aVv}-hl&^jYFnxyy`h8(
zt@r-`H|~MFA@?*NQyJxAgWL{NyB!vN-XBe##fHg~P&7v&{!~eM5}ZvMWI5yLm;m53
zT9_SH^P22L{S(N=lnBp?dBWZ3<DzaCRhD5HI(|u|y{5HH6YjKdM1Bj0t&kX4GvalW
z-h~TzPf)Jv^f!93SkrFrQjW{+mj3{1ahK9NykRx3ZwHA^G~cK`A1G9brss^ZTi9|P
zG*0m!B_DZ82T&v+f(R*Dww1nUI!RQo#!3-rvIqnK!elsSekh!{?Q{%ZB~aX(@m6=i
zeCJ`mh2Or&FwycsuslLMQ^jRIn=&p6e)0bR27k(r!NgF*jpG1_Ui*G~BOhoEV|g=F
z(rOy6aOj>5XZ|b4$SA<x&f@6+^gs*0`YmJ8!bjM!9h9<vR!9c9tb87LbwIcQat6nD
zJ(;rq0L*0$=8Qps5`kAhpaVu^n2u-%x_oNTpD$q%D+UTw-C71xavr-UceGg@AcK({
z3KG`bvsUvQzQioUflYx>Io@`ir!v<4c3jmK9Gj}k9hR!7?lr)Ee`Ar^?K}QciKW@t
z7}oKPQ}8x(HciC-F32j!4*_BDL8919a@7$IX=(F7g86Yo2X~|X5uWeG=CpP%?Gm#u
zB>AZO#=tz*VGk8E`=AaQct!Pnkj>ZU6192QFN|O4n=?h&e^zPkYq*02VX8kiXg-Bm
zjz}`78rZGZ6O*z`1T>eu%(#-Ha0UU%y9e=GhpNj@B`|5SJr+}RK*3yhTx8}tFXc1f
z7L925BLqYtyNR>Zi>G+QZ_EfOvx`LRFCNZ>sd=XJJX#Krg^dpH{{Y#TTEP0O@dc?n
zS}=A##f{_hI4?bbEb<<!+Y$BJ%nTme>=T;uHCFdXDuER#7;A^L6CTr+@Xay8=P-uT
z1Z4@c!l>~MsnQ-%Egk%wEhyT(aPJ@|>av~gycE){iN}jw$pAyC;dHOVG>B>AbSIOv
zTfZd;f!$E3>+!<@3r&v|O~m(-VOL{%0^+Kd78xR;-I2)*H2sD&VJDv;tUK0#Cz|%0
zOmuq6=ir?Hxy=Vj87#qqt~Cq8DnAW4hZeS&{-Y=4jw6IO(E=ykPaus))lg%J5MJ9D
z=&JW_&ja-fIbJAF-k5u{K1#Elha(xzb1A#0c;3Qx6iFHpqLYoe{+pnC>IMnrqL(-p
z#~{3M41dt7Sb!ko84N0A?v&>SmrEb+yPAU8j6&f$X5|c<Ah$#d`{4pO<+CL*tRU#c
zVsrCOE@MXM>bp6sAm!|pNOw+rm+GGk7nJ9c(Q8DiSXxx#YhbMc8K#_cZV2GKLv{Dh
znr%8@pJ9M5*71yPK5{)(YO|A?=MyRe>Ws^-2Lx7fnkBOQV9qRQpLyJtwf_K^e)|UG
z9pa(4@kQrF;<WXI=7Mc5(sfS9-MR^s^NOSU$1}@Sp)5Z&&dWvYJcHAY&3i%l1)eR`
zaS;CUqq4q88#Dmq<BUGt3Xo4EH@l$Xeh7H1jA1j2<0#7uKush|MT{(19yQ_=V$iJE
z@O@W_kHilxLhwA7`8y<L`x!AVX(KKxS4a0*T!#@m9_F%lxGz?O;Bz+Qr*+>YVD0Ag
zJ3<h%bx8XGaM>Z)_g|nK3g2M%nQ(%Ed7g^KbjxH7jU|UM#R4hF&)G+JAG5$Ao23b9
z%pqr79xGC}XtA5s9bhEMP~(FDo*Yx2_P&VTJXiAZe{I%oAP+Rdo(432TO!xgj3wjQ
zlap2;%!RLHKNPi;$<D?~&AY$blF;q`J!UX}M)a*IRJqf2V($sCZmG22UEUS}X$l)l
z*$vBQk}!bojW+JiWQ5~<_G*F?{;2-|{ye@Y2?ph6X}pf1$772~;HhD(&m3g&l%Z`t
z!QQ9Z3t8uQU95ko5BwfmHFhn4VQV|N<Hb8COd*n};0ukj2dGg_ueDDh{U|}$*$`~0
zoD<_Y1pIXfj9LN7WG^VoW*9UBZjHzNs7Wst^Iqr+THheDsw8}n{1Je4a?Ci5hH;30
z>T1Y1O7YZlp83uNnvNG@G5yQHLtH$>>^hB_9ftk~^%*LKz1<f(G&|PZ^8j5|!BgCI
zkNHic>YO|zD!2}S-{Psb;CXnB;B-L&YAqo2*w8Rbk5{8?L<MkNpO(zS!@>dq#p<G5
z<~6=$I9c%a=u`Wi)*Bo#j%krsvi68PLtd?tklbLK&P)B<W^Z_Jt&#j%?57;4jk*c-
zLQLDqQfL9$m$ZFXZlOiL-tR!D<Eb{QTz}fs{8!Wuk~{eD**u4_poHjr+4>aYl6z`X
z83@T?Yv{407TZ;V-Buq&#xBj-<AwyrhM52WyDMHB;BnD*F;G;eGr>_~TbQ&i#Xp)&
z(*qmB=$j#8rvhXPuGQK+gf7l%0xVx}gK6USSkdaAEh0e)*iH;ms~l~;{z!P=y-bM>
zcQoQ$b0~<$)96vp64a5$F$*s7-!(EIJL$6|T<FNGSjASz(~`3Tpvlz<Y}o$j!erYp
zs)xAlhO;%<XAsPb^$M%zqYMDir=m4jLxWwrGGnUkByio$GrgwaY!~%}JwmqylAI*m
zAOOQ5j`%MCvE?a}$(aIB#Kw{!<s{n4{lSSL!qdtXW(KtQjNL_U&rn0!C)8zh{_>mM
z4Q_AqPQc;|PwZEZv(z^DpfC}?#3xHgM|kLg4X9dg%~W;+q*{4OsN&CeDMWzabdVMu
z@o-1UZy360f`(RJ?rVv<CMNw92D;##p}@4wBdeRi4D13pWLcit$s6HyGyMk3_;mq=
z;h7x`)XtnCYDLl4<b}zyq+BUI+Q6Ad6Z_W^dWEe(eu>j%mU6rgFUAK0WK3zyDDN>o
z-u2O*(C_*O?!BfJ?yP|d_JqrKL9C4BS;XIxnvcVToUmfDxGpr^$mSP`?sZ2GfIu2$
zt_>Xcs|9>)!hnpNvUn{Tz-^%pyc1k{1(7+9E8l9*5uaV3sPpQYa6OH42T-?U;gnkc
z05sZ|<98jI8eMi;Eoe4-1pHH?IV$b}c88F2lPE)-C(2OTR|Od(lAH&`7E4B$+hH(D
zBa+Q!?G7PjTo*?OIc>cM8=rD@3aIXlOe_OBy*Vw;&x>}Ocn&GO-70P@Z|1|GTdZ~<
z25})D;SuVsN|~3xAS0T`;k{QLaRE07dv|`Rjxuvq9BEN(3_kX%#l^&PDj+*yl7+=O
zkN)@n0EE-sid6%hpv2riR54edysYn#PLrU}bXgj)@K3<*c@*)~T@%SR2xcSBe!wKT
z*7Hu5(rZ{eh1AIKpP^RWC)A<1Y8dm_Hw~OQERoaGZBbE;bvFmZdBI(wy`ijVE_->d
z*4nPCPF~UcGr+FYH$#pNh)ZdVU$)O~sAr|nmtGOr9QJV@AgT(~vzGY0!lcSAi_~zE
zdIFB?MX&KG;u%f;pd&bGIcv1|nDBqJph?9x<hUux#SaO%<c+Gsr;7e_{(CQsj3e_V
zmbkbGcQxT?3~!Rr-zDsl!}A+G->%PTxAL+OG){)q5z*Qy2M}TsUOR>e%)dDDSWjsq
zdtUF5{LArN+z#i0(yxU3Lh->nyB+2Oiq}-B)dJX?1pv_MWuO$v97WK%M=H_sUKdqx
z>CGI8Sb#5rQ)S(y_*v4Q`B^ZEgR4-HCP!3oxu!xL-CB$o+IeJCE^;~^i`f(OM&G`{
zW&-~J*#Y-L@$Va*nwU*9dn~UZ3=a2+#P9Hni=55k<0f{=YyJft%B+1dhWB{_-tMT*
z?fn%X>Tb#D^WRY;2;-mGBO-n(jYo_P@Pc$u$F~&Dh}Z-jb7#Ebk`-3d-G;f`O0QAv
zrqPAY=6ET1$9c?+LcI#t{Utfu&Hw;)D=I)!AJVjAem7nw(ioQWi}2GOH7TN{I_w4P
zIXY8@egb#D%Cu-!toeyX1fP<&@j_|Tv%=8qyEPq9#fN-a<}zAnocKAm6MF1<?@I%T
z8cvF%j}mWWCk`)&L|(0s)>F$G@E+JY71?M#jxb#pOauy!1Ez}*Jn}-*;?ZwZc;9zT
z6-&Q#j;j+(n<cJ8ex-8b(Y9Xfd{(<0L)*L+a@w;sgS4FD5!}WL$ng{fBO$mZ#C1Zm
z0pXIVc*Hx)`2^DJZE$Xp36$SV=VCDOOvA_`;5tf(?+l_j9iD5$FjXpEEZsNtlmkcQ
ze9G^Ipk!ntt(tT5P*~IVbp7{pO>?Rk?x5v-p;DwDhF^ehLE=^3Z?h^@F@JTJ5RU!~
z6$j}>4aS)&bF$)`3!Yr+k*OA5!`kWVWZ7tGaq`$bf&v~H3&lz)o|+Uq7*9KyEAd%T
z_?g5`VPz=zZnZZFumCtmH9vOw{{R6TKmzB^K*DQ-9`?D$XC(U&*SCy<jNo8l>N7w9
z8b}^_l<zWDOmu*nW!V$MIZ}7Ay4garS~r|>2=4n%)&BtD7&t+2JY1XMIfdYHFC8&L
zhzjVCSjOWV2^k?`Tzaif!5!8NERF#@j*6I{cvz~SyWDk(q~?RJh{$UN{G;uADi0*V
zg}c2;O~leY4<yn%(}*5H1l(sXNbJueu({6e#ZEvwO_}gt1nj8QXzPfNsxiGY$Xuhi
z)415M3ShoS;l%=cCIV=sLAf!%RD4dr2gN#YQUU^T4g5AVSQ78sXR`3+l}qh^b4HhC
zrq@r1k#$Q4XKg3R%ORS&aBS*S1~9lZ4Y#P@!)G<Wjgmpi>A5tMehS+%pgYt3IU{o}
zI~@+C*_`ILu0*S}*o5fnojn(fUYR=b*^&Ukm?$(lwI>$!;qhIl_wJUuh75-{BXmv~
zQ@?ZNv9SD3CV8zUcxAVCh&Oj0K~TW#u{e@`Dcj^N)@5x`4Qr^4?gZI4ewB6)Q}zr1
zAO8ShP8gclXZ5V&-b?vEU5@-RFnO;QGqA*JorkosjbLoSaXPEvC4_Xksu+8+;W=G!
z?-+TrIP%y$P7McV@ZTTZeXa~)Xf+AWa+pqZM|fY8i=F;9cc!bJJE+Q4-A{8D>9x_l
zN4}(R_I`??X&Qxq;ERC-r|$?6qU_-O7VAmH9Ml|cB~dvHuaX1_n1$f(X}ixdt{f-G
zq2Y0X)p(ooMgVhZ)c&0Q1spG?^W3{w(Kh5n@|lpEE^8P>Z1!MJ#II@P-m0GNuXUOg
zPWXob4YMb%s6>l^jlm5bdovD^=d_VFD3gK0r7$(L6agUT$q6{36&j9f58c%*!}CxF
z5aD#@GN8=f3hmd_27cvMtm8iLU68%g%{K@GAlxa~MM`o?sNx#vo+$W1S>pcylxzWi
zt(Lvl{mV>{$3nHEfhv23lTv?0*NYX_R+af<(Q_pnS2#iUQ6S0;s0O40=<2Gyj*PI*
zh~jo315_c{jE;L6aGlbBFhNKpT@u&5@hP}NvmcvXo-Ro500tGSMrqh#xvTF2PY{E!
z2U}p578XxJqzzS9VRh^g_@aBZ@0Rg|(9A9b7T-16>4>;4mZOUd`NCy(yGB|QK*N%Q
zg#et;#)CMGu8Xibn}?b?anVp^31Ke5sT`F@2D`xj0CfJy64Arxj{Q2N%^HwGmW<*v
z{{V#@RA#>lsk!%071}9>zf+Q~);Hebst4N<QRaXFISImkYNK(}CMW~ECyF8lO&Ti@
zWm=iS>t^8n3UDmVuH;Y>PHV#UmY>~chy>mK{nzCh?5Tvh6_AmMVPoi@x%|UWt@kAP
zw(CkK<~AJ<!3*J;>W=5MiI5xwaXBXt;c!#iN~gURcV24dE_7K8mPF^5nxtC6X_+2r
z?=N}715OJgnNJvC;yjhz4|9K3VGvyhBcbAtPN;7a3gMJPCy5!Sw|$qCBAX7Ho+U(d
zM?kVVBOTMhZ0k9M&;%{Uz0r*2#azwGxZVSiQTLNbeNN2wa8Hou31hAbfvw#c^-iT>
z@3mJH#~8)(MfES6h8FrR%Yo72W4?>DzMooi@+-8^BY`kVze7JHcScBk+~uqJ1l%JB
z-G-RMhd`Rvuw>9a#mvm1ehYX28hs&nwwZ7Sqq$0)Wv!;z{O0PZrNW#`&+Mp*RPs8#
z->9eI#0Z`MuG!gYeo;~#@m?mm&%)E>t%?Vr&3+9L1DVw98P#viAHAVf&x5NC(;W95
z0t2-6w&qXaF#Sq(yQ*Xl)WA^!do4jeh!|_3#V`OluX)=WFDG(x9M)u#Q*v*MP!>{d
zj5ukg&lhQ_fPR**cI(@4X!Ejm;}|IpjS;guY|w&?hYU?Tj?W&n{{Un&X<Yp7gzqmC
zoQ=F7bV5vs99FyDsP5pEP0x2F1`boZ=Bl;NHftH|k0j9gx|5Sru{qlZ@af<B&>i=A
zNwp)|!;$+Qz>hcCWNHcWNB9mu)Nk}c-OvMOl$=V`;eTDDevo%)dE#Rj_#n}~F*a@;
z#Q|z?fO98!5usc3n&`#Dsv<mIXJs<{jf9l(NOSRjYNAKysSa^JAgUMlj_%1iUof(3
zu#Szk%&1a*LXF9ivosI=#OyE}$^OM`PA*YVgF~OHmWG&qFJ(Vi4-<!{R9pczF(cIq
zF6v;W;-oU=(HjL$WxL%%?6r6u(x_B!5-PGpUn8F+{{XywCWn#lu|TUNG5iQ#CJa4e
zR4q_U4k+P$SBW4_YqGMVqs3jqJp%3hovim;X!HxVgS{pK?KkMElE=R8UA^`k6<Y2U
zTP-3XGP~`ozh&9om%Qv>vg~YO2Xv7>w^Z0W*35zDvoopWszWS1<5YL?S^Rpck};Vd
zx-M;TcenlNu$u`^0hJ4YQ8*naZ#V~akiU}elF_CRwg_6*!hD47ae^S<B31W<IuO)!
zgl&w0&@bhM7dxd4k%S`^F_#h(#!fcqpW<<nooV2u^qxe;i0G=er3v69exS3PyN9A~
zSj^~U8+eApcqxIB-xwL`ba4if$wPQ%Rq)jgC=-dqdffw^r-Jd&aH!6RT~*Pgt&5Qe
z2L|}#FsTN)r+kaZC{8vQ9P?aLtkd;JH`sDqP|--I{{V61n*)GZV)=+p7+o?C8BGHV
z?hdHN9XTh>N&>@+7x8Jip!3ap1_<~nW%T{vnhI!W)jgrCzWIU4RK(U22Dxxt3K>Bg
z3XwMf8gFewh4iO>4+XzSn>=Oai(9Vox~-0o19WH62R5Y;S{YN?92DU?9S*~1Q)`c1
zQ?S#iH$L=P3xjn^=5Bf-jzRB#rby=N^*|QY7$;FHKnBhWKmwdjg5#$|%sAVK{I-DU
zqSJuo4hjL$ll!KS{(5pv52pLLbqJ6~V?Bp@p%mNmDWv1<*trKpLT>jj@Ushq1P1Lx
zK5EQhKO_jADCC4m$#7O^tUJzNoYo%H{{Ucn1FsbQVXYR9fgSurIY<tEB~W*CnHada
z*ClDUC`a3$?Yj1Dw^DY5RTg)Quo1fMWO7c#;(#%sR~olbuu71wQ=00mHfhV%3pC<z
z<Pgl09d-*Et)Ep>3k9Uz$IF_gX>Sr7?A^|Iqa@~LRKs@}`ikmM`?_u<^5ChqsCTi+
z4f>uZd!vTY_%xqW`$EFXRu8EBx+-`n)-zG<%<|X_2C@MCAvZ3{;)sf@Ypuve!<@=1
zMh}hFXn1Do?A51L;W{|oTnmdMyV>am+ljd=Q49Y7kx%~Rm->pIJ%?|Jo&(EKu))zZ
zW5hT0cT{9G+3-v)YiPg7krz~)$6?(L7-toyV3Vg+;Q|nB>Cr|od@QgCU=Fd42-<}T
zl@FwSR&H@|^Vxa28H!EyD~%8PwqIep-HF~=A!am`V0V&F3S!8dLDBs>Y{~6-K9a2W
z3{;2bW{TWUB%Kh#-r%v8<GK{Xsr*9BG6$x@*)78GZXJi->X>QOU<RcFtm?#b88qB=
zKvEsixVxvSGETanOIf`g6uGSiID&E|c-i9S-YDsH7~Xr4{ZX=UV0k~^8!UUaupiV=
z5(&H}?#xd$II7p~$E2s_Ow_BxkI5a^W(bxO^i76M+cW*AP`$TcBdP)2-A&Q?0foUj
zFqktxMD`gT)xK(sG#P=XMAwfsCxMjR;$mStdF0A)%P3W)q{&CX50XC4YdIezQ=DB%
z+oOX{Yk)qhOY9enA5EMJAH&CeOHqI-w3EtEjpGr{;TwaZrMyP-9g*hVYNKPjEp6_j
zHFDQDPth9;g0zAMnu*KFI}q;ikLj|>)!uyo8yczN4|TNjM;qzcj?{v!frZD&CvQoX
zpQ0Jl`B`SXLoxN)trmQf;9lp9Cdd^G4yxR?IJAXEl)dc^s@B8GF9DmaJldOEylu=z
z6;O-*6NS%3NVE}SnD`;=n<96vJdgy$A^a@3!UJ}^PN}UpMb8xI?^s%7YP4~SCrN0)
zd2nCLUK)+URf`>}blze(s~8UQS<`pH$XGu$uZ5g{!n-sIi``AW)FJJJP6ug{aTmka
zs%{)?1ceeZ)F--?P;p=)ThKzNSn7ijV7R(-m>XlLCyW%zRX<x-{vAaAox~!QdSJ)Q
zCsSb!yG$U$3AKP)x8$@z0=p$9iMbvn+TDfrEPH-4bxF|QXbXC;4|Lagg(QbgN{c(Q
zX%;O+RHg*u91!qTa}S-?mvQE?ckRi>;DjIxIR4AR(TV4SLa@P8{?oJHYRJC`9Tyd@
ziv=)GwD792*q$ah1>vS~H<01Az%{gBgTQKz<1+lkG`w-0;pcSggS;K5?ucmbK*gPk
zton`pIDTrA+*wa7SvlNO;nds%THXnh1cp3j-dw^so2+tHXdbItpCE^hUOq|M2FV#h
zH=Wk;R?PiYt47E7DU@{=RRCyqx~7LxrI*Uv4a*_kNu}IV?lf)HGvIb^;dB5EVJGGF
zUiRHw@$yIP2V!9^Kc_9Ef-+RuP#to2s7@{!kaHUx00K!n8cWPzKTU)HIGp+~3j{WE
zB{hv9=J;>%PH`o_<pNyegbrrVh;XA!_oRO?=D*ybyznu3D>^Kt?Q!1+U-F%boY?;R
z9`+io!rBZw?rwYB0ZkO>(-6itAhTfCP@DH|Hb?k$zw8VD0FQ^w#>fi?u*V}mU7F6r
z0;Mo~#fV<3N88P2kAvr`V`7U<N1TBT)g9+JljejTDk7UpzKdGVKOLsv!5p~k%^SAW
zS*ZNOmceGdvQJrDtVNBoR9}X(8M_1lsTby%Ntgk=f%~r2<i`C<rg0Bx#a;1Ww_N%)
zEzDU`Y}!rdF1rr#Z8i@#N%?K^Q8gcobPfS(XaW7lWZ7F_Efs20qF){m?8>Mf$K8f^
zmxl~K*PN$k=l=k-rk4s`qFhInmFRYnj2;J7Y5S(0Zn^j`6<*QsVE+Is*?^1^%c^gC
zbqx|z&LYfhrrhVB1Op3BS<h71rCdyVkJK-8?BEBgJ|m0MiUGG6hJ`_CyLu>>caL|O
z+5Z5<X+C6~mdDz?Knul;dP3_8aKE&<&iQ>6Dulg`8RdvZNtHj=T4sFDMODGR;ob?4
zWkzptX^$k)+}3st-_5f=@S)wuy=jgJ6aJ=?gt*zR3GKl}#kjvtY5RoOn=LX!6By7s
zEXhZ9ohLZ_mk<%&sBh3MTs}ytftd@8PwtN*R|I#N+HK2cHmtyO`sBO^dA$l7%pKi+
zQIT2%Q$ede+<U+b`ln*1<GNR~UB4s*hOFfSJ;TiTFC2H2{Id#hJiNyPuGWyoZj;R&
z;|&Lfsv-1<C8*-0Zr3d7Tpmcs;<J=SIk;XYc2-E}6en50GNX7qVNDUmq6(KXj`3f`
z#Jn{D93!gDRXBaqaq(1LOx{g1q3WwPwxb4qXuG?N@Z_CnBo&jYk~+987GdC!njQ-!
zgj+RFyla~TD!wzi(`ijDA!PQ!>eMXeg&XwUPpTh?(g*4pEH0`~-O`-MQ;KgWpvN)8
zQQ<}~wBY9Agj5wGbmpHU8F3s6Kf-6pID^SH4iGx7(EZj8-z=`C`Sngdh=(g0FD;MN
zc`JqC3pLG^uhnOWr$TmVutsAojBD6spq~)vQ!h=paGqJB<_8mTz^bkD1R~N6ord=2
zn#y%UvB0&$8`n(TsPfl{;1+Fh2=CQ*b~wgo$rJZoo$eiSg~e#_vwcT9W3vW4mEpxO
zma>zNMAr;dUIv$i*q(I7fbVr((>WIpqNzxi2+dF}Z94&+&Zs)sHZx5EHCS5g_)5ER
zN&PL&drn93nSdT-Sq8{>X|_%l^<RroyQ5Z)B|3g6c%I|dEQS+ClUwGah$-Z_kVI+7
zrop$xd7d3pzUj6n-a3_(=%n2l<JD^wAJpix8lXis!NfD30RYNl*&6lPt6lP5!v(s}
z*o!^K);`SPeK#<ks^4JweHVs&I)$5$!)37F5y&FilHv*AQAO1M0MpS2`pii~yX{RE
zI#vCOmg8m;Ie`+G9_&Sw0r$QC03_*lePw7+%@JV$bJ)`7KShSWYmMrevXT6})5(2D
zIrUub9ej|6&4M>`Tmj%>L00V)VaM}Et0PT>dP2|czwS-^D!80^KHCW<>7WEo;Ma?m
zc(Ps);p&8dMV6$mA8DzB`Wc)njtcEw)fWNtRwcy@LzmfUB<KQQq~0|wtK*k&!l|@B
z-Z`feWZ@cLZ)y~t1ZSFd<OWkY%&yl>#%>LYGa90A+5>$WPuFjVxW_~{DRxtY{)0ai
zoKYjyG?yI$ZU~^l_Z*~)s&w0dpkg}0!5;oADvj<Y^jSE1&TF%W948MMLVMAII^1mF
z{{Z__h9lZ>ICI`c9^Lv$xL(lrzx6ol=)J=K0Pp_*ghNZ;?OP<${{XuK1j34R{nl77
z6*{<2FyN;FC<joe;WO+XcqX0tF7Dl<?J_n++)vSH07F%W?+)qrUH(wD3SyV`u8H6Z
zRGCi?wPCV<8xZ(}cq1f(A~Fct81{}XH-!PFvuA+1GlAq!9fknx&mY}nF$+8n1Zo$%
zDwMY};l(+`fs9{<XiKQ}nLnlNgm*-~qrKu?5Q*M(NBm)XRN7PI0zrg3x|~Kws`l+|
zSG8N<0yf11qG&C}{uA(?4*k#}J=E$xwiyC!1l?Dp_hcI|Ppm3V5uKCWEOTDgY1FO(
z3$%BEX4gpQQQx5ZewQX=qVW|O$xFLEQ7m!irp(&>W)7PCmLhd;x_GA#!el#!9aGIp
zk#|H7Et>Xse3or@&0+rlPU3CA9Z<;<FmDEpV@XF1Hg91ZPt_AODjRZWOoGZr$_+Og
zPc^+lrGvQ?2Qiv=mcU78W@&NwY&(@jx}gQ9WL;<4Dh7z)#OdzjN9ww9oN^_2g@*(Y
z4XD>hJVylRGwj%co*vRJ90k{VO~m9b6S0o=V<f`U%}(=L`~G1lLGD04M54Wk1NT)4
zwT#Lkq;JuFp~ldR;E_5ib`a;DN_*b%&6A^lDmN~12ZG$H(fG~wxi~J-K;8pOu(<gZ
zj-3|_*#`XLQ{4JM?i0apUSomcz2Xra^BaVWu>rwAAlwxaT$%S@ha*G9Z#0Pqh*V39
z1}7b1HkfT8Y%PO+C<1FfFTtq7bDaccwStCjpH;yP&=1K{HE(K-v)lWpca)A#8g(32
zV0_S$`VS|F#iFDuGYl4;2QCQad=9&Utmd2~%*ECc@XH*J!BM9CUi&9bA%CZ&Aa0u(
z<X38?h0e*t6b~?}FjE5W2Lp-th5rDEy{&k>)0;2+QJlW*%eVG-4Dhs^N!dy~lUHS>
z8kYsx=&(E=0E>aDs|UGu2x!bM)c*jYuKX_4Zpx``4<LZS!-{AgTO7uNtBfGmH~ll(
z$1vi%ki`Pyf-vyuOx$trCz%-|d0bRWi*ac(t-f>Qveg@w$xgy!UI8-;2Z{Q>s<7DN
z=kdxA`?^OFCsN%IyW=G3x;u;}IrA#rHhZr5BN(bjTqEpzzdeF^C|RbH_bvme+9PIz
z)n-kXWf;JTrf}uSc9Gu2nWvdn{JJkNow)bmX({tHpmZBIDbstgMs9&&=73`wBAW(G
z2xUP0!pOG+dV+?eBX<*J6zBsFs_zR>ET!#K_Ud)uIW1GM3T+GrpxON9e9<vZwiC^>
zaI$rlwJCr0S$9DCIBXdO;Ak&*K(#Wpl8!~TSxOfs!4O3THJ0NNb{qWpA%^Bb(#Iv9
zB7yQ<oBE73S7;oB{>rUa9!ijL8okr%s#3O%YVS{$O78BBOYmB<OnW4FflmeIjVeqY
z?(CHOfPgelb_TXCeWJK5<`ix=v`z0e+cUiL=V9_j`yGcBCo!jkign|;1FTA)!>9iM
zavQD~7<lnnoqo<t?QNeVA-(Byqef<l!$-UVc^oOid-ni1-8ON8)6q7wjf4B5J}QMU
zX|a^WL%i&h%gZU>UFT+n+SZ?j#*+31yg<+xKQ$QIFhBZoS;{!5HLh@XwN3_wG@2yC
zDnth>i%-pED^je{Q~{;R!d-}hKB_JqpwLS8oKDFxS<0As{L^nmfsB4_V?%yRDil~8
zKQty1%5ja{Cdr0g*188Uj6t~73->|I@=|b*MUu`6G^rCU2A!SN%~e}b&%xc6a#Y+J
z;Me8EAlH(|Hay{f3YqvP_?}4zBCPiK*qbdpSGr&!yh_g7H<)v2N4tJ_DjPeYOf5VU
z%y7|Dz-kC3r$Ke4f^6FM_#)b1gX7>=X|sW8!rnboF&zt$#A=b<l5#u{hcv+)A$ELi
zJ(Mwx=f+cTf*`fW6pjTPRO>Xr_jxSiEuQEfMW9FT=ox1;(T;$?@6)8O6=xqtm`2ST
z+}w^FG~4~oy34o&;Tr*~N#iNYm{Ms}U?IrppdLTEcI&9MrZPJ{mj`+;5M|FJaEQ6e
zZ?FRDOqU)Gb<<=N_-V~k#XPlG*d3N8qfp%B+bT!9sk`1j{{SU<Ll1LBv(*baoEXrj
zV;mp65bkPV!XNCIR7(hH7_39DHX$2X0JT#pO+i>S?ek78Ybj(o4HFJJhVDYc5cutH
zc}d1&pBAU$rHjR@dZrn7Xbe2scp`jd6ePPFTzVTyI`mYqxyDKY)WRZSA;QER6Yw^z
zP4`8vZdNQfcTC9YpX+U!G%Z^j8cU9d5owegW04X(&;z{R5y8m*A7^GFwOT46>?6bg
z5ArDXkPAqBwXd=13cISfc-UKnv~fZqV=k!R4jo`}Rarr#!0#P@L>*S0F7A^rV{Glw
zR%JFc*??GjBb48wy_ZA*bVyWbwms1at_=b6H_?0d^Rm#8fpe#Is+kZnKsKK}v<*?i
zPc%rQQ`sp00H*DEepjX(W$*{pKmP!2S{``LsLL30;-cum3}>ol98D5Dkcql<{nLDw
zZKmQ6J%VFIZiVK_#|U)0n~)I#4yn8KDp>we_5dC!^jpF1CValZmVhs?XSSggpIc9H
zqx`J?&w-|T#{|yo6V#K>WyIL(-iok)79H1*Bx8Hy7EhOeaW%Y2)Sw&BGOC<ZT-v=v
z96<~&{{W?0D4F#O!$PaE9&==M`X`1_Z-R?_Zviw@qR#~;<Uh{L+(sf+yE6nY=DX&d
ze$`3fKTJ)v&?2GjZfX98{{WO@?lFI9ou29dm%|4xI|Z5+jUO~@Wljnp)^vVLtGUi3
z^(#qK$(2_I6FZs6Oz$4!H&laTlq&3=12if5-{h(mwEKZLxs#voxBmd?{{a60rc=CB
z9QJhr>$-RPa@K(Ni7sfpoKQHX;IAE5<9D9MZ@I$(8sf<x!xaHM%o3!CimlbI{kD4y
zYkdaILd~UABAsTn-QNc+oM|+3GNLI|?t~2>M#2v@PX~2|-&K;ye>Q#z`<<$lI<AgF
zAoN*NqyAR&oX0(<TtexM?@I<F%;dngsdl53@jyPA#Z(ms@55WrESPfYVQ{kWJoj(B
zZob7EhY{x6FLZpD{{R}V{i&VjXMc{L)N=~BeGtq*jP7Cz;<ZB9{%M87+6Wvkkkgf(
zBkHXi89miTxKHQZ*d?qu-Ow%F@$ywFmXjs3x_QFwFEveEH3m=z?6oBl#?<W3=3_Ov
zPlq0;abEHQ^<;E6M;;ouZD@9PL_5BvI7@M4VKv?-hG8ZFZSpDb=ZFNVV%Ei?WQOO2
z%#H*qSF@ha+hYWcQwxJLAZ$1Gf^acP{-sV?pO(&hKa}Y5PTyg{8peszU6|mEmcvfS
z{{T}))BMJ+#fhx5cZhIb>KcDE6!E(8Z`EW7#M6j#oi;}GMuM|-mxP=r%{J9u<{pp8
zg=aB=fO6u!r+}a|KU41TLPvJ#>Vp{rMul=r*e|`t&nBK7S8Ht-GQpKCV1TH}@i53t
zc!mpQumW)i1!N8p&@X6w5Z#;f3)&>?$p8bg6Fms#oKJFG**TRsX5ks3Rvpl)t^2+F
zT3I+Syxa_jROY;QWQ)i!h7x#Wo4CguJmpk?MCCG=8C!z}=?mIz`h@HZoAFHU+Y!w5
zUi)3>qZB!KfwI2#JT2z20l_*;gvil2&LrqOT2rO;D%k4<I1oU~rpVu^iM>M2R6Epx
zn3dzEFp=*8uFSMmaIn#t%t+H~gvpSd2Ash>c031xWWxJSSt9mZC*%*3tBsVKATw5u
zXyI*EmVu*qCXhy8DpfIYd#&&Y&OZs6Q3tcQ{{U((6(@J1eL7Dd#Y`X*$du}oo+~mh
zp`QwICsI`7aWx(ET|)L+2lp6S04HYi8uy3!tfogz6UeKN;I%XBb>XPd{oEBV{M~AE
zQla((y^w&reNfuj<hwbd`4SEBbFu@(;s%PJdfGzH_Lw;nqNr<8tion49W%V0l|X%L
zoFtW@k4q*Nmaqdkr;J#C*eSPbq?BV5@b%fsHbJU$KJO*PXE6bP4wz}{uh=71mviwu
za>gYccHFwqthyrN;gE0cyFENN%EjGRVFb?ZCz1lf0ro&tu-2UoqndkM&g|iPO{a(1
z3?<J17dBv>SF-FUsMrB*f4V+v*m-I4Uf@o1o!*z#YX)xSQ4=8-970U|ws|jRnLf7r
z85fchp!R;!_IbN~0S52tbw(WG%1!gR8$H<z+pJHZjhZ;4{6DBb;^eT?eR3NoMvI)d
zs>Gg3i#K&~{{Se9dyGP)xyL?NRZbC6&^*(g;M&cRIW&Tv9>Od~*PhZ<o(0>-rpq)@
zEgd_h9@ohTcY5r&%DY6p%fm^B7hIN%DY%>2!Fz|5mFgFl5_7z#2Q>V7I4#<Bo)|~C
z?N(jmzI!y-?B5OQAffo3vPVddqI<inTkM+|5VCNxbaxEz8ELr^L0y{&bYq(s<fb|i
z^FcUH2*B|zJyE8|QK@c@IRquuakP1e3E%mL?t>O-Iw#|E34K@bdHs-<(*^w#+8p=P
z6hY&`S;o8Mbh7rUwGHNsF7n8%)s1e?PO%)y_UX0V5zdo`s?0MBf3DvpOgfX?%5VK9
z_Vh&Hb3>T(t6gwY1Ixr>HjatVF^M9`k};n(JCDTDF9i!fd~|i!nc%Sf^Wn3sCOEsD
zf@@~0F*}Gn7xPH;w#jxH^MqP)4l08w2et`2?)59$pyq+h@Yp|z9;+<VPBTjbC!VV+
zm7MALFX2{wKV9W<P42lG5R4a87o3-9aqEAYrA_0U{_2>+S&lZ+6Dk^dmis+XIHOj=
zAPY?7sM7s(&KEAx2ZP7eQfA`=3^-;rU69$t*n*ltB1DK<$l1+e#`hNAAf&+{g#c9D
zM)F6Zk$^}eJ8GCIR&fnAo9%Frn+_iyXr0z#_@E)%%{(nAWV}7~{WfTta2jCMcju>N
zlcE>!!=31zk;Hvh^4x*LIilcBZc<-C;lrC+tlF#<8_~f!Co~xPZlnogn=7%FJMvg6
zuMWjH3=m_Yrd>-ODbR^l#By3wfz{SWX}C5=GrTw;BZ}<|BMuWhN|`o>=kQTVNS7S#
z$yRfvq#H(Sd50$j{NT~^QMkhIA#`vJ3D=@%-=fzNZba~u<IQ9dvxWSS_8yFLK!637
zxgtRc)=n642tYZttXspH13VYm<eP=eA&(<oXtXvk5S<p8BnzxFo+gN0_Bgb}N}%To
zc%=10<1j*}B#Rk{LD(t?#TJ$AHg>(mu5YG@%e~*-IJLER95b`5jKLySt-q4Y7)}mp
z6Wx%jvXh;<1BQimngmlBvi41JaWF2P!pe1UwBHerqVS8AsxTY~o(sp?J4PWK8)P{w
zi3!-LV#Up;_D8}45Lw`Aw{uXJTb#=5&#7MDd80z6Y0Ch?J`g^^*3zo?zi{a;9@HF@
zT>6gQbJ~0ostS#Jf&9Waqad6v96?2t>YMjilM8T;3s+yt<xUJl`DnFMebN0%k8`O=
zAYS}c2Q(4|_?6nbI}41Kx!U<A3caJ>BF*Ttagv1ZCQLgNbXmE?pFO#wlS%5VPMs5P
zHrV(LVR13RJQY=<f8EpPfd~fk)ifMO067uOe-KFG0tPtj3qCwLY;~T)Z1;v)s$s9?
zs?TU9^KMJSS9pHjTQg&U9!dCnHCFeQJI+4nRKo$S0zJ?^E3@HqjMk`pMsH=9!c~Y3
znBxlk8?!e!d^Yi1O3H_6r&s&8PzHrZ)TdT&nJ}L;Nyg4|v-wS4I&Q>#_-vwYnbA~p
z>dx>MXiRG(MvL2u06Fx-$vRF-wVl8Ki6!CL^4Q`xkHjx*VqVi&f3?W<98~jf5ApT~
ziR}*a)n)h11oCMJ4|YDN1NWWdco1%(o$cB<4HU&k!(4jG1He<jsZxX5Cs|r{X3t%k
zQ#F~%?E4<8#a5}P2jzqS4|_-hOBvZ6@MFuZW<j#8f!(9DjBnv+RfaK@NfJ|uYjq|`
z*vK@ayLk4oXsI|B#9NXhzNqZ|-YM9LinQ^2?ODy$w?lD!(~sR8^4?Lrz-{k3<$Lx*
zTI)F(MoTC*pP?#doBsgmC=<AHN5u1Pi}}L-N@2a55ZA;?&R6prDbjId6Gh!)(N5uY
zH|S3YKv7{je)^;MOq12=#6XuGL3=DX{UJOtrlFE^&p@kTb6;*T#Z(m=-<{_2S@Bh7
ztTv>3H&)+275vGRz+NK>ISI_7CfG?r4I1xf)qe)HI-lLTv~xbICW9O%!A5H|A{y5J
z0CH$FT;2V1PY&$v62tnmO~fEePFX701H3QDbSjHYylhok?pmi1m>x9ZIx2#oX}e8`
zK<xLi1?`FRM-6EKqep*~BCaCJ&gr5PG~5yPh0&UnDY^1>Xv`I@1M^P5bR5?87<L~8
zO*x~4w>IQFmj!a%cn6YeON}?S{GP7oGXVajkf-xmntv5LwvB>DqlhZ~z!cp%;Wug7
zANywPCw0KtJG|`gbi?l;@J<yNPk?3<@YQqNTo1A`5{0;owh%g_E-nd^RmH~=4J`!O
z1mYYkS~a$-5hA7a37g%?jz%*=Ta%b_DTfOxV<)YlFjaMCoJM>t$^!D}g^NTELdwXI
z!AO@8>t*5;o$6De@^OEK21H2K5W790!5s5I5gMvZs>ZxaNX|lL09har3d9DKBNgw>
z0PclDaj@sbm$J&0S2x!3iYg`~laL%r!-=f>nvKcak2QVcBqN^{P?~IYhQ6yjFjFs!
zch5x0b4Ad=14iiCF^YW?M)4=4sP9?6KQQ1ENILRYabDAm{FL5U=sawO_7WS{ikXK$
z$=~pe+-^B-U<b3%_~Beub7ei<Qy9mG{e<n=vEzqe8{M2R{-}vTrk!Fbx^olF2DXkZ
zPwt#9!s%1u!YA5}39qW>zoc|j#VSr?piGm8qHuU!hzHXcd)~HLgfNz|zPYMW_n2$A
z5{_9OikoXO!r}SHR2=Lrb}B{-m`M31kWPCvtR%)s?rU6Z0pP1ta2gU$!`+IMgq-5!
zBfQfV)NxXbVP+6{oi>|J%Vvp?-29YVf`npe5$m%}7P|&QPnt4{19WDxi`!_)!QO~q
zW56-gE%s;*ofS&Mqe1$G+Ftn@Q;6#&RV@u>czD@WxEPtBokqt+Ie-U==YvxqP^M~k
zO|gy%t!OO{3}u5xfgRyT6HlVCcxhc{p5AWt+wTpT#V2|?<u)<)CsuB^56AKdeg6Ps
z%ZXvxO#T~m<c16Co(s6YB;0O{{45?w{o^pZg{FRp_?;4>8}eng9|a&jy9E8<TGINh
zS_j5<8-yX+kK1VbuFS_g^MT<Cm^6SmMv`<?FuB3H{$Y|E>}DH`+7aImYxzQJ1*RL7
z?!B|002i|P@?HZ^6MXhG5a!@L3e04A8|~0;mF)Ib02Xw+Tbb$$kw+}~h5rDDKBs5g
zz#T0-+!hv9jlkyNcgJB1-)#Q?gim<-P`n+TlHtwBU6h_%z(7NCuKRrMN0R<McX|)_
zN5ocC*@w|<S7b|kC+LXPvB7v=3tiBs!4~SRS~Ahg-!eRx{{Rk}u;PjF43NVPAsJU=
zX*tgS0QiRJ%SQM-Le)okSMI>nH&TV!M|AvAB%ADYyRi7?>!3wKY<-nP_ka+`aw{uR
zsrs4wtguRK*L?$q`C+<wp7ql`bxj^7lOACkf24cAw1TpRpGijXi23Xi8YJ{qij8}^
zuAYd?TfWXofPxwU;IKMd1*KDSsNZxQ_H<Q6S}waDGw65~P`SMGKvZCKWC6CR@238G
ztE(;OH-tB-oL|$baUIhOfgbjVz<#_6_Xt`Qivx?pfXw0%3R#R8Va_?IxMNA^PRWEp
zYu_M_dLRv=>CT)ak)c3$bc3!;M3vfcO<4k%pNWLlv<iP8HJ!a>IGf62L^CL|12t-4
znfcinx-%4j+C!Yk1oasy-f-N}%A085BvPv1sy+1+?K1xW1-R~(UaK$;4ynWtb559b
z*PBri14~A;#3JAv#*@thiOmoXL}CrbsjVI331?JJ>2Lt#6H8vw8*GY#=842PrV;sq
ztjnCH=rz6A+#K==m>d}djZ>OH;$!B!GtL^44ti*$9EmG?h|gpJix*OvzXS(#DlZvy
zM|H>16bqY8rN_|{%2jf1B4Numl06rSXBW{JaP%{wPH1404YWcM98vYnS-8ZMA+cyP
zDdaTC5yz2Cai>%RI@@cAdxS{mqNYIO)n?P;(0QuNE-jg-e^e}TE8Qe|q2eRryi&h~
zLYrQC2`E6*rq6BQbXO+14JX~tV6Y2$m56gZ^<Ebn9w4d0I42xtp)4FAtjG|rBX#uM
zFK1A@MGn!~-Xn3_<ET`eK-~ehw^c%@?G-^7pAg3WRaL|>w<X>d1YLrVbHE2UUfY24
z&3oS^k#UWKb^tJhPtiEF&D17$l*qHkiL#!`^xSM)m>kfO8eLYP%1|=!9^ctzvSJ+5
z^F-?oP2K?68@{1;rM3%g_V;~J-tZuTH?Y{qX~{PeRX7v9bSg*YLGnz>E)*CfolhW|
zDY2p&#^{`O@lM1ByFGS$Za$6J=Q5b_RKqPDcp%Ev#k=EnGyee9{^*zZq@E65%Rn6h
z$@DNguNCZp^G%J}_1o-#-$5I6lj*Yk?`5ple{8Bwz|O?Ryv2cn6>$(o28qNE6yxKQ
zh=ALw40EH#E$N!7S~U;?CufGK?%p`H%0Ke#sfK;wV2lHwn$HjlaJ1#KOkeht(caM?
zRdx^@Pcx>#HUW*SE-pN{rr>wxuwEP8zSk7jVynJq;G5N@1Be?95ys&0;oiRmA9n8T
ze=uzJdm{$JnJ$llAHfWF>S8-ZjWLoQ4&5jFHr2w$Q=8qm*Jz_!v#!$C-GJ$AzQbt|
z?A(I0#}@pl*n7q~pWG8<(<Y-200!_z!iRbN=ww3RQ8P~HztK9Vc9sM#)(t>G?ws5|
z8C1(A;6x}BW7nD&#~}%Es`&tPQ8cJAhVziw6C(V|%s8ycUAwU~(_m&Cc|z?pPT^xn
zn-k=_M`x)20CBJ0G1Ml8(;_js!U6(WNsYC^$C;g)l^i-k-Yjnzuvcyy9OI&J?rUCn
zJ3@pLg@Sphuwf`k6&)7N@;+$dW5|$rl_s59PiTwc4{eZ#zUR$p1JG=(haOGh5Vv?*
zJcKR*W!<_{W`$>A=!~`3W3(YiD1hUe2;FiUuQwbW!2#jI-~{BFKqTfNOc!C1(L6im
zo&``XE3r_!NORgiCVbOaTH*;ZG*u}Gzv4IOt1+yqTaYBoTp;&`G`N!u7D226uXul^
z>-dlPp)uM3>Na~c2^~UU@3GGeQ>tMc3icR(=n#N?BfH(ZEI+z%H7T*UscU`+9no-j
z^Fnvq8xDfVNhpE59^x}Rl{__Ry93DnCpGSURzYx29qtlvjQ2`piWHwv^1?O~hft0R
z&i31voUApdjaz?$cXI2S_)x|h?xC1K977L9lbS*;V=1w%E_sDl6?$iOZH<FKrxe~)
zc%c6PRhC%3Xo)p4P+;hoLkEe$J1n@T99T8X#nqmBHX=CLFuY)h;muU2#{$^GVN?!p
z0?>XaxK(3i^tp0b@Yyp-`25Q7y`soQ;PcpEJ^@Au$b=g5_TJHbNlk;rI!9FB#1?WG
zp~6{|j;fDz%^5hO%&e20xt9byDBIbRc6ek;8o&yp@IYh3?gy%Q+;!Wc1AVe_G&+4%
z8ZMc+WzYlef^pxmBbAf!OYnkG``14Fn%t34v!zMy9by7EaK?JMWSCtoxSa;7vtHK=
zaRY%sxXA9fgE=EOd`F2W+_A^XQ(8!-=)@#i@}6;%yyq15XOM7|V)tFUx^_bqGX++o
zR;z{vV048=(E3hc;dCxZsC@18Rv=kwg7G$+9DNrRnu){ha7Pti;#K+uhD&D{5vt#7
z1P2Zr3U($)Ej)^*;L~7Dm1f3(BGZWaBQJ1ZZ#7Kd4R8){w#l7#iM_g~Vuz8m>L6@8
zqebMst(xwE#%zpX$-iNR$B<8DZsC)FU9wP#J`tkyTxVv3<Eol;h?cY4snBQ@Aw#pb
zDq~;OvphJbqSPPwUhS8-i12J2<DwaocF%ysaa*&m0GI|QL8?yjmsEGN%ZXJmv^P&`
z*GKQWDZenZrBD9Qy|w~&dF%o!)?gl6#O%`vK0#)M#%N9+$yCn>8L7ikasH6idTy~C
zmYV`HOR$ipFxY`Ew**|@;k3Iw^RKE9*1!W}j6kz(H`N+|whk7LigrA8ZP7Fgbw{EO
z6Di@?sg}s(c7G|q5h@~^gZ*vx2!_)DdL!&Mul8-C$5a0R+*O@s!_iY~MCos^(wbrU
z^;U<3FW;7d)WjhTrA4i8M1Jm!5(Z@G7rKbU+9^1Bs}{J8v097$hvfr=<f`C%yfoYl
zPgK&|@J`<EoEkiZ)~F$1;8Bw7$^iEbYy<B+z*(<p?s@qkm8vvn`n=Gzs*>i@$K*6x
zgntf^j;^p@?k<T<0q<refaxQuXvg9+{_;og+3|ADr7Qf`i?hBHx1<)B=MsnB(5@RJ
zm(h4YC3{XKHU~RTMIUm<K^k*g(^KY!q7+UKs?WYdl{cSYrt~@y#3KcJ0M=`|W~76Z
zaYUQ~Zk_j-d(0ev%Bh2T2<6Ny&m<FpF>qJhzlh7xcA*%t0tDT5<9x;r9&5jM-8&0|
zA-4!teOzE~zyqKQ*>iI0y`}=Qz`H#e8h}U0uf(Ke5@JAAoLp$sv~lLLFA|(XvCB$y
z_ue7-fL_~4K1jM+V*J+>H(Ref)?}{JP!Q4_&t+1^@GptR%NeVt7XsPpyC!Y$;<WLy
zXfo{3`}*{%3wu~bds`z=pCRqU-9wt>$a@=csOY<6>?DVU+UeG7gbYoar`kMBKSb-g
z;}%~9OyS0I<<U(nCo^Ok<B!C19SWf45J8M`EP5v64r5D(NS@)%x8-*PcZs)}%r-tL
z_uzhrKs$$&h(NiuXkZ|7x6v3ntVBAHF1AE$I9)b`BC}Wv+r(j+2=8!&1}8T@nkU}E
z5d^T_I4reZ9`Y55I;Y}wAsBdHXbxv~Tt^5_gF2(I!+0ypZWbwNnN5d#d<o(cN#qmH
zdz%{D<TY8NY2I>n^EU|d*`63gmo?|eCV@(g;Og!QH=jFws)BknD_IFAD<=po)O^!n
z;m05Vw*rapM-OII@jJU5&WrJAJ<vZUqsePc-y|fGfZiBEI!>!1=si=zS{t8E2-tjf
z11zS|i&A4%n~o!X7DC}>az<MA2H}l3sj)esZVIP&g@vpfT;(zoh<9*KK8Em~abuEa
zP6ZbBTFhzZi;c=-hz1Mu9;-6r8y&v!6uDq4v_CA3{%Or~xwJ@BsJ=CGFYr{{$3|^7
z_^R#xt<`Zv7dVUPvo0rZ6b!8O*e=%Opp<S5qn+04c$;j0bRxnf$DmTypBEP>lFUt>
zEz@Ru86Z??dC&>fFm|5OUQc@O0|`FMQ_I{<$=jx!mP7}RQoYsgACik$?DlEfskXqI
zg`7$P1>EA9Hq47E#Jjn7cka(cpL*I~AO8U5Xjg`Cym4!nf}jPRAvVB}PuO-Jxf$g;
zpg~RcaO~mrKyUUs&G<_322_K_+dNGWdTlb-`04UT9Wc)F$+IqF;AwO=2|5lcraJXb
z`5ANyA#XV4X-;KFIfl(3;B<`PJO2P{+DLCltyhL%TS1ckD<02<mTe>*g2CZtyf0y4
z^IpTtP~LN(F4f%*&xMfO@<h5dJ4|eDlHNs8#Zki%B#{z{{B~!=l=nKI?FFD8aO8wJ
z)xJS=M1e%$Y5ggo0JcC$zttWECjv66-)VW0jyV_68IKCl{{SndvGm+59x*^XI4nKc
zr-`w|#wkO7nD1D54A5WjsFLP1<2ehNRYgH(KMt=$Kbj&bG&Fyd0cn;!ytyfTI+vz;
z=y{`u?*nbYqa{-v8k^LJ;ls%fYY{5&$9$7XB)}X|Ypc1lf_y9ZuAEDk$q@|Q`YJu+
z1>L|VneNTLNw#%1k;Y<GYIK2YVtIip59(I}!2#Y>03TEYV~<qPCsj(2Z)c_%hz;Xt
zy7$&(r$PP=Z*=fHkcSUKIE3hPaw(W=123bPJldaz(&l9mbV!$y-b&T@&7b~J!qHFx
z)M$)7jm8xwJH9>J-KaAPnZyf#Ivgp755A4<%6X$`$o!MA3{q+H<gOxCR{-)0fcfkv
z6Ue4MWnH*4_(E|EVI&aQxY0T1s6e|X4xwlqeNlE8_Es+$@p41N>`c-R`BS}6G^RCy
zqbBiJZv|?Tl%POD;lT~g&q(|S`B|o!O(6(#OYr`abX4#7bXNP^zKVCrsOISe#gmLQ
zVr}#Y;g^z}NY+NGo+5PxJ?$XQQWQD{3WWhtuRF|?;`s4XJ}|Kd005NCeM}%g#qm~Y
zZKIC`PMB~yH8sC>!cDUYp}!>8PZbi{O>Z1fhuu|@!kFA`Z~)~q^<Ec=9d$<5LO>S@
z*lD=jXql2=e|RvNJQGMG$4`eu4Dcv`1riUQ!(`k%#nA>1N%tCY2np3SxNr#(Zi=C3
zc(`qlaDEaUi$H&Mav5NxZwqlk_=Ea|c?;bLX7Et}ymyZ0uh`bad{%*E$W9Ff`M6mK
z#P<%1Il7pr4iWj<Gfk3l2LsT`cIb0}D%%G2iSs};jh*9})RKfU(7X&7-Q=4%iMLgg
zMi7s8Y{0S-7}8+~A-4)fF^LcW8}B(ZBQ&Xq2i`nanwc~UMYyQXHJCD=E!B?jI3t3i
zPA=*E{nDCQfPLXQFlvN$b~)6U<@OH4nLjPLXuW|Sx)T>#pd^@*h$iEBV)>zm^rsuR
zhO`XhSxsvpxMU2+nle;<(@TW=+9FGiXJ$mluGbngH20s$se`jn5Ca>V$@h=h%p<)I
zYkFB{3*1AP*)vk4e{cMz!+t*%7`VIqAU|1EON^Uo8wO2z_$=MpT2K930p4VPZIGJ1
zk(5iiIhmvFO5Hx`KP`ZkmyAP}s9vM$d<uys(u6@X*JmZIR=4}Cf!sB5`a;TVfVahY
zBW(u~vsE6)xE}5`{HTuh8BBc;Ev*va7WLU|PICv9kh6Y!12;#AjTKBJ@yh7SpW4F@
z^xb;^ztZ~&1Y8iNDl|LH=d{VxqHa*}Zq(`!%7eZu?=jThZx}_a!%UOCsL@nxxK15M
zBeFKQv4OWOv9dO6amnBrWxY6yr`3DJp^5IC0SPAMR1X!Y$Rj4bs>P-V=#9~fF21NA
zc2kdeMecPm#ik?#W9|!oyJMoAHW$2%;kpiLn`u!Fm~L)AnCx)I%Q2DhSx$a%vJWte
zu#NUchlfOOzDgim)TI4E11^Y15QnpKpq?q%`$@G*{^;PvE|#2HqcZdV00CWv(LBIk
z!>cKcdtPP_6iz0!$I291h4i}ip5nY-*X)kvA(pNuK%jTK_;?K$^BeO{i9U*VUOuRY
zP{F?5Jvpa$lqy}1`W|}@8m;^a-~CDuT5gOPu}mHF+Tk;I%3*7UdLK0+n{MX>NNDhZ
zqIh;DmUDzR`#n)vqx9H?IJ?d7>dhDyBM9Owj{PHUh(J2<DeQMcD%hI{&Jsvo<m_~Y
zvhl^)s2Bqz^3@5a+A2XDE^e;V;CB$CahQ+E0B{I<XPt_pf$x^F@?ae$c-s1b-9r9j
zQ2rymOu`!)30PQK*cfeeBrxMW8oWi07=KpBy8P8lJEY$$C%Cj(x65br`KMqHf=|1}
z8E6fTkfQ7&=u2_i&0CD~PLq_xCYLmLu$7E8uN7E62wzt!kmm3(Pz<W!A^2?QS{11%
zoK*1C_#i&|gGoH~U;K9=rN~4&m<M^A(FyWfRPOSG;LCl-0C+1ou94ItF|&R-5wNQ`
zp9&r=(>z;bY%)*6;Q)@DRDIyyYN!|@NhTBA<^gi<J0}JqiZhN$^(wTDc={%9U=u3G
z=#C}~$XL6!UKlr=cr4yA4u>T;rpSNFgJ>Y?6Jf4<ZPjIXUhuNXt%%zMI(=2$9}*R@
z2n#(z@t5L)kK%pcBbqTL9^Pvv)DBs;t}iDnp5Wj=R*LsKD!3Yq!q$(;WH)KF;yMd#
z(uNWJOE7+0J$Rr_47ho$Eq_e?5WP7LCnk<+dzyAG_s<k>J3ox_PxM+AbF<x#M8aXB
zNYd$owl>4YP6MiQsxUje!hCIZSNm4P$5iWLnkS0HT^eMX^2y#3z2dUhV|U{}bZv|j
z+5nG=_jU~5^4pjq6*oV7JqJ>Qdsk`@ai79r<;7HU-cF|>$wu?by)E_)wqz>0dKi_c
zLpGr<$~qOg-$YxvtFrY@#&Je3WOGE9gL&Db2gdt7iZ66=(E~2+Urm`Zu>;IX0Jg7W
zYaRap?14hT=MPPpjM0wjgU_3U1?={N9J5~dLl7@Bb#R>m3MUI*9AlPA(^tB*>xkT~
z2^%chV(wlMklH*9#q??oZ)xwqdCptJs^N@#F$Rmg&QPm4&2?!r?1P`GbP8_E-oz$O
z!*W0#vW{gN2h%McaI8)mf8BHPLt(of<~Vh}-B=d^@lZ?y8lZ91b-E!vs;$epBP5%Y
z_Ln?|%TAGH*`c%uX?vZiFq{M1Rl&!IHw&|p0f}KvI36|8JVgEB39#0795OkDr59}b
zz{W&m0Z<#GK!A&bnkLD(TiR*jKdrWQ&M=_J!}L*mqToa15TK6oDvevuro(>F>;9H#
zjzn1-09U&2k|TI=&3nj0Lm(^nZNf*zHY*3>j+!CdQF-qTGVSZ;nf{-%<QmHp`i{qM
zgAvPR+G?2}vhd)<JwL)R0Ky<Lr5W`LK_026yrMUJ?J8X7A5y!fkUsaiZWiM5)q4#A
zd*NjZ`G{QHs+eh2Jz?4u*{C0a2Bkfg@i$PnJ3|{3>T!(<2NPX%NClzO(<MxHhL3o|
zhnP)pWkKFKufHfjm=bv=_*@U7@YN1@=;0~AG&)P&loeFduT7!7=^L+(DWUY9BNI%t
zgTSc!z#I-}n;RpdkGmjGRTy<tm|LdkRLdRtN+sr@ZeHj{_?%zLOe#il4<9mwg4``L
znga+3T24xT)Kdox`)y6QblA>Fk-d$l^<5v~Ge3A|vE{tNs^?<N6yOWDIo({%F+O_=
z@ouma!00x#33ZCJZkuH9*%KkH26+T*K?~Xdwc3LUq=xa<6h<QmG>43eGfud|;lTp|
z+q0-#6-RWH*_it67@9?=h_a(npCIXj=ADL}YIp~j)g9)!^w0E^%B_ao!&O3{ZyZU1
z%*7xb6%Y;1G3u#}!(uZ-1xSE+^-Z%Wl1CK4JLclS!BU3|u;hhI5PUhxsOL40u;D71
zl7;mM8Ky|`Rwy)vI}=?S!;-4g@Jx{2kel~}$3d`KAys2o>lPE3RItOtgZT4J4~(9C
zKy_FKdR=R=z`<d0^G@RuvDwY=qV~}c-Q2~MF4*zS%?lkF%sYKH=XyZOK8sQmf_yQ>
z87D4fNjz7w=P;j&&YQ!}Lv5FE+^RL551L_Vrp5=)6fY)5%=hip$dA0Mi1CJvj+0ub
z4&(-iW*{#V0#7a*9}UHq612%~)v8O2_`%5lBJ<a2_9w`%2y9A}Z;Dk%3eO`P41iga
zDnpv#22k#eQ6i&9%qO<3B5d}ch8tse8t_vOEtuwjV;R{?3DLNX{tLr7>UQSb$1W==
z2uqG!xvvwT51zquv)mg6!FDaL?kSvs68HX)sfs<AI&h5N`p(Q)Lq=;L0Q*^rgwIf}
z_O}5Y5R;TASaaP}gv>R>+tF2Xz$Q3(4Vl!YB=Z*8IooEPmESP3HJp71`Pk?!EqEMs
z`lAJ{WPfZZ$3^a^S~TIdM}1M#(S!V~{{Ru)N)URldo?kt7I57tZ&j%?$-nZiX0!W>
zf)4Xzf!VE5@EbQ<?-Wf(d&EOWL`CSe$I)s)Oar=aie^KuJru)xB{^vKqEkfFW+xs1
zE(_mML3of6f!$A-a9Yd#RolTE5+!QFWAyDF!%=7y{70g1k4`|SlbQXOvq`v-#66yK
zk|LwSSkofIMODF%ej>nkN$7_24Ez@rTBAdoa2$B4B?x<{h8}znnywJ%eHNu?b`0ct
zpbb`i^$#x-Q-W<;gb9JV6$aBCOTKCT0UT4@1mvL5?G5hg$<UnqkRUYO!uH}6hB3HW
z4t+{<T-Uq;1o1%O#Bh`>+76UR7GH-`eVq!;?<eS-8VhdXaDmlRWp^5Zj;+J=rxW*t
zUy{oaS2Je*=%2$R?DvtmXc75!P95FcT6)4cIqdqZfhWi#V=N=`oe>?~CYLbiop&4g
zrwi(|D}c!h_m^6-Q$FdAg6YVh&hnd@Di6%tgXXArG~V2K&LFHou~ee1F=vE46o4Gp
ziy{?G+F&qShHA6A1N1?rpd2$)_ndI_wbz1;UFP!954o-2JjP))wP|Zj*B(ll29e*V
zGXscOHjyrsqlolE2I*hQKLsyq-aujHtf#q-CNRG*RBi>=WTZ&q)l4*S0SGbXnCD9_
z08%%KbESYB_}v+6fOAhLT|0S#7*7rfute$4ZQF4LRSkS%b}XgI>~mi1M*?6L&x*3I
zji$H{W*9AS2bwE45Qv602Q&-uy|{<_;ESZxI9-w>-UOm|nIF^^Y{=wq#Yc-p;+vK4
zN3D@_#xn<2^iH|3B~)vVwE+_%?K}!>cA4=D2Pbba*hi3FC)FgZ!v<Yd+c_hC3PXdz
zw*rrfnU1JU&^PS19!tjBH}g%AK5CpebIn}VAYa*p@;Jd%g+MYRb;I{n$rxp{$hx-B
zH?dDUrf(dG+fl?2*8!J^N*l6p^jQm|ylONLsy0py=h11dYGJ}b<IP>Jt4v45XWqDa
z7+GvNfX4c#MoW4QSt>@H3pO$f-3!EfOb;Ng-wTH&cDs3pi%v?1dg`VkV<cSu@$pjp
ze`Lo#9zhN_w?&4XGj`)45#F<R22;^C?Pd@^NJG^f+Jg}NOUCTE1W>-AMqq$ZRG#+9
z7DkZ2yn_fC?*&EfAC<H6QZR_a8trVhlO{5{9z4<6A9szvBm<kP9dP9S;r{^2eaDea
zA>7nODnyv&Wj-t*^}o|-lAv;&)0$b18qR;?V4AKS;pK}Usz)Z=t<H-gQJW34-1Dpq
zx}eU}+I^~q^x67&r#a4Zu;QI|_4L2mrd{NYGadn#nqXr=$OXdbyRGd|9md58!VA4N
z5u!WHt!|DV;b;!sI~7&@+Mg!uJ|IHU2cSoHwXwV0DrT%$;?w1^*t^OtbB8w>Q31iC
zG>$t6xCT6s1s(BFv@@;c8`1@*t|EXSiBf9O5b>!2H}4})X$u$pR{Qnf)be;v&t`)b
zm-M>1b7_J7Kqdeh{^r~U?5xck6UtV77I#zQO_w^CotzV~VF9DbXt?OCa+u?5t~(C%
zp{^k?xV6{jLTIVls>gcobbG1$%pt&XSqI@A8)V(y&qo{M@??en@&1h`Vet^r{UDWO
z3%6}HPKL%p%O^gW1o9y4(T*ta*be9}^b0mRe{iF@t@o6Ou!E<P;0MQK!s6xPFDKPM
zA9UXAUxv*$y%!vlt=u|7Co3Z_rt5MG!p6k$gwoSN!P%#M<7+mnCtNwBE^#EDtBtmJ
zi=)YDD0tWk_QW5W1KNq^QGnmP^s**h4W6g~d;|?o6bmZ0guj`IPYe!tX>s;vQmfOf
z2e!<Xq$}#OCjN^o0SDreUMF<$FqjQ0&BE|_aq?Au?w0cdj$rUdG&f^DnjwO<AuM_r
zO(v2jS7@&}?9kmG1!)KRN+ShV7Mb`bnr$a_{^R(bMGK6MgmDO-1aV9(rB}1K@&#!5
z{^hMOz0aEP(`Fy4$<Mz*a<XL{dGdkrU7^zH)Y=n!!0Yd3j5ub}j(H$yRQH-JdW6=#
z`G*8)O9mevoYX#^u5H57gAc=Ifn?{biL$8j-tDe)NwxG|6O`M_s%R6eA&QIa_WgX5
zu~YV4$usl=s>usPIRo5RCj+A*a`RipkpP38^+aQ*ya&-zX99Io-s9Z|LCH1<LkJZc
zh|QC1YPvm806I+b3fiXtd8b2x%oOKdw~K^Is9aiRPc>ej;a+o04l#JSR7nFnWQC0l
z(am(pQ~}yF>WKrpGJi>hc2r|aQMeabrxF3);uFnEPH1yyp2BSH78aKqJ0R{ynrUm1
zEvQ^f9vUD(Z7DrX*5|UoRp4fvThUNs8AL#Pq?>7E3)@q|Tg2s>?JRAj?tQY*LhV!>
zad=6?Jth-N9l(nog%AW>ry1kPF|$Yp$QpA_fkBdd!3nN;Ci<q3b0DC#a7H40l~7uA
z&314tYLxQ|y*|>y+=$*DN(KkGRBtnpM&l%1-r(K+IxiESGps0cTtlh(r(&pHcVjE6
zjxHON#~vUo6e)rqjm9vL7ip(nyV-yNxfX(Ns%dMu8z^yTYhWXm(jnP?%9(N2KB}|^
zW?89DCmkk})dGv!&|#mLqSpzSEeo<TaOT5=s<o+(LlUozuc})232c&_(aA_Mvei2(
zmK=&ZDT=h)k@kS(QF#?x?Pp}|<5q9bpwr}l^hFqG5=tfn#{h#|XqJjW+P&X6M|k~%
zmcr^ZVp<1|r|AVVxS>84=I_*YWM%R2+A?H%nEMUa%{cj468C;x7iRTx5sBX=n?9>E
z+kg5l01q>__^q+#+n{rev;P3IayK~19&xkZA2dO}h`|lCa!_&6Z+rTqHJLCvl-R=X
z2S!`ygbKpkoHF|n-qHc=o>{EPx)F)6dFqcJm65|$&Bkc)jPR>Y^SH^=>>QZiD=fRC
z`y&CNuO$9P;#uOAlJY1!yJqDh%w~@8#!n~;H+wxoeHKIPUgeMS$r~)Gw4SJs_1&nG
zZ{Y%obB<@4b)M4S`V`J0zZ138FL&r-5gnEJ^Fn!k>Kl%&XL$x4j><x2B1UesdN1X+
z-diSnqS4jaCV(naYqPh6N_Hc`zq)KO_Jn7e{9es9sdJ`$L)B{p`4y?yY#AVVr&^C^
z$Nf`b@!17&!U8MYYduObo;&Bz#A$njn&6|msxUrzuVJ-3?5%xPKy3aa;B%dy%ULzy
zdrWHfnBY;iKcZ+X9y!o)SKm733TYJHOivwl>c;$$_RMoeL=CQ*)Nf8r%YD$eT?l>v
z8Z_TtGVC$NS7v)y+^P>3UWkg_pGu7SjW%Y#oEbfAu3J(u4nMNH+IRYBpJDiJKF=<S
zBne0En$JsUjw%%?%O9bSs>`?wMuT+Hz;iVC`KOn%lO|$3R$kr;W6JA;zG$NtzeH@{
z?xN|K;iUJi$~lDK;jCthhAUY<S@0;qZV)4oMHq1wPGJpXvNa_OAksw!PYH$MoJ{H!
z;bM(Z#Az_pW1IqWi@ZksQ6&qrSxz0*Lxz3V4N$w-82)5hR$<mWP&iG<oQlgEf_HaX
z&Hn(D1DQS`WF}#Kj7;Vfkl;fA`w)Ao(iUQyUF3(j2ddrWDZ~{@33MDf$_zL}v9Ah+
z^&rB?)2QE6<{I>gE#95Z2oOeyNal$Xc+oKTVY{X?Ko*S+)i^y>GV_dK<eiSEsBa{2
z&>pT&(=roe0~53o!J76S52A1^tl~in!q$KecUYd!<q|@ua2At*c!wp~YX1OgpaI1u
z;#Z2jNIpj|f-#2pxGR{#9g+(`tuwG?lGOoGuc>Bgol~>5$HeoAzu{CBnMOmL&v8*A
zn{_93qnRn<rB;!@789dWaa16+hbxe$6D?MwGsJWBSn3{0pj0l!Dxr4&0FN>!L_Z9;
zpNxHjl$+WSyNK@z$<e1o$ZbZ40KJ0dm6^#|ltA-$6R?C(oH0-y5n{O686#bsN7yha
zQjz2k-|w&rj2FsooY%1A=KXdFB|2I&JWmzF=8uG5ui2#Myqto=+Iv%f{hJTer<Xm(
z<m}DF4^##kY-<MJY2WsylAi+=$0Z!GI_t9H=PIeg?u!k)Unog&zf9FYMMt>c>1kQL
z#l&>mG9EHs`97#$W_`$K_a`;Kc4U(ga#M?$p*CDrtpx0c_Ij)haN(q&T-p;-+yKNA
zuQ-p8C1wnw9Mil!(1J%zjF&o$H{?^k3nsOoc{N5HW_=T(-Ic_V#k#L;#ooJHx!yE5
zoU_SkZ$&;P51RIW@beykY`@+7!pLg7JX7qfbDU?6s@0mu$LF%nr3#-bE;~5v0n}=k
zBZ3k`w?=b0C_Sp9u-_&p68XHKN#M6fe%qd@xbfG=VR(J>Ta2MZG(eb?@b9_*0PGN2
z((O+2Lp+s6MBKM}7B@N07dBjD=(1&YkNQ+%cW4GVH9BdBVyF5l3||GJ@<wAIgK+PR
zBZR^EqqtaJ!^$KrHbh|0^@SL#x=$vvFI7>K=Au@c4ktRG-3tv)*lro6KD^c4RnEoG
ztjvM(KwK1O3mhHO3`!uE4W?*zHnN*}x)%k-!)J}u1fgcrk21A!KLmH{5ImIQZL;=N
zok5PM-e_z-5d{q2W$zunCXhm44XQry4}2$P+-1KcOKO{!WLh`{4{UDKBYBCyvBJmL
zctCb~{^2sm^g1eKkDR|{8<h-o0ba*GC;-E_K;W{>9w4T-y#{H&BX4A>p64a_9?(2_
zEb#mg_kL=_S7;aEkv@xwE8JJ;hq7SsT2sHOGS^X$QwqC-!0<-DvTHz`#$Ob{AX&+$
ze8}eH4L1V<eXfWXZ$p6@DpJaa!f$MqTu0o>6JWBNS?CprSvnj%){Q68cwBHlh8H6^
zDq$N9oM449j}uy;!I|cbs?*m;E(pNKn$)JA?B*N(RX0T{A)uW=M|rg@nlinlN@xON
z!ZsfTL2jv}Ft@urkiI-qp#v?M$cHJ4d(t^9ok~2$ic_rWE`_BR%BFD!iF9kS_f#?9
z7)DS>G)*eK=H)215`Y8V1204|H@wp@t2Lk>2{2RJQ1EF-yQmGCbwUp%7YHwH=hI_x
zF^n|;<`(VAwu(#2W4qq$lZMgG%Db_&L=(F1eNZeX5yTE+GO_|(^5}N}Nndi~r?OFO
z0chE2g>SrGtFFRGU;=!V$xpG(niXmdKYK@mj=d9Q$0Q_nc0Q+@7H@HJ^G-GipgEOV
z$m$BojV?!dk8gB6>_|Zd2-#7LV{>zgGe|ag5_zvBo)@>trpw*NKcyLWz%1U-@3EWd
zIDJs^S(jmN?u^3X)fdlawD3Jt-NB=xJKyUq{h>!Tn}gL&I<fdiCm+bAwVz-zEgW_X
zTcG)7wKMh}+B83y{eMW+HPo2z8_C~)U$89Kf_#=znn^<pd6iM453=G4ruwzCeMp2y
zGBro+n*u%|X7_>Vb(2~ezlOlo;)`t07=@lQ^V|Ob=zeOOztlLeV}M2m=r}FlJaO!d
zuA7CjEi(xJ0130V{{V4LmvZRUfX&ym=pJA5u!}b2BAh@2dCI`{XEC#&7y2xUn^YjL
zX~g{&VYo%T{iFu8Nk8vY2xDB=MyFXTLB0BxXS7G7f6AY{G*~_){I+H(w0Z@=qZ672
zN5)Ea{XWiEBTzNloZDk^GERf4XWx6rPKo(A^UNmUuSTBmC-E8veL{3}5VFQ4c(8YU
zF0MPm2bGudTmJxf*mLZipnq|Vj0R-RtJ!0tkwzNE2OlLc>aD0c61|#J{lz@@l<9rb
z4b8Mr1OnsFVd$sA_a8U^0EA}0W|z{O%{^JS@abW(_KsLnV-N%Z!%deCZd^agG7}`d
z*jPA}@l&+p6!H5kIh;Kdl5;BKy#Rkzm$3H_iW%gFat}yHevNWGkNj*BX2CcIm$lWM
zt~VUKmj&!p2kEfgj1<q&PGxak25z);LcrvYj-KyAvuW9*f%QUQ+-SY?_81>c;5c<D
zptlC(<YB1Eag(vQsGHvK?kn(}dQKS;!>R_YD6#gN(67CqUyePbA{JAM{MX{q8^4*n
z7mK_Qggn(a`ez7&BM>~Cj*7y{wK|QD05|kh-ohFno<>UkB_@N`6Qtnu*JrZAvouG|
z7-++Dk^nWMJ|~L5ciG<Ss<;~KiON$)eMtWR!3)G32rBs9EsbS(7|!JNXB6TOBn9cU
z!mM8EA57d3gb_>+L~iSOBZm0}y8iP8Msou&wOSO+8-`9(WZ60qm`ss<?h(y$1sKK-
zAb5qrW>kxMCdJ2xil{<3oBr}xr;OX=gcBg1g36^l56<}&62lkPQ;cv4&&ARcakQBs
zG{Z2}XVp+(fp=>>Q?A-}Y5dDvO+@BFL>8Q!qbWl+h(^}<De_V(h|AiQ@YrC3VyT={
z7>DeI*U1t_YM?E}1~Y{00iMqusf1Fgb{`~60374cClJH;W<A`t8E9zom3nNanjyFb
zYlh7NrXMjpr(J}W`=H(xL&!Ww$qT!)GZzU?EfbnEdnjuT>)Sbwg&o{YcXyPIN{8dP
zZ%D{q%Doy*Cs#>bt(6@i#jb(%PNW!WP2N10X{6A1NVzb(aj~7oYO6fN2bYhEA=G97
zZ@22ZH8%5b#ps~9`SRK9_Ji_SI$B2=O_PctcmptxFR%eL+FWNS)j$suB|4aGg`5-<
z25uEr;-3a;R%<2#??oH##-<%v?mq#r!&$t4WZ9n$KsTBDrwWu`;HW>|Yfj4od2Ixe
zYzAmHw`sNt0NK3WpU`ODKy3!dMXfrgcVH@%*ct_npf1V}0yjQNtC@(vV+jE`k%<2Q
z=zc|$fho{t^O0U6;!e!gLF3}Tn(v<9YNZf4#FRI%fFp{(X|GX@V<#{-tb{#}EhCsj
zcpGuF0y|6I`lEw<A$WVqWxeeFNJ{s%7zEQDZiMbUdbI4w*L3+67Y_NMcfGOYs;Jhf
zat#b5NP+WA!Uu!R3rabcxcry4YLnC$LR(F^y%)Rx0Cvu3?$QIz$HKji%tX%;vuNHQ
zR0%>}O@O+r_Py6_CT6J`9i6v|emC_fv|=#8G~~UPT=ZThoqnhg*KbD*TsCAoB{%x#
zvr1q6hR4Gh+1xLejG#cqOL`lHpjMX%^UVW_u?%c-x?x~*008Gb6+ur4!He|UMO->7
z0^@%*y4+_B4HJu5ImT!`&i9SJ7eEJ7q22O#k^)$61*B>bnl-n-PAkII7Q>w9K<g4$
z^1$@k4n|{waer!cJS5Gi1MIh&_PbpAH(<Yb(g($VG_D~MA5DO^HFV~#D|mXk;aH~0
zqei3s0yo5J5>y7^7v{4J#r?O~%zgCJ9dC5YhKyZPWSoaSOSH4I_kJMn5$LKM!%aLU
zEv&2I<H0-e1d+jI#4Zg-!ou&dsR|-MkJ7OmHQel3hfnSH8;y?fH2o8?(W>zWQOK%}
zU<@z?q6(%M__casFZqIStw;Hd`h;MNl*za$qH1=A$<IY##s>WsQUdoee{J?ucK2m@
ziOJXcE4qpL%F>fZ!BQ?&SY6$mVYh@Q4Im(>!0N+ypxJ2buAfn`Nm=Bh8!v~LS-bB8
zZy~`ngTSw6v$3T<2E0Bf3N?nl%aCX@rH7Ws29^O=5%l_};@#+&Sm>IkQ_)tI=q5-U
z!hkFY0wz(G8m~T5rX1seS{w3J0JYj|fdl~w(TEn4*2%Kn9U3Ewnb(IRf&|@NrOmfg
z4k1a{e^6P+cIo2OCkcW^N#(E7*7H#g;E#=@LDVMQuRuq1*&a#2H@+T)4BsQoJ9c+?
zkMOQVjB;5-DspEO&Pp-GqSB}mTrF-1yV~;VsNs8jL>Ug}vE9)cgwwr~qY4tKIyyiL
zw1yoTH;MFA15Gx9^5;vOj_c_Pfaf9IQ`<`&GQ*y_h*+w2f(T|Iy3v%OPFf@oz|-(f
zI7KE~Opr2Y;)Er~#P6cwj!F>Tkv#&b6>&q^mLbF*DWo0=*jncRPXKjJnpD3BUk$8<
zrt5LIkdR*_aSB?-iyX5+hQ5vblSwm>O#=v%#S-e6W}}JMf_ZDIJ?7T1vaU-3^-pz1
zNc*Q8O+H09_iZ^&t5Sgy;(c}<=G6p_L_*9Jy3vL|G>sGh3}GD5&mgyX9Mq>V;nXTm
z5Z~P&3mu1%SInsg3T}BG2%;8@!aB_T7qPmpnZx~P&GTLcX77F6b^!WSGimvbYM^r&
zK(??xA+E}0*hG_{>VuzH`0L>KGE*-5%bW!IrUg~4BQi*UpGv77%S4mac6w}J(~qPo
zbeZH&%~o-fmWv@DErQM7^xGe6+CLz;KniTr@v;&X+p{w8??fzKcINKb27i@m;!{gs
zNuv>t14LsvD9C67V@@Zy065Ar&d$csXY^)?mRa3S!F<P-!{Kbdo1B}*-dhasnJ8r{
za4<mIqkeWkOWf9p=2lY(4caXh=X5S8x&HwE(bh-!Wq-I@G2ab>9a+w->9lASalcE#
zI>KSa&g!V_^oXAAxt@V`TrP+;s$IoB&IDMWi1OK+9vlxuWv}09<ooOxg*?aQKeBX<
z3dy~QG;**CJyvg`e|2pN)}MNMIGA4DKJDFUr#78A@w+c2SiP*K+GE)siRLM|;f`wV
zt6j9mkeNnHpMNzOnDtE+OCpeKvJZ^Pd2^1ZXX1CW<r^D~tbcb|p7l4}9-~D3bY3x1
ze;!Ag_GmVGyL;+~f%1A3caedq;EYzNB--J>zQ<ftdz?KNlJ;uT_^h#?P_tPO8$2~Y
zjlcos+bh+lYVGBtlQ;hW(mQ*S;Stv8-@HEs<AX`(>W|?UU-Y9DDE6u7)FA4p_m61Z
zoQE}s8Ga*cN}$Fyz+%~iRi#a@1C(4ZYd&eXCIli|6rg$ih@3+->{wWD%{F9uY>k0k
zt~grWe)5SF{43L%s|O>fC^xe9iOhZ*{IpDr%LvHyD9c~kz72s0c$SU}Q68ogVQ4RT
z;L)1)u@8b0A4X%Pl`%MO4^(ZOWDuRmSBA8F6lUVARp+u*#nEV;ymRQX@8|p{L*G*i
zdJNMrJ|NCS@>ESmi?*vQ_JqM+iy--Pguu{!mgJs5(C}OCq`=Ctb8S<@NNkeh3)=yQ
z{rMv>0`@=y(Gt_#Hzd(X?Qe=K?qwb9E+lm*T9}#YQJ=XmR0h1~z=Y=Dn^bM%{3l`=
z;t1morq^9>F#A8UhqUXLssS*&cM*<D_=ZpQCuH(lrzO2l5yr((RHS`JQ~_wY1v(rv
zYroz~m8!UZa7-<0KnfdRhu`*L1pDnf2*e$}GU%Q}8*m*{CM-u)VLokA`_5`Zt3F5o
z=fQSIRCHSvlLLqQ$rhRRORk0<?d>pcanFJwkM@|W2RA%3z2+4;YmDNL3F1>u0C(Op
zgaaE+kZn2E6JfOcAp397Cf(hgrzNeHQv=G$r@nV&4J7C|ptB=mPb+Gzh}~NbrzFGv
zrk$F7=JV*O33)rmK0qN3)TSBEApi-0p3`j#Z~^aYA5J_j0dMXSXU$L*TXx?ts8gWV
zT4G8$Np~`@+|d}Dz?<xtzFiYz6N!Wz54|d@oK8{`*yGkdX^?PYAdhu5#iQL+?B)m_
zBV;$EZt4dTnFkHrZfBA*c0J7`L5>QXJN-r%o;H<x6~PgZl^eo2jewGBK(_=dG?Gh3
z4n0;q$|Ei}Fq>8NUuQcFqkvS5d!@h?*sGqE8o%($olQ`vwTyEd*DyG~ZK~{o4;%c|
zR??-JWft0b!f|_M;Dqwl&7kDFO<9QX6yi@3e$VCq0JmY#5B(AZKjsn1#lPiPx!Li<
zv|en0ig<1MQ><M+r~%9^7H{njc>aO6-FL}*YzBTC1k>#8H67$PekjYF;!m0cg4d8v
zkqbu=>9fKecKgRWZ1D$+A^b>>HLHn4({6)O3UC<jbCPb);yg7$?%vno_oE=QH3|>_
zVE+K5Fa8xJ?9=(KzN_3JZqjAy5%NUfq`=E&N#LF-Q7prRUGX5U&oyE@>X#|y&N`Lj
z`J)Y}>l5s`M#d5U0G7i)r)P!0wcQHQ)+=<y2K;sy+;N_QQ$&-dt9ZAvF`wJ2J@eS-
zJPF*Nsxs#g5FRNYEb)??55oG-{<CB(TotarnN0jf*zl3oIBYwWn}Nfs{1ct03#KQk
zG5~BIA`qDv0OnSz4M)0K-cd4~OOCq+V4se`A>!XPbKeu{xUc7Z6pS?`qm%A{#O>TV
zI&?z_{{T-{cZJjj)5K@VYEg`ABhu(@)?oEH{FIz-CfaDCXH^^EeSsj@gTV+BE$Fz%
zqKljHvVz_q?a8^J2fC8DAF*=chUaEI+;lmo_yb_@vT>){V$aF0>X{rl>;knN8vQwi
zVAtafCjS5xA|=l9TDn)f-}{h?m1V^1gF)uK@Uku}D^c@YQGsRoen?n1KQ(>PA!q)I
zr_*4&K>gJ3<1z3@!AeNrUv=SiS4Yd}fpau=pemnw!Mp}e93cnxTKcmv?u)b;;PLw*
zEREtW(l``5s>I|>4<sGGu8^}ZC3cSR%sd?D_C%O$Da-1h5!o8>?jki!%v|zBed~7#
zwaU$<3T<^j(gCx7*;H_9!JWAhy@$EJw^@rK^NjC-lA$;LAy`C%V4BS@^a`O4adU7z
zunh1`j7<m6iW}ep5lkRVxN-~s0LSe(qev>)i1{cM&qV(Ka9gUT!Uqv_4|Z+Ss=H4u
z6kx3Z^TmfmeGpkpvFF)8vJifK5JZk({E;q`a&uJ!olrpJBP4L%@D)-Sjw*Ra?zd^5
z2+jh-&zjwjZ~O$n`W@JA?d+nZH`Evp8TzR29aawp-9$E<N2<wiTBnhVPKn*%xFC-T
zW8!3BA-_cT8d?k?eiUM7G|EB6*t^2OU*#!{6&NEJ8Lsd+)U`l<Xk@%3--<1>NdA_%
z$@E?mQN((yOMtRA?_i(G7qnr!90uu)oC?0m+vSj16JnTP<@=QEby`S$O$7U@>~`z=
z6$Phxq-xWV`v-VbFL#>EG{>gNxZt$dZ-X7bJ<*FQjSg^-HI8UJ0g&RU9Q5F+Rkl}D
z;&d1Ir=HW=#%`axVc<BT>eOH3Vfrea?HM4QM_;qU?0#R6*{Rw-*S73O)nw<FLL+s*
zHRN^)!E`mhMU;=0(`+@*#uUbzHEYNHC=7Irj)z5?hoT?*x}WsY{{SmAVYO+xd1Enj
zH(co(J(5iIUPuygh*of{_aEuH6Hbo%(U|QFX!P6W75%P~ss~_eMK%rNDVRk{yQVmv
zDx`;2Yi-R^YtM29@sVOihzwP4{Uanjn<0tD!8`&!@~KSjzcd2l>YU@=_L&Ea4Y{Tg
z>|$e74|>e`uzt%_a&1xQVQaJap`v-@v#;A39K#v2`A?5TH!+VmM#7x>mF?9o2c?j<
zIsV(D2f1-y#HaxNqY2ih6vvip`He>>87q2?N|;{GFf;hCWyB#PH8_d3%-UorAL^_3
zgY@zEr%7r50Ls*MVm*V^ekXYjW}uB1j(5(DY7P|G(b=eT;#O|RY>&Vn5>COo%tZPl
za^gW&F@9+0Rn9($k{ZzV^1{EK&zc?~R{m=`@4@8@E-2luSqESGg7#T|JElmSkoITS
z>_-c)6D{OcZf+suxJt<8IIY9Llp$mYC&>x>w|tXc9X`scJj5q4+}cxj1E7#mfZ*Zq
zSsJ*H&2sdF0hd6O&ia8d4$Zv6cY9BO@s6Qoh)VFD$%u}(R&bElWwZHhccDi6GZir>
z2=0b4!Zr_YJ~FRn^3fB91-2N)+1}q&!dTKI=!D6V6J$B$4ofU%{FL`saB0w@166ZA
ziH{G71-bbwPXOT+0FPd2;q3KPS{!fg{6Zv;WbjSJO}7PgD*ZwNd)QO>W{donkWn<u
z1M*fCv+5BTS_44nsrR(m;L*+KRZ`Zq!O-U9Pc+(l_HjNaM9i-d&J@F>dZGALh_VnZ
z5|7>o2t1SABjOcq@BaYNk*^||X2^VSUI(&AP;9Mc%yC{4%!<s05Z!Er(G6v0!6(@|
z6kKWIj6)?+j)9VC4g{>uZRg1}vv(85vYi!&;J@gzX&~$yqcn4ARa^yE$(Cg%+NU_T
z7AcXwe75CsdJCzeo-$4_?x9n{UhiYU*OKt!#6lO5@Y!hz`5+7qFsU1vEosGnB-3pZ
zu+uKy{gZ)%4Ih*y;h!5uK=M%-;HfxVQNMH+U{9)a8gHpg$L6~5v`dO@HVk7)^_Bks
z=|5x*u6n7Hq9S)%I?dx|T#&V_n>Vm2#DjG6E~w#ecUAn7eKs4@u>B|Y`v3@Nfzk%t
zjlEFUC3$oNGKMrN#np@u!C=v*DAgIj!}5);G-yqXZ$I>e&gO=`^Zx)**b{Nn+?H{a
z>~tz#oBoHN{)Z9r*nNTV{{YdS6n$5Sm<y++wrQ&N@8mWa-51+%J2QNX%%HMb6mn~i
zQ43V*Z3;o2d7YY2XURM7yC}l&ye+vLR;Mk4d%SE9HRA884^s=rRAa0AEX8^HA^R)z
zOd-!P2b!%fi<a96;D85D_EU+zB1z$N=wNYiKcjgc#c3b)ls7~GIt6L<Ld-dFFo4Hi
zqp$fGxO`KrYSeRjW%dY;^`M?bJARwu+UUG^vP~dwkBZ^2&vRM0Tgln4=KT{%eysyO
zVO{ti0Awwyz8n;pH}PKHi`Zii$xX5!vi2yu)Tp~Kk2G+#gBu&u+AB9WxcRSqeA7q|
zB($$|y#D~dC6#W>$v>T&u3Fg}w20d5L;XK=>*VNGIv@xBASo7tY#;f$gx>SD)e}+N
z7W?RjRIWy@ckGT(oG}eJuV#fY@YpY2o1@7KN+?Hn*&F9<NW}J9mtW6k&1z~1{-i-H
zr;6OupbUTr6g*Uj9YM0_eHKO$$j7JI7@h5TAq{g~@?PlqZ5W8N2vLpZNZ`NRqZn~d
z%V{^x`J)aF1ac|gdhrgAig3k`2podqq>xkG*K#nv@3aqXxj(2)IDgRL2h9<=OwU}`
zM0cG|BUB)cJ2I8vaG#<XYsjH7E?h#88*fDClkEtFM*YactO9PGlvVVxSv!(uF6r2Z
zD=sL*Lv7AU+h+$jJLVIixP~@+v&(|*J<U!I8--CgxU|@(c$pv_LI8&gwCb9BvNxOV
z?8%R5^|E1xc9uLIX32%E#c5GtkLaMfl`=<A5Hd}ODbEz<A)@Z&h6-(>P6MoQL9ib#
z13|%MHeNbSBrA+iiNzS338lapY1AhHhT(78;un}tG{F)NGy`{zT7sHdeyP$+UGq-D
zM+7^*(mlN&*P5q#v4D%th>2*wRpL4$cWQ|Hw)c*{cvem%>}TIn!W`kKI+aDnmlZij
zn&zz8w~~U>;$g|leUo}(aWYd#JnWx`3x0N1V+JQ4L1gDiZiO-60yE1cu4rksvLfbq
zrnaL)hUxZaRYmNm@dS<K+8XRyKXCk0gP%`P%u1<;m;A2IZhUmiA46qOJXC**2qC$Q
z(|jyzI>Ly{=&2V|tn-nGBUBt5{J0UtGZ8L`IRpSMt%b3m#Gr(KFRB-N+~ehKqeXMF
zW(oCL+tp?>Sr(D&voy`dy6$y?!MrX`v(Z*9=)!(*zwsW;JPC!`yD1>epLJD$XMi|j
zPjSHM5Ek?q1KH}X=hPGChGfT^(+iyJfJL?YSkQPWpQ%*~o%TP7`TpPeO)u=u)7?Bm
z-lF@{s4{Td!)s;OgbpdVU5v|kf9YE4JQ7#%+dT?9^Xs=DBWD5f+X8!`hmddB_89Q)
zTd)&llv-*#OohHE^D>RGVT4ZT0(_DCy5b4oP#C+(2lDOARB7}o_?%qtn#9^!Nwgzw
z`hJLF911qzjMrze?oOo<_pTrT$m}3sW4<<7HiQnYN5f6+Xy=ThytQ)ns(h4x<>|0U
z)Wzf1W=)|!Z@<{ddrWOcpVYG_CB7Nkq9=<Ju`%F|oQ}f{s4z+Al4%U5Qeb9ANEYF~
zCwS#k1{>k(rc0es!>xjNV0HlFcwh)_`z|*z$qnDCB1_s^>Wsb7O!5U~61b;HZ9)L#
z@Ayq=v0(It<D=T4yqfVpMW@wfHfm6_Tq`tig9qk>!X#FyTNvoe1yEC|+883K=aLau
zInluV6k#qoEaeO@8?J9nR%=Rw{Go37@#>(0Lc${pVYvtq0cG5}Iuz(3&^~g6I-wsF
zBmV#{+ie2QArEf|S%?eTsPD3D@P?!Xo&h^ZJ)y$G`E&I^y9hY-Ssanf=hY8AoHiUp
z(jfJOaLe$l!=Yy@B3BgaH?cR&@lx8L5bg_#JHR$P%BK|x{AK_b-%-yScymN)@?XSh
zXP#?@6a6cIDdOd~;IF)PU%&g7fyNi{M|OHSWlWZYd`=D6JS2})MC{GiW%UZeuAiXF
zZuDKhvUvkmoRD*7n85mHg03nICwn5UV&jP5RlofkzKWS+%bVi0mh);>CZPQ=k9k6=
zk$k_%M-yDbgz@UB-NQ@S2R59aGs$E!91#Qm04(-jiqaGC)^1HXJN&5x*on;mB(TB)
zDor!yy_Z7%Tj3t=_Se-A>?Y)Bnhb<sk})hK5>W&KTO-vGh!Zoiu^4Op-{l}CGNcUR
zB^~`6Ii^FY2*auckT)Zm;)(N3mepn$`me#kh9icGn||{%o=bv$ra{d?u~k^YN50#t
z?Wzw)j*65&yA~g)WRCWANcn7ajS6J`tSWjTrp*))Y-fN@IE{M`%Cek5pm6FNnxDKf
z@^gvR9LdAbWUq33g0%d&CSi3aL*t%B3mf^PYIJAK=3x+8XxQ{QFB4rpiNV4Bu0cZb
zMZFY`Azs*l>##{C$V;Hh`JI`#!tjd@g3CgMp2oO(Yy|3ToHwr4Vujhmu<JHX46%R}
zM;qm|?@^@oy4-9N+;@hy3UD&ObY2ikulYov8A<P8HC<o;xk1cn@zp*z{=@BiBj>Tb
z?`C#%&YZ$H=sUpw0HN8Y*APLmKw~a6>vM3)Y5xGF?9!$u;(^5XhL0_ouV(vW?<ovj
zLJKz#2b%ZY@VD63k)I|10OVF{e5u_G#2`)@l+Ck8XX4R4)lQle(gzb^@l^+v(?K$G
z=%aW2-5G6Bji-~80`HFg!Uwp>lJ05P%~nkLqiSsV?9zz;0KKShc9V_gfEgn>fOwqD
zCt0E=^y;oK$UPIGq0)YTL;-V}UFKX3whX&F53kv9lTGaMW0$gJcu#A~fx=LlZnKBi
z$z`GBn=GI)p1Cz$y{3W3Kbs|+9$V;C;S|h~&dUiD9M)mia7H3HA(&_Vp>f>kP|Om1
z%09*=Q+eH?o4UXT=;1syLseqRE-mYjSKbB+{vAqBZ&GmmfM5fDp*V)r2AiXUge=<P
z50#d5Yd8*%{{V#8j?Bv^ZvOxVwzSXx0Lp-2CRtNK{t96Syl5kh&>x88Cz|)xMyvg0
z373l~662D2mUeR-z|m!i3rHmhZ-t|f*~Bcv$ST;3lsGL(`#e;Cu#R-`YyjZ1XIB(t
z+>(idpOUZ!;bn@&Ww2$lRtEn7(!KSQnhMHxM7pCuN5R>^_yjL2U#k3{_iTN&ZilLh
zRqzLQdOq57O><t|H48B2&&Z?fth>gx+||UBvo=*Ok3>%9BO^(&5mk=D@HHOsTK35J
zY;?B(*d*w;oZQ`{rx_fPOd`8mVm+&Bj_wz;5E&Ht%n_kaZ7%Y_ag9@&?qNF#BgC-0
ziiO7xvZ-OFbH|rehOl#T36TtT=(>}K5guu#N*O$~j*9Il^eE$lBNDzCp0cxP2h9T&
zskHg<P9p2VPk7%n>^Qt_8WijYj9F$KgNn^baEA}w0xNq=(DMs1nrVG1hV2=H#3s3+
zzV>Rxr48q@{%8z1h-o*>D{7ifCc<Hf&J(cFsOfWf6-rr5;Bf|St~siCa3G*To9t#f
zF7Xej=BfvFe$_~_oqiL=Xko)ORn(us*B=F#X!r#(ehPP5N;Y$Byl`A&gs8&zZ*e@O
zI%&Unax1Ed4iPBh4#>CYgkIrv1D3&xm0j15s>sxIdZTezx8M_j6scd4b;wzQns655
zYHaZ2R;7D18V{Dn!5-A+owFtb1pCB9ZPB<7^r{84#0}%0s%Wh95&@Rmiga)dupKs7
z;|uWzh;dP6a&k`l(@~8lhefV8ly}}|qIeNS&ut0baD+o?w8JSj&1`+Fr|dVr?DYoo
z579}a$6bc+XI@`tFu_!Ez;E_={gY!+v+B6V!5wo}eus+8EXk9$aZv}M2%$fE)9H0G
z<37J&NqrZz1^g-v4;&tDSGy}QNEzuVzy#B&mmUZMN3<U9py4{H-8SYp>?0R;{{Sgz
zM=T(DpYWnPu}-Hh2<X%?J4T&4qjHJDUKruWMGzkY$v{HxG(hqx(ALupF61-_ei`kU
zf2Ed0lfKu=xM;-H>~?%7%p;o!**mnDJ|AcK)d#`5EPvNq`w)j}&)Il=ldBKxoE+yn
zyr@Q5SSiBlSU~*<K&-~p_$!yx3?50xdt9?d9{07U`&J+c_yyqt$oP9Zeq}&&Uf(oj
z#C(=(+cJNpWHVmhWqA$!%{JMldk=_-9T7Hg4eFvRQf*6f%{O|bMjs(6H)t`t9lqbd
zfByhdgR#@71vhF8_)jDu#pZm|fx)}N+)(@)4<o8BCC<bT1H~W1rd$ZhO_rpE{Dbod
zdtrM|XhM0LFMv-4$HWTExo}yq;aWKTty_tnOD9qJ6fS?<sldw)eAZ;Bj_AtK#ag>Q
zBW|N(gwv;({{VcJq!S6ChdNuxpl4l~ih>q>$oT|E-c&C2SG;;7nP<!@l!-cGx^Jb>
zaK%NraS6Dpdo@LzI;*v;1RgMCqZph5$+d_5I|E(T>XWIGK*fsF=!~}mo&ByxH%Kf0
z0Fdp2CN;i+32(_f5mAA_jt6Cog=c)%h*<(Vy5gtwxIzT42%yMSEx_C&1G57|3R>p7
ze+sPU&l8BM4SCA;Ov6a_X1{c0ctjECpSvS=bwk5;#g#)d;Buvlp$d>7Uc?m*Yih)6
z=$VbBtsvxb%~U^f{{U{@B*WK40jWe-@=a_LGso39MBTpWPu^psbXO+jInwGPM_m_b
zWlr**BZoE~1vW6TqNM8WO3W_K-q6Rg?LP8m1~`%%!;-@xm^~F%y|{zz?8wO*#vesO
zYCLY#8IDShFjxrWkKKlsKcGy++OCkoeoCKm(oXDUum-)LTx9R^skly7S-e3fL&-kV
zC5G)jN&U}hbFWS2Sn1$ZBFp(_#bu)Lz2I~jltW9Kt&tmnqtO6ePjrK64!Xn-1mXc0
znqw(MIyfl%wim%bHQ?u*Cr$JEuN0ZsU^Ii*k~4|8g2>lr?5b|#xz$vu1;AvfSX092
z;h+c{9?EHfns_CL35%FKRI$1=ZL>g^6QESwFf$Z3*k_8XUmqxN3Z-|k$CMRBQ^4>@
znXyhWIem>B5x8<2#N-!@_}}ykF8zbx<Oy+XJPfjh6}v=Usk>NzC{D!ZG6_GJu779)
z1MIr`@NnQ$V2{P4!1DVJj`5Fa^9bhem;Q!m#`5R;ASIUvVBYpm?!A_V`fbZm$vSC~
zEi2h$&;2DCwtrw*tde*jciu{w00`)WW5B`ZGqb`04Ic43KNgd_nx2`V)2cbRBh@?Z
z>e4ueHr)XXhzHu>u1d<AOP7X@@#>gZDsO?;W^en?*$_jVOONP6?V;M-f2pN%D0nEm
zA0iY80^LF^r!$#=nqZqdL(ypH^tR4BI3wZh2mYpy*>O#~>s5I_rrRv|5_BoI#{s<g
zBOT2eO+8L&!Ox=i({UNbZIx#<HN<mplCmtNKiUwu?apB6(2n4w>^^JSlDsp8y#<s>
zZ~a?1gd+=LK07nQae1(vn%iI`-~{=nK$$*jPx@+wue4eBmEpqa=me~Zw?VMQ?69-8
z(>du_52!~4R#}dB{iQbk6Uj`PB)pwU$m*JLRCjpe%ODYlH_)&CE6<|v(rlD$LEG-1
zk`au0!s5NUn3wYo!?YtO78u~OrDNug{$i%{<Je)-&t;$?XSciQDlsyo{{V6T4?*z0
zY3|19BTiv8;-J2A9^NCK&INdT)}n1!0P-kBhQW4$iavV<`_{**zuf$6zk%7$s^SP-
z0rWDA&(C9=V7a{^OwL?U7)u<~H<~4@N{`!<FX)CGSXw`*rZ%S#;HeGj&m?)3*{U+x
zwe#~&#^yD+shq|eCa^SQ8Wv1or|GlHV`2Dkk~M@LakRE2Q>a~w*)W}nk7G#Xq-;09
z==4Onu>>lC)Z-R+PbZN!Oc3UpR`ozY%T(rv6P`mP^MnbBmZ}2W3Sk0zs=0fmL2xo0
zb5+YK;r52_!8xuKnPM`;+l1#1AvQs#eh8Mkfyq?gsMy01k``$<ZSzpqU=s7MRAhz=
z>ExM=m*EZRs%|FEQ<8;Mz*KZMc3RWLI-}h-!(j=J9UYRBP4tULB@v3w;8%p?RabX@
z>L8K>PeQX%F*M23h*X^2CM<l=)jhy9h2ZqeBFkkUTg<M^*9L+d(YjANF3m!h3@wxK
zPIMO%2SvpIVx5OJ-Vi<ITFpGL;!$gmcmzNdT+CzLb5NFIFK|s4b{4mg(PRt+*=1I^
zMCLM5Sv$={I8+#cI$}omZ&W67JW&P=yY+DLKu32Y%;bjq4L3CKye(nY%X;=l#ZjZx
zXN*TkLK{;fz@kDq(J|GzB1zZ`lVFA}(D4txp*+j8*nDFXKU;rwZ@ZcOCvBB7AMb<o
zP<ayxgc&3RCGQ*6HJ$@ysQ1{9JcX0szUDY=)y*GO!|iGS`u(bfuH>*&?UBDsSUmy=
z%pEp+;a>}g{{Ya;<c3Xk{FK;}=$s|ZBM+9rI8^iIw|3^xUc=Kcw4sB|_8T%gTroQZ
zIO>g;0JHB$m6rL%c5+AV%K>QNvpV$Uomq95Q8V<}kYj}NAr`z0S8lP@XUxKLaTo7j
zK&lCOmw9A}-t6FdCsGWNJK&#!8_(TmCE;Ty&DSb7Kk(Y*^(D<&RNN?k7C*R^o-(zv
zxWzaQR!#fI;&E<w&fj3Hztu2*(smr*UU^?;M2uYqYT@0h82up1)3h<ZQn#4;rNgTV
zyu5+J%KP-ghv_T8-qT&r9AvL)=zbF$@lI#~<z2z~Elh;cTGJ&PlXAGMP}AUOmt}4W
zk*Q74_DL-Tt`ey*3Cc=qN0L8=(9s{3+cK|3%^Wccf`<Fcs0MsoR~se}4<vF~P@?Dg
zq8r-1m*gsPcRp+G`)v&kyT<V9fiHC?h)#x67?t{P2qS;=l>n8Ss~><uO*RAj?fzU2
zvKEXhir~Ed*YjGa0NQlk>CL2!lcmlwO4DX?nqm2`<`psy<Nk{DOTV=6D}h-uU+9h8
z`6sH@gAUAyg0l!t@|!+AN;j%*IW-SxH|U@?rC`J63O*MY>MgU`tQ>sAEgTP`j5vd%
zs&$(8&p=h<z1VZn2m?N4fAV46?Xya*1M9QG>0#%HLeQ_o!;YBa8I7<OM_vfsro|ol
z%n`sHN{b9_Ju_Vp)o4=#*{>wv;Tg1Ca&T0D7^J&dPwvllZ^bt{m_g&s8@Z7la;zrn
zGn(Rzyt?tGii2vflNhtN1ozUY2slm0k`f(r3F2}M*Ub!^SMdJ;c~a2&f=8NVtN~8@
z$~3@p95jU1xvw)BK}^Ik%bUa|;g;_N$9IrMR_c!Lb_n!Mrsfmqnp*lzl0RkPAv-PL
z5LGa}#~3|yLJBwHt5j<mAUVFrUQ4t#s~wZsgs#z2#_e^-LJDVhQa9=7d9^y@iBy`$
zczecJ9<5q4V+m*R9Uy1LRde7BBZMv=G%jg}kLXu8qEX#paORj?Gdz=tYeWY7yoSJC
zgTmO0CSkz%<#f>VhCuguuN$55Kviw;k(G}LjBRdkE(lc#soBODc)l51!jntSkW6+c
zm(HcvO>ex=P5P#X;%v3&F>I`k^<BU`_K+CSs$>j+39$oVf((SqycX00iP!9pJ_CtP
zcXyE{<=oQ=c{XcApvt|Zi%|Ai&FY~fmlm)OcR?8}@{R*$t|f5$S<ZiKkVZrx1yT%G
z9E9(USA_UpE)SX)#@itq=i&bVpweRHYnfirM)*uChRuqWhg`ARM?bTnxazI<wB^Sr
z6SCzxUCU+NQO>Q=tg<LcOvCqNo^!HsHmDv>ALC@6?cW6$Y7X(fXn|*$;bNqoDaKmU
zbWU&isQp@mWv*}&$z?c4l8xTX9sR{N9^vpS!AjnGtxBEB3}^RLlBUlG(5$KoH9l+F
zy2;M@ESXL2CxkT{rorK&;eEdKCl6R!AtAlC{H2>SYXBeAVQ_c-r2&Lgd(Qy~W!cOf
zL!N<jS*!SR<LYd`nB%eec6e)tEFl99ZWsQx=D1y4q5ONI7&_kklw4uNb6T_%No^;0
zLUZD{tqx05I~|0{zX%et6>yH89gZMOqo9z8#Oid~Kd9Uxosj$1J9enl4G-?7MsS+Y
z+(g@1;-@I}R!0MBVs10-NXQ^?hVHvk<$y&(JR)sm-R6mXFROWM$<Zsq#u(&|<x_sV
zc4}wr^FUd^^HC1(GccM+zE{aH!^r|R9~*@9SsJcfQ*lzj1Em>coF|fPyPPw_6_*v^
z?qG4Y-9Sbm%Eqlw2a>pC4v-Pfvt*-k{9c3nrpI{3)E<f;iS=5xf3-*5(sS>vp=(YN
z-m4!J(Y;Qk^|429{{Scd0IItlej2a-Q@&d>4g`5Bn8TV6sFY$V3Fy7)Z$hIy_6b*B
zHUjD|;mJ2r*ABK?6R_qbGRDpvmO15|D5UDAKAb{umQS3bEm-Gd=^zgZq`)dub6oHx
zz(S0)=9!2BCiyK1SF`?mD!H{OlFqu`R&Ms7W`#3Q7&zljWp-}Oac9%L_#n;NfS3d5
zy{NarNvI!ybBiv`h0gIX9Fq%bOx-sNkI_{OA(*dqQbiWKo?H=^#_MJ$imAFp7L$9T
z?aYqO>Z@t6;1vYGJQg7GCk$CX3&aN1{{Z=r^UNM$2x!xfGK>&p6;B0H+}o46mEkvQ
z+m!z4_&ZNO4NvlzaN_xFcm^#i6t-B`wClj~LI|b=BjR_-7$>v67P++>INQg9y>9kn
zQ!5Ql@csLSoPsh4o2Le_ykaF(#e~C1%E(PeRL2|>h&+=++-C{!pN5KK74`Y2TD3CJ
zV^X(p(RB3(CE=@HoiC|CNPDBnc&6$Mam%8!>CDpGCahnQBym4tU>nAxf{5`qgcSzb
z9iC-WZ{E*&#>yE;XHoV76v+_!?(rYfz$14UmB0p*sXKklU);UX_Ou`R7Vi5y@A;p)
z6Fc6{{{S5`^BmP&eUlX9)JBe0#NvL%zQdq`h>{5lkXmfXpan}9(1Q{7f0`NO2l-9}
z@=(#hY!c%DF9i<BD2eD0x%h02+AQmFv)Zrn3qiBQ(cPcvvWRULf7Bypq7%F|K5H^?
zg$31oWZMZ&r!H^;9u9pG-KP|XM%f4r1;5#4?US%tv#B)y08G1Jg64tfvMl<ekdx-W
z_}G8Bz-<!QvG$X}gW&f46G%B3NB;oFF8<d7xLG^Fg01;X>?YKE&+Q>((DHg=U;J9I
zjq7%iPsj;U4ao#&DlR}iYl>^#Rq=?825FCLs&;CO0Ep6lhyvQLY)`W6r3<yhJE%b6
z2geo&3bl=FoiWv1&CbC-6zB$<+~3y2Aq{3%4Zn?ATS%Q7sb26|f>)A^AO<|~cnOl{
z_M64*0%4Z&$fo!(umkyuaDz$L(RLa*1@SUkOL&8-?ETv~V#KM_cyquZ?U8hF(_{A<
zGHXJ0+gaD+3tF%Frxv(s3@^scC&$y8kq$=XpFM`{acb4p`%!lU+)nSXM;t?CN5=_7
z5DdW+E3&ux4isy&;?@tzc8YG^$e4G<Qy_8w0EK6oz;#mcd^(a;edT7iov(Gp01U^d
z+o42AaBuf&gv9lfcgdHZqP*5`qT>GmqBmp9KC9U{yqB~H^$33i{LuC#9qMl)d5set
zf$gdT#p3$x8$4g?jC6$JFj6>mxw6mwOReOA1ZZ|ZZEnv^$M@I)t{gyixHf2xn+XOi
zL0OH;y+)`Qa8Qo0tkL3fs`j<HL61~%5yejE1~@8Db4&AOu&R$>{J;_F6=rXcC#tF;
zrv_QVsf?abtFsYxrbW+^s>1qjjO=Ar?BK_PyuHwZTZy{w1x=?pWZqM7yBUz3X_(Pb
z&oRXLpj%OfVcM?fxK+j>+RUmiYmS9gVB~EJvpS<jR+;onfKKSe@I|8-f-{*_O&L5b
z>KAIOR^H8z##Ki+VU76cs#C!`xTQVWL4!Fe!R^cgn^$ZDk+SV9t3pl-%xuWyvuJ{v
zA4&cuJ0*(|z7KcJ5J25$9-AX0qMSkf=UB4J0?w2=KT<o#vc4C1Y1DxOPHLbmXfJJ7
zSVuK`;y=@M;+Q)Kz|aZj0eGra7vvV}VoJ(G7~TsPeBr)NhFN$Doh`y7cWsiML#ouK
z=J7t!PXWF2s@28p={+oq>r}-)>sMziwXlDb_Yc;bH$l0wKX?SWpHP@x?9v%e5{6{i
zXv5}EB=8EWM<H^Xiut?tPO;ErvvnTq9&EB5P{iYqM;zVdx0A!7*5ZQ={{X9kfFNOG
zd@Tz%5Eq56A)|`Wk+(Lrqu{s?w14ygnxdQ@7#*>t^qvkjeN(Yh9yWVS{{SCiKE>{|
zYR?h$8!~8}z7gScZ#VoVIn%%<i5cQC382(rgLxkWe;TWt$o#fhg9mAV9gcrR(fmgi
z^SPbA9_60_8x4cWcUA3M(_zq5Z`F9~)+BzGTWh<Px%hxLCfpMr-Vb<j))ZlGLA*-#
z9WFdhXctwfSLTQfYjfHJnIohK*dTXobae=L8=lOcQk!R_ow_)oVx~acs55U~Arcu~
zwHh2Ub&kgoPP0un^m1<dT?_vJhgaYiv>X+?%8aJpBZ#Srm0wZ)ES<VI@LP?4ZH5Sq
z)`?o&`J!XIaJL58K6|kY5hDy=HARl;x_{|~PF&YTW<KKi8$uzWrqtRYpmFMm5Fke+
zb23mDDoxq(KzC-fM&F!(vjz&{8?t+fd({sir<xGZ-NtZe$ZWRv%{T%+NC^J`R`2^!
zEibC)PFw;uH+KQdufb{0-**y>xKep)F2D}uPvsoF5a!nmoH&HZ6j<tx{*f!#Y_DnU
zclU`yI*vaT3D*WhW~mIPp;wljnu+zg$o-tr{vh>03_Yw}WDnT)khpPGUMD-MVxwJ9
zh&?`OwQ1Cx*>Ukz@e1=;&5l-7-4kO46D_JF9-m|0XU-H_%^cOqH$%c&K;|qLvg7el
z4FUfEy7z>ld_j1sKK%kG9ZbPS&Z_TOParn78r1lKX@m!7s@pcLF}Uub0QyHNjJVh?
z!v6p~MSl`pe5{ULv;P39CMNt*CAt%3nC#67*g-ZYMCV{*iw8ixRi?{5ufi*MPX`51
zG1M_f&34Yq(;faPqq}G)9Pr?UoAKM5n*n7P8X($b(FAS<ite!{#4JY^P?~BH;xdi^
zbCnnl@N-aYW*s6>J({x)V(mRbcih#FiY+4^MN_AH7zhD50iwOGo4Q{h{>jb--V#q9
zXu7Q`L}SD!=Bc&;9uTTouQ+T1adVg^>XTkH(NbduC;<T2b`s{~o=rFSAElMBu)C_J
z9gJjig_Pu+=Gs?lZ8ztJq22XCxF79@3<u2~^q+9<sw`k};fp;<GP_PUKxhqt@>Ve?
zso5(dpPJ&0g%;2I^Fnb`cs`3Dor$k&ZNl?qP_e>fJKE@&KrSRB0JsTW7QBJvQIZBp
z*bE%~Qw=CHj2lloAr8b`IfAZ-ih!Q2vN2iEz{dNdIa#7$3r^1f1iCsd%JDAVIF3uf
zL8L;_9oJOC0M6G<Z6^L*dY`a&nOw&pLX2UzGvZ9gUZA<*DcJi%gjB7gc{BIfmIi#3
zZZ_Rqm;h#HnbjN4&0H3wt{-Mazw}6+^{w)Pc6+hvjrAH4!16%I6H<@6%yRos1Ho+d
zRw8~#`(xr)vd|W5H++H!43i$`wxj{<rneqK&>9zNfBJ`;fon!~S*+VkyZg+c7_`_F
z$fMnl{{V<kxB8#{yBp?+@Q?=xUP#6*coab_Y#7RR5fxf#pEThVoc00IGuZN6J+eMZ
ztDHOcNpaH14nsf|Oy538nKdOwH`MOv4rem56vp4+(Pck^_O~1JD_+ju`%}D30PHuZ
zUDW{O1R`1@&f(Qu9EixQ^-_2R47BfAHfa}!LD2ilAn9*Imdnc;9;>#A!QDa6f2So@
zljNTZvbQyn?Rd=c3otBsD;Hhme+rvjbFE8~w1oJm_mc;PYFjNLd61o|ZZ<p?ICa$x
z_B!ZhRB0O>2GCC>d^JPQI5Ldy;{NOXR&J_KpiM4wj0EY!dc+$!{{Z7?BXAEy?b0E=
zRN|%D1Mlv*N6lnu7Zdu7p@oxw&DBQjz&fM+Clm5aFyP_;0NPRembAUK5AeUi;F?;=
z#P<Vg{{Z1smKq?_aULMz2wul$aqEUc)RJ}{ms+&1{yVmRDTm;MxxZv@9KUt%{Lq{=
z8t+vHylWdg;{O0Iv6pz?n*L+99IDStZ2lD%x6M@y4)DB*jzuxGNvnAl!l_khkKvIY
z726vpM}psqqT=2yzsX`4OHZbV<Y#2Sf)=!${{ThNAU(KDEgVz#nD`Fpns5!{JW<@c
zLFQG=xQ?pMH64r0<ezfT$F$pdp){M#^1g?XZ~8vaGi#3F^U-#4+TH7*$A}&WRpF$@
zuzD|IyB+Ioh8j)J4r+@xSQ;MTh}j+Db5z4?VcrvGM+r<nl*HeFLL-WDnNE1}PB=R|
zaW|X2l;U4=B~xQuZc9^v8iAKWgfYTE=cq)-O^)BlGpc3g5Z-HBP!3#^OQ}07dURD?
z_if5}^i2evazupVh%!W;8L4$Ib>BogikdiF6!7O#bb<<FoZj%<CW75UBq?*i!)-34
zH19W*{TGI8;Hp#&eU1{UZ#Djl+wqPL3pYACr+{Nr)4O?uZcL}RGG~fC<ucayWQ%dS
zFzAae(kb?uZw{u&%!{BgV26M>2t#_C;GReq(|w&vd{VI&dtIE}!>1&09{`;tK^VmO
zZ5g;B%?n*y{{Sux`9k3T0Q*Y69;aoD=7}#XX@SgKr`3te2>Hs#j~?{IC*rH!jBo4i
zterCLP%zskcw9w3F~g!^%EduFp0IRlfaZ;=w-aGg1kKzNi5%>UZOu4uuxF5Iz0^|d
z!di5h)S-@b7m>wwd^Nzf2y=SGuF^x_RMZ8`BbJh$DzM~R^-Ybj9o{ePDjERYF35gm
zMaSNGF^N!XdK2|jT0K)8iPWk80NR0boIdc*Wgigw&fjIE>_2Bg{{W*n#b%fCY!Eha
z17)f@8V@Z{V>?D=g3W*F=A>Nw6}IT$yg)Y*U(cT=iB0hv;n5K4Pw+fse`2qOz^wQo
zyyAew<AseHo-2N_8?731Da6)s*Hj>k(U8V84@7Pa!q1M+;?{fr0Jn8UBI8l<@Y$3g
zU<0#6Gdl#}s2%807%G79daDWb+5F0b*Jfu3A2q9^)qW3We(#mseAl?s_1pZyKeosZ
zyPUX`Y~IYn=n(dPXh|jl5ZY9l(Xay3q53CCb5o3rwi_?IsNch{z&h~tU9>|uZ#n+}
zq#!`2P%*Qgh<@qBliC_D`C3)W918ZS;zOD8RYX<86TqfnrqWtGti6)&fLfA-fs!%1
zj04W-j{CH?%u@dVDhFty&GG!kjaR&*IrTvav4Q2V&woS*{<c`JW{@Y8-7t5~V8>ME
zCxRkR9iG!>7LOy<aV!4-$-n^`8ByH|48jPJ87eIATJl~mmYM7jOIp_X%BCNRm_AVn
z?Ro7LBCKhHAdW81@<@xinc_cXzQEtQsyr@cXhlW>F>wz@Hf1>|0|mUo_i!IY#uVYF
zOb>$8N2pp7Hsm&FSGLB$K;ox~+@Z%|8$X!en)lAg!-yX>d8pr-;yzn9gIQvDa4PrS
zrTZX38Yk3m93~Y^ZWbq+?M$Uydld{yq+{L}9|Ut~1Ic(C$FuwQR&6U3Rc+_qZICAs
z{{UrLsT=O#y_$fb?E~tvCVf^x;G#hp6v0|Wi@aW{f!w?~(>%vC`FVNAMcc0jl&=&t
z$LY;9mc_tan9<hjEu*~?yE%fg20cE-Z^WsBvp7Y<<+4SjAZ4c(AU<lSJdKK^@XZWZ
zuV(Yf0i=#cg1gIMfTx!NEqzgK*?;yEOHVr}vqIIb9|dtaCgD62tn^Hs?y@eX<9V8c
z$#lATy*cz*jl_D6DsYxL@I8_Qt>(ML4yqd0+W!Cr>`al7oriJbcqWeSsNEpXsxZNK
z9!a?9KE{zcazW3ecbHvc3Yc*c2r7;OMZi&o&Bi>?-ix^B<cS1iRSI=`)1SA<mD!CF
z9TzhvX!eH8mYtNfy<$<bDLkx&*DaYjfy0oC4VlAy$+|B-<_y3bO7P(rIznmQ-M17P
z3kDylM(YblbbHQrLliyN;3r^<1l{fmFt+A72-#BN*UQaeq;Uy1!*z(UuI}XW*z<Ks
zo>on&-h?UcE?C!D)gf-Rh8az{2>S#705MU3eul<L--cuRZT3CL_5T3(1MO3vj?GEg
zq9hS;Q-v<j%%AK&c=JsyaLgUNSBOJh&T>b=Ot|@>n~y|WQHJK8lg3kGkvWf%LPtx4
z=I&C4x7u-W&>=myv<Rb5-3K5PckiT2ozqR8H#|2VTU`j>k4r8aJVQQc2<-iwV(SuC
zWd)ERBb7GW%VFMi->_$rJK7K3c>5z79@Cixx$Mj>J5e9>?C~1pc{Cv~?}@Nq!)_lp
zFKSvge?6ZxEsWHEsuz3m>c9Bx#(HS2xv%6fQ)VA0_fDJJV7X1bH)EbhKd?zSVan0}
z0EU8prb70+bbrvj;_o<KXk2z?z)~FhTcXa3r~E0+3Eg=IZuB+45^e-SZs2HRM+UT6
zIzZ2%(Rgt*gZW*mVa$;lAaNBSu<Ej#A^1J7;79pX<;`PTMY?5~=#9>6R}9vL5Fi4D
zqvVMJe-7vmyQbFtQ8I)bd8YvShZqxb)}g%)ZNDx`?x$6?U(w^}nlLu7zPAaz=VpJ6
zoH@6eel}JC)Q+~j_GC7D`K+7TOGlCb_f-jZMwONneVJbM_F11kh(OS7zB&TvXSzmO
zLh%E)`Hf!r+2h@hRs7a`)wlK7d{l_@PQv8FjLH1BKn>B2<J#!Gk{7<7O*}SXzVf6Q
zBTFvR-93>On@<JXDY3^ZScTg0v_B9n87*3FSBMBZCQBMU5%FVT{(`fBN0C;YHCZDN
z@O+cu?;Y(U?{ey!d>@J|MB8~0Qk&anvf*n`d=Oe}%Mg|CljL?vn2XO8Hy>hn9xL9@
zh~k9u*{O))nZ8W64S*`C!-gR8OaalM&(%M7bVu%q6sdp*_)WA!5b)-x(=s=7zXa?r
zp99ayHv_Rn`K~(~4-N!*EQrE-t=Dl0pL$P-7?lt$DscCPW0EyTkW9u4VB=Ly2b<u3
zhsdF?`-RXIeSnTg?^U@Rf~Xi6*(W^tDxK$NEG3Q_Dkh~T0yk|A(5qcF#F|YHfyoO_
ziii&P@hYx!rc*_b*&EUDTsBw`_M384&HFRQ!pV)XyK%NvI<6cGVaVWX2Jv^v2DD5F
z8Vr*O!axP4F`f{Bq}aX@2+AAG7@5;hj_i{hz(fE^adwb^<h(8HuTO^4sAarV^HjUG
zYWF(H?x|l3v(gY9*~pRtEio6LlGew`G~%e@PeGywx_uDhI2g<;jSj|=<B$Pht=c_`
zGc9lHPm?91(k!PpM$iQ|{u@uUR+O=NqH#Ib=C~cF#a1KqiTeGq{{W-nicTI**cPV!
z7N=+>phY^P#Lw`HYSB4VVUe(VG8Tj{YLo*S0G+#{7j@CycZhLDT5?o_sZ**ZND_D~
zCap;B!4rX;mCKk~2VH=MzNEDAz^J~V70Y)7g@+UAVt{7H{x)RlfF7Exv6^$s{f(M5
zt|5DLoA3x}EgVs~+3XGo%KR$Lbr(tyT2y5Si4}ox{Xm_A`1$_;N;j!h&+--vH&rhE
zf;T4-8#uW71Oz$lr$6--{N{YuvrVJwqwaBw{$w}m&I9>Ew(;6|A2cI+-43b@TVT|p
zfA;U8=Q0+(zDM?KGsPdfqd~8KI2KK_D?0}^sNHTN2j<;g)q8m_<v4oVVM6SN)9UMN
zI0L-OqSuy!+HWo1=&BM<XprHQCll2)ZrG5Tdzyw=?5@kBE++c{FyUda<m-gu80|EI
zgN;rAj46T(pe&s9)eK>D7rMYHid79J^vEGDrhCS939{HNs1LN>1qh<<(&y70BSn<i
z98~KzUpiypx9GU;yXbc2Nh|oZ9Pde2$2aS|v@p0WZk#%CM|1&%aQh$1zmZOpidVIL
zxnmN)m|+AhH|V?qxFe*kNIwmeH^{<pXm5$fAqv#>I=`yI_Tf9O%gG446L)I3mX+@P
zQPqDSe9F9TJA}6+?aH&3K%?MnxN!wp#oN5W7ArI&Dn-)+jV{sN4v$a)2Ti`q9DWG3
zuk4%b;#y3V7Z@rtjk8W74)YF2kqCEB#zCe{r;$O;aO1Z?uF%HQas0r2k%iRalfcm%
z@lBV9T<CU2JUARbPRx~$cyi<vJOX4LQ%y6AHr#wv9DBffL@8jTF}uky7W`AGJaBel
z2m$6gDdvm_mmUBw!f`#*zjQ7MvXL9dlqjgjxPa|PRNFZGe#1M#O`{5^X*=D1UZDx*
zE)go9-?Rq2^9V);h7Tn7Q-}nP3$}{Ss^Pr>pan#{ftzlrhPokBYeZO|@}G!n3j72n
z<T6oV)o?&&sfP^}CPZ*R0;tj=OJug{gK4$DmpD#_G*5&o=HHc7ZU^q;utl{g&7Rwj
zG;A_X#7L6wq83wp%7Id)BE}!gbt^-c1(G|Dtxc!O<vwb%yPt&Emb68YB9j}5<Z{sr
zE!E#ZuZR(Q#Gpsr4UYDSgb0>cgk;ktpmHe2FAVWi$ihx1n>0r?y;;_MbMj`-=Cym<
zEkcmRQ2h|YNuCBQo*J?Z3x?CFKELt*0PY6=0O?Q9Y;My(AF@O^Z#3LHs;Jr(3H%qE
zc@;_=GsUQ0*$(wiKZZkpx)!Ay35}NrMO+*~&lHAi20m%HyEtSQob94A{NsQZLklmy
za#d<Ns|Sg$vuA>JcQ)^IKZh7qO<RES<0md_HI*7w9`7K5#OjWd=dRqA5%&xro{K;S
zrok>Iz$ngLvno)0-(|hpXl~TvKtl7o)nwZA*bnFgY{+(oN0=y%@3K;0FFiF`N;mFC
z&&~e;X-C9rIFC3$&wSbSP9eCO2?Ah1Sj9J1X$By@qpAU|r%MAz2wYaA?fhB|6y43b
zD9v%%{N_jXXYxkuswb%gqOGDbO)*KmV>N#6nLc@x+1_ltoQ7pIG!bFR9~Kslvnq?v
zMv|!(+W4*WO)W0%Q^wd-`K<B|;GJdx2cHyfQ?NQS=7613@KPQ(a3AGTG~G+Hy|jQn
z$+xZ6L~PF(Lp_<ZFWCs2`l;_7LG`1M2-(+#NII!?fyfRe9)M__<7;sr%*z#(T8#(v
z!u%?)4rv^;O%``~ihMSMC|tNtL@vrLBdTbt+51HrW$g0Ib58F&JxkK5^gPy$E<FOd
zrzCl+88hT1K*!y(`rk#wt}8b;?HLWBQ2BzhD@*MF-FxS3+`;lu6{y}gjf6Itd=Jgh
z7;9>{Mw#rk;U20))So7V&ia9M+roG%o!v>WeUnIgO8!V$u!^nu6&8Gt*%;hvG6hi5
z2=-1zHvpSj>Pvf&VEfRrtxe7I&R4z`jyos78|qhu?!e*6*1B;4m6_(*vW#;D*-uqc
z&t$ee_S_+0cWVz5dhrN{(szQRaywo{I>32283k#xL9lLCxI7#Jwh114a!e-<YHSb1
zT53yt)7&v;gnLt-Y4ZXBHd0;QV4g_fP+*L;O>3~b+Zg<=yA3)=i$ZfBNDpT_`^&|r
zmD3M-OLnjBnn=TdyM`rEuB1$p7add_Lm1`J5aB4u;I25JM|w>E08Yn{V?|CM@3UU3
zw>7PadmItRN#qM?M^qkL1~6t16ejAIa@ezY_1RITqfMhhlq#4<6xUU$5r~_76+%dE
z7J_Ugeq8*~mPd&DSauAG_d?Jkd{Tr!Ya^+;BpR=<;u^BVBqO@|kAJ0<5OW6T9?tla
zt9=$1QUi73{zY2(s~EX9Rw5IaMqt1>tx<|J>z6x;AP5Puj0b_q-4u`YCwg?l!*B9|
z9(xamE-G`yOz?O4O)}RD^+q@iRYL`s{EkCwNx{8jd_X(YEY7D+Y2R?q5gyyPtrQX)
z@+*Sn8-3CH{k<*!0HdG!Y*@A2GahS>&*Ho^bZyVrFq!>FG-WfqV6!TFZ2tfXrZ$F%
zcNG{(-ubNzY|RW!B$7G_Oy6@a9)Wnh-_-2W%<({gaXC?z;X3Q2D&@5s_}!YQ2M#j_
z3KBh`#3{l_EkOO@Z6JOnKksP?G|#uKwv=R=Y?;yq+txOwICI!zns)6<jRTlv)>$}l
z2opl3c4Ka=GKO8V8+`4txW4k4XyzqGH@kT8C1nBg@LtPUJ{v!p8~xT=GC%4iX;pyv
zBR_b43GR1*vTq9VO_p+^NxW^1>R9LWY%?p0Xf1DOrhj$d?t`1p7OAs>o1Okvc!ihY
zQ){X}$y$c{V~vsVp7N;~Uch`7O@I^2MG!Lg*J-SOtf+AzJMNxD6O&)X2yi!EH$H!)
zEQ5y5DZn(@&UQ@)RA;h}3x=-Z?r3<6TlGLiH@JGn!v&3V=k(oP+9P0fTDm<D9pUXb
zeO0N_?C+$|I01xwQNu=<x#P%lURwtE6&~aIjlM`<0r*)@eCjQAJ^+!DIA4S|I=blH
zrUdg@Fa{4GwWWvqN|V`Y_`f0df;+Af+Ku+X)dK;7e8^dMq25nN+a+=bs9((zP~DU9
z`$A1hRkmfW-3+Zs+5mehSn&mZBTsur>JUBHGV{Q2Ou>Mh49Wn=EnPmhT@blHB^Y~$
zVe@LrWpGAag;ixY8e$~Grek3^NsAEWZt3!WrzQBE*mHiWWz-B0%-f=S(c<zz?^5(e
zA@}HPRzdC_i%<^9lT+oe{wHQ+o-Sjd8)7iC1QY>oEMO7F>4Eer54@!8f#ivOA8EeT
zKD#tb-Kfy<KbTI<c2qdT9FXxld~-oG;n2dRE`bDRYsx0=f~;jb!r+GC$u<MV5NBue
z$LxfoaC1$&pzu$>0u!+FR6%orCO36eT*_7V9g6o`)QwC*{{XtoEb8`YD8jS#XY7r^
zSv8=K>Rqw53xJlYPIFtC0(L*e;%`LUZDVD@LyLvm!FF=W)MR4!wgoUj9;o3BF@#x8
zcD8G9*6_1ZIKmd*o+inREC(0ny{x(+AqA{QRU~(UWJYSJ?;TuXIw6a?Z?k;UNVq<W
z7kNeV2P`jHApo=t)2*vb!Ri;?RT<OM$zo(12+dHa=D$$cU6uG?2FCVX7iM|T!%0qS
z9e2DNW_4YvgtfQ{#b_BL-noy)V_D#=d7*MZ0ZdY+#yQ<HDxs+CnsDxpWgK8!kh8@h
z0P|Vb9_d4W78vG@m>SXK7F#ELieu_fB;jc4jw<dSeNbun2HqrF+Mlth{{Z(B_^dR~
z^(?{JqcQ&gao8vA4eBO5it%E^z<sVsrLM-}K@r1kLfmY^Na32N6sV87wcjT)oDEuz
z0yuLh<_RCV0tm5H@K1oazz!F>XZ~r`<kWA63}qWMkWn9bQS}Jvx)V!W4sx3gJ~+yF
z>=q9SY;7o@c{hp{o~Uk%Pd$X%m^&N=2pFe{Hdn}l%4KHb0UMH>1%!2RUdU@#z499(
zd*+`+asV&pV)VMpAIDT@d8;qoPHQPUOktq&@Qs}omqJ`#ehp3pp2{0<%A5F<i$eZu
zXVrMCPoin=#ZbocCp0~r{wTp2bystVCvK#Z@<qMnHsN895a3VAS>7Beq0M_sNb9g-
zd)pv&P7b`(euVPm7WF51+nNIlsuDbvH$z?bqlh`G&8tY=r(5V#PVF0YaRFHDb1@qh
zP5P#9hWCs=RsR6RdIUA2)!LP1;&A|Zqc{kr-z3X2kRT4XR%-)Aw8Dw+S{wfWaye(@
zhIJkDKus>u1ECuq_l(}`=jO!1!fLUHsFMk^Q}Ev$bonet0fHH=lLJFu9KqFQA<H^1
zpR`PZ>F9IYpS80}Wcq!9B=S#(V{A3IDECYbnPy~>H%%3U*t^6!6vAkhvwO=`?=NY=
zp;{OK`E1~{CQq97XodHUn+IMxC$uuoaURox{#rYZijC;xKn-|s9Evs#T0Ecw>Qe+f
zWjy@Q$;G=YV4aZc40Jb79IU72Hbl5#C!2OV_HgEb5%{!qXEDoT8Y$CbW8N0XBO-QY
zb{*Q3%meRegb-kacSM*}x67cvmjoKugCPFuBpmuI`|UDbiNuDPji1hw*J(ON!lQDS
zV2(-u0E>u_jJlpp_khu2V0g2I{%WI$YeQymMlV$r8<^&#9YOjkZY~akY}F!IPGdld
z4QqoA1C+|5VsjT<6NvklOfJj~{CN_Yei@-0HWz~0W#qUFf@5PG6NCrZ6JEX<qfD7?
zKg{phIrVCnd=M1pvpwc>B(Bd%<~&cns6k=d;qgGUuLpcLctRjGj5QBAF$u%G2Y(fa
z;6kfVo;Z`v`z_VCR|p)$odWF?eP`smXsd=s7Pc-&G~8}(rLsONaaVU<3b{MhI;R=`
z08LLA=fP)Ar+MUTo@WVE`?s248iBKZD!=-YJP<Wd%tYhDF7B&>mNqW@f~3l)6bE=o
zRLe;+V4(i;shR8bRPJA6D7r~RI&y_C?wc0X=QL)tG)|1Rh}`G4B3uIEv$nxIIIcfq
zkN%d0UL|Jr?3@68n}6sF8Z$h?_nd|>-QM}Rw4y4}(xY~oj_nmkb=lhE7+3_&*J^v~
zy}(rOG92Wp?LC^EBTE}^XymFIl*``{n?kGraLCx*RHasfOPtEA8P0_A*d|CLkqAe1
z+3B$LiP@2{S%iJFWMtw@bE@bMge^|XwBOAO6X$gJ5OEh|0wBk<HAe}kd+Nvj#-L;p
z$8$m{_H#XWEfCeex-*Zo=he&K`E2)!<bUZ#@b8k(ym!re;WzU6#?D(uiw!V(-0TW2
z(h7STYO(ftAb><nF4A31f#7#-lIb@9o(hdu34k2%K0#B!+7zT-ca4=+us;wc2J1$*
zM$Cq{rxVX+wMWbBXclRuwOLKAB>QlM#r-aZ^W6bJ+!WsS`*>`QdmarpiDBS*qjGLn
zwc$J*q*Zl%1afL|JyROu0E=;14ddjA-ypaTaM}~Fx~An`LnvHru)yz`KC3_VgZ|`V
zwI>Gp-9W{7R)6}25_KSgphetMjqlZGQJ}TFOK-55(5A2#c9GscEQDezvBE3id!yLQ
zly|7r#AfCtdu=1?v%<V`?=i(@57^0C(>#vcoHjN=rskefHoFBe?H{6U<E@KTP_>w>
z?hpNt-o&_~aK4DPZGTXyI2z74n>K7{s8enIk$+OD<8Co6nAK9O%jCxgeAj4~pW)PQ
z6Y8{kmVu!i?_mPrA=rLtm!@%#(ovUDs97JBi4uTx?mNjJ;kfw)s_{{A1b$cERw!u!
zwFk4+Lz?WK(;%;9p}w0uO>;=|TD7z3vDaa1!+7UAU4_9gGOI6Ah{@5b5b?}}cYqe+
zvs2*+r*8uz$v1kv7Cgtib6G3Uao!yGuLFi4SDRGgrP?kz6`B$pAdX6K=vH=kS`CBP
zZ2D{x0SM<MI!5pT7$H)`*PLy(TtWm4G7sodMoH!YvUJPQyl#Y8$2`Hb8}U`#>9lHt
z#xr4<C&^G0&aN8u0aTLqX`<~8YvRS?-vv(4MMv}GjfPt&68eV$sCXVoebcvLqAnBa
zIu8g`-$umLeX`c;A9vljFmeHZ;lajax~p+2v}gHrn}lq#n=k}VcEdh_Hq@M0JyvKz
zSfB%Noekmb9*KdBCSzYcgiFY1o+w6ybP=*QL@H(U=<hl`0ue1~^%8-e_W_OsTVU}p
zAEGh3?>l6x8a?>M5r_olmT2am7ewQS-O*EM!Xid!gIxF;eUHpjY8ie<o@?KBeRg3T
z(4Fom<LwSHQoqrkx^&;{3%HjbF|(G<7=LfK!~XzHft2boqyfeRui;a19Zi;h`W_zQ
z=bk}-2x1*h%A3CFxb!KQD>mkiaz83*YldKrxv=~eZ*U%{Kn}tO>9Tu+CUDJ_H$0DY
zN-n2Yzil>CE%8dQZA=m6A5DZRtnXUh>FmV@23e+V_qRFao=CKsr0(Y%18|cd#m3hV
z)`DE*Fw+3Xrl=e(5@9|n;hWzzGS*`B1wb!oagIA14em>?!wUXuPsJHBZ?l3oG}$Ms
z0v2sJo&=Sjc<-A2YC7UOI1Raq{{S}ahu3xKP~py}?1?4xi_bY$-E+^Xi6q$#{?hDc
zjuDb<xwUaT@CMMbFKqg*2*&v+bSs8|7zEu5O@h+vRX!8F6NGb54|5pj)vLw9xpeyt
zn49y=tkGYO*@1h%J9Rxq%}E}L-T5*4TbjLVU>?gYnls)3C-X(tZg#<GvW!(3Mb9JN
z2LAvh5PM5$^bka?2?^dRZX@+DP0-vFa^`HBL|!w5AcH<@EZTU2j8mpAdFVXTKI)|$
zRa!L;LwCB|JV4B$y!I13P?re<O(8}$ZoGX_I9}NRb3<~r`1?7u5InE;2I<$DKE}a#
zma~suNZ4cmh}gq~j99t(BlonKam9_;P9crGAgP&a*iNqCvS_@bA>F6tvq31_f>R!D
z2J4|y!vZDOlu3if;Dy_lHlg=@;~&+AM0{1dr;4EGx@?YAoC5b-@<LeQh2x7#Hh^2s
z_D&*PEut6;jktNCZYbqQdEI|1pZ&WX4hXa|t1$xO03AXzeKPex2u~r+HYf)6hL66d
z?|x>W6QBWho;(vAS|k&;3z>?U`6H29=A714qR=m3=7r%Q(0IoL!G@!o<%Mc)HaLU<
z4sICYoc8{|@T)c);Vu(pEg_7DXy(RcIgdP#?tyGLOsQjLLt^E2YDKO0gs#m=zDTjB
z58c3v@J+zyu;Us_byS^%dq7A)z|Aud6C<jmLHQ^5h1YC*D9Hp6noEWH6%Ll~PZdqU
z(YtFZCVi68Db}d$LA)nuFawYMDsf}QJkW-`0uhJScXU5as9~VcZfz<BA!wz#3VefO
z8DcwV3e2kfICw@xA-?l{aJSuAyyH7Eg0p>qKQu|(A^r1k^R`KargB+qb{a4P{{S>-
zxHtaPKM{q^{Y<(LT1Y9krB4?|1#s+;1=yb9`B9MX5F$bKPyYZ<S3WIrqUVhZ208Rz
zEl@aE*AFr*6(*cvumIkjMUz@88{o$Up$KLl!fml8kbA%g;D@$;$B4iYr`XlQ;j@qb
z0JwO&%X%;3ybmVd{{Tc!dsURio*m_uEu^%Jh(w=qvp~e(+#(%G_hm-p?VjmcI~Vqe
zUek&7*znd>1*Dv(MTZM3WA6p$2$XRi`L{nkfE%tToGtHNRE4=zOt(i4T=t#clDW|W
zW!vx5qB0@hC2witnb50Zzg4Rm-|mov5D&!Jd!ICa{0eMuT8&f0^#M$Rc6*^~+3A1S
z7v%b)0X0Y@<SweysN0skoc3gQPnyt?>9#KFX@$>mfC7sBt-b);v4@`$i7s)Hc$92@
z?9aMT1G!*rx!n)M?(*P(iU+%<)5K;`oL!yd^%|{e0I~$P(I05+vf;4jWI4IVCXQ*d
zAa|Xho4=wK?xXvh2E(#2((Mf+l+Wy}9DNT2;XDwS%y9*Hd`;+7&wqsZriRk&)bP<B
z@j9YR8+1d$aeCOH#E&E{O7;wSuVs#(EJZVY_1yCA9D>cl{z}BlXZ2ZG#qDRsL}cN_
zB5)BSkwk_zjt7yR?unwECX6j$$bXgW_9{yFLz+KHRyc?#Wl)u=XVr5H#o0+~hhMVn
z-tUt4j@XGW_$cO2CHK_^zQ{&N*JfmZ5Ws1)2hOKbsRmS~ax{a*Fw=4IH!<Xa1-+y@
zj$vseo+TaTuooUJPrLhB!8X$*d$Qc@fOMFIbh7?o*`X=lhVJR#r$l$~?*46Hpkz`s
z$kb{PvuKs>Gyc$a6W-Emi#fM~_F7KDskVEW#paucJF`<U<eCvj!f`k2uys2OI3|Hj
z{{V>3ao@Jbc`P*Qa018>s=PJN-DZok#cD3yeLIN!$ZVS%#|=>XqTBHkk~pAe*I^-!
zcm#~l05#AffZLz?c5Gv67dJYC5d)HRldji6(4sF**nr}z5z8^?sA6l0GT7IO?HFA?
z659z_DhYsE;i{%X0p4M7GMH_gh=p^-3l+LXN|5gwFL?*m05<04ynoVAbc-8&)e`nn
zoJ3{<W2-f-+!;tWL%O6X7Pl7bT4RFk)CUggU+RTdkyWx?yYLHLl%PY2sC-1D_nX(}
zqPdLqOfYgegu+}(Gu<_#;Wj}71ZIdU_?yR+WWMo{;dY%9orJJB;?H>m*{14%C&|QR
zqew>VG7z)yxO0%7fxXrjxbStMSP_0`Zwgc7<12;H7I1)`_IItm6A<R+7i)0DAf5>K
zt(yn7OVu9E&f5HgJ(b=+w785f&>&v=RxQ-YU63{S{{Rt|Dzke-LAVEae&`Tp;Ox0B
z3ix|FiSipbZ2aGESpNXi(F+PK`0M`wqka0d96ldJeWP`+*l4Lrcd5G~t2R&Y9&o&L
z;+R-w;Mr#0#Z-<*><<TJd2d{mngD$B;k0Na9qf2kZ7ai7f}=<3+wF^vOD9|^Va%k*
zJ^o7N#!AtkCmF5Uv5-B+FGcOT{Q`_m4F1+M51Qcr0Q*YJxeJoAn<4)I;!{A*B0T~H
z&LonLj|BM*vwJ)B4WeB%)1}!|uc_qD0XR-zaO$&*`L7Tn7v%o{v?h=ItsFgFlJ_7l
z;!t}BQlK@UwwW%&zIY?N-q3v(rQ0>W$ptx-s4xaPsmexi#HJc_0lA&(oZN+^1??JW
zCuypv(biQ~86eosNSl!y7AB%*@)O+Zt?cb>LH_k!ovfuslb^?U3nE>PlgS5C(K`=_
z9ds)3rxe@+fLqc8%FW=04o6k*ACAn^WBo^H4v!==Asj6Pllw0RcH<p(0}Q23pTjii
z6xbS!HlfB3&3I}s74K7ziuP)&4${Yd$Zs^k`vgF%ztR!i#$fI6&B@CxRaZ-^5bY#>
z+dML!FtEXEyh;J@8*+|SE##UQPiOgTG9{lRciQuea^k(d%<#1B5BTfwQ~sf#1Nq19
zvJ2fG6x^NSYTj*(w&|w)lV`nb4!SH&cX@(5mbR!qsDBN`dyM}8v~2cWbO(3ZA>+$U
zrNDPHZcG0FiBD*;1Z?B9Vxv=bqYj}R%-hiIaSYAH3?K**qP`{_(|kuD7ycL|wknzU
znNtBera2LdBxSI|K4=i<l|;6^APb%IM*L9TZtY*+Y5xH6nh#Map~1RyS0w^L&0VvR
z&t%6X?Z_kYlrkue=f1$~Cx6Ko#vSS6j5&ljDzw>+py6hnQTxhu524rX9fu3rY*Wc?
zS7O54%%g#oOooGNXk}D+XgFF4$R`_8dFy2aI$@(f@V@E4lHR2omNrjNz0#O&aI|qH
z55=l4<f}c;1{@kVA%&a`(~7(4Q*)_5+O_cWTi^}xt2~jh87iWX4VgIvL3Sq-^w=|I
zA4@AM7FG`GB@#aI-!$1Vm|uAI)4|Hd$@{On{{U|#aVjvl+B9xJdMhwyRv6$2QH}8G
zO*+P8DtIckpqCu7)kRw8$6V?9Rw{t_xcMl1UOUD-=Y`oR!QHzM^0E=$JOFwl5c3hD
zV{5k{NCCs4U8$|&%Et)`kqu?ZIeM&`$29){ZqIjTxIQ=ge16FLZZasgM-TlQf8`nn
z^^2^f3w@G7(5g)BP069-cyw|vpj|@)g-f-#_!V2T)^YY(P*27cCo`zGuFSiz!PUwo
z)bS7KRG7xlW)X*3)a>BDBZpj<qPwXuG{?F%*d%aTpk=LY9$Zw8@uLv=Z4kzGQSsg=
z*~CXH#4OL(RqPx-mPf+JpZZo(v;_JI+5Z5;radK3iO_n06R?~@3UDw!n<Dz5B15`D
z1_ota-11H{QHSAmoS`$KIO&sr%gC(U=7H3vpBNG43&mEZH^)5?(64RnZ>^AuR9QGW
zln(V$W3AT{u-fskmiA_3HL7?84P$J^445`*{{WZ#tld(55ZREtbVGU1C(B{#j^!cV
zKciIdyp`jwaliEF5YCIoNnR!rBbZ(WoyMPUl8C9jWHUvb=yohV&}p*FFclsw$0eb%
z7Mzfpj%ldJeH69XIGq$>X_@zmGCQBC*lpflqgB}FI!6#!iJQzi+c1YOMU!1i>KC*F
zmyNG33;48was1z7+9%|=oren>cf91#q)=z8&k~#bN6F)ow#?IMv=_HP69<lcZiKi4
zyTou*@Jm4fNEKS&SXwxRmItFF8@@MA0}bX?6OI9SreU&$fCxw@6t?lzYh?FAHwMf2
z!@M1HQ;Cm0U@zr4eqWZ|Snzs-eWyOoOf4t|`VzQC)_~X3ZO0hC!^u^}jj~&jZo)g}
z7=Jz+eXt8mUR$C&x&-%AV3~~klPZ6raqisg;oR0m#D&E`{{YI99Mk-b)fX`2T+=>j
z=A&oCI4=!hfULnk4UviqOeyfoy6rGf0mo6YnQ5GjiXVjR4vrflK<TpN)5GaxH=|=o
zM*Q}Rb3(;-VLH5-_$YG?5r<taoxIW^Q^Vdv*^8R)?M>t~nJ0BQ<?^ZMgce)TaJn}&
z18koi)gb8n>hklnfjEKl5VKvKkEmiEcr3LFt3Z7{bn2tX%J-f=D>5YuHI6+OyjDuW
zizltn-lXO@AJu3^2B&~W6&Mir5Hk{;ZdX%0N^}zGo*dDOv{WNFBVMa5m2zeQIzhCd
z6>7R8gF?xVlzq2+_PNHJK(WXtS=F;z6~kvC>GtiP{-w`%bN>LLuhQs|YYvF+;xV(!
z?C3#PYh1|n+1hFhIRa#Q6+^REF&}wIe8eUm&d$?S#K!z!U8BAN*s04O=$PiagM=NT
zM{s^&dnDhABoK&egTD2gC)ym5mW@{wcSO18lJ}a^<}LErH;DiQ;oid=0SktIls75S
zig#MPq2TQABazEyZ~*h!n}u2Oa+#}F>;4qUAO#<UJZ=rsnyW|3pm)i3icF3&yG*^3
zMKB1&ITD?@oDZ(aj6`3UT2*lPuXrfFZ8r`a3f%^)OX(kC4;~4!d(1<C=2yH9%*Lq8
z_h->v&SfHe>4lZ{33elBK)$iNdpm#or)#*uX0f9{7Y&~KzKg=smtC=nqa5?ljnq6K
z4Uoz?V|US0Ggg)nQ24rZUxp07kO=NIxLf3>4JH?q*)DN2)jXUg!!&Fi2hQn}T1E~C
zTO;PYH~^Ir9)@x!qRnEOeVve7rSnq{yZ-<&+3c4z{MKfxerQN;Hjb}!PQw@^;rGH8
z2fS+GME?NPvthuv6z{!bvD0rJKVaO|bcOE}aIv?0&P@uS?gjYC;#Jp*jbab=BX<0@
zn}-4qM9?u%7wCrT94Df1Amlox#s@L6`fR_P{{WiEQ6to$_e>-nPFVp8A`UJ=t0N^;
zb85D;yxS>_4yRfw;So5pao9pPqgVs_P1H->{{VW;)X4ZO!Pq1LB@5E+evNS1NyK^&
zihDush&NSOV7Z`xdZ+vsX*uN9nfR~u4)j!=hMR$cXEmP{#@k52S-45ybSg2ob;FEm
zgp;BleBoihDLghe?rYO6Hj#=fRH>Lld6c47rcm5=erU@leTOF_V^ka(wP{h%I7!)k
zPmx-gKn`e6MMU9MTHF~oGD^h5JGv^koWc*HyDPlo94B8T7}yEDvQ4{lg!0@|y%VCS
z=TXg=wAqq@z{2<<PQbKB$D>9RTI1QOiRe(Id&6dy@U(?kRK(cIBa(do0OiX^$`zWM
z;>pM8j(wrgeE`_ghrSrPD|bx;;I$;}xok88`InlrO&~<<i3EY=WpTi&7%H{KTQ)wb
zL}{vPX|&s|l+VLvZp1#R%_Z&(G|w$nGcYFVw7HImc5rwqtc_4OG%75@#>$QD@^vP5
zw|Kz{vnsqbw`q_$A_ScP7(wU0-fQ1=&n1!B;(_ugiwpHb+9hyWU(sex&L3xo*=asX
zgP?J@&33NL+4r9Xf8sO&BiWY^RsR5pQr$ktE|uDv=D`Pah?{D=Z7eu|fjF<or?Sq%
z8eIn5B~!&2;UV87(`^34rj$3Y0Q<2LtVGAI$N|>6%oH#7ZQfRB9#{Jr3K;(Y{iBIh
z9@o@3hy;raio@C&?$s0VRr`vE_*Ep2BAOo0eTNWMbW><ON<Moss4yQjqIN_y57fMM
zx8KRS1ljML+wj4Co(enNPKlWE!B0;#;W$l#{#&PIzTl5tgU0Fp+iaUssC`~h{th$!
zS6Vz0y^C!yP>uWq4mqa|@!yH`3BjZp1n9EG@zpmhqwBElgC%=#2SBfex5%vu8NU4m
z%J!U3lApn*4DoK_7o;&X{1z5cY2T>x4QiLwIoET4><E(LZc6a9K=RtzFzFno7vT~J
zCp6LsZO;;%1u=J?K??!8I0?xK<;^fT@jcnnc&8FnejC04N8i5yo)MbKLs{Ix9ZUro
zCWv_#;RucPSbk>Z2;Ig=;TG6r;g3W-e?)AvB^YR=(QRXHjt?Itn0)$O9qN^@{UUz5
z4bZU|e{?}_`bUsQ<shu_VvmCUS_Uy_gfZhAeD?nUF;r_Q;>?@JMZ4MBeBOASqrpv%
z>#7Kt(NrMx*>WF*^0r8b>vVhuN9igzo*g_DzVx3}7ju0S8a<)g!00%RYf&vcM-(8|
zpF^q{EnZ6SSEfhx3$*RY?fERQv;LGWGw8fEujjKj-^pu8RQ|$@S}dGTQnLR5O;T5~
z=lvCzuE#)oKJf5rqyrkz7#YD*acIoONI)hKhYlGPnj5bW@y)rWoDDy9w802ZB*>`j
zqeXV#41HCArxw?$Hy<So-UAb|Rb1zTaZVRxB1{*!%b`On=+AvqEq66V)ZFkGAC=s7
zK=+aOld7B`?-9XMYj^}qgvQQf!UA>?hO-N2fmCr9vKsN2g-Z*~EEQ$8DdWDSN(O|;
z*Jb%2v{@ryQ)gj?<8Zem!0c@-9bJ+lDKVlrx)3F=ejr9|(3MjhQE?6e5RfxoT$XRd
zeM%ixSd4^h=Z2r?M(ZQ<S(TRM`W&_ZaEVRb#c(v0mLw-s8&T~t3CcnM3ZObVsKns%
ze9plfkcw@7`Rcb?UmXGv+?qKHEeJ8uFf=&W1mZi#0Taf{Dnk6_YCgL>l-PsRCgE+N
zTYaM3HAY8x>-nL1nc0;=hI#_DTnEW;Ugq8(WD}GsW}85g>YJbsHU9vK2N9zgE3-U^
z_TQhP?DT;L#j399h_#hRIPoTa#C_|&4NqyqRk9F1DfPOtc?6=z4h~=|{{RhRU8|`(
z55ac&)K3bmpNmyi*5^*7{L?Ei@o*tM&cR0w@0z(X;trcxFJO}zg<`Y&AIvA>e#UfY
z)2HxRHH{vFYs*}Nu)nn8d0+sT5W8?=^ru{5{{X8cK#`^$>GVK{1Myv&xoN+E>ZxIC
zT{vI6i-5ZAU7nURCSN)5La%Br1>P(T;Wl6RRS5q8^pjAkbm_d@wT;7s6xpimH#rPT
z4i*T-kuDjpX@q)&e+w~^wlF1Me+X0Ew#}WH{{W{h{^Eodc3=uHDcr4Gc8MtdBP>5K
zK*McxV<s}$lXbzr_Mmf3yQag6G6PG$sc|K0;#PtRiC}97T8@Mxc#xV#7dW0{MP}3y
z@st|MG>Hsja_V{PXm%Bvawufas=gdvSJ*e|R?mxC6v%-+P~4)^S>yEZMqJ`tPcyNk
zx~Aas)g8{x(81SIifc=Y8rJ!uxFdy(c)D)xGqXzHL`QhjiGz<Ih4nfC#H_pa<E!uA
zPD?`{4ZhP#&Yxze;pm1yxbwxgAZN&_n*E-uM_82kkXevI^6RYHK^{sbo#*MWnlz%s
z=6MB7v5_Kk>R0h8TTLVEWmc4R5&jlnki3<Zn>TiWH`=vFTRl;jIir%`Bg`!w9*Fp#
z8y|wR8hfLohXR4c*{A?dH!dlr$3e+7m~f7$0XxDt6jqBWcWCMti@Pwt9*NZg9WX-Z
z-zAf!hx&vp+TstE!xKTvN{;z`im?&7?=0ao5OmQstsvWytmjld82Y1(Xu)Yr6n~b%
z%Zm0J8)UH3h~Q^q%(z-~DluXBtCG|h&~0FeH$ocV96Z7T268|j6#?}r10d=Xhj`Hh
zgJKjHIASDm2o~0epJ%EPABG+|Y%%s7y_^+Vu&+C;H)^&m(}+sD$+<fXwfh;UTAhq8
zYnzC)bJ1`1f5Z>r9C@Y@KN6jKy_~h&i3n!LQg1gENP(Fo6yOH~nr!{sZZuk~+K%or
zV@-!9%X;hrDpYGnmG+y51IozZ6N{bhh6eEC)d@meA7k#c=PA?N921@(n+82s6(5S{
zIE`Z@&}zP(@IJslRfs>~vm`A_nR>{$$4#DSbVUONts~JGhf2F@Zn+RkemxdQc&L&s
z$~Jqe-Lm$G9-BZm*K2#Wd(YhnzqC8@S*n#PHV|4Q`l^M_`*MZR@Q51+j*}0=?HDVx
zz1*L_^Ffl=VX%wBi8nhl2*ZLle~5dhhRpWF_e_64c8Lb7LG?xldVL#`)X&(Fg@X`2
z@!r_-(lrJbYI*%z-!)pRJL(L67MQ!OqJAMxmiDsu8=aV*Lv=PC5GIQb<8*U}?2Uu*
zR(~d56`E@YUxPp4AH1tm;_|#Q=Kla`gbD8en{X?{F#J}Oi65-xw&3=96a7wJDs5gW
z#QJ5k;If^E_{{j2M%dh$hzf=88bX`AqvVI)J-egbY1IovO3|iJ9aP+Wl*9X7Ab+Ci
zh3>}ey6U30YNtWZcLqlU{7w|AIJ=rTl07yb9nY5BxULDpCv<9~Ia6k0{^gmh(eNwV
z0*L;rloh1sqHU?+hjBWg9pfbPjgGl)C-o`2+Nc9nkWUiff~B*=sX#6ur$st-NqIxN
zrt(JVJK+qR9sR4?aXU;w3no(T{{X$vv}hiKY4L8KG<-E$)6vVtOSDI!Sz>m^7lIWY
z@~JY%J0KH_rNNwptb<^AEops+Ujq#CM&#TMd^UScvo&`!$p{)TxgmEZhPBl2#{$iS
z3)rFKf)|L@1j4g;m1dR+Jf#^O;0@@Hv`57`%_aPJqG0Hs2ACm_JUA5+H5n^TN~>x-
z+Svqef0B$f92($O@xof+uLYrB%xk_Y`L6lw{!w4e`X=9w%1$lfslW|5daHx7=l$ff
z;r0ZS0B|QR3o@Y;Ku0G0(Q<U0ikPAfKg=`>!UQyMNBCXbev3*HQ7|0g94AFZ#<~1A
zg-ldy@6e`Tz$BD@z`3;bKz-;H1wnrzrOd8F;MyaG%#<5!kQRt|jnUgiovaQN!H0&6
zv-t!2F3i@E&JzG1x&#hd`k_yu!k7TaTW3@yr#L~uoJfW2n<KkZdfz196~XCkfXQ-T
zuifW}+IRqqF^?4{+QU$rB;8IUV;ZJzzwsc$k116S3eCUuWh*>coR;%L#D<}|>c&R~
zi<Hydz0n+^Hf9C37<G3BkJ1&T8!3e2m5$FFsP8Tg`7c!F(|8tfwD1TPmYII4(Mt&R
z*~~5o=ZMDE7ZTk9#MbHuiBq?|Qtnk8?!WpGwCxrE-;Cew+Y_N@TCsnng8o?h0$A|`
zeNcn;JT#lxnBG7yJ%Y^+ccW!V9C^F!cuS=D30%HkV*SW-0Xh+O{L`lhYiFQ?uwkUo
z4M!V5OgUht28|l4zq%BM{{Y25Mf|eg?y_KibjQ99QX6lI_DjdcRpgdC$qm|oVjRL3
zbiDkA%`ap(srG6h{{Tdiv@nf^(PQvV{UrhS6kkvS?+XQ*sfDB#lhi^YL$Hz2cm<=1
z%(hRN2xq+Fq&e>RAmJih-*=+SD!t1OY4Ks1wu!yfINqu4y*&Nq8eXR|v;P23Q2Y_z
zmtq_?SU?MkY&VOno@&cXfFJ^>)o<x{?wVu+jjnd~$cj|jz?efHRpq=$fSLf2VP#>N
zCqTHLL~j1jOmPhfx2EiL&kiA982|ytf)FMk@<j-4$UCw;ly{7AVT{OJ1F<PFP!hCQ
zaN%)108{o2>%Nb`V*4i6IdE4dt}pJo9txjDd?H8%Zn~{W@=X}SF^-zGW-7F=ZK2+%
zTMT&(gJAyv@oA_IJ)NX^A&$GZn!#|^@hf<o)Z*PB@L801MgIWfU?xSnri@|2dMvQ~
zT%2L9HdL`#1~ljwzURlm8EeQZgMM3Zx=)3|M^p$er~`(V;IM{<LqIyA21r)>+#{-V
zpA*j#qLo(*@F$w5V(@Vbgg=VyhTt>-)c*j5NzdM!Cp1XEBu!TiIVgdU7T6<@PO=$l
z6Q`;Ym{pOw%|SZ~vt>6F_Hyayc4SwIZ?o8{K;#aHXjEbEO^-yuIg!Ot(~8|SLvYU#
zqIMo?se5!Yw>2gWfbiRj5wuzbmP>-XsZIsW&f8(niNZ6wU>KPu`7YDlv98E9KXIbx
zG}mBtLf1LN_D*}u*a&D{oHX$z5z7_wP~P}@YzUrQwOZtaoZHn~yQ0<|FPx%U*s#?Q
z){YsTsAeb0Kz3vNs%Dpb%tTMTtF)b7G~}I&nu5o6RyEV1!<wG-7d8CjB@#KJ4NBn2
z)vcWq03-#UEsZ6|KosDjHPy!Rvr({nHbUb<oET4IX&f<L$U$uf6;Zr_O)8tm3oaN=
z#|t|o2ZXIDkB!hy!*2$q0Zp@fIr^i1J9w@?`iFgv!0#-++{;>jWDkL^lmQrwb4RGy
zbJ**V4sQZB<Z3onPIC-(e%&Zx{_+CJ?e7nt*vg$ol8@*0$qlD{)s1l9$^<OjNhn<A
zbOVV*cYnlq_KL89Mvyv6w|m;lhc>h?ZT|rFqZY&JjhRw>h)_d&VZSFGpNk$3d!hsp
zqKU0j0Je;|`K*}8^6u5iB>I)%Ylv>qvpSV~uOYm$*a!Y^u>xm9hd+YJq5x3@Mc2Nw
za{|znEoZSy+o}5m16tTPMwVIXjE8oaK0#?g&7UMb`Hj!tX!@-IR_Lj;MzJM0xUK~H
zT?l{?iL!{Nyi2(t8!nu(=$>5c6()1>KvOXxJ3B)5opd+h`@KqhmtLj~N%mA!YaARv
zSsJb*p)!gCduN5m3FS6GNO_aO6N^W*@8*Am2fFrfD#4W;2ShA;F&<{iL-swVxSv@2
zIkChZg=oglkwjc(hVCexUVmlbqfMjD9GxxmTg~rxzjS;uJqji=`v3--0z8lb^xJ?R
zc{$TL&d+Eq6hnp|R55QtINn@Z97UQrb4Gi;L0iGDXdq{6t!ofG%BKXoIlQ52UyB+Q
zoj1p+KpCbp5(=T+eri@8V5bA9aN)BmOmpwa8@%Vud(yM1SkoMNm7P7$Efe=$;u>{O
zDto8ssMC9Wwz;yX(xS%LIiehae-Px*(aY>vT@);ZxF(OZU@aC(9M&*54_%piM}Svo
zbWbe~$h~2(n-L*qhVj%Z5Oh?S_JNax5;)2tlfB1m%otfCx^&lI8wK=Gvz@wyWgSz;
z;tvh61b_Te<{9>rPvE$dGjJ90QFE@03O%KjXRVhVxeRjSkh-110QA@p0{u{SAo<5T
ztx#P3{AS>E3B;0}g}B->l6fpZK(Ru^rw-}D32t`cr(wPpwomAVf4y*d)J8!*Wzc{C
z3r8y>?2nSp6*n5LeT?bC78iE64fCE3OLMlgc~yiUgK&<45VKdlX}%eUCs_7(0#1lp
zU?<#YN~m$XlH2GOmg-f4<W^-oQDbm}I*v-2XrCLdN-}u;oaJXK-})Mzr^5D-IFt7M
zr?sC|iUWJLgZV@!G@V=RY|UF_VSSF=*+J!}o=d}ql=Z}h$lrYt=w%3K5YfmdwgvEu
zxfc`CHn#Me-Ms#tqjwO+`gKha;b7o0`7QRk91pr3$Al+K-T3vm{!z&c=~A~=1b1ot
zTyt6AsObGpUdD?zKjekWX^&Y>mpY4Tj-v3qLv`IosF6{(VHwD}Ev4FfF#*rf^J}Yy
zh7<j7lCSp?4^R;DLv`Ej^ZPCm&eR<pM$FLOZ6=&r?`Va=f)|O-CEXUKFuCrKBa&dR
z+am5h3P+bT;`)y{lq~Bv$Slc6dw=3!BXL3ae3on=pxhO{@rAzYwq~tm#b{w=h)LiO
ziY{~+&#EAe95q^YDofpW$vQ)U#cYafxH1P<SwX8xmIt_L$vW#b80mf6`g<p4mRW#>
z&%>%Z;&ZZ`LSv%wG#FZjpF}M9aP&mQhsA65G;w$RQT6*Lb>v7unQ4!rCl&%vtfRX|
z?)oEgZk!J7`mb%PFg~fLFhnLiTRwc(XeIhZnyOJF(H3H&*f&pWd!cj9QFYCrTggy(
z=HUb4oYxmOye=M)j`z(sEX1MR#Vh`!WGwRk0ELhvaiuy7caea@kI^_)4yob}#qwSn
z)k`1dbMj6mB1z*bHk>D>!a@EM{nO8q5JL9IWc*4JK?V@LiUSQ9H{wcZJ?MegWn2P4
z15?phN7y?>mPn(pBIw%S6gZ;-S09=SEF&&kG8FKG%&M$wd^k>LXNcE<<Tf4>QIc~^
zJ#<YrIeyBfHZv|GWI2xT7bACP*X%@fMs^E=j2(k_X-$DcvzD_w+-;g`OW8=dgz(iE
z4j3^UgrhAE#N)&Se(t+)dFYMXRJYRGBNLRGbo*#)kG_57nW$uRM5hIs00OrRDsbGf
z=bAPZgQtmwp<Wz7kcjST)FAQ%<~l6cNZG}|Bt@{iPb6^R%#KOs!ZB((s)e*Pdw2dx
z^<Pn<+{!Kg095wc{s5$sb50<KS`hyL&M;|i@xQvx1o|jfE{}?(-QX7d)2eZR_=GI#
zC7|=oW{j$I<*jQ;Jr-6zrEw{<#OM@N?FYvF8PQZO6K%SY{{ZeI_;Pvz58FS8@PD|%
z$YcG}pp91TL#~cZEpXH9S>q>h>V?$Huv2O3Wc-vxZN~LrF557BPMX>fD#fSQs%{?g
zaW{e5O^AWb)+o*w5jz|hD>SfiE!GtV(qMSUP?B;?ab#~jwkQ`|+uDwb(b*}y{-*As
zFMF-bl-y&pQe`*)074h<bL*nY<SPn<zsj1I7Is>AM-z-J-28P}$=VM@Wsb=Yxa~|c
z8WD8Sj+i)I!$q5#)}JKXN~H3}&;Ymzi=6hA!GHQ6`;kKDz&@zw*y)r9-m$NN=%!D}
z5E{w=lBzmnOPiPl2Y;y^FaH2YIQ2s!<L1yx)~C!Y!Xjv~l)>kk)IF!nm6K|?dKI1n
zN$2z;vm@cP++0cptQv9VR}kbx7hSv-Z0wYQS3Ef{_)PoC&xRAjig0tEHsQ?d&q9Fi
z`$G*gJo1k<n-8etvofSK_|Fr=q9ocqHgJWV+dn1zTJhwJj5GE!9hKEQ-zV&8v8AGS
zXM)P3Od*IquXBeiuPca-dkm0Ztfe@BE@4z~6K*P*E*{%0Wm%*+Sq#Q#wxt;1aH_ak
z;00Cg>Q;=m%^$tC6i}Dce4i@?1%cbVijNEhoP5=dMt4l-AsF#bqNm-E^Ss~KuMt2J
zX_(-uV15g=`A;H;SWF~A!B^cQNke)|oDUg=?+lTW*I`-Kem^Y{gQCgeeDy?%n4J0e
zeG?k^Y+Bdinp)_R)2~F<$#ex$JHTcT1Y;Y~ctdwZuES(UsvC0HccUciAp@pr$+5UL
zI?wFxF|t7<z2l7KvpkB={XqkyY$5?{rvSolc%DhkU>?I)g-V8=C`&XtBfV;j`lexb
z{(xC0Z9j`x4<@Sz2Ll}u`@Z$iI%>Q?qXZcR8D(3#G<L?3Ql&E5f7Pmf7d6=1%;W+j
zR;_mqrAh(MrwhkV#@yEH{oRuW>Pu!^)pnKaHc-;&Zym>;ZP#HRi=q<TTf>M}YpUhJ
zMciXxwK0Hma7`iH4_y%g>dHfbkW*#$6<9N#DcIev17@BQgbqsL38wv7A4O5HaFFBq
zN-ZrooJi8kRhE92Lpr#M$T#AwJ*OOkei~%qd-WvhuZEk?)9i8o0QV6foFs9ZeYuKF
zkL?hMGfChNs?hAcYJMNg-|?_#wV}-CqBg5itK+8VP2C-;r8CvwN6mXKJ2h#je944&
ziv;ZUDmCNI+g-jY3uzgs0pL7;Qa^Ss?r<hN(C%qr0-Z6Q9s`1CU~#|z&}z6MacDwt
z4q$6())(K4hjl1JhiG8`0I6$L?j1o+!w<`e<hU61QV9P5!=F>h?g)M(nc3Jfk*q22
zDAX+-9sd9blS-wCT0s!F#H{vLz$wEWsF<9WW)!NC`1FU(sl&8T9@{>nF}E{Ay$AmQ
z)m<0>A3jU|5tf<(qxy7C1hf_ru5uRRS_D(}h#4aouHnl-joaB_?L1l$g4akRG|V+=
zHaH57AkUiiJ6sQ%%N8U3H$%kdJdcXh{E1C2Yb!m*I;VaOOh@|u!4r@Px$3jkYQgZb
z9@NUnHI4rOq$%8_Um4#eYHU0I0I~o!n=<<cCR^2SJ%hTf<&i*^#m@vtBty>>Y*7|D
zb=mF}oj7<xH`sj0D!|ij%>}kc-Oz16`aZ^KnCiK+PLB?5QM%wj<Wb(GQv<pb01hto
zULfp4rwyEEXA5luSUTpK9Oq}zI{ncu3NX32Nfs)j9Z2N&S~{i&0iGNA?P(Q>3>>iL
zM!{);^+Ux^<cSkVdWc>S!cSOU(~oq+tm=6v#$rbhx-6Sx<*~%NL|;^Qn-uG`<{UNi
zUie2drz9kif;u1rph)1lwR<cdzQYb_kcRkfAO{^&4&Ze8nN+}L0vn0C^4Kh)2}A8t
zrr~fy$H{gE1Mf9ca1x&!@?En)GZ_WjLzPxKSXvC_Db$GK9J@V7G(ZFiR9-)&Q)}N)
zoKvJ2RhTIXrNErkPWn7TAff!fp3epn+0HquV!r{^H_Uw&iXO*!m~vNTOBU+fN{gI9
z-WmLpOHU1u7MlqP(t>h>%qiL6qR<WX;*PFA0=2nb+i@)vVGVTgk#?cK?;HFRocD7o
z^+Wt5NR0|<?we7d4l$rv8-)Yz9!DDmdqL)o^KRicM+^R{%P7f1TH$f^PVvM?E-KBC
zrhCl1v)Dsh-hxkCl;FkyUiY76qg3-&j6)y5n_pvZPy*07qBn0@+3y_FoI`EBng+`E
zSrInTIx#yBs_`}399r{bCMIDK;{Y`DUx^axKmPzKv#>z(Qvf*p6WYp6c4uCRtqndZ
zoM9OgqW0bx4`2TPb@p5e_mA1g89fEpjIzZ6_ibST+D>aXIG<gap$&h}huGB&P5%H&
z9nieWb=le(S%Z#+WZ)gJ{sk6OJR~fD17U#XnI9d6zNp(6`k-o6rvC3MfO1E?s67GY
z#H&+j!A-jHGN<@hITL-6o~XoXymZ}&REwN9g0!eHeN#j@wZNmP5R5jL{u;_;k0okM
z?M-V32w_f&O=x{euF0T@B&v4sn$NqP(}FV7;c?Mq=Yz9~KXY2En0&G;J_@Ut?n|?b
zQTWuiPngMe+9&c=J=?xHs%?kReLFmRVH#AJ-+li88!!Ayl$|LK@iFzqCOPE4cjUV*
zF3x+hTK4ZaMt3RBbDZ}Q=NYgISCYIPqG`vJAqAv@oN=@=px0O^VedP9mhd=oO|%Ma
zJlvW|EqDX^R%~thq4?E)0T0Bg(e+13K4@&PnLL^{bU^rsLPjsV`l0Os=n=bVVG+%E
zi?Mm5BM}3q*wTX)EPa*YD^5Uh>JcJ{s7N}yfhtMZE-B*&^z7z@g4ioc?H>ZHAYtjZ
zEOLCVjF0sh@|B~Gz(@-Ze18U!<HV<zJ=v17sM6o4UJBHR{-zmU!+*_aY_&!)Nduzd
zxQ30SVsY0&DEK#9<cZ&DXyGS{5OE0!;-e1s_~Ny(Vr$y|11Tz*ja+gF8EKBZ_GU3D
z2YCQq+5p@KXVp_?GTCnICtojAErthGrPPUWAn`}UH-`e7BAaK=MS68xh5rD*lDWhM
zGBur4;fwOJar?LW6;<_XKYF1y0PtCt1T5pU-R00ZAvIhM0G{`K;FgXEnAwzU(Nt+X
z5xp`eXB~hRVKNp#{f9z!@B`wjJRj7m;~$w*JHpX*y^F*Jif@m?a#i=6rf{p$2B8wu
zY>*BE-MS87W#EXJOaaYq5p5TZqR8AsPWE&4U84nBeim_54QwWG1=-CaPDbeYq6#1d
z8*R-xRKgZQP$*(+p5)wZ6dZUjWJjVnNo4OQ<94gtV~!qqqYkyY-Djf)Wf4-OZqsf<
zs;F?VV~mGDt@l>+Ln=@7jgEo}5%08UN^#&XcsvvPz$;;c!>Vwe8m3BS>0kyqT^;H+
zBUm|X0-^6jHz`J5)&NHa;<|SAZ@j4992Ek`2Jbr*;yiAg(%K!7gb#V{m`3h0N^&wz
zsp3XJ+E5|UaS8P4fggzYx_ezT*@@Gr5S>PU{cAsU+Nt-=@Y81ViBzfpBso@X7I(zS
z5>8qL{{V?N7M%N{7iFbU)c|uU9rEuj3J=045q9AUec9xi2nNSchHKiXiZ<rBrVi-p
z4DI3n0H?0e2fJ+geUWCeKe)m_!h7T3o%oN#8rccjPUibZ&+Kqn)1ytl^vW4kZTTQ<
z*pw&lT6~4pM!UXij>GMN>aq?aW*T%&MmxNB<&b05IBrHGsXbOKGYsJYVZhh5r&)Qq
zUOMkJoycf5XQs<{bR7qovY+(>FU|f_NGG$qKX?dPI;&^Qt~8oDV_E+IuM&OHgEhO^
zm~M5d;j6Yx<|Bw+A_KPPjIBz~j~$P}LXeuT;5d1#Ov2E{6L0rq;&|$^_LjY#t{CfN
z>@5vA-@p+IC;iiW^<2|z%HiyM_;{?J4jy4fD&3v*+a@zwcK-nWi#pX^Sov<NQ=-fD
zh+2sYsyeyEY{@hPOu3iy8%{$L`tU+!_<d}@nnH`xoAOywrspl*(@IbO0IYBCvxdVz
zbjc?nWcj5s<L{r_W@BMEY68O_n#gYlN0`xO2t|7p+TxBT$l72~qT@W=AZ+74sig5d
z6jXaW3~z*WS-A;@Taw9@8c&jNARJ@Oc(CFgN-{D$)@^sq8#SVQ)<}OBW~LAn@Udo0
zrw0AwZgfH!k0Gd1>`j1+qH>r|A+s@<_^Qoukt{H~JqnCBYq+LWr2QkpQJvHKtp5Pi
z!1P<fjRv*Jmt{a=bW~hJorp$~gkfwh5U4%Wv6D`$Mg!M^Y&+-C87=~&`o>!+hc<&{
zufK@qnK<gUQzgX3h3=@lUM>m;cK%}MK+fsg0sE>=W4KPLt_&?8+I|X`c~@5zPX-ek
zavAJ-4myNMWnI^P=*x6Fa+{$6*^X(uxT1OO%M!>|fO%VJ9unW}J3XRprdg`)iHB%G
z@>HFJ+IAfIs+bt(L!$QL6Y(%V0}0`_uXfUL6Cbjv-i<8@fwQn7_6nF*8l$ixq6tsJ
z1rsN!RD*a_BYE8VCrg|qXT?|(1NTmt7#%i~qG)I7w5p+#yzN|h1=x2cO<MdqWUK?l
zkr;0%?C3`o9w`9^;}w7-g|kG03IMk(TB?n3BQSx73@+vufV#($AH-+H32CP6d^1OY
z^j~zy#fjWGEKt@@UZ`?e=u-ss_@iu-iMB;SJPLE9QZXFWDW<`_eABmsq!ns4NjOE?
zIJBdRjY@^U@U}-_hqm~#Dzl!^OUhP2WH^<@IkFvLs`hD9pPFzDd-D9MfZS{WrIRJE
zBJKT^W)|@>AymZI1B~_Hg-f)T7i{7G0Hp@n?YoQt^I5b*{eI83{{WJ?W7x^$3Klg?
zDUggFJ2Y3O>A-epqPZpl(xGqm*&h`~k5Z|_nqQ0hOf4Gd^x2WP*_a@v7Z<pY096?0
z0X^K<bz8JRvqiFA7Bn(A;yNQVuJSr&7F4m-cRovvhXKRvXHuYVrPYYZJ<f1(82#3f
zs_isi-BNqVVUjHhPW2sNW^-73Gz{B06B3Ia%A#2I83i;#(4!!<7M@F7mWxtX0rJ>D
zYXrKX_FKfDS=hTh`PFgM^+$f7#@=oVFiHjs2nqOiLtV|zJ|9Fkt2~+H7qKLTU`@6Q
z?=i=wNWp24Q-%-q_@|K{<umwD#;9v(0ggSlVuDxVF|8d$Sgk-qa*Y5=%5GMc*mA}D
zY|5K)745XQ%;bz5Q|KjsJ^uhS-~%W;zoLTb%%5DB^BePC@kKUVPt8U*HY&eA=?E;o
zJ2EQJvwr^os?SshjEU%@@12I|WmL<0XpFJsxxcrT$`vtIbua{I6)YfLBucDt$Acas
zW1^-RN8);_I32~PgMK$&96r3Zdq7h2YVf;tOkqQJ2|kNg?Dp#C!DsUvLVjEE$q2(Y
z<2x$QZAXZQx(h-5)n*#B*)+huATC6!cW%S=PM2y{YQmi|8T#FlqSv*f#4PWE$54aq
zXoOs=VSXXiQ^e!qa=5EhFcHmEhc&qH^2s<l87A_z7E`(KOb=<K=VCs{U5v!$3Z&R!
z%-;M0YcRRErMjX4aWT~!a!^|4%oNIP!%VsW%~4@5oFga$qU;PTBE(rxq|=KJ5}Nn9
zu8sjY?JRC97)Dh7Gl|-5?Q~T!jyp316z#{}cobrU5b}eIwULq-oY*7|8mqa*IN=VM
z*^yKj{7%LkZv`+6R9jX6fo;A#4Xn*~U1PC@)mjMwc5dpMUvV>y!8Al<fi&>I{FA{5
z4>Ocb(J+A8V}f!ify3f~=&iwNvP!xN%9|beqYJ%4kkA1<ZkygS4!U$sbwDj^MDbMb
z#9XI`z9V$xQ*vP}cQ$CEL!^V04N>oEPT=-Jdy5#?Yr{kg$p@;Fi09O-v6$I2qknZ1
zpoKA1v;*}=6NQt&;swfcn`GIa3&2D<yb~-&_mDsbIN_IY3YUG_4zKVtqN-f_#21oc
zDb8MNANq<FOG*5PpH-0~V3-#pG#>01jz~+sTMmD82V0$u0I)?t^-sdrjpn8??XLHn
zE*vbW#USgpabP_86hSj9D(?~pv4pRBb_bj9wEqBwg|xfa8rhac!);>fZV`imoH{0A
z10-7S%dh^9sBcZr*%HUFvEF}XA%HpEJZdHHpHw}PeSXWxVgCT8bwcC)$VM!`n$Ma!
z)N%N)<^$<<h`F_@6VT|qKj{f)F7_j2$T8)xV5L4ceHAcj5qhq<Ddo;Ly3a)6gm~ju
zEQ6lpMgY({mpXO}OrIrF1E<3mD3j!~FE08fH_qrwYcSYa;_A+81vvd?{{SR>eWi`V
z(WId``xy-%pT??TrHc~}xc>mz2y;!s5{$W%O#K#?Ts9p7%%OeLRr=H<Poj(r8{FTi
zDfmlVE{r$5d%UwumQ!P@5y_e?-P&j1zlTQ=`KAVn)Z{^*2`x}VN217Pt|W%9WyNb$
zlkh-y<aca7?-CTrf$MZczwZ2oMR8yJcMtk@2;CFSXg7MJcWg1<xJcyGM1v?r-RMI8
zd;V$zDt0-JAdlU9%>$w^bQ^#74;JFa{{H}DDOnYoIT&0Q@fhJ93O8ZJ9$_%xd>?}C
zfsL8KGhKzQcfV3fs95@?<l(o3!l>+E&CH<%#$iU+fcOS<^FbMX8o}p~eV2-xnnD+*
zW64G!=_p!p4vQx=6`NmWceKSb%iT^tcS`<yIKzK^o+g_n;cLwQ09LCcZkqw+^&QO2
z0<xUfg{=m4vhiVYXkI3~d`u@F8H7$A&sbCw$3%bpZ>WTKUT_imtg!Bq+h@9m;<g>m
z5xq=9$Q9s!&rpW*k9tp0V4XXX7GkM)qG-cbuUk6hI5;)rA>sjmey{jPz`)Vup6p{j
zVSg|q_?@Bw>gD{$u=Ey8npDhna%gB74W{sfY@l4py6nWT=$deMt;P+lC=K8)H3}vT
ziVzEpzQ+)T=>fh?87M7ymJpvjL?8$m+e4xot{9kjgv?+z2%abwp76bz=W?+{fJ_Fn
zyl3dRf9Qe{%V>kBg$Vh5i9C}$Z49yVMsd(;lS%E~X0gRdrQ&33eNdA&0X$<azaV&X
zRxWX4IgUF6u{vJ%0rNnDW!Ly18Z<e1tKoKg26F~%s$Y^G%S`lgb*nf2tdQOow!0tT
zY&E<BVK_gD%YtAfrt20^157Cnqi)dCo)M~Scb5b5<N_dfv`c!pTopOxvn5cs5tD~M
zRbq*@sGLn;dw-`rnPlO~I!1)q87;2hJnslJhc+D7ToKJM3A}KXk*t18j>bE@wijs8
z%?l5+k~W1%vW}&rH6QvBhLdFZ>Gpl;+2d)C>D#FYw0{U&-nG3JZY?L)R|xd`QQp#T
z-5p3C&d9MR&`!-UAA-3xOMJ>7f+)}tt^=}@p7Qpa4y*4RJ)dQ6Q33-(9>cHwNM)LC
zAkUtP?x_C&itrBZn?LQJs@9?SE!L8n>~#zSJIUeBi7eF&vrPjF8a}%;s^6Nk>(uWm
z4AHmxrhnQ|#39%Ih1%%RXg{gMbPKgssPG|`uvl`A3%Am{V3)VMQIEdIyL&5SwFDqn
zWUGJ61JtGmcA_SYJi|XTljgNTbKr9;jLywy@6Wu;DaYD(M?;zxmsNR=3;AWfwm?N1
zwHf{U$onzzM(|42vYH*Z+hwBCxdYKUTTDlq)y*F@1&nZPeOEPkq+9UW;v}Cfk)SH!
zqE5mGGJ$i640H6gtiPQ90Gd1Io)%9G;c+Ku++p2syyRuhGJeN|=CYlh>38<s3zT5f
z4nA_A)E*G37Be6R1yaWNj)!Nl*%@wT<pq%dk3Fu!*4&lFGaWN*KeBzQc4pI#4;Kq3
zhT$CHWcK{lZ7^5q^9wL8g|WdydVcMnRfsaK_xMaOGj@Z`I61V~1dNHyoxU2!asKOu
zi*&TXLg<%&rZCVn596@GeJm5qg?+&#H<=t_>x#~+yT>1*7I|wY<FH<#gL{*e(BAKW
z^;Q%sVPX8PAK?*)yf1b0{{V2D8zu(`7Fb*(47T~N=Djewkub=)1~UraV)P<GUL#fq
zklC6xYh3B_M<-&R({=6CU8}kQ`0z{z$~HRfI$UgEHZXV+ppY?J^Ibv!K4Hg_?TjoL
z0>2E%U>SL#0};0b5nK+>Y&qxlRS696jR!>+8w)0dFS^~ijTab3?J}}^TKZ?MYsC;b
z^V>gafAk6{&dt`BYpP*5YDDXA`9L(`5(a9U#vTU#Y3~Ku{{WOhg3}zhDlB#pjAFiO
zpkrnLXn@eGJTO>qM8>J)p6^uA0oI?A_pY)ox-!<-Y%%wLRqPP<{ZZc4QY~k^MXtZ|
zHF5&tnCf*z3Uv4Twg_kuE81}=A~6vLRV)wU)Bvp5EPk>$rnraQ@uRg?O`YLAI3@;y
zavBAq<0X4+dr0GLnr#07<$v{AyIEVV4VIKQu({-Oa6_v0wcr&%>_kLz`#}K+V`v@~
zNvsdP7UUEPZhiqdpSI(L*^V*xebe*Vt9G_<Z$}`F-dywl0BBP`>LUCb{{Ra?u%EDQ
zngwnn+W!D6`E@_+J3Wdn_}NS_8jk*zjr6PbhrR4(W+AEF+jMWVe`EyyT)lNfk{RAL
zYTOR$)sq4Enb-}tk5Y$(VQ=av=*M|o7ZCu-=dwG-0UVm0XA%rv%6p7h^57L$)1>0#
zEfQ~%s47vaC68Jaqz{7AwJ`d$piM`d9h`j1Ifmc*c0}nvHC_wrFbVf{B}9i{F~n?n
zXmgtOk-)Ch_X%|3Hi5~08>lhN<kfBiZiS8m&1X)KF6wTs;>ZOXxy>I{mvSnDz|%kF
zY9Sc4{MQH`YulnE^9nK5s^jtq*y^Uw^g=AP0FN-WdUhDcQ~V>OY!`cl`XUGidE826
zGC76lpNG3W<@qeT$0HH1HSD+Nu)}k4;1!rG#_%6&QGJricb9(2o%L*9Got=*vV00H
z9p5t>Mn=m(!T$hCnd8Y!8e`$K<~P4>hs9B$X4<3RjBl`lvy3HWP#{Ygb~E27Mlp&w
zN-hArNdpvVDAbxG0EkpWjF(e$jmM&S!abR`#{+GTAkrP+@<V$McU18r)3L%kO*|99
z+93oOf~+dP8>2`FOn5n=osUw1ClnY*c7t#M(HR847gR1x0G+u+F|GcL%AA25k&7|j
zhCZlS%rx7`xk2VGh2W9$Lhh{avZgTq0F*#&N;jPMOcjR%D><Wzrf~5=do&(aW3p6)
zeYN0wnTyp3Zl7hc>CcilUe@7b^dKm0u4PniAm|W(WbITzpOm8`8?Uwz(Hp(2_}}at
zo+ej>lIHNf%;`hh(@>p=V4q*HQK||so#caLazgt3uv8<nF`9Jl2u|K}hy#TG0K%SS
z7<E1Kh~@$SGNX)!jEK=Qb+-T^jIrNz{EQtJX{Rj^;0)ZXSf=YQ^x_A_G?E^M0B$i_
zQ9B+1iv+?&uAX7Qh;Ke6c6KwF-XQfU?s4KwthMM6@yi%FYN|As<>b6E@kR@Twtiax
z3J6r8)8c4?s=M@&^-gPt#Wa}{XqzEFKZ+3;iJprh;<#+_3^iN*IqU?|55JYxj2{xS
zCdiKQ@HzqZvRNx2UHL+y(AOC#fk~F8*8JCIb_@W=1zm;jE)J#bGC2K%E}ymU5H{(v
zR)&u<t{D`^k{Q+zz0v;wp^)tE%Zs;uyCUquJc1&Sd^A1Y?q(A~se^Evx(~=Bg|l-o
zyd|bnPAZgM*`GAQiy0%!L<OTnY|D<Ql2&mp9LmofKZ-jK;ubItGIK|Iokvt17EGlc
zZZMc0*1R~d>ILC;VNVTQLNWp)2n$z<S$Bugc%RyYpUMT$`<~9;>YtXY+w8RGsM*3g
zBau<!1#n)^XJ^c;@%JpB&TsGwI&Xa^`R@<0d~d?>$=M-(^9b_E9|I9MXD(~OQLm=%
zVZjUHAo(M5R#S4brB*zjW#ANKdarnut#6Q8m836tt!aGr_}?u9fZ~V%W=b2AZJLLC
zmVM*Cdorm+U!oc0hu*~1KmC5k9j%IO@jT=aZ)mIR&R&638^NSQw_C^rB!8)F`50c!
zQ048^9|fFjlZvZDx=KIAa2{JPf~f=^1#Y{Bio@7%ynkhx*eubBOn2`2s~UAp1M>?o
zKf;FLGUNG1Xf!aqQDYmRUdv<9E*~YAc;%r5cqUuW<d2Ikp=Yq0oVfKwwWWo?Xb3h;
zhs+bpk}z=yJUOOeh$c0SRj@V>njR4#bl5H!VSCH<H>v^wFi#jm6UjNP;>t4|aD_#z
zsomqTv1?46)`JtezUWNGAIebhj1Y$FDmeTl3m24Nhd<)g?Gpb0Gqx?+-iC}|^D@1c
z0)(1%LGn)z5H@HO!9QTQ#A<`w<B)CDoL`4~LaPTbJ9`X+3UIh&e!v8i7eX6PUOIh-
zlLAy?q9wdbQ(Jv{Ey_j%j}_r)fwU9R9JG~2H?Das#z%@5*fzBlbOJMkTxyu`9T-e;
zPKWSyr5(|kUeJ_g7~kDRnhVL$keD-><IQRe`Yl}B_02;|!;9Q>0HKBOcfH_Mpv#Xn
zB@hNSH2H++IHZuc?1r<6RK-VeeqM>k2ywQ7L2xo1(PZ;G0moeTRQdbGIyZ370jXQ`
zZ4UncdZ%ua6V+MleTt6pg6{XWWOLZz2O^FT?2CoHQvl|_!lco<MS`RO)lhycS+|1l
z2YXT^CVkl$VrRviqI-h)ac#&Q6o!*AsuwK+{;KB=EIohp0s8<!PJ%?29!Y=?7Enoj
z?vBHiGlX$JZ{OR%wt8%gILp7Vf0a0(;p2$pjF9#RbqzxC{{SEQ85&K$JV--Ebq|Cp
zfzE8yE<dt!yRv;y08?WdZcQ6B2jq+iBh*5Pd3nD{+0>pWS-akgHyQOt?W_-{{Ok#>
zC8R)5>Sg#Uv)a0-T}}0yfZT3@KUCwgLuu1>Pt}D`d#6~ps(Vdd(p((z?17-!{E|Lj
zV6<+}Q=ZAfDYQxr?%7SRXXkUmJKdxL$9{HQ3xfm|=!Mtwne$B(lyl;*b??H>js$rq
zaH~`~r<1p~c(LWtTsELy%@Bp*0w9^{kBXyP>QS+I_-xJiTlGyb$3+f*y`WY%E+u92
z+32)ur_pNSb`NgHhH1H4m+Ir>jFM!Xxg&5yDrMahvK|bt^-L7$M0qUMMW^9(M&=rd
z?Qq1h#^dOK&S7L!7gK=I!gWX6DBKGp9pi1)w_aS(nx4bHipOOB9LmeQ4)wZlxD}dz
zGcN<l9G3*^Fk@)u<)jZ(Vl!NEY;WFCaS!I#WPFi=75J}3nJKNsR+b0U)&_`TYfX=|
zJk!IP@4IJGvMmS<*YiY<Ax0OvlgA)$3)$@W!SaReID^%D;bq>3A5_7GvRUVnKFiwW
z@871Yev6Lx77@i{;C>fTB1!T<I|W1WvZ}^ZYlt_o^wl2?cDCP{4d%VgcF`lbk5&9$
z)rfQgHVo*4{kKtuvFQa=65Q8n48qGY370Kan@V;Uw<Df*)gAKeZ#?sxDr38I?D3#L
z8a~6vHIdp0gK)o;HfgcMNP`-zn(L}x`EckFk#Wfk3<PDtWwSgh#!dDgf);LDDi1{B
zq@lA+)0!knCqzT9am6{5+*uttr?JeVaQ$qbh)xk1h2h5SZi&X-*yM-Zhi<o0i7bBb
zJy9f$IV_g#BF*<`*fJHHO`)v(-|~bsR0+0mApWMOSY!ebyx)fvk^caN0lwLFu=6YL
zU+)_*bQ-B8JA2(wFp&TV;)k?`M7h7R;|OnxL5<~WsKN|SM9%S!5au74=AK=Mj3np;
ztuC4cCpHA@7qTI>NtQ;Vf&&8?Gtejn&2(Rq{tJK}X!n%;*WNmCDqZ(vvTmFBLw=tu
zt<f24g_P`s(ddjIPg0oUimP$Q0IFg%--XA38h`1Ld&0f4Ksne9a*wJKPJuo3YQCJj
zSYdYsN%bJ#2$)R5>@r6I&1-M8Z;iI~+A?@>`E1R^$1B|nNOAs^{KO($eLmIIGmPoO
zs;N!*%W=<uVNz)T>`u#02>3fYsSi5@BpS^5tKzEY>9gHm&S%Xy0p8-afarn4Nyp)q
z&*DE1kWC{X0zTaT09B`LJan4ELuN0C1kx!~Xf9@W5Z=P)pX$jlx!eM+-CA%rdH(?6
z2Y^Nvm!0hSD#WyVIS+!<AI1vef!u4pC+uiK_Qkx{jiTORWXfaAfC4b)kU{8%=V@Rk
zfOxZp%kn56#c3m@_8UP*+9rI~d7&7?r18RFB8&ocSH{3?b)jhZFJMad%&Ue}YmbqX
zE^57VN5EV0PO#HO=(3Z&KNXnSm`x1P@u?vQ!txfS0x=e!q7g=x<uTJ=Ke0{DzCl!f
zdj-X<y@G40h~F~~a(Q+U0cs(Bk~%LAN6yQ|PzFZ;+u0E`pKgU84Jq|2Rt2gt;RE80
z@33B+6_r7Ze^SvIcku?JWKQVyEBKw3laE-1mw3QugM<`pNgFh<=(RFdc!N`ApxkmO
z?&;91s)EDjhVxuO9T2_=+4zpNHt_t&;&FV>RpFG~(BV{a_E)~unI#)y`6J=&A54(D
zyE{jrUK*_+PL9y869M&Jif7tCrE&T%XA?o@<bNKEnVzv^F2)<1VLVaZ!>+2>n&N+@
z6B9mY*@L|EWzZe*r!D4ok@!&_Y1byBz9^E{4EgXzZb9>2%a%@?rn2U7MaS7Cw9Cda
zG;XClzL0Ui<ETVOa$D=NtDM#i&`w<u(=DW2Sff93KEVLQh8)OG!%d7O+91Ny;f*0Q
z9XE3+^9=*+AFwu?YqHXP0&sxaPqc_@c_8y6GE<M;>%~xO-Jg&|vpH$dg$9(_`gy6q
zDwr-`idiS3l~$G-nF%t8r%MndYT~2rfDCuYAUm;GV&M{<KYA+CZy50}B+O;4u#HNn
z3!B4EO2Z<gog*Z6XxT<Xq;x7I6}?HZQ!Nd^M8i%29C;&;81Ti=Bauc69iW>PQbP%D
z050?)n7g*q&@|Z-_eL6ZU;C!gb7$WL5@MilkVkR!>^K!0#mDG};x`B@`L4N75A6@5
z7=}%0GMMOY3P8@VOs8+Oz~>u0mT@qwRY-BmODAG;-!D8mtoG)!*`L_a0zRuSBH?2H
z08xt>r;r1x?6@jSc8ZZ^`Gg<e8n-P34zjEwT;ZLTF72*?Ios`LW9hW#v)H6Dey3cn
z0e)hH2{HBswK@b}UFRW7DdBYSLYcUt-x2DlvYUA3PgLUPKeZo5xBRPM?2o@Pv+XsE
zo-L-bp-J@*kXkH|JS7}~BbiQ}^L6}cY^m_sX47mB-8#Y9t`c@?b~HMXZVXZ32b%u?
z%{{FRz}?m|K531iLVOg&PhFW3_a9w=kGn4Z?UUj9?Hdn<5dxcO!^aspF9Ye=>=O6I
zN;k>O&(tXdXuP_hWLc+Sz5@x+?hAtc9a_xwhb;=sBm6(A8R0(!d}e(tvm}$VG92$y
zuow+4^a{f*k&+Nvz&e4h*zt*#;jF;$dRcM#315id>J8<@?Udn0o_9AXjb)=2XVn>Y
zyun%%wl|6wn1wj|GlO3`Vp%$oT2p9Q#B4r3*B*jay98spJ`4FEgyRse4kOWPRlg#S
z@JkQ$6*)8h)$P+`?dY{=MBJ>gA#i7BiMy`)Dcz{k@k}EdiIO;{7d_1^fsmeRuyt0L
z6mq{FYMW()#0H8S*N6b<R)mCYS8(~E_;m#JMR6<t0Gs~+)m#dAC1`Ab#ejE?_oX&k
z!-+6l{nbtmK!|u@>JW^5rRUKbw8-*TZFkFDG9x%vz$euS3<NutC6d*JzM%mklS_KS
zJCnZOcW1ia`<6qq2YJ5hB2lqvF~o(FfFU>%)`wdoJU+&$*CJzrpes|=r=FoO%bmL1
z&qT)x#+d__r|dN0n8g~b;WH7!bLDi<(Nd0GHk&o$7fn$7M(&<SiK|l&rS=ArcWyZS
zyN{OAk3=JyZ1Ytkq7#SODS)0RMj<QRl~AzB^BmEa9Z`8>?!a^*WlUil%;tsZI|GxD
zY|(BJ3rqT<;9M|)q`QJ7Q<-y{ozd@sCwC`sy6q4%?y26S9y$d}x&-b<qn|Vm@FcHk
zyQo;@uxO94R!<v;gTgU6o{KyX5#A@gJq3=;E+xQ89C)DS)bB>*K!hS)@X1amwgBMn
zD9MZ@#${zqjwk*3tt%7etwrY(eoIVCM}!O$l59GQS{=+}oqB`4cMa=(7qs&4RS#rj
z<lg1a76j(K)Crut*7ZmLDQPD@YA}`t$J!Fnnb>46J&!H~l4Sz-yy0U8r8TbYmCIWi
zA%?TS?^~fMvBf(Jb-YTd!&rELbjr=HAo+crTH)~9p_T2t5AH|XAaoX7DSy;4CX@F@
zJodv!J4z05)jhqJvy1O$e3b%S5#ApWP9&{Xe|qEQ==vezG@1La5T^H@k2Xhqv6Ovt
z`E2xBB1G0`^)Rj(QSx~LkFr>Qq}U<4(J`|*ka#1$SOHBgamWEGqiBpaW^XbrW!Viw
zqw633p;do$-RvOR*U1}`C(UJVh}rB=Z1vb|J2Q4@(fjPMn`|gac1mTUeHGtH(@J)D
znBd{$ydAH7yqrzKkSW=K`$viqY)8*$wCG-n%lI|fQ~Smpzso~>Q<r(<74S8!Lia}e
z7Hp)}pCG-8rN0SYCj!ytHXA@=uQ%hfOAd=8vpLOPuVg#iaFwPnk|TAc&g4$b%0oAl
z4NCt2&2Y=~rx!bk+sAL36IrhfpOIb~?Jd8)H-TTuGKv>2w!vz(t2lWw5rx86BsXVx
zaSOn1&V?Ay;JxC%nm$X#LcTBQs3B(NQ@2)y;-%Vqx%{uX9K3?baaS#KgMr~HiC}4h
zCq8PEAdDv-@amc-okqBZ`lk{xnCY>%dpp;ER!dMqHw8!DZ+h&`s~F?`t4a7SX#NwJ
z{{ZeKsOL)`_T34-NO^GNh2LTFUN5}Anh^Jr=kqyA1_R+lHP_;XIwWGieuWbbs02GO
z0%?6Gy91fTAZ4$1DdnOk#w9u}0QoAPb5fCG^|q+ZH-P$oq^=E*Rs1SUXQ0`;b<8Fl
zVBOQ6$;<43OHR7zgF)^e=mc!fW!Y0sV^mb`Q8-sWr`V|Hv9cH@VGt`)?GB!Ls0GGt
zk~e4{5{%0Q_IV~-iV?R{vAKjtD=0@qdIj154FrM46CBb;t4d><$-uEv?-&i+l&4{I
z<-r0-4v#SlImt;br66%3Q~RbH&ZmNG2Rct6q_EW2f(HX!WD^*F!tElXzn4e<0Om|<
zY4(6vN4v}v(kfH?x}VXi7A$NdBAq5P_g*HI_>|5Xjah@UEH3#dkyWsK0M|e$zmtCP
z%ovWhx^=14F~}Ps4QFEvAROhGiq#mxC2vJrKTy@a0|0o{sQ7^`D;Idj%x0=Fu6-}V
zF5kZ-_Pg)h3@-wC-2y5x#lAzLuxlm6<k6`rOB&%0BQg*r;+tm<QZ<wd+~yXzp1Q2r
zN}!zbB!ux!n|x0?bmpi#KgD^HM@<kUQy7oY7df$Et@tU3wCrdkWQ={Bp#^1@M{98y
zutYwyxp;XjB+TTPTuHE>-4jjCoIyFqdT^4M8P0Uzso*kBCg!_F8enjeg=bQY_q;{=
zZKUSS;leC>qpsfzTZsPvOLiRlL=KEY*#c1vY1FBEfwRc^EyjN`ser!!0JKGmg@So=
zwdM9bnhpN|t9$CbzT5u*)kHRfSUiyYYVLoNH&90{j*a=AmtNDp3&!V{`7db4rp+v!
z-F9|V$0t0M92y$t?>#st66d&*XM)cWlw)-4p9uLQ7@P$4Ui1$o#YL$ABkQv;*8H|`
z186<vd1V&x2isXapWz?Fbpj);(EF;*6<PgT1dhsk#+^WRX}f{r3poXs&@7zU2>xZ3
z!7VhJe8Yen2JGDf-~=8^Stx-BXti()#fVBEyZuwA&158>Ab`yhYT<Ch8qL1XHQ=vE
zu%qCnOAlB=N5DrFhqTt=>@^%T$AyBxEWnui9DY_=BC<eu*zx9x!q4|AbRM@}<9<j%
z4cT{ZIzL&Rjo;(TJ-#|OAEc>}b6eVx!3&Ho`XXYb5<vXS-b46wJnR-gk5IGy_Hk1N
zqniGNuWVmrxYGk4O$r~w!at#0xPUs8A+&z!qmwMG@Kk4Y768u7zthPpF@B+YE<Y6L
zr1mwA=c)qxjvP_DUO+|~1i11E!bKwQOMnsfR82ZfYJL_RE@9y4LnmDFD<*(F6{ivO
zPL{oZgUne+-m#>g%@^5|n#2D9NLRyo9_&IfU$e=ie=h{sa3ae{`5~~&no05&`&>4U
zz3vVkn<|#~cw#t|Y#eX2f;*QUnJnZkA7Q5ppPIPQ3{I%bAbGE4fO>6(nsxdkBa#Mm
zD(b;RY$#e0yKaBVcn{vdz%qm`8V91ds%_`8u`L750M=o@nMA;Kigb}<JUAzst-*`~
zlVw%Hcr$zDW!Z^ya(zQT0IHg_oFokhJd}kvr*<<}?A4q^gKg!#Pa6Y&N+a(oA3>7J
z$PzIeVHs<-dq9tZCjh7%M@5-caWb=7@Oa`x+NKbCS$i)d(FlJYD;3O<Hk9adKn5~*
zRYJ3IusC6G&s4_J#39CHqjuy;)!M8f{hiQbTuPsLNzG>NWu%Q22o)GuG%W|J0>@Ci
zA)^Qi_=YBrnyhzfM|2x==9(Eycvvhy=V$VY8@R5#BkX2Q;Mi_*PQ-W(Q=2aK2i0)c
zM~DIu+Z>fv<8km4@<#kZXpk)KOLT*7VK|<skOu;#V#AuIUPmlSjW!KjTAp0u;fR&*
z9ly#gUekc}LJlHFn$TN@Ny7)e^8hD~*V;7qN0K&S++<lBlCmN`!5gsKauAO0I*vcw
zi6b12nIK1XMYxCNcJW?IF|pyN7~VMqV0Xq(DL`Pr+yrDqFJ-C#Wy{TjdZvoE@wj|~
zCnEa%-0nL$qp#S?oi{(wE-GY{Ac{<#nV+W30&XW9f{KncfO`eN8r>bxaMRKnGFcb7
z!=#`vy{-QMxLtVrAB&r673O#Z18DY)be;Ml0=N2bemO7Y{Lz=uI8RL%@(0YItP^Ff
zp?+5vS*=1I8cq#80@RV|vxdT8v^j^8@l@U(L1*2&<+s9M^!phMMo@MkBu6WtMM_<m
z2Q!H_M+-gCv|MrLWMpV|3p%So^+Wj2`Tc|V)D0`><8Zu8acS~go9MmPpMuQn(v2UQ
z1UMX#Lwal@G|=CA5x6Vx>NgX4a`Hrp5L4O(N;qC~g%ONjLJG-&k<bMgi-~e{3Y)Rb
zbX;fAYWEdTHmxJOI`Bh%hXa;HrKh1=&8G8(->pU_JUDqT6OJ<<p8GvGr|#4noTk@a
z_0J@5hR}wd2=6tw<_KA1EAr2x06V?{+ytzuus_vEHR|8=ghPmN4Q~;Z)M2O#t)WD^
zS(gQ#7W|W~+%p&ILM47Dc6}-sUgb~wmx-qvc%C6j*RwF7ciRJ!%vBBgCc(xr9x}c2
zuG=|)kM3F&*l!QEY*;}YWqU7h^i>?g5KBxcCsR7@Inx41HQ=T&0n_ZCV-fzPq91vV
z8TK++@L8i92$U`~UaB@dngTIhZn`t#t2wghb5pmBT=Pc}sBzAVw6*2Umogx<@J@`?
zAaP+C$IW(=-Zi1FYa5Gr4<$kMVZfJEF>U|^=%o5>!5JGUu=%X}k_S?nT~dqf!wbL4
ze(^bL3l=JFE)%CTOYbB*Clt=<?6@s9Ko-t?kSkV^AF=_=aJWMiBbfI}{$NajB$-%V
z+#+!|5VuqVT~4+F&-%R)@w~i8H5=_|^%AVsLmAD+<qh6@Gw)o+^vy|~(!55#AUO^x
zm#L>yDByV91E7VTFxPVnQjO|U1~?-G)oA|fD3(^L9sSl<A1#fORJDVwr#BTWBB34@
z&T_QcVWB($iv>3Dge+C$EiRfeFtfrFH=~5BVLLKc;G2tr0nU>IY|DWP5rQ-c$D$h=
zT>V|aF~eJhlFHu^v=HZEV|3O@_#oqk_v51AB{RKqZxT}lHa8@G$BK}@PCGrP0qT$O
zT&zFS1L}?3Ku0$i=qwhA(qBogJ2R<Sn1?A}&T#ZFkKpe&^qj&nW^nOP0eNs^N03B2
zwcy;P?qcGdiN^uI8~u(uZ6GnH=dj!rkdKlcCl4v&qFCb79JV{Vpd5Go?oCv?wlsLX
zw{<J=D!U7LH&THzQ-Od7qV}qW`dv;Q(M8m~P#yE?VI*t8S+trO5Z|dxV?1M2d~U*q
z>2JkdpccE5u^IX7Hva%mKlKfoFXK{q^+v(VLbz-&*3WePTgdWF-E*+D?YKCEEm*R~
zZL{4UCFiz78p*}xgt&HUh4oH$3gz?w`2CS#_jA(w1eUxQ;|M`D97yxnR)$^VoxO@d
zUJ9*kM)|Mil6-=`>qPl&%7ZDfzE?+jej04mWg>bkwCKI;Y(JYQUd7PK;GgPR6b2wL
zxP5jzKu0B&Mu&Oo5cWw~JezP=U^iX|fNVXjf_uTa$;qTRW(xjacm11B-coiZ^g1E;
zm17wq;BZ8Z+4Mtt^rw&#y_%v?#VtA7>chRu$sOiZ?q2W9#W!+`Fj5)v5P(kJ%#9QP
z=Cp_rWDXZKt}4g5Fgl?!1B02IER9xy(h8UqM0uG`AcG*PF<Fczu>!{VqcKiB5##b&
z!}=8M*RhV+&}|(QtlrpflF`%TfAkDBgLv{ob?*Bj1E~CE836r(BACQN{{Ufmrh)e@
zw>bMI5k__!phMfLA*3vhO719Xn-It(YXN3zVphajI!bJAtjU0=V6RWa>{1;(l|oCL
z;{wqZ87~yLymdwbBksX{GHnQA40ibiVTm4Os!k1Y210Iw3sY`Hzlz{dj3H#<kjWDo
zuLpANJ}blYM-wWL*SGbj3?bp=y%B<1kCGB;Z-0csDODF5HQW<~{{W|Jfacv3jz07`
zt>dDmVaGmcq!lfydqFI8TNGqd4k4K6r@j5PPrM6`AT9Sqv=NGNby$0#a6X$_0&~cm
zoFP=BOf+B^%t|c`Wl7I6-2hELk3SMsX0-4+r()vJr@gmy*3GV(3*9C7{pPHm!0%AJ
zEf-&ROEEUGW*TGgT7G3#gHo%FCO6?J@?71+(FtVXl-UzA$wme|v7F&%Rbs+KIx|MY
z#8BLeaVe${Q_&lR;aLTd60}ANb3r^&A-)o-nf${wMW#k$#pbDqpKM49lbrSpCUPZq
zS4+4P2$o_u7y7Q$K*^#%%EwZt6JUq70-qP!5_oL>Ykq6nc|NQ7G+4#_F3k6jExF-)
zPxO^xsy7b~MjPIB%{Ch5_jCG?mlFU56?<tP(?ovh(&xRz`6vUuZTRfse#VPAaoXmL
zc`#>^acBZ<=$$0k{{X&dVG$yn??3hn!rL{2CXP!i&p@<$038$>wVGJL!Mc_0J@o!Z
z>YDP?T6Z)=+1P0eiQ<9#m*XR<Gg7GD>oB|qmL8e{z2}r*1$uSH2+~T)l^oH~6eMs)
zCV}`6cInyLJ`3I}J}Z*U=5|zyefu7HDCC4(A5EEIZ^?UB*xWQjWVqWk_-thuJaLpf
zeU_;I0Hl>+P_n~>PuVvPW5D<wpUfXLXL#Ri@9xge6&JdR{{Xln1peru8vw`O=g0*+
z%xS+(oOW2p-8}K&nlUxgCz4}e{xcdi8lmKV5zz?mTTI{4aYT1?fP%TS`7G&FpXqYd
zq#@##$<vyQ&?W4*n}5d1%2R_oNE+F%kE+wOxDV|IWt%rD?gr2Q00={z(BiUtOICg1
zD|lhW>2)HXc^J5;A9@RVEQP{%;=WczSYZp<mIKHwDFCx`AOv??NKcX$d&xda!p1eN
z=!_?re6aJ7fZeD@YegVF#2zZ;rVl`Vz_VR`i`rl-FSd89x(7!6kccD!ZoE?iONJ;J
zUui;q>j(E8pNEKJjuUk7<_ieeKF*s$Ye`v?F~NJ=Bw}d@=%>ciLER)CyCn5dEd-9D
z=sUb7Na~xq2(n=&qaA-F_b}yS37Dz|%VI!txx!;w%DyI<H{zT3v1#I*;Nr&_&q=y?
z0yE7S5Cru=;=zgzn>Dk#ETw)e7hS{ZxTo4+$0S~U3BUd$@b$@Ej<I91m8CH~N;pL=
z>c7*Ms(Hj*M<4=lnqZyoToLwzqoS<FObV=hupGS#4Ro+Z8--5Y<Ot9N(p^l597}Rp
zxy8rLFgf1C^=c4*(~0ZF7$C?f5Z=<pJQnH1Ds2)awV7hm&xlY*5s^e4@?lbr?an2g
zR_z*1aKwG9p&^iIx88RX$###rqSH_?*f@<7aWUt@asf2w0iZ>>F93^QGN9yr6q!tb
z5mOwH_^OUCHWC$)P(j;a$vSvQS!`$|8EH*8n^QQn1YjwSYua!|ca78VGfZdX6*Ezk
zXp#%^#B3At@Wp^mmo|Z^Q1f;{Wez@ziQ0a^@L$Pf9X^*|%nK}LItwd3&1EM408x@Y
z8*<)vt&qYE<nu!OO7RdU(J|fQjvxlJl81JhfQFI$K8VRBdkv4>c&fSBK3h1Wuh{l!
zH`J@537d@(*4+^rSuj4FOT)5(sxs#pI~qg1tLBVjsMgO@3&TytgQIM{j?mH%QT`Ao
zen<A9W@H9;gv?B?T-uYv{gqP_8Tdz&k+5q(^ivL{eyGE%)o~pKia&Wa{Hyql!B_Q!
zFJi0>2haA($Y{99@?OIaz%5FW5fv@E;hx?le=3<m+4}6x2FKx`!{+nAEy_UduxPS#
zdpE!#GH}Q1vO3iN0Hna%oLgp2-9Bo>8>9GCpHP2J&O0&=!g}m%xwV^7r>^QZbwEBF
z36YmYk*Lw?R$b$d#YBc+{@sI5nvmh)r>W|W@G?A+-KgA4Q3P=u6Q-46xa_jYJYb?p
zXd`&tJAMBEC7pWBrrG7H{uyh}nl^ni>K1KbJ3MXpg(bBGhvo`za<pI@C@{3uwA#^Q
zWlgQ={!@+-rq5;09}S!v9%$PGg!KrVERfGt;{f126{3tDr5Oy}m60o4OWD;BAP<s_
z^HO?3I$?#YMqCk)Tw`OQw4wb@fd+}EiU%XG@7f#8X08KjlgRy;n81A}?Qz(B=SR!!
zdqd@P;AWm5uzXKvoFX=4^CPd>Foq$?3Y+F~%c6L5VsS)5sBuoJFwK@|Z<2=1N`b`?
z41yI<!rEK2sTv^;1cS1JaaA#oTbNwFTLg+Uj%H^?l5K(c@j^Gvmx?k%Y(euvW)nfK
z9}S$D%{;m0n37hzBgZ_}W}duPjkKqY%5_Rlb+HI6>Qh=82jWibag|ga@3WVa7I6pU
zgaYWO&vnRtEh=(`He&nf8UFynoF{p_7m3e104=NcPH!Wsd^xVUPXYMBk*MN}nhQgl
z9TBO33EUEXE3<c^Z6@JJCrsoZFysMQ(R}{^NxB0QFt~Y1D8Xrw$y%LA?^-%$yG2T!
z<c{dJ_f(xmjV9tCUo}e$8%CIAw1m+(M_ExgqzL(|-e*K?00YTf{fD%>5Cl&hfENc^
zR4q~JhMQe+D*Jb((JGN><Csw1tJ!IfG}g4m$|RGiRyF%UcUG`Q%-+J*`7dUZjP+R(
zNJa;;?|1wyZV1SZTQk}uYC73}CC?GSwOO>7=O0K$Z<+v#wW>V%ZVjI4K8Rv%FcH|o
z*=r=bAR%tcFn5~61OVPh?@+Bj(C_lHRs!yK0EXo@z((<Kz2LRkqR6~TKGnKx;=S^<
z5{Lr`ra6}%@U>YDj3iSVhe1<B>2rxL>Q=Q`@C9dqu&>=V)5Q-3I7DxZFPz)%pkSTX
zaYoH^KB|82v$jVEI^|}vsZK}qsfO-}{I*`xaQ^^s!o3z$={%oAM7TNLL`Kp<87L0d
zp3W%$00o~U3EYCRmBoGyAus)t8p=%YG;Gz>{s8QVcX;nuKWaDF*V~#f{48kb<9&l+
z_Jy{8EXw@fO|6#G4yb&Drn;Mn(&1Fb=G4gU=$dJD`XMA-Jgm*m!)D~OMjGM^54z>-
z=2w6i2xUVbo6#m(W62ob`Jtg&IE9iH4hA_P464Sh9ASPFJW-kCI;d-EGjW_dynxye
z-4#A-Mwsf3^-2w&ByM{wdA8dfU;~L9f**NSh%e`uQ32;QuZiEXi0sXq>xiuIQ~65q
zy4_(P4Km}@$w7OKZ;?AP4aDbrt!`_;366Zw7pDe%!s37-#{_j=!<^5iz^JxkIVfFZ
z!U_3(oQ~TT(qQ;48}@uos6Hs#oghBPiS2uwv)J3a@c9q2+ZCF~Hf*DLus%zi6-~?`
zqWpQQt#kV^c83o|m~k6(E+%>*YOT8PwhAH`$6IDnfbRpa*8c$ckuvF!blAYmWP;}V
zSW*5c0Ug}E7E6j_U2Rbv)*aEzr(UB=eim7U4`^mA2P-F#?&ZU}<2*Sb_?Cn4P2Xdi
zJnjAvnsu9ru#0fCzz)1hX~Z6GQ)!a`;uB<UNV?)aNEtLKLc@WK?%3bxva=i@^I8>z
znkNofj%t}}D$XIE61)PQAL=<PQ+UGgcohcH(lrakia*K#SW0$yL60>%^afl;Xv?q(
zS!l!n8y?Z-pNPRQIWGfa5)o^XRK2Bm+q#0ejv0N9Drb>S)f=Cp%~>=;-==aL6&ao`
zD!bUs+R=OL)U2q|YdrN=gk#0>L{0$M`0cwi{Jj%{ff5`(&BJ>Tj*t|6<J(QXS33?K
z@3S)(B5qIyVZi>OD3C=re#l(hosJag7hwh7+29m3QETwCNgJY8p&K`{;r{@rCK6mB
zr=K+Gd3WABj3aVo0zo8f2rDbpV35<6-D)@Hj&CaPB6$Qb(r5in_m0c{`!lQe#C5Py
zdtDqSyb`~b2cbmbett_=;R?ngpX#UktJf5o=7X#Yga{qg3Xf~Fhh0>-sNe^l!<jAh
z2+!U+{%S!2$DvxBK#fDLj2f=)ne!!I?qkq}$LvJRIu{f_{C0ehkZg8tv937ZpiIEh
z0X$D#z8qkA?C=Ud8&BBRV7?cypVDlzt5^P(?8*kn&D%8)7qZjwUg+2l;R=7<vBWvd
zF&3D*rfXBs`)xtbrx`i}kRXi;C`!oY3gcuT;lH9V9rus^qM!!Qryn#gGJmOiH3&v=
zQqP*wq1l@`<`YgX&Ita{v&{5f2K-MIq7hBtNcUf;*kc~QRWK_0#;Yf}&OS;fvsQ1R
zSr(gcm7zxXX`fVc?Bmu}uf#e!`vuH=*RuRRi!o6^G(n_zx-~0}mVf|^(yH8TkCGRD
z8Z13?RyY7~<`fxuY%IlyM&z`&s6aQob5E)J8G^IG?}l4**=X1$z2jvNTWgMrJSXht
zDPkQ#LJla7n>cJUfB+CdHT4cXat%T;0Ap@kSA=Z#nn31`qvBA60B#XVegk02`z^D8
zLR!iVW4e^gJJ|*2v(J)oG#_X>PDMocQNxL$fc~Wgj>*e<J()vw&E6aWtRRUR!hR<4
z%jSV5m2)6_q%Qz{xvv8bT~W5vyW%T1Hy6cC&DVmn4KwDX3>8NYWTpo;o3qpcosE@m
zi>hzlQVx9+^J>ZLcJJm1<hoN!j{$TrU}i1vfLI>aF{RB1co;#=sK8lq=_@|zpplV<
zr8fL_6NQ9WYMV4)XR*9FA}V%jtzoA5&J@F&X+_19NWw;K#HS3{^4T#`t6OeLT<UV>
zH1&eJtR&{+A2jCm22+E%CPlriJrz^9z-?_|Tw^Re#X$>D=Kla{7va@9IRwx-qdOQ6
z6SgIAT79FszVKrhhz84&BMUEe?HSpmqG!D$`)r~dBwt|3HbMUYc0+aE5{7$4o7r05
zi$eYomvwc}hQ6a3c-+D>_s$-c3Na75f0j!7h6f%<Nhc*uwJYgJ+z-^9`arO6Ac)1<
z1|k56i!WqK{v+DK<-Aj&)tr4YQ!i+#<aVD63D_|1iH(BqcxdJa4ZqHB%{ENrjJ?C}
zU;hBqY|O49QSSqcKrnB?P8v~3v%!|wM7Ika<^C3}b-pUmGKT9hIUVr)Q_nRmzB+vz
zvVUXWZ#b2iGqd^3`Jn*3kU15>4erVrNS-Y@F9jM{-_+c!>apYMvs_mlnfG|we@gd}
z-k|_<2PJSHbO@z4ckG$CW$xo-GdX<_+2h92f466`M<2!y+3K{c<M2)fD7JYMf3R%}
zBC<6@7qHm)tnLoNaa62adNf7Wxgtaxor4f-4cP1kIh9H8Y(%}I5c3PR(>SuG?82bv
z;x~uGXTCi(Rt|BVi^WmkG=KUw<g5T}dIjt@Ppklip4PwMj!nW>y|7Oe1(jSHXPRx^
z=m-A*(X6u*!(&6}Ro$x7^YTQNwZ;k5Xj5kmGCbX7?Gk>7Te`(<3L<+=W`I8{FDojP
z>NKqT%qsQ`2lPU4miA)i{{V8;rM{aRh_a(`My1FGG=d?#kk7PVqQ83b;Jx6Qc@^wD
z9+JE@D?R>z1Pu3)@L95=BkwNY>y9Wy87gk(4@3@Jb{W~BBdYdZN7{)O7_zd?SG%*0
z*_=2>IY-*tk$YteV=?NE%a@xC6JrZN+pU(n4e`R%kCx3FK5N9?*Li)O3Lw%X{{T*^
zo4dn$ZXYAIV+py|>c9vb%8k|rybRHYQmHO)CCsO_laM+3BLE%e6XF9k_>8JS=<Yi_
zc#YHuFdhfZ9e&5PZ$)$6;__KWz(4lRC;FMQQ&Zqmt%k>#3-PJ=$Gi=sb5)D2gfW)O
zikejKg{;%}Le3&eacLTYsyX41WyV!lYkNeo?miULQITh}d88_C0OUwh0nJjqk{%a=
z2<9aaNDMweqDiL0M$Kz{Rfls>Io!zEX*MajRs^0hv~+qe{wtmRq!r<t^5(kFQFLX|
za*cvv!;k?!!2bZbLI*XPosNP<lcas3p&4Vy$4uYNI%G*hK0i&Sz)4=!N{;ybmYvt)
zY2;A>5>s-yJOEqRySbhhSxJOH0+Z_c3oUh79MKf;!=Cr#(Zv$yn;y_}=+UA#&1#SS
z*x-&uX5(|f4NCkfV+&8c`HJ2<fu(p^3~1qmM$RbN#7~mXZ}8dicHibTekDhWZFd0@
zc4f1OT32uV%P#f*05z8t5&obKc`S>*i-I~_Y(Iu@bG^cT?QY7(G;@hrwZv?V(odk3
zr8b42Uxpf9{*B_b-Du=df-8aXK+ZEIaodx(Pnu~jbeLVBc6vuKne<G43TG2>>Yo(O
z96~iH?gQ$MyEAixN1BW*c=ZDN8Y2ZuaxP8t;Jmr*D@PTO4f!q?*d@YdI3TG!f^z{<
zf+}J^xlE89#440&4ENppF4x(~d9*rJYH>-eWVqqTF4IM@Hx4aTR)-vhYP~0G&0vvD
zpp0CmZkP$q#3~Q;pOSIIH)DjRK9LUb)5|SF?UbIzyNo_M71dpk04Qr7mR{9XH_}{;
zgF^)Vh5Xkqc{({Pvss4^h1O0NaK030Rvif120qRWI-jI?RWc0XbEh@o6o<usDi%zj
z98VJal-qY-6M0NA8;2pjE?2k@(O<f3HTuJ}=(BA|^;S|wEK$UZsgb?aHuU7T(#T)B
z2iE&F2u?c@$z7D5_7lz5Mh1R}<p=15wagx|8v(UpJwD0Zj6r+T_65kX*&aCcSqhFH
zAGAmja)~3J+`GWN<FH>PILWkeUMYZe=utq))l+kWOh1x>M+37l^I61mUW-I*?Aljn
zd3TbeL~=uATmkPOo(N)NLxl0gwd5GJlc#epIa%w}p@<rJ^A=Scd*<M9(&`Oh;}Mev
zNbiG|XR0JH0^)fE6W(Jp!$jb;ArEPALh*65={~DUO&c>(arQADn;E>uf(&K7bR0>z
z0_lW4ogcezUMj$}%Z`NbQKTIRI-+p?+N^<98{X94JozXLsZc!UrI%=Ym%$X>#Z-8c
zA9;4J%-+sT-f4{b17+G7R-1DZ1bGz4HLq?KoiC=rA<Zpt_kmeP6Xu%i12<@-xxJ$h
z9U)cR@1(O8v*5j#kv$Yac%SZ89rLHirv?F4*FQy3rM0NkVU7eQ?_#s1R;Je&)F<M1
zp!lc_8Ydp`9H&G;KI&;YPuLsC5U=)lB0Sa{0<1U>hQ>btAY#&ZZ5z@PU^o-zZmrcz
zxdly))?!b^R4L@kNP>B7_L$&2!a20Ge@NIfrWic)LP^mWY+~-4a$}DLO{^H*xX&n*
zz+H?G?f_}C*%v4S-qUk0$4`phtnbIE{{YY}0vLrJ%}ytY8U2nsa*$6&k;`b4-nQOp
zu;#SA-`B^>W|^It$VNW-x%FSqf6Ztm$C*y>J&yyAvQY5VD?BA}`D`|Oel5EG6(IA_
zqA-!Yuvj=sHhlnRU?85%_A}B~zGuiR{{Ymq{{YmYahdZ@nY?8ie9nMzDxZ5>TF&|>
zNiFXg)t7*PkZ=|J_76>*LgI$yAYSh!o8ISa(u^%y6CCp10#h)yZ;ZrxuHAb`4aM<Q
z>-jK#>ZfRa_E<prstzL*v`krl;p6`R+BtR#K>lK1^Ie;>(=&vYuqu{wiHHiju!E4d
z$vSdJ+h58gc`pSPx~uv$FX9|tyF7|%z}?=j%p)#0CueA=Bl8seV;Hl9>boU6fVlor
zsJ@f<V#F3iEc?;uznbM~rYbv`$!FR7MEN-!s-_YFxR{kdgZia2dx{)M=)Ib*X(^lR
zX$*MGN6|J2R^n8(&g}&LpayI4DfrvPyh4)Emo@HWyez`T^k{{AI9fd+5?hVboIh5m
zZ+Ca<pDuu2QJw2~FJ{XzO~iU9LyeYY&0@*4+R^1?MS}L52gzB{Z#3@<nBY0&tkb*e
zW$f7b6*(1|Fc${jKeBBh4--De4Tj*zS7m179(k{O?92rbyYkx-!VjVsGE8jYG1U?X
zHX(MWJ=wTC99WYgsP+Jv=$VEBe|9RkL;aTZPh;Ttm~k}pQxgc2CQDcHr{aJXD>*<C
z*8>M5)lg+4s131HT0?|ns(sMNDJ0kqYLnXRO(8oGaA(KtLtjawylECZ!Ufs6;3g5@
z)j9j2IkqxS1(cv9ZgMNbU54K2C=2#iyVGRO5WHB^qai#M@)O5qNNJig4Fl6@0raUF
zXP8fAI!!T&+MWS+R))CYae2gD9*DJq;|m)R$SSX6v^+sgruSrV2qdn^H!KSb7OJ4o
z1INRW3%;vRaE1b?7M8eRIOYgW2B?3)-54VQAMedcq~p9e9Hewzsjqz(5llykrA(W|
zAUmGV5Y8obSJMv?*U@%=+^Vv(06Fd!lf*Cl#X@{7Fwb397E&I=1O(5+l3n_&j!(n&
zUf%xz_gx#piN@dh!XD9H!-x4#E&?V-Q=;(=*T)dEaTMRxS=VYe;-*2}cFX;c0a=be
zXp>G1w=3vXJ<wqQ#x;%aL}u*O0$IHik&y^yyg<*Wgk%9fWKQt1ZG)p>GOS$C)B2sB
z!p4fuIGp1UC;TE!UuwA@_JfjfH7XMKbU8|B#4S3B3Y(lMHxtOvAeuwm?1;z&GB}SQ
z7Oo|Gd#T&3!aV}M(Rt5qu+jDbKYYS^10@*_d5)ScJG*otW#2RAy_&C&?%3#S-Wub{
z9|dwYc&0OprkiV;6C@#OOl?0)qxUsLbUatE>vYp$CG@JAUPkC{(PlW}as$<P-CG;M
z_OSw|c8PnJ^ICo@+D*LW#><Mcb$Q4r!bCoy&HFSf!%#?`S3qxM4$dpyEBKBS{;Sxs
zVR2POF*R`?6lKA7e)N{&#Zr1?6<+P9MpzF-;_8`#Oq0uC(_#J%Kl?%+d^QVX#nus-
zTtdtM7xN0poF~m@SSXL{H%^cQ1t99H1~G;sQg{T=FjQ;6_-xN*!(IvXS$EI*p(Hv$
zLnZAxF>@SM%c@(=8!$D4$RQ!rG5NK7@#wX2+jHGL0vJrkJ}864a^SROz~HWGI8tn8
zH7UUNd$K)NYe&3!f3mV|9Bx0R>)WU&%%g7k*>bwdWb(QaAv*P38*LZ0PvnN{oL&gv
zSF=R=n<#Gjf+QBlC75s3W;cmE(6}g}ce*E#N5a`zRiTT|Ux;4Ai1oT8cXZ3iSNpm^
zmEbLD9%JlvS(aS=jW%e!R2&U6`n{E}*=}_#d9FWT0tkl=NaCdb015gOf7n?xVnXhC
zOu<pU*w?C{9cT_Iu{tUxtt$!dE+EgUV8%&yCY9{5qu{y&wYQ6%2UOd~l4&%9#xON*
zp-*<#hf|dB*|V7CP!S{3U_mxVa%4BjQezttF@II~l<J2^8L|=sNj^yVDJ+6W=oL-q
z#MYgE%5RRF*y+;B@t-cJK{tD~Jc8s`M17DaXBCCRgPI+GG~e{~rY-nq_Dn7M^w>eG
zy7vwN@nW5jI7c$SoAg%R@o<#rG~F!b(uazGbm$KRCg7OqC=hE_x#Y{o#T(+xsDr37
zqwlCv-Wr%6P@FfLy~E&}h0g6CMH5lYhUac8dkdwm*P1d*(*;UxVR&KsDsEt^O+sCq
zXcicYv)w7MMrccL&om^usDaQYZacFuNc^@T7TiPyOBr?#C>KWH7ZIu0F}?Je%S8o(
zt{`Vb49Fv>TZ0xnIo&i#<e%jU)7Sq1TyG4=y&O>7syErB@m-r3I!RQp1D(n>AKE>W
zp!*}j@MEGU1NVnLkrbF;ePbc&rPV6-qF4ank3QsQ=9`9?r(hR_w?DEs#C2EvHB$ku
z4JRMUb5#r5Q;u>I-B8)H$D(X@j?g~xeTGI|b~Emq;2cjScR0_l*gB1-?w67B`wWp$
z=9>r2%EOA%!|Weyc}3S~&Z%?}#Z&|0obO93<7-UDcGYo0!n4WgD3IJn7qHuPM#i(6
zPeefjnrGfY6#&!&6SLVJ@cxv-(&yu-Pr5Qk!d!+J?D&2QJVF$b!X$;qG1Wf>7P<@1
z)0%I0L74&Vb3b7FLB2-IHpD4eViLHN+cKsz^DBJ4<;<kVMY*a>qe>$SKBW?V@}NHj
z1b09$C750rw?lot*Jo_?ZMKWFU(F1c6mH@Nnl?3DN10iJAvQbS&}#WbwusICt6t9Q
zafmAWMBRTi$nOJ%s`#@<+7%o9B3F)!i6@Zavo9ro5*sQ<ciJ84qW6j+K|{kx{MFjp
zhB_UW21p+@BAIY_>P^@49rM@>_p{b`D?OB$8yVo6uY4;LxX!N4W!<h{6|3ChIwJv;
z$@(o02aO{|EyC6U-190f1-dNS(LdC^&ywT;LRv0IBn)HS{VUn0ex#M7W)b_=7hwlh
zyEHBcKSg3r7amAh_ig^to4rrOc#Srg3oQa37$@cSvQ`irM_q#ae!-Ut!Qe0EAF>0f
zRCc-^A!gSV*?Z~?achQ#%A0$(67tTv1Y?3|%gpsa62}L6Do?~l@QVfI)eAM3C!+Vt
z_MI`cnAq_S5}5!tE`b)C?9LDo-AU8WVc74*1%ccY;<<M@O(eRLhfP#tC7`@~wmq$6
z7@bu2n1&uokKcDiwT^QGH2vot=uXAS+scI&ceI0dTM={%Pb;JB3!$;Ie>Gn6d|L8U
zi`oa_xzgW4n^Uvzj7>Z2j;nVaN_a1r3MKQJ)~P<0*vV)(2|Dsq-M=nD$U0y|qIca@
z$EYy8P;bR)3ZbOtI%(C+0CXwLc&6+vdwrzy;x2-vw-6&HjFW~F2h|8^EQtLf9~0tH
zWC|eDZ0I;47`I#sw-T`i3SMeA9`?VCy5WEL^7eCXzKAwsf&|icvoU@lIm3jY1ILUv
zE~pOhvH<C--!nX=Q(!!B-|P=Rw!}AjA|&Os8A9rtvhq!Xv$cKf!NQ(#7Y3Do7ohuD
zi?iw#AG>RVuh&t*8gwbwelq7;dIfhno*_`@h2Oe#*Q^?F@SRE?7SH!C0KcmD&(T~4
z31<+Szb$iLF@_aV{^_`{%S6!U2TT+mTUou}^BWDZm>$1kazkXsWeaFFpA)?~sqX2$
ztfmLLEVISDdTI7dV500N^BY3XX17uQ0Jg?_$k<?s1J7rP?VlyC@ah&(B=8*34W`zH
zh8v42Vuf#QnKj(Rra*l+bGCXbInQZv;u;aNs=)PDo*dqcVG_JJTIYHrxEnFi02tp3
zN7)x^@hujEWU_!e&;64HJRy*Mr%74t)3p5Bhq4qN8$5K(9x=ynh*xkOB6e$*(I!#O
zwEY&3aQLjatlQaXgVhb5$d5&trr_SHBf~uT?GyKnx0-2^_Y=zL(9k?Q3b+RtR&Ezh
zq$9ZF-7}o4w2VWcM<)xTXEIK}j<CFKrX4(*y{!E97`@M2(FN4q-cvtC?KqV=TdK>v
z3B&Kh**pV;tlCe(G>^Q*dB|GWtizDAOM&x7#*t|2n)fkJMXQ)TYs4}L9oDcqe<)hC
zk5n>Rf(YuVoL!G`<MMIXZ)hA*f*vlT8-WL!JD}n{SHJZ{h)uJz92R9p7T$Q1v-o5D
ztcm%3kV)XJ4Fk}9sTiVq1($m7pV`f>2hn(I529mU%A#S`iz=8MsTn}Im8&2}G~vQ0
zf~(!IgXVZFBo7edI+d0w5V*JgR<9*{Ed$~9DVNe2v}&j}oiYfe90j&};WquzZ`das
z=B_Ns;6j3C6HVS!z;P0;7&lmmzA1!2#LN@IQ-jAAqKkb~Lx9u}P&0=msD;I8AvW9;
z{%JXN*y+Ii$<7)k5F-yBcTFZ_j)=MLCIvIQ@kDNKIE`HI*?SFWwzgfF`=@mO0E7q(
zt55;MBwb=4La=s;l0>-9R^qYIcs>sk9-Es12d;|vNa%oWHi8wOP>N^H(y9iD`^W0;
zDaFm~;pLCl2+;vjoW}5y-h~+s$;xd+<h`oR4IigpifL^;I0*Ut({4y;J#|8@LeqB*
z&p>57-`RM}%{0rzFA!jod91WMM4hDsCI(1+6R?<BtMX07h9DGKrs%XR@9|X(NRYo|
zdhE%gMAHXGh1u#~%`mau_8}`@vI#I&7E}IX&K9oL*ZtNPGo`E(iIZ+zf**0&sSJDt
zq%7vcgk%sB7y$HHg`LXGwT<;V2nEJmcr+}s@(VV)r9b!Tvl$hu-iO%GEX~ln$Y?)5
zvG|U6bk3T>%X&J()cujZ*B3v5C-&RmZ8<)<D|fWsI&1Vt%{CuV*CSX#M(qbgX=S2&
zSo+Ot?qD&65A_v*@gD<E)jv9gl{#vk>?5?qawqme)Wer(?<zIlhT(<67iOtXy}=SW
z$wCW=3Gq%E8o`;iNq{w;29I?B0hrih6_#Ma69P&H4DPFkurAQrcfGL`%Jp6d8_?F7
zLkPi$fR1l#N6%@qs*@coz0Caf1S~`m$geGuU5LHfMCh0YJk>lfFv~w;s)Ey#ow});
zn-OvFO(O_1)o2^@U(J8bWap3CQ|2m=Ev8#C26hXIt@J~}L)p+D@`HN90cG9~@QDK(
zi-_uq_Uspn4ZT;gz)w+R(~6ek{)@vaCo%^sBbLu~0WM>Q$#}9__?Qk#h%+t0##gf9
z!o8Of=#1fo{4<F9t!vXhu&l;DspuECRr@`GbYY#R5GE&kL^mirw4Gc{oKYAs89a&~
ziBjLw{{Sme0(Z6HLU2TRl^z-6vIs74cs|%Uf{fFs&heasv|JYuv&SjXa|c8$Ul#pa
zF5O$iA}~JD?(Nm9kJ-IPZh^yS`yav<D_5D?Hr$Gk-b)q<7^67W6R}fmPVSj|d|YK=
z7@B852wZklEg*43WtrlQ?;cB6E-~q`o6hk++Qg0~9Gshs2Vf1DPj&{A3?^fy$&e+W
zYEyySz2*(<rpIrZbvQMH-ZQ-FpcaFM0I`@`wcEQ^HRhZ*gt`aMp3O+N6@|k1o;~gf
z*j>qSO_PA^GHw>A6VXzs5J+heFgc)HTr?jAO~;G_9%{2%>&s015p-0qR%6IQm^bM>
z6I~5%ddW}SsCOLRN|$|2{wcBQ6IuS9;9&!IcZpAS2fFFTtswIt=hPl6v{G|0No%a|
z8+?;Vc5-<q8~suCLKF9Rx)7VayYWXp&dnu1{{S-TjSpqF8<6-Wj6k{%<|*1oSx{Q$
zlhE)@52o)M4qjZ-iyCQ8{{R`uI6P8qz{?TN>Yf}oY>fR?G2c=c9P$4Ea%l~5#3!0;
zx!EXt=Y8DQ%k7XAMv*y!wJ|8okk=#?k+NiMB6Lm@!2_|N9*N@GBRHzV2LZ`xH~SGz
zo&NFmZMmb~)f2w)4H6>5_f;J4lEaFz5y%96xik50Re`IB^Wm}5JGMX6APjLpU7B@X
zHop}}(}J_AD>1SK_##EOd7*k^5j@S*_e^U^jt3xsZ8mrsKF_=UP?B;8987NaQ}zfA
zYdgYtV>+ubpoY%~*g)mC!rkX|2mvN`x@M4Z^C`A%I)waG*~O+%oHb=P5N<(xe_Yj8
zww%OjPwa^<lE)LpyAJ6y9MC^~+_?1?FooSH<&vsIc6wk*)SuZll)b^Uv9wE^yB~Rh
z<$&WOCD~m{-uZH_@C-yYW>re{ZIBpm&2|gUec>yyQ=C^W%=#|RNsRYmQEf57jR6ux
zoU(YvzlhuNgDWR?W*<~{ztjtI!?*dbl^K13c_LTiym_#G=V^koMCK(plZHh3p}a0n
zME?NeKIf{5r9sYJyM4nT!m3D(LU1oNLkQqA*_)s8NBA-D1!c)-z3_bz&BkM@1e8g5
z$PEe+je*NT1AMbX@x%kbjF7uu1!m!wMUmJHXt07`hlI+EPGlEw2ZGY*3A|55QW_!0
znMQu_pH;vw%B@k%Y&(Q-Sypd!@$S6Dqin$Drd5w~DZ#9w>+^BRc(J;A%9(8!VUAZv
z`JXl8Gvpy<7hFX7Ebsy7kKx3x=DX&<8l1X`)Q@pXNCX}`CQj(%bWwL0)5jYHhG_14
zx+=lkSZqemX@IR9e#gB&iLr^(95&?x>^ad<rKEYS2_I-~W=}njJ<d5ltAwEv+YOW{
z)op5c2zSEit}QJicQAOb2=N{YWV1e{9|KF2osRp5oANwUfu95_SlC@Qu>}k);jyHE
z3Uu#-WvSZc;%jY7Gv<yF;w3g3*l1CS<jD8{t5a^TY0bWhyz|0~n~HnBVO@mFo5grs
z*;*@yI5hdE;5g4jY#pM}c(9oL*J!CNW37nYS39`?2hmh>rD>8Fg;zdCvP=RKfp!hz
z=uK;_{_;X9vlU8UYIp<)GkHLVHkmC0P@jXeFx{m1hb7^vGT`k(acF&}bT&~esZoVK
z3tPE+o=TY^wt`||ca5n2&h5u>&cy<I>H@<<nlo9=iVWfTikCmUlf>OIjx+5v9TOT$
zo?Py5sWpvj9aXg%n{e^sk9f^^k0fKk9;iT{lBi1}NOnpCYm5>y3Y*1BK;dvI9Z~tb
zXgF-R`zCfb7878az%&3Ne3T+OuMoD+j@zbz<eEJcAgZ#JyhS{^>+;X4;jx9s{pO>z
z*5Uj%pQ@-`ghNI^bOUCq!%4UYk{|{LXVDT#am`)NA<sOt3NY2|G~EY~1L%Y{Y!Lvr
z_mpO(NQe2!2!32gMZ99<<f9+5)AQKkF;HeO4X5pD2vH0L#cFos7mo`&)6iswRGsz(
z`lEN&6uJz~-z9emZ$635?;F{k&U2YS8ZUMEE3~wtF*>A_*fAn_N_$>nNolf+oKw#&
zqJPux(>y0dkvCd6g_-R@6Tz(?<u~p-Ik)uQRz|!2YsHV?h{a9VukP&A?0alKy8c(y
z35^lleHP&!>}ZYTS!S(d*5VzjIGrV7bn5{ZHQYtQA{=eys&X!MX9I`br=*@~*rfQl
zqDI`)cU=4jk<A}t>#*3qeQu;<H0iYUmB(PvJJV)(eI-YDTFL&+rT}Bf@@lh1-0lcr
zX1rb18!G_r><&!|#o^5UtqRSg5#+UX!ps1?FKoa-o7kNZHWpHGR&EpIv;cZrW$-oU
z{*t44rbkPwZ<OC|rTrHceOK~=@ir>@W>ujK%CQuJoKFpp)#5@cw_kse=C^D?<8YfX
zNzELIC3_Ulsj|vv{#HP*#3Cv6DskrGkBfWNe=rYQFXp}vnz-b(hkOyup=Y`e_iP4r
z*e8;3?=_|SFB60>e51G7r1S9md|^Cd!!g2EbF^$1LIZCLDG1t2p<M5Ish%5RTtI{l
z_82SL-TMaLV8H_1Jk$(CO3}rn?H4ct!vVNsSVfUp_^vL{Y!EthJrt9PQ8ek1fAA27
zF}PScb3!?zC9Z?esq;XM@V%f4@U^4kvqqni%(v#UAc9j&2~qcegB)6p7Ez?ew3}ir
zy^!&JER2TIZM3+REv?_%kO5MPz<ux;AIzU(Z_x|6!PJlGUN*kR$yiJQs$)PG^L^0`
zG5{b%b50YBBdU<$h8Ky}<b{hy6UG9Z4U^{?EZ*XMP%(!Cc{_@kAcqg4Fh)gTCj&=@
zZz;czOp`wFI7%}5uZZLj5Y~ZhN-Y%~P7C^IRJM>C)270LJ7;Ufg_@6dJ_Dk2Lx}TP
zo;s#9Y%4G>R%2j9r`v>V4H$-J(Nue$Y5->?8?B}fmDR{Ft6~2D^h~>t?5T<reb(mg
zr|b>dOfHT(0G&9<zv^B+U&QL101nKF601dHQKGwEH((Bk;*Q2yOp8tuMU^If^-4p$
zIj{qG>VT&9n6|Bi?CCk+ew(3%tfno`ir^7<dl67*BESR=Dvk|r1T4+L<@T}#6ON(4
z2Q2=_g{Ks8It6TQpHxIPf%PR1vEembK;w|uatO!KQ~}4d3D()8A^qb=NZFnN`zlST
zEwFY$dtHw&=~@X;PqN;+?H)ReU|;UZvOCaY^<91?QHb)!zlYhLeoIp&4rohG>%7*F
zKVTd1PW#6a1wIlrUh9XZ&vWXjQ#kT@893xsh|n$xwjHKt%V!m@g#isCJ}3dFnmp7z
zxO+*uqom%;)lcK%pCvo*!|Ah^$#|;vl1D}e*x1AYg%MSSlfqE5W=@t_m1i-(A%|dC
zKs1X<;fYzS@pC*-8!~lTn7#|bAM;)cjTHy=FJuA&JQuR;2hU`p-w`rwEo9Z=CIWW!
zed~s#=$!^^zla-hyfoW{bK+LDzCn0P1}`krNf;}-p!}F@vR}+<5*#3osQX9$lt^^T
zhwP8uP7}`AtgwPS)tu_BXVjq%h<NA4WE&^R8)9=X`>?Vhd#wKev@hp3=Da4ezG%Ti
z4N@lAM>NxhNSH&!DaWc9pQCggQNGea(HV1u0==dK_9MZE*X#s#4MJ%Jw9C&pTK$}{
zBfTp?p<i%~;qpY1bvr5HA%HsbCyKMl$o&_-eil)E?_Grtvfb?c7G)lOdry~|@Pcd;
zFvT$6L`#J-(|=TB5yS|!ih03MVRk#`s>lfkLOATR9>eRV!efHADlJ|rpLl+Yn&4y6
zWLxSdV{uK#k|PZ_nveaFcXrB!;`0%7*A}$FIj;+;H{obbyK`AV2hB5dgAO(dvkh8g
zZjKz3fKKbptgAY^v3O0JJkh;{T6(np$QTDcYu;Os*~SIzs)~WTxW9A`AgSVOT1)4m
z89GlugkDoV^Sl_NP>kVm9*M?S;3I^ihY2Q)Uv!-hB=F+V6w7W8H8MA7^LhQ1$#jpZ
zt3VRbW}A|Dtx*VtDyLml*65o;s7oF5RZpro-gP<!MUO72CDUkyO{MQQo?H_!#OI=_
zEEM-mc@KQ}le}gcc%fn-to2S=@<QctQPos_BX?oGN{d^G<9jk-*#j@EOlt}oIgy`9
z`yn61sUA&E0OAg7xN<-T7M!^uxLb%GCQz_Ld+$9O^-B|;=@2s3$d^>W8VkLgxThFv
zP9kq)Z8ki#EAXjryPRkhGsXePB2EqB*zn*@jpJlC9lUSIab6Z~k1f7ufEkn`lPure
z7(`pT9~4cH2dF@`-k_&znlgK!@_$y-6u_4zlTPpoKHAj6IS4*_toLKhS}C=@wnTT;
zS}^Nl1zykxoUFs43zSAD9`yMy4QdP{mDl06966XrHi-u%YGboFIftIj2G11Wt>uwg
z?6m8_7$8A^yq-x9%->a;Y5evd;qP@^Rmjh$6bRtI`G5UT*f*tQ<_Dgo3&<nv8AAnd
zBNKclk%ganWOPx#SwuRXd<wSqsrZFDL)6@&jGL^6<2jPDa_qvT^Tb+y2+bO>*9Tm&
z{0i5G!+M7i=qv*B+ofusL_AFv_=Mg5%}}x5rPm84v2s!Oj4}F~bkl8kDww1T58VtP
zQjzDgCc6>xy3D+|o~sPBk?OT=yo%JHg16si<PiQA0p^|emFVGU;KF3DMZ5u(FxC_4
zb*$|6FuZ!JwlE)}kwn%r%z>h8QY@C8PgHWG=|EweK<kZI5M>V?2A`t$qt!>^JLbK@
zP~BmH@=r01vea%HMtuI)`<48^uh`7j4fuV7m<le@D4_+K@hRRu%Ik31!|Z#@-a7)%
z6PvIt7H8xTj5_|M?C$z)KV|j|HtM4?t+X-DEo_sb9yiKqVW(rj%5WqYDDIj~ghX{s
zW5ywe7{v*h`?iHyj}+Nx=71#xN8yR`Ld_UQlF@1Asz?}1PM6upw~}d^rr)F{_?u`>
za4u%@W5}fOcK&qO#k>LF>xBce(>ZGQdx1N3^9>m`Gzel5an)3Gm?ebd(xVH`HWbWM
z-pF|5Q}I(LylLceO_JfkKfA;*oLmCL@ci*w9Nlb$5fk!S)5K3gh{W*?JdVM9O>e4T
zaK$BN=Qxr2asfH6d-od2h~`s95_~)-P@Q&r53w=BMM=O@rpGvaR_lAg@m;Cb%$*vU
zOARsO!*eR6bPBo19vO*5lNzE;ttUh+RnWu0m1a~q^Hyt`2v&VYz;6bW6Psna%~fNR
zTHhsHfbvEaQhSq#PJ>kAIpG#12qDm}fkrK0vK&=bf<|u9cDhweS%&v2i!sok=Z+mn
zPbVEhaTUjGp&K$|ekU~FVh5h9S}yf*P9|RQ(jiuBd!QYF`$+xRt56<7LSTDo<T)N`
zxN)49ilKrz3#(P^XE^b-=F*M4AP-eg1BaTkt3DckD>L}06#>ChnHv48IKx@bkJ$Wv
z)O{(B-*3N5>-KX!l9cs`ObVU20;u2wXDQ-~+oR2$=CHcq+oo;C%8`&~F{f1eEH-Rn
zpOQF(S|IUO#7Dsy3=LrZkqg*vFN`l~)9SF3Olidr4Ho=WS*cI6v8`ae#;U`*ZAQC{
zq8bPhI*uysZ)SQEPN1{69toy43B?xruVut~Z1GkSe@@K?Bz5^M7ZWJVIN}^RqXa94
zx*j-lUe9YE_NRMUBcjbaz}B~<1V|xFI%ldH?1KE5->;H4Ueg4Q1X(^Yy#C7n0C>UZ
zq>AOS=Vha?{{U^4U?4Nkf+xDBAagpoWHt;;?H`FJ!FZLCyIe9MG~r;E734SteXTw^
zpG3mmXH+d-jt?=BQHazMxFJ2&Kzxmn-NhhjdxbJ=eNcl+lUg=g!%dw{&%tVpZ!E<$
zGu|I0L5EAv=2I<_jQ4bDw0cg@<-lIsg9puB&8i#f5yRDZU1xJcajb$BfTAoi6Lp`3
ze<-)s%P{E$0IWb$zY7xJ{!T+CIi&Q2B=?PvR3hfc`v>5)6X?C)kyjsbJqOz2vtf^4
zvEar$%Hp`}Hw%u;?2X&moIW3D1A(q_TAiI1LC+}donb)~DE&Y5M);pFv?Y6?WOCUS
z`Ym-UEJE6zi-pPhrh!_EFo8da(e+OhfXE!&cmSeXR6M~B%tD(41CNBxS4<Sm?{X7y
zTQ=PR_f3F$?AJs+lOXcdHqLD~D2lC^;Jv*01V`LpV@z7+EUR6mw?7bo-PL$r$@6X0
z3hWpTESxk)ew|}bbAfaZ;_e}ep{C~NX1Wvs-7&NMAG#9B+<&@Lccwb3jjCG0#}T0h
zVO1bG*erA4u{p$LnsYnPVvO%!Qik)H@J~2N8`>MAY#&u93_-%7Crl~PIt38wM8tJN
z8S%abMCgIXMTQ&=&8@TXKhw5P8`!51z$N?<0oC_dZ6}UNxLrRDp4}R59*X|}@U9w2
zUMANBd8~=V6ANl~W{z?St>&tt=NvEq)!titQM{PXy!ME=PsM=h6=rX+5ND2xpt1H*
zA<mfKym}@$QZ8-@zL!Gv1)b42y0gdJu;@MVj`3p~KdjwYZ@hCuU$GMR015i2dz%Dc
zyvVZ8>Qh6z=dcDu;+rc{rP<3s{Sc?_>X$fja?gw>czaW={UD*@{{V1^W6KF^^(1L^
z><m7LZo3EW6G%5R#4f8QFrV%+fyLi6v(XFvk9ZGM4jMp<@j2~o;5S|d)fCt{G;Id;
zSqI4djw$89F`yoUW|=F3wMW8mwCfX+H%Bvat~jkTULx{J{$8J`A%g}NydD5b7pqoE
zXlv=Xv>EYXS&9_L!GG{WXY`ay^TK#9;<pI-C%~*XIAaSoxP;a_oLv)l0UVdTi`MHc
zR%d7O$ypZ|*ywAW4&}{R%_JTLWOE+sZYGh1?e~Gv8E1anF5<jeg~4TrF9|07R)rnM
z9QYn8CA<*<qcVlIc(VZ?0ckw=th@I+`)+_P?AV`uL`n&=Tp|`g?~&~lmWnn3<bbP4
z?v@+C<-s}3iiA0l;*M&lCUOB(-r)-wwV_i3&Qyr2P*0+`Va4QZR+kGm)Ehmfp?n?N
z{{T}sp&^p!`6DM0zRF__D$Q{}szugvyglc_Q-o8X8c#$3r%Y}%=JTDAlIrCA0y~u{
zLF#4^_H*fNlcz<Oe^h!zFJ<g-`6IjqxuKC(FM7?V2lm+?b=@Uo_LiY`zcoMm!95FB
z>wna1(K)8LaDkX_RpN7tk5ZUO!seTCMlo*sp_zzBD&T#sY`>U3$A`T7Ek4EVun!nV
z^0%rQ{gNjTXFZ3>YUx>wQab&J@chxc-WU&0{Tz#nPnc6Ov}^=#uFTdzCyI3JFXAzH
zY7%N#7iJN>{MF3nSB9f-@|_inNkw%|Ej*NBe)H`a<+DJi791`E>bF{Kc$C&Sv`suh
zb;&)nJ5Ok2)Tl%TjTvl{KJNgy$;2B$QYkW#ejOHXyey9C{;Mw&Y5?Q`W1it3tU|)U
zFat)R5+2vtGCQ&ap$(|ef2dfg?-=2a^p9w!6MWOloYyu$;*WPryEMb?ix(tnzM*z+
zhKuUB0GvC@QcGh#Xj-FO*LuP>4lz6sB;cPRv9_a1{)oxNerphAS6V+clW>qA^;M~`
zicDYvTx_3Gql@9#azeB)!(@WhJJ_S3J2LJtkLfVI@DP}iU62Esfh{bjI`ayiA>QGE
zWtB!?a6GhEWbV6&S2zGFi@|g>NYjdcYG}-W-|)LLX;NvozIv4&5tmJdLL?Qv$fEeE
zyui%vn}wq$@IbnYuWZ3jiWJS8J|m+-H#%_S7xDW?{VfamB>w<)(B-tq%{8tRDGMCs
z=Fp|iFP9wbgGd!FTbOrM4-`8-HfRVI3BSNU^(?C}(>`n2$a7i$0ImN3=&;meZ$?B%
z%~t~xzU}jeJrsA1b@k5jvi|^7KkZ%+7!RT_(^BVyLcQX8Cziv(vEr>!f-3zE*96>H
z-01RJ>^NF3=O1Jzfrv=ZnX=2g5$UvyC$t-L1MG0J5HTIRc4<^0#R+yEFs9X=@(V{n
zPYk9^ldqD^#`PWI@?PCGG#-t*vl~^G>#1MPf6V}6veRghGi9KAeO!-)<OTf59V9Pg
z$SMB-hyj2#jdl_Yr(rJatlsC%5GMUc6;NW}jG+@qk5qQtwXjf@I75OmG$;cY96#TZ
zAm74FIWV`Ag7<>Zn`yPgWxJb<CWg00jhU2w`xW^P3BC$Bqm!zv-wX>glB6+Y;&dFA
za<kQHgmKyLggvwfLuP9#b>7c21$but5xd}nJXT|81z;Z9z<dfPa*2++XA`TidR;V@
zfqymTpf&WyF~gc0w8jSi07?^zql|gNiJK0N)kWw7@jD`FG>h6M$bH*fU5ippiq6=2
z2s<@LkwP1YLF1|e#L;d@_!>&MblCxq{am}GAP5*9#{E&XRyLsiptu6OY_&`0896xF
z%QI9F=2egS&sA~7Y4$jU;s6u&Kb-H7*bH&oX=cWfMn{6)HG5Vk1(2xd*~4##x{=gn
zvo)fOLCzh1!I-kvTzvlk^mW4))q5^lE)%il8Zj3}O=|4TC;62(o#nwixVRE}!WYI>
z*?)kPVMwOZB=A*T-SKcXB|(%lh2b0S=ocSI*i}H_@=R(6G~hw19LEh%`>I*>HUS%#
zPp(S2V+BUxFkR%UkSf4q-rT~0-HafPMLQluJCbzbe6;IyNi4$-Ci9vEHO~(h_KKuP
zuF>R1XuB*$jX9OsaDeHOaRY1Spi}J?ySwH93cG_Gc;t&owl=_=SxHZdabokA5SwF5
z3H?m68_pO&&v{^HcGEVwm0h&oPOk3h-UngMgV#g^)r|cXgoRMyqzN5LiEV6;6%t==
zXZ$Z6S!+6Kc5rw-MYce5Yg7_=XZ1o)dwKAMf%L=d*s?mh+$_KOBmUBw>`uUG@*`9<
zzNDrIpET4!4ryrWRfZw)SpYu9fmCyt&jWaLS%(z>X6GE5IF#HpsfcXmXEJsi+F$+V
z%H7)-z`f(N?>HEr$|Y1FP8Wb|=t0A@I;6OeBM$}G*xvQS9rHX?OWY|jyc?P@Pj&~z
zXWq3P-teC_8=bALj;(|);{Xq$sovBtboO^N!)##P7Pz{eAso=XPSK0V1(|SB-ag-?
z9gZcgrX1<^T{$U(A>)WC<i{x4nJE4nB#+E?7y{(*38T8CaUajsXPNUt?5NiR`mMLx
zt5p90+CIVYRA0<@*=Uj481d*<XJ_-z$SnKUg0*Q2N5LnGF@OM!&31g(^WXmfayB$@
zKdS=DomujUUen3-S%WU~zQf|2B6j<wzJ*vrBTr;)88vF1b)KhgfVa_R;P3iE&O|71
z=be+c$+J(;tUr3)4BTd^xLZ(wClHgvlGL4?5a<!aA5pXBR(J~jWmG(FFI4bbIg+)u
z&u9cE1@`{{J)bmxis9+@CncwA{{ZW<WHd5)v0dU??xSY}4)n5kVo$@~OHavYb~uMS
zw3lU|5UM)~-W=BPCe}vu+V+*JtfYprPdlPq=SCUksey)=!tioD`6dwV9C|2Wd;&Re
z*>*P{m65cof_y4F*ScEGLrTn`bJF>QGl#$Bv74pav&3W<iJ}1OqM4%eO#Yo00EgW*
zo8zi7CdY_cmJ~!ft52T8klr6-Lo4}C`vS)i*^oPO1~zCA-V%gg<~DHJ(tGFt@L7W!
zZQ@$&;8aVABgiA+0)EC{XDXy0FoD6C^Sa`)nAmXRq*;DyEOJ83VMK=15@$jaz&w1F
zR*@&F-J}hh$Ep)e;v5P#%qy=IM-~IfAsjXf2VD))0C4IR*-{HYzI+v!-SU0+6u{S;
z2a6{)m}>{Q?$xH&KwulK92$TX4IoG7x6L%mNe-O=RT$9>a0fpi6WICAE%QV0orq2B
zi3~OvMoFg$U=l@v=AGbn7JyK5snGzPd|Ilf6WSlsfKt<ivFj+vdvZECHw{)hPCQgg
zF@wmPuM?av$$2je#s2_MQU3t8c?;eMnD2y7R73|H%CM0xn9G87&7`KrOoid6xXytU
zNV%0N6EQKOFad&_;eAe|EQOOyPJT!MhLn#ae0Bbd1Dxw(BZ6~?V3H}=rV(u4aDxHU
zSwPcg)p(lmg!15z50}{=#c$0tlKQ5};}7v1J)`>#nYe&F(A=UavJr=40UKg5w>4#b
zB~RW=#+Y(U`>J%nF9OD)&gdC6g!E|t04v!xUin#nsArvuGUmCYYrUFw{wBGNZb$wV
zLr^;B_FK+pX9V?dM{&+>E`*{mvnb}Xiy_qlTzn(yn}@uj$9y&o+9F3cXtOFLt>%ww
z-#@Yv8NE}PT9UGJnef`?vlyuP%zPDV@C{G&2>cV;Ahdq#+HpR=V5r@~@A`mQX|p+N
zyph)}n&`DMe3$c^^Iy(?&1M=1JT3yw)to$x1UD(cckG9=i)6?-rv2a>{{Wgb*mv97
zNb)U|>@pc7@xl{}-#j2DfdeR3-WYzs%SGt3kVovIDE55+05g{nw&I;|M=VH!4sACD
zSWlwLPQfjkjwo1@0cA1Jqkilm{91r{mHak=^+7mqXs<R4IU#0lc8Q9tG3JGhM-l7x
za&2b2)DW^$G92HMJEhrk8*>0Sc`S(!Bdyl856xzBUK%V7^CC%JNXOm*>V=J01@A1W
zHJ13N{v8tL&pQF}S&B`cnum-~2^VPOr*|pgvo{xf8?XZMe(8r(#HZtPp8Q1qK(7OM
zb1V2wbo`cG$WE1zh*J(^{ShERa2!il4}y#?XURS*GD}*0p}=!T!#@kHD@M$o($8I{
z>sLa^(?2h<kjw10Wx^K~A*HZY-H3@rxUH61eVB@F6Xw155RS31YDhbM!bec1a1V<2
zkN%A3qK*0u^0MQzQL<(9aUQC13WMmJWuy`ES<Fh6sah4L&k1DTozTu#1;ub*i%GrP
zPa|)t-v0nj!0K+)PMtP8974!Ak3{1vbhnf*#dwEvjZg+VII;6U4v1OJ6U8~e+atvs
zOf8rxn2kW;F1Z`JFAYjf#J`;$mRZXO{VQB5#pHqpOWCYr)oDm9v68)-)>^gy0QQyQ
zV=RXPn$v}*0TMIjn9k+Yox2<aro-w!9nF6r--M7CG$b@+sunkcY%ISPV8*>skG{hr
zIEhmj+w(-k3oP5%>Jim@Z)=ZJ3s<}2D_+X=lgOVW>{Q*M)1nzk3;E3-702xGcm1Up
zb!v{r?O<uQ-v`p__`6d9^K#9PG5z0T-dZ%T3={B`JHV2f!qN_$HBB4h513R#S=^V)
zb0?WjJ<oXiafmA+UYYd;8^YDUHJN4nk@3=@KiRNH-L(3J!E3YG?DX0_He~0C^Aobh
zv34{1bY9COEnR+bvr4q~Si3T`ZAkv@zVKhpA2rA9_+LoRkz96rRGtsbdqB?4A!oil
z3jT9f^IX4{Kkf@S{{Zf@E(6hjDt;$!Yd6ybq2M%}7G&(ZB3JWA+Wu=+jCaj{G<>!Q
zh{=1j{{ZgU!3(;RNsgO6mKajFtjG)b&-$<CynInXOqETtDhpmNgm3NQPo>sm&)NR~
z^nX2`>;3?&+6f+eMw>XUGw8pV?Vce20Fn?O{7|r{@E7wB!pNLhgFJvncU$s>?my(D
z)u`uUJ1*ViB+{pepyev$y@%qjhLu7mtS3mQ0e8U<6Ix2oc@G2-xFeIJ$#8hTLcf#;
zp<d*p1RE65-r%bwTNSe_{{WTr*~Bk+{g33&!|WGgcoQe>T4KR*Ucq@DL0sZD*~e)k
zaQUp4EuQh)?xJ-60Mhru)`*qYXA-~v!~iD{0RRI40s;d80RaI40RaI40RRypF+ovb
zae<McvB410;qdVw|Jncu0RaF3KM+xKH5#TXv|(H6DKM-gvscjgN<NcZ&(sZrURKa4
zM6E$~P;L$84J@#>U0IN>kP$1)0Pvy(xo7awj6WX>Y*YyYXk)J_@<+CTQrpO~xKQlT
z3W>05DcJa&<tIluMTXK46aeYQhXgFjco<Q2!EMUE7np*aM4v<){{Vf&U_n87RjIeE
zr&PzrS8TOTvo?t0E$1bxt*)HwVjQ(D+i)|ziNGt(d432z_(tz-I#9$9C%_qRr;nQ9
zwf6x`7Lkn3qT4Mm<0j1y0NNuRMng=p2!=GP@T--Z#A8JzhM}adCz1BYb6qV*sy)Wq
z>#cD4ZZN2oFU$s4i+vk`SBx(2m?7JK{<?tN0R3dP8np*F<d;YL8i<!PS(@@L<)zJB
zH*<(A?XmFZQ-HVthw@#1k|{DPp&X}a9-_cf1=g^HPAqVJEowJV)Yq%@xJxlEJGb*L
zQYHM9d4>irY+YFB3A=PNfpypCWT9@tQg`&QX_~w*>ZZ=Y_{akWF2axSlzhKQ5(?rz
zyamKid&9n#a-hXB=r7*J^{zP3h^VODEL008V*daPk2g3$@F<g9n)6=|(>Eanoe_mt
z3Mua5Sfcg5t{@(MRStFJBclC5eHYcr-HzGgP3OFeSLG<*^N6cT+*UKe-~}Z{cyv6f
z#QqJH(jUr!RB7%~k}Lejln6x#uVk%#t6EWozeOKILL8c=k71Djr&NbKlG>%uaR%%k
z1VKwm?QQLXn8?1|tV9EmcYKi1Q@z?Ku@Jkg&a8Y(n1%<CpZuI*%Ft=V7#CN54NJEa
zSF|!|spw%>)1vl?NCvAP0@lS=i))qt03woX3-cLtG2{OLu>kakf~q~o{{RAKE|(Eq
zJ88FRbV3E>eSlY^5|Xz9>21RVrCe1iDmnrHG5D#z10kErkI;+Oi{!XjO|L0(bU51F
zU9^#E`S-=jSl{1ud`n2I;V<U6CAr`VQNqE2sOuji23F5IeyS;68+FA+XtY)D5LnGY
zy7CxWjGDMjqA@0<X~HZ3F5ZmF(rHzwR#~(_5~Q&kW(t_~nmmCT>6ABS1ucoHRi_Ep
ztNzQyDqAxD0QPN4_yslnkYaMK1H`CykfW`q8FKiXAcfSlRv!TLK?Om4Qw%W#<S-Aw
zipccn2Zc*5mS9yxmB1juP(25VoZ^7MqyGS`vQFw>&ytXS)l{<>0zJyAe2g9@q@Kba
zLWNZhl5F~8%JEow3zF?)(E;AA(j7#+{-^6cuSXWg?NXd-kHvv5kygB!R|Xe?0qs)}
zs;#<O)<rrZ61HDA1PBE2YBlLuei7CkV2vsE4WS-h(fveG0kSNkY^4^4!p>-VNLUL4
z&$tK(wYi&K;Joo%P!WaEGJBFE4wtwNj#fFQE6RZ3wunbsLhhduv)~Ko8ichtJh_h0
z=ni_wH^YRp!g54m*%G4dONu2Slb;Dnv<v)z@+?Je9p#}>wU8{ltjC}y$-aBI1v(nD
zzx{#B)*5B%b@pD<6q60Re3Ngzr!^|LQv>QX#}`Z^mRRH6^0;w!zk0U$Wk%8}Z{bQJ
zthpBOUbdShJr*@SN*Kg3fx@=YETaA@l#W+>Y!g|HHF4YQMpBAOp}RY-<@TvR$z=_7
zo%to#k*}%?$~h+*p=W~3<8MAM#CNwgmwzr$?*b}$s<}=3yg!Wj;;%Ecp5slYxqLcy
zS9mQRN-lKY^U~FLXUtgoRs2Bxm|;F=UdY?E^k;~;VQH+f_~^eLqXBbn=Mtq$bB$^T
zWfgjgHHSbEW(G4QmYH@$MO)8_M7m&jTWSl3qd9B-%V;8z&PAfPOKhoMcA9YvX-Q4}
z;VojtKpS=9V@~TIB-Z%)QVHLS;*}E>C_4agM^MnKfR!lM^39AoS$cT+g<XT6gxiUs
z7P!*~gBx2*ltZI@+W!DoOkE5VI0$?}u;?%yqyaT<jbTug#|Ewnf6_*6=-6Xxk-o$$
z8GZq!$}~a<R5)))&^(AuHE^idd<z;!CmvnGRp<rmVPRK>I}<0e^Opk66mOypuKDy#
zrysA9;-J_EEjC^t^?-5(y=kYX%%;%Uw9B=_*ITmaby@)3k4-3AuLr`4u&7q+adA*B
zocU#@Ir$2nXER3IYf|S8cM=G;;?Gb)RKUWF2>lio3pGVJw~(%}*aNN`dIlo|odzm&
zd*_JuabsGXRw}(p&!N6xE%c=E?jI_R7fe?U)YD=@`ocOmXPBkC+i>43z8FR&J&{?K
zq*}PR;W<Ks;aOM78zpjGnXv1reS~t$_Nb~d-%?>gTVKg>*Pb3Fjbs5@v2W_-Pk*U#
zGh0<%t7LVj6s9a8?hmQGX)lsfufKA!WrU4$W|gaZd`yBRidyqgq&&a==IRa?k7v21
ztb9A2Of!ea@CFi$zA6dY{A2DN+Y6xQ%kH9WSw6NE1ndCFo=cRi50t3yndK_|>jP8B
z6IA9$ssz2-15rr0RvoT1P!FXbXcNG;du+i4mBaSst?kA`LBYmrE-)sG((1ef0ZTxC
z0yj*$4$~S?E94#<fnkj$LT?LF?++58AdtGXMA<q=j2P3X{o)A)o}^t)8C)65rSVp=
z5=QVq`-Cj5h!X>WWlzsyu9VAth+bi*5a+;omLU}9kpnpae!X`UNX1~H2(r}aZTvx6
z)dgoza9I0@6hjAcDC=49DilDKd<^f_ZfVwOI?3?@`i&iyO8y1EI#|NI<ME1ZrzS2g
z<#Cf{5i2^FXo4U!0HA!f)clI;9|S<=OuIv{C4j9LfXh09y<|BqQf=zq$NmvyY*XN7
zaV4U=YFp^dciCjHpfnVkX4p$;<TsPl(X#knm{1bD0@=c+{&i4Tnh+U*Hl1qrCkhlP
zOAoOn64EGYjeXP(pev=ULGmMzE|F;8a5v`y9>ga;!mfx9*vjj?pB%7vU`3QRuA#m{
z2KenFLDaNfh<l+@^^hf7M&vp%nvU`ziOK2}u(w^jR3@D0!-oe9No<Jr>X+a{M={Sv
zRP$U?g#{|w9H}Flqo6E~;I0Bat&t|yuru=Gq^_WDn-*#6G3oI)3nAz+aLCyTz7ggZ
z_!v<0ErqPBH_bdTcT#Ai8CL{RXSGB%7fU#55u4M1_;-7I&`1`SCF7symj+SgaSH67
zhZp2LhG{_-3cf%~qIW?1p}zhly9+EGFao1gc~<^J3%4cF4R%H5putPAu7<S$v?Mi!
zE*W<UbK<4qEXscb!rtQ3Y4(QJ8!@IU8<)^+U@+^=HL|>~M6zXR<lLz9+eKM?K;wnk
zL@0nCFE??pZ_xct*Bxt+iN^U~!q`gbT3ewi1X3z^q5<|o>gMXe7Fo`ZwhD1d5aj9D
zqguuMyAw&YN7w9jUk3s<7}elK{h48i2kF#w=Yml>7X+|kEudgv;di1?%ZfcshW4Db
zh+8UL;w)df*WAeM2NxPps7;V4tVhJAD;$ejb^}pX73uCfrBFJ#igtYtR(sG|+~QRU
z$~i=@LKUI6wJ8;)C8#!ODo$R6S9%3EN3*6iYgLCfN=f~QAvNm0ARZ|bw-m153>8P|
z3~CJxX%F+cMkY13)x~&#5~$2PLCts|rDI1%>@?KFSK)x>#qsHk<t(N2`J2H`Z>4KQ
zJrh$iKr?euQqU>LFO(SWC9QB)8O2cK$N3JjO<v!)`3mQOlG5&zf4gxFmMl5Wx1lYp
zT~uHb9=NHR{L2NQ=SC}VL8uQUN?tM1{f_kYumm=hEOm4Rr<5pymfaP}new<`7%x{c
z4nbELQPyJ}XV8b=kzMt*%A)$=D^r@7MVbKMLn`38C2rH?4D;d-!dsByzT<<6oSImG
zxVF{7c0+p+F;@1&hlC6BM}8{fqS}dDx`kr|?X2KfH%(*gg@j+iDrjy)pc`0?QdrUz
zm*rau;XyeQCOWaRN&e47V5Q2r@-G^oixr?{P-~tah}^f>wGuheZAXaE&$&yOT0eZl
zc(6?bEFKcVjYs`}wp^ffG(>~NhWHGNs?l5%Cn*%?d`C@AJe3L$0DiDK(<|6}ZxGWG
zI3@75j#G~`$D-~tzu>@;Z=+Uz;NoAXF6S^@;p0(u>MIc?x7O}6(Uw}nr-O-1)Hc=`
z;Zo%TdY0K}UuHo{a0b6}+zgpFc4XYP*FqH5w<wf4AK+rD!X-H8Vi}}2x=vxz>bUa2
z4kWSi!P?QvFs+OxhMdm?<=9X<+oqy$NKwT;CdI2-y~h@-Ff>2gfB-PGpjS4<_JCOA
z@Ix~$ADDbE)%}!6KssNP(p5`+u&bu4qTAvpUL|lSeJ!i#BL=1yl(vyFFuT<c0UN<P
zen$2hs7>ay$koB8IZz31<!Fwc&gq4rkrdt2_=pQGxDI&16|f9YSXh4pJQY1z!gw0b
zf|mBc0mEa{di|9kKx5>dO|6-9U_lxo<Z4nsKMVzY!Zoaw(xrYzo)<ARbk&gt8o(=q
za2IN^y*}bap+GFd@Z<ybd-*97s6lU!sDX5N%60&08eL5Ox6HRtA+2$}q6xfzkyU|T
z%Mp(!684Hq7w}q}Gv@#@mvV?hg2+715QzGKLZI_E4F!f6wgdvmuF$wD;^PucDt0t9
zS9}pN*+@qRhFX8k2$g$p9@rYoeiG6CM?SGX9^!p#WkS-*jEl)^mUflHDR-&e@+-4!
zwxFHIoXLMQ3)Ke1l&6G9{q`6tkY<G&l(faq{{YnLERX%YMAj<07u{TB<>>NCgG5-^
z#g0hfm19yL3w{i~h*$vG@;!V){j(o1U{}_HI3WVigst+ovYiYq3cLy>Y>@(q%L_XU
zf-)|titv$?*8yXvy=4?iLd!&|&6ZGV#3s>)T2_e-LWSzGtW?OWq&Zm2r7-3P5RLA*
zmF+UHF2i(SymaTIXi`d~5km-iVX_Z-KA|7(6as;k&xMM%-7(+Ds=KFEH)sWlr<x@h
z2-Nl*Zg*hF4Vy#i9^}O2Lt-SiAT3z{&@V#vJ;o$6(OTcR%Xg5<ha;)F@G$K1To!?q
zuN-c$)Pge7u9|>}8w)l1s6Y-e@7+at0M;a^^!XJSIO%6XI`YI@>K6FC34fuy_z10N
z5wdL%Jx?z#@YqbIh|;*=iZP?SgK(#<qG)f*wr1jJFt;8VASt{cJ031r2)G-F`OxB0
zL*|9IU^Q(~Y!#B4z?}`Tn?e|k`iGDMf|qfqtl;CqIRR%Tv@WsOk8|>crs`BM0%&#l
zAz&C65L|TbtPfQV4vT!RAbdiBWC6LkVH*yj_OL2jgLIT(OiN_9tulz1wvof6vf4Z)
zZ)!!5;lrb=E3xclx(liy_e|!{9!}$av7&NR=AkSxrS-z5O@WGEZp47K1_x?B<*px6
zlqr@+Or4Mg=hG5yGW027U=6u;bM`>Wpd7A<5uEbthsz`09HVt;6V$HapL2^gxsvZR
zcDHU<0t@%S?pun#eqqB>1X>t}ppChSU<1P4Q2-;VWqVW+u?DI&R>O}s31P!IRW73*
zTfodLJ+c^IBmijT&d<}m+^mFaU|HHmL?m8H<7#X|D%Y$-g8@i^+*0X$ibk;xZB>ZM
zQ%9PRy9V7)^Ggy<UP#nb3lXe;2-=d1frBETFBmyMdlUm@a4ei@VH^smg3>F?3U(Xt
z<XWrN=K5*CV)vVJp-pfquU3<Ht?~vktDE-UFvWm$_;y(kXt-muNEtIOnj{EE{tr=X
zbzT1e5d~fiXE~CiL&}dbi4alPUtCKKcnO%NgXU#`FRk-6EE(2#_-tGS7;3KKEvr^`
zUmvq-1gmcviO?ApPK#DQ6(8VW%2nJ~*<PBt$$+S@;ybK_VOreYTs{`&04G?bAjmHr
zF=WwBHD$$46N;7v{yod75N%hDV$zrEqfn%%^hJw!FP0{2GJ&J1LMcml;%MM7z8?9z
z#$Fz%60aC<qy@uyW|M=ILZ5AsCrTbcs5xv=Ab^W<Aj;1{5H@iMz_Wmmw|q@>gY&`k
zP7brTcH44}xaVaKUsf{cHnvY@8L)w_5?oH<wk<`749a!)TM@;-mNRLEOU{qvR)zMk
zO|!ls)%uo`JnifxXsl7`Lk7Z9IImi|pr01&qA|7gcJBCB&cUPVP;OB@su6=f<g%ke
zihXDdjT@b!(|H?o{Q-Ua#Q{+>&<9~U*H6Ge3zK0bX89?qNN8&7ej(~8Ia1nDfCYPA
z60oKvxZ^X>Pnu<11qc5C!!D_J=6g6VpI;FMy9a+TIIrraDOLu}R#?%}7u-IT96i<u
zaWG!V_f;15SLqG$Y_d>rLP)C8gL&sq1e<6JREo3_RlF=nZJ@1E6>|U>_yamhF~?x=
zP0=W#sh;4VS{ho9F{$#7tDgALeHZ31xOTJDzn$e-1xl=q)_4?_^;W7{e8$vYgQH~~
z!e5}ncasbRjZi3HxAebarO+F&-ZfBkR$5tqa{6|&5fP$hChGA(5rr_63xX{Cp2#I*
zax1G(ULY$hF(c(_;mr_gL(hTX{fBC0e45D#AQ3IF$;Oi;=6H@Y@c6uj&mM-)`bDDc
zg_`J1%ytV#FGMe+NgWFm^|4q#HNf(tQ3|+)>cB?74x#~78uP|7l}q6HyI1U+>Ba1u
zuAcLl6;{KF<&@iwEQ3uWc!GwNr%!1()qS5cNU`TD+_2a%NPK#f9N(fnA&yuh1S@lR
zZ~GCc1zBAcZ~;0uLwSUH_0U@rE+AGpOV~^Gsen$*G4N(WkhctLS}W-Zde)Ov7Pl?P
zDLxCxF@!?YqQ}gs0efs0E2<6N!rZY{jsE~+@;5kY;GA#)QUx8rZex_Qg})OfZ%z(~
z;?1sjRN`HKja8$JLc<EJzm<qv!uR-KUVUGyT_wX?K=HB-;cmD{M(-aGPc9tjqf-9>
zm0B;^8!hFjP+N5<YYI;i`tqXJy$=jFCpl0GGU!VY7mdB~OeDhz)q?M{P|d=Plt9MT
zZj|*<TplHPc!SJLc&b3BnS#51ab!BkQ&db6^bTq8omf-KVx?(Ba~HSE8iu)IiBU(E
z{8tcCis-e&Irfpe7nfqh<(OMX!wKj~Egu?P^^l`i1k=*Q15p%C<CfemMJ6J;DjLz7
zyHk}>I~H4RsMNE<qba6Q0^KZB^i$-NXuHMuLS8EzlDrVDHB|r!gmK$MYXPGhwyIwG
zU;!yZFhiujEBmR))$4UEGff^W*>Ni>1;921&@l2-xwVp(y}cXuK!U536%N~1+c|{6
zXzHE=LB}j@Gnx&{&D?-H4#eaD1SDy`u1;J^6*xr&K1%uKScbRUaS2N0`IY!W)5sRF
zL<Txg@BseXF6-m0xS;O3E-IzO-JT;HLi`Z(fC*HrxR#?_!uj|jZ15TZ>G4pb>VlF&
zwW!izg9K&eA>Dx+$oRTSRa|ok=u%R7YqY!gAvusSTKQ5P9o_!`9&QZ-Rv8{CDpKsy
ziBO<~%}N5Vb>PHoIJ6egy6R%2;b1tK>f`4ETf>He!3P@!F869t(yaEasbnD))JSxo
zQVsdKB{<UViq56#FDM%DBULR1sj(oXf}Th$FTacw1=f^R=W%}D*LAj4Gzu*D*f7(o
z<o^JqZqO9dE2i8dSQJ{-$*!E;?tH+uyYPrNz*Bt{@hd1&iOgmyuxOlsXqFa{3m%$<
zxl5$rM-|nvYVYOhR*cU;JjM8GrY7-t2ordrUe83e;Ci*uZI0oVp)707G_ofgxFI<T
zZwf5W5jJ_c@lS{pJZvm&k7tX<Zy5B3iOEUC8`SkSm<8?9@2&bK3fs7PA|pi#rSS*T
zP{jj<jzU&iLh3r>U_o7i<h=XxhB@=)`?0<4)#l|O)&pMWk*j!*zppU}Rapm`WpWad
zmjUD*g=@DsV&nCi;la#=U;uktxdA1mdqWwr?m~lr{{U`Nvg}g;>_ek`ml>#aWOCuA
z#sO4L!hw`=)lOH+!9`%BWy<fU*&HBfE}n0~8#Dww%!iJn9eou2RWK1_MJOc^QO|7a
zm1R}4@B!2}Ef>jU1l8I*x0re_9zBha;<=t>;*ML#CHkg(a1gr#$g!9yX%aZO*M4Up
zi>*t%5N2Ena0(w#{m)Mx_B~?Mp{IbT5+1ve@hhP=XrToB+jneKG!6S2L~gl0fFr7s
zDkl@?Ak_-Ubdl84mBpu-&x%dw7^<w{dH{-24L2ztjUvV9-OwpR%@p~0AW{+*ZP(mS
z4t3#cm(*Pd&%&vgxjwoj0UmYWm4?!?pM)#I{_0WiuPVcwR~&ScX>T&ZvXF#p0RR{_
zbX$tPWgMx%I;=;y8Lo~MhYZ2vPjNMMc%-nUE<BaQ%upWyfBQRWrVbRSltPX654B`;
z9a!4Z0;*ozZMK{wZTp!GD&19D@`QU2E~}36BZ2!p5{XnXEyWai79hGDN^jW`;)Mce
z8Ixca+`n?r*`aHw<SGqqT(LS#j&VlebXW%`;DhFF3O-(?@*$##HhqBvjfz}-`C&}C
z0>81S?@vA4IC;eFaQ8t~*%;TvHulVO`ab6iW|LdVp^frafzVQ34+s^<I_^1Xe7jQU
zeF!+Kcdw7SmDh}`0}6b>pow~2N6rloL{o!zrPr;U)>Uw@<WU~grMRJ)YlDjXl$ySX
zvVkG1MN5=zri=PTErUzqIYJ<Y+@-hi=2gzKLQpn>k5A@`r?stnh((m!dYlofMQ4kF
z@2oZ<g8Ci~WkR9Shn+xU3tfiGe#E7<1Ax)A-BLM9LyujGppg_D0WQ5BWzyy|z$AX9
zJW6)Po}P>pH!B9qOQpUbR$86_h>EC+Otqo&DY&0}?e1IdDk`AjX<(M}QG`&yVdb79
zYO1NxF02>2N*WgNDO*BU%V8acI(LQnW%+S7ZEv}EZN;4qF&ou9sf()kL{}(OP*~{Z
zt7Fa9L5i;Q00{MTo&)n7BJwFnE894gnA40TuxKMohKS7~xN|Rk`m*4NjoErUOOdcy
zh&@~>wKg=-y3SH1S}NlqF5^?XA<c@*!_J7BxyInEs4)N*N+97n74LZ`J+|W-xP%Sa
z$ruiwZKP0Qsoi(0z(|?g5h4rNuNAnMw~O{GO@J=AI?78@@8`jDlfoSNC#&~jDaP?r
zjlcs=V}d{`&C{|k$sYSfy)rA6#sF|5?po2zb7GGJibvTLIw&uXGJ=zA$_Z8S_YLv^
z#j6}zKulZy`;{A@GgrZmuIG#Af{1D$;ZQDu7c4{R$^OVlB<8=$C6Xn!Kh!WRRqYA!
zLVK0ut^0Kt2hhe{My#qT*U&5(+U$@xxMJ}^OhKt7XPDyr+zr=wRrN;8*Ye$u<S9aq
z>HB0|V)oYMA7rsAg;iPgQl15=KxxePpteBH`(>NW3pim!6~Wn!7OlPF2v92UmEa;A
zhX8K9ITq6{_kOAfmZRq68LYw}6^+=0wUAPf;PDSo8jTcPvlJo4rU&H-bH6M5zD8+)
zWTzt&3WW>vcL7DaODd)TQx$tUncRH3Uo&A!wRi+w0n8vZO#6Z~a#s7BTRz-ub%U!}
z{!~?5UQ4M_gfQ}m6=uC!L${8k<X>X;IaO{D5y6zzJjHY9O4^0zw!*k2Zdrqx)LZ%w
z!Zc0>6?<<YMK;TNYJDvkWeWHS@cH#SG!Fj&;TWq0({ynW5esjx<;4(yS+4>R6*Nj7
zZf3yYs*fT^92!hj?Mb@@IT(M1O1rxTTDCZnz;>Lxa(vfU4N0|yv9q2vi0g=;WQCe_
zZsN;*;=|lo2CX#i=7&pByQxp?H_q{1X1`4@=*4nv4=56p>hFnN8t2q%@wi@zG`j-r
zC1|N}xO;|};@vv96=q$cw}IiyhP}Gt4U?7@xRon~xT||}^?^NKpjwB0%qek?RuB&b
zKYHej#|8#jqxA(_n+|MuwGnDx2~{Wxh#aAR#Kju>ih5WNa=2<QZB#3>y{{o9$T3H1
z(ar$?gs6Z{a7EP-q#!(_ALO&g4~Cfh#Zjy(nAZ+k792BbYj`BhBGIl?)r_Y+7*j9I
z1DVV{M@lxALV_>uBWn4U?V9W=kzQ^_-Ik!S)+}u(OH`)|YW>-0mJ6r~9qGE#Hp0{^
z8;o}x>=)6tSW}b4bF`Ax2!co7G0p1bg3DT}%>^x57wC(K=$sF9$F73GVFCNp9W~{S
zdz``hQLf$m?O*afT}7;?MTiZ^ORt~AHzUzWuZy@-@lKcw18cleOXafe{Xw`}Rae5o
zo~d<5mVyvITt0Y-E5z~4&h@}lEvv067*rn2aQP<s55!Vs5EqF|;GCx3!phzeuNe|{
zx!x+)V>iORZMQO0Bch(rw3)H!e^|bCwat_SE*zCRE{IitYka(!V4{E)5erMPco-hC
zni~qL97f1M!o?Kgr4Gh;<%L;CSHW@0r+bzpr3-1+Y~Dw-8;J)2sp=<%P_QJFO6v8m
zYvJ)5K)RFD9um3k_me6cn?Wg$<U)LLFC;i6wO8s>LAG6>YlI<*Q<sWER?9JUY0iES
zkyT2yF48hpRqaT=@D>2UjgF5n;T$YlMN6YsJ#?H4og6<S6xn)jr>L0;1C@CdQOtY-
z5tPRU=`^yUi5wvXIB<xjLqp8tQRWKWMZs@+IjQi$hE++STS!+T5~0EAb-g%lT67fg
zoknHH?8{WV;IqGRdTkXfOrpue_oI3H?hvQ}R%ulZ;Naexht%g!1vl1T63B(rZ=qKn
zo!CZ>weUei$~>6B0Gdauh(Ruph)byA*bui@iY3fq$wQZ|f;vfGg_YtZ)f&8}D+(r-
zS4%)6e>_4mqZOdkrHu^yQ&jw6W93*Y)f1XfUx}>~8u2~RvlUN|9_HFLPN0_igW?sV
zM6&t2fO<UWM{pt`gtnFVrq~>?s6+}1*j8YmAh*=Mgsv@hi;E+DhiT#+c~c(rH5)MV
zcjc9e?+U7d@5Vtfrk>IqF$g*-*YD2Qo8(XAM*=+Vb`J9V7dbO6P!b!jK?^{Xbn=*D
zxfVK>i^-kFx$_cTRrnC7KGzWPmIuGu{{ZndIa-0L2l_tAU<TCXw3t2;CsY3Bup&tV
z-LDt1Li4q7ca}h9tHS=#xC--XrVgxx6H2oWov)}VN~?sG#nnJ|%P$vRmAScwDyue%
z+k)4ohujw(pCj4wK}9#9e__VDo<(w`I<uo%m~Q?Vw7=UM*!;?YZIadBxe8j{EtVkC
zaaHqoc`JlsBMSn>=}kcLrR70rpfNzuq(`sWN|klLGSvjumc@2D+?ocyAhskf%}3lW
z3J)OPrZrZN3y;UR-<^ba#_AR21WEJeDboc_a@cPQDTgulumKHKC-lJqOG~IFXu7O_
zko~b6rLD?nJSy?}jFfRl6y=w+C36N-s*5%;08{23#3m#c1?~q30rUR=+`t+f(b_No
z0=wL(+!2`+e{6uV;c9^5@0MkciM3akfprP>rYr2N&QW2UOAcBS)lM%5h~hRy;udA9
z#0YS=1UwSub@Cwnx+9mO5h}*U3I0O^%57G(CC#`1B00o*hL=$KfSihp$-AAWq1);=
z3VepU3)T8D?N3zB#6)01uR8kpiX^FB3|FEc$z?Vmr@RYw0_kT|l%NMu1@o6N;a8{+
zi0=tIBFpXos-<i{paY2rr%l~@H!fP)(qB6i8Q=?WLg%7a2Sx4)0l2qg&&0OK)Gyh=
zJLz<`{KAI?c=?n)x^W%-COp{?H5%T5vi|@{LLDy+#Dp!-gQu7|WH7%{<3#Keyb-NT
z!(zi!1+jE9ekDaNLuy^mn-y0D3aCXv<npNxH%p7Ih8D2P`Ciz7Ema!kD6&I$H$+vt
zy5aIiOcVeL^il!li_;Y)PS>l+OOO?$5K0!XFCK|PQ)sp7a-VXBDv!CTp>A=tA?Jll
zf#or~SzW*=Wop1Q`z6^K1(jaS$0z8tOAIcWmCnrqYPNWy6}JIp8$I2^%(K+40?Wf_
zd?v<+ox8}D%{NVv`|>D4q5IEa2>n#3&^tIB3#<?69<}o;i!2L6w6>J55L#F-gtdu{
zbiXibsu>Eeb>cNG%9TtJb3LpQxuuS=xKtdF{gBkKeQ?y23<n-v$lGqc@f#z1CYQlb
z&172Bpg7xz5Ng5msX@x?dw8=30;W4F#TGc5%(zy{r)~|2ZC$s2F;Uin>R2gMv7Y%g
z-KB{chCnU9qXJ`U+7$2Q2KLme?@iMRwB9eCUS*VCF7T}cu_+LBEaLOa9)K*Cf!hfj
zf?T{`#OSVmj!YGojTMZdL9!U(AT{N13sO<V?dlqC#px{62S-&{O6}4cg4!fy-Rb_U
z-I}~%hTqWlC~F73eQ1Ru<+tYu_N}V#!9Aucq2*yCqB|_yTpU_g;=mvkjf2R*WZRWB
zA~0hO<*J+TDuTV7spjv5k#D1)tB^;NjjQJ16sQhUHLnq%szf$Zu<9u`dyP?`_zM69
zg1{QUy=#_NiZ^h{52;j4)C_d_U{9#6sPbD4FhwuTM6Ij${VD^#*T`6J4dXTB77Mq8
z1zs1xkT}pRc$rw)Xyl`^FD^A%O;|yq1!!u^7WHLMtu1YO$GF&5ptj!ILbqER3-wXT
zrK#@Sgci8y#p@a|+?L$4(A{y5<uYYvmp>wAM?5u01sx0y^l**YOV%rHS9~dlP+tIq
z1q?QD32{$Qe1|c^T}rC@hqE0NH(tsMxbXGjdbf!ZMC%W>d8iR9hRP95vx(;03M&p$
z@SbNNzZGO9^uETi8;UxceHS^YR^szyRPN5!bVU<NQ%Bn@z_fA*O@$O<ZEZL}Zf}Y$
zUl9#r%bQ;9mP+D?FTsRjGR;ji3wlC>WEhJWTxt%8USa3$qE_MoD9B_Lw_eKIi0~DB
zoAm>3Vz>f@EOLyVTQswQ*=eL*0zz1zhAmP6C_F;#P7{_7?2PO-c8OxSTstJC9ddCw
zMVHXNNqPlB;tuto&mn$g-7pJL!i%bkfhk4`JITV-tWnF)I>WKHHC1my>f)P?@bBV9
ziDva;Eu}9;g}YgLfJ!--YPI8HcVBSbBK~kBtgb7Z6-U6vkQY`w5aH$-3=`=Bdq5hq
zP%1?oZ<fYh`tQ!{)9??ka^P?Wz=Fz+hd?$c8w7a{KmpS2c!LnP%P<tmwg4SfXq<Jx
zuO&6v7bkTiS?#z}8{+Ce7gN9Wq7`tfZG1C-$kz8()GLqt0(9pl*pDNVOFHF!%PzH&
zq2}5yQ?KjY=y?e*Yoi0C`eImVaN^$L6<>O8?Uz@FEwQO;7D97%%0t|eUXEr(x(&0s
zFg#RKf>laV6o+0mTt)u?YyHW2M`Fr~xX0UV>civ}ci0UI?)D5&3rcOiOIF9*Z9or3
zcao_@2t4liC3p%g?3_3!R*P@ii@O5pRnEV${soX|4>fRCgmO@Zqia^Rcj^SFr~qr#
zMPr4O(x7Jg3(mQ?-P8;!whb;N1v-d$;Bf$<Kmmfx%-xmjs6ZucxAvpSh_QY~bNCDQ
zw+BR}EB#A1>~dNB?lhZOmON?`_F9(N#f6aH-GgtlxZpPXm>zVbdXE_!^-G<gR&Y-Q
zC0XTC!=+FIMs|bk^#@wq<4EHx?{U^5@n)<gUA|HG1~TJBF(_dSfzk2>k*dd{Ioqf1
zD?J^qqR`*N5m@8F;Y*i{0Whl=6(QCfw;F~T$S9GhG^ha*zz&d-KCp%J2r*dEcDL51
zXK{|2=Jsni9&~WBLRM(z7r>%$=@?k0wj1STg%W!NsTZZqtk;K1)kU@#6}sSE$n~ge
z$jk#>yg8k}o}!{46n5Iaqm)(GnK`@Ta1z&!E_|*2p(L`nKwg{4VW!sjJYLCQ4S#A`
z2utV~{zQyTvV!CYBCC{)t!Vq<S`ofy?DRgM_1sc`4%;kt73>>t+Vd`gdf`_|@LW@A
z`;{o+<Y`+UkT+p4G&WJFHeK+>Hf5LSDg|n&)_FYEM)<U!RRNQ%+I4TJZ?G5krFd9s
znq8`6rK%@zL*yX9wv-)gj0J$}-5Bu&k>V1?Tw%*@`+6lSlVgEn;2T$>MPOpDkkv&b
zrkBd5n}<pCD@9#W`%_Yz4*Q5{)Fk2@&6l$LB&9vTu^Dp>YWrBY(Mvz?5E^!Cwb&*w
zZ_2Q9?4~N(JR=zk(cqz5=ST|7rT}YL2m$g-+u|oT3`AO~wbg)1LaDm!&zPj`@;wEx
z4}&>pghJ=bSHE>J6!-0wP9KdG7N6^dh7zy@YL+1YfHbS;BA_1L;@m>G;<o2_i6P;p
z#FbH?*U%z3(%ozPq48dqiY0P@h%$)Zqjk&h2rOAxAW3kJ{O<fwEAV<My0?PhG9LTj
z!YK7%L#`*nvdUP9<pqyB3}SdbNmr^+&m-bXiN~9a3TxOfb&Db``?eHiQ=0wVlC7v8
zRf4buG^KuQ2qokq^k2QjeU(kfIk<|cRA~(YRSUOlK3P-WTosL}6wEW=830tZ)GS5K
zN{OV@k^sy&;v8e1sOx5E6uyPACGz9#xm3aT5)5n(%t8$!-5u@_fr9R<Vd7%KZHJGd
zamHLIFC0FlQ3ZNS%so|$$W_Qucc`cZZEt$75K3^b07j%VTZWoGY8xEUY3JEEqXFe4
zb^-4~rPN!u3S2A(eo(NPh*mr6IFw=H0Nt|61OdQdM7{%5BPy|96*@3M+Uf_RnQ&`!
z7e+6+NAlDFwAYw3>xr3Rb<{v3YV7vQtX?P+)8S>*qjnlZKu1&MfcdMYSX@ofmGV4f
z-B6uuaE#GikA-&-D;yf7+$IZ5Ou1E2-WWTT>=&4+W>89nD2sMm$tvb2*yOm!PYz`J
z2-Qa>SOE4|9wj9<S;8q;RTwiNex=`@*p|_E<`RWC4Q<uBmVzEstI_6G7z(K>67<U2
zA%UZ9p!udLDuM<C3nfNN`IS}(XSAwEjw6<>tB*0HqH~`wM5;BLtEIZtn;>hGvJ_mc
zD0_`Alb;dR%fen~5R&UtjyTxb)-R0@*cIrmG0X~;!mt9b!BDwzYw3rerv*;XmRnK8
z@PYkSRcT_wC~*Tb%o|$$GmS~|2n|sXGL;hYlW%5LXgl16#Q86kW?(=P`=VPkmUD98
z^kvQ2u;jF5lK69W$JLEH@fH986sZNvh-F_ok0e0C*gFuksjW`4n6%<m(4bTZF!oEh
zwLijC2<%lMAFVan1?XtyU}WbeNHXt+CCA#|+H9u$)o%~Pj|hV{dB*<$S%yLd*ZQJ#
zfV5TekTs1!dB$?MzGHiLUoUc|S&-u$Lx``7Ert~=D^KGP(%9Z;TJCaTZn`6dd6)RP
zC<=?E&6gJ;ZigCH*JMCzwdTD_x{`yCT|$<JJB4hzbHr*pZB?FvTPfLZ*25GyXdgQQ
zKmC*9Mf7hA8KUeQea_)NoGg_Ew4A^q!my@i5!$$Ut|B)Lg2F&J^D6wdi7lwAm%s+=
zP*&Ym#=ElxPhcSt*u@@GbhGedcQv(WcPwGKgQD>4vzvWDREns}Jj>>7r5L?bE-R+<
zn~=)_D)(+BOSc2!hSpd@tS~wDF&k~F>E?-osZrgxvZ<R<;d>hS27pUxno9`8hf9Q}
zQ#@E-qKI38u`L?T`*(nsbIGmH+XjIZn_TWd!z>onlaA(JD!vE`+7$TlA5Cj<=42Hw
zIcc%asq*C8>Un@@Db)!U9Ue>A;46?9bwbv#mHrrOYd1f~mIjpfa-}s1_ZMO-eNh-_
zZljCMA>k>dUa#eyuJjAaRHq;`<OJrDVMHqHf+axXU>lwXq(vGwU*6^+WZiXq1dJ{=
z%P#@c4LN1d1yoq!d=s0lxVx!TKHm!!ilo)5L+Y<G=LNd>zG;^oRn4%A$pPtYgPo#J
zTtJmT1ni6XKt9{0@C;>Uv_Z7@?wpZ#qu)SHg0&VdY6z?Z;|(C9hYHBgXb)0V4=;&x
zs@Om!0t=jF)%;Qs{Q+DSqsmq|J>0aF3{_X(a4iJBoQu@E4A$^2$UA0)=5vvG#T?R<
zsi>RopC2#*;qx_Gwnfv4?gf8?`I41&1afHr@a$wI@xiTLiK?FgVe?Z5cUKFI(B5?$
z0iuel)IXFC7Vce_b#qw1i!WdbPNCaVeFoG&WLme1l!a66!!K2Dx$_GcRynPd)n}Nm
zc1*5`_M9*S<3~J>6D7Vz)b_-|iDsaCgJa`7!?-3qEdXvnk-zkFzNN!(IzM0`wDRV2
zu-?;fK(HmjC7&0DKv+_ku@o^dDuHA5&a&R(cJ<|h0+Lzr95Uu8lAtWEZ)NL`OSVBx
z87AT`D~j#qB2IO%b*2XIZC4Vf{3Y$^)NsoQo=u#7vFAeO18*5anzV>pYYsVpsukwD
zKQe{*GPYP4wztQrNciu~r74e9u)OzgqEMldyc#A&H@}*`BhFW-s@f9s$CRq=ei?91
z%UouT8RyTaFjxb=p5lXHk0@nc1}MDlkT||`f5}QBOi=4a5VOx)-Ad2OUR8d=r1LPd
z+UuK0YM>F;ngO{AQW2sX8Y^9tNgPsY3MB6r2Kv;&N3<QH0A+M1Si_Xb0*_CnjYy>~
z*vn6H;Q?&5;{HiVabL=b0q}(dp?K#|sxL*n@>X8Ak3#uD9Th}!5h_Ok3)T2ZUIF5;
zWTLy~__%B=E-g!lz57RHTYLw34)IHZfoc<UCwMTmfY8VUz}Gb>u6u3<aJR%&evef|
zuUMT?Ft^NuYjMj8{y>I?ro1p$S86nmUmIlG)pvmyQWWP!(o6jXXGyBr4>uYIcc_;u
zc#Fz}=aOsRqCbu$?u3iPa=NV(YpafwU^P{*9-<+NJ|V~K!OHVfuzTeT>YyWa8hh~6
zCRZhR#x$oax4bd8+Z!#m1+N2c7WFT+&KCVj!g*i)0zgm$pt6S56ej-Mn@wo6stc5$
zu32H=(NHe^_IZNiRD1C_%<(sJ&J{oh$!&o;xnvTKYKf?ADCq{HlBF@*v^;SMEpC9|
zd4;4`D0!hBFfZ?mDk?)o0Ztb$0XlO@ra?VXp6erRmhtPWgJJ#mG9d=OrTq2es;Jf6
z-B#5CEmH3?Uvq}Gtsj<U1Mh#-b6};eRuG#YA8&|*rqH0D9l}{m;5hALfposdsx5-g
zies251|}feu7xnWaRe2}7zvgS$|@d4rGTVWk8y6?rh(vg8D7z7jRrXL40Hse^0E4L
zjnt+&hqnEca?uv11oI6|f$YK*^HHg4fb2y70M?=uzQ3~k64a~QRfo;9kX|>BHj;o?
z(GX(5c5xMZ;Z7Jv={V=qBfMoR1+)v^)@Ec8Eoq^}xV~b(sfNSCvgix;fqaa+s_5<s
z>@HeE(m)c3I0uu8jCs3C{6l|g9tYCPN~2E;#gmOfhicrI5$w^$>`!XkLD)JMCTsb!
z3Z6#S6$&{8ZSHa-?C~shwCmy49&EaN*copiP<e4*2m$sy%!B(0YGVQWUQG>W4K?y?
zc#&t%QKh-%g=Gk*oO{9K@<hZDrZvl55M}9Y7kvm7V4&jBjo85r;>&(gRWU#_QAOJP
zE+yJCfdp&;OKjyex(*YjAN@aMX_c^oZF?YiMjnJ~XqPr87??gtrnPK+q(P~z7sLYa
zxaHigLWLgbfhM}WqXL}Q<jPfJ{{XDA)CRQ+5C+7=t;i6!q0MSR@j_nRLjbEE8?W1n
z>e=?yM;<qCV5>uqOwkrZ!C^t!+bUk>(#6DsRQ+H=g)2v}G2401RI!9WuHGQ^5gwA^
z%H4od82IzRvl!WDJAgv9Rf+GF$L}pi1M>i3<~@}wRwZ3-vcK08OJ<7~VN&U7@#cu>
z0ZHa}J{VX;Cy8Fb9fjbGc-ut#AtLJ(=)wqb)lN7VgITNL9ujzMtF?P&gc97q<|@U{
z;UE;)JQ`n%sPI-=6H8D!fke=9T&q#BVWmKW_fOP9G;U3Zed?EqSKVAb4K*8;yn|X8
z2LRGHK~9BDHgM|DRe-C_Zc!4$O4;LzN`TfF;c-k(wmNt{9;bTa#?LtiEm#Xn79~(m
za6Jlo3U8;Bn+AFD%Y+S%88mDm#!xz@qFuW}tXT4%gRwcY3`{DHTVoQWIcyfzUI!}S
z#6~ELGM0=AsvNDQz%c%ziB%LofT|{tphME^2MmA~7@|EsO)#NYn!o-Ap_mA-RAqNw
zVwnV0oN*hmYEhzuX{?W9O^iS`gn{1SinOhjc2pTk;edFXZWg2=#MvgRw4)a)G6I02
z)#xDkZVk{+_5>o0dAe#_P@$aa4;DK(7+u7|5L4UKEFlC1Lh?qLjE&nKNZ3uKU;UX@
zSm&4-S5X`4-qi`HBvZ=6F=5ZR#^wq_x-XS=eAF6Srvt`#;^r;9^HKMrh>eVZ3zD8L
zqAF!qUPIta?T5!M2*oad_cSeEGT43zx(zbFZZ5c>nk{f&M6owunxI~CX+w904@4{m
z&<Ml<NCUv<wpbOi#BH+j&WlssQ4DvEZ<s<tJ=;L_K&`J;mFa}aMGDp+gXh|@j0j%q
zgF9^_^@bsR8mQ4$9px(!ze6o`RXn~A5jDDAFQ{2u<#<DOckbrYZB)fwdDK`tyL&-s
zAgEBGhVFqK9?q&EcG>FJq82k2a=X|7v4UD^+gU8iHCu3~%YvS8@`%Aa@w*S?`e;=C
zt4e<ufb;;qNMQ#Wi(cHnS5HpVt$k7pYZ&zviuX8ESCX5WwZ`eQf}C*pjpe$M!KlPI
zcZUsz%G<wZam`_Sd9pKth(|&tTA_WG#saPVthFCa5{KC3^BAz9l4|TI3*zO4M;~NR
zv|xC_q6q-EQ$W=t$Qzypv7@G?zD~JuRM};U7DNd;WozlNh%9ZP<hgze9O3z>?6f?C
zP*LA5Ie_DW`Tqd3coOlzu?d0AU)9{A^1uD5RNlr@4+1>V0E+Da%-5=xihHWNh=Bv@
zv;ddRScqY^t9%7RP^I#RtBc!tIU;1dhZcoemw+lC0~B>wueB8d^IMjv#gwBP4qx^p
zxF1%koMZ+{J(1-VRx;x@1*-67O~*xTqO``Vew+c#2mk;K0u%sq3Jc9+NP2^avp`QV
ztZaFXddqZoxyy+)gfxvLvw|@(Wjr##e>{*EYR@Uh%zKt(rzy)VqAp#>Q=SsHRb5H-
z1u2({cJbmOrD>&Hs#hh!x(~366g-Tt*uqY<_2LK>4FkuimsMx~0A=8z9C84AjvF1*
zsGDdM)VIw_fVt41$Pg}-bn^U8E$(50sbm`c8j#@iDb7l8EGYFb-(b>>DVR82cee>e
z(4&7YYfEt|($FA0*3>X{aI=*qfW8K=sQp*yu|>z3OW?OLuR%-sh2ahZe^|Whiha$C
zZQt5cmZ)ol!dqL`cn;v`#?_m8WdXOJkuR|vc=bQh^Qd)QgqM4>?Wsami(gfGjG&`-
zS#ZY`<;6~A;5VZ4Pxiu4St_euerb#@7bx0oCm@%OD0E|CS2Ad!>YE8o<b)vq02tdX
zf~cFN>&siX9{I_LrG`_$Kd`2unN&yZ$TfuwJe)&oc*gdirI#z~C4e*x;jYvmlo|Zy
zEKTt})jPl-((i$RBUHD{(D-L1&JJzbh~XP1(gIe}`H+=Rx`nIcc$h$>?z%!+S9(-E
zE=imPoRA|crWk#Y=m<Eam*|c3MJi|tGQACOg6K<}72G{ImUqOxY;Fp~Znlg!*LO4)
zHvraAl<s*fK>{xGC*Xm}bpTlL2XUi$dGJKH7GrLOrcu5>h2|04T34Hj@ZF9PSjkE?
z*6D|{_u*V6W#6jUtShcFU4R2sZcr5{rLu?(g)5XJTKXd@0dZV&80xNj5ekdS;-JvC
zg*Hmi;0HM_!qUG}UPqvYlmx0rI<SQpzC>2UBPFbo>}8?S1vhqkCLma+x~j=(fDg#z
zYS2*S9A&vCwUq_|m=_=6aaY0mafl<xF1DCQ=vQdEE+!N;>H?C|wJ2+6Jczcn#9(Yx
zBO208GQn%U*wpWpS~93cLME760x77Wc=?S5F;(g+jxT6d?A3niD1uI>WT^#IvFyEx
zzScEmIz>SIxdp<HQ@;iIBqPu#lg#2U8M=wgzbOv&C!V)=X-BP}9>q(PN8x~q6pn3|
zzQ_u%F902~v)Y5<S7cGNZ`c(QSi&VzbGHKxEEdZJ<l>^<tFJPHa13dF;AjokObjL_
z-bNpbaG~I*+00)E&4?dKAWLFj#89e_&HO4f#a@Cc*mHfxRdyO6(;GjJ3y*+>IR)(Z
zSui1ACFELFR=@;<w<6tKjC<3a(@ZnBZ$|3D5?=d%$g-gswJoDz6M<ZGdMu@E&*CP;
zjo0k~o5Ob;)UcY=UG{>EFTf2WRlLR?*X}Jt*eFWW!l-l<y^#bkFrs3*TNlVcj#}Da
zs3HiEWo5MtD8UaSrQD+2cUw=nS}3k?3u$q;Y2aN{Tn4_XA~RVZ1{_a;6?A)Lgv*<x
zI3ue4?BbT=a-bkAw$PP%6p(o?<=uie(7X?f3Q%^;`SWGq)2pY%2JoEj#D<}TWfpOU
zMv_zu77kd}1y%9a=HURY7<^=*jW`<d86XQRx~}C#E{cb)pt!Nwl_70Iq2Uyd8CR8G
zsz-3y@UzQMewwP_M&|7cMT_R3o?@$pqe|V>%XryW%q_hs4^S%tm2cZ8H`20O6f8nk
zc;!Me)Z56hpwMGmRfz!*AaXnfvbM}qY@cC=ExxR)bRHuhO%Wcob;K$GYv3}<2)A3M
zGh!4_Vd_LUz-Udx2+eA(mGdxYyX=>sN8%}GgZ;*ARgW%N$IlS)v;+k1mKC0Imi95^
z+OLrH&o>$5Kng?Um0MgX{0JL<0ec<8;*AltVh>Pf4)X|$id6eqP)!^6cPjy{JfewI
zR_+U}unGm*aW1WF!EJz4D~P^(+BA{&Ee~=$({lMkNnIBO8F<(p*Ad*diK$m=15UOI
zizsPm^Fuhb0PU{WubLgZ_Qu(`u&d)ryxL;i7fLLTp#K0Zlrq8TZZtu1f~P1R4!CmF
z#&`A#c4!p5!)kq-5G2q6!f+r>TZY@qShx-I>t(Wn&?Ru)swFwFo9>rDw8yi{+-j(&
zRS9MWb1MXWp_0($roIV_9G+8{dMqa_s}%T%DH63lS7V)ZA4q<76zVmFtq6cdW(4?e
zt0SnP1BqQqU?>mA5y}yB+m?slk(Ln8AS*>1z@2+7n5E(UEB^qKhCp~gO>9G&<eSzs
zG&FKE0+cUEV*L;T9OTYf0cEqv6aFj(%`4Zx65e~cR#C7yt{^&H9L*KY-JMO^2o_in
zYG3IGTCXuh(_MpDOVRcBy&;r&tvAYltZ$(R__-3^X+`C*r9A9_cn!YDsh4-8M?Bs*
zmDi<tFAs9?H|8i35Qiow)CdxdCV+<&`_Vq<#Qj5NJ17P#eB7rT6+1ad>5f*;;Zxs~
zJus(PI7IiH!2oroX&p+{ugb17Y<XVDr`7FsZlTmlE;wu-<o^J=nvfioZ{Z+^Xh$vd
zAs}5<Uz7@sOGO8vXpePuLfW39(r@o{%J9C#Z+=Rgs#p;$l+|6CY2I9HA)~4xsnLTq
zzj+W^&<S@o5^FqII?RnN#IcFcn{q`18UP&7K<xrMwM2UJKc$-PuglzJxC2$3#5m*L
z9u-cRTyfl5U8p@JIM$0Swk|aP0O>7Kojf6Q#?bEgdxqpIR#S1f5a1E|f*^a9T^~eA
zfMN#ju;7UmW_w~MuM&hQ`1L@AWT^+Xge*hF^j;8c02`NSk2<)#96M1^;2GfcEYblo
zo>Ih}{8mcS{&^Ei@<mC-V7hR~{Eb_4gE%u`_ykDQ2(y8%HW5^%Is&BeFuGcnWaA@y
zsYvRk+Z?d##HlTUie5u@Glh-3H08p15l?)KQ?h&Y-63Iclu+Hi+lF(S8&we1asL3%
zGb7CRsE3l+WL(naQ;fgK?n3T0R6ur~<6IC6zam$c56bZ6YZxh+=p56{L7MK0!ReY#
zuHm+ZVp^k0Dkn9OOaNkB?MSj36&HYZ(v@|}3Ty+wk$f_17+O-C;qeNaKRQ=sv@5$+
z+hYSQ^BSs&k$$B7DK<DSGA7b6yzWxu5WalMiL%$#L_nA?Zs}^*xTSm2>*Hl0Ik6St
zh<U@hi3N1g2TQHM3{90)VZ>&TBH*OjoB=y);3HC!7Uc@Oy0tDXH?ta3-A7T=ZIm#n
zb7~G7!*Z(8dScu~h&DJVHt7*c^CJ~tvyOLX%-&Q4D(Ohs#{_L6ORYrJ$dj7~+%JW|
zhp1f8bqP(2*H9M3V+)1GVkt_^Xp3P@RPfNk0~Q$OPV9+w{3f&^@M`1fDDjk()zZrb
zsMT3+7^v1|x*IFySj;g(cADC^IYB5U6Y|M*v{r%&>0_mU)CV>ym6hwUfCcZk5mTo8
zl`t<0cIF+mkSbSWx0Ql*E!!J+#WRw%-YP<Ji<R~3BJqbDL&#D{FO=diQgV%?EQL36
zogc}lE&&6*bn@?ttAtM$UN}pk<=UzBE_%uz7Rs3-?|}dpgaCwAjW%i=)C*{PD6eoP
z-ROb=XtcQlHNrT!khgURdqP*OFc)FjtA;goNMCC$&Y?z>iX1$}ooaf58*bh!WrE>d
zbKOHwI5k(@97{@zvWLY4q9UFYA{%jTMrrD~og%lSh9e3n2!+XbR%_y2lp9RXok`0P
zqxlsVqv_x<Yb+FORrA)=-NjSTOI)mqFuQ#+ye$aeh>dashNDwJT<^+8S#~1NEqt2L
zMrMyO_Us^!wV<N?N*fAGOXSiL_}&}riN9=Rc?j;e6XFZ89cx;X;^6)k{YIKd8x}}P
zRp*L)ROghOX8Nyi4M=K?y3i1qiTVi3s&1I=)~`7ETq7ziQaAqqtP;h8K)x@;CWN<3
z4Q>FH=93y>PbR%=d2;yTTpK<Lun-!pRKMO2bsWEY-^?BWGNEGZR0ja&qHkM!vDS6A
zS6CIqP_Z3LDf92lzX`j)O+;gbe5SrCX--_T21eXvPAS|$MGnhu@UPLuuLYyviYV4M
zP|4}|N<?j_>WZjZ2mbWKS5^=<@~?-ayUEFEVANWY2*JKV`6G=z;Vbdf11*g<c(Ql9
zIII}YrN~7l3;<za6|6G1Xm|jcXb7%+*3=qEP_;iuFmy_IIIbb7oW}%W7iyIhiS<z%
zDvR2|$QqP;nZ2Tt8}(e@4U*=T!~pVpSgT_yrp-6eQzQj%O<){8Zrr$B$v0431O?-J
zi8WfV-V(or(!>Ep%oOFW&byQ-3hArAG1O7hF5B}^E54$@SAr31Nry}1g{>eOPAqy-
zeM{`dbJqIbWKMrU8*GHAtO?v;D3@2}2!2cK%EBA?URGS<z%3NEH3qNAwmpGW_s~?f
zT5MSd^qXIO^D?lq%Pb~Yd;^Gy5>tc#roW}vO*%M8olsTZ!HyAUg95UmTa%|kRo&;*
z4<<Wy1G~yExN=mWc|oa-6n%dsu9X(qvN!9;NL$p`@f9Cc1V)|1eiG80QGDza>*8dH
zbaCC>K(ZxW0cNKV=Vsc<6=l1)5<=9(&{wrVuLW&NZl6dnQgA|@nkN^eTKbk;0G1ZO
z*;fMVS~5Qf97?Z=eprK#oEbDcKrw1n^cLsDqK!ye18g%}dn3w&g)?zcYcpKiYI8eJ
z@2iC7Cs#Sl>1E(iyAjC(iR=zUr6N>pp+!>Y@-ZX}hpJG08Z*{8i1mf$#z5T|QBO3I
zkX&Zu7QicICJDnFbymV$92NxzzOTd+Rp4_uh(r>VstFCiss}aUn~1oev6Mis&k9oV
zQ*qndcpR_vkYSMrE)ob;Fz;%UNFt!EdfS&}mqBq2vMqt_$%Hk<y+<Wrv|-5LgQkT0
zFz;ma+u!h;6!!-VJ4IASboq?*YC2I%HwSeV-^xuWPR$njBQB=SfxD1}i@u(TJNi1P
zbtdSwfY(=&32e3KC%||62zE{R(11E%4&_tF4&(~pD;(DZa2trUhh_ob&RMU=tWwl{
z<maxi!`hxME;YJSrRpnX01H~j7p@5$Jrj_hPjV>hDUU!weW@iz4pAKQuou_JfdrD-
z?<`>O2f>RqX@_Zj5V!DD(t*@Zc|#0f3wR(L9_7H+Dn2bBkk<&D4aUsaH;Pe$*G~B(
z?yi>Wyv%tp1H%oM#+3`!2!`=>UBI})c3U>z_E9IiE?J53@Eq5VBzsF2N-ERb7(&Qb
z*%a;tW`%^I9tw*Z4KQNLTk6=}(H@f^<~DB6>fla;ME-HT!7eSj>L~$q4PY@@-O=B}
z0;s1`ceCQ9ax0i&M0i(_9k8&Ywrtj4?MI@yAFM3S?eop_!m7F;M>huU?%*8&D<$v5
z$)<t7HDWMIfh{K8v2B=JV|>0yHITK2RuBV1mT`eDu03mGN{duo-vI6quGL_uNQGX+
zq+{k@oLuxfAde+tz<5+6a+NB|w(BNx9U_B(C6FbGi|vyACHF-+rH$=A0W9|fr?y*X
zN^z<$EF~`}7Q|Wg&xkxl{gts;cI|p%Gk^RM>|!~DTm%iUuO|JkFhYWlzU2Z=L85{F
ziD?CEiVa`L&SA~1TtvuHa&mjgP!^JntJU!gaRA~cR~1thl8<MSWxqW-Ya2i?+FN@W
z0@rJRtb@~#T~|@&hL7ITE$33{L3d<TKyYn=SZ-dv;;aWAI%5Rdz3mBH(f)DVE1C=H
z{{SNNb@afy%H|E!<@V=<u`O2CqbNV+1+ffIa~FZ@+%bf<=qvqL=mJK`W4nZi+N^n<
zdhsX`c$F$uR11xDfUG&3I&kRo^8(X%`p!nkuHaO?Hf>7Itn!`cMscR)TJ6-ZWi(RS
zTG*6a{^*p5z2!O#JJWwy({*x=l}kawl9~wqIl?qk^-vg_0pRdD6_8k71Aj+wa)_Or
zLs-SxwTlaG-N9u)l*L*oscDJs3rg+(0K`419r$GtJC93&1D1*|(7A%?!`>mk^AupX
z1uLn_7+&JiOK5}_2xt!5iAx0N0f6&d!KFxf*e(-;iNSt}f8}E8b!T^AvL{fGHTVl-
zJ4N}|*YHFxZQ<=;7`Vp^`GP=qgbcd1)CYs*3j_N&6dyhOMX33#7*_4qNE)PD=AkTW
z)6}XQa^cQQ#^Qt)7>YaywUPSF;t&dkff3~H0RzVDKo<6)aE*X#Sb&GPw)BDvzdZqX
z5SIu<Uv*NDEbabA3?>wbC{X@Q`e!H6R{$o8^}I;9qB^9sqykqr14iG*2Q0EXObHO;
z3jqPytu;EcJxXL<)@M*~L-sT_G&q@Y8WTwKJndK5BrHYB1`>#@H*l8XkhoG%sMm-j
zsi$36k&LLe2&&&z8zHJ=vJLvVjpxR}`Uqh?23eTKHB$|6QC&e{WzUf>JZZ?g&$gWN
z8UUvYZI*>{ipC)t!Bn1XK0OiMs>8`On#S_n9AW4gtslY}K!Z$)=pl-Y0GIbFkXn>&
zSsl8mLBCO~A&a2^Ohj^5vfxvqP4R7XVttZy#YSGLv@$zaL{O%UDByT95JXT|T6&hO
z*dDkqh}4L2{MaI_!+2CU+^fRW+qH^rC<Q}k2xqV@M@tME(F;nTN_!QC>nc-b+V=`e
zMM&XC3~Gd=fI_n`CAha?n+DOke2g7n%N<?;Hmrr@O>Y)iE8z%t+hWqevF7y{d->Hx
z&IAiZPj(fDV?a}*Coqg`8h{#>xmKKR<uqZXSD0_#Q;<5=B);U%2I52oVg>7Lx!}vf
zhNbO4J<3hsUFLG@)B<HhJ3^ua1z!uO-UmEDHOmJ9tyi#&jl(mp5CMQ_IWl0dzI_jI
zd$w_KZCP#PB;-{w1#H0cT|lzag6{jdOe71sBI_*;-$!!Pnji{vW#t!7mQ=VCl}w5I
zO9{`??yrQG?P)jHGg8wF%Z?#?PC7%tdS2r}Y1?5~DRIW_=L9P%jE>wqBf3kZ0MPU&
zGiBS^L2LbsE!#zigb8u3Hq5H?G0$z^Z^;J1P^GTdseUQ$3fV_Q27o|vFD0)mUWE(W
z2a{ulhZBrOacTD#YB60&TGGZ{^Q<l$VHL=Jor6NGujL-rkkk@av|ml5!wZt&Eh{+H
z<n);krY$%T`oi6PKqv=oYMji67P?C^3800}a>2$NPcS(z#+FVjtIO}W`0Z?M9-c^G
zx+T!MP>1(JYOp${s$Xck%xQ8ow!tp<2&n?y{Y3Kghn{0#aP2?<J2>$x;i5dQa?Ds3
zxIJVAnyRw^0a^~O+lts#+_n>?QZ-gM!ct;xg}p@(D5oHFB8zuuvg3Un=Op0V2at5!
zMvE^MRkE5OYpY){n_tlt7tS3^S|wM*<H>&riTEN=;P{KeayG!4_L0&S=*q5QBp>x2
zOuT9^C=Wmgzfm=iNH7jcLy3wxW98;y<tg${@LzJAY!{>vpzO61>Rc(S4D3f}IE;qZ
zOK9y?8%Bf!#oPR2?h~VQCQC++B{dua7tzYt;I<X3YvI6_D6X()1#~=Sk>UUY=u|$(
zx>d$5Npq8{(KV`Xm1a@@0MINrCt(|%HLPu&2uOJ>W}y&cKzmU|J8y_C?V!FTfo_V}
zm1*F)wrMv3d5%y8g%V)bY~+n>issuPhk9>`Vo&2<)ErRg%b`c%3Sb))?1fqL;gp^)
z;2U8LY5=06P33y=8o;kR+}ve%^BYjE9&_pmL1>!iCSU~x;<-*EOcH<?I`bJvO9f%o
zEC?f&wZRWSjnKVOr^InymDO|$V67qm`GdJuxkowC&+-D=RY3d8lm)#3<P|TclGyeS
zIVxVb3KPjwq51@TS$C`|*CF%s2wvA!P3)*rlb6F#@7#qoyUP$Hz!cYumB!BN$0EX8
zb?wkxC847FYE^WwVe*aqkX3h>;&uexg7gjuGVkQ42cmM%IIXT>$Ne&l0R9qPu&*#o
zz^<)SLI%uL5{4_xR)`2pvWm(z8xQo!#{vB5b`4v~8`&m-PP{b)X)>iBEh6;aDSM!=
zX8OC7ovI=iilTSfGiafR%5!WU#t1;@07^_bjH|SrP_l}GbMMVXC^}W=n5v4b<_>S1
zh<vi=`!HG|*RjZL?5G<b7&qvQ+iL=i=zyy~9^ivpo|(P%ko$O?F{SYh$SqFZ)P$wn
z)^Nt~P?ltl1u?j3ZAJ5ddqraJRs3fU%s39$!3)#f;5=e&wj1Pg15**lxV%ca?!;mx
zwOgiYQK1Nd*R&WlxcHX5x5bgYjlVWRO;n-Atd$S~&SSLdH!WjE9;yWbrehj&JZi>3
zEgwb`1s3_eme$cYR4UyO-1M~s2HiKUDk94`Yym9QvcwuSuJUdXp>c7Qd<r5ln!>MR
z7O`=Ywe(Y9z#v)zXVEQ#O@)PhNZBl?R4tXg6mDCt1T`k`uzZEhTcud(9pD1N^$4+4
z5mtIFOPF!}Eak-V=h*|8kO^h*kY2d;lUT4z2}1e01iZGc>Lbj*6VTxjyhDW;Ay+aK
z0e%kzyXa&&6bG&k=Tdi(7v!Us5IyTC-<FJbcEVBy9|XFF$xYc+hoJ>tFBq9k%(+A0
zH)8e&VXFTCvy6}$GneiwPL<=>dnJWaz(MB8#DK&q?KoU=bKV0rSr7SpqO%#XIE?1J
z!406q`D!9SRef4&R@(Kdfb5fo7WD1R5FLwOsD9o6;P-J?#n>k4?jAs%p}Njd8Yb#1
z9WR(@BE+=tEP_#6T5oEYCcTultcM;HNL?Hs6TR~+&I}iCMMBS2hOmq+4mF(G?MI=Z
z9*^~AF%{u2>bP0Bwe2GUNVjU~@Q4dIM>!XsAJ%gzLjA!aWCEGEW6NQP78iXzjr(d9
zrwaS7aH{$=MQ{oZo>V{V1ouYMwQ1G^P7`>b>n$M3NV%lHX4{KRNK;|tWq*(DrF5~J
zu2)wItVSN<RbJ8rv0!do(^8*Z%Xxs)sn!R;>}>Lo{{YD%(wzhL<!&?T5QLnP$SsZ5
zwh|{)DDV>XLV5h*d>5|N;DTd!nCl+zrJXDfI2IHOOGDRx`XFkUft-Sirb6fuhhy*|
zjI+Q8)B=G>*NgAe+yTYiN)m$KJzQ4$06O4~6emvIvFKb4l(`51r=7G%Cn#hj2WM3x
zfhsXzys9-YReo)$i~~PA#5G!1242*0P__|C!z*SAhPYB~z(*@l51t^=me?rj1HqU~
zsriE|er49d)lFfATDyGF0vgmTzcBG|>~v8XVGS_etj`&Q;14wL#+Bx<J#2+!)v|@-
zQmZR~w$Wv!(h73c+syKSUArF0cV$F(mTvB~Vdr3vRzL^~Ie~3Ccf;Y8+7xNs{{Zlo
z%9nrUTpc`X#arbGzimtL5%|wsb&L-4Ju63>X28@+E!96G6ArI20A%6{Lgr0&0@t=U
zUvi<Dfbr7db{{>8Dg)pnR<5oa^STHtDj9s2I`OOuG_}Q^Y|3m%4XY{kOE=;)2Uov{
zt>U8OUqzAN&@HO&_@k+6c606JMaRbI)wG3_`zDfqW&7p!9fx<ZT23=9{lx@2YmOzZ
z2$oX;cI^wGCX;D&0Q|Xl(=eT5y&wm@7317%dOjc~o~@xyi9=JcF1cy4G|VWrk(RpU
zAoHq<hz1t$#s`X`3Qzw4*EME6Q!eZ%P<$|HkJp=&Yjx`GEn_R>4Htgc{il?LR69Cl
zr=OM)^jd0{E7i>*q6L-l;grj3D5^u|S{86L7UbU5Lzd7KZCMZZ@&{`J%Zwo$p%8Ud
z@H8J8DWW99@JxF0N}|N>Zql}hR1~pV&BKc7cpe~KCu#F4_zYHi!Zk1-B{erD?nPd-
z7L~Oe5vH~<%Pmje&D^RAb&-<P-pYsWqN{vZc@RMj%j9X7eY`5B^-(6;pgj_*i)XDE
zD3xs5zbRn^znfO(QORz`fld34cNxGME?BgSl+=hNa1>IC-=(O1c9O3|wHi`Jm)}EB
z8!5#n)-#xZr;>gor972ejE9iePX--`MQwIP(pTJ}wL|c{YCj+~soIjGi??0yN~*10
zaWNhy!wakAc!@1hV^|kwETvw%cvVG=0cx_AyOFpp91L#Uifrr7?SqE?+xs$T8aeJF
zb+vTOv75Pkhbxl`EL=QkP!6)q85vw|Z;96D-Ek74(!KL6$KrfZBEW;Lc!ks|!6AsC
zTD}UGczK~$*z5?AU%X@`KBf?ZEiQK^3J7@v295A?rfdoa)*yvOQZCTl1V{ojNe>IC
zN@kD_Rd^vj#T&4ut%<a16{b8p=+GbMV2|n!ZR9xyq{L<gOG0u>jGDo6-y1qWmlZ;g
zzRXLsr4;YN;a(KCbW<<DaL>L_mWS?G6|QfN<3Y7}qEn0U;?RoXb#L}c9I1;;7n>&!
znc4?{@FVgiycTGDEOb6#5LA5yf*6S!{{SIk-AuxqGRqpStd(QkU0m?$yWOmBFhc%E
zaa~LDegZI{b<L|k;}U=h!=j{83|7{5RoDw{la;9-*-{Q42iPkn9gZ)%D~C$mW8q~J
zjX^g$Dl&uAxz<&)Sm!f&sDH?CQT}r{)6K$MPJw4=r&LSIfuME!QP$56t13e{sZWg=
za!Cu!wWop&fI0kC;~T_!=lf!kVHYeGudxIo<)VF1df|H2{zE`@S30bz!rkbp;x0i0
z=xN{yY{_%G-}RI@B@PSRmUijA@6@LjgD;WPxYY111*$Fx2I{YwL0c|--4!^qC>Py8
z7>Dbob%?*IbOive7NgCNrk@ciFeUwe5L{VGbwA-R9^pl+z~2O~*$VRQ4A^gD4?ePj
zbicVr{Nz``4ZToSN1%*_-5E)Ap5|#(PsHxXeZmFv5(iHYoS^|A;P~J51aQ*L1+(_c
zMo6Nz{%R|5o_3US{vkt;KSIBfpp`{xoDk-<7TZmFAw$4B+&V?_gjR!+Re%<PtqT~>
zAXw>(C?bAGs12zO%u0y(Syu|SPk33peaq2)`El52bZeZ8tLU#s<d(ghAo>#HZoy+7
zc*j`X_5T2kQGLKUI4jKGrzfyTS_{K`RHe#Gd~E_)i_oCrg+gTD96)d+bfjV!sV{-3
zv;<!(mJ}4aSye@#?E|{nIi{Gkk<G20_FGy2hOMVDrd27=AmR(scGGKs2*Bi{pp50X
zAYL}FNIL`d91sV0FwGTvhkyiQ_#){ld1@-#axz}3Wm*9BvufpB6(@A0yF!&fDOFK+
z!r(?Kt8}0KsTz{Nlu9aj3n`RPNmE;iw;5GBYAzDGo*3Ghq^{dOqJkO@_kLpM&&<^0
zWxG;>6%mV=a_G8?!tyG{2nvXYj95TYz@^|f2MhFog@!~)>Q~-Jqh%V`3hp`$a$vLl
zmu@ZP_M&%87t3T;uDF;wZUY()itDu#yo?6?X{pN5E;c#}G*zks;+F?L5H#=}SWh8&
z3eh&(0$xP`u6)OTaZoW9fS0Q8>ZKm&oDE8WzIWoh#0B-NEoHf){{WlZN^f#KRLZ!!
z7Yz$;w<kl`=b|{9qAGM*vWN`2BVz+lyL>jeWtcoI^%pYU2NeQuir)&{kJ%YzOCX18
zmgLG)eOID4OEi^S2(os=c%_vELa8qCaNq(p*9v1r8QaGS`yk}VFJi@a^-EYzYcvA9
zF&BdZu9;!%vU`vj3BLjIuH0luwaKSKZ6Mle@`-6B_-rWh88ldRr{u%43<5$7nrhb%
z(=GzhC?EmdCwN8BF|&xvP{`+TnfBE$ie-c;ZwgW=xHi~P&cHToXB}^_h^th*t+Na;
zfs3MS>p;;HlbT?%D>s?+Fg!nnn$B&F_6V?jmMCF@5u$uv1XpwcuY_5vHB_r?kR7{}
zUPR?XdFzFEJVe7rvZ+qus?|^h0lQ+DaKHZmd!`wG^-`1_;vHmFlEw2o1lc8<dB?Yi
zB$s|)P9gHAKUyO|)}`@C!#XU<;EcO=_?5)3sGl3&%nAYfcjik7Xn~ygxl^!~vhp?+
zc(0?VwMC|LbmD4&UfF@H>@Yuea#hQPbh5*>;qI7$Sq@<(z33QsK&~Xf_>0rk79XY@
zWv%}JS>QvJ0<=B>1nY6rjoPdjBE0vR^0U!xuued*tdf<eH4@N~+OeX?U4TDUlGQg<
zH0vH4q+$<IBm_7Y`1Kr+d7xJkgSVDl?s{WAmQ@|K=2-Eoy|puXPYY(%SUf0*A@8}c
zT*?n*sD*lsrET!!luGoq;JD9TOZyGUxcO_co8YuRq%~=x`PNlZKfD9p5Uq4{TjtcN
zmnW54g+yEb0H%=dA_S%M5Ke$o4AZ?Lc#Al2TWefIEnk0f-w?3@c_T>%s>*MYYE(6R
z6N!UF2n;dukz-!_9&j2)^mG>cZe3WorBlUC)zmd#DiwiB>aUY)xHhZQimy9nOaXC#
z_LXo;gpJ24DN1~lVU`iMB@Po_$#q-hf`F2O@2okv*38MzOD=K*%MVnqCPAR9Pl$*B
zwOs!IkS}V0^0-vGK=Y(5g-tlNX)QcH3-UJ7HcXLby;;dZ9wW-r6bnAY@3zIS6^}7A
z)o8Df7!(NA9&QOaXyx$u1g^U&KwU#RM@O@$7o~UNtU$%y<OLG9tedbo*=PMjg6UHD
zKxL5ngj-1o$5?kwQTfEHAB8SNXeoGcDct3Vx6HAj3KeSzJ!-xHkket#eIp)j$6NvB
zs)3E@R)Pd)n{9dJ0Ems&;dS{01+;scRGRnwiMbwS(m1JSFpXU<2q+{xt-p{W>lbQn
zsg`SncKj2~(G#0$2)BedqD^WdFC(x1<y+D4KA;769JF?z$Du&xTD~O?Q7Kqz-;i-*
z=gtX^pO+9SXmL%RRfEGC+JJH}^o{~;`Ct=TR{FNwBCs?uZV>T9UGvNrg}T=_@<8?_
zJU22tT3Wy5#dL;UhArY+4mk4SHaU3!Fp~+HyLI<B0F_RR2&&xFL50^fT7cUfJ_`ea
z>02(P61gObJGsyyE1~6Mmq5TSRWv$L@P<Ux(_9;gS_nj|-FUcXKyXz67Xwi)m4V*D
zFX`jt@pl@l#ZA*eDR&zLXnQ?CFF16SKLns6pRA7}jpmjE*uv~?p1P<9DSGT*@G<3>
z6vet2_5`(47&?wqVXh#IFpSHWZ3|kB5JVd92C=q<Zp+7Z05}wt)lfhvQc%9zh5__l
z-vY^Q;I5_GrAbk+{L3M}B<enExB?bgqv;dn3HX&iX^FH76IEs`z(j72EQC;nEv`-?
za9WMn)<HqO_vMJK8|G*=eJ>s-?)OAPnl0UL1emg4bT!>&8rqCf9cTCpwSkE@SVVC1
z=ob$Zi3n1fmo7j#_j=jvl%PCAcbo-QGy1@J`1H_an1#r-8U?5S0Fg8+n>6N3Sgxk0
z2+t$7TrEl=6OoTp)-tio1b$I>O*Ua*2S6Q#-f&I`b5;^IkhhT~Epqutu0lNC)O2&C
zL{65cd_DIn{-xSB0cHh2sxGf|q-y16?54CL(Jz6irMsNDt9nooagXrTyVK7V1W<!Y
z-@Aywrv-rEJVe^o4tN6RENakQ2P+c5IBARP%nBk$RlihnJ(L8{6>^Kp-xnMxKM@G1
zRtEr1+pCmzsx$<rSSLIzmAO4__(-nR^HyJ5#6c8NiA|liuxCgF7tHE+1w^X~ix&D-
zy}<Z=i6{blV6s69rXFZl9e=R{K=696QT0&kHt-v=@24mp>)1$g3R7$ssd{b1mz`br
zEyT7fc9fg08P3M8+S762XSV&)EPS*70C1M2Zb9<_xV_FN89pIR*QE>+)*a=AI_UJ%
zP+ToVhI2Bv;$13<RZjz7l?9kcvEnvaRqc7Xd~uQ6^(|*7d=9F(cECk!=h#D+hd9$5
z(7(_Wojg%5D5xc>(hA=Eo}cv)1tKT1XZAyyQQ5?W9eGaR6XAdjt-_k9*sIUa_6CTL
z;VddV9(?o6UE-ancOM+Jz#x1O-!++wseGm;4FH>!_b+JWDO2h;s<f#`vK*zWJFYlX
zmeh84K7B-rjE3>&`lWzP%_g#yX&t3DgWn?kK@sAPYixlvAU#&-92F0RpV$#883Nw0
zTWI-677A>1sbUfLql-vy7YV+^*{pADwsC@RZSlmqTsK;k1-R&jiofc{EYggq%P!Ou
zFi26TzThG+X;ZsV-R`Q2`&?||z5PMHs&Zc6%aIbq9jp5g(pBi@w?XNOCEZNvOv23m
zI_n;yr@gF9r>jVcK63-Z4Z5Ani3K+JR<A<Ez^B?fAmW}j83#h<Q|6;(bxXJ8We!|<
zDIf{8T7_|OupDh6$`eiuE62Bpx<R9gEh5kmPlt?$7|uw`*sd#OZRSx^9x%eChZ5@(
zqQFfh-h<qokA~wQMV`lVl}w8lZUBA5Tdz4%vb_ec&crabG#?5|S<ARq5v_w4M7m7J
za9Ro{{i%$fvug$)<8_fXl-M;uS9efSzhJioLXW|R)i4$&swnc|1XFaRe0Onh!+CF*
ztI%&Pt~M6~czlw~D2knpGPrd?>Uq4S`yDDV^9^iv{{RxCq7-^^c`4npJ^_W{Zjp*X
zIVfz`qlM{yAmZPA-WDw^wQMr!JAtuiQd>9=QGsfQU>h&P3>5pPq7|a!BF~-y47z+r
zexl<a@uabFe+6FSBUQwBYX$bi7e!#nO!nw{h^ST3D5fmRJ~jbtaeUQq94f9P3vT}a
zQ=u(o1P3o$IexFnz)B9jdZ_gT_-(!;7rJs;(+};B5#kh%I&-L@_QLtIQm;7qE;Aih
z>@duB2ZEExhXgd;om>DA&IIAE=lxOj-B)ChNInWKV0YbEMfzg5KtV?i_I1e6Qa)pF
zz<FFl=jtrBzL?^pl^)Y*Eq=svLoj}|9)MJ+%23!p)?35?B9XS2$VyNmcS5u_8>6|W
zBhX`h@h%kn1b2!;oziGmaMgWEkAG6)D8O)>lyAcYlr2%laCr{i`mh6V{EntQ7KdP3
zr{)!=+p@6N_8d!5(=QUbk#(26@i*+MA20~et`|=bBHIOX%B~TxoK#(DxV>@N4hSup
zwim#-h3M`=U3p{kD!g23#-J+lQSO_ARNy`f?&gxV+oeJVpw^yB7*}BleEo%dEV%+u
zQkg+ZKnUFg`Pi6r3E)|0%NNHUBf!TUUR=Hd<$xB~R9A@oJNpRmzV9=Lm3Ce2#<jh?
zk87*^u<%6|uy}?iil_ti78n{ARpuQNp060CM>Kr~!vYBu!A%XMeJK?NuS*?AptZm2
z30M``sniY5FM!)+!Y;(zx(85p-m!!oht)*jBJ>o4VS%93v@{ZQ;IP%U`nC9-3~Xgt
zY^xA3=!<TS2ZberO*Wxs?fHrb71zC}1lxB2Z3y0nfLi<FTS)WIm<k}^qORz&1R)Ow
zQ(8b&7Ap;S<%<a5JYg*+1>tRLOA92RLiM%nwz6E<18HLDIVj>ADy=$#YsE3cc@P|2
zV@D4`%51l`2mrT@H|BPo8rW-=Sny2+naSU|+-Y@H6GXS6LJ=2hyu`?2J$uw>u<0kT
z6lzeyDj7$g{{Sdd>A-yxYiPU`$H<FnHVH~y;UMTCI~OBP5r{eiNA$xizSnKMvH1ta
zBzkRGN16ptskgDwewkK)<%1BQ+1ged8&xu2`FkkTOuQO~t4-~l$Vupvs<lskV=$1T
z?PLCc(yI`h+g*k;kRqIypGH-7Y&C95e+gy<ves=zLN$;+v0W%<=F&1|>+<56Oc2@|
zsXiDZmA7mpU0~M*P&j_tjcb+5DD)L5;PISf0RSi2bYL1BPn$1Tsd&6m@iGkW)T^Fv
zgB^W*Ii-(8RPU>A$z!xo-ogsMM%twS1lWDPVppI-s{+m^vxw7&q+n5H_=p13wT)Zh
zVWDX$Eh}-p5|k_s*s|5sRPpzco{^^hKT&%uE0N^44h=<IE})%K{9wdq<t$!1sn6p4
z0MhQCGmA2`1jyjk6P&m$(12yGMnrghG1gEdsJ#CGE#eVn+SIes%C3%K_9*KI&oc72
z=py<mYl5U9>>~ujLA%;4*rXa<*S25HDR8Tk4+Ez79+^T20TizY_8b>|r}A;21razf
zxZ8|Zgsr)C+Q6hKhkfC<>=>p#=CX7V6AQ@}Ov+Zi4={-n%?=lcuF$u(nj`F-;39_j
zsRXpEaZER%RQ8j?^v*>e0=&HB6&BU-`;?}=4vhCLLhxf98CeSvWt#|E9=(yW#cw4G
zEdK!g0_Bxtu&1?HU-oG{awnji2WYqxjFd0(nYbE<mE3AbFP5E45`1jJ;ME1I3{pB&
z%I2?i1BK9e7be;LP7n>|2Ble7ULrbhD4vG>lHf#XQ~7KaKqBA{q|(X+-N^4E(CO*!
zgL2rlXHF8i)<P1{$@+J(l(?4?SHcjvc+YNahTaRHR2Hwh%fnEq#?IFB0ga8AE>WV(
zo3`=SaH(Z0KLI+wbQS&yQGq#0NR|cMUFdhFCk_{0E>m=}G*s9rwQMeLdBRfQ#}Yq<
zQgKcIwZ5zFQj`npL<>+FddPYDJd00PQ9f8!kDKv5rF&bYNR*T#*}h@Lxi3E^F=ZDg
zx*<!a1J=Sject>-W3I=*$wuVPg(m=UP@BQRssUgfLhWTekx80V73e1-5N{oXFti88
z%5O6C)moon6poo^qsOUa4Jg_Y0s!0p0N>0^wKyARgbFm<aH-AZ+%|CMTzy4;y^y8S
zta)x-A27<QDJ@DvDoR^$FJBDGF}2BqLfL&tR{%VvZVk<1CRPb_G+Ywe;>yI04`tQ<
z#84FYFMY<xzJrp5!`V4ve@TnP9k72|!u+IFtCLMf!U`J+-4IL7X<OIB5Td7%-f9*~
zf&s3jdxFblYyin_u;-v}UvR#GN_&=SyicDov*GAiBfl>S3ho2sGHR$;>12rWVHcnx
zsz9&XYR{>Cg9o+;SxagHuWP4>s09ihF2TMoSa(QPWpU=k07}sI3zuN5fx`PEZ>%s+
z0E+=_7M#%*uw5QWaq=`5>^@$hElQNrb*uyfOtaZtv4F96&H08mRg){(LE=2&&$(hH
zjjG9nTVytH^h+y_+8@arJ;3cg)TM<LFh+;fkw_Rz;+bNln`FLG_-P{TBL^p&hF=B5
zGnSzu?)5_qA#i1K>kum|m2n2f7qB2S5~;fhBQO@SwD>EA7_d}$?i^RjH#p|yI&*Ph
zB)mmKp>0<=FMei-p*uDx30z5$b-aua6|1*x(MB=~i=4bJObRQ@aMia3VxdiZ)Lw&r
zBNyzcvw*aBczp475`jBEc^a1fqQp#T7koxQN;yENmY-o;)pdA}MddzjURE~TRCp>G
z1X>;v30)a#FJGcN>~(q&3rO`t{vrbE^WaAQ1hdO|cEhX6L&~92HkqYdtOyV`wIKkt
zTaa3|OHQJpUl4?lt+JZow5=TJWK(>2b1k^yRN|KP6$L!KBk!aq8XxloP3$=uh!fJ`
z%rJ_yq^eeKSA6`y7B6!T3uBAI+-j4a<yIyGt?B*}$*mRqr)}^3Cb07^eI2U~!@(5}
zRae^+v|Z@Ejbr}+Z442Y$0p5(;hXIcM>JL29N0R(mO0fm-Gvdj25iSAZ)i$elYEL=
z__@G>j*F);V`jERU16kC*-kh>Ou7<tlu=(4>I`o6mG~rPu;F81x?s1$j~dR1zoy2y
zpby7UyB{)86?`$#d7gG2S+OnJ4Gc`xVxCumXpp6^Y`m5}&5bXBaDVK$ibU`%<5DcR
zFXE<feNRp+u11HIx`4g2N)iI{<a=HS0ter1R8a?@dkQf~0K_E>DgaJB-0?NQ45yfc
z)NnGHKx;wsa<EO(Jv^{;X*jywUqmIBLZHf@DH_gBA2x;R@QtrmaaM&{_;CPhtX>d5
z?p>fwoULWHUAR?_XmCQWP&D`jcjuUGq-R`r)PJZZpq&V0sQw2G4cE^r>4?f`<Y9&O
zxGRA|2Fp78jOWQA(#jUwQjWM6_H4UI_joVyVpMFT0pP14q2-0Or;5nfq6-3l*j)GC
zJn=5oPaxJpwwIwB$TbP7JTV*LhEH_d{XkS)PPqspEVI5}F|5I4c?-kd%s4>e=pa$C
zsbCb^89}Ebs;M>Z5`d~Z;iLl~)wiU&+TFtBiEVS{KglA{bN*tYbpwFtWjMWZR~7#N
z#4+Bb+I*-DU2{0)ORt)pbH?m(HmWRiG<n$f$es+;H^=B`1#Sapx3Bdw&AA)0`zjy`
zEa<!*i5i10Xne{kZmWJpL_BFUuM7xF+I<|$0TQag<R@lG2iYT&hs@#x3mf^+xCB1M
z5r1Y4bif;1WsQj7z?fFjZx22tRzCpC%8)Jo>&1IO<D1kQlq`$QxxguBqBRf?tsIF=
zXDQ691G@x8jn|S^4q^ON;W2|ys`+<$s6bG01yqJOvelr_UpUTW*j(L65eA&DAfi(G
z7cJarQ82pt!(A^ig`rxLnz)7whIK9oKf2A_hDG#2h#_bpPlqg(AT1|59r}nD9vmu~
zn{GHS#wC{vIk7>ZrOZ^lqv=c3U0qk0GO1b`Jkv9siR4MGVqMMEUh&LsctAn=re8(@
zQt|E%Lk5j^)>Q8!r)9l^JD&%G;SQx>0QQeC@fi-s(R;5XO7wNhQRHC2G;r+8{9{lV
zvq9TK2n%sdJzmK}=!v?v;MS@G#BxZrvX`Q&`Qj<2)r)tlhh9|t(%bZzOcsuRE;UrC
zS-G})(VR}DI}M9bi{#e+Dp7s{kb$<=!A=VSLOE$b{%j(r@a#I@8_LQQo+~ZZMxkO$
z!6${*HEWCEX&no+Jc6lwz<Q3UbZSM66`<rwhZwS|HX8|jbrv)Vr^#0YQ;Q833jtIP
z0o#`b7$L#+#e`+9*==7|l3C3v6<#M*oXsy%scKt!7Gij!Rf?un{YzJQ<?2=7U8mi_
zOF;F+Z*;erAg*y#U*VTJR=}GJ$hw`EcBwSozYq#VZ@Z$_1>G(Wg;CF#g@|g{1$ntn
z7P8gt-d@9;V8kCw*TzFJZv>>h2xYo<<`7%MkTWP?YLTb|%C@p9%KC*8LLqQ_$ij6m
z0wqAIzOCXGDIP7CF5M`hR+`+fO_dM2ENUKxs1(3`Tboi0AfaLT_mJDFM_AElfh<)H
z2f+vDtVa!U)bBP7XzybafLK96U`or7YU=pr6=2bAZTV$`3wNtN>xp3;1||{qr7HXd
z*x)dKSi-d|5u7*6ul8+WP4q&mpJgzjhuqMP#6q^%aHn7@vae?BsGb8cg$CEP0Bb;$
zzooJ1>@wNlgAlFtvDZ(($w&esKZT%z;A&r2!vkD3aa?i(&_DW=i?`U<U<-gzeHW2z
zul2KwugF%{t@wbk*na3?+}APUtFYOp@E=Kh%2Ln%yemx~x-N+8Fz^e3C=0tzr^Mb}
z4d2TdsJ<T&DRvnZOOi%S3I-@iR|s7OwQy*JrH2oBIJK~+b)`gC1*){aY;wq}8}8Um
zb^Zln#1R5z9lr02ISFoczY_b96fE>WzC{dQ(F>_{W9WjS(X~xOn06a1)-eZ3sk`O<
z67}JJ0&~N<R`&_8VQoWnkfOuqj$G(95~0OW9OtI?wE2s9dqRh-m*mbfh}u*i-PR24
z-@K$Z0^o2i#xsRO%Mu>ThVPmyk;OH$s{5B3?5(NOSJ^9Y<J|}jEHQXYMl-QqSGhOC
z2>4w@QCZ!nfg!r}9R*}ct<^gXxA>eb?gPav8|yx0s8DL|txj2(rlnX8xU8C-wepJe
zfLnTl`mwdGYCK$fMpw~2RmF;%zxcImDh9}MgbtoaXssAgQB;@Y@iBE%!jCZ$ZJ$ih
zHzd|J!EvqRluWU4dDI>og04OY%G91N2IQsG4#Gt59)VW332!;zMb{ZQ5Fs?1Lv}Dm
zxKseNglvdIz|%1wtx)Sql|I6lQivgg0Jm;yMKNB5Lj7zlt8vT4K==z37P(V~Kg#oz
zF8L*1FD`pUt7E$D)KnIV=!5X40V@94cJRn)LfKx}G$qh#rE8mNwUlHFOTJY;$(F+U
z?<1TpH|ob^d_k@nTQBNe(^s0A+_#(NFv}P7aGfdVkC=kuIFug=va7nTtLchY)OE;^
z;_YjK1pu}9j+&+@JViPTEHKc9+aT8#>%!a<ylzFY4vE&PtO-F!%k7n;s77lb5hLgM
z*$7)Zxvp7h_BnFH7Sn60QCjpnV=BA{0pRrne0zeooDc!38wJDr6u{DRh`EoVAVE`8
z(ifiP!)KW2qx%RBdjh*w!M)8za|J>Tn>8sNA~>$+stQtjo<zG5rhY@+LeH}u)uZKS
zoI^YH0Dy!8>Hzg-nH`$0ilz+*C`@3fT#Io-tX|V=Z6MUY4oJG7$*zG<0w~u*YP_N3
zg4Y~7Tg65<WzH4g@dWZPEL4{&ac=@(D15J*tI11yIb2wHxbJ|Hx;&_6?VWIKL~I;c
zWt)uH1&;GP3@=88;EZ9!1Xh&RmPOKniZo85wKThJ7X;KhU}_mQz~VTBJb9dZJ<;+T
zQNkhSz?&uA)M_==#<wrR+O=1d#yURXC)XnzfV=IzGJp~1p-?igqcE*nrTQTdD(Nx{
zb6u^jRNTwzOwp>U6#(~?vcw_u)xJpWtSwvA34)c^WpM|(#dgi3>6Kuh1!~eqP~)9e
z0w<E;EKvOTBLivR`K*_sux!-JqT`=K%&uTsykw8uHC5aXdPSDQyH)~~orGIhRk|V+
z7~JJh+wN*M=eUpqP?i;9!gVr2Vj7OI@7voR$@WBPs?~JylDbB;n7}DBcbULUVRmXd
zV~F}H=9TmFRRAKlfzQ>%tWvt`C3^X4Sap0P;+Ad@@LA3kmQv*jfkK<tkohBnKoS6N
z+K|ErGs|Rify$xBrIn>r$K0nd&bAwa#qj_D(f0^ZOsop|SPqtIjCbl(Yu(;kYU4(p
zo?utt<>V<94&o+|f-v6Kv_3@Oc;yp{!^*_q<|Rfe4X<q=;<HslSIXtrX}hYvJ7D{s
zx@()I)Up)gvBT659<?Z!#hu#)V3py!_i;X$jJn1yOPCHmWigA-5ftx(tMbM)0ey``
zRf`^AbLHuD<Z9F^)42dEa^WZ_v+Bx=m7g&ddVJJM{zn_h>qH0-Tkrgq^46ncm&zGF
z<;1lrP3g^QSu%uNMfju{6^ekR5@AI-iEH#pjJn%xwTPvvV@7v3YGyfQfty%0A99K|
zn^#dl1?;04tzA@n7z6QfJ3lz7re)4mcvv(o`Z3%}p+RdMZ}1IQ=HpIq?^>qU!9FcB
zGxJyq7T&HuFkPW_MZ>nu?LrW)qvn*&1s-r9v-(PjN6VX0uwbu-;uNxj&AYG;E_W>{
z2%AhIz}BWjD7X+a^$mp?ht6PD)#{SgX&S;m)(<Ocd@CV(4qnAJy-wzdt`{SO4cz4f
z<{nQSul^G8^7%!n&aq%Eu(z#C&36{eUQw~X_8)$_T`ZyZIIROk$88+~>zDh(6uJ_M
zyBSLbYj6%>I?yL;chQd0R8Db4+UU1V_?*r!6G^mdi2<D4HlqBC?ryXij)u#OJ9GYl
z0<NPxUmU{I_-O_Wr7fX0b#No(S;=er31x<UZ1%zK5*>om;S&6{;se6su(XOU=;)eq
zj^fuOkJq)08nJAwi5pqT#mvFPEn4K3a3ygRm0^mcO?a!O25Q&_d-ly|eh(hVQv}#4
zRB}6TZ0q?0^o}Yo2iURVTW&$=vkyr^`11*;fJ)i8H*|}lqG?6ys03GOgG>}cYz6Ao
z9m75y;w_>T(F*%(`2cG<&2x&2l+&Qhv1a}9NPfXaHUz8S>yI+oQc{zGc!2JxyIUGI
zv1nEH!Ki%&vf5lB%QUjV8kp-CDdl-xFdo|HTIfQ>1uf?)YJngJfTfk{46rlPHP)9(
z>!z%_thC~>HW^qvqW+1MWNJc}eng$B@$i=vP8ZJY?l-S6yxN&o)3TlHPd3CApkA*~
zKBn&lNBJ2MTMkfbFs2o@r#uc`gzF<gLDRp!O_;dT0J4ao*d_W@F*X?VB@;<O@4I%+
zDj3qNvHE~?($<_2$t6jl++xm0)$T(Wo0S<@PJspEH>iTS=j6-LJd|F0hBC>Puq?5%
z{(Z%53gvr)ODt^^mjbb`^|GSP9H)G>5SM#nJw;0Z`=|>1wgEu3aJ8^90*dtI#XT%+
z?~O5l600$aohJVPugm`cvnI6G^%k~16S7%hh|HjISdhhIJ5Nll12#p;*d39rMSP&~
zWUxRlEt4;@5a9^8=@$w!CddyI{{U+h&Jisw!^U+G@*LIa6+(@}+UGOF>lf$<3W3c?
zedRKm==0#};;XOXpFHsh&}|;+8vB5%VZd|KQLAFmpBo{%h}q0H%vJy>38K(&$7x8b
z66L2VQdD;PdTSywp86L4v2bHMdc=Lu15^qntWYm$K$|&+<4A)lvr5@4XOf#@oq~)a
zh~&;1V0F(lAG>M{b}2{=;|eq4p%2pGXr+MtM+I15D6TKXabZF$A#v{7q0M=S1-m@|
z0NF!WroWua8q-LDaSefc5Zo%9TLRbwP$Qp9r)X$FS-B-Ai!)GH2PH1$ny~S;<?#Zo
zsxB~HK}c4eG~@X;pzq-xbR0NS94V?kYGAPInuWT`e*|$u+_g=#ylUL>8`A2dRn#b|
z?3W(cxVAYM69yA0JUz`N5!;&xprcQ>;Q{o%<~*Dk<{ay58xZDXH7!lTHN2W$$;#rA
ztEzwi4K`T0&Nijc5P8_5_S=Q9R}*&fHXiyRQFon+x-~UMcU;fi@C9&8z8%7TwSp-P
z@B;A0oIlGAxMhI4B6xs-%W!APfwLQ0zVyKULJb81HmP3hnphtP0IK@P4v<?yTI;Yh
z1r<8Y8siB-4?WGE<^KSZ3^{dp66$ahq08wKuV$`QF(6gStK<?tWD3wWHNr84T-h=0
z2ZlG%2oP<0k0Ql2PcUU7$DUv&`oPscbDzQrl&VN++O}|CS;W7+T{)`atwtT8SXs${
zTCa+Mh$SYgN|S@D+$D~LJodyJDRx=evhAo2t;Dz#=w|{^CqlM>aC+Iu!XnjW{g#~p
z1yUDv>R!NXz@}1oloC?E{2D+*&{mQ8AV_^mB<ZanVp|r#7%Vm}S0RkG$nURl)p&C*
z2~kE57PJsGDV{3ZL*gdP<LD6-AwYp2xmP$T;0rRNldY!QG|f=<Q9hjO0Ng9&Ew6xq
z;s@FyApj&RRTZWF)z&grb!LVg+FFB!zdS*90#nWA!0zWA<7HVDr(sJ(>01@H7N<kZ
zuyDQ_GKQ|GwMOcq(znEKJwsgsW8YpQ4JPp7qZXpjs~IAOG+<~A)%alO79hM#)>5O5
z%}-*#q=y5i+uT<OpUBW`sTh1xVk}@(r8=X`?41WdRA-;5lE26SB{|)^%j)RXApZav
z11+vxR4GHc)-P33xBzwdE*ycMKv9P{HGqj=_#s!=M<iY&XFKWWiB0-N$F&magN0U=
z!BO!w@<>!Mz12*p1hl&qZtga~yKLOhV|zyg0$0J99mG4kmY}afOsx$sTC+fc?0MM1
zpwBN-{{XcIb-fVy4Y5<!LbUCHR6@tVvJC}|OYm<CbY3Ma?v8gp$dKT6RbhKC_QxMZ
zP^rA8sNoOUk2t=UZnu#ERc+i<K9&m$off5I6yQ*F9#OJYfTED`O%^SnZszn0hSjZ%
zc|cbI%Whk%{Fr>qA{%8AIQBN5NUaD~yetMMS9k!ZD+sN7Cr5;1??HGpY>nx8BSlnm
zaUrRtqUs({mtkrb$j8Gpp=(+rdQ{rG4v&h8w5WhR3W4RBST<vxB^>2a>WY%Yl8Vc3
zyq!gtEFIS3CPfdF2-AQb5Y!w?%6#xe1>c$+v(?1v>$R^aisIN7F@Qx<j+g~bO?8q(
zS`Gy$)GiHoH?pSs>ppH#+mXz0agaJ(3njq-*z`G~9`)RqbubP=tf;b0m@iCG=*pJW
zbxCHBZv1pc+}JfpYMT8HuEZ)H?SVAptFR~RqJ^LH?jlQpuIhXgJ+*OP96wPg8C*dd
zej*A#?6F8)J^)!N()IKhwGk~ET+-=qOLt?M05m{0XgzqQ4M4Y-#RxkdOUuVnhYcjM
zxDT0mD%Wk>BTAN=erK4|9|Sc8QY*!EF40{B@plDe;9Q~!c0<kBHLdkS<cM;h))!(h
z)9@q|PJEY>7ZrvG$2+DB9yVyCt3kr*BtqB4xTc*a%MM`m8Y>GB^NXUY-mI!(qg)CK
zCfXfPaZG#O4h}*JIA-m|R3fRmV(CZd5Xy{Cz*VDybr@411KS=YB?f`@Hc=Gxg{fSk
zxePu7mQXFA0|iFC4N%No*bg!HpgD+;3(+j?{{ZnTqK7K>fq>bg^}o8DsdW~E7FD%!
zzTsJ$R2NHdin;c+b!(VokQvr;K&Uur=202szON;tyTGt$l(xj{`63hBtWl~X!(CP6
zHn4;YRdjb5=jEI&nG}!g2~DdN+jO~0(%t1eUvW<`H%0B#qpM2i%=9!9UPhp3H4=>%
z!CO}wFwBCY)dP*^2~=UQri^)=x>m~L9gMR=9S;!wV+t#;mKnsQeTq?YG^O?%>}B2c
zlc|}f+|FKa+7kwz36U_!Ei>e0y*&WQ9D@UWN<{!shPY>lfSPN3Fk4#m%jORP`%VXN
z=sbT*qtxKD%26>_3iK|M=?Fe$b=k$P4-(u{YT&kr`g3lr8lvih#H(G_t+?O{e-f!u
z_%ivL`(aZ$Q_5jTf`dn$=2kU&i|`kX)4f-5tBJtiqlnK?*Wv@7xq)jcoUU(%Ep<mM
zxH9X76?rc(Z~`jex_$lBtwAP~0DubPh+bq=D|*>b09v7;WzDWrOYMNV=zW7JS&l6a
z5byxh2hVx*k5jvoX!C2VFNiV9fd2p{I`$&ZJ_Q(!)21`0s3yUwYEa|z)A=gbJ&GA(
zw&`OO5Eoqk0MuZ3#j)6HQ-`)bpJYLH>#6I<hFN`Bk9c9>LoK18v@P1NLGBV^1>SlB
ziG0wgHkU6b0rK?_NNZ0wZ_1VSpL!D`vm2z@P(`u>wwK`%{5{I<k>`*0CHMexE)xQk
z8Tj7B-97K6T3iR1(Q4mpsCL`UUHn6OqPoC}aMG#xxOybz9s~j2px$U6(;EOdfN>Id
z0PsYiP$PS1V;3#TDx;i%4Ipa|VnIM8gGk<|S(>|{xGxz%W-s<|z8~(XJbEe0d+=ji
z19$*&gff(iZf`O?abAnCP3eW7Da5S2bmA)`I&ZEg9Yv&RXoW#U3*tWLmBj*Ou*7T*
zaflfCVjT=K08w8z01!sk?=U~rt_>sb!7UAW99r`QX2j%r?k$*%&fA;2YJLDccuKtz
z&0VeSY7r9QJUx$a41B(Z{-rXoKxNyXMN3fSTL-pZqSMeTV%Vys?R?C#jem0B5=-(d
zBUVxHDp5lAT&qx?Rn>m37odHGy~2ez)If-ql9~$lDVUlxdo-BKYr-BhAhfFhpsK#2
z?q$<s(hy<MtnQd>PZpk=I--&QgErK!GgY=*!-Uh&gt${CtsF|FLT?PhduTbYZxYG{
z+VC7bF{ESF2%t+m-Uk;gsMg&<QV#7y^bm!IT?(W<m*|-IEC!0Ipgqh2m{QSg`ZpHU
zRuN0qrXlT>F_!FH+J(31ulmGJSoU=(R4ePwN_-L13S-$=gjZjHBu(54ILZS3vh3q8
zxgHaf!NypnDhs<-%z@V%M@3%|)kx8bDh&>G8CcSl^BGo@y7RbDbrY&D0%?Wl7cWPq
zqiGj{)4J|B5EKoudELBSAVN;9dX9XVgJuwd@fWpOl)(Q0*W!qQBAiiQbFd<B!zlLv
zgKhYviFK~<4K)DLHWHC2qWv)4fSh@RDwkZPQ}R>~9`?Kr)J0WKf^DTQ%aJpe;yYZ$
zw{Gg=8qrfM3NZ}`{+7d(0m;J36_)7pIb}fxZO@>&=H%$$*Ro~NZv+wM{w0Z?#|Rty
zVrQQP1gZ|6NcK=uY$I)2{{XVF6_mRy4DeM~h|5~is)9l&YTv_h;2gDE>L3|n`Tqdx
zA;ke$I4`5(6H<0K$R7)py99l_<l)V|#<;qTfTOG^7H2oUjwg&8u}?Hh^Zjx=6Tp@S
z04jCUffuw~-4WJngUw_H@S$n)*Aa-83*)F9hK9f^lwl)yl1f*iH#P#mRJ7R^FKfZ{
zGb3J@Ep1bpkErd1(dH!%?W>N-1)>T<mc_PBsj`;S6kPrvgvPPgK4T6nK)%~VqjEQE
zY|~^cPgWORYNbZz&r4_9P;8;v7V^F#Wk!H-Cl(TgH0yj21(hse%gihVg+CFD6bjV0
z4^fg;P@jt(lUEIBy2CmyQ~<L5L9<L%cGtOb$B%g_TzoebtOG&3r3~8=+rlr14<-UU
zxIIKRrrkMS4&VY66nKZd*bD(jR;0BpjPhC`&0P@Xdo^1%;(oI^?`J+fV5n5-#sQ(4
zLJ*@3r&scjkov=W<pf;1#fWm8a}{_aPh;jSwnCeD3L@`<*u%~1Gv!HEw{rIvtl(g=
zC5z?Ue3IzSfVO*`YIgV**mIk;MgHSLj6eb4)IAB}H{}em+<1b41h^Q>D~DU+7`d{V
z%NM~DaaAV<`i}dD*OD?tL1l6FOk1(y5s-`CZs7FTC?N7ye~`QqgY7|6RFmh5jP51j
z;smRf*Lw_6Q>gPShLW>&->A8ei|NB2AZP-p^g{`X#5L+vigZ4R_YUba_?8uwDDnUt
zug7pF>o>+A{jX69d9h8v$H4tT;*L6m#)CVs>qQhkE2y^(5g4fzO?pBs-5-~*`jv>b
z@TCIZh7aMmIcrx0A{Jbnol*0Fo&;3SCH56#n_pd?8ECUg66h@e{v~)Oi9#sS=fD2|
zVxPNdkSzDdxVj?nK>-tr*}(ePhAAu?e-KT?*lAv}*l(uvUy@qpKH>1juLvwams@d^
z`?ON4tNf)BENX?JRbL%S{u<r<6OJ<FMV5F6X3Xo9T+b13eF^4K(MO|@^IO@7Z{(am
z2f$(kQ<nJ7<C5RoBhJ7K+K<Mgc))l|W92^t;Ki}h304=Ugh7y2r6287`v~_Onrf-3
zS!{*?yic|v3-K2vMHatK>9M~M*^BK$P_5T;v9MNm3<i8Ss~G@^_S7x7v%nQC1h+Da
zzIcFFx~14xpHmG51_f0+krKG{S3N=>6pKp~KH+v3K;uP-@no5Fo5Th8bE<B!=S9we
zozoTTaNA)Jy2(ZV0DK%6eKFfQ)hfWnD}jFZ4ibc%;qEH%nSZw$gbzE!Kqw5211kOt
z$(>*)!PwkVr46B#(GvlFRL_!GlvC%+D$5Onk+7wjw)s=J%#~`;EF$^`TMZyVCmc*;
za3-C@%>?o)iilq><shwi$ZHA37Vk<gEGN#@hhY^}1d463JqeGncs1Jl^(mpri{OaL
z)$9{)I+@8h1s^di(ZEX`sccyM!0B)vjw(?ux)f=CM3zs!)kLTkv~LoD&8CugC>58Q
z6nA^bgbpPHSeLgQe(xPGA%EBlclk4T>sT$<QlXHBt=jt{Z6a8uyP+$ogzy`jbEYs-
zS!3g16XF0Mps?DvNDhFcw7pGGusJHu)<aGRr^XQ$R{m6_d={|E0x=bf8I@Bhz~Jh^
zkp<kcx$;QC2%vFB;X7GenDkVAgk_b*Ky1hX`mv_b;4?!(D)=V-GT9mz!*@d&D~t?R
z{!7qWN^gj>h-R!ATcbjf(^54mh@YpXUtlp_JYZF$EUyenK-KWNLeMwWdY7|kW;Bt(
z;{%`wQj%Bz%wR)R(u!%97Clyq?RmKD8bCXxFL{d@sM2ebIKY6LTQoFzXShJ|GW4i}
zg6!A}125p}8{o6oZ<F0I?ZsO5%Um7lZ{LWVrZ;;1M35UT6%UK<b`w%%JPGnSC7Ruf
zxl97yJE;?SPql5ZE9hd4gSb|L2!&Zefrdg-%r7}&*+F9yDpNqV&GjptV(<uUlp%Y3
z9woBywa7;2Ao8k-6mnv!v08Pc2m{#C&Lymhim!LvXx?>tPxy&AT={G6SEsFP8_(il
z9ZO56EV9v~3V#_!+PHyQgyI1j;`Ra2*AY_rG^^UO_5@lAE`~LIKK^Vw@SN1J>|yv8
z%bV0yB8XT&Qvx>OJ8?jq$|4hLRuF!|zK-H`?aaSH6Dg%ej%c<LTkn<H7Ce|V`Hk`z
zBC}jh_&_xO0A>2>9$P#<f*iqz4i7AJEoF49emRsF);bWjif-Gm=`Q2;ytP)qg93x#
z`iADJ3f6IC4{u*kYP~=lKD%YSQo1iYV$i8jP-Rq6i2I56^9v_R;TlGz3}8oz;x9YU
zv22KD5Lg#)rYw_D5lU*8u{7Cr`DA&0a&VqWSF2)+M5~+UEtUN^kjGvD{{YDsj?-&^
z5nU$b+PiTvyq}RY_+kg9A#3U%O^h^TIU<N{d<>*AJ$Hew3^|*k!LZKE0~L#`I5{AD
zwgxhaLq%Wxv8cAG7S`86h7=A948CU?NIV|r4r|gHq3n-CXqEe0g7lXS7hXuHIj&_e
zu|y6BG~lkt-WytRPC_R)2Klail>p%_W1$OLDMDzOAElCvn6P^7RC8fT>H~^Zpv}OT
z)oxk7(a<Dr^aldS3Wy3S!-|$2ap&Cigrf{+%Q)=<mR1CYC4N8PCrDg#mCYSXjRUJH
zjY0~~4l0DwWukczhumEAQkstrpS2w=-QakAGW3^U&jeMJs_^8mLQ`z;_+-a{j}E1Z
zq<lbPNxX>$FLZ?sSXcz2r9sURs(n-{E)KEs73N$P1nZ_X?Ibl`&xonHqHTX9Lz=(C
zX0;Ss3z@(ei1BB}Lli&pF!^>yyXrmDbqCp4o-52YG(juTd_;JgYwlrLtf$&k+vDQa
zThnAh!q}Sf5#!P~$wjK*mh-yu{T_xTSf;-Cu;FypDJwt$Zdm9_%|I`2aBXF<THah#
zy0rrC0t*EffG!-N+tFxcM(AA@YtuA%BIHja9u#K6)9f(AHD8MUiL3>Oh5kYrwY^a*
z*uvgQb!Q3QHose(@&I_QBj6K>WV)G(t#-Fn9wEV0<=n-th7Nk4;Vw{h7#h18vRgB|
z;1beSWp0Hhp_h;c#`1RD!NXNGO8e>qDpdGTsiP$HhEO}vRM|Cb3oO}0yCtf*xG6Ni
z4Ogjc07pi8_>S3=;gVYpL%c3yN(hYVo=HX#wEqBU8ZEAXuQ78Q`qOdJKzsTX<H{(i
zu@!Wlr59G0utZ8&egH?pqMBC5rexlLRJ<21-F&D@4j+|hw1HzAx5^_p78Pv~p$`!8
zRI<ZsOTqArZ7F=JJH7&M<FFtWG@3(1GPeue50Y1I1vBJ8&_7dRx|RcNF00@Y`9brQ
zLT_mE+t`kH*t;TekU>b;Tg+3e#i96NfD{h7e)0na=~`*5_Xe5_N(!4#0hUmTMMYjX
zM4wU=@*&9qkZZX)fls{fFU7~tztTmRh8snF)>TEm;(=ymXXL$qE-1qzW?!U3L)zM~
zuGGe#66ED=!t$9#Ao|j7Ev>c7bADi^l_vZWf-LEZnzjhl@C~!_8n?#4WrMzw4=e$A
zaVgSa9DYc{M>hs&`y#Ci{{ZPxDDEi^22dj~EGmNdm~az-PikaEqVuG9r+iaWPVbxU
zFb@GxSMH^+#-oF0>g6{HPlLdQRkAIQD2I4sr5@lh;kHyGg?s=kfDdU^X>%AJ%~3@&
z(}fe>WqA>fxGG1DiC{YWm&k1!S#wt}-9(IN4`$9$ExloFR+<3T5`e!X`aKcjjp>xY
z+UEAQ64oLQfoMHPJ~y=)LLPNWnkGq2ILr4KY|}nWsbhI}$2y8&N9tr%P2R1SNrjvQ
zcgq8TYV!3|H6WhU<QtrgHf-mDWeP!52zfp*L<?SzGh8D{q(y-qc)X&cxl<G(C<g8C
zpw>ZX^gq<{%jEJFj!Jler<wrU=qe9-4HXx7St$*8Xy+r2cG2>e(k=>KHvC3ZvCx%N
z5MNWa1^Ok-N&`3|eNj5neUmA#S1<VwVw5y`+q#v>jDd$k;wGIHTgflPr15a&l7vQy
zk${stf6zfsWelQR;p%XQ0$VD4e?js~!Xe}<d#PnetAz84%gTVP54x0uXzeOk3I#64
zf^kaa=azSTYpR5+i(vT)&!Cv;`5O%sAIpwet#>G;w;5B2uFJyT(HgsWC%^bl90U0@
z%e>&7wJv~Q51WiBxmMp70rLg?_a2y?kvj`Rd&XUHJF)1pyu7m6X2C-z1S!Flcyxoz
zt6WzxY4aWI-BHl&OI`T*lz48Msh>x>Ie7FitTG)Jh-6gf*7QXcS$r3=fv642))19m
zy80q3$UjNG0Dj_Fs-h}O9-$pb>)GqW2B}&N5#jbrF;sSE=ZQnuOBSiy%Mv!Vj_YDn
z*r?X6ZsieY!kX9-!-bO94NfdwuDKrNin@YE+ii(grf;YrvdTcZ`tV;KSk=@<=cL$0
z0TyV%Ui_FJ9Cr(hSeHn;<P3V+%1kW}nHyGBT&9M0IC_r=s;;%+h`A5TcSA2P0D5do
zm)6290SE-4(2mg%<S-5&{{U&CI2qh~&e_LSGX0VD5u&=wAv{G-X4^)vW7xm@DVueo
zj9OMhM&geNHDoC*mYBNUBnwtWes3zQ=N4LAia<`#i~N+Kq^}*KE#24eY<|J{k;c#V
z7yam>uEwkGTF!ltsnr2+q~VnJO*N!Vsd~!a#-;Mv@6iWsm<Nj1{9JIlSSW>KD<qV0
z!9Efl0H*ou2;YRI2%^AkisX5wRDm0}T%s(IK-GCs7p&KZBrT1&z3L-~akYyRfyKEz
zL6BXkF1ac%SqIX~>Q;s5mY`vTz2a4bV(w5&4W))~T~5XuZO5oO4xz|ehuk9Tb#EqE
zoH~maxVvFz-O71Z`vf*Ym77CZVvSTC0iSZz7QKbVelZO|r(gyKZ_`x|Nnwp-E^UA^
zqAt|Q<%!m;a6>L&tGksFIC>+ukCMfufV=|&BG`BzBx-pja7=!00WAiGZMxFS`c^MY
z$HIxojM?=a9tDxzqr=<o3s0D8H3gt=ZZCs3>eWxueKE3-&~oU5E&49dq4ZSBv=zmh
zQ2wI9@WBOrF8D*%`80*WXF*x<rr#2caqL>LU!oS(JOyX*;ueL~TlO#YfC%(==cyIU
z<y6$*W{F*Hg`+@7>Kn!x@-Vz=;x>gwU=)p5mR2j|o8pBCsG!>ED<g;mbHO}J`<IPH
zP80;9HIS<I_R8?iz}na=o&b}ba4#|1ZSNz$A(_FIy8~=0Bg{yyj~rY`ti^g9ZZ+5c
z0Ocjv#wD*2u^~iao<TJe?8l0$6{%QqV|k4;kD!LhU@DNDzHTD`@HYM<bIG(bL*d6U
zqhK`mA^oj-4ibknFM&gXS(`8`fD81+G&}vNjeJLaHh#q|D2lf0+cqPhB+xFtY+<W4
zw-|{^m3Q{<zc5vXovRRIl~lj97hBm?oR$?-EnIJ3FdzoUrm+INs#}N@1j*1v#9mj_
zKr;GAP}_ktEJmKUkzgsD69w`zl)jt1#$3DT3f+KN7_w^Pq0~aiw`z^$xJ}aMYE1&m
zexXd>xml>pi1217GB+EV&^ht3egYAzwfkjOS;v?Iy4>*<0B;<+>j*D$vz1h#1HiUO
z09Sr-T`Ff~y8^NQ0MaN;;p+CR!Bbi1Wrz<7u<ixOUXG#7)E8nqh_<CITr^R6`ud4{
zvlNesvi+!<-XuwD7qmQFbSRE~=AzkR&9!S8n1zDwGPz0xnOXA<H8dh(otPo2U0&=d
zy73rpg?TO}--x50PCIE0grVyZYexm#+qNAu%dc&PC>BjtrVzrH9B7-M01MMd`6_?@
zY~u}S@=RP+g#Cf-U3^qZtkp=dU@EUJ%Q_V2vVuK(Z|15>jw@5)V<=Q(o4Uzr@;djD
zu?K*8w``nhR=XA3mW6ZA8>_;xdJb~FU#(7M+LxrDJwUW2H1&Bzv!Y{GXPzZ?adDT6
zS7`0voV-{*VmIo8#V!8KE&76!s9jmhW_y@pQzrq!<*pFZ4P53V1ryI}5=1)}U#VaX
zo1w$hq0Qp7K4m0~31kCORgIu4CO0j+O^<aE2{Po{kSwpcSQrJufJUb*pb7=|>O*y9
zoJj4iX`@}}=gvaZO}gQHM{%{7+i9B`tGoBi8w7sLsQe|-)qLjwn>DIEHnoF!Ir@)P
zZ_r=td(&I8u{6st7bs7HTu6YAI`swAcQIuR`D$`^0SfY`C67RZ$haT|^t`|PhUysb
z@7&iA6XKZA+8Zc5d73}innb(sBX2vA=tuRJpog_mS+2-{9U7q<<UOA<oo=tgKfKOZ
zs#Q^g_|2la>ZJlUX?e^V1z*glbz0{wyPy96h{edPWw_v1J}O(0SU%l;;iW;lHL95P
z00neu01B4#t}R+HEbl-arM=pbUeJ}DivyLIWaoh2VxQzJu@>ve4>^E&Jv`iK+)=}m
z!lEx*$I$Bt{-vou>jk)Fc=Dx3sZwoCZkBEpG@czOF49O1__?^Yxy$<m34i|pIuJ6_
z{a7U+$8t}ryoe-lTn?38V3WCSTd^qZV~(pP+e6I+3vA5T&_`KVty1A7hM_N}$K%;w
zU$|hVTpAwD8DUehvbhbg-Ac}bEvXs^s698Zj*Sx-+y^VH(~AOwEZW4X6^+3E0Pluj
zL9Eebp9#)(s#I%2_B4X=M&`z@Hr82mawApOTV*Y-!4@fj<b<AB-o4o@v(Z4A#ftMN
zV`Mgrp&N{-WDF`smYz|zLM<^4d+Jzd8E-1ejY%l+G}I<3@eOV2m532x2?zfG%1>^9
zfXeW`9jdbP@*#b4a+5yl=K1EF_Aw+nBaR%4nu?*|C1<ubcw#&h1J%SmWomR(Jp9EN
zFC%3+mxTI+ShTfRY-P@B>4jx{!-Um2x57cXf1wa<PoJ#Y;guH)g1ByYfaglXN{snh
z>xLXl-7MDw!zsY@t7HY@UjyhonO^c~Te-520fY+#4RSRf8UUv`0#E=|$59=9wL~jS
zvfSbG{ED=sPJ!5|{MqlB$c)0M<j#vd^P0{W?(QZnyQ^j#vvj3eS>5uHgvY$8!33@c
zcC16?FPUhvm+}>1g|&y;5g%|iw(AD6QRJ&2=md(G&S60Y*GidKVyoJLPa2^8Whw*0
zc|jHK@8T<!ZtNfRSo|tp`0K_pcg2`p2v<k%R|>@q{6rMhvuN;tC0_QRdJ?-;ASz(a
z-S&RgDpP>VP7)ga9I+7^tq1&KHPU&7+3AhIa!M@$<QZ|;cEKzRYu|th`z7}TTh0j-
zn5%CfIGZgB8yM6L;-37=u2p4WFi!4Gy$R$Sf7rmlYIe0y)11rM@J<7}IF}NfUnf)O
zsJLv2kXs}IP(VqDap8+VSvTV@J%O)SQ;_Py35L5bts&!eacH5T=XsiGYdBC6l<im>
zLuQ#Y4#k?0R|U1ZxsD8Yk1feM{{ZzbL8y6FRA?=WL`Wr?smWHs?Bs_m2b>X!5zjCd
zx*tgVMV@`atH`U`y~-MP^={{cb?A+hbSsRji=_jmgY|bG0>>Kn2|&)w(ZMzZ`cu^U
ztwtuA+&-z(I+FTSzM6;Z=fG?rJW{}NGPbsW`U;huWaiW+OZ#G>&F030g@u)LQB1c_
ztd#_3s^19mRKu&|9nz~lcVoJ3W|jh(P)+406)kU?qF5J|DNZ9Wt|s@nek1zAREPyp
z(iuT3seB845zR(}j2T3$E8kMPZEWD6GS*bFwBV^RMJpYI?akbeuN5$HdPkQq;I;)V
zWVq$o$#L|TptxIj9=#wZ-w`dKTI0mT6|CWH;tmgRt-g#WJhH^{#$5Ur1I5cbusc&)
zDk-JnetlvL8d>ChWKd|}7QYoL?=sMx0gh7P-ca*W>r$_Ce15hWVxY+X01P+_TIGm=
zV@X#m%V=$so!T2)6LpgR089%~x%jzCEzV*AeqxkLje?eo>28WQ;olL%NDYbuz5;Rp
z_TP~SRyiDS_`V532x3~lbT(v)jfIqFQQFY~4?k!Dr`+UFnA+xUbZBFg+h55>?)_w;
zobN$TO0p{sRUTK1dS${VP&bbRS=ZtMT9jM0E#x`cCr>d~)=H4_F_^GyO)tw9tAzuM
zFoBH(8LJz!!ob$eYC0qebWl_kjVVnGN}9EHQHD^Zfw0|phXdRwnU|*&9IJ-8e@8?V
z1;$Xw>>7cx&2RL-spWyH_b2jPWAzJ#X2Jdymk}QS06@6gOEL>wm2f33{_a1Z9vDYs
zBhp4+cVAAB^s3$B+hr&aPey)`E;QT2kwuQDvazpH_DTdAK-~8!m!uR}8L?05`x?Dp
zpO}u{>>*8*M+zt)XbnHuk`IUxqxhw$fZS<<yY1ZAwaUYB%vcRlE@g}i#Emv{ZgNz!
zpmq2q4}fDl>w8I*I5hc`-f4KWs3AJgX-=52n{D5XfS~Ajy<%~zRP;UFLA1*?zlb^z
zj_n$@h(a-_CMuqY74z*UK#bTJEVxPG1ZBOgk&m522Le@>{Kjj9@g=1ml)*%KK(f}L
zFC-U?)}=sF?;5`2b(T%qn=MQ%kLwmzBJ-;M03M>6K;v!O8GOcJS=S+D%@vg|LzQ?O
z*h(61zE>6H<kwfV?pxF2?IDGfQ(lDftZEwpZJg%XOKlTIr~FE+DwT<;Jvtk|)dNbN
zb1P4ZBPB0SGpG<vFU<0N2=LhY*x^0#3;ih;+~t9*i+Iev<=nhVsu(cl#Jw!2>U7c?
zYn_y^)SbEr458y2x6L}s*(}s;m~XeFE4T>`5{zJoW_1Ag!H2JW?~gE3J#xT@NmUPP
z>DnrciLe81H(6-n%%q6sP1np$ycG&*8(dskeCmlsU&S)}&_HImCkKOf8W1QB@1tkc
z%QQ6AT^nE|uz)q1e_*TP1u89=mTkAHfYICqrastjg3#JtY++$ngNSd!o<S;)5a6I%
z!<B1mO_mXdj$ASLH=B>JmekinAj&9%8lmbrAR?d%&_!<&wK*#J$J~2iOEp+6v^btV
zMU44MyWo^KNk@QYqmH*7>$jPQY`Vk#5(R>CSBpCHQuv~*-IGv4x(*&x_+x16^aY61
zOh*|$N5lw)o)!S)(~Ax)F}r$j^duP;aBhX)q8$na@D&|d5A5(tV(z=SM=(C1G$OCt
zu&Ih4kt><tLekLgh^?jv(QENLaegCP@sPq;6lk~%3QzOn(ix;>-bIbBk48YH8BohA
zEecTl8nC&-mhHP`1+xG<UlHOCQ0nNuuBPm;nWK7OSQOYh9V#F-(DMR}WNELjH3dg5
z^<HHeaO&>h2%V8p^hP$~CG=Pr)|vyh88LEj_*);qC>~J?bOk(8<N)@45vUe`-lgsM
z^$6|+%T&5OQiaJujMxwo2%G}N$hXn05F*g;ULFxv&4)5I)AazHZ&jq*Z6u)*z*W+w
z%<q&NMWR!|<B0vSNQz=|CmT0K18ZxBAZ{bTSFa?jtqa{;OGr1`PA36#meYu$D(k~j
z5l?>}rI2>YRzh4|*j+H|XeP`9K+C{Xq1rqAobqK?T<hF7x&=jjPy%_W{jqI<qZGy^
zd+rn}7n(g^3@+&@{f-i?D0T5D6PRjsbS)8aQ<hknDY~mZ@!GB-rm86cyRE?n`1_3k
zyT22I*sJ4BU|6G0UZU$zcuwq9NFWY!0QseWLFl%ic;G}bMK##2{6Itn%<dwd92INj
z3Mx3+PubN{zG`m1-rz&&5?Bzn?&Xbb-JCwBC9QC;618c1S9vh!C0snPL)B5vMvycI
zKiPAz4lJvP{oia^PFF6!>na0=&kMwA-t^R{Y`H^Ew<Aa3Awe2uc58>_#l>EM+RQE`
zQoCVH(PMAFNHdmxN_J`3vzS#bdnZ<aOln>~`am(!UDk^#Jk%Ti0JO)W%BhLIz~%Q8
zY3W5rfR0b`7FrtHL9{|xsR4OL8D5yGAmC(urFdx^TqEUaiXY)(TLD7_DwqyZ)Bpf%
zJ76~QzK~eD5cJaEsqF4>#d~)i;Nhyl;OZIQ=C6itDgG8+Prg{I_8l=={{Vm3o<_V%
z>VdBW@c^Y%4{k>{MVunlGk3>bFIpAMM$6?if|US!chupH?N&wj&5j%y>Lkk*Mr|9+
zyK%oTkh5#XaXaQ4T&BG95%&u?=%Ajs{{UG+@QL><rJ!fs1WH!jxC}JuG%bq>OEA%4
z?ojZLTWO!{P_p>&78Ky2eE$Ic!j@{9@+klY)F^#$bzbsxev5$3%cZ3zjC`rP3J2X&
zG&55yx2r{J>aJCR7_B^U(d?)Q-GZa)sj8Y$N46`;A5e}C#wk?;WhX+bj;JhSNx{)H
zXt?lFi*c93DpEY4;+SJyKx;*e9Irc<P<soo_)h--Hany#Pj1ppKu0lt&m`PMy$|Nx
zo=Ja*tw}(#9Tw4%#bW2@CHS5Y*CpnwsX-%D55A@EV<}eKaMZ=8=3rb3gTfp#l(St_
zBa%NTkQ^INsbbKU_Vn2|0L=rXo=23IM^2uGlhF^_Hdvm(mvpWu1ny6Wk`-r`3$3&Z
z^#DLxCdFG+G*!S#L#v?`yPJBujs_e19^a6IJcS<$noMj7AZc7IlYUFMMPLPf4?`T}
z%Xv9C`GEiq0d)taWO5o?<b^dDDvI+BZOZP|38-o1P%!x=rVQOSHj=+Zc-Q{`NRiGY
z(7O2|B=Dwgw%jkJx3n#UFAg`*iS|=G7!hyfp;y~c8q1JPiG!$~8t6cq4-dHD2ss1U
zj3W}#)C*8M1roVBP?pKMfq5AH!Lh|{LY6WFWyA#xJRS(CdH`^R&}$M-qTgf<N_*kt
zriUW|7f-sv9COJiK!p{n;-C*)J7I9XZY-*(sqW&E2;UgpFwLg3)1<e|4`SImJAQ47
zwAh^xXrPN-24$*az%wI5trq_PLN@XHP+9#{V{@nDCG(wB0B{v>3eS>I(~;gBKnB=C
z&Wv6=H^j3jIPJ25J0>s`o(1tNJ)$*?dR4q7#-JD5Q$N$AjF8rD4NJfyGTcXWwM!eU
zimL@S(iH&k;?e<D<^44f3HBQr4LT#8x_LW;X>ZUloDy~?99`TzW`-y_iD<T7!zxOu
z#RKeH57ytx$1O{%t>l$%iAMzz$YfAcUI~R^UVQk9Ro}ochM-kYuz$i<zBk!oG0>qE
z!m;mTlql=hiIX=>Zo^07<27xEqUs5($IZQ@FS1^3F%@Yj?>MMfh@cBw@e?l;Q|!wX
zlPsATul5Bch)bho%+b4*d_~WSfqKO+I91u@gV0n;&sVjacreZkQ_bTHP>u2i5}K*M
z*^#q?S<MQ+a*PM3$uPwv`!>g1mbUb`es(3Ul=&`dBSmlnwxZbCZp2l!K1Ci!Rkm^E
zE7Y%0Y9J)6nP_$}8Tc0?j4yK3uc0dV-9@9!=hD6N(;agJE3k6=l?(B}_Z;RQI(YSE
z38&!*l+i`*@FUY!sO7hqLH_{YjT~<h&Es8pgkr$fmd0Ga7PSs~c)7{<Nz#sNO0QrP
zCMmCS_OKU-4--f&W!4ydANbk5WV98@;U@1T^=6|1mRrVt)>XBofv3!02vHLfwmVC+
zu31CC-w}w&t5a_Mh^Dupt)s_-2MpNE#cEx@p8+b~I*5)!tca8FEFQ)1kTGkurv4j;
z6^v_$@HgO>N6xH#DkWQELbu`5DWlX@63bb~k6xsA9wDW;*ghbdsEAwCxLQ!l#8tpk
z%n_<Zq#C-FmI0Nk$oiauftmAm%Lg7Kx3HcS5HezIoABE(wj-<#uF7Gh2~dgxE!HtZ
z0tz1tV)6?U=rEN^z=D##+X3=XENyXRWz3FDdA99j8D%xTyCUGL65P;Ec6F}QQyRMB
zt`B(<xk$F|Ti)ADn%W*LBju+Gy>McyZEcE1lm5b8panzHQ7jAIqn=arg0Ruz*aa{o
zsHXh9F&2w;Lv<eL18>MMvCCijM3KGqQZa}EcIKWUHovZ=sjW12-Pjk^{H;DNY%hor
ztyYWps@R3m3_12Zkm{=@U^pu&-}!M>DY$%yC#xkK3#@Id;QNK-1w2r^GL2HHRRUGf
zN$T+;;+dZ@5}KW}0lZ#P33?vo&8Sx!4}43=wCP+~C?fU$0Nkx&(qR+QN{CIbb;jlH
z#V;CcZd=I(itm~OePu$d5D0t)u?}Nk7bZDuY9a-r8pS}Qr)bpAQ|uOP3cH8|Om@ra
z0BdPRz;-NBy8>I=7Fh@(vpc~bLk+!ATSlSI+vSu&iU4iraV4L6^b(!Wbx?v|>@JJ3
z^0>9FsPYVU52$KX!GonQ$mFac!891DW+?Z2d_lI1@;-y&0F^DB9OtGjF7^Bjl(oDe
zz%ZBOK(#1mQP+4ya2^OdlTg(ZtUJ%Bi0Z1%#S@$ucv$kjn!q|)v6L!;k44;TKsS4L
zkpsJ5f`tA=r$)~{b_!T29a^msixZLUg)fk5n;NcKh*%81)dtcst9U#;KMalL=w~Kv
z<&@!VM{%x+64T$!I3)=LMXkE132l6Q0h*#V7lpS2O_mx(CS*-5a)wEKxTLL!@|boZ
zyhv9#Oq9DOdmq>~i)XlS$TnTo#F`Mgp{?>opr;`J00tI?<gaN}&J|s2<I~i3VdV&0
zK452V04k3WCaY*@i$u!-^;lJmA<)!?rT{K3w$v_2l(OP?f#unBxb8OFoO3kAfb5`>
z)ybccl6B-^6uSixq4DS28mm&}@8%0W22g=(@Yz;R;;dS~U9*^7`8**9bAi6@{aiRz
zglz_xFm{uUi~?W6WTE34Rq3fhjW1JWuZCB6=Z3nNUoU4L*=-5O<MGzfET_xDF#H7>
zY{;ZJL1+rE?A<}9yK1034(=AT9^$=NLGt9HEZy*<JPUrUN{m^c;@RNF14#z$YU(*+
z!WUDsk%Uoi(;^6<JMQzYnNQc$D%(Wy6a%1mlu^qsA$URM<lIZ<UCOkT)x1Sow~1O*
zf(8vBkOiCb0i$t>&@+ToLW8PD9L=4zxFlp`-&pdqJvL2J@9by2imglk00doyvalpC
z0@Bwn;Fq{7h9g;*&UN<@p<v1&!a+{H-5U`VQCz@FMdj4gUOWbHnWSJWU$;@7h63`m
z)G=DalCLnl0ZutNO57HkNh~i7l-pwkwg9xM@=F}hHhBcIVQ(^~tP=i=QeV#sa()d!
zEk|fq(gY<<Ji!EbNrpQEzy2{TZHU#lsb^470|bFW0}2*91;#j(SXQ9E(jHmNCWlLx
z0XbB+m4nsb2n1hogcq^%3>vz)ggG$*!<$4inBmcN$v33$E29B3HO8+r<{LcR5EqA?
zAgx1o;ypzwcozi&Xw>9}iWW3o(CVN(JP}X0J&`sRvq!|EupwfLh6$(`_76!<>j}Eq
zQtIPYm=#TXzGu0LGsM)>b%+E^2^pRxXr%E2t+(A(#3|yvG%SCp!Do$osQO*t;5VC$
zlUmSuo`Vz;VA$CGkW5{Ajq&BlD)#Q7g{;@i!W8y%EK==z-sN~yUI#NVwjr#epUD<b
zobi&m34j7nYq&HgfZF!OoU$s^E-#@9TiO+c;>v39n~mFNLWB0<BL#N8b8Z4Ls(4|e
zO1Qt^8;y#*u&u94NUDPMX#W5Jot!)db`9CE4pALn{{Xm{=_y^;3ySF428DQ7_up!_
z3snWi=DL=w)}KX)x7xX78#61JS7LnMQs^r>y}eX_6tn>>c%W}jP*`{$$mr14^^*wL
zXx5RtkHErXQ~7kuIYOg)M4~g_0^11n;_snUrK~PrW2!feefW<r3kR;E1*Kh;)VVS~
zPB*ghnk8=37%k}Wz(Zu^JCFi8?BcFnBJ;b^a>sB2t-~9B-Gyddb2)r~-xuK(v6SC}
z6GjX5%@ykfo`Dz)q?us2SwTYCuFKtuYvf3g#dHr*d$t#q_MKHyuK<t%Ww<b_1s`A*
z8m9pGU|=ze=Z%k)6e5=0t|QjQ0^srzv|~j^BkaoZ<u#|Oj7o}t;3_B>#I8)9IuF!m
z+pw&@3_0dhNyxhQRzmmcpclv_;rsL6B5?d|K+$4BmKML``a&yfW6-G4U;#s)47<>u
zDao!JB`2jr6*BEo!n$zqc~PwtE$MCkKw3&%vahl_D=v=#h+9{AWo|^Kga=br%o72(
zz9*t0fvvA=?pw9cR_|KWr6`KXh<F(qQWA^~7x^_n3IUXL3K-yeY`TVt&0c$tt<dUN
z+4&zTqRvN@sBL_{DqXrYY;8AgxR&)qLEBd{yA?dgKD(7<99O2bu(Y7;B}IWye1wJH
z2cHW7{{Zip$OhQX<xpbws?`s8U|m}RctuNASj$GTj2bPD2!fgl7O@uWe&G<Pu4;c+
zQX;gy8l2*;mQAX|%La9(S{*PPZE@sG@I<dO5wz`q5UEbm>Q{H-yI1Dr>1(d<#}Sv9
zZ*EVh{{UR1d`o0oRf5z)xC>&GdFW-KJeg(|4T*;1PH6Bm&0H0zpAM?uC5$L7wS}C#
zEz6z}t7o7CGEv_USxbijs~fAG$yN5j1_T$Yd5mT3awYOZ3YC9jGk2tzia1418ghfj
zga)$QI;R%~trYlki1rk4JIoYuxAh&+7#4Sz0zex!t<x9^R_3rE;0;G8K^QUicAuGY
z%7@pY`X@KL%<1qvn+(yK?clnjsF!H5rZIcpFw+se3C}2j_fu3LC&j10lrSZWMru|C
zE3tKp;=uZjkB3o0dlAtE53GrJCDHDzS|-+WR`DovGo6J00A?RL9D;{@v%4(195WIt
ztl@&t7LHeZpm4$rIEuGs_Q6UaS6ZPMjfDz6R}SeOQOo}T7sV<W(A*VsN(!iUspaYd
zz<sbOXuCt1ez)rxc3_n)omB4I{b(&kYnS7>bmb=tCp~rXVd?{gXxE&Dpwf}|aNFP4
za$FXL*+<<<9oyYos|Rdfqo^D-K>Tq9L~&~|-YiNJT%eXcH+-muCG0BNZSe=J34Xe(
zBmA0)l#sxD%DV%JJF9hiD~q)eW2xoH3iMK5f?=@KF4bw`QG;{_=w-+c3iI8inl}cg
z($pcga4IOaIYI+>b_M`5?TQXy<iZP~XOD1&sul8fYhTH2nwpP^V|qJ8s^Rbo(ZH$z
zy<jGX8rK@n6_(dM%?AbtAX9NA;GhfJC8?mRaBPQAmOVg<Yb&96eaDbc8utDn(p`6L
zM%ND(vN^pW0CGT$zmQAC7^CG0RmUR#0BfkEk8#$$#FZ6wzE}j*V)*HA?5B$e+PLw2
z%`8Nfd~2Br;6~8{+-_Ze0;LkF-z;@evz)(<Vzp|H{K`%@y0wZ^<^`-%EaN;tU9D>I
zcE@Ra_Yo~pESSKOr=bnNS&1A1n_Y1*+p55zRMEaFWvtCn^z4-kny%GhOnYZ?Xjg*b
zu1je7WomZg+9KD5P`VP*5G`B6LzXDKT1uL^kI@KSmKk4X@oM6bx8@S?mjs}_DB58l
zf3pD>&Xo>o2q~md*$}~pEVf~4Hn@_!%53F(Vt4$#3SjXFkS~auU~?r1^sBs7abhqs
z=Vj|*htWzFUzzay!+-}Dh5P0`0gG*GC_s8?Z<N*vmSy3g6WEM;uZ<M-!wR%7&Yw)8
z0OJ0v%WFinl>=M7M1^oYXpH3yTXvwSSyb8{rRaWSDFG161Jy(l(W<`VTx<c6YhD_b
zwklmx!}CFOZlS0U@#BzZ9@}ftf25A%j##KJYv~0<U}U~V%B|Wyrf(I=8d^>-8N~Li
z%$c|zgWM2xbe-$BDI}Y#7%hAc9w6)}ewx4;RfFw=G&yu<SaKAv_(~XMmm3-a>#7UB
zwqm?cmOjXZuN}tM1kzgNv=K{zKidRAmMhOU%%z3BtFgIZNR2ESw~4{Zhc(4UioLX@
zV#Pa4WoG8`7ajs_&T^U`BpNzYR96zZI9L`o96BPw<g{q{wz3T>k2(dSsK_<wh%trf
zs)uZ^pZ*0j$9YdZtS%|*I-EUHx`k0RLL1S54)e=2S}SH$+^C45I|_gTp;wfDT|n3>
z>zw$FNp#}9N=saJRpJB6I0LtG&>c~pQvASm2!`)Kq)=9hzw9jXD$A(hO`e<~Tt?B+
zOtP(}9qG#XBTBU&-Af7@H5Gi)vA7O<)RyX-+LTM7y09m4s`pb0idx$Hh&QKLCl_7L
zUbSyD*%hSsM#8+t(15o&gk_0KImMR(JW4c90WvZwGCC4aN_;gKVS1M_ssuQTRS={r
zXx<}+LFZ<nF;LUA*+hHVilA4dS1<yHcj74K*jB1r^RO__vDV<zSyOf6=Bhq+eNQGV
zSXS>x5U!aH<X*VDAPU|F)uLM1%F8_a6Pu&=o9<I_8&SJ%G9pFULYf?qdhW0W6U`8O
z$7nvt5RAo|v*cLkRjRL6<T{_e0jQz?luf5FS0p>y>~n-dTSndIX9oPo;XLd>^4DN@
z1MvlHAT=!5c;eWFR;|%k?}vd#wF&K&1Q*%q5OoTne}3bmF%f$JPwQ7>NmIcVw~D}S
zVC6W#HH=l-UNh!O7&i?;IYJ`}^EFxrfi?|?i-k^XzH1_=-xL`GeO?xrN);m$n(Fs1
zW0+b8^gXh&EnuTs0CYwtMC|1fj&3t*xcrXt<=;!%oJgj{yXJ8~qSzPQWQ{G=X#jKm
zim=yk3NpW5ej$Tp^tz@p3ZIdTAh>?sCT;yxyu1uMOy~MC+|}jESO9rynbL&4Wyf$<
zU1>0ms{vQLIIkf&skaFQ?@fTN*wBsC>9&WqJ`!65dLPx59>6qwEVt&_b)x?O7)d3y
zvhItVO?<Z3QKkwz<c~h#Otcm%-bD>X?$Q+!(FIjZrwj6~1jL1z#{2nxNT@(rc9u!r
zQup!zHBCmiea<*t-yY>Z2aCr40G3&#M(>sRfFrSSZWjVoQtw;2eFWoDz27JpIulpw
zP?t=;w;`(f-s9DbutZNLEZQp@oh5=8@I^dSjiIi=Cp9;rjkZQGBMerD+#wh6)HcU!
z0c$}5DL~|Kgf;jKx6<JlB3)6)9K;i7@hG8%*nm>-a}>9_QIxcy8*&RHFKa8UUg{t(
ze&T3x4NEn7uno0iQ5f#p1GMbP-m3HaE%Y$26I|A&s_uw7po^a64GFM|+*!qw1D_iY
znB0R70M|=0XNir{p;`sh`}Y@sZSepfK;$YrS}t(`z9@)#0A(CY-9Mt;JyQV6X;$zz
z*)io_Wm71*L=bI-$GqlG2;hLl;njiR8ISJ}<scPBT?9rN0Z4`6Fw3VgQ;fHwBW&OP
z<jfjNmg=NHBZf+`>=%Zm@Y?MvfRqMfc%u*NEKpUr8yszsU<M@?Uej4k#sag<EG`Y0
zOe!ZWN&=CiMtnUI@~BrNe7I#y?@_N%`Lb(I&$u2J1-q*KI8I3g*Bb!{XbY(Q<#v`G
z#<@fwK5t-o2Jfw?3_;S?fdRrQp}D#*pRz1BnBVJ~f~S56Btby~;I}FTmn65%vglZL
z3OcUQQ0O|Z5X0R~{D{|Kx`FbC`!)iQdn_?eUYnjPsdtT(e#vxHp35-!%hgEf3NxZ{
zB><>W{{W-q2mB)`m;}~0HnPU^eOVq2^1dRRPyjvNN^}c^-IsT&Akb^Jx3&JVv^OF=
zdYFC8yZ-<|E_J*TToKg7T-9#4K+3n+p4oX0EG7qpkNh{1s{S%<M9||914l5iw>Pya
z7doLsrdPxxWY-<@t|0YeLzU>q`2(ch=w)4sKvk~cKIzrPmma#J(d{iTmW3;u5~T4h
z;j~)>f~i2Xz+9Qj5Y$xjx*)G~LYU;TA@s(vAT`yx!Gz5OFB_a5$XZWyAS9zp)o7i*
z2R3)tsF^BQdka3cA*Pb)!k$Qt61iz!BR<%|&CB>D8HLry!Ub(ET9{8xit4-|TfW#J
z*k{Zvs2%(wZE{t;9i)s?BMb9p3uF;XyAfaPDUqwZ28&b|(#BuRTxGMmUHgujUmrB8
zk7>t0GS|EZp;Mo3aMYVe9C2Ju+g0xZbGi>zfXh9Qm@W^rKO?G$iCkGmSU^?n9XYrj
znj7s1wWfdoPe)*R?joparFxoqEaPL0RP9A2H<1^q3w=4!Ol8Rd{FJp6@)X+4SYf~T
z3UNG<BmyHqVUCyOVkv4EPOX++N;rjpzGVmBFpzZk)69OW1w7caQfmBTEMOV7Hk+ak
zFM-eqB3?Px8#r9Iz@ekM@eO3t6{*kD1wOT|`WNa|fXw2Gt0P=EK;o&<>;~KrET9JZ
zWj>bhrJ|y-PqMb`JiXHgK@jmq%1BNKp_#G8{@)WU_%2Xh&4@Oy&Dd6TSmvCd=LWL$
zBJy2wJA6Qvm0<JV!YVA&)Zj)S^B2lpTJRZUXn7@N!NA!U5t~yY;@)OZ8*6ZxQ{WUV
zVS=SN3u-iiTdqO|KEg!$tDI);ny#?nxEYv-bgxuF?xS^^@~os2!i+|m3=wD?!V3*i
zRcgz?#W6(y;B<HdB5R1DQB@ktbS~+ZN;)q|^(s*`L3HCD)z~dsj-L~t#9_gOP%mvj
z(RcdWw^NjoN~ERius7mwAk~XOebhXEiEM?!pe?4p5}As2C3n<izRMzmELxe3bfXu1
z5s?UMyPzXQ*;`l+;-g6R;MfO&G84FQjQ|8ofn2LfFy)QWBt5WRLp${!lp)>48DPan
z`m~@0B`Ob^<C$a&R%nXHpA1_XLL>_M$mXdPYV5|K1yoM#=HqQH!hnpT4DUv{e+fYq
zH)6v92;%5j@wMf?ZmTjMw1{&`;^Lt(kg9aVF#>ON-bxYWk@XaE{{V6>F(&}GpNEL6
zbJQiqR64ceO^H>{M{cAI4MG)hk8tRDf1=<vd}r*xjm-AGB1B<1nCnoR%jK3SV-fUz
zaTFLV@mgl~m4`s>jI3U2A|8()D`%?sshyV&Zm;H9C}nVFT;M{q;S6}Wddm9On9%dF
zNyAYz^2pYKz%~(1m@ocytwe)^wv+M;j+OraBvk~U@a|XxmP5%MC&1;@9j#X=7TW&+
z;SdncH4wamp)9H#ZtrWvYMlF)9+q{p!3bAfqCbgS^==PyL<Bb><8lj{S89Y?VaP<;
zt)T>XQk_*Mf`^T_zfoM*4%D%HvZ8@wdK60~w6FzkftPjbK-X0C)M&Ov9A9ls@lyx@
z`dcDlu1;?M0Ljq;qkK6jbTH)e5=Dhf1q>(|9ZGqEs5mEX8$xa$8-jvZ_75x`yS_p8
z^nr5LrTQX^2nX%&AT)(81@}n+5nKg_i=3C;_OXk@C@JeozYn->rvS4F^ZEcbsLKcs
z2Te;8xm%%Jm0-5Ui}>bkO5||jQv@pD(Le+;J3!m3VKbDWwUg~UTNfZA=5%w`x-bL}
z{{VHfZw~$v1+?QYn5E>ps1^^l63ejKyx(qJ1<%s50S@$W>5ZpFqJouxyKQ?d5MAq~
z@TTFTYp&N!vQZHlWK0|e@(yJWMZdmc=rXI<IbjefzD3AjNSbrch*kOyUXl=Il>m53
zNzDB`W*i&8XHTXiv1KrPHxTxk*73TsthfYoGgp@F-Rw8?#dU14jeiJH8ZXlL3}C4y
zo<-Cu{GOG9Cb&I}nSJCDag}I45P4MSHBMEJ-SrA+{8cv`1br#vuS89@%WI>R^^j<u
zG=KHkY;L%EW%h=ww}nH#p$!AXasVa5^nCk9W91A6F;syvw`<^pHF8x}ih{D&IkGSj
zf?;mCJqRA+(^9B!G6bLsC^ynY2yG|b;slFqUfR=;b6cKws>74XD92d3%%O*!pMfX)
z6n4;MI7i|c^Z|WPK@9xpTq+c#5aMf<fE~UJyJ`g|1bPTxAF!@PqrXtyp=u9vbZK>e
z$Q=Iw@R+^{`GAV3Sx=hOO&zS&*_caPOe*<ew;Rp|EBHrPI9@EmsaDOZ-LP|S5&RoX
zS%rH@HpZ#wokF+*lHyOaJ3OpZLq%d8G+9jR790ugAW<%a-!0QB5p}YUzk~*;igMN4
z0vmL~oBAVFs+13n!aJp20KUNh^VU?lVy?ninRHK+Tc`q_@vFZCF9`Mu9J|4SLb@Br
zq&n<)<I4bO7tQQqo%5cMSiqFkkA|lJQZ9hhWCMx|e0T%|EypQg@eu7jQ5_5kgc68n
z2v$lwEtO`Bt;QgD4NYJ&+wdRx1cK}rlY)dcaJiBqoLVf^-bJVUB6%Ah0o#0xNg9`G
z*J=RIB=!_X3x?k1VQ)LPm})7@wNyi0x@@Zd0EsmqQjV2Vgsjq^5s#_vv4_!oqXwB)
zlvp)5S>SF~sD54J)mWiN+-qzF5cAdu(xle8%k@?!K0VDUA?_?rt&-4~AQzKP9$1?S
zl<-yGo45+9Z5*O3vFEr!*KaO%g>VNivJqd<DygcnJm)Elh<6^FKmk$<ibKVn1htjH
z2O+{|n}fg<mQ+rEa9=$W%>_83oBqa1=H0ShA!8Qc?+ic%m%{ykX27nT4%913FX3gA
z<(#mG*_2oCBrw=5^Ic18349m`cMsM%0+?wI)1)<tW{IhFUdgZ;5F^B3n)8M560+Ww
zqP4*RBZwReE)s|{4<terLA&RK7D_zf{I?yA&@z48z0L4ZW5m3~2g_XqV4@T|8Eh2Y
zu9YbE(F*mL&u!8v*>am?VWb7NWw3AuiwMh4C0Sj(N-h(tAPQM}$i0I3FC;RuW-W9z
z0+|}TYW`l?e1C_3tw4`(?3s7EE#4hM_hJe+3Y|C_i{<-j2-jg=cf<)H);z7$bs`06
zwWw-#Zt2PD=5K!CSAUV>RNAhxf}r;BoUajcb&4zvGUYWcrUJ|*9t}N`%WEB0CFp)T
z_>QVJaR?4g7P92h+=ofX!(pl{@LSBigM@OBi=g<Gr{pRIbH{A+UobQ{f!K7!9k#ha
zEKu>==2ZtoqyD?R9zdMr&V=$-<Qf7~g@=&v%b;D*w`n}OVZ=@;he>WK?W-y&cJ7qO
ztKjU9jnr#n6E*b$5ck6g&l&KC%Uo?%<#rIcFQ|<~+u+3+rGS3Rn34mQm*yR|U`zxk
z<6w4y>fBY_EPCL(t!l-TUu){vny944`u&RFsfwnh)@=^e_i1_LPRH^{a7$`Y!}WU#
zW47bOD22kz=l-lD%fI2=%dz)lhLBP8$TgG*{ErQX?3G9{it#jd!Y~C>k#Q)Fh=m_8
z6wybr)D`Fe;_m)M*_ZQ8d1gsobh}jIlJB?_YvMWe^D4c_DypAy&Tji?>+rI|4Y|w}
zf7!*Kaa&{+sN@f1t;-uy0TPjZ3<|}CI8@JEGLCY#p~+Z0Mo@FB1$Ox$TyJ8UtU<#F
z4ecx<US+Mf#G+TLpt0=KjL4JQtJznZ<_EiG?6HrO{2h%1iUn_$9ront!QvXwcnIzw
zaejl?v1I}ZxENO)mpZVm(iRI?@|`1xsaVb|fDNwx!bPaczH1-GG?fh9);kK5m5=h-
zYo%`Z3U%>N4?P_y_D*VeUmCyB7Pto;5e8o?s}6qQk6IgN%W~K2I>)!uJs>XaQBXli
zpmRflU79s~1zfO2_pL$8Wq1<L(pn2Jt~kW#zy^@;raM60gamj&Dxm%)5QJ3nm<+K-
zGzK*h_VPB*Fq;i_Je)_k0`Ru)5ga?J^)J&Wth>V-fU^tk*7((_Pb}v}&<S`1)^QFL
z8iP<0p%n$`)v3dET)}pdlFyjJo1^5LE;Y*E+^MdzC|D2VC7|ngYX<-X`J6veN5rW^
z#^F%M0$FzyPJiv3kyx>&G~8)BO^&F*tS_?7DDY*nzgx*^-4Xou{{YH;VlS(JeYv50
zZffeB%v6EBiuVqXWA`TWOUy_&z)DMHAWEG`Y(CW?-?E^Ca3ROFnr(GOOX(G*&dW03
zB-5n!khNNJk0RmVv#P50HOeycZI6g(#Hk<OwkoN-O7?QXnsvoMOeF_9AeN2MR{rG|
zIgSXud4BZ<bT`G0nRNjWctQjD78VLLE5Qp<scW$y6u)6SM<xTpHTeqSEIU@mJ|6Tr
zi-O8j^+d116b{O3?SLhrPD=j(C}C`^(SO*>>SpPPQL!oy>lXc@e@lU%SibasW#D!J
zSKJkYoGT6^gW?N<yOnT4z3jS|i+MFXMHT&*`~`%y<tPHxLHw0y>rc5aPh|zpd@c_(
zO4>m0?2N#oJ=Za+q4O*n!0wSO{zTAUaKal?_OhxR^Yw9lUPZ*q3%VTAwp-Z?RRWC*
z5YbQi1{45MM-a9ZmedE4x-lI+%qY>&@a>FrD{bjcx#59IEol|UQ<=yH!2|6g))@1|
z%l4fS{0unZWh{*@HbpYN@FBOBe7#Pz2sweI(H<$oQM&BDuA%zJT95wHs_$Jf5Sz!v
zv5m&L(3XGIO%0aaSD1|BbVaIE6*d7uqjl;ZqdV2Aq7SIhnzks${k3gg5P@W@HCWhN
z<_l2l9#~C_^c2+#1xx8e?IBPV+Ae9cg~Be8DzuC@pe&t)CFz@uI-H0#dCTr1TC3fx
z*Pxa{g3*?e+Kx?4YbD~&;NVt?t7YDY3!RbhHO4j<!6>bzsiambQooqKYI*J$7$W$<
zaWbai-K!ZfD?q>c%dqAgP&PX!EQsSr1huXzDI|fVp@-UkS~13{ucs_R1#U*m-Xq5l
zi8OlZI=uaQ1B(eN6ntR@3huX?GjNd7-?J!&bD$KyP=jE;_6Z7g6MOCrXLDd<(9??5
zjV8DOPnEpH@QQ~eOh%=`5;0b(T~*z9g;K*j?57UvMpbyJn$nVuRUc~Ek1Q+KtAZ?}
zXlA|6Ef&#itMz2A(~OyXETPO_)w?Sm+}yk#NoDcz^~`*AxWX?tnfVe5lhqWwO-be%
zh-f-Z1j8gpYS#OeiPR;El54a@fzp&XrOBw3uw51)hmWhA!q=gneEmYa#uugdfqLXF
zBwII@l4__dEWvPHz>z6!;c#9`k9cui7UjwU-zd~;WunY{Y9JD{N=N-B8C&x?&jfCa
zjo5k&=H@RR+aU8_hopSgN2c-{;oj^Yk~N*R*UkYZdJ8;C%e|TM7h>Z*q`SjbC=w*4
z6`zvce;~>>^Ps4IV!B#g@Ui?Q`v5%J<wIUdr^^Om2=t=<;s6U`)M+v;P*ok=XVh?E
zXY|y&d&|o47>san!U!U)&{wC;!@^gfO_ml~g0;26=)$|+PNmXH2={-H!GBJ!*HsO$
zYNy*#p;mBPlo@qq@!^30zeN`sn$8C+s4!W0Q@%-B*Ix=j4KXbWKf#3lrF~{Q+6|E&
zA-U;9M~mPG3vN<o${{=l5T`^?QOgsZ<TjFXmVtyMdGmM3!a?<8V!y0SgFKc|C6EBE
zEazbFEDtId{hAGV<X<8tvYVsD+_u7kmHaU;3+PiS^A_8&vG_}O$!rkJcG|e0jrSR7
zitQ98hs*+Tby{EW`e$?ko%1Pj)Q6_%nSc}{MbXWa@v0^#yNNXut$~9&3~BLJ`IP*C
z395LxRL18|05xx_k2h^_zG_s4wL-pZ$7MLHC&|gNEsV@nHEDKKqy*)>Jgg6wb$GQO
zl@)nZ6-|d9G&8e0?<{+s9K@zwmisA+k04yLPF)2Yv93y~YFItTx*&ERwU>T0q6zFU
zkkb@uoFalGH3QM^DWSDg?YUm@IqdNpqE&5>E1kG)1=GYfppCl<C8~T7jf0^jqVv4X
zDU_B|%FF5_G5`-*I30XSDMI{>*^0wm4Qv@CO4RgM1$0WG_?vouhZSjC32n;~t=O{0
z*rC7^rNffuhfNNp<>ZvEY+}-BLy@rS7FyL#V7n*?EVgr1S;HP6Y^u9?=6BP88hB&0
ztyJwIWxke0$8O9A(H%9_NO(IRQNC*RTHhg0Q%gZ~LCDulYO>YFJ1FHIS7^l2K*7V5
zlQOBrb8tf|N0f^--!SD&pzNJ7K))p6Qz>Prr@Dh})Fwgk4N43#gE1WTpAmPhR}NyD
zs-tQe`arPGd0{jMb9UiWR08ERSVY)wLA89or!#yhc-%m#gKg#QL;aw1dKz-}#ipgF
z#MQu8=x(CmQWYs)Diz!GCHg2!sfG84bjAbAFUAPODSyqPL<k>xrq_qu1Wn`_)Ik;d
zl9^@)c>;=!8suFtD`8C<ol*0UlAb~^8fdmP*C(@lqfv!!MURx<zLml1+_6+GCW$>1
z=`12QsAtGc(Yibp>gkEW*yG`3uXwpPqrGwzgcJ$^UxkIrhlZZOj8(+;%gN3qN}8dI
zNZYB;n@Ocw$VfuDP9O<dy*#fYzHIa2AvbHrmZjw2TWekHH?nXMiFsZ-sNAPkvctsT
z3O(Jn02G1?ymcwkzuWLjOUkadoD16i4C#Qg<tf-1M79hdn`fZC<F^xgg*}Fz$yE+8
zX*T|){{SwdZQ|0<{T33+imh!APqanJExLjL5;Y3CvZ1Kb!0~E=Pz~f0!>p*k>=EqI
zCbes2q&N&FC@sdI@M1TR)DNj`J;<;m4?UzJQW`3bNbtI#<dFseTBCv7zGqy2XT^t}
zVcMm8*op+IJ$a4W1eMUe`QlkNi+g@1D7-xQY1l%nX9Zi&1R%zW(${JSOC66)=U7=d
zgSdT4;J*Y|{;N|wUoz6lEUowsv$j-oXC*!!<>Q>=hy~hN?<*cMPPHDJ0obR}?K*t~
z5Jw#-oVKQYcv2RVVQT<O>0*YXk;fx2uxBr5B^nlR`bH`f!5|9rhDEf?ZQ7u>TcHc-
zgNCBbcyO?NF+v57JE*)Te7Z=vPHlqyQ1Hwm>Xk4Uj6Cbh6UENOH&&-stSjXj<(De<
z(bt-bDRSp?ZiQ*d5K&|ofo7msr_Hj!EA9GwiE4GUPMncsFe`-RM$KJ=(;Y3q5qI4Q
zUCvpWitK5x0dZg`-Zs}(#ImUJ*J>gT-$H0|6u8oHvAoBd^5A_E$XLqrb#mR(tJq;Q
zrE(R@%eO!%C<>^>tgtSqB71v3DUN6iU8Da15bP)lcvc4olF`;ukT{DlfO-o{zi&({
z!Ji0ar?^Wo;n1-CvC6x|II@uXXuRb4YSUKlm7UJ&9AFLO`qL;AF4xkBn9*gosC+kM
zU1xp5j|NRe#U?WYZ5>|dy{Hct**!3-0$5-DL+y{nIj&c;rgkOLg6vymKuA&It)_5z
zJIWJ>-p>^fZWboTjp}M$V-!(^0B1TY;DEbXMX~9il7V0YEb_L%ZDD2G5hyEj*wd31
z8dqT`#Omeoqi)$(aEYNs;f}%DdbWjM?6^G?xVp>rZ<?z`hZQX;a52*xdHDAA0!>SG
zKzVWzvbELUWG$PaJa(1Rh4dHzflaHxUBK-}Yxj^)zr$);6-&5MS7(+!Y#moT)KV<H
zr*<F-Y$pU;y_R87OT-zaUBydti?WCku{8Ioun6pqMbm$9PX<eYv{L-N!;dn4wH1(O
ze8Br6=oNZa&Qd$v3x9>csP(6^0c&dAct|+*>&KZ`4mqiItIC~Hh*Uwya{eV@WryM2
zT_|jLR;qb2WLD6;zKO|kQykvi+C9WWfh!zkTFM>9in?);6kV0u7Krj*(sVmuRK-ZY
zYmPG5Se#Dw!JQM`K^^b=4hk-cyKjkQ(L%p4R4%1?BT$aAt~x9f_$J2+;vTq$fMC-2
zLSDMXC?QnU3(Y<Xj2;9;yI2#wEdm?hyNGOPHKh=%h1>G?0fI-u!+x0jLZSzl;8U5m
zeFM?#N(4|?Nmz$@Lb!4*(kLteO}Bzvq}x1!4QaJ{80bWwLIBlMdISB95Dgrc+_4vR
zdx?k^Qi~c-xm3>(DJVwMh|Q9ydC0bCTRh{kf*A#{N2~kS4JfI6?h9OE9&Hiq=!e}<
zdU6`K<taQ5fU=6B-2m9%h<3eE%>*1m4_K84gP7I9bE<<#`MZaghzn)q@Q^MR!CF^R
zt}f&(v-D6jFE41cd;Xn3+P9Ni7B2UiGN2{R0cY2^v1+-c?g+Cz4>c3OU8Um#R*43*
zQE@al&z9w?)INaW35NEzt%0Qyj?QKBqF3pRHHQtixwOYkOKy2?enO@!L}<3R2G<JG
z(~zZO+LWuul*2g>JTbt59)a4ZM_AXD7%iw}oGdeUXCw7f+OQ14MP2And_YYM-ng4b
z5rFc)L;FbSwZiI4b{(b6z7@kow5kc#qig+X7meZq4Yy~OE_zi~<D(i~3WP<~@z<fl
z4<&m9`5V}v?btDXS;iEg1?kPl8cv}b>*T1Mj)MY}K|^R-Cxxg=6}jNGTLSGG?+f)_
zBqo+&Y+e!|oCtJbl#=g`OGqEqSVIT`>f`A~CY=cigP$9CzS(B4g8GkasaMkOJc3iG
zzX*8-1HHLOI4hWM#<1`(H(nN8mkmTq4jNE=zz*uF0s;=MqlS{|maEQ2KlaI&U6`Qr
zmGhOb9l4V57b{wq`6ni&TuvYhTXNjWY4J6n)yT?Z8y3CGrN--AW>BzVfg<06{YCUg
zApzj12Za7s5fwpJ+vT{=1q-foA?c`^{OBUyV|t$}7E^X@$#T{84RhjH5UFQR3XZ^v
zeYZ8Sdx>>OncnwR5EQ?;eif<mmMCF$-Ll|_mu)T+t*pB9P~MZ^yQYwJw95-D@<%xk
zuzji71@916%mR4zu(}Ja`1y;vF^*Hk?iDS3IwzVRuA)Ib>>nm`YFfe+r0{u`Q<$|`
z5}QnH)o_qi-Y|9uH8ETLiU@)bFjuxb+S?tkH|8#_3b>?SthV&ALeYsyV%1sB#%2Wv
zi)MB~!8gG0#x)i8;?~kugF{>07C`_PRX4}1j>Cmejl8bz2OnUsiLjS(&YHEA_sY^2
zt|g&{5TyO+THGbt+^24I$hrrAxMh-;1-zQTRiPJ)@I~3ec_3ee-g&@sBC^IeJM;no
zW;JjMxz*Fhc>=8<o4hih-KBPnx93_jPwYUm6Q}~BwWKW;HUX88ID3lCqa&q3elvV2
z%?A!f7lVtJI+$Xh-5J6+JgVm7lE*2>+2#R!h-VjCaK|cxPJ}>#L1>S347-}Huxr4T
z6-E-dRM9CTz=h<YEqgMqJc_^(OGl~(A?;$ldIeBV6;_T7kQ=X&UF-f!$qU#*KraVX
zR-+(3>>Q1pD7O0@ZlgQ|fyy-${CBOD5sdJY#4wv+#=}yc_;JFWSAd0Alo6>e*OtBu
zqBLP`K))>Ug=KgQqs+G0GE%He`-8Mz7q~1dn+#Q>IfuUusf9*-#J>eEcv4n0r{aL(
z!cC!P`Vd&V%F-aw$rQdK;ucte9J>z^g9W|ZHztc)4O|bX3(C<^7;_y)T&zUlZC?*Q
z$hb3XwJ*f0nvnC$3~J8w!ljKAa<}foWVLwWs7Xc<YpHLMku6b5m>yv@S_X^R$E*6d
z-FF6}wWmcdtmBqG>t*HhIv$kUIT#=bSEvXBR-4328(ro90M;oR9M{{sA!~sP1LX+8
zHN#!<;!uJ4yp+YAN4_|E0o%)e7Xk(Ks7Xa!!!|rx{{a2r?<$)>%D!kKh7sjL{kD|O
zZKBugh!~KtJ4z}fh!(VWJ#v=1+uDyxluY5&Le44~xcbIjK>F+nrzu_J@et8frm8An
zR<DijNJ(G^BV$bZi=k^PTQ;oXHYYpAnQB-)LuJzJ-nFD{$Fq!%nuHjMc1vsnPPDcT
z!kx*vUMXt~ZSG3877Dl!*<Xhrg|cAV&OmN+O@bI)B|eifb;j0@i<&r+{Ibe9;Qs*f
zVRG1wTF~<S!pxD$d+Jm<k=M>1M5dr9!_$^oD~0P{6)4tombmQ7ur#x3e2|@@AwyfR
z6sOrZ{>&+4c<Fd*f7w%GPFCa@TCKnJW}s{Wi))2Kl2sPDmk=$-iri(<VgCSF6?fAi
z<nb6oIxxOV8kb<LI@WlJBc0cZyug@9Q(YI(MBJ$;^IWxr(2j+cKT5;_>r?ZHr-UtC
z1QP03+4DT-BqI{dJB)%uvPDr!&#~}8>zXRY+$xu{HvP+6N6%V@?!$<Ne9b>QipVJ6
zDSNqvQX=Y%5XE3u7Y`6)!x38HQEfY-dsp1NZbQ*KdlH&xipBUG62O}X*2c-c{36-_
zKGy{CE+rW2p%oqbNY87meJnw~=d+h3>@Y-8rNOrN=?|CyK@R@_uV9H%DmhfFNc*Q_
zZb{N6KB<BW33sB;*%~_smgy(?A-k6)7<TXbEDn1afsl|h<bicL+){lGK(r<1d=r?u
zZfmVTX6UQhjO`UD)uz}N04;^IKaj1cEeLY5!V6>ILIz5q6?qBJUOl=ow3}%7XH{ve
zphen(f|URpY&p?3EE`>0EH&)22Vk6BEm`2wVR@lf-%yuezFt~fTy|3Xn1~ivpO10m
zRG<|vB0aeF_}Jff^(dWd@D8Quo47Bsdg64bU^oPD&Y`W|!tkC?Q3|CiZg4`D#XHk9
zYfzV~P5euE>QX3q`2(q9gS(uhRZl~RV37^G8>6*z7rMH4(H($G$$Q$69EXOR;SoYr
zP6M0sH2(D(Vfmqfg%>upm-@xJKQ7e;xg~pSIs*E2U9bw^Q2GgJ>>aNIz`SLdTsB36
z5lbmmliX;9-d=G(><hQYNISdXmf1+QgZ#{g4HDLCY`b!Fs0--dQwJ=jx_C*)Mj`Sn
zxeB=tHz*+anAIWvO0o4cVUI>#va#^f_(xN>ZylL#RDe%LJ{3_}ZJ@^Sdv#N47l2$q
zS4aoSi=H#2ir&#~I3`oA<LUrbKH(O#Z3<<GvRVlhZm`ti5e=-Nf5;<t^G{uhMD#cf
z_Z>l296Px6a})`&c~|{JW|@jWEnEQ=(%0kYxmBlCyB`s>=i=Sd>q(owa8g5}v^LKb
zD1{M1+8)q>PJ>XSC@X?MrBz`Ar7f^o$dHX?tJ@7*Tvp~KEpp?Q6GgU{;-b2Ao-yHs
zP@wF2@=-NZM~iI@^f7{ZfvQt$5Cw$4<kpubA`c`>_d5!W$5l$p4}YApJDnUW=j96O
z$wB7yMp!MLpg~|dEKA%8y8@!zYJJ-zVxKDy3|XSttUi{-YC$~hS1QavobTMG+dXl|
zc&7u(8(PizV8<VMT2H-8r`$afve0Eethz-|)yX;9HVdZsM2u@eaZB2Z!~odyl~7A$
z;KWS^H4`q2%JWTBDr=;ou%>jlYjCi}jq%YR4Wc>i9!9kkBA33lD)c@sNc|7HE@lNo
z-W5hD`zF$VlA5u+;A+Xj)9cfJ<gAlr1B2DU#&eW!S(h^%`YC8IwhmVL(k6g5^)HW#
zke)UyEd(e%h+n!ogqW`f+)SWXwPkM3fzCZ36-#QdLteKq0$XO?ugj>am03W8<b)JG
zY6BsARaYCiTXrnmtM|l1%WSK{yY(q)z#5l%+-FGuKbO?1i&?HTjzdS_puOINxnC3t
zlv7;+29FQ$IdC?8MSb3qm@6=IBP&enH$v&FC0ojNtb$>tr$kZ9VVf8ygi6|H2Vuem
zPG~xNTUoH(62QFb+wpR`HI%wJ7c>39-&BDq+s9$65v?I}mf{ac7!)rbsA8~w2>$>(
zL0llz?11q>wf#3Wf{wO{{t>h-<?G79GWH(M$}`$=S-~88_m>W5hC;eHmie1>&k+vf
zCcHM}jEC;#77j3c%4k>1YbSD}70%wr^$nSnl?pX+qF|nOMz@}A5x_kxYI&D@&1gm5
zpGjz>%vcSW+AC|jawyBZm!3^;QnYQGM-ahQBO<Rz!4*P@ZD9BhA`e9th-IEK9j4jH
zvqntAm4Mm^zoH7Cw2RDMV<qE{aTbn6>UEHg9&h1;4#u^?0g{a>t@j%OMjh+{3nVaD
z0JB};<!V|v)a7>oxTfzj`NVJ*#J>qn99=+#6xn>1W$zR(Tmb=a{2d6znLW2ITuYv5
zqjB>KQK{a9oRn{4nD7K&I@D|G4X33tBoG=mjUzSj3yEuZr`O2^gDZn%EK99`Y7uVx
zLV4;<Dtf4M(gT+&t_yR#!M}x0?b3LUJP7T|&C0C;4;V0_iEq_a8Y@QSz*o+tkUh>(
z&;e1^#c_OJQK-0Qnzp4hdJ-K5YAW!gAbpaDwCB?<Zs7PY-+tgavbgOL3&mj<(py^g
zfMUjK-DzA$ZBko=76Hb4kH=b3!RP=tUZ6^{;d}2?kVg?;PtlG}UmRQJr4YI?Eufrz
z$_h3A08nxr9j6h<8cKaui(nN=&g%UUJpo66I3<1{Q1CeS7S)9-0Zi0YhI8X%X9s`;
zrC^{^$6?bM`3@fPGh<7Pz$`?=APrC+P=PK-qV`<wBa8?$DDvI04yQZ@^L(hw6^?z<
zjAN>oc3z%h3fjsm+Y8&{n)QX#9h4YiZS&?e+35f_nFqKwUT&v8F`}4npw`)qF<=&z
z->1VL05@1VtCYAR&<a;OQwsdF@J04nxleV(X;P(Id$<E%y@sDxKA(6V7&^;GCHM4<
z-0bI;3*a&qTekd(EQ~W!&+Z9Pr0mM5L5VQhC%6z{1R>G0?h*|^_-IDZFMu!7fNWaA
zB#K!`z1WZGz0{O&!cuW<(xidbpL0(Tp9GIVj#{$|N9w4BS-#rpXB0$VbV725dS*f5
z;kxaCSl2=1q_{IpYVi`FO0O$ZRE$a^%ftn>bp#^d2&f0#Y_#i`_FIO-Rux@`I1Xh|
z@MGAlHIZK=aWPd!^i~B*935r0)}ZfBp#F<Cp#rX}d;~gge5w!uINo*w^w2$&-Gu>g
zr3hCxEoHDo>lk?{(}3rVY9(>60NaqXnfM`ahBm&+?PB({1ml=7<W*vz=>qT7M!}R?
zTBiedI4#W;i)68jE4NjXkIWE~TZqAWs>qc@9Edwr=zz&@HU^Kw6}5@9pBuzU=MZp;
z1BcvVXEGZJE0?=GoD~!{uza^ndf?a#mGoJ>wylafw&RZ|+sDja31Li8&)8WNQvFd8
zxS@aF5kfs8HAMo;b`@~0Fu?omQfi^7ZLaPY5F&*X`v~Sw<HV}sz#>?iHI1d*(a|Y1
z(N`LewvE}5iNKS!P!tPSh_nb})1Shiz}y!l^!p*9wCuiq=0W)ZxlxMByyF68mtU9H
zvn_y1jT=yf)n;GRE+VH}K?2v>y`vuTtL!4+<s+FN{xYrS4wVwa!K>m``H0d5;}r}V
zu~KdHA@60h0qM>dXU2;i4`fRd0-K5HB9rg|hu%w-SSFALz*rdD;wc$VsvD@i3ajsn
z*n`@cO++;Gj97L<m?0o1n;<y_hLEZy;C(EiZxtvg=5Yzac!6}p&BfphJ=^aF>yRV3
zR!yC+s7ygjXdeX-@*&x4D<fA@gq3w!Ygg1Yh!f42#XQfl3Pf^=$UyWbi8<7(lZK(t
zIMn8`mX37GZJVIk*L;-+yQ)gXgq2QtuW){+T=vgaOXfK)Zqy=Ku`A-!Z&lo6A!<4o
zv$AmraT{$T>KB_5cv8T7P^6KuUggy))+Hj+$HH>fgy4C6iPr}I0JY-@AclzWfZ!rf
zy#xTmE$#!wWi=dfu!<cZhIuL%Yn5V4fo*&-!V0oh_wdi<M0+~@xXwd>QmgJ|Iq?|%
z*J<S`3_E*{C1BT0M~(tDmX<){wObxy`n&5`^SzK)FrutV4s6Smr=oK4$LlNtUo_Sm
zP;Umjuj&*~z7G=SCl$P0xkRJ9XA+7L92z4J5<X3VYhp?Y#WYu`wP9RaIFD2o8-bOY
zZHl7e@tma3w6jgQ+-5h@>#CXwhrnyPh_<LjXsH*)vjJf`%dC{Ebct!^h(+}D%V|O6
zNv*n=mI2|7(*_A&2eFOvU=p?2q$QT_cnlCQp;dl$kr8a}vI2^~cPz<v@rD<1<aWl2
zpF}3N;e(Y2c1LkKJghF#%Rm!WR>90i0D|3r;WwyCsd~5W4-#?P?5R@$k$8L_y`u&d
zqx~+hXdNXfRcC@}qFBzac<YeTnV4yD%7PP-+VRY#0DcSBRU3fNqxAq9+ia@^KGp?u
zHQr!rA6Sv42}dy#S3m_k5Szhj#NoV&&libkTn)Jj<;X0UqN4|<=%K3S;!nUvsWyv(
zl%uIcR)WH=<-+)*8_3AgVCwyR#3@7Bm1d}a7--qb`5L07*$YhM<(p+J^Ag4|CTS_;
z{{YI%ZSfq@UnDD}$<5z>;heopH>G|kp%B`}s|ewB<crO~5`)t`Q;5EO%POF6YHo#}
zAW%Q7$_J^{dPtg_yFJ(b;IO`{{>!0TEy3V_Q$|N1-4g5&i2Bt0mX|{PC6509RN?Ev
zCR<0;!OrD4eXad7CE&$d2a6u;7DLcb$7m>Q!t{dUmKWYzMXM|&aFn=uBF?wF_5mZJ
z7*%6@-o)*Gj|13DguE@|?_dNTU6pFkuv9s2)XP3qgS6`S?TGzeKNezQSGmH*t{Sew
z&x9pc5JP>*DyeM0If}HtAX+oGmn>@(SFE-jRjc|ON2usIb5$FcGg{rJBD}t+o5XBR
zVGefTww7?p6Hf=fh^`n89wPZvJ_2u6NdQ<|N@XlSr~>%5Bk=k;_(j9D;Y%Z2eiqZ(
zsxC8;>QsV4nl39GpgHX6C5MxU*7tFhc?(SsTg1O3dYk_M{rHbthem}F@MWC+8i`*K
zJSe!@EpHQBCuS}+V6B!(ekCAk*hYp9M}@|d3iU&h?;t@B20KEI2~q?H$h4P>O?Q86
zX1-1U9wGQMNMgN1AdD96529JN0>HvfEvt81HcL_Ht^WX|A2C(wr_4qa>{bt`K1+J3
zPpP3ZcQkk^%sB+=_`O;OFQO5YEC5&~2viLWJ2@C67Mg0$@tm!%pf<C~56N1sW5O#h
zFCf!R0dQXhBAE!1l`E=Y90-P8tn0{*5p8O_m})?i!Uj^JR%)Ql0&U)nk~l`~YK6@=
zu;9N@rVCc56e>{UBxx@1`kK`**izzn@5`d$@kY_Ms@sMw=v;fp3{bQ=C{gC%v@}22
zLXS%*2ZH5@Zy>SWjrzN~l#sG3S7x5>L^n5I_racy5xd|^r%|dI3I6*4iCsm}^)y$;
z<hTYcGB~Xf<{N{u;Rj+8jeF8imcr_p+0P6>rk;lZv{Au@0+7}0CnzW%;4Ml-kVZ~Y
z4G^pwg0B#xnO_z2sFRxX;rH7f2G;ewel3VR#slkfghHWEa4^xXTB!WO?b_18BJ*Eh
zPCzA1Koe5hqLz$l3?etSxkRe_-y!M;0o1(rG2m*zSyaN2sst>zwp%~Qc>=GMAn9i}
zE#R6#W64A6Q;;b&343*%%5{}O=pYGhSY#jKV)6bA$c<bBK{;vE3n!P%X^u$v<~I>p
zc3cOi4^U8fR@HY`)OqFj9iI$M0LM%vnODZkb6zSJ<C(HaPe_cgDR-vgq#D%P-fzN0
zdu*+%<~^<?YI(_IH4Z#_m3N&JZ8l#e@pTwmOy!q(YG(m8UXH%tG?RW-kPaJo($r6Z
zJR(aXskr1RqP>s_ryd%Cm3CN^)l{fq{R{U6C5DQ{Y6@ZfIA9ru7a|~N_<@ADE$qVD
zI4P$iDq6N^s@daq`hsi&&3B}Bzl4g#6b`GvWW75e^)Asa?fs9;EZ8hrMbI4&*zIcD
zGg_v;rDtfhDhPUbKWFI!Eqpuzr~4G&2~|tQhAo9#R`C{^L&aYw(K&QZ<*7_Dmt=A;
zIK6V<Sn0BWg`JycE>%XjVc}ccD@BuC*N<vpC@3C}HPl-7<BhicOks^>o+%C{n&6|v
zQQkADV}{WBZaq_y3@nYjf7*@w5eryTqB!uAYc<X?xNqE7CK;uAmOaCCa`j>TXl6?v
znqNpZTJX|Vz;4&H5Q9U0nS#ou@-*<R8vQWxlpYlM9~l~P{HW4OA>!?8EtR|*xpt7i
z=sB2#^iZ@>X07tO=3FYdK@%ZNw;KpGc5$3a=T9E?GiEAQ6vu<+2!30{B^Ls<bcDwv
zM-ZJ-sX4O*@lmGxA-IL&l&BCwbK<Xb++xGKyKb`B1b7j9P|d}BOfGYl#I21NX2PeC
zs4cH?TWr|H3Tzg&$oQ^a7&8h~+UgsRm7)8pRjQhfYT&3**vkRu-IF*R@8p!C6`KIk
zmG0_Z#dm&XeG;iWCrE5+03fffM)dkge16DPbC+`ODSBDvm=IBOM+ojMiso;68!m8F
zYgda)i(>k>p4eT@tav}Q4y9L<577(t6{=}A6?y>MBaem|Cu`U|ibeshgSYVVF~XVh
zAr*bWRttWvf5dX|d#k1cq3eLy^o-5Fh!GVBWtZZDLJmJ8{hJr6_z5p1b-tK=fD_=(
zWeaP;LcOs`DF;&i*Y_Ns$b|0K!ww<eLDgPt;Np9fU{<-*)AHy&C?J?ws+)K5Wr2*f
z(%67RfO<|Kk30VWBl+}Itg6e(Bo74boAv9MX<3YVSb`45ds-o9)>qr&F-r1=Fhxxe
z;YdGZ%m`f)8^;w4^xmBoR~sO;>Pm@x;#jVeAdeCCVaq;VYyL3t!L{I}_OXJXs4G+>
z{@3B$T0{w;Jc$A24lS?4*Aa?DSQWnn6=*Oj7T&&h<RK$b7>Y8a1O?q4N*t<F0O-;o
zn}_ZNEHMegaz<Jzw3RZlysWhf?^_yb$TrJ3^6U7mh{PT^fNl(QtfwneHh^<N6*Ao`
zH??qD)u?bTAhA(V4pd^bo9$#QDiB+|BW+z~d*<aqWRw>JoQ#IxSwP}<A&pdyLV;S;
zu4ziYl_%*sNEY;p2eRTzvK+vezAYeYYmkOEvEDf)YVyHt;Hje`xU#er3(;jgS}?}p
zUCtED5qZmnI~dbAUsxgk0NA|S8yx09P5%I>6PfPV3J`Ve!x)V=p=W5JVrN{zI=fmt
zHRdZcYUm=^LoQuhJrylAs1-;lP%Q~*XGKEBD59!}O)MJNhgI3Pgur>iiixNc=|vXP
zOmU~La`eLvbE)khFA9-<idWwv8F>h;AgCHB7@ZW`LPx@dgA2HLZ(Cv{Ehgb%_RGl0
zDbY%H$7A$O8YnZy6PgCrGtWpci<#XA_39dIC|+4zLKm0$QBViOwv^%Rj-BEpD;j|G
zqsPqi{S;`YW&n*`2%-1I&j?B?;qAnv9qk6SE;7Eu@Ex+X{AK?D**G5Q6j{Pi*Lc%O
z*kxRT*O%pif_O0qqGvUBFskG8m4aZSstL(q6~lNXBonr@)w3QR<J)&k;m@MqFp7Zy
z-e?`8KB=s_mfN~HQ!$<cspF<8P=dwyRg_y2wS@<!CsAcR@`Xa+IJKO(v)kUjiG^w^
zIOR(TC1s9_F<_>L%AfLhgv!lKKy-HOUPuIEN|#1$Y<A5-h!<y-u|0Qn=p0*8^H`ql
z(j};G%i%O5fw5UvnT+7J>T^%j5+`SUA2R;{O9-<3E*xFwR?*33I4;wTI)%NG4o=@w
zVU>Dal-YHiwo>qUl%;O!0BW{nfx!*$btu;sQo8Cg)r~8v?m7?}FyozqRA2(RhMOgf
z>9DcJOF8f=TZYTUl#-M!9JoLYI3hufkV6N)A(Iy!)NEE?HHS%f8*AiMxW)78Z$X$e
zrurii;to32+^UN_cEFcu%e-W1x7EvkD5cc|uotV3o0n;HvE;q?5Ubu{T1xf4G-tMB
z;W6CC;UW>Jdr8b_dCp<QC=#*@*z;wm!kSc~w{&xpk=n}(ZIpcmm3J063*J`wl-CYg
zi}w_)46H$vN{SRd23yO^@RhUX(5DW1t8NzNk7->_03cWdZSv|6y|~ziiYv|RutzM!
z08uM*qu0&HsbvazhPhvH1M*ICQZ1)rw}Kr*k#UX{TDX|v?S5uJ(T|0{Qi_0iV!tq2
zs7GjAmJMp@Z42OvcwZ9AC}rh;WmFZ*DGE-60t1k4kSUH>Oyw@hP9pe+%P0)W3sGuY
zoNE$F0cS)+jioL)P87hjEGvF0cG(pL)qhflHou3?Ljl9e11LzSM(v3nEgIcnu=r>x
zg11f_<{>F6&aY20u7O0;sPkxV4?NpUQi8d9&e*QE%=k>=obfZ&dz8Uc9n)0#GFXSK
zgPPvs5DJ6MRD&&ns{=fY6gb0VU@;r26<jYNs~-Uf9#!)ZZ4GaHIL#jn6lmpmZ!v#2
zlX)1(R+gk~Q_Qpys^`Oz9S~2rG_hG-opL*(<8O%hX=qbqytM_=uLS10iCwa}B;S(}
z&kJi%d5wD+&I$o9nG2jyML0UNI1cGyiEnTYgOaBP0uIz1Ml>8NfMxPVK@_ddejuAm
ze!)F#zR)T`cbQyPaHX5ei-nKbLzMyzc#U_e=Bfhyv2&+<p(vxchbd2WDvNY;{{Y9>
z#*$bU-BtGt9%!k8V12<rLsg~F#c~r7M$ckgF3`j6AHbZO3l9KCC<3h$)IX+Z73Fo@
zRf%R_SeJqViVG0C3F{KG!&5qD(~5AGfEGhSlq|cIe_y*YJP84|{WLcX6g3#x4D#D_
zKqUM?EephvgklITtAoe#E07Al{r$=pR;M+#4#L^Nu&a+q-$$B?bjvciTstMJm(iq)
zDX+AWS7L=MDCX{5X{@s-9u^hzwAk&}79f>hQil?qTL2~&xZgams#hw!ynF;O4c7;)
zo%uw}UJ4_kgd2x_r2hcaXS+9VZ~#DDa_kRzSP|p{EFY|(4uIQ7GZJRl7HX>h00s}T
zBWmrrhkb3&t92;3T|AP8C5Pmr!)3hiLN`*l3M%Tok(el2CJ!YrEQRHkE#*wbbcW7+
zoR8gIge`}Or=ANVR6ND^x=^vs$F}WJa5rLb^_<TrQI8JUSH!45ylpaoy8)nuOQ>)X
z^<$X%%3oGW_uIX6L<=szrASRcI_LuLCBj@63PkF~J8kvqSl$rKTAxE0?f`cvvq8w!
zzA6^d7AA#xiZq4nJf>dnlthIL28|R|f+$-WYd6^n>p<%9OhB61W8r;9ok%|`t11+5
z{D~Tqhc{(QiM3nD5N-y;+iW4=YBlk2^HXnmoc{pH#yS1we~aosObGnw0+^p^<jZhb
z+dkN`Pb=Fi+zoB2^SXW9qhU*}I<1Upgtp%mc;1kmS?E$;o`uw?E(;PR(_<2WT7K``
z3gzsJ5MUIwx4n=gybIR)2(Y;;CfL>+RB6Is&n_aVXaQax*u%@jIZ<Q<P%7djD!EVy
zMF|DIMHjK!yaiPX<a%#naR^h%Tm(P}HRzK?7XWI*uefheDweZfKN7dsH?K$=4${1$
z)_GcZvVt<9Zj>=MxG$!_g%89rt_ga7;#!j83jnaz7gQAd%#(vvcH@x}U5T?%TPWcv
z)mFl?QWE+MzYu#4Rs+*hmJ6{Tnm%RP?XGeFbwG;0zM_k|AT9cNA<2_gfTmQ?PG2p;
zP2fe+tYtdvTB|V0RN`K8-Vqy}0;UIXW0VAKEct<7T2|Xd7~D&3^nG}USD?4w#G--m
z)M--dZN-HRy0hyn3OEZ=0CZ7z*6g>Y_*J@(WvgGY5HSN`7<7o0Mll&xIZ;rNGjM^#
zN-qJzG+2Z%v1-~au^z!|YNmz~&~&*shB&aLdTJ{12?=`}5FU9ORB6wsRvKG5&xmFM
z{jhR)xD+a~-r<giEUbiZDdefH%3T#rJcBB>IbRq;*{zMfZg@|(#03)luJ5{xnGA3%
zc8iK}Q39b}PYehNid}d)gj`E&;!+(!g{Jt);7-D+PvYhfn(>y(z)@?CAi5ki{EV|`
zJik67qAs~aaMzhm3igVF)M&4#2)}UMCpe?%GFN=R_$3NE1&ieI3aZ{~cl{+T_b4>g
zv&z9)SvKxZHUhPN-snM12Vc)c+3FIe)JU-Xtbg{GnnZzUaEEY{YMrsST)!M-rxnXm
z+Aor-N)SN-tidg&tsNt6;1GMN^|0rt1|FdLLe`Y@%aq)FyrIlH*MD-t!DIHgt;1{<
zlNFA*wDmO#Dcn2!is3HaFC)uR{J}t)G}av}2i8PIsM_)I>Un2aF~y=!0sV<uR+<eg
zN~@!3ErMwURbC$q>@*l$0<!h*B21Y4LjG+Ufv{bX;j83=K;$Y5W{4{rUE5_#f<O+S
z;|2m&xXAehg?(Ls=aL#T+6Z)0#iY4ND5rli%50BCQ)7ob7X#YRM~2~YL46exWyZ5@
zj0^SE>5CQ4l|=T9{)WSc6?leekO&Y+)n{C3<&ozheo(Ul9Il)#U6429Sd%EJ$ce@t
z?{M}&-=USV?-5?AN@<Jhp@>qGfsGh>+l;6cn|Ll3)ze!|b4lkh+w>y@Y$3z!sE0-l
z<y4FlMeDG_uf{D!M7)}V^DHXZ@f1~)4-95hw*tM!wJ6MiS9Jn=Lp&Nb%@hw&xn04q
zUmj&OP;X<l?phZT3KrhDmiKE%`2#8!2Tgi`sfpfRFi1ojZ0+fBK{9O>OCBh=s^!xw
zT`KY(DTFNA25sOs31mQlc9l5@l=#<9OUW&j#>rm_8`1v0356)Z%A&%hi{iRk6gqaC
z{mn^e;zh`@oxDdrVONQH+{d}cZpbfN!4R*5QvJ61h-3piUr$iH{Cus7id+{!{>LI+
z8_nYcoQuWal!_3(IZvTV-P_OX?1ry8d_Y)wv;zz9%PLN23Wa%usTHlBB5$$mFBXC%
zi#s1+<`P8<bgir+v;e5O#HzJk0M%zBjeg`)0KEVeNQc$#K!Qf#hF=!An*Nj=-O7Fe
z1!bQ@DtswZZ>BFwzy(Z7wD>0|;#3~3b+eHLX|E<<D_}a}zUksx#H}8hf*|&RUcyb_
zb3(!%Dogk=o8{qr?#-`O9dl8?xc&7F(oGC)=}O*bC@3e3VvN(Y5v2(I#Q+JjEu7eG
zqr$eqhjb_o-tdB5k0c7g%$rC_-E^|M35nCdUj!Gbs9jy>EKr*jj!>7}poXg3^SLgE
ze>$jQhb*qbB>=XazF+|mEU!^JxNf@oXh&%LhKU=dfki~xS6?hj&oY-@p@6LmX_P(Z
z-|VTdNG3iKRzMPa8kZ5#E6ON9bk`#rx`-;0b^8Y|mK8M*0MzEwp8{1^VxnNC8VAkt
z#`sl4pf!6c9UR$YVvfLHyo&`~7mG|Ih#1@7NM40u^YKw0)K=B@aTpH0!<eUD?ecgc
zWEaMEC85+Y1KqI~RzWBc-Uhv}d0Q?6of?%gWD|b1c$T12NT8ocZ$*|Hpji&8(Dogi
zA@z$Tu>#^X#bAW3cy3sh?!{Sfbdh*vp+JC$T^6e061-bl1B;hBt%-T1%fzV5K(6nJ
zq1B07ZDgRc1RHKcSiz7;@_boVT+s~zQ%1{SAv2w_O?#-_k!7PqsZ+ss8v4aB+CCVj
z;c`i)!R{U6^iE^Zvez+jVat$0blKWH!(>xm9LQDlHoZ$ZLtnWA(T&y3Z@WiTTyO~F
z_DtBkso=*SaT-wb8)a9yWsHC@A3DDUW0hG^_Wdq!hRuA#&IOXWTH%Z%YPo_$q0`G7
z;Vm}qalIW71upg3LS1U@@}6Lngj!$?nEKiaHuRPdr?KIRin=eaGvMTum8$MFcsIsd
zchpFfa1Cu?nScxWgi)-0uW@USR!xbnBJAWDY>P~3(9#zzI%aVaNNlXUY2Yy>+ozCt
zOk#f_h<UaI8!42#zmW|dS+5I!ey5ghH@r=Cescs7S+THJ0`F*#`#D9NRCY4BY1vl{
z5VqRAhQI}%sM6F`@B;`d3JQ6bio0wLc_4uDY)gpr%~;Lp&%_c+HR!%5jO)6B=<W);
z6qY-ekgIR^je$&PjVW>+i7}xq0kJ{3z8K?2;}Qc3^+eBx72?zd;IzI`Om4*Vn8NVb
z2(-F9h~P~Y&v9~tQaIK&OF=-Kjo&oG;x?685ZXlVU-AOp*LK3&chm@X+T9uO_c>Vd
zx{L(~`5mN|puCG)h4x>rs)h~ZW^8ShC~<f4Gs?B=YPe-4lv4ngK?(b+rUe4{>{xeS
z5#mjE+H$=?*;+lam0N5&oYCcc5a@_LO@FdA74q5~H&M>RI)a>eg0OsYRy~Rt%f}%Y
zMndRn&xf`=@QGdlEI9U`XiKbX2yp`vhMx*w0|@YI8eBj%P{d3w2^F&nfz%LeKxYvk
z4a#i5uHbeklPzTkbim<XdqO||0BSb>0PMXjm-{LCnm5D0OiN+9sVD|312}L`D-jw{
z;8m?gc)Vk;$ob`n;7i8XcVF;=mJ%(8H7ezBHVzL09oULvO%UN58hYX?jjbzi@%J~(
zCKR)Rf##PuEV(HK)bT`72SIfKO1$7cp%xR*%(lvn4n^o5V0|SoSK5XEmJ6k)u)4FR
zJ4hH10D}R?-JL9ek7s&)<~xY!)uWKr7qE?ZnHJ6QHi$;yb#?)}c!9<cBG`{T@J*DI
z^;9uA+bJRDN<>_Gi7VN#Ugc-6p<JO?GUppr`p!nf7IEK1?L50<jIIflNn*_74RHSe
z_2`vC2oDagv)pfh@cf_)!IlSGiL2jG${C390dN#B+#PDms?Qr01TkS9ufAibhlaoi
z;JU%ED0y)FOV!u4Oc2wxR#DBC8Buqr>U0!zfsd5}!9p<!UDTF{JR><p;RHczsjU}a
zx{qAh^sq!NmpGoLJ8iyvi~-maophE8v{eC4=$x^#Em%yNjle+1BSNZMm8F09K*0nS
z!`TcJU<_FwdQK_pjzn&N1lwxGSne<3xrN+r%0OjTHn>7B_`_PIV!`3vt|6^eiW)6Y
z=ihM9oF)GEE(glO1ON+TTMQ`;X<H81Bc33j3L;%0wX1;&j677t2skY(@J<stuxr;*
zEqJZ0<@U;lYwTUM%r@>(rL969mgCOD9o}_PTOC{q9kgB@<Tg#7K&w<n+MIeq7NNB8
zr)H=Iw)iCgu<1j6RHB7KfG+%V0aDryYx>n;$hbTI0Lt4exka5ytH=oo%68DT7`SjK
zx@_<!M+VxVS|`6Le0s|O*Iq0o)~>gSx;bN?bqE8uY~;71A>HppEB^qJtud*_%Okmh
ziqc_BNDFJd6?|lG$Xwl2Yw0BCoRqRpZ(F=hC?di}Hz-SjFkA!$Tk}p$Gg!YQ0Uxdf
zlX1pDI17Rv@dg)jtM3}p6=)v=;HaXBZ7v@W(pX(C5U)a@2f$PvWV98ozF?M23q`YG
zb44tt)iTzeg?yOaAthCpEO9=gc8iy*lnQrtF>pF&?S3VthUiN3y9sn{8`8-BRv^`L
zY9Xs|`=}mqh*c1(r;{d?II9A(GWd44+OdgMJJbSmER{#Wj3|I84-kr18~_^v5W8Ck
zH&t+v0%$?qifqQN4kVDgixaY6%wuEDg0k3`lN7cUO*@y{b_#i1e<PW~{uf-Ub$_8#
zouEO<k}Igyq{WNxQO1Mwk&96Fa@Y%3h@xDwR+6b)1%u^C%G=q57Ex7suD+tv4mD*$
zyE#1qhf5264k83AU6Yy>$;3Fm>(Tg?x~Ky^kj)Xnc3J{92u~@q`xQ_qx9o0b1OT;t
z1PeNt+WD&@2Cx^9C<m#s4mcOP%|TeYtCvMZI0{w{;PU}Z7?@CkC8&riS;#Y(n4yx5
zxmf^{$&%(@Ja^myHb4HY_Ya55fcCu-l94cTwDh7~0KhWks26-}ybpveiY&0Jp}gw|
zknA*0n9^PzDi^MgU^x1wHppUW){n9Z-`QjWq%<{m>Le5?_1ZHq)^z}N6sLbN?XJT4
z&?DIX$Hl2DwTSDN=hP&X<oj<2h|4LBkAaBma3j9sNRQB$T&i)IU^c~n<R?21Vj+GL
zmM_FsJ8rHe3qWf6j;P%0XJd<HWE*CwdQ5H#)URnshMlJvh8$RL-P>d(PlMb!tx9>*
ziC7GLzC|o_TaBGnfxA^!^DP}*C#RP3O4Ssvky<@4eg#do(T!-7hs%KmO<p4aFf0WQ
zY{nJY*}{U|i>ej7K(r`SuBqa@##KsGaG(Amc>$lLLDN`|>k4zA9-ThOb!Gn35qk8M
zQ81uTC@89*aoTsxwOgrt`x5VhTp-G-BUYk?GQ&>LxrV$b-&Bj9$|Qhft^jopsJLSw
z%luB`c^$t}&XRDj@=9%B3qbS`2q|{p4&j<0$j1$v;w`&2?^UtCp;{YQxX#c(ec^6d
zWWo0#c06<Clz?b&gqo8XUg%fc5Y$rnSFsGru^s47aQ9Be>8mr;`CxW>6mVnux8a3i
znPMJ-HBbl|z`lF_Dlkfw+(Uoap!o8w)b%Kn7P!GIn31;F0ZtJFG}Vmm1%oUTPA43Y
zekI#?kqatQmf{Lm#KvE1UvL5?Ha3k<_k>460K<X+2;u3+F)RveF_~7wBTxd>qaImj
zT%x14vjzOJ@C<N-;BXragPI4(hqh6$1kl3doEfM)QMRh0LS82B1rs8!uY=w8f8t<|
z%+rFEO?V*EAYQbYupYtDP{p46ZS5tx928u7Ce-V+sgnLrX|c#K3^wg^P{&6M(_Y9+
zQUG7*+z#hZQQQ)Nwmwx_H#;fGT(Q&?3sX;T5kQT?f<22YNKxk~SVsul+ISYtIBUNJ
zw9I-gl|R^9*K<HaJbWdp-ai;r?u8^M3zEo>iQPa`Z+iyaRdL6BMvJP)95BMyMXT;V
z9^(pH>^FrClJL5X8+GAg+1*sJ0_Mtr^t8b?HfbN;V9(__v2`7sxJu*-pY4Njs8o9+
zAJP&iJ4)9a^&j4X>p<exTn!ivxmrnj`)zmG5sYf32Lckf74VK2xKhA85iMPVh^dkW
zC*x6hyNmw-#^Y(aye>3&MO3w9P#nOZ_OEQI0LY)%%SK(=4YE<<8|Ta`NtXUul8to_
zNMxhR3*`pfG8<fRvzH$X)YrdBAHM$p#%aROF|5&TgI-`~k*c;R$Ilk77LYD<V)sRl
zBtp?b#~+!%ApXRMN?D#!hUuyiLsZww$}P6qC^@+8HLIUXikVqqYY<I<7W_P5>74<6
zC@!{(*n*(~{8L!0YQ%9AxQQIngKW+=1V6U`&^i1jD$TOoT(NFg?A$F=Fqrb__<5;)
zxz&nM^jt&)rc;%$GISmeBH-_Cg(cE627pYyHOf9_OeXdWl>q~2t0kcn77K=_ctg}T
zMu8P!OIyFVpyXc#fw~ya{{T2uVUJDaKwq*}5|Kk6nV8jFTk`wIq4XdC@=9E%-vFYa
zga*?toDc@ahJ8mI4ZsKyoVP-vh1@2!b26HSZqZle-z?qrBAu~&qaDQyX<V#D^YTNo
zyZ-=NnNG66Uq3OE-H<BUwITzDw>s7k;45BM>RmpFW8+ef=RIWOuyR)O2bN=~B8E|>
zp~hfcBc@ll9Q1jT-QXp9LiFpF+<}~p+FyPclvPu9g4nk7)bC5Ykz_K!n^hRJ)iZfW
zdx4)aoj?A<%vwH2_{w_!0M05C9P=u!08!lL)=NhNrvg?0mXnp-G_ye-k_v-BG0GSH
zl|UtQh!k7MicOE9m{ba_IMjpHDA0fSVpD5wg^wFi#RYsE{9IkJi=P|j9VqKMPDoXm
zRor@xIp}VnW0`}Z(^}7?xnWiCLvCIyv4toE%A<dFC7Kt?sOI2Y;BO02EbKLlFD5Px
zzGTC~sxCKKLPDvuG(fg1n)A&iLV(S}1#;Vj{VwBqXD=q-*dpo(N(~q&8|oSg^>Kp-
zBVwLo)rMa>mAqn2zj(1~iz)ETIIk`~h>rr9>kjHN`0QSAuk5i?$H?UOgBe<zE#yTh
z!f0rJ#7Mk&;tjyeM0u(QG0hmd)7n(1m3T|p?H%zr12~RUP4%lM1Cx;QGW5ieb$g{I
zpwN#soscmfsaAhTpsQQLC?yXL*?p`~n2*#o#1d;O>ELI*NLXjeIxy{J%1QqK5ZTWZ
zywMD23mTx^(Gar(ui(WPLSfRs)-UA*^#*F!eo{^LB+vBT2#v8vs?sJ}h!ikWJX{WM
z+0PXdoe9?4Uqwv-Lv@MNVXuSYg^L#{Hmey}yN#`(>Dq}1DXVn-sYQ&oUwvFWia-S~
zxMnC<UfXp+X&53XPhTboWMC>?__2HV9nVV$ou{UaIKs&IO}1d35iiAf3w<2mn^NpL
z^pT^dPzJmtQrnHd0_Y+pi~#^+xXW^|vq$1c8F6io@LUHha9+8&QXD4~D<NM%vX0r0
z@!-G02H}V(u()Ys%Qm(U8E-R3YqV-n0zFh^9xs9F3bbdM4aDHuo`Bgdby8g8mU?CE
z5m!OV^g?(L*+)hVN2MUPVGT^E#oO+-QWj{d2Y{6sl=~CBqX2R(+imDdH319lCUwuC
z?*9Psl*PV&hLLUJ1we--0kS%1I04O#Z@BTp`~bJ!^g>DrU}?aQGZ^xGi7`{R_?zTw
z`)*U^@!E~l6hR#0ym(l>NC69jXq>PnP<XX$U8>LyS~#gUY$#)xjm81y58tRHAAdgt
zTET~?frzsemvrt~31{Kk_#)E<fTSH@*2Q_QB3eyjJc}DG@_*3CGSOt!d_*;Pc`FuK
z0qCA0DvukHZDr#ZJx*FpRzuPV1qc%dCM;M#Ohuqy-c1;a#d04^V`#VsK^+!h0yQPV
z4aa|~Vlc~t;75WTZSCYm>J`bQJ)!jgZM**5Sm~&lm$w@ht2#Hebpb-uTp%!z!Ka0S
zWo7<c{@K*!Yl_QYfR1@*HxcCB{=_mBA!PBVc01q*Cjzh~-CQQPyJh!muidknx*-<t
zs(-SX1r749v4FO>%Y+Z^Up<uzP7c6<wjE&{nAu0`ErZeOR3jZ8f>+03^bJ^Vp0_TJ
zdt>q-Ao4LvICkAP3D)-ag%}hlHqwZ^gs+$vH3IY<A2O9TZte}T^26;1WqjNLEp@z*
zV^kf-)zwViJC_<>0`>m@V#1-^rHerXpm5hb##eRjgUS%l=;`AKsMg`@#B}893-#L$
zk-g+qs``~uS1f**I3Tz=sbB@H1~=kgC=^etj>#_0)#^MdJJl#4DN-*S5Cs4{In5`q
z?u-)M1z@{k4WJA;qD%OIQO%wp$BL~-hN#DtN_!>@ab?MI>z;7G6$+}r8Cziqz|mHP
zxkv%GYe#&s0Fv>bwPAvKB;LE-LaJP;&!RA_!ACZlrUnO`f2ge<*WvB}!I2nMsdSYi
z3Ib4g`GB>&@%1>=Q0&_jqFgRCO6`=V@%M6_;v5xSRLM^Osl~)hMjk5EKV!RF$AV$b
z<aIW@Ujp$3^fWmz9jST2b%+zn7}lj{6DDPA;sx7JiL?h_C!#yT0itp<r!UJg#J0t7
z@h+$6fGKxhm+=FsLp<l0cNK5c*3i4B2g5saIu_J1UR<`_mYmoGVz7`4yR~Mro_jom
ztzz6dR(`{%;81ke5ZbEEkE|Xq%<0b>`D1yM*azk_7zm0tA2^J?%%C|O^BuJA-JXh<
z61ASIbuQwWRmb@#Pf6N{+z{lJDZfTHTL9>{zP!SX;#c2PuAQ(vt7Kj2x4(+PQscK4
zYjRZQ`z+O06o2wT_Z3dPo01$#4k@O+U~l$XoyIADOj<tzae*#rrS#H(s=BC`3?}xJ
z8?T!|joWSze#GK9)Y6MSX@YSoo=tw^(BZXpeqqRMbr(biL;nC{hxrpZ23)N_!dILC
z<<u0iYitW`5`v`fTwy3e!8{3FFt63#K@PnIKv#r|V_H_*<|Q)+XRz}JV~OEmx_Vet
zQslFzQj#0K)qphkmw~*5CcjC!G{MRT@J9nM6tDUaPzSu-v22U)UIo_(i}9d*qYhE}
z!1p_>M;-@{F`y7sJPfgmuXZPBDU2?c33bxN2>l^51OTZ%=5AHD1Hl5aI<&JkyMINS
zB?6VC7eJ=pI*AAk5vd?lyT%z9a0*w`2L%Gw`%3`3<JS=2(v`#H^&AT|VDw}ZT(rtu
z+TD-0t`8LWDN!U`FGZz1(HW?s3@hU%7;k7Qv$AUHyMORY`T!`+u!i7vJ_s_N(14O5
zh)0^PZEILfvdW8sQ)o)11p%OWk0)A~^udoo6*Q;78jLDsCpbbqfNk@DAUZeVTP_3c
zi4K_Ht9HO?6@HK&g#p$X#0{LcMzLG6(fgTgl$6D!Dn;4xD&}2G)@L6#jwxbv^t@72
zU5&<!>ZAJsooQHXIJBecUt?om6)uuS7s7}zT@7hrFyo#$fp<FN{gwy{#hJpPVic4M
z!dAF-0z6@E7e&tSNc^A%`{YGSx}#xnPfRJnI4N6o3vu=dwlyJ1T2bf;M=Ez*(GN^j
z)QCEtNn9}+lG*B9V^O~Cm}RhT6opir*td6zA&50-hs^Dp?3UMD7AOt{RZ>=v?l?z$
zOmMuN*%8*_7JIrP13>@<DdUZnQ6gYXaSP@XieRq+?NAai5&{Tfq_9Xeo(^F}%d+Q;
zMU-ib2C9elFH5J*=HV6ql+Y}o>8b1@14ed_3~&?xtKd7GfJim7W>kweZa}aua9}MR
zp|G{+SM`wcSV~@BqRgj9mixJV{zKrl3h-R7?MS0EFouHZ*UbL_<QhiMU0rO*<Wn!B
z<nE;@<{DmW%3RZR&pu*WW(!UH1Irh>dnkFe*8?koGT7!|b`%l+03_7D`)8QtOIr|K
z@K3^24KtMY-}0rMQ+6Z61*IwD2!*9^d-E^E`zu=Hp;6}QFqFg3Lq-i36PJvqd0O6=
zvJYSpdaIF+TFGMyc$VKzJeMdvK|SHbEGX3G3BUlmt=kZHh3epr>PWjqwKchZ+b#sr
zO5*YvSvV!_U(pR3oOpEr*hHqn$QnGuwhu#bCHn0bcSw_%*-Z^vfv)e#;UFdqE`T})
zO@zew1P2l=Zrybe2~GHU_}S%dMzZ+s;bdgiEzOn&Wsd@eVP8p1Bwf8lT_hc#3Oj%O
zcDznfpLAx_yg3F4MbI@34U{AtN}3yvxlDn{16LiF;P~-0&~5SL`Hmf{ot_ZTY%y!-
zVTr0C0*i)*JOw|*XDJ-+<As-^4W3Zdhyg%0&{XfDtS{aP1!GJ)j+M~%;E7+}^dnH$
zepuZO9ZPSj`IgAE8gNJ!bz5Dm5)>7E%MJ7ueX3?mv*GYpWh0Q9Ri?AuXmY(=<lMj<
ze9KFZ#v(0JnQw$Zx_QuqpdACLPl;UF#K<wG*~NoV8zxqr^#j7wm_2($AR0T-P%{<|
zmbnBu?OfNGa??nCvD`$}Sf43YlpZRG{A4~Yi+?O9Jz4W~XXG_hc_4s7uE%r$KiFns
zfR1u7A>=6^I~xE$9ehWtFQe2jIyy2SZ4}#>TJ}w<^12?r7_}vNV*%5K;t@XV;4UrK
zf{=W)M6jycR#l;cNFHtv*+D`&R0Or<Kf^HQVPNP8{kB>l{6uiPyK8Tj<rw5A*`y2e
zDEq!5C^v_sc9zj;QEmN*N-jSa+@jlut`9L!16;xyC*CDfgdueq9Hr0^N$9lDr;)w-
zSOK+bW64y&X=O*hQA3~)LXzhV8Oe$LVuU$-6Ab1QPa^N=fW=wnYuD<bVn}gq*?c73
z0CRVdeZi<cP0fGg){L~eJR^`_L0w-UGd>`)T`xnZ-R@n6-Jy!OeKacj)F4v1JBwi#
z%J|86P*$m?5DTa`u}9`KHC_rp0pzA6ma6wY7!VP7E|9}~G9U~ZdxF#u^RWJ8ZlYBk
zsUOBV0d~JUCcK2IiW8U&q3f9EEQN#Id?x8|zf2PVmssr;GunhS7Xk$U0Z^=XK-E8(
zK(13OaZa2YYF)y0utc@<Y*U8Yheeo5d-H7CTiq%E@R`0`#pWJwQ^-oCSXecGk-Hfz
zQb?O!N<XOw{dnW4wObf$)D{Mw3o548PgEa-?)&r`2%~j4(uXET)$?z#`5lO-ch6Y^
zlKsU}j4ivI{{UE55F&>7#=t<)6ciDcD8!@>^)A)FqqJav`fE_FD$r|Y2V7RUUjG0L
zR%$!xbqkoky^*F`HOn-iZ64^~=>aA8RRpCqPduDBUB-~jqO3<S_o*uDv>1L930IZO
zQRkPBL)jAhs_SmhNKBQD`(8}qK&RrkBC2yW&4&_$tLK4Lgwhcx4kxFC9Lv#<FFyH$
zRYj)>oRj?z;Oy4YX|nYP(^1Y~ajm4CJkKUdi%&4_)R#w)WJpGq5M+pv$OP(`c}fl@
z&4AX8Jr5+oU<pT?h()+-SsE?if|d3hM=e4@tnd`=mnWkreqhe^aWGZB)PbtwLeV12
zUOb)|H0vz}e5n#ii3b&mL+3?9YRc9&3M$K6+JvtmYy{v@V`D->Y4a%o%U&QCn!p3a
zqiy*}Is(EP`Ct{fDeQWWA!<c0eZdB_6f4V;lk0lfvYUn@siwf$c?a2#P0EZ5S8EvL
zI{yF_s`{7!YtX8N?WS<+3gqW-%aPv#2T|v=a-sH!11QU12$#(EBnLV5Fgt9y)5n!{
znk8jyaz(wU1hW%UTkvW%4D;vRLxu6ULZefJiL4G$R1BPLXzcjS^|qx@i&?XH$O{mP
z3s*V5Cn891qaz3POywecDzLeejBv*HNX1JY6e%b}6}sMNa9J>|*zyA8;_}<vN&t$A
zXdL^9+c}e}lVG4>-3xW_5zPQEI)m|H0jFZ%2Q*ss?gTN6cEeQ;0^tm7tCyk3&t)*C
zR30r-J;P<nao`|Jcvqf8I5NViq%JgIuBCT2gWK+E{N4UV{bMVaKiih8qv9ZqRVC&f
zHf4WOyCCH7d8m#oRox_v9r?Y^Vo8Lts%hqTvatt(zCtFA5M`T6IXfRz*MtdhPB}gc
zzNWrQgghbXfW}Ce;{O06LY<2{8}W|11oh&g{@?+ud6`Y=bL6CHa~QqhA`~5JAn<X4
z&Q!H<8Z?)A-^gu4n{-*>se4cys^y7oS1$B)##6F<(ZM)D!}|!i$XIm6GJv)_0-Gzj
z#T8zr){rjCQ^C;*Np|=>uwPCAl<l?ITgud|qIb{+cnpEW=h|SE_Y23P-Al>7=RVdF
zI{lzH6417Z>i+=k0+UC%@fzO|MCNs6*Krs`zf7!VpL<$>LQ;>F8CT$yT%B{oW&prK
zK5>?a==vEA193++`GrQXv7-36s3B4yW0_X!`-vD`mHq?*%skvbwG_|J^|G`LAnh8j
zi;i-oc~XS0+ON2ih$hz0N!CPV&Gzc-m7JuT4eGp;i|Rf3ic?PPZH1c^vq&<=Ak2BP
zqiyKBc@czhVN2LN1tMtKW2XpGCk5bmJweoF1B2!6QxKZ48wqFbJ}3&W7g$K5P5^~K
zugNG8qF!3)_7cI<<w$bmZWD{JI)Gaj;iv>23hwqiP)g<Z64I*Ih#@Fwqm&eeH?5Fq
z=8V$tl$0>)fIntIG1=cd`Kah90XM+AaZbV&g^|U1UzDQ&U`l-~_if)=KB@#UYMWi;
z#8*P`IEC}%Rg^lW5Xu&u6Z}f{qiaZ(=~Z##SU?q93fj%M?xQ=(jtRr<U>Rc+EG${-
z8Ov!|Y1?i5lp{~R)l&YkK>8slpesj_Y$ZyaJo{%*(E{t}w2s{hQ=rU5T&KCfK?22<
zl&NBnNnuF34&?<jh0rXQDJuv5#%@*)MhYM+uBy~aTd}vQqFBhfudLz@fvN#X<p)Vc
zZL;zc+XjhbE7sWAOV$IZ?5Xd1K@?KLqPK)1QYFf0*X$ra2s8C|;}frTmA;<A0QEJY
z+h;`)P~V>e2`<mOjB=W9s0dquCq#8q@P%E=iZqESI(Y>5GOlmR^T_~bR|1VibjP;o
zu%H^(n2CyB*uEh2&`{!v;nNq!pnSU#9M6sj`vQ<bS9RuELC?%lN@kzG5|oRg#-ne@
z#vPv_AU}zwE1r3FTo3;MT`QbHjY<fZ@Ma^Bh4nAl%3lLxER}U0rD;ce$`1gY)JU$I
z%@HVdc?ybrvg6R0qUe`QyFtz?5rP1)965KAkrt!}Tob1Y)JkIYD=H@`w)A=VoW(C9
z3(A-t;JYXxR8}be0K?Q80Y!-J^#JArO4afxBtKH$mn^`N#jUMjJoV1yxNX_iaZ=dA
zFWI<4mEk~r48kHVy@DI3_z`Fn6(h~ZJIwdtJcyi(upG6cFg~HbXo6A?Ei?-aOk*PP
zcfzD`mXI1O6@(U{QB>h&ga@jT1&5Wb+9i&3(Ny6h!DB7g+fu<**o0QXcnlk6T=$@w
zRX~9;4}Neg>auOd>^AYUnDSiKpwjy_L>!Je^>W$SEfL77gHWE@X4>bGk;yG1*~{3}
zDlu^;yevE@S~A@l+TYmGdVC{TUGb35PsmDx@WkJ&p#rbs@(W-|_9I)F#9Sb6T*v+p
zJs?FIdSd;jDF!~pWr>JhULc~O)uT=}1)8N$<}F|<QKEb7Vo(Q3)=9va5mcRgGPG?G
zyOcf^apER6PBdP8GOJsOr<MZ<XQ6JsMMDrxt`*Ri<|>w91_hI&P^WKTp_Ir#gaI!$
z(9}y5^eBj6*_OGo?<LG!7#h&GF~18d8BQve0nh>7X=nf&Wn2%7QrE)VULh0R34*J{
zYb?*W6?IW~npqtwY&P<NpuXbJg>t>-$Zt%SSEd8Crnk#c7Sr{&wR(h8ORFLwqSfl-
z=3bzoQ0eASN&S_aox`!-7goXm{I#N7qZh=g+w06bz>RA{9CUf5N~<Gc8&{UcsZOA%
zI}Qm%Q1<I(G%lrI5UG<(0#fV`p)IIwJ{swl^@s2m7Z`n55hYJTKB82|Y|)YP&hINl
zvw=DVi>;$2uU?zDD9x-FU`5;&NN8xc*K*ppX-2EOjK}hfK5?kLfnZbzx}3EVHnQ^P
zIy6LIqSq0wvM^P_0>rKZjjgBcb0n2+)f%wK(?W{CArA#n6bT8J+%2QTt~;D^5h9d=
zP2_NiP?WS4KB-4>-3|wbSW=J9+v9<{POV>|7vVji{G$H=f2shrY^nm%yx*2BQqx;-
z1vb1IQ>b|hC;@LfifecrCzVrT0b6$7#cQVniu{+hz(=3CoF$0W!AxvZvv(zoYMt#Y
zX4olqtyDJc!yaNQWG+Wr6U-qKcqd@wW$nMBLC<OdZTt|R-RnYDk8`Tdrqxou6J84|
z-_f6~A=asE8YgqtiFJbWo}l5VoWd+YPt;9R@<U*~QS2u$D~zdd;-Zn;9Hcg<CxXEo
zmGcDFS(Q$QRu<Qena)5`5~I>>!%`M&u_K1EW@SsR0l<nWQ!B`s!v1))O?VaXh>r@E
z(7C*UjG;=`GB~37Wv5?qwhnv^0;i5WM3taEE($|E&=T}&IZYRc{>7Z8nsA)+ghl5@
z8><x#EJZ>&tb*uM-ariC4$Z9f9S|W7UbespDY#=u({$|mVI#(*@WG;5#{lf7>mxJG
z{{U<n`?sp)(J1wgW3sj9e4#<@aJE6GD;=qT@>OB1?}$4YsitCp2=PG`HAo8hv?Im@
z>!DJ}5RuBdz$asT^<5cpARxh1Tw>#D+#n08ye%dbDG(1f)sp*5AU4knWXfTX0@ju)
zm+1r|J=`k4iwcHqqgq-f6@rJGoAuNbg{p`=Kl}?Zn;pUq#-PCOAY05iS|VUwqA-FN
z#F$DIloksr$|VYik}tLDj!e`L4nqoaNZmD!z#h6l()zh0)FG0Wr5`2f4&lfjD}n;T
zo=zpW=GXw|Sx9L%*hdAN`$uB19YA}kW6_q}YA?6k-D5rh5ilmYfc(sLp-v9%!%ZOU
z%g;Ur=Ay7)LbG500B*)>*uVVd3Dw|@9+bNF-S#DULU}L*<mQ7Ruq_IrTHE^60yXym
zvK+@1&EJo6OXB>fnxq@$j`QAaAk$qhhN@iyrUglL6Qz0DU5s<OYi99mX1bAkx=SU+
zE%0W;Ei`f?2P|*q*`l39PDg5_<vm7?Gr)!P5UEpI*qk`AmZ~eV2NpnTiItYIjh+WG
z&yH>+_kEp}%jUveX!f8vxUDUr=BvgU7VR$~hwP~po-0G?lz8`d&Ke^i0d6(;@RI><
z!4Uog#feQ^P!PeHMcQW!u*hRq#?-u|>33{iXE*?)SRPW~t*$IvZVWcYcu8{gOn~Oo
zU~<dwd`ExSQTQQ%zllZgQ3b4|wZ*y3Zq)Lz8azMbOx!i9ilLSO8+xwb^9AK4;)opx
zmRMUHLU=QR1$wp=qutB-j;$uOVwKYW03sJev&xBHX1Ya}p*ra+{<aix`v6{7E{JHc
z?C4Cll)=;xb_?Se%k^K&759kqRV)a%W{+ifgI<)E2Y;kvOTH(IDG315mqqmkR0Ea*
zqeplaS&4>o=OwtUA>#&E6oy;wzwEVw*JjsQFm?dnuBFDX<?(XA&S7IneNgnwjk9MB
zKj|3MRalh6&de;LvHgNmXWUs~albxLPz4BWu!ZA6)o8|23Gvb;N>jnvK~lMdptlHI
zhMHCokei@7Q`=mC{b7qQaKAl8VVx==<q7jspnjd11r>l0%GXFz*Lq3~0;v^xs2k>Q
z5Y87)AcWnd+J9ujD$qX?TukVa3uP&B6zpW>%YP#z-_w+|Wp&XZE=RgnlY12asZ1r(
zuQba#PlM1xAAK3g;RZ`^<$Dp;Yl>Ni*%rA-d_sk_ZrXrVMec9e-0PoSID>aham>q~
zc;~UBa{jmE<BFur8eeoUhTeEv^lY+4m3vT8T5=j3vU5Oc;?|;@9C15EG`3y$dbwp)
ztL>D&M6PKc`7V!&0_5e?yUqZ8Ld=6v0lf4EbJSfKY8X9SW*`m2%&#zQVc(5_1-1?q
zvh~Z_Q0U>w$hps1$_|TuU_Q8yRV>D@$w3B96zFcpW)4<7bId&vFdlIk5Ev~?<#4Lk
zuA-^0QqRM~m&AzD)<ScJ<F^S{f#g!SlDBuSai$lwMbiUbW6N2-42R1Xr}`t-r67y%
zMTv3^s~!r9UPg(h@=@@wd?<x9d;UvYw=F)dk-$?|)0y4UTclFM6qNMzFhmNkD|+?h
zh7E3#^utb~o>~?Nvw)X5MzR!Be^K^^r}HH)kadA?^ngdYh$<Cc$l$&@?`4&Vw*<6>
zjyrV+I=b!+6_$(mI+mQhO&74Gt0B(f_IngVgVg{<p@`=e#}-S#Fcs{LTcm|k$H_Cq
z4v&)=NP@I|pq8<xh_MI*3c*WtqWrUxT`I=!9KeI@2B04ZCSwa;e}&3Y*O6}*WGUU(
z036*%<jxRKZ6wZilEuw^Dp{3o+3Qw`@|3+c*DVOLBj_$PAU^sXAtGo8A;<p!A;~cK
z;#Fay7PMjoZD>}OfBO=LOpFw(Dm|)(l2mI$M<DOt9cn?crF`mHSEzA0jIwt?QEj6h
z-=K!yDPOn@2U^_$fUVCsu|r!DoE9*l4vp=0CfWYd#2NxFM<6fyVQ>n_NSZAc(?y0`
zt8Y(R)y5u6AB~k|mTtvXzF}C{8hjTj*&@B7$Awry%Y!Mboi6U8zjV%VWaa8n(E^89
zv?!)E00xcrOfJ&5!~r-xIrlE$z-^E5WpXFP3iOv4yjN{_jm7utL+yr>;+{5B+Brvg
zCqI1u05t+NwO7O9KFwosl7)Pm<ZPha3qCK6+_6gY?c5;Vi~ukTLWt3KgnFK3TY;>#
zKt%+;AJp2#H$^~$APDgEM)LzS_2XiDSH}wNj<qX^L-X|wr0pfu?L?JpTe?c=$<=|0
z=|Fd6x~RNs5Yt#1x0CF`t{2VPm;V4@D_$O0-iLic6?Y%xY6S?)3WYI35fPFucSnK9
zhD8?-bTLyvM%xrei1b<H_@4}2XH8Y~^v2-^R2I5RxhY@DT2z@r6+-z~nn(p&bVl2o
ztux(Q1U;pxJW;gv!0yd)co6I%uO+;GrIt8OxhJzQ<}q@-as=6@3)2=J=rm$lnXZ{P
zv;nd};^IIka`P`^#Zs(qCAEUI49ahRCl75`85{3<qY-Rk0N96;w3n_LU7dQIbyOav
zr=kR{ES1%)1@xmAZ?mG9no4fDl?c0!Co?6O0{;Ms$)@;a{ThR$tz5W;4?%oe3|>|?
zmis>d=Bva4w%521WbJ20YqmwSEJXC=N=-VfWMh@p;9EiTL8r8X-3jhKUzR31SwQWW
z@DXhO+#U(5sH*450a^=Ou2{&AVLNqg2jcvgcq0+`QaCqb*NvGBYdR{D@F2+On#H;r
ztSdv-%MTt`Z@6_In%f)dv&~sZY&OThp?HSE!CqLYT7Cm8pGeBLDg}ym;^jqgv*Cr&
zF4pZbk&+vMF&Og#tLkX8Uv+$EhAT)dP+obBgt^@o4hd^PhzPHc^1wpcWEYf89Z_TJ
z>QWeE!%CQTN1Jy40AZ`L+v|zS(ug~2`EZIsz1tBNnCVA%#Jc-6{NWBmd;o130`gHU
zx_*+zX`apxvOXXJesH|e0*V8g#!_O1rBcf~E?ve+gW+`mGF-#akXTku*sF~(1sZ}-
zBkcH#UHw;xLl#&ru<Fo~s;xkmYQ&{zAdO9D6?nk3I*7+BXv?-RrX;qbtAoK$9VAuX
zq)sKJs=99}K4IFZ-HU>h5e6a1`iFIJ4XEbS%{5nI?%<3az6P(Ym+2-m(in&j+zoz0
zG--2b@7&!qQ$oGDDfbfp0MjviKL8i<xc0><aN^0T`e<ti6<wNi5r8KK_3Ghhtz5UC
zn0d04@E#%JuHGV*i$Tk-E~T$T9(>*zs(8D<fPfVm#k=gRW#`S|Q>4a^t>SJ>S(Oz@
zWIJzqi2x5PK+e*Z!1|HQ&|4BQ+FJ?|`0*NE2WqNboCX2zD#o|!2wdLOq8dvtdPboX
zQdL+ST)zRw9)`i((|V!j+rbnRcc->+P@ZkQh3hGMM@+GfuJZb*sFZf^xD~*o+vJqk
ziSryVo~qGl{7LR-s1D3m_x>eMuFNDShpi=F888lR5Q=~_u7@m0e%n^gU(vUPhE$r@
zn%L)6+hgKHsC51^4>ldWNp&c0!$Is8TbC*CG<+MB@0@odkY$hwK1khe!5kjIx4?u2
zy{J*rFQy^pY2!M;i)#{uXn?_I2Z|B?U`m0fd}_E5S~@S?MiX3t_p<lCCN`|cgbkRr
zRDZCi3lxrhi7Lpo)fq}3`(!PmAK7D7D-N|@rk}KQZRWXZUq<hhmqu_H(2XS?psHV{
zA_K8cps@x(RYnA-fLlh{vT?`=ZN6-t>Tv~aG^#mSRH;q3cM!yItpkM5JjShHZ60GV
z(uJ%jLmalFUd*X_7C`LB<3kg5D1*#n)Muan0M>*t=2yA`Rq`ozoyTxc<?w98g%_hF
z@3-M{u@{=}peVc}rt6IRVg$KpaO^5)!l89^a|VUy1}x0j)>sldg}Q((t|P6=VM8KR
zN*mQFD<HNM_szy2et|cvp^qtTF}b$><q4*}A0SF&gTu69!C$rQ?e{pIRkRsrO9H`4
z8vg+GfE^Ka#5|-lB2oHlDRQVbW14q;#-;<y;v*F5uzxT}upH$+UnEMgK4EOu&ys$`
z#KI~oS_8Yuaa=wE-dNcMW@^eDv@hxlLV{3A{AF0eZH#YiM>dF~EkWOLNGd6$-ar8$
z4HD9&e!B(*p|_C*KwRa|8NAfK#}P`ac%{pnvJKU6#W~w-_X)SRAbvFJD^zBe(N_vI
zPnA-go9HamqW-HXt!#1YCDJ1ENC>J(Hz5+Ig!xAe>m9|_;xID?yW*tSC6*QYXD}6l
zf!%TVxC-U=n5BuUw(p!tdL`1OO;2iEAPL;94S!{I!VK;5KqbeyYti6fR{sDa!(rkr
z@>-Nm`{t!s$H)e!DeYs!D$`0Cvf&gfd~NxfwNba&imy-W4qkBo0I_mln|$Oo39642
zLJeF6u-n122-U5GS_eev`DVPJJVQ@??6tY?C2^JWHhp|b(&ciJfS`X7!BWxBxElhe
zYsb_I1sTh}R8$2GS^<UtalGGlPAsXd;r{@UhP)L=#6H+f=eN-bn*RVV6X2hPN;>i7
z=Ou4IVXO);{{YasF9;g~xp{D*!pZRaja=CH1iL{E$DfEV72D0IDo@Adja#nFmtua&
zq>fR77qPMpj|G15b0gb2+ZTTvivz)qNTs6@xIPhJ0v{@bEvOy0v{7-rC^p6xkPxLl
zQW)a~=&!h3w_8e#F6%469|U#jFY=6cRc3%#@E+ko7_DVgm$?u)Ja-MxJr`gL<bo&{
ztAH}A&s;pzC?z9hb7WR9Mscm8A>$pyImvwH$J|zsqZ+C8>aoCs*TMT$J=?IIqfLsK
zXcWd;F&m(+ycY1Rg17>bV+Aiyj3}shc3f*MuVTZsZLQfjjTD^K9i(A!KF<hvZ2=|0
zE%ZQE5CP#~6ZIO32$!0V2`-nr>{sqil`f%=xyr~=DCI+9L1Alr7ttEhI@diWFokU>
zL+y`WZ<f7ADorjGbQu-L$c3KUiG>BAN?*YM9yN?L@fBWIc@PIS{{Wt4#tAK1!q}c=
zBaQjIng;xL{{YrmaE&LGp(O-VASIWu6>U}Txc>l&s*0<Ti2<~ld}{<5HfPI;JkzQd
z%1Wdx2B%hWE}ux;VhZ4WQqfMdRNdCYo=EpxTnavcT>6~#aOMS*{XZ$%Fe|Jz`hr&o
ztNzS*V=2}Fy4}Dab5Nx!(`k6G+ByQQ=2aZK@pFVs1+1+XLDGR>Ji>TNw^?9?)NaIS
zmBTh_CtUmz>R{#NA+w-WItq;doUe&>e$Nv8Ais=)^GoZo5#pb)npe%sn>7*d->6Y)
zvZJEo($ReBJM5L!D&6d8S@i>f#C>ay*xDq?lUmhMfI<%Xg%8T2{1GBz%~)EcD~zRf
zx_xUMVy^`4rD6zoUHrzfU~h%N#6p-HO6b$JWEL<v7{8nd_ed0~g`u{Pon#`=ja0SO
zJs?v4;o3~u@1`fREBtR0u~#ob2alp#4n=~fX#A#<z~A(WjZ7B<xh;jV`c6h~5WK4m
zvj=B$gKE8x>b<uB3kEq|BFX_rkWVX<ajG9MIZKb_SivlE((0Rqvg&o1vjc1;MN}vB
zmJL%(`+(*G>B*u!=mw6u2tul?Dd~p>1@clT3bc3@zYI$2h%cE;P2s!Z3`=5S+?C9C
zk+AE({FSAPNVsy<H4&*mRSW%vnG(wBoJLTlnVby@#|6Z%G_b$xa9uP}(EuadzA7st
z-UVTHFj#N{+XZejOB=rw@hyV0TJqw(Ky_bTyXni>UZBrLWnG=YRkXp|tAK$aD@GQ~
zFt|Sbv0GkJJd=bMnKo8*&@DwJ@*OXBj~jp4Miq8(F7R)j$%HI761iCYvUNj;Hwn~i
z3gA0gLK>&S%pSLVE*gqH1|CUrE^7AAs~rv<lIo%ic}tW^Cc8m}XsK~WsPq2-XBK5v
zGn(;;h%G!!o~Mf_#;8!cLx;pGr!OyTFpVDe8hWQ~1RZv-4=u~H^4nyY{X|LH6IFXq
z@I^6_>4*L$TvW7H(8UE5`LhHfg(=yxiSlazL3hMXc^bF8!RujPItk6_ymwbfIDie1
zEblqNHQ~v@qqQZCxaFfP(o;=u15qYMsaM^_%rdIc6<6RX;?tNr#+-@@lu{~9;BO`P
zS)Ya&MW8mp6#mMD0<c5bT-A{d6e4a4b8s93#H@Q5fyE3dqiW^w2v<4I_biCnPCOS<
z#>JIWgxd&*1NRqzCtOWEY~$_T2|yDlTQuxJ@YpdF5nPE>S#frj&lb4X$}4!K@sisz
zIn&}8?+oeMiUA{@twIA}RoJ9efNCBbbuT2ppS=f_OL<k=tLPbE5ruf)NHj?F2{fcj
znL7HSHqQH(&upM&0a&({*n3!QDmb+nacx5UH0E=BeQXWc6?KP#uH{}LY5X93`HXuS
z)hiY%`4HMMD}fXx9J)bt4n28~)H3o1p)QM$TS6>Bx?NvzynA~+5iKDn>>(Fz{{TEu
zokK1X#8k+fMycI*#24a}prCgy<Q5IT2-U!1gp5j!wW{q8HL9vuKnt>snE5VU$A(oR
zELEx*3_6$H;K3UU<gUC#_`XSO)#7=svOQ0C%$7G=Fx<^xdecQ!60{!INDHcp><gwi
zxYrz^3ZP{;q_(d&7b(9gs@W9T3YIH$3nmw)zEsIeTvLCM^nAO7Yl!aL73feEbHg6>
zwN-E2r7s7rWi4g><RSXw%o=C}aN^;r(Xe<480y7A6W}e3K>?vhfUu?+au)4(q`pnB
zm9#i@9Vc^$xH9P!6?{`(r=6m}4cQlc;#usPHfhmtdo|~&#MuRfs&K^(3uxYQF4_aN
zQ-D169=<jjKoVB3sJksy7qgh2<i*?JA#bOX`{d@Cu2Lq(@PecHF5X0vsh(;HyYweZ
zP1Q&c;N9F2a>6V6l^#us%ei<4W4Tq$%1(>tB^ubqf%Y(HDIUEf4HKVc)qyDMKJqJD
zEdwr~?cse~x8$Cxlttp9auxyhMaC9bRm%ixT`@xt=zPsDYf&Dw@%lB71#j%O;`w(g
zC|69XUDOv*?B*IR{P~nvU2*j*wfU8JKjR7sN*CfA!EuH@<qdYLySr7wBBez%U;hAR
z-*8!phua7dWqO6tZ)~Bx0rYSXc$6sH@FIj6T)*dt+DaCudq_+%D(uv?Ss5f!o|q)7
zCV+p9lhD4!F+Dg8SU)N3jR{ENrKkd1;Q3ilS6Q`n_#ja9v^_G8qnuW(?e!YL4$TcW
z4rFA-aIdKE1{#e9J{4=bZR|j7AgRC;o*jXf?hbn`nyR|}p%ozxpeBDH#@BqqE@zv;
zVh5)i5T>^nc?R>Rh^jdjUiM2&3hE4~!=PtkJjH|t;CG(vd6a=&Izm_05DN73$i=+z
z-k|ZYwwtiKAyA9W?*i+@_DBceq0ok@6a=f?Tl2vS2U5#Y&Ylq`JKQvPSsk<!MV_)3
zoh5!GH)p77g9srvOIqC}_NHS|d{wJ__`HIm!FpHh9Z)aJ)C>~e4kNRjV>FFf0)e3@
z3YIHp5OKt^JVB)+mgO5OLW|pQNCQ#Txh8-Du59dEV9i_$Y76rU!+9LIG<0zXD?LA;
zIforwAd6+d7K%p}IF@pL6$P}S@6-xKoLrb8xsJ$QN=dzn-rI)p7!8#F0E8k9xLtz0
z+_~c@OHJAK#uStVk9?qFqp8B8EsDHte#<pnaP`v}byz!Iu2f^)L&tovVkxH^+#}>x
zy$V!&n_7;Wf=BFsBq@KBwF@=O58QsZL%e=RiA{m{l)-y*LRgae?J=dNiL{m7zG0Xv
zRpk!0xt_umiD2zl5K{djF9KJHw_^6+xT|q4<X+XoG1i_^i0v$HjjdLAiT9nbSDS#l
zXaJsTn2n4A8W$oCEet}3iZx}**FbKpF>vVZSLB558l-uLZWz`Pe2%YWKqAQ}0;Ghr
zHTk?Cr;Id!;r1Z*)z<DRDz(ynYh&uiBhM94D_2%%AFDGWKxmch8_C6xq8>3`-IRwQ
zZ8G&}vK%%tsi*Kmq+wVku%gcHB>Yc}-Kcm)YMLg2Hvp(ogH+2oTR^_?VLUiM=m2~`
zgTK}N7*MSNg8OZ#fjd+amAsMOkCAi^b2zZyP}X{U@dc52^&LnpODL?eYpm-V_u_x@
zB755az}`8i87`jg$iWoi;IyoIWn<e)Tr~AJYgdB&Ko^_}z~x{j)JP47pO#q?*S~DT
zGcLXPnBf}g80)ElES97hZLG7fAJ0S`K&q%~qKas)CFWESc50B}qGF+4$OTzax^JCs
zYBo%i70xPhnSHXTm&b`#RbJp8owy3})jce9Rfi2qZ@u|8TsV2y%?AO&xn6=`pi<A2
zmYkW6{EX0U(XPOx2qodZ=?$?XZQk9C74~*{_<_RS(M?B-@hF(A8n)5^jy7;mqJ$!Q
zsBf&MYqtUHr3ky6k*&gu-&ByVa_f_-*7zd4zjA(+Dle}B{@_`dYj%!0`jUjoWHPL1
zg31oewPoCA^0h7VaA_Lj611{8CQ>~LLJ%>WljHC}i+5dM+H%@ff`c;norSno)rQep
z)+~zhP*hq_YR=H|Ssn=00kw$*gcE>o4aN2T7ca+k0ffy(xp^n0Vm5&0B>=TzprhF+
z2TJVlqA2nq&xUfh557KPh`ZN38!iM?Uv2A>k)X_9EoZ%!!_!(Wf;ULCnh%z9egPS@
z*h+vj0liLT*55YAy}q|z!e;kB0Iba)Y@c-z1w5{nC=m#r<!1aN*%Vqe%@|CDZt~}%
z6~5o~;7A^$S*1^=pq_}c1$m@!tPSY!dWcXTC<p<8a_1#+9d5N`UwBSen4N?yN=+6J
zeXobaLrev}k1W$Xf5NK~H5=P$^X}z^M%p!Y`h|v~)Ghs&Dr62ZKAspNavsO{SYj@z
z?0zO}idr1<n;GFgfz&<uk0$}KmwEDYAV#Hp+JG@232OCzVo35J&f=cNmDh6|F?_P>
z+R|l$tF^9XH_gG!0AL-&09vVmvB;wAXkv{NP&-j7$l9uF8N#Cu9`;)Q021(`*wKBF
zV8d45cYx+vij}*fFDfrUoC&krsB~!g=12%#^crCp<)Qe!metY*M+<_%5ayt}3;9!i
ziG)N2V4^0}Dpk^4ceITW6vh%;nWuj+Ptm<?Wl|0ILDtfJ#u-)dk$5EhD6*Jh0qZJg
z*<qTQ$T5yAwME&~e0U#nqlODAX_W-4iNUE*DLJR%DNZCelqrqN+w(%?ITy948>J|>
z;4_&(S=m2$?_9dmWxd`+OTTxpj6kyvyrfiuG!0g`D<Y<{j&Iy+ySm`StCaB8>tQ$^
zWiJrZr=B32P^+lPPy>@JY{LHlVz{W`mKf}ERR;?JtI0R=^|W-i?#SKUGZBi;tn~o_
zVPLZ3Ron%MC8ks!CZ4;&5jYi4rO$lT9qG8r=5o_<?R~u*&RI~UDpTwUWat(a3A&c@
zyPHYk<$<>#X)%WAZ(2a`_QuG!f@E4FThCW0mbI;}B7`?(d6u=-K)lL+i;SZvKFSW>
zAr1CJPrHaN7XDh5%7tvd(iZ`Fjy|Iygn(59q7PQa?LRG4T#;x<{+B6?@#Uk;O>BYI
z`(4IAD!q>|(1)%TDSl;sESSdRF$>{a7on1zT5Ii=7xGA=z8KX~wHA)ei$;@(*(@|4
zDa=)J+4h!|Tob2;EQad4&)iZOf;$NhOF-r^zJyxNrO{4e(E3e*$N{lRab%<gH+;3%
zz>9`NU>A12;N%>)PCD=<W`}_c;mLW9lG+a_QtDxPL||Q)s%zktP@)JxTg269aR*Dm
zV#*{BQx^RpcyjAeIKeJ>Z?7^rxh{{*j%jP%sYLK;QlsTNB}d|g%w@}WIUg)CoAs2<
zA7lm9z!<xcG&ps*3NA1P9*XFQ13@1&HI`2A1JBaGvyZ1ohyMUrpjGg2JQDu(9Mf4D
z#W)M&XSu+X&@Y0DhmxX9p-R;Yu|laY%%=YU`)mvAS(qLc-IzSv#xN-rO5(->k5$dI
zL^(M(zGV~zpv$PFq_`b#%P<}~`i+4t`-y^J;=2oE9SkjeQVQGNJo~6^>>mQ+<?R45
zp-qf!KALANu3rHP$@rG_L<8u1&|7k&1#YpDdsm8KE}NjA3YkvqtKmsNyBp6Pe!}#4
zSLD7YHAboNNb_}a2O3M}1guRZkRs>WA!^{6VDK}<A%xxKcoxDb?63a-5GZ10K+c^6
zU>Jkuf(BXDT4FTCfgAu@L9pOWEqJoZlxREvdNHt=CAQC^T`D%K;8fFAOG?oI<0*2Y
z6-tb)wpvUhVu8~D94zH1EsBY1=h{C39r0yH7yx#_8Y1kqN+0ND{Ti`K6Gd8zpe@C*
zs@t%&P1ajOoJxFFQ4n;}PWDk4<p>&z<QQ56*USdmYZUHS{EB6zO&Dhzab+2-Xht3r
zrR!A@HUpD~_G}m8%+Xlkm-bHWK(BL9+c^X;ikX0jZ#6G|aTk5Z&WU^=;T?EzLkmu0
zXcbddn2Ma-Cb&qv<kkVOqece68@<+%A9oy#vcX=?BZ^=EV-K~x_}Jib@Y|IWek$|Y
z!zW=CKIZQ%RvNiPRHa+BZTBf&VcA4Mww@S@D$=Rm{!}&P3Mic>iBddrl1HW#wX|Zt
z5b!@B@B0K^wVZL>qSqyM%}3ufwaC(Uc2@weQj8x|Pm3FjZNbto;wsK6p2lw{ev_{t
z%DH)8E37C)C^=Q)5ZHyggghhG=L%M?!d$rEPe@ZYJxHv^%Kev5A3oEElW5P8j~y0m
z78YwaAO8S>48I`T_(|i-=3Lq%;VC6|!z<5VJi>GYtvCUVqPDs?_eqdgw|Qe(nYZz-
z$VRL+ZN1gQPO@8SU%(f-`D-Fu2<Ha^3^X71GOq-yel^@;l>WkkU#cTbwX(gt6oIs_
zc+^2uza$FR?7*m0-1J9Ub*|$~mxFLcSRhL_EcZ&;!aYrzmVne?`TFEuD%e@#g<+zG
z!Yfz{DEk4f2}qb_D@y+WSn!}noX|J78*YQsL8r+$g^CF1DyR(C_O`J52pl{@3n9-f
zrjB}-)D0&4S28)ZK;h1jD6G)>L2n85)G$qTDy)Rs0=LOz3GIe*WB1TXVDIu^BwW}~
z>KmYwe{x|B0{rQgiZnh6htY&$*EJt|Rp2pnD7$p-CiQLw3tJpQ1slrU^9%Yd1|t|<
zU1*vog@Vx3C^)vI?bk!qhBy%w@>BIepbP|lL2(ot^ihjMsdDuIg8+_>#vQRgfT}5$
zRm~Td$sL1ca`w~err_l_NPVCX`Umks>lmBaY3LGAZ9O3}WZ;*u;{pZUxzmM1^_S|N
zYWMYv?x;=q%3^i52G<Ig2w%+@GogZT@pwvB=jtdSN`vhpgU!B}wn~+TBIxH4lmQEX
zAqN*vg97B`%`|0q2V@)3*>gAL@;DMZHQ&TJTa}w%Bs!_|K&wvh$yF#FxTntOz<tq|
zhq2G(Q7!N^BXShIn!1-L1<>2$#H_pROAUZ+JBI>aie9?BXjGR()wZnj73Qy+mS~A3
z_$6HNcNKVHDY*Qq^$~mv+XUDIVSIokA~cFoi_B6hN5{oZDZ#+^9^;Cm<#YwysLFJ7
zFwKUohsT%$2CIiTJl<m6d8n-EM{2BQ)jCV^vDk&y*-+^4M*%tHL=ae$fOcj&oij(a
z2ky!mdMYDslxE4IXQ52elC1P=EritQa=F8XE(duTdkSZie&|<4&7nwB8qeW@AWM7;
zEt*GWGuy&Iu0-3QqFnS5+F61t<bqMB)KmWeDSZOf;{O2cKuNqaznDQScQvD2iI+-{
zg)k|{R+z~U(SB~KTnmLrMDR5fm(@}4uO?9<e8CG~&JU7mNNKG+O0c%AuA|+Bv@-gl
zB_KC_+`K)T#2gy8pt#S?PM|zOo5zR?Hy4@$k3c~Esn0otp}7u+Y^y1%Y9~h#yq<?0
zb8tn~r@Jt6hODZtQrcU`&5Tm&>`m!NHk10P*&424sY-8{^f)-?qZSTWb$v%sfcdt<
z?iPVnmX8)-BEdW)ZBX}FeoE$b*1}YClUMO6(;@=QHHF*!n_nD9C>}_wh3+`7e-mvC
zMLa+*ZciC%`AYnXkdJl6%6E6uhqJqlPU5V&cOq}t=*Mz#DRTE{#;F)=Y+Z7#f#4+u
z)V)fFSdzZ?UkrKVbPiOzcqfpK*0&yr-?m<mw)n8W5RDWwh)SH}6{{h#3G`b6-27l#
z0L-VET?e8e$Iu*tlRz*bVSBF1t;tyQ)oQVsYSla-M&npQ(Q>CP6lGVdV-l*wH1cJl
z_*JKdy~U-jQ5x0c!Ah?}yN$GUtz<X|%+^x=yx+`r(`^c4f<288k#VhOev@%e*bDqg
zQW#dKdKI|)s<CF9y@^2iW=5iHXgnO;!}1+Kd=v7lYzu-pBojc?K8bV-Dy_ACI)QL6
zdtludBddtYd#gbMySg8?h=}+V_h>_!>$0@fi#bI@1@F2*dt7jT)n*{t0><}NQxJ`7
zE)(QoVW-gR-Kz$?qkDVklP{M;2g40KUKQIK3c0ldp?hj$uwYDEdQ*@60(+5W2B__N
z7QG^qO(<O)-)Y%JdLG!8sEG>}s(>#jSN=}>PAP2~P`G-b$vCtYq#mhEZ^fzzPEuEl
z7%Q|wGq!Eocj5JLAd8Ni7|rXzHUi{sfnc-5vM#*b%^kHaxk{t<m_<L(p^k5CpsO3`
zXZ>c*bQrun@`=Q4AkxLfrkb%5xHV)xm@llMVBJ&zziuHPGP$0EagkUzK$@{`CW`p@
zfLZDIH*~>ww-$+GNq*I09P*(WQnz}qSpX>39Irt*R>Y1anO?H}19ux)k@@z;)(Xs7
zJHKNc_vYKJQv5LD^?~LVgnm3_oC*{_reCV)VpaRNXaRmG=zhbSfO2ZYA1=zMrKbC+
zM}(AB5{<^w(5`-=DG>6(BYX{|nmUh31(S^sCfS7>WN8yfu3ZT3q8(q9#oYz5OGr^d
z#W~dsDSFI7Dif{}iiy>6D^9P^<&Gdmu6<5_u(7|n9}M%|Fl~7VBq>m-L&_QhUk+6O
zpi$_({{Yy<Fmlthk@=RB!~Kjp;NlNjX9sw_m6mq0iU3RH!Yv3W^62lPH;JIO@~^1g
zN>s5M#3<0rLDX9yvcqB)xP%`CB`f4eNgt0Y+eWAtutPtHo{H`?d=Xq$6}oJ)3-;W+
z6s01SwBHAmHPOZ3LU0rbXg`+^9qw>ZTTwXJsgjFChw-ja)Y+h#T4oJiX6^G8i=wyr
z5*?`H_B}y>A3k8Yf2=npG}%`hYt#emQDW*Q17mOEp-@ZBm&><lPA;)FK$QtZ8cPpK
zN{$4P=0;cJ(k6*(M2Ii|Kc&yBA~^n$4}_#@D?&M5*|FFu%X%!9J8T=|i^&V}k>-S!
z*aY~r0)ndhwh%m%2JZ_RKA<HLym)Cb*FNFhoitX&@FBCF9hDluH;maT4bq#!t>Ph+
zC2p5s^$Y0q`Uq^}_t2G4VibYJ+O)0!XudgA8z7<XA^t|5(i`yzwv?yb6BMcP^a8{a
z7hCs`d<ms1ZsHSxe#kdbZ0E(!yjK4J2DVU|hhWSa_5fX7l)w}>tGgsxV4@LMihv?D
z96K%_`wC9JE-or!eEWdl%oM7y%^L_?(_0%((rJXKqNuehFt7^wVNw!WT7zvgK1Vkk
zro=qfU-kt<hsBGWUlr9uw5Uq;=2tha5X8t$G#NE~LY9t0>+ep}!2zT%Tc*F;hFNb*
z8yJ_V`=kp3?aCeOFTOeYhdFrZ&zSh3a_!<(O2L#IT)eUPj<|}padzyXrN<{T2vwHd
zYOBO$Iwh)k-Ssr4Tc(6APme!<8GpL+#^qk*9n?a(mqpP411sO$UEbdm2&fLxqnbg^
z(b`#1&;YwuF^d$Mq~yMlLCU0n9!+roEz?Wn=plu|$U^mm23ojJ#(R_(R{VvkWRu?o
zp(?bi-zy7>?qgLG6jpb8{!TX*gzX>uQG68+zSG{VimIW3@TVfRr992)n$@{}iFEk6
zl$&wrw+uE2*UVEO+>ieNN=sZXxK*5Q<`TAA<(CW|QlVBh6k}<t!VN*YZlxr&z@c{L
zb=kGy%ndqkfZ!1FF=L?`Vc;$l+IU*i_NO3+pC7SX$Z}MOZ3Yx&um)p}>1a(2lX88!
zAgc!VhzOQjmF`s3gVDM_a=`(5f?Qd}wjwp_Y9v1ruvov4OWMG109=d8#F^g3o$Mm0
zjSFmN04zJ&gHwQFG4Tk<ijbNiCz&CsR@Q{oT@p3Rs8CBgaHK)1IC!E9s(PY)u4UZ-
zvwe}gp7H}4Us13mH^6h^h}g2McM7GqOgp>gy0=i<YM@(-tm@tV&R%o8ochiuGsncI
zxnG4uWr3aKG1a^(Q2ziUO50>uN^9elm(tn>i%XuUx137x8}o_TVVT6?FaSIA4Yn#e
zG_{qb<fCcl6qT&+1|Jy!FzfU+iXeG#oTBT0ABN!0M%7*O>Q@ZT{{W|}kuu~T^<^|1
z`22<8oXwQ206yDgZHBdGRr3-`T}ydK6KBDA=2lSU{X=83Y0ZRnpYE0hnYZO~qx~5|
zz<TgpZY{ojF`=Zji?1A*n_q8$J*CzE061B|Q4WG~T`3?0fBeOKPBphC=)Li7!`0Mi
zEd#~?INITXDored#UW|%!WXjsTSx;`h{R0RHf)MbZu<xz)SxKz72FHO6n`b<NM-9%
zl(%3X>&p_d9{@gLU{PDFtvxc)Agh!?Rm7(a{Sa{iY|~=YwBSPc_YmS#H5}V0Ta|wo
z5E#F&%y%vWdgqmicH?OQ4W+1iQ3wRLw-NA1m^O+u1moOww-r+85pv^mc&u9f(f0fw
z0<HuAxC{OnIZ*Ro@>AfCge&Su=)kL&BCEhKubO~2C=bK{td?;1Rxy_x(ZH~%0n`rd
ze2CSlyi)HqSrgkAAhz17G{@0t(#9QCi`6RxyAB&@D;<|FNmzXuaq(<77;cB{2uw!p
ztLCDdJI{t8lsk5ePGoFngiC*M)G1%6D*#An!-|O7d#PBYt>G@H*g%QWhVm=S1mHBl
zVkk=8gTRE>fZWS!0K~0DJ41m4@kBV;>L*JUiD|9}Z}OuO3bKL02t5y6KoT;XsHFQN
z_@obw5a*l9@#ZGjq<LFNF6R$0uld0>fI74Kxe}Yf^R6E#jt!LX=pw5?5trV9czp^X
z2ab_O1YdwIj|4B8tWjtA71GgCmNzU0ri*=~t)sYW=kTe22X*+y8h!IM=xRO6h^S~V
zA20%By=oj{Tet^ERm&^OL<fr~T7~EMRB2M@qsIX?chy_~#h`Pfw-<nEX7IoWX~V#=
z7IAdsyNlOS{HCB@H4jFC#}@jI;$=D(@{nw`MZ)4P>ih+a)tz@I)GS4QcqOrH*Mbm8
z+A^O#2c-z&P`$Ao5#IM38uqohWs{48yYk(_xHnUS=Djf<EFo9&zIY{Pd6y_KUm6P*
z&LT=(Sa?|IE`j-V46aJi_KQv__{?z&RqbU|<m4^-j|TaL*rzoDvzC>N>f(9{p6(Xt
zhv0J_FXG#8{fN$WZ_e`?I9E?1zu0`$Y@w^-x$!8VtYW0@phPWR{b?-J1Y6_OO?U`&
zOCU>GdYnw>HqIQQwN_H}^j@}EE}|NZwmPiR7gGflVMtZ7!er&-lx|;el0t=hXpA>$
z=$IOnfptL}wB)FBTP~5nH4`z~_2_$r-v=i-=4X1(T+n5^<4o8Df-$H0QPtv7)}WN~
z*2UfS7lg_^tTP`ptq7#!C4F5i@XjV^t1q%NLUJ{P^DKIyPV1=jls&`u6@lX6s$Wph
zC=e_b1;)JEh5!o|Dn?(+GghdHDY43bNDR9{zT$>6k`jc8x^X2))2dleEJY*intb85
z^>_oDy@mv9U>49(ZAC)3rwEmCx$(o$!9-GW?RWnGnMQmVrPI$imeERyb)0ncafnvo
z#{>m`&vN69?`4768BQOoTb0D3ySp1$xkqC2*<jx84-YDVenH{{Vv2c0LgX*h`l1|o
zJ-_Orb{ufcqQy0cIk9EK16&Er`ibebhZ{PCRpiWV{zSnpsHQp(O&|Ta$e(J;#YC@p
z03I@M)^RCRspiTI7I_ou3Ppiw^Dg)Z<G2w*0@rF>EHj2(R>W9o!>eiYGt8=373Z6j
z5h+fbN>$$amDfh_^sD~>Qr!9R0G)`w1g-`%Wzo5+PeS+atYFsSQCfKzjw59$9}UZ5
zThAT7*wck?4jEualo+*}_zQg7FW5|UH03y4HHM(`p`Bg8=Y}eturR=z!rvw{W2^#A
zq1WOVFVS7Cv7n$l3;c}+F9fsXj@yO8B1w+G8OpeiiB~-R;*ik>%K25ih|AO4bQnM{
z7FKqHs-#{FTYF;LRY%PeL4l{ANRR;rsuj@%6%yE@qh95gd@Hv!U`!r~W=ga^KtGV&
z7gm~I5AEL&G7MWZKtW&@3tUqVGWBL+X{45PV93t4huyOnjIX9>fvW!iVkx@vICxwU
zB~WIc=oL&BzO}sqi0QP<;)c@wN4c=N$}5O3pn4=G!aag#9}yc18n+4QV6PGu@<CMv
z0)zq)tC{yyKtU@3;UJ#GXB9C`%i7cmfRk&Y+xafVY%R+Q=g{tk-?Ypq2Vsbx%NKhT
zh`z_v@-9MYII45Z?+D=Q8n5WOWrNvq{t~M0TalT^a)-B8dZ>o;P(5Z~A1o_eQfaVH
zxn#6uvJ(b_AyuaeB1dH%M5q<^mI9*|ZAP|u{{Y6_cGH7J>tkNkE~q?0%xqfkEZTu*
z3iSl86fOYp>*VOZn1_1@K%r%@7hrg~z+x2VNpGX2l|t0`m`Q|~PSc7cBim>whZ0;K
ztlNC07==LcY_M~u$H>tJq=&g0qTMj``!^hN+s`MYk(aDC56nO;2};FB3e7cmR$(C;
z0Z@EdR~5keGSd!!ml5M*E4`{yR<gYl0pah=psmwCsM8#|=|x!fCQiv<J2i=Az5f6^
zA&nR<q5XofDQ(d6=5Ro1rn~`F10Ei$%+o_(GM;B7iJVl^9T@U$rY3gComCGJemdRP
z>zGh;@DvqC6)TUdh02&+oVd|Au$nm$4g-9?7tKLYi-&i=H7-%`!(#-wXKhi^Y#w0b
zYWWR~K#c+TH7V%;71n23A0Q3?0AnO+ctvdRo;!?`;U5Dk;$){a8RQ3&?@-Z!X9rTE
z;b-$4E%K7!dW!}0h|#?~btCCX%f`^uvlKofy>{OOGW@DBWx#ud{gN0zE!NfDrR9Mw
zKv}03G5Q=_u)b@@e{i;1B`<MZ;Vq)KS2UDQMy`keMv(g|a%-kFeCdjdL>Blqtp5Pb
z+_=ffQmfxmfR`5fc)&Pn9lL+{OVUj(Ykp<CjqRxNaLw}hD<lqauqhp;!@W;;IpE?D
z;dCmc-V)v6)?sHkd)gv%TmV<;A7HvbyKe26mKAp5I<XWuch|TZMKIERkXMB!6xR+#
zFr-Z0C+mJWh|MhNpvMv{TPlIWEX!i_ShmVsA)>23?}&d9S5z(-vMQP_{!{_ljltdG
zEjTZlY_dmMqoVMP=6piF0ql&fuBxo(#+5W4SE%Y1y;`pbbs~;*8k!fLcu|Wq-k1Ew
z)xbPgsYDa2YVf%OH{l!lTV<3*29d=Xl@S`{0Ew1B@*%`3c8Y>wos!NTSJBFWC~X!L
zyi5x@ekLw0&_eO+7EJ~Blp9%LLx6~!E40qpHqQ_)_YZ5a8)H*<cPN3rbr;zV(d?M*
zE%V3~*rjE+ABQkwV1@KLji&51;NmkVKp}z6qQ!ulX#FUd?3BhWJ%)dTqD#W+--zY%
z<gQM~<c^f+%|h+)YBG1^j+E-;E0hqa-esdTRHx)jBWjO};^L@nJaq$^*e=8Hk-<oy
zpa}h;)666TG-|5EB8#D^>QgLD!N*(Ns;+?f#)H}BQYO`x^r0Ilyw&&~*+b6IurH=v
z^6FdW`S&;g*D4F!`Wgc0_o8h9<I6cM_j**RNn|eYRY#(h@9HUVks3Foi;-eN%l<0>
zKcuOJi=5m1mglW9K7+jX20{c4B6wl8JXcmz0ws(+bK`?4NiJxDuxrDqS;%;ib45ZZ
zKv9L3>wPGG8FD@+XShH`#?BG|(LI2W3@~c*{Ub3J_7g&ZJ<wWStW+t$A&`r`<Z-?s
z1MxRX>RZ)l9su#;av%V=s^+6w9S4<kHV%|fVH%+LBTjdTTFM2LZ^aS4Li8AfF*jrW
z(K6wIvdF(z3&kMpRVf)oO_)V{=NoGkZTr|EaEdt|sy>-KjYIrI_#B%ul&WjGfB7ly
z0SmvP7joHl-B0x@W35Qs1cU@%29d9z0u+$t^)hl$SM21|XUxX-pbSt-+g90L(7q4s
zoJkSz>1?toHLjuq4IEPi;+hQt5S~g;E?elPB{#NY2yBCj)XB3}4<Ba1tYfyv#*#~t
z(k@qm--|9p3p|3|askXhMipW&AyI$`FI!U0#IKU-bE^j3rs6opZlA?_UohhWThd(?
zC1@CnW*C)52Uch~y#n}(R}0?#hc<&kaz#Vfvgm|osS)Z2zEgt70jTapL0MF0!F@}7
zC;AAnxAt4msnX5>a4IzcnsP0O+AXIJzl1gOjI<)QKps4}goS25x(ofW97W<Rzr~lS
znm&3WrRB6$FnA+QC|YOKF2SFp53o#ufO37|d-4AOWVE~jxaK+KbxKn~R^k8^D~Zjh
z5#5>9iAD5wMx`qB98jbc0jl=TF!x(s`KyZ5oFxZ51S<S`dLomrWe0(n%)3b!be6~j
zCCFp-a|{Rp;su-)r$f)&t^msB@?JjUu4#AJSrjD=dC?quYec1^sM%Iv=aK_eWFf5?
z%6O?$PDO4BPGJx(i7A>a7?1~8p}(|d2ev|yC|{`IDwM5v$}*ch4Wc9kmz<b-fxI5!
zk&hbM!PHY4x7pCY6OF_kQN>H7-)R}P^;R6A)$UMz7dz>izWxM)xuJu5X@RPx)LW5%
zU{>aY2G$d|s#ftCucFv|@MDAGvoTm`StqD@I_)WR_R23*0e`#TB^$-w?&+bI8CBpM
zwGqClR_}ggK4)Qf@OcU0``Fvq2ddw_!s`=4sKyOm<0(^)74t1`HdWna9rqDc0Q%j8
ztOhtO9KKmb)>g-09<~l30=~5|VX~S$$-6at<$<-nNmLf~UCo6&v>LvBOLN?!F{iCf
zYkE0I(>k%xD(*t;`{(9)FD4_D=EZZYJH^I<SuI<xQS&vswN}B79FRTSb}oxu5Mg1C
zXH#N!F5xMol`ZnHElXWD0fkf**90@Ld{QZG`(|U@;n`$|3ZzEEqxL{=Lc}IPnj2tH
z)dm>yRFl^(91HVP&jP#10wEfe`F3$UENs6N8^o>KBB6KWaW3xSt)wM+?j++tf?CLw
z*c`Rn8W*P)q8DExthFd;(hZ}HfFGeRM3{vv06nMqH^U|71COhXogF$IJ&;>jAksn(
ztV^gdW!P|~zC6b+&S4D{ZyUD(klu?FfVy;HF2z#3m^6qS)-W_sgYarj8u1-!a{W;N
zuEQ(JsQmTrfp&`A9IY_Bmm~(MLIQLbPA8w_+qyT2Q-)nbgGOyPKB*Q85ACS(kR72#
z;*!PXfH^!*>0mcT0T;wq8`T?ez%%-OC|Fe1s^FYGb2QPS)t@N?*7JD{*qZ)yHkc#f
zSp9}g=WGUtnB{~o(xJ~ZC4SrZxSSY=o$KOWgXr6Oxf~r4u_Ejb&fv^PQh-!Y;+lYT
z!<fYgmLhO)s({M9yN7~#7;@dTTS|vE1rP|F?`Y_ylPv)pyq0BtNDMU6ayP1<kre3g
zMMuAW)-<VdNC8I<#AR<AMfoVWIblFGd_c&4AM6-BJJj7SMBHhi%2$ixH;Ct$t!G(f
zP9eV_#$KUk##KDU=6!gQ5+@Jg`S8LwC{X#mrrI0B%X<F+lT9?C$yNJ7aXDz)T-q|d
z6h3No)aX7j4dRq-O0q3G^zru(sH*xGBN==}PTA^PK<$=F`J0IaX<tGRg9}tF$ZiO0
z5I@*-8u99-fy+m$pA$;lyN;P4J773nt!Udw21c{bo+>|a>{a!$$GX(!xIa<Y+Z#C+
z1stqi<k`9B^^`bW)7J}Q#>C)+C_>1D64iJqnKW%pQ^4>t$N}&u?>_0{e1<vlilUJ+
zz{RGH!GT8q0PK!siVzI1hSm+oOdsN<z;g`uLtw(9W#L@cx*(ABYs!CRccIE~U4>B%
z8*5F252kD^9SAIIZ98tlyJe?<onRh)?!%+Yo&+}vm>9S51Z_B%&yL!x-Qhck_!R;t
zV138zP;wXKV5JMRJE&}BCl!YNqk{P<j)l4ocf+NT)QSqdHD#IZfaEuE3|A)(ZYsi!
zsfV1oW7>wU^ozN~)GT^}u>hV?+kKNb(R-)NZ$IFRZ4^op{22;&v${s^Fzoc29C#uk
zh!#PD*|2kRp!bPp6cz#!)xXE{a_$vPG$_Lw%Ifxs2m!4Vm?tX2FKH`6SA%lEz8uR#
z=t4MZVg|!X+`?CYFrL`RRh4@l*{X#U;T=@LPm7qVNFOyGYuguytRn$@5=2TUpLPft
zS5nQyILSx=OWU=1ooa5=xn=--dzyGBIo>|)z_&hHER&hTf>o2dv5~9w)yys$l!@bD
zMRyO5TW1BNTJ+vhjC|gNXbTbrDj5Q?oYH_f#mD8v<7ENDxs<&+F~=BX6`MyqY)8q{
z1LGGlKdxPStPDg{Sox|p6Je<Q%Ds18G)j%rc@;z|81B8p!L=btrr_HRg$Tm_Mcrnc
zg49}v$HED+;h?CvJLPsgoyEBc7MY9b7zm-Tl;!$m`+-X9%I6wJ&x<$e8NdZ4i0u-s
zt~rXi?FHAA0$8dE&lH3=SUKxzS3eO2w67aH3#z?|>a66f4cHzRqH}ORK&=F$&lf2z
z#EcSuoooK3#g@GD0=S?nbm3($-zrwySt`#>Oz~0Vrg4JXlg|b7P(O9&jHXdAG`#L|
zX_u0y$)}Go1G!inO7jyYIc1XkDmE!e)8C|RH2^qzwGannO)XvAy;Xzqk0U__UcKU3
zT-;$Ws!KV|-reaDZuvs8AaI#(0e*)u!6WHcezH*&WMxKy&QwaI(MxNJZ;}^_OBZZu
zZUvVqV)@=>u<V4b1;4pf&91^82-ED|nRkzkAz$)sB9!g51PlC)hy#EDl<bR`z29SR
z4>L|fH$^t#`WKWazUZ4iR4D15F@Eo~;-8fT&Gv!A3WN_(jaB%Syli6Uxh0o}DgYrK
z%F*SO^imuShs>)&T5Hnbv2i&x;vn-Ba8ioTeKve0G)^nMO=-DMeJC&8DQAh;VRtxZ
zD-girg-{&;ZzNmPYC1qF>Ty*!>Mb3)T7re@BhKDqhxEAQdL^3G8z~4xu5Y4CNU8x{
zOX8I*KtOql8(tZ{3R-1|&SFr2pmi%XZ8bjG$i#_>PKg|$qt4Vj1Hy4Fcm@%**(C$!
z$ilCb77mIOyG0*~?4|o99IE5@c%rDf2<RCzk8-@qN24hp+JfM_f0ux0T_T>?sA0=x
zl_3wT`UaN}Li~kTcuw&_Ud)F;cMq9uu=XAk*u)IWz1t?Smok7S@rDL{OKIk!D#q3Y
zsCj|?X=%pT2p(!R;`wm^pwbSNDT|ZVxQn4><dJ7IHf~jRFnq#O-dz`#6|mHaxzuPj
znoe`kfrCPb16I7UoJpn=*?lszQ+0vk!zh;l7KV{n2NI0w6dnm(^wguc3Eg{md)tB#
zqPCX6CH^dB0oh?+hCcBA!(t(&VQz%5D$!KHEmbY}i|x3m(cUzJ7)la=s^U&AQX`w{
zi;kc!Xb;MgzAc)osY;d^R)Tf{I0rs9{Y4cltn!SE4XwDnqqjzmax#`nOJeK=4T&`l
znQlR!9Ae7FVbQz!4?dADt(wj>mZOVT51GQj<XvNcuQ!nun^#Vu>PoISAPGrFFvY$|
zytj>?{0cxKb>6F>n6<5%@~l@d6FNlHh^YlC_%{eg%R|{GK!O7BK~T%W5_k{`nDw)E
z3Q7yM&EuH7Z_^g+v1L%RBNqImN(cjCX4P|d<*$X92WzmPXPI2h9JF0NB?Aiv04)71
z1S(u~1PWDl@Cj6IizrhumH{lI7##syv%zn#i1pJ#_JQ`5v(I^?B@2;KK|0s*{8JO<
zgc?~AdAw1|c6)~k>6VprzbOG>KpIy=Od6xaP7%vsRD#JXp+H;*LdP_s0riRziU3L&
zgL;+b0q)Msxk6IxBGft;arau{2tp7AgsyhM=@9)cD=@MqEEuxv@`+r7mxy@bj%rKM
zw7mhuos@s#xvyxg3$5b~{oLj@8+O6LJCn<>V6+us%Kn5WH5>4KJ10HAl$#kzg8t3d
zFv8jGyX}4CvRW$T5ki3THt{dR{FSm<(d*(gL_s~>qj-{FeMXAZKNnPvU4ac7gpTu9
zGq7~W$=@`~ZkqavfEG8pTou0)%&bK>-Mq=#Q-+EhtJEjZ@&d8P2imYQ;PvdiX{s+!
zC0L9Ja-38=b?IRNL3^o50}p^;4>_?g;a-Y`;COoxNa5n{GsKX;1)TxyL5zMlwcsUL
z<B$L_U|D0mt(s?!+mLZ6-r<*`&<7}5KG~N$7gU>a(g|c%LI~<lsguTbXl03&Gg0tI
zqBT%bn>le0i3&K^&cRhQeY=1o^vp_8`-3Wr^Teu9AGv6?;!`j}-<|3xpfbt`kgJxC
zN=3G!+S#$&SK0Hza2o#r5{@Z;A`4oV>^##BCAD@`rG&K`UR#H-ZPe*LWdXY12s(**
zRa~+W&H!rw^$useyXOEA{zIL3o+`2gp`6`kA9EvNfod0cf}(jPJ2sCXJl8PeFWkx#
zJV(UrUzjf|H2p(wd50-fgk|*#X85nUP8NK}WgEw+8JiH=8x}#GDw|=;XHRp~%Aw?d
zpwMXm(G&uVj}$|Wj;!KkGPf%?&2qjNhT0f=$AAN6kU%e|AvXQo1=M>m)!4C!kL48Y
zB7_Na56p=w&=jI>)DxnZ-PW-8p`sZ}53o+d0ytf;{pA@k;s}(MUTe2+oTMdUOtX5d
zHrTqHvLu_gWy*6EHLXl-52g+9*8t$Vs3-S~^I|13WouP7=EBwZPET?9FDh?=2-bv;
z4Pwm;X0OhYvePDC7EiuB$AD^ji$TU3?E?^{EJypWA#=8mYTiJTsXQ0@2~y;7zI#<d
z+CI`e9t=FQ%)<;r%Moc(=jBwYeajk+Ja=;30}1SbAF57hVT2ilIUi&?$#AeAk(iQM
z0Tw3dv%SEX@ze#mbAC2A&;)CNEH6M4ZM4i`)UEn#j{HF4;QRn=uc)cXZ>}bJiIbW;
zkuks)bgE-6uUCZKd#K|B;lpI26}s;`u(IUj8JWwUnC!#sxgMjUF+)nYi$c;kiOC27
zr2~|B^(&(2<Dlw3E1UbX`w2^>$2}yXl7+esS(4QF=TJewep>DPmvka2&x(lK@pziz
zEkQ_BxyTVxbVk5h`u4{&6_&0gu6W@trJHqZPndo8h*ovt<!#xX9#6TvAAyLsY?k`%
zu2f<UY_<-r2f8AcIgLqZA3Mq`HCL6*N(Keppw@OFPb@$=kSarhpu9K%XdMuBzSd;v
zluZK}!UKVj-fq0OnPOEbOB>iF!77LX2aCwwzTmdy*svRF_)xAiusO8^eYdgRl$V=R
zh0uR<-sBA6OQmhqPI7QToLnov94b`-3XL_P#ZEU9^AH(Yv%=*BQn(_thtRd*gv8k3
zr~d$8HB)`FQTuvrc(N}UT=w0}t4L72MmDSn)6(F-RF9$40)#KFYXfe-lmdJ#U(*(l
zrkr6s+^7^<79h6ZhA(Rnw!T=QY}_}#;2~ALaq$9TvH@}3szGgmh$3)q=oRxji4w1b
zq^||BL2bua6&KBMXiW;o?)TxSN~YPs9V?P>s>5}lsB@!1+wL&}$2QB<;kbvjS!E?+
z0^6uKyZarY7wD}@s%OnY7RiR?xrNj;eu~4|BJAAy7Gcfa#z3!9J{zn#1%;}3^xWT6
zPfb7mlad}zk<@tQ^V~Y+KwX}Cig`fws`D}4%r@`I9E1hF4HBWPa73yLc@&buuz9-X
zpo&B7?pr5h?DA6OBu1%c7^ufJ8{)131-5s}n(h$2f6xB_LL!A8Zd-PGmA!M%cEzHl
z977fl{4$pna|j9X$o)Vag;UtXHBoH7<HwQ#99KCUBF3nDmL2)X^eT<g?AP=A)@eH{
zMKsPwddXjyyclYPGldUmm2m~08)U*%BY<ip4Gs0P_=`M{Ruyu#R2p%}YtsEnZ0i32
zq-^H*OtpZYILv{Ye&Dnar)`dds1<%XjP$Pi$b?(AqqUkl;&=TcT$?)bfG`T%RRnGq
z>0iM7vE*%q)V3u&M`n=imKAXrYT++bp*TJ}TPQUd0h7%7h|?7(HFD~j<-F860=-U0
zA@oMKjP`}&3cb3SW!C-2aRvdw%rwyIbJXg@h>(Qu0XRnalo&3#$BsP9vf`*bL90k3
zkQIL73bzjx)L7_fiHXj{#>NY~@ZJ#Zey2!$%NQEDO6DC>&xc_ZgCtlr^E_9~L(Wty
zuMmYsg71gKJZqnFfom6>0P#)<{Fze2I!*R<3ih&He`hu&PD$_2nTnj$z@)(7+!sS(
z=@0n|6a(!X3F#KDd`gOA--@!Y!vVWJPCA&c2v4#tiCL=^w#F<7$H^#+-mhF#4`UAJ
zMrx7&0LWmmZuU;Czq5FHuQX<PhT|fskh{b^p@a&>s|n_<9`#Hl!R`?8V%b+Yb_?q0
zVc8bx867Q197GL7R}B~TNbS{~z`^>sg{PY}k212nRX8o_52T5Pu6lG+FYWbJb#qS%
z5!w9x0SbT}s;XRF1^AnVZYC7wn=ZD-(+Pism3y1x;e1RxhuFRW?R77VNgBxSYUz)G
z5{J2TRZ@p0n;gwHl{bDZAcDix5sE{nyZ%dP1vc0&7vd+vDb*fhf{A_uyMZyXb|X<j
zf)kdw`ia==4XCC{dMCIOy*a0-kR1>go**q|>D(ab7DQ!JZi3D#P~-*OmU@epPI3rY
zQP79gv;}{Q2j)K9@X98yka1u)C?QMc$cQ`2s8=M^KUVPlkPZL|m4><@^;2o_l7T!W
z!C#&o=HjBo6yFu%Raj7(4nis`KztMEbC}6z&>{YaLeXbgMcg)8to$z-T?OA5j8U?H
zd1+r)1NyJ(3qt-f2{X?)3sE(hwLj*!74;^=#Y@gFCBoU<WPVn07WNgC_#R6a2;c?-
zz;!+Z>Xozg1vaMA_)zL4+AqmoMR_O5_q4LF<2QYs{sp0!X*8}%ha%pAw#Cu$DBvV;
zwr(9Z^A=WSAIP0E9P;e=Yv-umF@;p&FkNMMxgoW+0G<3hcd4MK_6S^H3`%8NT*|1B
zI`}DTDX-HVgZ{*{m1^{*w*utd$dyo9<*c)qU`3^R6-*w<lg9|H1(egBcR5`<;w}fO
zo+N~?Ab`Uyn(`4}o15kg=(b$g0w~~XL?$4+M^FMdleMe;K|Lk*B$xq1a3)NqHOu6{
zf++%po@&$zd>f^l(h@5LtJq#8WzrbaN<d(5Uwe|8ESIWo{c$KMmbGwKcNz*8gOBMc
zFf_8exZ-h4dm;)NEG!-_k?i-i(|TBtUOoT{5a4Cew0CZ3BwyEIKtsX=$8nN_S<4t2
zC7ks4mca>X+Ubom1_9HjYXF!-El-!^j%ZQ<_rytB({x-+!)JE^3eA<))RIgV<$$~9
z-CVi<0Q_hCN+BLLf(W8sa|LvKB+VzFD6k)rnjNUL>znfwR}{@cGIG4Q3ZzR^rRh#c
ze$G@H^gB3tKcqO!SLWQF;99c&BtH)sDorY#U615dFK_(K$laa5yy!6X;vp>J?Y?4G
z?s6&`Cy)W+;j0kcb-u~9s6v7=$75|*SmY>-ZwitI2C(>}iNUe|0ChRmfwUkhi-Agp
zug^H3sZu1Xv|9NmBj9AwS6l<HQbZVl^EjXy*ArUdgw=uVQ-!PufMQ!YqHkw)jer7F
zvu$oE@}8wYD%nWqV{nNZXHJ&=-4MiAR29P@T3!t<>IMTRT+z%2r<C;sprVDqB8L?#
zq%aGg;ptEPBS50F`uGmmG)(Xoh(6Lo+3={!I4CH$1H`H)!m$wTdAXuJ2vO>Knsc)0
zanjHWXwavf`i)t2?3<;)S{?=HZUK(r##h1+f)l(p`<bSidWPGjLWASXC~#LjSL`|}
z=&+!vA?WP(5Vu`hUq38df0I^LV#NS{Rw^~*sgEBsNSI?~(&an)PyYbKZ}xbBM6lVk
zfnPHE0H8EnJ`x48?`@CxM)7H<%1@Wv?4&k)c<y-gm~(RLeoOP>UEhG<m!P(QIVAxV
zb#RVCh;j48c&j6H8rW}>=2%vy{jS?8K&z)vH!GSP`=SC+FTrsVFBzVRE0;(o;NgKK
z4*uLGQNJZbusdPW04j%QWnK-`Y@8TiBrZOQL3NiY!<m<7+#H7M$S_8ttQUHChjpbj
ze@+>3tLr@mP??@72&%<!ku}#m_?3D;z!`kq%DN5MDBL|P)vSCG*LzCJVY&}RSD#SQ
zcI;DtRSPT44Mh>iF#Br=)T9)|4tTc^b0618gIfd|?WJ%w9iwe1bHbsZO3L{%!}iKs
zA{E<?8V!vVpaKhgT(7hOx=Pcf7SJ`ktzU^-m7*SyhNYc3?^D@#Yh8%7FQ^mMPFYxh
z1-)((P_^uBb{D4z=@=Ciwb^2mcFV`Wa71(~zmDoQ8`aq^cve5x;erzT9DI;!goo3)
zw0iUL*s$W4Q2`x5yB(Fp0YuesSIzeoehDpfY*SLIhLY1}{b<HZ=G~6PdZtkIHLHit
zyXK<W3is3tLQtt0#A&OHA$D^xSz>jIWfGW<z~?(SrcoC4wQ4IUgxmwR{X^hnOj1vm
zUC!U+p!cwaPzN;k#Z6pybL%157(?k&e#M}gb=uA)->Sas;;9TL;<|x*V29f}!aKT1
z+9)oy)sAx0_p$azTObO<RX(Z(i@*N>cRa^@QRM^scoEa<OIX1Av4S9f2bZ_c%r68j
z%U;q}h_@%oyg}IBvGXVg1So>w9K64<-&#u?#qwXk7mrbrIK#c-6*2z+ODdRQF1HFA
zPmU#$uhB={qHvHe3u4jHQt>e=Hi|qQO_^1#>A^Eg&qVP`{{SjVwFM|Z)7|85^(1=;
zbRnf<+7b%NU|vUQ0T?OXjtYz6LY<)=hMEq6M8Y#OJxYZEqF<Pfvn=%sviLJy+K$@(
zHAGVB+i0$b)YWS)7M}{gm{n@lp6G+y9{RUX=Qni4EOF)@-2N%-yyGi+!pi`<fL#`F
z<rF+QwL$cSj;!JZ_Ajt(Lskk?!86Qi(AoP+ZmjCXP3MX<yT3vj(MB6r!31cB7DH9&
zU36-q-It#rc${DgWv1)mFDx5u7!yGIwpIegYCW=nqeBSMi3P$Iz+xnzwXzVydEgZx
zOW{l5bqe4L<r?DdAkNikE1O1kxGBJD<rU04>>y!(3|cFII!k4no&$@IU7~orz}(e#
zxtB*+6qJiFuYc}yf^S6?paDgcqSP;{Mf^hf0;&OOxt=41=NnP%D;l>yg>*Vj#u1hv
zUknQCEcOe;eBg_a@<3ikgXD(?_G;S6t~3GXQ5r-m*s=i5&(4<=R6idU?DizsgWdA0
z8u++dSD{lI^$9ANJ0W>EgS`&}+^9O|D6jUhpv*t)e_ic&fJ3d%Yq8N>7`oqy&rck$
zP}D8T)!}=DY9ZyQ##vLrovbVX4-OxTloFm7k8CBU9)4#HNcLpE)g%qT4I0p<44_yC
zmlWI|a5x~nu+jqK^%miMwKaS|y3!F?6c&|aq9weqi<gpbIqvu(E`VbXgouM@00*gX
zy>dLm(5m?xA`#4rb;*>$(bcdfk?7TPd%@cjRdguH1}*G75uIC~$|E^>Vy&vmvnxT#
z9iGRT&o4~8(jF&fO)ykzNvaf_{0dMg)>>?o;9}192gE1|x#bB)0JxmNp;rYaw(jE@
zfbv$0r^H0(g|nIis4J8R*s8tV;h&x*m4}(&-r}Kk8{+O`eEdtdnS-BRp;^NznXne+
ziNH_`(UBySr$V0Ko5X2Id-DyOAn{VJ>xrH5+@`Koy1~{fM=Cb9)I-qm7Xc@fTbP<@
zlaV8r-Io=uud5Q-tdz`ejv;Y(ub$8v3kK`V@dX(H!_%?S3cb-rw=8%Sf+)d_YO|zP
zoO2eOejEIhi3<lTC`Ob`g}5DvrpW0|Fvb872$~n!<bh=>Ts4d~{gJ)J2P8vKywak^
zUuNX1^@(jfjw3ie?g(%x7diGOTX))U-I1m=IJVoUN9~N}FhitknTxQg6m%jH<#RYn
z;%2!smV*EZpmgIg55OIWaKu!JTX2eLJcar<A4C*&Yf~WUxh7<%^U||{MZ{PZ;^cu4
zO47Vja0qj3b?A)qkp=dMk9LCL@Jf_BkiACJ4V*VBw;bNul(+)~#7nG1*0ES7EV-9J
zi`-XTSK#91>cWaW6*M$w4y*MO!KayE1<-X*Uko>)Odvhnb`_1kKH&*NmDVz~cJdlN
z<y$}oH27c&MXA~V!37#-I9GOF7Kcx=C`SvhYtvT%0h_Nm9EvonfO4_AbX6b2Vv61J
z&P6RxH{uV$2lAO(C2H~T%PUb<oZf;x8a4yakQvmf6b}z1tfab9A4(YWNk`{M`h<Ii
ze2TN4@iHtNxs<o?Kp|Ix!wdY6$^?Ac34-yxCs;-;8|nZz%oL4~TjXGh%tt#*9?V8s
z*xvS;a{4Fxq(QFIp5}JH;R1p$1y3nEVtS&f3qe$C0vcKz0&x$V;V!@dRSrKxld>f+
zfe3mx!w(u$4_W?5&N+YJW1X;3MNLNO{{Wi=)Vu8M2s?E=mWD1uk1)e7h4HAXm2W_l
z&%rgai`8h$JOgSFnm3e>Vm>%xE#PGgT`CF}$8wt#TluIrW~}2m1jR0P{qSKBJl5ZI
zu_h3zp(TW-yebu!%76Vg6?tLt=rp^vGUB9BbquYz;=Ibi{{Toi8rn|)VtM5<npnSK
z3A*FIk5~p9chR&AbHD&c68o7oBj=NSL5h8qL0Dm|xxxPcVa8c$MHYu;dU}@26ZAyF
z2g>Xi9LALZ4J{;Y)hujkE!U`|0mWwV8m}ViFkM=J2C52KJ@I@+*6x(Z9nmy4w}27h
zzzVm8l|V~w166#%BVfm19H%gPeRo_b5&cBI#jsbqkTf3Va&jR%RY$#u0N_VEQ5%=D
zpS5roa5pMcHn;R3*(%gAZGuYy4S&kfXnK4~dWshLfq0q%Wk*y{{dXO%Cv4q(-!{?-
zEvsFCMT7U19R&)D(m4sLTQ^3+-qiM6zwsk}N)#xiU!*-;vBZT`l>0u&>J6z+n`7o_
z(;}hCvIyXC8ndz%ASz{fe&U(}+ZPHPXP9qjSgyrtGoA7ms*UpMoSma6cPh-kOfW-*
zS{0|P6wMAVl)z_DuxE?v7N+7hK0GtS;d|tWI^6chwIi=SCmmN;!Z?OF=eV>HWja)4
z7sB?E-~J$u@v{1S!9rSmuiUde_iica5Pk!$#nFX{ue0t41RY*fV3p_eTUVTogo1hY
zDdr8({Ohk{_&R(NP=&ZBDiUrz6^Dw$R-8~rgTUt{M?$HI7bI_yNM8!ulgDIK3Bo^6
z?~DQt!+%QcX;S^9=Y0=(5y#Y=Xc-M9ob5nrKZg}{r7}pHR-y~KY@6Xaow-j~Gu}V@
z6->fX<@tyq%HrJlc#JS^-pWeyRtIfc0+)}GiO^24sY-`;oZG3S5h<*w!w~o@h;gSo
z?qgW{_d0XAM?H9#x^o*L(eo>f&^}5d08v#(vluLm0CF42*3k+!GEuF(!2C-n-I%He
zzYz^UQ)TXz4TUx3g+W&%R{FYxJZd7_&HHC3GJ!<OKpQ9qqPtTA+T;9XQB%=>o<y!Q
zDsxunQ;i0ra$uQtc@qk{z9Pe}-zyUWXORGVK!m@4k<3a5M%ts;O`T0C!%s5hJ>1-A
z6-Rf8EpAYUYeNCSAXR6$sA_7fZx(>TdaBhBL&c8oBHtx+#`Gi8hfhsRr%}YqEWzW+
zX(jSfq3e2=muo?+lV!QOgvmPG*fAKDvqJSiVvwWAthjqr{B3yr%Rg=kfYE+s@pZ6^
zTT6M#XDG4Ahr(6p^A^U-C2^Lp6mioap-}J$L3HJJE{}N@h~V2DihL5(wq8pGd`i7t
z9$>}|hZQtadM0+N&6)%C7opHm&@yqz?}Rx0g%?2dnSP^cwV<}{plOfvj-XCjI)lZ#
z&IS%SJy~_vh}21Y!YHG6YfkHGQo2o6le!b~zGEv}FT`5%D&FPrNnKIgdSNpdhx}!V
zl6tjIY&zLUw^f#oOvg`%O>KN{l}6-Pe<26NZJctUNK;=%LIo%w+~pMN9>p+LcBl*T
z2*ar=U5DnPE!Wd8`v@sW6AK?@Lv|vwN930>X>UqaV1-O2k#=g*+KBeWX%xY}=q$b5
z?ud$^Eq$~6gn$;hu0q%CV)q){cp(yLU0N<_fl*b?B145y^hBkNo4Q}V3D6H~JTyOc
zs}S6Z#?G^Ni~<_GK%jU4eD5%t8iLRMcrzw)+R#iz7W(RD(PTt;FY7FYdTPJvl}$)m
zIMgO~JNomqBA^RtKbfcu_vj|q9ZMIOTO*ff{T!YyL>DGUVum;a0$r>_a}JJH>TEhw
zdYsZ)tul-iwi?^=vAD-g6ew=u*aaC?U;04TgpEO0=5hzlxGD%EYN4jnvVeDvA!Voy
z>SgsT0#p@~2R|?W040b#0jHXYpNdxGA26Yaw?oV3HFTga4wQ#$7wslA+-=nqSA}NZ
zHas|eO7GadJycs-+7;L{^bi&UM%Ygwq*wW2p}6Ts?Zs8VQL=pRnC;vd%f+GE26sZE
z`mnz-Eoq#Qi&ooM5aJBAujo`HtB_i=N6ZYzP&H<hOOb9JT|Og}sEWiI5QxA(v{JFw
z3aq$i4j&n6iw5#IZU)l6DyJa45q?`1p)4^V-8awQiAjc1(R{efI<Pl$TDfUN{I{4u
zQ41&~CQ}@+{vu1r{{RMoH6ZpIf`YHOqjaa@2B(14_>7Ec({2&r-+C$k04F8eirS`D
z$)-!Xz2a7X6?X9%HRJQHlz=f|Da<gn(xsmtgbtB7*&Lw_h8S+YBmxv~JiHRuIxm`j
z#Zev>JerI=X<(l4e|VR8T`Q_>kDB#My`#|rmcia4cXN+!tRUs0HVQ7)<>0*Kqp?Qj
z8@NSBYwTEKm2-%u9PDYfMCuS(#<Q0*6#O9VSGjv9SD94&zQ=IGw0{;Ikghx)=xY9>
z1L=7VQf3=Mz*V^fH29!0pe}HCz{Xt;Ws-CrvR}bf3a~A=jjD$UvXJa<4WfV7%gh+s
zMdk4MAQYm3ZaOoF?8kxY$jEq6(PB?_H*g^YTA@c^fxrXISgWliAldgORn^OoXgX<7
zjMnhWzM<>lFjNB@s%&b?y@nSyb#M&Yu5oS)U)3^+Ob?xe95(TdvwTZ*bVJn(?q+RA
zPB?%TXT~8&Z`Z{3IDZkxHRoVmXEMqxys1IYjF@$K^#h%+9^(4Fq9PM(oxBLfpf9~*
zsDgUAs;a8$>Al*Cv$~8y;jsb}4W3T6LdX$ee`QvsU&A9r725tn$Z>tMZ4J}Che=G7
zFrE@3mThQ|b<+ACVwWSU?2jYI64G&a10xDMsNWUw6GSB8>Hh#C%ers}aii_2L%HT4
zD3x0*0hBO#=nG;A8McjBi^3%Iob$8NT^;4Iz=iD<?E@)bHM>V*v&HK{TphB5N*0y7
zo~9axlB`AkE(E!;8tk*YQq)x2EHI=rXzmgMUZ*t|m>O~u!U7oEJ-E*b5{08zvd;VI
zu(lc*Lt>?@kl?up9SBEIr>g~e5n55VXN!!y>HtFBNF!_+bh%xuLFa{LzyZ{@D;^0O
z>Ifo&xmGcf@qzucvx}R<Iq^*fzH^^dQ7YQkdw*eelboRi@>S2vr8*@g_17147R~9x
zc9Lq_I&IWlVJDSl$~SHKl}$x7YQ75kh-^jCvL4}YOmOSx#4VKM3abmqcPIup;hv%v
znjwO(AO;Q&YCOdkG#?AL#!w05J_neFV~1OQYFi*ZhiJ=`j3-x5DLFUF?6urq-Twgl
zoHcZ-24A2M*>(t#T{&KGTrbc3smq_fVjW~nSH)jTO7N7dim1N2j^fJRF^5DB5{3J?
zEfIP+5SiAjH<5YOfY&1zH5W>(OTOUSi-_~fEHjEmts>2V@E}-4j_SCsa$|}W;;tQO
z3QjSPzft3pZ8x0Cm34XI5V-^KBNzt$wp$gjG9|+xeG|&Y!nKw2_m_KN1C5?$FBn;{
zd1XdiXhv<be&Sf709n(fU#RWTTg0@1Y4S`#yT7sj01V5j`PDU^?qIZ4#Z0igIhKIG
zYSfI6r%><{59_X0Cxv!J>Vf13U}zPuOA5i=n8RNf4S<3|LhWACH;>=4+vT~dj6ib}
zVq829*Mb)tka^pPC>zeaKr`x3#KDVaWvlv&jA<?phZH-}rF}UC{CmG%VR=NU$#fOf
zj?e~(U9olXFhc8Zl{{oqwdWc}{j$onyae%f(`k$gQjOxDP+-UwK3ee!YySY*0ye!s
zmNv3LQnIZ>X<haEU_KNL<$9HEmV!|#+UJpnAx1zYI6$j>sNVS<AU_;O4+h1^Z9!^K
zwp_egJ@^T7oO+z2#Y>P_`68cJ93VR09K<US&0X970AtETAnC}(FA(6Pq7qit=!(Pa
zCL^kmy#-Pt*UQ@tw6KQ7x~zjHcdNQEkJ(|rsZpV+g9f3x`PeJjm(5ijsPMOMDj^g`
ztG1O>d^<DbC4xqAFKZLf?)@^ZJ%5QTTM0t?!17sBRO8gHbN*}zG}UNgz_`HmL_$VQ
z^<cQIddH#?WD2IfR6pcneW(KrDKpqjR_~XYSn-{9!%(`KoOKaNo+&lm{<Z?FEvng&
zZ+2BbP#x&-S2G3+VL>iZytp0&^AU(>s+P+jOvP^MyurtE98(sdK0_+1%LV>`rc!{L
ztrKuF7YYi<^$N*!B}uU_)Tjszl9yVRSQhce7{eC$(`($)!XUDAaS+`){I%2W3x(Zl
zp$dU&j5T~imv78amj|Jg-OCyoZ$e)~%j7WjL(nFQPj3h`G!w3TG36`Sl-^!MS0L)B
z#55D3<NFX6C>8IE5}{LUwMX0o2Fewo^~Er)0armZml>q+?w}w%tM;sUjSDr9CVeP8
zv>eV|`hjJU=IG~+z9uUSe1$}H=&n3Ugy3(NY0>U^&Lws{=fw4KV6}V<Di*&B0xnM0
zX^PfZ7WS@){g*a^^&Alsg|E=8B_`}RSHy4*#mlE7e8NHzMvK7n5t@@$9)cAN(#1OF
z#fu&#Y!zOJu#0->c45Gn8%r+X*&YzH(R>pK0h<LEZ-%Can!Au60RimcEYp$PhLY0V
zuuvk34wI)wrvP@rMC>0Sjvff@W_q{?3YJhU`CwZ{Ru|l$y#9)o6g);Kl~F<XV)oUb
zIB!O=(v)1<xiIV$(#tG+JWM>X8xP}=8#t*D@I!7o)a<?NqXMYD6xhU0BC%0Sy;AT)
z*n#q-C|%sVNgBag1lmmI5lbhna#xV3$fFYyDHelw#4W9a7A2PoilJu@C_1ZgTw(N-
zRRBvSBXX2>Dlcl$qFt6XX7<ad^2@j2^D~o^r}j|YzH~oiR4AF;$ILe;G=n}zTJKQK
zuhj%B1YY#7nO$0j&VJ_{I0|<l)EOn@<L)i4JJ`O0;0&N?HV>MLw0j$~i{xh$Cit>#
zbU(|C@=Y6O62ZBm@%)5)SGS+JKzjhw@~nUXzJLWNy7w}`=A(b+#kJ_D=+|Y+v2dKB
zmzo<5ZJjiLzxXo&R0EWGuE>yZ=t{Wr{k3}+h6^@y&|cO7I0~~i6k_1^4WNa*D`Uu%
zi?Dn<l*|j_m`-Eijh2WmJZyP-`ITv<*AoR)pUGYjbv%M(wAxx}cNGacF)J4hUy%UN
zqKLJ(8PZ4NeGGqR6LemKNP@03{{R_nAl7wD794_rx=+W+n313W;P6DSPmHAxI4{@K
zt>^kix%X4LaePIANdaywhGV?ufqq%xP#!~JS4O#&0!YDyx~}t)Ot^rbgw62*6>XdZ
zk@;-k(a-mZZZVwrtrVE5ZK>_lRm{44m;Oho0(eD=Sc?=zXcN(KuP18x^*O7*h%>+`
zVBqMD7i6%7iTf?}1Se+~@62YdPf9%ck7FPG)s16<j7&D8G{gJ|RvWS5qAHQ%B2%0R
zp4(gdF~eW!kilR@@`6h#nVzBY7M6s<0m0eJmo~BBC^{z3Lb&|0mJUSlzcTM7Lgi53
z60<?JHzQT;M$*v?z4PTE!YNdpD5!)XQzym0@-hKVlEz&?<C^G_u;32|Pw-ezRPD<Z
zm;NdTaHcbPIBp`i(s(5J(y#ln*Vmb&if}6({L1eh4;3Up%R?6kyxiz)VQz2J5pHhy
zG=`v}@`d_27?2MDqSl_s&%hpsMEmKfi9_~-iBM}s+A+_Zi%qSH<1NlP!K*q3qtFa>
z38wTNQXXoy_1O^Dgw=1bHYA@Xt4?dL65kpxVhsrK-<bBAy`WNvC=C~_i0nPSZOunb
z+IN;a+zlX<)&7u}R@bxfT{5{|VP?cYl&nd8o@H|+{0DH{IMEFEkhqpEMJSJ(%bsb$
zoF#cQe#VVyoC`vX`NO`9SDDMVvHt+h+3}ga=c;K}ZbNTIhZoGI+o@hqe8uO4r3j8i
zYb(@j@SB&y^fVu{mDfWcN?2-oy4cP~mcpkjxoEamFs|3K3iuV1o<@oVg25q=6&<K9
zTL!hVhSI|I3_$@`IQ0d58vY^3s9m&OM%*gmpEd`Ol(U0C^C4=#8z~|10#I}t6+j)c
zRbUgLlQj%&3GOOjO%6WsF_o?ePH0pnYZtz*ICNt*WtIpdp(;9Z6vP(FtHdvyn&axp
zV@B0Vh8o$EQhk=swiI^>v!<wT$fLG42G`729wz?I-!R{_R(pn{@W6!xA36parJLb-
z8aVh&=b7e<gRBrb0p!{K$ECHEGK4(wHRhW(sj)dz&vCV4xVFoN{1#`J1$rS}9|ZVD
zfAfg*g?gwdynpnZX_qy?zkU#{vZGYRlvAX3sc?Us+mG_#IK~$wJgJ9eQl_fjAc4;p
z#q%jC^4~uzDGN?`V7dU#+bm)r*9TJy4AB{BhoGks7G157^pyTwZ?=eZKUy@lUo^N;
zT;b0>OK8q&^=Gh!!~%g4ZGCeJ0onv!M(;Wm5a{nyTeS+3VXHt><s7h#&N=~7kpL)4
zURl})Uj%H@K?|WnLQ;l1H=Vc`V}W(5>V$@eMP4sdV~YCbkVrDhq+0O>0-}y`>dITs
zHO=rtF<49os1JEj8@p>Q0pxgs{XZ~R!*E}rqh$&}#nClo!pnMK^`nL6Wv?wENS!jb
zL)Cz1+_IyAq8!`HE{8P+w^Q+~zYq%Bm7Rpt)D+q;a&h4*-$^BV+l@HmiFj2l!`RV&
z$bxDvX;C7Aiks&AKsBiyT?>;i(CxbQEDF?hUOhosNd<X8-0D?=tjdWu$2|Ee5c7|+
z5s2_UatL$ZjPp<%Uiwo4pu3?wEVs)Da6y_VcgR2zDRJzg8fFodD7e8YVH+9D%o2BP
zJNb?+4JgZMs?cQu8uZ@TM}dx8onGRWo~$|sLu%=)=awd`!7O;bj`9Xq(CLta!EX^p
zBK?`qH85}jj|9KR!YaWc`?X4<!{Fyx)CN?9pIMjNoixe@N^BXc0FYYs{{R9I>UsXG
zqpIpJlvJZNY_`CzKrg>6C9pI!Q(hs{%hC;4@)rQ&K})injDwX;cZ{-w3rw<ShNEif
zy(m^~3dzu-OKR8rnNRE?!JkND<F2uEQEY{WR+!0v+6>sK9>ILoWKZRNH+$8KLa;Ih
zf;eBmj4-?#WT7c*Pm<gStQB>;KE=fk<~qCFHZU)IJ+%{|y;mrB{D(@*TNj;WisC#r
zK!OZ8!<Wc{O3^}ydcwT(Km8eLJ>(?Q!)_L-t*s+WDJ{WM8@69Y9SFYLSPi?6l(9=N
zE0)<0M9zRFJZh;HQ{q}bk?HXZVo~WDr|KJ9+SGY@n()p(=V96#^$W`ixxqMmL|GOu
zl!szYmAw;z?2ALe1)2o3NGTnm2Fk@`^OVExZFEyJ4J__g%%HZR<+hJ89srQLHE<_M
zkAYYVa_Um){aA--D)3NjVvxXPnqVHIaSW~CcI};?fC|@%VMC?`J&`EdboVao{PPeR
zb}G$Caox)%8$E3<4MlvgEf8_b=H)7{VDJ(bNqm|_s+P0^o4Bf-G@@L+h)M{XRBYZS
z%`ow+cSBbsWdKD=q)BpU>f5G(MR}}4OU$maf-oys1yM+Nw11@Ypk||3%e^SCff?p^
z{{RRm`INTtEf7G3?849<UDOJJ$=&7Wm`=z*W}6pPZoNyjD&ANC3wglqqhz+Nv3krr
zS)`&1Y}Hd(@>e{2imNQRVgCSLh}r`e=AIdF<MM3*AA3b^k#H@72vf$xjBy&Sn`6%S
ziB`uNfU-6Rrxeux04F)gEK`U-LBb^}P`8pO0<F!R2>wR+tt`D7u&!(+aCXrWj18?)
zBb3~&R+GoE1o=Ef3fI8Ow24=Vwux}8s<-ZIZlf1WdW?2YAKcA{cw`OAfq-7{n0M`*
zSPYJfqrM{@rtu}OTlV*Iv+4_a<}=a&9{f&7WN=;TWT0NpXhV9QJj#3S+#c99QU0Kv
z5p_n2i1qsg82}Z=uMvo$6jsz0`l}U7jxTit$qGG<$#9CNjnWaSv80Q4`*Q3zq|CON
zbe}Qqt!xm+(5j^rOMz&1?bvOM3YN6xc%SfyX+=f+33fXIlPUZzLpat$jR74n%^_nf
zsagv7>TU%=r4N|u7;@bLDnX%eVeu(STBl>OI3A_Lhbkus8#8U5V3wd414&AtboX)1
z+yKJ9prJ%A)G*bWG2<ezz}K;5Ufo3}$n!WWO0&NXTNSokEsltRT?Ye*taJ|t4@)x0
zXJM>M)}z46geWTD>h*w5RMbf3rF7ep!@Qz^Id?4yR}ttDLssI0npwhjhANKKIiP>x
zHWfHkv%#W4{TDAB{KZ2nM$60okrcP4psFAby4iQ3;*@!jN&c!GB&)LD7)>h2yf|fK
zD@<3&fzY&`T39gB_B_CaZPz|yv$~*Z;td*Hc;rusz{O4>uOCJx0=!#W0)V`{FQ|f@
zm7h10lz^pao^BF1v`OK{N}hD9p+<30u1lb&FK(e?mW^E}B(ZgSd@ul>Yte|Ztu5YH
zxO%zh1k)9`TEIn=oT{`F?1bWL<%sl!DR!WE&}DgN@JHXM-W4jIeNDFBVOi>V&Zka7
zh@#6w*~Dz%2i2sRs=MZU#C}Rb5Mf-Z4E`#=j=pTH-Lh0plcy%BW~1UdBK>%rxrJw*
zCksA~p=>Ft-9~tPGKJfa1bHm)&Pwgl9T7)PPL~QOrR(KNm6=#47!n<7<e-X=Ot$|3
zDt5fLGJv}HP%OC|)n6>rryp#?MIbU1a7EFvP>;32FBqs_z@ZDG6x^$bJXBFeLDbr|
z*aQW!z@*tKtP@U7Z3$;LL65B$Vu`^yiiFU@059TNg1}kiAR}nIUWB=jLa0|{r^%zD
zx+0)O$AKVgBGu9_*eo9La@)EQtyZ^G=WqZkK=WXB1Hs-EZ-^nDG&}@VxSjkz5Nri@
zk=Wc5v$4kz^I>}Zz<xLmR`5X5lvnw})&ZAgJo14TP2D*fBNdUUNDiQBs@*FXJwk7!
ze?l{1_^6#Hx+z+~yNIV_MJc@41im1SSPwLtp+}+_V;sAYI1(xcg|s*BHBXa|a<s3G
zyS<S^Z9H^%$b%MAqlGSR)+_v@o?p107UiOrnS7e+ycAxxp^-vqSV}<j4FZXXD2K(h
z9pBB)D*Bo}&;I~F)U`P85K0YLa0d|Pt@&-QVj;9o)X-_G?&P%_XOIy8000NtLxa4s
zmU%tE%}%Xj<kXuf8>MdvI9v6u1&t)OF=%&mB3l*TYP<uj@UH|3b>mB*DD<+)x^0<>
zF=yEK64+o+nMi4Zhv`3PF6zr^c|+a9rU6v2Mo@7BiklRdLqp-mH|FP{26lwCcm=v3
zGy%RTW!hyyJhhWhg%-HQ*zpsN9_E-6xUzZ2ICV9}ssjR!2}5$!vb06UaZ5dlGUa;n
zl=Kmg5wJ6L2669$$Hl@l9_|U0f%qPTZmkSn^gON*LhBFJ?TvjB=pO{Str?6$USGlS
z3978QaoFrd>^;W^e;5uHI9)9eJxd-}Gs*=J3e|Vc*wcJtlC7*V6jprunNgJ9uP$-a
zG4}z<41Cfa0r3z}N6Q3DG1^NrN!=%-$t@h&#2zA(Wi>0o8L?3}O=<~R8$}Pzp{g5O
z6XRTNU(IntG$5gT)IEiqtp$zW{{Wo)LEgGK;-V`S1D@sz{ZQOh@F`NZmH<|?zZ_@W
zMCYk`h>m%FBi2>8zI>TiSKQ?5Cs_BM=Z;N^Sh1KYdzCODyEGd9&4i@ttS&Wy*-ceJ
zst%69fE0k{Va`S8=Hi3JIycbjLh9h{i}l1zz~7bqFfWBHZDVmn?7R(K^yvlm-A42o
zU^)&MaX5X}*2CD}uL9s`rvc0c?U2jSj`h0fL2*)#R8dhl_$Of&M=p_}bwVmDWnCME
zPBH-f8FH=GDAA-kEpc(8bwR;)E|J6TOt$YJmC&hbJihXUA${l{h&22gehA6Mk$8a{
za9B;CQXe(hDMSHMs_Pg*uUVoRoFUJRq-xO045iQDBEaHoAVqn;2^B{rS~?W-QPV>U
z3;4RYIxA)(#a|stp&|-}1CU8|i6tL4<13<GvqF`iLf3?`K)hwViwHS21*b)1r$7SY
z!H(6Ue5|t7ZLEL*KB66YYRbdnYnR8DnZ;H<qAce8#_7AcKE!3>n{-B`frID7<o6%T
zT_+c~r8{Ax8|DE5hX{ClzWDo>xC?nJBBfT4_PYX<Pp&(NK76H6(#h8EZZ$R|V?)Mw
z2zeze-T<Zwe&W)q*KaNRflWv$;-aj>%@K8Hy<&>)S5a?Z$bc)@gnw&^L_J#g8i`Z^
zj>1Z-FI7w1aeUE&hzxFoct86ifqIX0Kk-8ENa6ngEd9Uysyt^hxbsrbEs6JXDbS1A
zwg7p^>=SZyQJv~0BebcPV146z{H9RcWjTV2CskEm9tl^jJVK~<OU*1VMLx}xV{w|I
zi1{F?9Uj_Y@~GZ82!MW}P1l&!fGj*(ppO;}BD4h$n@;u)samym%qS%VftJRh(_g}6
zN<RF@8^L=Svi7g>rXDlSz}0;MmSYq^DE)su%l6MTLH)r1^s6ae3x`zWHG=;DgD#vZ
zWqD7i-kNK)gWw`jLaVlNEl0kmKc#%LO8}=J)I1XzP9}kfIUWB12YkwBPo>PS%Yoel
zs3Yk^3qT*aBjZUhFDHl)Ddesb>NC;c5G3h6%vTWOnx?kk!z>6|l1qoHFna}7SvXp#
zyrgYroOr_uX6<!j@<$X+>6{)aDA<6zO0?0!==6BBJs^H%$%dmu-VXd+pnyBKt7*h(
zR7e5zLs4nfi<HQxCOE;!W=<xqn?9yLAmcP4?dBAqcYI&7+X1VmZdvLWL-SQP)DvW1
z3Y0>vwN$i`fWf264LnlLX!E&N!dM-7v3se!!N+o3JDmYQR+3Yi0nVZ6Y)10mkjk)K
z8$3M6I(IKK!cA-r&T4Qck@>s<uRb76De3htL4Y*yfK}N$IaIoq!7}>`tgZ()eES%@
z%e?bKC+$Kk6xYnEBmw%%e<VG6M$}vqmA|LV((F>9mXhpRV;Ifj^9AwaGZ4;eA!-wW
ztE&hU31-`Q(!cXC+*G;GAVLHL5o2JHByzKW5HY73ftq3hKt4|-b;xVE_(B8;^#G#9
z3rEWx2^reACKcv4w)ogk_S8W_p?Kl`A<0<oLv{^n*+x{i7^+QMWr}miikVO{#3ZSp
zd~Qc8K%u)7N{zi$$C*fWp+_qHSBR#Q)%S$H6-NriGK(NzG<7je$R2eX>wC*#a}*Yt
zK$Mo3D?9f&1O7zTxADxVyB;^i^pTzne<leRR2-nIlThj90<NF5%d=9hM0aLVm$ZBF
z8+d|>cvPZC_nq{3IXD)={?#`YAiUkl=o}MmIRu=L8hC4hqD(D|C425&st&2OD+mkT
zkt>*#7j@z_vS{;l+bHa=e4>t76i#buJBE=@48d{3DCI)Qn0O9PXWUiQ<rn_|JWm|T
z`Z$(w7YtWOs9%8&kVyIkOyJ0_A^8*&zzwHPZ!wrHj}~~kfFx)Z)mtLjtJyZfTIoZ(
zesZ!p0jdDiD6VjfB`Ub)aN=DGD>#gBBW{r4qdDuFnsm*|!%Jmie5wOnRiGtZK(B%?
zgx`<@&dR4+<l*poxCU-QtsWuRB8Z6%;=mbWtcUf1rTp<a<b$KokW!dsNCq%jHqLEi
zFsZyz_yH4fv1UP4VHfe0X-4pc63R}Y>~i`6FDk_yR5|KtsecR@1*tDQ&Z?rsu@%Pa
zOF|wp>VXF4Dxmdd9|ySAFNmh?s$bM25q00HF(+SK!nlis4%us#E-Y82wDzRP<14q{
z(H9H|>E}mWwCdKu1uqB=3l;JiI6h+D1L85rYmXmu+z*EqMZdX=ryjrXwwkNP_FsaR
zPM9?-z(*X(gBS98fQ-B3IsrF~Y7Hf1r0fn+K?^J0uttgmZ$$AcAeB0N(fy7#rDAb4
zv?_z9qP76FY8P?^1+k<w)~Ur!tLeLPB#U|ddL<#<I_!+clNsGaICnX6{a6mvcW#*U
z2f-k;)1#57i_z(5B!CZ<R-Y5pUch1prV(&$0+%a+9p!5loN?wl-;s@OBxt;)?of2H
zg=qMc>n{U?w!=A9{lL7k#p}#zRDCr605f)Xd()F^T+8uGcoYs+V>*GyD<RTC-O7u!
zMN)g0!TMq>o3Qx=3bn*rqws&?QEr-O^kqeldgi=@H|kYrYo_ZWg;rBuE)Nm7s;kbQ
zMGJCJLM}b6vC=(CboPK3G}wz*ONrEcwB*)v*)GmPovgUbx&$1yKkVLTt>Mv>8)8C-
zh5W)vd(wwvq!?UDNnjiUFN9qB)>rX>dxLuh1*vqD^NKywC<Yh~t-djb4Z7If<rcbY
zsI0_5%2$X`1bhZ0>RUnuPG?Jg_r>{mhf0*=#i*CSm5DOZ1)W<TWpV7GnqUsjfgvYb
zhu#c|v3*MbY526vO+j;lq?AK$M;7nyHKIf)B7WqKIzXt{Cn$40GV+yVwC8rNT!U?|
zYs5JTucW`Eu&nf!dW-F4mgIL8!RA<3o(jS!-B5B%8ol_HIiv2OzsqKMmMx)EBNVen
zzV1E3SX{Sz5%mlv!W7E113|)fA!S0ZHM4s+o?S{i3?BqeZ87tz$Lm*p{%7V~%Hi^z
z+`Kxo<#&GL6ef`_{{W$y{{Z+mGqWW-{{Tp<(w<@gHMe<5^N#CSE;I^Y0lbdm45aAL
z7Q^mZxU)+SZSV;Er^#p*287mOyNy{{@0jbXUkp{ewf8G}YWi;_#buX;Qy^+_4z^eJ
zU^d?2Bh)s~=z@W52Ou~0nK2vCs28^?<6D$<EE!DQR$xY#x`Zma8$8sq%{_4!Hd(i|
z>To&O*eXQ2C8IoEc0LTXEUU*&EA^3+i)%vO^B~HUb$-AA<wb)lTjaTyY%mpF4-K0I
zJ)UQ=ekW1462wFB3EB6ClpK(?@ip}t(sJ8HG(-(%pvHq2$nB5J03HfV`i}-xaQTg2
zEK+Ei@V+vxXM#A8nE3!m1|n9gL#<Ku6T*N7S?MqKAU*W7F;dy<D%CqYl^>R<ST4d?
z3+n3+dYaH7qOH)rU}A*<M%pbyf*KAn*;nDUN-=GbZx{!cyj`RO=NJV6;0Zzi9dmSl
z<6Ol2gvGM8WKq-91^DnvFD{9jr`&RG^vw3kS!sB@_YsA`BKXVVabkgPq1K8@E&j8A
z3P3PE2xP;aDS2NcxW@EBe&C9!%>{jgs;Rmb2HAb&x$-!|#kjx&#Rf~O!nog29_wc8
z<<9wu0=cdoM%bl{0$~B89%SROe}H(nuN820DTekUU+Atub)2<XZR|%JsbS%%T-y>U
z<fr|bMd9c#(r~~zm5``+@B{l1kXYGE`HnR0TK26>gLsMw_+>l@b9yOsm_4?=0a+9W
z`F<shoS?<~M7HYGD<H-$PkFCT0$J3A=`6dCpMaEnY97ifV_;JKr5k?6?V_q*97=K~
zUcu%?$2Js=3);5I83RC9MAGpt3E1HpAk$#TyCB}DIThBTngc7wL{yJQlmg#$%1vuk
zU#av7eN<PpcZpE)uTr-{L`A3n0K@2j(gK0W8eF*Q+6B@6N}qW#wM(vH<k&T##Qq(K
zP#0h0601_W)8CkuRo8c1haf4<Y%sLq6<Nr0?w!Fa=zlE@bHFg>-e&|7>`#v~gx^&z
z`x508ND8;{tXR3*v!7KvTEO6s_78ZI&4518k4q8rcUvf6Bi+LB1QO9MEg4C7-P8_?
zW#DvN_$n++Wz$PYwQN+{W$RTQMJE(6H?7LTnMMoYw!%(y{F9&1gSBOr69P7}%}eP_
z%2dw)*~(RMa+n=ONBBqu-kNXmR~!$tRtOEkf(DdWtQCx~X%MxhLugQdseds+KMBkm
zeh@#yED=Bs0lx*XtT-*Z+kLF3n&o8&me^v4D%V5ELJYwuz88T$Zr{n>W|voYanKa2
zM-2!2I$g1)bUqjy+OLFh5;(ZN2a$#yW&Z%70<Gp%Au@4sr*yB80toFrAr!pN=@pyn
z2Y)aENx^srHx+vmH1?{Bo*9}rAAoF5KCSej4B%Pjr~}hcX;9qTSnuSfAq{O@(A)}t
zuePTX2P2A*__S6JFsH*FNpL?<ml!?hO#-`s<ZIfm5(H|n8&o04iSC0HhU8TBz~|-=
zZch2x$Dieppf*~vo)$N&8BcgLALhxgT?N98sD#kv<d4r6*0#lj1u^oZ(!#$Xhbw7m
z*Bs8YEuGY{B7`9AteHg=M49o*4v2!E0S{UEG>K~rfK<xzK|@h_bAJdTF*S3;`X%ef
zAw6)Bf>`qER7&OPO1Ui1)`kB7_k>kb^FXYqt!E697qH+_UiK+gR+w^4mmx;-P`VUA
z7HI{6S-#_gK$L@@wgreSH;0IED|cn%j0<to72$m|GL+iylff>&#PDpv9{QZSLegRa
z-w(Kpx3qS@F9g97D%fcQoFdv;b-E($>wncbsg$<-Bxp}H3zsJ5h5$!y(z&Qu<YR$@
zj9mRT>vLr+h^EjhfT)Lo$A=7x>HWiRte=Kl60V!_?gd3zPh&{j-<EhVno;AaFBrHy
z!LqJ7wR~q#)uUGs>}C@*7in*(3VJl9%&b|EqU?xyj=42<`u3MsuZrBiHe55Kz0$|A
z6-D1LwP<-(0Ii}cm9%W-)kj0(JrE#jxS|jcVT$P;!$6UDHWa6yKa-|SB9;#$(nmKz
zLQ<pK8w0{H_63rr7Kj>LBlhYVJ%x!!ge5CgYZ)Si5#~}o;LA0IC=JDEjj-Fkf!*pu
zMztJn*h-Gqo7#neUXK^<C>nrAv3m!_GTiJAwBV><#KmporDJxQUrB--`~iK6$K>k%
zO+>iMj72G67}J;uc&iW=Th91VRo1`iMmv%yINpZN)Jk5*39VGVaPq8f?@zjh7)fKT
zA@y-(btDF!Uwjd<+(C4qVqkji73u=YEngi(Y%dW@=2;ymv=tvK2qTAQUPzIv+4Phi
zejr}sSJ80h!Y3^ckyu*2*|!oZz~j!`Awj;X2)F8?f&;w?d)-evam%*5xmDRJa_eSl
ziKJUE%0t+0eTdeyLhSzlp5P!fd?nnCR#4NXr4g^Pu9bs=jh*AQ9;1W^3Z5aGxM{41
zyK@FM<@9<bHdA~+%7s*UC2B$m^bAs}z8X|#GatX0<SfE~K72)2A&PoqTq=*T>LwM+
zk-i79NYhVhv=7F_5gLtGIBiY44Lwpkio|N1L1KvN{e!R|4-UTW1BdR?HHf?w;l{}w
z2m<!4temTmxrZhdajEd&s%8Cm^@!R;Qn6jQ<EyCNXIsfD2u?tfL;OSSA&y46i}Gv*
zKO(!#JdU{TX?#=#dZ69TEz%xv=2BeCDu{CKz`M>WQk&lqg>mAkQhSxU@|LIiLc1({
zx??e^+kC_;d3S^ap;fw}hAZk`I`Jp}02RKk%t!+}<Af%>%W?`>mNOOvwVauikp(fj
zMi~15LtO1Sm=~N%HOm=XF{**8TH?YhIR>HOQoSm`)sv}kzR*y!A=()u3Gg7~E}-4J
zKCWRnsdsl%yp^UB-N}OPl~SfOwS24<!licG;#4gbEX}V`3nsPYugt081+-&jMw7AU
z%)=_QuORBCRZN~x(#p{Y8A9>#Ots+6wxQ@CSb_65(FPMQWIh#bTt#)h==6Fb#2Z|u
zR>*wTYPqBE-?(%Ls!+Ak0Wmm)hYItb`y%xTpyp$^qt2s|U|{Uz3D{M>T&dq-Thkd$
z^KqL~sZ?!3Xw#R$vc{wNVkoXq<HZThzISF9L+<&g39It@xs|SXewxZ6->bz0=ut<2
zBL;w!3y_?c^j}doDWBgmif$_q+glYvOfd&nwkwgqvA+72hXsIj7SD-xGk2n#&Inw5
zsI93;T~SLM+rsoEeHeTK@cw&!$=li>lJvwqz^+i&MeOEHuqG{2Rl}VD6wXHt6!i}#
za+P?YxC>T*TW1lB<d>c1<%M>xpr$VLG=1=O;*+Qq90T1^Zrb42d`Igl*K+8?Gf#wN
zz1Aw^@p!Oc(4q<+Pt-mQoo(h14unPmfNL3M#*2E&GxL$foz5v)DeT_`NVxBtPdt*v
z>|S%B{{Yp`u=?f%by!TG18|CafvOFnA%^o_M8l&VsVO|1Rz{dLGE(Mslb4x%hlnx6
zzH+4Npg;2(ab>5CpZtg1;Y#0A&MsgxetG(+hJ*+-w4aTfr*R1izzt)dP077B4^}0W
zL9lgMeO%q~YQY$?qnS>uaSz=#G1jM|9wY^`W713b?V7u4P3Qmz2JsiZT2!Av6iZjQ
zJ;ciOmewUioRE{}l?h9So5)MJ2C}MQ0q2+72w>Q0V<8lke;~*;P*lmwt8p`<j(kn8
zykiX=R}4jJ`>f(7rfsX{;twTSW!F`!6qMCI0u>d3L?WiZv;x9$vW2zKHWX0Rdx*NO
z;c*oZ7OThdHm<6qpv#pL$VLYwuO=_Zlz)kN#61k**n3oQ+HnS7ARo>+nD>yo!Q7@z
z4c8;jrf%|-w|hdR7ttOQV^i0`q%|lPYiZ#aFb4Ds!kosHV8lGwtVKm<{Y4n&A!>*#
z)HkwtjliU(kOFDhQrQ#-WK3mf0s)1Pc!R!z<xvLOzG6^2UJf7-AbLFyEKQa?TL6L$
zqhLI+6*a`?#G<VrogW3u(v^4xd$?p3!Rx{5c+KdG+n(XJxl}Y);M_4yW;R3xPhLBf
z+HRoElU8m+O8A+u*2=}}i=6Y(+(Aw12Kbm)CNT;JvRUOO%`MT%EA`S07>wszW^&Y^
zm9CKJXJ=oYVLj_r)!1pBXHIn)2#+c6M(+)X>l@w%$a*0)!F-Mt6%dU%%5!_c_>;X1
zub4#wk9ZH-j+Fe1{9$iPxbm1W5O#>J^8SeH+0x3r3*cGDbG?S*z;*gOa|8}*hpYO4
zReIJklI8t0*O{LVtnhAd)WB(qB}KA`+ED?0k<wNuV5HK`gGQB6aL$KC6mVidQ18G|
zYz88Z70r7nl7$BAFsQZw-hT#H;Kto>30VWkPdubV6{Qzd)Wh<7+~to3K`Ivc+p&FB
zT%)#`^|9opm2*$*N)+|axG+_BumoB?aw$aArMK0ax|wQ&tZ2_N>%jxt$5jQFf@EIH
zh{6X4BUTm-A<CX-SgRJI=#f?=I5lU9vXp*|4GabTdX#7Ls1!k54=@VTi$cDRnYHRP
zY`)MewnJrZgHm$~vGOrBgKN8C1_tZ4ihxb0Q7-=grhyE61Q&UHT<JYSP<d<VwskaD
zxL9(kt{(1fV9*@x6GTBeF;*ki3SpkAV0siETii-8QF$eU?VW_y@_YPwVLB_*#T@QQ
z?B%=?*N4r)^EoZ;`|`q{pAkDAA>Y0Bmkmz5`54>z>7Bh_M7_Y!9!?B8-7k^D5#a9Q
zfTL;y%gvT~hj=a3BdgE>^kb7s&LA&fjJIWBhZ5GG%J?QY5%M`>`ScG_6cj9Qkxh9M
zc4e7`+EPk#5u>rf0W4HyxJp65K~=Mw4cQ7oK<beu+j#dm0=nu}N-2&DQ=%@IIaNax
zD3y6ACC&kS2l-64jU8-8b!Au0Dp{HI;gr^_Ef(lD?8W%d%j!1=rVwpOa@BU1LW*DU
z7xcoA4Id;l5fpY662MlV#sdy)Zr<8}YCtVo8WO{Clgr4yDQbe1Y&Zb{O-1y<C+f>6
zLZs}!Mjc5-`Y0>|RpJ#_;VlF9)&{M!zOE|o!dY{mYl9x3)DCP5vem1%f)YyK>xzG1
zb8f^s14cC<3^*HXSf=5vIi^sns({i|8A~#PfomTj7(l_g_!9FdsNBMp+y4NJ?BzN?
z)9{M6D0yYFrs-(B_dLPT0R)nqvfs#6T%df`Tz1QTZUFb5rtgj+)^QtC-;JMo)Jd$h
z4l#%3LZX0Ca4@mfiHGUq2sthK%SPj5fz8Oy#hO~Th9P<`Eh}{f7b2FggV8HfmiyVh
zxNuZYwAG8tQug7gmu38$5e-M+tEhJy+ykO77^;04lG>;zAqqqy`oUO&R$UbK7Ft60
z_qhC_Ea=#no1elu4sZRUElw;6bttW?QZ=Pvb|HBJL@SqiqWHMeg`wwN{{XCB!AARG
z$*awkK}}_0+;|2tmxQh;Q*WnHth*lAwo}2>P)yPI5%zQoi;ybsR3AyBvS?Z=osy!#
z{{ZWp=Hzn*-7wu%&@|Ix)J6iT-tt)pYAk;*9cwJ%O(K^P&7pWsys=MF)wHf+c+vGm
zF?fm$RpN>nuQgAyPJRhR0+>|<r@NL|sup;@GBvbHvzw?>i}KbHt^irfocrPwY(HQi
zdV;Nf7q}x7IHy;oj4^GvH47Gqrvbsr_QqxORBpA*TDuMyNX|$ITJch_7TS=%aS()4
zjr;PAdI`(46LMA(kL&1;>`)bH-*5yH)LJ(Y=Bien;i#%d%@$?g_$VOYwrctFFtYE%
zIYQ0ua)XtV9_zF|#0HJ7py&8c6BR9Aa1z<Q_z~I!H7>L`H3H~oCgY#3CCd1>Ek*j+
z0pg8*!=gcVN10l&u{@mNYA;9pWqGat0AR7Y1JPp&t2t&p4FjLR{{XBb6qGA(mJXo<
zY$o?|-5;PGBir1qOjW9@=5H^K(_;b56zEzof=4Y;N7P&lrZt;F6BF?X#UycTn<{4e
zw6u)dXt@}<$k&T^kdE*F0PmDpq;3I+^HAw0hntkz=3ra^4o0*LMsFb(iXzve)h}Q-
zbI&n#8a_#PfOA0IZdE0UoKVp^f~R%oYUA^~MZUIm-Cq9y*nG6{9{kJri9?P8X8!=m
z-i?C7>wz?NQBFE8Se|J33Xg+wlq4Qma#=}<f%iuuk!indAW=fP*FowLN~}8@tM@NX
zu|t<m#+CeEn1Cb0qbaBUy!=YDR^%FmhiTwZY>SfJWqA0M_vQ;uOT~NYB|!0EY-OWI
z*@i_a8sr=r!zZ5NjZrs@Sbrcy(Qx1$sL{o5-9$$K15k}Ku6skD$C+Hv**fi=P9oI7
z``DO5iZRz05ZWv@UZUZlDIr>__ZCeHmG@KShaEYqi0m_7u&)J|briO&E)~9_Vd8N7
z{{SgnK!98VRIfVw7yCFw>b=dX^WIdqli_tc4}llVFpg)9UiyXRs})|Tb*bOj*#!kQ
zQq{taquU=cJwmxS_$$S(Z{`AZyZbp3%+JXNYS;eddSBu!x{CJpyAk}K;DbHYaH~oR
z=mIywFav=>vsDU<vbeHUxnI1%JOkWY+Gqu41rd+B1t<}9wG?j|2qyFyUy$oHOK;b*
z6>ywG#m8V`AU~QGIC#z_<}DG0)iPbqQhyCbIms+W2hBoV3e}M!=Nu|Z(j3-zg{@$>
ztneOJ`bDnq!E<r>V^y)Btw*$2>(3z7P)%t_(5itQw%4?m_Fqqml93g>T92$iSWjV6
zHYHQd_fdDq84FZ+70h;PBPE_zTp`U%A5{p_uGG>8`=scC(638%v)COLx5Y<lxEGY~
z{2-lu^A(UhyLBBO4~feHHY2dI<EpC0%lVWdUet0pjfYY6M%kB24YX8iD2sVPWkSl}
zl89R4ht?lxd(<;)3K65h2kZ%4!H(}=luaxe@``K5lj^P^E0t(*7{wUZG;1r;f$(No
z{{VGroT--s=sZLf&(4iS<!f8YFlwJ6cnG1Du7d30$q#5%`#_i&@}kwKGou`8CN#FU
zRn+2X$+>>!rrdCFOIA4W1Qqsz`PwA1Dxr5GqX^))h%t$?noYIsoTU)DvSe0ZJjYNi
zyx|M`e7oO7r)4s!8)z2bv{ul7fpS%9u9<R_{{XQ0eact@B)G2WD`58(u4&qwuFwm<
zDk*WTubyTX#PgmiGffB&q#rD@qjlu%hO++v`RW$hx((LcC7}#xTgqQ@SWuSI1%yR*
z<%_w1NCl5X^Bhio0zAGNN<YO?ay}y#0keza9$*N7Im_e6lM<CXxH}=N%CiS0V82AA
zck~%dKom4S+ldyKUg*P4W79Pr*po3*wD=}hl2l8(g%ZAl;p*pqMCEuz1U$fo*a)Ph
zhJ*ru)I~vn3w)snj@2n5W_;}@eB2^9)xSl-4D%{PEL7vKrC-+K%K&)|VOFSEZ)B~>
zULBm{!Q|WsxCQVi1EL}2Khi2XQNAm0`%~k&$>HvyI3m-{ka`Iwm6GM17xHJ+c|_Uc
zHZ@HMwUjGq^Y9Rgk(L*&2o#nfwfEv}WaV9=R^PT$_Bj@vjrb`=^*qq3xYok?U3nom
zNkN4V2&@BtI(3{@k1nDqyy!OP(n6P<=Qm~vb-Fkz{jeAXHf{O}BB@7{FaDyrvXv3t
z?j#Lqan2nYg6PdyU2`jdL1iBe6E|9mD^kPIDoWE|#BsedK(N9k1&7H%oIa_lC%Wbx
zXV^Lh9z;@%?#?*Ft)~(LSt}mFV|C>t_>4f&rwK_9V7qK;Ermvjiptb16df>PHuFR;
zhRhRL#}?Jg08HIDLMq<nUAOz+#OJe-)8?Vn<_DCOe`Eguo+zJW;nJrc@?+H8umj4m
zDJ7r-#}htIO5$g<vyYp(b1HH=dxLzTCVJDkVIM(y^22ud=3kY+sRTg|x=__lY~pwG
z(Jbp;&WJk@4R;+;Z)JMs1R6*x_!1XvS*<=-9oP91<<kY-#ZUou?+;Ahx(SVo+t{@8
zQQ--Aa#z6@T5C^8*LQ*1o6Usi+w@9HCkW1_mcDj+lmrAmQZ$Bi7k2*uV<0TP9YWQC
zDZtjvK1cV`7#E2`uCA(aD~2+-Yz9}vXFa?cg{W=?M!4e00-D)X;x4x=<mlnmJ1M7M
zGPSjOdPn9^3oyG@q~QwDLHmF1645|k0jOG}?3`3S`(XyOg8-t$o=l(WD$%+rL{HNk
zZ4#V`!(<@t`XIhmKX>R5@-vo#i`jU?j<!l|P#*>)tSK8J<W<j-vtcw5ZBXuUWmDbc
zO3~#~=;p=gt`@wiR%^BQx-0Sl<|eboQOpXm(OxUyazz0tr#FYFlv?ZvXW!81#Fqa6
zdK|}`53prY2bep^8@i0KZk6Gy0FvCI6NjM;(DsGHnyr@|j5!dS9dWPKOBaPtut>Zw
ziWRwF!bU}M43-uPX&@L02*(wLY^iOcr72;3c^md8B0MSFSCMg=Tl!Wj2#+gl5Rq1%
zOCz;L`hXG5%cdc~KvA8L8YsG|Crx;P97-2}OVs4mRkaNzGRDB>)Mt9G?Jw(zRW@>-
zd6;wxrrHT%SSMfr7^V&)&4k2(Gqf@ml~*WQM4;4)D4mG04l6jO9Bf5Dvl5^i_(>cP
z4ZkV(JmgBNrd-Q@-xP?2S3BHxC)l9dT8*kx<~Na_7Gz6rwym(ZP>Ym2GV8@YiwljJ
zU!jz{rz~$y0BSmBmv%fsf*g#lZuo<Mi8aj`dKyAq4q#gwj#iST=Q=u<iF~rxmM}tX
z8yUw)q3cQJo+H`^DI{!dOm)H~R^e}3DcyqG{Ne@}7>nc_jNMQ?;3D{XT3~dv%Ii2Y
z4lRI&qyGT$$7)xuGULg)Nua5pWMNg%P6f$c?m7)B^--sAIdJA3sX6XBGWe&iI*YkF
zwili5aG6Jh&ijHnLj8rhM&J$I6Q8RBC{jAG`J}%WA8{p;5|0ATOC(;=io@lm1EgxJ
z+QbjKnjb&~1DKMsn%GjRrO#sAU*RJ~mB6oLBvf@O<CYs6ShwwC7UY1m(uX@2w_CV(
zeWDBn0n6vcbRQtn@TS=k?!MgMfKnpQO?KBY!yZx`Aj_K?6|PGc>tLfC)uO|SwM$$0
ziJ;YZYkO``aF5i?tY#@E$GDeI{3_}^u84%7!jrF8JXF*`Y&x@vdjlZ~X;z0>c@wtH
zu04s5Kd>!fXyY8OGu<q1uCF31&2%f`iDyyP(&nQ)qDPxD7n1hroega6Yp{YR0%9*_
zT(^9+E5&RIuMlugf61w#O6?*)62(+%+$Cle#b`g>l-#lax7Nfx9?Vb?8|nZKFJEv&
zJs?OOqfDleP;rPFWvt>_0`z|lRZ$=aEE*@K9!Xe3wg+q3Y}&eXk~&_Cu%%ol7c5&9
z(u5th9ZEb@9_O6Mj1#ekpONzzTa&`Iu6a=TKr8?@FVq(MQZO_M6Byjm5d#C5K@V#l
zR`=#5SgHFSKtze}^$^XXUzVeA1t*2AOVx>2bR-t};vv4J5^HVyu$^PK#BAVx%fA)l
z{{RbXPkhaEaVQ0jM~b+u_d(n^MdY9V0Ebe`qGnEPu|xV=f@nlVGKDI!VYuLBPH`hd
z&1D4fkRD>J#()rPi(ecESqV@8ry*Fauq9x;@P&C8w`(@6hs41Z3xi4{JK;Cf?8?w$
zAbrhRi>Cwa2tv}Pi0Fc^mBN={+HW8eo0rFOyUEYzP!Hr!@^?Xn&RAo^P5PK=$@q#P
zO-IzF0>%Z2R#RJRRF_IhkB}Wyy_W$*N-mYfYtDMbAua)_9|;pyrVG!Q8p#VFXd{(v
z*S&xY6q%kvCfqq5emkC$zB~prX)&m4VFilEQb;MF`1=wg)+61~7~YH@a3Vcv+ouh&
z^Ms+GF=1Ou9i#q5>$wvxM&Afmgji1=l(6*y+3kQ05w%%M`$T88BpUNqG~n-cd8Kp>
zwuAl<I3dTfpVc=8iyNe<M783SZ)zT%IF(WD2$n#pcncxE%D?GibJw_9Aa%<HvBK?$
zXF7arz6xB>a!X=NmqYy^ge#v{8+e*F@*<RK8%L-!DE|NoT6^I%gXH=A+)(3N#eoIJ
z#9r-L5>y3zqx=Z1^o<ga&^R!{iUH7wJgZJf3n(;}wL(}Dtx(z;qp-zwb%Ibrp<uiO
z2(Y>hpY&8!BWlfa$B4xMEB2u4FoZnLeZwa0?|E(z%rD1@2O<Et4`j3w*QbRsSPK9T
z$1p;rEhm_W>H)^yi%c!!3~rD|H^F8G4%9pzdx=m+?I?^KIAuJK)MTkw44@d7T><FE
z5kjfq0&l#ZkcJx@H~rD^!gkAA)h>0#K&0o%hVZbJMmN>LaEG{Wga<Yf`hjhCGK3G3
z;4yO-7gB1xzA}+i4jdQXG4OH^Vt|h7-ky(@H^x7Sulog^cskjegOPR>k0erj`up(|
z%OL=fKyJSoea(q_sgZqmJ^iUOM2^v#xHKm0CuFQCd?BN9_HBbrA*4uCgr(W!5#VJI
zaai?_5B~r$h$~lr2Z>76IAdNS2BU!2>|HJ1EgymlMoJJSi60VF>0>c%lYF^{p)J-l
z&+nPQ26Z30fVjzce?cx+R|hM=*AWDyA9PT`QEX#MsFn~=+p)f~Y-V54GV@48`C*8W
zFroxTkx$bSu%rI~6c~heTCMw7)E<{>37t(fBVV{KBomc=u&9<u>c##+5|kJJ0F+c?
z(4(LO#Wkd`lT@`c(D(p5mkkLWPrKS<+gA%nc*Vz#7G3?z1YTGtn;7LSDDmNe!b_-X
z+V5`=-AWs4-HdFy%-y6SxH0gwvBo9(!Px6Dun9xrVv7o%@XlNnZ3TZDj)Cwd6r(sw
zmAw-#G$~XcD04aIzSz9PYrYNP_3m>JY!^t|WoN8_I8d9QHSofi0xfVRRjjdADZENP
zq2ML{0CMRWpGxL8FXE_&q`6A-d-X4nqOW`jz;`w7jMY1zc}0&Y4l82oo-2E2Xu~<F
zS2#O;Q!yrhXkSDS^IqkJ+lYE%+4yipsSTN7uqOR8#Y}<Qe<R!}A>HzwM21mdWigKF
zn!Q~#L%C5Xxn{oixl;zJ){nMd1maUTF<~Ntf{P=RA;Q7-8@qN?q1nTQB8g%Khw+62
zMBfw+Zg37V4}d~V^Zh}O0EGY)sl}<RfAjBt7I+sKKqw+`_$6aWwagug7LhwNMYobR
zO3K^z=jAO*T926rtna`<fmHfJHpKEa9?Q08lL?c(^umEZ^Cuc*R1}yeG%s?Cj)HT<
zvD?=;-1DBQc=J;@I3;`dj*Vab{{T+LFt-<oh_A8vwBTG2^6>ysy@F{bUtjc2z*ny0
z$yDS+%&Xyr9&Ds-`06WXd^{umvoP)L!fg|+N=}Mm>6M`NRKnnQ+R_n0Yshx(5r{2U
z6)+m@M{TO=s#RxP%f7fiCT-+RKIO=-FgOgU*~bpD`YvPPYBwzmdzrMPB;GJ3cnV+!
z$ZLV03~m<b6NV)6gPSw6saB%hPliA2*zlop3#_#p+}*0}3}wQ!@+n^qbHM5wm@6_2
z7fTpPRjUx@_+>0gi%<nIjhJ!@lNQ;Vh$~3)o*InY?!6wKRue?dNom7n%AgG{i-nG4
z?_sRM*N4;G6@c5LL97OmHlZIw4~QGt#iDaYXFtb_fQ8Ct8C9L}Dy+8-m&~eBH;w8J
zVRQPN*4a{v+;6bf%UNhv?Lx55<%QXiE|e@=XKYg65oL^~_sO8Yshtq)?#qG_xHj6b
z;g5zDQP2EBEbPh-uuJ#X8s^+d!VIr;sKryPJxxIbbi!hW)5M*h-FKAuEv-P0Vp#>C
zNLLC3rxKjO=9vI#EN=Kj#<aEqBNs+2vbV}`?Ifs*yECF!h=Zn`_RFfK-EX*~UWMlw
z($<zB7?`;|j@P-Bk+^v+TQqu92GhsD;eOM#`TVUS;<tVd0&1n-3|5FXxw3L~`7&Iz
zc{umXS7j7XXl!Mbd)eqzjs0IeGf*k4eQp=EbLzd#b#269OZV`b@+AG?h0Z9a-!Xu&
zM-$WZWdxiq!4wXjUepjN1DRSI-!j1X&4-`em0UIKGR4L$vJnH@lJp5PVUNL7OdhX`
zQp61|nU7<Sds#u;EZ9+@OQVxv^wbKjv^@pOR0`0xt#L-AR+;j{R<g=4wo3^{iq_#H
z$gJ(~Q5}#?xhnaN3<iY?Fr0AJ5w^ZLhOYt|>e*ebo4mWV0@|cLG5-J{UGe4~&bWn`
zjt986MTQM5sC8gZVj%G~hMBNmh}uHrs*0{%bZO1NE>i{WJWpipW8U}FVMa)Rb~&aU
z`o|I!EJSFL8P~|&>>nHmri{cq!qHZT-%T4V+v^5qGmkZ3u1rR?&R3u!N@#LI`3bZ`
zVspvC!hFN{^8i#5%Z89+g{VrZmo?XiCcQ?xif@H?YE=TN=+YQ<S`{fv<9#<S%~Vis
zh*@d{&ke8{L!7{s4GOl-6Sexmwr+gw1H$j4ZZ-~vshv!xd#O-patVtYN>ak*UT<)g
zZu>+XdbMT+MhdKKA8cyR1IcTec&SyqJ;f;1OKON~ivVeD@f0rK3A2K{yhf10D|yM)
zjo-8!NVoyS$M*+Uj2HnMFJJsh93Vc_9X`kwOFGC=e0rB1O)nk{8y^?R;vK}ZNKTMm
z0YR}DqPb`x{xP#&uyuUo(Zxlncc;v0zqRNx=y4QyJ5uau4Ov%6*Xo*uHA#prt$67Z
zjj7_Gb^*GjW^eoMtAfK#tZvVx33Uw{nOJ1-tBi2BvYbzefktRmw#LHmDF|`Kj2;0L
zJ^>ZsgP(UzeS}-MAyI8HdtFp;7lP?1Ea7T9psa{01!vVkRi}5J+K2!L383K`<;msa
zI)z7=g4tEzjJjq~lkVqtOAli>>it8gQFdQd60fu0GN!4*o~zvJ3&~U`dCpV|XoY!V
zSfdN!o%@RDskO={{7g@eED1%d;3P%FuN@9-kLi&1NAc!YbT|pATI=7kH%8Mj5`v%t
z+l=w+y+Sqg@dWVi9k2~RAYSoJ=(X5(0I;$FW5|1II71%_B0v^AU7rjna1i={S$EV!
zxYlQLHznJSyN2FzvDM-`IH12}{{SNMfqhIJTluUlu*o`mS;Gdotf~-#)t|kX3bL2h
zoJOEPt1sZ(YU?T9K>jH<P?qq$o-k4MBJtbm*tW%XcT%AV6@Kz<tvsitdyXy*v&&NM
z+V7<<H;LDLO63`@qx%y`QKzIMsImvgF^R0yJjRD_zK9K(dz_Dyg#{@UoM^**g2%eD
zJEDv$=HT9~VmKP1hVCFIom3SD!Z<yB5N%wiVX7VM0=~%sWg65g*CkHug=cQ_DQ_K?
z-;$8ypHM4wVt5J9C#voZtRE<VvE@%O1JBOfRK$5wD_KOGu&aJ0Vj!E)m>S?(3-E`M
z6b&?cgk93T&=EswbSf?joN9)dNx0L^yud<T%h1yv=M<jgYi{hBx<AW7{i#d#s9eV0
z<x3=<HZV>zrB9kfAyFkcGinq{cNwa=ikQF7;^RvP#c$z}Rwc@{Rf$n6TN_9P-z5j2
zoCuWn{sFA{JxUqg0I_%>)O<t5{{V!1S4CC&cERm6=Yo{VIn4GR8A>Ul)groWx8&X0
zyaMh~ik+_Gb!1LMd|wQzfXeolMs{%v7EWSC`i@GzhysSVl>Y!(G>J5pSIxgRoqL>1
zFc`TZh6)G46`@Bjo*=L^gRl?sC709&>QMP?Y<2Dk<wL;)6dOwjWtWrQXofUDUlHMN
z39^7(QjHhoQM8b!Yr2m&7YBQa*%(k1>^ICUX3@OtQK}FWx1W8@%9WT577BY=&sRmt
zTwor8pf2jJnN+M1fDV@JtjANeHY+ZbgO^?!jwHTmj4SRd&b5!blp)6=u$=gT7Gj@E
z*;`E?J$%NDzqn)DE9``m$TjcWv>=bL2%yPV@4*vNP+Lc*nu*I(8%AzaN5fvBe}%YO
z@`b^KwHg$^<V3O$(p1i&*T>>1LN3BO;#VN>%ghYw+zc83n)!>UbiD$C=L*1cSBqk?
zN&FY`vWueZDi?+pG3~8<3JJal4%e{G*}pUgDt3n(ZVyx5;N@U(j+LTNX<pTlNWBPU
zd3uK<PLv7i4oc-_BG$uWOanZXn=Ic<e%KhCsiOQQN2s#3Mkr=jWB5ypQ3GMo<(k{~
z7T2#oQL0T8I3sM@a8D=pWT^}2eR+*wYivUR%7+vWiQN?*mGDstEIhzcBE9+&n<~0=
z2`q5)FG!bzpAj;;(5ivun7R%6mnC_H8*-0rxyTVmkk3-#VL$*?Uex^fi8FOU&=iNx
zyj1UF-0eVV3$fXOxSW8s_Ldy!Ez|`Ph8VLW*mz)&Zic!k?3Nxty|K|e+;7JVYk8SU
zlY>b^Mp+J~S~*|Y14Ahl#rU~wJ+|5)^GrP;%Z(fs63^AKfblGMi*5QNWH<s}6!mk;
zI7BWO1;I4+*Rxrf76LmM`*<qGuVK8<cj?O!yaeG}VgL>=ZqTN4Nmk3WRoP|yxBXzO
zP<>?SqW#Uo2rj#FD-I%jhcLYKm14GT-E3*_@dzL>j_y6x&lXH8IOp7WYR#u}#6sxB
z&xkjt{JB&eR+B5^`weVdn1AyKPR0H87K{d?3tYh1Qj*Vt^$K+zo=vZjl(WhW4lPC_
zkvSOEL=YdoHJ@=^Je%3JM*_-i#ZJU87w$X{I~JO$d<vyqjLJ57@<8s{ykL1~mgIBE
z+3AiGo-Ra3oeeXE9iJ+P!*G)huXny8g2B_@xoM6V)<@Dl%)K#fMOKIqTx^XwLf8XU
zm<4<N*dp-OC}8bLa70%qq7EG8K-l1$Sk`E=@Wqgi$Oo2OeSUW!HwKOHS}ac5@OXiN
zx}yuSmKC8Hb!nn7t-^uC40n)Fej%_33w4FgxbQ)36%0ZtE_<!3S$g|;wa9tcC!6Ad
zdFf6IoWyn|uXQPsfpPx;_(jxaXL6OxeK07IwdD*GI^PTye#5P%<HuhU0jKf>Z{h_w
z%JVD?_Dl1)Diz`MN>yGS*MeOt*0+_KCXtU<@I-A7&r**KWMx_JVA~w1Cw&4z!Fevg
z#k`G#a?Eue#>?JZDa3eTs1-q5SjSLv!T!q_8~$NaORWOAN4jzhQA$#(Z|GuUL?Tde
z_V^%Ot`kL|1#ua%E?}pF@Fl)%bOkZ1mGhpPO@j`O+8?|8hSBu(If&`&Jh!okA4rX5
zUI%dAif6UB+s-i#T*K&+kFrsB0IOOfGh`4dhTWK6Xuaab)6NAfaVJdVK+Icmpokuc
zci`86tACr9-4es6pD<hXI_H=vd_0hh3V^=UJ>d4fT>B^PQI?$ZOE3zxU6!TmO#3|S
z6thZ7PPOwoRx8JGd=>{S<fL-(-4FK#kkSBJsDwQ%i0Tc^QD0ojG%Z<#TkE)btBu@r
z#Y6&EJfAVZT~9i-Q0(U%*jj`PlT-;QTo&7T=gM5PEYh<4u(+sIc<m9&vG=7eg11G?
zU54=$SJ{cctlECk#s_^XeJ4r1b*hoc3SA;}1FpnQML^;wOatH>2f4Vfk_t0=h;oj!
zXy?-hob0Y7&j+3>wf%z+e6OuxuXyMF*adC^t-P_wwVH~;V>vMLu0ncy!v$u1#PE2U
zDe;S_Mrakt{gJ$AlC&jcDqf9782<q1;TF<0gvBQg;Dh@@TNPpF4<pMcAPLaeu`mDw
zrRTARjdoG^d3#t7Qq-YNKX(+Zm?q^9W(EP{W)27Q8R7ZLu#7a$b`YB&K$kN~!#efi
zI~O_GHTz+?m*8NgT)Mr$ODJbzpF|4P=q8o*5ac_-QiP5N<rUq38qGWrfaD9Tbz-L7
zbLgmTa<xBF>u%ot%Q`qd*(mF&rC@uW8iB~sru?um)63ry<u0}2X*1Qz0yTvz+-fx8
zlyol;6=n<CBm-}w`w-^s*0}d8nOoQoFwWMm!f-?-6>*uiY(xA+0J&RV4-&Y}d_`+O
z73gDNC-FHcmB$jUot%{`To<jV6)SH`^;*X+axq&7RH=vPfh*Fo$Wyu>3b|q<g9A0?
zW01C)Th*f32wV+bm>GhJW`iu-Awt{oLa!Tk_9_+{<_fnf516fLd00@K+Mw-V{{V<K
zN&f)u%T#dJ;v`_7BIV0`vZ-wjUy?IEOW6EH9ta+9h{Y1L76l_H3JRfb<%!X=gEUf$
z>kLJO9B@enP)016dRkh53FD7)%@192QZ0Gd0IVQ<!mgb29s=Rq5yLBN@=qGM{{WwJ
zK|06Gq$9s=K4tx;m#CEVHpadpt3c4;d=M;M8Fvl5@%0R$Dd&f&$+*vI)Jk9wUw;uz
z9HMcP!O~Y=W<pPTQ^>)zu*N4dzZSnrhX)=|e=8{5R?UMVQHyGa<WMiDz7K=KUJ%<C
z4=eW_#toheY^dTJ(-GCm<+qvl01qyX#BX6#k&vUaOJy}x&c><8-KsvNckVA%KIz1I
zubeBpmlc;TmfV(ESb27I)2~saO0u%b69`kUm;6(5UXioz3nPG3hzrD2w%#Yq@9bDR
zI6GC-=3MkX^UR1~f#=901>Y0HNiQMtD|wbi8x9fD1C1I!bQKIrzLVIy6naJ|XdhI{
zM6UPK?gE#wfs#HhSw|&p3!-M*TRBk#ylLBuB2vhYIBNz3+dLyYL^EED+y&7xyre6r
zSD1ix#(Rulq5Bx5%D<1V{9kF|s;oeq@_48NsE~NMx#~M<mcmiLH3Gm-QG7TX=R4vI
zf<a4EW~kviEXMug0_|aGTaaqCwgF2kG&@eT`0)TFWAY^}QZUkLJh!d-BgpM+ELXUH
zr>&}+zmhfE%;U?Vc!sHGUjktbov`o<Uf_}BijbEUR=Jzj$3WGNC?!7KBVEFaNtrsJ
z1^A+I9!o_XRx`@6<v>tl<^UPnxPsziOSMJ{MFPWAN=jH&a^OU-nDgsdbM{1Dc2t5X
zNEKG0$JmYr+I*!17V0khWL&iF{h}(>)y@0}B{wo%<LV_&M4<q&defPvcK-m>Si|e(
zToK#sKPtigL$<;QiTIy2#)!O5Aj)5u!P|JUF<QJDwp9>0>Gy&E0I+W12oa4vru@dc
zhm4KwwfRf%WGw-_oN(#i5T5KNH0*_#7w2SyqzbOJ1hzE4qy>&s$J|1OU&xSBx@xPL
z*5eQ=TjPXTQq|v){n$M+<W{OIGibD~2OqOVJ<Jdh9YV#b_>kB^kUWF|r*7Po^3I-p
zL!l}e!%&2zVBmOpVLEj?YNu~BP<4Lk!Wyw5u`v92eB63>DdrQr`_qWxrERTf+!mX0
zwF=1hYKVX+x_Zn_ybAuz$fqDW{^q(R5h>RUUXIvb;x4!a!OZ&!URA6e=>SpAf-6c8
zwi$BhGeYXjZ5-1o!I5Bd^2lvnBce*jP27sV5Li|UE<QN8N=IJ`5Mgl0wt*PdF=C&&
znc$#*;ed~HBHgALF{Pk{s@f}<zif4oBv_zqd^3r-0i|&)V0m3$RJ3-7mQ&o$O$B)v
zZ&#jYOYlJuEmy{wtOuX0C)XeN>h~$Z*Fo{Imf1UxztS`uzOR9BmMh{lpEI`c*<Uq%
zu+&!dI$VQC5cESW`vkQQKUw!HNhoOQH(xAvXbQp0VvA-UAqlqoJatnt{6qT6o$DP3
zn#LUi`TqcvH`PL{9(=$R-TW}wTWC;XkXSSgsB$Q#d4w%$7O(UfMX=tL;!uUfcKd-g
zTuQy+38;m7%${Lj+pEdRlr9y10tq<#(|fZP3&9OTr0R1xs%;^4ExpG!m|;+Ls(Oxo
zKA{!ggia(-RZtmr+Ei|M^#z?%Mz@z9bb}(4>EVZF$he-9F8x_z4PBC$choL}H4I`%
zgS*;M;-d0rj>B68;Y7EMhSJ&eRYOZ|UELf)3q&M*BG8s7Q4YGh;$^Y#LHcC_O3Uam
z{{TAf1x)M5an;TBk*+w%Py<S^{bEg)x*o0$yf;D6^)kAdceP*HmTg9yap{jfOTy(^
z9NWh6Hn&7J365a$mu>)wc0-2W0ghC!`+0J{AcL~m?ZO!12pctSA`uGkb#MeMCGvl9
zfPb5kUQPEYHOrAd@XoQjHE!j0lbFUku1I#HK-=btTz3o{#LBmuozolVSox`$W9|mp
zhel+mn15E_N;pBcWwc36SY1n1z;#8-+5%zMBKPqcu|@bSoD;dmItiL9(T-ep)m9l$
zPeF*G2w6-@<r<|<vKpTcFt5RvhXu?0vED~zy(IdKp_(l<xZ@Usq2JS!4xyiDTzR`+
zf)pu>S$%}0e~5?-B<9cu8jE06a@-B%`DDzs2d3rP0bHRGhf5N+%VCtbRqE*;jvoBA
za*e#m)=^b5+wa6&9Sk?22F+hZN;<GMS*YmGq@cHv*No;`;QULHs?QrQeYUD{^jCdM
z4H#OL@Ni4*X~F|<h&OL)&T)b-=`AJ1f&Ty#lNZO-vk?B^+}Hs0IN0|vYOf)%U#4Ax
z+!yb#_csp7Jl#opRs-1)54ha)%j79IDIn&Uv<JLPXB@=3#`+gj#S{>#?R}`#4Y#i0
zeH+siXl}zx%ZirC>P(UHhsx9c0GURovG6bs15(}mGT_f=I+D>YeRI5)X%*NM*Qh~2
zA=AoidvRXZ1xly^Y<up$Vs>G_7MKuV9zw8~)|{g{4175qDr-btOPz4FU0poNQFL?o
zE-i$Q$zZb8Nx&QuC0;E?0+t^wp=`Ub@o#di0Y+>+!?2u$1yyK5hs9Rgx5OOj1s1{R
zL^X&~FDL0oulxb9)m>9^tOx*tv{Eg5Aol%B4uwKyz=D+cjEPBJm9Y-^REeVG*NK|Y
z))oL33j2x34C}~cnL`8NaAn0*9$!+ruA{(KPF`}l3bR&}@Ecn=$Zc>A)KiA?z6Nr2
z29}g1Zx3^Updp-D1eH9tpYV$a^a;P3PGZ~SYrBK3YQaax)NxvG^(C@UrZbyOzkB-1
zK(ngKU|IJY0A2baFMIrpAd8E3%34c5w9^vJ!OnglwmtjuEe<Dxo{l1-fVS7%7K?|M
z#dDoP)pyXYVBxe1rMblkzqTbPaHAQsgQk!hi^EgN3yY7#!L|y=yh{{rCs-|sQ38M{
z2HT}>I279jaRfZnZ%}&j%DI(@P~IAh79T9MO*QQe0>E=!2*mV#3);dfR^@|S?L^#G
zgE!YdcQbA<PbnQ0c>|nSX+iPkS057~Rb|#yvW))#WjP*3_x}I~^2XwslLRLe@BN^}
z7j;sV%PM5ijdgn{pllBT!3lYRaXc3G=7f)gARu0%q0Mc87;3Ld=H@%HuPfBizOkCs
zn35E&0Z!8UbzUaY@H={gXs&`#ZtxDSUD}^nEe>&+^yYjCSAV_GSdm51!zI{B8^Rjo
zuP~OGL=7>qJ*un}UUAq51P*fxAEk<vxj0`ZArJ@w6O^?(prBRFvz|GTeuNBVYKpJs
z<IYd^c|_voQ9;96SeS@^@DOfo;4QnUwYqAexCA#T9a^aKO&?O8>1Ay?*Ef<{i`sz@
zF7|K>TVx;WFXPqf@VBRnn3>QTdgya5q&J}d02DpPcoz@SmIe5xlB}+6fR$;dVce)5
z<<{4i#3U|-r#tZ%xiihRIR`F>oPbuY@^=N$0ag{M#M`}Va6YE_7H8jZYEsV9!AyI~
zX87a8a4ucb<}2j!$Eel&mra~pfAfmo$J_>-M$y=X*F<Zqiq2DSB+Rz@bVy-NTZZ8x
zak);NSl6WiMVvj*j;@aTqB$cxF|h1YR_nG>!>HcHJX&5J0lz5riU6cF;P#Us$K_>K
zQh{&_T1a1=qU8XiDuGGJ%y>boXGZ6Vl*~JFR?XKOkQQKHu`=L`e+{dwYZWTJv!jX3
zAd1qxjo+zi4!CB3jX+f&9$|J6L8MOZJELM%TJ#XsE4YgmAVQ{zWjNz;%VCL5doC?4
zYs)m0YCJs2QmgaeRsCffifQzkP^zy~@&5pLjFTJRdk`_OEuE0oN{PiRzj-W9AFLz@
zeTIP0mU6^7qxl#nsn@7MKMX5QE+%We;)t4ghprGCz~kqcfcNz(y?sY)@+V4*1qI*9
zV<1o}it=Qz(x20C@pwK;-;AvKifv;f!nKvmT5Z@X9ITc7Q~^OBOl8nrs%9=RuQKG6
zWEc*T=<)TJH^OWUwetrV(luPr<CxifOR~L*5h^qba11d#F}hulp)`;G0P0BuUzm#o
zJ;JL)(OPC7%`OC_doX|0vhU?3Q5}iM3$MueDL_0?7|Q`rfoyGV*7HEN;N))%H_E(<
zN;9eFb?z65c=ZodxnxTdoC}ugb~|V^$4%S!k$7pA1xo4USek7x{fKU1ZLS#WoaNo9
z`GdoPPqAdElEucs=Ewzfh<A{A7@e;<@OMC_QsR!UtduL(pNoovI%kXj0Lu$4gGY<j
zR?QN2(Q~g-!R&Tee))q!JOD3R^1$EnztbXx%!E-6(8AbGtIe;Om<Q7h1jM9h$AKtt
zUBbDikWg7HDezXVRE?@JI;Y7U!e)nsAM6TaQjl74@LPg2V~bX0Xs#!tMhfyEmp{~*
zg2H@0;4ZBbz^+Y{op{R*&bjG?60PgO?T+yq;#ST(Lpe}!@DlN%+^4P(a@Og{U8Y=D
z2udh68^4*gz_HNr%vDMeu-b4$;Ar|UD3zkTPE;5A<{Y6o`CCLIXUB=9RR-Tvbwgae
z&Q#s?=$5N(N);fuCB6JM#vmvf22;c;GXjc*Z+P=k!#ygh4_Ot<6<AfOwxfE=QKjTz
zgAJ7(X1+Nxutmmn3Q%u?*~%CEUQ~a`LD+ddHbHv>g!V9uAx#nAH#0ME(bf7RuP0O}
z*90mpww3l=RwJ!oyYPq<&{;N8$wjTFB{hOMiG3j1e~w{wVdLB_7hl~<ACDCdYh^d^
z%_s_%8y<W!+=Jr`Yo&)0&bwcNJs}P+UzyYbvD*RRjIDtLCXp!(3779)>X_dAOQjTl
z*cx4NS0u2!pwpYE%&O$>`(mzKW=9IRLaSbTig3Kq#j)^I*a}znvNVg)A6}<?L@YbM
z72NJFu{-W6*0~^5;<2SU^hdUNJO^IMM}>esNk&{1MhV5NS|hk~#A_|=;`tC_xE-<<
z;*|pP7@SBX*Wt&4qS02TmrMK@YcGYG_PS*)rKPj-c0!RVs4aMTOvA$Fo&>pOZ2-Zq
zK^C%s*tKEn*)|1S+Q-aL6_Xs&lG)4D$hbp06lK<N9SoG#KtR!X>ONx-AVbKPWy=jP
z4%L$iOc){jO+La_80sZ(gaAh#7^>>_bW6vHxIKfj8z2Kgt3-hzT}HPQlb(?A%gj!!
zqN8U4Zhex4aGq0s;;T)EbD}q&*<}_3`>|8Mb1JrS;NlBaZ~|B-k1_uMkb|Rvr{Wb#
zqcm~z3}afWoza&w>I$hrXL#Qp2AG4576`Q#K4NIjgh1d}rPv=oj));>>F{bA<;D3|
zQl^$Zu98qlp8!Kka`TLK>o%vke*^^pqtL+=qE1|r1^&UWBBK6@n&fP(;R<|1lIigd
z63JM9&f?Zu4!)T!t|@^yd_izUba0eQRUa?Z>%q>(?hUM|xIq_9t(=Oc@FH$ySqDyF
z5af04C0|DIP`8?K6%rTaMFA_$-NkzLLmt+s`Ok_DIchE$8Y1HM17Q|7WvI(yVqPci
zFv6hUa0WI5uOGq|0Y)V@DsZP?z62Hw<*zZ22GMPAhe%6Y3G+fddXE_2n0%<9FaZAm
z4l@4ZXl=LM>dOFn4-xNuvafId01g@eUXU>>LFdQ(#!zc({{U8HPF{S$Zwc#9DtjPu
z@ac_~72O+=7F`ZGws7v6tF%M-e_I<8?~94<vQ;Ms+oY{|pI*c_ocflmR}m<({e(8^
zTKk%e)e2+ehq>}LR)CEU6ZwXj;@$xta`Ow}w%}S1<Dj?BSHy@pzfD=aiB84YwF-r9
zsb6HQHV%^Z`(U$xeX-7fs<o~^<W<$MQa*e3BXSae0ZkJA$`Mel%0}*5KIuR4CWOiX
z!uuuo2Emuem9ln?0mi2pz-3TvuW%?A*H#5Zwo~+rW@Tqz`ymAbd}*4)@EW@EN0C?y
zeoSDA^$%$VwQi48d8u7MmDOHDh~`=+!pf)H4COIML#tO`u+WQadyBYp8o=7Url=ZF
zQsx*10oU&{=r8w~ccB2R@{ZUB@)m|6kxI^%QB*b*MT^>z7&6k19YM?~4Jyb*Djo-Z
zB{3-_`-a3;+yO=SxN?B>=BNS_db@--!uTbm%PRl`tSrjnz^>v(s7G$*B&P~d3SD<8
ztK-Y5`y;%eTC-vTR}~5qAZbqqPQQM9iUh^~0RHAG&3?llbKVay<k8##NT^DB-&GG5
zHa`M?<fbbI$D>#cX;;?F0Tp6V>aRV{l<OMH&A%J{o;I~W%WQ;$SBk)I()v#%bq!!?
z7&_0=iip8gTi*lTD7~i~HaR#o!W(m7rk%A7REo+%YWi8cM1~W^n2MTt>*FCmM*=4L
z6(eFj%~5D{1iLeikTfXumkdFw2AvZb@hF9_Sab+a2nACa2%yIzokH#8qzeZ6^o0T|
zU=Sl(X$~tU))q#T`6l>4jzi9IC}WJiYakz$Xvdb8JD3ksxMCt*e+mBpg)}QQT;fW^
z;M2rbSG}XCtblh*1WiL`t^16alE}8a6L`Dv9*U%u&u>Jjp}3%nq;9Bp9PJP;^~Ant
zBjwiTxGbZHF5BWHA0*l1>E;pPV)l6v5#Vy1%DStJNQ|#oeya{tc4732g4a)jFKTQu
z<Mte`9u`v2pIiim*%_SQ765|4@T6&Zr#F(xuSN2OSUJA^ctwr^xQ6eTcXua{C=!=t
z4ncRq{L3hCwW1y@>62E1{4W0hWo(vjt*vIIdbSl+YN&(OwQ9J5qzaBp{N@N&H4?Vd
zAH$r_pgfUk6(|P-V*QY7Vmi)_TIXf3QUrLvBjQn-ydY9KN)&n;D~dvTJ)3ucO~#J!
zH8l8$K|9bn7a6=l%TaK=;|BmFZ-r_-CFjDnifLvWUjqFkdN-A4y5S-}uZ2<taXbgQ
zp^cx607{$Lo~JXLu^YL&PnX2whaXX<*0V2R)ss_)#l{VnwLQe)2P!ou8qIRE0tFq-
zU@G84kk(C%D_~vZ6e4mpppJ&ZCFEKh4+?l?qIXd87MiWjuvtyYdU%SizJxO3K)R!r
zn?E9q8Y^ng@}ifkqAI}yQHFxFEE$NX-s^sglnr%!s3BK)HyOvKyiP6kl8%?XLrBf9
zJglVE#L6WwPHPbV0I|Vuvy-k+ioCvrd2Vu-RJgvlg$SKN_5rFqH{_fq235LAcI8Zl
zm`(*ZHD&rz9_18B0*Kv_D-#8&Q@U`+eU%@d7r6y<nx0P2vIPWEIL%Mhr!cGml=#4Q
z3l<2QEp1x43Eo{rU%0SC@y)#xN~4%+E`h8&l;+wsuE<?q=EnHv?kEsDsqOpyNwnx0
z7WJ$6ul!DUjUGn3>Ikqc<(<lDzh&cCStDZnDlgp;4>|sT0W0F>V#}#muHs>`_mO@o
z>a_1KW4}ad!B}T3DJA;64pCg(Zx>bO9CrvBLf^(Mz`MNJW77g)Q0ZJ%tP6b^MC$!#
z9&iOdK`a#@3iQcIK<}JW>qK;wRBIXtH|pZ0S?e{sj<;emfQmI+lcT8kL~M3&<G*tj
zZsH*ioG1qlr7J(+g^k<tOp<EBMfHS9j<bO%hYC?{NkqA~8#XNnETWpt_gb`54&P>z
zV5Lk0vbQ?v<m@^Hr%c(6HP#bKAb0(RG0J$Iyt@lm$_B1GRg~lucuBOeEvxL6Uq46L
zPs`<qoaL7Tiq*(uQapaxA_ZM=3uRl)-kzbzw#e9*a-L!@4N{~v1bQ-@hP&o|!afLI
zZa`P#j-}A5?aj1)${xR$+o_+2gPg&aP(z*wen<w0CA~~6n}&;JwRE^ZW9VudY=6}O
zV<OvyKEM(TN9Ehiq1eAinC^<%_=eh+&~C&ov^zE`b-IF>o`|%|Ddilps5Jfa1#cF3
zY>qH_H^>nnWpAMfQ+clfTZk7^sjfJ${jjRxzNehFRz4+in8FBVQDTgDfPm3L5+>6a
zVRkCGmes{7t4A0<*PIIGEGeL*)bSqES5(%9uw_pyf210db~PI+jJv2#!FL;ZAuI_^
zRi}AsRdJw;6eELTcKjpdyvJmA;`#RqIT*w!CxD8H8lVOFDVw`mIq^5(g$|k2sI6Tn
zp+sGUex0kPE`pvGKuW8r+sgw(*s9htxtEc}J;i@kMqB2B22hF^^;l%Ub+~$ij>Y!F
zjjXS!#20~s!j4>E&aAgJisEvCR@&0If$<ms4}geWta9df%15j)R<PG(T71WmYN_=-
znQ`kk+#uqyZM#|I#C{Bhr`lC{a<zm{JC)Q05`Yd@swbK^uiNhi-PQ*I;JDolVm%=U
zXi!{KnJlQfX5%{`VN;CKT5op&gg6V|0~7L9guimN4T){tg_6ZTxoIvc2sHeuYuvQB
z{4-JbfGYg|0FxXpA2_2J5tSQgp*pE)q2Lgt^X33hP^Ds*+FV!v03?S!Y~wZfHv(t`
zS6uod09Mffsvw%9+WR{g7AJ_~5}q}%lVD8`RFj<IT4P1FSHmp>X+S5ily{=Ah75zb
zZ)e#~SEAo78pdghwG{c4v%=3P*X>+Xh&>22-mYUb?;1~RxB;<G^~orDH#2}a+$t}G
zdl*udKI#IQXm=?*__<Uuu3$<8;nL*@PDS?u*cZveA2e5>mNwHR*8^OzrHtN}vW5d2
z-5A|+I_fEqzb3z8yauQ;Eb?D?5#g9-84E;ej6jrD4+aT)F@5tjpSWwwU-@T2N+$}}
z#`PE`g4yo2Qc+v5xj2Vgru{5-a^XHDSX6q=)KHaRQh^d_>b@C*bAKLEtf&%(bX^!Y
zgX!b@5mE@9@jeq;$3u@X9~W!mU?3p<&)O!EyIIyRiKkn20!_4hiEIiS;&yzTk;zkI
zsOarf#!@azWkRUBD5Or33M#<QlbBjdb~)n-ZSrd|t)9qHtk=zyNrJy&3PvkIJgD_e
zdPsVQY@(E(Xc+Wv?Nz_(pkWMJl>$`eF{Vq!09PUcj7vtWy~g?(a1t5|x7=3~7N1rB
z0LiO;pcDT9;LUQ*FT@wf5d_HY7fjf4X|HgvGvU7cTuWa>++y}Pg)g)1EHDG|GScaX
zv}H$1!u-PHu*X`O<F>1qn^C)JPzeNiAO^mb%-|OVt)cHcLU5-GP{k@<(fnk~-6NaP
z1?KucLKOj@dS%zR=yDKL(Cpwkt}Tgiw(EwVDr0=>pATu2xnZ`vLs#|yHCq*^FHKL9
zP$aHBDlR{r2>Y%*VVC8z6&!tv7YB5rY54pTJf8v(l=X+g-M-kI9|3GUuYF%13@NCL
zsP-Ld5z*1W9w>?d&mM}$SwNMRY#_aNkJ!|5QklOhQ`B~D8*tRhu7IuqAV3vDiy@io
zT-m>p72h1CKHEmU{y-I@!2C5cqs*&_3WKU@1-%wtm%g}_1ZF-<pg#|SCh>r!C5Ewu
z=;c#@<#|lXVgL&#Dnbqnn+`fh;|FG<MS&1Kl`5e7FZY-cWa>H8H;7fv#R}NQV+yn*
zc`?V{fN@rK%8mvb-hDGIU{Gi{{Glj4S;AFq5O5xrw*`JB(W(9$%tLJ(?On{&W%ir$
zmZxMReuh$(tx&mAI{yIYP)tiegU!_9nPb{u=FgZ)vwo&UVpx4euL}d%!H<f|IGme%
z<Xk>e?2XQH{{VWIM)~Q!BU`;gc@h5r^iKjj)(Y=s_M5b?30m8bm&e!sb|txbAW{q$
zQ;MgJE6kuZts&H#qzJ~T92<q6N#o?qp+GcAMP@1vne8?zU`2*UOX+|CkU&`<n`#bj
z+F>_<+gH52zT^1-EL!sM5d{*yDdu$B&zqdNnKu!V%Dg2a(XC!2#zZa(!$y#zkYTT}
zRO|}7uxC#Yq_qiG<Ew=BU{$OZ(K7y2t9iGV)aRP{hN6IY4>7*A$KZE1!<9#iidL#`
zZB)eJTR3zV4Iv<Pz<6N2g=_6tCJc?W*f9}5C3DA_(D#zE$L(8|0IhI1sMTC7^7(@3
zDRdR8#~(2{bqw)&f>K_2Lh&phN8RqL%Fzfm&4QQ90e+irC?dx{{HI2kt{{$kmtl=h
zDNo?cknoH>7c2W#j|Vy$F&&!-#6aWXCGBCVXxikoE(Ll4zy;@fP9cQd&=?>;As|tt
zeL=a%Ted6YRUk0ATGh!O12pQy1RDqr{EB|XEuuf^HW$D&*+lnbUcW<D@81;#aOJe8
zDJ4^PFC*l|kkpf#h|`fGg^Cjv6K!UIz!t>4O0%BhLGUG8fC^Y6r%}zwHyVc3O&ycf
zRS*w`@Imrd(jR3oPlOhzaC0ips+9Qbg=pM`d8vx81q8RiY`EB}(-<c_APCk9<F%hT
zDXXlUUxF&FA4J9Y<IJS5dnr6VrSn&ik0plB2u$eCIi96+TwLp){xPn&CTm`!ylXC*
zgRxdxcB9dDhuJDDJHQMVrB(j`Y7*ze@6>kIsPV{RRGoif8iG)$EyU|Dy4LP$?`1bY
zT7Kh06NhP<_H_lry}^bBwf9XKMax@QMX`2e;BAbcWeG)8G4VtzM-ghJAaS}#rwzha
zx{H96fe{qPr96_RvV=%=_-_e;qP=SIl-V^@;Aa8EzLmVR6m14m;G+uImSZJZ!nGiZ
z8<)NyZ4-l;Js1ZJp}s`Hi!Jc3%wChQX)l_Tpfc($%Fq{4*W4<NpvwOM##M4+$?XHo
zJ>wbjRQ+P0)dNw<&{`f0+;9zsLYl|a<MyY3<C*wO>0mUpBa)AgXR*X!Z%dD?PpNRP
z6UCu_QJ@_i%)ejwtOsgub$SuQFN4b;b0ny}u+5D2Is`u)<R`;N4}Sw0Vp3nD%TZHZ
z@pjQ;SP{nOFu_jQx*0{!)GClHb>7M?6nKlYA?$;D@poNrDYPR1)S!=ky!tAP^+idx
z@7%eN8m~z7j8KmBXVkWu5>0@kYB{128S%Z=V3ibrpvWB-VwDQ^J|TNzk#-jVBg6ZO
zU!uKYiuc1b5g;o@95n{T6rL^5XG$duv4W%B@G;7To6Ib?K+#EnfHbUQ+`B-sDTvGl
z3fWja-SaI%<;#t-?(GzmiO8O!wj5pb#<6E4F9E^xQ7mhbaFQLb49wcYF4S_z09Cl#
z>j#Q`Rhg<mL%fADHd2}t`Pkw_&|1cOoydH;1ciM<K<Tn-{(}+Snys_IF|Qa?V$MW{
z`##!~Rocuv%47nCq=QY}#q4Q(qyo9M?JD3%)0fmK+SOOY$c9U8RK!`3q6uh2aUqUe
zX$nkl*%OMFVZjk3GTXM=O3JifBzony8`vdn7fHZy1oHHo5l+!K3%!Otx|pkYnm9f%
zVV->bh6Pb)3>MC;w+|ksd6$(jQ-M19uSaZp!?l>BI5;?r6;#2rTme0aL&MkWI+j`$
zqP@dFEydajeNBSv7QM?|775CU^*JF)LhUd^Cq=$s8>rchH5}Xl(dbK_<*#>0rjcd3
zU#WEM!3yeCKwg>v8u=r;33CDJQV6W4fWh!#M=@?;^2EAv%0L3ohAI1Pg;?FXs;N;-
zq_&j!V9i^6!2bZaq$oJ6CCw(w%s4#?kA-YQT~u3kx3SHHpoha6%W6=H7G=GE;`99V
z6s;<GwWm-L3r!FOS8P14%Efv<)&Xp8I_hvv^L2^yyhCC)jFz#OCkUny3y`trm>!2;
zc1u<0AwC)6<i8i!n5CU8a(FMtsadBsgr|>iq;!gc)_9afTSMjI<q-up*@Rb3vlZ;Q
z#g2~#%2oSfy?)zOEGcj}WIfQ$1za}qt`}Vo3~a|-&fBjez{)!SpZ5KgEFlns-|Y-o
z2C(!00PzR30XxSVQT3owZN?;`?)W|PEk^s{*cT~r=(VzzGU=vKO8mh}Xbr6kEMS%S
zVkue%$oqFeug=nHJaW2_nc0!7#Bopt!NoAZuU4#YrZl?^^G-_kO1zD^@?P;-?A)tR
zmhR|%EW+-_;H<z_kXVUB;Q%N(v~!rA`Go@LpM}^?C!Tgw8_(ydMdpYvx^2(N+<s;R
zRrW*ZGe-6ZT6AlV#5S4~Tk~Lw(*^$kar496zA|v?XPL_^p}#gt>?@wzq7v%6h`gQv
z-*T$3ODz`#UbW491oC(O!l5WUh+XJU5Ll;Rr9P-2T?{-ygl#{hNT`y!Y3z^5?)&NX
zMBLUmwNP~!4QY!HAuLT#E#lc4ZEG}BFF~kjMy(z)b)cmx+sTS-{{T5yV6A6w;z_-v
z>o~GAmdeUtPwlQ)#`3hNZi7y-`-#(zrKrR!&N69Tf#j99QYm(mbV23k2~tH)N7Pus
z(4+qV$IJzqYG;DJ2>5Z!xYO0Mst!jlm|drCVl)7B9r>2c1?I8&fr7%bK#kX?1xS~{
z(K{69k{0z~sq+kUJOx=oTG7?~2~GV}i=ZHObU<|ZB7HybSckR#Gqy1oYWFq3t!lcK
zt5o=AUlowW-I_}7AvweLP>NQXg+O$Md=5+Hjx^ym=!O)hX^laP=jAhSeP4ubYDT~h
zGOR9lg7jAA;-vJ0OC7z&G`A5}s*UvxC@MP~<58$S*$wQ>pt!o7nkP6~ez2<j;Sr7z
z92@hYP7l$ivcKda>xk9Z@hBsdoXcde4pF7Xqu#j2MHbhG1tb<uMyp?`d#PF;x<aLl
z2X^Bip$kPE8>@)WMXsBiz!;|@fZm8_{4UsCi33&utQ&Awyq*{cp-ooo`ey@<U3FSO
zN-}NbgLu2a9e<BdSv3U75#_pP-4VP!%mx>Ly=SY~@(;495Y4U*T#Oyex(_A<<!~#v
zs)VI?TW4I?+#_*?+Jw5YyFjJK{J`3y3W6-Ni{?1&T!?#D2JS&KNP<L-x0bPrnlUxO
zOKKxg&jBfDSmF-@`$Db}c$CZbdLjfQU<bt#@H;5cNNfe&xY91z>_R+$it#MKggh6F
zK}{28Hol|Tz?mz3`&T6}Be*;Ygx`>ZXcP=HY^w|hPl;#7qri1N%UUk?#k0G_ez%#n
zR4dGT5lMc#VO%DuiqbpTC(y<zw(rf4@G~fn$#911DlZ4iDb>+=P_Nm2M9ABOp<)&n
zbje(@l+5!m7|%4jF{@hf0ByYnu3D%)92?YZfF1oFx||~45fwsUl7siJ7^a0o{;QGQ
zK|D?`MXZ`wwje9BOGH+<T05{=9BMmzIwhM(R{XG*wT^FKHx(QVH8E;FNainCT~MtH
zfiMG0Nnm%eq&?0cjR970#VJ5~J+Xya9D|vU5~*t?FYB41D7?7cH3asgMjVTKnqTa#
z=rr7W;8)ebYO+zTTRGPRUNC`DjK0Z0Hd=C*wjO`vjtfOvd9K-8R<1S}<ugv2H52A&
zwHJYgTt$?i@Jfo8v$KdI8YA2;oHv#_lBI=lg33bR$9}%48J@hC5flJl1TqsqqMGXB
zvAu>G3wH1U0bIi4Sdbwq1Ra4nwTn$IB%0O;7J;-h->8c13i7GTl&Dkt5T!1#HJx$L
zihNuzx`9N%%AAH+xCDq{GYXFb!*H64OS!LTOO{|TT$_7<p{ANqq;4s<c|w5;Ug#hA
zggJUOvRzJ_ju9GgH2Wo@1*fe}W?k#mR{~4ukd9K)c}O>*MS@aDiozLg5FB)se|H@r
z(E=^^5ayFT5X307UEFY6?jwL`7TK}%mYs+Xnw59za@~vL)LUNSHEp>@V`A}QAzxyu
zHS$Y<#}&J_s~kp)zx-lM9`ez`$DqVSQ51%LW{O_>grp#ga-$vKTUo{l9%ucIZ6F};
z%w7;h04e4!sIH#d^2$44Qn>T(S=(ukncSE!IHY4l;jkuD9F8%P^gSALouukRkG}-6
z<YJnGaB2ivyA_`^hOW76a^SAGoN_fE020Gsf7qY!BU}!M7rNr%dqM(T-%VYQn6jrN
zHVfoQW9y-_!X&3WU)0-6F^<K&D~@AgnU%2TOIL2ra#o$)^n-ktDMSXFq1_!l#1Iu_
z){2119A1O|$!GFv5~{O}ji-k#v0#~@;sz)%f{l!p)Jm)Y@Jd6C7E;(M3|tS`jhDK@
z9+s#*2tGH1TR8Sd$Qilz^oS=`Y68p;y5?fR6V32;rQe>Xo2q7RY|Hwbou}UfsF*fD
zm*j|0&k8Tr)V6S79A8rwmT`alcNK71v^jF9y71am6ti^T;kG7bymG?TV-s2_)m&;`
z8^Y9BqFz?M_6KbZnh+JC9k(wPe}r^aqyGT(0#iB_F<PL8?YsD8#n^$d=HYb!rmb7_
z39~pJ27-{It-|(;16zJ2+T8{xTK3%cS(`Ljqr19DvXy6@IfDES6Upo1bWo{u@S^(^
zwZRU^$k}E&>JM13s+DtM1zU5J7o{&TfRHOi68*q^A7RgIX5Thd-9<IiUo0I4q4RO9
zC>P3icv^mLeD&1nAbh#aQjn?#s2l$P8EJ$S&5Mrj=-Lb%HPMdSVr*-{c!3yC#(ErK
zD9cRGhA7iO{@jAAPU$_wkhjq|_=&l5mt8LXjJvYhIGt5-zf%Ag$8jsGD&x#p2@@JZ
zrB!POBFh|-oJ})mTLF|>l;8-RtM=yd`zX$@vM3T3h0$jD1iobK;K>l%0QXq3O3j3O
z0#!zkh5Ik`h{Km~Hxhy73eSg|6|p1XMn;-)7C{sTmxC}O-rNwluVyjNh>WQ2qju2>
zshb;X{GZLAP})Juk=Dnu(?QnV@LDl<e0}cC^jOYR^PV9bL5B~O471iAY2PBuAMz0M
z@xh(zEyb`9)eNm*!RFGzHwdDt&NAr#04FvLCkO5E3WBrEa!Kv;7-rdU5CvP$0C6gY
z-sy#8t39qj#L)FN*@UWo?%{5{#rrUY62VVqBtoT_wMXA<*4tBRj+tb;;%S&(iE{q{
zXExEii-xUO+E<8ZXtg*t>Ks$O2uv+Fj{`*q6)hff3G$@}s!LJ%;v52n{UDvhB#s=U
z+eJiLs0}ohRzBd`>_WldL^TUT&};V?8+@ECN?YHU)5-ZO&v1GO!&J2F8Kfmb)7o!W
z(K2CoI4&p?N;wi>WD6}^^BxQF1aQGAX_ETnq!C3+;cID<*1NC5K>)PpZtbSc?dnm!
zJ<bcwiZ=S#c5T`V;d~pN>j`=12p+*!x0f;ko1`Qx*io(;ga#ntLhQGRQQn#z4a8z1
zz)YGegp^Qf(hpX~=Npk|t(ijgYdKVVk0!gorM)a>0dSTLOL~nK1O7u~6)TS^iwHZ>
zeO34)ZMR3B%s}^`@unhb_KluJDZe}8mK-W-`=_PiSDmnj_EC*@OJT8aq|VbP^AUIP
zuGE^rg5Qrn1oK0sUcX2|nN0b$h_2M+9J}p}d^RiQnT|S&a)x<~o2C4@YQdztLIUaY
zH%g(ucBn7*=4o>6`8-ap%zhc=`?<s1Em4;@9Hr@rTU`5?x?ibW#Miz(O3q8^<{kyk
z`k2(=VW8#U50X>v`(JEj5yz*i?smEA>$-~WOPOw#_AJKRuTwB!egYAw9f^1_bt}`6
z4N@rPjj&vckgf@`hfW2HNNUOJ)Y-WaU86l$)Exq&tn_gdo7oO8%FccLYySY!J%16S
z=!`0jF~bNnLo)KD_rH3r8CT+Raq!Af<HxvDMB)5}sTWEW{GFWS2*Q}9WxPy<#n*EW
ztktN-UPisN%M-=}YVqkJ_=VRg_QnTmMDb9w6e-NTc2HG~FPSZ=L$6g*(%h|6TQy0_
zcdT;#_Dq1e(_aKwD5liA=<SLy6-`x#DW;~COgScUc~RYsd1;&(Z<%4zW0Fi0wFqpY
zzF-Aq@KeP`HD3?~2svf+F?Nnu-jQ=%G%R<L?T@<HEzgAMfiN(aLlp>M3ao`A0JEmM
zls4(`^D3C;#4PNi=OS+CLxD>mMAg=Zl~S2p-w@q5^>sr1ig%e_E)vgCZUn0T0Ae9z
z9}h|SjQFm=(`XtCu5SQV5JwOAU=JFWK0zA&WmIas%3hV)c&uCaYvz&-0f9Q5Hc@kb
z(p?JGz$6pAiZtG5z?^|+e&DPfU0kY-G-?~f)}y{l4D=R{mr=hAbJR`B;YK%;?uc7r
z6dqYmS2VMPyn2*S7xZz*9ML>&;=Dweu6UU$$}nKwE1>fLa=6H9L9u9O{U@ZUm2N3r
zPp!WPOtP;V)Wja`N~>6D^z#m+0IG%dA_zjD;Gmp~?$>|Frxl1*I4SUecfaf*@&f0I
zjWXgZSVdAHFz8lpkx_k0Yhh6*m(5FMm<~7OD66c(Dz`+_8kJ49WEKF5;vS$q9rC9Q
zH=}%rs73;7Rz-2yu&U_(MPXuAz)+1uAPqz>Y_9uR(VibIW}HP{Rv!?zZKuTCXKqxt
zZubTG1ZWg*$x{hbx&#255UYWm&{Ix1nbe>@?Rq0T6ydY&_2_}Uw`%j|4jW_feu>$2
z0B1m$zt#aPHCVMw(MP5iKUh*SmQoj*1GJRIlvUx2Ou|T@pa;Msd1k}BFWoRqo+_$s
zVzCmYh}G_8OQ~eEplO<qyrBG7{{Rw@fn$+ipo~SUJ5ueB99Uf@8H6{A`X;La*&TxS
z6aew_wX79Y>J_uf9;!o<i&S$i_}fb2ptQzavfxLNL-6Eds0hHT<uF$Targ!g3>KL3
zi~WU)rE@lgSv#x`)|peBFdD27J`&<y*cf66!7$5I4n?ke<Hbx@YySWN@O{dgxNkJy
zZ~O)>n%@0OTck}3iAY|U31xc{U3`!ut8N#lv80U}EK~xf)ULlUP$NG($c?pBj&ycb
z;dsT~Se42RJZG4dZVO^}Dx=PwIASWxqh{fSMwQyOE`!sBFD<ssVQIV06$=8rdxtQ2
zA&AwYdt#;#3+`KXRrkfl28WAqO{W3UwxOnbaJHyDEW+eIIA|Z(Pq2rDmjf0p0Zv8Y
zXH{;`#&43zb}LPReN})f1GobGY@(fET_60+xu<#ERpc-YrZoJS({U-r1UegQdn>E<
zO9ZqQste_bg=F3PiRg0ZD!<8F2KDly*)e$_`7|QyyQo5Hf=3!~WN1et8V^Sm859{^
zynz1z)~8ooO{009bM8O?0I1NyRx~cWK-h>K30CEE<>GhZnAHQx%;fm4V&Hlso%I^h
z8G+Zxau1N&f+L@ElZn;;04I`AxKQyI)LcDOM$(@%m5Q!1`t2~fCls<bESkJJ>|A!H
zu<}hg7S_xI%L!G%arl-{rC6?;Q5`F_3NHp&GCY(-zm``aP)OBO;ja?D{w3A$GCFdL
za$3x&xB<$83KvgR>uOkbne}nsiTrUM+QJMp(}E6fqiJ+eA4`{&d^1T@{JqN$Tdrci
z>~6FGA5-jCUK*$oaUfRZ?)Yn%eSNjIk%UIMwwCq`2+*J|;5iH>jF5Cv7f$?5cr6)u
zL~d@2!chQO7E_kv`BtvoF`+93<XeBk8gtKFp#pJPKXotJDQb<(&_lNs?*3o>Dqitb
z#q~YI@ZZ>#=`uNGu<U%n2GD(8vy$+Y-ZJ>z_c<;Sms+$7Lsu5eMmqBR(H%rmjT5Q2
z%}+I6*_Hh|j;J})#MA?&cUKwkDF-G00Q;ZIFKqYm$^acQ<naT{wPj!a5eBN|ZD3m+
zTzMRG{^hJ01==ub6IeNx+_-u&qL5ix<k<%LBJ&c*>}B)Py$@&?O=zZ~8rIkY)lTOb
zRC2mzB{eKs&b|%&CIMV;l~FazAv6u6aupYyPVrX}3)PFkMH5krKP6D0C;tFRVS+DS
z21>n({0OC*Q<XTYg{(K@8moN1*mVhh3IvtL6^5=TNd};8%$VoEcCb7P-UOE1;V{x>
zQ?852Tgq}e_C&T@oU%pKdm4eMVeO1Tp|gbIXDh*5jXQ93v&jNrt|^}<61P~}Qm)mT
zZo=^8ojy&-Jgle^sPusK8u9lrieZ#c1*)NU1ec%<%@&8}(=5Q~FaAueu=wE@k?7G%
zq7Eyvc?(fG=LPb{8qgwTuNUw_dZJy%pp}iaT#RS=9?Nqci2nGO<H<Y(7VgG^p{O1>
z*l!n@d@gewdRnADO5xhi{*H0q2|+KqhDNOF<3@&v2CpK5uvOUVEP@fW<;3IwoF17P
z$GCjzm7%3KvhrUozeIjQpM=ZdpAIpIKvtYs#|S_>N)o<@`vXWm)s92c5Q;ZtQoKj4
z?SPl;1JD);Urb#CTGkgDqNuL?`;2zic`m7AP+3G2nnB_TxuSuE=oO6l2P4Tb+ON7a
zO3Z!&*JTg{N&!GAU_$^j?B97Py0;tG4(@S{3a&xmV^D8{j(%e*El{PjD*)0peREQ;
ziY4>L#3}RcXo@JEt1yPy+VkQ)8APkLMLh9wt+mS3`B-=wkp`xyAPpLuOaB1E97wGy
zDbebALPvyf{idsCUUVot*K*6~gYI?uT%rI+wf2!Zv;}iy&Pbv?0<z78)yqbz)@kNc
zozkyOj+oVG0%rA&QlS9DplQ8i&@9J2M$1075gvBCDWhVD;0v@974a<{Av)m*;lq`w
z!LdD{-?IhK%5x&8SVh{>0>#%~3Kl#-@ak3ZDn`>UAUjgaKF_+N+J}ui(T#HZs77qh
zPx~n=Gt@@izcxTU)OTO0$(s+GHm|goBAvb%PB~mdQm-#(x#nd9LR0>s)KCG-36^}`
zO5WzrwC)bJ8A#pp5GcvKFS*5E<e={3&dy{qj@r&+_ZN`W{z~C7+30gz?Z*l@<{w1G
z&SI@v$Ew@S;&$nounej69frBY5#dl{UxpiLDJ#SZd`zUYm1OnrvMb={sd$2iPb6n$
zZ#>Bhd%wukX(mGB<1iVv_#g5#U^H#rDi^T>o~v@0v%NS|&upiKjvL$BGd{UyMDE3u
zjvDjt;&gk0nvG@F-oZi;@p*R|xNw}j2sEor)Pk0)#t-}4I9TK63Oi>4cP^P7E}m3f
zVwUE3`(b$P(D;KuH<sH%MPS41s(>lk<2fpq-9ou#o3MB%Ez<cQ4@j@Id1+?CS=^8N
z8EXQAkX?NcTE=Rk`@)gI4c+i10Jv%w=vXINxH;+|<~bFtKP17w)7cAoBGU%1O6hi0
z!S<<pTKk6=1=+w~iNo<4OmCkvulyQ`SI<=&TX}cfHM;pT2|xUGgaQv9uOFy#DLq!k
z>_JwE<+xwYU8VV0SmA9PkC=aK#@s4h15S@IwBe~~%YJ_57rjP=a0M>7CPy@Q2^5+>
zjvud<Ed*buh+tBdAFw-(C;tFOV>Gb&Cs7PH2b){`7|5;pTdcCD$o#OkmZd9pHN+hq
zIU`h~L&W9D4)g&HRDMxF&DB2mMEoujQwNvS9$*A{6YdlNWwbJ@{XnEOnhY!CdLdB3
zlg&%$Kiv|5E|xsfG4t0xF`)7v!gbCsMX`YsN*#p8yHbrV0O?V>JxU7KbwLUdlkFHN
zEhki33iJqHN4GSZUk%}1KSl}SN(GiC?HCE_Y;_K^%EHsn+|F7P+Sob1VKo~}KCz40
z^p>^ZoH0D6;%Ji<(D{nB`&PqjmwFWv1htu>6jT&*l?x5%a`^Eo>a5zQ;vu2&6`*3=
zvs*0q?4_u1AeHvwT89A&s9eoh!))n!uLw}F9rL20%|oLy;QjLxu<^rP#}us?upD}r
z7y&`_M|{VgZ_L^*p~oL_&W<`|UO+swflEW?ZA7`+)XGa$UVy1<*SE%7<%_6jbdG`S
zianRbRAv=2_vQ||2|HYcVQ>LL;xW?NPuvdw0Atr4sw@`}#7^aa6n1%Y>2c+T8S3J8
zVAM+r-?)W%Iq1D*&9@UmuVHtD0?@I|N6_)dGLNbb3(B=FR|5utQX17;g+Me{Jd~?y
zDksN)-(jiBi29BMQkbIdsvwkF!;<m1w(g)P36{EmBK0l})x?oNw@i&{unY3d^_?3V
zm|N*YYcjcAK>OgQRS;=(xyzOSVDWvlMk=T%Y<fR64e>n<#H!KGr6}ae4%WBPl8yR>
zXazO!cL6|(=q~;t#b=bP@Kktnq?3G87n9;E4HcIQd%&d#Ejg8yP_@mdL`UxBv;os$
zLsTMiF<1yzJPVf|x0pe-fR?5l7pr3)+N3qB7jBWaScV`0ZlL_)l6=Q(i&tkgp}LKc
zI6~vqb1fttX|!p;X8z6=#c9za3Y2l4JSFWZqgcwKAbU<TE*jTPYs1WP?7nlC3z3kv
z0)7<<K;LcMfrA%XJVS+}5h1F=JVyfuXA*F2zDh#08;GYM#F^&AAh3YrF!=IQWzfqS
zw&GDlv;Z8R1P<q8<3tMDfrtuaou^MqD@MyDTq|qtHS{;@n9j$bb8HGq77nk7haCBi
z1+u;(I&Ha>4|0nV?RVVv*=Et#dvNpbsZ?7@ZN%>-@I>9NafT(`POqJ35Wn5xb3IDV
zey2A8AUi!1<UK@ic!V^y;Ei%RPr39;WH@ir;%dv!EDA7LJOPJ<#VV1gzii&)YI#eP
zX269;sPq;*@=K4)CYysQ$~)jn+O7z^s}xH0hwuZ;nUD^!xbk=tEnK^e*sf)`j?_4X
zme>Z1QBCzPLY3^h2~PxY6aE4%t&P3H@~R<dlwE*aAQmK2CsKo)J#5pS`-Q}=DkYdK
zPYx=VtQON;Lb+iV&{}>l!oh3N!>{sjN?@*6EA}C@ba8bX%IzP%A#0}32B3@p53U=(
z{DJv3+X|yr5q&&<AwEgeHA#CUX*spCF<5l{!*(91^E)cZ!?S4?DT6(Z*8>jXeRl)u
z<9X~jb|VC*6#oE;I?+7XIXU-`I5!j(cb)M$dFPpGRh2VAe`hdy$J0=42~Im!bJS&E
z`%XkrO0PcP5Z4Q55N0ReMpFg9Om1npbNrXqgm@oH#3YA~LKQ+3Afv9XJEflsWL_9!
z*MGKRztS!;jGl0Fyko0xP4yJ=5}mfRwSDB>d7%6+-auQe1@1hqKEL=h-*FxjxMl%e
z>?Gtz>`0(ICY|XCN*@3kUw0xv2%2e{St-+|_{H;c4;2Z#biNywto@Y3*6IwNOn<8&
zHV(t8<^%M!OQ!mPb;Yxw(VP_evSO7FQ}mRKW$#adQ&$^o5y7f-z!G*KDxh;NL083e
z_e-yuG7$UW*6e>!9nzCLHykk$yS&FG3gwFY_<%SZ<l<p#P<>A20ow9^lNOFbRk4Eo
zdnwBf=s|W*c4HWSWk9n&0CV{(>p-g<JsEXvtG<+NP%}#$hfKS6B~){B>=C+A`7Z{O
zc^Q=u^$9zXMEeuL*)cS-$;#Spu~J(!1Kn7Z=G1t_h5Lo->y0$Pm!cr+?O~Qm%HL9h
znoZ}`N^--vv$mGe=MYb9HJ{jNhf-#m7KRFFLI5jNRRL5Uh@ys(-cN67mUY*aOs|QZ
zyITXWU?4LiswnT*WUn%j-4SfiRU1u+#*A-d5UDd~_ZV^O(AGc$v5cT9uY|rYhg$a!
z#ko}ni9ig^3t<TvDHf>#0Bt8Oq%h@2n3QWe4xuTA4kh7fe0lLGcCYkYqau1SYAtzm
zyt-Ic%4@ocZqOJ9zy}3Gye9*{3d3Mf17{CLrIRU8EY-0oL}!iGU9lQ&j9B;`!Hd=k
zR`7uNiEnN3D={yl7IR__hA(O-2=@7dyqwuEOP{dt{4rRJ8(B=MJ6@nU#tmh}0Euor
z#W)Wx0W(B41(m_VY};QD<q`KdwX{U5&#vHoa~IjxAbSSHxqvT5CmW|d3$N5Xbx-P1
z2ws*SF}k?B_J!g>mUq6OwF@uuqNL#^)`TX&g!oPtSs@tRNMZ{m(DXqBR;rcA!M4u$
z^8ymnwSAv)5-VN9&FvXsJ0ndKM1x`h-*6g2qp_f@gJwD&R|^9xt3J!YG~(B7*tw`?
z(W_T8g6IWbLe4jY7IzDPR|8n^HtZTo%RKJ7Atp_KkJ$uRBBjmaPMDEGEmDq=To4;L
z(Jbkl8b$pF*vz;3@utHz(NMAVvb(nSJTa2$6s(G+{zbzD>aVKUj21D)ms2;tGkmzZ
z@fXx3#RNi#V4|(d)GImDdb!npA;<d_K}_le68SfJA~mU;%V_JyN*meWsBP9&vuI!M
zSvIR}{+_0KAXc&l@y``G<)oOTAROSu6)2ObsUdf5F~S9DV*R7)4KRucBgqEep?5it
zD?vY~Wjs+-iw*QdWdtCL^Fs|o^pB=3fTFzw;96U*A-H|7RZw9~2RMAQ51w8gG@MN{
z=^S}ie>0DHIopnLR*FQ5Ij@lPNz4{2Z%#I|^d;<JK4rxN%jV4>T^A=T?iQ3$@{Yvh
z*x6NDCnh4T&2*g(HsCS1!+cZRCnmNG^Dnt{i)pwpH^HTlrZcFc3asRUExP;+(18E~
zTC(Aw08;qe@Z<=RnRSOgeF*Tg!lsCm=p@1u6O!8fg}|1@g5eK*xV{CrWoj%cR{hHX
zInF4V;5l-vDSY+P;Yc?XWec(+EZ}jlj!0EIUo(|}*K@@XF4~SA3_<3gf94J3cR_yK
z6vK0IT@uCQ6z$GldA@y1T81`kD>(d$!q`4Qf}p34ra0w#mCJSjP37tgQni-eReids
zm1WEKu*UPA`bup|dU-Q+tJ%Y0h0J3~ch*H=IR_S$eWDtezKRL)e)+~Yu19tVvwdH1
zfgr%fF?#X}{3l@A^;M|7RdgEegd8T&_s(6KICvAxBDj|S0A&Iw%D15{;unxmstx6@
znwGjYcz18u$4OCGs~(fMLFL>EHu20%P|_;J%;EGBK81jbZ^9KWl0MY}J1z?jV~XtL
z+1L7I5v7U*iHWaCO)F2_z3d(hs3=jkJxq&$fR?aTU2p+A;qs*-xbpW1#A4qs{)83{
zuadpSG%NtqcdMD@sh$+y+ZpSdRVn3kOIWC~w4hBbjjx1^ql!Q|tu8g-`T46{a+6rx
z2ACC@VM>mk$C>fr-k+~CHOfAWKvsc7K!WA<043w7#-c7F(%S{{+#BhL%0#xx+7*NJ
zHIyfB)K^PtYk`r!w^_l*GQZex-!C4g*_-C!USivhW4r{h$2N9fHbPXdcwwi>4FP=a
zQBlZ);lt#Tp1%yC@#KntR&xMz_b5nti6zipTJTEzFbo~F1lFkAYr9Eubw4XyuyonE
zqAd9p8hWPXwN^(RC@t#aB^iUEgg3B&UN1SDc-1|T7S|QmQSn#bg|O%p+vTp<Qh1_O
z(RGnt3u$^(#U=)h1uu0R&X$$Kg-fORMe(^%Mz~nwxUVr{g;B~{Gq@-fYHz}6k#|MI
z*=_-?Nd`o<c?78iy?{D554^&_kVcnSf}-L}v>S9=3X%^{)lSO@L>kA^PakPobLL$k
zEke&EA?$Dp9s%f&u1V!^RW!M6cdfdXXK7d9o3hU7;^P6o$3Jrwlc%}HCu7mZ1q46j
zSV{^I+a8v6wTpC>%x<ZJ;bqy!63aZk;YTp%uV<-cL566pIQf(fwMPor3*9^uEY(2*
z8Xc7P8zouWp}BW7ReVhmovRzK2*B2Uyi~oN{>W{6ma%d58^?sCF|pCWmfsQi2ugN#
zON#8F6rVm~_<UB@yDSRpb)J(}GL|z+Q}8S(*pE@>RhPK3SM-H&E(<J#MU4srrOXVW
z#j_b&J_dzNOfY6`#q|A<QP4kI2b3lK=>w^Su^PI+(h12%d?fZT(yxM1T{14Xg+3xN
z-O1@OV%k}O;<gpl1OmZZ4Xs4vs&y}+le?{{NdyzcL~scCyM3i))m3}g=$yKr$;<Dk
zG>Wyv;>wU2a5FidJ~C=)xZ9|7U5**g5v|+vD<0>>d&!KrDaSJ`I2|m>5@&ZIv)kNI
z5d)!rsLknqUrcv_UXB;sNff&nrT`_vdC->%=7I|ePb0M|72hAos49;3KtH53j}jck
zk#MYdA^4Sbi7klhaGVc_JMQM!$v;H!M}aYQD{=an$GGF2K&3xMC=;rA92SwKUu)`G
z!ZHo;IhC1uz$|f`?>^>PokwxcA9O(*KmLfvD`5k17OLVE()$+KSssu&<-Lc3!Az#g
z5#}IcVP{)#8fuYs&6tC$ZRE=Sw5BNpp<tFDtAk<?9SbXm-NwqH0RVW4kp$7!LAaCT
z&J<CwzRb7|4asqmc~^b*P@Txtk1Pp=o!X^0>6U~i)$TM<(W~NVu)qwB-?;nYdZ=ca
z;%VX|<yi2T)N{UL(VG_m=W141@W!#%+F5Co7OS#%lf%i})fQUu9>=LpQSKCD4yrfi
z=E7$!fFY)X8o>so0zu4JZ&#A9#jb88769~gB0BD`33B`+_)sbjgpbVNyvMs9Io+?A
z6-Voso0~sC1;8F*+Yxt7Vz9f{5{W=)Ecsa%RI~v+r<i`n(yexD)ysp068@&JWELF1
z0GS){YWWZipJS5RCCh2EBV{;8YNPEsM6%MZ4caIGJ;mC0Mx1~_zi;QJDDY$4vhnWN
za==oSn@z>))=zAtVziRoojySd1=&UE%xet#;6x9)yx@vIu<XmO6t5kKpggx5CfDRS
zzKs&tDR@;O4vD)}Z<+R%-$XcQ1IJ1)BE;hesRH!%PGDFJD85}_#T6O>d8fEaF%Bx1
zsc<76MNoZkT#p-l!Fo*|Ud5U)R>F!7KG45^*ppU9l>{toKMu*?s+Lr(>QNOOaDR@1
zIEY>N>_(1rGrI|b_+|b}bJvd%PuS)0oJ6xo?G#n6A?mGWU)OVuRjRJKBHQR6BH?MQ
z7;dRNPGk_M%ZJUtIyY!#eRmrp*7ofJMt~1sL9`O7u4QzEe&w<B*bhO4xC_j(%{*4H
zCnC~;V0t;OVzmj1QZRogvGcO+>$!X}c^5$ylvViu043z3K>q+{iikJccFuEy`7LGA
zKQA#$w7c+k7hALrT<TO&qL)E``x|Yr*c>-300eSY$N*)Xz$jhlC$JpG1yYpMIGYOh
z+?JbFMzw`qyhd1cmU$sqP!t<91oN(bu3wvZ>|Q01mjdjxFw#!z7g1#-l*Atbc)nP!
ztp(pDMovX7;+8ec``9sRS~1wJzEUX1#fH^@2qL!`Qj7s{H$}ZpeR`GE%j~)eOUZP0
zqr_bRcsv0X*<_=BNX2spl3J>7Sox<39RrJvCBa$=eMjSaYAQG)G>i&&<JEb%yeq6S
zqCzh<&pbp9wrxGrP@2%~FQ1r{gF;|(*n^j~7JVT12V-O|i~>Ev_i=N}kUZ3^v=?oF
zlw$=YhQY2ofmedr2W8%9E(HS(SO5mFq=cvh@NasHryIJrhwIF<h?kXk*%}wi5Y^XE
z${-d{`%Fz+k~e0Q#X<0as>P%p{{SYItVEhvS}ww}`b7zLSr`o$#}K=3Rp*eG1Cn{Q
zKP#pSqEVQTzRWfK$y$Vq=Gz*II6UAKeG*cGSht*;s#lj90=`&^8u06p0?qGb+gEYo
zXY(H6l5jdTf>}*USLepdqOIbXfK@Wb1zHiR=35=Ng?-I1u>Sz~5jXBLtCeq-Z<YF$
zeI^C@aWBMqD4ArR7RqkjfLra$i?(Nlwsh>bi?Y|*X#0M-3aa3ON>pEwC?)b)H^fxm
z!rcM4gfuLRysJe^78nk$sM~l47Cev+K*P%gYpd}QK@@BYyUUyjNO^5FluUYtX(^6L
znu!rQw$J#*W<@KJ@hZJaGva^#k?lpzM(kZ1CO6`~U{@|x%^sDubq<|l_M}2ynDLS9
z%B(gPP6a`e+bJj}2bZ}200*VWTM0d?s*PMxf{nVn7)CByx~ch=C<A#{8&H>J9lh+~
zV@7+^{!YnY_PB~HI|Qtxy29Ex@v9s#?7tBeqv#T(8h}MWz@#W1r!Z$0J|_|R%XU7g
z2Y1_8`4aSMFPbjc&z*L-d7=}5Vj2a`H)Cae4EN4V&xz?~HAnt(A>w!Vpz#}h(KE$D
z%s;%(sqw@P=;`LIGEkpWo7U%iM1`+1%kHC-$=l_bPRE4_VzwuwU@EH84fe07y@o0G
zxkS~Yt9y`_rL^bKh4CitYms>Ewv0~Dl)Qd3b-_ZZ;iW<)M)WwDl%@pu&>#ckei3>G
zror{@$R~#o-q>G=0cWGOU86ej#~D{4#o>eLj?y`HczBAZ3hwO3$dH)s0@28JM>9BX
zCJc%$Iv9OIfMDg#buPs{7cHMZ0uWUS6nMk@fYeoofqXp6sLikiaKlLp=<L6+x)WaY
zOjU)X%~_k<Z1(I8HiPik?Or|d&w0vO59!x7=y@GWY&QmwI`Sd7MpLSPV>I?siUo%=
zR(4kzel8-R-c@M<v``cBgsd93=Mvzn#bcS$tE@$+SWsBHJT%^sb%KT}{JO&-NnOy^
z3rMyD#&dpP;$Z6~VdRcQ;BRn(_)n{X)h6n<?iEKByHeH6u;4Q#+!cP`{Qg|86*N<d
z{{YK(-;Sqm*@90o8T=i9o}+<W-2;{bRYwWL!EKrnyyZkYh*9Hf?j*srf^AiZn@z3U
zpwk-|xT0<;!m|!is8;3yEO&4Q)}z6^29Dv03*=SV9D?ri(A=j|DEL@`tsK4Gd4Reh
zQ7Vb3caCuC$Uy>$Ua&}5G1&`XHeXAH3LAK`{V5-LbS{9bdLkVftZJ{^C^&sEjQ~+s
zj=V}07GYYp#kdDhPc$6fiD-KzGeV#`1m;9Lw!o^8K;m^B%xXOahLL*T7gK?L0$gqs
zQBSy`NH0g3X4A|!QoJgJR^lD8=I4*N$VXtSe7(jX^upI|Rd|$V(M(@ZDl+;%aXbQ_
zVFbZ&fwiTaZNPC#Mp_yozH?}QBWb=OQ)>>2oGz*cLA}7O0t#|(HR@iS7fWiQFQQOj
zF3ba%3WKJLRozFYf-bDSP(T7X9dDI^eI@k4YaH8rM^%a-@O9h+a0^c?5m*cJw<o)n
zcGzj|q7j7@>I-~8C4)xNwL>Z(eHAMqs{!NN^YesRd?oEe_}JSNB0lC%0A8p_3+(i7
zq~RgpkD`TATT8iw1}tDfRkk~i3^#7$w129ZDRR<P`ypgQZE?HYNEKA&!ziSp?P}oS
zBtAEvw7TH%KrOtiSSqzD`p^)S+TFJ?@dTR+yJyHnh}=5C1(Bqn^oYH4Uv-wCbcb2E
zf>Z9-2n|~`QKuw*<16zgD6wf>nNs91(Oree*cPb$hCx|($>ZuAhp!MD%ge+_gk|W-
zFgYmhbKf(U1<t0NIU`;aJVoDDnDw3fflv)5S03_Gjjox`acPfyK&fB_ufYIRE#8Ro
zM!{=SXVAlBQ1e546;V*oxbQ*J^o^oF&sz+6VJ?=%4tkCpgf&4FsT|<-KO~AEsywk-
zhh+{E`Rt+H6;Zoq7<tKSe55F_HBO>PT)&A~G(77454hs2`&!tcv$u&?D42^4M_1x^
zEdekFp;SHoRNc>iGeV*ST?=MfsT}IHn>nGMr2FFMp(!vrJD72|>9L@4zB=Dmwp4|x
z>{k^TjvL#nFZjw>KE4n$6oecLKueu~-ZlRKgryIb_$0B9!a)w2>3FVX>zm8RR~;G6
z{OG@q!lA7b&KLr$(0#DD%F{NcVoi3>Q2Q=$uzs@B@~lBP67xf4UAQ9!g4S=0J;N<k
z6otIQga#8~@5DPRj4GO3@55*a`=`6f@WL6LUM?BWJ!HvKmXnFSym^6j7v;P}IE?t(
zKa(%IhP?jvu{k-2t>@<B2_Oq4)<5C`Uum>&a*(?EfUdYG`<PXX^IYOTd>>r*FJj90
zA1}rUIQ@~@K+mA5RYGV!>hkF;w53%)!g(|xxL=7PX`yiuqL=KhCyPPAmV5Ue{-ils
zOYWjxcXF21gXjc5aZi>Kh}ffo@%EM*FFA3P1Z}!XvIFKVwS5F@fz>x+CyRa3?(rb4
zwovi%cxBoYUtPv&4ZG#27EAsQ5b|F$gkkRxw-R2XGU+RTIko;dOGH<O94TvhS;T3!
z-QWn;1K9LnN-p-3=$6w|Pl0r{WAcS>-Wc#?K?1qO`IqT&r=_+M^+i+5$D#*|Rd>IQ
z%Ik7Fbhf<`S{8G{8ErIsEJdeAcTu_G@jI%VNqzN?mLGc(0m|*dsO0gSMgd)|PzWpG
zfTE;jkfQtOg<%WmoXJ{&b&%Yr6y|p$*eD_+V6n6xkTH-@3C;-l4YgTeQsSR&HEZw0
zS7@s;81gfzN%HxeU-FJ&`R3-=9ZHYAzcQxYps2RKBjOvYb)o%b(l4KqI;pn8*f-?}
zK47q<FYI-kW?{qTk%SyAcrKz__X!-poprkfgCaq`P9jOvEj&%+t<9QI3@=k(8KNA)
zs4MKE<@Wh}TK*BMI;<Vu%cISPoiY}1uAbX4zWl^paBL-){ip_+x;b_0hz&YPP&x56
zA{EA5TWZEWIY*a)Q(4v$ohV=B4VZDimC=gSODxYmB?<2m7bcW5CptI;T;xc$lHQ>#
zj4)c4$7Hm2RjMpl%CrkA=bsZGuT5Its+0&cGyr&gTot9O>$z9N3-0^+>ew+L;yaOE
zS2={T?Ij0DTFetA)ym24eb0V9d76P?X?EyiYpRs5p+ykWAJ~$LuQ5u-4=R?RGYai>
zu`yRJ5aFv<$66o+VNO&PaO5?+6Wpq@(C{ugLAt(cFYYB<a9<X+Fc1egg31^F00A#d
ztXdlK9L2xsd*Nl^P88aJ11uYLmY8kyI)(_tp{r-g8-}v`Y$VDc+;Hv})*+&fRgngA
zR*-oy_qb<iXpcz+U9H*>uP`tLMPz)^yfH6D`Ee-Ik&}BNi9mC`;>3U)Ci?@!Imqpn
zvz1{v?f(Fpk?|f5wyW;TBfZ7IW}Wp}`xKQaYC_mAtzrN~L-pbl)-gF11bfN{(2R2n
zLC(@C8s7z`5ojGk?pI5v_{1$bHuLg5!6+qo*FHIj6Kd)ympntY=#_sm&F&eIq9VBX
z^MxzfGv(dG92euw_<|Kev&ekM2~}jj6$5O$dxGdyTXJPp3$-Rsw5lQZjM1zWY3QNY
zy9>mn==T@yo1RTT3`1+R(EB!T1lwC~F(T%D;UDX_78c;UlC8Eg3Tx-Q6bNYW%58;f
z&C<%jKz>INhU~g?;1v!Vf{lN(tm_)i5MBnqQY4O*D=O%i<Mk@aqCM+Z@WrMHwrjL_
zUZVW$SP&EWmw=&UjOl={1EAk=i?-(+oii=|OW7t7aa%itFFUF1v3Vujly<SYwFRq(
zCfp`MgkmJRRE-7GdL_&Y$D~6~@tvo{!>YlsQl4h*0d$=wFr_yI4$TESd7!I{eui9A
zFPtbkHtm#2fD?5_L0M<rk^Hq;ba-PWEQOFe20wrdcT5+rv>;8zphFe8n9{X`3ZGiZ
z=m}(3TcG?gs|Dk3ovcY^f+tBYLtL5`5}T{7-0=w2%D;Ci>Aqzu052R`!vS<E`kl<?
zP#0#yW@XKHNBKH=gS-tLNx$QXSa&?_h7Qsu#WwjKm}n`yxlgH$0)+mskC6FY#hGxM
zw%5^t3wTRY&Q1Yw*LjNh6Q!eL(-<N0CfX>MR~g&cH+e6IG_sCb$4!@MP(35H)LS=f
zl=?yxU0Uz_M+$79brL(=KfGcSlCY31SVIYVh`sbe3Plvp07m^)Di8f})5Z|&9I>b>
zfeq1e_7t>0Hf)q|X8AaaoCpx`2zL)gH}DC{sA|EOdV|)K!B<8?7g4J0np**|wzWXG
zT@zF7ii&g<DixGe=gJcGt4@L%c}RE5Gy=mG(^p%kxJv-^uLm;By<>pR;~qk4h2Z<F
zj;s}Rf;2v10qhkPE7!05qDi3^3a|TEfnak9#I0{%7;o9B(`EgLH(3?kW+sLypaFCT
zV1&feWi0tQ2-Jd2$42w4JvkIow)U;$7ZE2n#BS)}(H`&r0H^-|I(O4J<i5Fu=zaK~
zUqJ%h1oDUhY{t%Q^t3RtUDa-|uM9mylCx<+uP$H@<3PfanUwVLu{!`bF786F2XRWJ
zRP#AAPZ?h+ib0M+;gJwJ_Q!Xu0)VcA!7GUcQ1m{6;}SuaVp7T-<|^3$0SD|dsU)g)
z@Jr)rMuiHA#t`qkG~m2370*M68}ki7{QBxsz|BzIDatE03Pyth_@)=Ms-PfPE9~xN
zzefTaY9U^&3qmsv@d<5|wRgf(7b(ze51o{>*@ef<5|d`a?DJ#903{&X5JU-5mR(qL
z4jN)};ESRNd?*P}5fwvk#*l&5=a=>{GB`K~0QdxtD2xSAA!pw+NAY?$YQEuo3Ckyh
z4te5<YoTq+Tr#DbrELEI_}LW#4+3)qaO$d5rJ8$tzf$BH=wt}#kgk3rQqq$v#NNh^
zr3cggP@!N45LbH|Kn7Ewa$0T$kOMSfIjEXo<d(0fbwhY!o>5<Sq2TC_oZIo&h$rg>
z=ysx}%2n8I?3$We!k?)7plxp+-%k+iTys`ZiA2DIRl!5h2*uKx_S`B;-qlYvp^{o;
z@};p1gFyNv6$q380Y+8)EQlkBT22KBDs&Cy_^d%{;r<nh$gx!13RR{fp{j-85t*Dd
zA{tzgRh~1b>VAQ8_ksrwIL4rl!6g#pn}X~<1Z8ux9h1Uzkg}x!6o}<^$Dwqc%C(_6
z?FGy)2ds41Pdt+cJPPEvJQB!wM>B+Yu3~dLZ1F!nV*^Tn@RHh@J`D~cD8igB0l`{W
ze#J{-7tH>16}obvq~u^2f9E_y0d%%rc|Z^sppVitCZ-b1qs&!_gBj3P#a_JM;R-YP
zC9syb@~RJkiLUV~-}WplX<8+_A!-3*M@wQnwV)_wUzm!7G5-L*xJ_#QYb4%g3vt+g
zjCSRWR$-w+`b~j>844blAMjslFD{;@_^wpL_xptqmylVeK`d+EiAZ^gq<Y0t^#1@6
zKUr=+#hj}>C5wc6FOs?8aA~&}C=DOzL3Zl_?0qUzU>3Yq%Tosf!S^v+%F|nj{4Ys!
zw$aFrknqR<0GtaFP;%37v<X9sOT9&RL2;tq&%SVT0>RjNf#SEau(%#q&v4vr5LyK=
zPE<iEC;CbgQkn)F3RkZad!+LbH1m-S{NEFyRnq~S2K$$WzELwu7XJW=UV?zS_Cv_D
zqRYPaq4OyJ04S}@PEI1V@h%eNe?RURbG=J#t1qwo%hTPG^#aPRu21-5-K{C&f4;6@
z^z>Xt>c*p->CAYbn@wGKS%7yZ1*AA((B)H`zWho&FPTb@E~BiYRe0`p1quhBaKp*d
zNO$mI>V5@XLss^wJCCyDB9+=<wEoL@xw7i`#dZ9f@SOYO^=u(T5gJP5JcS4CfO!o8
zt91cTIdWSv7~+`Vr7ck5wm`>9=EvCz+(4;yY#K2RpeqRKNG;%4)xnYLkg$&}>hM80
z%LeO0%EL&8-fMs~OH20f5z5Iw52aLla9tkbA-sFwhGfnqOMt)_+Xdd=qc@SWgp?)F
zhg0SwFNK*}>dKU!ja#Z?*-(J!SrJ|y`W%WP;J;7`+P*$w9(b1A6y!xUR?90A9tO{n
zl>Eyw%x+rULF}ci{Kt|S3J?@|Xhmb{_q{xkL__#De6q7{E&%2LzU9SQ-IA>=RImk|
zS=g8wsnGAx!8AaR)h^zlUTlG4NO8{I(3EjHuRN{+3n6IKGT~fD4mX3-0hz?8kJ-<u
zg;Z4?zG`L149f3|jhr{=?>$2wJGc%~EMaSRYV$0`@Jp&ctRcSIDhhLa=k7c}2EAAa
zOJzcNl>Dw}G*^Y6Pl9C8(Z`ij%skTSjxLx`KVc4H=ahF8b501JRoyIbs#}zXiHouK
zuCqblMNsDIsG%XuwhK!x@0K8#TqsiD3+lyi0K2BJ+pFY0HZWAd6hUzli>PX-QvD!0
z&n?e2@x%qxUuObXTobBjKO%GAgd;_$%}23WQ_+W8P$9JxeM$!wQLdYFiZr;P*~?Jl
zxRyotZKI^cDubzI7ZuG|aWqZH-WM&1bQ0<7vS8s7oCWMs%@@Q^VlW*gkrS}9VT?yi
zMBElHK8Vt$xb(xsR%WKR6{@stRjgH_)+}kKGK=!k!*TqZ0c=*A;DDMZ4mYZeLOs((
zBW)9v6c@goh>8%uV+%rX>y|Av3-1v3H*~feibX76E!5f<9~Tx`(Z>?%E}&F)ig^xY
z^GAM_74vImPSyqP;W-1IWuhFde>IG?Lrs}f;)?#8PlC)f+HOjls5MxDM>E`9Zpo^6
zOl20~$aZGRs@h))#x`e=qTAzaBXlyLlFsz`I9;vO&;@?GEwa5_bXw$7<!$#IA2u;9
zb@4A^jwxY5GAnr5_@?F7T|u^`NLemYuv^hzBorH0nRuZv#Iz7m!c+muoxoY21tV>(
zjq^*BT?An5Z`Ljc=K$eV0~@2!?1%{+OUQ7VCHdR+0cZaJgVeI_c6mHP%Cgo{LOyC(
zD$%`g#;e6tu8w8(!sDaz6HCQK&`rNd%>0lGXP31wj-KkqfsCQE%g-G`QHoW%JWYV0
zDKCJfm1~O-{?NvXPL-@lT0UXqH3#yg(&rYc!XKCy+Zwi*-LtS8h?XT+OtWq9U3r3~
z5qK+eIv9ns2rzbr`X+D#uh^p#e4GFrb>OPg9%K8a1h9fbBHh>b8i8l*wR8P|a3+RJ
zQ1(rWWxeC-X*31E-0C%4$6G4eK-1YSlz~I41L~l%5v%2i{B$Q=NSxDXryJjaF3#;X
z04PkW!Y%UfNB;ngei$h-RVSjraGVX4#22n<i*0W=25C{O71QdFB?Q)*zY>G!#d4lV
zfIj0?beL1T{{SdtzX4t-q~ilD25nf8xuIcox`nd*Vw7S8H?K2}JV$t_1m{jA;tTZv
zjcY*B)8k0XkAE(uVhvuws5p9q{I_EW`JTj4l~Pw)sQPawkAivHf(5?-g`qFN1+CGQ
zZ7#e|PLAVku6UG<VRb(8q2#5!E-_XDI#BS}XNSzDDmHc>2gIPYd)0BQen~4i78&~p
z3R_=;BLL0``5*Abv{}51(hh)P%MmG$p!R9~=^~7Vwg7y!aRmwRH5I<v<Yus^vV}c5
z!WW@9p>F;~#%Q;NL`*5{E){G=z_nTr<b4Lv>{Wk|3NUYUF?%Gl?_Xgll&wa&tgU6t
z5I8h)22^pt_ISCTH_w@9(g>rZ;&RjW*4%jo#MK1|Y>!oItX4n$kyAs#*SNKWF|UM8
z3MeIDa(B&Gp5XEUgR*(7?r3{N97UixHu{BvupbaFzCxD_j$w{jZW5fWycB6*Yi>LX
zPtG7ZuouZFb-^43PFRq|R}JeNCkk%+8iJ2^^&C8l2at4*yyb4zD#Dc;+6!Fh_?cA>
zsvo%IsF#~I*=AFN;pC4O4$6DzC`*)<IUvVY*lx^XLh*0(ISP=i`Db1mhh`~?Z20uv
zROD3WW*i$1J||a9w}6AJpaE*U1hbKdkV1aij8M=s2Wi0SEe`!<iswC$*7aOhNQ+Id
zPWwwO7(2m>o;E~N5o}DayM4t_DLqGu<_pUdH}f6^n?={))L{jdt?0x)fVzzX?P&Vj
z>SY2;G;CFA0Fl;L=313zA4@JlUK;-Z00<bf&r!zq&rOE;CTo;7w$f7z=(ukuuOuvj
zJY_7Dpd5V%52mJZA-iMjd`dffzAwxY%P47`)NMGEs-l}bQp91Fc~YrbB{Z-rt4#zQ
zw5L_1tEi@kTXEBuzE;1>l$sZY(;L_+$I2;&SPe$1MRP`$f;7O?v+G-WF!3pEL{<_r
zPL}{%dG~WXni?qP;mA1g@W-WPZ@RBD6Khn{I{AYKB@O{9Z4?8Elt-~plyi}6QkDg5
zPxO@l0WNs)5#k~ppFW5gp&MCR65ARU`|fpRHm!Fm@K)~9$}|8kmxau>%jnx&Cd{jj
zXy!GQ85RIKYPjyMDapIBz^&hGQX4JsYyR9VaoNiBYW3|Plqe1&#?Igd5)PN4(nir{
zHh{(26BE%Y@UGPCRygvi=X&NQ%*BrK?~*mr4N}$3i7b7HAcb8}<H+z~iXio4e2DbM
zboKZ>%(9G4VKOc2K!f)*ylgtAGhnn^Ml7@tlV36#@z&P%Mwp-*L(477X*H^=Qqml8
zOM<niL)J0DSbG8>5aB6;9|{C~2n8-h!0k~U=}}u_mLRbgP@~Js4j&E2REztGx+V)i
zve;uOsXXTq1S?~A#WMaWgzg32R^0&ss&rTFMNQot=gTm#GyqV)e9EDS?^zZUPR=~0
zH=E`n2Yp7>bX9yt5LDhrQ2K&AYM&^y5lPiRf0t5<lp)Dg9G5lll=rlJ4JOPhbP3Zm
z-LJ027yy7c7|c~Qs_LiFF}3tP$Ur3C1#nP0V`n4Ci_22FH~t8&1>><PAxM&aIK!~h
zu9k(b4Q@U0^g-$}z|w>;0jD>Y2bB;&G%-y8cPxI4j$j|Se^Y~`$;X4|XIbY+`*5dv
zMxM%*%glsC!W%jRc79HBTEEZy5ub={aP=RFH2AO&vNVRPZU?B=b;WXoshqJ>R|f-{
z1~Fg;*`cYoYl$j&Fk(yP4MAR2fcrhJGFj~d^@0Tpwm6foIXm|`7IQf9;xur&5G+U{
z<|++1u&g+h-J<q}!RjgqjuFOe72Ow(X5x*vj0D1~O>f9cIw{XLIRfK*ZI}2<;*I`B
zlFw<XF*0)v>B41rYt7eF%P4D2x0JL!gsmdDfAeo!53o{7n5#s)zDe6ps0RY1wApM+
zg<3z-;2;-t9T_H!0#2!fqSW8IQx+AHyX=CNZ*I#@A22MH`}UY`1%J5?#=s{cc#IxC
z#REg~#yLXjyA+n<^|HpZKAo<+Ah2q#jl3S1RGh{&#(A|AL4n8yo0K=>bTEb}eimEc
zd5Tt}%CF68d6Aj~fzzoX&H`E`s=H%p%alYm0@jfkKyYjMz^LZx8`vch;pm`f$@&NZ
z9lFafdu}o6gl>?UB+LN1m#28t+n=`lqP2P3Fx$!X6kN~>%#Fk%1Zwx<c|bQkc!yR$
zbRj7%r|Mk3Le4|LjzGJ@cjJdgcle!Syk8VsO0N~RRjD-UQCFEOk|iU%Edg;I@x-=`
zr*s}CMB~U5OJfRSjox4!pu{W4beow6)F&{}S<3LE?l6AJi#c)H`k;Ddb1IJ~{%rHE
zc7Nd9Zf*oxe&wf81X4wzDt>d|gaGBs9~xm!+pJM=)UFn;d2dl1sc-qp5Rtxe*ATv)
z&#8r?mng(kUdrFM10@}ey&)<ILFmA)p|}Oxlv7L{f#zH(^-cL0*;u;<hzk6xs@DZ)
z9w(NX1JEEZ2R%zYQM%_JFjbnv8&HIB@*=LV{S`rcTRE&Rwjk0q+vO2?k*oCPCtot}
z9|V_IKq(U+VJfrI8mdrEsx!OJtV*U`Cd6EHRia;Bi^<x^TdcGVLaFVCakdnCRSbk(
z@0S?(Zcr<N;i=QS3ZPfP0N-MYyo@!D<ESZf%|(Jog^0V`TY&JfeT*Qgn9j}_&4Q_U
zPk!P%OF?n`xcc7*7KvpGWMvl>3(6H<(y?`MuM*W}Ux=y8lKxP;^#+a~pKw*vV|Q;~
zf-)0_yJ)E_J4Wx7vn5px@2OVAmnBI^JFelD6b0}%Q>p>c!QxWrtQVKO8=a2_e?(J9
zh4Zg&*hYLvc2$U|1x<2aR3WQ!2PgQCMafR@C0rPb)HzC8byQJ<2)!npEANpLflN7S
z#5<=<mw!l0j4G93WA;VZ39iaXR@R=oj;!7bXx2DKJ?|oPCnZuF!&HV^^CORUBg&0v
zs(sw5A>MM^Bm)5ZO9s!F<icyHAI-?cg4!5^tFa=?+QkRa@L2`)bnxC0lzO7pktnd>
z9n>YGMB;Obz8b#as?f8T0JJ{RfkN)~)~xi>9+i!@`=TV+B3nmG{{Xi*1FLok^|Pi)
z7}NByg*72YY^IBJs)8o-kxzh9C&?_RFoUfqG0|*l8``y46Q!WqUP$*!y4*%|%G7V2
z*!E5=)-)edA<){mn#&G1U$co>$}TJPMXXPWU5@OEG}^AOii_Y0+Jp+0y-qEtN=v4>
z(Wy%3W3~rns3s<1*C5#k%;X##UlH7ggYG@}iL9q-xEB&aHEdu2OMHoNHa^ish71r7
zf@7jDxKsneA+eZvydV_2HPnjAw$56F_`3098{(LHO6$E~l?ARg<3p>4UwX+_M=V!C
zvj%uBb9E@z>KM5MJwyw3{TD*cil2HU6)*~pXx0M2Y4~ik^#yNEnZkA`b3vIamj{dL
zK0Iygt}M7174*y_zbi#l@epB^eX*7;Y-IuAV_LnOPFOs4_9EJKKTyKr4j_!de3u&^
zpD}*!?HIU)1=Lj(R73{rpZa=@+|YPjRjOfSc$d`ZvjdH9Crk}}m)Zu{*f<X)G?o;&
z97@<m$>mcBDy2RFE7m|BWpPtFt9<y2PGrbXP;A<N1SUk4+LJGsS-HuOj|Rsjwur#|
zvIdA%TH{k9<WO*rgAMGoM6`Y}L3sv8zAq<^7z+;pe3i_f)LqEVi22(HF|!Z=Q4=e8
z7mvz_j>+F#j0%prRd<oC+^D@l^A??oNk%@FExGiZQm}0Xok?c;IGJ^J^YJm2P?vaZ
zG)Gs6D(KW-w^y_17!2`Rx$=5r5^cJ*<Gb!1?TK|9OW>rvn>7weWD6zET?MWhmU&3s
zLCi*_NZ8p)%uuENllf6j!})xmmk{-Om-ZaZ1@<zfVd8NI;NtRlM*jdRq6t>YX=r~I
z8Pr5XX&3?u`{H5pNptGznmjLKDpC(0w;-lGxk*3KDN=?3;qN0=^m?x{rO}lrsX60K
zZ?ygyi*PG+XEj-njeRxt#va-W8CUX3u3ZPK>IHhs!;m)P&Z<ea^5j)c;gb)T;#bMV
zXv5pjbFkv)5%eVo`uZVab)OH=h^M^1qIfI42Z$3Xe`li%P@%NQ3u<kT7Zw0f?mOTW
zzOx1kLHIHn=3}1chW6R1!nvu%BSh<dB5Hc$m=6?ntqyfr5emf?t$bW3-~)3WhAX^e
zMOe~oX(CEe)oH$YiJ;IJN)0V#GYr^(abJ~T2)kDm+XBy0(w%RZ-Nm;BZ;-ctaBt6u
zA=MU@q6ty}4=MyLqn|`p`-Q@`*QohTsmod~!8zFQdUp?9UHiTmxl|1;a`BGjC7up4
zRfsB5W~P%<HrU6kc5u0T&fE4d%N94qLutWVloij3OU3gHAwUR%6`u<>7Y?CZ$&4U4
zau<#o@hWN%Ikp<h^3hb>D&-aF?pl#2SdF=?5#a6Uia37WS+VQ-z|)aYb@v?xH&{iT
zn!PYhdH2tM_7q0E!4d3^z9p*bhFm<o0%DS1R~$QUsl@?tf2gUg1C`=6N-FzZxC->6
z?2rh8gb|7#STjW3`W24#G$2yTDHE8Lp-O0P3RnVc3aaiDmr}eD7%OiGs1cD?oJ=ok
zg&tR!X+=(8II2>6($V&F1s1W(CxM5trD~TA(oJ?F??DU53AVn<v>}`v)~RjF?^tz5
z7P*E47qO}mrEond=Y;g^HrKx-;tP=(w5zPLzipA$BaoMKPY?@Op#yoQ=2S*Ltx%|~
zoT0uX%5!n@5boz~LTpoNdoK~D3|xuB9gO|<<<X1d;!#fx%cOYXZfHxgiFw1qD$eX;
zd_27}-axAzTcKkd2W)nV=>e-2>eRN(Slccob>}WWCKA>?Gnq~W1bel`_p;|{cEgaU
zde|M{KW`>J*9wD}6W_oM{{U1n(9s(lelTjKduhxJ#x`SZx)@b;2MQ9bmGatN*O~DK
z__D9o7lPmxVbn1vzQ>CZ6@lUkLY=DQrvCu(Q22k^EZ?<eQxj}XybJ+*9gidajBf6^
zwlYPjeFl~l`i~)%@=588%lWbNpcPrl&QFwc6Q)bQ;9MxOU9H0+k7QopTcWD7)4_}=
z{c5>K=O>KGSCOjmd`25ezO@nYUphA7XnRpmgEL(#qHD)gYm$vZ?w<VjG+$gi!jMRV
zqG-z84sv5qp3zljx#?XGDRF0_p1jQCJk)K-qb?F6fO`n9bls~)ajQxKq>HkvMnOm%
z9?vw4jzJUJVBc)^MNqZJv#EVJWyD=n7IFp#^$X81tNsyDFcpC_5_3wPxN;Fq0;wKG
zQI3qh-<xwx0_z9Kh?ib^jC@LVL`4I6ZPiQ5Sa+Z=ff0E*646H&bE=nkz6uf9VG?PT
zX`k_8A&aThZXtMu;B7R&;x2p_oEdmUH|#@s{1TMki0X5ayF7YtfE1>qw*G`_M(1f7
z(6?UL)!yJ}LS8Or0#J0pmM;_NxGYyAL&fFGIhvDBkuZ4NXmrVqP6DOyu2zshMzo+c
z9-~s`yFLe~ei+r_HPKI&PljI6HO*~Ae-c+H>gcDM@f{tibUh8R8C~s`5<uH@V(>|4
zH?AmzPz+zP2CegWSaIE=j==^tgGvSswE`y6*B}1?3S-}B{TgY$g{PX93Jn3=sKLqN
zzpa9-EzWjNt!t`lC;6P_Fk0S=T7D9Dj;qO)O}2<}cvo<(wKrZxC8I!0FurbIz+c~r
z!vP1=pcAek0C7N$zc?Dd)bFnQWdsuWMtG0ck$u)fSIe_XFZNvl=cq?{t99dJhPpaN
zu9sI1(*w%I0Z`{v&xA{qp7OJE_6p3GvpGKz2@L^Uq!(5!U$jarir3b~p`?1e;o85O
zC5gYtx`K*{67Z479($g6*aM~lb@_T>y}OQN0`G0yTyZwq0IK<w+&+97{hYl@lMNt<
zTK+OfmEa3LE?BrC=Z#*8SOZOia{C@7XnT}iS1m&6^jQzw4QLLnd8lY06M63lYoq%0
z16A{k{%H{#iQ@wCE?#vn2gSjvr)Ht3uP{_uvE;C2F9fd3{{T@01?Y;>!NVK#!GHEu
zHZGT&F9~JK!uK1OH-OfHJD>pMpg`Diz_FRFsCS&6?dOPL;G1k!m-b65U|!KH*_z!r
zL*EE{A0^3#K)J2bQpCR@I}Z?B<?#&&wo7VV@B-!8oOc>Bqq-3Gzy}ujimP_T3~9&2
zxtwEHW~v#~8rO=*2f!45c#KkT?MDNXk5y0`pko%HQyjq_Hc%U*Cnzy>f-PU{#o@Kx
z;jjm&>f_8bE!M$hnOp}-{G#ozwon+3&NRtDoS}RRUPJ<mD_8AdUJ!6(Qs~b(Lkg#S
zbp;`5!^~<1qoVtm$JKJNzf1~FFk(T?ajfHY5gg9StltZ5U97o+lox96(79)yTuQj?
z)u2CP1k<R^FHlvZ>&Di}uP7p|aGSMpynmGzP!w1>EV?D!o#oO5Y}MnyEIST}Y9UL(
z#f_Et{{XU;_ALA64?GmRhtUq<?XTsS6i^7-`Bk?&AWc5PM3`0~xZH*#R8(R<H>mw5
zFhn~X?=v04^mgJMt5z|ygt1nu(77uZ5l%1u<Pi&(a4*L^M}ixrMfz%XJz1Wn;Hcdo
zd?i*e+1-@Y@4Jj;0eYjwOP$aZTzgWP5B(9veJ$5NLjbvU1;l~-gQNFrzll)O&JXHT
z?yBS^oBQOa7Tm2HyPiq-#gvI&C?%Y(6Vn7qO#Q_CuY8!Cx?TyP*bj0sN9m796pK=n
zm3=4NC(VGZFb$VJ8dE{o(ZwnvM?#fbeB^CQX*4na01&(Dey&#;P^>Kf0EM3(es&7H
z&J6A|LfXGyaG^(w?LAHhKeWMLXz`BAyn6K}m)`1eW+?cqAKH|p9xbC{XD5q{T++bx
zo%LnH7KNw@N62;Z@ew&3OW^$o1y-0N+sH!Wl{~^{h*b_S{R<^(dkd-7^)ObzuYgs@
zdb<-u;UcBWw{Z*DO2nv&sB!RiTsnGftqD-sRHuaEs$1gH-Ip_8<Z+)CV%=@gTMJ{O
z0vHznQNW#C_0+!51quZVs>evd*SS_LarHE~-euaC23BkDVvj_3b0j<iylh)9mxy$s
z?kKf_EdZo8<sP`rRape7?eTFF4B`6Y=pmzd3g4SnEFn>C4<p#b)VaNU3_S!P)_H@i
zV526N*Dy8^7Aqy9I8x7~kCeOi+xsd&kvRiR5YT+pfiHTf+b|vPDFWe*jevUiNsh_z
zUB)%pI&Ofr2KWOh*$SV#3VQlGV$C8&tJC0$S?9&$T4fb%negN`wG*`;{%L!h7?|Zn
zLhWwlWV$b)5$2Hr5bQG1^B}YpY-~`v-7Ttve*XX%<z|BfQ7_3|0}oRXd6SNtV@H7V
z9x8`0iFp>Exh9mgiCi4OkErLR!B>I+cE9kh4R}|;xmT@}I7nzIhm*tiP&KvWwRy9e
zVuv6Fe!|`(w9prb2PYz3v0(5vHJXACL@DtJQR@ITS08MD5a!_rgcgd9kv!^BIjiu*
zJoTRA?`9&kIZ27JH+l0TgIMGOZ`g0ia=k7t`w`Jd%aK`~2xzF@E;saM9oq>CMm86N
zNW(VqhMmem`jfa@96Ldwzz~!Tl<D96kI#c3Q6kk_9y*k&rB?;Z$|e@%VT>A9cKj!%
zR9nbjvQv%6d1A#XPtIB(9H>B17OoJi`~+?ZIiBKMk>u)Hba!+`sHIh_KP<OZtAf1B
zwM%1#);9~h#M!I&RKN_746F__KjSF<Hka^xzmgW0M^*5&u%%$)^KzB06?Z8h2GUw?
zMx=OYLIgc#CkbLV1S{S{Q_%?m3B9m^G~zBwt~Bd}EV}5Fl8lt*$k^8B!?l+NseG1j
z*V{BJxY7iKHIYHK@*-c6>kAD-%@yjC{+1~0VPkbfj{vG|A3yMgcd!o^VgcicQ(!*m
z?S)EqTgNll4r!e`%%!%4z5tbI0^%tFU=y5hP8Vz=ns)1ESBTics>tkx0{OW-ogY9t
ztUgcyZsszjg*@0d@iM(}B^WM;*iUkxel4(6h8r|tf*u<z0JXknSBj703&20tRns^#
z)C~Z=A{ST(qua!3X=(jlCf3wj@lu_<h7Va<8xf$q_fs^L=ms7V{(-6lPQ;jyC{|`L
z6q3Xmp<!vwz^$!)4xZb|l#MOA`eTvWo@&tcp*=z)ksd(lVUR3rwB^uR>LG3m$hgW)
zWZQ0r5J6z?0u07Pt*yuls;A-z5mR|N$jn(`&28PyQCDqg^-|Lwr++HBTthJ1n|4CT
zj@m7V9}Sadi(!kux*<54Sou)b(Fj+Qi^vy^VQ;B+<+xdW$3hb5uM1?oO=)%&RIR!^
zwpZ{OgE$m=$oRWRBJmWGrrf81eG;fqyG22c*tM#a)6Z}%A7GWx)<+k-Do0i~eDx7T
zJJxQlFO~pd%WJd*x`RW*7Ey2yoQMT;e<Y?jShrjy$KbST$2~IV1Xe2-+zo+A)nxt>
z)2~-AF2QUV3x!tVwii+I+&gd4VN`Uho<LTUF8;nC>qNK~NwH{DQoavTv5s*1Br0s~
z5!a0U7?}S6giZedVnAm3oc?n%&06p~2prw?{4sQ9PFiLZxd?Fa9)4rEHVvufbC)ju
zE?!i&IExGv(19%vtWjL&CEcTI;OCLC<Qn&rm%mGq;^PzLzAT0MQg?4XY_PALeiMnU
zyDsavYpK)3verwd_V`k!8iCSxPcYHqr{=?Z5oyj;7Y_#~HyP<^EZ!jajLl2(GOd8M
zU@v-!6D@#L%&e_>1zwYjR6=;2{K0@}?cbSnHV&YFLn=_<_G34XKEhG};rx#+^Br{T
zyhMr}54QV62zjEi=Kz+l7u?szN?20q<)bBAd@*TU7%nu$n-^7?yhhk+px3>@s`&5+
z5(G**6IB)95<eFxq1qc?9Px6Y?4#3xY++V`DN6a51wq2wXe+kuD59-K>e!NqpwpnS
zaplHUu<5~rfOQ^8g7(V3A~*OzaDdsr(vi=wV^tr@gQJ6iWBn=>UWj(vNin}?UI0LV
zoW}SA$ZITe>lGWUF;&?wiwT}JFBHWtq_HalRyx!b<S7i(Cqo0~BUprJr;arWoHVPz
zfpw@W=4ym(ba1LbAjQ!o#vpph9_9ul{lFSfQx1GYRh+{nwXs1ew0qOolyt=0y?B9O
zeM=)1Wr}FvfL8GzcQvA+X~n|2O}mUrxUHh%Ml4@M%8b0oRAf8n;8ITWv}L6g)eK&8
zDd9o=Ws@jYi(dz1RH<nlN(0dyCGPQb)xk(dNtZvf6T2zBrm>;ZngLa6E6o{zzvR!E
z{&P}@YlPPo4@BASJ*Yl^`X)$)yi_UZNt=YVZrbEX77lo8Z345M#mA3dQx)+HM#yQ+
zasKfY1!`}W0x$;vyYMg*#lwVXyTS9e#~KdJ9==L)*z=-rC0wZ5ef5^PZWI@ebHq4M
zi)95m5$YNmLBInE!9hob>JDM@1e|$I4figiMy_s+d=YHEb+3zxoIc)d_lO7tK(nA~
za#@qD(xQbQ9AnWIh`v{4xo+2kl@Ig<-P2-#rA)coEh#}v*=~)9k@C*WX8=MrR&$Xk
zXG`sT+;cHPYTI=x+4q>)egTYp6%R4b;Sk~TCiMiCAkPg`;tHF0**GSmP0}iYgn*>M
zeW9;f%Grx|cEbo2-kfi^fwF!8r)IHK4{D6qtApm|LP_N1ipw4g*ND^5-bjZyCfBuM
zp;_5?DqQ%6F=!tG6U$<=-1_O1f|*#P<d>FtI$3R^^I)d70MmcMrR#y<Vg_{KikH)(
zbY9E2q>bX1x0nMQ;L2sl^KVl~0m=_?bp_eWsQ&=F4Pii3_`4hFNM>zSTk(*t(@NGr
zaX=TvGPdtT(0Ny?oNJlN&JUtpsKt+tWjG=QI7I=DDMBYAaFIb!1IU_yr=kYhxTxtl
z`Slx(uXIuzO$=}rJ<4p;v<Xk5GM*y*wibtQHDDzJTloxbI%aOc$;8|zTL>}e@bUhz
z?!Z>O7u;}fNEdw{M9LP6Jh?}zBRFLwFlS7zEY-{4z9G^U3f(AA<RU;3+xS5dMx3WM
z!0!+x7kmXjUFprvF0Dprt<lydHYh!<L1|tONkY(rW9Cw++zh(A$h!d?^V%x{+OI86
z<{%8qLhI%lL8jukp$8l9c2#0vPdxQ3&J3b*<+vIiKW?BBEyxa41ObwOE8&SZy|U+n
zl96VfD;hn7-$YlAAwo_+RwpL7d)<Q3hetL1MF)U(Jk?4ApS|Ex*bq~~4=mxSbPMAZ
zL=e;JCY52A1VhN;zD?p`=)G2tf@3b;;bM@o4^=L>CnuyqmO?Uo11{>-UKTpb@(XpF
zy6B2&R;R~_K`|qMBp@!!942HU&@CPNPDM|=HqU1z^8HQoJW;cIWKSIxh_R-?H=hbh
zb`X<R;hRKv*cq)Yw7z+Xz*`-lFuek<ts8tnVOdmsV&Q0QuY26>^ATt^sZjFY2X?TQ
z=2U96D11s6)O6}z-7V556x?`*tmHPrsF{pf`K7Y;U7)ZNPP(pVk{T6y@G9j&ev=B1
z?CZw?=rDj=y<&!d@C}SF8G57q9^th#-(l?XMH^W7PYGEDqsIZNjv7=6ge$(**+88Y
zr`?JG4;%r}0LZDp*;d|v<QBL-=OSH9MBC<%KFP2(-@{)hjLSn*-Zqz&xvOGQ03FLO
zVDXewt|}X0)YevieYIn12Y%!!Sycp80=@HOdqTIRE$z*^Qjh(Lb*p|$0s~i9%4jmf
z5KXz#2Df*?HOdKLbI8U8(Z?-`AAFjO6u}3<Zho}(RNflWX!byiDo=wDgB)JiXV$9#
zy1H;!yaw=0zi5<>NqE63%!1=8#6fU@Qc)N^sRcAHz6@@c>;xaC6%(MGHQ6(3kQ>ow
zfRyW%N$^L-%!=I$e$Xcr+t)}V)17x}EC!(~1`lq=Y$A@KmWUZTZma!DY<{rSth8lf
zSh&RgZqe@Dg&CeTimMMSv8ieCTZ5VEU`bRgSuD$dtXfxcfY}%=YM4a`nipU(fHnX;
zAlBgdOpK@i>J6|0r979n@s6EQ9IzwBN*~nSI>QrN+|ym=(l+hJ+p3q&qbN|nxyjE_
zn`lz%^9}l?l?`o9Gnr1m!jXg!hAY|Z0s^oAze(rvQ+oo?6?hF+DI2hf;iOYsDtT>*
zlFs44YFHuzI*^FhdWvxP@W(Tn7HM<s*~%Pd5vzd%!9oRgypx9o^1h{w(#FG8E-C8V
zSzs(x7iQtj62;0%s#9@Az8ik7xWYHeQzCp3p<pW2!#Vty0H&{HTT>TLI|85iN0yjV
z2IKIK%7!I&2vXf9bJSeOiu+9<TQ7VZAq(=)Ai-)nLbOoz5x9hKQB6he>mtg5d(^m5
zOsW?g>h=6FYju&pCyB?BER;$EZpk=cQB^^E#YzI3*3>vjc{;CyjkcCYy{x^*xw0Iz
z)ea^o!*A6Sj)o@R`R-{T%SlUw(!O>pkXiIz=Z98#bl0B{Q#=qt#pw>!xO^U!5KQav
zhz?jF8ja^<2io2u7y$l(u-UK{HG~~e*J+7MpA#;^oz3L%&`SwiVEBx~0}_rBGgK-b
z0veXb?*3s*(9d@D2M$Z40<C|LFtR-Y(lpUmNp{=<DdmhhgLG#RZjL+Z9)Y2jdh#Z#
z1ms_#j3&W&-kvy@Dd-4ueIc|ZQEu!jxmqKydUlPpxl}dC6;}FTh$3<U=iD}Jg<j>E
zd<7jjeiw;OrIDs^6$dJU3l)hORvjG+YkUZ`a*trGI!Hz#AlEGAn%1P>h+@E@){a{d
zLgA&u8k9IUwG>zKH3B>0wpyqk!a70@gW!Sx0NgtFR7U5R##mT`x?ZL44R>gEyn3Ac
zw*9-hb(yt7ZLEu>cllgwZ=y2pM`*w{YxxM(Tt^F+jPwru5lrIkweZwILi(c>x8p$>
z>JnP#6FP+Qx@z%4;v2GuK--;pfqmbPaY|~s;ygv=2Sy65$IP;Wpnn8bS7#g<7B-_$
z_^uE~Y`_+=MzYV4La)TlQeaofZGmYnC_|#BaK3unABL_o_g4KNlvj^(*|5{-GCUO>
zajn1PuILU`xixxA(-IB_nW2SCMZzNPOd2l*r_`d5iu5%r7zU@a2&Hdq!8OJq^7%Vi
zx!e4ZwvyU*)KALkeUzUKL%ppv+!`a#10i4wU7Qk@^r!qnf_N@&Z!oRg62sy2GtB~=
zHy%Na09xn6R6zv{@ROmIjVKQ*!3KcI>=HRL=!zG1e24!4I?gUtKvLFr?_<D4TArtZ
zdFPp3`IuL5s|HwL=|_}cHx91c`5^q2X5`QlFFv0UMuP3-&GB(cP4pQ}-jdTx{YRT*
zDZAWwV%RyrnEH=uIA59sIY@XVwUmY9LtdEJN}*!{B&g!wYzgTk`T<d}Rbd?~Jk#GS
zr;dC?F7s!y1US@`Z5Gr^%TH8PC84PD9wnk*G+&Pqq+zk;!SR_MH)G(?1GVS=K~hVo
zbzyTw;I@3CcL`9VunUzGh9a9U)rvy6w{uo0$pU~104k^d05mG)2dnl$ZVb|6-gX6A
zE-dO*P^$09`h@ddg=kqrjI&*6AX3=ef%rzl`pZ>rxZxxiXbg32c955l;8<ezltU%B
z$+tV1m~A`iLfAyt+C}r0)hf`IuDgvjn@!mms)|Hjc_3{Z$wd@x=#*u+@I-}JspP?*
zY)Cj9_E_Yh=+!k4jZLTtuxpXnK?tC)%QzKW%b3uqc~=}z`v=Sb!HsEbL1Jj7EIWVc
zF(U-Ci%iv|-H!b{!g62wnugd@?v-U8di^3~QP}`Cv0U27yo@axls{38Jj<R)!H_C@
za4h<6vCE6%IO=x9qJTc*oNKK>iW6$cBZ&|KT=-T9Kn+fcUP@3rK3G<1^)sk$1XHdq
zI54T3#q&H6aSzaHp6|F6X_TB*RC#e4%9Jyx=K=?njzE2*%zx~^s>bD~a>G8EMQ&7A
zPv2|$fa5CsfSF}lAT1LMf$}fx!W0x<E>)HgrL>&cO+Gxqa7C&L^wu5S%l`31q!v|I
z36_5I;$rz(E#uP|yP@tk$%=lfs`C0ErRwPoFR9|bryc3>N=ucEOWYzwBDn@5;i`t_
zt*moFiBX0d!}3G}YmlxU=Nl^3?OrYuym&NTCNz1=nH;qgdm(K_q#qV>JaoznfS1hJ
zeUr|d(g7_4rvma+gJ~j5cU@sOp&8)@;B5}6lHY8nD;=?CZwU>>w(abQF<8v#)=&tc
zcnJ@TX+y}u+^ZUJDI+j&7m$cvz(LrPl>oJH=|el)(TzOW(JZy+*VI9tgxyaP;eG-G
zjoS_qQO}b+RLbHURWH$cBQYxaI3;j3?kWweA3e_*oXnz^<h^?s;~_LL@Wd%7%nCp{
zy&SQ~1*rmj!!3;1CIGsJ%SX<l<&-1A#I{9OEwk7t)O25^9HZTiN~1OxvChk$y}99v
z;E=SNNat5qOrfh;?7X-YwIDPU?;hhSLIRsVWlAh4${iwtC}no%F;}$OtSMUY5CZ=I
zXR~vfb+AxA{YPrp&JrRf;2{*S5I(%ZC}K;-(DQXH_W(Xp+d#F#9{#Qo3wJ(}G3NGL
zVFAGnULH|Wu$1@Nj&Mk{U#6xH6kn<lw>*hb*EaH1d~*OB9*!>)nt5Ap3%ez|8}ar)
zFgD$5j;AZC?T1l*&|Icgf$e;KM7~W2Tig|4aMxtH2&tERc5Jaj03L{|tHXuqP>Ax$
z-I(8pC#cwQ3Tt~#vS|cdPFB4H;vXTa6nRp=`o^@c0(i(MSzvj6YMglzntJc_^DOXO
z+Ap<Xp!2c$!gqq3mAKkOm`X?vAhibV`zHEciLMJJ#GI+t@h~aZW=!zzG)7(Dfu*>Z
ziVAD}MWU!NtO^ZPe0dyU>f))%8<Fsv7TtGzU$#++@(uh|%6&(GS^`jyr(z>7>sGks
zFez<e!T~sgO3>=*V;>3ScKP;!{4-p55Po`;bx8i15|MWD3;I$J+}H8MOXolV!6-*v
z*K!%4za`0VfPKyos+mV`<8qS}dKaB#x8hU^Xm3`Q$BS#==l=i|uEB<if)?{s;BgO^
zAWW+>oKnU<E|P?EVL%i<;UT(Xq>Zm|-<|_azULEY2~|t}z)g!N%&s=-5CKr)c@w<?
z?5DDOiO0-h(Ya2C$u0>)S&!4ib6XE7>KFP(%|UltLi@SS{Y-m}6~q<nqN-Bby8<vk
zH_yO%W35vq#?c<ix_!a}uG$S<p)WYwa@)}olx@{!i;HaW%vJQdeC`8kv1xCXXJT`&
zF!B!^fBZEpyP&QB9(~6m+g^F0J;tCrV?P{7_(-69MGlnemHZ>Aq~I&dptBbR7w!Qx
zSuXfir&{lrjj@p)PT0Pn5Bu%_Ag{PQb+ugqW%vt+)Zh*&%iYJSR*K`yupv-&sb&t2
z_VbCmJf^>^xa~JKm2P5yRaUnT5iGalwcL>$pjJ4Ek%pcep|F@ckge6O$biG&2ETT(
z`ej<E7lLeL@tY1a;OVQ&h<O5m9@9HJb%Hk61)w~Avp>O4h#&sNB0%k(^vy0x6@AqN
zg{LrWp#K0qE)=>Q7^|K{0NHo{06@U3F}5^Ct=gF1$qYg%Hi}`4ePMbTWUaKydi!v~
zQC5N%$d26*yaR*pCyQ#Bu8c^RUC?Mp5Ei~1LITCp>meSOFd=qNzlli-!lfCcjyVxT
zaqb>5ibxSdNnSoBCXrCyr_Cknl;8o8rBmimD^iuq+WBLeZgrtsvAnzbV3T(#ZzxqT
zfEp(%BsW6<DamXwZo1xKNZ7s8a3!xWkt?*TR###IM_jdQ?gd27Pm?0FJe9V9OQ{b-
zQZsx|rC=h11KS{`H|3T6)09dNaZeX2?f|8Mx?H}$0|ug^r4|EfJSSuuXDACkI1!eB
zV--^U2*Ux%e555+?0k&pjTey$j8V)9RrIp~7H?$KMFtkQQv?CgLNrGJv5Ahn)I1^w
z1IKad4d`*zu9ySTX<TF6Ep$7K^-QM~(*$PV0vt+qRn!j4)R2n_b$)@OaqlKPb)IFG
zl#P4=meybcRrNF3Mg(xYR|#UH6)kT-!Z7JKx56=`#!6@-h%Q?jg7LA9K=Q9*!LW~z
z1};!_8=s0Q8v?8q<0Z;<*ND|~C_==ug>rimrLAtUM^|1S+3M~t+8VD%mQal_m)olQ
zmb8IN4Yvcq1K~yu0I3|)Iq@^4{IuFbH89$JK{OWQ#c>y!YP<@eJ}AK}%i9B5{2plW
z&BWo2V@qCE1xKYuz?LO2b>i|9${CH}IXKLpl8(cO%5JNWoP0nl7E$O;^7Y1nHrOjx
zfV;f29WmDyS2m@Cq`f1M6k2qV5U(esYPy9+@drUWH+XP5wgXc3I=F}#0#5Sx$wA+2
z1EV)0P$8p>De|(*R3{KhnEGkh4=bdVxPcv0r~+7)su;f)hV>9;@td)trp9QB3M;<z
ziGwtL8t1&32vTc)>Q-vq^U-`IJR9yb`OB0Rkk*Y^)S+$u>1Sskp{l<8%_{hYkurxh
zwG1nsj^*Di@$@{wESSNuc~mXRz>ke9=MtxC9;#nZfjrevUx%0x;~cEE%lV5RS+{Q%
zObTGpx7W<6k;ZgI0m!s3KBEX)sVb-sEoo47+wI~nKs!Dc2{ape7Bf#H_gHnNgX|Qd
z1HyHp2xZV6Mw|-u+~r>I=whvrR(1-Pa4@rz<7E&;+-AWNpagv0Bf4-2%~GKi6`@BY
zS>-N6TVw?{LRpA^CGv(UYRJ4iAjY>C%{k{mxqm_0`X?k#(R0K=vc6Y4@6_hoeeA6w
zHBTo)9*t~S!1jrCY-a}N#BYyr#SlEkF;}jD?PK;beHUZd31V8TyqU$qSAcI)<1)`z
zVwm=|NT*jRO0Yp)hjW{5Tkj{F#JI<4=xP9lR8_tcW4`Bc-TNh=y6FK-JWf`=R=AZ}
zTB21q#1Sv^tZ+imKyq258iJ86GB6rijUm_pqPn$mow#m`0RES;wFq=DoC3FaL}jb9
zx5T^dRU4io`tSBD{Ff(oH0edf8l_kF<~(r_EZ&Q){{V|93tVr5%xI1lS2?31E3FmB
zh}l%>;vUXC=H32`taw`bjVBn7YBL{O9g1MSmL98GY^C~cJEQaU9&$6aM7!RHogz&U
zS|r-J{zY3Y+wF`eNI8PFOp~q5c{TMfs)&DO5O@Hdt7ddJ-Ji7!5(+xl*1$`{{{Ylm
zTGinwUu;Lwm%+(!vjCl~#j2<g?8S{b+{%jK<9lasXCz7?-!KLPK)j_FDzUGEE5*mC
z6P(5BWm*(+bY{;%{LlhmKyYTukw*$G?#*touy{r(V$uTe=%r%!QvU$Z9T(#P9^qMd
zJ_a=9jOAjWRE2MNR=@HJ8?W~YyS#lv)b(nt;8uwdL&V*tN~`=OMsPX;Y;%+wx!<!X
zYEPBHA38-?C?FYkN`oJ88K%f*5`E#Ma)49PL>BZkR{%I7S-ow=am58gUF<w-B9Y6>
zv9Kr;+RCn9cg$b7D8X2>+uB84v9T@qKAAwRR0iI-d`{&TFxa*qd`3|^She}9m!(W0
zi?ZIz1G<H*mbu;uPF3R#fpu9xvR8P);0svu3WH#sJfU+`HU?e<eT&!~pqp_sbCIBx
z7^qcNzDVE93dg}>8yqYwf50WFkN*HxE^Zdi=|Vma850X>#9YEr-jw8o=S#1=#~@3g
z!ukfW^^7>So;RGG2>XYcVI4xStP0AX0}oQ7G!fGY1|<f&dVLZ34;(<ij`hw$Eodt3
zMa^Dr!ki>MC4(!=qCv`g&m4RiS)*)>SR`u#{tU-_9-+RnR7Y{;VVoi+$vKv8!D=`W
zbH3w!Z4&U%P`mjS+?V^a=2y0C&%Y#~akc{P+A<vA6?w9@G_aA?ITHdza9U=O#HgS~
z7Ha6%lYAoFtqKJT<TfO!YM!EucWLCZ>Mh(J=zM@G`X%dsFEQ|<gSe^x0P>OWL0!ZU
zC*OmgsPv&(aOhFXQ1Lx;HW)Ho*rPn5f5nyAW2dMM#vSM`?j(R{YL4aEY3<F+peSug
zQ3@LL`1*yS4}o|<?jL;%xL{?1bRH-r8Wk%?EANb*VlanSjlvV=z_Zr8PJZAt3}1#`
zJ#AZhSVV0%l>#$Pv>_f<gMJBt*lAL|X&!ZUE%=+WG}J7<6%w@QAS@L<gL4FIcbBl<
z&zs|9Nd)L+>x{NTY!`C{mP(#Lhbqgr$0w*Py&dLQ_>YzeFCDi0#nn1{DhUfB+2pu4
z7u~xGfr|615h8-opj=)l6FCrjSekqd1ZIdztZa%xz&l;eG{@OaWgyV58<h)aR#ye}
zD%Z08%3w78W`a--GGF#nx?08$%xj1R!=9Bl40WU+3#AC_@ys&Xz;G5WWIh}P1X~mz
zh_OZ)qB51LS_Qus8^50B-m>XRk{*`|oDEthLtnRY>PJB{j$kkYKz887VHQoTBGCwi
z)ha;Qc-r#|11o5?^2%^+yg`KCV-2AIpk84WS{Ld?r5~UHh(c7t<K0i4@EJHp3l8rV
z{D4)g>bt3Ui<w1*a5eMxcPRM4{K9fW=l=jHDNKE3LDcb+T~UvI6S*cRy=-B_Dgb%8
zk*EYIs6aFaF$Pk+=4v!l9x!A(OE;o|2g`rcnCuna@o;hXJx%jdZdr>`@I)nTmUaw6
z;*ECs3`Jim_XeX85NO0~l&<=jwVENt_*jrw25ee$vhfc$7#<-0#YID{paZye4NbCF
z!~mB$Es@^-iD*uQ=b;;h>fEu<NwM%k5+fGyL}mr2DBZ=QD*F<eS&~t^Mb+}e6X708
z=3btM*AQIdQI3JzMX{_~;iTOr6Z5$1D?u^rVF*E6DjnSCup4*1{ah;Pao9XdY7}cU
zhIRPyp$M@+3d*!s{SwsJMY17G(MsJyZ>%eTi29ln;4!6YgGE&dSS$x0D{{OUq?AQV
z0o@aWRh>mz4oS(-r_E81C!^{A0FWcWwgI#-d)UvT;v_oG?Aoz)?&4+kt48k1s0%>8
z4JAZPO3D|72yNxcqBh*6#@9%`#1NN#_!()r?-lLEqxBZm%jrwPh_S3eg3<{Z@7BX@
zd!EWv4sE;ZVEB~WEx2)F#s2`sUQ`Xovx<tpK84_fDppQJY`lwyofU9f-5JV;Hu_<4
z{HevPMjTMRab(!by<Gi3N{Mi~Wv&ee=>_i?8j_2CA*A~3AVXp1LWDqzYM+u>X86$-
z-<Zfh#|>*N9X23*HJhwsnPcd%$yp9A1Hd#TUg8~yP$$Mc*W*x1-o1&qZbZ9o5`M`R
z&w*V|1ws65tBJ?`n>0W<Sz7o>#|mbiurC4<posZ9^8h@GqQLS8l3yr}gSUbxpzgDm
z)C@|kw~U2%_talmK(gr|iz}rFE`y1YEQOHyRm?C3_hqUMQ0th`Q)uYUCC>`$rtzn6
z5TjtQyj{Yos#d#^gEXOCYX1Ocf7`E5`+yG%%W`txO{5hJK(|6%sr5g@8`PMZMr{;4
zN(tfbwOR2Vwb97}K<6)QV2!S%H+?pY_&Bg(Z2-ilc%9?Yo_ZncVz3&{YW5tP^dLI*
z9jx2_%^EHB6zC!Tl7#AVuZdT46_naA+;)qUww8b#!Vbty9=S&9D3wF56kYRBSfvPO
zaGSO*vBjYXU*tRqK(w=K32tC-ii}>w)5T6PC8JYl@o;$R4eMXZo){Pr79X*jO6Vuz
zrZ+_L+x+2cQ%bt<Z-NshJ6;c&oqX4q&Jp?bgptU2;YU7Y8^Hd>Qm+q*Imxh!mI3ot
zeEF9tMCT}U`+}h{>_IJNZ*;{2OQqioa_1p{pHSJ11A_B7P1SIScwjgiy>jCQ1IXs?
zD-09D9bTA;QC_Cz#OkL10B}Np3_X#5inr;MPG!eNURX5+PMho%7E9LQ*cMAQswd_y
zYD&FT-K_rr60<<M+f+Lp6=Kg=sJ3ew{f*6mPs!6QBY{rfC~~e<SoxbpT5M&nq01{-
zES)Up_BYq;5GiWQdg?YGrcto6S>slS0hI$47ev>|_$Zb(Tos&07#~Wba9zURvfE`5
zg4F9Fg8m1S{{W1o>U6?1WJ79Ah`SVbT~nByR;R_X*=|v0UC);tOX*b^d*}8b)M8zE
zgY6WpFh{+6e}uG@x7zKpm%=En$xdn8h+fbcQdO26H5=a0p#8{MCfQH+SfcO(A1A{H
z5U+VGdsa(8c^RGx<Q$AYj9B+=)VPW?C{#f(a7S0-6@#tqTn-|NZ<VCprMCS$P%4D(
zgYgJ$O1y|s9&ttTew|Mw05;SfT!;rNrwIfv+(0nURt)Oh(EW1YsQt9D^eg_M@3R+|
z1f~F3mQ|rqjaYuuYKbe|Y6v!#r!09-FxgeU=4^8Qi;YwoeesYgI|NdP8$W?Av)rEl
z0P=RXgSZ=hMEhejB4G2(Z`o8!tZwcVU2wW|d5VL+yhB1M+9WUy)M$Axm`aF98Ao=9
z5ADE>N3mXr1QxRGLc(<k>c#ZAa4W`~ud91utI{i3%jx0*Lso|HMz4dtPpM2`?+S=r
zNF0k5C}r;-nq{nDX{sl>q*Ny!IxFf{ia{H7IF(l~x>`Y9IW$Xh40I_~ib!)Q4H`qt
zP6!?5<MJG%Xc{~q_{*B{D0L8a6~>1A6rzy6X6!^s`XK15D^-HSMIGBI``kUw;*F!!
z52NT)VxS4Ahk^4faDHre_#<SX9{s<JvF+RC2u3ij4xiwO;t*co;q?>~Zc!1#W7V4u
zAY4jo%jRC)z|*3m4|5#lVbQCmrC?U=GY^O!T-()TYtY&_!=3{KLaf@1Ew;m$B#w^J
z@SOQT=ZE~sbX5NUfRPUFk3=pJ1=x7}MWcA;u*DBdacEHZV`rL0_{D9E)IWK{`wzu6
z*^SHM0gj+{IL~qvD}%#CzhbPN8;KZ&a9i_>U>5pYF=LA}v=KcIMj62s+HV|G4i+dJ
zXv%Zoivos2Q!8{n?C2OfB7xO|tpQlD<BPw~5l@<}hxZmb{^`SHuBY|?01_JIbr@P}
z5#V$ZjbieKEZvl$Tb@WfppU>*Rw>`e(l6C->_s+PhqRKzL{}W5zV0q0-zB`j`6lM9
z%s^miZT8ec+Q{F_d@PBFxqa%eQtD{~_^s^i;kUmjQV?+(-~1!9e6u>1IEAt^nOwG>
zZ+Ohe1u!Y?Y*ciQLeNbFE}E4M`NhRLJok(XSzzsiDkv?K8#W(#;8Y)#c`%4oLH-f4
z-tyXoGiV0^k32JrRL@q#SD*ETdvTD>Gl!mIo`v6ma7%)H#$H9){Kz-W+U3Rg5U@I-
zLIoP)*3Yy}^2Hy4EoxanLHI)_>rwddV&!>4x3pKTQt#DV6ej?LenN;Ijqa;Q-4IfW
z;M^%kCPFY)k~x{oAbM*zE!izn&0rx;W}QVMBHnlPD>hMa8cr#Piz~e-+e-nRu(poB
zb^Hc4R*Jk$bE+2ZpneiG^cLqfB8B%zeU*J|Rm)*A-AQ+fe8}^nR@__LmS32{oR>{P
zRlxafp%GbtC<}i{PVPS@rGdO(Qw|qd1|i^`3>4Z@b?<RN9ymr(;AVWQsYo(_cp%1_
ztWF#&Z%dsnhmC4p7-)?1HfHtJD@V<;&Cre*4)V{qqm-XaEch%2w;Ps}0O-J#T&$e#
z{s_9tk*?7CnbJY=L0Gdi2<1#iL&p<FxnI^Cn!rY>z?U+Pjo_5$QoEp55O@$hUJ&XE
zVybWCxM9iBV%~N(ND@sA8cdBKVDNkOIWo#{vKu7e5+=M`3?F4c(Y>!lwG~h|V%!O$
z3g5;`jd)opM~ZI-%ZHk!DLs@pVm)hv(GOa&T`E#<K|TTHu^6YT6-+|%{_<!im%s(3
zq!RI_=yzaqEnMfU0JT!~Lx8G!m_d}vK&FF$ZV?6H@3gk!16H&O2v>=04X*im<A^*8
zPXXYgy5>~iCA<;CID?J6iRK*l$An{awrV^m=TP1`rsdKL?XIDPpjWd}Q&DYvk69TM
zWT%I|V50d$#&-|a!jDUl;)bYJ#K9}#FCU#{(H;K)DrlmrYr`=Z6mx#_E$lc%2Edr%
zCR)xB!Bu-Y^~4I)m2j9e8^KAm6Dg%&;CP&Z%hcW9$3K$Gy^hAMKJY*^yTD{3g2_)V
zmUv|*hch_4$TRDRVOG6f>HZR%c2j)_9-&|NBAveHmG(-mx^K(mipp&z(AtH#0u=Zu
zmi8Ya%BUGbw{;2ymE)_0*s6oGnS>|BRfre=07yMQ><AXm{BBpk*z!uaj-Ck)psJ>V
zQJXQm0{;4kB;(>;cDQy^n(;Wdq2=A5b#qmtD$SLpL($}V$SU>d0|ZDGF1l(7Tp8hb
ztS$v$q&!PdK0oc^qT-`BRA0+UR?uq99Q4)%PNP8Z9_0ln>;Z=6+lBpVz*t&C)stxJ
zYOuYl;&J}?oS**yw=@(+#>&V&<XJJVvrx<h@l|9p6{dmQ?S4#wN+axPjKH{==K@ec
zB}KLVFt0UZ-8B5%(lYMJ<XkD|-I9l!5)K-IzH*}yR%jv#Le0b#PRQJNM}f^eT56&r
zZ!^`xC5gZ#nQ^zksoU680^JsLpuSjU@vKmaFF-cGVcQGiZj0+YxQEzLwSBPVE5Xk#
za}WttB(Yo+5pCPphT;xD*x(yv8^vS`F3p^P7x5Xytm^0X2k(uAgS%ibMZ<+^35bF&
z89+cCCvkFg1aF{vJ|`yI*b5e}Je}mRE?qj;=phk6hnyAx4?deBMQbt}f@J`6xS88m
z@coHVLYk}Sj%!YWl=#}SEY<%2Y7t>gGFIYP*G*Nvp|C1YrU%|Sidm%aAbE}|Lh)qE
zDo}8Xuhc`!Om{6Qps$CUpFmU1Tz<V{ogzApV>lhF<^+s^DvuHuV2u#&4haQ&xK#0+
z5HOS(-J}QPm0lEEV1;nukuTy3YqHjP*ecpY^YQ{JnD^8)IbD>(%=UyqtcM6~MpucN
z*;@3IA!)~gCixZlW(S&2xmRm~zT@P5OfSnjPynkBij4gycdT-WHp`EuTcAbI_v}!%
z)pe^S8NV2u7K}#S1h_h71zlU>0<7*-aWi>$gfJG)7m`yO+gH8_;>;|j%(7k$5-<XV
zEA0hbF52Fgmjq43Xd)}(6WBVO$_<CXZ~@gk%y=u*GX`2OFs4(=@K|*UuR#^C0NK0;
zu4UoF&h?x|jyE|3iYVHvuZAK*#qe`k^2)`q2hb-yx4*IRu&ysi57bNx#M;?Wm+14`
zD&EmbbAXlvJM(wMziss#VQpAzp_CBzBCls<L@JSU#o~bSqTtsQ7Y(^y6yD<C6k8Em
zUUG2o6fUy0faMik3gcz8RnZG08meW<>yRsvaLw&Haonou)<b@;l3Cg5;A@!J>RoJX
ze~{jgfahI6Aa)s-O`hsA#K(@Hz-Guh+5%+GuO?<aRm*`}ZQ3LXUJ4u!M5l_b=F-sA
zA3|=>$b?zRcB;O6U|02j2s=Rns@5b|sX}lX5KH4EGp(i$y{!`5?x6|4qvku&ye7(`
z@S?c9urPWmwJbx@z=$bl2#qC1U%cvthbj!cfmVOWtg13h9<nDu*y7JI)Yg`Hsqv3;
znKwXe0osq*2dQJ6^0Lad<rYwJYmhxQ9yh}d>CA8yw-86c4q8L0{T8kH%e$Ase=EA`
zV-Rmp2gch0rL6M>jTz=sDfb4_oz@G%JPp}fvyVOH+sad&T%&TXv&cvY$wCE#SxXww
zo_$6&!7UQ)(JdIg(Lkrn<223Qmsiqn<!i?%7u*0vBs|nBaNwgTE<POPeoDDO(lEA@
z;e|%jTBgmxCg-l6;;rByIRFruCb<B1U+_Qto_XSJDBJ-LQQX=eVNqF4aB3?q#aBGX
zV=S(f&N72Y^xdxJrVPi?BqiiZtoAGdsHMjQ6C=B1fsLv1Y7hddo2#o9*xjq{f3UNQ
zoe{&-ZPLB_Is6f{P8_?h@P+>X#Gpe7eET(urmXHaevr&KKrMvk_O;-H#kbFSF2~9<
zeA!{*2ZCQW6#7tImTCN8;jdXMp>)52mu+QkZDD0*ZnbgMrGB&@9w`e?sH!Mf^f<3f
z^OHP$MdOr(CXxJ$?3JEpQsb6i4-k@}(>P+g_rJplG;gKAJP%E;{{ZrqQn<5`?wNgc
zf@m%L%+gJ&DJox4m+&1DlQn5sfU!axlS&6TDtl>xoMcrf1|3h4C)Gm75>O4w#9agE
zv34W_b8p46fz*w!?21d4nEL2OtEbcz!W+BKXv{q`iAfi5y?H7WbF#Gr9^0euW3Zkh
zf}?7St*u*slDch*3JS{NO&kV7k_IDhG~taA^)K>bvrqHG$dlM&qX3Jq_c-}6@ow~w
zvLrt+(<<_t2wGmOh{gFU=D{|qMRLE@P>`@yChlBa^%pB_(&0f8u^ksi1}Jcu7?(P7
z45~q(1_5WG6{i{;@K~zsD0~8^Z#?I?oH=J9EOx`jC5ZJE^Rx#j;mUFf(h5ar^o$hR
zMaaEaQXS!!{{Y0P?S~kS@g7y^Alx(&t}>b?sPTZiY-(MhH^Ya6s1#Jxng!Gk{_di!
zQa%vGP4V2dzzTbAB}LB<o2;iUM0PUchO&wvC?a}#SwsTOvOT4}>{1wP3kV}t)L9ml
zf+NB$rHE%xxsZCE#tAk2Q^(kJCB+wI_-7oa?)h<!f-B?^WjftoYG)Pk9>!a_f$Q85
zs!-@G!FxsX#VRF00b{vUsm5p?(v_p<U@aa67K15cMyz>`(%r`l*spmO(;Z~A_=U+m
z3sX6DJ}wJyBu;WRTMvd@vi-1+EoH5Pwcpfq^!kZ>m%QnpG3Jv(6voy0I_5O(T+hiU
z90IT$Dl5>P1Ae8M(O_5*dSTM>U|dD63&H$?2Bn3^_ht&5D#nkQMVH$)0;_J}U}IGn
zHWf!ayviPFCD(RJScMaZ!WeUa1*q7n_%`|~FGWHM@h!oTP64bnMRf9b`i-#zo*ti|
zDqG-j%yz0PC<m6txIs24RQPUU>%{SqEmGzOFBluc79yuF%GB%RX7>A68$Cf8uI>FW
z<J*53y9=*G!ZqQ<typ>*VWlHQyfB(W$CQ*C3+YR*j^<U`J%Ra9nQ~I9>*p>l1XZPa
zxEo&3swUk9t^WXte`}}`{v*JZ1*+l?1R~_)(pwQb7BHp92%)2>YnlGCkxGz{yc?%2
zo}sYKU&(6)TL$5w2z4Xs0fZHoSkf2Owu%r<Mx1FjF?4vbDM_@<pKi-=-<UIqVRw>W
z2co$l0*h>Y=a`@#9wTsO#L%grcat$kG?A4hqJiqVj}qwpCZW=fR4cDUF>fa+Yez)o
z$Wi*nQrz}`$La}$Qt@ki(afcnfS*wwg}<##g<4Vb7OJY-y{Kw68hfZ!2iJ&(!cpDw
z%Ji%Y5dQ$~4oK?ye}Y-664yIBb<b=mfGRu-ijCOPV{$(vN#`cK_3<7lLq%}-hpYqx
zi;TjU6cjH85YfAM+Y&H^whgT$u56RbE~b}!{@lIQb*BNLGseZQ+P-8K?X<511t495
zJxdxh;PjQmj|a#=5K_=K?t5xW-ah=x?^Vyz3a&B=hWWOr)DkMaNk42YfuIgOL5FAL
z4OFfD^2-l~Hu8apE>uG6mzD{k7nFDWmAzFd!WRQRCy(s)v%c}v;ay7pSIIIU=V|Ei
zVzq+(rHIOsytw+9x0IrQ{F`G`-gDOy%~4LkcyY1d8GG-_IvtTUKpipPhs|f)K^89f
zB??t=x`_utM&45HT{WwPjf!BZwf4;Undj=&b1Xj*$l)IDQKTq*B}9C276PD|0S1bC
z0^<~iE&CvO@dHDdr`JsLZ`7y<Iq)z}X+w9H`z}&IYW601SAz-Yf&#HBDOSjB^$1!e
zZy~rCMY?JL!2bYdk(CGGEDs^r!;CNBjHhi2t|(^KKu7_~M!qOs=Pi+(Jj!^zpmCvB
zv31md?C7_bLE(>OLh#^_K<er=bo6Wr=FWR&k>+4Gvi+uUQ92j`y##Kmrz_0j5walT
zq@OfO-{B}MtqD0*2m^6>>nnhsa1SbCL457?lSS>BK1hyXqYl6^vyXqqW@!UXtGQD2
z4W`9gs2O$^*%-woM?&yTElA`PFVB)4t;lbaUoq9(OJ!tVX=#f(S<loZi>rAQnt@R{
z$Pj+urnOc;d{l5VmSIrTx7@_2g+r@QqPJ>6`ngv{$k7LH5vt+us4OkM_?Qnsm=Bhy
z%k*K^P&8O1S?rhMYlB$f5a1uc@bs{%*{G1YF{&}}R@U>vK!ga1zhYspAf+<##`1<*
zCt(qy#!%X%e5(kR#7@SIAq|LYJ(n+ha@Gc7^b^~_fM%+$4L!aibz0O!m$uvETs1CS
z1sUvOnwR9xP88X?RX9=;1%NrUvyS|6ZNa>Ad4ieYy5A8FqfjTdZ$GT6hL4KCqILDW
zynMhz681b8O>8Z@=~B;`nJPVq8>MC>G3XuSvFG>9O%C?_M&_=x+Lt3Cz6_wO&Ds1h
zzOFUYc;K1;05YMh>%`%Kt@rZ{cxRABybI2Ea<H2Wwls52p;j?jiU2A48l=5}>4v&{
zGQhjW+#n0I`A{LyI02fdVq>xVF`}~+cXwwFJ`L(qSblz@mq&REBKvr^6P)by%`B_B
z^)0JZ9NJ>W_yX_c!Xgi#a-G%!V!ns67#-8MK?dj)xyn#w6D~&v8f$_lKo`d$+hZE5
z3s(H|u)8cPUe=b_L9JY+hW_U{q4$g!PXcSo!N!_FPq3NFAgg^5T7@??@~n1&4x^WL
zyNGx>Ja~?iep&bi9JFB?G6+u?@`9XQ3*V>!tf5hrr9ksX&D~^nJ_vd8!x&cxI@`Dd
z!#E?_Lz8MsOP?vQFg!@}p_=>yfypfw6GhUnL~KO+AhPj2>xA5G*WYUr`o4Oi4m=n>
zbq-A>C>=@L!t)E5jE}lJGzb350c|}-7@)M~s_t2)jg&~%_^?fU%jgp23hr8^^2Z4U
zpcXybSv&_wc=E$lvY(TzA8oIWC!~&pl~D&G5<Ms%A2AD623zd8e<}9X>zbMIcW_jU
zkminj+X%`~ONze)sEcd5c;``cFCgMLLwx#=aSzyqwnni*{3CPtvm4X!nNBatc#RGt
zFH!J&Q*%&`2C6<QxR}<0as(~#_mSifVx6Oq6Ujncos}E`QM^-3B4JW`%^3SmYgAyK
z8MD{M#>}Zl)k8y8K9w9{DX386IfCsk7}A}hEvdZ69>P-4Eo)*+;eTlp_+<^M_Rjf>
z`I*z<fbl8ai=#GW`Q(M3)d~TSr45LlC93ozgU6|*9T5Xva_KF0;I(OPA#dJfH4-uw
z2~*Ssjsuy|v&FN)#ziLIj)<xrgb+jJ57CeyBtHb4QGZ?_u<35~<`pWNDaxppDaW|O
zOHB4759JM^&jVx*)S8vHS+?;TJgRUC^XedhnP+#hx2uC>4si4CRob|OwqzZQDgsWp
ziMPD)^!gFXTYG8H#_R{2hY@6bmdZxN)v5>ag?Y)F&#AgZrmNh#rLU#NpQ?kAOVf@8
zACbC~U2<MKQ$Pj$xa*YG{zLl_P(c$BJB(;Jy;93%8fqCI{1!pRWJr(%^v<4Iv%(hu
zBP${OB@F>~Bf=2GS3M0NAUJ2(3@A8$Ti>fJ4x+Bx#X(n9py*c@X%AXUaBSyFO1S_(
zCh8t?PioMyWd+3zFrh>nvcIt|*8IMpy2?LPTH>O1UY9PxRunI>(}|Dfd0T*Bw{sf{
zwmtjf;Ar<T0pQs3<;41^1UE(7mg8vPV{fu5(3LR{r#lu^WCxLB4+3k!Qw`cTVY7&v
za!uT#Isk|a8_p|;xH?rRc!1j9D4=sH+R>$TvXCi$o4!~GDNPFif8>Ee8}nWtYZISX
z!A=HHMvVp@qq^SF(36R2)h9?B5^m<*8fz)~Cm#LUc%O0m3KF^1*V7pvbSx5`PSq;>
z5UL}?0Y?Whr;125^Nt0!vxm`YXjiHZ1Ut)NJ(zwIs1Vrm<Ag|DOk4958a9%H!XV4&
zi_q4rOi7Xy-I*xGWKSV~!70!#ZFZOSp?u{IttY4;n#%YVCB`hHm^5|89YyAr$f|-3
z!^j3$=$=&~5Lnnt)3WbZv5A&&{BBlVJKF~UnY>0(`~l9SE9m6ix0uQ!8>Pv&%MaS<
z;a!<mS{;d9b)g?MaG(iF)!xXVXj>g;UomS!vZZ5#oWiuVK;UQl16Nhj2s^cL>QqR$
z0e1O-S`ne-m|9wKL~hmm*O(WIR~)@X6)6nk+2m)LO<fhlz0M$9&2hsOm^;9CWm<p*
zY*!n8f4i|-+hWic26;pQk9Bn`#QOz01L`m6&M(b+hXGcHsbfKJ;#q4iW&8z>P}(6;
zCWml`U?TI+nS}Dn!NRdPA&5+Jeaw)Bob>r<fT&&Q2?#s{4h?e3^4hlT{zi!ctLXyR
z3t*4b`8r5<^t4;;he;c2cap`h-z`ZLAGJkD@dm95-rY!CS!-(8F)I?f2TKZN3WJ8s
zRXGO@f){xW#ojG=dND|MObZ~~Q!HxVF%@mky285_Ew0#jsySITVV&wqV$eNnRLdf{
zgV>5f^rRY(N=Mf`x`RR~A+dv^1vjj_4XU}giibgk{?DY?jGfy04k6yb{y3GneOJS9
z7Uyzy#uz;N=AJ&u)x#iu1C@(`yWR6kG%f&WK$pL}huaFZ@j>Te^mG}cdA|{~?f3vY
z8%<tiTMG(%8{uAvf~%{R*-p2(n|BMM>4Lp;-6f9#!RMk<L5B9i7YPFG&Uz2F=17g(
z#HaeqMMS4QP~3Z5k1~(zsA`-oS-TZd$Dp?8jt&@H7!(&NQLTqH^>X6@RLR8+HvOQ6
z!9>+Kq+@`u4up$UqVu9u%W)dSBXMCs8b70m*Gi_!C?KwbL~;>jOLX59oRR1#ms+&7
zmMONpT4?-J8t1f}=&mtWV{_(Sd=x$$)XL^MkP+CyFnR^$ZXlmz=S45MzyAPlL_xr!
z_yPe%EaS?+n(BY-x3PH6VQmCIKWxlL(S!@8p>AsBDAVSnu?Sp=Km4y>o)~Z*(pn#D
zoRgo%#u=*Bqll%vABE)kiGWaOoKC*@js{e@LX``24r6i^Lu8q$uc86gswlIaJBf{4
zr+4}#v>V*w9ghSJX$|#g#b35ofwRd#>=bxq9qsl~i+{oj>t!(X;$RIKDOwkd?YJzN
zKeCBV3lw=ST0j=)Jy8s0&e-M9u(j@XISg~eJlO;))bK9G7N!gfO6l~$BH+1sQ}9bY
zFxcHRE8-PgH}gB$nKTI;<9v(Dv6G~M(`!-A#XnXGK}6mMt|AOOR_m7G;JR{qCC;nc
zfBrm!MFF6}xjfN8hZw~m7lCzw^tSxWq=8xHf&8QeK$MzzA+~gR2%JRU$x?(@$Y3?R
zLDPaGjy@qMSF9{RYc9%K@6GIB;KxOYT&nkxSpqExdd0CTTa7CiM@Am<S|$;oj&1=B
zgf&$NWn;c7hzNk7^o#i-+&w6Opep2Ons-kUrvtJGQ5{HEV5z<*Mt_J6qaLGy0+TgL
z8{3cFD23q?9f*`CnuE$5S_l`S+n%9^seXYAdX+m>sde8m$ye+hcbseIxkPBV?Jp_q
z4iSd~i#HVvj(~7jFY3X@3|_v+f@=i{sGy;0W1Px4zMVAUKlmi6D?*Lc9wKe$X2o-+
zx0(r0G}-<1`jtB7T$Ny{&_Y@wTU-oXjjHg7m~3nse=0atuo`}$$#>KyUfW`&9<ZYt
zNo6*tA233qGk1w>2Du{7cF;dJ79jD4=spFO4h3--5l|_j;G#dtcG||W*54wz(X&r1
z$L1`eTENA(ih&t-BVcq7gpC2_U5VEMlvGOF0Yn`JkK}6!5URR(hr<T>qyGR<kPegb
zFx}dv;5*4&Z}u)Ls0#FI`D%e$r8%lGE@FW7RmlZ=>$%iJO8)>DmDC8U!GH1#Rg;R#
zjy(EHfYDwM`crLLf%d?lIb7dUL?<XOVxBge5HK(wNLs?G0r0vRNCvK}^f4)A@>ITS
zZ|Ka#2img?Qp{C=8*-2=ikzohM)T{DtE9SwMgnkgcv>=|^JqNy(KhSCBF^t_1Rhbt
zh0s+!${Llaiu?yvIIE|2At_0<PJtN?-7BYBV+@(Lg2%)&7=gC)aP!bM`V4oZ!ojkE
z!h!`SlpoR;JGLOf;HVrBQ-Xm>L<)ON1MU{Ad8qg6tflMJ8=D6{myrY|av|cQKTQ6e
zei<w(<<u*@#BW&BCusvniKK62YlZ@-(8o;iDF|45hO`4f{j<pwsa|5?=B1SRz?gDK
zl{V?1AX$p?L6s;^h^6Bi;oZ$74dRIU5D6REI_<u4p%rkWst5_^r%qepg3x0pg7}Zd
z>-i-Q3<dJRj`H$Q9$!&F6(4Y><=(2PsQapA9!gPq7*(WL9eOR+<{wpFQOC4q9x@Ct
z#{s_#a5kZ;%n?4tvfr9vd==4M)(0lSypJg7U^h#$>S(M$=&p~ZGaG0E7v`=Lu!<|g
z=cup+g0$a>Nwm7yE3@1=qeJ2+2v}Fq1NtMVE8nw{1~%?n`2`n*_gA|sN>=hUEcW_f
z>0yY3J-xhC>GI18Cx%2A(6x}#S5s0==Wk8H$tYE2+NVG%n^(@fFl`OsSiYiMr;~T)
zVAZD_urw0*^DB+db`Fsr7~duzq-8=d8LzS=lfC8~Lz17_Ye?b_r`SXaK1b}?Lah$y
zbiBe6IlAqoFkUY+o;yz*Va!5<6ZYV2Tvf#kf!I8jz;;K=Y`y^%5k;1Gk0ENyd7#(Z
zfc3Q@)C<^0{{SQYP)$kZ4mh7PEc`Afv-2)rO1!o9LC-R@w)yuLYEBg2-e6_(6cW>g
z17)-jag6;?aeVbBgh4A9a62SeD4><BY1eY628&7;Uak?78c+ejDyW3zWokq(iS5Um
ziXtYbgj@l4khtq+x+NCmZB$i^8e01;_r$!e942fTO3&U?o@H{zovrBWftx^V4&Ecc
z{YoNr_!MK2&I5UtBri#wqB?8kloM#o>T&L6f3f<57Zi5JV<WB_K8VJkny1`BR$8HZ
zxbNI|cKMY!3smH-z~Kr?IJzMbtB)ZM^qiw&RMG${sc5%@Oi=a^jT=&@6HnJ?_AJwI
z<W`Klu}nO+T=f9ERv;2Y7ueDeYYNz7=#AppmP1SnC4wF%h(8w*^jBI6fxrgY!m^<x
zd==3bIin(>9?VjjkpP^OB9h{ZK;3&Rh|arC3n0|Ln%&<}BU-rfztfLN82d005muXp
zeuPUFq$YY?U<>JZ8)<!_Is-wil})nHo-QZ?(W(CcwP$@$q^=u{6%K;aQ9YqX;u^__
z!2%=f;#ih86k@6RNWc`~*k~iM;0!oX2+H&3+CSK=-liExxMJIMpa*$&&P8gN?*Yv)
zoG0S8&mou2hO73#YTCa0o4n5-SQwyB1htW&IW~GWnlpf!t*??)N~4tkw}Y&+6engt
zdQ^A^TV?EmIE1DL4V=z@OZP1V&JNfF&UL-|m<sHM)WY9sDdUNPR5qkTAfC$YRn&vN
zEcC^LU`o9v)NHHGX+9WF2!W_r%1)?5n?);>?{Z?rTX=_Q%K>cy>L=GsR4e65JfZJ0
zzs)hb4@W{V(tm<rRa%ltOU_xL_mFODqtYU#u=V0~aRI*+kgsyD1M@z4jaSTdaJZ#5
z)Dwv=uM&aHa}^Gx(Zvn-3+;+$3pCOa_E5>@Kx1(E_p;lwLZSx!gTr@_*Ry{V(u~)K
zlk&9{OZU?RuCq}m$AYA?sfmcu#*&yN({}>P0cKwMiM{uaxUF=e>CPU)PyqyMh+SNY
zCR_Ol54)YP+Y<!4EQVJ#irgd`8>;KZL6{uHdgrD+3z$`qyGAYmnCPmw#VxzxIE7R=
z*do7*AMB?mD_3ixp}Un|mLO@Ovit{$S~Od}V`)iw0{JvW90Y%CYMWKc!fwG{J*&;Y
z_ZcGb<=%{nt17>8un^VvFMX&j4i1;enURb*e;bJp<kgX|HJC%nBPf+lEEGdQSX;s$
zvZ~eMk}0(5d<<Vqx+Ms~ahA>ziswJ6C+jQnhm1Luxip=bY&S2wLJ?ojFrnxrB08$j
z!154%+;}Eb(AD`D8~6mQs*ek`beGa_8*$O69C#2Q^>WvD2td%!LZjfN(x*pv74*Yi
z4kHTHyXFAY4z!jOC6SA7n-+(VYq?JfO;gL}b94YB;iE}LYT)*c(6#p3<14Q_=H+e!
zm#jI2i+Eu~8WgvAQ<x|m&XTI*VPMdV<?QbDDRYAm9YqQ96h*yIe#TU56rGH9Icfkt
zX_RuIjx=0QLeXkz{{WDt?Zz}!O7LaVJ>;lc-q6WMMwk2>G1A3zuR#+gziDC79m4ho
z{H66=!tv%NJNvBZ8>^}Ad0fa@9$iy*H8GX57t{<nbLZX9JCb)^mGUBEWoRJf*hHRF
zcH6#VlEn9cEMPCeQKr4z9r*}nk@#m`Avp)9)K_%#+6ZOvyfO1oMzkxr<7VJlfV$_u
zsCOYJR~uA1`fP{d#VQJ-dVoD!uCVd_oCBIfGnd)OYzHRKxmzvRQT{UV_dFuog~uSr
zm0|hKqJwbYIxeDD$63r8c%iiFrS0=<Q3GY@)*cyV$3XD}j5n)gJE`wjqn1^Urdpye
zwxfcy!t{0$(O4qm1XAy$SjrV|v*IX!jc`&V1P1>A(PiEuJO>i-4bdq9ewfuA&h0))
zXs`S-imnN8PXyelj{{YM`b9t?yw_SoR<ChxBWKsPY;O-V32md#a=xfrD8O~p?~>w`
zvzdoS1h@E9E~#jFWA>$$;RcAJs~0Q)Uem8c`8>Fc6>a<;E7T5^qzl6Beo0znqwf*9
zayflokI2dpw72cDMbIieB>w;jnQD>kw!b6e7o?JGsvuH&vcICHNb?-7ldldA_Nw?*
zaNiN7o;!)pkw%eQwQkQY64K+~dijILYhG^*Z3hHMvR)TYmT^(I?X?kL^Dm-t`vm~x
z4l{7$j5jE^E(5(*v*WxSa9w=COa}X%?|4@exLT|@2wPj$Q@el_1qXRcjkqockf<CJ
zE%={u#$G}Cn769(+XA0vu9n1`Z(XLOtE{;(^s+V39f?jA7}#~?qe_4yqS6J0K>$u`
zCC<1eCu*mqI+##+!wX7lAD8mNv0ICUBeCkBYsD6Ib2DanJI5_7C|E~;1H6}6pjP&b
zyx6>Or`YO!$^j?;00E3a)l-wF*)7fuB8v?R<%TLXDD^WrfK7leuesEno<*FE@3izA
z2A8U#R98wtDOHw3u>cGcKa~R-=p|K!Bi8{b0@p0M=iCk_1-Lkh>b(wB4R8@^3zTPc
z5o|;FmqScw*E?>8g73IbT|Ov>Ap|E>@f<%R=rV3D$h`BE88!*YgZq&UnniqM!nfW>
zTsd%v;*6dvhd@?O6jJsqO$tWzPURVV<WSge4OD9Fi_5Hd!wTE+!~?kMbA7Xsa=cMm
zy*Pss_G(`{>KC(RGv-P{-icXzWXM4;R)`TtZt5N=pc$NZFGkn<Q42M`d6#I|m1}oW
zgy)@=lwJ1L=RPn%9rm0Lj0;zHHI7_p`MxKdpAAZpNJ|_HTONY2twey%ws7}ac!2k5
ze8*mo7l36`gPP(#Bn`=KfZBys?4W4T!Wa*-<rW`xRu~o@;+=JJuA<eY<D3^e<zLa3
zm5zg!m(_GNbn%kZA;3nj5o2u95o%(C6E;W(GJvFXBHDT0yH#f%pEq@95EdZuuZX}*
zP1N(kq;M3~)eoA70gUf1+cJx)G+FK9H8-s?dK?88A&K#LSu#gR2vxA`)t%)znap5`
z&@qEw_^v5wNabJ$-9>{`p>A_=0cdC3UopvRs^8357sRIs-`e1_ZruyiF3DKxoe`fi
zvcdAlTBclFT_zsN$|^UD=Is$&lF=~uAm_q`I`L$0_Glv2k!ni|V|c3{5q2QtHmVw}
zYLyHbaANU)_8@$9HjnhjotdSmuBQ`Uu~2zQXAbltoki-8-QaFC2q$_e@=kh<3a<M4
zsG+SYpgGLfb~<1khxGYwR3s&OGy{xaVT>;J0<mK!+szbVG4z1cZe_x%#5@5s^gC_Y
zLeN=e0ZX(PO>4L9MU{#QtC60$ixRU|r?jTc>%p!q_HW@|;LN=96xQG@qVvRjYoK2v
z>TM_B4VEgLK!33mwjZ9zJy`sl7_aID@LaElVu@mlcH0d7`w3L5sLLNd5kRuN`GuIY
z@PgBLc_ONZ&}kb_OkP5NASqE~1zWUEJc-8PB)S_^&8Tso@xxexh!l52mNgF}_Y|VH
zDTshxUFz=@D*3qkMkurzQR**(J?S*w{5Y4C%LUh&UOoZeFtkom#Hqn>izOxvD3kzI
zr8`Gtba@(K#6z^A{Z#O3h8yLWc-<_|f@kx?*!=k=LB*<F5ClRjA>hy>fZi?{<#T30
z8Z7lJRTW%IZqqN=L(L_TA>#5Z6*FSzqY+Dd)ray@=;7HcDLtnt^Ba~Fc}%$S0f7j;
z2$++xH5B}aV3!_=4kV(3U2wcbgZZL#kBGiMVqKL?Yih_5%C+rUONNR7pys$n-O0Jm
z1oIs2d;*!{x*igNnxWb9-=T`~;jY5eb{7EwmUdXyx)ZW<uLJa`+;q3jWgcO7vh)O5
z*#JgdTvH|5hfx%|A)bvCywI{y8;W%HVxm+ip(W=*5(^+bjf}#;fp$3gv$j>viyw$k
zLIc^90klfzfwcq55O`Qxy+o%hJKB$L4M9W~QP2z3g+fA(R$dZ_VIzj$X7I-})ow_R
zUL`=ZD(`|?#$Goi2nQ)!4<JOXXdA23?k038abH64LcUa*_;fcVm`E1kuDqaSUa$ik
zX|o@EDi@R}hISi_HxCtYFH41KejpLE?FcTvP{wQ$qV4f?%22xQH8iDTor@brRXu!9
zI~wquKnFWjp=pW+!Atv(ipLz)f2_ZXcrMa9rD**!t~`YC(e}ipqRQr;BltqUvZmf=
z7p5AnEe~#{F*_2wSbffV&TGW`@eNQ2>)OJq5p|gZ#6BrmT~c%GaE8gQv$_WDU(_dY
zro;V%WbY5GaRpAGy+aYeJ6i?uHq%3yZ+IUqMhL;a4@5X_>dsL9Fhc(7IuUHErL@AL
zF+c86x9cwCsAgQ1C@U^D%x!zMe<u2J$GY6!AwQqqq^>RB`!FY)^IS|TPp+b6pMIjF
z#AWt4@ikksJkPU3=1wAS{hr<`Imr;aFQQ5pI#TqIKoCa9&m^23L3SC}bsygwS52w9
zj!R<8y!1%k`nR=@%nIf_9)FBS^nGg)11g}YdAbtdb8v1%;HnTJRmPP`*5oqEy`e6z
zk}it2>1&ZK!f>bc5>5g(w2|j?6OfBm`R8gh5foBt_A(w74!DEo)KRwAuYqQ`DdG7T
zWnI~RIEv|2?6SluuD+GEhYkcDTfOKaum!4|9Rxeeq&|w43?by8Sd)AucpTUOr<mhv
zVRXh8(L#zG@&k(qw{tjD78UdiK9ZTqj>I&v5W4D5T5jb_G!a78kSVNJ{2pLr5kRIY
zz%9~?q99aKf%rynN-fdofp_rmAP7lf1TGA-#h?j9HdnRhn}(IFX-$j&01~Vbx*aAA
zW`dF8$NvD5sd%t$ea+#E1TWgNSjv<`V&lYcO%C5s&@eJ0xDn<aj}TFTe^+;wfQqmT
z+YT2;WaKu3#)iRfZ|qyzjk>xok+>;v7Lrjgi@=BiUd-q)S#x_(L<7x|qU|E~6@C}{
zQi&aI6uDG!GtFJBt4s8yNZ9I<&g#C$!V|qD<-{gaRNnEzHdD_f_$o7!c>@i2i0B2&
z;fkZfjS2_!i3snQIcX{m#@=#$#9hmI@<0qW=tBCi+1j<-<f^A6?KZcVmn`yt<*gne
zE!AMSS^2MswO|f&ZTB!Prwev{A^@}>h-_7P<qKt0ym$wO0ufXJTk$-_FgVC{j~B92
z1$Z=ytoH(z*fd0MbA_dG3pZZPv8y#4Jwh&ky>;;M5xLcC338|i;h8AiX2acV*>F!p
z9jeHeeo^wN9E`ujqhD+Rk<1B!lC%ZvzM8A^pX@!TW0Lpm(J#jB`XmPfl67i6NU!u3
z{O(%725gO12pTMO3RUsjEOADHvvxE2Ud>koy@vk)+zo9ZRq`zW=|+1GFt(rtZ6}|Z
zf12X7tLMVaQB_uUiJZg(PfM<&-(lE{0yiC6CLqs3&gp80gq-YHeH7Y(wWHymF@q<H
zD&BDsq%ccr&w5e?YOBPkAw!&V5|lM_K@7g-P<ili_Y#5BloH`p9*_P3oN{Pe_=FI4
zEM<>l@#a%>9>(;+ufUVe;Fj>&UD#+KgENHvvR*8UXn=jBglK4w$Ow&iW`tCaXFWrO
zkvg|(a)rMMRvtoAl^jc=Rqq<G2!Dd=Qh&C~Wu5e!#ra*@hljo$8FC>>I8m@Di`WM2
zC<0sqbz=FYOJKuXf%Q3A0dQ*4UMrX-6*VugBiINF-b$8av7zJIY&5jnQ1IZ$cU_zL
z?l8AaaJr(~N>7+=cN$%IwJ5OP;dEH4@Kca|kRY-M7WPD1gB+pVGX6!<X+xUE0<v7K
z7i|ZdG=^^4bpHU>2Ju4crjMcqS4Xc{SL7*cs3M+8F=?uIJ<RQD1HMP{KCL`S7}K92
zfe*N%<;ZE>Gc?LPE1HA?NIpy%IAiBC%mdF<RJDsa^VJ1qZ0D2A7wJHY<d3dvd*O_A
zk8I!aB^R45(eJXMF3saGVnu6VYnP5l9W~9a$O5Ijrl93r#2pEa-fA_)P0!0J8jGc(
zQt&nzrDfh}W!F}29gYsrD|>R<2un(Dg~j>_ti6D}ZlTj;0BCu)43xV<OQo$aJ1GUU
zD+ZtBWx!f0_M-~$AZR8li-l2TBREVg<H(rogViDJ7h$elY(sDyn8@;g9RiFmxYUx*
znu~cA;r>9HNmCCNOkUa*m))TTomwfTJ_c;Gpuq4rX0WI`3(=aX8}N}#mK?L6Aw*Lp
zS{FmY<;J_UalB3dZ+j_PTw``)wC)3H8YPY%xC3Z2Yh!D-s89!pU2zHtQkV8`2f{{f
zHMD+cGv35QqTL2V3gp=B1A(sg%23unJ~)fyLhnF@krakSyE`a_VWGjPK-thZcvq(8
z%mwG_yM&sL0;AI}m1?d)mCJkqZ_8676vf^?Vb%)T*;B-~LZuFDm1=j3HZgDs-W)x?
zCd(_;;_Me;IuInmRJx%~c#GL(&hip~dbL63J@^Xc!3kJ){0s?BI~|8DPg2hWP&QvB
zEb<Povb!gb2E`t*FruKh@5vvFkSK2y>KeIrFFSBBHMk~J<UuLbRX2{xOfU>qJ*@IT
z8uIjbJB}iaDM3$DE{Ncbrq=LEQgXX>79(68o>!OyK)H_%%7uP4N;)qR0r3Ts=>zg9
zZ_Com&6q*=Vn>!(Dn2O_SX8JN<|ed3^Q#l4T@Q+=If9&grI1<8XWmN`5neE>{{SYS
z6sc&7xoHDcZMSo|baB3ZqEeS-c#JCZ9oEYC7A9Yr^5g67JHn2zYpx}LEj2t!=CfoW
z92Ov{DP*}fA<Mu>`38;?ckdYymy;N9WL-%2umlrGqu;qyaBjAPaB^*j&3RzeC7qzK
zcTBDwuu`(8%f*#Za5W^A!s;ht!yY5B`<86O>8B9j13U|aO$4jZgSpceQSF?VE^{0I
z0N@=nWv+gb>h?>gWugsjkjD1cmJU^52F7J)5nJvtS?YZ+gi}@Tst4LPj_b3iW<~Uf
z>X$$fDWdfph5cc~B02v6B@J?^uHyxFOuO3S<#Uht%4dDP!^<@SQ=(mo;w#cYs5DFf
zXQ%@V`!)?FsDkMAEMa&>RZ#QpUg;ajQg``y98#z9QDtu<fuVtMc>244%EF~<u-sJ8
zLWEVrpiMk07z;&ySyQeTb^ic>iptZmK_udU_7PBlx!BT}j6-Wix3^G|T6+*&aX1BN
z*VG7zjl9cO)3d>^_>|&xGnz5t6oWTVczz>}z)B#rC5!ndJIBXS$R@wI%H6o}V{bgb
zT{Ht+6nTYs4m?IDfmqQI>u^i4dcj=qsk8?5u!%;*ca9IFHb6XxnPOAM`sO;Cy<31B
ztT6oviKWVr4I{~3U{?Ya!AS^?+<@lf7zx_1rc>QuQB(B_yNc66;ans#h&4{i@8Tn8
z(~)^lT<BK?Rp5Rzl{Db0rr(;Ijp;Vf*CLrSLgY{0Wi(B=;BBX}XRD1+j53HU%TWTE
z%}8AZ%qzTW9a<c)Zo^TQ*u#{OKPaa|HnIm)9+t~L%uX3lc0Nj*QwHi^J<{3HC^bO=
zPxP2ntHD2^FnvVRxt!e|#B25o6QP~jZc=k^z_L<3y#T1AwP@6F8!UkrvM$z{bb;~o
zM)Dk9Tcmtb71P5hosS!1sv*u}l|`^p`cP;LxqfBANkgyZ!HGy0A>Br%;NN(`e4!@L
zb4r@L!&e8ZeSik6J-LiLl+zvR-F<{xiwA)#&V0|ZA^u7690H$u4Pg+i=R(|AiLv&|
zzKucV!%~2VMRnozE4ChwP^j0|Ru(QFdMeo97w)~zf8?p1UL0h@z!^_l3)cW!h;2_E
zK|Qf>txv3(-iM6}jUp>A9G}=(R|<--Jzpn-+vtb*4KKl{6?EDuL9(i7r`f_jYagfw
z4oX<1QLSx5n7sRdrq%o15Yz}Ckp>C5a=Nn55`)s=;828=qS?$z-vdizA%iL9f}E4Q
zgMyV}arY~BT`7x}XQfkum{uKp**Zn=+Rd(^RmaT3am>{nz*Nq_*W5mz@odIhbZs7X
zD`LbI)Z1?|KZ45}9>&jCQDh3b`5gA;c`GH+XqKK6Nm2?db^Br`I`$_14p=?~7B&L{
z^q`8hIBkQ7L;|t3ETQ`z;8?q85c9u494l0mkt~gFya5qtVK^HaxB6$s!jR2tj-GtO
zvv7?nhiMsdy?g!EOQG)(&u6kSiu5ns5v)C0@IHB!AjZ;!st435yZCxx?u<=*+_?)3
zS6*XTZTpw<+9<vW<+2<Eiax%S=GqdV>@*;{jCkBn2i#+&TY2q{XGCi}IQfY!>&B0w
zG=X7{rJ%CN0|BM(m5oOF`?OM$4N|Na@MDPwYWFnx2^CKCVjxC4jTpArK(W}CE$QV^
zxHBQ*Y3&f5UEoZYTJME|T7fwIHa7f7!3Juit}vD!-b%n0%VDMl&lnFlTarn!a@9<I
zrJ~qYDOyWB&sGp>_=`M!LzwhIN2Y0|*LP<%H&Lv(xSl1Z2~aPTP!X3QjvW604kO(?
zH~_$N26^65@_9G?lvUlHyvhaLYp)YmhX@F18cs}7?SqntNwE80fD)}~QUE;OPCHn(
zQ-TE*%38D`Tn4fafL()iwQnczSctAF^Yc=Mq5_>SOSS}Um4Kf{E~UI9f=a4)j~uq5
zlrGa8HRYj#rnk6*wCx0<ybK9oX=XO<oWw5?U|5tME0rbu!~$i7R~N)<%|%4MgE_i&
zUlJ6bQ-<6(iZB(FM?~LenwGdR*9&MS;M@ppne`2BLaQhpzFV-0+^*donPwJdmNs0g
zcJ5k>;rmY!O1*Jz3_C9Ugt3aUjpHc85ttqY{GseDSCF<F5w`glA6YCSI4_~uN)`a>
z>y~FB@I(oGY0Rf^L-`B&s9>SIilh|fe!(pSG;`u}5L3|BJ&1w$6<L7&L)C4s4H(*2
zWuj~YR=IqeVX&+amGDyTW7l<z5utJz6aeum08+P?)Zo%1(Ln@lj%!80Y~@rg-bth@
z<c0_r&>*|hLKF&jH0B+c!3kSVt|Es08>g57<TSQ&Tsz%ytP^BcM*Mdltc_{{dh>13
zzcCR2H{pIujdr^_uTiHgPpI(I=L>};tp1b2>g@ayq;6F=XP~N$BY0=Ni0KEO+G{hj
zD0vkEf$gd-cmCE#K(g0fQU~^~r13i7710Cbh(fQ5=p*784`2ESX4H6EExt{T`K|aG
ziOJpqSoo(V#7KjrZ}Ez+-0D}Sk|8uhMmB^W3c<()F|=b~J(q}4s>!2QerP2iD4aTk
zwUr``8=IBdeW+|>Lg};@dAB<+`(d2H&0DPB_Hf=-)>?|5!!_E{**9Qjokp{!Eb_l0
zfbIAS$571Lwov{SS9%b=Q+Y?40cLP1D2HTa{1PJ#LN@qE67SK4N+&GmZ#0j{1+Qte
z;+O&cE~L~Hd{>gv`!g}DxnEcVgbmr4yLg~R@KZbB)TYUJKFIMKfUPd47i$*U?zT%p
z^ubANm8(&%$HIs>i%*$`$~M{8-H;5jnvMl6GQm3NORJ+uhm;5vq!>|69=kEBvOjAY
z0)lhN1kxZ}`Pqwy)T^#)H+OsLb6ZZMv5&Jvor5Y1%mdBNG>(U=W!Q9A@QqSyXXB}C
z<?+SmbI%Ija<y+JHW&?&s>lztDFn8svRt|+B41|TbijNz<#4#x=r=5ALV0325~1T)
zKyL5Ecn~Ra{%AiUG5}c!msI{hV6-Bu;Aar-k9jG|D?WINmvv#wDBB~$&)lMy71whS
z5J#Xhj|SJJ#cP&bTzi%+fiJSV0kis=8h3Q*8Ad>QMWYR0QH9W0du0O>!df9>scB^G
z3rdN0r#<nq*dL~A`!Ate8Xg8H;Wc-L$o7f}aZUFRTqU(tMRjA{ZI!VSx{b?3vEB_s
zebr7_*#<(dxkJcUm%~hrmn2%tzhoLPg<w9dBWAxAlLTa|$0kxyDPTHCa(2b860dj5
zhsn%x#a-&X>~$14WsMTqg7sodE1JBU^%9b&pnW;>DEBVAH=^bAIU1ADc1<ByS2NUB
zqn6k+&1Vsog#lKE9iW>DjsetoJ{3nxxlXLqAQP~FU<5f+DCut}r13To{dxZ7ZJZW2
zW?hvMDj|H2QUgc@r$+WzUnG_`)4s6^t4b;N81)T?x@;#1aW94`0%-Sg(-#z-U{TTs
zs7fk21>3y%5cTejr{rPlhvcVE@stVYAf84`*mgUw3AGo)xOm)`i9@BgW$G7i2h<C9
z3P#h=E=Nj<tqdwIA~V!eO*szPEK;Ry!1>160PJhF<wIr=OZc-8QRz@FbSW!_8nW-U
zl0DWbr%7y}l@)Y@UNskK`n50Z=48Z3vcyPwG${;8Hz?%QpSjeSs%?GK0gt%<0M!$Z
zrh*o_L;{sOObUV*-8ia6jTvSZBTPeduZBTu03Y`sTB|s)Rpf3(*6<cS)JJRxyxJpd
zws!L#iX?cx;FfD_<1kpN`Bdw~GRm7ZcF_^h2p$Z#`7w~74LA!d%r$BN*VW9>-wCe+
z8<p6rAUtcIJWd><fH`GmoLO8O5P+ZP5aW`5n8CIEM|C=gj1URQTX`*Q7OIf7dX7gJ
z6229HVSTv~!{|wNFwp86V7LWVZbJ&@t>iEuVfOk6Cb%QlmJC9^_9#ox4f9%6W{*JF
zGpfq9>C#u+#J3$LTQyqxhg9u@fe%NrZWT2!b>>Dj({=3O`i7Tx?s7m;KWt1cIPYTH
z2B3G4tXCnkyo2?@l#6|caRM;hfKK_dI>a7}YT{y8vB|^p2ERk;-6hNNo{E^P09#}T
zWr}!)zO2PpF?|*D<|$wv`)at45TH|o@g6EgxUX<PQAHleJXzeAVJeUboDHywzyc{&
zR7%QWYu#OSuoBt6%4RU|l)SQ00oQJ;bh+YYgUe6oWgt)-1vcEK{)rA7GOnwbKzQmu
zurJxcel997Ah~rw(ptM0aQ^^g1_vPFZ{hb2O;$_Vzvs(5hVKi5;$56HL~8<B?1H%w
zRVcNlqESnBS|XhZl~7e4nL%7G$es%c%N{%y8YZDYZ!_#t5ST~Yf$l#;5G6{jRGuji
zxi!A3h7uCOGIJaa+SEuB-<N|J9`-z+?Bv88)y30qea}uAUL@Z*vDAkPi(Uwp8BKmK
zpphv4F!YyLv+x{Wx$qx0X!xQUn@y7N1r|WfIcJLGBHhHVDK9Ga&Z~u$MXhjRh*0VL
zSVQNz=fH)B9SZ&eSm>xlfN_WvhVj`1j&v*Dr(9kVIop<qt`^vQ^&6X7=ODkc<*^`@
zmdD9P#NkyV#2+wU-QfbOyQ6Jy!aUA){-*W-U3Ll)K*#Ph`y=zy@P$o!1>>2GThMX^
z4=u}7y3{wub&1&dmF}hVLW-Mo4d~n!#vLdYD1xSeR;g_~4SD3KSA4pbS9y7bPFDF(
zH~HppGyedqi*@{+-OZXto+m6owDJuitolb+g`Ohtg7fK_fug<^TyqZJ(z05#G+hXF
z>N?b(ecX>%NAE*b$G<%ys0-(=Cmc2IVaekmeP6<GZt!@>qdRC9MoH(d-mfH8aiUZf
z?0%UX9fyD_Q;-(K7ZB9>Pcw_@?&DQQGep0xj%7=@?et7%pA(dz4k)7jE;I?ko6ku3
zqOYUGe#J6ckwgyZR~;v^=-Ak(hmwSCT9r6A(c(}FTP;*kuZXXGp3zBX;3e5_`w|P+
zy_^z-X3=9i9=V#~ucig|O29CXTei9(WKCGs+KmT}xn(9j;vFW4TwPVw{Im=;aCE~w
z(!zMbMatzs?QBG{BP^XbN_b_+ph4!hQwzl46x(AH(dE1D0Gbz8`xtQw`AbAj;BGwu
z2nmkz2!IUtp>=}gIbp$pI%6vK*UTZN!g_qlYoV$y-E&bQxI*B+Qe7O=2yI&d*K3y)
zcG}ds(lB$7g=n-Gx|fmF5lE)m&c=H0$cqgu(iPGg4lF{5Va@KurnOO(*ATC9%UDt`
zFcmtv)kDE<GE3u;aZ@uUm7U65NYc4QR*(ZN#-LskhN6U4Xi<KBY)%Xqk1PkV3h9+E
z8&D^}V~KBdXYCu2QVNkt=~!X>1Q)j0{FMUzW+QyMnsFQyupU~Rg@9??mX72ANP+Mm
z-`T%}X-Al+fBKv<>l=-s>K|~$743P9I@PFf>~V@&9&X<Xgp^gPt#2UJ8~AeaO1KJo
zs_R6hPbJqi4j$+z8OSdp=}jQ=jg8B(YP!FRAzGGP=u|J33Bl%7<+@j*4Nri4uZdC>
z#x%6tYpsGEDv@2Eldum8TYIP!5LD)XZVn|Zc!8*-SUf`=(jLwwnyyyYnUbq;PAWS9
zv(Fi@%F>8uHyY1y{I18%^{{EAMXlm_7;3OS5Ftsn^3+Av@f4I{qLZ;;oIt+Ezle!i
ze}^hXrG0XrLxT&{q2}}$rm(H|YV)dD0YH8UF|w-}*zR;dp?}15=y<;nTo+sa0ENT1
z;lsIPO<#UYu4%dk;BG(8mBUmIK3Ar1r00+%3=7tdL^Nxyd3NPk0IE5J7E$}qK%7)F
z;rI@vY&5OCH2x7I{50T~`jZ*?6gDsX<j8oOMRg$>dH(>u6+pMh;Ord{=;k9&otC-P
z?^MMfNlMV$A~*}U_hw*H9sm?dR8Ro-9K{B-<fH6Cd3e!of0N>LI=)Mo(vr}#!vca}
zyIWa`r|v&5ZA}2z{>_+Gb62oY62vT(a415kS%0$4rXxdj<AyN=z*u%bTHEreK?E$(
za~aD96?a4Vl*egDAjs5(w0}<{7!c4;T8KO;Osm}fPC%J_`R*T+io!Hd(Dq5>gfBZ{
zR2IRgS&b+^q#aCIWeWtySk8;ww6+c--Y$`(QiERAH+@U~pH&5p&P{+HJcyAc9FT$*
zif8Od8+*f^WDjg%#*#|(YZfxmJMf+oDgaZk_PWAjMN^XL`s5|N8KO##d22biYGN+f
zrOcADaphU47iOJ7$7|})Q-2Ev-8hQGCu4j^JL_hAx=Y&mtL9Q|jul(2TK67cg_6eT
zs=?OTsf+{S*V2`<?Im*-<7@<951wYiS1W<p5=g)LbAcsR(&a`5qm>KY$xaXhoA8Nn
ziRYCe!Dsj_;Rn#DxnXQ`K{e25Bj`_Kcbx^sEG1*ar^x8WPHpJ8Y3&41QYew>i*|kI
z743pRuBNA(j@+s5nmPWk6UBa)5(XLlA_TD%CEU7NGfO(>{aEKi*0-DBlr)8diVGqX
zH}lm&2$@rZDih}t@gatC^1a0PNd*J}(Qm=ZhI(4?D*phe)H!?fk=ArK;MO;L+8((T
zRR?@F<vy$?rJ9+N_tQHff*l@8`I_yot|4tNnktS<7<jd;yyU^*YK8A{!gaNEZ$DA5
z?!0ltjXQsEY&C7T2L@fOs@wAlf?r>Lm_wypQca%mYnD=9j{BRR?r4)tFWf9j64cBx
z;iudb9!W$bu5-<>N#JF=Re6G7rVy0WuC1_C1ZjU0K=@xIJkGo{8x<~>Q1ls|8G(*{
z9mmwj#)Cf0dhkoOhvML_TbQ5HUWQRuttia6o>l5o!U{yX%MV_ImG@&bD;EbOAW>I(
z`5|dLA3dVoq#EQxlP&<Z^6Tx7a|<e|j8ug1k$P>FaZ^v?FAM7Be2Db2wqV#8aKwd5
zsjbI0R8UY-rq;^+THjSwoP~1t{7R;sbsVr~5zW<y@A5y9aKe0H)f`SpcG#)$7$o>#
zs+Rx_im9y^C!j+3Ly5f;P~Swi+f0uV`72)gN9v}j0pUC_X;FF59HZ1b!5O6XeKjh8
z0AOpVkS-xEqWlc!G-;m=jJy-_uhFtTrSB)A$!1*x=hQ-qz;qnL(AH}&bF`HQs6?-l
zIH_Nv@R7TDm-Uf0Jq558wJm@7bqNSi?M%XBmJe;zJQQ3W+zD$NhO;cEkT&X>gAP?6
zc!T_6%L+n)L0T59PfRviBfR&GkC}Bi@s0x5n<<~N&ODF4AWGUTqqr0fpm7@n7k#q!
z5sT2B4m7<8=pmWwSqONBwDL9rbOY7RqU*Tff74^i0JQn@80jLMqXKW2D0&-%P}PUN
zXe}#t<jelmil3kyFTI9xE-?TgDAj@ylF<hN>|h(sUN8NZ%r&1EaH7>N6_n?IxM@C1
z^DRll+eZL<FmJ7!c-!nDdN+Rm03|*nR)Kk!o@vwL2gD7bk`MwDdZ{>)kX<D`wz-uS
zuXG|~3g~q*-})^~h`{q&xM?d>oOjII4pWP%wqWS@LK4EM;1~NYQi915J0UTyN9?E<
zpF;aSCH4bh#q#vZJ{-m1FarF5jpiq7B5}wcH$(nM!OJpRhK~d&9WoTU)9@fi)Q<j#
zP)1zZ@CZhyZ*SM?E5?nn+!x@}XN62a2fdsUD+(}P+bo+~%ND{S!(=+f*;emL)#hl~
zDd=+@`rUx?qCo(Z3#)rb>Ga~~KBG~sDuZ}2Gc;k|By%^vlLgMXplD+(F1m$ln-c`9
z=wmAGBPF_WmQ{1JOu}MH!3s;9oyK>xB}(!$S|qSMQU?%o$-*E#0cmED-2k!*V8nAi
z$ThhyQ_Yh!o&<{S`wSj-rZUS2H9!&8ma<+7tHn#D!zY$O(xum-rG8Adv6E;eza^*{
zD{92_7`UozG`yle<+G(dFT^9X6#FT-fd)R&FvmeifSEBMPur8F6<t|Cki}0-0iodn
z%L3xk8{r0lX~6`|XY_7tfT<GIa!pd!n(Js)aH_42hyxj>Cx!CW?6X;E`Em>8jnR0C
zHybs!+u=BmQS!?xVyILfD@S4u4X{eC%%;OR=sgyT9qIU5r!v!9%3h`#gjho5Q7x2}
zB44p{JmQ{h8A+6z+oL+n5$RIhMGwvZOOoO;hed=gB^BY=a<1?a66YwXmCmxUK~%Ut
zd;}Ngig*BeIL;Skkq0;{v7AKRb868vn!9xb%4k23_kn*5N~pJSf-fE{E|B}k-rDEt
zU*rl3N<Q~JL2bQ=6=pVx7kTI0anQtLv%p+~%F?&&+@vC}_%PHhb6i-D?Q)0(l?=84
zZY}NxJQ<XZ<WHOsxAg%vmvmVM#D&4JSCd6Ym3uiQT>{@vGTK9-W|w?R^Om1vI0aX_
zUJ$$H(`i&ZXK>xmh`K~7x!;L+SIjclWzbwIxr@Fky-S%+dRHyDvMb#kDYDNqW-ken
zk*&)^geEu-itG<$yTe_0V$>3Yy@^d^xbT|zU7U;vxagdKYFgGU?@@l)>&rF-@Zs3C
z(NtKm<rgbmeSF8ymE>PO$xCnBC@^^0Np9x`Jb2B8ni*G-2M-iiXLT_HzN|s7%-#$&
zPnm*TUtP=r7+5hbwxIradc&nEr2?JVK%SRujk#mD@3>Y+L#77?-b&-BG47(-Z+uY7
zy7_+uNfe8DKmNl7L_M9c0hHK9=zvQCEPS&g@x;lPX*j=8L&G9LD&E(qluK<pRvA_S
zU(~Lk4%B~AT2oxDXa$GrkPg^pc?b_H%gL8H8l`!-Q*3Q^PZs6{=K-pjP~7NR2A|cH
zTpXj@Vp+hUwdCg9>@h|u;Uh^5Y*DttvTp4?t_Ft)R>%JU_JJ57A~32G*&A?p!9y5^
zwKu0)e5@TUMDMVT2^BmZG9sCao4!^gqQ?cZna-VwOt))TDYm+MNR0$XMiJrQVdN1+
zRP0t1)CUt0f!$mUDWsP%s5w%U<9WWuCzg!y%E@=Y72Y^j_$FwAd@l?|*>tj%W4_3x
zP|mNUI}#aywOSPjX>PQ96Bzr2)e_uyH$@L2F8#F|x;T=JQrNbF#^n@>@O?uuRykLw
zYC<np<&_V43C))y905fN$O%u8-%^Ztj+t+VU0N1SC}gF*CB2<rsZdk~7S=}r5SNw(
z%Lw^Yt>ISkdB$jHcVyD{8B=>KzjFMDiRHO}&5Cm0F(?!*uKXyCn-q9qMON}^4?r5F
zr0-#uykkr7!)#dmt(Jh6H)F&d&i-ZF2dBC<$(H{BDe@euhep9&#pjzLx1*)!FwibQ
zu2wJ=cYnyk1*^5fo3Ui3E_GQA;AD5_8K`U@v2<VC?Q&Kk{{S5^Vlq+p*O|IobL=ib
z^8Vd_)DR0T{U8#n3F@|3YF6{yWi^+>$LJzfM^@?n!Xj`D`~Lv%8mysRx3IXm8;fZN
zJYjg^_M-`I`DF?a9JY9!+w3LE6ggH9!Jcm0aD2HEfN}%ikLrZOf{APkYzF@Te9M<B
z)w?dqQ__(53jY8|lYk0-_x3y^c(Aj~vKB)?irzzLE!L685vubM=Yl|0{D_JG+6!pb
zwgP7_N^46jq!fu?X#?mZrs-Lnk!-G^zFZN4p0tX*ksv$-rvBq=Uu(0t1nt1$>6``i
zUeB0V#p3-DS`~R+xx|-dS>f_9sFe}4sxc1`RVo`w^C;j1Px_>8uWA-*?xQ>pU079(
zQ4sfNJdR4oBi4t<O_GnHnTYV1PY2b=U!d$`4Sh^`!q}*oU6msXTy{L22wndG$R1pS
zH$l5vx$rW}rYbLKa=dvm>>jA2F*ot8?)MuGgNtQCHI>a=NhMnPOSK(9TmB%MWdggr
z$Z#kTSK$aRDmx(~A7a>bwnI%)V{K@sussosO0!rwGOU&asdOoDUFCR$U0JI#4fP7C
z)ZGIGaNly3A9F0zcQQ0lkKj%bv-^v+OjYeT-)^HzL;w=sR_;fpsf=s#Ag7DsF)*Qe
zc7--vE7f&600JV~1)f21VqJH9(DzZ=$`pCfbs6vnpK&<N`lS*Fvr8PYX<vr}MhGDC
zD?H}GF(Q9N1;B575ZZ>%vzSQ(k>b@ZRoxivKy8)cIxx=qlxv1k_E8iMvjs{twk0n<
z;4hGVbw;--W@W?8aVY)X?Mhq-nC}_!`oHo!E~GkK!R`%#@X7%#sQcnm?3J8d8r0XM
z?f5-T7N_>0dT_ho*mVIDl$*Z7rC{|2jWXsyXfSW*+baIit(OvG?}g;0uMTMmbg&$N
zDk$wNUx1g$;{7wj{hfAmSy6uow7On?EKX!2^^g>l0QUUjrcqA?einJ+E~t=rZSj`~
z%q?&QRuzfSIIvgC3dVJ%zDZWJgdR(6h1_XjN_&Xj)CQ74ck7rv`kA6Wqg4><-@GR*
z!z&u_!|`L&IV_w9R%@dLYjX<GM#y*w8{ZX#dJ44_<KSq(k61`rz~-=sJ_DD@a<FcN
zy>n*Vw~AP)A8^u?TQUvpnPqq`j@YgaDK95jY_AFedI(a+5Gw_iV+a-=3fDv0Mc~9P
zkA!L!2vAoRaS)c$5>8d}q9S5ZU%qEESnqnMMfzK6&!fQ>B>*``ik6wWCBEOG+^1=d
zl80=@CGq<m&Po%6%6&#{4tp2PO=7Kn167M~t8iGs*rikw(idgPalZ`voiS(hNo$uz
z!JsXqMir%vZ9H!>`)J8%N3s!e!CD=nbi@`s1nLe9BpL`^L#FXRTua0M0JrfngPe|;
zN^U~3?dBm14z4=)9|#kVnPWZh>Tv+#jyGjkXAm>V89S8~b`{?~$=$L*VZlF0&jz1}
zX<1nDdp6~#at@*>w1;Ri(^Z%%y+puR>ZvwAH&Uz-(*Wg`qq(+2#mh^&KsKe^Y&P1@
zFPLu>+RH~+fDLry@FMK<Ug1QG+uB`%7(}$SrnUo78l|ObWvkzHlS#D71rIW<1W*{&
z48pf8K8VOaeM4wlEcXFzuRtX<2CjmmpaC}F8C%%IZ;ZFYJRfnZ$Qs>iU>l`>Fv0-K
z=t$dN(w~wKtHw!i71HtQVVL5hiku?2WRf>PIdCer)G;7Ct2f&UQj$vB1iUJN>}(nG
z_&oiYM;%9OQk8PL;sf0p&2fVPN6~xr6>himWNxLKuYQ6c`h;jp$c;G8+YdlZK@{W#
zz@l5fTLxUgR3cWs;*|o~@IaSWb-bvHTHI0Dzrr1ej5hfCSfuh#!6wiyeo&0Ur`1zl
zIE)T|7$5RKC^yYKLL}MYLUv-0PD|{Ij|rxn*(u|B!hvNlvMk_wB_Fd*{_`__qSwqV
zZ50g@WLoP;*K_FzQXc?gIp|%!)kC4HE$V%W(k9AOxt(0=70Sc_$$us~BXvuX0Ewk-
z?hq^4+$t;4YW+q*ASiKK1a?M%cX7rxRq5(WQuSA9_qQ(c&kerF0KSF}LjAVT?T!$$
z&xf*Aqp)a)Vlq7&!`Ui8qml<1#gpTOd=M34J{T7IE?wYP0HdsV?dhe<hVJXRD7y3)
zM0&BH>IVFbC*c>1^0JPOi{e-UHIWrSP!I=q;jLIsG`<wgK?ReQ?GzL4Cb$Omt6mA>
zD3^&iJ_z%%;cFZGoWKEz4xrq>UgJkiK%4_&5G5CCKHCi2;X*HPttmD4)>wyJkUgie
z6SikNn{iUxB)h_icArQ>$RLSfOHX|eQ#^F@;qx@eI*!`18=+tshL2=+ZCSt830~g%
zH0cMcE9yqkp^M90`<uFc`ZWaFqf4U8hS!6Z>Xg8i3>I8fs$JOQJff5o;Q|}TfO2tP
zSN{OWD%x4u&oh%Sy!RVdjpn;9sg<Z@cu15eXBSR<N-lL1n^KIuK|#GL3jLIVsr8O^
z%YQ6+j_I>(mBk2tcw}~~oOJ|17zeNX#^0B@_OO8W!|obMP&NBdjpB!aOJbW`c;7<e
z2>=_k@UhmRaPqST_7AvhPfWA#n_*gkci0>y1Mq-41@NO|b1FXpH>}IcyjpN7E^2ur
znrDVKbU6*biA_bBer+GHr;1r+Z--SXB9Sy}?lBQX4tb%iQ{T8N%)VeA4#F2|4glrL
zB1#G!t8~+t29@YLAir%9FI3mW!C>R=Xo%8oa}^ZOSLfJ8NTvZZi!z5zG?6rB1?tW*
zNQ=XxXdV&urYAKJE8LaI!yQACQs1u<>@?MiZdhnmuZU})-<q_rMpwSS@n@KJm^ar#
z4T}|5hV>4f&eJZgx|awH<4)$!m~Y6YUJ2<(r6?-e&^dFRjNN=L)AX5%@O?fZsM0P7
zNUI{s<>?RRB@|1S8or}NsHCVlRi#$!n!PsLFLK7<5$2d=7C4c(<-BA^G5v<645v$k
z<*Xp&JSSh}QL34h%G{MjVu)11%zhM87K*Qr3ej*lEWainBR3`9eUR5ZH;~5x@<3ux
z<G{TV-Mgg>0ab^RFTHaB5duQ^N23a2mYoCUgj3NaOSRg2AnFg%D30r}i8dXz<{S|~
z)!>bq1w>bpj3MZkk%bYe&2t*qLxQ&`VFh)zfX^?e7QHSxO0~$6lEeyAe$Fasx`jC{
zkit-F-3_RBRgFCAgtznf!#O8Tvs?Q`;|a3#EhZ|13B?f99LjbKwen3wviR%jX>0=f
zmml^JE*GlpM4DGVT0Tr-?WX|oIfx><r?}#6UQj7Nvd*%?^5c&rwy}L;6Pi=LCQ1{P
z1EM7=d9qRt5qxxKpB8jBAIqJTSMYF)L1{?xS)O3vA4UF$sATZ40Ou|d{8#&mJh|te
z5JH2W5v$bG<XapC-LNR)vWSkg#crXMOlVtwBUTiOeit7W<yPT@0u4d2395FF{xHXt
zc5{dHV$h;PM9=)`2vMsZm{ih$cSPe5*B~c?)d39%(SfcIM{X(&cUaD6$I%O8@>^Tf
zXPe5YON6z_3j^B^f^axr1Tms~F#rX-UOBIulzFg+*+l9OA0iW~>I6GL^i?C4=6tMI
z0QV5`Bikw{S5Fxddz1eFQHswZ&Me?&y`>lUxGQ<2MOKEfG(-U}_KdBq1#!R-fBpSu
z4u87XDTOFLHkw^0e|H~Ho$4u}%b>Q@_9w1p<k&LB!<=~i!Z@sqqLm`m;mu#4F`_)N
z1;@%;5GIZXgyVfifTWRC0f+uCX>@W;=oWO8dAXWAOD~Q9k3ew0WCj#2MfiFnv(OjQ
zD;Ba^W!4VkX<*N)aLlM^6y>`Sx#tS(7bDV>LM!;Y_X{0X_?r#|i$S>9Dx|a=Wy@sF
zWyMr4lVdmQ&2t67Mk01HBTHJ(5cwf_g^l~m(m0ORntvt!XG$=(+kC-gd@`7nI-405
zyoOvq3u_G(x{Q`I-zF51t4Lxg742G5(TI}7dllF-&^{I{!$g8%RvoaZux_RzI;(A5
z`czu)ixJ_VZ33WuD-s((S6@&(&o&g93_!l!K^x9zdcbSi!%YAJKUqWqg4hJB3&blH
z&@pkA6jk9P4!7(j#quf4nt;K#iQ1$D?Cx$~L)zw0+Mh6sc(ry?RG=d*j$CHX9ja6t
z^v#_6Tg+MqKr68+l)|t09$Td>UFG6CY8wLo0Kz}@hw8Vugz-ZATwf(D{SWqN@tuvz
z8mD|PP^(K(sX?W48BxIAb0Jj<6A|uPGGz@HMe7FQG-`-G%9RDJE^1;hm%go-ZBmv~
zpNBEZuqh9OXl^s-{{ZtskURO5k*R83VQ^5;vW~V^6ctnjWF3N$Ifsv-gq0IIs&Gqy
z16#@`5RDIC>lBN(UPlB<*s4R*?rx~eHEFm+>N#7)miVP-0!(<j{{UyqbnBhGcMXCz
zLU%caLsaW8@ZPk(11NAd_*&4-&%!XBX5^?3-BLJ$=V!&ncF=sB{{Z1tZo45|PZLL)
z$g6))wT@GwcpeyXeL}_FOA2MiV}ZM!^o%<Y-KD4#i5*R+teE8YWI~a{z#d@a)dscG
z!!|uuH<Wb0USkUd0}A7^6<@7i^pWZAIk&uPvEAntD+C1#2zVu7bT9VfE?12S6btGU
z9&-NXoYuz+2f>1BdkH7Vp<<sU%CoteqB+V?>(eee^43R43aghzyC{qG3skbD60=`n
z>Z3~|+KQA#)u6hhstX?RXJFFH;YqWSo-<<i3XMpGH?$J3Qo1_^!lzZ+w6}&D_@KUl
z2#1<CG)wW4np0oS<@{Nr049TauH!bXQlCwVfTkFFb&yAFR05PaVC}1lYT!MIY!8-S
zHDt7K(9W+<J_0nF<`!$i>HulTIss7&J14W;E&|X>bQmgZE9}8_R6{rLBWa6kSke!{
zs9_q6Ch3BE2!UGA0BXZ%gvjC^f>TcoKQI899KX}aXr$=}vL&n3z8{5;vaTdu7xr-p
zBUfK>OLVw@I=R-~OBQ%wdlVgKd?Bop-K)Q7DYNiWb_r@wR#J61F`R53X@1xMM<vWw
zK<kC29B5{)b*Lr$SS4O|0NA?X_xyl}>2+IU%xJ5J3mP6qU6WT<LLdXNZqjh&L2Tj-
ztEEJwUI@(`Hv(`jLz!`kY86eBV!;Zklf~T0imo-uB?~Az4Gi2ND;yRl3{<(sM8!!0
z5D{^vE(qNlH+<315DW}pXCDbyN1mz`Hg2%<^BB{PPONnT@LO?p9I1fRzmPMIrBdq~
zPT_?w!MeskRVJ&1q;76yA~~SQVg`*}o=;3ihph%AxBFnMW_xuNE<2iMkczJ_f&f>V
zo{N~FuxN8WAGn?Dz5^_{aAy-$o5bYv-ri`&@C;wESGa)p&MgU#`jz>u{9$xz2G%v}
z>biU&gNj`70U=^?QisuE1iMp~+#~HpeXywlv*pq%<<zo?<kzSTH2S=hw)8G95vdUu
z&7`5D_7!^jk5<sW8*@kM0pAD|0Hch6;R4R-lnR?^Z4gfZwW_-KIU6*hCp3*h;%+XZ
zRsgSeItmqSq(iEq7E9gY;L!s52-WGz)CxMb2g#s*8Mk|2_r=d*`-UNaW#4gB2K(}W
zeWby)uIku33=}$O?pfm(xpYNWTZ)u?<hHx#RIU=LG}Bm3T}~l@l%UG;z&T^8?oFKE
z(Wd!8wdteTiFuIT6@iP@PPfehDCuPtw<^3daBP0M)6Gq;s9!HABUa2d=w|yOKBJoD
zEJ6bW7GagMsO4Hbcj$%vQ0W>1YtZi=pukx)W0KCL95acKcv_G8;h(khCEzGKTf1ef
zbf5nKA2C(aI38NGunT}T-cD953D|AWrq(fL-s)Rn1(!&JLZG05oy#QppkXgTSb!-s
z(gT^(L0;3#H;u!7?g>B2T6gYVn4tL)9Y=zO-b)zF$p@p(PO$UWG|A$w3ut<m6k?;*
z{Ud@lr&MC9mQ{wE;MovGobs?r=u?Z%@fDaFjt!S;K6joYWhbTu=q8HMc^IbDP3Quy
zEy!9>)gqPzSXH)Vbugm+yD5~YDOi0tZ)&MXlmaFa6AuD<Obrs%SMd<kY?y3D47i=@
z<Wl7&7U9u5Au0>7X_Sy9W|XlOyn0sDDf>3*2zP)7p;1B8KLLNUCgq@by1SR!>%F;P
zb#vml<FH+}UBSS1Dcy2>Kp(L0_5of~<JgHolBzdMEb5pBrIzzWNGx*mI)*(K<!C-&
zX+?3Bd8vXQS0}_NO2IoaRd3`JJQJCvS1tI7VP3t$i%fJiI_6kcg3}1J9(-cBe)IRs
z8aAucx2T`kKprk%QI~N69vo#|<@MmyQnh8F3Ix{j)GkWY3fQPW!^7}vAhb0S!ua6X
zUj!CWf#v9=yhT=cN3+QnjFjV+FLfcc!QBg~yF^Q$S}9^R$b$TzNH$tIV_dX*EuaGC
zS|Uf1sX*kql9l7>Ei_yvo?6RBa>nqz#|?T$Q*+H1Y}rd93ZF&Hu&}po`N)>z;jL#Z
z?)6AE`&JOc5|M9EE3+gsp7H>L49ui+5xzj?yQz(C*k_urjiM-S)_RcN{Js155fFu2
z@YYJRr30$9OaR|FB8xe_TvJY<9#RCavLNT|4-l#dQ2^y-$6k=(i1cNbsJb4320((_
z7vklzO7;zuN^fO6)eZ=#8hZfQA4tHVs;0!?ON~9V%i;$aR$5(g3ogy{H)KANngu>8
z&1H%KT~L4_0j3$&0eo(>yI=UIB^ufO`i>>@Ol!GS;yBw>1{w`Ju3PM5o?f*r<k6Fb
zNwuM_Y80)6e7y)m^&3m$T{cu|d0xmxy@L&1qA?;?CHQRwB^M_HR1l9AW$+I$)6Urs
z0<^^$St}nkc$F)u0zQH?0GrwBA27slAk;z_^TIOBt&1wg6}orv2VmIgd*J2<8~WkN
zXtG`8=C7NLwm82v2HjqK#<MglS^kKutY&Dk93IRmw-DBfapp952S<n&fy`HYbvWl+
zig0_|mLLxemS3g?G`x72$2tmKLZXb;bd`?wjwUEDostGT16VY{lF$Ijk`98jENSCh
zZ4Qtn2@H5{EEg}7Tz<fnW&It&;t!_@*C#UxdiS&B0#6Qd;HbR$;0?A0FY8q*jm{e-
zS0^g|2%-vRH3fB4Rb{X2qPm}YYnSbU3!{sTu`WfP2ZXc7Qo)P2`jxGLawy6Ps{a59
zVj4GpBBQQ`&uW`+NYlf^ntl>4+v1#kF$c3tz~)p6gvA{*nt&(~j_RDR$|@RF(}Twl
zZdjrwB1fPQr!}5oV4xsbP0JGES^<7pQ&7AIeUjP|h%8S5?q&R$-C5|567U`!uK*Jn
zei~=H%ym<tT~SiXE!qNo7=H4SQj54jK^xxCXcV)Ccp~J*d1&~ia+KX-1={B*P&!JW
z;|o=cnkq#{Y=c2eE~H>g=&_s-sJh3)Ozewm*)9R*9nM{`-EE;PH3AG(5B~t(Dz6+W
z-ie!sf!aivqyGT;rCKku!B(X_;p1DZ0j*eR;p`B=j7GzXHDHGaw)`zc$KGY-w_Rt#
zEFjYipsHA1!aWl6){d}z#$CWsUrmWA4O*hPG*t8F#MY_<Ncv(b)Xzj$EABt0J;i&}
zEU_2>;i%-OYnN$Azv4n8Y8zKpHE`RCY``K#{{U@y&+HD~sZL%({un2K{{T+{v<wyq
z!cbCMu^v~EnrN<DQ7}-g!HU_Dny%_8!+{fjHETE4MCt|&3E0HSATJS@Dg6=XG`PHa
zg+x3;ppmH;OtE8vx=>U@d?VIuZ!p>Il%?3w^OYVbCVJtr1_(_}UB<iJumntdNZ_hx
zg8G8N%6>Uuo_5pR(Tcz~4U~ltL9ZpuMOF0Ofl7FXs4W1i^Tfiry$MIYE}^Loeglz<
zOU0-)@lmP7DDi98iMlEQ<?VqT622zqnPv8jqL*$dWhKf%&1TXSGf?o7hCz|*+~183
znqe&3!JH*^vhMU;UAq?zYG4|R-XGtHBd2QSRnqro5VZ|cc6#q4);V89%BbCF#}$FT
z19Hk>OIqv1H{7756C$JGg2Yn4DBA3z>}|ocp@ec9)$mo2Oe76>ve@EV<J>Oy0(2{2
z{JRNP{xBhQHP=wgBOZ5#pc-H<{QL}0X*?G{QwFQS;PE*i15Jg0C&|?Cbpca#GObJT
zI%w-HZ8XIT+$5K+jK3EOajI}ef>3voHpoN6x<r0RWmhn5kF(v?uXI=>)GI-GGv%8c
zdmmF(fPv)JL1K#6SK(jS;+#mgPearI)S``C$P6LM2&4ktJXA9@(5xPAT!S|mcFn>9
z-hd)iy8+q(H1jV!NWHLtGM{WVQof?_JU(F^{9V!6+Kxd>XkT-+50nRTy<aF`fL6(A
z!E7ULI$x#`!o2~1(l~a0Ud6u(O{xa+a;s%<*VtRj5Mz=Kn?9lXP4DnW4%;Eebgszd
zKsQy-jAf1G0~f$^UWkyicGJqDqJ%me1%bBMmCb}UN6NllU}!C`EyGS}Eq2FH1t6ve
zDh_=UlZi>kZGyKHTK#h`DyWZx>QHvE?cpfe&NkXv=eAqS2km0EE5a^X<Px=_fgGNG
zQzK8YY_`y_60(xrODZ<2D!i@(b+YA!ZL3&u=!J5zgTkItalxuuR-r}Pd`Io6VV2So
z_ITo^CGjpSyRP|MrB;27rIdv7Z5B@|k($Em{{Zb5nE<QbJlrg}6m?(kO#$YJxS(Nb
z5Y(z5vFPK7UP1&LJYB^Z@LoqrgsUzv1&^w8L^<bqh43ALgjV=^OAozZAkl9ON!=6o
z1wKpWCmaX7vUs=Rwlo4lOMinEMYfE8<d@*Fcim@LFG|toC_SGb!|zZIiZ>|$ywHFJ
zn>9wDEir}e{_NB83gkZFQRK7e3ypQuxlzZ(g}<UP;x+q8icRO9Byww`IyNfg5}gq}
z$Pzr42GL%;+$F7nD5(2E!UZrZtAK?}?nAqIE&+S}Y*3iC#F5l(xcGx#@erM_cE?fI
zT<jAzx8-viuqF(n{k;|aVQ7Yv!z$#Zd>G)^1t{s9&~3rUDhj~^Ellg1oQs3{_==xh
zXJg3+{{Y#>kBEi=e{}@n(Fu@>qem96{3e8ehdG4_dhk4g(tZ#TtaJ-cb6XILp-#QS
z1>K~f$C;F1d*i&E98=ox#>+j{#CWIw003{PuRs$l$NRzTu(mC&%o_Tr*bg9;S88l4
z*pRV6AD`-0uxAREJ7pt1@w5lXctpIkkS`ciJOst1D1x^QR>N8r@%t<=if^w8u=WnP
z5Xl1i5o3u1RCubbm?vzMqNv#~kljG~s6OLygP#o9qblgKlE;+OO-QXQary?RKa5yc
zfS{i-6TDeDvQqC<9za;3yH`wn<f;d&26N5TPHE4erGzgnr<0{X@v1CzyOmH-*Hr~y
z#}=SjeU4Zn?YUU%p#x~yb~t>&q1CqNHF?F$U{OO!qZ`A1T9Ip_{_q&J8$PU655DT!
zlpf8M`?{ME@*!QPLdCgeikJ)q*lVbAqJ=rZ0Ho0xv=&)R45HRAaBia`!(ltmHClkh
z%W3r&Vh7vB9v#e;kTnHyHu{G;ChP#T5U@O5RK!r*a5zhbf@rS>^$pn&9QVw+^iRD<
zNxFx{$oU6<i9;4sJ`mGrRXm8NPS^edrFMYnrkKcobYWZEaFjz$;y?oUQl51x`LU#w
z<8SR?mm6vnlvH}S7w|0chKS$`dFfsHhGwxwmqNR8%#RSp=m#cn&It1k4stDz87>uF
zLjb#%UWbE*60Z)|aoN_+8a33QyjmYCQM8OA`#z!u=<-1Ev`>>Eo$R-pxoRAN@W)po
zQS2`7inxh`WLOwQSW6wOZQz%1@mevx@=J@&_iI*5^gz%s=?gBNNVhbwzO45Y{jeZt
zu7T|B^h%Axd`C3-V>+s#&$JG!S#aGKr;g<prCr!wGOar*_N3+(_$%7;==U!<I>}Fk
zu$@awrKp1G_=LSn^hlZOJ;$b+Ti9e^0DvDaGK4Rdv9?5@jY`IpGRX4j;w>WX>qxa3
zu(jERZ>A9d4?2sy$M$6!UGFtB-by+SZHr-Fl-h0{HY)%qcwcO@OsEV_cPZ6*Z$Yb9
z9f~kC_p_CIfM|M#vHsR}&J_{`V8@O3Lg30Cs8_rxNlMZ9V+g<v-8OoR)M}kCu=2(R
zprMN&St5a|P&^NW%k>wX$P!n%2FXXpu$GF@;L8oDcEcADDb{AM)prfVP<@&$l9!U#
zO1*^`U;y9=Hx-f7=_URZ2WVCCa3NCoEtkvnYEhpt<~8LXtq_`#F1Fy+kNRkXafEKH
zL@Ead>Iz_|M)_<8t_55^SSGEDrG<l5Tg5F{9G9%Fq4O=M+S*H%9o<Wh7mmq761(Ec
zUhQR3t~BU@xV`$GP3<bzhn?^QUIuk-`UYSa+P!?iOcWOjG<3mqkH?f3aXS_`VFkFe
zm4JDKV%i6gH8flDt6aK>R+mC2ki#(hsQ~e6r7dYdOcMgXh>p>4%~l25xW#T|b}ROV
z$1QTzeQ<kaU#tcIQAshk&86T%%zydRB7n!&R5FbnHuq!1TO_|d@dEaplbE;NR4<_F
z9<ijRBM4BeIhDp6CkBt-n6~IKhiVR1KVxAn67oD3kt}8}!IxfX;p^g6vwHXh1HnnD
z0{sxTg*cb_GABNxYD0xRi!V6uVK$G9CI0|r#5?@pHu4u{B?;9-bbKR!*e5K$sNuP7
zs-v8-Ve@@O-ye`ozYJgwBQRh1OE%ZFc+7y=@T$3XP7oit>6ZNM1^7kC5~>HXi4V+H
zzRBR;;v)N&qWFl)*<BH82~VxF=Uv5_NI}H`-<*kSR0gf(K>O)^UecwKOiGOP2p)pZ
z_7!_$sxCC;d`=%eBBk^#r%_cu-6LoVA4>}cZud5>SjzXv^ftjhQOBSQD@}B`E8H*f
zqFr)94D22XyMjv*b=%-$R7!w<7sZHgh8#4?C11wkfmvnWE)y&U>>Rm>bfg9=O(oo;
z5K5X1Phew+qHQhwK4mVTMXE3YSHQN|uDz#5tt-k!^c$I0n_h`-?rXIvQ1bw(Ra#z8
zp;%y7RNCgRNV9ff4<bcK7bqgD-!NGzLczY@rV3lE9>w6s^b=JpM3)AFn`qLdu-+|`
z!at9oy6Ra+c{-}ChV}-H0lx+?yyU$E+_Sh>{{Vy~U>k6AJk&hQCO61aDege>2M9-s
zzciJ4X-VW#j!wuDUbAN6<wZ-p46g;mbUx7ZV{&hylrjpll~8sB-7jH^HyT8S7WaMm
z8CGLVX^9&`oD>s;Zb<3$T7s(<d_4?GgWzd0@ACHxNOTD4<wE85-W$~!WH_pzceLI)
zZ_R*!E^)!Krz?3yw%h2E@^i7s4MTG3QJ4I|OvfkNw-v(jb1VRRM%7ZIQ$bPn>jaA_
z1tiL~>Vb7#j1(5vm1#qE!^)b2o6N8Y5Bid9sPgo$67Zsbe9Fb1nwYm%^KmiM2Y?B!
zR5O*H2stek^DJ3w<rX1yMTl1)1PY`zY7rQo9VK{V2AXM|N^R$PffcQ7D?yd$`H5U)
zGO64uWvNr)brDrmdth7l%!#evw5m<&U!!wgyMhk8J38rw;)qegRi#Upd+40Y0xB2U
z`l)tc@=hM|B}I6p3{iID5TvW-y~K_XTe#B<v(L(1R->n56C3Xd=pZ;tR=P9)04v!u
zn`^iibgItz*O;oSR_?)4i0L8KvGCPOfS?w&mpHotVCKxI#2``-3LhaV^<H8Wo~zY(
zAfqP;z>`mXF(N=h?L`O|F1SY(*8$2!NCj<f>5Jv_<{pAAxW+2EdTIfBEujlxkEdtq
zzYv1xYvbl1Dr|?GIS?gUE^zZ!mkOGLgX4)yz?3-}J!CLk#fx(;?jHD}+U}xAoMbWG
zQ8e$GBi;@4(&I9{=q&YdN@brXij^zuOhjR}^a*Sc8NO@C7Tm6iYLq2Waon43-uMr=
zx)+deOAU=J(DKu4ZaP~Xd5lscM6wq+=GDsqYJjc`0xMNtpPG+bsI`j^T8M=?I9Jk3
zNNP1g&^R)Z3?Z8|aC;K#{{YMM3<eIREBz1y8=4pbBd!Ats1!$Xfk03b%H%K$*|R($
z%oZ~ApN;^HGhC(Jvo8?-rJT6)1tbZ35z-A~#FeSF_j0HslGal3e5E>^jomp6@euKJ
zd6-sF;qeFCR!jm?zatss4e2F?PGX|#mb|`T*To^da~uBvMUxS9;kaho$FolZY!~GL
zy+lY}2k!L_k2xrTh^IH(9@OGqc_@$69EO5OhKGhdE6S{J@Upp5QC4J$mi&t6BUyyA
zb+eIjTFA3|E$lw=G&C$hzT>LnOnB}Att`#+0_4iAZnCE%3h72h3#B#kg{&Qx$?YGB
ze(qbOXhL8jc!VH)iadlfu?$=*${SJr5n)KziDCQ)$uA0vL`RxZucL`|rp)BI)Oe3d
zReeWtJ_b8l9|vsG6DKr%07@_`YVbu%?o@yMQ0Q9dg{d@8LvUJ8<<qv{?;;U3N*eAY
z1gIjut4QohP;bUuKqW&!JVPBUBAvwI8IDl_bP#I=Mp+rne`?dvz#e&6!4dv!ZRj5d
z$ofdj3N+NUK^|US=4p0y?kxi3;rWN)In(8ZQ_!HB!q_XTE1;XS{rZYqM))lZW$$Qo
zbKzG#JQ?VeEF3P=(e6^9RiT3V;^wY1)%9<ovMI)v&wVfv@Lfu+;?5$JT}m-y*H{Xn
zcyhv-g=(ngvo@(n#UO<ZHFSggwHMGMa_IbF63a`Q8VmE>m`BQ>#rG%8N_~V^f*xo`
zqA@y5b;IT>2{3fn?#J@>Hg|(r1W$ci9Rzu*U&4?_Q2<hodKOYv*-bq$My5x8TiO&k
zSNKP`2jWY0LY*sUzDZ|^7b4huCBzpyRW=}wP!>gQ1kiP#M-V3$-4@;+;8$sY;l2~d
z$HbMb5rzWWYkprWvRNPzg?(VIFRT}QF_wfVH+o<8Sy<L}>Ifps8s24IH%fKFjU^}~
zZSgzcbW}9+8SL!wyo1+MG>b6e%CyUT)F(#u;jw6ee2-Ehp>a4<P!Eij0Q-jqJ-Mfr
zQ(Uk=i-P(ocT~LkhDRD`Q=&W~Px8yEhY%_+>xp6|*bJt0_b7_kY#Qtc*e5s12Ex-0
z5ZTHlOh&5-%q@SZZsMvwR@`}Gd`t6Xr1FK{!4ztKOboRT0qB+f!==E!u9?^cMdg7F
zD$zHTU#GZX%+1f3;BDk55mon;XNj&cvypPhm~;G*Am|GoAP0eU<)%Z2IfAJB_KiO<
zAqCYYJHy<ol{6ZOLcrOvTKVb)!;YL)OuygKb#kwN`zVVB>CX4upb+*(6mMFPRa8jB
zH!6;LV!yqPukOYI)~Ne7Gq#`FMYt1oWa1z|4>tC`iB?YhWD+%!v~dN2-O%I|=FzOJ
zQAkxg`HmYHDoeHyC9~k%_XOi^F4ZXjs&eVI8;dL5Swjuc9mA#pTxY4S@KH@8508{P
z-JE`Gt5to*^amt0a*A6WWlS@b^dS*y<}g;$ROXkoD11g72mYRBD{)2Agmnz>ixm8X
z2!U#YuPmn_Sm?I6xl}lqPwZu+bu6vCL5z4Ia~MWBc~htiNZrm2U0idSKqiN|d0hgr
zb&}|+<Sp38vB3yKiW%Z%mCa`t2v?gbtqAQ3QvL)8Fl_|rdWE&R;8?UzlGY@yM?@8M
z${{j|{X`)40wSGo>Ixp@5k#*gIVp2UmEBTHM_jVAz(f24<))R(2YAY>D(+T*146GS
zmJ9lDspMRaIj~`;yRdpUcI^r_#q0ydn^A(4@6EzCqk~Kx{{V}QeVto*ym*iQ0B~Xe
zRb#rtRYSe<l8C0^+WbW_LWe+NDV2b-x}nnTxFrWri%GAFOT<(FPjCX&zzf+*YE|%c
zDno4aMna>Dg;L@*lf;d7DlLIps=#_+-X+*A_#zS&3(vf|oT0m?5UEFZ^vSfxx2wZp
zN<jrE&qU#uw6OV#sL5>FhUsNfT*F-rpsLsv_EQ^kf>fr=1b<1$mMT(-RCyEFu44;r
zJeflloam>C){>2iytY-pFyr$o3xNZfXSi}eugfhi*O$LD%M;30l>k_qvexWV;8<np
z!_>EsF|3rOJj`3I-mVNldIw@brsx`1Me=C94JU4PoUN=Qz+nU_EH>qC@i6zyc&P1B
z0tcWGQw4<{?;?o&+#@=s^Lt9rB7*h_e7h&=Aj2wnnF6F11y$@|Nniwhl=B1w?AID9
z)t9guO{6ZVS{2Hpe-T}UJ>NAgE9F1!XpkwkYYi53%R1UrzQsY9rKn!pIXL8#{f}og
z((BMcKf6&#m{D9W1ex$b^YXvq$NvE6i{&n0hd^lCVHJ=}6M0n>@<Od!CR|@PEz@d0
za9nQkG|I*kuSbCTGAKHX^X|M#SO8X-lew1^*-6kbaGbK++q+R?h~(s4I+Vq;rm5jl
z750xSF@bA-BgY^OzFaJ_k8d!5JtX;i5)EHVIaaZUEg$NDHm;=b-Fbw&PMmDM7fWm8
zEG(sYmmX46<q6Y#&C)b}=I=~<a^;q-hRrqh!%vZn*SjxQ#O-6BNXWxyE%=LpAHhRJ
zFnY`mI6OcRhwwrzdOvq9$zI3+QhOtkaepf?#rxAj7ic!6quD8CDGiGyAfs(Jvrknj
zL-T`sB7ld~S_`~3dm;U1pb`On8<n+IIu(^qh;Kc)1+5}zhbK1=4u8Ddv<+<Fh`_T-
zzRP&{mOpEIMF>|=V7=MtEKMQO?yq6(abeYYx#AM`OcPshgvD621>%ecwY-5G0T2!g
zyXt3bR;giy*V3H{fw_wk37djoHk*|&7do|nZf42N&NQ#K4NYb1IOT7srAUf79x%89
zV@u-Ft^8{TA1P#7%Xl05?4q59ufx<+wS3S20DwFudMV!NO-lg0+sdncBrG{q*DWHE
zv_8>oHr_r`O^_lBDw}TAj!z5x*jWhCwHr2<c|<$iR^P;N>J%>+dO*2#E3cu~1b2DK
zDf5m+ybqow9u|KN*^v&;`GLOQtO8codi={=XmFj%afSRMl=0!iGyQL$NIHgxa5Zg~
z{TCdjA0%DL!s}z67R^V3&|)r&#=bcsI<!*OAM)6gM1?5<@mW(1V0-A7*eUcS2rtyQ
z)oKra@?dYfg6I>e-QP01RsIgbT!CTnQ2zkJ$4MSI4#Jp`*D|ZvJNslNOu9$%0UChl
z#xE?=om+F^>Nr>k67|0lxQr!Qx<cclm#LR`vC%<*ynC0&#=X7K!*Ne`sQXlvtTzN`
zc#JpJN+Zm7mEPYr%Y7D>>q=8-xK3peKxk`77n4Z9$^u;E7Myn4g4n&KX233LVyJZc
zED^OKum)AYR?B7M*1~R}0t3RtR<@~WN#S*KsZ?uz7_k~k=$5L51$|7;IU4j~0*2bV
z4z7DTqc@Sva4Suiaj{}~>?5R`F)Lv@b`X~BWivApuh|Hf*7Vo8B`PMyZPnzBGjs>b
z+gpy=RpDHXGW$i9*G{){genckJqk67lM-5Jytm}nSm|!LsPMuS_Ar1hquPi((9mfC
zF+$w<1}TMhnsz<6No2ix9tk0oEPfm+$Z1MDEf&Y_Dr;@!AN*y+=z8y1o3wJccP|Ld
zqO8iHHHog?gr{{5ErZ-T9%d;b3pZcLZkcKWk?;4z9TT`(fXL+s>_dhX;B2_jWbIzW
z3btr>AL0_3=E;212=;CqUJ8bW)Kpta$$6hnl`TZ`7`!mGTnOm=Ww7FhXb&)EYNd{M
zdk8_46+_W#hp*kwf-a(51OWgiZAt_O_P3jq1Rhi#$hPnPQxS2@QtK5#R9;Q$CD^|;
zw5r*MEa3s{?jvBkdWqO6@w-hunF5vJ9`xakS=s*pjbs9<-c3e_<iNOuP#!OEiZl<3
zcAsRW1qKv{YOw8lh0gvALdr_<Pl;2OmHd?o)D;kkDXP3fxx(SK(Q!6|;yGjAYzQzE
z)zL1`acFB0^-~Z80J8K&@i>9eh7O_+h!ombm4POQx{Ou~_duIT7oO5H`*OG)D?Bmu
zZ6Hxg5mJqja;Y#=2$&az{jjPwwcCryut`>bas{BQCF`MsD$sJAR2wSRJM2NcCT&E3
zT3_*dmO>P;TmJyDcaZ2hy}`%r0?^|7i>|I2<qWlQU${Y%`=#%QRNw$O4<B(tQ7Bg*
zD{@?<39uS}4pFeig)fhDiRnjfuwKn&=$g05FPeEEf|id${#S@|j<V12Ncz^zXJrn(
zECVV;b%lbL7x0}w!3rgI2X=FHE80e*N~jIOTtF&$UvQ3U8^UzIMBO{2vAjf3Z|bJ#
zAiKgKE1@pXqSK-O0MjKrP!Qt(0L-eCaLU?gvmJou3>8!{Am_~WlKo{4scG)kl9T0J
z>9^!O8&W~6NFkK4v@-sLoAG-94Nj<Kz5=?r9HB*vq*4=IWGMKR%>Mw!lyM6H8o&OU
zW|(5dVBU#^LpAnbJ6Bv5Ux|Bd(z!ODi4mef&_xK(7b2E6h@n1j`2w{qPoXu7h2R{5
zw~`b;(H&c%TM7y<<cOR|^hLyDRVtN-5K1j$c%1DjpZp}}`$-Qy$epq?N1{WzFQ^uS
zAFMKzL0IX+<_K#gnzV^>Z>wWVAr9B^;c+~;Wh`?D<<v|IfxdbDA-3RXSaH{>a&p}p
z$6<I{zG#D32y4WfHjYnBtQ2ZJ=^EeU6F&ug2xG&y4P`xwA9R@xrnosqDB$+|7Rc93
z99wCX%|@<=crw?=bp6cqAo)=paZTe(>f@9iDUrSf{>{RG24BnrxCip1DE%jdPMF7b
z%C}X)UvaZkH<u6qr))h>KiD=!*C&|Uy}debe=|B;+rd}il|hTVM&8&o?oS{t;#&*P
zDuJVjRmhmHS_Hko1z*7zLKo_(Ma1Uh{NBrwKd>%-KmcBDo^u4Lp<QfKLKH2p+SFlM
z?Kc+OtAm?o<Y}h+rWI@9zc&=(<#tTeSRai`4!WFQ2btu!H`Fi5qDOp!$p)y{rvmLu
z>wy7~5x$eT(KMXU9K|-VqA^o$-JT$3nCG$MDtB+*H=xb|*%D~Co4Sbw+4ikiJdDFG
z?CRpO2Md++29zn`0Uig4+zRMYE10-=0`!iqV{vN0)Wf9^HR+s`U{s*3f|0D^j#$O6
zm1tAN54q*HhRkr}%xrv+!2?v(HK_8tIH$SXE3nX8^D~35`OE`?ekCgF)G|hz{{Y*B
z3{m%HGU8Fy=V5!m31*v7y))=QsKcRc(F>#!Hjd-$O_69p6>c6-pC)+^0zR>rRJM$P
z;9bY&S<p@!-`sv~Ufgw-x=3wF&!1E(puQfNMg5dDhAT*Nr-7W--BZ1`%IDo+F;dyT
z9b$#b4iel0WpjoTRIQ-n?p)_9)yzlOENwy7W<^(1m|7|vN*WTv3Tp}iFXCFbT5U=L
zD2+D!y!qJ`AuV>fVxA`mw?UT#hsqwrWvo@2n$%b80<}Si*4$}ZNIOBl8y<TtY!-+}
zL~xf_Tmzm^mVvxQg2Ofr7RI<~oY*YqDogH`rz3Nk3e>sPs&6YAB@(kX@;wxD3zD@g
zQ)T8W5CO>$EX1vA#T}di!~t{{7DsZ3bZQFU+b9(|rz(|)@-GB6`B-XrRrEmMFLGPD
zfD(;Xh{ezhqWFOHT~p?G5K%6G(l`?P3=+7;d9raDm1qK=5d;zWn^09aA<(V0lG3gg
zR2{P+_9W0zFEX~%L@x!!zc+G-<)o#3EW4tYfMe6HN@@^b29O0t*4=GTzZY`RV{VDy
zv{qlRg2Dt;Idl;ENq>l2Pnr=}6atm8V%)AEU>geXB(;IFN6ZT3*eh<!iv^Be53)IR
zZc?IzAYK5MgM6L5%5AWlVj>Izf(9Up*4Rbg=IV%E7moIf2rYtvL0)wp<DDkz&Pc>|
zR2L!gU>WD;KjA}u`=m?p&*WrqpaY}0Okn*6ub_xeYFl5zxF-NweVZ3HQcRIFXw*s@
zSobPYDo!H_Xu8qLk4!AmfDN*+B3NBf;Fa?ztyc7PFj+?K5DxYQhM>(Fp)X$rfpbBC
za(-<g2Ppt8;2<;$TmzQnFSWF;%UF`cQdg91Dm1BS!agO4hEIp!-lSjaZmO0w8Y(@P
zRx--$@DzX_0?+w_Q>ECPZ%ShJZrYjy=z||F0r&LS4(7kHBvs3+!)>tdKs^T?)JOcP
zV99D&v9wYL3b_wXDGS@)K|(7;O-~}ykmafZKvw}_Q0Av0dGiyOUG*b9fGLeW^#jZr
zbXnnuuR_!V!JFnFMvDS52o<gk)+KnCQu-Y1C9TO;dxmjpHEzcTnW}^d;D$uTo4QV;
zK;rCAMRP7=RkT)HATr5F8?E(eM~{%pq)tgm3K}plKYery+JN^I!j=<<LOiaP<t;@A
z*_YE_{{Uvu9CwLv)h~q#5#lyd)4H?3^KgAd3;;SJIMkrmfqab415rb9ja_0WRb55d
zdRHQF-7vg>)F7=qQ$6NDSC=W$W6QnzA~N{g*Ebre*N7U`@36Un6$RfXm<2p4ITqjZ
zWtl3v^DOX%o?zHQo6CO(2h^Y$$4Pna#qkD_l06HQ6A(P}yArTf_2KV4OC`zuR?@iU
zssfynRdU?=*}=9RTip*aUzo^3l~AJX6}Bf3lm!Fat}-?8`O#oSi){QQ=>10z{{WNZ
zP5s_w%Fwv4NOJ>Zb<Pw|go)!64^X^`RC?+jVYICg<YsJ^uZ5AggJqQ&Q@+CaTy?yy
zKJYBOeH!tx!Ozc7dZL~q^tg(6rMu=gk%+~z{{U=LR)+Sr#H(0UF)OQ$I-Z|AE->U8
zPchaStL|SL5GE5u9L^w#mv3qbE>*k<ef&ZgAhpYdVL=Dn4#AbUeRAMJI9ay_uxgs4
zh+$nh-X}-VE=5EwRvb(lq`ku}L<;I3(|`~hx!0r=EGYpVpsPg;HV&ekWv&;-O0p=5
zW>rjjXb&iK0JBo5ps>ab31)poPEc=}KvkXHpH&$Ng)Oatm$Y-oO~Kz%z-z(2d7S#!
zIoJId6LV=;rxpMQuH6R_yhL98K!H5q_9M)yE!4S2zCg`;DQLr6JYQ2mT51Y4O2Xv`
z^Um@`Wv=$B7K1X8^BooU378Xkb*&>{3{~W9fzuBZcb_nIm%~`5jl1`eI+`!(;uOOQ
zU;>^Mc943qAJF=`$eAI8;YK&nHdbn?`60mq&2Vd8C3j0@g_MbkKwCOwv#1qgqIEWq
zN7?RYBg<S;c_p>1O5dL|1*NE6J)i+BI*m&@(%|VSQujOyzRa`nAGGr?R1XI@-eY0C
zSLv>pvyaS7XmVyT3t&NA0?+#%1T&aTDi4sk@t5u*=bat&XA4gq!!%DRUvm;~{{SNk
zn+5Qk<+l_*xtS?Y11H*C*?U(w^)#&bo-^W5)Eo}Lv%NecqMS^j#heYbA5+E8mG>0$
zQk0Jo%T4l!K=ApYF8p`WrA1ycT>zfgL*-RT=?%T)f3(NBh9ch~Bh(7uACWe&9E2)s
zb+sB*zZd2Mp;9o}dBr~7AqGXpT?8UDD+1nX+yJrC;7C6!CY=Oo7VZa-TrVBjolYZk
zB;bCF53!n{T0Y{_!qyssOXwV>U;f2OjcKR>KBIv{9v{LK;a5*PcM+&cD1BXX6_>;_
zX9suOf?N?cc;gaQu_^a_32M^o>k%oGz1+=B4<qvs7f#VJ5>!k4r+;MYnU|`teDm7T
ztw-3fk4;%HJ=H|3jop761kJGUM#dn)<`<rRARc~jQgt)QdB(-oF0jWoS#}n^A$m7@
z2bIwVtbZq@bw>#$oIH3Cax!kVW#kl9_C3JzEQ_WzLxjxYlSm?)@(<EDrF)$gAmYJR
z(X<!OQO4!FRK;<^c?ttYR76oJF&;$b9@_XG!nm>>-E~7TAjxRjg|!{2KtC}ZY7{P&
zuUawXb~+3LLj}mN(A8e58Lul!c(;YzsOv_V2$W$JHHQ?^;}0(4opfaY()KDiR;$9T
zgyd&;V0H{l_Ec~cr3QCMlsE9w(E^f7IzWu0a1FIxc}NwGsp9esND_A{G;gPvJ3^=`
zn6wR-O-j&Gp`4*5lR}WBCpkz|btP;+s7s9mg-yC|k(9-`Fl6<rC47)cU49N<^M{Lb
zudp6lKdKOP^2E_WUcf~Inb-`1tDRg<i1NmXs_UzHl%NH*?PlkwY+c$c@<)V@&1@7j
z!k<T(Nd<KI>@4c230^0EaiLbK;8TfeeR(Is%3RQ1b|?D-dfRK|QtX5Np+<t_e1jX!
zRI*UHv}9_f_QV(hX3YpJ%)a=9wwxbeSv+gg*$P5<>&$Gn?-KB;94j5OSR^56rM_EC
z?!r`(&+E!@R5{EV6U-fhDk3Dpt@jTznVc#2Qr{sSS48UqXrO~}(yp|L&p+4_n^n~4
z2?N1)U1D&*UWCf(IJ$Y1LqfA-h1BN;I$k$bC`D4R<?l{lDBuq|m^~V6eG54bJWN)X
z5iYD&`hrS9z*VavM$tkCPcgV#b>z8pHOsUsY|>i;E4}7yVT7;jtYs&86B3juw_U{#
zalGz}0T#=a3+}Rscxa1F=l=i-)9xkw^$ZDDCjf==Fu)YMq+{fg@R7E!5mgB~`UpC`
z7PJIX)LS7E4pG+%?Um5bOS!;toe;Erq(VBvSYCwTdb$fMDm|ej)ri$0)%ct0PFSGv
ztC>L*Is)-T6c)7SB@)k-#Yui+*@f$@)k`b|X9P8pR6~IEQ);arWFrVDY>S72rF`U~
zJX#AgPcop`XI<!mc($sC?Jy>}p!{QM8}7Chd#KlKcX{<UNNGxR0}_qa&xJ(&{w;Dy
z_sVH$+0R^z4{9Rme-LB?afM<qJ;cc5e87)=+%-h4cX3~m7PsiQctAiU%OPt118CAE
z8vdIX95TKI<pHV|J<2cp@bOrx0~!^$6yF2>Kn$P`22#KkHJ_wl)pXV8Whs?uA~cMT
zVMZEt{#<aCXYxC5^Mh@E${qgzD}*~r6ZWE@mIVB+wQwiqhNEE#Wu3)F$8I%)_Acf4
z;&7|%g_JmS_poIObOrvaI}ZN<ii`kVfp^B->r(MXjo5P{QGGgmOzn$LBKK9CA%REa
z(R(d=TG2x~<0fT2_b(~8Y&$uDw=MNzMRlbY1t}ZT<hCG}fFDq|EIXXF=c#<J;o@Q?
z{bH5BP+yBnJf`7=uK>9Ht?;5s4<$=qokKa1k-{6|z!&xqyXHsIEZOSAba8rOC|isr
zI8OoQAGy#2$XOUZ8bTMzZ^`&e=nA!W{vyO3C+rCrzlOcUI&{nI)VQ*N212~00irK3
z?6ovA+C{2S2LVBV^^g;=D5WpNg;X?_fIEUqVP7JVRzarK+$FeizM=@E3Gn0?q7YbU
z1Et=Gyd*g&9z&1(E8GAW9RrENA9Bwt*gHv5<x$Z6BpB^o@shs(08svuo9REu`m5Rp
zA%+X>=-e>S*^S-D0hZ1ug_J?fOH}^W*utZr=x`8TUmLoSevx#nH{EP@M#7I}L_)bp
zIxQ0(yC50ywN@8Ww(Yjw@hF8GF#GB&O~>%M{#f`V>pwR-fwBij8iqp-*a6uVFhs8K
zv0^y!<YW1A&`u(H0f>SNUN^7dFe9a^S=f9RT-c?G>kc}o5z5SFw$MsJR(C<AzDU?8
zn;pBmhKdzenacj<4DWyfa85NTu7uC1KHM;dnH0xBTzV={xzl#OMl01y+zFUSimd5;
zg5}Y){Sfj@t!Lp&u#STpysrnv$I99ufZyW<^v)=ePF&#n89^;)(N|K8whG8u9HHNX
z8pyP#hFxgEO)d7Rqz$L+_7<SXyUKmJ32nM4?P_Ai>o0@Kd8oR|(5S#1+BZc5?PT!y
z??pvaC@|hjUsRSO;r);PknvY(ed~w?(Oqi>3vwW(*CM5AS@p)z{m^H^Onv_C6l{w5
z$yMP`mnEB|uKLfZX7S5BD+-9+uYt7fsyvZ-V1EJbQCXwUEHw(or%?=PExdmc@6!jo
ziHhza!X-Bdtz=3Hc9UaG*)IyoMR}~*d`&@HgMg^xifW3eic41y09o9X+k3~joH{Gc
zPrngdrbGOUm2-3$dzM3wn9h$lJSqq29#*dn)k=-_@dgC<)wFpD!1CGMlIJ*aE~!}h
z8jO`yN5TQ&F&+I>cJ<=;Ry8gL^NcslH}GhdP*w(zb{@&jzMhI=6s+uA3Ee<#rD}5T
zF&t7k8+awLC<;0QRQrp22~1!4Ow0Vr<J`fi;#4^{-=3w`G}oX-+$fe%0js>`QjU6z
z%}ePsj)sUT6d7I7oQ*+mnyK?1U6r^|I~aWk<UGh#2Dm9K-L~fp4>K=nZ9%PtP&_uF
ziOtdut#ZNo4D06Qu<()L^mF`LBQQf=f{57lcE091`LGWJyT{s!<fGstk%oH?VqDlj
zlfbXvlunBvv{i|OYZ<QE##_1S3V{3WqVk^Yph7IQ&V?#kM%eiy)Y*sl>LJ>c*t}^K
z>2*C5U?4wf-EucMvyr*Z$~d>G6GdM3fZ!s$9l`Y$I4Xb~XH_nr<x?o8DzR)X$Fcx{
zv>G563-v`bToj4HFCE}rYNbKH<f`FiiJWq!K(+(xBjI!frS?jve^TG<B~nKm(B9QJ
z5opnXmYTs84hPf~t$(qU(dMVZUaZZj$$Z7o?X_kw=Bnoh0`@gk@veYPBG-3fFmBkq
z0HvzY>Y$%5ATyxyjXPg)A9RkOYJO~YjhS49a)$V~tGctXQ1m}g5Xp%0*o8sD>G=mK
z4V$A)4Stb#u&*)e+EM=iWx8SodP21ZG~2ym`p*$^kEnv~ZWgq6v8ZdA!B5BnUp4G}
zEt1k?#nwQM`DL&)N3s6^ZI81LYCcdg#i;;`{{UeDDe;%`TMZ{C_hCI|gKNtu3f(8N
zb-Tuvze<Dn{ZJol!r@-UsI~kP1M*{3r)tFoDT8o;HX#cTp@yqDF20F5_hRW59LEX>
zLjalNrVBJwZM3<3JonvY3?DU@!SfNT{q$0atrk!l{{SX9r-vGK?&Z4Hik({W@d93O
zIsiXRu}ZkBl~^4kZ5FP|)a92j1GnpQAk3fO9UknIcpsXS6#_aNu6iDU6+mgp-zZp`
zx~=yNl9;<EnArJC6-vkA4skyCh&g}Bxs-n%7%uJg*mgn+WZZ6`@N(<S8%{#YR7CUH
z2-WyUrmaPu$K6bK0R^^vqd(Dw*~~cZ;39&BEbR>`%GO4fHR4oG>v2t+@|ev_OruUy
zI3)2DKWDaE(~Gs0ErtDLC_QvoTT;V_R`8`sNvn-=BC&XtRQQNdT4q`)3;jmg0#|{1
zEyOQ5v1ebeYit%b$=|Kh*>kioQ<KHcpnF!Yi<}_SI$wV<8W?o2AV0`=(@)~0vui(M
zm_z-+GQ@I(o{}9>=w)zy5oj7ZoL~^$2L^eSH;}f`FoASosA45OJX(seK(6M~h@~(K
z_YfTho(jl{biJN?jOcp;+(w$HX%_MB$}%HE&yp&x9pSAgc1oV?Yuxzmu23;Ga9OyY
zBaEF&%G99O4oSs%(t*#wnonRDmC^fPc(`f*09XRlpG*^~Ms#cvZsL&?rjy9TR4?KR
zL<~)4$0F=9A@V~9VQHT)nPsjs)fAB-Ku~4ZF*IGW@Mw)DHG0)y0TmZV6Lk+Asp=;V
zH>f(uafz4rI2zU$lDt6fnn`QMLML?8WiX8HLY?0iC?Sh~j-t0i>eyK?#oIh4qV;2u
zO^Ct@!JjicDFC}>N*gZ|*AoJ1Wi}%M68;gO@QR+R&&0$yiM)3&+xG#aHN_F?18R}0
z$n4cOc^;cQ7zQ<=O}XP>)1~-Me`pt@%NT+mgH7A=gjk`gYV{DKN44htOPal(4Ptcx
z4+fDBj-yd2CEc?Fg)av<nf$cd&87~e!RCq2&fNBSi7u)uI3m<aPAZ_hpfIQC_XE6$
zjckP=!$HTBWx$I4joj<Z3PzeeFl)_EH8SIuvj>L}MvX~kI8b0uD<}eBydG8181-_8
z_x&Q;dz=3Nhkp@&ZUgcE06B_XP$(=EmtZz$!KhYux*XK1Mx|B<62Lrlps5Jd=npAp
zJWT<s$lJIfu6U-U8wp~BMOoNE?{98~%LnEHK=aAq7|B2|Vd$tHHRy3)eoqkV07NDC
z1&CC@I($a+@ba($ahmVMwt<fWxo{<UbO3pNO&;z;fmaB`14DwN8HHa4Bb2vV6=K!Z
z$_bG4`j_6*qxB*Iqg~zjdPEzYE}mdxdgFoZP8g(BWlBRrG~JD|gd{;t0b&wtE>2E<
zp%=+E?exy`qFR~-)W31+H;}Tz)?jSn`Ys*4FUd6n=Ar>hHa7Q&3qj|@<^&8ktbEGr
zEi3Nx32v$@z-Z2Kzz@(q<<6Vx8WGjgGo-n7!B=*wJiH6Yc&m>(eZzw<4&AC%I!jOs
zl=GC1<3}kCkSMPklJ9Dh^vr;<%aMfwk@b|yhk&~8OmcpK7lK{_t(n`#LGxeAJ&@VN
zxa``)K@u9pE&l+dn=a^j(eipqhc=rYR>(B5=_{3{i|nuY5WUT&04lJjuh^d`M}8HH
zFbl~6{c6XyZWKC&>{*Luc+W$STgKI}%GBZzjdKG7`A}HIAh*ck9%Nu@i<Ad9k%RCc
zOOFNhI8jQL9wTa?t@ISd;#*cUOsVRs>%?d-J`vGj$UUN|iOtMxr?iEDk0)mknfBw_
zI6{|Po=k`r7sHK!%v7o8{^%vw3mg!&xuon}{7P=Z9p{n%0OU%ozjCD}1y?VM74LKL
zgd4%1;2@zn2kaHS?Jp-NCI=n&kMb5XOmGD76IZt1sutjZ(Y4bmjdsq31Vc58yHNY}
zqXV}<{7Z(-EgaOfvc07rl|@1k9a#o!6mbc%ukDrp02zMzL4sEM7lL@T)S^Kkr6zK|
z$kKtUYlv>QDJt3a5e=8m#S5>NTXHv<HZB*4ilI*l=iF%n6OF;$j6<uT;?**sG$(vF
z+yz`?Lj+H5)yq{!>{fvSgIEM?rMpNiP}7&TE^ksi+%@Rf{{Y?p0I4iA8-Z8PiNA{v
zf9!Y>q^9W_(OS2m%jy8mHAtt#FOuy+QL+@E4I|0w8_N*Y+~H^y$H|lU0<WS{{ekx-
zMsE7}XZVB7f$4lps)h-A5T=z8dnL=n7&sp%hBM`U;t<86Ai&22qg$nFfuhe>3t?MS
z(c|g!3bK_;bNE=L{f?TUphiKnRcDAg)r|;eU+(0^irhtdb(j-{np1{C`kgnQremu+
zr;&fsT00(Ph)vcv$BW*|E~Df{lfsjnw2cL5Wt4QT2C2PEv0-@393$x6_$_vf5a);I
zhox`F&Z6pk`IfR^VTz%Ym{8$sE31t>V=MCFafl8HMPIvsF9+S19vfnrc0q|a4o^eK
z%TX|i>3pyL!GrV#LhsS}0$guUUn!YIz&>v(Ab{WEMI0j+LiaTP0KotaEW3O_3Laki
zf^DkTUsnpv_b5s=-FucS&k<a!-@^&BchHo++pIoefZoxV$NEI2-oh-Chf>xW$wg0+
z=fg3or(M;5D+;R?Ep~Rb+(RjkLf`f!BpH{)wJXB1?loD+zlK&|5~3tK1PfXoUI-Pj
zX0b%!c3uL>OjDoElvKxtU3I(th`?BEMjgJqL^|;(bISx}!Os?l$cK+RQjym7y*OF>
z#Lc_E{O5_2jc%qGA0*)XC4^pT3CNm8y~aQ+oO5spMSYHp{{ZBq?2C#(7PGxQ*?6md
zt7aZDY5Xe?<|u_u3<9gQ<|e3B5o%oP0FC1KnR~GSfIxr0L*+zZUR~E-m==`r<okk=
zN0slyvdXsBbJM}aKjdHyWb5UQ%F;Zn;$bhxs4?=VmF@N;=8cA}Ouu4R5s+5!1UpNx
z>)g1sN+SjRUInhpb3*(jf)G!Ozi|(=MuBX&BfFI{^bnLzHxWQP>WOp+pNfYQP(>0e
znIOW;hf0CcIE)uZf%HVNctJiXJzGR^62pq}A&ej=glT|5Rv72*aH&aScHK)dzbDco
zbUa4QkgWr23sFk#Nc!cbzz2aqnZD!1Ya#-a-=8^K)wW}hQyz!@k<Bz4)41dz+K<^y
z3hAW!W0zzpTKN=XYUTu^3q=u8_<FWJt4Dx-s7%auzb|R1lE#ObPV~}~>Muy?00+ge
zWxd-#pqlxLeic}zE8la`9K!biek^+6K{TN7p$u2nqv#0g-q2|uWneL*R)Gy{fYE3m
zP3*NI?M8yea>x1*VG8Ctgdh@EiWM=CcG>^}th6bes<*ONM%d1lTv08h>1mQxsD@h_
zgZ8O|Z7ADsH|AK%6i+me__<wXY_xA?f$kZN201_;A;O+tN<Ox12Z>U$6|ZWtk3^E5
zW<#JZPgx{zDI)*~?)GD`L{ZXd9+oj*g@i2F`NPCK{2Rx<+-I(s8VAk5l4U1;J~G05
zIw2hks`w()4aZB<<#O#MScx%+k5v7OFGI^Z9^*2({uq71=fXQkG%Zo)Q(O<7RMt78
z<sC%w{mW9$Od5F7dIaw&XtE0*vc#yL7=9Qcya$1YgyB9RRXO>ju()jwx8#V#c*)ID
zTxHb;mqAClOa;ToUZ51rHIu94nkqPH9A<H=;il<@4;Y0a`iBCqYs&WfnOvT(UsBoU
zHE)B7PBq1`;c^l^Az`E)H;q|iw;|ba5wIn!9_d~s7fp-sBl%Jc<?y{cN4&Z(2`Vrg
z#s-eap|%A0EQn4qjh>vL#MwjKo42dX@sf^*D%9e%l?cFRKNhIj3Go&{^G5;f5F15v
z3bI~a)}s$jig~u>9<S|KkH!p^TUQBMUit~epopjnxUD(7iVE!%+3hixt5^WfBz|Gk
z{{T9q91Hjwa%GIoQ~WDwEJ4b4)D~>x%C#=9HIsyTLaXW(0hT<{a8z{RF@XW!>paXh
zrSh<V3P1`K=!VmPI9(;?0oxiOB^bAVPqG}Ixp-3kWy@9ONXGjZFNL|mjMALQ6enRu
zts@ti#qj`sBnv4*pSb6Hb*`4TF%fk-0|#O){zMQmf%5`Y;k^*AgsXP{0BjhDmR?HQ
z%yQu8Baf{u>41x>K579#4Kak8oQ<5tS_keB$0R%zL0T%h`?3a1)i|*K0A=I|7WaZR
zYK0ux++i(?!UErsBGUs?b;o=(671lQ&rn}6PUkafuSi1oY<-Ep9%78$T)uZBP%dd0
z*9={_A%3u^n6r`E+ScNzzxf-Z!NhA@5;1y=9>y2J%t{CN6Dfoz7yy-7O(yS98#-Pm
z@(r!9{D?7B6#A)lIR;sn#VXoiZ%`K79lV@NGhkcQ%dxKSgHrF2M5~bag*4hLOZF?9
ze-eV-XMDq$-{G>73v49e?Rz8n^S&mg3Y>vwKg)&(RNsUJ8V20eqd+rF;Sofqg6ozF
z!l|pm7dZl0zZEGIjQ|X|K|{CpLp67gZ>nj2o8hp$F_bYc(O=nOwgCb^7FKDC@V;sY
z@aBIOi8178OFeO!>nXqV3jxtRA(%qCc{bU?6A~$Xx_B*$_iZ6HVvvo6rK>Y$eLJBL
zeDRQVXlOWH(j=tac&C=*Zs~$h78}~jG&OPuc>`HktQ6WytAthJ-galHL*%5LdtkmR
zW5RR1FGxz#mjd}+bt+i|LFX@Z6^uA^V@gq@DJ6iEsk2>;GK-<3m0giAj9-lO!%s0A
z=Imwx($`?3;2aW`-7`a1uucB}$)F2YA$`i&ryAI766%;=Nx0gc)vK|m#>HdBkA{fj
z){>!P7*pI!1!+>b7z+w1Dy3L^jpLsjNp<PjUEluzRuiD-;uD67u7J5p1RRQsW#Z#U
z+Do3a8^Za6kR@AK`G>RsT9D0l7!Sm8Zox?Z0JX~15L()mBH8Lbsy7am#!?K`pteUA
zkb&t%#XIN}`y{fZkgrxd2L|zNi9~y?4JAqdz+S+my1)4QUxo;5_!(uC$Y=|RjcPwi
zm_cfwRM#yRv#boS3Y1&(>Ec=~*c>fkta|lxEX}+kCCx|m5(f%Wod%&t%N1v)T;u{8
z1|jwz_yABU_#?~-q2IDS0d@~-jK%=?beHNp!3zVU1a4EChBs)usJa+!d3lz(MmlXj
zawV;a>f*f`tOBu=`}rf(vZDMYJ7S1BnrtK%+!d`}i+v+Y*3>j9jB*%*I@DY|n9D2_
zdE$C*h_kOgtdhc70dk21R>QO~<qBb?-1tkiDNZ{_l+G#Yq4Fc@sb85f=NgnLev`@?
zL06;p0LXbM#lp(U{G$FW%OPjqA62jXm>8;fUZ*Dw*?&2gx?0#i5+rp>a#tYOi9uW0
z_=UAZ(Aq+Ax<TFwmo=D@!GP*494~>+kGTmp&<poLc>_Ip1;(2D=^VdgGBimFPX+7D
zT}|?B)%n2HzQ{hP2MR9Da!3uAYM|oGHK|v9!vK2?_!w#zeQAH{Fd~(5ZCvMN0s-4@
zHY6Iv5_+CBb@7x5MBhs^^?v24DFxc{%`Z~H%r4jSVg&8S3RCpKcna;EuQB%lBOJYl
z_{PPhhuJJ*uSx<%Syc2YUxY*;04&^|;JfG$)&z~>=TI4~u-BGn4c@A2ksKM{dm&6#
zD$DGgR?%^_Ho$z9ugJwgpCzs>71x--L(-uY{3iocxMx_u;#zUrMIL)yPcLs@#pVfh
zwPKO!ARJJYHDkY+HX7|%`7CSanK&^|Wdq2JeXLhGK4F33C0jp?j0!bBk;u?h&5sCK
z${c~TY#D<ig23Gh^}zCv3~SV`b5j2R1&@4LJ*By5fSVg`AI*Ne?-5Gg1Q~dex}k$i
zu&)rMputadeEEjQP@P>aS~?XwQT`0j1ss(hd=HArjzwHfD-SXi<Wq-z&RCC?LAwgZ
zQhgYJ>9=t-)G^Q<L^r4i2vO}tMiCLjPcpCO8KQt5I60MTZ+kMDY*@Qi?li%b$+UOX
zK~zftbPx+6<9Hz2U8%l#u{8nar<w>|xFU#E+#t8J_EvHTC+U{e65;LN;vMw`lzp69
z5S(AcFBuOHa{YkUYS3dTLHPUBYhZ7yiop`X(}`~urSkcxY}t>H-4niDuB~#Quvr?Z
z=2iH4hCsrx?`5^ed)3Poz$L;&1*66Iun!ER03CGrn740&f2$XCzES@GDi-v7xsK4g
zK~ECuQi!<cmNje3rn`Vr+OEa<s7-lVVb!GDBAEHsqljE?vlV%A9+-vxD*$y`^>3T3
zp|LH#as}c<7O_K5OrclIc5?%yZJDfE02iL&(DyIOKvy<vSOIFqy8(CNUuZ^w;Ee5y
z@z=y#^m=0a`-!W^8<#5$34odyF>7{>H`Hrb3k|+<=UfaP-_oE<W2vlhNcmB?p-&4a
z*2<hoXu;ZtscF>S0@xoT6nEK%FIv`nxdSv;i>H{O<tqe8zoHC4Qv(W(^>GW6d6Xhb
zz}*jkxns+{gcLfGo)3s}(Qh)WeiLCTRKo8z7L#;nmr7bRP)FNx*plt`BDX;uSGd7O
zl{JDnQ--y^zO9EMY!>YPKr~*wlJ$W$4@HhBh}PZa_FYwLdz4ntJ`2!P9y~$CI%ll6
zo_#|$#-(@|G`QebX8!<}4M~KSh+n91T=3B41=DaW915->?jF2{-NLfi=S{bf9W1Un
z&^&~`I%<s7;Fg`RE#^bZsCi{-t0g6t&PZ!}9gH5kiqY;qhhR1h%=J)+6@qiDZvxc;
zioF7N^E}=k17Rv?Yv5&;0dlmliAx5@z3MGAeq+(m$lF|&oCgT&>f<VEUJ&*zo-mUr
z=t&j1twPR-(ZyTtq@30uk~MlgEwj(!E9l}pWp{UR%HnpauQ3@cC&dP=;+vaZcJI$L
zP8CRpi>nx;=jIN!iPo^17CArq5R%4^HgyR3#aK?8=SWYXIRt;wHM50C6uNPuFElq;
z@cCZ}OY$&a8$$6>d7Sh2J$OgdkO<YBlOJ%EkQZaGxIf~pI|x%wL>oAkHlp#d`6UOS
zB5{f6ihvIgQsxaYJMkkzXUWg)CU}<@5B-uVwCgiv48X%{y99e$Rq#et-3#s=V*7^i
zqm*uq6f%w%cZddUmpK*TmNgX3<gS@&ta%GxF=(*0wMMT4dC^NQ7ec1X3}gQQ!G-BP
zZ|6Fw2t?L{PCyo->PpUkWl~3!0BCq85#luh+Jl0Cpe0^7mpzQsqf&5*V;2Brs;~k+
z02cFI-<iM;CSpBrsM6QjE{8EsH`G2ym&Gk?MJsOD*9&zVOHzHi5Zm!Myd&UR{{UeN
zlnv4~HX14Q1q%Y%ZO@2h6;+FJ4EHIk=pRnueiI0)`VQtFakG`h%Hs8GuuV8(?~;*u
zW5}?$)HI?Y@~~pM?09ylda{(Fi%o3=Danz*7FGM4SezP4V>hK)t+1IOa@^qJEsz+R
zW0okQZ3_&*?@de0I;ifS{ALJz4GN&}Ow2Z*vQHDvwD$*RhyZN}&Ps-x9XYYq8V#Pd
zAGB=^swI|>hURk(@PMo+^ddpHsvOmmkObFb%oUwoi*E$nYE%HZiTV30MWlB^I~r+2
zZwXXn352Tu09GH!Q1XIbGhd8ndS2OZ+UMl7wRCD@6gh@0`&=}YuVMZu%q@!Or7pTf
z43^7ew!G^wfh~*|n(-JZDB-P-*i4&4d1zon{`4t-n4E%zdhrm_`sz>Agwmr1s1d{2
z1<UcFDDrGl{%%Xsl&h4`eJ*Ch1(j9u`i*h7+^{KIkqCv#o~V?4$9dC|Q$%0V6%nwl
zVJ*KdXzNp6&<3IWSp&meKA^hYPpy?Ne6q*TtXaCBVN3f&b1me1S#^j%USNE7dA8*+
z6ZLYR=lo<UJ42jQqA=98qlU~@QVP5jjfTh0ULK8-{?`VZX5O)MwynfI;T51~XG}!{
zpqq(vo@1@#shhA;3&;ci055PA)WjTHZvHTNW!~wyMFiLtV2ve9yLzatO5g=DwFgN{
z5)VB$77&FNSlLdHDk;GGPfv2V3<9&t=G4$$S|lz-Z#8^CpGPYC4JVkvElBj1F2HQc
zde7C_WF{;=qGL@Qh_5!<OZ|W-`Oh-qtd+A@mNu@SEjX}1U|JJL+X+`)RY_G`XHZNk
zDp(Tb8y13uJacKqE4r(QD)<^5g)H-omejKK&xnW1ukCR)jm4ANxJgI0c8@R5aCDXE
zcpD&#MgVjX1q@uYh4Q{{#ML}Xc@IBR42&OF*HOZHmKAhmEr3m$w$C&XL3>u<m++5y
zV^VhPgB(!@f3FnF->(R5zalKUq|0<t7N-RKIk>eRBkppLGLoyW(<_3i<o+?~VcuW%
zH`iY{ev;0eh{{4Ub8++#0DFI8=ZU7UEr^4fKY11)k}BG24=JL=Aim2kWh($7&~D0e
ze7x`AoLA$~9#l89{{S%_ji^$ceT$e)E%6N*QTV|J%3I51GE&P%u!A<v5wJWbgp?9M
zS~Pq>RRvA9?iFFCkt6NN`H24jqEJbOJlRP#sx19e4h}T;us^K4qHz>IVy$x2WN-*?
zv*$~=J9^9reh_#Pxe=#-Fa}+6l#Q9r7Fe$Sqo5^S4kLZ#RZVMpn6Lp&gVdC!`-59{
zke<L(E02iObYR(Qk=umvN*Go_@N?%N^_X<eAsPq0V5&3fG@rN@!2X1b7hrb+@9I^W
z!<N{-5&=Y)3hfU7a|9qjX~P<#?t8XW<>G^cm+Y#?Emjk5<xopOo}yEkO{d>90Pzyu
zHuVL%RuI^WOxKHY4Z^P1&&5jl@D*^qank(9H;7*xs5oSK*x^fipBa2ysYst<1%3g{
zQ&Z_UY?kJkQXOni{#33CCf*EyVN@PBe<i2_9)Nc&T{>34@W78#d&|@gWV8PO`vkO*
zw#rGpLJt8HXla}T&psm!CSi$Lal{rf5^wn$JsZ_zv;P3X5m)N>ics4C-wyFL)Y`=z
zE55ffB%Hg~@+wOe82<nS2j1&IIH$i9x1x|K0bg)36>8{!yMKl{;9mY^)B-P}F3Ld<
z17g&UEfU?IRFL6{`g}#lfYZWiCHqk|MKOp#a#7X6wv}=<5mI3g(LielsC8ZKx!7Z@
zxKZqdeM~kM4A`jMhN#GROV3qp&(-xepSS#xDienGn=W&FyfBj=jV~`-XAR=Soymc1
z;$&Pw$#U)y5fYOT#voyLyem{oQCv2q{?dlQ6^fevBowJf&7R^*myjzH;slob4)H_V
zD<jg_(iT~#5FkNI_$Rnx`@o>&gE+66F9GndpI|2Gt&0j4Jrl}<mXC2byhItWPB#dv
z`Dg8C_y}gOUJ`N<=|qI2aJ~7J$#=hcL&Hr?>#wHv1K*Zitl@^C;MamL85fxKRx&E{
z{;WvS_r-!Mzvv4XqAF(b4Mu~W5g6(dLMo>ru+#u~%Ul4>2Sd48dw}vHRsBk-ta%Oy
z)-4BKYC3aU8}OnrN@`HGT`=aWr@=BC7*9UtVL51<RZ_ymMR|@k0=$LP{0h>=KxyEd
zM+|kz58^tOg0(E6N`1Z22TF3<w~3#)xpZjt9$BeGDMO%s8KH;yQzbSFg*BpZK{ekS
z2NdVf5tf{|HrJo{o=r#fo*IE5Y*wIz-VX4f6q)9E>TBYq6YgrgVA96&n;kQ&n(=a?
z;BZlQ5u23r!e!j?a4n<6xjg|5msY~zuRhlBh5Fb?^=^M~BrKw;qgFu?rWE={7kx&T
zx=fHRFuca4Qfpz$G_b1qHa$#ih8M&Vl?Gzr8i8DsJGh`FuPs+Bd^!<N0-?Mcuxnlk
zS4Hv4_+<)$lGNlAO*9Acx`$Cc^DE+7pqRBs{f80sZ&0yn+2C&y+f{i5jHlmTL5N$I
z&nd`?=%FjaPLLB@rMz=BxF8$`T(1wG1_j~E4DlUailZf?bAek^d=UW4S|<V$0@B)>
zEf}R&E=96cw$&b38Dv$!c@prm9CIWFR6x^o2J}@$V(H~jH~N+HtD(%gY;wRfmxELv
zXEQ0S_Ut~$DXmXCSVFG$o)kNXkNttkTY@sdQ(X8`Byc=C&(wbdiXvlHY%I9Ky&zd-
z4oAecE{}lWfq*sJOGGXso4TPF6Vw28YM=l@OQ?Y-u^8E4wgUb(4ki+tdR4>R;X&)J
zQAu+exlyp{Cc_dNGAjhvJqv!3vKXPQZT_O@REM_mF+kUN;@-2k)p2l;HjPspwu*ib
z5SmuEgNZzBcw<9M0k6PE)yExsj0;bQe3l6&?rl><n$b*tu4V;`rPf<X%M!WBf>VTZ
zT$u3=r{iXpmjl|T>H+}#(n3~FYF6QjYV>kDYM(n5h+p9p0zd#<p&EY*uA(Hps`@q?
z+)5*$OsG+^_~`B)+l*+%WoRrR^&2+b-T8TsQc``;c>e$^zzKRG-e9jt^<>L}YR5$4
z6M9^tudIaIljY~;h;?ddciZTf`|Q?jI6y{g^?8?sUxe~K^klgO&~sjZi;z{m7R{AV
zFQA9zybvoq_H(MDK;@k|=3IQ6Y&_HjV07Y=TQ9+kEl_nvn1XeaR^JzpaB_gic=E>N
z2y3cRuhDR7uiUg#U?{uEVskbgJE&rkkh*kWGp+>DI!CdHsH@f)O>O#;hN;rf4A)SC
z(!MW=lb|_Pr*laYU@F#R=H`S?UxI?pdS*V<FsGI(<-G8toYg-2m*BQEx?Fdo`W~jv
zuD9Cu$WCz{ii)2Vj)BTJ3d_@TYQg=A^>y#t6pvjVC2v9{ytEjvEQhg0uiX21nQ`yl
zvp8UgEZ<__uE$slk7abNaXJ>%mti{N)Jxi-;i~;3;)>H-<%7j|W$Li{GgU0xz;S7Z
zM}Aq{d8{YE$BDq};fcqky1sMT#bx-^y}ZTnxQF6-A(G?n6tpvefi$lmb$Rb4@<ein
z${hp}f{$U4CR=iV*uPz#`D@%*!v#>6L0d&<q-@6Gl2kLU)S{l=;Ib;xz*tnP<=A`-
zakH^D?{hDu_aZ~S+4$_Z+C$??l9Inr(6m1=k)%?b2wDe(t+>iN$4-R>Q-PLsak>b%
zffb91ab$qf_Cm;c2ZyNT?GBYFk5Lfa?tld`AprihflC1cEfUAR{?SR{mw-E;YK*<B
zymJ6}SG%Ss0`jOtYLsRff54THKBO!QD&M0JfMN57g2IIc3V6Cc9>cOzxYOHFl^P2b
zILZx^%%a`Eyl|g!A5E^epl6m-UK|r5%EBcJomq;nd_G5pDi3Y;#lPX=u}JT_)So|4
zW4+t*dk_p!dCHbc!rBAIh<F|&<ocR}upMnhQ%YaiN$~;zcQeDxC2eArnlHJD4juFh
zPN;6l>JVxP&n=WjQkX&Dj!uu5`{p+`rPl?egtUc9TD{8Vo(Wbd?O-E=KVuhOAPQa+
zgl#XD)*8GVjBrHWtm#1Qa6rNE7S5#M!C|*vr#j9Z0WX-91us@=eQdjn$cm}itR}7p
zJAP(2zI?5~uD46=oje{5a(ptcl(r6-4(2JFqnwFqI4pxknrkk+>;M>iMMX!U@h^Ej
zt!y{xP6_bJR9?_1*ShSA`W(<c9;3CRcg=oD3Kr%URNgisT&k~w;-cRHVG^U!E?e*s
zB-D9P$B2O%^-Oszmx-K5JLaPvs_D^0xdjE#*&(+$`UT<ah0wL+tG=R^cCFzoA<~Q(
z16o8Yd#?%q0LoG$NOiGt{$m{UT3Q^R8v?Sf<aYW3z_7yF;`)?QEfm!+AXEirC}_sS
zrNU^m<Z6|hcRx`XYt^QJk#&0Nb?u5A8vaihivkc0IQB99-n;|gL_WS*=S-{Ahkzgl
z3d6<6N|xV%a>&*SPTwXvgS~UYNVEd_m()5Ew;8B&TNRsI)B>$kN!YTvzGI5r$d$uD
ziNmoJzDTpPCe>UEmO3H_*<`Yk>zPAk`(^i)D*!>Jk9TtIKnLr-)3Pc2sPI^T(bKdi
zI-pn9K%B|P`&7oT7&M}KVVJ{4kHT$|{{W#}#GzBRYLNH+<`v{@bHz?mFCRZ~6}8p(
z@dOG~&o{TnsGC?niU|Jzv0rb_4Mq)gw-k0+Eb>7{?EE7-U*BHYP?cA@RPhZ1S3|Ec
zXm=G-`px~ev_~t`<mo+3t$8JC@CZ}<jiAo*?+?QdVfbIDaRJkIQ*DG$f{JY0C2ABs
zYM&x5s39B_sH6xwG$LwRapRos6&J|+gdIK`78Ffs;$B58X`CRvuke)$L@TgsDCSq3
z@yK(e#!{~S;Q2@2(16#sVP_Fc^6Yx01qLa4L(n8w5(Nsc?U>&TYhd{CR~-VVXb%$k
zC>%UmOnezvL#Gc?$dGf$jTH6QmKHdqO#!MjL8sC>TB~@5StdA~sp5;lv7_Xp@mKJa
zP!L0a#VleE2n=iw6cJ#*XT*CB<+dx-v6h?#`9Fx4P-yiIbsbiINFV&iSh^gm)H-XF
zY^HpOb|Z$jZhlrqH625KAup8sd(FtPVQH*;!$wG=H}2z}Zh|~UL@KfFwjk=8MXvTH
zs-Qi^Ux6IkAH+jQ&a+Wbu~p|<AGky;zR@JG0Ym^y;E%MooZem4eho`tuqm;+yIH}b
z0)YXz5`y)iqNx=kl7qw!U!md)Kq592J<QD833(`ww{_umydk&w`I?e}_8-bPi+I-M
z5;E)MXc_x3`t&KtG!Kg)8DPd2DqgBoL1XjgP#SXKV{bJ~Jf(K|$!Ih$z&*W9==t9$
z5aEQ+6Lyer8w!JEZX#tq6i=mEh|Zuc($^GtdO4^yZ*(|rF|tr1>aPC)ZZ`BjP#1-{
z0gZa015YrNY3YI))L2BkqY#)T9NIBu+cFP=)WM}Xs$`|oh~caLCk4z9UN#tleRymv
zh!*EMfcPQc4lzW!Q8#Iwb+ZnN@E>as5Nf#%h4TTNdSj)47gE7r1ALe}K`GncWKGbW
z__#OdsET0+JRk`N34rp%0G0&ZSdlMM*`Zg<ULw${!=kY+NFW#DTJMhsh$CV@FPg+^
z%8@>Z=)U6JJSu+5M*tTH{#zw?3BN$J3#)UJXA%m933^&7c#WUy7@V=SLMijc<^(pZ
z<|XlLu&7fZu&gUy$7Kc6e-LX-<)HJ2nT0s?6Nzn>f~x8z8`+t0b-uWi2BCF^x%5tt
zPoYjiX8R?)z`r+p5liYVNvfjkhcmi6fWA{y%0&B_9aE3~5`9DlL@l$*5fHp9J7iE%
z()SngY&vxMf~{der4NTzLv*>l{#dGSK}9*X4tZb-SF&{809*4-j)&A*M2Ij(2GzB(
zEqLN_HVml+0p&W(At{Rr5+;p300<Sy#*weGws#~bq#mDih^4dy6|FvmTH;THhKApX
z5%`MCwjqBJ%&Rn&<r9rV>lE0vH{`b%${-Z74<yroUuMp%gDoD|ur8ihHyoU=Gl~%B
zPwG>k^|IPK?jZnpbI(%cL?NtUvS4c#bxV($d4`BZP+NEmS)w2GNpgc<Z8!+pXLSto
zFP)NVmM038#?f_kxC<{a&cL3tMR^=YYNplZK5Hm9I&M}2hKF0?pw4bX%$A1%1qs*v
zfCK|Ja2_CLDiOpxNMNY}X2sFql813LLDy{6UPKq2MCQs6*L)WOyuq%h0<hu}v{#$l
z;t@g(JOTys@pHH^9hz4J3cbJz05B5y+ADJ+iQ#=z+`S})AQ#+cW$#wjU%YT`x~0z2
z5vrGy<b@mK=kpe*3Q2zi73_zhtHVRF4L3OSdXZ09FICP72DiTFBUP|Q#cDYDm!V5d
z<S3|L90{@r!;;Cs;M_I3Bcy6NU3fjm3HFkdIKTYmb;MW&E1pLo2G0=}$#Bj%#mFM0
z^J3w_QdZP9uC{23nhlLwT7z403NM$steONbN2LD%;=1h%8bDKXoZ>q|JtM;}59^jM
zlw$Kupko9HNGRm56Qr|M21zli6n+YsNs)BE#H&WA8Zg(Nh~d!6Ks}H|=~pNqQ*MTm
zlN!68FnAQa0stl5Z@*-9x{PvP0uK!GLw)d+i;q6okopcJq1%ekh&_dZaZBvu&qENZ
zb-`?5Sz>Xh;R~_oNn|Z^!kO|8ODx~LLK4gYDMf13ui&q(1;QXg(oE3Boaqdd+jKdZ
zfL-A&7}ZYV#0+xzisw*UNbhX~YObCqj|;@Pk<R2`jjJ`@6PQnUuFp$XLv#K`49jyd
zg*7WRo{5U7RsR58u}D8P64nxEUnPF-7p<Dz%!BhuZ;`SN%~HU%#&z2qqv>M5$s(1C
zaQi`tsd|w^Q!J9|jwG^hE^;r#y@E{vJdoT4wt-fK!5eTVlO}&<f={ul_$A!hiB+u&
zahSI>QtyT%FnGECRA*_34v?f5pwjetduPRQ5p}On&LP?#kdw-4`*`$sGlP6wJw}y{
z`0zsG^!jXmdWt(97$gIJlT2!*8y3l1E#L$fmk2N`+;!0s1R!AnvrM)rRNkAjFP6+|
z7F)Gh_b42T%H!u^&}>~Czi|S%jBT87AT8twlXT$^{Sw+Dp{nLvbEwhi&`J`oFMLG`
zitqmb#Uv6rX~|CM%vXot4Wt6K#MRx5*2JaowaEE#Q(d%*@f^~lM1xwXW#feQMH<Wt
z5U{^jYiN$Ykv43PWqDvM5*FSTgz)u;6mBbEX5J<Rr1vf4QUS-3IO>k(MXu|IrHt7^
zQ}ZV>+X7xyM0v<G%*Z8yE(^Pq=2b_fMqG1)NkG%8hBu-!jKPnqjH%?eWNQj}0PvRd
z!;Mou!Uz8V9wI&{-L>>ZRU1~<=b1`a_ZInwwr>xv-~{qP?BrGyy`3T!QUof-MVg@<
zwPzH2*;ZRJ^A`C^^8FJBg9o*!Lbg@@ClTHhJ0(hZxho~9k9U8_4W;Dsz)u@h9SW-5
zjXe4v0w(<1zT#@1Qo%Y&>t|KQ#N?xOL2=Ay%ae`pz#piV1OyAT;0Vgl_!O`m!B`mL
z7DcNzAA*TwdmH1vS1MSbANWO*5k$8w?BTEs=aDJOxPGc!8%>m_JB!qJ+dM*7?Br0L
z>!dzy`@rR!3LYGlpYqM|zy>TH8rowtLj(fiGp)}LIM!fXBhZ#aO-jfV+s&|btVAMM
zg0UcqHO~2L-c(ErW{0*nKyIHe!bSi_GsG{-bB1NkyW+jZiK11o$gJ-HLV$r_N(gL2
z)6Z-i<G{Xc-@YYCMG;bx+HAxHqk0{Gu$SK0pj?Y?$I&{Htx5`kFfT5*?q9B;(d$s_
zEjPhj2h}0r--yqEGXDTNqS@t2mJ6VJagAIN>fqcsSA2bZ1Pxo21$NgmxeK}O)xA*^
zEK+A!VR_&e#goF9oT^-TVC=IEPSMubSTw9E>EunE0Ha;dj$@6uP!_Mn!Z6!>H63m`
zBFo@JqPu*;ElLg7NkBO#2<5~z{C=V@7*>-4(@-cdMR%0BRHt5Rtmi%++Ah<wG$(?P
zeMS?n7iN{<rW2YHy^aPM$Xq}@M+XuXmIVo(8r!Kh-ElAQZO9xeK*h<_b?)gXSTym=
zKs*FaUMG`A^I>)QHr*_EhHGeVAchz&rMbH&5UE9t=Ys)oT0Eyxn=HXp&|q>XFE<qe
z-9CLqYyflN1c7Ulxdc_%m*o*K4Y;U*TwUgDdEb(1zzt)7P0yYq&BnSTRqinDPm08>
z?M5mlz@*;{qnj;io_tQVzfg@@u3vA|U<Hk;rz`x())leX*SKs7Z(oy2qt1~?6+T0M
zPy*+8*z|aTbfv!XiKs1)88Pm5X;rL9T6;f9;>d_IRZENkFB;2sQaB63+XEsNkfFRe
zhO{qsfoMm1Y(oAMU`3J~sA7T9N>Cw>E}T`;gv3ge99kXMeLanD(#NGeJ<7v4gNv%s
zaDsbsfYk4)0j(NqOH!+4gObF+We3Rq0;=U>Aw!2$CE@6<Cj{YHu%5TL(8~KPWq>S$
z!jHld2#Y&!zat%?B6U{Yn0*UWr2hc=nW&HTScvkF0DQ+d+Y@HtpmbeIsP@rdzvPrp
zdSNUC2f<_l$4Y>8Ifrziol9|PDcE#8!O)F^=)z<aAqZ70!G0YQ*OT@!w2Cbyd%kDN
zQERu6C^E}t4Zl=3{{Z`OQrTVzXnmBPAJc!B&?~xc2fN#c3g2x_%=2#%k*mK${hcWP
z0LCJUF3cv#6nrt^)P#?kcwit#xMc*pX>{#RCz3J%CCjY#hPT|RJ>L0-bTNE{m134T
z%mfyxz%-e3Dl3{=&UoqMoCx0^@-X_FY>z2WLn`KgDr<|6VNY_H6>#@FnR+LbWs@v5
z<+q&gV^P3^Pm?Qc8w8vDN*(Pg5SLyJSz;nLHRm0rxo_wo8&2Nr*c{-`%q7VvId=wr
zrhOF1z?P7e&iUkSQCJ8ZZx<Go^RWZcWP8w6)Lr}?Bhdo`#)|;2Oa`s8ITMeu#Gp%3
z@||F^5lV^10`BJh^-eVl#YudW-}^a-NRRlzzE}pW&&CQV<a5?n(FAK^uhc0>P{YXt
zY22@8UO4YL%THG~6Rm?$3cO{6zcclZ0tN!G)&=Kd&~~xJA2d%CMWp0U;VjK<?9QKY
zgDQnM-Nkag7f@`K5UDw;U70i3p346KLPlf?)2h%y{Ikap-qZzOt&~YZHO*`dsnnf(
z5o@E*mMW*i!GdtQQV+J4YU6?9Saav%I&(*5SA|`u+PpvbC_>818+Jb8L04{YCY%5o
zx-JnDihi7ksK);QC136HcM3%hz<-_M#(U`2#)p}tv<pCLn*g!$L=?MIwsE`Ex_}{N
zw(Z4!qqx0|P++Oy!p<TERZ5(@M7k#^nk@q5*s<r~q<*3sm84^!7~mAwtK4wdv@Ev@
z_(%m{1F;}R5f1W-$nKx0+Flhlwrx~Ndf}%weegG*tEoduI`=3wL#VG6DVVwSYrQ(T
zP2r{Ofy$G1TaSJvz!^1I#Y?v!CVEXIz6+^>;KxjK7$Pxj6aN7A9l`|c(f08f?uP>C
z_D8oLpr@7r5H9>Oj1ey^#n&WeEg+}8l~&W^IS|c38mn5W5ks!mZxV;}3RP~cYziEP
zLzK@boDK@tU8OQJp?8&+I1v7<NF~7Xc}>(Haa9fB&8)yY6QQtQ6>Sl9{6YT!*uC~O
zYWj^rK>JGZQ}BlJrl5<?B(MHU;u`mUSY%JMB3l`AixA2bQu5%+O9Ej#e!QhitWc^d
z`XUs&iD}{o17>!-54dKczB~b4Z0*0u1r-U~dm&*hx^4b3>@J4ifXmtqjZtPQL!S1v
zN8~i$EWiB~j7#KcIpX3J`IHYuiO)Zcg2baYi>$e@`0rI0`yL7c&2GA=ma~i^EG4iy
z!}JlZRt_6LEL3=gj?Ra_Y`4NDznrZ`SfEO<^$-P4mejWJ;D6YV#qmDQX9*vFsG^G5
z<p*pHp&uS1Rg6gKQ5E7?a21`G@YG7p1EpgJK|nBvR0&}C`4KouDmLJj>??za2Jx4C
zMdi@J)sUWh{`-`>`uP6<%j!Q32@qvrD?M!Aa9u+Z)T%HtMWsOEsJF@wcE{OYpfc*n
zQQ6ISg@!@o5+^Q&PZUMUogbznH3}4(Gh9K-cLk~p#e?T*!X&OQU|1-fe8GS)En9k2
zANu@(V;6e-m__XdfceQO#c7ve@pSa^OaA~PqzansYW>u`)OP&3V=5N4&%^F{-Kl_f
z*1Q`$=LNtyUWBU1`5iw>juwE{vsSGZ*Fk--Q*CyxON=d*nD8)u63I#{#6bfU)})3|
zxo#VTV2fAfm#C%$G?es>NS^tSolk~5)OX*%h9|_f-HQBdr`-ar6IGIyMh=|}Zg~RY
zxC;e8TfCHknm{nM!!(UURk!cy6z3^lmR%{O36kQhEwo0|Gze$WM31P}uG@q%NIX;)
zAN2a8Oo^ZPTh5pIieWkL;U&%mN0#QBx42n}ddL#iKrhaa6|emLLK6c6#uw7@3u^Yn
zTmY+3kGJXSQgQ=#&ZaK&{t!Bbir)};MQWkGMMwZ!R_SW;+!(6k3uu8f-M#yl;~OF5
z;!_aUP6F@aE=1A5NKK(hcUN<Wp&u-U(g;mjDGn=Q>8!GKiE>y3e6%6bP2swNKOEn}
ztXWP}@P#U`wc>Iha*Py2J2S2GAPJ&!x|H=7m-OULLBUNY{U=mhOT|wPMpHN~vVk4l
ztD)LT36`JXDA5>B9q;XCz*UovGzC4BWBu<?kYdyS0QTXa3HcI9=9jqCwz}vw=B1aV
zMU1J&H?=0w7vxdfW0aTO`<7&igmc=+5WS&1*r^)2ma~EA1{?nXoIYxwscsJ8_8mt>
zb=>M?&8r$m7NFOcR<zLMLq|fv=eDYU{ba0xy#D~=vr9~`BB5--#>ygOE=yTSX;4?^
zXNXD-fqa=^d1P`BS25)!5`_mKdVyqXgMabRVCB7M+YBb{Z%x3+Dgg8qD`M;a0I`Nx
zlRL1!c(=HPOTbIL%fir=v2HNxOHz&=87YIRE{Cp2c@t@3PO^GAlqub?vB3*E1%*Vb
zvIpuf8s;P22(aeBh2icyk1UztAyoM=6`2LSMUD>)u11$BI{3KF6dBxhg6J;^hpkEl
z2i?Hxu8aL&Ysht=Q1Sa2;-FEe)zxu1ZaIIS6$czr(gu3<dED-c(e*6orA}_Q#HOHD
zDa@|yA%Id+0l`hqEaKwfQq!CyCS%Cvg_RX*CcLHT6f#TQ52&KZUPNvI9EZVcM^hNt
zF5bm-OJa<ms%!}et6Xu5Orr*r*9S7cf0ZF!Cp!R`=o^e@<sng^T^POVZKV{o8=qd$
zx_X@Y?$r43NQeiPjfd+fRtvTcY!R0!X6ZuD+!0@b?O;a*h}A7fjv7cYJCbM}{1~oM
zC_u^zC^Efa2d<?IwgeU(Cpe)`kPp-lq7^r`($JinE0II|MlObK0-6}Csv_@rVBC!*
zrKZkemzxnuir@0Nb#)k~8_Zp8!S>*0gsN4@YE^;saLS-29$=%J_?9sW{U$N2c8`;$
zRP)~#Dh*qt1-eiO9-BA=k*i?aK;*nw#1V{bL_=^JckVV!+k3F6KE<o;#7h%7zXRma
zF&hgE8onTkn?77Zj>^C}uW-`(HYwNSX%@ScxjZ87)c*h{EQKp-;AVBq+#Q_{?9Qz9
z9a<CzgrwN~gP`A`^8)=X+prcZvL%7ay7hBt!kTZI$bzWno@Th#R+x-#yH8{adD1Nj
zM!qJ@5T>eh!EVNbV+^<Joj~)W0_eVDM>M7GkpBQ+eIfXT76m{k<PoJ#9+evsnxR~~
zeKD*XpHQq`#E{aw(W_V!OI+48dJG}}LOn2m`3v%@Cg}0|o2I8y<?{-)Jrp0Bkb;BA
zqxl8RUJp_CwfT@MrZD(`YzU&}#10nfv3Af!_`Fx7-DC^X)VX=slfl2#USRVb=VVH4
zeLTe!q~O;uHE;EB@pC8zL_I_rOM<_=r8SR;Tz3U}_7dlL8e@fA-9JSE`Kp*i8l#t}
zDhCzvW=a-}RYJ~Ibd1P=amb+Zyi80CJf0?vB|K~!Xf}+bo<8I_kygvdx$DeZ1Xr#g
z-bB&17xybf53VF%MJ#Y*23vUijDJ9v@tVJI1QQ?GrTvJF@nhNZS-a^MLH0}U<LjaX
zb+B2-?TO~EO&84@ikoiKAq@k@EA~c2VV`kaWD#>3_cA0nPwry%R{_c8dL|usuPhf0
z%bT@w%oWO1IK@TBCv8kfJ$1AFB82AyidZRh4@O+iH2lmOPY1c?I82wKMF9_D;p-7T
z@);dAjVlm&7^6K^)L#>J1CO_2-<r9g=uWoo06B%nIa%^Xx!Uk+!?=iFSW8z0%`k1F
zJVek{rBIDt*a&(~7)Kdo>W&Cy7+M{3i!WQJv<Xfg&lXBXw=K)QcX3@yE_F%^vd@PG
zRFblTMSPWT4T#s<DMpRU!j?;&?wg=mkl??=aPth4MT;cZ^w9xx9TQ}I4(tmn3Wge~
z?!>fLo^<~J--S7?XCMq<hFtjSQjTM-XAOq(z_y~V`xAC5I-;=jC^pTJvCZ#i=B7x6
zyO+{)HYi42iA7^`8E5x5)C8PrRbws4sEbsK>6$GzSq*}p_7RO$d&{}#I=I1W`5>(;
z^~!9xL6lh8>4fQvlgtK)hbPL?QTT>p{Yr|#XHExeC_(SI9=*yE)bjXhWcKJY18TPX
zM{58gFZl~Oyfcl+Tjlaj?rUNY(`n+_gw|7v%n=oI-EnmvA%L_wRG2)a>|HNbB=dNg
zImA!~EAb%(xEQe@ECCtw0otWKT(k~|y1`Lh<BJPh2$_R5*tU3l&J#kEac~iIaHDG0
zs^Tv5lG5Tr)AAjX)NA>1s{-7ex@E7{o1SP^x!`dt;#Qz`tRbx2Qa37v!0w>p+WGxH
z8N=8S<z^I9*2TSx;g3WFo-Ok@ocAo2X|!Ul71Lye06ciw6bieP?R@>THLT*%aY7p@
zqB^6n&YYXZs1UP4g+h><T~9PbcLuMzmTL+*&F;C5hdBA44O}GC<ZwqZ=|ZtikrJ2#
z3gTKR6rkvemKFKpna6Q#y9fL>7DyBq?Bw!9;WS>1BF*n~+Csf#b!T|K&jbf7&wJT>
zAgubdAp2t9+aVh64M?zoQkKZpv177~BGL>=Ho_SFM{@rF${KqNzS51H(6D`j3GH5k
zI^1k0lCmLzpJZSZ$?H{yrX;}5nEY;Vsc{tBhNzdM;Xozje&JGKALHfVU&|UuoLt9H
z>QZ+s1q9A#gMsi$m*@3IThv!9z*x_~1%x_Onh3jbm9U{6Tz(iFrKD-Nu+rR5Y38eo
z3|(9Fg;cHRsb$qS5~eT0yM?)Dk#5#EZi)b`0*%dej@^u@1zJ7xKai+9Se`4XSp0}0
zc%_1mSWZ`6W{`AM%mY+xu7>?W`Z(B8(4UL}{bj+V)<r0yj^%kBPFHTIjoB1a#t%i#
zLOF!jTf48~GKPub-aa8fatZ91Qaf>~2!50f#~AOJ*HLpQqa33ArZ5TP0QSoE*!S<3
z-Af=RtG)G+xPL36><Q)qR6(jnG1m2_g;>Heh970hLL#i7dJj<>?4XWaBibe*x0D#r
zwg+@dhWeR&Hd$pQ2GuLQ@fxXKnu9>n(%SSqGrzY{=wYm44~5NNT{Q9_!Aot<BKb~U
zY_*nh&XUbEOVbkdg3mqVJj!r+nxoqK@tdS#=Dri7s5M1G^(h6ZL95TGDWMQ}p(=s3
zSmdGLj$XGtx->z)^WPE6FC)ZR2I}E#uaS>vqP@GejWe6iJ`g{#C(e(b4}lWt1uf`=
zP*cdiz?H6bqXcX$=k67T$y?ZrHyrB|(mlbgLxYj*?L?r;>Q%Dmo)5dZqvv|Z=>)6T
z14YcaEaUAx^FP))<X!NfOYSU#caR@wq9wDupKwk80PIWba*$oiY8!BpEu1h<Cn$E^
z{{SNm-8{a77`k}iI)n>)#SuPyY*LGH5Hh{zl{UWM-GGRwjJYbR{X08%1eadBe+bb~
zK~kgFhQ5<YFF0J8W4RVMwD<=JcocVfy7yBn4SmIfxi73hq;stIAcG^vi=npq$xI9a
z))ydvL2uk4WzK|ZiY^WWsi1s%Y8ag|{6!@O3r&$xmg~)~A#~#MR~fl!IzoU7-|c^K
z$t2th9YflRFq?Fu)8^{s-QaI^N+5L!H4o~s@Ek~a5)Ov|`-+y|lbR($+oF#|p}LMd
zx%K%R`&1}izT^QwW~oeH#Eo%^muRtOgQ)%$P~pzvhyfAGv6N%gX4Pr$aEoq-CfR6v
zQlp?!@3x{QRc=aXbjLEuY*4X$%dQ$yc^9}Bv^ZC9sbo^C<}9ld=4iYMypx0Pz7@eJ
zYjKs-AoS2Jb}DlkpP~U8ny(;C=Ml?|bsF@)vn-Ahj(hB<{{XXtf}`ATg55}uYWac+
zYn2?Tzko(Vq9vnzF~JzZy!(B#%y3J$%!5@j-k#tdN-63Z!Q}d~a;ojO9wX$K=o>_j
z4Fd0IY*1NFf<M%pIl5;-2q%W*ZLvc<L_D0#Qq-#J>Ry}Eh4W-)fTY>KTVRBRngg{S
z!c#&mW_p5#7NLL!4Jc{_M%E1%aqBC@%^JbE=9R@n1Fw6aEFI8}3`eap5rj6?Y9&`!
zhQU-T^%N4sL&w+I5w^WV+pn^1EI=W2(56@}1)>wgIH>AWzr3aRirOv!X&xH6Yo+Kq
z>TQJ<N5V_BA&JzZmho%MUbYLT-^8~0u?5zyQ7x5#)po%bEMYl;*79XIfpmeXGZHfy
z0D3Vf(C1BR;MK9}Whh<(<ai||njaAOFO~4Fp2=f^y1B|TYQwFD>^?&+O=@b+RdvK3
zqeiN(`_;!GRfxZNmk_X+UA(=;&IrFLw3iMBx3`DG2^O?wmq5m&!V?4GDpjLh6-I+9
z3r@J}l`Sf`Bv1qG1?(+acheCJP!R_@>w2cH1`^FG3*u(kt#gzvT3lJpYX+eLfml+R
z!u8qrZsEQFq)(w8k4s2M(9nIF#*VQ#0^2VS{YK<jWlLsILfEw53LNFF>?K#i$2DON
zB_+738>qOTrtiJynPi=p2$KjW2v49|mx?zA`i>N9<0iFo?J|$3L|yS^yhfyTX-1gA
zH@*laY<xoZ(-Li_g{`DltIfAV@eTmmE{7_L_NH9s{vuteF23gpMQgr1&y0fIuj*Dn
zL$iN({{V9BSD8mMv-@()yZW30yuHe(w*r*)i~-T7!xbt}KQmpdv@>QXK8!YibU5^s
zRxkemz`@<$QR~C0?Ws*U_$ZL`ZErAGM$U>iV8O+Sp>00|gxy1_V7yhR`>rTidM$!2
z?EVQxCaLU<VPzAhgre4dI9^36-k#TC5i`Xg4d!p3#*E{2JRA`cSUm_MxR%lGQAHN`
zowQm+50QDXW8^Ro@l1~oYp8dKqtfzkizU_7#daE}#8gCbs2WP{RcgO@3I0)ofgFj6
zEUtKtJm7dL^&!LlnR>A=Z<6**>R)5Np{F_so>&Y*%h6%Qu)Tlu_+{YUspRs9iD$%N
z2W^xCHs6UAm!BLHiIkrZCm}wDbiATt1#7c~=y;J|>-n8eWF5DMsdCYpDCmKBzfB4y
zJ(wTXyv0#asr`pS?aFfwecD?Tn*c3Wz)Lh})6D@5PQvldqy9Zi5(-tSJSDhHn7g2)
z0eM^E8JIz5SMdpg3l7NkIViErtKWmBR5+;AQ7vaP{3=xJ;1!cwjq7oCQxfBHc0vU9
zDWa+h@Czy!=7Cx`JFibrtTueI3zeF-h=;Y$m#7ObT}KFC$my@iF9m@u;8Kae7k|s{
z6K$3`566~x)$bx(Nz1CWU+_pFEkK;Gn^!Oes@4v!nw+~q{g)C7?A_}#<`aI=7^{_o
zehDGagr*8`^oV^2;Z!jAm^`T`8VLjoYB^MEqF79ofdEms4%KG|+W;tAkF`bRx8kPw
zV0_$1>0EKlRSLSR5W@hMfVs<WbI0~;@RzlsYq<E=?Ltt-I!%5QN|7mtx+EL)>)2uf
zEDK)Z8QkZJsm+XIwc*8nT|x4SCJ72D)nU!t4QwmaPbQEaE*x%5tD=(2@WmvvTFe~2
zVvM1niu@3JB+z5#kTx0z^UXp~q%X^+G-Ewm%$Ap02SS~T4lE%>aQrtKuI><f{l#PK
z>LXqqttgh*x-XuS7Ik#cXwhFzpgD)&M?zIX{Bsa6__Aq~L0@h+1l`!CQ~}%QIwCtE
zOYG4tP%oH6!<IAR334fAVrVWYdlo3F-)kPEFS@BoS0m_$xP(S%^kodC_K<G-8ji8m
zN2ywk72C6yHL~lr_Ubu5$SUOaKU^%h4^0%XkUFB+ls^z9?dNq;0YQ9rdWJ(^;W~+k
z-&MsJMXfHdyO$QNC8+42%e8Z$j;e|l)O124TkX?!lGby^@!+ooPIXucKVIO2vzJ9p
zY(%u?j^UD<0#%?|^m7u0R!SqnugVnZ1a`^L?@=pMe85ypG$<CaH2(l#hF55GVAcx-
z_a9M7tqxTNs=4iTs(@7_YHI#efTd3xQmD>pc_WUrxgv&E8j>p+R=D*ktaSV@KXVV>
zp_12=lLdjN!8)Cv0ws+H)k1A<o-Grfoyl@;fYnl<eQC!9WXo_)o|0V)LEnfXDwrri
z^dBHgwhhOUBgl;qsoiQ>#dyf?$jMeVexU^13)6c!LKoupRZbMsizr|^9QgCpwR8)A
zfdo~a0sVuq1>}|3!>%k5D1<eoie3PgR6sM=qE~>fMUS`*u9QCB3l<mJVCEG$Vvr92
z4VB}BW&3;>CuLv?!@^DO7IGu0Ot*pn(HAWmp4&(ir0`=7OR?(Im0SXS3F;a{v^Z~q
zToTg3=UVp<3Z};Z+k>sa8e(Q&q-ZC+VGV1*t%(hUPJ%uZ_(Np*3}_Covc23mVauYC
zJM44S$FNAphuqF*J;1EruM*fN_thSj=#>GJNzlQ;;qslvZZl(2We+5j5eMR2scUI|
zCr(8T#;{b8IlnMRNkvn9{f;xl(V^$=R23yo6S-g^92#i(EUw@kw5TL$e%Lx*;7p=?
z_PoVuQLgpL@sW*^ht9$-@6~dU7N742^neu-iDO?4ns2kAdWd0YFVGPN{{VBmMCw;r
zU19-39+@b)@Y|Y05H{WRDe5ZJijMV`ig$9F<oq8m1f-gd1zxx%N*kmfJnW@tTij|6
zzW2T^HC-zBLa)9T@tFFBHeFnIKq@H;ewpDt39PGPve*Jv)u7|<03h9RV1)}_9Q_eQ
z$rb+q*OpZ5No5N(YmpCHoZXd>QwVK5L&UtR{{XsX>Jrd^@+6`jiMqd*YF!gqI61*d
z*7_uz+~L%wvd;~2LyY6y6bkU_TAQfZrV?{xO}l9x<&4N#+%?Bl5=p#ntBs-o9xbXL
z5}<EJC)gtd_-&850CPZ$zn<YwJP05GHuj6CXcR&8f<llhIzA$>Y!$Y}XpQg`-WkBM
z1z_BzshL)Q8qPkn<?h*Yw@g<^YEr<Zx3`$#$~c?@+t)EAC@m*7p(3?#a<=~f>LQ;=
z$+h|;o*?D>42q`jQe_k^aPJ$W$P|cJzTm7_+NG95Jlf@k+!l@ms6tZnux(77)KUN_
zoa8XO1`4SpIIR0M0MImDQD!vPZ;l)JF)LMmd`8+#V_gCHVR)&v&k-!v1@IL@&AB{9
zs)18%u5K#9)nZcRS6pl}*jWA+xQ%1Pr>l6xu5J$sWpe#};Y||my<ty$Zn-=7!-$EQ
zwKXNndqqhtp6tx){{RGr{{S2?WeBXyUhiOu8ZVjhLgl#(!(%zTaW1GkJas(ha-#<`
z*p}$2<{XICx7)PfzA1qkcRL;*v;P1&BEgoqmc<5o4v@jgg4cj@rPcSM)8GsNs_N<9
zlXtOWsxzhj(;rb{{qsn_W{nuS)wZzc3|s|*9=n4gpYfa>JO0JWvnD@kM(T5He)EHx
zfL){styTt~P5%I_d6F8Q)k}9`KJ^ZThq5WzXtCGC%d4+(2M4d1P8UJLoY5BdEUk~+
zvwxN58&crBBgi`v0UC9GsQ&;zDL`7b;#;^z{ni*n;W+?wTqMtRwOiH%A+0R;X;eeI
zXa?OZ@l^JMV<$%X7YTC^ca?G|(z(s0x{buDH$XKRLp-l4?Jg8PKalcD+Mh~|Zss#z
znbXh6*`7-fRV^pHR+v_Z?T*vOnc}jnx-cTCop}3{>?}M)Qf2IUD4$h3#bG?etsE|^
zgp?y|QEcPEQ`kUKQlE8y5}g!_(+&6k0Ha}Gn*tsHL8w4XH3so+?1TkfhK1pd3eXH+
z1K@~8%1*9rFQxE{fB?c4bAd+0y;aMJL|W7!O%&DQb8BxZ;mXFSTUHTCo)wb*35a;2
zA0A`SRkEA^0OQF9T-=tNE$ky08fycRnPHy$=LS|cYOVnw4N=Dfx{X?1ZAJh*DYSMs
zMyk+!%$I$fM55G%9>|tt*kBie$#SDY?~x-*Hf>?|ZdTJ_lJCIhh^Qn@t80Vuz!bK{
z7x*R2(n8g}dzDu7%IclJ#7%52rq@BNUq79w0<pYm3N3#XFR(;IP6x~$YPaia0r4w#
zL0&@+YZ9)MlC6ynD`>@GqC8aG6CLB4<RUYK$R7SdHtV|~Zie(0WM&zcrcyLT>By6|
zFyuR$OJP!@K!e`-@2D6p74fQA3LC5K>R3{USI|@gk#%QLb-<><YPkCPit}dxiGaUx
zbwIIzQBl2oOju+}21Mm7qts%6wB~Xnwj2X&1z3UCqYa-8`XCx9?TU8hlR2FUkS}Am
z;%bA}9Yk`XU1%764Iq>UiiNtkY3K4)6~J^Q0QL+>!PvGZgzf>gc^0klaH!#X?e;xX
zO#6T>3i4flqy@EToMlG#RtKqS4N})*M75pR15_cWD?$}lREBpb4|Rr+F2w7eV&hbk
zo856PFjX2G^7gXr4nU$=_*qq4*Dk!4!OUNQY6Okd8axxelOwcW^@Q)R56Sit_sU)|
z2$gD=LW{of!NlBvvQPt6@DNO+?(rt+bfrZ{3d<JUoSz-Rq&+c>Q!&V^a-G0SyJr2v
zYecTV#|>TGRP$^M4m_NYI^R{t(9KBw0N-1YI|KZ0;y(2Ev4||#4Ep~7w6>sQ9sOOs
z398qcxn7COXtfW7vNDxSy0cDWHb$V!kldo$CKR*tlTPds>xD6Wxh`Cl_`+P~Y%Pdx
zoDd%lK${gAde(*K+Gzp6ca5m_@{&q~8(w~R6AfT)<v^hTeCeiL4FFFbBR|&<lg#0K
z!k|icBH3m*yNBfrX>kQuP8CAK_{V{$6D^MW1ZBvew^lfW{W!l`Ab-FuiS8y0LNxu&
z8DE7Ld1a~uUjet$Qm$+(b6H5@f!(z#X3t#<LA~dETGR&)tkxaSTJLLOp>|Vi{{Ut-
zxYhC)pm!yG_QY#^&xDMwIQ#(R`msRht{{lmz8;YPUeIgiU)q_pR9boe07N>0NwTn(
z`xy2?N>HZhrC&Bp>rLfMlh?xOaK>uY<f^I2bH5i8Z+7}gU-@rDOT_r*O!5&t_4Qxa
zv0@@+l}sAyX+GIy1krwU{{SGzX-&>H#}+A#X@%%3nM*Z7>WxEEb)3iHAgC|6g-#5}
z7++!$wMWZ=0nmHW)rK9q(6iDpLJqw9Vhe-MH4x2+Corl76=(}c)olo-{5>#jVX`_x
z4R#3c{H$KF$I}vWxG&1ow`m2vgHrYZ(aY(FFOL1hiP5=T{{V{y&Z~jXglE7<*~k7L
zlZ%;GXl&Lm)+bYE&+4NRzqM%K0BSFVnjt(ukUlaX*URSDa8)f~ENPKKQ&9}UvFM9&
z<{ZvIw*LTygPs<MLR9m01q$0)rA7fg4jW~S9p6VE+lM6zao+g%aS>WUtnr>1M~AU%
z%s)|70c?g2;UG-R$J-JCYLuq<jh+7hW7Um`Va6JI7=hZ4{9^N<9|NzbO&O)KnYCPa
zTb0vJ{)v}x>BpF2-U}N~EF81Br;8=Z)mklrM1X!&h3K#E+_k4T-1C%%#6K}vJ>s?D
z^)*^xjhzzm0a$b1!w5y6_&^l31*>eUl+oVAXr>8zjibFlk->i}?iBNc-BEoU##O^r
zUa>8OEhM<z?k!W_22<BkVqlzCo%}O#%}S4&GT;g}qt_~E2v#e5$e*(;J_mnd1cBN-
zy(IC5%zf3!fAF<aw4Hn{N%SoX8gQagd6CuBQUiWr)VBf!G>2OmdxKBDh=Xs&#F;-f
z`ivboK8#txw1`0yilxm9iCehaFZS{nE`%{*{lpfDhera|v9?gQ$<U}o$EJa=ham@j
z3aKye8A&vz-;kiDP>uu<LQa9OtOx<E16J{t3E~tU6NVY6YErIK4~15A46zWEyR0Rf
z9>k*<T&aO`AmRWeQ4mZ`6xLL_7Zwo;17-(j4gUZ-f?S|FTI+s@=-6~~aUm6LwX2|l
z6z!Vh;FhQ<-URs>OlWdi_?LHk1KxKTql(+jl>Wj2cb}jALUFOj`P2la&q1?}2z{jL
zyvw8_rowzG5Vqwfo9%X`jU_m|`D2f7gBxugOJa?#v0R=!%cY*(P4O4k`NSHjSo(N|
zNoI-Uaffrq9}&Y<_3j%h77A{ALZfT|)4<sM)VHgCpe2o3_OjujRMG5@a3X;UvE^lE
zhi99D(+fXk-c(eibqt{qdDNulD?~spgIok@EzU%9eZ`?F<e{<z7Npb}3Y-`wxR0IQ
z8jkZQMIOjR-EXicrHkM0O6R)5i+?^P3s%oAeQE-*imS`T<wWW@MQ2c?E!=_jf)QMt
zD%85-rJL?QdcDm<$1u_qIZT(eoeow68Wq6td6hbUYE)X2@U~1th@M>c)Vf-lB{kuS
zxmWWW(OM<fU7?~m$N8X6CO6Ea4U(NSfKd90<-ftwU)0ukSyeizWl-D+$r7e;?UkGr
zp1iF@aUc-=m@}$fN8}@QOD!h_8`!#^nr_tQ^-*-ualzeD?ye9WgmLjwg+qJ=9J>rs
zj}38UaNV~<WExOs=(8Jz5TRJLE1NwQ@juw~JhLSb52SO`taG`_EKkLfx#CjdYtiP~
z;=_|;DE|P!mYrW*p(T0^ig}1GC&c_D<Mm9b)`2Kf>ePS>^h*-(O5t2Ap9D0<0_<oG
zcGv9ZvO1{1QQeHD6=K*>Lkww0fhnWRqE4$_Dl+qMenuIEDm*Hqgx7WAVUD_r<y9#m
z0N~5uvR(K80LCSCSTmwm_F1<jUaw!1pcxRY9JG0a+9Ny*-oya4H8pk}K<IEn($XP?
zh`E&*H8gpol=e^Y!9S)Zl8b`w1t7y-1840cXjqorb6y}bbwqO8cK*g^<=NmkPIwsA
zzQ{nnuU*npCGs1{2=KV7kA*{lA=Y~fGzT@H1POw!cZ!nWA8@|7AGTP4<Y;|DAleZ3
zc@mov8jVL&5E3u=Ows{+4NFOFVXOQ}VQ7~56gYWNL<I`kq=)OVdpQ{Ld2+%$$mOR3
zhve_-I~E(@1!2#QpcJc=k;YCXfreAcXtclH?)|lmjDs5R?E%GQ$!lB6#<#Ovpo9jD
z#uZAxrrZIATE;6*-#(bt>k|0J_94R?0lyNG_GwfD0Gac9R)AV473J@98Knen@Hrt$
z?XuAGolNMQoOcWf;cJh{9|YxK=ZMTeDQi<14S2K^5muEAU0aCtEJ*Ilp4hHAfa?Rm
zbM@Q`t`D>hky}xwFVp^$*;X$5Rsg~DZ7zvtq#u|i(+?!oNEC!FrnH+*MLSE38m29s
z(l>WX-eML01O{0F0ty5Rr2|5mUNM|`ZGoh8wj_Y;6b%}(`ZPk^;4`!>H^>atPC|no
zqSCCi7lR<3Tng-Ljv-d6VM828HraCO9C==5bITngw*t{m3ZWv`^{4cKLK$InaO%qI
z{f~JP*waD>CEgf*%T1mjEV-9Z52gnpLv&@h(zs7QnAT97dLat^_66GJ4u^mL02Umd
zT8S&=l%3zI;TBLVx_Jz3#G(bjdyBZ8gsP&cflJ{f3-wi(xw;SzsklR>t!K`OWq%{-
zi4XfY+plCTmkJSf-%#s8OkUJ(0ccyH@@jCEhj;*<Y(>hBf?7h*e`e4ZSO9L3Qs$3t
zEZWZQ95qMzU~g!}(4J?{WnpxVyX9n4NWu-Qv|oW+9!L~2R=c?tmTU9X17f3Zu3DNF
z)#d~ZILH-Hm9vfzq3l)fK^Ng`AeV<#9lw@{^2&m>Do<6j2ZU4st5_Z&zXe0Xc(Qma
zHsu;0dt66{A2-yywpDqU=y3=wiuxn}01+g!s8DZ+7c%_#pwuk9i7u0lviL1G=hR*z
zmB@DNjV-Ms*t-149b2)Pimm(5M<t_Hs=m3nnbz(eSMB0C0b$S&*&e<i7Ax7UaSb_d
zUoxl|yP(qHD(<wLn#q1kBQCuzSw|m}L7(9aZnWaec|W37u-QD(VKmfSFcJOQzM}#6
z2<bFSOsDBosHUg}2vQ?O4(MwKROZbDqTQ?jEl665Dv6gohP+BwVFBfUW5sNCjg?q}
z!Wcv5IIrwStMO7LQ<-9O+(5p0mVl<fLzX3y;nKM|VeK%c0ru*0Am-S51cH`yRh3@2
znIM2;=?caX;-U?5^|mS_O?hT1XIO1UTDcDlFQ@(PD`LX8@0*{pxE95=g=Jo>d+0~V
z+&hNvc`y9sIGp?;oi?ldiCoSsr&Gb~DcTr5qbQevJ$sERJR2TKr;C-;exOnC{y9I{
z%kdAOEqw$>b2kS0MQkhz0sCQK!+@gl1h#LCeZ(@C80GHvjO?&N#+ZJDu(P%9cOnVR
zs~olP9TUP*{g}$C4Gq<Ci47+JY>S9WNPBb$9VQqI)!=pO>gBAJkEEul@0;#rd+Jd-
z(fsKWF|~cmpi%xp2DnjvMw{vAeUgs0*K*u>c$MUhUvXnX6r_sB<(HciSOq{_To<>+
zX$@IQqk@g`>I2jo&sZbWx8K7ouoO#pag4t*@sG(f$k^GX(vcTG&Yc1RI$|bUY5xG&
zrc&5YhprJ0{RMH&+`pS}h#_Knd~yjkQ!_~YHjCpor^f5!AV4n=?%ZnT`-EkIFJm4t
z?NVCSs@m?}V(btyIBqv>(@QtPKu^O6_W^0_#I+#r4MjeE%f1qty7A@@kgi(}By^u3
z#TQFOzO>sSmD(2j#dMa?ScqAE`-?~NrzoM3YQiUv5F@vyd`q%%9?Pq!N5Q8H-d5WO
z?ceGZNbRb=1XcaC9Mu-s$7Ug}d?Z~jjqQZqR*lUJ527pucw72CO5mE{M5oyRiqP;<
zFYK{^fd@v>81Dhn$f-koNL}Zs%bBfQQ^aeGaY9Z5Q4>Q4xfQ`KEm)V+XJq&bT4frF
z(h(`8@K``|v4@y$xUgmz$M=(={v@3$edr_<xo9NiFsHKpPTbWu;CB_yS+d2ZEJqgC
z0tST@a)?&V`GQDiiZ3+uMijdX<bw*hv49Ip)Q-;_bpHS%MyBulV@;=o+p=<lZ1H>w
zz}a~_ZKebzhI0ztL>EvR0t|-H1aIy+94OvhU{P06im~CA3rdx>`G6qNfRw#OgA|V{
z<+!Up40DZEux)uxarlMdxoa%YHrq+>COj5R*yafDRq8^42S|maS3!{@<)vCg2=`;}
z@>CUCtc6-!@5Wr)98dj<V|}U@adY&Z{{RpV*2uIO2!))gX!G8MQ{a3Uxq+gWmI`GJ
z=D<aF4fA}!(%7$F-w_wEY6M2lgsEH?DRyw*s(<2_bQGd4P?Ciz)|kNxKQI=?VQD<$
zx5Q>^Ta67EL$+uN0+dM_YR`%v>?3^&ev<?uYsb{BBA+>>0*IbjDO%rOt}}wS*J@4|
zWPn&uu0l{PP&l0N05Wp8pxTwS1!Z#adl9?6bR;tFXZyHj`__Eh@I$Yvl}Z$YO+ub_
zX(#~fVBRxQA1|X%))DTMQ*(Yw>5tfPJaY)vlw&y`bM794wFVwuV4Sn~%%jRP;MCRH
z@g8{E6re3Ih&7F6b40k{%24$78B~pSE`ri36O?)4VMV}EV!e5S#4iay{9|Y!Iqp_)
z`-Pk2lr?#_3qjxb*^N}r`}GSmS$SN+B8H1%{mL)%aMv@@_t>og`C?Ho>2N9RHnk8t
z%LfIBw2xB4i+K#YVQjwXoHsIp<aoB=tpuj*_3y+LSY8nS0O>4gb~P+l3+j;b2?BxN
z3J^=bw)cl<U@=86yw6|Kl@WQXoltmwTqb2!_Lhr2lI<y1QnS6dAM@f6A{WCX<S*z?
z4EM6mr4Ie0rBVXoCJ7p&V@CLw5@!}3P{n|O3tj!**dHEdRO6Rq7R0(nui+zQ3Wzj?
zccU6n^K)A>==V5))qw*11$~l|WqpB#>9h?)2iq8Bm-$*2sW#<UH$AVZLEeq%+Qq>o
z@4D91Eb~61PHr^#iUE$fV%rA}ud)>s1CRVsDRAXL`D3tcXr)&E*=z>u>JS2G`3Yie
zz1Nb)SQi>nzEpnV(lDxb)B%GVRg89xg*0B-S&JQgsRWcbj9)BjxjPjVHM?^Grcl>^
zitQDw2su`P9F9J16?4~<^pQb7ZB~`DR>>d-=}67wc;C2b1Bx(#Ao2kCUc=qhLm5z_
z?-9#&k4C*R9VgskPiMutMNSrpk9YAeUc+H9%s1!6$d7ZEnf6Pn!O|@X4EB)5EfmO8
zEF_R>Phrw_E7a^P(ylV#tVP$fFq2?`O##mkmd|H!kSoLSb7AErP9<M8)9?h_fJdS_
zWKi}6lkk*9+0!11V?>(&073&xRQA+O04ug5P!E<8{TRq=yhIjCi=PKl!AD>zYli0!
z`4n#Au|S?`@%%&H)ccnVzGdVgnh0y=jLL#|V(tJ8AkXX_%9hgoGPz2C->1P1WiNls
z3L$6$XCbQ^iZZCD1QjA%2RnzaOfI!EnBo5Nkt#8hDxGq$1y}*am>a6SLPeS?kw(g6
zwQa0e7~c>O1ZiT^;bC_@j3BVg7%2Bb6f6oGR)O^(%0~rWyrOMu7Ta`qCEKz>v{r|(
zKlOudnRTBD2C6xIMp~;OX_OeA$(KmzF;98a)9^Bw?0*v$qCbS^MuTd~<&-1B7|Z;s
zZ`zkN9y1Qa5nBL0g#&PFcpX225J5|ANti--Y3ebEFbpCum=0OinRO2gsAW%q@c`<o
zcx#-2A|l*;d5y@a7h9qdyVkmaIZA<VcUnB40NAQ^SW*Q~XCPnR)LZ8C1ByX#>5YI=
z_|T}I3>cbWWELwcGsrCQX|;Ka>?)%vdbw7LjStAEJdu!?MO{0}@O>r~p}@Bk0>j?r
z#6c473V=|F*MjIuN@_j&7?)Ph!plW2=J^g~Zt2Du0;mQFVk#v(r3TDLKzWC>qaOxK
z`{Q;Tl~S!z^%MsMrUN0P^Eeo4c!TBqPBnk*Yk+E7i)dkL601ir5U-T&#v<WjlxUFB
zf<A7&K&=`O=|Wv9pl^Vv@5pOZcxf6U)5)D*%tr(9xL3W~w7aQl{1cSWizPst$~-|;
zDMQlOYq6X2TDbObPi0^U5Y-GzWpu7hsZmz=sPqjv>xMS=n?u1Z3Xkya0xrk~vkX5~
ztEb`jWIIBBws{kY(xZ;vAgZt*38bW*F1<G8M!Z&DD&pPRwO=GUw_}&L)K|}Nh?6UP
z)V;B28L4El#x%OpdU$MBRFI(QGVmJG?d87&LzHo4X}1M=M&`1fej{N34Fd~arHx(f
zsH);!Sva8Cr^4f)4>jdC0&WZ5;~EH3^prbK#89}|ymG-kg1O|WmmB>Vj2j_^7Pkay
z7OUACpEa?IU=9E%hwE$y2-2CWAMRY`ui8|yqU?MB0K<~{sLGtmPEQfVLuqqhN?SLQ
z$#V7$XIBO7<1+j;<cWUXjaB}@gbJkg<nhGl*yW>H;G9^dQs=mZ6kEG2C3USG!yB3`
z`k}a@xt!%~O0{93tF;B+dA|C7+0S(|0ND)?kui7!%-%+2(iCCG?GzH0B(bZigK;)X
zRHCRcxmX?xRm~^~icnK>);<r;wOd>A!1sZ?K^H3?3?MWGzNPmAfz|o;LB^$hAb%%=
z!h2>OEx3Amz(w~AdUOQ|(0~@6EW3j%=q;apN<>dBN|tQ};=jK!c(Hz2;QI-63?dK>
zr*+4~Mxf(bt04<ge<f$#u{O0(UF<Jjr}irjoB`lhH!J2@nH1Qsnqtu#(Ya(bk~%wH
z=0_q;Z6bM)>=OVF#cn^(9$pR$3PRra2g1fW?uP5Y{bIVB-us5AY0k>tAkgAD$CxUW
zTI(pjeZ(vd7=AUG;RS+%)80T&pk1=vj~L<Y;I)cd!}TW!%KY#~+t^u3b5nB;CbkcJ
z+6AHqZuUfB=ASv-EZDp9)CnZB$=W*rbq-0(JG%b>(>(k^NpG`({{YffImnc{*k#fW
z8JCS$F1oV6*_*l#xK)3ss62!CW1->VXf;80-VrofF`%Aag|^kcpeTgWiP}Gr0E3};
zN5DW8s^CBW07Zj=t=VqF<&aEJL4rCl8RDvN+)}{Z^w~^z9i|p#FoGL}1Ys9z6_+Ue
zic$e*d#40zlBJ#3aB?W|p%X}>%9%@Mzj64-281}fTyJmu(m2S9c{=tIu0cVfJg8QJ
zm#d=B`j44WFU%6xrC&ursfcro)mN2eY}=}ve9P<_9sz+B{(y^@EFcdnXrJI=IkiX?
zx72O6SGDFf$usk7H(Xdu>i+<BaZX&fiopAnt)+FUX@ZXS7}PF)FGz!cC30~TIFz`*
z$l0J1?XsXzSeGpxomPd*A$Nf@x;cnKvWT$Zl^AlingX(}*|zN@rjZiVb=T@mO3~zI
z9ITskGV8<zC}G=HwN!$1-R8JV1qPNG7S)xe=<r;#adNZ;`yLrlDR3cPaEcvWa5j9(
zZd<pT7^ART>zn~ng=dY9q@dmti~54aC^P0emXNzcu?>XicpTJ7I=Wr?iBN5l#roK`
zc0Uh@XcqCK)xe?nK|3J_yL(aR)OdaYP~agx%~DP1061=*$go#&V`#xZd6-4yZeWGV
ziFi!}yOy6Myt=doq}*j;(At`IjHqGTc*QwPwv;TOkXVl<(2M(7Yc`E~(nX_1Z1Y@9
z+lbTLaHE#UZieKI>Pp(&%FoB9Z{AwZCeXYz?`-;j)=^Cm@?BXC(8lYQ&vzT-`pzs|
zLtd3`6joeyy?(|Lts70cjd2c9wl{Ed>OX6*yBmv*5K6SbC+s233Cnig2}8VHX$6{E
zugE7>3XdB@z<22=buXwuzfwm*o$VVaibjnu({8e_$iRZzhW>^B0K!>otA6gGwyE|A
z3-cR6uM+5VzXgLY+%2qY+_SFfRpxonpPd#Ew0I@et3E$hA;3N}@`%f|R{2gLIS>@X
zM-Vx+Y#8(rdu_}3S#qS}&?=;T4orMi3Tt?2577KVKnyXhe3?ov<z-++a3%>$5cNo`
z<{w(e-rK{$G!gkj9c;%hITGHWiN)Q8^b)ry^f8pWZRQ&Za9m-6ic*N`rG^9c#}w*w
ziT?l~3x~%ET}4KkI#4;-=8JcAC@gBNz9OqUuIedXuM#w1E_;qjM%<)dL{^Jf;nx!A
z_^4~i4%plCo{WJMCWTLdafBQJV(s!-9D5=3X#=ua^6@bis;HO3JQB`#jjN-?XfQn>
z$a%+pGtCUE97{LMs$5h#c2blLC9ebRfQic;A9iawR(@GF171F31+XYQl{-WNTD!`L
z;N}1zaPI#AS$=@F!vPH>sr!@eEmkkJRq6Ofg|}S{!bA$KcSHww+tPv)6h^FTmCfmw
zaHuq!XM*iuvf}kQ9Pl7>Wy?r&BZwJKdT8h7I?=4Ff2;_ghlR%LMkuY)-SaEKCf0!W
zPyvH(R&M!-L9cE{W5lm?<@@@T{({Dyk!2`|@GNz}x$D$uhAKbbVu#xlHb*|=RfMoB
zOXO?ynD-f_ZJtkLNl0`C>Mk3QXmNit%7+z0moIpDr2bSv`vb+!D~Ca@lf*&_fxtuo
z;y9tG!CuUnUT2R}B(4u>=Az97jc(2{&#qVtT(@oUucAJ|Xr9Yb;alP~R}j*q9QOXK
zUJ5oCdRYctzvTu~5sSQp!`L65BU=z2E1bVE;qGr8^#U?AfLI7#)k`3?Jeq9yDbJN2
zA3aTgKj9^dOYgmBk`0m}22yax<>(e*!(<C)t<9d+TF>Xwrx2ayQq#)Q4IPH^z6z~n
zSr-U6e3u{O9AfaDc%9FzW(adupy&ep<#gc{hs5O~bW123a@JjA5HWp=fT*nd3lHt$
z!Q)PTP7)~bJ>m0Ho`x<;4Mvt!`G3f!0ovUUWM?tiylE;fif*AaD#~he9~M`gafw2K
zKAd%D)2opiTH5mY{AIk*R}D*i!oo{l3A)4OOD2*GDnP0Q29^Gh{{VPiA#x*uV|cyP
z-c!_1mM=$+YsT9fp`har$-9bW>PoKVlfWBwhFOtEo@E9lR?YanAa7$_gb<8VhWatc
zap7!vYBP{r3Vg60&%;1%l}rzVt0(26S*pEozi>EM6l?h@I{a7sS<M=l2eMH)2`&?j
z+EpmTva7`#9vl5og)lY`Z~h>#gIV;Lq;mB6VOk__(ijM;q*53WR@q#f+V>sZ4<WFq
zwYsYLurY4S4eesGS0cnh%cZK?2gu9RR4WfjtAudg$FfqxA>@20N@#%FR@kET=R8ya
zTZ!By*-w>qHbsm-KBLkjiX33+=hhHutPKuIA8}2hIXioRJP`7DxbfIYu1`M^dcFdk
zghjE4uxlwufD)5-7q-PvTc1&ed3a_(5d{jZE1YX$@+!Dp%ZgKqG^i>|)8hau+&ZC(
ztMeXZcPau$2_<2zUE2<MEZtu$V3Up8Pd37h%FuX#7pHzAUSE2e2=>;3>leUULHKzl
z>^X5f`)13*U@5Xvhn82?`$V#BXcO8^BbHf*f<uDk7%t-53*}$+=RCmP;H8a`{{W#D
zCqW!UnUt@@rE;MIZv?*qV4O-SpaOsi$Wx}W08zaq;RlQ|OJw*?Dg=~XB!~eq6JcP5
z%?@3VSCC=JSrKKmEz9e~Tq%XxPBGz|mBPvqw&pDBO{lU3d2Zne#%yT|y_#ADt8&S4
z8sCFuoK`m*m<k7hy3i5YDt}ZFK+Z6iBovnLR!}olt!$$z*v8918h>E*R;p`CeMZ>Y
zBLQX1z9Gb0QG-P*+am-k4n!4R2@^m$)KG@ng8u-fR|8Y>EyEfLyDXIO)l4c}{j08*
zXQmT=m3Ei#WdZ|wW~jdsld%bBE>svrA8v{$Gy|MvJk@tBomO%_Ws?fFf>$c{6;ndf
zS}2soXd>cZ6Jt^@OB$#E2Yp<(wm{w@f$fS=DueWAh!`W|`M-?!>pYwx40~4!@(vy&
z{*XBgEP8yxpd=F*jhz5<K}0!x*ERf7!Us2H(zvlha&rJm?ux<3BvM)&+?nSdE9%-j
z+@JC0V<Nst*PDTPxz_x@btzyidnoAVWp#Vz7%uWbHw~h`URY-mjxQyHXHgWYdz%Dp
zwN*7<B{;8F&0QX_uUB<F+wSL-VqcwlgchA_wJag#i1@D{{+s<W<4IDoD#U44*8n;v
zi<i+Ln=4Qype;Tnh=CQjP$}?BKvAoT;CgmsMvYgI7ux&4XnQ9B5(S~{@IfGNXV>hY
zr(dR$YW_mtRx4nsy6cHXiU(_y>g@B>62p?D_{Qq1h12@O7SJ*!0jltjYqq3Qlf+_U
zl7VWN7l9VOh?(bVFskX(oU$sk+B~{Le|uhniN=zau2C|KfB~f|lpTP>8iKD<S{ubz
zo8*J<l6Pj0nJ=B`K1l5HD*jED)kv`BuVANXU$uY^uOVwU<7K|?;@8CE65aFZ19~LB
z^6Cok5xVxg1a&&=upVtMcMb>zv_OTWep)frYRg|KJkA@75y^@nlx%6f2PJI7MBimc
zRO8}ybsJ_FE|nmv%fB75P1*22_+VZiBnAHf%AbK~3HiMeY4IAUM&62Y-+>INl$r?q
z7@J}1vl&;SfISUM^v(RXpoo{fd>cA%#M^AQhv9HKOf|MX1QDS?FNbU^v>O({9aAV(
z53At+00{p8wmyaWkf~s$BigpgWPvB3kM=bDy73P!Hrm)n?BlZBAhoWXYw8mdK~cvo
zWlM;KO()3=5D`jrgbK-z{{Sv=UohbcEi1494>WOdg0T+_9%nl%jxA#9G4f$t6e~~#
zSce=hJSFtbDE*5LkqR}$I2^`~qRCaA$)!#8d`8UWN1#W#6NFMK6a`Vmmj`2jlq_Ae
za8PB6oPm*ahlTlAIlopepQb4n8u9f;8%r9{Kw$O&E`TVdiB%oQQW`rQE!MP2W6ry=
z#0|~G_h*4L<^2%5_Z49v${sI^25Jx&qUmF8BJh<`#<W*|Np2$5&@6f|&aWkTS-cq>
zJw0#sd-XI*ccjgn`I6C5FT)<dQXo^@z)j-jB1xmsr357w@AlopD$R@1XhUsj8l16>
z-l5dlwa9bHRy%Jc;Y)Acuy$9-7LSXLg#D6?yNMQ3%0mADxd)9=8mGiPdlOp3j8;t#
zo+3gejmnaOmUZyU?oFMrwwskmZ{)@~UbdHLbt;#_X;3C()1sI$d6paDk#yduc!XIM
zD`z|AS|nS$C^vir&vB~z6U0s1Qv4@bVQe3%jrxQzf`P2Fnd+t3rCE&EWjlcdmA*HK
z3<3+TAYdkh*TEHZ0h~ok`wd@|g-tr_brS}Fx=GDT43&?IGy?^NV3a@fNqH00K%*@Z
z-0dt?j47AZVe?SQK~<^|fNc%o03KahE}(YyVM%W9OzC2K@g0EXR0zgwaP)1<5Gn@>
z<|5ZQ0eP`aCCj8BJJHrSVjvWy0zNsKeR^z-)!2SG61h@~Y;fgU$-jwfIX8cDtOFes
zs26k-dcn2sA$G7b?}TOj5FAy4Gk0|@4x%?!2=oz8njd9t!@frYrc)ixXPX6e{3Uh@
z=kp+d_zxiie;_D5QguYx&73Dmdd4Pk7_l51A^RyJyT7k5g%=6IV(6jJjOb+|?*wH|
zGJ=ceSW@krTp-8FfFz39VXV)ZSAe&IC$13;D#PUWC^2e_+Ld|YbHWX~Nnhj?mE6A9
z#6du^l%u2|HgfdWBvCe>OW=v*C<at?vPT0gK7tlmvq#_Ok3AO(A4ddentH3Q8h|g5
zjHHw{6&)Z|Ew@hNc9fOX&U=i_YhP1?lpn^D$T%DH1fV$TS3G10AT}yi@#=iuPRw;v
z1s@b9`eNa(8DX3#^939q0S>A(1|+x|J1_X@FQnUo3wk!GhdzhP0ENyM5JnY<0?Q94
zL@P|FEp)A!A?ro1p~76fpaQPWQpGEZ+8r0Pxa8lM0yqjW9U3b6MT1rpO2_{Ii89m!
zgUE7A3$)$>DV6IEXl>!VV5Y9t?bb-GsYA;PZbSLTc!mq-X!6Uj*IPrPdL?$X-#%bH
z2ILx%P;_0@ArgIWSfH%(BLEkF5xVxOtjr@@Zv+Gu8Q&t%sVzBD+Nl+G-K5-$oftpk
z4&t`pUQqKHWL=zxa#$fb!b@%C-W>52K~nFb5Ldu6#n1ezpwTTZU+Bc?&xoR}m)x};
z{X(l+NGl*Vj70Ax7@<Y()oPfrO1}W;GLE8Bwq^M&crL}&2o_2m_?KV5#S}kKR?al%
zj~_K3tO^ych2#vMYWd6`z)^k(MF+gYAJ|ou!`is#3!4af&|Bwars_UYRr8Ssdxp_n
zw&K|T012`R9fm~Gj@D68Ou#q4sChe9s3<W-b3dvJFBj>@jI$p#1!6^k>Z49t0d04l
ziC0cBf}#C{w$$dYEQNI{{nrYstm5N-3U$0R@EO3gN*tkz)wDI8TS6{KgiuvNc}1zT
z`(d%A&ze*YQ&U`7JutaZpdRgcAr7l;#=wOTuXM0ZnEQx)VuAdO!-2_t*n;s=MutMA
z7v+`r2>cgOI;X3kFjeDI#HNp)5QYa{3YPBEYP2W<b}%2P0<6sXX8zz!w{<mTsc#Bm
zJ+^^ecz_&42Jh6{wy-^~l@Obu+eNkWaa}`A2aJH`V}=~9vXSpvbVb%;tV@oG6KKO&
z9@`Cl3y7!v)R&W$wx91f(Mm6WFuGVM*d7O@A-3bU(%^U!pR9>(!*|tuKql0Df}_9G
zR6bTMC1VW}buQrX)f2QV^7kUsIg~CN0l1ZRypwecWh(QhNl|$Xp3a`(cS>j5WLcuz
zuBcFyum#n42o#Dmc>!xR@CY0skXqx<CG;<wsX&Nn-gf$lnKrs8rr+e)wky{UvLYS1
zzQ$W2A}M>g33yO%vk0<j;!&}1EV7|Owh7z3ISc6%v!d{{rEQ@_C{{TGoU*R2`5NLV
zzRTp(Bl01#IO2!_X5c&A0-|?R8&yB#8Y7JlLL=OL%G;`2N3KC16}Vi+;o;p%np<S+
z&UXSGG!D=JdxOy9w-&}A@a4y0^0yHbZ*q!WFrL*E2pCB!wR$blWIS!nbyveVNQhXh
zjfvvnOq5M^cxgyNqRQ#nM73BfYstiWS~eV$x5OrD<qiOGL|*Zsp%|NN{2I!Jt3ulG
zn;)@lv|Ws(&Z!#);HJ5BMD*3<30m78(le~tzgDp?trta#f+~lC!|;_G8@B|)qb-%o
zOt39SYdI;826Bv*TXnnhsp0m5%59`hrH%+u80R?O!zu*!ptpc1BN7*~Eb<6a0X;Z!
zRPLBG!}f&+?FnFuTkJgCw3S>a_V+95{Lb!4qieg3x*;PVD1YKGtWapXuC3L?${yy9
zOA3u{X9YwB2RUL@<NcwCf<xCSR_18;3i&C$sQ86@QRIik+(sSYuX7t##0(rn3nSs?
zqF=hZm0|VGHeNgot)@f20e{vPS1c9yk%-BvoJqW-<sW!W?^AX}L+2s0>dbH0ye7oW
zu*AkCTZxj}sm@oDC1|@9uVOKNt=?W@6&0kcr<40PFlhUvE3N7P;;*>s41=siz|}q~
zF0CI2<`4C4tB>Tm8Ov?LrHnlyAh&*OSeylGQn0IUFNl@MnD(JU!E`XbEsE+n1MV(V
zMw}Cx(cxK<{@>O-qPHlyt??1)oN!+dj92)CbK4B-)P0WfIu<-zlE>8<Wvb7^3!>R~
z-jT!Rj7Y(I7Jz;wT8}Ac{l=S5?7IN38Z0<Fwo#|#rlIWPbrO=x2-0_v)ugJQgSafB
zE`s?<ed8KmwX{@C6?&qD^pJ6cJgG>;^7W4LVCICU={aa6`EN-uDI<EvNM&|SSH0;K
zwe^&qy@`8<u>8aTrpIa$qf}og$x7f0IOSy;=L6zo8$AAJ)&cqDfH;CYw?5ccljgdq
zs0qW_Pjl`tWxU`GOZb>j^KG3UW*(YKpekq%TZ@J|Yp!ra_M4!ow}-hETzOZxar0e8
zO=$h(txbPnFdRhx0QmO^9Z($%ir-ShM!5;hw!Xp8&tjNYvJLCbwG2ayS62ptj>+XW
zVc|VR5HU?J&GHOYBk%mswzORqtW-pGk%}>odF={>?ubnniV@zc(JAQm*-`lbSXGYi
zHv^NodSIladW6y^kQT5zm(8sI0LGRbc_A2Ryvyu31$YXJaZbb7&^JmD9U%urM5#l{
z`=~8?OE6Vj(#HkALpT~2!s&pkOsqJ4C?aqwqW=K>#A6}KKez%C&PR@-_)3>kw9A6l
zH5_`eU_6)BbS?X2H~25)$LA5B&7W%j0OSu!wd}Y?#3<<3NaT8P(s+HqR2R0=FVPY7
zpY=PKoY`GP9Bm4BFeetju}p%mBG@$^FTx&w%yHvK3k1g$!SvSbJou+XKS&>P>S8G>
zZrTY@OnGi1sc1P(J$ja)sRE#CqT=kdBUr5%sz3agI1rT1>ft^r(faEY!+9oiaXF-K
znb~HaEaFnHs)ycdzauG*R&&~=Kd2+fE>naYil?R-y%OjhL-T<01ct<MS6y5!iord6
zO5(!4beE$8ScAXA5RnmjcRwJgv*#*Z3aAP^PLvaD>+j50k?73^r!{4I-3eTBBnJIo
z(!w*t2!pI~iwf%OhJ@7mtKy(F+wz8LBwl-ZP@9)Epyn;9HfVETG+ivFr~<|iG}Y@9
zx^QRhR4&~lv?96*hz8?+BNb(UtM^1*iE0-_D^XMqJX|v$8omgq;MmI1eAREv73hIi
z)=HTH_-V+N#?=mTo>++*Exe3aw*yrPM0Q?&Fh$4PUV=Q+ELjd|FI0=A3#}MZNdR-{
zg{f*s#DEjapS$iK1tD!IA_&Ec1*+0I*%s=KZ%$!ACgR}g*yZa)l@Q}!TLVb6H?W*#
zCvRfqr6P|SrlpA1Xg?5R!Ho!{PLryFj3TxS9UF=6SUn!V)2av#BTe*qRCPpC@DbSt
zk213mkz%U3i%B8aHIB>I&6vAtgMfm`M=vZVU$&pSD6kzCKvQOo$Q2;6s<Y3TVJ9ih
zu_}$lzO@^DM$Z`)4X&Yt!%;8ca7mT~(4JzNxx_ZRz)<;(V|H?yMuoXHEjVFarI{P`
ze1xcSu?-G{x80{l8<lp|E1ffA9^;nWVbw`J)5#2EA5c5{P__(Jiz=gCcWAD8FVh<E
zL~WjWhgKz{$3kAn1KQLD?Yb9exmo$mVkY=3KtsxM4M9T&e<wA)Nx6CUEinECnvZ2-
zRp`*B{lGNttAMtdM;5<spTQ_Y5eT5=h2mxP$W0>$l{a7uOa}NbsCJF6X%xy&gD4IQ
zH7UIeIt8!Y!(IZhTf=9vYKfNULWNKOOS9Tx&ld1}!>hF6>&S9LRG`8QRivuYGV)cD
zpjr1l;Ilp^qEufcsZp>oCi)v2*^2={3p-~l>1mLj)QEZQFB>tyVO#R5tMbe)mhL!9
zY~arDH}ZO7^l#@QE(#nb{X#T5<Ql)^0@c9}NOo~C{{X=W;p8W3BL<YNj%63`3*pRa
zier>r&_iXc)rwqS4O~j%`<y+aY5xFFq^H{>;`bDVQ3y~%i;O_^=D$#q>#KF$SZ3}S
z>mp5p&sPb$7LIQ^=eW4g0$uy{USc<rjvgeupECIQ?yXnR!=&&I0#t*BD6o672H>wc
zAt84kg98P6>-n|E0?inP8v7zDTl0i!RuXkrSwH>#`(fP0XmO$t1<eEDQKGwS3`Ihi
znNvS?d)=Gj5Lo#d_Ms8B`CJ~)bDr-aaq|=Me9);S-AemdIYL$PugQoRt7i%6n1&TD
z7j96d;=vtBhGN~BXUhnM<d1fiICq8skWfn#%2)-h>f^xcKada=vi=w?NCGRpMsSl2
zo{5wKN_PiP_0k=H>-tUKbK-myQq}mW#>FQs7Jg7FvUkI07d#7MURI?Af2i21>!WF`
zRvZhy#4&c&V}|=3N0H2RG{9DKL$6~l6<h9W)&7W@+TayQ#I+i=mZO38(>_5LeQndb
zf3SA`MFj?P2udQ*u(@9{{T^AxO~af^zl4iRA$_7)#(5})!!hB4&?x1S0>Gm={{RTV
zI{N1dTz(*$^*HbkB?0g~;HSEV2m`+MEFl}T7Ys)V!nvp;Z(j)v(zb4!$o5pAsY{8&
zMFRP*3()++;jvh*1wVw~6n+#Mxad1$Ylx~_bO9{csq*{NhNe)oW6uoQ6s@%PfV1GV
z)m!PGgSkiAM#m$55og2wH2(mYddshr(g;pP0Xk*V&zkh9qVAjy1&D1jN4VLRJnF+b
zWGd3oz`thW@G4%G8GS!}!nv?{O^2n3x4J6%O$I!IF;x)JD+GC#6MJ&Y@<K2#<1r^B
zZspOitxd4)^&bfym36!FJ&~3mbzr+h@bN1&iXhsb)NpZ^SQmDJpn?k9VpOR}ZrEcq
z6Pc&0NF7>YK}UTi5?CTCG-!ej2I%%7*!W-|Q1Fc1mln#fx6EE&F?W=MOu(L1jiL(1
zDQ<t~7*&?3>mx{{4#rYp&Sx~Y(LJ7Gavt(nxLd}@GL4LVjBwSTBf~D4S?;4wLB(GX
z^7V`5HI><BE;|R*RscCSeGC*l?z%zLv;vH&XU0vV8w<`<zYB|irAoCayKiT*@YD=K
zcb*F44N;DzVa;=6{h9`rS07T_5`@ln()n_T=z5EdJiHgM!T`4byKhs)3IGYV*sN1p
zH&gB9mPM<LG@}`PRA@AY0Bwm!KLMgZ8Qn0~${T9oX^MS>n|WmRkm3Y5<2a!+<V!i}
z1rYCvTrhy=Z@7fYrILIY%HYD`IAT6&8wVOAYeKExE3q=8ngQe)Y=pMfRrrPiAvWXI
zvW8Tpm<a(;?D||vqSX+2Ca21P_NzCpJtOfLd*`CM5vOpG=J&F#1n{&i2zCOfSIGMj
zhn$)GVKkU_sbvl&Kw(K;>adA5YAE5GK`6+bZ<2{{ZBI{7gaXvnReU88A=<Pl()f=y
z?v47947|ONJtQFN%DCSb%ox~j@!KmP7o{qMQ(oUD{!~f}i(QEU#4*&J;o<0k4bwn=
zfH76vK-SLSIVP)B=I00<^fm_IQCP@q%86B*0>C7_C@n>-%Y91Ixhpb}g;EQ0xzT&&
z%Z@PO2pwC%bnfaLg%(f>$lMN)8(1Kr$f4=T;iJsJbcW$6Yoh-ELUnATOclZlBVlRL
zDIKfF*O|yk<WY4*rsId4ihE}zFq@*NtCrGDs4Je}VQT@2b{8#3I2CaN$>iQ(y1u`h
zS$$O(*Y+jKrhJTe$>K8k<zC9rl;Wr<LDDY)vU!(MwwkV2@@E-=#zn)X@6;9DB5D}Y
z`}G0kUU{fEg3NCBpAz3I9?H~lNVi`qBsEJblpE89$n@bhP^ZOnF$+IJGK9f-OS9F&
zxVKxajB1FOkU+KNfs8i#n(qPs09j3<-@KEYhd#peqWb7@3cvX)qgM0=q1OY#5BkQO
zakJX9(G~vyp$V8s;i!MJPjM}}AB2!_OsArd=Vfla5sJ_Ro=Q-D+8q|T*P~q9iS!HX
zwTY(V&^Nx8Hxqpxh8m@zeeHkAb7srrTM<B^yYV$+Obcz|QsvH$kIm0Z1mXZWN=}2y
z^v+0}I7u+p^OiZ^aIs^-RsR4cX$>*!pZ@@*b%S!Ap%zi=Sx;gk2zR}|qF=+PEkX#G
zw*`<_l{>tG3SWzrDVYf>k`+tpKSgM$#UBFAMNaJeIW534mg9hq04cBS!z2kkCEQQQ
zEYZLP2H?o16QUCIE%rMin0G~a%s8^x=W>Z^c2k%+3nF@)aDKeRj*d9@lF)*`T(b*L
zH?riG&PfJ&1<O@cmX?I~Env26eDf~D1DMp@2%Qk$ipnKzOAbC2#fBTtHBfn=qm;h{
zDD~h5TW_=?l`7ZwVuo7FMeXr=oP=gn__#wfIvpjTgB5k>E?s?$b)^6{r(5+<dRmir
z@t4MI*LN(&{`<tS53&hc9n35kxpugK4mk?3D}rm@tXmr780L&O#o$^4dsZWG*e%w_
zcO-Ifz6iLvcw4vrq!-22^I!N!ea&vRa{wf96^a)Z-(D(uv9sV`;F(5NGfEEc?Wj@<
zAk3DEJ{MwPpHWcuO53EjN68cbDMH=JQRM^RvE%kV5FUB9c_c#@HFv~9CuF6ao*BX;
zf58-8M7*{wDdL{UW9D`p3lhLeE8R+PVxizTmkUw@Na3gma~7)DGWMmCEtN{eH6=tU
zMuA))Mqz(&Kk&s=`<_aew(IOP>*$e@w1f-J1~8zMj#%bY(PmrQOlQNcYBdVw-s(aw
z8b>_!28gTO3`-EJ<>3AtF{~5<`rsjg5m@81eQ-nOgr0~|WpnT?L_ymZEw>^#_7O<4
zuQgjQB`Sk;3@AWQ%DTU3MR&E<v7(oB^%3p9GnLIY-TE|&bTiQIq84yr>R-V*yEX_t
z%U8LvCtYV8_c%~MZ4vRy5<9A=Cx#$*m}K{HDa;FDInE5RPGRk1vBi&tHC}EtO5;7Y
zC#Iv-w|+C`C0!Q|<_QyN%P6ZrR2p)6^9N|*PU;QPS1N%@=21>cSZX}nXHTm^xB@Ov
zD|Ig85}H0z-}ovmT&Y-|NO8&Jgb#+62dpH8sliZSlS5VcA!wkDm59uxVL5+dqQIQA
zHs)6}`^m9FTMs7$HX>@rAfwd+x&w$jOH?Pxyu)dyqiEhMc;;4?>C_H=g}l)Nh0D4f
z1Ho|GGtl3BF|Lwz72)BH0t)yVRIEbu_?7SuH{kF^*p7#UeKDNVg218<f38+ac`a0b
za%`O4igC@I{LIQ{H*=^RG6tyljFd9bGiDmHw4YG7<%L}?`Z&>GwrNd#^C-Z!)6oZH
z>onav6$TuNec3J^CdL;ofP^KXm&TQ#IM`!-4?8n3^j%)Eoj_E5csP}5IxSgSr9}=O
z$Rs09KP48sVBD6OL|VZ3zp@N=NmsNFQ$U6x*xVUeHA^2Pu+Tmc-WO(21dbKGJ85iH
zq`NZ0C_s7~sFX0i3{oyhyS4WiZgj5lYjNE~;IL4OG`;yv`h*y1PNBmnWgEt+=zYV#
zGljxDAoxp&gi=NnKBY}Z-WN;$ODL<l%M^fWEe$+oLS8Bc?v7x5G8+S})T!HF5DM~}
zUk6VM3cUf{h8HrTi!bJk;|r+nt{Js%{j~^2y6ux{p!ly|pz;H1zDq(bfv!;SojxTf
z@`CzkgShb{Tu6di9S_kaUHn~eNL(!Vu3x-B*95-dtF3tpCa@@s__RYwlrGRc3|St3
zw|vEWiAm#uK=l<!s<me_)P;)jGM|X~hn$rf2TIG)u=+3iAw3qJQbg)khkdy%i1h9A
z+B%1tq8@EkxpQz=-DNZAU%G)~#dZd~`>5etFR|h`?|2{v_$Y;-)D@Un;(In<q6`G+
z57H2riQ41Z1ifwW-#+2;KQ*VIQpo=Rof5A4bp`J71x5GF4_1=0Di_MaLu%dF{t;m;
z7vvsqIW(y2WDY3n_(G|^tLm;@A8>8J`fek~!dFD*3^S$A%2SKD?HV!Sn<6UebEA~6
z$;QQxI&8<z!;Oi6^x)Jbmyw*(N9p%+w+{-(BvkAJ$yPwZr1J1gq9cS!g4(n3vY}@Z
zRi@q`zy0}d{y>dcx_{~?q7K?d`I=a1Xe2tOgiwp|Qf#FN`BgSsh;8Z8{%#+d$wB3q
z3oJFac&G;G-@Df!F4g|3U%Rj4-&`N#-=YyWM7R*V0(07G<qei=6-PB62~`#&)WT~~
zwVr-Rbfl5LT~7Gqmpr<@%j8c`ADRRe9~@}1kC~OcgqYnR>kYk<YJVO&##Z-icIsJ4
zo*wEl`~`2{$ziJjZP*oJ>HDxaW<i9m2D{o3O5uk@QLVOzo+1Jo;}O)G*ev3dw9FTu
zGua047b|r0@)ldybsNM<O)4DnUCqAig2HT0ZQ(sbRm7`<bu*#GFIx&nsd@D@+))is
z<h|}?ZM1t)WHc?3dTlbe0+cMYZC?$6jK(oEuB&GO0E<9$zuZ_K{<D-9z4>O-)l@&?
zGU^8_Tr9%S+54{J#;xPLiJWAjWl9I&)-pBl5=@ioXTNa9H$NZJ_vKh!0_gcd<ynW}
z#Zi1^lHv!6(*nq$<wy?Iz*ed1Ka@9Wpx2aHT}uJP+%%tqGzd|ISXS?4MQpx1K4Q6p
z#x3B);A?#t#nlGD@eQ4-rEZPK@Qu5x=$TN!<-1~9tI2C>!J3pZ19iG@@;Z7;b4kMh
z(AHOykn<6Wz~x4AN^LKocB35|foBdDaBBkr_o-aS3)_rVc$5z#M<Gqo)ungy4m~)M
zmkXJymArit+sa4#sKAzwURLiUJriaw1hMqPNZnik)z!n!Ld+<}@vpbkNCrGA%chFA
z#2493b_dxl<kPJp31V7vf0Ei@H^E;^5l<j~#DpF=#XV6Ch38bjT?mzx;-Lz*v_-QX
z#)8l+%kEgV;mr{2sEiI>szEcgjXlQ+5hc}>@=h|mmZ5<tb{eXeE!n7&6;2*hg%5c5
zDI-Ffq78VtR0lax^b@9#meDV1%Kmstu<sTJW)wA=XVt5M65ydi(2O3U*8H%yK~(yb
zByK0hR6Utt;i&<2bl(G+$mEtz4?J?v5t=!<Q<tmkf_eB115Ww5ShPQKLg)%*7i6S+
zvIoCEc+)L(X*Prl595hfyiYbo7gkl<E?0Lo16&}GaB-=&kBFSr%vgnTBe>NuXaU><
zsMUuku_{zRyX~%}hnzLIcuOmp8%gPgq(-Qq@D&mBMl_Y4qTdh`pl*GVs$Tcv$Ww$>
zK9qR;VSpC3M=;Qaa5edM%M{ajhZj8@;-Cxa=N_rzvVXn9k`T3Bo#Pyp;9YCxVJLW%
zQ^VZVK()LLu?Z|GI-Y(fN>ChonnhIhVQ!J9x`Q26q)lk5RQpuDk+#zrN^&(z<-;tL
zrFBt+t#T69?#pl-iG;LKNHz+obS2pVRjK9XCAd@)HrcQM<C9jkk?$3j0(jo`0ol^{
zo%d56u9$Q3tNWbLrQi#GwhE9LT?E*n0?~>^R(C2aYuXF7rD$)n=2+rm-p)e1m(lO)
zTO;}OUIJktdQ2F<E(5mnWnb5cU$&b(I|XLa{Sr}1(O|v^d=Nf|ARG$6&r~I%*b8TW
z(zM7g2ySPdkP#YRA@@u)8#$BcX3-5^0LB4-hyfCsHz$<89KhTg!tgqPHc^EULM17#
z&kw0$Y@UK|DM|FIsZun>3LEgaurBq$OH2kbCnomCRH-*=vyE_d9^m@t9!RLpyeQNK
zfk@~cWGfnRFhE6saQs>Yk5a<A@xKhNSG-sKob!K40UXNJ{a^cwgjpHQQYoTVoKi}p
zr0FfKe6g@GT785Ob)#2&-8gKcO$eMk<iFxo+{#BV4H@7x(KHB*;xKBAtg(IKJ{UyG
zChq?LVAz4YMdXVO4Tr(0Y>oU4Wl9>T!6u`tuyK4uqxSbufC8d`>6@957<5Pv!WXnt
zF0{cts@3iax>d0iO*|o)+zudO2McQUr(#fPQ_)>KLrlFMXwa~(Dnwjcs%sjElGgmZ
z%&Bun!1Dtwve+Ujz~34A)*piFJfd_%UrER)3+Si-68ek@LLU;pLJMiI;Q9%Lo7F8C
zr%}}<srgFLXdV`D#j-OX1eAn?VC`1jQsvqy8`ww}EOi<k0?IeZ6of*yIW0)yp}g<x
z4&l;<0!ze{Q*8K3gB~Ez__<`#v!RH%Q$I)oMweVyeuS_MY}d1@fXk#dH(5g~;|Pfv
z#E1Q)PtxS}XNNn?Yt*|-hQ#*}#b_=!8u1DD16W(ke~?4aa%K}AYeR~MXsk|0b!YTC
z9!3H{Bib3Wfk?L85`}NffS)*ilHcB3;^w2XyZ-<f&`(bmDGN>em03C#;Z_yplrf_=
z2-zc#{5_F2@eOh&1c`kp^_7uZQFDs_0EszH(C%x#Srl03RpGUdDClT^6qK0r+Nu~;
zm36&@Vh;u)u<$~`-Pdd}^$g!_$&uf<%7C5ljl#C9hbe}(i1bOW8=~~8gBK~jwR^5a
zb^9kp!xOXm839gevWZ%Gf3?IHVQfCC9R}K2jjAG`(ifMZvnX44LuUal;4~_ZS7FRL
z(dhF@VWG@?-Ae~M-w!-QD6!IlDcQI6#8=$YuP~QNFWo~LDkxiahw2k!?f784G1}Sv
z8GpzWoO(#DDyQiUBga72dNRob!(Ey<(o&2|s=^9y;}`@5Dk2Y)Q1!yF2G^GbFhtP4
z&4JFg<*?)TRrndPwW;Tw;tRdwGOtu?(>9e>aYdp_u1aZ?bEt6HE?o<K!y1DrS4}~}
zYq8rFFslz^qb&#xpzG;is4Yf;T1$%cRr)4N*myuI-BUR;aw@oa6Ti*8VG+!}2ZIRD
z-a0%mQL+bY<MzM_fq2?dRJX8t^8HFy(1E=*UM+Amuhbk|X~jB-2VkJSytq(Hi<md4
zlDG&p*ara)e4ZhkxWK7Z6dVC&3)~^G(}%D?Al_6uo>(~OZ5N6ps$s;yyq!yH(Gd{H
zG_*1!4}H7_SA0XjdPTu@i(7bFq4M=|*fJF=Z2=F86@sT_*#RvZv3wXBw-wxDo7Oc#
zwD!9Z(!g_(fwigoN2B*8ap2pQ?}<Vq&`kM;s4%>%2=l<Z?f{6SRYh;y?^h0l#8{y?
zY#re~rLrlzLEi|}s4&>DYg0_!9}L5r3QcY!I*%onD|}o;Vt8gnu(hM};!#@p%gJ5s
z%xxat<tw_b$Llq~5WEI(Mtw)@>I<}3x_g*Q<^x4dX{e}2Oi(W?+J_H*<IVl)ai|}X
z$)o1uG<41a0Q84DmOA-fAP`m$7jdius{m2-fV#f?63RXT8Twv2y3ZcH^E?+hiC{k1
zD&_@}?q~<PW>>R;f$CoqelRNIi>zX|R|cUIVaT8Qid0OA$ZFN9v1u1dSS+fA{3_zA
zE)5N(N~v=X*T_wlK-U!&a**#HV4P^bY6N~OG<#uFf6q_*D$98^9?7METN~$G++QCP
ze|$|8(Y)r!2y~Zr2)?Yhm;?}sQ(#pJRd-T>R60B=CcSvK-6G3sW3H5BhcA_eD!s&>
zy^%K9O>!f49CLm8hTFsx2+~8jpK%KcaVjFGNa^l8B>1G0fN^@PG1{3=Z6vw=L}t!=
zr6=-KXX?%&HwY{99SfCWP4aPdVok``aYl?-gn0|XUPiaQTS(g1s(E)|Ly~w4V3jxl
z4-GjL05AFiK!G3VgBgG-pAToUP?+%f9U&oWA8}FuOkZ8UNghcO_g!om#)q~*f2=5`
z+w@^B<v6XT4l|W_uW;|}m)XKuK#`;0Y<CBWbUBisuUK^qvZZMUo5L3%mhLXi#Km8)
zUAB0Y1h&)L46d^x(%A=AJ*(#$kVHa|C~Rv`Bp$%+_ZP_8oDhUOugU3iE~=dhx-LBG
z*3qSv%o50W!J!CSk_Wa48V_RCgM2!VvMYV7*(?}T-;hqoZMB6*BtY%8F+EwZNu=~1
z1X|&_V)B~Bh$y`J*iJB0#KUuRJ~bC`uQ$P7C3>@GpUR44;V&Zw`Ws6B0Hw+cwQo_(
z1$4r=H)jIWksEC{D}jAOscUSy<5Y(=v_2~T0OVpK!5H(bTVWjU`vgp^7vZGoXEVUv
zXO9uu4#2VsI*_<!)Y(L(LVB9J_9-|Zf4UhH*$rMy;a#uI5|=U`DhMHP9%shcMFn+G
zdE)K7!&yZZv3S8z#;wJPgby2NJ~@{wPe;o#C1N>xYWAiQklonAfog?CiyKq}3GU9D
zFNQvU)lb}IM5NIyx(IENfT|86QrwcgY!F~yjg_IZ$SH}BQ)cpN<1Dd~xNqt`<=hgK
z2U7zW>WL)qh=mf>a<E(l^(oD)69a(`bf-S1KPb;9m<ZVrJQ@+Gl;xZ~pX4q9OPQDm
zg-ku^EkRAh(Qd-JHm-GyA<;kI$pVp=KyN1`K6T1ULn=Pgp_x>C)?7HOx}svCwjtCg
zL)@h2)I!#d_W6x!#RGMs00ArkV*o19rmE<861Ocmg0m!%8^}B{qGd%;yP}sJ+Lw~*
zCAn(`%X>Cd0-D=^P=q%q;^J2CznIt2Y8S@Hm|h1htb&z_RJ;ZsCn%<wP$9t!Tnn%O
z;=c{3yC5W@!l;~U)Y8bRA}HlO5S-zE(JcP}VUrT6@Hgez{>*g2J~X|zlet$FO{m=H
zi^%fbS0zs_MkA%z;<hPu^cf!>gefV7UW39m$nwZk;uK{{@l#$4lrWjoR6PbMK&k^p
zShpn?N-8X<s5Av)uydEqi*n6YB=oZll9u>olGS&PCZP6q(qEtsA~zGrKn1Id3Wfqw
zRA@N(jab{EzK`*hstSc?P=o5TP5#5U3($f&3XPf?il#4vc4!&MJ38)N{?v5U+E?Wg
z;0>oPxSSQlypS~aaang54dlNV!)v0hFH+?}YOWDLJ{1vFL$2%<0BgCWT;t(=5Ze8O
zSO$fSnpqpEaJu5V1Bfjr6BtZ!(DxKf6%_@8$$wEpuI2|_h1*?2BT)k?Qspfs_Fs|W
zKYoY%?1Nk3$-$Dym6!avnu=?SIPJO-syy~bh&91}L;YgevYk71f(jP2(2l-j*A;p6
zq|7ei>4KthNEK>o)yMwiRYs^!^8OQn`l-r9ZdH}L+-navEksDb>Q<C;Hdqh1D6wl?
z#ziQngqGpP#i(tTSZT(JmV|;bd`L1B<1Bhw!@P;5TmDEx0GLIQFG{E9sd@7dmio2V
z{ud_4s73z(`U)z(1s|0^@jMq^<#{w1zgv3a?kc*Z<~T>FG5OsA7wdz^K7=?1mhBgy
zh$S^LVi3{iI{~2|Y~*BtghPG=ZBap3ts-A8$Jqh6dOU7=GAhm+w;OV-BUFGVjM?8g
z5&^mc{>)QeDE|P<ffm}+e99TPJuf2!{tbqrh~%6yDwq!;NKwn<z9u8t>yJ|MCd;}T
zsOHf_$S~}3^OeG*pujtH`k2r~92EibYAJiA>p%7!(h|MiMlnfuc!jajbk9_`E1wJ*
zolgT@!4{!!2Z}$e6g4VtCB5L5ve@LS%HfW*58eV0ow*t=++;Yq9;?4Gl{XDqr_2?P
zl2%r@cB~=BoL^|dX|{g~f;HC8UXP|W{3FF+sF9%AcG!&~P~YXurHL$qLLV_1ZuU(g
z3ij^r;R=DK-jpJ6dOQ{l`!=6I`Z35r@5T+BUveW0nYHc*S)rZ%A;UVH+pX$^3iU9y
z9D)%C750bldKopm+}UMCp9K29#D~}0Q%^Inl00`$BLWGbfhVPnOR9jiQm3cggl<^j
zybRL|<g6`(H*ysTOPgEXus~=LgG{P>VsS%@r%Q~BRN6MyxrjF)kv&G5f(k?w#^ctE
zA!)TFsFmiv;k1&g9ha=;hMim47y!OSd_nO_!sP)NvKx~TcC0YBo7#$2A(3r)Lh8XX
zOvobI<Zt#9idZKgnx1=MF2aKSwPlyTM~-okt&IWca(>5QW06hL)}o4#TeB$`rD0Ep
z!Yrl)3O_Jnz5R6gnFS9NvOBfIEM)%xA*Vdl$OXl5Zn9mKa_&Ly9{8>BFxa48-^{wu
zDCkrBSCB@JM`h-M2_75BCsa@^4+Y$AUk>i`aeb{{q}ea5HiWyZzcC1K0uRlE65N+z
zL};sSbh>G;EH@q#v%oYXZBr7cIwI?l#3gjWYzG`?;sT|?$9!Z_sk_J=To5L=4pz#8
z4;s2K;SF&_zSqe&ORt?iU|Ch|tWbiYsA<S1X?nF&C_or?T&$0799VMl4C?UiA!lj;
zK02>(UsctANTih%%)MdS<tPVigUzd8@j(e~JCcOBd>=OoR>19hf#?&FIw3cRyA)Cd
zIab_nEH=A*E5Y(&>D8p<Jwx?IK3uY)co*k5(ADTl#E6GWa2X6zt+ad$33o1|wuRAY
zW@ir?GAb*T`SUy0s|{+Z0lqecn;sHxnV!DfVaHXi`lv<^XDK+<k@K?&Q3owCCKH>j
zDbFbkfIL^y9U7qD^9YJc70EpjS>})dfp7<5{=n1f5|-6^vrGW$*4$i{q*5lCP@DB3
z7}2qbU<0ldGic{T3D!me;E|VAbD7nI`Zw>^z)B+`G!2Pq4d(kHYTY>ai|jr?QT4K%
zwH7s6!(JjiN5dCC?4edO(O`%oOQ$l$nd_->*fT=5IPKIP8@MzjK4qGeAq!=U^Lw9>
zqMmD~Kgffvqn<hDHLx6r)xODs*r@cqtKpV}b`fQGV`%|U27qwVW4}v&0xwp0zfu9q
zdws;(E6Xm;I3aF?p?d_rNdotDOo62`==LU6{{Vf&1B=-6!Tn|fo91A3T~oub9_Gt^
zN(V*eP>%MBP-Yivu0n`|Mu_*2jHp#RGJz_54-QzWIebPMr~T@qirGhd^D3#teW78*
z;4v4g59tqJ^H-g53!=zfX3Es{5am!kQNR?IK|U}F0$JYfmzth#SuEXTP45%Xr~9&{
zJRavL#5Y&k)ME`|xhogh2<yT)0HaLkEj93DVgCRmCm>N$_-*wY*gH_1AyL}&patE)
zs01k`N0exHhr-g4uHbk#F_X^@TDwtHLzaxyv7{n(dQ1gp5r@Y=MinrrSLFABV86yv
z{MI*#meQRI91`WWXFb7`Dq|_3_!<i_!biVTBl`{x2vXbz_WCiwWmhM68!q46!2~><
zMBqp&ub(kDoi&wUm`^c!LN(W%+<qogUdiuqsG-H|Wr5{s0nLcuptAWWem2W0>$QF(
z8)sfve6&k3nuIBOxnwdi$UBSp3?=a7^8oa;!3j1j<gu1Z7V2)CfYQi&KM#G3L<>MY
zyac)NBNBLWS_gjnR6`YdjynNSarIS%&osn4>@OvVxFm3vX6PfJEdX!@1VLLXU>_|*
zPLiRva}kfIps<S%4@@`|?_~nO6OYdVj0Mzb#moLe<$jv%iqeCNIaA%lwgqWO^kTck
z_K^@RDA|fff#w?U_lgN>qSjw<aI}Idxi2h!SzK}NetZHb7FqS*QLgf^!;tAEK#vdr
zxrU)oF=aH?^vj5a=tFvs1|o0y1g}Ckd{|dhik9EG&e`e4tw=yjaBG3FJDGHgTU3}w
z(W6*6Z~Tf`k@4{$Op|K0^OZ8wIMF2(@O<5A4iWgUETCA0H&~+pl&^_BF}19g{Hc`S
zg|GRGBQRzcX?K~BTzjKI1c<D+lssiw@W%72;Bzctc|qlPoSTS53{R+ULq7q>&QKOD
zoB2vurd!c)`bA$oL49k$wQ)U>?2N8iYwTw>uDb;`m=8~9xw<nMV9L;9(NStYYSM5~
z5|wAIVj=`vc3tMfyJ&l{vKs;|pDxM%x0f88D%zV1(^+i7x%P|03iEf8-!Kd*Um8eI
zfDNy5S~Rs<X2WX2@ggB6@MFg@5(H16<4Ivmz3RydMct=|FYL33fc(u({{W78sNE!9
zhs~?y7cBNk&M9SPRry`lmUIn0BmgO+fZ&`Qp!Nxs`=hsR*pIb@PN1RL9<%SDlU~(j
zjzL>+G!-8h%>oYt{w`FgQGL;RXKYkY%853V;^o*{+kvpA?X5;tw;&dvVcnZdw$3F+
zq|-jKSOuEZ!%lQVq<Pt5vwV(tJ<EMrJ=sBO2wa(fyc)2+ncE743^*}6daEIPRS=Gr
zg1A_~45_7y3?$}!n{8au_Y1s(?o|yOUf1kP(%h{CTa=Mkzw|R91dM3^0OWDEO|+`6
zz1$}oq5`|+EuW%U#6zT&w4dN#N?GS`4g^?wAiHlvv#9RdQzX)l#IfU>D_-tj)DGBS
zUK5ogTG?=x?uio5V?ir8{HtAVTs)^rU8xcEN_)(N$f?~n=TfQysIQpB)dj*VF2GTO
z!YU0HX78BWppIpl+ij@fHu1NYUWg~qu{OI#61=Mp5KK3s-Zlt!Tvciy>itf}6^9<U
z<`dikWsaCXq@!EJ^P_-4;`>rBnMgZ2cmDvF>xe9hUR?Uu@XPL?V=n|D2Wx!FE7W{k
zU)odNaYF!<5w5N9Q<H-bNq5;_$k<>3k|-(xJY?Szzo$A^x4|k@)NQ*J<^XFb7#@#P
zr83}Tg0Oo&BZH9^qM<v!<IUt7D0}sCc@}e+qx)UJL$dGU5C-IWI!IbD4)|i5<z<h#
zPF1(?xlGd{MikaTurAF@@E@?ZJZ{XVBj{>%ZHgh#4r^g}Y^OKZgVYY<#2Fh8&vK&B
zmXueQ&olWAUl!BU#`jc`?aWRPficms>&&!4iMI{G_IQ7ybVSDM{7S|GKEzSs-N5;c
z4Iho&4V8oH!~VvV-o01AW9lNs<sKRL9w4DF#5H9Anx?~S8JslxNnVSf11creAe>|+
z>%3b5_!FLa?D={ksuxsRqeOMb4}=R7LIHG_n^4UGf#zl7?q0j6stV%&0AO~|*|)g_
zqUBw}C<C0SR*|=Y(K2)c%qyHaj{t|KS}EoViP;D(?W%=wTyV`_sFsden_0I6L9TU!
zrnl^rKyU#OzF`@7EV1rXncYVOFn|7;yn|jk0ui?C=O4xhUxcT*cXZ=^!;&6S3t??g
zZt6VND}dLBxTw&6AAizYr4=7F%mEE6Q>k^8kc})T3#__0;#&fp>j&1eZY#;}pPzDo
zci53c0Q2UfkAz-{T98R$3*~jxAmCb_5vX#AvMNP{S$6ygqyV>c)!#C=ybDTDBGD>x
z6?ki?eDe3yk$qpFM+C9I6h(D4<^a1LOmPJMc)ryuQkALEvA_K#Zga*}=5=mhK+@1N
zl+BXOOXlUpu7=X)y8?U6$VY+s8<n#ckWw1O4U1n1JCf@!iD(-FEgW04*!ob4-x-ek
zwz>o;hErGNka42?Qk;P;+fsEOhBOKg%Xh4c1_W;&#F(?2pYIqumbHW;9Lo>FEyJCt
z15LKYZ~N8d<qIrftsgp)m2ieLg=<M=sRLpf7{1gkilL(b-M*z{4tP1K14Cb4X+s#>
z4g(M{mQpMb)+&Sh_m)7^lE4HDHe1qJ;MOj(_^`zb^AU?fepWigZEab%nj-_!B(AJo
zXYCc58wY_@j*0Gtiz<)=G#98Y)A>62gUk7zrEAyp5XB7vB>`)U=Tue!^F(o;0Y1W*
z)CxUY^+qw<NV`u3QYXD)wj4N%QWy_AB}QjqY&v)tYt+T2&KLmaKak5A6L(5$tpq1a
zU9zAhg*f5|Fwd1<p_-*q`9PrYSOOP<)(oogu_q#otb%7wUd2LISfzs~s|rB@fxEiZ
z=#RK6T`xoi1liclSfmUeCXKS{HDdgl*j5fk`>Y{boQDJAQJ^rYJm#vw?VLR%Ns{A*
z+Gn*ZfnPG42b+7AB&%<o7>_9E<J3}+;0wvgE7MpgNXuBF6f7WwF0@(5Qt+#?OWcyL
z%QmV{5DRgHY2XEk=}8FfO%By@Q!hQdA$9zYWNF&)J|Y;DvBdLSu@0vP{{SGSSdX*l
zhUEe&o*IMz_|<Sy;5a577;1m8WBy@Z{UP+XspN0$s~t*?s2VI2$e5_+ui96L!1xa1
zt#^)GUL(@0r1i@W5|T16EE`VP2AhPsZ1U9Nsczs20c5I85pfmKWH7P~C|P&xh}b$E
z9_7Cv9511nKm-DsIE*`8!&Vy>*fi)N#$2CqSBf|Q2oM_VF(L|~=22*?DqewPUsvY$
z$uxasc_3}ZC{pc2asUOKC6>Ri4_}j2ED1PLACYA7Kz^4<01T$2`AIDR^H!AuK%l{F
zK31{zw;Zyjwr3Hp&0hdO6u6k_M<(G~4d5j(!Xdj}W)Gmd2w*V}yXq_HRhP@m5l2mU
zdbqY03!TDJ-i<ym{bvT>uhJ5dYlUkBQ?uGwX*@-JToyYlGCE@QNL((<wj96((vrBl
zmFaGkTDcfnx3SQ{tw7FuO37Pq7eXBCOOWBYQ+rYr^49+VU@McTTP!b$ek@!rhqIYk
zPXK{)#LHX{S$>XR1A<P_!1Hxp$f%~&aX?g5YKo0?h0M0TqFgXm_0)O@%e%1E9B%NB
zWHw`i(`78`e4K~lB1&9et~g=Gn}nQVLq^+ja~F|vIA=UF2RA=^;+skc?15=6&$;+;
z2!R4_<^DWIX9Fa^NEbUFFZ?3@PwaimskKfIEy9`PEbbEQW8#ZAeUN2p2-%MdPN;Uk
zZfGl4=s|kEdKzZm`79KId8TVSus?(z*y-l+FQ!}2SY3Hw+&Zq<+7Xl;cKd3HU_r#N
zxO;npX)k5V^B_*fA@ijPT{bG4ps{)0v7=D*lU)A*@S*@Y{0BD_i~{rCX7XbbSJ9B=
zO{*6<RAHm$W*nzl?I%g12Yr&9VyzcZZld~uwWF)E`vO$nmek$@4D983<{Bl>i?uW*
zvL&HbErHrG08D!elq>9jdsIzHVQD>syCGayy4KTaRisyR;+_!nJlZy}RC1mz)!~Fr
z#b9|Vf}zsG3-*Y590Jt!Q2A3P^*woyfnBTN(3|wX$&SY$&tWJ5A*}<Fj&<>%BBiN-
zw?Kl2NjnXzdt+!PcbBVQ5}b5-A1o9$Yh64ddM-{50~{M0w@Bn4hbhrT*>_x2zc~yt
z{hvSBj&HPi>3Ie;U4cbHxDaLE6dCZO3M$gVGA@&O0idj>MLgel&mB%ua78n9JsSQK
ze!WCOIIpkTUPz9Wl>Y!iQ%!^jqL~RxwR#um%Aq=8!ayhrL&ggOXb^O^GVx{q0IVE5
zK!^@Yy2XLWDHkBgDdQ^DP<PMTiT?mur&ScHvxt?6`HpG|tm5qS!)}MAZ3@B(&D>rw
z&tw=d6*&di=bUVaX|;xqhUU`Wd`S^_hz~T9{Fq(aaH*iJcya#1*)FZOwg|LO0?LAc
zZIwi<8^;yzNBb9cdz^9T3J|*)z?HAlxaM8>;a~?CL59Y5o&NwCMbNHn8K7-lt&A{5
z<zhUusmZY9m$uk3&`SQ4Y2lSIi=p|Pkh5AYwmUc|sImg8X|3a6{+(fDD^@!OlcxEJ
zW!j29BqH|{LUs=2<i((QD;R+bPXH;hFW1FVjC`y!pQKco^C)Q)rw%kPh%GFSP~zpP
zo!}=7c%lNEznJA33W4$>5gJz9b10M?9!rGZc53>{rh_}`A-wkA$!u^sn)+pN;@oc~
zUNLQ!L0b^TDatuObAm+!6v!9FOl=~!v7v(~+7~Ni2QYo4UO^rYW3>9BSP|D%b$ic}
z2?VPx=F5RdEvM5ibZO+0aec4cqs(fWW<wQY%V~#(jQ7_Z#4^Zyys%QT`40@Z!EnOE
z8YQ%^cd-Rq381hEf9vk0c+{)-@)i*)nC0*8aF08GGVCUqoJLbc1CodFGCm6ds;U8r
z0a-_^0@TLNTdCEdhcr`jGDWpmjkkxRQM96<abTAUl{vlGU-lh#DXToJNY+Hv)*Wa6
z08rn_*qe|p=VH2xg%~X6Ao3CG+~lil1!aN6I+3M?@_hJ>!S({GIo-?m3O3O{viGC>
za;x$;8tyeRU$2NQIbl0s3p8kXsdTyr4K7~dlcH23Q2}45+~KtbU)wn9<{Hbhg4wJ3
z_Ym`_^;TN5f^j@rwWd6Ev~6xV5!zLK38jhiexjXtmzuOyZtya#>`%RJaA0A@c$5`e
zH_qMUReobTxIZ03HKn)4-^G;93Llf1r83{r63=BITX5o0uEaYH{{XO^p6`l$h>K8d
zpYtsfQ}#CfOuS(LF-Q`xYdC<i{{UGneW-mxVGR`TTN~Oh?<$fkDOgn%&QpG}QCJ1I
zAD8@{J1f%p?|;M&R@Rn+)ESYBE9zl8PoJ9jtc@f3uJgaR3zq_iwU6AC_*W*bm*ZXW
zrxkzF@(iI(z1KHzhz2Lp2Q5&!{{ZB6#LV0Djy|-M#egDbE_Fe0Pa5?<NHrC9KC%LY
z3BC=izfg5vb9w5qY1lfVSrk!a2(UCP<;4wn`J?@;6&0^|&nP#mZ!l1ZLXnCN%3f?N
zP8`RWSfQrz@W4-r_Ahdk63s@aw*LS<oXp7kK>;R#&~U7~xnxmV7u-dAYZvzbI5M28
zSKTCQiS-d43a$4w;1yJB7%y~}?WVPv&~w*xH~k^jR|ANMA}hk-Y|-qd2bEaNZ+^-e
zR2h`2q=Y&h9}^Ox7CodO6K%5n5s#h<*A{6K_(k<syd}$uua>MOBn5r6cD;_GW<Kj2
zEbBOV*t_-j8IonId-y0Y+P{FsUAmmiw?`?%bvMmZE+(9_N$#bd$j%NjrPompVfIiG
zTRgGLlHDWQ5=D~V0_BipW1m@M!uZPz^!xIyc6pYe{b#;v8sYviiaUqsCHQY&j}eac
zz*<y6(b%Z#D9YSj+CF07k0+OwRBk<<LV0>ps;<5Qb|nw{;}Nb%55Y?D;yEh*#d#vp
zF+-!~0H~)#FfH1ZP@W967us|lA1pbUxX1pjYZ~TZ<s5~I_eEg?$Tr;chuBI1C|Iv8
zRMl>e>|}<bG!EZTFe<18CO{J0^AWa0X84(#W9~N*F)8;ISvTy2FM(+5Y!Geda@o8k
z64rBri4q3UDp*pgj$u%A6c2d8yBrd_(4FOk^ke@3NTm@vO`t{@M`v-3fC2EO^VN8b
zYKmq3$zYW!-Io%Y6Y$6NSRr0Ws;KQ;p-|8&ZOtNPEe($fxY17#v0g>t6)Z(#^h*>f
zf7N4ZD&8fN3`jxq2=i#D2NbP|4YUM+^PI!D`U_8;s0t#6=yWl4tMEF}nLF&_!bc1O
zFnJw5;U21nn=_jA07bheimICkLFdRKOM2MrkhfCAxseyxzcBkLy`t(4QR^VQ45-m9
z02#8ekB6S7n!5B$9Cw~2Fm8w3XGPMS<B@7A6Fr2BdlDe$atyz4>eM_ieTFzf;1@_1
zk@9v!=v)mO6gS?GG6?C_+%D-{8@}thf&iK~;8G&bm0iT~;7@CY2@1CiA7odv*LKnM
zA$G&&^D6czxU<^Fd3Z+lDh1sExg{Xq>x(9VR`?6T8>M^xjKkW9DG#VQ1BiSxI7AuY
z#mma*aI1p!3GDKee^^7spF%uCo&c556FHM;m3yfBP1jS_;@ccXu7`F9`-$gD`xpdg
zBY@fTxC`tR$8CB+P&=~SZSfeGvO8-Fim1BXzp?yu`JD^`I0o{oQ5XrUT-C%he7n`f
zBT)xGNt|SGP<&K#DdM^Gw`q=58g6r{117Y}uaa_nTrTSm(xh{ku%86!&ZXH~YI*a4
zYj;&$k2Y35B!Z64FpE>NrKO7auM)SD<`>Mdig57#L|!1o)FcwUzE{0KdcLgDOxuIb
z6$dx*#G#n6eDf($OQFTgh!#9s6E8c59L55{$Ze}%Hy8!2_u9w1i0v)DsDJx2x$`S@
zOXM*aH?nd#@dUlbh@<G7=DIt7P&yTj&wR}aPX7S&ov11}a$#CIekW0M4<KVF)`?Vk
z{ix9-I|l?7kaTIt+fSxy2ezQTRW3Dz1UHx^EXx)qH64HYrl0K2YC0{y?ZPU>0@u@L
zU$Lr|&Eq70C&jS3H~OhhVa8`q-w`Tuig`Sclm(Sul7`4BDac`oBn^0ZggcNG=cZLp
zN$Q^w0NB%>HlVL18%KXdir?M{_F2qk^WmA~T-faM0KvKvWCaLtXPWZP-GncnxMr>T
z*1_J!0x!8c@PwRQ2M4w^<ZJH>e$aVJE3GY3@h@sp&twARQ$O0h8c6GSCxn2MsCrdT
zgaCz6g?}KFq*H8&6GeJp>akCQEG@UggxwwSlxeRh>b>kUT2v+!7nWK701Vtv4rBh>
z`h*qz29{MYA1|B<GI<bUjC_YU$lH`gYi&jgwNw^vHY_0eCXO+3l`Nm(2dIiy{YrQj
zgmf3wFrq-IMc0(^@=ZG#F|Q<PPy%UIe6x}?&3*B<kFvpgO*nIQ#xNlL77?@9{{R>(
z&Dz%{>Riqi%niNmNm7h>?sA;AF1z7KI%gctJLO81K@iWA1V<?`_i>^rR_Wx&M6F9b
zLlBJnxr%ev{r>>*2SUZG+Y6{I+^v<{{UDBlhA-&{#6$`VFKThj7YDRM*yJN|i1o6<
z+G*xc+O8f-xt;(e-y~w7;pBGZ98XsBH0n8;)_lt}P(m~wXIcqx3<nA1XOOVLW-kbG
z{{TqZDtoJj)Agyz$4Xp$UP4aDw9@!Xeea%84G<|%Oja@7UOb{lmQUpq%u2w)?E}Rm
zHqh+;kvS^WZ8fn>0^wTA%LcY$T+>pPp%r(vXcdpc+v>L5vyG{e%^`{IpYY%)Q4Isj
zFaBaaCR#>L^6FY3tu$fZk|GduK(%#@aB-<?Fi4ln)S&KH(*vN1{-BowUUe55z17#S
z%wNWGTWkJ8zTh;VJ2q8nw;*|75p)IfE2Ri$gz*{Mt$A5%<rxX^X#+fv5IKQ$WE?hk
zov}N<Dz&Cv^pVjN!1Z|#%HErXD^2;rEii#{vW7MXlqduY3ZWt1mwGgmAMXdKf(aEB
zG~VLfZ}VtKF3;8x?rc$N)K}e%Z*4oiqbXKou=L1k3T?l#tP&ZHgYy=o-xolLi5YDc
zmHz-XDG{7H)mQeY*NE7me~vAbM{rh=Dgnq%I&)pNufZ(DP&ppux@_(bmt<HEo$dD$
zGqoHCcH;S1M<@nVC;`WGc!SE7Ja=^~E}Dg|tqVRD6*@~zLAV$e3wN%C5)jFhq}iiS
z4YvbZR4wUPY<O1%+bLjE(7N*)%C|>$cQ2A93XZorEi_zy1O)2XzXNbFmc4^^_)_Gp
z&8JwkgaIt*jaNz4Wp(3K*$H=T3tj|z#UQ<^)J%=IL!EAIL5u)`oFi^uA!30JQIxP+
zu(NuNFbEf#+u+P<_$A;A^|OP2WHFTIqSJs}EiUbtN{~Hpo(^}j{{XWD05?xIoKQg(
z-YI*qiFgRB3<*gJwc8vVq~D0)SlIz*N+_Vzsc9B`s?%UsPehjqTJw=AXXfUbs98#_
zYkb8aNLNFZHipGj`6zu1E28*LpZqzqq%O<KGz+vi3ltSi3c;GzxDhyeG*El-KKY0|
zMA%lBaK?6m<<J3_OLp?Y@b7#VYn)!QsdEI}usy`9<gwolaL2!h-M}v^RX(NohdEJW
z(vE^BYV+8@DQ!gX6a*2im9E&2S#3w({y9bQ_de|N#N}7as#mIXMOW0SO+fFBvvLy_
zt~Mpn_)r&|Ib}VOu6i=Lx>YHCoD576<{~YlS0`!YMX6mncJNOnGWNiKOb5{C$-zhC
zELY@jf5@maleii&gILdcf`qjIJdhlqQKO^w!G*dJZH?aW!1pN+NCFlPw7*kM^8>_f
zPKQBW*da|Fq&Ak7MHX|6z22ijJeoX>EY4>M+e*<Ys6FEirQ7nt+lS9J2<P+rjYe76
zrIk(eC2C!M;Y>tRZ@g09{{XTS_Q5o{LDW1-t^5Z@U+i;8l)nm(@(ROCx33b~Py@+v
zsI^h;uDugnN3!Zsk;MF*#%RIwf`)YkQ_9q(4fJvB^oIpnw8vGP*akL+;OonsbVdA8
z$ky+#Qr{^4^BVK3VnH%6u@D;tH=4c6&)%!z88$zxqge6qkI@*Ry9GPmW;d~yqUm*D
zNEMJ#cIo;MVutREw>;I=z#I*98uGTJy;S%Z{ivJ{GlA8NQ7ks96!?S%5n+qq!6Wr5
zua70ld3m}GXzhaPfAOPuO2rdFq#ULN$hv{RNbRNR9MLOI7<6CJA?id{t`$f0r~pa{
zG>QiJI-9i?z3@tEE+0oRtD}FNCCGWd=TX~LXZ(n>RP#_Ib*X9rPnl{Gk*Y%^iOLJ`
zUvam;5t$&D2nTMit~Vv>Ic>Z~l3uPboK<4i#6@7(PliIaEE>8Xa~5|3`=D{HPk*=e
zQ}8AilIN3SA$k~K3s&t%jtAp|o0#rJPbLrCzT-k(9z)y$BJBhpVrk$vQK!W>2)tp^
z?%Oe(KrVLo7ef-qN<tKOMTGgSbjl8}6MZ24&?l4PS{G`kH)0*Md@U5Qa4<`H1;*FM
zcuYkUF+#%$a!Q7lMBrwVW%a{p9eD`FBEZp9TmIAvj3T32fZ|KNUF~?WFP>6zg(nbn
zUU^>!Q0UdJ`<C0MJ!Bb!1uMxaFtFoLu|cIYECR^+C#L)nm0l2S{kJJ%X=7Ve3c$8{
zLPa+Xh}-iLg<Wq~0m^1d7o3*zVi$XBL?(p7!VMNd9T|u<FuT>7M)1Q`EEFu(vZ}{<
z7+V2b&~QOXqYsT3C=1_PReE}xq<W5PLgP^LLy7Py7)ek$1ldSt4kFRDADU`R$YL$)
z+SI5gpJ9AjRL)n;noK<z?*WPCx9t>+U|>yP5xACREpIcNgB*>pjqmBjVGmPq=79<g
z+VAk(5VflXsy@~irYMku(xs{#az(defm20dFtw)jP(@3#i-&TtEJ;r#6ZvIbTKorr
zoZvaXxpX_nK4-!0fqLYul>{n|<w0%1PsFYtl8RbIPymQmECqlIV=%CHKm~MN-AZ#-
zkyJ6K$p#XU_OwpGeF+dkgIaR<h!MsCweEBFh!;kf!4xYC;usLFMOfhoSOrf*(dILo
zkMowLq|*ps3lzJ#War9WQMDY{cujwn=5Hwo0nxtD7RTt)gpMxN>cep6E>#C?&aXwn
zio!2jq_RM0P~nBg!mt+r^Y>K&pyi|0-^&rPeq{gymsi>9Qg?!K(lr%I;6QY{z_ydr
za|o_Q{{Sv8nYnZh*ufamq5Xr}O5OvWVM)5nuQ9JRGYFvS2aj-ag7pg6Jn?bk)y8GD
zgVzaq6)I9XMmD3^t3F~m3wz%5!qG_H0=GG$C4;nC@<5&VmRuy1;2^+3w?uX|<~LVo
zc5+HhhIOk005qBj_JM4)00+6Qin1JVGs(Vf=3KxKhmGP^%{I!c;yD33Y|!&j(e-LT
z_PQa&@O0k;wB60!D7OBxWet;oPWgpgbI-xC8&>si{{S+k62bKwl^muBJ4h1~a6@Pm
zAO(WT@Eda$0_<K@%$PnG45X`xh*t-e{7dW`pPYx>AWtoQ)<cdqea?DROMHv`CReCf
zQ|xSkP!0i=K~PEMa-gKEOi|j2d2V@~*$0n?9aKH*(-)rQp{EY4Ld#d({{YBukukL@
z0S&la<CxSk=7*A#V=X`-QK9;VT!FPjap=)}3g_-Cs+&qrUS@#Iv1s)v<|bGF05wac
z=Wog^X!^Z~AX#WHoNwJjDi`kgGFwgqw-C%l&X}iz%atp=j~AxKL$r)5Aq{uCTwJFf
zTMqD^V5PU}Jtb1A>~v-$yJk#m04}zOOI#rB0UDuruIPrtk6vN^Tx=^I)TAz97KNw#
zTAUfKcz{APLZiy7EG<>N{o-R*A4^<loD_vL<8GrPcLY%6vxC)s76R=LJrocD2MPI0
ziOE};yh<<|+PctV@5B#d$>|h`YrMTX_+KGTt-rf}$aajdg+rFlb=0%)!OFC#Npw-_
zf%?Um3^WC>eJsJ^Y@lvA?;GEi+l_$|mYaRk2%ctH=CD0s`mxj?`z#nsNGKdfiqT_%
zPSq*cJ8a!q94@R?m(INjRoB*6o*}3zdh$m{z(K4aq2Nhg_Hi--aww0*W(_i-mYm)~
zzQ_u1Q-_QDIJWxXQ(_cDKEf@#jtfM6vAnxgt36yA{GY<2w&7GyZs}{_^QmYa*rnVx
zdmEe{Lh<uhf!wz_<H;_rmwq*2?%WOH@cAWj7sE%DiE@&!E4MlIEG-5C;8)5EBRO4)
zC^Sna%cwXBR<@)yTqk1-aNa;3nOkuta#>AH?eiw6U&d>=k!`5{3ByQ0aNeKRTmeBp
z{{Ti14V~B;rS}Fxo5IovV^vivfa6q1M|Z(|eDg4I>i&2?TBfY8wp)nq-7jP&g$E3R
zwNimLft^H&c3{B4gnrloww@J+6A3LPwEQ7d)%%(`+oBd`8nmB$OH-uf1n7H*@63f-
z;^L>F0@a@4Wr0`dcnA*9$fE5|P&Clcy$&L1`8FWh8Bfr8L(B%Sn316kWsFN#aTby^
z-SY@W?%IND1ZihzB9V;<6(d^26?RaEnBqKjfqi5`Rwl2~GD^)sP=*l8ElI#F2H!JC
zPc#E6_##a=&LoAjAQn|MC{k#$ppF6A9!59;GUO5j=xrwj224PqY|<{R(~OF)AS$9@
z(gv&-OuH;+5wL8g6c`x2TkU!!=4gYWJ{a7n9o%6S&{ta8C+M)M%Db6mG#YFG#OeT7
zGXcpo1AZ4GfQ-5wsZF>o#Ld3ZiPdP?mch8VCt-{_Y_fPcCGm2pmbl^+6pcp-P!w5_
z%6vpZP+H#Fh2>(<w-_q=ImLcWX+UqInR4=1;Y?cc6DUNL&wcRI$jtM`Kb7ec0oTiL
z{0MYPDRsT$%-d57D80a!$pb*$@Z3{g&<vkr6I7TMn|I8tR9jUq&<IY;;gANQ*zX`9
zqS{Wl^(xp$byl$1!fnEOmPLKnaqiW<5jlhb&Cy^zsIS#ZNDFa6y%@mwhlRSxlv=YE
z>*5d*s*V7%5HiH8qR4&F>nF-bPHZVyBRIlXDpmM|kQIqf+#7uOc!NzjPSJM?YU}4X
zk38Zb=c^OOg~DBRsOd6Pc{T0M!c$h-S#KlQf7xShtAh{1INHH4jt;e!h*3OHoDe{>
z6f@T%T&}29nMZ<F6W0>TuitTK3qeilF3RD!1JuE<b7Z+vz4EW)h};qJR=9r%98P1y
zJ&ux#SWc!MrJgnxZZzb77ja_mf#mmaksC-Xe&ZMQn!9u5k1B*=Men6Xcf9=&Iv?J$
zzXnmw_?$gt0NtIzrdpzFZi?iYcPb8OL7d2z++cr`4k2I^_VL0yfUAE362M_$EYmUJ
z=mD*m46McH0s(*yI3QXIQ;j?NH37)Ok_*n?=MtUxs5;&A>_VngDeZZP;C;Ovd^1M_
z^(eq@3xKXk9-wjn3sS0~RC<8&i?#Gipbfq;XonsE(%VpcYX#S=!5sxE{{U*-D`cYC
z$OX$t@8qDXWlSnV8pt9VA!~)j#*hQ=j7t?=H--Iu%2LyAzT>Nu-g;xsIy~G~w%0DL
zFl}J-$uGl04OLJPDpZi1x{5ZjPRCd4gZ4rg1(s4a!cXT7K{$UBwY4D{?_p{L@6rN7
z8SHig=2*<|RiY+<14Hr(R%H(K7!6mzu}%~AxCk>blr_j}hCcpiSL$cYYF&pYv57$h
z^kXEq8THHKQlsr~alsi6L_!3~WZyA}M`2=)U$BCb?Y8@3rQ8MK;A;DMmhRE;MyM{L
z)n{=9g)L#r>N>)}ZVB_864JE(uy(P?149G2&6h&fe37fQEMl@`GL&kgBL^Vn?ivbi
z)o~odN+&qR{7x{>w}h&=`1M@RmP+!G$uBII?cOQ^SGfULfPNxWm~<6Mof7CgjXg@I
zlzv4CfvMq8S4e-$NSr15h^F-js=2EB<f%nDYvmmvF?8n1a_GlP>X@0jUHrVk8W8oL
zx(q>sZHG9jxF91yM^=Mg)M=KB^AA~OQM}{&s+dR9P5%JtDD`Q1XI18G3Rl_`ELvDA
z&g{PwTP6N9LvTV~)x>bI8e-5@NqTWb6yXKwL?r^xsw>L`&QIJzgW>9qrpL_JAheiU
zZQU_J!Puf*pj2jnt62LGEF??qmqXn;{gBWMZxdo{U67~b?o&5Mv`pA;UmQ2Z!2;c_
zVJoEqYJ+{YPmdv<xP}C-i!`XVJ$kZjnv)654diQB?_*6wYUOi($>NFXxq5m&jUDii
zK3fGh?T;~lLUL4C0MuCX^uTArbmL?c5SBT0mkEfeStUKL0~QF7dIp7~aq*n$2SUpQ
zApG$gy97wykIMon9O{?7H(^P)B<S=`+T*<ZkXwPTo<%YFU)(kocL^5NrB8!uCZGy0
zLd-$4kS}FS@sf(BVjNIm*r+93bcN}Ftw_p%yC~^5jqYPBu|N8_t_kI*t;CvLxDJ;D
z>ZQ=P7DxK%09K%MH1aBkAvDtZs;HDucW2zr)5?vra}aO4Ucw*Smk+YylF(StJ$YbN
z<9Iqrptm9Q+0UJ;T&77@-k0+_eSgD^fzbw`QX!Bh%aZgS-yS2(J3Mm+!1OPYG}ysk
z8+@z8cFAhz#7bndnG;fqA$qK0>_jfxiso!2umjR37gQDWtY{$MW1a7qW}y(Sviw6i
z^B!NdxP(@DfJz`{Ye#_LeN2wEe5dr9lje09b+Pb;`E8HKW3QkH*$?-#X_ww#TC_q8
zji3tcjY<(l;jp>haUUSuS0Yweo;^cy3e#<~>|r?@c-);XJ8h?^_e=<c>-o5PdY;M4
zm{C9{4YXFnqeXHedLX_BOf_`LdFMenG;BWmO4oICK2q3zO_%1s^W5TGZ^S;e<z^WF
z0Q-M(Pxw^~;!-6SdsQurq<jU>Gd$PKZ`F7I0M8pP(`J-e)v(<phEn=5qIbn(&_+it
zFIY2X*^>cb$#Ki|dU}SZJHB?sqDb@<mLU)Y(^M|-uw4f3A(E~js~Wl&%mShIm9A-e
zL#G+t8`*Fy=iqVmIB87C3VmlNkvIw8eM>nC9hqScfz9FznRw-F<UK?R6-}v8IWzCI
z_B~Y}nPjpQF!-kCEnlqNL>XeQl+Z4@A2HWNt7*C*!9iZQN(I7iKoZdf73>s4X&oLW
z=${cxCO3=SK%Di&K8wZ`g|isN3@4xv0Sj#OiocMZ<G%`7#XLbSJo|Yi42>TE*8D?d
z@gHKPs8nbTWmH?)F3=_Va8>ZIzea!KLM<>#=jH>AZ|v}qL{#zygv_!WuNF6!?BGYE
z2RSI|El&p8kAniSb$tl6j%zi*<T@>mzT81{tso6;H^7xOFG<0_Y+lCG6^=5c2<<>m
zqT^wRBvPz>B@7-T*hawiAGBh<M<`&oYlxL=M@8ViA>Sv(jrnD%nXhS9`*cFS{{R#%
z%B=RB4|3rQL{%GpTwD1=cGA(+nZi48_V(0P3lxor_<Ncb8+F?A5mYwnys>ms-M<q5
z0Qlt}pD08zpCcG7%zLm;Itnk;RgcfEj4Nv!-8Z-Y0Eo05y>qaw^lnxDmSJ;L-qX2w
z-XZLlZa%#nP1sQN7Y*2t)KFm2<D7I93}FX#;&za)S4bjPFFnMIIB^MF&&o&LU8jYq
zUo@!s^%cAZMB<kHjhw<c36weEGaiw72*Gfd(jmgcw-#DA%y^?)V`JIhzHU%$3n;|t
z^qisyeL^gxuK5DZWmdb0e5xO!fZA#j6RZltf|Ts>14hu|Hwzl>m7eo*1XFodAvSQI
z&1EU&P7c+M{#&7M5ofv%o2Z~%_iF-PRl=oXcn-s`<>vbPg$>vTiz`0Clr=|cxAKfH
zLjZlenw!#M(HQH6d>&QtM7%ABAChU`QW|@eqvSi(AwJoRHGxyN6tZ>{Sy4R|d;@cD
z!z3J=q;R<gXUa78H%HY@XyaIyRLm`+Si<@qa_`VUc~5C{PLfi*k59G|!2q@Ai;rC0
z^s*u0$yMCGOQ0XiOeQA{$kEs`w;J~FoYWV$ry+dYRZkdz))6?I-z*S^U#kmX>$h;x
zBmV#uSM*4mM@ObyErk|*FkNBpNns7gw^!v1{{WLs%g4`FmvYX{gUR59EV`D2?o;xv
zJ3=&ULN|~MFT}Hcyr9o0g$4D!^<fW15qiwzL}gDo$v3Kp?}-nJaSb_f1&2*vdMZ6E
zSUgHgFV)K60!lRiF{<QItp5OGw!fTfB55c%HtK6x;ntomSx7HR#S8j{Px#vKBN!h1
zehw8lanWNxzFa6*%<#Wbv0w_-PrBw>ugd((SUwg7d4#Hs2si1LBZHc96{;m$&xDT@
zM`X#Ox$yy)5Cz=Pu4%LP7W~ytw(aIueN3*~ez}3P@_7mf<M2-wt&{)><<%}O<XyF2
z!&3+pM;sabi8A#{mY&inrw~#nBQHK->J(~^Ko+Y2-I@v`whm#k^U^bty-qkNGOhT+
zsbUg)M)*jI>3DfcVAGPKGz2=G)qfij0-|~F{z4N452cp~f(E9k<%a-ElZBdf(T^*!
zYSzW5T=&%NpopAy`n0-X1@LW{q?#Tu0Czx$zcwZmTKbGb@5>7Kh>M9KNk_`Q7FH6n
zEmgvU4z*e@z5f8uGWpp}S=3DGayi<%%jX<Qy<c}XeM0!UiD4!=MQuvV@YdakY;FMn
z3+4}FT2hkmCau2KUe{;{{6RTeZwTg{sjAcNW)6+Y?%?Sf&Lf2h9<CtgwbAU6M>sL!
zjsCIZNM5qvyIFd|fF6DPz_InrbUORjDwl87Dc$id+Uv^8Dy*UU;+`&&U{hl(_ddZ(
z8d|8knweDy3brfVTRE6=c#Bk9`b-=wP&-K|Xe#`1kVc(*shavC8=wkDxZx65z`DHW
z(EtUzt)#xuMp%}@!^CI^X!2?w<Uja5#8STm;M#~c)X40$Yr)axFq7SA()yt872G*e
zy+6#l6X{*AvX{hDKwbX;R#MpP_$~`sh&S;Vs)1C?4c{=K?8NPrL?{!@3Z`<@<>k)h
z%QQv3%oO`aXw%b1?L_txQ(2k-!!=y=HSsWBJXCy?HWz>eWm$ea%Sz5a7cL8xD+aDp
zj)&_x&j_S=sPJ>KKC7pTO9}?-!FoU*PSJqM7~W6>kh21X=vK48ywWxv=ZI=DVqi3A
zR@;(;2uz?SDmb^qRVi!SX5OZ|*zOVPMkAK_S7}i*l_tu%uJ|F%MqgG1{=T9RK0OS<
zE1Q*4FP?iY$EW~8UmTByUAaGRa(X7AN=r#cu!#Z|j;pP{VYW_(s-x#>Dz3<@bhC$m
zd#kZ!%r~mU>-fRb4T(hp?Kmg4fUqs_ls}DE#Y|@Y8w#O)6&uzq^p_kSQ$64lQ>>Fv
ztlt5Mh_;U-vMp4!ZHp0fusbR)Tlt1tc6}j(YEw#aR|B2m9q}Jc!SU;mh|1Gu`&x-<
zq)#h3j&9<-7ZBJQC_IfEK$onbZ-%C&fi`waUJ}vDsd~g&VH`p!sm-YG%#91v!rLNk
zcG?OJFPLJfa4$$Tjpz4}X4=&49EU5ZW}0oPDcLx$h@1mKz}Ns1s1|@`aCx27F_l+V
zO?b4mU|a&5X0A>-epIH)nXy11HY=Q&DDqMwr#_PmwQC*^Bnbg_*jz1Fz#Bi@+&j!x
zrtCdB(0YV!G#a-Fr;Db}SvDS=$|8*@C@aeqb%Gpj5JiK|yk|1xHQcMx;c^V!7WA^`
z>2x*3`eCvi75xeqAf~Tf=bDu*vk)ERbE?Z`*%;cs00P*~g6rNBTQO}dq3I_aJ}J+h
zB9k`K(p7^BD|*!!>1j@GN_nxxBX4R_dyUZ=-5xC6J-QWzH++#t^_H6@Vs(rdLK1K#
z$fBSM+;b1u4Ni*f4(5&JKZ2?3%h_E@AeXx;2CQ%_9p)iG(j90{cu+Z7Dr|u}!ykj5
z0>YY`0w7y%K4ll@?mCl@>ZZDyIQwFy0=8CM6HcCO0wD0Vt?p_)%oG>G5jo`~pgjUp
zY8_1r4p!u?1p(0tnYKn0>!=8eH5+BHkEzl=HI7OGqV9{FV9!lXCAmDLVjbcg<T_cW
zg^V!*?~}~A>0Kw5<e)Dcj{T&C2s2Hl#-)%Q2S}Cd(uY1G3~-{XXzd7)ni{6uv(>Fg
z5n^??h5%;_f%*!2MK^2|KT5ZTT^v_N-IXiWwe7MkYcHq3k~)_akzSF6EBRI&jg(B5
zZmbX`uRw~btlnM-KIG6Xx+-d!UBIR}Y1))D4aVo2FFXGL%;@@$q?FMr-?MG-(o%_i
z)Ie@*R=Blt=ejQw9J8ES$D86M3pc!}qe;OkX~Hw%M648hI`|2CtLq2<00O1j9H`hJ
zYYOYX!jsoFO88EM0fncq5B*Vx!nFFE=Y=nJ*vZd5Mb`);6_7N53q{f8h|;x{gVd>w
z^9Nr{#T^{RkpTu5=qgHC;RLHd)v9C%6EQv|uVzH4#Jd_b;ymz5T%2-(uUJBONOtIN
zl2p3+0d6~vZ~GMA)nwzIE}n2hQr3ig>U{AUrgib-X|WK<_(%}Bd3CtAmJmMK5Yp0F
zoRJh&3<RnroJ~)NvrP<hk+Bzd6l?JYib#0q`H5#`h)UMuGPUXre`n-cEw#QBEv@cZ
zufI|g%j7*5h=g;TS8OwuDw|*6#au8sz?(X%V>fwDZtVtvDUzmMNR4Rs$`|=8CLYc(
zz^Jgrqf|if0blVOWhfQ-sNudCq9H)5M1%KPd%D7ulsMbn()dJak45B3U~|_5T7GUy
zr}fCN;C>>(JR)0yMsq9tmI$GRj98o?>_{qmn%8A|O+=@TeI>Nk)!0y5C)y1-aBP{S
z%fKlz<&swReo6^250{prMF73bLi`r^D=fr;gS2Zv!cveef#8G(FO`sHY=#$Mv`z=R
zcr2GeSRpILqOJ<h8uC72tOW^w!Q_^6064aAE)1|stOeO>{{V?Z5LIJ-ZbT)y%kWE8
zYNdHA?p`LCb9<8UmX_vxk%gSzST_Mfj-N1=qu2xyuIFxAG=3TG-rQ0S?F&#44Gc#c
zA_+lHb2el_Gi_JW@yz1=5hXN1@MzN8G==3hJ9V~bOVyUSphKY$;a89;4FnZz7^{RT
z%{5~BBGLwKuH_ZeTj=|YsmpXAAg})b%6s}{4>?dep>MWlWl_*OV)q0S{{Y2f&tLf0
zBT*Zrb_c&{O50U;<0~wsZ-Oy((%bA<+#(1z9N?i+S;{R~aRA&=#L@o%Zc;^n<Si*;
zh(91HEnF2mm1MN)82J>1QwwH3=P~=@p$F!FNODnywk42OnundOW*6;a;@7<VFPLcF
z*9CG*kQM_exueF#7E^N0S$_ifadOEzYsX|Z>fy|?FbS3Bb~NzJlDP)00QNGY6<E^^
zt!{mtK<w9sls==rfd}ges^D=<L~oz$d<^!nq(4wQQBMLj#I`N*Vh_w^aU9<xG|0xk
zY6FF?(3g{0c_2tj&C2%eqAypn+)C}myD7AU=!LAM#MSo;X-Z!dUCw@h+>vB8E;l8?
z@KFnIn}Q7Xzu_<K5%AcoJmn2=g9Q{<gNMwap?2(cGKpDsO0KEg);S~P*!X3$EqJ?#
zoO}|y8BGe{=E8RbcIh@%O2+9o4Pb?Gzx6|nu|l&Sl@ZQeyi(*iuT|YE0$virp&+ns
z8CT`$Yq@R5dxdderAMvf5ByummA!Rz$3%9f%F%?tvFMY-hQ30%VT|s2KPw`_KtJ$9
zni)MckU9e^MdAY^f<FT1OaxXUOA4}P%DNhslUC@BRx2Lm`#$_WhT-OOpjkk!Yu|_=
zD7<H1ITbyeauX@wrC7HGNtSRzpag>I#srstgi@xKU=T<wFMP$>DMt!-36P54LA<Q+
z1kFPV2Ve^U!~{Hb%2qGMLy`1BuHOr=9~Wr@jNhw_H)EA5!X7d>1MHConOgKA$qP-*
z($8H4u#5w9l~+*nS3{T-MdCp|NcAPRRgjX#_YiXLo*{Py;B#=?9vmy`3zn^x2Grt<
z!Lk>plLowND#Tu8V3@N!7cBU)%}aM-Us_Vqj}RVX$FInzYU`Z0)yJ@h*)9*LJ7$m`
zm5=5dofd}$e?eYZ%FFznkzQC{V?ERSVdpk=ZKWB6@WDhrQv+x8;p+aG-M?c#`Ex34
z9!6?{f5T13n7Z9-+PI`xUAXfKbB+`6S|MAxPw+xWs^Q}-;pPiswO5m5?D=kCeM0<8
z74CD_xrKk9`L+E&@Xc^1?KkVn0Inv_ctsE>@yEk7S%9GzY<47Q)GN-5honuUL0j6C
zqieNs3BE7u=P4lTA=CDeNEYJqvN<-gVwg{Yjwg)jEr%Mdk~g)p%^Lop*dJ$65o!Vy
zNxukke$FyD!nQ0DGhuI7p{S7_j&~PxKe26suSVZWu^tM`=e3kfbR~zherf)~oV`Mq
z6fLeK;cW-qv(Co1A5T^s;%#1OA}_PU-o-jJej>CuxKeLi$gO3ReHyJ-c2Zj}U|w}z
zC=8a?ab7>9fP~hX(w<7SS3(!$z-o&J`(4kh%?h$`<NWfJycLm=wAhrbN<LQ;z9-}!
zCW;86+`K+P*SK}rmu6Bz+golM@WtZVc-5quy8be-mHV};8$h*U$?*jrN>T{iXpPcV
z$xZ2YrL&pyRd)k{AG4$i*DmTl=>DCwitt5YrV6%dX+sBb$>aMhdZ;mXe(-1T@R5qt
zez4fkEdbCeGRGAdRVKr<0cpWGXTODr;=L(B`7NLbY!-y@wm0p3#v`rshLTP?Dn21+
zZ(xaHIm5|lCyat!Cvw|YEFRE6FQW<#6!7qm97dle4GXH@N{H0f?AVEwnmtWNGVJ<8
z+6)%BXt!^femh_nd~g9qs91W36=X$AM(!+1kZl(RGhsrCT_Zl^>eO~M3wddu_(02@
zM4JJJfm;^1qP*eJvCs0mi5za}^E<kL#?<jI55o7n8$-6#(%D2%xrQU20Ue>Q06P(}
zBe>{)z_CPYzb$FpGdX1h!2&E{f{xL+QBXhR%3|kTe-t~kb8s6{xIp|u%9_YOB%kXZ
zgKrfWj?{r;UI^pye{84Hoz`S4i{7DZc^ZYLTjntWrt*P;P_=V$wTce1)teb|_1B=d
zd!x54H-`~p!I5%2y|GVVFH)3=9n+(Bi$KNFb?^!&PR85y`h;y}R8u|1GNpk-Iy~I3
zs;W9AkJgDu02Q6l6LPGc0~OHI4%p5#W@X?5ZgXSfs9Z2^+$We*7u&2zU5Y501$MyF
zMcrq?^XUOGlv@&J5|y#?KM+9yT?|FUJ<lkMOAmlcZv|oPnMM#mmxhwyJXZ?%f*)Rf
zbueYZM#h@6+-^5kbNDdbr3WjQIg|7U2Ge?_*+i-y5t{H$g#iqXs4WT%vf&Y)BH?|I
z>Xg0n1>Qk4Um)%UNfHH*Y9SbQxNBU@&gl!Jl`*zmn~yWsAYE9-t3`?U!BeWn#5^Kf
zNVGzvG*Nrl)~Oev73uc|;`LyMxG69yv&19RZ^VG4^4tZr_-BFYGo!B$z($54uO-${
z*T)^(1X>xq8dPR|0x~k-8*O`7fO4{T4tTbsB*U7wp_9c)hMnr-Fa@@yxpBam3C3D^
zFbNeLASx|mTuom@DY*Q~^IjuR!i&NcY^ol6n9iJyIgiqAJ~9ypEM;)~sO17qJ%p@r
z#hlz)H&BEOn+FD85gNgm-foujg+`zcY+6U)X!@vtEG<Zebi%#{-}0*!6P80tHbS<*
z+Ge)^+HrSkgd(T}^eeo1AoLZD2V`{-RGqUM;O;V|Xwg?qaz=1=7oJes42TzEM`G8t
z{KBZCB643+_LpIML%G$)>yCJbqySmT#Iz$NEmmAVHKZ1|O-fKL<zBUG?UXrG{{XkZ
zcF{z?@rMH3;*9Pvd-!fD<wY)xw#9@xVo*J*mlNz&NoS?Bo2jW{kn4j$n6(+(3l@+(
z%9*=4O?yA#H|8+Nygl2qYM0nvOS<@ne;YRM#OB$|zfq+33IQAT6-I%FmDT_g_+U8C
zY%$Wa?isfVr3MXoh|_~grwj5dS6O%tJejCqe4j8epm~j6Rm}h^IlE#S!|S+2DaSsW
zaVwQVm1w{50m+`D{pNL4_SU7piTPEmUK<uFL|2<`Jlm;qoJyy{LG1&ajW~zbTVX80
zSn{;UK4W|Oaa%NQbFN#LIZ6%d>C(pMlTX6S4zA55^KdZHX~}94fDN(KzycF>dVSBK
zQwoQ!PIGR+`<xA{yL`EeUNarpSuYVU09D<kiykd>s_@E+;ZUK`X&&A#TH|)BQf>#6
z@#pqhgLBjeL)_h<)Aiw;Pn2)s;z*S7ZR&RBJ7Z(6wg#mAPyYanpyPhfFvk$D2bijA
zU3(xp274#H?gUd9>x!Zrw8SL+i;^{Jxo80525_%9kykI=XvIl#eBm{00n%ORIx8;y
zQ^(XL-3RVms<v&17a3E+X~@CGkTVU~-x9stc~Yvnbe{q|yOIPhTEl%&qhqDN6${W>
z$dq&XbzvHcwzXM76wcu}vG%IlAMzD<Y&>ioii6phoUuUit`rs9bIh~I=k*{4stYJE
z{ag*oE8xY?3=*d_@WKQ!A*hbKEM*p=vc0SBg(dmh*zoreQZ=UNE~8y3RB?1Mia-Is
z*pB595PKm_W+5ywMOA#B7_~*>@C%MHRc=KHOGUyy_LiouG2;nQu7_32L|%O#HsZ4H
zilO^I@vGulmVQHaj6Ggl1q?&wlMnZZv6gwJ(00fi>+OUGlgTRfQPDx6W(&x`LkJX3
zL8a+1;Qi`QWF%GD`Emd&U*$#vD2BkXJyIalhd;Fk*x1!;oGJ%pq8H31*na1?PvFa4
zu)Q@(19c?A!^q2Ff}hywibz@V+Jb@gnj-?w>`27294kv1o!H(SEc_}4&j26)0AUZw
zX#2ZP_g!0#TkyvOw+_<#fD~X^dN(>bD~ZVmfS`G;h?2MMy@uo=JbMVsC<YMZJr!Nl
zR1~Uxz}@qk&RD1Fl5EmwjClIxqsU|rWZ?B<3hG4IU)?cj=DzLY%!7Pgq~oGBkcLid
zZsSJis+(xrh0KAekCLj$OsdfUqU&zR2a8gaQ)5Jao`9}Pl_IlWd>A|08&=irLpqzF
z^Snl=G}5b?7*A-APYCaqY;+V5avFjWBtoNip)rTDzMP<YwiA;UOTF^CntF<ML{pK7
zX<)P(6TV#3uHj%V2K`5PuW4DAY~Vev#x>6lDgYE5ppTe+r%7(Tz{t=r)o)~LHsNgg
zZWyXmVvmvyTv`iDK?KgHnkCWjvheSBOHK!ZDjOvRC10jjL<7N9Pk{u0N!8g(B8(Nc
z*_kr{2fr~X*RHrFyjs}JiB^V~C_^k{8&g~{K@D>DVcjJnH3F^I-UQ`^wjhhru~Bt3
zbTWcjNJ7pn2mq@qigZW=K>!XxxXT|GoLawRRwtw>u?e&b3bN}au@<JOHGy#DceMWi
z0umy(Oa4ayz)C?<-QLce*=9IgKWosx64(uJJ<AW0Rw?O<O{ogA(ZLqyqb=ro%n~T8
zr%Xc74P$^qK;?7W1*$Z17{zksj{#hkTVQ2OdNqQ<wrKdy{{XW0jOCV*;s7oxx4&?J
z4eSnwc8e2OiN=n(@H0yGO;?c<jO>6ujEz-J15dLDas4`FWO}_p?rR4-FE|clMMXw`
zd{S|%D_$Bxd!}UR_<#aAANj>c&P3lg{m4N(nDRUNAn`5zh&CD|CSCskCDly3wTqta
z0F_V7RPm6S_S$n3O>u!kwmqO{qui$4sGLm$9x<XN7$ryWLeu~hVTe`QT<SFZm$bJK
zLhmLu?xxBK-`qs00Pj}3`0&M$94E2F#0y()_RRAb9GxR&7C5p8(V`GFLxv+P4<4X3
zibL825Jb8oaf$+grKf@m9`z|ol#59kaM1f<gtEXmZosqWA{g=4QJzQPGA*=umX;nQ
z$|ygH03Fg^n?O+IsywJf+4dX7h?F@{00Gn>sen$Od`GNAT`d~sYQGW+?aGI^uM3n2
zs$LTUz1(Q&HKfMJ+?CFBWp&3gfRsNmYS9X$G>3{|4u}`(Q>P9Yw=6bL<^KR<4j`iL
z@*Gk@`)K*B*~8m=l~&X~r|ZjaSKB~rM9W5JkjuOQRp^OYJzPLXGo|R(gswvL_oTgo
zF2<UcVWFo7uc{|0s~;i5G%7SkkUGm-E5A1lXum!q!9Y(5q|3W}1s@3YXm5S^JRy7t
zh7nv9U=(3Ci6&y@W%!D<Np0;2TEg+Bs?imi0pe&poRG-mBh!?>SAezCMBIi|$MSjm
zh(r%yzzl1{QC5nSJ{T#Pf9I61Sp6x&BtwLF*_?Y6G{XGLKwq<rXGo9M!f%SOON=?`
zxolxU{NUrM<9Po7&G-(HVSU0xcKKyhFdvapL@8k<S`ek+dzZ^R`5%TPU1G>_TOZj7
z)`&{J^#G--XqD`XnrF>OPysk8{iq6D&}oE7r5@4(#TVft-ENR{aM3kf1wytP=U}f4
z*-u=94%_z?dl+$5;A+uP@?yy<AZ-)GoQo{FUI|gY0)ko!X%kLvKAK0(+)K^!C@fo7
z8!uN;gcy4&5#M{J%K!z-<)z^U_B4<k&)8T1UbP&rhF*d21F5n>ZgFL^B)$fTs<lHy
zH6XQnZRg+!>vPQuuM8HZyUz&+DnK%)_z`T&c|UQ$PJ-Vw3>R$_)x;MeJiwqAlkJJH
zFM(ybgj>~J!2mSF#ppi9RjaM2Q^6MbakOVc(m1DB?8kh7)Uw^FQaqj?QAXYIqlbvH
z!jkB8xI(%>x~K~5YKx06qfXu<H(U1Rw&x(aA$O&}Fc?ks%FTyi@XOmycw$FOWhdZA
zC>`ynCjL%-N2uWh_YF#7zR<Tc%L^LGWvUY2jR@jbBPzigrz9%5k&wp*jwP0h<peJh
zWmLJRWL$0p7Iv31r&52pPz4p_{{X|^qtOf4nqgJN#)bHTieZDm4b8!K0({4Jk5H92
zXn0i-tip$^Cfvvd&e{m`hIDVTFZ^PqcB=9(K)M-9_!XD)6i5+GoiH!7mRI&|dI6^e
z(;Edn_a$FkU*eWzW$<yI2_7+$!Uvl!G0r7r474LxU&u9b?7FupiUB!4QK(ePj#mpO
z7lOD8j;IV>m7!K*Y!JQ`VkR+hu)Nzp<RP_<A>Kv*06GOYs24`sVNS_Kfh*7}NbVlS
zPq>s*3e>C&h^qlbI#wl6nEkSK^DkWQmy_{C5npo^ubt*ToXV_IhZ}~tzq;l;^PUD4
zc9nU}OZKdk$KqUq)n{ta9MUOIt|4T!$7U5R;te1fm%u;PR(x|B>N{5emPe+Sh7Fq+
z^B||6Qo$_VPHG7@tF^jWF**EC<RH0B^OMYLGq3#P@&%CWBg!COVIx%nyQF+xrMs-(
zaJIneY%gczZ+Q8X09v&<cp>T#(FRBS36<b$)tu2R#^~9`t3uhzI*7d&+mx^NctTEZ
zdo4ql@i|oAH*$lU30nhYqwnF0PV-lX7-k?Y>(~!B2C%C5<zJ68Pc-|f2o_P(D{9Y;
z`7+gM^N0{cE;Wv&N}*GQ3kwej3EQ~gQBcV^M|lw&H|^e>Rk!=<LDbKeKT!fXb;ySO
z1xt8cbVb@@s7i`ZCxOP<+hurDu&t%B{#?X&yYL|}Dxv8rq624%3Sc?iU|Cgk@>NeL
zw^hl}mY6Z{drHBH(QYIv&pekFv<R97rX_4_x-E|;N&yv4OmCtmHCXvC9+v+A;xTpB
z`^8^KLfzp0-de^vaL9hzZgMu%(tk@9SE3Nw!n5)-Y!;kvaekRW@r=B7a(u`(o_{Ym
zC|2V3t5}9NQTRm^L?}g=18+k+t}`GuE*X?j1fV+Cs7G9rcxMeRuR7*at|mSTnPk*T
z2sVtQ@lyNj{{Rn%RMB!M>|C>6vX!TpakZ1^(hmjBXdb<g0Maqwma9JH-Ys7iP?pd>
zqJ%9`UHr?_Q&q|hP{mXg{{Z&paf|ebgl|eRTpKWOgrFAot$spxYO2^Vg#kgqqe5&e
zp`-2~^ZMTcP<C8pUl;!XisW15urI~_$7HRBpWY2ANSY@-Maw{v*P({#QxuiJg8m$j
zTpOr8lS}RpzcQkrdc^?NSJulF(eN?E5%I!{hcb0zg;y7#E<t#CW6F7b!k&I;3dmvc
ze}f1tKrIx}0Nxc$Y=iVAa)@1}^(qd-({`9hrAyXWAywBiN-Mn%rTDoVhk=)p#PBih
z!l*>5iC5tfE1^(8>g|~r2)8*j5*;2({{W!}@CE2@RGy>CUzJuu>@Fk<`PC`5D6?{+
zQ3VI`>~uaD)9_<z_}&Uw_2|&n{{Sg<5KY>Fc`8(@NsV75$ws_Cgd7wNw}aJevA8$A
z!J#}U6-#o^Ay*2riWlk^uY7@O8Dex@UmQxs>cMwn*c})tQ-vL4p$W>d;c-j_l<uKG
zl`v@tP^#h0;Vm{G^eQc5Qy)*nq_=F>0+Eyjb{H?B<?S0SM?!jsodo{?NUKZ8v)qgb
zzYZAK-yk6Y_S<ydw1+|{1Mc)w5NRM6pa~VB#dxy^SUH~}76`%)-XdKen2ttv#!Bgz
zxqM3@5j0HF30uIn@fv6iR-GU+RKdcmsMl?F^uV_mEsIK*PM4ZdEp^D7bck6!LEx#d
z&owRZOB*Z~T2!kG&(m9^g0K<F8x%`j3AAtvEqrm*K(TggI{-dU^5ZHN>QoJQthEa)
zX&cU~$Mqol7j8=V;S3s&jDc`eFUkZ@fRf8FM|)&G?X^$=kJ^jGUbN<#bQflTSQ$ud
z*sP%<87NV~EVsLswcGVd7`ixhQfCh!EXxemDx7G07+la_lN}p0(c^9^Gy#5&WAzcX
zQkw|303!YJB_-X%vlRyFR363?($<{t&SH{-8h)WWw8~zNB7xBq3-=IUyHZ)nNwyoH
z`vrleieB6Z#8_hSLvaP^QKf*9Jqxpov6k|Yp=9aoVKU)y=vehShifh>D^Gsp7J|!o
z4*|eRY9Uci6$J4`^tt_Gl^XVyVm!uVJoue#O4!vu^S!@tSPR`32rpPY45>*EG3aXw
z&a3|bGW{gCV8~WezxmY3gkBBtE$0M|6)HGNyhAK5V3o<fZp2V|8eyn%a3=O?d~2Vj
z*+PepQ^vFS#G=2*{Ey5wF{6M^O~^H!O{a>2@~k}?vcs45Q;YJ_0}D2yzm;cPKnCvQ
zBJvb%P!gBmXB{gt41P*1mEny>J9Qq2C=j^&P~KBVC=YN@nsB$=VBx4&E5v1a6;8g0
z%<h*HtcL!D6@?aGb=Pq=gfAAc#%A5)NN?FEHq##o>rK_f2_D|zTW{}J!Iyok6JNHY
zao-92mYY{`9~B15apdWkvh8fXp?VFaz+gH%$D2J(uFyV+!9G?Ps<b?lg6>k<3!&5f
z2ueZfUZ1g}4{#T58m~k$KF)%=QYj;)OGlmNUg5wJH@d9*M@(xA(0hazUp`7reqMFm
zVXBlSFVWw4$T_a~ihsr%$^tPdX6kBftIxCTgvLtWxD8d8zNIgY)**)h7YQzVAIfo%
z-8^4V+#$tLCHHVqcZAw<h1qr1_J@@e7+}>GwE+Ot50)0F26LLUNkY6FfZ*5jLPa1{
z$AEB#L=nO6IyBq_AaGts8(Db}s^KE;&Ik+f#gw$Rab|B?SwYdV*!qYdeba5g62PFs
z!UL9JGed9tIdWkedx$0-OJuqv-@<z(5{O=EZbmG~TnySCUwAvlD>gBXg~LmXkvCfG
zqcWTq7PYX5;vfTj#`%;kj(Mjx9=r8$&Xm6S8kSh2b4$6m%$(_YA{Mn*4r<}1uiN84
z`h-cuRgW;@D^2)goh|1`5nIVc5H#9Xsd&aI0%+re8^mMbAcde`a3I!I;q=rr%}rcb
zBHs<)0d87KS<OQztMZ#{6BI4~00R9(e&@`(b%WXj61iMucP_OqE3;z)bI|RN-OPr_
zvWMN|fr}Yqlq`{zO$c5)UhV{e%^ub>e1FR*SsKu^3mn^-gPQSGKxvMxZ{v*4cY!Gr
z1pRiXj{6e3H+t~{%ybPJu#dXE1tL8bv|i0c9g{w=?nT;7TBa0}4}a`AQn`Ti9&zYF
z12FJcA92pu%3*ZWCZ@#o#v>wLFTuopf_~@BxzuUE>V!7{?6y3W86YZbs(mv}5Z1^7
zK%3^?5?A14(QYk=rV%VrjwnZpg9E$0GY}lCHV&Cv7TQHQYU(o(ml_*tSsn%(Sl3ee
z!LfV4QkeMiawWp`mg#tdgEj?-)c`XOE5RT@6Oi%wHRr)chdN~{LxE_z&S9;V+7=&d
z;j6V@CDqPbPsn%rORQB|#dI&CHGsr4io7Ze4+VnRp{brZ_fQs#_17}gHsjBzZqQ2P
zEJZ>b5k_--^hj33W|e4L*2RXGF3>GZ#0ZC?G{qE`{{Zx`=%WB-bi_8=d0r~SNLS&b
zW7ycCS5?I#lq>8XkpxPLoi?&u6oulK{=<PqP8jg0Ss<-m{KuIubAzZ$qk>Wq0p(&E
z>5Q_T4~TcYP8yky3ZVX)N|T}d*{Z4VlS!5Ridym(t9z(oO++!y&bFwiE?7L^Vu!w2
zg6g!@GNNNf2XRh>MVq`&8-iJ+Wtz3|GbxbPFtQc(f!8Q3#K~s-U?2Tvd;b8>Bv;PF
z-@Sfy5}acD1-;w1rUycC{(B)vdHuE!WvB+v$&XgczBn30?jfSwWeauem8*1WU8(MI
zO-jF9!@%gTnd4{QQ|K}|#H9e%dsDwfFbkCnf`QaCw2Px}nZxrw2sY?WE+pH4Z}dS(
zGo-oY#Qxk=s$lr)dEkG~{qr%+H{e*lM)!%a_YsR^ziq`d*M;m$-Y<2`Ypb}YC<Ny>
z8m^@G9_ayZA;G8aOAO2Em5qhBMHhJcV-Eu!XA~o`kof_HQvs|s!jmZQ63kv{cY^@}
zwSCV!&Q7)_{m9|iXMACis#>kGg7RT^7A;?K)F0Y7m~zMHw|_AI03FRp$6p=Hrairj
zVN;IGmB;UynS5MChr$tG>=*?gJT=tBz-!(|9FM|0E4X`W3ukMFElfy_U8D9RNm7no
zv)ImHm0nn$I|TA6EpK*2FCe8se=Kem1r<hXutzN)mKeIM2+-Q!R&ZhE2Cm3KDMy|o
zBw;MQf22!5SmIfg{oq2DHU7h#YL78@e3Ijd$mw8@WhGUj5R)3>)FSV&ivIwNAx5IE
z9ZF;j=J1~?T#GL2O+M$mK?<S_XGnIqF{r2l&6L?&y65;T3rfBi7D?8N_St(mB5=2m
zi>MDCgcX{=eQdA+`Y<bH4iMx4{{Z5+(1M#*)UjW@kztTbIe>tTZWVsA+Ayvs6aN62
z3iHe}-G&zNzur=pwQJ4sIxp%q9I%T7*FGbTOAb}-l;G+{j7wmM5!i)pq^MYfZcj0<
z&oD*(9}IRgq<@b|@+?eaNqN-M=Hd!BMT}8*6+p?!Yf#q!{{Uo4>M_4_u~XQBDEP1D
z0Slts+I|GIw7KQMPY8aAsHB$_EWeS0Qjz*C#Vcs9K$b%dZ7J16(h(t!-@~-e+Lok*
z@w=e?u=H{uxpEi==vUrgU0t2ap(AeM3};T?3Bv%W%Eu?EXsg#=JVUyKSdKwf{#O%S
zo@{QBiwD9!1`{3F8E&+cg4`Pj_#mUcZ1o1{dALqImm4T`PR}*TJpTY%1U+muG&7_d
zwreLoJ<Pn{!Wtns7d2&5GsQ|Ds1_utby=1YHq1y{$r6)FLD4=U^!RcsVRis@qQ%9t
zE+<qN@>idYF~EunEbxqVfHgR!aa-)Pa6OzPx>|^fdS4YSfa7pX(h3SHe8!O&{*0t5
zW`!5zg|StDaVisV9)Kb$m53_DLet#oC5)2elCifh1h*tx@d{^>He66%Jlr#ykt(@b
zz!_=k4C9*!ire{cu~krLC86IupT(RxBU!{}qdIpcn|cgSZ$bvJW{+S_<H!qf3@xLi
z@4pBm)hU*_pt)cS8%ldl71^z5CB00qGMXrRQbFtXwLsAenHp8R2(XtbF3R;PEng_^
zTsXwhF**dKZ8;Rzh&}~ya3S-t4RLj^f$U(ne#}}~o0pVWeyVYF0Oi1<at`7lA!^d?
zw4NKMb?lF0YZda;Cc@SowjziWInEDpF?-^U;MH38u@?n^01x0O1Kz?tK4s)vRc#9&
z>{q^kK3pK@9enmo5?U3CokM2Zai!M<@0geZSNcLC2hh8)SKtB)O(xJH)Du?H6D$z_
z03nA;YuI;VNb!S_Pl34X(I%KGVHy7a0Oy}^ygG;nT75^o<%V>|+-^j2r#&AqMP6(B
zBe=Qtc4I!G9=L)Hv|Qvr7(Orr6;3>V<wKBp;qffnX3l<RFT|yw3&4q@v{38@66j2~
z%2IKKAMe~tbV);ROz?3HjkDW3<t>olV9Yc~cy?p$0{;NGHOBtubkCiiU}OnoVbQ!A
zOFt<$o-KfLKp@ht5<Q`M+-Unr;ui=d@J4AteM4J>{{Rs>m&@|HLMnj!CA&VNzM#>{
zF*4_9zZTpib>0;?f8>BI)EB|gUZtNOA3H1|n_{6l%T5>868+xuRhNk6sSbvNf;B17
zvDl<ls@pI1@bvQlJ?_p{S=ivavZ{5&eQBSgDs^-J0Of8<Q*u7+yT0g-bQNc*RObDa
z1{oVKUHW>8dOSZY?eZZW&&Q~5VG98_-n}y$miwa%&$Xt;Iqg?%-rEjF<GR24fjZK@
zPVxd^q%Wcxh}y4kE}KzD1aPQ~dY)?q!^B#6=2GJ{?ydf;HV0h>eZk{=+y+s5a^NgC
z(=KM}K~8ZTmzNkcZ?pDG_$vCQgZ4y7Pf%SS*2`gAk)usHZDg&8H&b8pdVpXhRXktJ
zJVx(;y3P{!f$+iUw25FyOdc-&Ti>!B!L)`8xCzA}PN~aLTDPG+)x>PqTJsl+l$93M
zZY$8RB?9q5P*G;9bzBuQlm(#Rmh9vDji{=g5W0=lQ}oVv_7yT+(*0zmd9LN7EOX_{
z6#jtwNwJUc?Rq8_wO-f0mjo(bBfxr^;sV>`o7vo1PmU<{mXhSwa7sV3>q5W<t3eom
zZC=p(hhSO23Lz<w?(*n<Fr$l)46>*yUFHLL%Bzg}RvD?ZYf6FM7x+5$9pntJR#4h1
zWP|4FA9j?gfLD~5rs_2cV8*%?LSjiJY|lxdKqBEgV>1DLV1T%4%cLmhV!R`hxXIth
zDpQ1dIg&8_bdD@}2ou7^`66b4Pibs2;BSR_BM@k8S^;qa=xXcgAt>Q1{{ZhX-WsoX
zDw&4E(zs?&JPkEZots1?B~_ORY%570gA&W})`cSjx@l%R@XFvFVABkm7nIa`SxX|1
znLx}C=~9j<h6ScOE#73S2jWsy@P%3xmPCs~49iV)jg`B$H%a%BAqkhVl@$ak0OX~v
z@iJg|(re)}RVf$b3=j>Ua&O}=$UyB{nOFb>*efb%a|#2YmD@`NgKAj0ow>wNL{x|(
zbdsExZVU!f;Z3mh3BpwlX;FVn72yM&i&JzFY3^`dO2>#YtOvyGl}O-&!ac(4zK=b!
zAWG_7gE=?qU;Srl%{q@31(y6nv*I~@VUCYLdr@R=?<(0{W(z5%1t9~#00eu1+g3mL
z%dG{qO=!QUG_uj{8p>*-UX%=}w-S|!&D_+WM{E4Z6+t9?t9$AxvXq#n0rtzDY_;*N
zkya~L%^(XM9hU00Rddg&K%RS0ls&PoNIHt4nH$&*C5nc5k+V+X#6GZ4wA&oBup%ff
zKzWh6^IgOe&1uRW`ht@;WxAG*4vnJJ>g~yIfd11Ew~-aqWynDyV!javU9S;bskGfG
z%mkZ>;0DtdZZ|jES>o%7Me1|O#CJ&h8<?k2%4!GMHPlg>a-}T0*y*B9(z|*idE<RE
zGvJuuEXv{jbx!BpJt7^oMsX8`eB8<3{!)GHT5#XYu|zvh9tbnd3_{w&sEcf(@WA|l
ziNOosDPvyyg$c-sM;P5f-(mrAl$}N@ynp?S%gMy14;%9ZH}+RoQ|>2b{G(ZC3~lF#
zl<2n~sz+#;Zs7a$LaDL?F0dNAc=CXD5G3xED;M2=<>FrtBIK6hS1F|7<Aw>F`SQ>;
zQ|(bt)8Clrad~}4mAMHBSgr_)vd0Y%G%gU0aA$xT#rK_R1KF?}bwiO=9|IpDuWu7=
zqh8ZOi8h+9f#ohlrZH_`GP2w0(I48p-Vq;VN9hMRa<pe`TKTy^HbLzQ57rHCCtb6V
zt+8n2JkI{n6n7n*2~mTXM!g|#dDk)CJIZIINaU>dxR<vj%d%V{j5@euw2Ge|o?^Lg
zr|S!>M{%lofmm#~uCdYUN{?Mv&iIe_8n3KEYx9jkf+BG75Ht@G)#z}M8qA@}z>1wj
zPP3I?iC9fiE^VVW<*}e*1KpJyaM1I}vEFfls&d>vk(n9$r{G{&v`cUoS~HAhDYwOt
zii5;9uA^oCa|k-mj4T2OE66nrNli}oFF+5u96Wfa*u9R+(G{DCpNGJZ1OEV!mc`h{
zymm*L?D&Yl>v_bvB6{-@k>B~;Xu+UfUSW4{zEV9@>^d;c2lC1C&RKF*@f}s><IuDy
zeA1YRjV)cgMoPD-?3GtnQ4sXqgy(d$`ci{P&q4)AcOq<eHgHu`Dd>;PE0&(SVwcCk
z9J_*cvbU%$T&trv!3upeC15iwAffw!;=A`4mJnE-dQT{fV_slrkQ!A84a`Vcz3PuA
z<|WRE^IyaO6;=Uvq`3m#lBg&w2I>V{EAf<J3SEyW)1^XOn|0>tDZJTO^p#TW_ngaA
zM1WD{8`RZ>&O)Xz8Ru?rHm1_wcM{%UDqJ8&As{^dF*7-Z?NUS!&EVKtpUYsurj6^&
zpftg&_b+uY`&(ft4EO<Tj>D3yJz{Xn)k|&a6{-zfzd~3@?V_g=2pkd$&oyj#M9p=y
zv+h;pFc_`!>bjfglwr)Tu?x{=DdwWCa0QA>q>PO#L4(pQ#r<s5gJ-E)s8*jVq911W
z+pqB@f^j?n00A)Gi+namyAfBWgfJUpI7&fiSJL?^b57hEy^;0G#;CU8&Voa<bxm+C
zkOf9WRptXlF}xI8X(??=g>5&drp6;xvW{70gWU_aq=RF*mvzqWKyz#w$TJCWY3OND
zQB+4$ky&WM{Ya#Cy3Apj;H1mXIqC|1(828hHCha!m_QOZuokZDL02%1;JeIboUS}I
z;^xfxwGF%>^R=YPfWiA-A)Y>2q}T&p9<N*&TB4^+NgUwPN8^+K05~R-=x(e`IM(Pw
z0q*dR$~P%c#a7YV@)N^iQqhEW1f{k=&;nddvn@LH31({AKIPam7^SU!%3}b1a2U>$
zKOYr3g^zazSj74X5dgg^GEmUx!6tbHLBu-fo1T@UsN{gKZl7v})2Kae-**F9X8fvZ
zDHzy0R)WgWLK+DJBy##gnA>!?gUL5k6N~OlOyJtOup{;d{J05f<Ep$8@vz+`SW5(D
zlE1vb{uTOv5{|_lVGM^HKlswO;Q<RhH|3FDdngz6tW!M0Gni~Zc+I24PT4JH{kV_`
zYlp8Z?I@_+9B0+3O`0x!zxliW0EJw=*vuSfIk*1+h;Uc${%4uTQi}S3S9YrYNP+><
zut%0?3Q^N7r^DA#l%iMrG&C7=Kk?5{Q^HVP`JY)mT%zGdw*cN?v#3G9!W*OR$!E_z
zx}=x|H_P!Ju}bdigyK$ET#J4`w3h0a)j)FF9Erby2&!WbH;_Z(xl~czeS3;j5w7BN
zUMe_L6#$HF;8TM?Wguc-$oW00S}gVwXF{;2xp(1K7byZ|Bm+O&QA<TE5$Q5(1QSkh
zr3LE9DplE>B3*i$pHsnLxBxDTm&!p5fP|T~^Q<hvAZnsdN>yssv2@Z_ztGrpL9f6m
zVh^<2Ys*4XA9CA%#TWkowKR=a)$uN}O>sq0Fz7U+;@gh?TRY^m-7%ueC8F(5NA@lU
z9Qc%e5d=lYaM-Rh*t3JR?Dy1Y>W>v){4}?ETCKGB2y`IZ$qumB*U-aCUwHA$deboV
zs90Z94$LydwP`x!mG(}GTRUqj7*Xj*iOwi9;D>+(1Aum+GYA8|iONT(mJ~vXx-Tzk
zY~?-DfwTl10iuQxLh}Hr`o$<AYRfO|qJ4~35XHN7=iDcF^U}RE$(zy0m3Mf;E5K=Z
zzUno?TDJ>?o5xydDVH^gf0v90^&D}Re^~d<19*sTGRgq>Z)u95RIFe<OSM~GrB+BG
zc&wDr8hwQq??YNxAc{pFQMS}>8~sqCuF`${tl{YfCpGF6^SHiAML0v*B4aP^<9)Wz
ziNmtE?iVvF^s&-LZO{7{o-f<WFWl~XXN`r};azNNR38Lo+MXJHFsr)LUwfAZ*}^HX
zcco1UT1LS3o1BFYab3bzD@>-YJE(P^BNGYig3;mMVhp%C{3)@{!}*#jZs?5fj?%J_
zw@QF_3$wNV08_4F2y;(O3179mqih$yODPnBnhS2&6NrSr@{|^Rv$~YEH=VWHg)n*@
zHyB-S`zvD;Zs0ZHJp=;`WW3nOjzQVr`Ke>cTPSG$%^Cm<E02ilrvlsbi)6A*aVSE_
zodA`6`5;kC_Duo7!@8VlASfDn4@^U1U9q<)kr5k1570S1$MMK@4{*P^9i%d0_a#EC
zK^D}dDj=8^x2|EOawzIH@v2>Nm%%K@FEVuxxLhIuq02|OgHsLoQ7_g?8Z0=AD!9Fc
zL3OO;yBKFK6yn<=;tWPfW$;1=j@UQ?+K8MGn^uDy&E%Ft?^GWvzKRHRxu~5iGQw|h
zj5O*?!}p8Si)>C77(WDBoCsY$cwtUK_cAz&>lS-dW|G^|y~?dt?%c5jteNE4`9<~q
z63?$AHA$&2vH+@^adhtA@*>s1wI_YclRKclCH|4{)Q{O#uPrIn#|h;^=Nk$<K;-xk
zw7c4D^nzbw<TxvP=uO(NA*9=Tp(y!>;@}RQ+ZaitI4gtH4Ovbbp@rlCC4uE4HsxVx
z40MG9?UKcMOJ8X>+I5vZI@x#$;v#n)Nb&>Jt#YcZ)U(yZ7zvxptNRV3Su22i1q36r
zlhRR3n?P+<Y>OFj49)%-YkqPDJFua%gwHYM>Yhs71UM50zy&$B_RgG@%0%#+JsOCI
z`B-v#oPXt8zuA5#m!Wgu2(47Ktnk~zG{FG3!Le$EK(^@y0VDEgND!f94%xB_13->u
zISJ(R-2hL)NKYly=CZPL#M7fCfb74*b3Z4m{{YO{jAx~2q)zTzk42=pK|lukG{&$(
zp2P=}WMP7;{7P%%{{RBo-D47OUGoe70L-aui^HRfj2)^J@6iXUzckB<Y6KdqQdcB!
z2KnT;O3YR_;n-!}4L0SPhkZuTlvzSGV5S}wkCwSE)c1Tr0{8;sr2>Gmd|agHC^(d4
z^!{73BNp=bgT$GA9>_{}_{^>};^j0Eg>Yj*^A%!O3>DzDe+CKKZUjkSVk(6WWR`53
zEYbNZ#b8esS8;lzrLedv8on5IwarA~`pUb~kPD&*(WpldH{8a1`zoUi#m$C~D6(;~
zzF{{??{vs(0rY9)l~@IjpunJ&!aze@8|yp%#<diE(Ka+F1V;AG<9ikGZ9UcT2%)t|
zt{a&=1%rMVTI3VMWXF<SzZg*!?b!%vX>6f6x!SEuH)noGk|lEUy)h+nT~i}WT;^cg
z^R1*LoYVgRt+*IE-QdTc5%_}brK3Z$-L;qNl2Zm6d${OWVDpgWxLf5#5H{sG4SWvD
zi~<3@={U&ySCVbeo##8cB@clafUfK3q6=wYuaIo|cMRJ90L77X4<a4pp^hEV)pd4d
zR5ZN!iReomZncVl)V*eXMKoFh-g~pe%1aKf7yfXX!iI~W#ves|HoIA6FT%=eIYk9N
z6_IV9;=&DF1><?aZOd{=Z$TMssLC&jAKC}@VwQrQEMoy@wg6ay{{S_)0bI#?xPL^W
z&)5ExyEqq1>JTWkO5_wX8wFM7=?(3K9yt_(r;C`CThvS8&tx^`yCyfz{{Trc0*Ce?
zod+f0W@Be@^V^5qafjzps6!m01Uad904Y@hPCUe`{{S)@GnMvZ05+jtrWGB>On9*X
z!l9hzUjqHJmQeuX-QV^OAeB9fgL*<V@!>D2=KQW2XG#}r5A41SPw0e-B1-5Il$KOo
zpgN_VB{km8fOP=v?P}`6DH=YW4nC!x3<)QP+^<#3!zsSPF<7$pX^tQT`?n@yy0SU6
z<LV*6u>Bx}M0ke5>J07QO+2VuOgKIZMiJs>QR2s_Ox4Bm@-eA2)M&=1vRc7pE1}X7
z)!`kNT=9%)wuit&5@D+4XjtyJJ|39k-2w;#b5;h`jV>8UI%z?QmvqX?OpJ4(Vw%4Y
zNQ!8bygeeO6oqQ=YO-F}p(DYnHasy!P4`-mgbT3i#|e9zVD@SQkXXBZ7CBbfsJxK4
zNEQ6`F)gcZd<d?!W2&6EC>#ot(qQ&pAo3?TEhH2wEmn5=C0TO~pZsoOC;+M}d|>|o
zQx&S+j!FqNj1Lh0vH}K!!W@jYFTP*tA7vnE6S;wdd-eedPa4<gzJxQUR*nq3GEXTb
zvl&%pbDoG5vshynmg(ivz_Q@(0{tR5L=U4X_F@-?fUpkhvKtKGCNvtT*_fuSVE&-i
zm0{$D=ao<q1`UyD>@w-nDdKB9uJtA`Q{Ylyc?H4fSZJi?no-v)od{S|WbM3gdix|@
zu-*A(iAu%YT9w|`*Q_>D*ewZ4B7(Uhs5Z40yuyhM>8yR})UE>1!K&)~%1~CrP>tJn
zKI51~@B*s0)dxGaSR=9-S0JG!EAPak7-~2kS$R+(4r(vK6dGQp7q2g(0anD(_!2LM
zk|(_82`Na~YwC3rr&0Y*lkoh1=iKpg#my*|NQhZnUe0cNySer1SHG*78J5yULd9<%
z=(if9`bXmy+L!}=!s(f34e@QtSo+vj$6L>cSFCqAzcW3|q9X#^^>YE>4lKxgOTV+2
z%&A{IMR4V8`C}#il?`FAj-bCB$Htr|v!_4vivIw^{51-jdWQ?(_>at_fQO-|7oEg7
zfu3Bc%Gw4wm4~>E0=l7t#1N>jdWf^489k}^w1Kx97TSoQ)FcbG`c9`8uX8O3LriuU
z6|%lICn&jIYCs!-L%ME7Fow+oLf$GCz%<&rF)#}wEx?=%;^&MiKbo~pExCd29B@tY
z5kK<NUPVU5A7W#ztrtRWrULSYGPdGs+`t#m!Q{AA(gMUj1k1xl5?bINa!|iF=?c(h
z>=hX*>jmUux*OA--kC^)geiOpS65#?9RS<Fi|SNLsuecMn_berDpBV>--!<f^?lK}
z9uYN{V-jg>HaW0lQZ0h~k;KN>Kwu+)r`ncP!)waQT&!euuwn!svuT9(3;PDL5QYZ6
z@+SEVkKM;`qT>hs*?hNHd1Gn3v>Z8m)0j)Yk=|ooHipH&`v#<qR;*~aQdU5A7xoae
zG$rH7Gu_M4maCcE2ey}`Sf)gjVbl&KxwpV3mfS8>fdHuuEpvyCSQqSsQlKgD8tMee
zD+6v>1SHO5O^Pi2G?v)amVqnA&l~su0FJt4aR}v!Z(9nMUYZu=uPk6nd>YiKoEk;p
zzDSP(x~@G9!J6xmzZVQTw<cdeE&*2xrJL>%a4mL}G(kwT8T=Ya+p5Yk(va=d(0oHR
zT?dPxo<3pXr7gV=-oYejx-D^0v@=yFJln8{Tk%G9a1R3!lRPbKd_{^7-w8-E8kP-J
z5$QSy+(K*WJ`BY}0-FaQ1yXOqE@?rcctL!BB|5cxh}2sT>m9;(Hn`&-8!VMJd9PT9
zlYvZNzD3gMsv#dQNZ8*ADP4kbEB^or9Kfb^7cHXc<`rt6Qx1H9jAS;!dEt;1+^;E$
zO;Em^M|CvM4<v5Pwq;zQW~~O*x<%|jd%mt6#SyoxVQl&n?6j$FUqwkhuujxNU`kp7
zwvmfrg;n{$R4$_-EnP!?{{RU`L27w;V7UUKR9dBI3}(r&N+6w`(Cuyjy+@qI4A%{y
z$|<Bh1{nlybVAaP1{FrPj{Q`=Di)2zUyW9OLN+dB{Eqz@n<<MUYIv5DwA2F`>_4o7
zE<p?_RNy6Y1b`Flrch>vnAodsT0&$ARQjZ4(r*NsebWk10YIZbGs(`voY@t^zffPr
zbx;?kcaM|9G-TBJd+bX(Dp63gQv=tTVZlAzJq&jU+HnO=thYz5B_`TGk&B8=0r!J%
z?T)%$k~5459KHF2O$yy45K0b>S(#Cm+CUmKweu6@Fm|C@`(VRSfL*5S7H)`g*iCzk
zZwN5@mjF=ttGUW@Vues+v!fstfgG`PRKjL37HexMZTVbOvT^|akkKW}(~4>V031~U
zl<k%{9W-siwguyrTYbdfbDm77dBZ5Xeh3W;f6qgPYqbF|DD<3Bsc?h81c$!k0iqJY
z;fS>~1g^%9Q0ZKVlShob!Ms^^Mr;Ty4}RzJz_!F?^Vtyj@g4%!aDP$lgatS+0G~i$
zzXb}F;%D@&i5atwZ1Dnt%9Sd%x0r|ie~|(|JQ1v+K6Z8S5p9s;Lvo9AbL#D&I9x=;
zZx@nsa@;DjSO?58IaKanEZuGp1`03+TT%8<Kv0yZ{7J#$0?R}C;txEm^9y}DgUMoz
zkvR8g)eaW7ejzPySj%OG6ZnlD83hG55M?8>laeqLf0a8+z2*zGo5GCV?FqdteM*f@
zRk4@mZ;iI9B$TqeYF%nee#0!~bagMlC;Unql#b1#_bR>Qq?%tw+|BEO!v-Kaq#{)E
zy`G?DPys?6k=0IAq;6ff68U(4tTb%D>1S=8STZql9pY(>An9L#wp@2L=)IKN3aqV^
ztIWGbyX2LCQM)=y+cqtuR6d%>oc;ksUv`P@jtDF^wdhnz0=?jZM#FR=Qh*kX<XBc9
zp@xxl(bMT>Y58H8Cx9{10L&@3ape&mQ0tmvAj-M&MOjap=^x#<R}={VI9CdfRUgI+
zyEwTVdJ*!vz;losh@*he`#1A8F$)K(^92AzukeZ?{TvNSZwtX<n2HKlp&FHWwLW%L
zx+!_PgKz}DSIJ7tcp`&TIm1n#A2GyQ&z~tk&|Nq_k@e;BaEb2GYym*aMA@@ILXBw3
z3PYBO!3{2htJceVyU8hHlazlC*uzlza0L;9qRl(WP@EUm+JxW)381E9YCF~(S*~F_
zfZZtjI~Xn-BiOY*7`JHsw_!e6$$McltBz%G$CoxzfGWWAbBU;qT}7e?_+!;pHg<AM
zyKmI%sael6F=4BeOnnByH){h0dtzn61TbivLjk0SAB=YfxPlQB2)CG|<JinQ<pW6^
zs8{T@XH?W92Y(g=v|WzeUu_lTVM2Tea7AY|`butv->yf|62uM%PcV7I=52zlO$G0T
zK3RDtxDezB^iD<emR7Lv-FP<{>pv42XiQmoIfIE-`~q7`J_A%DlfEGI%er|5JNf2b
z%ivm~;Oo5>1UWVRG`mxd#i5J9)ga*>$jZ2H>#}9HhO5uSuUtxgP0>N;EvM{G!zB*S
z;%E*FwSEXP7!{Nq-Un_MI$vw$JkEM~i({3SvEqf4Hoy`kSXwG2QL468z|S24^T|kX
zy^*BRVf38^0q{XyDnL<B0Rt|33Iy|Wct_x$(gfrczbv$7fU*n5)}JIAriv_y`PdGf
zh{4$|NrDpal_}ZFvcANU$JmL9*)LHQ8YF;A^#+Y}-h=9S8{!<7fX(X8xhffbRP>1y
z0`T0iE2Wscm4MiIC}SENV`XP4>}5O#^lf)XQuc|fBQ7g2KVmp1rCkq-#rT<~faRo6
z-AxrU-RvmyKpPs<F>Z4f7H2w@9*T#`z%ADCa<`?@hb_Uxt93T-aEspwRUv|n6!RwV
zz3S{s#c%`>T4Hg6(PkHLtZ?Ho{Z5tz*l#Mft|tdyS~0%zH|1f1UPly!=wZm1^59mu
z{7l>jS9Tg~fLqgd+%l~#aCXB8H$%SOYQ_mmDpjbTN5pcnTra*gtRJwg^j!nyTo4ui
z0LgSTs!kl0iVrZQx#HmkJeMEtWqY}QR~{}a4bB)LG0rW727l;8VwDF&{sIwZfoMGc
z0NOg041%xByTlfr>KR>u4!0IL6`J`@=D=H_HT5Z9HE_Kv8TBr|C!Ig`rbJDj`EvgN
z^926@%ix|CZH9Tt{(?b^h-)X@An^%Fz2WUa)z9to^2PIVzlZ)T>w+<O0d$OE`*^pY
zkcjdV$_<fK6(Iz38`4ZR^-8QDX_&Uz%+C?kvhpHtye~0#GW<EEIT0sCHmi3vj#tFD
zSV2ckY|#&h>2Y!>kKGsbQh-iXy_6;g7R3t@O}53HGIWZT%3ep0W>ishJUUho_Ll7h
zS)F4F17}Nt+0wy_9@vCD<??*TWH!P4H&XA2d#TsrC>`O;1_U6-YWBIG*`)QP1pfdb
zbCU!#pWI=$7$QUOsJ8%Ny8QMx&{HZ-zv&38r?)5zFooi3U0bo6#X(}^M~IbzuSdfT
zNeH&@dVs0!3tROT?JNhsMaEX~HKrv5IFB6hiyaZ_*#7{O#$6q#t-2s_f@1mgpCB0M
z6;&`1*r!#E3#HXcdka`!MA24~R4Wc2;4TvHtChIA<zNwDasL3|ZXkM^RH_jBz@zBw
z*DX96j&KSWz{{Cn0{c?x9Evo*h+N0mV<Q^BwiNRPw~C+46aN6_h#{d79jqRZi#LVr
zn+wK?$2`XHX#yi6(ru!yX5w<y_>ZOi(5<g<hX63(m1H#{?R44;NjV6|iOj&an@Gq-
zj}8dqh<0DizjQE#Hd6CvHE@}eS{TBnC57^cw+e-OM!P-r6|h?H7c0`=^HTUOko47(
z@+g}secE$O5+C3Eb#l9?rEx$@m7ubm3<APaUpN@zQWMh~?jUfX!}kRRN-}3|dgc}j
z7oSK%vorl;vV=~eo>9mZt11Ow9uaem@M<66!PhG9u;hi6Dr{cTX;*BfkDU|hKlJm-
zsFx{6!)6gNS5H0}SM0<nWzQv(nbQ9N;zhjmwH7)zKrfoSWle-4?5nXE8~e2{d{H*S
zXxEwANo+5ovh+g%-9v~Vr=*NA;1<EXJSN-It@=y>C_E~)(GIZvW2^Ua+;juQ8Kelv
z97A9^idI+J`O{%8M`7W=Ttr+5q(RC=JQn=`<RvAt&}h8#C<9A;4aL#(oHEc*Im-V4
zyg?TuV5^E|{{UO3R1c`N_|Y!;vJ}Q|N5q>v;B<ld78D2FEfp2W@|2_@1FaUo+#k<N
zaJhRl2FNG$A9-CQ0Ji6w(P=H`Zkiag8kd)En#c+v@3|m|6<6cmC<$>x0A^?<&RJLU
zH=HOaM1<N;4h&_C3ZoiIfq;>C3BpriZ<xMd4@#wqzYsMNY8RsX#o7iC;>FAAUCQdQ
z{9q7#8(<-BRe;dG24%p}B=ART5SNu@m|fQfy0|8blTkndV{8uq4xxxi!^9=P@^<e)
zLUze_HwwG(#6Ux60Dl>ML;D?rVzeNLMzluuh~1`?ann3WbVNm5?|?~t8qDcA_zp!N
z<WZefv*COO_+$zZUW1h4ZuZ<W)<H=qusIXB@DCt7i4@_jY&F~q<9_z66*i{8M;}h$
zd}yGyDS2Qx9V4qxEVr`8vI~8otRPU(Z0wf7o4!DgQrmC<&C->@PlLE-vC$EII|F-O
z1#9Xfol5CVPjJ|6!<ncphQ~6FSioSP6mzP?hQNIwFWZDv*2ksr>5S8As$aiy`w`tX
z4Yi^D*z8`MWyL^8D{TH}h6qCnrr*t&U<2fBe>TH<3r?#^bD?Hs&Lc;gu?ue9UFLo9
z3*`+ngZ}_4et2&^O^3n6`pMixCTaJO->J;W>SV@q-W*2`c04H3upkx9%M^C6jb%Da
z@04N|vrKd~{h9FdB4}J%R`QK62bAunZo*r**QPp(xUg-NQFV~zK#8J|G#KS2e;H1P
zV-+SY4(qvh!gMfPw1a><UqLG9N87~I=<S`<a#&d|owvgl4Y?5(0OBN4MZb1~%+IWy
z6!5{O`-_C}Ed>3Wp;R7#BjTERErE(GbNh2P4HtVWAQOIXBHM3>T2O6t@sj2onZ>X+
zt^kx(aHZ7@CfMUCx<=x2(_&EVdS|hQ<gTA_;dZ+ERezAEIn{MkNyEAy2Y1~jEj#^$
zQ*H}Hs0Wpb+B6#RjC%=U_T5-&t#_JW782FWyg|V2UI-2Yd5%|ec_{TKehlJk-z8uB
z2lyeC@lxj9<=Y<d;OQEYt$GH&sC6YE&k~NGF}0i<MS}f8@ABnC{{W^A<qGiA3;;==
zG>{#5YTOqWYZelIRFZ7G+iQtVXoc<GjZ%GwlW!PlQ~^@chpANy<bTAtGL!OuqChEH
zZo;K}Lcyp^9tOCJ&fhF5i)S0n_vT%$i_$?9(7AKB{eTn#F1@6!k_5f;9RhU6XsoW6
zDqPM~Jc8m{&EZ}OhQqG`mybK9Mh78)<`)s>rNmsNhE)J~qI^gIZ87kA<H~$&AiD{v
zeZ)h&-$nlb(i-nPctCx4W;7`rpdyOus~JQ>y+Z?lEOPuz=z`NqvZcPkP~qV#*y8=E
zQA_P$2*mfFP~1P@PHKr%xh%GQ)%85a%=LtE`3LcwxE9yR2l}E?lFyc7Ig9f-?&g2~
zY)w_~{h7Hfqg9&KWkYPB6U~W2x)bQc=VJ9<<oLdbLU7Ip=2$CIibzt1EeG5K<J4H4
zAGVe#MC-5j3cw3vyrG=l9_;}2S80X#rQoGaa1aU{>YWRcOI)Crn!SvGwbP+OxP$rB
zShD{BqasaqfGCfuhby3{zuOJ?s4`bgquO!I>Eu)`BPbB54qoDqNjGTQE;Fvb9PNKZ
zMwFtTAt-FR+MB!^yPKeo>4s<|`)^G#uHtg2>OCxp(N!U)#9FR}{miJ-yK!ZV%Xuv{
z^Gt}IM+=Z-w0sW<8qjJENE0Biwo}7b1kLxpfvw1^lUwROh<Yvh6|oqWb;<0$nN}@g
zw6wTFt;_bI>`hAJ`3QL}*X+3Ms|0<PM?Bydmq<5se8W?M+SI?ojV%CWX!8EFnAc6O
z9;{qYpP6bz{Y%&SjNUiF-wv$SA%d086^SVNmf-$FNvbeB*zrdh!@pSK6eFLi7+v8A
zbO{JZs8#mA4@@X3h}YVXrx(Y`y!&OIMVmtNXr4;Jq)&X#GvLZAkUem|8OSe*bUzUs
zsG}BK3k$U_shdQljyRf-dtjCmT7IemVZ~nMED5EnjV@)E=SlkAp$7Bju08G8?x3y~
z$9UJiv!}!hDhPuwnV=g2?4R~6+9}AU7b^l+gT+hA>_?comLGK&u&AiB3(t~q^0E02
zVk(5yEw%AERYVjm1K@zLT=6*rek0KF6#8JQs;{(dBVCTf+CS{2wEHG8oQvjBs5iIx
zNFYw)oZs0xDO0)X4-i>=b#kgYR^OS0?{T^gZ*ySkJA9yH++zTz)ggj*H@sx;i@3zT
z%U$*%UCNO1W^t3gI{yH`8@u9o^X3H>scrOiK2Oj80OR6#-?`6RO{QFC89NpZIp;p5
zck+|t%*2gq`4t&~P{dBpa>ImF3H(%h?-nieldf}_YZ{;sfiEr1u;?wn)c*h_JLtQi
z-!igJj?=}355xf4FbubB=EznW=z`Bo%LZc^SbX|CAWN~);V|TSB`LMNb@2=s3nsRu
zdzyn^sUw(jGvS@{N!%u|0ZRQ#X1AeWuQ31?UPX8{=dx8xL}hfTcD!Y|{{Z++6hi3~
zJuy@rmEJ|`SS$D6q#J3MAsM3U+^DjXjkpOrzq?HM$(H3d(M3PlYZE|u77NPI7u`{f
z*nNZmfX;V+#<N0d2Zro6Sim}5>Wn)R5aA8lgUI<@G!T|FH(ccNrc#bV+ti!sNgRb=
zO9{_?Ks35J?1I!;S0w{^OIr}!`AI<^e5ddjl=XKXfRqNtz*Kkzh04_toToObwz8gx
zHVTH2hpJ>zSd6>@#hphxQ%x)N7lq!^P#3{)j?m+p3Np+ZR|UbQ%F(v_v$N<j%L)Lz
zGj`NqTG^6-%GWG(VC)3^I+qaS^zZuvwCwXT-wnzlu5Dl(3Vd|2VPZEC(!o8W;-wl*
zII!h$=|HL(XR`GUD@Ym8-`RQy?UJ;8;j`P+>gje`gJ3j1NMi3!M>y8Ju48txJlY=|
zKwWQvTza5L@2H25Y092q)s_7tA+LkPeH#g`0gXq{Tq*;@;baALxvOhoX&k=9jL}W0
zIUVD-{EKdbSK9FfXc<A#8?wmba>KMgWJu8@t<NY36oRXY?iJc>&AnavOC{fD95cxQ
zk8$!+Uxwe?GsJ)Rr~d%z85I{?*ij&t(NBld45_jxZFb)r+~6p}n)!H)^<lq7n?73-
zoR}L$Gyulnaph(_MYd+`bdn36+F=t7ZpHXDgWaO#Wk3mEh{hvK+X$HAd@)Iel&d(U
z=_$)tJ&|oW%ZRNn)L$*aQe=wNdmb)ANpVl%31OtYVasn!V87);@%t;nn^yT^t4#<@
z^$;pH^ci|uU}B9AX=6V8K)FiZ*FR9xIXti{#bD=%n~W?ac&>rrjnb;RZN@%;^boAQ
z900sNnbG1$G%!I?T?tWNAHsoVn=J&tRgPMB=_?&*=mUhOp?AO^?C0l)EbNt-q}GQ<
zd3V`96o151!m&Im-yCz)Dze(vFrq-=>30c7J{lNwfDUs`*y8|&LH0|kR<!aa#H6}~
z>yqGA^P6+@jj?ul9~CdE^h>pL6E-Y2)n6;J9z7s*13Kh~iK;xXCdb2^MU;nPo#JYP
zxsB*b&3lVi8j70_I^y@pH8+M^_KF_Fk2&Uv#f6_ALd5t_kWUt|Pa?h--cB9@h1L{T
z96K}Svk&VW($04txM_N{g(lt``z0u?j;i4=%U5_?V5|YHbzD4iI!rYOuYV-u`vpav
zqtn=yv%`D@`WJ(NU(}1wh%HgYy3GcT*4y(1lEz*KXNRIF!+m)u%P^S^NyQ>+x;;vE
zxBmcX72C?@$TksRFgbaRwkTgqM{GPQ)CdBrbZ4gG-DR~eQF+?YcD<M;=2vUNBA$%`
zC;Nn)3ww`oM{nja-KUjah>G}O@-V`mz!tzEsbJXFt&2TeX<NiVCBAQ*A)QNVt^&ch
zVO)#fYhkySOy_}_wD%m8ZhxxL{{V_$=TnLOuD|j5<B5+#)xR^%#pfnW%JcsKnHhz!
z?oc6kRIvWXq!n-Pn84v<@HK~KrBAj|`71f<RXNN)x6G3cG7$E1Glk~5ja#8gw4|0P
zr);%DF~aJ@R>Ni}!;~R8Q-6ZO1f6x^Muc)5mB{;*?)?Dvx2$sRnqQd3xHU1#D&b!9
zZV(*$y@g9-RH^C~!3L9xqmQx*-GN@T=aUUBUYgW@Kkx{=tn({NKm3^Gd>i;kCpA@5
zZ)CD^TK@p$7!WpyTSi2x6SvO94!=qg<<i{wBhj{%&UV4j2|R$aYw9fy)YIh+;coRy
zB+8aYyP}7kmYy$|M)Je#D+{zgl9Yk?MiBo1z@+~G`oAERB7x90N}UepU;SA31-Q5f
z;yS)3qL70OX~N3GlW-7;N3i%KGeE5dGK4Tn89bfFy(gcRUyNGV+j|0&mn;7O<T2G$
zPELloo6qJ(30koK07%ADKG|)KMiZPzw<c-@<p>{gb=$d}$1U^==3t&v#cKN@e>2eZ
z(?^gZ%R_lvn!hs|xuj|8qg!#VOnwdWu25XiK}?v%=}Col+a@KHK58i8M<Y)J0@snM
ziNvAkPw|g8uFkTG%GXN0Eo^9<5ImD-sHebSLn&^)WvdjOFP5$JMNXhsQGjS#-y8}?
zrHRkUc`h+Q<ceSH6)INvOYVA~$v3vsN=R$~c=7ujs0<N|a?yTaUGe@{w;6V8N0_ha
zRh-hs&Q$^sKp;7N`VxZ^hk$~I{#0fAjoTGvAWH<!=T^%+z~3uxNsGf?qF);UVO6Jr
zfK<zV3v$Ham$JiIR{3)qs&j9R$l!FJxc30WfN0ZcX1r^}RX;2};J)<B^zcfE2Hl~r
zqaL2Zp7)5}!lVt6NX~YQBo?Zo@oLQf0R09P&^R05A@+=?+z6>rqT;;xFtzM+H?)fs
zyHy@$JEJG0wK`V=FZmjZ8~|-80JjtSvpCpp^{3*QaO@~f7=A3Hts&6bA?a?M@rX#6
zy=vM@vCaTSS0{;Y9GsJY@;g}w#cyhIm~r<$V|N{_jAW=Xf{8ChTZyfKRbQ}TdJXF<
z@(T@>T;50W1&EsdlBKI=#w!X<;BWyKt+Xu*?2K2q1?$qWme-gX1;sOB+#w`N>v$fH
zPRS34^&ZEmk8FbwSXGP|E9NDH@QvYD2&oFoAkhIa)0PF>8$zhsbqp|C_#g;2oGJ6n
zN^Dhmm3SQWF92YugLVwPVrZ?tq`awCJ;#wHINP0+mf@P6BN!^2t%p<7cF?CBVWdqy
z3qy#5jUs52mnjc8HG>8;ZmJYT3m^`bS_aFd8g%c8hC*?)E70xg{mN~2d!xi#GrM{4
zX&p_@sbW#eq$24<BkByWw$crVldX3PY9D|OMgIU22G>)-d8yNkp4|w+crc*zN4WiO
z3&@2JM5_Vh=N=%7hkk14g4l`xV@c(eyqns>luJM_FW<P;qfbe2jj_C1VN6;+2;@-l
zMa7LALWXc*1L1H^+PPU5YLHVeg}Fx!I1M1>5p`qR90t<Z4kfTZr1xE7&cRMtcLiHZ
zYOExc2v`CvjkQNvVaMKP+~LsymZAE+BB*Hd$W#_K)&6uRh|X;3^~A{Lr9|F#W%!=|
z0PLWR#3}rrGr#=$ha)fl06cF@Wj7iHVg})Td`vxhHUr9<yTX58`Funo6Q~}M&}L!t
zkXbJuoNwdQJB3<SgaHLnXac9*5Xdf*tz)&P9+ePBBdCw$mBx4TIUaWbR{NB4gNkWD
z48JgpL=knk-!&-!xIB)da*mQWDMvk31?*KW1Jo^VX|9ljp?!KiMZ10?QWr@+X4?Y6
zTl)+gh!A+J3i1#UxmxTtyb7FCkx+ng6_0<U5v1zQNrh#VIL){;iAZJ{B615be6>jv
zH_pj-#ju{TSF^*=@DL~LtN}LI#vq~6bVL!AZM)kph5kkfQ+OgmpxaLXC3|ewu_*G0
zJ%M%#5z_WI_RAZVo9kztBQ#Do4IFVe1e6Ki8wd^&o^D}>9K#eY<K*H(nt|^6aJX*v
zvZdu{g$%x;QKxPl0vx5-ITAlovB+B}E^5Dm5W^@qfdt$4>8Ifj!{+iIi2757?EB#;
zHA9Pcl@WR?o?pr1xUt$i!6HX(u#N1hzTyT308%);oGBIuA9?0(16dc?2eeTibcR8{
zekAs#i5h?n9b}*_6>-@rS{6>~*yBpOuM*5UF0RJ40Hy#{z>BHE*8SovZlW#Ionm%n
zgdk8wgs`9h4*>`OfmYU=X8c)RT4gvs2rRUleWlO{XnGV{`B=d>?TxJus44ZIDn262
z;r_w=gfvac&~q`GX^+%6_kDi|qM2MDAQlC^aQo^Vrq&+PvI_@<o+91Rj!{y{TjT!#
zHE&hl5FtxTwqMS}mhL1K?BYBge8OwMI7)^U?e{a^69SQ)o?bDeGQquBvkrBQ+>7OS
zgY_Fd1rn*$xWx)z24$VPWpjQMO@bx8Y7wq07bc4Wq^alM^@IL#>AzAsmbDC3^%4zu
z<s+8+xJNDs7~$BIy^5)|lWvK<0;}>j#9RA4YCjs6?pKOfPO5kE0!%7sb`e&lcAh=H
zn59MZI9y_-Lpn_qEVl3hhpr8^U*Nql=CdMk{>q1N%M5@cg^~H4!Soaj<T1MVeME^-
zz%mZ3Q18Z4tKTs3kW2WOX>_*wdVXU#g|jNWu#E!so8iB(q=>Mg;sFk^QkHORW%{#t
za=Ic+LR}#79bUz)(*~|AtKI}t^x^}JqTYH!xwsqw+d@YUtYtV9hqzG1=)J9LM`kqv
zmFnh8VXc&7S8FztuM3BWrB>ePBG6*3U5p;_9#En4x)^-QB?eqvEqE|Em6@q(v&22X
z#DnA`j--;#EE^%abV61x0x3ZuvznIBVL<Mm8yj9CL|m*<dBnFvK?_R?hnEi1h(j%$
zo9-^Lmg}sJ+0Y*+OnO`%XN8<n?}yVI0dbTbXyj&46|>$I6<fVmbXzD40eK=az@h9O
zT?3YoZ<)|0S9SxijV6aw@fh%#TNiCailkwf3PQR-&{?x4+TvDR-nG9RBRr8xhOwqr
zrM^kjE>Nghna9Jsx-(+uRSL8!sG_yb%H^y$jdT$_;3ItTP+>`W952j5mAjlnpAn+C
z8*r+mfhe(Jmj3|c`}MVU<cM`XdWopI@C>*>8n*7}WkK7x0M$+R_G)|7vb6Hz28adu
zSVK)T`EQskD!>QZ*Zwur6T$xgXzAam>fQ4_+{v|9KkE%J-3)q{$^QVD%$YvweD||C
ziLeqq2jE5kw@+=(Ek(8J6is{kWDY0)04F#X^#Z216l)tZy6qL<>Nc>sR#CS2OXJK_
zG}Kiib4WhbP%_+sQTE=>M{S%dY2Ecb@6-m7)7UHI5*BFJd=$TsG1do{i`ZQdo-XZ$
zMG~kjJO{QqOud;54lFSX;(d`uq&mcSB{P%1v-~13jzd$XM!iN66`We*ON@edT^W%Q
z#^alkfp}fBTH)Wn``k97?M(qT#}3<;{*uC9v-uc~ndYalhLf@$5ExPd9#>h3z{#CD
zwp{-J%uMFc_U-~*(*9YL=MwdmX<*g$c(>2}66Ov`^A<5;EBA(EOtDp#1WDx$vQ@+a
ze8dW*_%2mbT=5tU7zeYsX#W6U2O2r>_=xE+Mf}3tTiOgG7Ad&ph4-LvQC5t@X$pJp
z7u0v+)Tb`1#{9vV6=LlF03iV-Q7fvbr$}#Fm!%T5SO&WbZR&UKU-8OLgfmK@ki=>F
zlw39~)O^!A$1WzUZ9U`_4`v8AW)~$q%FXb(-2^q!bia}|NeyE`7ECl1?tNYXq#cEy
z{{UIh(Fo@wb~-8l0ND6~KLiDh#SN@01$l>q_txw#(;s`Aw$N}w)0Oi3l}`F8{{Rr(
zMfLhZEL1Bj-MPsj@4yTbh&^J!F{mV@(lw8KGqFL>OvO;8bT<)dkIZRHKu7Ux7j3F~
z;%(*X@Kce`h$ZhtOW@-UHa2rTPU8W(eCLT*OW-c7Wv@eri#ne%>wm*h*VWJ44qyCF
z;n|FqwY&?;89{sWo3J`Tkf|CX-n-?#F;)#weeCuTUqd2@QBca3!cxHE(}~O~tbE1p
z5w^7qSzTd^mtLm<)L)lERT4?<rKHrwP0E2kk#-FSl!V*hw&}${P8hk<{1yWCnBBUl
zh?;(4&6+Ug>5oW%{hT%_xcqkq#zQvDunkRbOB<qZlD43<(jUYgV^uPx;5l*d!_IYe
zyg>P!<{>^v>&Yl|3lQPss7BJ2XLl_rcKqD3L7}BGl46C)#ITz0!YmLbmV@fzh^JC2
z%Qh?we!YozQIMJe_mZIqQi{7G=ycc@7c<<iL4+@Nsg%^oz0dM07#%M%k3gz$%&(Uv
zoagQwI(ej(?qBc4c*`aqIAClUdj~pDVI2m3@)b}FC}YLh;g-scPX&>OK&Qe@vG1Ci
z?L=`yg0IOmXcf&MQI2B_gi>?vtq05(e~FL-8XdDu#KBZoNZ}Q^64<eS2uh}Q5Gu+d
zgT++b)T52>R0(O!t4cmOoTo(;l=nC+>lAG}0b}k$3cM24=Q)vz(D_Rr!&6v?@gf#L
z6a{rtl~z6t2oZy8BCDl_t7a&o1!Q%&x2s_^Z;AIbjV8`9Um{9W05^var)U>tL~c?R
zujn+5hsVYHj*7J(mKa!E`L0KlRh^!3Nf0xf1KQe{zPx{`@`g2fT1Qx&I<etZ1)jF;
zaDunt6zd-4(QWy*JW(_2%{46Iyc^hSzN>(*DX6mfQkN!;R0A#QU~(me&Jyd3m3&;!
z4*VHWf`DgfCT3fJd59RTzI{dKoyIVLD{x={#=&PF8#(bdyYm3rFy;pg4Ic&&v{#s|
zk1xb;xx+YtX~b>;UyuFfc-fiX{&lbZcwlnu=zO6ER;v6XvEn;tI(@THrHU4xGS6kg
zyi0EQn05CKVX-nJKdRJ*xjdkbLjbBY6M*OL96_Y)i<M4x=#N}Q+%gw=CzvHC`W!0-
z9mAmH58^Kcp!eO)24c?$Bc`Ldw8d~5r&U`h!*Q;2W|~}qh!%pBo0*daC4s@y<_2NR
z8BnR=V(f~&31K^<ia<P9e8AP6k-$bQ8`AQkBY?pBl{Ne&t6(CldEyOKzgb!Ys&C2@
z948;Ks3fZ}XbKOSEa&AcxPnkA@C4~udO!u78@XQ3?a;OIv8d#~;_fGQ?6=SV0JKjj
zV<@DizL;IdZl7my3S}^QV6LIb>R13iyg=c&y4ldEDcQkhy-wXf!FtWx_;e*vEs~2A
zlm_j`qRh>Jh3q`Wh9LN_;3kTv-@GL`g5vNs9o2yOV|lkJdEoaI1Z_7@K>UMXJg2M9
zA=QKe<4Wb1wq6GXm2-iYDlvKPUxJ{@4Q~&x_IDdDDaOTfDErqks#OQfUuhS+RtohA
z>l3&&@{vt5v6NTm7AK;^>m?SQ?RbK{1pw8$VbV*!6W}I4Lr7RQBL{7G)%OzDQ&cNe
z0>FR>+k=U~hu*PK9@J+_Cx6{}XbhRYVN^B+zcI};lzJ1>N%<gN*xS9)2aF;rrJy#0
z`gIEaz2WewCN`{Up|9#Kh_(SjD&J(kbC&Zu$M%f%4O=L0G(@E)&b}w4JBai-d_x`T
zWiUJ`!Q!xn)p(R^s~5or+vZU@uZc}*O-!%^LR{WkluA~6L=|P~<+S<)Z;ilQswmXJ
zsTAnidCH)Mp8yKws9D$Bk`8tajdFm99cVrUWUX4G@Y=;)98<dB+`kkcBq4qPoZlJg
zmK-Wh-Xgk-`YVZAcs27bYaRzmYeu3iu+V&X@=rb?QrN$@ALMkIUWV+Yb6qSP&IUMN
z<J{bFH+WkqWijRosFowF=yo%eTP|1ime<e5+!rId$TpK(x~s%J%hG_IgJp@N3(DqT
z{{U5vU$keBA}M9#!&r$mMDdwI^b`w)ZCzH4t{m`QjV*}Wp<e^`1%;0=yt$+IJoG4?
zCWV=f?*3&Rxq0ysg;ewqW<Bq@&`(u5fOCndr~tI0Ht(&q75s>)T|ye+6GC=M0_$Y4
zZeBeh&~QD@HHQN4jS*B2PXVRPPy0f^R@r-|K$Y$?db1C;#($9@TvP9v%~&j}7<AEX
z7R*2}O+x^Q)r6&o1YzEkA7eG|wjmZ;d5;y^=5Y2sMas;ysS9Ni$_J8VSPPToQ5dO0
zpv11=2)sqrP_}?5>ECFvl^vzVEz@ePU550PDv_mgY%^_+OSq^3!NjD3y8?SziG#{I
ze1T!iE<GwML~i=Dc6wAD!Bmi0o7UYgQsL+w8tPA~uhGp>4ari+VKuW>Qwb%Unzxb;
z0F+c7+U#}00!xRpEYvrDd+J>hC2S9DI1HdZTTl}Y;<f03$4Ni{zTk-p6;=}#T5QaZ
zYaM)?WV{+M-J6yrOhDrwW-8fLRodT}_r4-z)%8>8jHfNUPEWNaO1%ZGEf`!|Un_A_
z4P!~3^{%|kx%GECbmS@p=O8^wbn?$N7}t-v--3Jp0L<Qh@!cW_og}R4v~QlSP)*tB
zt8QL^9v~3)+`p+>{{Zes)GBK)1&kcgDjQEf{dYAiR4Lda!ol@(9{RF5`_bW|unEUU
zn2&%|9j_L*=Y7<gIjX*RBMe)PH>wfI(nYRj?mT>LY&~^e{{Z?(XsrEXf3c2=xj-pS
z+m2i|N(RP?OO3qXJt&FyO}C?@9;(_0$rmlQU${|LZny3%kr?nhB}kzjp`_D-gXOkf
zxi<Mr2Bx3n#sw1?;s{$|u9<BlD}@IG!3%U>Uu~ln*!fHbwv-q2n_T;V9*cJP36E6*
z4+s(l@B{m~%j{nHr43d1n!t(Nt34p}i+RYC#z&4L!~B(Hznd@eAY^#!MZQ~%svwi^
zxCFK>Rrf|ewB_d1AQ57>V1*r$!cXs@(p<cf%{5O6X09BezJ;KsP^<&cLmip_02E+`
zZJWVzl&+ESZFnFw!jp&<%kuXIG_{D0D*2X0wXeo6w?^w;ekJH3VXy>xAyhZaIRlB;
z>zp_04xbu|5lO!V&m!{(gJNdzA4~-!7AqTX;bjhMWv`E!c{(XkeBY>w@7j=77nhVO
zG)L7grKSP7gZ>ztG(FTsk#yhzmBoKhlz|mQr>Y^Fs4YXn^(hJscY=?kj!sOBp|o?9
zrFYF>#h@vRVA3ZHy;eA=)6lK-Mhbk?Q5OYRnnma<{e>ZnG^_Omio0em5Uk<{<b(^t
z@iS*+L?-CnH8XX<P70I19Q88z$VNL2+InNMjXa!WJAH%-fMv#u!2(NZ2VVoWS=!4l
zUVTDn>|EGyaew5GfWo0IbOon!Ja*{hx$rR%>&n@9A)||0eUxLlF|b(aA;<@$j2KOp
zl81|;&oh(e*_c=Pkx+mIJiW?Tzk)JG_Be9HX`YQkC!op<4KEKHeeB`jrg~$YZ6ADO
z8!9$9!5k(6TYe8BGqb8b+!l&&`qLYJVI8S@b7HQp+ewo0idTRNL6j`wpW_g}4Lo>m
zBOiE;Y#-m^H+g;%1CT^@JD73bJy*Dn__KY(vV^U$+;mluETJ6^5FshEO6wV;c3hk(
zG>^b&!`?1xsYO5(=A4OD&?W1id#pL{AvzPKI`qMT{YcuYTtC^}2bk<b_uBse;3~l(
z*f#*7gcqC_u`+?`ff;rc$0Bwz$la?h0|AAV<Pw6Q9AjRhcCCw~hUfYyH;CbH-ZMI*
ziahN@8G&W(Th&2Z(S7FyS~>HHKHw=}621ZFd9@Fq_61Tg37b=em#K4f6X3vQ6uvx5
z11wwh7+D%X>t4`uq$#eY2KDaPQDx<*7j8)x<6b}b{l>_^L9|&=_$mlJ4PTZpg?<fk
zT&}TdI6!S3o#v4Q(_Kru=xuP+A%lI5DTT!)6=SokM)7*pNt<O7l2*7-=5I<W#g+;p
z70e_lql@{4FA)RJ{h0T`-zqBL8LwP&ewIszDwN%Do-*<4BcBC<#>o8I6P<pG<_KPe
zz@DRu`&M=3iVGF4I8v-Q!+4hdo(2z=&>KWpVqPs|30(*xhmM}3`ZUpH`0R)^FO$#x
zpj1W;T}qaR%+LHAYj^7Elq*+Ld$=#{Z~g!guN$=@r?pmbDRjMiIGya^*_lqkYtmuX
zf93Hqf0OH(%~ZPGiBNvzA1G|mTtg%F6AE%F!lYD1rq$(q&o`3zKlh8%{{S2+4Fqq{
z+CJ_Gg1&cx>#{w!taKLDVn3z`)wP<pa^IhZPmzz}AH-MH+WE>P;oo|g!`>xH6%1Ks
z{M1fPv`1f1zPeacggP_sD||*(-!ae$d`?O`22BxZ`;b9YLc{2Qu%sw$g84U_moGX>
zvqp1MrH3^ElQPFnRY1k?s63xn?UhkI%CZ9Y?!BsIK<g}20{sVmD7u5}k<mv>80iB(
ztuAU^oT|Y{4bUHR9wUC%7wk&Q6^$@{E3?r@_aLu&l^u!)=!<Em;W`3R)HD|QC2T{U
zXmA5h?4yqT!tceuj6ccGc@>~r;P>ixrWfhzAbRsguN=Hzy<o-!GUE)UC5Uc52#Rb0
z_KY~JC4DgY9lrKwx@((EJQ_a<7vaM2eB>xYi^1Cyh&p;F1_N8@>Nk2U+PzK+IpIVJ
zN5>2yuCW&j+fiCo@e+vBQmwS8A8P?W57GlMXe^;7YzFo8gesnHc%%6Xj%ZMMx|8On
zQpM0fAiTaOB(YUsf3{%Kp?-~s^HCwy(PgiqE$g+KUzl?VIISDyFE4YZl(?TknSg@A
zB$VGPSWYD@aCYZcDRflooS$=@7b<45rSM$$D+k(%Qll~wmTXhcr{s@f7x2|b2%{Rc
zS_s8}9{7sX2jV#p+kd3nF5B}rx&Hvz%eec>M?NOa*<VR5#RAAFK)gbm5R?Txc$>NT
zLE-R>@6KSiij9I>=H=H66QPzz1;wMNU&$=#o@Km+DcThR#{Tn^#bE%vA+M{6*cfqe
z>{C_qSrj(>hS;%_5Un1@H~`+o_0JV?)CJ{d!^tT~-{0ttZWaw#`xA7BLFjF#O_J}%
z8+O=^Iw<#s6hnaLkWP8u^E6TJm|9OPo8=(Sgv0mj&E#!B;fZwwS6Glt0i|@<m(=v%
zos2Lw5kQbpPz}Yv0m-U4`B`?x2TZ?S&pqTF=I+(w`JQ1pRRfEV){qE<9WHZG3gyEX
zwSaI@dQK38XhhSDi$cmBI)Lk?;4KLfnz`*N=L;IN07G_B&PuB@)KT<9Qkr-;wzi7)
zN0zpLL+%E8MUYok!T>~%wKWcCT^>G+K>Ya#hjD%l(yh=41P^FrFT0G|JSu*biteaD
zJR`)CzC|%~{#*x}E+u`m77&XFaTFDh$+2Scqo`~y5oJVAl6gCH67S@M{0y*Qw+nN>
zwi45{Z{eJWZYqKT%-N^@kx(CtP^kedGgmSH0998Z6iOd264@EpmCqjo;#aY>{o2jP
zUTA}{Z^Pzrh1XtoC-@*+xSvwWJd!lh#H5t}0I_0l@5~ov4|@+GXhn!~?NaPdEZ{Dl
zYBUhmg{*;o3yV@%ThE3f9qFFQXKlAu0TK~0D+jV*{0{mNu8Z&zgsbwWq-mB7F0}<4
zX1+69q-&O?#r!sbqKI`>lvP{OFG(xipVievUOeIK+Uj<MtW{QUMmlNjvesE!4hX`c
zINifTAmY7NXk+b{FwkSJ7>580TdMy6)U*$cm6EBefPdi))r;dLbu7E8$L)XE25rM*
z=^9@tlb@dx%Unt{AH84y0P*tI{(Qu8=$KA<Wp`6LHMy%3luY0Se77p~aN=D{-DT$l
z+zEb(oJO?<rDIiuR#~rIfO}}70-Sfx%(AXmtcg_Z6rU9UUcmY^&_dGNC71ksj31as
z6hFYT`bX)c`?R`R{{YIl&)gAPSwL!p3*InNfP+?mVAZX@Vm;Tk9S8)|E|H!g)L%*~
zg5Y7UpMdh7ko>ubN9=8i-66JxQq59aQEeK|K8lr)0bYx}A24}$LMcNMu7W5Blj!l2
zho7?%ytZ`sRYem``FoS8aJ^1G$(BiWc?=<)0(rRq0Q5?JfHn08rca6{>Wy9grLis=
zJ=Op)48edoh?+4`4)Z9@{awozyWXvJK4v3qU~ZO#+*Ve>Pinx(%vZNvF(HMVqpL6F
zUCtn_IERFI+IUy07`g{C#FOY|P!qlALIbmf!muBUgr5oTj12J8MUb+kZ4Zv&+vCT%
zR)Z=Y$wj%RUUHSlf}sa%3$PoWXd+f=pEt=W7Zh<4tKU#a8>>`D<P5=N3g+0YXpuaZ
z+@ucH@M=hPeV!WZg0|J-C_%JVQwK~}a)+hbfg+XL(gf6a;o_}Dc#sclURchX$1D@l
z8Ha8aC+X=>k{+SAynkw6ddLr1w}-T_!AO~Kv3VGp#IUOiNX<^Rtw5Kz8)419U}G^u
zoqoYKqKF=jNH@R$`Pp|l`Ut1&+1wwP#2@*PP<%!zi;6Cyr61wk;#G2SIA{U(YFF;{
zC<dFMl<i^>xyoNLTfJ&-?iFvuG%V|jR){qqhoOKmF{d1onofHn(%)`DgbFUr8N`T}
zvD+i4IikCo@>D>|N00)c8dR1|Bz_`Y(OiQG&*lEV>`Qe(SOuyC^fLIUqJ^ds-haZ7
z0{xie1ixZ%AK_3kgOpGqK@sFz-?$PT?T`(D{IHBE@f7h2A;6_=UvYc1>{=8}#NAVx
z!2^$6Z`I3jOXi}^T1Dde{N$_jMNXY|T%kFKQr<T_Cx%z~DsAmyi}jK1%XJn%Of5z`
zQP=G-<mx<&HWeV&J7McK=eudz)f)PY?Wc&_A?eE42~`eMt9hu~h~uap1VbH3{DRc2
zSw0*D&l@SV?U#c=)6*{qiQjR=R;B22T)BAbZ*Pj7oN+VX%wW)Ub{Fk|YmS~k?7K@c
z?U$Uaj+&V{bQ50vD7l4^I)*9Bcx^JYPf&dZK14va{07{v$+=^xRt}e3OAktyh>9m2
zlk9`&j{!jg&oZ7&l}qWoz|pv3zYQlyRX6*pDi%#CLRPiQu~q|8g$;B|pxd|GNc@Bd
z4)tUgxC06>g)DQL_<$M*BU{X;KI-0eFH63gWWHXY%L62#_AnG^;iKC0PB6-a8WRb&
zj&FG|4I3G;ki*F5_FRNVzV*_fNZ&=4p6?7*-1vlDC=q)awrt^q=hraxjznzWzx;(#
z>alo0+n7f3QOOJFXbygGtSD{l0ylA2CTzarXzew##CqI7m4S>hC@0Vfc0Pi#XNjl8
zu<&n2$V(FD?<Vui6$>JY$%>k<>*ELH&=iT%d4*gosDaL`=}Hf13qR^2pCkCszq!Y=
zZCCG>e8Y%qWa8sx4ourG+{@WW=D&-X{F#;a{{V%*a=f2FOi&eA`=2Ze{{Y81Bdh#B
z*ol#F>3K&Ge9ocF^nT1q*8{lM?0YbOE&PzHMY`I%%oHeFPZK;f44PsFg$<VtUkqBK
z(beMl79@Z+g$<AW;7(Jx>%kpL)jD(^>`$rz2p?!MoPEEThOwGS)Dn^@Wt}jtUM2z+
zL~DD61B<Eg-mY^QVRM{Da40ks5WDvkMMQa3xmdfe%B<oN5WC|=X9LmN=z?OK`)3-}
zy#~-H>?(gBq!e<veXDZR5Ct6dk)Gp7QraJ>RSmX&rRVz^^$IoDSSXbTwz7um7WbK^
zfVtea^r+4$SP*=L2D-+w)RcKz0J<+hyf+6n=s2q7G%9Y--oa0?`6Mq`XsT>OwY}k6
zV{f^`yO*LnskE)`a)rz2%X7S5x-*H&^G$r45m7CIkt(=is?8mh*6*m5_Bh%IEnE=s
zS8Q=Ii$}GE!DHR$_EuuuQlsKGC~NS7yd`CauXilKEFKS+nW{&<zuX~4<rHz@JwfpL
z{#wD>=8Jm@S&)~S+m!c_c5`Fhk37#3nW}UV`D5K2X)42QU+uDuEa%GVm;rop*^0KS
z@BooExpp331W2HP&o(m~FDHuM^oikBRkEfJK4@GO+Z+8L*<B67IW<*pQ*Q1Lg25KC
zQB;_${f-L3JDgJjupxYsRYubOkzdMI`lPCI1Hwbii88$Sj6QDLJV0|FmKN9kaq!HD
zE;x|0X>5!9fK<q<uW<oc&{fx^jUm1T8ZL{-LRMphrZ5$-g3hrsYo?JwYWVjPOrP0G
z>7b9k2p_3=Qd51W{7AXvka#}`YM>RxH{93^(27#nd<SC;j_Xbw>B$Nf%HNj6I65aY
zsAYX<(Tili2@$2n7j{$v&??M<X^H3}lrI@NHWB2M%J>DF{X(|ZV{qC7jf(Cj*JhPR
zICt4NcII&^FWFF1I4_GuR?Hpc8IJ{AU>_6{FXWb}e^!*s2CgJqv3CF<AuEi~oUAuQ
za95~&iCmb7$W&{VyiWXqK+7uh@lw*xC*&Dt1W2H26&e!LeKhchZ=aQLBx7*(qvi0*
zt^ihwod}8y1EOd0pkux!)NB|ZV1s?sDuNpJuHvdwb@6gdTyaKL;34eMlv=#~Bj>rp
zbWN5w3R$KBOhh%R@LO_EVmd#Ge9rmV&rsvzh#5$GfUL8~P3(mOR#XES6Ag_sbO56r
zPL(b@9W5D`E0I^Iq0uNa>`pzt5gNf%ZJrHd-l5rcimV-~O_CYiBpivKZ6>vb+d|pG
zt!gD}s`6Fx#kK(E@o@*soULrU7~yvoidtyuaju2iE}&eH9ry*ZnD=VX=%}S*3NYr*
zR>g;r<0@{!>C4C!m?K=MZ5<uuc95ppQq+yVj}T^>Dk{;>gd3Oz47iv5HH4}T8bGkn
zJp^3GfR!EqZX}I3_V|iMN;$cK3*eHLZ3}0tU)a;PD|Wg_`jRWe3}fu@ScUq=1=7cb
zzgHOxe(9stM5-6$1~ZCG+7E;&meSIjoD?yRcJPkWLJReW`bG+qbd~O3<PZ;u$GZOj
z+on|(xn<1Tm5j%RW!;OsXBi&{+B~y&+|T@Z<#J}Hx`(wby9WdR0FTe9&H1Tj={`tJ
zmZQZ(K9y&iAz!(8{{RTkhO8LZN$-ynCsj~r%E6>h3_}kPF7ZV72B{!pdHw#fPTLuJ
zJ_a#nTj_hc5!&~-8{Xihg5ctp#B?=OdG{=7Tb9r)E3Y>DGKtbHcq29x!?milhoNht
zf}y92W)6nLMeK4sgK<xRU$~b&xKxux>~~6eQaTL+7aTzPa2MiuNK=9MDkWm>Ms5%C
zS`xnamuw%o35FVeg^0$e3&Cw$735qt+7*Z6<^tLWgNU){R`Ri7?&Avp^dB=q-N`BN
zZNWGWub5Sk%|9b)rU%Q>38!mSFN%7G&(&yDXTq0~pn<8&lC9!p7K)*@O<XqAD4rQ;
ze|*^*SFI0>x7_T?tDZ~{@#-#3(mW=1gI``C5Wu~th@#VcW;lI6kWkN-Ta0HOBhSWU
zqt|b_HW7pdIOo&hmT2pu3!rUj;e{wa-1CNE+jVacmGU75Nofef^jvzWuap{*SFneN
zsZ6j@Gz-_%?qNS-`iDv2{{T>tY@;l_2?)IsjY@ArvU`kGu~C_N5CeOD)ceL*8w$rG
z1t!vwwC%XK*!uHS+XmZJ4KH_oi~j%!Fln5(He}qC?9J(g-xa8w7SG!Spy3U9Dt00d
zptOwTd`5@^$6Wsa<tiM`0I`4#6ml^D+#j02>#%dPDsHvYgn#hIVV%R99|0(E;qRC`
zF#RbVIxZ#m7W*`>M4wk$wFGb=3mY$vFYO|3%d4fvZFhZnWuio?M#<nv$OHPQt#W{e
zClv_?V#HM1Qvs;JUMgP~>|p-@5uso%-<X{PfLl~Rz0y~N*t=)?0T}Hr?(fvkOG%q!
zPx1c%%_Cfd@{Nb32U{FIssL46IB5loI%qu-AVgYz3Z3shW?!EXs!A5lqv4B!{l+D%
zEawC{E$dktMm|Dl6Soz|=q0#SBK+UEfu~nFS0u84&kiG(?MX^-uhvq0piB1zv}66)
zv6#`J{Mw4@(G^gYGzUd=j!kXCy@n;Cbb>39ITC3MK)*+RswL?yVVWS6bi;aV6O^Ua
zr2C&dL?s6a4f&yg_DlPPUp&W|9ThC8tDMGSA<_GeoD`C}Lr-&(K-YF+iaRA8S0oJT
zyMUs4>MkmHiuxH(n(;JRFOFfW1wAe+Aw`#@A}a9|DGW?)ZZ4E(HeL^E!1}C6GHIa=
z)~d2vOcqKj?Yp?Rh|txh0gwpKV3-h;*4te}1win`*-*=aq+C-Lu=A%gqrW$Ehd#YT
zQAl;wU;hA7kx9r-ex+Uf9VzOz5Z*#uU2`k}F;pRkSG|#b5?6UE_BI>9(VA)Wd{D99
zlu<ZaZX#m31fwe~>2iOu+zL^sl%={v+`#3FaZc$7J5fS&!{_J5%XM#WJ^BdfPu{bw
ziGNa0NgB@Lr6sOIIDo*uDp)f|&Lh)cA|CSHb|;oz5l}sXib4rMLis^dYUzAdmHQr9
z`NqZ~VAkt##Ttz~q;?)d=9-lbzdr1M&=#$G*~G9Y<AqN{&ryLY_ma*gd70NA`O~#1
z=5=f1>S5Kj+*JbH>SW*i@oxwI{mQ?}_r}eu<|UL$oYEC4r!m&<S33@8M9yK!c30rq
z8mq30criQ3vgTe&<_c_guAd)qElIN(^D?gso+F#qeK!aI58Gre7PwO->q0#(rIX-e
z;l_)F*~>*#hqp||y>|ZqCKczGV@<mNcFvN?9=Sw(kf5g{H>#74uVPpLab>S88_Kb3
z(FLGC1|c3%S;1vLBvPBJmTC}ku0ViSZ`n#fFUm#!_)^6xTsd@vu2$~|LAU<I{JWov
z@8~mwj;C5}dWo#16y;U_03=jZ!Fsf0H?5tUVbz7q0i&|`rpm$v+x{qK4b(9EfoBVJ
zh4W5fA$_kva<ccPXBH?+d!NEn3{y?`#fb?Esi)aFp^kA?I5V%nKLd#;BS7gxS6XBh
zRDuqZNWOR2d}?1~**mhzg<1{>3Mx=6w^hVAGQ45Z<yV2iiX$NabVefTo?=@yUcNy%
zu>sg@u+nqy#B+u^84B+Rne+|V)pHx0)^O3_a<bonrS0gU;L>M~Ld~WYNK<%}6Ex=u
zP}lW&6$@is)Ca^ajw`A(A{%>kehVFD%dqgPslM>2tO|kxDZge#a1<U;fU6HH*NH=K
zM=7*6u;ZmYuf#}d4ZiuK{>Kes*PKpF1Ja}rGFLa**8Gu}{{UXlvc0-bsI><2`q*RP
zQVyy&G#bRm&Hn)7gd*&LD`@;)*z6UTuQ6Ee8c5r_+Xm8AEyfe))P!EtroK}$s>3?}
z0LoT5IJIOULeOlqu~f#%svVl&doMg<E6WaCp^m{-lj8BMhwBOttY4Ow6igU5Ep1kU
z>K9KDZGt~b0GL2$zZ?n!<r#BP+Au^{xQf3*ZcJ0ad+EPP8pv~tU9vUZr}K9KJA+PZ
z37pr|c0iC{0g|a(P#JzH&ql(#W~cO_EurrY3#(A{;KjnVXSwhmt*mG;Fw#C~497>Z
z?xjUfC;Az@g-}i1t{ZPQw>HOk;QBsem0Ru(8<klM2FX;Hsh5=i8mrU*KK9DhfZ%w9
z{V0IP#T7_dtQy#yK)R9yWx^G1*b%KcHSJ(+Sb9?&iSsHl9kO$W)YxtMkIO!I7=Rq#
za-J@H2+Ym-^$-i4+n<?cTC7}fUoOPB)Jl3jVn_pVQKmhP<s?;x^Qejhvb|Dl@mIR3
ztIn@3R-Lf01hd1J3w{WSt?!Nfm%uJnmMmb>a9X9Uno>51lLNed;H(PxCCPN!rT|T~
zQiF~OL(#=DpFNYq6-~G%w?M%dFj>HMr2->YzVuK56-vggaaC9%dLYKlRIW5HBI`)&
zAV+*yB0*#yvO>6|n&5S*oCVEFyob^5QB}(zio36TObkIT3LrGvynM}wDXurdRu~&q
zjCpNF-ob&lL*%TlPznJwEtiYxbMjSbqC5wfV|=|rGFg#Jg%)yoWG0(Kn~1c&_}G)v
zt<nO7tkFQYyGrmtHFH#P@{y66uXmYs6%PpKQQnJ$(xvH_h1}547>SC)UqwMpP*wgz
zrqK3P35%7z9u_cFz-Nf9mbK@qvz21#dRr$^x45g3dV%tYb)kM(c%1d&BMbik!k3ym
zm~ro7Yx#3`wE+6_E?*b*E;x+^<)sy6t$#R=R7KH;q@2RL`WPh^)C}>g3|e_KG?p?X
zfp0(w$uAdIK>!*0Aj_*v2nA3?N8C`CW@XEq&UU;$<HMurxRj%)II%)wt{2P?*|}^U
zp7JU+26s_%!7780kB6y|wz$a5^1a<;ANaXe#B)n986r0UQlK4@KauFguKfV~Gtyo^
z=Nk$^nq1V~*Vg%5=C(6xPYY&ZA}L7x!^{?f){h?nIsk#}rpu_Ok^cboE})ziu=I*%
z!@pxeR@p)a8nCidzzy^+FLbr{vCwGW_}n>S1>xK1KlKwKh!4=nIpbg4I>Gd)OLjys
zne{9C6M!}*Y$uSQJT*B1C<U>k7_D&PgmBJ8ZGE7$3RZ^L-k~fOU30e$gksJ^f*s}G
zea`T(w0R`=gTTF!5eRAN%={y$;$J1GB@eEp!T$j9ErfqyZ3}$>NdYOeRs1Ie<io<Z
z?&2R&{6mzUSn)gHO~RZidbDsQ=gDwfS?2as<yd21{>(=7r$`7*kr)kB^%86Pt>VYU
z^%c@wvje(T@M(X@4;K5SVqSDy*FU{McpxrVz&t#wexvLpcYZ%N9%3SuZh|Oby*b_o
zyP2pitdTJlZ{}G~4c-Pd9XMYL8H>6;de~>*{v}>i4m&I)878=wNcTVJf}n#1va+LC
zxTDBv-9rnMK>i?kXl+a@lA-|=&>`coIYv~TPxy%y8_M&t5`oz9@pxBrhaiHDIoK@A
zsA<veU40X3Aj7H5O7nRZmCJbgj=DN0O~Fm#>aA!3)|Pb|WIKDOC6PtTVeqaX;sz%|
z*?O)4xHNd8y4Vyk_!EMcRj#PU^L|^Jul}aWb#)hy>INR!O<iOHf!Oo1Zv8NQF_l*O
ze4)8eem#g{sK`rMQ1xqmQg&2U^?NP#@&Ie<SKkMkHakV=H5`@*_BM+E^19gAEUqGx
z{%lu{9T7ecqRGSm0NSXsAQ5;k9Q6g@M0gyDEop%s98HO1#*6;|VGB;+Vi2XZXQ_Rz
z@P@gh^(Yyy5qm0n;+<+y!2rLO02|6<CoxC0f<0oV2D*?BNpbn)D1L1cS*SO93e%#P
z3cOwzoV$j_o_0R__@>asrArTyk2U3f1Tdgb1Af*8;04vddnSz)WdU#)^Bda=KeZSF
zCdGK4nwQ4aO`%3!GXzqcxJ8241OWi!isB^?E)i6tj$7gno&_rqi=}rj%Rx7RmcTwc
z-*CBQ7wB4+HC??7xXw97xbXe4mari7RK7+Iz*xGh{<6NT9qP;_MHtcKVj(&xZBe1E
z;FnkwGnRWdj#Fw2qVMfZ5i!dApcW>f9VMPKZ&W!Y0euqWVzKXs+GTt1)kGH1!0>}~
z%c@j65^*ViFJ~;zJ_Bp##3Hr`YpV5dL??MwDL+W{;DU37u+Uvr9JeTpzf%5zriG^q
z>>}8~mHBr531U?Zz;umUX5*V&y(ct;IR?@Ne5yT7^z^taC9W_Y@UnnEhylA(y5a~3
zQ1)p$Rrk=&{X0igrg;a@v2b1$0oyvAieC2P{)*oM{2C~#{M1)_yMLTZZunCL93Z}x
z2vr$4W2GHI<z%G*BbrZ{*D}il04-O=Mn!RF&n$NI4&Os@x(!REPi-db;)C(qmbl=z
z52$m@X&)CgDj-ZFuukkL^en7dL3vHw7i>AuRt0vKT#Zgvu#T|B(Nb+ZIXsZH`x258
zP{h7?!d?KU2lOGD`t$NjF=N%V7aJ;7UIp7@SA~~$P{g&%Aev^~9KVv{L`H`1xy`qa
z>nTqn(-cyUdWhBk0B#?j{SY|!Hh)p~-?JBmrpTTsDo~9%D>$`aaz$B9k4WN8Wh{`O
zGA>bX;Hhw!b=eSA@q0-~9}MS@w*1?{188n_o()bOth5~j$j*Wy;9f18oQQf2FgOGs
ziyZQH)A%KH8fd9|5U*b>Tv2*2ZbSt+z?Ll^cW8>I;D`SJShFX)m&oWqa#lO>!kF(&
z{{ZO$C|td5?BIi?y>O>XJ(B}-dmO9^4jHxk2f#!fS#3P1xZofyt1@ttVYLyc0j+WZ
zZZEmv*=TR|m5)`nT{U$ry%bHM7W;voMgIVjz$&@9wFN~_1ptZf-~Rxr<yA^qRE~P6
zO2cjCuM6f7QMI4E#Dc5MaRUX@-f<WOEk5ly^tE}SVO0=YT|7VfjBg^Wb!&gfc;k+P
z{{T_gE?uRp!Xm7IWe(K`?~W)ipg)M0a_2j6gt12ou4AY<yY1zh7CbGtbV`xg1TYsP
zq~CQPfe?V9vV`!;_-j)I%JdNXh^(c52)b5;eFA4wpA!ABGxjr5&hU@|rMt;P@;lwc
zfZB%1{pI`G3z9nBrG=0Kb1DAz)UF1#Y2epzE&TY8S3wrMg2uJ1d5);_6@|8ubS~xx
zx;)DEMy&2t@a9S{zs5BJ@Kp~hV`}6ymBT!FhZH=u3;zHcfjhgFfU;4suME@B-o$wu
zhuYifz^b96SkFWVNmGkmnHAm|4+Lz%cxh>#Bdc&dRN0FwQ5Msd>Im!V1&DZ^=ijpX
zB^c@*lGhHfp;$8L_PjqWL`+&9&<2HEl>~uB{00JZ0fWuNWjo+pTBrX20P%#{wTN55
zKPX}I{D$3fLek8Nkknqqid8R7t`C@<<WmJrRU-hms7X4ngAGh2?S&}N1*Z*Hlf!bW
zY`FHbdyoZ7ve<eU7-34gU9Nh9_|91akEqZBp{}4WoUpV~JVJu5&=&h&F{_}ZvaZyQ
zD3+l;{ffV6mx&HgOwopu5rti~+{b@!fdL>`&mloj6^2}SZ=L29$Awf%6=_5p)g|=t
zC|A@bs08=2pUW7uQAJV^%@X^27sbv6=OZ11eG;~#NpWokoKG+}M-X9_F8D{pdIQ;<
zL9<$J)JX+nu$7t38`yuORSg$#8?O+?{JT`Ds<6Y8)w|VLP6<(dj4?S!pQZ!ENNe<J
zTjE+7ZDROGN+%BxdBukFX;Eg?BFihYNNq))@|hJ>Tg+&D10#1j4p@vgLw1OZFR?s4
zjci49X!5OsS}CU6UzI))sq47E{V}C41n2Y#xJCvTD7wD<K^Fr>>d$kI*JN1>e#$us
z1@g@DAmkY4g}|U~cmDt&iVE_tbNZCokkD&eu9BB$$xyPMhyMT|@0+#UO*v(JLY7-T
zA(bB_cm;-F<(TVZeNz|Pv=0-?Uh7uF#BL&ZB7`cV%C`}-!-?5DeWL#W=;tOi#ZxNY
zYt6<OPEfb5`;S3vGFmNq54eCw;eFhuprFJ~FA=i=Z-A@`UgmsG8J=cJb4gm;UmZ+<
zeVyh#w)&#(G%(Vzxp8<~H~Ienoc2m38%#z(HYPVNDjv_{a0obQJogJgB^;$hp;VIq
zHP92MZ)*|e;GSK0b+_T-bK+D$QkOXMIW6|aq3o{C!7AuB_~K>C>NN5q;9II&)<KTd
zt@?~?pmlIId4X1+oE^W}Ur>J=l#j9u&|PHw#V(p#W-=QkdWD%>Oz0qGt$;pem?1U)
z01_&DeeubMQ%UM~{y_s2>}ud2BmGT^n7=YAvThxwx8hl<ZpE7bhZWRyL3WYzt^5)=
z(}(aelfVt&l!rX*6^Y=!Ke;)et`6!YE_3EQFroA)my1@;*>P@TO&GQK2-ZOJ;^LGP
zUywU+KFVNU5l>e%wMX3y=Fjp<Awp{1H#>mzWp=nMZ9#anwiYi#7P=;)%`KTwg*wfg
z$+eb=-VOf%zz?wlkiiC@na3SRDlIkgt|fqmSI=x308P=wS!4q0DwnsaxoU7JzkYv^
zKm0&xz{B2D2hc^jq8O5priJ@pfyLg}1uwx1DP@0=@*yq&>?7C)u9#l0E`<Ta926Cg
zFQ2(!4I4%jPi@0+?}Wp9Dja8PN^9|sSk21ZAPy+NSCR)7xy_I4zxa3g_ZTij`r(Vn
zJv`ME4-R;u<MWf^{-P5W`V_O#UUdbSK%X;koZ-lP&S)ALcZ_|C3)|p|>s=*UpeqP`
z(ah)-3hV^+U0WDCsvad2EWFbSAP2<Q!n(?Ir89!*CD3>=LZn&*q16Q3cn$Q77XVYr
zD_GXdj0OIis6R^{?c~e@Twd_8z_MLO1l5PI_6WKKo1*;^!i^WM_~9Df3W!pS+1?vt
z^D4op>RNjIF`r3RT0D=rLyb3S%dBoC)znpu`1yymZ~@?8&@quX*(R3G%tZ2flmH+v
zi!CaSHU6QjM~MLWQl|TX7k;yEGiBTNWKG%h@hwWKhb%(H-kxv?cmu>r+lQFG;WGZ9
z6`m()#0%@x^HD^Fn=TywE}~kpFJw}4aC#!S!@Ibv?dnmo8mpoefSo>85HEaJdBh{I
zP<TVRh&D@ATQ9g^=Y&uFVfj6KyM3Sspy>#2%jNS^)<Hchq%#P5E0Ty#KVK^Ci=DQh
z9uKemcyw^&fy4V&Dt90#EwV85#45Q2ufh)88OwYeK$H)QIh6Lk98@(BQ%<f@Brm@c
zi#Qo`=gb923x|kmu&U)gAmA<1qkdoj8#q)CQiu|?Eq3Q><11*_hsnfIU2YLvFknzR
zta2Nn&qO=xaT6uG5TP$wlkVLW)Dy12@nkv$9+ACX#(g2)5gB6+9<E(!x8S;ohc`fJ
zqJwZoIzXtXEL(vFLqo@y^vu*>LauLTD8iDtX-C9dM$X&|l8&^1q#mou7uw3@qxeJ+
zOARTB*vp5Be|ko#<|8s`E=nTb13TEaHCg6euy`&x7|^X&)o@DzwUIpa4>P@o=ecmP
z-TO`3L6l^3wnZ@bNUoJ*>>z6DZ5qTY$1{)_dAqHwb-+t0qwS45dmo^+S;KLO=>zy>
z3SOuKL)8Se#Nya|923PCE65+($W?JK8GfL6BlCIG7&u==%;*0A3N9XArKqnhta^fn
z1bC|@ORxMYUmp|8G|C!%GX_yvR3ekzL$p!!Hva(N7?;d&3a69AbhqEl^gWWv>AG_r
zl#d?vAPO}19*$!Qy?{p0Q%sI(U7O)o206;8mqxqDbA8Q^QCD5K);ELA)rmE!Zu_Y+
zq6)awrR#<-{v$*Lh4I>Oc&qBA>E&2qe{3LIAW@~2YJn?O*1s{|4NkU@>><I@v+))0
zKKZy4paIE#QiJFZ1(b)}OM$G^I=NEYpy7IP^>VQfoJ2sddm+fCbIdi<3iU$<iqCN$
z-r#Ng4}QcAL$4uz7;QkWV}Sj#xGo$(YSVFWBphyir#d(*mUKY?eYz?uy7{WCOVQtn
zHz3$3xTO^?557v3J$iVu(puV4&HSuu90#qwl6KNJ+>+(ErF0M4l&Y#2MDhFuO#!wb
zW4+KP?7cvd?uwU~>YzF`_eN6TR)ISX&xor4=P1dhiH_)j14y96d=qUraf8$%^<M~l
z+~e`0iu9sXb+NDjhptd_!GMFWC`QyY;H&77=!16hu6FYA&+O?d?ja(|+-vLV=T{X5
zG+1=t^_103>c102mv%JMl=txvYe?OQVpkJr-xYI;$>Wch@IhwyUd6$?Pcz2O?xLh^
z^D`lHPXNG^Od94-tbQXbwqesj<uYLdLqgPzfHXBDF<?472O&*|9w}@M*uR7^DS7@x
z6_phCaV-~)J$DunVlcC26=s&>3JnzpAMO#X^4P&tA1AJoH@fzBacRN3&YV)>0gSQf
zJj)x5SCN>&C?VG)P?vFk7!A<vWsO}zJF8`+a!p4Q4_syQT07gk&;J00;gs1`S79F@
zbaJAUwmVhY?p20}qVs$trKu8pN_%TP(`F2+EH*YV$@PIbC3O%2kC0)a^lR;x&xTWT
z%I56%Q1wRiy2^)-2ehu3>K~bq0Kk+3(GLk(^YJUup9KAoX%<>rk;u;JC5&?4;ZS<7
z!l00&%JLa(_Dmvm+JMTt9nT05Qzg#R0xRIPI5uD51yeQ=XfU|;1BZBr6XPT7c+Vc+
zvjv?hMT?8J)-ICLmxqZ+G>BE2_K`y!UCp-!(G202^C^6gvkO9SyK)k~bic?f6f&&o
z0<Z>GUs0rg_*4Lg@q!r?d3%A-HOxU5><@de2HY_^CNW{_`BS)Wb|MF1loh^EQZ}WI
zc%_j-y5~tOv&~H{dcE*rH-}$%_<;;0H7kKSTpyl+P<g3^-7Vc8Qk>BaKtUSqP2wX9
ze_V0q2OT&mXG~h7fUyrA3tmXqv~bqajN<*dAjXddwK9kYkcId)f<=!fr@@go0s~En
z!0^oF?jF}~i9rbF(>V`tg<gJozcHF}B<6#{FsUq)cw6>VqIeqQ+)EsN3x~>aE*HUS
zTxHb;u|(qbaE)6_^hz@odub_F{dEYiT^#oSE$4`8cD}B}sKkPn1;X=`4TYFbTEV7-
z!y0P?57|6c-FS|*YR@t?+_6tKz+O3vDgY_Eo=HZGNxylLs=SZx4F!)u9HN&M`ta$L
z7*0$4!m0|@YRdTHb;P8Quy2^HD{cP(fx6WCk2^X40L~wXd~*!H6*!Cs$5)q6`GLRs
zj{Ma5C;tF0DAZW2MZ^7v^uLkH60t|{11s5Zy-y-aDyR>uiOKG90NKd{Ju=j29B-M{
zfBec^D0m>-bG^IyNeGs5r<{nU_9?_%RK#~|m=c9MdA^Rn@wp)(r3TU{ayRleqKfdu
zsP$0d^8;<?@QWq2acbPWE{THYzz{dcJlR)8dUHnnH7q(`447H<)qG(80A|pZ*Ju9#
z<^hh+)JKjoy;c6g(6ZQgpl?ryw8X0@q)$w#IIkyxQ-u+?FQJCHMSZgsnBf715D-%A
z=2Zpq<6^UE`r@KdSYOo*jB&(ai=U$&!22_)hsMK~?hFhHFM0m}kQ=7qTu@Cb0L#q^
zzcj;fZSnnZBR3v65-C~vdzCvcfBJ>aAjU_jY)9SIY5=ZyLa}tL-8hZr#VAqVaOEDw
zVe;L^nm}7{9Xix=Mw0MIS>ux6D-un-UPH+0ucFseGS#7b%XLz`3Z-b0l@z)*t6PF5
z$i{I@O$-Eb9kH!ZQ1P1l)F+G)7nX@PC|jq`vIiY|eL<Gsv{YCC2RI)yg}$ZT{4Tty
zS5uc@#jk=VtzT;L`0ysg9$Yv!3#u+K+(##3L2Vcx7EJT^Iqwhr0|EW;%-8;RDN|-N
zSCGa9SxM!S7?B=;I}4Q6PEA|mz?Y{=d87|B$BDiuUm8~*Yly3{?&yv(6v?r@;D8Cq
zb)CvH9Qd1rjcY7X3Y{afGOiaAaN)L#G>Af#Pss^XJuZPB0a5DqSoa>arrrC(p-PDb
zSAM~8;H@fTs<FkDmK7F`1C=5m*1h#)zR}afDIoZ+K9EqvWQpG`BHHV20=T^SQV>+G
z_-k$wX37{<d8AxjL^c6$>~i^=$Z$`g2qoI;eWwweLZLPHHF_wjJEyYEigu6y@l<H~
zsjYE(@BaWA?>$FSfT||3PBX{c8N=ze94zijW;uV=Lfa35pguwBC1I~{vNiD$x03}~
z004Pv<tGcMcbu!5^%|%@$iQ!iRk`LX>T(W^SQbTea@$FqR6sVz-VkY5!x^D}pt~*?
z$(Z5`AZp+n@<b~3i-hcD3@6emMK$bFr+VbEil$5!bzXnZa+`UrpGZ@D#BvqDgb!tU
z;#_<}mXqMO2&vI<Wq<>_AntCYd#Gc3-+tkW;`mnn!<rfhQ{~y<j}B|xi&0QdEEG5E
zV!Bn2l_X7_Lz*nv0Yez+`{r+OVi2l-L$dJ0k0rLeGZe10;@G%XF0e8hNTT<XcpO}C
zP5?;Yav}Z|TC1)LjKQs4iKykPFjPae<O!9OQpUcG3w(z$lzMTuqhKlvWc8@p*sNIM
zDc=}Yr}Zb2N0gVv%qjsiQi>-b*V-9k!iQ&?<$_g|a;FHSJNsbx$BA&J1S%S2DIa93
zrLHavxR1CcWyi6@XN4M{5-dP>-mdB`-^A=}g1L^H#69&9=y)IwC>FxNi=u>7@CC8x
zHQ?f0RiG8=uloo_@B?n)bkwqd7>FSk$|dbxQ*1Xjteq3Q{Qm&Jgs~1L3Gga5cs&pN
z$>sk5&iLkcJb3X15b?}+Q*x(1CH27T$QW>K&p-L`{{WjrAqNTbcK-mvSdhFlOAskF
zt;Kc|8s_4Zzn8cLjgiRa#wZ{G={QehN0MVY)7;V1#!Aut&M*?*Skyp(<!@1>mD?3Q
zt&E*rt%MeZ=N!+1X5HJg6aAO<okuPj=8i*T1N(5Q@l%zz_CIU2dJdnc#<E=DN|f_r
zQ`~i?t~UqX!_6(Z)4|&+0p5D~NyowF>nK(Gl;Bp`Xcx82qo3^GB4^A^dISr9>NzRG
z*6(CoYfUPx#llre2S^X<S>>X({{TW`rUE-@m=*jp$^QUnongI3tBG4n`?8`g5#xy0
zZREYh=Lx$(RMR1z0yQ2xZ!ZP}HKi@jWUUgBYQmqL{{X=q#h#+2s>D!O@MJ?mz)|iO
zaaNv#SV0tDC4`n6@K(7R>6JMx0`@FrP05QZ)KaFvc<NMPWiQP}UF>);xaZ&{)B;+<
zYBub=aq<(R1YnSfI@`ORdu%Wv3rct+FJ&Ct56FbO8d<~3wOp3~fu~GG{U;bL+ZZTs
zll0_;_x}JzOHOk<1IqA4cGwYSA3`0vL%}((k_+7ZQ)VN4(&30#4@m+Pa8Hvi93eb$
z8qDTcAieAw{=rpSfA}_-64f@PYQE6vn~^iwt}bwa@l+7XOTG6ObH8scz+^YzD0UiA
z;j1~F>zcnQLZbU%zy(8Lw%vwr{wDz#!0tB2R5qY9RB^ugR1E>!itj`@z}k{LtqDpd
z@~wg7DrhR)96$X=_{3NRJ##wMUvCjNz~4EF53^6}QN$Ur`Kp)vw1t++Usl^I!5~BB
z)iD;c8?%m{){s_AS_T3`51QnnJ@qa?FR#JupUR?ro|u4|c;q2W*&)fI1fwq}6NkDR
zi!qeM>^3sRk0W1m6K8_gQUmJ-E!BL-7L_{3?q#aq@Reb(1yoG;Fb3{c*;-qN5Wdvk
za3=Q<r@sBvv8o>gX^`}Lnz-*zY;#?G#aSNZ75PzipN+8;mbd8KH3z3R6P+}>r;5&t
zl`Iy|IT?do;Zaa8i03Nr$z04SN6k^hm@0KCa0-K<=bqVU3NGXQR5CjU!`vYel%epw
z(F3)lu9qB?gz%cpHTjLs?ukwx4H;E`mcuj_y5bRH+M)@suh{_YZ-*Eamra&-EVTFW
z2-voHC7Q9&!>~0fs#*C;#Z;{lRyMHeh84I$2KbZ?b7oAf`h4dtzCW}ws;07<6`%oC
zi@b~;V*Uq$8a1Qz9Azf2r6%EV=bu+9FlpqX7d#ZMQ@Gj+C{fRx8E+O9IQei~5$*4R
z5BV`x<bIr*aG7COunp5bC=U}FgLL!ZK1oh));d*Whr~%xLb0G)F8YP;vWIny@#H}2
z<s&v%PM!lyc#!d{nxH~3yA{?_)F2nz^99msRF&An9|Rf#^Xo1HO^T%qwKy9KE+DN%
z)(kHD=C7f@xw;3!!p>nhk0^+OY9t;yg_I)0&dP6Xk|mJZg#o)U?i9=_>g!{B;Iz+?
zVQI_g&{|F2H)}x3IG+_0!;o>zD%oCsqIoMFhaY@P=Z)%ZDAdd5=5O+AAMdHd{{Wfz
zI-jv^cwxMJ%B7H*#Qy+%O8u4YJaZO=e#A_$Do0M(a)?8W9U_j%_C5GwG#ybXM)na_
z71R>As)29}p&qy$GWMs6n~(Ak%3zids#}OS#vfPcSV1~~KKb2`Zl(3<3C{gx8(BG-
zx8fDfB-W@>0iVE4PuPdUQq9EsV6=i@1&dWR<*CoM6!XXz>_x{C+5p-$K17KKfOfq7
zSl+ZM5d^>t1<T2&k}Bz?o^K^o7e5qj2LQAB7{Z`X%qhZr5mRP5<I@88b~2HRa^)7-
zf+!vHQ#QuvMgzgbXO7@8AoTGvy^$9Xv;Y(j&fzXLwfa+y3qo3O>)m_sCH;)Ir@U|}
zVEZAdLr_dtWV+AB15%om6<TUDLr_z3{(X=?Dwn8A-_&jMDHo({Yqopz431CRAC9Ng
zStJbkLJKet7aAw~8ZVc#xns3rk1swalKsP;nnW;C_%aZ%c)3uzl&7HH#x~XmZmQtC
z1Lpd<K`lv6>BP?j4O+hoW8i%pkwChjcUuOpB?@~zdSSzC{cYQZgddir(_ImQ?VZ%y
z2BX)NmA{>_`;H+^+68YhQ;MfZ2MS8ytD6iL{-Vl(S3VGMB~+Rvz#KXE+<AP(@dDLy
zKGr83!EGm9#Sme))WdHNie?f%?crh%f?d*0*?~PQQnw1r2Ovy6XI$-@5~s;<aZ{5?
zEn^CB5ML;j!L0A&5^P(Ke-VPlfJ+F}6@%TvPzvGFH4_Z@H~#=pv8}uycHGmmyLS+Y
zT@7m_ytgSdmq*+_5p}$i!l2)iM8Yv17itoeS|3*`mrSjf+Hy;q+d!NrB&xs!w1!>9
z+IdOsNuU<jEgS5ILb9}$c@I(D;1s0bLQ-{9QTXriwH%3Qd|?6uXmm*Zi?3Z@Obwmu
z+oWHFPb<pOhJEMl4n<TGUaR3^VfJib+?h+6F6%8_xJRL=5b#$4xO(9fS5|gSO2fDb
zd-ccc%wRy@1KF@$aH<#BteDTAC7ygkYJz}8vKr_<OCZ~rB-+Xo?hO$TaCX340{udQ
zQwsjWIij!s023lyc8bIj&1ejRa9OKI#oWX7ij{M2f`a}TV*;=PO~UqAnm6>tNEV(1
z$fa;|;r2~OfAdDiv0!)|&U@?5#JobjX(Ha=E!13fZ~}k^Q4-=WYOYg!1bhq>u&g|j
z%q_M1*{KMSQjJ{MmlJ#sRMRS8;L1kE9xlKrlJLrw^vlFB`sNc*kx_NfBc&XhR7yWJ
zf7Uvt$5ixGCIi^VA@=(@!ypkyn7nl_N%Ik^2Z2Qi66G~@!_dM4?xj^sRjS?qLFHZU
z$f5E?wC>?iW`h3!l2#)}CAiFkYNhf*z6MbM3*cc2v6-B49Bu?^w=-hUhhuaPNQ$gn
z0Rdp78W=P1&Znx4oz>XlA52d;ggwV(^(q=uuP7#1yPT>XaS<>xXeE4Ww>Kg%6`8Uw
zLx0e01>840*4)ZBRF6ThnYkL~#SxSYv}4-AXaK_Un@fdvFKTKR6uf%RQ&h-Tus<!3
zA1D59)O24@`R;MG{{WBI5%_wk#Q8nX@_W0Larnk}Fyx-cqf|JRNfeI|Rq~J3&N!d^
zkNl0b4)U4s=O1tg0AlS#gek})<j9shaT2dl?cw&qM$GdQ#UJc8Rv8VYa%K5~T^xB|
z!wi=D?8q<;N$30`jLy(5al;#!ehpa&g`;=_B3Dwte#rAeI6$<=)!^ut7&_HhAYDwI
zg&VNxQuf_TV}QIR92<>cRKRJ?ho0g~K-xFzq3OS<p!yJSFT?0X>udLmbbBgl5Or%$
z5N|h6(6z=WnSA3%Dhs=SsurQZEuzx$qM=t8Zl5F`j?}5Iu9)lu)vc0^*3RLy4>cG_
z$Ba4Gm?%90u{*zIX@kysz89}uODbF#U!AcA`viA$1MQEh@CE?j33H3D3vM}bF~;!w
znH=sdn;jCdeAh#b9%xT-ET0*opK{++V1s4qk)XQ#9ziEm+2BoIjp4=cmU>^EDE2;l
zXJ4!~tMVpjhr9m()aqELo_-_gfL0UO>zPq#tM(uWJqP{!mvl9~yF(z;mCy)`D~j>x
zk@_pS&=fw|dDvf{1{8vTC@uyo)T?UaQHH<fEX(oo{jH-M6BH5(Fn1Oq^g9V!9u9}e
z1!kK51D}W(N5}?(xnY-I!DJI)f>D52v%*Z4P*sHqbDWyrU3@^+-`D;y&_>+t-SLva
zjpAXG=|Mp?xQ+0vgeBrr%cWqj-EVB9PI!)Cb|#!kzs4Qokk6LHD&Sl&6nP^NMMBaE
z#_dg}u9hH_js{_ZrK{?e=&-i|1i^>7wYg3ZJ{7S8_78FY0NRcmk@pyM6=oWXl`Ag-
zj6;yldsZIMh#P~t3b?fXrArl~zK}}FO}mbY<yU7aGZfBOeXgO{g4cX=30tTu2Te!p
zodv4~?hsL;Wz<cnvhOD++*@*=+FE1nS)th=*aPxHfj8qpRtwS(T0;b<>J<;{ySLh{
zJo|xlAED-0Tr$olIQB~-xH1bPo4b^?K*XW+@_%EDa^1S_0R?a{aXZ<SR=w?~D&ZcM
zp&X(puZU4r#>S0;_%MOMjakjqMfa|eZwi+*#;PD9agln#v7i$7eAD7+TRg>eb%yT6
zRrzPn6&+7tk7@}cLMulXtsn7;T4%oEzNHer;eq1RXHeyGOQ5>y2x|_bk%$aDO)16d
zp>9>{BT?z%+KbYMsYB6&xQF8T;wGWH#S)WOnM4`kL1n5Ajut)|Uv{#S<yg`fs(N^p
zQ9`&1rn~ZJqDpW@J{86YIc%_`+@}FSPTNmxIr!xpWXs!>(vaYmMDQc|4L&IWzVRFh
z4#=0Z1+Q=#3+v_Ni5svjZ$Otfx_3%Wh|CIsEp6>(k_Wdbz&+0!3>fynM>#Nvt%VjX
z7|dKotP-nE1hqJXENTcA3cfgO%ViBV6Fhl?RgRw2`Ct}~DR=3a{3hKL>tIvHWk?`i
z;5Ep({kVgQ2VOfETJMBgU7eK0#jPGIR=#9IsbuFMYWQI&67O4<cmnM{Th`PPm@M1D
z0OSht!{(q;2d0Fq4X+S6rH3=7bGBUv7L!opIP?Dil?o}zO!t5JrTCO-FaH2<%kepN
z#`iwqYCJ)AQsbev-wZ7F?eOT&yy|)JD?6N?CI0{d-}0s37T2>@HzQyvn`|Bn4QA?f
z5Y~fZ?ken8<OeWe82<o0%0_hl+;0(7r|+n&@+~X2Ei_$s5*Ff8z8R<R6D}%ar)^7G
z2}*B*3zk@Q3y6K3(NF9J?|i|vh5BSIj3^hiNDOg7K$dg#U~h_Hs+lJ`1%Sa&hl-*P
z9K%s13gy0t&DVg#AJS8ZLvYodjEgL7ECzC_vqk~rIer44`w~yoKmDv1Pc`9;3oOQr
zz&();7WHt(?_-$y%(3^{bYUu-;E43h1lkXmQUxQ$rFA@*1#|Lnk$5Ff>YkuAt^Oo9
zM=CRl#UHBa-!`;ayp>cuBg{*5VMiFjWF_TqG|di5pKMli-+{tX=yiRmPot?t0a~UP
z{9?B$*LI`STMV%3M}pAi)Dte(Ytq&^P}qkew8G23$&v&OSRB1U;@K%5qAi|swa8c?
zD7-G{>I<ZC6^MW1K!Q#QU(@suUQi9{vOD2`FL=MmY5{giRUAw{EWps><Nb`JSIElL
zxVKIm146L&F&EVeGwdsG_E2TlxLn}_lG;uws`ox-`&h!uPW}<e>XgYO1#LQH?TM#z
znVGP=HYH4;MB~RjD8CuR^*|lH5RzIB-vfxj77Lgf?;#=QFzm=>e@jd1Dx}g2&2<TD
zz$dFN8xcP=9br>_k@QN`+RPA*0UYr<W!k%8aO(QVTxV&>@&)!1uo@77F<>mo3u?3;
z9?F*K*|dVH2ok<lTCXnVKg&62HD^zB;#+qBI1Ba?{V~kIaR%|whN<Y5bz5GoDTa1j
z{$Y=6pol}Va4cN|q4~qqMW_eG%DmTB^zDT5qK1YTJy+F3rkz@#;;IGywA4Z}MRG~a
zQq+%$Bktv59M^=zfp7)aP+8ckq@T{8^kohPRSmYH5+g5|iwQ8X&>j5B6GTE+J3SC%
zx=uAx@Wfk)UoLo+I;C<wzzSQ`J%LGgokGz{p$^C@T+1$-fi`y=otm-Z1g6c!A`e&b
z-HP><Oj#EZ3m6v}FiT3?r1?7;WLE?t9uSM!CZglY?<uUfI|B|}X8de|<G0?YDjQ$m
zVSeR$8M8M)&1Csk64?i9iNz7F7_+u`t!63VeZ*S9y@(vYnLDxaxd7khmp}W|0_pJE
ziQW`bQBK_$6?NZ3$q~x$<Rvyl)x{H<CXSGsXXLKQEeSxJRnBn}sgD6)60p$D=rLy*
zE{U|f)b_^}YODEiy_7|wxE=^Du<&~k5@kwwx|I-O4>?{Mm2Z9d5j`^UFjm4E*O88O
ztyZ0}Lkh2K`bGzw@1A_zTEs|ls4q)eYD9{t;G&2u)8uH9C$iBf15=8~Y^ttkTADL_
z5yymg`}%W4-)E)D^28y#er9|Mr5tro&cl2&(<?bTVu@aFvbymTOp6EwEe%~x0gV<P
z#6``V%|N=HcOOgXErB_)Spixw=fw}^Mg>ivspOD4k0Hom4g$I<geBiqRRjb_Mee*t
zYEqqE0wHrXK<jvdS&_1ha&I83oxgz-ZO~Ee347{SKA`FF^*hQZFB<;<;j_MJng0N~
z7Ww}G#2zc(#Jahxz6cxe{gRhB?rtZ2{{Z2KVBP62@gB0?_AmY=4A`J@4nCvIUTUrs
z-0H42tPmXaX^2lJ<HW09T*grGLRZ+1yoL|5WiioY&Gs6OQ~-HfL_Av5b~5^<O?ciR
ztiT{HeJWyLqc55nLwwmA-KA>kPdHA{v|HgT0f$yw0xB2Fq_+JnLgGExrIOO&rqWaF
z&zK9!kX>hCLIWNkPJUsRMa*9)oyr4b3bumd@PbdJE&PWwb$%a84HV9<VnDyT=fq)D
z8~a69x~S|fh&<nMA3-<rTx*+8e(#vtFj3Ae*rin#n`NB2VFh}WJ$v}^y~3s;hN39$
z@Q$`Pv#O2tE`9>lmE4*uE|Lwu?iOEZ4(5Yig+GE@S*&BGBU;KNb$NH11%>0xrPYXk
z{*yNV-=2*n=G;Fn=ww?h4-D?kFnQRgGREEWD@(Nn)%-z916!(Bwk*?fd_)^k^Nu6&
zlEsR%@pGP09*UuQUxOk->|Oyhw~Lbdf=;e)@il9>vyO@J81~TO6opsBynw{7EB^ol
z;H%DZaC<k&4z69!{{VxRS|71(N{5pM3QZ*slWBJh^+#&#a@eK-^BM@)5h+Uen=BNU
zYau!dZp}@iuXVUGyIlbG#C)Z%@+i+MSzH0Z2A-8qut9NfF9^5@K(NxiKMXgA6U(>>
zfN&igY(~YJ_8<9Dmaep_O`|vQo6NefRy&|`n0jp#J><B+@18>Ac&JO|jvG+wv&#om
zTY$duGD1}OrDaMtN>8|FCZ<X10E1!K<RLG776>zORkk)$ig`kg;7Y&gDwOWF@mDMM
zRv^fgvgX8w!RDA$O@n+5u-EQY#w*K$ZBH%sn7+we`kzCkMKIv1rH`Zqp?1nAs3B78
z>RI_(Ye2HpgoKVL%LrI5X<yh-i6v}zQ{ZJ_33z|E0FA5Eb}T62WC-J!+}kP^tiW(E
zsozf&$1C?N-hE2=H1viV^Ph3{vTYrTT65*<435c0^E+#@Ac^%p07Q-PppGK4%lh=%
zirCiYFXkFR15*RsDy?2csRU(64PBzI_TXIe8{4KQ3a%Z+Ha}W8I)XazP%7)Pk5gmr
z--y>m^xVaAKLOk@XsY#o7_@M3(#5OXs#>Mo73jh!rr(L6i??<2ElQ)ep_%S3*-8l4
z$Lbd7H05Q~3$zVo#pWlb9L3DgQQUBLB%%Su;)sY3joW#G@Vr2jvR$jGdmRGBSZrz#
z&U9(2rk)`QbQY($<tk_0<68(ys#GgS2tkaxqv|Tr3bce3c2?ANq2apIS15%Ely?vT
zhl3`^0Q3&jIMxqrJus2;M%a%4WK(>_*22@jAOUpwCbi(+XgROgkc*(QnV^N&7e@da
zE<`86#tjazqWPCwoL8dt>v2;pLFinr)Iom(@6#(>LY&p?S%R7=#74joZItJ5MNuA(
zGmjj~=y2Gw!+xXg<vFmp(}G;=kDahZ3qtiNnU1w^Lau<>efQKgys*q{r6`&Zf>2fI
zhZ@63jsrJv0_gf7kTiqXS{&YAbGr!wk0dcqGmQ_PF9KVA+`ic@J@FKhI^1y|0aHn=
zw$GP}hZD_4b3A;1@7>Fw_1r=_fgCSn>zQ>A5|16Nv;3*O$*ZkW&xvKu7pwmO!hl_!
z707FUV-A2kI4t8WNyc)<XB~%7y}5iW`--3|Gu$aJxRxSPMuy7hOXK96OZ^eYy@5>-
zdvc{X<9sT>$_u_ICbIhFcZBcXuMCZ-x6Su_!4(40^>J1ubR)es7^X&6_ED9==u20S
zU?c^d6~_Mn&{TXbunz4YI_>#@q3-7p$KV`&2wO)l2b-Ek<sX?Yhq#>A=$Q@*fkRZ1
z#y`!uYGv5vf|n=kqQaLTwz~B(<CN8fE?-br%TPoZh!s}1^9(qXgwY;R5oBplQAh2}
z*2k1(xo7OAxc>mnajl|RPmjaq9aKepcfK|){=UKsg!)Hf`4`j5azl}WmN(D95XjLH
z-%t<j^VL#?>0863Rhq}^CaaU?#Ti=uBcgDSQII{kKgfTr1AA&61*Ocz?-H*MZBoLZ
zAN)}5vI}|$`U!e+t|_xTK&TgJK;Srw?5q%Zr`))_-*$j~tgDXc@PE`ja(DE~fBl~1
zQ(|r!#&}xvViK<D{{RFIk3Ih2TWKz&z^?icokFlKKLT(TUyNY3ZuLuw@=FSK;GnCi
zHd?_{N3-0rMTHKzvVg2-{{T>%BmFor&v#rY*3J2LP13b`WrPSF3(MSEv>eBU3oC3^
zQ3;I_iH>c{<pEKpyrjQuaY>+SKE^E#VGKQ_>R|}>=t@yy-WR*7B(RvT#*IKZw>EoO
zbSq51I2v%Sym6D1ZULTU3J*Jq!)A?e*xV;$1FhhhUw&mTBYw%pI_G2at56l^xa6oT
zH-I}iCCN9rh=3fNlbxKbyNlDB$3pqMB}3gsHHtRo9h(vFgVv9M`60Z3`jvGPL#zUL
z?qCd8YSbM&va;c9l}A@?Onwa=>{;uJYTBDm8wuJPQS{628&jtjumlOu=nH^;VJx|J
zr*RBSw+R*nRT*~+BhtP?;Hauoo6RQToj~Ho8g1GJ3Cpa#Bcmf~`zeYWCz_zNiqu}%
zYSzNy><AnJx8@d{55!k!n@2R1AZqQx>NA8#9&7Sq8IJhk#v}_vS+9L#PsTbA;D9{*
zGs@kfj5xg4P9==OF3`QyR<AeL5JPGg^#mzen1h6ujMN%0_J|h4p5+l|5dj0Gp_rB5
zn}~eA^lU0txdN^#Ab_V*xR+6P1$=@$WftwlT1Wo?7-F7WtvWnp>>$D#Cr;c5P)DP0
zxu6EPzM>TMNk|$=y0ldVCE|u9R4HEjWzRJA84>J+h*grps{a5uo#!rL`Ws-Ncqkwc
z2`H`<)mT(aI$T>X3wUEoyW7w()Ml6?uYk4@)C9mvZpd~q!+-n6D=BnfcBWscus^(3
z2gPefl8{DEkq8EmCp!<jm2m$6D(3Xsyb$);yldf4Qz2BP#d(Zs@u4oD<2MRjDf3XF
ztceLBY<OGQbL5y1dfhx8d!_<KPk_)|emGbJI!H=XdlH4^$YBQo0v!u&Eku1W7U)X~
z_Fhd{AKaXE(weD(9|#vq^?qfEE@RjK089%)o$(Vsv(C8+0B2McWGE&E4RJyf%Hb?>
zyBVv=8Uq!>6eKi@jc;JU*y4xJxLm9$@-dc;d82z)klMH*&I-{4$RA0tI8{^;To5=g
z@JFGSG0?r?*gmvTK`)3nTe)~1r4;`FYG8<kOSB_vBF(eJ)GvSYaOz~vqcVHk7ZUZw
ztmJq<@}E4C?xn1GKHvbumWG7(At@dI0OkJx;@B_LZmzMHCiLLduj*breHW@afD~SB
z?CJcE&r;~CYqD8X^z$EwbIqY!J}S{36T=;G*&boD8fv%AV<Z;j;k3g-=zns)AyVSy
zf)^~LmV`g@QFn@X?G~VwYJasO9;#@QT&YY&FQXjXc8hR#THmO(I$`J|<4OB6GICK+
z@*7Avf!MWgf~rxw@v4)W%E8(@LMSci{{T}G0hn<{<WDgF0H}hgM7%a1O2;C+{s7DW
z08R}kqTZiz-MAzLfRofttvAmxlJ;>vBH!Z+E|M~!rw7CV7JwdB4vz+Is688sZpu5P
zU<d=%w8b0<uaw;o6QaQ>X}IauO-)(-W`$@Q8lLhgiwfQVxCpN6>5zs3R}{uFwFk&J
z$``a>AR)ys?FPZ7)#%oxU*t_!5#4Yg-CVGo$T)zB%|`4Ma<Y`Fg7d}8Kofq|>c8+Q
zX&z0F?02CLRk4GkBXy)}ZnLBtMRX&PQ>w-ugsG~CbLQHd-M8*juM<82s@l#|r3+eZ
zWjDf9bPN9g>De43swa!$Dziejr_31KWYnd1&zN+)(SR2%zyma)Ek~MQl;eD%X@77b
z13<3hW;2KTL1LS=cS(@2c58ZLZz|^METY}le(EJW{5-<SE%cnlu#DnV;sMZKb};#e
zeXXp$OQ5x&n-*1FDC_Ag(>(fsl$jnir}92MW2~kz>$U=dcPU?I`b9b$d!~crS;1&A
zq#IGD-M^%+M%R9BF7;*zAnLA~6orMk<xmwxIXW0i_o#LsP%slE8b&=#+gyw_bHUf4
zP7nUb<I?qA(;u^MqckIZ;J<4MkLxK;QJ1dLxPsiwic)#Vkd+TlSZ5$?u7@PBeQR70
z4QMRKJ;Y-(@<s>X9{`~9Beu$A2m7L}a7sWL_^o9`WIEZ$&*LpXcZ^A=`jEz=>90ia
z=qtyD03`qv2clVBmN~-S{YxAtQHDWq=B8=%L&AWXtB)~QDXa7>rr<#PXXFO}N7HG{
zvx}?3LS)zn7V#;{-#0PZ@3@-E-Cq>VvKOaD_ZQdry+#|vO|6gs(iAHi-bsH1iL#w3
z@N__`J97LOjaudc4AO8p-=hRsg?dwW5~chLfj|JET|mxA4-Kd{o?>uZ!$;h88!n2z
z@2RK96G^btJNb@`IW9|Yu>cP<DgyFK^}(dE@+_d+9!gkK<@~*ph94D^j+*zd?iE+Z
z5a*rf0dp;J%n@B^_>Al?-NK5d1HS=fZjcJwVTSb3auAulO?Psd@q|L@{*x8TyraLC
z4y{`Hm*dDQN~R^-e+5;sNJMxkcS;)?p+lN#=B%OLsme5vSvVy$Pk>#V#8d1cIG`wp
z2T-J?tD+jX)o+NZl+01}LsZ~g3xy9k-_9}DJYu?WrZ6s0%d>n<vR+R^92`N$5cy}W
zKS(MfAQ4?VIf$q&^;<Vkx3cEZ2Exh&brMR-x0WYB+Z`+>6V^hn<&VC%_7@_)!{nDW
ztrEmGDp~4<5g>5;ZiFqD$Xy*~viMT1`Y|;d!t;{oON|??7n!UzAn3kV%Q(n!QRb^<
z8*Hj8pME8rC4u9NotQHf_P|wq3D2y_{!H^eCv%CgP=Z>UdISu=`?KmY0$*`k^54Zy
zrO$a!N-}hMh1ds@KRyBXFN72gthGIY2vwpEHUu5SD|#RNgM3RIUHX*Q7|LF*SgjTE
zPyLb8$5o}ycr;TBpyj#i*FK#1Xn?5HT6iYukyOAw`MQ7&zj2!n;-sm(tlXCNsCYJ%
z8^Z^RyfJ{FLg@ESG8KKLnw778D0^v!1Rw0JAM7n<C8e*y+(HE=)IK;E2xZXH2-vj)
z5r!~pPc?7r29tfw>CpT^<RK5@F_p-Bj)Tnc#gVL>3tuQs^(&MgLAJs&?ZW>6nj#8M
zRmK2Z7X3app>-cYuo`Q^z0@<xfZl+j8_|&_?lj<?>1ENW5Izx3rC+k6Uw{$@%e*6X
z4IE?7bPbgErEGvTnU7g@nLn1*jSfgZVS{M`PMR(^uh;2=y+cCST;TQGvWpAq_F}K&
z=PN-kad#Xl;IGS%JCI!hiVoJXKoJy9#y2}$@NzZU+1N#aEw_!ASa=?eJLcoSUjT3i
z0Um%ntpV;AJMzqjMhXZ&w7Tuu;I=VM<cy;B>Ob;t542NYdL_RBJ+ph-h}(0%;3{KY
zQNC!x=Kxz?yOj@+_^TEGzzB!TeNqhQ__cNnx$?QVj~TMc;S)jg@^r=<hCVL9+LT{V
zcN8a}!l|M2#L;)SP&#Ju7VDIe&0=^H!unwslpQko*xU2ESl}*F_Bl+yMC89`@EOcg
z_$tItFvg|~Cl?DfZNndiF7M7fFm((Z7kOAga-#OEP-{=v#PY0Fd7FA=PR9gRBJalb
zakE4!1Ud@rDj?S-<lUvO{{V9d*h;DgnjyPIP{u7ia--gl;ud=wAI0?G-2M?oeBwdt
za|(*41Q`YN7#xOjfdVg!hEfI$1TFd$Z%M1e1*CXd+iFRVogaZ6^fz5|VqpdYNa;+8
ztq01x{ewDwp;pBF06L@y1<%+r*J|7^^_XY-f-%4e<vky)!|#|_1t8;gVaIS4W60Pj
z;WAw*)1gxWG<2?|reu>L&X?tczv>3OAmcK%LimQ98N}BqkP-Jz+iRTl376t`q*sFx
zn-`%jjad8%ffAFC5~|E-4?Mz)Le=lM>#F0_zpuQj;TlK4RYrva{fWEX_buDO=2;Zp
zrKPL&P^$3Qw=NrNxouPqd^Hl`JBfJ(66yMuO5v7Vy6Iea9v!TVLRHG@f#ml*Ep>0i
z=833IDQGwRj?vRyfHp8gMz*VK79W#Tw(X|;DtI|+6DJidfh-jgYIp);FV>L7ysTqK
z%FWjD{J~DMI7YXWW#?2p!&RUQnzvn;lO7C!S4}^%h>Jd70fPNsl^23E8V}s>?g%%c
z`CbfnwKUXx<iMZs(EfroHhwMSe6e@b(MalYOWcleA6gKV@MYQXJW~ZOCB@iN8}#t(
z0aa=aq1X&7l?MSXz6Gy>SOyEWbPfw@RW>OaPmZCbYRx1;AGiwFhxEqMMFgRv!gVc8
z<trD%QtH*_U;-xvEyppmwyrEr*}?V2XKj|>w&feKER2U##Y#~2`<1~4f}ZnR-qyyv
z#6`dw5)`6EBcj+nWE=0P=Yf^M?q?Ub=O-SaYAhB6=Bl%}xj@bR*i>*!d}s3Oo(aRg
zfbxjK9GfbnbhUQMoY6nO_%gqwI<BCvs5+UNdsUoo<Wq1L%P*4V(@YR(4HKk}s9A7%
zSaAXDvHt)dM(GUoXpM4b@C(1$LC7W6NF#5K{&@%RZw>2})u*<(zi`t7`JKNcb=6Pz
zK~cAg;_fd64EaC#1FoHF&cB4I=u(in3|ol7rp35Nj@h`@Y)NekBcVnHsT6oz<aB!}
zwPEorUt)>~vQMVqA;y1F0v+Fwq5OoR28b<h=#PG@`5~2@dxGI^^$E5+DlF<CmCVQ<
zCvnB=Jcp(#^Tn?m5arPVdzusIGR;*Ui0ggL92T5-1A=AW?5RanRd>uG3i-0&uCGfB
zUooE;RbsZrKVzgRHUN=8ZokxUdn0;akCho#Dwm^NE+9KRt|GLYzak`DWd4eR`BZlD
znv#Ywp~*uzmh41n@0FW1K8&!~2&8^DH<}yj*i>-=oCgsP$gPc{`F;h;zr`mPAo#eO
z=M|&s;zk9xOas&#6gjwM)Ut$aDCkWt1R%4XuU`;77dC_VBg|dHMZn=i@8V@wb1J5_
z>TQYE#7^HsNHmTT6}>Q~qEbb7V^+x*g(t|eECZ-&(u6MCc30%#>N{vw(?S0LMi3t#
z$U=}<zC+>jF)-Kdq7%=!x9~G1H&ryOOaB1Dvq6>i%l#?5$!eT`NJ?0-bBhAzC<T7V
z$LpvJu<VH1Y>96HeFRX7xFIw{l~~2L%pQRv!>)Nhh}CINW>R{Txd72^O4eVAN+w&q
zsV3q<&f_U}mC?kZQeQK9Tuq=fd@|Qx@SIUJ3K+4X1m%=cjtTM_vWAv$_=-TqEEZhq
zdz}42tVmApf>V)Na_LBUSPJ0xlmH5R_<?PuG;;3W0XldHlyAa7EqZ<kwea7WUZo$j
zZTn1;mhRPGAv_xu4EZI)0l}gO(XC${PJFV@f)^I5@U;j=7(?74iZ7&<$~LN1o@X2w
z5Nvnc@R2bdyi6_I-=J|(-js*H3@xgxf}?xspU8QQ-7P{?5g=?DEn+?ap&XaYdk!jZ
zIXSoX3l3a?d+r+*u{dn0Qm|q4azb#A2&pm}Dp_iai-Dc=iyM6Gw|V|ms5#dGdgPG*
z0LKlX?sG5JH?VJ1JhaBi!caF_9-A&yhdiSI=+LT#7eBxFr>R4K##%Hci(GXC=7ZoF
zUdD06(+RzWnB>Uro7Vt70yU|&6?IB|L1NXv{TM)AFsUx6fuQZ;orhW%4Kk0wl|`gW
z47MSI9k$Vsd_}JB<N;mRxt0Yo>tSlnn|wD_5RaBvE{Lna$Okl%gNv9(T8&vmHENV2
ze6siCz3HF^T%$Pf<-)yDLYLLeHj`_>TE@oiIy$4K2BUjB;4#*|kmawW6m}`ywwQK$
zg=Nnt$J7?1D@2r**t<dX8clWfc!wm6DYNpRF*XM}EHlWE0aKep;Z$G)_n-jaqSQVX
zSP|wG<(v%BtVim@{9~P3%EX28XAEl{F+5Wbi!csb>M_p%+>|uSjfu+on8&Y~VACGu
z*f@7NtRVwBA56$^u3>%3{RwqnYXrKvl=j#&G(_SRZ?>fkzI${}jrpCj;;xq-0&-6o
ztV-J&wxP-QI(O~_q(DywH7Vz*Y3WV*js~oHPLgH>xT24dDK+>q#cRimtUHUlIw@^~
z?4vfW1ihSkul%ZhCTGO#Z*sn+P2!H1RY-{-8_#aQbna2BXznDJ-Ri4+Bp@q(N7O@B
zhMp?p=zk$fV=4f$sS49;hT%XkbX3+p3#9NyT88a!Wd}g6_a>hK1^ObVEs`-cTno(@
zMy1VaeYg0hJ^2h-#4>e9@r2<vx-d)TJi(op;Jh95*QD;nOX$%a+ETg+x7Ekge9(LM
zlyT2~N~K%psQCOZI><D30y5GW1-1765mj41hDT^6%n={F6fyD$q+lverhg@a;-Kf-
z@=Prd40SyKiw9xzD3g}UR4U_7l-*dB1Qeh-RWda@Xue^U1rLB2d>JtF3NRRh+KBQw
zg8YGX25AD{ejn@#ZCCvz0u(R@qASC<`4DIrH}#qyekJq25y~k~nl(nu3%~?qs3}f9
zUvoc^4+Hq`8bjNyF42{*+{U;+Zc@N5fH`l75DFeT@${StKBdQ>?tci?JNieWAyrI>
z7t1_8)QpFEV}XADkPc2Sy%?EDKk^q{57wt5iUF>`=2{=)7awUvd1PNsh!DkA)6Ag3
z+hrnA9vt)>!+Bg_ka;6RM!X`ZT3hX6*D%5re?Ud#MYPn`v+?PCATFOj8iwlzZq=+f
z^qP8OI}&sz($km`ZB%~BJfF;sedyiZ(OLnsc8-l@SrgddDrU=e03(6o-CTLYo{o;k
zRQKT&(fXgm#6XbCUX^c_SJ(;;JA^p7K=7lY<=Xp(h8O!8ce=Hl*tGPXe@S1KQ$Si=
zHz1cudxQ}2q>2=&u9^@$hI6HTRbUUoAhCMn2UrBJBff6`0Ab%wqlUsH_0iWAd`7Z<
zFJ=UIB4v67XWlSS=z@^3UzTQSsCc}8B{%;75MvJwS!IapM#ugC0LGq#tMcULz&F7Y
zQrtP6XT(ABjrWCC517pxl-e%>WS27<R+7QE6*QGew*t9u@BaWREjcKJV95NCn>-45
zOf@)`I^wQ#%X~w%LQSK~7N@vr7NikThS-$6eajIgf;EHK01r?l2CW`gd3Djz_Q76q
z-ft(s8C)(BjI?-qU|xsoXAS5p{{WDn!H~8Bu(wgsKL-#CQ{}ZARiA?8YbygNO8)?4
zHW{`b9cp-ml&u5bi&7AxsIAysC9w%^?M@Q416HWk6JR^n<3ebfoLyL~VaF|l5q0{h
zO}7a`;dSONDG0Iu0180_H@j{H1|6;}FEew=3*C^=O2#`)yjC=P#De=zSMYMjSBim~
z%L5b&_e&+4iIVx%7c9!*UoNCZj*-Uz4+Xz~s+5|5HoGOr;0e!T;wv!qp9*8i&?1-7
z2pAR31A&?K_}wAdUaQ5$!f!74XGhc6H4f)u9mT<|STi3Up-MGeI|#N`VSd`~oJ!uj
zGrwgnYM@pG{1O4N^*jnek3(e(ZeV_*(AiIL9h8=?Xb|0T(U-+xe8kXY!SeZlx^e#i
z$0#->QT&9_`56_`v&C!`0+hrw$X#}7i*m`(r2YGrp9>mKjPDus5IY61E5HOwE>Nc6
za=1;D;I=lm+N{!QRJ}8={A@^LSG_2ha}p63LB{j`T*4|A@WERG{4v}Lp{oz+f(#fu
zqS=01Kal3HG4;gb;&6?Rsa(pK9Q{miBzy?e7&rEktc5vT<%$?L!b}YU*}x!ebhB-U
zv@t<J17v<!G<1~)twYOg`;{OGMsCS}&LX{6qc*k;`D2r8emiHP;3)%r)S>kyxl_dv
zO{2jXK8PV?8;)^6VnJ(7i$h_<0-O(o{{Xi*%!OAKmAn)}Knj6F^0ICx!SBSdw+otI
zGvYKkmrUOy&S$Bsn&~!PW+^T>{jU&?TRjb9)D5T+QsTd|PFREPQ1RFejUy?Zt;F#H
z{Db0cJ(KKZvyP~6WQgBsq3WXA;ZgN7J<Hm%<?)~|4$hj(*<M(>s}3L7Jg^U@>->Xm
zSkfhOfCnwicpA?nFFKr5-tkcc+Xj6qCH=p`3ymp$iZ1h)hwaHn7OLDHgyuTh@ikW$
zX0*54%|v3hVxd!-aq;FG924-oF><HvmYi!5S;%-ne>mtPSMdlTbaJmO;&bkDAMq34
zG30EEQJT|>^2_Fhp&qx9!_H+81hdNBzj2zT_|;ec0GPj+9tETQS#B|pg@c%-oi`yk
zEp{8V@ePkDM^AzxT$y{N3~z64-m;ew1zd+rzQKQ^QXRqL9z;ehhC?1x7Rdz>IINjE
zl+4vq5NS<+0Fde49Swupj6rp4l9`v~L0;vH91|)o7YS=TvYsp`W!<erRZ^;R%dz0~
z8z?*TV*V}=M2!MsL_HTN-uYmQJi>u=!o0ChWDT_e5wT9Yh3N1h>r@7tw{=JnQr4^N
zawQ@F+P(_rF8c@Jg-G*(e|;!5D&DZv&aN1{PHo}M#$GgA`#47YXjU`|m#a*I*k7=m
zk(J$9-{M^(XMSih%F9oKpX93`zB3C<Kv0TNA805Ion5gG_MqvMU*xaR?6Qv;IEu_z
z`>bLsln*hu4W4NW<Pg<tfowNd2?orv&hc1PFrbFxpY~Pe{!=-h)tk??QL}U@prsXo
zCio;JHf=}|!ClXBEtXFet;>;egW(z3TM!OjKe6@{2t)F-0`fhN3ar=}-AlcdVyN`P
zWoSOfWGFU=lu(uHWDPV+oWkfMcVrmNG^%q8to0JLzT1v3onAHpFw`%CdtKX+A_v7T
zAX56dsi${v2Lbd%Ir9fzsl$uOM>C{FO72X#qDnLd6kbK*wi1qr+LhBDi1xDk7Hzzd
zFg)SqcMlK&N?w}qM}`+YVWLjIA9ow;Rwu}lOa%PoFc?-UE`~3-H3C0NOeJRfdu+7Q
z#lBu_jNoVk=rOuOM_F*n2rPo2DdGS&y+5!Q7lM@sj{tQO7S`#+<-uD3Ex|6o1McL<
z3X8Kg1`UOyNn_2_EdxG@q3vaAMm}6&jlQ?yb>PkFrYprWT^6g-4Ui<Q!(SlqR5Y?l
z!Jo=ToUbA!H}tG0%{rCj9I$0C7AZSG#HDOKoE^ddc&?)&2UxMbycpkDem0_R2fTuw
z$C`@Ex$d9#4RyX~27D3sKMm)^3+-eEL}0Y>TmayMlCqABiD8^<H5r+Gv940(^tFA$
ziaMC}=H(8=Zu{yZzf{g(YX^s-_ff}B3kU8mVMbWS8Zpy*TZgrg$QY29iBxf?5NfWU
zfw)#ZO8guD0EaI;PySAh3)rvxJ|)DJfD~P;`Oo|hX&W_*Yv_#Ig}fF*J8)y?M&mGT
z4~7#rGoSqC{{WLJU2_9~rL5n`iE4WXzXCXUsKg1fN{u?Mh0H4Wh`1UH0GySIU-$z=
z_1w8@ab0`KS5Wp&RM15Y*e3ut)vCA5=1EnDU4;zrsz$!SesG|I;%`HQa!RAJ_?3Mt
zaf{u#ECES4j@T(CTU`8%zu_$(3r9rcAwk@KkkE;YpsI8wLO1|7&?5-L_e(@X(^$T;
z31fX*y#D~?P(CVF6#Vs5NOv+pW_pGu%b}|yL*fGqQ)inQ=~^tS7*s7)9O6Tk0j7kb
zvh~v9sDov0_;@9}p=H}8jJ96h(`=S(j%+oitCW9y6BHN4<-vid<cH?mTJ&ltQy<3%
z=)k{H`#6Zn=jAOh$V10rLAn>eZN*OF_wqwqC+cGDn}7=*h>B1bY0M2Kz@)nJ_<;tr
z&n4Uyp%TRE9$HJZIkhxJ7<r+L5p)rNg?0cfhQi<#5pT*2%Krex^~ATOr5Cd91^58E
zhi|t)omZvkmcK0==G3ueCU>5F0Mu@WA9AjvkwO^hZ#fq+hr=RJ8g7*FP*W&V>Zgv?
zdkAY!GH=f_XD6x-2Wx65lLcH>stw9el<`ri-iyfIuq_)*sY?c4n@FJ^?<l;f3*>EZ
zOA2I#@BpC=vzmewq)=r+2evdJg2;e6v5Uf)eoI?<qR-<6L=zsV8%M}RObFJF&p^)B
zO@O<!JkRnqd)WEY`4Qs%n&^Zi9p=_K$zB{~8{_&RIU`?bs1)L$UwmI|C2ZmKbbiYV
zg%<@phT(K`U?i&0J6Vi!e1IkJkq!O8*ssZkLHroxX+fdRp`_&9pr|&st3<NvQ=>H+
z!E<YkhgLZV&>((|fQL~;0Iy&>M*KmDuayaO1-9ujy4)|shTNy6cZRC!_mG4Wl{3>d
z_JiQSU8yVS3DmG2I_ey8*Ad5C_>^$wPHiQn5{z^p>A*C?j>?lSFPtS-y?h@QmC}Mw
z3uPeg`;8Pze0zvj))Nsu%Fs{^oLHkf>g%}E8%eV5)5S`)%Jc#oQ+1(Iq*hut-O&L@
zWraoM*Dq`T058G-<4=MYYiAOP3f#4NO0GxD7Z44amb4BPr?`OS`Z;UFP4|bpMr5I|
zjZdWv9TTXw8c}H?H3ibit_ZY7grbn9&UKub?h*lQYUJJ6DD5AiK@B+Dj@gpmW4|Ye
zTg{V2v5*EXRg`k|MJq^^6wH+zasw(8&{45Zrag}pL%?+ydZ60#KQ7^n-Znn%>InJW
z1qDD0w)Kj6jU5cCT)dkeOMEb!rUzFf<lK(`0A@bGDacRimMVWKVE_h*w3pOoIOhcU
zi7+MX3k!wpk~pyGm9@b#VCXywhK-TP0gfgmeURgYUz8yB#Z!xg{J^pw1lDRF+3Jmq
zTF@u*F2g$Wt7skouX-e34_ax)fJ3^D;r6qGn9NYfb5Z6~LAyz~%wp`0L?SjgVMO+r
zHn-dV06JDsY7c2Z_q=y8VXkTf<(0Q9kkoyorDBVH6%kYA6xa_n+&(}ll08F*UUB8c
z0J-?cnAMDU#keIZ*<q+?b|G{Ze)<)|vQ~3%P_vb;C;lD$L$6R5`2PS^=l)qRnsHd>
zdxQWN3fPtTkL<!Ft8Lt(zw7{%RP49^05a#EC7Sfb&ykpCigf*fSZ7x~{I=Mv?*mEO
zb&k}c+^vE0EMAX=;E$n4)T{c=J$FmWqMORN6>C6{fvMx`)HVXI%_b2R4aip#=9Hiw
z=s-2Vg?i??Z<iiexevugnimxu6qmG>#`%Tl8UD?-#I5cmkl$+jj2!;JKIzO%jlbJ3
z8CfquDX)0{0OW7cYyq<8zb+*>tGEY-RrQ`Q@e%Nq2~^`dQxbWc%vu2e)w;N=+_(+)
zstrLS6NO@uxxWyXh9ItC)-u8KUL~w>CxuChLa7e?RIUU{Y4NZf!XGhQ>0nWY&Uc)e
zYR7|2bwv-J%zWia9UYhER%htW0GbW@@evZfVS76YRs7T=W+ye06-$GT?rjuxp<sA`
zs`sq4je;J6hq<0(*-TnF#%Bdc@&yg97XFduyggN}1ii7S1_ZnnYQeR^i1m|)Yl9xy
zQWs@zzLPL>X;0k3_p*mx`KSuu<hKUm#Hs9A)B=qu8Z1GfwKKxfo3sx80m;+8VH_0?
zRmGBn?Zv@LaEu3wFMOv_@T|2>vq^p~3xy*i7+O%(_M+n+Q~ZwlFRH>;>W3%7r9oZ$
z)uK9@Rm*iLTfUAUyKkS!0}aKYJk!+5Mk4U1#L><<B+#6-3Vz4Id*zD6yAbTatz_Y+
z9%340&1b+!6-z50fnuDecdBb;c|-c$TpEw&Dx(l`Ee2KOJWs!Vr82f(E+(jmDCADz
zE33d#j$g2rj-)Xkx2jy0VWTn0r(MLi(rEE|5A3XPTf?#p(#mW$lyKa5_Ns$>W6&);
zAg&NNJ_6$20-W6!G(}snJ)WV??8DQ=Vbvh(llc`l7zX?%Ms-d|zl3O<L|aF?XG1lk
zu4QwuQ$E)K)z^hNat70Y4k_+f-Bma|U@i%belRvcS15CW1LNWfvdb7b1@UuGTME7i
zC_(UzDmZZpoI8g7#wceH-lKTEw0_Cxda$%iMbAweg>!HlOTVgO%%SW92nu&hUp4;#
zi&WHgA$H-@-%`+_1wCUISp`)6FjAuYLM+7y<^xNv9vLYL7pn}MO{<2)IY6v4gcr@{
zWt!PXZ-^#nS@Iy^BTT<**%>fSW+t>g8DU_lCw<_H14<X>@)p;07VZ4(r&Tw^q+LS&
zc9gE)fJ*m4L0av?dLcjyECEg!(i{?7KuAM-U9)eiR@@01E&L|SEUMdw=|$U`8j1qY
zeCrBWYTV~A#0@t5RBF_<IvBq)rY>yp%3Lc6(bU2YRAbt!Ktdl$(n@NEYwJs}xYL&r
zazD|H>Yz8rkbs9Cm7Qpc>=9CfRfe#${UU#{U4@BWLoS6(eBlLEX#3l6H&xVjUdr$U
z6eXTTnMiJE-=eAbKyMt^wZ_m@9~Y(?klJu*9v{MWR|^E>8E;8(1q-Y)s#Ybx%GtFy
zA?BvLm=(rFsfPCT0rq}pU~R!xC=KVj$-r^UuTc77&3T!*#{xH{rJY9sP0*~$BV0I5
zOdmv`%EH?c(b}riHi%&IcN_N1!uXZ3#hgl2y<Z{rtSzhOB}`Dm#NmCD%`x>o6+`7t
zzBB&-9AL8-#%#wc>n#$5iwBmF3FK_>kljEO^QDHNt5o+F#ORhIeLN6o29!)+0R~T@
z$8L9FW>(kx#=Kx_L<%<YZ_J=L9@LkQFFx2Thu)r35;NG#KO_>u;`L86pu6bt?q<{N
z+EAn0tJ9Vjv@3M5%#Syahpq?&6}M5z!GJsn-)wJ2u{&iXpMZsnR^ymKnB?0{{{Rwl
zqg8BCjF(tyHLA6Ppiz~^5B?R*Kn(|L9xJI*&{O4DnCxXMpnkzXyn2`DRZcPDZ*Kg-
zLWN1h69GSSZdCzPhb?Y0)NK5mOBjvE;L>=tdpe)|6<=Edyf`m*)od~}Aat0cSFQ1o
zZz_UTq)rjFYMx-b5{)m4mLxSji40#&$_rN5L_D=m9K>_j#2}-fyu-w+*<}5=O-OF|
z5$GkYn?o;0KBI;|VqISAl2g(5#G@f4SMDbRUcRm%m#BJWK^MFYVs#$iSzx|}Hhs!m
zQqyDQ&P>;wD?$wBCcLgLL)0*STSY_MMTZvB_EFm0!KdFAM6wlU?6@A~z_j_CBfO3&
zfn;TNyGNQLO|z;2*+3Lve`J@wq_2b+(3a4j1bAhwY9ZEIwMT>D@dO&C&w@Ikv=0De
z)sN70g>K}!b|%WTg!H)X8@i_-x(A;sl?u?WSqIqw=NsVABjc2XAt+TG4-oYQxpS#r
zN4k}G7mP?EkWfCME1lDO7ZCW2r69n>p;HpYHL}f>9w4<BXR%aVeS`+*FdIul;U_T^
z&g?BC!_IXoRXWudh8dPoU$G4n%awWt4=uAFXtVDPh<j?zU5Pp^#pe}STjkbYOACD{
z5Do~tB~qVB#p!D0C5EnO7M0<L_UqSbUqdfS+>TaV%~5$yAZC@|RS?*udl^?8fsIZE
zQ9=M=O^RmR*z?0Fb)^ke*?;6|bo{Q9DTb>R9lT(j>8wIStPfRLQ_=SU*n?Hq<f!q_
z)EWlyk@+3sGh-pdC8E3NhX(}?iC5+Y0JinFImc6?sb5@TPz5D!D`fT-%Y-xoejq;8
z7^j1xCBq>!FqLGf@J|HO0!D1eUC0_@OWnt@-|b{Mo^|I=U;?d}=4gM5=JGLmuNygQ
z^v>J?9z?Ph0@Pc_)J0W}!KiVB^>vY((ExQlGlB`w*5>o#9AqVEr?b>1ua|n1(&|;<
z=23t3<S2+5xV;0xC~^=NR79fEyIL`8QuX2H7N$z@Uo1LuE|!1Dh)J5YP7t-Ej0wxN
zgMQ0TwUf(t!omsLCGZ2?Ko_}qP?RH-Nj#;dS+A8>cr2@dt~7(;XlF!rD(R=oCB}PD
zYBGUUG0sEdfv~pzvR7=y9Pim<Z%Y|pB(Uu?Yg1vZcN-~S4^Cs?QY%uqUee1tm{3#x
zQ8MDlQHdfs1D}8qI=75Ka2p9VaG%2c0(r3LrQe~H+W<GnV3};k6pM!E9zv!Q>f^3e
zybnR#2KNhoSRg4~^O+_FS9N?vaG`QZdDV@A^`t<PX)2f$N~znLmrggL4*5!({{VP}
z^GkVRATRpdJfN0tbvX2`9f>-O)e~;z@hRPHRXYRXAhk6vht12hQnMgi%vSp@6+JNR
zg()&Vqg@q3<zBHg&d&1$*b#G=H9$Urp)QYbw}bLm;}*@v=uS?8<A!|inA9kHl<Q$<
zKJWW*5-PKMDj@*wS6so%oVw3JtZ2dJkSoK#?7w02_&9`JH7POfP^&@WiCk<tsGu9^
za=gYY@!w6HF8sMYgp-O~H&vF*kVk6)`my?=%P+*WT5}&P-ebDDRpj7_cMiV~KQXT7
z!^rddQ3<`J@umvgst4SD%Krc{h*;tO0P-hwE=}^KE|UpdYwul#$Q2)kCB{-Ic3(L<
z;vjAI_oN==*43)zB;f9EGE7C#RD8#aFk1Oe7W$6Pt_<;wQJi(=B~|2Aa8!N7FW-r3
z1;TNc6n%#j_N8ua{Uolx$D%wL3JHP=CP`AKbb>@esc*nTyz##!%ctbe;Taxw{4pNa
zYq1uwza)QPWu7+307_b>w+uBV!8ZjvY?r`rOGQ95_q|LDoTAf|@3vUAghTwvUnLR6
z0FR!|$`W%OjrGNRO#8^JO;J#bqc6XS=V0F2<m+Z;{X|gky(N|s=TJ#_v9qyL_`!I?
zUIkHThP!;=^MEn4(Kl6oScf$;qPwV<jw4n1hoA@m=>%?V)Fh~RfCw$O*${;#?>eY}
zoo;!%M5KIi@Y_RA<z(%EbLIwzl3u7W$AJ@WsfFpZv0U6-LWtcmm)s-0m7KPpODixt
zu1^uhTXboQj0zr+AO4{l0sz|A_%Q_DerBvlz%@6&fTet(#PxULiF(7JJlmBxa=P}x
z{FLs7KW7X|6;a&0VHn)kva4B4Qnh!%gH=27ltLW=<3zy?kk!zUYe2D%J+LizBb2I!
z(X0$2gdS+9EhV?JRGD>u6grfeQ(g<<xkgtt*rq9vxXK2uJ4ET)XtLBzXriY)6jrZ|
zVxT}2_!WWVO9B^tHw@U3^(p*^H6na!5&r-v;!4tvMR%jn&;yDP$AR8quFuoJ4h5%B
zBFrZ@m${FHjdYIOvnjCr6SAKKaS_<7PVBW-DSvaDe`f}obvj}9J%3{x^x1r54Syq$
zy^j!H!d;X(s_ZHfAR_VaC&M7Q;!(lX$|>)0$dfmxhQ~X}Y1J{U<}bDC1>kWxri-;N
ztFGd?APc*rFe?69rECyK9(skIR*njkfbhM_nubslqI;+nzrrea6L1)4l&7e?#nCov
z%P3n_cf<v+;`@#Wmyalw38g}t-O4}*jt1ict8al(ht~XMG>vwqA-18yRWxO`sV!#_
z3UbFrQJ7#G?-w%FE1N_h>4p*<mXQ<(U+ARi$b9e>z)5C0;FS8v3%;9T*Ogt|P1@w6
zXMK@sv%*qYD$wzFL3r9)wknEXnk}#}JVfO3GxYNU#<?#lB~@Z$$Nt=U7kn);sON&u
zBPmrSa4Mr19Y8+JLrNv^0pr|4lVX+u2u+bb<H%*sj2KB>A?=6vc@$C;h{pB#Z#5Jp
zmsLgn#lqQk0^F+wid1Q$b(vrdSTy&1%?_DG6rQC40sFx^zIMjGQq)`&nNfl5u3+S+
zo;<)^3H{g+gz;R&37|kU2L@J4F1!({^GpT2`j%gb*uDrm0`hQrhwOQsdxn*G4o7Z5
z&Glf1h@n+qF1<~qTh_nGqv38GjxwTsG7f1J(OnnM)C#VEx(Jxt=?xDNSk!iP?iGVc
z3RNhmtzrP_MAH(M#J&OqHQ_h#OkrfM0xN17xHQ$cbt^9N<6x?T)#USW;sgSa9}NpN
zs@3^aBK3Xo1OV7u!1MPTRV_6<N})D*e{pQvfsJS)&`Z8KSPpVa?k)+PyHlm-7ZRb|
zD54Uqsy!{#a2q?pc+j+Cq5lA+Vp@y7OX!?j<UBqhumqF%*Om&EO?I7bJ5Z^#Fo6yL
zr^~EpbuRi!jEA)*{K|t(wbk3GDl0yR^>UTsy3B4;R4TtBXKdw+2v~2Lzp=G)+7=?f
zE1fC~AVJ3JWdjhs55YukJL$0g+-)nM<zws1F8qfn8i{zEz6pm1$q2RRRCy><x4rzl
zsN{v}a2&Hgf*qjj<g8D$)n}B*qt>SHr<(eq)_{Ll1Bw+7X{=&`&ba!TH{7gLrVl;7
zU=<h)ScB$>8XjZb7${PcSLRlLd;HwTB~P0M6)I?89`1}$V-Gg2B__+IIm!3TqV0v1
ztDKrN!KA&Yl&78`7$@5(tBqAY-pdU*$;&_bpB_6`MQUp(qs*>-bpaD$Lhvg-7$<>9
zb~;56^#Te>>7qgi6i^-q9H!FTv8Dw|>%NF)3SnzjC1?QqRLw~1ce(JQr`6*;v{&XT
zVZ+Qz1}ML9rdA9`(=P2RtG75Ckpc>csfLA?y&flw!18uKBttCk?aKC|ry2&XTp<A4
z_cm5L>(u=tIqt~tItXbZoZP%2JPj9VqEIb~26(#v0HaYF-eWG-V%v#mBc6m=<hqI0
zE9B{&`VqkZgYO{nugxvkGUkoL15Pl+-6nfwmKvui6FHLTm4)gA?g&j+JD8W@)jZ66
zTVGht1+5Y709>Vo8wekm-wGb1L%9?}Sthze<{;b9C5MV0MvOuMyfs7};5X*6{82nw
z4Y61%G{^CY^mtX;$ZJC<;3y*Cav@q@^rIUMx(w+QpKMW$*l<y-y#m+EcQe%1`;Kh9
zyy=X&RuQo6RGX}Zko7q_&Ill%NrxNmAEZz<*-bDIYMQ0_Bv}5yN#U+ZXFG<G+^YMO
z2wb~i6#xn_dJ}*r@}!|2_MvM<UsCHTc57Kff|;{nc~!Eb@~ZJwYDP!{+&1OJc`W#`
z^$impyQnz5iBvP;q4OzI9@8!(gns*$D-PW^JB@*k`$G`4-vb;wo1~bJN~qNS{soJu
zIH5BjZkJw7MyYOvpl|~zj_~+KEgEZIOcwxafo-;>ddnSxEv$2rCMn8veX{kitrZv&
zv33;O+koNz5y^==g6XdO2(ou4eZ(08rweMOUKqmBY$|zbQH`oC>$FKwNmN|3H8>lW
zUvR87O*)*?ULEjwLCcVdcgWw|28){%owC>jh$9ByfLp;XLM1c{H*?3#gTiVm;7PCE
zJ_seyDs<hynvGhKx9eEoOMTl7s~~b7)^D&l83-txn-$uiOOZ$m<gm@*{8@Ida1+QE
z?Su;88^pFcKE+{}YeP;!Tl|>g-iG|8A;f-;uxQk|YgLp~mzYA9V{-k*z}{qVy1y{c
z=-&N&!B(2Thqf>;{x1v*8e?1px7E|Y{{ZFODq%y)o@MdLk5t2%s%T2QuuuhEnK_2P
z<EhCA?bLbAxLH!R>QE3iuOOhS`4qWq;XLWH<|66GhFr@BMSaAgit&|ceywkbUCMRh
zGZr-p81X}PLi>YiF4q(l7rVX}{{Rm3#^sbH5d`fCM~QgCqkxt(Vgd^|>Mz1zAo-?c
zW+~!4k!vWpWacMPD3*)`x4*>dJ(V~A04jyt(Ecb*QPiGVvbhwFn`5jROQ;R3b#hDS
z7Ov8#nBo;b0w7Car_*S<4RJ1kDlULi<>lyM`zJ^lMQgVf3=0Z*J>yhqubV|1Zu0w;
zcDN~@v$r_$0_uIg@zwQdUge&BsJ92?J<3{IAs$c=Is-Q(E2Yw|oe@>G997bjVYs>{
zQgC+UGV+V}_EGFB)`G4NmBJ0cfC|yv;9dU8hLN+@e#^y=J}<~%K`li<gj#?H{{UcV
zlW3)um&{|L+dUg9I}bf{mnz-{zDTUCLE;nRw_>3>k72^9T6>iEF5-x#Gvkc4IIi`}
zu>-<`x5RH_Yr)k+qiEIUiFjLr)0{a|&+`LA8KtZ%&A~1PN^m+(m=IlGPq8S0(@=b0
z&P8*0$g1AM3$Hg2Zu3-KDsLtWUP4Bh?2n%_D_M*xj|oDG5p8+uRqs9)S5vh+MJj4g
zme!5xwp)@b6I#1M<%?)qMyb2F?h2ofz!Z6ZK`W_V@~ASdX)LHmn&*N=!#=u&pM5is
z4HmmLT7z$rIfi|K<>kb|#~8O3`;cHA_vCQ-yqBH%(70y6);H<I@hP0Y<bgBZq1hjC
z_vxt=!>{-lYFfTH9l(GR(%+Y3w&=Rpm2hg>K3FN^e9;ZS>o*+wShTSLNc$yS-K=+d
zx9K1mE1-??K7X6^V#Iud2gSJ33mgON=>Sm*zN;g=T0dbWqTGSdDgwU&h2AR;Hp8NT
zz`uyFiP34$Ra8m4<a8H;LDz~v%K|<?l<~x%bph}X{zU?7MOIRMODOPD(E*NxDG!9P
zD*9!Jl9@12AOa9X&~Jt-fzRhmc53PNODL3SZdtj@dz{ZG=(4Hms&Ozq?EKW_!w_N$
z`3MU+VY;tSs~c<7^O?*zbw{p9IFN)aTgzya*SHB4g@_!7+)O!snPj-BZZN_%hhxKw
zVETb=Fwt6yx*2DW0l*H3fYHnLs9$iPuOt=J4~SXAAnuao*n)2Dl7(_SRZDc&z`$G%
zYAeNrb}*3T4y7(#81DQ<RABZ+dIBkXOIOIAiGze!31G%;@PKtU@FId)g21;6xwoaR
zgJr^<DWF&YM#vjW#n?s-0`m9pM5_;N0_pU?p2#az-CeQeL>${ZUu8wKC2g?5rac>p
zHQiQJL<=cgjW@w4YQn~vz?9;%UGtR|R_*YLfybzzYmz3EgA-Khr~<6B#%>ul1mPiO
ztM3ZXh9po|AhSa>XWARG%C8_R3<eaNLX0*}oE2eBywNg}vE_wO_C|F^%&$eoR}TrY
zyXc762KNC*_g;`b(dNr>iddlAVMMur8iwjp4Y&XU!u7%vSc|wE;w{+;o7LTI0s<>=
zUg#6(<2sPa9M-ly(BwD@@ftq-L|LRDU$#Zd?p&<UA^!lhwgF8AfoN88-y$ari`07K
zEqI)q!Q9t#xk&&8J9;NPu~!uGlaB+QO1K}iAQI<p4vLh9Y_-+xeZ>P$5H~!=a1N>x
zuZD$rm(s=I(7y6vbz(0iL06N{Z=a~qdo#zV6VQX?VP0K^a0fw6^9gWSTN;Lyixg`B
zj&Ar;b<7j!b3o3C4-&AIqk#Z~&|v@$8WLRrZh!U}(zMsE(Ko?ThoTfU1OlI;Iw&lZ
zS0gwuSgx{B)C#I5DvdvvI`b~mC0g*7>`G0XF+-s8LJu~R-60TE)4klbjVXSMjA*oX
zwm_FC?1uFf@EFYMnpO1;*>F6Kl^%xkg_l!GYK>R3YaK5$3h_Wpw>mn%^~+37Ev*1w
z)-o<N7=coI<+(~tH1l;U$<;+#1~1HhLUTh%B->L84l;RWY@1@>3(~4q+!qDj#62pW
zaSAWXNn~{EE_BX(ixnP|gz*6#h6OIGlV(cJxCT^Yc^)=Tqi8;=`-gjbAW%w4^~JY8
zh<zM_7jzHlA8r9Wv0lN_mTUZR+y%0kH`4qA51n@)nFw0CJb;cUJ8zYCc$E-a9}bc1
zKQyUEgT%JeXza5VrQV-&D^UntcSC+90vR@5bVQWnh+IQg8E)*CO|jv)a_IMQOkDLU
zqFM#>hBC#5zL$n2mB`d=z?r3RuQIoVh1F`siGL{n0G3r^<AUl>ork*KJxd_vN92#^
z^hI0xMM3&+!;o0|exl3R+sk>1gu1+p76BFe3piWj4sIY;?RywGx4&u{KBpzeukeM~
z9bbr)X7Lux#S=<sv7{{xJPE(bf$&;6c1kX>TS|K0BQa34a__0v1)!w47he@-S0mXF
zfQVpxU+%5|zy)i@8}%O$C~&)Mq;Gvoi~OMj)HtxQ8uCWLLW@Co!O0kV1O>*wv>)su
zSdE=ZHKuj%#4MfIsG83bf`8vZ_eT<e4Fa1B?h4Hcs%NM?cw<zu4Vq7~bTGc1asL1!
z{{TY$6%{oj!G0xNN)KVuCEhLTlVNVnnks;c#<?@9oiLmHk=SeI7!hBUQ%$7mCG51y
zix$<j`GfeYUNYC;#i9y|FPL=mD!i~CxpY0DXDnwMEoe$dq}N=ck*$TQKlwEHi}&K-
z{{SWDKR{B;zEbPd={|GZtl~D<+U9xL!OszLYG8FdHp;d>EcuTIdpWjq#JZOuK|Ap-
zAka9m0Dy<2Zy>R{y2;xu2q{%30nJ{JhZQKhgI{SgBX-e85Uc7aRtPVWqAsP9T4OtV
zrqU3nseN-AaBZiRI=HI<<}gt5U0<I}2#X|T-w*&U%YCx%X1vEH#HUQSDuHdUj-?tS
zR*Fp`!6$A>OQW2RGVlVgh}%(bD2VKlBEt1Kpa+rSR1{6s4e;Kelp>2K)=J|f>K+O?
zg5l_avwYrSkb|xLE}_TqYeGy|(EMuif@U+ID(&Hbe<qX2x}5VsyT8b5R~2@l^rM=@
z9+_0_A$8#(&H|I*V{3ryYCzD@vcRXF<BR13>kVP<hl!V>Y%R1^L=SGRz&~Xgy8abO
zMH2QgOQ~HgaIlKnLe-Oa$Cs=soWMhFw(=$DYvP1%cN6jfD%kCU)v~qwPX%PHFt*D|
zTiOK4%LcTM1RlqiO=O`j9}I<%(1wTO*uaw0;^UU;N1c&8qSKRm^+rok{tw--;1m#W
z_`CLjz9359(4bl3DlP1$y^JjSCt+*?cf)hYClIi8m;MlI$UdgPu5C;)(V47HHMz}i
zIlop$hd5M03Rg-eDCu}fQ5FU_1Ebba1LoI!KB85Xv{jXO%24<T)M*Fz#Q7AlgCjcB
zL3pAS?*0!jkWbN3z^WP1G5vXT^DCu`O@X(g4RiogUIWalk|<=J1BkgUT~%!_(;YK(
z{Nkg1ydE`$0F4RpDs^l-@WEWXK<?C(S17{m7Tb4jaOyC-SKOi?oHo9O))LD^yx^8D
z!uns$1y&N9j=A#)o1#4BvHXp5q2&WA3tv0mqo^!WKFVXqD>p?m6C(cr2%N8J&n-oD
ze5(?Qs4(8U9I1MTg6j&SJhO|L%DA|;tuG>9y^7ayc|z!|hk&($U=^G}(!x3`b&lnS
zoAC%>ElX>+lb>2Ofxo#Cn@GQb&J#_|*AS64-Qeh{{OFZYag=|x61IhV&9ptgpSmc~
z+;c1|3<>D@e`21Ds1(BTU(&!?Y?Tj03@P9vTLsFvp|^fmLb!l9#^0DpV8Fi#WVk2i
zgZ}_TF|DUAu6Qc`AU6r|`ORUd^nTfAK*b#*uppq(QMF!BgDSTU;HVP^;W@h6_z~}3
zV&oQ{XseBs-jFVHA+yg+hq#1rYv);RuKMsl`<F~>eMlm85j|RQb=2v-WeU5?jLcn2
zKpscL1u62RWbMC~CJF4^3+2e_TLEv*1tJai<KhCo+Z>`T&4<NUPFBkGq*_}1@&p#w
z_R6C_r$gHp<YU(T%5Igx+tOYQ$`N`~!7({#>evvKG}%CypU9T&RlE6xplzm|eTi`i
z1Pylu1<%!{_9`|3R#V=y;V$rFd0QtGx9>qn_JeEl7Bj)dfAt_~stysNU6Kzv{{Yym
zFOG>DK4B|Wlt(oZu<MIA`e70Va55uY&QQ=tjzl*E7w;k3t#WjWj2shAVmAb|LcA?2
z09W^i0xeUId1_c?A+@Ygh#Sxs)kW+VwSy_+$WdJA%xu<|kTB?IUoIHcO;976IfJo5
zorLQEg=+d5B}6rPKrU8;lWa)c`03CV!vW4?(zQEc()gw|+EDHgrE7?$Z1^J=^-KGt
zj(C5JN_<qq+_CyxuB(<<RU8%rge#YufUIZ1T|nMtWt?1jhQHXsbQzsMK-eyDKGK2#
zUL2ekbER{H`5q(H3ILo^qy^+TYl(GQ9sJ7v9{y{@t{Y}mL9()G=jJ7_IgCT(=WwqL
zcK0DvHjBQ7oUzJrV>p||izeoC0`z?dm=IDpMQ<YntNfmIE2-v;H{7Z5s_(S8$9t1S
z0*TcRwKi5(xRBnBN=Hs&b#q%b0TL$`j*keGpfzZfSxE9=oVlRG!Pdb^OhwL%7OE>g
z(x9LMGfW_A1K_hvp=w_%m3-j}fbXU7OL}<Kw^dn2#c=r|=I=Mm#gaQfvrG@BkLd^7
zEjd|iagv)CfpnJKl89T1FM+neWgDgoZDuL2`ZB*IO08+v<}Qj$5qFCC!2w~v5%=3t
z!Wth0!o12*a(T3<NEBe@j0o?GkSSC!vlM8eC_`Wh{j7kNnv@^l!;E;1<6L|mfavdg
zj;~5jF<ENrnh+g;4t6%k4F#~{izk^*HHikBV&suQC6<UJ=Zg+$a6(tFb_?e+;QCmV
zYz*lvK@{j86Jv}=wLGwHcPJ{-4ME=$!q9YGBG{_KV@Ud8t_b)lqk4kEgUqVD81_rL
z8qU#TKsm49aaC_>+m}cHC(dl3w(IMN2abFDf~B5#Pl!BR{{V?vup8@mPlS|1wDgU&
zYh$p5K)S#!U)DKWYFhlqs-lyoqxQfvd5J&GGH8aY+fG6!$LocebJ``@^m3(mG0Q83
z60YT$u2=IZqIHl-c07QzB(&0eEF|WFHQ+G*u&Drp%VpF84}w6l`MBSs@h&;33h7RM
zu4<Y54+L=j%Kg+7rPdRuM0NTgstU`~o&)<1#dc+ut!s(qwh7CEueo#Gm^M=mE3@!P
z?ju>pO~sZE`tDjXeRCE83c1@DOJ)PKHDFN}fFEq9BLnPdf}fWTY^A7Yk`z7#DnXnj
z7;XKl398op#e2F98+geYUEQ&lGrp|OJp|VR1TiTmx87Z}p?#|=CspVW1HbZy5~D&K
z=_1}$Zz-C^YF-sDtx?#nod&RqiG`i9zv>0(mC8Ial?{jPlS6MFx{2Z^{wG^>a}Cfq
zy7q_7Bm&`f$RY+yD@9kxFjg<3`KUpiI+ws%{MM0n)dy5PzE)Oq94<M2wLvr@HYQWd
zC6p;VxExG!3u1CXd>0>eERg8f!jiyr&A>H}XM{{)s)}QIcj^mmqQ$bo7Inb^T*tLY
zujDG(wtPB6LnsMj(}q|^yj)>EFsxPbWm|*DyZCo4j*X3}yjnzA=N;G|9PrKVi~CFg
zt`ucge{mFUtVXKR{0DKCU7{;^rIKP<;X5`HdcYq>8KdQK&m)se>=~jE(h6DGa73}t
z^4<tqyJ1x{dvilXw<$yUKIlg<_?<s74~7<AU2L+pfV6cO8C^b8Omb?kn(zo3)Q9Yh
z8_V^gr{#s~e(qrkuwA^No8#?S8DUpo0^?mDvFp+?FkG5=1jE9E<!oRFjl;YlqV!4`
zxpTF7dU>c$s`m8|SEXYZr~R;pZCTsLOWi{zhk!1Z0H;ex_i(fcn5{LJ<skEL%a>WX
zp-S4bM4Vf0)@;XGzD+RGVWfYuxY<L%UzD9n3Y+&3s*XNOLTX)!S;XD+W5o`4D4@UD
zSmzSUAiQ!qv6(vqniU0@EZgrQ_0-b^4mC=Jbyf^LL<RTUp)Fz_e2~oW#ko;Kv&DBQ
z7;UoVAQmDt=0>7XaA+^GxTBOthqhOMLO8`N>h~V<l6VwcHuq}ZGdtXh$njO?QiDjf
z?`4T8U@C}KRuR7p2A1FupX@ilsW}%e48(d)Er~rMXFD$lhv#7YL<N4nW(kJb<eIE6
zHW>lNJ-Q{S=Q)Q}VN2rLfN}slps;)mae)_9EIj$LG&)tHEfQ$mfjKkpDs_EBlQf(a
zM9VK~nGf(~U0G$`8KS&4ycQ#AsPYx%I{|Y^z~_sAgDsb;Po@hzRS9+&cJl58U<_6_
zO4nMW8A}u{NXu9^+E^a~MBuDp%+@%*8Gnkzbsb}@a}z#<jkCg53likotCsqU@@Qbx
z0rAf1h}a{FeHduoE%a_9r(r<S>gGLO=B!LHn(Z7}2IErht7ixrJ;-^701BY5sZ9+_
zG6S$A<)G%a3t=jz1C21D?*b}7v_uSr1u-IwUu|kZWGq7QPKa;?uIM^IP}SvrM`777
zgT4?-o|i;e_A`<Poq;Zoqzwo47sx*h*%Onf7?xZHC|>#i{FGbz%}Q9C0Dz&d0*aQ&
zVbz07D_sSb(AZ+3c}rPTKFXEX<e|rJ7OvT=0XRfuaipLrr$6}dxl+t%-zWW;{e@0j
z*hfS47l;&gJ}<a%Le~HsBR~$rq~R1Dhf<oz=S9Io7pdtuUGaWW`AHiZS_+<VWe^Ox
zg@XsVX5hu!;%t?ayEj7c3mbC8o?VvuQVXN~*dta|*JN~x<nY%rya2T-t5&l6lxF10
z9a{~yR@KGeX?f&MQ7pbL-erl;&<Cq=rqEI5x?q`e$EfIWl=it};xH7CjB(g>zldKO
zC|-`TpeWws^8kF0;3|qIL(~mT2A)G+d}4LsShYbB)XLid{=^I^1-dAG1U}&*Leu65
zP}x)Mset14VN+CmNq~$s5dg~u0bheZ1YiYuLYlS`s@?A-Z-d6!E`EjN7{1umb-T=S
z{{YrmYfXr5S5`kvyaDCmBQDfb8W0m6;cl_ntZ1{M5N&k(QVtXSm&U?&aIT<5w;4AF
z)|uw&Q_{{xul1Fj`i~&7TwVRoMuxzd(a`0m{{T26uqZ_-{{XnrZ;|IhyPU<aE6}H~
zr<ihkTWlylQlV)LsJUL7OJ1WkMG&O<l!_24>nPgTwVV_<h?k8IoQGvkeK4rX9C$zA
z5J3G3M#49|n2UACKMctszbaMDV*dc^mEB5W%Zt8C)I=4;*1`%kyHMb2q2d&*dHs|X
zh>E*9i>pjdNwW?~Rc8aW=#J8<eO(7P_L|ff=%dWkV6q(!%ZbW!%{eM<EBZq&nv5@$
z>wVtr6HzGR;!!sl^oT71Q1*CCf|!2EQii7~qDTiL?pUj|lfps>QsS?lkqsRG0CP;P
z(HLh{$6kQ^kh1>(*=oJC605j1L)~7HorxY%gz=J*Mzcgjt#y#=s-aw|V3a327234I
zb9l#Z1JA#S%f_<GEJMXv5AaFonpnM63eSv<lUHe6aW9<YiR`}~AcwYSN4Ds#bq5uA
z!c8ve@hm~Bf!qM!C5LRNwJ(vXn(|*fLm<~e+HgVDezAY~;D~J-Rk#sR9E6S1tVzuR
zxn5HdUVezRaz9rg4m3O<Sq)R%CX(P1-|1@SHqnD`vSv4%`ji5<i@b)BA{@y0Zn>yt
zXXQDY;W!f&rqcPzxW2ocSBZ6Kqh3frLDa#5a`tIY?Nkc-cxWe^fc!<Rf>qnuJ7Ut&
z_W@wGgZx0nfzd;5AuAj5P=6p-oTFT5Wfe5S{Y<KEWhLso^%TQ3RaP}EKouWq5(Ad&
z1~%r+YPzvvJNDEhd$M@@4#8}fkz(A<3G9su+!X3EcpCwDREQ#Lygpl<3Am}R6%Gv4
z<7;#ea*DE=9Th%2G?ab{f~|KsDIH^03gZ^Vzdm0vHCW=ikw3=x#T4>IJE1umf_a58
zW=5*Ar3>;Q1zNoc3DZ`h@0HmZz}z8a(EtN`=k5*p5}=T@AmZV7)-;~~02(+liiHiA
zSGJ{Xy;3`45|*?70C6B*TJ1E6=U;2D`73eF+Vt6E6nua`+46{Rr)&492g}UbMRHOk
zx;WX-Jiy{vSqfc*bwGlqJu8V)(^+Vz9zDl=PjLbV8mNJBp;FsEs4)SmtW@))3WzP?
zh7pj(QS!rT&RE6jX5Zv`EsuZ_O_mzeT5q|{Z``1<OYlYoO1+-p*6(KxPCy{0QgRfL
z{Eo+>zNLH2Psv(aCvIplobcaI{0Ki(Zg*bE&1k*vP+>v<Emf+h)ENHPS6@?{;k+vB
z9)eQaXE-v;cRswFMnvf^wP)24n<(}amUd%&7l-*eZfzTuweDR2zqYfF`v#r*c!W`*
zc^+;fLg>~wK$_L)NI0J?e<Xf@!<`BXZnmXuedYc~bEKK#;a&=YzW`!&Z*aJ}Ji>0z
zu?ADjcHsaNt?T$A16y$(T17Ce0OT$+zToBRpj*&g?WS02TB`0PIU8ZgE17Ip<!)ns
ze0h!%?7x_Yy+<aBP4?^R)^l(r&PVkrwuWn!%oTsu$V4?$;T?ohxLi7mNcpMoz;7qO
zT(v;Zg_+p2Jr*9(ax2Z}GvXBt?6yDYVsO0t1Bg|N{{ZrPLLkJ2a-%5clsC-`m;&iv
zuT#{x<SAw56i9#_n1;eSotChn3T_;(GI-TSB5XsV-G~BwlI929Ih2*{`BJ?UX#SGu
zoFWK%l=(9ewMBZ0Nd}IG7!kaTOEt?^z!P14zu}Nuw$oo^#szgx={fHEl|Z|xIT{#@
z4$-I;q<d<07%RZo<s_@}qwLLHfOF(yy>H|taw^*|#6<83i^rK`0v`?x2P;o3SC4Ee
zgJA4ikNl>I0m0Gt0sxQy0HP!p)B(I<U_MR@kiJX`L7?(U&F_%BOyp7i<H^$d4KV6J
zalC>eo<3rhnHRG5^h*Q*?7^(D&k_Kh5QHwGycl<d;EuRL3*fm>vbV?tsEhg#r|SsI
zD6hF8Tmf#W;RUg+v#YBA03~_$1FMl0ZBfT4<ufORF1$^Gm%6dfb&>D{7$`W{SM>pK
z_`cJkEHBd#{{YQb%;JsfEa;Y91cfABM$0=O;kA0y+50i<G(IH;uIq>Y01HJUGe=~(
zK^ECIuc)-=5%6JthzQl4U0S6$d@d@8?iNF$HG<|Ox&w+7)T)4s(QxCnRrMWJtXhit
ziL}6XYY;&{P(dR}oKBH#rfL5GNxuyI@OV5TO2o}}#noLhx82^Kh61}Zz2G1|Sv#h*
zY$^bDC00XgMpZ?O`~v>~C3iOgq3wpU*+E*OzEUMkXFReYv8w{FYh}VY*6hTMWvwHk
z-VDM&yZ-=o8+>E{vOrD0>#0KTm1>|0gtgm5-Ef!XPHDr~%AA6_hK)ze<ifd>OIPGU
z>3}Q3T%5hB0C9aWQjx9Z832izQqBmf@O3w)B+>u@*w%%Y@^k+HQdFrSEp<{^rix0h
zZ!z5rLiF0WuxiE#{F4qvw6;?5_#_W6h>&ZZBiRk3kq~Rb!0oEmUpF1l(PH*Sgs{ES
zH9KQye6f|V@7;wZ`a&-SPC^>9dtIu4OGqdEDudm`zV!3l9O-2pm=QohjlBY&iJ}R@
z^r==_N8Fplxe>~|hNBjzX|6K21pfdah0(~%0LpWS%Ubj=T!QPBm;V480FC38kE(*I
z>7DtguJZF!gMHzJYSovzu_YDRW#fM54PVqw6|=t*_$J$Atv}R7U)uH-`fshcMO-zI
z98*esL^DVIQS#Vd7ZQ&R=TmwgE@dpW<J{7BE?r$IXI>{J5eFY(z&1+_`3}ODLDggp
zY0B>*hWUK(TLOAT`dG23h5NtMWbJCLRp#Ylny<@FAE<gw&<5d5ivvL&^pgJo$hVeW
zz~yju!uN<8RlZl;xSG#E+w>5=tU{H;@=Bx>@-_s=1Lr7ICicoK_P@DY+EKPzQlN5p
zj9v!Uog(F3yyOSqS?`sY&l9Fm&T{%;n_ZN#O!jo#Vk?5<vo-;suQLfplYasTT1;Qg
zZ>}2oJxdj%o5|dH_#af6AaeFIjq8?pt%oam$##WScwhd~ijR;bq=ubFAw%Suzys}J
z9hpd_a0JEURm!aWMhMt)chG$Vw^OgIFwdy9%38U@ieTN2-cEWAt1uDYcLL*{z9ROz
zk#4jV^(uRgzOs?)2|!<VXqHT&3I#V60g(5gMQ+7g8vg*+6%g|xA2i`ZsNV&DW^AY0
z(l3jtX>GR71EwWA9`u$blE={iE2Xbk%1o+0h{mrO`$5ufB4`U;SeBg%f#$%JSwP1N
zzku^L40#S;a_UsWe@&t#crKxhAP}+|xu-fqtCM!4A*&uon7#6@T07gSg9)$US}(as
zCK|&2+GFBd#6$MP2F(~xjgFRRt#2q^f9d*?H7=|{&3czMw<`}&MW7M&A-l?{^dfOX
ze$ZA)z&64~&BZNZ(^e_lDSi6ov@l^ipw;3lpfK|LAkQd_?xT4WNHSA()TZ(uL6$H5
zm%{QDD)+%qFc|4vH;ew1y|a-IT8Me&jD11eUAy-Jb(}te)T8!cj~rYEU`LBXE(HTj
z5p>ER{1UmNmcjZh6M$M)dkBdt#eERJN~^Y}<x2|ESjBsnqp4Hc%WpOR0P1+5l^VJh
z7vHgf2S@1w@}1oO0LqEA1o_Z>nJQzS>RjmO;#uG9es24lJ3t!Gb9RV&*%VqU46oIU
z;H5IPZ2{_JN?o<yu2(CM66~+=OU|=&xH~@Biqorz=WEto#HY3M=27ZznC<wCWhutn
z3aEmbr<q7SN_%3JKt&m(X?&|JQI-*ag84|hr!j(pwvHX3$Q&nW1u=S5D{gVE@i$1y
z_AKMX*3f<sITO!1_K+w$KNmXUGmOM&7dT!@SUiSN=Yn*8;_z`p3wkgRh33-V{Y5xM
za^sVTSnquvwfhmrV=zkCWNg?{d1rb<$@oW$!cZ`P33n?@Qas&N%iY@V+$R+6&Aa2M
z$kQL@Miq+sDsZaEh4pl`7(&;6+%;_O7VNfAE{7xRiq?YaxtwSm@YQ34Y3iWYA|DOJ
zzgin9oDmjLQ?lrR@_;L9#wlP`S>VIrAacSbxzY@<Tn&TFGPI>@TnVdedQp!Ru@Sc3
z6fr^+&zX<v(STRafJVVo`IO-{w0;;2l00myN94H@!p*DlMk_^<*g5R5Iam<U+7v$E
z_rL*-FbTpaMmnUHz$v}AcBp{Te;5`G0CY1PK8-cE0SY>F-Is0)tpVr<RW<}HRR~yz
z1i<ow1RXC_OjbJ8<zcaWu@?hck68ui(praqMa~(-z2jF9#-Inw>QSjjTQnsv2Qyqk
zp8o*y>w1Y`X;G_3d^nc!3;zI9NxUkPw#)*&{d`1#ilM!4PF!tU&k?F^{{YVrFJt@&
z^->Vs3t+gd7B9pI@i<C9Whkd;ttY$37jKOJ03wH9?H&PeY?PGLUA%XdXqMDgeY&un
zik<@0MgrCK5NgFlX2J1!kq`%(Eb?Lu0PgBPYlsgQ%yHN<hFGbRmSgL5%V)X~?g9hB
z8tZE1I@<i$gG0R4h_zI;y?ptVrJf}a;k--T@Qyu2O}y}PJl*pUa6b}ua~+iMRIi9O
z9?vkkLk3$(Q>RticzaNv4|M?4Pq@wuFDKLndz8>?z6NG4U-aB%d#A|ejG-Cfe7_4K
zb)tYb%NQ|k0h$;`L@tL_!Wc=aK5U6hF8VOPIa2bC`Mt5COdEqyP%j@{!jV_<2)I6A
z)F69Spp3-5!()dR=z=J)p{>2jwUtUNoq}(ff8+rU75IFla=Z}DsrYHAJ&?f<azKNi
zmim*KQg~_Y@~K8Gt<Z7(V<~aV(%C*0D9-QAM{8Xl@rRKaa}R=O1|OnGE=*hX-5iZk
z1-%sXv2c;XI`^fNxOKB>kYqAWuVx*5MkO4{eWeAX`a}qqY9CPg*XHt-cONBJgMd=Y
z${d`?f#M-;y%kplPoZ0iqWqnDiT8bqL9l&93fmr8OwI|EQL&w1d?#V^noneZv7mpp
zQZ7RjmNhby8ApsiVyTLmmK0%f>F!K{{{R4!gKpMZ8n)xB>Yz+q?YQ5nh^C?Hf&#t0
zd2F&?apf&G3FT1@bdsW8mVE)*{6pK3`&_7xIzRsaC0)(W8@+C>R(z(u(^bES1Q7<y
z40<SsZfrPIDumq=`CiF>P5%HW9x`kVxp@~D(YDd)CP2R~S}<zWzD^*Mk9%00)oAo7
z66D~oGM93*+}oDfN@x`a4L!A`X#%aaKNLYVEFsRBDnmn`g}5MSe<Ks*uExb;?)ry2
z5&?VjE1gkc2|(t&)aqqJ3#kt9fT{S6Kr$SOxdNH;fByinDiZTY&lx$Cz^zoJqUApB
zCzGgh!cq-g)sQ5N0QkzPE)<jv{{SMK{RQ0OB97EAbDjuU3aK4<FyOIGB6dM`@L>kV
zP_X^t^oz8iCptJ(rhOQkb*kXIL<3N;yf2BHiQ2KK%cKxT+K3>YO0wzm5QQvyhv!IL
z8$-hsa@ec?0Oja-t1RITAw;-NRn)WE9!QPL<ysb>B(K!8Z;rNSgj|+HJOeJGfEocJ
z!z3pSPJ7wgKOED+KY=pa_fa(qwyFz#S7{4>ao03G$y;m%ED!?tYE(>CyQ-LAyazX5
zbsCZ=mq6wKV8M1YB6NW!V73%Pu&6%*kNjgg!Cskuv(lbwOSV)tQ;=SqBE>X-k+v4d
zIk%uAGtI0LH7cXQtax(}HsG==d0v5N#vD~VFMG9_Vg|kMk}t(@gTP{%Kgt@a#u-<E
zK22T4&b-AT>4r;n*+3xw0NX<j6xq{<N~=IAzSTK2xp@d(8|Id#Z)Ho8An&|rN&@AK
zMyUv`2hc~t1v*Aun-A5E%$N`d1Kep7Qf-bwbOnOO$W_QoVL+>zq6G_Y3;P%%W#1}G
zSt-Ck{Fh_`V@UEZs2YwMc)z%vDdBxW?~)*!v#mTf62LDj<NSau2d-GEXy7S%IC1k(
zA0lOUJYO>^0uR@w22xg`$Ke+tZr2}+eCMfs)!U1Sum%%e*fr3E`T$INsrHkdJj5P&
zjkGCzaW@an<;((9Y1cTGwQk(kWHi<S0BDYk(8LAu#eeZ*r%La8vJ2j@FTz}G>CLM{
zWDL_2@WiAAA1<q4SQ<XmhIhsXPjOo{sqEVwdZG5*PmrY!T1KD%k>~M{?hmw8bnl6D
zd^}f}bB<3>P+Cp9zU65&udUV<wrp*$kNiVWfD4KPyc+Tvh2{zmZ*#Y~@!~7nY*<$?
ziSu3Xm^#gTBB)j?cdbqBVpTM@{YT<~HuJU{qH;UUSrO0`s2i=g0N2ri{g)Bx8l_Z>
zd)!zV&iH5u-A`_6a$a@(_c<N;)xuv+?O!?ekw4W*aDJIaH`TA2gkfplmJG^j;cb^t
z3ACIzCSv1lo+mWFi^58kN~xxx8Eir;`yG>3pC@F^n%I5YU|s7xgpy?c0RFBp@cZgp
zf5?F~T!lPnZF^aZK&O2QjI?|7MQLzLD>DUC*C|nfx^!DC4T8E6LNE%jahKIae$6ZH
zMCPtgVyhG5W%$;R=Sfi6p`ouc7+*03HocDuVx`RaRnC27%bq7;UP%Md;FdVuu0Eyq
zW1_sUJg}-Gu7tEVhhsv!Jb0QW*1>TX9H15vSy>MSqiBlwOe4ArL+hEtVxMfdK<XJe
zv=Lj=B%0mC9(c)X4ZoEbi)*46Lywr--w*`kGWy18EZ2NkcXd6Q#Y3aGY|NprE+NhE
z*>NK&rUSPAvb+IjoJvR3qT>{D=s)ZQq&J}0s~*9vP;5Hph|EL{o;JwUv3?hj^yu*3
zYwauchHXiSmdDC1EAkMUX}b8fC3yLO^BKaaMflBOtWEhg*ed|DZ@ZO#C{x<NIKIM+
z3qsCZgZi>FbM4%TUa*e@BCzN^gg!O#c+b9}D!Kk_JOI<UsEPyR1{`_M{Oi*!pvzaO
zTHK)VPbbfbl|GBDj?So7_3i_G%B-^P4JPpjI&;i>?x;J8AgtV<gvPvaEl&&LF1_zQ
zW%H-r6*~(n_4p)rU5p3NsBQ(n0Zs^J@&kAarA~NT6LG%7D7iIExa&cKb(DUvW3@4#
zqmXox(i@=0mIlQG=+r$;>VA;o1)iIb*b`*IJAy$p6HxeKqE66)L7=;yy9z&pJ5r_r
zPVw?%P^Ee&m1MFPuRdXo6Wj_F5k&-9FE<RLh7HnBXsX@>S-@MS1kY@5mPtomFKgGO
z(HFp#8X@6aT?ggqP&M;trT+i|8+&X%pyOq4%L3<6zMRJ{=KV7Lcn+H!7Pw&$ZiV#e
zJSQq9Way=X%Iu4--Opa}s~8_r?cZ7B!-5oa=ae4wst1QCMRBdwJYV8YkT+YP2w-mt
zP%%RksujEt2epSBn_HLdmDiI1E)R;c$eMcMf-jxSAX@lFCQm(G7<y|1yH@V0XVt5<
zaROSgJC(e^jSy~)G{pTD4TTHh3MIo8hS}kq84epCe=)>YHIZ>81l3Eg*aXV0ixjE}
zjv!4b=9pESU|l-tHqC{=>rZtZ;H;LNrDzbA)Ta;_G=wfN;>^qS1RKND8!5R_J#DpL
z&CX(>zz|yxIzTwgmN0)9wA`|DL%%Vh#$Q<)@S9!P{zcLAGl`>^pRiqGwNOKa!Y=E%
zH%g(^lgKiIpETS=^6ObE0yIDDqrM_XABnOfepVWdK=$l8-!b+L&I|pK$tpHLj12XF
zP0Q3Hq%zS1_P#83#*JBRkSexqtWJ+Yw+RZ{0z7JC@q@$!(Zz|`b^<D^381ylM;rmi
zPx%mH-7Dja@rtI0OZ6<j*;NEAi)O$c_0{K?EtGR{YWBA~us!b~Nte!foLoBU(v)z$
z6Ar*FTnCtzUIWl`DynP)xmv|*;*QKRuF(e9d1SEF^+RZNRlK#fPF2~0gTBp?(H!9O
zEKgDNQyYfacb7*o&kHooKth!>2R9mRak7c-oq{#(#SjqjEeo^xMKLlQy#VsgMo|x<
zYM}w)DGPb<b9WX?Qi~U^4+vn9{{R!p2S}?s3F*`;*D<ZzoZNqK_hPqP<PcKLZN1-e
z)-j{!)OOBWSIY-7Z;VSmh!!{SEUmtXiMD2^>aye*81OoxF<?}^7x=^k;na9x$V9$6
zWsUP+<3Xafrzaw6bQ=80!I3gux-Z#bI7?dJH8O*(d4ktEN98Oy?c{?gLQiBoV1EjI
z8;1v|hg`?Ba<Z?9{33NtL1mOL<E5oj*&XM{{#7oA9m^sSOpSLQ!n`S<Z6_lHK6v}&
zdP$6AF1Ei$=O=30<$_69U`S<e%A&7^2LXmW!Mrv!Q#ZMw9%x4Okz1SMQSCBi6Ku1{
zglpIBa|K-0f4Wd|>)h@_PYfsG97=8)10X7o^J5h1fN3pT!US?^m7if6)O9}=kHVcx
zbs?NTPhlhW4qTxnMqbCG^#tfFr&OpRbfH@|?LHJ8v3~H5l(g?kTZEyaYiVQmaSW!T
z;ieZ**{*y*UX4hl!FfgohZ)%2u3b=k(OE=sP<{?bFWu=<(L3M7qg9F6Jl8LxTgV>E
zq9)sZpj@7(Tu-`>U)WooFPMP`AQ-A@c_IpBakb(DgJ5XAPl(j5a<I5;NLFS98Grn&
zFFo}HbzDlTZarm8uZRtP5@8vTTCA-o!UTO?;t;Q(LoXsg+f;EG&<W99?5t<0XY+9z
zt>|{RgoxJ22@ET4UjqwfTE5(K1J@I$Wi5QPV_i5q)Lga!O2<cK3g*-ehSU=yISks&
zv;vD6RME@C$gYY9$+}MsQwpZPzCeEoK;n)p6MF$Ewz;fNqmp6riMv)Ykc95r1>G>`
zfy-u9_GO&e7A-y{lc?}M5tlmzuR1Y0bSgS8ns~XsB?X|MRF)+f50rRDa5RSs9p|}j
z#gJ>V5@es8FTm8~h?QvQ!dx{M%ftWxFvRDK_bwxX-x3}<xkQ|#rfH7qTe5am>Iv{f
zO3)6a9K7VEHDjtpAsVcLsRhAJ7U(=LbsaTna@XybJDQpa%P?a#jnG_Z7uQmaQaW%i
zbQ{=rz(&xi8v$=WZ7>&|RI;j?%v3Wgi>7M>8`0|Hr&duGUEvv}UobU~(I_t2Q((Us
ze-%pwmDwIBL!|ikgu)f&EctlYYf8Tn)gV9Xd7$9~$wz354;WEL<^|_|b}lthSC+f*
zMXFjI+=`=qD_A$nD!7$FYKi=N=jvcKZ;I#CcCh$s{eeLVDRf*2KS;TC6}~OwCNRUm
z#9AVqlAf-`*nSvSN_lzsE17~R?~L;h0dz#XDi64K3@h9g%6oxZxBmc}_3m*>SjVE{
z>u;mUIHCLNhmz9AyLY{#wS-|oc~yQPOrJb^d<1bie=q|Sp2?TL5fpdN>Y&hyehy`A
z><Di;id)~!+!7L%`r;rdFQ~U`Dgjwlx2Y+6yH@Y^FKd_1r4<AF#T?2(v*IiV3H&al
z3f1Sc?sr;otb)A-24Gxpse_V(3DvwzI``rdp&@eyzd(vub$o@s<EE?NUx<|R;x+#O
zjQ)4Y)9|v8vbhhEa2R>NF*Y<GPHJele>4fTw8$z1dkvY9Jqll_Fv(ZD+W!EHD;5`6
z`C&A5Vwi22x_3z^aC;Cm;KfjJMA?VLQ9uru5GAB&ApS#jMqDR2b|%n@^1$*X?gii$
zYwcivlESrta@^u*cwr2#bDB!}$hAG<3M>TX>JFV%x8?})+dA<E<qtVht2r>ca~y!P
zt{#XzEhvN6GnPGuwpqGwa8Oc;%TGn<gGi8TmE5Vt^}}+BuO1~e965ir;$3gSerTwx
zYP{cb!c0cfi9ZP-;io>72X%d{>R4hQClpkOi}sqXcPhNfu0XGj6&*0AS61K$v9NgO
zaF(P-Z*BDmuHX(_L9he-Z*a1-ys-YUyM&K;%PgaQgc+#LM!sTN#AV^G#-nisA>{0g
z=&$vhu~kuO%CV(}${MzZ#cZa4FNMlYglnt5Q%J^#h#eS;EHV0S1#4YBt<9lN(iI!E
z{{UH**L>l_iFt)O82%E{bN>K=6OhJTDKF{<{{R?7`gGMN7Co-ggfP%?<<8=nk9*`n
zM#6*0Ig+-Z?DPyReD0;_WoHgFQKULS)4-^CD|x#vO)vdnJQg2e*w7T5HsM`^^;ZXe
zVsYv1txq7o$hd|@)5+plpkfCs7*6D=+LDJng^hIsp9k_R4+HNJ50X`tKgrR&FnV7D
zVb`LsGNK_?ITnK4_2zTQ_YtqICZP!b05uCfnP?CXmk~JY;&s<Q`IidDHqR@TN`lXD
z<iTwVk}}_#)SzENiBrQ_;F)V0O0|l($Lddg7Ms(fQk@VWK%ha!(Q=vyBYmAxce>Zg
z#4$_47j&F_vg-Pn@K$`q)r-khk&VK6_9dv|6(GB`^Rlpa`JA^5CT*xq%|2p^jxyH1
zBMNY#ctVkaEq=|GjMwS56r$@Q$x9rdyRHT3xb_XmA~%-GLLTJX8%Tp(stgzuEem)q
zUC&l11^Kc$8QnF2K;=YA-7#F`P7b)*S!NNy^W<e46)4LXM$?T8@dYgq<+nLOkcJH&
zoVY;y4d(}i61X%?t<DgNyLUXFag-OrXlv%9N*wur4~EOg#GG59F9x^)XLjB}T;ECO
zuwyW`hQO_(1UUpx_$iLb7GtArr4s-pl}^eg{wa<p0>XggO8|gE=h}RMW9na$DwD#K
zsN-71guK&vEd!=w8FGnjRtXoI+bWRXu~BFdF{xGR*$*4L2fd$BiFV?_XF#;vF+2yy
z7{}cp_;+Fw+d*`TL(t1OaIkrPrxz59a^`r2pgD4^ZEj4XYVpi1*^cxqh2%!4C>Qa`
zFb*o7t1_Wfp!jW60`h?1*g+tqf#L~`i4UR=ZK4s<yG;&&V_lmTg`L~4$!|OOL@$&f
zEp10w$sIMO_*)v)>U5$o=qAsoT|4qj%rawd@_omH2}a;r%E+~~kA>!3)(wDymdCB&
zTLZv!L-Z9;LH5cbxq1W^{<r#GaPlkxeB32dVK3WX@-g68zH1<-+USV58W+(%Rf3?V
zhF3&wXsdcbKeKQV(}AVt1^Q4|P2ykL*NKCTOg#I8HukFgOT0n;)cwMp1Yvna+U5^_
zKl0DyC7OD8K&T9veo*3A*Jj@=Ih?$GPoL~~k8n^w{{TibIwyz@lZ~#Sq&T3~oqd>-
zC#?(q7@~E5sOstXp2&kGSOS3bRuD#YtLxptyaHSsE**H!zK1b?JCb7SUreXj%5kgx
zjc2Y(8Vf_*A7UtP1WQpMGH=ERM&n)u`hFsZ7XC6mgs$G4OGQ+5RF!$>ZeJCV38XDv
zyhD<dhbbim$QV#|+%?Yxm0{Ghn=xKfi0BZ2Ag94lmFb4(LI9+y0zUabnziGXR<0E_
zOU`QNOh-<^XaJI%zSbgNUlFzB?xGgRM-C4W*QfYmh%Vq42-<KwAU&{|6uh#Tf-6Sw
z<4B6S@{G~BcrXg={MuOYy@;W&R{n0yuS;)2qG8FQb#0?Hlnw^N{35u|h)Uln<k)~5
zx6FTMD|+oi@+#{DacF;#C=pjq%w?hQ1bzVh3^LDn!jxCiU)u}~27S|*i8#9!tiNd(
zxN#VIT}rkQQd+<+o|t#_s>x{Bhq+J^t5@aNhW2-pfKCXffLga=O0m$<K3la0kw9{N
z+t}B!hyW>j<y0l38aq%}P2fJDK&J2G8$ghJ@G45l$SJVHC8BxiYH|ZM(55AS=|IK)
znDC{nCyR7+$)y!GsLR3R^uhvEz&B>t?uE213x{5)14@ubL<@n9IM|!lVF6Am#mXC5
z6;-CA%PSdb^Ab0~>X~51`<aI@$nGMQdGT@cTBUG_wsq0-2X%2YM$ouN2}f*`tNzBI
z3ROt(K)>+63o^d=h2O}59VI8i_O~mjP%or*E&=G{{{Rk8xDNq-auHP5ZHZ2j(r)VW
zZQSq8Os-K&KkJ!<UbdpM+nME^$2Gald=t;v-9ocJ*+sya1!F~6PHGKt8Wg@FxlWoC
z3wGdhqf361IO&G~Zc+$TThV;SukJFe!5awdX`nVBsm)GYi7m7OurbYC-+9Zs&iRJz
z^ML@ssSPbrQ!N5>kKg`hcawzLHS#jp3K~GD_=yyivS6ekDJ{_s2Z&M$LS?2}jP04%
znCJ7#S~lt(W+^OIzeu<QRg#m!6#|IXc$S($7fbUi22(I#x6;Z{3dCK3K2&l-^wP*!
zax9{4uXv*ohq}I_)d_0^G*xSh0|@typF!*?FLw=J1=5}8^|>47$wH*uwDWYPWHawd
ze`qplDhr$BhO-)0kZ#5Zh^QeNwV;FLwO5021{4#bsx%y=rvq8)6LuC9b5Z--+tOvX
zK&u`I0kc)1t;uFaVwwv>wgTP_!9!NBB*E&>J>z)=Tof{azoR~2mA&{tNY3B@n(;UQ
zmHDcS71yDKo411m7LMR>KsU+6nz;VmLK_cCAH?M}i0$$rLjm#s0Eyi0<r6*noc{p+
zLP3vh7yBEyO4tZXLn?B{CtA?%IzH98Ei;B(1TACL%TkPZ=2Ot`!#3b3*uq<nab1VN
z*yn$}3;2HNo^YqDwgXn(lwhd_?Awx#aPgOjZ)?cr+n;+1Q)SnDL$Og>`Nxy!iJq?E
zxEpUgz}KSCxW55}5eY}j%jbn<I#LS-X8ujZ4?cOPAWG@QYvSi2O?m=22n#~Ymm=Db
z(PKEAF+|nbsz&xXHuNrGE$(2rCk2hh3&}nY6)F`#Jk$JC<ny=41(AYUg@gw3au#to
znBO}YD9%;bFg{2C)!GS3LkFv^CySg#L_@s*sk?-6E2e75z%NX%q!rH+$;~gm_OjOp
zL)&nxajI^18>GLoc({AW)C(7W^(A<0<Rrd?4E0GGm~c~K!+0ksstr*h>b?f9yqFSD
z;cie4;0aJ3ELF}O74>-qQC;q9R>U$}Sk56|wXE~Oc|>cKMxj<Aa-Fu(2T)Zijz$(D
z;p4|hMVxRD9hf9iPQRZBqA~vfq~=|5t9qwu!ON*c2;B-hV03}{fc<Fju!{GRqmPH(
zVVLi2<feL$5Q+`~dqJob9GKrtt-$H2K|FUAwP61MlTVY&HB*Cyt?KwaLE%bWven)@
z?x0FT#b?;foE~2>l)yDH`5>l7$2khOrl(fRf-7J@Dp5@dZdiQ5!R5H5LdAlwj&cZd
z=;}>;)b(+oDbap~f7phlaULWNiu@BgJi2P7HejCtsAp8}j!1kH;1rjh{7Uw%@Wsnw
zfE9DayDU*eym1i{s1efC!5I&R07*zbjD%9Pi{En*a0OL#LsB1guvY6CSui!xxLO^#
zO;Mtu!!F08VT(3nq!Q7zc~h6I*!vBfVW45>r~#;o(hILz-&BthyX!9h00`q1Zs$6(
z#p$bx#LfAjlRS~sf@=MQHWDdn){@H#XOW`yiOj#YS%tRlw6ffmpSY#Ml)hzCWywKr
zva)0qPfT0;wzvhbu82+tgoV|Em9@lNQ571qyRV6|9kIP{ZX2>MXR^}TrE8y2fG$PN
z5NJJTDjuA;yMWb5Y^q_ZOjfZJ&f{TM?p~|`T7c-g=TK+`K1WgH$9(e#8_YHis;@L$
zD<Q$Yyb(})60U8<G~kO#ShiYu1`dN&8cZK@!k)NcXD&!NGwWtdrSd^4$_s|@sOzD$
zMdWs;R>l&cn^NHzKMyDx0~Cj~sU~W!D`B$f`dmd2`tl?m@C)^Xy(=#6c-zRJquTh4
zV*LtNIIjJmC8U&1RgsFi;BjRW5?b%Hy-2yuoZS&Gf^(-=2)r?+b!_p=P(O2F1zWe>
zM4HN)sI(5H5~?gMkATV<3)mGnEXJF_vHR;$q$40XwQAHx1t~}xzW8Nt-p`ytU}BwI
zAY>Jzqr`5=W#mS#sY(PPqos@-PCz1*SDZ~_Bv*=dY?df6Z73^c-V){Opj>I2W;8^p
zj`0NiMmDMVj>t8cL4@;@n>V=v<upRNl`EwQ^fDRIY*Tw&69>+MKlT;?DNDRYU3Ycj
z?cGE}jYiqSfacrg01ZCKaFwrgTi53$RB_uv%)d#*8iX~17K4D0GOtL(Xz*a#A;6{V
zV#vmcXM+nXz^nU%Ev6R^SBPHI&#}ZB8;a*UXApS3dmfm)7MX~w9=L`E##7eJfe2}G
z$4K$U;=5JERE$1gj&C+hpk;6YI5>SjAfXSBtjUxm(O@Z@0MT4N&ZUWc!1%o44=ag&
zNl+t;bqKF99+B8qc?4+OdJT^qEOZpswd$tOT`P%j;)#6dD`A4(SM6h!xl;(mwZI*y
z4z;C!wJtL-fc*d_tCeYM3gVY18q@cXLROzEajyw{qq;g_31fe(q3NH4IUx@B3oeN5
zjMedD)Ko`4M-SMU6zE_t%dwJIVW=;H3=|t6mYlqm#7oY%`*A6xrGz;9noU`B+yv7z
zzO&?t{yXAn>uWfvTg2ttMvYgZAT?X7wl(o3k1)moQz~#qmYL~@A8p_UPOH_?=42r>
z)gG1Q`j=^W2Gc4jC=<bq3xjn{d|4|6H+!=#7J_OzAsMZH^(!gyRt=KqeFO~jFgEg!
zw!iWGm-+ty>g-scRjUWl9>e}SQEe6cwBEjmb;Y~*c3&w3-c0*!D}MR2p)9~&T-O8c
zCxj@@s8x?OQP+93{gv^|2sM5urN7|-C!hPuC_*ozT|(TfF9TCooFU|wkSc@W1Zk9)
zx9cV3hNc(YEiF@MuRR8m_luzA?I9(&-lD8a<&9t#KWu2<M<C$4hS57(?D@TkYk|*l
z&Cj`}l7vsom<PTi)?!hZKU;uz91p9xi^y+@7fK=Ul0<imJ>!hHiW=E<0bi@v{g;JC
z8&D|GZjcHCJZ%FKkyX7<4olr9yAmo{yzpfLBWwF8lzoS*l*s!e3WQ$_tk2X&QktuU
zSkwT7sEX1z1ObVAS@<p!tvwA7M{%;Q!_-;`>99p)by0&{QK!!mrOGa!Ln=}meE}~a
zQ%=aGqQzEq4O>O3oRF}1{{Wn`=$RyOhqr%(U=6i%o)nqh!X$7Hs#r=y4=q6EnW}Z_
zCi(?)lLd*6OrSKyBj_0TQkA%VdxSTTUmk8b;H5RCSbB%nl^fHWPnb0VFxRcdDvMn{
za3hbirsP1SF+)|n(ZqK&tn=e2yES~722D_30NHdq^AJL!UzYY&xpvF20oB7Duv<kw
zShaH8PZq{_t%K7uu}_JX?^!<K2p6;TnC)!D;fYgAY+hQ1<rMMiE^9o&A#8g$91!zr
z-hGL24gkONlAKNCiqDCk@Jqn70EOysWz^+}xUC&=5PWJJzcYISedv$a>LCiubYsWV
z0ICJs(-*^q7*TNvd0)|t3Sop>tLw5L+s7W|oqm@!eb+TIG+zcxp$gB*xNrk~)wC?T
zyTl%+Ir#(f1?JQzy{MNBfwe|{HjaU1kgn)7S<*3Z5Yp>ZiqR6%jndn51^KcL{h494
zO02S2cWXG%g@EI|ACQ0&M=Ql&6)7-*_E#j+t>?^D0axR)D&uiid@?K6VXN$45j_sF
z7ZL~s9$^@vAQHJ&{$@$WXl9}c0{;L^sN&SQHzm{#;_{XsGn>EYyaWA}HBd{!X}Qgo
z2MSWYMm9F7RkT2|Cg(;q<$Tlx#c@#)rx=Xg2ykmfp4d%cmBU~|;Yr7($HTl1fgKGP
z7VAUqX6s7o+z@W5W1TJiN>B`iDO(T877mF9x(9(yRM~!FRZb{X`U1R5GD0Wy{c0L*
zZ~YIsR^-jmmPaVr^i##b01N8S{gia)6=X^Nq)Pex68eTw@cV(_V>^dn{XrK+M~7?%
z)rA?frkALDRJWQKaV{V<HIJMM`88A!ur4KO9wjvCM~mW)=ZyyYg`y&Yu!@J~2Qwfy
z&>&`O)VhNSs947_wqX&J4w|!=ix5&N+(fbq{BoHdMX&z=b7dK`Gy5|o&=4Z+{R+;J
z4S9oR#fgnfSO%9n6;kDH2M)Ogg+1D34oKFg_e5o#XETd19{g#__!^Y15{a*sQtcej
zRBUkCi|r3Zvvkls7NFFoXZ~}cndO;;dLVTrd&oI022-m-!e^E>Ps9`}W5dBr=+=vL
zt6-sL-Qpl47E3JbV)-gPMJg&4b2%Wt#s|Cmolp@~=3!_vs0s#fs?=81e4DDC%&E@c
zSL6*!^31(_S2E<_7iuEhY^`M%a+fiP8MP0Q2yYXM+I&TJzM?Ud9x?PHAMIF*5v~@!
zF;H3XzYI3DOFO=0?8d(<@hJ4)iXQI&0BLfSrgQTu+&b!50mX7<Oi$c|E={?uUrH~O
zIn>_kv_uG!;c7}qTwSLCOoCOu1AI!`A@JJCRufe@@#<Vwh!E%MZqVg?s_HA+7}YC+
z0vO(V8o5=>B|#JHL^h&g`Rw*ff{w~#UOY;-=c&V^%Puvy$}fRm8qTrA*h%rZO29dK
z$%kA&dw})Ayvjusjh`{EiiqK=j*g}*dl|TvgK6I5ou<W+Z)0dP99>_S2@8y=d=pwm
zB}5M_d)x^8h~3AFvI|Oncs4}b>#kFS39C)j0dyk1t&Q^s1-NBIYJ55(Mb{?8E!8WZ
za-csQs38PeSz1%!Aa}Y)8sRIS6+A8Y6aAn86=xq9AkE#iRfp)fq6@1149P^-t;M@c
zQ#1#%9#&~@k4&?>59|x@iS(RyjrIaBOaYIWE_Y#t48;<{bL`3mR993odWPiNWuwz5
zZw+`i97xWTW`*{n9azNI(@(?-%^&Eq4y1k<;vpTg!Mh(#U_8yNCh2}HRb0n$hEcnz
zP0jnSFvTji=6@&5I;FHaPCPjQ7>cS{o+UzC$-%|MX<Aw2zXU9*?m8jT$FAj$s-nZ$
zF<IL>iQj2)md?rU=!D)CTe7v6w^h%(mcM+*U%ukqZeJ4)zM{xkKVYF4MU~$<l^aEN
z^VPv|<*dZFunk+!f>ts2F_PKqoQ@f7@MgpF4k>g}oN*4WZ}F@<9>8<hlsC>7K0Fyo
zXlR^P{mi~Y+$w+84n#)pb-E>#gGA=3j9e!G?!h@<GZ2C2FGJGm<47S7CSe#!qQ=+A
z#q#R?l%4fEaC21H&k^tn#ipg$C3IlO{6H;e_~;d%WE>CV!kRBxerhVhKoHE;>QLok
zCYJEomFuRY`i8?+2g%ZIdhseGumV_9qs(+>w;kpM&%7xwhozU9_;Gf%>Jq>h9Xov(
z3wE#qp!~HrWq>;cK^JW;fbA><_AR=7s??Qhn%%iUjV`S#L%LmrTld@ohuWyNFfKSC
za;k}b{K{x1=b*CT@*Wz~F<8cFOMfka1Wobs0L!RS9BU{=da4NssE&ExbI)e^v{Ztu
z%sKmxmbX`N_v0`n$PR7l5vN%!Grr+Kt&h(c`l)*fE|sx_;I?lutGE1<O@3P%EiYkP
z^B)YRtl0d7k2#3Sox=hTypIEsh3IOU!*Q?aS0soDW-?LVH&=m4KpMc%qH~bhREty;
z_X^Uw2L=HtD!XE;UP>wt21ZC$D8lLmGJsElyM|QI401HtJr7Yh*_mkT$l0;*ayX)U
zY8FM=Us+3FjQfm#dcecGbG1t}TlPzu7l)Wy6mJ*ETwNonbQ`p0epiE{<*W<y1#G>A
za4$mjGaWwg0;S~2ZJ?Wf#IBNk2=^XyilsNx1QeI<ehkv3;G7RU;d@{N)p<qTd|byg
zBB-Js#eiQmtZ{of>r&p7J!=pwx08vZ#a!uy1G*g?z|qjw^U+yO&0tH*eO%+vPTCjm
zYxNGX?NO1?{{TrT8E`KMVVYAz0c9nl@++oY3k+DQ_X(L;F1bRle&#DqOL<whL#S^k
z$6sG$BZ)@*lNXb_m26(zcl4Bia<`ymMR<9KnHG8x5i?8iCB3QsDiIW>dm5I;x{r;3
zcItT^6PB?@6G+}BHRvG}tv_~yAyjfm9ns{-Fs3Vn;;n%`rzXm8ct}u={{V{aVSL3<
zvsKXY`iZu~lKYpz1cRv*)N8}&H-K#o9Ck&f&;xmOWgmE|A>wVfg(W88cI@&_ejm(;
zUJR?tbI(%H3W;8-rS7j`S_=>ZO860;O!K&qK_%XhGQc{R7CjZd2t^L1Xz)FwJf^l?
zQnqd)#ZPw13XLp$F(@qs^e6@&*9g5r4bd+D05&802J=S2AQb`^<4T>UZ;JIY$I(so
zYEcNHc}Cds^=$<Wd`9YI#e>@x3Rag~r=5=xsek)P<BS68S-<AaRh2QSlK5BC%sRzk
z@YVjKx&i8Znbn(Ti(1=?fgoZVvA_!J%wg=m1c(J}a^4@cD4K%CcP*Y3Ch_^4jV|Ty
zz@N0ag|j_hSzJ^F-=iL^&cs`u3goL^pWww+!f^p{m7d|r+<Z}wtE_)vhf)z%0Bi14
z_Iu0Pj3ot!)Jq+E6a*{gBLo<7kc4n;`z}SUhxZ6hMOPbVC>K}&+Ean3b(tT*U+SUC
znps1P0ue-W0Cvt+PMDR<^YyZjIYZ|Vk0~FK!c$k`aA({#^3;4<9CjKv6}1(%NA=gZ
zG6Fn)N|seZgTiD%eiE^$Rk#)4@d*S>br3;)T|t)@;!c}i{>%>VxBmbWIMvTv9pzU;
zHsD`}#O(+c)N<9`L7?2k_Y^6QeZ<$3i}FX&rpEXP;d-|1oX)rdXvAMX`O;c7Ys|B3
znOrwhfM47pG&L3LA20ePHwztmA^@bN8|EG^BPIsZtDu}@u~%B-2b>EP9p9!ABlxWe
z&xRYSHY*sr{{Y~WY628_qV6)r95H@q=AiR8O)bKDx%a_m`B7?txI(3@6KPDiLXbh~
zfju%_dWudO<Mk`P+C8pUOgvpyGR5xHG_LpDXg4pb8g>`>Dp(QVQ0A-oIn|p4K3>Ua
zR;x|_0Fc5-+fR)R^-ib2ME?MLv>FlVkfygJva-RVI}Tlow7RJIA&+{krw-^Utyen<
zY+B;!pb4m*R|r}iCrGqCawAuRCz6T}O>1xQlnp3gR8J-9U4;JaX5HCIO+CUoGWJHr
z(5%qA=vhoUQ{(l?ZaB2JiZ9`86fCGO+WiDd2{cm@*2!M<8Mmm4^a)8p7)f`Alu!-Q
zo?XUDCifb*5Sv;e3TKF}BGp<pPyi7fi^L7@PDTcoh3gY;4w2y6G@+x^T4Dh7ME!n|
zO}1epVmUYqsYNbnq^_VOItI-yAhflBYaSUE<m0xbkcT7x0Mi~(WE3$PFmz9GB@Exy
z3gQYdN`47fxu<xsg<Ef!F8FVzWhlhl7Q6&!79c+53CVsHt%u<Y_@Yaui;IIIbN<hw
zn@H(Kgmq+D29K6-XSLIyDh`pLayTpHm#tE9E{|c2F;F6ayio@rioY(QaZWi%)`#3!
zPXOu^A;iSya?E-9m=D|=XTnabjONJ-U1NmG416!XaR(87by)ysb{>iQMJ7~2hxrLm
zcDsmX92~aIvq~&-dv0zhKwD_(EIV{9-TBJW^oy5zLJL-{3a^z31%*;8)!{uLO{BV3
zuKDG2_*!v}H&VOGf7BTE)A6znT1lcPq80F2gT$O`SzbjLaE;#~Xy5`J>jZ{f>_r@5
zgUj|ZxGJf5;$8&Y<;(dBmZS2jIC_nH2$^Q+^?&$$+Wo@-7T~W^QQc$Y_cmiO918IS
zm<V|lp9sh_2?YRodAVo7fmJO@>7=ye$K#nvOdGE6Ac19J?p&5JOT}i(&np>rq6=0I
zNUT*?&xqSXuBx=C8c%LJO?^};B&}DqmG)ZWFCh7-`O3>UL@?Ma`?MvBr~DYnT!2yG
zARTT79pb|l4VA$;Sg$cE`RMkssty49nNsR1nT4qy!)03%{bWjmpBN55tRyI^4g;yB
zMiD&RC3j=QUYTkPuv^ItdM#Km!W!>f@OXq^n^oie*<sQ^JqjM(D;^*6Ff;Zp;~<5A
zzFHRl0A{svx!G0a0X>2x1;NLZ;vi0hYoW#mSU{4dwO=D@ziLzw`wA_N&5mZNVrw3f
zRexFc)L&AO;8~^W>+;+9KykoVhl+rF61gthxIJUVFhgqozErlEJ<W|$?R8r|xTwVz
z-CEs45qf&TA!H#&2!D;ljW!k(e#IrZpEs@IV*TgZQJ04)^>&K!=x>^$7ln%c><|3c
zxkPIC>Iz&y+0-W8gXBp+zT&5QP+S7a9WOU3<5PZEhqxkbR>cEGb;Xj(bXkjIuAgwj
zx@pbsWCBPq_i4&JBg|<<-<PPL-sdB1?Bd~|1|Jn!FYzpXFJRP*S69-((^yxYTmJx&
zJ#tsNnpHHn@2Owka@JQ-n81<jtAx-J7u7|owN>tVAyYtg=DUuu=$)H9?n@`c%<#JK
z#Jnq{{p=wX<v%5k;*>n;0pP6WbH23m;}kmZZmaVyHP^Q%NSoug00Qn04V^*cfGHH;
zRtB`Cii4A0=8wqgh^l~lb2UV3(CpbSisXzI8ztok7U8{(q^r90KDdBXFgA{uCv9FV
zps>b8Ev_9|VOI>5ajJk;Vew9^;Y6!6$R-~5BI}7cf?|%cm4vln{FH@llg)gpc1{BL
z+|)?9wOV;hiXPsgjfHrQg0QebNXH1F*kAHG>j)gKil}5~wYION#oYoGFt%I<*7OxL
zT$LOed}Txw-YssvxTp)$Jr5y>1C(E~3Y@R1g#zIc$j#Hw0rl08M%qPfcs}X^gl^lx
zUj%&XkOI%iAfhUfvYOwhpsW{Bx7f!TGVlh3dtvCIKnit9b78aXg%F~OFP}3-240d&
z(@SQx+ENM&t#a&UyeuoAe=2K+r%{CVxOY^sX8iylt7tyym!a$JFl&%S1mDlR)?={;
zF=n3mCFQicRd`@bPKS}S$Xkfv<l4M5K_r&XvMmD(8n1C}I$MI^>I&STrNZIE;2=BE
zjl5Jp$mUe9S2#5VxqOzT6$N6a$&t)=#KO)apg0IGx^zo!c&%;Jk6(4XuS0NXE!z|y
zX&1p^2Tdf$MPJoKyBaJT9;--->B}<M5CAyUWn^>C`zSRa?H&)(<x<TLv22-k3vNu(
z5E`JFU?mjx>gA0_y~8sFyBo)jBZg%gt0{w6Yfrc~>%i4DHuIY1QOm?uvUSE{fQ`Wx
z%gDQj?4f>7qQ#v{zsiBgN;g$iZ;CcLDR}s2q97`EIoOtR(dqRR>n6M7%(Pxj#@2mY
zvSFs*GW5LT1LlCcZrB92nD#_%hWKvM08d(nCg81$xIh8~H~bX73D>feRZ>P6MV~Y6
z$mzh7dIrsdD1RIO0QC_7C|s?KTIt6)jWrRRtY*Jv#cK`@TiX@B7Ct0KrIbP9R6ZPV
zWe_&M^O&C)eDl$KE?0<^MK1tYt6q>F{A2Pm?O*(3s9aARsAR3M<o^KljV8i?aGps1
z$}-@nGW;<Oy`tRhFLu-#n&}OGm;V5`E8#U`W!yI^S>@mZxwTS&SA!znZ4;AdOj@C#
zDgt;iv5rcw^~Aqi`a%cI#6L?`rRM^!*NA`r06H(hGRsF?a~OkK#U&L)qs##XtieH2
z`FEn`Tk@BDz_#_i1XCERoO!+>XnYO-0If%6h7<wzvbLF`rS%=q3^S-Rcapu8e?}Cu
z!0*&hEjPb+P<bq)%&mgjUMwt>xD`(iUu2i;F1{+B?U#~C<;GO&g0Fpel}cOZHbX@(
z31B#_{*uh9E30gzSTI>Z@LO4B*7Ms8D_}e-6cxy_cp_r1>N9xwP~~9LO3!AG##!7x
zB{bCwo|{Z&2TJ&sK-442RP4j7nDjs5WLBPm(wKZ0PZnb=u%eHOmD`V%0vWJ2dt6in
zf@lt_--x(@hf3Q}9ROO`ux*oG66DpNmQ-*>cqL9?6PW7Enpi{}TT<Q;^WldR`zat>
z=#}Lm{0Bny0Y$sf^L@s*eh4Mm5VW~O^6b1pv)pe6fcasUsHI%9iN|*E;x8pre{+z|
zGHm0W^~A4&q3}}Ah7b$BBbb#&mAwZ9s+AS<A%wEaPjIGRj;A$v5H-EAJHZAO9*_Qg
zS?+b2Ka;L`n*RXL5ytU5fDpJnhGM`@(c_fNqzB+4E*C@H-b!BJ)HikDZab>td0u5?
zCB?KLBE!-k1?M9DPJ`Xz&v6V%4xD5R(tvKqf4I@MLG<`w2smyILcc<C$wWU^xjPwA
z$rW%}g4FUaK47a+c+XoGos9xHKstiJ_oNhcyvUTZVq)vtn+U6gk>cuXHe6GdL}F+Z
zqJUult6Oi2qF{}wal!2#8w<)rT7}DAxU@(1DyKTdEnzNl!CiN7I{58vHbyJKD-Zsx
zBAhNbqCj*hj$+><8sN4LK|!;)6d*#)I-fA8P)sir7P6O%E*W`iE*y=VjREZ-_(R+j
za0O6T+&LWdMxJ_QETV+7)yj$i_#;R4oel_mo7fs63#?YB!6-0Qq9YhY9-we6B38j@
zBmV%Tt*`y)NI*eRdR<51QMFnL*7T1s%>Bx4EL29?Wtv0~FBW~2ln^5+3urBUkniMC
zL^bb80@5i_K6q4nI)jCBZ|qxa*H>?EvWIV-?!(@x`EZIVR+%jvpjJ*25gy4(Fk$6o
zF+<h$50)K{GDdd9XmtA@j=9hrKn27{+N=8&1yeo~36!d*iYb}(>NvuDpsKssdlwy*
zC?bzHQ;fi&2sw@*;&G7&GQEF*m6)d+^l?gxwo}B^bu7Z{FXqymHHML8FQn#`tGT5O
z%J8r~<RS)?53!A87M|_Q2K&NF0_IQ=9A&M(38EE81}e((Ktx|4<-R^4Xfg7HxwBJX
zT)RcyWyW@9%I=-8MudBH+ZG|Lj}hc$sy3tng%0Vl8j26>mK;v5`ir&$8%A{+o4DUB
zQ7X-!D==48RaFx3-r^7!N8)E^);1>l$<uk?DOWO=W5cNQmMfuqCF)gv3!zvW{zfKj
ztoSPjx^O4Z5kgntnrM;p`pbittb`y4tV;L(rF?;RkW%LRf?&o5lma7s%m;NZ1mR7Q
zOPi|V0EpO3MjrN$GWQnk;kV!w(*`fd4<9ppel436y+7hI5|(~79|MzRm9-bp4MBLJ
ze(Ei`4q}gs?45y{DMYdx!i(Q8@(|zAtTZ=QjDJZ_Ft@9LzK5T!fCIGjO%PUHUB_SL
zK|nNhNCsRiCF&pjXWEAQASeQ*S2KZ@)3~p*do25m_AVBs7{>ul&%1;Tr302Dq}s~A
zIN>9uY+C&LirPB9mwIFxD7Jz0#Wn{0CfVc35X!la4w%OAZYcCci-6mLQ=iPXx9U0t
zQr&AMBo`+60bp4uYt7qWeOp0<OFCFXD_Y5+zfb@|))-?9xw*5ne;|VIo_mP{^@#ER
z0I1rinb%32zaEc7r0nVM%wr{L?#0jC!|B(YB7_JVo?(P)t^<qOE9aR*0V`m1v2L40
z&lA8<Q5!9o_%jkVG{OLLr{v~18B{({tA@BNm;@;VPzyLPCo5CUWUr0t3u%<fkfFAx
zDt*BwsBNNO)MmUexU?_!052cdN)8k(0BB2k0e2c;pt%F&iImzsEs8A-z_=!nT)kY$
zs7_C;iGGrb$y8Ps*sy1He{u7C#R3MnIKE@ZB^2hTzwAXW3jpN(Uaz@*#Mi+cu-*h#
z&qFa15m`Qf#+%)SfIX=7Ih412d4xn&C*KOCi0xGQBD4h<qtq|;N;tIy6?+kU(A=tP
zP=H?quQV}x2Z}XREa$wO&84-Z>%Yl&-cC*|BU@ZWYS5yV8$-StViR47LLHB)C5IU|
zD%>m&0m8q596-wEyoD0p0QT7~As5PWh>K7~JhI(&k$GP}V$wZqH&Ve)98tKP990R&
zbB%3}(=>tM(qA&UoN(X=R-1u9>E1c$%f-A40wPO14t(aIR;7=DW9VVh%+M&H3B!_O
z%vBC-HJA`Q$dQw0m5a-WKxBlP(U1prFD-}qX2&kr2GJlFOrq@n0Pzaww|MbdNGoVx
zi=aeXRy;3?KXJ~}gcOJ@5nV-lR~#2r<yUbUU`VEB>Coi8h0e%X5Vff%3N6bN<@tgs
zg|UkkVc-|ca_fT9#n~3X8W9SVsGhPnp3!i`2~W$HYb~VH5=Yw6;yF>aI2Ag{ca*5R
zZ#6YNRG`2d0)y5SwMFqRvR!qSsRqs>iVFa69TSyEkEm>ooVzxo+yh=z`M#KFJv6&~
z%EMa<F4;v*9zqLHR`ODTRTE!Ct=RaXs8?M1octw!v34aRWwM|iHlI}0My`{@9%OsX
zhLe)*W&obC_l|gBLbP!~YyFH^ZrFV6{{TEnfP}Jf*Zv}43yV*6$S^oo{{Ut}HVSK{
z=ES<5PsolQpS;4kD4*EINOF<QQSc;;RR}Rw`4>IIdK#&u$mC<9;TwW^_mG(ck?yl{
z`7pfI-hU-{yOV0o@0bOJyJN8ryAX|V(qRQ!;boIQkkqjNcDXTN)mRLjWmuE{+s66R
zU86h3XmBG0L}D8;7>x)>=mts)2#82L^hPg82?qm4sJ~Gn1|m67kq%KQk%0j^6cF&)
zn|ph4zuA54xWCtRp6B(s!U!r`qTl&S%ckGSJ(3MPrxq6z`Td0;&u{K2D`C-o#Furp
z2VF0UPqfY&m-i0y#gy7!O6h-vTAp#nx_c&?m*@yo4;JXnwL#C-OfSF5FH3PAQaK%)
zi#d{oT7sFo!}O+0?kP;pnp&OxHdyR9y~?q0+;n@Y&DKv=q%g5Z*oDRV=aWzKHxsgX
z^%JY()7ePZG{s`f(09$pw|;zR!MRCo$h?FJ{tnWiRU~Slgc?77uHMvoe9846OVoW1
zKZ(}~TG{?wE6z7xcVBz*i@B%qzrz?8Y`(7$spDYr{D0w<zE+&FNtrL1Lmg%Issggs
z&mwGHDjYxaL{#tW-vnr4XnNm|7bPTvC!4&*OKvUNUiwlz2vLO#6qlUK(TFkDf1f2F
z5K?<wRLAMpiu~`Xi}0R=#D?Tl*xV=Gaq?rO<5FvKn#|~RibW~%`z_N)=d=ZNQ&l`m
zJ^q@%Rn+^*EBzvZyrsWxL=$t+jEUrkSlpbv&ND75G~>2WF~jm=&{ab%$<0};2V}iX
zYJO>B&jgo~-OId4A3CZy#GZp*2@pds@!aTUwR1Bkm<St<7xQZsB+*|<C`9=~Z`mmm
zciv|l9R!Jub9|;EqPMztcD9lmb@>IQzjsMq?S1{Sb8eVA6aCz)ZDdnA?GlaV-`o+U
zT4(+s#oyV&q4i8pnN{RM7pZtHQR>YuaTyk<5#<fP`upmmH$Iz-24P?1o^0{8Sljhg
zKS}hMLC$#g1AH!D)9fe}s%eQo()MW4Um@Qctr-)}cCmnLxK~C_W~(b`2p_mV<L&+`
zHTU85o3QgM2SW~XQAhuGpDi+9^;uY;sz>$C=i0W}|D~I!j>(>5e`l(*p^oz^PY&um
zkaPA^%loXx$@_V=?liv{minzUexG`C^enzyz+u4ayfyD7G|5}XfNO!mTvqsuS+may
zo*+1G7-_$};6Rzk#ea*1jI)uay|0@t`cQtbLsrlw^*StkcdoZ5WX7FM2H9E!eWi~r
zdPLs7%FgdsCFTK+eidGMnUHuTC2u`q>V+7nq`>umQ{kL*mY2R#`<gsEU;3XDCUK@C
z`EC)Fo&WE@SM@*cE0pn~=A@f6S9l!fOW3@mBOcP-wn<!07BoJY=}4d3ArB~3#vc%|
z=K}~PChzW)AMU}Ge1+pIcHg5G2L7&D5dV*|epR`E^sUcLLh<Kdq}TaER!Hv_+ecQ1
zOH#w%eYNXNt(NS|b;G`@<~$8Dmw8FuHM(O6T;&Uh4WGC+u&^A%;A-HjFq%SM=&jPO
zzQ57&q_2n7sB=_=-BV*xx0r&KMKiHWzYZQdS@-b&usVEN>dQZ;z~XexuxyU?wM)y3
zu=?z|2KT8oCyk$1-n?UrT)D9$tz=|VruM0E%XE{b!|mw;O*rxE4gGUh_lKMDy?z!S
zZz;FWb9+#C@}szC(3ZW3kE^aJ#MY~~+2+96VUrEqPTH9LTi<UFh4h48{Le<IIXNhN
zc5sl%ouS>}rLG13UI|-5CRA8e9B`QyuSg```&sbz<I!B<lTn$1*P2LI%h@JV<2!M_
zba{EZH9eWy%+(gT)KAGF{gf$mVMG6lv1Q@ge=O3z7o2XFtYxk8eYO>eeD(>X>95+B
zQW_&ZT;H}l`WhW>mGCHo#aYYtNU@kzRsKnn|7c&i^=0kRczt=PqbqfbF|o@6mkok*
zXli`U6MSEJy>G6CvGYEfI3srl*lgjzEZpyH_Edw#!<W}rW#1y+oH+c_>nJt-6<sry
zam-}ssQc*bzIxqlbg33)3U&WO{?#nL`MGSaA<a_w*A_MS`N^bG!Y?^r=G?>i9!n2J
z>{rOT%@((X?bzf05>kXJq$j}1H65`7A@b@&8x1kmtF_5*UHpf?Ly|si7c5FKSfuVI
zzJ3Yw-sH%)xLYmE!-2{?n|P*|JnUeX@jr)4P5!p8uB19m5J}lR=-*G=K8FQBmx8ac
zCRg(DQ+U~RKaaJB<SJ>*qSEKR7`Qv^G8=Vn?*@cgYPUSn%>`c>Q|$}*cI2LAOmyai
zz+6_l3Kz<sxL^Lqayreg32!XI(E_fX)9O0<-e|6(yFD$u{`Vv)XJOszkIamxpsx<n
zI>Vi}{L)3_dgJ}_*8>t8PsEYlowZxPubCGXQ!ACfNgL`%K8oEvS;5#mGk<83PwX8&
z-=XZ0RVQ$t&yyv#eQo*nf7ZX$jxWAxk22Q^uKw|H=*GEkwhvLQ9@{#PVXHf5Jc_r@
z5NtT7?W*N~opYU4Xb5%a?9Ke<>8ru54%AoZv5>IsGQa7(i9}!C{j5uuQy@2;41_e=
z;g{$CH+<y%%CIZhQ0FD9zw{Gd=Nn_0-ZBj-1!cA4?iaX}w;K2uBKeH?w}#ASwe-V3
zmair$Z#Z->)9aQV2+liSbMvb4g!e?7k+TC6n|opTJ-3X4lPCPRtX8q4_-`1UxEob<
z@$ZF0d>)E|>i79SKm7BW+pnviz4tOC?Efg!KYvJzJc$gdKbTl_WP8^85ZY4$U8HVa
zlDhh__)SH}w<@mC?d%ru#RbUmPV}!-7L+2YOhPF9P9c*|I*iZXYHYjV`OHAsa{&_r
zb`h=^d^q9c$YEQY$9wOfJ$xfr;)U!H(Jgi{+W$?|H`g_*go|ls_06ePSK$2lANfo&
zJ!X2nlwg0@@jD%tnm%TQFcbLa+8ZKxg>P|5i!@Q38ggiRVAsTjlhSaG%cYK~VcLCv
zk|%D9V;W8VNc?y8$xxgA&|T}BW~^2}9Hs6ktc#_pj9{*V$9Q?mLi$`Ut{s|s3^|sa
z_K53tZ@27oEBj8C7a&4XEb>aa>$c7b?hXD1B$|%f07oqk{wKAd`a!epJzOU8>HK#3
zNWsZDpSz6{eCbLdb$TmQQ+At&xcm$5Rs(L))aMx@LMFex*d5Myhu>NEJs6>+7_l15
z#2GrqXhtY(ye6mYcu7lWa#_H`E8MGf$|H(zxGp@D+*Fep1?~#RuhaKMTEm`CjVV~X
zIg5QP{NE5XI_*y>NUHAClm5#DKF+YyJcv;;)6v`ucuPzYUo@?DTx|G$L%!>1lQlxF
zf248wq*MH>G7C{}^e5nIWcfDMeXX(~SA6IE#I4dr|E}bwtNLU<<p8(D^SzwnYOxx5
zcTuvK<m}27QA<;;vMST%@mV%+?Yl7&p~Ih&!RK!KxP-H^qWZ_S<=9h7%~DAZ0x1&t
zgdsF|<}KhK%e(Y?m$0h}&F3VKnlAl$u|08GR=J}ZIsI3?GOU$nDL^HAa;3ySsXXY9
z*oe~Uf4w__UDc|M7s{iJOIg?7ahSNXVfoe7l1lp(OR~IuPXP7@ys<01e@7QC{uTPm
z@<sSRo5sKjNfpJ<O2~y|Xtv?$bf)_&v%ZY~SrPwmTHQbNY5Np9t$HRhFb9w@A7}9=
zj2%g7+upn0uR^}`T%1!ui_`0uMnr_<ShrA??d4{+@#dW=dpmw;ZrA@<hol5kjJR8d
z#Dzhq#jBcMrXvu1S*#^56GvXOZn^CqmDHt7{_lcj!`Sw?w;hwt_WrwtklPhnzD6&n
z)v6-Epaj*TLlx?CJFm&}^U+!KmSbDp>p~_9?=KPOabe~g{6+Z|zm?WDKSbzd=}iHX
zM}AMzn3}`bTr&2Kq<wO}isqwJl)&Gzko&6NxCbq-*+rHTzZ>qciHUw;+?V1@3W<_%
zlmyE6b*cU&9q?76pJln-pupB86zka@cUjoq^UnWnn0N6NU1GfBd26GQVq7rC=QB|g
znVjD`wxke)0#<qQOfpDDRy=k+UOU;H${Mg{o&B0ss@T{-ho;B`8ud+{dh2ic{n};%
zw%Ptu`JY{exnHZsv*P7}-Z$-cuKEZ3V_7HJf7Fjq!@cXJo6I_$j_dC^DzAyQdjB6s
z5yw#$+wA?<SA>XmJM|aBF5bfkk6!><G5iu5M%`e=w(6+@gxk?Ze>kyYHIm!vlCSG0
z&ua(I2z3OqQqDKxTD5u}|C|_@Wld0id|)d2UXo+rPN7(U<$MEMzou&jBefy>h}))V
z=eR!Y-w=zA2n#DK+y9ss=9T%u{Q5r;WmaLZwWyNYH5JG?Xbf9AV89ATqW)WE;r<`%
z|Ndvm;%MlrmXszc3PXr7*LqC#I2>|&(xMpneDR>gV;iw2Gh)+vc1f|QwkCvLowBvc
zSkoxXvRg=^Z)7%lS56pF9xvndfjh(}(U37A=A)({VqeZR&NZX~f84X6w01)~<OG}G
zC{?;dbXUps)6<E%S~r51aT3Jk!)#r!y0O{N&fXn$*<Vi6VlSVn(o5^FR;e^OhPzJB
zo>0B?cW2hCl+}Ucpfd0AnaYcj`ysdUpCX;!_9MIWgmP)4Jnojo{AFyz7A6)(e-&v`
zo9nMHlQnO=D(TZW?4hNjo@>vDPu0wOVpS3+)6ao6qhjh>GwjP}q=q72<R2Sr<f}}b
z)2lR<Gy*~1D?6Gy32%<i?q+P5tMTL?*D6m?6bykM6cyo%Ob54wURzl7Y0gABu2=Tg
zlr3B|JKxE#Qs=VN%n4sszlNWR4J;&U_S?JO*MS~7h<1O(Ea-@R79w)u@yS_vZzLFK
z@gCBJ7KKB6uO~J&O0`lczA^iD4HAii3J#utWlO@|nO{0WI@OMF!e?wgTVGWlf^_5B
zPiw-+q~X&t!0wa9`<ZJ;3XUZn;80}(4<IBGP#}6ga>wy7^+l~<Viw6~9>{pg>4}$(
zjdO$T7B?BTM<Q14qM|24I)*9-235Fd&LZpR%|fi}(ZD{h?{kN+15b?}bq;y8TJW?N
z$f@v>)Qbrf{$S#S&WNJR?tr7p>X}8I7(Q3ch~gqEt|j)mPU3Z__cDCAa4%A96}w%I
zM9BoH4^&WMZy2hZ(0y&4A>d6_;EY)@b}vm<6>;rQ3sHlX&~pTIZhWm1SuLTa)#!;x
zVV=l<19fapG>gss4Ac)-bM6n1W)3c<qZX;d;4g-DUmOnD&d3tl77Da;ZBLmC)~nH-
z4Hz&)UNsZsw!O|V4kezCa|EM(>&BK>>-Ht(Ym2TTy0W=97V3*?f@bC5!ajob!gH>T
zuFEHahfw=4(G1)5ZjB@BxxZSU0WIic*{G~#f5|Px_vaSKs=WY_s7a#0V}ukL_p23J
zAzO`1vcOtq8yGGiZ)G_K!3bY8?wEAMRi~I0AAm2Z9CaS0+F0Um!O9OUbS)Hhm@I~|
z=FlSePiUZC$GJe<)lbdCWyc^&+OdT|59;f9(T`WOs+r-gtTOTK6%6JgG$Gh0G%j!6
za*@_Fsgo2EN&zDnXXH{I0`m`@%5nD)R;G=|FZ_pUMc#kh7?IM1b{K2ZCB@O#VpMJh
zb~kcBgIt=4zB{I?hhWY0ai!>L0k@Fw+>8-2EluXw$Q%W@)R-sv-?b<%CgtA^6*xw-
z))RaL$7}MAX6_wXfVJ=;Q2*s!%Q?UacSx%yBq-}^*Cu`6Ns((qf2KZ+b1L^sW;FH=
zk7N3boE5=7jmBeaGOhj-T}*SZ;E68=^(KtO3LlIFFD*zlyg|>LRxpZpV~rO}82vIi
zYBvnuhw_mVebk1FfaFAYDSOd11^Mje7CESxnH&ggI4h)`DPbxa<Tf!Qaz41y(zZXZ
z1N6F+{0w}%c@=-}V)U4p=>2UBq9a%n;vAo&t=DNtgz3-7cxjW?-!%rEmxqjQs+I9#
zZ`MQ5Cz!It+yV4oymFx?_2mn-qOatF>ppBvS^EyO(Y+uFZJ*}@uLrJaDGnV<8+goB
zMMdGyyn=~dhDF~D={U)DWPe-4h{;IRBOx0+^18g2F?kE`^vx$M2<J2UPyyf1hA}bz
zd>M(i>#&GBL5LOYmiW~qMXT6^;O(=ViZ$HW$H5Pbuh`XyrY||kWyJKU&IVBshN*5^
zN|$0_UkNG;Hc<aSLJ^;RSR=MMWA%IX3^C_3A@KA9MES(ed#!(|%!zz|L`-n0OeCbD
zDQCukOSp&D*ti%><6*#vHbNLD3dBFOjuBThpxIj_ZAzoryV>~_4SUyEa&6}xst@Jk
zo*t*Yx~HJy8MNuT8l`baS~r|yzK@<;eh8Z<0ztG|Vq6#_my-L4NUkIxVP!w|G+IDG
zPPX6UMr{t&{8dzj@JOflhAr9){*1oVtbT$XPoNZ_bvj?Ok=?OXS3@R}Z(6xVV@G+n
z-9eYYI^G_)rGtJ3I2SHvjNh!@s~mcwEIfyESJP-dzV39a=AI^P@00j$-vay!g>9I%
zD8@XZ1@Rx!1d$ap=s47hwBa|^7O9q?A^MR_Bb{Cfon~eM)lVWq6ECP>q7AAl|FL9d
zC5Itn+)bE_ieqyzHU0r$b>89_LEH-lbFK~*Tq&q@aI+_n-NdYVO+*UX+mqn?1*HR^
zDPFD#K(uiIQyk(Bg$Ba=Tr!+1nDD;oNz-PYYH`587u4%loAJ}0V#9gq&;*M-tji+@
zuq-3g5L12C`%>{?eGo-$mpimblc!PxeF>YN%l}rwGk8NH&=7i1x!PC6Q?&Srmjso^
z<8(&c^k}uk;%jt-)C-aZB!Uq(ek84N5wcA!4br*%o6r{(zh;>F<u(_V|7?*QV|~7b
zVv;R_j&XGRD|sNBMUFJQA1H09;c3uY8&;&6@2$>XCzQ|py|S`+mykSVh>(3#V)0RL
z)~J}x2&Do7AemlAhI!@*)_~n}NYWr3t=Fjm^7PaPsPFux^p%_X`5RrJUc+~F`^YFP
z4srBIkXLE=K*S(ONT%#H5eV#rImr)8&D(mPy_WScGzppb$71O$%6!zqgcHFuwihZx
zSLE`pb$CB4rlXk;?2@!;x>dhQv(DR9RN%0{(drcItfsPwt@p6tm@d&}JHcgG%X~I6
z=?uHq<H=h&YcMD~TpQLMljP$kQF#PQQ(dngUlV$ZKQ$R+3NCLAmdrL!a7Vh_p1>S(
zF8nZ0ta>lU-4xnU?gK>GxalU0C&OIxkCk0)+ueRLfocHlA-g8siRI{_iU*K2><JZ+
z1zw+iC^lEVk3Uv-eJ$&3X6^wQmH^RYBYMrLnj;nKp<%-1G71-oHX=vDJGl3<U}jvz
zhi5O$xF8>7SQ$J(sGx?eI<(Ub^0nzBR<nu9gEE;Ms>2U;?MbGxIBWX}<JA`g*bS~}
zu_d}e5Ev;B5(~|nD|;*K`riH@i|2Vwz~Gc#UY}Vb91J#*QQ8fzzv4k3+*JQLOuRhm
zX=-!EcipSch095x1-y*N*9fYV>V-6OI>aE$dUb-*a!~Z<c0aI3gpW>3fitI*wPwQr
zM0&^(5cf>(`2~p=ZUrJLkJ-NO#{_kF>>&g%3x5l!Zw`<L;bGf-fvb^FP+Wieou{*8
z@6BSw8qwig$dTb_6sAoyiYpWMXh{PBo^>mI2=FskE82%f*^t*lZ#fI%*vD!saKkbl
ztT%HIT@`c4>b3WboY$4EI=h+U$>K$6>}peXBBNZH;qK}QsOhqx={QX3-wTDcQq8L0
zsUhPPCy=uiY*CQMe$qX_k%wg)^=11nr!y{JFC*TfBEEHVkZ>Irx07;r;}ogCNc*RL
zL+b|sr{1MPeJ<c7#(p(8kR~lBLJZThkL0cd4;$0wwddn5!$>V2oFeG&jd&WKojep0
zR&w+ZG$~@7Fw&mqn^h&vH+2QpfqiboDl<)md<4Eb%vvpbZZ?{6_*A8<Q810&IGWb9
zS*3wYcYPv!A;?f8Hm9C*9jCd2;%DOOX4N(FY~#5H-opaReTS9nx(9?DRkf91x&nv-
zt6m0?amQF)KhxDBFK1u24E6M3N%!|QZ}+=6!%1wRfu)gZQia8()^inTBIASHko)~6
ztdlBs2^hxh2$)J0$@8+||K)6<jlfz*+J92|;VjMpnbt~ziq0e?njY54C@LXfVq!*;
zGoO#ue}WP(_f<4(F4r%KT5Yw(gG_o}(-{Yl2q)hQFuGmt=EMl9VAk66Mxm;>Ao2lX
zP~r@hLW&vt$D(c%$s?eGWn@Th;*?v>MH`NL^e6BVj%09)EV*dU@upO9!}|1wnr_-&
zWHYx@Cx7|)$h;>e-FsoQ?6%AC4tIe+{-UQKb6Q<cbzpSxPxG5tE_KSiIQJ1`WZXE4
z<F6=nq9Koii|C=VVHQnpj~W%!!0kgc!p!w-grpd^-lV<iHSzRI#k9!J*Ql|%k0EwQ
zHoORT{jY|19%9|8$4F@nl)F|4P5o|_>`iukn)aGP#UMHoI|%XAGXSUvDzDFi&u>+w
z_pMpr<C%7_a<7{5=VNI2p8E0hX{GgbHfr4MyL&fsv|=LmT<yQr$$&?E?8{#PWEeUB
zShlJH#UB#5Pu}4*r7b_(y1y6aRnX<Jo07hT;P(*r471dTH3_vU=Wf7635`{UMXXla
z-@o17z5$@t3P3?d=aTUtU+LrPZrl#N=3Xj+L%GZ$A&rTLlP@<>ell4ON1^@e=vDe4
zpb2vk?OC*n4ykhBcmsnZ2k<qC;|O32aKw@2ncNdCyC@ESIb`uojO&BWXEG1eY~2&{
z(|0TA@X$EMgSg?i7HxkOR|+A|=149G6Otzz#-)=!ERS@XSJU}nOX!+Veh-%BC|9ks
zRmPTb2!W_(UDV*h9yOh&8s=nimDiJI%k(4!+uEU}YBWxDO%)d6C#q&OeM71P0SuZI
zMAyg9Y&sI8FzJ-e`s!=aLCRP;itL6MK)0lf_{`U4eRZ<KY}i0u?~YA_`hb7MP^FQf
z_H{pBPZCxGkmI2;Dq_F)({Sj4sumixp;&(l8zbZ3L!hWmt0QW^Ei_BcWJK%Kcgr{y
zc(sSVmVSCk%(<%j;mdm4zjoP>Yre9@93pI^dlUYW$c7=r&j7|aFVT~ivx+2oe>6)W
z@BTz1`MgSRuxZc~{c}gecOp!t+LiJsUt>ZmU3W6WF1N%_jm`TQy4RJ21SbgP?mOGv
zeA;|Ng_A~flN#o3U`;yjP|#blTq~#)RVp}*YS4+4Havpm<_W)5Zlj)M9k4yn26(^l
zUXBQR%R?!4u-TP?eebi!))_%xR4~GqX3vaxNNU>^D^H}?A_kgMD@}t$LSj_&vfH+a
z>t-US_i2SmhReu1#u!3wl83w++zIPqcdWhf7m$7da&th_D@HM$peBGN4TXKBf983W
zhSS86t+_)}UGj?R6qokLH41Y7v9J$~qcC*BFfm{3*l5bu{>UMG4?V*8{(IScdVB64
z%W!)z!b@FO8a39Ew0G$r3)iOfXHC`I3ObH0JU1J1wX8<CQi11HHuD~8_qyJ7&NTD}
zUu2rtP>f=|(iQ*tiwkZ+XNa|oqvV2y!OYIgwZe<39H)(YG!OCc5p}j$42jt{l<eP}
zI;qcUT~h22g{E5*HDUv^n+&%f6<7!GQQ{~bY&h#}f=sWdOpc>DV6R(?!|}ICx;r_!
z{m9s<MxJ-7c=b*y)<TmJ>SSLk802iO1^~YN2?*fcNeV3HngYMaZ$$QV=Ra2unFPY8
z*wX+RZ3cO>&RWb^`QTY?2`0N+7e`LMhhrdwG|3jA=(IxL1!w)niudTkD>Jt^Hu7h#
zc<GTCIFB@Vs$;0Y7~Z=@tqz#WA!e32AkeVqU^&<MHZBiLT{gM@<NQL4KcKcql7mUD
zL(J5xAquD7fRUBv1i+EDnWLR+BY^$6dVW)`Vut6f(D@mn5rdkllsHg;@Jh@rAa7P&
zcbrbc)JG&k!bH~c17*QMaWO!?zBH;1%2DhMU=#Z)6aWnz?$8)5<nhUHGMK718*K*n
zAA3J-6G6MGOYOuHid4M&a@O*cDxHg8(!+!}3RVp_kw=#~TJm(2p8)cdLXxNLR>woR
zog4g6NB)H$oA+$^W})f9d{qxSTnHge7Vy2{L4#Ybc94C>1YZZ?J`+pL{H*#dcdg)x
z_qZ-2`5QH&2OTmp8467<#T%|_?D*G_)r29`&g=i6G00FGF(N=1FB&MRz8|AO4u%cC
zoaA;0BF0hVEypdt2zCx;z0%@A@VvX{obM02)jZsE0eY<8%%gf3#(q)l6&LG9XXbc3
zld^VtmyKx=4h!KO4#eU@!q~4sP7MCB9JS%}w}SjtEOh9D&qeh2taSzYg==f#GL*c>
zJF5^ciS(zPS&fCH>s6Qx5gza$NX)*}QPxkIb!0Rz8wh`SZ^HskXaU4<A8B}+*Ubo0
zYgP9&CwLTp<z;U0P8R6{{)6SH+xJYBQlq0nQiYce%_{Nsp7q>5BL7%M)HX|u9Vig6
zgp-Z*m!F6*BL@t~qC`L8>mMh}oLQmnc0WL~uC|0u4os&$8IqNyuDD-Ek<M$-g9ng8
z+e{3HYF=Om%Ywc}acowinnX@Y;k*|m95~+#zdup&31)OC4*Q^J?vRYBd97%_%0}1J
zVs3!b6#(whfS5-70Ps2fa`Oa3-P7^ObzRb+cnPrg%9T<cp%_ll#l;1#L7}Ju4+4l@
z_>%{x9&5R-apAgdGF}YOlyn<0%05Hq6BQ#m;6DE6PIhqJt`%KPFh1|-(M|V#;8T78
z$J>9UNGv0iJs{I`V@>(RQvM?ir9jf_0;IFAoa4T9+@3S9oJPY&zN&P6s#*_BOmUC7
z%8Fuc$z8z&bcGJc1b|!7XZb&^d76Mao&-UOYQ7DwA?%gP00BqMM3>KeTI8f)=i<Y(
z>3&Z5#k3i@hb1dDqrV-`h1`KPT*&46;_Yb3aJrxfP{}?m-h3wZ;@$KKu^Ix{K5(<I
z>Krnjvj($0Cadq0f?GuKs#Cm1?4ox2CRo(n$vqu=Q3!p&xaZ`eY?-ROsc(zU%_2@B
zNNdDK)NZpulAi{_i4`@gj~>1*#QB?DfzMA}x6iPP2<$-AIH~Z8{G_S{L7tnH^j%<7
z!d=*_3m@@Brw*+|!v_v!z|6$bQ}da<=nZ<t8SK(bCSr*1zqp;^k;)vW#Gn_Bzk@f!
z`Di=?MhVa$k&^cZqO}^#mTnD8Ae$4T^Bw?`;_G$m6knHgVM0@+Er;++{F!^fiz^eb
zEJj<V2p%!=giNfHsgz4aoX{PpAMnBFDA^Jh;2_krJlPX4_)619KzLahd)dJ=m4hvt
zojRm)mChgGfISr=o7g9I&q79r5J`KCN}}elyt}&Fbc>*e80h@1a~cuR*>VsPB`C>K
zD$ml6lCqp8fh3{)Dj5G*&|DET?<`a`n86rrD7)Mjit|^tpaR>|T6Em~mZkN7+SQXa
zWId2RpuY1KIG`=LKBT;<RQzy`I*?bLd(%F+VA$hs<XbWmC(L6oVpR$_mURUodlmZN
z7v}`@%kAs0#I&69N!wjnz@K*v5Va|0Sf8uj<-gREyuNy;Cz;@-j|GS&z1z=Uez>Dh
zKMG0Ad1$NmZaT#R{E(4cFK1irrnwi--~22l>NUlwDG0Rzyo(z%*qLrJro4d#raMrt
z)GwSWSQ0YmhE~lZx|%_D3MDN9Z-5#_0{R1c4eJZ+9hT)3B9Tu`h)%~R_CGYg8CE{+
zXG{GtWeDF917k|J9}wRwU#J<BQ*l=I5o#E}kDsvB8*?{0T*!nT7I9J{NuW<_g%6XG
z%&V=Voo7O=MhPf)`*sYdVd@CgZu_39O`Vae)u#G<u9Je&A^C;ymYK~^pS(`cvfP{9
zR*tD*d~&Kcjdgp9K!Smr9D((BlX@kMh}M}993^SA%$V%mk0naSC|^dzn&12WLN6r#
z12|sV^e#?Q7w9vvq#L@ID{4=Ct_&In#wOD6U57TjIKcSybyq!cI^x~-&ik1z3!Dd=
zCq_&K*sCo3Q5PGX_&k|tdkXF`9#?m`UeFQOYNvW#&5C+rpyMM@(F!Bw{~qz3O|>~z
zoe!RCoKcs`^MD1=;{`j!-%@W%m?T^!kH4QLD?gnrmOAdTwG_G4;NrgFr4c%z9@#P-
zDeskAupIQ<Sj8wVc-FbK+ugM;vw`CzprIZkI~}oen~R-s515Aa4RPSE-Ldb`bj@FJ
z?RpYW397ey#4(UW%jO{nDC(*&`8Lc*SL$7jJ1hQ~Xe|r^p}tU}R%1(r%90$U%d_*#
zls`^m>X83fI;JB9rEmOIp1JGHzA)U?P1kqn^BDNEo%OlWnpe;x{nNdiU<Zp%_HUnT
zi?fA|6fjO8GIP|r&A!U)-AqVV*4I-07%rX;{wM8lWpY>R9rD|^t$ctOVRS*K(f$hk
zU9IQ!l_fY7k05KoZmk{aH`iTBtcYWp0e+{;mhIf$UBShaG^`~abea2`ASHVVm<2at
zA7qyA<S3teWiPOKYn8X@;apE=PIkp%vrc8bS^)a&1-N~+ys}gpoh>$?TC(xc($+nY
z@EVDn$8el_kf;~-R$%!=I(k$Za!9t2r`v_ltXbb9lCG?ah-r!DUrd74(SD})<EIOo
zUA<4F2mY}PZm=z=j28lPku`8~OyRidRLK}x_|Rq3r>o?JPm-FK31RCk>Eww@?@3X7
zDE`8ANaej>$S+eSLzB{o>W^A6L9q0~{m{eI92%3@=^u=?wZFv1JW&d)R^8Z@Z~poH
zh>UXwO&2$a5TOer?lQfw_V_c3gWG_nyP;L-e7RjDCbSJe477O|Dgy1YY}Of9G_#2Q
zIh37P@`-Hd`uW@Ch7SHq#e-`6o-e3CH+F+r)J|@Vpzm^+PU54n83)W*sj-6`t<drA
zWWT*iF2S2{5tbp%EI2zY^U=tBOEfOw3<vcxLzn6JHLHBB3?PU3m6In{mV9#$lHB$%
z9s1Epz$!fVjW;LPR3+y1+Mmt>i3CF^zB$>~-Ntby?!MnGa<ltz@OXz(ve?~&kxD|O
zXqCBI`&D;GtZu!+Gccm}y7ypxWk#nZXA5A%#wIij5!8?^HC|zNs@BjN&qIa7v>S>_
z>bUZt4kCoAnxulNJ1HV~N4cxN=o$Iw-Ra;C60h>43m5kfE`v55@3#hRO8!#CMm9IQ
z8N{+>$sCs>>)qpr0!wac)LywnxcoVX_z8WaD|4<BYaD!PbLu{=+J}Xb3=}_O@IDEM
zhPIv7T)V9CLJI}aRR_#|o(F_J6?7PUuf^`og^b8Tp8?I49b26n(zvu!?3tU^lv%!u
z!(^W1*NWAH9mNG*Yxo(z9|b*^eEV31{V`Ut%mjqnH8F#mp)aWmo&U$et7SBk>0qYi
z52O{Vah;_t-aHE3G3+Pt0-KLAt}@`YQg5&?+izsi3Sh@@1%W~Q4Ud=>1fPcbRNw5W
z+4k9l(`AI>&!Jz40jFZA9K-dvzTgR+a?7MyFbG$mDRTn;7IYZr%p@$bm9sUT(+tnO
z(4LiNv%87<(&YALHCS_Ct-Q+XjF_m~H4EUTG%(<*ry`2s05L`yjzqSa;?$}p-A)G5
zOResuf97-YeDIs!AEJef4wr^(ZzIa{GT&GyIaDe>*Q<<kK>l|)DB7zSS8eb&a7G$k
zse9XF<}{%oxM6dRsr#I13cBQtC7!q*R&q(K-ITmNs7`KwV|c(tbZj!csF&p3Psji9
zaY_+YQSw*3&7^NgfApnA`!65sN3_Z+0zh_~!dt-J?T>CkC-U@Uwa32$XPFzC(v;iM
zF5gjA<Hi}t!kU|z&Ikq2s7iAT05hDXD!Lx03Mr2Ddd~ndO~c}T|2_oGz-r4r5@07`
zn=bW{Ch&M(s@ikZTBRvPraq+1<Ay~AoccIzC{mOl>rAshrD?vKM-WaT#fANNQ{ban
zb65n?Wh(mmMFZC_Va)E|V8F2AcnJRJq0_XooUiANFNeXvXcuJX9<~cz_*gZT_KD&z
z8(Hi<K)m3aFj~A1ca)pua{#^l-f&CKAea%u%V|@zZ}$qtPjGuoDOGzN`k6=L1N7vZ
zNe!@y=o@T!*-^C1*!+b(0&%%p*rH@{Vb~EpfeGUhLg>T$Faf-Ysl|Dzqz$<n@`X)E
zLNX-wta{Y*{>1)c+VsLE?eRg(Gv6GGotrK>et|bFYON=G^u%gU!R|);s&i!5`(cgp
z5ON|~BuMlQ_=zPSeJ<z~0Qi~r*tEC}9K}=mSEuUOED*jLtJ5%0KH}M+&FpTJ0&Vlp
zrAdMkjJU>2o@+bG;Lf^L4?<t=w}3I4#r<|;%*C1(`S?VWLtQne-I3dVH!|_v9Lj^!
zI}imO8eG5i`0d|^y79NNzg0BK%Re^_cXIa~wsCRE6`pnOI3#!BE72*wI3fDn-~d{q
z%RmM~Rz52dH0-GN-AicRE;vz%BU0aO*#y1p|L+(zW1or%6H)01d;81%y>3O?z?sdP
zv_DRA=sDpJh<%e{;GgK+nW;aKCcgbe)Jv$qPK=jF!iD!)(K)scMReXgsoOhxQB4w@
z9MEhpc|o}H%e^6hmPrhT3{O3|MTxm^-7<>V0?JRP3U~kgl(nQQa6YL*OK_xba(4u^
zK6)f+cinK51L$0X>Z%(S=+O#^vVU-OrP8S(oBOh1qE)G|AI(yhQ7P5WpI6g1mT06y
z)t+{L{f+-oQU5{!&&ye+h6oESZqC}Bn7ym2<|+9*5K*S*{9i5}NrMK5O|8B^{@AO=
zWA$kaqaL{1qu=9Lu=ZD3%(o+`XYos}A!%w{uEL$Vew_1|?$GrN;snA@yWtJIi)FUy
z+>yPO=vy}tgR-YLnU(bOe=GyWF*_S%>R#QtGQ3bw`_}b4?(-LuMyHdd2t~rrZ>T!F
z?uV_T(kFWolM}(G7ZvYyAk=q(bQAfd9LRfv=#sBDFs~Mm$MoHutAZPTS3HoLH7q|=
ziPvI(w^o^ut$_7jSo_gt-&BCla`Z&4h9EjOG(znn{;~88wEITfNv@QLhKA`1jx)R{
zA#wPRT2XeBO)iJ0mfOop4pI-ILzAG{XqRcFvV}RUTjBY3lcCw#d`-7z6+3A_tZ}b9
z0N#}Kj|DhiPqS~np|~st;hw!{J8RPkkoJ56ii(|!FBpK{yhTxrD~%fmW~sA1^&Ka<
z%=~n)$0s};z#6ura-NXMp`?q=@WmCJzWqqG^kpMSMwlEudbA|TC^BE}iumMql;02b
zs_PEmPt3-uP+4qDEY#wG<-0!$g=dCOpCvI`__{21#Kw++qi=@lRYhRKL@oDt0-rZ<
zAD#Q<{bXBlLur8(QB`adpf2)3GpueV1Gby)b~uYgF5a`Tp6;2HnBN-xD+AszK{?ba
zicL7vTs?t?c0>Lk`<8j{O<xIWPf(c}IZ-*@XQjCzEmOvWUUrEwSa6FbQEvNbxvMXU
zrb3H$bG_5}EcB^t_E4hoAMYV5WAym3nFqO;C`M3dwB0*v{}oW1R=n9L9rROA$%z8;
zUU~jl%3UQD6GIM~07p64r6_>Uv>+9pc70Icp?7PC#W^~mB=BZs0Rn+H$v!&wf|1z$
zfr1dK-xP*n!Q0dpNZ@!S{_r&ISH5CG4dsIR<>`_YOtO9gMsl6=e$Eo#Mv7)M)Ymy?
zMjRJ{4WwdA`qiSt^2CaqYa>B3RAlOl$w~4&sY|W6{S4?_DxDzsL$mP$VNgo39w#k2
zETR&|{?$iAF>P3v9DEKAM42L~pqaTcooB(qB~_WzQdC#a)IXNZ$bosVLJMfbA*+Jo
zV_Z|wg1*gQyz;ATs;wv{A{9bYbS3VvwrJoiLOT{3j@Jw79UiE1n$FmJGR;N*0$)No
ztNzCCr00+Qbn~T|BT|IQ1dD1}GvoKuC&C7jZ5T0CjajvAd{LeFktFAr{xv8>s-?k#
z4n^WO!V4VQ69-SZE9{Q{1XRx<zSmWatVlhqTc8`Yi<Hfle8TI0NcrmCA(L}U)Bzao
zqb@^j$?58~?**#Q2-04en4vp!GeRN_hTzn=Fr}=|G|Qo+TrwC%uhZCG+4JaHlb=~_
z(#VsbD&E#zIuuv;jT$XYacAo6*``UhzP^6yZXPQ5>-jLMam*6rU`j;(x#J<-LrqUO
z_63%2FZf)(96jjWN+*WFc-}uH=vFc@Tl|x0>MSJOgEy~E=ZvRo4pY@_Eh<jpo<Xp5
z2QJEkRE{Y|&Wc3-VeQR-ELoyXp`pJ*J_Dw-6gkx0G^N!H80l%3DR4BiDKmG;3C!RR
zycbvqNvI`{Y=^ced4LdI)uFmb#m@SN#a+>ko52OqxWmc#b7N1IV+w4a!&5_^$hRE(
zinl=8J@d*csCBmeGOP9RbL|Mv94uv_s(e7xj;kARJ#co<FJ!M<k`nXC@B6z$J)ZE!
z-!7Ral_qZe`5F0G-~%;%C+GWA5r}$p@GJZ$i(0GZwWhRIQ1=-nThz7tt>P1f&nf4g
zsNBQ9t1P?3Dk&7$WHFB?X+~QWT#GB$6DmZ6RiVBw9H>qSs^p5Ir}k{Y1M_7~B7|zM
zk^;}qux7T)jgM@6v69@m4{?pc7OpMA2W$8<ZO8v&?4OBgi<d2K*{wb(+j=YNVmSLm
zXT8aAX+Ix-MQwK=uAV7~e2Dww;fi+3|7qjB4kxCVwxqw-e82t)^UMA@us1dJ9h!G!
zB`k;V#YO1lY?vb!>`Fe(C+m1mJH&mN8jo{YvgNU7ba+qU3TVXL+|)w-4D-x48&mJ4
zp8l?w>D3iyuYEbRxPJoK@XtISX7tyrAR^Xsr#lJJG856Ef0<LW7qmL=X7<aO!<(+#
zlGG+a6ra+sB2gfvtld4j=B>uR*G0C7f<^QVtyFsN>es05eCk*Y-g{V4G~BUy%cpcb
zd!Q3otE~+8;k_mh^i0CuMPf9nVy)rxxq{gwU8Eo?e)TXrwEitUE^+X9wwAun^X`5c
z!<SL76M^-Y>bo7F*eUsgBO)2TgLq&;HKaaJGd8Xqp1P7?yq&j@?+i@#y@%J?bMHQy
zsnM$l7AOjeaB5`uj;n<~5PC}UvfSGbRY*}$Trz=G;rP8b?+Qpmf4O@v><iO(rdHqv
zW6Q}dxx)slT`yerv~oF}14@P#xO^ut4tDmzn2CTwnE66q2$Vh#H~26PKoVUSMn!J<
zLq|lWEDK&KiVP(sTJCfm0|~dgHXMI(6p{+}<;6QcPZ?VezQxiNaN56faZ8dOpzF3-
zmSms5()pb_sQI7IM5-{;$M!4VZc67^ETK;Evm;*ucl<&p#42I#sAao`E;V-}wg+f!
ziBzt9{ES#v&>GqP{<?@$w1cl45BdItOn;M~@1JnWJ*B-P?`rVql=23*pM*sR=e&cG
zy+6|xU){C$x4*TcJj%JS>9j9Gfcv7uu5-?y?V-Lzul}{qyBG!;D_RPqt)Zevlt1?B
zc(^;|_ZS63LGfG9*76MQxF_wo4C?J%p3f<-az0;BsT|Cfj7~6O)#A|cOK&~C0^J)|
zG+Yswt~XDY`p4px<m9$bdw*m2A^0~<?Iv$BsrzS?#x1+!rl3z#t%06*8UB#%V#Aun
z<&!*sgtJV@5w>9}ujE^PhyFL<=J-j6h?u6%Z>i;A$qmVIvpZ<_A(zwwFU=4}ITx3q
z=}MI6-p&0t==Y{(#jVj4+Qc&cVD0Xon{lXh<x1oC(pyz$npk~1<aG;ajlw-wy9T0@
zkfdm0)>jPp?1}JFBI)r!y!1N3j+ulQ%Cd>eG0li=wMZLbpBbbR&a@tXr>;Jcaci8>
zqglFSgdm_Ng{1Yq7=n$!*ovOiPx~Vtjf>Nf%Az<YMbedNl~K>G`}o_cWu~(0va)OQ
zAaa?f^D`U_BqFJ@VXovD8LHB~iKXsN_T!yNR2_HZy<j>d48A(PMwfPkM;cI)qGwC6
z?%s7srb*C?o()c<=c(Rii=e37d8L?*H*P>;6xnIiKikK~>P?y!2$3DJm?9wZ7F2OH
zt{^5{42ha7AZBH?Pc*ce6RK7_UPxaSI+J{5bR)w(x<!VniG~aN2{$-8W~@H68GHkq
zcjzL%ZT1_gQV%>?q3I)}HReb$QJf90R$-ggzDLfQ0(3XzPfT0VuYme{;&tkci=tRb
z!!w*zlaWWeNz4)&Hed*qQR|4;-dQ89Us+4Vko(dUU8<w+oDb?ruINO?>O}0MF`PvJ
z117S>^PZrgPxYt`wNzfEM=xh2e?+TNoW2P_m(yy%^1-`@LlwAf#-Zv3`yTY@Gdpc@
zMuj~w5w<*SHc8Z&mNS8z0PK_rf9LWPx&MS`RA`d=Jk$0%_mV8vDdi7E-1X_i(`4O>
zerY^LPsk<in?m@hSCc(lWf%xydQ@Gdau8ZsPKQid4%9*{JKuY3Vbi<SvJLE^bEM{q
z?35^jVPyjpcx}EGGm6Mi51{CL$Au?=(pOi=jte=u%1`I4O6h+B<lscq>bQi)UHJII
zzV|m{g8VDMPN8m3rEk4)`>*XSTgqM}6kdj^+*r;y_*w7qy$?9@k;FVFOU^7wr;5Ty
z!cMKLpJ?@>un&U^oOFKQUlCr$p9M;?a@B_u-h!Xl>a|bQcWH(#Hsg~$MYzXupnfR1
zLif&$Jw`B(>l}SHOx59jy%=eMJ4Lq!ZO4D<+aM4lM?u}fua$m&VT;eK7!cm@2i-pr
ztSvb!J-{&H%AYcx%)b2LF!Y@8N_udB$9rEi{S9wr*%sFk7}I%r74^Y^0HhoUN_aXR
zO2<d)uMgkI^nCHvk9xQ3Fs@1s<RE5e`_^hEwPfln`==4>J>}|<1e!UJ?x`o88h;$5
zMiC~JSfBMdet@XNugR?bY=mMVV^`o0L!W~c^Jgz=uYKQe^wJ2ET1k-wW%a9uJ^)5X
zW!t>02`?x&3mwqk3#iY@KE!EznQH}6-IupNI>qb)v%M=@hrd~_FMP<q=T&qo8N{LA
zfDCaK(H>l1CT#H96VV22nnfCS9OvOXa*Co#J!21=hyvcD=e%p8ox4TybUd0y6LlGW
z>DNk#Z6@!iPfdF6N4mkqN``EBk=1k)^z+d<Z3PSZPO*t&VxFU<EkUYV6fO`t-Jpr^
zQ7LbH)97UemouNH=cmovk{FS#GG`hS#~aU5F3$VEH?yEim^g0ZuiKS*A_dTLDmfoC
zMVT3)sxT8v_Uh`LS?azuJ}W0@7MPeX8tby31Pv%$&YarB89NCL`Az8zJzW^430z)~
zXlqE5JPyhySlUD+j*F7w65wUU>??-K75m@3hfe0`pe6eL@3{M6@R0&Jfw4Xd!m)PX
z=|wG(!ysC@1l6T^q}XVn=l&g<>ZkhX&(bigts5ww^K#LJ5#RwP&HFvsLYLZ$y2a+n
zShnvD$*Tn17%%2HbyLs0eo<q5@!QK;1pqV*(Nsh%BMReH+_<XN0i+n^v&ZGBGzXcC
zT1%HWg6FL%?10{R8GIM!;xT#@y`59cp+a<$`u$d3(qYc}RSrSIa=0MY@U_47K%2SJ
ziSncMI|D~gMQ(DSU0pU~d`~K%m1c{z&u34)k6siZLsrMrlN|zt>w`vftK}5{t{417
zr{j`KKKMY!?1;tZ*H%vec-v`&BH@kC*3a05<!fVW>#4U@G8IonDUk7G;g#J1=jLx{
ziP<yP-M;S+L?`+ED~+aYrsHMukofQ1F%-haegZQK-{ah|18D~(X$QI(Weq|S;wIj#
zJV*>~{*C)fnYEuyOm5=T-|-VFK?B$@I=4COX^?g58RvFpD_20MxT4)a%O|(38s#M2
z;nSc>K=^=p#U7^2RweNf`@-r31Ji<=fls>a2S^rwP?PNEfa8EFvc6>)Mu;mcZJ$Sx
z6!$0Ac=()E&CAbnW)nd_2KO#NoeYDa$5+?tj)9ef8j<0c;l*#%aX8_(9p&J`o>L7H
zwf1wrSepdeZu|hz$YzVv2(x1y3MCmk+FuT`dLUGXpc~(5A$8ae(dcSVRrQMHFsJ!)
zs4JFOGxuS%6B#T8gH_VcU9@efUXUin0}d)hb({ttI1}<#MhpX<OMjV$_;y+zCl<)~
zD+VzblpDFLiF4q65tFg(XGuoQ%o2}hadgOXFUg|0H{B5au*eIKLvvk}AqE$yAxD&M
zddBdYm;s_(g!pb`V024Hs&<siIcnn?4uA7M8nC0p7-l2u-mPJm?)6GMyZGrlGVm)h
z36UH}zKL*t!gZoOWIA){z^%o6E)+blVQ0U<m4woii1SmlZE)s!)CBFaF%TL=H}_lv
zgTj4c6hh-t=52^xcvZ)qM3X%@6*z}=P|*I>uG&nq!LCl7cx*3+<XI^8njGGCPeh+y
zkyW!}zXyf=<R_RostINJjLP0vy_pfw!|2QT{O!*!*Fi`_firkIZK#qy*>x5v?0}g|
zx$4=y)l}?C#|%3}IV<m^nbmR6V3~1{gdjDT>&B&~`w<nkkV$0qDTpM!AFe*kNcr3Q
zAePp{Xm=ik=2YfN<Fn12>fw`kTq`p~dH*J;G4E>9a~NvGS#$~QtP_8buo+PUTa$yX
zb{{g8!OS6ei}s8eLf682);QJ35-C=^zL|E#-mCcU;dI;?VHNMM<J_LANLr`1l-ai5
zngUL#lPTbSyHhgb7cz|I#@dwh+CB6^<6o24B$D(|zuhCtSF>0vBDD>*mY*t}0uC<}
z!Sd9dDxQLH#&AZ6o4RW$WM&$<Ccgc_uAzu{pMhAK(nMtr+>Vj)n@yRG9ue)a+mF6t
zaC=JmIYdZ!BOuf)q~|_G#q-o$lpJ(FR_i@@M?vj5a5eH&f57ycRIgowXo?uaZF^F<
zdyTKY?0SQ;am7MkiW!RVG8g23oOtJvMOjzFI%;lb8UAu{dCyra<)VdyNpiMM?ja<6
z&-FOxYqCiZ24^JU_pXxI?=0$~@uJ3G$uA;2b6>IUL2~LZvdO;asxu(iNC%t3l;|#*
zVUJtVT=Ei%BK@RibsY}G*HlGfZ|qkA>g_%R&TyRA!JsjA$JATSmziC;Cz>;M!)rg?
z<jU|r771;FKA9f<i>~h7J0{w3QT7*|&rupOY*o>xgSUILcSkU;4|K0X-_^d?lW&9j
z&b9rfpgCR+yJMnX-n$(he>Ly&X=VLR5msCrMi%14W3qhfilgf-&mRHUE)$gKQ_DQ`
zIATs-6h;D_T4*+`;pIpvdR;l^wH=g+_zP_23(Xk~+K5t>gkTw5P?^Ta=D7Oy+WAL<
zw)g+B<cj6F_I~vPZ-`%zGd6@<?W=mHF&8_FL!mBv%mDa00;}Td%oWVjlT$)<g|3#o
zYnmjI^HK2+sXQv8`0$$tjEaSLLrvcbuP<JU)yj0;vGsVfH5VW4`E$o$xG2`;)n!VI
zT!vy}mytp(&y=Y?qPCtVq{e3k7Z9q2+7OkU*@1j7cP!K%;d;vpjBGLj3ZHRuPlQbL
zVHF;-TZpdO`nhUJZ`Tw7Z5_S)3gcYW;f`Vmc3aY<ecz4Yq30UniRaZZ(JOg57!&g6
zop`&iO}E`t?dvT*nB-`r@8&-Jg^1$4NjJKiuHkFH`heeQlIu{at7}X*RL~t+2Ks?`
z1sY+NV|yvnt3Nn=hsi<vQPtwl(zaKCS)~3Z<6xZ(%e#BMa`d=yjimHfAz3D32AWW3
zGvKsRqRzF7KGjElnyMK+YHYVK_#hwdao`Eh%q$EyDM(qsCmId6WOFgqx#EGEAoTue
z$#C%%>AO|Rp-b$Ge;FhbHNGa#^69vm20h$`LZ**Rw<H29HgTpAowH`aysIe-m~l}V
zS1rhhd#IxLei_U$gNT*!9EVTs+i=4&9%F^enrM+t#^-eyeyETnH#WrH2;idrp!yNj
z7#&)U6&}t@nbjlap1St87-L|Uxi|*k^ye1xu0Thp`o7yKS_)*BuQ@6c8S@$)BaBj!
z$7Di^`Sh&twSvkS(c^~&t6lqH`s64hqt<M@k;LI5c+fM0alaq`SQ^4O-YfVozaa+`
zFn};}2AQVeBy?QPD&D}It12tJQi6fia`%c?YAEZ#$&ok@#^I0%tDK@3I!7l=jL-WO
z9oYr%h3u=hdzK*opQH1RXR`|%f8TCeT0zw)4-&*`sa^CbNhBdi>{YFx#8yR(dTU3l
z40}C^h^<9zt=dAzlf<Z1yQQT?wN=IQ%lo_ky8k?%`*Y5@uXC>N^}QOqgch67*k~&F
z!K#m|=8WC37Cqks{uaRT)FWXrDhIry80>DZ9BUAuAruF_d^3?3e7>Vw@BNc+Y}pdv
zEE1L6xNDY5&7Wyx4A>HqLm*_i^iHUZc%NsK4ZBnHNXgXBAQy`&P%?m(H&#!LCa>jJ
zGV8*u@r|T~#4r6<%F9{4xhdqQmzCoAP4bD;OUJa*?3PdLqctjInqbra-wD@-Hqhrs
zQZprpAhdDjMPGoC<2niQLt+#IUp8E*6ePBKto=O>U$_7$1?(tvMmi-L+(cY9F$wk}
z`NT~G1h<PAsmWEMt^tz0H89weUOm%P+NG?`7+a*+)p6Mfbc65S$ASX@1tKe`I(D68
zC}jB5@B1^MeGzI~*VPsYa+mW4hDd5I^uR7GE_r0Z8tq@^LeD46JI!wo4a>oU3ctfk
zWZ%-PEF9pmQcVDOY`OX+QELmn{2$^T;H%3QJ^blekF?D9`OR)%ZZ&Llm@E-AC5Xyi
zkzsa~mu+RyS!R+Cp_gp`)XwFcG91^C<7=E1)@Z%;$b56$H!i7J(i3dA@!yF$tLc9y
z_<#+=?Q>~w164X7dE{y{=Wss1T+LwAQeVv=^Ky&++-NrY_8`9!D~re9d%$fhLgm89
zMA<zE($l^ZR%9sxn{=lhTbJ$R4jUVVLs0@2WRXX{Ri-V9NyKXNYO*nIAqj{W&|<bD
z6Ie!9)Xh>m%*^^I(s+f}oz~euK1=rO+@$4PHt;RbkAu9l2qBn0tY|s+NS=2oz;-u(
zdMWZtLg+x)`cnU16a-d<mTx$}-#wn!D+eYs>jmF$XTXc7`q<#N!MkkVCs~i=eDjzF
zDl7dU^Sg5VvRV%d@n+J+G`PouQRPef+Sq|qmqAqV`f|Zdbd8KfVU(LDVf@vhsE+>j
z_bqp)Yk1uKf9L4l9aS98oWn%HT}~YG>5kw%-+Bg%*EuMt*F~c3BGpBch)h+JCS(u}
z@Yml`@?YyX7O$MUct1;|gwM1<b3-v<OeP(03rdi$s`r?|257t+)bzT1>uEbwL-=$Q
zuLOm0Y<3z^is+pM5ZA(zABt<BsP+m^WIx*;iN!|%5oW8N#z*pp=sioTBT%1LMav#R
zuH~H0c$rZC_s<+tuYIQ-3&h_8DQ(Pkq>H}6(@|`UoCZmfH_2Bt5o%68dxHW_I;K{Y
z26{#E+fF&szAU^8j29`(gZP9`HkwrgM(X)k^~$C9C|+#Skc*KlkfdiFF5N$6vq3WA
z-pz*a|41!uhcl8PzPxMMlh5e6_e~$>&1>jEUkw;va!_O-Uq7jD{dd~Hgpw><F7(ij
z5wjG^63Vg|H!_Fk61@$vA21{{BjF{=M#wR2LtLzZ9^;cW!p;a3GWaY>8<dx~IdwSF
zezO;75s{2SkVP-022Yal4f)mK7V08gFLDL2|JwV^3)aQb88jEu2Vtw9IOcJ1;fA0y
znnvn4V<Mbd-Y@f|_S#MME5b}YB<-x+eVL=#2ZhduqW!%Z;(K{#fOuzKeptty?sO_V
z`5xKQaU7b}Re1X}BP%gGJC7CSONGJ@WW7rS@d-6>HO=!r!HQR3l?M+0@6J>Y<(9f)
zZz!ZP^^@iD(v?h_lC8x+8O4zXYBccb0H9=l!9Pzq&L08ewXNMJ9)8invVT1hYQRPf
zS#+wWL(C$SwdT)91@*nt7LmPMT6|&KNbk`V^^48#-v1N$BWsWKO>D)q)j}gqc17?a
zMH86#n7JhKcgf%D9;1m%g5#-Sbx34sfie#o;v{O}j}<qc_3APb^E|77yJx_Hu^PVe
zN|LLxxYa7T)WoD-(cX@!GjE(g$L9|}#|@?(t48c0Gle#@9b3j@iV)iLp47o}&~s1H
zGbY6K!{ob`QnT0XLj{fgt}Z6mZM$h8ZmC%Ie0Q-FEalO0HXm2RS8X+(EvJ|<bagD?
zB~JWGEvsY@sLmy947ys^;`yGFup)qqG$#L+bj}D1xS7cG;JQkiy@nvZ9`VqZI0T4z
z1H^ApbWl52wRYqbpE&%p(RTnuFGdenD%1V#%{YV%)HNAvDp77J_DcDa%f@QZOy)+c
zK}VZoedX!&?VSBvHSxZe%qu@3t(*KR2R*ge>Pea@Cb988g)obb+y1!ZE(4@n>8qjl
z-BGR_3W4+J<l;w|imArlcVwGa&@*}u-ao67k6Zq(+*swm6T`RgaaMdOHJnue4Xs>~
z)cc%RZ|3VLNUIJz^XP-JUACOk3-xjpkDFO(*bkTXnWznmfD3m;Pkp5&7hk6z>BW3Z
z%fvq!PGrVB(XJpV(s^|-HD*Uq@V!EUIDWn0+?l?A;8`OZIV5Q{fOW|=;5IJR@BpMb
zT7BVb{zzIJoT9ihbh{TYGS1-!@c38y0z0d}PhoW1PHDva$&oumtEe9I;Nzp5s`!X3
z3zC)NfTnlm`Pir(e(~JKFrAeRzb__q?S0U{tA!$$BxR|3k|@EmTx7B-!@4Q$D<fGp
zGfq(k5^}X!wKnMve_|f})!@%u@W#z!&!u42%pYCn5~Kbq312?r^n`>f`G;2;Gc(kC
zPacG8bus9}y?bT7A$3vdMP$&|o#PRfsZxH1m*8zh37N3G9ZcNtS+~injMr7}c71$T
z#*LtVa}WZ?Y0HjVuQ=4oFprTkGjL9JBlSZG9?h>BA|+$rEp&-@1ThcHpMFz6n3*^A
zZU_6{iHpzF3=|-+XXu_YrUCCOBj-kbNyKO{O$Nm~?V)mlYFd`v;~RqkSD%?p>SwHs
zdryt6v#{X`Ry+1_A1j*G^Q+B%kd><C9ez0v{}Hc{OWn%-IA#q3_J+s@<R6>eq<=nV
zPVc8yb!h1Q<CkC?6^R`(L&TN*otd6>minb@FJP#fw}DORREq&K^1l~lwb}0&P91vM
zSKcj=CFDMs@rb#W{lmj(Rve|I3>g5<(f=NNW|7xpwzx@Ab?|(veDQV~aeMF(HtQX_
zl==32S-n(ahsFk}I3-glk+>`z|3WzZo@eg_P(!x)>J_VPor8Z+KeAL{kK5^xYKgJ8
z=R=~}Hw4%*GEvT6Kg}uvE`~#}z^p@+GALWSJ8_+;EPR=KHKj_Hcy(mJLq9b`4brIO
zAt@87(6U;*^oB}e=7@e)7MHs`F4{UDD_SiB74jNc7RjgGZ|uh;lLv3=_P?t*vXQfh
zO3l>?LG#r|V)vz0X;-orL(YxZ-xdlr25`J`A9jX0sTPVfmw&&T<^Dxx<@=XJQ|{8z
zVs<;MK9;f_Gk!JfU8jd|Gc7ntea!zUxq+h4#_d&Pzh6FoxiCQ~(RB7R=WC-oSR!88
zazGrJ_*x|EIpjJ!9_4b=Wp62}N9RYTy;t^ILKaSxr8!i)&J{oTMe_0|H}$j5@FTMG
zlBSYbXHFZXqUUz+ZC6``A=Y0{uhWqSV`d~?h5mONV%g7cYz;LJ-rPA*3yxkES}f7H
zWNRy=x7Rt#*;p(Ve|!>^+XQEI+$f*C3m<eUOcPoxHriVi8J(?EvWL;hy@Gg8dQ28k
z8#_VC!LuJ<+77NafN!Uj^s3#Q2haX@VqfM9qh&GmMI<WLP$@Axf3L!P0j{S)+*gk^
z__aQc@0}mK(EIZx(ZKnMZz!TfQK_HaL1ex!kxuhA`z=}W`oiX0KEzHW*)-#hh<X74
zm1Sj%4-ob?CMcAhQ;z^>1Q$N_OG94G&SvvyxJ;viHS-P043o&$U#-p~1n==ErB73A
zJt+bTZv>IfLSxi+Hhy6gf1CaA$?I-^SuE^>lllo!Dv;IM{Fwcxo#<=E1p|4Dv&!%Q
z-h|Rjk@y}6&3KhPyWKv!6dhx7l##*6%zi6<O$@dg<L)%E^)R%h5NTXu$W`60pT5qm
z76;NUX>s=J+e?7-u|5#q@z36a^6{;a9qas*pmomE850b1`E=GuTS9T1N9=gj2Y!p3
zBmGK~k>BWDNiV);8P8H`_86owHC6n!)rb1vPbnksq;FTo%Ng+oG=vd*!akO`F{sPE
zy~UU1*8{utS1Q+<SuDWFx@fz#ae01U)-<BxnQ^vyMo?8?r}~lo^EiOl!bKV{d6oAF
zpm=d9-XiF#6yn_RBL(;OSah{9q}8MGM>dQ@mY8Yr6l5lk9gIHJyf34m(g@^`Zy%Pb
zJDmNeFyYM{?qzDoNo32-z3+#PSRLb@XGwQHu{XYoduw+Xa)PNB8-w>gc8k`dMeefC
z8gKDxZmVQtEV^ZLwK0lxJ&Hmj4D30Z0k1CSK`XCmeDx1E43Y<E7kQPPOQ&3*xL<^(
z54lLb(I~@(x;Uxy#)LQM!76L$w@&n_4VjPqB?o`1bJ~F=ioZ>>E|D??7;noIs_Wr<
z1-td;V&`+CpBZP6qD(W?GYf9tCA~KABKk+NpAf4xKg;SRh+2g0m6Yqx3?KxJtIBw(
zTf`Syu#s&ruH_TwEaMEMFM&?ao}-%46)aT18GrwK=(63Yq36HxzXy8b&vxTJ4exE+
zTCxA0;wq$^Z2Ai?B$<#f(aCdbG{gd|qpPOQERj@vqlV{KxWJK{bRq#B;UQ<1=3#j;
z+B;S>A2?S4TuqK~wcvY_dC6Q{B5Gi}8iqh!27>;ml6*GKmD%}crtt+*F41_v$gujz
zUABgs_&2s?#h0_BG1R^SoTp`#)=tPO|9Gefe-Y*rartioPrX&tFWq{R)+`cI@Yp2c
zhpQpy!0Yng#logU=mtG9otU=Q`pv45o*KVfw6weZXk0@}K2zNIf)dOk$UMRGYN?RC
z_1-2D_PGBIBsNFr|IX4vRu;CkM&ryDcK9aa9bv9lxXSvGU>;leSo9P+<%@sx2LBnw
z_@x`^t{LxJywa>E0^_pG;Ldpl{-m(IVl^`^=eVc5*I8_HY(%R5_(!}|`HIO(;~mYq
za_Ni#(x02o4`BH@8?E3gc@_+qJo~F>2@p1YfxD{r;LK&?7q7A#kBCbGe!t~_juV=H
z@ycCAZXJy9>SCTghV&#HQ0tM-8`jC`PUkMq4ShX-r4i%BO;&a)%m7*NW}^)l@Ouv#
zk*@XHt^o3VW3jF!&8zk8+Y)S@GtAy>y9A-7uki;ss~$e*7eBUV%>fb>*gG^O^rYt|
z1ftQx_MDVItXI>)D!7$^%-Wu%VF#a>-KYx~(ET-BXc_i0wJ>h6ymPEF{$qYy$T@9~
zfJ^1;#eaW8y*iBgT6TJ4t5jNAI#x9>tZe8XnA=xcSeDffBs6cZL_T#dK@9SSPv@V-
zeYwh@czMUYSnMJt`*vr(h86pXbeVlqfZvE5hGP#p$Iv(NDIlpPN7<7lP5-<W!1vy^
zrAPLSzqq;5IeTDcUF$o&GpXfSu|X)>$bCQPlR9I_9lHEO#zJH-Rzfzfn{ty0&A2+a
z3Y?XVN#bj2IK4PleoP;p<m2pRYzHfz(SW?)+`cRDsuR+<JzSWU4}e>fMeY=)dW&L5
z?^csfBIq1VpC^Kffj>@~4H@zN*m-bs^PlO?uz(j257m@rzJ9S*wpR&_2DTn&xtgJe
zZp|F1A@Z)!iK#4%ho5NWZM!U%e8(M`kx>_)!l!y3;gGSvl6AqC2$m9m7AHpjv>wl2
z5%L)FBC`3$`Rw?PA>&W7XZ+EZbt>tjFl%K%@cYgvti0twSjgh<Du7Qwq%IF&jf}w)
zdoC0Pdw%--?sQ!|Gbb)Z%I2Erj#zj~P(XXaV1;x#k2xm)FiW$jCtfNyI`EhyD2>Dx
z$gPHeF8mSajK8Ya(y!*xt_ND07q<7k08VLn^wDHFWnvaTK^9aO>=d!>N%CL;y?#q*
zgkk;1=zU8$#fCIUh2svdKC|04^W}nE$bTm;(ImyRQ@47$CU2yVDlKtE48}H=WR?aU
ze}Q~sQrLr+oP5uR$Xh%#cYMvWq+FB6OL^bL8Sl6I2f7$?$(`23<MLdOrLFIl`_ACp
zao^hn81h|Kb5%X$4kb4dv7FG$cW~><ZIZ~BvPE-T-eftPAX38(dE?Fr+m{8MGGDoE
zHIi=IxS_41q~N{}?5hiW5OCRv5O)dqaME+$dcS4v?)J}=DTsj)#IcYUne?vaXH|`L
zx+6YZ8LCg+!&<nF!!#qcyXyCz^}-$Ms5e<0PQIos-T&L!_DCZvz{tQLD_&ar*AL+;
zt4nn-qCv9u@wxtfamheBZWQyl&VA@Lh*g-xsO!kk*oqYnb`p@1EX6^3(nmh>HLI&#
z|56pAaMn$v$5NnOjO3Lr4&tj<GIPp3`g5;7>)AR_K}(Spm%d1WSGuabXTa)~g}oid
zu@|H-x~lPI=n6BE2!541$s@UU)UAkW|DA|8-|m+x3_UK9B9Kr-tkC@LhNCCwrIW3n
z`YSI!es)MzxxE+p;)U6#Z|fy~bUu5U)xSkz78TZ60KMS>Wo#=LB`(d+E(~FFGIp%y
zznv4{jiIs3%=Ui3ow9SC)X!Aj(u%M0<Y+!Mz+X?fT3|s=v7*SYA|5=#21gvi3(NT&
zUs&$CCnV}9bKO^Bd75>YL9>*X&~IsLl4yP0UYy;Y=d^R7`e`|5Nn#|sEbOp3+e<(p
z%DAj?M(k>*mzs<i|069g84;Vy!<XA`Kd6AgbY3$s*G3vcVcjKY8+p9K_{BoaWHNyG
zu&{O<S^z7QNQ7o$%+1TcNF8CnrE(oVYujLHp6kg|iCbWI=u4TsAq;hO`eP2zv}yI@
zM6tNDIZ|8Rd`0d-A@4I-O27mZbiOhtX&Pe4#z?P|8}fCKwbG5_v+WU9Mc;knt{j(^
z`Uz&W?G3$OLQch7XWjRGN0We{6O#Yksg`I57Cr4irj0Xrl@Tf)$7-brnBzef;fnk#
z1PJSi*tr&ad2VSB``|J|hwJj)-iQXI>OrIQ$RfLo2fF)m=em8qJyN;rM}DSv`*U*E
z>F8>X3Ec0edY1cVk2;o?I<-cm>YA6&m;_P9)OyiPtr;ZLqx<pGKB360@Kv1shnG;T
zl#^+s!OQE&vrEB;8hS=@nE(?t`Tl<CJk@Vbr>G=zi{Hql^Gi$T2H*r<hUJ=up3vm=
zR~x@<UDZHQPmih~?;G6|7pn>^u;V2FlFYm>_vstJF^7G_>+KawVy6A;ijmInupL%`
zg@*W#AmA6jvLI4o!5iwII=$gNH|fG^c_nA79~J%Mkje+P0)5HWWs48VW_&!LScnS`
zkqb^_6r8@I1d+1U2lz8vtBru(q(K;!wbSTWEdSG$eClBr_Y+4KutzPwy2lz5bDX;T
zr_$pJ9WU|Xp?TGz?CV?}2%DCx0l9>97L~0LyNEcH(IEkC^}Ll`xN}MeF9rSl%{;wz
zGvX3<)o<?`t#JjVrOW?2VRh+Ztj2Nq$AYJMKcifwUr3jk4MbwO>fd_(uCKSOr_A5h
zI?hTp<xa(ae8D-c+ur*j{ojf0y}iBd|4z79c1AgmxI7@h_l6TR)(@hvN1+EEQ5qX@
zHd8-b{}?q3MOVD5y)D7>V%_{dFV_E@KKVaqcuuVUdp&dFe@+U?o&spxxr~+*{!8<I
zh{@B_@;7c0O)(;Wdr%vD|GyrK=g_)+;$SAA0=E0#33#C8AE}lo6Uu5$7THNr+0IM(
z<w)PHP#AzGeWanHwS@~0J3Q<}x^V~=XK|U24{NOadvTmh`TRUdgccmF3&i0%3~K&r
zGltM|g%nlgUf#|%V<Z-1AZ}DSjI;gh>cK2`rjE<$P#8vnM#*Yromw1ptAgVUJd8%L
zg+)vNxLx5U2vrr`C;25mV0>Zr{EB$VTpLl{Fg^VzsmzL`r!VHUl=@UuKo~Vu)iiZf
zb9WQ1z@^B8o|EaO2n9ZOxY)S0IP=RLQUWqhhH8gubg8<oE#_(#oISQ6gcYed>C|+|
z{tzX)l<7exue14_N64+S+Y_svjqpS7sD>fVI3m0o21dF!x#s}-Y5<nto%ox+Q(E4(
z!~8kr4>K64?NP>RVOsUfswBM$lDU;lCSogn3pT9E16f^`Q>4*XR;u!PE-%)Yrj`6L
z`R_!HZ$+||^1r(oF9vMi?xv0Q?kz5y<l1gD`m$QLi0M<_zXu=4O*UXn#<@AdT*4b+
zlAX`Oj-zhH^?8w>$J4;$I)G!31i6Zs;@6DXxR8iohxM@eDVsFZ-=!^`@L4c-k(7>2
zot8YvxvM;Xbx%7?TmF0$vvFj+<)$Kmrjd?8SnpJkKtX3WHR>)2HCyVn-WHIcPyzbs
zF0ku*RF+bcjQ9jPu2IXJ61q{~+ASKR8S-1UyIa&rUWJTdh1jxx=x0uitjv16oX<oW
zdJzwukWHX=cTu?IOq)-w&eoz!$!v{OU>13}92e82Q~MIHmNgU0U+OGfLgLX1Rh+~c
z`nH*W@Twi+Kxgm)@vR2A1(}M2U6;~e{$5PH#vBCSX&IRx)EZ18-cocUjiTay-lLSi
zR9*CS4oBFM8?Deg081M}B}6tUFHBW6i|@)@bsd_&S8otznOsalNbp-XA(1*wV+X`H
z3PyUH3|~}VwByFmh&N(CsU9IuyKtI0>${gE+EUxQjd0QJmBpDBXSt~t{Fn$<Za`J@
zU-t_~e$D8SrT<nOt<!9wIx)wD?Oy%!p*GAJ-Ku8yL$$OPJuH`4f^;ZNou~UbLn5In
zp0$4RxX(%Y<^&zfm~LV`0z1g}--($+eeXPDy^$Ys9a!>su4YCNCN|TL-eDx%H@}e`
zXgNd2_+<{Qm$b7g*^FbMR(*Biu7Nh~W3{mmqvwkQ2PBpPnHU6+2rsRReNbWdS^@|E
z+;mirz%W?gAvd%{3?5{QM}nu+i2W=?>-%{jw}LKL4TBcbP@gwYK|QmvemkNum>9km
z?}{a>>x-?Cos2!2GW5T$k{t)9Q`0maOWG4_aeA^*X_YWqI(DC@oxMa6zH+$j*481~
za-6|7u6%dO;W4PSX0ZBvU6Z>iJ*JR`?qmsQ7llI|ylOmhK&G>3&t^-!Q8%&mIZkJV
z$qr%dzuOt;wj3|j^ZcvgG%f)1lFbuSuv^R4AG$jI@Tg2Qdf+OfufAS1o)MlFt@hH4
zF=1m|wpm)DSKBX<MnVXu+lFT4EytQkX{mWV{S2`*60CCxB62IdpbDuiY(*u*THTJ}
z^Jo(D3)xa77jBn}11asA+4I@W3r`_Jkq;^7eFBlFwpc!xGA-%pih}z(UG3KR;~l3k
zBvJ*~Qe>OWjL=#IlKLcVxAH`u{nW1z8Jb&(SL-i5`;$I9(uSDTc21WTFbEqQdtkJm
z=2G-BX+Lj1L(y<r30ikKE##fTdy?I>w5Xu&NV%(|j6&rqzVcdnbZaFW9SXAF%5e9v
z&k=RSFK7R*U|k<d8x2=&78zDVcE}dJMB2sTxB6v93+1Z|N!0qcgW~Ey6R_@P3_j3u
zpml{t+tpY2xf`%`FBs!mkX}<FY)iHxfg4p&H#Y_n!DH9bM2n=0T@=qmqpFqY2$d$g
zmN@uefLx8`P&w(txV>FYi;K1teM9bsV=JP5uNeZc(RMRCA}+Hm&Cx##%B#C-w6*T5
zw%TfaE)gOXGB%?^3Hwyvx4+>be%BZ9L(AA?Ev@7kpB1!3Jre!DpDx)4rhUA~{I-bb
zt?SJWahg^(uJOn$db_m2C^AW>H5JL_T=H8JE+p*N$f`Nn^g~*tyj&1CEU?8RS6uD0
zz)OMa`#bfolPSW1b(0lR@|NZ}lm5kC``<TX_A^`_Y(~A+eDKailbPCg!!~<gsuOG@
zEh>RP>KyDuTwIgCE}8(`xRX5(9r}I!2e!5SFKDGtp|3R=n>9d)E(9>^W^D~^zW<=S
z1WwAm#h7UTkZMg<O59$5^DY(_uvJG!y?JoToZ2EiFiwUepWDo5o=Jg_Q2eA`#8NJ|
zf@0LqoGE_<t4ho1^4hM${n<>%klv&f=qgn*v)<~Yvf5h+5LX5n%i9e(o?d~bUA;z7
zsn8MgPdUp?Nbn$WpN@RL<+gD8p{Qpi$Nv3_@*5r8<iH3@_fQnc8VYaj1>Y_5PA@Je
z<;>qjjT+utXb-V5jYE7u8O8bAsWL3BLQ}pCw0nI`_jf`RUrHQxDR8Lvpw5eYdAbxw
zo3qo-%>UWWwKdcs>Ee``gLUn`1V8DsFP$LH2vCB3P!cM8zG`F9**czyw@K>0++nt-
zK-3L|*m|(EnnpSv!UDt%w6&>>b>~j<(!I>I#~y)tekUcm0`S)%wMJJ%B3q~FJ1uoo
zW_tS$gwpk3ecT)i9hcusbJ!{tm=Pu;fS|&k!^*Cg13p#-LKXmWTh|5GQMdV2yy<~5
zeVQ&d^0v|Dz?w-RnL(|G%KF$&ShT#zGFGm3NqU2?T18E)2=J!qHraH4)yYI#*VEaa
z95Ai4a!JWyOog4K`D_ow7PY!?da1}V^;z=OoSH~axrxDTMdz&3Ejl$@+1xj6{>3<+
zbV7ZL9AI{Kox@av%-h4-{bRnz*D4hb>jOd){a=uB1$4zkpY9ga{Zw?liHrmM5_ZZ^
ze6=@@p|}HiIVcT^1TWDay#Fyn4F!KFmJIHD#Oo5^B<*%NqnkK%)_8zS_LQ94T-dUc
zLAUG|wdgcxDp{2b<kDO-fMv~=JP5cbtOg%Y;WLl0uILCh`Whz^3)T*VrC<38)0*Wa
z%|RFf;vNBaGNiLp5!gCuF{&>w^*aFSRmgiX|MB&yRGTTtOWYDE99f=3iw7dYgYjVP
zyCX104~*tpDcUOze$Y(UYwZ|mXnJGS%kf8(GZq{n4#UO!rJn&cVB6$k^l!V!va5f6
zReB;}QMs83bdAN+3t>fb%J#&Jx$yAyh25Y=UFcrem!~17eV3n+Y<m_=?O(|I5gO8?
zMct;scUp^t(;UN%-taW*XPqNObhN$^$3u}Q<1=bwT*;Z|sb8CHxvm;!7`-MnMNF5x
zav09i()=QtStQ{}#-%EvbOuPKKis51*#3DZ$pCBu+8~|0*o7!xQ7}UXqTeahOxMis
znu;9I9wDZ|^DVh%NW0K)@yeuO=<Q5CMxmPqxLPOo{Ozpu#QRr%b~U4s_Algam0yS{
zbl;VmG*53TIFHmMd)mw$YWo(NU(EFUmlKXbF6(d{MV>#nxIc;d@<@pg{>hV+Lk!y}
z>jStH#^gC*_n|G*7toHaM(+fIk(>xyg$*F=R+Q;mn#Ib~a04gLmV3F<2$P2nit$3}
z_egG+5p|iI$Bn_=n)HSwRrU8<g}!U#C7Q<EIGK)&me^B+Pr_|lr*(dI_J-2iD{{>Y
zHSyYTW=fDu<2J_X{FtI7zM|iuUu9@jKf<*7=04u!R*@`X7d0gw1|%K5pKZ<a9<Rb3
zY9|NhHL5Sj+1_gh<;FU0HyI&DH-f=9opN8<!B$eZ+SS?S4XBx5665ExMK!d3y3r7=
zz-bEbhbI{V4s-B|s2uF);n#!^<aZK!Zai@u<6Wr{*XW(R9$mEl;#npXI^TFJ(@d}1
zF&C_fu7oFh<cR=@2pq;qTecI?Dfa7p_`ZiFpzaoMLa2DHG~*rrpPP?7XEBH}y{@h-
zPmjVt`S34$+G-gG-i(ZZ!sRAYOjTz65~b8eX-z{J9`BBUj_JS<8H~4L7w1SngVNS6
zr-ctHzgqk_Pv%0fN;UpTfxfG`!h3fC)ce7T?`OlOCH<5F=pXd78Uk2s5(aH=W%uqf
zDw~7;l#A3=wn_+}AQ=$t^FK*nfc=TIx25l5liw|P6n}Z@cgt7a#vbjS1(cf$C=7P{
z16Hu2Oi5?hujaCc{X@5!WTZPJk2FI&MJTCL=Qc|!;{hbwiG54@JMwDCCbIMTYLaq^
zq2OJjt}&d&Xu#iBh;coyGpiaSic1P}z>cR$*UeT)NlI=Wper7G%8|+1cduRcrO(t|
zC=zS3s!=BDknqy0`D=&;5!0)Arz$C`U9nfM9E1%%w4BhgUcn`a!YOJJc&%sDJKYR_
zQcNbsO}Mx?M?wEZYtFy*bWW{Ggbt8AfSf&&c!(h6=BWk-2kwH^c5;0tCXf($HHR2k
zTVGY~wHbeUNG5*Sml)9c22$)+h(gK*s)uctox3qFM)7fFe)u{vXp^v*OVRS06{!Fl
z-b;ST(i-_(E!UX9FyYhr;}2Q!iJxVSig@6l*C43SlA;-R5afZ_jMVI_ZLVaKhD*V}
z)dLaFlSY4AMA-V?LWNkA)}%-gKI05)ARgs?GN)~GgCD}iSGNN!P5crQNqy2G4|=uS
zeMJ*HL`^Jt5p(I+d{WI7nLl7fZ_orC2gCUV3Z3r=HGnRhCkLX?A|GcRA;JmK9g6x&
z+xEgSd70-wDvzQkx~?1Fgwv-HuaD4G9O~e1p;Yd80g7I}nHav1GMMF1!BRt1t=&U2
zdpr=z)X+>fhKjvQ-ZsVe-au;|an$GGtR&U#y0`Dbsf5@)pVo2|sZv>il(m<5Bl+hW
z=r#*FshZL&_XSvldf#$A9$-gu{=6aEuI;sDt|v@bTEN#iRpztglMW&3CHpw;Ulr1!
z_@QHSk|xSV(i}ZBP|xm7gOm!t9GVbl-Pbnox1>E+DBPPCAH5j@=SF~qgJ6+y;-RTX
z&8&ftYU~6#tyr&9zN&Etnc=2q$?r$HwUXGr&f^lZ+ar%O5C{>D#+ytuVl|#%VO1Uw
zZoW70C5HlhAzTM}-%i~^t`AHIacAngvj^YL`ebk2VOK=Er51m-IvqA!Z46b~S-^YI
zb>X3p!d&SMHK~@f0cf$SwL!UoJp)x0#NmhFDJ4{8ZjxBF;seoyQa3*OTl9epSOA%$
znFsQ$2{;}g+2U2-&)|rZt!`O^oU0bR3zOA^TiXpOQ+pW8uuK8R!CE5Uo#9588hL9c
z=~kT5!|~ZL*eaRa1;IE%n%ae1gwu-~@3mMSm1w!$+cW6C|MNaoHwThKx5V-PcLGNF
zzCM2@Voc}fVE79_y4$@_6#AvMh5gVQcoo-uGp%(!xMFA3h1Mw?x0;drVNT$!NGVhs
z2wh{3>a{FwBXnv;SX)1bB?)1^!!bpF{e(Y)IP|L)d(e8Sj*AQPm_etDP#QKAXGL^j
z>`k=(XzfoJcs5g9ytcaTkBZ-u{4L9>r0W@g_%3sW22ybV$j8#zZ?!<cn*_)Zwr0Ti
ztVymnyR!tV0_oq_l75<CMb@s>sfdKoZjH80J2+XsxG5I2iF}SjK_!&GTJ@)Sf0DL8
zk1*anG%TmOVAEqIL!K&21oa3CS#VK`y=vo$%Au0(X{%_z#%eV<S2Fj;19qrxh+lGG
zP&|SgB<Iq~WX;$rZLzMDDNP27aAfQG9wy(tF<LS5L8XaLaTp6M&a;JSq<Mt+Slf8S
zqd5AOBQ33jAp^CoHC{I@XNhZcJy)`xN(f5Zl-`4N$P|CP`rAuVg#IiM$%kq+5n9C!
zmD~a;wnSS*a2gTX9Ss)<n}@zK`4yC^g;A04s*m4EE^aZ;%`WZTCN`<Wql3pYA4`%X
zai&Gc4FY<!$kzF>s5k!5!?%v|67t0Q$s0-Zth)brE9ovTLq=+YQ|#Flzzk3eyFOYi
z)CcialHN4p5g@4S;90Sk8=96rxxYFd5a4@3DW__&IAYjuGfKtk#;DwjU9B$R-p0T#
zWu%o(%A`^*=24=mfNQ!gOVtH8(%or69&NgnnPlF`E&0Ir8FnqBLR6vQ&F$gW3+JcL
z+U5xk>#J%LwSHp*Z}u_A9`)v~(<~4ND(V9qbA8E9*y-xd$5+HQ!x7^-p`HQCtuBo9
z6uy@(I|%-~<wP!BJNb@Ly#%|&j>sjXSrKC%>niq3E$6kW`7o;%6#I1E9OLg6hi)d!
zy%6fB8g>^O@&V^dI^9Gl_OK$??YF`2NRP2Od7;O$wU+sm92Ke79`7|}iAt<fZ?E(l
z^9VShPh@-R+G;Vz;jz|Qc%bD91k>laqBe&glq-t-X%#NiQuFo_1A$QG*E2oHm>GqP
ze{hYITLTu|>RU%lnvp%-<umH;jlWT0o$qXQ!AQ>P{#g&4LtqH<`?RVe(wpq)MKh-F
zSLe(>3dQ7i=Q$OpUjXVq|Jmm3p<=~?SN^ROAFh=jLP+&o_{rw=u0HJy?J!rP?sm|V
za-ITn8fT>fD%c>YPc*(++y~xWt!y#>^W+>ufr>)8+`6sdvlU7MjtGasd4E@dy)*_!
z5m@oty7KyZh47>8w^R7ZNnb6l<TW||qS!pIAMfW;?$nprG#pRgU4(fCD8aZja=yqr
zHt~)z4=_Gcp$Lh`X+H^8<PCWo#DiR;PSodEjQ@rZmM4Trp}s<v^JTTtp5({0c_teV
zl~1ymtG=Xa#+uKzoyPhqKowG=A%$5wq0lbs_u~xx+NJNyDAkN0M1J~K(G#3$fX3i!
zbhp;+bZ0ji465#uQccaZD@FaH>O;R2h!<*}yl(w|S6XjJ&b}fXtSw4_DrC6ou|i=;
zcv|Qf05^44qDzZBeLNgOj@6i4yPKSYd?BI3ekLum>6}lmr((Mc+mP+Dl_9m&ch9J9
zMS3PJ4U0L7im5z$^>M)@f<0sF3@?gA6}K!SQ+s)}UbOip0CU~^oKAA(qX-f|4Hw))
zQ;P<Hm#nu``De-#)?($#ylYUyCu>wraq+F_ukUKwX+dG)|8z)Stn_^7MXg}9g{8<?
z{8lV3B)O2hD$QUM9m2C+ssRYTfMY<y2lG!#Dq#6yLSMiLj;DPQS!fEau?yF)RIbt5
zo)_CS^}7o0;Q5TIYLFd`<ImZt27vDA-go}L?^<3+FM=LguLQDyWU8|-k=}8YPOY|$
zg^tv()rBShWU|#&D^!VXA&wpd*KV=(fpo?fsy@A9RHSje-(*(o>6Vi}0vNo!X-8~&
zu0prM+_0KvP?l$5;ZtFt5XkcOt@t?;#6aZ=o5AxP92I7=D?q?0>XMGnXV@yvh`pDg
zoXNNaWpK3P{znyBSo70(GqJhVH3x~;7A3AU4$e>|g%M7jC4BP&_gdD|>o&>ZRhX4{
z`y+H9pk}87dP{5f+w6}1Os~ics*!K@i2*FO_~EiEt!^FpM?~zglvugF;EiD&-=W~v
zH@}F?)LG!1y{325^*6l!Z#p`~E54HUr`2b*71dU4aN3*SNXXU1+&>5^A=fzA>1<tu
zMQyz18hj$!Ge<>h`$%ByDoD|RX(!rllU`3Ms&Jitrw8&0P~HnoXS|=EJiGeg{P25n
z2IEb;a6k)ZM{=xjO&-m%{J9v9auZ~`F>O2Qq5xZfX!p&9o|Qg%fdW^}4!>NLwm&At
z<s&6^d_aWx!#$9=ibn<%tvr+3V<;CmUl?LrMRBq7E@G~%7a?R1X(KrQKoJ)MubAwz
zqk37+E2{JRrE<)Q7;-aF<gqI)#12-rR*E<9yJCr|qlucfv{NFuN0-MRTT%38Ev=-}
z9j*$82Ila~s5X0IX8bemp&89SO>0LG33KA8@@~fDdRkDEbqS{kNT&pW$N{RW+|WnJ
zjM_HuCR;Nh$m}fdP-QjvyH{;?>KWi6+$1n#f>nPzt2=E~Z-px|7yNG#Vlvb@y=m8O
zccg2am~cKAQy5U1Je_w4x1PV)rL|1GT1*bmCddOP<}W>!X)<pDe}gV9KD514EsRDz
zP|MspA6kL<)hp5^@5{O!31Zq<QFJ=x>gS6mF3^pK+I_}AuVYjY3%biH=fGsE`<2?z
zRZO563y+Yc4~hb95Vdj(0;@IQskL)9mRxz=_cKA~eU<G<2!~Zy%wJp!69wKjH8=ZG
z2zAH0ggzSQaC*7WGbo_)7+*_qvlF1UAz$EI-_y#gz3CL+9uLMZ1r*|~exIaiVTQVY
zywf9+?J~+JQN;D}QekFR)x1(&)}7D~khc4~=Hah}_J_vid&TcLNwjK&4lVE-;$cnA
zo)5%SMheT(V+|v=_K>Z{`Zq#5*Ndy%<Q#BK4nf30duSrG=k6n!Wt9q;1sYO1v&R~z
zm5B`OcGO%WMK%oT-|2GflvjGEu*s1ZnJf9JZ0gV0`sH;<Is&vM>QGM)Y+#d30<`Fv
zGV8e*L<YZ`bb2_VQX1YcFkz$U68YEGeF=O7S9D$|BJ<zn(#n$v1F4|gZ5HDGlXnCQ
zG}^H_u@JiDvnDf+XlF%<Jq8=T=g{p8U%DqoUptJW-z(P{O8ULeBs&llg4ZKgrz0DY
z=zgj1X*Az=8ew3Ru`0o;E^}5#VLi7vd?5^JlZKLr!q-H4i)z|*Y0+v=w@1nyE0N!x
zDH3)9<rA0qQJppNy*jycSlh5sv)RhE7gSdUckd3yk1Ij&u~O5tY>X+)Ymo>E`;Od)
z)bQszm8%4a3>mgg$rr^0CpW0fD*N7BM^$3NY+x?fxq5ysio~S9Q3(m4dT=EPE3_OH
zFWcv;t--p8Y^P9=N}twOjb{M+4wV%X^S%hSK8&Llt+{`_AH~-CdV4|8O%|BFF(WYx
zTp=P?0wmPA$NMd=*(H^p!2_RX6+;u6g!X#{>y;%bQN2hz3-mjqjPTUY+P-Plg&+~K
z7W>~ViRjL2Hq5{d^#Zj?dg6*Q7C8m=V(Y46n?v{RUfmk3=w&<~vsVEna@B@%oUv)k
ziKPoa4KOeX4Qw5$tu+PbHO)&)V@~yi!*o7GZ%>}HY<iT0<q_@+L8mITE%v)&iz?Zj
zVaBR<%$W))KIqW9XKm<S1$9ZE7aiv3lpJmEGk^;llCJKno&+39bGB2{@5AYmzq8g9
z3MwNMlt#-&(ny_Gipaj5a;*l6DC2eRc(luAx@QTq$n(lPY^upssMa_lMggoyQAjam
zGzB?8D_{MV8CH!@ih{^3Np)XXtuNwrEc2}I;pmHCo+t*A77@(6^Q57Rz%-eQX)$d?
zHvwk*wQW3|26kbzZ}trqcnPctN5Dt>!w*C-P8#d(M1r<4_`sXujE)sEr5DNWKbry9
zhFec8t^RjnCHJm@92Xl{${!q<ajPhxyTnGMTw%q8bCjpoN%h`&+LtQ{Md}5zE5$m4
z9<BzK04#GbfW9VWbNkLFG>V3%Z#0Si>5{0vQbgl0?|5_jCC&4QfuiBsft{ihw)rwJ
zhe95iXju<{QeWGIW(s`oq~c!w+tS?oV`cqdr;dvA<@jR~bFZ#k!QatJ5ld5yZg>th
zc)+5mBUNj8u@*a!sJpj@+*Q*m*&6fQX;qnS(b~*_T2IhiWmN_C<n2O0qgr2|&8kk+
zWZsg91f3I^FLcH$V@sC!(8rJ*YVanT!<bze{%v6u`d3xg@g<bAFCKb7X9hs&k*$CS
z47%|<HqPdL>NP+kid>r&)RkPDkU3H`T~`r>wkf!mYr}TZ7JgbW>CY3fQ#(~JK@9-u
zi)hQ|hV#wnsx7%^*U56WgGIvjIECer8FR6xOtNC?<w#R|)q8_9^c+;L5%~dyv>%vg
z?J%aHM_TSRn~hUS=s{%yLT!&}(d(p~&<tI}8rQtH-#kkif}umTBqY*aQJt;zB{LP;
zX?4o=lN<uHNqJ0-qB19lWcugLz-fNmph(knP^bjRvMB6!-Zp9mR?%vPq|J_bV%r2r
zNt1#%Mk-7bMVa^wM!DI)Pu}tV{R6)%;%|;4i+M=R%%Ga_%m_u#BDgIt!7o#&?POB8
z4b!k3Ja`*mpW202rNcg_gO%@USR}#hR$O-6xol8~clzTtuXe8xujx2>Qs9<4@t;|Y
z<@QiM**QI&o<>&=Oe}&wYAur^J35(=$i0eg`DAlyjjc{{spL=HbV*TJjFTb-_yy>h
zdm$)ZZLN&UOPyCZ*%}0G((!{W@p^Y}5CFBF#SirMt<EqLXKQTj?&V!f_Gm%^j}gjJ
z7)Ot|E}6#Cbg-r}evN66Hmt~Hrajv+v7Ir0%9zrZ7YoP>Y{%lSj_Euc9a&MBZl#m9
zJVQvn^Z&L;=81W3sVbPDv;zu)$f2&e3M49!Ov?cs+(;%x0@gGgB)-YF^<Jl7Xob{P
z5LokrfHR><Geh0Xj-)xH2i%E8^TDd}n2N3Qd`{6Wrilu>L|)6@71gg-(lIaIo&M*5
z*oFbe)FtErh{tcCBy;8WHQjo8-3Atz))!ZANt)u4pXawYL~1*SxO2(|Hf@jrBE^xi
z>ew$&kKNZJ-Pte=SzHiTNbm3~>ksS;+~!qfWaX=soki<%{x3vU8_Gshuoe_OGQo0k
zS}m%EO{b_R%=n!eK(zSgq2JLAJ#mAH@<h8H-SwqQDi^1>iab)1dfn1K@kUlWwIOmK
zCNAkITApaN9VxrtfOn%Ox3Xlp>q+|X#)oF*=oVx6P6QdVc>reJIwpmY%=DCZqvfh7
zA|>-$K_RQ@^?ZlbS*79uysgTiWvbj<=UY=~$^G5YLzl-i<?Qd1e5&*$QAmTf1rp_g
ztx$ihl|RemNX#YDlgmuq8;9P%)L>ot`h{#SNrh$J80uhMcn?M97w+jQ;xZ<a&Mt4I
z)u;i?L%uuMHr*Y6rbg<ls#WAWGO&_MoM{^sCOg?)p8<a1;}6MmvPiFcNUgMz%K)lb
zaG{`}ru8Cb?Q|=r*&k(FE_sxf+USxSsm=UqiP+|u#q&9ZmU06*d-$ND;d9(^+^<WY
z!Bx|3cJ8B`>zKz4=dIBHogkEdxm6SSSB@Z3^H>)3RuBBrk@SCi0T@3MUQPAhd~~%O
zFYD=xSlyCzEzoA`#Xx>xx;$*7!0rl4mivo+{;m5vapg^s$nJaHIpGHYsj>;Go4B}i
zPx9Fb&3}svfWK5PaGNfpdjS2WU9h&b!L*<N>+%;a@)0e&2+Xj;2KG$LL}pk|0I^ak
zw@@l)3MfK}fGl)y`(YZS+-cdCy*El*kn+%GVaGu}v=5b81{!Rzsuqa>?kZm8tW(N2
zqq3b=51L9b-oKImoq)JyhNhRu8tqub)JVP9#cA^eHbBNnp2AL__f<b(h@fgdccnvK
z+nR%zG_C_VI_T*dCrp7mQnX=j{?h4(Ot;YE!#dRR>ze_O9_xJY3GSV^d74)0q{Ir5
zg`#KdR><yw<7TNZgF~k`i<KWaiX9nl2OW@IA~yrAbHbbv%-80DXFW8$^Olj6It0k$
zw~KJaQ{_g(N(k@>;4;hV+2HsvcTug+RaNn~pqgMd$dlv4PSQEt$yMV%A>GIk(D^Ow
z_PFWs?9ujiZIeb_+XI+U8v5XE-uW(XYc3--xc;gl!qZ9U7)ra|G&EeOu;Fx0pB2@3
zET0zP+Hiq6>mk+SDpN8xb=swZN39UiZhJ6<G8E=^HT6fcrS>1`dH6USM$4vsFlaIt
zOf?r@efY!lVS$<&!rnhKaY|E>k>~HM;nuhT4qrcbWWSwseg<M^s48=9w>gt9(6=z)
zCBIu^Z)gUj5+D#jH$gai1&+zAC6Ob*yL#HuPL$?wMavmU<c7c^CP?5OuH9aXQ5)}!
zoYK?TW`lhBIgIeCfa+V3z_sKT`SYdX^O20%VMydHRg|$n7$qQ^kcKiui|hJhSkIK}
zT(qzCMtJ8I#(S$VY*Mxa&2hbwo;i&C8oj%C#`1hRso{!X(kohRJZhK*Mk`etW?;ps
ztha`e3|bY^E*FYbJO?W`Kbbph!s0S)P<PFI5NTi?g?phD%b|`9<`fd3;bImQeep9g
zJJ)cq#&nMULU{C3j|gmz9%h#HYuQy0**-|Bgs0p3%Dy~LM`|rFpfixdZ&&PZ!xQ#1
zyzUEI`Tc?j&pa=Z-_OuvA|*K^U)7v;$6CRkK?Jc0+~B3EqBf6oEI_>;X#Pl<7h;h>
zl=Odd<c3Fm-YJaF|5_pZHTf}l3eMoRY9~J{x3tt01uwvUaHU$AV;E3pVm+W?S5?s`
zB*JI@kq^QFn0sc>Nr&syd*x@A*3dJ&q~zlV8Z?c089}>=+}~RBRO1+o`PrIwuBRP(
zA31d~%x`e3e}p;9`TY=<{+z3Fsz!n0vggY}++(b{H>dcI$NgAbs8Ao)=2~QaY=6b|
zBYw*Ojy!wpS+8h9u$<MQ&kHWt!MWk}O?4ZQ>&5aQPa4O<o-2Z@C-n(qu$|WkndWAC
zLdVEN61HM(d7AMRRk5NJMACz=QiN$G3#kop{_EVO{h~fd;iL6L8(lAaXXnU_PGz#m
z;#?G{fW8k(T_l7V+i0r|(f|m)wqCG`Fa7`oGQ6BN6JZke-wCrJ&EpKUF?P7~Vq1=*
z1_>}I2sL;hM6JyfXt%YyemUi>{rxLXOcUO3s90pg1lH}-EK&DGndGp<83^rJF~0qJ
z-kBkDj>P-jO-yC!zWnt_kZ9+4hU?Z~6S!Sgfy@$bz@>13wW}H0%3~^udh+wxk9;o>
zzCYW)zEP>$4|Q%0qH{r^nM;NWM^_WcZs~}hxL!CX2pP6rAUTFzAAgY^OxPptl~3NV
zU4A|YR`)Q19|#jhm61X0yQ8C?t5DwZ6A(MtQFPQd-5+F&tJTix8dv518a1)K6-y}s
zOPx2#8hLi9zt5Z8-ewmxIPGxd4y(ea6H>O~pD6^<#q3C5=moBS_}U@Im{*l_7G~mY
zpS#_o1ggzTTz~Wj6rN|&599?tqtc0#i~pUV)-DO9%_$Q+?}}cj^Di#_HbD5$CaV|(
zB-d%Gb$K+LZraGfwl}sYo7f4xExBY!*lEP|M~ADyUtTTw_*%%}g(E5QV-iAcy^h@X
z-wCe@(=_r&yn1P<mH5hO&x!(7JS^mh=Bq~^Je#BFxlLZm4o{H-qs3v#Ur8QSo!8#a
z&IMTMXtyAUm7*)BNg==*#0RC~$(v8_fVP^^F=zJWo%I|Sxe=!|5iuyA`hPv``GT9L
ztM}7{B;UwiBT~!^N|efU`{2<<9}A+0j~V(NGrD&|6~$}^d$HKD)nm;fBnxJqXd-F7
zonJy9(rp1|7nhwk*T1H?q+6&tdOb*AIB*-)a*}UI<&-Ic!gE+3+|>pamkR+&p#FCd
z-#d=$$h3%&RTCi;^R6`u@!@<;0N&=M6R;v?SI-{_N3one+UBh}bmY8x9U7UbFZ}vN
znfAFD2G|9$3*%D(JGB8<X_J?F!O?0MuQh%(#OjOga;Eu8(RG0wi@<W0Jr}b{tgWHg
zig|{$*}~TYHghQl*eJXme`rF^OZXEt;y4QEylKm_CDZDy=~^=_tU^Fzpp7rTFN2<1
z5yQkOG6}wX3^#c{lNMuhhxYK1P_9QGp!+tqy*pSDH`;vH7f5C?DymCU&To~YW_<T>
z$5)_-_a3%$IwQE^+DS(M$}e4Zg%O)ojj5O~Ory<ued}E8lSh$!Av29%2|l>r`X-P<
zlQ_CZw{E~{Cb=tAk#B}!7UeGgW%5j&k>k%KJ$v!rw;KDw#Zm=(I_t?itPvyP(2Cou
zb1(<7ywEeEk7wGRN#LvQWnhdbh6*}FDJb8I5nGIuOJtZO;%`TIM86{~Klki9CNPOC
z2oHdviZ;woauL*E0@SIdL8eQMnFJEGd#X!-J*fn(lE}D$k_U2S<}@<Ahz`@11b!H^
zBb&nvyPGp0wdKYsWUk`rwOB1vuB{mi>;&ZW0}3sNwNEEi6`F=%j)Zb}03q4ly>Cj#
z18r|oMXBzA!pMM`4Go;)k>v1Zg{${Si%dpj09cl=`y>-NV~H@kq{>YXTD=33iVUfC
zs?J|b^TP%uD&_6{c>JnVO?XN3X4R65r)=Of$nHqd#f_3f?7X&X(2^4WHRbb=p%8fw
z7m{G9ccWXcHca*2Lzmx+ln)=!|CF?D(9kczfOtx%8pxGj(^UqO3x9K4n)O7LJ7C?7
z?l2>c&&FEkzF#7Qb=W5LN!fsFs0uo5^nn+efwWA&eqTUIrrNeIE5L!}+y4J3I`4nB
z_do2P`<xD{w00Zoki;H`CU*64kdP3X*t?BTL9D8AD0kJU#Ap^3qhCqv)x;j9ws5Ff
zwQ9Gtw5X~oJ$LSZ;Pb<0yg%>P^}ep>MT5*CDcVbBsRu|GQmjmPLVwSz32A~-ed^8G
zB>b?Er=G@rL!1Z83=lvJgej-!msn}rqVCp??|$8hU{+ehD`AKWfT+RVLD=VXdG&Ny
z#elQd^De+MBF@PnWT@$la3@r<e<h0&azH*@3$82;R3jX3_@EO>@>Lm?)~Ow8N_uH<
zR+$En=3ZS*#g{Zf`E$VSQuh1}H)pk9nYIo<tqI4!@4iiUp7k&AGg{~nBjQ(*+Y(fs
zaqgim9j%kalPK$;2jDWT8UTZ+CR$TTcTKIS+Xmz5$$jl=g74t}_y&HRGK$fD0UNm=
z;F*&bz?m;i$&XP9M5>7<5xpHkf9KxLc)QwI)1j}n(`(KMdMqrNoFVg4w$u^Ny_QjT
z&RIM`gPOEYnLnG;aMHGJ;kiS>7^0CgX;{U0D{X9<3C$2AF6PnJ7@Y8Koz=)Z)zd_*
zrBi#Y;5z`YCu)!ursn=?=5~=D#;&@6u*3+Zo$Po2+p6NojnH2>1KMN6?B|l{vJu11
zHhS!ID?ztwrQdHKV^hEf`_L!j0}NN`dhY^^%6_t2>36Gt>7`_OjsD52yUKPll!o%@
zB%3w3UJ?d}D;*iTT4`Mk8M;)__7`0&Pz!|6=Xy4Lx`thinJFj<GzI>>Xlhs?h?K=S
z*#7t1g}>*0mW|s2eKT0)%%!(4a#MaJN~aKnxDQZ!LkSuK*FlU*8A{JaINl><)QB*s
z{E6;5%bed#uQNxcK_x~nd@xY&*&j@uw2t~&tYZ7V+HE78w1Pzb1XoWL^Igb5gMmp0
zz;T;?$%Vg9U5eZ$MiSXBeaAhnev31F^S~WyxEt_3z4Pk->S3N}m7YJp^l@$a=iMYh
zi0XTs1oiaD@P=*9yUQ+rpxQf>+|%Ksf~^Lt9hVL!<r@312I+FCBuL~MOJ{2xv|uk~
zh1p;!s`QxK&1nReNzgN=-6SfXz1YgVbUHpeG*eRm5H_4G%0k-~%Vs*2YR>!T6AXtk
zRRSR?5OX!NSh-3m58wN)+eGy&o0f(_xqYQ=2Pjlytrd5NCDmFFBanPJ%eewW(L$&h
z29kAz?5rkS8SJ_=#hk6jFiM7%O02Czl5225R}T4Y!Iy+h&O~(WMn}bdD5;eb5Atf*
z8#6cTE(yl0m?KG1Pt$BeOS9xJ&yYRm<qKqMD#dGePJcKWBAl#gCFiJRI4IE3J>em#
zYip+T$zTNi0QTX#C7b5aLB`C`5C0}mt%GifnQY@Ic>S+b#Qb`1@xjw$oX10@EduAh
zg|mKz;$7i3P&KImDZF$w>)mOf*B#UZ742l5N!XaASNKZ7pW`SGGRP0oP02ZK&K=(G
zXW^dwXh}7I5=p)oZsEG$rax>aVXm%2z3=LVWw^#@8McYLO6J|e%3^tqg7+w>Ll~mc
zYNDa=Il9MjC<s<f=}skE_{d(HPoA`%S!is+!+)WcpE%ukUhgdPKH%eS-wVyX)3CrU
z1`fxjnxl?jcR%!4_t+9q`nNl^GCiqhF)%x)c%>zgdGh(l@K^b~=Y9qn7|AS0pPl;q
zZPqb^8JvxsAGhbbW7qmR5T|Qtl4@mJ3knu#rACGa)mbGS{Em|+OHH#W2G_0K$r%Ku
z@W+viYuN(-{icize{4?4L071=VUyYedpj(2iI^@z-4<`kfzp>#k1cG7JMnkeeJ!?$
zQuAwr;MiTU@(!-Hmb;{Z@4jk?rPQEp5~9l)_76A-sZTXy-^CLHooq*>E~ZJDjlu$^
zBDo}$<T(k(pHqk{T4cwpSok!RrM$MZx|&{_hnJ}U%typ33i^M10?CuVcx{AsQ?73P
z)u!P$>`a4}wW;JjUI;Y_AU>Ff-rAug#YccB2pvFT_D|`#MCGm9m?Bu|+P-3lXE9H`
z-np>Ldfc7t{N_rPL<<mX1Ab3cqDSagl04Be)il!3lFlWB5~k77004|W(Wnpc@TFvG
zKWV9L)l!_|rA>H^zf`n3O02t?XS7#suStJs%{BEYSYf|GgH|;?fR+7_9}>We(ZNMI
zXRwf`OggBX(~3k{{gFf@Y>pT9L(&3`e0F+ApExf_xlYpR#}^{lx)Hk$I%#W|z-()_
zhnMht(M1hrZ9X6@sxl9NM?aUPrfxMAq12|g3rvMQtizYIQ5<@R#T1djpOt298+cAJ
z)29MAC$t8u;MH&XCQ5!eXBt7~TIc;gMb4(@&}C^{!xJfZjT5C6cAKN{mO?orzA?4g
zmy$PuLcYUul`>j(GCoMswBlB?f~Qg!=zhoXdJ1U_8AyIaVI~wZTL9qNuRZxCs&H;S
z#To%qDtd1c;powM?(7(kj){s?9}^rav7l6=Th(7JRSRWwPzbXZS^ReBic4;#(T_Q+
z%V}F^PXFf*@>)-Fl==!9@)L#<L=To?@_*DtYMEr-2&R|n>0;bANTHWQmb68LZRBRI
z5cNwnEZv(ac4#^oLtp4G5DgsY3=Sf6<b+YBv(jXj80mPPamc`8!xS<zzp3=!{+T<k
zyXxJAPg%r27g+V_#+qY?&JmgECVYHP!|W9@6vO~~crgg!PV1OU)l;_-vzjzn*C{k2
z4hUK!$&k8{JH7fJ$W0lM$$^1(;^-0!M4dU_^omZeb2+BhtJs9;gr^kE+fFC@(GKua
zK3vm#y{`V${)Dl31M}*pzx}#!^R`RLp3g63Hjm&|Xx~Lk(_H0ROD6ZW5Bt~$E%gr?
z(uSGZ<0mRM%AZz{&QQWz8m2V$3sYh#y?WV#5>eS5(6Kjirl4J3=YMC%ZegEqX}_$C
zD1D*Fe+y`t(M`gPpkFi%w#w>k>SCjndV^hvq4e+z^P^1B%3C}Jb-|kdd1`SKmDg&d
z`R*r=9`i<kW;J>A*oiiLbtcN{(R*)ldA{405Ao{!DtFP_oq2{MtPi@GFO@;s?lmbF
z!Kjx4{`*Zwj7W)6eOZ}O5y$|7@d+JvP3<`Ky?(<+iwrPNQi9>F;EkV;^2?0sIst;a
z)r)FQK<r~6|Bmc<6omW>VHkt=qj&5BT^2x83GE$MC#;=bl^lp0L7pf9G|%Ld4<1g6
zAu{h0#RwlRREr_2Cz@*h`%OAE$PKU%;JFHjJ_ub-!<&*<Z=XD%WPtoAW#R&EJDwDm
zUPXMvunn+ut}NnBRObgf>R<KdJQcj6?^^P9{mnwbS%G?IyLHL8`xmkJl66Yeqgw%w
zo0f1h<r}H_`OV1$Cri1T)m}8^^{7D2{y5*D2=7cU>tPRJ;{|V`ZJR~K*sS+`0=egP
z`Y&sTc=Q8{VVtB@lD$@^sIXOq)JvszeSLdrmc`c<ZMIkU+(ULoKBvUrK<@_$I5jqI
zArI8SGU4|ULfCl_=9f8+iWoFx5BWjeQ*?^xLK|2Vfa<3JLQ+g(tu}^>i<?Q3$I}Sq
zp~fvY%P-F-N^~-BL^}8%y%Pn8f<0lmKP@<_69#x=`M^y5;{-)Vx97r#+M}>A|C~Sh
z;EExLZ<By0AaV0dUR>S_;#(vbMhdPa;&wr)@28Zi&a?krB^h92RwC=+R7}D1wAs1k
zH0fcT+y%ffIja73YGgFLD$!YoJ&Fgj=So0_Q|9}=t51*r=IVU3%YRklJ5V*hdo@~O
z3@yt<scAAbv>1$_IEtI10`Va$0{pBVcDFDl+EM$`q5g|cs+g!f{p!vM!8<eqh9TAb
z^AHr_9XjBZPr#ApKQVtw8-i%eiHn)cp1to*T>Y;nd&i&(P`bY$$8KE;)oL$*zRkM8
zT@trjnGJaS(kjPgcsO%~P_lzT;U0YO(5<S%MQN&~L*M2cCK$qJ!S^hw#l{G9c)M-s
z0mfdr<N+=;P|R~q4bxrn%;(Ea(4#0+ygNj$=egSPtB($^vjkLR5_o<hsIKfkP_?kg
z7d|`jOQd9z;>K-`k(xu~-zrq~nL7iUGsSM&!WTml6nq|rgdRFjTpkT}j>Rh-@MrtL
zk=4d(1{$4Y?d^BWMLh&9Kn|y|&p8Plfn}3ik=yL?<SOM&wPF6`+UKPAaY2#;dV#2E
zjD!)q@3(jZY^R(8cRCuLw6y~>$drJrtxpCS&mDrIDcV{~4&ug!7R0BG`n*zDDHVRT
zI3ncKPwKmawCQoK^{zNInGtJ?@d#8!l~y-9{%b1!lETJocwQx?mT^&)TJuNudU*n+
zHFL?WY>#RBgL{+cCG81e%RTb(z}vcsn<Iksa9bqa-{OAuj-8gfMFgfle_>OEF$e;J
zhgNL%drR@Zj1FEzuYSgVdyHsnqBf~-m=iu?zORJ}N+PpV5%3OEWe{sb;T$62W;&pQ
zsX*LYCn*{1OdvPU_D+7b?tR{seNH;N!D3Z<)#EF!@jetg&m09JRL9rl9G`N~4`svj
zqwGxR+UPPBS?l2BXuIJtH1vVB{iV}DV@7uK2adPToZXGaw=+%i#Z}g;k_2dyYiUad
zS5obr>hYX4>@p45p43UBhYd{GG-8o^=x*nOOf#R1ro63bq;;}bG_Lqr<CmWDV*TkC
z0V9EwhgX{+e5XjxLt@R99y&?jjRy<C5Ug7hYXtqol9J~&BGGg6ep7)jU~%e&A_g=f
zk`YX)CX?_ucZyxf_{Ng3vEp&bQIyq!cm(YXfLEPx+ZQ^Sl>VbkweAYd^qQY5rSn3r
zIZAlNK|Er_yy3We=r|w=45=LztblbbXPtJ{cNzN4=m>g<8r(;ix#rCH_}Ay`Xaa23
zACQj`#mJ4NToP2K%1g+tylGZoDvnvw8@!eJT_yCQ1wlEbEJQD~OTxfBQEbfike?L`
z?v1ju(}M7wpJJt`GFc$%Xb&|_$2ptCv?-PQx+o6Jh=z#OlBL^K4j+mUeD)>?pBpK5
z<nj3o2VZL7T$vqc-~Q+>CGO7Oi{iNNT2`L=Fzl@!<k0<lxK51|0~BQR-Q4x16(p0q
z0Dbo+(?~g&(@<m;l0J;6bXL>v5~oiA9jElV9{=mQ<iyB#Z&R%Nl%!`*mA3e*X5!7)
zPtyq`#V7T3SHqW*m1d`z>1{C^PuDhnFkR#auR{YZ@2rpP6D2ts=4=C4X{-2;2}9f@
zUzUyAciX3Qj-7T~(dHYDWE6YqOC4E;&;@V|Q+qZK(aQe%oU;elULsPAS?;1F$VZk1
z-I9nj1I;wPrGq*~9;aBH+?q&2K!*H5Gkq3u?uS}jSN*#ua&OKpSX1m+_Z-7~#$Fa5
zJ!R(O$$=vI%whbpm3A^EIBGDNkW=Dwp%Mi;hA`<&^zqlZDVEEAjN-H6-uSIb$~PEO
zWK-w*^q}?ajKtEil~KrcSoHcYb0;Z$Kcui=pO?#1Tr12&wKI1WXNY!N>~-7l#Z|Ge
z%Gx1=$bNh9hcRYcn0e5;1QM@e9T6f}`!;>;@;J#U6a+6i9O=9qV%R~fyTEKSk8=#|
zp>JMMdt0miWPi+NRPIjQq}o@@1*Ssg4QK|rIqI(T5hu~qLH`?WgXL4N!$R*qjx^s`
zix}TEfnQexnG040kuPrwLv~V3v{Wpdc`63<B#rH<EeyulCB16K>BzL9a}j{Cb9i!m
zGrhHLh1K|O6*A>)SpF_JgUJfWavSG;M(9Gp*jfiDvrKiO%=j4L;4SC&c1f|qMhS{3
z9nx=V-x-ae)rw@QA(psWUxIzqT=#KOeipHHSfqMxrq94jt|*cCwL*9tLi55@@&mWQ
z3|L4Os@0LFQP!*G?x$?7rWX&yOxk7Mv=#ckPN}(JZ&&79iQ8j#*lVj+7l(zA8nFpa
zb9P6wwx}iDzi&58XpP=Mh&awX$Gj7j`kFNyjZHZ96LS%}R5!?y*FA##8jMWW*h;A*
zLGuwHyz<7GlHt`QFEf;5@#Rgf)c=xoHSgf#By`lcRmCGm>T#)5gs_dBPcamn0JKMU
z)k|+Kr>a6L+TVVDRzPPwX#BiOiRA3$UIQxc?e$B>8s7Wf8wE4$*c<YGUxZdwK}Fih
z)XxJo*(b7;fOqR^ce!djeq!s6CtxCiWQ`6KSUf88-<aJ=C}#vB_TbbFq-Sy41_{x8
z^5=Iu%-5=$wd&PrW^#o9mv6W1N4EG(MS%*K1`JAWm~he#pwc3qcyh;&8qG^HIT`x_
zm>%)3A~MRK$+!9w<Aac8|7Sni$}!!709{iPXTWzS%wzQDIr=<(0=;zf<>2K?U!Cjv
zyd$`l3VFlxVU7XK?J@rj**sXiLtX5`<wwV}1K<t(N79F`P)qpB(s%#0GJA2+x4DD#
z+9EpHeER-Qlg+Jd04C5VJt7{ExjPK3;G_2uhI6IgJ$_!#83sI9>%y&Pxl&4s(sEN_
zcYV2`!4&<{+@0wRCu2p>B!|@{Nkg~;rNuCKuWJ<Bg3u%kAz@VIzu$I`5Yw*pQgHmh
zbA1AMmulw&+Jo+Oh~^Fy2^u-Q-FIsm8VtsKtuw|0LhpaoK&(tr9Grd99ef)|Ju(4=
zZ#d=*a(Izq8EB|8$!bEVxA6q$D~ol+B9kjsH>THaO*FKsU??oVYED=hP0bkpbS39s
z8Ej#H9upXQOO5h!h)`lNqO*>5p2K~2FH6dkLAd@J`!)UiExX`3D1Wj14W$}zzlELd
zQBym2ZPx^hjt%HkU^Ez5r>)A!+fF|Tw@}`rSP<#1Q<-cSfe4RpeB8cA(nH*_Tcc<|
za&fW#l^!WQ%2Px!e{3B^`v&ZWR!*)dnM$8iK|Ifn@jqNr+#Wm`HPY;7N!SQ%0x=Z-
zqVKX{m_@2r0e}=4fbY>#T|sE6S30X!!1PdiWOj*~-`&`)*-WI?Myl9X?dy`#<%N-9
zcXhv=?bTElFINk$1bW=E?j?eyxb!?W@_MhC&$LIzHaX$c9?}tpT6~Dha9S7>;5~Fd
zJ5I%#SWhp~pA!BScLF_KD{r*6EO9huX|b4<nYBN94eP`kCW5^lR(|=yw2^&U9<j3|
zg4m_31WG3FBxABnTSDKw7Vx>~b*tWCALqfYVh%bEffAW`2$Wx<ZnI{AHoaA!^2p=o
zjU9aFsB$weDtURDBE2;|oTZ`EV>#Pjj56zR%t3$KC_)PdphWb|O?<9UT?EPu7go^a
zrLDmB`=LqEajfp^15UPt@$?1i84&R@d4BP^vxh8w!sP8uREwekcWWvnlvu~j{vmv4
zs>iF~*3>?DKg!g`HU`ik?1baJ<&VN&&IJj?SotEp+qQzcYc6_xQKo&jQr;>a-V9PY
zp?nYsEa$J)>Xyvns#xu9@@~Swws56y4zFY)b>QiYTPxIWK}`Oy6i|7rtJb`NJQ)71
zt$f7?7x&Wx$-U|K2JsS+VE8AS8sWbjX(m|a>Q74$JkpF!F-hdG`_~Qvk&a&<A!=A<
zkNFhL|9(?VImUNhys!*fJE}IbFFoMr@as6?-wk*2DVq$u(Xdx`8`QV$oO`&f8?XE$
z+G%93qL`hhrP}6a=wtx@;7ABT4z)Gt=lT}Zu$}r<woU~3t57j&##9dy=<Z_f^Q}!*
zVXD;JMFM{F2)V)ckFj4M?L>(-6rr`w<sTjx_+vYPc)@v<t?;F0S-J7uD`sl*dv88?
z>4r_odT&e>?^Q>)ygkcKqac4OqT`7So<y_p-c046t<g!N_cHFov!@q=otq_ho(mm~
z;y5Z3j)~TrxT4#YkjWd}_)CcW9m-+fK2=!AkfV0B#OV5mouLkQPp*5qG3bMmZq5z|
z22MuRK$ewOb;O7HZpHQHBppG?0Ah({p^~7pDXNjClH4B)mBje1G)AGCW)F#!I&VFb
z!_ZtbvuNWBljhg<<EOMj5P$7Bg(NRPb%N^PDLcFvs9eh(cFGUa+x0P0f{lSKpk;y7
z(v7K2F}#2B+DO)azkSl9h&SR6T81TP6-@iJ{wInb&Z&iX=XD!28CWi_KhpXZw6oz{
zI~w$WRF5hlicSPP2m*SQhM7LhbX_XGIWDs#;ZB=yGjjukhmbRCX6li4HXr~dW<N_{
z+AC%zMp__rgNLLm+0hdD^=;(i35yt<*5L1(23rd%`Ky`3TAo$~Pcv`L6mqbCtWI2a
zk6|t;f?tvbv`=-#W^4q)b4t;2#&`-EJL0We!!)cHQqcod<YB%VsZL^jWTjUf!{VUY
z4k@NSPqrI^iBb37nem{G`ZL0A7pFE}9ZM_t9Y5coKaKNJ()fb{&nI*N3Vr+Pq0}QY
zdlDUuOb5%RS6Cu$uLD*xnk|WJBw2xv%d%y7cmTnMmEGO@?nx@N@mjPC(O^cfv?g{X
z_)Vfrm2<$GFtGPuff|*6Qmk+-^(-OqoRVR~Pi^(>i7Wl@hfWEi(LB*^1VmLOh<gLy
z!cXoKe9hc#!zrsPtw^-@Ql|dFpbbX#f$eiq6}*w@@pA&e&WUF56G*ZsZpYV&kw!Xl
zj;17Cd;@ixUsY^m|MwfvBq0V|PL5!#rIgkwIXz|_f_6{#4Knmz+$Rt2tEh|HMpc>t
zPHLKof6jaO<k~iRQbIqO0|(Ey^-7d|)7uLTSbBui=@PqMq{%>32&u^lWy}6xIZxM3
zNab6`s5lm0r2&=elsAt3tBa&}hT(l1c_(1J>v3?}I=Qje4KTl8n_3yjCYtx~H+n^*
z?bP}Qu>N+)`t8IiL%>p1l=j8r<~gb59Rz`CaNS|`EUP3qq~I<*gZWLFzMB2-2d<%c
zRNcIEaJ-z(rPLRJcU9g_HBGeEmZJj^U#Z!WZt`aEs<+dWQ3e4@h~3GL^SbH-J%J-8
z!~MiZXPLFSwh>Q0Bax<}J`$R9;JMb@UBD@e=tzXIpQ*+st_0z&x$MgR+cRG~bHXdz
zSu1laf?aN-X6$$O!-d!z`0;m_uUFp{tsL5h&(>31k*Sl2%#akuLrSa7Ve-3KY(pO6
zz{9S?m2P+9XZ10$PPeN{^G@|~SH3KRVLd8#8ocf1kz;+DpyD0jlhy-ucT|g?5lX_h
zv_anNTF&zj#@!WiOVY{d@XC_8GnG6LqP%CzZW8LUc6P(rPU}wj2ufeArEODqAaJd(
z0zI2)g;~G+xL-GC`k*T#O~TGeOfH7elklVw#L(PWy({eb<_4Tm<95gMvVY}&ztw?m
z@nz%la~kXkS97L59j#G9J;y-k^FAcKn^qxIbxX|Z^bY?LC*|-nkkxK~bs#pC1dR~K
z(=|;h1of?l%%~Y1%TL)jOolZA(jUb$Xh8l|I(VkUh#V#^szs=&OM4GgX{Q)gfsIfz
zcihoia}qN^W~X(2!0Po4#~<JmSB(qv!*X|}t@v`!?b(6MF|D6)M$Q5*zri4;qxt=^
zy!y~DTV#jDK`#9VkN&luysE3v6Dl~9ix*tqNq{<kbT(K2@3#Oqh2NK?32S}3Z;#1B
zk*G?s2;?w=Q9J*n29yqB4s}(qb8)!kKIBu1N5xh8AXvSZ`;sxyauo#{L5WS>kA48B
zJ-=M^Zs?6hztW=IgH4XlG~qCF*tqMBf!)Y{l?MQ_;2zkY@~F!5S)D@h-7c$0!_TB{
z$-u!r@-;1HgS*aU8#4-6hHIRis@!U9h+??d_1P1ZXaUnX(|=d;Iz^)AWjduUIboyh
zkdqd}E9?o!92n{v@P{~McxO72>Yp{IR5AWEze(drc>&kyU{hQ-X9UUsxgIOXl1||%
z4|wGp+k$Piqt1s*KNVF#`Q2sq_rd%0oUGWZd&#Pf`JHZCt9qBH3CqvE>gsQ%>P1_-
zASb#(YKCi_3m^KXor^Ck_vMH7F$)Byz}?rH-?Ahp&h1iO4h%K7<~;qF*N7?pTP^$d
z&L>7z7({XHco|qGaIm7x)Jw>YHJXVSpbT}Y=XIhB<G>=HM=!RTUS0``fGqLK)=!gb
zgQv?|hTDM})e>L6t)V?|0Zpq;Z;MRB2jyiV-T6H0e2ISm(PU#+<Lil{Uc$TS&b8c-
z`5C6x2s4D7vbxlEXucT3tE|lheY_exc2tfeBQwkE7&j~((BL0BaASp1XOUPq6N3Pt
zi49&~sp?gapsmYhz*n?p13!%sIjFU(b;Acsl5N*OS}(ABDJ#?UGm-EV{|foyCB=oG
zu**B_+r7Hlq-4ijfGl;#lFFdF>0srt)NN&4)qX`!5Do6ZN{nJBAP(_eByf~ZeY}Jh
zxvgQ(N_}n;aF*UNUQpB&15iUqBFOlG3FUR@Pq{W`8~8`rY7|nis+#CPD($bFD)pjf
zNUJ}$wT}eUBTXcI(gkSuTQKzt5cc9mrgMc)J!<*Ns7WkjX>Moi&EJd2p<^80(lz#G
zftZT~EZANdpk-p^IB^=OHM~<GXW~C-!_~uU0HH>S7l$=N@~*$HUa({~s_E))?62OH
zBTg?TPi0+vR&2tWKpgSy5?_kW)04r&V%-w?R2jCZHnNh}q+i|EY(5s;zlWqt<XZIF
z7Yp;%kZbQ;XCQ%Ch#{Lf)ZVoBbQ|7RrJ*q<FH$4K4hu@SJXts$KltBobf(Dnn8a+N
zv=kKQd)S5l5d)JN4Mvwmmh4`u0&AQGpj{tkIf3OmC_bWLFP!!gf7~i3s1@W;i%Tpe
zidDq$$S2L#UiIir(P$cML0;5A+Au#f{5rvEAV{;o_X2)XSUch-FLK15?s%7})<f_C
zEt$g^__u4DDg{8h&?t?asj8<lg}PxKHmP7_hnH&$h-&E&%Y8b-%7QQJmy=M9mXr_%
z6ttdVsr(b?gdF%>9!WN^TV~l^+F8{{wGl4e-X>x66<dx5D<pyc1d(r6obugEIC@q>
zi^1Dtt}uZht^C6D9><C#4SD*SS17e$@rx2-=*j&n{0rE#Rg=*|K0E<CTCGt+|DYgp
zJN1zv<8)9u_HwoWV({X54e%pnZZxmL*f9Wnkefqk1^8sAKIfk5h&gxRpa23S65;-}
zCN)zHL{^=D#Tqt8!EpAZjpu7a?(BD_2=?@orVs0EQ?Wi8dG<unxR3OTyW4qfvvDPY
z{cIHWl@y#obxtqg_r+#}(d$2&+o-0#BdTo>dmG86If5-b7hn7BaxSELWSW);r(p}t
zLPu7n=wmj0Ew5Vk!I4=msab#7??bx&&$rDoc|);WAf}3YJL_jBj7{O9O1iIC;w4jp
zL*r|7`A)tZUMb5N<y|zj-5r+PWf6f;2MfQ&6$6GxWgxR|W(Am#Ay{@I;?jP2(};I^
zq5z2bvdJ`jC-PHI3@rYMa@r@(Bre8+_^!T3-U0GSiHV$-*F!YFn*0Jp=>(KDJ*9Nz
z>igBZ?5j`OMq7GSN#Y)$j(eby%`Bt|&?(#and6gqY)wpJy@D@@gOjT=`dVFM0%6jW
zfT_ym7-G(31lvMa6w>rG&^{mAt~IYdXD0d(KrnG4Td7Rfz{`WaO+ZQ{U_3@mh1KtP
zO-fuD9uV&NCV1NCy74f?|9w?`&`0>LJwCr=>)n}nLEQgsQg_pqEykCqK;EESd4cY0
z{XN%FEELpWGr>#&F3w@%J56nfiv9n5ouI?tvk@bm{TE|#{%c2b=~loRqdTGhxB(?4
zDugs?)Ibn}gb<IAQhZ2sarGSm<rjN-6i+&9YRJsV;9{EB?&ERUBOuJ`M$BL1Etavq
zYKjEsPnyL3^oTi$?L&NjRp&bqQ4_qbD%*orH4u`v=M0S~nUnSrb6#}VEj*6%UF93h
zcC%AYED3i$^nIN4<X#cJk<0UbOV?5L7d^I_^mEvcte&~wwuSs(>hZpdD88oecx58T
z(_QUKDjo($f#-fo)*nJ2v5R1hh^%sS4Ll{b9%>oW3kAk`fC^rio7~=q*zc3e!rp|B
z3SSyrJw$;utdpH?_@OqlBfatYlgRP1(x1|YNjaacyNQ2wW&k72I-vOU7WRL?{rq#v
z8InOvht!YA`l&~TZm-ZBhBt_KTme5nGov0r=*yM@Uq{@)1jTu$t0<=)^<g5AgW;Yc
zk<>pIMvU6cDQjt%lJy9vlo5nJQ2v^~H+CBuJD+@as1T&gaJ&~J-e}2Q7YP|Pp3mR=
zB$%$adVDW_4_Tw<$bW|$Nk%4WrV!=txX=sv&BL$eWgN4dqMkfK-3&Za0PD=i&L~XI
z+7BvEWB6NXegM7Td8pZ6a~0~jH_1`CDH<!JcCAqGlcr-aQp`h9I*Y=X#;(eVJUc19
zl^KP&_j?mry`?F_H%N@;REH~!vLaOO-x@d*acN3l3x7z`Ud}T8M<&tb5H`Kc${7uY
zSp8*z-j(zrO4Zv{0r?KOGnmPCsk*&pOCzsU9d_$Vos<Ue`ujTIfQPnDs;P)aC6VDO
zp@o~1o|3aVhh@k!Y7xFk5%X!#3pIz})?aT66;CV1DYi_TKZyLYBEw0hXYm@>>O1zA
z_|i@?l_=xsW(6vQxY6Dr99B2E)9`l}s>qJ{hshOS3s8;ka`LXp-X5{~#ytpZ?C1s>
zDobXdiDyCso($|SMxZ+8j2p=eY;F@8Jhx<Z&gE?MyTt?Bv***QRen(&^^e-iEN5=^
zQi|hmA1*NHN9uCvNcEvA=fU$`=jy|8&VO-J9$(Ipk5^W5W`N<opgFqoiA>`#E{LWX
zBF!fn-{*1EgIHHoV^LQhfcXtx{A?<C?s1(+ela3+W!gEPSjqn<B24%>Y#;GDVP|$w
z*R@0C&Keo)iT>SmV>biqr3}_b^)|C=;qjw*t(OB^9r`esrtq?*=fm4v{V4-R7KVTL
z6H=|`Kyg~S<T1;_Ok>RQi`<%nF`m>17w|VHBs<ZFE1Lv8gt3h<nQyt}^@fJM`)C86
zl&#~2DuCgI{mNhCZv=`TAxe_GbgU3_$Qrsu)Lp9G3)RyASBsFr;|=Vp0(Z*aL%$2V
zL^w(xiS*WmlW5-zS4DfcS#lIax@seF=!bRFdp_8PmIrP_zdrBRkVbnpXj3`{iiAPN
zkVD@ER|bMRX2c$d>AX+#2H<tEs;Fhlx(J*TqTy68M5|w<nH9Ki@&M-KX563e)MU8(
zOaJfcI@?Auj8l^CurxMKF7g!P{y6BL&J<^7pCXw^*voBtAiTiw&#lKf2f|Zb2{NGN
z8DiP0gQd%ut+(hhvDXqy*<!FiQLzgT$tj>^U{%NX60|^2B)0Y<9YxLr5IyKuYg!*>
zQxIzEU^|eXYq8CJNA>mMVO>}FC!0TpC1Q&;-?>&$O4?<go@$b|aiwQKbd}qmQ&#7K
z_-RP6gh`Beq<VF3D@!2SGre=5Mm$D$RjWmAE+B2>A5e*$LBaF={R_)l1$TB5HCU{F
zLQ?Wt|Iq~}awv`FUg<M$YgS3}CeD#M`o51gD90-i1P4nXA=lLi9Oqg%2r6l0qISEb
zCY7uyq;QTuN@;Qf?nnWjLc!=Z$VSr&9r9$gUFBJu;!ktr&TghV&gZxjbx*b3OU%WP
ztFe}b<hO;CTPNpZ`()0pwpyI1s(w^I#Mk<K8MLgh1J&q=_CD>m`lHwTS{*2#P`s+K
zpNbj2x0YM{%Q>4$+^QDCbX>LL-en2u^P}`(cWoYld2M^zd&Z9Lex=XyiA>#Ya6~b_
zUtP>5X^ByzPX`1tbJLn`Dd{a%!#Jf|Q7LL+k%^4zJJn(#6ibXTHfnNz-eS1etQ}B~
zKp@?ArgNJqLidS>JV^P7)P>P5<$cRg9i}=z4Mui(F0A~lh>xA<w#{c(n>($x?QLj_
zT2G@);EewI^ZWb;Ukq4Mc^}IN4^*8~9W%NqPO)xO5<GzVTIZr9<^dtvqm@+KxEfPk
z(|qjDzY5V4a}%H!lS9AI38SOK>M1>dt{eXX{H#zjRUCgkURiu3^nT|gWTSvxr_meF
zAbq%S-y2Bi`jVWC4)*ED+_(5}c72|zJ#u*8&+i|w*QEf=nXrfc25r(393vCXASwuw
z+jumc1N~i_rZLH@!%>jWZDRBTQ)IgY-&KyF6pkjwH%4!fN=^c@rQpv0ihPg2M5t4-
zWg35ZgdV0u!tNkTms8ddzpQhreV1`1%??*6x(=jHmkZw|C{Jqb|7`)zMOeqcJ-n~a
z*{Gt?34B+jkc5uuGRdyPUKgig%N{nwwtqP9#~IL_8N?l}zJ0z!X=x%+@J_CDFx+9K
zDujoG)RX;mL|1=`H9yo-(<ivR+K$i`>3@}u44;0`tuTCgpL`q-*h7Ub(9B&*%}XSd
zX-j~h4+g7@S(_89N8IafM=H^)x~qb#x2_dKLxvXfy_BGir{UyZ`uHk-b`z6xxx+)E
ztgY^kSM}qIPLVvr(!ZBgE<bQIETz<+w)$E>9^r~DskvPE_z8sl#ma3^kMLz9!;5EU
zh-b%C8N_YYeq74&Y$Ob7CD7Q4_Us&_CDS~ZQQH!94HNjLr8}K-a?9$Te=W5l+|u{k
zowfjUOC5hOt~}7ustk6BBP)ML8|*8}Yc@S)1nlIfWg8j}T7HRi@s9BhepMtm3IY{`
zJ$YSPksG7XuldfViUVJ#K%;)}mCjb*xyyI6pwc@X@E_dF*UWjki)s$2Ms$r_kUt;c
z#5=#w6*kBg!&mwi%{pasbu(Wd!7pi7S4y8hekkT5l~=mxk;#Z|m~!4&<_gy!3bHS7
zTk5vDB=jdl&YjK_juWLkfF2BmF1bsFvJ$}^#gk3o9G$mIdd&ZRV;ARRBcUN*`aLAP
z-OMhpZDpEkMPt`~0wcsmB-B1=2K_;N_k71OjjR+`<HB8hp+8NRy4&EN(UgAYXy_hQ
z9`fIBvT^Fmb4flIQ<B8EaXHn5h|@zGQU7U5s5TF(?(gpU>Sb|%=V+Bghz<3eAK`f6
zpk5iS?sx5L6&R1c(8O}c60mvuiQ*Q}2tr<(g5p?zh@k`MyMFP$*fufs5F1n3Ll`v5
zPb~i*axFk|Q`F5%dMkV<aH`?&b<<-4d9`bf9}Q2hV!uxCa+ebi&#HpVhbwWCq$AYR
zT%n)okq7W2CPF`Dh;wAPICantjOz$bBCW&=EB9^0=Wj%WtMS~rf=W|&pSLS&4PG9>
zHfnO(BU8x`D$(4P*ZYcv$nzbLM<>6ze4F7VwUbBzrv6aRGjCRTD4#VqtOoJB_1Ee;
zC+5Qlk%A}N+u_|b4d>1ARmQ4?ZI*`xg`b&M`ROyoS=)>t4xQZi_N-di2~#QFm|*C%
zUiMfp23zft-Z-yFxII$|78rQeBH<*1St)EPFNlWEg2Y`l$UKY_x!PAssm};(9E?94
znAt40QMpWOs$$-7uoB?MtPus4X#t3r$XX$Ew3S)h{(rxLVJO5?;)yvw)Y7#1-tm7s
zkB@<;Ex^}*NEe;z`#+hf+W6dYkBecUR(<^YU#EWbEequeuPbxoLUaBea|%F$Fc?6O
zQc*B6A3MLGU(GR&(F<2m`pkvQneb5rIdS`2*hHo6Izv=p!Q6#~=qUw@{V4;-^WAQH
zEqjYDngw^&hZbF&EnFUympuM8&<5uj<mrVG-lHZib(HE?XGFvD@ptRvY)Il66<O8y
zlsv3*`7S<u$!bijEs~-|S)BiszMB({?eZNqK&m4csFr@t+YRPqJlDxKj)9U4>|?I!
zz?>SukFlEovX7Ke$S}Z8c;zvom_52I?YEoxQwlvH_SJj`H4u7D#!e67*LtOAze;!_
zk&V}1q3*M*qDgj<fg1dicve00g^tGa%RVIW<1%me21F$<&QC%^7S$Sj8xi*ZM?{5U
zmG~^gHT&?8F`j*prAH^%eFx<+3=<ABMku~7r;dvFfU4><V*D5TDbCR_;hQ~PG%yiR
zFMk@Ew3+o!<U{L8cE6F!8uFiJgreHf>(YfEi05|th9%@hCqQJ0@*JQJFy`@MNYeu>
zgJnNWC7?D%wB_avx*XNn1I<b@RQz$(USD`*n0|RxL4-|;myj9X@F}Qf#he+RjBuGS
z?lm?vACb2V?|${|&uxkFz_qPO59bK%vMUrj-@B2DeOjaZk37J)-HNOI&h2tj%HDKh
zm+<(@<2MhkeB%FaFy4^Qo5As-nPR7I7x&E*)kLM~o4NauR#)+bIx@^%H^Xq-5Qez7
z4BG}RVnPM}<6y%y&Q&1*h;LNUq4IzSzXoLh1U~<?ek+VPKgMgmr?4z`n!5h6zw%j`
zbgvmZz)suda&T*NPw{8`#+<<~9Cq^GXH!RTsiF@d1N1xd6n`;`jHqVo31mLojI&nr
zBRF0ua1z7_U{mVEx~l6Qg=LyctgqQs4XVAZ^vu%M5E(DJ$PjNu^Y!Zbg5!AK-pXOA
zr4M%Wrx8JhL4C~Urjm!t*K`A6>}ndN_+D=ujLO9@3({O2O*OCc!5<N1K_kuT0x}6D
z_VS$^di3eaG-+9?1*|#{?mg=Gan@3PGXY)aOoafjkc#1eFMWKgI{wzlP9|C&yz13u
z(q+rFjd>J0Yl-`$8ug=_BSu1jc<uqoo1ImCE!~YxhCDwv)v<t725B7_jPX)d4`&H%
z*$C--btu_wFq7MCb2ZM7FJ*!W^>&5D>J7o9T8933SE}lU*Rd7WI&zBuLTk(qRT3@s
zS`?C@k2<7Z`tM4oSgaCFzTMEi)&}sP0HvB;Y~8GnI?PlT9Ca*<<L~B74iDAtASp1H
z^Cd_6Mg}_42SNQfN{E)|TK>>%Uje-v=Xs><ZAGb2%*U7Z{j!M0h6cWxsnl<jffsZV
zD9t+7?U|fPpoZF2>B|+NLA9BdbmNuxx2Ex0HG?CO4{weRyB%g;M$`e+)KGN^-VVP2
zF;mh4pI>&#{orqvB3%a3G=0f;MHtdjNS#O@IBO;Ovy-<mKwcyU+dK5}-%@27LoNii
zk_AH!5Bq_3sH1e>PZz2j%^mlWcN>;>94GGyaEJC3b%fs!9gN{@9*M0UZ`IXHuSOVP
zDz*A2Zt{!+#!0REvnm3qxVc`etJTFeT#ngjEQB6qegombG2T3`Qva6~(ZR?{89ee<
zNr(N2wMdyd)L+DyBj4zyM?7nN*ksVGNqSe8vbqs;&OIQ%LJ3W9yBH3Z<1Xg<m;HV!
zStxqSU_O~|@?sY0p+fkOCl-1q{AuBF{uZeLG(AWQfH#|b;nVdDUlV`CGE<gQP176d
zEuy?|9rEe50)t%!a3<l7$E=|Q*QXyd)D=+MPp53(vC|(6QInYNazr|*7MHga<*8w0
zI&YWBxJo#Gax(8UHr5cJx>^aHD+K9}FX2AP6$o)Wq9<_Ac{&pJUYp6V14Dd3fnR9M
zb^nus;DQ%>b4)g?XM@n;EGjS`TTMxUPw^k6|EcDs>MzfQ$W_YCNM$`9?6X13YOzVe
zwXc>RhlU9ysmZfAI#X7NBfiSGUx~WoG_>)O639=|%PrsI$GW;9-&L^fbrV7=bjy6=
zJaQW?re8XtyiIns`f+<<vAnHeRJlY}FU%qH25NHt2aK(`S$vl<6<(-x!%k|h<XPKK
zrNUozgZF$_?0;Wrg!{ePMc@J_JLk-<w)?xeDwXg_SLxs*h~cto;7bKSDgU~!&ws{A
zfe4Mk2sO8Vt8oS4=IPHmQC5KYuf8SfXQBfD^D^;Ed67jntAe0o&D>3iI7x?l);lBY
zNX61`(!Z{-Gp+V<G7Dd^8POC3PyL3hcE37_5CyBL9GD_0>QJB1HFKydRru$1vSNY1
zuRq$9t<Ep_t8Zt=zf^1Df4{VTVqF0ejD~q^rJ=<t-(^ywY6786;`2rMP191oeKjjN
zQo^>wnwbr-Qij6t%gobtToHyzmui-(B{{2*;f=B{>z$A0Hvz&LzmBOHIJV9Eb?*mC
zoy>YCM+`ivJQf$QW3PF}*Gml^DgTH}J6_G4a`xyp^3n-@S^H$OT@qyaFw0SGZZ^{u
zF)HId6WK@oaxP$PBCs6)=G?a82cY*;s)Y@Ck<FPiG-=?%jq_`mf&&w%16~+K=EtRZ
z<Fo9`_79dX%sTyl4HQxGNULjEH)k7B$35K(0X1tfi0Tr`%Q)#SOK|GIx{7y(h#ECB
zOgkG_=y1#L=l<(T>A@?5mv}IQgXH$D0NM+qQ;*Nt1vzMeX)#qSkzdXouZw_^M|B1?
zPqIvz#(VertE|ooWnaoqfNER=KGLf1^0lVD+nNpwiFn5GEwLqa9B+k`iB{P@UNEvG
z@m(#h6@N24Sg+-;LrTg=VfzRm!uVj%Vt@bb*GFNgpB@MHXESj1vJu$HfU4(v`G|f-
zUgnY3u+ZFf{mjv_6GbCu7%HaF3oS1^rZX2~xj}Ir2{BZdK6JtL)%ft+{n`wr5WZaD
zPE}e-0qJ3Ok(a14w>&!jQo0ipQ<5=fA24Ua_MnnXM!IAf;pi?-)&7H3VV1;#m0l!W
zDAhOvp<Hj1q!;H@>;d{zwU<ga0S29q(<fd2K=YF=ZGmjY-1{bu@eI=lpfV^{Q~8Nh
z7MAFv99&(&)%N1)@_eOklH=+?P>Hsy`~~)~VOe#KQ|W({E2Rdv5+O>WUro_`KAI=;
zI}_{*tX#WIY%{#1O#4}=MfkRmYR+tVZYFLWG9kTmU@xi>shUv`4&*d6`kg@=B{gxA
z)Zmx|88+U;8>}9H`8J|%pQGLe`j|4B37^o81x77^nvG%8*2C+$422boEF*b=F*2@|
zgLwN()uqv`yxvQcZk6Ol^y8Rh@1WQ>VdlfUwoU7(dAV#2<pR7|J6Rf`^xga1uBHf~
zt$1^Mq2bxq?aVO4HoL)gPQ&(fr^jilZMNALY=8K0JN$r`>+dh_k>xMKLTs~?o_<|T
zwA1McB!wm`xmXYHKkEPNG?<U?2;;@SXNavWWf6O2(5r3|n+)b{yDNt<(xbK1^r`?)
zC8}OmzzJt9!zG!z$S;aO&0DDfCPEkwiARSoV7{hwk0(yT?l+~1I46A92h_g+d~J%S
z5!m0OW`=0Wk!0X`9}#>)n|Lv1KFZAi(eyVv-LA#i2=UG8OMg67VdE}tY4;Ap3I8+%
zal(ZT?;Q12&JlVnar4MFAOSLJlgYRBsp75iVo@pjGXM@XoX?;VBs^Gs@A%?=GK2<S
zK&Ai+f2BS01JsAZsUa#|I2I<G&)geAPFV&~(-Hu~+O0YV4~6C$jg~J2+nplZ55DY{
zPKoX9y@>z5S59K@6tNbWxk|1G-CI;|4NA=ls!nvwMnO>4N(s-*x|p2zIvuN=iQwh^
zDsHr(<y|X#otC1E$32ibRov4S_12x^cneWhG7fh2gsy;QV2LoxZ8$uCNmRYK%r+^~
zWNGZ;oKQsbg2eOUZX1f&2?jqR{pcgqSc<uX+F*~CKdy4sw!gN4_>8EJ%oPZy4&xTr
zue~2ZsvQl5L3Yds1dW~5j1YWK%s?&u;4c4sN44Qezm-x{dITk&igG`)S}LHfR@U=@
z<(X}A`I7!aJ%bYK77bJbHM8>|-W`l53lxz&N#)vYaJqYN?8&Gnw@#*%7N7pGtf`W$
zkipnCTKjOSKg)6=o%%%yrR8kK&50xmIF7Y>R?ZP2dP&u(_P%NFF|UPByJK0kHZInV
z(00LeOfkBE(x%Qh*h|qM-h%|a4#u#4-85`=G+GxxjA)iL3uN<SmJRsI;+p<SaJN)`
zX^Rx-%ZNWrVu?m>Tr9s&bK$vtV{QGzz&PUJx~Px_$njR6v)ctteO|K<K#@(?Z7?8#
z1$c9ErgIf#zYwAXkj;L(VxZ&KB#mEhwCXU+W}u^u*+Ro{E)(nu=7Z{kmvsL74c#;}
zqyLiPtO*Hxx3M#YUqLjTNy8UjOV}THa$tA&Mi9bxFoWqJDvG@7P=CMPIRZKKk|*BQ
zO}BrHnRlGPpj`gIPiggg-AN5WITcdC$K+FK_7g1V)9)&R2GZk`SjT~PVt`l+yp;-z
zQ#<5;BK@WRbduk&+L0|tA%H_UOLwSvNI-wn+ozv62$cr*tdgr3LRM42WsWNRkaFsF
zrT-cfo8JzU?k5xZhjHK!$%Zc{9>zTBo1$7c>s)Ua9~JtS5^x&>gASvF0%M>CzRKIq
z7@XgXgKTgCeq8{i&Yu`|0qOIl0iTT=;etwu-v$e0s09iQAvONQ$Yo78OA4hF(#b?+
zub*0sxd3Cm^>m|%4|N^VD{Iwi(WOQ2mjy~o7A%5Nk-2ik4Kx}5i$c7Ak#Un^cmO@{
zCs2arXlz;0|Fq4bsl!fdP?n5RN_$`)gDA`fQFf{Y!W~}yk7)x8D&p1dIMZpH_DKlS
zTiD3Sot=odE=PCsPP^osBYp`E>IThBupg4H(oo>>IGXxPJx=SmYG|#{tlu$#>^;0H
zKi1){ml0>6gc)2@<K0yZsd_aP!LJ2f=+eWR)>FvVx$@$@2G}95*0}xUyl@<$4^eHJ
zo()~61;W%qB&LW!+2$DEDn~9q$CpYQsHx+l?i))Y@vXWIDatb?NrBrk`oo4jy5HpW
ztkOjRO$e=zt>EyE6*mOaOELg8J|*jQkEV8{OzkfJWeD*s@FK6JQfWw8$9ZSvX3*dx
z`}sdV2bZ?1L_KNWpfptTae?&OFZ@I<;g07uY^YfZrCqjsIieJ5BcJ^4FDrS4KZu|j
zP4bwXYDblnSJB|XQ)Y)T^Lm%YPU1a_9xke-d4l<-Z~W^z7jXzi73#?MLksfhD}0_+
zQ$)j(5K7x4!Arz$TT}W=F>*F7$qB)6n<X}&CSjj@Ku0f`*#{j_9UXa9R&GGI*I1q!
z!5dluQp1$CT4EL+g)G}cJgZbaSF*}bQh7lv^v$eMqexedzvmZrrsZPTFg0o@0cZ&L
z@3%Hkc{bFd**T=xPxClYQ<RuoKHUoyi?y9%Tgh^oThYBggyYlwer;_ks)$K<qDm~B
z?Nv!D4`YZ()aA^7Slq36MRh6hedTgOtBU8<C{pw~b4HloV-t|IX#vWaG)#9{joD@p
zOuXetI~%XFPVLt(EDXyQ{O^n}q+Cy&{q<`sJ|w#l%LqN!jOduShFu25=s@njPgid`
zF4p*R*;7|D%pFs?H%W)lm~_E!@6jG-WXI9$`@sW?F&k}(#U=`uowq3Leu@84H_3<4
z$3>_Rr!a#(l(xXl;nA)m-Fe%oQFpOc$RNNsqw4Z~N)}eDAunEcoV#f`pK;NIsXk#Q
z8c6!Fd6^Fn+H;y$DsUKjmfrJ4GfQlVg7{L-yzb_v|L&qX?P0dAK|JLaKG$Bw`3Yhe
zWnjL@!R6ZynBKxsBQl}pB(LL;v<Ql7ES?+@@W~L$K6^?Ed48D~bWtGURmr1vZiTKW
z;a!}<9}!HE@h|7eLA|Stu=x(?ddhc221#US6B01Mde0jjFocZ-O$b9OI-Yr|gXZxu
zYnkvFJ+lsQhA7dJ6_{3{JQJ48FxTllB`-*O-;*hg6UU@WS{JWA@t}UV$Ptf2^V|O<
zUA#EUz!2Tw%#Q}R7xcZ*Eb8aHvYd?|#4bW`ikOh@y{}st6{y0sxttW9^pNBbZA!-H
z$MOuzqzLE~GAXQAdmk9$<okP|!&w1KObmeSjgfVL={Dsm6;@)Y|76Zv>fEo++Pzee
zFDW~cA>1%1F~rm0KuY%?LBT<#?#||g4G4@ut*~{OtT9taNFQe}++Y-?=3&D`$pI)x
zqZ|5v8?S>Q)Or@JZwa%N^dK~Mwc`c~Y8xBrfVF{CrY>nn8f%z=wuJ_LmnNl`5|nF1
zJv^*VV?!`!x(%%cWyx>@o@y;p=bGm&v35sqOng>(;cQm5=hXyVJ}4qSV-sjXzV7*U
zzCI#wHcwL$Jg9Il%ANJN8c@TVnGu;5_s-OR!w8PC<m&+eyW1jc28c?Q+_#+h2z%gP
zRncGE+~Rm=rXM^<1kph3^%4EgXLkxf6g+1v+;Mz?raFbP(4-OE1pUfV;d&>dC67v-
z^tCTk6UshdE}?<vAzjJ7lKOc~>aPE<qI3Uey8r)px9h4dD#^KqWCv_>xYV2?-E!U-
zS(sCFF*1yD$>B=eDyNx|F=~>JnK`EoQ;6g^qUKOc4r3EVm(!&w>HGfv1@9l;@7MeN
zejXn8ySi6(mL+M{yU>@wGzT;<ok4|&)P2(B#pY`syYTLNV^rzQrePUUj(x<!RYVX)
zIF@jR<`nzMfi<pRfx%4uI-nKL=Riw#rUpax{AE2qB*Ug)>$PGwtxUJ+j!b@4`w?@`
zblngxDR>pXv(UjuLTiR~7H@k_n|eQMNBh|tA-hXbctJzZ^#MK_p%|b|G>z_3i_pur
zSZ_!2#_Yh7!t)h0yfZi-tu!8<`meR+=|95TeG*L|Btg{|tU}?T*Mu!qV%>=)r|3@(
zYwhHuvC#Uj<ipeRtrlKM*AYn#pt*E+Ip@rLVO7cCDY5Y5jb+yqw<9wY_ZsYR_Y^j<
zZIvWVgPz4--{vOg^VLU(g`k-WbLAMk?%giSBKdb>0g`TV&rOTtN99yZ*6PQfs+lji
z&9k-y*1Ru~ng$i43!#3`b7qVsm`oiS2lk?I-}dA1R|Rj>tkSe?LK$IU73)dnPynq(
zA}e*~R6c@?MJi!+5b>fWNvY=1!>+xerR>Be%JTSay1R^?L#}<pu_WUV$DYN%9mAN;
zvPaOS)ivOc8B<!XSOT@Q$UL8oZnr6G{cJ^w1EVOCWfERRs8xkSO?Cc-1aA!yVx~AR
zBn!i}S*MAh5oVR3MO{VLHZ`7S67^D+onP+d=kJtkpOEzeV>5!*M+w*~=zkzcFE=^s
zN6OzYPvi_ie@>1E+DZrW{)0Zje)BMt)mMJ5(%~2Z#!f@)i+B;H$e2*^`Yie)8N~FY
zC{8Q3XalTw_IOQZo2611t^KJ}pGSR`HTYj}Td-!U2@GAVQO!e-kB4oW&uG_OOU$I#
zooaeGX0Lb)(?cUtRAaACq0w1S^HxQm6lBEIlM(w*xth#Tvn84a%<$?Fc*4X>!9E4C
zXF^|?k>>v16*nt0MbgaQ+f{sRSMx}|OY^LiKGOy`;4UP-_6_ISU~eCDSV$#eh*`gm
zT*%t!^095YmJ#e0Qzo5=sqqMFk1^SF?T^Vj*;Kz3lo&Q%gMCo{wvV-Vq+_6>`SN<*
z#{qkfPQuMSlZxgFuVUqIrc&@{bH&{lB=S?RU;S~#`+zJ*@Ny{1L2?)lKQ4@QST32h
z&^>f)Hgfcd<kdWj>(}EWtpmp{q;A=>{n`jy)`v?SYtBUopdZ$8p(0N;X9j_UkL;wI
z<ZXuI#DProPbc0FC61^N1E1g;-}HY(Iy>Df*XH!t+FkQn)T7f@=$+JRX$l3<*99mt
zb$<_=H8XrL{+O0@1g;WD_;p|xva}z76kF*yZaCVsXHlB0aTg7I?JtIMS;T-zTF;Hl
zZFIoV?WpTWok3dsy*IvGd((CsWGpLfh_m^~#nGjeeOm=Rnc31Qb)vB9@;wHp0Tw$U
z3EhnPZo*n<ROi{4Fh2El4&fBQ)p)8$&DTum|FQ*E7&F0t;Kk5_{ER~^$~JPgckj>e
zK5^}#L5r2M=WtF1aG^E~dMPC52>%j#3^hGgoME9ZQ8|S%S;mDIkb9rEYMUk92;Gcf
zY{PX{$`LPBc<~7qU3!Dh24?)sk~4>Xn=KlA_-%m__p-R~>pw8w5V=|wEhL!1iqF)W
z@~s8w`4Cq~3POqZ7oTbdd#pqc&YNcINpIcfZen<nceQCq6{8UTdRAz!p`S8tYdIx~
zC47Gm(kq>&FE*B)_qgH9DXVq3!q+r;Ieab$J-9>(@4TNc%&~w)-$TAlKKXXOb8yoJ
zmC==2RG9uOPk&UwBW>4M0;Ve#Zqq*7(R-X@@jhxYgvHi_6&<p=1^?(LzBhs~Z@Scw
z+wSAcAS=C0S-nxai3d@6?74}D6sK`kKFA~ENE~&`Sue_{m7wH2ZSB<_-eprc5|ebj
zrB1nN?msG={gAnHqOk1L*EAYY;tQ^M^Sl;z)CJ>J1RLY4ZhlXOeldAfu0wy!L|daT
z_0m%>pn9{Rz($0X+L$9N!1(ThxssIanBLc>_%HWaV8&f@4j=qaaW&KttN)W63WRM|
z#{a`(6<#~{C#y*PIehI<)6D6X1)Nttc$O$P$|HJw^-FS-yIc_g#<X+kmB}!2<-1v#
zPHIkUZnYntBW_OJi}Rj2s@LFxbHPV<)5@JAS$jz(tRH74V2N9PFer>Dcg;;w8f2lX
z{6$yBw%O_*#pra0QT~u2%NCTckdF0FnOwmsAVjoLoLe;H3H#m%*1)q`d2(hh5xOsb
zP=&>Jqy%-VSHkX`0DL}$|6^Q7Di2`12KdI?f*yw-LASD0W!D7^hmVtah%kzm8I~};
z37h%@B*x5?imyN)7Du((yoSYW*bu=>-^)p52ydqZO$%QqPxPI3`Zw`R%BD(7oRR$N
z#a;pcMg;7F!yhu5FZAdzZmm9!Ob6##ed|`mwbO{kM{^D0MW0Zvj}Pv!vOLI!f5KL~
zm!13VN}%>4x#rj$LHPQ`fkI`DphA<wjq(`vL4Dw}iQhi?Sh@seovK%^90HGJv9A@g
zD6d)9mTs-6VIR<hR!chD!mh)!tdYaP>2G{DoN5tgu~w8zj__i3390Pmr3g~a<z|m%
zT)?oCANJga5Y-asvUk0?VG$*qeb*$64Aj;?Zu;wh%{=|G6ogf~*I;ElxZcY*qd<CM
zdV3osmAYZ+Od9;JN2K?EXw*gqq^Tu_9*s#UFSFO#o1B5TS~YfdbgT?tBDmwCh&Mi@
zEv#5>%^u$An~JkGUI<?hR2(|=BRDP%T3&bwetlYzW!}p_XHj;25Y%X5-;B9>-2JwX
z0xz&f1Z?^@Swa5LOE|M~Qs=Y2KjIV!_em9+4+O*4yI2yvTKx~MWA^4Ss1B>HD1~+D
z<K^@YS1OjjG_1DaAm@i`vU$wH*@}t2fA+5>#y(&5!dRBZ!AZ@K@L4IF*e@59!7lD5
zOy74dFmP-^{yv_~tfjfnC<FG&pqPZR^MyXjKdF5TrRK25QH=qJy|d2dkV9U*S`;lA
zues0UTJ%wSYZ%2kjs{HexBZl(p_U!!+E+A>b{M`CRaNTDMHEf$f7tnA4+I4;TCyo_
z0ih+Tdxiw`4P*Iq_wYxu{>3XGs^<xhymwFR_Kf5NV5j#lsi3$OV_bmx&xHR(nkPd)
z*Ido7n>t8yE4bZ+4*7loM2(~~Zsc@hCG5S=ONuDsN=+!9ZSum>(8SJfEas~zJUW{J
zfRaHuelv2;i!ZJ+!kH9@rW!*k+-x{u+p-DRQb;lhYpcu6>*L!7&5yYPo;M)jV^mNa
z{X}MzdS1iphDX7TO`@w$V@w~=#di6gMb|nl2v>EDjtzTR<vLLwud@_I<mu|vbJd!k
zP<9Q;at6=4A-6p3eFv(1=4;}o)Dqh%rN=shY>8k)$*%+D>oZ6bas9bVUW|1*-R^k+
zK6uEMb6ZR7qxVAS4Ux-p9yQVM^DxW-(gmKYD+%0pQV7hUj>k84{U@i|{b0lW-^yz*
zhCBw+!Vc!fPOPA(skEl6^9lT6(D20VP)3z3%xLb>(%F5`(fwwT#2Dt3?RjwpVE#(1
zsTP0DzuKyLyDJA%LjOAOKHND9^S9F&j^CR1;u;ATYIRd|md;BzlyiGEM(<AzuQqej
z8Fae$@;!Nx4aD=*apOc|V*eBVO`IYIc}n9-AZNw%T-2xJbov$WSg(>a=XN>q=sol0
zz|0p;j25MIN=N$o%OV8gJD&leM?M{xiSaYH57Ze)`?8^VANHSC=V#>yGEvOx*ehxW
zDR0w99c9+!{}XM)T%2O_O%M|aO!t3x%(enfWOX$=-pADdlVai(yseY>tIw(H2<WV$
z7?XF)rB+S9eDC4Y&MC{+r>e%oT_{qH;cQ^xvtg^GB<oYBW#;%t??-%Gp%pJS+7Akg
z06x`&mJc2SNgh{3Oy{Jqv8>Ce8lehO<BEl|GH!jMyh0-03y^c`;t@OVga3n0hgW5<
z)plRx=jSX+&`jb_J1N0azc{)8hI4KJ?p8TIPOl%~;c->MJ3oETICfJHRX^}gjv+9_
z$LBSZczWx9UF)Q&XW2Z(zI}hFAkfapTI;t#eOydQ%F_2AI$sU0X!?5XDza!B3Sy?f
zBr=YFo^5}AV6b_?fQ!T1#G@Drg6hn2x3l@{8=_Wnyt+;aRIfH_o!DwC=12sAN(KrN
zX{`QP1ZFV)UKGQiIV?(OGe3@IJ@OV~#lwmBcyk%us*2Fho@+;eU86$?u;gWPS8YL2
z9LZIWT8$F?p*P}=S^&a_^aW?Z=#Fovfq*}xUha2365VIM_P{l@0Enf1gn5S4TvT!o
zaB>}O)XVWpR%bp3G+3K9a;6Q?b$ZAoZMbaXu8*F>*xFH&4^Q8nz-DfyJ<^^y*`M?5
zCP8!NrfA*1#zxU^?$?2L;fTlf1={%ZL42@8)g;ELRq5mg&B}^KJM;Y8!VKu^|B7Q%
z`uF_WIyK4|rMW*;9Q4MQ^k~`PP@J$GLch+e8qy-SpmmE?>Jmsn0?<8=XO8D-+d5BV
zHv&1EX~brmxc2T&#Xaxm4z(3(Kg>+`^;#KjO}goTR;BCd=d`wPVAnrcP^-fQ;MXp=
z6=~@LENoiiq8w`kpUJM*^r=?k2rAiMpf{?h*T0)=+WYchM6mnw=MS>76FGyOVQFgt
zdbmn#8H(?_ymEe^J`SCyNrq<2G{c(&&Z#7?v<z_lNvh$=iOpE(G6Cn@gS(QyHMf82
z$MxIqm=jkvLKjw(WJr*IaA@Vrn|!Ow<#)%3mus~u7R&eTFX)%=qfCjr5}<ymJstSN
z1l-c&*)p{F=J3-DhwIg;>#^-_4^1<$5Z8`C!R57eq=c4F@m5J3`?q_Z(;u&szl-5F
zHQIs3$4DZE^FoB*O`AR3|Cg<K)kc)ql)iZNGUf`^S5<+cPYiHj>jOW0z30*#g9?4i
zn_|uAm}LgmPn(yaJjO%|IObWu2RwMb(WV@*<a#8x_v~?DOX$gZ8E5en#jJk(a%#S<
zch`g}S^$dZi8eLj{5tSyq+#PuiBxiwOQ&zD;!}eJIv-^GioI7wnMDiXfbv!U8jyor
zlNYCt<9N3By%OzwZ~ov68j^LP)er7x1P?VYj6E1-XD%FbgP-)#h8aw)7FT(*IHiAv
zOBUUE<Cp32Arm>MF+eJI)5+Zre!^96qFIk)1@_5&qG6;{5Z9Av6IT~;bXCmvf+gq`
zRG&R}1(V$&Gm|_b;D3ic)gN7pad)9s<TscuP5h;zyoY`&`+AJeE>s9GSjzkT5Ohk{
zrtJFRJ9(O>W<g=3F{bL~)Z9KEYt(}N`nYsbsscRZK%IO0)q2|FVt1gYtlNLMxYgpp
z3uv)U$IXx;2COUVH2Bwn`+nnB6kjdy#2JgZrdOH%?#j!_lwG~=S}>E+$u+j)1^ILb
z;ZKdr@6r7vRN7W%xrSlSTEbZU=*bm)dc9Is0i1wyRi3&0g+#v4^zdWLC%6U+ng^x-
zD92W+EREV1`rS$BJh2)!zIUoj`F}++)d8muZob>KY09~+US@MSu5E)P8{B^y9&1{N
z{YXZ}1jh|b1f~^lq&b>v?^@-jXedxOp<_?m5qME8g51QsgbUnlo6RlC&TqZA*z&jM
z+?1gjJwWNjMYUXcoyjr}^+rq-cCI)odQ?Ax`{X^eS}{7x0`*y_Ud6>p8S#z%r@=3{
zrFy{HdlsNB&z0pGm=K`fQ14~GIXpSjF>tl%4gA*uv~%P;VUCljcm_|7DSbG&2JuqA
ze&;M&&(U1T-6^GHR^Q(U^ly0MrM)Z5|Bh&-t_sb?lgq54(;#kmO@>;8Zb&6R;;ubR
zRH*;6Z6zl9)u*+hh`eqMhA-u=?51hjh{I}!y`k04h+V9uxY*mVyz}I_Z|(T02DqLG
zbacKnvf-TQNdJ<xa{aqGuF>Cjw4oUIBG))PSx3k;U6Za*ap&#W%+WP2vXk(&M6QM^
z#x~!wHP98s#ilPmS!*Yxu20;!6~vbgu-QMAdWc8w=Ur54S_OUq0}P3Hc-HvE{YCr1
z4u2^FNP#Yvp3Fh_Vk|T<@PAAQ3lGX*I^Q`r(uX}H4s<<Bwu5W^y8T=h_aR-8WTlzN
zX`&a8zdw@XlV<m9%*kzA=Ld7oE8)b56|hgCg^gJ^Fy|0^K{<5A=WV#qqCL9acqSU_
zn_&FBNY9BV4AJ>epiOP!ysB4>8hNmCPT#?G|8wgP6Y&aI7E0UVe@LI*&TE~^tidKw
zYB}}e-xZzw<yb>DMA~4tVuh$dmW{O^He02*9adtS6r|Rm>U=EE5gJl;IJV-)PqvHf
zaNm~PqwfmszAP@)>|y=wFN_cI;U>s46FdVao(05p?Yov!|1ls04?~M5?oUi_*638o
z^qG|sJbMWa6&OVn{1w_$VJXkW>C<X}I@5Iw71+DT`Jw(AmTdS!neyXvUxz6Un?RpC
zIDXr46#JLLcZR<{=$|ZfXvyOi5gQh*yHJNKym@aumXKf3A2LrbO9l?vv@hDcMwPhB
zN0x<LtD)~M*n$fqe}Xtsa2?{+>r;CE#RM;PqivE0BZzP7+8%tcoOD9;Zl%fen|d_?
zxSS-@TV0%um?IVBQ1DI*RA=FG?jT}78lc{-=q_FX4b3x9qC3TqFW}PQnpYC_rYP5(
zlO;gxsZeB^-^#BJ=MX`OnX5%2<aSCQeX+rigtBOI%;mZ4bDt3%+}l)7Y&aF=IFxMK
zv-bZ>9cqO=E~KgRR!Wx|2@68(sRqGzL8FH32(Th-z$fReJyK!CrN5KvV%>WUsc5~4
z9h+smC<OpzXfG+Zo!A?+EP{GXd=i|Cvp`8LPOGC@JnG5Lg*e;FpgS4~1vGK1qbyf>
zpq?9RQheF~pu~ZzkrpSWZS2-e4b>>U%K4|=0SJSul%sPI#AqYJBE9j3SVwG7&l&{3
zQSMqVH^Fe9F*Br+G?&Nf^o8ogBe(GfL&t{YHXItCL#ar2G>wFFXsj9yUryzDt>(Cw
zh4+2l48MXlbu{+abWDrxCaShuALpBK9q4NVGbnV&W$`E$32K$dB0vNA?b3$keG5aH
zR+-0@nB|dK3(&6jb6Ygm^4gBn_ZG?6`L~yqwl;Rv(x;jl`F@ouBBWj!+laIKrT-wa
zqF!t4hgx=nhFX%`$yR1HPCxgU`=sPaQd3<a=t4qO)3nJg^Xx<j-L0^vz}<}Z$ie`~
zS`Wgf&+NK{3tlVm2X*#|MkYJ<FGnHPO3-2rKiZJ$V=u2$)i2iUCi*g+;eo#og%%H=
z^YXXXHmFwKJ@ag1k}r|1%mPif9Bg9X35W!qjlt%Wx$8y4I@;?nD#kRovfI>hX`Lv#
zVV{uCpF6x{cAwL%JXKzOs`o=$Z;?v?vRB$axp}p9I|Sep!iu@hsnkHe!rtXN;#JBY
zdJIo=-puJ31E<QaAoxEUZd1kQf`&B`Q+A#2$-NAZ+PnVx<k+pek>^Ucxo_Nw<d4HV
zH0p^{ACv4D%S4Se((^`Nebh7wiu;grBl2amC<T@?LJmF4so+3wD!V5cof=dt`-_H0
z-7wPBJRVU_THnMu1>iXgwmozahYw|-ggha*{>8Z_9}UxU0e*3U!)s?<B`R-|5$swn
z!=KT4pI@oGs0?$ZRU{+zF~v3sT89)g)ffurD?_s|hB=2fpP8jPru{mQ3$8o;#h@73
zDB&NCn>JAbYOPN>kr?v8%-WH-I+C%$%fiyorQG_j`roTtZ5fcwAhZ$h*-hXJx$6BP
zuq$!7fM(MX`4o**zUioO<eO;^AH3msRyk&7m*|t^{$zK<B62u}XgLIpXwftcGlSL<
zN>I0guUi`jOD<YxAV8$eNa5U?Y*M?S_e7b{d$Hf>r&61jYoNvn+-1(qr9isesBZI+
zJ_ezOmG2bc3ZH&edFa4JO+YPRbwwHX-0=kBwdkSOkX{am&Y7z8Q%eWnHzs)q3)8?*
zmjZX<@vy(2ri*7M84-)00dOK=lL0R7s@I&n3c@$*3i9S7y*>}4?V-au9S$FTs^NfS
z_gwhRzC$2U@nfI=kY(mucJR=S-B?(#+t<*c3AYc$+c8-+0Vh{&JKT@f73H-{Q^QBv
z2lHKWtz-xtZrhpAIu8Mq%|I>k#y0e(G?)*njWnE|5K^C|j{+pM3_fg=qWJ3oClmYi
zL9eGxTX`Go5G<HoD1-37wg$Y?ouOmh+i%c5zYa*~pP>J%IHK(b*sgihp^$b01?LqW
z;-g%xM6`TWQj3^%d<E2=kPSioLw9zKf~C60RqcdM9{M(P)QgN^J{-kDjk4$*lZfri
z=R=KmS@3$(pGOfN2+y9#q6br}K@YMSrhRbr!a<t`N^)<3<t-cLWjAm-=Ev^&lOJ;0
z<1iTbi4y*_enm*a48wdQYII9=vtuN6Es^$IjNJ$@F%VhHVDdDy2}u+KF4ygFv4a&j
z1Hs^+hf?1$={tMPwArxKaq;&so5~Pz-ya5CY~4}QVWNr0B@a*Lv=SEbd*y&M^zqYE
z%io~7_g(#&q`jc6$BY7I9^9NbZF~EFtY_0|cJq%aGRb@C^(fn{|8~so)+LOKQtV%)
z3&}*Lk=FIo>fK#&*uTJ@UQ+d4a6-gy?7EL-2<XT6@-%mEostFWDSU6EdxsLZ+7HtE
zpY5$Z4luDcQ}?UNdX1er-Yd=P*Y8qs62I1<sg_6IFMY{+p>D&bD<&cNRo_u#ZLWR!
zTfz&Cc?p1KsP4fG>EhX23LAlCS7A%L>QT-Y^7HRK^&wqK?kOBH9Nm~w8)&!CYf2q+
zP@_%}WdnST*1S<GuFky|x}AV|JNwdRp5DK-w};GG_L9d=MURG8`1?cdW1W$0V?ZZ>
zHp!f%^M=EGbGHkOTuaML*6*fT=*xOAc63XRjL%I<LxdZ(MDWGj9G~WdPBYQ)4GYD%
zh4iN=+($Anz`k?*_*xy%3@4+e?au=}btFlnGI&L)6}$$y5)wkVf8YHo759p6=i0+<
z_tjwb|Ar`<yLWSD-*GIU_YcrO(~3}2@8!T)RyKa}N6?;U_VQ53*acb1ZX)p*zD56S
z7T>I;{BKfx&@S5H*@ML!&N~hn-?ffqEJl)Q%G877ZN%cZ39uD@@~y49u%2<#b?SA{
zpj-FbgN27+r%cAq)Tmlm*bE@;IyGW-iN*`HeOxM117ZL^vhcyf^f6Mveli4|fDt;S
zGYsPwe!~f|VzRKn=a9ze`fgzGaN_ta<$;%i0K?T~|HQwuZYAI7yE3QcVX4H%vh`aX
zc9xj$@nn84kexHc{{N7t-ME7RrG<@S>ZcCV9Hvuh&Ws1(gW~K>^xGVSk<nRZDATEv
z_L8!M0v-jq7W&>J$I2}&=*Ovu-=j(t>{wC<VI8dq$<bAAuj6;N;q^voE#Dz8ES2JI
z(aak1P$cQZn<T(<4xZSwxRKZY+q{2*<L~F~z7W+?&zp{RUu+m*X3-$MpeP(JfNIp6
zovyx(r$+arpRsX<%R-BPQgj+I-aBUNf!%1-)EmcC?ChieeKnYJih7(p@@0hk{DG%T
z$wLRdFFj8j5fk>0>5hb}O)-etoxb|Z^?z|`C5=qyTUn!J{J6B&L^-OwM73%x!Gc5u
z@MlV{*mDtAnpTTU&$G0L-&N=;ho}&Ja$n|rAh$6BJ565}h7233Wq26=;q_<;qMn};
z^falxu@gj{s%(q<Na#Dr&^qR((C9we(p6o}MdS}^8}N<S(H8n=SvKnU)p<()%mipt
zTJ_XN#Xa-Oz4a&xQxM7uhS%k7KdL<02LQS<mPXJo8=WZ4i&tyBK2IIw)u`wU#sX26
zP~NC`oC-IsbDe*wK*vyq@fqr_FUrvdY}s{yp7$l-wbnhY-{$Rn3H3%AU*tD!XtpDc
zS%iy2y<|Ohf5M&Pk^;0Q2QF`bfV|cXPv5x9RV1~}dne5&O>>;{_v&1lrfr)w$WI2^
zFgYNzvx;q<dpFE}9SASC{<Ado-v;?~n7!yB1}LJ6UeR&qLnHb_rm9B4Os)>ECFBKE
zz)z)O816qA!}b77yjs_mHeX|mEs+~l@g$c+yv5T`Fe*M5YTS(-w17gXQ{9v1oXB2a
z3!KB5@)F2F+AKu~^p|M`wSs!((P)b^)kY#SABUxt6MlX!CF8Y%aySNl9ul6|AYXV>
z35`pJ)}PhNE&6nITtWV|uDQ9<Z^ZMfG5(68Mw^etVUosDIX`eU5TN<%+$p=-uoCU4
zBr0oo(gQo)4N1^-eZT-g+(~ub&vHn{Ly^=G2auX9tp<L4N1WT2$LrOaLZ^sqF<h#n
z%rLZZ$IR~PvAFWw;CXy10C^~QAlwiJXcfK6ze>v@`MiMg-+%mziwv(dyBS)`hSJEt
z4!|w2c*ngED>g0w)ULH)4VT4%V`2k;S35Ws;}Kiu9iYAo6Y&jQI!p4W@jgcLVQn1x
zW@OQzPH_j-=_~cByUJx`ER^_O)Y3{0`U&#T-Qo}uxqcNeML4TxBEd88y2+w1kL9=#
z_*Ndd<Cwi^=5`lY?r`g69AT|Zowgd>X~cf~Th#kCQ&_9Q_cLNS9`Y|S)<3SiN^v!{
zK9Bf3RWK8xMW1}mT%nOQSXa=vKTCgqZR~|;CkN(qZiHC#d+&sO+v(44IF5#1r7dJy
zHlze?1VmV$|MLm@ZS2I2D!Mb5u2f<^Puue@d&$%LX~F!rTr#S%gAyWW>m~7XIoX`L
zqj$e*Eu<}%)d@HBTc48cP3*vhhgZ;`lg$gqKW24R+yv%H<91JBxUe*EgfXF0mB?a`
z8JgYogiIa%J7_h+G<7wr`l5z5q-~>maBE7H5g8v|3dIIyLx+!b@v3r;0*|CU>O;*2
ziHAAM&WGIg<UkAU{w-sD%Jx;MwN0EK&(-U`=k_kY>O*?fec~0BK?{}O8HM~A&wFb}
z9wAv)j@d_zUHns4w9ywrB<p>`oP(>FVhv1?2X}x~^a$O5y2tD;!c!s-tslJ#A}eG8
z^1Aq(FFo%A^b6>Y2XR^DrRRvc*HIw|dkz$cr=R|VBq)_HPZ?$wJFUj%SoxAZP={Tq
z-n{%XwDR;zTZ{2Ovx}uZq^jI>z;FfAsT$wI`yh|JJXtU+d&$AnsO$8UYV~<-BsaYc
zvfH-7f3>yf75&-%XxbwCrV<&ph+hYPg&U=W)=nQgvTq<weK;NDRsU#+Y1ofuqnl>R
zZ^t;WJeUr*vLJB1%E3lvi^IUaeXB-I9*+gk)V#87%xv=+-i8u~u<S3#a4sLC00pQF
zR}jC08R*~t5+c2$p)emMuBN?KKcyBi_-3wRQR}))R90R7o1&Vt)eizy|D|2I?0iA>
zaZ2R9_)TSQk6|FyTj=ENZS*?gqJS*ia(#cpUf04$b;f;Wjx1M$Xm@>j7KS8~Jhi8X
zwEL;m;Ytyt6U6zVg@GV)g`U-k-AnGWNuQ~X?gY&)rJjSdEN!2yh#MmveGPsW<X5A`
z?ct;76Iq@!MqDDQ&a*%55ZaJJ6l}J-RMJDBmfS=ih*}ul1JzLGHK=-Zdfu-hu}g)h
zV=-Dg-z4S;pyKOAgV6W0Q<=RQ>h;>6n5Td#S{V!~c=0&<qfYv$*)iu&Z&UHXsuo(U
z;4w=}`zw)6;jS2SAFf$*Ae2o9BM)0ypL`y0`h--&<3@DRLXnM|m;ZWX4P?mf?_{Le
z0v75})t?3U2rEQcZ*&*1upf|usUu-TV<Wkw>>o&&uG+#yp6|2^8e!}*zEX~p4+>?}
zv&*GaT;_!C9;B{-(56jpn7Y_oZjbKugWYy%kbqprwxIj0<fR7OhoV=5foh8ZL{!_E
z8szGDphCbw{qvgeOyXvmO99u}*vy32Cw}rZwP3BBTR|}_`a0+mZW4yBW5(QeN@7lF
z^^rq90PM_qHP0Y3SlH0~uu7PF#sw-1_*9@rQ92TfKnhxYJ=Sx;t;|?w0Pe6Rb<_{a
zYP+x-`JZS3ECTV^<QK3se!1qeC~}Y~+$bMa|9Y<2Zmm_3)edZ7PdVjWexc5qoM8W8
znp3pV$8&1?cT*bRETIXkS#~yz)bd?TMhFz~O&Ia$&l7Hog!*B`Ym=+?DUSW$Pt#^s
zvMgK*BkvdZ>b&nU+o)U?^!RR>wlp-$)(=m`<=tmnm2J02cx_LCDsnmd`pz<|+s>PT
zf|xz+IJf+F&qH(1?$h?>w(o#CX%|us4ecwyr03i=GTXGozYg3JQPK_n7~Ih6xz0>d
z`0_S6R&nm4{5}5@^Q-;?YYJLvg334PkjeIV$@)lM?Jn3ZMG8!keoMH&{{XOvI@)!r
z8lC`<=jR~(nVbECCh{Ao=fNVWf99_Px)MfD!YQrZ)70pF?-aMqz^<6wO_NTQpXY%S
zok&R0&fU}!DdcTk904yEiF1SIlgg5>>{HT2`w)@*L+Qk?17e7@;ppletzT3B2V4u7
AVE_OC

literal 0
HcmV?d00001

diff --git a/docs/static_site/src/assets/img/chip.svg b/docs/static_site/src/assets/img/chip.svg
new file mode 100644
index 000000000000..c21a6a5e1271
--- /dev/null
+++ b/docs/static_site/src/assets/img/chip.svg
@@ -0,0 +1 @@
+<svg height="512.00001pt" viewBox="0 0 512.00001 512.00001" width="512.00001pt" xmlns="http://www.w3.org/2000/svg"><g fill="#b3e4e8"><path d="m119.464844 51.199219c0 9.425781-7.640625 17.066406-17.066406 17.066406-9.425782 0-17.066407-7.640625-17.066407-17.066406s7.640625-17.066407 17.066407-17.066407c9.425781 0 17.066406 7.640626 17.066406 17.066407zm0 0"/><path d="m273.066406 25.601562c0 9.425782-7.640625 17.066407-17.066406 17.066407s-17.066406-7.640625-17.066406-17.066407c0-9.425781 7.640625-17.066406 17.066406-17.066406s17.066406 7.640625 17.066406 17.066406zm0 0"/><path d="m426.667969 51.199219c0 9.425781-7.644531 17.066406-17.066407 17.066406-9.425781 0-17.070312-7.640625-17.070312-17.066406s7.644531-17.066407 17.070312-17.066407c9.421876 0 17.066407 7.640626 17.066407 17.066407zm0 0"/><path d="m42.667969 256c0 9.425781-7.640625 17.066406-17.066407 17.066406-9.425781 0-17.066406-7.640625-17.066406-17.066406s7.640625-17.066406 17.066406-17.066406c9.425782 0 17.066407 7.640625 17.066407 17.066406zm0 0"/><path d="m59.734375 375.464844c0 9.425781-7.640625 17.066406-17.066406 17.066406s-17.066407-7.640625-17.066407-17.066406c0-9.425782 7.640626-17.066406 17.066407-17.066406s17.066406 7.640624 17.066406 17.066406zm0 0"/><path d="m59.734375 136.535156c0 9.421875-7.640625 17.066406-17.066406 17.066406s-17.066407-7.644531-17.066407-17.066406c0-9.425781 7.640626-17.070312 17.066407-17.070312s17.066406 7.644531 17.066406 17.070312zm0 0"/><path d="m486.398438 375.464844c0 9.425781-7.640626 17.066406-17.066407 17.066406s-17.066406-7.640625-17.066406-17.066406c0-9.425782 7.640625-17.066406 17.066406-17.066406s17.066407 7.640624 17.066407 17.066406zm0 0"/><path d="m503.464844 256c0 9.425781-7.640625 17.066406-17.066406 17.066406-9.425782 0-17.066407-7.640625-17.066407-17.066406s7.640625-17.066406 17.066407-17.066406c9.425781 0 17.066406 7.640625 17.066406 17.066406zm0 0"/><path d="m486.398438 136.535156c0 9.421875-7.640626 17.066406-17.066407 17.066406s-17.066406-7.644531-17.066406-17.066406c0-9.425781 7.640625-17.070312 17.066406-17.070312s17.066407 7.644531 17.066407 17.070312zm0 0"/><path d="m119.464844 460.800781c0 9.425781-7.640625 17.066407-17.066406 17.066407-9.425782 0-17.066407-7.640626-17.066407-17.066407s7.640625-17.066406 17.066407-17.066406c9.425781 0 17.066406 7.640625 17.066406 17.066406zm0 0"/><path d="m273.066406 486.398438c0 9.425781-7.640625 17.066406-17.066406 17.066406s-17.066406-7.640625-17.066406-17.066406c0-9.425782 7.640625-17.066407 17.066406-17.066407s17.066406 7.640625 17.066406 17.066407zm0 0"/><path d="m426.667969 460.800781c0 9.425781-7.644531 17.066407-17.066407 17.066407-9.425781 0-17.070312-7.640626-17.070312-17.066407s7.644531-17.066406 17.070312-17.066406c9.421876 0 17.066407 7.640625 17.066407 17.066406zm0 0"/></g><path d="m162.132812 110.933594h-17.066406v-51.199219h-25.601562v-17.066406h42.667968zm0 0" fill="#8aced8"/><path d="m366.933594 110.933594h-17.066406v-68.265625h42.664062v17.066406h-25.597656zm0 0" fill="#8aced8"/><path d="m247.464844 42.667969h17.070312v68.265625h-17.070312zm0 0" fill="#f7e4ba"/><path d="m42.667969 247.464844h68.265625v17.070312h-68.265625zm0 0" fill="#8aced8"/><path d="m62.171875 366.933594h48.761719v17.066406h-48.761719zm0 0" fill="#f7e4ba"/><path d="m62.171875 128h48.761719v17.066406h-48.761719zm0 0" fill="#f7e4ba"/><path d="m392.53125 366.933594h59.734375v17.066406h-59.734375zm0 0" fill="#f7e4ba"/><path d="m392.53125 128h59.734375v17.066406h-59.734375zm0 0" fill="#f7e4ba"/><path d="m392.53125 247.464844h76.800781v17.070312h-76.800781zm0 0" fill="#8aced8"/><path d="m162.132812 469.332031h-42.667968v-17.066406h25.601562v-51.199219h17.066406zm0 0" fill="#8aced8"/><path d="m392.53125 469.332031h-42.664062v-68.265625h17.066406v51.199219h25.597656zm0 0" fill="#8aced8"/><path d="m247.464844 401.066406h17.070312v68.265625h-17.070312zm0 0" fill="#f7e4ba"/><path d="m102.398438 102.398438h307.203124v307.203124h-307.203124zm0 0" fill="#f4ca62"/><path d="m170.667969 170.667969h170.664062v170.664062h-170.664062zm0 0" fill="#a8a8a8"/><path d="m102.398438 76.800781c-14.136719 0-25.597657-11.460937-25.597657-25.601562 0-14.136719 11.460938-25.597657 25.597657-25.597657 14.140624 0 25.601562 11.460938 25.601562 25.597657 0 14.140625-11.460938 25.601562-25.601562 25.601562zm0-34.132812c-4.710938 0-8.53125 3.820312-8.53125 8.53125 0 4.714843 3.820312 8.535156 8.53125 8.535156 4.714843 0 8.535156-3.820313 8.535156-8.535156 0-4.710938-3.820313-8.53125-8.535156-8.53125zm0 0" fill="#69b0bb"/><path d="m256 51.199219c-14.140625 0-25.601562-11.460938-25.601562-25.597657 0-14.140624 11.464843-25.601562 25.601562-25.601562s25.601562 11.460938 25.601562 25.601562c0 14.136719-11.464843 25.597657-25.601562 25.597657zm0-34.132813c-4.714844 0-8.535156 3.820313-8.535156 8.535156 0 4.710938 3.820312 8.53125 8.535156 8.53125 4.710938 0 8.53125-3.820312 8.53125-8.53125 0-4.714843-3.820312-8.535156-8.53125-8.535156zm0 0" fill="#69b0bb"/><path d="m409.601562 76.800781c-14.140624 0-25.601562-11.460937-25.601562-25.601562 0-14.136719 11.460938-25.597657 25.601562-25.597657 14.136719 0 25.597657 11.460938 25.597657 25.597657 0 14.140625-11.460938 25.601562-25.597657 25.601562zm0-34.132812c-4.714843 0-8.535156 3.820312-8.535156 8.53125 0 4.714843 3.820313 8.535156 8.535156 8.535156 4.710938 0 8.53125-3.820313 8.53125-8.535156 0-4.710938-3.820312-8.53125-8.53125-8.53125zm0 0" fill="#69b0bb"/><path d="m25.601562 281.601562c-14.140624 0-25.601562-11.464843-25.601562-25.601562 0-14.140625 11.460938-25.601562 25.601562-25.601562 14.136719 0 25.597657 11.460937 25.597657 25.601562 0 14.136719-11.460938 25.601562-25.597657 25.601562zm0-34.136718c-4.714843 0-8.535156 3.820312-8.535156 8.535156 0 4.710938 3.820313 8.53125 8.535156 8.53125 4.710938 0 8.53125-3.820312 8.53125-8.53125 0-4.714844-3.820312-8.535156-8.53125-8.535156zm0 0" fill="#69b0bb"/><path d="m42.667969 401.066406c-14.140625 0-25.601563-11.460937-25.601563-25.601562 0-14.136719 11.460938-25.597656 25.601563-25.597656 14.136719 0 25.597656 11.460937 25.597656 25.597656 0 14.140625-11.460937 25.601562-25.597656 25.601562zm0-34.132812c-4.714844 0-8.535157 3.820312-8.535157 8.53125 0 4.714844 3.820313 8.535156 8.535157 8.535156 4.710937 0 8.53125-3.820312 8.53125-8.535156 0-4.710938-3.820313-8.53125-8.53125-8.53125zm0 0" fill="#69b0bb"/><path d="m42.667969 162.132812c-14.140625 0-25.601563-11.460937-25.601563-25.597656 0-14.140625 11.460938-25.601562 25.601563-25.601562 14.136719 0 25.597656 11.460937 25.597656 25.601562 0 14.136719-11.460937 25.597656-25.597656 25.597656zm0-34.132812c-4.714844 0-8.535157 3.820312-8.535157 8.535156 0 4.710938 3.820313 8.53125 8.535157 8.53125 4.710937 0 8.53125-3.820312 8.53125-8.53125 0-4.714844-3.820313-8.535156-8.53125-8.535156zm0 0" fill="#69b0bb"/><path d="m469.332031 401.066406c-14.136719 0-25.597656-11.460937-25.597656-25.601562 0-14.136719 11.460937-25.597656 25.597656-25.597656 14.140625 0 25.601563 11.460937 25.601563 25.597656 0 14.140625-11.460938 25.601562-25.601563 25.601562zm0-34.132812c-4.710937 0-8.53125 3.820312-8.53125 8.53125 0 4.714844 3.820313 8.535156 8.53125 8.535156 4.714844 0 8.535157-3.820312 8.535157-8.535156 0-4.710938-3.820313-8.53125-8.535157-8.53125zm0 0" fill="#69b0bb"/><path d="m486.398438 281.601562c-14.136719 0-25.597657-11.464843-25.597657-25.601562 0-14.140625 11.460938-25.601562 25.597657-25.601562 14.140624 0 25.601562 11.460937 25.601562 25.601562 0 14.136719-11.460938 25.601562-25.601562 25.601562zm0-34.136718c-4.710938 0-8.53125 3.820312-8.53125 8.535156 0 4.710938 3.820312 8.53125 8.53125 8.53125 4.714843 0 8.535156-3.820312 8.535156-8.53125 0-4.714844-3.820313-8.535156-8.535156-8.535156zm0 0" fill="#69b0bb"/><path d="m102.398438 486.398438c-14.136719 0-25.597657-11.460938-25.597657-25.597657 0-14.140625 11.460938-25.601562 25.597657-25.601562 14.140624 0 25.601562 11.460937 25.601562 25.601562 0 14.136719-11.460938 25.597657-25.601562 25.597657zm0-34.132813c-4.710938 0-8.53125 3.820313-8.53125 8.535156 0 4.710938 3.820312 8.53125 8.53125 8.53125 4.714843 0 8.535156-3.820312 8.535156-8.53125 0-4.714843-3.820313-8.535156-8.535156-8.535156zm0 0" fill="#69b0bb"/><path d="m256 512c-14.140625 0-25.601562-11.460938-25.601562-25.601562 0-14.136719 11.460937-25.597657 25.601562-25.597657 14.136719 0 25.601562 11.460938 25.601562 25.597657 0 14.140624-11.464843 25.601562-25.601562 25.601562zm0-34.132812c-4.714844 0-8.535156 3.820312-8.535156 8.53125 0 4.714843 3.820312 8.535156 8.535156 8.535156 4.710938 0 8.53125-3.820313 8.53125-8.535156.003906-4.710938-3.820312-8.53125-8.53125-8.53125zm0 0" fill="#69b0bb"/><path d="m409.601562 486.398438c-14.140624 0-25.601562-11.460938-25.601562-25.597657 0-14.140625 11.460938-25.601562 25.601562-25.601562 14.136719 0 25.597657 11.460937 25.597657 25.601562 0 14.136719-11.460938 25.597657-25.597657 25.597657zm0-34.132813c-4.714843 0-8.535156 3.820313-8.535156 8.535156 0 4.710938 3.820313 8.53125 8.535156 8.53125 4.710938 0 8.53125-3.820312 8.53125-8.53125 0-4.714843-3.820312-8.535156-8.53125-8.535156zm0 0" fill="#69b0bb"/><path d="m213.332031 230.398438 42.667969 25.601562 42.667969-25.601562-42.667969-17.066407zm0 0" fill="#b94137"/><path d="m256 298.667969-42.667969-17.066407v-51.203124l42.667969 17.066406 42.667969-17.066406v51.203124zm0 0" fill="#fbb540"/><path d="m256 298.667969v-51.203125l42.667969-17.066406v51.203124zm0 0" fill="#6b9777"/><path d="m162.132812 128h17.066407v17.066406h-17.066407zm0 0" fill="#deb859"/><path d="m196.265625 128h17.066406v17.066406h-17.066406zm0 0" fill="#deb859"/><path d="m230.398438 128h17.066406v17.066406h-17.066406zm0 0" fill="#deb859"/><path d="m264.535156 366.933594h17.066406v17.066406h-17.066406zm0 0" fill="#deb859"/><path d="m298.667969 366.933594h17.066406v17.066406h-17.066406zm0 0" fill="#deb859"/><path d="m332.800781 366.933594h17.066407v17.066406h-17.066407zm0 0" fill="#deb859"/><path d="m469.332031 162.132812c-14.136719 0-25.597656-11.460937-25.597656-25.597656 0-14.140625 11.460937-25.601562 25.597656-25.601562 14.140625 0 25.601563 11.460937 25.601563 25.601562 0 14.136719-11.460938 25.597656-25.601563 25.597656zm0-34.132812c-4.710937 0-8.53125 3.820312-8.53125 8.535156 0 4.710938 3.820313 8.53125 8.53125 8.53125 4.714844 0 8.535157-3.820312 8.535157-8.53125 0-4.714844-3.820313-8.535156-8.535157-8.535156zm0 0" fill="#69b0bb"/></svg>
\ No newline at end of file
diff --git a/docs/static_site/src/assets/img/circuit.svg b/docs/static_site/src/assets/img/circuit.svg
new file mode 100644
index 000000000000..4ce59a3ea024
--- /dev/null
+++ b/docs/static_site/src/assets/img/circuit.svg
@@ -0,0 +1,66 @@
+<?xml version="1.0" encoding="iso-8859-1"?>
+<!-- Generator: Adobe Illustrator 19.0.0, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<svg version="1.1" id="Capa_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 viewBox="0 0 472 472" style="enable-background:new 0 0 472 472;" xml:space="preserve">
+<path style="fill:#689CB0;" d="M462.232,435.304l-37.336-96C421.309,330.078,412.427,324,402.528,324H61.472
+	c-9.91-0.013-18.809,6.067-22.4,15.304l-37.336,96c-4.803,12.354,1.319,26.263,13.673,31.065c2.782,1.082,5.742,1.635,8.727,1.631
+	h415.728c13.255-0.001,23.999-10.746,23.999-24.001C463.863,441.025,463.31,438.076,462.232,435.304L462.232,435.304z M64,428l24-72
+	h288l24,72H64z"/>
+<path style="fill:#F7EAD0;" d="M376,356H88l-24,72h336L376,356z"/>
+<path style="fill:#EDDFC2;" d="M64,428h336l-19.144-57.432L67.24,418.288L64,428z"/>
+<path style="fill:#5C8EA1;" d="M1.6,452c3.359,9.587,12.41,16.004,22.568,16h415.696c10.146-0.01,19.181-6.424,22.536-16H1.6z"/>
+<g>
+	<path style="fill:#84CDD9;" d="M224,224.688l-24-24V140h-16v67.312l40,40V396h16V36h-16V224.688z"/>
+	<path style="fill:#84CDD9;" d="M336,60h-16v68.688l-48,48V396h16V183.312l48-48V60z"/>
+	<path style="fill:#84CDD9;" d="M432,159.312V36h-16v116.688l-8,8l-24-24V100h-16v43.312L396.688,172L352,216.688l-16-16V172h-16
+		v35.312L340.688,228L320,248.688V396h16v-92.688L387.312,252H432v-16h-51.312L336,280.688v-25.376L432,159.312z"/>
+	<path style="fill:#84CDD9;" d="M32,36H16v147.312L60.688,228h-8l-26.344,26.344l11.312,11.312L59.312,244h17.376L128,295.312V396
+		h16V288.688l-112-112V36z"/>
+	<path style="fill:#84CDD9;" d="M160,151.312V68h-16v76.688l-40,40l-16-16V116H72v59.312l104,104V396h16V272.688L115.312,196
+		L160,151.312z"/>
+	<path style="fill:#84CDD9;" d="M184,92h16v32h-16V92z"/>
+	<path style="fill:#84CDD9;" d="M368,68h16v16h-16V68z"/>
+</g>
+<g>
+	<circle style="fill:#F2CF77;" cx="232" cy="28" r="24"/>
+	<circle style="fill:#F2CF77;" cx="328" cy="52" r="24"/>
+	<circle style="fill:#F2CF77;" cx="424" cy="28" r="24"/>
+	<circle style="fill:#F2CF77;" cx="448" cy="244" r="24"/>
+	<circle style="fill:#F2CF77;" cx="24" cy="28" r="24"/>
+	<circle style="fill:#F2CF77;" cx="152" cy="60" r="24"/>
+	<circle style="fill:#F2CF77;" cx="80" cy="108" r="24"/>
+	<path style="fill:#F2CF77;" d="M0,268c-0.003,13.255,10.74,24.002,23.995,24.005S47.997,281.265,48,268.01
+		c0.001-6.368-2.528-12.476-7.032-16.978L40,252l0.968-0.968c-9.374-9.371-24.57-9.369-33.941,0.005
+		C2.529,255.537,0.001,261.638,0,268z"/>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+</svg>
diff --git a/docs/static_site/src/assets/img/clipboard.svg b/docs/static_site/src/assets/img/clipboard.svg
new file mode 100644
index 000000000000..0a4abf1377e9
--- /dev/null
+++ b/docs/static_site/src/assets/img/clipboard.svg
@@ -0,0 +1,47 @@
+<?xml version="1.0" encoding="iso-8859-1"?>
+<!-- Generator: Adobe Illustrator 19.1.0, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<svg version="1.1" id="Capa_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 viewBox="0 0 67 67" style="enable-background:new 0 0 67 67;" xml:space="preserve">
+<g>
+	<g>
+		<path d="M61.559,16.528L44.432,0.713C44.064,0.377,43.583,0,43.083,0H25.446c-5.407,0-9.134,2.81-9.134,6.877V12h-3.358
+			c-4.946,0-8.642,3.124-8.642,7.369V59.37c0,4.492,4.077,7.63,8.642,7.63h29.231c4.677,0,8.127-3.288,8.127-7.63V55h4.365
+			c4.41,0,7.635-3.513,7.635-8.122V18.555c0-0.029,0.242-0.057,0.241-0.085c0.001-0.03,0.134-0.059,0.134-0.089
+			C62.688,17.541,62.293,16.824,61.559,16.528z M44.312,6.665L55.462,16H44.312V6.665z M46.312,59.37c0,2.363-1.938,3.63-4.127,3.63
+			H12.955c-2.323,0-4.642-1.381-4.642-3.63V19.369c0-2.6,3.095-3.369,4.642-3.369h15.358v15.381c0,1.104,1.396,1.619,2.5,1.619h15.5
+			V59.37z M44.028,29H32.312v-9.878L44.028,29z M54.677,51h-4.365V31.047c0-0.562,0.014-1.097-0.4-1.476l-17.43-16.059
+			c-0.324-0.667-0.94-1.132-1.732-1.132c-0.036,0-0.039-0.182-0.075-0.18c-0.038-0.002-0.044-0.201-0.083-0.201h-10.28V6.876
+			c0-2.063,3.02-2.877,5.134-2.877h14.866V18.38c0,1.104,1.396,1.619,2.5,1.619h15.5v26.878C58.312,49.276,56.881,51,54.677,51z"/>
+	</g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+</svg>
diff --git a/docs/static_site/src/assets/img/clojure_logo.svg b/docs/static_site/src/assets/img/clojure_logo.svg
new file mode 100644
index 000000000000..152078cc7e84
--- /dev/null
+++ b/docs/static_site/src/assets/img/clojure_logo.svg
@@ -0,0 +1,50 @@
+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<svg
+   xmlns:dc="http://purl.org/dc/elements/1.1/"
+   xmlns:cc="http://creativecommons.org/ns#"
+   xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
+   xmlns:svg="http://www.w3.org/2000/svg"
+   xmlns="http://www.w3.org/2000/svg"
+   version="1.1"
+   width="256"
+   height="256"
+   id="svg2">
+  <metadata
+     id="metadata4">
+    <rdf:RDF>
+      <cc:Work
+         rdf:about="">
+        <dc:format>image/svg+xml</dc:format>
+        <dc:type
+           rdf:resource="http://purl.org/dc/dcmitype/StillImage" />
+        <dc:title></dc:title>
+      </cc:Work>
+    </rdf:RDF>
+  </metadata>
+  <defs
+     id="defs6" />
+  <path
+     d="M 127.999,0 C 57.423,0 0,57.423 0,128.001 0,198.585 57.423,256.005 127.999,256.005 198.577,256.005 256,198.585 256,128.001 256,57.423 198.577,0 127.999,0"
+     id="path8"
+     style="fill:#ffffff" />
+  <path
+     d="m 123.318,130.303 c -1.15,2.492 -2.419,5.292 -3.733,8.272 -4.645,10.524 -9.789,23.33 -11.668,31.534 -0.675,2.922 -1.093,6.543 -1.085,10.558 0,1.588 0.085,3.257 0.22,4.957 6.567,2.413 13.66,3.74 21.067,3.753 6.743,-0.013 13.221,-1.127 19.284,-3.143 -1.425,-1.303 -2.785,-2.692 -4.023,-4.257 -8.22,-10.482 -12.806,-25.844 -20.062,-51.674"
+     id="path10"
+     style="fill:#91dc47" />
+  <path
+     d="m 92.97,78.225 c -15.699,11.064 -25.972,29.312 -26.011,49.992 0.039,20.371 10.003,38.383 25.307,49.493 3.754,-15.637 13.164,-29.955 27.275,-58.655 -0.838,-2.302 -1.793,-4.822 -2.862,-7.469 -3.909,-9.806 -9.551,-21.194 -14.586,-26.351 -2.567,-2.694 -5.682,-5.022 -9.123,-7.01"
+     id="path12"
+     style="fill:#91dc47" />
+  <path
+     d="m 181.394,198.367 c -8.1,-1.015 -14.785,-2.24 -20.633,-4.303 -9.836,4.884 -20.913,7.643 -32.642,7.643 -40.584,0 -73.483,-32.894 -73.488,-73.49 0,-22.027 9.704,-41.773 25.056,-55.24 -4.106,-0.992 -8.388,-1.571 -12.762,-1.563 -21.562,0.203 -44.323,12.136 -53.799,44.363 -0.886,4.691 -0.675,8.238 -0.675,12.442 0,63.885 51.791,115.676 115.671,115.676 39.122,0 73.682,-19.439 94.611,-49.169 -11.32,2.821 -22.206,4.17 -31.528,4.199 -3.494,0 -6.774,-0.187 -9.811,-0.558"
+     id="path14"
+     style="fill:#63b132" />
+  <path
+     d="m 159.658,175.953 c 0.714,0.354 2.333,0.932 4.586,1.571 15.157,-11.127 25.007,-29.05 25.046,-49.307 l -0.006,0 c -0.057,-33.771 -27.386,-61.096 -61.165,-61.163 -6.714,0.013 -13.164,1.121 -19.203,3.122 12.419,14.156 18.391,34.386 24.168,56.515 0.003,0.01 0.008,0.018 0.01,0.026 0.011,0.018 1.848,6.145 5.002,14.274 3.132,8.118 7.594,18.168 12.46,25.492 3.195,4.908 6.709,8.435 9.102,9.47"
+     id="path16"
+     style="fill:#90b4fe" />
+  <path
+     d="m 128.122,12.541 c -38.744,0 -73.016,19.073 -94.008,48.318 10.925,-6.842 22.08,-9.31 31.815,-9.222 13.446,0.039 24.017,4.208 29.089,7.06 1.225,0.706 2.388,1.466 3.527,2.247 9.05,-3.986 19.05,-6.215 29.574,-6.215 40.589,0.005 73.493,32.899 73.499,73.488 l -0.006,0 c 0,20.464 -8.37,38.967 -21.863,52.291 3.312,0.371 6.844,0.602 10.451,0.584 12.811,0.006 26.658,-2.821 37.039,-11.552 6.769,-5.702 12.44,-14.051 15.585,-26.569 0.615,-4.835 0.969,-9.75 0.969,-14.752 0,-63.882 -51.786,-115.678 -115.671,-115.678"
+     id="path18"
+     style="fill:#5881d8" />
+</svg>
diff --git a/docs/static_site/src/assets/img/coach_logo.png b/docs/static_site/src/assets/img/coach_logo.png
new file mode 100644
index 0000000000000000000000000000000000000000..1591da5afa1262fad59ba8390da88d81128dc7f5
GIT binary patch
literal 41911
zcmeEuWmFwqvnCwe-Q6uffZ%or4grF@1$TE(LU4Dt;O=gLV8PuXxV!5#@Auu2J9B@`
z|I1p3)u+3+RPEBI3PL{1OQIkWB0@kwph!!JDM3JhN`Q|eJS=brd2fRr_|0S?D*9Pk
zRFwR)qn)XRwFv}-R7iq4oQCosZl-o@bo4kVM+%`mMCqq23bVhzO1Fd*2!W>Wd-fMe
zTpSG~MoNtdRSfYt&_FIFu65X#Q#2&TA;yC*qT6zqVD--1)!WhAk&M?&UZ+vN(G31I
zWQYSc;%XkE83=O8c%~2(6k~ZQdBa#z5b++A?m8+luTC)!IV~+FnBd@*;j{uuY*pVO
z`tiZzsWX%|igg?0-wRIfi*s}$7lMa4>BEtAge1QLx8H~Bu=R=!V4BO4iv%-o@rZ^9
zZt=*4P~mW$SHBBZhWwo4$P<eUF}>B6?6HizCs~gDMG`}S{%aG#7ySzVo^N*?y;M$)
z1tMj(P}OpaZocd<e>rUC>4M_e!V{Y5#b~7Ham<`dpR=~_)?(wBi9!ugiB!oE!V-4h
zRcg=H!Sfb;^p=Rn#X+gY?NtvH2y7=7#u)g8341JTpZ`vSqYDBe|AI#Z1D)ujHSJJj
zDDm`T0g~uEde36s`7a&UHb<7BxKCjuS5c!|vfnm;yqC-krfFnIG@*e;epseouMP6%
z+D6)lix-%X_4w6GA3Q_e!88&YhF|BA$!Z{uvLIz#?KpatW<;)wiCJ3L{m${HsE45*
zh=Sh|U2+F;qbJ^}nn5S>YZ_^9iueBa0g6lk-_KR-tY!YJGWc6j2_a5UMsUZCA+<)Z
z$JM63u0=X_unw&sSZ&R1?O1fIhrAugM<YVi9ee6Ic>l7=pf-^NL%&0ZP6oqj6DGs5
z2UN8e=D>9@NPdKXlLEu8Lj{S9jE7VI4u%)n1Pk3EQ3^$)jv=XpUx<)S`w`4CW}%Yn
zvZWR#$G~KQ)1fHAT;53xrXfG_XukG;rg~!F!*d+@At!Vb7g;RylL{8=Td-j-dM>lA
z|KrB0JGJ*i&rZ14dZuVw#{yO+Iv+e5!W?|YcWvcBm{_u8j&7cD1l}LWrb#zGu1z>s
z2cd8qLgT_$5ghhV>;6#zF(7+k{3RUA&Rs^L(K~HsIG-4jL3+)kBZY?hn1Ixb)Wa$~
zkNl3xz*zM~`+AKYD&d{(hYDYTdC4d95aw=F1Dkn=qj$66oDj6G$X2~929jIu?luBy
zWf3{%)rdlx<56dY?&;mh7bVgpRwS(?!f25JIxoXI_hY|OX)|dsOyM0<AnhA>I2I^R
zief&QNzyfi4c+=~&t4?v%uiwTHQTj(Re|vS1PcqB+PP2pSI^lT8X{hO&6&#?!n_cY
zN4YCO`XZiB=Y<>s@pF)0sL=Z#UP&m!0|ZTwcI#+gMR<Z?&&dfs1-RgLxiCU<bP-pf
zV+UYXfyy8g46x=AUB3z%2%AHUtn=`ojDC|{Cv1Y6lfiR`^z7yzk0OUh$f0l+BV!YJ
zhvC&ra1Qp4F`&i_5s!^gp@zbsU^hmsfoBjIik2}(Qu_83Nh}dbWfp_Fg?j<9FR@CV
z^%*%ocd-b=GB8p?a2(<TC_<7hk6_RF0~xbeQVxzeXA4oeWK`bngq1nn1L(L1q9By*
zz49>RWtWD=hs3Y>Mwr&5YT*e+Ga4kb(4AK}Wmt#ZLK|Y~ksJ<iO?o|N(CJ|c{kwnj
z-Dx_oD*F{S)X(7DSzi*sRQQ-j2zt<5h9HV>@4tN(qo=c`Bc@FM`YMn2mn25Y7`;5?
z<ZMlr))dnWOE!ck*eis&7j=W`3t5QRB;7)sx6}kRDnpWrn3m+C9G^tXrvpl#&x)#S
ziVkWBiqeW0#n?shrAJD<C61cg#WKR0->ju?C?(@Owluf4w`{g(FT@@3nzM4Hs|)Tv
zZ!o092gW1HkZfabvuu}_l$4~D6dt&%<{W@#5NCW!%v8vVe1BRh9v8hkpe_k-32*tf
z__1BwM>cPw%>1PeTZ_epp!|pHSJ$i?k%$T|nTYZ}<+Mt*e6>nf&EtGN%@rj<uurIG
zhiCHx<aOAAAfYh|6G|eiq*10sLZ4v-5us12vmD)s`3Uid(ui)V8E2M7OkG*s*LruY
zKrQWhd2_*BBWEYRu+e<hT)VuiTsI4>`ttgP#gs*wMX$Q0dM}qc=e|AagW>7oN#5M4
z!7h9?WG<?->h$U?hm3RSIVo;%Qe~@3qx@~niB~&@=ZmLz`<M21&$%n1eU9sI*R@yb
z2i$vW99<|!$bCe^@8`|!hqBGS-@u<@EMxqpx`}GC2_{@5=p!8ETDElWt{NMdr=OOx
zt{dI^t{BDEGrz_&<z9cTxM_>k5>gp5Zd^ae7PtF}#1w)fO|x5I@Q9Q10}4k%iY=QB
z_c3=Zm+X6{_e)l;W9eT`3^Q#z7Tadp=6>~lN&ih8rWLl7Xe^5+8?deQhwIO++N_$i
zT39(@S*GTpW^MUpxmEe;;<)vbb-|L#l1@F%qUXHnf^Aj1anNG(BEnJ05zAuUjqT0H
zN0b{9Dg-J)$xsQksJ6dg7kK*try>{k_n~Zzjw;<nKNMLdwZr8J+8v#j_9`PQA{Qc;
zh);Pecmw%ycsP069U7c=_|$pzU2>YgHx0X4IC}{8`6Bxb_px#7?pJK;;I%fi_&PYb
z#Wl?|yR>RI6}D8mb^gsc#@JLmaOjI$C)nd%t6jLvOG;C(nDebvUqxNjTy1U3il`_3
zL)L0@L`B9y7CDBSC9hv#S8{5)CNHck9I0QwChF(pm-*`YS_s(ynF84gr3cFgD+5A<
z=`i!C-l@_0T9rMXy^(!mP`>_seQ5oW!jyu5qMo8Xry{3TRQngaA%-DSk8M@NypAhM
zEBCUXx9!~^yrr0qm`QJsRI8M<@f44<&Rvu0=81%+L`W`SuE+;|Pt~h-{KMpZHotJX
z$S4AR&KK!2<)ifZ1|HAl=_Xx=(+#N2r2aMOD(PsNI@%ICd6_b4?=0+imBeXY!>jMj
zSfg}A)JSne%2Es=3`@!nw6F>~9Mx8_hd*<r=jgN*GI{Thu)i@ZC`W!$%^FJaBu|J=
zjc;MP=AcVe9@&>QDlAtx+^%V|YG!qR0R+Le1FbXs^}`BX`@3h&9}0aX;kE%;**fD?
z#t2M-l^i|n+$J4kp`(j2oY5Ka?c9<ybey|YQnK+he7qy>i^CQqU%VV*nw(nZt~jT>
zXfR@rmBjco7n4g-&=&BFxoY%{(Js6%OD=?lrP3V?3U)qC3?tPZnKEZwe-NgAZCtps
zJ~^7@EpWme^?1*1aFXaT&{pSBemSy>H+rgt-eR!(ap+fBpdT&|ZamIYJH3UXa*pEP
zufHTazrQGH|5T%9XsMp}IxnEP!~7e2lg}bM$Na1{%p|0~p`oOA(HyVsd7@sZ&*V#@
zk=Rk@)4X&M!4%9S##B*xrQKZnsrFsx(L{+u>47?jI$q_A*T^)d*`NM+J1tpFCha9n
z49$tA(Jw2ZE?G=433DZ&(whoyolfWHN0c^{B<=^hvL)lXV&kd8kzXU+jo)pHD;r8!
z)?}7b8?Of&dTnevJ)YERR}8!cZ&>-o`1#!WP6RYJ^^|M>*w0;P)v2j1wR=sT$a;Of
z`7p~nZL{!aUb_bEDDK$n#p~d7?pE^1PH^2J`y_k2`#NYZDD*JqLG!vD<?D~pzOBB<
zp1(obX>Dn!&uj-#BqIeQs*=u;?}TM|wcMT8HD0Paa2;gF&cipOv<e$mdD;H<&ctmh
zG-QSHxa>F&>|@zL+Vqd!rCSIcy4;-cMmeGEu8hv<+qc{ADa;9@)wkBq={tAMyANI}
zDfiDu<|RF|kac1@Z(ph2%v{dpR=DUWH2Gfk{UplZ_TbK18S1!xa9urq;YT{bJi%R?
zYEt)IJM+Enw0^B`oGQN@_?8WaN7^UYb{YJ*_uFWHb4)i-!CYY|D^;M}W9O-?ng8eG
z<YmZMt6s4F)h+gO{{7rT5*I64$AzCuj@w7OIjh0L0PcLd>$Q_Rui>?FA2y%cwe7Y;
zKm8|>*PhWRQnDo>8XwO^;Z2Txr{8!>taAEP9jhb*1o-Gq)Wp9PA-<u=DZs6dpt{RT
z!TS3mWRlS`o|+<x#NSH8fL;-pPVEpKpt4b2FN}CfVf=lnG`l6fonBD6w+VxFb$%>m
zOHJvfCv+5@^?kg1|Nd+i9zqEVLZ2E6>X`@@w!`|Hu^r?%yRHXaeAp9e%Yt+yOWKEJ
zXyJiQ$X8hhCel6hEg*Y9u$R(wf`GuKdHX<0D^Z^Ti3OL1iiWd>+$TOGI~x{5W4kXV
zEbcb;z}pZI0`7dkrHzTRA-TJawXGAMyCCI1Z}0)vZ?{<~$^Uu9*-DU7L+&%VsGXw;
zIVZ~pmiLrGh~(tt0*=O}d`e;x{~iu}6QneEcDCnZWp#6NV{zkPv2!$IW#i@LWqr@i
z%FfOVyus|`Ve4$@&TQ*M^{+|(V;(URCnHA-duIzfTk^Mg4ZqmAI15rzzAf}WfBv;k
z6L*XMwUVvVzsUj!WPN+W%Et1Z_5U@@*~0X{820w$U&H>P>tD+WyiLZZ>SW?5YG-3(
zV(a|BJp<nOhj<a||8)019#~tvF@?|C(AG?l(w*7Z#MIEm+L=;F*~rnv#Ma5&&RKx<
zjY?~a|1|4AcmL061^yxHzu1Yu+ur#U94&wi8on`4h)sa?|GM{YCjM*9|BciCI>vw2
z1kO<iQGoS-%7hRi(<Q<$2naBQw3vvBJLJ&{jJN7G5!{*6+=|28h=jGXTi6FoM0)bv
zpX3eS>4W_#HR#DHFeg_s@?eRKs;a;E%&izbcEWWF!!OyUJ1IP$>^hmW?ndlBcl3K@
zwckRE|AmBv2Sb3+{UM;qA^!XFzxnXrLh#?p@c(5Ax~lxc|K5xfzu6B(gz4$&y;w+s
z=t2gM)R;nXONj~(Ps3($Q~3aKPuEOMQ{NB|U+EMF5Ag(PlWnTJ&=ScX+X<=<3947M
z5^C2)GKFmy7^<4rdy3*FswO6!9wL&JlVJ6m0dUk)X`!~ILAH8HDH9Xt^c2&?+#JK)
z+)Tb73+MO@6C2kAt3wq#LxOf{63>!$B5$g85)q-6%KVaY0(B<iMOosYG3S=Pz#D17
zG7uppMFm*j0wADOFvz8gP#Nr`GSz5kXyniL16eG1VKPJZn<JEJFH)ImkkU-Ha0m)^
zv6Toswo<gkwz1=jCSeXuR*1{!;jEm7CKlVqyBCi#mq*!$1{VeT24@9kdnVYWZO!5~
zP7G4yKn%2l?=q%0-s~*+JxEFghC>w|8-)H@(w{o->M`BH(lYXRB_6p^Ox(6-Yq(8K
zI^|wanf>xPmi>bMf?xHsPJ#U8*Wyh}J8_SJ@;@2Y%oQ!XT6(Va^&Oi#=0uy8M&;&f
z(+Tp%f2tXolJ~{3(WAp5FjWUXal#@(o+Cy7oc1pZUtEn;yIT%Xu4~51da!iY(YkG|
zF6O(ZR1o7#(Ip`v*xt0(C)i>Z#@Rde&aIA8hQH^YT^os%H(-kHvdj%@LHGBk1-8yP
zA*u@x_UC|%jVBaR+2Yl0%^IitKx<+zrgJb}m8{})Q7>r57E-;*g|hM|&7GeN<;rz=
z?N|SHYG;^!ZXT8D*DclX+6Xoge?MThdv3a5co6zs6oj;lOBllt%a*+LCX*ZK4ML<l
zPo%(hiih|sLx}zY?)c0^&R6er*(Es7qoes=FTZ|p*3y>7yh?0SV(kj|Vh{xMPda}w
zRZpi#3{n4Ekn@++)VNLWzc)Ro-53a1mCF&Qa%!^swL2bUt=Le`b!>%vAI?>r%{S^x
zY^WN0GQN4|No~lyG;~1$s_$?5M(Cxe4L~R)Bp=(zAcM;)k$2(d`C-hdP7Br?2Xm``
zK;i_nI_Q|t%lOK2!9X<%_a&&M@hn7aV`%16wmv+(HXvYCyJ!=l;3^O`Z#|Ferrx14
zcP<@SCn@KXtxl>z|JSZV-1ppUKU=tONA{)`U;Fir2~Td^8p3Eom{<~2xrrJ+LV&~|
zLKt140J&iTU6ok4JLmRSYfnHa==q%u{;qQ0twDV7ympt8>-sa8crZ2Or=K+o*@=3H
z>z+Yft|64PDDQv%5iAW!T_W=_2OpX|y9Gj89wl<cecX92Mf>!$)7i~hO$b$6MOX2O
zlMmLD4&`9pePMu<8P#K`98-Eji}^*-=sUm>c7P)ZiXRDpjaUTS-MQyg_2%NXvIhFN
zpDFcLwdE@1ALf!nYu&XbqMlAVpA)rf+el1?K0|`g!MUM6KyWdx;a`?cc+7{lXghb$
zpGDl^SgEhbP@(dZjgc@r)tx^9^^~yA-Tu}zEzM)7dNR}p1j57y=Q4In_eWE7eTVQF
z-oM#wOtThrvl<Qf<0kojf_Bj#Yy<MGxXJHn<<!0Mj51C8>aD9Z@E4M^KH>KvuxWSb
z=%P?F7B)=yDi(08@<KB|8;c)lvyj8w0>9J1P_fWqMlKZmlVr9W)$LDdD86CR`IckF
zX5u-kXEZe5K>wn*@s@mS`0!O!=qV`{9fxP-B1BQTuR^pm+N1KU<~X1d1dUWeMWcs?
zMQ$6Z)=EN;mhW+@YRecOIX}k#fH;MTF1qjyMJI=D!pK%vXv}~=UvuBs`K8RA_d{KM
zGfmCb6x7a0HQrHecE3e&+(|rJJpt?Fbct+okc|vY-sJ}z+*1~n7(8S-qE2(HaogXE
zgn%W5wU_4XreTrNj64o_b9w`j%9<Nn4~GXU5>u`Vd(@u%iVx7@03`FyUo2r00ep+T
z{tcqt)APvkXMauSn76}?+7-1>?{|Fi>|*dOE{daR#1XsA=b*kvsb5+J7G6bYknraE
zfb`ysOi)3)Q-Lv6GUWR08XkO?QiOkPPJ%qJ@Ie6(-$1LQ4bzW3{FTX9mWQXG<m4PG
z^<Zr#u%YL1!MQ^X?*W}egOU1L()nfg9#Z(p=Ok)tSb&K1M{qQGSCfBp2nFZg8Ll}Y
z;su-B*5w^i7Kh(FY=qw-HUUW^pcDUwzFrBY<@NHr&$K#y={UV$ckHJpd65VmAn(fb
zkBkiWtk=yeTA*r)JY1bTg^e^a86#vC1uFv_OGV5Tf`%rKj*qvi-8(fxQYVAYWc0Vk
zpoFfY9q*cvKR-WgU-dNmyu?9m_<Os&jy5WiJUbmQnA_=}5kX(EgJtCEwd-DnH_GYk
z<UCb$nFrX&&Ed^OyJT&(dCYXiLa-hi3l-W*rX{lW0CQ0U@XGR8f*umG8{zwJdg4S|
zp=#&Hh4FK#&oJCR@X4Z(Xw7o>>D*qOoE8SHD;kE|_LSqC(3$Y$ioYu$ATbbnf9Lc2
zcI@9>WSH4Sb;CS}f2hU}orPH@DMu0}nsS<sryz;i#8)f7%oBxwV)7Te)<=ATfc%TN
z93D~s<k}Os?ZvX3A~m{)cLGM|g?6W1x3o(*Ur0WoDnL1OjG->O%L+5Q{AS<F3fRXd
zr*Mo=4RRr&)x9YcJfRhbn;Q@Y8kXoch+Postm!!t?G*`|iBzGslm>4WDl|C-z)#Fn
zYh+acknxO)-Nh|`|Fgr{L8>I@Ow(|@djT%h-)69XP|vZxS*K8)8PX|qW>DxEIXNOQ
zE;m^M`6DCfD&r@v4d3}eO}W_%%R2_{grO!jdH6HA_4*3Ax#t_-S4~t@RB`Vxlyfln
z6L9#yKcwM@6~S<2AY1V4-iijru{*Yl34TUK1%WW$vyT@FdF1tPRnm6UL=vNZrire?
zpoNB}0Z98i6=;x8Z-3+3HUlSoszpSY!LAd!*`G|&<p#+OSDKz52Z!vNr~c;;k8i?h
zZQ$K^T40bl#}9WJDll0P-(J}D6q+8QK1GU%Q8qey6}YvJl-u{!;BMUX#g+j?3i~k#
zq7pC&7nXeJ$k6nm)I~L{FFecpOLB#%=x9vIKnC>-Gg;v0=GQe-v{%?5D0r(?bY|f<
z3*e-Oo*)KOv7*pG@;ajLbxQZsEs-Ab2MVMj{D94{Q{ou$q}ivaM2cK|bXZ8pNMI31
z=qOu?voZ)x@tNndIiJ&tWe-D~6Z-2iWZxhDRWJ_?wpEuwV?R1q@YUhaZ^S4)N8biX
zL`71ZMRhF%<FB0~HKfaY-tz2%2BD*8`G>@aTqkWlgg+R68e8F6knz3@!>9&CDM44i
z4mCgvW`MDzn_sq(Rom_EU|0*u5DJ={oG<{)T3EFv>|L(VU(i92OMndvf=mSHsX-3k
zp*Uj?z~ZLlkJPT)cGr@pU|D=+CvSs=j6saXQMJ6tcHLC>;c>)LQy1qO#K+Bh)9Gz7
zBlIHeH_<ac(NCJS<t1QmIEkG{1cEWmu8F><#g-_EE242;diCMW-g&?{0U9X>qeupl
zLDWHvvf(`VBRrOGjB9|SD$RlY%|97tZ)Rp8uciIwd0ND)x`lBkrEmTN1~|7Qg!JGe
z7y<G@V;aqO^>r$$b+l8`S1lJd6gVO!O!I?XZ_md`1W~A?W2F2Kw&6ISAwU&XB@j%=
zvkSU1=6`l60?ccD{cM<Lid6&)2`>fiiq|1n-%SyH>=4X;W<Ws36aDT#`Q{M|K8nE8
zvx4>UT+Vlt*H|?Cjy8u%B!|?+qya}c2@>UpD_^}F`M$sP*`HBb)Zh%=ozh<nrxgT^
zj~lFr&DVB4(;};WB6MZcWbpy+pOr?s`7BO<IoN1(UVMt{vS$;6pJwnEtCuIW{_cMP
zGR9GDIN7`ICVOf!von-#zQ9q$1Ok>WS?G?;yP+bF&4-iT5H;5BZrNCX6O1Xvec%P`
zY=vaP%WM6#hRhbu&=KEpF5qyP11d(m-JZ{V*Q$MeeeVv{wUnbKgn%)jx{x43#9nHX
zJJgKUzgq4(g!0Lg<CrrP5CM2FTU<N5t$1>lQ*7gcH^0l>VL%(W;~2MR@A=@X=)it6
zes@DF79(q#q!H8!f@XEdki|&!j)*ob?XO0|W36LR1#50UPXf+mWg+<6>UN0oOr-o@
zN?vPcf*M$tJWb*6Q*zWM0KW$j&;s+r&8Ee5_t&}dfJ13hy3~rwJR>GxQz%oUkVU;f
zgD@}+RKmJ`LFqH6W$k(sSZ{;;pb1;}C5Sc7Y~h53mq5v2@X?z&5$eGw%L8$r(!rqK
zdCLV;wtmi<_%Q(lLlBTS&;ux-p~hj7B~PijY<rxN9C~Qzw=mLP`G<cqCV3;6g!i()
zeX03?>=}r~(C;d{g7RAA9()(qU7Ehn_?euwYP`h=CbVp*v(S5Scm*tLoV$(BNq9B0
z2!uZXJSDpTe4FU2JzAE`N9tCqgoQri&J3vC+|;-ACR}pqlF{g~*ZHbf$6jrn{;w|-
zKxQ)i=A&4SmM<FldHDI0rqy68MZv)UA%Q$r!J14UYU!EN_uKt>-56+ImieO2ptqy~
z3X)%TDxFGr%zC-+k4TOPM-d{V9s;@I_KY(*eg`}?4GqEV!%?M`VhBNicL;*R8{AW*
z&@}+=^j!MNy@Y&BjIE@vX&vN`4v>Rrn24UI22&v+sg0cey!$-dMycZ~Q+lz73_>^f
z=uccK!<@!$KiqG3H9sWgWex?>K?GJ(BJ;VFUq2e5Z+*$C3}ay^t=iDFhX;ate}6>K
zrk=&+aUbhyvMlDD<v(n8r^)UwH3uvy5B9tR-*o<5P-z~SsydMqK5WYy<GO;ovid9h
zGLvJbl8NnJ2`k{c;28-2f+3hnh_mx#Ul86cxi9X<38M;S8^@!*=8B5F;WpEF<{QV@
zzRnYLHY>}<`3H?(Q_<HYfDoGdWG_Jy<TOWW*<?Od>?MGRkn_d^1C_3&1vLk&>u?D*
zHMQn&uJAtct{TAbBnC_fbi&ZGuFX8%`Xt$&^UH_<`UwU*0nXfIKOtf2Sy#t8?t>i)
zGZ@Sd98UdaxSl)xd&BdF2BJv4)}fwpbj&viQ-jtyVlHJZOI;Y2w8a{gfiuSjAtSB3
z3;Yn7NPiFA$MR4L6{})6jFbBv0y-H>YH)aN|7<ms|8{e#g>8;e>nns8NJG(N443&*
z#o(?DCBrQPskLkB3?27v_F0$#-Lj%fVFi-O`9CJYhZ#9g@aE)z{6Yz+;AD1njm<d=
zy6>;@{uPf5ev_^DOa?Z#A2T$0O+H=1+BUI4)Yh3thyZGZ{3Vyc7pd*y?}2=O#aidU
zDLn?s+XBZ38p(u;g=+F1wQ~ObrpMQzndI4RZ=q88XQeKXpy})4-{FY8KOj;N;2wcf
z3-vFKfppnwmCkG7x?Jl1hh9R!yv{(NsRmM<4Fy?gk+u4sw!F_3kAFL`I#w`UDW0E4
zr#h0~+|=SJiQva<S71Z+O9o-^&=SK-=uMY8^CTF^FrshpWLJrQc_4#}$uS(tbH*{H
z<}rMPC>RD(CsPLI9X3v4*EE^y9i=qRY&wM&kh}2*9DV&#GB&K5v#_v$yDT1%NpS{j
zer8wjEKLd;(AC`r%drId7-YDfx5=|{0-(hs|5mX!>J=3iPt3>!?LmXC;SD6q;Na5&
z>8cER4Da2RMtc?6W%S)AsJ^^`rpt)bKX->Bkgu*4P8Ys!sy1gJUdcn-hXXmABEnP6
zZtC$B&JyN8_9t=9_sFns%76=V<?K>q<>$RVH#x^F5w?=c1m8yPU-_a>-i`DTXWrqW
zU^_g)Z_2JjN-2;pB$x<<z8(mk$+J8dscg*brhsTmMwNyO5L2C;V$JL(Yx9`eiV)YP
z8cZumaF+;-d6VjO`E@<-)TL9e%J6j-&=oSI_jQyH7#f6ufdM;GP}!-hc|D*H-P4T@
zoG|>Eg#WT>{l#o$+`G26wjp;cY*0)*gnU8lv~!qUmDyP4%;DqqYS?cR9J$}zeBo~j
z4bccP!AWR={XrY39{Ci(|LDMv6MIZTr!4Jy5GZVa!PyzPJ5YH;M}Gke@Am;8@=cO_
z5%Gx*l+ol_I2_^zW$p<maxrWDGn3(hszcQMsf%k+mHnP@q)Xg)HQ=dg8`<;9Mj{xP
zIE*eT2jl!+?^8Nx@-goHx!K3PF1#OVg~a{N;zU)8YC;)H>e^zTWQAW}ouoBs60|ig
z#%hb=hnU{wJBID8$rfLwf20^Em%u<6f86Vnqg$mAeb5x^`Slr^(jP}P;1l*QZ#{$g
zbQk@jX7rbl%gPOI`1f>R{mlN6?$is{hWxg4>&z!B*;k@%nnr~BMy^%`&oG)X?2BQV
zG0??3S_RLU;#dK)7_;j*GnD(=b5Yv3=SPlbxVfpM!Op8Og{-TP3fL_RsZ81}3yI9c
zE$OUxd(pZY)8t&dt(x9iwypE=@6wv4tr=M&1e9xweufK_v^=b_NI3|QXE#Imlv$4c
zwfi*qB73^z4hCBRhG2$}lkbbndhHr&q-M?ahgg*YFwJUYM<&Zm*GQ)27DFN7l!Bdy
zi4od8udw=Kt*0j+#do?)f!e{^U;(BqxetS<go2qChbAc|ycTV%+sY5<_)3xTmiyml
z3))JC4n@95gq`t-p9bGepJ1Ht^SN!}s0c_K$8=#*2ux#=Gd(@{k+v>GsR(7OK@~wm
za>8W3a8{bSsC@Hxg_$L3oAuqOu~DyEV)Dd_1Cj!$nf7L~8HRy0%biW))dAU#wD40*
zkIBa65$dr}Gfb(AQhPE5HS2v<&BMtO#<8>Hx*NlZ!8|+3!aC$gcd0;$sjzN<Zr*^G
zV<H-2Extk7cdvf*+?MehT7X5L<M4k;z~%8_8qM1s&LU;hg@mC1LoagPWCO@fE?|8I
z4~e%f;dl$K@I83H3e*n3*(PAeNP%Sa&wnQh5Wz}PU$qgWT+K!dKu7)gd%krB>3NQi
z2H&PbemT!QdQ(tf!MYZzPSMo{(eYrmip^X@j!u+jwD4t-{UI4FK>oS#Erp$_v=Oy5
zjxr_iS>k$H7#>)sF9ug8;u_KHk@0PS$%NC+w>&bR__L@hCARd}ih74%=xmp>TsHck
z?)RhNq7-f@D>oqn`awY~VplZ6*3fX!b#JMR_T-+V4dlbumv*7oYgb=~l<LKZ?A#)l
zW#Y1;01_f~w0+{H95<#{IpeCFEzRIwm3s?1wJVsFQy~u)06g*mspoM}C{ji0`n}&A
zPk`@vMA#qV4vx*m=JBDj6d;(FP((%jwvi7|w!2+NUQ`I$>QOT5Dzu6sM(vP%#fgH0
ze!(%{`zwWVNEpxd(<#nQ`@Z#*iU?D>Mm)<T3a$rq^&0e3dQMxBp{F6MUGTJWliPZ-
zW<QL0oq`k6j9-?~WDKPH)Ja>PvTNHWrP5q0IVJ<|UH|v4hINkYStBHup3)KWqFbuo
zVox6B5HtBho%VQ=@90g?K=l6RCw<qxwgJv2B2sCs{V|jvr=05NhD_v?&O@DKJf9JD
zfuB_9eujb7bHYq#lLQ|mhEf23G`ct8-~;Nf6i3;AoUrpcWC}Xoz%}1Lm54Z<<1;fa
zq}6ZY(U>yZGo#|&?oSt%(?Zkhc-+~+q6l+MN6D{z?;kV(bK`xVxl*Nv;xL;ru$#i#
zBbqyulmaFmL!d6$y@!s}#uSZ|^*M2QqC|m2Ql_%}becH5)lJfYbm5>UE|KBC6~E@B
zKt>v^tuk#sw{XV#!smM#A-jbL+T!$ui`bjSOy<bRdgQjdl~XEZXA}X8L#BVqr^FAf
zWd#c@e@d0VV?mZ?e4O<@%(0<Dvpg7&G^A?f$}%zj>6SuW*o1_-Mq8ZdKA<zv4>v_;
z6hZrn*7^LNv+{IDdCqsu{i+~+S+F!w%Uv@V#1?{>KF`=2f@MCep@_5B8^USrscL%k
zYh<?}9eMxVEZY|yR>U^`B0)|k_c=Qv555;EOwx3%7s?oNeTrA%sVp|R8(n1mYRBFC
z%w5ra{?QEz=YT|%m2V$shf(n!c+flFLs#W8hI_<*itjvby@H8VvG~-Ok<Y+s<i1<R
z0L3p~-N?o-g{kt8nByKS<v+~uyds=2enlx=`kawmhn9HC^LSri#4YwgW*>z{F(zzT
z#B*wNA-Fnsh(M6<rLwEO)j@7a{ywDRg@%+87l`P@Mo1@^zkGb<vWqiZ^}a}`4wekh
z5o7{ms`Xr@|7!GI^f{kX^<v%M#YqrqB@jjeclFK}sSbwFP=MfZD1f{#qy(N;5m}U+
zOs`Lm3eQwnWovz&j_uQ)Y#*4e=npNLz)GcVPqDGnRoqNNQhcwh5<2<o7||9^VuW79
z@zp7lYdZJ4(yqS)UsK#NQn{w@$*SM6BqLzCW@~id-8_X*dR_$35VhL>v867~O&-OA
zodC?S)|LG0CRGc!PZV__0xNbGQY<$4<3|{RJBf?e*F;UKmWU_M&1x4bR9GT4wFx)3
z-{0myGkh9b8`sbwOgz*j8>tE+k*24=`STjm+ZGg}yQ%iHV1Q_{n3e8I@MXvU=9KV{
zaY#LGFRV0D^!EfknU#;2v&UKC`{xhK^^z~t{l$kW>YI~lv8!zF-Qfi=y7a~8ad>co
z73qmnqqJl6G`?P%^JKuo@;7pE=j^o4?B2lTwM0@%OMUnzm&bqrAN_rXo>ItXG@Q(B
zOwbFd6-~HMXEw2=X@gJLNL1wuR##}ryr~L!lY&f&?|4<)168`jpSC>mv&%qZKpbqH
ztkwsMuFw3kaoW_<zhgB>^yaS|A#2vg-R_emSIN|lI<c!gZ&<y8`tvtvrC8@Sc2s4Z
z;tsrRTO=y~4gBu@iN<^J_;OSKh=6jBiMH`sKU)-QY)Ib}m272s%>+&Q!yZ!SCM0A8
z^nh=?C?UQGrdpQW3tzp{xjtQ5Qv5_v{YH1ae}1tc3Z`m>nF!xAm14rE&ZXtg#?2`K
z?MVF_lFF|i-!meO_vf&r2pF8tua@HqX(A{$qp0|UbwS3Rp)`Y*f85}l_iiUnA8(#r
zjtYeC+bbC7`)d4yGQ??6`OF9iMqdAo$YnD_UQpy63R+IUq`%8yc5r>TcXl2lx}-zH
zw`PpXZy%J$1MuP~7^xZ=vjWKhub}OpFE)PZXUA)CU+lHjMsH9R%+iL!(yt>U^WTsu
zOODZR=aJfNE_&Wt4Mb=F&|nFFW&T&a<6&wJ?vZaH-8~juG=W$G2D?DkbWXUzxi^-T
zncl~i_eZj>dJpC0VkXrp=_=I{MtHB-C&7-uD{8|4fi<H+PSFnNu~O^~^~L!(DYfxC
zZXNTwv6V=<x8Ght%u3qCoM^s+dp8s)9Uh-xFn8dfWGwiEdDX*gzhUhg6{#a;kP82G
zV>Ls}!91JoG1B^l$|84?b=1~3@iB+9w(sG~lxBmJ@b>GTEqv2u7`m4oglnaR#l`!~
zkf@LpVH*Bzl{{ra_S?gSog~)j`r(~fb!;=mOTug}uC5X_B`DTcB8K8P`~kCX@%c4Q
ztV+29$0UZ2dhtqFU!~>7wm?`}?gnX6cu_6}EzE##ib(8CHV_|-;NOnSCYaeDtI~St
zoBtW<4s~>N{G|`gI<OAY0OYhaG-0k;SWTEQTT6x)7p$3H7@cQY2F8u1zteUjPD~99
z?BnJFD0v2vqKy+es28JvLZH_yafic8?HZa#g6jGyBbRHqEweYP(79=zM;$@{9<;Nb
zI}9t>-w?89F^AvqT;B}e7I&nYzUt+Y+tudPGo2(>1vh5m!zPH9mR6c0$nCtf{v_|7
znXrj{RfzCsksdQ)SpXDt9f0nrrY&L~Ta7MKWr@+4uD|nx9PaFFg}OF4+^v+`n=_@>
z<m{gF0wr^NziWe7up{HVaMr<ZJLsWxT0PJ$EBzMLyc;<EIx+yxB3xu!FM43kriE6)
z>r#I$Nz3_x2>Q#$U>ktdp<VV72YXw}!1^??x^s+lX=3<O;;1709xf;x0iSS)au2Iq
z&t~bYtIC7O6DW1E&o-zKd6rv!4h{^2IWc3JA@R?1*i?{<T+bh8zK<UFa5|rzQo=8d
z?l&*0lze`f2(I@VAtLgpRvq97nSW&htEtQKL3>`KYTr&gGC!Z^kNWwjJU@^4+7y&%
z!EI9j@Sd6qCyv;&7L4z*gYOq}=TQA}n5qCL$Oqh_prp22EoI@t_AOD8+x#tW33P}l
zkiyMj)}KFr6xuIWBCY&L(Admc&=~4=cP+DK8dagvH5dX1cNXwtiY}}__F~FAItG-V
zHU;wRW%gq&w$}TN-ZXEfEzRT8UGRMR4hty(jf{eF8fWK0yu@`Oz~l4#Bs2U2V~&K7
zU+J7i?A4UII#3DJ!U345wVfzow$%f^*?)NUa`3?S9p%2S${CQRZB%%db@YQeA<OA|
z(L4tEqwjuQPR0ZDE0<MFdjbJ}MsSrX9TC#+@9K-`*nh|0Q$xZVh^s=xQP-i1QpS1O
z@e)jGnmfo+=oyLn9Gt?~*^o|G54vCEJWC-O_2C2HZ&#dZw}%D6efP7Fwo@{3=GBqu
z(-UR@@8$!l*Tzk3xjO>r2yq>^<mf@B!lfqFa@ufcsBJwMT67R4a~H}LC~-!z`km$#
z2(bu9qmd9t4PD-UbU#9hPfYaec)3pGe`H<x?NC~8@5R6LqyI&)kztj?67tudG9&}>
z1eY%sE>(x#dydq_PidFrWM&6@lWtzEJX#qW`p!Vbb{QU@h)9^)Plta*-)-yZm#{*^
z7A>OhZOg;v1XT3T3$MstQW8wn>ai-^eswjqo#WRXC7lkju$>|2nMOJAd58EWXfVjJ
zjeT1Y7Qkf5r8U^3)s|pA_+M{!xV7p2&Of}4y<|rQTzWNLbPp!J(se5k!>=1NqK^Nb
zr}4;}yEoB4i-8uezz?FEXOknEly*3Jyw!>RiRg-m(S!>vRr6r(ertNF`(V=7e?`IW
z{yDzBxq9PU$LiWI5J(4U9UpKAVCrWxTx_&6g61;=5w??-!6b%fQ>E8=*3}~%F`@6W
zcbHfk{t#<0UzBQpIA^?EuEr00cLWVtdOzaIIg?Ftz7Ls^ALoINM+Y1srWCzWBZQ{h
zjDFMKJ5=go7k&#XCh1Sbs<lBuG1T>Ju_`vjd1Te_cYI67txtch5=kAc2K6|up8x9p
zAeYAZL(2T*07%E3)4)AEWgIpZlmzR2UITbI9ECxd3TkM0Yp`?6Eml?$$ih_=rSFAb
z6BCsKx6A}S;LQuT9sOqW(Ym3Qw#?rt&w+uLQYL38?ag1RBRlCYFqJ;gfN=b?psKDm
zl1@9K*<W-|58#hcwUkxK11T>frDUqwJo8$r+9^ayX=(V0sYHi~sr!dXu|`y~gZ7LA
zwV?w0hps|in$(`JdFt2uK=p+%AwiKA#^iV6@BHn^K$Gng8k+460uOPU+*o$L-h5-C
zzMsa5)eH+N-dbD?k^O!!H!PX%=2C^UqEfM;RzF|x;5tRY;sHlT)?uipm8cOi!)Hp>
zCFEgoOFv27tFFUxww^_6N(aBD^TfbKh^cO2y({<|BZ|CNsxsaf=Ri9qffifCCq`$6
z94a?>k%Ki4C!I2V{TQNRn{$Oa_>*<Z&wlVHv!M1vdmtJC+wnC%(i~{Zr$2kJiN=g_
zV~XFA1S#!#NjnwVjQFX5<7L1^nZTx$3NXSXoYQ|3Abfs9Vxm7^rz^O)xHJNJlbvoG
z*4ouajdw0W9};CoFkhuH@O)FM%blXRPkxV-3SxB!i~G0r?=|?kZ9k;NAlQYNd>V`F
zSsbRmo4hq@;j~>&ez`$HgFgcrP8cf-pt|4FoEcxwWY*1HiG1m+qMD=ZCqs=KFT|P~
zVm$X|vdylPCDFS$$<Zq(Lhh4cRQ5t!4Co32Z3&9c-sa6rDd<E!?f)+S<?~zA)@AQ+
zB&$PHw0+^{s~9RpoM&t!|2n{Gs0ZavhO(I13cCBP{XUEI8ePk|fbr&K7StuNd~G~U
z_p8?xeduL4H(r}Jq3ALSm_T~5uDzZ<HjufwoA)G@)bcZrXe059n!j84@D!uni6dhV
zVB!h<URT385Yjp*18zD({VYM9=eH^23aC15R#N(bV0tv5iNtd5*ExkKA6x?+89Ps_
zK0Q_tk2u9yPoxo7)|2OjjOsO&Uk<AIK~-esGoRI+-o?xL5kkdB64VXcY9NQhP+<8{
zklqzHqc%rUDB5q$?{p|zvuIhQBWEkKzc)}+e%UV(aWL_<Be~PQ#>%#7oC#V3V_qau
zJA=!9^<wxbH>J$EODPX1V}>$c($)PgSJVVa)P^IGE1INZqabSf%WEMa{dsoPX?p%U
z8`nsiffSKbQhHy%e)b2hC$<3P_(&mE<zZ6a9l+3t5wB9)OIC`B&_-j>-JjuD<gRW?
zZSX1{kFF~Rz2JPB0SJH`9*C#DCeuO>20}<{MQkY7K0H-zZm=Gr!nGzOI=!MieplDV
zt9Z4pL%Ppq*!l(S1Jn>$5s3lF&Uoim@|Akdlk{an?L^;V{}i|5C6Db}8tlMka}+EF
z7|aPwUE+~b2Mr_ji}4QwqvhS7ia)~7b%$TZ08CRJ+q2#hobhBB$}g?t@M<G(3X$=K
zn?Cq_Sx8j2@tgPfYwjtZqk2qJ-<TherrX!%@EjC*JoPTQ&F<{5l;W5zn=Sz03(g%h
zrS~ynG#XV>LhI=3)kqdykjb_ct09eBc+N*PzwyCezqV>zCBs(%1Wy%u!0Y>J0~Upm
zFTcIX^>2QMbYfH>ebSfTyPe(BW0sGF#&JtgG65+jx&er|2R9A86g&tBq$-4uTAF+p
zW^3D#G&*_{0*f(lTFy7!+P5350u8?51C^!zq~578g+Ke%+gl@94Os}+okU4}jA3!*
zFr2wwwb6=#Y4ZEy^Zd{VjHS!wI$>{kdvHWWFI}|e=k?C5%Gy@-<M!#~awCngSApjw
z7CE2Qes#BvCBOHS=Sbs0(9w#Et6}QS!r0!Y*HaW|KcW-VNN87C7e6=`4p4~Tc{N_?
zPn8FrDqfJrF#9Tof3^e`$miO`XWax{!70k9=`{utDV*rL-IaLEQ0;VBJetf}nH8cO
zP9v34g)0yNO$&4&1x)pJWeNw{!Tp6k#gGMw^L+x?!IZ#<`@sB`W|-f6SG7NDVry#I
zbILwNSQ1+gTOEzak=}a^FfEfD52bIL<{$Gx<M1k~5UdjMq)0fIhywzk)hnUCflE#X
zen5+5HyzgWGQPKUoMXCZm4G5$X%(ML^ssw~LCbZFApB_ZuMS>+Fi@XGblyPc@&j5g
zEM1QEHnOmiY>BWjDnB!ggY9;Q_=$uQ<ES|$%V9qwyg|7@Ef54OMVTyy9FPUV!Qo`a
zv*q*LSQ;s`R^cV?iWTy(1pefqow!W&o|jz=2B?7m0P}dHf23=R2rGJYrhh=dyRE~J
z=PmBpQN88qIZXzLhnu3-PEui9=@KO!dgy;fP~rjA$P0wNvnCvJ{S|etTc-@HmKUSS
zz|*U+a@N$}aj@?WW@rs$eYUGe{=F+00JUZf)a=9epa4he&LBj|rQP+-rOmb-&Cjrm
zcsHiMS}dkfH_sfsd#4wUEAqB2fSM1$LM6=<TK@hrAZo+<moL{jHnNQw``WtC3i6gV
zrry-M0~-O<(k3U1YK-E6zy9F>fP%jbCkIh$Cg>uE*&V(&x22u2m^~rkcijWMq4ofq
z{`FEDMm8E;cAVNO^mp*TZwX*_!BI!iitgnINH<*#@C;`Pj9_s%md9axG~mprn)0y=
zuB4_~);Ma2CgltuoWRVmYl_Hms?z5<hv}xvAwCfkUknsrUX*pst=D0azAe^-<^B-#
zAu?mv7WL;U2c~NADWqj&R+|SwbV+2R5sn<D62}r<!LF<H(7+-1Wrl8$LEN`MiT!fM
zQrFf<zL{4;`6P07n>11lPlwoA-iudj*|D)c!fK(H`P&DraB4MXEtRit1&=xik1THA
zv-c@_JlSM3)Q0rrk~6!pS^IvM0~T3<>?DkxEd>S9v4f!lr{e4>^3`Pp1x-InSjsCV
z7>aX96JlpVY6xZ2X1o3{hqt-m3D}~%2KdJU%9HRt)|2hxhl%U`OAjrc=$D}75Lps8
zZWcLG(xg5FSy41e#(|}Kb0hsi62a65SU2Pg8EQM<{B-DV@^59TV3qZY@>ZwXOC(%&
zP60dhLMXYXHdu}s(#4n`fS<4!t{uvw4k=z8d6b5PtN4!AjE*b3>1`hVZ79OL`ePya
zY51s!-k--ttF|oa3I}S3^64+Pdx*d>0v%oK?Cir861~{$f<*x<i%YS$X3K=B78)3h
zL1L=Cais_2dzBuMdcZtD*(qF9{UmB=m`SM~K`*|s`hCtrqn)?6apR6~{qg+SSEzfL
zP`m&N_&4DJVKfnYx*59KcIy=MxVY45&*v(ByZ_V}cGC;s1FCIKrPBj@2~)z6dR8uX
zO1-&Q+_|2K@v8Ky3iN^D$X7)jHXs(X6Jj2}kDXlV1Ns*xA45nS-b&8vAN}>}c-&d;
z_N$A-gTQL45d&ThyqhR_B8ouO6=%C{R*?RJmYUyPaLB1A;1AHk5}B-fEk!t&JO{K)
z>K%{Uj{zCT2+3l;G_^-wFG=>ns%jMu2t7Kx>$+7j4m)MRYM!tqsJ4el?%C(nwA81L
zi3P9+8>wS_lA@KFz7019rzk-cuQ4&&TRLWjF?MyeA4c50z>f^d4_rbU+uMPvYB6Kn
zKY;2eJsntx&Uw(1(Z{-^+L3dbQB_J!wS3@9zy~$yY08iJg@9ya$pqi6!8?9_nw+7d
zvlS${Akh?@!8`w~N}8HkfqLyZwzKBL(=fHMqo;SpsdN61ebNjFE}ECGF5bsmfIYi(
zJQ*LK;z&OB9q%}MGj@KcRwN?U){)zfR=4kjyzft@f&P`APS7(ymu9{7Vz5WV$~y;%
zgHFafpcyebwrc`u?o>sn1ku=Q_`%Z?qte-uBv`a+J`vaV5vZ{X2eF2Ob3P~gHr-68
zm(z&~(dlotRgthxvw&AW6j%FbRp<-YuJ_wYX;=mQ!UYP#&LC-NX_iK#*w}i`s_Y-D
ztC@}-Z4D2i!oS|S7l%E(ET*X$IMc5$S2cxC+N!qlVa{@8K(!LC8N$a26n~zc>|IM_
z?CFsq;o+6mmm|ivVZ>=_>v7sba=J(LNVFoR)%^A(lL5{J^4g7w%VDZ4OqQEHzI!?B
zefhe<-m}ZKffoT?HX=8)8-Dow5Jc5V(iQMZsF=$R6ibaDFL=Lz<#Q(=X!7{Dt<2>}
zd4&D@A7STmC=|CnVLu$b4$Qot8(cf(k50~9(&;Jq+>Q##O1E@ZPZo7BQ|j`qi7(Xf
zt+fJD1o8W7)Ja;=fIz|Vx(ze)j*In#aZJObxZkh0K9lrjPZp(%xysc;eH*Q|=d0tW
ztR;Va4IKAUyTjjpqXf@1n!4p*>Zv}U0+tpm3%Y7_>}OeN($`eouFPl2I{cwiG8Elb
z7<y>SF&2T|X$~~WsA3659-QzHZSdc>>LxiM%98LPicGG=T2+}opPv_zinhQ8$`XRU
z0aa%)itOXKqL;9peMT~Zt=;y^&dN<U_;C=Q0eU3FeX08GG0jp3KJRO#Id$>!h_;$<
z^-6Rz(6RVvILGcO-xLNdDEj8pbja0WdiH6Mm2noV2D}(9Fh&ZM;+cW0jC_g^H_smb
ze*1^4V#vNP2tgCmkn7T^x%2q&mXF)Heju^epG^uNOa(+OGR<$#)s(K25;Y~pRurHh
zsesqdl>6(q0Y1jMbHA}MG^V-npW?KYrM5>OzF3tPIahOMnI1JPToaNu;?*F(2@1+L
zbK-)u?1jFTc}SfQ8LiXdA}F0&?XGs~EbYjIsY<HA|ExOjon{F)^4{_?<w7RCwP&uF
zk)<>n*0<s0ZkX)$$-lDlj!!li354#R0{-GT8^!9SyHuGFpZHj~yTdi>+WC4MC7%e$
ztT*cl;_I=k^ONrdKkDfNYiy!lzdoxLta?N1^t4KxuM>9`e3<-kku^#Dre3x#)>y4y
zdFsNL!jCj)dtstr2+$K|lQYAlKq0$XX=WxVO=JDDqOFPd?}dft@1^t{9AOz>Pk-b)
z&b<0Px+E+GeLy?2k*k2nzSd^!>+jdj-@<KA)c!0rDwu7Z*|$U6C_MeH%MCtce`0Fx
ztkM<GZp%6C;?>+-rQ;Uwr={ns<&XUNjM?aw&;nbt#YyVk=v$$iY95@d@UTD+3UP~j
zy~#-4fk7<vzA#ZxK<10IS@Uxe&OIr&c73d>P;10-R^?iAr$kH!f5@?~ip6U}WVseT
zKK`;=eq@Wupk=Y1xv@2oxY{VDqeaDNef{Rnp8Zcv(JT=uH7l$yw=UEG)Kaqi5K?;k
z^kaAlUr|Zzd^s&;2qgzp2;#z}P4G$zw_D__>h_U`64?dZcp>OYeXyT(1z%rLwZ5YR
z{3!xi-QA1tb&Da1uByF6YNLkAEqADlmDa}2Zhb%X;B<dy)Kn=sGxG#LE|ra!snNcp
zrhm7EmzUUP=aeIQq^Zt^IsOIcCp0g3;qh+%!!PTaxvMAzFD@DYg|ueDQKTJ5M9B5+
z0Pu4VFniS3^V>vgDr#yz6<_WU-OPeCQBk~6`TiJ(lM#Y9CPQT9`SCC=t7cWja5hcK
znnGeIW@>fAPNhEHAs9u({uoHU&fSdT>!oE<QxhstI+Tw*aSYm&gYtf$(ll{2J+MQ1
zYkJjUs0PG&PWbV8oU7dVhkPAvb{<vUODcf9u(fNYPihtJ0SyD|I{xyoQMzsCbkd=H
zmYB%W%MGoAHn<<ouEHr1V*C1SClR#u49|Z=2NzY-m~5wh@6*z?Q>Pmmo4Q7V5^SeQ
zjpSvm?Bf&SbX9Vc=*xX2b<G)tSl)`HOM`L06|E_W%=C1LE-KmWGcN}^{<uAVM>=Wa
z+h9kW%d9^Zf0OjSlDO4vLqzNb6srtA+W7PBUOv$y@LvdE-_ME2;qq3Snbtyd`cO+N
zc_bvrrz=lRE%j0}xZ+_a@NnSac`-K+l6)21Zm`qP)}||(L?S4C$IQ&XZs_@NQP%e4
zTQaR$P}UmPvtRHcR2l8jwe21cIaP&>sfq&0S^je3wj@!>YmXvbQV<O)>U6%yo)aRO
zGKzxuC>{))Q!2=8Fx2EeKTVRcgwd;XL9BgdfbkHQmAk*PT~{afP}|{WC3&2t20J|`
z)vlT2uV`|Gahm><%v<~Ftdq?hT*Wze>8~YtSXt9l6jYaJj?GMso)nz)>&9kI4XRkL
zLQ-m+V3it1OjVkkdHNd+P3dP+ref>yi-wUkA^Zuap{htTcOr7oH=uJ0HybP8D9JXn
zsI26u!j+0qWqNLE#NM&WXRUjAYrQt9IaeJ!j)TMJ3)bWg4mM6_{>QjBmXgZCqyq!9
zYrQat&P{5Or&~k><2KsHyp2_vv0+W=Bb0hhcv+6AOI<CP>yI@<6+D=#u0B>aK7aeL
zzU`Fr*L2Q;=E-FsFMdea=)G^4NhW0$72^dXBZ4q-?&zf39YV{rT(`e=^$mTAE6p2;
zotzB)A2fYsKoxD*wH!g|?vU;V0qI7%JC*M4JV<wWq(i#9Te`cE?(VK{yr1v=bNIu|
zTyy2#Yp=B?7)6E6m$JWas3}yGz$e~FuB41XR?x+vc2Pr7_0_kU%qW=i(MO9IJ3k+A
z5kNllH@-398EFj`gW!FIXfL{6N?dWZuR6-)Nz*XkLjjn@;mD8$QZwgW81`ru3o5Ln
z=DJk8XMVYrgLKl@Yf&yP_O`!eYKmMGCC9QHK<jm_Y;L5@<7Gk)hB0XIf|lp!iYZ=^
zu=6D#8zq8H*XNZ(Sa0U6BCw$v|Lzk_QPdcb<zCRV8W#td{Z?JQ1feK-`xLKeM~SKU
zPlBW@JAyehHdcS`<K$s!`#p@7oiQ7W6ydXdjlu%2nPfm;6oCe;2Vp1?NExfq<0{|R
z@_Vlb+v}||M`z<3vyi%a1#sUnN%c3ot{w%=StAjpFwOykl}A0(7qoB+H8_SaOpn-~
z2dqai2u}m;{%<IhY<sx*?$EaJch^6=!Xu2uB$a#F%kZOeD+?P>R+2)_%x7TfI!M4M
z12~dNe*P3>)$_Zye^D=3Mk5~PCbMH<<X0mPx!guKf<!*OHw*z|!i;>P@)c^2x2Er{
zG8(Bvz#T?Jo^8j{nfXV>jZO)7X$PpCe=`ui8>yBCZ=$2!n_d+$M?<gmUf7@~+aanD
zf_6WcHYQ|!$&as6ij8RAzQ6xM%|1th@A*%^utNm!%Ou|vd<7f*cWftGJ7FYzALbYL
z_pK59QQmKe3bBF4#yAJJdGf2`@mM}+SAdg~2MHa$i~Aay${NVSumCB-W+3(K3~r3(
zIZk=vV0M*e3KJdUJaM)C2^?Vs-7mb}FFi~`)D+w5Dbu|I@{tN2P#QS>D?^DAQYOF&
zL`R=z$o<$j<4~h+Gs;BMhxbaiBvt1w)|!1%3uzIEKfGtPg@u*Nii@E>WuvZ@GGf%%
zYOT=C=+(YLx5W*klS$|>-@0fwvlU1AUgYtaUfEyGi-~!~>$bi?G9=`?Fc35ttJQUl
z4PAOEA;AYI$#nkdd&KZQsle82gTxlj$>e$hOy+Knqi7=aHY!UYa5fd=T9D|wN^&6~
z5<vZeEC@@X8aS8@Zb}$s#ZFCho(8Da8NQG7Z*ekn#9iqHxWN!bC#{EGUpv1RoA|(B
z0N4Q(Nw>GR77`M{UvPvg*$X|^`y_%t;`4sNv{*&gb_e(rvx!CK=S$(~dylKWu~S+B
zREk)jnc$pxzYbTyMQlvqaC?K{+d-bbC#EDbmg!uXbP&+7$<c$qOEq((4vj8nO{>`*
z5TIHX)0x7;eVGk@xrT3ZhGB7pg%#}tcJ&WowTiftWYWPf;RNnYf$V>&QoJMpX6N`f
z^T(~O6CIqyUmEz9OUQ?!VC+!2!PEQcF&V}zKt6Y_15OiDIXN{Jgsdr%@o+*Do>&WH
z?`1~SPwonWG|Rr5E5OD`IUuB+>ye3${>Fpl!>;6HmmlS>UO=)7Tp%Usw1c`l37P!k
z^e68*8hw(Lae}q5Ew8Ms6nx-1x%viEnf>U8Q0JH3k&*tomGNT91rA3o6enpYoOzgD
zLq;k#Vb=%PsqSP#uSpCwb+wv93HGSL*Z}pnFgCXuB}xnewbeaOrjv#eWiuwEVh#c^
z!<PPaIXw#v#Sg&YT!#Tzi5ejP_S43Ex(d_4m2b~{X#z$KA8X|SG;Y>jFiS&auUw!}
zuiI3S2vi5Jsw%+td~$!<V8sYV0y>=;nt_3#Ix-S5`kV|G`cE;qy}Z1udyqCBs6^Zw
z0cjilDeM#T+7Tvx!eP#L_CjQT9+){o_*clYQlQ!_52O+e^cUgtwL|8h4`wLX2I}j_
z)VqO=PaM{F0k>7z#ghU;=pWOW=ZRK-E0Gr>LQz5w6c>+qt=ag=0F)hoXlzddXiJE#
zjLlL1e23jnG4W5qFK%w?I8*x7B7CI)kV1<8l-u8Hi_HT<z@`s!S#;1pu@~Hr`{*wF
z<JzaN6j6aNyZK$)(C*Rh4^fv|riwP?6B`EXSda1<{g|m!xUk}sD+S`z$CY0-M>D0-
zmZ*wa;L_s5aRB)$9Kuop61a(CaAL`GetU-PFLhpVGi{9XU(024+GhK()G9?SD7tN_
zs6u1ccghU{hr&Wc{+VtDnsUUB+46cbfY7s+Tu6Pnm|t&OQ*9QxLEVd$lQS}Ycn44W
zM0GQL<GsQt(YJ@_8o^>MtXYz*dLe*cKRfO<R;Sbgr3$cfYspa8cYJ36vcCa}mAH;q
zx_iqPpR%^q2@HVItrqnnq@=lDd8Dgp$5EUO5B(Wiv+ZpO|8W8%ia_K&Fm_h|*7?xt
zlII62qO9*_2(jJVGyt?guYIPuNkpckX7q8vd!K66d&XWA%M_Yy6U)CLCq6C70;voL
zTdd(~&9@uE%IZTwgp>d01u$8D3-fmy*}33h=}c>hAGn9Z>!9?n)sz5@cEu|Ea8a<5
ze_cqq-1l1H&wz}fl2lO`pV2Jmdu@@A|8ursvx=^V`0%x)sv2|$THMUDurk$ORcBEL
zQ<(Y;_=g{_Ibn<HllVXyncI2tG`S+>6)amHR^Ny*Fh-wsDI@*2DMBcPe=9(~*JI6*
zYMUObB<l+_F3tcm(=@j|Yc`|BJ=<1PjxdlG89_aMxdcLs()mWx432Ns#*)dg1$oJ|
zd0(R!y%%$`3ImHvi?jE8<gH^uq$~mklWfCnbiF^JpsQh-hO}NJ4SpOyD*%euK|mia
zkN?d^5k45#{%DR12xtL02_<DHg_W%(S7$=_HJq3uFTqgX5Zln)lyy{5)rALo$`8H+
zhT5Ne2g{)XZ4jCl8*-l#GmOhx%JE$aEuvURqM&XZ#7gsgTw`zBWJ6$z%~61)sWb7K
zav+cw{=0azm|D<7GcNuzJ%58@5Izk&)GhR_KD-|XAY&OA9xjP&)1LrR?W|}aYvtxF
zW_UX*6XB*Iq95rerGj(Q(=Ed2?2qotkDr_`)x|R?x24w3Ny7QwsKQoKUo&i@DNRh?
zZzW(N#80uO(U*LRlk4&Vpb0iPNrgL-xPAm?&+ihEMvQ7n4JT%zf-1kigo}XH{HHFZ
z4_&2w>D76Kw}23G2n6_goaUMAT>s|&;WYtg`D7Y3=VCs$FxEZzJD|P&)r2x(M!R47
za6Y!+M`>TV&GFvBQk%>42P8hdv8j?e5$~R!f)O0Uzamk1^u^L+gbxuD3nmL7;r0s}
zZZS-Ztj@S$C3dNWL}*+dkzlYn3>k)s2CR+!bydM@M)~{zWm52j6s69^v=qGsZwjB6
zs;cVeh)@~6)Sth;_p19@*Xk&%3kV1bzKP1n3=?uLeN7z)D$F+?G1`7D1gT4W90-XD
zC0MWoU(go(XuZEYkV_{Gqo;-bR)MXgSJYP7%7EywM6T#3*`y16sFOL$7nO=%${3}%
z=};oT#SP@Vx^ti?Y3P8pap9n2wL$SX>y6TQ<t<`(21ZV5rdxyDVMUs<Oi|Vg1Zas>
z&2vT0sjbZw5c!3{{7{cEeMw38qQ^I#-otX<{98I)7jB<trkC%G>k=O156$DQwZ5C#
zaOY^WV6QL^K%fIJ6^(bC8EH$BB8(v~c%s?GfvhjPPTPlVRnnN^Xt3sb>kFe^ZMCbk
zJivaDfsrLhO-D!1MMYl?%3SW8HrU4+nUe#Jc{cID#2T{(9I*^mRqi#{!(TclEc3S)
z3(Q~8O#68|9?s|foi{#>lc9Z|Z;D5lQw&Ym^cp;U^8_dTQw(^D!Me2AU)8B2;-xb@
zegGq^6_Sz)lZhw0GS_v~Ch>VuY!}?8sPn0z@?sd3m58N)a3yMyRZ+y|jzi)PdJr}m
z)!5cBx%FL`xSg|!!@Nks1f$NJn0xMlV29Pp?Ub^fzGVF^H3nT&7!3^`dd+7ctyStU
zj!Wt|mn_WyF{}d=6cltjDHU!v9`ZMxCJ_3j$T+nVoI@c+G+01!V_{8^&v)rN2QDs>
zA{!JuJ3ACdhRPbKF)=rmJv@10wHhrO-F+O|Nvlv4yus}50MO#3-u2LXR%uL8G+2AG
zScrivrLoCk!q~+;A2mBzK952uo<>X8vD!}8vG2~zE*J%UX(1KnzHdoW8<7f*)Q;I$
z+Hja+?DavM5+%qv0Zbv@M-k2k<eLsta<*?4<OVS;0<WwjwLaMWYBTIc*QrxG=k9;n
z@MhT=tg(|j7h~`9$q3Mc-vf%7*m2NH<v@}KaymkEOsJHnP<aAf<2Xz_pIU;M74Ulr
ze@FQz78hgbGcqn^Z3)zbXi;JDF;Qxo=h1r8di)jFnKyoB9BF}5SRx_@Gc75g-14PJ
z&_RjcQ0>3~U_MlA$lrwS(@73!G5Rb;8p1I>l$R6=A}lxyysv}eLI*AAaHqls)EpDz
z5dzU{8Z>_h2ndd?=O)||6*qmP(t;m?#AUk34X2PZTJXL2pZ`?Oq~Jf^R=XFHfDt=B
zl~z}?8RV>HPU2Aay$7{o?JrgfUmrkJ2+lc$paEqeT<k3Kiq4&pc7fSmX=$-1ML0p5
zv-#Y9YZ=7EExvFH=P%Akz(~+wREtv3_7}xT2Ha$Pyi*4U=lh|x;Txu8l-0D6zWH>K
z&luy_=O>y$#Q$PGSQbiqUY^C6S9_CU{@V9YwD7SyncDjBQAzUZ3K1$(>;b*;x$zZG
zwDGy^Otln{)sIpml>Y6jTfMb{*ohDnuh>{jiQAg&)D09<itQb(2n%o77??0i{_Ksa
zqNpR<AEN+%KvIwu=$VvgHs(PB3xMqyB!24$V$``D0$7BO=gOMK`tr7~<HoS9g3G#C
zyB{)638cFEmu!^O!o<_7B_tkoq^i^N<n=P<S>;EE9%+wn+tQ~0kQQvlPX+A1#O<+K
z@$-RaM12KWMKKMYX4qw|vHd%{a}k?hgZ)YWDeoyrS)#)gJYE*^2$Ju%HqYXZz9Uaa
z_iLX9>dSGkvfb+rudpo<NmUa+cs5SVM_``>lvdxi5?~NW-1c7&GzOv>zhTZ2%cRH>
zGAXFV=<8al$9RT+V~2Jpjg3t^k_Uxti?hIjKzvvOBO{;3nHY40(Lus`PpW$I8ci=Y
zq!sxBL=DMR7f_DheI8Tt6b#UJI<`GqT~W}LU3&_0r?<L2#Gs+*i(QTnx0&&^bfxR%
zh{P-5K&@z!lH*nbsozjxq21}uS;z}Wxdjx_rRaR}JJKOL{e0>0kS?-8*QS=gzCqFu
zXkAT7uGnv>cwv?+U7kS6@rcR440S#1oz{z8Z-OG~2WWSCMJ?KNf=2E*EXm8pq2)nE
zR+g{4sZk=W<;CIMVi#|K(&q(bpr#|fA{23$@;d+ac;TYv92$N4iU)z&QVc-3D=CHA
z>0+qp<9e;@#)Vtrwhv(log<i<0JK~?#8({VVHFw$7FLX;pt2`GKv$e(gG?5PnN6T{
z=p$Sh+<wpQN=@kr_c_azq-V2_0r}Y+D-24+CGdA6K<*iRV2t_q`>s0G2M)&24*-Kv
zGbwrMC1Wqw37fa>GuQI4;gxwx2mq!ZCH-t2;mhISG{S&(r;OD*zb6oQuMZRS4ZynP
zp=DnmL|DQS#<Y%Sre^MWB1Us8J^{s+G9zU+c$xou6b)lV?)xr*;R*}k&#3ESon6Nl
zj5}9f4$^w%Z?3ODRWI<R?EVHaDIO|u4T%k&8khTT7;n(%)z$Wm@NfArXu@E6|9-zV
z$60QfmtWO0Oes5n;1&4qK1EY7hRWgjAF5ZlZBr}&sb8xl0SJ|p=b<IhTE`8MX^Or7
zUc>Ic*FYl@#RIPLt_<@7g=aegb^)o_z+3=SEQ+JLD&|tK|NR%G%KNlL<&P+w7m6fY
z9Y9u!vIj#d3?_$SWva*h`3vII6K2~4I5lX8H2*Fa7AH!r0wxaz4HNbjsYR=igb*rf
zI2cz$ClYvhc#}6H3VDiR7nZ}r;s4(k7%ZHVB^w5+05J@8q8?zN^!MYa%(|{I+?n+|
zKAC5j{nrRoA9CQb^KbtZ6kyL)*9VJp=oPwEcuUh-yG$)LB*caP>*OUw8luwwjVA^R
zD@*C-|J_(fcyfz(wr;|;!}?A`No_IlL|{Uo10Z+Ku@du0%r@%l5D6^w2tpYl@E_Ow
zuOG?~RMpj#V8na3YFAk!h~@xi8k%ay;K~J<{~MfM^?rf^?EhvDMkNOe-PZq}wk|?K
zUy;Qwb$Z>jpPDC-UULO+Z-Vj5{$Ys$=x)=#>4dKN!v75^;b`eaKi2<-6a%XIQSy$U
zQ)-XFSzA?kmDudL=H2iWRb1g5zi<qLti#cDb66!r0d#u$3vfUAOd1;G26}v)YGUWq
z^2wDRqBFg+MW2BUeO(eH<*=#3SA)^&m9xzx9q818#SgP2Dr8G8?d$9MTpvxg01dSY
zFm(A|-}S+PTa(eCyT1CXN8yXL0C@3^k+pTt&s<4jg?K(A&ZRlW)KQ?)$Ekg)a<tOK
zy?AysL073%^Uk1Yj(5)@1XvEqxPU_b=mztTDeSojkj|$>i$>nv?glacKn;1P1s<^Z
zwEkrIMCFTpDt~Ktcyti%xkD$QC9I~d9*uT+94DqD5kE6~4QEK^^Rs1`$kf>q|Jy8I
zr(I!s(}R!aZ`oWUH4n|H2V^mm6hAumL7P*3#P00k6z{@(aVQ1qRGQW%3p7GKIPK2W
z)%IA#Z|qV(>U$_CJp^S3)8v@q1=S`3Jf+T^9dQz~HO2dy!deAkZwL~xQ9e=Cc@LgL
zzo0*m)Ame{I!J&+`d!$#G>r>QIDsAqU0s`SeJu$s5Zp3=J=f|KwRMUbzaZs=Jy%*E
zBzC*D?D-r&;$wex`cht`<uPo6i8avgJ{uMWWSm}2JU)_#z9sTLOh>WYlTn^Y?qagk
zN{ias3^z_Lng{FaS?g2kG(O}?WoG$&gp5Xq|D97o;D#I*7G{tzDk^GWX98A#0>yI{
zZcrB~lYoi0me#}kwwtg4dYHZ%QbeSlY<#<mapVu4nct1I-s;|t%>hDgMSk6L^Ye1C
z%gb^$bDd8!2`|TMHut}6fz;mRNpUs0{yw7M6S?(n2skFSThCYQ2Mq~{H<UqBn*9E+
zxNTdHftRFwE$*fWI=YfaI2YsVykg8`?t%gLlsK^>ZP+c6st<Z_QTO@-{rUI=1Z8(u
z&idOM`}eVpTa79lu6z{5H(58<7>bGtXx701*nr={OL0kt3JnFARK#xo5O6)m_Grh)
zOSZ^ROJSv<=6d@mEdgf>Vg5|VSsBqZ2x9y7uswM6QA&9<=M$A)0do~)<5wie#_8s{
z@AL<Oj{~m(Eijf}{I^*M`3^B+Q8E(C252!~Ojr+{I$dAkV*@UW!r9kS*X80moB{`W
z$p3|X&W6DnB9@mSBD_$;T_Zm&=UXqnk@L5`_fALKv=SS;>>$9$0}vNn1MI}Gs!1kh
zjS%itCZ<VEexKx6&x-HjYH$rCc6%$~X@ye%h3NKVsM}kzZ?n<M1&-M<@_AI}6mVhW
zTfW|)xt0C<^1<-P9Y{>EhaX5?Sc%o;#<j=Saj<yo@$oV7p3}`aJ|$fTmF_16M4!UG
zx@KjIX#J`@NcPt#mjY`~fS6jC7UHD?x639KfVUSa29o>lwvV-3;P8FD-&dakZNKq8
z#w)}qbSlSURN9L5tyC2Kc7H)4OfS_(ZgY#HC~1?d_Do^Mkc?NwaAY_cofy#cYwuCj
zAgou%RxOe6H9|r}tlxZTV4@wZ-}vz+eeYs^_^DWo71RHn8HgzL@zZXplQVF_@?pm&
zU{qR{GMK^pZ%I{q7FRYN^FJznC^w>FU5KlA!m#}tWl8fhp`cYa%@hwG-&Qz&=yVlm
zKHS>x^ps5sEXp*+KwkgT(HDCpwbT1ac0dev9=Ck@a)*=g2B>utZC4)n`RUzIULd?0
z8|td6wIn5lMcYTdyyb?9bp}S{-R9I@shMhJA(zDfs-@Ny`CO+uH_^@dKM<)50r0$_
zb!=NRE^~`S{%0!ypiGukVR0M3a5&GeVLWdxA6EuQ?+icf#)?8UTR6EuuJaO-(|Mny
z7hYm1#DfJC!u0WfRiti7#uLD9vKcTFfx-G>*~Qz&E|!OF@`Kql9{{@?XS)p?iB%T*
zx+Pj3puAy;MPlabr*<TfVH+wZ&o1C{bGz76JAxW7#QBZ)J0m0GO)C)|0lxR%tD~oh
zh9rt3NG-k~LMAEJ5Zkd7NKw+-1Z?onZ<RkuBSZCy(M?p?bXJfN;RnM2rE&L`<|((&
zn14qNKdt?tqsxCdC~3$ZfH(UhrOul`ZA7h?Q11*sH2mK7Yab!E#|>vwjn#*<ium(t
zqKU~>QyxH6mW;?-;o$p@gSXqTeRgC-)+uBj8z=T{GRcmRQAFsy|IEu3f0~W8Jz?F4
zU_jZQ4`VAXhN3Vw@ORL;WyxB>Kp`f6c~MdRcJ7GC#zsA%{Vc&Ou38HJp*7bDaXNlz
z2z-3}tmHsj#10Q10pr7~z2x_;;Z~w2EA15z<rWB{iogB6Z6ls4Uy=D)q6i~S+FAUr
z$=A|lAvoq{FqJAAVyK1>=&U)Q;&btc_;47CThJ#HjjC&E8EPw87RxG1oW`a`7_=1>
zm}3So`6pQ^4GwgEsjI?ZV`957GcgkXgcy9ItF5>_O0m$PzZxY<{n)OhfoE-`4z8l~
zG40+ovue}+SV|IhsO&2dQkY9KQBe2BLs6uh<`J2E2CSQ5Hpk4zS*@mrvkQK8ldT*(
zGwF=o+~<301(TBS{4{{Pa+i%4DfHook*Zo!;cvavWA*>A5*Pms{*+0B{(lCS=Zc+!
zlcsPe-6E2f@Ub$=N=(Se*BdDA=1M>n27ZCjXJzw6_v8Yr1m2WpC<B}tfHlk6`GV}l
zp9N6sgfO5Knh5)&tv~}c&92)MA1^zx)NA>e0qpEuL5CsHV$A~`R&f>$v?d1bO399A
zwZf)c0>}5yh)|=@mzP6-Ijo@S9w@Q~hVr`6M&#h`jSHk0fNN(6R_Y$?=+uQBU@OU3
zioafEYFgk%0U3QHET5!&(y*kcc_0qxdZ#5GD>=s<Ei&*2P*T}6=DLJ@tw*-+H=+F>
zT!-&5IGoC54`)`dRtw;KER;fHk6iNOL&*cGUgjahASIN9U$IT%r@6fc@9M)BuIJy*
zkPb;2TcQiU$YSm_b$%po@s>D?;6uUK5EiNixfu~7#~-hY^PR_6J5K*Z{JvT!R5<+u
zfH;%A+Po5w)J^*=XT-2z&6&PgSc*j*PQ5aZ>WCl!5GMiXI_NZEn$7Qfj!o@gn~<^Y
zklWv{r0KB03}c-)H8l-)sHknumrzsn2ls(69_zU6zLO5mHIjY?NMDs?sOhoTLeXYD
zQe36`j%(&pT3Tn%&)x*5<Z%+vlB%<5d&%9+?F#LlYBik2!b1E|o|&lvwcXA)mN|eK
zLKc#LZf&jj(*8#udur5AiG0Z|t9i2kcaCFkd3g5Lw4)yBK<D2~D1*DUTcernA7vG>
zBFZKQ2A9%*WCZ_ev-<Kootnt^KXAr-yUY|J?)wN#_D~-p#neWnauke{Sxbk%aFpcW
zL(ibQ`-wHc8LyQn{nK#*HD!dJJn?ALHVo&^rgsmHGSr{xcGbq-15$jzMJ{PuW9fm|
zVt+%kSQrCXyLm7v?%aDfwg+C?2k<nofYp{7xZPUG)AiJ`%r$)M3x=fhsBVA^Af1Q-
zY#fZLtJOH(PBd00v*1*N0MD5Ss&=;EW2cy(<=`Ix_@@rjMQ)UufGqcKo7<8XlHle`
zb~-hT6&eQ~owhyS!nHg(prT*;|7!s$kImair|9mjV<HQ{qwVQp;~^2l*bY4Jm(KVo
z*+06K7$*~j6$f`-7az`_wel$cSBG$c_r9`yL(`NiOMoRH?JN=!(a&%JtQp<p1BCXp
zaSFXvfwYCPSMG>Nrce~{l90W%(gVfRA=}HCWYK<x0ycO+2qb=IH;)SV2$2NfkaQCJ
zH-3b(yP(Dw%a-#G2@9{NTg;q!6v-ayj7)%(AI=g-<Bt4a_2lK-HfnXuhU^=T@i_?k
zp0qM2U!tM1@m|MQEP$q#QOLH0M}UoOT6g(HI!_RI`lz#$Uqh_hhuUt|Ktzx1WIt%r
z=zYhms?2Hrf5h%*EOUEis!*|zC(A3haLpKo7BSXeH}VuqQ0QvGR2ljg+AKS>tm>xj
z6^5azm!k_h$x8HQh(0QpsPDk<#$l$ao$RUUGRD!CH%#_$%1IK7r%YOiY3pOeAb+&n
z>M95H1#Hg0B65c1Q!tz#mCjSe3WDJ&WWm4xnG)jR;h92WK5cZJwPw=Jz5k3D6=^f<
z$&7vsNCUbPUR~en>03DN<i0g(eWqrGax9tIN8?YiiC?H>)rEzXUl+FY^!o!71=d1S
zG&Xtbk?uyf!vrqmeWwNGRlD1b#{W-%CMDT9vJm^|(^q+FY*74bqLNh=xEAa^OFukF
z-&Lmq!{u}gt*doYXris1!p~bK)1XFC%rN5f9;Lq#aLDrQ6`3|y27$KE<M3>4U1`yA
z>4IUQgc0~gMx1jkE$xxygvZHh{!p9j#F*BL*8*krJfHQ1Nq%fs^G&Q?<tuabT#%3U
zQ%D<c;R{_HGD`OkfOGkYDz}xY80tF<K0>a^j_gb$+UIk?Z*6w(OVU%20Q9%J=LX(X
zd_0zU8g2ACa9xws&8<g;Terq*c7`dUycj4*Ja1a;Ga+IS6ciB=(ep(Yc&kQ^M9D`z
zU?Vd<ZG_#=+iyTII91$<3~S;3;Ao|+tn5mRhYt|}l#OBUQ0u4T9MA=x!DgA6_wxBR
z?>hOfs>cjeh98x&clFc0WWBs{dDdwT>qeMMMS_UiR!^S(=mju^I#}akK=;s1RL8BU
zX;8P&8WIur<+Xy_GfscBr!QG+w11GMqbE_RCmAXNETEvjm_Juno;TL^{d1i$#M+;l
zp}v0}pXS}5_p2IX>7z7-dK`JHEL>;`v%oWm@JPeeIRSm}<gt2kwS4;Ifzn@AZe^zI
z6_K&Tp`>I1_2@o(e0+?&YScaqlvLPNo5|qA^*a~G*vy6tL_GnIlNVJZFd0@>0>)+5
znXHLW{A`mh^7O(0OHOeyU7{}4VCL|b+S!JnWLh;XHF3x<h*ilj&Lb&(QOkhzp)o}f
z^90K+2wkm@=E!znZmu4Hlw}ia*?l9*f#vM>-5L`3&)&dgxBtN3*7i0p9+%|<9ANIn
zC#AlZ5O|{mfXy;CDAPdIx-hZjnY<}#)TjAu``CJ~N30i!HGV8EMF*X{N*YQIo>(_{
zv<$x0vT)?}>!kfkUZfGA+$uS4{c&o2_h$X{*v#`)|6s-x6B|E6?fa{l_sw{#MLOeY
z8|s|Xei~B2AwytPJZj_laP2R>l*g6$o^JrtPErUgfVl2ze0*#y-gjq>(vTlqd~09-
z)-}E676p<0^;G?uos)xvP+Ta0r^Ve7ucj_+a5g(NV%=6y@`7^e=s_WcXwbYQ`aF8p
zFL0b0`>VaRx_Ifqgi5x}G^@g`)~<i<y;Vd|k?#buIDo%=W7hPdaUaUfTXrauMLvF*
z$OG&Lm!3xN{{--I@OOr|qXKqmouxpPj5Q%16VEEi<|cD)Al$GtB*I%-l=-fP0${@D
zqwx64tJ2{2Qkxw!(9qcy$`T3uBsGe1t8GX&u_<)x0*Xnpi%I(z`T4tz5(ShBgUmFq
zs#H0+{0`3-(w2Mm0kOGsyyPhmpz4sEsa<#Q7KUxd!|FFi{1vBg_8lFZI=Hn;DRnn8
z`O$Puy#vk_6_yb2c+yx`H@?o=t{r)~!B(vXeBd^)1?}b^PLYu>wtd9P5?uGkGyZ00
zyJ5s*b<kHk4qT)&>m{kOk==&J8QGw4INqfl9$P1ro@!=FeC;d6!N*&ch=a5RMmAhc
z&(FSSC@9=1cF5ZR8a6cz#T!o`rr|Qm{qPcev6#|5oM<a>14WLMjV26R`fA?u^$h8>
zQ5W?O**~fzxqnMlSlJeOLWR|YWKmAp%}0HIC>=uMYBHoHk8-+)XFQ@C7K`D4kiqe`
zu|$LB26*SsTwLSdcrpO<(UQQlE;JYz@nOkj_2PQFtb&%9v@bXKQpzQ2Wf7N$2rSKn
z<-WL6B~ovY#_g3?V);)~X!DwofotYe9{bgn0G?-(xDCT3)Yw)b4B|Tie<*!@XpuY&
zsv8%V0H3rSn~$zmcB`C)8<tcFGt>zp@)C}#1O0H-EbL8xB;uuGAcO+3^JOLzOFDZB
z3p~8boD>EdcItEMgNrYc$`BZ%N<wv2ydKp8T2GUkbQ8a~+S7@;gTtAxMBib(*{jxY
z3?h$e#HK5%0$H`i;DCIM{-3c3UL3ARK<n<^@zMCJY$l<2d@b|)zl0jR+}tJrb1^D&
z&@4s?sGvYKN&5>BoRln^0vUBWpDrPDiH^an=S2_c&TX4O)5C*c>=v!JzIrA_5iii5
z3L{E@{&d^{?9)D5o?clAvki%_ZPwizR>ZS9xG>Pv=O<q83%mvD_s-s|&IH!2c-}rn
zXENYXkcp6q!IA|o<kV}|g=6Po&OR<m>qr+&eTUSWmRTw>F5y?rkFU}P_6?x2nG8h7
zJ!ek)Efd$FyQN<Y9=RAa-DNms>^=Dia@~15<sX8mzd@t2q;!U1P#<Tj*u9a+e?Rr{
z%c-xQ``Y$5yiF&kcnwVncIRlj@F|gN+r!&XI)?lYjyl0qFl!=K$obmA%rp1bjc>OB
zW;-|Mc}MeYhKA@L*QCdv*UvC2d+M|`I4TGh63J$h$n`4gX;cw*O{$xA1moKi5@Yn5
zwg4ili%7RA&b_03fj-7#{cgRdX<~sW^iOIMzwwVJyh{SiTyFUxEl17@I2CK&-nzsd
z7PL-c%$rq<9^1~|>aO*v+>c(`#H*i9&t{7csfh(GCy6%<y*PN9eO)ESuAm@8e!_8<
zlN*2*{@0FQ;~neT^QzO_QX3{G%5>o&(M2!SDDiUJWb9ZJN(r6)5Ak<UdT_eot*Zwy
zl~x3hSmy|y*}r<ay66y81Xh}_;Dn-|?AU%1Y)56bB^^X7FWf;v;$L7;?P-itFP^sj
zO=y)7z<s5#B;U~drA=h)(5UT9G;^<>cwC%moMXvu1W+?x$%TGDq69f+M0z$2#?2;t
zfH>ARus`l_|4hi^B&Xfb-u^q*bOt$GYW4id$xcBJjXDtz_U0>0{CdH~n69FQf-<^t
zIy;rGeUK@l_M3dM#zg2iNW9y7$P0U|VgeQJ$N&^o^5l!t@!heE>XmZpl_M+>mIMqo
zgaN(KJyRLDbpC+ay@xP6P5pS8uKNK)ql(4%<&tr$OWki_v$t<{Rl`0hkP3SBU^cto
zzll6#NQe5J@H;5z848m22HO@Kr{r%%Niq<7f^oNkylcoBv)K#1U*v1#Iq)qetF{J`
zeS7sWA}>f_X#@*@IClv1OQl9m^v!!impB$FGQg)A1|1ze4JET9%`$A;a;ErjcgwP=
zgjkcz>~W;yosR02oyjEkR~I{99`9b>-c;yE+ZuBc=hqmaH=M)Ka6Q2ybn!z$3O=Ak
z5cr=oN@Syhe|(dhy4{>B*Tmy2ZCzY$`mxnqq7o?#LhQyWo?0T`nD*}?kC*8p^a(b8
z(VfQEy6c`{6A`F!7eqx4fS{mJV{?ZSD-xUBUsALbpvyG-O5|sycjPx>I=7$~vLC<T
z1+_CrZw(SGwk<Yal~7|^X;fS?1qBxkRtC0xF16aio9ysAl!_}JB&DK-c4ky8QcgbF
zR93qz(;vM_P|M36$8&O#5In@pQdHY(l-h~Fl39G5jpZB`Je{6uGv8o<<o`~od>Q_Q
z$X$KTxaM(ojY|(d$6E}SCWI#R&L0H()836h8N7&;GCRF;LUiF{Iaq>(iz$eZ`W3AA
zw(G!Y!JNo!Q2(|$k3r0|?0qciU=Eggn$p?2b**<3@MyESgY#Lo{@Gx9#OQYk%x)jJ
zP?yk~zPC@|2^92hDrT#>!5t;3smqKiccM>+*gz}q?)gz)!Y0h`=BeJ!yL$&3kx^vV
zd)eXm43_Pl-N=``^@^19(#$NuL8wB2O(Q%Z67mUtV$yvJt*D!${le5^(^6@PY*=Yr
zJ*_GSTkKsymE+TabSCp!{j-Reb->fwp2sWn(o6}TS&t0k+Syc3Cc@obOe9d_5E2C$
zmK3i9^M};*et)3i<*U^0h3D{Y<Nv|YBSA-no~6ivR+^UzsX(s0SPv>TC|zj3l$10^
zlIhP0el@UFH`=9s^LVWhBF;4C=#h#1H|vX_kuwAYnOG@ZifNY`>gwdJ)^h#n`wLP;
zG`wt-3oY$ri3cQ^G*QxHF4LPG`;VhS#u{)ch?C0iaH4Nro8I%zyqOz^VAs4f-4v|~
zB5%jM1Np%LmW^2a_O~&ZDqz*?3(uNjyC%wf^Hk0{YN6l7ZLB`l65qc&d@?8#Jf91<
z>8JiXG~j#e?U}T`rN!_#Dv$KhZ=vFZb8K_IJ2ZhD1dFM<zJ3sk6-L|ei0Rx^FuK;%
z>%_F_l_*-0@uBxx5Llot_rj<;Y6vg2TVhAz!u{>5Uc1tiZPq4OX6G_1Iy1Tx9Zo07
zHkRs5U0TmP&*uqjAlPjk5^nBAv$y03v<H0>V)jP}ea%>h_oD4X5=0C2UIi?o)=v!m
zPzf$^aG+GT<(~Fh{I!)C(ocas2Ye~+u>I+yt0l|D&Fu|J=;qI54-1FbK{zPrNG6gB
zauKGlX~->TS9c!Bc<~Um_@9ZXbW8>iI(XO%9qHkO(y3lA3*h5r2TdLx$;uf&Y1^%O
zTj!EJ?i_1+udV3`N{+LmqZc`zhcf7?%HP{wf3r}m0bv%W5H0<nr1B%R5%jGad3;~c
zT)K$ytRy1sIryV4tF*~V^?GTtNvV#un`e#>P&Pq|O7}xQ0IOFq1l(#HOKyVllc!H6
zLJ1gB2UVmIsaHcDx095k!*tRc*G~vU?%N{U^dC4aE_)-mc~`904O~t4sMO|=j^<=n
z%f?r7WXt700@LLk2sTL_EHYc$o?@<tSw6Rk3Uyb5lk;A|AnL?ffG_{8sf4v$!|rdb
zq;K8JkmT%eB9BIIzmb-l;F%tW&vUuE`*TxCm<+zAvDiK)&^TayOv5S`ru3;@m4$EC
zDFQId#WY$K7E^G)21*mY%AKy5_3(6Jd7^?e$5@7|;2FB3uxGWUM@(BCoku2o;kv_W
zh4Y{lh)d0ywXMLz7+nn5h=Fyo2Xq+E+dEudACIFF^J#T!t2%|yR;}&ZHp0E_8Chzp
z!6m8cA^LbP=8D+A1&}YBv<mSkY?XuY)~9*Anv2FfE!cz33_laqd%DTHMdP=Zf30pc
z(r85x|JUUI{V<TQTs-k3WAFcB0yd)my$m#}du?Y}XsGgzr*E^iB@d6?&6vsxF+a{3
z*}uN~uLDWGQ9(I$22Ae_yD?Q-J;-P1<_@7G;`Ht5MaUF=%SYY+H)_BQ)nFHj$)taX
z3B3nhqBZ@V2R>=aYre}&Et<1!<ni_(h1F1d@^Eoy*30*4v+-}FAJsYZ|1Km#P%J!j
zKbKnZ24%lv<Tjek7&Y3nE4*sd@4c}^P|&WosYq&o(%^7ULq8+W(u2BaV19eAv)woP
zd7PeWQ)W$i^j1<@;jR_(>%^I!eff7`f~ad%c65=gcpADirc={~P9H~oV1!JXCY7kS
z*|w!d@W?eor`DV2Cm%<yCUEO!6DEYEc6@usN&XoPQt?_phRau==kunddqHBLI}!-C
zgz32pIH{VDJk#s1s9o{e9g@7zLnE;@(aZAIh3A#L%zFm;v?0%xY__%j${auapf(g3
zaTWvAUqX}76bs)46^ncfuAE_xO;6u@x>?fW71VFedp_ZP=s31gs@mjkb3dkUHwmv$
z?*QSEfIDA6*<b3Gd>Ly0x)GqnEV^EA4glxfodW03RTpwlZpQfheV)ef()2RtxuDFX
zC8j!lRV?s~p!Eaw7LcL%wlfo-t7XTydgK?Uhdy>n?YguAXZK0-j%;M)#LOZnY>{_R
z;&1~x3W5*5I{kUVU$ONSBxx<m*QK%C27Pi=y6@IbR(*e$l&DoVJ?0TZM^Xy?wq%f6
znrs7WqG?eVm|j1+AI$As%@Ng(jdC1Em$zxp4r$&<2`W@^Q0Uz?z9Y#}_Ma`$SUwK@
zsjpkRF(DoIBZqQ&f4P1v=Iq9Tue`aU!dsVK5VlYR*tD>^#bTwgW8HRE+5py{`_*p_
zVurulCZ#$9L)_!|vnPM|Pe(0lj^qb3-x-RoPl9}?87ry``@=>z^}Y3DGP>E$+NX-=
zT4TIw{g_FzDBhrTOkwxy-T~_grqfUz`V=P?LG_|}DL7znzO}<%WheH0;fb2y<YDeW
z)YoD@%h67$6u&-f&KzwL6$HyC^=<c1G@6)sv66zHz2z=px8}O`J2Tgb8rx1D2L`oc
z?>m_om={5Pu*l5?r>yXL9LNu=a8~2wOWk$N4)39ED2tr`quZW1;Ypt8l|&nkX4Q3V
z$J!Pb2$n`A>vdggoTvr0>8qGo#Rx2MWO1w6#q^QcwH4O>R`*2-%G9|uDbQ|sJlHP9
zcCa^MacK3QE~F5@q-sZvydU}!eX)rAf79}|0Qr%|T(?UL=}9F6u)j%syE)`bg+k~b
z9U5t3pZd(7R6k2A%G1u$u8?Yze-bo@M$`B%$nwvqrid9RTc}Q<88T~f+Wd0Wp;a@^
z$U4i$6<xf!MM{*xp8unyL^NqE80?IuSld}*P|k2Wy}K169t*)D+E@8;H1*}%V>9y_
zAu}@j<}Q>%m0|S*G>z)0nVbS0`n}a%`&TjR&Hdz#V;?(*2{c;zz35(c_jcF3G(7_p
zdPa2$m=?OmUUF)H+)JxywXVS-ZxGGHR9!PI63jvGX(Q0)d>paKOZA51Hz%^Wouvr{
zuhxr<<gX<nwCYJ`yEo-H>>ryP!AVqJeQ*vlI58}X#~UMHb4XQoUl<dk8E4;DUgcnc
z0t8qyxGY<BH5wIfnqYBN){-wX``?b6r5JnNB6r?+HG*)3Q<OXBenw4mf-F%{dyAgk
zP*Jfm**rJzbUu1FyvAat{>)#P&^Z0FvAdy8q1mG+UMlH`NkHRInx*6qBvslKa5b4^
z;WJg*vaC$BWex|40^E3?KIwOOKU^+qkHn?T00}{HMirSdIeaLY!m8Y!+u_s=X@cK^
zfXJ$&=kX?oRI6Q&zU=(Wh{3ClzV*!)hRrhOaJAC|8y-t-iFGKb3obL2vbUMpO-E;D
zeimUP;6thT>UJEyy>M}Ma_vT+IUDI@LiFC~2w1v&T%j(4j?yyH_1yNt=*c;W+s~(h
z-|yu$8t>S0k1FKLP+za!dOkf8^@ay-4PL(Q<AcQ|A!!$dpLO?nY+o;#L_#!|P>Tp(
z=B_|^xM)l4af{@6jnIHF{n`Br7U2+-9?6l0s#119_1AbLXDh$T7bUf`5qSJM`10rb
zRC>!&(SdboS>BoeapeY(YV2TzxuUD=P)BFgF_5$?5Was>;PqkRlbAKT6&%K`D=~P8
zurWY-S=ahyf(MO=1%!X9u{&6#B<f_GWT+Yr^wZ!gG_EBgL}Y?S!7qQE#t(*-y{qfT
zx#^6}$WKx@7z(Ab>@>pF#JvgOM2!AvfyGl}w466|H6ycagDmBD-8}2OA4)d#eA&%1
zuY<r0LcfRhgw%jFnk!FcekVw_`BE-M{TVJQ@(k@$sw($qy4Ue7Srztcx8n})yW%K=
zcD$KvSMhK)HJ@xa^#hQFg$1Qfq?Y@RvH8!MDI*RS?-FVxy4e~Nu!UW(*&&6wfeWx+
ziwW3vU|KIGlBXKZw3am(l-Mf72D9&^v7BPvgohJ3;)pN91TIB=6$JXE+GDTR`gsO3
zJ35Fj^)k0H(zch8hgy!)a}1TGS0&{y+dpM$vocT>EkTaBbab0C)_pm6l$x>oS6%}r
zMFmsyU#JU3yXi1XPzKLGfM9SW2Z!j^JZD-bF#N<UHt~yKaNO;~*eG=?BwilFG8de>
zk)S^1Zb0B*z8tLuHhNktjW3tz%(knTt_{DexoJ2gcEy`Hm`T0b*ra#7g`BPoL(r9{
zdg0^`lpVG6_3tB7%o5=uc*~ye>yQkGop7qyHZ0#s%;vH3!-byx#+M86_L4Y0omW{>
z7`gEjkq)}UhDO3*ANAOblojg?1wE9IL@toZSttl$iU$Rc>ZS_#tq9li&=|WXMtbes
zcgiEBh8seTO6iPcVMX~t8e6vYwoh|Eh=6!ZRbj$3^7D3qTlhzJFY4qfndYe~AeVIE
zyYG|M!xU}UidQjsa5%bHW2?3!DA8JC6<Uw7=$Mfi9%`a_4THtp$~mbSM6@?l0uBIF
zKW6n!&AUHxAN+U)saS7brT97&zHM&m$@C1{-nI3rK+95*5aSG>fIcAnbav5y69!xX
zkZeOgL{hPM-Zo!uVMB|DLn>M(@$*QbKhu%C%9qCIMLrg9tXI$~XR`HK-KCx26XNYX
z{aj3n1S<hKrX{8XKAO&nV@*xX^7zY3XC47u!#kzW&g)FqCcVhewhTz(-kG@x*6-L#
z*!RF$97;B;t~+Gw)9(<$W4vSU^;CcqeI8H6yjZO{a`!yFYr+ExhOZQ#RTTd9_O<iR
zpN-4w-^@&pdq^2HA|(5Yp+3`ZBZwWz@qN<bXyhWO(&(t-8gV8%(dizqWgeJCFt|w^
zUSqYb-uzXvP}twqp);g_^DB8lbKe2frZewbQlH6{O7ZEV@$b9d9hclCyRpAm=xA5b
znygOfc|m>twh&5^TacQ&-D}^%e1|SpR_VYD+GrQjM*+pJxnf18z(&mxlL0cq33+Gi
z^Q1ms4v#2zM>;s4;bS|%#qOHcSgX7`TjYiM&Y3YbE^ei1uLnybzidfQa~|~r#oNoS
zk!X)wnD>g6FDgqi5XTIf-z?j29k%N<H^B0h8bIxsGOg8>oGf2?`865t(Rw5nIFeJc
zLCcbp2;9w0a;XT~@j2cGBYhlC&BsWu^}JFKzJ6N0l5>e?ve1?Um(gMWn0IV2EV0yg
zc0P=-dLu~7+Toi`TYf^|QWNwfW#eRIENHK(d4b{B6FG+6))2xhiZ)|fS(w>q3u0_~
zR`s3D;9IkM8kfa(I(EO*l@(uKgaKfLV^@X;Xeegp#MDfbLzYdr8kWH>&=E=+GvR$-
zZR`~qYj-bv+Lk&a-zR4~ZKw;pibnhHjYf&?j^C0TWH+~4zmWnNTi&9~rBzVi6$=IS
z|Im}ex4vnoKFoT{1QAq8rLa{Cxyxp&o{c6u&7C5x_6+q6G=46Cg?h-WoGC4ZS`pCn
z^f-^49n<w4YV1a5w)OBiwL}CycAf~CJH^OSY(ohs=);PwC~Nli?%0c3rYpnK5z=?K
zyWfAh-Q;ZKs;<5qB53hh675t5WlJ(TlU*+zcKS|t7&N@4<#kPXcPuLe*Ux*$Q<$>?
z;~Oz~l(VI+CM_O$x~eM>97Y_3Fk;{~!DzpZ^Zn0vE{RYqzBMFxV&*QdYVbYcliZVq
zn{3YSHP@KXo%(BqIX3IVgNY+M{5g;e`MEn(w2W6Pl1d~6i8^k*06*0VYT{Qqi(D#;
zz8(;Jup9Tw7(JtQMA>HPhMcwB74Q;ogW(;JTi1?3LAf>8W0WmUd8-)A!c!e-czw<>
zd2fTiKHG?`VPGhbH3#dv81X9rYV=@Co@fz&-8E&Rgl*+@^C{aU-=pYTkv*YOe|{D%
z2^1y`E`Sdmx2)RHQAj)YbAcB>RH=*P5|tDy{i~_8RVOI}`>X)05<hG{_TNIOJia&v
zRyuC=a4lID<Os*SUTT+TTssXr%L>q8Ah1<A{$k>8BsIw!J61x{)Jrd1M8cy_Rkb>>
zi(cRt<Q2eg@9s(^8lf*5-Kx@>x?@AZ&zdP2C7Yq&UvvwcVY<<dly93&)r^Q-!gs8h
z(epZc+D$S##5vcOYn$Tk-@t(`)n_tYGyW1!*e#_*eH6`Lp-zycMJ3MMv>3Xz(CGk9
zp+_RG8Z#zjsK%?&N?XQ}RaPQ=hDsn%vU+Pbc&`7lKj5H*TPeb(`SSxJR%}Cd->US{
z?9B_Mln+&zdD_n*byZP0BrSng_54KA;Ti@|3^0X!(h$#5#j@=BoA-FQ;MP&!CG4lv
zD?Qwn1mwRc7c7P@vVLa`I|~>2(^tzs#RH1LM$P1}SnN0v)PI1CYlIPK@Oe_La+C-e
zY6nMX@#eCx0GaclX(1$GKNv7g?H_KV33*3jCJB!@_`1$A*s!;KkonU1(oBPUIHXe#
zKl6^A{5%r0S!3$9Ez*<Q2{mmS`S{qt)6gTR@Ju60vUjTouV#W6w40r!IQuu2PLT8g
z6%0A+nA3fTL*Gb%7SL|(tr4jhk3JK?YW|9eN{D<;!H|zf0Swa5pDDA(1CbnIQNg}f
zlTDSl0ZT^Tw-6RIwVW*IwrZxbQL_|xi~z*<*9~2UU&H(rJHr%rI=&nU1CNp=ho|sW
z^cHrnZhOW#gocJNYVHdXDG79sS@B4WPR!Yz*G<jKn$4GOhRp9{F_+NC)@6=Aaj?)V
zgn?uSA*XPPFdyZJPgs}caU9{i=rc^$tIW5nI0R*(4q7{fds`z<b#-|ICEbM|0bo+l
zCcEmkkp2CS!}s~(H*cE-eOz`XxKo6~DVm*qbEo-XEm<Dv!0vJAybg)2_a+A$P$QX_
z25N@>vG-qgJC#yVKdrw#d3m_x?l_e}3zE>dSP!J{ws4vKaX+#UB+-L%#^>vxov^Wn
zC@^m8s}|W|h4zt?r_eBW28SZ3__0q`1?}m<J#dt0+NU%>e#(Q5HLPfAz|NOh_c1T5
zWBk>{&-*r=*GBx}k6YB3AdZ5JfwzS9R{YcxVn!8JndKX;(+^|^xtW4wS_@CDm`w-i
z@DR;f^3KKPB|Mo~Q!*UZP27HV!#f9;St{Z67%UpC($LSRv+7mdEuRlg*1xqVSWAuj
z9FNFOq=WCD8l=XXS9VG=-71pmEn|#KZ*5la%t<$XM%Db))hmmyi$&n3kpcQxYwP#2
zx$Vl3)78<PgQ2c+iY-!YMx)nc*VO&Ib^+qi(mqnZND$|chTnwe*!G<}lv37`o{Z$-
z<w?qWnY|a^PHy^;nC`8*pMsQjYYe;(_Dnlpr2{R2^`N#KYVl>&<}0(S<=v?p0Y@5H
z5gZg34z-2z-L9L2vmIN$CR#zB;m$9ZNMC3%|3mj3iB0f$qTQS6;;xbF<DhO!@?3>7
z=k-EY!%JN%X|k3wc&m@0NMQ{Hwzll7;~?ktM4j)Piup4&$=c!NR-VrBP&=Q+Sj!cs
zaUXS_0$MM^{P)?vl7dOGxLcbae{@s1Bv(?@D4&h*D&-37=j>`Ay&qy!cY5wAw}z@O
zr1SE2#z}AGt-MKk&~Qs{-7ag{c1b%AO-zwKyS4O~>aSUl{9`X`ZPHd?gglzd^qNs-
zx7JQ{zICfov-@1%F}p}VnKBq9_DWrO+^z5OvDV7{((mjX5o!xgu@HYe{#PUgDo*05
zlGVWV!hUK)<P6zpp?H>_kKaL5G?JrdZRtaFs7DZu<^yV>$@yu73TB}^PL~nysGgho
zDT+>Xvzh&m86Tb{FZ}9tpFJ=*{$Oc1GV&)0kU9k_kpzc8-+nZ(S#iEUCPI;0N793J
zZ|0VHzciotdS%%CvLn;vM-neU7Op;4uXMiuR(Dgol+yMmbL91=Q|2qP7~L0b5%ym~
zSi~_-I2y9BpVhr;13EhZ;Ps8M%c>Vv)}Zbwr|ODRR#{dE+7QiO-Q_vA-R4MgCFKJX
zjdsuXU$C3uG}=u)5OImEKl0)ZbQPj}u9>ZW?+Vfj1a<0;a`_VzJtriOD*A3LW{s9&
z$E@lys2{ZC0ie<G_WBtU$^$_zqzWORp0I7<m9~aW>m#V1b51!z8!OLtwAie5rnS#_
z+;FTTw6;R2HpT7yjZR)hw;gTG<{;lFLETnRi;Yg8w^eu|)e!o2oM>zA^F+u|0iCH>
zEe>o@@ok@~3SJn*@b@J@^&1*ms;a(9SX|^E&^XM`gl3M4j4Q`!e0CohoHyamG<Fs{
z6bu0-#I{1O1xl?)9y^bt$VVs{PtG@|?E3vo`d;o5tCJ8RZiR*yG%Eqy{E}R$xntlk
zF{Li({sEsRKy<48cEUaBcDm}suwOk=f#P4h!<Sbs_ujGPGn_6>`2h`1*`C;B%I}zC
zxr96#`I8>Jr`L~T;<t;=+g*U)uF8{)5?ua4q`Dd{i;epp&z5H$Pu|cvoHob5ya|ua
z=VP<`%kOZzF-;$>HF)DpVyK5-W&GMRKlQDZ8Cz}IhNgSjbb3Vv#K2``3h$n+N-T;B
z8*`~z#&wB4IkV&fAF2GUY$<Vz@yjq?9|oQmQ(9_Gpzce^>i!MJxAs=r|DcHv@U0!+
zj7pf*PnRsdu``sj`%BCYN!7iy=JC9maL!-dX?;>0+o~i9#irNPGY}WT%;)U--4iA=
zZw0U~=iU_U@9Cccx^t6yRMUM#B=5AK|6hC8`3_h6t(ou|y?0TfglHi|BFZSEMTs(a
zF<OKeLPAC_GZE2^5F!X7M2i-^OG1S4>Mf%8=xy|J9>4Ru&R=jozVm55%)Vyt^{jQT
z_1yP;JzL|zb#i_6^~A6u{)<=$Oa)LNlv!;JEUe4Be{dnD8w!=Zvsk)I(lCkFuqNsC
zirMyEOMH)uVMynreG{=&b`ge7rZ$&!6r;cDbe3>p^Xk=aZsU7OzIO}${OMgjC+i85
zQ#@~%6%o}OAsjOfChw784;uo@b;@K3<f^*TDPTtK6L$W|1=*pf23^>=Eh(dHaT%wy
z$2Yy^AUU)nnqXP|<scnA-ofzpE=BR$Iv;CE#4Mk?eHR0+UI!_4QTcn)v~`)6yL;A9
z;f})ClIwB|Dpz9aF*s#~Fzh5TRa)QX7NGJS_W{B~aG7x3$<#lgeiF7o{}Ts@q@Xv+
znuZXMGK)EabK#4U9}QV~El>?(2u<i(_D;@t>&Ke;Rl)_%BWK)0)TcJgOnBWZzceH;
zTI-I!f?H&mRd45Y_YUu+*HL4-+i`M;$;Qp<fORw9sP(B4OkxIp>gx_WA^0LGO7F<-
zU_?y``yl8}geLv=Q%2f@@o_6$P4-^@Oj!i;wb}T<@9qL)5uy*ZcK8*9wCh;jibW*Q
zwtYV3wY37c++H4`Vl&vkGD$Xv>PwLV%M|lLi_?*nuB||?Ha)=${zFJk&VT87IEL<N
zMRkHz)E%9+wD+yww|ka?o0Ed^16L`2MYh{CZ~Ctbq%V!viawf3;_>D_758ex56l;3
zi2V86NXKG0qX(EUFD@#5Bt;O#AxA1Lr@%M3x5GX7VFtZ>po{YZG{?~DPZwUmG>$O`
z+I{;q*tK?pLo1Bwk~D`?%8>6}4Yu9L%a5dqp9avs=`l-2jGzcd8hBSpa9iibz1OR_
z!N~jgQYH|D*wd;k=Pjn+GGUOgLur9_rxudmZo=W~?<5{r9o7I(^uM#ReFfGH1Zx&-
z$&_D?c2BzE-K}3@W6wRBi9%P-I26osVBl~C<7Up0oOr0UQbg$giUj<?#cf?aG2M+`
zNcamp#(!}@%@|6xF}|~S`b=@bJ~2u)1(JLl9GUtSLO5;AEk1K?@j?n2^heTx^P=HY
zS5(;U+s^j$PDM;@HlRN5KPCeVe{Uho*Pt3{SE!6C99$(^MO*D9$yu5ZZqIV`c+a-z
z+kLl2nIh(#<GF$0U~5EdAh)!YImCm`)RGms6|<xUXeb`7t}F?x??dalLyfS%Du<Y5
z`!4l~PP(n<qVZmC+j$(srl!@2^z??ye}kCxd@Pw6f2+G3Sf^QNK`jING#~i22CJ~)
z=b!J9Hn<&8!z}fbKF(Yfl5`c!Lz4{R82BK6?~`xwOF_y{H{$%R2^oQKw@5?WB=q|~
z)P4*&!!uO;K@br!jcTi_nE1Z36oUA{r|rNTAExJ{`4Cd{qsygkwKXS@<9^ih%XR%o
z&LqCZd)54A0gB*Z%^EW-OwDIYLhN<MN&H&qjd-ccaOv5T)e|{0uQ%#pKxr}8$ShUk
zbH-PwV43ohwsS|WQ<lOB9ZI@Gaj=uJT~7DA41O>*k=GQLF^a<^2pe6T5VlZ+D*>uh
zsPItwo38;qqDOMSypwTMI&k?bw>V1H$l5{u<)s2<)AAt>x$#4i43VuR+pMU#XoC?i
zII6bSkAYI9)fH^?XMSv#ji+(GlzT{7gl8XTls`L8Wwsp7d*0YWSxy9T1Y)-=|81m*
zrfqB2(9qIpkg)k(dV2E9o8iVX6vL&xf^U_oqHU(`?CxnvLKW&ks+IpP%kCtdOy5nN
z>dH7kzWFRbgEc%dXHWuSra`8?^Fg-afTrorzC_BKX2E1iVmz2+K{-u|i)}3&Sa#}}
z*R97xzjuH8(5v{L#K6`iHa@Ht_pA4KCR%8c;H5qXWitdVH#}J;M;}{Tc;FM8Tt1^?
zZEbCbc7zQA@v0?()MA8C-|%p;*8;q8j@=+VAs_nRAyGw!g~6Pi$?9{@V9rhw!kaP_
zR(+{2Ym*!|K5*}$-E2O7A#CKHntg=>-trQ(E1}jLXEga5d4y!YbC=my;B{LVV!r#I
zJjAiEP3024NRoL6s4?Z&;?hIB=^s=PU4Jd?9}wOh4H3Vh^NB1_E*tfSK_d5`QE}w|
zrXyWwzRbn?22a#AG%viM&)2y(8schr8RvX87*`?6(z~}SGvR~b`XIfO;bp#GFve}(
zu3Q{A50t-6arh^AbBBr;4Wd@yIUYXN3du6B+)xPW$jVe(B1@?eP0<7*U$oFPvL7)f
zjed}TV!qEKWARbuC59ACLgL{DBb_V&3x|G}j~FCZE;eU~-ZRQl1J?-Trw9M~e03n~
zubMjfS94g5DZtK3>G*j&X>6#9rXTgYb9}-0xIzL!<@;sG_#lcF!&L9&zFc?;>X_%S
z_tW67Wy%27ASVX8Eo@}Fy4`y$+1Nb)*zvZRx8XbyJrhKpInE&E*!95)^X9>m-8!Ej
zvs|j|B2Z5VYFdeWcC%@m3%T|Cc1fyQuEc57c#<<U021LEOhEtOnD&)dmG0(`u);>a
z7L1>&COyD{hDh;O1YIIO?^I&}{$*R392Zr7aavwD5E%D_rWdSbyicO}s0$IoXe;;H
zgpwxkJOtTE2Nx1zfXM-kew+PLp2LJ&s-7}fP%lhcnTC}feU`a2pJ!%gJ<4H}(zhTt
zF8v-ene<;4NasZ%Tnaq5Yfi%Oi|s<sHeM`fzJ${C0?9^UWpuK~Jy|~6n07nu%3`JR
zyn`_KuOl{ITMW#~$j`Q#mWAiubER*S7)SF<J=rG<CgpV9na~<^#d4&uA$ra*L#s_a
z229u}SD1GDDa3xYV0YA)bHDr0*Fb#tKpzL?%|v5g5i4mIDhD<O{Y^VkXwj2+N_SWd
zDy|vs6Vq}F8g4&ik!DNyT9|QuM<nFFxnc-!6exyf((_=mV0>o2n!Gn_kLS7K`w{5!
z<*t<Wqi(DtV+}{DN#j!36ps=+2V&IqeLp<}`dA+}In=z8a2_%?bYS_dIOrd0NpfZw
zMzxa2yekqGMAABpXg^?awYnELluk428^mJA4S;i+huGKb;#j0)<lyNevkPTmg}x%N
zMs8v0t$HH76efkpcD0j+<6n^r+Z}&CtwmOWhN1?UJE^eVUzm;!X)`tHUK#1FiSH~2
z%CJ)X)%(}BOt3GbsY;Z1&JU;7ud9kGq8%Lp%1%(2h^?pZ?&g+M7F+&W!no0EabYz9
z&7cGct){#A2{T%Uy5+y#<lWvqbw_ZCrh4}FC-EnI;P7r27g1;39u`YSDt|*IyBo&q
zM|8@NAHXdmTP3{Cx5b8ynvq(l3pzY&>UG(k#Jvlx2KT55u@^0<*qKwBSTPf+PG|h|
zTxKiVf){iF$TO*1kXJxdH46)<^qTcvr44#)eqStx2!dkqDfBN-u)(biYcS<?pt?gw
z>2FqoXyk`F^MHF@Q}!HkpB~IO0DQtUA-tm@lnBDXEWGqbsOWUQaxw<eS;PWI6>AI#
zh=XDVO00|yhiaxRWM_EDAjyqg5xua%&Q))5^WLHcctJZDCtrCzY4F`ie(TR#{Y3|1
zdD<F<$pJYiE@;pUJr$&5VO{DA-jPh*&!8^60=mg#1K!dXW<MIFCVtD&hy`OF8dl0R
zIXGB=QXDu2tj-ZbTuFVM>*}`KX7}ICLQ=Bd<!vaX7=fT^(NY=yQRYv~zNbdh_#V<Q
z(}U6kj$`>Mf&e*{&#z`x#}tmkNlJTd$C4_$-ksEZOC%73`HN(J;`D56C@Mwji*YK0
zgv199tq3N%(qs^lw&g!;*mlabyYwr{^Q$%?s{C`J3PBQ|0LJu_6r3b=B4vurF8)H`
ziy25)6?57&6`*-~;Lo1$Yc$l+(}i6z??HN(p0o;Dnocyd%@EbG`!FDKFb$~}16au}
z&Y{L%=HM=8f)KISR764RKtz00)rF2<=cO=hKUj26KKf_uOsz(~CGe&K97YVeXijb3
z=y<1AhZeOS7ttZ+qlZ9JfM;oF@M(N>5^3?c*53Kkz8{IrYf8UOm`W`46UXTugknY;
zGj97o$8{4<@oS8vqQP$p*koUrq=M%g(}?y#i}TuCojwc==iqlLvGkS^=+_)!6To3Y
zN#-px^x4Oswc^E5LDwk;>scs5+~|8l!F3X^-U(C!jiR~EZL`*EQ*5+UXzj;8-z@OB
z0J;ZRnAE6*J;P?NjM$A|t_8vm%HOc@OYvpOf)y-^5k6YhfiZoqDi8jB@MHeby+y(7
zY|k?UGDfK*#n@K2$B|NLWx&4S(l6$@f8PeWj09oX%plqsd5IA=U(YQXSA4!wfGm$U
zx;tae7^1@vNw++iwKyhnX2WJ(mgUrN4RQ@i@*E^)K{@et<P%DDIQ*`}7>o2up^cY8
z$Iqssb%zcbVnzy?JPWrTvybw$j~O1!q>$Hta0ciX2|&vj15^_Z(pDDJT(BjNPCYrn
z8hr8C$de+q*J!y0DOx=$Nj=m^trmVD{>>m4svrQ|mUQIqyqQ<A%gYM6J!WV78KEj+
zO?93J-0h_(r^g+w;gI2BQ=@7jEMBOo&n^IMgS*3jThAB_E14jgSd3UKhJ&P)Wmr|M
z*<!czi+Fo8zA3QXkXnk7AF@ct*4uR6We|ow>}ns9{l#_GM+RDgF2Fon>#i6~0qJ}p
zi7v9K{%j3>k~E=Jv+!xf0m_YaVtVQ!EYR3j_Eq!yiOk85X7z{)C)ZbPV2q@?+)=|m
zQIfn70-3g&;hk2jxH_Up8H)@r_*iimPDX4D1=Av(a>e~QJ{y`p71~y0iRILKSlv`8
z8<=N@erC#qVa7<hUuq<#AMd6c46oh2wY<@2RT*l~098O_dgVy_gBqMIBEGtkwq%-X
zuwefe2@|kw6y(kDvXO{gr{m<bJWM6DA4`ls-Aip9r1HnZRePXp61Gk*{AhQ>IXpd*
zGjO6t173_p-4PK7ZdZ^!QKbVSkWOa0Fw)V_zdVZ<XEW6Xywj<kyh}~Z9k-33!UmK3
z)YfazY>YkbDm)u=Ep~X6xLeZE06a~Jpo0~-wm?~lJt@>bWiwfpo%>8}R&%}gTlk?@
z%wnq_aC{>UR-V#i9Oi|(Q*HL!Z{$~_4X?bT2CNT6E0UZ0g&K?<veRMaua7LM3^*y%
zR^F7Kz@|clA6o5k!=JI{ek`dfklp_+H9;FSyH`A^_>+fN9Lz$%Ee4Ejj1=KnL(YGO
z`HO<7<dvSdKQ({E=mj4D*#ox78N)@+(<dcLHoLj39Hat@GFRLI!h8ZCDJz>M5Q6a~
z%`!HgXa?`vVJ=G~;63f6x);~qPwri_b$3b~+OEBkK|mQkgyV+MS)-LDZ(m<GAQ6aA
zO*9~eK-t82tG-x-wh8!4PWqIN^xW$SVB=2J>O++lG;x-eRW%KK@oLLg92+#>&)ARo
z27lIBIk0#3n%DooG_E!$u`&5i_-fy@Xio4X=Xl^k>_CP<c6NPAf#ldwTFd*}gi}Xv
zSGJn1BJLPT<2008fHo8h0d0~cIhvFX#@h_>=&QSM$X<$HC%fmWz_w#;gmTO|mB0ra
z)8$z-nV1yT1=)C^%%$?4G@|5$^$AiN4iT^*Fc9vFwgrF+DTQeT1ia3?`jt=xY<7NI
zR^~WBC9{tUQRm%<rBMl179gE9e=k8C2`HnZxJ%qXKY~?&3-v+9c%lYaA<$EP-m0oG
zH>Lfs&0#fG`86f<BNHuk-J@Wk9d%;}`iB3ME277}o00$-uKbKR&l*MfSsIhaQ-Pfd
zD6=7l3YloI51|i_jrAJyk?s#}-&U^MxEF}_3DBvtR7rBOUVxe$h$r@CIfibI4Ifzk
z?kzd?_4XD)GUblX>fTjT7l7tz02q^1gJ(jQHDO9s1RbOJ-9BS_1nqlT7w>tic}}Mi
z4gV?pLaV!A5XFs?H(kzu7xv@CT}&*7O5JL2J&Iz{`*0I6t3}TUS-E6>r;_QVAazW1
zZH3>PZrM$qbib0l%f&Ex(R;MD2zY?Hiqhx(Smp-(;a$o4)1$G(gQ_CLVnKcVJxg72
zMd3XhMgv4xR=zYl6f;b!K7$HeA1lhzEV15gWMdy#ws%;=v+~cS*hK2Y)wC5p)0mpd
z&1QN~Y!W}3cwt-_nXZDsC(#to&ZYBKC3(~v&Cv~j&aD6k`jiMvSA&R{0R7ok7(Ohh
zpj7K>?hruOef>=DB0PGPfg^F+SL&K+q@IqkfwW_EMI<h4?*g8`bWUVbLXE)ta8d7x
zqTw&&enuELazX1<iLhay40x{1P=kGY3P#xW30z--z1sb4Y`8v6O@LyPwfS?Sfvx3K
zbgTR7R<gPX;9WYK0ySOoFW%vbVk%9VOrCZ0)W0;zkM?)Vkrp&C-SfI|U--hU3D)ja
z_`9sOkcop7uE2Oz*aWEa6>jA$uuHi@qE0LtOg0uC7UIJcp{*;);-%Q+W3Fr85q>C8
z@^ZfGOLhM(yg;r~qMkmU1xMw6R5LnXAtrL`_l}wQ&Qs4Mz}-ZKsaf=Wi?u5by~>Y7
zrWG@b+Vd<Jjvx88^>ddIKe{h?m?V-UwsW7E_9%0*K5bcm=@3}X5nxs<uuq0E5R<&Q
zwgnt0JodI%`69==D9Nk-rg6S4BIo|waeksw?|x?~zPsKjA??|S{Jq^`w7%CTw2oh_
z-j8*w)xFPDsbbsr_(j`?`@W79#5Jl}({rXOXkI_Daw7ZP@NIv;Hk6BU0{!g9^O5|x
z??`{mRDZ$s_Z#ypW%aABmdkDtM*7~!jhZP@gibt;K2Z|OtGpwu5DHNSyQ}|mVgCE=
q-)Q`s4F47c;KKf&p<r2i`hv(mkW+74=(`;e_-MoR)r(baU;GbOdHb^f

literal 0
HcmV?d00001

diff --git a/docs/static_site/src/assets/img/compass.svg b/docs/static_site/src/assets/img/compass.svg
new file mode 100644
index 000000000000..bfa36476a1a4
--- /dev/null
+++ b/docs/static_site/src/assets/img/compass.svg
@@ -0,0 +1,47 @@
+<?xml version="1.0" encoding="iso-8859-1"?>
+<!-- Generator: Adobe Illustrator 19.0.0, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<svg version="1.1" id="Capa_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 viewBox="0 0 59 59" style="enable-background:new 0 0 59 59;" xml:space="preserve">
+<g>
+	<path d="M30.5,9.025V7.858c1.721-0.447,3-1.999,3-3.858c0-2.206-1.794-4-4-4s-4,1.794-4,4c0,1.859,1.279,3.411,3,3.858v1.167
+		C15.177,9.553,4.5,20.55,4.5,34c0,13.785,11.215,25,25,25s25-11.215,25-25C54.5,20.55,43.823,9.553,30.5,9.025z M27.5,4
+		c0-1.103,0.897-2,2-2s2,0.897,2,2s-0.897,2-2,2S27.5,5.103,27.5,4z M29.5,57c-12.683,0-23-10.317-23-23s10.317-23,23-23
+		s23,10.317,23,23S42.183,57,29.5,57z"/>
+	<path d="M41.78,20.378l-16.97,8.485c-0.026,0.013-0.041,0.04-0.066,0.056c-0.068,0.041-0.12,0.093-0.177,0.149
+		c-0.056,0.056-0.108,0.109-0.149,0.177c-0.015,0.025-0.042,0.04-0.056,0.066l-8.485,16.97c-0.192,0.385-0.117,0.85,0.188,1.154
+		c0.192,0.192,0.448,0.293,0.707,0.293c0.152,0,0.305-0.034,0.447-0.105l16.97-8.485c0.026-0.013,0.041-0.04,0.066-0.056
+		c0.068-0.041,0.12-0.093,0.177-0.149c0.056-0.056,0.108-0.109,0.149-0.177c0.015-0.025,0.042-0.04,0.056-0.066l8.485-16.97
+		c0.192-0.385,0.117-0.85-0.188-1.154S42.166,20.186,41.78,20.378z M19.009,44.491l6.523-13.045l6.521,6.521L19.009,44.491z
+		 M33.468,36.554l-6.521-6.521l13.045-6.523L33.468,36.554z"/>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+</svg>
diff --git a/docs/static_site/src/assets/img/cpp_logo.svg b/docs/static_site/src/assets/img/cpp_logo.svg
new file mode 100644
index 000000000000..4c4b9645c261
--- /dev/null
+++ b/docs/static_site/src/assets/img/cpp_logo.svg
@@ -0,0 +1,25 @@
+<?xml version="1.0" encoding="utf-8"?>
+<!-- Generator: Adobe Illustrator 16.0.4, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 width="306px" height="344.35px" viewBox="0 0 306 344.35" enable-background="new 0 0 306 344.35" xml:space="preserve">
+<path fill="#00599C" d="M302.107,258.262c2.401-4.159,3.893-8.845,3.893-13.053V99.14c0-4.208-1.49-8.893-3.892-13.052L153,172.175
+	L302.107,258.262z"/>
+<path fill="#004482" d="M166.25,341.193l126.5-73.034c3.644-2.104,6.956-5.737,9.357-9.897L153,172.175L3.893,258.263
+	c2.401,4.159,5.714,7.793,9.357,9.896l126.5,73.034C147.037,345.401,158.963,345.401,166.25,341.193z"/>
+<path fill="#659AD2" d="M302.108,86.087c-2.402-4.16-5.715-7.793-9.358-9.897L166.25,3.156c-7.287-4.208-19.213-4.208-26.5,0
+	L13.25,76.19C5.962,80.397,0,90.725,0,99.14v146.069c0,4.208,1.491,8.894,3.893,13.053L153,172.175L302.108,86.087z"/>
+<g>
+	<path fill="#FFFFFF" d="M153,274.175c-56.243,0-102-45.757-102-102s45.757-102,102-102c36.292,0,70.139,19.53,88.331,50.968
+		l-44.143,25.544c-9.105-15.736-26.038-25.512-44.188-25.512c-28.122,0-51,22.878-51,51c0,28.121,22.878,51,51,51
+		c18.152,0,35.085-9.776,44.191-25.515l44.143,25.543C223.142,254.644,189.294,274.175,153,274.175z"/>
+</g>
+<g>
+	<polygon fill="#FFFFFF" points="255,166.508 243.666,166.508 243.666,155.175 232.334,155.175 232.334,166.508 221,166.508 
+		221,177.841 232.334,177.841 232.334,189.175 243.666,189.175 243.666,177.841 255,177.841 	"/>
+</g>
+<g>
+	<polygon fill="#FFFFFF" points="297.5,166.508 286.166,166.508 286.166,155.175 274.834,155.175 274.834,166.508 263.5,166.508 
+		263.5,177.841 274.834,177.841 274.834,189.175 286.166,189.175 286.166,177.841 297.5,177.841 	"/>
+</g>
+</svg>
diff --git a/docs/static_site/src/assets/img/front.jpg b/docs/static_site/src/assets/img/front.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..87fc8989f6bb4f5f473e8d7e3f1fdefd7be2bc7c
GIT binary patch
literal 280416
zcmbTdbyOTt_bu3H2bVx_cMVQA&}eYC#t8&>cMBe@2`<6i-3d<6Ai+Jjy9OV=@4b1y
zwdSvx*<I`QS=INPy1S~Y>eShFUl(520oZa<vQhvzH~@h0?E`q-0HjKKTA2d?ii!Yc
z008g~fDDHWKzOs@0Pt@O?*F#m9N>rnNdH|A06^hL{*P@4$M`?}!>s^_|GUpy%r_s=
zw>a<q=No{K1NT1>bKw8)-tY@K2>-W@5d9y|*AoCp#nQpm!Nt<Sk&>I80{{}2RYd$x
z^f&z1ru(mnJwq0~7z?OFJot)|di+n^|Fs){^9~^zegOfF8UT+2hkyh3+7F<38w3*E
zfAL?pw+0R#0TBrq_zndX?X5#2HUJ(D0RbKn0SW0pn}PFt`yGIYgM>@PDTe%B)fh<a
zgvS*am-~)JytW5l?aw(a_ZR0N6x0s{gha%2^bCwl%sjk&{2;J^#3xB9X&G5Lbq!6Z
zmbQ+riK&^ng{76Xi>sTvho_f!a7bua__v72_=Loy<doF3^t}9n!lL4m(z3exhQ_Am
zme#i3zW!eWgG0k3Q`0lEbMp&}OB<V8+dI2^`v->?msi&}w|D>UAO6GZZGZkRT5s+D
zV)p;wh4aP>9uW}%5%?coaPS`gA&!HHM8%1WE2auGc6v|E75ENMJTAAk2Ze@P?HvD$
z^B>d?v^*Ph7yqI5UuOUBhz0%sV)lQC{SU8YfCK>kzd(eCM|>MMA`%ek4L}s&e}ICD
z@?W6-Z=nAdSpNa`|H129khcSh@a7DBTX4`((Q*Di<8}4zZ^{0;1VBf4J0WloZ~!8J
zXOGF<<iGNf5Xn51^;bZCmJ;D!0t}_7&)CGybzZ=31GD(I23QB%bqnI8-?)=ponM_6
zOdTCF#E&&d#^T&N_0rcj<yo}LF+tfc-3ITSefO=}uz-q~y~50G^74GaY80QCJ0?x{
zbWeGnmq@7+2c{uNo5^M3oIhRf2O9gbHfT{|J`Uu>=+s3&GacTE0FlHh+~V7arjGeI
zSPQ#JnmccS&5wGjE^QzJyYKPhn7>QWMi4l@=zB+Q6zgFh`~uth>Gl~jX*#R?eXz*#
z^f360d5Forfkp7pta*s<dGFFgxD9)@F+Y=<{zwydohCes9`jQwoDQf`;I|REC!|=g
zqBoKr6i5jeI1V{6J1iy>6AIS<6&7Cd862*(x{LppyGVL`FJvO`E0Q<9Wn076w_+Q&
z`O<9@N4zqq>#~y{Yn8VU>h9s%xK{yeje!u1Ipl+liOD@bRXEcqf3#|NI-wGwW<!`1
zUHY9)J-*oL<|I<@ox?z~N&3-mPZQSDi*gR8@s*kt(SslxQfm+0one$qv|OSVie7x-
zTa<$pxU8Ge@O%RAIH<f!P?Z++LrCVSovsasF}{S9=yvspl8xR(L{IxSl%KUJ;tRI^
zgcaq9)1bh7J-@#k@8C?yf_<_I-1A6mbZC0zYv&QUg5Du?ilkP3)9jnm3Z98<wWmQn
zp>ta~2;EKaAhb-=7~xB_C!{zp%U9rNCxpWU0_M!?;fISTc%*)Vb!Z5|Pt4EBxlVTZ
zJ#HV*$ik3c)}5ZpbnENrH8M<ok36_Y+@`VFuSiw#1<%+&z{HB~o4pm!7Cvg1eIPQq
zLRMda`fcedR3Iu;QlYnd-Ira5x}kBJl_p2-!lf2KP_z2o9@2-4$4rR$W{;K2EFWwa
zyQbc`={tOLyQ66-;Xg=-GZA_Pm@T!w8+6|LyA~rr5?Cq5S}5kmyo}{WYH=hpbx?23
zHCWNp#?OE$%?w0zI@8!bHcButj=$_#GIbN9+?hrX!Q`^Zn9pr(b2u@zjLKCLk7-3)
zai?$h6A_y}nStcDL|^xeeD^D5_oXUQI^GlyuL2P6*6yfz@D;=-jVw8YHMIYOKDgAb
zATL!2UO^eALI_lsmy&(qx10tiG4pv-;9CE%6Zan9mo}uPdXNnWLJtk3jxhBY_cePC
zJ2xBpdo@je_>k3jW7@o9pNTp$A`KY*v=Kg!ul0hn+$xj4wBy$dkF^KIr<1~={Sion
z6tV52xe&U5D8Uuv115YrOp=w<;Z9f4;>3(AJ4N$A9fv#W5!uo@X()Za+awt^>oT#<
zB7gkQ&|i_d_0zoGzTzfIy=w&5rO)r|(%P@wCn~<r5RAY>0ARssChVp;nyTOWW2o9W
zE(!qmfGD4>M~_mnSv}nMb(9ff4^$*81b=HteXH;~6d~4{+tOOdT9d6jOU4j`Uw5MS
zWH?gROmiUZ-m;=#9GA3iEKE8n7jwqH`-tc+oWEA<TD?laXo5Bm@w_QhYq__zTKvVC
zxE(j3Osct<lZGl}#4p^!Q!59bK-6PDxRl$TQ6O4-FMcU)BC#NlXdbOh`n~SNTqt-d
zm)KV&qe@C(dnVnluO9y1s4o~=kQlF;NIENZOD~C^#l?v|j5pET__Hd39zdkN)M&>>
zDJotvp$?mQj3Q)5plK2|%&#dLHrL0T4-EqRl2Q_UucFOOyI}?l{u5&=M%zYSNsH8n
zj@9ubdnlabeFhCIg3(dd^Wh=<B16uERcnZ`XPsfn!*%12OZuP&+KX*?4OZ$UA07}E
z__)JOx|Jm#pe0bo+VJzxtzZ{v|J)uToDgobvh7{vIsZDc5`9~OkuDgne+q2YZN^Q-
zqKmCp?+EI@>LWDn2ufIf1<a}q)Yh#x`2|)8rnIIXc{9+2(mc!@u>Hmqo@?C_`G{Ol
z66J-bI@Xl|>vt<PY6Iq(EKD^{q<hC_ZB`AE(wgEw)mga^>y;u_JF(j0BP(yoo3S=c
zz7rdq(U_k*yZusASB@!0=2<}^SR5oe&+0gc$WfHWWe~F%B?n+sE-e!l(^(!dz7~R(
zx8WnbBRdk%zWSJM;4+mt)YvcKW9M4GA$JYp^&immVu(}`b&AWHRViH_HMUw)w{t--
z`+DsCsP<m>doM4(c<s}OGR7e@G+jB1VdWjo!A;q1%9yRtPp;sF+w$s?K1!;DDmb1h
z6wE9B5^1V#GAB(`J*)(;Th*m1#?a<>_bRQM`FuDEonMB$3X4^fsjww*ez1j-&O;pd
zmu|d#e<w194wdt>(bXO6)TQ2s3Cs|y5BLrnKze`_<LN8>{JDW9D&>KZFd5nXW#vXK
zens5m6iZ-6=>&xw(qL+olPy&8noLsAk}hX3@;+?VaYVp8@1q~Z1iQ>Z;5@G*I7#Zb
z^ctVKPLyjs)^}Q2yh7t@yCp<#q^vt*qisf5WAZm$6sk|PiI&1WNgNksOfx}M6ys;f
zkPpA^cPFYS6<Du9Jw1Lbl8-<BboH%&QKu4ARGLO8P={+-=ktKj#W=|A#TSa_K4#(g
zC1OBXY8;Da*h#{k=booMo38a+c3juPOQSi;mgYwR0{!*4<s-e@<V!=nQN+q1FTmB%
zb(QnlhU;ii!%SQvIbk(rtgi1ou0m%eUMfe`Ux{?zrule!WJmAKs-$VDX&dIJzAl<q
zK&5(fwtXL^-LY0~sclQaHTZ9xlAbZp@uoB^v-CT@W(}sFjZ+oYT0*QhOny*v$eueo
zl4K^YfR8^Skov{VSJJ+VHHTuL{)b8?Y|dG_wBS850#DTko(l&G>~rrK%xMz_hfCh~
z(xF#qaf5T^!#tBsG>hy~(xAM}#)xDnMjC#m^p2{&H6QlNPB6xb5fra^o{8#;pZ>J}
zocXtXb3Ap1qsy75Ma!u?HKF#qKvAz1ES@aaLAc<qNmFS5P(7cpKI*XL4!Q~_&B7MG
z$7E8iv2l<TGOm%ypoE6~k|Ox2JZz;pNck0@+r)M}6nkfXl;y0*ww9P4)0{l+`wNWj
z9j(FnfX!Nph`jlyk7>K)NLBB!%sDoLyiAHy^;gb{)GMGPPRVPl(q^zw^k<XojlES7
zPjoJ=O8n$D({_f*O?mbb>QU{Ya=pf&d>G%~UEi)%%FU!>yG2JL;E~e4yOfETt-t38
z)dZ3<xNHJ)tfZMaa%)ObuB|6&?-3L}ABi!k+VIpjE5)3^6OBH!g!T$Z-e!%4G}XLl
zl0LB7KAsdHi1aaBmutcE_c-<elLxHL(3(*tu*8{@2jYsNuN!8m?%|mbI#0XgrkX#}
z`s??HUE3>fs30=CyiX$9$AA>Xb9WS|0ZVp&T(XJ6eyOoR30n!1obDrUgdXS9<Z<|K
zQzFHLU9Fv7&a0)?Y>1Y2Bl$#LC!gKxX6Y;!(<N<<kfDhDCDawxYm{>qb#}sxs#{6!
z+*!yF02#z4{Sn-lxxQR)gA<uC%0<8O)F2mq03i>b{5738Q_#eD@{!F}i6L4~A*7Vu
zeUEIwv#0#;dGZ8$h%XFFyyYcn`apHe3$+{|Vfy{3RrIE^#q|i3SlLm*${F;5PDQ%(
zx@t-L(4Mr>$ay|*RhqGWe6y9f;Je}JZ`^h<7feTW+Q7J%$!+qaCGGum;n1X}R;TPv
zaieJ0$NrMC6aj|tRgngse!hBk`ICucr-k6S;FEvqiqC~(b$y;y6<#I{>*+h1W=J4a
z_o%snB&LD#P}$Q4?z5YiJFogzfC}c`IKM2cu!eJDD(}2A_PNl_)q{|FL8xTrl*UQu
zS<-UV%3I?CNm_&xi0YK#Jx8D3tjo4+phpgtW{3+4NMhEGOl-}Yi|5M(jiU^@+B1YU
z>}%#_#`OOR|BVU1{$d(8SE;!1i0iwILJTC+D{<(aGo4p<qvWb36g*E=kv)|u#msfR
z%<KjLy07u;en6efxpw0lZkP<L$Z0jq8ZXVH)~k-;g_Crr{`TL-v1Oj5dAcN_O5i`Z
zOdlE8dZ^!vn;sk@TT9JseNSBz#VRGlTVtvmw=&w`*>vVmELIfwk#jD<k44m@Ci3$g
z<j8!(!Cc$`sTUy-bm-sm3P9bZhdumNi#s*>tv39<+m!TyxGSaci%N}1?@(p%L57x@
zKtnC+_0A}**JqW|u<R~;#@ZSKy#?!cru3{eh@@NOFR4|<S~b~968`0FC;-epVDQm1
zhSzO1?`b7rvn3rr%AJ960+LVIR&j1sw){omSYwdo(lwO^L$drSQc986+Zgt;8=_mS
z1@BY7b@vv6W^yVX@;uEMbC&tK2hkd9ur`*Ol3xL8h;023&<bbvRGM&Lz9O!0wr3gY
z=_rSr@E|^v#AYAm^7IR5@STP|#RPRzP)q~JI6U4v&t?N-V6H_6Cnd>|J5pXV$PBw7
zZUneWd18&8!mPA=G&tHV5~}s#(ciDV#*5N>7JMy`M8>X@t=QEZsMyx<Ep4RP$R|i7
zNfx6cX~wH1Mr=@nmSQfR^7dr6XLrB{4-DLAW~nrsn_3c#>L?_)3zH;7K*S|<6r+&a
zOr{o|UhJ3<!Orm8sr4rzRI{)X-k2Yy<Vu+6WYJ+iqY<t0@VUD!BUr*C;QFyDV8%kV
z`gH|+Wt&2-ucLAHIwnD8YzMsD_dW>XJuM4S=TPh1GinRr#!it7*#g<3&tMd3kDu@`
zYC*&|LXD-pQ_yw{8NXY=g#(Xt9VHRbI%m!H{z<q!sQYNo!3h>d`!sI=&yjXpZOWH*
zir(Kxqg~uNO|=v51^L9M#Nj!D)|}!LI?rFS9Y<X(g-XSS5O)Gtr;Bc8S}S?M%WN9!
z`Ct2l_~o*^t`NK_{AZ=~+#5(fp105jm46RlZZ)yzeV4xZYf1$~1EPEkHu#`i8sBEN
z45E=oOeoU0N*`>qDH6;9qk4(x?iS2BMDX1y*J>Ou5eT8ynw253H8%Sv$+6lt3m2B^
zhP#f70fMK=8_g-+v$ji4rDkICGp-a6m(7#gn~23fUu6)tTG7s1-`0wveE#{4^qGnO
z3o!w-J_=gLy*O0lDYf79-5CPjZOz09oh!v7kQilNByazEpxb;KVD2Pj{nPJ;26$c3
zRG;p%;Z?X^w-K2zWc<{VH&4gY0O68OU*q>hxVbK~D~V;LV5IO5stu3OF|zS9{19gN
z^#&?D*l4@3I><u}a&JM%xOuW%uuAg%Cw~h2%o;ZYDbviH1+35xmlna64!P<-<pv%$
zMhI|?=l#*fKbz-iZBI-Zi#SbR<N8Cdyr(^7-AsT;Q(~KeL}p_+A<>y}KW~9L2=RI<
zQ&g6!k6aiD&3E1A7|tE1v23fn^<xzJrtUPLY@%)B{zpb*s1gRMM6y$`l-s`llaRUk
zOFdbUOa3#Lr@M&S#r2rfh95=}6RQuM$VV3q`sqq#kG<0eO*?^yIWGmrWLDq>Iy{S-
zc}n!d1uQ75{LA{Oz#8c5yX|!+=x607w|g#Yq*02vb4i&>KcW@A-{hH`a%ZLXjK6+1
zB(G{ye!-&rQnn}yV(zyisXe>b(z)&WD`m(8H$m&n!t%LJPd2R3T8Ut9-K48f-aC$Z
zQD;tKkNP{-h+iqa9s!uaggiWFOs96f;qh9Ni@BAwJUT#_MNrqr$LHHd^ofqxxYy^a
zbl-WdRy)?GnnCZ4uVB1?8<HT5>8W~L-gpi+rD33o|3Mj-qMMa#&1#8$bSW^>>Kb`;
zDOCaukpeV0SG7=BIhNji5T-hTFC(53z9YhK2p-dZRBXf5kuh>~dbWDN$DL`zNZfN}
zFg|F33blR6wPW80JXBK@T2`!YOyAGudQrn`Rafh>YcYi+6IUo1s^~#{W<#1kk~oOJ
z<ISa;J*pl-R`wo>-+Wm~Q^o&!6#TGR()hsuC-pl(9<^xB+$CN~xFOEkS>_ecsRnn0
z?>pfztp0*mHDc2WUF%u58<HuX9<;yxO{b#$5#uk<j+yIPg_HDYVRV)EO)2&7p;{`@
zE26+-A82%P)BGi}Im!H2KS|TocB#XX?og3l@ROxY>q>Bo)4^9o&EYGaZ27NXz<NUP
zfer@gxsqMx5U>afIMuM#mNY&Oq`f06mjZ2GE{wN_>ncV-6dGbRBT%`#qRz$?9VnwI
z1U>+p3*}<b1KKIqFAK;y)%X*)SWo`B$d~GBe>E)2_$g=Y(6}t;_-9=`qA6X3>Bn_X
zuIrJlZ$$txD`UJgS^0Tvh8x~a*rKcz4TeUxQfl*Qn#?Uv9!sj0!$B4!Yn$r{@<yj4
zP6u~nuGhwqn#Uqonhb8=@`H`DyYOVdT4`S03>yNznJej6-KaWSBgoEdaDmpJGNzZ0
zp9dN#E$y=b=rHe_VtGG1{?m_@9#Y;J*TOTyY}Yb1a}@`%3h7nG+AR+u*x9tTE1+(C
zxGOWkyCLr7oU*3d+b>WT8q$Zzxp$Eq#Vp6xatc*KyS>~3+3LzpJSND}B1tl}Qc9u@
z<C)tT_Alm{1H5!8gudC85;QVk5|Q3qegqE$5jht@Y(4jB7^o=D>OD<ukD&5_qM7o~
zsNfULB#VS967meq5EYOI;K967hYXd@O8%?d>CmyaI)2Zt0>brXnYE6N3iG)#ebuK8
zljkG23Ba^e1U<V6Ms)O?;>#CkGIxf1soT5R1zB?Ln+n+i=XTnmo8^f;UHx*Mh2<tM
zy7)1#?kv8Er*~Q<>+`Vv$k8&1opz2Z^|za#R)f*QocfBp$m^fy;9x~X!^>S1+C?FD
z_%x9S&S$2nfA-p9SA`+P0ofE`0<$)0dZWKVP%{4a+2c928f_j|@@7(#C<kZa)xw|6
zCw$_rd_Ij=4QQY6T-F<lZUqhi0Ztmr1hr>WNrI;9CNpzNx!y8-s*c#`xZdf1Ym-s(
z$hG0bx9k+y7)E~4F;+QOIDYshwa7AqFzJtXqgLUu6?;1S)jUQ<^A!-tdGya)Q@z1x
z&u9X4rpT`9!Us`X@U25y@$-J+CG9dbwUiie8Ndt>NIRvm94QYI-olp=rMJtsd>~fy
zm}xmF1x+1)64pBzzJ75_Un5<nvSf$}&&PfD3LyT&mmt*=MUlzM#4()W00D15Gd)Tr
zrGIGp6WUlDcd0N$06c9Lsgz{vD2PZMwpW6P5Z9dR<9x+$c0Y-A(Oe)LK;42kdg95i
z>E>yq&k{~aLTcEn<hJcqr!T?f=fgA<?0oBS100f)PDrD%e3TyKsl0K8h6Xx@X$-?)
zs^FVfz|B-hM@C{iPkkxS#pC4eLtTTET7?$VI9AKi!BpLi({nj|QJy>(;8S>T*tp2U
zjm0mVLBFC*si8RLbtCkf+X@5blbd%}j$9+TSh)zfRVzFRWixHZ#=1g@t@&-{QYl3S
zdMiUi{*qB1WUqkdPta)a6+$0nM2C`80CRKUXA;4yB7>@_!EKWj@}zb*6H-qSF({-R
zS|(Ik0P~K`nqq9+{sFIl?DTwH=u^!m#(@5_l<Z1pDfD~YeNrt+)4C7Bffk#?Z`CAI
z9ZhM*e?t$%zn~4R)=8{fPHMrttT#U=p3{69HNQ8gd-Ky;To9Jk9^NixJ_)!N6WgY%
z5rxCS)@1J{hzesGFjR`OACqHgY=l?4Z-Kd#mn;bVS%fXqNp3bM#4`ASwm0IQtJlJL
zEu47O3{c|y-cmWk*+1!x<2?;gnZo$73hZiJ5}%ZRf4x)I_rP!ba3Ucu;GZMvL5K<I
zJc2tqWC@)X`pmn+Q8}7gxSfXiryYnkIO(kHy>IUpr8ny0;2cUeACZ9Q@N&yJxS>So
zC1to2)aB~pXXxZV(zfGAvY56+Iye7**ouC;HGC&d+vK}<to5N#6Od)bZwe@P>Dt<L
zep|8>+MBli-8ODY<Y$Kj5b-5P%dF_%^r!Zg%PF-GkAxlMcFS3{nBsha3nDXbhx7~w
z3O3mrh|~kq6<5VC;Od}tGDL~5QNE@`>tEQZ#)D=`*Pli+%TL2udzA10rRP(GF4|?u
zf00L8`+2_b3c%4F;Pm0n?6rCv8CY+#`N3!M;TqDj*&=x4d0oA>r0{2CkV=}Ayi4__
za+N?UXrL!}Xe>GyNA;hJ{>=C1BIn=3@v_^|>ko11OHBDP(~9MpHii;A)I4;sKk=dy
z%7tnJ>3an3(H(asZy9$y=+vz7k?xpUlEVFyzx07t#qkOCy+TC=kOmQJu+gqCB9le`
zS4-`ry^0^qEK<~S#XaGEHcC<WbT+iN*7WnHlJYLMW@9D9l<tm33fTjl)X!Us$^yBS
zi2H!tymtU{ZZG)lDCy+ij2ywCa|=(k9hOSE9sLQqM|CR<OlA|<(bzJ{E5TNNW>_{z
ziw}vh)6Mkfg%MTkB^Jqk`60_NvzMW1(K^d^x>`*MGMhLPKA=bj<zYK~AvL&`jEeCe
zxRZ;kT4#NK7P4J>EZy9-r^&<D*wRFxYa2MLhX^C|!BJVUy5%2#1(?zU9W_I2iNr)E
zAJm1w`2oGc)6YeRe34>}U&Z-q_Pw`ggf_zorcGp26BM=A5?Azbjg85b_{o8DjkXmQ
zjU-zsKJVj#lPa{S8d{K8Pk;6b?07ya>dPxy^Rr-@igJexDi?mr<0T*)TjWI>+Fo`n
zQiYYyXjDQPS1cw3HKMUsII5f{w9}CVy|=@3q>J^vHIrv5cHDyYT-i9g%>dEaDmGxT
zrA+}luJ?90bzH19{I)I=hDm$6J287<4r-f=izh`xKq7?2aOEN!J(cs9X3I}YuDy=j
zm;HR8L0{8gC{Ks8{JQIy0U$-tfKH$F>qG2@gMU)@h46rTyixA&sx+lO)i0>Sc>0(0
zu?Xih)x5=Id!jiBCu3%cIV3()O{X8YM{xa(MB1dA6=sf?qA8d-+hA~{#Mc|cpi;3@
z0mpR1OP-&7yAEzk%IaG2<}Ba2b@YlC&=_qWXXb)tX9jwjExzj&GJTpoR+y5-7W60q
zmhg;@scqQl5|~hTjf{8=ZfTsI8Bd=X!PMVNd^}xdNMdMxfF|-k_*Knk@!E0{iDe0x
z&Z$8;6WV5WCG*$hnr<&XnCYJyf?fef(C%sY)hLJ}rcm80fW>It1OKgxpWD7j%v>&|
z%cP!tHKrbaIjEh?+mbNUs>xjCf8>3&RO8aDM%MN|pjV*gB))e$M9BBKgZo5HHjj6P
znq;nrXu%Jw_7p?f!NtpVO(C4hhvO9><gIZ#l@u>dr-8U<LJAX6)t)I=wZS!7UyUSI
zz(CHsO`$QNPM81#bIE7=Y#ix~*|ET5)~n2aB}ore?U8q)f%kgP8IQLWEeR^)_qho3
z)u5^}eEltt*1RYM;yIB`b=7xMAJg&8h11F_P8};+iV0CMh87;6!(`Z@BS$WQkH5?w
zGNw)ca^m)KNb5VLyC2|RP(tr$@HsVT7&)P^P&E80HRZ%Zl+$;mF65IV<-rG&nnb5k
z*<AWEUvogl$JQ&dHeP}fwnvQvJIr1WYL*ewqLEA(<#k`(T?R=t+^cbjzLOO{-IO=g
zRVH+F{1Jj-3e2-O-!YZ^koRz4jp>7M>a6sfx<*(O4^x4J9<S7f3m3LHk<`z$bAA&N
z{-ZQWZ!7g3?wADKAg8uU$qJPqwE|A|S3S#BMrowr$OI$9IJ1a9P*lpWv(<>(>i`~e
zh-LLzO%Fd@9$XGOwpylzuO9m+Rq{+y{1xwM+3_YWE<MB`1ss!z4(kYH7mAtB5jUo`
zIjT0h8s=3e7AiKnTS|IZQ9o4>-l_K#{I#R;KS{%~y)Mg=im!}{pb!oT5cnf=9jZ9$
zD06%APfcF@Ienj*Cz3CM;9=%y_r|YFU(>|IdXGaNpe$;2v9$42%~MNCO3X^1dQh34
z^7T+8lUd@{k;H?%VAiX0UB|bSKl!fYWk`RMW8qq8impHGYBb4rA@~C@!<Cm*9u854
zFI!=z2wA+8UdsEV(z3Er+>RDIK!69oop1`-P}Kz%LaJ``^D_Ic#P3p!*T&G2rxs&d
zHEczcSuk?a$B$bxcp?z+MWD0?xI(OTi7C$QVZZM6$)b~4W={q4#YO+<)A?___-N38
z@QqxMj%?`o^gKggWI?2g3Hgbd16$&r&{kyE_u&pDB*k0C-VlTUA_k&on=B^}(gC_?
z{D~I)+&_dm#CDJpK|>8-GvX_NS))aNz3yVaz`qf7Jc?-OowRYVxM3g>D|gmzin?|s
ztCJRS-HQH@CQP%Y!%27Y@S`yQE#VmDA3F!4LiM0QL9sHeWVIz3d}&(f0eu5vWosqR
z*BsktKO1V9JxsHxfUT*VNH!_e7A6{qjX#kAHfyHSQW0bGD#kQC-E-yCo{J)JTky-|
zZb!m6dDu^}qR4r@>7Cdxn-DdiqjrGqZFbYgI~##YCJb?d*w?A&khZBEPx&PdwgHq1
zJ*zjdj)gh5=e~wRwvzCM#)e>tU{9q-h7>2g&A!6zeX7#1*kg6KNNlA=e*K{zx>tO&
zc!NjsQnVdX_rh8@EGw+e=x5N#_AqW2DF@qEzauFhnZK-Kja}^nE(&poW-3h@8Dz8S
zc893LJd1yH*Ywr~cYE&#RebzicbmeDG&2GRS2tTn4Qd2iD9cHifI{jY-C~a~(~z38
zI%ixui+syo4r*9wRHQbgJb@205%6xR^V5;T;o3h&5%Y1q_NE$e|NS)JJV>WbFlOS3
zFqIY0uz*?BM37z4qmb2V-)YSkKDF?j94-oH-N-?#DmT%O!H`yb{fIhnCTzjL-aZ(D
z$?{PZji02+q7_s@c*D&}>yMn5wJv)(nh-Y$ElaQrr-c=*F-_qxsCbTK{T)eTmTcz+
zEF861L~fthD|S4oN{S?senog*d1molw&qhOXua<=xuSYgyF#{TZHQ>3u&8P2sk`_!
zS9_kY@ME0v<!@_Uiez`nouzaClPLHkevcU$+KbD;8YnMZ4;+=|s)dN{;XCTRXVUvp
zRk*P#cY=N)Pcrngqz6co1;Hv5=re@*C?+(Y;X6@6-lV5;yG{8|l(kn%0XwO0iTXBs
zGT4oxd5OMz%Y_N@3W&O@YF2I6a{&$J^KN%E&n(hqip8M7dF`z{&PEjkexJa#BU`k|
zbPMCt3XD9PfJiDMJ>MjT!WLD|y92Ts`JKAJPFi(Q+>LGl40ov2B-lC;%MF-YFh#dc
z)R`MU@2LRvaTDga^>!vq)jH1WG3Rtxz6I@oWopTQ4oMJJbED?e=?xo&x`B@jZLT_A
z4(uIi_TF4Wu@pX8X`|DZ1dZ0RkIJ8^rYz^7&DDhlxw2nCYCog1M=5Xcn~SAOt#Wg-
z=v&pp&}zTO=EF-tq-;DiW-6bg?Z{TF?M{>Y)x3q@33^~+HF*^s2u%Ie20IEZ1Y%}l
z`Y9^Y;w41xeE;Fb^f=6OC)vTT)fkuG)I?1)#JCslo#joOyw@whb~E_Hu~VL0Ia&M#
z1bSu(P1TD)Fu`cA0#PX4Obu>aC)1@7f`v}^UIEogT+T||T37gThU0P?W~OSb=M{7F
zAIY<s#{+fp?(0_EL1*%Lo^^MhGL71Y!$Hh_Nq0mqnu6MfI!X*pLZw4bHg0Zcc2X2|
zhjWK!O2WRri)3OI$z+D-&@XNpTcer=SZ&kSg5}fYJ#IvS1NX>|2o&}|AE2i5P#c%+
z1yR~>CVMfC0o?l6P42h+F_!E2CYMBDH9?Cf8$*2087deL(RvPbgXS{SC!aqkn(nlN
z$a}*$Q!INzYytS<Sw_FKgo}J2GdQmE@3y;?E{68IbTl@<(fjDfS3oFFFTb7J`VCK}
zT&W=!L2bcWNnyFN4cWJ#_q*Jgx$W#&SG*HFegTDYN4DgA47+{!<B;;4nY=hQGi<nE
z5^^c0U(y?AI2Cg&CglSk%mAE}_>R(sVm*U~Ye|(tlCokmi68BbaeJpCHS%7r=zdC6
zec<{Lq4E*P_RDgjtf{hTLNzJz`7ej*`|J%!Yq@S%oR#;Hll1}WfWOLEs|VKR!M!(>
zZX&!<4~?p1nOT|C?h4PY-=dV#Wuo$PL%ycrk9#-nF#KM_2fAR{j`|jt;<g4KM~&vO
z?9YZQvbfv9x1nBeq<I&AA<svMM>zB!Y=eZ4$Vq(y8YAaw=PZ7v7Ff8z^|_0L>-MFo
zC&f)}xGsMh{g9s1mEeLF(AfR-%X;Zw^ug~!G@1N*vVK1-e3Bz%p67=z`W7^F%EeQ)
z!n0QHuYj%?3G#ODfQKW2as3h1<O-_rLNzvxz<EWFywCNYpK~O;&S=d1?YA9ml4ox!
zsr8&N)4XmY^6cM_+G2ZdTm(nr>4woVQUMr*;Qq~f<$sp;v19$t)gDTTq|c1*O5UYw
zg9D+Vb&+Su`;o}*cx%Wo+o0z<8jcoZVw-_5LEq?>O?aT?USn-cCqJa`8yy;qxn4uq
zeaIcH#Slz;WE*qEbta-><2}GQwSu|slC9e;pKi~Y)Ii)lxvfcupe}bIoKm?`;S6kc
zQ!Jy%z1i!q)0f7x_<co170|XEkm;OYmHgLAemfddCJ{3OhAHUsOBRyUaDju{oGM_t
z?JwNu`drC7%wP7TaA<7FS17APW>&aUID|@Q|5-mkKwj3|=SNEdY99t6;lYu<t$T`U
z%sv3(tpS_&&@%ZA{~>}qi2-zj=usBq$1BC%Q}jHYb75*jSAzSU=$qzN!cltAsAfH%
zm4gQr>=WFlva}&GxVKv~ULWsFkY0H(JxEQ;^FbXJ;S+Sb2R>DH$Sq_pv%0+IeF!1N
zGw-y}G_%3GQ&^G@Go@GT;EI_sx~9BRU!{$V;g6>PQ%NI+0BQ#n9<(qYG<QWj*yn&?
z#NfD77HdD3{^X|Ir*bPk_P_j@YunLV^sT%3piVazSd$hn?f&Q)k44L+P;1rGuO?r@
zY$Ak(tL)w;|B<Wm->8as85{cOFQB0+A!~kNs_D>-lDPQd(LoDCDX+7sR(U`1hhB$k
zFuF5~asEl%FyX$WF^PHubWmbAUybyxp$lP}ooD%jLcal-jED~<`NDzbKrthzC2lsH
z=$(7E?{&rY{Pp9n=)d>1vNdOBpHn<+;TG;H>FV@qi5go+YKIXsyJEcYI-;8_vd-NO
zgmwA@mQT}K5{k_Z)JYz{Iu6pb{DSaMOF=hK-k##pb$9|kW(&PP#jxH}HXYj#w=&3k
z??iLa`#|iPqKJt?8LSjJ8#mGtd$=kh*M95ugG52nkHdzJ&Lw|w-<0<^ffEnc`d&<j
zqD!hBmImyCpRi8kVGDm|tYCHH8(Q)~bL3F#)W<4SH645$;lp@YhWVAb`6%5QjUVo{
zE7mx_yc`@nP>5LHfetZTbgkj4{}KU4Fv>d0Dzl0`3Yizz>g4e(Vp>;3CJL?emL^<W
zgsUwJ<NXPWLARg^6EZuOiBj<b!+|NKKmGiyrjK#IXs=`@@nWsFUGd{ktyz-dN#Q0a
z0~BW6yI?@eMNyiTG4-%QsUM5U>vY)RwxsUc8s(@XCoHXM<xdP0nQE|8uqTy&aIogt
zsRTzP!X-=(*nufa!c1T~Ey`}h;^=WP*E&XZa_j~^*51$$N&_R_k<ZqnhtSWF*`V$&
z&23(th&ZLdn3xtbtz+9wDi*_sCHGqJZi6UukTlQYncq$ufeLjHS#bKS4LzJTUXrv^
z&l9JX>$rzv-j5a)14rem6-{tKyqD%M0Jgq`-%371;x+jBN4ja8)L*`ZtCz`A(@PKT
zyhop=8)~u1%-*ID`=e+(ku#`SgH!QnjfF^`u6^9e=oy8aEJ}As#B`dN{fc^SlF6Pu
z_XO!I+BXt+qs$bMwJ|X}p>A6<cCi7KS3r!g8W=78ca5P0Sw-s#3A0nAsr&6vu=Ul-
z=`Xd;8P?BV6kG5hoQlNBseQYweUna#bjFW-*3V^#BpmBov730Tw*)B3Dr3`GEZ~m~
zL5_v<yzWvprfVEdYsq1enava=k~qvXOj+HtJ~KL0>|f&r&^K#$=C1#d*ve-+x@gc|
zP$dy_4xa?Z{hNleNhVL9IIn*?3~o8JUU5e)BMwMw+oLo&uM(J7?TD0Os$mkkh#g5#
zncq!Alp&}>xheC(<ed3C)d&IK41{p%mjXQ$B4=k_CRhi30JZB0S>S3um=Wz2Hpary
zg3oj?5%PiTl9JTv%Rjs=hRJ5M%*uflHx$gwBF4*vvmqpN14CV_xjKEh{*b5PV-_+u
z1S`14MvxU2=tx^VtfMs&nYKXMC#(M!ueCDg{=IiPG2KdARkgJP7aakoEQLLPbnri|
zKjivZNgKpMs#w@Mx74N#FAw<F>;!W~1?nDVSG;*5uhY+_37SDD@QNRBC4zuN<g3jH
zx=5~iqueeI0_6-e3_#&f?|Cr(X-uVEs5TISckV1;Qp>BQJ(W}2AxpbfqtqT;0>pTd
zhY2_4his9%&g8uUu+;xXMeiNoL^t1D^PRf>t9n1)<Xj;=-rfwb#-3hY{_vi4<ma)_
zONXQ>vz}g#n}v}UWi{gMnTA>9;oSMjWBB*M$%jA8?lDu3A?fyG&->R{7(D!Q6{s7N
zmz>M<_`&79!pn5|oKzeu*Mquap{Es@_N@Blvflv*Nyq+;<sV9|Du!s=RY{k{vpj8~
zb~<!)C3MwX3kfIgAF;Q$F*mEb`6YLyJS^IsG}lIks6=D4?^>P?LsW;&<s_-Iz`!%j
zVmda}jQHlZ;g3*rn79m>aH*JRu+l}9ZB&Y!u@9e+bPr(x+F9&g{zdzH)nHuo1=Q7Z
zLpy2G0wQzhcbGTjonZba0_oAKoe&?iB?LXz;vB-`gOsjO!MdgK#@94{yH|O?)gy40
zT77;L)ftQkO&A;KPde?wcG<a<elN{e&~?=>TQ9`<o1J2^Nc26F9|>427N?kAkQIyg
zz_(b;e&YOgvf62qkQEFp?&{E;p@orzWQinesq|MXS1}tUNu5l#uHmTpa67+UPtp2z
zR_<%xN5gaf%d!Tx!mH4KqanX%5Gk|>u++yz?BaSj>83BY*1C%Ow%|KBJyqJfq&=Fo
zC(V?ECx>_Pa9x&n_{r}VehZkY4qUPNc5yu@n64_oXfflShg|4xPLoP!SY*IG9fCi^
z5D>%W%;68Y?_U8XW=PyE953(n;?~6Txdyu-Z4}-!$$FkOMJsk_K}!4-FO6a^u(&j~
zFSKwYn2LJsNVJ&?lS2HJdTkuoL;3EfF@XmQFT6kUihh*pj^*k69)q;H5K%BWK6B4^
zA!-RfRHk-XuJOV64oQ)h{6j2&*QMK2Mq%fkQQ}5N#G5-bTh<<qAh6-fY$i`3?T-D#
z<i3k)aHK=qiL1^dS?`eK&*|RoFOtBWp?w+fHjWwwg}RR9HbM9BU(V2>vkX$=u$N1g
zRecIx16^JRei25<JDOJEn+6)b;XPr#61KP%9~`}neJe&Kt=cT=c5CjSX3m5UrKAa=
zt)kkss3XdoPI}4nR|^TG_Hs@`H^TXi&d~V$;ds9LXjz2mO3ULkT<|;eI!=UUnkFk|
z5B(wa#FV!2DGaFm*X2|$7;$UVs`aCCnmP+L_jd3wz;&=g3NiWg4%(eClOLcvruVNA
ztuW}44yL#qhJ`MDQ%FbK;BKz5v^#6*{eewLviPS><6_G8cfb7w@4KFTA1#rB&x#~5
z>`yhBB$iOJcPCjlF&x?Qb3#YZ%TclspaOZnM|pb8lWykaiUDJDk|@-4=q(dC#Io*H
zze`-w(z~@#6tL!Wov9{hP7tuNxTIaBu18oC*7lPwo}5wrtS9%&IK{?82f6QIop|rB
znh#4HVoxU41;3$B@LDxbQ%YqWSjKpka4S3C(Z=(atRaRsH<PfIRr!7OCHtEGipi)^
z1z?;zBjaMarh*7<lp~HawFh1F>>zFsAKd&^|9&d@%hxJoY@<6{|0Khcg`D~ZHTM!h
zjSVTUC;y__!hDy`PD#a1O{FE_-SK4BqUoK6cE<z<)@W%&oWq|tc%x+M>eIVii!y;V
zID>1Lf_~CR`q3$MJ;ZGo<IX5T#?tD4v*oToOf_^!$ir^?98!lwqKHJcJ{?Hq@5%k9
zQBM}C*08Sr#<MENja+Na?*WJ4-(i#5a=u!T@LVmM3c%DvVIIAo<qg{w{v~bzx|abx
z%s3CbcF{}_Z<W<h)%hLhEd6$pwT>z_XrD~eQVAamgrQ;mENWtFq`<O1?xqgY$Hf}y
zTfv!Y4e=_{p-lY%@cq*aCgT!Lni#ch)4bN23k-T6gk|VMVLivQEL$19L!TKdHp9+S
z309c9NRuUGYOg-fo->3P5SzmeXDXZ#zG5l+2ZKs<B`IYQ999pOkMxVWN_2<qZ@B>D
zAbrxzy&ST(n_>ZWs0XT>TSJq8UWw@=t!`%5!_fFf?55Pw3-6buf21w87PTja#s&ZK
zuKE&+;wpkBvjSV0vD|2y8@<JqEqLI!c3i*Ps|5=No@w&qRnyMSuZ+scuSOmHBW`Mo
zU9u1HLNq#ulxuWOtwtQWM9;^sDmt0@)@y@}NgPIkzjpLYc%RT+Ac|XVrE`_7bC}Fn
zw3MdTsqLG&(N75&bJAh&B`lQ>Hf^bA`QoEoUQNbn#;gEPsEqtGW_j8y7w$i0WW3F8
zfRqu_L6jndI@(haUM@<La%s>44<#H~()j0rAkyG}8YY@x79r!Vj<3@Aq{%bWnYM0N
z4no1t#BQI>!DZqBxH>`cl4!B26BOCk6`uA+@zf@cUXKM=k-4QYa-f$#mQ#QN6XfQQ
z_qzFDxj}h5-m!)t$)@FT&jFOOl-S!JY4jh)K3t*@o<h3uhjctUclEMsKR;E-qCSJk
zU^WHmkI=7J^j6r}8wfmw;fkvS#3xvZza8~x$dK5o3V4=xJie&51<nJj$@5AAdbKKa
zaDG<T3t-xaE8|!RQ#SFXrx$pDemjQ(-tWr{8YZz|nWs;>^p`s~<}Uq$OaIusq(n?J
zL<83u;#SmDkb{>rEPS)08WrWVD=G?Xv2}g2s`PNhwC7I}=?4#PavPBMbbZ@$mN%JB
zK7kwI#VT7_zCizK@%VS@XOYiQ2_PP9N<#!ia1_yZEr$xzW!c1;c=wP-=bKPI`e?K%
zf6<6liqdL>K3|!7HxoT*-pp>X1uaxja&=)ezEyyHKt;Ss2S}A8RelesDT>GXx&q64
zk(;a*G&T6*%W9p|Ft!4t{C~3d0nB^+pK0(-vYn=iG<-4kb`a)ylKAusvZ4>zePz8|
zV-S^Ug}Hmc!oBqr>a%S1G)-dNbPwsjT0$qr&@8e?s?plh@~LV7>(Qp9e%2WV(fTHn
z`|-C#{?1?;#D`f8OR5CtXe{q;tJ8eJ9~ea2NMy_+O{AwiG&%Hz<@@@s0&`gmLux3;
zHpGJ8miF_j$FeW21@%&k;_{{7QkuQT<JWmdGgnD0CxASK?=}9+Hn1LC0;COX!1h&@
zh0nu`0*(QN0xZ2>;l(I5xD5yXa+d(^%INxA9(JpSQ{!k=XJB39MGY6+LwqYz<!9*w
zeqt5dS8^D|)&FK4xcF9iTUTHn0HCtHRZG*gsk78ler5Ut7-qK;Bydu8b)*|p_Hxq9
zxCJb&GBuP%4}1mi-rVx$@KSL(k^e2`;ko7B<}<pW`*}$7!&MO`OG;5F;o-v8{oMZw
z;LickLJ?0AlGL-d8np;i36`F4X0`~Qtcp&I+x%3$xpwLng`cgbL7IZy51Xy|zOKu0
zT=wxqKoEFu-$|Ek6N7Pso|XGhK(h)OwtnU!tGniq#Ey+~7brBNmEr-#-d*xsB?<E0
zx~L$^Z){O+ToAsQ_{}-}^vr^DhLn+||EaF_d9zC#(s4vTW4JSrV0}O`N!aWakDov!
z!gTa+Cd<_5J?6IIzkTL}Wl1!Y%JKc|T{TRnPw`wd1QTSS%0z)Q$g#-Dpd+gbj9Z0h
zgS7&%p7G)B<@M7|8I^{SLJjn-;4xL$?V7Ayt{bH(@;27M4_K~?(@?w^eBAS%a6?_b
zELhXl4#7!iYXNmE@`km?%+Zut+<p)7?z;SgU|y59>S)HPISp-XS!8>SOeQX<o{4rZ
zI@>#8ENGmY<s9pGKu%dP_bMSjI>Y!A6k09le>fY?MK(yWO2))^<hZ-<CQRNwUTL+9
ztt+28d`l9>vcD2}AKcd|Vbdh(qyGsR>ruQj^WNIJD3$wl=9#vm?oS?t&Gh1iY8Vb_
zE0mP1^1I)FlufahLpAHvl6;RF3S8V*=z!;d47v!pP11~<!X_^XYP`~)w<;-_@a>*V
zr(Oop7aEg(WDhLWQ;4G5cql)`YA5vW3X-~6w#r<dzUrM7N|z|9S>S<$O^FELfFR-M
z>SRO{Aa0*R28$bYr%#Vh?dA@Lc2IXTqPHMYSaQV`*6LQr^`eor+@@7rX)i|eBHZ#x
z@+R_Ee8*fTMvYE%Ma}?LK_{L1CA~r0=92z`zXD%{4}@NH%2K@BR+8g6n*%#!30dZ8
z)*4;^(qB%GhQ=m)p}rfsz5T^*Z=TmXCEJb>#8SEi;A*>tzNsjvX8j&DR9-mm$V89s
zXa9ULq00k3m=}5$7*U!Glg6`X7tZu54RGtDbu|6L7klhZ@1mR5p&2U9@XdjX_c2nJ
z3{N0UkpNnUepU<UEvc+nvr*(kmlhTDgOnu~xU?_qo!YURr}EzPwYTGTlZFuPL#}>V
zA4H)i$9y?eGo>xhFix4y+ANG;N!%(qhS)GT2A$D$1P(YIDUlyH99k|V+OqyxhF2W5
zH!7fJ5IdE&O&^~w?)=%zPp7M#mIwo`ZoxO2*0)}o!o@2%qrR0}b-c-Q>`u0)3klQo
zzeB{1^gg=h`MvX=^1wQI??5SQmyvtytlY``r=$LRD7sb_9HB@h+s;XABI||y5_hZ8
z6!(Glr6TT@5o;eSKSuW>G=62zVCY63Z8&_1O&jnIY)B#!YQN|iCp_NtP~hzK)u=5C
ztF>a;Pke4ijH1myJj3-!4eeVYU8UXA#cDRC#f<li^Qr+?Vr9OxyaS}-Nt)hg`=*Sz
zs8u+m+?8Fv)YewWlwz*_xUd6if%HXn*2M5-?Z)tS>|@0m%9<$Y^WDJ4zpd*+yj!46
z`42rd-haron4-?ALkL1f8NnK#6<VgC@;{jF7_SLIFO)tw`hP>KZpO&1u=`CL)US}Y
z7C0t+wajeXFdiHD>hj1(R)>$~FUxk+bSo7#c7UeWj3i^+8eDqTPQM&jE=c!LH@f$`
zNsYsmgp;fXMm4ANE!gXpw_bE=Y+nK0+F$Cpny!Wm4=v&AHsswQH9mpMQ<d7%B?mfs
z=D7V&co3{kubv7MtcKZF0J0s}r|A_CM&#r`_cMZ4@iEW24r|*F%S6N0Evkd>{2*oP
zPH^}>OfKP0M|O+6(dER2(H&>U{e%-U_0;dBG26Y?psa2^iNrmtFrZ&k-5{czLFC&h
zLkuxwG<ZO>et67@zwRE_o=}Iz?kM_(VlTbb#>L5)u*uybmIzFm0|B2hFD))w$ANoK
zL1@&^8#nlW?9FVfol6-H?fqJL<q6O5kw23R?Y+%uv!7RKpftWcS9D$@?QC!S*?N9`
zkw#3<-tr27U|An+{osOw{H#g%1LFwjZ8czNZ*VQEVh|}fv?HB5AP(FYai^$%z*2}j
zppIWU-8w!ANyL0pAEy7#wXT}<&8MjSIO4<2@a+@f0~V^vw+G@DRil;X!kNCQTdw1t
z<IMTw%2A?S4RcuP6>Sc6$X~ic#eXR)+j{S2+W6i6u)BzE1trXwJa|L(bM$-pI6N1^
z%qBo5I?!^q(DS%uYtrpzN?3Q?<g=BtW>mYDg70L!_ma%tR1P%`4rzbp_TD^Rl-`nw
zfE`(<O6__IcA@A{Oz-R*;f87NIu`qWhe?8Mxa;z^j^qK|r4t<FzG`$hq9NM1CqZ4k
zWqxI*8kBv4WRVC^$uKU~nn)^1^hYx5pL0!h=;GphJ~vy{cydX%RGPAfdhKC8PGmdD
zXNeniZ=!$!!Dk!ISX4}|Zp`N{)V?e>DYi;07SO)*=U0Gs9?Ny4W_mzEOuG{-Q+X<+
z5M_~tMvHNr`GoJw>Sx0qq3_yHaRVOmOb%(o$3W2AtJyPHiMT$#FDJoNLXX)4ZT#=q
zwhWj1t9LP@K>&}6<>9r>zjudaE};OE>~s9d6q8K3LG_A#Z->Z+8B2o?ZlVVti@Ycw
zEiS8wc<ciDoN>`c(a=v7k`O%zcNAS8SXHG9fKbFiDkNo<;s@fN?1WgW3Zt+z@Ycav
zL*n-A5OiVd;|<z|xjox<+%^>G4nk-VZmk+$IzJMiC3y7U0n5T)%xt?nl?f(s=RO}I
zgSMYn9<cpvnCIB^BwNfQ=`87o9K&5LrU}O}#2exo@4vLCY|iY|Cg6mbGSJZiNZ%$s
zuvcBRXm1(A90@6QbR;xT7uuRf_^FgZcgR&g47gQ|=E6t5yT6Hd%)i9=?d^PLL;3D%
z{oFo5m+6sDF^{)3Z`vZI(HyAjIR;j?sB3=6w@X5^Ad3)LkxE&<ATm6%60sG?bBC2~
zKX_#_Stc)GkEO*~L({TR$4-pu66LXAx1p;};g-#h_(X_mt7}jD(sGCTa;P_hB4E6V
z$#B=c=-@4J=hUC*)YKliBq^=*F|b`*e&>exMf|Cbs%Pj;W{f&MA$FkVJXF^A)({;S
z*F692gEYY6Lk&K)vC<tYHy_p14^DWT_<=Bqqc|f{V?uJG@FN@nCY%)Xr$ISYgIx-R
zG{c|QcBHm1zCBiQf;L+>tnEW)<|wCrs@5IpRuD|!VYkqxg>8?n|2q({p``MHPn^0L
zJDSv7f9W{@DGlA!!U2K0dvEa8)1j!zX2C920WYx*AT9D>#Ak?2#5{>gLyLR_{1CO>
z$U3&u!7tlGVrQ)DAI2}|r1*EpWkT`y{^}p>1#g}n=aLmh1h^*~R+Y%Kl=T*(?2Ly<
zjb`|#6$w?yx`mJ?bT@<qo$VZ&>_69!W#)tWF!PTLDn`*tWToJnb=7KBJNgNfCTew}
zT=D$s4v{&71`%jUJ*w1BVhSJOb+jCr;U<8U$}T09{kz;>`A)TNN_Qk|SCcOaP2dGt
zN>-$+C+8wse>TOK3Z^;T>AlS<)3;lhPoBqF3ru7Q5*r%qDs-D`PHw(scCTyp^?uXp
zn8dWf{}%w%Kr6qAh4jxbn%gKbh6m;qW6gq8JJ(}%KixIV%&|o?$!x|s0r}%S7*yu)
z#Jg^Pc^UNk-+`?8UsFZ4hNyyelh3eZ5-<BhpU>8-!>U`hqCd3I!rA$N?B4ZBCDWu`
z(8_@I**8)BYeQABxW2MW_>o!S0bHrbWjzQ!-Rhiu-3pEGW?a71Vp*9WB!V-aN?4^g
zFoTSP#dDVFDzb?ccdsBZ8(4iu6~7sBz|9z3_EV56o>H?kOQB$darS9gWU1PEeia?L
zp3zE<R2-kDwK`bVXuo!F!13&A1-#mfXW^7!gU)I0`Gqfq#JlIf&9t{(MKG2mJZy41
zgVwTVm_>H60hD#f$0nr>6B1%1fz@-+)wX44v8K0Id-m_feMj=7nVp6hlUeLph&en{
zR$?Ry1`mE}Z2Au1$$~I{14ElRL5})9dIny#M(TN?d??OOam7r=E2zYy2tdKbQe6T`
zSaaxXo21h2GnM<K;Gb`Liqh^GA~EXFt1|qiMjZY>N~jTp&k<%*vnbxAcBlrVVj?@`
z^AX4110;QYXzO%o7j)A)c&$<xTkO2WAn2zf@UCO+@~i!lR@_y%J^i!oS`ghqW(4p{
zggwV2@vMInTKUnZh_u!W7Xm<>kFWUF@r{+t{w8i)HI}h$G$#on@WA%^Rsqu{wva}u
zt<FXV-ae<;dQ<PLq_y%ji&+3$kTMm9<!<1QO3{l`mfAx1v4I!x%rZyg=~a7esgi+$
z@_A^*yB>4?c%*_oLf-8qvpbQ7di<n+jdb(cS!(fbLKVp2kmMTSZ8ZzMD^U9lz}*tQ
z!;$xW=xQ%p6x&BnCZLIQ-)n{AEW3Ag6`6H4oVU$zt>!2jM<ieh+?1q<(1`&ua52-L
zb64#&n;TS6BMes`Gjd6(=VYi{&G5_2b?YxCGFR;}eBC(SE9z{&rFa&Wk)3YOXe0e1
zKKTAIUqWNs^{lE&?3S&e|JD5{cwm${jzCqE^yBHDYFDt9Yklw#+j{}dD1gZz^K*|;
zz^nH64+9dXjCB71Jl9t->osjri)MV50^U-NI5^|4TIhziG%@*Fh)K}(4myvoO2=_z
zNhu-%=Oi}(8l^s@wz7*$+f{Tw#EeNJ+o+_V<o(i|)K;foquTg?Qn_}vi($hnMBT%5
zBcD#S#n}lgwFVZPNfeBtH_DbwoM88)wY7>{r~4(t!c^ut5Q290BcaIXMQUibcN&G<
zx^Atpu48Pp$VMH1gy8fixD`{UCm8d_&$!h{$D5Je9k!itm!ErlQV-#8UuxTm&UHH<
za}K<Z>rv_2Tq%Ys#Cqcfzc9aP-`msT&%nQp+Q-8m3F(?8&4!$|rYSF>xOtDCZ4~Xe
z$8#F+(hxACjCDNLFHO!ml@-s_V{EA@M_fk39Q)N5(@_BkIO;3$CSUj}XUBP$Yv^7W
z{{X;y&;CrRZT|oTLHO?9^9JzW{rJ280OZP1otcxWnfpU`ZV|qntChY}1i)v8Bv<8s
z!*BQ~PsTqTd`QrIA*|_o40f8PscUCDK@G@}W1dw;C|c=&U>M^djP(Y;Q9MB@n!sM^
z=X1o_bjKijgIy45tC~}vGth$DP`I<WkrlT_P7dM8`g@AW)=~VSa|g@w0$aE}i2k)g
z9d&gZFEs#3qC1&;9C}sRbp*SzYr9n5QwB)SLF#K%li8ZPtLRGe$33XJINq2N1JwJ}
zkfJ)gXuvSRTyfW>VCmOz+{*TWh9_T;`}$PzU7Ksww7DS){D(iLYgn#jb2<C0eA^<r
zg4*LNyCWTP-kGhIjoCtm+MsX%=N^K&KM)yBqe{SuAbsfF55ML)txFA8?RH6a_>3^;
zo`>sBF-}s4QY4y+mqVaec$6vv!Fb27uoUDw*`}SDm<;^g5PAXYT#VYrofXmZq>wf+
zs~!ns{VL0AP@xufOP9u5Cpagk)vGBr7HFd6uFjfipjgDyhA6y|+@8Z9TEmvxY8#t>
zkd`MO-Os<ZP`SMFBU5n4c|0z16ngRMYNz&`unb0auR;$)?af<!@o{61xh`aD+-l6J
z6k3tWuiazU+#g(2%LVW$d8IZG^SM4`KDix76^wNaCh8xvYba1;C~?pe#(xUVidpol
zS?yAM3~pA9w<Cko0~u0jDBni!`I@HGU6r5UZCq-)&WU>0cX4mGmykww{s*_xv?IUL
zwWt~3ZITW`^dSELQS_|q&1UU%zp(2HC5~Vhc5vey{SGUGn)^!A?-TFI3F=n?(E9oj
zRZ=ZAcc+;&=g$`H?|F4S0>@gnx48Qa@H<@Nb9Mg!fqQ$_R=KI)ct=x;dlYMk=K;3$
z1xdpUj_cUeKiLp#kej_C=z@3~zG3t~#3`C}_NAdpnm>tRV{Xlurfya<kDHO4q4er1
zoMjZ0)s^&;{YXw1a*nRr#JPLo_|wGDTv<j**pbMe#LFfRAMGxBSEb66L|NqdJz0nw
ze>2zGyi3G)o*Tc_p&DH8<_Fs(u_wq$`=k~epP;KZ<50bHhVJ3#5sWXJjQjKgh*GO5
z%im7Jt0?lqN-bR;ivB5r-#=lQBw*h*4sZuwsj9H}!%*@~rRps3W0Y~8rz4)Aj>k3U
z@o9HR`L|fxBLPVu{{SpivuUiXV6;Roc~Qy57O0;qdKs=qs#^Ct`&;cA;&7?vS-)^t
zl|M!tkH)C$dZ^MGTc|EKY-C-)sU(q+z~F)0^{!oIj(b*`;FNgSX9M_2#z)h*r$s%7
z*(B2SL{?(N9Pk)7MpM}1kxF=IO*JN?)q0v!tvFgyyt*~ycN%8BqiX*Ezp|Ypasg=A
zfJ1k{J+sn@H5+X{Rt!+><nQWF;a*W~qUp{Jo~oO?rv2T_KP!RTBclv{BD#^I-OFkg
z#`o<JfrVKxe+|R>*0R9XrkAv{^F}WfN^*R+^l0guTd|pj0O`2kkbeq>c&}D5bEH3-
zkDc+HZu~RNdHja|08F3kxA2vIxH#k=LO)9CE$?hEU|7-0NTB6kjN_&cKaEO<Mkegl
zjY}O%#5!>ECymS?o)~A3uUfReAxoK=r)Av7s^Ec&#Y@d&O7h6uV|L^>OnquQuii*6
zEeP617%AxA;Yt+m?l))ImpYdBk~;70yIAue+aot-;C_`A#;vDZpj((50B7X&HO#`(
z$O#F%u*l76pmbcaFwa~LhO5~)8GA&`l-0$Kq_RW2M8^y;2_xKBXl2oF8D7%i6u0oO
z!5`sV#EEHi84Bf)@<`-ozZF5P0DXfejt<jR7<t%S{VaC!>2sCeVnnVv$6VGermHQS
z?2`!~Sq>IO&d}iUtUjK#odi0Ll*_0_`*tuRY4;ps-@O|3#nN6jr5dtFaD2j~WkYu#
z;Y1p4*Si#>*4iB;x{Tj(#5W1Mc5#o?am`=0Z6e_|x;#wWfO1zp$E|ssdbg6y_O6j`
zBP*T2`)xJcX?KxJhM5AX{v<qpMy9HJjWugMk4Dp?x$?n{i>F{tMn1i3qyEdcxVFX&
zZP>8mKAEiT3N_jdqlZ(@-~P8zUz2|oKj5RE8oUMZL&RPR(e#fGwXTt?URXlX!*tR#
zcJn-H&Q|GEu?vzyfu4X@6-udU$!^;pu-Zkaj@o-UhEc|H20&q1_d0!>ISd?(asr;-
zmH3{&@KnE#DsnVW4Cns6?*9P!GKoLnsh=LC8%~Siulw<N{{YFA3c4asT@TnB{XS-P
zks`tl6do&-x_7sB1h6>gALCyEe$KzQ55<3tzZvw8haM%<v|DREL|qvqw2I|rUn1IB
zmuZsbMdhOa<xd&L4>kHDtXWEdz<0;{BD$#0nOzF!DK4TsyvE1a9`$AKjjT%2N{aF?
z1E~w@Yn*uXx40^>`KpK8pQz%Zdu>AIM{9tJ&NiH#qz|AS1$5ahTQ1G&jrecUNj8Vv
zM?To8<-KUJaHA^6fmx<|D-AtuKFaRsGv`J}Ute0MZF>d&l`KvOWcjjt53g#MD6KA9
zi`?i<+_1?ErFTZ-ocAAvX6mxXYqnU|bS`oj91-oB%9~hgJ3C<}OiXdR9V>d;?FthR
zNv-4<Vd<Z(X%})OrlxkCXQb+^=H7Nm0OeQ={vN-DX+7N5mmXcjrOabHeSqp~k^axL
zg7fVb@}lj0?0l&MKDD8#Y2@60YDIG!MEDM&vPY;H>+C9}N>;flYWBIkeqQEC=0HoZ
z<PI^Ktu>0h+gpRT<k$f{NUC~<y{6b$h0{5a*eKnd!#~7o#L?W^6uWstV+6?Eez@;a
z;h|d>^gGEUoLJ3*y9`7&4>bpyd!^k;2xZ5iKPmqJ3d_4KXL}Z*ZrBd{w+>e|Hnpkm
zWxARnxPAS-2&B?ml(*2@hJ8BXCTp<P@&nX1_0ROJXx?~W3Rq7UPzcE9(2C<D7MF1S
zq8=AHBalb2t5%ni+7c|^9P+(K<^5`TQg>o<cICOy-p8ftk$kSP%s5coWcnXk&C+!}
zL&LXUX_hvY-M(2<JAhNyfzq9Ee{ZNjV7reFdNTI@wToe>$z}zm@FygMIs4wn-_nz&
zr1UBilF{sX+0r~Ws#|@&$}Fslym@R!;oqlm=~;<rwzBzFF3gw*WR1xBS0kfpPouD(
zOK{dw$%l;fjGnx9u8USFEc@-<e6n(zxd-v;Ij(x~an+mAvwWzvJ@eTq7g0JMqoEl3
z)@G-BZFLe{Sg<k!^Qq&Hdbee%>NYG4idsBihxwHK4>fH60ApOgn)b2t$;V&Q6%mVN
z$@1=u>ltjX{?72R8(V2p)PIPrYwHiN+%Q&QpPK`o4RRXokkal~QPV%v5)aGKPp8tl
zIlMt0ppncJqu?t8&1j>gnUtEn(IAwS-rczTszz<j6^FP!wVEemGuwvY*MMpbGgg$u
z*0-emt%Pzo$E7RSmafaUHzA}E=LCGfdeqjaw-B?$RJL=D^_{N9Vz>U#jC|4a$M;QU
z+G^3UTavDJ{q48}pX*k))Lji*JG|Y#pLz1La*lt6yAP#gODWSe9rtQ*TWc?2?^kc_
z?DeP(&Y3PI3V(Lo5(jUsbM`uu&v3SvL8S~f;f`C6L*MB~CaG*|1htYoS+pxF)bnhd
z#>_Lp%AcsN8hc$&LAh@=Hmrc{Do)4zIv;xMAh?pr1Za0d*d7^A^{H;>vDGb2q~qn!
z%6$*0^sQDRFqNt;3HDBE(Rz>2?CiB0GpICsl@zK*KfjK1-vhN$it=cpXwg9oe(NtE
ztw-T|w$d-|EKqr<a7t&B=*#cxTQ@g)PNQ^E*!gmf9UtXy%ls>qMN+FMEn@Xd-M?v0
zGLo}eG-tzqWelEc#$g~}xBmcIvbBpfI-*@f2up~=5;_{_r?R#S<js$prfVopMA6%B
z42a|q-*|drw}jLmzKmxVDEp;r89vDrm-DvNxA|0Kl1+3rm%e?nP6i9~_8<Lv&c1sK
zotkR$h)j}?R`ktWidgn6Y5)LmD3zYZQSQyHCC~QFqc8^-5f9D9eFx@ySI=5AAMKkp
zDsXNh<RA8heGh@?YnoDhhd63~|JMB&)Aju;Scymi39-s_ApJTKO}*45(+b_PeW4Wm
z#W-)uyfRHv)&i4F9O#UGW>Oq4<$`OZw((Sp7>iGyE4cBNR?bFx4#a2N*RMPr)vR|$
zs&3Bbr@^afdZ}rw<QuVr83S<q4OcqWixh_5=IiY2cs^<nznJ2@XIAqhkuL4ybwUAg
z%WP&Ofsxpbl*umTy@6Ky%~@9pOJ$Li1I}@fY9)l0siM_*?-Snc>@PI=l39wFWnVN*
zpE5e*f~0VL&uY%_C8S!;yLWMN91?ehQ-O@}lk~-Teu?4@XHV0YMMr}6%@4`Al$8T@
zUD&}G_OD6sYd)KK{jFl;hW`Lt6rP^Mb;r`O`jtvQytUU-s$(h6E}Wg8Pc!H(E-7Pk
z96;>{9e>8ZoiF$(IS2N(@kZb7Y2W%mZC}v!h4bnMY-%A!Y_T0%^X9*u@AxPpqJGwX
zCrJ7P)4%kB+PUiMsyz8$W`30Z&!4fk?8)PQf&LotUx~ahw|braogDX5{kHZ!?sJk=
zE)@oS6aij=fBO*r$(H1W@PuO>PxgkP{{YD~>>si<w6p%r9uJW=;k3l${{XVgKc#xM
zwW8j-vm_h4<v;q=;Y*olRBhEC80r52vZw6P;O%D9#QIN!%okP@$8&8wSJ$#^mL?93
zweAX>Fb4z?>MPT4^(zK)G@0a$*d2mFw3GePdUMcMp<8IOT(cz15j!5d=hC!w9{_lE
zZL>Ii_VAfz2yy0ryXlVp)zfM*T(up&45eGy$s~O7p|zBe$oJD2XLn_cD)i6yy$4ZR
z3vG3x-1#>cCm$=1y2^bI^A+pfBk&HLt6Ih49}mDs8)t=joOc8J!@sXuZjIr@xUn`G
zig~dQ^=?KPeKVg*s(NWHdme+W3bE&hUX1X+Gs=`gIU9+L^xAzh?^{}^xqB!sF5_+c
ziW0*i*BR&Dy<7ej+CB0@S1JzRPZ=Feb=t><p}x}%nI2eDrFiH^_|-+mGkuAil%kKG
zEpM(a%Rs9G#=wk9#TX1@2RYByvh=M={{TxL*;>p<in(a+e)<vFG6DI%we3Ifgh4DT
z6yxR(xL|XRhnkMV!%@K}**5&a+atHP@}gLn#%V3fr-oG^@1f`Rw>FZWHB~d%r~{7t
z<BI1-vt=E`kZ5-PWT1T0w?)TN2d8YB_W3*orfOwn{p>-B5_x2H;DS4ISr;D+>@?_B
z-r3pV+mhgIAe`iSaoV?4@c4$I4|ZLD;21hJXmdGgx^_H(YRC`#Ox5=jm6j|Xp4|wo
z7`06<?Sv_+0g?A6U95jT4PDjz3v&?it^WXcpqEBY8$aVzVel5CdvE^$6Bq-p@VllT
z^Zx+rRZ^oCnosW(>Pts&xU*?uE+cr>?%HSze8jg4_;m;AT-L99;Y}`9vw}(HnNA&I
zQ?rj&JPPXVz7XnmF^jD>MBLaRansWo<27eW_(Oek%-X3aI^#LX^`qFYg~SzUtyGUZ
zi&wY2kri3DebtUmNMB+y2hz1Q4SLRBlthnjw`g6U6u`$^de@@=0K%GzHO-`hcs)-T
z6%hDK_JZGTxWHb@Kc!7oB%Os#u4LqSmYZR0*0O6F9qENJ^NAa7(mN6e{#8Da;w@1#
zD*pha#K-rM&cvg3%D+H;{{TAm%O8T;I<NZ1o!_*dbbbSh=r4W~2(3KW4*26-6+y@Y
ztyMbIBGaoYX|n$SEsZKsoRf6*zkNR)&jr`5bZhI_wLLIBg~G6BSphMe9D=2H43EmX
zi2P>KTrI7{aZ4<#or48i)s*(nq5Lbkv-opj(yhhRjrL|Z@_^dsjFF#Dezn*^;J*jk
zz9ZA&-1#fV&5v`QhPf-t2C80@Zu^_kSe{E&>9OILdcA-~c_S#f`?%$O@;eH2ej@uJ
z%q3?~K33oUHS`po9?`9n$&S_x5r)9y9-oC^UHm!lo}zcgo<qnF+&KJjYe>$Nle=va
z;Uw&pk1w;;G!14K8$=S3^DcPL`z_Gd9`U5k`h3zXh#N7#1jp@>)O%NbsC+imbZ}#I
z^X=KN41Eu?0ng!4Tlha%y+W38tZ0WAKf41xxUQ;nu=0zXs(Xy(m0IwXp$~ZBp}D%y
zonKZ#CA^#mPn#>#aOiRBD`w|Nu)K_FS{$t#H}OWy0s4-a`qyc3;4|bxx`oQjV{q>Z
zAjUZd4b-2gskBc5>6UR4w=&8YpPD9JzlxF6Rx!m;qaIa0YJEGW{zlIUR<-9S^3U*S
z;8OQnirKAik}W|-3J^h%0ow-#s$N`ahSBchL$rK`73M{h{m@tIUXy*`jb0z$>9dJY
z@*9A0?lH&VS)cH&_3N9_HT020sMux$<`}`_*RL6^t5?IqpR|nAMpUr%olj=dGa!tu
zlvcNUnU!}CdlT1j=rL5}Fe?T`L}oizap&^w_}8H79}lnARgS_zDz-r_@|7d5dX9an
zZ4=?*-2JTTlJ0wAp;<WxCkLNun%ImlDwJpbW2c9%q|-;0+)i9%Nt||EsN>(gM;N#&
zCWO0Wj<3o54SfLK4bY>A$K{5|!6La-_<Lm9my;Z=<*41$r?zXFIGQ`X4u?Xet#ifo
z4PxU<)ROCHJ68^X;h8@^)z{CgO19C<ZzB&mLq;~_0DGG3wV#Ey`lLeQ)^<sdU@_y5
zLs-y!Gu5HDjRc7x9AkG%))nZ<d|DVzgx1G3Hm)Z@738Q&o=Yy(C$>#iU3N=FA7z#|
zL)BTbIR5}-{*~#F{4KPSBDeDHBpd}i7wu8nd?vGEIUpX~98&hw6t^qb)M)co)<x)7
zcwiXg8%OARirvx+Uf#oPZvy#@c-qB&zN7H2(&OPgNJ+J9`evfG@FVGyB#Sd6FH*Up
zb@1zEWU%#FxuDMk`i;f2ua`6!CkF=}zlVM+%e61q>-JyqhQE8_UlVv|?K*~|aV6#4
z#`5(G%`+Ew+LsEWa}sbA4?$k1YvH&>(YxNv>_+A%8T!|!+FA>g8OUWAR`vGJdgGoV
zjGru*%ylre66QzEwx6&^?1JsKPYEFhqkX?n81^IWRm1#h{f9qgp9y>)@n4DbFA62S
z{+*#}H<puqy7|1hpth1o-)mg5w$@N^z=P0&EAGn+JJ&d2z#aXogZ-fGn10WGIo#ZT
z!ZE8H4{JGVo}_85nS@mOqxs<dgXHqRYM%_rjNx?-KDHMkzo1=y&Eth`ScHwnRI$M3
zzn{<8GA5t)s_?{1)UKiWSX|fiE8^WV#agwu+2ea;h;j3dfPR(Hf|aH3Jq0Cr>ACa9
zr#v5Nbho}nZ|)8o-=$>3;tSXY#3^#0-E)!$vv9(-X83)q3)yD4@{`O0G%=1sgOW$T
zYR|jyRn?R!C4xxoFdPA#9=RCp?Ox<*;p#qKZA_)@>BpJJLt4+p8e)ytaL$T)mBRt=
zp0u!N7TS!GNul1uBv1S<#5m75BOk40LGa(jcaVLhBzX5{YOx^W-m@d{Pl`1A<<o9m
zp%(z|kxx|}Mt{Pwt45ugjFYjnXEg6^%R270r)kiqhnS?2Zp+EPawq-slbTI6;E~ZZ
zo4Dj;<8FV+BAVV5^7J!ik%6*8=0nS4@$XKX!}bcQwy}j|97tbl6Zj6EwN$L)6%*`H
z*EKmb=^o1pE-qB6jQpynjokD3)((lS+UXjsB46Lz$-3Rke84jdWP6I*)wId3K)=)k
zZzM_Os7(Cc*)-n?_*pd#a2<DREHU|zMxzme4oZ)3J$lz#<Z)-6B{e;UBAE55G#m0W
zG9MyQ>70RDcD^FJ({5Wv*Fj_)B!Fk-?VnLpJX2+H2EV#+NR7k9;NvKH>0NEjzJ;tp
zmlq>Ee1x5+p4=MZQEuq<DoI%se$gyQAi4#u8<da^pF%&8r#VZAN-Ha`d>)_SS+k|a
znTuG75Xq9C#*Or-;PYdB+ahD1O~3tqDE4x9x)ZW{j<cHQPMT3{*xbA?Rc~X@TIeCw
zZEkJDyT=@Z?*a%Q{cA167wTh@1%^PRq>-K*9gSC#&idXzHRB<B_o}B#GLDSSq@<;)
zEnRq<PlzmWM&uKNAw3VZU(_x<f~NviV5B#GM+9-(y>qq}+Fql59iFW16i#v(TW?Qu
z#d;pM;MDsBcNzg$#9W1W0S}MItxKltYY8jaoYk%8+8^z4A1*<UF+lkw9;9|P)=w^%
zsAxB39fY>a=IrP}?~hT6=By-+EiPMqLNn$&lIXY$52v@aQ(Z?*hHxV?Zj2NTafb)^
zTn@gOrzp0ZWVB*ZwA7=cM`fj@)wY)%?Y!;exZG1b9Q4L>R|cTg&%CzMFn&>y`hK+!
zfv&7>ZROCl3<&pT44h$4@aNPXmDSyNTG}SrJD(`9?S{bYef{f|%7h@*Ni}1&Ms+5+
z7Uw3SO-2cA;mD3&Hu3;`@r>0Ot?q0}MIvo(JM;Nhtvq@oL^Ml%u!6gg{{VRVXQ>tE
zdfnB))Y{Kb2<9VzagN=7wT2q1uTn}YMX+LN`xqw366huRG>UdGJp0u+mvcypSe%jv
zsHo+h-2%&yO6LPTYL|y)d)x0c?kE)QBm2IU(<)ln;!<|GwPk$-bNN%Z?j3xx4@{cI
zjUP|ClFn`b`G=t$s~Sl}w|{6V6&=_h9Re?3{{USNtzXFPaFL=HA1|0Qgd7jfojARu
zjU_I|(mPw+t;8n{oZxrI_|;8y9>(t9UegFEBjJ&{D|a7_VkW58<UR4qXC<-Hx2`oC
zVW-RG?Uoi@+;j9lQBB4vUct>JFEed?Nn?Bh9LD|8vH(8K$g2l-&C1b4qo(&6&$+G^
z%k27vxt_vhRs)oAxP$ccqjko?Z+mK9K2G?UfKU6Uiq2SiZ*7fMBBIdjB(b`ZXs;~g
zjycff8JhvUf#_<swWu}a&}+!n8<B-zhpF}+p5WIXrcHHc6I#u=Vk}G{j#+>D)$6)<
zgJ9Nlc(pMzrOW4YvD^+lt0__CinBF^S1D?aO|@yB7_BbKOB1#MCj=hYH0iHw?Cwpr
zqc@Uv?uJPQLG>9N@lk7@8^6)w`yQyO*4HGcmLrm<vFXKf_WGnM(pl@n&WHV6PQ$2P
zH)E(i;=1WZE}f*zY9^-KQPF7Horb6+`wXF-3CKNuy!8g1b*sIl%G;(UZMh%=Ts7Z@
z^pv?xLqWTe+CXxQzr0`Z#dH?l9q|UR;qGp2OZJnLC)*S%QeNzlgsR5pb>Y2jn_sX<
z%dYFFLjZW?#eHA)m-RLAhJ&Z-x<89_>rF{n7~R8yGqr~G<Q{WhV&wk-lm7tLt~$_4
zMe|={|JVEe_u<}zzd!Hfh#as7ECB3w@;<fF>5zD9NwjNyGSVq6*>O6bo<ckC2Mpez
zgT-iz;|m8=!yMsP)sKI`bo^>N*^*@vDhTBVB%lDd<Ll{L;xQF!*~dnYew7%*N3$?*
z`~`6tdF+$SvY3LhA>53ortBV|dedyY3vMwRk^^?_U5A9cOLyT#l_!YL1o$kV;H!Fp
z(*$)C_Sd1*43b17wG77tZ%p<C^**)V{h?B<S30veV}x|)?&^;_)&3V;rlk+|RH0&(
zNt4VWRd(YflhhwUUeDn_320h8alCGBW?0FRM*D#O0J6WPPg>@sNv_czY0bb-xw$@L
z_^)BxBD-BDY9`ph$;Nm*;-bDg6ymA9mu|kKeOiN)snpT%_MQ9LRgTgej2@tNujgC-
z3KtlQ_PFs(YtSa0{{W;G)%_&FY|kjUwj(=m!@fm-HsA12wlDi!_@$5cH1GW&wyr9(
z<~gdXAGP1H=C1Mn%{~#fb;ufSRQm}ZiuY^Xe##6y%<<r4GW^{?QC~iO$uej5W$<bu
zg6pRoFdUEGzv1{-sp~q8p6$AX3h*uqvgKR@!RT?p>sKCkAKkk@J&mZwIytMOrqN@H
z>%;PRhBa%sB;ADCM#txOLU&`eJ59Z{f;3rUmAT}yob$sBdV1B53Tk!`SS6O3cNvZ%
zSScSrKBqlL(wpO-9^3dMz&hWKTHU6HMAL5dDG^8yB3i`rGO0WQ`$);GX4^~KZgEw>
zIZjRwLsLf5jlY~>$cfu1zS%#nD*EY6<iw;%qozpQzTWluTHo+jKZjDp5>E)~p}Uoy
z4u3&e7a#Cf9|yv7r}!rNPx|S79Dm9etrcEXXqwWrB9+hAJ54>7aF-(+K?6UfW$N~2
z$wdV1?kn<Kf5Bk1$81mF39<d*s;NJh*vIp)TJSIY6+8BR@iW6^<4r$S@bt1i*OlW-
zyI9w=86s~u`+)}@^$KluE!0ii{T<S*Y;Ux+wUKc&Sp~b~p1$I_D74}Ipz+)<&Y_h@
zxII1d+O@nJ;=c!cK+xjxU&Gy2(^RsM;chJDSyMO+QHqnbz+t%?47ub3U9p1N`N5tr
zlApT6gOTq_SLRb)4e4SzV&L{?5ZZZU-l36>GT7&ipI^qOYbK2dnRiDZ!-2+q$n1S9
z;}6^0_M`E4?9Jf~9?!tm@SR`8Qe8(C)ynyi+ed1#?`4l5G@GVPo3f(^a<@wSzxZ+g
z00lDrqx?JK+igR~*Vg_a(oBlCc2e3~+p&nS-eQv8IEu&@J4^0do>bJy-SaI5u?jxy
zwLf_$@Vg@U>O_HCjlX;W&m+I#QAnDGGC(WFHl9!C#d=`2Nh1<M%E&-?-A+Rx<Pd!;
z3iid~lzBZHp!cix(x}^>=CG24Qi;VS&Ehb|I5^`O&;I~gwe9Y1Buq-BkFfq#(WaYa
z6bT7I$0YiigusmDV);sR$E`1EQZM2*bEPG2Qyw`iUQZ_Daz9)*8Tw|gNfn|;`^}&p
zn~z$W>cTsLzThhn-@QQ|pAG>W<AIZ$R`JBmtxkDD9SX&5kIN1>W3lZ`wY=2rCRNju
z%>EsNp<i=W-V0bk`$)*^h3iqpeFO?cyiBFLlfV_tG~2nfYQ-x(j63;nERtKVC<%9b
zshp}4-|*}zrHZW3EVJ&CM&!GkXa4}LS2Y0FldNuXm?W+^`k$>qaDL14$Z*Zk6m%Vc
zs*GLkY3-9%LvJ2X>1!IOXXVK!0cvHoRk&7FB!isythkk>e>!&gk0W>K`F5_#z(Vnl
zHw3Ou?@)ijof#<9YDRdAx@n@k4{;2^+hmG4=OKsZ-mlN6uC2c6(*e`~QM&$o*TWyT
zZ|yVUU)k5fdPTp1t)V)nh@`qmZmw6%OK7cDJ?^QH8bZ?{P1#X{m2Q>!t?<kK3QhY%
z_*2E!+Lw*5ZT0;q$g6i{C9S=?7>f<&DJ|oPmO#1MTXN^Bno07zEsj}Y7M;)CYtoj}
znI?$tUKx55io<2t80pdij7fMUO~WUt9D)hQV_w~JZKms1g6k_ROgAg3?0bXst||>4
z+su~Y+<c|@`VaA~VL3zGX*BztDy%9lX4-2*gN9qFL59jR8xC1P`@`5aaDB3KT5$M`
z+&0ZZHjVN=aJdJkQTTNg(cD<g3~zU45J(0|BRR)E)2&mnwY9yr^A&LvhwgVn-v_A0
zDzs@kRHCirxm2+-vyEqd&1lJ`xRlvF#hV8z3-Gukbv@~PJt9cA0yDWMZTLMwJwdBd
zXcszW+p8z@V^flZatHMlKAU4__LBAl#!p2f82<no##G{@tgOvGvF<*ol1(~BB(HJO
zszqeR1uerV!(ew6*~1L2g=P7@dsPpy$qej`{6y#9G&wyvk&39bvpInogq35#>M_(B
zfJ!56rE`y7E2g=#W`&WsBcZHTvbi7!Qrvc>e$3HSO=^l)E|Mr4HkCQ#=B19}K)cwH
z{>bL73s8&5VBJWm7SnzS&#x70T}X3FL9yJjkDZ&IpwdXz@*kZPll-agX@S%8ta;U;
zV!%^z>LpQJzSg#9q<kKuCz^vvxmfNV*%M%mxWULjg=4;|&>m2?JRIOwhKZ<J>JqV4
z5z4K#(;%O(y$Nr05}&)HJwnFdtUIJ6VM*iCyu0J|<4OBI_~<ymZwu<!$8YpGv0d!!
z_I9{&%%ye#+*g|XdD$QA_2Z4lE_^YoaoB&M%S}Q&t-a0~vUkw@1^)noeYsEC-@>j2
za5Wx3?jLhs(NBmbu~uoWAI@b2q(I~kd=4{T&R^^l?H}z~;f7PR?bJE?*j(5252y$&
zEne#F#9`0w_Z~<cPtvu+LCM)OD9J@#oZZKZbkV1sQ9%J_5V*%H&p)MgFx*2tYjI>m
zisNu)=OFj5mTqq$w($kwb}Ogc=XUIYoa8op9P%qx*Tgp(PLkR{xr}9xmPCUkPfP%7
z(T){Vg1+Xj6yc-T_AOJz@x;!SXvMAEd5quc%J&|*t`_U!7MXqGL2r6m1Y|^spM?d#
zmhW2eokzqM^FNX{sA&|qPJ{!{`sSC!Qd{b}%;c`vz)1RI057MguKYF%61-147mSrY
zSuW?(%@lh?a{^V47@j)x{40wd8q_DaXiEf5$L3}LV?1@Q8PPmNt=LSuWLL5oBv0HK
zb0kvWbQ`h(_U&HN@bAQuc&kj2Z&WSSvuu$@IrPBo>0Gs`)x*0pzYmC`iC0FIuYzpj
zlu0zLH)F;J;auLWp<QTqctFb=9nrY${{ZT*N0LQ~VF+#47|Rj&A{~Z%v8$2FFCuFz
z_{a>T1}8Zuzdh@h>wK>B)g7x18R(JYy2Zp=k+z2K9%&^;c*y+!07{Qf({X$Koo^W2
z{QQ9D>s!7n@cLR=tCq&r4;+erh;#&3dEu>LEj0DM)8R*+b;C+j0KZe$t#-z&WWCmL
z)TH5N(TOgFYAlxCc=CvTO~(bKJ%_z6m8IzymY23V%u5`Ifz|QKl|4b~c<L(Nr93wF
z@@xJgounv!M2G-O9?RGh>)O2X`^A^HAjt$CQW1@W?`(ZWGJchWXyRtyBX&j3wFtxC
z-_Y%4@WqY2sJOOJ$`^1Wy-||x($!WeSL9=`o}g!@eT`KA0EG8Tu(q~}2q(B;Kv=ez
z$!v04JY(o<lGQv%cd5rb?`*d5M{yY{4o|KRY9U3bw=KikryU2!ZKp-4U1*ollN_Wx
zEJjC8Kt7_L{uRE49hQ>2DCJR`3J2D+8vYx<^mspcB1yc0ag6t_hQj99<|bGF0BEyy
z`gX0V#kHx7Wy<c2$vhpXr-vZ9n&DPL#3}>3a6YUlz8Ua-mp-RGuDFdnv5m<Rx0Z+T
z9GYyOW!Q}o+k!||WBL7R5ve_m>{rFkBVfmz@%q+rtx-eS^h1<l<mPoAAknQY<%0K3
ziDFVra<4l90AujS71wD<tz<2yU<Nq(w+wbFPp6@-cH;is(4w%G-r;v-9>@7GHCoC`
zi+m--i6j930IUue<FI81Aoi{qRJ)$5=6VsP;|MmN$lAZq=9ny!M;z#<<#cb*j>frX
z@YCO2$uyD(j{pgCoDM$sdh3!YARlPc<!LQs=*ZlI=)mWZP-?<WI>|M|C!cQY6~;d4
zKBBRs8nmXRPftUlq^ZshGt}X4^bJc*m0lJ5L}YMKX9V%cuRi;Bt>H_S*#wVkAS%LT
zW$V`ly}dQ4+Ap+0vQ<AZ3jBfeJt@g~1@v3u0zlnkVmE#&cs1E9O<6zQZg4s@qkc_~
z54*Xvzk5iu@_fC8r0l_auj5rF)Si2vlxHUjqnrWLHR*RcOKCd8>XF;GlOpXtQjDqk
zd;3+@)s->j*hH8Z+{uO_x}#bUPn^P3WZL9BS@r1bZv2f&13>&7{pJ4v5myrO+rs*k
zbGk@$`=OR)0nlf-ApT;z{{Rr^T2F|q9i)I;%Zx0M<Tg)Zj-tG_O&7&FPHc=HX}2H@
zCIXS$8R*s1M!gy6nZ;g>Zr^byiL|?Y4r_gHLYzr!cb0|MJCv_gBCp%{mg?rn`Kk=6
zyR-Z(2dC4xs9RIhuY5fI8@RW+Td17N0(ounvFbC>gIx3%5oz}6E!Z(gq=1L-A5cYa
zLj^)ge6zFs1zQnOMw@Z+Jp;nOXxA<-E-s@UL@F4t;0E;keJj%ZHRDM1=3O5AZH1fn
za(ZXi*w>$UTUW5Q(I&9qODh849+~Niqkj*aLa`f{jAp!wu;z<No}4Gj^+(bg#1Lwl
ze70<-%Vo$TuJ2J?1b+<dRg&iNR(Jm9Fu3%q{{RiO8@*php3>>ejcnUSNY38hTvuiG
zORYc{EnQKRa9HD?*EQwJt$KgEPeatF8dF}^spH-(@RyeM-|&qtW|6$Mgz>lm_p{iK
z*1Xqc<Tq_+C{ZGk8A-ty_pfl&uC>n&>PscuNp*0lSSmOibkEkjpHa5D)HRtrLH1E?
zJb;#r@(A_y$@Q;e1B;C+DPlXGMqz`MTHN$LX8!<(Z?zjeV@_FQW?#44D8z@2(x~Wq
z{RMr8_UV0J^{<6=eL~Xv#u`LujRVD~+hK|1pD}U>^gWGz&Aw0X{*{J0y*X9=-l)$H
zg{Y~neHs7I{SuqRx)hDGO-W=p$qEJl=m}%gnr@$}SnBH~y_}{y#RRRq;~rogm4{mM
zG%-AD8IDkZjq!tp<JZ0gH&(T4nIo1LSeP&F$R9RGM&qDU(!JG%gmp;nmNu)hI~(0{
z7t`ign8k4s-Q`MnBpyb6G5FU+nv_>|5b74g$`>3ca!FC&r%yraUNd`rbn(G?bOSW#
z-eZySxkf3HzKjPyok<<et)f6=(k@(BJAlYQklVWq<L1s({{RW}>s#Womgz>VH=|md
zbTf3TN{wZz?0epetm$_4<`$iN<zip1IsWe+mFQNw(y}Mnrq2EbdLQtwoHXro#SnRN
zvA4Wd{x2*cVf9i)de(t{*H>{Tl+iXxD&S+MVmjBAR+ONWWVSo1RgBw|8SFL}mru#T
zBh-Id{&0Wbp)xeTwZDp8zyzk9)2IA+TUYdRX=!<!l*gtHYx%|h0D^=g$NO9Op&zJC
zJO2PkEvu4gBa)%}EBh#4c|Wr+gW^mt{7X*vJ%^b6E4<Zit^Aqpr7tSHW)xIkl#(!5
z=clc4-?CPn70>L&;P;bp2Twc$f&Q`U*ELNeNHF;`#QQlp4ng$e`Qo>wI&f*s=%X1)
zS|=IdtBr+x$W~Px0@&Y=sIPI-?kw)2HkVL`f>E)N@W81UAdFyx>FHi~;lBrIQP1Z~
za_pTz925BTuVAp!bmWXQO5uAFdi^U*OsGxXF6J#%hK&3`{{Vued>8mt@Y>(P-X-{b
zp;>76TD8sUUYiv1W0XRWl>O3?5|CMmDxjS6n*8PcoqRi@_`BoZgM2OGJ9WF$^&NV`
z*6Q8!wn$~270}>=hEhiczgmCbup38({0bCf7n+a$i?^@K57|flPHVs!{{W$zP5%JW
znCpPW?te#m@9ZJ_ChK;F&VL11!lep_wRx@NT=xwWib)>%uf^}%XZ8=&KW7Nj;s=1|
zxYYb7aVUmUI0oXvSK$_BP@&?G@R9`>Z;^&dm;RT>2DpET-T=|QDf~UwejRv$RjoCf
zDQz6GhuCCN5<m~}M)-9-m;>6K$7A_2_yzkyd_(=6ylD@L{1J5G26X#=oa}zjc@myd
zMdW7$>~J?BIR~ge)n~$gh`t&4$MEk+_?h7*X0X+*#PO@}@wg=<iSfqxq*g}8MnS<N
z2EUHoPfoegH7kuK^>)o`Gr=PIJZx2m;6;As{{X=s{vpqM;lCFAD!q`hSlnCc)@XUa
zxD6EeJ%BMpaq7hS(QzZ~FWVRPOw#`VYM%w^`eD=#m#k>gO*Wl&$YqF$l|+$Bft2i!
zqI}~lyK({LPv>XDPxvHni<8Bc_dgXpN2fNA1dAlFX=^l-PO89|%Sw?fL56a}c^!v2
z7$3Bc?cw`q_&>$khLLZi{{X@prrO_6YiDsLl;y4?M`0V8Z<a{hx-Q^jMFesMe1G9z
z+Vl3E*M1@B^6EZ0*BDEwTL+0QS~yrVtCcXO*ou+CQn(|GVx7>B>YUpu;bTx0M%}#z
z1z}&?IhlCjPDOlq`*Z960JX1$JUOHIllEZq3>Jd&O?hqY?QW*o`;W7-MR1Zk1^xE#
zDM=g_;Bol0{{Zk&pW7M!_luPO0NgW8{{ZA#lT}j3^y2o55X*6z&uey({$3Y6SL3(r
zSMjg*==lEt@fT0l^{qv({1s<(@aq>ZXQ$i5O2k-1j*uj+CgzNh8OQ_{z%}+)?Zd5p
z)P520R-59V*{HImlU&QLc~Dy1N+1e`CRpxK%PO7a{Ix6w3E&)4cN(i?eRy>J$C{cg
zKLY?)^L61r+n@IA@z%d@;ZFtY)9RX)*<l5xrjw`3E9_G3g<C>!s->9p01|i=`;Fjl
zi2g44Kl?FSd`JDQZkF3yu!4DYW>tfGyWC0kXroJQu(Yiai2|lRZ@hl-rE&XPpFm5b
zBmBgF_3EUWOnAXj^u>QZUmCyQr+*#%d#X=q@Xkp5C!><_#IQz_UdV@|Nj!?%AGq>D
zZO$^-&2!!&i~b5h;a`W#@q@%#pNs8uCW$9E52-O|;(xmGTM3NOp~&Up00J|?r*eIv
z{R~~{xcj^e<m0Kub8~5O&pDN{8S(-21F$vtYw$b%3Tg3&!P?EXo$-O!#M&b=M>L*P
zabLk01a68*#AzuR+*VLa;~Uo`*Y0h<igZsC_;T08UK+Stt1U*|C$yeC?UE@}GPuqN
z$UP4uz713xY}OT3jDbD1jpEyc6O;GbsZ(7wt0It8XL0#dbv4LaTVaZ*IOuWI{x#a^
ztcrIJzm1@F7_DU(^#@W<W9Kj0)Amo({{U)F2UzL<0NNkfdd7_;vg!8>hDd*Wsw9d<
z45w?9Pn=|~+<<up;6H~S@JWvr!{V4O{wjEjPHiFy7D-^z#L`VVs{&))EO9JBhH}Gs
z9fxto{CxeoKWv{0c%Ju5i%$OlggVL#sbIE|^tRnPE4P;BOXZauvJ8TXppHPVmV7Py
zS^m=c=fn*nO-IK1+X;0mVDT-}M+*j(a;6m98B@@yToJ}GST?S6w|A-iLVbf|eq4Vl
zk&UCKDkwDKu5pYJTwllU7x?Gl*TM~J#(xcThSzo57~*R^IboJ_F?pkn{{VFcC2*0m
zISNM$$;NB>>iCiScYfHn`h~`kuIkfxahbO@m930+az+n88c5B`kF1>c%{JDBdq$7w
z?tMFx8e|Um=zXdPG+}M~$cM}PL;U(z;0Nrn`*HZk;m?TtN8-PY9w63yM{JrLi>B#!
zS8_}z)EeU4Mi<O1v0^_kyW@lh0ge>;uf)IGTlTZ@?ZNRpn$Dr1X|l0fc(inTD`X$t
zSy%UxA9s<D&~Z&qpt;ZKZYVA+{$%;1euL1|vFV>^#loQE^V|HI{$;)!e%}88w8z8U
zUJJ{w5KpezBZEDTt&4?~2UYV}8Ia?nD~@`CIIrnD;OE4B3*!&NJs;w4g~KhDv1}(;
z-<FNkl$ItuWuuW<2^<nKIH}R-%T-@P!oO?3j$R}En!XM{#7_)aTWS}teFcn3dR)SW
zQ0!fm3cFw-0Yfib=Zqi0{{RvG-Twf!PmHyzd)vPiPiC=98aOogp|*@=fx`&UG>gYc
z3vu;1?tg1vi$Atc#N9GkJ~L<<uZR3A9qO1ZTT7bG$z}kod7fzZ$mC>=@)4Zv0C8Ue
z!ekR6hb6-exEbd>aY??0a+7Z7_JMV1uMtDg)?Km*RK!QjoE&!@jeZQj$A9=LndS7~
z6o26@7yketSMC1*fpuL*!{L^b;=hOz3rn@pZLal+F2dXSQiv8yhLv!O7S`NYU<JY8
zVxKCKG>ul7)|)rZ;Glb&s@Cdc+u!=v;;-!y{{RI_@m`^;+<0^LcY-ZHPlfgkX7Uj&
z%;kEtP{<5%MtR4WuEb!FhblgK@Rq6n00kBJ%co1@PX=H3y86ktOZJOfmb8KJc@Zdh
zk??<pH9o?G?V<@)x&0MSZ7K|f_O4d`A_Eo}$E|-l-X8t7e`}9~`uzGvxvt*a&1pG`
z^G?^I6S}--4IBB(=f*)8Az4j+zkbXA0JblLKWR-iO&<E)be|GhN9Ijr8+3A@>m&?$
zAa_&B`jO3P87Aycv_0njHI+$B9OI>P9vhxJyX(7X+YE;*yMvzm*GJ+xAdHs*m529u
zt|LvE7HO{GM)M>gRGeccsn1RYcR~roJ+1aVo5S)O&63YCJ8}YoS-&&Dte=m9*Zvvz
z#{w|K{{X@<t8!2MIm=V%&|T^As=SM}{{XlF$KzPv9f&`({{W4^X5+&;ul|wCSk<JW
zwmIioPIgTE3H^g45q{NP7?7xJE}_Bc{{S8rHT%)5+{HRga^+;0I0a8=*RUVmHTWm{
z2{dW@Q+Q>iBq&`&Bz6A)j|-apz448np=~L?v0}^DdUYRBRf2rFm|_!`xpT_3i<_Hk
z*ON{jU{b@%IQfQp5PI?XS3KIau?A<bv|*50T|`d3vyt!kR&JxL+-jQKdTh}m%H*a-
zR1$j;&}Y_?;?C~L{>yo}3CfN#q<UxiX1<P`=~YQ5uWqN5<BXv$X(w;MZO@q<+G@IN
zTV6L-F-d|r<aFSQ(7n7*6J5R4vIr(0IfU_+@6Uf@O|X_RWZG50RV9N)=cAF(F+B*Y
z9#pooslBrb<CK1*^B%Q>bEP|{q3TnNaTJoC=MgOPBeW+mNfe%9o|{;A1JL_dpm;Z3
zxY4{wy2MedLv%d5g-!yw_4=Mcu8Q5XJyAuy^|BXkGn}8m^{hA1Ec`#JL3~+`p3^LI
z0y$89eL(fC=Te<`T0~K!Ix@OFo_`)b=C`%5jYdB9@K3qLZD==;Yx+sNw8*-NikP#X
zo3;m`uQJ#4M6|g6)6!qgwO|=a9mRcd>rU`Cw7NHnZD&>@8J}c?@G#z^)E`Rnt6@2*
z$)|RYYB;`GE^RJ%I=l<w7>0?eKiKXPRo+O!Ipa94#XKLRcvr(xO(o-8h)K%hW?1xI
zr;mD*#Me3<^rlT>*`+a^q2l4rKT<228n4)NE4v$OS#DBZQr=Zzf$qa5yu3A8U0C&T
z6uF^xd4{cZsa#AiZiBQ++gSz(MLpCH{{UTAx6<_s)roEMF_7(zQE*$(;~mF(rheP0
zZbj3j2)$M`Vib??ub_S$+J9!)$>I$~3}}iJd8@R6=sTSD0=+euOA8rJ8r<{hVCiBK
zjQSn}t7v{F@UMxiq`$UlA(S*l2P88Oy^;A;R>SSO#omc^jkf^EfOX1_E9uMYukFbs
z)^&Ii3m8EgUAC1`-#^|bpcV1=iY)JRopE(ba>$dhlHOeKdFbDTJO(OQhbh$G`q;-4
zN;HyD(6lUdOIgLe#knk;Dci^%zUH<x?GsRjGdrU(k+%cRK=&O5NnzuZ)ZklPR5iTg
z<r4n@19tn}jci7@vdkXd`cM>OXdsMzMRrMRWO1o=W^A+ESjfL>R%AQ?Pk-l%+O)HZ
z>dH&TX2TCH2VzfNy>U*u(w|giOWT}n<v>6`GgaklyP0CsG;-k~U8ASo?%C=1*PlLU
zH!kO07Nw`&dIyKDba}Kj(k_2<cWO&y@L5~lt@$1vypY5uMVI@_{YOgWw7nMA#l_wI
z>LmRzK43G~C*Hc$SyD%j5xT}QN`Zslx2<zlwXas4&e|!-{^qTVHkvlCU>$6oW{uPm
zzN6P2Gx*j0IgF9qX?Nc&Qv;agQb0dmqNqnD;bZm)+#GZO{+QyoS5}JTq*{pb<v78A
zk%=9%(Ec^&%1fD4qjh7wb0pzarg>^<GwGL&Z*Z%+K7eNgb~)}VMP#*>LbIK(+ja-*
z-|??X)O4FoQB~y)9I=h6!10c~JJ%(3;p^B}dc?CvJAt?luW??@D)ezFYiV^yk6Mjd
za&2o99i+I0KuH--@Dg$l(Ek8KTDE$X#FqPR<J`lej9}4k;X8ZCS~9a+NO>woQ`q41
zisafGy*laa<t-esH{K&4f$i^0+0H)$DlRs)NSFLTB?yySqlo_i<)bJcm$3J)Y6;EN
z<Zw+OPcY$51{8jk*TtwM#Ae#@LJ0u@)b2+1>6JfP;q_f8+dq*ub|0E4SEwSL7tisl
zG$}=0I6V&L>qKa+j5cz<@;>oBPo@V!SvH;zu<;{Jb*(_FKl-GRe}E59QJT)V*F?H6
zm2;K7jA0oMQPX$$itKzHYpGj!eh0c4GBXefIV+wI9D`Y6>P|G&>Ah}igpD~(#!)_h
z)im3U2T*-BOoJO>G3SoQ=~jN*adB@fue8en!zk)k`@+57#Qq&VDYpj27tV>fB#e>h
zdLPVJl<VFR)x0~VW<^Do?D<O0+tonoPCu1xLlH`}RIg{)!o4b#=K8MX9|dby5Nb0+
zZc570v&70eFdYvy?K&Th^h>=dF16WO-spU?ssr<|?lJ4$yvM^@O@@UK8-&*^xsqla
z0qCQ)YL%R}ZEbK~H!l>0wu}R`o)0`?uC*#tl1+|;=LIPv&~?p!!#9$x&7{Sca4@Gl
z`jgFR=n&ao+CAd!a?gy8a0hDg4>~E-!m+Ro+3-i{SekVD=Z3Yk77~E6g2o_lu004p
z&Y{w3&>F-=>d&O~?+y5B=U=icon=&8UAKkPLJLKUJG4ll1efAa=!?6%I}~>fQrx}8
z(&83^1$TES?(XgcOK-k0?r%oO87JrLz4lylJ+moyiJQw%V&sDwg|o9X9dz4R`X9h*
z5#;qSKh#ck+0`_6^JL4y)*I)6QBT0s<jmcen|2$q{10%L4Nh~LQBj(>B79hGM&@)q
zy3VW3o)Z2bkfD9$-wKTP*~#YEs#BxV@BK1Hj}zrQ(lBIR-AL25aJym6yZ6P2Zl;1*
zwr-ZhnRDfc#s#f?$`yp7&GKw*TEdd@F?mfVd_OLOv67$eb{u%TAKuYIrdu1ery8;k
zo6wo^=uQ&Pvm+i(S(Y->*)#C(^e+aVzlwdFTk=l%ud!j-S#9=_@8|2A6WNVhekc%G
zsW0iyqVTr$6rn2fKV*myRxwL&*+*W|MBakl)7j$ppt?%+n^$qTYJVspL*mLqbQBDM
z_U^62g;^%Y!^2;k9#?Lbh05Eg55Y>=r0GiJpWfhtS|-UK8@@I+%$%CEt6&nL5}oF@
zi5zbyVCx-b=)Gyx6FFgbtAq8`HK~mb?4kU*+iHvH^6l%-VhYDsVSj^(UzGbPWmoac
z{mI77uXtYYd&zGr>1SW<Yovwx4VK9|Y$94l;DP51blSbtk%vv9slSk6{>Jfmts^G^
z&P3t#Qt_-Z@RaF=NYvMk(B!%0bw!>{#4O0B#1><^x;hT1K{wyiPMV8qi}k~kxA16j
z3MgqxL}42pHA21XwZ=cp%)}8B&th{TzI(N1R&nV}T_QEBcAJ>_zG~HrU8Yvm(g6j-
z69gNl<}H-g0!XsqamAUD2c`ghJla&N{<l!08anf!Ur(ijP_3zbA3*%csrkL^_C=xK
zmsuh;C|~cyFby2I$=JR2h7kec#eE@p6R3+AHokH*LJIWN|F7lT0udNDC;p5Z|Cxrc
z^U}PKI2osR)MPwo&j*Q}SWSa&iUd1%{lN2sl&Y{=#c|-zOK2<)_?5Sq=4EB;GRsZO
zXK1kiAT<~>kvll~E#S<ViR}4@zkkd0IYQ8T>PeOqmeuQ9kcj(eHSdbl2L@!Ie?f41
z^hPAnV{9wj_||v<#p`L^Hp1z!xr5O|=)pr#Zd)DsS6w&Y=s&=@5V2S(bd?=WkJhtz
zf)uXSqxljOo5RN1Gi*O@1HVI9;8b0<=47{OXy3%qZ$WUN*K?Ls8ghP2u^5qTG=F`4
zagC6E!9;X>f*%EdkN@i_HqgNPAxxl|X<MXYCIj~T>@UK9c^4kQE2R8FnTicw;^cP(
zK1jVZwxi(&)6}s+f7?zz=t-5VFD<E{VAS8U^!B#qeoPhq#wesgFxOGu%JyX+toMw}
zgbMlnjI1ES5-xZH>1^Mh&kEDf8GiYj?)R=9GPq&!U_sx+h*5&5JHdjFAl@-t+Sq{c
zJSIXjQ-6y?_2MLkbinY(TC`D3s#nQxt<0olB6uH>OTim7cwQiJ;7IdLeKP5}$7wTB
zDS+Au#b?zQl+S`}1`o`DcstNU>5jzE+dm(O;or3N8zS3BMUo8ugK5<)|HV&9>Ec%N
zK7K>~D;4;F<t5Dkf)MVsu{d`(Jffti&fzHf{qeWf50l%z=KjOrDRnpr-U_ITUKA^!
zR&%~x=xB16mE?&+!AeCs>w_+vq3jM9V|q}0o;2C9DyYi{!EofXrHtuM(~y)rPVr@)
z%^Y=0)-qY%TOJ48qEonxc!17{EkZYWJ31#qsHx$0(dBODR?xta(Do}c%SNGyHEMgV
z(*0cw?!G{Bq}-+hfqr}`X4!8NhzH^5wu%liHc5z`!_47-O>acCUlN~bioMV8GA^7V
zcs-&{DKzBEe`HbX_gO()j~+J4S^j(P8ybzM6qfP-xRrow47b*9skc{@nzI)QyH@{M
zED<kwuf_WukC^Dd_}<R<Z1OjdX#J>Tp|nDl1sJdsP(0!uF8kic$U?t$;49eQ+|#EY
zc&qsjuo18Nd*Ycz4RnV253qXkBap7@RU~DQ{UagrSNDV`@AuNwvgYI#e)ybs#Fx&A
zWbEoX0vOw|_Nf=B%KIEa8wr!kpS|EQo&NwO?Ee5>^9%m~0MU1gh_QbF9O<6MTe=Ff
zJNy0SC$Ytbx<4AY?4ubJnqSAS!FEfj0lO~wD`St9l8xP=A!FwUBKGA|QU^U%=Uqhm
z^(0AaMtcrrF$jns@%?Qjq8~2FTq$^4SyY)_Fu-S(mvq7ROr*p9j2v$4+($6SD0SQ(
z`Wc6UaQe?9$=~13vWnffYM+xl6KRybWbr^cZn&2~hYY)cxc&nF0Lfv${{a@%kV=j;
zgY$^1F4FvsEa~^`5g0%Ifb1!ry{1mYZVW*Rp_RY~r2Q=9e<NE9>&TC=_51@!W|C4C
zKNBo(IEA+hA|?|L1G)ZdoVt<!%o`IU`&MhGk6u-p04Gd%NHd$7Or!MJc$z)RA9vH3
z7%p+#srM2~fb8fNjq7s-N$(`pF_(}UHoifp`3#7C54->(-Vtg9PWmFwABpyniu8ye
zBBJ5ZO#%g8aw!_a82&6<;_a!Xd&0H{Gn0D9(ejc18Td$pyg<bxZRCl*mi4ycwCQ`R
zypI#9Sa+P0Jsa9HhLl)bAo{bt0DNR?O6))knPzvWkoE-q2SAPe9ys27{t~Ax_9$sC
z`)UEyr9lYl0XEUxP*fw-=C56f{s9K1))CFch#$a5Ddg9b@I9NgJRN(A4eKEs-Pa$`
zk-PQ0#y`Mh7#8knO6bIa{Z&2!qv=xWsSEt}1{@*yUmO9v@=n~f2M%G8kfrxI;*+Nr
zxCMlE@jjN2a*PF1B3}$MlP2=wAhw?=nBU6s;=5)cn1LqHUBv^TmFJ)fv5172R@(w^
z4N8!$@$%x1=XHU`#Ab6xjWRl1ucvE`xay)k1z+63)PbtirJth=@9T!IRYzh+P;FsH
z|2g^yqj=>4?zlV6Tt^KXdZ_{)oD;cPxWNqzY(?oV2KsW?NVghi)dGZ6EmVrM>(M7=
zdIR<s#((5`=HUdHU!8As#Cxzu&=9&SliIraINAFVUoZ5J5_M7a<G2{tBfWRqb{%NX
zEC~t1$D``=CiNMhBdv~C?mNkb66kqPW6W<JlQ|X7=pI|<b{b~f4?Ki~AGGmUs#E6e
zgL-awQy)<UE$i^WzZk{7_z58QE;{{Oyz0i5i?uM6&z1?wb+-J76Z!c|h@rtd`YeS;
zG<(s56%VOXIQh-4sw;gDF3I?!cDFB2qTRWz%g3eit3VNE_JsPY<!6-3Zn=(YmZR2q
z@Z}+^C-Iw6ya`&LSb#3)eDq$R$RNd?-{n9CJ6aEU!|?xviDQFtEk)X+X(esz>+>fx
z{g?8)l8do+mo{EJ@RM^Bbmi3N+@TgfG<8s5n+Wdq^!h`}UDZW1zV_#mD?#K_BD&Y%
z<1hL4kO80Hgv%9;QM3tC^OzV5Q8!O-BU3b@0E^m5E%M6TVh*bO{K=W(Jh)#Z4YF%t
zMZ}fq4w;vL>1AYPv+;KMg*oXyKr3DPzT2;nr7z0|7#)3CY*H`@4u5Ex_&p%Epp1H`
zvZz1d(Kfq<eU<HqRxA<-JJHU4SID!Jd=$r#>SD)+%}CO0ozWIH32esnIUP_(?OJS`
z=SMr#;(1#9vq~Y|PIE?C9(*HAe*7%EX|FFt;I>&Z`uzvlc1$AjNybCtr)whWSv0nr
z>^NIB@RCh&O+s9B(WDuYut*ZAXP!guiG1y`HK$c;&Y<Q-4miu|FNo+Z#sN;*B=Dd8
z%w&Fc)9L^rb@fj>%)0s?LXU8ZAxasvr(P6J4D-39L}E%e8RS1wLcimtCB-eErX2We
z_|35qrB{C|*2}V$J#h8ZLoPUBm3WRQKQSFFKb%^MbRFj29(ZNq{1Rdv&k#=J-t02|
zh2t}htMH^PU>Sf>T{An2-5QHGE+B;<{s(Zs87w|t%5B;lnw#oUKk39}Q~bp*)|2~-
z$YJ=iMnW(C5A{2TRgw6u?cSxdwB!||?hGqomO>hHu#~}$^*qlhP4ALbEm?+CNBY`E
zHgD?>mf!IU!hD-XPI*V1#&*>Qk271Ce*2o-d@@7A1n^s^OFLYD;}jpUt}ye7K+(DI
zPYS~~^T|8LD&C%`a=BQMGOY|v^A>EuN_$*JkH3fzef!5c88bfIrUVu`j9$d+^n2BM
z**sDD8LG&KbfULX#`jGCa$|ynD(}EKA;|d-$I{r4Nl*0vw6v0#27B%JO#fTvJ6f?d
zA#~A=>&zqfs-KaPO*W6DLt9KkjnW#LmpCxDEs6uk2dnqO1=8<CJYz$8nzq}lj+)ye
z+bPp~G}nRs{{YVHqj4w73YstS-PE*yKZW~jG97Gg|Kg^%jIbI|n47In&v<Ri3v}n2
z7%_2H{%&0;&48gMmogJ?H*MT1owT%!XG@s;87S<R8tc2A=q-A)%B|K>+e%FQx2+qe
z`nLde!K<kThAGFo04YV@)sS{9jFs0q*AphdSy5^mJ)%0V`@&599RqGY3>2K0+rtUo
zcIGx}N!8(LL-g*+iZXtlCS&ypZS{sU^tdc6mj<y(uTrnRH;ZvGC9$u~V$O+0+FG=o
z%DjohYC2#QM9G_^EnlTkt%KUG1fS*^^WIh59#j^o=~|4uMM+jx6A&vjRda@-kH>Vl
z*|{I7WT#0wu32@3%M#JG(^vo;-(QUTg)CAAFyH+mIx><pFIKX!T_9#^>|!MC8a!NU
zqxG1EVXK}MM;X5UPBtJ~Km+xyE$X9AFXM@v$Q@O>4$+k9+-u4eOkmkmHOw#45sX%G
z_B$GF`zAiiQ(oLy2Of?8Ku>RhcY8bLG;xQEzku_f8%m2k4p#uK-hl%v8#R(Z8LuT?
z7rGwrjnLF%B^J_+{;%CaaZrRzxwy>-Kh;a-rUBj{@X?{%R>s7%O<Hxv?Z9{TluspE
zps#gfpd)U%@!7ui36SJPcR8I465Kw!xx_jp+zu2#DM5LdQ)Dc)ZPBUqW&iWDRks_Q
zmb!xnpXc2Y5mnR}@U#1&6jT3IhN2Dq)Nn_S)?lgM)KfxW00~5T2XV@8BCZ8ukF7^9
z#pzCE@Q|f$k_{wFqgZZ^d2VTjZP|1k4WQyy0w<yVtVM<kY+$aJPEQ$JrhtHZYS<z}
zD`#nx3<XKa>AB97;K$AJwd`xK@09m&_OPnwr#R99ckgK%Z9R{xt^iUDdKF``m0!0v
zP=mR;ff@JHIkfLd&xi5+RPAXZGnca8O>(~VK4XEvsk6H5CiO7!j-W3=E0n&J>gf!c
z$)!YA4ThxKlGBRrem<0DT_0PY3^(5oMm;myn)zk^0TnGDsq(_sbh86buVckZZj0CS
z&TC12KZi@9?gt53)tcujR>Uxl%<viD9p>i3c^hV}Nm&&69P9C+XsV|BmBaghkKQX?
zZdZc-9egl5alEKG=`dsGr3so*vVZ}Vz`QoxenCUnM7YEAe_RZXWG-DO9hCEFJ)YGc
z#c6q@(^X_A4ioJsy|tr?%8*4@h|g7TfSx14%y@g1qL8M&O&0TEyBM;_!YKAKYrrKs
z2}`!=0{q^9Hr_xbE|>?(;khS9t{GZegpP%TzSa-3PaWZzy<W8FJJN~+`}=1J#t_CB
z*&*4*UVBUr;<TA~n(jmTK!&L^TGs3J&WPkjQL`Nj@+UX<_J4r;zfkJQ8wWnQ85Y`T
zuBmS><RLw~BOEI|Ywe*ch|sG_@}`z@HfxkTT|<7|Pk;YS9eX^OcGMUX+7#$dP@A<z
zA<53$CguXSkT_yRA-(D3I|SaxUCjMGv}roBO2J`weBqSe>3kH1XcVcpG&6oPT24@J
z-AeQ6Jo50=sdsc_qt$0r-B+C1<WFA2nTj+-<ngMrIemz@-%ATCb_53Ugerf$R`SGS
zw~=XbUiInMgO|7wj;Pal@65E+Dz-Qy)ZXoEmk7@(<kH386GvmbV@P<_q}I}I@!=Lr
z#BUZ3`{@8--KZ6RA1$jNU)I$~cO>{YHyPv5M|lNiEi^ZlxlB)}9NV>@lUmam7yavq
zOJm^Qa|<o-XZY@Ds=L`5Z}XMHFme@s|F&KHZ(P^qiq#sI!!7^9;Cn2o%j=fhfktR4
zkB}dbj4oO~Qget@i7W88RinM6A79zzJ(G6!QnEf+(xz~8VR%zlh7K*%I0W2qQz~|9
zyyc^w{|WrRS&rMM+RcuSt1j=8qMpV@M9@r)L;nF9s!;CAE;#_WDasq}$o>U|@570K
zyTYA~+#^jDa;&5uLX~i`xvBWP)g`Uu<0Y)EzLh{>2jTvdJBCjU01(_B8tFd?44NU`
zAWYD|b-Q$kCsRY^XhwavI!kRE0qR=_x|^iVv0C*KT)X#i=ZH2_{Y);5_tf{ltVz=t
z>el%oQR;ctEz8Ay*$K>qBSl~BNPG|(oI>ZqU0pPHsr{EnaZH7i4fd$w`&8!U^v^BM
z6yYv29(gb_qS4GrI1K`$ZQB^bJ3{s%Ff{+jQ`<Kj>{WufEW;Qt3mK9DT$^jL{Q3~w
zh!Zi@!7FBEDA6PPlIM?GW|uPH;NY~yDym!jtJkGJ#yRZQVkE|IS6Tko9TKL<S4DHc
zwQzpmAAoZ|hpq#GW_?ppeh#PKPl$fV#ErQr4?De!@%b$lss2Swa3f8ixQwgmNxw93
zk_OdX=u5|JtLg6^j6bepCCgWY7N@TzbRGfkMBW54|F0d|P#<?0BLxZty)8((K}#~$
zm{}#~_BA)T?7`r`sVWZ|9YqlW05IP93pv3V*!$|K9qyx^#Rzqu1|1Fl0oFeeB0SCl
zUm-71*Pm~i&Ab(!w3RhxNC*_E&%w|wI(w((X}g-zFpSr-qjA;VN5A+Wd%?Y7oX}xI
zL$&Np(dh}HlNokATcDvGf9*W2@Is_vjdITUph5gW`!?(%W^*Du5c8RLDv%VeEM_*v
z+RJ=?zw61coA%_GEM?8VK@i;Ma~~p1b#cD27DW?*AU;gb)XK#%0=lGoA#N&rhv@M7
z4@v7ekZF0pzir72%)3X7AP4mBTu=D>XmUXpS{?W{YW^rlB+%W6O9>cHZzV>I*Z&%0
z+30hkbj}kB$D8WdMDEOItP8_ltz6D2$70L>q7kc$3mZ(j8p*O70Kv?c$V<Lw0yG4H
z6X_AMercx_94a*&ao@d3;r{$<WTU$MO?9^Q4N({pfQ6c3Z%`K~%FS+;$#+fTv{m@2
zt6O{FWoU}`1=y?S?pq(>hP~Qa+AaWDwTPEQTnQ#uh=%8sEZOFMm^yPuM*1|uTEaFm
z$=+(o?5I`73WckQLxVu({{V|c_jh`A^Q`l|`1ZEtZFP}x_IHv&%TeD<0`g4yl{qY)
zJKQ}^@06bzw9L#tVdDe{pd4lv%jxHBI~0Hz!F9OZ_UFO>0Hm<<aP5{~Jj?aOHTB%A
z6+yC2AG_#S2|k<V;z+9+t0FqVxUhr-8ra*EEgC}ZH9fe})EdGXIaZ02L#Z`~hY}(x
zB~w*gCN=xW_~d{X<OA1DMDiGudt+)$vv>f3UNs#7;)RmwD}29j*4aLbdM_3rK`=eJ
zz$r2FiA5wq?j=na2r4o>s<LbDuXRl^T+yRL)!8zv{tpFTSEVkl1w-lCJXmdO?m)F@
zr6OUH&l7jBllS3&t!F(rO_?{eTTr?G9Jkv#;FZyUhQ@AK_E__0mzRY?77Ci{N$O~s
z7v`4Bw3POt;x)G*z*0w4(NKks-4`|6`^y2|wr(D^KH3ObynZrWrEfr#5FBx${Md@h
z;7)|NKlhf1eHVhZ`qDUp(O&=M%brk+Zc3Rv0h$zz1RB-x;}^IX@*-H_ueQ&zmM1RX
zBCUD)r15*>djb|gjg+n2d0Y?HRf**&iQ)jxho^1EX9D)k`Iy^x&*B#`+D<qloYsO4
zy;fTjKKpo$mdS+2Nl&97bd>0aV9-YTI=P<m1F5jUF34Nn13Yr_I}pZ<e-N9S1uifq
z1a0JX)G~(E`JC}knHj%dF98ze=Ah=gZEVy{<w=w#zA_o@qzrRRTk^)-QtaqUK(P%J
zXsB(P-kO2D`CH#an)Zs`1myFMOqz5pOdo|G8iJq%b%`OU;ly{W_FJL_YxGS&!iim^
zcXT4io*t~yopI3r0koP(EiLJ_7ZL2U{{U4yGSF7h?#1mmIvGtV3@np31FycGE@*pF
zV5=2C%@~Y0jPBK^9N1~AJM^=v_mI9KaibL|%$Fgwha1wL^C9_@0Ez%jun2G$)0o^C
z9xB<;1l*Cc{vh}=$0TU}RFF-6V~hchKU(@qt5&%1I;VE!$-K>Dwi`yRo~d)-w&>{p
z7kTUDFLC16U)_MQ+g4CiML)uth^-{ApVR7F)ITH$-MuxzG$;FD{iy?|^_>fLaoml4
zd4$e(jB34?=LhDp;eupkz^ftN$CgS^*BAZ%cKR7%d9Uq#d}D(VkB=$xTpV0Y---Et
zHyBt)M~lH3c*|SukPc?g2$Lqnz#;ev1ZDH~E!QUSFv>a#JUm>Q%YiKPmD<eX8R;vv
z<mIl;$*cxw&ZN<;kRxnB8-;>%8I^&vf;r<U9@r}`I*Xm>s4=t^NdplQ8i{1es2*sL
zz$9<9JIq$N*M)U|2O8aF?=*W%dr!?N3;9RuJ6VN<$gpLfT;IgF>td9gRp37IAQK|P
zye;+~$&sF+C`KsHDT?;bStlb^6(-m!o#e|On9i+!32WwcPp!T_%M>%2m3@m8UO#Ev
znM5*?tMj`7q8A)PyYzI=D<h-9#2)-#^hpcMv`B)Qc_+YC7TN<u?lo7oDP;}4_&6v)
zY#N%3N08l<)AoF)Hhld;ObX6}U`X#gR9hVSlIJkiV;GRUy7_VIdK(FfAEy`a?4o*{
zi%x}<air<2&5PwgW4t_<BKnL8j28z}zKOy>EUr>rNq^7inTLBq&;?LEph!IlPCpUl
zP5}xbpATPqT(DE#l8;HN;VNFj`ZP86UkJ<jg07BZKNtF`u~-*!T`i2>9sahx)r*yF
z^AGT{YRDLl)HKt*kbC(F?1?6ltyb4yz&TumssAA^)P%o%I9Kql#5vVVDk(XmI{@jE
zc*8&K$O}c%vD;v~?x%9yN2Z7#`5n-KIxC3BZZww4`@;@7qO;ZMw>M}2fj`rVyEWn@
zq&(}8JGDzux%if=AT&b#n$DcL6K@dOmp}sQ0=Bv!y~3z}{RJ=CvRsO52>>&5HjYGo
zzz0sbSZT!`N8eK_=6xx@0u0#}n!>eLFC8E&D84uIKp!>jrAT_32SckvfGK~XB5EJV
zJ30$fhNy-p#hDJR%}4tbx&gtwYkx^+4gBm+{PS2^vN1z-dX1?-x~U;97jrOKK$Ct2
z!wFESKhhoAXuSsx@3@e)4gW)k^mwXMNYOu3+~^GbFBkzF)w$p{<HI`O>>JqU&ky(8
z0RIrhslC7rW+hOXs?dncQ`1TJWH}k_trg-#B2=%osvP%*aZ8_F4m0D{l1lZDmi&J1
z!_pX|1~k1FxWJvk#&gOif1l)^1>m<AM<Y6aJ<!c&Ag8k)m}CX2!dz0ng}73ZBgk;4
z+HGI{fHtzhXrkQ5Zt#|F5-x9f?dFuxCVL$%dx4+(Zm?>byTJj}I>+DH$%Rq87Xjzb
z_~zR`+#2q@B3lJmKIA}wt5UswWy}0%%Px=8wRT&zN_`3c;~^vO`m^%oc*Mni=Y5sk
zmv+i{A1E8d0$*a@OCK$@vPp}hz??xA(4;<`D5~-jk9cbjqHO0m%+##l0$JN$Go0l+
z=K~2<Iq2vw)A<3^$j<jF7#FmW^tjVt?{+EFr*-HU`>x^drVqK~nebt+1o!!FTOh=r
z2~M|>5WGfgjPWc~72Q4aep+Un+-C*LDP6%b>xOItgPhW~{#M9-fj}`TzgqD}C4h<$
z2d!aJn}8TBoJ`D2;WDm2*D|yu`~HF6#A8CBV&uP8_I7Or>x5KIw-!6E?&MhcW}`H&
zZ8tW*dF&?%+Y@l@dAz}P0+!vtRc(5k(X8cn)1U#EPsb_FvY2%T(p$^e{n&s=8k5h-
zp4BS89(V$r(!espMzR5!QO{CfSLtKpNz0X_RNDoMhUr-9?D^|k<ckCs0@;Hx`0|d#
z9-W$@2NAl{flK}^ub%o>`U3ne5k&Rm&gwf^u+hzT`~uQ{w$>O{0|<T1lcx-a>U^;*
z;zdLLz%bdeSs7ArZ4k1n3!aR*X~d--UIoW@B+aqkX~Q=Nl48T(_&sV_RXdd6zE@_b
zP|(^BTOPDLOnT6UlJniGORZd~s0jg?PI&Hp*bCm=?Ihg!B4GRNTvOlKc>MyD2Dgxf
z!hC^Z_2Lty$MVaGwklFwSgz9^O`c#HNG%q6b=K?Bxq&e-eWTx|uK{b(_{7N}v2cP$
zB?$1He@pF_A9a`L^&I~=ljhIIpeQza{kM?ks<k!+AM1>{AV{pxE0ZL&&hH>uoVCkv
z(JILw21E=A?28-?#0Qev=}C6n)seUkp<2jX$n5++pKgSVy*+b!&!g>igsQr!_OaUn
zSKRO+*IF03rYurB8V2RH9hIFpHe75=x7S_s#P)ENe#Jb~o~U$tm`@OeZ2{jp;U23j
ztxA(ETn;L@`nO?pC_K841*p0Z)!B^)8s^ipQwh04`7fP*h}Apf&)vj$**EE`>2$94
z3vSQ282A`*j+f^fIT?a~&1RZzlrY!*DHE2u<XP1tsna*EZn*fOlNfNB=mQmp%|iVZ
zi}Y<WM+*$~V<iRNgkA;}PahlRMqF5dXoVo2)?w=iok~8pN$SQ5n#eUwV=P_F99k)r
zXR2a*HUTs`WH$UZr_SfKkeHOCaY`Hlve9y>Jmowq$Bb<%>;GCoINp&As~xM!!1zF<
z^K|0{ICJX$L^^AI(fZ{{=dDne^Sc|W$a~)HdTj@)wUljEyG5&bjVF<AqBnB!e6Jp_
z^+Ciu*g_JGb(grImg%!>^UgXsY2uWRTUEtj_o=q^XP#!4?}l%Q;-Pw%jBz*Nz1<`+
zv+5xtgNLeX@O1fOo>->Hq5GfDy|{PMPW{!-@3suJ`#h5mwG}!XgDho8uT$W`RPP2v
zfNQPn8>gH+`^N8=`xW3Hyi^1xXuRgC%hlqkXPN1ez2jKj_P{4GFCgvUpLnr9FlTVp
z&yNiY=b{*j!8~$3uBd6!QX)|k#d>!lBt7KcNotIiKAUhQ?q`?9>mT`Qg%{de@@8GU
zOOxq5PUO@Lu*>nyD0a7(sBzh~xje_PBf++2ZS8Cf_A1}8w%tbc12G@cB`CyO&ID;?
z6zy&#D3nrW?)wXIO!NNNz~mMF*@Xftq`f1}_fvJDjvnJ(PS*1eGakCqyGLy*SuvY^
zC9N|*yqX`TB7T&eF-kQVKIU{gRjAGdk7jia<Pt^OWGudOW<U7Q&qKr?f~gR&l`ebC
zzUh*|*MI&D-I_xdWF09v03LU|rgUWc^z$M}8*}p3%5!X>iO-n(q_REpJG1X8Ga99g
zfAAIU!bvoCg27O|Xr0qNp`AE+_i|Ty907h_mElB%JP9o8N|Hj6MCdGmaQ#I;2B5&5
ztWT|Vmc?n-zwcw7z&*{2-OBp~ncvNhXn)4c*KFTAuA9j<xH}@fLb8mLsayAq$k<}C
zsCF#@dauXXcRcW2*H{_#X_V+K4~kRep80})GQ0<-9%c~pQC)!2wDAuvC5K|8G9~dx
z4FP+HxCinCE?>(1s1uk2PVbs+a!!6(O$1G){Rl6s=e`<DJ}O8ykKf+{4du^(nNakN
z6M=VVFS$m7ZD3NXd5|s+DNV87*8U~Y53RwXDW?6@=SH$Ys($;WKazg*`<NYc<K^n<
zd37hY-YELA&`(phJa0aY{5HKpSqbEwKWe9%_F&R==VIMgifX}F>!sXYhzAa{vYt8d
z&PFJzHw{<4duD8RS)OYPN05H#u%5sQrN{2zpxj<+s$OqVb!W&wa5pLZJk|u!8XcS>
zKIw0#nKS!k`YrZkxY7?L)_ko5S#ZA968S0$cE+DYcxoAE`(aVv&gieL)qPlzvxvPU
zuM{6BC90*`mb7Kvxu%K)e3hlIKAda1Sa_Di^^C29MZ|PXm(3K#D)RLZ8<7Q7=Aitd
zIhX0Ir5UKA4r$2G82lPbmL1zb1q3Ik&w8QQ3mC4-?$hMh>gpT5c64Gy#Q{B@>d2|3
zHWi(X&E{3a<*-_`&3?3uvCg2S8t9;HvmXYj8etc&%X?My5OR^w*u++&p}dMH^wUmX
z>nJTIEhp7XY2W`h{rFcDM{j`OA?OTk&-kk}k|k6GX9YO{jdT^3{Qw*;XMXc*8Sixp
z4(T?8E~GnrYwqfge$<pbCi7P=2^0qStd3_cN)f?EC}_ZU9ax319QM6nizG_H`yCt{
zq3bbs7k@_kQk)vrs7zte@h>}WIpC@G-p0wBr_$$1tnWg<Ul+lq^6OU)(^*_y1NaMH
ziuJhm6qW>x@CMR)zHK^u+Z0|xI<%J=-U?8~@GoglEo@W1q4b0rUs!hw)U~CcqKhA|
z$KT#i7Z;4V|6S=1O6BK2Zg`yAOMPE9zzM)roy$hHV)O%Ka`C~;)x(F>L``*)iLOc~
z;QNlp+2{Mf^~5aDQcLe-gGwd{>8zQ*Id>q`e>;kIYCO9BOsMeY1^TRkcF(i^!ndqN
zWH)TTCP2m1qY+q%7$ax<<M+e|m(#bokC?6!J8wWLrH8G`n{h_l3fFAk5C<F|;*k;>
z#L)W0qX9t{+O=Gbw0B?s1LRvkJ!bNdEZ`7lbW4G)hT=%~DXZ#`WKsUfXCglqmzAJF
zVHmR?-n3Yu-`k8$jzq?#`^q!I=o^UOyi&~-PnconALfBScaQ)npm)KQ1tsCQ2%4K}
zbibV3!5Sf<VTI3);HN_Ch7;>?tX{ttWtfAc!xpLfL{x}%l}KP7?E5#VDWlI<+omwL
zm)RSta;<U67G*9?Ix%;GaS;%CwD*}!$;^MkpCG;04=|w(-E&hd;HoR`4lt*2=FGxj
zi!)`?B=q@sNpHBfA@ePc&uQi#k7Au)bDEOf30Wwm2%me1C`{?y8sBHrrh;Z_-9nr8
zkF#3x5D`?FUknBGZ*NhAGy9t6VDbVeHK{mUElO_Dcje1N`0FQM^fim#=;VHZy4~KC
zus(mz$JD6HQTXEq%L+{y7fsU<4&eQ=i}{H(o3ap_p>*qTJI;0L>t%^0O%HPj?)LIU
zW!1B$Oe$ebx`Z}P*bKW^>2vvSa1Xg*-_Z1$FkXCUvF|7-B7JgA%EavN2*nOHdOy%s
zt7~>{&g%W2=whJJ!PTL^I><%fOZr*M&l4^GV6ds)#X5XyPb_n=#yTV)M+E6+$}!!f
zFl2~LPdsxLUt2JTTNk>gpVNy8_s}(NXav}$8df+~P1&n=6Z8ts*7YNYt(=D!C${@M
zAQbkZG$i#?k?b5_HN`l-Z(9kT(xsbso*Mn}d<+;>WO1fb5NqcMY&yTGWZSyYH;|ft
zkA@ewj5b`keu^WjdT5|?;vm&zQ|{5!#zLvqwAH}(7@tL_cl0V<=>Q2Rv~XJ&IM#As
ze{Uz#Vv3uff&)A_RsVMJ4-m~GZ|I~K#IO^y+pAh-j0WI1d&?C7fE?rEdOHrt5PMb^
zY$PIWhurIEH(PD*_7{{bF{fK0(4>tQtqN<NU5ng^F#TiS9}6;DDJB~iAe!F4tn-oQ
z^rG%Yk~O6t7}pLb!52{H4hKO)$(v;!^=G&5D0U6XlQ^l1)5@7>(?sHHs3yVV`set*
zCK%WIS!xda{R0|wMa2Vp?SGuBSZl+3-05Al#WoGB+Ws83^E4)HM5}&BCWe*1iJ&At
zPB1n0Rz^-L9@*755dT%Eq85qVMopL}!ahw{{Jz9W&a@e)tLa$+h_6_90T%g;Qc<WR
zv?@}vi|8wxZ)F9L=uDrcC^j@KOa`9uGD{!@{4SbGH>4Xju*ssaNr4!}b2|6kAu=iY
zmOk@M>m(^dkE@L1^R*^KnAO@UaNc*#3I`9+AHcl=wmU2SDOCt;DEp)<7bf*6lxzK4
z_gE3j)-jB5H)p{mWdX%dp$Z<p#MR&o>t*l1CAFaYLt1%n)%o|b(K_rPf=V>6S)QW6
zJ;cM)rZtc9bU1^i<Q7U?X{klTt1YeQPsKlN5IvB^)+V-SnxZCcm~U&X#`kLS5nm`e
z6&^h?g3mfE!`)r(D3)P7-oj3(Ctc|sD8oiG{7K~C*;L0Bm%0tx+bU{f)%8GV@HOg)
z1v|@X#6N&hb6Y<6+$;_J&7YNYIHUdK@0xe9TKpXo8><D@k2i+x#SL?ndWTvri>OI4
z1_G!Z7SusrP(Ey*LW4+lhf&wk;aVK-h3$^HI{HiO8BS!{Ge=Cg2zbHQMPGI%HDDDz
z9zWI6r#xqoM8}?rmNh1+U5lSZ`YU(B6pZDj$-FDqbzESHpC$t0_D$Ovp_j`+O3=^6
zy;dl==;uQiB14MPy3N~pBwPRMP#PmI<MaAZ0MVS8kU=b()Di!Yz@c7!inuMF&Zfrd
z%r<ypsx?D)X0Kh{&G$FnRXGl(`5biBJDkF65&fF};FU0&h@vtq#h~JsskvW)SKcLL
z#OUL|1&}5Xrs(m#8cW>wY9z<=@o`H?ra?F?NM@2+7GbQeKj-Td3Hk3H7muCnVKVk^
zJTNuYrvNPRk{oMzFG4;Aml=J(+nxOCt}F(EUc4gfD1bKCB&3Wd$wJ0K`iT;G>_HK}
zKCri2R;_CaS{tpZIqh+Y1+~RTtkpTC>1LnhwhqEPwm6jjCbr2=7&{eO=BTV}$jRr*
zVouXvpjEzcf4^ws<=GF+a@lR5wJ~-<y?JN}<lUnNUhGHm=t$lLyU^FiI#1B7eanp(
z5Q}3fQLHP-C`sKU8JRf-!8G3gslS{M+rNUeP#hGzLdrt~we4-*9M=Oac`a;8ARKH@
zJ<YXhLc40hMiDN4r>VNqAEk;$GSkO|MNR56x5sKFS&3KnV`4TMD0lvs%bYCh7cocA
z;K%Fq79eq(VFO8t%69Nlqtg<;+IsDNyt6OdSZf0DYD~BAPoL@$NVMZZS#$l#5Uv`a
zt@;lz9WV=$o0D~TGa7pwh5k|Y^yYE?S{r$iy*2^E_J1rkQh2bx@U3d!6qbBtRMV`l
z+SMm}KG<z<3kj{UZyGf=9w^nz5mOKQ^i_^2NnC45{4xAPZrY%7vErXq;9}m86D@wr
z14eNyjf5aZ&9$D$^5O(v>i7dj4?>SkO5<r723#JrAa=#x66U(TN(MzHs4i$FW!p(7
zwP~wQYV1lG{GAn<KXRM&21$X{o*ahQj$ZRH0$z*wfeSq%45wHMh`VLxM~=#a8;%zO
zfWsbbMvCNCS1$C|)ouq(2g;>4m4WM{7?Uk;xtY_cl$6yCkKTQEQ>rU7tex`^(OT~F
z6*(=~)ztoA@(+N1IML=DNT?s>;hjlJx-3pA4s&j4+R4q<AdxefqNX=}FS&Ea_zwUz
z0zu$e>h{VfW1I?F8{a+29u{zH)&B@fN-Cj)?Mk}U{iyx{dK0J*>pjo&4u-FcxB=Gc
z!DMK{o}cRd#mc>*a+40XS&Pa!z`9^d6`j(~p-w9M&Q_}89PPgPjwOd0z)tO;sCq-9
zXmOz*b^eyvsr=kD&!_7><5W4&tn@NeLfVAC92%-IRR}aFBP|wilRfYfyZE)lP-~~R
zI^!ZIyNu7K&sbCG!<(_i8aSdkH4esqs8x6su)~oc!X12Hy`asB)Vx<Q7-nt-$oJfm
zEz=Uv+9!drh~=s6Zwz_gv2;_dS!5A(hq;TTJ)0}mPh$jZm7jocp*+&u2kVq>->nDF
zn$SCGF%RQ<ieko+Q@wW1#$*u^g}?013}N*_N=kbG0`OOXN>3`^7fov{nxUWkQk3`t
zWRKSqg*ul6%W&j67q0KdTDCm!>koyV#6aacIy!)>K;_VQJH~iU=D}jJh8vA**{;2x
z2j)!U#T>-Jrw~7SxJaPcGQudpWn!xBQuOnoZ&CPNu4(vXqEug_k#C~gT5GXd^1Q}$
zmh4X-IlxtvNd=j&wTF*wYxMg^KdHzA_^(2><-r>h8Gvu}HM42?eCa6z<o=eQJ6_H|
zBMwr18}lrwjO!gJlvUD$%7F5b@@$`X56PvkPzlJ_=*S@6l1fZRveq{+u0W@8_zqtJ
zh);LlkA+J`o})`6dC~#Q`Z27W6(@D_pN-@#SKXZ9OF9>T0W&{sL#%2DK0n9LlIx|%
z+{FT(T-x}j-m1``MM)IahNefqd0p)1=A1gMF6Zh>e&mSSJnxPt`HUw!>XJr>HC|8{
zNl@rKV`$R6$ni|8q^*NPiQbAXKc@lnVDiTS(w$B3PmQ%KSQl5ZEnwyJJ-puHw=k)0
z9X76C*h`g_1#{lA-d)SOk8IW0qo=6b`x8SqkaYJC1wnk91|%KouyP^dxK?h&&1MZh
zU7>cQq{FAa?c=%90waaOya(}pst)FwJ|mi+C4)QnrLcu8p4m#=$Mzg}p+m1_E0P30
z`!gjEP}ZNr+TG&7vNlU^Fsa=fm&z2E>ai0Y{iNO0V0iI$yyvNr?zY13xJdK$XUgp=
zWZo}MebPt#Cb5&?F%3rr$jmx5^UDYu#QdWaDL=-r^p+HGjM2}JRbkcEo_0x8iGdPY
z1x&grITtn<6OyKiRd4;~bxzidv*MBo!;SY>^}CAnFBzcLd9zK2L=v~Bzb*R2r)Xm-
ztl`|;SZ`}bewq#~#AZ}mAvgIVu={mF#fR?D;KT@L{&&pFJ4md<y}^5Z8ujTzUB1Uw
zt;bkAAr?`uk@rmGT;fly*`+s7wZFUKb`$g+mKa={IzHaFOAoK3)u0h=WSN?U;T8Fj
zMZ-ok)5wN9<|u<S_aK}=h)F$k0jDz%Hfc9sFKVOn%aA5nX2{q`<TyP==c2#Zljlij
z&L=?@Pdq_18z02Hj^sGB{AwTaa;+5=-LWWrR@U1B3-8-<Yo2UWHk0EQ{VM4sP;#b^
z#bTF`Zmgu~<qKKUaaYlh&ABp(Wjf6qx%`Hlk7N~l<m9U5JNs3`nPA{iDK$l938`#V
z^`Yu51)g3f(|ua;`smYH8SS2&8K-T|oW`m+@#nxZ<*<JJ*on?eaQpYO?fg<-;h@g%
zhrb%hJI_tef-k_4KP@45ub7Q^M=fr?Rwuk5`zd=dPxHZ&?dgSX!9(kzrvtt>%$8GW
zg{YZlF6#Y^j3iCVHqCV^_FrU$1lE1tYxb|~cJ41|Bl!p4W1hee|I!WcYj}d%Jj5f%
z+W*VNiu4DrfPLo9AT-lkXvP2hxBmdGvvCHz{PJmo;~qt;Cm>{4c}#P?evAv@fc>pR
zPjt8dRJbtqvp<fa?Ffdbs?1?*u1jVSYe7sorFF?oh3YBe@7}oU<iY^Et~m709`gp@
zOw`!y4902N!n7w26Su=9i0&Dy!cV6qAe2@;cv)=QkcXplM?f`w;=_e~Zss_dzAq;l
zDGE`oy;J0@xuYbnhr6OygVamiJE!4Nlo{=7Nu)qY@BuNu#!LrJQT*RN3Iiz+({DST
zxeY@1LJ=6uoKGg+6`<-Oq*J(kscb(-<E8Q?K~3@{<C*;E9d<Fz3k8FpXeswLg~rBy
zN~n?U6RF_<?n%?eEPV*i^?=iDjrLzJulo3}OohQuy(n^S&DCw#tbg|Kj5#}kcQJQM
zyFT2Ed6J~3-=}a(Er<S=8u;3#yszgnK^aSDk${%`I+i?+RSkGZ7bgcbMXo4w6^}Y=
zt}RW5-y!RJi<2f+6%=j@ua5PT^uN5?f@G<P4?f$L{1U9#Z6EG*<vy@y)<;WLPhJhY
z|1j_E@OEXpP|9Jwo>Qs&u3q^0kQJR<Vuu<|HgrG6uk1hgF-bLOJ88_5TetQ#>8mlz
zA#>7=QeHNZxNTjr$b1xHARVAGf7eWidRDkY2!&15AZY-4Q)2WNFXYGZx#AO{jcc*(
zo#ST)2h2TXv#tN)vy##xo&>gPph<Zvy>E?rL3-}>wclAW->;@}bQOfirrjA7p;P}N
zQWl3OFg8bheUs`rj?EHd>rN;S%PKiFluohddfsrG-z6M8aY#g*-s5xzXU|<a!=fD4
zB~k=em&J<}TGT##`_4bSU;HF6{_Y0S(V+K6=Co61qdRhdRBXogzG4t!!DnwnhQc?>
z<pMTUIr`(^NcU?t$V8l(f8+6KKE()g=}S^yRJ1}m`c|-iCECxvWhx!y<890Ji*_j*
z6DM~z#nwCYBi)ejK2kIIkmGs2fAHCL{acdEkkD@_m9PK+Wgz3RhDC~se~E{tY25Gl
zw1wWk)eNb~D7%U=xw)ZycCrO&0ZQ_vG@<VvlbLEWT@muKI}0gX^_)qQnN37FD<l$Y
z{xSp%`cy)=!;SXki@ka1vz^+jWN0kCuDDc<dku)`^7Ta&=YEugUlG^|26vUi+_3S(
zzB(8)Ji$O;-{<=&hovR+vM-aPkOyOC&(uv*Q3F9QnF^Mx%We5x(!rgVgsj>14kKW2
z(Rk!Bvn^oD&xWY(v}AN73^T6$#9mSm%q~7o_@H4R*X)j74{pgCjUB}NI6-<MerVX1
zHi}VB>5Z&~2n@8tEke$=Y(5P+iWVOl22ANMv9Zo`Wxt878sf!nY$+1+oU3+HpX7n!
zNvZ8}Y?#thiT?v&%}1{z`;m*yslJ{?Upa^QkI3{pwt#aEx0qSFc<kmj4Uaby^F;D}
znONkrrL(LuJ{u?>D9f4lDZOqWhg<aNS-;9$+KOv|-W^iWYpcBcsercws_XH%Gz!{a
zf#juCR*1PI7d4TROKw<#nxZ@Yk#<9U$Kphr>BebFN$2V5p%&7zt3PIRvcAqOj`f}F
zb#OY}83HACPTj@T?aPl<LC=!0Wo7g(EEh>PL1~zj#(v7mj&+;K8r<~6AIV#B@MdH&
z2d<;=)3!<7Y1hXp1$zO*{9`S0xp19!4Xop`)N7L0j<DVYFD6S;TExc+f#?jb`o!f9
zDeI~UQcvBqB`5R#ZZU@c%r*4Gbvo8!zeTHmyT>s)a@8P=cT;$QD1X~h{BZQ8u`|in
zWV__9OfNj=Bj_^;^ZKfz@*K=<Pg^QlId_Uxzdfu{?`Ne+mv9v&$2}UvY+~c{X;jlB
zn^!5~9deGZT0~4RL%uE^I2Wdn6jwmx*b!XW!e#sWwO4<e0XT~q0UKlaZyQLt5M7zw
z*6(v#*bm(-RUlERvs+2(pF<v+H|D=NL3`Md9@`WA;!$byb-G-U;{wlXjUE<T7XQSA
zi5)+!Pt|{x0>G!~9N!%)B4xH0tceCRyYt&%{Ds;%tJ%LUq*6*&c!=j|%zo8$qZ#Bk
zs7X8Hsz+x?Hq`$0;G2T_H=c!)c#5jG6yYk_L;phZnqm6#-BUh{?vCC`xK3G1wf?5o
z-yQIewy8bOSt(*0k5l5e=h`U0m_}yCQ!5>~6Z-s`UX`a;!Bi}0xjq0;9ghSG2Z3uR
z;#VKRn06bW4NBOIP4&u}60(@QgR=Bq>KfOv;OPfWhzhB2A<tNL+l(nlc7U6<rPS1J
zbw#osc!K7c;2`+Pz*{3X%Y){HZ)N@_bE?_DX{B+HE<~)qBNN$%XFPw-frf!rXFxaW
zNIqrDT4CYBlk76tAY;`8V9t4MFI9769Hk$f#e)g<Rm_e3O$#zqNq8^k6U?4xvd`x#
zoitv;nc^kc&rfS0(jhm1d0U!EH|9JlXt?@F;Pl*x_fw&B`sEU7x=czk>1ORpf^mPd
zt*OsSlj=H)G6VlLcm15%q*Fwi5A8>zc)vh8h|7JZcvYR>vCnKiS_I{eCh=^YC+O1#
zde=rW4%%FC3UUO=uKr0X%vY0QPtv@ZD}evhkg#-ItR-h$ZkCR5)a^0+w-c+{s%m%X
ziZZlgsu_&x?YQ0x`4a5W$WuHPVmcTXVc6412XXUi$Gx<ykg!z~Fi+VP{bo%Q@wP2t
z^Mng7AwaJw-IzxBdY_hay;%LGwD<aN-QkF;l$>*_gWEu>?xqtS+|g_5Hp^K(fmw3z
zlZtOVIFfh57JN`iy(UYvKy3YiIWOU?EQm{4?kP|In95HvfDLJ)`lJ}^5}#i}4ZAuk
zyqJ4rC!)bR;FEBktc%*CbP$cGw>2%3QqC-jb;;UsNk6!vI0dP+_{-C$bW=p`2sIvp
z&2{nYiOc%Qyd-uu-bthSS{HBYXAXq&zTj^{Zc75kRkmHmMSst{w<%?4I4_oUbtQa8
zR;ozszwN-fUV+cwQZMT!qpJbqWRx;*R~g}hnezq@UCqujk`Fst^W87(2!hBn2(Rng
z-=Smpox=44-(tOt?XJbhbU_xRx9hlK>35^tRokRT$Xm`A?C9~aRC?<nVlm%~@!lFW
z<-Q8dbUqi>7ZtenS(3-owCHfC?p`#w`hqcx1*5M*oOd*b+^^V*($6JfJZDQ8&D%+D
zl5zTm0_f*6V?~xnf7N=O9C+)&aV$8ymdMWfN&m@eVu%TiZkjRaMtGi|MzXe<B>hUh
zNWQ@@-!k8lN}CS4YeWk%N|TpRI=DSj&5tS^F*M}f6|w&|>y8<f*YS<Ae&PCk?x^P-
z!s7&JYgwSTsJBYTJJK0epOCe`!$g;oeM2;UL>`x3ywwELUl~Fj_4?GUCyB<)d5iM4
z`Dq<*=<d4Q@v3;S`aM8bioffY>e5&|K9^a8Hl=p`+rH~4HF-F#o7}O3V#ME@7t!6#
zFY{h4L<%GC#CL14PKU6Yf{p~W#vxl`7~;03w)qUhJg5&z<6o*)e0>8Av;_)mL3mIV
zZ1OeUu=sN>EzKcEdCKf$yNMWK?s8ol?9!u#Pku?XXBdCJk_{BO7!d77_kxJEtB=(l
zA!L;@x)0Pbxal=4!}*l&1L7QHz41-6UHSWbB0^!MusEhA!!K4byNt8aa*LTh3=LR2
z*adM9`zTe=FU~T@cNu(aFde1am9DOeG49@~J|TVMoqS3lb?EV_P7FiOs1}j65C1-N
z3U=mC1vOawWn%of`DO^&<RO#OS~+6#5JKO^FDTJ|N#<5eQLndrLs~DbxB#V}afg^I
znJg7hbZ(?R;KDW8VG8uBR+VJ&uVbqp&FXcu8QjfJXHUlOkn+Q!)hK|=9PXt}Ij^@L
z%`Q4-N^$12sjw;CbO4-mPY9pa&OjIbPr-pc&?XFbqV6$wUEC<4$hO%r1Uol}yc11D
zmi{SwC$o^;#mM|F{yu77Pr9cihLD83b>C-yBsTORW&Hwjs;(kPiAk}+9ZQSvjMg-8
zQ#qD&#&Er)IEX_et$@VAl-s5+&6eVH%D-B{rF@K?`|0(DcPx*)Y@ex3nedW22Q9Kf
zL8Vo7jBK4GUAE^%1|>5~;?n<1v+|vBl*7Fd;}ESViKpNfWy<Iyc2?G#JG_Om33j)k
zuYs*tPg>!no5zIJ;n#W%^_fSKBaV`0gzl)>*M{mj<g)>3pM_G;HS=+8wT8q*l*Q^V
z-=?rk*ls@k*N}8ku}!f6-c)cF8MlW`Bj-&%tS|YF7LS=;<za1$xU7JE_w%y2B6;*Z
z;wFYsIhmX~!v-Nb2*RVKY^ld95Svjs<&SjpGhd9tpkl@A`ON=>EsogaTy8&mCk(j3
z;~D-!AQ2gKWdkkeXN)0eaP3Lr^eVX1*YLU5Rgv$>3>TPa)&_U7GatZ;4RgquWpUwj
z&K23Wn>*vZY+L-6bYfxdSIh90zWa8^CVEe;n8EN>xje9%5>~?PhBlkfD<$LJ(zA27
z7(BIt-=(UdO<sLkf$!&efwp!1Pr~!48A-GsDcy%u|JJUx(BODWOa7f)!Jp@2MCkEv
z_%WT{9&4SrH#)qj%rR+-=A|F{ypuA65DhI<MiF-r(wPv5d8+(@a)S)#^#0YIG5bfT
z%i+M6-=F!1#u0nqQdU$mu*FLP!1cT9c!+4t2P}7O$SvXC5si#N8y+s+6Ht3a3RQU$
zoMY>o(K`SkBkR+in9r{1qy$b|9GO*uecU}ov&z)%Kbr-87VGJNXf!nGlJ>FKQCe6k
zNfV__=#q=tIqcNjb#334SjGyo*43m;XV2*SSJ(aM+RvVTrOW3)C4wt1RhU=UOl(+j
zB6GMf_xvS8A>WZRc3W(?sbqKAJ>PdQwjklVi=kxUCID|7O=-4H-DbG<#JFC<?0V>7
z`vcz5|Mq6&h<-t?(<x}HR7Cy%&u{WIE&plVq78fh9{?jk+`fMQ0G)p{Kk!j4zwp=I
zDk;#WnnpkDA8k^O8jk4wzx|B#`Dgu?d>bro7dm<49+JslL|kbagOU(OG4uy%{cGpX
z*tyI8%l-|8Bt>DTp1IHZ*(>PVtHW#`&YTGW8%G2mUV7I(S=fbEQMHWZ(zQ5Z-6Taq
z^MTF<bKW@AJXs%rb^GrF!Y0??(r({Mj{$_Yi3^)`D#Qbs0*n#??_GVSuQHG(d=|+8
zyN_<am1rbUi=lA_6gUKd&>r=4<;<>Y8qRl2{&_r4{{RIH{kD7uskVpXOKWW^@;|)=
z{*+*lf52a9!5o9qxd#6Lf|dT)`mF0`rs}q9EUXbDx6^IinU4S{WO7ODE9~$1EFs<D
zj{&jk_Pt1d<CgXLSNkb{$<25J{>E)H{{Tv3t&CfDvGks$`*r@_{vM29Yq$4Skde4G
z)`@Kr5$l$<UOyV{yaWFL1!4H-pvep#JkoTHe%)U(x+B?LGM>TXW`%Ls`3K)M{T0g;
zR}nNbD!MwAWnqv(;DQfOYw>UX3L*OqcuLd4H-8?!4e9aew;FKswXHJIHo-N%Kx>$z
zQMNaWtf4?gOur1IxEiDGU)k68!T34*OTgBi6S#dpT8RTsqu=dIjBN#;60BBe!N}aF
zaK>FzBR^12X6}Vok9z(vyf@-acftDopNf1jbtRUeVKQ7>Ttardav{pFKVIDrL&bks
zAF_AHTVL8k;Fg>51;lacNLt5MhAi!l_7FGV9*ZKfN~0$V;c`ectW;*tXlpstg@2fO
ziiz!AGIsEL{{Z!?sy0phHOly##C{j>g{Z&rZlz_VK+Umcy`DG_w_FC1loO8GqQ<JB
zQcDP=<c?3L6<6%b0KqIiyw)z2@q^&*y>_~0iQ>%$`sP>J92VM*w6e1vfmI|dPp}o}
z?_wPs4sf|0^HpsMRZ+_%IR%R*J;(IzR-+N{3n0&MIQ;6@_HxR0@DE}|S%W~8+xNdE
zE?YfD>uI^qYgRXm@Z8!*lNryLoc{nSzBRdJk_&sdKt^D)WO4Q9-<suhFN}W&{4;X;
zZT|p_ygzL`Zew_EH5*isSak(jKtUbB71rol{{V*kcVqVb15vZopjO&0ET)ch+&YcX
zv0v7)t3}Ckz|GZ*R+27i8dbb^8{tq8jNy9|=~dvgJs4uT)_{3x26BV*0;{}s&_rf0
zvNcld%y0l4l1b<VWy!71RE(i$bF^U6OQqUR8IDxWM^oCl7(Oq2AG+1y@cp-n^eHt7
z<XB;}O-9(yB(89%v57ac6Tnb1O?2KQ@kfRHBWN%DS*Y1*w(NF^Z*CF<C$0lX%B0q@
zgT95u*v7Tdtn98$#3OT%LT8+NiqF-pA&`qpiT1|0-l>z5>GTx79{7Xs<Hj?+zkz&1
zr0UbE?lD+iL2}5s#^SL%5IyUtk4v$D7M+=#@=iT}kfEt94#iz=%o}ShCTK;lfp;I`
zJ#k#Vui!g5*`?FvXx)#^o<(%mI)$aQh8r*&Z{BPG3HtY}Z|&*iA~a<~o_6z}&aN|d
z)air1sL9eiCmo!PE!52JqX<(VinXKJdD@v0F-^_6<d>2F^v|t6<5#;y-wUxA$qKnA
z@D)CZs@-4e$qS97k_P44M;Yfm>p3f}O=Q}PnbhifT59%B<bYN%N(WLE$n?p{HN@!m
z@>#es;X;gTKylA(cdqX8dzec)sWT`jrH=|aoDSsIF!N0dw)_TD)rV8`6`mfaw9~1@
zg|6jzNVTg@+LVYqkscI@AGlvZ@7lGkZ^D#U8xV8@uj5>u)~fCVa+hJAe5C{b0I!O!
zFAAOBQ>yd@vMZ`|+|BAJ^76B?)TUd_J%lGEWgCa8*6hn=s6x<78p#uF1xf3H*YGvw
z(ce!jnYJohJSjXMY}U1=yQav&eEA`qe5NS@1rMjDE6>C)WYTAyRy?lhnHL&W{{V$G
zX)di${hiOq-B$;(_Z85MLh+R)n8w?NRBYNu(AK@p)yMXHh!tEo<T1{D{VJ@X!4g>%
z7XDg?U~{*D)O8e7sT*!|LlMliT8?XmlVs}-*%aWmK?EM7*0e7r5Wun(1z2Dn-h>*b
z5=9dWsij$vf&_uPdR2%X8H?$6Ie4FxCy|5BIOs(xjxJZZ#fYizlIeRyZln*8@;z0D
z2CnQtqB478y#D}D{?hRD;HxoAZAUzYUdqGNe=6=Y`?$3mB)W_4xaZr{eie+Ps%JMk
zZrx5k^88v!a*;mAg-QCae`={`YMR9V0NR?QMG)-~A{=hV@nij?UU#qRSJs{(md@@#
zB9*+L{Qy#Poc$}%Kj9(L=eS7p$ChMJ12PPb1^~(WS5y;((cOh{XQJupWo}B_Cf;yQ
zZ_d2i_KR%(&we{N2mUF2U!VJPn!5{4MI0$|!`N4ze$bZ&Pucg!K}o^zzODZNq;l74
zZR*V`B#-Cs{t4Ux{jfYH8=+lG{{T(bRsA7ro*esZZT1&XPa7Zg!LR1){t4xu_-o$@
z<NLao{+q6=`c}KSWH`ZKJv!Ac!lK&M9yxg~yW!80_BIL#3nKjLMtU52eJPjz1-sTH
zTm2j%g5wXgHZ~LYvz~)B-uRBqH9ay>EV4k#rM_hhxE+c2u2VvbRDC`Po>;a*yOe-I
zWgL^}e;VndT3Bvr#qMp1s|iy|T3FK4{ugO>@Vq+u#WKjdNl}JIJqobJdG*hP?>q&r
zEEaOS+HLC{io-1ofM=e>8uxux?maryuO<EB85N2D0A;_zxc>kW>sC6Iv*`AY51Y(#
zF~aWZ05!#i#p3GXRGaJg9!sk!E=0~nt)$%dx07L1ZY=r0LFw3kTHriCuWOzo@l@%i
zLS)ktGJL|ks9)kFdY;~u#(1XJUY^EPxV)4^l43|*hyV}fE7X1vc#?k)K`xx}p+-XF
zp11<NdhO0uQL)=bPLyuh(L<@SF&3|~LBg|)rhftrS@5o@ttX4p<5?EJJZ%w`Tje9K
z0|f5+^{!`2yw$u(9M@V!oEut4;Hh!f^!f_1XW`!*nHo!rHE<3<!pHayMPZ2gw=1Pl
zwRCh%IYG@H+2U^w*?7Z8lS|Y$GB7U_1A>kRABVMkDRrgKWX%(utrHM&&ImZKu#?0Z
z6~&NO`(2ixoIFt$R#pQ5o~l>tUU8{>IoJGEaF>>I+IfwWEYgNX-QPT6zYJ958I>G7
zrH91M@#uK?iWO%SC}__M@c#gVZFTF$wbibfORE)y5u{=dcLRk_)|scpJR$Vi&>yn7
zjBPwJso;$E#eHq3Xc|Amtr7J5DHh{UzFah{IbwJudMNBG#xHy!saxv{rulJyq&N{g
zjq@%?py~Nm^mC_Q7kNU^)mzKEJzORQDiTTFTiyO9PLZfyX?8DcU9m4EGs@$k<BXc*
zHH}qo+-a8vSBcw~Y<!3L<LO^Qcz59{**LiI44-OMJX^$AC)JqpeuAlZ$G{#DhW^h|
z(_&a-jsF1BU<^rh&QDX3OB+)M&gC25zeBGLI+2GXMe9$4-T<1)ITh}aomGT@j!7B$
zayj}}58|JM`i_$WYF-_?YwJWiZWC@LkKb>nxUZxxuJo}Ty4pkyn<ir_IuXze^dr4N
zVXfOW!H#DUOv7Mua0u)h)7reXoZ_Q3PNTd}8k8zRa+Fp3o(HDukeg_&rgEheD8n3J
zdwSKaP9@gurO~8zQ#xWdJp(TzKGo{}H1I{9xopzu@uFYCxm~0nlO33J&!Mjc(=D|Y
z*KKTORv2~#PhdLnUbPHND=v5)5SKr^Som|GUx&T7Dg!PNob(30M%%#Fzh`+o($ZrH
zPXHfWe=5T8_OETDSzW@=yte{($|%QPnLUMcIzEZ6YZ%q_iHa+>Rt3mV2Tlj`tSeh{
zwlbBhnQOvc8MTJs-^=rui9{d!G4;)QHqhr}0e#6PqPK)Y7>Y5pDD6tovwZ6#N9|r~
zsbNZYdYJm(gyGfV#L|I|pacBtJ4*0}hxBWuys?1KGK_{|NDb?bmD7m~of~jo-t^fn
zty?k9Wyu)%N6U(bh^uZ}=w*BEWMA4`>bip7d1WL)wVhi7j)Un`rEB?9?3YTfBDMho
zx4HDLou{?8LF6z<_{yH9nz5_yo+7NI^#>w_C)CoLF_I%17_NW-{{VKE?H0BuVvSI<
z@yO2}^)2LLM-r*|M`N63tH%nzQJ-A#n%eqU>54?L{{Uu(65%9}ebT^SeFs5UTK9yd
z*L4W)tzQV>2?lYq9-ms=@*xsrA`|?oNAm*W9p7B>TVd+TocW?Euu_ef-9JRqG$;&K
z00$%HeC3JvG|S1ZZPi{ajO6qupvMGMK2^go5rTbb?QB)N$H6${j?`6hoVnY$Q%Pvd
zs0^^QlU(hdSqLs$7~`P-0QFRt1H$tmTiEBeW6|T>>JRJ1U28>X_eOL6d)8xl(T=2Z
z(={$IlK0ikZqxT2jeBj0ZhSv`vSr)43x(j2c^S=Q$6&yim}efNJpFpqYpKa=8H#ws
zss=zj5$l6fTWY#%F#X|8-8XUudlOEbCChcNr6!%3y{1LB<{S==zr~K@wR$!Ua5lvj
z@(`>E-o1J1M@sKE4{A;g9Mu|cL;u$Oe$=jREpJjgg>*3vSmcA>@~C6JX|9>Fs_lgs
z;~@G~*|p`;^%!k+47?NN32)(2I^*k#%X_UUJa-P6Y`{nMV<|ni1IOW6r5EaZ7Lr$M
zq1H)sZQ^EA7V*c-Kso;a8mT3wuc;|E{z+g6;zb|Ix#6QveW*z+GOSSvFtRzpbJXB}
zQ(F39v#}|4btV>PWw(u+mqpH8`yRkoHB3yDxolyHs|qWbJF_S3x~;6pbr@u~+NM3<
zd2yVK5ypQS?R3jk+OHJ*@ySw{C)ga<HR7L$DWmAumv*v3@<ARx%GqK|4mutP9M>n{
z?~6J;^^9_AmhwR1KzB??3CB_w1b$Ub;-zIxY1d6oU#)v;UeeZAvF}38ON%23Axn1c
zU(9#>6fPMp{{U-`6vHpxRMUa~0Kf}s{*m9saTvpC%4Cg`cbI-qH+1*>>-n<(0D^(d
zJ;&{3;(2ZW4pUCfJ*+K&{VQ5e?6W5sC3ms=f%_k}voGwk;M-dh6ex7_`{@YIJlQj}
zSJ6;g*xE!wLH(mXe|al#qw#J(D*2c8E!S2b+0VlIb@FaVw6QcrN6oZI`@!|WIj^AS
z@jr#0<V4d544EKJ_Z!p>!25Tu3`OBl#%|5*MCsIO^wrMN)5{1Vks>PJ^UXm3p4lBS
zQ^x~ZPvy!9Rse?jS2?BIphmc{5+W19iNQ>LIpF#eU3^nq7*-jPoPT_dMQ?>|M$!s5
zkGjvtPxve#AMlHK5QCre%}4(LMcdcqSL~<%04FoxANv_}-~A{`{Z;<}g2EtA3wRI@
z`TW#>^j*DvRes7}@^d}_{{XR@P5%JWi0inwx&3^h=N_W7wVfkP@eYr5pz9F{?C)Tb
z*5W4U@k*r~PCGJ^4PK1=*z~92mAUEbL(n{r;;+VD9k}p+i}ha#UU4>;eQ{$Zf4y%b
zMt@bW*kAY~$BS;4Tk)@eoaPyAtu;8te(S{V445D7i4!ONkzbU58uiriN5)SU_?j>R
z>N@_TcA#_uYndEl{`#-d-}oclgtB~I@m{DivK<K_Z%xwN#>4T%W`a+#zu=&Mwzi}D
zKU%@?!{Lj@JX5J#{i1ue0zs$SIV!9a<xKJrN(KZfjm1vouf$)4e-6GZe%GHE;qbhA
zg!+!Hf4buLQq-lAZzA42Z<<$P&9!o>pmk!ZF_Bh(Yws6c_}})O@y@w>A(v*kcV!b3
zpWInFwuldJE+xf%CI0{f<M`|FQ}%!HZ;8BH@o!O+Pnzz=-p0erwwmC&G>s(r@=^BS
z#}`~@JZA?07(cbY><8kn*>}R$emb_+<M9rWJ;K^tOt#--mfmLsreiZrxgTi(gK%&N
z<W!%rPwlVckAdDf)4U_&4MKfG!MD<-rNy<=w4Y!O*jep~B*r6V-6U)By6*XR?P2!s
z?W6l|e$2lazh>VM{5<eJuPyGedTg4`87yvM8=<^{M<L{iRY-|nl16%W#eNOY2lk=k
zPZRiO;r{@IH7^}_s$pxVX;!jcT}$a4N?7236C7bwa>Rf+HT=7N)1S7d#gE!AUAne=
z$@HHLd4+8>TUi++F@uzAlB9X^oB~UHs(#=d{{T@x;Gmv6)$D&^9|_tS87J{0=+IuN
zKQp4*HPeRm{p?ZZzn@9~{Wtgl{{RHb@#n^$g|K`~)^+H7JT)l%{Vvi=R=8>5IoTwv
zC6CHUI;IuLQX3qAej2<#6F+CaiMQHCjrG=v;tdM!TS;cInURCWO12#c+>AYVWju@;
z{)9Xq{{RIG{fzt{;T<c&c3vX5@^rf!dl>Pi-eYCFYa0Im%O6Fr!@t`r;x~c*Eq>BI
zB>08!=3{ZA>L$&gl*I6tXzbvQQc=}dA2lC29QE&-27deg!v6rZJ@4%;@VZ-{6r^Ls
zS{#XKsu-7I%p_7_xK$i4niYd6;5xAYDFA*U{@4Ei1HWlsgx8iD=f~ZD`+ap;C5q1F
zZ|A&HVG7KlXH;mw4lp)>>0eNP!62@#hs8e@>8YP0T^1>y(|lJ7srceX$Kzhf{{RJr
zTzEgg-}U`}{{W)lqR2<%7s8JlkA|NUG#`k5N7_z@sN3o*0XPyhvxyZ6>=>v7_OG+`
z@7aU)<o&83Z;rkq(<amgrl#V`JIlCj+wAU3ptiS~Me?A*%xm(71Tfv4pOn96uM6oO
zHu&%0?+EMZ=Gf{w^@X*}f8UBp&^rGBz?UQOuk2++NP;v0RYm|D00jVl6G!`V!n)aC
z^o=`F)8{I2x*6b_Cczz<9T<BMMSntn@K0~rw_Nz+;M?B-d_}nXUx++Bk;s=%{<8YX
z{y_4d<CYe}Nc?ULgyoB|{LTLWf|htz$HO1BuD3p)7>Yes*3VHYqX5YyA~_g)yT;6Y
z$*Vu`Os#hJN&T&KjWXG>7Oi2ZS=^^*jV<2f1N4#vZ^DGq)<{xUS|8AL?S<v!-c5oM
zPa8ns53fO0HD3r_=-Nwb0#PK<49gOZHn#@{)C2g}uS+y`(7PyDqX3s91CL+Ayeq{%
zH?!3A$+YV)-p}O$EXqo#O#Ha$cOU6lPY{~rg0lQhyeGAtSjHXB=GyY!#?BVDc+<>9
zVqL&EBmDKQ7ghL;py_(-UuccxWj{2~G83Ptab8VjqiR~mh0^0qa<ZF-Qx@zfj1D^B
zWMes{v+$f&amlXhaw`jDX#{by?IaB4i9dJPb+1B-!%qsUN~N#Ut<IYGdhx|e4;0<H
z^ge|D0EE-STGh02wc;eel=9{tQyt%QDjek1JC=@H^8|S(JOjZT3i-Rk7k23I-`oJw
znLPNT^jz>*j_0pEE2F&lxv5?1M%zTQSrJ1ho>79M_^`wHaqC?8dU0ISwxgb3>?iKM
zI^6mq%tvV%l_HJF$@L(5{{ULC7O!n}A+?fZj1J_6C-SeHtiB@ak0@xQhd=EDxBmdu
zR&{TQ9wat_JS%N%w;01rvE@gu(0L}JrXH*sl(Z@G%IYrbx$o1!mJ22Pk^=}|or(eS
zllP7fQClJ_XyH>P*AKKVR2<}FV?NdJe~3Io@jJwq(didg6J1Q7yz(u^+-<=u!9WBK
zI+I?<W%1VdSVs=4r5n4uW&Ptq!B>1^7$AlLy?qUEVySbrGoG$pUQtq~tL%LwwpRBF
z$e%9ZjDgeet|m!Fk*M57C(8;2ib6UZ@Ch03-nfs2o;&eB#2*mdOQ~90+3Cv}5u`3z
z#DfO~&NuPb0Ajrd#V@nZw3rzckbLKY7pM$+9jB0b*A;3Ljg*$>E*>x1I$N<m+?Ki}
zz>J9FNq#`x0L~68hPv0|hJQNTg>IqHxH%p1)B0CUqa+%xn7R(A{{XpSW=Wx98`N{(
z=~<eG!rOy4nWEmT$<?Ere877Z;Mb{ucx9!2gjR%AX(s&+^3zbT@pg>St{t}GL_c|3
zu>;)nHP2}_+QiW}_Hd3B2Mh^i=f4ARHCITswD9%P3mKY6W>{rcLVy$KNEsfT^IX@C
zt~LEHUfHF{Rb?Y<M1T-M&Iujs#jRaNy#B7+ihDFg91Fx-nQCk6R_4}KI+|NuF6<R%
z+&Jub1RqN5w5@ka8baE|VwU@X0U{Y5*x+?NE15c8tF7sh>DtsDUEzpaMIitdWgKT7
zy$_{w-wrkS^;?;A+qCkobombSZo7v=?knjs>a;P~H(Dyvi?d#b7qhE}#rqrR(y{7|
zY?cU{vd+trJ1IW3$9#IV+xs|p=3|Y=!rHk$nEM>n(lTAFpk6B+w&MLk&%I>+(F%Xz
zojh`2BX9g7y0_)_DQYC;do$Opxl%u!{{Z+WuH*Y%_)~9Px{v;w?yLGmyj!i|RT+Ui
z@PC-E=JWmu=fD0M_riW&18vl?o}cm#y07U{?(17s+iW4vsct_JO*?87dmeA&-wtbc
zx*U?*r`h69nbjEOfbM>kcTAQ${XQhqrB+cArq|qr@r(__2kTs)j4eES_L%SEkjR9T
zF~APqbJcmyYin54EiCNduxpEWE{aCtDMR5mDC>d8Ju3)d{k-KA*%|FmV}_*Np08uN
zv^Vm^`gW%aDl%;a*C6_G3H<B8yi*0{i+tL3(thtd<8H_4^y^)|q2o<&O3?S}bHvcQ
ztZ5($j1GAtoRN;F99G|pzu_M7j+UBwkvK7{+gw{PBs%s7q52xf4Nnn=$ERmyy6(E3
zTbfu^TcoyrXUsa)?Y!ZI*=ZTiL(t>tUC)KnQiz>a;@WR1<1r9asoKEu0memoZ-aab
z;Qi~UlzZ#g+jD)PN^B>%kM7`l@+-d8jFBQr>9~+lP<0HzjN{N(qm0i|s;Ob1_1(#A
zX;0cXYR*&O#k^WxnR{a!27fz#SYrx(tJY<J?lTuG-AN}W9)NTe<$f2@r|{M5UEeTK
zDwvLV+TBKbVEWf-sQ7|AV3zSPGCy2@2=ydpy!?FQI`WiV#fPaU70tLuaSPqN(!{%o
z!Y&4Q?%Z|ssjbU-rvQW39Dj(cU$@4?Y-TVAQpX4I9ZfIpZWqfXhocR|ek4~1v()Y8
z(9_gBEuw2uiEo}$AUu_9XWQ6)D~r|s8A3_9(k|p#Q<XAvk58M5)ws6TZIHZDq`-dw
zBrqS24Ns?Qu`mgGS|%Uf$setE;ii$_hjZn;8IPsk>ek=ro*|AznGOW;AC>*cAFXuo
z-RW1i14h=Ojr`G=-GKRrvs9P%@?1hLZ2YUZPZA-<;5|X$*A=Vj;AzE_$qbOOk`;Z!
z1GrL0L-nqT$;vKqdm2WhCsUGI4b<+n?*v>mvq$IY?!=-$Z2tgs_BEY(sA(Fx7gmlG
zwl^f7D-X+$%DdYQO?BNET51X|qE!M`?z!pBLt&zLF2#=Jc{9!>MqmfAQHteKgjc*_
z^@UDNO=?hGT1%T{c`v25^4Ds}s-)n0mIpPFq%06?%dXyCeYlWNwg&+F05M%;{t?pd
zr5AccvPP$>0va>_00BKK4)ekHu(z3Ea|(mi8A|i@86Txu#6~~EX0#`VlYK61UD;{&
zm&<)^0yI}6CBVoeROeQ-RM-ezy_cY>8daZ)Ev?$zLk-d%K;8%k)O0mg-V4Q_$kgLV
zloPunErayu=~~XSYVrw3mhn#KLZ&N~8($gc<~aPS=av8pnB%!>$b!>Nk%_lr25<)_
z@~s(c86(>ZtCD#KgT->W-Q41owIy|E#`1zsp{QQ^7~NN9-1Wh%`>jCP5F{Am0a$UL
ztwc3ZFxb(idwS-ygdBn|L!y)H7k1yh+zA*M9X_?s#SNq^P&9!-!8sqwt;cZ^0Kpg)
zIpMn1i6qf4$dSv(FH#A}{7x$69$k8trj~_tmc>|+BVd1ipC!_^7TkvQ`cnO?LUv>d
zGM!1u2B49xq8qH(9mchby1CH`iJ|MgpLCEnKGhu4N+ejLiTUfhkIJkDm4-JH$E{Om
zDJD0Z_Q~d|)-_VPEH$)_yIkxCw?HbnuvmUnY_D3GOYbWro3YRyd93TLMi|(sV0QJz
zX&K)^G+tPUIEjEh=K`~@AZB7B<&k$jIRntst**`7-dqEA7jZs-RN7qdNi_FZ&f&Cy
z)Q?J^XyGK9(VE3wHjZZ1l<L;;UL3<I!yihs;@=I&quwp$vIe%0j1Iv1AFpa=rJ}W~
z8{Nc2K4ZWoJ-b&zBX1XYdMNb0PB~yF1&n_%+t~BL6;<m-oTDq~a8#)%$v3gZXu70{
zt6Rr0R}xHtM*Ni<hhI;nd!L-XgY&LuO7QN5qnLoivX@_;MGVD(?Z*|}Vju30`{uKF
z`Yq`b3Dr&AAOF|=PtbJBj}-+?KY!T~zt_kJLH^2T1-}80dhPWu0NPzVT8@KoVANrc
zX?Zz{Ks!3;Dm@51cLt~rh<Xp(zqD=V7Z%S6NYXwXGsoRM39k+DU&aZw6LVvvi6Vm2
zZuKNHIA#iWpW+|v^sWhF<tp-=<8MTJA6Ldzlw(UsnipRb?KCJarn-sd)V36Y6NW&*
zkV$OgZymWKiuwNY;$MyRonT(-vdQL<5P4DUMO6d<sy@G;t#$)e(Wi>jQ<V})G~1so
zkmL8T?k+gbVopCQ^q&>@qx(xwn^V%m7^1h`vl!)6u_ufSbRUg+SZefn{iSMr*6FqX
z05b9U9P22?6*+Ia-~5ge+U|WXLxE*wYq{g|-_2kEcKe`?q$uURO?18t(JjJ8ivIv@
zxMDXrR#;GVCy+N1PkO@BAn+fH{0F4!+Ivj}^8p;wM8%1eFU|-&40RaBJDT*r2I^||
zZ>1ZyF9d}aZM%1>o(N&z>ME-HdTOOxTc2xwOXZ=ZEH)lAp+YIOPfdIGey6LLTTi<v
z(Yx$#-L5`Sf%lI=pHE8ub^icw=V?D{4->m%B{cGIbN>J?7WMrZ&8(-0G-YURBYDxb
zI0SAu?nkfEznpK|*;zkopAxH(PnW0k{{XKWe@aq|<x+i59<t?pto?KSn01&mKiT)e
z*P4W@3_`_5SQEJmxN(g0o<@6Dr1+}KOS!#BZ3+t)2{<IXd1&JxGvE%K5`U#|KeMg0
z-X#5({2SrzJ%m?FrN|?Z_)vVuexshi4|?u2UlVE`8iqR!VPt!oNkLU+!I2x~l01?}
zURSs2Mqz=ii^22Iij?4&yVb7i<)~os3YApnqIzG=9SyRtguc&VqufXqWm)37wV!-W
z4-A9Qu5v-oY*(}Bdd{aLd(G6c`j%18;a)YQE{Uq?ab4fs1^ZYGl0+00lO$}N7v&NH
zxZF9%2b%7#@3fBvX*a$i)$SG@KTm>N$?gdxM+{0{Mqm!_l|du{+OW!N(8OTZJX9wg
z8vRb3eLB#qbh(r2er*2$!Cr1&<HEiLGIbYvhire#J9_-J{gn9qraS_F>Sfb^^r0*2
z&-f?@?NRW%_Je~!@c#hAcD=Y79nIU#cRW5!60FM-sf%-Yr|uGW#+|sYiT((9$4U5m
z@r%O#JJ*D!%SYBM@9sn@8bcHktG>k~0@w!tk<WV4Nhf4^_v(LRhjw=_2N|y)`1A35
zPxveFhh6xSsyZptY~8Jqx$|zH<hPF*=X|W7`V5|ez9Q6r;HzJ=hMjEp8f~t>dnUqC
z5o;1<vBI-F;3y}!&OHr&NB-5{wm**l0BS8>8++(Rk>TqZ43=6<E@U2I7&A{M7tNOh
zg*!r`k`w}2nKHSqkC5h?TZx|HVp-&3%*VMWB-ivw{{RG@_*Z%0zk<4_#En`%E?*X2
zp|(?un@b(>qxy}~yx0e@2cfUcFWDdV6!G`Ry=K$JJ}|Yo(mWp=izL@mDUMs@Q<EL2
zU<nQPOy6~R#^xJq`cu<1?I*&TO{RyT+ec?%WdwHC*6|Io#T;rC5kTk_Kmv^~U{P%T
zM*je5EfQZ2e$ak5(PagEj}huYH+3<!%#9m=z>tIZ*VljWP5%IdKLI{Bc)!G+3HZyT
zCaZRqcK5naYrC6bs}s6hfGy^WetgJ}&5{C~cdu&y0D_(V%$mb^oc__C2eo^fE4X8e
zUXB&q&`k=2OOgjZQ^pk><K<<^KR+Lo-wr+^_`~5pjVwMOcn<BXbxk@#@Js+<^C?zV
zRs~rdSgR=*Ak&muc4VK^82<o*bN<GC{2D*-WvPGiT{M5eI=^Eq^Wf3{0D~<{{{WKf
z;;#(<0Ks6aPLh%M)4_86xncrIH2cBi$sin+Nn_m_6VMlOJx^Mzq5lBEMgBf~fB2oE
z_z&POg>3ZYmhLGpP2;`kHtOHKX{1z%V1Oz4*dcs@wX#B#u3x)j*ni-xJ{E>A*}ucL
zI#gvK(lzUN7B$IO;Cowy9G^m{qdv9#^8x_`jQbk?z<xRWG12}yd??m_7VB^2uB~md
z2}15LTt-8|E<5azRfq&+WM`WGPJSi)H1VIpKN((l568ArSn4)TRnf=F>yx%f+oQ(d
zWms@UHmvMRT~FxK!9TFK>_6kr25Eja(L5m>mb$H+H;}Swnv)H)AaF7+`i$WF*Gc~X
z4($Dm^Zx)MX#W7gmZkpy$#wYO`zwFJK>U6CIp{y|f$KI4;x7)zxvec)U$f6>?#0{6
zmt=s(%*Bjsr<0Xs<R7s0&-g3`iyes`81O~I-85%wUSA@{l4F8GLdDb`*;0K+T57^S
zOg<0(%|8SF0O}XIpTd0%X;bPkBsWj?o2!E987!myB56Y4ocy@Ydi>}A0D{AnJ`nIR
zU-|yO{{Yc&*ViAhPsLx4Ul{xYf8+lEjaD|6Fkjx8bVPy|fz7F0u_(lo4(teBiFWyk
zByA_;FZe7OAMl5G6a)VNFW3410Kah3X%C_KQ~M_I`%ZWR{>Du^{{Tm&e_oOhITieE
z{0{N1lknf;Cx!ffuO!c9qiYtI*DC^sl*I(9>=9X%7Qo;ZA6ov3d_(^L1uOlNbgv4_
zuW4HB+MUwHzAZKjJDBbyayU)W#E~fns~6fi!8~ONACjN&R9$8*Q}(dEv5fPk=~fo6
z>{>HF@|ac+{1azL)i1wltt(m6V^>`w#{Tkh-*qE+8rT#600W&z<6kFyQTT<g{9*W&
ztNcaRS2x<->)aTl%LEQ_97&w9SkNqJI^YWZ&;5ZvW{qRuM}sdsaq$+>?L0x^6pRa3
zOiyQKgtEsVI4dNrhmk*r2p9%d0ZMU-y!JGwN>wGv6Yp;kYBv{pY?ik&2;nWhX-;<F
zbA|3}=iO%d?aJGDempGlstN8RW@D3qgd2Kx$**y-ztOdQM;g>+A^EX1#{n=sPXJfW
zJ}vl%emq+GUW;!H)|kbTHI_Z!yo_#118_mf>D*RWDiyJH8lfBhABo*lABM`5eYWpo
zV(Q~V(4%L*nr%vGq$*lMQV}QjLLu9<cgg(gTUgd@rtplH`oz&HT)M>+9T|Yeenmfb
zpdPgE4QiUpY0%B$CV}EDw&Eiis<3aH%Txnz-TZmUteNy37W(>aPV9*M`3}`U5GwTv
z80ez|k~-6d4werQ8NzCN=(V%`PfHDrt&gE6IxY*NmhFCr7pQA7>yrNfWav=LTVNAN
z5UYsC8&obx+;}I`irlpDywchK0K~%Q?2zsGkt5wE)&3s4N9$dNofeOy*vST(^IC^L
zHPUY*cWk<iym}hFphK*|cO;NrS!-TsIEsD`KTxEcS9kW;vzIH9S9<sKR|l0+aOY{e
zEj9ekTFMO~>h9Z3)8k~ejf>1%EY26E<Hu9jcCMP^UeSCd8PxR@SB%RPMLzD)D+S&3
zP<mEwuL4J`MWTx|wSOefFp6Eg=L)|zcs;6(&w^v|ouBp<vdawa-ezSjEO}3x=3qD+
zV<Mdj^gguRNi`Wg-liCuHC__X_>$A1sjb`Ukm?$R&x0mX@<@?1=)s&rkbYF-hCOSM
z)igL3!R;)XOKW%;q>|*aDoX5kHshVlH)QPt>Tz9K_-|5e1~0Z-!}hotWAl=3UM_)-
z-hHxgeQV8P)~q!hX`#GG=JI7WR&g^li!*M)XWhtDBb;^VT=Z~rtx?7kv{SwOy$Z~;
zC6{29B}F=oB$Myeze9gS*0guGw(!P{6zU~`pA2@ik|8X^uSNu54&O@7)wL~GS+<Ww
z(sfIwc$qfad5mIJ$=%Of3=Rq6xL*}ZbK!3k4-aTlE$*Xk#hQ3W%L2K}DD+|uJ%xHN
zgf#2>cD?Z=WLl%f@j=^boEB{Re=}W}`cz#T)OPLM_<kIgDz0X#En9x~ZBB#0J~P$f
zv%S)mV;r_jO2FSZJ+`KM?&N)Ivby+{@dHG<g3#Mq%r1Zt2}2~Mk8P`rpT@jz#G1B+
zpm?_O*7nMK%Z)+dkT772u~BywY=gM+MmpDDt@w`5!yX=w#IfJWb9gY8aOyKOtqVUH
zC`rIU&jXxS1za5JI65%DHkb20hdZH3N_E{{R~37A^gS0}@#eaMTYYO#(`|$VB(eD~
zCLHuocMg7)!RfynHH6e8@ZO;DYARuA#CGtsGo&fifF*+f4<J5k=jYciH8>=+XOiMu
znS|0#2GZ@%Kt~z+R}-%`yQCQWQ=o!p(&uJzoRyJ4B|3DGcLp6gSGgV;p*k(qpDkXR
z-|t<Ij=*O)tUe)CRQc)YeIMZZGxRe`@zQF#Y*+pmy^=v0#>w*Pxg8|P+@I%~^E;0h
z=o&`7F^VB^tlhZ#N7#vY!w+(J2fcjjbK)H@UH$%r0WCZ}V}U!{x+~z7M~r3AbSl}$
zZY$qD9r##9qAc2VsMDTNw5#ERalu&>=RW*rxUV5Y5r>+mUW$yL#M5nhrRV2!?p7Y1
zXv-IbtIZwanzLR_`FS4o;olZs6y0}i_wkkA{J|Y-7vs*k48OC_j-ZMbP2L#Qhb_}K
za@J0lr`}r4<lHWKJ6Lo+--n^9Ume+B_I~lJ+p8#F4Qh(nIT`yTyo&3@QisHk9agO5
z<1~H@{=jn>KWi@wO0Ba4Q2Dy!HWwnlpsyNulf+td?`LhX0*&5mN=`xVlaXJ~SL`GK
z{i!@RlaJl%CqGLIn*G!91<ab%zDyyE`vwAk5FBtlYaBiqR+3IuT}M+DIxf*yJht=3
z{ym#tir&XnoV2VKIQQ-aiR0wpSGj5WCyMVGZndp?YllD;;}FIWIT;JP=3*3bc;>vP
zMeu&7qU$ZM-a@EjQey<=*q*13*v@;`sxOG=mdT=sB1a^p_aH3JU$D;r_CKX@=9TA4
zvY~^oc%`kESJ|Ha2Lo3Vg;T`RN#AGso<HJfHCx?P<ntEghx$BnkalN3F~WhyJx5yT
zv>5e0A~_(@<;2VY@}*tq?fu}K5Pilfo$j+?pj=;E-@LYk;5bMZe2A!avjk=V5S}-6
zu0u=K?*1HV7g~+Aq_f^!!mPI!QmgGo83YyFe*wjG;joz5MNU!Iz4d08jMpck<%6vy
zZr0I#!d?79*TwO&)Z^K45h7J>=tpdf3;|x<@Z;jB7t5jRw+vvAA0}7J6mRa5KZU<T
zURf@uXYp2RUk}GTa%xgWCbphYv9tdGEg^Os@;P4KhN09x5qPV^-@eU?LJ#`cB$a}(
z^+d)`rdt)?h|F?|m<2;0E?SzaHLjaI51Pi{EB$Yu0F#5WR%+Vzx%O<*CGEp$lg7!s
z0v`mgJ&5g}QCts*EVX?MDw&Jh$loiY0txibP<^Y*{2k(N5O`xwmK|RCwK0sToNd~x
z$O(^`K<$!ixYK?s>6-LjP1S_g5u*t)8t%i6g_(Wp!=aQ;r3Hwlz1ONZ=Zlpp(!<SO
zN!xC}Be>GE4Ql09?p0WW0C!4xIPZ?N(p>1vd8ZjP+lKRSN;B{u9>bvYt_u6&#kI5z
zcXw-Z<`*I;6?kRGb|eAsoc$}#wLgq{xYJ=8JTO`vyH(to9aT>*asq%l4l+$DSPG6!
zG<pu5S-~|MU%rQ=_<H+L)lAl{D}x9jf-o<U!2mXQ4{CM5(W5ziLo$EQCPB~r@_71J
zn^@|eCBMb>_1~0?s!2Ik-PMRx9^*V#Z;P*WwlAu9LiOXC+y}Ucg5p7snS$g0039{g
z{hJCriBwI#hkjj3wdd_3(p`I=@uXbo$q@!J8G##_zDyr%X02*^&YCUWD6j2FBWsf;
zHi765AB}up{{RU#v#Lp(%b6IFgNG_J>;VHGg>;&3fvajUnJjSm9T|ZBb=Uoz>8@|U
zxwyIM_nw7u;(aSe)Ea*e>WF39+rTVxM{I$D4R7dw9PGT)XX1#=BLK&9ZVMx4u~D3T
zMSRPv>iVCCraF99$oG@8#l4PpHv^1y>s`?B-KL#y9i`;)M6t#LvwX)m+}TmzIqGW|
zWz?%Fxl5MERwYJJj2h-|XVmwP7JOjvo|kN-S=`?=qvQf2i~j(9o|VjaXX4JG<858M
zJ!Nfg7A=g3va$0)PO8Klo;%kU0@?gZ(IvLh5>oc*n|p@BB<mQ%4meZjYtF57e-wCr
z`YlqyZXwky!z^PP*rtBz01)7UeQD6jaMkHbttz*-O}0K_q_KEBWGL5#lY8#0eFHzl
zSUf*?Y`j5Zb!tcaw21ks^#mN(f1vn_M)4?H{V3bs^MYB&m_Dk+8u?xy9_s^AORpR}
zQ`=35i=qYcn4gro0J!IZMRt0JfplA$&E}ZW*<I&yOK<mI>btx9RuwSuf?WDPGwS(O
zX5kw*<b6r!fD9=RfRak7z~u3f&0U#5i}&iRS9=0Z)7OA~tKz+O&TlDRZ9yiyMcgA}
zwVW?fr=OU599H&`@oU9eO`LWUG&j3=A85`(_8amE`W|bV7z))c;i@<>I1gcFr}v*v
z&NK`3`z-eXTyh5ds-McTWboC>OaA~8_gP~90A+(5{{TAji+_xbo!Zuvf)RjCw3#E{
z<{15JwXoK-U2RtX0K@l4$lYR)gqaVaQa~X3ipsji;UUk-bdEUHq}{o-I=0c)%pkqE
zF}$2F1mo*aNcXT#usfiZ5saQy!RdlJel^j}ZXP%!lnlohaLmLioMfMK=~?qYHM=Fh
zge;N+kCUS>zfP5fO4Vv6+9ESfrKD0CVV@>cmNj9W(czar&biBr!KGXaS)whI_uC+!
zd>ZN_pI6e96}EMaPID2?e+I~>Tx*^f@j4qT*<(qNU@M)HsrCl7!{SnQnb8K(iw3I<
z-!L4Ef!n1vOQ(k2Z{`00mZxlBj)S?bQKWRcmfgpa(B~OBI3G`MO1_g_YEKehpC;8g
z1^9LzyGK7t@0(9mvENNQ>R6LdnP4wy*(J7ud!F?DGef_=3q9Ki;$zqNkI;XOZ%wIa
zcKUYKxAN?l{#z0N$nT$8^O=4t_%BZLu3@xxia(VmVsh=+AQ?G5slh3(XoRNMyKPTa
zw$to%H8-s87X&$xl~*5?d0wyaH$~Bd!{Nxj(BzZ7vLnx~dY{9J@@-eeT9=J3-&xlg
z?X02sAhu>CWb`aMpIVkZFG-h4{?*kSh?tOyxh2$a00W%2YU=h7jUR0}egx9zm0x6r
z>z*gowfIBc-8=c`Y&P+HsC{w=Z&O*G8`V5R@bgs~k&$GMR@o)G5@K&|Ixy^Mf;P9j
zlJ$WXQmVxgc7{H{4^TSSr0e=54dDp%Yf`4-FeZ$iNq*$#xvlD9V&e)Ec5~}%*%b*>
zsR`a3$~V&cv(=*U(2{Esx@_|_(?+4=8CbWl2Xm`ujPeTCsDEoq{{TE6^EL3FgmqnB
z+r?UXNh>HfaU-&vG5NqMNa{Nr*V=z=m;3U5bvT@or77~Jne*_*VbbO0dUrqn(EATV
z_+@!Nh+@B*<)F5TM0Ljw%%>!6<Zbn1$E8W(U4KkIJeuvE1+ux7t<~fJhD%(X%t*m5
zN8?=&#s2^wSa?+%!#)#)t<BUy<+$@a*p?YrDr3*hy@BK$ZLgiY8LfDeO1rnS@Z5$%
z&O}zSM6zJ8Ae18vJCmN3^m#T4wjz~GRwgMXwCI<cxmkuPl@3ZcX8E^$-`ss~sQhR6
zaccz8XnJfqmc?z)3@EV%1CU&SllU6>XUBgSb+3qVriEu1w6$sf0C!_#yt`w7;41DQ
z^c@F!-PXJxuY5A_?Z=J0VRo}kJ^XRQrXqrj?iHD&Va63sa5&Fe=X8lRPZH{q>$)rg
zOM>hg<&cDooB+<iV0@<l4!FlkycG;QY0ncRl7f2l`5s1NTN_go3<WmZmA1Z~w>`t*
zhOcFyX`k?l%^^jNWR`g&ARjrGECvTaKtX^w$s?tDyZF`#WN!;;cIGRc`{aL@YQ_)F
zigHiNda>z~Tx$F{@GhC)t4(tbt7CPWyP2O4HdEzh8R#9dsqM{O8i$5;om2Z>-qz~N
zG3-g>g<3gBM|Qy{Cms7&%49Nl>eXw`l(|-^^t*c{<hvgBcaEipg*+x2UiQ-Or(c=q
zwmM+6h+Y{~tg-;8oM%3#sr5MGzn}jA+v&G{)t)8e7>`a|eJm}7e?Yn{4|yC{9Efq4
zOXubt3lFV+0RI5NK+s3~SooP297U$|{{XOkwRTr<a(usehb+_*o7DZP{fqP{ufJy>
z4PD*Iur`=uhq1|K$^Mnsc%m5Z;=0oOOpGP51(NdBL&y+w^Tu#a1_{B>Ojncs%zCVr
zzq0p)?=7w4xSGpPJ61yd3$qpqK2wd|>rUgw+Oq2{tz1c_3vZdO*6j{vh>fmtxo!sv
zqqnVNL)%rV=SkVeXXsulFrk-W;_)%OXVGu!b6RJCej#d?5yjxmUe5PSl0$VAQ>X|;
z$8jNs91_I!&JB8OS61F9(RD9|zZ0T`lIqrL3zmvf717YeEKE_AzGY&&l!hI!Ys(Lf
zygA{EE1Tbj*7p!aZbP%(IgCOi&J0Lbb7!F3M;w~1VdCEp+)T0R_R&TqB1O7J$yXzG
zdC2**lapG}!%nOxQmr)^H?5uieqM(LJBz5(tqeX9^V9D1X?F7>(SKo2*#i3VJ1P7u
zrJa~%H7!HajDMwd&#3y=4~#$HoBsf^{k6$U3mskn<0vh!9D$!xD3-m0#2z-f@bob1
zI$h1Yu}dbyc(JUTSab||Dt*pHcOD)1kTvPo{t|13RgH1yTkTwDo$fgV40h{UEG0z=
zI)3$wo@0ZnIIHrXENS2HOpn>38wb3<mtT26%Eu+tVGp;-0=Mg4oACbt_GtZ^{uE!u
zscAkBvYKeX%-V=+*%X2Zkv`DUDmWuMkT13?>lpk|ExJXeD$6Cr#ac0pt3pOYWBsAh
zxa+f`YZtEovoJXhSraKCWf|cJ<JP=fE@wg1bnB+S%<ZX{Vr3-gKlo$SzqO==gx@p7
zM&R354Uxw?vOTH#ma`1{BVMB&s^Lnu0SEfmh-y0AX*G?dfH9@KY^;(UrP)Y4?&mno
zIthG5{h2%$R>oLnZ#Fq2VBs5f2sk<ANgXO?=T4)PRnJbQ4pC8Z-R^qcp0^sT8;YTd
zL4vY#y?PP17&-T^kp3fo!99Ow?}>LaYGXxh2L0H}A<`!{`}oE@f+V*<di=4eCzD>)
zZE4~=y=qIbsodCF1F<3&l5Qxy4(7s-uWFL-#?bg;;%z?G<Xy<){cDY*apdkGl3N|e
z0=pEk7^=#h6?gd~&c;;3VClKeNnfRpjBG#PnP0O`n-bq&&Egx074U6t?;=EOV4Rq(
z<9(R!7#~AlTl_Tmd+^`j{+Tv|@ZU(Y(qS$ma{}e&b;;c<tXfHYy~Hl|VlqIeC-GF5
z?AOvcK3eT(en4MxYm%GAvftbNk|uANh|Vy_wsY7Y&b@h1juMiVA^Xp^r<sJQS07ot
zJZ%(x1#RMsXqlk4ZSrmi@*Z$cTzxaorEuRAz8m}`_}gg@hyEgI7Fxo^@<j7xC5^L_
z<xd$&V+W1f9A}};dCtG$yKe%;1)aZ_J<O1hrY7ASVZQSmb!_Bg)K@d4_@`98i9W)t
z(k?dRxd%D+u6k5tuTpXQ%gnK~;Wa{=+jr)8j;H?s1kwGPu5RX<-s@ks3QV3v7m$@z
z&ni6IcYF_aCmrk6{uchj{{XW80D!0d)wI!DSG|e1MK-f1+LdrSLxq5z`Dn=pkVShY
zjp8Y8{8wtanzg+1w%MS5Fu-!z>9K|h`qrG<qG-1_7E%)&vd&UCv!5+9%O7t_!i?iq
zt5I7RLc4VNne0|tn-@@@PCM7-2mBT7-1tktqE4pwQ0@N!OyIB6Js(xEhzpc;mu>_=
zA5L<^wSPNbwIA&n@u&8tys(SJej7S<hLbGsWp8P1aPB<qp}g52@0=oxsv!2QE2Yh$
zz8<5LPXPD@;f)K%zZ<+I;SUqT_MJOZ*6ghA;SSOHkj*lquH-7ql_X_IBej0yYM<~+
zKiP^I#IWA@i4sQ0L3sgH9Pl%2TOU$Sy?zq-KHK6?fc#me_}4(v?-Kh-yNdSNq_vTh
zOACiW%2xzr42u4Re`P<~BgX##9y|dzh<+vMkm(n?ZTngg0!tcP4=p4PvC3JO8}P$%
z9A}P|5w}xkd+2rF4*vjUAKBC3EyP;4gY+om)gWJ&Qq&}CtB}C%44coF6OIPxkq$V|
zCqAdI`2OxVBeS<fn)YsDXZgIO?pS{jKf*tyWb1mxwXmO9)h1Vt0L&D0cK#LoPBJUz
ziTr1Gdo{)M@)fw472LdSQ`lpt9DOU)r-X%Q#xaf8bIG1MRA%JmW{uAi{6xLgZSFNI
z$$rvp11w2^h{aDpagLci&MU3bd_NobR?|__O118#5td7a=gUSUkPjh3C*D0v99PYL
zHow+<N8#A~F$a`<=}{SDK^g=sH?j0PM_#A3bTIhGR`BMCx>OU0Z6hQ{3|k31xje9J
z1|uYXmDh~Ps$yuY9vZK+inlelw)WGh<l(c4VX;44t52R<+WUXQ9-XTEL-5s-+UeH!
zP~4@fZu=#$61z;pmvTtzILB@)nA83u>6*3Rh}chL;zme(`9{`}h+H=$Mmg(PzX<jF
z{c};j7jQ!Z!5hwzz^bF3nK=268P8n%aa47C3whdGqbQzPJjv!yErA?imv;e(Rms4|
zrEt>AFnBt#r3TY`KR@fa_p~!iZZ`!iQ-o4m+p683!L48T>%*Fau&w(q*cdxoz{+i%
zB*!5@#DM3W)sb`JdrO(t>J|GVH!i7`P{W<dI<7jA=~<S#mDi2*dv?>^1?&-}tby{W
zbpqpIC#uN3GtkzB$AB;1DJ5`YmNX@mWJwAF2hMhrw;egHsL{Y;C`yK5Z)q7cx<4xu
zio{~+PM#|+yQe7Kt7+$<?b<ho^uG}6)A*TuNV}wpJ>O)CH&oiZjpcAL)wtt4*PDDj
z)%;0k@k(Q1sa##y11xjPJAUd&`Q&Ghmx2m`UMZ(|&&186KZmcDJ9wg*qY&G!z`;-6
zkd9cB&s<lecox#?$3(o<T^n`WepG4(cWrF$$@{7?$^5I4VXI;Ab#XPLXxZ8_wy&qm
z^HwIVHy=+At*^T1x|*7%ymT0E^-r{yxU!yGSb~fh8hxcvf$9@EuPM=dHLG1JUTQak
zO|yxCis6Ro<zvZoh-8vUI41ym*FzAOQ`RJ37nfMrM<fUk4eHyJNT<4!#^YH&H1S7|
z{4+9X`X-SIM9;D*<R(I&?Q$@9{4292agGm~q%F<8k4uQ~{{UI7Zf}KY`P}MK_RB(v
zZG0_c>l3!?okLL{ytr>gP)l>%<X2JRUx-q8lfpNC7Se1Djm5s7G=@kyKr#T>d2AEO
z$<AxfJTc+RsV#=1;;E;(mP3*Y7?bEY&OVqGsj1!E+IY`Ru+gRa98&=n(S}q7a1n!K
z5X5IEn$s17!(u5bC(R$o@^V#M1B>UIPA~KBaQdH!VDT@8^o@R3o-K8)L=yRbD#jD#
z&m%jW42*Og>aT~s9bMf!YCa)Ls4Z1X$#UZ)amF|D#g712(n)utTUyDd>PaoN<bN?L
zh|2gmAxZnZSC8vnES6|e-%`1GStTy9o(DV*2OJLPG_uTPTJ&Rr!1Bgd)^AtxM|Tiq
zu)MK&mgc%gYhSxJJU8H94e5R}(`<CBsWk~M9yvlvrAndYiw>JW_U~Qih%}E4_-k0X
zvW_R+c`He9C5f}tfG|$nd-bbc1^@;@`M$S`-$#SQjVG4Zfs4LCaHw#gw@?7*6_2fW
zuE1QwrrGJ*h55Ub#<#a4YP0nUfRM}p90eTL&u2KyMSNPQp0v|RtuNiX9?nsN!r-Y!
z61Obu+Is8r>~p$j!y9>Z+lg%=+i7Lx%Ob=#5=w?;Q~c}IBiH2AcE<t;Z0*#PL6F6l
z8%h2^R*#1~W1x6;#Oc>9Br(kA&x`EN7{5jbaV`!z4z-<gec=dhyx$Frp$)`?dPx&1
zsrD_-J*!$6nd0h0+DYiGuaVI#G%&QPxWjjJ`W~mEM|Xa)%XYvzCg(kaHy@pH-yGvM
zKeO+SZCk1E)~%fP&7`&Hx<oeit^=~M&gCAbw;l0Zr|k!BCH<d#b0o?`FNO7D5B<7J
z)~-{vNk!~<wcy-2p6B!9`vTfM_w8TddsxOxuA|0roN4zbt$yQN-B01odM!p28|~#_
z$i!|cati(;zY#y+p3JHHU3gKOINPY_ANCKruitMIct=o;u61-pXfj`Nus<(;I@Z`Z
z!AZuZ);!wSc}+%edZuD{x5c_{jXYjOx7yT?G=lAcxDmJI2a(?u=Nfm2d?9JKRk+n-
zg`$u<TKR0pZgK!4oc0yf>$<*!F0w52bejI;C=4)CBm?{89Ou{(T=lKhj<ezzH0x_f
zQb?JeVu^(TI`!m}(zu;EN&e|w`e=QPIaY*YDLW>+{znV&_eb%a?xm~io*-CLO}trB
z-Z_*Br&c*1I~~V9nZ<dozjJS4;#hn)B(v$RKX(DRB*Em9mnuL%bOXU8SJhrF*CRu0
zw{Ww3!D50jlY`r+#zk_vzkob1d3K&B)ni!YL^!sQ{{VJQI0T+YxCG|CthY0aFC0vp
zzS?c%e4cy5{{UH^x5C9m=$^J-zNgQYz9EVoZ0Z^Y$mO@WEo{T1Y(Fa%=dY(q`$t*v
z28$PjlUvjzH_LMx8<mDKV-k!OPrijPc;s<jEo*6|n3Z(sV*5)lSk^ez0b}@X1HOGL
z*u-`={t+G$c1Z%UmhyJt5WiewspA2EI^p7)d3{*ZsTf+PeeS%@jvvaa;hTi#%?tW{
z$DDYt#6E10L!rZJx_!hb(L|_Z+*lAvZ094P8RE1w%}-Umh+F82Ev+E|K#g{*J~_@h
z{{V$>*BV>+diZHpWXiWpTFO@(tZ%^}b_0`MsSWm>qUg@{e8Fg&K|MG0!1X=ODPk$5
zhNTKyG`4yk-3Y-~o3^XE*Y7bdHCd&(T{?00nbBF~5sZP?VE+JnKc!;$W5JSLU$olX
zdyH}U!Y-o$fCT+B(yrOSx_!G&Uv!hVnIn_b=RJJ|Z|T~c)_x$41a)D3Xkj28Mh@YR
z#8K_1hNmhN_mbRhnyV#NO6_zzo83R`HZZhp{*@^P7zQ(s?>ABhrFplBq&^zcn$F&M
z-%-6jUc_IPHDi`zoRUvWn(SZ~r2V4K7c9JPEBr@+mOY1B^8Wx9d@|Pc{{Rrav!?2?
zB1(ccW|M0MUc>xFa^P3*IjTEYyiBSpcaEoX;n{EWs72#wmh;L~2bvwDAa}veJ?i(0
zJ|$@S4wv?e%Q+*tlx>dN3JVd>QT!vh#c~=4!hLqt$C=_Op)7u94dxG_>s-H%^t}g7
zhUZU?;!FKOW5n0b`@)-f*$5nN{5Y>q+S92x+3S5?-<jiLq^Lp4tGAa{a2nkDt?!F1
zB)i;3*1wdZ;O86#Ja7kU_b&?gUf;)7f7`w&Z``(i&t{FZNsjN2nYSNG@Ok`M;z%^;
z^*gJJXt$|I%u24qjBIx~3dD6@1$z&HJbP!U+KmHTwOC9x%{{gCkO)~(a?2tUv@kqm
z5nY*vTZx1nY7+NaJp8YS@O7rJvrS6&Px|vZ+lza<A1gykn5`p>0xd1bNaF|Y$K11?
z`L8<hKg8b^cwfagI%kKj1aD~3tmGkYn5ZNik}=OG6)wHw>r4GQDLgc?!8i=FyCh`;
z4y=WGxE*~f%&pozGUDc0U1n>A1!rQ#q%EAW_c_6?T$e6#y?VOyXur&^C(e{yFm&{B
z)$PAyD^S!mZxP<hJ<LD9otfGtI0rf1?}OI1bgvzFhr^Z&X$-|t1{NEXE)yLvcp!c?
zf+iNMvnzojAC<v81J|u)>N1Ow=G;il9^f3D5rf8hSJ28Zc8yf6=zMi7O?q;3qc)$Z
zryjN9Tb~%|-YCA7#o)J^Gc05>K+30+oRWI;Uq^Ve!Vj)$4R@x)93o~LVTD0Ja6an}
z$Ee5^=H4anj;pI_mbW@blw(*?3?%K!g%}_XKMtmupnOu+{3|S4CyT9&uv%m6@_<%&
z*B@|`{;@qX(!8qFrqi#K$u_<yzNgpV>sMd7&97#(mtz-ReOtm>ps=|!L306Ddv^dV
zv7W2QBXJz5r>BVQt|z#}P35l1m-t-3<2%%HH%I(S+;p#J)$~sed~dTpCed$G?N$*4
zJA-5all#Q$><GxO6`#R6)q@Ev<c{RU%Oqj9Mm-r%Bz-Gdc+3tv_Z43yO`Kku`5!%s
zz+>Yom8nOX3#xa}^vx4d@f7mOqD5@ls`*}GGEB(cS7uYl#yev*$oPZBej*e90B2lW
zULs>-LML<O_S$i``Bs01^=Hv_2pU;@h|)lUL~XuUJZ(AX0sSk}bxREk#j`XT`Acm@
zI7`IlIX}C+bUEy6&4vn&TZ)Xaa=aVT>u)30!8qgal~pKy@+htF!{QH!bS+-jL9lss
z2yZ0v#hi{=m(`V<kOzG6Uio#eco$yQc3!~@l0)*y=jD+OdJqUF9-}q!&X*3ep?H>P
zY(gdE3zuL(0y22p*^6~1x;d}>5pR1Qx2`OL<=KgfIX6PAKaKFgmvQV4D;)13t2&Ux
zWvw{3s=wmAjtsjtjR!0~J-Es~SNSvRElWet;k%8rm3U<<xctPcFZYz3=ku;3U-8$3
zd?!39t6j%)JGKR)A2L?QOo5Zh^)>U(ui|^1b{Or_<>RxP<`Kgyd1XJ@!RzWXTuzta
z`z-@o`)#3#;(mPA;DOf-o^xGLz)BR9WYy14`&tzs{f_qQzsUAq68NuO@wTTHfepG_
zB#aCeF!bEr3wpQdPSEtE(&slC<+I4vkMk)Tm=W0%fs^Wg3gx^97NL3K=(Q_H-{eYV
zX-OegFgp5=YW59Q{?6Vf)Lo;uw`FP8OQ{B4Qh$plCysztImPhw<A<WtPgiBHqdRzJ
zo2=)EuNQuezsT_a01)ai>ygVolBOWiZ)Cw-bI2f`qtd+>QoFFb(lo0^IJt-iQ5eYB
zqbGN#*0ATF!+NH(CBBf#l1&Gg{DoGK_E32w_pVxPLsIarnJV4JB)z#mXGUX{jSoTU
zO?uTS(yxQ1c=G79eavZM>eI*6j2-OWzDJ_M`di4C3`CPkclnIq1s%>iW8SfDJX@%1
zca7n{3>Jx>cbR&_Awd2l9fzT=CS4x$MbhH6xo48<@OhH(au+`;0rm8+NbuI5XQ5f8
zt;X2y?ohceoMW<*GmKX*YemA0Yd5aQ^m3ZYRi#cVMDERB3ivHFttU*>?p>PU^=Oz#
z*(KL{<dAx2t$jI<f6}}fKM=`zE}LSJvOnA48<dAkE3ZO0>0b4Vk6+TBTI#GUIoof|
z`JNVYK|FOyMQ+dk(fVb^iF)&D`lX%hw(&?~x{?4J$_lDig1<KhBz3^zy`#Z)z7Wzh
z!>V{$<DTB+VBH1zhzA8&k(Bgd#c|#co%|tbG#Ulh_Jxi9P0pXF5M>I(?xe}NaslIi
z2iCeB2jL!>rp+#)WvMG#+ycVVMp#Hw&e@AFBmw|C4<o-dram>P;Z+Pvlx_1*)b=Rm
z*eph1<%LmkYsod~{zm@*isrKM7M66qR>C{1-F=}NVq=6Uz{;>VWl+5PS3TmN9op!&
zcK#L8BZ}hccOfm~3lu9B04~adJ4qiX>T#O#&lmW!Ue;zB3_=@g8+S&PV#rpM6So6#
z0Rb3M-1A&==y%h2?I5z0#>ORS<x{#$vFN$a&V73i!n^6^H7AGt<h|QpU6N-sv%D|X
zq^Lsj+Us`Dr#x$=-D<xIbo3IX>owb%0;p9Ra0;9pV|PMxR6HZ4>i07FGe_h_Hd;f|
zIT$QEmOTN$6)Fu!NYm{!y)3|yOtM?N9ZY^#-jT=manO_2yZr-JG23j@fkY#8mlFV4
zy};^o*q^{s%j<np0`Qo5bKSRnRj<?UF~()kmNDZiD?Z)b&swsvh8;E=1Ox<+F}H6_
z4_f|je{bWT+QY=;ocy|f{*c?(^gpM5=0POn=Z~HG<n|xnEBW93zX~_)U*dBgO`3i`
z@B-ers#A2LpxG5_dt1)d)cuA1mNlug-`T^$G0PYc{{Uz2M;sjQaKNA9Mjt8e4l8O&
z{1JJe>Q?D^wzqK1;>jl-SqmT~LEVht@sM%Sy!-ZQl-&N$o)gnV>m0w?>d`Ck8|Qqe
zJu*)<&iLb9zVL>$E+UU>qh+9!23GZ6atJ+%92)JRPL<N6`DE6*XwR6<F;yo<SbEXs
zh4k*XJpTYfySCK~+GW#7upgMao!N7cS-W!IkgHl|oq1z$EuwH!I5D`&oPVCR-s;ve
zc>e%Oi^SR;m9?ZkTPsTtSi`qclSZIyk$J(!2&`)@D^OQQ2dL#wBO<s`uM9-+@S4%R
zyPrCvNz;Vdck?lPL*e~WOQe4Wqj{48mlm+U%6sQ&IO|<4zlbF8_k%5TYwIaszDtH@
zm6VOav4B_%5HZuGbb1Dle`k00_z-1}Bn;!8wd!6t@QmIn@TI1cWwJ63B$K0=I<Gwd
zJq2{EZEORJ<x+ib*zJuEtb3{1BbD%%$4y5|@>>2SoZAAaCP0-?@K-0Q<FT&m#D5U1
zylHBDN8uHfZ>{!QUE2uBolKl0sG}v6=NTVb@oit=Zn*>veWBeQO5ey|I7@_{89@Ew
z>;Po}`@KzYS`F5tqcqo6Z7-h?lG)9_%P4`=Wk|}9QHk1miuW<uMqQg><nU8boR!tL
zPtWo?GW_com15&kt8$9br`Y$DvGKL^Gi|ZFf<%8Xwd^D}m~+bH{=S{7JHk5Gh&8=y
zQqeS$`ev<bY-N@S{{VkwbLEkeGlq@1=Le;0_!r|X+#d+^%XYk;$6b;ctc|VI(X@tm
zODK>DiR7RXU>-r~UOnK?_*g#&ylX#<^>vfQvR^>LQY9Jk<@?@O%Bb52!vIE3Yvb~)
zzO7GfK~&|f1@OJ!nm<X!aUZaw98Bqc>Q2gCKd)2K{Bz^Y2TsxvZtbPA)qc?X@>*1t
z$W&1q4orL#j=8Q=;bpYH5j<^opn3A!+v%3FT;5Fo0DYw#d1$?Y{{Rs@=Q!zJWAP8g
znrFl-*>&p;QDVMFKx|}<;FbUmK-hW#j;eF&d942c1nWBYgtb<?@WiIpTbN7^%a&X>
zUv~8w?_WWK!q&s!=ABzqmabbRZ4b>eEb5+PnMS84xg8XKd;b7k4{^QJZ(jK=?hD7J
z%Gk8A;7@kSc#=WJ*!!qG`8Ct{I@?O|ABodN6P5CTa@)Z~Vb7Ky?wJYq;~mX-{<EfB
z_(x8URn{7LC)E_l^FKEpUP#&pY?07o1A$xK4Dk&5os-;ZHpyph5JwHNj2)Tzna=<c
zKQ1fCo);5RFjXsa%6(VneU2YDrArSrho#HO)B5UrC&a%9=~@NNzle1>9o9fnXo&y=
z5EwZ)Z=8B{Ij@~QApCaHyfYb;FtjX$#cyS{AOPu>A2%Mk{cFiSCwyM<7m78>d^O<-
zWV*N0P{a0{i3x@PI`T;@MmSOF*jLEj4$|*FEc{Bb(+$jxVzRXQh0KQqk~YF98Tmt;
zk%7RkvB@(30Aq%uPa9L3O8WV9PrZ@xn3+Qpi>XpR>|U>4zwl4EJ{MVPx0ZwAZo8>p
z!*hA&&1@#QmPCpc%C1q_f!qdqXQy#pRDTpF(gnAObvRAdoGx}aXFg=CI4!vS<x+VB
zj%ya@#hwze)NSl_4M)niLL!a{0ggD)K2QT6l#)B=rFgH2d}H9-JsM97+FUN7HN>;L
zHumkk6`6@2IN?=(@#iP-q2Vdw@rrcmq_tXT?0s5TdU!VlJL&o#qrVP(M`7ZaH62dc
zLA9Y~hn@igg+7M69}oDhC(;_$<8*hCN9K7w+%H@Xy*hf==ik9Q6`R5jui5Gor02|n
zSAmF(?HK^0b>MfeMDWLn{C3kx;;1az8D~$kNN3#`x>e*D)0V=4lEc=#9LEnzjary=
z+DY!$Lo7B?i<Bc)l#{yAC%=*RMc0U}E>7jRmLw~d%y=uuLQhY`Rz3BOk8ueNEU`%>
zWzt4*&N^oqJ4y64@O8(>KNUvvM|~qjG;g(|&fqiC1cA92bR=>;>9!xVJ6KJ(S+{8;
zJbvqfK7*kAD@f%vXH8U3o-c1h#l>T>SZa=pqst`qM`hy=66?bCY&APuxLn{x7>SxZ
z9x{b<ob}1)*0|e=?riSkv%g6rnp_ys(StDd;D0LW@4sk&3`wUo-kYflZq4T-J48>a
z0Nr~Ym6fIZLb=y0msZpD7%oy^Nx5yT*!1~_$&c=UF<qEy_>4WN$|}okEO^+;ilcR8
z^?x(pJQv~19Vbl_>q@i5bMxFu2on?le5%SmWh2)Z&3wPF*jw03viADcGb^&g6UY^U
z?mI&Uzy$h!wWdBIXnH)2s=l>qvmx3R7iX1@dIdm19=WeA@el12@aI<eg=3}bFC?$A
z#m&w2h)Ymeepu2#PD^^aj2fIK8ySSe)W%erlxl9fKA(U(vcy6(Dbjb3UnA4Bi;Le1
zJhD8Jshu&EU6|v!Q^=_F{eM@sy-WK`$C1$I5|(Ab1QE6Kk&Nb}gI4f<oo#NH+NHF9
zc|Keb<;oL}mm7{T@Ay}7ed3P`T=-ykU&F}%0BUJG{{XhIL<@m~l5%$Au<u><XG#){
z-R0QV4=h(Bh2)o&%nMCdOt#b@)tb%1#UPSsE#+Yoq92%fO`H-r130gwFZC@tO)}p1
zO2y5Tj*o6gcoezlB~`+Zoxwbj#eAn6{v@{#YjX@Y(GUSLAOo-Px?>eDkA5J0L)U&E
zr^CMh+gn*&TFPD<mYPyyJxM(m1K&SGUIulSEL(+KK9j%ReMb%P6z~;2jxpYs<^KQx
z_sFzeA`KpY5NZn=wZ+Vj9QP9>aD-e8vg0ktBw*K~cq_%e1br6vFA!_CY%U=vj}apW
zw$%%a0y+*W;z<7hXfKE`Xtur+@jrs}+pC*M#7#VxXf0TQ{;^AZ<2^{n6`!emLh+yc
zA(z;;uv^=Ryqj1o$#q#}`A{qIk~d`I(z+|<)$urZ(Wu?vuHE!j^*u+N;G>I^p)F?g
zvhI3CygIzLmp(m-&CF2E<S3DwYd{$93&-6ZfapzhcmDttZ#3ODDD*g72VDOEru@tX
zFfgZ(JqbBAwcxJ>Yu3IJwTbR5k{f6c#+NefX$lTxBWW9%vOy;%u{Av#O3`)aZG95^
zP<%MGhkNg58<%zhfCx3)fyZR@aDLKNRHGfz-u<i?s#&HhFnDDPH{WE8y(7e4Ch-;O
zA{Lry4%ChmB%fe8{VUY89|PX&E%vGI5K6g|%FoQp5&44k8?rI^^TlTB7kd7ar^n)Z
z>&tHq!DWdNVvaKCK<vLW6WgcZUIDNCV!6JVE&MX~dywxpvXcy?jGvKHm0okt<y=oU
z#N(vt<#ZH(y!(94_mok=+NL(QKAR=i{Ew#oB>3C=Kg1De@<ka-%Ls17hlY+o3dp|U
z@D2|-uOF~WFA#XMOt85jA-cIpnaNd9#N;U_`@j$ZuQ$-Oj}-XBRlT|KP2{&9Wbz|l
zG%0XoZlI_HjiWg==w2M~v+KGxq2Zl=MV9WuWts^#g=dM0JMy?Kk$_KZQHPx@wvR6_
zHECULt#0No4T#6paptu+wD!Ke5%iX`r+BW*TYE1K>2Vj}Gu)8e4x3|4C<ENrhx}Xc
z_l_;JkL}MJ>LNKUo;QV2Scncs?*d1>ji-`!(>yINi8VyHhRapDj^5R_6j@v`Bd+N&
zoDPErHKi@Zv{tj(p(R3Wl4(h1G6Blvi7lV0_phCyn^eQrjS6%VbCOqgZq4d?*|tqf
z8HAg})OLz*O)k>zclX~DJU=&v(^2u1cNbcXtWm{1&4R7O7kI!9-^CdNlb+e@#dPdo
z)HOXm$41ly)>kuts;;s|2ph7b41fXXO?=5@lD4k0z^WsTKP`X(1`8>Tow{X4;D5Y5
z>!a}2hphNd#F1+{Tymzy`HB<dxj7_~G6r+QSI}nId=>)~of?;$z2chC^0Hh`KNEm(
ztx2kBB&Dvuf98IS>DG+ktN}y+0MA0bKOg8T&b~b*#DBB@0FNTP`@_QezazQ(9JHPu
z@pZl2F#iB-@3oRcvNG}(S8lu?r+R<K+3qC$o&0dGhEd^tTL;_ha`&$p^EGm+$z7xQ
z_WuBae)*s9*1i?;fGxLC13&f;xv%Ie#QKyILoStQ`<cNKxC8}3*B{Eio}bt>N`Gpf
z42WA}Y}9by_Th7%(!YE)7}^lFaq}ds8b&<;$-w@!?1Kk;>#@peDGXK(YhfqZqhNOJ
z!j3zW)c$q7@Y4H4veG9@_>fBZZ#9SBQP5=b^shA6FU7>J6@-#ZStAFL94XbY&}8(@
zXI%J*!(wdhBRkndZ08tN<L-g$-nGMFYEBKMq3l<!7t0&&TD{aGpIMt!W4>t#2+VV}
zbHD?j<I<_$-A{bmQ!dTjjy_)f>bq^XhwUFO)@_R8mOo046-`T6k4^KE*J)>I91?Z_
z1`$*e#Co0udLF`^HmXZRyPTJ)E7Oj*{EtrXe}y&6>p3+&8|+?p`m{;r*x2v1;jx~X
ztGcI!d~F7oCFZxMM`vvS%ec5%6!zVLgU_xiRQQ$PuLpmk>FqWBz(BED+CXx+>RH`Y
zK_?`WRByav;va|-Dr;D`((I&JCbf?Q$oR@e?a_~O*jIswr7T0MTDr2no}CVwII6U(
zLk~?WdTZtu8g_}QYhoQ@=Y7SjA}e-`>_%JyR59=A>sIwkD5s3Y3K*b>`DlZ`_m8<J
zy>mK>M|&m4yL&aYua<?PfXKU~Dn3;MBOi@P;T>00)glk7VJ;Ju@<CvW>FJ-RO6T^~
z6*WoAMX~HkjX2eHVztub9wgB<EgJD4x<+W8HI+f_zp?lF)sF&LSzP#*-%`Dr7-F}T
zlgcC+2_yTX?gO3}*RJ??RI!%w^54W3*B4^s1>zQvVR!(s#t7;$-n=5?OPAs1r+=#J
zwy7?Ub19QEIEqAE{Gdp1a-+9O_M@3g6NOY1e|fd{y8REIl@3fy>Q$Aa_iO$~OXAkM
z(mYgc?k^>}wNNC58TeIh#CskMR<ZFNtPzEkL}WJ^VUB;hn#X--!n*gw3k9{*A-T4g
zM4?2g%u&ZHy-5U)J?l62jMP3D=@+qhR>MyxP=u_KNpB-YV^;gPV~|+&!5H<f8W?(t
zvZL(Vlj-H^JjXY};V8nTNnLkZ{#{Q@@gImaod?3x>eg1zF6i6KDV!{XU>C<l$42}s
z$2=0y{{Ulst)`f2u*P2JcPi?~my56?IAQ5qR{sDOyfNZW1Hs`ddF9h??JbN)`$eNs
zbr3wNNWUvM2k!7|p0u$RO&+NP$r}Fv3l~ftIvgHB9c!-%MdImKp*6`kyCv4v-1xlj
zFqG?3tsQB#qv?D8XPfIOHHM{e6{E)X^6r`7Y;E;9Q|fwGL*ZNFsWf(WkwqVy3m{04
zk}#(|&OygPT(^n5AARB-PUi2!*OvHo3PZ6V1IbK`l|F{O58&Odqv8Jm7uadH0NTpw
zBuvB!6b+~5T!I*WweDi7V%7mGTT5T?IC2MuS(21xcAf0cQm~i9I>wHk8o7&Xke%Q%
ztS*c@$AQVu{{UXRo5xyRpN7!gTgxQIT$K`S8Eo<iP%+1RS5f0{3G12;sytJFui7b!
zP3V)bizIXkKItdDbUKZek>ei^MX5T(w>Smb+=q?Fa><eb&!^>F9Qf=t1y%{!Z|Hrl
zYfml3DX({#{pp`8&#Aq{LgE<PX<`UC$j7+ON4<2O9@6i;N2^(BA&fEHx!lK~ROOF7
za4Sd0x*nO~wXxKc3w7Hg$uP=Ba5!GLJoVzKX+9wS#k&68pUv~_ZI!L#nHVGeqq_R8
z&wlmo&oiMqH0w`JZLhoeADdw?^=e`r3VPdndY;mJW1`QmuZKJ?!eeTiWtE%djX(>Z
zcOY@;TGx7Zg|0%;>INpag-C>M`Lad{FW2ZgSI^e}01-4zK3lzKSy(jqZsR+i8%H4a
z`_b@E;yvq|)4nSFK6?pmG>uv}EDql}8a6+JXEo<x@|i~vs`Dkg?0%1n&1wBz(3BmL
zTkLw4k7XX6;yc|ZLA)WF7HH93fg|{ky5l(Oiu4N$U29ai4vY=VX}9NcBw#vW*M{~C
zGw)ni!aoRU8oreO01zhDAreN+klQOR)p6a9;!3dWE4zbLx9}H<wC!(FiJH>sgjWD_
z<jvF&4^$$!GkTIz=XR1$x9VrYxSAEI)r1wKx^!k^crs5HM=q(Od0%La{JYCpGOVt>
zSbG}Hj^b@EQjbl$^J2JS5N^XDBc={WAp6&$d{5FfUkz)6;%9?Ey0^KEJ;Vu}vc`Mh
ze-QPqJ5Nsy{7ko(TkzG}TI!IYk?bENP8X5}PUUgin(m%y^_)CqM$XM9n$>?@&o3;)
z;bD!aswSnatlxg8HLJsMtZSBbnrMzKR`Or6EUHLJ0qP_f$Rn{@<ot8tZxZ-JQj<s5
zt*~NDF^@7v+2<gPe!SO1ZKyu4<0~74SB-6mSjvx`Wd|jjj!t+r^x}Ba!S|B2hOsiw
zb8rl70LfB#C5h-yy>m-5se`E9INlm*`P)OUG|6dVDaLhY%Pz0KW8#~AX<1^1G*F1U
z0<Qxe)jp^FrE6@dbV;|H=85^qUf}zW*1gZf-VX2|gy*)omeyCYc?(@!EQRHbPff9d
zw}aQcZhykK@W+X4qPo*Bq?*QN4Fs@lb@$=0LG@m_?Om9TBEUv;<mtXf?B8#Z^E1hD
zIC#sNTJ-bzm>&<dD?bf*ZM7ScGkNkLW(pf`80Xj8q0|=N#ae@<%6Yd@A@bu=1V{km
z8RrAJuUxmV&?M1Uz%j%niV)2lxKd$|41ic?GB<EKSIt`Qigd3JYZB@DHNEBK+o4Am
z@%*5kIc6Y!RmqoQF!=mcY+WX~Q?}ZlMzL!Nms6!R?^*er*NGrEb}PO&E{AsI7*JQP
z8$bTKcDZe-T=;KJ(2{v)y}4v{+;<eqa58xwyw_nA9xm}_oo!^c31eqw30QZpnJk@)
zXRxim4`{35y;dDh#H|2A`7P%o6(_kF1Y_GZ@72X&>P7oTuh{t9HA=C>B^g;ax$4#$
zZQNJTF06t}sBP6lj*b*{Q|@}#8?Sha!#48my0{M3#QCWZ+{N3UEl*%Q>eq?>Bx%vc
znr@eQ2F#Hp$V|*h?6?PLKAEh)_(XI&>tF4CYg~rj&B%9(J8-hG9{J?^;=XdCsXu<C
zT3>nk6mtw78FIO=m;3{mwfKpsY91-j^vDzJmY>>I%9imAyAvvn&D8x(eYN)4{{Xu$
z>t7(*c&Ed<XN@$^4|rcr7SYLjY$Uk3Ik=fjtAYsVzQEVne`sIw#<6aj)^#1vB6-)e
zlawhdAOF(#eKS$hFYPV0PY+0~ZzM@1j$|Rjws%M6079~#y}KCnHLa+8W3$j@)AYSO
zO{;1QqGh%)mWtW=8(g2fNyo~(5!8zL{{Tcr@iwb}@lMLj*jvYM_QHtfB)XLqH%98D
zIaxEf61g6oD?3j8tF54sW!0li4W+mi@yd|-kgSeys?vexE=k7b827KQS&VaBB69uQ
z<+_qj?thWuV>mj#O^xTK-D*+R@pgWW&+oDH?xW$~h!;8x{yy<`gZ+sU$g&t#Rb^n`
zH<%+IG-EvA=LV(tUwrXvn#QMb7Ihh<Sm7>&tXAOxSpdM!bCH~6_pIUKU29sk5qQJx
zS5~rtkdmJ*m%d0O5OMib9yQeLb?armj%KmANJMc>ZpUy{7}|FI<RJ9)tg-kUWo#eS
zl)ZG^%O=fmm|Vfu#74_ocV2evcemayv%cM9r8elm3nkVw=Bl0tAH$!${p{DO_-@Zr
zy|)^c!Yv^nmK+Vory1?}3i)%xv-o@a7SiwSLKx$XM8TO<AYK@q%%qMoFbOs9-VX5;
zo+gZ2S{9E@QH4b~GLP>$9=&neyqZ{O(1dBOpPy6T#OB`Kvz6|j<a=h9c4cS0W(?UX
zt=nkpfzzDxU(cuQ`5VRiS9qBu0b6FDkUM6=-oJnRI?>(gM%Kp-1-T6DI%l$sb|0;N
z1OEWQL5V-LuZjukQ%?T?(hF+m#Yt%xnA52lPeb)b;3`?o`!x7A229EQo*REnzrB7x
zPPI?R`gPBUwDgUDn&J#i1af@a@Yo1**K*+CnydRh-CHm0_2F3j!@O7^Dx)N2n;Fjn
zxgUsM5iYc+mOUF!`((g?t>!6RxdDg+<pU!e@_Tfy>;?|CXBQiM`yV%$DN3t?U0I><
zuUNd7#UsFXkQ;Q>)ub@Y0NP>YP7hp-$~&A_(~IECOB;`~$VVrxNk5Po=iiF>)4&%$
z6LsBOYZ8kqSqMjpLJK$~XKJVe?qidmrgL9XUTL2YJXPRLXTuGD=ichNZOxQdp?srl
zB+)!+EO=nV#?823jz<I<E5ei$uB$0G+e27z{v}Q+)3%ze{(eWO>HZP2F^C^@v9H|+
z3y;dK%i+sw077m>k$DA)3^V;P_*e7C@!#Pu?I-&=NoM{#)^5JbsJxNc+}dg~x=LXR
zTx=|op#g{ALG7CGy&J{f6+BC<+4y%#*0o8lukItYw~XG(p{1FEBITqCGQ<p$2d#M(
zrmHPaR-Rs=vp=XvS?sQsZ#he3e(^}%k<gLfBZl=J_2GXQ{6FBWKt+3`-6ox`+&e50
z`GGv`gp#-iAMWn!UmgC=zp{VFp8<HX4~SnDHHAxfBAPL!z1+7FO41=gyywbLySIWF
z`2&H}bzc<x2-@zwY5k0_i=uF@W@yQDB!iN<V~<Zt-z%p}3y6(awx!tfvDhb4@oz)W
zJRfIuapH))C-EZJ%3Gk(d6OXsT16_3uYeHe10alb2ED&g@Q;o@7sCU1PVP_a(u~Fn
z#54H?HiD(k?+I?B`qz;7W`Bsbx;%P5zXEEJL!1*jvB3I*e@gV}HE)PF`fZ!((Wq-+
z-c)Zhw2Ph>a}@!UU|?kWdsh5ahK?f@M;fPR6>DvOJNF(%iLFYFYzth_mc7^9?s+xO
zg`@Evw)g%pw3kV|xp3(vnOV0h42EdfZeMlgy_dmW5YW6h%cw;(8kRByzE})|4vd{t
z`tevAW{cvzT4@fd?w1qolu0Kf$T(#usP?Dd_*U}LNmANION0POz>$_bkPqpKl~3zb
zy^EVkt##AP^J9gnLc6ON==v4Bf3InpmFya(u`E%vDq&3LZqP{>UT}G@oxDxr8~tk9
z{?|!wvswt)yu|I2PJu$GA0Q{+C)&FI02yf4u-)6;Tu93BlO(4&3K(N&xna~*&kOuI
z)_h~8T<Vg>Z7t*f08qFsB#b%v$3VF4UcLq}!o!o9zTTcj^=Z~{cF{f0oxTb9Ikm`N
z!#cV&GFthP%&bD0)<+~TC)6LCzZLF&9sQjABjK5@wVe*`>dhS(0f0iOZK$Q2)a8dA
zI2F;pi*=ytHhSyZ0M@c9%FvII<0lLVKQf$k74#mNWn-+)W7C){h<B_rH<g@n8Bfd`
z8ROEq@z}g%Dk)N``F7DQKLhD<Ts{JHT|7l4&40Z3v&H`a2=zT0C%4pYp_|Ow(eF|k
zMJh9p3EQxqN$X!R_{+j~eiYU%FCs^p8*=F&;Y$ey)+#_a$MDzdF1zscwZ9I3X-6D}
z`pL@7UA)GT80UWDoZ`IL_ybMV;5OQwpqRPAM&y(2j+NDd#rryrt?u7*=W!S>s^Vn`
ztvtIQ8vG5`bsrA+ka&4ZHI}3X(%fQX`;U&9Ve+u`udgm1{`*q<CFJjKc8X(&vBN54
z7uqp^f2CPZf&LY;jyW{=UOA#5sS`F7k3dQ3T_1?FVS57JUqbO+AyXW?w+7qCPzDJe
z)i~O-ty9{<-K@`~$#W=Dhq0@4le#>w#U2yTukS2ub!&Hf$x)H6fcc_WzHNXE0<18@
zgWtV;+2d~jc!JYYOB*=a^UuhS$WysaKvA><l5>!2^gI3#{Za!I(x|&sP|&)tSru`>
zAn@EAR)>c)$+Vqywd*wbm~5IBz;M2)?mt?1e05bWSMc`Pege#}_-fE{YV+^Z{NHbc
zmlv8Ut>U${@J}z#T#>pnf*E)lzq~shm8J0CN7K9$sNQJ4Hi$`UAQHteMtH+>>Cmw1
z2(QwO7r~l7xuo1!Uc!9Uo<~v+2=g0oZ%+RJjdM3&3<Zp)(W4Bx$Y2Lf#~!27l|0sz
zn~iN6@3G_KIDNGJm%Rt~qw|Br1w2J?%b`Nnkrs4qfMAU9LB@Jlpm-DD)`za5S<7f-
zjT_9FoaLP45Lvx9b;W&|Kf!%3QB(F9;z<;I$7w^);osi3d<)?jd_5i2p^OO~K-(t)
zm$=VwV^>!ZPQ4jARXyas<9xpbEEP!mNLnYFT6idFSIpDL9J3a{K2%|tj;A|v2dV8|
zeeoB?pAEhhTt}+>PrkOdd$rwf6MXYvXO)2!pJqqj+l>BI_C#7$v)w%8jqWkDazNvc
z#Mj{;{1h+3-YU>PXxsfuT(_D#9YWI5O*U7_jpB|7(m;ox+cL?ydV+I|FejT4nMRs|
zrk67R03)s#M%3Jwy5H)3?ciS@d@cB+=0)Hu_^zje_lDcek~cs(E+ch~+n+_i^{+rr
z56Xt({{U-5CqcPcx2mxIA&>~qeocN({=@zf@n4Gn0BFqxd^H5JMQbBXeIf(0JAI58
z$VUJZjt^S?g)jUEWTeG(cM?Vd<f+f7;C^+4v!?lHSzb}qSjJg0#7~t!b?$r`-T_@l
zQH1Mu(;GX-F(t_WCjQD-uG5dbRy4l^*=oKxTX;^*J4;V26(bT%$C5HL@{R)zoY&|L
z{{Vz6H7yuvGeZ=J5Gt&LcEApI0x^S{^W9&-SIH{eczVt#ieo5P2-|P_E6?C-%CDQ_
zahPhDYOdVRrkZv>i!R9U7#v)2l=bCXJ$3GQ&&4kb-KL2ji=w(V_K+-g=Gq(?n*abp
zoDII@@lxnsESJYxd^$+D`zE2L!iyc-#6vt?0!+C(&~OO{BdD(LSG3hNOGdHPY|L`~
z)+$Q^Jpjf)_OB^}!+Pe2;!Ay3OtQLh9Dqm`JS#@W3>zn=D;jxCDtIRvl1f+8(9Wi3
zS~<73rxxJ!dVgCTR;l4FBgWcv-W$}>ZLfmh%WBOeM?B{}aDDj|=US(Lbk7T4nJ+DN
zvcv*{H*Pq<DmW)N&3z?#;a?NqDUGh;wzqSU912OtyqU>0$Z8)8&Gr0a?ROK;6752P
znTO)s0r=NO0yQwz6rn45borjnClMS%bmo_n>UdAW>#qaqSDLi)CCI(FcSjB}62%|4
zsRxfr+1K@t4|sP{7tqS<bRzPx2o4nwA9x;qhrN22hCT+~_+wa`SF%v@%^ZS3I{xbn
zJK*)M!tcR$_VC87YNk=k#cdA_=y?Ex_|~;v7Ipdgv|H=>9JDc5iga8XyM4NzbMU^~
z!)c-1>Nj_Dm}YWjdBQ}Aj!$w}`eUtmE~Da2D)ZuH-R+&N>Sz!}w#}lCCETtV&QA)*
zB;%U;X3N1k-l)tTz(HuMgA9ZMTPNn)0R@0K<ky|+x&(PP+cpAmk%7j4pRIIY>fq{C
zm1<i>@6_kSOA^0#Icp`ddTMx%kK%XmoLY-%dvBKHmJ2v&rX#wXw2IgrI0C&N;J=Cd
zS>i7cHU6ErX}1seC0)K;VD{i{Bc=esuTqoYzl3hQA#f7bKQYxnm~$?Alaridtr|Y7
ztY~@_%qs#*be7vJ?;?xjVh$I~OyOGt2aao-8B+%Rr`0I?($Tp;$H?gRIMq%PuU)vu
zq*7n-==uWp!d9A}k$qsOj{|OKbMiA0%cwjl_UKJ;e;iUvPk^2_iJ6iA0O1<dLnlA;
z=`RMlFNc0Mlf)L0+v=a__a}nDVm4vff$7al`$X8Qd<5~m(~yolGpcS}<YZaPjlKD<
z3_Pk~?S{lJMtGGe!lQMlJ<sRU_7c61PW`F8Gj}Qp6<1INPXO+)xL@nVe)?;7me5)*
zp*+zfayUS&;5j+@IO=hn8vFqLh%H+4_MGtLvVz5y{{VsQV{m)ozj|bSGgY#<@Sdig
zYi-F_P&WX2sp=hbo)5igT7Jrd#9I8juOoaTl&h*eALe-Wvmmm)`#r2N#APHFLU%Ua
zc`c9!=~!s$SX|0R)Z~HBJ-?lB-w+Rrd=Wgi)~t06H3#oi-2I^<$sCpH7ty|K3f}Oo
z#s2_`(m5<GZm!`?tm_^Z9$?%Qvm6Yr4>jvh#o_UIZh8+-yHnJuO12(pSC32IeLI#f
zJZGY468)1=K5Q_|Jf==gPdFpDrE7dL*5dIeiM0<9UpR}+Q)j2f4+0d!yZuXX*ZS8p
z;co=^e?ZkW3;zI%_VYy)*DXDroDR~<<#go48FG8>U_EQqSbQ(J(Cy~7w7-DMzst0=
zbTTYKwu2;?3Yp}SgO6JAGhEt?;~XlM>r|EB;CIo&%9P!9z25ylGbdE>wA!ut@aB^n
zrlBN@0G3d8Cgap}4S|!Mee2Zp$G7kWgf`a@toDIRO!J%>nog|QBLw4tUp{!(Some(
z2h;968+&sd{jJJPaTLzie`$z)=!CEG#=f{2J$hHmzZd*#to%yV{?(|<JQ5CVE#PMY
z5(&cqFd6auM0Dwj^ssz0iLFL4l$;XQ&#%d!J&of$z7po3x!cj(Po2-R{wH1dC&T*w
zpW=&Kc`oF*fIY69M*jerk79cab-}Mc)xIZq-ts2Z^nFN0Y-1`SvBY0zWjVxwhTz%b
zo)2p9xA=$St#?q<^=rtj&DNW4As%TX&0J$U2PwFB>zd7n;s&b;)GegZT^99xw+kZ3
zF`V#D)+0SLUd0TJ(T~A)eAaiwj~56+g+{HvfA}Z6$NNltWxlaUp}U17g$JD?Tdwo&
z$B;iN;{GG+9y;;2gf$Nr-&{+nHLPflyY43J{nQ)|Jq38Zhm4@mp}B_U+WIkYpdT(E
z!{t2>UJZ6W6!^j6NUU#kJL|T#iuN*(6~Z!P_B`-N`qk3Q<q0Pk*!k+&g?g~G<mA0d
zT2<|}uYfeGy9L;Yr+HC=Up$^M%9_==*0n8aX*C;$YrBa6-1#i-0Km_w_v=)&%dHG}
zF8jj?d28kQpJ=p|q)=BU=3c$WrBLwijdb4%P9=@L+bge^m~a49KQRS{IUHAQ>M10l
zcE2yo!A3GpPDfQ6i8Po^!$m5~)nri7q@9(rI5_TcT+X3!;OR8z^et~pmd{YO++^9g
zqMcaeJbMVwUPcKOnSbI7F|A(f5bAN-*v`MawpbcR<FQ-?-S3*nn^ud)uzzOwBHn55
z7v@PN9!znt{xZx-QS=zD8PUYfr?sa;Rdun|Cu%&de(wH8j)A9XcRGoO!18Nabj0C|
zJGQ^zbNY(xweR>?UJ2DKZ|rrO`S0U%h%W$mwvmIo00ECo=DL3a{6O(HgSA_I2gQ1G
z+lz(~M|)`NJS^D$Kb2WC>x@?a0LATQ8|^whMI?$>R@xRZlk<Axy-LJmB>t;b9IMTL
zTY4Nk&XZNB%U<a5i{BAyUMaaZdcDF)sVcfjZ!6$}dNe`5haQ#AUF%w|gKn>F467>e
z1TIyb$>Bg;9Gd8*@cqkat$E@gqSiH5+T$*}xddQjV0+fLhP*wcS<Y{*?i)~*BQ0(j
zw-p3&w0fHN)+!LC$tF)FS+`b4C#3i)DH<reS1r`{7p*1L;;X!jfQ*Ra498#th9j>y
zuTuDXs9WCnhU-bun(bk*w~5v)ZrkP-P<B5g5ANi9SCbzeqxhlKgWFvyY%oZ1lBcSi
zatPp#wb0r4lTonMAlH00=^fk>WuccCo;KvfM^;~P)2(viaMUq4SyE9_?RBmEj@+9Y
zQwfiSdC8@Hf8md&Z#1{M^5(FPJKIZ^Sni=f+a%)*$^b1CgSchpw0<XiC-4V^63#6W
z7;fOX#7wg=5+sFp0>3EAo|s|y*PUqp023^A?RG3A-XFZY#k+l~EX2-!?#YMA&t}MP
zQC?s17hSXPo}qbVt?F<s+sH#Bv&kgnF&ltD`$~a<#xY+rkWj>XdbI1#OWE5;s<Z9X
z`(8fA;p<^&%L^qZHtyr)Z@Y7e)u8b0t@QSA*$XM6Bz&2V<{iicS7Tw~uN^_*rqish
z((_Lf##Y|#S&GS?Hp!jpKiTAZS2f_f&kXpl#nJ1!rQBxT)JF3_%OH7n^dCA5=Nz7R
z?OkWZzlalPUl9KQ;XbvEZ+ty;VsG?70EDXSWn{<7>FRpdy;~QBRQ-)ge9BJRer+EI
zmsYKZo1Px8ElBO>_nrOc?RVmB1HtD^m%}neduJm^viag$cLab?4S=eVlk-<cuY6zd
z2aDR@#9t0<FHx*fF7+)(PfW*bzyivr8)YEyRddC8&x`y8;4cyA_C7GQc(olp5go1E
zu~rT;ts({hx}FYl4R3f$QiD;|8^W4EOKU{4aInOoAwpA;wLnk+?fKSm$uM-|EJT%(
z)%=G`AB)9esk}e&T)h_i9+lwl+6(qux7BYo&l+pcr1O=MHQOXEUmWd^ZsJ(=9V?B|
zzAgBF$A?+D@THBox3;=rZ#a?s$x*os%0R_%+SkI{j|1DmcYGG&(qI}pffxbQ^aXK{
zdt$iFTf*KN)s)C0@+EGPz*+_a(;)L#gu!7kGKMb?E_gk^;GWKRjK=dkE+QPVUq86h
z@gI#mRQiC^1<#bT;A{nm#&O>@=sp_pFN*#f%X4GlTlsGwOi1?ELv&I*E>6+_Ao^FC
z=syU2Pxf{34UOgGmR4=Kp6g_b>GHIge0>S7`&H1b>@H)q)!nXl$cRC0iZ}E)Rs{Yv
zwkH#n(W6ox$w}<ev$OR$so?WUlZ7`Y6xH>=<aQqo{{U)Rom@*|nr)KLVLR`E{Kb4X
z@e&E^(D7Wpt?_?bu+{EcL9(#2Y56kldC1Q^XKzofd3T7u8ED!siC<gLW}8jF<QKGk
z!N1;C%CP#@=YoD6>t7IVZ)dvK)gZ7yZf1_kH({QN+epDbO61FMSWFff)tnS<tgY9v
z&yA^yjGSjVIQ-4nzAXGsvx)8_yR_8;jpo*Pwue#42<!JwI#ug`i5ee_^k}?KrlAN+
zLvaMI5831#5ZGP7dJ|nXneY!o)uMYp5BQ2(yYx>mTNu92F4g3f86@M_8sVb&6R25g
z(`w!u)Y8WCL$>NUvl`07x!M^^egd$om}Iz`iAJX@U0#<tY0{g8?R%Fb@om@(Yo^T#
z#Sr;I5rRE=>-9ATkK+lvL-xP1&MzRdMvi%-V8tiKRYH;v9mo}f;?Dti!R$WE5^3&&
zPcC?tQykJ3=*Z)0$Bwxooo(TbPW2)OMvz@bL@9dkrukidT#4g2B=pI_J!`U-3o6FP
znNIfa*YDjPolLtl!{NQJBjtMOt(TeZo(tEm=J79wQt|T5d1#J=0#x~{?ZtkH`HSd3
zPw=nJ&xYR(d{mx4(tow~*_(Yr2XwfQv;OWyVgOy-pL1WPf7oaKd&m0MlZ(P-6l%JD
z)nBf?en)m+3~gE}YMW^P)9ij08y^g5!8IF?v|DLuy5WH$Dej-5DL;IYGT*|W9!+`2
ziu^00`1i%yb-lf;X4>7KQo9>eLaOS)eDC2wx8?y(d-kuQJ|%dM;S=dLw^k8dCYh$h
zu%p@A<~NdTMoKJ>o8)eRuue0Mwd4@%UJcg#J%8b!4Ng4S2}vQ5Og9D>VYrnb7Hy*q
z$>8v7=klspj8dsTdfof^A>z27*g8wC_44y*)v)-_@Y751R2mP0yf)U%?Hj$u^O>WJ
zvKI3klhc(9qdoCm4v(#Ax|f%zyRswY(mS}?CIPtz1PqT-rv!Ab8}OEflKg10(XRZf
zeI`I-hhhg&wMkR~zE*ztITh`<SK8xBbr|PXF&SaDYnBn)+s21uTuQwi$;b)@IO4vO
zB+s5*7~s?!jo&vjyVrKV$(m;TKZVXAEXs{rN$q6q=<ano^Xi)4gI7WEjsA&Yr6JvJ
z@IXbv^U+Dk<eYTmny27RV?pr#+j}{6O+pj$0?H#(>=&;-wZSKdyeq9+K98<>x7uvd
zv@dYZ(7fzEW3ZuCQSLi%E49${t2<p~o5LPumYnA?$184@6yqveCwCdz+2kKu_OMxo
z3p1@!pC>HV-fPRu`AB7WoE70!w`Z=FNuOKzH^e%{&AUaV#TAq-g(L5`9E_fs8Lz^R
z+p%O{+K<G7XEHX+0$q+tCdS;??Z1Q8>5AG}#Ni3~ROGSmlkR$&{73z|<CZVli^OR%
zm2H+82adKj=D70;KFRW)whl78li2-A{ha(Qs9$^v@W!ex%xfl^B8n}%9iC$VTa0dC
zM|zIOO24?b{{V!R{>i0RK(=;1SPafcEKclvs58kR5_4U*?BQ=6{{ZaI;R`8`?*70X
z);?r?uJ`~~L9a#PjaKzw(sb#If+M#`TpubW;}R(Q+q$=4d)G6^)mUnjA-h)eU40Lv
z!DW;xSEY!YSG%vP>2o*3vEAx=k~3M_+N?kbZSBtY1dL^hf-*qHE9nmi$7^S7$*9M1
z9MR;3UA&KQc?4I^-VyPV{50{!mxFa%1+=`sh)mZb_rfG|gx+`x&UhVgGn)F&8yz(@
zSJp07p5j-)GB*bt5(gbWD)J*uN8FRXojRSkyi{trI&SLv*`JgD0Pt3(GWcJ?6EWOP
z-Tq1I{;mvWzb1YNXZ{yQgfagBLk64w0Hsma>u>xOx)qPYz6q8;xc9n&ANTGI*W{<b
z)i3d{!RkBshfN>(H5Gadru05_D`<aFJ}9>HORHu_1Aw5mJ%Aan0J`vDis}4Cs>ib6
zWo^WR9#VA39i9320=~wY7;T)O;{z;xasL3<uQ}E<H`FG3iI*>tv^Pb`!2Elf=!P}Y
ze-5V&Tq~Mt=g%4qo$bxiSZNk=Pcr<Z5LHJfB-gORVWfO4(T=y`-A)S~G7uW$`$A+`
zV$WeCk+kQa2b0Bn4vVAdejBxUu#l>*`Q~Gc?HMddI0L<ZGC#GC?Iq$5+7IG2)zo(q
zr-y7Oc(jWwkb)&8MDCpo(vO*m$xuRrfljtErDglXXNAH>4)D<ZSMi7K{{Z_h=z64<
z+CA2xsz;sPWrdq;-RZjFp(h^09`#$opSM@+x#C;ju-{+b=*7-Nx|O38EKg0)H2C_q
z2fcjB{{RH_{fj&a@y|fizA5}#f=lfx-tDyOEjcBU1(sAqd1aB}WRq&|RY0yXcV~bB
z@vqze0QO?=$L!(pBTjz+tkGyX{j8JQK{GU-M9?r<qFEf4bphjHxRx8RNycj!RFoCt
zq1x#|y-(b2Gr@ij@l}i-CerTU)vYcwExpCFq_*;MM&)!RUy+RGsIAxV-IkAQJ>=~a
zHp-jil1CA&YKlo>P828uB%Xr56Mx{FUmUzc;U9}p{2sfybE5c^NF#;Vl!Ep++C-8t
zbF|8+V_-43lqc?r{R#g7f{yqXUe>>68@*RZl1ynDm972aD=_md{&*M=KJZtW9D~3M
zp4Gf@Q?<#R7(aH}8GjJ}0BWDvXTk#F!t=zC>Nj!l&}nhWb{{^H#=mDIcm3^o1)uyB
z5B6<}80{{s_1E%-)sbys=sJJPz-|Y%esccDe*xkBp}sg+cqdj_PLTvs-|DRq+QQlp
z08rhGlCnpU$la0BzfFH^KiC7{r-S|m>c0@a6X~<*8kT_y-MTHr_c7eStOTLN@<>#w
z65*F0cLZSZpSP&>8lwp4eJSvR_Ja5$@q9j&@Xy3W075~%zV!!)v+1`1;JG6Nuo23j
zNwf^Hh_C~xuV0Hxhe@`Y<5DRU5uuVv+=Ns_8DhlX1p|_E$9n!!bRBnB@W!=i;y(}E
zNojp!GF)3v5!&&|84Rqa)Dd6T290N@{BZamYWnK55O}-8v73`>KxorawHy4R;lL^+
zJ4*)bkZ4t;^&+MoFITDZH;exOYcJVv!ShLHsQ9kZ>S(`%PL5m5pGA@cc(e6zD?8!O
z?Thd$<FAdh{{V#k3bxh-{nE_V@?Bd+94+L;^FCQ-cLbs~GQQRMSNm>$%l;Srm;7m@
zcz?p0jpn0ms#!^Qo6#Sc9?1%A%xZzS8P3u4uK@TZ@n2Z@C-Dy7;zx&LSv3nQTiC4k
zfXb6yTFY*d6*&weX+!NJ2N*mWms)(+Y`EdzmWT8i@niO#_#yj0u<+-PwI`cXMOR%u
z!aqLgP>hc-Wl}tX3W*&_2Ots6Y)kRi;4FRz(!L~Xeju^2(>0q>d1x->NbbsHtEhoX
zJ3MX~93WsBNXf6lDZgcZ+QY_kKZ~z?A+KJ|sj<A0=He#vW@(P|Bkv*0F*!TA=Dyqb
zXZrwZ9y<M=?tDY>u1#~pn)il<{90|3h`f_!8vU(v8zFz5;#s*80qvem{bFT%D4$Mh
zpYTwx*=xcYfI3#AscUFg3~a1i40;oD_I%^pk9zt$!~X!ZZ^5q?d?JhEZmE8BKM%<)
zki~PU1%_WXGIJP>T0<k9r)tJojt&KXD^>UR?7jOh{89a{^xIuCQ`MVE@P~#x%V=d}
zG7matGRPr^a;r$ihnXQGfUC~X2Rz?9yE-Y&(^gv_q1T`AQjgi5(@|?Ej<t6fI2UnQ
zC_+7cc~U|Tx@X$Hu=qLg-{3dx32Pwm2B#I})v^bk(qwpU5q(T<0~PfuNcXSAAKN4L
z8Pxv(Wz8#2@rIGBHnpN(+<l4UmJtlr_XSzeSO#TfbVebDFjRs$1pKq`JK`>{@T=ms
ziQ|19W`(V9BNJ^n4+F4SRQ4dLJ$vBxsO9Wmx0Brdv*(M$UK+HMR=+m(me%NGlHx`S
z9B88@+1mj?9Fk8J@`uE~+Z*;Kiq?Gx$GYB`;uM9qi#V*?REgYV0<0pE+_1^PE(cy~
z>8~Al8$tNN@XGtaTGL!ZW3O0w6Fsvk%84q2lxIg!!?sk9YW`~f0Bs-H$HV^svnRys
z9|ZVoP`cD@ue7N3Atl&BA{U-`wq`J@zzBiP2VRujHMpI2*D60@e`n9y%l2&X{{WBU
z_-*iGNxRYKog|($T|VB<+4cduh$K9f^+D6GO8Z;JzB%}P@b5?(-^A@|!$-E1ksDu5
zkO>O%!Z5@$uph#JpKAVh{{Y~cvH731R+c?1uFw8BEB5d9rTvtAANx}2`c0pT^$Yzz
zT@kJgqu7|XlX4>u<}8u9U_7P9)?WF|DO2V|Myrm;i1@4i3L*O=_-<0VeyM$}5J)Sh
z+0C9m+PDxVKiww1&*1m%Kk!fESHrzm;$?)A=$cN6FoO2ySn{nBGQbrjyx9|+fIe)J
zKm#VfmQNJu@%Veh`j&wcnHI+Kdu4VQEFzJR5<N~cUsC?eKLh?Ne$+005%_xi>E0UC
z?vAIZ&cTc+G>RiuF@ikVcBv79Ku}wexaK8BGA>o7_h;)5h=1Uu{{XV)iL6Uyr#7jn
z#;vqnT3HV*k8_#ih4zlvPI}k0d?o#-egJ;Z7we(;8&!;Ig-d^D-$eG)xbL%Y*#7|V
z7d81=`&a(}!8$L0XWxd;@khdX^XqmtGDRMx4EEv)Vo8}}Nl>e}W%C#-DFBRvk~5Fa
zFAVsLR`8FAEc{QQO3+?dPL~kKrvx$_kHGXJxZ<i*lU9*X#La4t>*0JO9J76e6`?A*
z2nYcG0F7sOpI-1!!rdACMezezw9+hN&St%pVhq4%Vs1N~$Q&Yq$FMbH;kSqn#6JLN
zo;PB|Y8EivD9=*p2|Qz|ujQ-ujQHc?e~-Tubsrk*)5zAobdhP6jN6jd&@uAkKrl16
z1=s<(vQ29!`-%<RQmowEqoMna;@|iwkL<6cTnl{;eNR+XKQy+sETH4M{h^>gn5FRF
z{1mhHRPhDJ*tIL2S4mb}3AGC&w?A7}oxd;#y?lTF00jH}ioO?oWbh5&#ZQY?vg&a?
z<d$|eB57cVEJ1fl?F%G~#feZiZ2)o!Ja9koQ2zkg1HhlN{CY2sJ{LnZo|UT4C6&#L
zlDx?b>YL*+JcXphim$jk92MZ;;9&67+Sr)ZntPwBUlc!VU)k^Bmw+w&Z~d_zs~zb7
z0Ev7$Q>Db$Ofk3x-l8!cesdvRy${SnpNxJC{?~e6?HS@7Q^FT|I%t!seXCDZn9gL4
z2;7d_5N;iE02t%mzaG98XucZpC&X<R!@e;4NYix-=&dddoQV|5CSZW8x=5o8K{-Rm
z=U>pL!|&K%;4kdm;sfy$NV?WF8@S<nc;eKqAb1a&IXH&i7CzZy^ruBC4f1Jnop;Qq
zE{CG(_tE%I!gJZY>pR>kw9?1(MFJn;Q`MaJ$4cis4XX>=37Ta#{$a(fyX4Bzr}4U;
zL37VN0Ii=Nc;CT374U@GzPY7Y?oo(iAhSES+zN#CHS@p0UmMA7sa?zBhb=Yt%@|e~
zwuqC3#yKOsb$?{moi|xo-|si2#CT{_Sb1G}v+g|`!*IMYU*pR+3SSF@^Jf?%vwLQ-
zemL7gKWFb8hIKB!8P#w(9W3Sbs5~*KgtZoTGIJHrn;eUhtO?F|@AatuJV|LMz&{*a
zD?1b6y-|4p0!^f&>sZ27oOx%hyYw#<s3zwQy^p|O*na8kziH17T}HdgE&0d&xZDc;
zr13_tbYzdjT3c?qu-)fEa2Thq2i^n{EAg-P3Yl&H0BX++O4~3OP|o}nIk34HIrZsZ
zxc>kUJ|F4+AdKq%97T%dK@!_Y<ua$P#vB~>t>|Jf(Qb|-MEdkRY*k9M;HM6Z;_tj`
z;SEO84GpdT0DGuK<~pMVnofT5hswJz@UC)subZ{60#D&vgRXok)?V%#o0O8`!W^iD
zfIE!p+kB-R$;chMR~u*WljEO@{{U#MKTy;3H`4q+V6AU=bA2Rs@j3v0c*@A`Un48E
zdj2))%F_79SGtT%7M-g>aV@>Hw`;c7Qb>=MRnGu9BOIJJUbW(4`LI=<8xJKaZ>_&4
z-22R$y$sTVt+hyQuYaF=8Qv=Wp)Y(Brs;M*3bnQIJ>#QV-CHqH6H6;(h@>dzaL65i
z>P~AP#@`dK_0IrWUTS)6u9I~E^8UpP`{Zq>YeySwPcmE%4$Nk|&ldb7*Cn>HlG8~#
zWzDL2l0vLZNW|p%ZaCS%W#<CC=S{irufwfec-zLMbxlU{L-uhaE13A?q7Re+dYlUT
zY?A?%DiF+Y(EaRgrF-b#Rq{VH<NRhig>S3#e(p-@-)S!Aj!O0kqUwL!R}x!XwZM|j
z!e90KTc!^}F^rMGBC$Lb;q5}xQX1~1arSt^v}<jh`}co&o7D5gdl!y%==@EtBYmJW
zj}M<~846-`JdLLrR^a4jyx+u{q!yZOz2ZLAd6&!?1feI9{uS+4#ir7Oy^qhSR-1y-
z&He>$z9h4_SK1576r^?sr{PxbwKxsatP*6bfqxEbp1He=?QItQkDbvr2;2`sD=yXV
z&chs#t4PG|+F7!vo!LEW(WNG#tJL4w>2rHu)UGt`0{;M1Jbje`CmF}S4_eF9WYaah
zE)8B`1kuX~WmzNVc6rTn8pVa@h_zMGB)fapl;NR>`EaQ9mv1Ni+Vm}R!WX^@x3a#P
z@g$5eiPkmPB49EA>&6Gxs!CFKYeQC}7^vS?WnJh#8IsxV?5yL=YFRKg2Egt};+f%{
zXW{;#Zt&}q%ce^-U=*D~g&^^<{_74p@mwc}d{?M=y7>4<!KPyjYvx`}BNYsz_(44Z
zJXdu3y@tPU9-nV)wvkyzkjpfKEEshWfsk|VYdE@bcezfClv0=cF~|77UDFCapQg;O
z7npu%n|AT*kMOTt_z&R?a9v5_y>4&p%_Sjajb+aHaygK%2WpSIoO4q6b5!uohdGkg
z2%`!P>xq;z9CiD^^#1^7y;<=GjHlLBbo)e=ElBemxcQ_Vf$SGN;-YxjsB%$SuBTdb
zy_Y1`jP`}`Cxx`uNwm*0dyA$E9JfJmu@0^i_(OK*Cb%CKd{xvGog+eM*??owL`jZt
z6c3rd8u5)UQoGg6?{NN8NZC+M(olS$52iW_neGLgk*tx1P~5RRjxn0^)N0}vwyf^R
z#8reXN{nogm3QLb8ro>bT-BqF_Ri&s%+ci?N$Ziw`cu3isM^kD)3qR2L<IKEq{kqQ
zyAnEJ`f*(sg!LG;T?<n1RqLVi13kGQWcuUUyv|FDuL<8<*lQ7$k}b&%#(r0f{{RW;
zoEpx(7*wR;H*57AYB$jK>-`r`@u(|xZxo3PVVdG_1997M<hl0Eaf87TcyCdbT`E*U
zagtSJDu<F3w@iwY!`>jo65B5KSHqQzZ)F)7V7*G?t}6@Tr;aRi?+Y}#Y=CNV#<r2~
zMow7mS0s#Z59wUkY&_N#N-f_10D;XHIn{7oPW!~-O;W~dHC2)vArX<wa7iP$E5<9S
z_+Rk?Yuy2~i+vKxd&mn#J;An&&nCf;#lQr%KZUyb*Nvszx)+CR<VTX}V?>4SzVf?;
z8Os7jPg<qnPZMf-M~JR`N2VVyPr8FQDI@G(HBJFiI&sN0>SE!~AKiRL)L|$m39ULF
z+i~L^N@;@H2pE}F7)5U$_0I<@v|x`zTs7XM;@=kQR^AD+ztSU`_Czln_G|+o>a0^}
zQhi9^)yw|?73e-Su^P;_x0c0KpZirKAr+&?%p3q+c^DuOSbFU9X;&~_Utcxm{f{9b
zQJyeEa=-(eax0SyR#?0(MJOmwRew8fO&WBmVk7N+<$X_}bf1DgI<>Q#P15ADVQm;$
z(%wh%lRX#b11;#kg?hh&JO>VsrnZq8Xk=*=Tu(BH*@se%y9IODcdt{hzPi?-wAL+_
zMMQ=>va%>x5VC@@bi|C`!;*1apNsDGEn*3LTjC~@>@kua>6hep90fqUyY}E$=TpwJ
zjBPKfVr1^Lv{kRs->bR%c2C3EUJjzhQ;J<~-Sv8}BUXJj4;3T$b5*{ywzrj!pLKe7
zG-|(eMyrJw1A~!V--moF;Rx=ob!|TFF5=VHQU0k`83GJsl1V<q5znnd;LjX<IfKCW
z9wLh8PP~z>3N528yUw9L@L<_}%z6x0nf!40fui`wPqDM`RP*SHRfND=swC25<wheB
zs^=tSvs>g?9LA+@s8Mo-HvRo9mYaOY;v8-Qg#NKwF_e~(wYTnj4WEgj@t1@yG{s>9
zmGYD33;<J*19utquPe0Dr`6=Pg5gqW;kjtol?(!o<az<#zIO1hj<38obsSoWme2+Z
zBW({00C{iXLz?v`@#e1;mGy?33~Fy*Y;vw+ZI8xC9CY-rs;`vR!qS}{TgLZKuk_Cr
z8I2redUYc0D_Qxk_yOZRULdA>2+}JPF^DoK-!{{RJC8-rYT%~QyhWmGko-royS%oB
zX7UD|6OXgW3iLM%{AbgSE4}enhkp-;Z0&URQkJA+kWNqDBc5y6d>!JigD1ig>7Fbq
z)1nyX02g8i<&Y@KpKi6`=Cy0#;+!Dln^#MFdK${M1>x#KrL|2a?W3{sP4|uU?Ki|0
z`o5Vgk<S|kjgaA!o`8<t)$}&C;*EF4+B6#0sRT)N08=&CLWXSr00+uIY<DDA8>IL@
z!G0pt8(+{wx3<<7Wg<yWmAEDfhXG3s-lvRLN#ZGW?;Ja&=(o}|)jzw2)^jAYW2ny2
zwFjWAvkBm3R+FVk`>)<jS?SR5a$X&+3T~}==_Pd2y7l!rh;`2q_^VovOwetf=5_fU
zh{OSZ+is^H<kz75P12jdKMij+?-lB<Zs+o&gUTgRIRk<`wFfP<bu~jz_=Dk}hx%Mz
z9q{$J`%HxIxQ%?phXeSA4);G?SDg5l#X8r<om)@Sme82=w)5PTBs_=if-%QTobV{Z
z(af=VggMu<qaS(njh4EfQ;fyYqn1k#hJ5pq<@47=(0(2G%fz#I>qNBG;Fc&aF6|v%
z@F#ULr_ClwDkVJQ?-AFE{WtS1zqsF>escI9!WXw*GShFoS$_l)#j9FP3@`?1B_c(_
zlk%0veh9^Wk9~^Y`_g_*U0AC51Fa`bxnFjZvuDm>at!vBB?`?&_P)sf)bg(u_^ZY~
zH?p#~xk#f;F7hj{DO|@Bx)64>gz{J%XB>J~F0-hvyyoLr((W|ZQ@Sg=ck@I{IuRsz
zU`KuiYlGEg)?<xh*K@hDK3)}0KZFiA{c1f2#u}f7E!xT;v&PT8Ny?sk9;2T1^*MB~
zxOz(rqss;5bMyK0*O#=Lyt|z>j=8F8PjzXiT@=(b*<!f0oHp4cBXL;({yc5u^})ff
zsk}eqEfeDXyjl#lX4_D=kIlM+I9D0@q(TNBJ<XCjj`i?=#EX9sc;4F8?KGCOzqfBP
z()QK<aD50nNC)z+i{UrKUlIjPRtaIT@eZAM*72D#v0O;17{w}%2b#GU;ACgDRwsw>
zImIfJX-}4-PnE4M`X+r|L7G;}pE_y5wRpasX#Illzrn8oYFej-br*pqhe@!OJKIS&
z1s6pB0IWTnuqSU|1!8#9M7a2WtU;sO*%s55ed0kNBg=od2N?kl0U7OIH+Wn2v(!9i
zuS2EyT_)1?Yd{wN08E|RA(;OF40ywa^#Z=$(0qTQ_`1?PJH<;CleuAY6~sqmhZtWk
z3PuU*j%)J_784(p=1Qh5r6_%r{$DfhCn|WFQ{}sa)8Bi&e*?1c6@%%PzuR}Rqe%Qj
z0m#d60L^|1{@yaQ{{Xc&i6nO1N@@8%;>O&5mHWkE;rl6NOFa(w%^WHnVUs+m<OF8>
zz;qerzXtyRZuwGg+Ly$Ri?9T+UAV>y*xQ5piu&wkB6aI~Nk-j|osBqCr^yK?uS50+
z_Ezw2v#oy5z7y0eWsW>pD@M)91zm<Vp1C6*jdva|@i_1;g}uhCAh~PXgKvo9C}7=?
zT1Qt_P_KXoInPSu{{Uy76kTW^vIm9d(j<*;mdPUw*m9etb|fJkcJd2k4!+fe;vGi+
z0LLa>6Hy6ydo8=nhEI{%qa84q1b|Nf3i9ySXN$%AI?_s>w^wTYjB`BVt``X^bW`SO
zS=&Y2=JeFP@iezyB-LhFZw=Uj=^Y4@e0z|#Nx)Nu^1uU(oSfI!{tS*sv5I^7+DSG9
zK*x5~ALkYE?}Kmk+x=_%E@c6>EH0r#F4)H3oDq|O{6y}}eR<&vTezM(MrfH-07|j>
zK=wYK_32ZuNzPQNB%0XpGRkv}B;ek=pPWDNR<_TB{0ut_-9rcd*}-3v9|C{mxA-?7
z^f76l`c)Nvvi|_VTPI0;E8uY#+!vaFAB1po_}Apuz@Pao{tf>Cv5QRq0Me+d8GKI1
z(ZyL=pVu|aa77kC-79i){&*G6YB3~s?R|YR#S|)DM`9K<MEQwgcmM&CN#eSBft9eF
zdz$!n{t6B8QT`Qv&YGQ#l)he};zBfAU<eK6%n7(`b;ppdnH?F|o|UE7E~YlTu8&Cg
z%kd+@+RyCQ<G&O5A6u~2R?|V$Av5W>F<h)uNp8x4nigQJ3kJdG75uGN>v#MUvqih{
z_rQOR9}TR>nLYlM6jrf%&vR{k8WHck6ZEgiH%E=kassTU1Re+@s3NnIxvW*RKTAJm
z{{Z+Xo%ihR@Rq~jcA4PlbuY3@aWt2$e9)Pmd0I@A7VZYsDxY}gZg}WCllHs)ur)8*
zy?j2mo*zvQ`vl$*<=nN;o%X*Yu40dF&nMqG?T&v|f8dtC7xa%4`~+`>&nOy~g|4Rk
z%MrP)FC}@>MeabA<U{@34E^C>rSe^%&P@qi8?pSIe#dr~w?DNHf#S3TIM!fu$M7au
z{{Zp8Yx<D*fp_2yJHWc5{7<>Gwa_KClHX8!=>$-iW>Suf#E-rkXaMAsfzB(XHzCgA
zewF<Ff5B6}DQn&!_|IwZ!Do>)tqQ^!{>&TXd%KX%(y-$sL{S$x$IXmxH0@+7-Jad>
z^Y)?rf&Tzxol93=f|h<D*KV~HiYczrFwwHZxP(bAo(T~$$9CAx;sN9UYnAxf{{RI^
z(7q>pF!2Y&eILR1R^QuteD(tFYvevqnM!XltOs@h>AZbw<S*DG;Xi|ZF@DkdXNJ6a
z3SDWow)Yx?+DLv&hky-{xc(&E!I^)EDF=dU_m}pz(tI1>KiMD0Ho7g9j*)0|c_2vb
zVU7s*%F7&T7Bx|pb_xbMnv##@Hovf6*~$?Y?DgO@Kf~c|AbwVG*YWjV*pI*<sqibo
zN~iAlZ%ZGQq*Tx|@u&U@_%ZP}!Uz7K^Zx+o^Is!=!5$jWyjlBL_)AOi6}nwpB)3;$
zC*@4GwzE$OBfi;DTL*)Vax3d!_$$lr_?_V)>7-Bp0He)(#s2^V>mYvD-W4s=W~KiC
zru&**L!tdX9D(vT;A@Teiy)KX$A}McrCw+L{5^CnakqkZ0r}Sn@gj1c5B2wq=Q>s9
zNByyS(ZtP6{yl5@e*XZ1ZQ8*X?2({6F{-qa-m{ee7C&f{lj>{vfv@TB{t3B0e`Ku%
z0sFTXqyGRV9RC1X%^EX5{t9zp4cF|Is@zIGYi&bMl|SAytbh6on*L=~im+ws4n==W
z{{Zk($+!N<+VcMZ)83!|0Hsk^^B+S5e`wwp8SuuNyZ(IO{{YczUx5Dr@K(|P02u|K
z{{Yyp2mY2`SMQ&NXIAj0nBKNfAN+>3_!<8I1#Mv8v=)Pp`W4{+0Mg6KY)f;@{{Y~b
zV?VWSoBse_7oYk7uj!v=7}xVF{{RHfeV?^nntg092mUxK`eOS>_k{>0x%_JUP5%Ir
zk>U^g3toTd_pjZb_#}0-3-C){x>QjkYpkr;8;B4^fKOZs{3ZC7bMYU<2iI1-{{YeN
zU%KD$Nh^)N1hx1<=lgJ<&Itbi!jdc0`!n{)u!jEt_FM7X6Mfat?q$dOQ7@Rk`0|SW
zN$dLD{jO(I`!e|Pdj9~$KYBm%6Ib#_LlQr(KiL?0pR*r<9r-82_IdZrLcf=Pj-Ltq
zL-0%DuCwst#BiA8u(^LB@ODjX=X5fmJyp<y>~aqvf&D`M$Zwz7i@=`VAF}@d(Mw+-
z{{X>G{v&)0@YjPh?+W<m!*F<u#P`w5E|SdeZq_jml+N=+RzijNSV7w5No;KyIsxW?
z@Jz4TXW<`(eidB&D)@n|*yvh?mHgK>&8W<l(9Z<>TjiEQvF}&h3Kq^VMtR4Yf5B0I
zX<vrF612Ss;ZMU|TH9J%8`sqJ3!4`cTO)|fP@+nNq>Z*o3vDDX134rQi2nd(zky!~
z{yJ)Uj*0OT$9BFAyMRP&Bf5zS-G<%f0VJ}*fs+e`+*st`abKw(5&r;!NPHo;mOV$s
z9xT;$dsYF_Y@>$N9CZY_`#~y4_i%j;13x2wWzU5^BKW)Ut3=Q=I2uT;uHn7Cmj`4p
zK;hWA9XDfW^(Mcg-xge5T-yk)H?_U1gB)RZ5=TMF=m)KI-VFFX@YmpmfZiYYX`|a|
zR_}uE5G^dkbTZ22q>zr=nB%p1&%_@P%V%jN#H+gi^x=u^$8%bJT62#y4tlkeB(9H*
zekXXlOVvC}_RVy-wA1$(;{$Or;{@Pmr$JPH9>RQEq}+JpPeE^G1inH>;29n9%Qv7N
zl+P3Re$vB0itAE)c_qA%{{YKTyu7SV0^ppC<M6Iaz}j8?l+##fm(oXW%pO~E+oCLU
z&O08wiu#;9T|6uzyB{Nos;XlgwtWw~d?5FB*6@J;05-*7eKY(g)C%Z6HeQ?0fPORc
z4%c4|>YpM10Ix}T9<|2!Usk$?HI7>~1SlS0C4l_#{Hvn)$uxI=06b$M`@_SUq2ssu
zB)_G2_@<j|_wbzDsXdRvkJzZ=_LT7P>+)a!0O=LQe$?Ignq57n)9+_6h}EQx*pL9v
zP6*<^6@OyJ+xtoQd52`bKlvqbU#Nc)wF@h#ZU(C1VvJ{Mw!*;Yf_XUlRv278JVZT|
z`<i2%l(g(}9w7aq^vxT@$3<J~S(1I|)A@2LLdppRpJ~WEt~1viE6Ahxt#L1tsV%!-
z+SyOK;>6ry-MrQ;1Cfap<R>UW0PqcQ9s$s%)cjv{;-3&&s#sYJSVbaYIV2|m_8+~m
zc?9v2Ju4f;cUDpSTAl8`TU~EXmd4{hD!cvY+b506!z=G!M^_JljA`I%u4OH5`g$KR
zg~rb>$GVsqxX#+N_wqiw)jlfe9xd?g=ZAFNY3$NnG30_fnBLlGBno%i?RLzOk@r{<
zYvum{f|@sk^}mOA+J*hQCGMO>xSANo=}tz%=Yx);Je-Q?d<S`<YBtm9{xNG?h;3vM
z!uOkZ6~<2O`5bf=<(l1xg|vM~Qf~-ei7kBKNP<^HQp0x86okM%2<ci+E~S@Yy__F1
zlhdZYhoztA{{Y)6G#xHU$L%h^f$P#nwo*fLbmz;n<q>}QB=C!#xi#f}H}MMEK)QTr
zTJG{tV}N6fHcK~f2dzIvw)mOitri5(A<eOowqD%KhswtXaR+<t{vVe<_0ZaUGqdom
zv&!=e%~EA0u4d$tKBqX((z>ZrP>q~-K4zUL(U<QX-(y!_)AWEMvba8M?-CVjeyXH#
zlh9+(^ImD<8->y?EcDp#nP9k(iCMPgK*n&uXX{uxcZy=s?w?KZ6cWQXnsYlbU{7pt
z09^FPYS+Gju4XrPk%wT}ivuGBb{_Ssnrq$YbSiwZ*JIG_wH+oIBDL^zWQdqE%A;zQ
z9XC4+x2`c+9}#>{tX^oJ*bb=XYo9r%QgElZ!Rmc0PRe1Y*hdk`WlhWvCnvD_SDQ!n
zja_8Cx^+caaEsS1oScusqIIXW$fpSNU$N^R8PN293+as>mZjyG2_})w=aA!`dt<Py
zZEsJt@bq$Pw(ewWxm1YT?kUIK9m%d%-{L9L_0+W0<BY)g0$2$l4^Np-IQ~`JYj*Zt
zCei$@5>%VYeao=t9^SP)@oPeyW~F&`IUf!v)-Pne)YYVp*$0&h^d~v_zaDCD82Fy!
z`zA=E{{TmpMiC<+>5?<gpcNN|Y;~5iyS2B4jEx`|ljR(7l1@J=(!B7koaQ+7yRg1n
zWs2Y~PhrmjrOqjOgSS23Ly*<4Kj9m*PY!D;Z!9HGG8vHk(UbSK6V5Z*y)@{0R-NI?
zJx11fWr5?|@<`i&9kIs(xSfB(u-so-=oXXiS+__xpSYlQ>zeE>?%zo8{{Z|Wda`eZ
zAYqK4<@3+C=U7pv%G-+2E7j(T*Hg?twBw&rFKzpQ6ua%F96vol_9ndwEA!&5HMIS1
zG`r6U@*UuxOn*xAZw`1rRMzz?n_ER^`#euGvU9N$o_)_j?_WrKEuX>G+H{X&5lQ0-
zwp)hz<R_?7I0W_pbg8LYjAW_GJk6c3!t+{BM11ey%NzdyhF&hZxABo{OIc%cV>Qk~
zfWwvD+Z}LCUAFM{xAAjSh-uKsx>N@Oa-=QGcYmKFXzV~0^fsYq;qMZ7&2)bmtSYfa
z(prm%mExI?V6HIRG1IMd_nMA}sx*EVv6fLf9lm5S`3f=JSA_s}9M%-`XN98aN(o7A
zbnJbG6Nf2Eq;VH*HRNwtco)NVJ_3V4(CuM+UpvdWNfV>v{b2)~AK|Z^ye06lJVUA9
zcz<7O+gmG7F~UaSVnzoeA(Vm#ToP;a!{OJ%y#Q#fajV-CX|wFjB9<<apae-M8903X
z>~UW@{AiZj!}@)`mtxHi_Qs&L@<?(=lNtG(vgasN08W2e@bcWwt!GmE+3ltO0Ltgm
zXBl=5oi$czS~kDW^+$;6z6J5dt>J4+=hQ6@jb}C;k(w!<IR5~L%TU0t7$Hc{rDy7&
z50hV;?b?m=$rOkJLV;n%I}*xRen8jJcA8^&)J33LT)}ACQCAvmuOeNYBv&MvLF<m$
z=~~lxC&V8ZHM_gd3z#nMVk;fr%8<zmF9UdBqzro+oV$qYy5Cl=;-zigpYR$-ojw)H
zt5c3U3s+0(lhWUv+2;^^N%)7Q_)^Qm>8KlhF2s3Jh|cMsV8B5$FYu1aqw8Fkhkhpb
zt5}Wn9ZKxn*`?sQh)fRf2V%z=9=RF!uc|IS2KaV=0Nv^LckcS7oUFlBl*jgk7-RsR
zGC0m_<~R69<68?O;wwuhbco@X2XcYm9App5y&M*Mmt$x;w5J%&H>*l6{$5u+>`gpQ
z9}y_hys4#YCv?%}I=-I{mGKJh<3+ZNeV%bNW=PTUkMjUY8;A!ZBD%lq%|`P4Fhzl{
z8*asSjxY(z`Gg!`cFlEqcCBh2-%ouWE4kFkBVF7{<<1xmVrEogQ@F10!<r9@JWp);
zG*QO)7t*UYls|VIW2ekULXMpEt?~SP>E&v6O1jfjt)uv#O+}aGSX@p&p0)joSM$Gb
zmvXnk{{V)UTCb1pJTK#@ZSHJV&FAu?>J*Qa(6=Z3r8)Mj7_>_d4EWRj5v!Su7U?54
zn=HHu0saup^5uOEdN0I%fBq0Z7HK!SF7v2qafl~m;aPu)2|Ym+--y~AJ|xja%iFE(
zoPj5dHU`rt@NPH(l>6qsGY6k$6)`ZxS8b`TR=Ri4RU?L_?Oh3J7j@`)--$jHSYO_q
zPhZrvDa)UdBXY6F^Z`KqE0&5a5W?C{k!&o2mW@$~JIMa|JnlV?YO8o&eKIS(5icc{
z(q;K==VKzo<D(&Ba7VGlbe{*l8r^(B)uhrjEB%pMqnT%u{gLwX)2)3TCZy>@>y@Ku
z$G-2#^D!0HGMl4CUGCqJ=l%`x4gUa!HOsjE!Et*G%jMj|8#<5_@Z)C#9{p=~UDWLL
ztH+%f#S5<yTgkLH{d8^%A5r<&*-&_cPw=LJ40@tO()mKhHW8tD7#waL3$JsN$6VJB
zAHvT9_>T5neO@7Etjl(+LhO_-Nr}7rsUJgJIae3ub+Pi6Wk-?^;gzoM=j+hd3x>z6
z8mB^|cHLj6k@EJ3rfN5rCf`K2dx@BBSm6M+J=>y>diS3ScwXbc7E;>yj!En;wG$a%
z?CFu_3;ohaXAFH*ej>V`56$5(4_d9oj@X*wUn(v#?A;Y`qdD(`!0%n2rK?`)*Rg4u
zd@zkh>kB2+h#A#SWnwe=8uIvqjq2j+)xzMZ^3dA-W%u{&eYQ)(_<TPlda}J;-nuWl
zv6%;nJUQX773ew_g{=Zz%c$Bd)y%9VxM1?AA>Hyv9Y8tFeZ~E$d+T2dczfVSiksq2
zmwhbOf;eyByDZW(O3IQepWTH8dUa!8pg-`5UH<^jRQ-R(y*w((s8EF|O*WFd*K@|j
z%QLNBbuDDuvcE(B)$qTF(#K>iWx0_WHc1_`!3Tv?@89vSEVy{AFKn!}2xNopuA&)a
z04T}s4iECLK+&|hyhq{$*L+H*C6I-(ifn}>ebIx)2W<B>$9#CxZ>F-1q>Yf<Hw-ce
zJ^q#T)gvgT=%e%eG*o38Qa$3s?ADXpw5(?l>_m%b0Fl+0@=wz>;(rqKF9-NfPQCHF
z-c9Ffx1VU9AQVNkNy4JzAdW`hdGx@l{uA*Bh;O_Ed`Th;nC)a1(vWtLNZ_1vgV6nb
z>sLk6+r~d-@ehhoXK``9-V(W3z`~{zuwHiO>q`q-aINhr-E}UHJqnG<>DJvyej8tF
zel7TeqWn+Nty@U9(Iygow&6BSc^Nop6rPZ>k_#O5`F+1xVez+zb!#}Tv@(9*aXD7D
zk~}4%k<Sc1d^Zb&j^x+o&%_&@a@sgy)A@|46R$wwf#Z(D`PaDoB=K77dR4sl5^cY=
z3b!#H44Xdduc$x7D?H;3gT_~ddB@*Ne*XX?+QDX6wMtZ}J4N*BzJ7gAyL=_9UPG(L
zYa<CAqd~wUKI9&G?_Y{Pw~W!x`&0OuSdQRU!=G_wZV&5UuKo$TYs7h?SsMO#JDs>x
zjPwBgI@jaR?YRou{?h&+g*giyfX~0!pwH`GE-qJz>fsf%KU05aeQQI5;FpCoy;PO+
zG~`U<9axY-KPv!x8a^-Rz9i7^>@}SZR3^xV0YQ#Q0d2&$Bo^lx91)7*J{eCr{hT~K
zZ#|;ipwkhcb>ZV}$2mW)MR{k4{6}M~5A2)mb5OHXAbCtY#zX12@q^nH(Hy>xA=9ee
zno8$3D!ic?#xFza?*sfu@a@&XmMd?uT}`*oXWO~h9FR%ubHL~;+5AKCJ3{bhgmr%$
z{hh9xO4F`ynh2i;MYfHkDp-O6ZMe=W^E$&o(xZWH?6u{&Lgjydx;EUsK|FxpTvxaF
zTgKic@Oev1&~9xX201r<(f;rUBkNN<mW3H}(y7h*9N2tnlv9uSADdsb7wvcAuiAC=
z`#oPvv(oG|nWvV{C>i9N_u&B&$iypPAja6&y?oE%KM~(}JH<L@i?qa!OPwnA3+Pf{
zOl>8+iz0^#RFx%0KwxpuivEy1JMjZfyCPuH1nR{`as35*RhNyek_8aqbHFTeex|vl
zgq8O_U#-U5AC1lb00mq9p&PD?;eY%Fy#D~?+WBwxi~Xv8C4SR)T2%f%io(`C9ywCl
z-%gH5<J~b*?%r&&v2qbd-T(o>$t3>(MfYAY1nyZrxiylWJW?7bZcyW|V^Zi!_H0WP
z4bnd|zu=NL?P=oQ8p6jBy^WKfP01voe@1G@{kVT&-xOJRzfkzW@Ls|7EiY1)uJsKv
z(F@OMJCf48aSWG**p|WqP?9$60FTrOJbw?Gw(?3B_=(PV>??9lAHtAtR~ta>PwLLr
zAJ(ILpUZE9d~4%BhCV5S#eNIExzcr8C(LueN;;m5tPc#VIAmZ|hXfH{V@3Y}1swgY
zuWeUKhgP+g;zq*Ex_!BsiRc1^*nb4gez|zLJ{@?rZ|x5h=z3PCFabQ<+el}9qoCa)
zF~IcARPgkF4}2X6z3}FXrRj1oWm#<PA%V7@+oMG|G%PIpgt2g451s!2W3K}K(z?&Y
zDSv1Wk9y9h2bpOlt)`iF<~EsRZcMhIDnSYyvPKn2d^Q4)n}h!Vf|CBv-YfBrv1Rec
z!8UJvkXWsbt!--=c~N0$Buk<fC6i+mbB~ZJo<Svk)sx2eix~2Mcn=|z<a1pwhCFkU
z&2(5l_l;Dr?JE$mYWF{x{{V;nKk<LU-xyhV@4&Z@r|P<NhDaws4l)X|val-3%1W}Z
zAc8>^_jJA|{jC20ZeI&(cfK^bkvu8ktv>rycDJ?`*0bH)Tf4&&?E}x5oG8I!BT~)5
zAUEq?so}qa`rYW&HBSv_cXx8W@3c)}ZzQFUQeC4hpM2L{7mPHmKHvKq)gp%8A?2(n
zp-?6<v?u{W<BVe!lEcc^HCULvkL4T!P6dA4{>Y!TSM4<)hE2bYJU8}_1o&sew==G$
zETy)QlT5WH7*cj|42=5%pOKrKlh(f5@cxPLyTe;GZw}}>KAUrrQd>JHA(zvJL{dLG
z>@NIOh|46TFdh5Qs7VB|5kH;p+OPJX)&Br$FBw_*n^Dr?(qPlA-qzL#<Y}MFXD^u~
zAwxD#@S=}u^6!G*7In{o{{RxS?}^?Ux3{{y(|q9^H$jX{Cxzi{+ZkJhkdcGe9Y+WH
zRI&J%XC<+VdEUF3f$4$oS$Ez!vbwcdBydA?CxcB;Z(ETpR92QbFWGzct&8@D@IB{=
zZ*<S>o4r2dT2Bhi=SL%XYWvWYR#0~zl;<DbHRGSQhwY_t@K-+!d^w}0p>?ilFDqW$
zOjamkouq)MjaPJ!g(vT*9ChhlgQ@u1YYF6tu`2wldXMq-uFFFBgQQt$@rf=0mA+;J
zlat!C_ELRJm3Sw6AIwdD#Qy-nI)7_R-6~x(_HNX4lzVw3o=aUqDF}3DV(cVB2w{`K
zQZeXj^lMM?g^scrn6i~1b;n+n7PaGxPYy=EXP<M(c!`ck_u`}K(psi&u~B+h{PX_+
zf|35!*Eg3Mx9q*AX(%Cu?5Dic<&?8ID;3f#QK$hx+ygT+XOI+iug2Q`tSx>bFOwSG
z0~^Q#4ZMO6>MDzTMut1rof|5r9epW0Jhv;`q>qn3XW#fJy~l|>39XM8_;pg_!kRg~
zR<fLgxkywfLb3)ivB1l2VeQ(#EPrY*+CN$Sr+y*B<Go)^gGv^bOQ+l0!y`&WDJ-&r
z?!k9HWgw8MFb5U=70u)8JDWDVw~!K}JRV1}qe1a}dW^-1W{xqF?oUBVR9afGM!Z|H
zNAp+k2jfn!@B`vqr^KBW&I|diqmm<UaLlU7D8qcq$T;Y5N3pNk--uuEQV6^!@IS);
z02H)u3$*vXDQnoGwVvw{TD_AZGdrw%r3_pZP!N%vk>Bk{@g0T2?P1C3f(3GV-;RH=
ztl5EB*Bl(~W<7>YOd-tdnNz84(fq6Hy0rTKw|TDJDx|l!65P6`c7~P9kTa8jPZ>4(
z8T$i%)>pp<b+q_Taipd7hln)=V{(!dnh9M(0s#eGa5)?wdi})FykTbcF~-t`ln<P<
zVD3Ji)uShhZ0`$c`hv7`vf&@9x7Qsjl2v`2oMx?I`&A`%srmK)00j&EtnPju=$AhN
zG@T_brSUe3&px3nq$Ds-^1xLiQI?bUWaO{|BZlqx9beZPtQNOHT2_&9mRQIn6Vo7e
zHQ3neP_`ilYN+X3MoOaQi9X#Y%6?&g!9IU&Z9~NuS`X~suIQ0Qwi<Z1v(#;+Y?QW8
zKYbGe$sfe52m3XF{{RI{)PH9`7F=03{3M%vbD_lczhJS8?P&0`7V>VKGNrqtmMfMn
zHxOeWhF{UIjy^TjydCgc#hxhe$&>B2S|#-M%^CTll_7Gn5BlbQbM&v|(&pyk_U22=
z$sSpyb0m^EEY8ewS&8J5c_j6rn?_gE^&bcP3HZzL2g5V?$H3kg)pdO~+Fv4DiyLR0
z2>{$w1O=OD``barMsbl@ej50N@l)Yer1AJuS=IClsGDq&+fOyK0y?XpE1z(~@UQ7>
z_6OJeBdUJN-WRv<l!(H{+}mEptP4kY7_{NQ!BWn}#(#x@<Q&zn+c)8d!ap8-5%CX*
zekIykB-%cgb9G~>NwzDCdC?omRF{S!<tn~WC0vj;lZq{%@2!u?pMxLpQ(uYNEz)RT
z8`XS4r9KqiLlkh{z<O^R0!Jtv!vM#w?#K03(X>y9IxWA9G`W`U#^M1zq^f>GtTG!O
zzskRsiv9lmm}b>9Puc53@Yb{DBZv(0Zs4Rc9m<`saoe?RL!M8PCq5-bN18{Be$e_h
zqdXRxuZJRGbp($jMNSq{KJtE?6W+6a2<rCwj4@gaiILRYM&PW9{Nyk=@6hzGrMzpU
z_+Q0Z1^wows6hwza_xf5k{fXyKp%f2iuoJBULMv4wsqSH3~nVTjt4BhjHjyCyO-2a
z!^$&OxAmdr<1i{*wP5a^&#FH}d<QMtqqX7A*D9;n4Dnq@#&{r4fZjB97$?J;r%(Fy
zmbgCw-Cv@mzNRrWkDb9nsUE_+kBriX_!Z+%LE+6%5Bvm{xGTv?T^nJd(?18lV|Qr(
z0BF4d(BOo=$Nm9NU$I^#gG!KjlG?@R9Fw{*NF$PYAC-PQ{>DQ;w10<(`!~P(M?HSJ
zd_lWEylCXwI&A}%>&ANuSP0%NuHwx*c^?aWX7MhiY2qto(`9%+bCLkclb(chH6*a<
zzA@7vv}j_RFpsqK+#ejU$OU~yI#x%FRtx_C8e3axY-CgtKEQYc^cfwvu441VJ{XSa
zrHOAX-Xo7XB`c5Dkz3-hx2mVtq4CvX@5>Zewv$-!725ej`$ob!0hGA?`l;$S1I`CR
zM>W*`(0(rPw~c-rTU>lSyF(?Ef@`~Qn<nzr2;>|QwDa#&z8LtXEB#{r#(5s;CXe@P
zDvXf6qW}u?{{Rx{Lr&F)i8RQSKrFWrj*S~|Mmii<rB1dplFg}ltyiGv#VkZ;Q+WO+
zN5IdB+KhJhdKA*-5BfqkbDp)mto%yVA=G1$pUsVC`GMn%<M~&WY8rC*L&P>-D$$_w
z?Iy#tY4<@td402ANkOJ~E5y2F8npI_EN;8}!If`(;{@Q>bR{~Gw^iKf#d|o^Re5x0
zp6YszpW-bt<SGe5WtSr*bKmf+?+t7BvDnD21gma5m{<;{-~4NL#l9Ty{)u5Wp`o+D
zzyx^r7hE@}CnC9hIt!Z}D9Zw1l`VzjIrsFe`DY1g$&W0dbZVV`V0Pc1ENuWtKItc@
z`qOobd9-^0CYA*0kj@zTbYbsY)|aLDm&J3WdYod)&3<j$d1LL9)cf&W_1A+g;<X3E
z^Y4i`c;+LYToaM_RY#gi<CiR6=M85hdYpo7LfK?zY=;~a1Jw7eStN%~k>`cXPRLbE
zPxrIHQaY1X?O?K!85S`lW;3*qcP4w_@m%kV;IYv(;o@a-#@}OYA9apL<y7Nn+@$%X
z%63{!{k@-t-p1m2qP&pwZNW}`&TC&zxA5ZWj~1x!=P>9SAcI-fUMaBFEMwI)NmZhd
z1u>8g$L<dR<X30lZ->4o_>Zd%J3+N4oX>LVRu~gJv$6E_88w|*Ql(GYRhm&=VNVTO
ztlTNZx8!lLYC1oI47!c9^GKyOM3dpbWBtxE$EA8NhrBi7Jzh8?)otZTFClxVw++&H
zL#veB7<%pNUtw$i0J86fyanKw)%-mIY8t(;NlnzmP9+%uyr8EFqp%$M)?dR*-}pj4
zEk~*Ny;^V}J5U8X5=Giq9FdIVSIX4l{%Ue~2y3mc{{RH~ytjv_#!{X+U0bJ9nDADY
zqv<jIheHvx3OuQc@hp?F@gs~4tJ@#pSRNzL^j`z(5H0FQ7M*uG&oJD4xSW3q@GzvD
z?k5AKeNW<ud=;Wyf5J_n-&r-S{K`Li0?iw_z+4}`3F8Br@yRW16XQOgWvshh*f~b~
zGkw#~BiMU!Uo#FP#QkAbts7f)t-r3v==3tIw-<x`%1@PUx6}1JrqR;rWRiI8wM%&A
z%zkI@RU;=k$j;OFiuC&_uD{_Mxba2Jmz`s!4=}VTwHG7+1Ck3KduJ8&HP4Ti;g60l
z{5gLJn@Icip&|xImDg|psLL*TgOiHQ@n4U;7vf(BY`eLXz~g<?2%AEMY^;2NmSQ^q
zG1j^<d|QdE-3oX_lea9c-i>~DK1w*89Gj@7?Hk=_p8j1A8}T>CFNwbk^)n5nt(TK~
z<-A^Tb&*+A9lM4@f({QAgKMJrr{fNhZLZrsm!-tB%%~)}eD8CY2js$m)GwuWUN5=Q
zzBx$d@*nKq?CWNlGa82p9AkxSV3Nf0YX1Pin@_NKds)&n69o6R(#3TeOa|FhDFh7U
zu5-a1>neEqlyL3Pmn}=JEqnJkvbomGu?~hJT=bfH?W)_&*u&Gl6Q2rd@LdgV-bsS*
zEo~IzZ~MDbC4UeNcV7@ZQKfhuT`yO*GPU)>iKM%Tc`m_Qg>XLlwmDbkvHU%&YOUf+
zX}nyGiDoj&7o(7Js!w%Mz#f&g@m?J-#ZygnW2fBQjf{$st~O-k6*(Ya*Crzuh{n~Y
z^}BM%qDt=n0OWl>BZcVG!_J;vbvZ3>Ei~!#IJ-X?Y1-_!lC{8Jw#sKLKoUmn+Z=9R
z*1gNY-Y(MY^z@U(PE~EK%nLj#z8NFVM&L5a-p0N~(>wv<O+xA`Eg}b+c@6|a=4At^
zbI@YGlD+s_Ew{qmOG%RC+-jx`@x=~647}{;lT)1K7%CO0!Bk%RNvrCYe<SDemyfjR
z)~3^q?Cqud9&_S<hq@<?ykNSQf~PNUd9)be0625hWDo)FYuSdW;NOGVL|R?V!85}i
z)q(;VS!D0Kh9GBwlV23+H-00tHfGUOU6y5ycCmDje7P&e2<#1d*0bQ-?+JJ&ZyxxT
z@n2SX77KXD541llN$6a4$lzAFj$KNgDmXb-=8l%PdwxeuQ-%gu=MG)l{<>V-xzkUN
zFH-LLW=UC$@+@06!4W)fVi}qL00CG0Zu)=xBawJ#M`;nf!15~}G%y`ApW@GPUS;6#
ziuXPc(q^;L*Uh=QMiNOTTO|7W{x#GOh5jq??yYCyJzG_mPPO|llGgU@$s@BJ#QFh>
z&W;**JjRYM8z=1G?vsC#Je(w9nA59E47XeM^*pn~x;5X6{A%74v1Ul^*M6aLqm{||
zV?)z%?_XSacfdXh(dNI>MZ;TLiyxVXELd_u9+~ttUrq2ohrAi5L!<ay7@lM1G}5=s
z=b$CeJf7yE)MNh7w!AXMG`A}oMs_eUDmUII9I-Wdwq;i~$3;c87W7xp`)scy!{Ktt
z((e~{ZGBG-Z6{Lk*NY-LdrKvil6{^cqacj@>@&dh$u;z@gKwlkr%9p+P{9&1zV1fm
z7~tcQPgeY^&b&3^ySq(1X#&_wYZwc@+yNwBo0pBl)YgCeEUkCK8lA<g`fPfImHAbB
zlqb!OKSfe72>dIbGQ(AJR2t<`e!qCKd=46oKWhndIV*Ou@-j5PfWA1?HQ{k@cWZBJ
z1ioZWA0>O|V)MJ}UZp0L4uh;Fr>kzfmMyV~2nrMqt+Ukn*Q9t>9TM+J8oib2SjuNJ
z0)a<UxB@aoahl$#;mu*h*H`IhZwvnCobSh0E0W(qUWIBgh7Hcvk=ch-s8w>+t-q@t
z(czn$8E@_GETm*vrirDU*Y}O{4XB{~)a_qDZioJQy?iI7c)w8A{8OfB{uSITMfJIw
z;l>Lp$fE<(Bk`}XwqE}L&u`^f;Nb^O^;WO3<>F&e&hcme*5y;cnx3I7pW9PnAG}UX
zrSZ@p?0+hQ#X1a8_-Y$HN<(yx!80%3QH~I|es~7FQa`pxiv_Nwd1y>whS}VaoMS3a
zBD}%%e-rAu>G0mCs@zE|Px6VUZH~*z<CD~M2ENj(YJA-fiBYFgHje1}JHXx)z3@G(
zmJuX(7I#d~HM{URIT-88``1%HhZZ}Pzwoq;Yb$VVxGLZ7=ZyPTkN*I|Y4Hb30BSZ#
zbffsug8q2_01DO9z9sqBMJ}V7%5@;22O~Yb#cLHAXpUJzTeH`+tJ8L=2C)iDlY)Nc
zKDppmr)a+q^t-8r_M9Hwhar%HGI{M^8u-t~ULuppI$h&N66krJQYgqm3l<wj;h$Ri
zZ{ZKb%bi|Z3msG}a?076)L=M&yOGd(kSm836tp_9G-^frNVDvZ1!$fmu#!1eMVJwc
zvBw_bza0MnZNEJJ(%vEaK*Q{@&;9|yU%0*)yNXOC5~JgTgPd35AMMU_`%id?{{XRJ
z{{W;=SA~qXL+UV+a@C)vUj;4Ze}G;XP70khGN;_0wc?)_JQ-oC*~xI$;U|qxmn!90
zemVUOeGTwWORzrzyeV}fI=IszCuuzK>0FnOd^N9loJkBU)^>O~aKF59`1G!<HD$=5
zBynP}_mh%#J`wn1;(L|5lR)t!EQsjww2m+wC;GD8w<rW>xHaf@9}q9T9cq#2`j~li
zyM`@n=On@pB?#`LwOG{r6XBgIXVCQ6_ualw$o~L5nf4@Leii4fuj*QSbLl#?NqH1`
zLFSCQ9-JQa*9vjH(>!@b)f-Dg=`Z+Qx_!GxWv8;-`HFZ<1sUy(R<@b(9`z#<YBwwq
zIas4T2dBM!e>eOi+Qb%GPlZpHtH!2D-b1lm1JOaxHJ7St-X_q&O<FdH=K=O*Pt=YL
zZ}on5<#R~l6n)fwg+cKqE9-G_Z7<5--s{J;d7b|N#Y>xi5SS3KB>ltGjCC06$2IcB
zpNM>9_RAft*6o49GBRWF<W>%w<6VEjUO2Y#4~lJ!_WK?pVj=eK<9G-AzMooEFsZ1q
z(y#9|bMI@98K>ArruQH~S9az*RD<qnm9NCOj4O3#DSLTx26Kas2<IP#eE$G{rg$I5
za4x;4>xeC8D<|33QU>n-03kV3?TYLCO>3Y@;AtPjSC-1w3Fe?jA2!j-kPdQtd)Cf^
zz4Wm%ttczDdR@<rmR3ZDKp6AoliRWSQ*~d76I;dP>Je<WWeT!Hasl+keBa;;=kaC$
z0zv)0trJBgeqvkaX+6gyAam_qjFx(xlx;4!VORqeV$2MD9{H@RLNwy4Em5D=q~*!f
z^^0E`GuxRhW%CsmaX2Fb)Yn<z?-|`XG?31YgkYf|zfgFu3DP`mV{v5501>ES5GVm%
zI%EuVHRIZI-C205O)%b@Qu&fCr*D{~@E1H1IuS~CoE4+Fg2gu`u6>t3#G8#TU53`{
z?mU1;j-2+clTq=6R~I5xnSst9i~t8yUjz7~Sn(QMNW$^Nw$HjIkpbikf-%;)zX$w4
z@h6RS^n+fM+(wF2EN9A0`mh~;8ihqKce&LJM4UO3*!>xY;!Wl2nKbER5h>b|9!Ud?
z0a!Zc#knJxX}WyAWy|pLAL05BTKH4NUM{-u)~i0BptOsWA2xnOE!S_oFM>Qna5UXR
zUcO?|Z4o0e=W`xgKD2#Zb6BsfK3;S0tA7z$+UZ8tDMz0)XD&}AkN*H(yuQ=Kek9ax
z&5Cb~Bkgy5?O#*>0P3#@weelW#4*~mp*E)Wk&oO}=RZ?}#aYrd`~LtFUP(R8)b`qC
zw<wCmau=&Vx$BCGlvI7#txV&Kle=fqx`&Fioj%?x8`+vfZ<*Ne*ysox<DTA?9?e%v
zn6-u6b425KeCIjm*1lQ0yVblv?Rg5kcJrT+$R`n}AbvizrQz=s>oz_kwb1S-0@FXf
zS0ISgkCc*n0(ccwaITaRj_shQif!8G&{BL<*StK^+YuaY-~lKk1J7V`GCsA__yff@
z)7#0fYedM;F39AQoRWDw_RkrwB=K&Dn)Cw30tDj@zwHC)dLAnn>yt?>zk@WgJgXpr
z;#K5sUP}IbYiZ%sHq_ddCDc~f`n$y65j0H_6F#pXTZU3m2Q9$<UiIg`3-R`stEcv6
zprtP)1*4NZFg$_vub1_GLi5D8+Q*1*3oWg*Ed{^(P17UWipcQyhkhg9_?`<N4WO22
z<4b~6kC9LJv+0afI&qcVi*u<6w$HTU)n>Vzrk!??x{ux?laZfMT@9~~<<T!w!;rGH
zzbi<@l7Jpdk@^hR#Ges$e-vr@%G%rRj`Gf2$+d!p_9r}p^sOHTd_~keLE@|1o11AN
zXR~n0ko>MVTy-a)u4=ehHlq<?v5mX4>^)=Sg^r#sWz}6GhScs>2bl&q1O1|U0bI_5
z@dEz<;t@?_T>k(?)1ZCO!JnDP<Q6}!E9D08mZz-hU+|Q8#h%W|95uC*5M`CR0>B&r
zT}G*|HH>zaT3BNDQ=cl`xZxBXrv#0qyVqmFQ>9DVNc5?DO7r$ndY?=9m*OacREjG$
zVGYcC$MImEym66^)djc4FCTbLZ#v%M*8a?NS((9A^ce(<dRLg(X*YWIp!Z%OmLTw2
zH0~k5SX&rH7~p2S3r@fB--<N{u<+c_&-QjJaMQ$A7=4R|1)JCdT{t`j6CFyWYWgU>
zFKe~vI+#309#K%0@45A^hw+ofDdJPDY`1wPkd#-rDjXl-;W+?$^sjT!ek<!z-LyK~
zD{V19>cPMn^d}*Qp|6=dDWT|o1@Qj4srcZe`i-TLZ!oUkFU=VF6QYg1JNnm`_-o>h
zv8YKEm7GA&BA+kJJV6=$B71t{*NX8Yhr>I(G#@SAugF;1mEl|2&d$kdkJXsGYoXp)
zHiL4hc5dTn=ZV0P860h7WjO^%I3V@SeqwxH{{Vtz>lb(P>)!+QD`Rgi>4uM~Ge;y@
z!4btc4z4quqFseX0d_ex^%sD=LE--Z4_KRvo4C?d8>X6O+wM-|T~)Wnq}FHq9-Su0
zNgzmgE~CG&^sZ|7M--asJU%l+^6$fcvghsF@OJu5N5IxPj*~3IWRhu`t*Uv74@OkE
z1&8~~T{nn7X8!=&<Kr2%(LN(x-QC-V4;*@?u?(=r-8Y!hX+B^(ge_mFH(wQ@mUv?G
zPnL_Y5_!q)YdO9k+s&@SZ3V*l!w$#~Ay=tBpN&evHjbr?SJe3v;m`aN*F*SmJYFdH
znW)3A-&(MdblYTPv<{~rv#}2__9}za5nkWoe;?fVg=Dj{TWvPd%N}HF$0MM}1pNo=
zUX$XFA6RQTE9=@Vz?R>0M9tF-hYSvTgI_dix+SlOZ$<W?vRg|D4R0iDyMgPI!35{A
z71M)+V|3QXWqc&7Q|E!oc$ZPul31^_mML)q?u^L1GWFU=Y<ACXE9wu1H?a7pNQ(A2
zL^lC~$rGp{B=pA_L)ho1(!3Y@4^pzbj%&tSh}`T+d$HUY?*L#EjB&vg^!LO4E%d8%
zdH$)TB$YqJ2{`>Kvs^7aT?IJG(vGYC3G>urR)t4STOPBf=_sShnjw;MuMx;!pgfAL
z@tVToAAp`S+QHO#b5tiE_315lx;!k$?GgOI<0@+t<C{d^0X%F#+JE62)i~+@04|c(
znx!QhNc8DLl>UDI0AoT|_KxuRAKktG0Ma?@^^4*(v5BO%KQ3ct$o^INKl>P}H|-zc
z@Z^EA`;q?uUQ^fVZ^WjtguTw8bMh*ltH>XPcIDDbPct0Lf06NixYB7}BpxBNWtYkf
z{#)=E9tx9+^UXU-yzyn!_g215t-OwRx6I?$0G_0Kel_U2m9~Md>J2hndA7(&eXZGf
z!vpgHS@ycj7jj!@x*{lPvRQz_#~9?`dgK29uU}H(2`I^*A5v{e@~ufUtuD{QnuFSD
z49d}2yGuV)BYTs;80;&^pT^TiaM50mHZA)cU@5^k$Rnur$ggPdu90J{YB%?@%I_DM
z<SN4Dcl#1K?anI`;t#_uI_491;kZ)XNOCr&es%|u$?H>A4NIPz=v_AthM@R-H8tjz
zbjhroLn3^x%rI2)2+t?*uQ$}9@n6FP-)4O48JPa&KuA;6{W^88SMcAB?r!wBto4WT
z<F-)|X%l+oIKjuv54~&Nd_eGZvzu#)(c(N2jqCYyT29Srxs#mW`FU=S0M@)q2Z}A8
zQ5c3<5E7xc0!MDu-o*po=@H2m`%TdTtZ~$PbtB%pb+GV{i)5D1M7D{Zc(7GqF}Xk9
z1cAWzs5Fm-em%Oex3Oze6tRPrc2I$H!Ond<V!Zk|xO+*$vr66G=X@}gYg1FB2G^m*
z{{U|_o5yLXNRheWT>k*8lkxr&?0*X5?j+T%H3J@>G_gI*m|rK6mOTowAmYB+(EbQ`
zUc$it0K!%+wF#ddVmBduG0>l=uR^=<H-~h1wpwYCTTKE2iL+@czJ-T8b6y5Fil|1X
zv85aIPh@(OGAg#`si#-e`2PUIUkN;Bmw#f{{D`c>Z=QHpYcOx&1D}}s^I4w|{upXD
z^IPcJTRZ93cS&z7p?AD-vB}9CbmG3Nv+*{Ed#Zho&gwV1hbPIDgkN$2;fVUyocGrD
z;X&A8fP?pu5IOm?{VU3jf0)y)%P6+HTlF;hg)UxbNcr778Q`ynTD<UhJQ(*C-4vut
z<+_X#g}DUx>t99u3(@smeqCzk?N<vtHf=Ocq^JQ$-M~2K827Ase0QKhDVpla$51B<
z=5@#!Je|wP&-ac;t$Lq@d~@J04@0P5X;O_P?=1(J14ovQXt`DS2H7KXk&*41GmJ~j
zDW&Wh=F#?F@aMmk;%H&%&MLA~TBQ1}wtF5+b9H4V=A1~<lBI*Yf~rP$3~+viyK8M0
z)@#cxIy@vgoCLu-^%AK2r26NMYv$|iTU^t`_qJC?);So>A&?^|W<SC@fzRE+tz9SL
zAB*K{S)$b=)opDhR}uMQFue1zfD9Y`?hSHL;rw+K3Yd1Bm7ThnLy2&5RVdCnG}32t
zZKTb6;(an-5IV<gX?BvZlM1nz+wTI{1%VkA_EoL!ldpKD-&elTY&8kvn>Lz*Y)NE|
zNFq`K?IRp=dsoKRZ{hy{6#PYLjf8(^oSBG}mvGJeKrtbGfEA_i@8aeE0Ehf}sdzs0
zrS6MuHpKwqNb)n7y93YN=y67GM!stvY#lyXN2BU_ITkV*ULsJ%Nx09d>G+=i0P&Aa
zgT&q;pIx)Ny^7#)@FMQpv|}MNl0DYGUDYSNjU=?x=8>=hshfKvpH2q^b_Tww@g<DD
zFTS_C(=KlJN3=tK1T8dVNVx)31RO3g_qa6{so;+p>zXyTi{f2U+WSnh-dgrE9A?<$
zlFu6zl(%dg@zT8P24#wbT^vO_wAQI>^Ig2ppwD<e3G6<(gsID;Z8xLzZ?BQ_w}d=l
zFNS<&7OyCobiG2{+uewPmA+xMhA?uVfN@(FmU^Fnd{Qs0Zr(2-i8p`>&2KLt6+B>r
z$Dyvz;q|YEb?*)8ehOHS-^0G;E>K|05I79Gxm}&t@y%`N-YB@zd?NOKAWKsEbWa-H
zwm#6%=Q}Zi2=_SltZUb*@oFv>Y5dvMlw@$Df>@druJ_UCzY+`IixOSv(p~=mWa;Z=
z1hK^%Pr6%)9ga&EUA%iJ0D9FI_~G$NXkS*)^#K}L-NOu4kVcCU50kUYRr%C`gPf7q
zHS;IhF14vgneutfy(9Dor_|!T*TeR9HYOc98*?q@5iQ_O7|2FmS8rVL{Hxr>LXKS+
z)WmCkollvgUm2QIaD`Tq>vnw(o;B02HAwIDxgoaIuG4nJ3UWxxM>IQuB~Czf!TQ%l
zr)!#oR>J!0Ow<}_9Hf_Patr|e3zYT*el_w(#OZHi@fC-MwAe%4K!eQ?E*Q+mJQ2=E
zsWs>tR)gYy5a`pdjN;VdFhW`~w@n9}M@Zam0ki5subX*F_A41mX(?-IJ#ByMq4m^r
z>awixm1wzg*>kJe+rMMiwY^*5K7pwnTg6uodwf7GZxzG7520AXsXn#ho-S*@j@}?$
zKf)HxZ((AD#ID$l7x<mo$Q!!=4S8OvqUd^L{zE|(n=u<wTd4Q*`0{Jnd>?-of;>3?
z0NWNCjik33ae4N3mfWvF^C={$9@W=go55k$DwMA_?6$J*_Uw3>rZTo867Y4Gxog{9
zdm9=*!Y>T?Z%$kHvX=5xBW=X4B#6Iph|b0K?_Crcyc%l+*Pc!@<wA!lK{@&4{*~o^
z1^9=i=zcM|(k0a(Yb|g0L4gPqsrfhXG1mZ*n)Izx!#*ChzCLBV6Sj8=8C8ithC%i<
z<kfX&SK3#o<$nDStI3@Tls%m)DxZ?u)RV+MAinW7y%*ZFxQ&v=1XDo7?8j3fH~<dD
zx{nT8=o-eS2Ag2(YZQ&-k-u<}&s_3zp4CrG&~ImWt~Kq@>2n|5#*qH*QoW^hIRJG4
zj8}^MMe&WtiDaL{9}FS;IkS;j8rC&No<MP(*;1-KM_TKthjsnr?cJ@{{1ei~W;C!k
zZ)W$`m0Z*D9)qgCj&&U;!kezv%5Nw-g>9J~2?w{mdq;*mJ7M8{CQU=fB%MNWvew*;
zwomZ|=zkt7%03#wH-WV4n>(<#dWHE0;^;B`+>6NoAH2Bl>si{5#3*3BlIGDF_Bn<n
z&RTf~Ry~$5dkW~SkE@E5D9#t<Npmi~r^3dRsYX>jqxYTc{{Taz@f>#Edg<3lmv;=i
z)g{1H9<R3m3iH1T_+2%>6|JqT@!UeMzU14-vKW+q0-kpgcs)&K#r>(`$uA?Ga3;7T
z?J|E5LDf`#nd~d+4Fx<Uqx>71O(H~tREf$!ib}i4r-i`5+D9ac%MmC>afPpUuaZ8F
zjyknGzEX<UWF@D=cbCT+XNZ?oZ8p*i$Uo7<g+s8BaDp&--P5IgiS`>$`TaHdwczWG
zM(^TYkiI0mG07&OZe68vQ!462Xb(b80OZ%{AKCQZ`T0upse5@Q=|@j9=ax3K5`^Wd
zKmXVGheOxyRv4qYie{A)ZsHdpb{Hol3gh*^3~O4hr7opn9(8u!*!f&I{J9;mUA5M>
zX2C4$yBNxh^T(}i=<@0|^PTevEx;|v2L~AdW1hA4JiN&(pAe+r`J#+Z4tzqo(k<6Z
z@nyWmSpHR;ZdifSa3hjEd)Ah-@iW7-+B|k}OFYF-moT>{V|FLG#co|{`aSBchNO!i
zaHkBXB;!1BUSr~a3$2-1d^*o^vN<gg`C2j8uggvIMQF!OLUO#7nP0=75bv*bA!Ddp
zq=BT`lEjRl`iva&>&<<y@b&EO*(11(l36epa%}@0ff*l}ubjLY;`=Kf27kgM;(a;W
zEW})?UF*2y5!^C%AlIaPKE2oMFH}6@Yji&>!=tDBOV@Tf)>wp^OzVW=rukKm(NBk(
zlos#yTil5d?a!#~Uy`4;<dMt!OL&4vj#S5CgZ}_-P*>>Jhx}irPKj|0>e5C)+PV3G
z`i|A)zY~9Cdq0gHJ-hgWFNqGH_Vj>xD`qDP<ie4C{{XZ{ob1ot7|tuf#M6^@N4JHk
z8(ot8C;hv=3wRsheuv=AV^7xJ^G&ge+INE9-J@qgf>gE2u^&|xcU=9w{t0SPu!6&0
z2RR$9?eKk%`b}^W{{X={G>Fr9@ws8`rjPzhu5VENfvhb)TW=RUt>|>{zx#A+DEnyW
zk*w7jd`#lJMf*s6In}kJs_C}coK9PEmWm)j?b2ggI@g9Zc6l4ha!c-!&jgdn>(`T9
z_5T3ucLtUWlX#7?hoQ0^zwO}HulSo^iCvML4oUCDdUz#Kx9w5o<DDduq<qh-=`d+N
zBh$65vPJ~zjQJdAr|ZRUc+bSvy3}XEmJ$#ZgI!A@`B7BlsLyhLD(8M8YLZ{uMQLds
z<udLrI*q)K%-0#=D=`iA#pbGn6M%~RhCOrNKaF~dO?$Cho=3A!_mbTj`gXgiczW(d
zNaVQF7~n7`D~{`))vu}emrB&H<4ajQ=)!#J*aQ>m4Q|PBjugwci~<MUQTSGDarjDm
z#k{<YOo~q9&p!NhtFvD1$>vS-G;%JQOlV|+HQkZ8h1zS;?R+6+cHU&}i*5!%URnPD
z9)MEZCxCD5q=@Z~W=04y%5#o6>6+yHC8+9JJSnGYpYiU1k)Eo}&=cHJl1tsNn{tig
zpzjgiSom+mF+4KMr^f0$yNhC1ZU#BWZ(4`Nw!SOzC7g4<sWsX~hebFFLGSY56ZNRP
zW2VEc+Op|##Lb<{2{_3fo`>=ko#Ev16}F9Wrs_~S{GnjvanDW<O5>>p+lyKiZ*@s-
zcbZ>}Cen&nc#;!t=%5LdA1r`6^x~6V@yCL7n^7E-`LjlMLh-3AjyfFRe7UW=eO}Ty
z5M6-G;46&e1CHEur$Kk1Y1Yz2x^QP=%<>(WP<!;wJ*#MQ*)E4VjCr}aPcZ)ggwFd{
z)a~xtCTGf|$eAkm{w#BXMK{6%9S_Fx#dOUKLEXWBlz?(Zf0cSqgRY-Qj`7k3#P7N`
z7{^Y7+O}5L!@9(g>l)p-OQi-sEzVbhahz~Rf9YCAm70~!oke#ZT@D)WOw@dJugiM?
zF<G;O^N-yDM{iPVN_i}_tseVIwhCjnV3DhE@-}+pclED8ZC29S(TkPDV2~H?06X9e
zl6wqsUNx*-ORDQqY8obPEz#ClBI(XBP7Xel<)b1uYnP%r&kGG=%JFY?Z8l4rXOC$S
zuF$@i-S>r0E!D@2bt{c0K#EISIB+Bw!#s?7Hzx>#lj~jVzAe%0Y(?v{DO3Q);N)?@
z9l5N32H4n$C%M0sl;6toNeCrd8P6vlbdC*UiKODWqVCb0XD@lQPj`FbEl+-l;G{uq
zZoX4p$h>@`AG_>G^{w3tL(?>W3u<}-GTdsC!RF5I^E<Y4&V8%0gH+LUyHK%#3=uCl
z2WqZ5@Ch~H3itZgi|+N^FrOl6LTv>@vW@@%kbZ7ER}~L!QQoEW9W=H|$4jB=I`*5S
z8(1BhX%5R++mHd{jP%BH?OjHRrOlz~O=hx#aVA<xPDjq(gmYY#<of25sD^tovz(>6
zN^pt~RyfJYrpu<!J>YK_>T9{iR@xk@W6=87Up%+v)ap$oW^dhV-W#^EOH1uR7AQZ~
zLX54wl;;3|3FLIG{{V+t^~Z@d$!@d>O}R*mF>VgU=kl)v@okCJ^%YGH9nf1Y*D<aY
zN$yF;YrN3yd^O={B(uNNWwN@OARU3j0K=fdo(*c{CakwQ<5n&<XA!Pzm-Fj4cNfwb
z<nzdPJd!!@^sbxXsB4?+?Pf2PAw>*(?kAx4AaPv9w}hKg*C(>k^%>^1kgGJCFj#lw
zj@9%WOW>~ucxG=2_)^MkUh3fPo>n_T$$|kbfx@1`x?yJh=W7v12;7vTspfj-v8vc=
zf7+fP4=uqvY-|H%$n4FG;Ci3HS7YI;OSo=_iu_8$?OQOhsKP-X_f#JKr>%0@Mu7eh
z15?-3$#o$4r*aq~{{VGCImfuocL`~6;#~&mY@eA_`A)!r$6k2jj=d<1RYhqY%5kSI
zXpFn@ABY>m*HLLI1=goHjxav)QQYIQk8xdxfwUVB4QWfG+CKga)3_rn{{VTI_u;w8
zJ-gQvW8$qb^puPw&pa`Ok~kD)LgcdfgoFT|;O7<P591wI#gBImr4nhnQNsB_i%BpI
z!nBG`Mn-TjJ<WNT>@2IrQNzoXJ>UEio@**Gl%=V;@ps~_#<8jDJ`K_!o_o3GOI9((
zB&J1F4A|fR*v2_Ny(^ON_k}(x=@Pc8whL}qHmc7uh982nepODw!^8IeBa&?zNs?*s
zs8&{8uh8t=KqrC6;a;z#c+<q1WG!jq$Wj$Uwi~tEwop1{6rOSDE4Kwk7)(Fa-mk0f
z`<^Z<A5x>{g7vv3iQ%n&XItMC-8}QYSt2JfMh8!qpgxtQ;7=6Ed#g!pb19F=S0^0w
z<E3U?_{Q$XT(j5Yj5dnjx{_G%*<w#P>yhb-^D#aCo#Guz=E4#VqKuZ!Aqc-W@d8c-
zN7d=8$+Mo7N{zQYk6#}YJoz?4?I91fcJfIEx+`B5N#Ly}=KlKNO{-kOWDA_EA^L8|
zR`kjIF<x=-=fE)dTT8p~4!bmyPL^{Wr9MS%y;Pn{dK&Wo00e2bUM|#P(4)hAhT>4{
zM&PG_Pb3!T59L@T3(BWNZwX4ZMM|0V?}c@3V*dc)P}8i~lRd($8R1#a?<K*;PC4CH
z{5f~1PU$oyTh$5SzYkhxf;>s4-Cf#4rvy*z)Ofzdow7hk+6hJ*T~B8Rk=L5<G>-x3
zSN9U#!6Vzd%4Anhytz5YTx0U3N~Rr3YnMlBGwR(OL?}~-RePSZ;X8dZ#9(O>;u*ML
zr>eU2BaT6@o%9de%iy-1D#@tXYx1(5q6>4t>@#vJ->ogKtYn5<gL44|Wdm;fbkEkl
zNbuME6OP-&a0{gInTb!{&YTGc)BZljYtF^FXsPUC(r=!j^kt9y6sPuowHa+U#9_15
zZLNR#F1hcC{{Zk&e}SGe_-W!F2WeVvzL$Eok$YhS&2Mb6td|fxXk4w+5TTf4fKECC
zUUzr@00hFYwTI6hIcHJ#v84fz{G2t9{{Rlz(+q7Q_~D3O`i2kwORqncqte6Xrj_n|
zh5I61Etl;Dp<OV@RK3Xh+0HBUXX19J2B4D3X)xoeG3m!3k3(KH;2+s@NccPP3fsmh
z;$Q70)7Ed@eUHfwWr^a(?><az-=2p!#a#G_uW3@GhA9XIiOO}zuIwENeWZEV`mt|A
z<o^H@X*z|bw)&2TbFr{S-0eG0VtL#?wKj|5?QcTA5~Y`(!V;t(kbvA|9!L1qJx=c4
zO;$k@2KjsEBp*>!ucVgV2sKMG0-*^Y>Nfp7tKO&Xtq%%qN!g>i@aM%u@cpaDacgmE
z>`NBg!Oy*VHkI*9!rI-E!xa8hst7U22t1D6j%&zdxG`E8#EM4Tkto9cRnBQk4!z<8
zyf8%<*%NsYpOm*j$FH?hsW`P`2SyT3Ry(_I6~m?LqD9BquBF5JDbG>$?_SGqsOk33
zB)7A(nnU+p)GiORZLgg5drc~9$!%w4iDZbgrg;t6sO?vDOTQENUN~+1NIcCs$XMon
zpf^r>k8o&pTG5JhlS%bG>sIksjjW_k6KHl;?)GP6#I5G#Ncn~_z$3Wkt=Rm2&^4$o
ze2D}Txs3gl_>v|AYp=NUHRhN0R{Pgd(&m!N<w#|cGD;t)9gpQ){;}Ywejs`GcQHe6
zeE?9mHkne+f4)6<%|%=e8xi};?#(Z~9-dd5O9xJL8}}vnGv8C<)uhDzq!PXM4r|f0
zAB%8mcE;OHxND%Fe$M0)#X1t1&reWm<1Y>yt9v7$c%m_FsYNc-oxWJ-jPMvRI1D?A
z)v<+dFK5%V+n0v^!d>e;3?z@HI)k03>0VWAqYn#8j;PMIKBVO)rEgBhPvWc18&C0j
z+Oml4?%&OT$_oI^)d|l89`#F7y^<+lXIx{=jide@*vhs#9Ag#4c-LOL)bzt=;|q6+
z>KDv+aHweH1G(f7O--eEpGMYnrGU&ss)XGma!BY;y>%+ioTAcN982DhGe*XvuU*<%
zK^?1<x5;%@KRPpvI&K|Q<0C&x>vTUCYuCDap+V+FhKqhva`yu`&#zkI^le?PSl!z!
zZ8e;jmNy&JdyqR3$Ti#D_|sFmRD#aN3FX~`leqx)2OQL6V7;rFerGLK{sxbpe1EKb
zQ;$?{6X;sZfg)_JnE@hPbzCpX`SoF+dioc@o;H6NcuL<}gzeq{xK#=^O1v;V_^bE#
zS{I4zCermdk|_d(asb(%OykzRe$t1CJ_UH9>F@1c>%;nw5|rdbPe=B}L?o&tr7I({
zooLpl?4=t?ZPMqg-h5#2_lAZ40Q@6TNp-5Kh*#tx6c+iJPw@`Gjx$|1fb{XB_$ueb
zSNo>a^#&8W^(YkOK=eMO*UCO7x0cRRt7)^avU$w5H?aQm%P;q%n16M@oqC6Xz9W1}
zTagXjrLFaffD2lJjgR;d#(k@*jvkE)uR`&3C&?EHZQT2d!e1J74JjVaPmV}U%v;bb
zs>;p>KXJGO9&`CuF|F%fD!RT~`R+{6$i8IkM&ON;@|DQP9SwZ3d#`<$R%?rBr?Vx*
zsT`^SQh(hfV!aPsy47_X2z*uHy^~x*2l_Ps0CG@ABN+pF^RS*hYs<{>6=`B&EB@{1
z{GYF5s-`L8aMGt$>h0)yCY`V9Iu5OCsp#_<=2=LFU~+|9{#m&9=t1C$?0zr&XkAyr
zPh__3V|Ob>6wnktQ-FSUibc+Ij-5xPcpav>;hz@xSv74q&tr3D{#>?~zq)skI<O#+
zd9BOMX5!C8hs4*+%&{qFiO9l})Q;TtHRj?lbSu=SIqsg1{QH-e=2(m*Cx?f^^lSA;
zd+@W)pHe<F@nHKjF~kMEw4W{!dCt?$Soc2F@4gP7RoBJVzu_4mfnX0SU&z0_40!U?
zo)^%ZXFV(74I9Ues_QSNYmu_ec0*do8{}}jsvkq&J?nnUU6V)g%oc)cm@TywWtJG{
zXw%J6kk|wvBc~#?&T`6CV5iLPxAbP2Mr`m^`5bQ+za#DKC&vC4{{V%hZ>L{dvO^q6
z58GE5HvoO+C*~lx8Lw;AeiiE8G%a$ve2E7pNiQec6B2r;2ttp2)$oO;t);}@X_Y6D
z+oVpXaU}l$69ZeCm&LoS7GonHC0PLbJeRHzk<)50U=QhEJBrADw<s(mCn+UsFY`Uz
zvx)H(<x&%jedl!F<j=70JO$vt4tzh=G&Yk?H#5e{c1DiKRD8;^6%5DJ?ynvAckxri
zo*nS4o*LJl53s}<C?pZ?VHgJi7d(^P^aHJV7yK<x_)0WjnkCPdaXD!I%yvAgSFTk7
zIBw3QpK8h0k#xTbOuEIyYkzkcw~zO-O6sq}F)xBJy@HDL@{F2v=+&i)qT?7{%bWFm
z&y~z-V{;73F~7eylD5m!$sKit?ca(t8?O}HX=7G{?QaB262%ZikM9R&Am^WY_wNJv
z&)_D9;g?~jYKy2#h<?v>l1L+FILd8pfDXAGtMU87e-M5!cx%RXp9uUrdnDc-mgnuV
z+q65vp(H!ADwhWXKX}*H8kg-YZ}4|ghfnamwZ@^UUAXez;S3g$r*>lh0K8@$fKWY6
zcv8W_G5yuG*4=D=gABVJP7etwHrAJ2{Lig?Zaht;=$8k>y4BX7Yazod(lSQKd#><U
z{Q=E+$Ae>E5n0Cumkh~qZhW~lIEaa6UqTp`<J59${{Y1;cxyfo(mYY(CO6uR;(4A~
zW96!!LFry0cjKQBUunyuTMw|J@RC~=O@r7B1NhZp@R*vEQihRw-rD~FNcbg=wRap;
z+@zhgciZwk(^1wfJUwT71+Jqt_4HYeE#NdUGY;f(NbU*dykEh7C|fBZ@Lz~8Ev1+=
zmoUpEx#6?9fN;f>`(ms-*1jC@$hJlE;k$6x4&OYIA5eP}?OrGGPT$MZ?X|ld*7Bvo
z7>OG;<l_h19c%03mEaZ{G;H~|Z8KU~g?LnRZCUJ^KDE&NMd9n~h8`HdhFic+_@I0-
z9sWiDpTJdJTgPAUk6giFVvjbZa9UO*jgdyD0G{Wb-OYU61?8gM#QGXdcX%*VTPMn=
zzUeX)e!NqxZ}m%!a_?EyCV6k-a2_=SZ`r`cBkXpbxUL*VPl2OSKGIH6y1n`@Q`o|0
z_?na_2*pL&+s*nO_2KUp+3H?D@a4aW?n_)Yw`lh8ARF1t=r?Uq!upe6sa|!L_aywQ
z@rS@3C-BCH@vFnOx@EkVH&?oqk!j}TzIo6dPfTZ^ui3BdhyMVcYhJ}jQdU&bO4=Sa
zAD(e?p7%fh(D*Y?xwDOKrMrx|*r5p-0gYYKwRm18hH<iTeq3>pj%ypwhA35JI71-a
zj=Ai9mA|QJVo8?v7hEa^Tz^6<?2++uOR2V=E7LAC$TS^tCD?}va-)?8rhDVp6q^44
z!_98O*lL<p$-9J&!)p@3PxG!)Z93ZW*EY-L$}#)4IS-#h+uFO|hTb8(TL-jBH>^xU
z+e`c-)qDO_)~?K!oL4jtBwPE4&zCtlAKvR-$A>&eHH3*YEOLw(u5r`+Yt}FHF9&#l
zZ;(clAMTtm`q!Q6KMk*J<}&Fw2yk+t&Oct1N}Z77S~u)_=7F#2w=OSiWswA*F$a(d
z>IkoM(0qDyl@Z*?C55^&=YwAY+FSU)!$<96Fv!1oLxYj@uBzL|Qmnyl)<}Wjm4-*@
zT$97y>~=!Ey6Ap^{{X_kwz-QI(t>fEXPV`;pBK*gWnz2qYvXx*V%zuWk_H2)-;d|b
zRk-+psmg{Y-N45kc=fE-8@Z#!D<1K$c+PokS^UhNjhgTsXIOjLo-oHGde<>^;yp!U
zl6VOyN6ns<gKH)3trVAO$wmO+{M^??I9o%SrAz8lmFBwCmJFN&kUQ~O4LB}$U@`R-
znG8*BcO>B8#t0eeYMhr%EAL~^(zaR{+UC@kG6pKx`Ma9X(J$bVCbXN&%9FL2b^L3X
zS*LOVILA!V&22c07h$v<;*07lEsc$S&N!z1-Zr}MbI{go*Rx;S$1p6f8i<&IkW_Rv
zntbL3*|!xL0M_@1%1JXM%YfrJ?MClG?2Kz$NGIN?NtqiX90EU~t@~!ZV0W=NJQ8^o
zO76*EmF0oB=OA?zv7uRB-pG+!k1((;g*}I@32G@lE>b32Thx+nRfx|g`Bv_`eF0JA
zI+9oB$N1MZsKa?@s5Cdxva+_|jp<FZiaX6gBbaTCk0U**uWbcoxLRsA7ILfFNO&1#
zKXm(7WvA+^e!Hz@UBLX&<EP=?uxHclA-QI^Y$}tq5uPg=Pd>tL?Hd^olsP1J{{Uo;
z^d`_Wrg~FcG}0LE01Mj#9M>5<Yo+Q=@r<kSfsw)KTKYeWto19Dw7gF$Vm?!j7}m}1
zo|m&NyypuS&d-iO{3@NA(2F>`GS-QpHT}YB+Ve4qaM8h^PK4Iw_2P@T?1nag8cYmu
zPYdilYX<Mc7Pc^lQt!c7kOPx}*XS#Uhg6$TzeSzde8&Cb0g|A5dsNA-greKKJ57Ja
zcULI-R-T)I7?spypHQH5Ue%1hW|bor`IG^cWKbmIo&h-+u8z)qBTIyfCm4~i(zo#*
z+4TBUTCJ_E)LYsl4lu)S1dm*;3N|l6(fG0KW`<d2dzn;z=_Y;NT8{0EpTN~EE-gdF
zcTK74cS~%7UFJpya}an`9)MMi6+AqiV%u3MkrysOEtT$Y$0y#jXVt9Zg(R|7WnY#w
z7*)ycd(`YE`W<|l^_HCwh8@m<zVHDRnEHGE6^^>Lr!3pziX$Vp%6z{vLQg;oXZcq>
zADq_<eR(LJY++XzMbBVAt!`*q-l3;!ChE;2mf)!hSaBbz`qb?1%7mkP9prYp^`(%T
zRYysrAR@K~7rt@Qvmm?hm5!#?b6-8R)2T89-LN(|2WU7awQ`;%@!_{;d)u$H-=CFG
z3@Yd8zyq-BTo;0TJK~*odEY{ToVM)T%66g>d1iCmdIMKZNxP%B>rN18^!;M%!gm)9
zro$w+S8v0A5LWcuO=4^QD{FfQyd`HCl4OVm$>9|`vvvS}wJxurY2OVzCpNWlFu1)W
zDmPNj-4#2MeQS*HXN0vaX2G?;5^uMP>Oj+O%Dl(;V^h>1_T-wWO}$y4)@3LwY+BHC
zpA-B-oN2H*wbLRXOEDNs=aNAC&FH<UnueDC9M^ArI_>8Dnoloq8+UFUar71G`p=6K
zMbj5ZvW;T6QodRs0a7u}NcH26=CZyZM0G78JaHr*P37i3RLR`HV|X6*&l;&z<+g@3
z8<cL(CTrN{zK-HCwmS{pYn9`6axv;ar#tE%C1=#<Ss1e7+B1{3AbDas44P%Phc#$y
z1iI{oD`frPw&+Q2?tM?DYIN3hZ3j?YLLwI{u|@$xpWVl$Ui<7vk}F$9sKl*k1dSSl
zD<*zkJK)xwQ%P#E+JtFfo90Md@{Bv7_w=uF(Bsj(MW#iqLaL7<6_r=2w{Su2+*bwT
z{RA$F6xK`@Yi0oo^~X?twa+?=?@b2o?Q@{ke03G?f$qG+vCJkggOW}<der_J@TQ~S
z--PylC9^;;ucz~FFuR;%n8rCFcAVq!uMD)hyZbt6s6WzfTNpf*1mGwhtb6tXzRt0;
zy0p@wu+!$=mcb&BG0Q6fo!s++axy-hD8<=QP=<%KkVBTds<om#W8$=t=rVYB;x&_-
zV-v=glE>G}VmD{E1f1r+h=y0Uo;^)%g!b1UOAW$Ee6p>`AdZ#de-kvQ;fB=6SNFt;
zQ>tY0jf21^o|L{3@iJaKdS0nC(arZzd94^Y;{j6&I&q$~@fcN7lqVjGTXa1r;-yNo
z;@*wueXpnMcmDunk1<Jez#-1k6c6@+$u)A{#mS*u2_4EUz!D5F`Fk9W$Bvcr<*u=7
zr4a?Zu9A>MYSI!id+o^0avn4Ba@s%IVU!n48JBiOdmLbpPrq8^r$N8zm}VHCcSL>H
zru<H|x{t}0V5~;dyQ2#7?-+bfZwg4<i7n?Os{_#bX1*|s;=R?YhK<4XR>2%}BDs$k
zd_y<HYBs?6XZ>PlDy}#<9kW_}PD^9SSiN=F_HAFr4XOeDum>kHCPC}ait#@a-mR^@
z#o|rA9EEj}O0YTVGhTH!jIM405jzgt5-T#|{%hsL%vgF7YkG7nb5^Y>F`j*<p>^l8
zAY>eF?0tCZxcsZ7dl_fao+yCa#~_YJ(zvxq!;+(qrD!F@iklFX9*dD(X?Ho~udqpH
zd*Y`50EB#g;q8}uXZV<K2hyr&_C7DR)8*1Mn_&#1Qe>U5Hsg*7?^YvEGELFDEQf;K
z@%8IXce#PQx0X&@vo%YDdX>!WWOT{k7-5a>HCu@<Ze$8sk3pW}Je+%)&G8Mtjx`?-
zwu>dgU0b2tjKgfZM?g+6YMl2M7R5`*`06rEOAY+)*g5TtnqJN=G{{`{=6aB5dj6~7
zjU-xYaJ;s_5SI}h#y@7pazB-NXM^?M4!?#PEh=@ATZv0Z5whTe)Z>r<_4TeMJwo=$
zPnM9dZ@jJnA5OjMdp&a1ZV9@uiFXVZgatVC;)hG!<+&ALrk<8PQ&LzpNCl<V+z`@k
z#&{U~y4Qqwnmu#j?}=ma90PTv$>mG1Wk?4g{cE^+G<$#+Ii3=DURYz;^NQrPyIZ>g
z1d^MWCeI8>T>FAZ&2l^&j?y#3!NJ=5p38Zp=-xfhQag-C8>nk%g^wGD-fZ#sS0`cn
zKHWl|WE!GK0mvpI59o1RkA?Nytrq?{VFX473}@~U-;SS^dUf8fXRE-%DHv`Ca7Nn8
zpI}EGhLv1A+KRZGD$`Cdosq>y;g5*F54=<Twc-0is6`}xT&oz!Vdz2UBig9xe-L!t
zS5vsP)#LupzlK&bTS>tf=Ocl^^gPsaY5JwJlLgho$GBnAXAOdR$j_y8koZ?Z)~wr5
z(at21u=|ghmInuMZ<rr^)_%@VmFhhWBTh3<+G_laOYLgP_QEOD{IfdY6rOqgPo;Zi
zr>I4!>91%YGNP)18Nnqnh6MVKxvvY=JU@A(&kD?uw!@UzcsT9tR&DRsR`b5i6p@(9
zqe3Js-#1(zO41a0mo#3T45vX^IP@_5Yv6q|!ul1?t>Jkmw?I70n|$t#mpM2GrYlzO
zS-;Rc17+fvF0AES!?GA*Ajr8mP(b^~*8+<xNVOa0x6^LV+9AMeS@VY;n_GiX%VNGO
z@x`8sY;CNb(+?{`n?^fuIunYdr)HDX>5RFdYL52T<WDZMXBLNjt9Y$sjHIy)$hcs3
zfwf1u&2^p-y}7^e{F>y1DoOjhSh8>z_?Y^fS1UG!KBeOcJU=mei1(IZ)L?ycitHet
zOwn}@vgyjutQ-)mN)itst7H+_;|96$ICVD|$#Z#m_c5u<%V@n>=Jx&~)or|6KZdpB
zRJWc*mc{S@BN$~Q@z;@Fhw-{e^p6Ya5lTPN9%$Zey*B&y#ZkWS<(>ARHGMW^W9EXW
z*>5e|kTK2y?V7*iWV+SfHr7fjLW+hWz@|UEpx_Ws<yRaOppr{M<5fw=@i07Dr~d$H
z_*rjs##pYbw%l5}XABph9{B52HNPKd_nLl>t`uo*?c_=B7BwZX2H6GxIOo@j%$j6@
z#863;QMYbR(mjPxwTcUeHVY|$o)cieIqIj^KJ{-3+jEoA?mBeRv!1qQwbzY4CwSvg
zxwP=bt>v|>=Mham?&C!M@nWO5TxPm|3w#Z>)vgu1NqIJ{czAhBNfops&@T+<>&0PP
z*d?s33eO=ZgDMFa9=PdUHKe+mBygniB4h&0<l%iz4r{9sijt%3@XM9={{SNAiI&ur
zm*e-Iopo(x@an{98Vo;a`%c%2SKM~EILeSdQ=WhvRyV|72mEBU(6r0D4KhpS)q}h;
z!b1g7!|%%cr`?Diwa(vJSRGAm<G6dPZ!MTOXI!>8&JJ)7YUTWWu6V&B(ywf;E<RX|
zy1=qYwC(C}8-RP)$>&b0!Kg+~PWs<(pU%g%!V#w&(NwFftnKUn01R&UPf^u84im%k
zy8i%cP6?htBFZ6)s0D@re?ii`f8$o5_x?1725YsqjvcoXTcDa!2+rkGf!C=Aip}vI
zr-*d;qSWNR5zawlg&tgfgOQi~b5VF5wciy@Ik&rsZd2uvW(E56Y;bm-*y&$4gQ0rS
zsTFtB{TnvUWrxGIqo;H0F9P0MPvEUPM(})%t0Za&hDrSCl(*i)GczweRPa43QukEW
zw0RoKTeCK7%fM02J7jPxgV3~1KE~D$58mD@Un(lec>+8M<}uC}ZzO@iHBZG}A-WPD
z4ES-{Nu&8Cxpp#R9rEPjzWLJj$;X!3Y<?p;JlKg<Qr)(6noo?Qw$vrG)D^=<rbt=O
z$Uopc0k1ck#afoNsWjS+#^*!5BVY>P9;9?Qu6j!*hHwneBaE3Qa!F(81yGgbiP<3}
z?%_vPB-P<Cv%^~Yqq(kUt38jy-aFUz<d|Br#i!b<8D)tAmm~OB0|0$1F5gnrXI1eI
zmd_X`aysN=2d2~19{g7gvRx}k{>`>_S+3h=211WtV^htoT-n-1;mr{@O(57Y`krg6
zPkG5EpFunQmU}P4k2A*q02%x({ho<!b!)5INp?uup;B~jm$~1L1%Bs$VBP-!&vg8a
zeiVMrv+4Q|#Z4C9TT}L_HG3%1Qa5sELATqU_4~W_`%mvzgIY6$++!;vyA1u-fB(_^
z{gN5=Ng3f$8f`lVQG?s-SK-uctZk!?c->|}$R{9yOKBj6)nj5xqT?Qhu2{!!s%k3*
z<U(Abz|Jv`rG1g{jjpCF+LgqRZHsDvcPkuZVB~b|TDJOzov9e^75>M4w+!jRGY<TE
z(W`h?6m9kmjG=9f+>Y7zt(!67FA(V#mO?hRzCp2^5%VAG%`R7cLz+CdMts+ro^z-v
zo*}`O9!UD}PPe_f^2_O$q^Uf{=cm*fqkX07{v5r}B}m3cD#P4%HA&-!R%yuENzYGO
zq}Z&Q8LI03Ah60!zA(ou7!s}DzaN!sUR}ehvfXKH-!g*n^M!HO@TE6JZ2rp4zA`h1
z`Gs@wT}NxE2DNV?U*&E-U&#B^-L1;9=#3`1yS7Z-#kgnhbTwi1OJ{tc*~fGGR9cOV
z+XC$%oOR&m@~o)sgO&{%<$9CWhS@qZPwiH0#yOOT+pZ5vr=-bsXCvKMW1p9io)1G&
z^B3;dZqw>HrX$AK8QOXrbmEZG(5lxmIV~d>%VdMbed&)N<irRV=ub+zVp2;#Akzdf
zGYov8vMD=&mH^KoMr;7N=C%ZAR0ZliYE2@+We$=IHr@_;)jLx$x$=;XNh8yWwa`~3
zzP5(ZQb@DUC#PC<jA+*FEOUV-IbMh9%~89ONu&%xB~MP(6}6<ggwmpb2vXl%QtO}^
zVhh}2Sjh*R`+FL*X(j%e%RD8~<2e$j{vV+0S@xQir>EP9w~{})Msf5tqi^C<h7*$9
zae<G|)|z(e4KkjotlsMvvE5u9>;X~7I0v|?i(NE|&y1+xsHt?zcD#~%hyido0|URU
zX}*ttJc`i|B}Z31GALJii%pFAwQ28dJdLP<I3#`JT9#fJzP_^;Uuc9RoB&v7@~e<o
z+1SQo;zBudCx4}8Pit)~#zh+=3SG9G{{Z@`y-B*gk%Oq{e_^;;W|BEn9A-0;r?*c^
zS$s!+_N!qehn^k5<cjPxIP_(<GF&?4_-(^D^uVcnL!|h8>FX>=R^k5u)#Iu4$m{^6
z%cap=*LG%Cgr3g#T9)$mQy576w9h?w7#%A@>%p4NnW(+ih<C2t*(7A=whdru-d&xF
z9lv>tZXHe!7_PHN@gAq<i%8~Oj6P<V{olm&G`eahd+2g-TI(B~XNE$rki#AQhfXTx
zuCafmMlI+4;wk%%FhKS_YtU|dMW;f1zcfhe{9dQiHJ5d&>9?<qLJ-IY1dNgSb4>;N
zqneKH1e{*!(ll<LFbp$~QSJ|MRxdQm`^AsMo*V$oN%QT2`E%cB^gYE<)O7o6Wg3Q@
z=+KkU9zm|JO7Q*dgej=J;wF(pBCk$IbJL|~Ekxv!xs7FaE%SMB{_1sKo;e6i5PK7h
z)Ow}%t6^#&xCIhU46%?ydu4mo%}Z3fyu3EaZwk6_S%&=nIH>g99`{p`V0lpzQW=$a
z2OI;8e=5#ci!_OgPgYCeZwl%9o}YK(EnSQ@u*B&Y!7U`LK2R~&pUl_0ctcs&tb7|`
z;oG>>&XI)x3@}Ffs#IWa#xiPsCq}pOBZ>(3#iz%Vg?etz2_CM*_57>e;rL&Fq1ah#
zdc^U;HRk3(sL~itG8fZv6~|jM!qaLocbu<f-^}-N+>X9Adr9d{HFx?RBK`ugxz#_m
zyih*bJVd-UsDE(-5s#SkX2)DtC*$2CSGMscoj!)GDV7MXV2C%%8>l(PepLYVucL2t
zn`>Y+i7z6NW*@ke230aQ@Sfu**VeE+TjA#gd{J#~ZDNWQx4E}xE@E`!Dmgn(1pal;
zfUh@MeP*WmdLK!fWYkp$95gmp^*npSUJkIdCs6S#rS<fz)6V2?RXuU;N99=hhQ0l}
zcrWfZED@cML>Hp<+<Rn?$XCAnJh8O?(f-uDWK}KXM$hu6nEl&#004UNjMP3M_&qh%
z%=*Ta5EkG`E5wcR1w-={KAiD`&uZ5fh{Mvwx;15@TdwDXvJ7QhBykj#+iy;`K76yV
zpT|0@+gu^H)9r&6Vbz!n0iRxTit~HDKIX*DXy{!dc3+rhfza2zX&P1agqJ#e1ciA^
zfO!G2kVhPXI5pb*HPo~Vc`tPT01!ZpEP$kL%2<`^(UFYq9k}FIWU&#aPE&`yr$d5<
zS5l=&(x|z8PZIEEnF9EK`((`M^32xEsq(57=&VR8O9PyCuIA&w1H`sd!{Paiv&RXM
zp>c;BdgC7{6{V#3X*@Tn-&t8YPo`=My%QfU0}?W<IANS;8LYWAJImh@{{X@v;dpK>
zrEEs`A<BgfjH+$HBODypEo#e^*Npw%-fN++)FsdCTs`B0@SlM%o5s@WI;wyyvPkdd
z5w$QsGhVSJ{hXSlmKGOYP2_QpG1Uw4>X_qj$OrJQXW~DAyjS5r5-*3e?>6QsVUG6Z
zU{2;AHW1^t99JXYyB$wZ@okIg54KJIX<^O~ADec6FRm-95QZ{>sGgR(Fv3@>g2Zwy
zdNtho0uKS({{W%tw~aj8$&n<RE#|oSdCx+pjkw~xtKx@)2;1U|T!cjeUAzzh`C9|H
zCkDOtO=nK<?w=i>itnJ0%#ul9NW)0Wy}4;vWFqIN>s35XbYpE6lMU1}$*2~IQDs@c
z8+UJDy#{gDt$ElA)!{Wa7NYx~ZJA+k)#*x#n&wv7>vQJM2Y6dn@uUx|-rPzeiHtVV
z6UcS$ThMl`DSR~q4L+VssddPd$^1?4*@5aktJ70azqhyk%vfTA3vyzT7<eL884RRg
zVD;xY`qvk&Y5IqWV;ato587Z|w-K&)WB6Ei#dKE8D%Gtxb7`l@_`H4^G~*q0{Lh_r
zEhAIWu0(fZuPQ;vVeL>`O*{e6>}8V$k%tYAze8T{Zw2P5ZX&*m?Ogu=8iwS0;|=`l
zj@P^}idyeUUnWFT=0L-z9alX52DhV`Q<GHcZh4OBU2J(yrD1RKUD!vpSmBg&gW92u
zMo1azarDM(y70D{E~%-BBZ!EtR6MM_fI1B4wtH6Bi+nd@11_mxm{H8gm<x~Zb^P&K
zPZv%#6dzFL(^Ji$gu6rm$&pKA=|qtM@~=5Ne-qNRbqOFz_rtYSQ(zdt1J;*IhAmVR
zY}qK`T~BX9G277BYqHUkce$SpoW@fNjjW?20nHLTuXJsuUoax)BZjX=@eYLgHme4s
z3iEew^MZIRJC97)FExu#9Mi&#(uBd=$pfbp<I3aBaEo%4k&4nliwqZ7mJ`^InMc;P
z?)0rTCNk->EYV{jl;<B>ONe6F4%{+;Fh@KJ-0)X~^^XzFac`j7w3tJ^#sVhqRy+!s
zCkZ)8=r){|i&KQXiYTEam00JFd8n<VR=8-$EAj$Wx%y(htni1y3oE;kXRV();0&}e
z9l-}}&CnCrS6{CDDe&BSWQO&2h**S}5E(Z0Kzo7d(!5MZ5oPpoD-7p%<aaE;6YRP9
z4~Zp?NhA?l$GO<xDEW!}E6}dIHFKqDR(4(^gUkDNB#Bhv@D4M_PHWp!!`d#5WQNYo
z;%Nu&=PS9}9Z7DO_FDH>@K1?6U!!Spu9G6$!eVXn0K6*#NX|$HwQ^U_@bRh5e|OKX
zp`ARx5mrhuhx`NLd#?|^p3}66wb-l2AN=!(5QFh2lU#N7m8jg`!)K(##`WYrQXu=l
zbqCaAwSIzK{2|u0t!hV}!Qy$Cs_jw`MyG;KG4IWK++PoV7yK%M*5>t<wKj~j(tY>Z
z-4Z11$EIs6PZwaJPSm31E&l-F&u1^;d{ue=@ua^q^A5vC)UNKMn${U!a;6gs43JL+
z9D3)uskGk->zW{*294mHN?u4n`Lo-eK^^ikEA%H%@XgM>adWO(Y3md>0lreD2q27P
zj;9sWX&(nySdB0B=6Nkn@7+W;Mw>Y*-ovQ}t$EYqY&XSHlI2TXdi~#l>s8>lPAz*!
zd+FQI{KEj)KqtS{rJX{?Jl1&B10ewJZlr>Hj)2!Dnl7^~)A)uPdxnBCt0{^y`N_az
zjBq=V&{yj}i2e`!I-1^1M^X!?+QPwb-!nHHVOP5!x}%`$T>RP(g@v1FUKz6%gn%At
zzHq2|v+W>cjzG^I^>sMU4eVhoB{uzZOZ?6(CkoYdCr-5X?0#v>r%$Y3MH)h7xNnsm
zN6ts4cq9)~UG9_M4;iJzQD`>NiSHbsy}6SKx-rOJtmQ`-_3vKe<BP8i_#sb)d^r$#
z=NmWLN4iNt`^>zOH)kA>1$TcAyknsFS^*ZbZvnku^+_uR`zm9F+sF)hvF>Z##pg1D
zrz|Bj)8D#J^*Zw26Q@!&KDj$JdoR`bA2i3|9~o#q8N6Q!Li&!ad{rjAc#B92oDzX}
zjYfFRYeU7~vrolO4qV#l`gOZsdA9M&^4qDAQIAz~*yq=+eLFwJJ1rv0_gS}{9yC)U
zEJ08mpyLX^DE6-y)c!T=z9F%S+fSM7<hjh@70wYs$C&Na+;mj{D5{1d5n8wVJtU*i
zHva%I&xdmQ7<Vj1SIo6-CHNfnm-{JvMxNbn?_Miw<r!6mRapxE0C9*UehfIMyg~5C
z#Wv-<KjH|WYsjM#hEJT7Vn%cF^O1%<E9q~9za8v+LjlqJR|Jyk7Nvy#M%dNV0#ZO5
zCvhAcahmRaEBs0LYMvgkk}0m*_E{p2&6X8YZws7BmIUtG-G3VK@Hw_^Paf7Hi<}kt
zqPIzPS3Nv-MU>RbVH`BnWhSjTeon{b7QLb0X!l0mWGn&3K*#v-Jw<1xp3bIeQKDox
zRv@Efat;m&!SB$D{TA1J6QZ@zyS<tTNQ|m8U>7WMt&lO(raIS{c%Q<WPQ7&mden@i
z0act1s-qi6Vie<nUaUBS306rY(tY|KMScm)DASXZO|7lfcRoyt>7<z>1n*)pPwGCD
zt8_l!B+s0WkwWy_^sjij_(P(>3`srLmprAUigHvTLC|BUC*Ku=seB$;VOjKh#ggY?
zFB>=@pH(Z`y6R%^t~yBYC6VJ&=Jsb1JHDTw-1v)bBD09d{FV9)o-%7bdztnB00-On
zpsL%t0V*Rg!A9yi#%r`+gZkFLq&|h>ORJkGC)&)AN(SIPbCHk2pQ(P${u_ebY;={p
zYl!3B1nqFrJCHIktadW`j91QQPNhZ38KoT`sW~ntmJ<|}NWOd9ZI21m_3sDVSX}9P
z&Z%n^#nDoX#J0iQ1zp5_D_6l@7}D(T_V@-?)o$icvfZEDumt_Z4U8|)X1vX{Ep`oc
zygatn^4=B^E-jS4bB+kcJ?rXU2Y3qi!;-D-w!3aMDNvuXGDc&_<bkxTg9o@^Yv<$7
z1sKx!n^yBbPQ+ud^l7(=)p}mXq<E`Iy71Hk?RHW@a)=CRbC7_MfMYyw$*(Vy!*c1m
zWOA!{vp6zb9CNw3&%P_&^`D8Fhlxe5hw%nT;=PIxmNKJDaU8&P4(x!!-M}Xw?N^lR
zdTe&Cl2}RRZ$j4@kT7@8RT<!W0&DaYLl;JjDpgjWRoMJf6BtsjDA8@g`a5oUb<9et
z8Aq6`a0uz2$G^2prhS^;X&(yA02`0+`r@ozD%zP#0#(Ke_RnMM#c1e03D-PLY;5$1
zn`s}uxo}lN_r`jhdQkHP8_KG+jp;?tNSv5UyXoHaV7p}@R{2#|yH>W7abcs+=EkOJ
zn11r$ju+D&ifhyL&xdUUT6Kn+OU)t(-La9da2Wbxx(!#snoozk7<9`xj`<x#()DAW
zr#*#ZQ!=F`9$u$RskEK?3GiaYCHR5iyPL@K_Kj-FRSn4-ZW9C^qniD*`z^osPv>8c
z{{RWJTl=4i9v=`!-dtLxnFj}GM3p<&?6=q@_O5EUnl4)IZG@#K2G?`{()eCrZ8S8v
z5tmut_l!pXj+j36n+&2k18>^#<fEbQ*1gNbUKg^{bP1z`j7cIGc+UR-VeStGsQ6Fe
z#j;ENq2d_MOJX>Q1MduE`;7FjuX0IT_^7ncKV5z9tq7Ryb?89rp0$nP>pSV#U)d>n
zE$+9lBaCr_`1h@E6L@xA2T_&{ujB?R8<E%08ur~QP4NE!gS;heev+($Sd8~l9=o{0
ze?jlfJ@pmR(HK59xLaK}GHu;(b_G3$Qhy5ZyVpaXC=Qs&HG9Nb-lwhHEce45e5k<t
zihF;JWlg4P7S_vodlRHGs4Cr!TVsq;MFeJUG1u0d65dg^SkCXQT87eJ?D2U3$WnI#
zJ0A6zv&`$rJ$R{pWfK@46Ns`0PJJo9ZP%DdOt*F%@lwGfO_9|8wEIixbnAe@18?A~
zbo}W9DM=%@9CO;ML}e~PI3w1#o_i~+f%5DR#sK3#TF$z)D#Rcgvw?%whJiAXG993t
zXSQl#6~ieAGDSopcln6Nrbne&M3iF#tu0U%<J4}FF>dIAkYE$RKU#I9*0@6=vO1Cv
zInTe+p#oDOl=aOdsLFS!!Rl$;Oy7$7I{*wQ3uBYlKb>bobtt%Nh2XbPDy&Y#<-b~M
z$k{s%JMmR<mnE9wJ+j0=DspN=eFVxUnG`=mk--MD49t3t*!<~5qRhKXVy9b!AEfJd
zIyKA1Y*pcq{_zh|2cbPHc7GT&zq*1Qv4h6qNv>fu!5ilw{VP&CSbCKw0*JP>339Q6
zTAq8hg@Wc{KKB@|f$Z*K+~@-a`AF@bUwXl~v5r+fSOJIybK15c)ipRJSZ*0xYJ97}
z991OxiT?m}*Rh7%MzLGl^X5BngSZ}xShrWwU){SE<%ZCp@)n(EH``|Z($oofmP3)y
zdjVBU*9Th{%Bn&(?WAO5(Bsyd@)Gkb<i~3rlI;P0Vtf54lIBAnmWdn#00MZ<d*s!~
z9n;UcFviA1*n%S`u=l8-w4YF&P(wA(mZY-8H!xAxvBw?FJzBVoX1H?xSux-b*0t?#
zzju-4t8_f(y)(kU3@k3JQukS2CL5I%@ayTG$BNmP;eLl4!VxU22rR?_o)2o~sfnje
z`{HwlbI0@c$XX4m0U6_^dVYnVYBxR`w!OQQM{5rCcXRV$PC#7#hPxei!#*F={4;%N
zrrL<w)(G7nlOxFO$tUUCr5-7@CgVu(a!Re|ECO`{l~4h5$81*w^9aqQ2=&~FJuGrs
zcZ4+!Z$`M+Euq9pl00F~H-zeQ+dV7L{v7;1)x1w>YpGg9Puf(N(2O6HWQ>2Z1$vd8
z^qM?^B`^q-ZH;=WC_Ms>KNH@&e+AEbed8FT((WD>x&Hu1hGsc+GJWg-xKMG%21x5)
zIa55EoZ_SI?7z(Puvu@loKw}ln-_i-co{Es1L6iAW}RqNt{ABQ09X&p8P9x-{u!^S
zwfzn&%^D3pR*b=ZOD)KJEOL*yfr#P9smC?odY^}UY2d9w=HtT}q|jUZ#_kB<SNXC@
zc1H}2pYLNezodAJ#=3`w<<RUd;M5_VOsf^dK?4zzSzR%)h|g?x;=CM23lWQ`vGJYK
z*{vK>^6B}Xt>CFc1xgrK8*O#Eck}*{#`u!PO*&KI?-?TTArW0b!C46B=T3RX4pj46
zuq~#gi#<SwlL4Ace(GSn2LyCs+#2C8EOk9C{QW{URFeddo@{OES(7}EUMk_&tsv3H
zrKRdo$#-v=nmDjk6mjzmaqpV?ELI*;q0a{7^mkqS%<xqytVbnZcK1m?(4WP+Mw6%b
zcJoWokt2l>fb$!4vri@%vj*g;+ki%KSbq<7e;fHzX*WvO`dY&NYR$p7+PMNZ>QDO|
z@zT5R2WmbY_{XF?LKvdeZTD>v92tlEDo!#noOiBjZ9m0c9NT^2z?zM$gf!M|8$#;o
z!DT<+>M}cUMQ?`0;o*g;Ll*|--i@u7;jx}KEyYhAO0FJ!v5K=@-=C8>T}s=-8i?{>
z)NVA%C0qk0){8xcc-xM-IQrK;p!vQP@y(ie+9bNPxNjuhMn82tPrxMq01T*bK?jk=
zcbB?PhIKLL-$AHbtmh&YiCQ#rd&)=(2T_{L)czc5))rF#0ASoS(8z_nfHwxmVcYQ)
zaH$B!RHHkltlIwoG;`u0)YM#T*1GgPkH>!ttY)`!;imHS8(hidNKWs&=-9|%)C1F+
z@?V608@#aBB=KL2iiaVV#TbBH*nG#12|zFhUtwK`ggzqQ+vq9bol8rF?#0T;bxW6W
z_UW`JjHt<SafMKG?OAhtSMZ*Lu3Y>)_>C3J5L|&Y7lts%jB-Zx0GNv&Lk#1-E6Bm&
zGYCR>NM3PT->$vRnzeG+%AP8_<!?=T{Q4uC_~+w!mqOC7JUc6-o@tD>6p<W!m6NF?
zwm9w1D~b3iph@Ci5^BCCxQpztg;kqkl>wgxihXgK^j!l=(S9I!(c}1CB$w865iX%^
zaE1{c{sRV?Lg#k`p+Aj#WWEc%(Dj>5JIs?x)F2sH;b`3*2`iAf0mr}Ay^pTL2ZH|2
z=H;4A%eqYRzq0VSti@y0;^EONYS-p`b>mgjr}4eyx;%Sq?Es22L%0&av#~t=;%l1G
zEF{yVg7;5|m$sYD+m1@~U~$`ReQ)D`g?f#(>grlHmimiMyQY%LHdNj_NQ<4g>zt5#
zR38rh8sEOGHBCm*Y+*ySxWl^+suhoJ=BloDLK$nN7Tu$|XAMppuZYa0UNcEIds}18
zZ0+>BO+xbiNQ{<qub&gIKv%EeMR7hD&@K+X`qc6mEs{rQ(Bw3o)Gw-x^NRZqR`^Xe
zy)E6pgYIoDqLCzJwZUmJMm(}d8(Fi{*1kZ!{?pN}f3Y=jBy!r50gdtsH$pkDBQ3;H
zs~U=gwX(n5c>GI7rV@13MW(uv>$-dvx`*1W;td7QETfD+d>+~ADxJrNt+fcevomi%
zOfokNGCt#WVOpA%?6cikS*n+5qGypn;|s>`!1b#)@>}U&VDnI-7>trIIVH1<^V=A}
zrzuV{SKS^G_Hs<&ZM6A3H4f-4{>(&x$=zhfsqUw+HEtViE%lqAQ6ArKpA=`H3<l&J
z;~DK<p>HL*CL8#bqmmF=PeP|1zaPS=j}LfVaMo}ZEs?i#6$3qoTG?avb$Sr6PD_@P
z-0{1Q1^95a!`;NcWV^|?xETQp1M(iqLFi3#I)B4`TfsV`UBR~Q&g1|jRmoA;=f^>h
z;;*G7@T`$p$dJb}+Bw|G8Z$5cJTb`iuFt~yZ-?}&IPD3UVL9I048(W990TdaO@1Y*
z4qs*O{{UT2M+?LC=B4d!k>Fk+(y#nQac5(u+8Or49FaP5!H-daUf1x?;8dDL(D6r#
z8DO_VnTZ)yP;wOTeqevXy~o1WI##Mhu9nwPA`jk3(JVqF9Zq_VJ65KrtN#GPUvi!v
z&>?7aEjlvtF(16Rkn|+_vF-TRk(zNPD-%iK=)%ip{{YDQESrX@QfgDyUy}a-gV69T
zL*Pe(d~M=6wOi)6vw~K@f(BUfbNASgb_4$a*RHDH;f1e;u4M48mjZpB(1w%j&*nyV
za=YXoc%BO%YV|!0G|P(?goJm5?RA^)pzXE2s~5ytww2&*J`WH?(%f&HB=YI`i(-08
z92H_u7{@-fgfY16T{z+8QN1*~?fIU)ESE3K<t#;5+S|J}b&_aa4e*D;neJklW7KDu
zqPn(OjsZM+bAetLXRTWJ{{UC8u+{9~wY0Q|sI`RUC2)TECoS_FX0m)y<LxtF*CV^M
zU-*~C;G_rTDoFW>1Gzo96w3`d?e&A=xUJUS&K`v2Lty9S9rIXA2JrOk{x3f4@M>Zy
zVsR6x4PxKmk5ZdmhJO&YpQ>8heUD6sF($x*Baf9(``4}MUNMJH(Brr<Lb`!bvv%hI
zb;wRhIPbu(mS?`d*ELyn9Xcj=zQGWM-bUUA20HQhSFP&WMb4L`+3C8A2qe3S!?9!c
zfbPS%&q~5rCn!?qd;ROV*MoDa>cW!NI{7}x?WFk2;ZU-r#P?Skf{^i*Tq`$1p!Ocd
zv%CS|`+tZU=C9(tanWvKxLAhr2GrOH88`zS>%qJw;Z0-2I*-`2*&`_fW@*)T1ywmD
z5%-TAFM9O-R^LU^^}DNxC6-HBRV0)#F~%eV9XhcioC@0$l~cp(N3WjeUQb&SR+U-e
zecNrO$!>eZ_Hm_@npT&oMFOBINQq>OcPDo2el_BEe-`!62|+!dhIJLy(Vxq?5<<R6
zToIMP8=J5_E6%mw6KdMFoh6;S8(le{%JQQCJgwIU13f>4bgZ36LEYd<S51ys-T>2H
z9CM6(v9GY|#-{;>p&U#WEh~2J`WR;zy45g~`m@)+p=SR8#hU*Bjl7*o<02C=NTUH%
zDn<ZyY%T~r2TXd_R-NNxf8h)71^8o4j^g%frj+Ulkdb!l{9Jv}TyDRtNIVN?;r{>*
zm$!Sv_IZ>YyAg6+gN`?J$fo_aZwPo+9Xm<=*_7dSb=tqhkaNl4de_n6@DRjSaHSNQ
zZ$pbOpRmG4wB^j5o%Q>S&2vh#@Ft6KEsHhAy*xxKzs)X4`^5Af%U>|~Yfik<wKtzs
zxp5?!XEQMc<n+n@B70ZRao<mMJU3F?Jj`TL#ls!o9&p_D{Hu-EG$@X}C6<g9<{6*N
zUp2!lWr@nUAd*!2SJ>rJqlV|hR!Z+w-;v9k{c5yfTMD+_Ej<o1#0ja(qz@0|`Pzi5
z3~QVm6Ow&!1ymQhb-%?M9|+ly(OTLyw9naz89ROROz~cg;yoi(g7)+~EO3()fRd;f
z;1T`bL7K$ycAadx)A*Y3G?TCH?Uw|d#9^NUmN?*MyIp8o+Qwe*bLH_b7wsiNqI}P%
zbF{gOQSgU|Y<yYamIiBi87GYLC5)baqrN*DvGF&@(0GpOTUOg|6{WZ;h)FGRA?|;R
z`=Nd6&ba!ohOBM8L#jZ@Z#0G>3-K{#LPKMa0LDnpYs7pV;T><pdW>^I18kM<W}aep
z30aQbKZ{`KJ!tlrdB3ee-=)m$kE2RhDz{5VW9W~B9z4{p^m)7)sU@tMg{v2}`)Fnj
z2_JM7Jd`89YVLLK73z0zM+c59Ze>_T$feWvya+py9swWDyh`K3GiaCPY~*2k1NpGe
zo=a^RKA0yt9Sw6g{{R)fBMm}JIVO$(pCJ)KtN8rexj$T2m0K)?s=Ac6mgxGd7ZTN_
zg^e2N`?@FH9`AJ`S<3~*t;CX9T!3SiBCGf^u*do7S2bT7=vsWM1U3^vI*c@>BT|3b
z0ZudcSIk;&xvj`#gHyb5Zq8UP<-vbZ#yR${Mzq#N^@p2tFqU|T+autTfMXnY6^!s0
zT2W4<IgWbVOIA^rvUc@Hw0L{P*7}yAZEGW~#_a8Ow^eXYW{@5~0aD#u>AFRQ#1P%Z
z8B-%$nUBhh=O=Mo@H-J+H{pq`o5Z%0%BUe(0F?#+kz0Zoem!f1@fU;qO{MEM-|&yg
zEKx^r>WLEslV?%GWn1#E131f}N@{p&Z7D5!*`vw8Vxft|y0zw%lfKruB782gp7LF5
zR`E<$o@ph8tZf^(Jdh=Sn1k#EcAf^0;f$J+=@#)j+}o1SO>jz=J<7?FNh8+2Z}^1<
zrF?8!33WS*^<A@C+*|yf&W#@*om101EAbZEJ8gJrcY$qWSphMy$OoKccNN2jl%n9|
zmXEcXKE|V`T{l)*+vNRE)$0!gd@9$i;&^SXttM<1AQ{f?;Q6Pop$8t-<67>S@P+(&
zsd!Jqi*acy!?C8g*kTtWvIKTiC%EJ3Si0woJUylSG(Icv)ym6zI*8!3&e;;-xZ3NS
zuG7v)uLQF2wxOljhPu5o-#lh}mfiD9bt7;SJ(s5AU#L^b>f+q%Dvk3kCDC8X{m%yz
zjl)L-uv4VB)meULu~~Q*{t?Y~3%?%eQ(aj?{{T2Z?2bD&V+7;W8ulGG#M%zGBiiWF
z#~ECz+NybBQ_(^Gwc-98@PEa9cSM&~(zRQNL<FkB@|9;OGC=glsje2=;%2FDc3V`2
z+WuG@WShW>;Yi?u-~dmiMSSivE5zN?r&cqRcS$qLo)(54F={O-ZPfafTPb5A)-tHb
zaEMMz4!8r?&{9dGOR1;WF6E6Qlm#Cv6T7z{@$X+W_=n;>{=a`=s`yslP_)!^{{Wwe
zUPB%_WT-02Yu3Ce@oqhO$ZDFDS25qf__&QDBr|%iP7k5M2E2@RSBj;|?dz_n%9Ld-
zB^R%j=dXBQ#4%a?M9@4trtr(E*(9z#ra`rRy=(Os&7aiQ<Y&YG032w^@mE7w^h>xU
zpI5X|E5-f!kY|!e>&dUzPwjR80G@CE0DRJoIi;*lx>WQ(|I_*Hyj^2^be9%U2DOZ}
z$c9xUq=W!LKivdpj;5IYF4W&q`)1MQ5w+tz8G8|qhq$ju@HCnig*DmyC2ff=RSfqM
zJ{3;VFpJ!-)03Y4t8&*s@H~Dix4DJ_U=p)DWbTjW1bm~Q!ycUldU%Zc3r4J|$_=Ys
zHPrcN<@G0L7j10(k00?Th%^Xvd2f6{hlt?9pnT8r<ZaJ6&lTp-Hm|H&uDz<n*47J!
zva@ck-I>0fYg+d5&fnq`{{Y)?87v^4NMPeB6PDq&_Z@v}(Eh_~;;mY7d*=%+J~i5~
zU^!_3C5Sy0N2PXuXi!N`dLxm-N_>%5JgRwZV_7a^+GSFq0UzDRKZ)uqUsToO)bx2`
zj03m{S{7ZU2^{VAC(^pZ@VRd-WVwpz;u5a=13GP4RA(WGCkL=UrE%UL@Td0j&u*7!
zw^0qAOK0T(<K{hveKU^r)BU4{lw8#G>Lp57O2?nr-dt(W2;y?_OUp*%{CO1Tipk_v
z2WJ^M>6++nG+j{1_CK<;o<Jb{xghlG{Hr;%&ClgsM<Qg7GDp<o>0K~!n!H`c+D}sf
z)+paFum}9&uIcG6r!whMx{cf`Fz83Ub&Usu?O4Mtt;1k-GJr?niphAigCjkv$r_Kk
z8wAyIJB8J*RMfN^?K(mxUGeo&y$9i0;sRq5fz$M^mfrGM)LY6HQPbu;<IwVRRHU-D
zo-)?s%W>2e;Qc?spDl%V)WdoFutc1IF^prHk}0E+*JuQh-mk@>Y8sS`kVMV10#XOv
z&q76Xn#Y8u(d})djxhfK!z7E;6VoHHtofD4bA*(DzH*QTd-~K(E2$`3Cxgc|)m!Kn
z*HIW;a?CNF<nvvBhCC4=ntAmU{m=sku_LZMswY+vS7FSLB?Y7agG?pIHSK;P_+xPr
zo1HVRGv%JFeFwffS0ko)GWSr8B-^yKrY6Wa2dK#FQmaA7n&ESZg@d-hGoN~tvM3Hg
z892r(tGMtsrwiPbQ~r!_PAfZ3@V2F^YL?nr`3%6e<IpJNnx$G6y~69cm-2FP!0*zh
zVX`vrrF}AS{&=qE#GVJ2N_4r<)=6!)?38ZIf!FVnJr5YgayHD(rFnP?(8^q^jz~Q^
z(~VUpWFn#~%14wlo`e4Y)l*zsU$NjF-*k*~Uf=Mi!SUNoABa3en__1T3@yoS`NyU@
zS9#)>(k`|2*RAaolyz?`;09Rwm&bM{xu=b^oD@q@(&+JPZ-!UX*g}TjEy2S{5$L74
z;P)U`fAHJ(dDng*-d)@2v&A3TSA3!RoSY55y(?D2*Ts6XNr+givKK7CBCdM^dH^`C
z<4n+I(6M)Wa$}t3QY%>kIUP>v2i+L!&3U-2$<vM0_3gQ}3Nw`K)`u^1@a$<aw}<17
zCoA&KBg9|<#$CD(uTx#;g!~bvygzEuwm?_qd2O<xm%5UE^8IOb9UY?Z+<N`=COZd|
zEHEK1?#v4Zke*05IL%YF*YyjVLiSO4amFxzg>mWWpRIVxreTXq*iEF_z9HeMHwiC!
zbn`h|O<p@+A6@t>21NtSje-9FSMrj}-yq{Y)nMJ-+v*omyB3YmDlRxoVDa_EXlWh@
z@swUWi^g`kWUXs^Zf#_dl$2A0hXaCCjPfzky(h#U4z*7WY3ZonMoUU0ni#H5-!w;p
z%19h;1nu<gT~)Iz4iX<%t5cd$eKpZ{IH_eg{9KczPRdqV_h*$Pm!`%1K{OH@7^IFl
zWZHHYVhW)?ryrGj6|aLVbiH1Ch^?Pe)n!F_F2;7kBv8Q+bo-~C_1<aT9Pr#aZO(~!
z4Th1c{g^-7Nq;ItT&#hSNWjiB$4+b0wF!JR@R4nF?HYI0@2{OBiddWzJgf%67$*T#
zbtjtntWI+*RA($KEoY~i{SVM^JQ+L{N^!(W_l@6^G5i(aO-30mZ@gQid75;#k|cq`
z6B}SMv0cHCk}>$#116r@KZ&8#yc2Y6r_<+Ite~A2YdHWqGXl*a1B@QVz1PROY`V6a
z4wD7bdCr1cmW_7?k&ZTkNW!W0Jk{TZJ{<6kh4HY}LN29Z-ff<sz?N3W&lb>e$mlWG
zy?IzXd?7|v-@PeaC@rpiBFmvcJV&#?F4tQ5m^x>S^#|5&uY73@v3tnGE%t1}5uAln
zvQOd3!D2;ur-d(Wd^@IVQrpCzXx`8fkVI=El?Nkm0Cgl+zWi1Ao#E|EM~=qM&h}RG
zkY0K5gbfU5lB1K?oDw=$h1uIbgX9qSZd+@pFIz6G4#^{vCoUJ}z|T?0?knlC>W-Ef
z{{U!4+}#$uw<d(^;^TPPHMRD3H<!g)MU|51x+s$V=?VvXRQ=E+_yhFna!;*s`u>CA
z+w0i%4Oc=|yp>s5f*~bOQp6CvbIAnO?yumR{a;1$^eBqzISm;}fJNs6%PLp%VV_#z
z^sOJp5%_`pJ*`}{moY}*$AP~pGJbF313h`~UG#8tYF?%T5gA!MFK43da|+pwN@+(M
zN!>qR_+#0%--R9v@V|u<#9kV(7MiRmljbQLw9&6Fpl~t=AY-VnB)9RVr9HH|hlTD4
zbx$_(&PCpjjv7SVGCBL}!35XPx`&T1yf>p;!>FdEE6SfcEx4W>1sDjbtX0YHf%UJE
zqOsJxIpWB?Zo6c%nn#Z5Bn-QfT!Xw23bN-1Bd5~5%%RuC;ijuknYP?r^;&*KLaU=%
zbyuajKk_|l(o^D#eYWL5kw@<!Ic0o(){XfpPajJ2D_<4rT5Jn*qrTKhysPUjzE<Ku
znlAfE7+^XY_e}#*@GOfBy_TD64Wc}fUR}%`<o(d-vIbDN4Uvus#eB_edu=YUajWQ{
z#-C`NVFcy;mtq)ywlH!t{6npJnQmVgQ>jwCxp{48-S+vM*`{LUR+SnMyOr**)4lwU
zQqy$Ed`aRPTf3S3gh3~lC-C{$`^ASD1B{-P#C&}4o~LQzt36)g&}@4+p?M?8jf(VU
zJm9~%=Dvs1d_Ah@-V={mzk(_BiGOhg%xkh}(2Nf`0183R-e1P8cxzo-+i$gMk*iru
z%&J{-S~!p_Wt5x{PDrmmFXDQ*TvYLt5|kd6zTYFiGs<wd%p@>Um%Sdof7Q-!;SJWA
z;4KC;)TEH=QmBznltMpWm}eV`1GNXQCb<t9_*ccAIPo-^#+!8&(!8p$w9@^ZVqf9h
z8znaLkTdJeeK6WqlKNU|TD9yqk;xE8iqTASp#vBSK^Z)J+*cE=UET=hlfkybPPcSl
zGFiZyVzG=W#v3ZEPkd)Qn&Y8}uSXqQ9-L(AT|4x?x)F@XuvnZuDirybv)avDO)Tz@
zI<e9<FAv_G1L8indcrw&3tc{4{%0HyKLLu7{5`wZx#$}A!^^uE^mr{91;Av2K_flN
z7}q)7oDOggVts4m4-xp{{{TtVZeH0znk>aN#QBnEJs1LbRqc_C*A?OK6hm=+BzpD0
zjwOxeKz>DLQSwK}ILRj-*y?KvnQki)E^G3|>$gtF%jTK&92F-|4%?J;-R`tL!0>Ox
zEepjuoVrGuhI{)+{MoIq6Umg7ATdc|<BWscW3_m{#2pH675Ljyx{FPROv5V5X1Md8
zBw#RH1C}GH<E43LhBbM7JFPE@d^b6=W<wpjUncdLPF2~Iki&WVpdK(Q)^Bv*9qLyy
zYJMcQM!HK_<Vh`*`A-b0#3agc%HVv!=hC?)gq{+=G$i7uWSjp0Fv4QqZGz^+B`Lu-
zD}HwLJWpHDH60fBiCSYcA|;(Vs%Pe0c0RR^v)*`zTaQVzkN0qewmZh~5qh(KKDFr6
z%X_UX8b+BIdG4h0;vnQH$IRsVDC=3ewyWX40ZjfA(GXu=qVBl3XFCH9N%?R|2cWNG
z+D%3<sULf1{%6VZQgU;rJ4NiD<Xn?SvK}#yLA$z3c-?T4Il^Oa%004puR!qEh4k$!
zTY}SGl*=o&)QSQa9PzoEJTH6?t$2?4WV2mk#Vqlgqm^Ys%whu|FJMXaJXf^*IlJ)w
znY&cfhuLJ2cH6|qc|q!N$&ijQ)7H3gl=)~$LAKvx=(4EZyd_r2F0V}wrrTY$(=^#`
zLdW(yaB~yJ8BWfe9-y8Dc&?A|<5cl9hgH-VSzE~7c|}9=hHUKv?x^jW<uwn9@mycO
z_HB#JWfOeQGad3W^*dMPR0p{y+P!B?_=S6{+ZI@1M~wnIs3thKbWxM@FkV>Wit{N`
z#9*mgm*VR8KE{;rn7Xf(-rsT0`2HPtPr0=4e}`gtY-We$WgqL@wh1rP<EgJp@cog{
zbQpA0F-tiF8B~)bjK`7_10I<tw-wHKPgA(@R;ZS`OpkWWnC!s-1szz0;~B>l(CZc&
zMu&b^##eJS*iHWcWwJnJP&yZLg+MF16Rjx5DjF-_%=99#^rtD+tE;m~cI=ViKM?$5
zec(AQ6H2#euH@7P<!H)C$_FvIJyD12(!HMI9UD}!id&eXSZ)CqR%LgPNDK%V=sI$E
ztbJQo(KU;9y|_tbxUd3SXfCG(%ID?7e+ln`2(LEPJV&hfp7P&EiW{vu-sgXu?%`TS
zCm3f_^R6@XuDnDjxGIyp67;{w!WCraQcvDbroWBPGPSbs&9{%f#|Sq%YD{-*=0LeT
z4^>XNCcQ)Amx}ZY=&gJ$;eYt`wqYc*DBHY_fO~q>7e5Z|G>up#i?Uh7q2)ivE1$ZV
z>JT1qDSR#P@%%-t&SO&|S`%?O!8ri^(0`w`aZeXR6H!)-o8?D^OD&ZM@>wgt>*SAE
z_;;d3;hh~dtL52oc9~EHb}9R#>U!2jp)KaPvUoqmF^g-({q?QH!)$Cbk;urw9+mAH
zY}RR_jZXgnbNSPG5qWEsg;ah1dh^CBnem_YMXr_om8!^++)wjaJUM2`{^&R=0qPGN
z@mNunSV!v<rrc$-?frH=S`{g&Iuzf%9oKV-u+vY3?SHfH3t5-@ve~jp6OZ<`Vd?OA
z8ebFJOFhJ^97x+Bl{wtU`2E8Eb+zF?0MDpjw0gwK*3ho$W<WxcF~BX3oyaxkTA#*U
zH^SaNlSQ&rpF~*%3`oI2$M2&#7y$O}Ij&g7b!$p=B-@D@j%`YXAFEGJZ3h1U!mjZK
zp2A6Fj!SD5kSSgPJz7JP)Ykt1!M_uDTUpTcO%uco9CAsy-DPcs;Uj6rdx2VC68u$h
zHissS;oa8)=z{XXxjtKS!5w#3%KFZaV{EZQe90c9uf5@B<s!!lar|WRMQww_&mVYI
zmoB^Z^FDtSiIpX4P`b9={EM2swYH(EPj8{3$U)g`6sr*mH!Q?`+>TCq*Qofv!EtIn
z1$|#n3~w~}kgBE#Y~v`~fd`I999D0Jw3+-rW@XeZ-Nn{7Ne~6&!1+K^!-3R~TJN;&
zZd<D)irt_VHttqOVq*;L*LiG`{{YLScU8t!!`6*VJfFJOz5f8=&!@|<bpEI8Dy4f}
zuE)vx2Akvm02`{>#?5J_Bkh)Q0AshRf(}XTzyh{@AYJ$?;kK1&YhfEpJdz0GfFGUv
zvli{md!55+77q5>0C!d;+yo00dGvm-lhYOQABL@E@mIu+W5k;HSr<!}%e6)sPyyou
zyBv0}pvy7eT*b=~DDtRlWN>B`BZR`o56yQsW7l+jI>v7cPalbucV9=oNX}VfjAW|k
zu?L*j7x3S}_L?|8GS*dml_hN!P8S1!aom&Ax~tf3FBk3pA&=~~igyU4ZLG{b=pcRK
zdIjy?vNdg5TaeS;0StEWU@CI41He(+aT(98bz<sH6rCA;tzVKku>Q(bRxPh~8egf%
z{8)JWLwBaFj9zq5J4*1**b+D&HcIjV?TX_X*`t6PduBnm=YUC#hyIz6Kw<v&E9qGL
zJ@D_uR~I@rgs*317O_ic*T5{EW`BBvpd|GJ(zxinAEwyd7_O#iUM@h|P!rc{2LO-4
zwxgKf-WD_@`DYvG)$itc7`j!lSl5Zdu4qYX(XN&}O7Si+7351J%MLfkDv(+;*K1^q
zisyV`;gf%2nzo<JhlU_v?8h5`?hmDX7prJ-Y8GuHGDK8-q!6HeHym+XewnQ3x_XOE
zU4(P5$s(0I#(#}S`A9v+dR6^;wJT2Escz8o933Txac}SFe8!#))g~&;@w)JFo<a9h
zR^+m{(b2<_0x4p{fnMcnrdmm=&vzO9#8ML$I1Su?z-yTCBy#FH<6T^^onuU-Oh0xT
zvBv=8(zm0S)U7+)OKG9-xSLapP3oD4;Yb>GO;Ons$|}-Zu_9K9zllaV56-;@#!GvB
ztLd`G<*mD+Nu8TuU<b<EyAUa0@Xnuo@Rs{tj7vVYw_vG^?Ij4qC^*3O74rl)_VDVk
zTk9*S4WD`moS-CgfKC7(L0(N>Agbk7l;E^_J*<5N4jrdng)B`e@4Ej0Bi8g!5GIG>
ziRRN``&lXEplJ>O2RRw(^{&=`jJlK$vM8XqmpJmr`m_VCU#UMqUpwo1$(n!oli3*e
z97tmX$`jO*aCquPa?5|H>i2Wl>5nb8pK7?0=V%f$pW##YN8oc`J2z6ioScuQjS4iB
zVB@l}^tIo`ts>`8v(#qN^w}=sljlcJ%s@N<0_5cPCcctSh5rEXljyfzGVwAcg~GsY
zEp2TP6(K4&k2IcHPoS@cz7+UVPrLCndVq}iT6+l|L(iEn@a{Z<SE(7VzoGFh_2s3%
zg`@JfUVidPWCwD>56&~3bM7nYGL?#j8g->!e7DhE+fVC5u8u1QYS?Jfi<4TrFS#Be
zp?pX(X}334D#?D}ES8(OcI?Z?Dcs}nuPgBHgl+t9;v1W7M&QG78X1Fy1)P(}=N&tW
zx$y%;eK*7-Q?T6~wbbmEimZO<IOEV0%~|-PpxyjDZ6m~X@;q|f#fdO+^L>5AeY3?u
z)#jlqI*WTF=39!5t0egz-bZJrU5|&FYw32D+stpeW-NCco?GdTK<QIl>$e(=Qd`|y
zstHErmNfyh)E|GYYbW7t%)T|&uJ83Q(j!IWlsPy>AA9Rvt7+EO25la8FBAjhZb1r9
z9Qum!aaf2`#JJ8kdN$8v2})R;I+}J`*xC4L;>|}<_><xLE7Kfvrl(|txGKz|WElXA
z9FId^uYP3z0Q1m(RrvAX4-DU2d`|G<CBbWWb$cmQ<RrHfq1(-V!GC4{0MkQT)Tu?;
zrg@U3RY|84|J3>OUDK~+__L;6Tb;vFjAv*7?FvI@+iq*JhQ{|!y?Z%GGDiHMIc$G>
z9-Z@0=~}3|_=z#PfJQXCa3NMXWfEX+C!xqV80}2dENw47A%DVCCD^yNkN0WSeBuEF
zWc;9d=C~@+=agce%SZVh9XD1y(%$c1)X31hJ85yH-N&xQyX`kqI!Jo5f&gLY2X574
z!!tL9HOs3@t*;!Bt1QjQWnAs{Zn!npX_}9QUiIvuoXI`9x0YRaC<a^0AmPVcin)Jn
zqwA8sk+cb7K_o<R@{llB+mVr6wQAJ&snm<q(P1N`>C099Sf!(VmOT?#*0)LaD>*kr
zfwC2d$tpVnPh(uyf&L#)K9diNZ06Z}EYC86avCyz>wmsF)E*qxgc02MUsM5YuZfOT
zZmA&12R@nL{&l<IQ>xnNpYV&_69Izn$(4^Exz2D8ZaU+rsf;-<3)<tCywsF#(YqeK
zZK>SLZEFc)a8A+4W#btf<ItLmRJrhmy`{@<sjP8c#0ZL1Ua}60^7lO%Pu8l*4Dsl?
zjfaP3`#FXek)(@>W((1YJcT_lI#(HMZ+WSBM@P~vBl+!aBza^UWdY9SJ;})#Cnme_
zaj7YLN)?(({EmD@NK>^sw7uq3lTFiYG>A1dwh~8p?-K_(DC*c{Za)?FsZ+qd7#26S
za|THQ8=HH*+(r?8WMR;${OfbWHqdJt_NU;Bi8jXUF74QL4uA;&`ivTb#Wu%I({(*0
zGfM7}B5lcOM1<u)`gJ0@XyY>lh~%A1{{WHQOAUjoLDYh^u6fmufbQ<?t?wFm&|F5c
zs^sNWx^6$-J%Fx|d@P>ZOS!!_Fv)QmZ;mGf7Qoy`IOL9kx}OSoYgBZHT=8IRsb8{N
z9ya6d<rk~to_bYw*X_JV<4sRNck&X=5TWYASdI=l^vSNQ!#>10a$%u#w|_I62a~Bd
z`&ruhBys*4@a5%|oO;FFb42g+N1e)LQ_su?RUN%6&|xA;Hr+zFd>r+_9XoT^6+^@R
zG1Q=*>P=rzoJ6O}S;GSolGqtuiVi!Bdg8qg!?qUs{({=8y7|IM6{Cy*t>v~jIpZMb
z(!H5tC`LA|D_^foOrAlElIEhXzL(ce>qDQh@c6jY^xGH_Tg;MKpK$3EeOUhhAyP+Y
z{h@C=G0Y)ZWFw#jimK<<y8R-?@*A7G?+=eQLuvLclKi4Q`P_Nr5!W5-Tfva%lG(?1
zs7ez}v~MmyGb%18{w4z-HV*>4taPPLEy7-h6*@24a@38jz0L<%g6_t6O9qJ=1d&5^
zZZI33{8b%aOP0e?wbWwyoJ6-1fH(wt{doLqz1Q^!{8RA_mNZqh(qRGeI4zv0{Qm%z
zUehDEx_Rb`F(Uk`?AQaYa5L0*HOlKwDl?M1wy14FPEd>4=y^1k0jwD}Zh7QsB`2>O
z04f)>hflX#+gQ?hCf(*?vg)K^SEg6+uJc6GygPZ~i<>g*G!ekgg-^&PKXprVz%{Pz
z1+A=inw&8$(0?u!Rr#MhWC7N+tBj`xlvBBt96X)enmmCHp&YTrENq~`^SS;L{qy($
z4PCUi(seCM?H6!Hv09;XB!e4|Mb16>#d_V)R*c&SmPzDaBqlOOdi`;neQS;JH-s+q
zR2L|QNDGvB`2}4+g}sh*pTd<a$~#r}Uqa=Dn_SejT~1>2#S?v`&t+`yV-VXkz+soO
zFd67Ok4p6a01aq%pJj(syLfSW#M+1FcuW#h5%-D4K9!Yw;n}=9q}mNuA&o*Ji69*A
zBcbd^@cy-HQt<Ynqgm;f&`Q^Kk;fm|!Oqy(Fj+C%oQ&57CpC<yO+&7Uo!Cqqy{hKu
zba%J*c5a$<%(7gckXyh`^O8y4-3A9*hTp@DsOl{Z(O9kIQX~j7@{V?c*PiFSc2<)`
zs%CA`#}V55#A7NLWG+C?bDHAy-x_M4@Q_X6Ee0s!w2C~>aUy>9LZdlXJOIZEpbVPx
zFqnF^-z^`^>BZtH&`+J4TCTa_h%7Jk?SD~@817;VWo;NTJaR{krLZ=j;{zw0;;wuz
zlfw5}V{00=qibsgqR4E;))(z}GGHqKw?0%sa!BfWSBhx5WV(*69j)X~B!sKViiO#X
z?}bpm#BvD0>0d;z!W$13Of@||;cOBJ8aZ0<9nQ#ci*?2qzEp9>YuUtM^87X(Wq6!R
zi<i9L>GSAx<ai8ycRc2m6(qgXo9_4dbv-}8pA+<p&l1O@Xtr|d5DD13M~M9Wh*x<m
zepO@7IKuSnR(>Hjjpx(t{w6d{q{XNCwn8}O`%H5w%MewUX&C@?&t69~o(S;Pn{RTy
z8}RW<R+ci7UR*c}5&?qEo=Y5@DDU*IOYvun{270zN$|HxmMfSoVq05qfRK_X`JP4t
z0hs5E^VYs|Dy@XWW(vL%H-)Y5B>Au9*`I8(X;+}A;<c%Jw;S3krM%BK)cz>gc#puz
zYYu`S)Dj@?6J*=mCN~oU51Ac@J6%Z0#%ky6@8B;D_@nJ#5uIA@7$jJ2Aa^S8oNjzC
z7-7PZ+PFUi{215f)_%*W>ayBL=dY6#3LFEtU>)b>L)-!Q*RH|va>CbBZA(XrYgW7=
z<u>f;8xlTZc8~!l)K<9c&n&~rbnvRz-rX<Je2&`Gr%I2ob$<81&*XOgD)2wqEpBJ9
zyDU-$yN=dzvSy4oJ%&tw#m#vpfQY(`x4O*D6n5$!Y2^bgxZxE5<&W0~w@TzRE4@ci
z_`7v)Wq$WD>9JhEXPQTqSsW}x43Z8Ql_YVIUH6GJFBe$YNqweRX?m2l@tGi%;GLWx
z{{Uq{&N0t5=2pv+N)(z&_tSHy51K|8Yt^0fzSsPZM$~jIKVQ-%)bw;Ke-a0p)^Z)D
zj~w8FR7#_$=DhR6_8$%9lk5>&URm8LnY_rH62x^X8yyc&I#-8yclL|aG!Z?IiL{6>
zJi{J-$1A}ilwwFt+p)L<o=$O$SEWlAjeKzhzluB)r8C=Ktd~%`PZE-Z>^w6r(lNDx
z$0Ie@TL+%uX~H<Fi<e~d`<>Tqk1r?9@|<N_u%lAX&riGNdbWq+KLOiJ`c2K{{4UvX
zD@K7?`v(d0a(Z^G9~Ia|;@e9}OrK`Iv*yx4)v|e4{vnm)10Yw0UTOX+(C$sHxeV}1
z4hp)QsXUM*Rw04op~ZIV;r{>==-M@gxvUSf*xnLt<(QBMIQfD3loQ;M>sLc2l?f`;
zU%hp&PUorngxnmio%;Rft?Bm~pN6dBwDA=8c1OxD42-VvK2IE}-nrlcIQ0U&GsK#$
zuf?An+IT@f;#%{?GTdHB{%XjuuHaQbMrPU$0q34gdIp*Cz4WPnu{=!w0Bo~?l>y$a
z@sL5xZa(lB<xk*qUH6Q<d+?UQq46%BmY4Url4}6)x(L{>B+G>e4oKSB2C$`D8G*<9
zWkZ%yzbi`XUgw!UZbwP3Yw5q@W<l_CPVfhTr<!ZAs#r|O(n>#c(m?+J#12j#HtM|y
zC-C{J{uL{DXT=L`e)2_a<|>JyQz|mK+!dWNBPypX8@Em?(Pr@{i@aKo;0qASmr@{+
zQ0`ut4IgFo{43OaDQkV<V7HzeQLSCPyC=DwRuo~hhRNJel>-df&OI|;^s@@tc3)c&
ziK*{aR^_sHY3{pQxt&b94!s_A&B*SZ?2>CszxW5F{6yCDFAID$Uk&N%BunVUwYi9g
zLm3}5fwRz$#1mdq;hhTl<yTv~jcv5(5M|}SR8=?|LB{NQ4Ae2|-V|RFnY4Q(xxAJ`
zvdF)g1XBM15eb|OpH2^@cfLB(b@^=}x!2|_?f?;>!biAtP%tn$bgz}mDpGM&IU2sL
z{{X{oZweKuOXA(%&7LXazZZCVc?OZEYjCs{sg@9fx>+)EI2pj>9M?Z*;#+HbNW9r(
zWtH~Ey9=|DFgC`cap#<OuSm4`Wd@3@*4|&4$tGn0=c)W_O2bl*LDeAE?_h%RD030Q
zg?RUWafS-9#|QAQQxT2BdmdV8x9ECw6<ax{cj|nb<Ie>6lIO<q3)@SJTPe0oZr&I2
z!jJ>G7zZoYZ(pT)r^a7}o)h?E;Egt44>6W<H_LSDyXIrcxjg)!aH<YHa%<=h7-~KX
z@y)a`d78RFn_+2TY$7wC<Bah8dbSTr@vT?IJ`&e_7pQzW@ipWZOQx9;!uk?OAu>F&
zr{)~})5t!wrH{wrT^Yh3x{Z{rugv(2XAj|*w3S)ftlMcl9=hq@^FCSlW1#AP6FwlM
zQ*OI!H4n6oi671oF8~JUJ!|j1TgBcV(X?}`c$wz1p5U1ywT>|(5<v&eBY;AX6@P?r
zUp@Q{@CDa}yg&Z{34^GT^Hao#ib0Yn5Vpk;l0Ik7;z&J)E1&qI9<8bCRyI?ch+26}
zBHCsVBrlLvx(-;Ju{i@BE4L7^S(ax{4LX*k9i6SVuV2910hHo0%$lwu5xrKn->sL+
zx#Qj;*R`ErP@HHGu8*f&VSLn)o@P`4WU(7mFz0Fk&s^3PpQ6~=U8bKA5*geA?%y-B
z90JYt?b4_5CX3=<1ZxeaYjK#9KPw}7krABbSbU`NdR9)6brz#|u49r&V~`ReY)i`k
z0)jA3e=7Cy6lu=>$@{(5r@>OCUNz$ykhHsOzu}Lfv?wigi@4ubL|r#d<1XL345Jw*
zrcZI(>sY#cdS1PGsOqBOlSzU{A;OaGndk>lJ^KAC&3r}TJx2QNbQuUM1{zCfcC2yd
z?uR`$6Y0~FTDnfZZ)0ILfoB_$GVOWW<yS4iC_3#O0q<TFOt%qA4ZEiQ00ndFDdto?
ztYI59AH~@8TTcpjXU0=aaiQM*j@l^v)<YR_gyRI{73ZMmCZ^SH*2c^G3rqh1MTt``
zrL!Xz>dt!>=ohX!)LsXNM)1VbX?mT>it=TPY-LGI6Y{G!MRCFAx{X&t@Xdwi_Lctt
znIrtE93htvAwBw%NjWFAVJuxwXt4Ee;+58#v)04YgsHqu29uN4&$-Ze8vg)T_-mwE
z_|DMViRG2{t;xdqSlTc+Cp?<+j}m-C*7dzcVd2=>CcK+<)!|+8OmoC>j&`6uyVrm5
zH^SPsxvkjP>C?wAl>m*c_o!$<=OuHT5$Y?~JOS{w%fptC-IcbI^x&j{gs<~|cb%*C
z9M@ee&j*9UdwS{0Qrau^G_8Qe;wz}8qZsb{`X4i0d_VCwhV=FEw~X!OF}(4#kvxNR
zs7^}0KH)t8uDb8Vo*MCe&AzMP>q(=FPX*dbk+CBOfQmT`bHM!RzaIQ~KC7rN!_6d_
z7TRSpl>v;ic0e=tS3LXuDRk+yPlq}czlY=zTxt$P(nEtL*;}DyA1}%}ZaDTei^IHb
z8C9ySQM=h~XVfrxg-GKnLY}l@wQqZ_iJkX^^i6-pmbY@<P34HAG9Y3%lE>vcNe5u;
zdRLd|o;&d`!yRsWeQp^<H#a6t-5E=8UNejkI^gvcjiGqoSNL!7a!(QJ(#buxv2M^x
zC2SrSBW`jRZ}8w_HSLx<hk$-0Xjic|ryaHBw7zA!-;o&*sL6b{51v$kk6O<Wm)60n
z#vIh-mD=4i2xc^~wHzF|XBVV<quO<ki2fSTygdwhB(YC%br6)K7-&N{$!AlMpY}z3
z)&Bqqd{=%e{?pWDEEd?wGdceNSChsG`=pM5bO!>we)r-$vRlh(;rMW$>e}&uC{=jG
z0OJ7nY;)SO?~?Y)#yRHw<;D3E`Ct+idIoP&PeJ(6hs!F}s~R!edvyDbtVUrB4jxrz
zwfs+B)IKL`8nnM*w(~7Cz#2`tz-Z&-vt;My=m))W+9$((Z^t@Jz9Kk|=JMnH8VAS?
z2pAiE&#hp1Xj<wvPorM$fnOp^#{evQyJ3j$z~>$7tJD4^c$49)FNeHA8Pp+&i0rmG
zie>)jRs#<o*YmBGTaCfiwiYQ$>qc{7=uph?{?fheuD`1EJ%8bkkGvVB=?USl5?o5R
z_7Y7DN|7{+DU6T-Rr$js5;;=7_4B8PWj-YFoY!|IK?Sq5%)&eqo!=l}Ncow%cH`Q(
zEqBE_rH_g)wXJerD)#U9ijYcO)+5joc~vdf*0#I@G@c*RWqnFUfa<b~nHf~O#~Kn9
zKqtO)&*fiBjN!U?9JZz}{$A93c4+0mTCQUn6rJhQTD|Xc=nX9RlTPsi>v|lr-sN`5
z_UQ8(hUaE7La_ByRt2mUvU!^7%Mh58aAC{D`Sl$*{Of?Z_^czik3!K8kQH*VWEUAE
z4gUb`zbtcE7C#krMb)Lzb-4tIaUbqTP^LE>GDb3|>DInJzF&)yQl6iI^;54xin4#L
zj=RO0&xSlPp~<dUm}C%*un1%-8b&x(V8@Pn*Ug#?_cGmidQQ<il9}$V75+%%JmC-V
zH>YaZ_^&X&lHW-46^P98fHBT6pySuxu>1*Ua1h3)%7#9##|(S@K<2)SEy2qVgLPZq
z`bWcJv7cJUMwa$xuw8g=86uHP?W^km=s*|0cOaa~-y|Ek%XUzsudQ&tCbb%D7P?NZ
zk~|?dU92oT((Tdkcs{_Moh!4w*EIhC0%;*_8H?OL$dLefZe(M?B=z+@g>t%Xxo4{Q
zl-=EF=68y9j{45&mPC+XWQh@x)R0IWxWz_Prrk=FAo*>knznXvE@e&O;e@3xeYJgb
zOa2_2z%kp+;#+$ge=_0;Nslt3hFp#h2e+kZ>(Of(I>~b@6pf$DOKFMUzo+i^eA`c<
z?O#di{vGhQhGDhwe}L`n?Vy{RX=KW>IUIsyE%!MDf%LDOXS}_W#nVl1D{ES|3n7XL
zBnU|3X?_6r09Tjw+&(6(YvEIiT0UzncJn*(JS|);8ABCOJMC+mJ{kCFXy}^1j9u=o
zkM9=Un0axJ;o5_*;xk-lj<gFm@r)NfQp;#WW<^1_<p<_cJ8@m_hO}>r`t0j@G=F1f
zeZqNOJp88|FC^fMV-?)!z7@8)_?c}Fj~B|YpUe`=_hZOhaK`{;hH9&06$$gse9v8;
z-*MNA%U&jg(&?(dTOK#!y)qqINbY<iXu>i3qKGm$`=k$HR8!$*xRy<*Yq2bBfDoIM
zw>=A}Cj;wV$KuZyXv18eLGV4bvc`jI%LKzJ!0^lla&gzzu<o@>t6NNrTA)p{+)W_N
zfc^4EPJ33k3~nNHQclrbUiS4qD>Tcgs@GAxowP~s<Y#DB{uJ=ff~HM*{{VS+DJcp!
zIbH!Bhduo(ht|9&r|PznOX4d%_H2Z+Hs6qAf{r*R@vakA@t1_WU#(eqGfKI6733{E
z_$Xrostka4A6nG$PlUAi?-ng4=IB`}0Lt!m2FD6Aa8Kb%jy{eSwNGnLoiuxYP_0`F
zLX=yBPVD%PTGD<YSZmGU&k4tEayKxFS!624GwQto$6cnr+3<IRJOus$w(xI=B(sKj
zBmL}CO|ja3q=JKS1oQxoE5@adM7MUIO1(jG2_WV~%Hy^Vdg#18@k2nha|gsNE*)ML
z8%5H7>h3IbWsyz}J7iZqEYhVIw@!+8T~j-8IaMreNUF1vig)*AW7TxO3flNvOI;0Z
zVzV%@F7StK`Q#7|SCQ{sFNd@{tzzuS9`f^HCYUP-;(&h+NCR={fnGsz@kdwicsh@S
zH4ANPM!9hBWptoQxb4}I$EYM4!13?xS9=8dcBSBDn$_S^ZtZTD%z=;hTNwNg74<ld
zBBP1G)1gNX$tzj*`7b{+g9DLN!Ddpbs&hwmcYl<<4`BGS;SUh%UM|w^w2L{U)AYpo
z6%dm052rv6Q(VW1^u0sIazPi5ZSP9k^98I>I!4X?=93)<rfb5rf7+ko{{V~Z&Aq+S
z+C+?_%d{R|OSaWsK4X%7YrfI^Y4LMcfplLF-e2BL9Ghff3&$SMzF3e{4Ds_Gl@znv
zLY+C$yILzgo`-HPhE-o{UO0sttF-oCGq$op;R6BCqVio!FjMmqf7%@Iex|f-j<e!9
z?l0$oys7ra?-~*T&US!3eR>MezP;Ao?@YAT#f7Xlk}P)@VO%7QpP1+FqUWwF>8mX(
zMvmkD5%_mW5)4Ttf~f#wkDG8D`(~>*&MD!g6$)PS-M-zACx>v=9XU#+YsUH|`T8D7
z@ZKw%uZa3X+m$gzs@ern!C<o>IM1biuHs+$<lmUDj656Qod(;*x;(mMK|alU3j)uK
z4>3tC>c+nI`!auhe>&l%ox-GfoKJ48D5k90|JC`IQog>E;`;y_&AFK^)nn*laG+!l
zPQKO9-bDlWg?u%7?*+!$npk8QUCKUObAz5pHQeYnIu4(zYQ8VjT_KtXU?8)7#F0T_
zgRV*R=e2QqwUc<W;sw`;ZREsyL}`ErGBS+gKK`}C?V%^{$7tK<(DLxLYEXl=x0TBF
zS~bY=j-7283d2v8mm`hpt<-cTrn<J(UOhX+5wm@wOYsU6I3)s|%k(verRzQ<*E~@k
zr4&F2V`#}hb|4_+WcTNa?lhai+Gdc9Lkn4&R^7+S*%?0mf}&1IMLTR`%%tSj?XyqD
z9t+hq9byCF`$o8$Xi2zK`>M*=49B3z@9kO^OC8LT+Fh!>)wF^u$d$fe>bT*%f(HbC
zHLap}vr^UOl6z3FK@khMH_q6}IqAV2tIj+HsmH6`__I~J9C0E`-*IvQA5q@9V@21X
z<NT|0c+cwin7`)edR6w3V`Ff-hlVdl+BWTy(ru<)xa0yjZ>BR|bE=(N`&&-YZHJQ_
z&m^o6;C!cc7=N>z<JPzISG~V5X}U5;9Jdk$+&+0oCniTY`4vWS&Pl~_(fEq$^Tk%0
zbZ`yOTu%eA=Xd~(+4jdY@ci_f<=DJuEq5EP#*F$bej0+`#tXgm2AmAY*&EqTc=xQG
zf5bOl7x9LZtlcPNyo+q{E=gVIW=FWrMk}_D#x^!La%mnQh$^G2q>8!Qo^nTCI^<WK
zSZZ2#iTrb^_=%654Z6mbh4@t+HvoH{c|Th3%c|6Oc#E}X_?>h1aExI_$>{#$xABLI
z$A>h_&019<ZB+R{XDsdVuU}F{aUTtIy;H)lHm|B@?e~l3O5MUg&5x^RKb2@|nmc?(
zv!1{gHt8ZVY*2T)@&gw8qrd+EuDZ=X#vU8B*JrW2hFI=kfi?$l>RfQZU=G}h>8D;4
z;NeC*&#yxaO;+(SlXhJlx|lu<^L6_@Ye2k1=RBpEBpX;O@)&{#9M-3RwRv<O3*Bg~
z3r{7b<jWCO3JD+p32vhzs7rVM00}pb?jF+H%l`mD3o{l!H}_SD@7|%%7JWJ`YfQWO
zHagE3J$9U&=cxcz>lo*HQd&DZd7h>cw5mx|QnO8>zLDY&5Lo!0dz82_UCcqcwq4I2
z&A3!J>PR^)+P#8FB)7MMEmF~=jwsj5`#xMor|$f$cLVgzcqWAnw%h$)Sp?U&n1<RV
z2XZ2emt%|!`X5U5JvYbm>e@^YYAqkzt=VCgJcU`%AC>(uJNxt%!<f$(MJkwec&+;G
zUOtu*yrT;GN&KFlf$27D;r(A+eN0?Lqfxkm-5~(NR4U+?2M0ZQ?^HZ;E;Q{T>rb(N
zD7Eg#LH__ejz}tf2*zv5JWVd4adoHL`D?oJ;neIWsQay-Kswi7b>g29>Dq6REOwft
zR^T+PBxSA|A=$IZ1N+0DTIZvWsNkwqjn`y+#wRI+s!EL+r2PK?%+>H7i+N$;OPx~M
zKb}d*5wHMB8gY@Hz~}I#(bD>V5yg9S8dQN^Vxy3u2Oh&Giu9ij{6qM2;-s|G;*vXA
zRTQeYX#S{4!XKwv>*TeM!ba~@wYOW#3&uk$Lm&?k8Ngl{O7cz*6@;<URVJ!Occb$2
zJt$-x)Zr>}wwkX$BhNe^;A_oZOWXLR2JY6?F5-vySi7C$+$!K7u4`*w(mYLRYbLFA
zYVT`z2w4ng%@Hs;`LUmweS3=f7f$fcf;AW|d`+X>U9Gy}2fU9lOK&4DG0Lh6l|5A8
zgVMPF03Ue%`@<SUz8TYIwryFRZQkk?{lw9Q+}qQp^Y=>u>s~~$IGWhGLZZFD5BPJ{
zser>^uqnew`S)9%L*k2<)@RW4Te6K7pR`Hy0rNj21F70NpIVMBGhVpU<<MRNBaY-U
zPFr9MLC0PSusOi4Z%B&vJCS{6i9OjRL%-Zodm{1qo@;(vpBm^E@2qR~ND?8q&33LM
zKzd*XRpjsvK9$o_Ix2BVx1v|E<5HAV{j<H6tkk}pya}pV_;n+PQ?|Q=O(AjRJZ?z@
zh8<7=-n^s2x4-b5>iU!m7ng4Zz*~6ZW0c(6cIV$Clhd_hwxQz>9?d=Qj^g3oQ5CY=
z#zLfOf4q2A>P}CtYp~KR=lFf6!YyWs8;i-mc{FRei=hNAPhfvDUi=g=m{>d7qgK=U
z(DG@i<8jUp;BvmFp?EvP3E^1h*LC~bOHDf7<VOwMlMT{sZRcSGvoIVU`8ckF#UC0x
zGWt}l;hRf~(+DMFRW{KW@yE{KHU<e|7m|9{q<AO8dPuae)O=eNmBh}*V{0m^fYKbu
zHuX}+_<$7Oi{AzOJ*?Tcj6NNKXz_yb>BYWWW9HiA;BnDTcXqE1zGs8X^F<75w3VW_
zOHS`&^g7h>89q%K8ESWTZSQOEV^71@-W2$kr`h<PD~HqV=ko35v<$GAWeB^XW@RTN
zl0h9o2Rzm97JNR?J{^fQ%g+zNduCCF$Gn$xZy>;sX8`^j`d7`r46XHz8^r6P>I_oK
z_vE>RoT()kM)@2DQ-PlTmE2kQKzM^mYp;lN$$c9_!pl!HnH{miODckPD&qhiN%pFE
z3FVoDqgGdhwO&VVLyf7LVR^39){<J^$?t#iJv;V!_>w$J;kkSltO;hhwpO~+Zt@K6
z5)xT5LEYtc&UY#G6>sAgjC@(4MW@;6C9W?dX9Z#17ncl!YL`?Y8IMtr4@}q1pAF&G
z`~|I8c&|XdZ#L2=j@r)bi6V(61x|M0?%9pJoO*SywzO?;z<wpRx{`fD7)18QS?#qx
zThCrghyE-cn+|c>jwQ(|VRI#q!O?Q1J2bafYhRJ$Vd%F?SZrNQQ)#unj@zCq@MGcM
zhx9wUt4XgeA5gi1Ns>s(L2mInx5~uf$u08}!96Oxo<G&}ZyDRceR9yUT&DSuY(^Q{
zT0jmD1mxnnkA`<T55&E5U($TDqf4ZeHj+Ze7dEmEWmR*zKyi+}#%oUJPVq;DBYO**
zZ|!?yfQ)4p_ZwTT@^=Xbe!0bXRk1(qa;KQ9PqS85fA9}_ja)P-`#b{Xjgs%`{O{QF
z4O8K_fP8iFS6BYU)nmVrU^<bswPYS=IAjhBC}1+&VDzs>ms`^#@C14$f#zw}w&G~W
zVqDCOrOwg8^8<REW37D6uJ|{{J`?f9w~hQQ8>}q*Eu(>q#x@u&6o3G5-k9WfuUPSa
zjeIw0;k#>C^vml#PVylI<%~htMKN+i1|TZu01k75iu#P!H8E7^wTV)vWo4#~d2ihE
zcz-R!s<p8AoGXn){wq5*)m{EaPj9Jsr&iK!uDm6ygL4dH&a=Huo^TyG1-S#hIPX-v
zLE<lrUO3Q4gY{nyNn;dqhc``i5r-f2&p37m9RMfRv8}G{JTs=jtm=;yo%5k+k&gCK
zI8omxx#Jak{uh*M0$WQ6npBltC5NLG2O)!WB=iHf0<%>ym|BbKP*YLvtkdu2d(|*>
zxm0R<t6sea+m8l(E76--@qN5Ew=zu}5D65#aWj*a*go?BIuLp2D_-|bhsAcMS(5(%
zNz>%IK7Fh=_W~jyg??6e_O`%VE_056SBC0;AHEq)1<i+uV;7c_WId}!#G~h;vkl7G
z@18v?$UINu?*hxE$ESD>!aX_}q*-PUDVj+_wl<e>!hz1-=N^^cM=Z>7_>Zh(lZtz{
zqPO>AeD-6S(8@WZgtNL<OI=rY*!!d5ZM;4j((n91;yLVY3{rUyJ4lZm!H^dlBvbQl
z9l+^UwH;@}>uEAX+G}3P!kI55*&KVm>LHKHhmJbuJxzQI;D3o0+8&(xb@PE3l1U?D
zkg?7(H{gBckq^wLjEX!TJYNtzTy1XVltBBgEbJYE$$28>+1-f;I2l-!V{g50{;psh
zN|MLUbB{)v+4f#X&(`AH!x@Q!!$vV~*G~Gszhmz!?H<oSy|(e+h4j0rJlTv%z+}QN
zEE!H4C-}L|dLFg$7h2aeSpNXFq>D_BL6k;}H14bD8}5wsC#kQK{7>Q!7TM^~={^qD
zC5=JZurY?#(+j#*Z~!HT01WbZHQ=5j_`@!mAhWZ-{{V!CQS*{Z+xhp$0FELt8UP1U
zc_WWn<il{qT9M|!w<WEcwe9AS_SLcYd@GB`t2J$|?E7o~00jLN(>^n3P_)tORx;kk
zS6~hl$${=0f`3Z4eWz$z&Yd5L{4iN9Czs2XDT5C!@sXTlF!VL~+2D_g8h4BJDfIhf
zXzrm=I<F+igyFCcbw8bbGvU9B9wpIyF>&CH9@YtNWSh?OX9O5m7zCpK02iif40cB<
zAG&JK&-(uWGAQE|+N+0mOg<z+nia9O)UW245bb9IW;{7wTWIBpILkL1Yvg|zru+C?
zNfPZA<?ioS0ydZs6;s>~yiW&?_3IXzRqfx8f3x-JWx0}A{KuX036b-_$vFUZ?O8q|
z(;rX2pF*4hvo1E{9e+-62(N1=uM8C@N=oUyf8ZQB=2QKpB~i-KinMLtd$v|@iGC&V
zO`VR5uG~P@T66*kqJ}>#JAhA}DIAic01ktiz|y`ad`;1OETBnmaSSsQjkP?pLy?>z
z<=c#7HLc<eB1=CHTiN(_+A!-XdFrPPAR`ASs|@uWD-YtXi!PC(*;$DZaE&F~k}_l}
zG57((fnK#AsaKk%MwDWm_uoUomS2m)(v@k~lalRwek7g-_=)3>75Jfa4OZd{yK914
z;T;1a#=N%Njy<by#6B~!{{V!8`vbz(G27Y0D@}EBFhUmKk-!<-_gAKQtUrZ54Ad-i
z*>%@R7F*n;B&iIE{CV`>+cev+16=7kbkSS`E!FkYOPKO@=Y&!?&%JrozN;K$WepPT
z->dl^u2qDpu#$D-c|BG2)BXwT_MR8`ed0Yh!KSih6d$$1<IH0DKfR5*$()wm+ofR3
z@aM%I8lUYMfHI+xE;oq?j1RsPecnA!9R+YYb&rXBJ*m7t9G4f6+xgcF6ckm*DTc^m
z0U-Txiu#}7FNtk_BiY_tc!tFcjb>nyeW7ife5C<iHZujsp%vxl3lV~hs^Kd8T21SH
z)!#w7I4r`;6G76fle1bUZ8bh$(zU%l+g(W}%fSq6Op*}ZY~<uK@_736#drP&fX}M8
zjJ1=uOw;nw4uV;U`9iPp08Vq<`wF4)+w3q&4vFE-G8;R%k^Jv2KPX^eDgXvH{{Xd5
z@U_L4j4dtnT_)b@S*^k(){zBQjDdr=F#rSE`5u+$#i`*{X1P;N*XX*O_-rbriFA~?
zoLarU{f~O^<o+1F)UP~YcM!6IeZFAbneJS4MH&7h*?$`8F8o?-<dQ3>ZS4y%%pm-h
zvGvOz<CVMmbgwJd_4^NpI-GVeczzKiaR)}fn9Agz;u{%92aYSl{8i#zN5qgP+NF**
z+mi}mP_y&NCnxGoYnFyb^_Z+o<hgC?*}iMW7+Nrv4vZhQy|q?K{{TPGmp~C*d_%v~
z;wD>o?j52y>%5#W2hzTm()>ND__jFiH8!@lc#oHDG~er8M`lpKFdd2JzFXBV?tCBP
zJ8f4?ja@`h&TYCVC@{;9_PX<4^Lz0-!xw%9{{V!u!y_}rlpv3pU5U!DK8Mrrs>c0x
zRq52iCZ3AxYkC;)&(&QDxO($l?DRZl*Tb5Rh<+tq+uclVmcW>V<Yhq5AbXN|uS4-i
zg0%ZfIJCP3X)flJ0Fq!MAO~|e&)r_Q703K6@!Dx(`bnG2zLmW1EtKWC=%5UePf_V#
zN_j9?THO3W@hOCAbHHC?fC5MTz`cn*d)C>eCm9;62-{IfU3X`@kzjC?={!n`l$&n-
zSCT$u@y>$&7kw*3*O?%<x-9cLkC?$51Gr;2-^z?vQJ}-6c>7<M#5btRq}sG_!x!E2
z8yqhj;3y{@tBvtQI^T+KZSQr)Z$4&IhfSh5Sb>EHCjcCRKN|XH!!4$GUdi-N48ot;
zaj|i8E0koDA2g9U1&&8vF<WDCH0WXJ&{CH>R=Ty9p7%UFmX%CyKE5KX5~iK(?|pXi
zIFA>23&WZ=hkdNH31e??jFvX{a}z5w1Aw8q9-};(<NPP#y(7o|60?(8)<v$NVH?DV
z#4@~!a)pWepsxoN<o^H>d~m)o@pe{8CAGE8R;wh1_aEYyJRGs;antKv7s9^>P2w*S
z#cd*%wuK(z<zj!k5)ATtWbw~aUcM75#9?sHF!rAGS2fwLBZ)n(E#-8nd!G%~o?ezX
zS-dTKABrx#L*gi2GYdy;G>QwVq-3)6;ekB}>s>9ykBO&^A@GH~(8DOf)b7&_wDmEe
z$WRA!l56UVuLchkIg2-f2!2+3B;FNQJ9EKNjQ8(cX1SuTgLG+id)-3mbi{C;RoW?;
z*^UHhjC{;^7_J;o5-i%hA1Y6x@88nryF)3VlwoJjJ++g6f%5_QYjvdRvo)ukc_qnK
zSpu_2?g<2p3}-y^UcDE>`%emJmmVnbWNUXUQErwgrYh0r?yBq`eCMTlM}R&hL8;z1
zgS1Fw)1#QnC8U$e8fgy)U~}@Xd>_D95Ajb}zIKbkHzUZ31UFZq4&qpxMjY~3w_NwG
zJVt4asa}=_tQ2CNud7$?eEmuoI{17>VM2V)OWjwsox2}6{fg&Po@u6Y3e4G&hb$vr
zh`Hc#)s1%gD!H(GZ6OsQL5;-xpbk&Wda>_aCXL~%OG{<(4!Z$(l#e3S&P1+$?(o0D
z4<r(6o$+ne*1LNigtLXVf>8GnLV_6Cy6j9~?NRrQdo?k5EL}&1r+Y5F4~~T#W(o?t
z?RT>3a{4d!wzaJf8qC~TNNx1=Zg5B6lLyy071sEA*5hB)7f0~(h*_E1>h>hdv1TXB
zjhnx#cI{2P)34&5J2<138*<Fg8pree%B5I!A}8oE&3fm;&jfg)%T;Z6P}1d&*-?yg
zNDN3x=*o|R+j`(*KDCZvo6@C+jvo)CU)tFB@-7{oCUr3w#d*hQztrbG2w%l{ulS3>
z{vm%l?%L@g(rxE`%_$r>Qcf~+k}KWk)b4y3x6wstvt3%k@=maX2!%%qT?yQO9xLXJ
zQ^C4|>$6?jA2#INvkxvZ&%B(16m#_JT2POS7Isgi>6X(ZH$=xG+}rFelY&<u9DDOz
zIo}Rar3hjvR*j`?8{g3B%ClPas#L>Qp(N94>N|Se_pb@~_8nWm@2F^~Cb+gF+^E7T
zJCax3lylP+<lZCKH3_3#Ui_Pso&2LA11CL~vaWa=N8?`Qp?oG!19*CU64D#1n`uN+
z>Nn9TV<VN$#Lfalry%tvycfp4DDcR<T_VNjZ9dhuwDK-Lcm@Ul1LQ;c^ImoWnx&1~
z-Rl1UuQSr(3DTpJIaHtTZK7W*ejB6ae~9tx9v|0Y)~sNf{>J44nW0US+sF^hq0>8n
z?YMJZd*VNgI**EGO&01&THf4bLoe=SVg2SGE=cWPbZHu=_7%3-oz*5DVwjdUM$bGJ
z+zx$fTGk6s7ee+ryb;}5y7`kyBoRo_MyL2lT!(Igy0KY(I#lOfyG3ojzDLjGyj3dh
z6e`oalGW&}yPuc(X0LnwptHj?l6{&@@#g|kJqu>LzZZO5(j|f`opq(M@>GEkZSpbo
z!gSBBYxIM}9}0dA>;4{+*3ZJ$R$$;qbd9#^GufEur><0r`0vMGvzLH$T^|1cS(jF8
z>Fr`O8(4y>9lU_dKK47~iu2Q_q^7O;p4-hRVlNtQ&38w~o)}F};undavGB!%X*zx7
z!p8U0r<)^hILZh4a(^27hvR?1zX|v?G~F{xy19;c(N=q|1d<$N?hEqak80ukJ#dm~
zuWb-Wq>?!95yKFuvG+sjJt;g#@e5M%RH7X~wcn7KMq*INSB=b1IIj3s<%E|lwA1?T
zapS1Uomw?{{nYhc`+Sdd(DYvn>OK>;vHLt1@WmKc5+Dq$I_DVLeZ^+le$RdY@g&}5
zw}+4+KfInI@gkpj2?O;eyr<#bt>Qg9TrH%ac_cz4h=b)K`k!I$Yw9nCek-@}FOF?I
zJ*-~5<s=wPl(#H7!i?kbuVL{uHHM8ps;4z&exA1OaN#m%Ja0O$B?kGvf05_9=j{Ig
z@RPzbTg|5%m~REdSP)9gjNs=VIKcYmw{2v%@Tiu~&&;t!mAuH7PoEFTaBw#^dBuDG
z0FTCreXGfMw$aM)625abE#N&qKu7N!diJjh@n?kQ@g!?;p-A_45u83317~6CTi(6w
z6!3KFy0s%1$?X3Cr(>f(!Q!gm;HlJYE8Tbdk;Lm7?x!xB2BR#O65Go*=0_utoEBB+
zGu#ULN5WqcJUd~hr1}-YNQ|Q~k^x4?MqZ-6Rk+c7MIE|nleCW%EZbv#HXh&(zl9e5
z9n~L9eR*M3js{rXKtLmL;E}<sb3CS=DK2V>K9}=8V=c#3!`(s+t90F1`;XuckKwuT
zz2=+Z*-zWAbsJeMcOTvPVnrJebO*hDmwD;!Ux>d7@8!DqkKx;Eh*obeRkOp4tEuyo
zqh~nw>0hy*FZ}a=`|Q_&J{_GYILX`R^8F81o^0x_Xx_*F*7ALK!vn^C9@BMgK2I)D
z2#VobJnR@7fap}@b4=2qUk=N0ET1%YP^?!Vo>o;D4ekgWR!+HbtZRC0&5f<8%&sNe
zmjN4b$5q?k0|Pb9*=stUlYMRBzZSmRdz8!*$@~rv-DByV4?XM4lvL*@2Y#oSE_zhs
z3h}XTM!&hyBDv9oM{tzNTm!<lQ}o9}SA00u`o_4n#aUp8JcKHj36Zui*zBX`CcEDc
z+{J2w(N!6i;z%Z9GE6MFL!U)IrDOa&)X-afh8!DfWG!)l{<d~wfKO5Y#ztx<Q8>G!
z7(uD4GCg-szVJqgej&MnebE`>k~8ydKPk$dL8SP1VW+;6Erin)x4W~qGD{c?>SN`G
zKfHPmT-Kz`X)0b`c#1?wr;Pd1G3ACZvBo_GRrqUbrA?#ix@=7?wdb7;%t)-hWRRdL
z!E!$G=PU;}<WWwnqlc7eKX}{OLZ&VgukiIYd|hu2hvBBb@b8&p3P>ZDbCWc|`#A>y
z_u4vF72us2r_^*UN*wN*TcI&MHpn<Xr&{X%DYl=d_(m&BNsG-q>l`o@$4uq3{6{1L
zPb8YT;i%&9zK^C}>l4JQ6dM@*<GGZA6b?%P+Z}1sb)6>bD@yOM@jl1v3+nFiva!<I
zXucoU^w=lWqK-)&r-yR<tai3APpBO$&psLWb3nD$HEk-`rNoiHl&dE@p;AW!xgG0!
z!<SY!I=p%u5x9uSHN#CS^+GZZ+yJLI=Db=vonuJX?{zC+L9)3%T&t2mCnqFz6@>*V
zilrqk#aEQ6dn%J%{{Z2SO7LWt+IESnLnKBpmz$8BgSE(9eKFFwxx6!ZckvqGt-ujH
z{$e>i1U!$(SFn|}F<V_~*AZJ=Ne`OH$8PCZ;1YVA0mX58Mf@7Ci8U*`Qm(eBB98}R
z?LT#aC(|{q2C`KpHne7#iVkp$V)ai$2G3HPPw_Ujtm&~Ctu3IA86C0_rBZMK9+~Y~
zI+nR}uRXkeQ`%nTV3&46v{IfJbjs)TuFls_p59l{^dyu;cIzb277naI8=M|6G1j?{
z9cnP271-L_C_iVJV`d-SEC%1Ec^R)u4~U^Dzhw)x)WKE6VO(W7EqWcki+^u%;Oosd
zOO29ycwJurWtis;pH}DTSvr1`rfR+ngI2Il-^K`(H}P#hHsE?N?TWc^;qMUmdsv3<
zD8z8wu!+84$Z^QerbScm-1fHD7Z!l9v%8W+Q_y6dp4sVKlf_krs#e$J{Eg~z*KShP
zTkbCSM7&BbE?7>s!P?q40IKd8$s?S0_N@N^2KbX#*R{Kgy-dc1qF@#vHY$U__w=mK
z4BpM*9~WF`H)zs9aUc<osrguY9)`0mS#(bm%{`}@5uN`4aEsR`g~99oaZ?8BIayl$
zjFR?lJo36kTA{SlC%3r1ZL?flgqkyj0c1Qn0niHc$eYBz0@b{oDtQbt08Cl=CPo=5
zROA!yUL)~$PuK3(OV%zT@{;LHF-D+*8+JzH&p*n&hg^>4Z3fan@;0Gk62kLG{Ch?Z
zAJ)AJ@`|S!MID$tXYZvMJrA>f5`1vcd?VrQPQqIW?;_OV0pb8|f#Xs!BA%+*`B`#)
zwc|RS)z+ghztjA?*0^?=#B3bo9m5I3sZo*iuNt<R?^Q@AvE6KJ0FmydV5fF7<qz);
zGlC6DWS>*<2Aykk!LGMUG<OgWDYLgBa8JtF$pit%YV#^MJVdI+Cgsxl>G~ZN@R7yK
zmC37XeHzsI8^Jy}(mXSC_j<*nG-Y{@%MOj62q5-3q}RM{uXx)308i8JB_~X_k&JOP
zV5+a)k9a&j)4<JneV>S|r<F9j{{XUTbH>f*+j*urcKQ~K@_onSQfoG_+T6(6I~#b|
zEBSL0(k~xy{{VOn#8*WO4j#59^c|;b>-san%rLmzH6;q`nm4z*`mgglog-7Y(rhj*
zU;$<_wagd*taAhNp#!XPfsi{I%8v5O#9k|~j>0xgI`-urcw3Wcahy*XCzS+z$`9dP
zSHcg6TBnQrOBKGb&0w;`=G@ArHk%`=?&UzvRfy#Du8-m_f#J`DT9%;|>_2SPZX7y=
zE9J_eaFR&Ljidp{>T$(+bEuXsczSP|R_V3<PeEX3gThnAViS(4-_37B=x-Q&TJXQa
z4FYQoR>s{eUIXP>TOu^>y9)<rbDxxa#fism^tvXYrTEuIi&5}%!uDE>8<N{oK#m_9
zTVdR|A-W9n)Nx+`ynZY3MZ4;kx^>Obx`)YmL{G3r%2b`$BWP3Wjw|Vphx)C(zr&@{
ze7N-l(k78E(MxWN&LN{<<K+hka4>716O+o88|xg+%{6^)_1yM&cRQ(%%)YD1XiE0=
zUGMo^PZW5E!~O#CZIbws!Z~EPhst|+<7k9UE2vpU(75u)sXP*Mn(91Z7sA~G#MWAW
zhxIw3yqQYLdv2fTNbYuRkiP5^#Ahd=2d7R~#adR8<EUVQ_UaiDG*fSLb0~`P-I#)k
z7w*gpV3XJboL7wam%;usjjsGVszWTKk1k1L&y^qzl2i}4%0^D%--1PYc}5PFE}Up$
zD*MT4e$TV@=z6?VvrM}eOB<8YyZd%erq_>af5AQ1!=4Rxw!G4{Q4}}&aBxxB#?nWU
zRpac-hRGS?ydvwz9vK&UmXi(GR<XG9B-~1bWdM?=t0)H<=bH813tI6Hh&(N84yLkN
z>smM_8D=Goz>HvSj|X~9AIR;T1I{bOb^id__S0VRAn_o%8g1m4A82SAGdy`Ga1N`~
zbk2CMqQKRwN(wmXJr$FVyKHGZN7oa?(WPlg-cgo~H@&`Qw}(7%G`($ayf=1Zy|-ub
zr2sJ?<0@5#*G3<C4<4M?s_Wkuz9wrLBj0E`R5M#fRvG2f*Ucj;7_!3oDi|Kw#w(e<
z_#5MI6}8T}r%Pw3-bSWpTS&Z};0{$)NZLpo5<eQ@eh~abgH64<_-(04_GmS!50iAn
zDNB^d@?7K<-a!MnJW*br12O*qYUNcWP1)Uh-#;Ud+Ec>fBZrr{%(T}_{{Wdr<Mx}>
z{2Ssw?9Um)J<gdjq^~Mlh%Rlm@K`DY^8oa~VV<?$YySWcZM<`!{{U$CC&ba}5zG7G
zZwZavN$9ec0zyY{#<4y${3_MHBk0WzpKN2ku>`EFpDKVkjGTPi`03Lbua15Qcqd7Z
z#=6bl#GBFKwbPUcVvwpe%*C=sPDnsIsL4N-Z5%FBiHpQjl7!ajZRB)1dA<>>Jo%>=
zs(pX2neJMSp>5_qpQ#D$FU)(=N!CFTU_XV&2Ofutz|;J7A~=F4Suk<tIT#;9jz`wL
zUqjLK4<7iAZ8O2QQ%o)Grn!=P=?S-ZqFj_kWe1W#2Ojmt&*7hjI!x#<ZY}LD8wVCH
zWOd!sh513xsIIyb%Oy@~q}RK<U#D}Ltt@h>2q?7Wt*?K-w{y)tIrtM<xx0(R))ul_
zJKHSRQL+C3UA*CDh^|8^0}b3BPg?o0C)ISN@??!;UZ9b)KK}mzt$vJngGPtLx+I=0
z8m!Ym(XG4?>JXf<#zJtYSORn2yg4<CEq35R;slEPvu;(HM!}w&R~&m$Lpq~893uwh
z%ciQ<`_GNySj!8GP^y<SwP`z^Kj4ik#l9!E`&FDCUB2SN;&c1HI+a}IvFVIfuZiz(
zG;MO*SJ1CnXR$HJw}gOv^*Jm6;4XV(rxogYp1W+igx2;?vfoa&C_*qr$Okx4fy)q}
zdIMFwDd9^PAhp+ZKqQxFkwkzLkKCykBoA8RSlV`zsJ=(d(v;lQXz63q{1f7dJ}2oH
z_qy~(-|YFB;y`vYvgM}3by8Ofp1!r^9}~2lMk|Y5M@F{-8`zH2gD!l<$RPFQh6!Gk
zW=|P6hrCHAh;+Eb_V**~_i$q?EEzapJ2K>v?ZtbahV2fo;ql^a6<w^0yf*U~_n?q~
zOl5e(fN&Xe$5B{O$44v0EM+C`Ni}BLv;P2xKTW`9G0*blm)3Tx7tMPuSNZomzu~9C
zVWN0)4PNG1V$~f(iNB5}JrijAp@GN$0A9E3Z}q<ic!9M@)cLl~1;jbu=EmQ6bIHi;
zc?PxbFZDe>6_-@Do&!E$mu><uJs2F4xa-eK&hXcQbxl)JxV$%UJ+x1-$n0FpAsJi{
zP6+N#71x8rM-uAP#noPx{JYri#$gr@4NvOo+OK=h%=*3y{{RZ=T1t4L)*rOPG5}<S
zu*fm@cXP1h;N)}CzDMzwh&B8DWZGQFlAW24otcara8Ci3p&8G9mD59^c%#Gj61Rk|
zmfl8v*A{XyMP?j^0B{a{h&1g&+r!>CjOo54xwEv?(Xx_7Bx*rDcP8KteMNCmcy%_@
z=8OB$)hAigX~&jJ{{R7I4uyZKSVFHI!b$<*Uk4t=l=GVOTWwE6@m<VTH@2#m4InEO
z?nFx_F}Wl^c~4v#so@`hI<39N-CXT~xeXEAAD%n$UrAYf7t!BN`#QsQ8BUTb0;RhQ
zk}!RRd6?YW144GEQu6X!8q%jyP)@4<03*u2%Pf198D2YlE23sR0msR^8-Vtu@aKlL
z-w<muy`r?xz@?*^mjGl9s!tg`cK7aU*6cnG_>RiiZZ+Gu;<OGDIPTwo%tELQl16(B
z*Qlk&o8bLF>~qAnl0hOYo><7_{LO>an0xRm$IWw^H0g5TmFE8C^Y7?wOX5~(+p+UI
z*l9X8nW{;uk!>WuyOu^%`@M(m1sw9(&N(Kiuf*R9cw0fTirOhw29Z=*#H$MIA9{HQ
zF6WK`Jo;8=$1f1UuXwKB_f3^zf?e}9q<ACD-L+k@)!&{!{dL&r{{RR)I~+)!A%pF@
zt;pWU(v&gh{gJ||jxv6LR;$_4!_%H6zFF&KsmB~edeW${(T_ZIyV-R#^=}Vp9z3#S
zEGB8w?Kao3xQrxuz(r>E`@`0}{{UF<XN0^Fu4(%9gx67O2t;tDyoKbK9YH5QC#gIN
z_KW)+C&Tt}>2TQFExw+tM)ofpS^1du4BQe)ubgJ_g|CS9yRR3js>f^+)*dj*x%omz
zjAhR~a4XM+Dakr;ZNc8l`q1ypXN93k74a!4SzWI$KHZO^JX@vco;~nEzSX5mdz&Ql
zTJr#Td$aqn7>&v?&U&6};*SPgNvrEEtzW<)(lqB)5-`h1<D)UpQ`9Kncc*y!Q`J0c
zscE{?e(KqY)9tO)kfaV>`vSzDnUBy{t9((ipTpk_bjwQwQ*7+9?udf9>Ot?<u6oyQ
z6Atin;e}Igo!Ksk_#9PA8HOUZCY3d4E8nX)J!io>-RF$1H3%bX%Sl{HxPSR(R5AH-
z7yvReUbW!=0ERvbw>MVXt_`-L>vtWPapoWco#bSM?hgkQ2f>R=y=zXn*RL6#JpGkG
z0B&Yd4%7a~>t2a_b)$GsMw3O;0JjpAisn@X=8*PH-F>jQ6{cmF;&Ad*X-}3(S=oN4
z)NuC?;iXIKGllN%udat{{{RSnjc208d*NtdvW`umWsWfH1Y->3<0qfY*UKLhJ|k-O
zvVX!cV6i-QfnM%=hG6c&vK4W_C2~kT&3f*o@e9QM0@mZv?&4%OcHtMzWRfVL$5|pn
zkl<vGo3AImeATYmTWb29{;KnmCwKEQ7ZM{JfrHZn1QFAvW0YWPR_4UwrOg+!-QMSo
z#Jo}U8t}p3^pbk%Wp6|74;6U2&r;EB8Yj1gJA{VzGP_<#l&IVD<K!*C+CLiS{9E9y
zcf^_v$A_;jUea6G71iZYnRjD_3y+i!yaznjnfxHzsm})JnY7(1JVeir-b~zpN&W;s
z*1cz1@gr(hQdwPDxN$b&bYy8Zdn%~Psr4DJKk9WU;w2RHO#8gQmc9~|XBN5AZ`9>{
z5f_Dg8LV6QP6e?S!8R?#cA_?B1CU*SCA~A&xj%^B7x9OKz9LWJuL7}w@1s__TWl(m
zj#cA0M@Gg-BZJV^)}y0ZYqKnmb0k+W;o_Wq@JB<F$;ZDn&uRWY)x1X6Ixe!xx~`f^
zF<b*IJ(v!82d#TE$H~F_IH@T7QI+h|{{SX=nT;AWrwS_ZzNxDxf0utVvhdHvPZW4c
z(XW!pEUxAI?#_Id_gF^kdyL||SK?R1uMzxI@vW|(;z^_a%7ynw6mg_+n~2clhF)A?
z_dFhzL&vw*14f5QvsP*6oZ#|VK{;-EfPX6IhLDSKXw8_dtfO)ye1~~hV34@LP&#Lm
zMp;gk3RA_-KJ$7d+sOGW_2RMExX`BW6}$fcKIbzXlF4Nv-)`M5S)F(zA9OFU0|OPU
zq+8nkf_J%{z-JABuOyQ+U~L77Bq+{3%}r;g+d-hqJ(gAe&;WNC#Aoj_FXVU?>)#1=
zJs(Atb$=2<Zgkn7?8|o<Qr7bZJeYt1(1J72j@8kaVid5E#6p|=4>K&wXHOkd2kkDF
zzky3nzwt(^qCM`Z8Pja-A@Z%@BlnWKpOu|R;P(J_t_J$w$d+4c+jm)2)I4P2h`@co
zcMV>_`$hP#!QL3P(e7;TVYo}%g9&7RHWds&M`g%l<bXzatUm>v8&1_0_Tx{m)Gre}
z2bCnoI;t}=79Fu%)M`?rgjK6ke*Ny6`CI-7vwSWx82CC;N-?)9{cZXl$#HYx?}(5k
z=92zZ&7HrP7YZcGs2f}!Kmc$+ApL7^!+PWp*<5%lRU+S1-R4B|mJ!Y{K=Ro9qa%X8
zy({D|irR*Rb*I;D1e2p#<H*mIAZ+cy+s*+zkzAag9=r*lcy~y#yld-;Zx%~g^wmFU
zDKZSJDzgS&#4sRJd_P)PT2(0CP<=PG&$Y<-o{l2C@Yu+x&Od9typOu!@iv=sf3r3!
z-C1T=wU#}z?Z6IIuuHEQ_O9<w{{V!p*GZ4XUJPS>VpnV125DrE+h{7lj>9;w$iMh#
zNo>4hW8&R1$5AP#xsFts(el4`L?66nAdK<azMq5R$AKi&n^^GHzYdvWsKD8Uu8|lL
zK4f9IVn#dwI#CWC#3`<LxZAz-{{S<)JkRiWTq~_h-@Dmsd;Qk+JO2QRekt+4kM-8j
zWs>Fs=mCKkskcPQAe3w?U~Xf{#}&c+HNMbw4-!dh;^?zvA(9I~<0QX%A3wy$@~=45
zJVkNgZ-n>$Jl0}@TZ0tyB0EUO50HxC9dg|D&OLKpTcrF-(>@w_4@%d3OE25*?xK!p
zY+!i&>7<l{wL!^M8RU`1E7P6^txP(NCGRiCx%nO^7h4~Tb!zCO?4q`c{{V(Qgz=V<
zbKt#d%Gz62ni-Hw90<EqD;_uWImcmNWqdET(mXHVIkkOnPI=<hP%Fh6fa(X`&Tv5V
zHRe|yHt@9iEta>a%@anJ5njmBI!h!mvIbb0h~a_xanCiyc(3C>i7e6`X5u?Y?I*VZ
zt)0rq%bczh`+H)(cN2>G>V-C>oNs%(EjxEUn+L<S@N^|OS@UgV?$?>-e;588c&ks2
ze-?Oh>S^@9H*vQzGtF)fLf~)|dgm4Mj<Kt2ui<N}drNh?X;7(UE%J<VTeswE>HRzQ
zwYRXnn#O%5IIS)hWXmG4jga;@Jw5iC(C~Nc=c4>m6Zo6LdYjnVOj<>@jpLLSBRJeX
zV7c}Nxno89RTrt9Dzzw6e%=l~eGi&G1$-*8_^%Pu?<RdAtZZbG-Pna+F+n`%8-YEm
z$@Ogl>McREX=X=~H`yh`ahw1TEsnyv{{Rl^I$S>y?z|nUO$L^hhCwCNlSswniy>47
zI}~S=j<p`GeenLqJB>2xIQ2V+CBEKXKr(v)oM2adY&}Kqxm`Y;j@-hHPIxJ&dww3L
z1F3l1NYWL&CE*>y1;^U19Og;exxmTCpszvrNpGuo$H$hiX|cy^92k|!VH*&>rH(Rv
zE0XY?#CrAW*k~FplY;vszVew76aA@v=112x^*4Y%8TeMi;tbvn)-PpC<7_SxSmaQI
zo_w%{c^i2LJl5D;uMt`@tqM<;I<|5w$}|(D&+k2v={o-agy*}|{{XlAM{^Lcw-`u~
z%!)mFe7Q9wKM<`n2;1y7>1_ZQiWtTve@1iL*jKOLX&>6!HQkZA5a`+zUS-2JKYJh<
zQS=_2&3rlHEpZY%&0Mzkc*_ENdU3^jT+y$~3aZxrt?F{(vpFd8UtjnJ&bzElZx!!}
zuNq0_buBJfecwg&<GpuZ3orEF9(*>ugHMe&OK=p&DdYk;IULu3`14V{hv8kuwH$0@
zdsOoip<Vs|04nqk*@NPjh5R4jd#gVXTOwUwG-ld)9LLadLviwsr1TZ(W|((89DRqy
zb!&MuZWN^I<6pJ-Z13pN^e4mr0ESvWhP+*)+}~bpy8h9%Xw=5)jka|8QJ4+b?r<yg
zugi=6dJopWEdK!EpnN;xuN(MhMzg<;<Xh_&R`&MLI!Jd*a~NhjMnD}&Ccj7j0JS9l
z0G?;_{{ZV$Lj#Yfx$9JIv+3~>%W&>Asli1hweQvM=70a!@^296H(EBHn%9P8H<o~J
zQm4#DLU<s7(0bPk;#n;`N1(@csJY&v4yA(<6sM_U(yaU$@s_LMjcOeuR3zyW$*_V?
z?qp-bAqVAB+&JmpyMKzdk9*?btm$zGB6U!y5C9ot>g7iph(Cz0GneZ5gQk`4v}YbW
z67hITwPv2Z^*9u@)_f=7tJA2H41{fkbCw^&>+is=pAA{vcyGeuZCVL_X$-8fV7JTw
z0m(dj^`-G9t!v^-?K0z1iKDiLVI9|-g$6^=kUxi<^ZhHmc<vH*((aYM%E5%C&R1_x
zRfj>vOx@Myw|bm3=_J}uM2-e2rPeQFd*3QJ;C=o~ZMcFtP|f#v>P2;$Zkjc##MEN_
z$a&Wn1A@hRvSXnBX1vnb#;!%RukMy>yhe&K`<{nBz;mBk^?O-k(a|J%gjUwT`DSMU
z6b^*?`uqJW3}h`HT6?p%E}E3wp2+2FBE0b*j^66=%fq4;F^LZ1(h_!OwgLH%)84J>
zklS5&uGYl@xVMbUa@oikKO$$}l4>swYuXL>is!Y_7G+uDCRw^Gx{`7`jQi5)u*s)*
zYs9+CZI<fBcWj<RWMpUQ*jAHjq_(xK_?pA_5Ncg-{{R59;oWW#;~Uh}TkP(zBE$zO
zz1eu~+#H(qE8iIScR|x%Rq-mKqW#5HQqnd?K2gr&?_L|>zYf3lRko>jb+$W|7ZbAm
z?8tH#ap*bQp4I3+JMiy@yhGq|ccW!zwsms^jgk~~!R`s@L9S}}drzKQUd^MTGgGI_
z9oo4!issQiAdMpDPBO<7(KGz<h}luQV<Zv?;EpTIEOhJ74r`jT=?!fs*~$iv>}PU;
zka6qVHS7K&d)qGt!K}1CUB;gjPK(pzI2(BBky)Chu94z>GA&TRMZ??uqS>+mVb^P8
z?{W_mQmO2%6@FXU6Do0&ZG2X{JnvN3JWsE}n%%?;r@^NH5u(bLVn96lds93q;E23a
zX)lO%yO{}=Rtm8Zlsk_sdFQwlrQ&^XMd0?f3SN7OgGG);Re>Om;MTW=yfvt37c%R!
zmV!ID#Ghgv6`Q&L0Ce>HtG0Ee3N6LjO!D0&R}i_C(N@j%`Mg&Km8)Gm$!RL5C*%kQ
zLogr2N8_5Ot3?Ng^)aB&Kb0I(`SCdgmDWR%yB#spBDzb(@bniga>e}jn}3pw3%|?D
zIsOG6mBK!x*V;YywQ^bDx0EfaKhDj$@Ue~9!t>LuP8yuCFPg96z0RqrQT`kI_Bu#)
zJvH@oyS(xwzqV}KseQ??@)vO6k<@NEuC_1jYw5+;hbLxdjLequs<GpSF}^yf&2auM
zv$c6GMx6pcrqNBpBH($sKgd=801q46?K;xR!s1JNn;$cr=P}5|vOa{6MQe!5YSP2U
z5eDq{U+~388%<(mhMZN_#SL<6tKBj^J6_x#!s%|4%m*huM>zG|E0u4EH`8hpX`UL>
zXT5bLT6bWPXFW+i_2?J=8`QPQ9_PfDZ8(ie#EF2T1MeJlBC)Ks8*5EE%^o=6S>$B-
zaG;dxc=hYebK&bxlagAYVrbLn_O$n8J{P$0HklC9ZiTJQ#ni0frOqQ}8Da-@9{8?{
z>^ddpq->i}S?^&cNlslQ3<u4VfyX?BJPN{-T9(Gf=Tfr^_PPH6(QMCMq>+~cxdV*)
z)-nxCK%G9yvKxzbF7q~6yPv$x*oN=zTSEY`3G%x%jGk#MJfkMAx|{d@7Sd!7b)|tD
z0vbDuazjMpvjfP<?_RgzuO4biX9V`;Oz<j+;`_?3FgK|F<MlY;9M_vkc$%St@R4s7
z%v+!Rk_ir~dXbTkE0Xau-RY28>mDDS;JA|IVY9|}x^-Yj@d4kAR}!S=-8rpxJyn{<
zpDkV1jQVtZVb{EHZ{)Jhkh*DDE^Y1HmSTEI<`5rg-~bLk8s>EkS}jig+5AM|bn8(c
zp6m;|g<`=*c`S3Zdm8fV-CM+d1n~rZIfmEmFg3h!-aG&p<&Bhif<*-F=ao45an`$?
zPsG{{#j<LC7qt?@fa!U9${~+%>Zll*Bpo_eUKUj8;iXOxN=s+l*E+#{e;G><>NicT
zRrd1oJ)Z00rmwEwgRWaGzMk0ftSyydB}n-Mj4((esLnlV#l_w9^8Wy9Xkh8`E<sB|
z+rs|<afjWIcgH@}&3I2kwDC`Z2Z{A#gJ&~aN>xiSz}%y5M>*-9)xD#(m!@M>*Is*T
z2wQ4Kr9qZZG7uFk%=qju2XkDN{hc~+aBtc*XCAk{pVKQWp<CP4abLTGx|Wu;j<3i6
z01$M$FNM~vt!U{D<PxlFJaKs~G;9v>j)8#WW3QmDH^QG1A@OdP8Svfr_Ovid<?o4t
zUfQ@^Y?db*q==3?gN`cwuaCSTt?E<V>$+OVgu92nncg%&GJa4%_aS)qteqpnad?_U
z@qUA2X{MOmEHT=^zGc&rFo?1S!YbvN%M5Ti=N&T2@pLe=#MV~wy1u;+r^tA(4PLx3
z6#1nmeQk8TUG_e+@i)Ss70cnvE6plvn64&NxQa+zB=F6J-G%I<o-vGYMSRn&-gv`E
zzPi5FHTAl?>cZv#yV*G8qbND`0=|mVzA-O`{1Y5Hj0W3NH!?J6P7o+u7Gk*yxfuw#
zCmHvzkGw_WT`S^ehp)V6slqH?9Y$R}gkbD^C}fllb_N{sKSN$_0)}HkF`p}iIcuv|
zyYjx@E~f=N?++|}T)&-4q;9nT06kaYdiQ~TEH+(kv!~vomPz4q@kz(cw2%rnJga9t
ziLZ?HO%&hwt5P>mlV_?ElqyG+bfmO$p#WofIBtWS*98ZQJX_)2R!B6<n}{PUVQEMV
z3bsceWln3|{6FFewHMR1KN4vn?nusIlgTa<Ay#PcN6avCFhD%l)lkA>vYL{wPR&$l
zyS;DM<zwVC{A~<dRBI-s8|uFUL%?4byj7sw!=&p7n%qIQ!}hJqaG`!&2}XWpBWVn$
z*S&N%KN`Fhs%wv^c-O)|Vz-O$-dp)vgsPzRXkIv&`>Hb^t$3u@w$_@B)#Ontg_W*k
zF*B5n@yJ$eWcD~1HJ$doOU4>RJ|@$L+4f5%xme#0!~jO(diz$hz~C`{^1K`Er_A$k
z{8OA^5~Uiv$!&dpN9d-h@P|Ua(c$r5g<00#;?3k;LqaIOXtt3?Lq#Sr&;zxy4;ZhP
zZSQ^(cz48+TPCCZnRjaAWSWEp31QCfHGIGhardjL_#g2b$NvBiEUvsR`fS&_OtDJQ
z#K?BV00BY%zA;i&mz;$y_kiQuHP83~ZtwN$zY+XR@Z=3NOh}eXCNZJ^0CNQg)aTa~
z^N$OS%j?RPCZd!tq@!!K=jYJ<11-)mSm>uJe6sB&`FS3f;-3iKc#_`cZF&oxX>Q|L
ztnVzQ7aJM4S1L;Gjk)`rgI_ew@a|s-_;T*|SGAcnA3I_#@9sc=WaU&bVB}-(bQSD>
z8NM!fks-0sd>b@Yv)v(8o)z-U5UIz?6b>@S9+)-qpNM=#apDI}9WI&TmHe|j&$zT|
ze|sx49G%~E9{ktP(89@KqfyS$=(O+D`3^4noUtx6qx;<yo%B{p_PG~@?IG2oeOgFT
zD=_57i}zb^1&(_2(!B=PRDmxemh_12SMQd<1xV;Vwaef5mrao*x6)%TrRnK}@`e^y
zQO`ZVH9v-6hI@6_wNSEL%uK>I<zig?p;xAT&2VB;gLS0tcvx8THAyQTiKqB`z)A+8
zbEiiBWSgzwxR9_TvGT?;Ki)X5*6ZT3S@>#i3h74H<||C`0&NgWFHACgtlWBW>s~*o
z>H5C4KlXN<{JMj6c7E(3x!9+>W3S`|YFlagOj>eyE5q@hCgGM@rp_glN6BOT#mL}#
zeQO`A(UVIN??>|VJsc<2F!NO~a^;dYbT5kc31Iq9i7YMki1VHElCGnt%vXc;uS4*+
zh<*^qe%Fh9G>E5+8El+7s=6P&ziC1`_XfURy3=kWvDGY?v}{ph+`rxga1W+MTQFQ*
zUD{ei*_MAP*pZ!}3`xfz<P4Gc*QZ{N7OPd_?AFaTUK@z>N<7pm+G*~jul`?|>pm&*
z<Q^f`u4V9)>nEKi(Rzq>BwxZ4I1G05<mB>Aa~i*gJQs1J+)bz%@2;h`X(G9~k#_<(
z1R^K^ZsV?gwH}9QEOxWQWe(`!&c$Jjl5>s-!1TvTt99YsSZEhImBW)Xg(Q(c;Ui9p
zx$HR2V6nI<R*ZVy-lgO5`IaJsceE|C@;Xn2KM?*Q_-@k6!}hwfC90F>3D8Q`(T2<U
zZh29&I6U^i&3zrIe$jA6db(GI>>p2#Se8R2<N1#qZO51w_*w8+Zsxvw@IIb&%X@pP
z)DI@2ce><dVqOy?`-O%`?_W%KW56C1)pTpi&0;OF&KrHp^E|Gghblc3U?>8-jMlzc
zPZ@f&U#i;u&!yq$Vk+h8a<8g=dM?L7rF?kMHS1|SH6(IgBS(vumCNn}9P!TYOkn4w
zbG|e_A=ErPB(So*n&RevmM|q{jx}GAbA~(&bv)L0?A7331o(f)T8x_I+D!slNg7XZ
z%N$}r$W%s20iS3&!LFl5_?4^Qe$e`5zMXLJXfR!yaD~`;gs;l%bB<3;iu2`{(3T=m
z!9}>mb2jw7*|kjamKrdHRqs@KTcPG(4m=xsCXc9ihsH=?GAXx!P6E0G<=+GXxZzj6
zb6%^U%`Lx#zSV9fl6#3ty~2Ye+j<PQUB|bjdXA~6X;AAo-YC~CJk__BMlCrKt~2uO
zPr4bn3!Jg-isHO~@e{?v;qA@!w3ixHt<r|IHtML)hU9MmX9Mp6jP$OVP<3k4rHE}O
zW$E%gI=QtRvkA<psXgDD?bPy3b5yw1bx-&}669Jde&fkj^JPV145gI)=`eZ|^4C4$
z--RA1@TQIZqv6C${UQ=P*<u?eO{b)>l1}Buaxw_-ULWBr>mM2={Mwv_Zfx$umRBht
zFOsq&ugs*8$?MqWy~9cPk?{vewz}}0uBg`uaSq2aTP)M4=O4XAI2b>8<eK#HSZHCZ
zDs*Ld`RS?i7_9pbo8UQdblhq!S1!#SOLeMAZ3VTo@ou(;d9H0GTy$~FWDN2f>0d{9
zdqdN_Pw@Ls(!5z^6w0%`!C9nunpHn_S%<GtUpZ-BAJ*)(#?>aci2&<s&e>UVaNzK)
zeQWB$@j@%jHDT~}f-Wvz*-~b|B*g=j#^nGF&EL|vG1G)OX~iqv_rAn%#uiGf>s4A>
z+_vA={{VrD;;$E{hS7W_@b(*Zy($t5cvmr7q3gdqGkPAUty61FTF*we(yrnY>N1vR
zmJ`Bc<ZK+0!`F)Cyd|L9_<zJnsO#5gvQ59wx|rb>LZqntNXMY>T^sytx6ov|9uv10
zHwG7qd)Y*06_L6a&I=NQ(*V~e)oSAEIMIdswta>-Ii;4+=c??jqVH~t^*9}8#aFji
zH@Z%xD&2Vuma>-F=K@ss;IUJVI%2#-R@HQE0z@7syGu)3rYa>ZlKV*B6m=&D^Q~ER
z9V*7k`|W{dyPDu#eWA`t&)(-fPc`A+5xhy^9alnoZ6@m8>r=3~TdR9Aa@%AZWWdKE
zcwz^oePvusFt|@)ho!fd;?K#jczT(}Y3=cD>R0)8UnA;IhJP2I#2Vj=^e+#|w@A}%
zk#v-abG0DDw5~CexDYUX>!t92$7@d#=#$>0Oxm4{!Z~cAJ4}F_Z%()bbBg@&_;2w8
z#2yIoE$58j24}fviuT<ATtvWq)$dVwmt3)%QhykDYT=UB;Yg98B_xH<X4~^@J;!6x
zyXBM8#L$GcpI*n)<$Ob5E~`8=lbt)gHfzxP&d>IR(X75H>Yf<!eCF3ocv4FVW<lme
z>Y%)3lagDUXVSc1#-AQMCvoHZPZ4-t%1^S{HYJcqiq|+ior5H2pS*dmi|?-`zJy6M
z=2dbTE|Irkm$4_1N4;HN6Ip6@1#YaO`(3fiOK<zPa6gL}+H>Bwr;=ddCn}oVSggZ5
zsfds6)t&F$`xf)|kno3yekDO0!z3C!=&@#8dA9C<iz9*w;Dg$}ozS%p3`sDS?UAh%
z<~{AXr1T{81JDCsjyKcT-Q2@=idNcVu(%i~-r~LcMEH^7EiXogNxy-wY~oTPxPaxr
zAHwV36~&IpDO73D(%KW2=E{ZRTdTj!{VDMVp`qNX$!BeEJgpws+5T0G9_z4k$Gv%^
zx|M_JKWotdY)iGwXLLb<!+n39e4%UcdqtB$TRE<#igOgGW5X+MBjgzG&!tlEKgT^Q
z!2bXVbuR>Kuol$hm`!G}5X~TVB!i5VBi@}HwyZGnrK5}J)&6IrEOr{Dc-3+Dms^ef
zsPCTR#1<L_ikU*&nVBG7RksdEUNhWgx?hAJw0v4~TlhobQj>QIgtgQaM(I!wm&^x{
zzzk%5HS;jkJXd?DPp|lyVvVo4OgY^Gj)+Zh+QrSTpsj5T>?V&mW{gNN?8CKswetEH
zj2v*))$Q_sCV05KT<2O+Pey$S@dM+n&xv*Sy3=juwpjtTPd$SdD#s3S(02+t_pdR%
z)NOCI+iiDExO-hn&=ihrD+dH~y;u-OUNc@*ZKP`tc?(|5<s$`yjNwPHJ<Vy!Z9R?3
zl$pGTBjzMCgY-PtW)7V?6jcg;3OFH75uZAGm!`Mz4u}KEA)YBEGF%vs%)#3%bNnRq
z71(MPz8&!@T)u|{Z*GdA6^0~UISjr2zO~Omsi>AnuuDib##Df$4^VnjEyTKg+TU5D
zqvsM~lMD}D{{WX2*9j=ybZ2c!yzyM!4^z<oGV8Zr1LODvmX_>1`Eznqnf$HWiWVf{
zLE{`(6XAUhO)JLM!%;xakhHO$fH+>feeIxNd)GgpPPf|bqjhJSY1eO-tfADeQX2=k
z>s>X~{JNCUw8(7r2ziCc9$S&v6VpD1rwtlZq^6d{+ud=BTAsV&t#e56PJwFL+_FYu
zQ519Ubr9pG^<EAS89geNoAD0&PIl0KAV(k$n=N%{LroDFz%m8g-8+nrVO~7??y;u3
z-omO&^E5ws9FC=Z4Q^@I^UVt0S;%~;IYb2Q=cw9xeib;ps{xxvO06{+EpC;cubJCJ
zF~()`PMq%>Z1w*DhCaKJ+rf95ANH;OuF=A(h@WPIdXNAIG7{MO5G&60={z~4G;i?x
zR=E?&g2pG37w6qd@mz1iy>C{x*Y33O8_aK)%#I{ECPf%!+0$tp^Tm62hf~9vMy8j#
z)`@LAWqipN1uH6p*>J`(+3kw_?=i@yfSYuxb9PTgdNAS)J$b5iX<5F?==meX+P1sk
z-E!LVREisjCY59pVT7AW`GXvg$K2Q3e+>Q{co@vSBk`lN>CUS<EQp>_-N+5OfI0U3
ztAqHRr+hj1h2VL7OQgyST7Ko8HeV`u{{Y!&*aOEvM_Tc30qT0rr*8{QG`A30jK>s=
zN+K`%wd=P%$i;Hu<yR99N^#cqJ2QGzWl!r9m6BIJ{rJ4l_=d|*vhcLlntqXQlO?=G
zV=ev7s<<3==qtl+ZPs5F$KxygMtN^7%K35IGN#3aa5rQef;!id%c%I~&hYq!cIpvE
z#jY)7P|2~nvZy2GUezU+iuFxb!j~Q=ut+@Cxoc&DLg7FI<Yxfnlh__B){D<+QdB8u
zmYQ|-zUR$g{{XRK<yxz|^S|Ap^tXt9Ax|fRZ#+F`9M=B;+CW@N9zi2H9D=|%$`5M!
zC&U`H)~{;z(Mrh_N~<F`1+aLHKm-;i-=%sUfvIU9VE)nZ1IhMz!kMHIyvK6&3?n;~
z1JD7@XM91|ejQI?XLI3MZb)W-GUn-gq-i>QvE$_Q8NsEKO0;p04~m1<`n79(&g}On
zz{>#~#|t%eWn}Mr`EPU1qVdm&F0@TU!S3!^q>P(|3zS@PpD@P)xM3cPWX#u>bIU0N
z5421imHz-`6bvunQ0uy_^f1Mt=xP%3@>X9nuGGgYJx{r;%`U@9&@JXrhFh!@X{F@u
zJ!|V}#x(RkZwXRV)zh{A0Ig3+{hoD5FMd1ta>f<eANJOsaJ&Py<}$rG{44q~@)P{#
zzn=d9*~3ZG^xuqr9=erFw7TY><;Z*g0HjBp0q<YX{`h?<SH!m^snn$g+eiP<@y{M;
z@aaA*zp}NAc`GWij9>vGh9e$;=8Id|_1!j44_+$yp#im&lBaV0)n@$zeg?h&09J!T
zO<MNz#m3l8I;WWn?u>6vR4F9joOCT-dvC6Ib5m)pBb6hSo$(8<#Z)H%A;}mWj%&lK
zn9{F8Dm3pTm7V&W>o2Q9yya3>i*HMBbH^;ApIWuNv$~Ib_Q7MGIS0-#agI4S>}vCB
z8lA_7TS0|f+_3WFahw>(Uzqm*W8S&@%e!CgJDE~xV7V^J_Yi^s0P(m2Hv`z>x?L+?
zTbH)d^+W!UM%0-`?Zv+7_YKhd*F%lhv`c+X2Dxt;JywT5p+z-~-OFC7N#c!TSli_|
zehD7jbrsv{dIpiG-q>8<%*|_Y0-|T4N~5EC15?YUTHnhYdWteMXKLIJyuQB2IOjFZ
zX*$)Ou>|%+hc;3&0(dVNZ<X=-99BI1@%FJ>8eyl&K34bJ<aS>Uv>SUFZbJnVK<$8g
zf&Mk-15?%_(RIr*3tHdYFqN^&I-UU^-DSt)U6|HnhVm^(OJXL6A(6j~58*vKbTzom
zp;*Ip;&xGSVS5U@+eUt6mxU<aoqEwmZ<@_s=J<$nJspjG1_2h6X>V#{Hg_;V8(b0@
zV=AOFI)Rarc{SvhHyZDSqHPvwF&Z7pj=Q656Y~Oj0kPX3O4jhin(c_b@lKBuL#GKx
znG5q920W)dNFB#Dx#FAc3&l22F;<0ym;-`iFUJJ^^d}s36|$_ORnw}|gZtAWyxmF=
zl`F=icI?sJT<X#IkHhiKPn8a#YY?6z$jCf_>9qImD~0hy*EZI=eYLag1Q(BQ1y^<y
z4*faBQt-$6BoC!uleX62ZY$Iy5(ppeuq1tJ(zP3}4_=A18wEOnwunfI&Vl2>&JnS=
zWaFqc#a{_h6*(5qW1|U=rBa`^dNrdlH61HV(e+zvNfJA$BG{<xHvuDj#{-O!)SAli
zABc4srMCXXk9@cCNdgz;LPvqX_0CRfKGx$z)aH(7o?9tmee3p+8Fvn<S#g8)6>n3y
zw9+*_0gFc^ycZKpC5iV24T3<&xu-bPgui(fHByw9?w!ql4d`&Rw-d$-8)%Q1NFka`
zWbSY9?OsKuvfcRiMACI7NGt@+a<L%5cL0&NoaB@FSFX?E2l#!V-+0RY2fF)oo5Tp%
z3Qq{6^Vd9@&hSo=p?pm6rM|6o6I)*-@;>DtY+%HU9+<^<WzwliGpjXty%(vnRO?PK
zy_(nYF!kL<bblHh2VXe0yJ7ZO&<w8gh282i!+r*z;yohkM!L1}9))-23vy$UG0587
zl0eS~-`cvb6?iQ8#9OSFF$<kLB3q1y0SN>K*iX&h=}qw8g*+vvt=6kM9Z{eSzTut1
z{G*&8#z&wztm@<2gek&JTX^&%6M}0IQgtG=)kw{g#IoO7V@Otx;m9Nq3NCou<bN);
zlcRWQ%S)ZLNtoW<E@as_$`DV@wLN?1tx=Cx)Ab7ju6T9}i0qPVx-#T04<9h#f$!^5
z&vPG@V{DGw%<DT#o>R;?+C8z!tfv`ALy1(2S1N0gMQ5zX;z(js5Oo4I7zo|K9XaYf
z>wj6*Ep<I*G_52%+(y7Oj7p-Y8Tml!bJUvAy3ym*Y&9J+<ZUKwsN@tqQ2hz~Ij&|T
z)wG=;_=i(PhVITpgE<6Bt<d%A2U;O0*Y^|E9MvM}-cR9V)>_7rmRI)L<1jXtO|m$~
zTa_91`Hxdvg|CV1?tUOz++L$aZ43q&6^?d0f=hA6PH|dZ5{A=IlUCMcPc_8I?5)#l
zp1XU1c&^UI-$at}Ce)JLNYWV9=0JtG&J*Z55z@M6Ds-wi*|uW|PH|kXL#VaYblp8)
z%$-ba$W|cj^&3Yx?~3uATUxNyb&-E;7TUty!oeOessSaW*f?i$eo_4^BW1Ohh$FiW
zMVj15H=p_C$Wnf%ueDXMztaY{8%wfVT{RnFWAZf2N#1|Gv~Vk@3x$jusTuP>$msgy
zoOvf3FTC!f@ou%?-BLYP!e+KVd&rb|kw~Nvq0s!LvHV!b=LWrN;r{@S^v@hkH2VG}
zzY$>Bk%(iBjtPZFL>y%O?^^lxO<zv&51j?WFboTsWWXvHox>aeJ69K`+<21C_S;<1
zT10Di62Wl;24y^hmFiK4P(cQ}u{aoJ5S3K9YAv6p+Yc|z>gBTbYu=>yi}OC<PXeD5
zc!Kjs@Pt!M1*~f|!=6NOIobm48=R636-NWEI@ePkrDx$78WPtrzuAl^W(79Fx!w^@
zFr9jH>TBk&4*YhLOVt}wytR94qC(Air^xY=Ffx3+@#;d6S~|DIt8W(TdTG^lK`6A5
zRtWBxn4TFMYx!>=`M4(>@N3Wdu0Fmpn#W&~I{8Z0?f9MRG{fL9m1<>*)1IDc{{ZBB
z4~XxzeKH^PxnYNM$px;b9GlkWHz2B@>aDXWxOOFdbC>Y%R*OUUffc0Cs_9pAHKo<G
z#ga93P0O99lk3;kuXxA7_ZJsUYkU$UwpCja2g?CM85nYM800^(_7%eT*Ht$99o5Cm
z0z0X$mU4g`na2!N^&pCMGst7Tr7lF%+3T_Ll=A0+lyNbeQtQ<EgIV!!!O1kuMjLG+
zXf<^OEQiZ14LLkWjl%^~^I?U2hvE%WQjSR>FFsVVO(QPEaM&cTb|n4d-nQjB&Yj_<
zmKnrvY=F-yk>;zp3{`sV13PQXJl$Kw)0V{73^?4|oDX8$)>&>1C`$6u{a@sEV{tX=
z&I(@hx6Z}5wV30Lp=4<h#?lu6VEWa)8v9Mr?Bmj`<XBC$+TMhH#Y}^oF~I~8!4=(j
zPfhTLh6Gahs?oJ8nE@gw@FGvC1hE6xiu5lWc$>lgDDda@UxqZxdnxpIjl{D;n`CEl
zF%us7A2v>L#wk|CFKrAer+CTPvszhl_A|vgmYj7-cRKGDd{WYNyH(JvVo_zOqAc)9
zwq!{F%Dgiv;|w`djyl(r+4$4M{t?u^)uLX>9mo2$iI}`H0sJn6;gFs^M`K<oZEdPu
zYq1RiHJag0@&E$^Czd0M*70_?t@u*j<4nJ|R=B#4b-c(y*js`zj&OMx?_U00LY4+K
zFm)!RrK(og&oj(xV||t`3C3~JwEpWAwJ#K(9cmhuzv87fqC#yUTR>J81MftmB!U=|
z*P6|L1!?zp3##i)<;MyDwyH3J5OA(R{74nZ=@3b8b!nljra@<$tCY@FWd0C+Te%h9
zCBCJvI$KIiHkmS9O2-?3=L6f<70WBdX`)>Y8ncy2xy{}6Wlc|3u!bM(+Ys?1CIX0X
zn@H-sgO9@%XF+X8O-OI9v$E+5MhM3E`;pfj1}jd>N3)wu)I33Q?D4MKJ5c8&42*pS
zdJ5_FaQA*5v6j)ZHH$U8k+S^CTc!u09V?!sqT^<ZrwJ!`JxR3tq@2TVA^S8=WDp|~
zRpcBJ%sP|M8tAkZ({vV*=Y=AT*JRSkDH3(g6dt)gmCfoF*4O&gwvS}R;IN5IqBSIk
z03Cpiq@0p_RqL%&RBPdHU@S?;%{9i-%1=-fcNODGf}EuehrJ29N)<J~Q>E7A&~NnV
z=e<p-v;{cBZXGd>r#($m(dV(ztwqJrUo3!p)x!`m(5dJ0u6t3py3}!NK1!hjEAoIk
z{${bK(&v`V<yeEL1gQ<iM;$v=MuTzoX}b;2-u9N%^msg1qDy|dbVZ@KlzEYc#>od!
zwDx1@Y8gCDp+H^rD6S)hbt!P;kfWyUr;(51AB}v$;*DJ*@YMJBw<z;ku#O_EH%HEP
zl07<uU7gpAtc|=Emp1{{@nT%Ja#Su)x$j-H@`_jH>SFT>t#aE-pHo_Rb+sto;?2Z1
zcG0dSOfj2k<yJpWa(aDh=?@QhgGAE48rghBxVnZrXp!wMpf0=o)g*agN#w{e`jf?c
zFaH1u6L>q}mF3`&$0Vw4?S^{0aB=uo&G%kA)BHcBOR8&kOqS6w^5M?nT#k0B{x;xe
z(>09p8RMnRS^Kd)TsCa5RGcWr@{#lh#eW+`uIe&PJ*B3X5rv-C-rb3cB5)OpAL5Yr
zPyj!Tbv_gE5tVd}N=ZXaaU^I6=Q6}VU~)R~$*+gJMe#pF@lS^=W{w%HZx<HWkr)pp
zpCb*7AMvku@eaLf;V%s6mhdT23%%5rp@#xR0|9%n#}(*ibe%jaqlS&Te2*U|qtBSf
z5si1Yy7?b$d|dGaS}u;)ekaspi_3KKZD7KJUw~ByB;z@A&2jpV#M^Hb_)g2h^XjnN
zLuDL{B3VOi01~7Qpb^D<FROUQ{{Y1|mow?i<`-BIB)H|ljFMQB(>dv0S$C<whPA0%
zNX^T>NfD42<{aenT$n74(We@<q};yy9*%#WeRghzI3*PHvEAq%HM!7!Algi_2&}ai
zXyq;USxF=FR>ggpWvWXBsIazwE+dv;nH$OV$0T}Yzc0K^s_Iudb+X)9`2x|I_ixEN
zj(Pw(SJ#@y#w|C){v5Tt(Jd#xyK88jL#87cQORI=KGoM#4(im^o)s9lrKj9`#pbuC
zL#5tpZ5oJdqIqZXh8y=|AdtlJO?uD4e~YW}W56)@yHH52?vv!22=EmE;Z8<5D}(jT
zeqQT87i_hk3iy&antN?aQgspA?J5G00Uns*y1&`y#PIk^Yp)Ay@)#C<G{^xZK#(7h
zeSgNfXOL5?g_L6b<*M`YK9?xWT?)xm_+F3v{zuh+w9mzO{28N9VWkr`qka|}Xk!Jp
z2N~bAdJkIphr^#8?6kiIwZ6M)JQG_tl`^0W9E=c!!(<GpJdaxBJVoM<5_peNzr4{d
zVvbm$`zco&?ju3{z<FH%0DF;&)bQq^VQps?o8^TuMYP8%1C!TxIRxjN*QrMd3@!#R
ziqzub3`{X~TbjCCe_m$`ckw3s#xe^J59#-%raoFO&;cg^cg1}X@T1_Kv!z*H>OLlG
zajWV!(p%bFt|am?f=2JV=Du;qO7qVIOaA}~Z^XM>MPRLNS<w`2&e5FUeJkoMV)Mj)
z4ZNFGy0ws~4<bpp1OfT^20EWn(!OIj%qr4L4@s-P#PqmZ4Tt5HCZsiv-kndHejsRf
zz7p|${hj`v);BiHVtCsGvXBU7+Dh}D0IvkTd4AsQqK_(hK37mTB$?ypU+Z5%_^uxn
z_{!$<#Vr%gux*m!MdTu$06ool-i4sWrOQ66JBE@~{{UBv>{I+%`gX6P!OPg;slry~
zwzlqk)+-Yk;wM%+H>aWHuxWbEiqcPY13@cr(f<I;DFNr-2Bp*BNbeY?V_ATb7wQ2&
zc@MRErn*F$YcPK>s{<SL3_FjlaA8_SXn@)!%K@BV`ikgPB`2t%Rmoh=k4BPc<X8z<
zU*=V2`>$OdhpU^bL#AH<ld1WgK?Q*REc%M)Qtsf|g}CPpm1aG0*0c2+pwYQP#o0l9
zzbGK|U-89Km7T|KZ5hyAOJ%KUZ)X@`YY5#ui`GI9-3PGsuGT1Y+g%po{>lW2l|ObB
zL0~?<zP#6!i=ky~ZD7hM#_|RWF&+Mu*x2eMcwSi)jN=1$PyW4c3YJzijNEoJ^*}Xi
zbemC?Sa4#u$P4)U)>`TI5IJbA$4{GS&*xMb$CnrcF;UJ(uQgZgkLMVM)47KRvvX{2
zciomFzK&NgqBLd687<HQ+MP7TPzaSt5)G_*C)ev)QT?I|vZQVfN~y+p^jf&`%?o+b
z09QCta5?_~>ronwZWkSOapYVWBs)V9^8u0%a52)RyPYj9jpCTsf>lpb_2<1|&j_}S
zFO`m6_JBb<iSJYE_T$KkEQ31{ghk7JPo-?Cwu@5ebUF-HR`v~YWJ7g69%f^JzwC_m
z6=ze{bny_2Epp5{<ApxHwFEFqki~6#7o0FMNdxAsOB;(@TaVqwi@SoLamTGvmYSPJ
zvWq$RwJ;=D=9L3uDgeN5srCFT(moP+N)H%ZUt7a?E$*53rKn7Toj0k-&p&ty<8==X
zXxhEcn!aVMjnt+YKkuF~_!`jCygA~J2VUCvk4Cxk1+<&vl~j$sxd5IILs3?xRZdS0
zE?L_{Sz_okl}(R*(7qdLz8vwDyffPkk;*qtk+BS|!TE{E`ewd|@s^Qmr1*K8PPb)L
zA$Kk?Gt~3z?OZ;Y@k3JAH0wP_L6lnFqwcweR|<?*p;AT}PjGSdu47%-JZV3PE_Dr3
z;x~@|Cuf4(hhLOug&YifSLYd|@%Xy&t%-*#hW6R{n&s~a3eFL#pEk+w=y`^r;mt$F
zTI600(6Q&rq3&)R?ZjE(c+N@hU8U}st^72JH2e5qjRyd>>^lDdv$Wu6-nBjqYPv+4
zuAzBy@j<9f8S<o;?jTXdbLr_`ho^i*R@A?+WqW(*-Lo{#it6|rfEXMM{VUYUc#>EN
zx^kt>7r%S=N73hbO*;5xDN5YB`FziwpI_JD)tglC?Z`sP16fLUM$7)lJdeOvAt%I5
zKT5v1wAL(|!|j-M5&rfP>UeBm6(HBB{7ex40BQ;RDWaHTX_4nPYSJWVpq49<mC5R$
z{*~cS=-Q8pmGz&p-CaR!>}8fX*@9&M02laI+|a~O#MAcF=92Sj=jW6uQ<A&e@jWw7
zy^ikSTxmqaCc`<0_mPr%Hqpxa(s<g|@;w)A`1?dSU|mTp3x9ieYQxdI4W`{&J@$hv
z_O~dz$r)VxoE&}?)7w9Yzp|y5UD<PJtFfOdGweX;KZSXjjw_z5VNd2RdUkP-^<&KZ
z33DBsLsY!BYnDJ^6DwtrNBbxBt*ur)KT(~pbYm2yhwl*Z!2K#K-2z_?#xFFi+}cie
zJ-Ik^&t0e5sh9BV_pvm^qf^#SLnqXV!B=ZWB_$_xdVj%Pko;Wmd}$j3HO)K{PJiX*
zI{t$<8vGLYbEDYUd|2>p_M3EP^E^SOjmkp|eWpIRuikz0e}D9?DpreW*y^C)DgV*q
zb^Cp1S<xa*A65HX+!Buz>Nm+G2RjL1NOHI<p1!r^aQKf=(6l)`K?D%r2-peYaAT4&
zo<KZ<kEL{iRkYVFtZgP~Qsz```6RH5lH_{hq4eUo-xX-u^wV6}X%@2V)Mj}cqYa@!
zCvJ0$4Dx@ic)rf{x$Kqvtd3dYWmbf#tz2|Zuc6y`lR)skr{L4$i!Ii3n{<#yKI`ry
z<zdq!JTR{yxYM<bQ%h;ARh6tqoRW@%soS4?ab3@eW7jn88q-Xf{?TC*By*9C?vaps
zFYtgkJ!{ofw2fj*Xswzyjbif_ILHXGp7>m4y8wE1HLAtS4+jY=tLfys9QE)Hw<T8X
zq2$_MimrS?WiOu)hIs8nxa6ark^((WL0oh+?N3D3yep;Kc$V7PVt5yQ#ASL$K-yh<
z1_um2hOhV!`#(#2y>jwTo6eRfmDhVJ#^B*s2Xis?<AGZmcf?pVy>4p_M#!bC?C2$#
z_p@h!XH&)xLI>UAy5UB@ww$oga*A55I__<S!(rusQp8Tva&~&(%lhnIwrvl@+EgAT
z(_s5JUzOEcC1o5K$UU>u+PLjz-s~)PdV3TX&9YEF=~X<wGtqI^y=fVAe*@~PYqi%-
z)f^ZnQc5I>IaWiAk^Cq6R8|*j;wf%#ZBxpQIa1ale)hl#B#zk}*9@Yf{iGM))6}U^
zoMXvTUrm|P>33EVvT4^jO)A6*8X|sIVbPp-B=e5-o2S8NuXx77OZHo9Z#mjY2pDD`
zIwznffll!{S}u)kYo^A?u{oA?9a#?w?tMw==qW8Mtu;S~u5DNgy*hT4{Im09k%s(0
z;QES%6&R|m-Ln_(P1CiKu_euom4>~2ubI+)J#KurD!>;>Thp#o^V8P1bxSQW=4dsG
zC0MR4RK&B!+gZ?X10IY=LK~>BGS{x5hW5wp2&O$w7fCqa%DBT1O!xZK_SdG`Ra?m<
z@^|@DfOrQON5@R`=DO=Twc!e|c9QAQoU~&~oaat!-F<qZ<%(Lpz2%+q6rK|;)NO-?
zRZ+QH`HIca2A!%{+Uqw3Ev@5{S!7pW6)}O+f^vFytq&Go>Y68pu47jdroj|)^l+|1
z?P1Y?&j+q6rucVjpz7WdJ{-A*HkdP|)DmFH$<Fom^gXJo^Gb@TD7#<qHOnyE*tbDV
zE2nQa(DRQLc*^6&S}n!Ac@|=5t>8iehGKFX8RP0rb#@DR;U5h};pvKH;&zy-@VPkH
zr=elptC#qR;Y&?FN1D$}FXh`w=SFz#{p@u(=xcw${w1;Tr-NhrduHY(YiXbOmBv1A
zaKj&7Yu2So_(*+TTT4^k!C|S<#z|r$cGib0@Y?JC5q}qbnnnp_sBTgT!tTd@+3j6j
z{{V<}Uj<oe+Afm{PKCFw^Us#!o_&GMa8EPED_+2=@@bcf+0WlW=nu7ecB=$_DAz47
z>|%K!y^U7lRctQPfB_loyjD4+X?<!nB)Ti~S6h6~6B&q<(}fq;?zzx>FTSwVeji)t
zQH681L2{YL48-JjKU&S!JSf@|&2a?&ZN5{=^N3dmJw3pv^-qU#=|(5;tB_*d@`u{|
zc2Ye(3C%I@*Th~9o@?0@U+IkAXqSd`JsYRq9SE-iy$7v)Jk+B1T~i{3NWNM%<7T?|
zJIhHeHHT}^=F08_NaX<V4mdbHi0o_4<aoSSt&J>5t!W148;(5Erv-ua&j9*Xu8*Q?
zx`vZ+;%_2Im&y@_Z<Nb{jN>^V4{mFo(Y!AVURJHG-Nj{Y3wdZs^JLm^+yU~b>6(f-
zdEQf|?Cf?z4y2VkU0+kG)nv2M{3&oeJ>>~5F$#kksS=Zb3G1HV^gV0KKj9YD*H?p6
z@onQxDgv=4epF%5FK%mV$C4%ETf?HN!m%lmH5nZkZ7u8lD!+*?X4gD8n#s&^Mym3t
z>a4sl{P`Z$=+MJB(~GF@eg6PMlN$v~5js)Q%TGeB!^dX~mlp}XD5H>+$O8m&ee23J
zk)~Sd;_p_tcUfP2c5yCC5z}$$_*Zpxt;ep|h*5l)n1p=tSmT~?z|DDKx{k(oNp`gH
z5|JbDqd&xPUabl|s>hot%3GRRtoM4d8(7sWw*o{;#Ipg=H??!RKD~FQ&nBIylCj1E
zM#HMFaZ%r0OL1?DSli7lw9V5k=sl{W@<XV>9jQSb$jZp$oB`J!=lR#8hl`84Gi#Yz
zMV8ecU({gHwA7X)xNkOT3BfS0Cm8I<kEL~5<ktGEsiW!2w<aj?;Yiv5;3)0D=Z=-Y
z$7?Q<v){oWR-Hc27#y)*Mep^i&{>NcWk}A}Qa3vAOJh4nt}C{b<r}s)2&*;F_b&(h
za`E1eZEd6KvK>;=$xKlt$;61c`@UyWx1inIiuGML;$MWkU#eQ#YK-=m7V-%qj_3`t
zFytV7kbfHa4@|oIX}FT;p=iSVr|-5-I(PT0a;2J$vP8&-uUwCyHQ?eh$_{RH<6}8h
zY5UrrVy=;S4xNAE4Rd$d@8<ck<x~Jk0OOOnv5Z%J;U5kiOThP9-L=iU0>RwN06QGQ
zK*3y|PIHsqzA?1;kF42Q!=zrn+ik7B>}>3EGn|8xHxH$H#)t7s!jq!gq@pdsPnU3v
zxzAz11bsNKDz;l!kx_<=e^b@M;^Q0ClDpfn>bgC}mBp>zuX3*zpJ4$wI4wNveqHUy
z$_G0~6@SAQR#rX_NkR!H)Scv!R2F#|$pg1Qy$P(Lsd!(;8Z-E2StHWtV$wp+#K9|L
zjo;nRsmS_QU2L-2`8LqKwXBI~A|71nChen^MN&=;dDSRZqjxAZ{{R5#qbhYB`HVd`
zRlT*AYa2U!6kPuRs|q+c$-w9O)H;@iJXXf$W+FJze3xzsmv10@bv4r)tHA9PvxAs|
zVOI^G?`OEID|@-^z_37M+Wax<8`Inxx^-sf&mGIHLJkU<=UyN17xqq*a>R$em^Hx|
z>VWhfzWA%r$Ea&IcWwYgg4=d+jopY*U5=ObeNtftpEz@2BeaE25tG#q)b^@cwy~pH
z*&CUpjU%&-+Ck88HxruleXUtv-Mc)v;Ul8HolMWMX{+F+kL?G{y0}1NC+;@xJ^Bu{
z)!*q}Ux*~STmd9XI2>+JIrKI1t>=y5@jr)GT(LuL=`q0SF`sY1it9WZ@v_eI#M)Fo
zDZ3V{ZY5iS;H+|ur)dqy-I0PlO>|MiR<9PEY|wijXFEewd#8is7I!x`OBeb>OTJeJ
z04{lNfAOU8F01g@;pVL!r-`p*PqitUHHtFdDJPaABLvsXZ}D@+kob#K)^uo+crV+_
zh%)mU54zE=M*wxt*1mf24~exe5>BskE6p<HP%-3W=Ky_uE2jyO$yAi4^k<>0p&shz
z)ZQNWy=Sg`V2ar$ai+@=w}nZ<`HkF;`8)z^toYxr+IV;2<nTc;x0*?|DO0qZo^g(A
z;r%XO5NSHKwyPzZ?ny*|G8NoG&INUU5j<yq<Bt$q&8fm*x3Y|tf_EP~xW?=cO6&Ti
zJE<k9#~H~kL(c9zMIv}|Z7SYbWog_<z!S6|Qb_0XtwhzetM3bq687RT9jf_>{qvqc
z{3@)rx6@`snH3k3fp;(ERpQk($A7bF&@9ro{IqCxg&w#(8q?S-j?1CyR^J%4weXx?
z9r0o{^9EV1A&7qTU~N0G&^H{K;vQtxuLif{o(RiXY05u$89V~r*!t(KVXPPS^Bc(n
zoxtvpkRE0o5i$2rdJ2gyHH|uEyts@#{KxlEh2B(l$vh|(*GZ^Hmc_*-dv1DWr+E$L
zmLt?t?C})=cXI8HepMYRo|QfJtrD)OG?H6bAKY58`HyhUGDZhsT&13ut0X5>)MWE-
znf_hvxF&l6(>03CtAA^w?sGPF%9!A(^~Y}Xs<kUNj9ho+XQk>1aeo_LT1wDFN6w|T
zgO0op>s-CJi)D!hpQ2m&l4qG1XFotYRf%-VH$HsKBz}T0LZJTuJ@Zh=Zkq4?0b`G7
z#D*`GK>q-H{3|)dy-ax*Q$_6cEl3fqN}*j!lukMNcEx53o9lT~NtxV;z*T+U!P}1C
zO4k!bXrxJumB{BLpHWVZ@++0x$dSBjfN}FM9rHyxX*-Q8Bc0Lg6HxIpJ<7AJczh|y
zV!b<8PvRB0YsJ<SqFqFC5gR#P;j!yj)7;&l`#pr-RFj-JQMU{3dsV13`wKyc&`Gpp
z$-All01DbJ+Zri#X7;YSR<B`i;vG56cTqB{s9+`^?B}WdMk}n+-s<UE;zu%sPu><#
zuA|pI>&en@vugtR6p#l_2+y^5n$(_Ohvn6lT)9EDI(>8L#Xd(-^1Y1er!<k?d=xQw
zW8xjmUL&=?xt`(%eKc7@XEPt}<DkGlHhNdo`c9+c8+)m2d~a<P<&DD<mU3f!Iq9^I
zm2uFnYvf-D>s~L1SWgjn0`S3oq&mwI&GSS=gXPMT-=QOsUsU`t&~JVy*=joHj&IV&
z`X=8cqBxRi+o@$EZWA3i&u%N^IHM!RVPvtc9Mqk)TG{sUKEH>jL)d!>KYvrUv+$>i
zb!%B|d?PlO5|wTGe19QxjsbF5GY{^Zb6+WV{{Tz3@Mnl(uw__o9vt1o8!E1Pg1wu&
z0C8ToHoKt1Jk$7A)_6s#K3+x<gk(ELB4d!m_ZjuCnswN};%O!8o$^nT@;NN|JmeGl
z*0~NF5ajAt_<DcA?tK1#QwtjEh8te5$(%jy^NVJ*+&rh0vigjFLs)kgO((enjD0Hh
zs>Dp{dM_OCMRWVuzfr*8_BHh_So5i^G%KX!BDYm+t`A((F8=bxgSs^blho8R2HTa!
z0SANMJ*wl!i=fYpF#6)8)NY|DmD?+VR1!T+bar+!TzG0lgq#nZ57(%!a`}vw?IIky
z91)CVYq#*6DkS>`@X*{Y<plB%aZxFxOO=u5@W@PnvFg0>=}9CcNR7J+PDvuSHQNKH
zYVzBzHkJ&&+5KxaJ8PSVFhLtR9h7F0sT&3Qn>uxm+h7+=*btMkK<l2>S6kDSEj;xE
zDqtL(^4@~FT`|%<Fj|=LBDUk6IR5||qpjM{CXVyQzb~1VKEk3^ZK#yBITsR1dlIU%
zd1weh%O9W}E2RGbgg;rZPxweCk9m=Iv}%56m({<orC;zYW;@yM<K(1Z0^=kfYV=#Z
zM9pg=>?Ng)uW^z`YEiGQqxPEE@yPF|R}BDFIm-s;BkNmG>edn=ORHfKJum?kfMGVt
z<<2%7<7pUg)~;CC%c;z;T5oss3!Hvc)9n)0;N)sHn)=*NZ=~-MsNAQZ$nDp?dPjo%
zP&BOw>3$`$oKFcTZ~+aU_JvdMj)$pe*9~#0AW^iI4cH3wPXlQK#@d{E7`lShZr$0X
zoE?jS$3fqqt_sy!yzinpWeHc3p%r$`Zv^Qh#YrW`f}ThZv6aU5Q;hT-c&}U0ya(Y8
zOUEDBHhQJg#A4X;h|)6Um*xwOfHxeiahDd&@XE#X%@R2#l^n@6(Nc;?=nxLTbSJG%
zrKY{{2J+ug@XU)Ip#1%=(m;d}{{TIa#yGE<ubJX%&T2ImCA!%kq2PFH4Tr+gtzMJo
z-P-QX{{WHYe-X4O{u*m?Xp_xtb#F86fca%EGoGI*Do?&Ecf+Q_!$@0*rGT`jGWm?i
z0PduL)OuH|_=n*}zodAhO7T~ZH7K=6Y)eS;JUa;_W9Grb1wPp|xSDssj}vNU;_l^b
zXBcM-8>BKHVsY2hSBF<Irx>nR&E29nf^)>%4@ufP>(t=<F$?O~k=;Qu3nb=QGE}xb
zG6qTMn)NH)N-LND0EBx<w?<i`#NYvyM&NP>C+k_d-jCr3;bU!X_Xs$NZDijXXQt!#
z0rn=f{4HVP8+ai)U4{nj+s?xBW4FrO<Jec3TNzSIo)2x7$IoH#5T^MjADQKzCHPyZ
z_zL*x_tDP_O9+-9C@ZTYbRz^Fp4H<1E7J8#eI;00%C_#u%$i7o=N&VHpU%FM_|M@V
z9eDS|cKWuDq~2OjZwkf&OsTtX@c>-lpFv(HuK44`R{j*Y@JEWGc;;>k%@E!|Kg4)o
zN4Hw~ytgyO;*~#!PwiWC#+C*&XFqFy?w%zkv2kufqn1@80QBRvL3OCx+(greLKDwG
ze?eLoo*Ry9Uq4S}n`-^#e1GGu$gXiM(i0WDtj11Vly(ER<6mh!GmLJ+6rHY*tbb>p
zht_ZL1H$^%)5kV-iwUAIhL0GX4jZ0p_n-SV-_ZVMzXm^M?-569<6jKe+(^WoZpmX@
z6OGpi#{;E)^ZmU&8}c>a)P-eINz19*K}Ep-((yQ6$3tPRU%2w^;uf)Y+B3+{cVqfi
zQSoKX^orw%rwy=oD!mtunJ4izh2uRw;Au8S*m<JTUou$F@sD18xXonR>UwqMsy3M0
z);B*O`{|y<dW@RG4S<d!pSC%$wIv$vHqhI@*XFshytlD|V>?W8%OeuYo-hE%;hNFA
z(Qfp=2+eDz4<_y;Mlz@(PBD~5$m%`28sRn7yB7(m=?>A{0g2#|c#%hPy$*Z+mC|@m
z;#QsGEqV04LfNeD<5MI+KJ;>f3_iWbsII!WSIVbOH%RZqI8=wSleNuHhdS&xb{dC<
zg1NeiMY~sgau|hmC$R^gaB8--;am7GZlaz*Vz?&i+(y8HG5f^EIKlacJl6o%<H3Iz
zEgjsgj-nnZ`eWx9&JUwt*U{Jdw}mu2D|q!;)!K8m-*3%;aJgnv!sPWe&svQ|IZBH7
zk)95kafLZMay=R2cHS<v@fnN29x;fkMIaE|hTyOSMpzM*+_(XO$F3__UQgh=iF9kC
z_aACLb+cn7-A2%-7#ZoG&Y}2;@b<#@QLws(Q*RO@M(%@gEEp97kTcf=pYW`$ZvOyG
zk_aV=?1r;tTT>z5D@h;BTLU@ggWDD9!v#jYILpe<)G-w5##4m5=>1PlxxBfvylX9L
z(=$yPnXQTW!jI!tU*RW?YB$j4((mHcbQY1VFCt~y-*_oIUN!@ass8{T_2!-)@ph>{
zh%UTKcOTlNm6b1|BrM6ae7;dRzymlP2(Mzb@l~DWgwLSM9OKI*L{1(z#~YO3k4EDL
zzHczVSHsm&iuWGO$tcPeZ{A0d>g{iJ;*B#wNkcZDB%WsM5Q@V)ecK>ajY`okZr!w(
zMn+PSr$e7n>z_)%&@}54J<h*-daFIQ=9&jwIAU_#XQp}@&A9PAGYiZ6CKjYyf{NYS
zo}Q%FuM91ql4pYIHrq(-E%mE?F3vp{#Ic#Jc7U@Jp7|TThqyG~5$U4#XtiBEjB?08
zW{B<ifZ>4$xa*4Ye-CT2$EU51x*B^qf!M(1(>}+wE{myZIx8EPWH%OzA>S74<w5P}
zz*kio6)CjVsLD+}QnNiy*85hH%6n~cS)L{Z1hR~PecK)D&3q@T{{X@xsmWn>gj?Or
zs^9E3a2y_mNUquS%O4X#_FX>iDDrY)m|=(=hJU3*ZI$fg(k|3AWE^9W_;=&2YZn&z
zB?~5ZP^(t)mpi)J=DqX_d3|?g=W~T{&$l3|@DE<Rj8?zH%}iYjuL(=NFCu4aURMqX
z<#{6~Jb~2qu0O&4Cez^3Z?1LQq2^mov25des;Me*)sA?pUKa5_n|G{V__gg=S~`dX
zVM918gS#H5ABA2cFQ;D(DpG!jZX=R&RpA>xr?=@kj;gx+mseT)J;l6BCg5`&#A9m`
zI~D@5be$Jl*7bXhR_fg?qHC6tGjeu@AZ<f{GWY{H>7Hv3!#*q3H8p#imPxH{WHE+g
ziD5*~%O*HE9=!CX4~SDVO(l}8oxXOZ<gQtm1N;D<GmbG|G4)JF8gjH&t8*G~qgh5$
zZ%47A@fv>+ct(9UP<y#9Y~r0IbWl-us|F(no<Q$cJYA^hy2N%`^_-*ZcXI~bbCpHO
zb;o0ogIT9oe-Y@&nu#p~IT87vGroA<N$Kh<#s1T0;hipFcO;V&aV!?`XCrnoyBWtL
z9GdlTROMB2RbKM_O?lymt<O(X{{REhAiBEoMDuDIVw8?D-dSwr^ZY)5VzQT6Gg-aX
zn#MV!V+GZm*e9Mp9CZYCs~#V_m2`Vp9$^Ejh6gwR`=9ZzHr21K^)C=y>UI-n%OEt1
zz%Dq-`i$3f=vi3j$JX}KQ|wEsYW5l$n-l@=PC!ABs2FE)KE8svKN7XG$z?hn+ki4K
z{^>uhX0v~5t<}z>Ar~tlCHo#QIP~JBlU20VY$3H^Vw&AZ1bx$5LYq9sjm=WxI|$^u
zMpj*`<U|HRCm198*A;Br{Kt%t2Qs^HpHM2aB3q*sjD(0o9loRw<SDbtjSE`Dfm9qS
z=Z?KjYqBxwH5+I>Z@0>Cis2ynVjTuga(|^?wVLBUXq|@lIsX84PjTo?VXo;UTVzlo
zi0<I$C;IzRdF67X4tY4M9f|I|_^*Y<r|$NU%6sH@uR(7V*v(+OU}H3ZD+v$EJy?Bf
z&V1t`9Q`?~3O6^-KU#+iyOB7jx!)VOB!t9K6d>TQc0T@<wW&|1>JMTfc>Z|IawCnv
zI2;eh+PE7e-1|`E_Z@RrX1s?@lFDt1I7^JO@(IY{X+Bu4X&9vMWOnwKGijtGU<kt;
z!p)Tj{+T~jKkS|>ZVh8jy~{^%*3&}UqTTXI8-t&r8RQXOVXA8LZj$F!vbePKUvfNS
za)JKWrA2pt7*Td^5?Gl)SuxO$amXT)twyY0E6}wDo~Zg}*W%BL9wisB>9^9T;#A7X
zpKuNj(-l+x7mV62zo*OM3o#v|%ZcQVvijuk2t9LOGC||-vjYaR0$z@hNe&i6j>jK+
z>si-UekSp&?$m?WX@KQzl#<Vm?8l$;(zs_rg_DGJJ0VuC(w2u|H^qHxOSsg$De(D~
z*HF2Vc9fI*n3$iu-`*hC9jN%jQ@(}tfjeD6YyrD-k>?!ptLUmadY-klq%MbJVz6mY
zh@xzUz`-Y`N$O2`U4^82SBu)tc0Xsj<0SVdjMq&YMAjDR(Dk1UYF3(VpD%^34*P4>
zXSI~|1&1nq!RcI`p1%d+4F^+=7C=fYjnK03)3Nuh4-?p}jl4Dy5CCxK<M>JFb6lT@
z^!78ycM_GjIA%`8$3OV+uC8SrY-t(CM9zUcPpN5>Hkqm>91^fjP6K)dJpuRcT`z%+
ztU4^0_M$(u?r$~DH}mLELG-Roria8y_AN;XFBmA`atEOvs%nIO60}(l+O8#$h`?yR
zPM)K^5vbO~Rid{#T}Q=Y*H4mSH@V1<amn0$tB^pbf3!7AX%6AI%I)*9_FlkNrj==7
zd#*=qc%EWJhn!<A)K_PD1UC_RQ>lE85DtUaHHNL6>7}9Mr8NCP)C|MV00M9a<JPrd
z*1pVw8#HpCcXPQLzaDX0ms)<C7Gt(vi;j5f)~PO?3V;Cp)_7cJpIR*L4WfAOq4UrP
zw#&5bZN-%V>yJ}b42y2T;UK?vlaql-YO1X6LXO{uwN{z{Wmg>uz{g+Ks7Tbdx@iIy
z7(0*6f<{kz%aZ2e(^VJ5l85AC00Z&OX(^TnfL0;E&rULH7f_CSnbO#jNnw)RGeIXq
zGtDBIViA^qx)nzQ*FLpE7MzuYk&jPtSKcy=BF^k42^hx}k#5DDCNeh~E+&hNBUu=q
znT`Q&zksNk6?Wc$mOThGlX-D+m<0OND=-cYagN@!C1DU<3C!-&l^GbrfAy$FV0H!o
zW0C7qT(h($Xw;HAjAow@kXx<6QO8~1(-gwyFPWFh0Pc)71Jkd1@Aa#DxA5ZScFO+%
zyfNb$3+O#7m(yj6(^c~(g=g9~peG8x<aH*ySufjGw0%~{7m;$MNx)Eh08dY)bWxWp
zyEKmI^eYW#$NnMkgfYhU){x1x5d=ax8Z_*zN&In&`cL3Tgr)JnhvxBh=A#m&oDF9^
zkbs9~Z<U<Sy9=<4;{(?<^J8i^w;lnty0yq_ZRSKBah`EsMSja(C6mIFU)$X{TRlSC
z&Skj;l(uuVfEZB0uusyq;ygupWYuFzPi=hH^|9^bbHm3lqt2H4dh{#&c(U-uk#z=@
zqF=0+%NF^>j-<IHVNy(DPCe`9`-ic-yYrFp<VFK;;O_67SJfXL?R-B4#g3JxTiPTy
zQ9SQ&Z8FS3at>9aJ-Nc1bK1T()ch%N9C@*xSdvBzA(UZ&?Yp=>#=JZhCX2&Tl?3dP
z-&gZKe>l@JuBrelLDas=^5%M_W%6APdsbC>!nr_XUzY=u?tLp8N6;^|%ZGzcC|w(n
zPC9*R)#RG3jl%x`X>KI98}NQX#|EwVWnqg|Ehh@ikW}-{Yf`&+XvIp=(Uf&x3+fs?
zaveYgXh4t=$Zy8E+2oP1{CvZT_n#TvqxfFn1RFwyUU@m{YsYSF;f<qLTpuhP4u17#
zPOP4+xX$R<@V1!-rjy)U5wrliam`6>40=V|+b#)#!^V5y^NKt-cP5)}I+T6V;bX#r
z54onLuB~$v)0iFIU*>FcgIUy>oTTo1ZMB}0s#~Sa)DXCI1T=^nuTh*<ou-+g>4Hgg
z_#%o=ET1tH^v7ddZ^PS**NROpcPK#L5=oFq?iRY8d`TXWaABR2UoC-A(`oBobmcC4
zTxs__870`R<m6<G`{uSZ5f-szblLt^P|8L#kWX6X3bN_KF^rZtHLKvYzP!|>mei`r
z5WytzUCvtqm6JA)h9%MT`9-NRlY&Z}q<$UiX42m32q3xEQc0dMn4-u4W7pQT-s0*4
zXPR*tPM~)`O5=4MUhehE-Aq^m_p!&&Qcd*;zO3mM!$<KvsV(eGHZ}-X$JA!KsH3v*
z{+g#xi^zzuT=gxJ>)h83;Ma>#f$W=Ml*_pTaDD#(F;CU?YaNG4Gx;Vt&&sMu<LTas
zIZH$h>TGzUSZhlL+JJ7#Aw6(={{W3IhHU2k$^PH9o5>-u8i07mQ;}Rph;wy$Z8N_3
z)o{uWCq409Hl5;2{{R75H1=c6u#txGAPwb{{6Kno*M1t0?%HFDQJQD1So~3Y{VHu2
z#unz{KQC+-u&Fad!~4shoiW&QD+j>dB+>jC;wyc6-&fO~5hfcBBHXNSRik#?4n0L<
zX<j(fB)FPu*b?C<0g<iP#zFM}ai6HI{VPeZ)~??A+R6!>e9`6vE&%FW`j1Za<>wh@
z5}g@UjBTdB*O~R%7ZBp9)s6~lnM+H(()-Qd9RAWC9nyX#9Zyf6?J%N*iDN-=7(KuZ
z@?`X`hTp{A6!9IW_MW?@JXccgWwVGXD`$|*M(v*JI2Go)mEVW7>r->6Y2R$QS%X?e
z>?4H;=vp)bc?X_O1$D7__fC!A)x2AzJ=LVcX}GszYSIzZ0~zEF#f5lTb^|VyIq@-<
zD*jio?&le9B2bKIN(uh}d#~aiCe=K1;`?~CYn!`m6G*fG$7!do_mKL#oq!YHiplXc
zthW(a&tq`uEu2WUw-LzO;VaJ^j&u3fVSRJptz%4^TJScZJ4qHw*j+}f>PI+d&&)l*
zuDiiDeh2ZjkM@MzI2&bzoOx}I{@LlC4Q*C=B^b$5i?Z_Wd_7EqJd=#(?XK6`%;YqW
zjsE}??Jv>w*0$Eu%i3P!_Y$T$GZ^@Pp#$q*I{52U@kffhV=lN*&wgajmIh-~0~qA=
z_OGw6{1K$bZF6y^AKXGSBQ#FCSJUPgAo{5yzE!>P4xIPb^VzY1R#6;e<9ihyPq6f_
zpvp3gz7mc%1!knT{<bi!9AqoJHaB?wy-sWX5Uoz&zN_Nv*sWDpcas<2&tZ<0rJ;CJ
z!{nVxb(zZYzGHx-4`2cO>mu^)<VI-R$nD85*B+<b)|Q`rE7>j^b_RZL>0ZTZl1bSe
z9Fb9WJ(KotC4MGok8Ze&`+CSJ9f6%VuiqW*?_Yrb0J9FL_TL$FjUMGUB>KIau@Ezc
z6Q+L({(<)Y0JHj6%Hr4WrMWnLA!Gm3`8PrFUCyoHF{^4zoCXfaIAFYI2d5ot!RDIf
zwS%eXQ!Eb6lmLtt^dsN6HC1(8d&A8vdOMq&8Cx@3FGbH>4#fJ3(u-E`o~oN(*UD^s
z+rbW})Oy#@P@_(o<$|%}My$8F-B|eE?IeinuwoO1c@c2YlflUJ`c@KH+S`0W)Ac<D
zRg%`?aHXR^cRAd?yo$#uaJJ@4OPKB=P6^&svD=<U6^kyjs70;WL2@HcDniTT{Iae_
z12ZRIYTn$sBx^d-sc5t68?Ov$n&*lwwT)d~SnbrTX}9DDsb@dZxqU$_HOBHZWJuEP
z<1vCb5u*}Mt~lU&n)Cfm_guKYwzILnxo5gU86H+4RCPE!0gC8uHAr-cltYN+07C+=
zZ<K<^JpzoMtz}ma%&*w*!q$v$=5!k7yLS3YTg4*_YiT^FS&mw1vA5+Nl}CTZxC>ZY
zPS#<$y8>xs03cC;8UvC5Imyo@s@|o2Yc<xJrfJeF!X%PS-FkH=w>8W7ruy#QIQ9E8
zkFv4cGuxpbms+}1xmC?8S55AAI;HITHn$e95ZhyYw$kE2zbcjl$Ma(WfggygqVH4D
zwJ0@f6BLq0`DoaMX;kn)J^1Tiu)Y!fn)JUD{=%2v2sW?$C0fpwo-J4#{c>E&OW6)b
z%awpY-h{scJpK!5`l73?ma`X=aS%J2Zb1X4Im(*NR)uKF4QQF>;v=e=>pH%>r|X)e
zm$z2GWSwv%&nnCAM{!&g?xhx~<~*b<F@p`dsOKNvKDj+BwD^1QbK_^m`)iT#vp~MS
z(_tn#?jc-;d}m>m@QO(1ZtUWZ8+-=%+3?d*u+scn;jLEAYv&gBNhAfu#J!`4rsa}O
zN6MtPwR7@AnWS?{GWe`{oxSbc(5Sg`WRK(_xLkf6@l!#r>iSG=1)Hp}G5Mlly9D*Y
z>tCeb5Px7@Ps9HJvhRj`Tj320T)*+&r+Wmt#i3S_?ctUQ+2i{<sMrzKAWLPka7fMv
z=GTaP732Q^2>8GL5RVdRx0X7nk~o^mSXf*Jlq*KQUN>%V>QrMVBNb4o8Lb%=D9SHX
zHDrfS+@uANe8MA?C;i5){VP`0w7EpFJIDv{%)$8d^#;9K`}RHkrjCj=xx5*!m0naW
zvzc-42>s~%QoIMlx(|(YPZ3AqeG5*x)O9&sqmI(vDJHmQT<%dCDPl$rMmVB8t=vj8
zk3*yJ0JLpF4Px0tM{vvLMyI3y021@ky#qnid_FE7&qa}~<ZKxtmxXiR13dBXT?GFC
zv$w~~-BQ=#_P5~;PTyM8Et>k$6pM2-Q7~4Kz|WKwh*D(QGC=QK7sS7T-x<CYO$^@=
zG#mXO%yA6UNBgN6M<HZVP_XJiY~rcPEpn!7d8O|oLs5Ip5_`={O1Ut{afsR>_gtPc
z^{VjN>9fkQU&!;kA1qd?aEJlw$FTit&aHKgK%&~ra_GU=cdGI;lit5qz8(J6x@W>4
z*y_d)0L@|l00~w0y>S_WMUGi5;oK&gc_JqyGU6mH)V4-;oO4u-thX|X=Wm);Tb~5W
z<1JP@k2FgfTfzeQYPpC2KIkWtk6hOmH-|i5@f%W&yeFqkd3SQ;!C_|`%PU}VRS*^=
z<0p#!ZvBt{0BJAUfA*F5uyog-Pm9AkLnX$mIIxHl7Jf(-fZ|EWAY+wK&DB6Y5&Lca
z*D&~-<9();@RL)UOCApwHgRg!5*V7|;^Sbs3h|IkOXaL<*@$DxkyTDssmi0NvX3o3
zerId;{{Vt~S>vyUpA<eGL8a-x6*Ozz6kf#hLk;E-%&`y{g~>lI;gvWeBaUl^^I;ao
z+Q<lk@}t+&HTvcM00l1iapIqgUL^Q!pm<A9zSVU{)Ab2fD-<$Gw>F#IurR6#8D=01
z@_DbyZ;2lRJ~n(7XeRiBq-r{B7Yt*DTeSVuj0ScIS9E2u&fVF?I8^qLP42~rq~j)*
z$n(oPxNh#I0Qqva421pQJw3su%LKM#?JxqYr{-T#>x%8XHSmAOe;8&X;iidwsoukb
zaF%d8g!E=*1yCNukzGH;4}d=&{uOAkYySWfJTa(g)_ayQc`62o7U5OoLdvA}3_(4s
zMB{!|%p4nB$!K{D6U!<S9|cJuk=XTLr=?aZkpjFnPg?fxhrhG8#vh7yBUSik;r&NY
zw_;VMSXf)`UaBU2vE+Jg&1`r}_Fee><9`hg75HbuTBX&I1)`Z@!p^?21}bD8>IgMT
zqS>+(WcEkSY~~OOV{@K9l$N#^cPd?1fu7a5sd!&i(yt}4)nJm|ds$3#LozZ+6Xyk4
z8wFWU1QC&6Q20moTlo9pZw$q%cwfSLjqSAQzA4OpQaU$BLm^G$(5`b^-Z9opHEz*Y
zXUVf^W-BP})tE@qY$?a#j{MfrX_6u){^SpSf316jAF{vg6RqoWX_h_@)e-cw_KVk^
zBW{t$5|<~;n_?0`%Bry~{2<qnYI?no%l0Jmn<2Mn3b_4EbITD0eN5^pq^^h|w(~XB
zw1si<5_=qhoYmIS(&xkf0AM5wneIbF{ooYjx6oHNrt1@*6v?a414|wnJ(0g7f$Q48
zSG3>THq+qm?1$iA4tykQ8T?gwb$=bzw8AM~&fW`|$!X)sDAMnY$GCjUxQ><P;w$sT
z#Hv)2mE`P_x$rI92&`l?KIK@xZP>xW`g_;Ux-Ok1>_+0=W`&fN0g!AtJw5*b8vW`0
zjJ`L1)IYQ*jb**@ZSI{HhID_QQ<f_TQ_PTqvf4KzA&=g6{uCXHDX)Y-ZV%cUMEK+J
zf#dK@P|2idY5kjJJY$=-4os5_aj~4@FgmCW{8${=Xu5hGHDMJ?+C}f>zv6r$Wpk)_
zcTtw=dqtae51OccUvZ9=p?Tt+LiG*4pCod${{SM#u!b4$p1+-aBjK;vOZJ)ZW{Uc?
zkAroENBPm^h{5JZ*rLi&h$E&4J*&WObbk<dW5=`T{volOPrkL^Z#}itW+`KF)H<*P
z9*2?AyJ0SgnS0AwBbkA<dpli`IFD~F44^mt@<(Gyr+72PKN9sM(>xKT-D;Y+T#Gvx
zB${?WMo2^jih@Tt!0%q|;jh`t<KM*PU3cO4g>^goOc0lG#kL13eqbb%YBmo91Dd*;
z563Ur&&OI;p{rZ#9uC!SB};hajLB&8z-5okMRmf<9(M0gNX|3PD%{q|n!@@@OGAtJ
zoA9^dcf;)_$4U63qg?41?`$WEJCy;R0!oI!Cj>H(LF8kRUPo_p{i`f?H%+y`0yz(=
z^atx-wx94(?*e%H;z!4y4ESH+{*8C6T)u#0bhC`PjRnl3bcgPuWP!Pe8RMz1%dd#v
z0KOo6D8Gq5Bj~rfKiao>4LoE<?7c!rGK@!317v<Q;aSNmj;tLywQUbHnlP*`@MC*?
z<gYo%=k=~xwF}AB<VJZJ+i1bbC(r@WK(Au(7sC&UpAmHlv`+?Tx`wvsyP51@c~&vl
zyC~Yf@FFW~#J{rN$B%`^KM{OD@ZI*Ar-T0hA2J|lLpMcdLdvY&gAi){t(}b5GELa=
zT?z@U3+i`g%Puj`Tplx8_Sc7Gx5VKFQ80MTdRKz}1bl4xyQQ_q!=DRkdY!&Xi<qGz
z+-x}7CVjCM9oKe6ZTM&QY548q9{@q(uM2obSH7^Bhm|}~G4pqD8a4awpY13s*PbhW
zXssJ7mQPzA11-x=_+x{@<n+Z`91Ir7Qb5P0dZ&zj0sLk7f2OC1{CDA<S4pr}SX$=x
z+2V_PXKI#VjGP<_x#4fwyW_u%v}pWG;Xev$`i7r#xvlQ4?PPeR3dKoA0|WvH<G7=m
zJKTJ-y{vf3ef^4f>z`_>`{it)Cnp1*{{a1ZmB<gU?anX=uh-AnBj7i}Z-qaz{Qm&8
z=f^t;^;xj<=(>Hz8Z}gN6eLF9E6s5*`3tKAU9JE*B5F^OTy)^)D<72*XqwFH=Udv%
zGD1FgNZFJebjTP4RyxPB>Gc_i`GMF7&$WJwc$5ALQTs<p;(>AD>2&=oO^SWVra@^F
z#$gAD+F22#IUE*3yz&6)Txa|gE608T_?z*sNAWMixosu0yYStVw=HO6B-~9T^01hv
z{I*ndR}J$FVDzOK%bL<daEB{#k?`zJ9SLv?DC^U$OJQvZ1<6uTyLYKH&>3Z!6lFl*
z){ldHRpKuL_`6T>Mujig@9bw6R`Tpw0!W7{+Z-}B4`u{=Rmw>j=ySIOn}ZU7+m9X2
zY8ixE36vCZ+Z-DHhQ2TSc=)N|-`JbNzZWzc%WGc|>jup(uLYzm51kau<zjSb)RK#j
zmjke`!wp|dw!WQb)nwmsaOP6LV94%r-oqsNS2C{UeG07wO{AU7Uki9U#Qr0&y}!`(
z>#O*!6U@7o+8HHCll#)LDF{8-is$ZCEMogbkuj0MRr%NP2ESE5WiJ){diXc+%U`tk
zb>PW$T@w9d)Fjj{;gClYBwHPoj?|d~FDNs}_aeSl_<is@#s2^iCNTIjK)lrgJ|=5;
zOT`)L1IhOnx85SMmM@c5h;jINZ+GN;yK8gj61PgK!E#wpbUvW;&16M9e`$T2PEG;)
zgKxM00A9Z9wf&AhCHT7H?(a$P?xcRlG?tfMLIEG00~D1MlZPnaso9=IWN9A+8pFgt
z@Q2y>dTI5vV;QxQ2_(8#10;DwWDq$&FHU+^l=CWX+{lSH4|&C&E28RCSm_bl6jWWO
zkaLoM3i@;5{kD(c%@<GcMzecuEEm@Az6c3<BTR6vfM;;PImRpO--!PJvFC?A7wEcJ
z@CSl#yj_2*X_s;8Qq3z9ZFBpB6RM~Q84U6T0CXKm8Ly6fHQ_H6>3$%C!uNVMZ{T*c
z76uuv8_ka+9$Z|z0th1?De5cE;ynJWu>Sy8!NtWrCD!S!k9#J;(RiQV{7TJywZEB{
zs#<tIThcCkRp8f+b$J5_?O}cBaNi=xgFaZtC4dIJ2gQCN({z^+__8T9>!BDo5S+5J
z9?>x$IQQV!-ul<TJE$}R;vW=zJJ;+l<J~gd=`G~O5Dc-MK`#&kuN^DFw0#f6K0Vfk
zi{X7P=T??7T_BDoxsf9$y8uqmPu^|^wrh)r=wkk>H&XnG=$}vc2R9c>4)HXUV4U9W
zJ$oM$OD3n{`&l1IvYtrG5fk7AQP^iS)oC6E@f$@N-FY^g0L)JJW9|p|*W9yw4EVo$
z;yFAUY2l04zqf%Wk|>dJ3QL`aMjyLS*f<1&aD5GV{{V}A9r(-P_N2BRKCzoga=v26
zbiQAjql~Mcl&*L?N%XFo(yXG?Po7P!B^H;V;#!}A?=>qCry0sav<8!B%-Hk<{{T9#
zW8fVQ(m^JuUg_`^e9zsC4`J53{cl_lFW9t^=iFQj7eVr``-(?DQ&;5i4xK-Q#+Q3+
z>#0OSkg&1A&t}0Kj<o62sI{vkHr1M@naxdWVH67my|P9S@DXx&_o|;@ytscR-3Z=C
zCAN&3=xsE;X)$p$Oo)1h;fOxO)uo5TELkngO1(mq4t=su6*Vi-i@c}z3NWbF$0Pp$
z2o|GvDV=A9SsR8%EJ6G_S3ToxO|<(7G`rLS<bW~{@b_<_uT{9R)HKaM`&Uq!HQIKB
zfk?|YuVY?S<DUy_+P{cedqff;g)l(i9CAN8DA%PaU9y!qr>Wz{brCdf0RZkE^k^D_
zX#OIO?j<HGg(^}$PJWfy!SMH3mgxEM>_-6aBLm+x)n51)PSfnjC}W)$a=N%GPxx0v
z@ig|iqKoWu+U}<wqj?(>FpXCQLSvfZZ!oS_62&u-xN}`pnjW=rD7Cjo+<50dt!l5s
zUl2&UrJ4jM)RKQn(bS4pY&Eus$?#OK7OJzzLa56#F~Ai=Ub%`5a^%AD#D%x3Y(GBk
zc;lsd=Z1bA*lA2JwJ8?u{{V-KB9YYNImq{}lUDdmpm>?u>g{e;Yz#>h#aT!DAmn54
zu3CA83Uzs^OG8M-?<9QDYpTj5c}#<@NnwNk0M@Te(aZRV+W30S7I`nSh^4+)Ki$u4
ziuKJO;h%vt6-9kDXJy7^ykj%5`WztoV!fNf{{RcL-CI|S;skIDTRlC4g;fY7U^c3#
z1Y;+TDdrqULX9Tqub-Kv3_HZsP>(~5(*6YaEi6*&NH@tXp2Kr%A>OV#`D`+C?Ov_m
z{{RkY&|h3j;rnSKwTc47KQ70kG4#i!bmHSj(DgYk^>4E2w#Wjowa9mn^RW{-X7yvn
zYZt^mJJN11BfHSF>xh$UM)NGC8cxS0Ml<yWz9Oz`j>W}Ls@snJ4|<d^cnm~q&ZfP!
zdL9k&FHP`Ahi(${UcZvvE(0WS+QQS$z;eJwqaYsSS6|`Z_(%2s019cBjSt$aJkPY+
zrQCZ#l0TOvcwk3xep<-!Ubo@D6Y5r)b*7PaH5+GR_K9XjkyjaI1A~vt>0Xl-h;*GY
z()!k9o9$>>t!GwDFzgE-)lcDG_F0+I!O?KUH}7xPL}A3ay-dbXql9YF)k$4y`<_?h
zPlq~xiP&mdM60K2R_sWc+yIS^KxG)m)oXx`+s9uHH7idL`4Gz$%NV5*<fOR!!bKwk
zcWj=u?;qILx|OPHR(3a0tGQB$!PFJtk-%(K$nQKuV`{?MIirtw42%ZT>5#yK`EgoT
zg&bTxr53DK6FAK9H5Ck1<!+nb(b#{Del6N~gW<G(9nduWM@>m&0_F?WW(slt04z@C
z8#B`t@K&v+Y4-BH#1~|RW0foO7VpVFt$nlNeJbO`RzGUI)HL`<-P;m6k)FK9sUCfJ
zuOS}+8f(!d)x=W5c>xJI&-=hv(Bv4*4ht_-eWGnP-LKT(&FjvtIoFh2d6KpKkCR^F
zM`F-O2|q6^2PU_cY0@ZC@yfhw@~;>_h_9%%FN6LLg2An>ZB<KT2@{6TuNB8z{2S2b
zc;>jcmGSbCzi$JlHPbBj1t!zji^;<1sqi0AwX*ov;W<^*$okcSh}4qUYlz2gd)M?i
z{i*)L{zktWehm0u!`>J8mEqLWt(9H1YcOOdbAPW+r1M|3o?=&xQ!$N6IYJr^j9)J2
z|J3}ZlF_Y;!#A3w{{X9y{nkE=25@Qd>UZ|IgI0vH_K;wleS6kVnRNj_WoKj<;GN2U
z!n6jXEQ8AgD-KFXzc1nG`Pb1{@hxKe>yhbfZ!J|Njnpfnj2w=PJ;!?7&?COp3wV{x
zd60xuP!GzThrWGlmiu;|QK7xI!namd%9Hf!DJ}IC@Z^Gh9&L*u`IX0;7JYI#2C6dm
zmBntyZ=vd&wYl!(o6ERdMzF{*c8*71Q|VZGo}k)|-NoFEvUXxJ&48)Xf&D8cdrRFy
zM!uiRo6YBRZMA{_0A!y30F6`9WG{6ti>WKIIb<$Ce2&ET717SEV_3Tzx=z1mr1*v_
z>7$U^$c&&aG6)<Fdm8#P_IUVvrvA{M8vH%*>d_f(Zm)&jrx64Xv0HxjV0#ZS7hpf$
zVP7?B4GppwfD6%w$_WkYU$bAauk7FPui>}BFB$w?gGdkJzYu7PXW?6Qp50~pJ<5kO
z?=l2U#iNl`*<1HqkC<03CbEnjpP_W`Ic$A5;m-p8(^h}CMfb&<?GQ)cySq2gRmJVG
z*<lKXf>nS@TyD5eGaGjSBMc5jemZ{Mp9!P*J^M*(dQ1_tT3)onTCoT?^6r_8iU&Zk
z&axtb&;jdTJ!!fZji>SLw}L!8ZzI>vq?&!aD=s8AqEQmZxVF&VZU8yQzB#Ym@7s6w
zX!xo7M*J4=kBYR-9wzb5j|zWjY0Eq-xo#m-a~euqMCLisOk;isEsjnP7x1SZW~|kX
zU+-Y-wKy-?_R8D#js1dR_%ZNb#3|wL6zMGg0Bwpn9tU?95=uPdAMbpdm1WF|7zP`8
z+E%_4{iuE>{?Q&E(Yy=ri{gEi?t`!R6)qkPU2a{ZzAnhN_Ylm0V8a|?Jj9iWY?4*J
zQR`Y4>}~NP4}u>KzBc~L(q6xdF2s^*=F0jwT0cFDz`)5OA}mTgsgoym;8}6Hy=VRk
zN#m;~_!;1DhyMT&t}JgpBEc*ddZc!2u5H)std}LW01;-Hqbj%owvxG3se_NS(_6Rd
zzu*xyFJ|wfNqRF^_Lcaf@lQej0D^mbGx4U0;$2?LTD`r~*7H)gzMd<qg?l^3WtL@e
zB!QV#h-Nt;6UY_K{{X=@Z6)zet>d5CH{v|^TJE!<*+Q20I)u$}IVKreRxcrU-XexL
zk+Z^<QN?%n-wL&#+Ry$8+u)B2d?%jL<5bYDE##KkX+(d#v`dR=VPbek#G*yrfL8<@
z5KozY#9kuNe`c?Wekc8#emCk`TtD!V+1r6Vz2Z%84bq8YYe{8ak~w6KQ=FUvs=3Pw
zjXj*Ax4-IBzqN#=wZ9u57x*vs$N1@?d~bu|zNg|XM&M0;vuYL+PUg;AW`Ev5ASA}9
z5S5P$@*EYx8LzcJ;G`B8e+B+Ocz5F8!>MP{^&NW3Rk*sakij9I-Ih0f$+YH3rA2;l
zz;o2+%XI$$_$JTB?K|TnI!3V-vG8;^t32KyH&6(Tf-rV(EWetvnEqX+J-hz^i(uVX
zzimGp&--Ei(^@yf4}j2*5H5{vCA1S;%NoORIX-pE$0M>w<wlCDytWvrE0MIFF9%UL
zZD(^XS*mhV-s;EI{{Zk)FOU8^_)|WE;Qs)JSMQ<SYWC94d#Tz0myZlC1aKt4Z;b=9
zJ8dA7fCw9dadP<Y#Gf5M;Fy}<h<tZ)bsb|vx)DczI0j~GYp59{V~%9<B{3H2z>-NM
zn#unFg1C4C#dp_!8TgTFXwRcq+eu+_bR97q^GUiiC|iHb0l^AK9Fbk8!5@J?6=wec
zf^c|;;q*F7e{6V)N7Qt?Lo7<u+stibjrSMYh1ND9U4iMxBC?Mnr3cpL(&u$&9+p21
z-tSUY6HOd*DdQuC{{RZ<z90B%_u_YpEPMyysAZE<)UH<E+Sq3f?eF|5qa~Q|stG&{
z*DsAzOw?r3uVX7UysZ?GvE^h!Ll3AU75k<C00i6k183pihPPj|%+^urejwFhyoW}%
z+_9MAw|9ltdwi)a#L*NkcvJ(NGYaahK1lN%G-&%t@|^zw?FHhU2lfyBnyo))kA)M4
z@ov8ZO{r=Te6pg-HcxY&s?uZ0w;fd!ws$Bk_<6EIzEp=D#z+4ETE9(xC;r19w+F?K
zh+3b-j}_S2O{i*<8DuM{z!4O1ks_Bkjx+%fb^vue*Jt}oe#kf84fucXi{WR1^k_8?
z8EQjOhf$W;MI6_6K2%pS2bqcoj@$_ZW!nmajAZ9JQ-o}<cK0@`3X;1@$!vWK{{RIU
z`2FMm0E7M$@OOtk8{C~L^IWu;-D<Y@OSFRAL5O0P8>3fu4CP2aDu8jE@!l8l$B6#`
zXg~NShm3CiCr;Y7p`hGN1b3GoJkD)C&2<yA;1eKOc5dod?-P@kf5BCL9C))(@Q;K%
zac^X0()1g7?KKO9c<}_cEpD*_rGt>{gk@BKags7K(f-R{1U@Cn{{RH~@n?g-vl%rH
z7d_6Sqd@Y+4aK$HotnWWOYK1?me>*%IKdzSGmKOf7|X5y08@E$y3$=O`X3Sg$$u99
zFj#yI@vp$|gjxo#t7_WR@Xc#`r=v7>DJT(KnWR!YqVYx+Cdm1+bHLB&N5T&S{A|&`
zV{eH*68)lVu2mOH)F;!Uznv~*5sNo}l!+uN5>yO<h{I%oSl_cpfpxzV{=&NN!M}x?
zgnAc_puN1d)X$scO%>8!WUIVjx~#USArcIZxI7$nKeMO69e==o0KO)CAl0?4KT+|X
zucq96w$2Is#IZNFlfh`S%s|7)%I?JUTppa2R-*T<-}+=zguU0R(fWFNo-O|X1fcO3
zif7e)OW`}MM(%AI#?BcnEM=9N8+l%5G8BxGs9!lGb?;w3d_DcRelhD`w6}*|-&2y`
zLcZ4RH1F(KaJCU?_Y$lEDB=TVV~K{>#_h^E?O$8}0Kqf<3j9I%apFG~_>08Wk)?&o
zuAc?uHxSPZ^V-bfUo6J#N~z;^HlD<s<MVUj7wr4-UeorK(Eb@VlH*+P*12bUsoEK0
zK24lcLphtw-{rCh*f83}oPYs5SDlH(OPcUonZ;C8=`B{T{sHzs?StX}00HRVw`Yg$
zd|4b(=~}d!HQuNlN0jU5`3}<OjlNt%dH(Kex&6PP{h&w2O&|Ucd3D6m<h8etO&UR*
zMze-vAs$Ekw$9$X?c0tklK%jLn*22JR-y4@Nb$2+TsD(^XKAC_-9S<N+v6p}i*Xv0
zD3O%qeoQw&Nf@uN;_#P<N5QWI{3-BH#3?)l;%f~SKR-*<<%Z(+;iXg!<}9*#=n_S7
z8BB#!v?u@p*zp$Sw?$xdoTXNt+T8Hp+w0<&j{F7im*M`B`t|;o;#=EO{jUXu%fe<K
zW{oa9<%xi0^N9<QkG+od_>FlcopTbypCyUHwm1jh+P=~KsJ~{9kG~9cZ}?5VF>11F
zu;0in*I6??$`&A%jm&{wBY<`(P`nVN;=8XB{=#<u0Px86za0EO;sJfBrJ8D2HboG@
z<;u&Ll0`d~SXI7#+k-G83tV??m{wIP)Rvnco?0|70lU+mX_NhGF+D~(#dhBez8v_I
z;y1;87s1{UwKq3fw7x=*;f!N1E0LTeXbZXQ0qS_J1|2_F)8|8}#OV@^g~-SN`}$Y!
zhwL5jKIh@Lz`6c5==PCoeks@Nq-`z+b%`Ul3`9_w8)#oO)I80fx)cCP{M%hyLo7WP
zQl+|eJhS$|@!p5~D|`dA{g!?X%d-Cd4C`0Pfhxm4`sQ*vO?G(3en_|&1ql3M@NbDP
zd@16sC&bnYB%4gXx4el#>h{vCia6(zqQ6PLA%4RD0Jd+$pAg=7m&EpVmp3|<+g#kc
zUqPIc+vn&93ZsPp0l=@K{7>*3;K%Ig@x$Ph8oj-ZtTuM{F=|)FG>lx|Myg`Co$xne
z=1^6f{ELB*tHV-B9TSB~zG*I0te4+o2T;_1Y)_3Jv>l(uUj}$*#CjHk1oko5T3^Vv
zU|Ggg%w>j25nN2g*|<4U2RIdL_R;VjhvQ${bHLsby0-#7a?ehV`Q!Or<7>-^-0thR
zLYN2GdRMM~(x0=Zj(l<P!Y_(GF7YL;kAW<tvb!&FaKPFnutOwq!dGiN(kUoZoR{Y$
z0DRZ}3Ik>0Uxz=mFM_{mD8A1wm!id|+nay1qFhTgwdKXbqQXdaMvcOkP(aT(rx|Lj
zZB{Kpe97*&{Ld}_0D_PFPVvQu#O*KO*MRS?^lb}6(;%8lZ8lc6MtOH_-{qCc#~P?b
zJPt_Y@$>%x#~+TDKeZo;ejoYCBolaI(hF<mxFa*($s~S!$+tfwo>CE>bC7xp{RaJ|
ze`n<Vt}U#7IeyMMzMFqF-GW$N!8Dr$k)dZo8wbj`jJZdSK)hp+R}J~H-?BHvp9_3q
z@ZZAtFEss6#CLA@^Xb}L``*S2XGYD;t`Z>5rHD}EfV}<X%|WdU>&CY{yE}itKK=gy
zf|!2LKN3C{Yx*aGz8c=Xiu#p_yT8;du^sHFlDb0*%rNO1H<;up<hNmv**+ohwzcuI
z{t1yUjXokj_Li@sUPOlOPz;hhH_2|Y5(oOjZ#LF$ta48z*FFCL1zY%k<D0({_;=z?
zon;K#CB2Z-Wu8}1Mbk?xs+)7a=1xJ|jymSOQ{mUZuZs7-@J^je;f%UFTkG1a!uHn0
zO$ld(uI=s>gBREfMumd04ci$YQ;#Z)UzxPMqpemwEYFKSW^aoB02Ay!5BSUAzrtS*
zYZ|rx0EjLik}KUQNlO%!L{~2?ib0K@BnuN{bCaG1e);%c;7^M_5&e%oBY0=ybT^wL
z(zP3{JvAGIx{(oV-Tqb%<y9m=>a0c^BnshwW<3h);)m=Lt$Z~2AFIWsc+IYEV$>z`
z3=*Zs+JoWQ7tBSP(L^fX5!3_0$Eth{_z&Vw27b;yKKMVYYPMItFV%F5sP3SOe=<8)
zytJMhWtwIf%dlo*dM-P1W_1@myvd~rE=Q};`g(b@=D+wP$BVpGsB2y*@P+oHbvBb>
zWdzoCQcBGPFD%YvXyYep7d*BA&r@F<d_?`Z{{U$36-PW8KD_!ilW{g6)3o@Ygj&ZN
zhK4BRNQA59g-{4^PAl)P_$EKWpNM}CJWb;75crbP71Jg~(=G39?jfET?ky*Em_}oE
zET~2|<7qqsNHzIO@hjk$jy@3Ujo{CTcAsOvkfp=Ik|c^Fkg!)*LdwA7*XxSvgrMq5
z`jI(QjR<bHKT^MG`-#76KiJ2^KOJ;ABsY2#!L^$hoQT7=E$;IWILvZ{k}^g_Oix2z
zDSP6N68r`IgnT>UJ4@TUZCA(oy~X;X&mz6HnGsbh83*K9cEZFi0|^`)^ICuKO`jav
z>HZ?|5A5UPY)hqhs@_+I7dc-%L{?M-x1DdCpIFomwZ;C}9w*VfPw}SX!g>zY(tH(b
zZQ-jp_i&$OU*8-KHwj6MpF|a(H!I}mwJKE<srzNSR{8$`!ylWv#hlBk$@WO_s@r~1
z{uTRU`%itM+x`j9@YOWI`;RL@Td+9b>GJN+{kPkndi>H$TN_y#VG6|4HZpk!1Fe3U
zd<OWr@RQ=l?Ee5S?7Q)=P>)J!(XBMQ>zpDuE@XhKwh<<RTy0k%I0~SWNUNPj++DUZ
z!%FT@eNp(4sYfJtV7Auej=rDPu2|^uUfPXG7+?@K9=R3wmyCblnqRcFhN$*l6SdRz
z+ZiUr8ilpo5TS^k9pjzR)Z?nI{2l>r3Ftp+--BNcej9vM@IS%N2wYiNv*|i@hN*Og
zc)MxdDK72+IS#;wZP@uh=qqPlE!hfGW|M6npDeK2%<(Gi4apzQn|W<&406Io;l=^=
z{VVLh+h6ut@c#hp%c=Ny=$h;5<64e(mK%gbnj>%JjpJd*mm!et$OIK5EzUkf@YaE^
zc-O?XJ`wP(qg`q`q|0q>ZVIV+T;V`2RYp&CAP(lTbrhA+oZ#UuSGoHC0Q*4ADE|P0
zZFmk-%Xyk^dw<ItfnSsQ#Q1F_IQiB^;=gkKE&k1)7W`%V5_nJGzKdlE@eY*MFL!SD
z5vr$?9LnHJyJHYsbZ&cBo&L#xu@8@Y74bnlYw@E`wOegFO`01hgwb4Yxwrx%#3^6`
zMA8m>up^~%sU(@xUWH1PB9hX`kM<(*XNdek@O$EhgR5U$+}Y_`dc|XJJdE+%thU!o
z>k^VeD-a0-it-QmCU1+iw)m$%h0dXJETy)&m8^uV<TR)dd5s=+7Xtt`Gm7_**jM3y
zkL^AU`1@DA)67wLvdvdUW|A@Xl8{`)PB&!+_fQLk&r!!U<R9=)-v{en2KeOO6!A@p
z*y_4%z4g89Ta}$98wm`^SzC-?sUsZ?ONTZ5iCpX0KkIX-___N?{7BF~JZg{O-C*g~
z8a|~hq&i%3ptqCBl&q~RLjl@(1G_V2fo=%EuTcHE<G<52F9>{8@I~yJKCOFgJ-b|6
z&erq1U`Fw(x)5>%t`9tej+N#9EB%T7D0uJV-nXmk7I4{U8iu)deI}tK(UJC9r+IR!
zu3LFSfCv~rdjp!2_S5)*;ID|jGSWOPpiI|R9vruo1KUUhFD%9|z&AURHHiRW+_n#T
z&)LS7s&{YjGObdhqgB;b*H4q_@;;aSsyt)y8^c;BgZ}{G48FRyxVp2rH*-gEBw&SG
zT(gB>8M4O=+^$YKn#}#3t?v9Q@K?oO7H=o}PP*-Kk=@Qr#aR$Cvla!5JhE<+uIvog
zP4ScA&cE^7;opONP4LotY2&oECPk7(3Q}Z;IW2`gS%`;HLC?4~!u$*Phw$vDiT?m*
z4;Je7mb$gY<l@Aa{oT^b6k~KU97OD{xd^ODIRG3Q@#<B?#}_(IKJ1o{&fn*A-lK-Y
z)#b!Rw>8tHyRPTVpR&)#pNO-3YO;M(R=w1s)~{B|`p!unNRUeYTFHha2S+N!z#M$N
z4lCX~apAZ=8-CeZO86FOw4~QA>~1fiwsRa)B=dnP6;d*iG7`!`88yv(0{xjjBz!LT
z>8yCi#`bpFM}}-}jPT!2aRa2#!gof>71@xp;E>^VfP3H+zq|22$FJJv%f+7$$DO9=
z+J>C$&4nVu%Ha&sg5`1M62uG^Ab<vQMh;npDo@$nJFWX$U+cNsm`g5|c*dhztu5cC
z--+t~0JQhTeP>Phod?684Bl!QLztnuo^3If87GnWQzU?IV2Gdv$Opc7#=q@n;fr4r
ze$U<?)jVrFa$i{8z|DA3cfXkoiueQN20{^-4l+Hfiv74+!$Z|=Z||(9^R-Qu_EX!1
z%#ubvWG$bR06^#P&39iB{ulW3SpAVSuL((}{hL(N<c{j<&1G2(QAYB!66LVOBL-jz
zImiQyQ^VH9Rj*o<+fi4xpVsK-%rLYmVU{8hyK7Z@-LEh5KPHkp3p>Y`OSdS4AlypZ
zo=kcSU<%2+@ivzht!1KK1`^)6*l;$Gyz+SDo;p|DUJCt@m&HE?bq|OzYdO{}(&l(<
zlWRg&1!A$tbG=?&xeeG6@|yhax$q9P;tfeWMXycdNfBSN!5Q38^(2grynE7>KYN<9
z>7nynl8q?x$9;MpvwX`utgw*43^)XEM?ve&bLf*S(V%EbCSFBm>fN*4;8t;l`rKx9
z`3n)oe)n>%!Tb$$`VOp`bfuvArN$4Kpq_rcYQCd26(}xb&RF79q~RxKj2o-#tzQvO
z3~dB$ljS!gkM>Rp#aTvgE6uW3mM(==zGeEU<X55p0Ky%8;!6>$#bmHZ@&rwxV@ZkQ
zg<>*$Wkq@A-ifMd_mW$x`7=gJ9Ori7)O~9ocGipA@H;(};V*q|GB&-KA1EF_zA>M#
zr8Zc%N3!Z7SGXz|euFiGaGJrF%x3dXMrAw?Vn<KLtVDGSc|57ND`Px1c>E}}_9<Cz
zT87(9hyFcL7d==4PvQ?X1lE^OO3!N0xA%7gf<Fqy@n4CqbX^g(2xkJ|F!FMWOA*yu
zJ*t)Fxp;vuatN7K$s7Pk>5ppBP^l*!cOp|tEv=Dc>X&vBJo3a)kj)|vV|}BxMnz}%
zbI1Cv_PY!d#~+n(`@MsWpq>amqNIbwcACNsJ60(hI|W0#0m0+&KE}CQ?H&9luEQ)j
zF{xQ2C(s_5uTKq^DbkXw9Zo6NacRMyM%-G$zy)AQCxP>1k8Zz}dv(qCkF+m_R%!6I
z4JP(aBh0&k{YhYjvQFLWj(GL-uQc$4z876-bw3v^(`k~$=by1-&KVh9o3YM#Jk`Gu
z+9O^zz2K{v;?rHiB)8*asMyZe`Ir{RBa`&6l+0<%+@(0^o%LFJA6vpW95yc(E_#-e
zR!gsTV(S{FzpLn4pNRZPW2MJ6q<(ae!@KxKT!|Z)SFqrcYtHn|R^L$Y{JtjAAW5xO
z+{-*tNIcDsGQ+4+2W}MB*TkJR_gL_K$AUk#A+WT%DAVc>muVv>ac&MsC#hlpu4lqu
z1-vDv=vuV?6V;9UiEg4xgF4H+u;*-Hg8l#;S6wK^SZ3tX*F|ogr`6}2IfbiU6yZYF
zj`wQk8L2hiuGYRU(e31n!~;fVAQz2)gastvjw{ly{7lw*EEoPAnXZ1?z{O_ZPZsav
zR^${b>ycgxo-x%tJ29}<Vbs>$4iS8~<c}k_$$~T6HCj)Jw({En28XIeG;*1I^9V)R
z=tg-y^i{)7NhwEdk1zdQz)^}(t8VF@&u#GoO;0vmKJ4k&gb_4LGqic{fY>Yg)D}J_
zk5bt*xA#F$4uKj&?}i^QKJ~=tz9#rn;{N~-zN@L<Tg7xF+4gDV5ZpzB^D}eKdti}V
zhO?&VI!)i(JQ;A8D|kw)B%g8p9gywEp*7F-XiD%=Y5IPrCL)D=8jV>^N*gO$=g>D7
znq}OvMXcXT4YX%5TSpScSPq$T+2r*&uG_=@CbzwcO*-OlCRh-itN><=e-IoM!0VG<
zEu?sD#BpADmu<xH!U9JN?_B*yQ|v3g@b<caFMh*o91)9VkL8b+Y;(MvvD`S$PAl5M
zWYv;YD@jE@olgTZ%<vWA$xaQ;b?Cp$u?LBCzY<;CMW<awa3FnzM%m#<-p60Yts#vc
z%YgUK9V^4U7iFnKs6MUX^A+3(%#Uv`?&Xg?R634Frfb#jXS%tvn`{%?$dHnuiZI}I
zWx*easl{P^jeb`2XAK(M^xe7#z^@QKqw&X2Q6~6?w_*?zoDn($fO-o3+VgAq<NcYW
zE%Bd3$t3>(ZCMu2ui6s7p<Ito*1c(AV|@ytIdY%>)%=ZCk_k@P8yx{X57w^YM@DVP
z{)gVGgA0JHoF4h4mf9u%08zLer_#QWqs3q@f<7cw3=UL`j34vbrH1)KACW^hQaJ>B
zibRWQlW+;%I6ZoIsg~R>8s8u8wL>Q5h~~BlvJvDH)PDnh2A`{0-rVV{CzrN(NMvAs
z^7qA5`$#L~+#zM?LgN7YS3zYX+S-A-MU3Ma8?Zm6B3z6MTb7;yBN*}r&9|S-9`x5p
ziSrW+ka_<ACaX;dj{ZimlX%W~AOrYSk``3Uz*O`sry`nT<{|<lA9tUvG0;j0Tpl_T
zPd4g6`5PX&>rpH@lq_qKdJNNIxxeAfS69=~hLv@3ZM+q4B3432`~-^5nhDl+lIh7T
zbGeR5C)E1YIQ-ZyNMzZA$REnIrb!6np5l^9+5;xD)pac`BwaT0YjH5!BEsx}hofMO
z{S9Y=jHI_G)~h<qKIlAhew8dzk(D5f^`&bP%_FMna#=2wCB(+nPT>KOys&*jXJ|i#
zQ<f4V5~$!DQ<go=-x$cxt!jOo%cmr~9j}KN=kcRNMa&gPP!T{q&lH|&qVhi)*NPC*
zI8a7MAMT!<d(~lQEG)=Wo->2kP&CT=dn}SM92M!$HKjb^z*hi}eQQ5WjbgtsNsxB#
z9XYEvBd8o>k(vg^!TzhvjBIjx(=Q<`-e4m&a@ILM%7<wAiKl(2rdaJ6$i_3$hh@1X
zSYR?QIO44Rk>#;@M>{zpr4KwhfA)-#gApW#JRAc|ypev+F0EUM&;o$;&2rYnsH-L>
zbiNb6)g#mY0JQY`rnZ{h8CjM_M<DubAexrbTe-9oLkm1<bIrrCC(U3<%lhD+D~r))
z-3z0TlE8313i=;XD*TKO&XNFnka+~xkyf;)pqjHbJVAD|Sj9Rq+>!tY=t1_blIGE+
zNLt{0&mWe|2342Qz3b2JZ{8LAJ^9}g43eU(X_4yD+ev$(UEv2lQ6!Q*!&f9}-dAJ2
zmWRIn&0iDrkAr_3v^{UeaJ9ytrCzPZR0kq9R!zzY0JM4DypBK>_kZmt@TcI<?Gy1b
z^W!(e{{V|V60?t1hTPoQ>v!VHNLmO|5wRQ@HnN~4SrmpRlb$~;JRRcOxwSX7)FE4)
z>zwRR>yEy}S9s~*uMiV$cOR24X9=|ZILNNJ#_MtWZdB>YH(Q^fAGBY`4*=*NvNoOj
zG3Xv8kH>n}_`cbzTZWR}7@@fPLMlw;?<zLJn+f>=-M584KdStG_-pX{_I0%Voc;`5
zUg~!`?9pl-B-QUBHs~Z_88)y+#IV|9RA$cHDJoljSKzyyDh)SSYb2e5FWr%N{QFkX
z(5~1ANqp5~o$>NP6j|I;#L4UBZ;|M6-o*DZSj&W&6q1r0?ily=u1XtwDFQ_#B}=g+
zq1#@4a&P0m#mRS)HW+s#oO)NMLjG>M5c{L-!HVOI{{Z#sqWTy+C7}CpZ*OTVme+wW
za0H9n(>2Xas9Z;OQ6%0lPa`Vc-;H8iT?iU@{LPqh+ZbdXdmh!Tr0Nz|A)~yH%iK6*
z<!QUx=r+{p^sf@%mbJBO>19mp`y9^26d&#>I0NZdJbkE5eWlGNqVh{Dav(`1V(o*#
z<oj`4z160JsHlp0on(!G4gljPu6-*@#8y&i{uR1wtF^dMu@uRS@OZ(lXw>)Y!MoVS
z@P4g#X=!}AeZ-JmwiL8XP?*nNK^Y#kx2|Z~R60P0{{VjJ7k{2lD<K>ZJpr#Wu$_hE
zZ*F#Rf&u;$*w@fn){^?Kf`COWDnil$+=cY6`FpkrwHsXX?+!}Z{gCjsqx-#D+lZud
z^2`AxM(=UJ&1W_J?W)gZD;1IwQ)Gi7SRO~MXn1E|iqiVde-T0zq1&>2ZEOr>TLT1Q
zzMZ-Fb>J;V?n~`kPm)KM5*Z+!;@`WF*uw_M?lD<Y%&B4OxMFlx(@v)9la-o0hv4tT
zeM7=}>geKa@!z+Wu}3D!B9Zvs2<K@2G3j4nU1_Fx?%=q*f-894mgZ$XY!a0NXvkrb
zSez5zjApXD6{q-X;icRf#+RY4pKCjnj_o5tCL_0*CRvysr19xq>)`K)-Wb=R)I3qC
z9qDmDnr&{b)*DYX@L9qy-#*}eRpC@?HOy5kB|cmA@7=z}nCt{0hj__O$~*b_Ew?^9
z`zW?8XJiCt<{=3HZXEXPdFH$)ThxZ7dpye7XOs6>JZ3%)eT{ul@xI%^_8u6U#9khg
z?7D+u3GCxSA|?3q5wdV_c?0WT5y^74&2EI@Rd7h_)34W>`fN5?R|@4C+nktp8n9I(
z)csBPW2yeip8<Xw>3_6Vi>%G6c=}y3RJPG&xQa!GP>~u3cY-*Ckj9608b`rTyOt|o
znp#G=sXnEs>$<Bo#P@PE(#Xxsk~u8Q-ldq5O>*{QW}7^Y;S|ZsX9a-lKBlIM<?rqS
zuIS?`00V#(dj4jKM$GG+?`W+OI><FUtIL>gE@loELVjU|T#kTOAL1Qi?^Bj$ov{=x
znACJQ{{R}0->&Az3agNvxUOu;x(1Br=WX9!X(mneH0-=XuW69^8h!1>x=OfYibq91
zP^1G{QAH#QT2kBh5_8n&zvotM;`6mw;B)e0ARa#|w>$;skf%|fbH{45?k39js?WAd
zhj~K<P)W&9yaCT(I#nz6$IPsF<MXX6v{{62^5BePI3S9vbG{ahlw)_xBK_=x+)(x<
zvr|Sec?}$!h(JmDR4L@oeqbbFq*hWpb6r)wn`bS9+*tr)F>NfTo!#;}{&mMg0fOx#
zb>*9H<5G;9H9Sik*4mMtUN)}4-HG?F)UWs_f$)cebvF2!;?EFix?~F!wx4CXkfiS<
zil=&vZP|`9oRiwWFML4K+@ryD{0E@yM{4zdhW;V2)4VJ83$~S5$Qk5iEgGmi42<BP
zdczY5TijZj;bkQyD8}|a;P~8hOK*stA=a$?Pid@KY8Mw8rHnRHJEFD3@-T_i;Y5fz
z;~;_#dsl-)djy_cf;Rh`vA$={N|Vt?&~~mbP4O3owQWx3${C_(wvaF{yAmf*K*o6J
zYpWLR3~hO4>c)M_vO&l~!=TR~<JP<>#&qf>C1-Pb^X8`H(b8!ttQ|UNv>|%duP}(R
z{OHa&Z{4Ul=e{_uU*Z?T9~1cBOBxh_S<GY;LoLFx1;<nKjl_C@E3)wQ{-NRRi>2I4
zZt8<+59N{A<(mpyzZt7n{x<R7kA5%cIxmX#8LTv2$!2vK-F&tUg5`qb4aemijP;`x
zJbH|+1mnH+y7_E(aKtcE#M7x6IL;~Qb!X|+r|{dsn&rQRlfze6cWrlJ6Urw?%0@$F
za2q>fE7V|C9hR-9YMPhYb%ip`Zs2)nK#8!~W>Ru_?OvJjBT&+t#5#_P;EiSr%Q$~=
zas8gh_l&0-WNeR>dFjD5<1s}gtfJo4ShF3wdvV(})q+*$NjToj@iTPl;^#W5&ii$@
zL%;FbN8>F?yhmvZPk-daWd*7z#>C-DfCd};#PN*Pojb;UBe&7N;T65qUr)J2Xo-RT
zRz~2*$RLsW*KefgT8@w5+kq6ZuC+W{;$PuJVZhuvG4%eG;eHnI>v*31ZoETsu_d&o
zAp;x+al;lTfsfSJlRQm4J~mUs{2Oc4^|ALjJQT6n8l#PV{B>5{X{FDtJR9*J;orf3
zgc_!a;H#^xL&ZKTghQ#_-62S=B3<dQ6ZhY1u~NKzz>*5>KQ^6O1OT%Q$;fVl@UBN%
zv(&YHRpHZcwszTyCLoWMPcC|rPx7q|BEsz)+S%T$g@9JdVm0?Ay8cz)iBXc~dmkZ2
ztL9Xj+xqTwSK8l!mclu;iCS5S`60C|U6}d-$JeE9=(hd_{{Vz~UlGRFkz7Lm0KBr2
zGc=JX==<^%t9Q@cs=7ylv>hJy4MsxO_dC93assIC5pXf}s+Y4|9Zjwl+()}{D!D4i
zdlBv`aPq{?l&Ik*)w>Qjsu+A^D&i@p9=BhKh2qZ>c)!G!%N4wa#uWYJ0USJ%6#Jo_
ze(v6(Kpy}kLEFAlT=YI8*Df^mhgqI0iDf4;y9IK7ps7DoRPMATzqq=OO}CyqRCP}}
z2;T}AZYv^#$OnKx$rZrg+1OZJ9W7YooWdKUj^Jmw>MNHfz`B(sLFoFQRh!U-c+<pE
zmD62MQJTu?H9#GrLZfq`5-HD8LiM3E$lCQ;XY*kr=41s}eg?b_4;5;j7`+#|RK=qb
ze)7q)v6rK9&N4fWhP|sn*KBp`QGcZtR_IW+Ok{OZykvXgz3fI)QjD&io`;u93mMr@
z{El1VW}h6kR#$LfA3I|x=uS8|_pW|<ZktLLw?pPVM<9%J&!MW{5w+Jkyc$SW5!#iV
ziuWvgdSF*osl{ig#+M5-42+_Z2qC{(>Zg}ljXrm(*5uN|MpdfaS#4v^d`qS0!!YV`
zvD{b{Bl51M8`ji3H)@w5q@Q`@jO~vd598}xHoYbNyfJuC26efdGzEV0CUV%Xr}T{%
z;eUn3j~9q8=hQAQBa!8{gnsHRfDw|1aa{bQup>3;;wsa^()QDfeoI!)8d)7$)g3h?
zlIr^%2Y|dcs-Fs4c#`rd5*V%?02zp4uD>HLPu=&$be<`>)O;1BY7*Pr!x4#h`A+E~
z%f@z<B#^{$xN<Ahp|!ZT_<FuJ&|$UItnViBV}!hjVlnnB^6g>M1Xr4_yWqWk8K>2B
zv1h4yq?FseSuu_qkI0Jr%9d$Tt!lBU4|e-LpQ-7rzF16E3iv(aZ+SiYpFiuqBAZt5
z?2~vp>FuqV2it8WkSn7<P^A6jKE|kgFw=Am8${K#ZF15%y!97QGRh+=0K)*sjOUv4
zopa&$fc0yiv|GhBoUhD_Wa2PKsYV1h;47N&t9&`q^~>EWQM!>(p|`i1<u<FH{{S!;
z3=bTE?Ov<QBOEW)psufXroSeA);Ap*-|bPT)lOR^_B<#472l7wXzs4OF?Pb$9OY(_
zWedBa?p}XNxecF)G%I*)d`D=|g&1kk%(1ccA%{x#FAe_yYUpPE(${s(n@A<d8(anZ
zqdgcOdb2jM{j|H}FtiAyI=KQx_GRjQt9tm{Goq9pzt-o-tWIGmzpN!=$aKj352<*H
z($`pu$y!MOX&n_tJ=*|%Ghbxbd_C~Z-H-NmmmDy}>I{BX){UHWWsx!!y_i?QUNrc7
zai`uliF_o6YkV0dvSW-da-ZRjDt`m~O0q~&Zw_2s*<3=|k`-c4%k<!i^YI++la(1h
zn)W$8uQf$Fi$&1)-xO&&&xbV`tZro4BqtzAfZI<Sf#7~3uq^MjO*xiH04#v_4Y^Oa
zCpBBeK0CYd#kH966Zw+DrPgRu923S*Jmar#YRHc7N+dA|SPX6|6_3{#74^!kTt*(8
z@e0z>v*fQ%mLf_UT;u!~t4(k5PHS6s*%jI`Xvf_wIUIV|zg_FFX?FA9Ut~lSmpyPt
z7!~t1s$AOm^4njrwYWCU!+v8WMh*{7E9fs0L#OH~EIMpbU#z$-3~F!=bBu%2@~m?@
zRHarDZs&5WqwZM7{hOLp_^+etw(6+_+VzlEfA+3p;Ct8a=i1x*>-ksV55f<J-x4+7
zi=G$Muc3oau(Q^z9y@<9C<vDkwgL>~oOiF?-}pzazr3HFb$d!L?ITJb3IEpokvV1a
zBaLy;<2n54T<{zY-MW6YHMv$oR4_fr6t<E9DYcLX40;y9&3!Y8;<QSJDWOm|1{`}+
zWhNHGki*uj+#|x<?Ny^?A21xyw+%c5${3F083g|TF<DD{88W}v?QfKbn+!WQ2Bm9D
z+Z$K%=LA2?j(E?dF<~PTs;)P8;}v~etaxSMk^mj4bSCyPQp-?{Lr)g<QM3>+e!O+B
z)1TPi_E`OzKWV=X&!=iyuZpaEMWsZN>PZ_~MP@{@GR5Q;hF3fQ2yzbW;A0i}<iEW{
zQpHN9GJ0d)zV-cxHJS7ehkqCK?+(r7-Pw3rE65}rQdp#s<6-n=&0$(iM#<`JLe75j
zx;}0FsXhwH`#k(;)qWYrB-a)&2<~+2tE?F&w3$OGB~?LXZPBp`0cAJ>w0~r;+2wy}
zFO3$S1ihAPZ7Mr?ueB?iO`>R^h;3bk)s!>J@uwj0Nx{W_hJV3B{5z-oYxoJ^pW3&=
z(Wq$Pj^6k$B_O;Tm00ut03P>2qW=JTLtL-yle6G=!Ve4hs^tV)@5NsmX&P0S{x#BU
zuVeG%=c35l<8M&td8zi&bZ^&hnWwUft!|tAkDvblZC`*t0KaE#YR|y_3B2(Y_laCX
zbuZh^t5_|ho?!dY#T2Gu$}j-h26N6U;oFEra+s#_;$nD0aBK9d{t7+fPYmcEwnm$C
z<6SaMYe|bpvU|NcE0bpPp=rj^I;%2Z;F1PTF_D~!{{Y~k{{RR66ns0-ejMHWF!24Z
zj&++``EEkHypF22O&Yt%%+AT?xC~Bt{EAhbqMoger4-zn(IfKq=GI$TqLfBB>-<3d
zYI&_9mf%ko#z{Kt`9hygYxV2HpASE0{cH9I@ju014fr{9&0k7{+CA0H%$KvY@SAe;
z#?oP859S;qr~q-zepBg?+jxorCYnT<ahT2n0zF1}&#y||R8qR^M6mNqnQD)}e`7z{
zzxI0lqwVJKkBP7RM{DAHD`{^og{ie$7PncFG%+*9EStcQHwFWo<BIv)_J#0g!4C}h
zqF)L84!YN_KeK1Kv(@F+KF$&5g~BS^h*b$tRrfQ2o-y9OtN#FkPkD*^Ncfr=10i1x
z++_&|1YTNtSH?dVEsA_r@iIU`k+t1OkC<`|#GK>MSCu?m+?|TVQdH^2>sDv{4%7Y_
z>R%D`TThD@Qd@X>d$2W2h|3043fKgwW>w#`F;S4YBD>Gpt3mh=qWoAoPwee>Z*(~=
zVrO9_lB9PfpL#_knRh^;4oN(cGI2}$7+Cx_*8Vivcu(SnfcpKWt$hxi1V-)c;<uhz
z0P~$=8JGZm;bk9M?*9O8&jt8D;eYKdf1!9QMHX6Q7M4=2y^qR@&kB|ybHOK*>Ury1
ztV+6Xx5($@e#y0M{%6DQWaq@|YM^E{!(*TTXP<i3lS+Gww6}ngS+?gN-Zh<NdmR2I
zn&fY`O{~%Rqn0NFlkHxOqv}yfc;9Ioq)nJ@zb?>uB#P*UA`Uk&sV0to>r9H;{ONXX
zCl-<Kk(6K-&q4I8D@Zh}ol%x|KV^*=&n$e5{{V4#>U-5s5nO6E8im9$?uo7+3P%}?
zp2yz5R^+t(n0^6%&YlVJL>?5e@m{;)D2$iOs$9s)7m@PZe(es_{rAfnl+H7f+j-!r
zj`osqk3&dO)0w`9<Q}Pc_Lq_?c9CGnL_^RZ>00(nDbhhCIXTLoy@C9z^%wTY_#^OI
zPuc$f!QZss!R;Y@KAQCKTi$7rxiAxP46<YbaIF)<$VzPt2s=R)?Z2~k!mrss;-~DD
zu6X|dS@6_e9Bm}aW2)-%PWSg2w1NrexPyKcCXh%~BSiU52hGs(yUFTr>?GyMb=>?n
z(5H6QS`<{<a0meQBfV|k$1S56r%rl;y)j?2pR<_#g@0);72bRp(Y_lW4^I`8ml|fI
z*DTj_q(mz-O0B&n0fU8Rkq#Rtj311?AO6m|_m015`_I|J=IBWcj-`2PcYm}8TT3@#
zB%yk4by2W?d4_n+3LVI)N&Buk-$UkKhMpntW{2WSU0>oxgx)35Q+S_Dj_s`Ekd+}%
znu^Y<8w7!j;~5=m^pE}u0q}$2FT$Dp4n7%ZcG?_Tm4)oHrM}{1^3AzT?8S=}^Bd+o
zoc$^vi~b}1kH2T{8ee=O@JGW-KNCrzEO2U?maBa*l3DhHB1T62thvLog#q^ffB;XU
z{{X>LUR#Y@;iQ^kd220>pcX9T1#hxpeeu$+Fx3|6rADM%O{;!=&&C+%pTj;Uk5G+w
zN9JITfU9*Mt$N&6Q|lfsw$U_bT3NLhhAU{7?=;e|Sk-;mh!xA~npB$Bp&y6uV-}Za
zz{2c)Ne*#?^&KnpZ}unnJ@#)MH^onhQ3&+!09sG1NiPf>#~XP=gVh)`tL~KK)E_O*
zSW|T*qK`lQxqoHLZ`t1G!#Da2vfSHhmOf3?(vU&Fx5u4~D(y)Ft461u8<UPcXZTy<
z4+MBu#n!slj`Vr8jWz{ibEd^~_MUE3g+6KqU=@fY3}>cAez@z`+NbT6`w(mQn%W6G
zP2s&l=T?Of?r+_@+#;ashP0MQ5r4d+jxk?~8a9ilYuAkpoPr>!x!sB5w*$ROl5yrm
z;$WdQC@mhR>A(CGFX0!%-vAE;UwkysE%ewmD?7)#5M`bA{h6XHSdzszzdd>SSLa5T
z5?yLjYKbSwZyLm_)tsKddspjk{1kpoNfY6=ono*^WU##r=CMz@wtR!lb2lFeJ`aA!
zp9S=PAACdb^7ymG-Y0P)Sx-A9t+MU&9aJd65v!uM;KiK$q%9=<>lcWl&02P{O5c(B
zH?H_5@<S!Xqki`!nArZ6m*I<KxoJLSUoYjBEbVkYwff)V{{V&`3H~*H%dh)Md?C@7
z!+OS<Ifqlzp>~o9HcGL|xPBC>$g-;_A1+jaa7TCWC*hy$%kkIYj<u`!L%>!Ro<GuT
zUMVamyt>4$$V5{kG@vp{K_4*rUxqj#jXvgU3n@Qm%(YiuXXQ4DrRdt6@Y?DkOvjM3
zAyJXoKOE!Mxxb3(JQoS!Js#DrOKg{NBRrWM0)@^&uh!p&pALQud_VXIo;Uct;Ol91
zO+xZWbc=hrCxY(bRDi~G1bImt0twrmpWa;Wd=c<3!JP}iULg2~<2wlTEn3D%%IVf~
zI>eFPF%U+I85zpp<hvXw$;VpcjDLB|w>mKSby=j<uh{%b@c#hAFNjwXEyL*7v8Hpn
zSd3#IM*#kn^xe;cyltpjEvAQMaAt-R69`Z+1(O(jecX}PrGCh2o)!2b@rU51z2k3(
znp#-hX%Jk?4AQ{x?rUh-@g!``zBk%eIVZRy6~+F>>G0dcH@6-Ulf$s3+S)duEy$Uf
z2}g<}8H|D5%LXLz#w((YOigNwo}KBXcGvt9pB05str~KgQhP0J=6^l6w>q<V`aZi8
zM|Ep1#z#$(DCoe0j-5q)mGFbb8pnvd4;G!PqufWNu&|Ei859?dK4l|tIodE7=Dxo1
zAH+}Cd&3_c=J;RYuLTbg%c^ST!&LD#)8SI)X*{e&4tTeOkr`xlJ5;a^d2BC^9|Xtj
z`|&z&hJOn*X1&pMI3d)uduc90EE8NwBr;l=B_*9ik?&GYLby^+GhWpNm*V+WE;=j!
z09QR6b{2H$UY6|7@VAS!eJ06t@9e1VVMpC7+y#}`5;OBS+PrkY{#Evuj}OA#YxZ5A
z;!lJ0m@G=%#cvCjGi-IbSR3Z~L0prb0QTasJZJFt;FrK31!<7{QPQCCF28XHpK%nD
zI$FeV0|??=DNXrU%c*wcmK-f~o+$BGhxL!xf^C1pJ|2fmw0V^y(ybqSXhrB!L1i2h
zmn)BK_pTb5o(`2qLoTJt<F1#xU7tsN4oY|$suNh6w^rMGKI{Gg_|x&T#5XpYZI**~
zAeQQ4Zf;#h7mS5aI|0pn)2Li3tOX897&zy>eztrf{hxdmp9VF5+JE+f(dE}=GdvnL
zh9+nskycS}8@ndgUDECVHbSD8D#U<2AHaVA{sMl~-w(B@?fe^I;V%;C%Cg%@qt7ct
zAa!FHbzq9&o-(hMByd%-NUr+Xge4WttFj`79mWxYYVBPk@#|W!j%f6~A_0bcmLs@4
zl75wX=vsMi6{rAhBO}moKQmvhKe0c;{{Vph02F*9;(aH?{uhBYuMfnQ_j=Xk!(1eS
z>+J<(juzV-9!kVA21)8#zG?Wi<1g6ue}y-H4*n4MV^mFE-gt~YAh}rgTzOJrMtAc}
zfn`m^AdNsIe5;N;?`d{SsM-pnlCwSo(UU_O<)V0}?ITP{;y0$xXW9+E@#gtHXUXPl
z&duL0>}QJoKJf?amHQfaf8Yh5?IAv|<C%0j?SkG2&4=2{yp0f2h5&=-Q<J|K=O(`}
zrPEt};*MB<4ne~E*Xy^$J7$~y31#7Xc!9~ggCF=EJxrfs@Xa+ltJz%qersV2rr`_a
z*Y{+O{*)wAHk<aQATvHst!iqP#7S#n11T(2vFTdVSwkM4mJ*P%#@IW34|=$}9Ij`s
ze$D>?v!=ECMtnz40_oQ=*ut<i<>ldDGs(9?pt7b{a~5?~862FF05$d0Uj_dF;GfCy
z0lpBntK$p3M%L(AO{v88t7@}{^4knz2Wq}SVH*r`8HY9JKk!fAjGi3$Kk<6*Zx^z~
zW2xGk2f82f$9#zCBM<IwOfl>Zdso+g9e-h6O7q22{A&H4?k)8zeMOq~eKX8m&*q)Z
z){|$;b!_EPh$Hw}vH*&cm76<g#Y&$vBX@m$4}-sHkJ)O^;J3u9-vxLJP`SLa5JPdB
zvZ6(4M&g8^RX7+q+B)>F2-5x(Yud9U(#ncqBO$%Huh9Ph3+u<m9~#B?#~Tx@U0q8Z
zoz1n(t*6MdCzlFt2!kdyTp0dc$C7iL{ekhrM*W(;33#^7^H%WOSM%Ihua~DIc~C<f
za!I;*2`9{wQbH=QJVs7MdH(=htIFjYD|ZQDBR1z3WPT+{;9Wl3RCD54RY7nfOyOo@
z*g50tUFF;m4L)ruC{hGhDHK5!0YWk|2k`^mzf`|yABBIjHk06OALHh;r99dTLmctw
z5IKwO_ZKRyZiMd4K@hvl6!H^*a6@sL{{Vr01$=A$moNS*d_T}X;TF8mq#>fWkhotf
z%V$z}Sy7>oGO<-5zK1=~m08kGT5GyJ9Hyi&l_gHAyMK}R#ib2TMDdIl>fgMMP!4)D
zWZ{84<YTX;eS>qU>DJfL>GJt2=imk_g&_1f=v4MStJ}Y4pV=?Kz9zKQ{xf`6p62Jn
zUK|Z@mbUEB$#XO@`LZjOA-1X&!lNm6U^f5=J-@+kiT?nz5A7Y|ulQ45Ebts28=6Bb
zT5H2~C!USTKwk-#VaU$my8x*NHMTzk3~VD-ly4|4dw-c&sQ&U!4RW`k#r!jWhxT4G
z)AY|7>2hn9*Ow65CAhqj1cF(lMG_F8`GIryLjZf%seaY}01P}I;eUu9*<Kpc;j`0@
zl>w68B|BP4UT|L|oG=Ky{{URqgWmio@fX2=+HT9?^{v#iT3hQne3sHV3}v;JSlvqw
z#4c5_kC+^BUsHbD{yotABk>A-KK?oGF7EV8N4Aa>1y!S-f1TNifG|kwUOr)k#NqI5
zdrnm=G_TXQ%;@3R(Uvhfw3XAf?YE!E{NeZ|;P36<7HT&dozK{;boF?eM<<meo-@XH
z2Nm@l*Ze833+M@bqZ|E8R$;!{IRZ3bALC>}k?J#nUAM(=iJ!Aqz-zA#_-n&H60-3i
z)-?FFomyL)mv>mMK=Q}Sx-%;UW4CAkZR6(6X@1t93~as~#i97u;LH(N>6-1cOLGf?
z?H<t+D@LqI0oGrbWbNC>TJmeh5g0qw_hW4p`u=CBm{h{zDtky$_wBpiPssU0U-8T~
zx(vFi-P&G9aXCeWTnotLVT?$sakn7x*i}CZXnH2CYip<2P4=O=p6VdmcQbs)a~_GF
z4}VJezvDA$S|yg2ntjcyUR^wsO0aygMpO_toM4tDkOp|`UYYwf_*y>@>JsXD&Fe;x
zL?n*hGNM_VKPbZxg?)2eb!gs)v~sdXv6e;qCsHcy^uKP0&_577OE$3-P}%9$wrLWq
zE|z<+<pvjXU;&Ubf!C?@uQt-{WwO^E&r;oPcVYJ_`Fr-Sr!^l5!E<WY7V5$o8x4}a
zWPQrM!bV5%w*b}}cy7+(X`MXTUju2BscZ!R4^lX<vBX0O92#_e=-cl<Ae~%Hb15uD
zd1ZH}ZGR)fHQU+r>xO8jX<|lEl~as2Y#ie?+TCdW7qXriAve-n%LH>qjVhqSbr>Q+
zjEr%Li^V#8$>6*9K*CQtHH0c<S;J(j5yJ7@kSkK>!+t9N0EJ-%$Ahe+y73O3GNhKH
zY|~8yqn|BVf)b6_&~&de4~N7o8^qz_*1FkrJKi2RjBF@Xl1c4-5$C!W#=i~t8QW3O
z^y`yre232$cpy5GH$uu4PTq$(&rU1mKZ?4Bp{n?U?fQISV`mo-6dx-qeBh2w0qNGh
z^ziS3G&z17+V~U4wktfgHjO3ZOn-R{ld+Rxe8&U=26qAJn)q|YQo=PR(==!v-U~J`
z!4nfCih%LYC9Bfm2>WbZX}D|F=jj>d5{456Mx6^Nt*v*{=6US*v)@kFW)?zuaH-Iq
z!xeKv-E(}cZdoK=tN~US_Z^45S-p{t>8|IV3=9wvfG6oxbgPqN6M#goa@!Hru){v8
z!}{0Q;-eJo&yaOpt~XKDBbxSkE=hSNJ5=NNSB&J=<T|CrmYw!gUp3Hto4P3f0Bh-0
zHBAcPbr8B-FL5fwtam302SbkLvJvDsf>KQE`()fW2kBgCXpy}-v6iUnbSSSiZ9%mn
zzHFXZjLNI>#1Ccn{A+7L*6n20ZggvRWPt>5OL9(GNN<~OO~DsD6W5B!@g}W(q1dj2
zp)vF30y5{e{{TQZ2hzQ#!rlS!^{;^~wC!#=H$>|rJW>>tl5%oKP;*_FT9oNkQ>(V#
zg;l3I)TK%mJrBY9{{V^fE0CTR)LG)5;3$T8#E?o0VPujP=tA?5L9CyPKN9sDc?`qD
zu*rVRV=`$>Jc$_&3oLHike=(4Q+P`EOivHV1S$i<?2jDTkhjh9A2aU}k4$2r)gjPt
z;}cy;J+;am%a!vHkF%UH{{Vq=Uz~9+BMnnGl&N9)8qwc*d6?6oR|SPn+TB07c^^B!
zjl4<XIfbM?AJo?2kI%bxxL;uB3J2nAnbo{^KA(9fgpPv}$r%eQEjTk4JdQ{qv)3Tk
z)-&o_MZTDqH<BWlPWF+TmfACgT=lQcZxr7yvEpr0T)YNfGE(SYhFtW)^~Xx)!%~G?
z&BDogo`zYDo+_)MJ6WUYuL%4=@b`*r=J4y=Txt5HiWUk^HwEj4Q^_8LRqZ=klTw;%
zn}Zy)#-F<)FvXa3+z0cnC*c>vuZTK@?YE9K1%V;AA)zu1UP~XjUI@qcx%IB^$G!-T
z_VR6S!wR<ZE0ZJ*lvX~f%lBK`rDtCa7*%|m8dt^AjFl9>;rkjo2Dhl%&-QIn*!j|*
zmuLfn=o__r7l%9zqv~=s#g3V6D?G8a<-$g(<*M|JTNy0J?_$1J@V~`n&~BT<-w=$+
zYKMs?wP~CW_9RWm^RG_v-|bi7jU&U*=zj}*$s6}*Z(1^m5#7(pAo-3vk&42O3Z+a$
zA<ORFUG%Z4r%t3LE^9kGdh|Uj#J>tP38cN&H1&!*1h`beJ61Cx!mAQ-TH1fZOCJJ7
zHMYNJ1^%Ud7TZ`*tf3VD0LwyGAOY*1YsdTn<4@W@;%2nBw({wE&4r_V%gY#s`18t#
zMN+vP4SiWYl&q%i?%AxPlOB9Ae61S)0C)YNpMI6~Ikz=R&V?4aQtq#z`Tj2nR|m;D
zskYv(+J9bWhg#WO*j`6#5g9TB;Cz|xKM!i()iiMh;StKKG9SK+kN*Hz_BGf40A~p`
zYi%a=qJiczEt24<cR{!e{m^mH6UnU|U&FeaMz5(`{gy(%F>L(Aa7Y9X(!7^WR%%Sn
zSes2r$4C8WH{ojCf5OA?`&=w#hO1}uBN3n9CpgC+OxNr8&FT8r<p;pu4lU2bT|WN+
zQ1aRf9Y)s-$E=1_+86zy-_pNbv#0tG%CV-MosU|C)T4N#|JVGHvvvTTxFh|n41FqV
zoG$53+$YJjj1Ydk>C;(DBrQBpo)?~bk80A<&5PT#x62@cQdl3n7og|3!S7#DIJ;Q!
z`V^At1-6YYUPz=a865JAr;bl*wJfiq{^CLQi)nsf%5qfu8g{*-3r#*dyGgv*H;s&>
zHqhNc9)hwVxRD`{LL-_WaLTzKh#kKQ=9S|0Gj_4on`?Wh5_<w9UZf7VtnHhkWpCjp
zgT*q!I{~M8jdb>N9F^RsY5xF$59?BB_g9y9hfkjp7X&PW{P(UZn8?PWruvvP?Ak0z
z1IKKTa!tZ<o-n6_pGx~5_9Teg{4n_GYR|-85c2o?ZkL+*8(O=!6UQC6dE|~DuB*{N
z^*yWU&x8K}wr`F83-}suhyE7ue}U(=mKRM%>ibTbTbuNe-Gp~emobULhS~!z0)R2p
zo?Jw)G;4;MPu@QG{eyKKH{*Bg@#A0FSH!Ylq%zH5(v99+H-E;f-|Y8Fvi|_RAg>+x
zr{e7!RsFTT9egkFiy!PShq}gz;oBu&x@EVzfLuBM0B_729QW7>ua15ce$pO2_+{}=
zQTWl|`wcr!xRowq8hyMjx^gAgCN+pi7)24<$T#p+bCHl|_$T{9>fZ=_VR7Q!KS<F$
zMX5_6oi4SjSGs5>4rFPdWtJvMosTb)c;gil#KxSi`kJb#!ZCe1`E)(k{tB)v2kl6$
ze7<M=NG!I;gUR1Nr%Lxf_$ob%Y90mnMdGNIL8kqV<`Zy)?2YG!%1D7dGU*$Y&-YDy
zFZ)e?*1j<Pq)a+rj{H60dpYf=Nh8)Y+c~1t3@WY`2A*4qNrdm*QyAO<80S9i@b~@-
z5##>=1AHvg{t_J@Nbyykn*v+gzPS@V$m1A|7%QsCv5W-Df&c??;~GMyk&@_W>qDAJ
zz0a^b5#c+p3;zJYGdx{&s(~H0vFB=btvTVXq-$u^*kgd^B3IQ&BdvZ3!)JeSs!u0^
z;+f9yA$ZOTrVijw9A~C`SLxl4{1h+ti-*Em-kGdeXgai--P=y0_7;{4XuPQ6TX-$E
z`9dTS232s`V#T)-O@4fMb6nKZTfMZrMUL|3R=F!02#_-!&J_><tIk5G01kr!wriQM
zX`*ej)WO$`Xw<0<c~U=6zu=O-7S!}ljhfx|vnFSg!*eN*uF!7n2_?Inj)J}d_^l7y
zJ~sH)1aex}T+|vRCzZ{_gpfOo*V2CqzilsyKePV;hu=={FNZuSb2#7UYBx4hwe+bY
z@7W}=+{9TB;|q)c26^U~{{Zk&--x=7uKJzF!{3K8-AwHy)5)XEW_M$jV&?3USdvL6
zIrXk8vE_OmRZL8A5%=Y<q272C_IlJmXOG%1;N%*G%(7niq-om5qi-k*Ev}ky9u&q7
z5m{8{KQKJw6~+Gm!A{sO?P;h8H}7i;1#PTGe{i_z&1mc2x9`N?5qw9}J}qm$8Ss_v
zsiax8t&iF)?j%ca@+_ukgI*R{flvd6;Aa3HYtOzoe$bvd{ieJ{q3aWPYebJ;g6=OO
z=R&lQU)?@J#S533c9;gomYB|X9dTJVrHtoNmn)24Q^Wjs@R#Ce!Pc~s;@5}ow3zi6
zL)lAp9})}#Kw*{MppEEAb;#VJu{;TX6~4Uiug?|4Y=mH(cLWb?SL~<l&Eku{7W@<N
zN5j90iEREZ6UD9S)|R9ox7!4<s!4}!h5YQ0tU+WXVR4_#ogYu|?WVPD{hw<DED~1?
zqa=;EJf5{ss-q=MYQ<9x&r{jOo+;v+yIJl(vafv4-AJ!*A}&Tr%PV&S9M|jr0PP##
z?}NXwcf&sowa0`kJYTPC?IPUj8tIa2M3NPaoCqUjOcN*W)pnh#a?E+J$$uBNk*9dR
zM+ul43{Z6&h8VA*9Yubc$NO`9T=2KTh;)0KEfd5mVQ&z#yVdRFl3)-8MwBe7l1QVY
z?NWNSNhIrtrl>w=_J<lVr537up67A<Z%?jkf3P3J*=EsgG+V3vZ%=clY4+`HZEBKP
z-#YAtnVL1(gSC!9Jo8vTvv!>ppZ@>^)cBWWZ@3YuUj}9zvwfP!Cm!|STK@q26f5Hg
zihdcl!>xYPNYol}aA(wSt<3ifXLoGIjC`2<u^~hvKb1fWj8nWD`*VCk@Xy1s@TWlV
ze}daccm!84O{d&EcSmBQ$#NF$R#_wp3$YxL*Z?VVjp8Y}^IuC_Vo&%c{+`43rP6f?
zBXmDyzEH$qAM|NBHT4hd<9PaS{1m&#uxcR!U%sskscddCiDH-K?oXE+@#&iU{rC_2
zK=|ACcGa)^LEzmZ!@7p0sY+yt^*d?gYc%<TC8<k@jA+5Iz>-J+XBp0y#BbV9$6hP(
z{g1{t?R0+)Yx;ZnzJ7&n@cHxSCP|iAX7e0_w3GLg;en@Xvnq9?2uEAA?sb2(&%>Lm
zZ`wn|9v6>Ec{Po1#In7rx|8;mzn!Iou5bu(B91r*Bei{{{{RI{((U5VJQ)n}skyed
zyGW#8;4AiUaC+m4@Shcb!AQO_U+A_={g2`3HJOv_@oOt<BV1c~gK21lk~*W|k36p@
z<;N9^@hA4X@$c;e;8XC!#CmR<6!0u@G|35)>OmPs^A>4YiWg)B_H8GjBpg<;gVD0o
zsVZ}fUHOmCy=zvu)FOgOGxnGWDJc8d75iP_pNQ@72lh<x{=Kbf))!jek6rF$v()1<
zK=x@K6uJVtm5|2aNyqO`rG9q&9`I(J;qMy7;(rzCwz`3^eb)MBrx2Fbeac4ZW86Hz
zq~~sa@C4V;e;&VSkBDA2(T9ZmVd2jTO{FRbY_F_rri$*wsZt}2<hhk%$WWvz;ICoE
zIO^4-Qg(yaxy=fylw~iq(?jcD*=P2xlf+*X;`nc^cxS`91-`8lN9O3(#^21iMK2I@
z<}$J}?^Oh6a||9xKbb$Yf5YYY2l3CuJ`vNc%r@re+-dmxpJccmY(Ng-19@ltr8U+3
z4*1YM7Wke`XGGHUom%!d<1n_XZqplbjkrk;*&`_+I0d;Q9AdpE;{O2cd*UA*>sn@`
z<9`ZxD*k&(1eWq&=~oiDR1O4BA-pA_%a+;Ga0HH^s7IAJ$(qj*PPHVa^*#Rp{t7n~
zcUQj*5>iyPjq%7C?Pp&>e%QYiJZs?(gMJqHr|{oK(lxCvK)-E!`!Ra9(cDCh3#cS{
zXjw`-blgxLabKK%KmD#eb^AhDL*VZlX#NoW#tpUQm94^F+JMN;uspXi1X%(EakPRx
zz#_N9-`i{A=fPRe!_N<`ma(H5-XmqI#`3I6*)AevbY)f~<y&&~A#zS;GH$Js(Arpv
zeDc>xTVB>Nd{g^sd|mPP!%bJ=!rAE>Hko?}`)~Gbh=eZZOfY19sLsl(mK^l-uSxxe
z;n%bajYsyD@zv{T-Wl+XywT5lAIm18EQl0Yg*aIOW<qoPepnbJGWc)DFWUp+SH(RB
zZ-y57ZjIsXBH~!&y|U3Qyn}eruw^qdGczi<3>9!P26Kw_>+jlj{{Z4g!ld{ot$16)
zwmK>p-YaVxIiT`nN8Tie8;3z5CAT)xPUg>3T{ww*D04Jr4p~h}$-8apeT(}__(ngE
z{v7bv?Kkkc{(G$|TX?OnZHpHBG+trMf-p!89E{002ePop1lL92ABW!w{0;C=Quy_*
z>DL}1h{|?ba-c?+0@$&PmEDHRFm5)E2Xpe*hQDkt68_I039fWM4rtmWnr)8aQcHPS
zS>?DmUo@@4DJLO5@7w@(uBYIS{1hcMokC9xtQJ<wc?7o-Sj}x9fo`AvSX*s;h?|`G
zgy*jrInxD2SD`Alv(x?!j^D0251ua9TKO~W4*>Ym$Hrd)JbB_BBg6K(CYG9Y^mdDD
z5Q(Qq?IbK;48fU6#~covoY&3&0I)6NUHo0t%r06L(54)YtS)VUIq$`Bx4*Xk0L1+x
zTYV2pveIrpwq7eZY^H_9ot#nmo@bX5NV|)NJNL292Vsigd@1`}c&p)Fr>Sas4vpdF
zmh$FF8tzLs6PRCV@}m&T3Zz*;!9V~WPEIPZRp*9(S4-n|@1jQ)4`+y}PY~qWjrv=5
zeAV$|;f|ZDd{xDS#V+4odx=^4g!@5bg<reK029rAoc`FlmG+tYMR*GFHm#+(w4Uwd
z17L|S3pmeWRDA&Ezce(T+AdvN_KW`jg_)^nS_D66QxVg}w1&l|!P_guWyz74?k9E&
zVB)@~_^10%YX1NhG}!FCO{QrHG%UVd<+Z$taI2h@SCVNyL;OldHRIxat_3<Z7Q3wu
z=~woYxn(Vx+<(DIH2(k)T6{y$wZgKtnRTWMXvhc4H!w{rvXVI5QdBArToO80q}b>_
zBG7-}p7zk{jP{pv%N^n}{oxe%;z-U9XN-gX>8~d6=k258n=cHfhxL6wOxJBKN0l4u
zFtf;oN6Eiyg>?gfc82w0YK{K@?XB@z=fjs;9rdo0crN65UTdff-deT_#*njlK_kH1
zxm(oa^I1l<Hzz2&HcOp8X-U1&^-sgU5bL);u|1Xl0K@BBJy!n!OIMEO%H|PrjpW?3
zN(@}(_7Nx@dJ)bm<!evd3*wfEZ*cw@({wl{bi<1~5RSnCJc!s6v}E(slU^6_i}s25
zf$%;{o8JiBLiRI!_ZGJ(WVLo20u@;pJ~7I$Bw!qh?R;JPZhTDfJ-ybM;Jp_@(j?Si
zc&{}ZD{~#wd7KsVU5)`>rv<x$4Q(0oINnyb;%6$VQC5>pD;}-?00j7zrSKQUTZY@Z
zOB-in+?I;?l6!qC@=DU-jk09zk0c_2#z%5{n*9&>Gy6t(>)_vmhlD&mrualhGL^fY
zdn<>zyURHrXp%dWS$2nIQM3TO0m;vf_@hSBd}-o)uM(RLGfWpH8|*qgxwo^3g#ei1
zXMMYf+(vPdP6c`jtaWJOtth&1f_kf;F4^hVx0eU(h|EX;pYIWz<J!Mkd~s_nr~DH;
z!&dhxwbJSx!1n(DOq|!o7FsQ`$hKN+d!%O(AVHGO#~2tHIVS@hdRN>(wO+5Lcq8^~
z@W;Rj<w<pvHrG0oaWHSP<8zdJ9vS45<sHJ4(1KxBZTrhY=tU@0mHJrx&hT%+{{V=;
z6?L!px7tPCt^Jt-%X-%7xnlD&R52tC%oOcW$-%%lu0jn0*HqR<n|ktEN}+~bc_bVZ
zAmD}vHTnhc7vevQe`lDrNc<kQ*D0$AjLCR~)n$Y*Moo_y2M%%xAo0?^ch!71ZF8(#
z>ssizn&Rf-ONm_IW#wY!fCCuZNx-OhnDk~+q?8j|^*dkL_x5PhekkgCzlQXwZY}RD
zv|F3#B~mv_aHSF$+%O}{XJNPu90P(6HJ|${d|&w8@Y~`Zk>Sq<eYWb#{`M<-y&8S*
z1cFHd$jYbvRDc}$i-X6_xpEI{@TcOJiT(um(KR0oO=??Ew-HGd#wJ#cm=GHUSYd$s
zthMz#KeUd$r`_CY{v7b{g|yvbWM-23D;5&I@4QPafr%%dn6S@KYNL!)*DTiN)S(M0
zMpkQcv;C^PCE<zwI(U0qwh=>mKF2NY(ep?a;H+hU{xTN;p1^tv^FQ0SUu$2FdPa#n
zXz)LWp_?RRhx<*?<MOW@(R_R3miVD3#4T>qP1bb@2;Ox_n&J;L<=Y@ccz|ev;xIT-
z&}SI-4~ah&N5vb98~*?rXu3qVmJ%za)E3e#%^YQqknYmCFtK7vXRjcPobpwbDMiPt
zHg#^M+qK>PM{D~-X$QnVvVX(afG5kjwU{ri+bfU<>s5bd?+o3^`y6=l!>w%638cS@
zI0sS<tXWaq9Oo6}^M2HS6TT(svv>w=1H(2tJX_$mku4_kEr3#p_UdjnM<OXZTW$wU
zq-0lB;r{^Iu5X9>QhYS<t&WX-<;Df{zh#zDBNYnLT)NDv@-PE;w*UYDuWF7Ozt!TP
zuln1u#PN84u5->>THc<gsQd)cPO1AL__M(N7&ij?-b<!fw+UzYqKZV?InMp8iVso<
zua5o){1T5}{h&NE;yJZXvg(?CrEPd^k^$#U9lVP(tb;gJD2FY8N4<A)e$l=*_)V>k
z5ZQ|>1k_|{w!sjV+Tt+T+2yD2S8i~iwnz7lO*>Nkwskl)GkM^D3}~7bv8*YMeOv5d
zZbG*u$lk>Bf_Lr&;~>|KiO<wrsL)Ot>vgW1rLB*$!(_ERj4DoQX{{Q1c{itH)&Br#
zZ;HBe{?s}zf^?AymJ97F-tyrQS@%53;v)S=4a)ZKT<7i2@Z$I4AMFdE=@u7j6xWt^
z7BR;l9$NYLQlXK*y|;F7c;dcN(|#y;Tj6)aIX))MVGoJCNpik$i&BWq7`SE*ZnlO^
zvt@FwPDW1|!5mM=Z`%XnSH@^8ue?d6=-MUCmE3aL>UvaD$9_Dm#Ug2*X9h6Mzi!cn
z0D?Ksj<G7dCwBE|tM=@Un$+GOmNT}A?bhelpB=n8@T>Mv@ivR_=i%SO?Ki|4j-zOo
zw>r;=Qfu!zMv<c0u`8nagr-WS)f<!quUh@OG*1|Kir3-Bfnuws+ev4r$*PD~VnVT{
zkM5LY?Os_x&q0jW&HfJm0D_5pT=Cw3G`&kt(N3pi3Z|%SSj-MECXE_Ki6uDBLhaA+
zV~Xm0S^Hghzr{WQkHTIhv9gjlt<u-+5~-Hv>6JidF6?%*l?>%T=e>7N6NZa$lkKIC
zo5WO{snk%Gy$&bgv|3!=A=AFqr@?WiB!U%!;x&<30RWMZNIlLzhQ59H)2;X;z#kPg
zT_(=%*2WPtVg<-3xj4X3dV}p=%i#|c-)lORilat^NRT9U@a$Msn<Q?@W+%U;UHFCJ
zJv!URZ>x=J%F@GCx<!iOSwg;8g=HJ?(iQGU0=V({jvEz>gQFXJw*0O7p8o&_;c=L3
z9e-%<q}7+-f4t%T3w(DGY12uhTU~vQQedBTy3Ddg&KN4b@&3;MgI?v~9Y4YzB5|ul
zuG?C<7+CLP*vp>IRGxi^$ggvQ;cvqIBf@iQI#lm8_JjAat1Gko+e--sI_D#U#d!v`
zq3N36`ZU&Y{ijp7Fj<(Q5i<}03As<q5M;BTrFvOj71hHz;U#!o_f0;lQ{#B69F}2x
zWiCpIEuM}&Jr6bbxu@u!6wx5kVx2EvQMwN_miY+L#zQawV35a;#<}yV#4gNsP8lv)
z0*$X6hl~U}o1paqs`$a}bh}uzT~qBkmZJ+1XA;ew>)t)w?Awmqk<*&<xa>S?vsn1A
zRf;V((g}A%ZEo@x+&UQ<i6`mP+P_ob=tnKfqN0?l>iZ<$b38kxjjtM$U%SzH+pUjk
zo8pdx;g1R2=~^a<ad?+fMQ<*vmrHbzutsFa+~~h_o)mV;&3wn=zY17fYquZTS6dk-
zWCk@MI3#rp7!Q7H?@RqEFBf<wNbHtLMf0SQD}~s1VBj|#sRUzfe7)np3$CGmaeo{}
z&rpH3-PyY33jjWNm|%j#lBbN<=bST)aIc7|Ur9FA?|pBr`)Yk>9Oe}0&W;}mQS#Y6
zwCt_&K6<$D#jS*|Y^+sE{IbT(Lj&LDCz0N@=kWcmo#6b+Jmyy3908sWdeQ#P^BgW)
z1adGz81)~fYiGn)&jyC@SwK=Pf;^sBXYW_DTNQeoyy^NMB})-0!&F1zITlM7yJ5Cz
z@Qb){v>wK|JKqYl{w9-Bf+l-t+_~A#Nc7LCuST_1Z5BH;o9vMU3%!TU_+qPBU*Bm6
zZEfy+$jQjuzc=UC6}<`FS)CN?xU-Y+-hnQ+;%F~*IN3r==g3(|<Zz>qI3Aw0^$&@(
z;SP><8!K}5(J${Ec-{2KAmX@h3BmoWXVi6RAIOAxk}i6M?UP-Gqp4bWx5L-6TQkOx
zL^eg{WNbY!KAyGNPM<Q@x$NQbF?NzC_*dc22e*h|zVTc)6X_GFXNfS3#Eh;Obmy)s
zr?c>VkBei!)~)R)w$ZI7c-7T%;ep``@~g+TO=0NvwzBEg_jX9eRI`a43jjl>%5XD|
zM>VakKC$5shgW);xSrC}QgbRQeanYKg4}WP_wH-vc+V_czh0qJmP*O4{)awMjHimP
z^(xf)lWxzqsq*K>4+%{UpB|%Rk9BWpDVd>@0IK#Rf-(<7#d!zz$A#{sT~z3BHL#Bv
zwdLc-T&Xxe&b{FH{^Is2C6eAt*rt>~r2#C+Mh-K_9rIr_{7tabJ{@ZDYPu@5tQX2<
zjxbf@Bpfo32Oi?SKMOkZUdnwx6X>uwY8XsgRAjle(DZ)}c#`7I1OEUBey-A5F(>;z
z;Xl>qs)cTG^fl=kU&M_*DHh*f({>j-UEz?Ck5G(1OxMdcz9-Rdtv=T+iv{7OXbO^t
zzXXQI;a$#~ujv=i#|^#Ekq;`83^si}VV}s>)gw_Rnblt|qh5U0S3Qfx`gei+T{Kp{
z6u*StKh5N@F}Bgyh69o4E6hA$`#kDPq0i#YHgsl%GHuo&^Q!P$>-be~5Na0I&_%1S
z?(S@ec;|1F5I=;re)pzDeO=%^b`KT!NiTG}xb5V)jO>o_3){%Yh8vfdpFQ~%?c_LE
z)S*{b%({PGWKWw5IHzY5l=xHP9~^i;!4PRW8<_O@m3K;pKfZd5<d9Fe;<}#?{A{uC
zj;nKRtV0TFK6lS^dHdMdAG(8i*gdi<hMT~CIq-~gYFdrGxw37>Nv$W|sz1DXboUkD
zUOv}sJVJ|d9a?xe^S0q<LDUh@@%b9_l`2*0x){i~->Thuo{PcRPZ36HdVDWb`swC<
zpW<8p015c_#Fq2k%l(5nV9aDKC7CV!aqP*6`(TRtAH;3oTTMMqz2bWt^t6E@WYSAa
z?7(nX$+cDg03X7?EBre-I(Qm>p*Fw<%UMv)yH8{cMga5;Ud!R#O6N_sOR;h0LWF&;
z-Y7oLa8MqqSh#OYj-HjlI#sgz%~7hKO;zrXm92)Sn@SXMG`-li?3+*1M18&BO*g_i
zy7+c2J6KD1w%07?5zMCEX~CTc+@yTO^vEO{{T%+xwf_L0rT+lFtKeM+U-0LQd{5zh
zQ^R&q#6I0-yX8>MsIsTZ4@~-=wfY_QcmC>rRrfTqeriyto$jx7vGNe9>}NV$t`=`c
zx<CKW{I0mTxwo`4IRa)X6KT$Q>yM>D;XPSm)Gl<{KX$V2nl3Uq;nyFoE3WZ=lO?s_
zd&Q0=R8>R-<gq;$xvwevR;L!Uh_JM#+D*g+CH5W&I2q)6_pe(Ojdbe8r>4gS8dT**
zOR3o3*;>su-8Sr6vyfdR1tVkhZY!SDu3t%!t+g2K=G-`z7RT~EtJI~t@WN?d+Rt!}
zZ_$y4<}Ll*gy(Rs7e%(yb<IxeS%S(Jvm?n7I`Ti;_RmV3O=O;(Pc}2X+gcsIho<S;
z--Mu=<c*FBZ^$GZk%OM%xa(aaeGgTJ*HH4-Hx0H>!~zEx$>?#?sp(eJS?W{xQA;dr
z;&=lS#z#-{>0XSM(%sswpRb=XHX8!~KU35ndhzPFDaLD|m6DT9=*-u;vx*jm))hq0
z1e}KVC%$U|Z`x~fJaIy&cQ_nm_8z=fLx12K+kHQNi!73Wa3hqA<NcG+)g3d#w-(w&
z&#M%QJ95%WqnG)6A4=%wQbeU~iZ}YC^4g|}rYo$M4TjpiP<`W%dfAIthQ`(~_5l%c
zfwD4j!TQx-1o(SZ@m8U8soT#Dv0$o+CgN5+akMC}SZ{{*wlG6BuX1k|GEbMZEJpyb
z1P_>lj2>$z4sBRYN<Qi4dLEB^scMaSnsK;-<|d3QsM>IHHnU&@?^`l_7x7y^ouVuS
zBsmaCDyt@YWsVLj)a<-P0?j4SiC)x5iemr)7$9SBOl-~vwR#I`wn~8`9R}hF10x+l
z>0CI<)o4v}EeB2W#k=Z{1J!;R_^ZJF9vZ%&_NAS`Se46?$FUv9t}BVsq8i7<OMC4?
z*jO_MV1WMYfTc#p_w+p1vH0u6(`(w|THS|RTT}><#-KkSeEV~P$JB9(ewpEq25VS>
z?jCnAv3T5hZikLlPf|TacS2Kmc*>*qw>of`$y0NuZ6%?FbA4;1-ZI?8V~Csy)k7{1
z_l7^nt{=rv+Uk0gOQXdcp(4p>RD8!gXB8f|ZLj!GT$980?e>V|Q6y!?Rzz-xpSnjw
zRD3$N*Ei<k*6aH|{xC~I<M(ZjxH%af)o$|C<Apv}bN4q-Wha$!0Zfs@3}7DBC61|c
zWpXbxsbsdfg@o|QY~>_L5bj1s9IGn;K^P$PHR;-hlP`*G!M<yCbQlvv<CE?yil4yx
zm6YC7g}8un8a4}`VsnvNLW5?CF6UjN_@47o@P47I>Q@t9YEww<aXrhll1S2UU7d+$
zBe^Fv%;-0>+uxgLmPr~2g2x<eMnURHCxOQ`N5nAeI%bm;cTxx=+!ZaY_)rgCda=i?
zbC!vDs%kB7U|Q1IA`d*k7G#GY7U1NPPZjH7aFK)NYg5w0E?BuXbeC}Hy0)>6W_FH2
zjHS2|6&{R909UtacPpxRUOx|ME9c21OR;mbg^b{qB=qg|rSQ+dZwgIg4yj|QOK}v+
z96Id<v(F>3^#_{tKln$qPY+xRI|bPwh$t*_cIPLaoSsd2INWUt_0=fK?K6uRDvfhn
zL+AZB;m3+~2J)}tTU2wyO2L?c{nZ@cR>azA&}W-QQ@iZoZjhWVTb`YdrFSs+pHGKZ
z)O8DqHpZk!6COgwI^*g`<6c|h9cml9>2)iDR9!!qu&5=U2ZN7sP~Dm5rsP!AbqH)k
z*UrSWhy-MIBaVFyWoY+ptm<*=I>}Ea)a?idC68b^C)C!h#+!4isB6LYOFaCsAHLoB
z9e)~@eOF1*ph)!FbYsaF-~rPl@_Fq<AH8;I87o-T*Q~V96h!|3VBU=%C^5G$+k@*_
z-WJqk(zOeDHBI*SDI%)e2PInv7;jAU^saS$X{bRlV37qU=1>nloc17AWV(mjm9xi~
za#S3UPo;M%QLLOhoiyOu)cdMS$mdfYsWGqvm78I7QhIaLdSq8OtHa^hF8=`G2h-kL
zyQ`SI`7TR-?Lax)2X?O`7y87KSj+Z_CW__=w}RV&BZe604;)r6g}haHto(OfH%gq@
zL$U1xPOBpYRR<&=mnDV<E5yR&?lt-9d@*Ixp*&38I!|9SFT~nDx#15Nn+0Y=ut=8(
za2Xh2{{Vri-Xhm^oo0I()+G?z$G37E?-}dQek<5MCTUvNh4q0w>@TS$&9GrRu*m?P
z6OFhT$y)i&JKHPR#k4OZPN$HnKm?q8%k}iHLe#1*cs<T~v}#eQDpRwznjV$mjVjl~
zw-P&}M=FiNRN#yZ^%c$8X*$%ptoqKCaE4oH@QE?`n~qLTulY5*@T0_fPlr5DCYi5J
z5Kj|EvLG%MpMYB(IjmdX3HYHkYm03$T5sMk^CQSp&T>yq{pg`hRI4PqAzx%e<0ZV*
zE?~a8gY0vIks|cmI`T8tx-W<N)RJn*myQn8lGy8k#~$^MKf=F?cQUMTwY-tZf&R2I
zuY8lp81G*}{4MxF27@wcT8kU)L8Mhk?Qmm7!QA6;4E}=y{A(=B7losTl)2QUvR-GU
zLjzW8nJXpK&+w;+U&VS9TK2bMw#a04*&>~;@f-&sln+C|;=LP6xQ=v|_fCQ&ipC{v
z%7J%G#{`Ztah?u&>s#LupHlG^w08F1QG-h+OP1g=w5MVYdY&-AXPi`!X|D%_t^7Np
zSzg`Wmonb@abhG=a+#5cUPd_1MSTWNrTw2r+DDh0bHb}1Yg(kD?_-|RbX3*#ojz1g
zp4wycXOp}f{${+7#-Sq9Z*Jpky|9hXQGq5#e*!tLrXaAq@b;Z7YiP$(xloXW+TKDE
zH_FYOtH2-}^)=z%GMi1a*NIz;X`+(Jpq4Z~a1J?8%m%`9*BQsXc)8T+!!oA|E1Q$z
z{{SQ3$}LL|Cy3LV;XX=^=rzLJq>H?r{E?iFb6;QHA#D=F!~!2W8`$zbVi$V-;7H^V
z)K<=e;r%k_M}+?XXX)}=T}Qi!6l4+8a2tWi<DaEgwXwgkw3kKjtm1o1c|5_V-Dk{=
zv4T{Oyaz*$)ytdbG;ygY!KWX{inOXiNiA-1uw2|)T?wpTZtuROff)Yl16BNAtH`#{
z>6Qrb%Xmm-;QXENaqIe5XtL|qFqm!ZEX=t709ev1?e_`8QEj5NqBPe<WVk<h<RD;r
zDC!5&ylBoh=UR){OIw_bJ{vIUE2x~xV)>Fc>F!9UrlDr?mt_j7y+`U!KOtQJ(LgT0
zUDC|bd8FWWBawhO2Rve?wf@Z{{k|BJe=JaZt@v|_r8jL#QA=Z+u(-a}Z&nL5Xl;%J
zlkMb>Z$b30N7Zejw9%xov>ObBBuuB~c0A`j@;cPEvPCMidPx~Umyd2i9-{;KSDpCE
z;^x~@h6v`s5eZZbj5d1X+XI^F!c=mp%C|XjYnD9S4?>s0-`ILpt<B`mBYCPM1BFf5
zBOUtVn&$jbG*>p}7>-#pIU}iT_0LjsUZXYSdiI9O&Ntjzhe7Kn2lEFt#aj3_PdR7R
zW%E@Ae1t|NvCbIrfs@S;ttq?KdZDUHF4WeB{ed@HJU188G&3}6%&W+8+auPr5zt1_
znH@t7*$xN`?VRGXW4$c?ZU#Wc3F+IXu&nb1we%9$%O{o_IFWldZ2thVd(=udWjk9#
z$ox|&zP_7Nvt6EBDn*V9Z9PZxucUlAa~`XrT3=nN%PYpLbN7RH861vBHS;#F9M*D6
zZFn}%4gfu!N$vdeUiqZ>XIj4S#hCEj!Fbs*BBNolc_5ymy5~()ibpLuI7!R7=ytQn
z?)Db8(K3&oStVk;`ti>lxTiOX^z9n%A*QLnxC}pcoLdGRpSxqfO7kmwO<LIwjVO`@
z4g1987C-GCrn-dCb#n)kWey9j(%^m7@3enETD6zWE_7|}6q8ce<nAN8*XOx{-PS9A
zG1_Bqkgp>j^=NwS2e0K_eUFA?@NShBiRAsBZAm=#Bw@B8{{R~GKLmIPMU78cPbSXQ
zKXY%;oOO3Tr>|On#LYPPyTv*diKogfr@3()Hxl4Rs@)4P?0+iuu-T08^QFlf!aKk6
z^f+gOr&pQ|c5h?IFSMNtSJS4@wPskOoEX++07)Bv3oBzFgV=THUp#nv-M$$3mr~HY
zQ>G0;^W`<ftjZC3q>9<jNI3&F_aBI~-w$|RMDV7e18NgmT&$OFi-I07M$iY$M>!y5
z;=I4&mYJb=tHf5ifPpOmhBy}>g_MEv6)eX)x??#9(!PH*#o+T?&CtU;Hte5A{XGw~
z%d%|WE5OSUQFB9=nXBr*rLKp^7kaIxp{;$c&eljR-Gc6$Eb;<zyq>>Gms8Q;*EC5q
z$sus<D`Aj<z&vAj8Lv&&w7o-5o!HuaoJI&9S{#Bu-pzA*Z-})Y7g|H$s5IE_t|6Sr
z?9L)*jeU3kpKyBDi;cll!c=tT_BO-g);A8N9dFq3PYu~i;q6{KD1^guGG*{LWM_<V
z!4>H7Xr4UrCER~tf?I@4E?JRP7ruEIABAw5%Eu0&aU6x9lHBDZbMqr9_2)J0p9f{u
zJV$+VC4?5Ul(r%F$kBx-_=h~7Zj~O=QgM<?vEx?5`z0j%-1aX5cspP4wl#fnXrkLH
z`Cn$3Goj>ycLF<c$2H_0I`IaF<DVNOI?BAUTTEcFxsWLg+p^i)gBx!4#eE_0)?bHn
zo39INn#4Bxo$SjHjIk#@2G-ijUnc+)UnzLY!7rhFN5AlemGFj3X?*D9dviLF$2@@P
zhR><SYaBErtIkeG==?cW6*9QAwNGy6>V3fQ4wa`^_)f+|w$u%sz6>EiZORKIio7rf
zl21N|HRJyP6!lF%NYtz}Z60W?^=9(}$!gzdnPZS(g-?{jxMvJc*1lwm;djPe4&Zo;
z#v0u5B;XiKR)4<PjH%c>#$1lX@lx7&A62%~?(ghxu3CHOnl{w$qIH>fFwc~%6yqEg
z>DX7Jf_c3<j*b`MdfM7wz24s=<g=LY{waNDF`9CX)$ez{)?4`_>@73Hp8>pM;OI07
zZSQUMINfD!Lf&K(M<N~@E(&8j9y#Q5Rs2=(`P#~DA{)^q#IfcSxKRtgp-xJ+N1@Gl
zCa<sfSHSuQ+HO^gu`J7OhC?i<nF_J5IKb-M4z+8;-xB;U<6npN_qN)MQ^7KMg4S7;
zr?i*&lY^8a)1`X&e8)V<=NgK#sHLo;?CslCsyXu7IZk6%bg;CXX{{k8b$4xDzfI3k
z_-wvA)3nW3!q+yJR+8ya{kjHm<w~bN!MmNn@()V!eRX_Usa)P`I@j8#mh8I4adG$M
zkpSJ$1C_}Bm5F2V3s&&O(D4qDd10u={{XqTvea(&OmKKuiOUduIp)3Zz;a*wa`9XZ
ztlR0H5sOV(0$R@G++49xShBX=s0KEib@s0sS-Sqyo-^5Us*dtW=$rEEsp@C^Jm;12
z!fCiI8_~A4xA`AGU+A!DmXR}+n&CpbNID<(c^s(eUSp|2=R9{-Vp&z(pR<GWWFAzG
z{@&H}e~&aPuk9O~oeCDXw}Fbh(8fw}#^(FLXQy0uubnlhHKn;(r;W>f)iEv)(~hRT
ziw#9onwxrmnfXo*wOHO0xt%_zbut@tuy`4Id8{_5?oCBwq~2;}FP20_la)i9<EDOx
zwRU<(fINS%TQu<6Lun6FCG%`8pKRAvWAM&sW8dPN$j&$sS(CJZ#y5_?L0#Y46zIQm
zR(eu|xosz7BS5hix^|)A^@{TDIioR6Etbvj=c!DCmL2^&*LC6F4fs1nWWBrdFQI`_
zCyAUwNFxDVKm_L;?fokDn|W#Bz-_d+Qrg@{<^Ji2d_N7_Ad%j%?(|;<U!t8BZ9!NZ
zJ6*4uqa5wufcQA=n((ohyw%s6!_?)B)vbNL{{YbXEQcGFx-o`5YZtwpntkka*9X9`
zeA-EC2<37iwqneP&HN#WBriN?r>$mcdQ3J}(dqs?y1I`=GB2GBK+DE*e4sL+kFOQx
zI@CAnlcQe3FDAKXF|>+piXIdM`^Z4)USo0M9~O8H^fl%F#E^`|Z*sB{r~QM$0;(CV
zGOk{*7=;-=l6SiQ0E6azE66hF;$C5bn@_TOf5UkmyQ=t)!%=F|$Kgn4`w(rGklY|b
z6Qbon_2#_W$6gPx_<^QFYvM8XR=17BQ$`8c`R4<p0(#W){7bjiG|#fGuVEC<!Gqo^
z#d0y#QP}Mu`eanMHW!!iL43DfVK^-yaWPaKFc9HE_8eEv)OF}oN^gCSCcX}wChF2n
zO!-Rh;D?R0`@&LPJjTB&IFZwE?Vpr@e;NmZyjf|!+Un9w{q7N@86DK9Is7Z>!zQb2
z$$L00ExfBFn@QUwK>Wa{^%bpssA<}dk8U*ElKM5Y(X93`qi#pY?Csy$x}%MsE$3|y
zPL4jbTvbTjGvpmZ!yYd2Ja+yc&}=mesY8~OBoBqhKue$>oqDgruMvDf_(7}9;7=3j
z?-I<dac>>uWrE;jouK5Y9PwXGTYN+DPKr&AgQy9fAWUo&Hs)T7j&e^*@-H4QiM(gy
zCGpRU<+#&znBfsb@Zk}Z3;?Rx3=i=EUD-}M6IQ>mp%k=!rr6we7K~MD%KGcoSiNnp
z>t7OVH4Q6K)-C?gDl~Aw2bUY6{tz1*aoWCO*K}_fcp~X+JW-}dw&>hQ$-NvO;@mTX
zj^euwbHuHqSPOp<>9fq9U|84$nl^L&$5OH(K7<<e4~5<$gH*YbO(s>an*vJ<&R!So
zBuqwe>ZcXiUNw_%5qqAd4!#z2obZxQ)Ab<u3*+dlW|P6UtR{t|FJMRom^V~x;0zvy
zzTA$};l{0Oww^K5u8Se&OF0%S1MUINJMmwiUN-Qifu{I^MY&jDy7RZ&+(yPMka7rN
z$LC(1p!`7bT-Nd1IMc5hF^@J@e>8e-5x&w-u0IO%s;Ej{{)^M8;O4nTC)%t|3Rdr{
zN9p&&Uxr>Du(;B1z7E{SsN0=J)_b(nXa4|5wpEo&tY|iak<jA4`}+pF{{TFb^A-5N
z@GnRBi>&-b(KSn0blWIbQnoWo1XpF*7E~;!b|c9d892wie#w1<TmJx`h5rD)5B~ra
zT^Jf!tr~7Pb$B+~-~7)%5c2x6QpD7cJ*@ic>VN;y{NuOKwa1bRmyTt*k#-BKwJqc*
z9F<n;=eB;e)mdBV-Wn;U*{O1ICB%c72pqYPo=YBsw>8oDk6F01h6rO!O>oQux!eM%
z>IXu3>rnW1+TCn!8gHIwkQa8&Tk))?Rn6U>F6Ap;=LMyBM@rWV>hr`SS*4NVT=Y`M
z0Q-)att)E?G(=bu-^+_@E2@s;x%CIAr|D8Yo3BfuZvE86ydd@`sQhc9ztVL%ogU8M
zZvgIA=*#I%w%A>?d7i6cA-RWIfrB(820hv{>-4Us-wNTPA>o{EQr#84zLiS*&oecZ
z#8_p>BcLFTpO0$Cw2^FNdlF2x<w;z9<UPUqQ{~pg9h_Ru#&}e1mMm|F)Z>D`{c6C}
zWRm{f^n9avs!ha;)q(22Us}hxl4eq|9M1^|ti4@v-1>^_Z*+||`$dW^5%9`4yLT^+
zLms*GtBiV;7iX!ZrRZ@+Ev3?A`!(EAj&sC{I}m%+*Sd}EwYAOWqk{2=U;|}yw69K_
z`d2Xyv2(K48OG;B@~|B7$2D%wTWwohhQd+%gktUq;!+1sd>XkieWO#tuu7g7nmd@3
z%c;noKx2Y}KCW}vjMrsz0*Ex|%(#kG@(cQvJxA1ZtZx=-7FICZ>MXe_`>`7ESzG(G
z-w<kgcCX;LqqdNgTm)=kkoq^Jb5vf^DNIzo$@3yzZ%fnsO$#QN;V&(|RBYl!3E1}_
z*FC3e(KXz9cZn2Q-b8<OIT#FmH+CoAipbM1VU?oOwUAcp7)zC4H;%_6oYtMfi>O-O
z@s{Kv=_f6ZuUgV8-LY+Z+8hS4ajI(`C%U-Ivr80>8U)AzSA`kuJ!;*bh9kJYGkB2s
zj;XmLPn9$7dY`RY@Z{-Zb9blQ4U+CIxjYb0Ps6Qf>J!f^EtE(1(`2@JDHt8TwcAdd
zZFULjVO&}mjFMXc3|m259R42lQ%=;Ld*cPM`P6d9o}S(6^s~g*!phY=&n-saPs^Ub
z`vJ+UEoK<)w(1&2WSR#uNXz$I9Y%UrY#K)KMPCx$-&{_MaU(W3P)5+J^z}99J{YjG
zv+&%CiXP(m)-rvQoP7mgS?LSoTT!U#!1<ENhTVXOI&<$`gas{SOARVEo@+%BB!h@h
zpS<jQu{`lz3c8CrBUUOcG<V(ryiFEa{5dmiNnFnp1Aqw_W&JWg8ndo=g4<BOxUm7z
z?BMd#KYBoT1Rmq-Tve8*X<>4=(ho4i7%o2ak`4|%fulu&`^9?eJFq|jlHy`;vBo{m
ze!z9jczC=lDpplswYkk2&st^<o8ey(c!;;y5f&A2OUiJhgPZ}}4)xSY;OV?IbQ)Nm
z=IUXSazS9(?HK3)>0Ybgy<X=2#$7hb-x66rmc#*uPN?`l-W`4OTC(c)=^=*s4WsTq
zHg`rG=dV(r{#E7A8CDfsYC}Y4QwHL+dY&#kGk>TO*L#sVl^i$IC#6)m_<ehD1Vado
zAYunUDvy*L_4V|xXP#osnOR4pFvxF04|<K9L9#zE677$~)VyU&XoysKdY(&f@Rr6|
znCcEn{{Rlg!9KaIH~3GW+sLvQCV0m<SuoB20M@TeidHQwu|ioQVIKEgz`t|HZ%XL&
zJtpf~)P|jAsw9zE?%Ry*8OOC;ahR0uosODrE2+jy;hSsiGCMs9KE*T*nB1;#amtg>
z=RD@Tufu-~JW1hgLTlYqPJ>Zh{$RMbk&@|7bGwpr^cC)=TkRWJpG9C$3CT=-(hmgs
z_O1A|{cFQvqQ87AV52d@uyes5U!`(U$Koi#s&l$`div^mJn^G8;;pv6t3!jk@tB{=
zvDQ>vLl7YrRm73x@-ThtpKk?t17EvarM8MT&gU_fi+8aDHSH%<@ND62JWqXQtlubC
zQz6f}Wdr<b<@Mi({2d3{tu+e+V!#5naOPfm0uLbjiu2`)l1j<kZ!dG7wR5^W<alr4
z=Z3tIJ*})!&UX2%LFcx6^Uo*Jx|=_RX?}jwqFOcNJ4~$0HX&5cCoJ1X9X~-(>wgUN
zpNSS$-Xrj{%WexicRy>4h=XGyNY#1oh8V_rdRI~5FN7AJ8?YAIw!baxAmrP@8(YH9
z>6R?V@Bn((vw_L6_}YB<c)nS6ZKQHxu{atNZoNjFUddgx<aOEy{42R*l<B%)jzx>?
z;uzezB%Cjt?%#cw@)bq}M>Xz+Yjb}Wh2ipMa4j0)UwBEB0P;q-`EqfRbB@BZJWt{u
z>^ZFbFX33?OPk2$aLPZ_Ll7I~=rY{=zLk&v00|F;G@DrTohZp9q83Q)qHKhBU9U8a
zkG{vAd)L?CavY;4!^*BPds>TI>XLp-ugvuE`K~7!6&gRgruu2B^Yah*NA2~C%X_&>
zLj|<EBrG-wM0oj#&IVW>yz`S%$>ZBQYq>O;Exz2(DG@!j%ZqkypgIw>kUzVV#d-bp
zrR9P{;r&8+ZXiVyBCtJ4<VfAddQ-ebbide^2F~2YJ(R*sAmg5dfA#Cl%=nHo<)?y;
zmv_<ney5X10SZaVPU&=AeNOL6@WXhX`E_3&9k8<uY4+z~kw-w}AGzqA`kMKd;?4bt
z@y-2~i4eE4g~V}0rwXA|WPW14sZ|=ygjcb<MyCzH!(;=;Jv!IKekcHIcW~g6CSjI7
zq>PhaJC#$EtJJMkw=KWV@i)!!KGGE76{7x^+<U*lzZO|sXcB39<<eR$yN!<~NMv$O
zFh9C$UTe)SRq=)8wv%v7*3hhraS$pc^(&s^q4gExp9(&~KD+&$sV3x*70;or>qJX^
zGsibpS0SMb8$leza1MAqfXJ=uVd*NeZ%dr?YtPwxy4dQj{6VMb?6&?Pj3d5ETq=cE
z_sAJj>MA3uX_Ef(IHbEz`RrVfJ+n`3P7?2?-=d?2`;)Hdr5XFfKXs4gSrTcH$Mg7o
zB#U!?@)UAavD9tt`Bx017`J9?3azhs6`NSOjzae8$`8wohF*Ib(zVs$yhc_F9I>%k
z5T`@fk7HEBLgbQ+n89UnfI#Vwr5X;4d#&nI#h)%R`<g?>02lyPuu9Fnh@7R&k~K8@
zIrU9SHi8?dnNHT0CkTapowHoGi~K{PT6o%feL~pWMQ?B9qVFq)^(1n6&3jWDUklqa
z+K(}!f$&d1I2p;WlXSf~n&<u!KNFub%yLrRLQIpn=PWve=kc#+A;ZR`<yvb*O?qxt
zgVg#r;hw*5;ynsW2yDL5Es@F>kSQ_efUVT9=N0I-`iF)jxG?>$=S{f-mAJ{t^d`LL
zN0QC_7Z#{i7-ov&bZKs$RuiEBrU~`uy><5D+r&`@^CG{54z0xTpF&4mdy3-B9WSg)
z*~ZAV8Ad#>p|g3UTIyiQ4a|OekCco6-rRpW<F75}vSxzd1aryt2eBVo>0<Epv=%;W
zQ5L`~al-+g)uE=h+AYnM!V9=T!QcL|Gt&$?$L1>-$C(*(bZH(>sc2GbchPBfqt22Q
z`J5c0XRcfQpFx`Tj{_Mz6(mO5Lor?@Rv1_I$5D>m>(l%%ZQ<x_1X?6Y2p>9}d71am
zU&6KS^c^=-Qpny#z`+l<86Jdp9^;zzaJBI9e3*#o+~%uBcjl!2Mwj-!fvq6F9!ky7
ztbs$R9f%{JsHtVv{FG5FG0L8+fuErqX1H6Q3+s0A8@VmgXe0a5DG#^mKJcnm^R0!-
z-AiuM!Hhg{ovnk$LFtjyWLK|8EW*wl(0uVimL_!7t!8?n+~0VE!y3WTK!t5>46YB}
zBRJ#p<kyi}KCvc;Z)>TD{>^0hj^<N`UB?17B!EL6oaA<|U(>C14I<8MYr{7a!!^~Z
zjcv6Zk}Q!D4oDw#4xpUoyuZX&+S=;}O7Uu=URvBoIS`OTMlis{co=2$73Jm`>dNQr
zzOLGPYp0>><UB7=KXa=rN{fQ`l3M=&m&~W)%_qbfl;7~NbZghsHArI+o2dYjIpa}<
z5(eR$A1EvHV!lk*t^8{p`McNjsOPb87~5FQG(Z$N5=Z8<l#@6=DeLK8@vrI@5$Y19
zjEf|&mz4x$fh2568AiZaQ=tTN>s}e6_=Ch=FV)&5hBOfG+LE%tA98my;a5HR=Du$e
znZprTVsO}HO|9ME{4zf8FT>W$=*tCD66YIPUfX_ujfy&l#eWsq+(Q<tCGFOcB8|}A
ztSxgA5=Q;;5?gOxc_ZGiZ#3ym$nmd*FV-T1cbgil=)>qRLHu)F9<`y`HL~hbymuF2
zw)s}BJebsHIR5i{=N{GIX)d8{;@7wE#E)qt>zQMZ0UrlBWIrkXbm`+hv+q||JnGp@
zAk)LXUnOPp^*zShNV)M9y6TeYcG{%ON19_2M&%EEgz|lGO?obe;NJ~tvP=D&ZF6fX
zu4MZZ;J-|s8zB3GT$jUth?83Ck!kw$QINoFxr2~?smW41;MSLoJawg8YtqHxTPv&U
zsT+7`!AL`Y5oT;0XRsXCjf``ZS5Ay<x;5DIF?d-hsJUOJyWH%&8E^3JEBH0-E>&6d
z>w!JW>N0GZfM7uZG6^`qu5ZOwm);=#pl+``GR&{%$8Z>p=&`5_OOET#IqzOSuif~=
zTGj2u+FXj)6QE_bg}@_$)1R*6iu+az9|C+Y@lLSbCApr(r1ITZKxKJ@BK)f3<lyuK
ziu5w9JSgJn{br{sdf$B(r?*E3i=mZO`n-9h)vfEL{{R!#^zR>dlTU-ndxW}F9%k>F
z)^r&AyN2~Eo-x+F8s5jlo-OdP@x9E6ahZ%)P~5q=B2~`{%YsK7dRM}pF!ANqy>lLo
z;W(s<+I}UTW{so?o=T1qGww+^>0Yt$gW~VP{{RKK(V)AD9?D(r(oF2FCNf$~<Vb%H
zrF}km#Pr@G3k7#YZtbtGx~m^AN5aOkYPBb2h}~<V^6bZaNAP!p_4}#p=Cq1M<pD=l
zJ6oQ1lY`DH;J+At%R1(hB;F<XcY5e;RJpgf9$(3v;ba&j<lq7hYxIG>C3qvo+WpJg
zJjn8|nDW@#$k4hDhh{v*<a!ZSwaC67>XFIe{{ReYXH${j4J>M}xMXwks4Rs~9E^il
z##l<tx>8rST|WDtBZ9A)<ye?u>P5RX?yENS*2!zB@ZZ5-33$W9TFtE9D6_S*o>kql
z<^y+kC>))Iu&}7^NhZFR@h!%mWjkuK+7Ql++oWE6z;X}F4nYS8oPJg1+Bd?N(>@_b
z;!S^38mzGfTR3lSCK5o;!pP+a7*m`E#(US;`i`aW?%(08x+jR|gHqJzlPL|7osq6F
z!=AkLIXSD#GYUC;XY8V-R!+^UG?MM}`_H7~n$_z%FrDE~mgzkkZ@-!0_TD-1exIZz
z_KSZS!*Lp+vv?LTS3N#n8z;Y|QGXHJ-rb_=x@6EfV#?O?1o``w$j9qXgU^c4qd^?7
zMQ#4BZg(g@FFlwJ^#+pZ4v?0`{{VM3WJHtZ^4~nD?~lg0t77U`Zf|$F;bAD#rJ@JC
zymRErCXsWHvN3#mXC3NAms7EkZtiTv*3q2F5mE*}m_Fi(BDledDH<)@oH-c!am{92
ze{a}G3f~!F$09?!k4$y0Sgl^Cp*vY=b>w;{hve`Ep*&M-DJ7(G7A1s!a)5s5;Eas*
zt{cR@IJ$t`X;MsYW3bC5t*yE$6=U*}Ukq2=6N=<w)-PgN=F^OE&C9GsONR)4ZVz$X
z4%M%Jtaw{lwYI!#No{T+Ox(MumKfNcv5?+kBX{(yo2f<f)=!hYo3+ib*lA;`eOn1{
zY0~b=pAz`z;pfCph(0E75<QD)_VOGBj%L~u)F@(ekEM1I{3Y;Lha`&QW!A5H(SXuU
zjSNvAMrS*YJ8iF|^`8>>W-AF^YriqU9!<O-JhEq?3zf&YY-7EA{{XG)diJd$vx*5V
zAUP_9fjoD12OoxM&QNhx6yBR9)b(&R^9(&_g{@5~Z11}C*HgzdZ8O2m;#<G?OfFI=
z$yK!yj6{mxd3R$2pW<PhS0Cc<1+Rg8M=X}HT-({nHf?S#rTLji$SPNA^VoM4z2d=d
z9s1Z{6C_|R<HUow!Tm*bUkNOv@wS(L;5}T4?4wd*4o>(=@;08MY3W{tOk$oEF{-W0
zW9ew+X<{8z?&P|ad@JLx6lt^DhrjzoQn-oZy^H2h#{jrs2_B_dzMj|q9C(k#7Le-N
zeAW@mr9eu=*^O8?WG$Y5x<6X@syQ{SD^|PkhN$mymgE>GF%~MI@L7<O2h>;C{ulA&
zI-P`Z==zt~wHTq<w<Q^v3>~cBE2-`Q2D#~Au=TIbtnH(@mTOND2+~+peQec@?}YyV
z3%{|f6U5#jb0pJ-Xrsu$^z;YoQ~YM|HSdjJ({C<u6_%b(!p8;XBRC$Q4%O)v-x77J
z{RZ1gO)}1Fi+J{#LbP!wt2oZmewE-}Ch>9C9$T63<GN@V#)}S86mi#LfO>k9UX~v)
zz~Hb^bfJ2!cF{lUq2x!0@mP#>oq5^|w^i8gbPopnFVVa$W2X4sKGP<muXQcaExH$p
zvkuXnp>i;AGHagrk*DZhC%hgc(KP4Npg^%b&AV+LX#VLRc}V93V!8cK#+oj%rrPQE
zwo`*^50vtkQXA>t71Lendg<_dOErj-N)3?r2tvCKpE4f8zE&98vaG6NTWobWdl5>c
zr-7BXw~^(3DEK4sJH>icrlxM}(#=3INpTCTtUD3`EBMy}JOlAN;l`;SiBjDpjshF)
z7V0F7<EVAUKtJTy(B3ZnqhqwT)3m31O)lErHHLd(BTQ#S^g^cx-nwrN`2PSv*X)}6
zMATt0e;Afbg&u`)2&%;6s`B$j3g0u*g=)%|v8ye5-ru3?AF~I??Hc37T33g4+j$~u
z{X*QsEPuRIm7P>82I!>pAmmr<Z}#4m{{TI|@xfn+J_*(S7-;_h6Z|zITUy-R-s<*2
z9^Nvc3r>JBCjgz?@GJHE`&UT+0G=cAuFMX7i;WcsMcH>eDw$>)bCleb-sk_){OYm3
zl%hj-nPNk^Nk4OTKSNvVsQs8S#xoSBZ(dc$BNfq4@bFvjTN|Zj?Z*UqR|za0T(Vlu
z2#`L)52)xxPbx>r^Axq&<hl!}!IZkTDo7Z~!Q_hUtbAV4K&JG0oPG4*{S9$Cgo5A8
z)ZxbX$OTw@t=ko5b%W%44X4asmOgTX^&jC)B9pSzpIFo%QIcIoPmoA9#0R2vsN#6k
zqr@>I51)5_I*+Y0`z};?pqnik9BuXECZf2tmjY=x{uKlCtrU8bbXBsrhQ{Vmc0O4~
zLk_F|0M`}OJ&f_#M}MYH3pO{H`aV5Jtz;x{h@&o}IOjE<vRvq;=hLJyqvz$$RG+0%
z&{s54^>rKA-b4u`Dp87bCqAD_v!qQ7dZ4$FwkURJPJ5mRtj!wLHGN_*(+IzI@=D!@
z6}K0LW4X{8<v)8eGc<nc4?p8kD>M#PWj`BBej<v_Xit?N%(7to$LrhPxt%8dcq9mf
zsz3U~9I5;(*|ckebE3m-sjr$IG8srLNE{A5aa<mw;ES79mr&DAZNMxSI3Ryo=Y>Uf
znXe<|O%-ohXR`ZM;$Q+qAPi^R8pyUU6Qn7*rICggxW_^G=DH0;Td#ub=0p3%lLIF`
z`K^5q!>Jv&_SME@xZA!Y9W&UP>5oGw%X^-CeRC>I)5y!cK1m#mkb5Y{Q=av$YaQ;F
zU~Vr)$*vT+-^=XB0D5Mt-k2}%MunlL+NWcg<pbqb&&(Is9;UimT@p!k#=r3lO%0`_
z8;0UZeY^qx00OO);}xl`^f^JdOSW4j8>E>x&QDRebkAz*6G>S7EMx<5`{f5Y6~ILv
zp{?G_Ev#=VyA~k^a6LQpuSK(qUADIU?X=sxvWfzq<yw11C8I4*OAPop@|o{6R*!U2
z4?P>Q<m2$k>0KAZ9}`$x*^dqBF1~nk_J)g%fq#RyB=xJ`4Ll8ausqtBn#SHn+}nTF
z$VW_P8&nU@u4}&qE^aR_HJ=Vlx2O-A*3JsdM^*zF413on)~PCqRF#SLmv5Bew9P(f
zbeGiT=IO1A!5IS!5#xWhYfr-SqAFWT#D--ug&%tzGCShAtC?gTVlLu92tfxRFYx2G
zJuABK4}v^NtX(dkAb4%i^75ep$8a-@gY8|lr%H@nO471MbQI?=acX)rI*y5<>JL4=
z%B9WBTOQxZ`$LhQ2^=hBb|<$=>)2^G8hwG&E#aDJ)>XTUaRxvSaoMrc73KOT!*3G!
zH&?&8v%7-o3z^#E?93TjB|MF#i6;ZGJl56C{l(x>Z~!qJ$0B@;4}M3eub-(>7^%5a
zcRKO&oMjayqhEQ}+jx7!*LLd-wcPf&CuB1&?WFYp`d2sN9}8-FS&gHU&$f39B#@t(
zMtS3|Na<VJb@klwd01Jv&L14(oYz%h7J;q7vffkkDM=PK5-R7hQ-Y^GIr>*0H0aG;
zT?#b0>FcMRk1l;~?mL-U+`MYceq7+=up{YOHu}b~Y31s=eCZR#B8<pzPIJKZ`VO_|
z+Fyn=^K~7Ui!SZ{%&nE4Mg8+do_2qEzu7;^y?#Fed^VdyU2oyK++E+nCM8SQ(`0#U
zD~2a#>~cnUrBfXjQZS=I$^GYM3nQ&vUDi=u+2ReVYPzcZyI!$K{H!v~wBrYpoRN@y
z1$LUv)`R0O2*-=bf8ipqx-dnj+QQ|HqbMQw;DSj#h{x8w3A8^A>AG#|c#UoEor1v;
z63IRZVq}@*ASrgn6fPBb1Ex<O1?889p&B2BBl03b(i0daNnME_HZsrZIpei^`DX>=
zvAngbL)k@L>%F{}nHaoH3>{h0r!`%)wTiTF=K9;q%<-!a34C7GFJ#dzboli93yiFi
z>PmxR^v*XK8IL#sa(jyPe+v92@PC1Icy4w3!+6$f6w?xmn4^*Q?}irQM+9JGl05;g
z_e#<HRTag&8o83z>fsh;w!Mv`GD6LPDz^t2I6QIQymR(~ZytD2?xfTU*vnz%NgQ^u
zmv=DAjv1Vd-d^0C0fWdn72gSDI7cWcxye4OuHJz1&LGVkDaj~Pw|gh~zqt0DZ&TJ<
zd#wvlwu@Orl)j&OT*o{Rw<RQukO;uZ&)qfSm;NWyZtsnTjuz_ga>p&in~YJS^Ry6t
zW5&~if$v?%fxIE8e0k9>^`8&i*xbiw8J6Wl=07uX=W>ERbvX6w!LBaSEh|Rw=B0OW
z5dgw$;W1!&Y}^tD-X!Di9tC+hj}=s>d?p6TU2E4~-HXBGalzG+%wg{&?60Psbl&|B
zA=LCgh&qqOxpmueZnWmIk__9a32hv&xS&9H?Obx)pI(*MUTD*3HW5#1mg#A~07R@L
zW+U&jF(iuK@NbAd!=v0@hCX0jfccwAPIzo*w{G;$?WyC%+-NPMZ}oDaI}aT&81wb6
z3i+*kN^!*NZH(&EbLEs@$mb)zhGqW%NMg~b`GXP{XdLB_BZ~5={vv73GudbnysTX8
zcR#vbditDxwe4Egw|S{)vsvCjZ*pdhp%OpIB9<I{%hV3IHQ`!s!Q0rUw?TBOs=qs)
zc?Y-QTHzyylzFD?#HA#UO0@A7w`rqJB9vV1gv%s_cCz-s_OF{S;J=<}FQSjn<gv#m
zIsPGELfXau00`=85o$t75?Hp|ylK5puHZU!uL)W8Gc=aAEdKyI6NOSM)WXt&a!a|%
ziEdI)p_yxccVhnlYK2-SI6NF+kEMMnpj}<+nmiiZ@W(&eVmsN_E4fb7lk4kWI_f$P
zh;5<!MeVG}q~~_RLHu!D=fiD7#5U8egtEQ8h3kZKGLsrH=t2G*^vJ8m;JN6z!q-NW
zz0RASytmWC5scg0Nf=!0Bo<Nr&v8_uf!;HyllFMyJ3|J`0(t|F=UUI;zY<F{h6rMI
zR@ufCc<y=VMOxB45_K5Z-MnTzl_m!)LodHWgVw%ru2pty?%%p$MGmcTGTKO4w{SO%
z;Ch4l)Y?ai^qm7;y3ntKx<)dSC(K!~mJC0ZDZVDR)U;h1=VP+GXcQFgJx6{;c=I$i
z(Y3^2%udM}vxW>fJoV&PbsRh@LGu{YtvF3tpGIo>rj@Jsdg|jyon@RvS7<w23}ZV_
zIj@ZyTHkf%o#B<Sx!Wvpk<NS9($M&R8{0I#(dGL*l0-I0d2Oya2aig@kHGpap383~
zt-O-sEbbT<&u~sfdU<X?4TOZDG~TA_qjKhDpM?Ja5cGsm;a?D+Dk&Q=%zo~A?SDXh
zYum51T{}*_TdN)R%^)DOT&V}63~)j3UOQpn-w$k>+V(K)z<ER{4x`t2#^OEe(7YtK
zW$gSrCCkYiLCkE!44CVmQI3MR@iW5LuH{PV{mkV$jkjab?eDb_8njIv%BaRi%Kre5
z{{X71={Iplt~di?Tp>_DyC+aRImZ>KEDT4=agsXchR0$LaBB})(LU1&xv`mr!Vr9>
z?iBX#n#!8wZLYn6N1oSzdFZUR6UZf)v=9CD$ie(QYtSRI@fMw@$EaJCytOO4aryTB
zdy4VPKNG=u0$biiJaexMRDuZl5t{FG7Iw<%ch7CS0ko5}{YPV4rkb!$oAf&?O<gY7
z3%Fk1UAvX-+dGGGwZ~E1X0&bI&hqJAd%*ine5ZwC>FQJfe>&yb-u+4b(bQjZpOi+R
z<MG;R!LSV)v|nquWA}#D-R+Fyn(B-&j+3V;@;@t>$>Nr=k2mIP>eDQ`n(6wasSKCo
zEHkqoDZu-vPDo!tj04iJJYC_h5NqBX7I&6R*P42%w*@|P9^tw$+!xvjD}$d(hfuP&
z)Zu+H=sb~b4ADwFxnn;=(>|jjxxW+Mc;~<#6`J=z*6&uy2HmdZe6oJ-3oBy>kCfM+
znb)Z-BZYcuq*l^frgre{DztMtRq(Uqoz>#+XXT;gtMJpq-XV>y{7<NA7Sda;_k{|X
z6cRxBnG1o&InFDf@V0~Cj|ZPGO1ila+%_ei`xA0_`O1yEeaQOP%a7yjXlre*=#Qx0
zUa{D1?Y?5!JDABMe7Ge40QIVSjYq}tO?9hyg{84e#s2`01(SN97|Rks8;_tB$(B}l
zPFj*l>9a|nrsmmx0~-lC>eBw}X#2Cp{{RSn9!I6k;%T(&Z7eJ}hUOQ*o%a6#&m}lw
z{dX$y-oHLPd+`4NN0Ufz7THf6Gr^X1Srj=?araIMKEk^#XUBdci^KMQMV$AM*kz-5
zWCe*l1=`;-any1TYUAVZ#QNr#JYnNoG_huIEv)2y&Np;s2N?QR4>W_RJJ3omO*cMs
z34>Ir#t#uE%dWqZJ(t0sv$gfl!+kf$`aQq)vzQufO2$?RBr3-$@u+1in@$_PdFft>
z;D3gn3;a)_URxv=I!?L}Ne!Rc-UOXN=WgM+oOTUfQ{fMYpAvN~07;@>Bv8Pn-NZ4e
zQRV!&X35V3&{g?$?Q6ukPxgMhW|L0~Z;I*_#?$Ttf)CQU@vjFe?a`IH`5z~XdDVGJ
z6x(;bto7XKd~@Ih_<gBqTHcd+aBOd;jvul)caa@%BvM$Az?$lG_qo*kf35g;QB}FM
zRuV;JEI^{8FVE$x50nG^Ao1G1LGj;@z9)EdQPVs{e(!e<lQPQ{yPe4*qXuOe$x>8%
z`&Xsjd{7@3yhc2I@ak(jJHsS$NS6#+;sOUxERYCq?{i(5a!{=b&I)n0wLb4ASiD|T
z@L0Osm6pwD^wo9L_Ji<>(A!*UdL`OLZ5!-qFVHklm;0)%9^4)<3kurNJSC|3j@n-d
z+MPalt-xz*{U$I;xIgmFMUhw*Jxe2eqpflpJ@3SCi(VsxLe(tv#}>=yUR=c#j0_9+
zpUP$^0RHg@o|W}H-Yf9Uowc`wyb)z{fWK!+H0xlccVm7PQwMi)K*z0ZJR~u=smh%-
z3QJ2Z+xyQ8JmRd&Bf-NB3f|JRyQI=ydUfnj_;=xi_*rNzyhU!3>Q~ItT!m~BF@yn=
zuF=n2WY^4|H@4KgC*p~2J{;;77MB{VbN#8LpUr){feK4xuRS*70=~ldk7acP*4j?%
zmA8)3A`5f3BR$Jt9QMZSSH|BPbsZwk($h+k=-EjoTwGf7At`_qmHVKR)4h7xo)-Z}
z2gs(mtgN?xaq;{i9Mdq*C7VtcyL`<nMQLSg_UwGU{{Ran=SkP3(e>MwwZ8L0`HVL)
zkmF-G<dh!$tMq%}$HmPD#2!YO;(aK!@VD4!hB0$%y@mv_ymAAy^$N+AugcAD;Rd0v
z>*=Uy+U)aRxH6GChI5XfWt5UV0nRJ!Z`tb0#6Jx+6!CVUWRkV25bb3ezIevxibgC6
z&nzF_zIJf-bk-t*zkM%nGxW^v7(B_WCN+NYDe~)Ux97I!p!oPTrNovNa)wjHiW6?-
zAKg8N;aqKn{kmK12vv?kM=vTp!S(j9L-F5=yfxw-W-HGR*xlMkEN>*QKbIqyZZ@dT
z$r<GO{uGmVZ%Vkdwv2zQbTSe@O#cA->)(Q*$r(X5p3MBC4~4|glcia!tr_OFx~0wC
z>C-H(40l7HD}otu)k*8_E1;81*KXn4B+D69$qDk7ImR>F@UJ!TL61oBBpQvG^B&Zw
zWY1iT91&f9gX5cPp(U=R42a2=SBw`0J^SQVSd^SnrjGh|i+s%IHN7@XF8<K7d2bsR
zCm^uh##@}=^sd9f(Auwv3dIPX8?0K-6o+(A480E@-9MFl_2R8?&*D3a)C(Gy{n<>6
z0nqd7+*hsqI@Dm19_5EPC6Eq7_QC6eTGyvFLehF#&*nxAwTz7E{8zg99$DjCdutC9
z+-Sxm5?I_g62tqcqbSFMM+^s{9FDc~PsHzty7G9J`wUJN&Je8OVOWfe!zX($>5NzC
zuBY(Z!k#VGZZ(~KUT?H2mPpZtB*qx;I3qruweoMrpV{9`@XTrB9UggPws#RUQ{~w^
z3_sq-rgL6p46?NfYE>hr-}66K#N)7d>d~B9QnyuRpJV4c{clY1HL6=&Zbx2YP<cij
z(TA_4dbjNS_c~9DXN@EmLReW`x60g(0pN4*UNNnBN^L5}Mw&?85H|wD_mQ7K>EG%r
z=zoIt`i86Fq}H_kg7Z%W4B<mLjQ2Slk?&bzr5MuJW2X+K?Nz!Q=foW;{4?Unyjgn-
zG!MEd;RN(<haP~}n%#JU-%Yu;)mk=NE4E~HAcZlLfWzHSIj=zYmj{S0HThduxRUJL
zLKPg~0taj^03+B|#<Sq<GsT||V}j05Jg9a=tW{VzA9o#k`&Uj|QVUtWhc;t~aD$8%
zt(@+!;a`Xvewf;(i>qC&n}G277jEcO`k_1zYT&#!-XJS9uwGo=YL{~qeL5IgM>~2;
zkTK7;E7*PwYRwjcJO|>L40o`W*sehmN9e3h30~s8W8r6ltgL)faja@nMI=fa?Dp#X
z^ZZY@0AsMtbYme-Dl&$uUzy~rb)hLV?vEk2)BJB5+Sz!9)_p!w+oEguOhEk@gZ%61
zX#N#^UhyA?lTNa`w78B;GR9_D)SPff-5IX(_s90~L!)RH0@~(#8^0(f13Zw*afVZn
zabHSZcuK|;DWfU2xk3v?4oWgG;gvd!bRcn8g~@5wsUK@AUuV$rUlHSJQ=F*N)o!)>
z&xNc%Vvme^>ksYiV@}g!?g)+l05TgUx;;<!E!VssX4-~_;!9mZ>F}~g<pf3_?{3E(
zd9SF~UbDUPlEV00t($pMq%xn9P&p2y3?I_GtKu(+JWKxo2;`F69gqT`EGOn-{0+=_
zAbMj4y0g3{RhMCsbLOcf?xp(J^03m)arleW_hWs3;hE>&0r2;Pw6BR?7t{PlF_%=b
z)+}O=>@Kd1e$g2P*OCA|d9T?oCrb2}{`s%YUxi-+{57Q9=vwBbVw$F;bqfo@b0!rc
zb8v?dfXZ2va!w6?n15u){{TLV`G586!OQaXR)VOjEqY&XBhs%WLZqQMNxt@4`R;%J
z)A?Ps6--ODaDG#RgPP=Z-9Fn@k>qA*+993FImfu`fn6Mp{{RTp<YsgePXp)6d~I(=
zKHinYYL~Yc*YjPvuIUe%v3g^;A4<Mez0Z?Q`<(H!yjPD*kR{9)1CK8of&TyyX^9bm
z-YFEvEL}qmc;l~X+0&ZS`$%5N6o`u0!k$$6=i0h$bNxP0u|o1ZAhT`*=3lRR67wO;
zLz>d!YnfU=jQ&dX&mf-v0F7emZV3A}2r;q9KDF4xrrpI5)O5LxyS$<YfxR)1NUUl6
zDdKzf61+S2l0)zZJ<qjW)K;vOcFau*Qu?Yxg62>GL(rdU(!B8c&wu6b5kk?i9%tRp
z_d)fqMbN$+$8N~ilX;Ujb@@V{LEM_t{{Vza)=TqYa_(5B>wJ8w#C_$#&&&mKPZ<RH
zVsGrF8#a9XrbT<E>DMCXD?2biSZ68F_4cmANz~?Tk&+w%wMqFv9rzXNUS6Fa`aF<s
z3O1FQK|Fp{e%ng$_BSQ0L9>i1pUWqX^@rCI(nzmj<+0~iO@C(gP}#+`nVd#k9j)Jt
z9&uU+!gAfSPpVBl!!}DSWB>u`0)x`J$^0fBL|b!kh77zs{{X9QqX6-=(Jq;+2_EY0
zRLc+zA&Y<k?g0I9(xdAzn|!;QMvX|rMRboL@gIk^8*MhzO499Qn3;(<OcS2RwOoh7
z-ZPTV`#$zWv9)kffIFnea0uWU_Dfw@;c^v<@;7IX>;5%z!V4XDP>$N>K^##AmxA9h
zJU2ji93DkAh;U8KXpB@Gouj$u`X-BK;rnrKqr;4F%n07c9m(YL*0t_5yQ{beytMPm
z{LB~@+`hx772MwVa^l|fjSAu$gp-vZg=6jQgZb4XH;64I5ZscDyJ*V*NzO6&)>Sbr
z{08u%@9!8EwpS3@tLd?Xg}Nk5vkW^h!0p@`Yn?_6$s|%LXM>){rhcETbc_AIwlnH6
zp=e-n9KSk(8MA|o^z}WeC?v8%TJum-Fo<tZH-+!$L2lWucZi&Ik)pvVyBM~XdgiAg
zYXqH-18iiRp51uOc6w)pJZEh@aJrbLVhSjb837#R;NTjCJVC2RsanIN+f8(#K2n1l
zT%2!UILIG&JPx(rUKWo-V%Jex-aXPqGrJAQfEO{wNy#mM8HZC@;wxh+(zPgF{#QLr
zE?I@AH7bwqX4i)P7--%hyp6nS_F8(&Dv31&kTa6s#>z0GX~sdVuN!z%T=-LaZKi6H
zB+y;utZuCs7_L=F1;RJXI`<uV99C|lZD_He7IMvO(W}bkHinS%urm-oTn?us_OCg)
z*E}zCFtfXZ?J_fet4-}4vaj7llOeJ*$y^MRUqy%EJZ3Kk53Ekr9=hnFyE6)vsZzwx
z>ah0}wOwCUk5oEU=n*uK+v-rw8@kIMmv46t(VUR%7Df7IuwaM4*Rx_*w318{B%4HX
z=l!HORzE^>>ru<%rZyJd4Aa(4LMh$1tdO(LgCBKeBWnZigYR4R{{RK!ys{VaZMSy8
zNM!)@#&U9Yl1>zJ)6%%`oHa@gqQ_!XB%Qt8etw6cDmdDTFAqnWHom%B()~{<@g9X^
z<vVzLYXr{5RUnU0bJb7dkELXIZ{hC0Wn(V7*J9V@`R$>d1hPUKb1LCT4b*~9TKi|=
zU&Bv>_K;jb@h0x-(@>4Uw-LAcD<K>VC_9`UyNJl(SFLKk5cnH%{{Zlgc&W7L91<hE
z(-+BYr=gN2CqC!ax2UpuHQfpml#*6*YU=+0pWb>7hG6jC&ZRi1H?^Lt$?f+a0}lyl
zx-hlAlF2SDu24%9w-P+kNHfB-<N=PPEqf+|;g25p+TBHxMW^ZLKXzusF)I5g<d1MN
zYrWSmd>8O<Q<K6Pj<0K}SVI??W?&oT3fN$qIl(=M;=C8eKeT?JVxB(<!YmOOD^EA?
zWf|dBLHR=-n^Y5;^>BHuEV_c08k~Kusmpir>_sfTg-V>2UE#kkPnq-z{0#8ko1+b9
z#rmbrt13%_XKMh4Hsd+-i7ctu<N|wQy)Q%YZ^OTasRpC0+}cKv!{%lc3`DGcSYsti
zvc#ZdfO5x^#e7lWCisEl87}-s;!R5Wacqn}&*mOwA2!gSh{4;`0l_%+u5ZTLeXoq}
z+W!E;)7#y7YNd<j-s^TS-zzgI^dNQXit?*xj~JT7R-ZD{$)~>EbSpA&z*Chx4Jf{C
z`=`>&x9WR`#&3-N9r(B6TVD{pi{{$iTYEN=S4lPp+}nh0!$?$h=s5MPVezeeZQ<q7
zyiI7A?WRT^*5ybSBHb~%5=jN{I&SCO*Oho<!jagg_J!`&xB<4L++gFW>x_EUomWb;
z(ZqU<rX><bwSeK{b{zBQdXGx^$ycKlC@5X;ey4##raPXUNa7rA(_49&-V~q15KVV?
z;0B(?>g*A7ZEG*to<MVx<&agg+^;m>?M)f)9P8J2c9R(Vi55s>l$0OB!Ge?g>MaAq
zULWxU_mJO79CsG+iC`pt@<|!FP`nX=&P8-`*vOIjI-3iS7jODP?rabC{`Lo{uF2u)
z)=IQjHSOwf;`0n-tGadPXZSynz+QNh;p&#K)3ocWsPZ#E%Wg>nvVcb(^w_8HjIr;A
z()nC=c?Lg0kJ7sMhk$|@6##CfkmsLbM-;zn(rjEjakEC_0EPq~Zk<Q#M4*<61X7%}
za-II;mq+mI$|HzfMb2;;2*~^eSrR-a5FundtC50zoBsf<T}o>n8+i8L+T;)7DU<l(
zvbC9XM6+A{3SXC!%!*2J)H<Gr=~Ck)wmM-n`jDhi&lr|A^2y<pf*0%Te@d}An!|0#
zRA|EUBtLi)>&AadsF%rauxhs8MEEH^x%K3Ff<29DF0-S?N0T{`Pof>e^dQ!XPka7k
zO3m4imN>60h`V-GkYI@cJ$hDL`bEZ>5~N6GLIxONm){`v^sV_kL8RM7=S47<R>0az
z437P{{A*4h5LinQiU}fj;etNpP!C>kdRB99WiQ0u7wz~SXRYe`qG?j4w99XGbpA@p
za+_25jxo><nD1B~2KzRYbvtb;W@sUjJSjY$4(0>4Pg=S0?^Ssr)9;c<)>9dRHGy!U
z7o3ySocFJsZT0KzEyc~W?z4XoJZ>KTwd_&D#-<%6d9^1f;yJq>_2Q?4Sn%Py(HMNc
zIof*;s>8ANuLe5e!FcjI5R<OSR~%>5b``B1#P+}Gb~#6v<a7t+Z+w4B<F&MFc@f(v
zTY|l^D@ar1Rx+FBcW2Q)2YZ{H3>0GMj#W-@K*m6=?MmuPD59SB6LSXe2_p=?xNlnX
zUjyow8YYew{aftweCorfIO+KM*RtudY1j5*YkQA288Nwf=jx}ud3CADP3fW8N@>L=
z%v;;o9^k{GhG+_gEZ=!S>t3nhtsi~tdW#TZf;>m%;E+h~_|{#Ho1)Ci9jrl^{{RU%
zKjKAt4wh|f;Wpq1BnKEH<ze-zrv|<28%5n4O<=b$k1UwwA94C%o^VBFTfU(l=%(_6
z#(GRS9QHM#C@xEw^5lG^91Qy&hO1h+yqE0iqt0F5BjYSO<Nd5xMJV?lnLh11QODb3
ziYbuv<#&Au{<YQHNv2qwTthN11ORseKDn++D6L3~%vOx7yIl4J{{Wtbx|uB<W|9cx
zKr#xP<AMIqrFB!a^%Shmsvi{no)K?jV2_`<Dfzkg2a1Mo80vDnrNWkV81kU`6nnSv
zeif5yWP|6pSf^dt{mYpD08W+A4R1r7D8AC=WI#rD20ZoY$^2_o2+8P(y^&he#Fm$q
z(b)Kb66w(G;fl+&p0Ub@c*mjsRpEaSd<EmX-G1}Ko)Xk2n@75}d&^^eCz%;^A2ctX
zt~me#3E+zLY2f=La?7DeEiC0A$%HK^9)mc+pxjSorzOR#n<2R;DqC}49u7JK?^isQ
zj$rv|`?WfDey46%nO4i??KPD?tGAkG#2*iQMX&rZTg!hKSRuEPTt@J?Vx)Sh`LKP!
zBD#HhS<yUsrp2h(+fO~r49_$%jJCz(0zk!m7ve7s_!Hs|sTQx}n+3Ts$L0R)v@JQ#
z0dpAy5IP)vYsY+P;jJIw?x$(uZ-@T?YfDLs39T;J$$&bim@&Ob`=s(K#LTih#|Ifz
zt4~Fx)&RypIltQeC)v=>@VSrfsL81{qJQSPo)4+`jyo$U{70oiBJwtXB|kGrcmOB+
z73a75{pH4qbARF|HyVx6REjA{{oG{r02~5OPN&+woA|?h@bASQ4z$($Ji#v|VC@1Z
z+#7;?%ytL(x|;cOLDqEl)Nb`nX%#1eK?FgAG;ykomIrP!$GPcVMJiF{b8B0iSX?~k
zQAVf3s(m#_r2GY2$G_Apty$uXLOlCv2|HvM!DRy@Af9`Q`ZK~e`geh~*>&AhR?|dG
zjFMa3vqk`6jsp&(w_5my>9w106E?fxtNFD1$;Mjd(oNz{IS#qQ3=WmC;XjEs`o60s
zh4DTh?#;99pcYbd{7Bg&KH04?7+Q7HsQRr}Q+zHCrV?Jxo9gZUN6-`N-U0EyiLSga
zB=+|{WQw*;11Zb5zhllT%dh_cW-Hw<T$U?a+i34BWj|#|reC!L9DwSlJZFG=S5e|$
z2zWzB4?c|yHuo{CcM(eu?%9L%WOc_m_sw~yhpucqL8$6lPl{IZdE%9B8g!R+&?Ig1
z7E|+X9Cxhn@sw9S8f`|~GrKmON_Zz<m2)PKr9K+`U-6H^iDYTC%{u1lb^9!mT|p<9
z=m<Y`Tp<}9!+k5=^e+~CRQQbTt9Xl5weX#q<VED$B273P{HmZf-*&*Sl>9fR{6N*O
z<J0^rcYCkJ2?gPvA2LKy({q8J%-86?ui>p9SMW?8CYsP%Ye^VO+c1Z8m>=s?f&m@Z
zJo{IvlVO%G7O7&K`6QaUy|3rn^*%!|$HH{uHAW7t*K5Dj@lO?ahf>$CthDW4Qkzz`
z(^r(gj|f&yrIcf7#|X^CjPNVXz9(px+SiV>yUh<lGw9Jq(g?KCx-?kFD&)5BGd!I7
z=Cl*XI#gPu`f*sCNhu5GF2Uv>lrv`;WgIG#$7=L132PoE@YTKL+$u~<8~lgmSIGH!
zKPe!b1L@wr=Q!aEH}-4A!FxGTkHp*em!F&Hb!2&^Y$bI{KJu#diqg&XN5k60`a~~y
zw(w79HM^EbE*o>B9*nF2Jajqcy{F*si9CI%Tqd8Vq;3}luCBM1jSo@7D0Ao!<6dX+
zZo+>M>hbH|82d4v)xc=pVIh$~96KPvInO_xX83pFZS{=OYg!G**mVdnF7&x@RzkdP
zUd{LrabK9>@l_zX>B;N8zf<Wm7*fU4l^TgR(*FSG-1@`Co*nS@{pZ`6W-&9Emg?Dp
zI4jf@1fU@2gZNi1bEIoJZIrU;8jIX0U5b(ovjE=QgNpFK4fw}Z@kfYTRPal*xARC^
z?#?LOqOt3}TMM-N;EMW_Sn$o(rQz_}u(Gg}f#!uIlsM!D-Me?D20iQRuz2WKr_1b|
zTKkX8Gd!YLDJqJ(mHY4bZ}}c9G<%4xR!f_L91cOo@64XH1P`rROZL4PrD;?ixI7-%
z@0yxF59q?>lET_)3TO9{OM>KgBb-#18hkJW5l1{y76L#d41ju$hl<{Gn|!J^ITb~1
z&VN$ZZ?7TpwQC6W{{R;La(zMSzk#it3Ui}8Qo2VHO&BNUW*{;7Nd#6|wYrg-OQ@Ud
z(QbJeh7_uE*SM&BDSX~M)MR^DTkJOBT6qZpoDPaRDpnJVrwPVy-F*&>JNA_$I$NXA
zCDA-h;^drJ7PYieLrBd30K7fQDLBS^;=W(_o8gZb#<KW*^=(2+GP}OW(w9y@x}*|0
z>0e;#zYtE6^6U!^n{8-(w&*074ip|n0O?;i$@@Zhc7GMNyK$tw&8u4*MunMGl2+*=
zk@FG{Y*){HYZXn+t$dx&zo#rZqLr7(@J%{Di0h?ErvCtAT3)l^65y`FQ~sJK$_d9A
z>tA91%34(ONvLS|wkcyY(G*3=+qjOZ4tw^l4(9&=#X8J0T}7phHUouwhs=@@+X~x8
zPpwU+>mDKS{3gcU>f+TU1hz4|AKnCK2k2{y6B@Y6EjE)PmU6I_7c^X!#y`X@E5^4z
zERi)B{Mf9Wqf{}fr~|mz27exF#C419GfCE?)zKtKCVwvI@<w|y=bga%R;0RTk8Jfz
z&0@<?dxT)pxMyc3Eb`5`@m}-cPlO&5@rQ$<lf)OYyq6J~A==7_tlpXBx#}yXl?g^G
zQwo{YIZdar^Cyh7>)7JA@hzOO+<<)Bdy&e%KZUm7hW8jXzo7hG(yr{#>V``>V?VqA
zm-7#B2s!7_{&l;c{2BO@YpKuS<h7M_u`wm)+vV*Ch8aCb^saXE_I~)MsOwi+kM>k{
z)~ZUwFPR!CLFl26Ub)6=u8k^kyk{k$ym54{oMg2ZPVsfU--%Z0*(Z;CW-_NPq;d%Q
zSJpaD$Da`Ex@Y_&ZKpo9s@q0Z+U^A*f)UnGarb?D*UDe;&wmiDe2BE0Lvwi{^E}wD
z1`!8Q^A!MhILWW6z8YN*h1zY+gFy}5-P!>x=XVkg+l~oE^y4PIY#mB8l65Nb!^>7{
zUQ}u(>A^~-mQV9-9&_MJ#M3?@LE)`>;$1dt^=RMiL6;KedY1DSYXW%rO?&pE@f*Yb
z8%gwiX>K(cA9~6ni!p=P1IB*}#PLVOyVx|yKeCEPcWiewh=GKJj@@yNz*msmc<SH8
zSFJCI{K+l=47ZUl%rn&`QI4LYkzX&1r-iSH=cjiSvw!dl%xK~!m1tYqX>_*vcRtzu
znq=|Ej(mBi>RN=iI(D6Ja{-3T<9Ur79G(u<J;3};e!PEW#s2_3CH(90EB0{l+3<(O
z4G708UM2m^Henu8-z>5!?!o@<d-Si`kN8Sc{&;@1(}Sajgc_|Ud-VAnHRp|=E2kSP
zbys&k|J3>a08#KIwy$p2QQXh=hEPY^2GU6A8`ILcC-4V}Eo4<l1n5_StT4Icp2T~a
z@BZB_tE9HlF?A;}yArMkU{9$1Yp1cfU0%@KX`7@g$%T#`PBV@<=eBy+l^k7It0s5~
zjAZQ3KhpdoIws4@4=K(L+~*_eIIe~t4(Xf|4#!jT1D(nTU&6L5HT^p7)>ehwNg5Yx
zcQ|D#eqrx{%@-aZmfGepf+=H?J?Z8XxM%oxV~=dsZxvCReO?_5Yu^r8+ZMl#JjPGH
zI^_=l=i0N^DV>lrFHC?>_*X!lDbr`T`whfzX&u5C%$-C`+y?hd^`}d&_=iA_O#@oY
zj}hGj(Cyme+;;?Yto^LI>R+>p(9KN?U(_v8tt_N<+#O%;<b6FywP#$$URa@=6`C`-
z%bl!_r-DBYyjFzz%R#8yvdr<pctS~%cB8idk^DJ4WK;EPrPt*C%3-?t5^*3Ic2ox|
zk&l>mCbN}9nw5cCOWD0UGJA1ve`RxPsH%CAC@@Y3Ndf3jPzd#`yB`c{ax`LA5k%w(
zB@OZ*^~YW-KHE*!CGffNO^dULCNWMI@5lYtBhdQQuMXSa>i14<ZnwLEh?NY1&%WLi
zeps!PCpCIYb4X#TDRRm>no)^kwwfzjg`PWUcH2w;06i1<fgXc{SM9YO7DW<HUe@9^
z-MD<w9@+l#f_-u;jkVMDIqVkIdTx^`xDgwcAb<%N9CjwX2jTaGv=1uvyi=%6ZFl7C
zv5qA3BX&P{NX_!7BcR9QU2iDF{8OVVc_X_EI&0liSM12{ZZvzT{DT4+2_HAg7_t88
z@A*}G`yC|6n(m7K0A{&{Qj$hTIq$d*58>9ZF3rWdwX~4tNZ+9ZGL_@DE2;42i4E3|
zH;DX5E}G8PUoLdE$DJntkfiqjf_+C$Yra_>cwSC^<(041zJ@rN(``mH?<>38rMj7t
zNvG-{z05vak^Zt0M<cldf&u(%pYcAmq*>{WrE2m^dwnre1QC&x&kFnSdiE9Rx}}$m
z{vz3r4d^;ldPamKjo?>J*FU@D7Io-Y^1xSVq<j$Y_M8RgpkCtO479y+aogItp_fvj
zP1VJ#+ozvn8WjGsQ<g0&_dIVy@a>(eiS8nkQq&|=SF^Wmu|<XJET@8V)C$<T@Rpv=
z*S;p2*3KmvOG^+-F~<P0IU_xBUd^m&J|6JilJ_?U?6xq(WRRix<YB_bzV6rr^TlQS
zKGOUp;@izbP1MUzb!6b}=#2{;sZ-x+J5GDoZVEUGt}t*(+W!FXFWmO!lg^#$!M2v4
z@J}ap)bI9bx?~S-fp#=e05P6G-Of%roYvi^fv@y(x0*GDymyWWYnjVUASWDx#Noc2
zSJ>Kjhje?bN$vE@bx9lv7^VOR7yzDwx943BhoR`Yq?)Df_FJ76b8o0wx5(&t!-3Jz
z<PE~TDCRN4NppL>QtHoEo>fMz8_!eapA7sG)b*Lhx#Hb&RFO$&(n+@M=c=dOU%+O)
zf5IONygA`1Cc2I|Z=`23ERsr$3X#})`sCMTs6idvg=4g~So77sX6x_iR5YIpcxP3+
zn$G^-&Iw|N%#CAEM6J6USTPtF=Zcx}6<ID?l&^m>>E!gOuX0wmky1@-!}4oF%ElIh
zd|<O|Az1fNMn)I#&3xPYLi}geei`dJcAw#kJv_`|7gJtprxx)j$oW@rGO;6&0sU*;
ze0Spi00DR_ZH})$opZP3u+zrCx&Hep<MD68yl=!_IruNB!*6@y{{RkJ&n8q`KBCWX
zBP?(*y*i$xAFXoI$6;yIZnZn<do4~Z-yJMI9jH{-$j<mb`&&)pQnvp95-ksiEIj3q
z&1Gh7{IUo?xpZC>9yXFkV_oOO?~fX6ek{FDh86?t*KS^2N@%v+H_N}uRXqS8jQ8j|
z*B3UsE`@%VcDrCm9C>itfRU<$_f#O-KPu(?Uu&gnTJ*ZsjWBC@$e6(_YMUlMd5+LG
zWRr|nn~ctBR>V4#C@6W`S8n|el*40j6N87emAf~4Uwd{LZK2!vwkw@u#J4lSsXXf8
zVpUQiEB*d)jsO7FsQw)IHqD~HnP-wqQ|7_GLRDlP8y(nn2RwDIQ%}|4v%ihxxkgOv
zb>uJ&#y~xBUEZ&u>q-`Q(kY`OcJU_DjDH9u4EuC7<|_Qk@^+IoRcpuLdJ<@w>UeSk
zr)cHEqi&X6!~-Mqj2wbJGCk`TRI~8Dy>M>tECIN3U8J{fEsuPFrv&HPx%s5Lf>&FJ
zWQ%?zc-S0$bJ+Ia@lLe8(yu4`Fp*=BkGkNieh2QKz|yBqok&}n)47Ht6^T_Gn!H)P
zVR;3$ylie7^4pOV%zt{g;G}@}UWYu^exZG9d8!w;ol*9H(Z2z(J7)ud+PUq2N7gjk
z$u)fq$yvT>VLYM7e0Rv?p0yG+zlv;H-qF6#cB64wLB>aH@xk@1`FUS8wO?~KUi@u;
zb@l3vyS+0{c%%N<Mj$TbnFd%B@6=Vr)8dN3btv!JdwAF@7A1-U{pC61*10V{ZA(gn
z?02&|xF>QGk&mIs!S=0tbrMR~T2#v%%BOf7qXYf*J*!_~E=1zAf7arbE<Dehb@-hX
z?t`W35iGEg32sD?0MW2M-Hk(K;Y%s)o<pgqjaaE|zbJ9phH?+3a2kfcb)iFZbQD82
z^_aq~`?mwG-p3-juLO9q-&>bbMqipqgsT?qzbPE_^!2XA98_rYJ%q57swYt^T=gf@
zbR?I`i&O>B^OSw0`)7)+Cx#oz&4!?M%knZZ{JHJ-Y60S?p^z;4WO63l0&qyj9ebM0
z*L-=RXg1N`{gs*{Dv4D|%wYcj5cTWRHOVTKrY=fU*5>V%mb;>6HcvVR7<B`3pZE#n
z8r(>9%f&9X`yF^8K2i^%UZ3Mw9wYHM(b5Stn0(UirPYbrQI|Y(oboE|#qHLk0&1Qd
zIh-gGK==~l=y-5DW11qW=~1mbh?A!)#XVPZTTRuXNu;ozHr$Pk$ev<!=t1a5dexHI
zCSepJ0OaJAU9b6M1N1e8rbVo#kZmt?DNg1x8H<=h6V6y?I2A-&TWd=GAAyymloevG
zSy1;rXZt@|+BDPEzsSkgah;O<&nft?s@iMU*4`Ge3%bBF$teL?7T}-*u03nVBGL41
zk87%4m}N)6+J|mFxU1U!muG9@ORY7f7by#QYa+2ycJ(2Le_En+-DU<c>65JR{q2)s
z$j`r8`dSoRDaABrICDwK47b#pYlgA88I}{B_~C%)aoE-duR71GZJ44Yxa5Lyf!3#l
zHMX**{l9j^B$(jsZvOz4dwO_R!#1TK>}xXs2zgnG;PgFl&2eHU%FKB#?2Zo4#PVI(
zFNZ7;+(oxwn1ZAar?;(oe}}ZYZ5q>TlbIxQmKeg1Vc+qoEoQflY>knsoCSpdQSHF4
z<`&YV*tZI$#tsx=zZ`M-*PlA+J&lzKSc=LJ_o+5m1v|2*k;ZthQMbCkiV^1{N`MyJ
zMsh&!^zU5fhHY&%ZCu9z#BqSw^&s~Cwd*!<U0Tl2NSl|cvT_I~Cy!HGMx-1R(ke9M
zuNKZ~3!gGD(E?cf!^`de00BH7!lRn@Iq<Ffxz&`TkXPmhfOE*rU({_a?d}Ay5~{_B
zjfU1t`h6=p=gPcLvq}-i3i&^-c^R!e*J&e2w|g^MO-5ffST8Mqc_G|FKHQ(916ET0
z((WZJmjYy6s;A}{`u40}wautPEOj6F_=sXDfyia-8{gis<DTJWF_2pWXl8J^{{RtE
zq|>@)j?HR!JGD6Wn_}UAy^Iz5j)t^X{t^izRk!k_U~+uS#z(&8<MXZ#Ip<i)EW#xR
zljdX(%B)+epfZceer}}!IsI`@>l06M93r)`=&<OXD!p}<(XD*Id0{(a@gB78QFRR(
z#%R1lY{(pL6NVBYZooHe`eL~24QhKEh4N;Rfb4!yexP-&uZnu}ABV9q$V-P+V~<0g
zmD@tSSy!a#JyT8ewTC*8r$$SwTOBTqV_`jwzNpTt1HvYl%7i)$;O$)e-hkB&V@2>!
z#Cs_;9aPKZ?3CEZB=Uk>s3APh?<qVG4l|0!y1$=O@Ya)M8nej^L;?XgL&)8<6F~}=
zQ2n+xj(on>Ab`pb1ytaio^yd;Ns5J9*lLuqQ*WO3zv=m&fA*y4N;POx<>a>AeNTh5
zOB>icTRwq0D4Z_QxMYHa?p&YeUI}j}i1h7KQB<A=M!Ausisebb1dQZkw`%)O;#QR=
znd7@Z7+EtXo5QZ%m|^#c9rp_P-^P9&u)6U)mwIlu2A?#tkjPi(EPX=^53fr6$`O_y
z5h^v7zt`q|r+~%L#bKk1ru2S$6nqWguZF%h)nL+mOLCeW(3up?X)6fIIs53`0)0WR
zsbc+?{txK!O?Y)pXIPldu)?Nj;v0!1l41e)QJzmZ73H2E{hl@bLh3oMb$EQf8Dx?l
zoVQ>K6$d`zzO?XXh$7Lnlt`Z2?N(E4m>9rtP;D)iJc2q_l=EyB9&OZ4YqI>04>G}G
zDpcmeS8>->+sz&+;@^T^G=o#Kir(fKtkN}WduxW-bg`)fEQ|pMKf*`{9c#rrN#dpO
zCyr(D4v(hKrp<F7+2@Y=H$|NN<Z;JdM>X}9v+*bUM^`e~mESQh!4BBz^8uUz*Vc~?
zYMO1<plmga5g{rArd3q+Cp}p880lVr_M?pFInhck`YqAvf3#J?{a&Q`ow}|4PhI#k
z;qMM7!{pVxKdi;5Yd6@rySI(D$XU49xcOKo_fLOQTl$Wtdt)nG+_aGyK4Tn&sRyHU
z<N|r*SDkD6&%&7AJ9`_6bi2!8v|n0+jF|37IV<(eak|&VuNqIO+v&a>)^v{*Nop`s
z{{Sd15Rc%QF(NSU)O%OfaQ7P0qM?SxQEE5%t!pltb^H&A#BhyT)u)K1HGYe`I=gEF
z1=aFtPiK1#@n1gK;AV6RPZ@E@B=UMzXNf!$tLy$2xzRi;;rn(-SWNRP?aMFTNUgyp
zRs+-J2iCDPwf&{OA<iO>&r8y+OKtMj?8x3;e}#&mI336}?Gku5#r_Y!hBEO=#Sw(*
z<~ir8hHMSM`Vc*<+0Ao#@f6-qfod^c^LN%(^6a~6dr_lHBcQVkMztQ6wu{rvqw}70
z@tD0fx^?r}yi6f6h?TYwNZP^201@abU*Z1%fGtmt?fgaKTljC0-<TRNC0&TZvyqi6
zpFjnDUHd`!V%JN!({+SMpp`^s;yBbRoSfy?u10t!zDCtNLYhUw4N}rpwp7G&z_|r~
z+Eb2y8u%(To*uL1v*dR2c4wuCr0US;ZN*JreO2~8(eY=9wI2^z1cu!=DF_i#GD5cn
z@sNjL<#UnVxL*u>LAvq9t7#g{e`lV^Vkt_19yAzVHTlD^=Z-7qeGB5nf=JQaUHR68
zW68Q!62!iU;P&)66-F<K9u}VAyg#C)+%Y?rGci{MkMT%2Y-iIvSEY>4u~qSY)`dP;
zrLx=3=bM$}*>zky!eZaN-^IK6Kl473xYOR=V9k|Q83zL&nCps*Ptu{k)RNviov$Ml
z*vNi-jsPPy&3qr#?seO_{vK*p@=tLx8=EVLKYF{7aM(QM7<N2^UG>j~Zr<%yz(`YQ
z4sK)Hi3d;s#{;$wYuv)-(Z()Vq^7rBK1auAc|@|jONTc!IzRYhgYh<~G(9!E38e`x
z^{Ih?$HvH2jte>G7##uYP0{4=?}NNP-VoCkORJf{Te)Gz#UrQ(x%?~D{8RAPL(#O>
z@dt(Dzuq=|w*z!cPgXCT{eOo9<GpfvY}b&ufpnQ3L5+&(<qRJ93UWL1T?(BXQf{Qv
zaos1kdz{p0)~4d&7L>NS-=VvsoihG8BD_NcA}l0fRH-ANa>oI2=rdj`;{O1HeiZSJ
zxVn~zpJ0yFJnNaGA~7Q=$Yoze_3mrw$h=YT{{Z2{5o+4RAKF^8g7DZDQwSh)xDm+*
zlh{|zz6SB#?xgyK{{V`t%yB)#d2`1ZWOXcCZr=NP0yy=rGy797#Mkz8^pAOdN4-3s
z8-=Fo!grTte!t{-$BT3gQqx)%ms+je&iK%y%-cpBaliq8O1){P$7iC?;oW;Ced^Zt
zoUj1(LUYo(zZ-at-(A$7Q!$7mwuCAx03K1C7Co>#4z<PIYZ_jUV<Tz|1-g$g<+Jn(
zJ!|Fh)mPRh95T}SSnj6e=*QbFQq<_I{2gyGxVyQ$kL=A4l+H4$NB2NK%DK-4YZ|A3
z*5gCFhwb`lk)^W=RJ3{OK5lvIio~+`r{L{U$VVKvalB{cgsSZx{PnIU#{U2u<IyjF
zv$RWP6TD&k`9hGn=v6@L_|@SlV`|h?smE8`!A6|rWvV^Ge~FscmaQa4BxXBe0Pq0g
zJaw*i@8TDTo(XQ2MItVKb^sr45A?4F@Yjs~CwRY5hfC9M9{J^zFPA(@=zyMCi2$mB
z&TFptqwx1t@P?-h!ezU(ygMVANb<yk?;#oE2dA}l;<CBnr^{YRws%pCG)Y+Ytuy0R
zl_j;b!eFwo;TRBA$GFGfYtd{xdEn0w+$F{A(CPQ(gB{WDf<_$}uOqKeYvmnV;P;F)
z?G1GcRf6qIerx#*NkRDi<Mgjb_ygc?_)8|Zz47U~hQilualA?q-H&2ebOX}4IVj4U
zz3U~|?B-Oed%B-&>AoQF{{V<z{t@qqx&@O*a)<2tzMP&>&NytY0ryfX;tz=y8cv6=
zNn;$1b*6c{V$>~1mnt`K$Bp>zE7<LHi#wkb-S|(#*HX5b6M-j~cPfF&<n(NMR{sEv
zw2d!Q&>O?rMY`>RL}-cRkUHnw_pFt5?$Gvnx4+2jim6VON~)#O**$vw=Rf;3_%7p8
z_{pK_O)Fm8M}KQ1Zx;)-Wl}dNAPjXmuihW*Xn*IQ>Hh%Nufe~9o*t7*__g8fPs19V
zmIwP@(kqCAZ4#%RfT_m}2;-4oxSn>Y{{TO~t!qv(v;E`yimP6<nsSfgfB)6+c89~3
zcWq;Lc+a?pj#$AenB<lnaDBPWbcRhL+RhIcXmd!nc8R+38Ai{vK-_L#ago#!%`*GK
zx>t!Vt=97PJVJy8w;wa(l3BO_0oZ1><<+!(JVT~wntijYrqdLl$W93#GV;VW0mgcs
zE0YDo_`Gh?<-d3SMs33l8_o@!Z;N8Gyq<3s_-0jNLaoiT;G+kWaLPZ3KN-hPwb6K=
zPrKIin@f!^OS`kwETWDeh8C77usInSAm^r671l}N`79t!O|(_g!!}eIK|%*PQ28;8
z_G6J)ms+i+pFZ!l`Ers;Xp6fX^c#r_`TkYagW>9}PwP`rY1>UrYf`4{>BZ}=x*Tjj
z4}4*DYaD-J5Wx{=4|yk-<*V}R$pql?IIe*_9j8a6M|I*G*Sdl>F<Ujc&m?7$OXqN2
z#Ag*|2GMTs0cb96))X%`<0P(I_#stXD&+jkeJQhA{6X<0`wbSu>}5vPns7Ej7auHW
zM$pW9u?NssRXn2!7Nt41miN;_CzsWnEfv$(?k-28cq;PAG+1pl2YH=BPV9nJazI_D
zkTQDJdyfg*ORh()_^l$hw6IeY4{<PA?g%_Qn@$;hfabb8ABLVWwJUpbcX2G+xM+tX
zK7;{}t#0VQ3zkV^x$zmvPv8x?hq2Cap6Bwer-#dBE@~0c7l+C#($==GODle-BdU0#
zLTMH~I$Mb_yIfjCL{hK!83*0~@)%>^HGwycH9LhGW{+U@(1*;qU9BpQ;NX0q{{R*V
zHT89co}FUJYo=QT2M84a$tQpZ89C{U)g1%I-UQdRy+guQQ$ug7>5`akY;K|vOsob;
zRd2jG?N;f^tiN?m*Yf=jNmGR#DoFX`!#)Amyg;dKY*rR?x!IQ)VsaHh;3)5(YV|vR
zfF3KgQrd>9_Mn6R04&BoJRA>tv#WT=#GW1TDYn%uB)+i9fFOZf5s-i7kw(u07zVxH
z!9F?CJbsO!=<Pb(hgVo945w(vnQoq81q&`nR{k#a+nV9CWrJ04^;@>Bc`n+XMjJBA
zDC3nH^pcDFPow_2o*|}u40}y(D7;UuGTF%B%W}$R0SfTS!*IdJZk5$)el_?R4XS)P
zN$%dx)U;qko>3~yS)A|2{{WchCxOsny|2Wt;oS?((R>AUBHldPhuiGcg{I@mxGV$h
z?VbjC_s7=06nr-LxuyM&$9^ccyt;*db$B34VG|C2c47;_JaBMv&THo>P2%Gi;&Gh3
zyI$=con9o%a8(p3VY#IDwY2a3Y*c%{1l;LKbD+&Nq)?zGt%4+yoP4g{U~U&5I;kF@
zkxbFNeWYkoC6|gcIPdLdZQt4!h~d^V{Bi^=yFmNSoDB38@y@H^{a?Y}CYQio3e@7(
zbhr*C)js4X$AB>5w{78i;Pm&erTjJHd2~%Nd?}#m*6XO7X0p_7twf$z%sFqfgSi86
zS(IZvYUyW`>Bg-rYE^ajTDMO7Y51OQd6ZzMN|qxguHe_j9aj3^Ub^ggm+d*F_!R4!
zcBA1-&$nrpD%a}-QhAMUEO=6q0i0xz%3H9mNA^Z-H(b;_M*5zyZDMR~Oz5}c$(B`T
z`=U(jYy~+Zk?G#8>e_F^PZQlsC&WDs<DS`m&vE74NosBioxrO=SIkxz&hC9{%e1eH
zUk>~|;zZEj!&Y`WE6ibAn3`9f)RTmmw2tCM4UaKTLCtw|u@vg5SEEV3OFJ%}ozGs5
zV;Es!hp9VB>3;tJBiw!|d|uP6B25FplATWKTeTC)#gYi3T%X--z-BH1Bo3K5u3O>v
z#lIZK@j_1)>lW5mPpC+VbrVYQM(7t~2QCN!ha{2Ko#M{~cw%|qS-6pHK*yT4r+8M!
z`qUg@8?Mk!J6AEKcwp%ovR=f{>XwZQL>Yc(RBvJP4!)JoJld8q`)UhsztHe;8OCKo
ztD(<mzSgrxqj-zsABr{GYp6Uq4Ze#ImE&U!hI^$|U<g-r0T+;R21w{@z+#hA(0(Mx
zqupO#O>27qm6CZ(j)3mqvlEbZ<az^&+KS%CL%W*TUZ3x8mNqC7LyeAemE;T>;7!>7
z0EFz{xbqFIzmhgFWo^Wty~*Pyrd4NJQBO-VqX~<xR;1-tSGxZIhCLkHXvf4)q{$dv
z>F3)p>dVgM=eRt9>0IZ;y(-Sr!<M?N51q}hsO}#ienz;@75L{|gIcn_gH47swPIqG
z*_gI@!N5=lwm%x`ymjJXuXqY;i`y{-vZON=LO@c?K-->sSD_p>BAt45X{)Q)^YLla
zp*nJosUDSg@aDtBUKz62{1tX0#kvV1<#&+3GbktTXCVD6i;u#78;wP^{oTa#55KrH
zcyGkyeQVGD7WlJWnZMx>-6X2W(_1QaC3$oMxg+Ks^P2Q2qn7wfc{TGfgc8=%sYymV
zDPh~t*A6a?T2-YQx+kjiIp{{8vr>s}>%*E9k%kj_Yz}{W(ESL{=4$i!f5kUeX=iCV
z&1Ro;ljcSTr_1vX!=-up$*NsA`y341ob~7N9qYEyw5@RhTt;5PR^8=qlWs`o85qWW
z>S0PPa!R64rOoNNCgh`a<Y&(hiF`A$uFshSpKyj|UN|6z>HTXKYdtenB$jJg0qyfB
zKSRhB>h_w(t*F5IPl>IQ&y0r4e5OSi&UbvJfW|OJYnJgApA-mgtdx0e$ZTPh<L<Ej
zN1?2Jq#^ECj;pHKnnwvzthw;hvV9*zo`yYg%qv=5oJYuLzGTPyhmbu*Sa~n+zEF?@
zA<T~(6lV0s?f}nVd7?Qlj3~cjk@7Q`*bY9JKdnZd9@Bh78tKqpOKR-FO}y+t&!u!v
z*tDD-!jx-9@v9p%tp35i)$N3%Voey@ftY;YbpZbWBVK=dHKvK;dqlZ*v1SY<Su??0
zWP1ai{8v*igLMxFs7srJGVrB3;2!nmUlO%_I`3A~t#t{@n|PbdFUbgTwMpu7MgZ$u
zU|mNwN?RD?sMEK!wy&|k`2PS}H+MH*+3%R+bA?RpBp##x0IgY{45zStc--A(V3ly}
zGCZa~#gUwIT;{!Fd#5ws+eAgYkG~^=p!5V)--epp8b655wc-&4<`j3s*U{9Cu)7+@
zY81H-s1JucUlWGY^&5MuX8W=N2*<8K#&Ou!9q|K3o+~J{%ZZXnE@UKU<>5wgpHp3c
zyR_3h$*kPL5gVO%3^&kMjCha468O7Ux3s%Nw}vv_S<fdUjGj3-;}!C_bvai}Y;a;G
z`CLmLBGokmr&(OQ6Wh&h01UttJADWzu&+|^?})Btwu1HH5~S;wicy7Q(3L$@=eVy3
zlUvlj#r>CaA(}D34mn@Pt$JsLb#bj}mf8g5EY{?UX$J)4{nPAwSABdtTCS#aD!E3@
zYJ07WJ{#1qjQ21G{{WYHWn&p9@xlCSRv!>}bHm!P(e=A|tm44(?sfwgBe-61=tgVI
z1-7{xc``*Jk;f{3uNBcOekeL{ig>)pla(&HDm&wv^J?JKB&8;fs#Wy0kD2wK3+oz}
zh^|}1uuXLnPUR#-J1|aq{VF&3dE<LqsGj1~peF@`bO+Z5`Pb35uw59p+_aXlGhtWe
z4F3Q@>F-o^jbBFa&YaqW=Nb7^`_9C0KRWlNoJvvTj0~e2$=hShFT4$=UCwSUBxxBo
z+kt=qU^yh#tU6Y>YzCDBk!_Glu^+?08R|f*nl`H=J-vkJvJK444+nD&r{22I(sHp4
z-k!tWyvmg-Ud@`q5#GjLoupmaMzKa>0eK`3n3Mf0sIk#j8#yBfeDd*;(0xCts%vei
zFhe5j9hpyGden(7Vc5wcfTuZgiiGdfzFVD*o}c}jX98I;^J4jn`nlxi*z~TJ!&td=
zYsdg_r}v0>9{J;pb~VZA+Jp|qJA$SE0NHLBAL=StBv0)TEwqJJMZ>7*#Cnf<(hcmz
zB$q?hy!|I$vs+nC*F1p7=2Pr?99JoGpw1^YQcQ@zWHa<Qta##0peyVo^&=o3<|^#%
z=3#D~gsKP48Qd6l;P<T~c<7k4de~*hhozMnWq99=0;dB#zb{(g?%|J5a}C^0=7L68
z{HNRs?S9u_sLv$Qt0?(Z)DV9jDhJhLR69@F5PZdDT$K76o_BJj)s8*`45V!eIPZ`L
z_}51TfRRz<xKs>ws*JG7I2}53PnTbUH8L%&vvKG~b5TpKM>H!B*k0MvMLVPUfMLk@
ztBaG>BMH=kwx@5Q_)%X}g4<EKfI%aG^0xA-JLCb8^{qdTdRC30OKIT!6H@z5n<}jD
zG-zfJoPZITf4q7V_||Tf;s%;!OT`BRjf=_js$M46wJYHsn{5zS8Cq+HMavaTs;I}_
zAo6`Hs|PH6t1HyyTXeDJVx>Ve;~s9d^gWAB)4Vz1i@*4+^oxtcXceMZ=184UmjRIh
zBXR5VH|1U*@o&W5DUw@%8hCE{Un<oM5!mWTjuA%z2vg4BGrK)|)vX)EHyW%l=pHK5
z&cADJyKQzz(A-<EqR68TJp%)Z<nDYG@Pow?81&s@;cg6$uWKw)?|_a7B^8j2_XnEs
z^J*Ek9~QA#sXbfeeJp;NfX3u_{132<ETWe(eLT)m4~bqI_>&YGUbAR)tw!D_-EOgY
ziz1P*yi9q)KBt3O9w7LCVdHNH`FFD--7}R?=sd(|_&b#{SBxC=u213*?ET`M9^?KJ
z9|hjWcRWZ$1z4k%>QJkI2=xZGJ{^2|@TZ5YKe2pIq|Kw<#l^(d@%^D0x)x%jxCg%k
z3ggUc*2E~*ioBQJ{{Y|yUqb;x6QzQhac^Yqk=I8&{{RgBAKA;RT(!(rk*seZ88R^^
zVuF8!a5?6@2Ve12V%}d6>hq#pUNaai6PF<Of9|i>lS$(bi81)&;uO9WwALcI&~Btj
zAZCojB4rs0Hz+@Qu5+4?Mey?JI=%JZjr92MBe#ecOEJq9AS)ppA7FY`SX>N}=bHDQ
z<azm;#KNQ{PA<<+nN^p>J{$1j>#kDW?^HX-5`iF${nbqLX6glby|0P&y%$uIRE}$l
zXWy{-j$e`e@sb654V}cg--v8v*NAIZa<L%*q5yCKIOV{`O?hv`&kVnXykmC_x&Z^I
zY%d_IjzJy8bm8Y1sX|drn&vq@SIJT{ag~nJ$6V6<UIhBB<=vdaA(}8AGtO{2Fwf#B
zO*>Pu)Z;o8yIxL@wi!?}NH=%qo_p3$!G8t#>&H=C=spp&7gE?Z;?gh)97JVHF*(5L
z`d8LE7M*M0Oi8UVn*5I;Ruh~#?pvTF^c{swSwfm|wbMB0`*l$#D{`IRg1lp@XqrSC
zW~A=dQVdOSV`Yh397Y`Bay_s!T`s@zOT<18vVs28`fH|2+&n*Nf<k&bBQ^n3(UQJ;
z@oukls}=5}G>$hAitb9Cyf=RRDvZ+Wx~773O*2pt!7}cG8|Cw28;2nr0$25~I<*+m
ztqSy=udUXeO&$LLUY05~VI_8*ntG?^XzD*0d{cXL-XQT`*!)7Iv5Qf&lXDNMG0uIf
z$gln@!91c{=Xq@>mt)&p$HC8Go}W=!T8HfG@iW05AGq<ZpLG;^bcYuU2@?f5;kKQ)
z9gk01r|{q5Q+!6fY5YT}#P;@brs(bB!w}!axX5<*8NtV;c4fI`ELBciG`XAX{{W<N
zSEG%R=W+KJQ_cP$OQKDw349A<FQ0WT!31hb$B~1xuV1Zt{{X`;*-ysWq%rHBEWQ55
zjJ#qiXp~KK<@j8J%Nk^K-Mi^uS$K2dKZZ1~6Txxd{VF7%?C&X^%Qu&n*%-?)0J4qD
zanidR%iC48zOvKj633<CeBj*OUr2s!y~?QIV}V~`gvuv}gN%}ulj?L*_Bd)*ob21^
zc?W<6x4}<{E#mKnuPN27;(JD)QVi}<@+3(j8&GriN{&72*?bS;ZBJeCxw-g-sZMn3
zyH}6SwN~8>rMCgcAx=(T9^RF~T0!Cu7VFF5>vhe-p}D$|^06o#6AOaO#{iOQ#pZ@=
zoY+|-+UhLK(nkb^(6csrADbBapd1SNYP>y@X0cdqPh~z-`IFTyzK54f7gmKSR)o3K
zn~tjg0Ps(8zqXF<Sv4;T3+oLjE=)1Yb2yFKBtN||$0j_*Zm7<4^cC}`iF|cqV|_W(
zHF<@L$nlIJmipih{ILkVkFRRj@h+|7%`?Nxp?HzxI$W;4ahq(>!u^R2^C0cHPu9F^
z#-1_pw}&HrPs6wRCEbc(#T~R86;yGwvKHh4&s^8%`Huru%jYR!YpS%~(oHXu*Hh_f
z;^|^3!Q$g8byr54O?0!j;BV=&={6<2b>k>j*58<|eDm{cJ$D=rnYx^PDM!YChnk$v
zZLQ9hnsvJ3EwM;91L?JJ0QIkzyjAg2#a|X&UHFH|OHD9=4W+1mFL5919I@O<_XFu!
z`aQh5ta9p_vo*GvXuIc=1#&wolfh%?D>!h4Ph_j&rDXQh`Mhp6y-ZKB!^f_tWvcv0
z(=YEeL8V&9dur}uw7F73g~taZ0!Z#FoV!aO70+d)tc)Y_PEoikC?Dg{)@Hq{Ykn%S
z)9iG+air+!KI>9O=aGj(M;|HfGtFXITI(7|h#|Pue7O_M-j5DcH@4Ht{VFQp>Cktk
z?_<}FQy;6hQCe1hXEUWi7Mm^17odqCkcl_+!0r#FL*bte>b@=4Ch-mYW;;N433g%d
zhvi6)dLE>K^sjjMS>T;B;x~(KHGdGs9?MO0`=S2;EoEbnua2s{NbYOXJ|at|={o(-
zhxCg%0z(3l@Tg@?!;`avjCU2hvq{oXg*bcNS><Gy8c!K2b9d0(&>_?Gzp!h1eqHUC
zGAx;5@i4<FC+}m>(s<)j(e9(0O|=VgX&^GH$T^Lea=$k`cCKzKxQB=1veW0ck&3AU
zc?XVgP~nAnhmQP71>MT&QrsjHx&$c?Asf}-)DCbf%Y_P+=WE>d@p)$?okiw)*T6pw
z_^(}-SS&7D*Gox!xn+^En~(6Kp7`sImEQbU@h++1y(&9f7Ri!fZ3MlUx-$%LaqCvT
z3wWEt2g5M<O2n0l(THb2cFyiTU>xHtd-_+E{6txGZxiUsku9}|nZ!|Km5heRC*1R0
z`Hl|@giwrToZhlsj{^-XU1`pBVB;0=`TUN%O1jm&Bd0?GpfbPC?I8JP&nGROwb)sB
z^Gebz?llcY-SH}X=&S)#{=*T1J&kF@s_D|{*B%?y5oD4uKX%<%e|ZmLE5&?$YpwWy
z!?!!`67Nlzsw@sR$TE5Z*N*2k<WjFrr1ecnJv_~)?IND3#jQyG5%~3B_|u`SrMH+a
z{6S!m#VQ|_9_BH)5C|nf&szQB{@J<z0H0L<0KQN7SK+tp`{JD@)8e0obd4KGNFGgA
z%W+3yR2k3~C5hs{dOq1N_doIBUWFvA?uuB7t$VgV|JLysr@YeUOT8aP)Mr=@e8S)y
zdSC(%9gS!~FT}4AUbEZ54v!3&$ci!Mk?$rm_yb>7>9AWt99H)SV2yT?M|L@4ex|&J
z@AjhjKjF=Ge-CT+T7)+?R}#S?SdwLt(*$e^`?(zCfCxQn*uvr_?+7G&btqF-ok<0c
z!iCf=SNm35o5@sso^uveC%8=G^R1Wod#GPY3TpPBXmmjwQV5j+P%r@t$vE#_hr<5=
z+9$(aF!5{+27>VEEr`t5w@nPJ&HPG`f~kNyWY^HTjpxN(AH`Rf9tyg(lSP<+qZ`|}
z+(t;?tDm2*VhP1_P*r1H!cObUsq+~ADZ$Zlq@&E&^||00f59mHKP|EG4AJOOY9}zw
zb#dh+g+R#Q<PnkA`B$R&o8cdeJQHFyh<4xUfNxvvK2s=AJGRr2{HxA>B7AjQjXr+^
z{87^GZ#9yp=SZH;Xs&J2RNNWZ5{<B^<wzjoiuw;l{i8k)ct66{w_Y0Z?=>}rcIk+c
z3xOd3HuPb!M(q5=;~g_t;$uoRB@9DRm9MUszQyJFwr^UDF;K(aoSn4R*8a5fJlDf_
zH(zQq$qbr=a;S_!{-yS?`^w`XvDc+nn@pC<<7LqhG>m+>rj>V`bRRL!N3JW+qxiw5
zU+Nmg_le+#Kyx6T&|B_&$0%VcA;I~>0e}DlHOwE1RubJMu8pGGz>@${T|{rWl}A|_
zOMHk=sjf<SgmAQF7$qewne_Fs5t@YSHx|Cn`J9*SA8+yJ!@eX<U%)AFH0!H-ctloe
zGLk?tBm`&XW;rYmJaJzzT>KjN`>g6i$9^ESjvYl<Tz#tg<r3>4gS>@1`V0_M91bh%
ztN#Fr9y7PFlH0@Z+re&mFB-n^4^p2o3+;->lg4pr-VnT&+Cw$)gCIz9cS-VxC$7=o
z(2DE9XVX$pvP*lORY*ctt1syuo$$~0h4JO3w}<ts4K~rsyGMM7ZZ1NpRtqUbWh9Nt
zdhyb&d|L6Qjj4FzUlQncch*-^MG&1)UKzI{^6bDeHV^R(k~y!Nd@&pNe@Bl>)D_IJ
zPA87t_W_I&Fh@e!CcQ=v7f0e~qq57T;{35gd<jA6vSs8Ct#~-h^E1R&Zcg9fR)5!X
z$XFaM8ob<gdTXlJxtuPn_4c1!(66rUZe=Vr1i3}?qgDfOXIyVX>J(MGPY`K>*|hs&
zXZdyqBn*2h`g&I#;(rX@YC4?Pkw9#(RW@Hj4i&M|MfDBew|dGNFZLV;;znm@QyP<<
zxnYhNWMmG4xGGVnUW_Fwn%`B|=5fZ1>0)PiY16fd{7K^ob$F%l#*MXYBatMy^1uVk
zjP3(GmK%W2ZZTbiz9YDnD?c0TF<uEgsbqxUxj;T;=vN@}GmMJxn0zCz=+UsYj7bq=
zCiOUV$82}ab-G82-aRwyMX>vh_#{jrI2dIkq4gN+M5+6~x>sFM=+LUK$x>Xsndtuj
z7(7uO?}is%8(6z4Ji`Ej#{{-J<BXd4^H;?80^Nd;++(QpJXh8FM~5x%d?s}LRv~=4
zh9#Qcb^?azxgT`nl21&Ee}}ve@bgc&yO&7QBUaPVqLx#ZMwF=mBiv5nr>j@c@b+tk
z%W$%VSUzb!n*HIA!78<H87@>OhaTg^zY471M*;OM5;JdilFci{xRnf@(`gtgG6<}F
z6U4T#X>mz3FXt&`NF!46E)PtM5D#qESLAq8PtmOWOQc^+w{xCtp^p;6Q-%#E3)elm
zRD)HESdvSNbh}yenPh}1E}(Kx_gmi;^Y!IPR;gPGtr<4#*2MizHDeg!p50ed0^`LB
zklfvy8@U!}k|s$a+6e^XCAi?@72y_ouBw*T9x8-D49xOLZzsq$AwMcV@Ei|LdRNg}
zuB_JjQPa`qkW5Bibp*x%&PG1*uatfw#M&&jnnV+ZTiFDTt<KULCm-y$Tvw%&C0e|r
zh>uMUygm!+{{9|L=Vfp43eRg#JPz@dE+Azaf@37MK^OzRY4QA0(e7Q|1Ryf1?P3^g
z9R>)mFw`Zqj(d?C6^>X;GBz?m0Q2=MdJ5z;uZO-Z)NRfDXd!@tV`;D%@#wiHA6ojF
zI7~mXZf@Gx^sC}$N~CYC(e$^5wapQHS8d`ey)5c+MpYq_#$gjg`N-a!az6}?wb=NR
zS<>${neXSex}N4~U7FHF2#Y3gFvw2fj@)x!BzSLHgTear8kODYvbqkDT%p{hq6dP&
zsY8N#pU%FlkH`KM(=Y!3wDkRdPP(|dI8{DgL2iEV`>X0e&3Jk5v{8bFu}3{+PHIw;
z)sK_J7tn?N)|YD`$hU!T5c)A4a646N{{R|Ea2DRtNF<EQAmpDwIsxli8o&G`?{W?8
z)wFDRQ5r7R4eG;#^{#eb2AI_jrEvjS4qMBUEsxZGRrB$TAH;dJCCKcVte!X4?Oj%B
zA#l0MGKW*_dWHNAYp0AgO>0quN(%+Gz1swJjBdd0a&wIHk9y`uhI}~+6jiuVrz3L!
zeUBUwnp=zOI}1Bl^$S$E!yhU@#bgQ(Lyia?t9Z&QTO6~*UuI}sKo~a3zQU!5JKG95
z>^}0JrC7MO)h}(ln+Ywin;$Ed0zr<4oD+)VHSZ9^1<k|hmY~TI%#vIZ`00`dLDsky
z@s6M+`6R;=)dxSItEGpVi)jT+!Z%9#zw7$Y^?RL4<43f8PsMK}YY+pIxHq6&de_W1
zx})pb#P{jImgCLCoP|aEp#29y>t2U{W#eCoA{#3=U$VgYX=WP~ztjx2(dkql;hwD>
z*Vr_fBAre{JHE^ia7h^CeJj+#dm2j9maOB%drH3aR*1{f(e-h0r_Ccv=A#~PK;xhu
z)#hn)6fi{bAKym%m)NNoA719ZlJM_=^nC*N7xyi5dO!-0F;rajBO@)3!m=0O?}n~5
z2XS|BQ+a<cYUiI;2cOEiCy$|L6)U0Cg+~b7>SDRPw$k8zQZ<e{xI+Y;ccIAV2d!qw
z;hzay$NrNB!?YhV2~GjeV0vP;<k7EU5!tQ5n%2;5iaEJeRvkWqx!bFIyH^(Wh^9Te
zo=oJ9e(%%1Yv<=Le)QRu?!Dv8wd)`3tyS&do6V1Q)s?y&<E>b*y3{PDS(Y_cLK$R?
z@NwTYp{nUxl+7jXmZ>CCXMA6Kk;Xtcu1iWU9phQtFoceF$PP2<?O#m@slh3BIg_)!
z&$M)#?Ly;8wbbXHLlwev^AqzAyu6%oSXX+5>fK8zyO9t+5;P2DBj`R_=KMF}iFCaQ
zv{!~XqmYSVX=Ey>_#hrT9)~8n?J{p3hVs(V2n46)DpLgZ`Iz+lYv*xTN|dU%%c0*+
zv?@n^Oz*WBFt*X(-ALU8%d~!c*E!-%9ehD(Y&Do(+0zn0z~rgubL;f4Pg|XK)>em5
zQ|6Dn8ISqJdFPMy3y3eEJqFIJ>Q7I~l?gdZm6~#>u4Z|(lgSP6n%Eyc5#~m7jmkQM
z^y(|q?XPaG?H5qD%Os!{+Hr=j8J=>}LhQ#o7-V$8$p*a#z<)lJLELcLLj%X<U2=yi
zCNsOx?yRBLBYomk2;ETgkz0{lgbU^pS00>x2D$c@TR<IlpaTTAJp0vqzY*%TP;Rzp
z908IskbeWv)=KS~-$T5#)9ea~6l8|r1A=o`uFbqLv5V!FG%tg}U_I-K(EMYj-9)k9
zN94<amQGhax>okLa~0o{b!_V@;e%}>>-7eyb8}eEZ6$NAM7EmQ9%U+A0v%5ZPp`4{
zu7AYyL1U%Ji6L952=Z`FM?8>y1xI15!j{oM`-ljuyU-EO9r0Y3j%AIt3p7^1SlbWI
zIV1d<-Z$p6Gv`fcdJl)^y1(%3dZS8YWd=Fb#_Ztt^fjZVE}4HL+c%j7$X&~hRDs(E
z+Pwb&!FSi19o5y9>IaT!_p7PO9{B`y2Da>VzdKlYVvI0~$o#!9+4cNs`!?B?O4cvy
zS60^AJlB^eaF2j-hB-JLDmbo%$w#+VV>nD>yB?p3s3n5iQTS_faj3e-IARAr{8k@>
zp8Daeg~Wkm1|6ggxG}NkJo|K_RB5O=P3&-eW~EB16pv!@G<tu9^xITsjkOejyonk4
zke|D}fsT4$^IYz^t6o~@5nkyMyI#R@7n3AsY2P5h8@ddG&V4JtKMm4<2kRas*QDPv
zIL78}?8%dsZaVhGc%GN3yyn){efD-2QX#ro!pN+vkT5ZvgZx}#xhZ1s_^d5ehn3t<
zMvfkMOwNkNd$D`I$nACS68Q5^@T6_5*-WcAWcyTM5ID!o1gfEw<GJfzHQ`?pN2Xqg
zZSDo($H<WkC<B)H&NpL$oN{aE`>l6W*1R8m;0+F0WVV79xhXTDMUF3-fN#9H`r^Jx
zEn{tZBDGhFRZxaF*aL?7zEIpAyph(tiQ;k9YB!E<>ubJ;=oKM>#Nek4qS8yUwe;(v
z)cPXt$G#hKvE3Q;`Q9ut6f&!Sbe0?p`T_N?&seVw&Y^cOVv}23GGRGvmL-cXAfX45
zk9zgrA9(Y`o*c6mI%bVL@<>()XT8|4?mQ{lj9IWx9S5y>wx8fXh`K(XbE;2#5?k3!
zmkAU?LWTKaTa0=DI#;EKr7vckMtNL0fUAdf9a?t1zXO-?9))ftUl90hBe+!=p2Rjc
zXR9F@Dt)Q?e~hoL{F{9S<)FK_JJ!nmen}IdP)<Q3+Nw`wseC@w65=OKMmvSeEXq)W
z>If$c0O3F$zbc-m;LrF??d0(nhLzyCiG0Slm9iKPr5FwfuEofbQfJgoQ;p>x_+!-c
z?;7b=GsSD-OB=m1Rg^Qli{@MI>PpDHGwv(U{B3dI&lT&DUHErVg6S?H^2{-?d|+hm
zUf8b#vhggI-wI^ZWnJ^hFWK11$PC<WKDft9qu`Gctn-@*?IUudiQ)M-4ump}RG-$o
z`q)I}&j(}4$7d6SefZq>--7x~(JrN{THVQM6{y*8@MTLbcmyfuJ-zGD?ye-&G<3Go
zV{w_WZ3gVfTpzt5>%8^$6_4Owg|~KAGT#3HXn$l|Ub?BbksfdkH!fI_vm=lWYY$hN
z>%)E`dt14T6GY$Z;U{3n-Ec?nk=N3^oJ1R`%guWehn*}%H#&MZx_g=)GQPRguk8Fk
zYCOw@&zTtIBQ1=Rk4{OcbX2*r(pO{d7WW=dn2Z8R<8FIrr9<J(B|J^6&br)+F4aSM
z+w<ok=)i-?^~H2L_5Pa#FX8o-8LbxzvGgu_4mxAKc2MQC6-{D~F0B@-ROHdf?mTPo
z$HrE6I-I_J_2r$+O7dG<PGWt^poR*5Qn~xblaAH$ZH}+v?JLBVP2rn~r@C;;(Of)6
zJ%D`6xkl#a7$j!Cz3~44#;txE7<4t!V!D}AVYzwP5QP}rKnEus$rbX2w~sV`0DMH$
zbsM+?$9!5d_C*rxb|rv}um!m!9C2S+h^d#(y&9MqH0`Rki1ji^VzBrM*n080Z%2Jk
zqI@&rpNiU*to9Sy!pd+al@a794;?`VJ#pT<XfCwf5-2P$_Cs|VZcYCHyE9|D79?)<
zQgdDv;LnQp`o^XLZnw3Gi$QLuX$QNx2bca;U&J0W{{V!y#!VgjnIqcaSAdDe3w8U)
zADwWl_YsD#JvpPbjy&H93{@!5f>Dyz`ronkUxZ#AD*E$LzbR&3-CijqfAx=%@?dfa
z&2v5?v9i>@CP}I3*QVIXZ6&G+<7A0$83;lpQp0ItFxao0G+&5XrJ}8!y~&mpQ~R6T
zGLWO4j7T8<HQ)Ge;u?68W-_skSir(7e49rcXMz1|*1~2~arN92adV4}p5J-%a>8c0
zWd%_+D8@=!e_MAYz4$lrX4g<=@n?u4vfcM!XeXO0eOM?~2e)eT8}Hf2QkPMg^!;8a
z+HNE^$k9hIJ)S;?>x|db*FGxLb-Q@19!Z@ejFgQVF2Q=LVE#DIO1GnFdOGRX8osL6
z(=;ebt%C+g$OM5S?PAV*jsW+r48p!&ms8}#UDerJ^*xNgH_x+axlvJ6msj`be2bub
zDv#kNwH~M9C5rY7hABD|lnbAgf*#=Y&2rx!%Xp()x6*VdyuCU<EL_N-`C*CvEOEg0
zuVB5^d`qe6-(uGmJwa|s^1S~5l4ae>sd3d-y#C~;gftnK#sP4u<b`acpWOiWJ+WRj
z3}3aHytU|$Y85Lgp4w5qn!i(?@y3?7x~7TYeK!h;3Z3Dy0<Tu}=e13!=-PC#M9{AE
zfy%JR1fHF9*V4Riz<xMgBU@|DadyXcN<(zV<b|`sHh+g7t$Y0XjfIRN<5wa(cEOfq
z`IiTs$8WE#Yl(7t8pjdsD@GI2J04T3X&Out4PQt5MXtk(h<7>`1Ngy40Jrn3%~MhF
z7Kvd#t>CNMiA~ytx>8K?G(-51K3j77X0E2Irs_A+!(_7D+9bJ(Ogf?b@sahZG)-D<
z6U2~dx~vWMd%UD=^0LOn=j9%sTHgy+lZ>fi7U!LqU~A!Otab_QznSGT-FQ$~t^JkE
zxB7&K1<RF?9{Yjie=4t|q?fwd-l&S%AQ^J(&NJu$8D=N{0M}l}<6nT5Gv80*ZwlN=
z1;b`Xw~;|sZu>~aGwEI<;ayTaJ6~4SI9P*j<E!TbKH|Etv4^&bzLz^;n8y)@l~{aM
zKR`yY;TwMk*y|cpe`meEMJ}+7iq3-toel=^>s)oVw`X^1`hKnnfg5^E0dFaZ=cj7&
z`z!c0=x!`*r`z^-M?4l>xaV#@ZhP0F-P?GFNzvfCySmfku}&7<QIpI+?QR(->s}6L
zg><nKi=Fk;zo*FHqghm;8kVy(JV~zE+evRFl;Y}9AITzj$4$pQtHS(ot?SyA?YPx$
zWtKL{0E{C0C?D3nTS>ap?xcCl5G2k<00(Y8z3NfnUk=~i&8a+|Xpms0>?#r0gIqMH
z8gEo`)rxOJci{w<8gIl82HROG4?A1a%a!1_`b2$CQ_X(r`H}hx{0#UL9i89DUk+&z
zNtms*t2U2uJngtcfP2^U5B3!FADOR8o%OJ(!6>e6&;QrO(sWOW+P8`9EF0|-!*w`B
zc_iGUals^>!nZta`!o1E;s(8WrFf@A`#z?T2~G4T%4tHj(&-`|a7TXJ;;}pvu6${@
z(QIryUvUnJa8^SE>WI=zW57O8Y-}W-;ut*zd3L3(c$U+}ji_Gh8jaL|JUgYG)P*F1
zr3V<tP%GY*4`qj(XuG%Pux1&YV~T}3k^DF5<ZS8x01Ui6;0sY}r}&Riw^U~G<CkoX
z#B~T2Jl5&P2flmPfAHf`_;2Ig9d#cMYFC%KlU+e=b8V=+kzFG+imt62wm`ukGLk-q
zx~~fOcSZ1rhHic;_=4Z<cS~x<IVQ^#z(03A0q<W8_+P}HM~dw=<&;HuVSTTTMZ?@A
z;!OJ$01ry}%ml0AVO3XmO7?w!3mE75ZUT&`)}+1pG}^V(OTGJ_czk2<K9k^^%;_3T
z7cqt1DoSMzT>%*m2qzy?Tz`kWU*Ii5(N|E@FD=Z`?fXTg;W7e2`^x_Sdv9P(ZTvy~
zsIG5dlS=rdq{wd;-*vU^f^Ld@FY(9K8=gvn54TGB^WhA--;1U31L@Y$n_VX2He51^
zF>s~WZEP0{j@*w<E7QWz!Btf7`GmSzNxr=k{{Vu07xt8$H!Kpf>lL@2hq%Y^%TCqb
z?H(WTB(TbIP~AYJ?(Mjo@;!U@HRHP1f_yXKo7v;>M~h&eUXtk}@&@80Mj0ifY$}oH
zPZjA>>yM<vcrC5&E~T`9O>yMPyAX9?92QrTX&F(-?m@2~*EF9A{9e^2i^JYrU_?CF
z_UyPvz#zJ|4}P`Bnc*-rVAdXLR^HC&@NxLON4AX|L^)iQ*QZT79;a`i!EAyAxw}Kq
zm1T6t(DfDOK0VQ4@U#o!tv=>yib@nj$_gFdd3ZR%8RG`J+f66N9v`xUOoi<4e#;u9
zZ8MdZVEd{_%C_EuyqDrn#GPNm+7;J_^{pLDcSTQ`6ucuT&Oj@So}FvJqZ)NJgSqBB
zP9lv}&9U#daNGFbN3rolQalpuQ60o<wUmyi>z|koz}Fk%Jugs-)$K25R-9s3?jg*0
z+t2(3>c_*aLq+jV!sz7EM2j-q#?nT-GJK7}?f8#M^o=Xv=fvL?X!l8DqG}gb&kIXy
zauXY5QIqpFH=_Z_{HqA_My;O7{l|TDs?l;wq2+pJyM3-`u|BChpKjCS^490>vqazO
z{%(WQ2Lm~!)NJ)FNF~m#b2G&c%<yb6eBa)}e5~I6`&KuHZY}hkK*6fS#@6CLvR$q@
zKt?weARMC%;;{8icf_6n@k4lhq+6>XL!e{#qJB}MW7{3S3hTvDkJcSpPeyr|9CCPg
z(5kgZq1ayAtRh(THI)ZNkYUHMqf@fBnMR>KsV+dv#nG47BZ}p3b=?B-gtxv_MD~$3
zwogozrs?{P#6kZ6vhGAGFm@IV86L#sei*2yO{v_u)#|k@zNI~lyWCj27IU&VPXNfr
z3y?e4t!Y6tuMIAmjpj)LnI*s(-cDJF9Wranw2PT!xoPh0qLuAV^vCYTPH@};r`XoB
z`18Y$Eyb>)7f#fY77>}g<i9EVjxZbj(^<DVRpDsqb9(rwSD@j{ak>Vl9;M@p?K<6<
z#d9B+Av}eVPf^nxb*{Ec2a<oY<SU+=iCmve-F}&`nC$QMODl-HL#NN>%@nv({8=8|
z#w+RjUldtux)0l=j3wfr^H6dd9F`wnO7t<9Wf^lqIvf~Gb5Z4}&I<ce)U;`)gIjqc
ziUM4hZ0%9b;C_|!{{V<JokLOaG4VD1v$d26upt1DhEKZq9k6*ltJ}O&tXpeZd4lj}
z7mE?IcicDx{{Sya@GUD})chxW-W}CHdu%bWiODgy@SL0wGr`BTdO1}a`|dVpy@$j;
zPGhw2uAK$+k8=dE&u<fFdE+j5AP&5G*RV&ZStWx=M#has6^O%q20_Jm`f0bCUGUeu
zHxZXly2qUqLnbl@P@~hY9`$PTOS;#XUW=t*qY0ECujTLUSYj_$P09Q%^)RiDwj)<~
zw0bOUrbwP`+*yug<VbQ3dt{FNvs{0Qd^6#l6|D6L78_Wv0}ZS`MSi?sAD0H7rd{jS
z*5gjp7T)4FQj7BiVn;#`2aly?_=?+G@lC~@hLEc1V;7Rl>;jod%N(EXjAo1lSiDN+
ze(GI&i!iNC;Tr1pJ8uczE}LT}iQ*9Q#33(e(GNE{<RQoK5t0vTvlNoU3eRm1mLH(O
z<afaC2d!~-cbbQQHCt<GWsZAEhD5R^QaQhi0G<`S3B`AE>NkaDx_1KN;2%1E07%F1
zMh6NAu3W;MFK+5mjgjKeoTnQ}-^|F>bsa*+C7D=9D!3!QM_eCjhSKTv&1T`$?1DRE
zBE(6^E!S!GJl7d}b3N1#bqF3nnIcBdB?-edZ(eG#>HZ@z*a3)yYPjS$$3i}p!*=y(
z>xCHF%X8E%G`nm49if?8=6T~Dd-U1><0Rl@W9wKjcvD8wWN5V+2x;&`DCf&ll2D%g
zkAG_DEXrM3r0F1vIWYW;oOkxkay}xsl5Kj&N19!V!P+-C!6z8O@5ub?Uuig}7)x>^
z3UZp}j;Qo&TeN$1veQ?{^FBmX{s>fo$GP?O&2n0QiQtRI(rL0TBZvJSFt{KE&U5RN
z)YguhKl~%o`PYnIGMiRY$WD3Pj>f#FSGI@4-Xu+8;^awbHOA(+UIb<N0kM!iwS$G;
zz0Y2UD|#)_rQ%Nz+C}0kneF#T5<wWj+Q1RZDIGCO4!x*b>S=DT=l!J%4cHDB)Z@7I
z?^wF-pAZ*1=u<R!GXCs>8{2nG*J0t04i(aqR3m-G>>@)Z(tbiR0Q6NIe_E->q+e0v
zTBS)!OMO0NXVqtSHpJi+BLcadW5h67=`seBmR+$7{q4hnpHO<&V01f+*=3hb{q@9X
z8N9!_7#$9JWcpSu-+}ElyJk&5$pe-xgxW&Ce_juzZlzC2TF1;qPX6;bXk*v(T|4BE
zN}D%~4pjBv{{Twn6UJ8zo?Nj-I+M~x$zP%EUW;Ymt$$02*4FMjsDT8j&OjaZo}6{9
zdryYi1Vt{cBWDfCWpU-_)7$Z{Rv7wJ)#WSt2|>$5wK*LZQ;)>&CXo{1$`G**<E~KS
zIbTlVySq;iYdXxZcxO<MM%K|3jC9<{Iv;YQk9z5JEd#?k7NVNAmt!>ZDavigT#lhn
zCB5n`Pr+U@@u!I&U9_@`PK`E@W^#n>?glbX;aoVJ)>D@~IeVzqo*!2U$9<U;9x3rA
zj>6L5n~0(yh-DdI5BE<|$8MEF#oF(O_3K$AwY%KPG9hKy=tpcGYl-n~#=9P~7PBm6
ze4uUTaK(Ycbt{aFds9;C-t30GSXdBGoG`$z*Qhnp=*7>TD^ogkr3msib4hn)G%?F8
z$|J#CuijJXiu8{H&3$QVEN>(I+@!Ql$7sj+R|J|Tg|&2RyD7Y>TmD#{x%_cf-%c<x
z!}hsnMP7^ZY(0iE#bZ;<-jtGh9@1~L`KLDM`|>fzKR2MQc<gOTVqnrHFx+-O*1UGZ
zQooMgIFVId3bMc5IUc9IdlsKPrnjbxo0XLvxPLF7o4sO@(B0L?FN1W)8!cU;ApC`g
zA4-=;(`>B#K$aTOMhv_-C3(jbSv-0>6_RN@tmFw$?LUuWSr-@3UFuNH4Dd>;GL;Iz
zNIgamTBxRBH)eY}_;bSrNsPAT{_MFU^R9cu8YY>j+HAFydC(2fi3e{(*oLY%$sxOt
z!@a;f9OEBcR&SpzysU4|#Ep00k<U0Et#r!^B_4Mx7()|ja;Tifn=YfGT-;w<1yyAs
z;bXZMvBz=EYxtt#=47yuCTnTsh~yy46(<B09dnKaQMI(YYxOrORwOe`a?XBMm!c2T
zAXGY?%0sQ`_c8|ZMp4{!%N+21f#$d|b9B;CdY+D5L)*DJh{V0p?C!M5?5`wgZY5Hw
z=E_Q@dL6%XALU;_{5|m%%s&d?@D{NxwWM=O*t~0S(=3X=ch39BtP>l6&o$;AEYYKf
zQ;K_d&yjP0OlZs%z3?)3R@RjyFzS%#+LWr%Ap1m{;y=4p2LP$ZIpk)$u{c~Duo9hV
zUriTYZiZQ0F(2A0D@!}SM0<b3Ux;=-BJlmshP+oZx<!|SO53*%iU~L*dgi=0$CeO9
zt)fLLL*|wl{uKlJi`aWtSrzVubY~h(j23|)X`Xj3WykOhfr0|ZwmMb^iQei<7n=I!
z#^BB{A-RWZ^y;MvJbPEj<@jk}>$;J4O!~ayj4{-02<!evPlIKG^6hPO=91y&K1hx*
zL6Dv>ao2Zo(9|9;_;|LmJen1_5+N$~S74kKJnSdtQauen!51DO_-CkF__oNwBf`Q&
z*b8}l_gOlRr#0DJ_|I1HXN#oQ_4kQ?v>D45jD%;$%Y*kud-ktVwSIUyt6LvGm(IRh
zTa{is-8wVm{{RtdHtC^V_<vK5OJdED6v2Xr&s1Z;KSNr74?ZX8X{Qekd_uc>Ns}ub
z%!BuHCtzH3Vei2e)O>vL6{Ow)zP2$&WM_&Oz$M4;0Qr9ogjdR13^xoPO7R>=$eldc
zLvw;S+=aPQj;rZi7+EP%=V#sEv9iQ2YgwO6c!xvyd#uWDZ6r79{3}6l9E|?}yPD^`
z9i^WP_>Juv8CKoZyst2JBfqHn*UH!06#93G9?r#(ub1b<>e~w_<luri9+m9=3Gv^G
zHf)8y*s&pEmpN^tAKv@Gel<9VNygE4x!LX1uB49J;+B!4YL<}L+d{V1R?{T6BGE#Y
z>$!mFPd=5`d_M5M!6?*fei?#i)F2D~zGr7*JA`5vlkJa6<+KfE>%`E==HCZ-icuKc
zK5x1Pdy~a-y2Zw?@W)X)Y*7@xEUODNm}T>R@5-r`#&L}E>s<V@lKQ_jowPn%JHpmZ
zHR#6i>V2&&I?j)#OQ>rk#U0@pwn7=kKH~-KM|$9VNu|TA>8+}TBKFQdIpj~`fOS$k
zk=u`IkHVH7F7Q3dYI^fpU0mF5M7zB61Y%5Q7%be6df(D~HK^+L(pYJiwzt}ztgJ3)
zL*=EkEONl`PJ3`VR|Kd!P<P#RJRGXLs%8<CC41=oKU3##7RmjYCZB7i`HORWq@t%A
z6!j`QXVSZG2>5$V(G%_Z`+2V*!Bzw;dG+pC`qxXXcyb+g;-$PAQiB$SW#lvC0iPV6
z-RpqVJa~(I`fitK_t$E1Aqsr3M;wuyn!0jRqh#&e_Ho$9OA+p)sMl88M!LPWl&aaR
zd!u;YCO}4Z@!Qnb#rlT5Z!P<3mxw=j>=TccH0gyM550RQiY~A1wNLE*NeZsjVGHoT
zFv$c1&NGZ=ysuf%m&H2c!6bs#(%C_iW(E)UP-h>XO7$=hQIEX$K8l4`Dr!rqd%&J0
z(yVT8Ci_Cfs)b+-7_*Xjz+Qg}x1?*AE3QsqoglqtJ7AtR+{(u}$6=3J)6*<8eKN_{
z!+~OWM(oM+NXorio^X0#S8*nd;r{>@X|Ug3K&d0BB31b$M(OgnIqEp=T~fpQSxPAR
z?B1m5RaI>D(@uv)1*VJQD|q0#Ei92^%`iu`OLy8>@IA<`(@U38v<A<^8eN5~JO;VA
zlzFX=xppLvz*mIb{44m4sN0)82T)t6`NsGAJLA!cgS+d+dOv{w0A|04n2PgSzP7T3
zu%(u7Jx+hTV%|aa;Mbh!x-(5r-cLp6(C4X|HHV!=cNV*J>U-tX=Gh^WFXkAQQ8G9x
z8~ujs*S&YTz22)ohGEt|A2iK<<!;pAwjwZG9tua0A0w7H&T6-ed?}`AUJJId@a5Du
zPh`6%Kpk35-^3O}k^$&P<6b%8ZDYiVuEh?KZ!C_ogCSG4HC+Dy8@3gA_s^w$9$|ux
zVMZ8?Q?#PBeLWfR`8Iip%j#5kRd~LhUD3&E9xd?%UUkT|X>Yj!W+!TTjCK4g<&PHY
z`tGmdh-LAFjAB>&zjips`zzb|*XX~*%L}FPR313dt&{BbvZ`DI_a=<+?~GvQsIQTK
zXx|K*ZAkc&O@MuhOc9QQcO##A@i3~2sqSlax%c^Qdq)}W%iWi2oVBNiCGm6<X`g5F
z?6%@L6NOb?a;K)_`PB~*cvIpJhvT%<G`(I)Z6r=3oQ2p2@V6OmE1B?*i*+3yVGe_G
zQq_QWww-sVJ^2_T>t4mEYkGf(tylYA+2NfB$s{ejbM4l;Wz9{yy$_=HG^%^n)bo2N
zd}rabGbW(}f_``#b0HqU=O2YZf8*&!r(!%oZwf@S4U)o1E1yt%X1BHJG@DghFSlbH
z`D}MLKDfajopIW}pW)3`Bcij&Co7bYuH{zrJmgYPgsmweD#l!nDg2MKY;QGo2`%Nl
zwdIQdSRrWT3V(@M93Q242gDBmMxHAE((uY}g5c&fBXJTpVm&+Nu)G!Vd&OQ7MevTf
zZkmK}ua>*>9ECrGqpwkqP;1}(58^nyVdr1zHjfR(vtgl=4YxntZZpST^`v<q)Tb?b
zp9hPUCks)!F@A{7xA<2kmErqA;l_bACAeFQsir6ABdFjWI{{y0MK+zNX||GHBnc{8
z2^kJH7snuHueEs{w1VE!QL0ESmPb;?MA|SqZEz2M)PKTNp;~D-Hg}gIbW`SA`9qEU
z8-h0xkHWlc-wg`zjaqZOlkRk6xrKLE>iDQyFS^;DU-4H_iM0gQbnU4OpeEH&g<X2^
zE9ajU>+J+q_OO|irzM|n0lDqlt$PoOHO~oacFAuQ>-@470U^de0!4YBi>*8}saZ#+
zX>QWq{JvWSDl&TKJt<J9?!`$SmL`mppDNJzFMz%dO()_ng|%yg_N%FNiv|F2v4e3R
z=0|g1ym_Dgc`biEUje=!c#0p39u~X5)C@@mxu>{^S(%6z5uAPCpdS2J^b__6{{WuP
zU39FI)Z>k3WB<_ikBy%XZTusnX}&pF>^0YdWshyVjTw<Pk`<8dPn6*91Rq-XLifcw
zCH2CO5?fzr(y?vj?ab(Katg8$pT@q^@gKy$4PEK~0Akd%jV{{uGP1=T%u>Y^q;de*
z;Z*k=Vz?vm@4{N?lTOuiOEpVUklaOTpK;jj7+1>>NX9|yPX$3nH62Q-4qe^#K6^3D
z>SAXJDD$d&X>PtpBd-3?z6n1JZu}OKPv@AHJ6SF*nPZg*!-I^4z~{AobwLAK&26hJ
z+mKuY!k$+IVBqw~9+mf3iaZx>;_nFSem>CSI%Tz{pw`|(5L5`!PFo<T-bvto74hD+
zVI;Q8G|{5DAeI9JW18c_Ia5_B!QWliQ(VI^mL<}t^wVy~*S``ibd7W2Wv;DphPbqm
zJgG7BWZ*E*p&s>@`!4vi!#XCVujn2n1|-xP*5*61yOB&mkfY-SD~93ErVcvSn%G=x
z*ZvA$543OPxf2;(u&Mya#(I<OP<R=IoKotRcxh#DBm<}kk-+pleL9Nkt4^LSi@~?I
z_4k~3Y-MaMb&8MO-1-~hr;j`}rpZ5s`~#=MZ3OWu+{puwB_QRB1D&CW$-z0ty?nWU
zF2ALCf*%;^OK#UTjj%E~4>Uv!ql{pj5-aE*1NZ~rSBf<$WB8S6e{Uw=GTwbAUn))H
zmTxnX2s!z9z^tuz!P-5>v2zE3(&_Y@eUW*yuxS;X7TWKDle7X)AEk0UqZNX}H#mEV
z>*m?MFENf|jd+JA&22Az64?~Sv^D5Pr)45EZhmO@Hu#AC7X&E=yzAo5lXK!<4>h0*
z5mDwxAw1=MeT`^b9}nsBd43z3X(aiU@?!r0yO5p$1Gr!Bcdi4(n$_z;6^j-UCKCjl
zIzJf)q4lh2P;s(`i>Vc>xi;Im?%%No#Emb&dd9UEi8NSsB$~}7jyXswj|-2Pau^;M
z_OG<AKWiJyZAn#Sj@7Ot#jJM{7?E+pjp~E}?X(K`3&7qT(ew>DzQuPe8rDmpDgsFx
z3>fyD?or?QS1YP`v&5QJ(~l6v_Nz;R1hGoFiSfq+q802{7_U+poFRs(3?x#h-m+@<
zUS57@Etj;5#Kp=BrP{ITUOkswZEpKo)gXoLE^cI=a|^b`cXGsofUKuH5Pr4H>st4R
zJW#fB>MU*Ut)udoGUZu8$j9SY3FBQ~Pqq^Hb5OaO(MaECcsGPo=uSf@=yThe(3<MY
z#LTxE)|4V<0es6@0+08xUJ{{J>Q$5VIz7EUcgdnLymb0a-h@B1=0}zp6m96l9x;Q?
zE5)s^r?++t{{SkTthvGbtLOyq){k#;Zel+uA1IvVzXCWp?L?js@V2Vr;(K*g1Nay&
zPjW}~t?1W<HOVk^+?$k6E5Taiwzu=eZ<dZ&2qBw+x8^^}y0Lq2uA|y&3a<kIM*QRc
z0(c<(4RxAMiQ)eM4zo{dYO*Vhj~n2fq+sBlhX;>Z@=ZfmyYSYpHl3r(z7=nhIhQI`
zuy8>f9^6q1kgGmu5otkk$lAH^$AGQnjw_XtHe;17Gk{N~PDX2ayg#GEAD3xvQ;rI|
z6ZqE;tZNz%i8QI;omSnVT(&+^jytcYuRgWZJV^wQ+gQx(#DN@&;|>Y#r1h<8#!|K9
zDrqfQ*!Yg><HL6m+uLQJbCog-fKOF9&2k#Osqrd71-b>fSpbjyq&-Kz-oJsa=JxPv
zO&)>bXv|j(1IH#t$1CaReMW18({#;h^H#OeJUtwXe+A!~Sad~J;OC|YuJ~dlN}Z01
zc!z#e^gSC)&x5{uNxs=@kSE)a!2bY!M_v!<Qh153FCiMwho%cMeBUoCD*7_|<Dva(
zEq>ZG@hrv$9$5LC94fnY1Ggu)YUSs@ztJUEw;;mD0D+bN0Ce`R2cC1P<$YP#SCu5o
z3wx&cvrF<WV;0wHF_Lkyzr3f?x!(x*+HVg?-WTy0F-Z%y$%x1~IT=3vLsp~Br(E0#
zmKKIq<f@IxN$00pT|>dT_P;E8rk87Pa(ujPm=fb@JOBvJewDQ)QZjO}%~GP8QR(`f
z9<w!{h_t7+x0M<5zC4_AJANXuE;U!V@MyO=^AVO8KH1>@LlvJLroTLcPmfM9&NyX>
zNyl!#)Yk0UW!9Carjx9Mj9g5z843RYRuuBAdUfPhn1^WDy-aYIbu>kpV7Zq?u}2$B
zYn_0PyV(9TUg?+mqe*fmDR2sv90P?M9QumYhS7rw!-r0a%0mO&wM!HEa>pPF9DIzY
zq3#ZS>yx4BNu=(LkB6G&-R79qQn}x1PFR3OGvCwFseDF|>Y98P7Ufmqn=zCl1a~Xl
zany?Q%Xsx09~Ru{)@9W$ag<YmhUDkkuK1=E(UQ{QHuGikzF3=~UWX&l)k4l{=2e>B
zJD!=N>oMPG5^Byv$!!}8m`UZF4hB6QyzflaA=K<ALJ=pB6nkk15r#bh-N9c%YiCY2
z8m-iEN?OI;95DnacE$o^{G=YgH%jF7EiOL=c+wlqXtUT{D=(OfkimusWzIfR$sa;1
zt~QLlq-^4PS#Akdr^#70J=LFK)?OG9z#B`wxw4MrFp?NqOk0$Yz@&hvKTh52*z_HL
zQHR5kco$N<yNVcBY%bQ}6ssKV5++Gg>6-IhMm4i3sc26X-G#})Qb|D|=nwHD5x^($
zuJ^!p8osx66_<w+Y10iO!w}vOC*CDXe7NXNMRnJNWlhylij;bE`4OAY#6d<gl$|%X
zm#FP~#acdvWBrGw-LZB#oT$d`F^qNMuxzfpOL-W%(xA97o{osvKaM%Cesr+8gh6dT
z*zb_xpq)aidTeq5>;3BFCDyO)BzQF^k`|4M5ZvI8_kL0jwRrV0ExjO*pQi;K6z+1C
znxBi{a+em3Am9+g2Om-2>rI~aOL=0R!CD_V!aby`^0Tqy*w<Qh<U)dJ8)F<9LgXjE
zeAk+MQPstUm1S{fFp}f#0%+Yw{PQ^n{{XE;9$8OVmpREs>7KJ6i!ASwGR<*ibqa`s
zBJPPm^dkfwy{lg5#`ZoR(u{TzjYjdg`O9DcGr5W91F)|+(DfT%5VLEDvP5oQdqyy$
zpy}Hg$*vRPESi>`sao4w{oB-a8$izCf%=0}JQX>=dsW!e>os7xBcz||cFC=2*O!kb
zxoRX^%rliGN|qTs4hS^{tMI!)xsT1#CTLWh;~zL6{W^YC<9E_)R+^-c-CW$j@c^nF
z2RP%Lk4}~Cehl#)?bV{&YB7&G5JXo9cJ3aGy${y9<yvuya59BIdGqc?Z}96#SQN`7
zvOaQIcJcJCzfZB#bhutUGTed(1b|EW<oecg&2*>$Vpxpf1_pn^oSMd`9Dv*jHn#wP
z3CO8+Cigod2(3~&*nY<@(k+x{l3G0A_oSOoSmiU>#0CfNV~F69{op;TG96O(c-q~A
zW%<EhO1*I%-O^7kp2XZo7<FYh$Q?ocwI0&ivbj^x)6=C{@`}y1M?PWPtPks&=Wl#5
zpryj<QO6i&kwS&sGTHPesHH;|v!uMbWX*3ot{AWIy8Yqn$^LbX;U5yFsVo{!rSng3
z<`;3(DUqMXu!b$!rjD85oRVnot~Bc#$r-L);soPtX9fPX&t2GQc6xNNTE#T8Nplun
zRt(G$w`?BVel^#_p$$!0?5|MDPEjSoqmTAv9S>|Za~jT{d*S$@pTv8tqn1`EzsAK>
z{K`4?z&`v^sZ*a1DJEVM3f3wylI~>q%fxd@bsdC~lW}_{W{nTZ6@P=}<BmFe*OY1+
zt?ix7+Or*ni<NVg1au?Nf!?<KMSiw$+g*8LB$iFEf_FbWWS?H<xIIxOZ89tTmPy(B
zWPrR3{cD?>+D0uNq#^8OBzo4B;#<G$m}I-ScP|hqsK<{^{k^NOw$eqNoO+g|i8aOR
zp^VJTa7Y;4l1T&7yuZM)PZpfIuCr@9TU(v56ig{>bCTUKI&q5jL8wLI%@xL$w*g@r
zgU+ErAaqt73d1n2-z*~Z>TQ)pLae1Orf#pRYJL~7k+h&D`*Un}bjwD|&OsayPh*@_
zuL^i><5$)LOE2#nW=ZBa!x6?w`=iiR-8C()yblJeEP$)eByxuS2ao#Vv2ASpMXFiJ
zd?x<Q)8YlZ*?>~&JK*xf*E^{>K}DZVqe7p&minCrp`&VWSx*J=2#TyFlW<_fbm_(`
zhw%2Nd#LHBN7SyQb+#l&17|B4Jwp-FjcWdFAHoy(t5LX~c+`k2qLqkOcJH@<2jO2Y
zYI>ftd2ZUIjWm<b@&cz~<n|apbwz83qbVovXwRI@YjepZ9Zk<3c&KVR@bD&pt24`U
z7H=811lYj3fH+_P<kzEk>qyh@bV*>bQ1V-i@w#;<@Xz8a<~z%4jSl+TS<{kZJ)TVE
zx&Y%D{Oh&whs94Drmi&WEtjcr@_n9o$3l97Fh)H|BD>{F-HWF5M_yHmrwUS1>Ur+5
zVA}K8U1_r@p4pAOu;7PAKTgKI_d@Vp=8>hvrP?@-dw?G9R_LqQeNU}<BSrRqix+-z
zNY*W~&#?S~m}eQ|rFx#LcdzQ6UzM!Av@&w30w8j|xL{A?T{R@rj9$ht#W}X=yR+T?
z6k2Ig{6*6IFKQL7t>oQ?!uhiSxVJoHo(5~C{ieJWn*RWc8(O=z7R_@AO@oONNPg=p
zj(1}nE9RXO!`imBufd`CO6KzKQb1%`Jiw(kb;GK!&U<rHc>Bel6nrzN{{Un7%4^%U
zx8>uz3TBN+;nDv9j1NP}^{nXO9I-0ADl4V8U-)C_Fm;#JTCGjl_2_T-K|EjL-E}-U
z{kuKHdVW0noBNaHE3o-v3RHt3?nxLOO?!8N8&~i*k8HKgP$bsyN>buTSY$-thuo*w
z<Y1cloA!0qbWiR3`(F{oZ*yy^$W5ixfuju|=W9lI3dfA(b6-;H*Y`Hps|K57hA9Yy
zMo3lq=N+kL)$7x#^=Zb@lKC`zt}iXa)~vBKl1@Fex7=CrPlE4;v41|3dhpun^BJ1%
z0<F3(%A{lhd*`QWx#3?4cwfVopJLNt^2BblNpQn+$XBsr$s^Ld8^j(9@s+oVC(`t5
z&+Pc)K46eLZReZ?2b0ZEo54OWzL^(T@fFSDDfv=Sv~%gV9Da4<%L?TlSk=(xuTiOU
zN<Y_;k@3eyh93|Q6xqzO*k4JyB8QY9e8GX~k~&w8_+L!3)$gvgO-JStNs$=gN$gLk
z0~PAp?u#Cgqg`lPvy}53No^zU5i?^1cftDCk=fX5Iv|qK5KNa~0Ao4NxcXPn<<w-T
zsJD9_-d%`{saY1a`<u&W8lH|(9A}^5Z%{gnVzoRSe){);uTj%7+el@MMl#B$mH>6*
zsIEs>k5hO=QW1#H9F73&2YSiTj-h3$NA@<B3)vVbDEn}yj1?KlK8;(}g5+r(c#3Lq
z=IVPdggzv`jbV2PkV?{k=p<HGS6&V`0C^oneG%Yq9%@HTk5Z2E(l{UGp5G+S=g=Sl
zLigUp3h`m!{{RlDHd^7hxRJj3%I;I#f)rx3ZoD^T;k_a)PfWkmu5FCITSmJXV<2P~
z`F8>7njyp3j6{>A70uq~&t=*E4y9*8ydAZ^m%nrE%PZdxUg+}bbHydyl&X<V@TqAn
z3Z1~5X92r503x9ACbi&C4)|R!EfYr6p7LFhw5`NI^c!-dIqnLRUmR%uJCnl~Lg&Mp
zmHZP(yKWKWS4YU@T?%bsk6&7jdrePV))U2k6qX33je!x}TCDc#AnH+x<J@zKyxWGe
zO($Mn?^``CyQ3)8$nZSQgq^x3w!cK|+4Ky0(`jX;^B_f#?t3@}U<y4$lautvwPN^!
z+rnNU(i+9$j$3P}%Sjd=8%K2n{H!};ioo#~i#{y)?*2VT#S!R+TU&IuQD6<ZbwP#5
z+q4oplU_Zo`1@DTuAuOwmMoK^30akZa?P9w+wKwS4_fhImg8wkGS=7m9=2aRrx`oZ
zwfft0!n{x6Wbn47F16s-H+pJ2E5>=2B|Sr_<b7$nuD`3@_-XGnK|8k#xl|p4zDOK#
z>s^$3CC`qvKkWT3-g|9A;F8Q8VEX;$0lu8o%g=^C68;~n-`*R$s8n>eWR!va(u|yX
z@m}RvsB)^BGt{0Z4@Rf3okXwaslfat@XC0z#3D^fKq0o<BgDt&m9xV2>FR6LweN(!
zF1hhs()eD?yc4$OcV$FO_UE2Gg>iazq?Zr>023Q0vY0kuyD>ChH=~lF4r}S}1$e?u
za>6?qCbs)zTr#mLB^`?MjQWb@t6j$G+K#JHT<~{Ye2)<EufXkb^n-VGc@o~*#$`r?
zHbSV$LU_oi{v3F-#TpKg4wWsPrOm4Y=PWMCJcRXP=lH#=+k9i<3(XbU-u^U{$b>4y
zc^Tu7bDHKnEu?rJ(?Lx_cS(#RbcL9%)+dlxt_69u>dDdNsIGW8zE>qU?s|@o;x7wp
zma*8wE9vt#KXY)#QGwWYtM{G~S*|72tuEmzfG{8h%Z}I`1$mSnGw^&eyf!wll5>f9
z1ZUGZtV@k)7DI8Sq>B;m<dqHFoOZ7!G^ZI}8XVF}PnnhcQ{aty_fV5q)9q3ye*2=f
zdG2xV*8;hVJuXopmLMAH5xqRTFdoD=8Lqci@y+bE&7{~)5;lmIm3~}cau+=fX81|u
zv(jR{nSNQ&lJCgIdXhWWr3$m;S34tGQ-q@X9f!eB80$KZ#!n1sZ*3LJCb?t33SyB5
z+9S#C2pwzp<IKAM0H1UD*YoM{{z0dFb?}a@;oGnb`u3d_!p6XYt#cS71ZSH4^ZwDk
z{{Wtc{Qec`P=p+A(d2U5OP~MI`MU4okBcunJeqHf(%R-5Gvvo{6QPuhca4Z*K9$U9
z+82m?P2yW`7Wj79eULO}NhX>ocQODNHs;9XeLj`-CWHGZXnLjHzx*aznYlzL!V4Ju
zwPcXzK5-c;M@^%SYv<?qd*fU08F-4{!<OG-(RAy!ipg%nA$Y^GBO@w&+x8Mk<MXWW
zxm>H&zAf^_b-t$^T(Q!pAE~R!{MEXL#hoL?-V^Yp)z^o$hWjnXWV^Apx$^<Z&&;dx
zHaO%A*EMh9k*auGP4Ns@g6VD{j!F6kLUED+^*nJ~7XBC1^=}PNG#6oSqG=(X$<z0d
zvJwX1-9rozM-_wc&%|?hU*YWD64bQ$<<#wNC0iKw{_bRU+yP_L%VW>CN+nK>3CUrn
zbi0vL15#9!ag^-$MBXX!XtUP;0I)2HGI>bzu)tWOBMO7LVh7>Yxn|XLi!f%DwqdfO
zGDETM7jHQ^=~@~;fwe*QjZ?(a`I?riCi!E9ccf*0>M84m_3vFZpMhnvfRm|16plG7
z6lIq^m78(M_32nbF^oCfz3<HP+LyKWVR)}2H&gNMxu@ulaTFif-e?h`u`*oAzm2Lp
z5;9L53h1<dh&uO$ygPqwt=YZKm8zsd>M@0$8Dj-vnL*(2aLbT;^{F(E1^9DAW367i
zmsbdPi;IQ~2M0R`O~;HLzLmK=E3a*uJ6Dp?<3R9{@(1VVY<E4Bn&+#G#d5V)ZLc?>
z*-_!=MN_9uZ@sL~JGbzMiS<hZsN0Vs-Bh~y?jvHpyAREeKx-eyI<AcNR@y=`OFS$A
zkq9AIZ_Ek$_7&{M7FP3Hv?QvAjd{*hlaPJ6;}!Aj-hH*xTNKBZ<d8-P8RECW;ytf7
zGE1@LRl}-wvF#oc@r|^)gjycI7?XGWd)zk|Kiy;4p2SzRPyLU3YT90~f#H0DjCnqo
zJwXS(e1)W?qu;{~>ok8lki~ES&mjK*O8W)8HDL=YB(1%bWJcaySP{knJc2#?*A6!i
z8go@6*YF)VxkblwGeG!x;8)bvIj^L!OfgAVw4q2JC}2MFlfm?^d&CxT_;ywDou`4z
z?ptN$N|AyA<719RcClGWYr`O5#|4n*9*1e<A6#a!?-x_i?@_L#7Z$|jBr)z)&t}If
zkF8{<HC0qnZ=u;olBONelvjT}4jMcC7D;#9Ur!3-k1+^8kEScHx3YABbqhIQxI>0n
z9EKS6<E?F7X;xa)4A+rc+(z8)g}!n0?hmN0H~tmgl>?QI^5tPYBT>lqJc0UE`&9Lt
zv6`oCDe7ixx~=_%#FrN{B<Abobwh%jgTVe()!yjd8`Rnsy@5Q<pec4L!~yNp)Aa8N
z=@zj`_H}E5>O-`r3>8Lr>)xr!rfS!A{#C&XBLEUl?#JBzG(xNu>{qgzwb70|H{gUX
zA-9ag=jJY3=EoUcc|Mh?2ZlT(WR44a1WbTaXgh{_9Q7W#tD0t#mba@c<2La)5UP#k
zm=DN$^Tumg>@TFWn(9?4BL`^HxhFU*M}I-mqH^lle#*y|-*|sZ)mCw-$s;|yrD1eD
zsTt&FjC8FF4FVqtY39%E6N|g$63ew1F;ml$I3Gh=UK_N7SG|S=l@%Sr%H!tp+rHt~
zIj;BO4~jffq4-|=O4MEkbBJJ{VwP5Jg-9JS&MT^|NjW5*jN_#guG-Ze1+9+`XqK$n
z)!M=r=U9t_BOhhzN&a=i&*BYPEEUe1{)k)8NXRQD(Zaid(>&Dh=sF&UjdR5hy7F#Q
z&H3a$;dwQo{{RVBmAx$@jm5zmo^ygg>=&(eVX(1sag)^2b#Jn|HuNja8(PvO*0kp^
z+(pA~AL^UXgW0-gr*5^x+-rAH>fhRHJKEga;aURZY%B8yh>ig{&JSAAO=Cmw^T({j
z5*C;=Pi+`1ZbAFX7pcckam7C4!?5_;@)oxXXLR^Y+g?bdkE-M^&AopditM45$}*C~
zKXLDE<L7S2p@YLITBS8-)3d)r>3<9BHy$X^Vb*1a3!}FV@el!F-P<{g{{VY;ewE5=
z_c~^?47zTmZWK0C%#HV;_Y676H6Mn&3#Vv$EzXZ+t1Po!B8hHehFGFgo!G--O{@kE
zdGA>|^!9!fK2Ebev%;ZvtPCU%#(rFN{A<9((4h*=VXE`4_OPqrs?v6pZF9q>k6!R@
zvG#bTU6O<4u;w>W>JLNwtEo>H*gdvy6twxki8_)w&V2`|tqo%1!u~sw7&L=4FBv6n
z8X^<bhTQUf4PLd<v`reyOIuYEO3D^Vm<{AP$KjsBr8Z|$X+<WEH$c0)eJ=CFY!BL*
z{IJKgUW@)T-C8HR)hv~r-Psl0uupu9b@Z)a;(rf9ZZthc7K%OWf>imG4ud%7k6N$b
z-w|n6`jomw<-}8<=H6V6SGwn@t*JXVp~+fzwui9zBU#ftYvK3scB^8a=-`%_eo{Rf
zj(6wtuQ1d#9YaslU%@(>rRL_^ma{*XyszCb$aq)BKu!-z>n*SBw9R2B(Dua=E*T;u
zZ(@1L<Q_ZoS^oeK{4=9?;@|C-lYO4{Fc=G!EAuZuEQ#uRWD4!Tazd+(cDZDc-G;@=
ze69}CPU!Og0QgFD%^y;d$gJ>OCkEbi@}&d)wIr)!vks%BeHZWzb%}MGuNC-STWP0M
z+cmh3QVL2olf6py=-eHqt$Zu^OLOAOQ*-eVEcFzM26TB9rDar7O8KBG6VL@2uJ7Qt
zfpjm1{yVhsu8(#ud^>z3TkSr19x33B&Lmjeeek4jUE5f6=dD(0EFz;)mKRyZ+V?%`
zxxE^T>bT80lk!Hcs}``c%X6$)8+dLH?$>vkppKyKP|8ni{{TA4T_;`Ct=s!9)vlC=
z!Q34%pp(}lf!nQgzB#mpO?Kn^5?h%p)z8^o*p`wU<r$kNaXoWDxA6yu{4;H=Ev&P_
zI!WjGEwSB!DszL3fsA^J`R{8|lp*aRn|dE7gThd+R;MKG%A4|b=y^xj<B<d+7*2a5
z6mQcbKc#uMk2K58KKoCa4>Bi;T;Z@z4l&Ol^Ifj7;{6WWO}Tv`Zr0`{FC0@vv58*5
zoP&?nyu(cRjd|k-Hk$kqhhP>tQBL(B{$GuHF{LGGEg6bj&0lV()QfE$rkcmZFvV~}
zA&zL&BND_Dy;tQp=e2zI@fP0Dd`Ho=n<?AKll>t<IT3|FFkJJ31$vIH<IPjU9w=W8
z>XFB&T3uU1FpaR1#?HUOanp|V<vOXe*F04=xplG|dk9UuZ=dfr=WL(u{n78ArE}#t
zO4Ttkk9*&-^!dNBjt-onyIZ;KR+nBK@jbf7szS2K7syq>$UQpy)LPGn^sOGrbzLl(
zEtG_oCi!HGJr7b!_RVoV2DyUmbt~Ioe5evOMVRc8LC)tv+-HtPYqZq0XfEv<&_3m!
zAkjZT^M4C|BOb(4l;;YHH@dx<=FWnYCaq_>zeAs0PSl;TxELA1YUaE@;=4_5DQqK<
zEy7I9vF$vd^gYF5MQeR{;y95&=vlbqzB}f%^pA#`4VjKqzms@kOGvvmlh6*Q>T9}g
zoHT6UhdkEDOxKqSI@~m;e`Uro^zZstZuf^t(!BUwH1Y!^#dGp6_kHu!*9N+bUKP~C
z+ikj<)Pudic(ae<&r^fbv8(g=gGsu!xU+c-7Y?NCm~oPG!wv}Myxe72{4}*aEFBr!
zo~jvXcsw(yL@nWakF;BPvMRRRF>K^#J-DuS!<W~|;;l<j(QQGK0mZz7Cuu)7BN+9n
zPdv9e$$_pOKPCi?f;K~w&I5J^uy3xdBGhD>7IkQ&+9ZvSC%T?_u4-?VHhZ40Cb}NQ
zW8!IMPqZbtfn+3@#|l6C=DA-JTwZEAy@VF|#DRjd@(`2GGw3ShF~Th(htG)$#=Di<
z7&y;0gMX<#?c7%SXcCxL%XwEmS`6pqW8J;0qO~Y0QIg%9cuKQaiSz7<J}BH;+v)ZZ
zqYT^68YmsW>VCN7*C}N4&7w-N$i&-BaTeU+Nyt4qR)>gfpwykTyLtZrdk74qF5$S2
zLH__6=>7&<YE$Z0$g+rTZNUtVo8yxwq0b+caLUxM*Jo8~oYkGYj{g8c_<`ZQZ^Hw^
zJ|jP6l_SJcusaDIGCOnY#d4E)vtF~*Bhl?OB)DP<tW2at!=T3<E57mHgDtf?g|?aq
z;MABelE`rzsXaXjCxM=8nbbTbci@dGXl8)4^G01|BY}bU7bDk$Ty$L<r5?QxPYH*s
zinX1s)aNaHNYnf})?qS2^J%)8%3dRtm!NPx06cNURvs(6@x~>%V{d71v?L*k1-gDc
zD?i{jgx>NEL&I9=F~O+D%e;^_vl77Ik<M$IUkhtqAHAN|RLpu?JhHThXeg)edK~t}
zdsXo2De~@g;;l+Cire)%FN!++`o^!M_y*8o7-JJK2dQrV0D!J*#c<eMNdVJX7CTk)
z)dwuu<A6PDpnOjo__pzYmgRB@=@bB94_<rK%O<p&OgB0qid6EM%d2!9$?u+VisqvP
z(p`_9$KxF~n^$aShU}f5FAd6`U!NOpzzz45o`;i;_1Neh6VS9D3;m+X6c;LAe4lvy
zigH0bfP0F^(jt=H_(7)ZH<wAY$j6<Va#ei*9P!lGVdDurAN_}ArD^ul+G<337ki6&
zPNaSDCg4FPo-S%~gkjL_$SKX!tIhVFhsAyz@YVLcKiVXYRyT4r&D5?44pjW852bhh
zCGgqNG_UQOXDe|Q(;}Q4?%;g@>?;2Nhi&gaw<Yk#kWx=5Gab?#qAySismRIT)cz^g
zMwJcimZhW3a>6tP(V_~(WGOBicL)8P*Q+N9wHUL`qnGA%Yx7E*YWuB@llEZM?oY)%
zKFJ6ZPbe5Y_nvWHgZoiw2gaWlpzylMvf-EuhE7O`@qvzVeGPn_@PAs>Ej)8=s6q@k
zwsIt>q$w`Q*$bXHJv#ks>CJ1!x6}M-o8p$Mq+R(h@}-!PGcb@YGER1?sp*X7qIi0c
z#Hy5|t(o^Q#m0sLqptncuA{ysd?2&%<(;MOg96286iaI~c+Y>4v!CKTA9{}8#@;os
z@YGffY`30UDqCETJoVerB}V(ebPf)2UEhelJZU=Cm#O%3SJ19wTey)TyH^sX^}!6`
zL1bkks013X;Z0UQAL-Hfc5f%ofk69VCwN@&b}EC%*Xvp0vkYBaEjk$58pZDSvEblv
zbg|W{VrW&eYisV#YQx4lzle1=lU=i#9ZG$P8bF1kC<i1l`@fBMcJ@AX$kSm0N!Sqb
zx!vWS;C2}9YnAa8wx{855l^9(GPH5;S&)?*SP}^U@$NEfnbUk#1*YVNMQH8U4<Zct
zj(uC|Ysarf?fX59UFprjqSYI|CGetni7q@h=PZhg@;S&NL%_~>`eM8%QTR9U4$Dr7
zG}0%QB2+9g0L57K89$wOL&h^(%JP?4vo7RCBuGf)aslXiWLF>JFOHrl(j<~NwBcbS
znF3kCW<I5O0=*0+Y-Kqm3pM6+VU;&4r>~LcHohFX({FBc?-4t##il&Va>Htq$OVBV
zv*;?P!@sk~;!FKz?LjuU^1;RfvB3IburGX5q+DCA)YCiPT+f@CPFaZ0<SVQATcv6G
z<T|Cq(fQX8ag~AGs~FGmV<Ypga#Y<|wba)z#KwgeCVLh4#a$CeQ5~FeUR<%~Vw8*%
z)UzH>;0;^wt*?nZdwl*N2#pK?27r8l*QrH5y?JWRwD9(qZc4>%1YqYS)<+<Z?vmL0
z*J0s{omWAgM6;F!w2)<$REN)|81b6tbg?l}be^|I$gIZ+2+gHwwV~r$w}td8FB5s!
z*II;jsQzJqTDO_k?iUAW9kb6B@7@=^lg81unWjL=ZyFHu&Ke|*dn$}_PfQBu4c*1w
zvmEmyx(OAWj(7)}^{r3Faog$=`N{TS84jq&qMiWEKr_EV>C+hCb+1o5&2tKL<8^la
z?G6mT3r8`Pu@d-t9EOc;aja`s8a9T%ZMf6iL30dmx68*s*~c7p{cF%XCGeBLz9#Wq
z)!&SxU1IhX0z{er0MX$ck|a}tRCQyT^7}0Y!^d7In_SXk7Pk&}T-~X4ExV{Fth<5q
zJXcW$rG4<G>i+;ogf^!O4X$GXDVIEw0VFmv>MQ3l_`Gf}4arH`+SzY&>M)dY+_IeM
z%A@v@R!#KkaQ^@lJTc*~3H(fk&%oYleTQwpK`TfgkaO~eJPdZ^V!b-w#}-<;MJ?d-
zC%luG+Z%FtEs=~VJm7S$7gP9+@Yh=L0&91AR;?6?3fnkn(8QreH{lq&A6^KiUO$Gk
z<kB@gD&i^E&3BV)sZyErJgz(Dyq8ZAC_*)qy;<edrS+F8v6^iad}R%rMAB(FR*lMC
z!sHNec)=$Jo`hE(x(A2#TwRB2b{wfsm65yW92&pke;fQggG{})n^2BPWpgAV*cm|Q
zI4n37#n@`vRn?fhw7ZGWE@ayO05T7CCqJcn6e!i?vpevSs}|m<yRTYncJr*J_B+6X
zgLI@EcKLy>Tf?H?#l9pO9-^_@7s{4K;fUvge@xdYedDcDUev#`bjeiP2?7FR+n#Zj
zCp><Y@A_}U%>z$`XTFcmxKIg@w$Zrs;CA$_Dz9Y^e{*WncUCp;G_QvK8<a#MmS#oR
z#~`17ui;$%#<ioS_zY5uQZRrK&!&2FUF_Z%n*RVnT`Ka{IQAT@>C65ju&*@Lw8=CX
z)pdCQSmJG&8Q<HFdh_6vp7&(MOR>i5p}(=3+6ka<H}4@II`hXkr$Y^ewTxPI+5!=N
zV;eBfz6D8j4W#nJB0^F<$(515XZreAtlPnHVG`;WHnx%5@wA0Ifb|*R*Gq0H#jD2b
z=e`SBXfN@j!qY^OJcC@*@Eq>OE@S7{ivEJUxWBkRD*R{oYvAo4#6J`KEvZ@Eq;GAk
z*vdSW0Jbh8BsMT`abLBc*lqs+J=^lGsukhOt1yyx*#FXQi{pFor=_=qwCl4KwnTDV
zTzP>DHh$<BI6MW-T^f&!q>ft)vmKwC%#CqzgT`<|ZERya4A+`3i8biwO#%jMFD-Yf
zL;xs<_}KCRKbbY1c_bj`Q<mcG3;y+;hBKV)4cDIh*O6B+#8iCMHPiag`Uqnw)2GQt
zcd6RL@aMuFC7GhR*5X_D;7Wj|PgC;@bMN@pz2C#VS4f`UOx7ob?dHNI-NOSA9XA;V
zAo`m5;(Lj{*z7YHQg+}K0B3+OF~IIB`gq$}(QO(iW{|@n0fdEGJfCOdKZR9nzP!0-
z%)6g8{<2e)r3$d}{Lg&2@CSi(yRx_U_U7pFl0aF*ZST9c9S2&erNiM18%bu>EZKxD
z_q?OAmpH-<5>7s1&EJaftv|&#`laZw(XPLHyXM%cilhCaynZ>aN!Alw@codzp7!SE
zB_U;&7h+;=xMj~P=)$phI!<a8r>nh@r4Au<W9=hC>(<|))81YDKe_PEw{N6bTMN~-
zxbhXs<c+6egm4P5?~L<bEBH&to+0pklUl<v%LLDhi&yFyKmq+hBfWZs-lL~@hf1D%
z$r9=)5=QfSm1G%ELt&S2;QQCj`hrC~vQ45)Hn0rqI-TPq<^cBq@CWp+$xw`AXD$1l
zUSXO2a#5W+ZZ`S;N50SEi#y~=p$E#HIF(1u8|Rf_-xc8B7BrdeB29ZrD)L*fXK2O_
z+=G(6@Oss2ZA$x1P3a*3^;J9*>x01e?^pCKL&tt5isr*hw2t-_1!WQhL-TQkazH;r
zSj!CQYPLA(VqEmM;85_!y=~$<39WT#)X0dX!i*d{1<L!WIj=+T--)ht%b9#LsyEtU
zv_jBbDH##}01k7GIs6TAI#0sS7I;STEpu0g?9n)3w{p2&>JHLD$;V3VJUyaCZK>K^
zYKq3%?B(557AqU{ACEYvEK2qct5>tJalrdrQlm~*y1Cv%b9bvo$dC)D!ON$b2g}FI
z8=a(Qnx}6ak+_x*3*AX@Vo=khQwbJ4k~Y46R>AHwU6+bH5#f2Hxz&6y;%HfX$XQMf
z0OTBFp2QmFd?=bNri9a*RgP7XPn|N3Jpz%AF~J9eT(PYhl2K9oPeT!vN`(nl_M2I4
z`GVZ&^3JJyzSdOhECT?aa5*1^bk|R-M`SMSrc3)^Gr2(X7y8#Ndv9rdr`+9wsc~l_
zqBl=EBhw?hD91mYXK9*^&YvpWK;aeJWUGvfp7``WqKQ*a-j3d93>>ZR8}H&<8Iv~_
z>K%UbAt>L7=xR&NMr621X2R|Y`$s~6e7yQrCEeY%>`pEt5@VDqHnt9P&u~5J2--Wl
z6@aq_fr*QD8-h7pkJ6z=39FmNQfkpxHzU=^Ve$rZlFiry(M4`q>nt5zcRG)ml|bk@
zJ$u&(km|BV9?sh4O{X9T$xweDD=WnFYI=Wzt~E$)rbbwdZ7Z(TR%5r<J?lwAEvTa<
zCe_+F8y!~a=foE`J|FWVmg3>0h^fI?*^W130A{<79%;TV65Ckox9xbwKQS_LsKcuC
z<blwhwd2|y%oY|=K)|RB9hh<)^ykwZtJk%E5nQeKI+_{codH)KgaV~~$N1IJlxWSm
ziru>ovXyu*V{bFWwRK$^QFx$*uC*4y^JXSS8-_*~p!V%mtYPtU-u<@z))!*99&uD2
z-zwzdyZ-<bYJ1{aiw4;$kR%xSijl@nGso7rd3Ec!q>Rb)Mb-Gp#~<ev>f!1sN7_N0
zoURf|X!O5^-VBz@#HYr7DUv&zmcl}axQZCh1R(YIHR1k0)vo*><J-x+F+BFjCO0q`
z<R&md2M&x%kWXA=t$hQcU&X9VZ5@=#ZR8EMKziX%QPdoOFlx1jglz9_&yB6&hW>j8
zXymh*3kbm(S=bI+Cm;|AUi{Zi4;cu1Y(;l<vq#d)FsI23G*-5{ItyQjnx?toD?c4x
zM>Ul9vAkPb=0=t?u*n|-<RIm8GmPMx!?y6hho0Q(g@&iN5#}R?MbBIg$Ln6BZL4UW
zKhq}C-4n}<D0mMN!6q@e7;%onkJi3+Iv%6p&l$Fco1=aC5+ETZjPtnTjAN<C74r2c
zQ-tqD`>$30M=#c=h;B7iq__V7G;iMBPqDQ9I^0^@MgT@Di4PptC8}x{x@?hKGO6Dz
za)%ALVF4RYxg=J;p|0O*nuF<DQjaYG65J7-0o0G>>sD?obk?;tu*Ay<`BB^Nvw#VF
zeQM))>T=en&b<tHBGTcxeNOB5lFG%p0E?eMdi`rs+T%sOit=4KEzGv@88YY;<J1B&
zaa??NR+?<`jb0_6XaPt(kEgK}Z%4heweierr^n}6-P;)C5|B#yg9L-d3br{tt9Zs=
z5;>!Uo04;mg#H=4)NZ^?C%lx1A+?s|s-2027{?p|mKB*EyW$@dYC^|Ll!iIlupZgR
zC!Rgcdex7HJRhLwvg)2EX1}<&Qy8<4YQ(FMesV!C$Q*Dhs__2+inM5w-$C%+g{SG(
zQ%Mrt%jNDYrI&LyHuN5*y?nZrNkRVOnqGS&qNM24s}`NQXnhy(y594`J|3~LxiiUm
zZW*N#DcaHzxzrwvpb$Tuc>e%`HR<BjFCP7bR>ZWc8*~zb$^Z_2Vd?2!l)f&zTa7?W
z(#s@HY^_be+TL3qyfKigj-J0kTzrDtPtYzV)7@h+hDgo<c_cixI$(dbUn5GbrB|L8
z=J{CqELKLb7>G}v*7@Jm>+bab00#Jm(Jg+}ccW@j&F6V2cEK!I>W<hO^Tq%fte+5E
z_)EiDdUyv`)-^WO?#o<Rz>;Bnt_RLX0f`3#y<_;^#q~cAX}6k^{izbwBb}W?DJ%g6
zxW`^A<xdjmx}BxNS=xQcnO@#h`{1wK`HvvRf5yEzV4(~>DYtd`dY)DmjT}y6T9x@F
zX13B^osRzi#4t1yvCM!bK=V#tXz8B$`qaM({5fnjTb~nZXrj`|4$c1nb?0M+P&%<0
zErDK_W#F6t02%l}d{3=U3|CoZY>lEwlwiMKzDOhv^<`~5Ev9LLbe=}j^mvSoDq=A#
zhc4~N{5a<Vy0JK{JE3|qyq=zSIB>Z>B91eby3uQ<sgyn=-*`gK#%~#Tn@sy7`D&`t
zNCc9~1``T0S0fw~?_MqNy4O<il3RFlR*oc&+G#IjyA8O@{{TFrWj&SKKHyi+o-Oh2
zx$y&9n)>m!K+m?`1sFt+IbJ#3$BoQ+&3$?MKiKH!N}gX9380LEqG;5RGB7emdLGrR
zsMo;ZT)y12epf!T4+=F_vA-^;^e+>~qgZ%m8^n%C^-Ft^Z{<d#%8(JuD|N$TCp`~J
z<ksdC)+f>=AgYC$HEyU_eCv_lJlA?HF7$miO<z<iEtDI)r*D+`VEz0bo1c2{t36oD
z;<2mR#v)l|W-3?!6}UZmdi1Xrl}j|<$(dp@^@!(NJ07$4SgvChQ<g^`GMr}~+2}?)
zR$a}*=<&}j$n!QXQ=S}j9D7x3e-qtW+%}f#vR)8adJ*nDewClETw3bB8Er>thG=9}
zBOfbnIKc1kT1umD$ngHIp6g?m@X}vulixuszq^$TjyOLtJ&$VT^$BN_R!tJ!S*D%i
z0Y3K9r<0z*itIcaaMstif<zo0gpq-T6_Ma__r5W`*QH<G+wR(ZvC^hGCvxX>&ZArw
zx+a|^#hL)pGQ}7ijpyah^{yh{O1-zdi$$G*aDrI~&Hy}`>HJT0-`J5~LU7Q}<`pA@
zo&o$TjncJ;hwV3a;M~aAC6|Ma!##(!B^#q}Y1JNxd`02=Yv{(MaUYeR-Yk(Z{Ky=N
z=XL)8h*tL(Yo~a6G}$D8B)fLx4$7yX=c%q@{_|AT=38c8vfDZwg~4b1PfF%I52xAR
zc;ZM|yvS@2$eB3-vGS6Bxve7@>T|a5c6zm?^62Stdw)8hmG`r_<LULybCzB;@fD=^
z7rq}SOSF}~dBDuFf4C1!_N{G6t)Sg>%9jp)NF#1Ra(d&?bv2D?2BmUtwM*hAHlVC)
zfwy?;fIT`=qX<nURc>=*Wm3)4G%kEk<Eb?bI$Im*QuUo~qDCfJ07LQ_3C}EgC>8c%
zd|A?O^y{Ar{6m5vHh*`28sP+($IQ7qLG=ggUmtukkjE9*j5QVWuH}#<O#J{so(Et!
z7_W7+@G|&|!Lnb%C41>4-o=~bWjv4<*z=s!<`KfFLh8*P*9hV&QB&u4PxI<?lYeJf
zTo~@OA*eEp$2FiO_Vy<y2k<q->-xpiHjSrVJ;j}@{QaKgcANu}xo&Ve<2b5*6`xzs
z{8M+}y-t1@d6vr|ATAHg1~Jfcip<pY8T@bJiLBjsT38RWvlHBX<Ig82rE61>``6RC
z>f<p=tw#v0jvre1uVvylk587}?X>%K*rf*ZIZiU&03Lv5x%fPDKZ2y2`wkeME3xxK
zbsGi)`^(72aqC{cYiXir8hy5#c9D_+IM^8N2aNh0RXuM(@b`&Q6}^mt`4KYY5IwP6
z@T&zYDCl^&j5DaU)t)k+6}8EHRi^5<B>=URU-v1q0G?N=Cy#pl0r4h}r+C9ijTzW^
ziYHdaeqFf(+upt?z4#^N+>>Q=JfqL=4jF&MS9vGI4-{Ew3E_Jyxa4beF+J2vmMAz?
z9{q`{sG(AEl|9kfmh)khl-7rxPi=23GtMsMwAF2AaRt@BRP956xl%aAd#{5ozu`6T
zd_%_e>!^8|RJtgsI;xxl<}L~Rb6y>KiKA(XW=vc@NM;A`G09_&n6Bqb)9kdpHVci?
zTtpE}k83J|tBzPHJpDS?Y$ls+qtdC2loIw%*Lt5y{4w||@LR(EE`Jv58kC~a2u#km
zn?fh9<`~Ea+Pv?^zB=&syYVLXP0|`T?QW!7Z8lu0!9LXrz)EtebI_i(^iRUChkgw4
zPln*tW1efcrIRh@Hs$$9J6U++)K|wp9WA^Wr~Fpdw3}-bf*6rb%;FXx0?Z@iDfg#<
z#;I19CbCKHwmmG<D|PbN!nCa!H1vO&^*4h)3+Vb>xbZ}Oe7689FPXY1&t1m~yZt*;
zekI9opzGRJt)bh)57|-Sbb&-__{K-#E9Y;9UOe%ihApjP5*v#-kV|iAd2zYcGxv6r
zmOw{EC-AP9;$M#SEq3DG;?gIzh9o7_?QBTd8OZDht$9<9Dp;%HC(Xb0V}h0+3x%Me
zLJ2>|?>z$2$H3nbt}btNsiwA*Xj2=?P>q@3jN!n~9M{M??zL-We|4tmQp+v9y}?MH
zBnlV<fPLFN>3m<~F(_R=0LB>98@q_tED8Io>(6p3)`6kw8r`&4I()W|Y`dFuXE<Uw
z+&MWO{p+F>xi2|AAK-a-x%*k&bS`MRu9vCYEu&tWTeRRLsu7ruggD^Sd}nK}>$VM|
z>aoQ*i!QRLA&Dc8ZYvkXUJAIqu@=_t584prSeysPP<z*u+O6iDaIxxk*UJJvE<{%)
z(XrQz{*}>&g1<6kJVz(G-p4TdLg@%a(*F83-nbpdO#2gGO*e<FykDT&>URpZw@nhX
za*%qF(2<O<wrJ5ufILfXsivewSMNmQ6J!oI1|ae6SDVCoEsHLRr$hGJh<H;Bj1?FA
zuaHz@+PddiD<hv7R#=)UG}fjDkEi(0N4XG24608ku4IY8?la2&07~?MaFW{nt5dg<
z+&KZZ{P@A?k<C=H@kPyyK4f<2j=+toSM?<4@v3WKf38}Ej%-_!GEvVsImSW8ex|wT
z<~1m(a=kiS;j5IwyNjy5<-N2!KMz@4YGz3wnXTX;AS~Z8IOGNOKc!XH{2Qt>JQkM<
zM&)AC$XZtbdh>ug3bmx#{{Uvdw-?t-0>%nNpyyycdHyCn>XFml(s^amuZfa#yDyM&
z^*!r`c&gPkt0vCq;i=J;>BS?Z_+@uvq3RbGxAQC^50=bN1(T=;r&`g|+fTdIrtw#b
zS+Og#TgGx4O#c8G2M2CFYmU6}w}m_}r7oGNTq(yUJ9YCIG31=!e8lxRt~Tf5evx@R
ztEJmXb8zdpO?E;eAQ|N4nEeI?EIcaIZF1-stRuupO)Imo@l*J3P1B|E7NC5Ht@k$7
z+8SJ*5j*ZbjeM1=OZ}~_UHFD;F%`PpMAF2Em5s*=SYYQIn%uLye~4ZwZAZjm(%Kb1
zR0_n$ANN9o+@49T81%?=?H=mF>Oy0=l*uHG*Nz81)$h}cDaVpd{{W%rW!X}oy}OFL
z+~IXQTTc$(%Wab^G8KDw$T&vl2i#Yn=>Gr@HNO$urQ9~63qq;{(x6gDs13_zxqBF`
z_1iY^Z0f&khcepCzw*&XztX+0!n&5TZ>C$@+}ylQiRH)$EL4sF+I_kj!xH5L+z(E)
zQ;M-}<Kb0>uAEMtZ6x<2f%1hcd;P$p6#XB@-ZAhr*47>@vMXliVKT?_sLvRDo!A|z
zo+bE%-WGv8%Z9qSbBH2S$@gqzs1?oX{w3D@LXckgy^Ls!{*P>kSgv}rcO>?&IuxZ@
zsMYviQ^v<rsTy~ruOxj#cdTi%m$;uXLmXmNoDu;T&pz0%mo?uNKZ<-qE~Tf*w%!sW
zguh0|d>>k^t$54Cde)A7DWqCUEI@2)kqa!ISnH5@BzDI&$Z6M~*_MY*(=4`1ryhO5
zQq7*Z4U8X5R&;Ql#WQ&O%bwOcFSOj;-hG+CGD^Wi1CD<h_8ELh;!AxkY-P>$wVDNJ
zCKw@m?P1RytLBEdkQWlB$)jwB&UySR-n=*BD~(4<wz`(pUQnB4VBvTj=~jbDGoG%C
zQ|KRo?ctmM0Es>tyU_2N3I71Ktciv~6~Ob;a`w(^_YZBy{P+E9`TqD>uV@hbSn#A4
zI()Or`u3g-q+t-7n8rcxiv8aEXZ<m%p@)2}vyN77_doyB@)JBdHNl4c#na96&e0GY
zvGhHN{t@X{ZM5(0E%Rs>D?Y``!7wq%S3HJX5X0OaI@gihX<Dw6I7`SRi1iGl6mNX=
zt%$ELSO?F}2g<<hV1BF3c~1u}M$@`H$m1K5PewmQNFdgy@b8EcTX?P?eX$uN6({8k
zdlARsUGjKWz~~BG+s`>}^Bu$R1B&Eivempvq3$<3rGilL7jPY253f<4)vM%uMs`+~
z;tP23j7EX;?n%x~b9>&&Q|8fhH);;6n_9EGx3bbKt!K2ewh)7EKF{TVs2Fq4u10G&
z!+O-WH=9d<$QZD0Lktkh#{_2wo|VKM8&`(uZ*@zl?*w=|S+KbD2)GsM`p%!E>vjt^
zj>xYL9^Oa*1QU(QK*zp8tmA9m<y2gedLB*jIBAckXGMfXd2=9u6!`;W64}QD4_e|h
z{{RYU`cw+H4ko&s`FS68d$tFl9=w|Ayls9hHJI!tJLI*51F&ET9|gXgU{@A(7^j_N
z4hV=5j^MWhnj<AE$FVYxEL54RbEoLKT(V#Nir#nz4(S2mh{hBHgVK)+c&^h*)c*jo
z?@C)kEJanpCN*MmyMW*y_J`wMe=eyezkB7|f5*MBA}EQ%vv+s&QSVS$EVt2xhBPt6
z%pPvdkGyh!gmkRxDLvkuhZ+!S$yuJu@efzIxYZX*goj~o8$QAg;6Nj9w|@O=lhgGp
z=F{V{bW;9e%WSQZN0{MWBhYjAfbCvUeKxD0YRmnY>ovX7<RqN!Dmej;2mlPOD${tU
z$#Ev9WpIiK-6|dGTh}d**P8Vx(x>%LX8!=Dd6ep6DdQCBE4BEp$Jp8)hotIvk9*<0
zMWCIRW!;qE6%M1TApnwkZeD5$yf>uTK-xy9Gs7>KSxi|WmAfu_{&^MSo*vXKyiKJo
z<QGTHkC>$Z38HcQGGP3})4n;)ZQXd|Re%jE#kPi4jAXiCK6@M-1IKVhaa6-pqbS*H
z-E}?cn3&gup)I<_?!J9d*q;gM(T1Byyew4w@ygy@gYaXufc)vuczV+I-KS`lWe4Vw
z8)+X>rm<{wpAKsvM`>`EOa@F0c1*bR$;tXv)YE)Fa+@N$n@wqk*$}r4^<LxksgtYr
zm*{au7;0}%^G3aghb-W@FJ~c+MIUzHf;r>mY+&{8S1;1YFPU<dhsiiHAqdAFxZvaI
zT*5=4=}`ol?&kQ#osq)3QGNFwzlB?gB$Cb@P$W^uaKt1e=0XP{o6|U~)hd&-Wy-BO
zZSoyO&Znu|TVqX`W@l0J#_9W^=tX!ZkG1VaeHLE{M#<$XZ@6x((gxv1J^3}+c&k>k
z@cxf2`h;1S6pv`+x$p0a`TjXtXq3tGZ*Ap|bDZb#u9_Hob2HDx;w{T{)SFS#WYDeb
z>}ShKaEjRO8IMnVR;*VsUwCP>d!kn6$cKgfR34vIJbfz9ixTr!pGDQJ)Zj=F**W1G
zu0EBUt!f%ozN0>$r3b#gc486%xnpMJZavRmlvhnzD!6++%rKFf#V$p3$*nG9)XbL}
zn24YP9f&w~U%bcqR~vKi_fvx5=F#U%n4~+^U5dJc&~w49%MB*wFIPyN)LaEJJV~6y
zr<}k0WS*6cb>kgVLGe_YB#{Z=gn;{VxB>wU!0Cg?r-zp;EYY@SN~9`X7d#VZs@iK0
zXDlxoFU=@Cen+Rx&>HlcTbthwG#(^IW&PvBC|)w1ai3K^GgRMM5ozLZ-e`9J09g^|
z=Jn1&3-3|fUfx|Sc6U>TiZ#kc>DN8+(?0c4RU<EVRNkkU>q4*b6|J$UtqAQeWbu!N
zCu>`xie>>rK`3G{#D5O%;P>fXSF3oI<51S5y7AxIVDl6pHiK+ZDE=M8J4dhcuUCb{
zUL2O$V`sV4rSgOS01R+McRQZkoB{N#uMZfcnPDsbjVzvAUTFDBXWx&T*0}KrDvk?d
z*uh>@6z9L_a<cfE#yd6B^cYeIfcb}3&tvMxj!CGr(RZyyITxvHK-$JYbM!<VO?AFH
z@c#gZw7@lsXiPDH$udOScQ^3uJrw@{I_Ey$2ZcN_{{RUVhZBO%5nAUGHsXDG&*58P
zu=1RjE~iYD;MzK!KjD^-s9Zs(>X$Di%!OAh!Z3-(<|Ee~0qScP#=iypN#Wf|@3bU)
zs5JXuwE5})+7-FnGthDMs(%aoK{TEzMAUzIadkFZdXh;eJ$c8{zVnAq*0raC)+Dnr
zAH5Wdlt8)5xndcZb{#X?v&?JH170www6)J?FQYotJ=f%XW3Nf0>k`}O`efcsqQ)oG
zFPSs3{{V&vyBBO8y(_QqRhOHfT=@IKnoZm2mN$adYspzADK6DJq>L~i9mArP2ZBMZ
zpNSew9tDY#Jv83fUhXkVZz7GLf=}+-f}{9IBhtFBgFZI6@W#EY>Do_+bsJ3&SJM~-
zp2Ai|y#NJua0pQ#;l@tv1J|$A=8~aTBlg*CRd;67c6REQpL3f5fUSkc^J1yHYLm6s
zsmvRFUqjOF^*wh|T|#|M@63gxR*c0Gf<m@){qBQ`%ol9F5Sv(*Q6$%s{f;H<h(2dw
zj+_jBHPl)7cTb-FRPhx0r2E{TEtJ}zEz~NwFMvPUtth++;SC-cH4Bk(B0(9r@~$M_
z6O7{kC?w={{A=^KXuCaG-HOhz^y<24`^E1Ic&kDl9D_mBo=d46JhK8Q!h_R$9tJ&n
z*U5e;@nz+|j32{hH1h1H`LoD%#sZcchB?mzw>9(y-m7VCr^Tviu_~F?c_UBulRO^%
zvzqwgcDTB|lT?)EYncqL-mFf4E^F7zsY-NeIc$8zTwJ4zjwUM2ZSy|;@Q04H&l2gc
zp|me<(L=CA>N;)4Lk<pq3h@sgxAtC^z6eM8u9|sdL-TEoV>u*xayYKr;m3zB<?xjL
zEw*UZSV(~tj!xzoA1~9Tcoo*Gd!zh8k5$$8OdVuGU!1ED9es%4isiynij=2)p50GV
z6`4*|V&xky{STe5bbl1-5y5wVXEnSR9K$5BG<(AOZUe7T>MQA=fV!8)TdRlEd?>AV
zquVa~%WHtXM2pbv$lP1qP6vASuZpJI#X2ND64a+YR4p8g=Lcx)fw7UebvfWy6Qp?i
zRq)@4#<}6PSklaSA|eXH&=4~o&+ZUEu5({UEY0I^(x>d#JML#ZU)5z9CurYOq4AKl
z*E~UP(N8RTeafU|UCC&&erV2mVTLn~mDfk%?+;jMvRK;N#{OdAywBb<W8WDE(-^LI
z#CrCJu9opVv_{Z2({T!o>W)rTl;f2?r@va_^&6iM_@*-?5=p07l1}SzOs+G~Ha=DM
z12ywhV<_I8Rc3nlxhnMITMs+O%elsCSHxY9vb!Thf7X-p7WMT4y&nD>hwx3j@$^~4
zzIwUs^{)}uZ-RlOs}`3#*}(1See2ckudOcZH2oqJm1K@EBn`s!tCF-&E1k0@()a8Z
zSC^Km<<@QI{oeT%rDLh-`gVhNs9Gq_p(X-!*iYU$?^znkLuI7gINiC<)xp3ESe6iK
z8m*Lib)X^$j!Ql;ta|hPD?7Ikl2^IVT(eyGZt~(%3M_j9_bd)U70XylZKs&5<;=26
zi9Dt0pW)x?E2h;gVYIN9Lba33Vy*M`ELWU;E6eno_qDjWmQj{Ms!x0lIIZBl+ZalB
zXRj>J9NtvhK0|^?>7K%|JPYBCIcB}pXH|c+%tq2aP`NZn;(J*PSU)b{=erTt`c}_^
zwOe`gl(iot#KjzcczV`J**g_-Tf2DHDV-M{M2r+3!>K==N1?E^@a&T@1fB8*UPBko
zGw)Zgr-#dQxpI)m9b}P4SpggzewE1hF41Dp-YHCt36P!mAYgsa1t%-5Ma$0YcUne=
zs#)3FM`3DkK5f!J1)Gw^eLXAbEepoFMvxL6W_7h8Tg%)!V0_L000QU*cy672_FGc6
zkThs<`{_wCNO}x<4?rsl?tNLjLnZahOFE<qs`GNO^(TSeyzEc!+fqAl*TbZubAJ|m
zRi*15AUfrWJWm)^w>$=CV~xihGw5oUhhbe3$%O10NoDhAa(MUcn&hu9E$_T>Yc0Yk
zhDSdr7*ik|F(1fQ?enGnqP7qKL`-Cu=jG$4;a-Y@jX1(9LcXxuY^64{40?={%PPW-
z#P4oK-ovM*QL*u!iw)RSYqpF?7{~@xVD$qafmJolCV5BN?U2qQIc2~rk6~PktKBHI
zwT)5SWkh2cBa#o)bgfh(!ZoD!J?F%F)`6>O>v3hM&%7MR8I6Lm@4*<XJtk`l3zw31
zNMlHsZs1N2Q_vo3pwoOG7Q1a^Y_A}O(kPTXG6G~0W9B%=IX&}NwO<HmZ*sz2K^J>`
z=!2)b6VO(1af38ca)gn@Yg)dUu39#gWx8Srkh(@n=lh_X5`Ahv1nAM~7tI~yY62@U
zjZaK;1oAR#qmxI_wCiaMI*dUa?b?mGw)&R!`d2&QoAIG(*H^KmlB|*uZhq)>U*adJ
z8T75Wn^&<gthrnf_9wwl7RTW|a_2<8KWEeChtG_%?|(T4FsQjCFyoP41@TwHS_j3C
zAJ4DFs3onV%&RM|8b;gKYH)MMLtM?4vvOvF(q?I|?-(&u;Hf`xayjGBS6Ab>^bHSP
zv9+2rX)oFFbs{MW%s3^~=REi8QlT1keXJk6&tC<d)Obl?@esTg&)1<glklg&T6NUd
z7gOEe-O4|9^Wrk3bTOddH>WkZr|BBEh+|zFP>te9m&=moVU6q9`?X_Rc`s$<=^CL#
zIANdO$M|(Bew{0b@qdrC-w#g(^IpkuEvXLhM&YCK&jc_X2_CsMh38gQZ&5<EIQkx8
zsc0Ae4ETZM_=X2$1fsegs>kJTemhp;{5ZO{)Eij%f2tT-QRYh=oMe-n5C+~pmFfOB
zb=U6f?ysM6UcjO-zkPGaKKRJ4J51DO(ywQ*x=%R6l`YC2m#O;uS50Zui?n`c6uq09
zaVuZw-xDqp8I8OTEDFRqA1f2VC!7P0)#qub>Px)c+AiSaN#!<Jobqwg^{-&L@s!co
zKlXjY#{!MTTnzN(dHpNK^#1^~+Ps;JiDxCP*qkm;I6Z;>b*2%{r#Cu0ymL^Jw^IX9
z(sc`qlYM7t@`LjEiUxhSG`dE$taxo?NEY4+Bs{AP#~$5<b@I!tTU!0MQn^Jd!$Bf~
ze-J-IT(s98+pN~*BQmxEI;aQp73oV2CbdLSt8JLKo;B2%G}9C;Mm|xG@vfJ{dasG}
zEe7XNyo1ZJn*fH82MpaAPy%`P#dKc`z8vYA$BbGHOsvuA@rGgiK569VKAZ}-b)f5h
zKJf+BriBoW$!?ZOW^NF9Unm3A@t!M&ZlAqQ+iq<-(u|R@cXck6r`liYyKT}~_eL0U
zG3)-(9M>PHc>7c3v=|$v(Il)G<oYj9>t5yJeI7p(=#Qc(QzUVXk}~6Kj+i~Oiut<7
z!rHE^l4<sq?9;?byR@4lje*BJa(#tiP88OsMJdUgCGN4PokYWS=1EzGLX48fumi6>
ztEQJn)~s~Nb-hp+1cVa_KR6w?3}An|UYntOKhg9@S*~_Vs0A$~&CVEqbRL7!u4;nc
zP}7<zq!OgRBd0ruzdVZUg?OiEp8gUItDYyJ>nm>?Nq-R@bMEs{fIU9$c*S<8ZsCQa
ziCN*3kXe<KgXzy&^C@hlw$x_Q?b26h2|HH@di$ebpZE<&ZX&q2-e*|$@Ga!rg?@nG
zeznro^+#N4eUnRHvAD7kYI<=Y?Hov>xE)I5cCNDH#ya+`6f#YA*6`eILePM_RQD$(
zzZ};+Z8Wijw`{a!ijH!m<D6Dt(TWM+^IdiyG61W%VEy1nTo2Nfl$NGbhb*+}dYnEU
z(|jW{Np!wvngJI|ce@kMeExZ<;J=PZyxV4A#PZVee?Ee_tt-TOVbaCEtq+`G1!H88
z?PJ?;>-bfRdDi;f9b{jWkg!Co10VLAq5lAMQ-&GTm7e6)oM8@ak=EOMO`Ar(Hrl1D
zT-hlfzk)^#JxLsNuTQ+uJUik|F?B5-7#vDBq|uUd^#{MLeA9VtdWB4qyM~PKQ-Bq-
zzyr_<@4grOJovC}C-FXqYa;0JZW2woe79v8z*2Hg&{sx3By_0zZgiSFnt6>kSC)SZ
zIyt;G;n*}g`0rtu>LGl{889ONb<eJA55qdvmj-}WP?k9Sh9rVHE8ipk0IywEhkJ9Z
z4N6(rhTBIZ;G7aVW9#_WoOnCKI##Q2Q^j+~6kr0j-e;CwiRjtJ2exa&P`YYIMk@~Q
zXssFQUkAP*+uwX=@NKS%XK-Sd#5#IOGslzw+{cZfTlGCF`W*XIzuj;9<NpA!UxL30
zTHeRwFNO5`NFk0D*R=43KJMSNM~*oJ``7QD^#1^3^{$F`O5BGfObbl^)bZ<0egeN^
z!z_`WW0i1t>(3*K&%eF!<@L$6oh7rKf90$y3~UegfarUJisvn2yts)P;7BF7d@PJ|
zr=EUqU#)rq+D8hQVi8ItAQ+_T#RpHXKVM4lX{vGNe6HT7ol(u&(qD1LOQgpfyR})>
zqH(r6h8aWY^DYXXX3aWn2VJs7xzR6t)X)60V~{?aikeL}@m-rw%&@T`qJNmKI;#%&
z?fx}PXgAKjWvnvWK6o%O^2zigs5I=Bv6axBX*J6=W>}BK9w4^NS8?0i?HJ3QkMua}
zOtRN5mUDTefoVB!ng?vfE=L7(kDEQe8l4WICL-3YUgbs<x}a7ddXdRK^+U&ZJ|1`u
zr=-Otl(EJT+Z4h(W3OHhekk{Cq6bmw7W6!?R+9W&%`NWna3v;1W6!RC&uW;+)|T<4
zEC5sUoM&@4bL-Cnv7o<}_7K+$H=mK4ISu{NeZb)QiiX+PnIbtJNelqV#t(05#)3*s
zu*J71q{waTbeTqFn5-GyaS#MFj86;4@O9#}+?`j+umoGhBLwo%aFIov<o6_x)Ye_a
zrNyqZF5pVat1NAUlCpKj(BvA<wAR+$&7PiB?;N~L?Z|@%1-a_PbQDSs%<IEJ%1NWE
z)+{usbjyh>p*I?UUGYSpdN4RbS0e!Wb>_Lx4fuj>O3k!{Yi61}q_*;gSIFz~o(+1X
zpM>rFF>N=C?Ay+c-pp=<{{XC=*oJ0iIRGy>@5N>6{{RPkIq^S9k6zMm-%Y)IKbTW`
zZs^L`+a_0_JHJZyGS`Hmh3CY{*}u4qQj^AeirO6agRUa*Z;AfhsXz{ybVPFjxTI2N
z<t>gv=fAysrli^)tvr%m+nE|eyUkX?0lT0-!|l?!2tE#K7WV65J@w7l2oahG+K1FK
zWAV#owV={x(pcO0nmO9q*#vg;K&qR9^xvF;k8D>qD-(ose!;$9GBH#tSCXp-?`_)W
z7`__6(DmDmdfqXp*~u2qF)$>B$3jP;?nQL)+z<gDDxO#oyXN|yM-`}(O>`35!${~r
zmRTeXx$rUa=b;(L7^p7w{c<}=EhV#x8;!h5AsJvjMhMSrfn2{@r6sd7hFe{wc`KBz
zHJ=YbuiI!^#OB$jK*C}Hi|05B*e4nF?OuoC{RdFc(p_mION(fu7>yeZ8V;L#5ng0$
z?Y<#d`M1!=Z+m9mP-sR2WCjJ7x8YOT+TYyROQGEA6UStqyM2VnWO4im4`JT2tr*VM
zk)$Zk4<@9x`JQj%oqFE$$cX{+RXo60@)^2}dsh`<e+9kt7WYJ~GB6Btjk~&ZuXj%Z
zcuE&UxLNLGbAYnRw>b7tG4GRIBlt_hI%EugvJmcG70GP<4>+ezUd@<dV;YNN$MtPL
zM!t+irfQbyG-x7AX9R=KP{X0DShPPDX!>&8>1$^4Pm+QLWSO?`#S07)KBMbi)#3X+
z653hsH4QQdBDajnWOm6_U$0v8JKqw&h_$1r%jLsw7%;~Ie5DW^_U?N7*F_p|l)e2T
zR4dh`??ZEn@b&vg<5Q;itla66UqmBGW&k*lk&aGA8=xaN{A=h4JTc+j0><Fm9nuib
zB(s3d&yK9!^ZHke>YDC}_SMn6PM&?U4bV&m=8b^_A5nwcW8S;XL&Q2o)`52B>1K(d
zVC7CE1ozr-NT-U0xm`JEZ&w-hhx^D`{sVOX00l{|SjT(zbr9X7xblw75>6X$LO9MV
zmxo939*SS>*3B#t5~Lm(N2=quw|egW3izr`YfC;H)ErB3Vz{?(!F<bptB+E>>)3Uj
zXGzku({-&&95Hq;)Spjt?OaN&T6GsG%~qTx7a2tGJFQa3Tq94jxQyDWsu=l=qza>g
zP7d7l9=&Tx7Rvt9>^h>fOCHc82MhA_AdWv=R|l*A0ApOK)NPT!vo2ZQ5Wt2~zj!VV
zK+j5>N7QH2qLSatX>GXLuGuU8&peUq=}t12Gv?e<gkuHDYWR~w)iv!deG5;!idFer
zWx(3_2P5Yh2iVt*c#7_A55djg{{Ru&%@x8-UKv2$8xF->b@vtMFnFPC@4U;GRWcta
zGT;TrML6s`SEO4>pkGLl_=irl)I?|GGsYaIK+Xv`JoV$6`dpe#!C73-`$kkhcTrs)
zIq-+!_ldqE>$b>}KAWb;G)DVW$sSZ-g4;@+#heYJZb;8OSLnZtwfJMcxSK(VZS19X
zNMj`KSd;)c_rWCl^Il!z{eIuTuLN3jYXzOnt-O)LXuf0lfk1fHB+CQ8@~;s1rQ^?u
zllWx~tR}-td5~SkHm&W%OnziT&z1<zK*o9#UOs7yo?~7yoMMz)SM)yb0ZNu0i<O(^
zN!r^SpT+MM>K8sK(===MXYkb1#hCW64KX_$mpI(GZOgYSo}#{xzPj-zfi%Ny;OiHA
zduU;2VDFgXCuu4(&N0qw1HfKB@OO>ubqy<2(M#zXdP6iTsO{vd1yhA2qXIVqr#x4o
z*!aI~qh08(Cq&9Uy}<>xo_2@f$NM?lr|VUV&Q$PU+0dYqi%$MtPxCG~N&RE%Q>ghG
zx6h}Rhadf+Z?4FEPjBYme`r8T4DJ@!2MX-NIU7`bznwv;T}NqbbnR<V2(m-uZSq6T
z>OdQq1IJD?MyaCcy8W@${vOIKE|1MMq`{Du9FV<JcfaLcN2lCbU+YlWUXvJC2IR5J
z$iR*PCnV!N>%^7nNy_@36<m%Gy<drU>Q&Y5ZS>3AGZ32MD^SWr;01Wx;I0QugVwlx
zQ$f?MJV9{{fyJCCE`ERDjfU-{`u4A)JWt`9OUw1W(%D)EiJ_TagMvO*1O1a<JnLxJ
zy5+=HJFYD4))k&bA1jFX+B=b+KMM46{7a|J7qyQMFrft)x@W2AmYTh%htpHi?%l2@
zk!JF^3RfQ}1O1VJDh(UMvH0J`<uz!UI8qZlt)2vg?ZW*}HH{~Yba<_^ZT5)gjBSfB
z;3@5#@rsMWny#SsxARMJ=7~y`E0iU}Za7oW4{j@wNhK*YXWZz5a-Eyf+a7_dcza2<
z@gO?I<K4CZ5i;zFc8o6UedB;Dg7KV!{{Y7NO^t{FCry%t#!9OL&(IFFKaDl{9`@G!
zA2B735yN(5>IwWtD~+~WZCm0T&otR+?U(GwIRWv=7~-^|<ff`8v301%e(yuqyhxT&
zX!mw@>=7~ofzab=3-~av3e;cXayr4MJ;AY!c9oA}fCpZ@n(MD_njt&tFP|U<Qb{=Z
zxxhK=SgGQTKsh>HgwsF=-;nunkEVWO`Bg$v(pJBz-G{5}V(IkU;;)3Z-YT=YhzWNP
zL`(@Ff3!Q-t-zp5B$r#BS0Ln{l~=9>c?N?e)~Dh{x|U$9v9mFA$@W_A<!gzijtGx3
z91wui)mtNuI*H>s*>uKq8<b23u&rMYX|mmTaTXaJi=DxGD%U6CsoGeY)SZM%2EpHG
z?^DO9>Q?#$5D6YNMkQ573uoSxBkrM9G-A0{e=Qt-=#_4rbI^L%mR{T3THM^_h+!r`
z^~uQk*G;ABF-K=4O@S|+k{EozR1I+q-Y-b*TlZO9cF!N<TE#TkgId_`En|4}%Xs8M
zt>yf{`kr$~g(G;cE^e+jDOTD5930YLT}B;|-HFBqI&;#sv^Oaikj<7-9fmo_Zfl*k
zlQlAYU9QOq{>HWCry*r-gl_07X5Uk^@X(49hW2PsDn*}{KBw`oHofxibwhi0<xt**
zVn#>+@%mT0TlinY+O6bvwle<cubUtY0h80_JoM}+sN)2aHlr6b>~ofX5jFeiTI&AU
zB#JP0M-bbC_32bCwSN_Oo-5m%6^_!@&M!7d0r{9Y#!DVfeFb`kvt?}(4I@v1QPeM&
z7#IaQcJ!_mT?txyiKastKo=v_bOdqMv8O736N%`oEUob#HLEq$TFs5a3@Bj4a51-x
z=jdzNZVrK?*v8wCL3H6>QO@Fd1e)_p%V~8V5(`*J`5^h3fn`sdxD}^$;7c2=9iX(G
zg~%m_*8SNdw%p)n-n!)1EN<+1)uQ1!Bz5|gvgyqo)rs7apSn56{{ULN%UsZ}?XEwz
zw86V;nF<U7r#-X9c3u#H+}n72Qk>h~M!6Bb3djdw<0GeIU1x}GEv&6P^<jr?jUImY
z)9F`b6q41IfQ?60`JR1g;^=h?VR;46gFhsTwB=u+^})|vQ<~La-95;XMsJu9`-5lD
z{{W47ezR)&9<43x7#5eHZYQEi{4M_J=xb)v#(K1fH0u_#fug`>Q@ykQ00J1UkE`2T
z9-G6tK1bB;ygl&iQt>_fw|ce0+giub7X{?*5k^J_sWt9anr)7wr^OH0)uFazjhQg<
zjiZpb!76*=zGZ=RsUOa{ncZ8gse&=>j-N{OJv-uOiuC(v>@DK6o+yYL7#O80JvrNx
z>MJZw>Qm*2VX}W?KWits^ro5N?+t0U!^9A2X&uK{%x(@3s08q9%04RV-W%6$W0u-E
zzS^!Md1g@>0n=&8Jm<ekz_j?yqP6JLb(?v#>1ALM<sqXEdu~&}_adk9K9i~6YE64<
z1Xpm%gUyZGA21z8O>k4gPBT>}d#^L5r6^7glIlZo;T=9JN4wLSOGykMw1~h4_0H3S
zT!Hv=C-!Wu1@M+BQ13^N1e4qlYp>ER^ou<o%Dskrd&t<I^?Q_#!A3y9_pR+B`%2Wp
z+Q3mKVhoYW3Xn&2835<nwv|aY?xUVqNnzlm=ggVoJ|CTJXScW1JlWRM<&C-Cst+6<
zpkt+aNzwc_tIYS;_ZKIB?-;OD>D=TKT;GelQx=)x78+DBJ;Z-0bC8^M2j00GzZ+_r
zzc(t1SdJt6qz<E+(WyyX<mO!5$n_~Mz8~q4rMT0lypZrzIAhOp2m|o19F|qoJZF31
zZ8F~D?6;AF3=F4oNTWX~=DkzE(ZxJd>T{`@a@d6%8xeb!_s8X5Pw2PtS@=$C%S&-3
z%0|!SpbS`#x-tfFSp8+;W$vrC6)8H(&t!go+-aJ|pA6b2u^KhTL%KQnw)Q2nS-u<a
zcZp}#;cHma%4YunTf1~)>Qs|n?eQPOv3QrlYpiM!Ak{9Mydwv2BeBK^2kwQh2k`x`
zinJS*n?#yRNrQg!*`sZXj-i!-828WPUtO2t>EbXo6l|vz)$ityKE4^ywC|%n$nal=
zZM->WGOUd?<=bw!mxTxAJSaV@2f=zyx#8uK8-RSak(e+b^*+4wT;7A@0jbI@HS0^O
z8Qbqo7w;ox?Sb;(AJV;3OOi_`SzyU-g;{uS{=IyB`RTbitK84pLTz%(V~*Cf33N?J
z+=LQAFdk%m57Ztgy`N3Bv|GZ~@xb_f?}*vG03*2c=DLp^=~r{Wnw$`T$iFfUK?l@&
z*Ug?P(qz{(+3l_OdA6inM-ls_ar1yX5!6zol-lw=%p!7wlwOBrdE);72x@wPv2x0+
z0{O&+8T1DoJ@Z_izh=5y53tH5XBiR7ykpcb{*~q$L9@S%Tdl)MCU*Hnc*pzay>w7q
zXwlBLR<PY&ynJkpi>mu%;19yRD8)xZ)1fsEwm3f)%-46zJ-jZL5COTPZ2mdmiuYXy
z!(J2d=YZk2j_L*0ZT@1?{F31H$M=W5VM*bgH&;@(_JYPk#^py1kFOw~b6U1O1Mq%h
zS@jFsWw-f?>|B-ZRDoK&L@mC>PZ>^~+>Q!=hF&H}(koe_nWB`HjZe%@dXKGReWrac
zU-C6bwoeful<-xt$T-gizSZ!~m*8yz*k5SLBZf=~=W{FMA5MUG;8zFnJK)}*;!9EE
zpAJnLux;0)cGW5U^#>%swre<J>ABjbi=oX+GmNJwrJ?g)f#92;7;8e_$yyj@k(yak
zpa5j;>~qq-qVPVY;TtU#z8Lt{1+{peY;r%`l?ma=$I9c^j(gW(@MA-^@|t^FarS7_
z%7vJNxrbK#I+N0w@vp+39Mil%4!+XvyfUgT(hl`Md%56OC49%)SNgWL@8{gZGOE3c
zoh^EvNvZzN9yFU;mgB=O4yRyT2-&6b=0$Fsf#HDu@IQrmewXmq;?INhq|<cWSldM_
zlJ>m3AmghJSD#!8#qf`awLJqzOQ6jpF8g5Idf;=nxdZa6uknvl2(!pAAA)xBFnw#L
zzG<4*g2PVEG3#w#%)<qOg=%hXZgKt<msariigkS-ShoNe48<Yg6~|J!B#_;DR!+R0
z6Y-%ISEX&PHvE#g3!HxlBZmBIv(-FvZQ>%AR*w<Ci{(d7S%>i>fI1OerN_gI4O;nT
zy1P_{f1k*NsgYZ_+D`y_kxt7>lhO4)iw{;5sJc}h%rAmAI=+MP%fgmcS7|Is;vG3%
z6|h2^sK?zo1Y^>_qd&D=`k&Ij6uu68A@FXS@khfqV9g}ytk_6}+$#dN5s|`-5s_cH
zzu4A3jsF0BS4}KMDJ$IbE8}Rnd#^+P)$lJ39cITzwD8}F-WR%*nEc(tW)D9x;|w#@
zdr_@uy6%T`7WB07Im*hUvtS-G@9*BVZBN?+L#f5P2Lw8DJ-8Va)o5{Qmhm)NRrG%*
zJh-M^>;CGw2OmbS1v;^JoOy|@hNo7_{HFsfx3^KpaK~likRaLenZ|HG;6U{?(%Vg~
z-)1N-z`KX<!5~<q$6mb~*!Qh_JqK6Ryk!;S(JiIQMp{Ii5{Eo)O}qtvQQow5j|+`v
zTe&Y|jwXX@%EzBAvBuXJI3RZw5l(5zLdPv?N|e2qH#eVS7TV?=K%0B{QY+Q@Lb)-Y
z%Q$bsybIzT^tK*6w$r9_C(Rj=2TYzi^yC`%JFg5xnrM?#n#9|ag}DsvE)Q%0gI-JH
z&jV?i<~2_XYEoQk7QiuzU=+Hb=!ACwa2v1Ir8z6Jo>Qi(D7U%8_=7-w9{&Kzw<pMz
z@zbaA0r*uYQ^>tpvB-^)y}|YMt?NC1P}X#5w7pSPr(mrFqyR&w1RwW@A6me*xRzkP
zXyqGganE0BR97&u4$UbQJWHt0YoRLH=j|-sL~ME5rzE%FI(wS)oj*;Q$^$MNZvF1$
z<m8-ZJ#tNFCyAQ>09aUW(Vxz^cu__~Xakjus0Tmq9<}URf5H!luW5CyY0yD52pMi+
zjmt^^86}eh;~axs4wRa*K92=SLMc7j=vOzFfAFlXwJTy<dc$a-a(77BKltc1pYZzs
z09dur^$BgzO&z18jLgMKnRvj!=cak-=~3O?Phs$G-%q!c32yZ=rH*$6MG6mN!DF0f
z^sjXIeFuQ-JScQ43!6zT+AlKj?<A7L4(`Xl<Hb|02Zog%UZ<Cj!&IY-jaK`sc^tl<
z;v~FLa73`mcsV6o@W%vJ@zy5t%rQ?HEx}ubIQ(&u`PCcE7r}O_Ev+?Ke6B(kK<I!B
z<e$B_wreX@(=2>CZDf~HwU}L@c>d2Pm_o1jf#4r(*Bo6r>j$dnbJm4LEmP%tGeYOW
zf7<Wo!xGv|$2;ITL+kfX_*SXY{7GeKBGsN5t>In9Y>>)|GIs;l@HNIkeQ5%j?XGz|
zhEPzS!_-$n;+;oVw(u>^rFhX^K`pt6T*5{{ljREIJTT8W6=_EHk~O6|k(6TPd!Bvp
z*H*ODZFNhW;(xUXat}LBRdMt+>2^AHpQEcuJE?HHob^-zsWw*02a+@G+}F?F3GPOf
z<44eB7`N1+EgQG@lx{2e_OEF1g6Y=w&!p-yy1mW^mY{|a6Uij>-O{c$Ng8HQe$}Pa
z`R`HqrK($a?(bO8r9%{d=+MN=m1Q5r^(VijeNo}PZcTqfw$_Yo16(P}HZk&JBxm?@
z+}DiWcu&CdM{q4;iaVQ;w;@=$1EQxmT=XPYZLVpWggzL9!}=tujDb>Tm53!5qAPR2
zJfB*cvV};s6w^l?DEm5#ovSmq*YybX9Tw)s&MRAqLLWQKkN0~A-W#$8abGwMN5gu4
z$&*e<V>mu#mU1)w<#Is>)Dc@Z_PWlosYBtP4l~?C0%A!MD;Z!uVq1WH0IquT!`>_s
zHkoU0vD9FOUO^oI`u47jzKk3v&h27zDwB^Vv+Hu7h%9_9@de--Ez(7GZ<71Rl;jb|
z2O0fpuA`vnka&4?U1A1~?33lq0S@yV@OF-Q6-j&};s(4Y#1<CvEN22kxf|$8*IO2w
zf2Z96rs+1zI`A9JY>(8?=SfMV^f+8=@^d1;!;goucz()H65dZNI)FzO@UI?Tcm<C+
zC)1~`XZY%UPgB?Qy9<e3<5;dGXOR4$fS|G9WPnX~mYxssE~HXze^Z(Q&#@r28&n_m
zk5hr#x|<J&w?<f})1s46Xw{1&5i{WRV5hOiB9!qI;Tt7(RvwjV*o5j-vErI;qiX*E
z4=hBAtu2JkcCg`*n<`jUt2VLLAXG?ICp@o}@`K-@CqL&E=`(mUPuG0Q1RrC2fKU(!
zUHyR_x^rCamGFba&;v7H+C(97g3=s}bDoC;d(_5MDr?Pa_cxXo7fwky-rc(%J>oqk
zcr`m|<QTV^$C=w{Tnzp`{p;Fv>8-ULS}SXK{J^pX$>VTOnBaW}73dm|f%M<_NS0}e
z%cjp7p1akhP&Y0|0G8xuv8U<Y8}PP;;)rIjNu!2nBvPTms3(l8K1TyQRpGNAs?wgC
z^gCyidwSIB#rwKl6{Yb`fv4+|PVuIkju@@Gku~F%C+`%=3!k_L<5~Xz6_@Qk0gF|V
z380qd>Nu_8kbd$^oScE2ag)t^*MNK*;Tvmq)kJa4G>?{<vV6i%R%~Q}*mSN3#C|5y
z{70{}1`)GJx_f{dECWOc4}sKUps$>xi08y8$7Q=d!xLUpjiP4wC&G4K9q^P^THIne
z?-0p(<{!AMFpKNEZy<Kfd6$nPviNHyuZVm;CAflng^FupbG9NfRFlX!<#IauSD-`U
z_;p+T#@LiExJSD)NJsHG=&PQC^{h`8Nn@^SR{kG53kVwM#tuMih55dNr?q+)ldl-9
znahX7ROOso(COhGAD2wG*P)OLc}JKg-c^qubo-8kb<KIVh%{)tWvTxFXm~;khcPbc
z7%tUfJNjq!tJi)9)3mEZxYX`hRLX$c+eePS-Dk-y>OdS<rCIo+!@eK1Uk&(7D#)q1
z%I9zgsbYC&J<nlUN|Rp-GQ{I!hK`!Hc*W<9d|P+n$u#?mjKd0fMYgDLq~MLf0uOIm
z^UXRqwA-6|9Z<`s%`QSt96h|t*K2=ug>+sij9%)mZ(xkD2YDME7>-?!usw}%w{mE<
zlPve~wZ)4Km?!wpO{2f36|^L|J(@ma6rEb0-v0n2Q&ZI~E~k@0(=&Z?4hzNzME?MR
zbZ_NdG#6TJtaf@myr|O;kc1o;bMs{P=e}#tXYm$`rdu_>jcP5FjzJ`|6Y6^L*jGuX
z+uyCgxVeQ<;@ZGTfD$k>lm69W>0jNpgQvMBuMoYu{>YG@zQ`ExRk|PkwPsuBH<mi}
zy`Hq&5;O8o7s^<64d{Djy4$@r$6B_I{Z83ibcrZ#3C?g&PJc>mm6guD=GvpfbkK#}
z6EG#m`#Hu)?~2kf<<TS1jCt3(XZWPYU_ie{G0w+32+3afCya{c(jN@T0hdM9ZY_a0
zFkC5dpTo6DsDY!!CX1{r)7iWJ=>6%fq#Z}i)d;C=^cS|#mj2pndy96+iNY0Zo^Wx2
zyV<Iu;S`g495t#`yVF&_`JBgwH0bYjSuW=xe#i6e$1KF-@~=<Rr&wBh@&OSkBOQ(_
zknnfe;LzJpdue>h*^|yccZ0?~>iS(x{g-!Os>P-Gjxq9q-!+8wk*y~AP87WNx4*sl
zjD(+4*1Ic-V)BwE%QRzmIOr>dwsR5lSnNBt0y>=gS95D?0^8>BJ`V(*+*Yqk1kXB#
z?IhJFxr||CH~{mCu{4hUC4*xL8JQS^z!>+cc1h&;jqD)|(Yq3;ag+S3)HE*-izhma
ztQz5wmuF~)x$bJ@)K;b|b30p|V7YUZ+IblD^sc8u(`K@QOZ)aBNmF*fasq*ogX@am
zHF?_q08om;47cwUPkqgwO7%TnYl!U15x-#Bf%#y2S1n7~MBX;loNj}oT;3PeZWNnv
z&e4X!!EUFYO6iTYz1%9Uz=ZjdSdZN!vGfFfwZhA%Np&_gSxZ8lEs{bMdW>fRt=wBR
zj+VN#k@<ErIF8=k4j89KVtqK`yCR`Kc1IOi)Ti&j@3aka&MkfkZmuPbmBNdOxlD8b
zobg_PtU?>alj;@;6tFfoy1wNE43q3D$NT`W>fSGrFD#z!*hMFn2i=T;g>pI{$GvU%
z?@rS+%@6H5IyJS`;W5kQ774U{;_5n#dsj7jbp5)5J>0JoR;?83^gLTs)UI!@<kjsM
zLm`?%l0@8ro);h8KK1IlABkhsJU?iXMX`di$kC9cn>^>XE5_yU)bc>n={Ip)Y#WSf
zrE&EC00Uib!L36|)*!mj^%EGFF?g0{3y?Tv2RYnNY*(R*p&w;+(C@~=6OHYusjlc6
zsJ{OIgr7uqj%dg+hz@#p9lo{BYJM@lzPFCgUOsq<@)-`t4p;bwK5SRFY1T1YY9C?J
zt><!)pR^(dVGt*%1Xno+!h1P&C%ua1bO^<xJ3uPL<d)AIb`{S;yeaEfW1j%IYTa}^
zuT#<WTdxm&s(U-QpeHfP@g0RvU#5Lcd!C7R;SUsea^FGJVukLbg;#dal5SDeSPoCI
zuHwSmTCst?+J4b$*o+h*n{F^mD{w*d73O{|@HK{+Fuw4@$cH<K1mGUL4x{Nrv5|98
zTOCzu&aRasXVIgKi^CdI+_jyx>9f3%ilVsv+pzu?=dZCJjbUmhTJV@_e-l_Tp*%{N
z*nPq0`PIE%`8-h-rPG!BYRJiP8iE*ke5{Sf1fB(YwYQ12Nc=vsYI~%O0o=#X4W}UI
zI5<4lbnw&VjZ0oH@G#2rN|mZh4^v4ky$_hYO@E?mFsoacqa<Ox#U*~Z8LwjaQ{!zq
z`$%65c#7#3=HKLoM{F#y_8X7!4wy9-ui*O)VjuWPJTograLc{ogn=88)T#ax?Sd-{
zPw<b6{8MoHEVcsYVuv>}uFotUo0>3Ck4}f$iu6~rv)J#gXvsNsJ>KC~+Tj$$;oY(Z
z?5SSFW1s0*y6hS;D;}#l24YGCQmiYCk4y1S!%5*9tgE&~DGZ9`;|J3OWPJ#$Q^%{{
z?Z4GNb;edvhC_~nE6`V!$*$%V7dEK!yXi>3xQ^IjG-3i4%QwF~^IE<Q(l2~Pcr2qu
zNG*YuSMI5vaq4Q9j;-1TgI%?WB3y)&N6s6uJa?~2_;aA8o`U`<)}-5SGi?bO!?-@&
z0J-(8=Isl!f^?eEo`-DDEJEJd5n@0BMt}_CyLyvUFFq&h9vZ%vNzkq1XAte4Nr^GW
z7=H*DJB3iZ@qLTh%>Es)7LqCz61(m=1FkXmtC#TKh5SXY*?pP|a`RrXCTBPRKT>^8
zD+Nw1^HkKvN?g3K(CRFS8ho)$Bg!W#@-rTAIR!{RDFo*Nx!((3Tu<U_uMTV1LMY>a
z+(kUc90U9(l5#Q#^%bS8eW7(|JSE|#it^&scV4`CahS#eAzm_lYsJ1HYLlCrYkg-=
zlG5fG6{Kd!m0~C63+zWc*R<(StrZ9>G<Z~&HaDkHw;4aV_dTZj#y%U;?PZ5ik#7`|
zVtXv_$o~4a0Iy8&Y|&g<X<D;fuuIlp(7sEq+@5=M72JN#e*u0E`18geVevnmsl#<*
z=FQ!T1)FZ<VybZdm^^z`EmqUP-UZ^qCA0GGiY>5Gko>0%{{S!`9FEoD)v1S5_EEcj
zpYX>`Y_AhaKKn`fzP^_>G>uDHmCmhpVw%Ez-??{GXvT0h=h$<TUQO{{#>Yw5;=J(W
z@kumprP3p~6?M)J9SE<h{Av3=_%B=dOQifq@VhnCH_pj?klYC)f%DS|f(LV+mHGGK
z9~fzR7mxKvlTEn3)6}$fq-Df@NWzlOoB~EqJPwtn69%eH)Q*jOk8dWz*QCAobo*Sz
zxY0CuFCO;8MZjhy%#s+_C$7=h1KzvO2*o|yy`7{sa6=N3OfDVw_AJ;1htyXyu6Vn|
znzhfLe5xUwmygREQwOHr#0uW{Wu|JA>sNZ!&9ucDG06lhhdAKl1d8=>*ea9~_Nw1A
zs}ouuSDfijRCXR2xV6@`3G@r8Oc#)6baJTJ<JDMJhl|qA-u5NGiR}_K3bMcisz~nH
z!5OLVw3sb?T^6|p5>TKt6RyMf9*tFeYp2hsJlD3ONORAY&xCF{!LOaD?N>*mJO_%E
zYJTzQ{sZuDg9V7zZnZcso%v|xV5+(H9Oj+ky>CO*H2L)VYnwZ5H6P2AgS4|RJ3{q8
zTytMLX_^m!-$ZMzLss%{Hn}jXkCggx-22vqJ`j=}3Lg|{nYy=<FEP=XLBRAR5udGe
z;4qYG;-L!l8cpwQj?6VW@`IffcDf$5p--!79v_=d@YU_V*;-7j=l}&rR17je_OA)?
z6gL;q+_tMZYl~?y3{pPQR2*=;6Q63y(L8Ja00?W`_>)iaMTO25?#-n@;q!%&6O5nl
z067)ZYyLC7wJ=Gq=-IWrK_#=?AdOuQ@gonL2eo?myqc~9665hj>YsFaxN1~!bX2NJ
z(!QwWA=EC?*=>c5vm<<=Uggjn`U0ek*5;4!ui_T51asTT1*Al?#IsN400*Ek<bn05
zw7mnv9x6$i&F*cF@xkV+j2`MpCm)4&aqE6E@Nb4imxZq5mf8Y#t<+njT=m+_2OibQ
zR<E;MvC>Akgrg7KZ*$IkN$_{aei_iN{A9Y#w66?)bkRnw84K#Hc+aN-y5EDN!y2Za
z47zpNOQ_1g%v^?+JohTx{yf(+sd%Tv{xY+g&sdpcw~J^o#TboT4(tac1Nc{n%W0^1
zcU_0ee<oa+QP(QUI`tg%t5Kb5Zk<Q3kyu(%oE)oaeuMZs#E{thZSdZQrLmEhUen`}
z<2^^(Bg&{fy-j}G{kH4-=k%}Uv*3=YcL&B_2wdIDeCxju>8b-A#3NnDl5^0vt$#*8
zXg}XItsE+hY;)DX(~PYYfB)9uqWFp8Ul?B8U21xDt;NDN$DJ_-2sru39OKw$-m`TN
z9gRNH<<i|Fo(B=+KKNOVag{5a5zk!LkZ3+2)NJ(oD5sV-mg++$#QW~dcV)4h4Epq~
zeL`6;Zf#cXMN3%b^Cx*2V6z;O$BsJ+`E`r0O{l@0e3T_GeH%7jGt}=jwoOLzTWj|r
z!^C!ANTBV<Ax1I9Qt{S_;w$EDcJEZUy4(>Y4#XXxU}tUt0DALYeWU7Dy6?o>l`Lh5
zG?O4d#jp*bM_hY*SE$K+@C0i~3lw9%E&{rY@y|vby?RyB=H%6!d6imfvUK{c$1!f!
znkR%VrMsNSiyH{A`TgS#0Qb*o@>^|r6XudnEcasH=N0Q$+AoDXLuw<`BMAc?=2(xG
zN&f(Awrc*L;7<Z+C?$s0IRWHND*d4TXCPzgU377DsZ(*1K64pG)TwjEG<`zO87H~C
zb=w@iYE2&~RQ4nS0ncvrwQp<Ttx=k5JL$}lzUNe5DRK^T(<hqrj}rKLZDKL~mYe*k
zBV%t8fU=+m=TdMu;{v>9@55gfZf}qFMw=Fu0`ceFG8r+Syr`_h-zK{9>R4&hm07zx
z?9W#ZN_AlxQ&(2pt*H1`>%tx^xAAU~_GzZ`+Tt5)#W-d-&fejJa*hX6UY}#)-ETzr
zp`m!MUeoSu<bqxIP)gYnB!H_f;0Kt6820Oq^^Ne?!*S?7AdAGlCAyyaU56JpUq8vu
zDhc6u`EW5_m*P(lrnL&eIv5=uIiqCTy-y6JZVW!76||v#X<_EOuIIUo$5X;Wl{`fz
z(^hg{qV9Cx6g(Sa;GI=JvZPC^JFWLxQWPcN^Ri?TE(sE;`9~c&73I2b!+Y&pR`NBg
z!h?oeAmI+v$O0wqf7>}eg1gTVN%4o`R<7PB)wLL4l2EA~$C9f#L>oa}qiT{l&N(=*
zHL~!mdOoi0Z!FS`dAB#xw&@GDaqd{K<aQO~%D*i(_h*rejZ9r=r#mF9dZKAj8zB@Z
zh@ny;6RKk@31ClL3;=W0jdOQeKZ5Nq(!*W1`#r>o=J}Rxeo>qP>JB>e0-l#%E!FJg
zheVbMUIrPOH6C+hV<b0!LyXoAmuO;=Xf-h>+Fv{5n>&f`ob$MQ(D5pERMNkp6f&x`
zX-chm-YcSS@3)by_6-i$`7L;G%exAsw%>3^aai}B2huewNvF~^i6e&KY`9zu<2e{s
zI0T-D2DPrNG{>@dts`$WUNO8gZru3;ob^41I#%?WXNPo~Ygn%%R|<kDA18KK_{K5#
zf%O%f=Uz?SrSdy4s|5s{V(xN!C&BG5dv(6jFE0}E&AiAR=FiTfsTcu$4Qbo>e@pRw
z!(ZQD+}+(N!~XSFRolik5J6u;4lAyg!xuVUllGqs+)Bu~lt$z@V0k+jV?N;Z73CH-
zQQSx8%Nr}EaLP(Zm^t8%arLRC8q<{3{R_-zN>GJZE3KcA++BD#NVb{_h_2;B8;_Rc
zXD6unml*tOM(<G5>?dn&Pj$SG{1;rt#lO14BNfhAwy|`d+48#T7S2BGr}xXB?y%v9
zp{kb_+SY=`&QUAdw@B_(l`4G?IIPs%B>B!aOPwaPJK3)8Ezk&nD}d27U<KXpj+mmy
zUb68dBnWn1z&6O4!N52?9;@HfR}*WeTwJ*R$(=VyqjVV|hI=QjDjizGUD5R@bpWP2
zsN&@R0M9_D8RPF9aKo<^H5@d2*SU0X^Q(AHJuaE(?`0u~$2nF}xwpSy26mpu*w+zq
zv$#5)wWL!hT%xB0f3y$us`{3vb#*6^s7_|c54IO7g!&Jsp{|O~{tMWOTgT-Xww4i}
z;l>6JOtIq!y(z9uVvN-nZd)0a8rGYsMI5t+vz;W|TjnX8eqy?7d-*h=ZX%c?=W;wl
zZgc2C=xdL+jtjZ2AIoTqHp%VfU%K9^M=PGcTJO9SW2;4QEoGKOi5qI5RNlGtW(Nbl
zYk1X+WonB?OTs#dxM<M@_KkP1-ma&3{{UyyqFCjTg5pL-8I9crImdp!qPZ!)At7{v
z;v|lE*r%J2$sx`+4DuJ$8qi-BL;nB?b^fMhm7-|{yi&7o$d7_cbv*D7rDk}y!(JKG
zB@wl_f>~6NEClCq<Pw~MJxRuBjX2c1ib(V&ijEpEjb_@@Q@FXh)U>@ZuJxG=g5U!k
zrL-%V7<6J!108c&+77UuD6zS_zk(wby0mcH$A#R-jm&Z0v9zxiCANnTgmup~BZ=7|
zyKkC2W2WJfgX>xz9E(oUY+Be|TOt|vNhg}SLJ%-AIX^*M>B&A;j+)itsNNg&X3gh>
zHBB<=*|c3u#KPGoD<D2lk%!&KLb%}Pk=DGEMALOWC&jlP+T_OGT-lExg3Jlyvnf66
z+q_BQoikp$yn{!WOtFQzc?SUOP=OF}!h%hCHm|EmrrZnFg)XCY0i&1`x<c4Zgnib>
z<|yP!pEMPjo?Dp8c!!6pt=68WGoriM-3>hMNhD!4%V6#*Tjc|=7{&%`h4^8AscT*|
z*8D>z>1AegjveE1k8$Bgs89uZri67rv`?vOQNtj(WocM81WVX5ryzQMwM)d`@P=PM
z*er@^x`JS(#i|&cTd~jaXRUW(xn&(SeNAiP9uBmnS}j6Le>0i8@eaML>n(jNc@1L4
zAh%9JgVmXjS3M7>YO#NF4A=5%_DJx%#-OT<lK8{8BlW8GZ{ifPI%S>hq|n^l1e)MR
z1di*L%Q5ND=hm@xe-de)AGQ9?(C+@<sagjnC|7V&=aTF=BR#X4&ZKK|Jr0ab940Rh
z&nIQw7rxyc&Y!4RStBzDk`$GR$WqypBkR=Ho9OUMbFRxh()rfbs$+x!l;7$H^{JqX
zQ@FB@Xth5q-)3nfRmeF9aL-l8Od7~%@cx50mHneMe<<4`93aX5E(pzZQ>STJpBX7>
zC_%l;w|3T79x8?fgwAcjo@iHsKp9c_^%ae%YCb3NE~v1wN%oVL*m9dx`sbhml3xky
z4<l;YbkOQcEBT9Y6on?o_j{=W@T$6Rigg`7T^G8d$ux?dNe*z}XSqGabUG<Xb8LMD
zEGvx*rLB&h_dwLNZ4TlAcFk~!<`^TBY<zQ!9!Vpi>08<_g)H@*GS^qIx|w|V#EJ^8
z;Bm;@xa1xyCsx;Nd_-k%z(j3QCP(47E>AyCTGjCX0EMsH!&Z@K*LOD4A|2Ug5uL}4
zq;dIFV%0RF<DtWsVk#-rg+or`!#cgxGFv>>_h^UrfzBrbuOq1=wP49;mWb`AyG8*U
zwl)FJ0F^uw_*Nyg_lf))uL<=y<}f?rS9UJ1o;PkeKb>@*AdQ;srd8i>DhZ-se}#s4
z$E9tTQH@6`Jh$p$oGJ6dq-FOR{s+6&?%=ub<VqUXah2N{F`V=tQYy!YBV8^7r^4l?
zm-miG8&|M3*8Dz%!{JEZdTzRzowke~xB$1MaK0^^&#X)2k~c7fDDDRz&b-=+^&O3{
z`cqdiwD=;pyH+jxxCZYrK{;<ud)HcTu4lGWaHnZ}fOsdhXzQ9`(5-AOY>`rRAG}rM
z<o7inl-u4jvD|^d;|8U)%Ac9!+N|GZ@nYOeKyibC+tce_(QT+(Ltv50OzZ)Di-F1K
z<|B*>@!uX=3r%V%l>&Luf^u`n;~@H1d8J%j&*9kZ^m_><Wgk1Y`m){s0N(mml`Um+
z8180h8fD$jj8^L0e4Nh5rUyVj6Vkm4QM|d9@5s|yFF8pPZagk~jCZLmw2ehHvlQ|r
zwZ+2b;m0f&vL-rlTyDEM=qm?@d_Nd#yPuj#pZ-~)<2dfeoMy3BoRyB0trv4+Lh#k+
ziF`-@00{K5#S<#|(zJvEQ*anxRwt!>1^)mDK9{B2iM2FVL;)9ZED&c327kk?ct4E1
z8KwABLDGDU^4&`(nDb=$tXTC2uO5Prh+Zo3Bsyh)ywx691gj3wfZTEzk@twle9?%(
z)sl><&qI?9i-Vhlqp9sFs@V8$X(F<LP8g=d8$koUaoV${)a1T$Ewr{~l#*GS3&`pT
z9c#<wx$*s$mmaHfE*9S7471!sxg___Gta$odZxAFEk^Lhw=>AN84<W%et_06!zn&w
z&u*1}XKNzo#4ifpXc5igDDGC)KE77{tV!&kb*@_H!*+Uwl3dzF6!Mi;j#MESu<F}}
zGILk%bhtGL{?Dk3mog2D=RQc!r(9N3cv8bph(4P0q_J|V=45B-UW{r<Q{-|A)!fe0
z!`EIRf=1EpF0Rr`wGqsP_lI9n$B~a}W9l0Iv8lYi7QMN&O+5XWJOuvnl=4_GD#JZ8
zDxZa}wJ7yJw_KhhQXT6pt(L~+&W!f~W_B+c$is6n&m7?WD{LJ~aIJcf<~bbspDLd#
z(_Y7W4~zaL!DlU;@>);3Dkq*16v}-=E)<_ktzr0&{t_<)`Imkrg67ISRwXevm}Owc
z1Tzvt0&$Un(~9;_+5Xo@*1TnDrE6BHEy7HJ66FDC$vhAnr?Ibb{i%E*apJvpKM19i
zBy5lOhCv$wr|&L7AEgyDEF^I%jXFvxtH1aM+2OL8eOKE$obtYw?0$LJYL>VDEVX;-
z1;kSyEHTI%o1-uoJRe&1*YO)@muExN(WSCEiHiN`yv*kZ_}7p~Zhb3n!r!z10EaXQ
zmVH+4OY5b5;bC#*qYi^3?+`lXup_ko)0<YgmffVq@s2&ug+Yy(+ut~)EK+mjru0Y3
z=D5FOQ`%8Wn`UY0o(8efiMG>qDN0TAvT#ry-!I<$L8jPi*FG4$meTeJwz<Y$Nh1yD
zLoP->wUMT3ur1u02B#=l(0`-It%e=8lh-5l6<B!ZS<&_1>^~IS!*>MVazLLs2cf|0
z@5ORe_SHS<>TiV1;Z>?~d!62=;)!*AF|_-;2(I8G{#d^E1O1%$ABA~DF>2Z+*8czx
zO*6nw;~Oaif3nJQ59?bxo}X<k(bO~x$u#K~oMDlEgkrF-d_m(43NbCy!FL-l{pQel
z&lq4w>sLw_Ry}CK&zjmDcDWXv;w><y!k&C3WJ0@kxar5&rE#7B)4WgOn^o}rxp(^%
zjULi6fT_sNc<1q}y7!Is>u46^QLuqP!7m#yARd_<*Pdv4mBr_YXP-s5F+IZlp>dOk
zUJ9HM+r4ipFWI{ewK%kzN7KLAdOne;{invaP`&hcQE3!=P$=lf0}WnF@mt2)#)Wqt
zkD^>6>DOvmULaNSe)5*%A3!RqYC4>kg6qTrOYNfqRV*Dq?Xia-dm7xD9V^7Pw>n{G
zSmj;>(eoR2WzQnHpwwl}Q&Z2KB$3d3B@t<D3Zs^3*fX#n-uLfbtKyFW=)Nh`?{%GG
zMQ~si@yCu-^d#r5dsm)(9q@OI^?wrCcnT=2W;aBn7mGH}x-ttZbRN9drFe;TKMLG`
zX6x|nlE&<`+ej-QOn?K7g1M@lMH-F5TG-bP;~PdxalZlX@4gxMlFHic*v(~dqA7oc
zqus`H>~UV>@u$T85AYw4rITE<EoF4yX%j_g)9n(C#{;({=L7Jsm!|li6q=lK&217!
zSY)UhlxL_Q4wd$o#IFp$#9x8dUOTf?xO*+OlSoER4>7l|JvgokkczAAYB%yYb6j(!
zR+OU~X=^=GDfk!0J{S0p@Z-du8~AsqLo21P+WyZP`D_G#dohr#IP1lE2B)RRXYdP2
z@g{~6HMi8|hIoRE7Qo@Xau0gpz5z`qf&MRAT|27V+`PJF(Z)VW*I~%&KrNB<uX|4k
z>oWX1@u$O$9>(KQTYX&&Z35?PhaeTlAe?bsbW^2LRN*V9uc`FhJ)Tj=(vnF=QEKS?
z>ecPMQ{X=mTg#x9n@qo%m|6uAd1RcBs^H^~?&7|o9xB&#-6qlOFD&McLA*sIVlOC;
z3Kis6pLoyVXT&>M;nVy<X!fSqiIQ1>65E5_(GTws^f(#!uPE^a)~VsUg9fv2xd;L|
z;Z~9o5Ag$!l>Y#-+}EdB#}Q3NtM`{f<oKnkSdV2x;d=b~p0lrbpxemWp0>z~G5y=y
zm5~E~bOXq)H%<7c);gPM+QJ*VYmvSQLV)8PQNhSH$7!}YFN*@lb$<=C>i+<_M&WXQ
zjfOos0bfeDz%L0|_ylWSd(C|swajv`2pA~m3(gew&o$)D;i<|nn%&sT4VKDSPjAid
zHG3Skm*YQ%P~OX~+-P@d;ANF<qm_96A@d*Qnzg9>W$+H9@!eY7NRhJOViwttLJ(kM
z+*h7M3t7UmLZwxKC5{T<{{XdFz8~=a0EXq%Zr{Xl+^v+reU*2-ae0UDunUc&>&1F?
zvb;Sus&hZQjE_00r6^7*rK)Ki_NlCVFx5;A_FH{HWF;k**+-iWM-8-qPtv!pG|MaP
z6&4F<HCqS<$k}%ZA_Jgk*zxq>SI)jP_<4P!&br@*noGMd3cNvykf83YIV65{#c94g
zy3p?h&W~|(3V;DDS(N&#=Rd7w3Nudh=HIFII9$S&YHdy_GuCxjJP&Js<y$GRoqvup
zV!{6afe<+Mu84Tv^F`AgHG8OaOHeWQfshYkI-jj#_;=zphlu9;4!sqymA}=wP*MGk
z?0{=(?@iI8j!0s)j0QhEYKyhM3g@qGZdoJ5#ME@;)J)$3`92{x`n8!TSnPr(BZceD
zalSLKwbm|fr?!<zwu@+&9Bv2a9{A`DT8`cy3HXB6?@RL`j_k;d5(eA%c*yn5WyNbH
z@pvv~`#qZc)1Cvf=c12#-wzil-a8$5I?|~{yB>r4Hh4CB?~C3P@dt>DB7bga?{5+j
zxwhQLwVywAeFsxt(bw3t{{Zf{=U>l1!QE=c)8hXCgfzF8&5BKXOcBU9<Xp$JAI86-
zA2!qb->r2))_1;#i;J&XHCk-{*Z99h(;$ahlI-fXvMedFSgA!|qXUzJfsS~t8^#)=
zEI()yz3g$eSxS)^=dd54HQJpAOI=z^Cy#nXy~gfIAn}vIBDwV<>KU!v1xXRqh2xAK
zE9D`qu1!SaB<^$eE%qDBZF1UKQVXOEw^62iy!B1Pqpzkb+@oDC_Q7v7jY(Lp0ESQm
zrH_2{ubsXVT+bJbe!`4=hK1RpJfUHMyJ_P!^ws^fox;SkK@$Uy;l?xc#dO)Hq0Nn!
zi5XV$>N;kjBzH0HhEchWI1GC6)1LnTolmO4YipuQtk|=`Bnr(ks~%GwvDY2NSf0i!
zFcQ_B6t*$ApI=()yepvDYqoc`+LK7=5k>=JhR%8lhfT>?<Bepd^gRCn!us~5EwB<f
zmK-FO(j4WLhYQ!JJYu@7UVEKmSZgbm+ox$($t=h?GS2)M$sfU<G3-rT@Lz*8UkqGG
zbipD208EV&&3)kpa&`m2&weS9+aa*kOBN^2QgV9jV~o~yszvhAiq&;J90k&rCG7ed
zo+#7pExa~-#}UZJHIhNVB#^4AI|T=irEFU^kz$eJIHYkOmI|m0R~X47l6~tN;&_G+
z4hJ!#;w6*^&NqbzJP}?c;Exw-_ej2XmyW<DG0uB)`PU{DH%ZAo&irh-qZ{<p+W3v}
zD(+2B{t^8d%uz)O+ZG2Ia0c=ZY>{0b!$@v54Gv9C{kK9GsxSAp{q|$+&{vXPSz2AB
zmvY88wDUGcVi@BU(tIe@uVc}!^o4KSC(f(rN$2zLT{Pnstc)r{mKR9$JwwA*X>l#I
zEYUb2x5jae!y`YPQr0z1cT9o-dQs$y{Z-H(G=Owh>ywT>#a2y5?7Pg*mS)M%e${JH
zL2;s7iE=`#Fdcy$f-0v=-R4~ahB{o{$?BeltUJ6}1a}&J!X?6TVvy}*Zk;iP1a$tC
z%ey}iTc_DIYk#!H-6gkSyb;jtIOJ9qk8vRIv)-=JDk<9AMnTVBl_rsQc`Qoz6G<yb
zxnk@|&TA=Dlvg@BJ%n0u>}+UPz9P|PfA~k7!7<5eYpC-Jp3VpGpHb5_%SCwFMbx(b
zFSMD=vKc(c&z3O9Bmy`q+zRyjX#BzRGknr2o_ij(Ys4C5rlqFbT3s$sDP_l1DmWne
z){(5GvbBxr(Oiz5M4VsVXUm{?J5{n<%>v*-BQE7v$PMkDHlKRGVRx=+k&8W6F(d<p
z68!21pka`I8u5<>Yx;yrbb?jk``|F;NjNwo73deYR}hdQc2SJr9zQDSrG{0LmexE>
zXYOh$UCypMts_T@U0EW61YDUW9aMjHLFj&*)#!h-yno@@t}I-vwwHTikb+AGT%7bg
z<MXd9hFLXlh<a|Bs2?)flO_}Q3Bfrdze>~aS!MAri2OerGFzc}(_t#g@wOC#I&es?
z979~KL2P@N93-NZX-iXX<4V-@sD90?+l#9?4n**<-h_7>hDW$=wb5yQ8nBkh{{XTs
zO~ewYGAjVGp&d6M>H*{tTwaxXs9eh4ZOp{E+Qhjz2aa=^_5T1Hcq>Wp{{Vo;iFD~F
z7k*G}?EGYpKm}v_$we#ZdNt|MjYZD2eZN!B^gj>9;%^&WM{RVZ+gkwA439CmA6`4x
zdtwcQtz)Q0eAraRlBZG_a2wQm8uG7&k0sUYH*U<b9D>S7CqA{%{7vyrsiRp2w({A-
zd46NAUuiuv#(x^P#$RFVb5?{D++?>$lz4wiv6kBJM${k47LM32)t!$)^r}(#kRy#R
zRnF91i#X0eQ^9O?1EKb>Leml({Z4hdja%%{tg-RV<LY{m^{!JuvbMU@=hbc$G}FxJ
z#PkXeKsyTGnZ|s}bIX(?Rt^$ZM&_jspQLzxH&$6%Ru0X(aQVJdk8YT*BHzLqQcCxb
z+%>;HaD^&K{_c6^sB2lbSv1JW@?$?B{_b&*52rPA!g__p?t+tDZFY^BRFDtcI0x(R
zSjU?3o%J-YK~lrPqn)(zHm70dN`~2j?m{I@ah!33)ce<4b2atlyin>^tpd6UJjh?=
z1xDm>dV&umSDE;_Klq$YDPnx=?py%Aod6XSzBt!xJPUDY5sidJVMle`!8zkO{4<V~
z(+NIXo;_MIyk{L5?3V)W@*nLx))4A206y6s5Kg1`Csp8<90tcV#Q2SWv~Qr*t>7nn
zEDo*4!<-D`xX(ja-VVRiHS29U<5r#KE=L4_1{-ex4^LX|HGLw=O<m<>IgulG8Q`2_
zk<+y}B~G_BJuG{Zp*r|D)2}XWZ^Fkr<4qq|@TZ0%yVO6?t!{2XnpKHi<I0s)0k8`;
zcr{Pq?vJM?lRl+wZefDq+Blt#<|PQ_+n!j1?V6uk)pdPW!nZmeqjxm+XwDJ|2xU;Y
z+k!^#m$x<Lx)+Z%{U^Yd)7Ti1p?@T0H*QdWizMXx*P&WUuQZ{h&g>2uM=$!MwbOr@
zk#niUud8YHskF&(tr869gU$f;#z>~wC)%w|rPNCbKi}SR93S>rexkdWwORDtF`m^T
z8BaKZxy!dC^Y2^)`klt9XB5+gRxC%zFfm=!Ft3-LGs42)qfUI&)QZze@xO*1OAihP
zmezB+BOby5KB&ZC`-9DN7y1{(9Yab>JyP1<=0sx2KH!olJ%|K@`PU_Evwf`FndW59
zFwRH=*d7II>iWjD;g|~Px04iL4#|j+4Eh!XkIu8I-#@c8$Jd=W-kH#8o)OgaS*-j+
z5Q6IN-M6)jydn190m<*r743iUl?&}EGc~cXn`=t&JAjexINkF900vJT>*f1T8SCq0
zZe)~5*w4<Ojz}G`>t2szr!R=CAk;MpWZq=k@@)eI^v=?MTE`cIjOi)AvUwSOdCL{a
zFSK_SUM;tjyi+<{DFm~H#C&xlBRB)O707DF?@x%y;b#o23pK3V!d1CD4_Mgu1B2XB
z=(g6Ez9PEQ=ZOp!;gw_O2ew<Blla%4YMPbZh4+YIwr#5(@PnU}00$nN8Zy|<6@8^C
zS)W$$7<p5Zs~hNae+jiqh-JF){p6)t13Dv+6;5z)Yke)lYnHA6aFRCiy#3yCJ$|+4
z-wyQnZ?$WiyM}V`BbC9%Prn@3t>}m|HQD9RZCvyK^{yK3(`V09kGpg`Qj%MleEAiF
zmfaeWxSvjaD-J845b9G;6{WIUXNQr&C+fL8R)>i7o82xbTFYv@hXu&X52kn@l~!bV
z<BZQExtEtExj&U>6hzyzJoiwH_<LEk)UGjbRhW5m{Mq*dw>>M}G%pY)xf<U_3^B0T
zoM0;sp?!F-3h}Wka}uu`4$y1c64DDV59yIxF3>If!|u+*B>VGKiH+%_n+*Q|d8wm&
zEU4afxkX0hoz4JN>VCD)Xu4b%RyvQ0uN1tK&mFzFBkrR2`kIHucT*b?ZcoV?uu)%8
zcwfSLBsTVzrakdmg(!}4rS^hdhZ~ra)Yk<{p2`~aJ-j79YV3Hg#oZ55ytL5P)DtvK
zv?w?wit&%9daL363fXSf{{Y1Q0A!X;I0DSdO2;7p?!g;(5no1pVv0RK!lwQK`|c54
zxhfy7-%m>UQU-hf01Lp^EX-w=N0Xnt!H604z^(8SooZaI>8Y+eFT>&JPZXoCL%Y8C
zr*-0O0{2L=*^uuUE`ylAg_TZuJo8^DYEesaerQqJHpcI{C*~{ZO*c%lw7szqt_|sN
zDeu4}W78P?s{+%*dM($EH7iSq6{C$KkwMQ4!=9C`I&|rCdo`iws*;>jp6AV@TEEh@
zHTxv8@yJQe;m5hJMeyFGZLiwAtrw9gY!V;jC%NaE>-=}&FAv+=c~RRZIXlnZAB}m3
zg0y=p%PYG{fhZ)v!RHmfG-nIgnI^iOwb``NA2ZJidALy;=clW6tt}$kPL3HZCAhcq
zCS_y@#O=pSd!F^sS$sS2q=)Sf0}`DE_QCp$*I{Yl{UgKQX#UNC17&dT1F*>KTR(+o
zOA#7#QflWmBMj?P<xa-u!V3$Sektg(K)Gg@Xqk&BXB?t4HU=~AUse2e)soiLq|`28
zI*hIoD{D`gh$rSH&o~3q0=_1W8MUPo>X#Et5>lxi{iwL^6+jvHslFN5N8-!<Ur5*F
zn&4U7`4LRgH=X5`&eHkh4%~5B;%G*tRgAu!k6$d$D$v2pFH7F<<agS4i1hm;8e_vG
zaYRhT9H_*jImSBtxZ^!*%i`1Zj}Xat=`YzLx|GU7sLx(KtF-Ya!+#FJqfI7fZz@87
zG@KAUZ~?ChyS%^f+sAXGTullq1u$+c6Aa^(Z2p8*a!z-t7ppjM)agcim1A^t7T*di
zpuUq*mi|^qqy|>n0W8OidJe?Ze;0Ui3q4706sfmp9URK#fc_=ga7aFwuCKv5z2Aso
z*6%NNsf7ecv5YP_IXNVg_}4R~Ygf9so5Q#J9s`maZ|@fepl?dLQmgG1<a=<-aLIE<
zHh0|Q^zB2$Lf1Ncsi{DpDS^&pQP}bW5nTSQ;+9BFwf>%{r;-$vBOalFs#@i`=-Q!M
z24p$mv-1vpMh$ivP0{gm4Wjsk65=&XtC5KYf7%3owdl$!Eogf2Nk^44Jv>>b-k-8W
zj=p}(`Q%`1=m8ub#<^Wm2Jq*I;nu8S0x5I3KZkMZ2d3fr*A=OVZ8a%0gpC>%muL-=
zM?SghUGIZ$W*U-cl446)7^#j=m<)6yJc2!|UQ1{aeE#b_de{3mRMW<#rCm;ezWNdv
zA?_JZOnX-ed*TgmLAx;O3YV6$93!vF<LF27H3q7Wq29@Prn|=S77c^JA4<q{OVm`l
z8^;F(p1!r`PLyLO7JU9Ltf4C-+I$n@O(J{P#3E><A1Fp$q!a$o{{Ra2T|(O8-T=^f
za-_H~0^y2r-zNkf;=U--HA~GSRZCqtytT-0Qa?P`vg@A`{6(Rdp|)5g4nAWd5=r&>
zS!>R#2}ZJ%4jPpeYHlxddWVQS5#e1<$XrTdu((zjXp^pggrehdJ-@=fjQC}w-|8O=
zb&m&lX>YEq7C$a)s85;};zA077pB~Q4@&&|(RIyJ$DT4xKU<ZO1#TmQ_haAG^YyRO
zZ`i+H)HDwtYMuqt2u(8P#IwQ40h1&!=Z5#KV@iCS&8GL)V}cyGoJ4T-TeCiL@g}cp
z;ay(W!d^C(M7f0#(o5*}WM}31uvmL;0>1t6wZ)Hvd;_R>w?|;MdbXQsbR|Qy8--9C
z45Vb|-@Sfze%3mKIwy=ZopKvUCb`5QJp0=K<YR{%dIMhJ<J|*JkM@1jS|2tvdmC3V
zERG}G%eaua$6EUw4qHj(?-yjBE%yHaf_hjA4=K(kMuw_ZTD$q*W1jfou77SvrTD7B
z*+uT19^weW<0p61rF^4p;+exVGXldTamOD0tLdF))(8ETbUj_yHwk?uxPJav=N0fb
zitjG&^_J26#)<>Ca>w$P-Pb3RUoA%vt5m5bx|rwGT}qtGOE+`U{@-!pEnL`X%Va`h
z3nYGXlj?8|+*hV*{s+H@!t%<Rr1Hvb*}S`7l0H9xMn+HLQ}_o+)ATJmD@`sj68UA=
zkG#x3ynEHVZxQQPTGZC_PR2Bka9j?*m2p(WN_BZUBh7^>lxaRmyLLWG({=mw&93HH
z!3E+O3P|-+z&^F>x4M)@(jdj07{g(PdHPp1;F}vu?Q>ASyp3d$WnwoBfJ+>lS5M-^
zSYQ&z!9qavuFA`utDP8n>XwQocA0ql#I`EiCKxCQ5tHt`>~DUR<32p_z1E+3CbglX
z`4eQ%7$54}zDJ?;uU_yy%&50Jc;s>Qt2)^+q`^;BInR9h)2CWgs@V20b?&*We4%YM
zq;~j{V;g7g;N$Q$>Yg6)?}!@1>{j-2UOZzFTy;_C8#o`WdJn^I4rpK4lIjv#MI`&>
z9%PHW;d&37BlND)<yQ4AwDtlxi9al0C>UPDrYpM>I&$}UPF!X!x%;cBi}15S)-QZ_
zZ*$`Ll3V+TBxn$TV}St4;2xxL!LBREvfKD~#ut!Af=DGK>?b=<sH!&}Ak=&x;u{@z
zLMs)-1fV2kSxLbRJLG*U*EFvV={_^@ea^kF#3hp6Au7Z;h_*Az_rdL&(+e(Nd94yX
ze7?Jm7HRZnlm5@J>fS5(@!<PwD8u<4BGaaCEF1(&sMzl8FbA!FOrO{~kM~FU{A=<b
w;RT(Qx5ZBmPiX{DGwPN~-byTBk3A1NK+S&G;-Xois`;TCBg3s#&TX^**&IORX8-^I

literal 0
HcmV?d00001

diff --git a/docs/static_site/src/assets/img/horovod.png b/docs/static_site/src/assets/img/horovod.png
new file mode 100644
index 0000000000000000000000000000000000000000..06f51b8f287fe8e43574e6ff36f92b8b2b2e979d
GIT binary patch
literal 50211
zcmZ6Sbx<5l_qLyqK!PV|fMCJh-4fj0-Q8tzNYLOe3oHa#+!tFkxI=Jv*Tr>#AMf+N
zRbSQj-%QU;%~bd4?laeYp9p0|DRdM<lz;yD2VF*5T=k!S5CH%D^CsZ~(re59)3f0}
z|NQtzM*O?F*Wxh<F<pITtzTq{g9swQRDCrmmli^+I{5)<WhDp&3xWRY<O&rr{qJPX
z$rZW1#3eRj7>8%7Nt%H+&b7wN-ld@6@v`qJbPqlTz4e2^k}nqP?G!Ud!{n*O$Uc5}
zhw$^;SygX|dqlHnC<?OLzYSy`;h7qsDY_(7CD6BjZ|V$u3uXQJ-{YSj0k04Jr#UF_
zW2CVELo^x+%EOXl(^Hy<{c1xJML4EeU-&W~#Myit56{0NQjX_TwZR>^S`WzkEO7G#
zNJo(9KAW(a?{L7D(<-Ni?~2K$F~;g6r703yo(4%|@du``TMYgF$gc6<ciVj=z5Zdt
zv8$G6k2o#Mr!_`w_1x9_h0i6dTGiq&?M{|(UDUl?>N(l0WKEE_n!>l0jwhfYomwd`
zejKUsa~|w`=!@vh$Q%6*=VXP)ag|c*pB<D>fh{=vp1X`Qc3i(@rV2xFN2R8pjz;ua
zf>-??e#f?SKD1fsaP0}KI?N{SUYD`?O>3Ky8k+6gY+W5LkzRJA+ON8y*{M=uTly=d
zW&01{Q(6tdar3xQ{?nsmciof{YZmb6Uj24wjIvQW;+8<zCkFv{>bRVwLAO-n=)jSl
z`=k8{2v0aS%buh9B!|k~K0QMtalTg0vo?u=`D5o)xy}=XkdM2`$_1EL-bEhN7^yjW
zxarRSLew=zWou?;TCELwx8K%;8<5SearkK+rqoRhb8&?QV2}zZCHRr!&>DVn+ct<j
zo|mogB>K2tRrLMjFvO^Rq-}U_P^<N;4#HE3=97bCn9)Zr)&KT1spMV`Xo4c_w-y6<
zjrsg~w&famEHzXgDyBS;yxakZFvUH>!ukAdiqHb;ep$Kq!0`xBg*et4mS}+FHqun+
zEW~?zeoy#tcs{7}-x5V*?HM(UWL?0x7nfku8!A_3v-SqzTGF3!gDmbW1}Q*OKE5Um
z)W)15YBjQ<!Nt~Zbx<CpJKCO}Sks+MrP(*O_85rOZ0fWf3ejK=HQLYYp^X2A?0Qa=
zoOH>|VOm(iFs|eGv`s?%!k*7~74v`MT{t4zs)=68bqcngbvRsR?&Je|deYBEujQ+H
z`i+Ww;~bCqT*D?qYh5UY`YK7&YW0q!*0K$rpI$q)R936_>ytsfKAZ5fD`9=q?D|IN
z(Ki`W5|Qtu@o&TDy!9W3EtJaC6r8I?Zb^iZBG2|u-y%FAoK5&*U+<^lXqoDNF<gB^
zu)1c?jcP*k@27qQR7KAu_cZ2r<hk?*guFYfK6vu-I;K~S$V_uex{g5+)CdMb?pzfn
z86>>DU~k?8Je!Yd{O~c<m5j?#Dw9lsnlOa#Oj48dEe#fG!COK>BQ_R0L-pI3Oatd{
z1?UWG5y(Tkcm-bMVv|P(3pM1d@6Xegmx;#v90Xj%SAO3SIbBMgG#>En3y4Cz&%5LF
ztY_>#kdwDB=R62KVi0fKRVuWq6}g1n-d<nha_Rjy5KzVS5u{>4ea7|9>y1L{CAukK
zwRXzyDabK=8EuaNTQRP^P;M9U_i;F;Q@L{T4rx)imxaoej&`yg8sXkK(G;BXnWTa9
z@=@8rZv@ne<cr3C=TfflceXLi#rG0U*x&@l%r^Vo*Fuatg90-r#z6c-^?IMoQO0EI
zWp}`4o|Q)1cR=S0JVwQX&C@z~tIb=KdQ1=74vNtqZ2|X;R?PYob=ERnD0^vLg5J)V
zYZxFhJ847uG!`bPVz2A)f7hqy_-c+mh}-y^WO`igvXt7Cwe1I>8D)BfYFv(LMQ)$o
zjJNY1eRy0>zJ5yMhe5)DL5hxqGG_wzdvBMg3%)V^ro#z_iCZV<2TegP`n0ZtYKYkq
zUMt5=f$P0$pwEMW4SXro07*ucVzfX!a)s^Rng0<U<r4Sq(T*#IQusw@K`~Q@{P2Gl
zmRyE`9fniCsQeNC_kk22{ud?R$U;Eh&M-R<l2dDCZgYAsTJqQlu_BbDeZanb;!<W1
zf#+I#9Y(wn%cx|`vYEak=4rEf?P}eMaf@#9-k102alNKD1#CjTEn_s))TW$3L%S04
zxq9j(<nXN*D`=ivKcFUH_%ym3#NU&*Lu6FMj*X2SGh-QO@{3fHag)cUBvi|bS)cnc
zoblYeQD@4)b;y+4%#-)IP%AX4Hwy29a+Pv56M_8rb<L;8aQjLu?2|KFvGli8gGjf*
zp6co3VXob*Y7KX;6`A7B?<yUwdjacoD&JRYOh4`}O85I)&{BXngO>1O9awFTnbDx_
zATCOv2<S+x`?uXq35#*uG$OJ|{s=&9L>C#-^m!8-jSkHTEY*T(GgDe`?m5>H3-a|<
zG!llUVVo>ZfaUNP>l{ZTb8g|^zuQ`l<{i}8E(As0lDu?G*9_BO*4V*1(YjY!0HMKB
z<IgS|Ccg@-8ppoyx-BsI$RmBF$X5&H^FI40t#TzWv?|$JH{frw9_H5br}2X;C*i$(
zJSJZEUD+X;h%pC|^*{H%1cpx1zo@90&+DgZp5}-JBe*O-GgBWTBPB_+8Y8~&CwIN}
z%F|rLZ1_S{+NN{Ymao=lZcBRXqVYZ{M=|QOq6b}?-jJ-lz!vMNEj28r3-^vIwJ?oC
z2**QN)j$%DZ^pv%*!E1lk=?KIZgPbvn2`CG9Z`tu#Q;g+sA15uz%Ooe`VLkD7C}eP
zAMzNp;)P@SJ%69ad$OoOhyqhlp3hei^9U$sGUEUxkQ~_@tLezQ+WVHRc)H~(oJ_h@
zm94!q!xlr%D!mx9mVosZ;1sP!`N!T1ix&fu)G=ZKtYnIJF_PV7+SeLGd=!sVIqqr4
zsMOAFSBoOQMi@BqJ4nQ&nQwR3r2vkWV-G@%dc04bMdP8mwK@+rO5Q6`nxuF6-tsX1
zsF~O_#r|nzY`QjUZhr7%mMB<p%S0OX(3{lvDSLZ3*(tnWXsWD$jl#*r$0ho~oot7w
z4y-Yi5ja3i5c<e)826uPur3mW)|S(;B=hNLbw~oX7i_q3YOCMuEF44BHoR@)MypXz
z9%GwqI#;DP@4Nn)9+Zt&p{GWsRqeiVLX;NqD+N1I2sOe*N&9IFpxdgIrZZ&~zpl)F
zH#PpZ6gRPSp~I7<lkeiUrp-dLix6K_G7a4(0#V~hzV>~o-*jDj9a26HYzCFls2(YN
zC=<ckRfgp{PWXYgJFQu<th;|MjmM7Ld<#>=1$HCxyVm`w9<@ljW|yWFp?0E1O(5uM
zb&@N>C4n)k<5zzTa;sVgos$;*37#Gv1B}}@j&#mxTQL)F8)XvgfwRbHuiqGny;l`q
z_Ki(9a2k!JBBM>9!F5lCGFDrlOsk^68p!iuIL81B@6FHe=7Y)nm0JQl#{HSp^`+7_
zOh#KmwN-0fon}^(A6gz8yB3;nDT$ZrufDYS*oyQ!_DpQHJ)nIN4#Nr!vY9Gfk#ndI
z=*5!Ns2T?c>+!kYpuo8Af6!Y*)&}-dH^77xXEM-BE;SM3XuYspqHr0sE_tbcChjmy
zH3blJmTsSfeBo|*f>S(V<P^d)>yw5D4{YKMcA)k<{38JC8x=cQJf<_%L8x!H?nnT%
z<T2GZ^g&L_)9tV_B6V~%C4h9X9@yVNMmSeF@YDa%`Y&UMfY$iM(jbJ_UZz|ZlKPu*
zq2yXK#av_Ecr7+(q2cy}1^wK5o9EhCHS@_Dl)yt-I2qYfr_sC7edQ6|&UI&5CAEC{
z+fP#qo4Hy#S0Gx^)L2A!M~(L*b2)$yJ=0%0G%-}j1tbLckrS{QnHFd%T5BayIeifT
z=}UB|%o6Av@*w(_n+y9BhkSK@p?_=HZTV%bIU9ZH=2GQ}XG&pj%a=!|)|>#@lBk}L
zJKq+HB`%@A@vudURdHT!SRZM+Q~FVpEX0?mOQ+e7iyN4+%cQHu87Q4`XRnEErxh^X
zTjp83P_0wy7EzM`zuLkKG3rp5&APXlYDRn(<`1~HK}XgUz#k{E2t7yUBH|<;kQ;km
z5ty%seMG6}A#HG!+i2yRyuA^Ej+dsCHQmxiikj?LuhzXWB*X<|0z*;L&Qy*X?De5P
z&X(e=r(3ypx<o_&V8gw~Fia3S0|~UmAdhpPt7Cx=EdKxSzy!@F=fvjQUpTKbIjN+r
zc9Nd`o1Q4YqXh2231Ue^Se_iDf0-=JD!3mkq?O-CnMK6>-uq!PXqI|>ywDaUp5Ico
zyFEeS$Kmr~nEN!}Z-F(y5RyE2sVTGPXvp73)7`IJsB173VrkV~7(+Q9Xe4Y(y0$fz
z|Jik$k@9J^(S?qp11Mg<h<;WhIay|BwHa;s=le^*Tjjg(Gn30cd)*Ggq){_;*;bX{
zpoP|SejC^3hQmcL$D^XrR(>i|M3Zwm2FTY8@MFTb$Fb3@afy`>O}hdxC_|UV2X(#w
zYBiR%@tiNMq)!uT^i@up83zSg)QK{C1a-OHxKyMm>0CPN)>T~fI{u`QR5ZF75G|%l
z@>@K2nGWFS#}<BbYSCl$#Or?W1~KSux^Mv6+lO1Tl^ZkFFLHZ|Unh>a`|7t^_BqZt
z>=(bSR$Sw?a94n>UX;9b8QuEg%omzR)r(bA#PnvXeQx>2{iE4#2`+j+4{4UkRM=X4
zewkryAm+cFtkbE8fLA+exE7v_=ryGq1?X=K&Lmot)zGbwqWOBBQ1Xxc=(SJm(`k-2
zXC-8%@<bgnM*%ow3_lpf$(q`oOnQ$PHnNC4>hpR8(QS2@Z!{ApGw6hP4DU{sIf?Ws
z8uSpQ44oepazz81RZ)Dsy7loYm!+?DVXB4Biqx?LV-u)61#TVBP03Z%qe<eb@ry$b
zF`3l^%Rydyn5YCoj`U75HsU*zxmSmip_G>p7my_|RkFUc+H%4^FKH6sW+?3cWA$x#
ze7-gx;dDjkv5Z-IvtJbDm_3F7M)=$OiC?3ZH0S3YNCEuOc@||VWx9NtRcpzydrN&*
zZ7tOfFt$>Ss=f<glBzch$xrYXEhO<aqpcrwhJ8iEsm`CBZ2sq`8e!65DQo}-&g;o2
z0^=8L>r)s2$&Bvj<`f9*a(sn<Uh%!AnJh1OZmH*Ou;LK%z3r!uF=U094Riar!+Jox
ze0&I8@?Qk7!o4(5L;4lT?ua)r?p!w+FDx#V@UYPZP4i6`*t3LIBJH%C5(V5M>WqYC
ziRDt65=n5RF8k_iR|s2He(D1yaaHPX-d|a7tu887xz^go>5*mg1^f`~9a6ik;Bn@s
zulBzjf457Qx`E#;3N{(i%4urD0MPh{2guE^=W0hI%NsnPQqYWiWmNjeRjRq}(+IGi
zcMEEOrS_TDCooxkFrgY|uQAtYL`>`jjlL0+jLtKgYiC?@{l#zcx~)rXQY4leju3O8
zgz8J4!wUaah}kKI8x=X`7wQWK${t2KtkqOV{r3BthpP)sd4GG#=5{EK#?6O{$Fv#2
zhFs6CRtmp6<Pw*QDaDTJJ)|RgS%*(EYW<jr!dJ1pvJziyk*w1h3{i{;m~&)T3<vqZ
zJc~58Y*k0Jm_0hHj6INUZJATXXU;D5+n;~AO1QBGjNNkDE`_IFm&(p*R;VU4I4nYr
z{t(dPPG1f`Uq*wV@uuWIIZIxc8H!@p8y`<`0(k&vPvLR-rW09?Cm+4jB;@c_A7f-#
z<n_RZT)-MXjyV$Q#Fb%r@hn^?a1sSROqD$fG*9`qo*@)mTHiaYNqsuuU%9sSnzo*(
zs&ffAP1`7}-CTp$bi2hgPu3*kZp*B!^RaMk*_xbuZ->|L`cENjBkBD551@tNo@iXD
zpQ-A$%OIW%O1*Yhs#sEy?%v_KVr53sex;n9m0Mj^s6HKi8sosK5NRSyIofW4wk6}*
z&|K1<DyP;z%GsktJT)e3bh@*p>)wil4TpmhYH4@2%j(pzgz1w;BUc9;B~z^eOqs1z
z!~|Pq=KD?1E4#LkuL}^Y?0va#4B`Se%u15Z!}tUSC*dKS!ET}%JMe*OyXV%ITWkwP
zU15AC<s(qWWcuHt5rgUhY~jFaQU`&+uB~_2q;<CA8$vB~29@+o8Lbo`r&+Uiqe<PG
zv}zebkGe_bZtG==ZA{Zw-cjBV6FHxHn@#RDBHV-d26VfC#nJa3!=|3R1pKb-R2LSh
zJ~)-v$A4nNWkQf1f2Gxb`ylXhU1Jca0|Q_Vnv1WZ?+w=^MJRB}qW_#RO(7h1H&|r9
zhlGpYxZdwqT;ISiE-w9>M|eXcQ%$ZL?vF(PNWR-1Bm}tX%p@5m87G<?{*v;)MK_Es
zqlr3iqjr_+N4)IuIM_p@T_}*GS1c*V9W{y#=w>F0<n#(hb9$8+b|-&(3kch5Q)sk4
z$n)8(l~dczG35eEtkCUL<vH1<1c?Re0#lJP-$DhSp3p$-i1kMzOZoQ|jipnCVp<Kv
z9pdq`Q8Z!GkSJ{I(PH+pJByRAsZZ5!q55HBB^I4|h|{)z(-W|F&E5bU^rZ^FApRW+
z=m?p!X%*Tz2CL`JP^Sf>!Dn=pq@xx@rJW<0O;T9+L9rnPULz5%=7dhJmKaxAvo@S0
zCRq4H!&fyq9G4|OocSl-?ReQVUjg0XSs<}FfeYV3o5JtRhoyRr--}ePiR&|C3+srT
z&)Q7-P#YP)1@-kODRms{B!GSeP(506jW<hh=@O&tKn7V?R|B$JmPU~M*!<poip!(I
ztY2}=|Iz&vjYIcwXmRw2PJ@?6EB541!Jv3V>q1SBka%VM`RG}|Koa(;`+#z>hK=fE
zi<Fd)KYK>f9?|B6i6$yiaD)L7el=`QVmwk3EX%{wnTva0Js}`}cj3ml+U|bJ7e546
zgXNjaN{fT9MIRA@N#+}}CF}w^Z6Pu+gizTHZ^s;(-E(3||9kYBbEHOvc<_;pT(le+
zuU=#``0vKiiI}LV`^AdIep_qeOJeTzIUbAkfB0V~=~pNs0oSfTq=tXo80qYHu<GA@
z|ICi7hP~Jm8Yk<|0^F5vz_BLz9po(?rzw>w!#lLevWtLY(eTr^3mzBLDX55>&VP#X
zX;e8W$uzXJwhW#tJax3|sjV<h%#XKiaOm|zRK1_nyjksd9C}M*T7XdJq@s!`AMXwI
zJGQ`QP8oM_EDy1pFbyG&Y|J!uGhtiMeeK24ja;IqT8Xn!pa^xWE3R1aS~0&zMuY$C
z>0rW*x_ZXeoz0R7_lb}~f9_fvfUoEo)Lf%Ui`zp*Ss>l6qMOk>H;fso8`Ew2m74sM
zNQF{u(G-;}j;cN(D0TI%q-v${jry(Ec@rM+s>-CzQ@iEypF>~e-O;{jy27nF6}1IF
zqQgd#ANn^L_i*fcz?CwVIr&KTV+E&;#oZs_Y&U#ngWrBY)pDa6Z9+31N4TdKLuaw7
z%(d%)RuWzE3JiQrWA{ASsv{gt6_xtc_wZShUZr$<+Il|XMxo4tQ4ORhY`+F7WLmtO
zDh-V$edLNtW0tJ)w!}}7r1Jm3H%Z{R%_+%dH%Rz^{`AVY+>*}j2#K4~0y;;Jqn}Mb
zf6s-T8RVp@MgVNmp3{};HN`|l{URwe<*6y!SHEZL?&CO_bsGKpY({gJ(`^aa+AB&E
zd}D<HmyPa(r>(M(Ge->cf7%{OiZhi!!M;CxmYbuK8=u<DVPe5WwX&L8w6Jcw4zawn
zT)iTg&pHje9J_YLlIKp$qu>P`enkn5G&P}8VKBF`eAi4!Ri8%s)ZDl^3C$;|%Hd|E
zarI^NjWbBzOdib25^%vungMA#Rb;e$x-D?=Lza<rG!!0Am`W?Cq{^Hb!W_1g`?`Lj
z8d|L1h>RljRRAL^p=b3clie>b9Jv`3DliVML5$ah$lMApq0wQR!A~R2QL)_<sTfsn
zp_s-A>z<6(*=#vKXn=r`n?u7_Xl;$p1Y>p&r8G%K%B5A3@ok|W)bw?dD^zzR?#O9L
zn<$Yk_F_IVdza4@D|+CKrT-i}{FbX6{~2H5E+#syjZP((J^>TvdiU^_u1e<;xBsB}
zjB-%^`a)u|t#3|F&Q$fwgpB6q=GIGtRgX36@9T4kvYC(Gd+eBbrrF+|(VC~LElTZw
zgF<$PN1uFe3kVMQjRH~3uT594bml?qB^WF1fC$PcAz~FIP>ToE;j;!7+MwBqIen=X
zefI{jh^!g8K?4^N7O&4{v^#%NPFS(^EhNdB)8%xXxpKw(mu@~-yrygZxPdd0PA%3?
z8!duSzh2?O_T^!VU1_BzLxGoA5q{NPoGi=j1V;p306leK6`PJd2FIGHmQlEniIB!s
z%zE9?iYOYd2~k>;&<n_Qf6B7|_D!CnVM{bEBr>DE^n(P=N0Qeq=2pPY{J<+owL?k7
zr|OMc+NkAiAK2?25j&B>?a=AV0wgMnK(^8fpU+}^uxPt(8-=Oo*)DuAk?vPWkN7N;
z-k-@G2B3(WYahpkIlEF&hTqv6qv#x^JcGXK3A4{!t~ek84{@qAa;>A!906GEu6%BV
zu@(*bF}?N$<}(k;jOoP3ebYK_{B(L1B;+FY8!8v_7(jWM+u2bdj~gQcp4UdT&fmLt
ztnK1_2XKM<qw1q0<jIF03c8;8euku$N`x#PG8@j)9MSLngt6Kr-{@RS9JYDt@Y{W1
zz_+bl1i7Dxqzf(}81gz+`hX<iO;$Le!)+-;T?Q?Hl;>Ijldb`BRs8D$ou~o4{Uk|7
z-ksX%4vX;@z{`r~*@t!#p~z={pWmwV@pK+&q+D2m6`~g-=*MeuRW$NN1}$%`{SfYp
zZ44Sfu2uD#C6033C-C%B1A$zXvH}ai439&x8U*rF!&ZDo5LPl54Up4V{XTxmh$-d!
z&+_(V*^54<qJTw6Mky9vnRd*&#nHi18^wK8ipli)8=H%R(v|~F3Z26jkSa(XSg;pR
z#Jkq!%MunK^s~}H(bSXn12S?S5FY(*Z(s~8Wyy|shfRt<3@5<8r*ryjCgs;vt@1hm
zww$j6@a?3wOm=uaBI#V_Nm}~F7jbv{DXjiJV8v2qy~TR9ou{$iPiDLl^w*|-ay!8l
z#<>}TVo`kkors%+ShgsZ*!pjwfJEz;Xt>`;-3pB0LE4J`Vx^ftSkDp*>f7c9<A+U&
z<F#6Z4o2viYy)Uf<UQ_C7~8|;PwN@kF4;7xA{8GrO^D&!LKc0ArQnW>`^KsLNhrsb
z6?5TH%_yz9u=((DW+i@&!|k(R#8bw&@M*9vxF;eq?mZ?|SsY5#M0$1$5gV!ISy4l9
z8jBGCk5OA$Tl5Sjg=LB%PtBnVQuNW`PtsbOv#wA4<-^~!Xf54kUbDHk7dL7unyW6y
z!Rw$Iwe8@{HLn={Hfez|rtNF*>3dx|W>&(L^C1g%8>D>s+~jsR)hE+NnQX+EE}2I>
z(#Z?R`83@gi59*u3`<DlDRE~wtwVgC4}3R6h>ijkTo_*Ov1XT(``zS=Pu$Z_vz|P7
zetw=nEhtSQQ<l<GK#3!Z+b+C1=#7+wlf$jSQ`YU+Zwkqsu@-D_*`XRV7B5lV+S+<@
zuS=eWr$-~n3^bkYMfN*F5|fid-Vz84x2ramap(+q9oskjzPykuQ?Y6?%y`WN!Lzd7
z-nxdVdjN<t;*9mQ%_duB^kjy$Z0OR_=FodrT-y}ihLRc6@2B)uP(~ebKAB%ie|DYM
zkIFoYgnAcEEAP-GvD<w_wt1@;zfAh)q$-K}wQ>^l4i66-7-cd0>eeh0Ckjn<n{vyo
zGF50M>1&Q3>J7Qh70}XXeb(}Bvfsx-Ic+XXd}lM%oO+WzrtUFq%O<z=)xf1mXin2c
zHI|!C38MuaG84<p=TM0mOc{(!Y9z#7WYDbX7z?&2yNS+dNbe&5uG;j<Y}m|p6!AtC
zvu!~7EC%;iGe<(#lcsDGNeqJH15-W+viaQi_kOabq6nHT20k~cvkF?kbSBMKe?PF-
zs3kU1unHl|K6t9#*<V;pHBa5?3I4=N6jN+)E62PSa>pxEcX-{N$TOdkTI4}S$EI@F
z=P~06+#YoA;JNY<Ie04uo-JqzD>%|N6>U|=*B=ipE6<)ex3c)EK*^q|Qp!a#4*hnf
zJNmoq8Tl3b!&7+gE|ONoKToSy=zm|V*!b(@i#(tg%kusf$LKI9kbwKa^C-GY<h_Ml
zs0>HlEo?;&stcRS_b@Xd`T1`;ANZTD?lT`?o7VGm<$Y)*JQtBptGNfYO$(t@1XytQ
zIhI&ptXoQ{fS@Qe5-;CtLs=yU@n4AllKWnP(+lB0r=4qOevvvg2%SdJ*6$GWgi+rG
zb)de2{{Ko8A=TLblZ5}3DxR^j6pQLEgD;_G<1G7`t2K4~E2x-DCpUAtS~Hdqnaq-o
zp}a2D2V$00tQALy%((?lTDPOwUX_3}tM1ESC>0YA#lkHV-ehlFuU$~<2V_b_lT<lC
zMEFkMgV6g5u71s2QrF&YYlH=I#U2B%CTl=MTD3d-3e<i=)7YmQCvwpwJW1mn53%uo
ztd6d(kCxKNjbbR2ZA{ML-dVi(%oRY*5+CBldh3gphPK)aY@If^Z-9U+O3RAO1iRDU
z3N@lu&I8z!O?~=&n+F9^z*CG9jZ!a7FJ3*K8J0+;L1%Vwe~kc{Rjw-9V^crslvyJ%
zoy&n58l};rRrIhlOn&pM5$4aOp{oU>3jo%#``i_s!qXGyFD{W9V2dmjW?F@&8p3`Z
zbOEdU7_g<mMN;mMnepPvsb8-4SE5?^Y73$L4Jm5c^#WO7)V-%%&gLE`zq~hrB5hvI
z@BTA1uOpXWgqhuG3V}GRsqnXIsa@3VjDwOGPy^Q;lYhpO-R^E##Swp{=1)Y|3(WT%
z);$edwOp4m#{v3r`f+%D^u*dmgWJ<K+(|L&HphlgU!Pz4cqF)APcv7FNN*>RcmHa3
zOR#ZEyYN?LT@m)<QJ4?e4@V>BN8_N!!omVqObYUX&|b<}kl6ztFyPVONSS;wza--Q
z@dy4*Zjg8kqz^Bcr#%&pHU0DBc|ZAdq`2O2FmI}BUFiYa>_^|I2&L4Iu$+M9KZnE%
z`?`&sa#}CnKw+>10SNx(?uc2>uPU8dW7~|=l&==0Dtalmm__HODUd5t;cU{`ijPNh
zDDOn)kze%`LeFthveo_Vwv3&HY7?P~jA?PE@K%$Z!R?dn`SYoe=@Q1!mBw2#VIR^l
z(iPOfuG3R6_T3(1kSzJ0qB~ZM-Yr<#+AwQOP@YkbZpBwpIFf7??aeB^VvaC{Lx)D8
zyNpd#VO|R1xdc@s4QpVw{`O*1Xr?Dg0g+Nqg<b1PlMpN!Jy&jRTn*&wy2aT@N>gJz
zXy2OD==bp3o1@XI)tw|o>7%!35P0ntrccvZP8L`xU$R-PE5F?A0uuOyi9GT}{yZ;K
zP=F^($X|Bx_G)<$yZ9PNr&s$StcQ0DBosHeOXgNcW}_9+iiola)->j%cDWj0vtg*;
zG@%;&f)oGDZ0d@*Go<xL3iRN0cfKMR&CPBzW?K6|ThKJDRT|c7`!*#A$YI%3^<J09
z_YV(b4lBu;YJjG`7-J|qP4$k(&WLD_WrflyRz8c@1qC`5?rI^MFLx75-Ph~<Frs6x
z$R`mMzEPIo@-${pM~I7Gr7y9OtkR_X#^z;Ok7sIe&{Ae|8LWqcG1?7GqG97VPi9g>
zYKxnjTE@c19)-H?fqg?lQ^;ulD$zf_|K$0h!w)xQHaw9^&a2X8<p>Uii1$)UH;ksy
zh<}j(cftC0mJA-A`>5GQPFm-cq|kA*Jv90rXPgubg_tS|FaF(#uLr%EV)X9ClUEqp
zV>EJ-rL-RPpi8a87R$8Fv~DCrW);ucI8d_YZ_V~#_I;?F{+G3mm>6FrY-|FF1XGEo
z0IDycfr@zY_ma(h8_rFQh#Wm<XD*Z;c7+BUQy%Tkz)~Cla_{5ZWY*f_Ff|+!{uQF<
z4d<0y?2~~VF*v;OyxAAlbQJ1p4MqK{n|IgrLMD9p<ZWxR=gw+t?3q39N2^YTX*I_9
zwP_$Tns{?H7;xBgKETCqj>2X56H2ScNF&7WNoKdo%6?zM>;3Sh9q7ioeVJf>`zjr}
zc}Au%MlELy91~0t@CEQoT$;;CE%4PW9BW&gvIV1nRTsGr!|O#hGWl3^{FkfNiVFRk
zXXe!O!zs+nRBY<4ClA^&MC#-C?BXECvpl>GNPT_+c)(ui&aP0X|0Utt<DTWsHcJ9<
z>tG>Y04f@_1p}`aIXvDq84w4|H=$>7<Bfr$#`MOu^20b>m3i{)+i~0%4z9iiMQK<a
zuQO8$LVMGl+B>Dn$n0+Pdn6>Qod;3ynA0N|EN-3jf5T{uJ|=d2^hrtbcC&<d^}04x
zW%pkwJksh_eo;DIH6JRl88PI|k@q1s!gva?E(;5~cdd54)}>a=iOG=^>__<5H}p@I
zW*1D*#0%!BeE05Cq7cy%0G99dj0v8q4D*;RHAuv#-Mm3>MS!R@n6oj;AZIl{{FuCl
zIA3i#^kLF}^)`}>BO3nk{FZ>fnB{xt{b#{#Y*$NUc{e?qs?iWiOJ;4+A8Cnq{X7S=
zPQ_OFFEwKCT?FW}=bPR|CYODO@M}!QY*~beM1DPY!7*rdmjJ8E`er{^F$^UmciYTX
zzJDM4#p)Dp@lM+@0iunVF-^-71Cw)kEBxp~*zR*%c%r3bzX7Q!V_V|SR~Z5`>vhDl
zwi8V#@dvuYEVsYk6Wor{9^96*y#KgGB-w5(G)#@z&!hJy4BaIzC6mvR7GlY4x!BZc
zGg;xPyT4ikHb-t5m5lp@U92MbKF-W^z_6*@>SYDr%<Qqsmrf7LFU+$4M#-bT_Z1IV
z#<S@Cqrx*jd9t-37@^Qb-~r@qNhKEx-vV{{-sy<o=EP=?+?}s^-@GM(Utd`P_}fFr
z+YN#jW(WOgq7oWN<F-#wOwvtB|27+FDeAW?fkhW|INgx$<uo^zK9S1J^+}0H9=O-q
zZ#jnf3j~{zpEh$8d-61#^2r}bYCt_+gx^Xk6}kU3j+;}m4`=!KZ#wYf@_j(3{oKby
z8J5vnzamuv%cnZvse{NLJw))Z2AR#nt$sUc{|rYz@}QRT#WwCJZqGPT<EPpDPe|K$
zb~wBV<ZAb<7Q6b6VL7`k5o!l(`3y!184?@%z#Oe)a|~wM2>!XX)t`KnET9RDRoQ)E
z>krhB6o$Q6$l0R?+H+r5)A%F2vX$>2|H9>wba?&rF^zDuVw!M{>)|H)5dN@Kj^S$(
z7==8LOjtN#7(W)RL$E4rsL$)0C>9=vjZge@#8E!mpP}CbGTG4km>51vg}`OYg(rrk
zna1m;4ZSA|D+l`jvDq`fQ}{9@AN`q4G?I^wJ5oHHZQ%$=SSCMu481(pY!(6#R$x@i
zE%t8IS<sqV25@9ZXQr|lEVWHKVk%1f<6Ya-Vj1kZBT=Dg+Q>JMUBtWKpR3i#Tz5Jj
zN~keJkv8mnfgk|l>+GbU`4t!Y&5_5;l1}efmp+-6{;A@6JjAf7*Sv1Mjt~l<3G(#4
zx+zd2O-BsxX5Mk0X&pSx8Zz%88WoI<a?wdOMHIUS+>sq}TK7qf66Q^n_5?G2gzJT_
z$feOcx3y*qz<g8+T6YgnncK_|I$yrZX*!CM^GwzF>y2OjcG2bY*g(;c!|$R|a};%Z
z=X`wjh8Ye>XIFTX+c@O$3SVHjb-fg2MN#;D1s8G%G1#V4PXxN*Pqw(}VUP%Z-_ZA%
z^8P*b5SLAYg64|axqcj@oN0Ck=tRn<bSH;Y-K1RN6-<1xxxRqk3}%o9f?_F?d`mDh
zXHwy-)k)@@6qiaXIeYH&_vM~6Umb+6PA4Mn9s|Az5@z3klC$>8b%NPw1al3BQ&nT9
zh2KP<UI;4|t$+sxG_KHfr|Gw#niZD{fh)8?G>~Rg8viu|k5u7^J~ZGS?EM@t_6`7Q
z?z*#qoXyY%1cpkpmG#igvD>YR0_<1+T->YaK=|Lg6$NHCd^P|a&O}JJEGfF!9B<20
zAy_Cq`6-5T9_cVE-ID;QbAs`h^klZUbFk7Egt~7b31-M5p3y+aZjHqjrhGbITs#@b
zD;+UtZu`Z8xCIg(0QSDz{n8E~byG!S(yh1J>_L{#;#Bb=))%M=uA&(;Io>yk2G%cL
zS4ht(66?HdN%A*3^)J|cn)HNQ`8U@%FX$p&osKE_Dn?A=z?Bklyfc68Qx=Zt{E5V6
z5PTWJ$Fx|e)yQ`CIBf5}K`(=1!wOcHzy)R|xU1xJY?0?2TB=9E4X1^RCZ?ldMl}M<
ztN3cmVpwR@LG14fiVVG+_XMpfTWBOgNS7i+bN}eQ@ojMGw65`{TswEuQuevusF?ZD
z-Isy8=C)@dA9nBCs6tG#CM@UP`u)NG{*F1jhbBRQw`H2@u6GXmfZG}8HH#bBHwpF*
z*x}Ed=>l1E1b5tJ)Pm*ABIA4xo??mbV&wTQFM9_P{I0J*_r=^}r8OiDzKRM4$z}Zj
zwcp>~h>D3JGb#U&|Nf?<thM>-NJ<s{wjEeEe~Iq<)a9~UzCic%)Vm(H;J?c(*KVYk
zUH+!ZKx8wY)U1xTSm#BEI)-@gsfEWo(O3+uZU$LmozGbu908@{03xIO>LtZb6e2{u
z=UZIBQq#oC=cwF3(Nd8)62j#M6m+V<Rl&PgM`B~fuZWm@XKnAou`G$-vwedQt={g}
zX~ZA|;C>sW>5#uFup>zNqx~oJGp}>}-mv#`9&R+S;p3c?;ZZ6vZ}OZX*oVpb+0fzQ
zPJ5vZ_7QjjRYAnhSIfj~dXZG=8+%Nkc1ppcqQA@83a5%vxihEwt9Nuh3$=zjq9gh9
zfimN^1~Pl&R4s<s?G~tO_n3ZhI<6gv?`=upIq^+cGF+FYx%ldCy8`UB#|k~x^3{jH
zp{|fn&EMZ+BC}72lD)i-2t-WDWMl9kgW_OMJ!zXJl7L8Wxzv2kvVv2=q=l|Oo%yeB
z4e4l~KN(y{HTRbJ7TvlbIrlMD{o^UE!me3YN7IHG_SMdveitdFitX!MS4T7G6}q9F
z#=l)!gttdY{>*>M`G}>Nl93X*PK({u00P}P4v8Y&-YNUEH9gbK5Jv8ecBKXTZk0{F
z($2KzuWApust<WXnk7S<Ie=>8jjvhU3<+_hc~{7$rz3Qn2lF{A{l1g!uawC{cM}`i
z`>1=&z1&Dh0b2aU(g$F(cW!nFXXo%e^a`rQH+F_KL}#80IV_zfUf9<x9z5P#=bdKl
zT*Q1xw#5}{c0cp#vE_7>3E#cWzGQFz1H8#P6ZUn+<j``YWTpKPGOsPGRW532M5Sq=
z$ce!r#NW_8+#k2VQK-tO^Z;;O8NN?ks)IeT`6FQzI<x>1A%E%A)M_l>K6pI;qI2so
z>vknocP*+*9{s`6r^*P)G87X2PWt)RsC`fFnD~h;RS)ee237h7xWBNl>I_1BX&|Zu
ziV(bP)<XkLg+gsT3gj_Zny(*1{t;m@*A-feB~Ykps7Qy{hfedl+b?D4;>spM{odMY
zGIr%3^{2NTpp6cPR&Qne3^>V5#Bs}c?xtaNdU&Uj7xW?&XJCq;2*o00F(^%4y-vWv
zCyI(&B?XI8bB&$5xX?~#sGFe6L`*JvJ_iJl#H?2@{ECXAnlS#O1&hmB)ykN-7hIZU
zF<&~Ti6)wfd&DH&I@(kEkz=TeFt*)fX1W#6ko*n_DQ~8DWvlu}db)M%bwF{7tc~l>
z&U7Dq#JNk@D{#WYRTN;GP@yCkdG`)c#*h@h5^mGhI`R4lBw8jy`$X}oUAU+D;&d-g
zl{u-IApdKQaT?w0?~Sy+Ur-%u)e(tH4V%~Fp$h%7LG~ItESy@&;sNlWENiEFY_{lr
zF#JkV2a|MYn2sO!Cn)SO&TcQ$`oKa?L~~DANoXaP6lq&qCnp5qmTZ{?oI^PD@GBpg
ze~LUosP&RZgIj9lc-S)349`ABbYt3D*;nm@U8^$o@{B$rA_t!TfixI~!tL)K9_1F7
zJ(lg+KjIQc#Z{5|7{Txtk&8g{P2%vRs+3e7nOLQgc;5?b0)hzr5^`GFk149mgPcj^
z5d-_y=B~J}rd`$Bx@}}`!e5b*sdz3JC@2?<A`~A#Jo8q1cCH=P$VImIr*82O+#zMf
zMoW26WufH4s*O9p99Yyip7J)3U`kc^af=LX#BUu{^u@z#IcNwbY&a+7#*)*w0SLMk
zv?&DRNr87~xt!JdWOpoBw?T%ZoJ)#SRA3@*IySaZwov__<ZRy`(ss*~E8_uD)jIOx
z&4ktx4<=x?+QFa=Z#2oN;Y%F+G=3ozzVjNk$&MmQYAP_4nVejh4>le}DS$~Lc=ln@
z%B88BE}34&8YJ>elv(?__-nWt!!Z1=186H;#KpyZD-(vvgoQO9Zig&zG8MHmK{QJ8
z1p2SBeQVqG>a<-Fws<z!G2<rmGL~ofg1u=54kTC@G?sQas%uD-L1h&QxH^OKlcn{T
z$h&j0l_(j%T1`XnKRlysyS`>WoiF!(&lO#Y;F&4t=zB^^O5;X>>I|ky(;U}OLXhlO
z#rp*Hib@(!X-!jfOY@pWmG_oK;-R(jI&E>1Ttl_5M&C%Bs_&`G-}QCwQ2%B?Xkrtq
zloQZ2>>KPC#7FTyJd2~?eAtQL%CPTTc;WCqd|Z$Q*st}~<iD?;Cofm0^1&#ViO-3n
zb^f`|#YzmLMTXDeSsQvQ;}cznSNf+A?7je=Zz|`D>~)25{4_EW6+Vp@99&X~g@>gS
zjfTgt^$2ecJJC?2(e1A~kwI&bs@+#Pnb&-z8}t$PSD`}mq0tp073Sj!Zg+_0c}_3S
zlk>!E0-9uZzS8$WaF#a4US5`;>s6gi9b-9PkIh$et%t|3ko+fn?_b2`+sZ#l!<H}m
zvzgrz>Gk=8oBwAaHo5JfJbpHst?^w*^Y_`y7D+w57M(`^m)_lxN-@j->*s#0NO<)Z
z|L^eC2k`$Fa{LRP{%!q#ybr#mg7V7##+`YOC&P|)V{|`@oi!RmbiY+5-{|9hc(&1R
z^T7@8K3_cNRY_#<&iPjt{#oa_(-$Kmccg4Jl$sQ|BWorTX*N}u!?tKXU9$FLUH<vN
zkl^-=ng@$3nK}NvK}*Gxnay$)K<)o@o|Ea58H!HCovTiNjyfc}!$be^b{~T%@E+Ic
zWrWjq`5%xjFt3IO;@huzf#V<icUea@N~tBrjRq6j0d@O?C$b%|NV42MlAyLhizB=0
z*Mj8Zg#HL{q=GLB4YjJoTu|jW%;Do-r#mb)U^%%?R?Dc>A|2+mHGvcpu}tsp_n7?V
z^Lp)I;wD_Z5RWw(!r1`1hqjykQFW$-;1(n%+%6R8@X@LUvoPyAgWM*d2YVZZ>eU`W
z#_2R*$KqfE`Mgklj;~J7^<4*|RwAmy7^dyX=Yu%eq#~rw^3iN5Q2VD+`-M6)ZpMZ`
zhEsYJMWE0sBM3JBEN?PZsL-^y{pZyLsou^APz!QroH#CcExs54a?#F6Dck|gmgWAc
zJdP&X94<$6KPEix7T8Dpol{(RZWD-zRS?g=E>ztay7Mku=s411Hf)gtMrsr*H>cCs
zi;kGRMt`I{zVucYEKieOwHT4+*2!pR{a}gHN%g3H%HmgGkCT<7YKtz_!R_g2hjOzl
zF5pCHz&GX)nqyH0tQT=8v`oSJ!V4gQ>dQ=2CvJ~|-rff~3(ZUwDVf#l8odzjCu1j0
z(WPeLb}612?Jl-?ea-&Nj;6&~MGCwIaNDnR@)*UMFGNQy`y~(Lb95-TO3JMc;(xoV
zCwgZ^pCl@+!=_hW%%%Hkp-mi@A}1E3?ziX8SG7~7!{t2&$Xi0{zBlVLOHr%+Rw)sT
z0S?+H5CT%@Px4(KHknREqP@MR4xKLmDW<d_>RE@oP=ciXnt%y=4E?6@-Zs0>0^ZcS
zG(0lW?0IUBK9xi_2SuNmBnj_v6GK39oY3|RgM+Ng6Kj&s6<+VP0&=Tc{2v>v!c*~u
z;j&bGZoAuyp>HX@aC3dEKd$(qo2#!;+Y7m*eR^7~_Bg^AN@3i*9};f$Bn6kvZk`^j
z_z5{;sr3htBO|1(L?b6$k#c^?Wc!W<I9Q}O=Z`JWBd}lG5hu}pQ^pp1(sJt+vcj?y
z0PbAMVRK*B9vTBimwB&M2cUbGwU4i!Fwm)0VMDK29lER|XKWS1`(}*F0y5BD>>oT>
z)cRu0527QoTJdD2f#}oLLh+2^rgifw2ieF1Q47(d8{dj%R7zDfk`2GBg519L+p^`W
zm#NUDlpJQsGfU+vlczzqYIGU39k@AlnNaSJe9q+Ib>0GNe%H8mdrCW#C(_5a7zu6i
zVubjZyH`79B_S(`5oS#wV<dkWnRywHv4iAEw)5dnf)Q~=UOTtXI3~B3N_N&OcC>6-
zO03rVT2~Gl!6*HdY<}Np0I>NtbRjDnM%*eJ6+Yz|=je8=kiX3U#AASfYiLk#N4Z(I
zQ@;$W&kJ1_Gu!T)7EI#X4=uqhjVjGP4h>J$iHp}q8~HzeG0;H0cv3~R;*zSwx!@L(
z)X_vTF>yW<`=gVaz04n9L$71_bPMzTzVR1YxBqOzXJB`0JRQzEL`0R_lMDNn8*$rj
zuoY;pKuT9JVb&wit6Jx-$qFCAZC7F!yXyy@lPgsp>f??=`J(?Ppr~Zs8@y3qR}+gc
z6$vp<Wr!M7QEhRSF0Gu2(U1Q(OY;x=WvG7~-{oGeMj;*r0}sje9*D;1A>|Y+O+X8c
zCaOafEB4J#p!IaYo2|6d_;=eY@0E&`TR9_fvPsiOhy-j<hKu5MN;rMJw!UKHKmGAE
zLNDsy%ZCE|FppxXTjx>T@2aQo+R#WzNoD4tj+-ZCG?YrT>~gKs#(jcKu`@^tggjT$
ztXg75du_XuYy4&=S?{Z)1IG!U_%s$Vf2(KPkK_nMtvcwn-<3TL3YUAP%8H(a){fao
zsacZzPB=Zl+H?(-uMM3QAqu$gIu(kf^3)8B&2ABk5=P`}8;ubDlcP@#yf>Yu^}&38
zs1F69rZK@0f{N#UGY<y{#VMBU^wpQC+vw7yoU!+b|GnHwFh{=1vw3V6|FZJSDZ!_h
zgw&#UHs4yEeFOvX(o%jib6lja0D0Z@4sl4+?oTFPQkz80rz=9%<S(5%taicojy*r-
z6g%X2kq{A;A5Ub<1)hJ|>oR@vbo?C%;p8gak=NMcM<X12lIu@zxldgZAaV(1bDO(7
z*&iq1bVZZmVcJR@JTDS_taQCb{qXy4iA%d7096qj)>Qm{K1=p++G&45a`HN`Vir_j
zCyO&8VP8Aw>v1r22ZR()uKUw#Rw?F?PrV3=6s`R1rW)8mIaZ``O^HN;C0lhj<kyvI
z{k&$Foos+vV|4|Rld=BF3LvD8C6x;gpeWID;FsM<!1@mlYt4I4MV{>qDRf(3HxVY{
ziZz82v3Yc9FV^Yw_lrrXv}*^@WsTZyMttwwYZIoni_h)}pQAe8Q7<iCEf7+$OE%H&
zI2D%=Chyv3^7gWfChlDFSnIX)rFBs9d|FoVb4$tdeB8|vK0{qn44G;&O_-DGnE2BQ
zh47v&kt0QuDCLq-8Tqj#>aa;tD*{ES+tOOtQo%NegIQ(&`1`ucY)n01Ck*n|Ibdrw
zROQ)M?BdW~V~|cq=4myKsh2*Hv@LQeVcam9tg#q~t{|Nv-R2uUog+JY;tLSRiqwyo
zovv+0v9vq;LhLb)F^X?Bscyaq%B-*oEkvf!giguVg8GKvw{#{u`t=>rIP((|lwc(P
z<oi}a#>SoT%8aXD{(O2xi!lg55BV>*T%eYhk-Czk^NHrRmWepW1S%&L$$(iRkYsNh
zO$zNcyUd7I#Z)vvVQuqB>A_iqJ(eM%+PqV`5aTfflfPke_Xbh5YfnB$cJ5h>HkpCp
z_0LgO-2E#bg1;&i)1fABgbF4{U8a#U2^?b8o0vQR*Wuvx+Gq6-^OJxEEdbdpzEmtV
z<c}9o6swjhKX_pLeZQCQH11f%Nj+rg`H>_c-sHAL?i=1PSgPc;!m(HN)IeD3ppi(q
z)}AXv$VYxo@uc`lQS8Cp7Faq49kSo4DoU@Zu6i_c>iN*hNgpTew-FxF-FGcz#c}qL
zW@&`s10TDi5%1t%lWwZ~W#24X4-2hQl{k|%=HC((tcuGK7T+{U6+?^e{y9VWV_Nr`
z`6|=*YRmlVwPVP=IR|?@GP@tG4qtMu!KOZOb^yYvELjTScPuL#?mm`^@W{a}Dbv|K
z6YcPaY)z`ab1N$)YC|;)b-ZRylWkI%>V`T82^8`BOQ(`k_N=BHG%lCB3ReqJs)C%`
z*Hcw96O{(<`8^Mqxe6+8_Lm~m?oal>ejPTLZ*^yUbI(+1XSPSOuUTr#lU$u<a?go+
z3uGvmTiOj^Z*8YvepC|co)^0a)js%oi?U>2Fc~72s&v>QA9xxU+6U?Y-|r3#&pS!V
z1~$S@Z;WSb;ld!K=sqt7>{nIh<U%1jLVRm4&C$;#d^V^%BbR5e%-Ta8kzwNm|66#h
z!Txf6(H{KwxKxe*D5Ivmi0GW82H1=bMJv~z0`bY$ee=mN=WCi!eL1#Mh3}WVD$NM}
z%AmUsJ~xNK@+CB7jft5`p&gyl+xZ`oQ@t|kPsQV_xD^&FpBh3R@ng-c8~vQ7dpjni
zid$Q47IIMyT5hidIogCth8{h;?%^%>>cE>7xmJH!S95efuq(UnvAw|MXDECJVQ9lS
zQKW^WWkxo*5SGNE=b0=Unq1j<$d&JN^5A|ln{o$WnqbID>2Iyczw^5Cy3)NZC=`Uc
zwIymp=H-tuUk0aUp4czUnPrjJzHT$A#5xI|FcCjKU^m8(e90oJKX`-BlKr~5dQR!v
zMK*`0<%rO~))D?k8m%f9iYFH<n({K-3--AsQ#(ZvZ1xY0bmBRG7ZcYyUL4^@n<{|6
zN2;yn-K6>itCKU@k_`TSF-<HI2!i`GZqjSl3N{up?V3AN=Y4gY67(gnedwq(lc$ck
z_g&QXIG#_buFlI6@Q?nP-&cep;J@h^Vg&uUdxI6-hm<^aFxrGD!s@YXtvL$ngAz4*
zV_BIEEn7c$INUBaXxL0=1OG4-#_b%7Vf)k*OMGJvf@eoG6JSfaJPet+?r?G(ma(A`
z-0&qKT^CBmZQzU-TEe%1JqyMo>1=2u?AzCU5p4sX^3ll{!(+R(@(#HF2yJ{TQLix_
z<DBIk5f_Zuc@56VHcq{xnkv7?g86V&Af1L|3-7UJbKTLmpNU{(5#Jyc?Cgjkf4e<5
zcy6%#&u8uO2K-sRqN$(61TqmW#U6mMRj>6D%nH50f$E6iVzqoiso3m7uaXO!cUHZ#
z9r?bqHt~}%u!e6ou=B}hGmU~Y;nN(QRt=zZ{Rn?-bEIBRT%6$k{+kKCMYzh;9EU@f
zbm|DOGgNMal5d9hizL6_JnKw-J3sDQqV|V*0WHHieh}qhfiDZFw}v6^Z`rZ+TURe}
z(6H@R`{zG99~dyTMl3RgIf5T=3;fG8<&By`hQk51-mvdjTQIg)Sp<#nitP}`N2%y0
zcWNo$h|eI-38dJ8GNj>tboR-tJ#(y;N=|TZPyPXq5y?>`+H|vE(DaG5Qv_k*)nEid
zM8(5N{Rlr<4oDx^vQ`V*xY)ZQSFY-`=FFe55sFD(v7};SLk8;xrzz63h2{8A$xG%>
z|9N<L_?pC5BLGGf9F|R2kj#UDEY2Gk&<<w8hNS{5R&qat-8h90+NX%eGCYU$P^uH6
z8|pe_I2Ovyou2cu3wI-xn;s~?YRaz_sGwxUTbIlkQPadXphh0TdR?q!C720!rjUXB
ztLBL%Z3#gHDKel>dQ!gD#_yck<!n7aJrXEpE7Rhb52vEH%mj%Qy89AkDGm;sOUric
z{vS=>93SWNh5Id%G`7_=PGct<G>vWB*2ZpQTN^gEvGK;XoyN9(x4(PuXaC-L=U@(>
zne!hh>AA|*s8x-@y)$@t(t<-{l&S=2hr57XKs0YEzXsIffE&WH?U;lBVOBWEubs5y
zu&aul3RNphXv<_=St4Q`s#IzzKeJ;7qba|_W1_Bp8#Mv0yjZf?gO?;_WJHd4>DcD#
zY79_9oOKEnwoZ2`3*-wG{yqU6hIgh#h*L6iNp*~$!uh><nyAq?p%uDI1adgvgOU`S
zgfNOccqV(MS*u%XM&wPI=g~!53QDS6g#ur*Ra5K!Gk2|h6&h^Tm{;X_o@SljS{0rZ
zi69xVS*wbHp8eYFu2>3}1}|wBx#W{LC#nUWqW$eb)hZ;|fE)VeE91JQ>^bq0@%G?8
zBG*ycWB_Zr%=AF&j-eZOz9f@;wqeLxbf1;1>V5`o*5*5c^+M(Aj9Fhtiqi++(lIJU
zoJUA513;QRm&w8jn&rQ|t9EQ$ew5)!eOhWtH#ZK*pyf4F3X$|8O>u~8{t2)dP2<$O
zy0Gy12z{jhPH1R*HKS^F>iRiDzIK1+#s3B(#pjo7n;j%;+GXxnB(pP8Iq`KhTU9@6
zVnyF$<!1Oq)HKd8Q(C~RE)B$Hi-BqsKY8GNxKNc{-!`hRwC_8>cI{>JMomG_gFc}Y
z8~6HF$(vF;s_aYirt2d21weN47P+tI+=uX{|Im)yH*-N-C5<0+0^?ZZM!Gc9FH)E<
z4iP$Rbnv_L(oWV(GljhS)(S2)PIM1XL!@CHJu2TLv|Db5->W(*=mWf7po@E-ye|S!
zxoL-6KL`R=Mow?ne+T5b9nO7prH6Oz(=)co7L#S4Dh^{QuIXR@o=n4u7EASII?i{t
zbvydl*8bQV;&Xq-jYccA|9&u%m<h^EUjljTF}yFBGy;1n++L5Iy#7>ze0cVr4F`N1
z;k<Z#;f}e$+dP5~5T4D8KP~04MOj_}f~&4@imv1wyV@R4pnXdus|>+2f~&fO2gm*e
z@2fGS<$JtQQS+GPO*dATEpczavv_>%b@S#?gr>kgK;UIdTkiR_fM}tB!8+M{;szUA
zhvjbeQ|-GF<qz)Q-K+oj6=8(X0?FfBYGq%Zo573qcF4aCDBK)h$Weh?JSo6F(fx~y
zPkw#skSJ>X_ej85Q$o;&Z)ag`%c+t^TR8kovg7{Ry+Nh!!~z>GvthUSs_!%D<HH&k
zyl3gdgfuC$-ARd8IU~6d^%wuIU;Hl!9r{f#7QEh3Q+TGUsb@8$4Mk%GGJ%6uGbG^o
z&L<b^L+@3Z)iav@$dj?*TWzg*eLiElff!y8O;JFCIEvw1DM#uUY&h7i&&uucWP7{$
zE!s;ZbLW#PsdDex7t^8TC3+1OUDdzXpM}sqZ-R|2tlZ}^QgFUJblKRmQXxmI{Zy4D
zKFb&37|Fzwx81%I`=zQH)s&lIwTgaPSlAeRY}XPuSH?BT#uTUUlO((2jXoyY@95Vn
z@;6s;nvOpywASmJIq(aY{rN>2P2O<`D`fxZ>VE68smb*!n{yvLFRJX926`iDFO(}?
zBdC>AQ*tJo5z5H8Dy!(^QbllLqvK*qds}e|Ke?LC=OQDWCQ5|lm?xZC8Cc8y{dH2u
zp2huf$|X~PSgg3d&;os=adfLrb^FnxCSo*I&spM3cwv=j%V<q$MjZ`G;vEL0D6NET
zPvQLf@-&|b-?ux=**<&NPv0+_+n53V)H60~Cd)-S%W&AOStg@3{9Pw&$a;cL3ZgtP
z<<)DU*K|u(yDj6*Y4|PW^>X6JFX}AswDj|)>}7usEP9P-Lv3V~mEVfY@NU3FG$a@e
zRrc&YLWuG0{O;^RU&6_WP*LQ%^N<u31Fc%0FeY?Af$h5*h;Cc<<-5;3f=2A=@DH!f
zXD&o6oKg5}X1|8|Ck=&}KdyvxUTkk+JD&r7%?9EnGo*=ot}ynBEiI{3hwqjl4BBpl
zy)-VV*scv;F}NTd&EWqOGLy*;Cg)6AUjv-pzou}ziXRh-cMinMvT3$h5)cj}6jzRP
zRk-A9%W<`sQN|?^Rrn_({L{D4HNC-UMko^X*>#bO=Hd6U`cO9y0!upmjUAg(ftWu3
zH3HT3%-$hA!($r$F(Bu0d76ViW&M<GP}UK17dR;NKcaNQc9?qoSMN_96t=9x7KwK?
z#!!NHvP+aIisH`~WK2w_*yk6#$G0@wFF1E58L6VDUY8S+cKUbgiCuKJ{-+={(5<lC
z6mHh`IVgc6p!wT(!~WqIP4KM~-O(pL!BYwXe#!cX8rC*#*wb2!P{*aINHLz!e<XZ0
zq9z)T$32$W8WmV5y{u3re0&*rS5i{`;o`$;|Hh0rQLYkS4lCvyUZmZMWwa(YYWCRt
z@0-vs%02MA$UGb<2WRmH*W<xiCo`B_-SC*~xojn31`9EFuGqJSp&}ZO^G0xk1Ck>4
z*=+AnO?F)jX9vmfI6eg(t6%qVDThjq?~KmK^&7=kdqc#ol#FVduO1*}moPBOsqIf}
zrG(+|9@C(DZ!}Vdl71otR+@$J&PsfWeo^C^)cirUe`<=th7tzv^K@3`&HqTqnXIPV
z?D7d!;Ei>=vMIj7sbj0|+;1x(g`{BMPeP)|s^fXUY?A%X)c(e1yrX)p_dKpQ$F;bB
z7Q$KcJNkI@Usj|!LielP`yHDzIL`+Bp{Tq8rv;EbYwo>5e0&EP`6WDWp=_?fiWy0!
zMklGhUZ*nVUX5pu8bQ0j&zx8yZm4At;zr^=;BmzcDg)J3W?{nZ1eh7D)%Fo_*&uP2
z1tBuE#ri>bf?8Zg@SI!-(#SG9A$kV=``N%{&3))?0Pg)+%m;^c1YTY%%MEKhM@7zP
z!9$a?$Nh-TN1)g}rdVE>(4#pk_q$Bnn^<90YUhi8j06z&Adx%vsT8H{;?(oVzmGz@
zJ!2jaG=4e_g#cQl?PUB@*lrdw*9pE?BCho(H06!gBC8v1b$w|YE5cjQoaw-ERY4cH
zU8s8MGbJ{%iEE_N%s3tw^7`%Uyq7~?(;Q#KSn$O8+P2$qOJ#iZePB%TlEr3VC9Z?f
z0%D1E`PPc*x#C}s->L41MGXMtyeiCWCi>0Y^RnvW+eqSAvHQD2C2D!7)%>z|QDK)?
zmg>+#vb+!3*C_t!tdQNd8zfj`V$8$DfBChbFNl8m1`9&U5ju@b_}gw)U2WTe3a$Ex
zLFm6vAIMAK8Ha~QW;yAkhaCWUp}?gQy6-C2d@e4FHkJIoe#XXmp=<UcJ>6lMOll?L
z>O+aU=he9fpKRo)ac_7-u*^}{vow3loJwngj}Frbj7&D}n)32?{=)lzC9-6?57}g6
z!$R1+bk}m3gWPiyp`f?EO6q8duX|mqnvPbz?Atrzxxlo~5c9oWA@d!sjdnM0TrbvD
zDb#-r@_M^N%Q^Zcrti#=Cw6PFioKv*YlnWl?>!+b&nGU{7IlTYzLWi4X})$9q~o(d
z?)Bo^y2hw}=u6@KJA>=M)nzl}2F+R;=ejS~FK0!Ho6Me>yfs7IvLuG9$lk=K0Uh&$
zXOU&bY869Rm?Q8%sB1-WLKz@plw~t=8oYkNomk2_QW`zK&c$cIi-P^=OWM$I%dM+e
z+CbSNCu@tGHWfKzs4hqk*^t-OC6-G;j94mfL`Q-xE~)6Vb1UWbd=d&fq(3=4t?W`C
z$(Yk;bvwm9%D5RP9_qX^S5PN3x_UvTr$ItvO&fB2<S9_6BVIN$i1qY<Dpm;bbircW
zJ(2*s(Zcl7@;Dt1$Zic+`&eK(kbG0T;dM3Wr%)-uaBBMz8;<P@O8Sz}MkDof(HM<)
z6z{b!oypOW*3m~C3>$&qon`z<a@^J#N;oCxIqH?`JyQ=|%PCn~|7Hb1Og9{eK|LN$
z@3xb#AXo@>qIA+a|Bk=5tu$;Ek=lHu-0$beuh^*Zw4XWp>Jz+qPpHxC`#m8g@oMTS
zNo0k+rNAO%8G_6R)<!A)XZlVkr)?Cuf5n5>t|HSUVSWmLH%KpF+OSk{n5}o;Q|oVR
zF><HzC*MrY@BnDpg1!B@Kr{OF(d>0y(fS^d6bka5mV)^BjIX&&{|w?c*X~UU53{_W
zjNWKE5AR!>)jP~OHNXCS!445mZvgC0=CG<cv3S71NPjMZ83WT!ocz22*q(}Ms{0R0
zP5p>e_pHjoKcdk-qXRaXWBJnignT{qla2_=G?d$7?Nd5N#0B|8!R2H(BZOl~w$G)!
zB%&);8cmIf>P5EQMYwSZ208X}SZh~G&|(+R0ID9)g?K)9lO?sO-O}sMTX^n8E0olB
zBsN=j>=e-6Z&mkC^5hA&1I3xmEX4bfP!x33dgRkfAMT0}*0Yp<nq?%}X)-?5%Ttjt
zYN3=vIp$y^ppL5AgekO0=&Wo0+PF)<d_fFFf-Br!wwR(8I_>?)wPAr)ph`n`UNsc@
zj<!6vbg5d*xDVGDvqtOP=lIj0i&URWlr55<gJ|XXHZOdynei+VW<?7O4hYn+84Naq
zX=U`DP&6-353~3q?=0K#78#@|tBQVO6E@aZDD%HRnz4!X{96-ml_hR;mRpAEaR?F0
zeQ~C%M9gTs$h0sy{DG1k)CY}POIWD;y<GCgwa8wMI9n1Pk~|TKiC(%n!1Hy1ueKA0
zzeX(eu$new_xZ$Mm+3qbog?c>gc7F12$g2&B7C>uMGr^X;o6>i5?_a>FMaChPJw;d
zuferi*s?^Nh2+NOL%JwRiLPLyz4DT+Or8Rj1^Zm<mVkBJZOe2L*~k^j9F_koOd)Qp
zlgZlo69b1g5Z#<7-GlEKuFS(GZJRHu&q}69%FP?afJs7nrd)akQiGhSDF<6J_T}7`
zEtc$xh*^DHS7yRtEWWf(y>htC|HA3(N17<=z`U?|-AUEiBxaw!fpWUEkQI`Nzb`C1
zR4(0?QR_lTK2aol^edyNganHk=$$1V`17>CD^KGkBORev9d<eq{2gSzMNvn$ng217
z{vy7RF7@3o!ikct@Vue3p?r}w22>_s*o`V5pE(&v_Gl~-V_gJ%rso6*Ki8XEWM8PF
zoAfRJEUYXxx+=YW;B&bdt7mBv=cEl|S*$Rlt^F4tqiBnq1RDFl+z2?29+dU_(WiE&
zAnF&iRZ75!kxXyWW8|@^#NfdO#J#wJoJU8LMv@b4wvU5E`2-6>kDfk06?Ijw+e*lW
zcUhqi{fm=CPCjq`nbGR>1mg>xbCSacfAoetA+Cd7X>OxbOI1&y#P@PzvRIli|4>r;
zm=Y<ca?CNSa=lspG3(J4u^|h2M+|DU<Y8^=l&dwzEYhgpwEi8o8qC0ea*reZJToJQ
zkQw|8zhEm17N6aLHs)QD7a^wC<!G}X&od57xvGqMcp-o^X}PnKets7Y@B3}7^-SfT
zeUVZ|vU*}JVTEo3^e3*`A{5B=H)UizUjO{9kVm)VHoVYc+*3b6hwd9A;(~lnJ_v96
z(k9xP3!!X?;P7*W<g^xn)qzlQxyD3qkg1JjH97)J^$Pu>3;|9ZdYNXA84JcS^R{cM
z17K8SMOI{KEN&t#g~d8mHZ8tu@0{7@UcpsOI8GGGaQ12Ut-+uCDNs%s;^K-Wu&d4|
zUERiWY1Ei9%4xIj-@TJJhh_RwsZ}g!LL4{~vfdC~I5<r5DHF1?f=jL~+VFi!knu_`
zKNs75QP_u{JV4#Ng}Zzbw&FvZc3U)KGrX)zi;{XPH#B6o6j_QA0gU!G(@Wv3abmiT
zxJuTuGuY4G>HQ~)169%*OekNCw~XL}{6}@~v1k0AzSwu>`F|zd*yY2PD&PTBH2e)p
z(PuOMHj~v1*M{tDog!xyNi2gyL=1}#xI&OQ3(Pc;65r_ak>9YS;Jbk7X2t8>MxMs|
zvUv?tVjC%8tU+Q;@G{E|Yhm-Wx<V1RdHXR(jptWIcqB0nISysqiW5fOso5$@9)TE-
zOw;M!>6XQ`R=$(|XQ+im2mJd9ao6KYOQYL_!rBu%&C8gKFIOdE-Hs9uO}^AQEUyY`
zb73JUcKE_1^lyl{Z1VD=0HW;LJ(aoKK9WIjrKJF?pL|2|)*LO&jz6rtXTm7jr&V!9
z!3pz5b$ng_QA|lag_+z}O~VOWeEX2ZJn#82PN)A@B?Sw8<R%$|=m2b5DV`B0LE}C<
zXF)Yz3$a=53A$utEuo{PEJL=otSr!SngUJ{s`USUpEv(Pm7>Is#(e&0LZjZ0K&>vI
zEs-RDpn;N-H=V|dh|#OOv<$c7@6mTD(Cbl8dFCUGu9*H0NDUX-VoPvZGwBwCPMDdK
zp}iZ?*>=Byq@CAfZ(&2z*$D5GTs+>i8<FQH01ijU0AY9S0eroL5%-)|z(dan>`$u$
z#Dj+bvU(~>8<ODrDKkzHAXfTkX8I^OZ4C_BCL6O=L&bWK1C1tov5|EIOZ>A1D}e}9
z0icds4lR%$QSs|RQF&pkOM&!~R>SY<qp?{p>x&1gDfYbZ1aq(m`l(E3&I;|TE5`4!
zK9h#?k&BTxe}F*P4^{Zv?}f_XgYQ3)=Tf)W99;N6n5V?0>ePsz)GSeJ+E9iI@zYPV
zNOfH}OW4Qv8YoWzC3abZ`>GpReq;{F+Zu}y^^80Ov|#k@<rLy4el8qXI!Htp)eDcH
z0K?=d{+#=lzf4`q#5+*?UufGgSM>H3gCxSn6iYy{BubZ8FCUmXevrFFsj7GlmlQK}
zxhaK)KN+dbt5;hrS1j5nYK*$(p42ow-%#`)P&9T$xICt46hi!aPP&tqK*d?q#v^CZ
zW~`(v17`a3EcK-?_bc`aW6dF(PUy5&mF4$!1*<(*3=Q6OO)4BlHQG@WBASD8&NQ1N
zp<ql?Hp#Ors6$(Kk_18}KZ$_=lk{NwWQoGI;Sm`gKGy4UC^Y<cGm$G%K~Y~SuL`BA
zh(MV+loGAivL5|Zj+~|9yJpE+m<#20eM^mIh6U`9&+^*J2}&(aGQVFaK}>dqs@{Cc
zd|hsJ^p#fjFh`_gszgNH4Cj(iD;92;BF>zJ2pWpHz=IKr^@9DYM`XT_AJaFs!r!k`
zj8ec8lr8>$qD5iaq|vMSMY>Kh(daTVL@E%x4r%>h?TqyHCk9rZn8coeBMYIFpi!~W
z7MXBiB3JJogIT-FsL#%EyShc?wZp>8|A>u?j4o5d90SD~i!(xcP7~AB-S_kY2`Ttz
zDL1%$z6f|fZ(C9Hr#=hTUV`IkFch4CZBNcLCkl+jE>Z>F2lj*<pZkPaZoK!}ze*nQ
zuKGHnVuwKMG=m<=5{l(tiD2uK{b!@wxvzh771QfXY{~0yHIxO%FBvoY^soN_>Twxm
zhUHID)jF&XP2Ov@QNqktyvLQQvK3d*bQ-g4N{@G^SQaemPfU;16<Zd6MZ3y779yIr
zm@5ljA(5{q&%!guxU9t5(xF{sU@WJ4W}&15ycdYcDzkhv&cI1h=4x}w*KQWmR~T9k
zp7!KS^hjm%VRKpkWt9<!Wvl3N4~)++p<WDg(NgKO6yjbC)L&&k1#@93F=YU`W7?iU
zJJ1kh^LY=ugN18eweF<MP2=Gbu{Ymj>iVS2a^_uxeY<jF<l}7;d!>G)`K=AX0#mPr
zi;?{dp_iT@Qi;dsQ86oiY!xmQg>BlrQeVQP+X9tY+k(OzojJr=_PpWCh}u4kZ3&Aw
zh(ma7p8=S=hRFEpUK)DX>8yOPR6Q3}<_CjqZ?RO};RAn`uIp))%kEy7Qu9hgH<seG
z0#^e+4Mq9+f-IeUmzj**R)W@Qq@vg2Zdd;~RC3{qZg7vPvY=U4$i${~alyW(dV=4}
zl1Ap4E~C8D*X;^xOAUUwiPEKq?~TPx9(S&<)!@?rN*2N%IsXGhcgu@8r8NaHmsd{J
z9ql=GB*Z@h+9A==ifmkx`X5=wKd;^mx@&w~yW_SogQLpZO^r>}ZTPR!8c9I~J((Xh
zeT3g;b^edmnU)NJ=A|nu#)(O<XyC*tv*9#_oBTi5^n7}s8=!}Ee9K;KIb$~Jip!&U
zSi0qIXjn7MiIR|<Rf%xNg;h@ZD~z*wsZ=9Br!g%`8zD?qf{Kzw;Oajs8-v&2sKq>^
zK`HqpHaeoD`qd1V%olX?5|ySGxIM@-)&}H_CZK;!biJCbnm13|sJ)>aQA2szNY+yP
zWHRE*BcJ>Hc%JXAjn$raadOT6t#Zw^4JebPWve>1oGz#!D)2@3EP-iKA^@Bm{)9ny
zES30xty*aey@)~?NTSc5R%pn<qe2XUo}H=aUYx^-+JVwZCbfjq;#i38DP+Vf*GDT+
ziUAe;M^4i=KTb^Jp^$z>%utkEEJ~tM&tDi>+1R$1cdRsmduR>wvtjm`I2>T$tR&=o
zAThQp-lL9tds-WBoj>!Mu{rK-z-7Q-vgvgE-MGi!vh8;4r%aZT4$pw&H6^vvnhV!d
z>&6eWv4?2DnO~qB5B$qx^ooSlOi;s!@6K$5{Z9Uy?oK4a09M0F_~YH>3awtlFA{S-
zq5Xt@jHJYx1=hb+G`V(<Dty*^Ti<Crv(}Kk-VAIsby)@q-{`sQ)$KzdH@b$+Ww$Cy
zdg?MBwu<?KV8e7eSd^k4lQB~k-Da-wXi0@f_kG-$ssS-HUPyA*^;Wyq980P+pgd6r
zSC`RJLT{|Z*}9H;A$Wbq0(I=m7GkPpDu8OGo>pX(J6IGxPjl-GzEuc#60M8k8Lj((
zX#X<vYW32J1sGYVFcyhH!cblTF6g?Iz64#cGPT|CzPmq}Hmt(n>W#M{k@CxNr+oJl
z3_k-YdIM}ZF9Yf>o1K~uTgqq{@jA)hUsZbLxc5S(WK2glHgYT50+*@;y3-f3eyn`R
zJMg$urPy)($#(V@Y!fa^={guh7gW{0EUG_KZ3LT?kTqb?SlT&w*XFB{0GuM467*6y
zIqNb6bE`@@7oDrUS@B`plr42Pru~#E<*F&Y5I<^5n&nU{>nFiUke$1gb~cYiOIl96
zPWjW_Dnj77$edno5ll|*X085=cUnD3J|Hu(dMZ@>`Wvk;i9`*1uCBB=x@2UyxcIsx
zcQw)-;6W?Lx_wF^rmebQ;~L@IMfK$)occ*OSLPVZchJMFNk=+IjCeK@AsCfGp)32W
z4bw{o!EOBw&Z?B3!M;H)FB{pKYo<_Ly&<Z+k;l6M$dXjRIL%r+%jaf8l=Vc%litk|
z!NwStU`IFs59aGfwU64L5!BBIp26z5;#y`t8PzV|(co`7x=l>c1zjfD=eTx>&_O(<
zc26{wmN$=h2(;C>WTqtXWairuimJJ;Qg+^Ub^3YblS)^>4W5{$9NLRo9Qt$j))(`|
zTYoR)G-O>V{f1bUq$rHjb|%nq1nZQs8PX&7dbHkL*O}@h$-hsPC|Q>hzF^r@n42mx
zNOR2<tF^5E2u-voL#WJ1*!jw{T-c&fr&(WkwkYxdEl!;M_#-SPN3zX0p}6+IciGFv
z_q4F$@%CswJ5VKXXWo$o!ANN2uS~48E}a<Y@9!V%DV&O!v*|KnZ5%~}^~a0yitg6e
z(JALO!41p<jmGWA0iIY9%wA$j<TnDqjcIcGI`UfgIU2fWK8nZ(&IHcH0knSyAdX~n
zBZW{%M8rYYS1mcxpUtn6Mr@NOc1Up5SCHTYc5U$_q|JAvG=$!AOR=^$*rZnV84bCD
zqSRblLd|V$k<6-iF+cvOj^IK8GvdWj#)d2*_^jY3d0A`Onf}uiPK!xTd=8tpwmG0h
z#UB2wV+1|v(!>QbeKzbj9;>*=;KEZ&hM7%B?Je)v2p;<<*mg@TnCxgIK)-N`2y%j=
zvQRQt(n4)cQzB29@GR%igD~%<93rTOs@6D)q9u}>UTjXEkzvC!G_cNMx$4w^>E-nV
z4arPgXtm=^&dF74zIq!HyvFeT1T`%?O8mP3B&N3e+c(+ayVG4-1_s$)yZ4UxV4C%o
zsK5tzUbnMuH|Y*BR8Q5(iHntz1=8MA=lCp;h>OoBDtY|EMJ(Riv+)T1vHhK{wMV;t
zovSO@_*8W1F6|ABS>bYxmeBIHmo-2^WkMPqGJT+N$WGk=%ij~nL|cua*`hPEOYN9x
z<I$uWzGEpOmdg_-4d~<Yc+M>Z$g}E-&)fU#^e_MUJ>t}-n&duZ1?A}qMk+d%;@$s6
zf751lYZZ||RopMhAJ>*;#!_K&C*%LK7t%fB`2&VhZTs^ab6Z9w9Q={&j3*jD*5S8G
z4?PKcEk>-bzi@R?OT1)F06B$+g&v#d2r*@L<Ng)NYaKerE<Z~*R`*;Q?aA9vHxCC}
zYb*E4SZ+DXG56=gV(dH<V2=WLN5F@HQZC-VW9hb#i*a?@ccZk~eoa|~-Z1|;W`AF3
zlc-a%!<7?`Kq5e@*yD&Ox#F^YHus#lV#q$FX*cX@NIfalShH&6idq53r1#9I$)v&L
zP5Ccbs4-oejg<q9dWMQY+7$e(Hpz#3I?|`+&m94sjqxkw#OZ6UDjO^GW{CKQ5V=WT
zS<?A$u3m#5+^>V)tF4UT#p<E}XOG{txhgiZ;NB|Mc~@L{qBqxohbgbrc5RQSfAAl#
z1Kz(<^CUMh6*``BEW{>GB|~&)NE1GY-AHVgey{AtPVW?adyHAjV)h`%idNmK16;KF
z`2<cVSA9NDl75a`clocU%I`ln;5^Ck=IK@cW~5?;O^@U*8dhb#+1;~>oEP@m!Vi^+
z8HZbfC?3X70e$m{GtLKJ1P|7t-IJnS+I$z4)(5+h=Ssz+rU|m+`N2IhuJHjROH(@q
z%??_I>bC68{NEBPOxfrI$SD0+c2(^$l-otk$Y;hzju=Kn1DB8OdkHN%kME0hH0(Qt
z^EQNajSH;2w-Mb{&cFYT)#qq~4;<*}iAD9BlvjY+SZC~AG4*(McE{xy%O^O+L#CRe
z87Rh|Q?Mq8ktq0-&hsq=r;b>=&Lr(rmZ5npmHoR}0!%`qE<Jg?WmhrK3wZ^`H6&yC
zw7CGi#w3NO2y)n5FydqH*6b?&y5Y{O-FHT>=Vd+Ks%5{A=}fQD^z|g>n92K={^bDj
za&fo4m}|s+**G9MbRITkvd*;NI(S}S^^`u#kS<f*g5~5*b@l-=Fy;GItW~<bdZ@wk
zK~eG8)Wwk)aekrN&YGCS?@O7a@`q%v2FHYyt#2&L!Apy?WnJh`u}Bf-3h;)1O!1QY
z6qs36j7surKV)wR8-HK2L2{pMtCk5aPzp1PFyI_AOE5SZ<H=^|Ilb-%65>cE%u}Qo
zvakmhR7-M4QH_d~#Y?6yO(l9ATBNx)4w`9vgHmpXpB9C4M);1-=BM$gW!~Ax4p)Gq
zlndMFsP#gq{njO_KAa!p{2Jqr23oW(C(~orP|TWtQHwvB`iA&E^gy-%pO1eNcLaHH
zF%?<ut(4+MUdRqiRWhCZh72Une_&TG(?Q&Hh<i6X;`8N9=3w^I#fG6oPF#5W670N{
zJ6bRAIQ5+?xLibB*`9!?ruN=CX2Kq0R6#=0fElZk^yZ8r-4~mNk3KO<il!%i2@T&K
zT@JO&fC5~rNf(e4YBN7ShdKEgN3^iV8x=g?tmawv`i1&ajsXQs`oJexf1$yM(;xop
z(HmM)3`W$@zIL~ex5Aw5_pLJV_aBExeMl`=dM%M13fNO;=OND3HCa!l%EchI4}N`S
zk(KVbp4nn56UwS<eEG}0o7vmg)3haaCZdK6hzsh<8k=3)5wi($F%47X1G4+07D`6t
zcpR2r9cez-@kFm>*~$$3IkX_ai{edF+z?<F6==Q5ujC%B-0(e(SR}6B2$n<%_)65u
zE=Z&kW|{FkfvVc~;<Dk_GlF}oR`t+R>Gi9Ptmq8P3rw5)-8yk7a?8W7G@ci7kyKuE
zwSsG*!GXwsFp#zcIuP17o;a)-_;0AYOMC_?zkgYkV2QG1*ARmWExDsqObuu5V!WJS
zE=ubmfhJwjC2zh}mfcoar&nB4<mRr&rFDPzt|^c%85u^=Qa#>a_7vC|!@N^T`=uL2
z-rqGqY{s=yGSObIXUA`&f9DcBbvrA4;5TKPYJ2y3^}`B2!I0TV)>eE=(~Y(S>5<@~
zJY9ZwHidSr1!asx^ro|hyEQX(3Y}cEF{H*Kkh0Teb}-bf*!kyzNm48BtP9<-x6R^w
zr@ud)Kqe%KK>oi3v#%`6Kc7`_*J^VeHKOPim@??agiS2M>n)&F(0^5sjd)B0=GfS^
z-!(CQV#2EUb^5PxOc@)Q_2UC9Tm2znazlJyl`QL<9b8DF>#iMce#QKlQ=3C0&*;`*
z<;7S#C$eegpNur)F$~$ZFTyU33qp~c;!KLR#x2sSfDPc)^9$XAG7Wp~>hQ$~+k^*g
zRuxg#PeFbAw?Q$j41xuNq58M;&thYLdMAun4OpxgzTwFkN;+$iJ3eQqVWOlfj@5yp
zN`)juNP2_=tdNXYrK*U#UDX)$d#96*57t`+J$McS{e8{k-=j<W(Wo;V+Mb9x^59-&
zUR_!B%83MlsZacJ*{q<&8@?9TLDiAA$K^5|heK1M^2Ps@2B7q}cWdj9gb$H2-&b66
zZzte;_qFfb;QK0cXrsb@nOEP`g#21273foMM9=$<BL4iLG&+k%KpA)){qb9c7#O@B
z8K@kF6i~V&z)GClQ+@OJaWkI_8B86+CDG0#5y15$fJ-8PDZiiam23{qzK$K%zc|&`
z&~LW;3J*t|fz>99)2jZ<cJ$DX#If=~mW-3+L1y{{oP@ZGI{n=r!7!}!F;ud<N+d|X
z6FZFj_(uxmciJ_No8G4D+z*)U9q%9ZA8tU9O)J4jB5(|tdVCM$_ug*$_-x<$`>jad
zzr>Yae{`k${E0%5a3ktmz&%X+{SD7azzbR4S$v$|<3gx4nBxC(;mBcXdvxRwcok%$
z$(MdOu`!WI&F?H|OvK->bD7><aB?YT$8HFuMw@%3Ncy=B`(m12qAd^=&vo{7$G$gy
zRSkQI7l71z;4lIY__#zu-()(@B+%yDUN7R$a|{?VcB*kKmLv(t8Eg`KaB@VVw*M;P
z1FeIN9D7?GT*4%&JgG*g>o(9KqxN9nuj@SklGv@#4I@L1O_oc6sD6yGYIQl1M6!!x
z1b0*(ND(F?MC3ftW5Q*U38d5ZU-RMM1HZDcd>Azw?Sl2y(Hur}%laZjYTy1yBGg&z
z3m{WVdT6#^bi_k?b$?xxUQC`#7>y2VB328ZXE2l#;zwQOfA|w@0xb)FHnh)fu`*!S
z9y|p2pLCGZ=(Z*{+Y9u++-DcA{Z&5nyR_vt#zcLo)Hu<Y0Tg4oZOwhQ9<f-hk8Gjb
zP~bV#R3)8ATgxma$*V5hHD)H*eIYje_&_*sM?1cj$#+(@ny^PC-p*50BZ-&vz)|o+
zc|4B`ZAj6EAx2MM0^V3NN`04)^Qq?wPk|v$yAj={fkBr#bt?0Naf`@N1?RmQ1_>%G
zJZu1+nLZ7g1wJb5x0w%+>|Yz=ljj3auX6DSZ_v9uJ+wa+EeAF%x;|V0Lv>w!+OHSX
ztI=cMAY0)dC3eF2J&KnbIWEIg+WEvahcyV#dH%8O@@b|1P%xfL&o5>wPksx+)X{xD
z4*8`~r25ZF^gQI09#epihw<+rQn&MC^p7X1>rGavr6F8bh2koXr@uXgZQIu_nrP`h
zsHdRQ1-(Q)NOAqu1jN+JlTt1!gi$sV-`%}B-t%CN*~BH#5Y=1vH5ubj%zvVctq3o%
ze|}CM1wq7u=%?9{16Zc3YK7R0kYdtD@noqYHz#DN*z@2Mh9djS21s`MO&SeqHJCwT
ziAMrJT7cR(UUvH}-%w_-;|f3ZMHS>V2!wdmsZILGO}|DW*r>Ml_1W4wsXoMBvg;SR
zxXMSrwp}$nucPcpp7(D8ZwG!lzIO)e4;P4U4u_iGT%mn-{AQ{tS`bq6(FBoO*I0Q5
z-*2ueLP;_3hZ}q^%ps38RBv<<>nS$Pnu7f@EWnA;$A(=xt}4S_Bu+trWT@c7NZ4`4
z&;wLPFrx{6vlCbhrJ%rGdJ+2hu5m0Ft~L;h!Bz~oWTL0sh%Cqs$PmxhPTO+VI;k>R
zI42UX8BsIHK{3Ln6a?Slxs{lMU)o-lS9Ao<lyajQ==D_p+qDk0UqDXp>s=O|p=B!o
zTAq6gygfK&puoYlAN_en?oYRCIedF?KNM6%3w*sOw5TaP8*0h&gkN8#>+rf`Do?+K
z!4bz|Ic$cG3@|{TmaVy_m*n9FAooofe$DcSN%RrO`e9187f8e&qWxK(yY#{6=qIPm
z(w$Qc8~d$#Yf-)>1OFt_NpdcM?~9r<lCv|@DwDrOb+54$z!pLZt{!wfS<b|c@HJTX
z4>EB+89Uf~Jy<WJW-Ig_+l#lVglu}2n?;F6q)e8X%Rdn>1lV^xh385!ap}6jaMp$0
zQ_1Xve`oA5sx?5Izv{V)%J8}al?L5)F`HOofB(|`p;?GiP(@AtJ@WBfFsQ0Sjidg+
zz585+v>^lt`RJ0Q;`c7#BIp}kA<L8tmleXnum4l;i#W_Qo$LE*>!PV}=IJf6+uT>9
zXTJ85(D{qHTa+7qxo{&Gp~ZLm$O=2x(~zu6rOR+q(EYk!>hqm@OJvJ0#v&aNXRUFe
zF$U{(raZRb2h1umyI$_neQ$}w)_bGy2@12pphYJysTf@UulLZ$A;HOYx?$IM9)SDb
zJ{M=sz<Ug6Z!k7~Lz<($`1{5%s=eTNs~>kzd6w{@$2bQC_}o<?+|tkWnj<X?-}}Q<
z0Mj4WF5E=F%Y3Ux9KZ`J(dly@$QHN-1@7d={^Ejas0s+!Pg$l`rw#!p_PS<*9{#Gf
zJ%=c33f^nK=uMC<6<t8i^on91L%2c#hrFfc?LEl@On=PY_CNDC0I#6y7{w9+pe?y5
z8A}46f%W|9WspAe@mz>yF}?9$Q@3f?zZNHFi^8>j<88il9^lHnt>Yrqa1!qiitTcW
z?Ya{De9%9e|Icv<)43ZiBMSsYWMz&ny}M_t&IoK4L_9NJ7eJKjDkM~41+X*{R$@I5
zAwx}%w%(N1OIN)xr5qIyt7Ebi>i>ZNgwUe#w(739_v7xHp4JN7OGiAek2XiW56O;(
zrBOO$8muhhUY;TGuSHBMs5n6rI&yva`<DyJ5&uvz|FD%8BaGN_v~^?SVc@s)V?n*B
zqQZ=YO1#klw`}>{<d1VO(>aUO>`{Trc2xUkOi-t;^w9g5*x^U|c7!o!p8pRmmI-Z#
z+L=f#xX88KZmeDm5dfbv2cN-NU1i&_HI`HK{vY1SR*LfL-bBXRHw@&43oZ^FvH1O>
zj{0R#!=U?cu!!r&gi7YlAGdHHq(131ijBqi{9AI{J}0Lw@%Q#y{?;bG>|Vfpan9|1
zZxo*RNvPv214!hu%<uD<$kvh@H0I0!KIDUf{y{aM&DmD=(!sA4PWp^qmN>V&1>V<-
zq@cGANW7pCl5myJe`Rb(f52MEe%dMvn4w3m-?A7474J3!J`<LP68exU^2@)0kf4|<
zC{V!vD|>`W0tL?&2G4O0Sar%`cdwxoMX#@4)fsS$0juL$bOgTpdX`AA$i^FYIRI9r
zK5~iZFq$DR%3I2l;6AoHS<>Cz`G1_Q26Q1}rX&q`$a~8f%NB%(+^;rgN*n`;s9!Gi
zn-Zo`&wM$M^E|U6%bscY2vZd9E`<ZVNsQ#%f{U~ASR}|QzJ7uGV#!DLrCX8Q{qBHU
zuIW*+(!-DT6gI1Sg)4IFwk){Du`4p5U76u|Z}3#8e++s(ZfSd#A$71&eEg5m6(Tg&
z<AGv&PTcNz2kg3!C+ZBf*O+`sGBdi8aHEpQ>ArN6;quju34v!A3$Kf7uo&NeTSDtq
zRXNe+dcvwPxSRR-?93wl$W|5#Gimdl9XXa3D%oYbX@b_8<C8ZY?&-l2d11tjWwcYu
zGd$$*{q}hlN8$(i?Z3&ZNGlo1!G7}BoZ216Mdvo{{K6)Fs%P0`)0^%lSve)2fEA*w
z>HFZDC&NkCG`m+16aAaQ#}zPSoAcYYRzi=RK5kECvVE4CF~f%ZwSaM~XQ=;VczABZ
z>=qS(;jlAI*Gx{^=2DM+tdonhJ%qfB{F9dpCEmr=x)&+%wt{*H1cj$n|6~IV9kHZF
zyaS@HqRMFBEVB?KbCn(-3TQuD!i9^JuH9*7S>=655YQ1|!Bs15GG#87n&KOdpbPV{
z>z{EGj%IlP=V1i!7RdKIFKqeYBOZP9T9vd(@m|o}cRm%C_}jsE*$)Xl$M_amcI}Z`
zh`yZJg|-}i>qLzOmY~9Owqns+wGBwbIn^xdyZJGvt-5_fG}#NttamS(QlOO|e+@1U
zdbM@RvbnUO?kiEc5jgr8`gl?C0e3VZb0V7@p{%#e>>Fn5MW5U1B<8HU=@hTPi$2lz
zySMttekO3s<1S$c>6knz07i(L&*>5a$Xo2(gkHQPc6)!W3^8H7TfHkK$)e?b{NsJ^
z%Cq8lcDKY^0rz5))iERfMABGw&K?>W_~L6d5<#ndkC~1;W^nngms3-SV6OAcap_d@
zd^KkE12uY5D#?%{c3@tHtB=^_RfI6DMr(K#<SAbJ-GLmw#1e^6AU>`qcnhfEm%iB0
z!NbYv+09hF=~kPJ;7OGbbQNyv)liq+cKhvG-KKN<)(Hw}pQ7p;8<`LoYUxIB&I1X)
z@9H5TW54X4$3T1I-1l3@2OAa0OalB_w|%>TNStR+Gw<l9)fbP?Rcub1Qa@YzN$nsJ
zk)Kmu2tErfp7NhK^z6t{_|78tB6Y;31?ek1tj=jDEn@(-faM@v?$}lmTC>;3Ro!uc
zSbxi!xWB*zXa@HZXI|p5-a=3+&8gUE$D^G*ZrCM0fqC;BDmDI<1<ZD+`<$>`;wEwz
zDfSqLC{Aov7KK9GTB`H3oW`46sW*U|E%)~oykXEgv=8iqF@N!_v3_k#ieJkrry*U?
zbHi-rIeg$lF4t=h-q3Yf(X-95-pI-D*i7=i-W>6Mn==_FUe2P_KJs|UfqOW5#dW{k
zKEJ3+9n`%S_|>OB-r)8Ua06h>|I<Gzj4Ckb#UwCpo?r%?|HJ7m;DkRR@F>6M#}&7)
z=NSNZ-X`b!5Ahgh5p7`vr~ktl;#9273z|kH?K}vl^t^cFz77#NnkN?CvUL=Tn{AAc
z%9JB0lZqyw11r{;G8B~I)>$nNjC$^p&GqTXXrYAJw$MyTA5KOT=d-itefB|%);StR
z^muNM(P*+@i4uxJr_So7Tv!!R^F#<DL|oKSdL3bkcz*H?nd*LhclUq1_We`bgq6_O
zzZk(t4bhd$@94GDV{-Y*Lm6i(jl<&f29#@mFl#K&6i?|GD4TdjCFq~UrBc#Ykcb=!
zHXH2RsW93cJ;&#B&(|`$=<H|{a6uM5{#k##D&4NS=XTUDO)8tB{|_hDwu&c%v)V34
zS-MX<Ta_v_=T@c6^C-Id2|90afQG&9G?ka6qA1f*lFrnXP~zOF%G&e4WQ9gUN+``W
zMpq%D?w22udeBpn#FbVmDcla^fO@F87|PIrlS14Wi}l`&Nl^}SQQ*mTYxBt9Q&r;W
zx|P_hF(%#x0myV97dp~mevx@xMjARgr*2VoQ{c_)4u7S#vg)|}cQy3h&g3XU3G!ck
zW?^kLY3x5e|MPS3V)-Q$0Biq?TiHZC$_%qh;R{5txPOkd|HAW$<i5SIFgW&Rm*z`%
z+&8YnwXl{0N512|y3=R3lC(R5Ghg!@7rZb#m*Edes4(AIY+Er?79!-#^<-(#{xp`S
zPB%!?YBt1@Qng9gUV6%iVZz(;miFU2@(?=~>=;Ca2jx|BR0=vchfKr6eo|3gsJGgw
zyJte2nwk>t-AENQz)HYv`{C;gr$XogN%0eW`;4o94TrIIr`z%JVpW6q#P_m$2B4uS
z?cS|#m`#!$9N&%;+-MGIaEYIG!Ur@GlRvp^cTnqZCnL%k9gXff0PP#GuwoO}afA<n
z{w>k)f<j|*u5t(w^Y(!sGBv@azw!eqa<}uAT6U=$=ECDKa*Z+Xo6b=}@(Om7q2WcG
zf8MnLMhhynhSrZ;s8|KBb&2@Jw$A{t$~H$61Pnwosj`b~PLji;1;>}>Irtnd?gZbZ
zY4tA2({a29T0od3v`hY7He{3#Tv}q!_{B!V8hqOt@d47yzV-gRws9zu#E~}>B6Sjt
zwI}0^>!*T)BArkOvd!;Hd$bz1VI*;Sv0GR(uUTnvL>6rj)AQPvr3bva-l<?A3cbC7
zA*H!9rilBj4wI55`}YRh*XLJfaGYko0H`h7hDTWGN|<Pusc!p)Il&wAMt!$AM`*WC
ztV~>2<TG%3&oATYAMm$v!DAOXm*RGte!P9Fad6C*Yr~#ZG7}LR;OWRnHdDO5`F?c4
zR*~}wipQ<$ixUhy_yFmNUllXzdfmb#CaMXmCv>irVo$PC#jP+Oi9e^+W69>25h!e|
z{}M4Hs<po@ak!Y?yDBGAv}@otK%(e!$KErw2IPJLaHV&D1l}N&7p(`3fLgYp?>dM@
z6**1}n9r4}dOqKs-k+s+<90Vjtv;m;Je=3biw%3<f6(QyzOOAP#w!9O2QErLGq;Nx
zLnpE!OAvRtm<u+J-BO^LV{BUDAI8o<8$Em775Vw*hG4w(EpnK}CQG&&_#u&2BH%SN
zwqN{nNU4VURFd&T*8t5u>P941UnjbVQW*X335)5LI}2fEXrC-eOkqY!*3<&Wu!TLr
zAEX{IfPgKz@lG6Ur^Vs*{|8oh8iRb&U1&b-KGR@;=>G9<>0iQU%E9;bI;-shg;d<v
ziC@D+7v;uUzd6A1!$p3D$ij-I&tHxuf>v)A#1BE)4J%HK_C|1@+mQKQz^x@Lz0B3>
z;AAw>@kI);C_H+%e+bQhJ+~EU$zdu1p|vghYVe`x=!J9*#cpXQq}w;PeJjc)gDZ$(
zWKTh_`sdtcU90gBp(ii2<B5?13L$4ik63fjz}gO9Bu2J^OVZ}L><W~pT~~_+P(@?P
z^CVQB*4?|0-#xnY9GCPYrAlWki6}Zcj(Ds8SVx0?8-;2N_3UDdZ*w+Xwe;Z(S!%uM
zQyw7fA;G>oZT&-lqE^5gj*(Jb7>_a0+63MLYnA6QmG9FYKaS56Ym)Xn6s*yglq#3*
zOC399`(F+IsmewlRl2Z09pbaE?WeN`EXFqHjF$191$P>(?&E921`XHNGl7UqHmWe~
zH+MD?`A<<%dXZF^j78Ee9F6<D{{rpq-xzJojy;TOC9Xphjk4|vF6lUaPZiL7&X8F<
z4si2E#!DITA!T)nmDu+nxkA5d8H5SCKl3Z5r|;Y^QSWQCea;p!n;`2oZR|OU?6+tL
zJArSd{9Cbor)`&0+y-krV=syt#C;uK#`8Y=y6XOIv_9<oa`ro^L4y_)e@FpMWe$4o
zjoG9dA<)fi{`mg^z3d3h`w2^<Y6_a$v`bS<=C3Xz+^&_0Kv9MgTl1u}DHq+L^|Zt6
z%h%)gd@Jb>9sRJm%C(k&zZA7zsML(DpVZ6~!)vJV$$zpbuY2Judjr;=CNfj+YjTbR
zB6Mw+aNe$mi})_8iVhMJatWCEE-zmBUUmpyZW~8-Y@iThA{6$u2U}3)P}3&%i)w+*
z^MlYZ#2dp6;uMDO%A%!lAwq@nmzFtpu`ofZnxAN~(LY-Q@akmN@!|$8hA6k2_nN!?
zc@1STaU~UTw&E146Xh6>y>>#%4FFJuXP`8n&_QhEAz?l^tyi@ewSb#YiOeTh%!PI6
zw5#=7dmenKWI}Ruiqn0Zzgcc)snke}2t0G8zd6E2qkNL&e&u9u)&9Kob(-Gm6XUZW
z$Em%|ACoZ*HDAE*f^3M~TH`m$J;x>D1LhMMC_9u5)e{da(=y8P-g1Kks53^SxpXF|
z9o1M9w$lUUsX4x<)aO6E(zx>W_Vy~*TXIB^RdY7jX|h_=p@hiF1w+MR$mpK7BzDDf
zk4E4`t;)7;GGn}NBxxnQb$-@ElukG8@P&DW$i(@yN{Q{Q-&1Oy-gGxHMuYW|1KU+}
zCbD;bCxL5+9T*MBTD;+8sBqYABi6;De~VtsXWx^_?#?iSK@7t4eQJKzJV8QBi0|<M
z>-1yeX2z_mNR!hsf~fo7rN;xI+d%*34m&2XdNC48>`E&n&#;^Qia2q^!_A9Awt3Q;
zB=RLh=`I5>sF%M$k*UBev0KOiE%#7boBNgY_gt?HflTi3L{gL)sq&>4K;pcoa2frA
za2bWmEP?Vq$Lk_@Dg$=e+lxWa2gMD(ym$Vz+U_zyOP7Vh{GbM$J&hED?pND{@a+v(
z2?zkiJ8p-O`jDs1F~S`(3%GgGh)ka6l@|Ysx`r6{fk_oq>*EXte-xaV3DX22=Qwjz
z5=K6^`Ggs}4URM3ufB<MVx^!b%m);awg(KndBO>gw})->o(ufA6aa(o=G0lwrbmix
zkPK@ynA2t3-dB709{W>(N&kXE!}_+zV_va{%qGmrZ`bpv%o;W}PYX*}5MRJsDF%;1
z$YC>^1%X+5W-n*v;q~)z^FcFh`Pn7)`bFmOi2)aV@PE{)d%)&hWyU)1ZoF&o%~Udm
zJPd&lPiP43HC4xN+6FfJ_B(Is`wE|@8^g`%0xuo6Y(jaP@5?eeX4Dwa5p-8b8D2w4
zs}ApV4wpkf&VTGN>G~9TM}r~P5w;kot^mG}M#yeOaI!(TELx4^sH+Q<;*puqzOIi(
zXTk{=mZbBEi5^1hh8`JV4$!iiSeoxwDeJGl!mQRr4Qb+#yV&VM$pW!@NUg#V!7LV&
z7R(^aj=xBFDw2n9*Z^W3J1)v}^_rZfjEq_LpE>_w3cmeU)I)3j6Ph2=bXzoFuDuie
zYqj;HH7EK$a3N74*7OW)p0NI;J0ZQBUSJhNBS+rEs#V03GDBHo!T-2;LAxZqAcb$3
z7Be#WYr`Oy9&2O|8mWMhHH>x)NMxMx9JVf3FaKgvELBncy{`7Y<XeCEtIXJP*zrp5
z3Y&SbL)4$?#OK$tE2eWs^HG3_EH4v|<gq9N3F>U}q>;b9r_nd@BKLs=zfANOsQ6yf
zV<%+wtexAezK$z)xdf5#*fat!zfv7_)9x53)5z|gfRH~BvB#GuCFCTbtycfn5}g>}
z7Avn}WQS@;#FebB=MG9+_RMnMGZlM(0|wvPRS_tkoMjHzFCy@1|M#Epx}=DZO4UZ;
zF>l^@N2(BYhLDFSwpaovf5(L0EU(o2`?>wUSbNE-@R8PN(qZSW({b#Y9jBtuYPn2h
z!V_2hJ|Pqzh%OeQjWUl;=M}fdhQ3tRB`Q1FM+ZDe+R$x5<o_HP<B{DpP_}dZ$P8kz
zfW14?N%b9r=_oLuJePgIiXlB~f0mnP$Px-@CDwIP-u<6R-Sx-yZCadOP5^cZKMaEv
z_@8wf`8+;EzmWAJwc|PIq!E?p{kUW2|LplCK<H?dn10>!QqszX7;mj_^f-HbQE1DV
zzc*IVoVnJo>h-(p`ZDm%`COmpG~+szXZJe?)9~qD7_Q#-P{>()-8blcn8+`Bc+%dm
zVMQBq2EDr~6R)Y=?|K0YMQ|(uOr2?2Og#Ml)u=meXJP?7L8&H)Jt2f{7=X;tNPY<W
zY29KH4s?>kt79bpzKm}MhvZm<<oKV$My>Hss3BqrAHtV@xl~63+<ddz^N|{Y)<NxO
z(hq(c*HD1byD$U!b7#CdFOo~zVv}}e*(1|F{1oEEg|@ZgY6o~-mj!&O8Qs`-`v@&A
z+_lW!xvdG#iY<7kp+Xs;W6(|348?!&s<q4hMa!8~QMD#DwBna**z*lrMjb6szb#a&
zzp?V^H{jeW^@oK0<G}{%de2Fp_0kMrTELAKCp~SX`V6GwGRHyJw0XOrEA7&rA}Dy1
znt=%|HhOS#n0`Kh*58Vh=2qboeO|tZiL<lxFm|n1GNlcD3z0(e{SCur4!nCzG2-CZ
zh2TeKcn*ir>oxidt*xAj*I3hSx1T(%@R*L8C0A)h&5_8)Wbr#{6aWjLMa0P)BCDhd
zw3&0Fq@!fn0^qQ8qTeG$4S8i}oZy!{S))G)-O;%)JRZ$2`*jnC3zQ+LG}KYZE~9Du
zG5@Eow~UIT>Dq;ZyA19GcMI;pgF`}aC&7a|gKKby;O;KLWd?V5cY?dincVlY&U>Eq
zz2E%kMRn8F)z!Q9zP8q_#Ms*QVWHO>7*q76-ajg%m7SU*_RZ@VKwf1qo>JGhQ7_9(
z1$hCZ`KFc#mBBg-LP!XnhCe>%^9S8krLLWX_f7to?o9ql662IUV-7ZN+lmBSd53wT
zb$Vuf5o9g>F?3JNF1u1k1aHqeZRb1P3NPzv3aRa>ZE6S@Lh2x@J6$R|Nl3=Y0?X~l
z*h1BP^kKkn;FeoyVcVxIejm`JkuO&*W?<peWZK)AzP~qH;%vi0hAQ9591M2<*uF&%
zDRFdTM=Gyq>WIX6hf?iocVeS3<zJR0wh&J8b0A<4Cqr^5D0+iQPx->C(O73KJaGD~
z%e-cg11m~)Z#+dIfGWPy!gP;Kr?Kv2lC~4&N8QwI(ZZkno%-&&KL!!4j~=y4E_A|6
z;3YWDpV8s*oDd3se;lwb1v{ReB5_Mf{^fGb%bHL$X@)5aOp?VQOo-G^GMvnk+ei5R
zOC2Ox7+61Ne=JU+z%lrwbBs!$;F9US>uh;+80yeGv$8OAD`a=4a!a5q^*8Otg~F|+
zGG6GmYNqO!{3fRcCg<^IWB9CSvFV+6Nd1Tureg-()8p&nvq`w=9=GmJXE9H81-G^&
zt?IU#qo46<MYv%?W~o2_&HI10Y)080Ig+`}c+2ztL;YLG8A&Wl20hBoI{T^Q&aJq-
z#AG@Qj``7o)H@5KYJNEm8nodZW%5tS00iyJ5%nosx5UH6vziBTvlzEE7`~;O_+VVH
z>T7r7hk64#oAwKVR4~@n4Fa3(U5832Cu>L8m9p?_W4PYt5V_mdsBP0;>99o_ONQXz
zL^Oo+hdDJaVop2eQ}2D1$5(@r^h;z5jb|BXViXw;fk~uaZI69#HAW!b^5W?Ih)u8A
zhUzPa<3jjDecax{`jftiHFu%Q#@6QhsbPY*>#Kx>uP>#ml@*NJSIVLgExFlp-XDsb
z(`<C>>8um7(J;TI{(W@H_3l3Kbr+IHnyp<`ZjMdf_C|)1RAITOuJ@InT>0MjkE3_O
zdH{s4sM5L~Qheu%1+J|_6qD{kpL9R7ZY(E$Auv>aO-FE5$9+T<ts)Jkze?A*YkbF6
z{CG#tHpXeOrda1?;<z@Drf0^s<J(2p35&LY`yaU5QqFc2sw{7AJ%+TRmavwV;3TUY
zXJd@fuv@v~2vg#s4DO`;w<oM^a<3G=vaGk(2l&diM{Rx2H|#^9wRRE1v$jXtA6Km(
z?$YkuH#5c&-Hz_hhRHYV7HSqL6<0dF&a@&p2%FGmSM1mG?k(jXI-g!a_AtTcgGb{Z
zoYx%A=k1RwTqkN|MAb)Mg7?=SbiE`0N}iIP-Yhp<3k_kJLC?F|AK>*#2{C?lus7XK
z*(St+M^*8|%wZ~iR=%h1?HNVK%Vo+~Va4HTRgn2DS;rPkkfBtc6TE!pnV^-`24ABE
zykGBUD7(7oY|NygEjwBpOfsdK+uJTJtNiv%Y;Zn{P-VB9sPQ|0n2o$&W*ykrKYILy
zR4|}0HYQ=HKx1tHE?KuXDH0BNzqYLFIUCYLTVQ{as4-n&vixRtCX$_3Oho@~eNlf3
z<qs6J?17#KF3J?naupZgPkVs>RK6@i$rNnOd-~$+_dKw$P+}hD6x7t1;vPJUJXcu?
z?3i$IF)`40f*}$vF0Q6`?Y}YvAzL1{o;hQB1*65kMO&$mhg1~|HGj#QrJ|;W^~fNL
zquy#IzcYC)eZ>sa)@<kyytRHq9(Ov7sD<h{cm~%kcwgJXme_(jElt!)F%id;_4_=p
z=3%ND_qAmuuS2I27(%)LX@FLD<jRpJa-hdZNFS9ioJ5f6+ueH3EJ_vCz&t5Y28`OA
z8i^ozbo819)B=lUt0~L}42A@UqufvwUK#}(H5B9=9`zpwQTOjjpG#Z1isv^DA58d2
zEP4}I(XM7{s;QAQ+no4q`c``eg>~%54g;bp4$XLbO<&J;;ymXa!k)>vztf2~!0!ey
znOp~!Xf_-3dpuWBC9$OVp<12FsK#-Vru}~Pbr}~%5GxUz4O`P}w4`~wJ_%{ULHRsw
zJ1$Xx1@IC^$|O6zG|^zg$WNV$Nl%AQ)|qyareZe5p8rIdE0Zo>5PWxZ*4@6!F6lFa
zkJRY{F4SV=;*dpJsJkKtHaHW*qa6fbZ*utF&bjr;(OjbweS5A0%lh^$(zF_JaaM78
z)u7y-ECddXE*W;y8RSZ3U@|}25n@d-yANpkelGA!j%G`BK8~u)Sn8UeN9gG2IKR7-
z`S|f&I%#B#gVrYbedU6nL?-CKw*@UVSE*gvSKR(>_>o%BR5>HUl<ihB)OK#cw_64^
z@ooL#!T4C%9SVN*L>}gc3(ZgyQA|wA`%c;iQ%Vw4ban!#PqLx0MZXmPOqe^8e-hy5
zC(NaH7^R_P+=X2yvinnmYO$^)1HLfWd<MhP^RJbUST=}Et&f{FQgAS6k+q6+Huo+r
zKD<^zO}x%l9!%tt=>~D7^Lrur^K&1`wjzcP!e`h$K|(5bj=gt}d1#!ZHBiX}NXSRC
zf;C#5`}bJZv&Wf6YmBbYQBNir#uw%Cc#Q`~O8C5QvC>~GjY0LBn*2UQL@ozEiEzEh
zl&V{g5I!?HdW~K-e=%}l4tY#`0oc<Zqgygn(k^YB7K19i=q6jWH1cPVdt<-sQ}kvA
zX_)`uvVOto8@VTlv{LWFU+$0zPkwx#or^NY_JEq4w`?@dYmfukw)#%55{NuK_)|v+
zFqsElL>g;Prm`Q0r}p^(dwL=R&an*d@6d!4O`>{vQIo?ZNhi1V@$RHmMf$i(G+T2Y
zo?))WjfoJI34Vj`@U44lG}>P?q0b8oIXFNlFz=nhwPMfqF=#5DvfH_K|HL~6=FI20
zCc+&fdqkq`M=myC!b$5$$o^cKGh(JJ-0!pIPf!PivOnI_qeZ-9f$KlCH8E)-MSOgz
zl7Eg;3XdO-3o);SfCh1Y=y3O3tgeHN8@3b40#pXaaX|rnMvzn#70ZYeKGXLkPL^#q
zOPmX@qdZ&?<jz(0MK*J}3E*|UeQUcxr~*T}Eb6$9Z1N@}DH%;WkGGmrrAc06(1tMG
z{BnN49=DpS(oy$uj}wwUA=AHr18jFD&h)Inw}gGjJ{H8TIY~Qds+=-9Ot`3CrfF*W
zmhq6r<4E-T$2we4U#P4J!oak*s0Wgl$8|zgkLT{9^up8KcPH<QAYkN}^B-N<Op9rB
zrOL`h#caz4yd*W9({&DJW(8y<q`{k@C!bfS`S{aSn5M~InDKhcq_ceHU61WkpY^-(
zpuo4xzH@2G>wByU)0KNuo{`uhmAypSiH9JOq+RxdRqp$nu0POc*qnD|RIM|C>UqrP
zX0`IjZ3mr1$T=GtlE{6A2ZyYl^xs_@+hXfTY>SdwO;e8{Ti%e+?NC9=kXg+9X-{b}
zX6i#bi+qsp=s5sY8NHShnom2^XIkiPcVvl<n;lp7OHwe3=)K0!p_BX0t>RPcLcb5=
zdA{h+Iym=;Mvxl<!%!c$j)mkzg&x!ts#t8?Ou^=ptVdcM$$*<J_RB9!n|-p|g8Ou@
zf@AubDW8vabVcUGpw6q7a86S7`szzKjecu$ytdy>R`*W3wb)1MDnFN8rWy)5|Dk!y
zz$1@*l~FbxT*0VaU&+42U$AFPwSolzaQF4K)(rUP+>jp~8th)eU1Pmp-w(Atv#KeP
zF_SZLf0*Bns&Yzv`=vl@?hH?l>u!~yw!;geU5N--J4mGhz<;gu3q5x%N1~);+_O$@
z=o~_I=x9$C!`CE@4R575Ieu+L9hFq8o!?e~mryU3Ak(3{a=!|M&FXqzJe9}cup-Ij
zkqbZdCBE8fpOx!mB7<8@Qj#Ww8gx>k&#q<>wJ^Ts_XlX=R!>ap`dj(BcxZo#<ztR>
zS8l=Zg=7PA6Yvj~4Q=6OWJmYJ-OX<yB1+0!tnI8iVu5u`*oYNWokK<4<$IpvLTVRF
z1sZMOHH~g)gx{#?@R)iMDmMC?JLW!^>hx(1=;}&oON-2{{o7maG?$v4{hhj_Ksk@#
z3?q$IKxn9({y;qSeVYgxZ%0Nr)0KXZYa{;zoceYot{SCOra)+~&YhT1!}}%=VN~)N
z1Y%L`P#Ck^*i4)dinCGac9l0Ul1q#vb1Dav3|r5TtEu(98(P#$EupA1!G+Q~z80~h
zq#AU*-drSZCaE+yN1eFGh6Eu*;AHc0C++wuN{+-an6Q$}n}C0VrCgVtw39?2NFJYx
z-C~cDI(;>qMbI@*|6tXVYVa-yzD8qg^IHVz(R_tpbgNjVdYwCQx~Ox?KeNiW{5>OM
z8bM8L(S98ayy42lbN1uD5I_l$OPI;W5qhxpbn{FE#iJd*r<sL~mkm_U@vW<4n))(z
z`mhGfs;Sdvw!64)o?Tq4upl%&|HcCVB*ul(Wwl><Ia0)-`-B$<Wo)k5bCFm<A|SOG
zp{QppZgAiuQvLHf=emAD$~hfc0BRV1F0?rlt6l7G9{HU4(cnDjrMMcx^<)W#?h^BW
ztbcDE7=*AJ@9+st?l8>3L7ot0?#k4~KY!%gHK`MOzuX_HC5W2K9l)!uQbX?6bpN$4
zF354B+S?1r?n6-@hx;Pk5gAdu%?C&fB&+K5^<1q=h4A&$gv?amAjkb}V>PZBA{e3u
z%K#g;#?f(KLdXsPG+@z;jy|tW0)~(dKiBAo2?TYfhV>AMFkykC-F9@j(^*yfj$oYD
z_%#sjHmy>#d>=K0*!5l)RXv1w2~NkaWkw!Vn_>xI*4LP_9qf#tRAcM%dveFS0M!dU
z%Q=emc_xh_ZtZ$UPr<hc$Szq`S`m;>I25}hG!$gKmT*yy2I-FHc8Al~oM}R+O&^F-
z((fa_^Q^IXtKh@Od(h)wK-u6pEv;rTg_rl(7A3f>OAa2u{%GB!W&QGiSlA&7BSNOM
zIQ)fvA%qmNqS=N^?%r!{p$L&j4gsL38YxV_lILeGpk<MvWUG{Db*WMN`wwqUY{Jri
z2+$Qtn<3wb$biMiFn~?IdOG8DCrD2U4L#TdApa1}|8TG=YEFvfqZ2*ApRsXqDM`R2
z0-`JoQ4s%Cn~2$L3SX!c9aI+}azvd<og)@*Hc{=82u5|m5_(|*ubYEqvaH3+f*b0_
z7c88l6VT4Tdtm}5sS>bp?S9<QUgt#Gdv%-V!}0o?71dzEM)AsHoNY+fYn|)1qVu2X
z#ZV)`=ZW0G#67bz)4USp#!If;JF-QQKMQ*r*+79(KDf{yL6+tv4GRmJX<c*IRV<G7
zfg~V^u=%yQMtq+*%-1MuyYIJFsP+t+zMh%b_UZUgUI@5MT(M0%XK!`>3R02hY8B^b
zhtRixoYf;c2mk=QEDy%h*`Ka7Lyz~fmP~MTp?XmmT)vEZE9P)_9nB7Y628FB)wq`a
zHD4rQP>FZvsb)UMk`_*_M^j&w(^op$DW22RZX=r;nslPy!>M@Y!~ZceKyRji^Zw;P
zs%@zfj>q<itW*&Gc^uUVy;9$Nx`ogx2=Wf3=?O#rMI5rgd)|w|r`E%Y9JJ!FvSCk9
z&R*SRMjdiN7_KawdZRa0ep&m*W(5d^jc=dNJVr(yi8=X5=(3Hru#kFV<-so0*hDun
zDp)(w5yqkV$^(Xy(&Jjfd=FjyWGiEqtJR$`SM)ICLzRO6(rVUA^Jg)1VwHBA?PD3V
zA=s7ejjdPSq0&o_z|SS7CJN=h8oB`8A*)x;LNtmHY46b#u)<{(=sK_UN#GtAG6{am
zbuXKmMZ$Lh*Il4WpHA=U#oks?rZDp-ZH%`2>lYoFuMb*3-|U~yMV{^NC8I8`(}lby
z9w2$VY?6}K3$Q}6aT_ZeQU?Ktn~(e3Mgl&FnWQq}7Ivmks^0VT5n1e;nI>IBTEo38
z^UXW0FI+UN$nj>Z!DAqR3<kMxNHT9_`V+C~WS0O1Wo|#~%J(TZ*!PC#@a~AU#Wcc(
z$f85w93X<A&)Yj#*l#aajN4FolPTL-(iu+=O+bT_UpV9;wTVZYk(GLOn^mF{HPa{u
z=T6wDsnv+K9hY23kgr14ADGO&X8i=12;mVs#y<m<=*u+K17VO8Y3Cc4k10PZ{uY0r
zpY!C}RsCb6^t1aMkQbX`D(xW+AK^jzd1`8+;_Xx0v-OoYWI=3f?D!|7&F_SO{K)P-
z`pIAgf4M6{0RX~#i>y|n=BPIBUIs>+UI+OemISLH#I?KUxlYV9rYGAM!ylqT&^mQr
z#;ZkGu##Dk1#Wzd*A&?CA>gv66wzgeAy*)Q@mcrPT~F&Yq<hD@=2uL%6SeS-Nx=Cm
z+?7@Vq)d6fw_*QRK<AFE7RbS5@$OUoi5_igxmIj)C82SRcc=m|;UGi7XVj`F#Zfc3
z<rdV}ElH|XsiOJ9$I+mf0^CZau%GRZVTsh)3_?NZA9Z)60`(Nd2<f=rkuyUwNw_~s
zN;;WUKkfI0NwamUG!q)np^Gi8w{AWRd8!;nFu!lXK)TUs00&Afp96=N5HFp(1kc(c
z@5?mKKaa1-+<)G2)WS@Vqo!Mx)DHf7svy;}O%7R%tLU!3)W*JJ@;#%_X9#2RpW%M%
zNk?cJ&-bk2Kd0BgK8rj>Q;lPHc*dy*=JMzt@nfFo%*xnUDRrb7qeZcvJ91_#k7pt>
zA0@Zjv^;6_9Ei0$Wc1#Y0q+0o8yMPMNwxAQOH|-sI$tomyw>9HGJfrkS3HD?3uQ^`
z@Fmn1dINg$9W;dUxgv|6EJj~`waojT=W*$bXg{e@?X@3mcZUXcRBrT-^P<KEs3M`d
z#ZiOmw$1tGEW@p=;Ja%e#3H<k?=L<Go%_bV+7yu^(Qh>Rh-NoaGP(H>buih0@*Lo=
z+EBjlwVrlHpRZ<duG_D4Aezm#)L<2Tds6q>U_HmKeD?O5c6RYC?Y8Oiu1d4R2qn&>
zYU9Kr{QU4J!~WYo?J=Jd`g%oLNOo$ZYI`}AbVOG0bq~ElY8@6^`y8AP@QvT)R1|qI
zJy3Oft-Ef#Gb~}Cv&L;TSr<Drw74g@RiUM;6p`eK^h0I3<j(UO$VSdM9RH>NY`Jl3
zwlR1pN2zcs=ju(sc>Fb>xr28dq?DrqUhPIvOye1S)o4`bZsOZtv(VogTF&zkb@TMd
zR*yWyqygY7kx9Mav2{(FcM%J>n5>Vj-rJ9>&XlU*aT5u{Ts7D|-=55yzkN326C^md
z3^B9Y>R|}XdPV^?=zy0;0Y8>px|*^3n`kiss)HErax~8#Y(Yoy?U?*`-!-@MwCJ5=
zG@;X1N$l?rIGPu4vNn7!o@vb0FmlKt=-o>5angf1LzKnyFWghmX(K$sQy!lN$gj+z
zXg2JcmFqtN(7o^Yb%Db}(>F@!)!JWTjbdwUQ8bEudwQIjLimQFNmun^jebf74?^da
z#S1Cs&3Bt}?msT(<>eVQE;mnM0&rurD|#Y!1!Ln(Sr<eUpcxSkHqJ^IRM(XgBz2pK
z5X2OG+)yrxk#~v901e(al1$mS9$lzt{b{2?y_S8E1I6XA2^h#h(B9tO1@9kV&;&bz
zreo_SA8zLB(k_R;x<j9JPSMVTjIVHyMf7<{TW>St@Fxo7lLdJckVsq5hjiE@TEix7
zmHX@6!?WQxjt~9I`jw!R=fO7v^i6IWzHTo&72_;mWYT9CVS8FeWOEg`&3)GH?p5rl
z%Bavt5vFlY3RZM<*34$s<^Fz13^_p6=-3k$s()N^OB(3l7m$%g@$NM<ljiV=0TvGW
zMJTNBm(zl@?$fU0$h{Z(Tz-2*{4Hj+)q>}Aal<*rxlg|B$3fjQ4|IYc-p8d~V)_q_
zyak_s;XXQQ@n7N|x7?BixSVufoUU+desHzC{n+peMXhZ15~10LTnKB;%N1HzsQu#4
zA&s3KOMv&PfYDy9m}ZV*f6hy%Ngp`ct|1Kii|J@kiWBa5+X|dwR$2ILd&tL#=q|r@
zPj>g)5XAEts+(?Z&8BaJW8|U{r`>e>e?me-Ly^x$#Kn9x<R5RoOVz9oF#G2tpRjv{
zkmZriZ-k8^lua4<rMyWu^jpk!=cRc6D1;r+ug1&kLl4j{>DsLexp+M;sQ!udEN*4e
z7YJz1oWuo#!TB*UF~A>zJZYqsHv#2#SaI=jyVpN;=8yRK`6;P{(Jn4z?Hufb0#w;L
zPakiNE+dIfZ7MH=6K&)q#J4Ku39c`nDw0rpRwF_{2e4*z2oL$pdBgzKN%NZ}oSxny
z9CNZ<`W(gWcQX}g=bSD)szJBj#Mv5n;#Vb8P>-}a;pNQZ($q#Hxl}61E^UR&eusyS
znsIY*u`XG_{p6H$33i%(Jh=n1#RA&;RR8s6t7IIvNgr+;aZo^;A!=~tY2ISU>39}v
zkG&@x>d~COw@|S%hMLV3U{$aIT#sZjnLn*_Fi{HfI#RfKFT;j`cUb%3cYNKIPvPOU
z^!+1Y-SRMvtZ#C%Yg8V8JBa?RHC{U}AUfK13|1MU#mN0s*^0m32Uxa;g8I-F%t``&
z5LPU1U8gj;upoiZYEK0hI$yr@6qcNe?JKbt!_`~P!Y1e1?g&I<&0VG0F$wLV#z7!?
zGy)W|SP#DrCT#(#xYl>BH5W}JGYX_*oLvANlwy`UR#ug6AntIo@nX?>j?S64L-8<<
zGYxVS`eO*@F*RU+EHhSvy>--ooTt3I<%Za(=4Uw$ME;2*y86G%@Aj3lSgjfVOuS8+
zloxx*7OLPH?a)94;RDxa4~*74NUI)b;;nc$wbFOB`I`!E%-X!R2}g=l59lrz9Lelf
z7upXiOK0m|!C4rYl2Z*=DK&1SOc1<K%zk&{A85dW#?E`AC{*zT(~g#w?e`1rDs%|d
zK2k?0(k}ph>z8^Y#rvZ|H8GQ(bU2jFZOeh1!dM_e>vcU>lbf4Zed_r|QU(F9{Ren7
z(x~(BeJv`;bOaHfWaCilVh<Z?TZ&SLqq~jUGceM#IW{fId>BBv^Ge5uj7=YEH`e=x
z8Xk?%@58QDwto>|vnU$eI~vI?4#8V0F898Oa3aIz(aMS^6FP4+0_Hx!?=L>k4>7Da
zHkLXmY1V&ZVAX353j^WqTEvM*Pf9%1as7m#ApN!gx$5l%7TEaD2%9Y3rV*d3`YfmA
zn;4pF%n;**C-iCtWs&$?cHYlb=&*^pIM@f9H`e@fr$ZAq?0XoGhrTeXq*8*jCTQoc
z{h;4k9@&Ow!=FIKu~e9Z*wGRQw}aE=h14TYc4+ePkXa0+hXb^-D6&%(U@RToX18uy
zA#S)Gpt?Lboea@5B~*mRb|Z98W+xRTGH3uUka#syAu}W;9V6q=MA2emJY?AH_+}J*
zW5*B?%X`3q<bstwO&pWz#3?g<w983nH1S94pBqTe0?X@M$%{HeQEopnSGC|b=*8E`
zIOg_;GiD(moFf*88e$*`d)x#Wat@Eys!q;x7*CNT(E<Rf7d?XRJz=&Vw6~uS(P)ED
z`~BSL%G(a2QCqP{dVYdVGpUgX9@Z2j(*?r=`lG~con-3vST+u`V+H57F%2Pu?ZrO*
z;0lWUBhdCJ<E&8O0ysL%0uPu_G^OT@0+Xb!2?p-CT?T6a1U!4fe67vf&A&+}YF6v)
zW|lZy?o||i)e-Q-dL3dyR6V-H_eN7C$eO@4^5Me_(3MDy!!)yP`uFOm=#fN9#%E^f
z2%>;juP4L#O-H__qc7&|5;<+|P=Xlg-%GF{_urxGzq?&`>K%Tv%#IvaWG?l>gHMdy
zi~_M1p?)EPW)k*uCa>xaaV#=gPEu^bBMVB_cgKZ!yz2$Q&}o)JRObYGxoUiHXE~Ku
zlGz|#7bC;Oz8&syv?Hicx?gpiC3f=^kaT8sj2acy<no7FDKMkI#^5)&dlAh@wTMEm
z4Y8FH8Qo@#`76X~W4Sx$xKMLu2^vSH|N80Err~t~%}NOlmPqSXz&KSqN6sZp1ZulK
zg-_5{u+%Wq*(um~&{q-1aKd7@zeE=4Q)AfqwcXpe754VFVF1x-<=7>6l<d!oi9^zy
zX;Wt;SN>-#=wSrmg-}ck(FIw%OVpmg&Gu@S-Vc19_gxO#IJwhsJwENw26VqT94F-}
z0g$(I=N(U5Ase<!);fu!Haz@;Tzr-k$bF-1ViK54a*v}+cWT&O40_pA>3}bcCXTY@
zVeJOVmz+CSZ_Cx9*U_R6d+(O=$78~<6;I{9Pb~4nw>qfdh30#GQfd?R0Z^i!floOH
z3DyLQ2SnN#JDGy1Ws7em9Y;#$6<+NJfXsvYPBfZ7T+F%K1R*MU@wZ@)fu|$U?TpEX
zXv-A!ZRk{Y+CkT`R4E!1#IC&tdDYI{FMJ-SjPICSo`D4N>Q%vpiZWTePXik75if-i
zu)JT7KJWlP0Oa5%8swGLaLRI3pKRDOmcx7Q&CAy=1^33X^z)lPLAbt$f$cM*ppR&r
zNsEl3RNLDaJsBE=ToRAn5X53)jK7#1Ov)V`c)}19XLdbTe>{$K^elgBlaG!DzdFjg
zxtbF{<Kn@kOjDF*P{`;v;3-uKq)>OIBpzqGT(o@Ehvs5rO0EA9&W~aS8f=9Y(QiRq
z_2>xVq#Mt6n3bb1T}VHpT~AZpzv#t4VE=ZHc%bGI@#~z`GRaY5mngEi0z&wSsdmXX
z^K)h2*^kZXAHPO`Tfef3`}H?2*J6n3Mg%JoZS4%!eNt8wzrIQH<)%84l?`0A43QpS
z71va346(&NL2h~sG8{WzG}i?|&89W<?q^v`>h3U<FW1%$bVzgr=y_-p0f#lj5+^X5
z{#>8mUUln8^ludDEjMTUeSEEN5hk@{>h-~c88@}oS52Kau4}nv6D^`~7dJQ&CGGtD
ze6iX^BMnPKmGAF`c;Jv~CO`c?%7#Dhe}!>4%3mT1k&Gw&Ik}(Xa^t5|A-)tj3@L`g
ztZ%MIZYiTNQ=$d;6KPQDmh^HShY70kdz&5SrT=raQbkybuqVfJBoDzgybU>okrc46
zAVC`fa5e2Z%7G@mOLR&mHkw@~Uy<XebEHo^F4r0ClkNAk;h~?O*`4kUnX?3OEgRtT
z`XWPS)1wr1erKz02fKM*e*~VtesfogzS!;`@<L$MtcCBE>3}JYC_XOe2G1I7Ht;^-
z>iq}~3X-Lupm^|@?hqEulYYUQpd>BRD3?qh49;wS0*!b7Tt<NCLBF5N*nyp*EgOOu
zL0HMkr-7@c3==>}Rq?bwsA8SLx~{B2+R}+NTbS#iYk&NN|6m&Zv41g5kVB-0!aYSw
zoC&#|ejIS$y4Zw>R=qt`a@;cXx>#p}RrNRyn(eVFX&fETZIu&VLc>)nHmKb*al1Wp
zShollIY-lmdQ`XYP;6}`{~^9wbEJAP(aoyMuvvJXwzg3M0s^L3j{3r?ai~0=wQx=J
zkv!Vuw?#n5`s3e&1#q?_^Glz}^%tvwlh!29Pb58pEtM{8#9{7V);4({hH16KZawvB
z>y{xTZM;t<ac743j{K;dZ=`-DUkX9py%#RxUrYayN0J2$3xCk039ixU$q)LCK0&Z(
zsGMW9;|)Xf-q&;v?=2E8@7$>hRl0Wpb#xGXky7OTEx(9j_M2ZwD-*S9Ph?Zm@o#es
zW##7S&Xh9MY81Kqhx>t(S;9g!-SBoO88cpYq2CJX^6<vSm10(QfyqUJRuJJCN^pGH
zo!~~g{`$<zs9q6VU0r?rC+hpuBvCU$YjJqd1?bq_?z$|<;tbt?F~L{g3qIFl7W09%
z?B|!F_j}i3%nv}`1VVen@+(xxHjy~eXyj4IiDlbW-7@EEBh84nuH7R)&mbivqM7!U
zw{KoeCUce8trGry?&01IfgAkS-s?NMxBcd}l_k#`X{=!M;{fhu^z~HAuHh=ILd7&l
z7|r)#4WqfOua`gOGg`03z0RD}Y!;DV3~a+;)@{L+S$7a`2S4@EGrQ*qqND*|6vmvj
zbhv<RoiqfDgo0R=sgXK%8NpcOevTTIR9#mtozBQXSlSh|$&K>n6V(7pC9;vsBtqC9
zg#-~~!pNs0yBrRmM5$@>zlTUxO!=Qz4<nYmq(47iQiv^hz4BpI5^v&bDrN2+pF(|+
z16Go+TQ->Tl41jdZ4!{7Tv`YG2dKhHeOEtI57#bQGoK5|004Mpiey_%om@m{0ea~E
zzB4^(1Jy9`eP{urrsLj2b-?e;zqt968{h7%Gv^M%8mu%1eSEmCNT2QQnbnZpV^F?D
z)|C0G=LIfWk0G1kp>SDpCm_Qy@0Ft|)rYGv&Nh2L-`~3}oVeKFLhNMlf{xPgD3lnI
zvXbCP*pCWqal14$wfdJsmW>n?tgQcN<Xy|lX`s{f^dVT|(bBK?<197eYrz?#sOk<)
zarxy?&L=zzQc!XQ?+Ub$0@M-M3|#ZmSX8vI-t&26e;zGeIA7+dx6&tmx+Rx>$ZoN_
zbCkNr?;N@k^0~!%9?}?bUewpuU!jC}rA*mrVLp8m+rfi|g#)(hK{eqGUdC0(zGJQ1
zxcZnk)ivLr2B$h<NGH2>!s+DN9HI1aEtL;qr;FBmdP|BXL5OMz8|-c|UJ#tlQ7=*9
zsYmyitMrBc-k)QTwe~9wZ!L`j0ry2H`vFoR`*;$ja?S64W0Y2f$rm0pcChw>c0j7f
zZVLh%6LvuCzM;i4aIFKJAso-`{1e5Au3l^Hx->vO6Jyp*74f^*yovTV(&=}=+Rb>9
zDo4Ak{@|M<$2QyN9W8z`d8~=NH~Q-XA5S6)h`}7dv9K#A{&APC`eB$2xxTs;j%^ca
z&2|2PeFkz75TKVyNtlubd19y>4_vtK-#<W6e~$#me~<sw_+L)_=lcKeE&t2)0}9m|
z#Lxr#i*5{2oFSmDp5A1(7z`dgWTX(#mVAGIFa9f99Mp$S$@uI32qFj|5fvMRf|R+y
zxMPcIIx@Bdcci{cm10t@V#yvu8A&x}5GxBwQYXHIzsiQYMd^4T_yh@$@!#%<#e7x%
zelunnN=CJ|zQ1pF0042iNU9xF_7zgB8bf;fdhQ5`Y-U!Yq=XlwWJC|=$>{xw@-yf|
zmF>PT5{)Bd5(7v9xYHQnpP>>z)1Uw1`8l8c7cUr^)#YywM;^sOyI#9regBAc(fjz7
zkCY6iVGSxz1`S~fo8u%vsyf`IZKZ+NQUkfi%u3@uGbKD@24^rjDXdI8k*HTAVdtwm
zG;$)|3j#(!W0@bf9OQkYD=0hNw_2-syD5=EBRVKVqtHbo*OjGBF7tmDpO*DL+TI%7
zfPWEi8RI|-dtp27k4RkyQQb3rK#QkU6z4Y+?0B*eo!X|l=#6?w;d1|w`9lqESpA3L
z4+zYGd~wxSY9zMjDq`)~Dx?c_Z26fW_8IH@`e)ZGb>(gFdWgLMGZxvHbQ1&*CE8=l
zcrOPev1BC}OxJ3`=td#-&!QM1R=x%TPZS%po$Z7VvHs!BWL8p3I8Wm@Jg)lOCciba
zIS+R`6D?_ub8+zmYdu4-aG*acsJj(&YmsY~1&!K-EE)%Br*SLhsEOnB>LzDF%nn8K
z09BACa}xM*wkrao6CU}`-p@-LhdW0aIXSrxl7CDmNd}o<zkN$-_TTfP{~0RJE`~WB
z{>^<*mKM*0Y|&bMrH$~diYvAeb!%_0&+x9AzqOg-a6l_tkyN+U0gh~K*Y~Jshtoq2
zgx;DhVQ5I0{xbrW=&M*y(D1q4oo`(kwsBtfjyrW<FKDv&rDGT}OO=(C5Y`apEA0sP
zQJaDU71{uJCzIn<X>U6to>R~x9@DJj;Nsv1o^2mUDw*!Lha3HZsNN;fVbf|}x!Pgz
zxo+a>Q-yjr=mi9=ZHnhM)2hsPX?S;+^TOwJfQe-zx&5Rkx;fZ)Q*C#nY5leaCu;qI
zylu+dljy)xuKNaeTw`SZxDsyuY%Nhv$_~GVPU+&+O59DW5Ym|pC{aDCwpe!M>u|--
zJQLz8!-x252%bQcxpAKodU88xgM%{XXY>+|{02N2!oJ?y4?s(i9s7%yb_rHP3k)b0
zGn$jG)Ho~}>mqjg|3OI_a&3KVI6=)i4_C3&q3D{|sqFbKzfUDZNu)ex0G+4AqO4Q>
z4T)73Rpuy(W#e>2tR5n<4ldMpKx31Oa8YCYMGV{?fx*FhDe9HLAVDmi>`DIjTwK)s
zTw*2CpFSa4#s1|cCcS@2@b5zZONoEk6F?Z1gbAVmgdQXb_7{(x`rOUcx2}Hme7f-m
zzPxoBFs&ZeNElXAQF_$ZVAJNt?GF7EWq%F$uEOBAp*?yrR;|zHeQq|Q-vZ^);OkNG
z8X}^XIqLnA)!GS;8+Q?}F4!)}q?*W2hyH?R{jv0G&y<v7EFNhvA^DG9g!q2EMc2Q5
zQ&K{2e669KtJFg&;KsGRCWIySFxpLx)NJu!;Yy;uJ{9H?T#$_ml(PD5IQn=qfoSUF
z?g%?Yq$as;lvne#%^#{P+k31f&*8n7PWgh4>hhOsy{msLkCisgqs(zQ4<1TFPuqCc
zKT@CX;2Tm#MEK50YZBc*4?|53?ma=a7*XkLiT}9hs;`wjmXnpJWC-sgNVe$Tp)umQ
za2Aeg`}#?&2do<VU011!owfyv4{@Ld`(_I$n>gK=hVos}c?$r-4LX*5r=N5;zf>>?
zG}x$7lV`d9GvR~QZmu(*>W_F}otfaoOXgv2aq^d-9JLv33PUHLKPkxtzk@P9bY9>L
z$bzJcL}Je{;*vPYm9N|jKX%OF*<DEAy*YOUp#Y?A(cJ#wp@?0#Y;&8>4U#Fpfe~jS
zoOx#lg9n##IuS%4U_BB;mGnG8sj<PY0l|jb!OjQIK$Hjqexf*j+;)U0Uu_1%xgLfC
zPAAxUcr*IAO5@wO8}l15@xQYsB?UdPS3qX|0-Qg#Ad18b>(WrET6BzorHO!VUwLk^
z-2L$IaQk)?sebZ>f?kdNpsupb(`rD|t*Ld$WJx*YS>yw=IsqE|%Hd7mKT_$yXm~_d
zE>Z6F&K5qe`(z#o`r5<m)?}M>{QD59a}#%A`{#mS)q*i^T^zT_zZ5x1<^QiZ`yZi(
zhy{_1|0VtY=Ng1`|KG;H|5fa?rCFm^x_DxoGa_@UtiYJA--{ClCdO3B*{H$)y8<Rn
zwju|gV$w}nCWK(c{Eh%JF{uP!HpWeAeiBSq&|WY3+iGinhZlJK;j71Z--h!&Gx+zE
zW+gjKL>J|Ih@i^#LEy}h94)fjr%9}SfVdK5`&`H%gq$lQS(8<ReWFex@SF@}iG7b)
ztW%W<teCD6J@JRr7Fw+3pJ9Zv(GLf&!Gx%=)(cpwSJG$s@p<nk2SQ2=(6VSl3>bb5
zvVX-;gR!WInXEEsYnK?3DQ5Uva|b?8*q|y{b`vpQzjyhJ&6k@6-DE8zDrRW*{Gl}H
z{$+vxc;%Vn_nB~%Lx&9kw*%L4xjbZ09wUjNR>w{rbTbuS@U=70<EV{~qSo2Z^7JtS
z`|iJeaeeOS22^C7MGWPD4(3YGyal|mYGlxjyLKMV?I&}B?`U-+WW__HIsXI4!@|W2
zq<cT&muo&!jOj;o&1gBsSk|t#Ea>>k(NGG|lC7Z$@65M8C5SBz#(%0~*!VUxI4$J0
z68lXK!S2lvA0Hn<6-VnR8Zkq9@Nt9+5`ZGhYXkjxGJW~CCNuN35Jcekuj1)Y4}WHa
zg=8aFPh5RWknCAgfI9voRom7R=mZfsT0hjO*F$0QpI=@f4n{XOH}(Z#YoZLQzDQ!K
zw|1{?;4!~9na8MT4SMx*qMhp;6$68+x1y!^2UHI_bE5!;Zs<JifSr3aK0>Ev)?srl
z@i;fvjXd$Hui`OJ?j}vc&Jr4B8yg!c-#|(wYPAzrEo4Vdz5x=kaDok~85BE^A{};i
zcCg1%b*x7>E4;g2!s=kqLCo3uEBN@q7j!DJRV72o(~GWwz<vAbOqP-6Dj|Lo#^?U}
zA;0O$u1baUug|jes#CH~fMVftm6hxG9xpwvyV_))&8<|4pQJr5!dryZeZlT}g_5j+
z+yGB3GayO5ie+k7l?=nCLAw|=IQMB%1A;D38l^tY(SG4_&YRWJI&jZdq0b)f9!&4Q
z{n$aBE_?rdo=5WsrM9aF%<Qm7_RvOSjnjjSiaBZumkoP5ns(a?EeY|wPej;__ApeH
z_rW{h<Kq6p^NBkuIVbu`B*Y#8K`b?K8#{XeuQ2etLQC@m>}~@S%T;mIkeD7Bp@-v>
ze(eAh$o{eYM3Bf8N=8But1X(vn+?8??$Y4TgUP<K($L~+`yGgj_#c1=IU4`N^8cc}
z|J(RqPW`=}``K5o0{cIb>R++-U&8CZ8vn|(|2p+Q4b_@BP1t|%Dr9B-%j*6;{)MoR
zpOknn<$#`X7#vTlCjC{d7ANv|5$Ja^d4(}k?W9ECH-W4{bMZ{5|4hf=&1>p5A91Yi
zIiV~TE7>}_Sk!m8JT+v3<#zQPVCRRHGV{X+2~c;TIE+_ahOQBWMG+j6QPas@ii%<G
z_+ZHIwIw3wuse_@AWP){=SIf!ROpd+Ig$LuUqhe~lztyWT)ux?I;}6x_ILU(*Ep{@
z-v2b-Q@Y79jg#?EZnNJtMkV0Gyh^IrP5lHJI$Wf)X7f1=DhWI0@Vw7MmYJ(}0~8Db
zT2Je=sYHTrqNADd{W&01Sw1<-K*$PuOj?&|&Q`@TVD<DzR@as~0Fogg0T;q$z2Y5n
zL<LA>gbw#DmRzhir2ty~?y21!&Xz^&zI#&oE{gB{sC+D(75H5~c{33&C#AuGiM4=$
z{m073;{{jL^>RJmp^BDl2mUPM6YsVe_|}wAdNsrn#y35R`VU)`Qg)FL0?9?dBLzfW
z?hcuK58p6cX#>Q+tV9;mtCde`z6#Rk3t$<uQr-@&r*t~e_>-LQVdhB<d*nD=S=*ES
zE|I&6tu${O_A$fD70(%I2j}Kdggtb&HW+wfgvNR10Bux{4a~o1tKurL;=>}@gRg=<
zGl{FI#a1H3uOSAiZBS7`#c2{;4GMgkF2)V|9c7@w#gvaG{Waftx-ODfa<V~1P7LQM
zapFgz*X9V5B1%eFrA=l<p3G6|E=q}w2i_1*-e+VL`atmq1&gqyPfQ7QG*VBVrkK<t
zq?l^-S8+YHL06vYq00(|Zc?k_0`UcI*)0HAD2N-j*(_X#pMpte<>G|~i6`x5&NA&G
z9htmB7pm2~J4UQ~qQ(0w@OIafgx?zv#Z-aux4DL8Kt@OvK5K?(xVp2y-Q&G|LfO>b
z9J1qG?nwE20|NuHD9G9rE_`ji*j!u8MFn@W<(b0Srt>?)&n|OGO1B^}R*>0<=p(+J
zVouahjFAXNI3!w$7pR-`jtmk!kKtJBi6BW7q4wp7%5)pU)b}P-;}p~x`_g53lJ$Ep
z2Lq^NHq7&pPEyr_oU(wr>8{0)$EjYl8sH6#;IVbg;R<%@20%i1Hd|KVMlF;2AA933
zwl~A+4VuZ0?TrXHEOF*+MSaQGxVj5EeFA#R&`T5V#D`5s*<I(J&KV6F7Q{22NxT1b
zcz)Ir%Uun=Mn$=XZzuUfum)K##qX_om`3oJP(1=xf_{!#24$-attQIGA;vS13bgBc
zr)MTXSAd|jG_VjqDf5aI4G&S%2P;s@e*SaXuau((GXF+C3|Sg#d}@ph&umXMkGJdo
z*^ny3Bi`k_0()*%`nL41);)}WIhF?(`#*U2e|QCCkwY4Z-{PpjHuzeYH0ktVjUFLI
zTgHvcZ(k68-B8x*%XOWl^mx$sy2)GoS>Cb}7Jz0-X6gQtfsm`3_%TTq3E=XqT>i8p
zuW8MrR=Nt?-r7DQ&afBbby*dl*>L)0M7xbZpjpOSkjLki<5kE7@7KfA>fjfMAIHQ9
z_eXwr3$DD~<umTboZc7!72x0`%(i32*WmUIt~Fn>>QzFODxvd7Z}sd3c(7Zu-3Z${
z*Eh$64!llvgg)^jtFrHQT^V9M<DV#5Y|xbL<8&!d2xdU~BBF^p7>6D{I|R)jrp~Gt
zW)sk7eA?#Dci*ipN%yT<C2X?S<f+EJ|Ll`&(=Xv4<BdmlANu?k>qNVbe|_1^pRdji
zOQnHHJZ$Jn=XX>Q96(EJe4!5!P6R<TV78A%MTbUzLgclSdb_OU!1op?9M;E@?!*@^
zlV&HbamMG{In$rQlzr(h703ceaStaG#t!^xQ1&Sorek|m!S|2un;Wk%h@y_D{sBxv
zx^A?^3%_w>#e0_&IUZJ3C$?Os?|+|k<{Xug+Wnc5T2YW2=0~T-MT-PDmumPAeiQ$k
zibR%q>aeZ4>@q$mNG<B)L;uxUfe~HRs9!Zl8v7e{|EA>#H+`a(*`BFlT5wLDw0OnF
zua+ra9pw^mWGVHL=r`D4G{Q(dozVjkLmaO1%B|(kHaGXIODFt=bVc%-?6xAM!3o`G
zoiD+%b;<9!s!>tOLw~${gY#bTA;C){if^46%Vb>|(X4Y|S#lwb<?g_i!Djp{%kZ<w
zVefs%l4*Ad6~g&r^-sjPwU5D$^!_h=a{>XRlP0D+6RHQi-td;Ne1?Xd-gY<X;lYV7
zn(Q=(6Q6p@F-*Z?dHz%ie55YFK$W2oB}l^)9aJXlhwbUp<#{&N^%%YPE-o}0Z`{w(
z+_BzDHxllv0)pgM;`Y$Z7r`y?dSdlnh{OWW20!!g9@+cV9ba<LfBp6pQZr)IJv9f`
zL{O69S+!o1xjH;oD3kQ8i<yFXkX=>|_Q6vu$60z-4j0phGj{iLgybZ8SubPOeT}B|
zB@gz&zJGv^f$ykI|L%Z!Q*$eXHd6Sxq2_H?0%t7)^3}vk1_e6L|4g|z#ToacfL0B8
z(m+hjVVMRW-P1*z5!B0YW!{9~*K0)x_+5lbgViOQYoQ#zo!?v-i6z<znP?0_Hhs!A
z%K}Z9&vcdi`E<+;&Bk5T>(5|AMAtS`$N+zHf>iK6TX(jFV5**RxPTx-@R@kcpdQaM
zk_=|u1DoxRJV153&t#z(Ni*I?dN*}+qTD4ESWR6$ScQn)NI1Rbg>k_^dvJ|TAZ&!i
zTw;H3v~GYo!+7nMhk(P#om8O*10!GdNtwtkslvbX)#LhL5=Q?Ol)~MPJ0rK!g8Io{
z-&WGD7m{nN0Y)-1GWrbBu;p_TY-3h9YV_&vYTxdh=2S1{M}?1k=NA%-TQ+}x_Bf1D
ztceyE*zfD}$Pr3>=`5;?3hT5v)j}vw!}`j*qArK=FChAVVbT8wBQdhPF0|t!C`k)l
z?=CRY0^3};)ys8Z>PEfi=NEqPq>SP<qd-&U=60cn{Ve`;^0@Z}cza`WO~QpddZ->-
z^iutubPOW0^ZU!-GT(l6qJLyO3J#tf5}7%xaRH&~);4bsF5y!A<PXphx5i0g)$x-D
zXym9d<ft8;t1aW7e{P-QyPr<hym?@KaMo^z(px9;4d#}xf4Cn{Y2K4z)?=r73iX`l
zCAMdj2A_B6HE&`51#gK$B_#}(=lbMT`T^3;`vaolJY{gEj%$yDant~wG{i(X8dqo2
zI&BL2L>0!`t(cYdr1u}Oq!x}TXAaXv9o5U=<O-0HrN-b<RO_iYvq}<%-k856gq+uj
z<xPH54sRg~chOsyoL&;+*SF%OGg(<^R4v%#Oc4Y!M+l%9=dzldk@xTHD_Z=tyJU$j
zF$t#J8-i;SwtzS+Ysp+%aSKtuMCH_%^Dr^vaqRC`FXyP1HB<g$A4!D8$~2!NN{)Vx
zU|GJ~@Ht7{ce{;m#?C39M5+?ilSem$Xw=<9G<9~D$%^+hEB;4DKAVrD)HAk|cx&+3
zF2`#qVg>yJjdbr9*1w=TZyJTLJDr=ce@{_uv>?9rxk?<-4CJ3KdVeVW;e!I9TaLVP
zy@fu;rq4@YWM`(d<%~P^GHJEb<~2#yO@Bq!b{T2sfJrJ*x)6ucjJdTI2QhwC0j1?1
zg!{QFXj4U^jF+g@bsN7`j&~AIF2ko4qU`jr83C_^n=-!QC4nGw$)F+b9Q9AtJ4Pe>
zb^W(SgEGW9Q|22QHMG)b!1yw(zfw=t9@`K*@!&xkUqP=0igalQ)#XpgpvpJRI`X2z
z{6VuCYdp@|w|ZjiwXEO|VUnoCf`qfgqCryRte~Z!=^FQ8UN_GJ9fz0}`)wjx<qRYa
zexmMvCGb^-Ko^!fX368bk+e5F8_D#(4b}LG>QnfI3Otg<Cnor)6kj3=Z+;Q^>)tP#
z4HgKdW4}T^D$@bmu3;2@eLz7eitdiQ=$<^@6*K=#$Jg%(9@7)g@K7G`>DLPfuNQ%L
z#=kjTNOrey*KXy~gRI9ss@-dS{NbxqFw!v7v2@R&?R*%HpH&)wvd?PA{ja5&fm-dy
zJr3iK2jnA>#jwkkHtz1nATFsJWT<!0*&4_PYwg7KmfWRt<-z0_QftVHltjPX$n-0t
ze60|H>9c&q_PCqNKE5F`^gLd#&A>i|D%_Y9<GA&0JQWmW?utmC^=-S;nRP_Oipa~3
z%**ZhmJYwwfc4-x!@$oJAFHOji|6;;l+_(==9J4AxB;8VRG8#mgs~Uu{0rZ`r&Gd<
zI2?99rLed}c)DUiGDS{3JbOvWi{(bO?`@{F`531sn^e6j#U-6I+93A3FvW5G_VeYj
zicrOdJye-H^VH`%MdV!bPO3LVR|S0ANNz?k75bf{Kjki6{Z-83{sF2K^oRi=k&r|V
z?Ao=5@f&ykWHWpzdK_2OFjj+3*S9kqwK;ql^f!h#J)R~3k2?>PA6b^jVmWQN7EoCm
zrJ8N`62N)~{!#>l0ma`$k?1_(+%ioHQOdrbe>n@UqUa$QeuC61sJpU?AP|gY!uW@_
z2C)dV5k||NO!_N@7pH+kj~ieEPa~c*d$;vU;5aAeK!(sGO57L4(8vz|G9)}$g!^3C
za5_7YEuvEODnFvxvhFek!s5icHzZVMeCjN{)?W$8;ruXy9ZRshokMpcF2A5EMz7dF
zB%WmQ>RwThy{Phwil|swDf&GFTT!b|$<?rjuKA8dfzX76grS{)fOT{dsZnk29X_{<
zEh)81EpjRuR0(lwR1Gr97ABrvEY;YlV(vxPWI3#f-HNq)s}V^)e!ijU)ZbpD>k(Oj
zAc^3v#Z?sP0DW|ExdwQT?h{UT_&km$>s0LmKc(bZ>e9J`jnnep7{8ZY)5S-YW$?HZ
zO6Oii^=TQ1;KQ}+LoggCNzJ!S1$s400<@Oa;QEbJw8`v8HUbg1cO(%W#ZF^xK95`v
z?iFkA5*f|dM^Md)`ah)b|DfDs(f9L%=?s!C@E5O;h)?=q>*d38-SWn&oZ|}8SvBYm
z-n}~KNT}BQQM`Q2FSOcJ2;A;}#6<z5(SmH0Wa9CZ9i=F9Tgg}c<9KIAqVza;ZI-`Y
z_gO!2S50u>3Rrs}TCwkAYINUHzuFi5LNl4U%IvYv>HZ>gr}KJpw`wytT5~J}oKtXV
zB^}aPt=!RF%gDOxRP2}RS=Y?0p~}q_jC<C&+J4FG)G2OVesV~oi@B<8TFznpznxgz
zX}XY!Skdm1di~i2X4uVt#{_(TQ-`>x{!y9#9*bdBzjF`D36($s043F0WZy8}y?c9W
X61#|IR?g>t2l>fLDM?m}8~Xnr?lh<v

literal 0
HcmV?d00001

diff --git a/docs/static_site/src/assets/img/java_logo.svg b/docs/static_site/src/assets/img/java_logo.svg
new file mode 100755
index 000000000000..228b75f6b419
--- /dev/null
+++ b/docs/static_site/src/assets/img/java_logo.svg
@@ -0,0 +1,13 @@
+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<svg width="256px" height="346px" viewBox="0 0 256 346" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" preserveAspectRatio="xMidYMid">
+	<g>
+		<path d="M82.5539491,267.472524 C82.5539491,267.472524 69.35552,275.147869 91.9468218,277.745105 C119.315549,280.867375 133.303389,280.419607 163.463913,274.711273 C163.463913,274.711273 171.393396,279.683258 182.467491,283.989644 C114.855564,312.966982 29.4483782,282.311215 82.5539491,267.472524" fill="#5382A1"></path>
+		<path d="M74.2921309,229.658996 C74.2921309,229.658996 59.4888145,240.616727 82.0968727,242.955171 C111.333004,245.971316 134.421411,246.218007 174.373236,238.524975 C174.373236,238.524975 179.899113,244.127185 188.588218,247.190807 C106.841367,271.094691 15.79008,249.075898 74.2921309,229.658996" fill="#5382A1"></path>
+		<path d="M143.941818,165.514705 C160.601367,184.695156 139.564684,201.955142 139.564684,201.955142 C139.564684,201.955142 181.866124,180.117876 162.438982,152.772422 C144.294633,127.271098 130.380335,114.600495 205.706705,70.9138618 C205.706705,70.9138618 87.4691491,100.44416 143.941818,165.514705" fill="#E76F00"></path>
+		<path d="M233.364015,295.441687 C233.364015,295.441687 243.131113,303.489396 222.60736,309.715316 C183.580858,321.537862 60.1748945,325.107898 25.8932364,310.186356 C13.5698618,304.825251 36.67968,297.385425 43.9491491,295.824291 C51.5304727,294.180305 55.8629236,294.486575 55.8629236,294.486575 C42.15808,284.832116 -32.7195927,313.443607 17.8287709,321.637469 C155.681513,343.993251 269.121164,311.570618 233.364015,295.441687" fill="#5382A1"></path>
+		<path d="M88.9008873,190.479825 C88.9008873,190.479825 26.1287564,205.389265 66.6717091,210.803433 C83.7901964,213.095331 117.915462,212.576815 149.702284,209.913484 C175.680233,207.722124 201.765236,203.062924 201.765236,203.062924 C201.765236,203.062924 192.605091,206.985775 185.977949,211.510924 C122.233949,228.275665 -0.907636364,220.476509 34.5432436,203.328233 C64.5241018,188.83584 88.9008873,190.479825 88.9008873,190.479825" fill="#5382A1"></path>
+		<path d="M201.506444,253.422313 C266.305164,219.7504 236.344785,187.392 215.432844,191.751447 C210.307258,192.818269 208.021876,193.742662 208.021876,193.742662 C208.021876,193.742662 209.924655,190.761891 213.558924,189.471651 C254.929455,174.927127 286.746065,232.368873 200.204102,255.11936 C200.204102,255.120291 201.206691,254.223825 201.506444,253.422313" fill="#5382A1"></path>
+		<path d="M162.438982,0.371432727 C162.438982,0.371432727 198.325527,36.27008 128.402153,91.4720582 C72.3307055,135.753542 115.616116,161.001658 128.37888,189.848669 C95.6490473,160.318371 71.6297309,134.322735 87.7437673,110.128407 C111.395375,74.6132945 176.918342,57.3942691 162.438982,0.371432727" fill="#E76F00"></path>
+		<path d="M95.2683055,344.665367 C157.466996,348.646865 252.980131,342.45632 255.24224,313.025629 C255.24224,313.025629 250.893964,324.182575 203.838371,333.042967 C150.750487,343.033484 85.2740655,341.867055 46.4393309,335.464262 C46.4402618,335.463331 54.3892945,342.043927 95.2683055,344.665367" fill="#5382A1"></path>
+	</g>
+</svg>
diff --git a/docs/static_site/src/assets/img/julia_logo.svg b/docs/static_site/src/assets/img/julia_logo.svg
new file mode 100644
index 000000000000..c608baf05159
--- /dev/null
+++ b/docs/static_site/src/assets/img/julia_logo.svg
@@ -0,0 +1,10 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="320pt" height="200pt" viewBox="0 0 320 200" version="1.1">
+<g id="surface61">
+<path style=" stroke:none;fill-rule:nonzero;fill:rgb(0%,0%,0%);fill-opacity:1;" d="M 67.871094 164.3125 C 67.871094 171.847656 67.023438 177.933594 65.328125 182.566406 C 63.632812 187.203125 61.222656 190.800781 58.09375 193.363281 C 54.96875 195.925781 51.21875 197.640625 46.847656 198.507812 C 42.476562 199.371094 37.613281 199.804688 32.265625 199.804688 C 25.027344 199.804688 19.488281 198.675781 15.648438 196.414062 C 11.804688 194.152344 9.882812 191.441406 9.882812 188.273438 C 9.882812 185.636719 10.953125 183.414062 13.101562 181.605469 C 15.25 179.796875 18.132812 178.894531 21.75 178.894531 C 24.464844 178.894531 26.632812 179.628906 28.25 181.097656 C 29.871094 182.566406 31.210938 184.019531 32.265625 185.449219 C 33.46875 187.03125 34.488281 188.085938 35.316406 188.613281 C 36.144531 189.140625 36.898438 189.40625 37.578125 189.40625 C 39.007812 189.40625 40.101562 188.558594 40.855469 186.863281 C 41.609375 185.167969 41.984375 181.871094 41.984375 176.972656 L 41.984375 84.050781 L 67.871094 76.929688 L 67.871094 164.3125 M 104.738281 79.414062 L 104.738281 139.214844 C 104.738281 140.875 105.058594 142.4375 105.699219 143.90625 C 106.339844 145.375 107.226562 146.640625 108.355469 147.695312 C 109.488281 148.75 110.804688 149.597656 112.3125 150.238281 C 113.820312 150.878906 115.441406 151.199219 117.175781 151.199219 C 119.132812 151.199219 121.359375 150.101562 124.070312 148.203125 C 128.363281 145.195312 130.964844 143.128906 130.964844 140.683594 C 130.964844 140.097656 130.964844 79.414062 130.964844 79.414062 L 156.738281 79.414062 L 156.738281 164.3125 L 130.964844 164.3125 L 130.964844 156.398438 C 127.574219 159.261719 123.957031 161.558594 120.113281 163.292969 C 116.269531 165.027344 112.539062 165.894531 108.921875 165.894531 C 104.703125 165.894531 100.78125 165.195312 97.164062 163.800781 C 93.546875 162.40625 90.382812 160.503906 87.671875 158.09375 C 84.957031 155.683594 82.828125 152.855469 81.28125 149.613281 C 79.738281 146.375 78.964844 142.90625 78.964844 139.214844 L 78.964844 79.414062 L 104.738281 79.414062 M 192.882812 164.3125 L 167.222656 164.3125 L 167.222656 45.277344 L 192.882812 38.15625 L 192.882812 164.3125 M 203.601562 84.050781 L 229.375 76.929688 L 229.375 164.3125 L 203.601562 164.3125 L 203.601562 84.050781 M 283.226562 120.449219 C 280.738281 121.507812 278.230469 122.730469 275.707031 124.125 C 273.183594 125.519531 270.882812 127.046875 268.8125 128.703125 C 266.738281 130.359375 265.0625 132.132812 263.78125 134.015625 C 262.5 135.898438 261.859375 137.859375 261.859375 139.894531 C 261.859375 141.476562 262.066406 143.003906 262.480469 144.472656 C 262.894531 145.941406 263.480469 147.203125 264.234375 148.257812 C 264.988281 149.3125 265.816406 150.160156 266.722656 150.800781 C 267.625 151.441406 268.605469 151.761719 269.660156 151.761719 C 271.769531 151.761719 273.898438 151.121094 276.046875 149.839844 C 278.195312 148.558594 280.585938 146.941406 283.226562 144.980469 L 283.226562 120.449219 M 309.109375 164.3125 L 283.226562 164.3125 L 283.226562 157.527344 C 281.792969 158.734375 280.398438 159.847656 279.042969 160.863281 C 277.6875 161.878906 276.160156 162.765625 274.464844 163.519531 C 272.769531 164.273438 270.867188 164.855469 268.753906 165.273438 C 266.644531 165.6875 264.15625 165.894531 261.296875 165.894531 C 257.375 165.894531 253.851562 165.328125 250.726562 164.199219 C 247.597656 163.066406 244.941406 161.523438 242.757812 159.5625 C 240.570312 157.605469 238.894531 155.285156 237.726562 152.609375 C 236.558594 149.9375 235.972656 147.015625 235.972656 143.851562 C 235.972656 140.609375 236.59375 137.671875 237.839844 135.03125 C 239.082031 132.394531 240.777344 130.023438 242.925781 127.910156 C 245.074219 125.800781 247.578125 123.917969 250.441406 122.257812 C 253.304688 120.601562 256.378906 119.074219 259.65625 117.679688 C 262.933594 116.285156 266.34375 115.007812 269.886719 113.839844 C 273.425781 112.671875 276.933594 111.558594 280.398438 110.503906 L 283.226562 109.824219 L 283.226562 101.460938 C 283.226562 96.035156 282.1875 92.191406 280.117188 89.929688 C 278.042969 87.667969 275.273438 86.539062 271.808594 86.539062 C 267.738281 86.539062 264.910156 87.519531 263.328125 89.476562 C 261.746094 91.4375 260.953125 93.808594 260.953125 96.597656 C 260.953125 98.179688 260.785156 99.726562 260.445312 101.234375 C 260.109375 102.742188 259.523438 104.058594 258.695312 105.191406 C 257.867188 106.320312 256.679688 107.226562 255.132812 107.902344 C 253.589844 108.582031 251.648438 108.921875 249.3125 108.921875 C 245.695312 108.921875 242.757812 107.882812 240.496094 105.8125 C 238.234375 103.738281 237.105469 101.121094 237.105469 97.953125 C 237.105469 95.015625 238.101562 92.285156 240.097656 89.761719 C 242.097656 87.234375 244.789062 85.066406 248.183594 83.261719 C 251.574219 81.449219 255.492188 80.019531 259.9375 78.964844 C 264.382812 77.910156 269.09375 77.382812 274.066406 77.382812 C 280.171875 77.382812 285.429688 77.929688 289.839844 79.019531 C 294.246094 80.113281 297.882812 81.675781 300.746094 83.710938 C 303.609375 85.746094 305.71875 88.195312 307.074219 91.058594 C 308.433594 93.921875 309.109375 97.128906 309.109375 100.667969 L 309.109375 164.3125 "/>
+<path style=" stroke:none;fill-rule:nonzero;fill:rgb(79.6%,23.5%,20%);fill-opacity:1;" d="M 235.273438 55.089844 C 235.273438 64.757812 227.4375 72.589844 217.773438 72.589844 C 208.105469 72.589844 200.273438 64.757812 200.273438 55.089844 C 200.273438 45.425781 208.105469 37.589844 217.773438 37.589844 C 227.4375 37.589844 235.273438 45.425781 235.273438 55.089844 "/>
+<path style=" stroke:none;fill-rule:nonzero;fill:rgb(25.1%,38.8%,84.7%);fill-opacity:1;" d="M 72.953125 55.089844 C 72.953125 64.757812 65.117188 72.589844 55.453125 72.589844 C 45.789062 72.589844 37.953125 64.757812 37.953125 55.089844 C 37.953125 45.425781 45.789062 37.589844 55.453125 37.589844 C 65.117188 37.589844 72.953125 45.425781 72.953125 55.089844 "/>
+<path style=" stroke:none;fill-rule:nonzero;fill:rgb(58.4%,34.5%,69.8%);fill-opacity:1;" d="M 277.320312 55.089844 C 277.320312 64.757812 269.484375 72.589844 259.820312 72.589844 C 250.15625 72.589844 242.320312 64.757812 242.320312 55.089844 C 242.320312 45.425781 250.15625 37.589844 259.820312 37.589844 C 269.484375 37.589844 277.320312 45.425781 277.320312 55.089844 "/>
+<path style=" stroke:none;fill-rule:nonzero;fill:rgb(22%,59.6%,14.9%);fill-opacity:1;" d="M 256.300781 18.671875 C 256.300781 28.335938 248.464844 36.171875 238.800781 36.171875 C 229.132812 36.171875 221.300781 28.335938 221.300781 18.671875 C 221.300781 9.007812 229.132812 1.171875 238.800781 1.171875 C 248.464844 1.171875 256.300781 9.007812 256.300781 18.671875 "/>
+</g>
+</svg>
diff --git a/docs/static_site/src/assets/img/keras.png b/docs/static_site/src/assets/img/keras.png
new file mode 100644
index 0000000000000000000000000000000000000000..5e4ff02984037924b367fc9eb5df7563c2564188
GIT binary patch
literal 13784
zcmeHu2{hF0+y9uRjEXEpNKN*AAN#)V3}ashLt`7v7>sPurbr<>*%^|EM50YmC=5j?
zO7;lZ&HL5z)bl(&@B4fI?>Ybf`Jeaa<iven*L7dZ=f0ME>b#kWF6~~ny&w>XR$ouc
z90a1W0=|u)dw`Y1(c}o=FDjh5t_G;G^T;Id3(QT=+!zER@`6B7u^`YouoN``0tJbK
zKvOOtkU|;=#NwaTfKUQ9X#DkTaUjq>rk!spP)62a5a<9m25E!0F*Z_g!}^M%+_A1`
z(GXvMfEol+3Q+)l^hM)Qun=D#Kb%5{GUrzc1>onMWid|JR}#FpGN+BP8B7x!h=##M
zrA5U#RrbPQFr`3u4+V2Ao$u_xZ_1n}@OXa(F|pv_V9{VHQEZ^6n1sB%yqLJ8n53i#
zKp}z)^~0k=MEr1E--P^-qlLz~1!Da17_1*`M=r`08-!Qp<lH&vkFRff;xQgSPx8Zk
zHw!ROY-dMILR4JrcWmeo%s;U0?EE8}yW1aD`3D91e6`8lO$_aW_C@>QaR8shZ;u0f
zYi#@n>z~vNLHU0@&e!+1oH)ET0Wkf$D}T$0Lx%dJ#mvz-Y*3&ZTAKi9!u30wad>m|
zKN$T(i9d@1$bTycu;xG4=y#|4pTOd=xD(iaVEtqJoA1A3>RYIlb^@ueW1dgY4k9f5
zFnAS7rLXb*>+(;4eB&|=!~otv?Xamxd}sM}?HBr;xGR`pLeM@oS{NXjxUW2t;^InT
zzuo#Pt0vY58))H=azm@gz~NHx?`*#;|CQtGVYWXHlaiME^{iiZe&KX?Q}DnB`l9eE
z7+;hpTFl?iQ%UUS;xCk6F{c1n>xuD0`DkI?f_5;fucZQPh`M9k6+GNr-QjW)Xc1X?
z8CMZ$Sy#9SO43zYL{?lzLc&el)dMXDxcMK&{Ou?`3=WSC4E-f^Kmfny1q*b*pI3iu
z_+Y-?Z+}!E4!wh7WzHWk{v#p$ao=}%zTO4}9M%ILj0!}ndjbZjsP7~)nAs0_!8A2t
zlHw9@7{4*f4db_iIRPcH-!c9);EyxD9}s*3?WY0=krk1U5D|wXB_tK3r4^)PM8xG3
zb^!5B+E=Q77J~7^;Zc5W=<goK{L3EyCjA%oe?9}{_7&Q0foK#S?fyft@2h`hQxe<B
zr9PPduR`H=0_Eq4c2^PmVcd^}zgqVb9>0kmTwFmy@_!mVw73)+<qDS(K}&f^ib%`J
z%Zs>5Nx?<jJjBI4;Ba}g9LnQ|mj9jT;ZTJCC$aku6L)kV=D$|pr+b0RiUK+UbRbT}
z4eLih2Li~0c?4p8VJLrpAB@}AY%50abN^n8|H$kxJQns#()tGTU!?stqW_!mDyaY$
z|DWPjN=n?_RSGRH0+;kai%3hL<wRU%Wh6wT#bspO#bwbF;_j&byLkN;(LYy%9~I{B
z)kEo9>Cyjt9{txW`y2aDfPEMJo1*yN5&aAM-$j4BRXRKOVkhnX*Ch_9JwU^yfc>8q
z?0*#VQ^#+YdFQlW^3+aSuv4V|)in1P_TNc&|37UyzS-~#`%mdV@S^q8!M_x=oxmz+
zel3SuKuP@1iuhkBe>q`i#~7G-+);ScZ=}F~y_|oe{fnHhx?ls5Sgg;#)~WoV)_11A
z$TP;eV?09tC9Qr^{vzX#Y^?ADz3M=ZveUTXesSXtKz2~8f%5$G>W>YeivSe;+D6LA
zOGxQR0bg>Gk}`4<5^~~lQW_F)Sy>rb9SKb>&EKc-Z>xWnuO#-bX{Yp)>}z`s_ynyY
z^(*(!<?p6{JMNdc@~c_jR)MKiWiJ4$9}}?3UWpTMb`Xg7xW1M;GK6Y2n>yLia{25^
zrjsO3x{;*5+N7`nzd0vNn+kzUKFuMpcfuK|R`e<{AF2V<D72=5LH7!t<E)-BbhJ?~
zqA|Z>6xP%2Io`DP3CG9eCoYv9IH$ZayqH~U^ClEGA~14qt>ezdOpWOrmU9=Uc`*e6
z15rWMT&5sbQ;eGEyrMu57${ntDi&@O;|)~<ftjJO3TBx3?td@;+b9340Fi848YAe#
z^z_3_pPaP&Z51!}V^o8LBjuJpO3c}>BF|Q}CY+qvV>mTc=rs~vRM<eAVSlnQ=!)3m
z6k)rbOEfsMXHV;f$Xq5k+QBwG8GAb$=~EN0r@vq9wAa}7$DE{X&)h@mE+sDVVb0n%
zvp2H>jf<o_+beKgDaA~t$yeIj1^QW#*C;R6Q$><Ke9RRKUl|tC^zI)hOSO~Hhgl<z
z_Y5!4rEkPz^*&hHpMNCo&0)^IqP$*u_vD)Z^O?!l?#-*>PGG3_-MhNJ*Kd_!Y>=i3
z`Uc+Kx=mQg=F&Gr@AR;j+V@pI-wT_&oEJ3BYe75mdVjPUa%9aUeWL?wK&~5X<s=mw
zwgw=}vGU}7%uh+WM^SwZtcs5prVkHWj2_l$805#CNE{c7w9Vk8Cr4fE%n+=5r7?bT
z*4w;^vVC(a#`Lx&@2idO?sw^4&+`?!pGj^#bSTNssrjJ6R+dIFjRQSU5bz;1BUP|D
z(nrE?EvHZEG$8}71Amk!U=k{7aoS)(lI6w>)8@N4Trt~Gb(4)3{*M^UD`kr7`J$>C
zZmU<dFr>ap?<dP`M(DS&86NAqFm+d`GLe)`2yCwld?r#fS6ZNT%k<!F2))(qO^otm
zNZ^ML=8MI$ZFjNZLh}LRqvt?}WBL<ysFNqbrIRd=MW}{phCY`pCo{gPT92G8x+#i`
zKT}@imGT~Y&sKp&l>4)rw4Ovn=S8^AE9n$ku>K)3!7)o1Pw?0k*sdF0Rs1Fjuj&C_
zZ*UG<eJ3f4lCso-kAHNL>Ug*J)qYq6Z8R+ei7fx}#=@>sQH<dRMRRNGO7pT6rg>gN
z;Qb<dL)+;v@N=7?!KkB!(NPR!<u8e$7nt=s*oWWZmq*@84km>iCF&`N-GLyC-!8^f
z@xI{43)n)8_#I~NQ$M@=_(GADtW8=PBDJV0u;~4#dd;i3p~d?r2xx=P(6@W_a*QVs
zr=XC0QuN9qzP8Eh2}gDpGQO1H5X>P(hA4AoPVIfrARu^h4;4?9I#R6Jfkb$9Tt1(;
z@YsA}>VXaoZ<G-1erujL9Zjie?Rd+svz{b<2J-ZcR1+=oJ57o0Vurqjno$#HXoYjB
z*V=kN)57|(itP~sdwxd##{($i%Qq*y_CtUmIn)?i_RQbuEO*hW-H4_xT&#X(vvukI
zW?e?h6R!*0%Y`{=Z!MH(#4#`H&6vzn&XFUt%Lt{VbW;NK$xHNVHa%`aL^d8OVCxn+
za?FC91hpxR>z;qv8(*dKFvYG%OKr^Ddt}oiy<F;{RxI+m&GFfO2d>b?^{%PNjGRK+
zaBUmeYi}PIf?G>SH@0x_T(2O;QNR-s8-ow?2Zm;g^sB{BFyE+l(QXx&2SAw8IKzos
zl1s_R4o>)}MV27i_0~LXn7hk_;D|xkMLfO;7rC0BOQ5M>v8-8lE?FSmJYu2T3XLNY
z{Z>|*#W;9PQqrdm7j`cQ#lkrV`KdBAX{Sk=beH8bS_*ZT%H^E5`uiyf-ZxTAjOWcy
zc-P3APX%g$P(hF!4-SUIwxyD`r01-b3b-KKz2V{A5zFMe+l|s15MQne2DPI0SVl7j
zcsXVKZL^maJWr=x=k}ZJJDqzpbuRA#6E}m&n`@HROe7?qOI~{x;;|8&_CV+R^`{qu
z0^h7!Wk4@hY-gsEbg1ehwq*BNItGt)Oang3qxo_Ol?@a3Gi9Y(F0-0#D1VMXTvwcc
zecWgHm|R{oofB5LwYg}!W%{~6`tlrS1ykPRrb$khlca|AV-U);5qWJY()7|T$DkDa
zWaLb#L4Set%VB_wr*&%*=AvaeW)W{%tK(Tw=}g(z)YMJ*kaDHJ{&oIdVV*&V&E598
z@U0`+qR4b^GS2d;7yiB=Lq6$LT<Q9v9B0M$tAR_GNu^d(Ig~pdnO<Nm@+vaDY<*$k
zwC?$;GZ_DDKk&46P(ml2_m_cZYeqrt%Ok7E&@I!I0{F|}F}G}fi8)i=uU6IBCz=(_
ziC}5y!1&iMPx)EEk6X@WNN1iFOug#gBlBSDjN<hHZt-ffA}O-hq5iDxnd<#sj43j-
zpQiejB6l;B95No*4noVNmZW`7x11~Gb<Y!|C!gL|WVk|Mfip&p@=?W-O&dMkdOemY
zQM(vx)|=IlLB)2x?*$J=($t50eoj^7$`y=W-e);}ZmO_i`*yid{ys!D2`#k{9t&sk
zSn{FIBU3ONpFTw_M|QAH-W{Wx?8cGO%7|Zt6C@oRs0yOqjREn`^{UN5kn>AY`!IXe
zaPGCwT%9P3M6IBbb<2Hgcb`?hAe@5MhYK=6PE{60Ma6k_L~PJ*AXnZ<v6fSWkwtIs
zpKrbZ_1=7r36O+b;Uy7;YiY_Jlqj&ec~BIg-d}vPwi!sK0P3j2C`sZLWb19KA<4n;
zt>sl@4+XC+366y`Rv^9N0m+XEgKaZ6!i~KcVI=LOHD~MX#v6?$aSe@e;Amq8n4pUV
za#ffWa<SB}IXtnjVf%$<_6E{eBgMqAbgvbKf$XltMKilwsxOwUb;B>6OxeBf(W5fJ
zsuqXPI~LqA>+2);LT=(GCCI105K6-H7Tz<d*(71PD8#f|r%9bAn5HFjC(2eBBcHNs
zY*6>asw+1{Bj2uE#j;<Y)M0c85&x(=z4G8O@8XWk>icD;wFX_qY_?m$q9iu+LlxUK
zW+~OwZQ;QTRx2{ssLufQtkn$~JbTQ-=epc{t(=lAJM?H;)%0T_kWSMM%G`5LiL%`q
z@nFZibTnGVhL<mrDG%CHx<kj}yUVB<7|qe-?qTGaDx%v|EM0HK_NSUl&tE>y!!$UO
zm!~Ldx@T{tzoppwUCtk;x~AqL38C+6GaFMRKo28^f}PXLC%<@EIyic6AA2EAvNE3Q
z4qxn$EUdBG3*j{c9h-J<TWBg{uu-)zzF0a1SeBikzxJgZkYR1>e~5(&NQ<XQcP=pV
zxE6bvQ)->?3{vF!nNRA9&~j=WlV19csb<?!^IR`RCyK5WB`jpiwAby*m(I6t#*Mps
zdLzTPWTh;*Nb#RS&MtD3cPpp1$0CEA2<ZnG%8DoT4Nf{U^gYVt=i)fBP?m6aTd{Lf
z^+g}=bvCI%-1POr?PpK9t`?$T75ddWRvT9e$&|d~68Q}dJ@j8repHk!WT)b%uQCRq
zYK@cEWq7ZtkhM-D<Ij<bDeT?SQ3s(~>QRr&`Af||ic{5?)sn5c1KW7c+my1+Ooj#(
z$CZ9g*3ko@;^`~<EzfGznp`R+aBMoic+|_0VN?{{2H4XSAwI^cR2dZ_w?U3uM+ybM
zQuio3hFeGI-We(;sgTt>%*lBlEj}#ItA5$uSmtkF6L~NdchYLm5^;UNTP(etlX+%K
zOe9FboSBq0x>6SqOSbRP75C>NjZ0h7mSuctl{M(BEv>6-|GZBuIcE_2aak3Nv-mIp
zIREAA`ONe5z=cfl*pv;U#H5qrDxP&WpS*J`Ar^Vl>_hiKr@HtmB8KoR=BRCH_iL26
zMUXJbW4SrV9Wc#KMur@JHfj*_IE46AxsBIFYkB#`0S(gB0mbKQ(~)_%tp{#27!W@-
zT#zHXtIDU(aY*$UWy+4;pL~8Ru-_5=WjBl7*kxUV%6!hzJx@xVtSKAhl7YPZfg%Qw
zcE>KY=qMpDv|Q*j2HP);1a23%Fp?GFSZ8!KH+Xp7D5xXjR!<Q{fDfqiANJt8!?nmX
z1UBOX!0e-3XP{$BF0s4QV<M@0VB1hb?~oL5SDBYw-N4Z4R;5*pFxVr{>zN4p>b<~z
z4*#gd(<F7rQm-Bd(tV9XHz;nl2_uh<fT*#Ccx6iEuQ&9!`6&C`c<EyibYv5&L3*xX
znYexcVLa=WLQbd&NwIu3a3po1Y_2Zzc{iDIhAScQO1nX3t!ag5joEU$P&Y{zxiTDD
za?sL6fL@OcP!PkcZx?y7bnaf`^OsGOAnKzNCyVVIZkr4+i(SnDuGn!8l75)oL>CcU
z4-Pq~!<Q(IfOCilh^4X(DcGA^ND-UYXdy>{WWvD?yV+50TI<?4%P3_r)S@5WNK2$g
zJv^)Oo}l^Q3|#<KG=(RsYFms+h33X~^Q3X7GsW3Ev3i||QJIx48wkTP%nI!W>vKg_
z@pf=lZZO$x<t_}bD5&NWrH`Zx`fzo4X7iwHJTDRU<!ZLD0tJF~9U|j%k`y*`e9^C7
zy}0>P`1uI3%sVGujl6}`v7sEZL^Y{yo)w^nU6Ik#Ie<h8ee61LlC*sGs34Ch6X{}-
zo_<iRbar4I5@YV`03M5N^}Pu+NpAPE=5>~qN8HOK2a!qi=5Pi(%PGb*v;$Z~glUa!
zMFVCI3<!lG7G`V2+^Hi&Y<7`PkDs?M9S~N}#$>X7SxPdwlGVB6UohZb8Y_P?^&xU~
z=%Rby+Q$PIylfR#E~KUP6<>;U@RB~Ijyxei?{6Gc@}hRPL#=A{`i+ko9jkAtnVQVt
zN3~MM4?L3psJGT<KZfNvAU=0K8T5cnU^v$5UPNAd#kMR*SVc0@lIK<d)5O(;vbk$9
z?qBYFG|+b&gWP+1oxaKx$P``Dui<P6_&&>2XVGPBs9~{2N?^lK?K|a9tivfq{Rg4-
zoWMxH`Kfd$&i8f-?LJF^<+6m+qOVex)4TNU_&q67YdHk?`cNp#?37PX!_jq?SR~pE
zxvOwWvC<roy?yhNeq~+XZuCHG6s#gg8NwqdQ<;R+jaMp(l+H*{G%0SH_2o=p3e;7$
zngY6qSa>=i&)S09OvU@^bA=6`vNvd!d`R)rD>qEDfVn2X<0Ka?q?gC6{0XkB@312Q
zU3A~&^%U2G43RL&4Ekso;+g%3Ang?M(>qEgyuEy)Td=6Ab(CjGfpDdh$d+Mh+(f8l
z0Lz$#-}ZaHBD+f|C~K=XOIyANP|N*aZeBd8*Ht?8A|7g}x*|X6Tqk^0#(7Y$C`Xi?
z+bdmhrM7w06RM>FTsDYM3L6;c3oAF~l1!8lL62h&7<}}-tsgKuu==iIBn7VNSy>Mm
zee%ie0<`>+)g{#P(}#q(Bd4!-e<>1cbFt6dU#56%W5By;<+84OsoA+aYIa#v-K<Qu
z|0(3BnggQI@h|5sAVAiGUR!rUf6Y#JCF#l0sZIlL?KzOvY8$&*o(`1c4$s?WRS5PT
za*+&8Df5m3NAuIbDiGbvReoF~-;wpJcrVdY8o?{A5d4DI5R<9i6HDv1Yr`yIFX<~9
zcF`rW^5s5XczyHXO1XM0Su9LfVc>pFjv8`NutK|O7|F_ZIC1z>xG6DWiHpHnl*%~H
zvEU>6rjWM<^QaU%aBJ8PmTAU$_vt->_v=Nx%tvSkL6R7T?snQaYSSPm+A3>QD4}Jr
z;OTSvtIcR2J)*CvSb1u}o88a9XA!$P8sfANZ}_&2FXofnc}BC;7HP*vUJ7IT1<J}7
zU%FqEcLgrdqo^9EEf*EB>qIB3LF;D0(|vsb2l^k~CZzL6pFMUp@BaOO$gaM4dF9iD
z*0U;)DuwOU$~?J%#Hk~<isFRqQB*9lbVDu@8@$yuNxXJ@f@G~WWbYmK&JFX+cdiYL
ze0`Bs(%unF-k8<RXBN^bbB_furg4g5dagxHR~JSa>UZSRj<?N5N{B{BZa#EOjv_RC
zUcQk!-ea12Wqn<rHd;*G#R7<vG2&f~1kL`m^eJr?{eGlYQ20Jz1nn+CfSV2|zjq&t
zYCC2Y{Pei_%3SaTclg>$THpIr1dvM#u4TMa*e7IdP_ox+r9<{vqSGM%>B>sjTu?_d
zW!8O64Wk(&J*~lf$KKZ_Nnt-#Y|2NMHxOW0d3fBCO%QE}jA_-Cp*w+$E|1>@U;OyY
zX!-V4qg)lgqi>+mFM&74g3Ip~t!YZ<X(!?+lFD`sW1|;}u5DWmKK3~>(YWE)HiA#e
z<fjVSWu&@(0@ED>#lhiqXLZAK7}RW(rB-9lmgf%uC04j!d3#8ZSTI|f;op?Gs)6u)
z<d{L_J-gV|7(io`$t$J09Z*JgyWcj{K(lM%Wjz4GF`W<Z;&qVlfcC(6#OzvU@TvWl
z2#doZ<E)b}Afhoz@zQCo)IxT4r)sL(m(%>ewqB@gVfh31JT*u_)y!EM4oclWIY*K+
zc*|J7;xxH2X_~D~V`N{YmSLitGFCGt9-=d-W+UbGs*KLtOv)m?H!>h@N-uXGTPHb>
z{XpN;3|X6ofCe&PFGnc1C>6_|r)rr{?>!e!1VPu=u>@o-W}vyU{6>Oral^~Qw+`^8
zs+~R0c$mkqPiIAUUUThvdUxUEZo}onzJP89AXGO0P2mwNWVV@-Vl7=P_0$}m6D!ED
zAkI!r1l|J_uy?eLoT6V{Xd5EV-|yZ$EtXiyLs*@}(xZ%<X=g9GG?j|>6d>1^vIKF-
zanr7K?S$}+!RpVKBJqqoE}c&@P!8ZvM3y`Dj;i|wypN%b)9-UmRx*%ToD$K_oB0*>
zln7a=M~FQ#^P?BKBOV`8KQhaITWdWa)FMfGx;45zo4|=MEG(?Cl;6pOCSKCJGSa;!
zUwb}_qduyaT)HPlfEi8hiWGHh2yJ<MJT_l6I%9~ClukSueBLj-NX<nF!0>`u`@Ax5
zA!+uCp?i`3snG|i@^p2rA}?_tv#Y<n_8h8*;H|9+=GwhhTd29Mom3Kz;x2pw(tvX=
zXmS0<jn^K!mZ|!s)db&s{j{Z1+2iyYx(G0u7jdDOr2?ql8dZ#2h1>-=sJG+{Jyr~f
z>|p4<adN$<8~2cwz(mrLW~>Rgrdt5{-0-2clwsFRmLMABY<<D46Ah0(i`1QK3S~25
z0m_5pmkc77?1|}K6d~_Uw^ZFoKYZW7PyzuCRj%?Uk6i)~d19^)nC!%sKSegD8AA?2
zFY16$9Pvgvs?G%w?M1m;6%{AnUq+svIw!wJnwaMpj85Mz%TlDJS(UN0I@3NB%xBPY
zn##PJ_dy1urtM^Lr<HN>Ho1Ab_FT-a&5Db8W{H-UlzBsg_8sI9sk8KLZj#Q$B_vNu
z3><{|0B>)=Ll5h=uuY+Pv@uRXzpjHYXc@U0!=9>!DU&`D5Gy?~PE~UrNY(v}YBtV>
zvAGuNE^j}i#)#hx5^H*2*j$v+SZm8d@j9TI<!EanoH(Py(C0pQb~s)~%@JH4xv+1t
zZ`CTvx<y-p?{3|R%7MK>v}y4>MgrMHS8txu%OK7`J9K+Qw0!$B3!iATi*?`8u>H9y
z<X3455qLQTAgkhi%H;Qex7a?sh<J^5B_!VA_u%VWa!a_pUXXC*$P>>-adpl?EgL2b
zvjW@?%ykE{l)TFt2&IC(ugap)JGs;Vdyz$1&Wd!|fG^aCeU7|XI#nq{cQ{q8766Ad
z0!${qrtXBw8iG(m!km73yQqM`8ZRD&@ZD9Is0yG-V{(ddwB_~+aEW$@pReM5c67<3
z8mQ?mGAeklliuoM4vxuz%B|?S28bZM1BL*L3MXdCF!a5Dbt=c`a+H493C7wbmCdS}
zH`gsQ=o@WugIA~0ZGpKTR<S*xlEfv;cb6b)$GyuiRe{yHbEciRF<A8O;V}>Et4#a2
z3lOnzR>C!wI%rz*Ikx@*-_F^<TLIT@Rhnd&DjJiRg<E)Bo>dBzQ}0~CiMeqaz^v`{
zX*4;PB0S13rjat&o>8>j2efN4BdEEjM*u|Pc+qNST3T>jZJ%*nv<Zs@4J}9Ta)xfp
zsy)s9Kv$}gc+Luwg`N<12)?*s{C0B+o$*CSn_9tYk5*EBA<3Y4THY3@KxYkIgw0qt
zIUZ!F7f2SOz^yzl$`RmjVtaWKlT$7G2R06ugf7ucDe9UK;0bH_=_?hwpsFTEg{4Q)
zL0zh!JXVQWhFizaKE)3yC6<>3^ibQyX9L}!Vj$bal$Le=Xy6$vs4n1|*6yJ3Ha<h(
zT@&}fyP`F?|L}vU1Z9yYS}n1T%U;C$!?~M}(haX^I8u$vrNR*V?%2Ck-);1uw+f!Y
z$m@VlSBhdN>;35L@q_;6Kp$4Zcek_e(W|1K62(bye5)?d99@Eo+~K|^8XaV+%(Is1
zcV%>nY(jKptd;f}?mKhoWb9Z?whina0P9too-5v9;;35J6`t;mi2_dItqT{-?VOKg
zrC2VXk)=QN;Q>gZ_Iy<<B*Wv1x{Lo>;G|8xS>L_Y{2Dq-znYHu08t|BI8P>!yL<*x
z5b-)KS~Q!Jrrl-dZ#ovSNT0Wx+IgxZyuX|^uaJ>#z76EYz+K&2=b510`=$`0>d7r{
z-#!wmnvbt$l{)1BPFego##q%Ng|P(Y>l*0R`O|9ZvUInygL4L%oK}FRWzoBdj<z>#
zl!kTa`$9r{?98F1hAmn10|6gWBon(7CoSJH$}O2;loqtwFiunk1fc%DzRAOt&QBG;
z-)61gnEpy|^R&;HT9wc=Q*I&~0d3q%Jbw>J_P0VO9C{&f8Ed5JKKmj{QEI-QSMR#e
z?N=He7~*}&Ema<%-Rkpsbs1rFH7bB+b1}C&U?|8{t55%>qiqk6m3vB5c;q#zveH@z
z1<-Qe{N7XrFi_AgjWbRlZnq>B@KYr<)=tJU6?VqDl)W(THU{l09*kHTW{lRb&%EV~
zzg)4sF`|uUp3YAjtI6z=(`>&zJfOXJ3v(Itz>9&`vM4PouB?scMIPT>I<E)JeA+cm
z<O!>O){-nvp4wWI2DO46V+nHRhRo0t$t`imC^ijqcXZE4--x<kZf+}Jd~B<<w_51g
z?g(#R&Wb)Wa|AQ=;Kn+4bI8q#^=ap!JCKY_bl*o6zC(k4&nhdDRgh3`16tkN1zhw#
zN9srSda_dkcd9mQmd9AIl82V|C2evB7cdCwoVgljVc1d+IeOCUGuz0Nph3%fzidED
z8LvV$lc8P7c%YH#L5mUDnWB%!V`lHo%GhpQij>3AB*SI)07Y=ufk!;_NkhKyn5qFz
zedTnn7nG$Qb<ybibAFy~QjOIaT}l@1;~ls@YPK9Xs@R~>=c{MLAQuhao>fiuJBt8&
z)Tm{TGnA^1AHG~I>HM@LA`9w0W&9MB&uum{o3cr^em2FI<3G*_C7`qMSCE$*;RY7=
z*V$3EaY^mfdM!RjPBbr>v*MGTUWi{G4)shA?Xxu>=uOssl+ModAVWS$5;hI#)1|>X
z;NlyZoR;NSfJ@smSz?&rXxnG()2I<kmO)HC<ECQbc~SIWVC9)}+`$Vbtu~}n1NhHv
zlC$#<^?OGjfyb~1Q5>p5b4yt*F1ly&4iR@M!q(89+=3ombomg$x{(}uVNa`9Cm%wm
z$5{iYA!leNfHw<lq>!@eYIKbQ*u%Q-R`^Ovv1w$6mIzIRC_^8F{cI)ArKq-{f$NIZ
zR;p`-HT*l3hFX<BNcWgA)avum%hz<F-Ws%;x33tw3^i{bs)smmXq<SOjKQldda4Q?
zviD7KFgv=kAGQ2yb$|F3h_u1APum+s7Gw4oy!(u*uLwkRy!Rv5_wgR;2Rdy5tJZ#B
z;1R)>sYpc(y8pV9h@<R?uu#O7w<iKDKXe|=SlipQG2~h)&~_YnA9%vN#EpjRNfk|@
z8<Kk~X>iS_bw5Vtj(zY@3V$?2SrzJi>Y}=}xsF-^#EP=L(jD4k+$lqs#vjdg$dap)
zr>CT3zYC%9LX}5<-9_O&VBiBngG&vN^`l`%XO8$D5%-JQGn7KUb{TvpF7B|7F;gZ0
zMZX04=Cdsx(EW9Fbh<QmKev0#U7YJj6T4f4hvw{>x>RP`<LDOay>)D!#K*B87vD>z
z{4S{DQv_cqPz`!nE6UAlhX~z)z(+zR(5pgBJ||dELs6&pL5*^v<jJ*$;n#)mo>$8q
zz`ViUv!`}<F9FkcWdreZ%)3kr-l!@Q*5ilNT+-C&d8+*Lmb`C8m7C|Q^s!EPUmw7k
z=0hA<)d#h0I3AQtUStqQfScFJ{>f3m$PjnSr%xEu_vSiUM1*x$R?a(E#pKOH%EnXx
zpj-lJD$JsTn0bIyzQ<VPRmkQ-*8J2&y1ETVbjj-(#>2f6QC|#2FV}V{jR_+R&yV|M
zhfbAA&iZ1x5Eo)}X;DF-Y+fmo9T$5it2M6MQ-=|=Xm>t6MDIL8Jf>5Wn~u3b%T;mD
zE+;_O(8b?ueXh~80d5NnL5czmLgmC%$HFgF54lTp8_h-m54S$axT?fuHZz^HIr`Y7
z0J2UNL0!Q$eqJGZe5QT4^U0HcFycjI)O1dy%IC8qkQ*zMkE)V|m{0r?^k%7Q&EtL@
zBN2^B@w<2hf-iNZ8E@OXy!Tx&vG=OcrrWgjJ<jr@kxG&Q@KDXhxdub4&H1yO(CN~;
zs;vueA4Ric_vb@Khvll~PO}t-wJ}Ou-kTgklf0e#)Vfw5BI7j2e!IAahO5$hd)-=K
z&~;x~ugH0M+Vq>2&%p-3<gI_!FBtelbAWV}*s6c6-u3iS^J;$Y%<bG{?{SKpfx2mm
zZ-2}IYvoPxNWC%8y#RKhz(^+67hO-UG)f$nRh@Lze4L<Yun4^Oq!%6ZZ@pbNCEI-2
zFB^Wyx)LuXYDde6$dn?n!6kx@=$FM~$(zUFkyQdWnQYEfwRWDAq9xU5JRCL3G4|GW
z-pamiWY4a(N3c6(O->6AE5fStMpE6g<m>8?^dOnkUwT=(MxA$oE_pwrc;wr8tmlg=
ziXwMd!d=SsOw*lz*%G1;bRgX```N(s=dJ)v29M*^84%<ycht@U>>gSws1`sFe?>G%
z69jew`rIaI#TbAfA0W`tPi;63q7nnTHK&Yhb$|c?5EK*|4$((Nr2^xGSV!lz<Dl;^
z8AX0jwH8C*?I6{|^96mBS~5@v`6ta~A+WbRz^jvCjnoOK8q5G_YLoG0>|pwNK%1$e
w*R0YI7(GA$H)!YuwE4HX|Mu?h!O%k4wN!oM6xyWZ>wlrr*EZ3r)NncVAMtV;6#xJL

literal 0
HcmV?d00001

diff --git a/docs/static_site/src/assets/img/line-graph.svg b/docs/static_site/src/assets/img/line-graph.svg
new file mode 100644
index 000000000000..f7ab812ee4be
--- /dev/null
+++ b/docs/static_site/src/assets/img/line-graph.svg
@@ -0,0 +1,107 @@
+<?xml version="1.0" encoding="iso-8859-1"?>
+<!-- Generator: Adobe Illustrator 19.0.0, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 viewBox="0 0 512 512" style="enable-background:new 0 0 512 512;" xml:space="preserve">
+<g>
+	<rect x="123.586" y="282.483" style="fill:#EFF2FA;" width="17.655" height="203.034"/>
+	<rect x="70.621" y="379.586" style="fill:#EFF2FA;" width="17.655" height="105.931"/>
+	<rect x="176.552" y="211.862" style="fill:#EFF2FA;" width="17.655" height="273.655"/>
+	<rect x="229.517" y="406.069" style="fill:#EFF2FA;" width="17.655" height="79.448"/>
+	<rect x="282.483" y="370.759" style="fill:#EFF2FA;" width="17.655" height="114.759"/>
+	<rect x="335.448" y="317.793" style="fill:#EFF2FA;" width="17.655" height="167.724"/>
+	<rect x="388.414" y="35.31" style="fill:#EFF2FA;" width="17.655" height="450.207"/>
+	<rect x="441.379" y="423.724" style="fill:#EFF2FA;" width="17.655" height="61.793"/>
+</g>
+<path style="fill:#AFB9D2;" d="M450.207,432.556c-0.922,0-1.866-0.147-2.793-0.457l-158.897-52.966
+	c-1.863-0.621-3.462-1.845-4.552-3.478l-99.009-148.513l-45.483,60.638c-2.69,3.582-7.647,4.586-11.509,2.327L22.034,228.314
+	c-4.211-2.457-5.633-7.862-3.177-12.073c2.461-4.207,7.858-5.633,12.073-3.177l99.138,57.832l48.25-64.332
+	c1.716-2.293,4.396-3.729,7.306-3.526c2.862,0.077,5.508,1.543,7.1,3.927l104.25,156.375L453,415.35
+	c4.625,1.543,7.125,6.543,5.582,11.168C457.35,430.216,453.905,432.556,450.207,432.556z"/>
+<path style="fill:#8F96AC;" d="M26.474,494.345c-1.323,0-2.673-0.298-3.939-0.931c-4.362-2.181-6.13-7.482-3.948-11.844
+	l52.966-105.931c1.732-3.47,5.543-5.388,9.35-4.759l155.168,25.863l100.142-75.107l52.345-287.901
+	c0.871-4.793,5.465-7.957,10.263-7.107c4.798,0.871,7.978,5.47,7.107,10.263L352.962,328.2c-0.396,2.189-1.608,4.151-3.388,5.483
+	l-105.931,79.448c-1.931,1.452-4.366,2.056-6.75,1.646L84.427,389.366L34.38,489.465C32.832,492.561,29.716,494.345,26.474,494.345z
+	"/>
+<g>
+	<path style="fill:#D7DEED;" d="M26.483,441.379H8.828c-4.875,0-8.828-3.953-8.828-8.828l0,0c0-4.875,3.953-8.828,8.828-8.828
+		h17.655V441.379z"/>
+	<path style="fill:#D7DEED;" d="M26.483,388.414H8.828c-4.875,0-8.828-3.953-8.828-8.828l0,0c0-4.875,3.953-8.828,8.828-8.828
+		h17.655V388.414z"/>
+	<path style="fill:#D7DEED;" d="M26.483,335.448H8.828c-4.875,0-8.828-3.953-8.828-8.828l0,0c0-4.875,3.953-8.828,8.828-8.828
+		h17.655V335.448z"/>
+	<path style="fill:#D7DEED;" d="M26.483,282.483H8.828c-4.875,0-8.828-3.953-8.828-8.828l0,0c0-4.875,3.953-8.828,8.828-8.828
+		h17.655V282.483z"/>
+	<path style="fill:#D7DEED;" d="M26.483,229.517H8.828c-4.875,0-8.828-3.953-8.828-8.828l0,0c0-4.875,3.953-8.828,8.828-8.828
+		h17.655V229.517z"/>
+	<path style="fill:#D7DEED;" d="M26.483,176.552H8.828c-4.875,0-8.828-3.953-8.828-8.828l0,0c0-4.875,3.953-8.828,8.828-8.828
+		h17.655V176.552z"/>
+	<path style="fill:#D7DEED;" d="M26.483,123.586H8.828c-4.875,0-8.828-3.953-8.828-8.828l0,0c0-4.875,3.953-8.828,8.828-8.828
+		h17.655V123.586z"/>
+	<path style="fill:#D7DEED;" d="M26.483,70.621H8.828C3.953,70.621,0,66.668,0,61.793l0,0c0-4.875,3.953-8.828,8.828-8.828h17.655
+		V70.621z"/>
+	<path style="fill:#D7DEED;" d="M459.034,485.517v17.655c0,4.875-3.953,8.828-8.828,8.828l0,0c-4.875,0-8.828-3.953-8.828-8.828
+		v-17.655H459.034z"/>
+	<path style="fill:#D7DEED;" d="M406.069,485.517v17.655c0,4.875-3.953,8.828-8.828,8.828l0,0c-4.875,0-8.828-3.953-8.828-8.828
+		v-17.655H406.069z"/>
+	<path style="fill:#D7DEED;" d="M353.103,485.517v17.655c0,4.875-3.953,8.828-8.828,8.828l0,0c-4.875,0-8.828-3.953-8.828-8.828
+		v-17.655H353.103z"/>
+	<path style="fill:#D7DEED;" d="M300.138,485.517v17.655c0,4.875-3.953,8.828-8.828,8.828l0,0c-4.875,0-8.828-3.953-8.828-8.828
+		v-17.655H300.138z"/>
+	<path style="fill:#D7DEED;" d="M247.172,485.517v17.655c0,4.875-3.953,8.828-8.828,8.828l0,0c-4.875,0-8.828-3.953-8.828-8.828
+		v-17.655H247.172z"/>
+	<path style="fill:#D7DEED;" d="M194.207,485.517v17.655c0,4.875-3.953,8.828-8.828,8.828l0,0c-4.875,0-8.828-3.953-8.828-8.828
+		v-17.655H194.207z"/>
+	<path style="fill:#D7DEED;" d="M141.241,485.517v17.655c0,4.875-3.953,8.828-8.828,8.828l0,0c-4.875,0-8.828-3.953-8.828-8.828
+		v-17.655H141.241z"/>
+	<path style="fill:#D7DEED;" d="M88.276,485.517v17.655c0,4.875-3.953,8.828-8.828,8.828l0,0c-4.875,0-8.828-3.953-8.828-8.828
+		v-17.655H88.276z"/>
+</g>
+<g>
+	<path style="fill:#C7CFE2;" d="M26.483,512L26.483,512c-4.875,0-8.828-3.953-8.828-8.828V8.828C17.655,3.953,21.608,0,26.483,0l0,0
+		c4.875,0,8.828,3.953,8.828,8.828v494.345C35.31,508.047,31.358,512,26.483,512z"/>
+	<path style="fill:#C7CFE2;" d="M0,485.517L0,485.517c0-4.875,3.953-8.828,8.828-8.828h494.345c4.875,0,8.828,3.953,8.828,8.828l0,0
+		c0,4.875-3.953,8.828-8.828,8.828H8.828C3.953,494.345,0,490.392,0,485.517z"/>
+</g>
+<circle style="fill:#FF6464;" cx="79.448" cy="379.586" r="17.655"/>
+<g>
+	<circle style="fill:#C3E678;" cx="132.414" cy="282.483" r="17.655"/>
+	<circle style="fill:#C3E678;" cx="185.379" cy="211.862" r="17.655"/>
+	<circle style="fill:#C3E678;" cx="291.31" cy="370.759" r="17.655"/>
+	<circle style="fill:#C3E678;" cx="450.207" cy="423.724" r="17.655"/>
+</g>
+<g>
+	<circle style="fill:#FF6464;" cx="238.345" cy="406.069" r="17.655"/>
+	<circle style="fill:#FF6464;" cx="344.276" cy="326.621" r="17.655"/>
+	<circle style="fill:#FF6464;" cx="397.241" cy="35.31" r="17.655"/>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+</svg>
diff --git a/docs/static_site/src/assets/img/logos.png b/docs/static_site/src/assets/img/logos.png
new file mode 100644
index 0000000000000000000000000000000000000000..e2d58a0ef3d3fa8d10181d29feb2c27a3e1c0fc9
GIT binary patch
literal 493103
zcmeFZbx>U2)-8%_aCZw9EVx^+Bse6v6C4_McefDSJ;B}Gt<i)A8n?!s#^Lh2@7(X*
zs{6k4a{oV7tBay*&u;cwbFDe%7-JKuq9libMv4Xl1B3DDqqG_f4006=4EziV((5~l
z<zd^eKVY5J<UYVuO_J}w{(|c8QQH{?h6wkc7pz6@>}!Yw%qMBdFCMT**~kI<GMz8o
z&48(<BY@Y(>bD`Qbg0xa(%6#N*YRRGQDV8uHAJingAlwg*aW_`QPdX&SF5OjZ{N~%
zf>yqHw?D1E-0)kRGPEC+B-GJ7WUuH>3Obwb`b;hhLMGh(y3Ye?l15`ThRGkQsNrb;
zdW3(MsG+MT_fr!4LBhg;gAMz)<2{_8@!wyK9oY4jdZUeFpT+)fzW^InTdy25A^_zt
z#}(Uazzv)K4*TEU{!>ms$f2JhB7y{h^xuv9l-G!^o$6(ozq?*KZw)WOVdb<u0Ol|6
zB0^#f_cfyVF}eM(Z`>OWx9Y){02}6Sr*=>GpNN!hC!)Vx?m$0fi9XlvYB;#RPAp&f
z*NC+X&6B^)#pzSjk1Z!RUnOAvaxtXy?Ei_-eIC>Q>-7_bMFl-|p`rZWWECpNcigZw
z`Y%Hyx*1;+eN7V<_HTDbU>E&sg#8_h`(Ll$4Z>T!n}>JQFn>L{&HqIFXLI~TzW--)
z{AH#Gpu8Tr|JfXWR|)=8IsQ{Q{(m6Ae=5iS4Xi?edjBmNd61-@Yru%~bni73A%5;1
zKI4T&34zkd9KVBmihl)k&+e111Fx0z;WPD|YdC(Yx)}AIe1EDEUD9V*F`*iiFeBBu
z^agP6)RaUR&eQU)yxZIF(zMx<Jd_`7;4EY5Rn}(X7}$R|ma5K;X*$2j)J^(nCr?8<
zPMz|RHa(6eWmJZg<~H#|z^C1iASN_3m5f6vGh^)F=*OS$M&;<mS&+8#I;cnNB>dv2
z?()fZsT|CVA`_K#J!cih<6}5_?4hZWN@HVV9;|L*hHPi$LH`yQ_5e_d_}Ha4YpLqI
zdEaak_G<Ex2S!3@1<plo!<E;7IZJQhXcOqyuObu}xYJoyC#rsz{?8BWVMk~~1$`;|
zb)a^89iDi0hi&?sTIN76snS=eZI?6(6p2r}57X4}t;S0Z!N%sGwt`ZtG26t|6#u#s
zhewX?r}7SMa(;~h=VF5imCRW{b?i8P(vzT))@0h*@%J0mk0t9_qi0Dwc?ov2Ttw-z
zVYxo4e=r}onb6EtG_Ykotzb)%W#4zzVJC$rpYy^5U$j~X42UXDG&QI%gDzOsg(yi4
zPl#FPFnht3vzh;X4s>7Vpd)VKl*81WE!5_)#?0TIfgq6M2iD679=6bVKYrbmu?1H%
z%j=H3`uK`d<Fy*p{=tx5j7BM=%xECVv34a9XnFv7Q2@mUR~xi)8`87gUD*jka6~aL
z=NEqZS~^l`p6ubwWb>iei=oi5+T7h~{B+-RghzG^MoIM_e6WH?sPhxOq}JTavKb6Z
z3=v?(c+@dJz_^OX?oflakFK7M{6mk2LjMOLMM)tmT5nDeeIijz0T+KHXRy4({7>q*
zT%I>cVJXH;(S$}Bt6k=j0KqhRw}exQ5)ivgXXL*v(k_e%zYN1i*y<N*mlV^Qo|o}~
zxeMZlR6-c}bK;Rbzi(=$<}VW#MJd>#CHxs)(WiK>FU4!a(PMz~K79esF8xj}5L8jg
z%p*5vY-<;9vft#!s`E7#@$jI=R`(5=&Z8v||2<}=mSGm@d$ymg775iw;_-l?U$~ht
zu>yE@p}B^0pL7%-?4U?d16JOSnW)nYmcr=v&TBw2N_TG4eXh7pTe8Xy_+7CqE2I`?
ztGijz))g$XThV?VDf}9?qT7{4S7Ci{c$NeLUi6lLvjUAdg)k-~)z^TrAQ2*#Y*a2b
z9s73CYleT@>P?dcDtN;eyl|7HTSkN2!RiC}5587;+f-uk`HTG2?*Zn@wGB!L_iV6b
zWcYmB?>g2rU?^H%XW;}jP34$$EY8vjZEU=WZ=)56I-%c+aXH^?{3q@W?kBWy8Xe_u
zCe=*9(Pn8v(_Quc1z05FVXk2cbAd=lb}rO|QXGAMzrF5#Ox+_kl0((Oqe_&snse2@
z3TI!d;K(>{rp4zqtF8xJ+iLYIi5$tdO8nck#5%Yn4GoMleXTWzOS3<ti&RX#JjQ-l
z5e{Gm*uzGx`!N=&ua*!W$3+*k#2?P?eixyXqTU;x33hTgk8$d7HxZ1Z5S%^+CkGUl
z{`+D(9V$T^wO73w?Q%@2Bc7f(3;6KeKyXo<JPm3PV&RY4HJjfaS)oWf?T_->V2ML?
z<le#2PKO3qh#!x8;MPY1f28l1WWrTzOJ$yrt#{1nMLAa;eEk#a;b(@i%WBnQqZ>eB
z*Kz()4hLmsEZxL~3wxKnp=cL~s<w-kML6As{n^P`RbzWN&xES%^9N#Glo*z&!Ra!m
zH_R+O0U}YL4Qj+g>OTB9p!E{EL8}m<+k<h_Z%5L==1VqGWg}<jH|0&drgu;jjiU%-
zeSO^DOR6ATW04`ui<Tgo5TE0Z-vp+3SE7-p;~u}DXIT9?j=ojp>xp+RNnKZGj4vSI
zTM3<0TZU9LF?(LNC_FZKc8W%yVpom;|Frzuv-L_H&Lxnf^YUF0sB7R^QwgsXY(z23
z^CjvJt-urfNb7~>QB=z5_zLaZke}o~{jva1=pznx;0eOSW||I1W%{rF+08V2d{ba$
zQ)CN%U=pgWb;SCwP_nc5VnwYQ-ryxm9D4?)h1pIwt^vVMY8{CmXTJ%hOJS{|ZbSG?
zr9TnlArqMNox!Gw351}^2oh`Y!4%^Mq5E(aDKgzG9xV`7pz<v)I*BCI*$X3@QRY(+
z$sO~m%dHKWYPWY%a8%d~UfO_bENu`JKRH_{$=kHCP9Zq$=Xbz7e?iEX#&$eO{nS_(
zL@(?s%h~1TMi57)eM0{Ifk`W~-%&X34T1lIbN*Jp?)1N9CayT(YP<d3{E~@>HHLOV
zNf#17N879g1fpcWC?dpn!Hg0dCTi6pwL&IBIP8@R+^bP-f>AoQPzBN@tNPZ?A*#r)
zxmy#DpwPnC3|0zg6f9qPoP~|*aO`L)Aaw6+f>*AJ$}7p9<%1+ck(uUa6<gv7$M-{k
zIux>BRo@i8<4WVI!)A;n2{8>%HgT{brZ=8sKIYb59<W3MziAkCJKZe=FNc92LFPNh
zsNF7{06+(pN(3j~c>nI75@)Bby|OIY`pWztvGZSWGi@@?7G`xOv>V`^oA0M7O|>h3
z_NLVfL5jevbd@9Addi@!9Zy)FlL;puuX$qpop39=6{URfDYf0KwG=8ukKr>IijeO(
z|4rAqzy^D0@_AqjCETpIRaa$+2`)TA;xU(>`h3=KeLVjVxcY$$$#770<nAo{D4rrD
zCDSi%Z^b#m+EU^qv|hyte6!BMa%^W`Nfbzz@rq;U?ysPKdIi4%QYY*dKPdO<-Lx1?
zboq*tkN<9#jvHCd&a}}{RDTb+*)cJ*Bct@PBYW<u!DQhF75rKD3%|fI7cpcFB$io*
zy|aq>sW9W}Mx#Wb9?2Yx&RHgaMS!@gw#-_}b}NwCz^I<da*QpbpA@hE1d$74Z~Aqn
zBVBcR_Hk0#KDKh@B_SXEG6t_tw05a{W*4rL&R$v2Le@N%Vz>aPcnf~Cel+dHv;`D-
z0b3h^R-R0wt?j{9-yTK6c+~U9uhhdz&bP)H)<YbVtMX_lLo+$SW=_=cCNF|ai2vUN
ziNJSM9v;4pst3xR;i@)r#Ji($wO8_G&lxVV)Gu%VPdtCn)o;qI*3U|bEZzHfZU4Lu
z4uh%<Gg33PPxB-CF^sp4?h<weCjq_xBWF-y*iBa0d{hsPLeCcrL>vJL1SWuL2Uq0e
zCs!o_ZeA9$+m%S&#hoJyH!~=Ad)Gs0q{Z%2T7glNMv=l=L&IA(BVcbmBc{%?D9Sdg
z*86W7l|N2?RwBa1>>v!Bcz#Q*WL>;?@9O6oKG(`lrks0<54XD5;ErBFC-TCq^QODS
ziTlLMOq5cOaB$wd%?K52gW|d;@HmQs#;lRc)rbIzV;hX5Lx1mSDTvnBz;%)qE!6Ls
zn-p_cQ75R`pzWyZ%QLfdVDkO2WBrc;6|h3o6J(oP&o`}C55C_ku4z8~G%0CkR+9y+
zYz}=p$B(=QtMRRTibN;<fyoGKoqVM<o~`$n>G*m2Vr7f{v)kkSVB>;bt{j=8*cpO=
zCVxDqVzeC<WYf>nhCFq(368(R2Cu5!2+0W!|HUdfyQrobJxZ#Q|G!zNw+P=y-&!9Y
zE{a>{JmbFtE=**&uGz#RmetrmW!j+iU!Yz?PLgX)z32gvsQJOHg4x4{;(+Y<0W$d+
zhjUk^C-4+bmcXuq0Bf=&0wxLU{+sYO*NM5R8n`^F)5CW``$0Lm478VKWWrrg*&rRB
zj0~{K+H~A*iGU8+#L~)IPD&lFvQm}D!B^gf-0oPFXI{<{@j&ivhQvx7U#iDY$cOPQ
zwqBO0yxw9ICvO7-lpV7>VhQ=h@3TH{s#vA9Jt@bK2_1YX-?4Zk?rCV@-3rBTu<hFn
z8q4$?WLPyWdc#wNnk`ApA(6CJBuUN#1<=knmq;I<7b`usfuz5OfNdGo<R`?@`DO1T
zx8xqqt56BjcuiSLeuO7(y-U?hJf`e3>$%@9^tN>#wd?)bT>*0}rYLrz!|cwk4QnUP
z$N@e<W<?MlaH<wDs1x3CPMM1vY{j&A=8K9~J@WPO#OZRHgwb<4qF%mRFnI(3*bdac
zjwm+MWz3D>1Qe5BsYX%koBKS~68>%m_Jdaa$u9&yyq93HpLSYV&eg4<bLZF9rd+k|
z{O`z4a%#BwVLT(MUm#@f0Nk%!|1?c9J(_yGld6}KRuskamrHCR&e&Ows3udBGDo$V
z8!QF#j@A9G+y&LtM57pFGY*Rus`v4Rv;rqTC++TO;pBu~DI~dj60wkBi<BZIPM5yq
z$ib9W@54t+KwO^zTP5cg5RYkpn5^yMY=~W!eRCIkkR?wf6e?i&DQeb7MgSDNCO>Xv
z=ff>;b<;`bDc<Z&1cMf+#M+FZFO_jfmRYv)nOlNab!9&N!Bls03M)VFJ#uI$e&aa$
z`#3<}urhJPFLfm2y#y0{M_s5&N5@f+>75(7A?T>#cQ^tU`L~3+yPuwbN0RwS7Z}ML
zNS_3^$uAL+MzJ7q(N^+!2S11oo`)~IULysz@>U2AqQ_~ir-0+8iUK(o482%AL(^QA
z=+vqO#AMj9PUFOBVDXf~(S7k^zIw66ex**_#_RKS<)LkCbJL+WLef}Y%l??U!%(g3
z?Wk|{=G`&8RiHtfLt9Rt3p(VUJt*D)-S0wA_+ZI!a7xTY<ig2Gl;v{Lo3qZ}rsTm{
zYf1Cp@!*&?a9w>Dt87)jK2e9y@o2<IAN>L^LaYh3L=j}2H?K<+UFe@Ze@wMfJWJva
z5J=}yVZRCySV|W(!qSXt=>*K!@#-!mFr6k}+4vlvmm6(tRP|SaGLC(YEnVm5m@kdC
z_z4z|KiEy<p6PiC8)rl#;(yO(V=7GlbE(7vz{_nNJvOZr=+49j?o`pzg18YTOfpDX
zJ^izL01wlIDQnEe?F|{aw3gOUCg|f2Lu9%uZF)Ku@s#>uR_e&6^V+7IzDw94R{GYx
z7w(Jg^qmy!2znTKM#rVejhXICROSR|n|~@Lm!J2X9<MPlWAXxG6$Ig8xU7v}ER<RL
zb{Kzbg%qNxjHs4rXlg~ToJ@u`mxIH{aVVFaim=@Xw>MmHi4>WJ9bY^NyM!!IpYQeX
zYlCo~({ym+knVKRXtNE&*1DP~dV72xucR2ME!i>lf|mX2p@lp)mwq89>p8^5|3-Ln
zSin2mNma{YFSJnhit>%-|9q)7{%oD;RM)n8D0_Syq2SN7?h-eyQFy-A9rnHbh0HG#
zSyvJv&f!(5i9f^Z@DI1xTv;7eGUx3~uV&LFtF>LktkC-!tf&_!yH_NbSdd^hR_{q)
zLckiU2XC7OZ;ba{G#|VlGLNkr_zNks>4@8GA<D=e5Ui~OcGt~)0`&3EIl#FLX)i8O
zt^~^u6)z_K*qL#(1q>ONkkr&^sgX?XjJGg+FBRCd+Mgw5)z*s@bWSnZ9Qf?BK3jNR
zRX;?-KkO&Qz7uz(N7HUmxg(_*Q2I7u`wqi_&>C%ioRs!<jWK1b**VG5vJ{aQV#|K<
zXQP30r{%FIP{7xRU?|zW#dO1G-wF++!G9!UYRA|MEe+!`t{T@)wo(Bm3?=lzoV1wW
z%M$QzAu9tUCo~nJ-7+tQLX64?M}s~;nj7%Gi%wk+i!Kdz1EKWlZ7}_x>>7C$!qU>x
z-f>lS&I#>NePp{>0=Z7W4g^w|mlrTs;n>ssUvR&t3lD{`CsiCf-IDu3Tlgw|^2@?$
zmMvm|;#oZ9flKV9qNuO2VRJQ8?))N0!A>TB;jP8m5p&9XWDkx^p7=he-0w(X2V_%?
z3EM{@Ud>Z^jksgIPWdoJm+Q{X5&{+twdtM5o^efp`#6Ro&ZG=QU~YlS`)&8j@-(>J
zQtY%$aA#l>f>mrfa76cLVo<l6`-Z)EOh-Z27Ozsc@5EMkg|FE#{C9^<SIKN^YN|?#
zj2nR1!dS8YRGKRm5xv=nrn0tlr5ii9^F(1nAdHBJ(ZKyvjl7pZnV0xC1P`IP-nTkh
z{3m{Xg3pkdW3Ae3tYtlZ0sg?7iJt=mjG7z29euD%T^*yYX%hik+HdGa+cKt}mKV20
z-uj_61B`+*!|xL)dL_p!K|&c%c4i>6RAcEZM!1{Ib!)sc-)FdG2UW3md$1cr4;lr0
z;xQf4<f{HOA0l<imi->L_hl6*x^Rgo_;c9%J6%>zJuGIC_gj8c{2v@n&k4#x&to!+
zrpf4cdXM8q(6WW8DcysEPPPL@tZbrEX||<ZpR1}W&N~(s$H(*Gt(L90{;T~tRaKgs
zqIS~?yMM`Huk;o=q*qfbN`Wob`*By*E!AjKiZ$?e7=Hryp-aQ_ZIeD9^dcGLn2Fr}
z6!(BFWd80#+{uaqr%?wX?#(|tF6Od@N={OB>^FTWckRp(c6tZasVJ$U(N+l(t6mLa
zbgFIt()G1gb3Wy9LrIfo2wMtJ$}Z)%XKNPvQpzHXH=uK66X)C<^aC5w6YqkMeIe?l
z;~CcHx!z*amxv0u#qGW|)W_;d(l6yMO@1SMN<AV@At~>T((O|cSm26H$5PPZ@EJ#p
z>ykK&=Qn`RQc<zps2sX0E4DuF3`uT+o;Y;6m#+vT%XVA}xUe~r?)HJvcQbnqIO)Cl
zv06BQ@~|v+OS#uNMhLbl(Vjec9?Cbot$NNY+VD75D?uEh$=|FI8?56Om|)RX1S1gC
zZ->O2Hx`VOiPg131m?JLvO+o)uK)M*Z2A**igmtwX^1!AK+iMH=X~bfTYiCn;u$HR
zvD4=-Z*V-o2u*h2uy86rVl0s}nBA$|DJw8(f&xSKM>}7>MNk!qmcJ78y4wE<9oCDR
zj2}R5KA9ECQq=-in&mWBHFN!6B5s~AEUDa`lU><oXeAWCMXYh}1-osVFr-9T!er*%
zty3H7vgi?H9DVNKz8fcllO+Eao}NDdYo_5d!s_{}%-OxAyzxvK@X6iB)mgjN*c5)n
z*M7K9Mwiq5M0QenCCZZLWZCcGZW{L+aCAp+>W9tBvr<iCb<XM^8Xc-g3W^_G%r0U5
zG!4yzj+axp{WTl(0?!K@J~fviT@}U9=Nyvb7br>^HQ6Q6c^7q1q-U(aLssJLZ93ii
zMlJ<OaEKNrd(B;XdItzbN3@aj=<8Z`E2Z`|D0S8Iaw|;p42hX!L~;x>X{iq9+`_&@
zVPN<3*b?SvfuuYYip>m|=i@%SGubV&+c_5CS*XA;np_0}SHmo{xYo&=%<q2T^Rm-*
zB#H&C3uBHq!lxHuo3O6`mkaE9#{O8mbvs^wRlsnM#PzG+Ca*}8a2NUASuII?0kw#O
zKc@Oa)GO&p16yD6d~EL@E&jNHrPc1n_TY4@AlK_<9~8N`n6P3oQy`NiYsY-jajB%|
zG)jNFNz@%Nn#5>PxUc@dFh(93tl<VVRO+;&#BGth!Lyu7Zm+{`q<a^Q$9GpYiY@D=
zt@(B_d(pxsdx?#IB}hS@>=7PqgM(e#=#dzK@tpUb={EeU73C2thi{kZtCH_GoYMyX
z)j_!`S{heNPW}KyWIKe;Q89Rrfw53qi1BIioE&ZLn4e6>`7T>3buDKg)tJl*ccZGe
z$y3d3esigb(&__XNiR;l=+YT?J!PKh<aVVzjnDP{plv1@I3n%sQgV!=ihv73Q2N_<
z7eTj+jgW#efsiZ(;e^ef7ZU7)zw_$(G1Y7lSC(#4jeH9<xR2dN+vKeDV&4mMD0C`2
zOos}}x}!Cfx0!op%PM7n?Kqt3eATmFIu~;LZrIbq=yi9EB{A?eL^etMV{#UfMz)jJ
zPx#eZE@I5H1ixPzS@Z!8U{1<vI1rBBo{KDm6Lk>i>kzajHVOceK<~hq^CF(LE4$gj
z5p^u?k*2OaBGNS*2d#q#BmFE=smt|<bN>%f|H}OKfT@i@YrA`V4R4zQWI^Fy*XgJ>
zpPA$>-Tl#C&%je@IF61pxUQ>W4ng!oE!N!}ROpSsL~IK%*si+$w2O?R>z20b>DYd&
zth{Pzt*Ej4Uq>-{B7%=h|02ZvsZtc7s`;)u$@zt-_4GwV$Nrf@4P3UtZPU9FqCgu~
zVDCeE?0+x*HnqyU+qTI$;qI)QgE6K@wI3(zJ?_S0DJ`U73x}NFnxEM^357fd!R<v}
zKxDi^?U)x<{T=H!viCNf9lTJ=c*fdWpmoR9tQWugW!(-KC;w|>Wc4mo82gHjyg=A2
ziin*)N;f5dEnUp!Q!B4|uQjULv=dS$fG!lfY9fuG4m?h)wWsAK>|WG^G9Q-tWfynB
z3^}#<cqfD)m3vE@G3t1nRo~0ULYfGq|7LF~&Fpjgc4bBTcE7JebS<mU_Zs^YZmD6%
zmeY36Ld#=drJ|}h_NIs`uU35%WfFsildne0+77i_(hoig4>&i%;C&{D_EdjF4WojE
zK&C6}-k)@sW^ao&|JGV)DH*fKT+~uUUL@_0TEQ}OOSWu;<ElIFbGm&$7B?lIhDxPE
zUfeCCWa^--=t0N&<1Ba|4`ZY?uTv(UtJLV}wDB}QXF;WCx0A)J$>Hi8y|AQe!LX`6
zeKlxRO*2Bp{znVT(Mf0=WMB%31t>{Kf{A^0?i~P*aSrl7k#>@h4JhQP)V^qU;|M0E
z(*k1E<HXRbllw@QduZG=tn@oI3n<69$`LVgML4nFNHYb%PVlFjEM5If+z{KveBa+@
z?CCN0X`vKA0)_It%lTJE97P69At5bYF9JScmG*eU<%+&}_lmSS=unrv4l8e;Z@S|%
zGDsnqk4EGn+V<7j^++HCZ??|MuUnV7FOR(=vR1Fh%|FnrM>r#(+U2KfEfjYFnP@`J
zsaG6Z=ukY-ySIHBdMG~A^E0)Jza+^jF7GVN2Af=*badxXAl%Il)WF8EI$k<o0fGLB
z5IYm_8HC5q;Y1W9nvy;7<i;W9YdI9t!42&HA;Hld(%;bVZe9%Mec=3DzEF3|)0&=6
zENIuh7f+eBbbToRWso^3L<;|xlprkQz1MM8CGYuw8~03*9Tkzn75DkVz=rMCp*x8q
zx;W6y*<y7$8J&T(ooBh-_w?iV_jNI)sL3{Fw@5C`mpP>bHxl=vjpBN4-=r+Is#JTZ
z@Xgs+Izf7R+^8t@BuK_#ZH0)@sh!qs``F|hh}YSv@7Cr0W|?bFd0?(abz-&7?s9gS
z#Bgnu5>^<!v$kkmRxwscSME(T<PHW!^$Z;WZ4VAC=a^!#qOF0vzqhpeP1#8hc$qDg
z$T#7NOn1cE>|0-R$+X%w6zzDn_Ht&8;O}omkGrA97=*O<EPP_q|GR*v%YWw=(;J7q
z2#(KU__=tnB3$-@d@@W{mtj`(2{3VF;y;EZg>4y27#vmye6swnupiC#G2a!<)1EnP
zhD;*l=omz|{`r)}ITEKyk^A7}R;dk~&J2#6_A6h1#7g@r+-dlYNm{tsmpsJy<mAbX
z9nNQPzbqTV-0MvIEz1XO_9FwfsW;_LD<{#}C%$Cn43$tOo$$1(_FHFvvb-Nnl+Kve
z5#VQb@n<#SwQTjS(j==8W}|woe~z&KeS|Z^;#GE^g>pD#H1A1r<cp>@G&gS@8omh0
z#t;XNv$r4RL4LjYmuqZ2Te?T{+uX?l)7L{4({SCJp5B6VSuZ1<7hEUv+_)y)m!6;V
zoC|DK`)rkVBk8VaQC-j4Jbt^G3cqy8<%Qd$)U)nyovdc0%#T7nBSfFLmXKDIV7&=1
z#4r3N%PaTA-lX?!yn5TNH@pGR_HXl(W@JN^x|sr+*GM843r>^z`r&P1ZXrPpeT60X
zMUB}>E^J+>bdUAEibV(bUW)ivzs;()yoR{T6)iS~d98F{bsSFd^?aHV<&_3p3u+@Z
zbMovQSIqH}6;bLtZ~t60@RBsIlH7Zm<;mnRDtH&vBu{VZ7&PFye#m5D)YS20En4hp
z-^wF4Es2<4ZTHoKwX<NUcduZ0Uf@FqP@(0nnhbxoS5I|M+OJR`blU&kRznT)b?+0?
z1WV~n6*NbeG>hWa2qhZ17LCc~__fh<+syDxwPbJMBhK2K>V(~8ZjKa3q@K-Cc$6U?
z|Dt|gL*2RUyTj9}Kgx2pK=W}c5#Vn3cHch-&Qpx6o1<@eVwLQF&mtVt;MBJEQ#q^e
z&$k@;-hOXwCiL4_-3}kC@>^>Va5aAXz`l$kGaP?Qjsh^}n@&#15?M-AG1PH@kWpaO
zrgO8^NZ`t}gVH(km44e^O8rAaA5xWQCCW|pPxq8I1}_UPg<M8!IaZ<<t=|#cVcVRd
zr_GK(<8QJva(Pv4nFaTPAj5<etL=P0;JE}oA_?NxwlXWwfc?nL$#U~Q+C|m>*?$Xe
zr&f-u#HxEHs>!aO2O(jF;wmgIc|YeoK8n7b_?`*ARFCO>+jqAdPwUnL$YKVBSLBz3
zcIp%9fvv^g{TCM<_{4yH<?KLx>MGTt$OT$<8*S$IUfncX5^eWjxe;cee@*6TWfYOS
z?WEf!P?y_B=|lZQSz4TBkF8jNr>i+mOKix+mw(KHfl>-a-gXpaHg0*1ow4xNv{wwI
z#i=t0_Qe%1i}4*DdOvEEZ5+(#ku<+jGY;Q(xrIs7M%sa!O;^GcE$wXj6UyEx<O+={
zrnhp>>vk{VqeZ7y_M}w)8QEDL=nuyn+)M1wMs7c;wK+VfwcW9<SgFB@1+u-LA~2WG
z(EAcGBNscceQwHG(^2sN@YYjO`=QtE$emJc5L^ki9?4s|o*}~D&bMBdh@s*}GnJzX
zT?Pzy42KYG1Yn;LEDE~1-**<t+4bEgHnYkaZ9i<VFdM&+3-4t))Qz*ND$NME5l8ty
z*%f7G;P@X9KyZChiADX*sH!3<yY%KgpD|~Fm6qLG2GS?bj82xm`nQH4Y92*6x2dy+
zrfO7juj``f_?FzAwcq&toi}VfS^jLjORg9lOi0<G`s^zY8PppVQp9NKq{gv7J1w6S
zvIQhn2kL|QG#jlDY{iFM=lxu4QuEgn^OAGH;fKzG-Q0~BC#G<6L&x?23&oVCA8Ib5
zWN+>gG_ea{QsU?5iP<{wB4jrKRhAa6z%U-^q%Ig4aWG5zH%|{d>^xzBH5+Sou{`H{
zGZVDXk7&F!uhcG`a{UA|pgC3&Z%0wvbXYlPzr{({>5C7mfu8{iJ-ug#Cu)9E6NX)-
z<6FfV&YPCiz!njOn<GlwWQz1;!J*8|+QM;8ACF(UHs_r-AFzbols}HQ(?~Qu*D&(x
zVo`cu*#-pv`INM=M^18R1Fm(^eIo|FO_w*ri%D`ck}3yPT>BuoRju6F@eeM9E5hOx
z+f5@LmY1HMH}`zcz_z`Vn43@L)Z#OrIPe*PFD)zmWfuUip+);_o-OSA>=)Fbdh)Fo
zjXa;(+gJGT`e(j}*$hL&;0FQgS(J*U1`5+MttsXJ9ks46Xmi7uy$u_UY0XuLByro9
z!u<09CNu+IXY?ftWf`$X+t5x%BS>Eut~QxP3TPJ!)@jshYX1eNnW<+YINw!^8s}_7
zI~)&}GY7*~xK$C1Q{TsTvu>L^aVQbC#<ubXQixuIIk0GuN#@QjSPid`(fA{Xf670~
zuC(!p!MEMZHa4U2$u_#uSM$FdwjSOHTpgu$^1t)xyAWe%6p6WvgpeLe&tM1RJ<2d@
zUAUX=<<!5i&_;S8?C}ofun4e3;5DonR4vIPNau-TFVvbM16nqac=(+YoTx%}##6T@
zg--`GyDpN&M)bA+X_(yMu*9JF1{wUhzJ2o?7Cdd&`VO31Z>qS6MSh|&Qn|k|&vhid
zxCAc1EvV@SQ$0;p<M8)RyX`!ICgzm-!o?jT4<B3;+d9)NpSo|B;~@rqe@N`_7VQOp
znoxJ$8|KR9Xh*t+gbgu>T-~atwXb6|G&XWS#dWzJ?im}*PhC8+RQHDLCdFq4-ed`r
z!84hkDr&XdDvCV*uGi``&pzaLefn61JSzNV{4h6OEHFP9dE+SEVm(G($tbt|aDOp#
zn*aiXeYS%?2NUZ1%iLk}>m=Y;#s(S27aiSoj<MpN2{8b4#WhQkgZ+>f;#g$)<cko7
zDZ9<#EI<TxyN%joSU#9pMe?pO^3RWV1yXVq`1|A6%H>F{em#}!wGfM1A2Bu%T>@X>
zD<fcXy>j}<Bo1o~r&5lNm{qquoC0^-i7l5{muC}~Ha}rAVD_YVQu-F_hd0+=j=vEm
zncxqi;?*1Gc9V!d+|5<h)(L+76}p~*MG>!EKcpsCGRX2ZuI--B=Rr8W&`S;-v5L#*
zv>oDVKw3%Bb^0vR0_h4J$^V>Epd#XCi}-xJ#kf(l6;9Jl9UdI6rd3-&=zq^Wes!c1
zHNmRP6R7MDU{De_!yl|i@VnhAt+hPY(DT^P@;NGGY|V@jU%eKh^l~AQ$I|mSk@mSd
zz_U93bSOzV_u7Wu&Nov^db0veg<v5$qLgfGoXb~F!-CdTGxh8cP6MBFm)B<bOAM*@
zuJmP76LF507q6c`q<leRrO%@yLVf+@+{>hBS*BOZb56-;z0QifGWChMHP+XADL!u*
z{%F8C4?dKKi5J~gLw3xHJR?9M;hn*7KoowcaSn$2hkI)!vHFtLCF0(t5^^0nFiaei
zK6f}X=B}EH!X~j24KCj)YzZz@vSuv?y$bHer_j0)g!F5=iD(omFbE`$LPMhNODr>^
z;J?c5#?aYG*+D$(+BZvUE(t9SeQ-Pra6Chn($b#`tJD*9TSJa<!*B5SYq|W!7{p(~
zcSHk3&QY<!Pi)lP9~1thd1{-vAV%-uHoME^<3WH-Rhv`GEn*qus!#SEY>h)MgdJE%
zTSG?98Mmj{ES<pZiLW*enx4%dsliZkoK~Z>Pq2An#(zUKac6IB*{LX)x!DK|%{He?
z+g;BoNR&z}xjh!0y`H)(C?pK*q)9N*CE5CTZ>dj5OMbpK89thVp*!8+C0ZbSkht4G
zD>*6omB$+4Gh?jG#u*J0u@Fit{P^v=&|LV)e<a2@R=7lIcs`!a5Z-HLV_mS%?}iF#
z9vOSyec~!ns8tMm_M&GGgDoRBXTzY4Dv@@mqT*#u(>b%Pb=G*Gm7-FI()oSG{{*Bf
zsLO6(JFNyw)tf)wjkYGy+Ef|qri1~2=@*0JR_^bp0{-`Nxo5wWeWQ@z8VJ?@RnvQx
zUfl%H`kt;g+zNGF_J`^`Q@Q2?-`1J|<wGx`7YHd;TA_zx&p1#|0;7Rxnlvs)azYgp
zrCFGsH_17^=LF>ZPnHVl?<duK<kPw>QDqKWtERxaoNo|<1R(kr(?4BHLA{NK{F`y&
zPXyq@0|9K##l<e2_Cuj;?JE3nyZlzc$kH5t)N#$qUPdvmWW3C^?^eTT!t?$jw<wf;
z7uxQ&^M1Ni-w~<inhN={S5Mg3Z1d@%xhI`ee;VF(Z~C4(4nz3%j@R)<*mgyJCy(&h
zZjX*yBd^pIJLYk4P?3<{II^n^fz8^l*9J|A9S${d#H_-=*q-aEZmV_n>6xo;Mik9=
zwcND=OMXz{w-64g{io($L!os$#$OaN>k@c-J4DWT^{QU>xe`qTCgnR_7r^!tuTQi0
zGa(}Ae|UilEw!lJ=5O2pJ!E8s<gQ=CBk|t7cO5hGxgM|I6Dy)T1UVy!S6(X|3cC$q
zD-at75{GWGfipP0z6i>B-Z_qQFvp~s*e!C!_r6*g07nv0D@SbkmRu&%obSrfYCLu6
zWaIM>6VH<^?d4UjE@;B%eUb@6SF<Vtd0y4t_4YL%{-Jtu2Bm6)uhIEZXpR$Cy~LvT
z<iBmZspM1D-fx8Z-PWkZ7<)l$zDNXu0qkS)m-?%2F*`dyl#UL@r6{!wBqTeQHQL{s
zc%aJ8+<R^mR1)`H#8LEbMbUfC88wBv!Eg14p5_Eq`&gw!uSq0w62{JDf8}KNUE}d1
z+z8_Up0jq=Yob!N&)v0zyZ84iZ%t)}5DWk0@H^$na-H``&AA!UYSK6EMosgrTMQgx
z*0KL-Ke%Ut*|JC6Xc^(FapQ@%Ytey!_${KZiDdtQIR`4U&f3#r;|Sm7JVn;v#(SQ6
zDRVh>aF*7`MENje`$hY!OE;*$;d_E%8b4TF_Qz&E5z$r!30{pg5a~;`^5&JZ@)qaf
z$DX*7kwwJP_nF;?nNIw4O75u!!Nw5g2qC5NCKI_2eb-_dvd(xpCM%fru}YWu<FW^Q
zolUq?z7S26O6^c101fSxkujFc^QV4?`x~+Fj!4s)CF5-Ums^pJXJDq|?;WDeg;O8g
z+K((LYqz@6&GQ>Y{?7=}-G0J4hB{+`RQR2O34OZ*aXA7=rE9lV6HoUq)RO$?p7?#0
zqJUjrhh7r7t*p!{$WNuF#?@kTs`!sHGV3GsMupH!jVejtc(v)na;T+VBWB_O#m9yQ
zM}SB57D!0+Xvr+s06wEPPIGO&F%YZtk-JuiCl3Zus!WB;io8roaI(|3C*oB*>Jd%~
zh@3qnGwk_(d}E@x_3Hc8S3I<G2V2KupSVR%26<>73#H8rC({^Ges&6P2c^wwr$urU
zG}$4hoTLTwH@zVKsYG9S4o?cu*Vm5?3q!(u>*vjnc6WDo6_h+|(G7Rg%5Sk!A#ZxL
z;B05pOmQSQJIlhcc9*rl_BlS<?Twua){<^UadB}~OjG?}{cYzU??zRPLuuoW<k2zm
zQba}Nhbt}WDxQ)JhqMzpXDfn~ZsiB_Ia_P%O{0?(gBxC@l;^H6A-BHF=)Dz+c#7ua
z_6h|$?VIG&u7T#YJZ<63gj$Y`W#_3uWU(_?g<P?fKd|66)+Q?7E7fIJkp4oAaevoa
zx&9CWB@yw<3GN#!aT&sYIvM!S0aN;|g2v>+cJCg7FFlT9jdg);6>ANDP{H%_#h#)D
zod9@BpQ}$kXR|j#QdQBD!esJ$P_VR7yI-yMg!X4;a{dh2GX2xV39g5H#+(zTrpS3%
z2<_vKt@vY+A3)vajrp~wnN~#pnWdoXEv<N+ogODlVzfC+mM{tzX8>sSXg!||f8O5}
zlIzg#`Vu#bt8)m&ru5k3+<mDKrerA(HA=`)@wA2a1Y*q4nf$o<gn<#OG~zZqfT>=O
z>3>s^4tUo?`Tb8>utX0LdjLl+=_iVZmh727y4<5@j}PIBGEOVzMaNkf>!RO=J%Q6G
z#p_`<3I5l`fZ^>n#g83z5rDoM(`n(uPhnmW`z*aP{fRf%Q`9eosr6OUp-=MuQVwG>
zX9Sv^jd?nBaN%jM_ARM5K5O;d$q_^f^}id~mDra`d98gQ?&74(J!0?wQKXyi1U7CH
z1<pe@2qVYQ&$n{LVo%pxD8)SPYJEKT&s&2&d|_jYeHxj<P-TM3_Gj!p9dVauIp|Ak
z;sqn88m-(5p)K=?zqj~)V)?##Cd5hCl}@Sn>y-rBEOQjNKg_GpahGEuMz!!Wr;8;j
zM+$?(-5!)Z_!AxrC*V;+v*h8W7KPvMMpf@-bneVBH~Z5KqbK{6OmuVWaZ~QAm*|?k
z3;(&}E1(myH0XQGniTsJ+*FC!&*Y_?E<${1u+|sd48K!RtG~!>OIn4_p0(`;u#MUr
zH}u6O<KtT&7H`3xi=zxuR~AE~3r`gkgY6b%fhZ#0Y$OV-PEPdB8b{11-ZRI%+)ZVF
zLU*yx2In<D(I$xg(P%!_D}@`p1<I4vt^;{no|fi389KrpAFk@vr$g+7V%#CrGV<oa
z1nIs9Jmb4*Y*4Ux>Mmgi^~wiyP=j;#QALWCqh0rm8&pF_v~CSBzj2Y{G?p?EovJ_P
z=VfIk@0%y4pZPs*YheaoCF-&!j1pxsO^i@I{Ks6Ikb=Bn`(lZ(uUkci+!M7NVjSt-
z@Y$?L1$ai`a9!k~vBgx;(eJ`cSJ!bPB*(r1>q~lkgUiu`PIGnkJ~owK#SR4fV~Ah?
zb&DgJjW#xXOJgiY4o*{Wg{n_j9rqSxpw0odVJGRKpFh?+hIr|n2lGP5V}>7mYSQQs
z)5l>Bxo!Q!+5_a~S|OzOC*@S({`jKCU%nV!F;+`>EHYF){4rdB!gE)>FgXp!!CAH*
zM?1d#K-o#o^h(H5uP(%3V2MS#zIY(GqNmbM3XUD!n+Ci2?0Q9`cj_oRXHJ!HkD_UG
zbVu<wQfsV5=#(_>Ry!x?8bpoZ#rOWaF$^$<WCaEQ<%(ay(W}29f?k(N<AtKy?t!hc
z!q9jPA6at=QOMgE0?Lbr`({af5xlc7dm*IrU3ZyI7Epr#?-X{yn+A4AtAY+_s=mkJ
zz7z7ib=6XRpf*_bLFeP?-n*~Ax9l)ab#|On<lngIe(5}HIIc71AhPuvE-0Y=a%S-D
z_fJ)U{i3Az-q2ST^O_-S&F2#M9(^vSNw@U)_b>JH5xRI6oOpLla=aha9T%TFF3_fQ
zv8Hh6Nkl9r$-OwamCbx6pAQWkpRPeS#Gs3pf9UMPs`e^w`Fa!IwTIPkv++H_33g&{
z5>`XJD@7E3F+4^k7oSOitv8r&scJQUJzkZH4|4dOM;lDVQ$(YtJ>WLYnb^!%XfM>6
zjRWF`b0EWU6w$*}{(2fM4{#n-TN%?GeoyzEtK&$xKBwMm{eLtKk69}f9Y&FaEf{k=
zXscQ_(e#{wydJNAQhg@$*2;CwPYSo{CgL;BKXy4<*uz4$E!WE}P5%Ug{SGKF1S}(;
zI(L7^0l>7iIkW}ry=v2&o9Jt?B3{|Vg0@UbVt!%H;OuScZ+Mxh&IuQp;~FMQ_|+VO
zx<?>F_5$OyYteQ}GU%s!$C>siw>f8>EQFix!!G3Mfx8f485@H+##pkg%~*1=AF_tV
zjO>|Tp-&AtO`c_JqAZ#~sLtb4F4oqX*kiA4N_K9vCpp&abqijhC;6&xC>A57Foi|7
z9~$JW>OjF;R}mw}-;>h7&#nuvCVa-s0~R8vArOL4fjOJ8v~qUygH5jR9y#uV(S$<7
z<72oFVBu~(!77^iM${sI&*o*O)#50UD4}X+eRMN>vkCk$9?o3!Sax)&lHu^}TF2AG
z#cF3u7(Bz{kv&SbC`Kz$c0Xt_E2thLGSxr+tzmjt<;TfRbivd|l;=XF3n-`^<qGGu
zOZ7$Y%dKmiRC)H~9$(7IK`?ig7Qfg-TfR;OtvvowomBMR-4|BgS3w&qZqV?1JpWqL
zS_MRe3hC$~+X23y)!b$IxGlG!Y2xEKG|xA*owmBX2&c^t+gMft56ir%tIMurV;DU#
z*d4?E*Z%^*(Qh(A4n3T~uZZ@{SD3S2I@Htdsw<g;Yx@QR`DSD-Cf2d-<g{`Gf1l9H
ziicPW$<d8|zhVPkUSQEYbuhcwop6&`DCIxI_Bsi5b%`{H!BZxU^FA%5|G;-fdvZ;f
zNJVB<kJM~bH3_O%2^7i*6}vnlU2U00w5J`sQy^Y@9v!~N&$0yGnlIa6mMQYZ&JEs=
zGGg@ecR8^~A&+=goiCsE7&O0;d+!Cc$O2`}&%x#d=o)}y4o~%C&>7VPOc}d5OyQX=
zwSnlKVk=Dd+@bhK9y5wEi702gEUO_A<({HHM#?^<8nLuhH<-1zxT8~m?g2LKY+jdI
zb6*HhsUUH?_Jevt$V@eJ^DwP0{r1j!x@sEnN8AqwDG9pUY~d`~?ZA_k(T{bs8O5!+
zR(EL{-(zgOvFJ~jM?1BKaqpb7&cuw3P>=r5pS7^II9Y7sSHm*_QH^A+@L9?)1<{fz
z8e&YV9;8OSI%*5+#!qw>xC1AJjIpR)1W&Es>U%-vLcOCb7(P`K(uk3TYDIpq&6`^`
z-?DNNTwshh(NYIs)A#z^>VDU{K3>a@IjQ0zXpK0wEYfwD&v1^DTbwAYJ%9Gk52a%*
zXBS5{Zev!esmyU$ZBNLd7|vYQm<<2PncJ3C)z2J<%p6CBDZ1r7R7y<ipn_U-OWm@Y
zBd7(<Y@d-nv~E{DbLR1O@TYuE4_l#2mv63Eps=?Z9171JDv`^Tyt^7r^I4H|#HV6f
zi5-;`{XqZf$2fCC8TekQ_2p8tqhlxar<LIz>R5*o_%c|3WZY?Zn*KJOSlbB+iGVV8
zbJ-Z>0o?)I!L6EVuvgMiKW;msY;*XS|8pWU3Y7q2s<+s#nju_Q|55Pj15f1vnnGC%
z3VPW~j0NZgTQRcn;{uOIiY66ey$|0w_zBg#v%_&qmht<F`<n0MXLrKTnJNG0%PWBE
zyzX~C<0Coo`x)JN3(b!EgYwDy%U55{c-7J|WU0~Sd^4J(pz)74<q4(d*bn9!O}+)r
zOnPT~8q%GBZa{u*Z6f?6S$pNmWr4P}MKEel^Rg4VXeh?!l_t?veeM%0gK|#R%JNpJ
zcK9h^)fDgehF9cvJ=o(C$bVzq{Hir##EU|S*}ESvI-{yTKFIbA5RrW`&h^HdAnUDc
zUX6NHhhpI5r{;ASzUlhD0PX#PohJp6udlDYlNzGyJ<c~f<H{rc5t|%pP`-ukS2)-m
zFU1s=uD4jpZP7=yjK*2vknWomBu^PI=;;c)9MzvK^$l9uT`qg+R%dBzD5nc_Im2kh
zfPiv)1;XB!X2BB^z}w%S@bpNb(50ZkqeFvXF=w*gJ#8K3EvT}AGjC<DJLcy}i%i=G
zjMDOp+^2g?+oifn^$tJR=MNCCW$6>!nm~t_o3pJG18(pM*m--0$S~s4qML;b=o@yI
zW$kRHOe`GM_aH5Dw=Case<7d6n97dR=rE0XiymH2b-UBN+wUi!WEp!Vv-ZK{PD6Sh
zZaH<T%g$hyy94w_NeFGQ`;pK`B$bfs$RcjiGw4#^*)#RfuS7JeCl3ulIW276sExj~
z{ecI0xW8jp#}Hly;IB;sM;AutLcSFviuT+GGYcl9JHG`b=s=0N*gDp}8PZ;iCHz_o
z*rTfcu=jXM?XnrZsspIR36zpHDjoBT;QGS%pwzQO3;MP1_4cQV3ZIwR3DWZUgXZ~A
zpQ7HORN7`Js`hxwpS)K3v1oO6b`gIrq^|@UpyuuvU4U=Sxj?R!-*hAeb6W-*Wr9@d
zY7XLxMtxDqpSsuuF3#<rI6EFGn|1mqZ1&E^CcUyVfTbhRA}k8E38m2YkQFAkLr`UD
zv_M;V<NG`TQ02I>>HTgU1$pes9&7C@3J3=K`w9ZOx9DFzLGlyvcC-)`3{?lu_P)lQ
zR-YZWN%h%~t$2#hpv}+;;fM91ix$sB3|d4=JQ$Ln$b$UuXq+AnJJB7d*4B(5YD2AZ
z{5CGi>~-#x-CI-pA6m+Rq8F4-Pkq;?9~?hXHhb;Y9xfG^H#dI@I8lq?v(VUW*S_3c
z=dZTy0iEjf{qkVGSEIgmZn7-jGC#LTDOr0uBe}&Q)hC)+Dbs$IWlqbGIa7r{%38R)
zIWTFGHm6zUU5IL~I3g{k8_F^DY+tmWK}CNAdRSfl)H*#{cw{XE)@((Y+XJq2Pp=t_
zT>r>GfC%wxooQu)G>kNU@+uwMSR>l5-i04K)yOk$ST|te3+uiIYpzz9dU~o)nofP2
zcqfbuc3U9dK|>A-I6q<2vb%I9MLHX*4+PNqxZDYxC#4#nbDZ{$Wdh_{Iw{T{Vf-aY
z8s>_<1CC9}sPdK;`0fRg;cldVT-OIwGZ_9zS#|kDG9fu2;(JCja-f}6DB%p8`W1xo
z`e+q~yyiwDnf*``WOueK13HU5gSI3@Rksi7s(mx%yE(payDlhjGBh<)e4FRzh2*k8
z>)?T_rSYIFQor%AuzV49D9#1D#GWeXr{pTVahFzB&RMptfFB}ys=iqGakf7;_i~y3
z7m4|bA0rn|8=_(C{em^Cy$=jxpkx}?8EzKORTA+R9=3KC#1o$^fo&ZHrgj+R%9pD8
zGo?$?RZY{0I`|*L=pHa#4VSAshA;O-IX)Mke0Eq29XSq(C&Sd01ViWKV>cuYc^ALx
zi&dK5?3d;at)hVln_qiWT(OtO%j~Znd`Y4d&dOl#y6wr5=IY#C(t9Y_et7NW+1+Br
z_to$S&5%|$TjagSX*wkZb?N%|zM3c?C*|(#w$4<UDqhqiM|}N$4KsR89NKl?s^b@s
zu+zNH^!=4Zk8L3bX{K?#GDP|z;?L*tqB8rA*~yIH@ZY8gmH7X(<YI4Cq-oqiM*8mU
z2AO5=kc7&2!gD;rQl=W(c6sgpEL~6PfJ5xjoed2mfV$-?e7a3fYnLaBlUfrgm1~cl
zZQGMb?UTDZJL^&Mt6-UiNkH?|Cag@$cHgDLxc%ZPm$HqVx=-iDJ)9R3#S)1t!AfSe
zc6aZNF$+-sz;Uj$OQzvuIe%+Ba+orBU!74{{^$hU;B$t08->K31nBfa#k?;f#4Rza
zuRfCI(f48FYM8#MtqKTZeujltaeBbAjO}!8Oa49P`mv)c<O6iFOE8m2_~k`Es-W5*
z^BK)+pjc9qVQ|uk)Gs|%hGPK+P0VFl%G7#}_H9lV{QfmMlS;LP!7^ciszJ+7I`(lZ
zEbfZ%NI^qH+E^6eqsuU#Z$wW%ea+?m>GW>-GBhKAkvvnv`ZDLweB(x(utD#u-nh||
z-r(%L$zLuKrAMQ)$)>9+b+bOTE?y-}@%3w{3x!w^7P*w5@(kMJ0f-~t99eEzg@s#w
zKXR*F2(rKMVWv5Z5M(~6|MA@PtcEtO@4#~1g9h?urfMK1HFCPLk~4->A1PDQYrV%O
zr97K`AX8!MEXXFF*+T_kwXK+h|7ji7=Xl&=F54Z|yhfz?c~ec~X@V5(fWv5fepRmg
z6r+Ut@@L6e*N=jy_Nnv{RS3EMZ)QDRNrAQqe<hRMTCMf#HPj1`@8-85sy-VyvTqk1
zJ492nqVjmHY3qBKeGO6&(vCC$P#pTAlt<QTDyY)4aF+Exicv$S49z|@;1B!f`V*lX
zBheS|xV)8BC&uwJ*O!ojmZO;SZ3T350~bMf<YOgn0Y4Ou3MHp{GUYgiEuGABp^?_&
z<1BX!d~e0=<E}QFB7>9`b;w{>DpP0WZk1cVmFtw=yf^O{ZM|&U=LK-w^3=}g(qP;O
zj5jSjK8~G!Sa#OY!8VS1SIK(r@YGS|$J1XF9c;UidDF58Y6!0Iq8>R4&ZYkFJjc1`
zz+RTC#2;K#LAHP!YI{JX-<^OZ^(4o!W=`0z{<4}qZhjh$xu~pPa_xnb3DA%|E<>@E
zWCrY*;>3}i>S3SY%YhwwDW*851iVvqJ0{d&M=r{6y(1@6pSK%xwGln5*bBGSjOSyi
z{sDWKO8HLMh=)02xO>F<ohQ%XGat_%W59j^v&>uGQDv^LkmB`WKK*YPIa<zigr_ax
zH|C!hC6GO48enVGk-Ml$F&;Q#n8b=fxQK#h?-jA6XrPL%5JvZ@5gQJB&DW>iqrK$w
zNH3dSNYR_u6!Y)#1WX4Ys>H9IRK0*Z6MMP`S?GDXhsQOBCB}3k)x)p2BBPY$wZ~ol
zmF4NRrxSN!EZ}Zt4zG<yb>II0nm}d0wMBgDsi&O9_O2b$oeAvn#qQP3lDW5F0-p)j
zhhwO%t;Hul`AKKV{e>@l;gA$@{`~oP>7|$UDX(XreHKfXE_E*4_i0B(-Xj6C{NK8D
z>mK7-1p6&~<Rc&1qiCH?%;KAOgMAUMZ2*W39I?6C6%HH$8=PcqmFpK|vf1yJgY^^o
z?Rm{!Z?D7SfE6@NLjvoYbkkY!In|Yo{S{WWgB>hykCWGTYQ`mgdiCoIBNIDMY3qM-
zPw3u}8w(3op6qvTi{5ufz$S5Qj@6<rwhayZY)`e5DvhfG`6%N((I9%N84xOQy@W=0
zPr>Gow%?Bh8!T+|K^GNz23^^G&z^Z2UYhqh>N(f2iO?4R;U}NLw8<0Mz9sSKQ_ps$
zVfP5;qI=xvQQ6(GqplunH*D;v1MKVMpy#O*C(wnr=kc^NVz)1oCyc|O%7NHgy&Y+a
zhL-oTm8+;L(pZNAr-j9DF5|r>dr!HI6^VV<ew@cStM?|DYjTCYA>b<>!cJ0}jL?82
zlZvqJ(Ku_=i3pwZ6&jO_M$><M3+cKhT3C;xRY?TtwpI-I;g=9P#v3e=Ep?P$*)lEd
z@D;Jn+BJdJ|DmblQGD|^kXrv7Ew+D-^!BxUn4tC(Dy9o?i^n<~Z{|9B6|&B0*EI2s
z#wzA<lkRY{$#?x^8O`7-@#qg+8ljn=adob1BrS=+!6hK!`rz{2_tfqBnZ4H9#kAwQ
z-P|l#xEVjb`57!*zTF9@W~FSwqS<clHi#Fl|NG|>EPb;EAG_vwocr#H2s7S$E}YEy
ztc&rbuRg?!C}-j24kI}qbvAvoxHj=R5=m0H2F8x5M1Elo+BtH^-Ts;{TFZkM$hz7H
zU2M10(s}>}(dydVkuO}d74ax<5WP~kbSeG1HZe#+{l%9y;NP#i7gtgAz2v=9IagN>
z&Y3-)uEi_xU)Mi?O%#jm7<l!ycOH{{+8Vn#cUx?=cdRUmpXIUR&IXG!ckU)9T-)5!
z<~DuTkfy@y+<$i*huwHs`34l{^8tDg@@e&L;m$kfl1(#(8jH{JGY=E_kC`?EpZ(04
zICjR)k#3GS%hGX*cFj)T_mM!Bp^u{MLmYtc#d}xNGITf!6C3f(4^GELlLk_dV=o@J
zTX1uzvv#q%(=|24YO4(VZfD(Wv$N^B$b7TScEf7}4Z2ztbdPC!Fp>q^)EVFEV|G-A
zZQRS?l4s8R-Iy!%Bp=wUPMO8OrDiXyhhFGj7F*95=6R4A=gA<<V~erwb8cf=mxtNe
ziA+1A*c|hH(UfP)%knrhe3&+wljSpa=G0FS>-ar)9H2f91_AQ6`qaWs^8w^v7)`<7
z5XDe`5ehzh0k%B-3Q~9fgMxH`gB+~31<*!c<4rtANiN&-v<)yy@&=lZM#zov#N`)o
zyA1{58Kuz89~fiqd&oWz_Oxf$-(gQN-Wlt9JH{jB_}&?3;oz2X^`HNNxnKPeEg3n0
zXdgxEjJ1LMje3LWc4_O`uG>@q)>*Q*A()?s#+7UF?6qIS>_6XvqEW;5c2~s{0TDRB
z1YX<vdpxoJMzkh3(_0f0=g-^veg@`F!a1XE!qm!3d(E*qwiP?tS71lm3XCnAjr<&1
zK62jcws;-tqwBD}Z3$m1I6pS0#^J4Ag5xoh{wBP*^~YG*@DR=${S!<mKd(#r#|*j>
zgNu&FKbHSHHq#AZ;s_J3?O=cS!yn?*Q%~(8AYX971^C_Xe&==Gb|K;|n80e5$wP*)
zvv4<|hVwt#yu7`IapT6}gcDBaZF8Tmx6W&`N*^_9l(T9+*af=ho_qG$HGE4;3;y=E
zze%`02q$Ow@Zs+K-*wkroc^pG<{I8U?bSVuA3xq%QFmXzyXDHteVh8pL~AFg>@HDl
z>m*=e>yFlysB2q`_CyV@1+|F88(~vW#FA|^u!z#@AE)Tzv%mGO*<gOV=-4wz#yktU
zXK$ObjJ+WAc$OO+fWq8Ll;#g}(^QlfjzekQNQzqrPO!4_G)6a}p?w1yB5TnY*~BTR
zYFO?D4ydN!LUG3Rz1aP(D;fJSShjGeh|^&V!BJ(WV@&xhmUrL#FrvvO4vydCcr`}X
z^ZQn`Mz<rHY^HT;l=+%)ZWGX%m`^V3HwpUkI4HZ6Tq-zK$58SZMLtIz5H68K139d7
z-clMO>sX#`Xrs8ANVl;(HVV!9k+Q)P(QS}Hz!zqjN>EWW7NdDDFtqsCY$Sbl5ZDn}
zkEQj`VMXISY-`_$CJJw9_FGv0Sd#s^v^%1KQ3fS>l^9bp8TIr$m*T*DUy9FH_Ro0T
zr$c!CjU`S9HY;iiCMchD+>Eyjv?->g>p3&ez`b)GbnThNU%hq%-dw(t9!^_Py?qDQ
zn_&$Fbc(?j(7pJs!Nl=6+rn&LfjtG9D7~i;=JX9?Mvg>vO)YDV8HA)c&1D1U$!<n@
zSt;9rjI*|O&zMZi=G7ilS%DElckQR%Q!d)x7X<u+C(x>T5Z!<efq&$Q2uwN~fib7M
zqGX~ml-%(>3fLV2GCPek9JuMD75=WlMC#~2pJ4|c+fLR67dYi<?)%T9Fuen@dp<=A
z77Etp?wf54Vr|GoDOS7AJpL%y7P3Rx{XTY0cemedzzrXA5|4fdfw5=8KWzGLqidrG
z9B2ajey4DtOQ7_+5-?@W-Gtg|yV3LHf<OQDb=>jWS2#aaqH~_+Zaeb!)rYduu$y-|
zPPY<2`Ps90=#gdk!VR-9YGfIvPald4E|`Qr{P9(HfgL->b2nikZ+$}>A9A8@?&pe%
zLW~?)jNIG++S{0m-)@k-a_~NZ*43@F&ThtZT27BXs*ECZn4{fmDHgML#f!$B>^S((
z2Dgv`_{fn393_^fF#IYWePk(axZ!L}KYB3BRf_jg$iDSgFL?X#!f(NiKqL!W+9^YF
z*|uA{EC&}Xsw-Q7qTs9ZHgaH6!imf;)6H}?c<s5R#m2v5rF9smD30I!-_JO4{^~1_
zLo8-Dx!xh9_?h5**HxAIxblkQaoMFuyW?<~?>xwC7W&+40^S+0v*DdRZ%3<P=KlK4
zZSA=6f9ImwHxj;h1Rp<Z6h40HPzq?x6rBtBaAxPn9!G#D7jH*(8)Z0Jistcw&Rl`V
z`8KX9AE%7z>PppaZl7G*fZ8UGOtb5CM?K2cp{~Wx))HImM+pa6jN@-&NO=M1X`jI!
zKCCunkHPzWWS1<47g!)xD6_n2izo5Vd8^$Gu^W@Y8{grjxj1FY2tHymKdwyk!OC*Z
zx(GKub^L8UziK;PUR8q)jqPYmlyHDv0OdI;*w6QrDHS+vH0PPNaThkim=8f#4%_$8
z1qz_nv&x%lUxqhF=6>4dzvgc;!?}?a_K$#_OMC5FE4!y?%^`j2BgOf~{lhB}zVsA?
z>Dnj8xs#6i&yQevq#en!0f@5R1%rHu=TlyK=42Gad}z<%{MmNy_-7pqaoz++PaThF
zyp5LIDGreEAxevXALlO*@WEd}1o{&JvvQvM<sY&sXIs9Di`rfpFkULoP73~}0_?;H
zaU^tz?&v}V^o_wOS4^n3`KA(Wbl>09%onF<VSGP-cYBk*3FkJq_PlTW1n;`@XKue<
z%c2b;AOZ)RKuf$DkFNd-=GXkeZr$AAFT1a#0~}1RxEkk={V|HdgWr;GGp&SH);)~n
zb^pZr`j-(&*K(&6uATNgjw<y!t+G4CDO7A}na??_@4}Mp_i@N#y|chJ0lOx(1b<$9
z3C<q%9a>*s&)Zmc2jF(CDwv87Pn&}WmVbgBz4v!oZD-T%%TKesw9esd2kx$ANpet-
zz4jC=j#*sWeAl_T^z_4ELiSL0)^6IgsmI;3g7l@A_CAonc6|BempNtDwjR^4l{Uw3
zQBl{<!(Jo5%?;MMUR71)EMhGQn|Io*%}snXA#hK@I@Q1Y<uBumGtO}7>?!`+-EJ1%
z_uqeikGmHa7vuQjk9Y3fdx~)Hz4zk!>#v8!Q#f!0?Du6{Kk<oAxcXxAQQOPrMz?_*
zcFye6<~hIWs;f{&>%x5rcD~H=T4MCRP}{q>n<>aWx9&&S(zt+qQ!&?1vFn4M^-a0Z
z@##LY>$+#b2S(QiV4oeYG&{9I{z8-$jB!Hoq{@p>Q8=Odo_n=y0`2Ced012XG&VKN
zqXq0{#A(sz4|ZhCa#?v@nRhK?$7U-W|KX#v#n-p|9+N6A#s!mp$^q0AQuYw)+t*@4
z!%Mt=K2KH)De#-9#{vBuZ@p`tF5BMnc9}CHZZ|IBKp~2AhGRt8shCiCK8BZ^ND+_;
z?nP*g)nFaVvZn5FY;ITpTSkB)rd^VmewIN`<+0?gD2@letZt;+>5aGJ!bv~Jq{{c$
zmVO@^V_Wb__1#!f_Y`8j7`r(#tZUTH2sgn#W!EvnJ__qP53;g1#kOM}+qw8)K8MZZ
z0Erb<$lYAZ_ArVU=gxEDwI@8Urxo>lUSD}l<w*+ix;uH?7?^c+PhsY1C*k>5UiGY>
zDN>tp)!+a1h`T;#+e7P%JpJSoXw=oU|FLFAZO1*DCy?8-p$W`;6DsMd+^Zy3%naw+
zZbuUbf0=L{i9}ILAv^0~)I6dNuBzlQO4`{QwtX4LKm1taeePwp6%?nt4PNgQtg{7V
zQ{>}7pY9>SDa4X*F7M(E(JZ%Z&g#_4=Yckg)?r#$XM^kf&Mw%eYn66gv77H~pv*!%
z$p?_aas*HM5QX;<yNRz&B5-gBsC7FyT;F!8c46{5At)rUov?YIZ~4{pxci<3yfE8b
z)+U(s6eu3iGWfFhPsdeP9Ovd@zU?<J;<h_pcJou;{Nwky2ad6H?`>{+%8J7nJji=!
zY-*0sy;!@uQQNqw5fdj=p{k;Y^VSY<*418a-UZjLpk^_;YHd9qOowCkS>xU2wd?9Q
zqRhmPj=-{-OdR7K#`&jB!WX}A78|_`<~;Z&zJB9FNd5RJ{PA}mM2POlhYl{H%X7M)
zpc-vrpana|B!%NR#TdJ>%m%Xn?-U(7SF=NzqlR|Wwa^-U8(n`7b=Kpay*KxC0=Ug@
zZ6czVzKy_)$J1zNh_GYFOUT4*lOW^q7$2w$aPdVG@S!VbVAN6NUiRGIV%m98c$)+a
zAaD3myii+xwz+m~5JJWmp>;_LpZxPmEN<h&bf6U{j!NRI7fh#{;~3|!E~Zs4NA6Ob
zwRx;<)a-X3ug5EE>Uq(oT_{JB`8g^%oy@@}&a8TiaBYLie(}UsJhYM~C;{H}+DUXH
zaeFupH!5Q1dOPcd)MJku<i|Cq4Z#Ob8BQnI9OS@J!xnrjpXJY^*;)VH7S?9#)a382
zt}Tg=-dAt2z4)nITsb9!lPO$hB}1mjI%@M^Cs5s*#&4e8i2LSl!ICD{T|RJvf;H`G
zEeQr5kd^=W7M6>HQ{FWe7au>6zat99=^XO0I~|L6aDpe>+wavv2G#A}rNm^-1V?k{
z#yQF#1t<ycxueY`aiqVo9@zP?!4<~Lo@uR%*4i5yGjp&)$I0S{{Vq6G$g86KjPq1R
z*oKAKzT22>3v7zq>2(7>ypw<d;as$GB>w+r?>hkFD$cEsv_-q>&9Y?KvgBSdwlTIb
z#U}I)q2+}ZNJs--LJ|`4L&AIM1s)+J0YXCWy#z4D26y9zyKKuvR<*3&w`u=#=4xe0
z-j!u6gTWemwR`WKJ2Q9g)bD)X`O;6GjGR-Z6U26)f%gwMemn*}^=DEO1ZkB`|2fRu
z9dO|EpMIYUTQ7V`@n-WTfvNh5pR$8FKfe)eoO@aW921#uw3FLJ3Pf2rwne^u_FLnk
zg6aF&pB<VMwD{(=P`e9?zg&myk7gCz{5AE^ObhYYfSn2sDq5TIThESU?(y=RZlhti
z2`q-GG@e$aSy||uDnj@0i%F??hLNC+<?wQxaA|wX%K>gQ?NX%REpjSgXCGHwy^m1&
zxR<o<S0;+cpzF$;|IK?cKX}cry5O9pxrn#1&ngJ|56xgaRN0x9pBX7hDPRTIXT=bq
zk;29sHAneWpA^PB>3_(_`CW01zK6=71BLeTm~TFF8#DB`7hFA0J<<BdGO)mUw$Fux
zL1q0Cg^G_qA!z+OHZzXW1YCLyd@liy7j^>FSu-YM&?z&KddyfP49$j<n$ppnr=rMy
zqc#&{pK7DP#9QBhy6uJ7vvd`T-u@7^TM7t7GhVliZ1;Tabj?sQZ;A1>o&A;Yf|l<0
z^dI-vKz|J!dJXKVT||rKZ==e)Ni2V6Y?jd6Mt<<q^L~YsbAG};9vZl<{A7K#tmGe9
zyXP4+_=?Sh!l}R--`nSb?j>{D<B*qflKGsN^8?J^^&5P$=P5J)$rpB8bSviWybYo%
zKY!G{oZl=ESLwt#(s9|iC-BDB6s)QE*AZiReeb>ZV%Dr#VSz7!X$1uZkQMK{@4nl;
zI=UP$U%ng*7A(LSXPjZq%Mw&T_T!H~-X&h=gZJNm9|r=i=gytm`Iauf1-Q${@8J$3
zMvS0quek;w*Pc~Ec7fI<B_)vMxaw!&!i7B+L;k)+>3rL5xAhqBP%ggn&O6w-b7$Bj
zWR?D#-~0x%XU`66k)5BPZ{D%|EBA-Kt$`RStLh_ATFM-1L;46@>HPg^=)1LcG4hwb
zE%H&H+TO-3cyZnJ+_Tgeg9*N_!x92fv<_`sN2ZT;SXuNiCT3lRS-C$ztg~Ilb%0ze
z8a88n`3u;x_ibb8EJZEqG_!57jd30ebduE_AgPYa+Q#HpzJ@VX=NZtr<3gt&e)?zK
zUOf-%%brJJ)l%+#Xf?<X;2u-?n)*5fQA2B>5#B&Ps=fJGRrCmkrJsZo^L|9j=c%1a
z(e<Y+%-57YhE03kK$EY`T<j=M`@Xg79lbkpx>~WR?Dc-&n$?wG{Wck99ztVVE#EcA
zFVnubcV_?4PTO+lsNK0b48PS7)E>0o=jOdFr3Ee13*pPELtt$-9((F#$}bq9eeW##
zZH<Yd*Pb(L2Clg1y#1`QLvPJZCAo7?KNSx@`J6eW@pN}_iMd~CXwN2!@o+TV4YbM*
z1&yCl{)vmD6>Kj;BGXj*okJ7BYlT*i$Fk%$&#yC4NJ;Kj22)LZA%gr!A42dh`-C*%
zw8Ty!>t?8pa4!-~_c=k1(H7gpA!J}~Wxon|+4ouyotzE(<jY~7`Za>qLk>0J{`>oD
z;8WFrWS;%uh^YZier7_|lq?ZA@Q()<<MAg~k(t3FKsFPY=4nmRor2<PViy=2FO-d8
z^r%b>&Q75ZY7)e@Pt;}Q4b)Qeb5oOq0qK?|PIYxNt*GgYmMB|kCx6o;ho@u1`Vv~`
zIXJPmHx+>@OnY0wUISblH#M7i2Dqtipx|a-nIuG4o;u^_1RqbUY=vQY^ou<-omAv$
z9UyW|HR*)7Adxlq&RtcEHPTR_PdRol>grlCc}ljqSI=BRf7zlT7P!+nH=ZPYN-Nf^
zEyv`^IXLCy;k<6eQCtB8I;yU<obG1Q|Mw{;4M*YbDja{jH2^m^dC*MX-Lgu*`oE6H
zgz=USIkPBX2?T&Bt^RN*HK6(vY$Gs7t9!L|PPA+zzkI6%E4Ej_;f{x!nw394_gG}a
z6WD5F+cS0FzBpxkHD{X|3_tp|=)$RtKtM{1!f-*0(@=aRpn0CHu3J=|G^o$M(@t6s
ziw5$e(iALtwixTE+;P_>laSyr5E%0oJjTT^Q&R~e1yf!=NHojnW4_U}v{BIWw-ESt
zkya}@sujz2+wsF^HelH<g6vNAT`sg8qy*RFdg;eUKsY;$>4&PrRi$mX`iVkZzuSY~
zU6_evI}2+Br%L&720?K_TH{8cxUzwujBMt$Ymnu)cC-Ox#dWXW!&#mqFKvw-qI1&d
zP4v`IzRUVw05M&b^L?u!lfj2yB2;4}IQZ`5=Uvxg8AmlJP2NN*@>HpX@w~x_`P+b^
z?TyaE9lTp~Xws($Fh|}i9nC1t34RK)eC^$$p44t`T7Hr=jZ8r?+yt^O5>PW1q7=@0
zTk7DaRi`~xKCr3F&H_ZYdO0RLIF_qzF>SqO`o^Y;Jc|>BCN(vF3%54Uz~{AdKJfE6
zT%RxixyEI>rtp)-Ks~`hzfEelK{oVgW}}YbXW95DrY=N|XXiNKVE!70b&r>MdfCq8
z=iT_~rc+QGK|MW7G}~p687ZE|er<ZCloTYO@1T;E-x1AzXO&O0las#%RQvc*xmE8v
zPkfy;WCZ;kZB)gGHWuh+{$bt%?FCZk83n%Uw<#Bo)nCQ&8bj@8DX{pNcN7%>cpCvu
zdZ^bAJArJbE$w~@Jv8*lYC-H~)N5snppp*rR8);Fd@2Iyq&(v>^?1y4M$uwkgPDlm
z1H5LJRlEw(9}cMoJk_-ZwDwZyAj|2Vf|NS=3XprznHYQH*D+wqMALpF-osZU%V|eq
zJmLmt5#*kM@!z`^TOWG?>;CUyDmIhiF2X!!rO7dX>$u%-%|kzM9kG`CFZ9<ypVq+2
zvPbAc`bSjgA`NQ07MZ%K!{);IBk#ww>>Ik|Cu&y#){BepF@UuIX9Z5@WG{(Bk~4>N
zt_i4XEk>hvPZz({R!r+}M%-9jG5$#mOPP%~xBifGo|H7r^n#TY50XZ<0hf;ZR~PWw
z$+hBzqwm)=iq%I5crB{!V~#nd^P_e7Eg!m9Uwt+1x#u4I{O3Op3vlZO360-=`|S?k
zTIbF^_uL+V>qUze84%U&4lgVet#VI*a9&>CXF?M$fLx$sTwEth89rZ5P7Y?yoQZFK
z^P2{gzV+5y4HdMWJzRL^nP+g*O*bKy>$tuSs{f~+q7p@faM48<nYxnIb)-7g{g+;P
zDc3e?q5Yw+Yrx1~`nYaG3<rH()}uYWwWZH-|5rnId*OCVeOEhLqXxK-)I8*iZonty
zkI-^@B`%uqfT7Bclv8!nPAo5a0GrBRrM!cb<xGRyF78<(-O>B=DwCS&o`y|ff$J?5
z@8DzRU0l1;SXKMm!Rp@UaNwAxUYM?)NAks8wF^+Pb~Vlz^;=BH?8?#+pw;23;zzJ_
z*Wcg?R&~_Hfph6y43%X_%Cz1^{?f(@VrALCv3l<lw63P13*U|0L&~6En{8`Y;|~b}
zv}tbtwj{oxW5(r%Ubpy$)}TJogM?*6;cSehtV)tAOGYgj4euajcmdv-w}6!3Cz8p<
zGD_j;E<wJl=PzE0?K=yNLYd~lP!Q{<?oCZe4xQ@wHj3^lNPCcs(ym2dFM>Uc^|X9k
zD~Nq2WNjTv>eDDBh3@VD{$kaD`^xjs^4uE;Y}yV|@mhta5H8A18JKT>_aF!TbOx{{
zn?=S5IHr7!pf%TP3B6+qIOx3kZ|JXqUe-YGM$TR))*tOqYk-p}C(+PG?u8fD;K^rJ
zlU`UrpJh`et6(!phsjD-$4i&&#FdvHi*(W&pLfm}96xO^eNbD#b#t?qzNU8?pUqiW
zNk~hh6(>VOgO}7%)Y@UvB_-tUX%7>|4aAFN0uvyelVeTBF)q6i$nPLEI9CiNW~;}C
zlF}M(kR8<YwVIUjce$N>o;IxbxCmSF_h9sxftWEp7tcQNbtEUc4dB>9D&%<|78pT<
zo%hb4zYUjMIFVp`9KQd(6L8JdQ<0cxSrV*YSAwluD=eUr3mH+~DhMnP5G_Bgix=<2
zb=ORz%Ll;@1Rhx4DAr4qtZ1pG_sr94FqIVN3Gpuc^3Kx@J-CZNhJyTi-*xAim^g8e
zIW{XKvmnwTCj%{TxJa^=LhVudzdltBXxV7lD)JY>wpTW_;OURHBH&6PIF^Y2J~4`P
zNz@$V!Z1h+UcF8Afi8g!*7_G}iPlwXQQIYE`-RZCeJjWy+m3=cV{YUUR)`{l`l5nQ
zzCpMsB}~Oln-hPXw+rz}Dfs1?IXo92$i+6sc(Ro4UD%hu;wzUJg5&Di@8)}P!;|?a
zt|xdG8*loLox)G~_V!Q^8O8n-ExMNWpjw2DeKWcd4}Y+kzMaU>aCsi$8BYM?;R{wT
z5OiHd0CVf!Cetqk{ISMZE3|@VA03;q&kE@9mF37yS;L2qV}|^abBDneY|&%}$V6e+
zxWjXvz0|TV+l%@F`Uu-yjMkbuv=MwAN*{E=v`jch4TPt$5j)5fz!qWJTK6ZG{6Ijd
zY>J~~!pyd;yor?qF195~DxylrPEcM2-;N?w>@Gn=U6au+PR+_d;;3BMNw4o1n8HOb
ztwT9p3YgXq>mvg0BtWg4Wie{3!zncn>LSNhDILWS1h$EBgX6iUhJL=P$-u#O8sEr#
zdEQ3=*W0cg>}OeQ-Y5*1a{{8%q#exGLWk%+qPu<~vTC&gkp=epTAJV^m9lR$wWfEL
z!9&W-7Nx`d60-&%ebjK+M`R<4?kH@uXcg^}j}jFp)01y2FHM)#J3|)jP6y{Cjw!7K
zd$l!nRaBv2XEDn2x1+wS0x`@pHYpKtnQ4d}ME^rMS#S;@EfVKoCsFR8n;<>wBihr9
zmUTN3EU7~6j$(Kks8~XQvpYQnvAJ2S&!O-Q&V-F%zp<X?3$oe8%D}Qv=)*7g_>g=v
zw0>7TM#ICd62n^dlRnzVHsLMVgBos-n_0#-Zr&a7u}H}sjD(T7bfTIJXKFI(qo^`N
z#WB4oW93Js5uqXhrhPbjKdbIbAJ+fn->6=nPX&(fKu4$o1R#qJeA@r~7{jml^cHze
zg4h#o{)S0w#sB-GX%nG5!hJ`xz13~q!Eu(%8|~|zaPj)j_18du4IEkx2!Q@@*YEM+
z?q6F1OKXW}_Ds<nZ_gWf-+rLAd{_Tx`+s9w&0NbWO~W*ol5SfDre)oLi32Z3dh969
z08w~w)f~LH`)A0BKN&Lz--Mi`8DR@NZO}JKL7s`1w|oorZRJ`%z)$_g4b@MR5;Gnb
zjJ}Uk!9Jm@tgz1?^;cSxHDmqWXN{0OY{H+>$=*<bi}~fJKm94ztXX5Miz5|n<;s<2
z-xyL<6*YENR#rr+@2y+6V)N$Bm@;Kb*d*7lUyp6uwuQZ>lV_cE)@Md--B}>sy+E?2
zz468yx&O5rO6W*+s%PD}apPeD+t;mI7ZH#iFkk?czt3Uch%&%*B;b19ym{t*j2kzu
z`y=)rf6g_a_9uFJeQIlK$xK&c`jy^gPi^yaF7_8Pdmk;?c2s@T`>|PjqtKJK|In9_
zR%n~4YvxZVQuuGp9iEmTDuV0r`ubaN<&>uoW4C02p*tkMTV4D(mhQd}&Hf59KvD+Q
zi?l=c_x4+9Hh4?AM6YexjgNNxp4P{&QIsLW91B?P5~=$^sBR9WURV~Nhk4l=@J{|u
zkxC!fgOVnjoEp6qn4AAI6jZ-!$|LZ;FN0BnLlQ<0IIAy{{lcZScF&{OQ27`Yc-ct#
zU(z=NP<MI~OA?~S$ZM2CNZ=v4;=c3NrJxan&<A#M3mRta!b!E~ATuTlHT8AaT~cbO
zwl$`emhZu9Z@))2(Vh6tbzetp7zH=Yxfh&y8t#4Y5hlLx<!cQxAUzecPYl0qbI8S%
ziprob$rg=!J#(h#KXMh)IjFBVr~t1Z<@XR?5Q@`1e&~Gv_m{5*Y?MtU{_0lLTuSyt
zH7t#Ll1H<>iA}&d@hP5Ln+lHo(OF|)KmHonCSE|fAg#LlLw^k%0X1Naen&t-`io%I
zfWT2nLkb8C|Kp)$26zySwt}YR(Bx;u?);b9Xq?P8Z{3UA@Awb>GEc^FQ-)A;JI(+L
z%RVl|leFAkx2~MpWDeAlBKy(DS2BShHf<_5pp%5?tJjv`(I-}ze0J=tHUO|J2A_WJ
z6C~2tZ&eNbY!MA9E~zCA`4YGYHrLe9&qif4HO<_3?1`0#i*@pPouU7xyq#9g&A9y+
z@6b>51e|>0Fr?F8a5E`tSFSF?3om_wRjZ2VU&Jg;uytD{etg^Oxa`tNw0O=m0Cs+U
z1y-#o!gJ5BA;8{Bi!_RQ2tu!4SH@>pVX9=;u39))md#uC;O`GEp@lbvH(ZQ}zPySS
z<(7qmjd_*erdwV%*4D=yWBKTIvEH<{$;wJJpMAB;ljcAFC}@#j{p$aF$TeWK^EO)Y
z7dJNJe_r278f01>5~LpDZpMwLOrS*y?RPl|%Z^t)z<|u!ZS`S@5&*~cZ7p^?C(QC`
zAt3EFg4UuQb_S|w?dU^u>;OFU;ci?oDUmeYu`IQ$EDa<Xv3)415aqpSi)AwFm)Wcd
z0-5|=r~@}XwGG8hi4;na!64AU=clAo6v9%x2s9A{q#r4jmlQKWdkn9$->^>HZB;B+
zDjr=>kMSAB`0-cr+S5LqUsi9}S?)ziofs2n{mM#{J$ZoEL^N^5IQFTdIcUTn)XUdx
zflD3&i?aN860~kAE<@>pkFoRVx8Pm69x=4=q-q4KD~bT74NG`rqko#X*~h}2HPBf4
z$}%_9YC6}eTA<p2lm&bor~DivWpOPmx1q2YHE%CL<%=JnZuxq&G*+`O@!ioMxvaZ)
zvVS$kL?L0~a7;XRHe$~{1p|+xj~t~zK}8%HH|nCvR6#1#L=EUHpRAJG2p)S0LdR0j
zOp`B^&RdT9k5;07={i)c+d{Ct4o=3mT1lw1I^t8N4#4#H9!6|t3WfTlSLOih;pS6T
z)q-~$=wG{P%_bB*@iyw`E{DIcgqF{IM*dS*5KtfBxE+%ai@_&ONA^W?kbLe;#H6KB
z352m^T}>`-DJ+RrOe`Zl{;=^`q`B>5`nB`dpzLoiv&^gDYpz2hO{@L1bd4j_?P4iB
zv3v{D$0PgF(~x=k3?z-rLtsx8Di*Ck<@?L9``NjOt8HZ6_z~CUL>%j<jsQUmwY7&#
z8Hd4_oPoiYpNqC3>1d{jdb~z47TnL@LEaNhzV9}^hX562oO}<q#uk*US&yA>%tON)
zi{T(p@AnY2=d-F;NpXfGkZ2p2j;Lv4F!ai^Foc%6aRkWeAi#`cj%Wvg6>d6=Hh2H>
zF4XD{_PcV`dTf3CMOsDotau~JZwD#gPWsz#`^@q=@8Ywu;r>U_Qofh#oF3E<)OK21
zIj?Y>=m)M3_7(TX@2`Pg*T8!EnEtT%e>gvIEbcS`+c-zg8F~kf&;C}Iv_xzAa{djd
z^X)RgvMvg=9+h$~&KdD%42T=k5kEi%lmPp1ov#S%Jx^f^{a>Fq@^6@ubyY{4&OgSa
zpNE+ZH{tzVcW@2KE1KUp<gclG2&2=^;`;C6&e5sR>&D4LZ^Nd&uTja8Rv4Yi-zDM^
zb|7nE`B0AJ*LnBu-3Cxg<D9P`(E5~9PQkO!J{$H)JXCmFym)a$;QGT4Ka7}{gu?=3
zkEC$@_1ELgH{Zl7ue=hrSbE+SD^?s9;97L$vQQ5fPCM;1<mKg=OR|E$>#n=Zd+Hvy
zfqpt=6)u0}{o(Ve0quQ7(J$ZdqHh=VyZph6mVV2YEyg#t6eH9JTe;TIcSxc8^C|I{
zFW0jAwWw;|Y97o+OHghFBx=$&OaHCmnqxM_S;P|bLrSWYI&iIT=f`40q!|EZ89uK4
z<sUoR!5xD{2HbB5@a-x1Xyv^Jt4PEBB90q!T}RYbUmf1t`U`BVc*)2vdJEF3{Bk4J
z{W+9y$K1&xsZ=^P&%_6<ZY=D`TR{HYt+#U@xZ8Se?&rb<<Dx@##`}#}g>I6f#U*B=
zu><Qy%De*KRWDLLi=q~>?h#wbf$GJi=V-x7T325{;JU$6j+fVe3wxT^7+^bGdAgqq
zy~pn7_d9NXJO}sTLz8DA$vtGhC`ZeQqPmZ;VXr`IO4Ybmw}?E|Q23Jh--J+e(6MP$
zY6DiBF7)B%9-h~W;8!5oi|X7RxcQjtxs#@AHO^u0(#rZj@6R{Z%WYhXZKCD%T%No5
z+%wxV>-sfe>}X6JHyZ2cTUhHhg=n_3v%fN%vhVQhCS7R3Sze8)4UOUF&#t-2u#9rQ
zjt95;R(!0*#u6jeF0=PD6~RXgqNZ-2@mcq@`j5YSH4sAs5Z71FLCdpmAc_VrQ7%f*
z$fQWCkA3~6w~uY^;oOlk4fYw=!8Yb}vIO?5xZm5P`{VW3Kws5BzhtwoYO+t#;k2U#
zAJcm7g|(<AxNKPihi2)>nP_pQQOZt>wN(U!*RL;!+s)U@2~}EXt*t(^w90Z$mfKMT
zh%4w1x`N{FHZJgN1j+=z0mX%Nxc~meTq98E&&ggssFn1ij}px0oeC1x$oKX`4=*$4
zv{V!gaxDSue?9Us^VC9tR480IOYAD@)55|U+;i_jJivRLJn!d%qLuWs9$NLOOu9$6
z*%^23#xiUo0PUu=v_NV<scvNrP0_r0J}K7;Ok7P1>{Xu>DF?Gi7WIn!d_VHYaz?Op
z@A!jd)1rn#UTPqTN_Fj80s@ry$Qpb2h;&6Ub;N`iRQvtmaMgg-f|NR`RY?IZisUCh
zD!}IAMg(JN>F#O5cTSGM2>MtL=@~i*-SF)ZwxP8o@9e8Lq;#SPM5iUVkeBK-N+K43
ztv*5vNCIU=d+X@Gi1|~o!b?BXUSq`^Ksh)2Z*OcQaP1;6Mr%?5%mlo`2U?`Ee~ihF
zH5!t^Ai*bshPfG9W-7-vdaicke_mXS!WvqiI%`Z<^U+$SO@6d}UW`m?!Q>Gs$jKUx
z7NUlqY%9Uqot0>`rx<I}I_F?MObnd%7X0<yO*rq^OpHk*=)wvUNU7cs>c8R3c<^WS
zPSp1nuis9A2CWY1JX!k$Z6w;*|C8N8j2^T<ZPLN#^u@hHib#|pm~kn{Hq^$!)Wywp
z>9UpBeA{0TShEFfEv*DK2{Ln|E#(aXtN|))q>v-qMs3^HcRwa=v=g-7MFZ!D?(b1=
zRwJ#Kn=prqZIsPW?J9<2VcE0qV%HxZMd60cNFj(D$NRO_a=YWqoIo3Xrc02PLaD|T
z8?k-WHbg%)AC)&=j6vVIm{z{=u+fErE5!OiAHFoxhM(`ck%E9!QlwTbU5moIpG48(
zRcNEuaw_|QKw7s13IvKpt1aaxl}clH(16KNq{f$u1D~6KuZ_YLyXFj*Cs169Ex&w_
zVEbZ3m)9bJ6N8;ngci;lO2t7iPwFfI4}t8~MpVD@7AoFf3_LRdlYaIsWYNEOrN0@;
zE)shiX%EXm1fQY{ZdN&eQIV<mj{A}S#`~lgDZ@aX6<dOfTB=@JFh@~1By|)A8A@8_
zuR`(S4JaNu5bimXkyucP@<q!K!}pTG{@E1gKytuIfS&+9r?CV+d3`IbRF<rPXZ2>3
zf3yNa?)nZ=rc5w`75UflaZdA+njnGQN0tQgaotu>gf(|RiblRKPi-AXgP@^5XRo3X
z0<UXwrsHoH>!@u{EgIfjjGZ5>M%mP{82`r~AbrLpG^?#~UX0>qT0$#UWq<e-H6V-p
z4fj7nWrh~mBT&YLk`+bxlso^s&p>OXD~fV|lQx}tiIIcK>7+li=mXH0M^2&tN`DRX
z*FbM-AS?bD&f|#$e(O7?UoNXS#M&?_t*d~ux@8BR-S}U$P}^gGZl$k1)3nTQ<J^&V
z8z8xJo@Ncm#3U#eg=@f8kJq<-2Z^)_8j<>y&QZ<pPs*RhveLhCKeO+WK=CZs3|eZ>
z+;_75SF^7Q|Jimc_gr5b&{{>GG-(os4<Bv-wf!E7Cj@*}RaKEWY@dHt`Jj#jTq}+F
z^XD7-?Cw@d+8g%d-+JiKp_nlvyfsz0vVB(PuD$kJy!z^^TvKUfw11GrbzcWr-%6n%
zT#)726<1u*aY^s*)KgEzOE10Dajx@^7hZS)Kl#Z|$f|a9E4X&9lf(U6pouKzMV-HN
z=~8_B@yA99LH$XVhuuGiyF5pG`c=&Z1iZ*R<#oa3NJ3mpDq{I@ac}Rkk-^EC0=FZX
z*3b#YBHC#ayP%ukELjWb7D&p>nu(+w?Qf%%ysy^ypDwLigB{iLQQf-T(1S-9B24v5
z#<!*7EleGJ4P|BA+j(exy5V~$X{6FHm6s0`1h~@%!OUMSd$MtPk?7{0$&tdc#<84o
zrbfzVDQUHN2qZVdLq+^nZ$0HK_1y0^AvreNWV4R6>F*cZ$@9&o+~M*z06*v7ILAO_
zCr?3U{3N8t4P(9;thXdnKLV~*FOrQ4v~Km+ac@>aO7e{;sQdtX8aEg~zNdN-xLsPi
zmi1A=`nVb8O$4nEK<Caa80UMw5AGvv1V_bsA{l9yz_MFowq}1R9rFcHf!S?O#Bqba
z(Ix89bl~yV(r@_RDKn5FKI>6`Bvj&mExaVj>R5nTr;Kd3<60-nYsIzXUc86rjhjar
zGHq?xfXBA|3pbAZJ`J4WaLM^+V|(FlLltgh#FYCjU9rmes&<odxqHysVamkuShp$P
zT+*?aP)82S?H;w`I3qpHddKZ&#LiY;u@_@Ujp&FI=|}nAy}V@gLFJ_~Cnd@W9NRyw
z!V~$sj_$0q9+VPDS$PFrqWQ?ws`lR>4z&if$Cvg@lne;fxsSjQf7>_VdFMj}DL053
zKup6;#!Jfd_8WJ?m0bg%pYjIE>TDxVfqmw6u#K3_&Ua)!Yq&?~KX-K1KqRE)=&FSy
z=4q{Ixw^UuD`|x+qbV(Dx^J?>71wAMasi}C-q4b80&8lbHEH{zNCTMYUIiEm=8*NO
z1?cLCLEr)xPTF`1gciMuCVc@fZTzi;7)LUeg?X3-0RacmQE))l3PWYo5=;UbesLn#
zkrp4;u~}&HzSd>}=++!;79$2o*Tm1~WRaN;E$%cyFwbTJ?Xucb&e3))0{M&Qt>ufQ
zQEIGV`HT`E@d<dD;Jq}^n2xd2WuiVVR%8vDnVCrH^dUIstZ^7SCWF4f1!JhVN>Rc3
zYkv0sIXpGM_abXeC$)mrp4OGu<K9Kprp<}*HZQgj7avdRIJO5_AzDETFnQ#qregb<
zzW9>0a*&$1XHLq*<ChJhRkPLRwe}5gL)b(u-?z4w;b%{-Ls1Jimh2x|P^+B;xl8+a
zM+0!$V6KU2XH4*#=-7V6GKwgCmMpfl55GT}!bm$>d9R!FU|s^venORmrtli6(%)TI
zNjD_}XnCrBOh3F@ECpI|^{jOK>eLhr9+=Udel&%m8F*~rHr(+>8L9<)O7k<C6v`~Z
z?xrOC<HODP(<S5KWKurTCp+02)YAJhL~)Z`*3yPo*U@jVgPY8@RMYPRv{{uDc4Sg5
z2Dj7v_hr6cthB5#MV7T1593H-T|aLnR{!cgv@ThJbk>!h8j@a)Pb~yPo!lrpWns@Y
z;$xeV<+OwCN(|8T9B(aYb@=yakD+EH$8V{3IH>GX`RqGb`v3lfnBqN1veV}c%i!f=
z)I|jhzo!+61f4yk1ge!{2sb%?rr$&bgv@O_vF?BGhR55Cu{U1@KV3c~NGicJgDk56
zHQ7cBoc1hOhZR4+8?6MbV<{+bGfk<pdrd#Fl%CY?q>-%Q)zEsf4x$|~#wWBEAnJ9k
zd{!r)Gf+^9^?&*Y>L0oZdD{R0KmbWZK~#SogD7BYigKfrV@0Yf7A`6q1!(dX#kOUD
zS@tWN>Xkx~<W_?8?<~TyvMQYR;IEM|bu^l|a8D$yrh-SlrnxYWt@mL4KVC$|L(d_Z
zUBVj^hidk>=r(%ECeR;Cr2>_xR%{PcsB-eT9keEI+g5}og3Gn+>kgi?IryCHa|smc
zkoBEUNaZRDWb8zkDNaHh%M``@chMrMnRR~ZgTH|%BOXol4M?Q6bQIGFa-NblxxJ(k
zEAF}<#m~QrOdmC<dCkWmuZ9~Y8;6@XD)=~9t`?3H^dP{wkmVxqZENwMZqX8~y~B@b
zzxW}NPM&J~cxzk@a%{6&o_YNKKL=3*l`GewWZq&@`R_lTcTOfii?$(W&c?{EceQZr
z9Oqzvd*~)$)&Jg0y8U{x-uEnc(1tp7;#jlxQa)`IRs;;KTu*PFYaMo;1JR#%F1N3V
zECLjUH0&xy6Y1-lX$kJBrh5aPZ{v7j(j&m{OiF+|BOS4HJrF;1FyeCt!IhTMIjKJX
z?Io>#V^J9zcI-w|smfMifPWuZT-sO{0{2CmCaZXNMw%&?m=jXQj)05Qeti{uw4P|D
ztAU1HMQARqK=U4!qlzPIdmZs5nfByNPK0~l0J<T_K!WOMNH)uH`0B{Z@kZq_<tkJC
zRI+VJF+%T->lqF}^0gjA1qe5p9OND$k?siOcRhx#2>Ln%sQ4smeGZrh891lKTUI6q
zd1IeM&m?d?ChY>OuX@s0H!D#i@Q(Ij-Ja*VP<zKZ(kOsV<y^YQ<jq&l`Ok^z+27s|
zv<_D|0odN?CcL@r2e@&{hh#LdfLZMY7w*0rb9d19GZzFrG=X|wNO2CsW#b-2LQH0t
zB%A%!cy8lW*wr}SED^h0Ji-s+sWg|Hn;Q|h7A2;rj5`O>g$u|QpdBs<sFOu$W@guV
z+OcDYp(YP^kIv1WJ)17K4qrcwl<$Gg%ZIe6xhpCv!baV-YggFwhjQ}0_uiw+uB~B{
zkPq`YbLMopcKPL(M+B~wjx60TxZr{=_w*n1eGQ26TS^YHau*H1fNFu*0&5~EKJ<Ml
z`cv#)1LNsKIWP5O%DpUL+5%j=$2z#<?4&6emw6#teLpn*m>2E5m+xBE^5N47P}Wh~
zQfL5PKf&c!H+&bRjUOM-=e7)G)ON%mGCX|_hNjFyYV2^%Ljx&u>v{dGlE_Nj&;6ap
z-#~yqjaJ_;8B1#)_obQ#_Y0g$9dv_~5jzo6hkOIW)8-(KGLznhti(z5qkRr$=HA45
zc?=7-|AF#B@x+A>sxSGpo?q}glr${u(b8J;yq#rBaO5zLvoJX6cq;D?HVOx}PVPID
zlb&0PApMiVL6DyS_rNX!*NH9*zz<)dFWy-_W0SX>>}(BY{S)WN<a#Ll-lnj2E+t|m
zh9X>hd)~_JIR8*Krwy%f*Jyrpt_KszEXa%`m+>W2XusK<Z@-yeRNSYa@%^&5aMFNN
zFgAIDNod&69OM`5F!B|xvl_TQuIKm|4!G70+VgY|J&mD*G6x#DwB+TXc=D+|=fwRL
z4LheR))e{EHgfj%d<UeZA~8Xx4#E~~7&2%Oin;e;;TbQ0GVVQ!N=h+2Z>WjW|K}iU
zfb*eu`5JgXS`ObvD*ccdG1yEHI5`0}?%nLe2Ejg>KA(?2hQC?IVcLQ(zW{#jNjtX_
zx%9FR%YkFu$cP1M<9^M%m_~vfvhPk&DQ|L~Gy~CeDb_vMN94hE#W`rY_c1u84W#8Y
z<x4e{EL-?~X71MpLKfCBG`^Z}HXJi<Ky=>p?n(C__t(G?R|Aq8_lF~_1{xZu9Y|lH
z2C&xr+l!#ZL1=Q<G_J{BP>GrQ4R|atTJyFnutV1c?P>yUmw+&yAYkh<zxiKj2~5^P
zpiSQ#>;xg31u%vC8nbneiPw=IV=FJFt@FCW0NXqefFVD))*bEXGcl!aC|*=xQzquZ
z^8$RV&|VnE(_JP_-NiV>e3&3>K$+<tS@O{u&&EY{60K>EnKTHepEd$hC+E-?wxy}2
zH1yBhYEo1Q`om{Y1G1n-kboT#;wL}YfxY##5TpetS{iWr@k20z^ud;0=>ChtJ}qCU
zZJCBc8%}e~Hm!Ee<#SS0D=r#003VG@$3q``C;$(&5H)TaDe$Q&%zHZ)*lbUcg6=dx
zIYFt=Z*@+!agX1Q7Z>b6J)yuTv%w*#&weI)<F8MO!T()Oz?)XMK?2312$L1IlV!Pj
z&KPP$2l0ouNJr_WkXij)f%W>)+8UIeWk+soGXZ}sUZ~6wx^5yJ;vdx+HER~poxs{M
zQXaXJXf;b3e{R5h)Do9;_t@d-+{7N{cX)`8aky{S7-JN2xWUkyZ=%oKrGI)5u7w+r
zMc?FAe5ZE$JeKt_NF6O5$rcKw-4rB5bK~Dk1&3&Iert^NaRW^~N;9^q+#0o|WT7C6
zZ2>eMf~<1iP(cuP^{@Vl<l+i$nB26!R$Jo+*-uJtZURXrM~!!;&3^)P+j!kU5GtKg
zaQU>(rA|ieZyrH)Y6dcHyb#p{Y!msu)kXX)hgdoS+l#R1j(ZTbcs<g2Z1gj^ankd~
zu$@ce+*mxD5^BU&Va1$5N;_`I{2aK|I@-tv0)pcLQ&!yf7^)tAf#9zbHKcp1Wj{=`
zlc|95)z>&H@?&NoHfR1Oh^v0v=x;(gEfSj7?!xAu{1ub`dOQ4+awx3fJis}}$+EKx
zp=J3dZ2iZxa8Ym&OR9Yv*%Vwn9~VUspU*|TWv0$+Qj{T(E#?tp5lxUehH>ref1(@r
zkltMNA0$w1=lhbY0D;jhoQI@vqko*mF(?p^hSwG#fA*Wm{r;tJ(N8t?OLQk%$VlL7
zA@K3Hr%?35n;7GdHP5Tj$1T8`@5E1LhB{IIF@7RTB`bQ?o@ozqUy#Fbw3b%kOI#1&
z<h0unKVi5TXS}Q{ml@BLSARH|8rbs0OH@Fn5<(bl`;K&2zjkW7Pq^iJvpFqz>J?-C
zoH!yEaf1dLKh+&^d-0>SvKpoHmtxa{PojFm7J}9X5c(;9`rghtcaBM2Aoil0?<7V4
zX0Cf8Z&v+OrWkeAC8pdx6|0_`?mbIap`4rOnhjeGAZ);313I$9w70F0Ql$~buuKZl
zOt(>CM1XAasA0&MF$IH8orSasqdV^FDenVaGcFcBT!zw*mKq=AEftmUQ?MY~cJ(Wj
zZQt6N1C}WlM<1(rbiY6a8v^VDjz1P7=#zX9sn$MCsNY`5`dNwcB`Z<0af_jLH$XMV
z9Fv*Kp@iG(Dr_AQ*jJm$U|9y8JOgRRj-v&A<oDN`a@OW=N7<r}QL%J2YBq0!sUK?M
z8=q&Y1XDkzU?#bJ<zjjD?g(VN={qfHR2~LSpNy=Nr&B><46m6HzBfha5~phICKSK7
z(6mh{7PN`Aqy1fUi5TWU(FAlKd)s%AchOm4VtjU|PRO|h8!KOC^)+|2C0R%dxIQW8
zM@VuFHiePJ*OaWQ@&4|g8?aba9*k~<=*F`WCiSYoO1ILp0SgNMjI&1k&ItD3-u_d3
zT=owm(3i|fL7=s)&MqDEIKcz<uTDXh)-P<n2HWf30heZ9xS&2faNxim%N-H|hawFg
zJQy=)&NLL-p>v(T1zN9KwF;uJ?i@rRUe^P~=6UCx*E#wT^E)Xi$<$FK;JUiH+5nRh
zaQ8)!0R71)pNyE)C6`>XpY^nWY`vG_;^O`8)B96;vXl?aCHqC{KXFJkAYl5%7hl95
z|M*AaQ(OukI@ceLj2dv-V&Qb>9G1e!f_qlp4^Y{-1Nl{SyFlN~VFOPKp!ISe_3qX?
z4+vUwi(}+#S#gtbe9pHGXziqB^}&W{?wOq28SCD{>c!0a=ahRy2CZcPGd260I63df
zl!5FI;Pf_sS**_(`W+Njk-4gFenhKsf#-ECrC3+;BIo=Fb;nG##Ea(IY1-g#Gu`h|
z*{tuBB76HxM`FBPr2XE)c30a%Adgn|4(_=Vor92<ayCY!TtGR3rC6@Aw-LB6teGoY
zC96JJ-<C@=uAd}OZU?U^eQy7)_o4ND`;(i+cBgA5etVpbdl*;g+Gs3N{1QedjW_ZG
zStxg;)1I6p7Lh_vZ6Vw}vbY{RC=0uG7n`wH<L}1&t;Xs)Gb6I%#fHsWQBhgd0bEOd
zK7Pz716YUh88><)maJGEIyCKc?)(M#Uf$Im=lXy2t_D2ZH#PqD@9-^Ki=g%?l&M7-
zLpZfFnrmopXOvE87!dD<<5<dO@BTT#=rLU~XnE>2H2vvch>D{zNqY#A@;&aBZ@}^A
zpN39#{bu_0Klwdco_Lj1-!zN}1!mpGd)?QakC@X=4xQcaJNEj^(emK)uno)RUX2pL
zYAP3MgU~&wg9)&XrAx|VzeXR{S0g(6nC<|OKySaX`j7i-;E1h(e&G5Dd^L6smWGi+
z6L0(EDG^xYX;crvxSZUzfRZp>_X%(g-J`p8O#q0%UWL%2zyMmjCJ>~{9g4>DCYXhT
zepwq}YYic=nR!u)qG+<N)&fI7GIMLcRp12UF;A7D^8&=c7A6la5%_KHFlAFID48*N
zTZPbRS%F6@eIBUn0>Bky5ibA1Mk|f+Qj%SmFp<=51VK+eX_)a_Ex)o>XqWhyQt6D9
zetS~war(awR}Dz<0YOfhQAt==S&vuORMY%Afo~xiY4&Pdal$lOn1<fb;VMOYx@uGe
z*FxE;4X7MGegqWR2=BRA-s~hUK1emy-XN`;+3p1Z`q}0MjHzvh4Lq%`V?IiOztuma
zQ76Dni||cV%~)R8NVG4><Z9X^<Hgdt^_yo-Kr*Q#JxFGJ`Xpuy^(2zwBc7Vk*G@~r
zvrCGxvzdK}+I8xepcT-zniy;>szdG=Qi&OeE!6(Qmh#|F@<Eyjv_7$*5G~w{u(W)p
zN;BptF}JM|rynzseU<elf7eH8;A<rC$+kpL#oOw^);~NBytfj5Qc|}P{1k&lkk-fb
z9s&#eXG=(hEj^AF^{r@X@F2F9T9!dKYMdTYON;W%K>(GnnEzVH0FXvj3IVz#<VGU_
z)K-@rc2Y?f{`o1`3rc96ODkhqLDv)fBz(=yV+$1|2zq6v!yWHHD;MYfqP<MZj#OGg
zH`^&RpkjoNU~)opGp(Qhj^n0{K>XB+v?f;D16nyh*=Vg-_Rt$}E?jARQwJ${pspx^
z>L97JNxaSeQo~~b*baflY?}hsHEstuaJMG0t!nCEdQqIOq9_pE@dOw955LJiN1J7C
z7Luq5-{_!H3&H6I0@u+ADR2(RpnC!O#IC4;lN!-cv;vQhcB8==gGO&Ft(9%qyKor_
z?|T9nfBT{F1Mg>^nmc@~@3OfcAg**L;-ln>fS%=@d^aAlfV9=4j+D#Mv^tEBiAGaR
z1Dfg@Y4xo>)kbS~C*o-hFW+rWN;6{koQ>>*jj^Qd8<2%YqS5htno!qVL+W+H-lSId
z@m>$#jezaqCtk$h)26^ReiZAEFgGn7-5gs==Pp9oy-y>F@3o3Pwi8%K<utQS;fB7s
znf|kB*&WXfZ#2u~-P?+mnmrsJ=|X@jHyfY5S%XP*9Gq``jO|H}VDv+`(*;HhX$1R5
zP<^y@+lHM*D0zR;0fA##HV>LT6X~RYS16gk1Up`O3vLQ1Tmwk;Nbp*|$K$gz3E<N2
zdP)-PwE7ijDmphm-2;dcys4zzfQtgFn@Y+!9&k=L5NOT(bbsDOXCi&Fm47uAVv63L
zZ!Dm#3C0ww>mSW`OkW^rxDep@RPw<hY=8buRD4XTdzOzkn2ozY%V>^S`t&L=ua-TP
z#zMQ8^|}5p|1xEjb$ZS@ry6jySLt+$RJUaZHb49fispW3tiDxO(d;i4P<{ZQzf(H;
zDKK}}%Wq>h-31Ij<0Kq+*N<q8o!u?g{)cT;UMcxtF$$h}&8j0>RjW=V+_371^KB1`
z16@*79kCp;6qnM%hWj2t%D558yX0&PyX-vEX1c`h;UK{FP&D@=6g>Aj_O4tHS)!|c
zH0Da6DllBWAA1!z7B|y6yRoPg#qTbprL!BU6UJir73U$BZLYVL&r*K)<PU#m8Vewc
zbiF4rB(X{LI!GBoeP`f|slC4Ea77%FI1Q5qehq8({)_XYl?$ti>!30$FL@AW4F6R}
z)>AUC#nR%xquy3*fM&_MR+T+MU)VR&g~gDLcs>2#bcH2Fe?xY{RIDuhHv#Ck4Hc`I
zG<jnPw7zoeGsvZ-Tjx;MT1?RTTI{NyCmq5rLHDs~STyFEfREr1M_SB-32O5KLFM~7
zT+lxBgAYE~54e8&?YF~4H|InXK701;h}S>Q3z8MMsX!GeG;<9ntF71wzBeP?7wNpH
z$LGzP7xuEt<-(<xUb^2kS%k|!_(KmpwBI$Ic;}sW44^u09GSNIL!Z`w6g7VQ;~yJp
zcloFeS?={|CjE&W@imZ>I^6)*5ev%sz)ec>6-5tYOXZspfn43MDcnH@uM=`_A!vOg
z_gs<3pNRQ&yHMX+ivMi>F|Bf&%zWAHvN@DB^YLUYId$Zn;Vyrwb1qV_YcA|52v9vx
zkde&HoSSjV@Y@a)^{BWmtK}7C4`Th^r@8m1e3oo!lH2+Nls5#nqTaual<g}qWAGhR
zK(LI7>RNZ8R<!n9^GRAI`K}B*G^cCLV+^Py;4<%vC(Q4z3mD19+NBC?sO@nqDT6t0
zZsi|YU2m*ei84<S(t?>-pP$dMk$|28^K(%akVwXtz6zSBXB;;Lg<Mn1GFt197VfuR
zeElta_ZwGr0ir{B*3<g-mA44wx1UfwI4BF5Ho2?v;{^KKPA1z`4PA_Bd>4zxs<rF!
z_6HvtzuTeox_`^!Tk$Tt@LV!I9^C$^ds<(H<Hma)LF2FQ=er>j6=f(<VfNu-ChQ2T
zYwyYpY;kI>-GcB2#dUVtFN^V3cu9oN#Jz3HGp{2mk!#*ggHn(#%bX`pL((6A5w=XB
z<Q!v1!g=cPh&k(2v=wb6RXAM(b+)+HCWMsWQ3)Ba&Ablw<NgcL>BB;|_kZ`-z!#<l
zI`yhAOqsvr1q~vDl%j*Ol2BSwZ|FrN=+q=EP)(q-0N&7K7OD(0A#2j+1x@e@W)j!2
z4y;S<zpdCEiG&VA5!>S{pN`O;j+L^K3*BUuNAa|d(4SD8(6_Gbi{Ji>7cCg1{YFz}
z3&7SDD=<O*<d^YJqV)NZpK61DYT_UbxSM`Pv$Enbn*OT4@|DpTJ30eHh7jm82w{6t
z9beri4kg+jKD!!VeFsHb9Kb6}C>U*}K%6FzZRFdXob95<suoZL<0xe1G=KO5+gE#c
z3AJc`OKn0*5-yfQM^>9L$`>6A3PRfkC~j|*rlE!h+)WGPALV0Wn{W05eX|}m?+u`$
ziFwG^J99K2QiYEj5X8hm9HQCSW6X_OG|__8)C$i>)Aw~|21e$jW5>pNws*GyF-2+K
zObglV<x~n7lgHS)S%De$S%}8_TWT<GGZ#m+{FDD(KLJV>iaQPu#-{pk!boZzYtt9X
zzR&Xh;v}R;*ITo3<FfgMSFnS?X{y6b;5vX>AGIj$ar7_UgoNx|WZ!rh9CIeZl|g{E
z)r;!g<=FfBLhOCzJ;YVF(D$YMgh`<yLSWmJp7-`QKE%!yD=|RtLKJZHAqEN+D=EQF
z7?FeVKe&oomLrjzkc3)KGkm1O&wt=qG|pQ^AlycJZc-(2;oVHFU|;cGtb5=&obs2E
zXrmTrGqv)iKGC#bEtWm<tns@Y%MG86N)&Mf;2UT`UQZ!uD=jUvuQ?m(<ER}>Nk=q+
zF98=?kgFdFU{1^%M2c|w^@=6PTd)g7PrXC>Yx?OXb+_!EcgDA|JT6pwn~?IY%aM2a
zS+tZ)<#<8MW&(bm_t#(#eM0+o>>{X4(3vIkvMlt<jm@vUi-fP9g7|Yz;zpUH3G;6t
zK(u4+22%zP`&v*|*W6J1Nu}?ZG7{syeL12hk3b5klUk|ay6vq6*z@~GF_LM=(%-d@
z{oO<AelL{)b`jtoa^V@6^0f<L8#R~!IotpCGL*mj5eonL0!DK@vr*B+Lo;tbtr#k|
z7NT;)HVhg!95LL$L@{4f*JH_V{)Q~FtFRZ*hiVjdvR&kn@88e*kE4a*&>Jp5dR{hy
zu`aZf?nTr5btu07X?XXPz~v_OxUYfrOT_>xj}*M~KB8Ve7ROw3X~&orp{4Ynj!*-;
z-=2ros+yjyt#vPl(%cKrWOKF#w}B^2#je-iBL%+)4TU8TXeXL#Hg+ox-=}E96yL0S
zxTe!}LuJkL0)%b76ds74JAOzOj=lXRlctJO_~!exs_fZHT-4bEjyneFR6^+<3g7qu
zn;v)^m8;jYOq}t!>9%pq_|$>VDIhmw=_i^rW1YN+;}Xjt3)-wxX7nm~(Mxao#}mdH
zU6$#x=I+rNz89&Qvbi~u^8FOP`2p&-?ZWIw?uKZ@BZcxsE3o#ShfuL>4Xr|`e4;w)
ztQjBfMyB5p+S^L!<IZ)YvQ(|zjJ>NiV9R4KV&cuD!lXsG#>}2V@!XHF?%sz{xn?7y
z@!dIS!_<f65_nv(@)J}nUyUt~JdcS#{3eE;cUn(HqP6~NEWYVZH0&-mb#yS)^qmt9
z5FwVucWh>4tCY_15BKj=a&E(>y{~gF-rFHymzS26r4M6j)^$iDO{zkoYY?rlZ^b)1
zZ?%AHF6){CCA6Ua6D}BiU$2U(J&rfnfT!1A$$ri?G4lxp=?W%0^KjYNCy<*wD|Fg?
zS2S(Hiv+Dpn^txKt<{W3VuF#0wA|pjV?*WhY%QE?xkNisLqdWj)ION<oqqc1NKQ_M
zsFA}3`MDN|C!ke9;NFrYOTtAp=T1KP<Su@+BS!q3E_{|?r2L0BH8q8O(pj@+8NcM=
zE?#!oW%$>>{uS2JSCrNAb^Xg<{xaOH{pUVq4eZ{%8`oWTo$+Puqrz2xIC^WKuBE8Q
zWrUU~4J~`{apAwISlnsN(yhw4RHYy81e`PWZj8w|x7*cEcPNl^-nQS6HLED1PiukJ
zLlaLTFdV^J@l#~o=&iu++GU(qBR_9%lI1cN$uW7fwEk{yq8}A;HdelYwN=j&u;%`k
zjf>63$cKd`d&w~8=ZCW{R+Ro3Gl$$kS(}40ur*w}(ocK)I!E^iybc*EY0ssNt&RI+
zOQysV{N1rUZhwvDn0XpWvk~rT{cLVF-%?`NaT{51-dIm%`K509h30xr`!xpbSw@W<
zM!)kk#^}Q^>-eddPuE`MR8|%HQGg4&)$2Ckfk&UhMdzG>EV=@eD>VVnI|_^N!t4J*
zY55)#SH%$cJ#*Sr49U*wQYr!LXP$Zro`3B>9nT<(?&n{96U9`9o^#R($j%}Q808jz
z<|*IWOKG9~(PFlJ)>8^uTh2Y>)GlcrZ3it+zmA4G$sQO_fAo~6>=!gZs$p4;i<h~N
zr#mXwrDwuUz_?eTqoM8}+qY^R8h-H?%hI}YJTd{;vNMqU(613q<%9!;gkS#{wwxUJ
zKm3)YnJVyH3w+A0qEZIKK9v^Mlds_KK?h2z|9XE79I-VJriUJ}Wj#6y#!1yd!S=OR
zPr<r%Wo9B0J&49l1vBYt-u;pVEnYPl80{r9M^LECMTfQc-~xu%16=F`xVVU+??;!*
zf#JhbIg#h!m}3TE)TlJ1rFI?IB_OtzDEihis6Tw(HDKNd7iU%87W`*r4K>?nT}cb<
zpx=fwCJ&`AUA{4bS@JQhH}+XJb>$_C@MwPIBbE|;mVsb{S@d?RIkrvO$ErgzZm4zb
zUs`iUeYd^;Di!u-(=w@tYA)oLN>ftLN()>qs`(Lcp6+Txez^~^JVjer#^oY}^yQ))
zS8N;0r(;KKGLOma%yVX)&iW17+OUlj-a%T?>ZHkQ|KjnU-UWe;OoadGlZxeNr10wQ
z*FQmX6zR>|nh0FdXE7K2^8JmLW?VUI1O~=Zv4m8|N3$-c#xKH@w0dbp)!d~>qVL*Z
z41G+~VtEjKP1o|fb@)(>z5UzBxaxdb*3v?buSA5}xs%2tefA8*jLE^?|9u#7^^LT!
zW&P^CcM5u~Qg?8$U3g3OqWHzRa8cWmwm5u0^a)LCUAvclqNhy6lzVQ2^Q2=<%d@f3
zH8Zp9Nyi}TxJlUfJ6gv*@(R*u0WB+MZ8wu>)m!z>GI;X~VVg|wnt3>B30?H~Yj6|b
z^Adngq4{qE+gmb$;5w%p18%+&dEdDNNkh0eR#S}-WQ`o4HM5?kcKHSb*KK8)xiRHA
zk)sPxZY1zK{+h30(67Hs&^pD$l>u4Qs1eAVH4QE*W~{v9Zlo5={+SfooI9PQ*tS=Z
zdYL}HGYDL3GpITZ?x{oLt`d$hq}yj%?V4Aq_!P)U#xw$H0=m`9v?!9UbrR`uKU}{J
zg|B@C%`7$o`s7zI{<qxpZ8ZKTHzH;FcvJV}kU-+t5g0UOG^&e=QTX~IdM}rSyB|rk
zz;0;tplJs!;MtAzuAHpj{P~L!x2c3K320GHD{L15@?>t@8~J{R|KJ)V|K#hilWtjM
zSDAdd*%*4laY!GNi{*FxADXvrMYb&oMFCo)v+u>zKl0wUm%wwmG^nXeatQsM*&TI3
zR@A!*VyZ5C3IZ+TXi1&@l@mLnr5!UCb_0ZPtQEDGJ`uv9ycU7hcG8c{yyurlq`xc$
zUqd5`-uZ|Ym{O669NGv#4n6;L3WxT^lg0PyU)^g!cMb8q1<%dAr>rzB=z%(Kw!;;H
z*F%=%d$~!Ucg+tl{`=Qq(yiaJ>MPtmJSU)f>5qPe-EYma0Bb2p^$?7*KuGY)wM1th
zV_|P&xdf`#ZrzS$Km9FEd;0$Utic7Gul>^l*!<6@A;0DV&M5`jFF66cqP150P`(`&
z>in$<UOuJGbt>0r#Sq0bqC2+}mc>{`Q`jKO|HVJJi<I9haQtsLW`_}4$clM2{h@Dr
z`c;!>sD8T_Oy#l4#Z^T|xyXRQ`mxH~`8vzMatToH2zqzzN)<Jmwqe1IKf~y6T#l)C
z{`Y=$rF&|(Y{$ay-cF1Ay{2wE()lz$SdQEaPX9D_9+rF3<3?e2?rj95f6@V5^L0|8
zs~q!p{T5#x`!KtL)x%|7y}sfF6k{nBS~#bB32>MH6C+bk$Jhbq_o{sAlbj{BATU8m
z3$)ITn~sY|Ka9+Hi*X^8*tV+o@amQuQS04h@(Z1pp$DfuOwYO*XAS>7saazQz+aB}
zg});$><Y8~ltIRk7BqJsY><Vt=)xmeQE%C@#aQ@Go;=wky>jJBl$Mr8%;re;zbqf$
zkpOx<kgTzty;O*bSOM)91Teq+^2-sUU3uk|9g0fbBb{)<379Zp0@kiw+x?O`e*XFA
zaqF$O;?pQ9giY`$KdJV7%{A8;(E8KRf=lsZ$dDn(p%7gCDK$0KD1(%gl;AIa`3uL3
zu6vN9yt<Fr$M;i-yu4vOHk8x-4drK%g0_?`z8Z5`b56LRxyMH3=`%+Cw$DIo<x^1k
z0j<2>h*&<&U3Rjz%^C4?D(Uu(G@;pBg||2V6it50ShXQLgb+<@>tXb@9n)?(d<aQ>
zDQ;~IG-B1BXW^sLyxNsSdL~G=MOl>gz#2VT$P!o-m<MM`B1WWLfSuKIjS_*HU+5Oi
z{<=;34XvxJ^Zd<|YLAl5X*I1?l68$gO|Z^cDTPw(^v#?7T<|Q}z5uqZd6X|(jX--p
zC-+BZ%{e8MP@jDZ2><$5FT!6RevEQ4$`3h)YHVIbW%~7-^Np|Sc(QWUk}XL-ss$<=
z_dYCvouIRT?TgPjGi-U}D|_>nZP<{%l|I|$%bs=5&(c+Eu#BJ7WJ)2Je+w1%s%vV^
zHRY}Sz=EaAamJjJh&$}t7lzH`OL{UurG<vO?xkO7jxF8Rn?WjrQ`3TSY8qRyzCbw|
zH%AoW&{$(!E)wpzna5pQ-oaK=TXhZU{`)RkNK^hxmd%c!IhJd;q`&_PcFG8Q6C8BM
z)mF9<{<W`K&*CL3Va6C5st~ZA^i`5y%CzM(2xLDDa?=wPoAeoE)}O`ERs;J>UyimK
zI3k}nh!am3j+?(b3-|qfG5yZa%8e73f}z?rzYcA%G)Wvmf3)n-Kf!^rDhv{Q;JnKZ
zg>C$i5Fdlw(Ww|QA`KHKWMLA)>g>TuTx9HP!WOt@)r)1B&IdGy2QU0Mf~w|ARTOKE
zkHV78HCWpyaGF9>&d-Sy#?2lkcu4O`%UXG|6#?>L4$MHy*?oav78fr%CxsEK0AWf&
z`rkHMMccUeC6qvLmUl3I*R<4M+doWCzx8DF4h$a`KL&UvxV5{65=~j7@@|VtpMql^
zez41ng@0LLZnAL`U}}c<wd=2%yi}iF0*6|32B`p`ILbtTTu~GKh?BC}E;Ad`x6D<=
z_!NQfiD~Gsp7IZGZpDIPHwEuJ8*mdSXWm+f)6zRH&5kQb(@(otTJN%teu~__G`F-j
zorcm1ct6<+>b;_zN*k(60k;haPK^2X70CL=MX2KrFO6$sUo(A;xuR(e%vZ^cOb#u9
zYnHA>{j2i~U3a*;QM=OHt=X~*^&fAb<uw=DY*X~8OiM2>n(0UBq&vQkz)6!pH3=j!
zk0`d80onj^)8@>}#IW11L+!G4Xd&$|iLz+*>!q({T1M|_MEQz!7&2v)_MHfjLc3!A
z#~2*tMiqTU$#--V-}`QA#h-Zl%^2{Lt5D^RLY!CH(cD;yEkuL40-w=rKv`e``cex~
zq9u#hAgaEeG}<<h9h|*_l;@4~Z%u2vp?7^7p422X(~3Ekz`dZWW>V1Cksf^DRp(+X
znFTiA`B%gcWcLZQVv@4IuHmyZ6qh5Bl+DWBPRr)lMuN{&<94tm%c{AJC_po*+2dwU
zqaRVuLbMExp>W2dI-!;Gpy|h=c+naHXjSY-Q~(fFJ0%^q%uK`$&7w89kJRYw|9sw7
zmd{Shz|m*U#>)3sBBqIB1L?nI4echVg<8Vme6|4T$pg$MpY+m$s0`b}KI&tKZzelI
z6sh0i&!C0ZPp?KJpE-r`1?ESyE|X$xtZyLok~tV#T8{O1-isF2k)#9iS3ZDGwRa6^
z*x1$v<PKGB_lJY1fvQh7p^B8<2cpAn<2%nHO}INFtt0hBDl)i9GcSteE}&mWj6?q+
zDsLC_Jb|FKXv#yOe90=v-?Bi@NI`(ED?JrM&OD_fN;UmqFZ|yBGeD!U&^^EgTvpQo
z3Psd^1>OoM)hz<kRW=D(MBRO$`qO<XkHBCb!4T1O8#acHl372zwe?u=?Vq9SqvZyy
z?{<0Lp~e_09jIJR(n>p%63sOa6*;{8><7f|yR+k$oeFdn^yyu;a5)B_esafofx}CG
z^uO3iT5+3{>;zhO56oZVTTEIqQpSuxDwP-#DQFN)yn}$V@)vOIZ)ir#-byrbOsXXS
zT)Acgt@Ddbopn#r)FB70?W81^V!}xe{H}`?wTDVW3%`E{Y3&ypa5!w6Yaesn0|&I1
zYYdeghLXlU1J1+*LnAKgbEPj@_r{_!<Ey*!lZ|L%8N<Ct)5e&te6jnRnla<gw3gvJ
z4+VkzD}M23Lw|lCtM5>I3f<fNJIHn=|KFLU$vcw5jO_1I(Ec`dVIK{*K<Q8RJVBP6
z3;DUg6jy#bFBtt-JhJ9YG}-8<P;)yiA70z~T_iX&F*vE0&!x$WL4-DH<Hrv82F@P-
z2gEtktqc4s!Rzvpe_-zR+ex?D*u^?q)ZmWjSe!Zhx0soIi%CN_jvH_>hNXN3tIHn8
z;@$s;I{OX-c1zg&MGgYH&p!L?h`_bL-h~SnA_QE&`|i6Dfw=7KY@B%FiAQX82uSP+
zbQb_AORc^LqF5K`A1(|UGzb@6bWylV=A4A)mtTH)MBsY!=FNt}y{9xHCf<LcOAW|}
z^$&me!%qcTOTj?a@v@%J&CP|V`qiJznw?|MiWMtx-+jGfjObFR{)59>13vl>ZuIU&
zqo)kj&4s9G-hs-d?c8$gMiqYtC>uBsK-<1th*(ZpJs+ES4JKq=5;4xfUXXlZ@y`3W
z!=co#eV*%{hMmCh=mBRVGjU?~O9y?dxpCXx_po%=-%#0-&-pHVrfljA+MRNbNLf!$
zfm1JK9{bjeJ;gm5u2r?{Ky_;gxAkI&V@2x4n)>9^O9&?Lnm@Hw_j=l}wfYqlRK3AA
zK){tJ6;*E~bc??AK4f{Vv-+zvI*hn@-rU7F=9-CS#kKP01;#h#+o6=((+ap~G^0H_
zwfY3&CMCq<zrK1Q@`eskE)j!~Rx?Q@tMc_WOc*;F*IfQpJoVx$loxopPUGH*%I&S(
z_ix`-$iyV~wm@x_-R!j}`yY~(i5su|8WOo~4;KWs-*D9xc=*ZZ40x@*m;uI_zDih0
zaFkr)=3TVu+1t~$b5S{BSZ+jxvv7HR+0M1T@FoJ>??iP|4h~YD6LTu}`InxJXuAFi
z%GE}B1>;sAIynKZi_V}mbmpN|2j4^e9rwUT*<e&6%_a11+C#9p6^Vbk9WkBu`k`w*
zef!r_k-5GEwqYj`u)Yem@#hf`?>pN2p7IV|^RIjczJ-57^x#Qwp7X!7@Xiih>i_Pq
zfkUo=zS+?naxwZ72-Sc<TQeE*&sA4VMP_y)9(Z68wiZ;;Zzh2<POc`xzVR(|>lgCd
z8mB3E<l;{gxcr+4q)ttbMLGe6abpK!%9J4(mX|^b^#pUR%Y!lj%U5@(LiDYLik1^v
z+_eAfa#R1o=T!q*VAAU==5Mro|H-gNK5qvPj6u$zbY7FxjMT#XdoT;a&+B;(Jzx2{
zk-cX3r`0l^zJ{s5VFK$(!EONTP+R9+{OO|>vgrd>6!X{Vt*1MaI<|k(3^Q&0+jm!|
zh`su$JA?GIZ8t#Z%OCH=eai_H5VSU6yy<H!AgR@(TWay0vj!t4A%<^KAfxW>{%pO<
z^h*&bv<?kcP|LNll65GnUHUiXJFBLI*fwe?a!;LQ&Lwc6Omzmd&<~BDG}A<U;i8{w
zB*h{1j2YNPdT!g^I`b~NSB@rffvo|8mNB$=mpVlo{p%#O1n7gBJ?f0<2+kUBHj?Sg
z-%lUQ@{t|O_SMEsQ!tJcaCt*9?AXaDTwg#p>FS38svb~LfxgW5=5IqDA4dMF{Doz_
z?qPnU^`>y%PpN$p<J8mo*nZIrQp4Jj&T-u4;Jar774T{`K8?rv%@3c=$GI%X^z2O@
z)NI?yvQhf)h@+5&Ko92vhnuV#*PVyDf$>Pxyhkf#ufSm{Z=|uU`3Xe(Xkndq*%`=x
z@^v^i?B-^eTAmzKS_u#&)-<AaT>+8^T&ss^l8NzYW7uZ=L^qN`$*CG4^|;^3^0NL+
zdlnMU=jR!?U9kk}n~)KVA>hw<Lx7(4^2~!u9QGj6I45f(DD7=2ugGt`optJN@gkPb
z(L~gs-q*q>V=p8<xD-&-7aM6w>MW~;$5#(8)1p^wQmQfC_+-SLF%7oVMEdw9fX^yv
zbq0)|Y!_J#5(N&@e|E;~;}JDB7uBn`BbMW53#r=Gwt}=Y2cH|m^uYb$U}~W7^>+;b
zdmz9~;9Yks(3sR@B#|D!v4|T+O=^7@1bPY_9yoKVvA#{ER(0pF>s9(eGg8UOz_n<?
zGiM!#_#A5kS+ltS^RNFA0m`xiTE!ZRU4e+S@XkExcw|hQgoNB7a8Sr2z}er_LZ8zW
zq_|#>GWu$+-n50wFOG7Zt%*Aqn7<_?vb$rj>7l2Pke7oo*I&^khAe?s{NnBdg4O~*
zr3{mE)*R&0H*^|V5?pC1X3A+M7+X4Tv7s54FKPLEju<#a$q!$}@=rQ|Yd!ahJO6Az
zYkMTxK=yq-=hz#*hSAqvO26h=5wqxi!B5|18~^?|){%a^vlV<tJeJ8GA8V}5chK+n
zD5kF<aCiy9bopYJ-}JD7<r3BVgqyC#@GCFuqGS;^ey5XOGJCB3<KL10uV*>dgi$-=
z80Vmsdcm`=v#w4;&beRdxLcO-Wgji;0<w2p?DV6J&yc|OB#M1ks`b7)T{b_}=Jh^9
ziU{4We_qE9n;Ykj{6GBrlhesEQ(*uBO=<Fl`PQ}{VNk-c^kbc4iYNcn=a0M}FKzt>
z*K-tnQI1$2Rf4BCUPkNee`9#cscgPIv`eNUk`;m0N%j#qZP+gf9(>)*A))85Z7sq(
z1wX?&`jgZ!&>489_YP8u4`&(viqYw3hi>WkcG(kg!jPLVG2>D!D!PZ%oexn#jO#{C
z|6ky6=9y<w$eW0ID#L{f@4x>(Zn@<a$OrU?AAT4via95m@PPvdM!f!cUyxO~o*`1m
z&CQK;>7dSQ-S*T|Pg(1?Fn5j_GX^_%?!=BAJHlMGP6^zWf^9h3YNh|=lTYHp3okUn
z`iP12UpQb5s2*h*e=wHTSy@@Q^Ugbsa)GSgrR)%n?ZB+x4_Jc!2z^}x+Ox?2v&Y|n
z22VLe{ar(Vy0*0tb*;q&sEfI8tTFouqu|8tlE!cu1+>ZXmGje~glKyd;_ZWRV%~or
zN;3Q1yS<X@g~l}zDZ;tb-~%}5M(*nU?(NNq09{)(`>IjVxDh+67Gg)$d;<9e=GI6Q
z;W8Q!pf_&dCCK0N9~utPh$2G$iN(Zi%fO(-Nxdn~Q4yzwuF15o7frZcpJY>J+|Yr7
zHrbR5m_yYo;|0kK7pi3Q&J~TH&@Fr=<~9?D5v=n}!=%NFj2*JP){(iBFD;5P1G{7l
zH16<90fGBzBQ&#0#<f)t?)W%lr4Pi!@uP79skqbAdbUcHWT1t+Z7=ubew*B+MRWH~
zzu3KG1DG|Pa{Y{S`mcT$@-wZXsD4Zxsmv^*-RvPLN8ldm#Oc#;&S|recmTI438W0a
z>4s}CZ_!e`zhE({C=1X&%%p8<lUcFu>}s?;c5{Dl;`HNi!C7SCl6>W846JVtjWA@{
z+&KifKXJ~SjFe~ZGX@@=ul42c=Eq+^%hPYr?=%H)EK4W^>8dW41}$;l{#xi%ufH8L
zZh$R!rU9<eZt&RaoBJqYu!Unl&29v0is=fm0j}$w<R+$T9rsaI{mC4KHPG{p`6#TA
z&-;-j_}&+SIBm{I977A^*WcKHS6<tIJr$%xrBGd3h_VRPB1_|)b(j9?K$Bp5IvtCI
z_G^r(iA)n%M_~NUAtbbj5~qveQ!p;S+4?69L@RxB$*ci%ma&vG<EcEMGrC(JE@F62
z<!qxyoxo`Ul=8XZbVeKM@0=kiw5CqQxG@8fot=Wgw5A@M9gb$gDif2n0y8vE()oEp
z*OYUo$Lu)W|KoG30r^j=rDf#8LM~JZ1X%+2C{j;FV*sh8rK3q{xM?$=TOmK=jLm{I
zOrG*+EUi_wc>T4$Wv|_Jkr_?vQ`7Gi!Gs{ZjubRIGT^0EtSqDn`I+xfjD!7_&tZBl
z#`1z1-2BpRv~nXL>SVtLAPosBpQi;ghq>{M6LMG_AFr{0bK^*<{K01TrHQXsqP>!j
z{)wYV1=L8b^(YGD?F2$)JMT`UMa<AatSkOzMf#%no>E*$^%IPAv(}^ObBFPU<PIa9
zbRwy&sX5y%C=U&D(pD}j+(jyHfyNHh5nN89<#mm(5tGM`B@i5E0Nge*c-W|A9LtRs
z@n(+41eaOJ0DZnjO&<gQONp>IH^Rk*X0wgrc2QANV>d_vX>43Pwr|}*0SbXxg6lp4
zM?Ol{s{#$k9W@-OLvr9_n^l_&`bigLryohf5}-nB-Z5Wh-tcg4;#Lx#(n_GJfppw1
zS`4VA+3C~Wg&o`&CZA0pob5+*hmD}MpYug6dq6^vn?-K!h=0R7G##nqMxcctbt22s
zCh0u^ZGS^OnzxlIV{S9~4hT#O43XkMt#T&d6wC6bkw%MXGoLkxIG$^xB1shUR8ehl
ztp7xU^OanPt6r%L5TFHnR6OI+%9<PlOqjIndQO&6&qJPWf`M@e(vs0ZYwK2NRkNQM
z_7KtqM-kYMBY+;aw~?wT4qBN52`pDD0Sp%@z6XsNP5OOWa56ogOAk&T*bXXmabk|A
zFL`;GjG?dc)J$4@`|^=MN_B6P2h9ZFquIuM)Cl%X8BcIth3OBysR2(_EsExnHjbcl
zPeJV`dGs)39DnS7(FRPJfMWVM?~5RdWaY($lL7d~e)DS>`Spvb6w=k=w{dp~$`&jO
z4={ErNEC$?c^98$&d5@B*=@f;Q&Fh_H#<e!@24zsWvM;!2RHEkvk^m!=5Qem<1pl`
z+3=J4dpG@Uul~*b5Lg_}YPaKl4hFK;-T2o>k#o+eUHlmDq7uRO=igvIV!w(I1X?@U
zcaQ(wPxn(K&@Hl_NE(rgbh=2GchxQM5nvZRcErF-vak+?om2=YAPsj!&{`4s3<BA=
zKli%v51u}G0+Q)6ATe(Ut>e=Tz#Gl+z|hZY@M9kiXdEMG-LP{vT^AHHjjr=~DF4v6
z*6Mqft?B@-w?6s;cK?U$8>Ebn7<i9p&GYG#S{C_f$BsiXt-%wiaNrt{YLpe!)-B}%
zK7-mDnFQ1}>I!xltMDGn#q?#>p|nH5bsOJD!BelWNIldsuc}<4te^hdpRz3bSPLRP
z%Mo}XE8!TNGvaQ%n17vF!)kt_t`%K>6yUWj-^bS`JZI>*RrE<&S5gnE8E<aCg|0Q4
zjLW&&U?Cp=<O0mf`5~rd-#}$89|SU+Cc?Dzt!AM#2gHrWl+0^!T-FVUcV=|dhUSxX
z70=<l9d|O{?Ut1_?^V#fva}wVd>+mld2fgBZR_g(e-mOdarTHmA}8?#Jh$oVWJhc^
zbMF2zzZhqR4I74O)210K-f$%rJ$NM*3FQYkk`=M;JOBLi`7U~H&*9SlEY8U{_doyn
z&mOZHHEL9k*L!<WbtKF5NI@3t0{J8DWptnFQT{t4h4<cj5Bd4|7|XS4fB1AYpf-K?
z-FNp`&)riN9k`Sdrca-K)Y8RvuanR7xYb{W+7?<$Hx{6(r2y59yU^e*L$jZ(C@ocJ
zrLwDyfSd3q4fm4Qg}`aeL)2mI^B^2;3c=&Jn0+iChn`6gYf7FbXvTRiLSi-o=9!#p
zvy=8;Z+E_>m0+ppzZ*Rj1oDee?<r#a?L<w>E`ADmt<uO$HJ?TTtd*XPn<g9gkY5>b
z7Y!<Wbf@t`kMp37O2^sBlgWNEpmTAK>fiB{I|ax>#?>_U!<6W$k#g*pEURN_$i2U0
z%9>fZ#WC$z`*QtKH2t)W_T8dmvwUxlplFr=Iuv=|jmj(9LD@20Y0b*Mo^I<#Vo~V}
zSXcX@C6Hl0a)*|gH5lKy?FUFrqD!tGz}qTMHCEQF#fqx+*xFcx!iEZz&#a|PfhG1w
z!|8G2_e}qM`z*{%rL2WAkDkJC8VTI`-5XFqD{E1F7Z#UL$&O4pQm*HIVL*Bsh7HZZ
z_%Wl9!QZ_NQVctD&dCOV&)-&nf*pk@<vO8>vKz^V+-?`sOGj?bV2q=?wTuCM*`Iq`
z$|EP9teX8Bwj-+hzMo3W&J(8|R?ynFb`!0wNl{LjQs)rlK2=uMiS$>kesi!PDlro_
zem>Wr4T$8v&IGjWUXHd}GK-}R`&^6EpZS-k2D<i`FVAy*NuI=-x-$mE<D1vdz*jFm
z2J_|@VBSXs*tV?_6?+>fprG)NLVV5gG=8zhx%R<Mc(jFITXw8w15OZ{+`e2KC~Ynk
z6scP<Ahk|gH(P1)*pz19AFVO8%;0$~HuRT^LV_R)L29>Ic?hgv!MKn}N^;YRI-b5d
z5|Nb^M>>`i3>%h?Vf0m<m>^$Vy=jgrK!5n6)_|Wv-jbRoY_B2BC>0}U>qyYWj*%H8
zHKuUSPhUpHT1##3sCd=#X)jH*{jDZ~>^JI8+L($a$@1$XP>?4&1#SAQUKjd~hTF3T
z4v*-_Wi28wM*Z{CRj4#s+gO&>+beLzqopWqi9wvL5uP^E7DX#}YMs*WaSF9&|L1~Z
zkQPs{fr4=EISIUy_M9<})}WzSZ?}>DUR;3~ZgK)>is6{;Yc&+p{v;<_6ZA#};t)UO
zcMJi9Hqy(=5-XZJBU$&hl2W`TiON1@9BcV5jDS#^Gj2v~{7^jr4)z&8HAP#wXG*0O
za6IqV$S>OLAOYA$`a6>qu*UxYt)WHh9@ECFUcTRWY9=#{IBMa_qBr0py9dE(Z!E2h
zX_1{y%WsyDsteqX%7+pGSTDihSW>Mu(Z4lqf(__uqf&}u23cko^Vf4}W6EtR`?dZu
z@K1nEGKP(tHpYfZJcb*HMpITBwWA5zv*byRM2-=$d?r$9$GBM!euDeVhnqVe#|}S1
zX3@z;6NHb8ccOvdtvx!9Ks(zbCqo<4k)^3xXae)%Y)LEeHUkz*>zFR&nGegv{zo+o
zB(S_vCh)QDU6lH(Pl<xn-b(PEez)xe>iyi%SpA8t5H=U%vu<gD=U{qK6neCAKJ`*L
zL4CHFX~z<XXKC0GR2Bl*^f%7gkThW)_EGw?=B_@Pb9e=Tixes|_~ehAU|tl<?&UQy
zwZlO{R4gB&+1?6|BbMdjxJgZKXEO6;xkNSY;d^qm5M(5kaTD9xK6rzf^@ju2K<P(I
zX)R44mqLS{f`Ht?XP#n!zwYtU2wKV)uxPUd1Zv?V%VMq3%o<38f$0F<?)?Akod;lC
z)s_CwXe5n#vn<KBY`Ir#V+^*zfGIZ3rq@tHLI_C~vSgQRDk)2V6jC<He*>F@BoGop
z3Za_r00s=k*kIgym#p5)NHg>QeRnjnB#*{2HVNRl_GsR`ci+AD-FM45=R1XYto#B%
zovS>?t^i`i%@)8n^z2i@R>Zw-)6bpHykxBv)d1SD0baST9(3XiSHiZ(p2M<uIaaYT
zfKPG?h)f?c$mZO8muri=`2c@ptv>S71-Qx{Zi}wD+5GjQQ8nVDCjr+@yGxyw?x@Sp
zb(z`$Pgno%eQv!_A4W~u51IeR|L9f+m`Baid2-;f>`A<vJ-FsS(ECjv61J`sfGcb0
zSZ(iKXIo7@Anr1h2;N|wV(nobCSeUNa9RB<@Y=<crcXOsSkKU?Z>uF}1MAm;+NAf8
zchq=So!<4@UnA`8EokZ9m~O|45x%Fi+;+e43hiQTO(6(Fw{|&24aTSTC4uRFlx^gq
zU1Kq39gZF`#znYYs<LkVW~*4fDyH&>ya!~I6x+=2e#I$7bZhE|?r_?mt8H)7YI|k(
z_YCsK5nt$v*@oIDY~hAm?7Z>+kG&}@z&sY=X}Yh#p4@b^)p~KsO};iylfAU-c6)2j
zZ*9`RO8_P&TD^Zadk!uNiI+JbWvq?PJI#jY%&{~S5xb{d^-FEh);n!o)njfykR^8{
zp(|y+gls#x_{(-u$u01OvDdW7eY@+I+iSai>cY$LdOy@jd+0gmoYN<8T~=0RD^{$q
zHEY&b8;TUsQW4=mPCDtNXqW!w=VVEK<Bc~W2iVwWThs@~9(!z`*AM2x-~RTuKCRbP
zj%dPYmedDRhn`9G+<NP+2Xk9IZ?_(J<dH`l_}}eR%tOUL|M8E1>|(<A$5Tu^wsp+z
zd`}kxtoId-Yi)b=65Cbxx|P+h0f5~LZ%tU1u;|S36EgbHx~!!c-DBZ`|CFRQBztmf
zVo^8*#^I{8vT>vBta-yaI%S5@%2nNC2cGGkxvjm<ngF22>lSVR06+jqL_t&=aED&s
zvd3y#Wo^C#Yjc3=mNG1A2{nbMIq}gVIn`Gp!yTy4w?9Tb$}d;w*}ma+>Zn_(^DKLC
z`IWAWXzl7c2YEVjz?`n1@dL@B6+#F;2%c6i&XMr$o6gcp=_KhB#1q3W>!)m_B$^g@
zE&f(wX)e2K97b+&-zrb?u&}31UDUkj%&fb0tTBsW)H%byWTSItyVo)<_c&YI@DfVo
z3<i~ijAC#`vE}FX4GfpJl-r}bAGc@9{$Zv5YUCA|fH02q2};aGV><XWB7?ZM;%U2Q
z=R%t~V3Pgk=!@)}!q|JSfby{;N4Sp~D4CABNAiF}O_^AcA2)5P`yB4F54CL{sQck_
zu)f$gz#@%9siFP#x3zfiLc%nB<kI2z4%VTZ^!aaEs1XTN_qf)`tGp$HEc@=;FmQ;?
zpC4*F0&9pVzvXrFPC3uK!=@c-aWU>^Q2Y0nZ^yVHektAqF?&F=>UjD<_kcDrZQW+M
zxv6&XMN{m8^N+H!vIg6<snV9O*k!BMmRdz6mJ+osR^Ql$U<^^Y1nCl(vVjYTmUVVl
zd~7y$SE{7zItmx>>d+tkRE{th&B850K<C}Ys@NH9u>2DsAwjFCYPmE?OH1Y(zyy#a
z3wPgzMcFoFa3&ViIW}xq4lY>IER$>5ISBlPvPVtf&HIC@<sP-k@ss`Tfi{4jHKlb}
zg96}aY&uve$;J-?1nm^^c~HdAhzpOpei!`#$rx!l^GDP-KRwJ39IEbn9_EvXbj>lv
z_|8s8(g-0e*W2O5b=(clALX|%o;!tOI*L9?5)lRH$Yk!CvZ^h}HGgN{CBdl$xh_rD
zjI<=cL#$pMa4qnf@-?~LAKj@R`tF^mgCtkUO4yd#;;py<|J`OZO|#Ju1b`)`R>)bU
z@@xWJ0OhVfdxD)drpUcf5A6|i5rC*a{A8bN)`#McdRL!R+*XFL7EZvubs`$NsXkm{
z1J(vA8_ZYdw<N+M_-jzy$o5$a!lVp<XCJ6#CF=9Z0ISX0cH6K@zg4>4bT6^T^Xu)l
zG2Sw<;I80(`H^fSV8SK0R&5aB%>Wn~SZ@c~aKR0z5^QaR!B4h20BnuF)`Xzd)6(*?
zQh<vF<|g1M<1_`NT7WHRD(~ClWF={60H<-UAD5zDEThx0tj_@YEv+oK(SXZhzEWv_
zfN|>u98VIbsgJlx%FwVA7!S^jg|o++QFKVe%@wK;^ieus4s|ou+gh4%sa<KQxx*aD
z>k;UVTd`Ca(k4LlL;!Xj8~a^7Zh^faz<pvpC$smteF+dg8JFAYtwhxape=77;dw_>
zxS(Ge?e3h~rS@rIa_1QPfIYH))^(MmhjYj7hn`9KHNv%Dx?hg*RN3yA%O5@;$)HPK
zAJ&ek^s9aVbf)?;m_yL_JYpsNIE;R2s&BD06uK%HH>y1cWda{cKk0cmD<?*IJuzuh
z0oW6H&u#uDfOzIT$^^**rqr}}>R%7=j<!Ae40+v4>-!$^B@>6LWWn61c?J=S_sqwB
zW3qy7z>>Cc2ksloDy<c71^#+0Es3ovpi(ZV-B@8v3b+-pnVgelX@d$Zd(3d$BTulr
zsS_*{t#}QuzLG%n?T<h20Li}c=)B@m3v5rK?W%bDqt7zV!(%+weUkc3{nBUq4O(|E
zD<A9gkr$tBtM7T>fNrDJ>$U4Pb=|I**44xsj|J$;UAC;WW#xUKB(PL*oMY0I^ov0E
zf?3mDaTUwnacznQ-m08{-Xm+2JX>gO)4Cz01*fH?5-87szySAQB_nr|KfKV+{8DD<
z^BL(By<fF`<R$aFtgZK8UEjDvmhwF<?^U{M7fMg6JJN2w=k9e#K|;y|{yM;E@^vn1
zJ=>)<PbOB;68C64iTEFP-DNiXg0s4E?Re@RuAdWeBi>ii_#cI0gRJ;J3h9hd-@&6s
ztt~4jUc0oqNCKso?!AY-BN><CKVr*cQ728vUtmS)lkLUrciM`w2cXlMom{Lwu-m2-
zT?#-w8!In_XuK488R?w_aO|vG;$m5^sdy9`ubKVKWp`O40{f9!bBSmDT`SItEjW#o
zFKe|WyMAtqcl;1#MEsek2_J!U#u;b07`;tR`@ASb9!q0?;lhQsVZ(;Vsr|kc+q$@z
zHTa*LB+yRnLueUsZGQXhx7(5>OJcTc#E20#b?VfZdHQp)v9Zw}eDJ~kWIg0O4?Xmd
z-F)-SCU@2GbZ|W&P+9$VFyQjsxpSR+_Wpp@YE45!18Yv0KRmeB#dG)Wf%?``TUY+L
ztu24TO6yia3yP+IUPBh3Z<hOmPhs*xB!=TdWL>On{!o&vkc+dAv+UHNhf>Ub_in~*
zchHN9GUP;GsU5}W4dr;gseBgq=zFXg%hv$v(_*<K+jJxheboi(i*Aw<SwE8NBN!!-
zI=%$F3Jto<jvM-E%k&je7oLpjSH`r?*8LvP%b_{5`cd0QJIfy8sc&vrW;^Rw+ur8&
zDB(3bNd=UoBQd2AuGAIQk-l?DvS`W4NYTJ+M=o<sIU=`+b8#YRUcPAF@Oh4P(*58V
zKDZ!t0-*H?<c6ni-Da<r-tWS7g?VH0P|)}omi#a7ea?Qr^?oaBC&U}$Fp;?wWMU>#
zr<D6W#OE$b8Gy&LY!v2w7$^8`FY57cRIjm%man&KicYm3PyUo;CiYDayT*0?anBNY
z$nF;o!}0rB_F-t=zpe;CW6!`Gvn_;_>BbU!P&D>0US@S){GMfg<3_w7lIL(FEgGcy
zui+JC`8r&LEB17F{6qW$V*3ECzhmB^gIvD%>reZOKezU0?qKc3o8q8}X74L-t*3aO
z2KM{aLGf}w&^-|5s6Wt6JklE{h?r^5rmQ2adjfrj;6_`YIdew24yvqd!m^?c0J{lL
zyWUCxwrc=F%J(){eM39vv1g;8pg^WhQGK{zZg*6`zJ7M~b^i1c>qU&T2Q9UM1G8~s
zlHg)bXQcV~olYFFWB_SmkY)i^^U2BPgC$s2R;<msbD{2}sKd`kg<9K0h{V^peJ|r9
zcGL{<x1<zM4WK2MHh?i%%h(R!(h#b_li`Tn5B2NUU=E3^0(=Gx6xheCg(kvEl?QWR
zHh^A;`6TU4*`yB7Lv<6NI_yeGjHdbmEzS<F9aEBS=}`38QUX7VBNKJid)XE^O&KKF
zsuHc(9qG4Hq(2krXK{KSIARYVpJOR{&3!VE1xs7NN}pVibMv2;SJ;>Tw$XOi^KeN#
zhb%lpSioXooxms1mSpoMX4zNHC?GpAbh=eaPaM(74F`H46=2GfnPn-sgX&-*7O>ig
zTW}*Lbzt3Avy-u?!Y!`A>L$iUBmJI3_oo3m3vg}PS!O$%s=!5Hp!(?10Q*APpnMed
z0lkw_tTxby`|Ttfkd$iW%T`&a4rq9Ajx`Z~Ij0>r-@L0P<m0m8^)aqe+nOz5<#z7z
zST)>#3hu8n*f0D@#4~Yy8v>Y-z&9I>Pz<gu57*_DcuB}b&?3t+&-$I#zIvM_&N&(o
z94qHm=0FMnwx)yor-p}>CnS^cBGAVJws{d!1^{Da_uLHg=f#SD2(9wrn){un7ulR~
z*Q4R=$8~xld4d46C}r?`0R(lLUu|o*TGi@JJVBdfrDPzuXoW$=Vj0jkb4)SwEIec6
zDqL}<H6IU@^xl+944;w%c@BJ=%d2PA{Ed|aCbi|@T>`KfyJ@TE9e2PKcT`td$F?$J
zY17Iq0B96dQ|P}|xb2h<;%UP$w@`G)ww>lLt;7Pnh1gnnR^WcQ9dA$1G#}d5bgNq_
zVznWDE!+e3TXx`zc?F6d`-4T@`$(>o3uY1b3Rlq4lEA`&(<i%6_v-@dWX&3AMNy@V
z^^<p{@g&P!)hWO<iFX#$t#<bUJCCd1U1wFe8;f}#^(0~STXODPcUOC3vo&nrP2GL1
zBH_XXa4Lqitd-wuO2t(+58AKW1>i^0*1tb>!21Nqj=bcY$SpCyYgTQrn$;U(_LJVx
zfN7H~e=1BoNio26u6w(5Uh8&ZMyefi>t}4oQ;XcV5x}hXm>^}$$h_w}vUbE?2~;YH
ze$tqb<+EIH3lPtlIM&90@-iEB`2ttIvem5K=<?~fdy<88O5p&Tbkiqn%b%aLs<&5%
z%VA8)O?=mq_g>n?GY3I4)i%Y=9yo2XO}ODoD>-i-Ws4qRJz{(-zIDv8NI8mEEiVEe
zl_XGl{+J)LcDLHw6~A+!wT65EVf^c*KeM(#z0Du}V@vI{n9fZtu?xrlpUoJ2gS}OD
zpKYvu#>FXh;VtMCxhd_Q+BzDsEZc^Mq8Ds+`QL3jz_rKgXWiFW4CmDAm0R%aq!Bi!
z<SRC<=;L?*3Dbk!%j$hBizt=(()RD$-j?OgiZ4=T@4O$z-{Z!OvuV?&*(<NS61Dk?
z1?<PdrLVML!GgXny|3rX%ggNtKlp*AFxT8Z!JHNNuK3Zi5?{G;Wz;pGccU)2-~zmy
zMGx)JJ8!JtFTVJqtzEk|R+htdO&0Kr7A<mM>R~JN2q{Qz@85m*-Gh4CE3UZ0dHCt?
zO|jys_qU8AJzrB>g)Q6lTYG2EgM{1wsz6o0Ld_UnN?^6t^!_A~814h_Bk-+r3wGs%
zP#8|cOI2n{zH?u_w|*UAous7JpOT~J5&d$wt%?=qVsq7tU3*ycnmJ3m_cXlap4WlX
zVjs1?(q{*o!hJ@6Nnw<J3$}Z$Fk^y^E0|9_>+>xqby!!CyX)S<g1a_m`{c%aV8%Gh
zOB>mB-;s8(tzn(LQ2Hm^P_q=ni#C+U;cFxh(0ii8CGU#33U@D)cjxG0y~Iw!SeDny
z#~GI;V$`7@!n7r#QdV8>yv2R%eabjOXWhNsGK;+_NY{G_-?6@W5i+fE=b=s%mfVP^
zOIcySY<~4jEr9NS+V(4Zy7WmxyifwP9G>|(VB8@8&Pd3$QwmPDIR(dCNqVv6;2BOH
z+&Y-ZwS;Bc*1XrAuXx-3uxGJdu=Gy5f7%ypNNV3!-92lM|N3Y4fLw$p&6s2zYd50o
zkk+$mWHimc{FC`#U25q!f6`JfItyU=z;t!bLVEu4!0Wd>{G_!!@JKkWb$H~uOraL!
zSKt4p`8qwL^}O@&{R%ZyTKke;Bg@TT|3Cq{7te9{YKoT>?}7KH2YPbJ?@t}^QV*#I
zB3m{)s5WR32HLS%@Z8?-_GOO)lk>6K>z*Whlxt>LnTgO-{t7Jo+OYZyyRK9j;f=lf
zePI#-0YBFY0H;%&D+>g6xMtKfeS1auMZ2|ILHpXOy2Av9qePUa+jD5`7674ifFfO<
z_@57N5BNd6*6*lt+Q4!!91wT~1CX6_m^9k{#a#nbM+@}Pcn^OCu}EzP*i$^{Fo})T
ztIH#+DX)#gD?tjDudP_Gx+M48OxRWB2o~$SHY2CW0s6WwXvHxCk$NE31Dy4`D87R3
z$OBWTnjBXfF7Y7#VpZ$v@mK=_`JX?(+3tA0hPYaQ?EslbO?fogsnQPG*v99mqD=e6
zB_l0Mj}uS(^?-P6x!m;+&o|HR-Od$uBXN|yN$J+UZLe+q?V~ng${1_T2E3}Ow;Zgq
z6(Xu3SuvdfYq##SZ4WNQq#8h0Za>{x+N~PB42GN5^z5U?S|Zw-&AW-I&H4=t0wCRD
z<%^ctru!bTk^l7xEV{i`fzUc1<%B|nC4LyLG)7RzQ%h~#KVG##l$XqVZexj0r}0g=
zP@8zdF}!duV+%UBc#wrkimdJ3ZRltuGB(<*g*lu|tf1B3`9GUJ=vHeVJH&!8dGh=b
zo(T@l4Ifj!*whe%IRulN1Yn;tb)3~LS`KjEjtg=?{Q#E5!6a+>?bFseW0rNym;i8#
z8+F<%rn8N{NJcxgsixMJeD7{c-BFDuG@S;xm>x<2toB=5ajp$KdK~)ss*Q2YxRqP=
z@IUpRI&S|IW~@2SWx4Hgb7}u<`_AZnxIH<1O7|!53D1&!*}5Mk=Ve>RgrVktHksJf
zO!r`mrNTr6>zZxn?;o?Gb5FO9i6bn)>&|USl3ojf6f8$^2_C=#YU6#6TJwgjR+OAY
z{JUnX-sub8wtsk`<qRLj5pC~YVf?ty9@vScXj^q{RQKQAZxRhtvH-BIFKg89nY$ee
z%uCM3f=r*om}G6;f!Bsuai9atheE*i0h7mvYZjf=Nk7JFiU3Kuv~Jp6M!e=>v9d<J
zCXj#E^NSH?9hk*XUAdj@Yd|5R?3FjMXkg&?kz|GJT%VsbvyU7-Pj7$XITyP+rX?_-
zY{`PtyDXJ^1J;oeWm)a7Yp`_I*fak2OWX6}60F{zv%O1}yS`GNIoekAF?rou(rs<f
zHAf3;9n!kNN6#-s7P*JdBnGu&at}G}cpG@!(fe7eCtyhuEva3tl{L;<f856}v0cx-
zVtVfKrSDiXuEOQMK2nzho7E=%yW|$=-tFQtWy-QUB|q1)Mh>%llo*Dbb^?kSQ}zR>
zRZrc9Eu0sH5<SUMy9loY@+$CAN=bx@IDhQj#8b?&*LVLc%xggc%JTZ{^50oy^F{}5
z56zm<_1HtQrkg&BXIk3$4(`J5w>6ayqb!!q{={0}(c}Q_>eh``j!V^`S1}5uc+6|;
z1UMv|6=<EFFxaLKzRqS9e-;{SNLL+w9kk<>WPR0B_S(*$+t%6_9hoSg{iBu??@{i;
z`vR^N54f*XP*7l}oN`KEm*3a(a%2AWuYdiJ>d4E>vrm2MQ-@SW<Yoc<eOd7q78cs+
zr=K2P=OQ=6_||$Tfc52<Uyhnf^{Bq{&O5K)WkxON2t2uY^JbIMLw}Rpgdc1?@q;P9
zzwJ1Z@@=em*8Z{i4y$e3jG~Yf!lFOD+nz;2h=$->zeO&{f;uT7l~5Adc$><#?6e^O
zuEnhTL$Qc12Cy!&jN}4KOU@&%bG7|;>3nXY4!6fguPf$RkqPi&L$cxj4r5X*>dMA-
zF)f-S)y0ZQ9mx~<j`W+>nMl9socb9KmA)Bh1(_49IOjMUnmx+~Wle@h-JjOnTK%%?
z^GLbTzP0xb%Q=2OWwU5`KWgV>dHmGwKikv0|H#n7SOU48S60dNbs{n;Z)CuV$aH|R
zdVu53f#^VO4QQQGa_F?UbjG~Ksyi<pnsc4MBX>t`VOn(FrgFma1y??02|({ah7d{F
zzCrQ~j{9(?19>$i&K!E3WhUiCvbgU$;-vp}<6XA0VL74F@K;CC@)##tSab2T!g+S(
z@GER^>i!n7NdSNYeL3!PT<$o#X~cQ<>mAS9Z+1Lyw~oCmzMu8z=?~72)QirvmOnfe
zwLvl-=-9N=>TbBzn(z68rGM^fOS$mO_Xc3Y<F&6`Wi1aqYO?6=N{q`8p`+6O?J7(A
z<Rx9F->-vE`9|W-H6YK^WMF`6A&aMY4}6GvfRP(dAM_q@TXNUN9BD0Q-43o@J$jOW
zDoLMivZRcRf1c)-6WVn)EpjZ<#5LRf(Z$GfM1O|(*^gEaC^pQ-N}}NE{o@ufh)YBt
z)UMAdMI7X_LY(T~2Pt`pb>o0*+LnOoXb7v?<UlJ#bShvP4~^7Ok$n}XSX!xS{@6T^
zm0%dLzKIhuoe?dO&|y^#9rmZ^*4VL^jUjIv@HES{z&XbVV715XIG*Ob$CmA>vkqeM
z&ML`+*vG{v@pnbocTYjbD0EG{?w7h9!+eCAZRxHKyZy0^_T<_|sC_gMaSa_Na0JIu
zGsU!dvTaOWgZ=5+VjEVJWlb;)=?5ylTTMs8q27^RbLtQ*p{I|w+9fM34dI;Jh<Z>c
z%3u!EJh0F@2(vKqo1ZrCK&%ucu;3oKX!pOf-d_2y?^(vX+bxIwE(74)FR2pxy@Rop
zJ$8iU&Og~U-2EFH?#siaHj$PYGesyX?f%}q5UkBM;+AWy32R@)e9q$5U~{|eed%>u
z_3fWqF5rhO{!^KkO>p~qr=E$!E%oRzZoDO8d6$rxZpBxeX>YCkzK!(JUh0*r@MfNC
z>!QWhc<q<$sGF{aGd#|GxV-c-rnUY%H(~mwPLb{Hj7NchBAbinonWO8K56NDYD{$`
zVkMpkGn}+?lP&(*5AEome#z1&jKxbsqScZg6=w@!DZBZ{4_NCHuecc2K3sCO1;Vko
z8;Ait_M+3wo0Se2&X|R3l_LW$>yO??=Psm@VkW2HMUKucj+r-dF}&AlB1FH-^#InH
zL(e(UmjCim^AX-75#ZTF|9MhSURb}!mVEYGcFfPdW=)fs`<dZB@&MR}_Ey;D+ka&Z
zzkJN{;l7D1>8vw5a6ezenvpqutfdSYhyqLWx+86je}{V@Kz!3}0Kk0#m63jum2_%h
zz7@|sF>>l(`mH#`JDz^A3ux`DF;ZT1ma&~+ds14O0}kspZ$0oc0}#J+*FBaye)K`P
zb`D_0xcoamC64ynG2<Pp&9Z9F!kfdOlft0h&Zl0q)+*KjG}HS^iWOgcMyKoZzA|;5
zms{)|lo@fI9W^_Blrk{ShRr{{>vA4Idf3HwlzGI|e(SdXab73}pK`qEqk8H#ZL=C&
zoa?)3SE;pC)rKwYooj7Y9Rvi5DXnLdYk1X}KDfxzi;J9cK>E<ZmRc|XA=$p2Tko&?
z@BE`D_4wV@VAfSxelPv{5A2lR{16xFbKIw`hOjIE%Cb1G+q~Vy6W2KxrXtcVt-*j8
zk#?yKu3dohQd&^^ob`NHWvpy&P2JZ{f%j69NKB0(1Yy0G<$w3*=~m>2`Y;K4Yr&YG
z5F>M_E!zHVJcOV|)Xw@0c)c_9s{QVrvuxIon*f*p%`%gVx}H@)^@yC4?a{UWZZ-Cf
zyC-r{<eHr1M8Im%%#tz5Ktg2~rcbe{g&(t{3$N^9Ef%@I_jf~EnXRsP1UKdP*$!f<
zsw@YbNQ(OrO>!6ho$q|dS_so{FlpATS%kM5b}+YmVDgIo&@;>8{7{o(Xg~MdbA1+b
z-F4U5op;{Z=k;h8Hf`EuGiJ=dBV0|i%Puat>K!|Fth+8S`-vx>aF*tBA%5wlm)hXL
zgQMPiB%j>5b0@UB_N)G-{QP_j9-!Y3A}w3C>>zRz#fNz(uLklSaHOZDJAP#^ZN3ve
z4KHPY*3puzc(pEv@UD~tD4pReV!s%~aj|8m08XbAbFR<<(<zBLfTrDqd%X%=Q~fwf
z$?$Vxc-~_YDUy~{!2VM59!rRRTTRPW;&@jaT-(_H#8qh@6p-kEY1*fAED;U_PekcH
z4Zb(SHvnKiA25H2<)#gz-%9u%?)on^DYlVBTL&~_&5{Fx>avhO+?MFkd+URBRy_7^
zHvK<aQuVAQ$O{kXt^jNC*N*LUZv@_6FUqY)dSN1FxUO?L=sXU48SH#M$r>C=4UOZj
z>wGxByIsYE?{?pjtNO8@<u!bMMdNBmhb4Q{ZG7%&R?)n{Hh2~zI!L!^11_;+h-2P8
zl{Ig--*5iDZAH-#R|w%}qHr}?@v^TOd4nw&a(>q>l09x~+G*RH_Mkl8#5_s2A!&s+
zf_dvhzLSXz<3C27Pxuvl+qviNy44*tA&Y5;|H;7%QC~@WUk3wCp&Y7F(jQ2ewn%C~
zfn7bKc-Has-h04z$_bW&_o&vtJY}A2x$f?k7>BaTCSD)j1UFmlXKuBmyC1NOuit1X
zXP(k6<AD!Vj{g<*Pu4COfpn#2zxmEO#WKJBA6=&o_dy6xhM~P1co>v6cCBE+Wbu-$
znctzR?G`Lu6J`cr2t1j1&dljWU%|)(wk@;Z-c{s7#+FrL-r}j|$sc}Lni|}Vhp^r6
zSg56v5PAbGVelkAN6}aNc+6N8g&Of(vTKzEtC+he{d!pcJ^7<7p=dmgT=&hH-em+U
zw^8rA7OLODCDtrr-X{ziX9<PS&e~vlr4U9Up~|hUT?qrn(1&3NHPqT*!R>EZuwotk
zg9o+&<IFqic-$p+Gge?N4DO+gyLir442#kSn|JsO+Bu?EE%BcR>Va^tA1GJ+`UkiN
zSOh;r>BH694^hW{NScZgqm{N`x#^WQquk>X3)vBNqE!2t7(`B}*G0ZY?mLJwBDWmY
zZ(L#ew4}26hbeRDbqHUG*s><C{o|eOcK)$LA)J#1T|2;8@w`ijT|Z~yKwJOH4ogVQ
z23Qm4s+gH6_M64KEq6e+-FV7i%j>?0v#FB98X+`ZSyyJaJh0R1v8uZJlf&%7ql&qo
zO}E!%OOzEGgo@}U^;ECR;WKau?U)MP`p6c0VVwX(X}t<0=Q$vv1^el^10PBga;xqC
zJ~iDY6^8)Iq-iS&Xnaed6S|W6SR`tYexGph8Mgkh*NIoX!+a32S^j`kC3T?K5U@SJ
zc+mDNU13LEeUbTy5$eYsZ4K_dOMm}_<<+!VH2`I^n<p9@-QFDlLL?=@thEz2x#X&I
zto#p8SYl(VH8anAjFlkTOP=~VTm8dd<6d}`jXe7_3z4^u3Ayv7H=Uc;yxKa(SR!CG
zbQx^|CdDmvf{i}!R7)6I=(IDvC}Xq(8V|eV3;_3M%y6lzw=OGxF{g!6aCrS~Tk`fw
zOFwxUae9X!h`}QOgnA;+EZ`;yB^AFf#Y#SPiDeHda6*U7nG-E<&T&@x;Imjv6JH31
zxP^gQ1faX|<)!xK{2Q&{$^|xf(l|z7hc%RzTIn-OtnI~jteBZpF88S@i6l^V4NPS2
zgi$scaNSRCocOvf(wY%OXr%i4tMDSr=?K%g|9pw`{JwAH(I-5|qTfM@fc70W_{3?}
zc=Bx9_Qdn_iwC#tyxKOr7~q>MbLl$!#~Gipu{T_1g=0sX7jV0#p~}`hxyXErS7Ra1
z3X_PdCzN80dEE?Skvn3fjlJ|7-bbg$g?-J6ANA1#6>qMznl*AG4HFk536v|EeVnBY
zW*&S1(w>*!um(IabdO)$yIBG~(~1W6j04S(9Cg{bw)2^ndS~D7x4>S3&(B|RqfNQx
zGdAQ@tV^QAR*u{#C4jP5m)V-Te{ZFWm&R<H?vapPZJhic*E?(DAOdd9g@o=*eWdWn
zH}@oEq}lNE=0?uM{4S#%as{t-r>~@UTRi(%%Pe8v>`eL7j>1y?7*jtd_IhS1IW$n_
z3s?M`O}_bhrx+k>>48{E>(iBJYmjxU%Qd{#64oB~d>r?cS}UtveC=J&zv5h%55I7q
zfUO8Oa(PFMiy8x5V_2@^pS$$4Hu($J69fEI*RKULr<p#H<oX?9SctV>>!8-R{<N!Q
zZ>3eQUT?c;*ANsqhMYFr6{+^h@;p`_3+Pr1_raa_MB4IE`j(Q@$t7R3L77wS$&LSM
zm2ImGd6D}xCDhnUJHKPgOMj0m@2hQc!6mpUJ&JuIEWm;)Lr@+Dq+OpdJTyco5)W6*
z6>YCUpXH~I2I!tcyzFzWBxg1P(r$v0D4p4XkZD)_GFw&g7hARW5o_q!?r3KL)(*6e
za?eNU)KNzrWs@gQwxvs#9@JxfSS@}N87$xW*0=0)pZnZFJ#Sx`AAa~@tFEq&dRbtk
z+>sw_8aZ;L9e@1sF2;DYdj)9!<u8BfvO2%}?z`>pfB(CS*Db5!mtK0we)X$g*?<9k
z3nZhJ`%ym^%Y#6)XIBWg{>HY}{k65Vws`U4XhrpTPS*8$pILa3I+D}dyZ>N|Hr?jD
zANB<#C&B~dq>QjBgDwLQJ<W27DeX(t`rbFK5UWo&RX+b-Z|y2pR;B`feT0QNoT;jL
zv%^i;p88DM|I{aXeDzrnYxCqp!Y3tU;0B%8!YKt<P^0t*m>&lA0s4~)Efs6?<ivFH
z`T8t7)`jxMHONwSIli>7d{Qou5<yXBH+rwH%paliQjWi8!~e6zHP4x+Rlfe<{T+L)
zm&6Z;Fz<U{#8~0;dWl@qI}Lm5RA%wxToxbDdApZ`pvoY12d^ESs=O+OYs#T-C4GzE
zc7%;8;G#leHYe=Dv&<9FT9>=AaO%*TZD#wmPX3nc#TcVo+EDwZ{cYP%tirz+iwtB2
zt{lpuEm9`AyyO!-K<hUu->|>zdeq*oSx1<!R-`j{sA7yJppsvbKFrP^a;jZ2WG>-b
zP@L~Wx#aa_D_od{IVa5Q){>rwtJiPDOKO#kA2Z6Hdj3WG)YX@FWq9G`S8d3kL3Z4<
zDP8B{2k)T=*ax$Je5-}3>#gmDR{>>F1kn5MmAU}zRhz8l@-JBG$Ih{=AAAkNf?i;E
zulsuZg#YW282btln0eb50Bqk!<*xd2YrFsB$d~G<o;ILV;Sydn+h6{fwJ-izcyeLr
z;=B4j^L7KS1FN4l{|i5`P{n4L8A-00$I_nlVU#lAEK5G?R>1CoJszvSKSJf3&A;$l
z7TEe4TE2u)Q#e1Bjf^p2$T5`tbxRmFz0cx8O*Pj3!uPFX)l+E6VbX%!Qeh}$tm9lP
zq~)4-^vRZV%FUKAa8y@0!Ls$%{`_4Q*zywd9VSgcz7`IF`P`aNe4HiC`>J_Mde$AJ
zo$5#BOBg=We3##C9jl+Rj+cKyD8^mX&my4BitC=-L6&^(_su(YjsvUPpS|4z+u!6l
z)kb+e<~^hkDDe2}EottTqK>()2FDM2^Z>6Yp5i?a?}2y^{BwFh1SbYl><xfN#PSt{
z)FD6=rbM7<7;tUpn?+qxyI#ow!JH33P-giYWJV4kHa4mtu8)<%j)2_ZKk<(U9ls4N
zOvl3k!~k)ulL+qH4pHPsTQdL{_pLv_X)kQ7wL!!j4dSkGN<pSwHhTnsZ_utdZInH-
zY_nC-1&M5GiuuzH!Rt>dvfCcrWlLA?uuEnRvC)I`0h1H01%PXFd7ZtqVy!J)7qlw0
zNHN#7Pydd%x0i?P(itP@Lv6-<;i_D%%g+VwB0hB>N&Q5hYjc;yricaQ>x9!=ae0{}
zH%n?a7C}kGX$*zv-vl)60-WOoP9BqQzq@R>9Rq0HguQJFfFRL#>HNdX;L@xQcM>hc
zC*Tj-;1gz8!)GtH4Y&W=GFxzFjpi~ahBXrCIUNoAz_RtWa@p<7jYJdy6oFc7P{?W-
z2a2te$U9F)+U(~Z#XxSCX5ZjEr)|uP0gz2X5yWsYJJAMxe7=<upC^xc6H%OK;~fh;
z&tXVZKfc&@KfVa?Gi1$-gDgw8p%76C#70vr>E?u18=RJ5warzQan{*Zc+CQUR$`Y!
zlm)R|W@ch-IK;->a+SUDU*EUFDn@=XQPv@9lhGhf!d-m=7T(RzzHN1emZs)4Ni#hB
zFJP@{c{cEzlO1qP5H<pO=h&E=uCyJ?)>`7`EeO;AxS2Z*?M+sKup+d()=GcyJKGB^
zD>gxMT&f#sXxL-Z!N?6*BLpuY*de+H+VoFf4beFi0G_;5-->&KG~C@2iQBmf6UPjB
z%f&FLuh_NDT7|VJfw?E5Qmytw2;-rge1Og5_lRJn-$MdAK^oi?;{^+x@)BRJ8B6aL
zye>2+Vwuiu@`upD+mw}|gdHKF1^7sBgSc&lc^LooPh0uct=7J7E#Q%lK8C1;nP}rZ
z<Ww}<_B(!Md(>t(B0`o&MC?rFGyWCa8kkL4UVod_W~bUw|8^N*__4S~C*HLNLT@IC
z_QX@q9@zHivnUV~SN#A?qrl4H7o6Gi=6~U@ZI7TdBBnP+QatTJC(m-$v)!{5pK&t4
z?-{n`;ip{u>+aWj9}5^4u>bO>|I2cS?JY1jcfuG;1rYc8d=|i>s=2I!Jh(J|^KAgv
z&CZHC7ND*=1Xu@J{Q$q8bU?b!RldE(YKYq%D^7CQ_4(v3*W$V_R#HEyu2@NPb40Iq
zE6{%0*KQ(K^cri&GNQLdzOJ|It+Lm@e21;R=l3>v&MYfD;aCSMr{p6z#Vdu@4EHST
z*S(Y08pVZfZ)mg@`cGEcwd*&V;+E@J?!C3_ob&VpJ#I~+++)7}dHct;|FNIdvofmR
z8@BJVS8x87t^VKNIcspevz&=zEF~|OxaH|Ii*-!vm!yuplKQHni4~Q0sSWj;wptDE
zRPRL=+d8K*CVuXkt~*Ns_hn%oQ>g)^1i1;9BE?5Njmev5pPsge*wH_*H%ouXzESVM
zYgu2{c5Jhkci(QW?))DclyS5T&pClujFSKd23gSG$lA?VgD#VRK84tcsos1WkU9!5
ze6kg#Pa~%080V5aRv!jJek*Hu7oqLbwyyGVVuWI0k?40;+D8Id-Mw#otWjs3b=E-v
z*SWdGusr3I?!|m4j)xB)ZnxilyIpnFRfna$if8=iKmWPUvJ^jj>eQ)yUhnII08=Rj
zEL^y-uPpY^Ll4;(zVHQjsU&;x#TVT>eER99ofWy>sT2{;KKtxG?>`bR)YVBS-Tx$@
zTcGmLp+oz1lit_%?c4j6H<C%^EADnlNlE0?5%;~fVKt!j?Es<h+<lND0%*q%JkREh
z{cjhW`cONmp>412ta-Cf%WhY`2n?kdWF(}5r6y%NnjxI|@ceCPErUMlyDZh4LcQY#
zo@u%1B>>Wc*&_$jH~Cn$4&YjBYxBNKKJb-o)vvH$!l#ABCsA(@m|2{CEFgJo>+PsF
ze~3^1W&6YSGzt-3z}-Z>BzR>6Nsg~~FH~cx*LzhqF9s0$PDD14<;&$>LSErLuGNos
zB|@T!AD1E}y^g$OfACtox@u8%DqmG}uS&nWgV*jl@`@E@zsB-D$42LcdAGD)*7l9<
z(C}q{u~*Cfj*&G0Ry$;ISNT|y6IN|<_H0`)^b%@dNC0p>u=RKLmz{qjq@J)g@-U|b
zmZ=}wq2M+&Y_dByZncMYK5yTiaD$zYA6>a&>yDi^Zq!KIU0MbJU4(ZEl+Y<N6<JSh
zeLaz-GOYj$>#-w;+sm)NX+w(#yEc^VEw^fn40dtf>>0<obFC;1w_>;=>+OanLJP9T
zCbM^L*s|3IK+hE7$*&oHL$rS?yl!nBA?e6l51k*j+&0$+SJ)BwCt*O2<v#cmtH151
z)^hKk-8${*rLY|7mWZ40mir&IV0pFW{Pr$O$dZCle^U2ROMc!*sI}D^fAUMqz2|!j
z;{9db{VDZ;c_b_FGrn3S*F~WR?}S}5sDnW-nHDmqgu*fA9Wkq`xQ-QnHUGo6@LMyA
zw$Wb6C0)W$Cb0a^*0y)G`L2ENpuuZ_#|})d-A#K0+IIrZZj(&Gf_a&>-hY+(uKcxm
zN1hn2v)kWb&2IDm@p@-L?n$QqrKkk&10zK)))Zv!)>va5Z~e&<kA)`g1g?WSmRsu|
zt|b;@4U=7bx(d~M(U<0NngzGMVQs&=(vmO!u_YcgKYWe9o($%o)_m51;MS$q`r9il
zRK6Zrpzh}*A)l0D^j>SqtnF_%Tk`2&GXJ6<nAMeY9a#YNc~Go!d0C_UFZ{?XV;}(b
zbzSwu4`TPg{t}hg`QumPJrM7Kcn`ei9*}FGq;}%Zda)M7Lf3~|rWU}Lx<*`?3}izL
z;W8KE4;XCU2H91cKr=Q57Jd=9QwDkMX*`!CxXlKpQ2DfBL;<|Jis*g7O>W=nJP+;p
z-V-R+_<?CQzQAYimbL);iqwW6U@uJInKlN7*v6&JP`mYPe5rQv<Z`?G#L?tQu#<{{
z_Q?}R+V@|mMF5-uIM%>(0gM4Ovs2oWn>*}@joV?AHbGbmA_f%K{*2Y7x+DqJ<xrr;
zssNa8_|r~nB;L@~CzQY_02}g5QV^ItB-sY|nyu0^5G!BYg?4J4!a{HrW+Bu@Uaw_S
zZy*6efHrsm*kMt)ir~}Oumg>D<uMs{`{iS7L}yTIwqQC@Qt&8}a9umXlYq}8Gzf!$
zr^Bzm*y`4Av+ckAD`0VkWnz)n!8`~u@O%)I=`e792rnGBA~3+cJg933K-dFQ66C%3
znJdW<J9Q}@8+_HJHtyz+S=#WyPQzF;K(4TndfV8~U1~L3x7e<Q|F8_N4|=YRxYY?L
z7$jO2mi)=MmQF#Ck^|V?Kz-E!+sT}7Z%-tyb%*U}tFpw26YS`(ea2D%soSwilv_Fv
z?MVQf_7H1zz=bE-u$Aj<!~Z^L*#HwhUz#;D|AMq3nSbeku$o<wDb;m^^gNJ8T=!03
zRrF;`d#h#6oNBYb^f_Dm#oHnB>KNxiYfDM8#*S88(1$D+w>&{S2`IjnlrH2hE=Yeh
z0t^Y19^9H@+n5=Jm(H`iYtJQSJOF?s^{8$Ov=_6iwmA1@+$*i>0QajrwbQvW*CN6-
z(fLYASbxe5nNLh9=YgO3C-+?d)p}M6^iYRXaG35Suj^-7tLq)=18GVfv_lFa+@s{9
zZJKk5v&P!=+y34D@o!(T#JzRE(g5A<Ee<SCq`wD1=myna5X9i()<Sz5AkLFfj7h=k
zfqJ+MB6{dm3v9@>=ehRCy0Jsg=W2@o>8l466L|NcS7W=8=H^ID<syK|4?<EL=)Ftc
zI$*oyePG1-XYAKxfvGd@{1P#dt4*Mzi~SrPmiy(^iHJ42+)Y=#gMtNPSQfgnFcr`l
zu_#u30(cJuTFa%iz~ZT2yn)YWy6W8i*mEwfcC1*`s!y)ZV_Ic*me{akx3d6`X$j0c
z&cs!@ESY;JSzXV%>+AN~e_|<xP^q`OarJ?hPved~w(d6%+4}qbYROqBaRFSX;s)H8
zmxFb9dbmH-SFDG!N^fs$Ha~)=wrW0n`|BG4@u7K`w+fx0ehksBL8qQzxmZ{CPJ=mr
z>}_APH^1>irzFrD=#PNxbsKSAjz^5OzxpHXV!fqJsRIXKg`ET0kQTO(cI{%nM~R@l
zvB~5{ybYIxZ8h}{R99?l^?A5mu!|fIJ!6h#426MLVr`W2$AOYl{6JaaNJ;6$(mZF(
zT{flg3P9?g*t)969e^#xegW7Wo<`fz@QUqfdBrLFe`3nhHZ*G{`vh}?cjASf@*;@o
zuhm$Szryx3zHOVU7un{T7i@1MF6k1W-Pqc+1wuOfQ<B^Q$4UW56U55V^IEL(<c3&a
z(1DVG>)UR-?LgVOuFH+Jtb+TS<jONvYL`p?Zb?cjt<3jBO#*=BI(y>8iFWR}=h`*b
zTw}$>#W9QR-Im^&yT2c~2w%Q@dG9P;zt2Dad}uy|h3_FLE_OynhBZ2Or~4Nmu=A;>
zp0bNBy2x3j2}GA$Yq<#DvSmx$_4NK-W@&^0Ihdp|xOVMYn=<9V7JKyoDL?(`Po3M^
zgQ-J#6|;Q){P|t9@4?>xVa^X6XG^yIFJg3DiHcZ{k_74%q)oB;6MhK4ad7W2N^$Pg
z&RPWT!D_eu_mu~~caDeZ0I-Oa!dynI90z|*p)_Kp1QetNR4<tHlUUgf`!(^{TdQ6X
zs}?)KACAa7Id-0p*wrl!+w8t=4`Fx$Sj+Q^e+?&%avgJ0{H>Vh$WQoCdD9)j_@0hy
z?h+L6(lCY)jT1ob&;)S2r|})zRQm$1yjKBC!;d3BNPyptSY8XPrWjJV&`zv8Qic1D
zTy^<WKW8DQ;g4Hv@y<JKd;P06GKW0}xBF^sJ&GOM8{W2cHH)n-Sn2ANf_oUW?rc1T
ziG#mj*wvKg8ek7^|D!!w_E!|*;rNwb=iQ~w9IBgZ0^v6Yw%Mmw++qJS<v(p+VNd>|
zt{!*vyY@I>`VW77)ULboQd_!Wr424Buo1(C+9L~}waYI!*A_niqFr&}0^7cGx1Dv`
z9DD1XckSIZ>rm(tbmPHwcsNG4%38YoZTr|o=h?%LKW(#T9&4{JTW;xi@7u<`*Ijv;
zEq(*=8w>4{p+oH9C!e;DUv`n*`@o;rR|i-jd#k|nBRP5C)v~dmPQg=E<DGY#f63d(
zSK)J05Ku))9(by@r(d>)Z~n-#|L5CLa`ZmM*pMeoxmKdwND>g<`p1RVIB~pXeBrvt
z>BIYt%WCh;k5eYHUM!~r8(u_Ig@g!(#5?vh^8i?zzXj{&V-QX`i7sxz*O~u`Z!*ln
z`Q&yxappC+;m)*<HBVSz6^e&GJcRC9WBzBqYrcyCU_0l@ff^s8F8?F9V5MA2U!+nd
z9x_h7$rAEMTL72g9dF#nd1y;$6#t`Nwv-#5z_oVnzG9IV`XBy+bMx&XuDpPES*UnN
z&NfdmF2*I3qg|o8&6afBb%60R-Myh^cw6qXCS>r*$j&<&En&n7mN<EV>yN<3#U=}L
z2kb-F`yc%ZVE*_nYi$o6Q9XEz3^jAV<f?MNP9J&z#S?}y$D6CM;(pcDA8vo6`5*r(
z-Hz-pbATn3oPb7FmIb%GjC=A5SEoStj#uus#A%l>*5Za=`(AS2M_T>jDc%F|9*FnA
zKcxpm<E6utv`I({FcSo*4ubydtg5$ZBV<E~jVS<=@HcJkjwbBZGVnHtW<VLPpE7B%
z{o-Yyst_B3fHyQaTd*E&#|rbGpF|E5<0d_5*H2HhFFsLf?Y=@s#2PLhX)Z(yn-|nB
z(T%aV<VgWD-D&<L614D^Ge+85yO!DW+w|ONfQv2GMwVa_!ed7~6avPfSqTv47=;ww
zuaaeb8V0D@Rjn3P&_%0u+5F>2Sq_~TuBFu`6=m2NlLp!!m(^M*1>jFoQ~<Pg?rG<n
zC@z#GHr0z@6=KDG_yQD{J#`W7zv<kecGDRJfZG(Ts(Zg6PQ}x{9&ncPbQE|o?-Qks
zybK%vjT^91_gLve&zXn#HmUS|8ZnRk&gB$p0;!CbfXA93?gTaqE^X$mbRekgz?wg8
z$|yVO+c#R)>Bm?rZldL;O0K#Sa6uD<OKgE5OqqZ+{D1$uy$%qP^5pBd-A02k!)wjB
zCpTPaCkqsYFx1G*)|}&A`myNl5J;*KN{rVDw}0MpP9TmA3|b;!cn6vE4AK-N;*Eu}
zGwtYGuC<m_M&CV8StG6lVK-fyDanDpe&)H0txlIjm_Mft$#|ELw3YmdKb^!H-~nJB
zeEB>W<3@Y?yZ2b$4lKS~0$6}E)BxDidA@cC|7t9pTUh|olYEve%kEYz{TZKCDQ#9b
zf1XYM@h#Sn!8M)@2LW!Fsu$HS%qS}irC3F<9>y4pQTmQtC~<hK0mXtmUX&PPH#N97
zk!McP+QK+*2sY7Q%oXnOlQ$^PpJ|Xuo5YASNgYg`)`nJdtHAPhXX&26yOo7?3uQEm
z8K#0%EaAoUwo-8Fg(upSvYTz|cOS5-J-cXkl4VkM0`H;)MUf!y-N*BJ0IJjEf+*QX
zd^oJE<vjt*_`%m+W7DwQ4&-MThVOVqz;!=$()@Ue(F1aCd>~6v-L5#yL(jn76qcqR
zgtYVNmmKIE3*dFJqel<N3b?1Mt4R9=uFkplF2K*bY|Gyky1CgKn2qF(d<(#ifY_09
z9>y73Igy_+zr(<--?GP!bXLQ|&N;0se_M5}?R@rSz|`nM0%a6{npTMG^E2mk-52X%
z_cJe93vTujQ#;+FMaisq+=^SC%vu&L$;w{wsF!~Ad(IMCR>i$zG=~9uQ7f!$&8^n3
zZI^Q;?m&I5KKzfQh~kPXgS9~4Iw$M(9*HqG{QP<DT95OcKgNCHQY@%*?9FfekTG0_
zvKxwiPHML=w*c)3sNLQKSdMi?{ibd7Bm0pS{BFs$O9}}RJa%taFRKHOJP%xWX4jo^
z!@l?R<@<s9T^TrViXSn?V)wiswd3Nf>2~>~2duQ=9ebzzk61x3wDQLFkX>?%7v5*2
zH<gr-ilQ0<n0}<Nz_-#Wn%3Ix`V|1+uh~vqUDo?|Ba4#Yx3k=yuecv2A9@zKjvj<?
zR4%J~CxOh7xW&D%_4WJAGtYEZx4p~L_)TW5P+bQ~vI_p;4}a)fUJLAvl?3d{k~cC>
zW95xpn>ll)edQ}Co<(_Pm3QKaCq}ZmZ{@rB=9{gotPBI2*zM_lqn=s!%I&hO!euEd
zfL&m<`l3JSoO8}`0H-XOyQj!{&^<1;K<Q}DrMz-6{jraIta}j$bv$a+DECfe!5ht8
zwfar2$kjiWU3QuM{O3Qni!Z*|UVH5|*M7wykEfnJaIo>Jb@%=E-|zbOK;!+p-~Fx~
zY<%lT>z;h_NqhCxSNEF-k?SAjZ@HY_RQV6WZ}q(xYs)=$=(UFpv{rv^s(2oHpzoel
z2(TfU(+M$DVRGB8JtJC@VsA~;4m`-U(!RqdSCLndSV;=W^)00UjHOuU9(aw9Ro1~=
zt8GCk6j$MaSnrE|_2TzyXiiTs@?h%uFy{UJ_P<+QsMWlPjY5vrWK6^J!VW{?YJ7{s
z4}ZhfLwmy4qkMnvuut2Jg83-p^?K*To=!{3wSj4)?Wp|oY<<ns_U4{n!CTkjDFAtf
z%7$0gFlIP(f!9*d7mrS5`X8y0qwsGvEgU-=X#{&!5TI~p!;7}H?nPt{GSfg}hEPM#
z(2B*hygkawTh(<hQQ^DBWK6N*^ijHCTN>8e!rc!!Ig8spU4C8BQ}2WEbw=krD4({5
zn(RN{`H4L^^A0;Idt~?$e>ZL2W)lbjcHUX@Y;VP%oCUNjuV+m^+BR<4=B%x&@m3+F
zbw5S}Qs5I<{p!-U>@(M1Y0C-sR^QOjRbCQwf!6Rh-g+AVy3n>^%{{2Fz)mL=*}Z>w
z$SSI;Y}cMrJ9*Y|wr=BQ%f;Hd9FH5?<FCH-Lf4O7g&pAszEh4jAKsx_o>*kf|9ijr
zm#&0fXYB45Zi;t~oA8!DK4EFsTx3bpV=I7#;0;qRJOf~MulWfB*c}k};DxjC&R<y4
zQKQW__vFL=20ZCGxZVDWE4O3S;}%%+i~~(^1!>-?7h2-fbN8#W{gwa2QUWh7SVJog
zv+vsbyR53cljkF2`n(0+ew2R6w7|;0TX5DVaqm5OzjC6T2&@A5-tiW)EPz@Jcaksr
zsd>lDaap~9$O*Ya0nKlvZoDm4Y_*ORf3c(!Z|Ewj;~nbT{0envvpAsan~s;W3+^)S
zxKq0FxC1d)-8RCG<qrYKZ$wO<&Ae~2#Oars?_8{*k+&%&PX8Eg)9<nNXYSw!@}4Gw
zH1gYb(OvGkVcf&wA5N?-hCDXur2oJG<rBDi4};Eucb{~Y)?o!6GV^@iNpmgf%v%BG
zN4R@KSedu|?&B7$-p0a)5lcPFGsNijjt1C{rvvstubt$8`QsVlJrM7Kcn`ei9!LZr
zD$2ydx*As-#Ci?@QbTds$~{dsAB$d~Y=9vIuKeqw3%k`O0#T%_K-ZH-=h>Ar2G}nb
zQw9J|J8^EM?daTGce|(SP{giFom8)msk<+xJzQ63EB4E$tKs|gr6lXi1fa3coIb(c
z*_&*?d9^VtkWLa9$i_$+34p*JZKgdEcG>jB!1Olz%_qj#zdy9q9$ijMV_ao9GtPtt
z2Oj&-0PA2A4gm1+*Y%G8Yf<s-Fag1iWJ?QG*^RTZ>`Rx;##*zPHi}S3Ie_m};L@*O
zJlVEZzhO(ZLcS~hN2r#vAnsjz#CRwtU?OFDU{pH@(umV$J9~7ded&T>cFHgj3b;9?
z4e`yR$4iX*Lkw0b0Bs}wq&$Gi{#H}~MhvqPzWYVnoRV$ji<Vd=ag5plRTZ~5*^RUC
zKox))Km)vv_m+^6N37#fmVq_#DBOUz;4;3Aax>862*BI~P=WRU=sGzlbD)*6m@|E{
zo$~#!+ScI@*|x`Cu*7XJM7(WCRcj%xP8(XE&Gk(VxYh`*V@_ok;F9gM<8Ay+S6RyJ
zN!A4OmCijP!UVuNUQ#oV0n-floLpRBv%mH^+ckcit^ebb)(VmDs}U?80O)ODj9@k9
zMwp%{;BE$n002M$Nkl<ZJZCi8Lei?Hv6|%_Sed1wB+*X0J2Dfk_%mo84llCx|BH6_
z;x{a<4h9g5alyALD>LY|tX32((8dJHwiH~Prye`mQm;JCre1%UwZkx{0CLM3k`fuq
za!}?4=<UF1Ylwbs0gR@D=v$5Zv}R(tHwV$i*1E!JmJ{`(W`2Nk^*nnrdDHY95c*<#
z6C2wtt%W%tdY6gcPTwa{rs9b=L5Ma1W~Xu=(Y%c=bTcfe!4LSZMS@wz$Aim#56oyI
zgJjJ0ms?g&5$@d|vfb~lLC9X_-j)1Ls4;$GhbJ=sWuf0j40;eg%R6qe6`XmZmE80R
zG(NFj;(dfrD)1r1_6m9DIpS&m9*79(w>^SptzL4h#5+ucsT{W8^jNw6Y1e`XF=UzS
z80i?b%BooaZ(@4i7Cef}Jp1S0aj}Zu{poL9TxIQ1&O({*!;#e2YKyFiGl~b<=#QOm
z<F32hQU-L>o~EKbFTRfDGQb<!=Y1u`z#cSbrlk)l>MKjvc>(uZ)lV|_=qtH*OT6q}
zZo+$KAB-U5%)kHI-u?M+Y|CF4Vkym@i4Y@V+1x$9z+(?{@K7e7+OAmY#dA;UUi5y)
zvOLe5GTz?(pL=cF<Imy91$u+IFH7*=$pPeUbLf4g|Gzc%vd50F!sGWTLnv0dth5tj
zxZ2nA6wgEP1B-?uH7V}3KBo`=ma~lBQMbf)HM|Lkz0Mk1OIgz!+{Y<E(Mj^qA%MCq
z(17=xa;t6K2Eeez$^omZTGw;D6(zJv2UIhR!(cUgmvhkbFYIIi*Y73nabHcIH*cQl
zb0|rcsJGlAcZcs!l7+TD{Y?UDKmYm9_c!}t%_r+~efpCQCU1Yr>z+qE*JnQSneM0h
zJihS43vI%L3HH`oZ`rliUTdeEa!S8}`aTQ&2wo^GL{a)6)1pO-?B09twd=3HKI)xP
zF8$Gueq=xS$xr&_-5^>ava0{aH@;zyKKkep7wdXY9oA!5Tkps6ck3v{Zq7*^-tEkx
z9X9)`ZAZ<L*x)PV-GrhAwx;YcNWLvp80I;9*DN-owsnhbsCXL1_X~Poe$T%%eEBi&
zRe$;6eAkpdVzY*SE@s}td2vV08?bXt4s+<MT(6U19mP8v)>oE~-1%M2W%f+j>(<Ix
zm2z}=xJY(}+(EoFN${;CNRmfseNRAH{$nG*YEuTxcGvZ%vAq`hWjhM;e#<0I`GAz6
zo!(`VY~p}(EkAWEh8y3uCeLmcUQK1o@)~wL{7zg|`lEXJtLogZeXLw0i#(-8&IVA}
zSL>jVT-!t3DB~E!SjZGqNM~Wn<#-=mG@F<^z4N!(t9ze<-a>vOu$x;{p2U}4#p9)4
z+VFCybNb8Ou5KyoHhY`wj<xsN{WJa>1;sEwvVPMRVpC7GJ!N~{x+;aV004U)-jE)C
z@)<k%_~UHNhD{FK-n?zQ4Z}_N9)RruTo-@8607U8PM_my(Vt}ak-g=F2}DM4`Gx1&
z{eSwKoqzTjR#jbNe)umvlNZ@U79LvOUAw_fnLX3iZrE&NMvid3(p6CWAlw7IkJJm!
zu#^R-S?lA^TjLM!C5|!*r&zG}Nc3x{rOjIY{Nw?FYhE<gy8|r!mg}wR%xj&j+6kIe
zpm;OhQ{9cXS>BWPTH@%E9@QMyA6nGAr{2H076PyhZd?Q~i~OLy*^;o<?h(hDv6OW3
z7XZMY6|=%(jVx}%3;hGGnU@_a{|287ua9>@?}XF4K<h}e5@&qe+TXkni{>r#4RX+R
zgf%+>8b$p?KX$DAt2^%|GZMMv^X}Lmv{qrXvnz?GdKb%WDPVD&KwR$RGX#!Hh_^2#
z&Bh31^^;h0ui_eR!#BNvg>?<@9+`!Bd#x2pgnIOjIm41p{rB+i&c6cn6PEnif;-<1
zw-F`L<a6&NisnB0%#$<3ywfkX;KJ`Xt92K4u)efMIq@HT_ds|R?fZKCO1uZ+JrM7K
z!`1^yfL}uk(rse}1REBU&K(e3%G+gaFnBo1g(4Irn&4C{sn$`U`>Izw8M(}r<#HNe
z(swR67LaC*{r0u(SZ}h)V!^E#w9!(#C{^}seeK(P*gGjRR_ah&xXEdmI%1gIl+!Mx
zx|2lJ{Q{HO>2dj-Xg|JuoDEIgXuo}BlkIF`vm?%qESN<;ON&|cyY}ctlHz>>1L7_+
z%kIBsn*CzfF8jZiHrv)3%EYx&2)8612{2TmltH9K%B$Jn0ge_=?I^S9#p(8MXHT*#
zrsYGZavvK!H+K^E5XSV+glvyJ_}Llu)2BArW6QSMjwUQ$Tk<I_LTpneceS^nSqrl=
zXLOc*YTg7pe*$r(e7LdZ`4!tpHn%D}o?`caqjCJi8{)MICQZkkwjTy1ke6noe|n1*
zZrx%vi&t2~D=V$~rRCNPc#{k$n}}s?Q+ApqjV-pK<0o0cyjhli%tT^Z7dcnm8TcRf
z15jX(>%h<e?<D|8<AqU5WsGPJ1p#k;<4bJh_iwU+*Pm<E%U0W70HWGgR#@`x2Fq<m
zMI>Q>Sz(^FPb{)gbB?j$XU;ayxKWmro$kQpbO<%D7zaGb0yqGZ=D`gXu#IKm;>S;X
z;3Ql=7Jd9Y8+7g*t6j0)YTsG~2(^>A)Rop=i%U4>j@)rO7vHoq2%+rF%wjylaM689
zvh3Gf0vHLTV_6InSc2AR@i9l)9zb06Pu0TLEMa|_wcujQ3n=UzkY%mI^DJxnSQ~oc
zOdBwB3Vl_A_k|8XYd~ZO)&NXgvMfTih|q=r?s3yjMZk=UQJU|py%RyHGp9`e?2)x}
zm}!kL!|n8K`@k&o&Yof|%_tAB@bDB2d#s+Oj&k!$vr!XF{}ks+R_PrBGR%9@RBLNM
zTa~I?smq5YcQWmB$}K!!hmU^cd=k$*08qLK@ILLzIW~IUB+DaieccOhTGMOqTEogs
zX7zQrCL`WA7V?cH`IdkBaW>}E>6Ulm6u`h?;W*wbV%a{$&hEgiJI@`YPkZrLYHB<&
z6d1F0#O>Ylk0owgceJ$j*)7UD2<_{<$rJj_^Uu6chKum>r7%Q{s}AOR)WjH+W$^GQ
zZo+%#mq260MQ7U(td6(Cpl|uhlUB8IEpDPwJ`o7ZSeI3GSeVoM?q0vy)-(s4r7;Q?
z0<DwLNFO+Dl9il0&xXxE-O>gX^ty?A$h+xJkK!i0#ofDK2A$|Ed-(ZhM!nqoWceE_
ztQ<>VrM~*f0pTaHc1$_&yH!l@8Fzfy#(w+~*GD^^e$g6n*{<;saqq1O-LHO%G*-`~
z2V<WWupjJvp91ekTyPeyy;++NkTQo4wG(htH~v#s*ycY!Zo8gcY>hkjIAwwN-mXqs
zfOe@(&V{-5e8$wsOBWCpEUXNnc_ScSiV2WI(Gz1pR{A9ffZ~bu9#^m>*(bP<`lACy
z#s;j)9$tHcJ-hwOjD;{wC70oCcphnOZ$Ma#Te<`kvD{|khKmELnQF%E@f7cYk9-e|
zA3xsoiKo7MK-TxN#+O^~{wBE#|Kb<F=)4hJef8DO6}#@$`IReI+CBH&V-G&~pta%Q
z=TOq(#f$BZJMJ)9ZXb-7fkUbDgK%$E(-ueP$7)LgyoZ3{VYxG7W$X8~U3JT>p}o}Y
z5z%gvEI%QXZlm+(+OFDVRw@>)&kdYw@O{s3{H7~ve8B=jSR6XvUxX4@P%*Sb|MZb$
z_59lQpIKJQ5Sv(ZULUy*@9C{oFCDNzVHYC?oD8i;M;;;RwW^g?83e$R@C;f|w1;>Q
z_W}JC5G<ZLOsE4|qwqa%@RePlwSem9cRyh7R6T>YunOcyK}!KZDoP)2N9WJC$@ynH
zi|m55adzg2JM5{gU$N$3sZ+j)SYE4i$x|ecP{mNqVKE-RbxI@2UF!T+K3QIiH+TOz
zcET{O>&7tGt{>q$C3y^&Ao`bIJZn+vP-Sp)U?-k(yvRP}F-`gO`m+fMbMyc};QIBp
zzHu`$jD2N<+rnQIH{YyXX^SgY*l7jFIZt!7ghE^X&Pu$+wb}f6r#eNf+PVf@gg=7?
zb|tblAJNk4Ek7^UDyyoU*O^>EYguJK{KV5%i6<!m*pZZ$l4@n;6*m9O)7-s9xEz1t
zxfhtLUOVg5Id~~awQB18JLQ)GmdoQx1?8TE1$I33-2>u(Q!hLVE9eug;lI9X%?~|^
zrL?c-4e-Yu>$WkBc=ybWSSiFlnuIsQjBkA28ou~_z|1f&<?i8~1j;I`?xt^A-Xs4<
zJpio#q7*FO3?Sc3?K}^%=O8Yz+uyh!h9e?#;Cx#n*Ce}R^N{z{vW@*}ZKwtFk(xc+
z!#<)uL)P50Y*ou5t7}?R7{j6Jy|ky!xeE_9VVzvYT%%ov5l6!CW6V2#-T~_l5o0=7
zqx-0r@Pi2>@vM{{g~z5o@3>Q4jBUd$dZ>voAM}TJ__1nO=Z7z~kLQk-Jh`LHh|%q?
zyIb_9XYynZ;e?nvJtqq)vb%WdUk@BQ1=7EI;<@5I5buF_51210!6sm}yXf7$Eb3UH
z;u1Lo;k&WC1D8|)E?Dj)NRaCY%l$h{%fFU+#jzF?TY$UlpItS<<{dM_9$d2BR&A-Y
z4B!5UjqaIHn23;&jSh`jM__O=1gbP4S8#H8OA43nNL|jzBGibDlV~RlLnsY{*~P3t
zz>X>afP_%Nz*7x!)|vqi#3Ueeb~0M#mySj&a+E#4ywcv?T4A+3UjW-rxit>}m`y24
z>Z}(_TjJLGJph4#%bCf3`|_EiY{3ab?3p)9?YWiJw!YGDHEmd7LpOv3+#y_jbx<6A
z(=EYWf(8u`EO>AyXb2V{xVy8sySr<!1OkNMw%FpZxI2q4E{nTep7*(5-TQu3UGvve
zO-;@GT27xn{Xw~i9mQ;SVW!)x)oe*h$kGJ3ecvrF2;9{_!@I(yS%<tqO)-A|bxQCk
zB0~|Fy%SfH0etA{Bh4+dPC)20e{)z<2R2{2Dv4{npfABkc_*&o_jzToR$DrF$sr5d
z_4nRC$S?dy$h$((z<xN>B^;;ZfrT_4b=1>O#LNfFoU|hWi;HtoBiaF6q>-31eaY#+
z1qnZYCv~94S3Oe_7lFs))_~|Z>c+`CaavY%A`fcOwl;k$xOmS-fe$80(p`|s{NWqO
z=NGQ;3%nct>lid0oW@~FM8o<ej7iI|m=Lp*$4`s8TN&=kwYi9>@b4Y`7fRf91+UL|
z2XLfO@TPC{x8s>Vr}r){YT1rgyNX_2@iY-yf;g1PGs<LA*zJzdb-(n7B8wBK#xJ|d
zzoSF5P+XL1r`v!RrskRt&k=>7?~vP4%<G#_DDo5apX+GMQwG(SMx|>dO0JpFEHi-J
z$ux3>n>PW6uffn`h`yY&0OKM)*W?1$j@9xyAreZ>?8biH)I0LMEGP(<IcIQ**4CMN
z<lOEXhiwbtlWP-FZRUJDK6pwOkzT=6mlu}HC>U|WbnI}|Y{u!uKw;_vj_=dSzKZi#
z6MG<yqRvLRh}6B(BS06fP4##hAF;9Rf_$>Yw;v<T1oVvY4+b-gR~IjN+ir)1WBfw&
zJW(a>yyvUq$oKe~L$S0Fd>*uSYPS3XY0_Kq&2qYgLf}sHhxn2wAMrr*6>tR{(6afa
zE+F2|5}uJL6|QNB;u3{=@UQ!pIV#S(7l&FY>(rg^;A-l0A!CKqvPoqo<nzojw)#oU
z97`Ts-nS3lrN@4z%`U7fsw!a^M;w1xj5GqRdi8<234fI1lHBS{7)l7Yggn-fxDt5#
zkj7>3=rvxLfX>%{-pW7pnwR@1{A3&qT5r054PSYk=su2+1(ny_%LUpF!Y?~<-P~t8
zd#0N@Jb7gMUbtyAoA7Yfueq+pK|Hi=3E8mUxJZ_F==8tU(p`L259&ojIk2Uy13ON6
zRVT)rY~?7(%TA<*@whxsrr5n_+cjJC<zT`wjucgMXzu!$-e&7#s4F9U&NCLRPqaya
zI-u2=yFAv1^2!^?wXO9@NOI?#YiJ*kZei(g0mpIoJ!rJn%3vMZq~G&X6@|Xh^1Dg<
zUiqqd4fI^<Kq2T6WG>3Aey^@E*-lEFwINI@Mk)oOyqPQ}>EkeYfrncD$LrA6qR1pr
z9_#gYJn=@`qa+=`akpPp02W^rNGzQ<*bc>V)daAb3Prx@T|R%O8$LFC+$84pfGta2
z|D4Fb7iIK<f0|%H|7q1~HxKw03aBxS5i`EqH+_$@D%93ox+!2uLbNFFeG^7305%GQ
zl8ZMdFGKD9cwh#mAB+hV?bWaM{*oP6<UH6Nb#hhk`?MgWsySVii<HZ=O{rrGqw+<M
zr#WlDX}*uPtx)#)*|(J}#6*q2i{TXvIcjk@62#?SS~376R6K(!syw^FSi=3tzZ3en
z=ES$YWH|=?QH}v0q)9P%7j<X66~v$oujCCy^^#e!NqAU)77koCM;$NNwdx|(68C4}
z+u-mznnb&VByCS!RV>8~u&p~Hee<Ae<M)q|!OMhrDN=T!&|w3okqy_K-A}QftM}P-
zXHRhg^>1wr${Atrp?;bd-jG(GHv2MN8_R^-=(|&L{`YRba|MrVuP@WtX^E|p4H(Xu
zZ*>n6S%%|?=P{pD+I*cB?uPwb=pQRCmf;9tN>*l4OQmFz6B`bQrQhOPE7dgNmS!XP
z_~^n!)lj*z5WlRJ<QR0`4a@48m*5EY{}B3dyn7g6!Tuc->KKlkBI%`9aQtIT8RoV4
zX8yd%`KpLmz;=&Df~v?JJ~i5fwcQ4mlY-mmc}xyxKM0_YCo`=WV6Cf3Z@-R~Kh?^W
z%ENcMz)Dd@pZF|veN^+VU!lRNP^QJ@VmHr5$Rk4#1ICgXktW*JmP|G#awnzvjAS|P
zQrW%IcblInk|<7P@ifj?{-h75m%1vV)#O2U&mFnjjk_e4XE4{Q@8wNlw(MBc(9trY
za~Aq%VJS&^B7{NerEIujgG{WZytRGlurgqE{2T4L*X9@9Pf#iWo&H*s8%y!tvSAus
zQu4rwUxKjx9N8KjQXA^;<|(m)u2jy|$MUSzUT4pZxEA7hNGrnjrAEi%L^3&txK6*F
zF8ZGI7+gPh|J)~^PDl9t4gcm#)1NG+n@t2na#`g(P|XZ064JRU;EypwKYXQ6$(X?E
zLw#O_Y;?M^eY|^nf7}_Biucc#x7mz*{mOkl@Be9(^Jh}xI{MAv(g02H7GQ63oc+Dp
zfDNEQjgxX<!C6QRDH1ol!n9aYfXLf*70<~{faOSzlpcy9f0rc&J-N9M+=~R9T3gC>
z13-_TbW<Yvv%f3s-RH=!R50(LOmdZjWlxV!akUN_KxPZ9I+p~BqxhZx{y~4*`9U<h
zg$>q^kD4S@nv7I@%jBa&uS?9ELj*obOcR_AZOd3;o#i1;7un0tjV*-|J}l8Sf1_3$
zQUJ=8#MP52eFPrQUiDTYbB9=?Plz$$j)t-Tm$|D5cWZ-sdfk*)d^#IqEuHRt;&H#Y
zSCC|7o7`ubyRH5ulAoijtztET!t9tWm+L}bwwg5D+1FMGUho@Gf-ymY2DWc7vx$)l
zSryrSImTg~GAU?|WTtW?N9AW1khGBEDjf!%oF1Z1>pT+BR*}r0!PUuW2{F4fD*f`h
z$aTtU))cq@Vua>4XkQO*1QG117j!xdYR~jA*lc?LEu)%mhyTlj(V{vu$Wakk@dJlk
z=oEb;eTnv)F5Y|yCTd*Nhp`RJE@X=>!4_^Ckz58_w-UtjSjJ)wMKk)Jepw14_WQWQ
zm)SYX50Nz~-}Uw~TgKjPesw$|kx0`J-<UC5lF_AcGWU}kq5p*8kuZ_?nPmb0_77%F
zVK79GczX4_USNsQ@eZ7T2pnc%9@m@0?o=nO!4#G864-{#Z96Z^*C?jH{*LGP=NP@j
zKl4OeCbp-SQ$zsh(YZ{i8`B`ozL7Peev<0zpLhlSYQFYpXb2p|e-w4;HS+GOt=4mc
zeZEY=-<%u##-;W+%_9&vKY<hPH_;8G;hauUORhlvmdA(N&h*?Ui-Y=`!fW66X5VQ;
z^0aVkxmk;S+;2_i<2OP0V5OEbC>xtI7iaSi_Y48tR_{^dKou6GuQ#7|qAnx)09{U~
zpZgPP%aa1f15@vNqdQhw&)~mU`ra_NDN{`Wn%x7$R$3XT2S{Ac6>d}{kv4fV(k@D?
z!;f2|T-v5>99cCRt+VfHe&agk2djRCg-)iwjz^2SRu@49^=rUtDkRB}>&Q=?zl59E
z86bT}8p{rJL35);xyLlu!1CTQD@}x&Qo;)z&T2wFVUJCcVTquRA@1hu(JLAG@~!8i
z{jE+I)~Pc4B2+Jh0IBA;Vs0EFxKpb`s|;n0TIm23H49<DKd6+W*;SxPFmw1_7ySGI
zowin$e(M~s-NL${;(C7PPEXd>C%u|VoY||F4ZG<FeGR3hYE=43OZDWd;l7ihT9l&|
zk${HaSTv1Hfk9_ASuG26m;ydm_j+|EfASX1xenb#1A<2pJV+OU#yG2CjrFX$smorA
z-Fe!c?AelZSJE<Eh%(mDr;DE0xc-J6d>F3>QF|#)<84Pf$qG!i0~USQJ{^>iBU$~5
zltPf9Nd%0JIcA8r@{z>=;ZmntRS8G!{qTuaT8%xf8vpGrIS@6c$%Y6rKy8hZ(3hYW
zHcmgxFuOI<4zvYD8>ToL!F}tVRc^Bh5x1F*(W!aBj@Xz0z$tpx4wpPn%53G@nO`xZ
z#i<k3QKgFU74@QjVz^w~N!Scyjoa?d**82(O{$q4G_-*eGDrbb`Xq@d4CmWhN8`zR
zhnzLl??^|Qy_uu#RK4{Lj}~9#6*t&V1Dt|P&>#VJ7&88GDzFCXcd`&C>3kraxtJt1
z<W4}+f(W@vy?6_^x=V1grxiz&!unr^njqv)y3D6Tl^M$2gXJ_rCaLD11D^8~)S$Yg
zw@Ew}@SAuWi8WO1@u7Wlvz65;=sV}-*4K+Q`E?C$wssyyRrtW86n#~c^GqH$G7e$u
zAZ6t~b3Sn^hMz|t*OlKqel}z>(br#KPT0c2$k#QS4Lxi=&EaZsAm$hK)pD8#P+Yb*
z_V?W;AY2Es{KW51g)^v<^`BN;{o5cLy`_Ze+G*1LE|XSU?DlWNt4IgD&-FE2x5z-w
z57c)Vkr92|61GqB$<xVhb#!`d>F$c3z`hAmjI<e2%s<U$s(#ldHHO`NrO(^RD|hqG
zpy(xxOfFNE;24Bm>5_&2cFaXHjq|5l*$qwKSk5E4@j}#*MT~%nE|Du!Wl}~W%u{7*
z;sL&ZP(<%V))Dkb4Kw3)UrVNzxGzgB)VA==_lJLpviT_OwPfMMF`FocRZ?q&z`qQ&
zMC-ERNCS24sWsY?J2&*Gb|oKBv1f5pfgGU`F%g9iaHA~@oRXrE1gF0!<scTgGKw|6
zlhkesDK^LYF1JjV#9cg<lWL6Y$+ELy+HI$6l!T`U4;mgHvE+y45Q3I+nEw0}PJ-O`
z%V6TcrOv8boP1iH2Jn>E!ERgw9*Zj}Cy)<wpeAk=jjWwB&GQ==eK$`jV^;+|G}-lA
z@KJd4+(Rh*Wduz0dXl$Q$mv2;ePfs^hc0xk&rt-3c$Gb0+U|q?%JJj(>vKsFCaTgB
z?xF0`T;}<MzX}8gm-dQCTdv?8L;a>%_2i@&YYn})>FH>faaxE}Y@@bJo6#7~ufh*o
z#bjG_CZ7<G54^E*5@vpGw$#?-?KS4jH@wAOoSn4JC36rsR>NRa_Ns|F5&qmk*UG$c
zHlb~$RV2mdD!&;;BPK-krDIxey9G|I0{y#;Rx{~@;Olsv<DN*Ol<&d_o+QP<kA%bL
zx7;s6k^6)a6Muhejn|GTia!|E)NC8S*E!mi1Pr3IhJHwm{}koLDDDlXF<rZZZM0q9
zG=Ya<%Y-z5)%02t)YM-fSsnUjMu|O+XNIPE6FI<heT_!rar)k*&p6=*$XH4Yq)A&U
z_SjvL$u8&{5`AQi&G<u!>&aT%n60;sFv(dUK%RRSWTgng`-g%jUQYQ9^VIO^KvP3S
z1&>*G#uoks@#i&zxyciQ?NNwaPXtcm-Ni1|2rJb+sHbK`vDPkdV<|I`F@|NMIT7De
z2QFN5VKtpF-f+Snu6<X$9hOT?q+g3>j!qR(b&gJ9RdHe^=f%2Sr*c=)G@R8=BV^pG
zc-@^2faiY+xE@33jQ?x*GG5`+m~m8W?|p(63im4vJNVCcJY4A+WtN4BaRbq(t?7>F
zKU$v=-;hNT{>GmQOboqT<u$KcOh0aIe9pfaY0F+Q!-xM+T{|uZYjd$w%3d2e$`u&*
zTAL?5D4ltBd-r)+BB=p<Fp>Tzq$*_mvj%{^=<xKu(+Rz7Zq9tpWwKYw<qhT5ho|s(
z`v}6N3cKZIBa8V#h8;j*81py);$d573%Ood8us<h-2<|<4Jr<eVWgRguM+5n*}Glb
zl8q!=`}I}`uaB0`Mobm?WTtodF=g>pV}Bgk*&aTF`M#%PuXOPSRzS7#@>9xWca<GI
zj{rl#e@j4)zQP$#<impPTlOUS9BA@;lmVK<U!O<ird&wEUgf(ozlothqsJZ1OH%ct
zDdOa-hZ_?qgQg8)%c*Tc1dd!sBj@R9g!EZgM1-ERrn^O1^W!cSqiohz7HAHgy{3?k
zd%!gIW!qPi1I5MmLSTZtfpdajfnR3Ic3g3?M+NnVEKzh1J0_c}*8Dybbc|bw@%Ufk
zB}Z5LODh&#eanXBH4I$}-ndc3joEuxJRLq=x&(}IVc4%WqD(`vvjpRwrU=V8+%`RT
zqlN39(?qj%bgC;%N|<lHdaa>DQfA#;$Dh=tDnY1t;q~Wd0)I*f7*h78W>EkoR%9e2
zr?LA{LLzC$*7|{~<>&#o(ldq^OwWGjV~QE$W9r^xc8@2zc;sSlH=rd~-ybt8FU*~~
zYsqZQIy{df#_k9nO_jF200mAsgOwQ^DEZ4svpv^rWi0u#`8I{SvN8A~dAldtW{wNm
ztsr`g2=9i_yT*BCQ&v-O2V$uppjJPF-rHcr<i#`hy-k6@FwZpn11{4nDCOzbV3b$x
z)^7qsB39*8Z>%?z(sX#ZZ}2=#QA3$Pv22*eT5?2xN%z{5lX16AcX{uU%y|~P$KH#Q
zEMP9tQjI8{_V*>X0)S*%H^-fPt<4tgY-GpaPn}s!4n=AZknX4g(T$On?y>BcUZ98E
z&bP&U|L&T6<)!JCimckKa(7$1m!rYKn7EX?jYhZYPjV~6NlYns+(M4>Ok>vy=itTP
zHu<B^n@8rFOH39?lBw>}SM=~S@Bgu?sKCBP><RgZs2^V==+~w8>pA@P^Z<`UyF|<P
zS75#hqJ+f2l_x?e<X>1m)=@J8G`^ul3HH3LeIsb8H4$xPhW+kU>aD1cmTx&cHmL3f
zWKSyqeutUIowJ&TaoK?Ock_k;BCn!2u2RPe*}PaCPsae`d_N|_DT<p?S^#+2eoZ14
z;kb&Ma}c~a>UR5U+1JC;k`||mE$ehi4Dj}TJ9eMNQ}514Ma~x5(10@~RL;Qd;UL@_
zJdL>kY80#bMTKe4U2a8Wf%U>csppTd^yri87!bK5sHxlPfEsE_UB&k<##p*837^dg
z*4sGC$`5kyMy@zkdD;Q37QaA;A|CNv7yhKE{lN-74sKigl1j(c>ITZ*Sk5>yYS$$z
zO7pX?&tuyxeWPM_HC?xkB)KL_oBT<EO#rJy%a7}zdL?R}+QCr!;scl{cZ`#7n5?r@
zQY=1#ud?ul58S|0^rN&4a$33B;MVDCSz^zXzdd$2=662&?$3hGbK@EJ@R9qV5)07U
zJQ%jM<A3VH4DHM3UPqtLUqo1q&#=w^Ez1plc2h!N{5cJ{UA@+4*)A$5m+)5Ix*Cr6
zEc7;DFW*V^qJE!Hm`j5K)>Gp9rmL9{7L#GJ^jmY6!l<X1bpcdSFpfH3aU&KB>=2E*
z?%$#1-gb~8A!TtrI75vmk&qAJSdrqRy{Bp8eB5*R;0_beYT8K}YjGUIqnv}?Olt4u
z1}!Uts7BYG2*b3|Yu#J4Ho0{FmQrBx`5`<}He)>d_fHUX#rgS+TCDm#$+Y-+IFMQ!
zrAJRGK2OZ!<Kz<S3Enx&lR#RjR>DrMDw}t75W_FG!iCfd9!l6VR*7RGXw}=Yh?#qy
z?R9%I7YX(F%6-5H?RV(f@y1AM6tg)jl%JgsWAbp<LW+D%9!#F~*SG8+bQ2YSwYeU>
zY1lu^mJ{#3%9b;<j}fP$HFEg#uq(xBY{f}5ukPuQ{^)dyRIs3*-8;Ucx#<I->Rn7F
zs5*{-qTV)<ecQP(GpLeMMrc?S9<3ximo2@Jg|v<yHH)+!3p>VaH@dBsT)3L3Q)Q0M
zHD}{NI<tv@8O?k(iUwYQ(p?Ys#<uM2;!slw&MpzoA8m~uA<JrYBJ(8;|DLUXuK?a4
zxQek=u45dw&@UFXTiGimmB*0YQB?i9k5PfBmkzJn#rCH2pX*%)pDw4BsjkLWU8fb5
z2-w-!z&QS=2zp-IY1~GFAQG2X;iyd@#G&VPCRJ!fj_NA@Y7^ZO>*2k=Mx%*JpEAG9
z=bP1fO82D&vSmxMd>qGKkD;H#`v%!5x6^~^YrGpTiEYiO^6pRJwCHl<@GTGG()151
zclGV5J8?bh!TU`%_idie)0)+-gPZc%8!Mc4w}GpcEaSGiyoJT)2w3PiE5K2vVRVAl
z%mtM17wIp8p(ZNzd2oB@EiSRcWY5k$I`KcSIyqys-!%3z!ajvru_P0J?5eav%0-x}
zU78QteHZd%FIoA^=NMjrQ4k5k#(e_+8@j7f=#O}PS0<{$qzFD0AY&Oh1I|51-l7vT
z1J**;V6XeAeN*X-9gIiYptVM*tXl@EMcg`K=3{JX1MCGZtm$9+e_L$94sl^P=Rwwt
zy~lh8K4EiDg-FDv?>tHNID!K)DyrpMvl|6AgtkI$b<GJ~V&k#QrkA*uw_9A0T0j*m
z)o8YJfZkpwQx2P64!Z?VTEPV(=<@vQO>=_@%K4s5i@za)&EetX)KYkzQf}Ol-MCA3
z-<AQ$PQYCq*2Tmy);qS6#z@7DOYQBUQ|n6yJYI;xFIMh3{k5&t>`p-+M;3FiBuFgg
z(>p<})#$|aN+*?>^cI2|O}7AXe)q1D);}M;xqT-Z8`Obk*h;9#zV_MY<2`tIW9>`p
z9`y3>q696oH^wO^X10*xl%49O0pX8N$Vo9{-sU>OZI4gbz~#}_?ViZ?YRt(yd1KB>
zuEBmNsbL)+R--RjU&nLap`iuj@WEBHOcSXstypJosoi!nA*I8W<k7q^X}KYs*op0J
ztW$1|y8u+KqeTvPUuK|OK6}U7uGj8^2OGn#uYaY)(VY*j;GLhokJEs;AyJQHY+jt_
zw$1bB;%#@X5bWm*GE~DmQ_B4M;VuR1I^2Ydg~`#51ooGK=_D;*v5sBpW=oz;b}@yF
zBTX~s*72?Qj3lj_-*xr8WwtMYrKcIG9>ay~0Jb*|tyLfXiAw;~H?1m{7aPZ^uUp)W
zu_FsXlMO!KTr}Txw_&(%77fvljoF!xg|<drI((?`fdQ`q{il+%lp2ovBHMGT5I&tT
zLy8t4SLR~H^beFQU#Yb2*K2w4hxJ@9G(y!|TUQY<++JX{nfA@|t4Z~?yeoNRj_nsg
z)C1X=;g#F%$%KGV0TejQnnQ=URz8ch*tDxj-SYu$5M@H?V%EjGByGDG=?z5_A3v0(
zV_5?^L@DwAvGq*+0rV}Q;i@BC)0msujJ{7IGx|H6D22$c_ZSeq!B0DBf?|*42wbSA
z3F+;0cT4y_9U6L=xQSu6XNx<W)r*})DU(-{IDpP%c3XTGIIu&rQ72GRwBz%552l-(
zmrgipZ&?m#wNYhBW+FBE>DRpr7FFO)O__Y(+0-!tdCMzsdq}rs?v3Zx6QE7!wm3E{
zUoh%j`uz|x6x%Sik?Qokm@VDN8=ke-7TC-9ay}XlG&Q#RW3psQoZb76ygO6B9Ht0+
zU!f%_+j`9AWA|X{H-LTFU%i>Kk{<0BLvmlymfb#86sdce(%1P|Iwmv}1X(>;vm{RN
zfVJ<BQc{qgM7y_J`Hr~NTzXs-P(TM8lWX!=@4PzMDZ8WIVrHfd74LR_3+%4vBQ=NE
z`!bK!@)B(uEL%7YwHBk%5u2EGX`@OBQ-x-6vT|)z=UlJ2a^CEN=dS}Az3u$y&n8+g
z7Re&&pBIP0AH3R{h<$q9glD+1HyCrpqML;R2K^d2KN)#<)SaEliFGx^_W3~<0PE8!
z0g+t-w97zN#lxCk`{uVyp1NKa)q8DCBV8>tE#z!Qp!>Y!=1bm5ukMA(+nZ?@m4-%A
zc2?aW!m{~|1e_NHA!cSykFJY)1?6=>f@sj=vK|9BV9{Vwd!-}K6J33lhlI-pQAbCo
z#`@5mK}n1&APK)F0-G{6K=hh`)cW(|r3(f{BymQ2>`IzCLG3&=jBc32V#^UsaT18O
z=tte~c<J%w=+`y?JnEfEokMaxm?0fwN!kBBwVQ49-cJCNY4B{{fdRkA=9V9y@nvqj
zC&L5r7Xp9Kd*3lR6l0#6mecDhAB|bz_T0g-4iI_$%bgSV%W37<Y?V)!aDs(QoR<=q
zcqL>31LytoR7j-gUBI%ef%@Ypdfqvn64AyN&KINNCCG%+8rFyhdvd*l5|6sdj6u{w
zotVt!WYzSh*Y2;@G)x-ao82#VISIYjbZmxJb3FBCJ7#ZUJSawFj@H&JMAiy2L36XY
ze-~pHVs5DK6WHI^cy)btL$M4<>@O7?t_z?qukt~s@X#N8UK#gim34lQk7T`PT{-~~
zPy{@vTHZ}WoorJDAir^a$PC--Xb-;a7D}=y{~c%;avR-I2u+}ZQqf+=%sUQ&mzCR-
zhOdbwN*i$#``iEmZx13+FW<ns$7GtkpTnRp=1Ap3O^X9jZz)QCB$N?O?OaVKad_(I
zeT0R}4Q$_h2;f#ca_gWGIj^z5mVEpgb%*T<FQiwoVk^{CZr^y$=Ei2GL4KOGI5w_%
zaq*gG5?(r#-BdyIVG3<1+`E1}x^~Q_Sx0jsT_~7eS9$TdXFFN_!8AjkqmSliz-JIk
zK9f<P&f|Vz|Cy(&%+1x`saDj;!2dS!Dmo7TM^E9)3CY{3jP_6zO3!t-E0^n|#oML%
z_VBtfK@4r}hkPv7$^*WRJH?rqne{v$*#2C&ed~6*u3qzz9k}p&cVw~}Cuob{J5NRL
z&JW9)5R?7Yoj5DY@;yWf&W9DuXYN0hHoD43Q{&ID{XDN+N7*2{v`bp%RLNf~!~*rM
ztV8dDlaEwL#GO39Y@jg?QO>E^=N^L7^CBjn#-ya5F0>kN@a(E|L&5R61jLY0pNbNQ
z)>`@KnCfcqp=yhvgQA7NiWrywaFH&oOS<3q`c!#B|88%Ll*QswuIB|~dU&k|?GmH9
zbO-I|k74jm{MYDkR`TalMA!M9AS{o$DZj38AC@w=p0gVNL=~bHt!yp-`+;$4G22Au
z`U;0%_`|L96We6WXsU7K^Dn}cBS%^p$AGgw7i0z5EHRwfb{QP{hKlbN978jvlF84o
z!sMskJg+8`g(60Cy9MY&ZX~;6GoidK@tM!N9ZXQ?<X2VUrHSffhhBSy=#&qhowfXO
zWAyH>ucJS{_lXIGHa*_;q3*3D)0IrXtQrvTk$lNn!gcqoCS-N{n>mrb=J9Cg=i&8d
zNNrgk@9DOmeDB*oc0TW3R<Hf^I3BrT?ejvC2aIl<7pDQU$E{9lJ5*Emt#RRW1i~v?
zg|o(PHHP(b6iHl}bGs-QIcVT7{?A24P3O4)Sz~|Eyu8N1c^%wIPt3CUiV~!2t4iHh
zrhfh&O9g`>Ner|^xi5yjO5D2Rt>(+>51pe0^{52@^)~XItzLNVF3+Q^-EUfn5&ikO
zF$sR_-ddD(^{Wyw2SEt;(}%(NbBpTy{!8B<FV0FFuZl8$mKME3c*vkW`4Xx%e;xqS
zEodP+4tQjaknw_t#2D9HGkB;Ty)3y!MR3o~!KAjkAsyl60;^jYmD<>PydF?(fDpxo
z_B77&Ry#_6taz7)ScD&+e^h!quS2WY-FfZg<BNaT`lVB(p5x9lR+ZD>8UT|u&3e&(
z9;H7`Elxf12^y0VG=={1+htj>OpyQTm>@e}#`o-mX^U9(Tct%iJGDi3RbOU&1Ah=7
zp{;4VC0_Fx`{}kf(RMp(e>&5B%VEqf@LOBTUv3vCc!C%BsF?X^R29uV#J)(A9X0w6
zuM?qXcO5d1Na&YD7J-ysaiPE8kg94wXrH_wz!zG0z-l_oNF*}89X)d~jA^qReB+fq
zar^T86cd^C*ZLv-w9@!UFKR1lzDw*8@pia`#`SuaF*P9Oc&qw_oH$@ByK}nHDc|!E
zLh89+C+4p)wJOehdPB>9XP<ja`4r|#mEhk8*e5;4Wq!@^BRk0?IxXbfzll?*ECexI
z5^SQ3{~)Z_CTP5HPvD()-8dVFwt3uZw(-4NR#}$Oq}X3naT<fK%XfV$4cL}6XIZf)
zS#0(y#T1shJaxLxUh5}ASltHLmxJ3b%A9VJk?pF`Ob<@qxSG#g?Hs2;RIvX+AO6dN
z_%Z^ch%L4s%M71}v}M-3c5^+S?{+6wVcOX3swWC|c|5j6ucaBOo>Lzo<HwH$nNxn^
zD+dEb+iS;-Yw=$`1RFk|XH@FcjkRCR={ogb@ikpl&1e`dIUw92T$D5uJ96n1@L1VY
zNg+VDcx7*XhMn<2pm78hI(W+N-%tyevXyddl<et_mVHqK7N~xECQ4I}fZRf#?gp?e
zL2d*zZ$FxNqEOcLG|`XE_^g-Y6M4~oRq|kWaWYubcwLKCab$`_3c2|?+sEQyY!wsF
zCe0*V%UZ{Vb6batF0;^q8a5#8jmuP!z2(n1!d8+fiixBh(wchMZbi)m&S)ELE3j*=
z@ne9jZuDO4#DA>s+bhVjGcj{9Lcg`M3xQG+P{1!oh`*PL(L9uo$1kgc4;v_S)G_L&
z?eiVZkn6{3Ci3EWYEtGka6Uy<8_JMk(w9~yyM!quiC$?_GIcdb?yk!UJ-<?%?{j{a
z9mQ%LBkGh)I#cMkokY*BUte`}YnJ$??^=*RG3c8$HLYT<MT6p4>qjj7h29cDTexk_
z5*{zQ&G~&6Sa0Xk**RBv`q4+tz37{RSjTO=14a%nWv&OU2Oe!!hRL-b>b+=~;za}p
zmozv+AV_R2bj$;UXvll36Sp5od12u4Hz!@ykNc6QKkDMJK>qWN$^E86FM$UO2Df|X
z%jFfUS?Z1RH2r*cB^SN^{Z~zZNi9spQC8Fl3D0Qwt4tNz+;x!gv<B$pV7MmI>91rC
zDt8oH@5$4&p<{P!>4Id8U3TbXNBvAgL)EV?PV1tFQ}B+(k*(ct-7~#B=wn%zZ9%Ta
zjIu~$Ak7uQ_oJmz=cj6$6=VZ>PBShUcd`KDBR*)9<*w<>sd434<GpU6Q(;WNi(38a
z#rvff9^%2jI)Dso?c+M9Z$k0=E}fwhsktt$SPo8AuMrXNZta#pf{XqpHYz|?b?0IA
z;3phe9v6O?*d4vEFgW7tqNYLYUT~jPGgK74??FFY_nF|(s%ZlQRn#2^Y5OeV?`GuO
z14^8%(K%9m^)oGx4#t~auCxxW_HuIY_4D4dpp|*fRs2ig!?8(pe}^g6(m+@Nj|aw2
ztK-aSy$|`EWp{ilo5H_S0=S_wy96qz4h8h1pXy_;rO|P7&f^t`5Si$+#mRlnTfgvr
zuXR8NqZu%wRXNmwd1KrS2OZPp48P|Vi5Rc+uVw9ZSf90dFdv#|KeR!&SSeZBL2Ian
z2JeWqEd|5Ing%J>7M3ANc9oW7c8$3b&GOvkF^&9<o4@Krqb3CMuf~EdHEup*8;t;-
zjn~&ILjof7D0Qi}+aY$WH?mXDMC6HjN2kbnCc=#cIk;wX(66#mow-<#P?4x+O)3>M
z^S7@aB53|grvu;dI2RzT+C~{=>}dmi27G4<^*4Krd=S8h7E_fT)OmdTf!1U)$AO6d
z_-j2*Di(*@J;di!nMCuW8?BL8#*ScayFrtbrUD2J?FS@7ocz$X@D^I@*A(DD$UAe<
zpV4t){9LKTI^H*(pA`uYhFAIL$^UJ)<FvSi98+_1;e^Z@Mz7i!p08L(wdFV^x9T!S
zW3`2}y+i}rSO_wfGaGnx^fr}AEit91>!=44XlYds1a4Ng!n$kChjS+UUd23U$a&@e
zN`8pWD<wilqQLT%igP~AG=+=x*nh?5EN6OBg{0hcSm9|Va>cQr#4ks%wGFiwH(K;_
zA9t0Nw%&~n^?HrhZ`&T3dkk`zhE>-wwuJ5@3thYeo{;~nBN<O(5!<=U4#SD_XP4Pc
z*knxQbDsZdY(*)37}2<<s92RH!%W~KfK2!+<`yTegAhMPS=dZA{lt%C{P;P^b`8@&
zLDNiS{AZZ`Qf))BlY!A@jbEehwKda)^)Nj{)<Q1t$xkwu2UuS-ux2Qf)g`x0)zyV^
zSHi&xP6Xw$q{x$eHfLZm)rPxA5xl1)kg;!J`IR+~{e%k)rmd%WdLGH2sPJs&fv{$I
zKKCwr)rYPI{n`$3?9#UUnMTxhk!KhiGbS?KjGZr6wpTm#R``62iyts%y`WKpkEN)X
z>6T;J1zBAUilFksq}Ln&U9GS6El|C_vi-HAdFIhVRJGQKV=9}_o1>-#9%g=3;O#r@
zLO`irLjyfMy6j0j%3bGg(mV*p-&sr*w6Yf=vW^#C5YgUmqW7j(eE#drUh#LoA(`mL
z{nOpv&gxnR!G@Y8&0#QCqT3S7$!|f#-><^@AwZv(@O3AAJb{z%QmbB~VY=u|Rf)+t
zffMH!5By&oEDbZb(qc-rP$cRs-al-*wAg!xee_w*u>rWV)#4xKtq-nzb@F5h+p@(Y
z5i03_+#wQf-%<JCQNDF@?XKK8##(}OVTPy;7Ta96<*o6eHo@2nZ}q2L@y3zfOT~H5
zv9TO-pgenGVs|E+xZFZe>Ze;OpvJ$mMe`z#epIL&6fi=LJ{WPmlw6QjukFgf%PR5L
z9Y1VWCY@>k%9H*i81&$Z&DzCOK$V!x14%S$MFYkZA0sO40F@<6X!tkaZzB|hej;|9
zll10yWbcNfjS?KAC>GE?3gHY98=kTM{-}A%uTp~=CP2L|3F_Dvt;{RelVrY($fV1@
z5kDrlVmHnf(cPk&5vMZTsKi8@_j_cSfW>r@Np+4oZsAD9@*x<v^xtpgH*PQ}&%Za~
zLnf%g&tb~mQqZRYI1Ixd-*#Q8Ij*>l?KneN=z&v7xTm4E?H9;Pn`zt}7X9Z3M33eh
zY>)O%B0wIM-uQ)46-v`P4tix-p<=AjE;WM63xU9&{{d<Kf0&cz6d6BK7BArJf&9H_
zRwdiGzkXb{{1SgY!tpesEq=EB;<9)4fZb+;_Fk5GLxoQRXX&U&#jK^<m07NRNc-TB
z^cOoH_|hL4*4y<E?c`?u3=7sicfZ9Dyv$qC_DcVikc%pebo7|c0REstRz0$Ku@jv@
z+w|p~=lIM`+qph^4a-p5n?X0~=tbaa@1bwYYOW4HcF*2Nh>0`+uo!p{Pl5Ukkr#^2
zB@@Mg{E)dE)#NpVqji0XuW9fHL>yBFD-f4f`C+O_ui?Va`LvkgX&><QwFQkvIg)?3
zRX|2l=zi2|OUB1V4MDVJ(7W)F0rC2%>MGULe0Wj%!A}9rw$9F#;JMHO0fu*hy3b$W
zcGKE0_+p!*UZv<pmc%AT^{;+iP};;iJss}@PJ#}7Y=*%WicMp_lz-#et3~DJSDAFW
zO8BWe{WBh2b7AZPT93jXFob^D0eb@^yc_)d8t&RY@6j?C!=E2UF+J%FcN{zkp6jcQ
z70P$|bEW=+K&@lZ8u4Yzfs3vu2?rR)-o-O$@|f705_J#Ch_WzDZVW>)6~t_ky>3SU
z=|*dWa?76H0=4)Lp7==#o;vM72)xYRdM<s&ryJDAtsi4mfCVLro^ZQ>BlAIrVxb!$
z>9Z^+DHeJZfWV4h@(LIdJI=8;got-ke3-_dIlBqCn1hzutF-WL+F-NX)>^m0^jhmD
z!#uGC_okS^-tYv?Y5xx<`yae)6%n2&q}CYAgc%^ae#<`9vK!*-?_)ogLjOuK#H#zv
z#>OW4m!@xnr97q8!07R7%jI%i;z5E)=v23$IP*sz|Avg__gGRS02hY76AeW0MD#v~
zh6bQJ>01iT#4ehiw*N=q^!fdTEo?heEjfHXE2;FdlMHS0tilV=VzUK1{%W$}#spkz
zunL;eHbGJRX)aJVR7`7RnK}SyvRc5ZV${*CV8Ck0gYAWQwx27Z&pVCzaQavS>_%cD
zcBAGiae&k*<;sr^Wm7Q@F~i+_b?ZPU?gxN;C0j<wFulYQ%Gv_?OZeogv#77ThJF0j
z60IG>taNk7Ek=b)89y9h2(=Mhh8z-BRKP~oZV(^>j(*DaSyuK;7hXmbe*3L!!UP~n
zb|n4rs8wv(?Df^`j)&%QS>UO+l&$SfLwdmM!_~)YIqigxZ_wst2}?fWzro`tf8$}h
z<)C#e%0Xr-A2_b}Jw6+@7Ryg%mO`LB+U7O|6ZF#5iu>gIJkDIx_LsB~<Oh#@D<GLe
zm-3iY15v}b4soxu8f-l|PhT@R$<nXWT9;vamAJ#vDUoGbLAjNrpb5bZvlFXWP|F!?
zG1A0T27$;D*w+-~4?pOrxvIS_rfW6lHrOI7{Blf5z8sCcYKL;iNV9Rz>V9D5@xT6o
zXEoGsP1t$&`n@`3iJlv_u3`c>K~Pa?kMt6%G!f52sp#*cHn%bKJ$#qs?^{#xOYYXk
z7ka+PSTAaXZ2|iemqjwhPqU~8NAOQn78TWL;AtFFQI|hm>kkwQy^L42qf~(Wp<@-C
z%;Q?>nLi-*Sx|KfUW<$ZS?Tcx{!xu(&s=>N6KScQ;AKdrmg^(l&1vYH`S<y3t;8D7
z<P^V1KahApEKS3&EklB9nfiH0q<a7*0uNjvAz8xv>7N43ml@V-r=B?FZLrHZe{+i2
z8|v9-Eo;=VLM)Y|APbJ~w`2`|61?zixG<s;UZ<)Y{`>j{bU`Efn`JD;M^y^%p9;YV
z-**+TEs4D%3(_Z_ebQ4N0tiIrDKr?>;%*?rRI<<fKBW;<-y5UMS#<?7Gz0D#=F_%`
zbUIqRO!uk0v!&BFKl!|>^lhi`A=oXSpNvDVx|ofHjDl&z^ev~|N&rMtjc-SKmg$9c
zAp5yrEuFT@Wi(^dO)DVT74;4Kez4Mjycqo90U?I6iL$;npE^g7n?}l2AEXENu<vX(
zq@%k}lLgvWq`X&9spK9@pLg%CsgB+EtGmWlsPxnHC{9Lj0jYP85sxMRR&>n4+5Yui
zs4|xaQJz;5N{pd<`z<@c2=s%mS=%@3p&EAy9^tgd@_O@5U&R+1yuokUEj~8>8-qAc
zj48Tt{Z-FkigE3u)bV9?s_6F0T^fgRzGY@Uv)n|p70m}JHGh^Wx%D-IGUD=$GMeBP
zjiLJullCv68KDVvQaB2g4k#m)jH{0jx$^%Np(dnt$NxtQ;I;F8T8w*2h$*voWJr5`
zd-X*_ILK_&`Bv62d_6XyN0a|ZaeunZo||J^@CF0}8{6si#>M^4pSdGHq4K6L2>A7n
zqK%=(8%yI3XW<D=2+B6`>#-9=6y-gjQD7sHIs4(zO2*f`F#eOSK|WXVi=N}Kd#<I1
z^CHpR29C=k`|D1kh_yhBA)I*pLv{DPn=`syBXJla%j$)$U9^~@t!X^7s+95%LVgr8
z``D3&1HTnRybjB73;WjQcQ~qMz%yvUfK)7tzmTcwO@I1*f3@4%^zx2mYC1GGq<#PE
z%0h##1zmZ_OPfAaOE>bY`oS`;Yqn4{=2x@nO=HD>xCyVhzw~R@^sw9GcbJIwy#LaV
z9}pG&l_4t4)$)sZmx-V7V4{NC^ykGosu|di5{7ek`@x6F?F8q06#k3-S!03ejsl<=
z8EZ`<zI<Evi1F*H)79dB?OGlU<KilDMSHG;L&)-+BY)&ZPqY?Rz`DKSPDP!lZWd8G
zm*Nkd9gPx*v*Gng!fcwHC=~#cv0b~*VC^=KX|5S3%YLVnE-bh8nwpd+Ti58lz|OV>
zrMsQjRG6ZQdkkgO$g`Kvu%P=~VcW{V>93#fF`gc{+S!K%iz~~I6<XMIhgd8`o5wUr
zEr<8rR|tVvv3?RApVHFetP+0cT2g(DuAQvno^Qz56!n>Ptr%gxN%!-B3(1QCKzyR7
z7uz*_x09o^YHT@))W54m02i~~gzkSD5yej}O-Z5|S(^zu1hV%uxX7%BSjK{zHgOqv
z!A&bd36ERZS*T|k>+@b3j$ICaM~8$H3$l4yr;96G<v#^h_*@&W0yTtNP1uR-D!VHG
zG$%nr0-Ep`w*<1*_0-zoM{Fr)alV2_avQF1211WR`i?_}tit{qF8&8c;-u-ctl!)~
z&AlP(&A;bJ_Q{}bzBcG}7k=|{dq{$FFL-;7OkM~~P;IE6IQo(Nz)RbFc5<4)PxV|o
z=v&OACcCrlQd#9HlM$3&Sya#RS_J8$=4cBdDd*pdHF@-N-EIT>YYL3XB8vD2dTZ>t
zzB&`vKPcQRfaR=XHVbi&N}7BnHZVZ}TUR8S92-0*swx@+V`R_f3i~(M<)u$3A4!R(
za(X1NYboOKT7&d}+hB8j=A-<lU4D;1Kdke#$cJw~xUdT4*(ANX_Q_E$o;9$Tgpmrf
zSHsn3Z;U;h1tjV_<8>5lWq1c3D{6<z*~o^KoqYztd&zhF#_M$aex2$ASWYZT|90Hg
z|KK^QB1<R9l{;Ojl&KS*-6YS&<aU?ixLs8=?XG528*LCp&+m+7h<OqGjE<pg+S(j!
zdOY8c%*=mFKmK+R9}0U~F?oE5!W!6&t>y<D#9-8&g^lNjbP?EyV3>NKo?RHS_68$C
zZYAxyKk3(g<H+S`$IO2?S{xWUpF3k9#1R3(2izTg9CLN)Sp9OU={fU9)pw&~T-v=?
z8WTK}=HqzN7E_a@QEhKepmzr)$GqjV`+_Ix2$kcvp5vGw*J248_kYg+vo_yN7DgcY
z*rnz;bc9!oRTqiV(Q@H6lHJ&MwL3E{vr&vQDnlFg0PuT2J1Vtt=OZ2r#VOopzb`Ws
zPH>Vh*P%*hg+x)kxKf(ri17K7-)y*83h>hF_Al-jz5w4Qhyp2FnaTL4$C=giaJzB@
zXwRMxEvebZ5$#ls#1zu&UMQK_t60U(X6PT!l;vw|mAMXoJC1@DlW^&kDc283#M(Q|
zWw!HVd>@3Ae>O)Ewa<y+*R3#QXj|RSIP1CfVPZ1zX>!cb`wT)Ig73wi8;AlP9zK4b
zum9F*L1varh_>1EZu|CDR_RL0IsHI>PzlTDF2#L)U|3r|J21a<>3|4yVtG;11RN+9
z7fecT3>mRJp$Dmk(!G3_B?C`PFQ_wMfwvhjJ7Yh434hSvS=T}kR(Ltv4}7vJF+Kg;
zLS$Ewu+i-Hs(dO*G51|YWn~C|$|MwN|2CU5K%x}8_B_<VqjpJPc*}30w(Y12@yfnu
zIO@%$7Dh2vl+h%sS`z+HL)zE>Drf$~jslRr5v@Be6ZeHNBrOYMjQjX1K`-*EjQAg+
zt8s9K?ZM}K6&|uvjy7xbA2gnjqgVq_NP#pQPzyxmj<obG@)NHuXPENaUl=c_FJE*F
z|G|Pm7Vta`Znujo9X%z_hcX19JkK`SiI8GXvb-|w5ZS-!MsiQc-+}7Cc$TC95VTMM
z-kiS4;^cC2%<<y{?I(KZQLP$Bq2~__7Z1c)DyQ_~J<9LARqKc5N49Ihzy$+REFMQa
z-k~#1uCZ!k-T0n!fTgh+$IyAP>6fl|N=?@u;Y@q0dcm*>X5Dnh<>PnX8!G!mVY;#f
zxwPrA9;2VRFZ?od)ng0F@kmrW;pvISKReC+gr)6e|Hm11s!&fz9}9fEDt#;4`9VX8
zRldXztDf;`sZdD^8<D9qtHCW0hngVnFO!tt&BM5fp`?(g`*dlg6xe2_-0kIRK(KtJ
zdLFHxo@X%u1|I&K>zG@*Cd&Nr+W0Q|YYU@O#|ZB}R;xST2AE9AU1P;U-MZiabf<fI
zex5sk9$^<3qx`n+owY{b=9+3WxN-U1V}=jtjPnTVU(^eMl-I7PV=dG-U^n``P-ODD
zC6)n3m%bhqA$bMzF<f08F9U4X4$gm>^oz3S)?}Lp8TT;Be4@LP?{N3S`UsqFeg^;>
zO{bIUxh=XX{MZ{MU7cTu-)8o5nfR=$^LKIVDn^sb|LRcsb>^;5$w$6sjs8#nAA|XM
zUK50{YgXNZSK^yN<nnU$VO_&M+5)gQo<rnz!rAu*WqhIz>n8i&I?#iQNNth+tsO)q
zLNVLcsXu|;t=k!;qDHf7F~;v<iLqXd-%{79rS}YFWHgS7y#s_XF53ET?FB|hBAc|r
zuidiSp5fR(PTZ2^d1e;tVAuVnRxh#c9B5xAYww2dfBWR&q#C3Rx#`-%4)YM!E4QJH
zj50^M9hDvLbL-HG+-{)$Ac2P{0HDdVF?+O)u8H+HzG$FM%Z*VSMf2UAv~RNXM`Fv$
z42e<B4@!}>D4bDj_&w~Dt)I9lg{gG5HMtp6M|~`{F3|B}t{Jv%Dh8_bL<t^VRk>_$
zpYoY6NN;@b$i6jZ&n7T=8u0bLQ>bnfL$uI@rB~GJbIh^(1Kn?{D~m(sX56&uYv5x%
z=xgdK5rkQ_>G}@7VOQO`=Rw_RM_r=KH<x$CyUUlX&j10IM-I7qEa5yPw2<XG2CrwZ
zR}V!IkNXFehzl8W=#G3bO*TF<sO_YkSg-VdC4B!RdEt2LdtWCxbPQhJ)*u<mIQJOd
zI_89YpJw}Da8LR65Ec}`j|B%dzzBHRhh?2$9WHH<e(g;x*Ng4Fe@I+DBbb;J?>8X7
z9fYC0RAcB3ZsmpfYHi=DoO#}&P<WL?dU(q0|KX}&20a?_vQhF+w+VW-&C8IfD0Vev
zA>)yIO2muBJ|~%6Vh~fCKsEXqw<Az!I?UdK({jeofQU<`isVq6t+ID$@jh89S`$>i
zoMf12e!WHCT@m?wIa2}mAo?Ky=+n1{RLiOnk*+0jSjdUr2P+pBb?$je#J>bL`TUjq
zzXe4+_CNJs1hlJx|7=d<fr$vv=Zg+b%K3?}!`@=_2|QUa&k`{YD0ecsNEx^5Zmr4*
zQf)~Z`^`$=dUIr7+$4t<{q;w}rkbozD$!(GrprC!S3QDN1;-*Sa|xTk0yjpf&r7{(
zl>i&79@q+&QWH?n)>gOL)RLB_{vPX_cu{I!1B)c*$_HWjo3#$a2U3U?=$*4eMHMI-
zN1VWu>h?*sz8?HdZCWbkI8CXEH;F}D{*=RX@BUrYf=RUJAby*|?*@C(7C4+AKlk4D
zS-4DgJ6ljV95*&dpKYa(35MB1@~dM6Bt9otTFFRoQR0Y#lKmRpL`MX@NXEo`$kSOM
zVg30_;z3+tmb(t!v<a16Y4ySGf<?An>)KE`o%TlPO@Iz8)X(MQleZqz>eX23LQ5M_
z0Q3xX%EoO4Y{TN+4*$CC(Tu?&TihNWULii8*XcddF+AU-5v%lr=cmwP!mHaW{c-4v
z3J3^oix~-c>8!nce#0<M9_3PzFVB2BQ<t46ISk84*Jz1CW+<+huki4r_Oc|unejzl
zl)X1kq3_I!QyU7ty^>YxWPo|&v47v9PLe-CEcLxLF*ds^!@DxS@ZCJCS7T@mtgHz#
zWPx@1v5)sSA%yVTo*RwiX@$!npmV+#Q?O1?K~~MtGUg(9yj1Np$x~2>F=O+o#1Sr?
zYuBmj1~oTa*uE@1wbcthsO|WU`%>nxe`Y@8rzxMWL;wUlEsy_ffGrm;qA4x@sA<V=
zvf<OeII*+pTB3;a^wK5MIQL%Bn~wJ5Ts?ZYYor#(%W>)u@hwlRoH7M@UDLN}iP;IF
z_t{RjErusN*gH2rC46A?tc(AX&$n=<A@w+#$wd=*Gm;gA!9Zv%9_rLLI0w0T@+tkd
z==lF({~*2)xO`3*<oDdW?!7T!+M3G<*y5O?i9@NR^K6@I*As`YdA|?Mb!5!?Fx94r
zXM55Lk4IM=Byr*Tb(o7v`mY7uE`aNZJl|!1Cn0@<7Ec(85^L}GLpOg}>2vlR^w~-C
z>~q6y+)?S$YsY~<8nKpNlqWMq_2|lio{MrAo3MF)@<5hRToSJxo#rw7@4x=zG#o$C
z(N7^U`)7{7#<~j_8U5Fr=zOP^_B;-ExaWtCad#6ZZEI}N=fQ|(*1*4QaevBe-MZam
zVht`Id5Q5M+-sYg<n<GMp25NA^70-NalFUOlBqokWhS3&6;nn>mKM2PbnRim8LVq@
zSzHk<<gd|AwW+v>OWI=C?pO;+W2`)`Vhk%bh0IZ73=ITL9=x5vVsI+ccI8_)MsMXc
zwMSts9VK1v{lws$)%SOHHcL-gi%Ec^y3(FAYgry>X3}ceH+sO>f-ugy5PZ`CAc88$
zvbYGAX)?IEWaI_ZbVr?NyOtV!C+M1Q_oYO}X6zJV<j9skp{plSo<O?7i1AoQ*Ky>6
ztL^W<?-DHUAgY6G>SX_MI21kqmEv@*ZJO)Z?t8~puA|>~yNDJ&9*bu;_^J7N|2f;D
za^8>+V>h3VvV!0&z`I}g@vLi`U>5<FX}72oqCzWgU85helaAd;wkSaDd#&bTRM|f+
z>VrwdzW9IGdke3)nrwYI1QIm31`Y1+4#72eaEIXT65QPh8l2$LNaOAvv}xSkU4H$}
z-1qxN@5~=?*J7>iQ|r*(XIE`~YCkmuX&Q|x%j%r70qj|I17NS$+=Pg=!1tmnR7u<G
zKQqiW5K(M8iLqzsgl#{~ZczsD$yIMVjARmzS`Dx#t3;V)OI^}}nC&DvYI7<E?($sa
z@Dd`^KO;D7q$x+yN9NLpCQ`cBrhjPA<ZhwzbwV*2SU_eKI<^ce+_W+MvA?3NZFS#P
zv7b<RN*Mrh*~7C3-H%Iu8nrq;H@Uj7rx`}9XOUroa;EBVQAFgluIgU;rEXmkZtP`)
z*^=R(!cBapf>{>mJ*6d-BwoWew3y#&I~w5qeHcQUoOnXBuS1+(Vk7GPmH~6Jt3yQG
z#AB-W&C2(F@=TeWL!Qhe!|#X*N8bY?nvOLm9#`6YeIPZwe*nnbq2Yp@(D+{x&r;<i
zp6=}?gYP6fSBH1&NOR6NSPERnB#mz0lBc|4zWA!F6T7o|Z#%6q5sjJeKlh_a0*a^1
z$PYP5)p&@9bT8w|HAM~&C<O<WNKhE?k3HDOs~ta2NSytC4X!dlnxBH!oMlA-$=>;}
z1|+VyskRgh|82!?<s%Ha4srgb6ipb{c6MiyTpEc4R3z8F4WSY{i=HEV@LrAah<)Go
z%%lhu9Ur=cuYdEGb1_}II?HQ4!9emR!Kk{OsIEjs8?mI5Bu<-hm+phGbqebqF&3+_
zAQrll&mxAn(a~n52<19$RBfR&RJm?nm^mU3FGPYDn#=sVE2#t_eX>%9{0bstw5%f|
z@3r*Ir|)-T49ii+lePt;@ie!7)bh_k=n4n7Dw9-*Dl!4b&5tV*Xl{7Hn>@vrT@e`s
zEE)j#W!r&j3zcY$1$lPj0r)qC&#kb`LdnnJ7>~xYzhnxiNgL~3xo_9k?c=zkVu#{a
zYMA{QMYz;H{cs%Mp=$!)>+F#0Yl-N4uaigNs&6b23C(3Nq{Y7zX!3qxZ5)x~!7NyH
zS;XWY#hp{nfnS6$<?*cpV^}KM$i~|?9jq-1HJMd>ePL4ddBO5T)jqhqs%(*jxb1~c
z&L74Vy@IL7Qg9^B>JAk;?`p@Io)-~*_c~5uZw2yf^0#0Fo$47qWqz%|aI#UBj9`ph
za$jlH!RQ>Bb$Bw=q#$iaK%$}P{N42qmz4y^_}i>N)cl|@QS8g&hrx+43C1d+kN21S
z>&;#HsoCdow!;cyZmeb>uLZ##77)OKHBlsi|JGg5xp!)AWt^KYDskncXJv=lm1Cf$
zt^qn3AZ=Y{`S~5yuit<lF3v<1wfDto=YDIKl(wj_R$E)4CK=i`ABxHa6tNnsLgfve
z2*<kuU=HP{+I%8dJS4C~?bmzxTX*k%rV04^J$h{&*T{>W6a3;kv_}8(&#3U8I1KmC
zMWKVYK{AviFQl7dcF&>nJRKgpGD1CoW2ckmM853x&*wI0+tEQ)uk2K2j|c;fUyPUu
zJ+60q%sBNWxe25;-!K>1{Hj1M4MAw1Qz<A?f;L_*`azvLuqWE6Qr@s@)V{YqGBBy{
zzbO>i=|H^che(xwX#r9;8qv?fay{#<dedxbo3-+{Dj=3i=TFwEa;zmbA}L0~(6BV-
zfyW@l5ab+ZTi0k?I)GxA5veWcL>ZEVv!0>f5(jFEzdd^tSI+g-r<-GG0B9QHd@*2K
zziYnFfKo89iS0Yz*g)W3=k>Wf4RW-!GD8#a{wj1-X>GsZ`mQ3<iYD%wZm6r)v?c4z
zk`ZVw$5ia6HKEHAp4?DOuc4e2?dz=8p4x2{mQtZ82q2m%M(!8Kk?-BdOi~oG!88>(
zb?9+rLOT{2<wNJbX*CL9CH_XKh!=!K=a%S?^6G1yg;8mvpGlcrIkF*<F=@q8FVD@X
z)DhHOv}?KSx)gf2!iIsP%N$RkJUISg>La*Utf6a6;m(Y3?h>v7aLFx_Tev9ZzGmK`
zBV~01XO_c{e(H4!zfx1ps-8I{<0Aj~m@NA0>!HwTYan+*UQur~xR}uFk1}b=C5||k
z(t!MQwTpOmKam{_uk%`yp>V!rFvbZlO!L{#wtE@NK*K)^N^=l}{47~kV(o~=^ls-f
z{y8_p654nj^nv5Td3|d0VuK=a6&Y~}CL0BfLB`dH#mD-71w*i)fi59acCyZz!_T_6
z{RQy3akhu@^^C)jxg=Bp-Ed?Uk~g3DGNUu?VGM~RFNF<&^{ES7f=yKJZC}eTIWbD^
zEp1|d3hqd{L>vRerWTfBaH;(3rvTq&+2KR1+(>Jkk|okV`U0Pdv_18EZwkYi8oF?Q
z?{qFt0FO-{leY?xK(dYBUAupn0$UKk!$hk`na5J`@WGb}N8Aed7@gv5A)5iEg}CZT
z6DfK8T+$IP4tqUWp;1pg4K@Lxn)!d(HTL)0MXz|@5_~?#iWS?-BvyITKG=+`I9suE
zNkJvdcfRzVZQ4O+)1lyC{*zi`#L6A_m1JuBj?r=|n7BRDF=B{aD)*K(2+@LI>>?|C
zA`8hb*|KUka$@56gB0LM^jDw+8jGOaOgBg%6?k(;VCB*hs!nH4>WcKzRZPZxS$Fn9
za0;-f>0<RkH(V%YZD`f+`&FOLF@me@T)^KIDRCiL$iNvtbNozw5gTsoLpy8=bGsvc
zH|7Y@PvI61S&qu)5ZmGjqDXozVF>L{##czMiM9rZvT&i4c4nl-%y1UR*~)=g2%L=J
z_Ah~^0|W?xohoJrwOIODQm#9ffiThGmErD*Pf4k*Y389s#HwZ#xHZ(KfZ6>E%kT)|
zH<}|YQ;Vv6%Cq)ELX~RIF_quHy%qc(=UxT<n=d)nfmY^9y+kG#TD+PqRUAqrvbB^E
zv&C7g38Y?Rzi!wK7e`vY=z_O;Jr5(FogU2}`+dE-e_YRDJn#`arrC7*dEYLrlEp@b
zd#6)?IKO>?8W(Wlf4N#?-7*QS31#dTx($}p;Xkyh{NaD`NxTxl>hNsHC=hmkNdV>X
zW~w`b)K{Ltho3DPPd8$*E|e|%`n`#HaCOq-bxj5_DafMf0X(DB&&s$6pSK0)F6s3e
z&x-PP=*?;(R3<BHl#sV4Hj$?pwC|ZS*4*qR6n}<86|{q%{uR2PvK9p!qgQtZcU(2B
z3ql8Fw^psyq;ltK7e{%jQGH)<Q3n*eJvtopfKw6N$da66>XArJ3b^#W+e6`MtNcll
z95ENyxgT9QREFBga+VWo7z3Z%lPh3B&Q*v?$NgkzERRRp|3^Ob?}@g%8TI9?Lcx>N
z@Fh7K^V?(TK|S0zCv@-JMs|N}a-bO;xmV`)HS`sk{UnD)MOOl1p{)1%S2P*)OJxfA
za2I_*uESx3?sH<=agD>^sb%O0+;reF+F8)~+ZKwyV#B$O=xfmARF;sgwgBOKYx22g
zwD8%E!s|;{MRHCq@X38F@r|`5ldKtYymet^YjiA7yD4BI+g2{%x{%mGx4qKt#3Ozp
z>&E+#2tU2iDl5Ge9-}<}EO}hmeo68ho`Mo@ulc$hDBZih8#((D@eY<?f|r9Jy(>@g
zZ-OuX6By^>g{%Rkhn=BpJe>J_iJKmV96^lLj$<Aw+mIgL^3|__c25(=?pIT@P4~vd
z#@#_szvId?ha{osb!!9F`zZ_Vna!+ex_^>T${#>F^BVY+JOnCg4rv-RkE~KN#aTvF
zMrjMSos}RYk4z=P&<u@@HdQVu{T%fL3X@tM^k4&R9*h<_FQ*A&3$*KmNGijT6y7ID
zAhdc=5PX@aXxEF#q`HRQ-}*8bswnQ8EdY1!zs*63L1Uwc5zZ}<FgCh5d_G8KMf01>
zu&cX*+IZO#ls)B=^<|eRZQ++@er+q2<V=LC4;D7#^3H>W0PDz+NZ{pp!zI<UatcG?
zh^DSW$|f*&g&m)RW6b@gS$;6j>YQ3PclEVX*8L*<L(|RDro%DEfQl9S)Vcl8HDn`C
zU<QL%%9?t&k^T3Z87H0PW5D4rT<8o^8r{a#YY@&kQr|oZv+W&dXfcA;6wVn(?nAUL
zEdK%{)@75=*>Vl{cF=m0-HF-Lc`$fky7SxqPktDm`raJ}P7z*d#6wJR3;kcg+wKgD
zLt_y0AM?ns@pJ>;rBAiVClun`DE~F)2vYWJyWNIcZL><AnVZ?JNSielWuHK4uv^Vc
zI$54?p+FegPb4vYw3~0I*fGs{671J3PK!1P!Vvgs$C~;m>i|l=<wIL)HrZSa53om4
zfi1K@Nqd_)F3pG$iuIG8PV^r1V5$-6Uw^lf-kV~r?wfigWA2?=u7&x}fjb*~+F1r4
zsy`lAm(UF>l>rr(z~J3H{sNs>|Mq1zM$EA^d*bv&j;%lrGI+ic#CCtgmc>=~>(a`z
zi&B(|2R<`pzWrt36Z3f+ToiXsY;XOBU=sD%uG=e#mSKzZT6)Aa&HuFW{<VA!hL>tO
z8Q(1NIczO%{7BQF=iUW_g!|Qx?<DP%-X0n=$Om&1Tc9IL)Ip&ASqNu6D1F<TJ=e=P
zqDoe8Wh?-*`VNUDI%Pb?Ob%a^#kIysw>3WxIk{eS9B!Vy)0wnl$Z*FK!msIwPy}TT
z-|AotIknJrqqKQG8-k3WY2ouV_F9-uYO>Uo_j$f7n@{vsuZuU`x-s!seZyE9!@BUf
zW$J6Nxo)}!*%>d}IQ1Qc+pSziH52U6rXF<-w0|wW>+F5L72|K5(%zv2H8SgZ=J@n~
z<(HV^mZo?PFXmjo7%5uJcB3&K(TirEcD=|Be@fBQ(+darJ`n_9-c&r}f>|ec4dKRx
z%v(3Nk~dl~QY*wFY-gO#vtHY9J07k{`LE`&zL`F)mzUH3{@u71D)b|g7x?tjJD?z&
z+Q=$Efq^q4n$I!%aDS4}_d=ws7nbw1r~GuZi+H|=VTEdH?U3PJ4KN~p2~|G=xG*@&
z3zii}08Zjyywf1PI&LdGQ1dy0Z&8>)p+ZaxG06aje!61nF+8xZK9Xkmd$BPeZsLBh
zi(w+6I~4g%PVPQ7t0etQuA72sdt1Xq_5pWwR-hGUnPm$}0-l?dnS53*MKjh1dttx1
zjZ$*mEJ+@VAWf&wgi%u;qKLIZ8s%yGN0lgWb=9L@kR*jM9DDa^mzSektdiz)uMd*F
zy5#-^sPW1JgZsFo!5mcCY$stk4RG#z#!S$D4c}U!5YCXl*s0EeA5xGTm(w_TSbmUE
zjMzWsEJ<Rgv+<<b#3*9=&aE4e^Zs>E_RfP9*6o|>dbYVfRF>GG#+!n%zXedxD`U5L
z42H`w?Aq|3H;YjE35D9DjP_}tPG0&-epyOT6Le>{Mc<thI-MtiR)V{`32?kT2gct=
z*w+IKnsf*6T3TGsz2a<pXn>roMUH%dJB)^-2hxe}J-rMN2>TeVXozHu9_mFfjrY^i
zDpOJ!!DDHQneZboThpd@^%l?&awOZhQmaDVci>TIq#O?FUmd%@Huw6yU{2b;!@u0e
zs)hCKYo#%c?Lx{VurEyn#P_Jga@3wJ0(Ia5x~=EwipGwe6%f%kpp2QBhoxm^GiT#<
z&DXbeig+Q8TGFQrvtb-oWz3d~3jHO>xq&PJx(@RZ$;SBfyWw+s_&kfBkoIyyU3ZR2
z5>3Jw(}#}MOl%xOXEsc272tASw(z(Q%UPN(D{;>FsZepPgo2%&4t<v$0`cs;M!RO6
zbw9i9DrvPmSPOx_$NpRe#OKML>3Bbv?XDj=wQ{Og{3`G|_sjTtma)p%U!MNi{i#(N
zUbk%a;wRjs<qw6bKz;}(gXrA`&$B}h4TZa_j{uQ&J8W!K#lT7bn<eN`j3eOy>^>Uc
z){TO5Kf9ayT<EXIG2I*!sW%ys>${^!8TIS8;%LPNS$P_7>-%wMKKUo;gdpK&2c{ce
zFxT-1V1%R^P*c^xT4>nJ{cgxvDa9orYdJ|<&h>N4x^Y%>PrqXPJDc>!x;V>`^;7hs
zG}!%+w|&`yhWj0FQ`=75aRO25#C@r+SplHxQ1l(2m^Xc%(lY}s5Wl7RRQG0CKxXMp
zXd5>rbY1xlr0(S-PPR^$5#AZ6vW9QZ>fQXjSjlD4D*NH@4e$TAv!SHhjAksuyXAg&
z0K318(|;}qT<hE1v~z%$=5z7o4=@whG_L7P(PlQL2fV5*Uds%0!T+Ts&H^fre|9!}
z`o8*oxVchclC(LQG-aY3KqJMVr|f?_4RC=R9-uO6ZgdjYCBD$q{63>m-PlbnFmgyo
z2eYde9=gWGiZDk*j{NeVmKOxdUue(IX6&WORDvNE%)`}xId!6`M(%TKe`MFv#+6Ll
zIPs2{KW)3=1}^SEM_}aI2vuauDIS%EsAp4R)-gKG+l<#Uqz@j})O_t|SPad~q|fx3
z<6d^TMf_$Fy>U*(1-!=NT2&vHp_2b?jF0_e2&2!RZ2~uV$oZ%5E|_VfW?&zEgkFu%
zoem(R>1(VmYh@XUVfzfnhJA*F@FeuKLV;PP-_3OD9Y0|VA2B7(&*wJtDaguBsN9UN
zNIjL1OelCjs$n<G<8;OBcQv6Cz*c>(m@TGJUh9sg;+Xhpgcjk@9(x`!rz+0A?G=@9
z3cZ+j(s4=0A2t)~T38ELUmzbYRgtdm0q8oaJ0I0t=4N~!BVlcLblwzu-h2PXj<L}u
zAtVbwt9Ty`;C{sFVUF&#iG2iIbLp0*GVrdpC-<M?S;SRK351;Lpd8E43)8PpoP;m>
z(+O%^v=p~07^ctr1+Unp+3jYrGeDk{1qFr^bzRL)Blpqq$W@Xf^6wKVNE=@3BFyN)
z;<8&RtG8B%17>ZN#4iBRw}Lp`mLCB(*gy<@#h!gFWJ~5=^<_54y`zS{x(4$-d!pd|
z^)sy4zz!26@8C^pomj_nfB>Sc*V|(T#rdo@$N?Jve}`YEwx3_np|K;XeBMnEsn_eY
z2GLI}7wF(p@=BixIq#?ZV#BR4-@7$fg>EXLM-8g4;ub6f$IM;x%lfBzOC~ONX!%$U
zANnlIsDza`#mf$vD_rFhml^=rvzQ7a!_?#H!rQ}XVxtL|Dv*l6z|yr>);mXIl~9ak
zN_hgQE}p%mQ7KKcZ~~K;Ys^uhzL$Ds{u`G5%i~7fh;+7LhnMEfnvj|wr|r8GL1|SL
z3_CDaQrO^VZ3^otu|3Y48CgmqXs^BD<%I1axG~0-(uu6?<+qUI3*LUen_=xQiOEl?
zX@MR!ezZ&UXGk~D`(ohWJN9u_vT>IM2f8(v8UKd1s1VrvaboLM#&o3u0`}vocr%>b
z^?8@!1%_g7Xb5$n_eH_Kv>z%$qF1%GU>u9%v4=xTZj42l0+5@2D@RtO+N?Zg6-`*}
zft322Kv-;`&38|Gp5U{>ajd7wu6K7cr&XWzY_SC&;#DjtIubG9xh?#>@ozkAoYhN$
zHOLKO`&~ndfxTO-rJP%_#xgYGD>DdZH8byxt&A}bdEkxL`)TQylp{X?;EyaA#MmWL
zK|5BU(pG-`Zxu)KFw!}nTB}EO-G;~L8}ZQ9`6i%=wno*CDJ9*Do9MP$VZS)v@XepQ
zRydL!)?9K)kL(w~@4yS*X%id<>29i5WyKN>nsr=?I_u})^yYTc%kA8=8h_*IcbFbJ
zd%33``7qhfpZK-E*|hF3>bw=NE@SNCRJ6$asm%Cfs@dtJUOuM<Hmf`R;Og%~yT6#x
zIr241*2~4;V%CV)DHHAsfr=Y!6gduKgMQdhrPMu2=~xY#uqFv(^(baNseKEo>AXkx
ze07;a#ebXDK;RIGKkB$-)sz(J1}1MC!jq1p(6Y9A{55&a6Z#T4-PS0-Jwj(kT4Cs?
z(l1KaEcTi<xld4yeT*q+MlxH8i12*c7#CNFs$2qX^_pQ+%748S({9)kW0^$v@H_Os
zCKW!XmvLKVGeeT*vJ;Sfbvh_FA6r&9U)HhBuy2g)&Vip#>2?O&bh#|bo}l!xwx^Gr
zSOX6@;m0=|nRI~|sk*$gwfQmUdABDvhI$gG+n>KEq4l{?nv2`KBOtb#@#(rM4P@~&
z5h~Z!kms84j;W=e6Jq>2YD>pu>0F9Y{5k$j#X{NsOKb8;(>-&a$wvOj=}jNqF=rE(
zt!!gr7KWHMtc_}VM+2}A{*_V(mU<A@+KdInK62CQ9kG?dTUOg%u;&Yg@Ibrs6MNeS
zN0!<!_B&nFGMf%}I-?QeNr1Qf-veFj^1^;7PJwmOG7L_D-JXF_B9&K`@Mk|W#E-Vy
z3OT;j@t!0|U<a`{UcAW^DusLe*4Ik)v3F<qqxIrq*oS1Bh4%T_;SlnM#>RYxkDtAw
zV0%>!mYAGmpomXs-dqwWI%dU=oLKIhy9G378N2G8^$~dYt;?oSxQA8gR2ElTLqpXg
zH_otT*zsiAClH7K^eaBB_7;-3$i!c+!rXpFV73;94@tdO5_5ah6d}HJ3h|B&PgW`)
zFNks-k0h%b{^Vdj`=YjQ16#32pa1S}^4`DZG5(FLP+lopiFp}s+Fw0pB+D#M*Kay~
z<}Ww>#jbQ=JlUY|`J&m949)nPxNg<{gf0BZK27Q#^CX}TJHf#4qb&%$`J(!R(*<>I
z%7{m7%4ukt@NVk(lG#WM?d80RBZ}}?(;4|lKj`A(Ef8rnP(UqOAq?<EL_>%7!)AZ7
zOM|91C?NEu;p6P6u|t+dRcWt9aZEw3SpYyF>dcnnIf^Bd#Xb^&Y&>9zb9*O*#X-jc
z$cRzFpk%a5s6ZOu`Q`m6!zjv(Bd{m@*lo2gt+HivJov6BYJ?CznEIignmLt+&J@z4
zBjf^e(q8qR2XlRQd^PIyc}+74UpvA)2OI$Q`dIpU|BZpL7tcKEN~^Ag#wz`J-}5t8
zQJB}s;%1HI2OcE^b}u!V=VhkE-B!XMT?@+|m|+Z+S9<-h9oJrpo86aGQ*T?L>C{im
zzEMPM!S7txwz%pzDJ)#7E8xx(YhtcPs~M~C`CjEWbH4__FVr_<)2Sc3o-AsEHqw~8
zj1`8KHAkn5GSz2A%&}gMoHDXxuV<q)3E>`Slm{Gk&ObAUbOQ_FH)V%n9YUH<udi+H
z9vX=TGT9`I?`sJc+S|tA7v`#HFx8V|1flqP^1MwQ@`8o%#93a4lY>;!*dw5R4lHfG
z)jCqq*jblE1aHny^>o?-@v7Fz19d+rl^5FT&P;~<3PEPCr<y_sNE15`+00G5ep-a@
zm)MKSm=8i}zA}hQBouK_zzw```jUdv-Pz47bFZn6D)##HM#h3icN89nClf^p_l9i4
zD8hj?B7E5l%C>H0rf%70T9y=R!z7WhnAM5qk7n6_`0JdaSc^_30Q0Irwjg!lOLvO$
z#C&8yT4!#1{`2<#akkE=^86#qFA$0;jPt#Y`FwvFO;VyhyHl_D4bwN$S{@JI!YxR5
zwo?_l{Gi_59SY4E=zop^so?SkVW(r(wG@dWOyyI1o-XJe(tF|vbU+ig$Jf$U1Z`sG
zR48dTusdP~ALbFTD+jT8cn^k&BK_pHp%IU#Hxwm!9l)cSAKp^I^xfqvUW>_L%02sG
zGJ5QD2MHikJ`%Dud-}f=dbr=J&CFnrcdBU+g^EYbwi>E%^tEvl8I<pl$QQ|Pu|e;U
z3sp5ga1-P-EJLv1)P|D!$p+7?>LX8>OxGp2r=1m?NjXGQeUkO^CSBqgl41p#0Uq<%
zF8$YW?kUWi9A8hiov~G!u?Y;!F<L&NXfoUPRTdB9mCJ-kW#r-x%}B-54EGp{cFyO;
zIPKNYO!SLg4Wq2t^k3)Yv<mg@U+$c%7Eo@u6w1IP;s+j>yNfR7HH5h%u8FS$N?R{?
z^2N;$AAS!}ArD;bL*oW|b1{5_c0WkJPUEw~0OfA;ftVGFGg;qK<*<{2Lj+?uq3n=(
z3?H4Q)h_Qq1Yyp@n;IDLiBV31>_TBfg=pp$#IgkjM32X<k(WQ185S9-_{Mc7)T%x@
zq1a|4w}#U~DQq#zzKo8vXCw<K;uE=DdUi54*`AG2JYHrr02|fz#rZ;caoVChAv>@C
z0WAHWOaDMP%*nRZ#}%LEOLdP)iHbkRUxnX7veR{u{GE3~vz0W7u<NhwPAo)sJ?Pdy
z%g(rUF)~KhNx*MYSr|{TQsoEwaOCr(Qp9|0=t5BHz77eF^7xR~TJ)t$Q(nEN2^#;w
z^IP-e{bvyb2sz*eA+E67I+<h&vlT+&^Il~dO^!pdVkSot_N+q+(%RkT@KjX})z<c|
zxzd}{-Jp7rzzAwFuU{+I^I5`?TO=>{dmncrGG<v=#%&ee<nIkX2A&GVGuFV?;DO@0
zQTxMRrfg0-?eJbbS>COsq?AZ|LwZ|HhkYTt1f<<@`s|(hjxF;Xxq^&ZCltG4Sz4TG
z*&b87!_X+WAIobX^mt*|3p+bzletgh_)K*MFWtKnKgDm~@Za?E{{9V^R^tM*^R367
zq&bO}GX-IoA)9$s@))s$Y>N3Wz@hY7ZNAqdyAGnlpRgI16w0isIw%EqwnH-Mu2EQS
z++SHI-g$(*y|4ZmOifdOIV{7Lkl!rKWMpl_hn?DPg|Hc=?He0j^E}?j*stizOFW{X
z&g#koGI(~Ci^SEoyb1q&{&JfWw9Gib3D))@c=wjbqAX0db)0p;`t>aeg~rmzc1`dX
z95s-7htg0M4j6yLx$IHvPnYxWmH=ejzmXHFIDXRflKf=npz)@%y~fLYqvDyXlTwR!
zQfH;PtgCrNL8*Q$Z^QuOi~Mhnp_z>hP7cW#>=|cy!Xd|#QUQ_Q4Rz`WH7xzPwl?`{
zj=<^G)-Wdx!on~s8nOAlW=sbL!1M>lq`7W&Np<XZ-+)&Q4RU83Tx*o`_cXb?Q_)S=
zbA7ox%53lU1k|q5M}4(gg8R^tt}i!KwLLDSPHy6T29Lmkz`n{-E3+F+@C*!#yTPZ&
z4VuA6a6s|o(an_Fh_7;I@s|@o)`o8%&DC`8qQ5MU2E@m>j~O=sb^)f0U5U=(3Mr$f
z@bkHk7X(R3CM`f~DwWE_%0?aYGR-9;6u#Td&h~yz)l(bSRR(nj3kyaB#2IX048WYw
z)~MC^^k5CY45@B{GRci{#|>X+pgB#D8xnz!Z-B4kLGM2>?qMF<kOqjissA%d`!AHw
zzg?kIItQX-7Z!2Y@;=xo^SvrXQCpt&vMV>whu}|(ud|$9v^5L`hkPWlG^Kc5zmxgT
zTD14u(mM|#h`+*e@tr3V&&f4D08<)GtB|ew5)O%0J<$zsZ^;uu$@bg4tJ6M?#CRO-
zPR5@8_*t`#yr-WY2aXiJDBiAaxU~?vwq)$f?_73D9C?{(Kd@Z_ipwi0FnVUT7eeL*
zV5<kvS7fmF%I95<m^}dh3_SeVn)bJt@5%$r7VXnl0^?$BvtC)nH2!*ik3EJ;D_gcz
z-Z~dX=ly&CL*%Cwjzc;-KDiiYp;icCYLh+nr)IW^7s9>i^eZE+a=|3CV@HSlvcu~1
z)(7Bqq4tZM9cvD(o2k&Ab9RsE*b!i4)AVRgM{(ARudXBg^Ul=!e`DGHe!1`Q!E6;b
zof23bm;RIU{0Cq9=jD|Q!~%M48!8q5?L+==?{iO4<Ltq1F@PJ@Kbfxnm7M<eBRnY}
zU|`!tb&1N~)&>8!_mggLkm5DpfR-2SKh=-^vLE}O|G6yzl4k39eEm)HU&#uxSRvvf
z*SXCyt`NZbdpZ7#h5XkW{PiIGf-Y73^an}*wSWmB0)8B?nahs&Q|A8|*Zjk{a1}Yp
zI#7cM+ePNT74X9!dp>=3zgj7Z|EIb5pLxmEK!?mC9ZDoG#eXYcpHPT^+nve*#s4h)
ze?2lu_Xh-#94emmUm1%(Y;^l)h=5ml=9X0c>sd_xV-|;d@)Z6>$oLPU{p+Rh>L-YR
z|4&W+?K1y+ZTLSm`9C%J4<_qRHTyqr@_*jsU%P^Ta@szj!qulozy5O&u?9d5I|YUA
zB-1IqE<1hGY{6dleV#lGKs<Y0Y(YMw9<KDN-G|?Y|B2x#3G)t3%(`U3OXCO&tLFR7
zO=Mdn7A74vEp=X41c|(7D0$MAo|K|1*K2d4+=8{M${zf!;Ka#j>*X99Umcs%ij&=0
zwc*(g@CRuw9QE4>Kho+dxRMP|eYD>fkc<Br`}Z%xvSPWl=!N0oVc)mVp1ABu=CZvi
zMf6~Aju9u;49Ghk8P#Jy2HR{7<Qt5AT%e=<(&|9gu<QVPR9MJ>hDMn9*Kg>A5);On
zheg$aFlK#$!GsbQ{$F3d#lWd*RSf=Mg9TA3z%<x}7U~NjWpf~k1iqhzB>HBbUpih%
zUAtile9xes9&SSgxNwNb0keO8u743}Zp2{`Y0*8_fBGiKkMYNj5~fI=p;0L|8o|=Y
zWoJajijUuWJ|6%`S>d&Z7E_6qHWA&8Fu~yoe7u=0Q;Ls!qo}`%9X0-m=%d=1X=!QL
z_bVmq)-zG^)@RNm3X0Gsm8I6dJoY0jl$y>dF#!PkKRoYWM3TEmMj~@~wCel$N#M3q
zPX|fS&Q2`TjYu>E*@cgDb?@8N4}Lr=+{>lF18V?usPH@E=u`I%_$>!gk#`w1FPDme
zO)(x2x|;ctx$-||x>H0{IQ%dWrk*qZuM_|8C;5+cKrjxc8u25QrJ_+SaZcTJZeufW
z4o6?-d!qpV8mNX0D0olapyx(dKm>pe^sA^wZAQe&i<$-9p#fY38_zqTlRCW!(j;Qn
zv|crVcuqLh!ogz-?z)>+ji4#OXL3d%J8osRi?bO!y7;;lTx?n0;sMt=49JO6vi(GX
zdSDQtvdPHs@EMfu)9G?EM*cp_dZyF317H6$c&gQiPEaQ^f}T;*?L+^<3!1#GC?gx2
zy19{uGTQhtMud`psMSH(1bvrAS#6xY7F1-$rg~ztXWuwN&vUCiXs`he<*NTQ!-<*6
zm5aaHZk{cp4ISL=<8WOw1$U#NHHYW6Ya>30hXz*v&nxo_OpGuLt+WY6>-qa5ReAgZ
z4tU~;xr)OkIJbAv;z^6<IUhRmV?)|nm({T4K`3zU507Nkio^zo>Vd5{yS;B;_1co^
zS5OvsHPAZ9KA5lGQ6k73uURAQf&mQzQg-9njUOGtHJ`Q|a~_|Fh_Jocyl7IM&bR}#
z?&p8)8rah7wc_5ccvZ1Xne2?Fb7v7@c%vj_yn1Lmo}E3(sV-T&Kj&CxoVNeT1)%Ef
zVCtsB+2oQKlA~|bcb8r>SPg6Nl0yH)<-pv^&p=&Ky}88HXyEWM)?fDty(!y%t(FBv
z5$R|W!Tc6+K&9ijJ3(%`gfpFTqkD@>X=qh~Y_w-4lpKf^8@0b2wk+w2O;H(pv{{v}
z@!!Y}$*7WBkccMdJMvGXpo8-Qqu*7sICsqbF2<v5ByW^bbpq;{+P}%Uj@9A7xd#XT
z9GOuxrc!IyixMIl#oc7dYfuNB!kO$~{mlIurkIVE*{h@>#4tpJT3BhHc9~+m?K!(>
z>>#2uzXbqQlhNSczoQ6EYQpXV>=Sk+o*&4Idk}%AR=>YKhwWakl@@1rhf`%Yt=2Hq
zptI3YHGLg10Js}<EAp|V1v$jdK}Fk^Wgc8R{z_NUi|R&TT4<szFt@Gwpk3M|!Q+E|
z=04~$n&vhM_rg0lfUypx_IrOp5KW41cx$~%Rv|L1#ZSzc5^GZ}d4Ba+Mo&oval5f#
z4)v|t9#@j=E!`h>_<vVNhyn{oiVLewYYIKRQA|+JYru|?)FFJFddIEtk;4{+i76<{
z{%x)AQ;m{|LO$)R902u<qzjkbp5#5Mv6lr6y{bVOAui0XFrC_;s#3%=Rb+bKpAZsS
zL<3kWmV!Fz>1q426utf3e^msp3BGPYrMX|?(eLah%|o;K>P2uBKSL|h2Fm9xlph)}
zfdN7`OYGhc`^6Hg#3n~q;*r>08AMn&#qhQq5~M<ag_xH1*q);d*v^3w2XlGGR5~+(
z9c9)bU~#a1tF1V!`{vAuCutBPP&&D#yhjexvDqaT$5TCVH#T}d%uB&~_lCPdk+GX5
zaX!D77RKY*1^<~9f7-MSj@m(&i65x)zxs;*aVOu_;o_WYlzh_J+$w)Ia7xnkrr3cR
zcFcNY(ZwNd<L7*aU3Va3x$Z|0t+bS|xWs!}I@-0xUs$dCG0Rl62%^I$74YHOO_o^0
z$|~!ipU?rJ$Q4G_AC(L#u#&eeEU3eygDaYF^DL3~rl42QJ`8}dGzPY5Xs9<skBFZ5
z+-y~OJ^{-!$-mTErHTkY28H&7K4Z@U)2@boWFV#^tzn@nCz^fn<YyF3#47+ur}415
zNt_7iyApxW`&k-xwmRv!aobr~&Jd866Gi4f3U@*uF9jKIeF2)d8B;mxdn+eLo{dZh
zAd`h&yQVv-Sl5nR_0X`ev;QWs3eJsP<5{CszkNA^Y!>JMtH*eN{Be9&gNyt7kBScj
z_IQ_}dyE56`_pFSIkQznqCAer^8$RL9-`9)6NX0SA=?{!KqycvUf+G3=0j7`PKeG^
zxV8TD-2Z8v{ua%hwiM@qUk#AY5I#1D_J|APl%Dy~`(8cBokPmF*jto767ihAf4YL2
zTDY)@(yWDIXsG&h#irLHMsSxv403*kH>kdp#)g4ji~x+C%6ihERHXH6^Qm-8M6~f%
z+wQ0RvuCe-W?1)ZnIRf!s)HwJ*_Y4gqy;78v~{Lrb-Y1NtsrV7w%2*pZB<-a8QoRz
zmEUo9?DM-J^Qjcj0@rJ{z?4HbOnt{bWvWS16xHQ!PW;V!D1@=+cuWwHh2Eh%f1D5H
zc+vLFp*`m(U}Tun2FQ*q$0i`c0&&O$e?;c#pd^w}bZ{_!Fint9oKC${#+tOM6+P>u
z37SvO8!h<t1<{H_lYzW}YUm<&y=UsVFty~Yq9UrwAgffRnDnx&>Pt$F9qNH8K^2qq
z#oWAJBr?}3(uAe*a&rz)7as~AtUPapi~&1badN31#(T@$IKtFHu(-ImFf@ehw(7$*
zyAB&;kEQ&yBocpT`xzVTqs?L=f*|37*frGFeS;|D_$w(A<s9086G&5~yz12%3Uj|L
z8BEIC+sJF97&sk@ioT|S626pJVr7f=Wn+nf9r*db(x`j5u#4s>a<z9@?J<<ju{c;$
zkCxk-nhTB~o5AO64d?HhAL`p4EZy(!y8@#Wq>V^7q)>4t<vliPO{FM?mTv1NP}$w9
zr^=}=#q}M6e+nwxZ($_G(VLm*^w3IonvJCgsscGvxR;f4#Nq*Q{0h%^#$s$Mu8ro|
zA|9T+4MI1#v{Eo5zMtIbq_)hS@2On*@5#IIdKy=w@Tga6-#MTNA(@!vIbWKQ^IwGh
zgtP0pA*{%>bSPztenwMsWxC<osGs>FbA82$KF4#~8fYB@0F_mdMwql(<awuD-L;$q
zz=F<*+1~r?C1_H@hG}^d!k=Q^Pz95_?^0#^MZ<iDIv-5u$6L>r%ohmX$=i&TJZihm
zO9<-tk}Ig%faq^WT~mh7I(Qz9kH+WAA0h4hh(3~ET69|ox&<$<2$S+(Pf5n#g6#Gb
z-U?JVTDep&+Uase`Q<;Jg*Xut@hmqZ;54q?UU0cGmrwT~?jnZ?=QQ}c-!+;fIR<oQ
zv2E6@jYE#55_hU&rtRko_Sozh@#>y0yV;62n|kdjPn6LiHiH#w`R{@|IT*2M9T8GB
zIOuRYn;)p!d5$YMi3^Js<gSSAuSA@4sb|Yh?}rFny55B(K*$SfKwwY0Iaaf}rh%KH
zj~*a_TLaW^bwywu=Z0D0*9N8aR<9xSqe{xk4*-_g5ouNV+cb9g{qohr+tuh7#Pt(t
zbnV|0BIKSUJe;2zBGYYA=UGXLY^ZqX3*3>gz(%5LJ4|2w$|r=l2G1RTWhF|}OX|CS
z;xF02#<&GLkk>n<*&1q{NmG5g7-ft0X+H*$K^T#%)1S|B5%^Nb8=AI|`R+~pjOj>O
z7>(=Voo^`jiH1D;#DQu0jke!7OyCe8Y<rkkKixr`K}h%^t&4T*vjDoM`}4!!LnO`O
z6uMgSI<kvz2JU)T@kj)TGVGe5w&^swMH?q7q*Wj5d~1Y(jyU4(JFvtCx2xZc5wW(P
z+wb2y68uIcd~5ntQK{XCjFl-ijd{4z#-}wi+it<5x>!8v<j1GHG)jd*RKT!Fuxr=9
zXmtO`Ktm&<@p7WYBd3QJ;LAqkvK4{v1)JRL<S}4FGgGO{=e$4N$Fle}HAS{G%y%sb
z`Hqbfr^AM+KZO9F@&z+?qVhnVYdc1T^#D`I8+~Dn)rungu%}pd3_yio=p^8{WcS}>
z76#u*gpGzI0`C33h>@ov?hR|h(SE9?-A~{TJn8<h@xwyYS31)wkOU`no7}y|EPDM$
zTgNPe(uZx?Cj(wbOX>_klR!>mF_`?hHjVPCmI`YY8@O-FVc-`JqNcn4L3v2N@>Vih
z6lr#)NIJ?U=OArmTA%KbjB&Ux%l%EbK3{mN^NoP<FeVvx6UwIIV?taROT!@~wix&9
z{Q&H3k11yLS}WH?-FClW>gZ%VZ_^ZBW_sjl660E;Y-UtfxZO)9FBhjER8U@ybol|(
zSh6Xfx+`H;T!qj-rr^Del@wpkxGDJEV!%HsCH#%AsvhD;tfTzklUcX+v$vj3Z{MJV
zsMr8Km{F_G37V4=xr(No{J`gKOTwFuCs$jiPi_iHd%e;hG_S0gU5~z&@7~X$meT(^
zXn3lS6*LFjO&7{K^y8W1_5!XB8GxHy4AC`fiaTOa&YmpaBH)`DpZO0;@((cc2W;|*
ziS_0%s9M{Q;oo81Ioc2@945zDg`U9Ue<{wTte1jOty>{QDMOa|ugov`Tk<M;a`oVH
z7xutO%eV16U*~ji@>F#k5S0!FVo~e61_;ehDzy?OIps@K5JB`5UEnxr?_GN!sNsvi
z(oH}9)c*B6n&+Hm7|9~1A7evXS-8QjX8{Kj@XmIkY04z-_J|UBy5q+R+)sTS>ymN`
z6`swPqDh;^Ch}B%(FbYY8Kl}c3!_?nxFLjJ<!sWfkrz6uzl(TMlbyR{<m_*AGtSTx
zcA%pAY!c@B$35H0)jiO3gNl_!Y=A1}RDC(_@-%@Z>>xu+nt@b#tQOQ)%M-LSe!pRT
zCC&-s*2|)}xoMUAENd_ji;hQH#2WFt72O_kN74#>!T5t<%f7tuFwl}M{^ap;U#26o
zLo?jz_*))Q>fO9N00}tH^|-GySND*)k9iX6#&q;vND*<sY@m0srCP9glE*8tkzw|l
zt2q~&n;cT?c!=w)jHZQT#PLCX%LOlOG>DqP+f8R_4VmYqY&fw6n^neY|2BPDvzHWx
zQ~4y>m)s~lER(a%piUuO?ZbkSFtYJ$2_4msG{F+eSq&TlDjI5GWBUHxscuS^#=Nkb
zr&@z4KZ@$`bg-%Q!eWp<Z?Nd?Z&j0)5hfd4+&EP1!n(NbcUJRrs36e?O=;%TguO)X
zJgq8J3fjvIXIF~wb^88<nD#5>&ziOk+vScN)6!x;D6LW~0~Y11zFClIrbFOB>>FvF
zc6=XmVhYN}MM=8ZWp!X;%FB@C#a&$QD5b;FaPY^JmRWb51eH4JEiE3{wa!V)JU`d^
zv^3mh-^b$^X^z8-$1GkKTF#4GYBRaCl9j`!mV>Tvl)N7)(wH%nE@6CUv4RgJF&kQ~
zP-#0&j23y**rrXWOUjZyl&0p6)BPKLk<r$bQjYsxIz`;)Rkd}Gp_~3nHfPJLc!RWn
z&C1iwvqKwbv^ACQ+GZZRg;|a#8wmI^6<+;YnF9`>$mjEmq&m^#FQ4oU#;ZSbe6-Q{
zLpA(+kf55CsA=ogqgP!?0M+OsNRAHuN?t}W1ZLpblfP84{!R8o;13sOw3sTEj<d-K
zDk&=esd#T&7EoZOGAeNk_8|WyDjDS$H)NFE-X7L|#Og;JuEtQ4JyKGV(zIks?isna
z^wmf>L(IW=3Byu)Ins+|<#HuW61pJ6DJ#V2M~#nwv_Xba*a#1{5*=c?jk0dSM65&T
zm<{41gou4#B<iywHx!NY5W^$A!h(#q9X`ByV71vQe||pNLRF3v*RriF58rI{G|mip
zMbrS(`gUsk!w?ne6o-@y-{WzY;Dfq3CZLUaWOn(J!N36nO`4z=O=bJH*hf#d=37Z%
z(~Iv}7c5vEV7*xTabwAb@+8iaQXzAXb>wZo)9YCeitdS1>fVUc{ly5ATVsm?u%uCb
z130cgsk&6|{@vnZr6A`=7PPA&;f=fR#=ORwqENo(t%t$Vrta&vS?(w0jQxTdE=}@v
zpg7HMg^%X|%*5s~wYtD!SkY&nvwJlGmTFxCd0-`h4op&$V3WI*6(otZhxrtjq!IDm
z$jQEs<#@8p=8dv(<oS?L3v;U^uIO9KiCn$_gQ=eo<@?7(!hR6-YdfUV)-D`eb)4h<
z#O%EP+puG<G@0g)d+n>yP~GNzRh8efP&z|lwV1JgLo-cE)Uhpxzt^g_z-U<UpbDub
zN^EKf_gW|R6{ZEd;jsF;yu<2%Qk8jhJwei^&9OmmOb<$yzTs>TkP)<6w&DVg>Y0^e
zHm>$|Z*>$@`Km@p>N%qUStRP?x<iJzGo>7qiag^8kcF;Q>)RhJx4h5aJ;$Wx2UY%%
zXTt=OUlr=<C!A-UwBrksS7n&wRZ=$03a}hp80+fh_<mH{&4lI=4B@#_TB@r%&6Zbn
z<0{o>AgRl$EX(n9!tT%Jhg&zSK)w&lOrAaAC3jc?r#E5R4KA1d>YHt9`rxMO)$*&%
zFOlT{AcL<Q(R#jjbe!gDL)@cXTGjaORCgIVe^ob@9DRa`L8=d4_p^iN>X~PmBVeX~
z)zwIla96ve>Bjml>xprEbs<^hS1*8$8AbUA`aCTBHya6vPQ~!Un3c=)=xf(GtSC+4
z&UA3hHyUYFQWY^Nc8%rp5qtRUh5d_SDR(bi>%+RL-0OYg^HL<<X-84-wXe7;oeD%r
zKL_}srmMn8W_d9pNONaUR{D&nD36Z4PZd4EPV@%Mm;&O4jsD0NBuM$pADjcFD#-mo
z(Fkidkn9I>Hsk$bn<vL~$5U<B1B&VUPkS(Ak{AICi57YlA0<sZ*(zNp8dth+I)w{V
z_J54V+O@czAt16PfwonhAHTqZ*VjX*Vpv)-EG3Au;J@8~L<7KyHbAz-D7QxaneaF8
zlgHA{Y3C!X%bwRXumlNm3JG`qi2^!kza29Z6B3Z!i3^~@U5>vw%BQhgpcl!d2atMd
zl*Bfr_;`4D#PsP_X^)(N9~_r+RKvQm0;2fAc~9p%_qz6d8V=OE-jRIf{f0`gA$V|=
zKFGkzYFkV8d^DufqY3GcF1P74QS`CMp9Hxpoz#d&opp)+PF;dXswvt~ow&?@1|^DX
zP;420Yy47=d{JbDj^D1EB1+1KkVRIeLqYf{Zjbq2&Bqp;b@Zyo;A1Xf2|C3NPAXYT
zJyP>K(?L4MGKZE!aU+trUnODHZ(x)huvUhNef0`dKW=WSbV1ykzmKS}k&<NAcR|<o
z2in$z#Aw_hQRU>LrFRamy2R(2Gt9GD=}x=ddKDL#^RqPM=9ki1kzO{4;q>3p>yGW3
zx=1PtGpT(mM4gFGBt#ctZBwlz5mKyvcu~sTXgWE?U(V}?edNPN59D2s<}xtO+)ta=
z3v9xgxI32&JG52Gc6(lf7Rz>c@6E>!P7Gb6t{Hyjbrt)`mQpmm#FGVbESG7tlVsT>
zl+1QXi6r=8LP{d(sbc-B*+V!rgC~5w8<ELc6~EF*lIxcN87qTM6Kcon3txW}p7EAK
zVdU#>&i56P3Vfy3cun{0uin)Rbp;_!DFHah5#7+_bvdt8(C7EA%WP~!`ud;@C!?+T
z6`1^KPwO0JZ%@-IN=#mR&DXn4l=81N-C+Vm{nB)ma!CiL9j}6U`!4Ii!^*N`ah=u?
zhxn%Lyf9*6MLa5IBoP>@{(2ad4Il-9IAP!)1$1?|!Saey*+eDF%|L)8#g2-LkLayE
zQa)dzKIc}RpWA*8y<G3i?rpi7#F8Ng^>Ng{C;>9O@qY?gR%`HMA&U7NhjXAQj`cM)
zJ9c)cc_Bf??<xTOh;bKD!%h+SyYNwgr9BjTHm5JpL65HdY@2m-6@@VHX;Ppb>!A@|
zBz6#0?7gzh_EyL|b(KEEq*g1!h4D@o`*`)rrTG!wuG;-J-MrWC!Ua(9x#p_9zFf~M
zCNx0Gvx`nUo!T1;b%K1le&(+iks<+z`&8tSy;InYi-0H(6t_g~wTc*C1frvk!X?7S
zE3N*$aaw7)-RDQ9X~9P%G^(ti;|amYfI?XogE{&;3GrsShWUN;GTyDb&3MN1^0KkO
z0dY#yCN@4Qih?PZ=BUr%R&LbC(v5Rei{QlL+zR;f#R~04=s7)}?>!R&w*(3sr}G(!
z+Pwlzb?}Csm=hm%r3oC{A=SMP=)1sAiHCt|?U_eYGrV}`m9Gm*QF!>5ES5?LCj_%A
zG#Wh_M4?kKO^bErn+%?MO;^*h>oHB)Ty{i6UPmm@ms613o~?&AjS>T1&%iwZ5*JC0
zIw-BWZ3QLoXOfVUyE<`ERAxM){eFRzY&#7@J2#GbzBZKk)nRQH%|g?;ZId-Ny)9NU
z6)-pEJmRE#S<Z~UggxzF8*dHQfY``?MxVH;;l5Uk^EL-gAE7OVwYD-+*R8Jx(LWJo
z_<u-$H;W*Q0(@8L2dn?2jT@}CvE=D~Mn-)u+eD{%OA<K7oJh@3Cj4>MkIpUCpuf@?
z#=-nGj3jCgHTWkX8S2M9F<)r`%Rgp%9uAHNl*0I{?)3>Hm^?Q)?}DTzT^`Hjb1$GI
z#I5a)w6zyt4~Y1(?5DwuZOb4i^Sh%}B8_q$@ss<feMY(ESYqTvwJ7d27M5>gU#Awy
z^WNbr@kh`(yHO4w&~M-lu@>Uth45Xi8TNdUE*~z@sW5JZ^Gpc4Ktc|wpI61$;63Wa
zSf6iX8pgF+4znE<)B;~ksRr$;JTK~QIF}J}!XXiGqb5=*RyGQ;vXpZqKL~hUm2j=L
zpXk(oLo;)z?GumMWyH|Q1P3B&@_v8uP90lznb&o{1-t2<SjidrRZ(r~y7lbca*_r^
z=A4TQt%nzOGmR2>=}I|^KXc<hOos6$$lkSos-{g*=oz!ey&|lTDkz=J+~_S8X8Mk%
zj}5A$HYG#Y+s7mo1(xN;4hco%F>q3o?TO@<$0`U~7K-&&d|TbSfgf;Sreh1dAEK-1
zoj&%!1+x(liu%N*$<9-G^S|!pz_faZ3*Ci{uyOpWJlr2T#;@vhrB9rHjnGQu0~}Uz
zyne)Djm2GhkPAAMV`4OEOAnQyAQ^tIGY1cYhbgEK&!c(9mgA10r9#wFglJYfvd+!e
z!xpWZ%X(l>VTwCnTicmDy`l*63nADd)2~9Wwo7DAJzpN$RHo4A0!M`KrJH$u6eh09
zZ^7;g1p&;G8>)ykeU*a)iIru~#ztRi3<E{-A_7{XU@EhMDvL{MEs`@c6C^kKQXgfS
z8Me0`NNVzBrB6=WFQl(LA8m@HW^3JIhi-@vycIq3=Q9u3hG!Eg7o3}RiMDDx^!VJm
zhaFNU3!Qj2(H^;JO2oG_A18!L)^PKf+HvnkQho-=923w^rY0m|Hm(39Rc5Wi9|<P`
zba_(f4K4dcyo^f%?u5=3({{ez;vBDoWDCRaaM?%t@1!quezo$&2|IJ*Q(=)Ol30o7
z@qL>l*#!fA;LzYceCRt{??Ev{xnXBGZ3-s9+E_{+f<bm`5L!;V{YDt(#<&+C+Y$EJ
zSDfPHNf7xvm;+VsWc;05mIdYh`IXK&6%~rl^{hrn#KYx;(7@gK=6au1Nd7xk+cq8O
zzN{wtmJ8q%dif2tc6XRF(qF<>0#O(<yFoD>TMxhz5J>hkbLV7*JzlWkX)*!l-9npq
zZY5L03xDV0LpjdJ`+$1$HRB(V#2+|sFN}_2#bwVPg(7P_d(4jHWF$lk(Y)^+`B?dp
zB(INyF+z~L`iuui*vXqbKAbQg+Q?|Vf6;FFsb1yn-RiI-P4!#GN&%k`)FARy83I=0
zEM_58@PPn$oo@Il%sx&Zm1S+ts?}(yDKIIBqstab4v-P2wn9ADiB32WRLxU@G2?h?
zk4ws{6W^2xzt@fWo)Z_d>Q-ck1b=k-=q#ox<+r8&M^xt{;<uZlFNgFnk><~xP`?<}
z6~&-ko!%>*bGs6V1J5xNH2F(PabW!QQK%r{=!WcmU!1itsM7yKFxhJkXw{SMq7><h
zG~UYC*Ge=%B*oRl$7pdm6{3Me<i2fEZ}2g3Ur!O|%0)g&8!9<QK78HyEX!|p=}0Qu
zf@&s*!8I=dMCFT_M`@tiw&}ZC?`(Ahvy6;1s2RCEK7LLbRu0X&4+^RpBD>cNo#F)H
z)bdp83tjy`vfi>SuB}ZM4FrM(5AN<B+}$A%914O5cXtc!?hxFyaJRw<S~$Vo-FL0k
zy?4K-&kv}1)rT?1e4bl>V{qLyt^B~e;$uc>*Z0L+%kjm>UZA2rHT8ZMoRJZ5F&FqT
zl}GB1hJ-YRly5UN#&Om9l+c8PYusVIEB8f}lc}(ic2m+rf24lKoa}jU+*hOiJi(Ss
ziI}pB0<WTjkqH@r@|9sJb!~v>^tfhvFM=V;k)1;3j)tSSsm1NQ-?+rpo$L?Vrta_v
z2^}3BJUqNeXlGdX`rLHH%K4MRq}zTt)$Oj`Q5VuDsjYk!Sa(B1>Xee??aN%Zzu_Fl
z<!7iFI!lH*@6!5CyDf5h4!!Dn5j8qMMDb7*PEXsdmxF>>^58@Z6V=`|w+)+taiQ_h
zh0-%Rhc()?um7d6K&FW6!`tOpC<d?cWKhxbGI)#2k<GZJZYBw#KiI0{(?8Bl(h5IV
z%aTjEqKZqzO+Im7&J_Kx0pu@Y0Ag31&-u08#mtU!TtdzvJyOCfN3H9%hWFbmW0c>P
z$Ey8dh`Z+&;8Xr-y+8FrWY1P|g~Q0{{ro8A(8veg<J5moDS*$lXG8woN(o?#?E{=X
zy`LE%?6@|7=GslTiT|UW%;nA(CoNXZlX2a2vXa<jK0zx!)0QAhET$is-oErJ`)v67
zQ1b;;sKBT+(N`qMzZIoVB*Q%a9@tq<{`$|m<pvMWwgoQ3eDZl_QpJ1cxqZEl@4wx3
zrXzCM41qscnk6-lT&R{rQ6KDbwpp_}QWK}rQzG}8ZE+BAz{BvDuz|Rp^5E{N*pzHH
zhoUbi<2xeR8ow7S1PCK=8w}Z=j-`!1%BTF`AHm^Y(?Jq#kG#waI=pM+PF~F{6<{tH
zTO}WI1R_jZyJpd!Mz8J<epODGT01!7Yspwe?<>iA<koW|vTkkt<+-w6uCA^fQxmmw
z(RMv4PC7s2BZFf8a)X(m??+@xnQUVw)MRXG(4<mz0$N<Cf*{eSJWUthl_mX#%GPdY
z<cA9a`GR%_sN9F+$nqF5u+);Dn=FEIoL2}IOJb*W{mRKcdfiLmv0-v=-=@diYhX;e
zVraG3bj*b5X3M)xIG_s2h$`@oHZ-V=2#xOknXH2!>9)N2`Os)%g|_zFF*O`&Yh5tj
zXWXCzcV+RMk{HyZ-}%{_A<U`5I9Af2INZt+xEInB^uqK{y?xaKe_YxKh|p=AjKE)y
zMS*?374vK+`<k<5f{U1F7VrIRHM$#9?s8%4>y0(h2Ln5O<k1O&;7?Y(Cmb>gmvkLm
zIa4DvV*0thPlsV!g*hyT6MdA`6QVB=l>K7cr6vhH$LWYn+RAh`nAr|F-K(*AcLtE<
z6@>t%OF#RU-nz^3;n4UrWs0(B2psm{?$(Rw^a1v6tWA2+P>{EdAypS3K9FB2!h!Mk
z@xK@Me<DSH{d<~D`5i`VFl%kcxI*WiH08%eFV`FGE>2Gj*j``3_E%xsM=W_>vG6cb
zC`3_o9KJl{L!Pytw1?xgNnV>F25@CbUY7*oC<<o8V|>TfHj3gO%O|OIOpmKtMFTz9
zW)aES9RiQ>MIjbfj#={rEF(p%Rv08(t_18Y53F1W$Lx;w{dvS;h)7AF|DDoM;lt!w
zh(?yVGyx&d3FKn_*s4BE)tyXg8gOx8He;`%l-811jC;!DZNjp)xx+HXx@w~~hpqyS
zCt6<;9+-_hR4B*Z{`Ll;%QP<;i6y}$dXGIn+i4*oes&ZH9sts;96oiw_sTg+h1rAL
z!H#(7MU8Pg31?G$3W>+T+<=3-#{&v+8>UxQW>T|XNA+UvNDd4Rr9rX$)k*{jYheu&
zT;An?qDW{xnawWBG`X@iX01+qs+DGfNAG2zI6R1K)LQ!5)RTMYL#%nF4nJGmab81u
zs$}cSNYRAYj6m+$vba>V7%jr%TQP$xUSvU?-EXO8R@Z8h6?DwhZFURNtU5cS!*amz
zsbhI1Rjly#)~CeAJ3)9k0Lb>pa~qv?8@HVye#n@v-0JaU892t|05A~5F!%6FbC*c)
zZDF?px_wyM|LzlQpIpOcfpPv6H0HTA;I=?fwHO%cyA;FDFjXkWYkb7LF7*{jx7TV1
zTc2d-$%U9^Aq;_yiT=dglpVE3{TDY<vAoXx*;+hkgo{Eds)wn06`HK!9?QI7TKr+x
z*@Zt?aJu;4UHs<|e5Eo|&-sd@djVr1@CAu)&GRbYd5TH9<)=MC)EQQIOO~3@cg;Wj
z+PuCxsUfpZlA=Z~rT+q2|A|+M*ubRJXot?$bisPGBUzsR+|h8=?^5v{+5tPf9hQ8p
zPcpMAgU|YniF>z_pIoxO(_tiVt1OT_*&aQzgdJqYw(&VrK9suXn@j!W&bSR(zGB5P
zQ!r<^B?)Rd4c_~Fq6yF(OPk4*s{0Gu9C)uOeoq@1v@^jjWJmMbjL>|080bq>5ysJg
z4EP5WUne7_{0YB(I$9!15x{6^Y<^9>iQ#%RG6eF-wU$5G^CuRJioN#rD?X1#NGBFa
zXkyz_lmvQb7>WHrLSW4OrErDFd#)_TA12|f#!0uUrBrQ)@x8+tgkmx?-;#WE;z(Z*
zJ;KMX;xBh!oB<bplvfbTC8Wqy@rX(Ut=nun7#;Q9HX$iSFn*(7t}TIkg0cj#yVfI%
z#BJ{w&LCeX?^h;2+Dd<19#zP@pWOA?5uE~XGznP=8@V^UG3D{>UN7GT<v=j%wzm^L
z(SV52r`O1~tt}R#c!ME+xH}^3)}T_5wZ;okW+*||l8$n5dHD+u-Of|K3hwe_j~CfR
z4GPoWMLHFY$cVNQci`XP553>!Zm##Ixb*^a`w_T`(=kygiQ5L6^~b{{O|wpZ4*<jC
znEyQwJYwjjq;#)Ker2U`>3}HU?r3n{dX^<9rjk5sk%MqHg{mM4b>j-8BT?-&x+Njt
zWy|7@KRW}M&$IYn*Hce?Xpid<xzKgS3O;4k#{o%VgPFzM!DOU{tD-6OZIXc*Z3APG
z^WyFc7nLGv&hIdMZa+h}V~E3wD!`nx=!C>;*x<jq^@taLI&8w$0MKelniZ8Hj_Af&
z@`?G?a-~mPZUnp!IZGC~G|BThZ~7NEWDbh;ZKLH=R^XE6uj;1b`jTeyyacM=9T%1_
zN~=ew0fsi@<s64bex%>@R7c~Gm%<QHMP=+OK2?lQu!y*x**4l?BKUQ`7Mxkts!Tx8
z4>Ncfg`+h!G28qVN4xT@XWutofx}OW+2L>?_%;dypS|>7j>`ZcMd4cb#YaC)PKHry
z&_|8T<NI#MZi_u&>j(vK(cPLVcg`522Sa#SFIB+kcj?>Bta1ex{sY&xGMuAhP~4)J
zdq#?A^X-;r8yRN(9Ayt+5X_Z4D;9y6e)RJ<e2jVJ%`XK#?V4h@z&c~40Q2VW9u%aB
zljY~1TA1%~qU)~l&Y#}mVcjj*0DHQJ(8(@5LZ9Ly%mJ0MN8BiyR7Tg8VSvu|%_G@T
z>84Q|i1`gPPVaN~V}k(fQs6^J!`!=3lKEC!%ew7;`y1(fGYj{IsZ(9gd0O(}q+!bP
zNoU~Uc8!O35A&CrJ!SR#(=m(0Y(=mFVH&?1vu)R-84N7!e3Df)LwSpBwY1W6)jyji
zGPECP_lF|A@nq7q@=SDiWCQ!UKVu%G^6!#px?PcUq3FwW2&y{x#R-6G*Pn3eA=P2U
zt^TwAf6Z?85@e(-<JB$4P&+&O?S%IS+cATuV7-`GWkS&{%3=4%2A5|gh4i<N>*-He
z^n|Mza<GHm`L1B&DZ^g3-1bUk%wux<OYM^WCMTaNqDdBdt5bm}Zn=Z5<=n#3MZk8o
z+Lk9tOmc_XdR!gB{p4C;;cxb~Pc^0FRTF&CZc9LepJUAXNf|a>9k(~s+DySf7OaI;
z!;?gu9?taf(KQw;^Ag9X*U^18KVe^-6K@)!j@x)e{W1Bg6GDAu1ba$^{mwc>&-CZB
z{qD=<ox{cCv_R(U<;`rI^ZvRZ&-Tjd#>UU*im&J-ewQ5ej@PUvKa{;6j>HcZtAS%L
z8Kv-zCNT5|quht{nwyD<L9S7^01^u(p0KA?4$1+07h)039?Q^A0Zl5s2TODq=^m0&
za4$YS_FLb;NAKUIZwBp<xGDVKO<z6LTCLpXj{`dSN%;iO1dYkju~j)eM3b5^X_N*E
zWfQ-ts;Y+Kx~j*fFND*-^*Xn*EW}#AIcyK%vDGjfh;_5w+g!b)iQ+uO3f^fUJ22he
zmN2{|C4DK-CmS-=twipmTXg^teX671u`*s}MZA(<#&GQP0mamYcorDv!6C|rbli!k
z<-tk=tqLIxuzqh@%tNcdDGk$Jox=bQuaI8NV57!ijrA5+87prf7u;?XCFwo3`cSlI
z)LMbgQlRao%I(f_LxocdiPR8`vHCBp5?8wrkrbOKSv^M;+3r!RKwjCu3G&Qk+T95b
zr$maZ?Gc;!#mc!cg!<wGfEKiMh~lU@UeV}>Fr>JBh|QT1tOu?*`^doX?D&Bo^O&@8
z<G)s7dYCJDNDU3BN@koPsERKqhRq@d_3BER(a%*(XRIxBkf0NH<Eg+3?r#b3(EatC
z5>l>7-Y(eT-7<xGuD5{mmZe?&503Gl7a`#xy!Xxg=hdr=?=C;$aPVpbFVW6A5109#
z$AkUebwe74)?!`)+v@J#a)hvG5^gm3>(L&V*@$R>DAK_-&1@t2_p;{3(neRlP=<S<
zc3XiQrTBc%QL)X78S%0FzH5O4mNPn&vJS-5EFnxU?9GyIC)%&Y?o+;d#=f##R}P`v
zYXM%co*Y#=1%S>e@ts*I4<TjE650F=mr&0r>-ksKYg|?9z2<Ko*yG#2()W`*)bv@c
zF#?Hh82p&C_UoLe`AIw1oH)_F1N_X={@n%zy|nw8@6AGQ#LAs77Rhc%o>|v7S3|NO
zl92O~jt}QY;bdkZl7mI=e-GW*ZdW$aC>^<nKHXjRD{96JGAOTEnDLv)_fjU8Um7JG
zuNg0s)cgMImqvjuLO{?O)SCSa69wh8_w}Gj_sjC5+D|(IVd9V7&nUiZY|<!Np-VE?
zSCO^t{_B!I-`<46BJ8)tbgCKfJT5j(D#I?G)N3pbTYq95#A;Mo8iY19G>{!F7svIA
z=|t!(PtVNGBBxGAmq}NLiv32!JNh{oCC>7Sj=9waY4QNeK{of9Ha45ITRB~^8Ts7Z
zK@V&|Ja(#PS${ut2ikyFAT{3cFm0&_?7P|WuZOuD8O_d`Im`VjC_8w3L+?mbVHPCv
zppIy-45=@h@8v|=Ay_{#Pd?V*+p*oIj!#TTii(ONy9UaC5M+g)bbNue`Yvg=K#7UJ
z)6!)8*-Lw>CTEr5k(`3!_~p~M<AlS~fPttJ;qN=`f4Sq2p*^t#>~VTOjXNyEZ2;X%
z9{qTk`e96EA-L0mge1&z68Va&X&1Tk8ZnEEsN?#%G4i1;x$&BQNNxX;&;Nnh{=|bQ
z;QofV2|4qo(a@}_uvjpKND=bm+6g?e+P-gaxE!TD9(Q~SKORU@8#id2Bjp-|^<Y&;
z?`#{4%ftH>R{Mv~g)0fI-54iWL3wPPefXv}?nh&!Z8QYy323QyHQ!2@mMf|)@Q5db
zphJSQu9TFU&9LuAnvRHk&32(H)ve>Jri|~p?ZilsoIfd76oa@=dB!VTJRSqA^u#Op
z&x6T@rL;s7*5WZiaq2PDEHpl4T#lx{+q9cJ1-U?jv(fg{5s#{hPxx|@9XB?^JzbPx
zeC_n3LTY~$f5+*1je|m2kRREV6po&ZH!;G>g+d+1`o+cnR5?j!ef6g8Ygvzbtj)RK
zPPowOW|ms*ZqD)jvcFpEH_XACBOQ2|O+SMqclqZ<i&8Cd;Wl=qV3fb4gMll}s;gkH
z&DwLOsN^DR9ZJX<sY^P0fZDQK$5p}JbN{5#0&Tha>AOX<A@_ciUxy%+p|8*Z_FS!%
zmFDf&{l=Mk$Bvsj&9If^A;ZPXBRCFR{d~eHK}V+v`Rk`48gSTQ!&~2MjY649&<L<c
zO8q$lxsG!1`*eYPXa@c0DE-A%yMadhf|Lv=Z`|M8O2T(W?V!Hj7(umVboOzYJIhU;
zuUvYb*WYRB7$6_WoY;58mKZR>zQMfc*&><k<m7w%R+VnNxGFFKQ%FF;t!Hl>?M~T<
z3PPN9jXlgyt~Dc#Ty4kJCYDyY1YUJdOui$XFI1uXTn=8?KPWp@_s=)A{9)GXCWejL
zrGIUVtK|si(EfCD7EL-7RJHT2yP3gBtJOv5x_9k7Y;KqdrB>X8&+|te6OK~RC>YN9
z%zB`bd8xHQbZeO&@$`OO{2`4^PKP9pV&*|kTpXG#_fvFtchj^XvRUG1o7El6-3ITw
z`2nAst_-xYD+lu`50vvnbQTq<Yug{^6^2ArvbgPyPj{`1wQk|Q>)6ilRyw$!RpiG&
z$swWbIVLp0ehLavDiW)}&_5L7@<2glJ|>r7KIVVHY!o!;c1QV(?PA8ZXJ$JSq-1C1
zy`aa;2K)Omz@Kg(v=o{dfIIN0t$ya0oksEY05N(PitTbPfPz17>3=aF0cHXIaYOux
z<$9QWt}W6EG%t29RWY)*U=Y9)YG?M#^KDIt`_;VL#YP)4bEL=R;5`Hh{W}jLk!a_-
z?XJX}@i>Yixtw{<C3Q6q9DAa45UC+{cYa}NxjEsml+oe1W*|}MrAS<7<&NGBYF_h~
zja8D6(%>2rz7#NfdCA<QR&jMD$FsozcaIzRKj)+e+fYtqE2`CjF*BQUJig@m@+aPQ
zd}A2P2I~j7<#>G-yE1Tm_5t1@AY`{2T2y@vmwfX(_YJl)dyNn)cRFfVUu*jTJ%7b*
z;311VP5Q{veeFA*^(<ZWdqdHe2q(t_fBnS-KgZ+RXJ_xc8fd`PAuF`%dTZ)_8IPbA
z`vhLHqi!_STQg5^{oBa3iHnUM*q}@lEq{RMGsN=y(;9c8EWC7JvLnWz6Vg^KSxHi2
z&7p7OLLOBHW#Wja=G8sbOD^i}(%Y`<LqitHywdvWoe4ErlmI_WAO1<GQNn0)j`thz
z+cssL&?$Skm}LSggM)P;UpPNLm*LVxy_5^rPKx$rZxfNbvxr@=qv}As(J!y_B|qb3
zx0kBx&Y(23;cM)dP|z6xC~ekO|3UL3CFObB1f&<Jbwuj3l5mbZrfnyT{M(9ucWNaS
zl)hmLDR;P4y7l!KNhGq{mOvgn$`!~MbI9;&F1I%OZ1CC@huCpThxO5?7m=RA30YzN
z?S?<gW%&|UL32;K@ls3I?OaOMno+K-e*AG8b<sode)o~GBtET_k3rVntz$o@9EN*C
z41b>m5HaEGcI@6X@{Vd`gf_W2@uqi{TqmqSk1YQWc|0GgJzFg5jUz|$QDzTj&6U72
zz6HA?ue|Q75V2&tD_Ll{sZ&OjuyM~X<uv<m;Z`j4I{zTnO>6EHs_VW4WE9eUnrbDJ
zLzl^vREA)VsUE3a)6`HB*U9{qf5()@pAhAX?R2OnTnS`yfkT99#d~vf!AGp}=Gl+-
zF|JA&J@MOiayUMi<SEmH2|w-=OjLUOkUpXP*PvQv(1!-}wMlN`pP&BE(DNq{U8~iS
zlYNFG<Exko0Wz@w{BvdCPt}TN?rY`l_rrQI2h_7tF4uGm77Qws<CgH)t(7IGV<wy3
z$Ok*K_lQ;`?9B$Seow2hl54s!71PK4jj!uWP-j{{Ul+6}QtqV<d_wUO4GI!k3?<zJ
z!Y`&%W5iW;+KkmXk7E-~u*sLQmmXDLFfkS!D)$ArjqNoTcmZM81ZRGgqJ7JZ%~ZZ{
zwmv%+<B!KA-rSGTp0Op_68oI@0#Ph}FH4O2=5%%A8#zx@TxcK<10Oxi%xPX@68fTG
z(91@fySHG6@Nby~pR22e#TF)%Or!1W25wLOIFnY|Zr!%zM75kQX4vktaf*9wGqhQD
zGrr4T(NXbwZaUNIW?%Y$x@?t*+tQKKI-xKDds7eBxe^uxa(vgjesb`A5}e}yw5vvo
zke=1j4x@Yc%f)BU_Jo#_Op>mtWw^+yfx4G|>d-L?nL}fzBExpO&#Xqou@y%sb|d`K
z$Ma-F7f?vW9PGSK*p3vd{L~^v$FzO3Lzxxe)6OlUBftI<NG@=7V&ch1>yNXxk@Izn
zR-xn7akX|=@~r4uz0LWaCY_18S_v~@_VVDGM65bh=w{NsAIp5RZESKN$!-s~8`Exq
z%+~pSen7lmSRVXiIm(0Q{$`WseOx8FT6OrY_1Y9VreiAp!d3*~9WKVX;ldMJj-Ata
z0RD^3$}c8Le<n4p^Biu}Eids&TK-OBlea4*zEu|-fd_s^^W^bVHY;o$A}_`WF1u<~
z07nBVO|?XhH~W+K!E!}J`xD`8(ORua0h((c0<Fnf`i5WZ)sgJ?ER7(vJ8gw41X1Fl
zyDMxaG3;mKq8Z(Bd3fi$-5(jW)%{aK1Z(Raa0tqRA-DTkL6ek4K{5O&U1~}i-}3d9
z0tF!gkND^7DR{!K@IJzO?-_~DdqGR)HuC>YUE%>fy`6j{5$Kkg|Fs`{fYG2nANmb|
z?J%^fTy87JWcLyvE5WF6-8D7=vwH93hGiM8A800SBrmYPG*Q8s-$YAna7pJp*MGDx
zZIt^@IyHk6f)iOwB94q8(zs3BBFn7`fSkiE76xv~+vZ(OaftnS`nJ|R$LF~XHyyz?
zZAa4UXVBu|FJp7}4o3+6R;IEg{1=A%`e!L>)yYeS+f##MtXqK<b=C{qYP4;B*l6qr
zeY$53y4|IT#}rjW83Tc)44-kfV=NT4ad37t*Ir431zb&k|8i|UFWGqS%gj~kAJDCK
zQ=gOZ^0+FACV!{8Hjq!*?q@OovwObFd~7v%{oC&+L**F5z^j$f<vN=))e1eDu+J5j
zQp<r(e{}m;ATni+^UJqp6OWIQnG?3@2elj}xOR*ivdl|nahg{ES#6geA;<s@`9CZ`
z__caDv=N>}p?T&F20EL{-#H;?(B60(JWs(WxIBiGfae)SmRX6%)sTo)mDB#{mXUT`
zac2h|;y0xcDMVv^IW6Y&4`-v6=<^Br8i9=#)WZo<G4&mlFbs6725}kT(>4HFq6){R
zwDXrhBY~T7ubV${AU5^w9!_5FDM4e2pzWk@P`TcZNZ2X>rW4zr-#+AYyi5H;t99Ix
zPY~5<j*c-I4&ssmZ27*tuc!SAPEy!*n*n|&Sao~GvKGb}Dbv28-;_<=Iz4o3ep{?T
zc2R)8G9m8gC-IH89pzx#kM%>xLyw3TCvbgc+(g%?UirmIu+2l=j{ea{<eV9Iu3K7s
zf|%_Y)<8uaX&PVhH)rkuFzqF9N4WIotz{4^%F)J#jN7fL@yWsqmHg-Ctq=lBFE(_}
zyEoQ?+^CwBxRLpug)ndA`3B)TFGoj_n|VzN>zkj2KChgrvI;b+J`;)k?%cG*bkK)K
z9V-kqq!5+Ly>y~Z=)d^N1PT;H|FK3ki}H{DBVQ^niFvY1D;=?7weH%it8CBkvX3N_
zovlL`H|7jCs=F!i0}HoZBZH{2Wm^4Lgm*V3mZ6t&&mKEVf{cHNjQ<&n@=Qrd%X!E&
zs??an!^@|WN5*t#Ph5wJoD-+UomyA`bU*kT!=*A8o^yY;ou{WB#VrJ0rKwP`S}w9t
za(_fq)?74r{|XAjv@z10G$t=RZGv?|6T4qBawKFfva->XJ9rap-|m=Ima-aRQpk^Z
zd*;2ywd3looYacI_gM9bHQJN5o0oH2eK1=fr0{Jt&S+&m8<Dg*9aSRoAXLJ0AWV)~
zM@w#|Es88XbKFf>Q}@ucKoxj*Br@XTV~4I0xce}f&tTgtY4knv_&$@^`!}|)>c;bI
zS5;~Y7oh1`1?aI=YmArLj$H5PcnNpxw43l!P_u>!o>^aPLjUB@8E~}Tu4y~CBJPRD
z*8Ki>=9K05h%BR!{Lrn~V8y~$V*#6Cc*dSEOY)NHpM^M7c~_elcO&9#$oq>D=kGz1
zSmOvoC=P-ML%6jArvBy)A;W%R>L7vPYd_nMKH8v0SJ<Eyv@;Ase_V{EeK(t+5IXG#
zIx<WIDlGNHp`~91yavbsgS$ID0%YoBEcjMF4l>x+{vdQ&qw1oJfpp*3AiKIBhn_)s
zoFKY))YBD-tzbjxbXn%qVx4E;$Oitokc6PmuB7pwT*~u$^-T0;!I6tJ`>hL6%HO)v
zuPxDMAkDv-xqBSKYyHp9n>o77qB7q|!*{6`0a~AVQ<a4uTYO@um<Ap}29wklim{Z|
z6&-zWW14oBUmO5R+?%sz<Ga9$qx+rx<LFCngTS5-!l^ujb>WT%y~?!nqu4{Ew}tYy
ziMCCwW-gPft@hG-k)U9KHUyk4{@{q?QOo3O^vI&w9zhw^XV;_PNmM}?fd|scfmx}+
zmeQ5Z(rR1Fal4Wumw$bg0cNrO7~qLFw4)D@)A{!Y1uF{|T;}2IN|fT-+}&vuY?HUN
zP{gP;Ppdi4Tf|sUD9ncnuJ$TIhD6?vS<HjVo4)@)=?fZKeQ#(TLAK%yr2a!$o)s^Y
zA=!4Sn(Y=vdDVO$=gUGSXmzQ~M_1xksNrjhjr;j^&4h_DUe7}|hp;DRNiI*l#uMiK
zddzHf2SNpxb+zo&@MLd;ozf6PtniP**M}AFmj-Xs316pOwxM0$y|+ss^%sHrp0$?!
zk@tSD*)FyQyfG53P}+25T&_Zmw$}KB9E)l(BVPm8$#gYVSL%SBaJs0nS5O}n=d-C~
z&e!6Zsz)FWm|82hW#2W+b2oZ1Cr*vQ&zKwL+|)`YjCTpgPN}>|EVX5JKg$)vq@!8j
zMXhR;oh&a9^Fv3)THC_1cIR}nH22HXqlvTSMzxunksQ7H4EP&Uj!q&qLj||3sL=ey
zo<yJyf2)H=yNp7NycBVG1*e++Mx7CoKJi?CkKOdR#jgaP-6zG#!Btzg(>bVpCw4J;
zE`l{Ly9vgD%2Vx9QioN#?$0zA9(M;jVlaugz)S6gNo5bk9WBkDT7Nf|Nj{zv!Pba?
zel}NMX#kF)I|b}LL9~QZX*=C<9d8ae;x2V8$<eo-)P*ZKpc6r4JiRPrXZIKqSE}v3
z9KijFm8g{%`Fou`?E2dSe?lw}!H*@!jv^FA1TspNo0B~l&{$+Qw}*k5^K6b;S3UYe
zD8s8B943WnX)X!yPhu-rc>6_^^dgi!pOM;3YQCzCOMmb6K5V7o@PqBj?CfcI14g5O
z9?i3Am1V|Y$W*-iuI5=djjykP_6oO{mouetmk#_1m&Y%rQrgPpy3&LzB<37lKpg-K
z<<}g){YiKlk8E+5T3Nj@leS7;r$2}5UVwzkEoy=5lHAYSYx>Rs5UhUwP^)>6f;EID
z6=&OKOj_~3uG~CC$R&Q8`Y!_~f{8ZFOmD7N<Gk(+_PwwY!}3<GCu{f<vAg2L29c~U
zhqzNDSC0-vw^(=+f^ZmASsTBz1|Enh{`a%~JGbKRuvCwjON=l=4-yLxDiqY#4{+}T
zm;37zvR0S3WF%=#h?c!6*=@^I%FA}HJ8}10=dk)TpT)W0eaoh*=Zse_56@K9=p7@1
znfZ*)oy^IhvVjLm&}2)ghR=8N7yoy!2mK&#pVNXV%hSZ@*|JsNl)|b8*~+u#+*cBf
z%9XHPD=W;N8sD#jOb#{+PUq3!zq{{i2AC8&VFB&A?!BvkhK|Qk`J*`dh!WcWn+32?
z-+&|wwy^66L&wnvBsSAAAW+77xgD!k$b7&`nXI0j_$*f4&Q1nAqNpzEc+z8<(K&*^
z>r6Qr_B7Sz);8uVO~y1af|mYH^>sudj_)aFr<XH(fT252`kES@dUO_)qnKXGq4(@Z
zsxvP$)$9=%W(H$Ov!Mo&o7=Olm?y04Fbj8KZ}UJ*16RrB!vHkKg2oWA+7XNSgq1~f
zK0dIb#b}93apASh<kaQ#2aNM)wnPG8k}PkpkbX<0F2Uz;#35;2jJl#fTYu{+T{(9O
z@8gW6Xm;dPk9F|YKCw(RZBi<^9<KKQi);`3`6a7iG0dOx&DW*zl1C<S-LD0BTyE-a
zQN&g)SOBef!jk|<ExP&yf*?jk+sCyEyGZ4MX5rV7>jfm7D54N!xZ7g$I7ILFYv{^#
zG#VD}(#kvTxdn`|ry4)7J^LhnTg3fP6*wofX&U{uY3+{^*&L1XD?+P)pQ-hF)*qK?
z?E<!&10iHAPQ5JU+}o1;x0L#~C>OtdWE{NeSpr7yU^CX$kX%2ZdKtaQ5j@itB!hNr
z$xN>N`JT43r7?(J17bzzTX#aWZh!2P|9^+?Cp7fuoV@?W&Hre@bgiU?EBpdBofl*b
z_V))0bDQ_ZqDFyS5jj_(@V=*+;&TW~F`hsZm{lv=S%bgk{(nd#5hQ7mFTg5YX)?Yx
zms92A1^C~p(lR+I)?!0*=88n!XdK-yt$7;B`py%+vsgG_Ij2>gayz@vX9k&sBa>sG
z7r#_|xeY1?FpnAc0?j&mwNgepDJBN>s&`eZc0*S8&-8qWYFT?7^yZBk;YWL93#N8%
zo5;|c!KE{Nw<0IiiMvY>VYDdW?E0cC2(^E&+qe2wn>X}GiJQ85I@5BE&u!q3sEaIm
z*?yx+HCBShBkV1glLcr2m#|5CDMhjGG-WsQl6}OQA@;p}N;u}xz%MYzHq5o*3Q0*A
zHZ|L;xOIs<)o~OHE2VcoR!boRCpMGIG+!7H3{qR1E|`nRdAVV(5^-bZpDoa$G}iTM
zh*J=ZZ&0t6(a&*47h=UA8oF7viW&%UpNbO^;BDa_k_hda1%XRDC$avSr1cpLr;6kC
z!rAZaUB&91syom2H&ts}3ctj@Zb;u0#|@!tkkhkAVX80Vr6IthtLdN;%{8x|r6i?n
zPN0^2XPWrAH_-jPoi?i7n-MWm?qc;gmyDqq?bXw@4+5Ux-J0y~Y!j)tHTbHqp_0tt
zm}d!$hgH2k6rz^(HT|7a5x2jd-=ABt73ViKnthsBk<E(acWkRN-{5PE9uVIq;D(}M
z;3naaX&X~RI^6%3a7GhNxcaR@`o>oU5E_FErQFtZ=Pc5vsuo;nQ0nQ%&QC+J%c2KA
zRm%!<clgnP;D6Yeudq<bmz=On6BQ8XLi#K8ij&-ht{$_wo8}<SDh@0CA+t9g7FgT+
zKsfxY$B!%=aQoY4DhRmq=-@-Gbc`pe-ZZ4MjMD+E{Lj<J1O-KAyH*xYK<<(``#@FQ
z%rG`lV*s32`@Ha{%9(ggjz5EB+o9`XGBifkWr5*<8oZ=Y1u>4$lq_e>ZHWcvy05ZL
zE?L>+@;SO!*(}99OdNCBRV3O{3wVPj_ADo6)b;FD%1{pg!iGFoi@jP67V?UM&&HCg
zF6RnzTt-2W{CC>dZ{@p2opm;!D@)E#c>=`Y729Wnf6dg_dsd|GSL4JU#aN}<O6XX$
z#%t#Eb;-(rUTz3i<s|QT`$G-a&V{#5%M7-P?uA9Ew=MZIT3B+gj;>>5nVhoM`(D5{
zy?`-{Z~B%H6+z~H`r{5g1pA*4pk(Ynly1pFXkEuHyl%GJZ!5{A@znE$*yQi<3N+Nt
z*B6>OptasB3#}JNtw^khm!Bpq{9woI{!;zmR|l`@gdOQ5P5rZsCC}Ulk&T+u7uj_k
zSr0kCt@R9zt-yVJa+Yl8I#ZwGQWDW>aVC1xYn&<V2Tzf3>p?7Q==m~AV|~lf4!FJw
zb{(%ToxePFmR}%*D|&Ne_4A`HtNM6OR$5}xDDJK-9*^5+niT>5oM)4n-u2AVs*P6@
z>Y8}R69!%(KZTZCc4yMM^|%qBu3j)rA?qsiGaceZW(}|A`~or*=$*6X!d@cVmycce
zY1kfzdA~b4*txg`DVGNwJCv3BSLVf11G>i$p89!fR`oP8r}<g)rH($GL++u?P~XO7
zK0E)mle{p`_s=<1I;T$D!NWDfqec&+o&<&hR?2Ov5M}+{C#dVzp3AN!Z>6~K_%ns8
z;m){pa9xKJUiyy38TU0<v)So!$Trib+>=|sJ;|8F^1N(0;dO>a(-g1^Zrls_XXf4J
zv0#DdIR2thkg|w(>1F=HAlPT%UQpyQ(9?dXwdDx2y{_xBBvl2Sv9r9iGZC00TLw*T
z`YxtxDpB1NtLj~SS!8^$biqF(A#w^s|FieM{5k<aLH-@2*VnfJXkTKQcx~Ia_k?O_
zO6G5EDT6O5F_IO56AqVZuaaOo>bge2AeGxVZCz8y57(Hf?40D&v??>eFt(<NyOO@X
zBC%7EqFQ0=kt?0gs-|v}Cz073AL@Ix7Bbl%f((;Ci+6qEYu$fRagw?y<>j)%EEBb=
zYNFu{>^e9Kacp1hkR%*zbf}HFgqs~`imjCgT>#5llLR$`Hkt;bJO>+p*QUCtH<J;=
ztpwec;cT~mX1V}_i$W(NMiu=yepMlBp!%Vd-8of7O)IdauIq&}H&YBZVBk?^LVuE?
zuohW!GPb+Cq=xc!xksV>>+sCq;6JreR9%5lKOSD{I{x!y&$o5-B|c-CJj}|&qucw|
zOyGU!Yf#$lrbLz%GcVD{Cs%2sQ5-@GDy)mBLjh}%PL>Dx1F6^7GGY9>@?8O|DV}Xj
zJ^KtO1OB$h9PVvo7~oZC@Fz4(;4R2td`~Ai;xVnjb2CW=ge%ea7fSGA!;U0h@fh9~
z>?9JEarB;W`P%HsVD?H~C@}H`BfS69J=c(y-$;2Ugm_Kkdet-*U!VVcvVvtSg=(WP
zO(7HuxBXi>+l^RNc=O%(7KX_IN59_iW5hy@^q)AgPg0JrRWi!by+hFlPdkZ<Lsbbb
zdvA|4GsTMHP{&w6ZsR;s=Lh>nB5o82oXpia*-7`U_kEVIe1uBLZF+BF$VF*r4+k&&
z&vzx!V@RyXj}7`7*qME?81Bb@)adKcpm%v<vEOxusI9q&^GK(Qkd`d!1R7<j<(orV
zc^TF)dpjlgFrGuo##~mYs`BHN%s0z)3d{hbbqFc8aX1nsN;0xh{37+f^EjLCaOib&
zu!)@nXm`a+%or^{&oweKTHSkvs4dY)zwv&=!yrxU-X!<bIamf-Wxvd%^5sVzL6WCx
z!wup1oDY69?xx9CKg*{w4_<;u&hPxJCNem2S$~KG-LJ&l@>L_*Qc+Po&-sC#oPFL&
z|N0&czLgnRsr&<S<Vlj&3OI15P~-GzsCY>=ANQ)`bC>z*6CYp=rssR}R75=OX0>Bn
zA6iaCK8L!Yu*uj8VP3_$PnFE*{1V=}s)cBJdj4OW+kZ2%0TeI+P@PBR!5sBYXw6Qk
zx-b$@?LO0XE<%Q6z06}fE%p_s?8X!rkrDDorOicJq{&9nbv^!VkB-;JZLcY9d>q&f
z10Yu}O*u?5Y<zZ=kn<|4qT<b@F}ogzeLz4ewVx##_)O;bqVrqwV_c!|w}p^_6?4$7
z<P<5S2H2XlweT~G^>X;^yMDBr>on5BZYsI&wBF0j<%-tTwx(3n>Bsq+{$l;EVIU4x
zUrsF|SFTtWn%ydc1X=B%y1RcB2B77{L#n16ZuOOil|)E9wjlEeSC~bs<L*VO@DzXB
ziIbpu$yuN6Ud=vme0f#C<WHxoQ}g(V!0P*4J1y;Rx%t^Zs7|AcZUpdF?%-#`p72|v
zb$x6??!xYsd_>t=lR@g2PIt}`L;m2Gt`h+a7iVr}YH<2NX)XSY&fJ3VOIsXx?@JJ4
zYB(pio(I=TSzGn6M4_t<`F(n0O4HclLMBQ4O~JEc-G}n#^X_dqab6qBxocD4^2NE|
zah;`4B^z`44SDs{y*iY>aw3yuYREfpRg56Eub@}o<pwCxM7g-2-Co6TDih8R;~UiY
z$`=Nj&CbW-%v!?}tsO|-2Qf=$aDecO|6TKXM5D6s;4+&r+Wkd;t&`DU0K1Cj{$y^f
zi(0B(=N|rvemzV!iO-iHvk9?WR3U%ml_nV~vhH6d*LNkkzoIE~vM_%Sq+BARXFpwn
zh9MCUh1ZFXPshV)Y#&1yG>s-;aq{9EdPDqUd5#&ioSamMNn$JMzSmxsSA6Sp;grmy
zgV*xt8sX%k3IQ8r#ErJT0MbVf`ufD+Wtkoa=u59sI}-FKH#{@BY|yaOcdNu?4tI7;
zC~~(`<J#(#Wo#e)SVztR1EC9S%f8DTKML5no~>Hkh|UZY5`l_tl&|1r88t?ow1sBO
zwMSbipDA+-WCdqXXA95SnK^QklHUr8i!-#No(NYGJd4h?Z(K=T5K!2e>lPQaULckf
zDPa?Setv@R@Ng-i#JQ9vc6N5l+N8EZ03`I!f7@>&z)I5UUw=s+9>q5!pfUdZf^(sf
zKp5xOFX0AE4Qc1Xt(a^6(m$5#O%$~Igq7Q09L^sLfU{T+Z3EDhCOF2<_x9f1WQo<_
z9YaTDVr^hu*OW%#)?i%##8y+1L2my*_AT6c{USL@7tn=YzJtn3OB-DN1#AnfFQuku
z+Ey$Vw@{&$<P;-welW}zRQ<oGCJG_+l^6)ikdBEMzS#%PmJ%5(;Q3JZV7K;*53f7@
zmVvnaxry&E0qt~aos5d9zv_vb(p@>rxIv7hd^F~0c5K!9rrxR~=9lGitB3=Ifu*d1
zjyaOy>Xk$xFU|KuTrC<!46#GUxg`m8W){}tXP?4Ca~>S4xu%wn!kfRX4lp{7LduNE
z95&Z#%-wDdr?EQQT?T6l;K*rN(F&Mb7uZ&WYQ}i$SQZvGaB@+OgTT#{TV8D(u1(}^
z3OVs<0p|~`HEuL|yn815j7)Bsg++pF#QH_NwJ?x*R0&gk_DYZNH1-b}(H*~(<7!Y@
zMt7H{v_LmCO3~1{KkC#KE#Nva4lC5$>QJ)kHGx#BD0vWbjF)Zi1m(0mA!D<7pwM?O
zx$HArZIF09MHXVKtn#XDT-!S%z$)syy%hmbjF4b^3sHVte_~lvljByvjvWzkl!Ine
zRkDHpPUgNov+<~AS=FPk1ay&K4Q`g3l#P023-SFb$PG_SfJLhw`x?9Q?}PQl7SQvg
z476E@E)71)Y>#%tp!?pP)Bgurea9;`W}4^x26NRE?2H2+rh6=tFlwROB=VD$H#<0Q
z^bHc}<a;6d3--eJryxeRGYPT7-4b+Tq>dg<^0iN&8Ae-((?0&KbKdji#jFws`}4Xx
zg`@8~QM+thN%tLorJDG?=RT2V;9_^<uWX5=D5|@?Ohby+-9g;7>|9Soo9dRngHVH1
zX861r=ZPj8S^yByB9S^DA{4&yya!Np&aSx?M44!{(U!ZNbFn5^aAIRztq{9^K&R|)
z7rG2q(ZoY&N~qFRY4qfjll}B*V2~``cCo$t;dh-u_m_YnQc|!HS#HQMmMU*{H}LLs
zh2S32jYqJmU<XB(^|d7dlSU%GBcr?7Ua1-_P$xxf0FjQ6h{&c;*5@(n>k?S>mE}G*
z8+SuLY?X996;XxR#V)TIw#;}i0_$L*N*8&P@f}(0N=`WVvruH>R|&<nko_qsPwKF+
zFxDf!y)<Jq&_-t?i${?@<Uc65YB*=Ua|ed6&dRM+@n}m7QQX3ZclbQJeq<-BdA>O`
zzL6hdhPg-c3|^$)*LCT>az{oVHzBM~x)QS<lfFX`Qq?$0sGo@urujW8&5F%fggT>)
z0Lpj$fwtJK`S5X8x=Q`OSht8C!nW&m?H?59dMoPr$4VZ!99=ae3!h5vt1^IGw(f8{
zSWehi%Y<!g6&dPgNxR6R;cJ0@O(xr;+x&T6g~ClH$qSA+%&2Rw<<;Pl*)@4<l*xm&
z+6rOt>x`P@-w)(Jm_TdIQT^vpn6w-x*;SR5FwKhI#8X17lOCycKA`OF%lp8PA!}dR
zI0Dcn2)SQbc-%hS0Q3Kj!T}WMP#)=OQl@`1AT_!A3HI$n4+*qvWOY)8Dv9e+n#}n8
zpIH1@izeaMA0C{dNZiy5*1r02SSe-mjRzmdBseHhGmoaezf^s<+~(SU>#S883f@uM
z9_-?l#df1+?#ec{kzYS3g|}XmbID8O?dow_ov+Z1+AgkRK3f=!zP}&~Iy&LNeADX~
z*Rz?q+C>$r64#MeSP4DvK)9b7Y-$m4Yb>e=7T|vw&t;J(ImHR$SdsjDTZOQgW}th>
zLCFY@>FFYtiw4I0gwl<EnoAu1tm|dO*uj*1VVf+EBIi68&5>W>T^f9Mw(*G=>dfys
zx?;kGIeli0<|};3Rm+6zlz5RsuuT|^Q-eW{u!h<0){UzT&-xy>ww@8U-}83vgY}bM
zP-6<tA*OA6(&&+|Rs$LFiW?QdVNMkKdbb<5lB2_`F7}qVp2L)ZmLkga(%DYDcx`(g
zu5c9~j0nLZY%M2jqOP5_)Bsh%^Mz~t&4;aNhz9@m+5LI5k_}|MokAqlb&60H7OmXZ
zbSg{P5DOYusel*Z{e6{7jd%(1pHPZ0OqY0>2ZzpQdm9tK8hMJkUk4pJ8|dHif8NHW
zUibAt4zS&G0)7oc!FalkPFv#=a(mjWzgFIS53?fFo6on{$$u2a_L$_AfmlY}<7!jd
z(RhieC_xdWlqke}bM^rM3FtDKk@BGo+EPpzXq>pl6gs`G4r;i&W6ke+t2WPxq{|aY
zs!f^KkNYzCU*U=DoCL$Gihq39;E5r}xT<qrV!AAmblp<^E#y29rUR7eI7Mq?Vxe$g
zqD%#ir_21<Xj8+S5%VQDo2ZaizgRXR6Eein537z9)cBqnqV<&UnURc)I`PA3nCM%*
zY3a_4cz7*-mUEFIOz;I(f;zkbSpdX`VDkloT(kOctBaqBY2slR_)P|&f;cABiZZ4O
zZ}B*6r!#CUo%kxog=EO^Yyxcq4Oo*0s5ZxS)F-)mO*3`LGRpf=D#F{L7sHb=uZTq;
z1uzBI&k<o_1~uK7klr#v0t@!pC<7Q$xA!`0&<@2G#&CBi3LCT_#uN0R5$fMB*kXDM
z&-dqcaeFf<HxOAchCaO2VeTAVn#;-JG%0WeXcambi9@hEAlTLdz~OA6T_|F1O*If>
z`AmJuWK&AS^G_;^{i5Xrgn$17jW!GkIPBm>a?M<o<FdxKGss$}fYJPHtDCr#Xghw!
zNB0S~h--YMFB3Y!y?6xq^d(xmC1T)qxY}?rBNYmjzRpU}VQki4nyO1bHD;SHlzh&B
zWsTam4JIKWAvzQEr1mv1akUV*tEg2<pqc*si^|;eD#Qxg?nSTFiIHV@f@RITtwCVD
zXH>DEB_ESvA4#JXUG0Ay@d1MH!(&CNU0ILEHLx!Fb7QB!S2ahG@=}a`{~=oKM%A!c
z)U%RVaz6Lj{upZv@x>hf{l-ASJ;Tv(vE@a=PXo3lnRqgt$)^3RxB}3UNT5Ln@Vk9Y
z#P1~J`}sk_!rCoO?OSguAo5K&Br%3$uPI0Py-J=C?qU!~+r7`CbJUb|jiSO6X>~>3
z<?h_rEcDvP<REySJLtmd%BZ{XRJrk9-r)6etGU?<cUG3ji~u`uXWvvH`_0c+PndCX
zwErgfa~S{87@wpuJXVSHZ#-}kWm|R8)dB}A33fL|+rce&jv_%&XlOI+n>3LYY(sMi
z?7p*Q5EWkQFnV9Q+%|sV^&eyvCXXo4$<a8M&^gT&tUEct+Re1%anIx`tJ!zVRY2&_
zI4Jny>aN#<2BkL}Y%uT+!w)dFGbMZ|x5`Q_;f}#;1QkdijQj-)y<kVC)<_?Ru@<~Q
z;2l~TbY?RuSz6nk?#Ihm%llR8JXh;zc@gT7w?T#jyn?3;&7qNxHOe_L<_6h_3`G-J
z3sXK(lIK>vRQoYpM@esp@vmnz8^)6_rZS3C>>X+M2Qvt<${i>;ufkrMP+IDbUAd>#
z?U7I`2WMv(STHIBB7=QmFX7~irv`&P8a+~?=!IB??ffAB=-yA2?c47i8*82I_h-yK
z0nq#NV@lqSIZj>fx!k{QR1Ov1(XEcV!KXdHY@X*inrPtJw}#eXHYpSm)jb)FU&c2O
zBKG9F<u=c^tNoAVIArMW=+f)jaTr(pU7Uf;A3*;4L~24R_HEd*A57e!As<!y&;At`
zq98jOhY^ex$U8$BI92VOekx&KN=>;PH4rDn;nRl5M(Q!wY+<NqQ(K+l1${N-dxa1v
z9UU0+SJmblSpcJhv_1xAfrypII#-hN!2#Vs6wf3N+5l!k(+=7V-+e#oMK`2t5go0O
z^3llIc~CFbpJ(DnbTd_rKloF^`EP%J5A+qo8Yu9mrk{RRJy1~Uun%ipKKwB5E<0Wf
z)$N1i+GhyE48pD0{r9>u{<?K3DGo{|;YQJn=N&uf{!2+aYe~5Sx!B({D=T2NQmzJ6
zb&tMW=p7~G(kg|UHu#S+mHLx=42Wr;$##k*X!h9Cfq^FknBQ91Kqz&Swqp)MbNxvt
zc9h+X>Z9w@lIg^1^t>v2`UZ^oMM9S!$SgwOW>A4{TP~>d`S3kVhrt7KkY_t5NL}IH
zed~iO6Tcefv<UTI`~<7E88q;UO{!ZUKL&;T+S2&a$yh{Vl09S)-(XK`%0T1cU}mi)
zqn~obP$t2|@n^0C^ONLijORKsgKP<4f#+gluO~}<KVIjjxU$;(M=&28O(U{%-^2PX
z=?@?s@A8t-%wPCGFsPPFr^l+3L4@vJON@(r$~50RjmGR$FqBZ~!HmF`uDlR_-|>|7
z{TuYzxw{4Td-3T)%=<H8+QoQ7TK5k&oFD4#B_q3iR?Cojq~7*1;Hp)Fn`OLo5mGf~
zGFK5@95aC)`j&=N{kI)U6((GD{MqeMb_=l8ctAnaSj+ae%4LN)p6^UlUktT_Jbe$=
z>&*fqb;>muq_+KsprSu&vcU03NBrH>$>*jK3Mac4)W~nYe$xq9A@Tb(yY7LtuAhT-
zHWq@(R@bt-`A)`0HGkyOdZ4hWlUtpA!QHF-{pqgR!`*A<dvWS`Ov&pDfl$U}!4eQQ
z%4fAw<)gFZEXK+mF>5=G1;fW`Q=+fQ8apX-HD2bx;`$E#`Br$aZL1-v*<0Nr<5ErJ
zyV%s^DVyy$zzpn0!V}g0&b@T99Fmn~=4~Z^G~XJuOSSz7$@9dudjX-eJ$s1yg87#0
zi8K>_`wj6udoOxXlCd-kg{;go42MaSe$zw*!RN}4N-Y^a@bUk!FBSIXR}8s^)?vW6
zRD-*UZ7e3on;l`IrZa1)@lng^obf;2nNtuLYgrg6!}I16@wwXSOeG2ZSXD9T)8aq#
z31*{r{ZnUq)A#YXx_xJF=wjbn0>>+uMXn3kx1-o}7(8v>p_dpnDOTF2!eY}O>$%72
zb6-1=JEg+{5vdZ@x@rivLdib1Z?3Cw=P5>hx~WtTv-8!Caud!BDf|MBD>v=`EOJCZ
z$d4C!Ii)Q5%^%g%*>aG5l#ZVU)xh^&TD!vfulPA!Y)q>*)o^iOV+-nrth@sY3(l%D
zK>4eMR@eQv*C_dKA3D@17Z$|fZbdrz0(P;q2ob(@{jIrmf8I`Sq!#XPKcVnwKR9PX
z`|%zG9-{j&NWQ}9K#vWA>i@N=kX}?P-~$yoknFbh{)#F)mP)a6)^0T(sk*1A(}ByU
z<CsxpJLhGp`gv+MGdWja7DLCom>s2wq>AI6PS(T7l&P+5Vwn_Hw^3Y#>98Sw$5#$9
z;gEO@-=2y2G~2?E-rp~oau<z~0X-l>3LnmL7)tv-90>d{X*6$bPr!WM_@NRGg|J7(
z6B0V8@xdG_<Nkc3#zY(%m*DOTG*=`R(47Qb1af)k`CHTtrx?fIhJ=PPTxgG`I1ZS)
zzCPV1KcBd&{Xl2c9ZhMH6@m(Y{fd2T3YA^-S(YW`k7`p~A^Mnk9ZD=&4>R_hSb~QC
zRj1c2qQneb5LNRIx`=jV3+zRT&~V)x%WKjblB^f(4mL5uh)=i+lW}TpZf?2m5&E(H
zh+ov^hk-u5$_B{F;<{Pp{jS=*gQ;)jY{8oc1x`)rPVKl+tn=Fp^|2!Z7@;g32;a!-
zAHu5H)4$R#fS3mWOf-KGZ)g0eaQ+Z7l???TZS}+t5LU*F(CYG(?>ZDc+ME)i?ODW*
ze8jyT>V0aa1sinKLFMGSeR<w)rN$bwvdxN+BdX2N0LP+!mTvf<dBUr#=*nO_A|HuZ
zx1)vlG{ernDqTaRVG-0w0R9Y@^nU>2KpnqkAQyy&1!rX)FXsCr+KiWkbPjlD>gO0n
zYocv*Z}r#mmDFy`sPSfA7JOk`NE#NAvRD?4QH^Sc153A`z{DvFDV_fpECX^1@W6=9
z8gN}Zk5Vf|<ABD2Ym@^bAQhpGSS8hzRC*DDUg30O|K1oibTH;DSze(hzb{>`tdZ{=
zej^6=>4D$?e_9C^(sgy50;0u5xB+0PklEzIgoo~}09?QS_4nw~wl#)XiD&!=LGuZ(
zzefwkd}AqXn>SUUwOn|5?0vX>+%v{|a<F98I=ubqG`#raBWBlSZ8q_(_py1`UbE|R
z^7Pm-6|U9ff>=ENOyJt`=|A!E+xTVv5)A0kl>$r$?Am`wwX2AN@{1*j2X0Bxrv>Dx
zNXe;b_;Tj#iZ`??|4j~X{3ywywNgeZ4X*h%(M`Cc_j%+e)XWos+C4kj!Oq{pTJaL}
zR$P1c3--s~afy1XYvWjHWe+bOhFrEnGJYH;_&Yq57HCx{-CD`|uU(Q*08>8u1zUC<
zQL+UW7iYZv{KE*Vs~4zeW#?kozDTx@MlS4AXm9|n;q39tA1iQ@F1mYmX`|e8=bTMu
z8ZyUh-mrlS_YaL4)a7{C9DRGXSFF2x+?#X{U6xh>&*k0s@^C}b#`Wmop(Xk<_H_GJ
zjp6H~S1uA@ee&yHVHT~ojrFiicC?xopjfVD(*peaS$~=V*K%*ewtd*XN8d;B(lz3S
z-U^(KKbM5D&rHFZO?&uU36`@d(|%flA3uKq-8<^7;RG!IZQ*L9q-Ci-y0TCA-hM-Q
zqb-hd2$H|^+0Xc7`tOYME%R>KOT+i7fCdP27Oldg_l-c~h)|21*T$*13-@K5>zFB1
zU_<X7!kr@WCX1laxnF)kF@Y(c>#nOYGfFATNZ-2EY|biVxyPP<8uoO<D{CKluGx$w
zT};r)@cG*DyO%m^1-ND#@(7gW5ww#2TqqTBJ&;KtUr4?)1IdvM+i6QsQ2uJ9aiF$2
zAb_=4GYj?X*|S3NZQ8U^Agk;P1eg^IXjyy8u_aXo#7Y?kl6cYv5{qMzLrPilYt*Qb
z0=}g#0m%(y+AaaL4FIs*H;rX27SKZB;o&NcI^jR^P4(&1N1Yn-3czhRHl*v?wX3>j
z=$lw=o2D_8FX@{$ZEAK;u61Jw0O^k?19a-t$?U%L%|PNVU6cYu89<{;%K;6zzO?+k
zq@~BC9mVuruOPdS@da%En~%~K)bK3Iz>oXiMzDK0S_gH#q&(3I*EmpH9FWcVELvL^
z(5;n#%T<vr-EH|e>+sxw`Ur5Z8W{3%b--)G8e`I2#?CoUcaGEuFQGN{y2xaV?OPwc
z!@aOCAy2$%S)mf-6x(1HU4}p3r~VbQnYPjq?NTjrKo*63iI7K4b4O|c+FDrqizc7~
zwQY4qO~zWRUSE`rE!uYIq<)V*`Uv_D8iaN0wkV-!P3rpe;}0oVF5o6q|KdxGGY7i3
z)2MbhprbGdPUNR!Md5Po)KEn!6l&SqSy$$%BA0ec<ABBijRV&l2a418qagBak#L&z
zT}O+xuFda6>()0>a8vb~)R0kxG(vS~O>i~PH&fokp<~DSJ5ixKDRcw)#ZR-<ufok5
zUbwz*FAVJ26)R0#a*KQK;SY@`$lHbYx~W*riODILzibtD?5A5?3!3smIZ!=v$aU!Y
zO>1mrTxnB!>4Q%(pUQnV4<105ygrCKmw>r{{*A*?75jB#zh3B1fcavmQ=3+J{N6jQ
z@?;BROv}%bf&=_;HX+IKa<%8pX<?o~zL?V6pG`+7l@f;&^mlS{q{U|*R&LmWN&ojD
z;?F0VCN&-x7FMuvGv3n<{yh%Z6c^BxGEd=lvQBsKNQTaZR*^X#)s9C3{OUZo{&c<c
zf?e4`_z;Zk6YPtHaVaR}9=lSqy3f|9F5!27h9Qm;Xr&xfjW?@G%fE~_1khcyY$Lw@
zX#t;+#BFxOL!*ac*kC<)A>c&J>GL>shF0^O7vw)VzHZdhNJ-CPSOFKbXc~?$-g^dL
z(3&~sR6MPoPpcm2D7f$5u{l0_=NScpi}kjUfWTAVdJ?}A2##UAYq8jt7sQ?+7w#B7
z0547$LvY;RY)>k4Qry`jJUnp<)^6tbL-~q;cp-tl827*}v;c35pXV-TKNcystTAWK
zngIr~PyBAyQifnqO@ODHGsfOC90JNuq^0?qO;r35nGNqtl5>g4bXER67XI{}vN}&9
zU_NW!-@H=-v3~8*xs|g1)|Y1fuY`)2`0AJVS<<siTkf@6HV((&{+;pV_Y09t_qO>3
zMTkF_c<ln$e0Hw1Nc13pmBo0p2K(SrdU%U{`)&9#ma{`yj9qi?T0bkL@>D)88%<X@
zI=;)Hk30hR4juR$^bcO#YL~KZ1IedsPfN*KA$}``1l&vcu9dxJH7-`tm#`0o$yEMe
zIUF3#@+#oW?22}xap3ZDz~JKAP^$shHJtyGzXI}_lEE0o^qS?dsd9(qJ;QlrX-u!1
z9!owsw|31?rsZGDv~u6HKi2L`8sVqJ3D@Y-bHM!Vy7cvFHPj*pR-O0*8TpBDv~#P)
zQft2`TN{Gch1pnnVh&mbbuj`HwO^A~qQ-&R<G_Yf>DZfK-SXMA7TIu#8}JFe>!MK&
zwaJ6Mobbl*#+dNyVVq5Kpcn80oJ!8a{?i%gQqL1V>^_V160xeG*0BW@7tz(%>0<0W
zlZnoCugzF%7A*NsoE9V}A!FYj*m-%wtw~eZxmG<cr{|{8>YnKdCbtY}3wOthf0iG;
z|1Rzrbt|@Q-+`ZJ{!Af6woy<ZfOX5(Z7_1gaP;Wj746#V-NId3rb+yp3)}8_Q<GXF
zjRP77G!AGS&^T~SbD%I8j#(RM#b(Y0dpjG{54#UGe&w3}<|(fJGj}&vteX8D2H*WK
z_8k$+!3(Fsy?YRtO~n4A$E;@l<<lwXIAjzoU0<(a>}mN`MNf>m3xj%hHzpB*;M{2+
ztMbkznuEc+=$c!8RZ#=R)}HyvyHzEr_TI;@J*WEn$Fh~xrtM2t=|czhq2>1n)uy#f
zaL=v7uy*SXv&*UUx^u@96V)|q@wUwpmz`uVMyQwP#jl60m8o6(ha4bSR6^_0QYu*s
zm5M-Emk%f3I0A;Wpf$&4DpY1eB^(=Cw%TwEyBuW4_Vp2~>oW<+5k+1ByX03z^|E6z
zzfE_+GzvLRYpVY`a4n#8<cV0k@PE@eZ<IhsMbSQ8TjPbN?!~!;6hsk#-g7Vtf3Dbq
z0`3X*#JHV#1+;1|gpZdi?i?|YfNU26*b*|K4W`XpgvoDyO}^2(o<Q_NcMq!o&N|5T
zyM|))EramPt6$)U-<C0bG2C376bN0fPJr3t7SJyKTrwW`-~VCbjzcP~&W_-5rxy6+
z?WfQvybj}YM`QQ?DCT1<b-HU03k@=Y57N&AhfiYdrrj#9#I_#LyCWKeg<$&3#f(3_
zNnIDP&|tY;%>EvZjK&#)>H?xK`Fk@?(N(y-4+6m7J#w(IeAC7X5I*6h&oFzzYNi#C
zo3X-sx5t+R_C>j7+E0s_T<WobuQ$JeYnW{K90G2>8QBD`4WyLj2<-dQjrfI<%4?EI
z<ZN!V3>9#^I=T>VG*y))N(zQCLH=G4$@s`NUvRz)szghyaX{n1e}Mzyo?N5L!+~;D
zrOQ*FR!c2$KqmFrjKiE5uM8_DJDnq9={CHes0z*3TH=jXjK+a~%7M&+V$3;`Xi)5|
zyf`jSWn=Y7mtb@Y^Qt_Zl{?}7E_mbSM(}pA<9BR_V(75qU?Ssl*`sx^J4Bn*pe<(h
zmt~bu2r%ma-Lw^1NH(q1qg}eR9LS73f_+auifto@;^5>7*gNhaY`=9VPJTWW#k4N5
zq;nbZcxmZG44Kgn<L5nu$LBtPA=CTel|LsVA@jUt@*17jWz3aZZ@m>$-g_6pzFtaj
zVaQlWjrg8@`{KptpGDt3z2NCyHK1<Dr~TD9pm9LsfW`rh0~!Y|HwSEyS5R8vm(Od(
z#hNHI(BBW67yN>;caF3GnyP<J!v^)kA2U8dmv*fyO&s9oi!BRhp?%8>v!Hln^e6~O
zY)N&30<m@BY_w_C#PU{^=RG>M!=^ueCa82_dkf1##bQLj^Xc!tM(bzmaa=v95b(w`
zPhiEYZwOLdoTr=nZo3)R_t9H-TDC#Xv+3^Dl*H}3fZ3K5b0*&Ml6L+tb3m+fZ3xyG
z9X*sGI*z{a&^hwCT6YnqS}kC(B)4TeWV#8vEIpAubFi}`I{14+!gwiCRaaiWwL68h
zoII4Cd0E>0Pbv|RbrRif$DdD8fUK}ttiVQ%eHonwkHz5IpT=`<O~?9e^!y+IK1sYV
z_36?YAHMcD)-C)3@4q@uf!1;_hgRml&0i&lv@*6ugZd%3u5V{`ZuyVMBhQ{s#-A&<
zuxz~*b5B|^w`vw)nN-Eye)QQ7Oh9Wp1qT1}<tu1H7v$pV{KKz)#rC~aN@BULe1>Cg
zzus6*0pG!27p&sks8{NDW|#x9Di_P~DWA_OtKSZ{4(W@ZK7UC8?gB0gm@jU=^YRNZ
za}L3Ek|-55B;eh@C#`rbNZRr0d*2eYCJzY2i)Hq}p6&4M)aT*t>5jQe)~Q(E!kdO+
zbrC`ST+3v`ImVk_)=C-s)c`CgFAHH|@aoayf>>iiz}Sfv+NR&59E%5pN{R1E0j;#f
zRd{WXw85Tj&@qngg_5*YKySx7brg7Q_}(tG94)=Z0gVG12Q&_tb3n7WHs{9`byiGk
zOmWwAC6KhTsI-uq7ftALB|NWdy^Twcr{GKutww1HT^T7bnH%DfEqpPog}GL7WqB;G
zx324n*GDwQ3%?(w_k<E`VZB*-Md%&oiA`}?T=eqGDcfY4<}EEQL{yp$mK{&V@TUHz
zx3uHS$bpQ5`*GmuCs3T03impp&;<sojdMOV^kZgxg{-J!Xz}ic(9s2(kO0$9%o~fN
z=MN&nFC6u~LwU6n8F?9)yMGp<k|OcN$Zz4};;YhM=s$(Dh$yl0z7)|_xJDWWD&>GE
zz|@9&P6`V_7mc+|BCN{IXAF0~+l~|zNlZE!fBy{<ON(r_MZPmfTD7>azn7vd*|`PC
zqw7a|&H=8hLxltQH#)4<Sz`{A=0u|;c^!&U_t3>)nHyXOKiK<pgstBo*f?>1AQdOA
zr;km#;4C$W_y`Kvxx&`}21>m2LiUox75pYec~1YH+t5vPshtmW0oSnvLq1iU`lmE6
z4y7sU&6W$2$Ht3Zj$9h3G$ncGQJPHaAyXm}VdL2awyq8JY0`F}`1~>yXGOtA*6jA)
zu=DB6dT)S@qdDkRT9kug_O~Qu6H0PonV#Q;&J7M;Eol{e9c<m3n$}e5aW-8_Mn}gW
z_VgKKWMt4=2Q9ql8Zww5Tf>G85x{uGLJ72zjX!(Vyc~XqjT$wCtE)bCYJ6Ng5|YX-
zTV;<V0(wbfE&iA&c10dLMnJg4Y=7cQ_sw-fWq#&k;5RR6iul)b%jN6CG<Ef95@}6)
z>{t}evVGz{)sB{2A;H0D(xfr`3ACD$Qaol~Po6vp$tPY>WDM~lK-sr%KREtNW1DFk
z|Leyt1U#p|_c{bDe)r37uxP~^71O%1b_ekpyf)!+Jk0dM>-H_1SC&*=H-ODEX3xdD
zUwl)+`r0&e>!ytf`i{r&!Tn7yn;q8=4Z*fWzhcJh-!bK@@2V`XQCK}Z|HN3_d+YF8
z8~3HpWAC~R4-xF&O84Nq4jf{P*K~N%Qlug4>)*YLa^)?JmiO0^ocKLfM*;-RpYbVP
zeE$=C@#CyYV~V$E+yKvy8;j8+ZbFX{qbtPpUPr6#-d(D<tgEg)+Wp$&fQ+Ly^em>P
zETbbZj;+pFaPp5u_L(O9u8cRVR$MCUO!35Ynd~j>xOVE#7|xp$)63c;bE$<4N=~a-
zO3L2p80VxMj?pf5|Gvmn<_`Ima|tXbr=%0GJx_Puu~@NoC)RA<&F<;@S6En#KUZxn
ze-1XnblxMdEF`jpCJpOj&Npwu-OU`F6PM##b{@puL&sHmdpjrGHmom|p7nFAD!}9)
znI9J|-^4Hh!e_k|^F5=uUa*+QvKh+ixi`MXs*Tc4s_K)U-8;6xj1MQPxYyD*iBY{~
z<1Teys&IF8R_&Byxu1}jip776Rkc`^+o5Bd#%SNV30{6@2E+xpR9U}H5Z-(FQCgZa
z9y`|(a?Rb<g$h6(=KXSX!tm>R!i{y9l8k@Teq4yJep;yN5fHL%i-!1e%0zg2xFVK3
zpZ24;W0w#}4j6u8FSyde{8}clE_RNHfM@saNdN0E^_7X@gfGEtJA&C4LIQ$&cIkr5
z<;%^pmA%{M7xiZ)j46F;>QvZro{D(&RclEu=$f>_seXO9Hfe$!#;WGZ?}EsVF|0iq
zUsp)pTa7dhXdKWupmE@zbKu`!h5hqBXeDSI&^VxRpw>8WCNm$4qmof7Ldq)0*1-vV
z>$&2-4nb9<s{U4&2rs;NQv<v@|2X2aY;ZU(10Cvl!PhAsDY@3&g-fa8Mtt7kBy<b)
zM1WhFMtSwsX(?)n1I3Je7WMxBp*S-G{w-R;&P1^1?Ck>wH#a1$TZJ>Te@6WW9;Upe
z1n;bT4Uq|l(4~GycsP3)H#7n6e(-bi!;aWp_-M^Lcx%W9wbV-UV)AGKzG?GTzB?3N
zSO{wbEbyTqzGH{>#!R_6+1R{wJJWOHWhK2oHIm!4soZkOSWqPgQ3Up*qfaUAWXmMd
z9&sPusbfWL`0})+*s)_LcJ1Dc{fspkf9|{zCb$s9XvUaZo#|GrWBc~-r4_nHm2iMU
zs=Eia!l2F#l|@1&sj9s!LaNmC9K87L8syPRMM;aQ&1F4-#Ejd#{}fj2I*CIk6L2;q
z6X!DXC_LmlN)R{H+m)7M-sso5F8X(9fcha-yP03L{WxYUqzh9AE_qo->jr^%;l94s
zE4R2R0>JHuPOG-;J$4rHsaZIio{N0>Uf7mkx>&3m=!IS_L)n%HG^lG1QdtyN>8bR2
z-R_fEy7f4Y#v~v%B@0P81#qLVxDNYUKiCHY+SbG1u8mPApn5^juA^~y{}&4Q2-;Oo
z1mUR+(59{rdbJKi#}=U#g^mQ$pZ;_i(sT0v(X&-;;oLf`cT?OoxNY@Ky8=>_GGtHD
z$&XNSZXxVS(jXfvL!<=Chk?Yo$UpH3>>{3mUERA;82ba9a+aegUoX(2T$C<JgJAF7
z8{%?NsNnciI2G<ASX!2bE>`pGn?dI{Kum>H8q<GDG7m#{Y$Ctmav_<{-UdYxQ(#vH
zT$g6kHFD%LDw{!;X6MK>VIL?^x-jxJl%AUhdz%7+&}t~v8^|RuKtc4E&^3M)c0of_
zegWT0PQHhd^xeu;rfpezdd-AQ>Jk*id;#0g`=M*_G==2W*Cztl?%1(Qjo%p=nSAF6
zjHrg`h4WES$FYU;PusSw(W_?<&K1$vuyHei<Z@t5(%<XvZv?In96F2x1my|~^-6~_
zF3E4~Ll0O5u5)Px`sbg^lp=~jz^LMtHEm)9t|iU#<tr4xEc23(KBieygof6^X3nV+
zrlUaOA+kO4lR8hHjK+4(PhGlnLN^LAZTT`JG4A1-HS2Kv_;F)@_33R1o}WbLF5L-S
zt17gAYQTXGty|#tucsoN?~eeUyAK{g%$Yc3GB&9l6^p|7e0p{6fWduwsApy<@}WC#
z#T~<_C{ji)R$|!7GnV+!^4oX#C^qlh%Xq0tNT*_=8-b8;DlK$t-v-SaH#8Je<zGiy
z2R?D%X!YAqH|M71dDAs^NML|!Ym)}ym6kK%p)q*$?%U1oOFwH$@?Li9&{q8_%M%-K
zp&Y`5A%S}35sAs2cF+V6<2@<UKoZCL!<Q%F#qkg0@AaE;n6b#k^|w3MWTC-<1ghGh
zWs^q6JYwN(36$HH#m^NN>EBv_=)%LQD1PT!<GR|APM(!U|BwUnh#j5v7KB{hsab~q
z$fI`(Jcay}Iw;O1uxT{OF0W6YmWtq9OUb-)xk$&xH6+j&?&63OIjptV%AUUb)wS9z
z*|(*0GLOp4LL2XYZy%AEl#0in`$)O{cH}xXiJ-Lrvl(m`ABw*1oOkrZPN{EF+*m_d
z19xrT5`lr<_;mX3N})s)AzLt3vn6OP`3pF|&Yri@xQ6lda>K}*`j?em@lO^3>0jrs
z<c(6bmuuRv9{Tkt-$zQ0d|EJn_U#{-wSei!QyDWN>ILJg_a-XPTG9vrzL;*jlT$N!
zMmOOE51X(oZvxIj0;sPdsC|~M&*i=Y$LHH`9;g8PIg8e)avcd6J}~-5`1yEZ{+}Dv
zdPw?Ozitr0Wk>wZbcVH=JL9bn8{A8kZTLqN*w${|i?^o!$nx~_c%2|Wd^P161O@nT
zUdh4pZ%<RM#)ZY9A-=eWuEwt!Qty@Z-r92Q>Q8s!0$dA~l$~2oUHMfTK3Bit!*S}P
zk6_EWL*8mzK8u1BdG6M!nCa5DY(bMxKCzzQBCgs~{t$G>9f%=tT@}gtO~tY<OC$<h
zwgkO>Z@jUpY}!4I0~!Z34rm;>Kn`fY^#w9aORjN1<G|I;0dWzYRbtC^D0k<TkdB~r
z3tvZ!?_L-7H39}IDX8pn|K|RJr{DnNRyU@ba96ri>sZGFD^F#kxFFAJW&y5K3LP+y
z?R~6M9jj@yi<gcAX}fkH<G?=nG-{$iYs<JwE<V1<^$Wn+B@0mht})cwKY@+W>(IbA
zT!Gf&F~dmQ_joybBFv{g)*N4n*wh%*^A0oKuC0TF#6%1oHiANge52sdP<BaSK6>}<
zk9BMQHe5o&xwx|Q1Tri(tdjo5VK-yx!nwxu7kV&b#x%VDJ~yZK6$OhW1RlEg?1@bq
zD3GwAY$^-PoBKO{m^mBUcI>8GyF5!=UFDDsZ<8j?FmC+gc;taGDxR4|QQ9dvAmH%#
zlZPuo|36_!8iDIKzFQ0HBUa5y{(aQ}O!;{ucAlgXf`D*>{3$d{FN{m3kRa*|1sHoz
zVdh`E;p^#&;XRvR;+;LvEX=y+i({vg@Y`~_NOYmlo7U3jIz?0qT#H5Z+|>v1@vob)
zE$SRf6c|qdp|nMzkv2$Vn#j}1`nD|F3okb(4DQqb6YuJYj?HR5CNJK21aJJX9y_B5
z7)W4Sd2!_*esf9u6T><-9XO3yEA}DK+YOK0)E>`{?!`EQj+HX?46TfQV|oSF3m@6m
zDoOQ;79<1>dbO&HcOD&t&duu@vk0(0ll;i#Gp^L#r<5T-sY(F8%BsvOB>3)rRRGr|
znMYA{U>xl7kD!o1bb%h%H1f@GU{@56lE}#@$cp5nC_!PK-W9am7vZukLxjllHc+XJ
z0}5DPei?A>!0Dot@gyrJx|e49LKEQHk!`V6mR5#bwiH?ms4n2D{+gsgVH!#g{tx!4
z%aBcX)cn5!IoO>+@!|2Xv3I9rn?U{HNfajLQphcW?ur?7wi$3f^C{Og`LJ*Nido;J
za8aUKyksd(oH(fj(gr}-vSI<L1>6>EYq4+^a999u1IfH(IycF&<dL6{oLjTBHsqIy
zLoBND2~H~kWZ4Z7J{n2V@Lb9?v{TQ&Qmk6NRw+*vb9^w|Gv?!gv~~UZjkL_71$IaX
z<}X-;EDBjgA%=I2Nu`|vl<MDr5;!@CdsE|Wbs#aWY13|gQ9Dx{@bqxUu<QD(Urksd
z?z1l>j&hskO(DP9qSka9F24&U0dA`UZ%xa5c=D^t!rJuO=n+FP`SFKLFBy;JJzlbE
zo$=go(6LQRbtrrjUROur-u#YX)eG*$a$bJcsH%I`(#y4)w5_td+V!e9V1rVfJ-vQ7
z(PcQ5*NjA~YsVb8)H#OC<L!)BEe~XdD5X+(G3S1rL(LT}EX%pjbMesAi*b!(V$Kn_
z=A57(V67G-ZLDyc7sa>Xo|M#o4Y-zhDJdlbf3Mlj`Q0%7vtiO;A+9G3#A$?Jsz2Ja
zXoTL~+M;`>mW)eX7tT~DS+Q;h-Y=Z3lG^cEwrSPCkj?m4>N<HUUZoYQXo(>`xL+59
z5%{%60w!<XaS#WOMyqx>*gFwS9$;pHENND(+eug6zm&DrhG4WiK7MOFBIv4HNa~eX
z++yj>Gj&aX>6>rt&AC(mz6hYcNMcx%WWEgW^ThRR+qON2k(iREmQR6n4S(m&gXlo2
z7)w{^)5@6Dr&~J(I<MVK5R&{7P`p!{W@y$pOqD19NE_nLCgR!GzhGDcdB;oXg~ADM
zOnwM0nuIGi=5J5^iE*?ysxq9Y>@@MQI}zybV=UupKPaZ`peQYkN+^Y}C2vegxeFgK
zfS!Z=VMhf$Ct53ebn0Z9q(;YLz3oE9^LpdPA^yGhphg?cOIZYv0~r#{lEm6N?z`{c
z+PE>?+qUK7E&m=Y(_YBAj>ZQ81g;a8EP*X!cjxo{C}GTQ=|32r%}8upetf(-l~to7
z0q^?HJ`2Yn>)(tqpLU>eK;wYM0gVF}!~qSsz94p8d5Og}cL^7t0>YKh?VqiUJ%P5$
zg`HPk549F+9H>1G>^+}_jc2k@Tqg9iR<f<V1A<*_@l3B!xH;C|2K>5~{*a%6derm8
zsu*#7C0l934%Kj%e|cS8nXiaWL;nbGH1Vwn{+XoK4r`eMxpAk}GjOCOiEMbSkyu`*
z#l@oFWDL&Gf<8Mx3xOW3I2KpjSjjbiS3g82pTeos=t}}z%ey3-cxkR!f0>eO4jn9j
z5qb3_y@+%4?_qfkNiTx-e?oe2z|JbLKxm!qsc2oZcjHDp{L}>O+JBf;OB#C$W%L_p
zc@CECJaPOOo*XwG8)>;WgK@__H0vAlM@FI0p2&E-_2W9c_F&~!73P_&pXD-sob@lk
zdV15V1r#(r_1Rze>W}RNhdHi_b+nKaDBp|vTUI))khzdix05Ld{ch<VEZ7){*T?k6
zxZAo_cn9o6xro-$aO96KwOFiFMJWVlAA5fh=Bz&=ABw)NviiyaSwmac4qCPfrLhmc
ztv!sz+oJH?9X&8{bWb=-ot2OPU9Zhpg|}zX)v~0aDTs~$W-D54i&eF^yAy#%0_{ne
zID96VmL=IZpP5ULdmUEpj>gQFM<SvUYjd5nTl!p70z?1+KmbWZK~&7|H`K?4bCv$P
zw`_`h<Kj^vmDb$}v?Le7=xQnj44g0%KfinnhV|5gHNwMoVSY%?%2hnHEJucuq^x`-
zX5>;Rs6)7~D*;)tptVfN`uLs%`?yuSafMwe%}+q#fybcBjpW!~v7_?Z$zu>6P_{)N
zlFv8xTk2!j6VR=g{@>`n0OPhNK8I~_3OyxM-hzUnQrHow&fhl<rA66#(7KBBB45uT
zIB$FE8`uV151n@xW8nghEv1$7i4)NktSu#xjNvjj$apS40jnkL$&=CKV-=MeYq<0W
z#kAf#LaSM^#+Ei#M)Hlz{WWXXaaglcN@Laa!N%Sf#a_Lck6=+**)qZu={UbRSXa)e
ztbXnKwao!fcQ+h}j51@w|9w6kBd#AvKr77bx}3Z|^-G*Mb=vH@SX=k(WNtxemOwkX
z`Z*wDvOD9~x_QEep^Xe7iDY;Zx6@9(aVSU&Kwcufwb)i1qh%Z`&86Z`dOma>=786w
zWS$-8k&b>om=|*vnYLW3slAu=@mjx%tJs=?f_xlE%Rm;DklfUMsA8H+c}3>KI>COp
zzJFINUa^%`>GzLLTwCT-bIdSDh_{2+Cq0OJZyQVn4)gVuSUev%a*}zuAR!blOYGLo
z8&+txct4o`*9N4^I*%kVq(_guu0lG)1!bN6hu%6{+8N;MiCYLZ8%WxAIz9oD-uP0*
zmzL{JP7ZkCse5UaUG7aw`tkAfIf#u<)GwI0;P>@*$DlrX;8V&u!+YCybKl2fJMR1X
zGry0whf)fYas`ZT*QzlAZ7=Q*k74^BuHTtf!ubpx)Ps4CFa|gkO<A^p;=|}B+;kr-
zajnJDIx3nVB;OMO?Qb2@2e%LJ&w0BL|M$sEeERJ?<GtH%>WjOtA!};^h?C~eM<K(e
zWbWDp@4kJ7?d8Ph@5t~dKK=V6cfkU9-*AJ~7}4^ch*h;Isc%mf%VnhFp1$7X$;ge4
zM%tV?DC8P2fq#SVxd$!;#0^wRS>3tWvyrlLC0hLOgW+<u<x4^t1q5ujBvE8Eki@BO
z{OPBN96J^^J9k3IumK4R7s9P|Yn4Pm@`7{ckh*4#I=81=^tvy-grK|bG8CcQm&?A>
zK+=CT0-0QsdvaYi4CN{HISgKy4EeRc8V58E{4)+Dr{_@)q=TC%jMp+1(h4mlBM*U;
zyIc(AQRX9MxQn4hEmB-kHZm#aX<E;_7E8VSMO7TJFMk`f8ZQwC1hlrL(?TC7e+0PJ
zLqnfd{970E4t<HV!X$#&<%@(%)C8?qjRRK<2l6RtolWcIVp%+Lvtx~HD0p;tu*IZ4
z^$>E2H{Z?uToLSMNB7}WKC$NFOf`^}TU17weJ}wp_o`2?u$D_+YrW8})d~l!zanNu
zt0d+Bl;5+G*X+qzxnG|7;*Z2-S|H<vM#r2|fOK_nb9L0p4`6J_n3!0`?5zDbyPn+S
zd3bm#SCS5NwO(C&r0089tj3OAd*SR>ZuKL;Mll5+Itp3DGFCQ|;xbOa4*}Q3+HKB{
zKQQ)LUA+6|i=6w+xP9>qy;^cou=CN}ZMdy}3$zFiH0#yrgmh)SP$kbeUIzHM>o?wJ
zxodv%&QI&`)$iN%R?+f&1lXgn$eu2&gJ@MP0yJA%I~NrcBauS2bONNx4YmNvOp`(Y
zd;Dk1vFAhrzM44nqOI$b(y}r9g;{h}ep;2QdcgNdEVXT&Xf?^Uh#<_C;A|1wmz<r4
zRB4N}Spad~%VFDJ`FaI1xdE9%%jL@GmFX+-_RkxbkKY!%d)MHWczk##v~3s!5lR|~
z{5^d>U0HxnnX?7w(sHo*;A!0a;;&fs(Y^4eEAdK+e0lcPE_h&Q2feVwT0)ke&9)vq
zaUN6o9{jmAifu2%gYPfK_J}})6C8JUb;9zgV~j#l%VMPaFHK*KPk!GDdp8|sy?6_n
zR$>7!LP&=)cd(Y_Tq)<$hk~dNV4q(RwC3aFIK-YXrggEc8$sS2*yf%kkk33~oou7G
z2L8|VwJ1f2!ky`(n%z>bvU=q$EUBrN!S^&tN&JXoE3066?kW#ElgIROY#{lBo$RPI
zdv>82aDDLLVa9_#Myum;i(|<n<F&X87XiF8t(i(#KqlQXOPK-`o0e^;<KOx#&tE`m
zA%k+4zAdE+T$lR>Qtt}CCmS2uOOvrO&l!Mu5mp*3$mQ6Uabj)U8G5AsuZHZ}UyTD7
z%>jw&{n3mcjX<LmltDM@U54F(v5a@!w_9h%xpt@P+j#u^$71}shVzjHJxA+od0#cU
zwmBdmkBx62?2=BK_=+eS{|Q#R)<2A*Y!4LWa4l%72UkrwrlYoN(YXxR*Yz~Ld`XXO
z2tfMgpGRBUB&@UX;aqG1s>?9VIjky@{X_=S9i*jYSHFu31yfb|HM!@(_|oF8`sTIU
zl>6&CLB3ecSkY6zp2O?}a=FhL)TbjJyYFVMGq@(UAW<|pc7khKMWrfsbmNG67OB86
z2-%n`Zns&cvPy2-1fAP8tB_7E$Xs^v)LE=rzl+Ifoy|SmjRU(8WG`317cf|?lw-~$
zFii>DX@?;LyW^32h8xRNF2FbJ!uP-asmhTXb~F%Z)4T!dhUgWwB+ZV!N0F96*XV4U
zSYcn+uQTUTTXbsIQ~}%@w;p5&gcb_SpU(KyIb8cneR8c~L>+wo9YJS4Y?)^T6d%~T
z+!|QQU&(&_x?q)RgQN)w^uhRtZpEGh$MEsA-|_pBwPk&v6*_r6<(0=({VepVCb2S3
z-LwfYFT8*Zg10gg?4#wgf46S%z4u-OjT&X_zqkwc9X1Rn=q}v<rkjk{<)D;`AIVFX
z;{1<4A~z`sI=(-KPfylBsVi3CAj@)U*boigd=qxGTyFa9w}>H7eSX?Blpa5h-TnF@
z=>Geab#(#D$lT6QW_;h9&zJ#+fB+;AFi)iQb*@;2v)zU-L1NV>%$<wes3<tojkimi
zHc0v759G6qQpyhu=?a-A?uQ?cMPS~!Su=#c@(SFUwgvN^ruBEiufHO0$`lkbpI46_
z$Ul1)=U89KnKN+h-5d2LO@iN$A*!z#v=EPFo+5hK$&ZayeU*AjSYO80t*Wo?9Xg;6
zEz8gU^b=CavozK#Bb21Gku~P2Q#d?k44m1wpobq;F~oUGqyIPuR_{8EZ~i*U^}HAl
z+Telfn_^(cx@N6D8k>sue%+(?K~zbF8|4e{jO_-WGTCxMD#H@Y*g`Rg%%v=cU*4@f
z!*FY#hGyl+Ng9>LUtrpd94d!#u4)<PjTi3jXqN6!bTXE0=lgUjjb)ney(FQup$-k8
z{_mk~_;lVrdVmw0NG8bYh~A>;4eHJNx*#;LTrO8#F3bBL&Dn!H`!_-~4ahbhh{w~T
z+Hu`!7$GcE*X%szYzF@Cmz{W<ee(Az_w4iD?A;6*=Zm`sl`Fy2ti<Y5#U*87($||Y
z=e6rq;x8BMNBhPB=-Rw$4-D(~$D?=a5G>jpgXs7SjK8Hd_8d<{kCwp|DivV-AfIRN
zHgzi8vvxt+H2Uoo7}}*Cf$MUGF>7i6>(?k{)97mDfar9M@BA*B__c?dg9iry9ff9j
zSa@UxyDS5twh_HrpS{+v3+KS<Q|X9I<D}0;r8N@wMRuG-pXwHhW<Cx$fBw9R!E4Pb
z)LQ-vz2-`7ur72LzVIXkx2A5TEeaBs%DWRvvFS_(decf<qszkq=a5jfi7j9pXlE~P
z3e_tv@bc19=q@T44iVu9$tb3$Vpk+&CL+wYKCSO$ds0p!uush5X2{v=l2}_yO~G_s
zwQch{tXsbkFTeg4qK-wC3Ej)vVR{r&Hw;@guEqKdoACOZ?;w(3gI-8)n(E?@pLyn4
z+;;12j1RgCQ$G5b7HmhWEnfsQc6Q~Up74DTH;SW%4TYz-CtdL7<LJ>N`25SS8UHO?
z4~A0+A#UBi`sz#Eci%l|-mw0~H~MPKN&aLKfPI<PzjNQZ(`>UakM5ygB*1FtES_2_
zzT?7<QVM<kcw-bgG~qXK5p*&xMFFM)Kr7UYN<_YlKR5o)?ifmoU$H6{x7ea=Ct#O=
z>Ff8#U^*?Ww;ws92aIKd!*`*C8!aoJVvSlQ?ghtvu*4j+CZH=ZrXLv97DL#UMs<7?
zh%18p0t#EsCS_pTp)=g1?!{`xsOHN_*UQCdN>|}`U#Hi~w^sR*O_6x#*DcBl+>K>T
zdu9mky1o)un|wZXgS;@|uAUgwtr2d2;}0B;K92)*Ek60n<@oM}5qiE^tBA~ySdVT5
zo~t6+ZU#~LpkMn4jD2qbzF)SV@pv=w#rz%kVBB@;u~g-nT*{VughWW{Lhw<*?yHqb
z>GHee+#krb0Cyc}eP80%8V=!4!q$r$L_1nO73ZKh;}D8ZO`|*RwaUHre~B+jDaFnm
zrNOrV?k(BgER>vI4*T>iR9Z1_DL+m1E(nfQ;xh=getkGzIG{LX8|=<6raRVrB{(tU
z;wMF<Y>x~Q%xI^qr?*nrD4?_{37G3jZx|BGx?zI`aCUK40B{-wkh}Nn!@hm{|1;Ls
zrtOw-ph@FK2n!42=vazVr(!w(@ZF$L)^trE$MTbYhJ*y8DTT2D&L<@$W8eM*%5}O3
zZ#Qh*q}-FrP%EAbx^(WO_+nb8cC2xrrW|M#ULWr~HvyC1`N%9w8e@XLKka+<GrM7N
zGGg!m6_eZIvUYkca)4u<ZJh|{qIQcWkvRuA_lwJLj|O{@7TJZagdI3fh@7dMbUZCM
zkpkyVb;~ba(j$(!>8D}8e+IhcJ7b;gK-MN}6qC6}zhtSl75P-0+LD<3uL0N6K(Q{q
zbHpGukHnlghk28ysyRnM)*ydxy!qV2RzYjYEAwr1Y#fhh(84*WP7n=RXqjwE0^Y7;
zeCMcWrX$GgK=61pEsE_p4_PBwi~q579Wt`@3S6SlX=<G<<w?BgIg3`aJSt?dogsmK
zbiMtc5%85c@x-Y(Jp0-g3UD^mV@D%_o?Y7*P6^i2CHeM)Jl6xy{<Q4w+M&6+@530_
zKYjiZ4j+p~OUCU!aQLLM8kasevJa2me+#m6^00o(Uezxhm0i1cYNf2$4HaZCcJ=$8
z&tg6LcUIaZam7d6_aahK(;-%Hvayi;=S|mj!{`4u0isNBEs$78yENh&=fxKpu13E{
z6A;>#&qUmTo02HE_zoQkC%V#hYiF^a;rQV~FgQT0k(ti+`R9#U<%^UuZ3r;y7!pS0
zZ9?L%yTR+PBZOeJ0NZH<e6#lMMNVX-0<Y^l`Y62m^ik<W!Njda3xHs{QnGpOIrYv-
zIa0p3H@A^?Q(;~}ZU+w!1$qnEF7Kv+#N(n3`)23s3mbkbys5zMhP1p6eey}9u3Ck(
zZQGDQOYc(YF9CY*sZ-%aAm7#y_JZwq4h==%op&lL>bm2{8?C9;=p*AN>#?UyMn{DY
z_by%F7#N5Eg44Dv(>O}8Ph#mV*Mz4UT^SrW5}ShA8)Es=QJ&2GQTG;sX25j}6}WDB
z^)DQWqaqH+6&b63c(xzIjftE^PjlCwz;~;n<;B%s;Qs08sU!^T9?o&id`!-zV*M|x
zB9S5DYlesFnV4Y)Tu+(33vbWfgN$66x8)6#U->)F6O}LA&k=7u*bOW9oWWmvsBC1Y
zTP2V@rew&^zr_WZa7&wi1YEBtSRE1MjxQG<!kZ6tQF5r?*B+<5Ckyux_!kiSU~~!&
z$E4uS0gbSDb2KfR^D%a43!FTg&ht}fOr^M+dqx-=xqL?~{@8F5{o9A)j{c2tA}$>Z
zHbrB9bdpNDfpvB#pd6Q+t;$Nx%){2hiMX+AT{NR*@}`655Y4ik>9Ow40gWM++CTk$
z6y9zQ>U`<eQ;0pEiTiG7rk>sRe;&s1_;h4bY05xr_r(!3ccDT;8qOqUVdv3AwxyxE
z_RGqnw0F><Nj=JUzS@k*x3@uqU{6E@dtery=U0o5;FEFPm7-QSuZhL9JnIbvxu-8Z
zg0G(HjRql}Y*Qqf)%V7YUFxf9CCyA;lYQs|H#WzTEis_Y2X5}w(9o<a_Ae!D8eOd%
zkcqf~PYd|E1RA@TM^}FuX&lfvV9tTGydunxO6K}OHZ7KuGcWO5vgJnVzP5qr5$c8`
z)Y=q95CN?(6>u#r>=x>YMaNUPDCfdJ7B`g<+b6NPJCw!8t}K~$z4kcZ)v+@?TDC@7
z)G;`?xH4`VH+1rh_$*{Wn3Ix>oWuk~+{R5BL7b+J&Cn~NFIGh@BPiz!cPBR{*FRGk
zt26R4=_33ThBUt!^?kx?uc0O-Nj<)P{<!6q5%_w>cQ{%NP%P;K0)sGe<gNJTyB~4*
z$Pw;0Yj4@$;NXacjhm?7tl#F~;K4&RDqr{}KUtLd1_WW&58uMu!ZlvA1`!zZ$P+4^
z^uvbVYj$Qj{#?GA!1etmT+t2{2Wa6tf8#MMSbrF|^yTKij2;@+5#KG}hXag*ZE(+R
zxM%!J0hYKyzF7Ku$8lOv=h4EM3JH9!-gJ?)=>P6S_f{1*>f!~UQ9VEPyZidK_;k)D
zy!72V6yz1)v0FOfvk60uLa_^N&(DAF!<@B;$}O#FQ9Yo2IHo;23=IfaTaq)|5>dxj
z{q7jl8s9D2fyX~xj=J@Hu;}eO&?wZ`GK)ITWm{kSZjBz`qf4j{p1Qtr(AuJemb8eU
z_xf!Z@V_4rPnYI1R~*3oH?~8sc9oAA7xHApg5|Xb`eDJAD99#mIiJm&`Te+YD_0h=
zXp-^Nv>t;iQRB_4?4USd860eLkYmzm0j=TL365>Qqf2L#XKzQh>Hd8wkn4)VLr=pg
zVF3l9CK>-l-pJCzR@&G43+Vhsu&*aw-7zTK{}>#Tms6l@-c|={*Q4H}aA@)}6Z4p)
z3%whKr`o{g*sBEQ^}=7dE&}tC+ypojW|Ak~ID7USk`j~5KxhGfU0hr+>b8*z)RlY&
za;6|~2;EP5dwXHcn)S?QfutJ}{tJJl-F^G?MvordjJ0>{(4LB38?j--CbK?D0+~A_
zsKj>5h~Z}I#s-|L=FOXrT*m#DIZ${ibC!g75>Q+KeT_5@Tr3ABJ@z1D&N^e_Tkoql
ztu-k}lv$p7=w7_{{L|`PtVtd%%~j3;uA%M1o1ti}e$1EN8jfTuBV(4mM>4_d-N-mj
z4=+VrGnjza0%Yb=;OcQlYZ>7#Ewi|nFFv~$rH6lmt}q!5OPmnql!oY1Z(1{o@V+9+
zELT~p#A5RpJr%^#BC{Uns(+Jma`W)~o73nefopOyUX*etJ#iOZ|A$uEtSo}dXV0go
z^dh?ms~5s;sktlCd@AkFp1)FYL1Yf$jB9;;|1Oo5nNI8Hd18f3Ll^;cyLW7fb}btl
z(+gPq;g_?GV6Y?WdvfeZgx4##AWKQh#8WSQT4s@5zRnN}zAo*X8_O!BGU$OL$9XQ6
z;5t>0X>4gvg2>%Ew4}0f4qkb0267mqTCA=6_iTqg1i&}%IHX*bOMdadb$$QNCS@0?
znBF`0NAh<0+C|F9$)$%GAu7$fxjEqpg5Z<JkFLNweWI)LUjeS2!@|_BCOxz#Anet%
zXN9CHc6H;%aN+NQh~tWLcsQI1>W1)F8L9FKunWju6LN3WswT;dX#_lWY1&l%#7ep<
zlD0T--5OveCbzYG9<;oM{4}~MIH1Cl5zJM<FNZaCUe>duv|QZr#&R4vozDAI*5G*j
z)sww2W^hwu-}8ww&REL5Kyg0P80M=&>^)hCRkY^5v1^zytsKbQ>qKLx49*SIx-bK_
z7ZIqP{0+U?iNT_bZ?>|Rm$9~dyyXdr%4f16Bq?QPNoyPrnbz12NlL{M`6-gnPWnAd
z*@cVxBLMFBFE(LV_xkA4EC^pNJb)EDW6?a!2Ty&m0gnx9i96p~hS%@wis?%Z;Sd3A
zPZ~IUI)6Xfg!@p@$_B6gunl$St+Z3qK+2>_AYk~L_jkcx+hPf5H^9Rmt-;_<^^i;c
zimUJsf7=6(`#R&N6_IFM$BPR0RIcV8^NCN_;hSgr<Dm~$;h`H_P%adORuR7VfL7rW
z!Y$C-53^Pt!%|wXHwpE|`z$BO+Xbr$_;+d=pz1a-LHZ@-{<!=oBB{_2;O&NYX6-^}
zw(%!k8&AOhEZumgX5{cWf(8($k(ynIx&f|g4_QR_W*_{v6XQm-!O@sxoMzhkfg(RH
z;d;IZ6Udt;b-nTR;)C#{?5sztV7xhV2a+gLv)9?+t)F+IUH}#7xmL(E0n`ocyVCyF
zo&@?z>-oRf-la<xQ#f%@4Ggk)zk?cCp_f3Ds+1azMP}ME(rL?q>R)7pR<*`~{|E;b
zpG-j#*BGTm<yumvO|!Lk#E=LN+}hliaTa4xM4+aK3kngoUP@}{<Aew=C!C}jT~%?n
zOW5wy**YwvHv6qj{Vrvbw6e=Mprb&&(WIAgVB&Zr96kt-I-!ubOoB$`WuzgKuA_W=
z^+D+9F)D{xi$2@`MVw4Nj%~3!(ZDwh9!?(WUV3giP9&W~i=Z}4BVfR#r|RNtTDIUq
z(_7}R?!2bumvUt%fPwtPg?>O?YM)vNV5F{L+%N{e{08Fa8NuL!Tr|4lVnq0%kyQ?G
zk^AzuYtX+#1iak!Zk5E!cFMy8G4#1v45J{q^cx$q{Cp;|IIk{tv{@dLlcVveEVgWI
zLIG8$mi4TH)+Slhq15;6-F@KW>4x3M;_>NILoOP$&dx31rete5IL~&pZxW2(X-V$w
zQT;eC8^;HScScZv7s7(QD+R5k;VXBZz^<rsik1BdjE|*z@C%`)1nytFhw;3pE}_d#
z8*cD+R0&*PNZFFMUXV9BHVnjy-7z?RHkC29vk+d#`$CJks?(RG?=h9hs|_dZdtl%2
zMRU;FgbOx0C)hWB3HiwzDIj22l5!cTNvg|xD4YEf-~MKxwX{ImstX&3{KQr4o5a!7
zw@bpy7dwQ&G5iThrN3DAuRX!*oY<MLFO8z5wmyT(M{Da+S{_L1GjVZhJT@KgWi0RB
zy=w)~+O+VlT{<H&G79l$<7w4x5agJqxV*<QrwEwdxwE-DK50egP93p#?|x)39-=5$
z3CZ{%)T4VhGtgSDg@=csLBj}IYK!}HgLQ=nj~I@CbCyPW4jjqI#<b&c7sL(^TBdl@
zcYhOi7j&hSo=*)Fa4x8VD<tu_F?V6a;DPw~+n+G^&%Y6U+BzR2>V;zHfIfKq-aFBx
zLHHHYJFS_Qg#+Bf*oHPhzN;5>nN&XF5HF*=jBILjQzy1?^gRbpo1Mrw(T;L=XUex3
zzK3awxlb*POh!p+9z#dy*O!;1-2@DZk2CIa)M73x3z0_wue&Z6T}!#1fp4Y2wSI)I
ztVP_{vH$DOC*bbzu*wo$^6O&dJc}`<7yZ47%+~|PeY>^AnA>ltv~E%I6_7bCJyWI8
zQ3=0EgSuwvWu7`36<cPNTnq<CCycy_7JwEC!G=r*YwD<zXLv~;`eig@Ya3j<%KZ)7
z_F>B|f-h{REfpf0Hx5(rw+&?o0RQTH-(uaC{Y+Cnr^{R^R@4o`>ZqjB{#3U8%vplS
zJhr34XvcQumcEiu;&^}c;{vSTc0i@`_Hf1X{~N<BrP#Ue2)E|)giF~cg4`X;K}$oN
zZdDooQ6Lcal({TVa`^gqqHp*1827*kGcetdUi+(YK;wYMftqkYlzJbTx(3^5D5XGc
zT4PTh)f$fsD<6-f9B~`H@1&&BkBb5%i!!*l^ZtI>kqW?dRdr8WLQfT(y96ZHaj!LD
zWGl35qz}Phny-}Oqo*r98d47Q?CtGv2Q7>R$CmLhkzo5XU#-_GA=%pDMtV)TXHZks
zCYk*uU0s5y|Af+W@^KRZd2yjGR?M3Yo~0}Arnt3t19W-(cO<0c(FM9M9-wve=(qod
z$ZTAksD{RkbT)zLv&lIs1lcL(72sOjZErbrj*8bMI7q-(+>gt6KIxA3I8Qh1_T0k_
zr**biD~rYT(<9r`YW*0M%8O|+&Cmsz`3Mbg$Md5*;Mkc|96Oz+ti3mWI+FF+;=w7a
z=xrpQ?%DI<PH%xLs8BF|L~C>;aJ`w9+Lk19<KRy9(Y(G7f$rTH+_@e$9*oC^17|Vz
z#%8F`x^C;!h+gjYQ30Wdef2|gT4MKYR|lWb-MBM>cCq}n=O$eE(2{_Dw-&*8=)=|U
z_jIC1PaV?fGG1uizIe=j`8wvY!=oRs$4I)DU%&q>S~a-x@qudsxQ>sH!;6zATf9T^
z`d$pl@>@?uT<A@D?s>GjK&`C>^XFsXtXXiR8$}xmcLod`cu~N0Nht;Q>Sx}U5-tGc
z$H-}HXhq3b5$_cg7jmG{_k}|a2QzsU7@LhicX<!ha8!1$tkWicMBAfS-2a)7EnQhy
zi=8d)ltHnkBnwT0pr^7t`E;MMV)-&8a3j&9S5Gu)+N`pq|B34v>FJ1xK8gJY4#1uo
zR=1BH{hw&pwcdyry8c)elSW|J{9QKiMd#pzjsZ@1q+<xqojZ?Ix~>%0S#EBw6$Frm
zJhk=Lft$VVp&p2iOygK#9aJ~eRmw%lLRx3{psSQ1E<QB6^c?VL*$QnxpMj{U?;&H~
z9<D?AjJU|NqkF6R4?co0{&cS8?!-boL-F;fAMwe$DOh)66;7qdre6#^T`;`WC_L5Y
zSz2RX`3s9n$@$@rjzyP|x2X9+<_SkPPcG)nEeE82JI-%@j*Jb=V^gO}MW$M#m$IQ(
zV%Z$m4o4^8V*<FZJ=o9KgTY-J;nu#*FlWtS<=WbK!*IY2z^gy4r~4#l<O)#3ZvcEi
zgTLTHNZf)O7am-c?>&`>EsWvZt);net+Fy7yr}~o#6QzkU%w|tx!>lFL{($YO~wb0
z45|@mZQ8CO-Kw`Hn6s9AR9jFRj3a1m5cZjtQ{C}hgIZzAZ<}$7v9DJVU_MW`-hsaE
z)n%*keIGtc5fT=16IVd3UX4sRAnV?O^Ga}UxGI{>B`yu<hOpedrQx3GUmK?o*m!n;
zL+Wx0Mom}Srl~I9u?QSWJuF;0mQ`=-+JK52evAuxmcjx3MLSwT6*;q?4i!BRP;(19
z_omS06S%hMf?+)`V6KeY29oO{NN*4kZn$LiuYlW)8#m+{>>Tao%7xrk6JLgl(w<<3
z0kNkwk|jCP^`oz^53(5B&=k0qHKSa&MnQBLDZsTxm2)7Kal$twrd6JxnmaNp4G#9j
zV-4#d)WxZq)R*seCifo6?3*b$@cjx=;W<pN{<Gtdpa4vHaiaR2r`LnyF|jJ%wZz2r
z_Vl17WGG|5n!6AFXEkcMuPP33thROafUa?C==Q8duF<Ws#gdQm9tWTE@N(RS^y95j
zlqs%%>GGE2zI|#w^7h8VwIAn}+9t}R#5H*FfqAe?IYPq?%EI{3E01NYt7CCAmfE*K
zR;fMZV`Z{2nTlj!r~wsF=CmUv71z^}(vM!WE;q?s5_uvPZ-4v~tJcq<KAx_4ZPJ6v
ziny}&$b6fYo<-|nTH`UFSk*PM0Imx-28r8mi3=?x%9^8w_gBZ({t>YE*9EJExm>@N
zDr@h7y~~xj<o?`6YmIV0M|vcCaP&}ytkElh=F#iW>mU4p?^ImtvOPoDx<Fi+htb+w
zz-u9Ce{xDXt*NurId7u{U4omE0OgDS-i#@q|H2F<47cHkiR11<+m;P!WtNXakumzb
zcJ^r6sJ=0Fwv>G?AqBhk9hHhyT<`wf+v43<#vw5|Rar{b3k^bOS=??>xZAL8KYnB1
zC7yU|#^KgT<ABBijRRG2K!9f%YaS(N{m155<yxC=8u8eWW^~QnwL;cuw4N=c;;Jb0
zH>%^Q+*$v)@;Jw+BK$@7*GEpJD%a2zGFrKCJ~f+HWsKD!)ZZ2FKibnuD)UQy+SW0<
z5KmC}*=ZZtZZ)B*y6<2->e?h7=aT>3(u%RQ6Y=~vTWFCSfQ57i9(5)S;X(A`K=<4N
zDhqXB%;=qw=h3xU2s$?Lr)BgS+|rYC0(s2;70ugl0z<onVf@Xl@W#xYG#1b+i+{c7
zkOG$55X2oyp#0rgyD_9|eOh4`;rqXi5ZG^{6tBfwn7G^)>-QW2*%BjL+;zV(bGrhK
zThjvjJOTYK&4SS~+#7au?fx@ee{VZ-9$C5N>-l^dL5N4fLVDNB&1YHk<j1FhIJ!U|
z)S(_eq!o9!X0#AzyBBRfNkG1cVd#qCN-OQiGwIlN_#AxP9WiI!aav|)(fgm?GW;+B
z{+*EtXdU5CLlY+j)_141wNTgQfyxTpo)|({fQwQ%Fm+$95?!JHTob@`4uuD6mMt|t
zqEfy<`MJ4<Wov~<&+<Z5Mxvo%&jk^6R~QpA2V?Gkzzn$l@{^BoI`*_$7+Nbs+=m=x
zd=6)V))K>|lncITpM47dfIxn1)@#n^LaBfM{<vZ2O%;n|7O_Axko3*pIT)^tX)OzL
zdusETk_?`qZV_;`=jKD@vt^9FES%4Yr|`%g_1wbCfNE(A<4IC;^=!^*qfFUQmMow>
zoIK&{7|2V8cH1I3KannP)3}fo4waK(ASu6Mek23>#tj>=e(f5>9zTZUghV(I@CgbI
zL8E5P(W7^7v@}r=D4<KD$xlzf{9k8LMs7>@w?3Hm(=X`QxpKEg<~3g4v*X8)W9)rn
zkZ|S{lDP4?arg)gxW2rPz-nzWG5vll8M&fZA%Keo>1b&k;bD*eb*qbvv{b~!$1B&D
zI(}Ck?(Paav&d5Gr=3FFG3RI!N;m<@_gxuD9PEq|2h3&sj3+vTRF+J;UdtSCZP<w5
z^>h@TjYIbFNa$SL=&Gs-be`5N6oWlN@a|0?BO&`7jwT-FIBbii0SxKj>|e_*yE=>W
zWZWtFS)-(+B>ke5Q(|Fm7U2z<Ag!g;u2sqbTBLnEZ!1O(Xh9cEdaYamm)?4C0Npl4
zBUJ=jf_9k_&o>-6&7}aDO^whCr9R>koa37!t{oES0{i+&zu^ACt#Ri-Du*-*qBVC#
zfapT&sVe=do!k`4&Ou15zMI!UA6i4#Hi?_??T6zO4}*Q&s7)JoQB{aVe1GQs{?EO1
z36+DrjCVb_7J+Ngein(Yj*0|!)a2F3gafjmmDpt_V~Z@OOYJ-<8>oRm+n$1alj8pE
zLmT@_6dtHxX=~?%Qg+oqYTIB}xd#GUdurWR(YX?C-pz;wipPwagj~7Q9&XIqRf7}9
zO27*lFYPHHH-Po3D7nUXn+ES6mc=_VY=%acmILy+E<2lyjRe2nZ`%-^X@UJ;pr!Fi
zc>gGcnI;rY8NuA1txc}f?qQ(6ALOTz#(@js0Nv%<we5|fJ?oW>Nlk&)#(VCm?Ojvh
z-EteUPd7sD+4?l9wPz^IB4jROeA-$Ct|_d}PC?PJ6|jlf1YK#4UV&1UlpJ^U(=_8N
zmn5T2X*|{;91aG#m9@58V~8rG1X<j#t){WWsQNWis<c+r#a<KF)vvtw9TJl0VwP(V
zM|$`gd-pK3Y8Fvtks@Eqr(%qN(?Vp4S$t=?mPVJ|3;rT7K{=#7LFPeyIwPXKbvc6G
zI{P#>Y~9QBw9dA7#%;s<!QCXJMJ8Q#Z`-Z6ww8G?B+wVbuG0f1;zIo8cW2=1pBAb5
zl$&wJt3DVR!+6peN|Cc}oj}f&`aOZPU;3U$3uqw`#)pQKTMP=Ayk^sG{EsfjbCrv5
zd)zpn8y>xPxJoZ_p3{s8&dQXMT;qs(st&m+eK~bH0V!!2>b}g|y$M`5YgAADB#nW@
z+FjyxPkjAL6fk^B;|8JE25>FwUH(caca@h%<k*&Eke`WLaclP_y?8t;<+C!}m**<w
zN*rm4Nv)B_fvc4RGUmSW{Wg5RBFYF_kM0+NkH_^ij&p{-pJ0sSzjwtl9c3*9w@377
zfQN=O$FGd{EJI*6f4{CdhS%?_8Q$UV$^DuQz1q>ZNdV~A=uW#ws}MtVGAz>4+5rOO
ziu-J9l*f77l!`8ayRb*tM_*586^puvGMHgK!j&cS>BMY&I=(lnFU1pd7cTVVEv*&E
ze4JL*Jz56g{YScE<?b_h=C-zI-%u}0ZBF;uqxv=^*zAePceKO#lpGA~P*=scetcL9
zbR_sKVDu|@cR*X_iC{Xhu9i5~uix8Ifz;33+D2KR-_ID^jYGZg$rHVmoAT7m0^Co_
z??4|{9EnY5eqTIyS4VtK%V<Z+OeWplR<%?9d4Mk9o6ySIm+{Np7!J?JgNAU7JMBd<
z`q+*Px(aWN+xj)+{OO=9y8E;Vp$Mv!R`LG0k9j37_e*zo!Z&{&LB9bZ2=;MN!2SJ$
zo8c5=rGGZQ7p?G*B83*>%EdeDeD&VW_>LgFfaQ<g)Pi7n5gxjsxv@=G^1(GSQ;6Sw
z{l_Sa6i;dv_a8Zm^Die~4kq>jAeTKw@riGZ3~Sr?xYO9YX@iP)EAcKYX~CS|uzTlj
z#uv~}>^e^3qCIGkrWY0D!(N0m6ky1tEepD(^XI9F+$>#Tfe}c{plb;?E+j31Yt<$K
z-?k<(gz`(Xab4r5a9x9us!uKs<^m}fM-NQw^)Z+1(ob<z4s4BEh0nG=4R`xcOzQO+
z0cmfQSMJ4U#^BYpW0cEreQJG=i;#o%fkW^>hZmVv&po;S)7~lg`@}3-yIBghO4wHe
zm-S^B{yk3%idUY04y*rOhJ0#9@|!2gKcb<_Kjf1;wZOY{>xri(O;!MQRAeNU&YQ~t
zpMo{=F#b#&X3w1Yp9QYP^5Qt-!%7T9c@t%!rqRF8fn5pN*q%rw7JiqOWNYU@t!lb=
z?j8!qqFidY#p=IVCM+gM?C!4D2DZ#s+voK-$%)JGqiKa)$8g0`1(g=&V_kd+`qs}u
zOFzqRu!@Y@t$)OUq7qxgmii+-$d?;pAgIU=LDiJ#knV;?*<9RkptVb<(3%F08r7Tw
z;wJpSfrD7McnJ~`k|A^RmhC(B${n&x;JlHQokaja8*6LMRi-2eozB?CFU?quxo_QW
zTnI~C=?S-W#Vg;eE{_3iS}<L*UMzKQ9Y&W;O|ekoOVc&EfY=!nw0$yn8@^b)3$0mq
zk5+Zjr)@YoH>*RJR0JI48P^iYT5N0NSqVg?FOk}=1KUSbP{1=(SIAA92x@B70=Coi
z9gIuNz`68nE^kWFIy``G&M$tP?$In1-!I-RvO^q+PQ>7@)mwWT`cT#1b4i)lb>h5w
z7b1eZ5aj#MT!2+o*A;nB-aQ*fvsX~8$!v<!_*}V`u&o+|ETy%!TH9U$omav|%XYG_
zm66)lRCbfsb!wb2`EpoR0oPvz78a-0ipZq3wiy^~EzxDXW|cQ(^;dQ8GB#1G$_fXv
zIKEHV9fg_QS|CC^OK7BVK;uBoI3UVQ;Vn=S90vQi6DTa33uHzx8P!!;lul9ciEcC-
z9E6j9JhI~&qA)oCHoFs%e>e$_&Htg|gw&l$&nMBFP<(PD9E#JC&$U^hv_#2~WS-V{
zOOi@#>s}?tu*tTbQO0H3X={Bj-Y+XBk5<c@Qj*a(FaQCSC@vZE)Z&56k3Y}-8_U*g
z<K9N^db=rs({T^nTBBOTBH1t}IdTp*^{gO(?3&Gc7(PG`?m99y^gVR@T^UKd=>>nS
zQ?6PijW1(%jvU(0m|W^fOi81W1_5?*T1Q!FD_SP|`gq_h_i7TKd(PrDsvmZgg%0Y~
z4lhr907FMlQe}v2*4xuub}2Vx?w7Ld***`(v=)+ntysSUk4*fK?!>u1;Qpm?cqra^
z=@FGq(umwQBP&M^`8wDjD6rh?iM$s%dAYJ()ZgcG_VMQa!h%v#Gx72CIrwD8?*s#i
z;pgp+fxSCfq`Ep!i?g$lvUV*_zx}q}k0;~28yUtf3lMF~uz7YgHfl6=D%@J?!6j2}
zl+pq}{`>E7_Uo^yRG!N)yY|G<is0n!jUdL04!-Ly)8yK*#(}Gb12(jho=$h&yQ30h
zIOGST!}aY$@by!Di3L@Zn|%A{VPuwh<8suM;P!rv7<$YLy=YClau4Mc1fYLdc9_=5
zZ4?Mv9i#5Qz8RM7ikAVDV^<Mg|9KbNvC}M@xCd_);fLqxM*B8`z5fOc@72Iq?U*u<
z^#KCBrgSTLAtW)w#|~{_yd{><-CCGSU2mcra09hu-&&TP3LtDsqpxdfJiXWCST5d9
zcj-dccL`H}<)2sXEg$zSt<J>_xh37uEnHoS2q~Xe>IUlPw^!~h_Y^4B;qo&j0ksYE
z<^!D#hw88Cy_fFkXm-zoAox?X95;}F;x}}yZ#cc8|5ij^ujuYn-gu+1gR2v7KYzUQ
z@h8T_U(n+DB!N(|T)yL;d(g3Cr!pwkm_!|j_TSpotFdYQT55-htD%b2qE&0iLd=d6
zxqzytPSHXsHC2JvB4}?;tKr0?M5L#sE1{??!kRR1fkejP%*djqAqO;BWVLM5mYOe)
zYLS-9MP=Rau!@!Oin#k08JdxJ?xb_3#dY}yV!X!#S+u#(8rzhdX+1$*I@x0Io~Kz}
zS36p6m(^ifjeym5)C3aCZd0<OwMnUEo0z~fuW9;py3Zc}@K|gnPsP2S2zU$Wj;WBm
zl4oYeO_Ci!`&_yRS~PzyhTMF!0<NXcg+&GAF<)Vx2p?rY{x6daCk6v2D&xn0x&2pL
z+j6OiG$&GAhj1mq_s5h(vA~rY6i;;tMX-|%4jzqW61v8xwx|<hKRHp;+1dH>xEd7W
z#;$cd5Sfu`{;iuf*mz8L|NPp1J(7r5d(@-H1?^2UvgQ&U?2k;x7r&guKK_;0NPccE
zI`r@Ak8yWJa0BjOR&(mPO#D548>07}MUiMRGK7LX<FqvF7>es2>I`?ka@XQ!8MPB5
z2X<`ThSy$uo$=VV6Py+hkRT$zS+Qs%LyhSwvu-mc&<?C~z{ws9HXg--b%$|F-}24D
zgi+lvk8ZB_#UzTxpY^;Kan+7v?Tm>-kw<HZ<y(&ftY2AR^RLY6yXn4sH)BG7_Qwvo
z4|GB6`hMt6H{t^ri@PJiTMxH?HWo{)q(pJ2Wf(8{w`F+cLe&W$FU9&j(R7zwwH0_N
z1zc?zEBc3*Mk;}tuqJ`Al`=$Sn)6P>5bItUYRbZp0It78(~o7vC5y#K?a;eFuczQK
zOO-pYLpWR+zeyt#4lpE+jZ-jebM_f8@=?Ps=LCw=_Cn`Vxh8x`_DLXpH`lA><CL+G
zY7VNJ&DFiC5>!+6rM@j|bZMTKmlyvn@4H+V;j?%p^7FIqNkiHuv7V1bk-uDnT@0D+
zlP;zJEzf_J13rx77GM!Bq=@fT2KUYhbjxRr^0;Pr<6ty?=xQz)vzAu7rg1>yKsg6&
zot<IdV<>FqeNLB_l-2N7<x>yu&|kN`X9|r*cF=7&*F33V$Xx6JhsMJwOS`~zB=_N^
zSqUgPcMK(Gb_0pWV2@nJ&!$^XnO9_>7tmTYNamTcmhq1O=}l>I-NYsdM@j>DW<Hmc
zr7Z8L)uR$*ve+eYad;piTwkBnbhUR;=E$h%GkEvYpINAW?sTHO?71iJftP2^lnM<p
zQ2FcP;>>+%c|LLLEv~PHR{}D3Y}*7~XaQXrrDbGc!Jiv=TW_5`WMDU=>up0Gku@6X
zETJ;Nk;rIFoi+!v|5%|cs|9@5QI6WPa|=xSV3M+?{(tta0zj&&ZNIa#-OVN}xpYej
z(xqS^qGAztd?pqKcA@wgzuoy6D1rgf(n!OS3%l&H-QD{?&zZTqcV=fc5EW%!n7wuC
zo_p^(?|Gj$c%LN7T8fHGwW3rxj7#l>_l}+}OX2lt*S;f&Z|Vjxf2ln4%4aI%0qcu~
zB{s|BRwJHw*Ub-_B{#1#Oiy?#S{gbltMn@H7d!SGl8et9u4J*9+4+dUy;I)(bg}H-
z58e&^?}2(Bxo2jRmhM{p|Maa9pmYGdUV3%vB&CUovUloK5hxMXQL9$c3n4Jwv2~5m
zS#s7Tzz*K4Fy!;@-CI%sUKf1!nS|VWtAx#(CGL@t`jn@9zpAiMjCgm>WUKC)vNEM?
z&1cyd(JH<yIa%EBOsHJopi|QW2a<9X$^x?|HD?Tr4TYSr;kr5xR<ob4IEbMYk`Le?
zL*VkcPe+q1;+k<?Ou#iX)sJVD!L9T$nE_C`DPnx%d2lQJ%op3C*kw+wpX}5ctV(tq
z$&$;S`W51YS_PIjRj%W@qX+(u9sufhW)T5bey>gdp<MwCFZ<~yd2Rj+U{Iiqh=pw2
z&h6sk?F+^U3>26hE%snJ1S{jun<TyZ_Ay<v(``?`v;fx01c4!&0w{aQ<ntsqCr4_)
z4EYBJ%ZFcoD~~?(u>7>}YjFqgMYkzWzPJFu?OJ)^iO0m-#~VKUD`m{YbLHmS?@$8>
znK-t!c?8xRgI!C==%k;VOpK$JpCGg?(vNx8*BiTLo%YL5Kaq8-Rw_oCHC2Pb9W?A5
zp#ne!d}8d|zfbn<+9|o&+0q*d1!KogP(^g;6f5UWnJV9a`7uICfXWD$L+Rl3rk3=~
z>3t{vhSse}%9mp$u<EzAVy4mwG=8t|17<wfP4*$~U=`v^Q{$Pj;26`;8!?QvMcH4`
z7X!k5<bQAhUV?FpAYEfr1FP%(`6BC*3uI(#+X^g=<#$|beh+Nfog?==zZdaCoFpbH
z1j;w2u{0MJmESH)m7~eUvf%!n;^$*JpN)7WU(EYe^3#eXG%8R$%yAY=3SrUzTcTtp
z=E>!cjTOJpdZ9w|TXSZlT(kUFnR>+x$;!?#E0B07FoS>w1rp9!Ae#tE9GJIl&tPXp
zU;UB0tc5`5!FN}}m37-Xt6*<fLqB!HXu0^I|A|rvK7~_S2&^_1J$r?``TZ6E)LY;l
zDVK6I0OWX{6mFUYoH+oQSHms&>Ya)5^cNct|0zJGkM1GYU(jD7f-M#4ntT7n0L+Wc
zZtzqdyhp)}SVAYJ=E<I;0PoG;yWM(Vnc}4ur+f=domEU1+~9NgV*36;<)>Bwo)~i=
za6>$$EVx&<M@FSI$E$X}2ArlVuwy53<i)Qy$@{<TM1MP>eXerrMV4B=_Ubs}&Lb|g
zA@`TM+vbwNNh+k`z&vs5`++#Qht*xvc4{m0rSjlBG1lY>94WPkxOS<jEW|nm_GA`3
zyQ!Gks(hvEVH>7vQB{9x;)w7tB@otELS@~}TeeChgk{muQTj`2pX;&Tep@f;aM5})
zz%~1h!g*UnYcqx(a?n4w2PQ=X%k8nPZQcOmVlRX%*tg?TgkZLw_8{)-Zn((p>EF=0
zPoK`Q@8|(X59oRT1_RDLhe+v$AI0qi+?PUe4&t&#2;UvNQZbE-PY(Q&W=m~uoRogF
zL7dxm7GrRX7y?>BVG-fC0LTJiLRnbrR+l481pv&Fe5o!>6GMKARA(lNp*US!4du#v
zT{-3s1#z-)3f>ftqiYPRu5TMaM|eaTReMklh#A=Akf2M>5}KlNfIwSN?+_SNboTx8
z#}2y4e)+AhB?BNTHKU!u6Q4VJkW3zTc2iBQyG|=*$}@Sp{%Es@he@W$x%c3aWLdvu
zH_E2lZ+E$3>Nv37+B&MP2(?2cGr9;rl9-~};tA!_E2fUONJQC4Xh?uWh6T&PqseN%
zKNz1ZbDwz^>B#%?9$aM%EUn*sVU7d``77(}5NvZXCbLb4k0mK%6t;t*2O}dwq)l`y
z1$t9i`MNuwQ=om~33vwLGse9|N3@dn7ThQOdUdjB5AQo7EJUDP4w-6=97{~CyUwyZ
zwvCiwgZjww-?m95c!BpmTP#agZdS5hwukEjje-MwRZQ_&m!GGd`uA_qN6x+oFUjzD
zFeKtP3RsO#QhDTZ1t{#*v7@*I+gJAGJ7EkA6yN^+MXtYIvVQqRB5t}#JUevw)B3L}
zFHhnhepqsU{83z^qoqA8#{Gs4{nP3@9Sg33b$klEk*2=-s(N<eH{2i**IlQq<ZZRL
z3Wjz`ue~Pi=FSx-Si?K$kM{r-rtY5FM}FN7kG(J|A}IaLS39Lcw7*<Aw%*#8H7#0y
zR1)C@l!`rgNHY7}ZgsL|#$k>L^^k;gqR+K(1s#X{u@H@@iO+6BIsQF*%>cPzNL$2=
zPLN}eUl5$uh%Tm=ZcT(7vkKF+vpoITR+$VdVNb+Z{;O#ljL8ChT_pxFt@SeY)a+u}
z8J{H+&T4IO{?BgTLd31^4<!RXFBi$oD-&19MHsU>6tWjTxc;_9jBmZ@x1%x@?!W2X
zq&ai~v8#6+%9KfXMh<Gc2mboEvhnu(C0F?|Fy_fmix<ly_uix8ZqTaN%hylRvvc7G
zAC`^yXT--|Qb0x#F$GGBBsMlydiQJS20-6v51lsC3(r0yiAUp=Wo8jHzNXKZCE*c~
zYQoSo3t6@s>b`QuO!@TV4<#3t*No-(;d}4O@X@2?tbv2{#W&cmg|(h;`~ef;CR>tz
zm5ibjIA1LjJ6<j!-t~f@^2$<Ka_|!=gaFM6gCc)P1@X97H^qcBtbl!m*t+#&oJzuD
zt5bDZ)itY&=<WvTDS%qKWU*<`#WSgbrS;8o?vvZ@xWi;gje?kF&+gsw?wfDO=rLmy
zV91IX5AWIeFUgoO<AiZdhYTAkU0}KHAV&{0rw1}hD&&Wwxmc%>{nisTj$LG0rvMoh
z<1g{?@rbQdsunZKEj<``<t7}!^<PQhaP!d(mblxq%Ml~2ei3D_4IE#-Ih-y1!o0=T
zPRtT}c^&7Q*8@exRWkpBBN*3=(xY<(6dH_m<0LIWec^&~M_j6Wvgo+ndS!d?p|!I7
z{jHLhRwV7aw1(o6$)g5q^YZo-AAc`75_epd!Ww(pL!(tA4j(xTfjxvfW+6f~-q+|h
zZDq{Jb8I&f<EYiptI|>V>EA#_1$lA@=7^l!Jj|;m7oVIXeEj@n-lLDmsNrWTMJC>B
zWm&OYIrU<R0~ps7WB+gHn^S%dc$XNDnQrm)<PY##zo5`x`=k4`dboYs5V>YzA6d5P
zkbJ#nzifz0MqH*M6WGfI8Ubvi)97$ozC8B+D)|^->&v%Il=IJNS_4_jt8J?hPpBFm
zI5Z+(WKVF%H0u7*KPEw`mJ3_$<I@(B7>BLjJt{V8f>KQ}QddHYcB!<-W{|4LWZ5DJ
z7T^4FC*nFCwpgs{lXAWw@O?NX7xNuJJn+7*=!4g9KTl%YK;Y+~20h>$HbKgcy@K(k
zNa-OkL3t(OP8RQy%59g6G3F)+eg=xc#Sd!?2xIeiNOi(nVkq1$Wk}Z)S$|{Hb#SUk
zk=oLuV({tSAbVBLCMeuBIDOik2oDdJkdP3`%!IcajNK#(s*8$><?C-2%HTl*r9+2y
zir44$tgLMLecN_9Z~$Jh@SZkB*8RpjRA5R?NtITi^;TF~Imxh`W18lat>tlS|2aJn
zfc5n3_K^sM<t)$bPi)Ze^_lq&;JQJtI8Hcvpot#vfYRK!=~DU8(_*N#trVn6;E=5`
zDcG$=>}w@#GE|6D(m`=4I<915)ks5YFay3*1B@@geyA}S5-4%S#HQrGFr;t-pF;(d
zGR}3J@K`qY74yX%b-)vKq?(|Pus%W={c01Wt+Bf#)CQmkOa8Vlll4x0A#9k6q5akI
z<8oiuF5(70?9Ze!@Zp=c?L!r&Mi4xuwRLm(N6v{<aHOoU9Ew`rSZ{iofor<`{$c5Q
zjjwP306+jqL_t(<%I!D7-xz?ylg13OdplGB`)2WKTsK)~59!}s`u4B{ikaCDuDl6o
zKl1$hDy?ceWRiTaF5us1Tsl#nxNmly%WrQl4;guOUpaL2gqpAS9Xu+#<Km@X&sY`*
zYij`N7mt?*o_<@EmtR;c`QRDYB@DCJyK6gn^_jb*Pmhi&z5Wk*`SBwM$fBj|r3&q5
zJnW>Dj5@1q*8S)`*TA#hQAx{$TnOz<N&&#cS4nj_?iD=!sI&XX+&iz40exD&#?ZIt
zpSsWPpnGp?vUlD(op$U5;M>{H4<4%;TAXTW{^*wRxKls;P_hBSQZ2Cxmf{Eg^B?KI
zdbJn<!k-C5>*<2uf0y{X?*@obtX}2BM;{e$cz*R8V&fafxK;^+y9(eCc;U~4F8XWk
z5o2B(0I^>_JV8d?^Sv-Ga|OoX>ldt1F^?}C+FrGTYmj&0CR`0V%oE}X*U@e3+8E&D
zE>q9xDD%JF19?aVWFzUaX<wQQ?HN&*rm>ygaFNZQExP>4rSj#PqY6|%npz-P1*Os&
zie!HkF=qDM_cqEg81+z5>v_bi?%X;MU^*7Qpi;zW-f=in0oOdHqP@FL4#Zf_F7!TU
zCSGUA3$h0+gCS-0^CdArdRt{4_B-du3e1Ii*X;zT-&(Ry0uYOu<#T`MkqpF_4wIYC
z?<ymDN2{{6dbKk1i^^re!d)_cU~6&pg{KD&kY-|AG?@^pt;^3oe+Ta5HQ?I%{&@dv
z8|%>OW05RFU%pn)zj<3P{rflHrPaMr;JpZLO>V#bTEt?8k|&s!Y16Nii!Qwkz;d`^
zng~+83wI_TyzweD0}-bhjPwXt`)YyDt%|fbq)WS(=FgW8-+5cPzotNDC;-;$5y!(M
zR3Rr+Pjqn|f_OQPK^yGWYiEj6tx<~eA)rV5o_pmr89IDKePZ1nOR0qmrW$hiW-=N)
zWM}$D*^%}e*!>FWA2r4TxUPT(>r%v^PAf?kXQi)786_480?iSC;I@e1cnuaC1oJhj
zLA~~f*O*>UJZ3q1V!XQK;|VI}9j)Enz)V~|{R&uLd+Cz_ydNk%;a)}qZ?ucPNPqs6
zDd+>b95QKTYS$XuW@Kat6$yO7I0f6#5Y(>w!KGnPkbT#M`Z^fHnwIr^S3*NuDJEBc
zy`}p(D60V4f{de1A)f})w6q*YdfPof&^osY@2M4VZG;=}5#b&(y<4ax!(C2BW)>!j
zYNheZZ<pf&t*$+F3M#g1IQ3@-NB9D`&S=&oRa;#t=@rhh<Y<me?`)e<IrS|(vr62$
zH&+fP6iN5kNCjGxq0k687+kS=N;q8k{<JntuDY;|l&0s&(H$owG$KfW))dxjM8=6g
zc1T#DB*2AuR#Kh>LmP9=npJY$wKsq>F)#3HE2Pix39=rFIvzSEO)ICeyi|%H{ek?U
zo-jYSdp8u=+gg^G=BN6sr`z7z6-y;<{~<N6s<xruoM8R&>Kkv$<rhx0Xl`v~85mnC
z6VyK!iv*5?#(Mxv`@ESWrBzV<;)RQ?``%g(;0*#O-J+rKf?9sn-v>O|*k0<Fo?R$=
z<I`l#uH&+CUy|&EoANBW_2&1(HV`BRn0^d#nJ=C9m3%VyBAGh6XNxLzhfvR*Zog~G
zkPq_$$}ZAbSy&eq1b5~9wjoiqzNF>~g1{6I+9D6lN*mv3DXani2;~=-sl^t{@U!7|
zOqGqnVE2(U*{uWSZPv-v562B`d&Y#yb2p5U34<F7ByHwB15Y}6brk1_Y2uRp0p6AR
zF^1r^OYuRej=Mw31)f=q2*p!V3M=bE)x8y%9Qewb*W-43&VUd;`UNRpcNe^$m51kx
zYmfgbKwQgQow-bE)4qeN%ldX`*G{)BJ=P2wI6#*C_>)D`2vV28I%L)AHL`lmT6oid
zGA(Yp6fs4){?ddCO^xTS5LVzrwkVtBY}&j<TDOjtKv?}~MDTpWhD~bmubp#j|D8Q>
zL1c(5ICxwtbbK1`^k890-3>GL^{Vt7*_u-zM~liNA0S5|<~k!jL~jU<!rfh^3tZL?
z3-Omeh;eGoFRe<?m9#QT#nKU>0TONoiT1-2&(}%mvK{eK^D#e614wjJ$0)0})zq8<
z)cZwZT3y?s;GKu{W<sd9JPlT>Itqas+%e!CN%4r~`VB11cjp#KI?}qJ?SYuvdZWGP
zg7@sEa}lg7!eTcY?$uYKAJ%2UVy&oD3ehhF=fdHEqo=>OoEsi2UHmN9=~@BImkyv!
zPooi2HWYnF+M8D--y~<qPIDhjZxbfhL9u|8g!yba^7C2}Jd?1G2vT}rzzjqFjy|3;
z6n(%SpNWJ1z8)}u?>2VsD;2%Z5zn2gr5NLPV^4%5vSKf=)kfzESSD5BIXXcw%!^8}
z3_eADS4<4vg=xzmJ5u<vDu4-bvMExPAT9HNxD4Pf<xoDgB{D(c<PEX3SY{S;a%Qol
zx(lWqdSN?W=^{&#PsqiXSN=>Q$b9+5W97-0KZ4L2;Kk&T(zR2YW>?AnWXx!WH>;>B
zMj0kK1`D9;?Zp6esZBu7jx5{H*S2Cl=iWq&Uu!nNBM+3J-CW(}s%aB#%DY@K=3IaE
zWJ!WW^e1rVU4}7*>(bucJ3;n372YUETC|@AJJ($`S$<r;QIgZLpfp<}TXyWP1Fl)#
zjLRp=&b^1_e?P25Uej|34D^;Ori_z&ZoW)tv2BZJZGPU^!7}QHRk9Lor~B{3<g_~A
znpW3+0KR|m)}t~P!1yL838-lT_ZS%HCqw#mldA!=j~j6ozyquk9Q4O}fMfTcJbu%g
zY4QI3c&h;p;a3eFusR7={IDoKlYr}T$T4dHTC)%M#TdcUl*Y%~1g;787wy?270AQT
zYYsYfJwOmN8lDutdko{qy+2554niJaOq}_`Dy-ezWMtnc*#sbZ^T7-Z#gMNUUF7mn
zonYx}mi?KVGW*;v@+JVzq7nm~jyu6ST3nOBb$LZK)~N8*W%HY>l@frWnR&GGR+FXz
zR{v`LifvrFHA%&frd2hK7=GE3D6f6LSE2#n&m7-Xg;=WqfAH{on<PFp9~R{8q!Z+%
zFMqQW;bm*3bt`Xq@us1&;Hw>Qsh$FmJ6eYI443&h=KVZ5d$6=YZ0TbFiJ$yrt7PPt
z%DtENRp9a4OX5^NbZ;9dkIx(+_rJYS4#Pv{;bc>Y&T(i{ulB)m;UhoGhxd(=+@cDZ
z_55-ud{xQ=SM*nX_Tal4p@`rtb1v-%rR-X{?Ul7s1YY>#`^VuvcB8C3NzN*k7p@;H
z{!l`=`~TLd@#&sRdc(TiMLZyfB0zo5o9ofWLK%nfkvCn?LmvAG9tczNB)_;)0r$DE
zkbi9EK;&O9H($_Q+DD#Qo>k9C;F`=AE!RfF^>=Z8u58`7QI;Tn@|N}M<S|(B4ggU4
z(Yx=;=O4bO0MTLyPG($xqnv-?1+wDTUmG3CjvP89FFf_6EdBXMF<|g1LEa&w$H;=$
z{}1tf?7v(bP$o{kK%SicvOF>O0pu%|)DtOk<E)u-^M7uat7cs*K5!#$O{QT+e-?s#
z3V8rZ^FD3y_eOxobPw)i&@o_`)EkziFZa~%(^Umy-xvU#lZmM;DV7viKeUdCv8aqp
zr3Tz?-Lge~SoA&G&wH#Vg4Z)<&60NQ+pCjXw{DdmzW+|8qlN7BE3Z<Tman`xUv|TS
zyQ;KAoS`v1e&R%V;NeG9`D<6NQpySk5${~PK56p#a?b+~)Yp%4`3|s6bU)a;XD?WU
z?~)}yEP{1@AtoE6L`Aoj**D%O=S`Ya-@s<>t^4ga`3MRR`{MRV1;B8|Wbp@helE(I
zb={5P=WqWVYGxH@e6mBirLsB&R#FNyAS-8jlL%V(2ym5~`b9`KtoRcWkDISiWy8d1
zQ|RjD<<&rm;;-n`UYNZ7T?~>}>#RZyG*u_T>t#uWG7RytqbV40&?)VKgU1T-9vCq;
zxMB^jAB{}a@D#!8BMF6aJiSajz_3)5R*4@3h_)=C$urXGIwK`p7zdlcwE><m09S|y
z-Z@fjt%t<N?UkhC$<nD^YjsZlXT!!#l9HOH#wYE(GX$=~&l#W{)@`lh*78drkj8df
z0N>^uwC?=7Y5nheRR{?WxS1nyY!8iylF4I7sAK&f&Z#w4%2N94j{Qb@0NSdf`?i%U
z$M>>G^udqYWj)+<s~FT4SN^;M0_XgV9?(%$k_Lc!`=JzB{(HPET)STqGYU*DyYVfS
zfuFeb)nzicM;nO@ZT?(I7wF!M<E(wgC?_Eu<=Uaw?w+JHaBW>ao|Y@a@A_EaK*lBo
zVoY$wBBcUx`jWHk*I_fW)yY(hDK$`3;q(+9Sg&AUkqiS%XIM>#Li5+gFjbe-6N16m
zHi0q{uHnZH>}cwbx~q;ITRq^?9^iD|R`IOeD<x&x%L8u@=J?f4PT&avSo6K2Yj6VA
zP8ic`U4q1^Jf+d+;pEv?obp#$R7+6YH5)?U%~Qms)o6g%-cpqpCp7@B1>ksN&)1@$
zzxkkh_pXwVkN_p50~T(&2{7}=vAej~6y|}vy$P%~U=8Ifd3pKfW7{`DVA_<v{yNLd
zMresoKKWc?JI6vW&vNo)KdiV*;ZcXmi4OXA_kbHdKE}aLwo*tUILfu?hsRRogQF=@
zVn?GS59yKtrf$tCl#dhA0APE{qutvnFjeb0g3_C_Eywg2D8>_5e(_+Ue03Z`NgWY{
zCXx5Drfl$H^AB(hVxo}%zzJNRKpXVud9@e7<Wa2x<n_bJ@)7Q%ek3yGWt{H=0O-Fw
zo-UWRZY6U%x3($7tiX5j9$cz_iu+>>a6MtgBy7`OfbX9trptuzAXSb(<_$f4folZj
z^+doq0D{QpVe!5&8J3JX%0k;p0E|C>AQ5qcvl_IWNvTkfiFA8VH}gkfzGmO|1uyNO
zzp)3vhr6A7wUi$}D6Uy2q>_smYcjvw+B<HU0z7&dAQczLq&!{B&z{~dSO{`(2XldK
zGM%q4&nlfd<W=)|?J~9u7&t)UWtA~j4%7r=g=HH5wIsIdvp#E(GPn%?H7irvLpIm@
zPjsv6hVf(0&6i8>ZXIO%?n3}@&$Vt$qsN?230#MV21#B)F?dsiG{JbrHQm;o2jsvJ
zj5Fx#Kq#Y3yI{24vKUt5msOi#>0AnhVFT8nkuv5SxPG=nzTRH4;ECI0_Vn}QSW=n-
zmV4nIyv!l8^x4?P*!I!#@oNvsycgb6bNGnCee_9`757{7=g71RN6P*~iK@IIXZ4Ww
zF-=<>64d_mjYnklh8@b<+}diIGQEL)yUI_WzaTpRj;E$)O8`Phv}+Sl2XI?7#&K|3
z_W<KF2aXvd>F>NF?s&Ip_~bEou(*ab4B>NH-^d?m94*>CyLA(nHf_WW7U%S8Q42#T
zp8*Z!L9B+cz|+6{QmUaqYJc4Q0}XOymF{hW<bye*Wa{Hf0VY>SUU9ix_3W>*`lXBH
z1Ax$#W}`rN=USOHp{x9F<so?m;H}Z1@nZn2!4D~d%kl5mACt$@3l->TuX}&qoG7=y
zu>qb@8m@CVy%s|jk(m#reR^gzy2+p}VbUtF@mT2g>N?r;ULG#;+JA=26QBMrpDsTv
zPt6)2Phy`ybcYB(iA&5;zn?ZG$jY53<SMvG|M=H~a^vLgLRaVS+&xOJes;O6!L^0!
zj>@7Z&r|Hf%;#4~pANyun=79zJ0K5Tb(VzqdqBPwB|oBHKKNz7jKMy^`)95lBsVNr
zCCj%Z%T_25eE-CGa>=}(6|l|wGFg9{th49;Z-<hzh6Q@b>j0(m0Aw?k_@qH?q_Bia
z0wPb$8YnlvyjBjM$dkB)9GP}b2Z=+h^F?@OG%y<5FItB7h>*M9SSNF@=!a+MB0q0V
zkd(|K`E2f3nexa_5(by#Y|9I<KqsI*ZA1r|@!SgeZFh<sL|Ks`4ROJfeFp!Od}kzZ
zO+m%v3oek0rd%uqg#~i&U3bZ90L_I3MRN0utEFS7SlPW}t8%?at%>O~X3Im5J&uVF
z3p6s)c4!-1a$I}mbjeIbEG=kz1tRY9^|#$FH{chbV`^!cXA;n05WzU130cTQUIu`*
zP1|<z(sR!MP~RZsnQ8LWqYuk=0H$w#_>l!*#-?y6vsz!)w~kiNHaPKGeo+`4^)h`W
z@UnAjJk2`vMcD%cRGUz+b(_?A^MI^1ka2=T!y_aUZb@9+0MMY0hwqysk3RLZT`TjQ
zJ9o-!FTNzcUWgCQ7MUAGMtt;`v30=p?p?d&b)@rwyWVP8v?m-qAp7^nNqS0(>O%!Q
z=VZzI|M!}*qV+=Tmbd=9K+2(+W(32dUH`xTzND<-Z@lGJ)$FaCHp%mkKaO(U@!;UT
z2!Ik7FUsXKo*$J4GEW?r+qQ0&j3=Lxnb%#f-ENcH*Is!=Ucqy(C@)b!7#9|<V79U|
z(q+M(-Lhf*26^+{_aw-Mm3WhdI})fKpgYxXVTBAQWu{5m+%HZpZW85Xl>Y#<t}HH+
zLx+#3Z=C`y6(1De3>Q{>-}v7C9TbF#JvPu?wq{o-z^<t_)Iu0rT<a=d!)5p#eIjgv
zkxiB1xNg4(oawFur2g`cjq#JpE!r5R602l35}Ge+bAhR3K@6NwSog#74DTV|KLf0%
zi}G@1!GB+q*A_gh1P@xd`MKHh%(KrafdoS-kSLJ%3k;UtolOcoT3T*<;@tN?0q`)B
z3-WB+wo{g^Tq_espQD{t+uzo#mc4uTDvuKS3mhkH6UV}a*XS9;9@$E7eNO8atqk@@
zDHNWv^9zLT-t_d%!tdlzN<%l{>O0mabnIL90NyofyFPvWXl0q9T}*@J^An$}gOJgv
zowD3Qqge4r$opqfSTHQdf@9^}A+d7r^kMQS+=RdV(+*R+08XT57RXnt_Q{=>4rz8X
zXr<W^G3WOl1F&HBWUyiHak=rrvuu{(Wwsu(b#Wtr^t?PQ{ApR;aGfZTXn!ay%9EiS
zu;XZkvP5y$tgvkr*vPrP323ib%cMh8knNOMl>X<!{&LgBgG|rIb}Ech)?b&sgpTuC
z4;b7+#I5&RQn}|g@iJ^z7SEbYfob%7up5-l;2Tk#I?hA9&I4jh`xx5Qy19S~oVv-v
zDQKuvCBLs#Rkx+Sol9+;7?Sp@b57u!s{oW^DE!9|$Mx~!$Emf?zPSBrtGNy5O>iF6
z=0c{Wr<>lrd%^oquq<1)9QRB65fho_#rk(QDvBOZ07YO8rj@~zJT8PX=#Cvb)m+MJ
zrZjx@u^z&)gZ|w;P-+8|rX|Llorh(0MqZ0<et$uUT)%a{d^E6|w8narRR0!Q7Bz40
zQTaJF+w$ZoK6()#H-YJ<=zmBPh&lRNvz4=!p0U91-f>uV!6Ldj^b78XmeKDI>?*Aq
znGabOKdrgD<7EXb(yfWS?Je65BG!2G>ScZ^5Zs8q|8L(|864bv1vTrs9mjvL2k`AU
z1q6%hMK{7__)}2Ef)bF;B008Ne;qjHDLnrgSP~h)cR9i0$R^p!Zcd1i8+)d|rc+jl
zacIrjP<G^1TPjlwyJfy%gnB=1)@$>mu6(q;1ar|Ra2X!r?k<t$@I^MW{lSw|lAegT
z*8H6ec>*jRHLS1fM?7n~*{+1;Wew)lAAVkk9yDNW=PW}9_LBAhYV8rNrk5;RkIV2X
z1Eqz@<Ayd^cUkA<{M{2^HGfSJ=lMbXy2>wKzKA)#8nU{EYYkV7T@y#4AMk4_`NP}9
zg%gK0nvIs-1h5_S&+Y*l{RCflp>+QIbIJMP2XSrPTB6`8oNmg`7{a&b-L<Q9Lm#BQ
z|Gsj~-s<YB#S39it*IJv?A+zcC2z?R@#xpj`m*D=g*`x?M=i>@FvCwBo68tKppCqB
z#|W7@f0dHgoyaMJ74{;@hI?+!+Im#)Na+aUl}aokw;o7?QW(cV77D5ZWNIb1q*lIK
zb40E?ubc8Jpw&sfN~1ym<j1ohlK{|do6K_>K<mgrHvqVUl?AbNT6!(e%GVWdo$REJ
z>83j($WwlL`aHSz#Z~gkx4TuJh4{Hk5Po#8O>d_K0EKCpOzZXgF7E>?>?##+Iuu|u
zf!~5+cp1XH5#;NxVo~S8Z8u$tUoj>Y064tlffjE(Cd;-ZNv9}3IRvX};$L9^>IrBw
zEiIZoQHQ^`E7qQt&yM}dwPJ2znX;0mrF<*od-TICFdpzkTRjvA?vD919H6!*+Qe?7
zdv98Mr@&j_iL6SvI<H2ZUJ{IZ<Gs^joHtwwK-<sDO}Qth2O74?A6EAtE<G$a;2B2*
zdjoWjmU*zkJ|7n6^eTC#(LYGkacUn80Y}E8Zq+JGo_P8h89aPAgcy}@AzdKbwrs)x
z06_;9a_5X1CHLHSe;rt5ElAh5>IAHXli*^5!1Jwl-6QXP_Jw4mXUKiG-3m7|(`4<+
zl`6THm#4V<`iZ-z58PvW*MZSLBHr_)Q6psj6Z0e-?fSp>Ka?@&oiAR9-?@L^ZY5;V
z*GFcY4-j*c&7Z-EAA&xIX<scLtn=`sx?nqfgE|l=$R4S7S(VR*I<Y-EBDga4yvg{0
z%T(1)_|T64V7~6kE9AzRGh_=uVr$|cLLiY_4rDGE$V@*hcy#TjAj$|}iPzk*pjf|p
zg`^}M7jN{XD;5_7^$0S%fr<I-y?5lb7oL-HSjPtfNO#3Rqg{8!jW7D<>$<+jZ78|D
zv(Z^SH@Y^g!l2_B7=*Yz0Lif!An;9V*9Ff$Ek}+30Br_+{lypZ{F9G?S+BsnRV!_w
zJTc|c>C&!C7Xa@Sa8Kka>sS6N^PhgI8KpUr*z1AK=@49Im*LxncA1&T;QB(Sd}se~
z2{6{kUbq*kgpjjdIBI&a`0dj}0LPBMnsT8Hwi&UJk7I$)cayQGn)9ovESD{rrLqf_
zc@8?YJ<uWA8;gG^2SA8M0M(i}4rb>ROIVPbw2kl(-&Vfj1_mS#7CE-l8xS6#NId=A
zBnS*EY0%ljr85>86f$T;?fZA%dP62(bg6v$`B$>+*X8o=8*j+?36o^ix-D?`W&*95
zz8uy?7b2WOo9IX_d);=#sF4U4Q9mYg%&dlc?b)+t$#e4;sB%C5>@&IM>KSsu#g|Ds
zVvXvpuK7JMtitx4@L-`sYjU%*Wj(x?<mBXHjIHk%?kC0{kllOsnHX$yOUf{Y{tl(D
zEabCJp98mETM*y0#O!{vUzc_mXEn<V&M8h(keekp-Fk<tTDwlgLS4OTmE3j59WwLU
zo23-WKfEtJm71-b^yeJ=x*h<~dBeHAWk9!R-AR#WKKV^fz}>wv8@0HC12Ei!I)}^n
z{_Rxw73<5*J#Hy@L=gMUdnU`Mo~>chYDS4MHqteAOKEto7%c3}-vbseKW~kf<c$25
z79(G+-q&cJAXv3@07%YHFCjHgb{<M?G`+oxRE}J>?I@lHgs@O<=@%PrJ1Od^)&LuR
z3-*4tb34v9(E|opM!WR=RBEE{5GVI=rLW-WZonei^y9uO_zT9RHuii^oLkSpyP9KB
zDnk&}8liZj^Md3QI8182x?^E#IesxChBam3O)Kv*xCaL-EVXX5qWtG9VmXhUfBqzy
zIB~oRhr_X)!f=Alrk`n_A%Vt>86^`Zj8jUe`te-x`QY&>Y}SZM!d<&|R%K}Cv@I2e
zIhR(zB_g-XPfu2~#?hCnrFU%qJw0#?R)Vw`wx(SGUu}Wbepru=fs5-)pgcGc*2Q#H
zZB2CN{Z{-5>(ds;zoupX>7X^;oi_)x)*AM~(NwrVuNPucIC|&y=7ZK+Icac%c=PWE
zq!e>eWAsyMmQB!_dFYDV7HAy{EA5M-LSzblgM<9QS6GgNl=D3VFubX{8*7*2+9~J(
z$We^3y~Q|bmI6O5pAyPfELDTEmKEdut1g7>1Im0Bf*f-y+qqT2D!pm1{@Ai`a}%si
zBI>2|k^~v$mI`;}_49{Svid`<cy*@!fHHw>y#H?>Jt|oMe@<7TEAHE`yF_MQI!<O^
zF&R9ghveoL%F1=Wql4fk9P{qv@n_p@KIhJZN0Ma6o<m3v_x)~0xZiHRa;V*c?Od=0
zTH8tLIPK^Gn>|3Q>sFUtCS3sBwtx0naf`68lwq@gf5Axu`oJIYtUJStY^VQ&=hs0E
z6+Y-9JQs44qjTm6m}BK8oN?nFbn-nwKrz6}MS|e%l3Qzlar&4}^2#k|19bJ0Kp0IC
zw5EJ5&>MNY-C%9qMOEGvUeK->)yY&A(gpE{GK1i1nm^`UwEifJH_9Xg-rNEKw(>{!
z;~Kfc*dWl$8RfEU%b)4ItHc7ty>@&@Sp&;&TXC?dAFq|q`|*O;xKl}V75)%hfM4+N
zFOa(w$#s*u$u;9*<(600DVN&?uuLbIJb6frv<vr@RlCw93G#eT%tPMpCP^muM*-B0
zh5U5dlPl!quXoCA7xe`2pMfxQ=?d^>o1M|mX*s15f1*Hw0dTv)<BJ!S<}j_(^W2zz
zQ3`0j;;H4519#%AmqZ{r0H88?l$$T?A&KzHw|-xS!lG`U(o>%OVw=o_HFN@8kq1ML
zOT#5!c#<J#?Tb)us8#{vd>+$Bb&xj~@0IIcS|is2kZuo9{RG-{c8_o=fcx>w(Ecv1
z0}xL;RJjNz?zIP2+esOPawXoK{jfkE4_D}CBAK4X8FuFsQtW^*eBGKgau8Oy2k<+7
zEM8ej*H)CnEvvVb6c;K0anrhWGJVR0%Dr5-9zEc;IT$aW?QZMSrw?Mq{3<<q^-@BU
zDhNhyo-sqRGSgv|TdDvAcJ(8$-u}-WcPb%_D}d8zXo8XG0&m;zTb0%bU-<LRzd*)K
zm<Shl*^&@{L|GlEgyuis9UHZ{Rxzb*aQuqpu0dYiwUe>9X8pEEwF~825Yke1YMp1s
z>{+sT?HXCL;#cv6C94x8a1{XCeqOu?K>q-DK#0Ez8GY`#a^uan3j3Zk9&yHFzF(vS
zP4B<=o-AMTg94@rT0=BwQ6-Nkj3UGB3MGLX@489Ozvv=7+Y-5H<_yV5J)vB|SE9a<
zR^f8@+(%?M6c~2z*deze);P6`s0Ew>z=kobm8*A@r<F^0^C4h_?z;a$88qZ<SVm{a
z!}s1Tad1UW>qT0UfA`f_GUveuE!t&$z<AdGeerp50{Byn1;oIiL*>=C-i1ry0Lh0t
z!&z5cCULMdcZVzM#osTK+vnUXVc~X!)YgSNj_Z1$2ovMC@vwT(y|)G|x?S<k-P}7&
z+G0ZAzH1M@)p7uFP(UE#W`3o(VVop)#E8-ZgLQ?U;vKrYO@`}{gK%pems>7}prw^p
zVfug=yQkTN53UC9>^DcUr5m)_T*-Yn=#=!pfSv);GuBrQC1y&uPH;non4EPl7v8aK
z#2BPo<6Ez22e-RM@oEzw9l>bCtvM>*a5L!zZx9;sl`Mh_x2)7$=`#^bYM99dq8q{)
zJo3OjGWCjCY8=ymT?+P>{rt21^3#v%b>>s$`|6JQM57A$Dvoa7LFU{EEn_R8AJYsv
zdzkd;)lIhTJ^%$ClTemrWFhY70}srtD;I;PVyKnJ5v_1+*@oZY31rlm@d{l3<>$rn
z*{7e$i!Zz+X()r5iTq|XV*Ip4U%V|V7Jng~I(3pIOBPAO>aXS6hZjhEayr&am8wiF
zJ@f8|E7>t)CQ5Wnj6DD9>oOX1VtDuyaO0L$r|`x3eA$nSWyLQ)tGKB2s6wR^wvTPl
zgYE86TKZw(xAHqePRx7eIk{lepC@?H?&BY|#kdm{<}deOb&f@|x9(4tx8Nq+u8VNY
zT~E#$Ap^U@MfcQ~q<|KYHr_dlLah$+3QJ|yQwwD{T$JBFt@+P|3OwCnLS*HR1d~=7
zK88xx-$FWXNGEyt!_`tq;VH_@g3`$oAFYwM=A7T`l78EHOukqh*LeJIwC}=Uon`g5
zV^V?l^PR=N%b*^un_Y*NaP9VEvh84s8glw|!u%C!$KqPc;Mo3YJz#M07v~Q5iZS|X
zsm|RZwS{}(!aY;*p9Z&3G5YipXIM~+Gr~kvmm=oeA*?s*84)gMtDS=}h?^`o*-(SC
zm$-DECpEigi@W42fZF;7IGqz1FZUTK?vYcaeD^I-Qm{^Cd2Fp5f+89vrq`#@@7v4R
zoHWc!{=5$?sk?UREXjx!nu;(N1qB7@n<@y_p^Xj;>$YuU#1|fsNXZDFq$lQAO2|-7
z)zh${XQR9n$%Wz*0cz`eW?J%bzHmK0Zp>);VeyYTMMZr{_4~HUsK460y_S~I()7)1
z#hI6iAng<Tj@i6%aMcay9Ju6ux@UXw&{_Nu+?})Yi2R1Q&w5&&k*nS>dx{S~!2Pu~
zF=lf$zAKID_p~!2tzjYg!9bJnl4o5wNPujm<$8e>3Z?6-+J;M4#Nj5-w>z&;-i7OL
z#;Dd4-GDQG^~4B_IiR<WoPZl{ON(bOw2@ZXjGY~Va_K644es^TL&?f@w^m*P;!?kl
zI&SG?Ux>E2kv?F4Yr3{wgo^#GKhL<?*KgTx;p&}jT9%q47XaLL(BIetROBBt0pZQ^
z#C_FQQpPdYsyRI7yuhF7ns&x~67OUUz-uOO!uzJ5bm$$B`C3G(lKPDN+*BFnYTd9t
zsP<_um$Vx!@kg>GsT8uJN`N-@Dyh3rw}X_X!m0uGB;n})5{}6YF){E!P`|dbSKMEH
zp5s$QSg^eM%$=%gDjU=7cruhqa0za={bU#{uI&-iuidmmS&A7kE_R2<t}ZQzvu&?j
z$N4k62WqfZs|KH@z9i0Zd>KJ%KE<&!6c>4p;A#PknF`@eldi1YLqo+IF0Z}2b+hO|
zhL)*<%$()cB0ps&EK!w@a$FEfrW!7>`OR9#ab~(2Fwk_d?d%6n9vC#3wh9U<PLMN@
zRO>KyIGcG=o~yxlSF&%P6v8WAEz)|lZ!bQ;0i02nM&#vc(C%vRw+6I{o=-KNfo(HF
zHerMeyb?0s9LRR^H*XdK?*Ze-OVF4xS~1WusgZIR$+0i0Av-p<Y9)UC`-@vtlvJVY
zLddWE&punKAhWK8EQEc)`bg^juyvuV9XxXt>MBNP%#4K#CH0+mqz1<jtYGa@E@kje
z$~LKd?0@WN@+Z@%hx<lbSh4}eJkBVSvdJp6NuB3C;+~n-6@6*Y$);H5zn!MSrSr%>
zQRp*b5Vgt@*ZQ7snA}|^4{nR~4#OjvuCmYgUW5nL8%X{4wsU0O47`I}@L7>*jbH!(
z?jb8)yhxSBBzmCy+(9w2qia}Q9r_})`T_xh(_Obcih@h(E&n&o^i1(keXU||+soJ7
z^FFjHzJCndYUeA<<v;++eLIE7Xt>iR&`ZnXezBp#cD{Z0NI8_0r!1IVfzb@?(n^8S
zZ{0OQd;k=`0c-aC$Kggh*b7$ECfDZ`n7=wi`m5$#f@jmGW3aMpW`0^|k3l`ObbjZa
z5dfV%<g*9IAr5)50=7NP{Sped-5)<NM)mE0&aLEwd&fu?fNjRvrXgh?w6_8=!CJ!_
z{9S;~^vt*5mZ7RmPs|zwIj{@7X!@(QKikE!N)ReVi&6dVJtLLtZ@LLT3h<l2^-l06
z&H$|kcL|YK7Vbv8Z*LW^dsxp1cnLJ!PjmbD-;%Cgs{C)cqh{QP8vwv>zxtoN^4jYX
z2(1xsA1{e)-A4MIHBg3+94Xy<^_E@Rx5=s%D`eZ&Es~miTn@zTL7s2qhFfnz2mk<D
z9F46g1QY2F<XeK+%A6sJ3yUBOsZd&|9tL;W2rD#N$q*zCj|c}C-bGf!3VYLrbtuUo
zzi-+A%?=MtkOs-eME=zmpQ(wW8W}pn@|FU6YpSWnplF(ttS?!(0=K?cP1IU924bwN
zGd8kL%Q|?SKaDV7*(F}HzD^Kbf#X)W0s;c%RRGY>JpP1y`x&fO;L5@c6DnOolmqNv
zjOVmw<qEm|?m061`s*bkGO`Y|{dwt9)t^>HS{`Boptaup_s^A^Zn;GP${eVBKv=dk
z`IrK(`7ED);YArab}afB+DM2g9p0v`BpyD52_MDbnd#javI0&l6)5-Wt7ph1mrPUT
zw1EP`eUHwQtEXP9+Q7kZ%f|I;zp+0nmMxW|h>h+64cPK>#8XEs>oBusAY*$^zW5Tj
zir*qWZazY5#w{B*$oUtW>S(N7$2H3ySO%9MC$KKA!8fT0&yb1|S9K1S0b$+}2bYzM
zdq~iW7X2i>G6bz;eZZV)%%)0kivCpLgUP}KcC?^E_GXvJ!Mrj_freR)5sU=hS88>u
zxX9QiP}36P{T?YY$m-+*8Q0q3R<9*}*H~Il4`-P(yOZ4U^lsU6AVnft!HWh2E*xJ9
zi}3u?^JUE75V>Mflqv?Vn~a^^AMvXT<mmn+2?-B`B7_MrE`{(e6R~mI^aztt*Yvlj
z_JT_;lcx_Imbp(puK+n}#`7I?HG|C-iE+S1Lpc^tZQ6B^|9y#=o;G4(GmnS2pFIB1
zgEH;PS;{h51DOe4E2Rx{!R1)QL_|f))M=N<D+?BY$*Z4-xR5EuVr<WzJ!&i__;4&C
zUfjKW>lQiYV${Y60T-2kc0;+R5{zVFW{T|F7bi}hem2vy{7NX3#bHsHk)0>LzJW6L
zzPsh7|IAT+ptX}A1pA$qbet!-xbwlnvUOwxJSZHfn>)FH<J_8&nju-aEw`rC-^;&p
zAE51{R}Pc#U<eD$M27#tx0l0uAJ%G0D^<q`Xo)bn6NuAW-fNedvn6PK`J-RSFPo3Z
zcN-2NhIOhuHhYxBga<U4Jw7>GzTbElt7GtGB(x<4-&RYBuC^~9-c{aSybT`*zDw|i
zZ~wSMLIS<zu^GcHz6o1pZ{3$9*F5#Ta!&;Y##WNLb5l{z)1R)B3}{(@0k`Itj_oDm
z&T3zGy{R2~d3gBUl~At3v%v4V$$jhQtfulhuAk~2fN%zUtHBB5vRjxqhe6SSKRZ;D
zu}lmlhlTnP8ev$HTA$7s(=3^JV;~ebdO)Cb<SB8<--*yMCSIJpJGp==@sz5tY2w!D
zL8;B#uDk*1*CrUsaj5@Z&DPn-i{Vm{sY-ns(|PSLSs|v;r`J1X)zWEOUSoPQ3x)R*
zEVL4PX({Tr<f}P`)ALdL_U+WK{;K6(78FPm5)v!`X3h@;wdqw!PqfmWcF7d^b@@to
zJ&HH=D?1Fg$$6Odwrv|DV@8isdG&%emcw#6pXiC__|r(gubs2r>ho~FDc(_^o@JTd
z2g`O?*Wox8Ny2!z7NDx0s25TIV3XF!OX1d<AZK&w-Y#t{Kx^hBa6Apx*Ve!_L1X?1
za@%@R+PO#%H@T@pqzpr>YH#!WP>y^gzM9qqS(RAFk7gQMX?dP8q(2_i4K9mKJSETd
zMC@yV*w<}6z(WgSzf8oL9Jv2wTxvbJ;3IkKtS-_8-a#~CobIX7t)zpur_2U`tfx;9
z6Ptj!uTD`>e_iVX#-_fdLzJ8i#Q-mJ9}M{w7N1ts+eYkax+A{^bvzZ9V3B$!z<CF7
zZP6uv%K<I>+{Rue<yb$v{k9PE8(dMdCFU1advCRO!Db`D%P8;~dKOL)K&y$wX2SY{
z>&w$>FlSJQylT||4B>PKTIJndj9q6-fQz5p*vUtp-*;H@Yv^qcd~S1zlZlzC0D2!Q
zbCUUo56gerwU_9>!84oImga29tFPY-cdY0i7XSlipVd=Zh1ln}sSLVq^Bxopg=A+}
z88>o(#aggAbvn{GdO+U;1dhw%<7MBhSyBr*LpkJ|2D8!&LwLBu&2?|MuFhM%T8_?}
zC*{YENqK526gkQJVvO+jm#~{|lJ<{124_Vk(5(`{>0Y?Qu1rdjN)5b4T7Ic6i0|AB
zZo`ki{<;+W`m2<oKE)8?8i|0lJ7Qq_0{HFt!V6NFpD)Lsep+%MYp#H7uomBdEBMXO
z88am2{`-aQwR!Et%P&h&T%35p#WusC<*i>Y9srcXAhQm??mBVCSWttsS*#nD-{e2b
zv!E~n9+T~Ih4<Ic*|Q}CAxsb8eu|MFY_qzS{^5tx5gzqIr%sh>+)oMs<}_H4yGKWh
zGr~slevd!%j2Hn96Ew%iEIq&dR+8q=m#pu;lPbtW2xya7mm7HF){i_Q!Kmx_OD{>r
z`|nE$Kx$r7>TS5kBFJZUPMSnUnAy~|N&xt^ko9Q&MPAm3c6*|~Lax15B4^H&Qt;<-
zc$Q`GG)gev06B~Y<l-F=2Gf7&P*v8M^2Zx81pb<$2!N|i5kcbS0L2l(^|CpAnFOR8
z1Euw8?d=nojSe*_5Zg=hN6))r9_kijDZ6Qp_|_T`P$sz{U5|G4iu&$mi)ez|Bm(^U
za@v>;fz(Cw9}UFT&UwOWn?!J$<PH9#qo*Y>Y(%-*b*7=R0Dl@q!i~2^S~|KpC-L4$
zoI5qrD<|+=Yq%08(G|MZeuC2@;5L5CfpitudtCo$dH1I{nSbNiSYz|6J@d%+TmCQa
z2U_%wffiGj9zCU`Fkjxp;O*h*4eQoi1uh>tctBc3M$5#>ljTyxoCa#7tk6<3GT~wX
z04yvYo#0x)nu-8aKXUIql8*_=i0d`5o;AQan}Qi9EYiOJ-{<OA4$a{qqsGWAOi*{+
za6JYjx)dNg2nG_1GYWoAU?O?w?%S2tbP@dRUkS_Pe*Mp~u2KnN8lE(rsb}b|QrNg)
z<CH}vUjHMXh$`PsB_Sc90FGaf@#jvIw_krve&4Vj_f><q*Y1i5D9Xu}Cmy&DKyeuW
zi`!LyvT@deC5r~@4hUip2&Q#Ai4$%xz-fYkiZD5chC|@U80H{aOS?d0wzd{7nHBqB
zyRKR+vIs0Yn;9mS+pAX}6%#uL7KdbDj)Ezp1)vipd3I>!ij}HO6hM3X_)7;^BbFf6
zHoJ=eYAY}Xnihf!j@PbU<p8ej^rgMisU;Qib5eoSf|=6D2n*23aQA#(y8t-`hL~>t
z`A%7Cwbj406mDwoh*riQ*%m-@9bW)YE(bt$K4Opn#3)5vdRl=qW;*KwXIxi>`u<Mx
zBZS_le4mgj{X!9g;}pAiscYaFxYMJPue>p@x4idFf^3h=gC#I5eE}{6__@dpm&M4n
z7sp^7QUC4xLR;>#N5{&V&vwYcjfqf1$x{mfPq_XWbWwLXXL@h(z`ixjnftJG?%qS5
zeCj#bv3swwBD0Ja%@+et8Sl3@7IT+Oze*l`@IL9#Hp;pvi{l9wPLWUFFOysEnTz)y
z8n))KOpW&V78pJwwtFvm``vfMSezv<J@Yu+>eVw|sxYLh!C1|2hYK=Hm_qm8cFCL`
z8Pq#oiLEq|o@H@9C0&2hEl}_&mw5p4>5km!!uIexC8z_d4J^FcckU{$zy4pT%1o6>
z2)R&c(6KtK59z4|>mXeZU_2VyCq}M5w~y|m$dBvx%l3n*5{h@%9@W5QDiyWNGpfC0
zHqR5ZzUaX(WcAJjn2(y?{g0RKmKCrlo;|U*Oc~KlVp|6*r4X$Ey1q_=+w!Gbj>z+0
zu9u`NEX>V<a3dB7lLmLvlD23&_nP4-GhPyN3Qgsq%%?tDD;sww%FP#?B}03)kx&5H
z8UV$1#GlBPFP80=SHIgT*-$nzp@)rE8yVs!cV9eE?t5#wltPF)^V#p^>&GsUA>CUy
zo)K4z0McBK^c(h{z>z_<Y3hjXP3ChX_;Y$dSy&%?UJPNEi&O9@F}PYTy!EwKWv!8_
zLyv*IwH$khsksE(7VH>dsqN(dtvKaC@iq5%sV+@dJc2U>@6N#^#L1_ds>0xlZJ$rY
zNb|Hh!dk_<K3<~}(zx{d3~x<+8jOUnmgrKK!(zwDQ(U?&z~Ebouo7Ax@a8V|EgYR=
zZjjorNf0iY<~;)E&f)^0pruf}7@m*5gZHF%ZQDv0xb_afyk(CFS}$JmBfQ~MsCh<9
zN1(TD+nBmFfp(r-x(uIs@kMe1R@w=PiOQoDc`9FDA88vCBduFUD<GNz{EOjLOYvK}
z0tkL<<Ox3i!V4yw=Qr)FDCMa7Bg?&W&KWBGOZr-zq!$od3dl^<zI{7*b*isZWki>U
zLLu5gjrYK+j9lTbr8FqWSB3=pYqD6CouF+vK<1W^Ht%U)h;`l=#K3-kKv(g@Sg$8H
ztmjS@O>YAMvJSM?%IzEID|EX}pjuC<h<iv{oRTH9z}C~n0O<9gwf-7G<zWDx^#DG3
zxaDa%G6nhUQG0k8dT&4%@zH_X^oT=Cc58YU?$<k^%|W!h)zQR=AUO;7qbK)M*oXRZ
z9s4KI1CXJ(PP|eo;VH_v^iu#Al_oI6%%5A9sQX$>?7QhQ-iaE-v8)Ae<pirF1J+Nv
zl$J%{i1IS+uektqY}&_PT*fD@Qt_NS6fmm%x{9&ORhX+`35ats2;X;Wr?&Fip`%hj
zLk)n?_DR=RwF{X6WqeqN1fa0d7_WoOvL^dw{QXy|A=li!|0o(lAlg~Z9XqJeMioK1
z`rwg7mEObMDC0*AY&5;&qN4|z>H%8ly2CoT*Z1Ee&V8}$nKn&I;l+(JLJfF3BY@e|
zk3W_K0JH5MeN+O+jg#U72V~#$=~4|Dc{RY~41mU-?b=BMz*7<}s(T<$70Qc$-~q||
z@=KxAkKeiC@h}*4Kv`t~Sa)4;fmA_8s{mW(g(u6lPd=$Em~kjB0E64iohxAgst@0N
zx0J&3Wjn~K{o$FDU~nS9;gsi}SH@Ot=FO96geW7JoxNz0?7!+NNk;wo8#hX)*IpA>
zfaZ+3?GNL)65PXXc>kn@Glvi-0NX_fQCN(h|FB_F0z;+>$dGx>4PbTpH{VF$gb9)Y
zci*X>ekwga`9wSc>XX=qCCGQ=uDhfhR@g41MnUPvQ(D9AcLYH5ti_Av$ZfZ&K4l-c
zLPkW3by|$KL7GtX`Tm(RrFa_@wn5}iy?XYPZU_@ci)*fRt08Y;{OkPy*ENoX7U-Ux
zJ4+X|#Ry)TgREyvjC4o70(i7L0MOnOR_C4n`(N?s*in`3_|vi;;4+as5=m|F0E>V>
z6(A0B1vE3MoXP9FM)@04THqo$!DmhuiO-R$mF}=G_Jh%lhniQh5<~)~;ntRCMqF~x
z-`E4RAiwuA%XK8Jx6fp<Tt%HBJy8jlq(d?BP<XQ+u039O{Bg<L{=0170;>sp{QSTf
z_tq0y{63htf`dclZ4A!mMYoof%YTKHOC<y`4ZF^W0U<oHwb|0T5;3g(;En`VlMtpe
zB(#f%H>{Qr#{f)iD73NY;w~^0t_9$hoU!R>bwG<-jv{0vYYlDyYfHt$-%kQD>76Q~
zb?2#|mX^c8)gIAeV$8VlGIZEEvKqkbI|Q&dZh$*KXDkZX*VXd!3(v{f=M0ygy?fg$
z#_oBG!JMf$Qk#kI0UnZesJ4biVtDHlT9@mK_a!ns5-xw!#2K*?iohTgLQ~BW46QGL
zj8yXR1WYUhEa5&5TFST1o~blx*di|33X2iP0oK^8%Lq{YK-}K?0vvnI>VbtvbESw@
z{|Z>cL=Mje1A|=U$}XXjmX;=m4jneRhUe_9UJla>qP-Q|(h{3#i}5_@a+tzQT6&Vv
z(=0!m)fglqh`c<tSTX?G5I`sM$=K#lQv_>a0@qawRC}6RY9r}Ter}DcEKbOi8J$nv
zSkg$1XUwHeG2ZgrJ>4ZUr(BLE72~}yO1mg8xH@e(N3c`8p`mx)twUtQRedD$c%B*?
zg5f^K6RvR@qsbRsh&a|0WYwzGvJ39AepvD|!urz15Ckn4M|}JP<l>7il)nA?%D{fj
zA0yA7HeGu49{_i$KgrkM!eSKfIXPihFCUq4)pWUV@_7;z7$A9B8S*7GeZe=YFGSx~
z0th~s&n~}gnsn>hMJes*uUqdky}z%wbQ^V%eEM-axU;g|S6V*KSZDX^p&iO?cibhT
zMvsw2-z}0QzbwO|41It$`}+CIrI%hR6A(8wDlA0GN{i)-FTPMQ>rG#@sR&xCp52IB
zyt<0clpXY)8~B&!t{tW3T>Whi>)lp%eRy-DiyHQQkiVzo!EI7YiJH7I@K&JSD!k8X
zl?COR6Zs|bI6&j)zS$(v5LQPcM!Y{3T*V0akepR4=>V6rkwyX5X3$yy>RvIfmkjJ4
z-O^^L^sV70{r&sTmnjeZPx8x&CPGUV+OI2jCaPaJ;!%ac8a)bO7r07H%qo(Ew0tSR
zI1Y1IQ+m!ZR8(t18}>ib2Fr>a39<~}drD4`Oq}zX+<*BHxq3oxi43jRGS+US1VF=D
zxFUc2!!@#zpf$ds2xuU`dFQ#xf=El}*#5OWP@VLNxE5`aihUcU(lts90Rv$D&=c=X
z1m0z6(_>DT@?@#XS|>HxE5)e>p-41I8Jg;b_}4XF-NcF3)pp2W1P~B1PW|j8!MhhE
zPC=vWUgiaZO8|s{ji=EUK*d`_;9&i!Cie~AupGy)DWZoPg33ur$+BVNCW(rQlxX}I
zl1H<kE{5=tUOWyRKB5#r8J<TYC5QoF(5;&t&>aam&qPN@sh^h5ddpavZQI6JpSCzA
zj~5fu#^PL42jNhLZ3z*yZU9`jqyR_S7WKeLSUx}7tAkQTZ%)QIxYGXbp`_*{ZII@H
zt}zWj>jsJb;E90%;q6f@Lep3S*Cpn)(8jF12H6NqH%9C`J#fvm>rqZ)!1Y}8fgZGG
z9tuj^0nk2zIM;gGnO7u}*6x(Ep;Ryc_tguQ<LzPT?Sbz-8ZjLmbo%yy0pPXEh>NAl
zCrAuGy(i8k`BL7L%t_Cj^VYym)<Ti8IuD?*4`M@tA8e@m&s8}yg7;@0_1AQ)4t0Nc
zogNHaA!n)z86?I|Qz7H4UvJU6vzx!K+zI#BA011Qqza>w-2v&UFGZ_PoxuU}7>rOv
zay5!C+ItQ_wvmwQ1=cD5)@MB(_sZaDAtjaJOazwJ{*Af`Cx4%roh#Wn`AXK&qYK=G
zcWiSy-j<_H|F9k)07_7|8eppt-zUE~a(R@+mnS7j0xX_mUwu`A5jK)!1mGF~`F%Fm
z^*FACMR5v%>hNo>QC8QKqtde43EmNnv~)%q{x~vdaylcF5<NO}kO%<M#~*%J0oQ8e
zfUGuq*)j<P_^SvX2VI8Kb+!}aeW5Tm3x+{kHTlvv-;|^$o=`5ojj(<W0T4~%J@^eB
zDy{%)D`16P04wW5kP~(T=u9Bk33C35j0_^gb#0@fzhUiKWuYAcOXnnj@fwlOr6qV7
zlrNJ30*3)`*FbBgq1Cz<+@*IwAMFMZ?+jp?#CEeTx+~|<rS$=~>Z3=w><3pU+R!Dr
zo(!nZ1IAu@_uftuCyM`|K~e;8oHbNKPE-y6z8D~T;ONn&>}ZP{@_EI^iW~Z*Bdp84
zx;vDr**Sz5@&GuORZ&^5R*K<LI=Qe4a)xT`L-_!|qOxkqF0Yk5c;76+^-|1%<yAFG
z&c`j^VxBL;d3v~`9Lw~BM^0SDCu0J#Ss8FADt}Q)n%@$Sov?vjjd%jIc7=$7*J+GN
zK-&Yd4j1hE!t&T3ib-L<Zg4thiv8>h83>PKLJ)2e6DeJH`@)Jl2(h#s<miFFx(6DU
z7XQ_)`3vi#px+f%V%?z;v~cOKl9!teYq?BGK6Xs@!!7BF<l~Zn{fxA9NlQ+`^*m|U
zzJsZS`1oy+AB5(g!9~Z6X;Wpx`(1OJ002M$Nkl<Zx^*(`nwhczT1v?$j;rLOM~swT
zmMoU;-MY(DFU(iK^%KuNCwJd?Ed=htG8Plox1WEaCRl>A-rl~l1e#lSKlF&)Ip^L6
zRaRBgogGfwX-_q-bNsTV>RJp_R_8IMVIV|I5Lg-O$;AoQ$8d9AURAFA>TB09_F^!#
zDg+l<pE_-I$uyv9xovOBRCs~e7(Zd6oCARRD=;R{Klzwck!ip+#(Q4$&DSRdT-&a(
z{;XAV?e=MX4%>UY2WMy-(~5<YOf@8wCS?S&MGdJ8+IhveFD(WM0nEeu<B-tYB4z{N
zsz7b?BC4{yF>T<6b)4yE;t(f#V`ed|QB6WmCs%j4yK|D8dWK0+VS()0O*hAU_n{a7
zB?;3vWzu5P9s~QZ1{%stS6W)8T;ft8fl3b^h<i<|d&U>m1KDIaZ4v7^m{%_Ouv~R^
zLp>;$Oa+(U7RFMxGXH^dHSjdCI=xzkNBT-z8&<u4plC-{^gu|UoBB1kGrRygdG)Aw
zGt-=WoU;gC^LOuk_cbR=v(nJ5_-(iUNB!(&@P`7|rI%lB@3K1Ir%!L;ues#jv8P;t
z-^oxncue8%!ABmpo88^ROD+SL?w}@mpsZ46UU-%a=@ru;VX!~Iw`NjpEr8?}k~gfZ
zKbSj326c;;#}HFG6>;H}OL7Px=&~E@IynOC>LZB%UdOob8i8TWB8zh)?qTA<4)QX9
zTzg}0bDB2ltPb++6PL>D=YNpIGzj%shuKmtwXjT50haGLY@VOl2C6gDiv=eRc=j0F
zJrc?tHL@)p!ih$RVFf;Wc#2&4#6nrR`LMudTpoO9r7T#uMf!CJli27$3G{Y{%gt&@
zK-hs@N77|)Qnrc7!tKVuE!C%wOp$KwTLzFd(j>>F7WP0*;eM&kSgM2&<b9oMlEgVP
zStN5ItaB+;Fv*DMg~N#J)f8HoUmaG(*O(G{mv(nT0M-2OtcA@y?UI*+hus0|6)FSL
zO9!_{j~;{Pn*?PQLn{JuPXwO%Ei;X!MKs-;x=Md2X$1o?aFC-1TG9hk5EGg1z$vuT
z6W_aI#ibGtAT<R_P$^IrN(R3l55=S`D7>ExMPLr=Z)GKXGUYUthpx!&k)O`WSxckE
z*gjCuaL&GG=V6QWzLrPZR&lPgB5Uwjf0^;Rd)aXR%``I+&wB3ecztrUUx5KtZ%OZt
zv7iS4>><H^a!#l}tYllpJ2$AnapF(w0UCa|oHa_STSZINqPN5|?U0m$kKtRxFV6J#
zXh-&%fs|Mm*C2crL2M)3r5YHf&+?PCm)Tqi65~JPeO9~rN_B_H;v7BPW*#b0#KIfL
z1Km2wPbbo3ZDzI<;yqVwMLW1|XBq?eJ%;Ntz$m31lqm)TK)C{Q&_4yR)^472YAM3q
zIl;qQM|kE639_%O!h5d-uu=g~j54lX-8uqyb10B#ed*YqnjYYLR!98aAY%&!AgzJc
zyhd55GraR~7+`9?$7Kl#Ql6eJ?om-pQcwEw>d&e@g25q|Tq4H-5<4*_HqMp<kfuBD
zXoQudD{c~HvSr7QO9fnV$HJ<c$3-xBqswpB&sf!rO>F@1UPt&wTmZ1c{}#S6#3ugf
zCrN?QKiz37GRt!7(zBh&(+cju2{bdNbrD>otFq~G{E9222v*F_u)<cLcU?yF4%*;`
zFq)A!+#u<|AlNr`WL;O?1=Eg2pPR2SKaG6!UpQlpvmJ*4T$7ll8m`n+Kl;c7^rH<V
zT65>FUM=3R+V(xmhL>2C>fiOJ3~N0abW}iAkx~L9App=t1g4ANtrsEI3KckAD;dQE
zq%rP77LAS6z+jQ@T@8(@@D5btS<w?L*E;m(#;xgxYq(CJnOmMCAdSDKEw7;*K1=*p
z=jeS<1Fng8sUO<OG`V2;nBR2Xln)2Erlinl5YF)taceFIg>eB3U4F>C0bKh*=F!T}
zO}sEC2f}M`n?O%#-^vTt+%7OQ@Q`+`ycNjpgLL|M+bq^Wjvn|UJ#fYX*VLf@9`PUc
z?f6}yqT5IS7|(FH3F!{^8<)Yw2i>sJvhpA_rnYX{EP-$>-4s#CLhvdVuB~ZB6pRUo
zF?1DS6!3%PG_A5r0aOv7RaUkb6e=)+8S!z4hJ{NNj;Rqt>+2x6&^F+>3>MjRNo`GP
zfI_((^lXuKU4IQx+EAtm@FrANM;JiMs*0h>QwXc3dW%e7k3jKt3xHNoE*J*=G**|>
z=8kon*p}uJpN$VD$*ZARR#1>H^PhZN-M=d)b_%;#l&<B?E%q--r*2bqJgYNV?{i^b
zngtp*fkHXcKPIQkDQ5}Bdtl+R12?O`-$1$!cTlr>paK*9x5sj&8p2^t)O0Hx;%1Or
zdxnckb%ku+zFjTGs6EI9na0GKURU!=t9?i~BvU~*#g(v(S4s+aSGky@24fkXl44S^
zPXL?GopFfOcaC#RPvSGbu1m1&$tjiNMb2^@La}tj#>y>&@(A*%?@Tc)Sm=X!<a=>S
z2<24Z`~UV>j@*S<$`<d~DXG9wh@%JUdw|R}V@{L!&hwk#)<LIP4`57d8xbH+&K~uL
z8|I4f@T3Uu#SIq?knsaM%In{5QgNS8WCFk@(Nu<GCmAC>q1{kjCIcj`;J*8&D~8IP
z=|kZur=b?GiUkefrvjO=WqkF<X!L*%vi7xE^86=jRa<fp#z6t)<mc2+mz!ll8`=Xv
z^z`%k$kR8Bkr~e{gv0{#0tB3m)fF7zE#EzJxxD!KI(c>BR>{aMl1u>izwl?059lf~
z?Yg0!sUthfW7mz6_L27aQhtAGB@O<Pi{QV4s#p*Dw*rD<Z7%&Q>ixUws7!nx&%Fdf
z3R4TztDrPDEn7tGcxz(3Y6{^DZRdzF%w$bso#x+qeBi*rI@jI2N4_frul2+pQ}3|;
zB=)(^;u@h-rw%f7sC_GSt89+LGrR{FBl&EAo>Sq@Iu>!C^^^dx`lDm1vMN1S3Y(Z4
z^!b{z-_6f{h>hmtZ*Ce|Bikcdl-eS?>9%#hr6)08q?uih+p4F#pJjjR(@Y2tk{l=n
zymAmRNgG+~(4G0#oC5Wu<@;bf%Q>A}OB)A(t?$m$XCLFVF(y`=u6<m}SAHYTTYePh
zsuBR$E#yVu{jL_MKsAgf4DOI%K@r3RV4L3Q`kB@09S5tX6vkiyUK8A1jIkGq!6&lO
z{^9z>7fR@t#zf1w@KA{>gbXmhP_h6%m18aL4zM;D>zFo>-*tq?s3=-c+WEE`t>`qk
z=mE>nnATyEky9-FdN%GMg>!C5ke`GE2S^SSH~aT)JpE~KdyXdj8+rh8`6yU9H%9#a
ztKrGC1}?Q*MEGvq5r3H$&RGCo2{Mxpt%VEnjIY0z)^NQ|VqED=fXIIR`zveWDuA^a
za6fK9NKmdKm4qK<>8+NPoWndWuzh84E1v$zClUs4a8CH{tciv%Zm<CNXx9$m3tXkk
z2Omfl>ZJR0-(kbVBPK?Y;F7!o$7yJ5HOdPbJ65^iriHUB`pFq%QUqLidv)nzS}$8y
z@&}I@&)N^J+Fj8`XqhUjc!uxH2S{BAxx7ywQ;0FPEgNyaqX7Q@Td5rDxkP}{M+&MX
zwFCflX^mv!mk;H6gBuOk;5`)vg1~v9@B{1UN|W+!73RKb{`gV=+o61yYaA}s>6w?|
z>(r0$p#qk%)%ML>oG{6RSq6ax?N}Z1n5T8;8|<sfl-$Hzu_-+pq4Juqt9<6N&=&JW
zB~qxsS}nh{N)lm!1?e9CL%t3SN$ul46u@0uDItjO+zD};dqn$6cNk2a-zQwc%rT^q
z*g=jSIH?{uNh#w=)pc5BqGjb0XjajUXb@Z@7~%5A2_TaPfGt00bNPq1k|<0FUEt=T
z5446TVB)9S6lGyDOV7*@H%vZZ;StK61M@IeXefZ_fPg?rJDwmXpuyD|lTmy;CIA3m
zA%VfD*B1jBY5}-S0bn@lC-5BycO3Re4JVB>%*hJWc60HBn+Uj1DTdn!2vwoSqsBPK
zU@ogHm9+d566J@9$xQCf9?~mfkQ~h2Ek1?-1+wx|ZH=0)wPbZ$HP+RjT&v4G-%?^c
zxDlg9%d5{rvyBCyy#<Ji$AO+;;OR(=uSzD$G=Sq#;yv?(yP6zm>)9eID0ukz;+`;p
z;aT~3`^#4gzr(u_V7g};nJBb#Q1g0VV@5F+pNQ!K0F6ok-Vhew221O3xO3jTbqADF
zN~8inG-HX^lZjd2v0@sy)&Il0sNQq@A&||`1JwF7I#sB0_<T5CxWN|-&l9}klA4P@
z{s>_E;~O6y3S|`jjDbo!FAGdhQgMYO;+KTD_GtjKa{y)&bf-HYlg1prSG+6gdtd`w
zNHvJvwIdhSfN-^YRwFTMTUe|kxuXXf^uXTjo8`(G*Fhl)!l0mF`SRO^(jm+;&S`^8
zjuU@<4`4o>JfxExhgI{@6x;Jua~qYF2hSE998Z#B0HD8c+eEqd%3-n^F|L>INRUGb
z>2feNA7Ridly)-5Qd)bo3H5<ha<Gi)8zU12#lpR|uiaXsLVRU(|Mpl^!lMR&+`h1A
zZi?tO{Ixmf%iRFOf8KaVR_#iV1F(2b&MZ=z(PRkS0MK@e4v@h;V&u}1-K2MHgetac
zRFD++!lDk1?a?ljN!1)+t4;#m56v7YGZ0(+yS4je#kQle_js0M<v|#OzN8Su6YcKa
zI!Fd~jgl$DyGq~AjSFB|PcXoOQGMHr0X${vR`#uFY_;iR&vl6oQvGZMPwna1P=VlN
z%ROE5Ifssss*2-cEZYLtYYYoSUZ2|W<Q*(q4fYlDKY2pzFr7y`acX<FI7gUV3|glC
zcOQj@2200|9puQ7_&UIve7)kg&~X}nqbWLZYvP|^Spu&kXZ7zZg9Z&$0QSGTryO_J
zoE|tA;3s1|+aku14#pVkYs2c8>3>Mhl&9hnpfqmv!CDm-iZTWOyjg;n&@G{0gpB$t
zh~rC3si}KzNqLQx#r|)M`0VI@{Hx>XvJ$b%lVR=N6m87Pm#a6$$y;Z2md;j+2u<a6
zT>tBOz~JRA?&n=CRXvAD&8lz2xPK$8wqTIK@m^~Ncba^EF)zT9t-1m#;%-<I!`Q=s
z&@l$ir=}@Yl}1AVSA^f73*rjjXfbp;SB&8RRj`h3F0m<rkj)I`FBr;!YHZ+HG}H`8
zHMgwOHJukc9liM2ZL)sLUYU0O$R?W^-YQt$dGTIJOiq^zCJb#dy(5962mVM8P{!*F
zYhwbl%^{6vY))D&jSAZdFLLPwq9F(5FjUT%(qDZg5wm6?<d2_JL3W$>+i%hdz%hxj
zt@Bo`Qot>bnTO|cSFBL!IZ9L_PbI9QxdP&-V}M+^9C4^i4jvS5czM+m(^J0Z0gGw6
z_9nT|kZ#5dwa-6Q^zI12n{`!VKO3&mv+%3LSVZe{cjWc!3-3cyrbr0fh5sppy4>+R
zTER*^<+<nT?!N~2nufU61g_~)Js&a33CISH81bhx<8&z04RcOgA7_d1G{VvvF72Vg
zkY2{v)2@<NQ434!a=813)io5ZvM|r(Lz%k-C^o@pdLwZnKteG-*E;AcD!x;$Yd~59
zWXdXJE!85_yaOQEJoT8~wo};yEEuF-LiGpx=5yMd$Cr-GCBvtpeQJ@b+FB)afZ_#j
zbfIt~9s)0(e&~-#gpBJN=_CGF|F=T;!*-!w%HrA^G7KNc1svq)fj`;<XY5-<HyC4~
zJw!k&<3tkTCFV*=ZkFT~<Vbcl7N+q>WEUorUzYr!1STD0yT}jAR{;F7uhq<lv1|J_
zDbCLm-@p**+^s7>X_LhnHEp6}Vx(KIKC)!tH?sKq@8ss&?~qL!HXv86^nf;3KtKS-
z1PlzA>Ih_Z?%7LOh$b94qS_P$;lD;0?o@1p8UV<2Ct`~z^sJ~c)vsMAkQy7>OZH{$
zz~TXx-_Cvzih4;wSspZLs%3x9F6rNTn3h~^r*^$o5_68op7d=fuS`vzl~r(mVv)*p
zKvxq4V7b*(jk5B#Jjd&_UM<9=A8aRvcUf790{>jt@3<dtxI;UgHC*XFIw>^;af_kl
ziwP(+6c+1n8AH%fPh4D)D2xk^2$$;LYvGdJsQRJ|uIbvfw{J<MFUPTOzX$RGP<)q2
z_p)FJuxO<hgqfX#CDz|vcJJ9ER5IcFN8%h^FEllkspf6G?TR5**Xz?TLw)-CedX>2
z-%%~fr4=^;Y$}Bq0kkk~J*}{rhB0l(AaER_f(B!YL}4)=<LM#85Jv>)wgR)W5r-<d
zq)Ltzlqpa<9n4U6DFoz<J&CeRZd_UhX4j%U-y+s^cWAzO06=$;qX+(s9;kv>f}>E(
zDgkiqUj&6Cymt;d4SN6r{kMPFC2#(?gQML^H;{80$3!ls>lQyJSy~7zkuMqDL;bke
z%PTIEGAQ=YZMG3(CEaZMK=9^fcH!MrHIs&Qkx9eMRwBH`hOCI)BHBg<%FR;;sb4AD
zPz0g+|Fd@=fK}D!|Hq%~MG_JSVK3QR5C<;Qsy{~^b=6U=ZMChfty;T(ZLQXhUt4S2
zf3?fiWm~Ov)=|X`2&jO7viArO5<>R;f1Y!5lbcHd34&g@@2_5R$GPWx&+FWA?s?Ah
ze5-u?YguI}`zJ3)SvMY$jA;M&rL%&^!*@kL9=<>FZ+``sufOP+;Id20s8FE3MrUjL
z_m*do=jlgaS2{jz!XSI$*H<?`r3)(@*{LpG|NKQq+vhJj%4y#BYoLpZ{!k`Q>UFxt
zh&a^}%NAMVj+d=r#~f>_SZPW1J2kX*h4$lh(we^?5_Mv%P8e-U(Rg#&V=S@fNgCJh
zRApDh=YF17w>tm4b96#yNl+eq{d)Zd)3|??l~ut$nP*@7XNkXWyi!M|vOV%tl6C0N
zAvQu|2j=H<0@R1{f{)RKdSG<)9<q8n>b8$tecR&I!3j~3(<JLXd!ouwPf>~Qp~~nT
zoRw|``V98zyBfGoZF5p=<WfHT>z<|UCAd9^7}-9h^4<OSP<lLa+Rj{0jrx9U?>T+6
zyf2R&7+f~#H_n^Nu3o6IuHP!zWoz{P-(D-%{=jXER@+~vjce~=?u!)xZU5v!Bdqa?
zuUq2E#n$k~Q<k*+O-oarqYta%Cq3+G&$DIk#lK6{JOjJYGqCmhI6)^5CMtlPtj~ZH
zU9a&>x1J-dY3K=-(r1!_Li^zay5{9}W&bNy=sW6%f47buIV8ArG)?6Fesceb({us=
z4S^3zz&{tFzMJ;zqV*o|fUA#5y-R^-KU(g`<m#3$x9v|o6^vWGLqTdEgS(ri^B`lb
z0@795Uk!g_djC8~z5edIgR!W?{male&*m33|J3qQ+uErOZb$#)L3$rUyQAi1&3&xs
z;J3Br5k9K#0nWyf606Z=yULgEP^S3S3ofuxzxhotesxFl+t>TOBzl2@;~ReedrQ<Z
z-Jn*-GK*!^|BGJ?vc9)J`l$6d>nxpI6bi!c>#}0~c`5qI@5gP!-wAy({TM(vq}G~M
zU#_vCsx(BDk5OHw3UQlME_|ng+!YFD`+ZwHTU+;)>$|K<k5}oqwP$qitk$s<{q;i)
zb&~eu#!Wir>&;`x9)Jnr2)aKQ!`dJ4`*m9r>#w#>qAr?;UevnyiDJ6#*L}j^xOjtN
zTB-)B@3&OVJ5}qGqol4>zpW&%OqB%pQr`pfGBlpGPDXDY+qy72U1h;DRVF++C>x%y
zVLUQDihTfEovi#fVXFGjndX)5oT8)YYgE^L`_3xsM*>%8IAdy>@VhLgI`u(x#>cAt
zn8uSh=e%?Eer~dIon^IM2TnF96MB_`)(aOdwAIU&+1j;hti-dZw`>Xmrr~nG;Xe8s
zDcn3`;NSmxSYzPU*hi)v6O4)N8JFICw>(W}OkI8LXYIMCp3qp<f3<NM1LB<pZ(C}5
zhFx&c#kO9{_RkY<4a&kk`j}&E;LxGAZpBKQ@$7SU(Pfu~9t{7hX+3b=7<_n!?`-{B
zs}I4=WovC>pCeoE)U{7cy51)Dofdou6lC>P$%kUQ^~D=Bo<)YuTJxe^IOdZYTC!Wq
zb9#0wwC|nr8(UGl*orHQb#hRH4yGpAu>8?2(}b@CpPAmLsK9lD-&TKH_WzroFl*-P
z_SGA{Y!_a9iJhU6Eh9#bvh?OsDqFQZ{-`ft&*1W(9<>UDOq{46BGEGmFZ@IJo1z_x
z$hK(FJ62q@RX?DTb#S}Vh7BJTWZ1U8O58wYrB(jjo4;mGJpKfG=3ftK$3<o2b>QxS
zd+xEH{Nfjpvkk7lvtWVBj?~)ZBjRJ+klg^fc*ipvcG@-t%^LLumZ)-J7Z1+0<N9O=
zRStYC#qfKx&205r^Y?U{i@$p#?~A_ekLfd{F+pd_`F?@V(D!FldBAsNd@rL(??n$}
z>r@TDC-gqh>%fEBevsBn<W&Wk`oY7Yv9k3;v3IJ*4(X9*$9gbZ(^qN-XuI}g)+sA{
zoyJ@BzgwyjY=?G!8dNpH&)oDK6yNy{jh!9&$_vfAsb1su)n?cWn|ImS19C%m^6&c=
zShseq?I_(D%*Vec`fI$UJPi-gUaKtMWvkY#Q)LZ5sY0<2RpaQ}r!`}!Qa=VZY}^!l
zR`~P%$5Ek%5Xe<|y6r_<Y{TZFV77gAHgd0CtwTGkQJ}cg&(75kp+SQNS@+!5EYxlK
zJS|cG{ISp%O@a+O>@dx{-j=+(R3(k;En6qq3_EOSFmL~7r-r>K+P<UZIsVx-aNr>A
z3?^GmRi&-lu&MdQ9QrIPEbOH|X4|^8>umMf4Z)BbDSD0j5?8HQZdTUa1`g<N6}!r<
zxKzVWGzYW&{Z**-$kiFne$v(&*#z%TEzj3ISL;!zvwi*ZZ|TzIL0_}FW!s2ht$l6j
zbn}%B>(<(~9i>6=WAKonmYbbr>(_5kIsYnsCMRn>2I^#z&g}$8&-+ljU9FR>Li-;6
z!S#OH`+ffI%l7dE^Ro4$-Y@T~HTUg(bTLCe;{39==GAQU#@O3r9X{?sdB42x<{vH}
zc+)aJjKzN7>)E_O|9s1x5L}Xn=xtPArUUCG`m54~4GOqu8zdxYTP9`ez*fGFZM5!N
ze^_0PmF0Q>+AmY49InrH9k|gxP_@S0^r2Y1`TkKjaGjCSdN3$f`cFR&Zh;5a7KQfL
zBUYog0@S}%7ViL!%N*VOc;KPd`YK?&N~O{x+vuE3JGoGwRUcCGKK1QaU?0`R+eV$p
z_4vkZ_LsF=EZA3x=+}F@Duc)S?T_!vi1~7L{|h8)e>-uczAJ}~v*aQLe-_QQrloIK
z!nQS*p+pMbZSceE`t=$lWcb@HIF#fM=;A+J{5ObKCG}zVJc*&PLB~_dbzg?6&<vPt
zDZ{5)V*a50-+AVUK%5Ks`!Pt~3XUO!F#NZBy_f3tWQ<O>AQ-LZ#X?KcF^}F?TwyzB
z%+QBTs84l@2W(arajwdA7i)a#-a7W~$4#1ajKtT|*EigIKLI3N$LS|1OFKowgYK;b
z>)y^p?$}ZDOzhmFj<S3u+f}Lzw|`sv)Zt5T%u%-!)Gr?!d*hTTHt>cUI-1=+-ydX#
ztE73snP*zDeEevp_q%cPX504g!`A)OQ>|+CY8$5UziA<XZmRy_7rk?WJI3U<jTuBb
zh4(LObUbE9RVaY%!EKL$=#K)~9>5O0Osf^(E>XE|9TE;=(?0BrAI9;Y{J~2}|9G8X
z5Aylp2o(Ueyv$@JEQb2wN51^JCjxj7K26`vnYz+5J5@!0^?mB!xBkxu*tV8b+5OMF
zt4dmlcIn9@tWOUe_ik=4&);a<G)%>%r-V)h3E$t*-<RIrr0J{dvX6{tcaop{{VmJy
zmS$f#e|)e{(e6at8IuC>8MxLP+5?0>u0*)ys9bZd&WIW|Vz`}f$|(vmH3Z`;Z&<h9
z=FWLT!R+O>Q<=dtUwt+B;P9Pj-+}g$;Ndn)+0)NI^Ni)>cDFBlNr7MmFCxE;d_0H~
zrcbxe-*mJ6>%qU;UBA3jLCzvO@8XZ!(bJ|WptdqRtDsbPfzI-}>QkSw|GoJp`{N&e
zXJ?&vUXXbZp2){-^be>e1<2ZM3CT8R!>jf`BQI+6aQBQJcJ#ngg8Ni($+qs=qz|f8
z1qy0yS@9Bke#PVV(J`$-a(#s9tePwvTX=*%23x)&gM&(KW~>`Qf7<I)@Zq|9KOcMe
zA=|KOrQQ9DpV^=O_y;>|*kRUpz~JE1ciED6Y|V;gfpLRAl+!c2**PD(AbOD>%5C4S
z$qL@Q_VUX%<*1{rhf2G6pybE5eqa6ZA7IJyY_yZ~MefbVJ`cVG^gJ&QK7Za^yXNYv
zRc5-zzVg+tS$_qCioL{qdYV0~*G8#=I;Z~68J43mFdNsdu{pD6*~>a3aFWW_|Lx&N
zf_d^!#{%nBs^!0%b}8#g8G0%$d2&ITojW8?C!nb8?6Rf72?LSuO}`#oG)uiyL;p74
z=P&Be<9^1M2hu!99XiX+GoBm${pbJdum9){%^`Rb1Xr7aodACqg+4Qy=N`OSg0txK
z+7AEq8Tsc*gARE6Ne$IC+kNujv&3IF>H54W(3zP7edk9dSiDv1jJmDW^|oFadTW%K
zy-_<en{~ZdXY4g7GuwY?_|CGQEgCK}-l(Aqp59nyNA=9q{8~Q;gBkKqmjqTTAp4!~
zecxuh^r{t?lm^?+x8abXhuL}OpJ!kB@)xW}ONLm3-Fwe{cK6+Xu*C|%hRdURQ1<L|
z&b3><`z`C$BhTi&G22c*=fdD~!#h*0?~b3UJX2~@js5hOzqWN7n*-PV`q?eF{lG3d
z_YAxByWg{W?)$Tq`433F*hY>XW54{x9d^?3#{}#1a}Bw0=iM5UwfW566rB`t&+mR^
zfB5|$?Y}epd#%y3G_1kL^nLWBAN{}zbnx=QyY8}^ZvUyJchm3^dflh!HTaTFHX1v)
zzb$(E4LkWPjoGBvm$xb{_s%=-uupyB68qSB=i7U$)(5-I{%~bkseSyMvxDHoOEaF+
z310uQU;O&+mXnlfYHICQzx=s<=2M@vof=c_<WtYIl9C-k(79Sa#xB)o<qg+-(r$#k
zc{tQ>`!^nukQ7OgrA0zg*6gH`J$rVgu@5Ty7!!(5Wl2KzeP0H{jBN}lvhOp@U`W=P
zv5lF*7=AwYeLv6reZKeid4B)QaXF6nG3I?;=kmJF^L3tQ>y~SVvYX>(xZxPP6Td@f
z+RWCN$m;D8^v1k?+{u9B(Z<-5ki86eOyrX_+~gK7iC?p6!UsiCOzr~VLN8Jq_?^NZ
zxv*?7Vq9xpZ*3+(v_{Tp&4Jl(+*mHXVozY#+3<3Gweu%;J2pHi<ORnun13)Q1bC{8
zFgde6<1>sc!Id+<o*a3AQpOH8A?aA-T8NJkDG_Ejw;%VrPW@ChgEpMe`wpFxVwKC=
zd@ATw>nh@_C1V>tni8^`ED<sl7}*=GtH`Yn2@i&-KDfM@lR<O*qtihFV(+k22f2Kh
zQVlz;Mo|6r#a~Z(@kIq5-r?P;Ux91w^-eXuDIsxIGUZv+gX=1_XeZzBc(0^zi2RL`
z@<JcDO~M1Oev%hE@|2D1##NP9m8s#kysil_pT2UANwa!BV)lYis`q#;VROVexwqDK
zhI%iZhTU=W8TR$p<owxfbXa5eB|gSKM`K2|E4H@$=cg#1$$;`7wu)XURVcEt*EmIu
zH8!f4Lc3QARwH!%HP-++e@ZNed#<;Tx(>V!p{d&KO+am<WHV=`A$4@?nv(84_$I+|
zpw^<hh%J28pt<2F!_9%eymjA-wY+n20!$>uLH3g@%T>IqdDhJW)IOmDn1m$Hrdxj}
zrV2su091&79@j>yS5h>s&Q_>})*C1zJ?TD^JmOFmyHg~nPt^3SY$$llZ_90&n}K}!
zi8Nf3J|<4Jv~2!57w8dEl@<8ogpm_eADs8_p6*N|a(dPtK2sdKQud?&Wky5rr<p)X
zB1@HyOjFbvzVWyrXhP4;GW(5`{DHs@-63>*P2^M1q`k1`wuhQjrS@fPYH9sUu|h>;
z|EewH=~~gN8r^tLb-GI&bIqb^ACc-b?O|Rl>>>8^Cva)KZ`+xw)W$%meAbu^--;5M
zypb%@v`7_s7dg#wr$Ldmdk3)F<5k_Dw2br^I-=QBSVU)}_H!yhPs~q<s(iE~ObcWn
zeToNc>2*SKD?c{JB2jwby`FFS23LG$%-&Sy7e6DUyV8&pK|+SykK!F}R<-YAAyl2+
z%DheR2!vMwpKFp>k^iC9qA}HHb56GrI9orJ9sc{nu#IN#Tk@H<M`ZrVNCf`u&8=;|
zYze}`<J5{UDwh+zSg2aAC*Lj@_ZYu{pzS={-HI>XqN|4X=Vauc`Fwdu-38``^qFZ{
zEbR~bVENUWr6bFhG#-0N!Pe%F2t}_-YwBGHc1p_fawvo=^z(9)xG<M20g7B#X_;Sw
zP7OF6zNi87eudMCya_nSQ9)MH66`=tYt8LoAZ%64ZfEd;pi=mQyH1Bw4(*UXhaiS=
zg%wxC!z^t@V=-fmd-?6Xf$Q~qoa$Z<ZbtAB=qmV+YW6^?vv~a?9MzqEa$+A>_%`5{
zyAa1S@!)}rct!1Dm9<A3+^Z-S))sJ#<}P%Pm9LevK33G31Od;cC&={48FexZ%t4pt
zr=BSEK9n4B(Qt(iO?8Lvh`vm5jM?M3e|9B1ub=qzu3cy4GgC{4&xuH@v?)Q$7hy>W
zE%|I~JHiw>aVg4yE+!SLP?tx_Xa{ZdBgit%<Fj^f+Z@YKWZ85)n<qm7{$fv-K2n94
ze1UWl0sYCr!np!ld>}M7%`woFCWP;_v7TiYI17la?^!>bkO9uBej=^vikMlI$nNY2
zP|nChNV|l&grK9Yi_#<7stmtv-t%g_KxADjic0aF>^$0qsh9B>NnBRec13AY2M_kB
zQv~_wDTAX1X4X!|jSAbWTf8dD5BP5HsO<MQV5ZR0cDIehSwp%S+(PkhFwiOsyvxz<
zn8=PpYMIcJIBEOKYtWp%)eT->jlmi>FKZ8t*=_;KL<L(gS*s;=-h?Y*qjY%}+!h7_
zgQ%HqCiK?GWl!wp8{`d#y50+W&2$=|pbeB00*qIu?8w0_@wF0lTOWnl6)XdXe8W91
z+e`&6C-|@n!A3zVApD*aeM;sqr^xlCN4Dcf&8elKG{CZx--lw;_a~95tuphJ(<=R%
zr$yhMU_LE+PKg<ya{uDY_ILX25$+YLlpXGr4KD2^YQ{3(?8`i(fT1`R=(>|QyGP}Y
zlNOq4z0%CKNk=3FtdFU~UxA*!P~TQY#ED=R+Z&(@PH68BnQ>LOp;&1@q3qm(*PAw2
z{vx45W=YHRY0%rWJ3Wf(^*s9$GlfYN4Y2Apv3(=~RZ9`<Jc$vOA5D9neKzoO_1&4r
zb8~!~&ToqKoiQG*OMV8U8;2ihvz(DB^K_N{<64_?Ybg8zS6o`5OMdOJS?Es9EJs&0
z1AhcuH3oZ>0Z|(og28l>Hal^SNl5dF`2!nI8CLYyUl#*@Ko^@vtixj0^4Ubr^n#a#
z!Tvj)Amq%-#9x0LpoW|Gdi|>{-h!8jQ&z{3BP*CsmgTD1b#ocP^)>~8AeG(eX+ma;
zMntOg5PG;+nd9lk=$1P)?oYSRn!>@00>hgjPqU)iHBMhqtxjMp?g7w73obcwz`q!C
zLvI4YRDV9+*{I%tz>2&KlEt#}PWUHg$%f=I$UhfDZ)3udW=yMzNMc$O0r)vS0)_C2
ziM#R{|K*-zD-7Fq)NZe8Og@@-TAFejydrp}fuQY!?GQi5U*aEpGmTAyVYb>K0`Ql%
z-%RN$2=BYNKL2~cawtb>XlW>m*AVec8=56h0n=xjf>Da-Rd&9dk(l{`S=7-9dgrkn
z$!Vivhna$e@X(4X&O71f@N$00kflE%%aH!+wsp=xyrys+Q&cny4HSa6HCkq3l}TRK
zv{gU3?uOb=p4n;eUC9NFC&&$;w3*~`fFORE>yAZ>M+b{`#P<M`vi^sJDe(^GjH4cl
z)$f%*4n+O(nis6hY@Q>Ac8k^P=a_JoLXu!XWiSZiCBH-|Cm8_2Jt`Dc0OD|^MZQsH
zyQxXC0EDa?iYd>8=Qu2+y`E^8(00z#UX1m+@TYXr^7$%<Vc3EyKB)r}d8Tz!?VxWI
z@u<eZ3w!FC+OT<566e9aoGxEoxC*O|WQGWI*y{zNd(o<T+0p`}i9PZoe*;80+Nr{Q
zVG0Dyv!+1f0~R)!C>&BZV&)K*!<IJLHr?79M&C`&8Q=Yu%&}5h8P6=~7+73c`P~t`
z3TXM$D@7{y^(@gNSKfvW$7@w}IbpmW1Av4z&z;OQ(O_j;akbzyJKJt%-;Gi;#kczY
z<y?ja^@Of9LvPle*-%j1tX+UKQ{9R(ON0y11R@<Qs+`F>!()%8k7yb^ut5vIMEi#>
z?Co%I=<m~UQO8~ReA#FU$DPdZ2094C0>WV!inPJlWaKR>4VjZmt<Q}|p@l7WoU_?#
z!Rmn?;(--)JJyx9z&HM{`}*YErZSb^028~Kr|QD97RSHh>enR#r-p^86ZeoradWmw
zp!uoERQW-gGFTQE7Mdh21BC$qz&@78Eq*OvCdg4ZrNRk8OM#9IpC~K8c&~RIxsgCU
z?t+Y)DuLVx2Hz+u#<{W<A(8vP&$N(qgPxU(gqdnl%`x^aLmK3FjJ5b9^Rmrsj(XS0
z=~TxWk;5J13^mFvyL#tr@P4I>{2<WboF6y{oCw~JhGO5CXnNp}mg4rWJQ;sI=Dke4
z23ktHOx+`(UYKZ7zR5ySmov|Nt=^M%s{17z`ucIrK}H)jAVcX*bes@cymV#0h`?(u
zN7B`7>C6uOji;9N-9ay#Bx(4zOcfj*w5T8aS_te}*6>QWwX&`n7e4n(W?s>C)KoEO
z$w1Nd$FQgtk#{f1>SN>#_rc;2HA6LgCE$2~Lcn9=YR2IZ<QM@5qaP;-1C&Z*Ct9bV
zQ=6U9ED5@UxV~i6?g1>2mZix)>!ruB?h7&zC!7&+O;9^5Ro^Y4dvD*0OD&-pdp<5^
z5JZlK^>hH*h*^jG_;6Y`+ifGlaBl9tjav>NL4E%(#$nLWQ0UG|*~SLPRK?(vAEECs
z%TJF8;_+#c_uNb7qEPNl4JuEJbWSj_oYNA$$g=%4<ot}|ZOIR9QjO{b?z%Axc68r&
zw6O;+V(_d^V-oGiuSWo6bh$?kk(%x%F7lV6&E>(EAYaw6h}4Bc;xsLC@p1MXLdQl1
zWecD6!8Fb#Ry|#uFqw)O{mol%|5F-QZ-WcM!ZL8#J0jV0d8H>>@Hthv4GG%Ds_$6*
zU&?ATt+9~s;^mRgU(mt~eht7LGT?W)K0F%UKZiwE_G*jGiN{|E+bIYytt;BA{rrXB
z$-4<zB4wnKZF(^MW{GL~q1e`s<1oxCs_=5A3wy8USrGt@XO3OqW7XXB8@uL}Zmuu#
znKrYv-Ec#GwbF$km2;D_4uANL4tMU=Ac7@csRucwv*mrV><yccMzF9O&fGMr3(uiv
z#vLrm0KcP-fVyp4(L&YTAos?B3zU102n8n}((6i%)w{b1cUlj&Q!sBK@9A5`t*c|w
z=4k|Z7V4w9`m&QGw;WCMtZmys_yIx*$Uy72tmf-cO<8Nf{B#SU&ch!b1^)fR-d%Qr
z{$c)=4nealcBraRVF<X$fuVVww9k1HtIrj+dar2nc^jW#kZoSqYTNdXsns{=T4HF2
zutsZ%kX$vb%|B^5>=$$GR@JMw=l6F=Zm6c^Hb&irBcAQra@{y#q1dYeAtw$Rc}>oo
zofcPuTrOGZC%sDIMwrDVIuyt>2(zl&?$3?c2T&%oo3=OAI4m4pdPllfV(cU1E~yqT
z&V{yh4K%Lr6OxtTAU*uAteBKEU3)!rs_(0S4-C8%9@d?5VD4fk0sy}baP@le(Gp|r
ze&sz0{-T0;5J0eHUg)tsG-j=YsM61ZLRS|;2Q36MHK#e6`JTC&t*})moOiUc_I$*b
z!B^wxTA`m}H&Q)dWm*#JA~FPBl&Wfw8$prOK?ec=LC7)B)Vm{c5B^k~{kF|^b>umY
z6hh3K;x7HH%Ge$Aq5C@8dGp%Q&k580)w@5E1M6E`SrDqR!m7=S^XA6NKi3&J{7D_W
ztFA}b=U;()$vJf%-8thgh%UL&84z}eZjq}&Kv$O1hMQ8d?g#I-0Z+aQsAo*e@~mh)
zI$j6xs4Spw0I?Ng0`G&i_CAginApCMbGR?)TdxK*7?PEdn0+yo9@yBsmz^&w-v;z;
z+;dgk`jbQ|>lT3p1>AJ7*+M&Kyj<p=yqo+~^RO@}>dUtBxS98>xHhx02rXfU64+Tw
z<J-`(K@I3zV0E36+mMa(qRPE%U#qS#Du;m(@F}P%V*#2bszpl6(!Og4QAmT#@4<JD
z!VC#>@A+q)C}VH^f9p0Xh<V1Iw-h*CjCm7wN`u?CgN{yJK9YDMY-_a=dalujJy(fs
zlo3b<J1XM4nW$P#M_iH1q5URZo4haecbXkqe84r;*@;<Im}<6@E%dWNv@3)Xo?%_Z
z1R=B4igT6zrA`O9kO+)p=twqX%hF&%yQRP1yX7c??u{LPD)ue{37RC!^w)gs-A>^Z
zTR&KIuqpv&m5N+lb_i`^94ZB!g2wPBXb#GbnGvxv)0Zr~f>(SYFhSI=GI)s#b~QZ#
zrEw4diq!vbv>q?QBBZug$P4V4PNV)JzX@m=F;<&rX(3o^atf<17Orw|48gl|K&CQ{
z4>Q!sd@bwcoVO96Y#|Mg{I1Z!al%di7hU{52XOF0bVcN{k>=quySC6Oq#`KZxUvkU
zn**M`)3j;Nny!O?Gy>)2PEWX^YFy+1i^3ES{V3Vu5Yt_Wo&M96?i?#51I;Pyy*w7L
z*%8h6lC5tWa??2TQmbOYGGQZ?>Ex-&-B-5Nhh&;gP*xhF9f?95g6S+n8#8ivW95Y_
zLhbri$)B>|ZKac;D-Dcg%^BYo|Hv3fwy<JoK4Wi)X6N&?ZTlr*tYvr*Ial|z>#>2%
z@8#b55eCuDI;ZqxB-=V0P&eIZ|NN`*NhTrne#SrXcvvp8aJ_^1d6gc8*16;PXGlhz
zSl}1tTH#4*<(%?QRgwa$g{o(p6D`~}&wA3MM7{2L#@<hyo@9=?O;H{9LppWxD)qzE
zrz(Av#CPB)V{F^u06^QhF`+B7dLn#M#iqgdeD)|&>N=Zky76hEr_^v8xeVJ4zA5>X
zQT1Mm#104$Nz4JNkC|0jbi`ZpwdWvQFg~Al-BH;B1+|2}S>WU8h`;X*HM&fid4*cq
zvs?m?`z?gV<b1p+d-mJkIjzbn3H^U9WVZ}F!CzhM?Vl-=o#qvRgdt)yD^*+50k%hn
zR>6mRR{pe^jv)K=csf;$Y-m8hK#dSz0&Z7nxj6cQSFRe2*Pb$QF0)qXR<V2rgg%5P
zd_d|bUOrCiq(nB4bgN?fTMGnLZvIYI@v?XXeP}bdr_v@g#k2Fxt%J5d*<ERy5HwcH
zhd0iWb`qWu*E|I)Ny)wRgq!j_%hwC|MpH~(yZdrfZ9DC_hI1gb`?uX(gd4u$3c!v=
zNCQ|-1PCmsT)#vPeUfuUZ1Ywv%$WTHRs?Fv6Yb`9#CQ*Y2fMdX`t>YBwz8=S{MM6y
zh2tuN9Gi>Q?qlNXp4Khp&Q8J08oy_!BfAv6X8`ZZjcxPh6~N({<ls%@((h}2B<+kh
z@)|pb-j3hp_d91cK6!`NFlgKd68AT&eWm(_`JcZe-$wolY-X+(zqMO+ne8&p5iLL6
zy5sedv&|~KHMTsV0;l5eM}D60fg^s8n-HXka^X}&0(*KVEzkP^IAip0f^x4~sHiFL
zem|=|q~rMk9@_0`XVZG^^w|>6E5wJa9Ibo&4&Z`1>wvA*uu9_76UrCH&3CWKXDmS&
zvHl{Q_xG2P(9{HO@b8@@;(b;{HG=$s*jk&<n)YArUOA+9?7ZI7jjyEpKJ197z5I*o
z(($nE?t?+SByk)|R>MV~#G!zrRq?FxQSO|ZVX_Z`Lyrh)(1OQta48Lj2W!e#){5M`
zobk{3S*M+3@RvV&3!gyk%jLkOs#|4$Wg21X+vIpOu6D@XE0JtpUtCbXze*;XQ5=mY
zoj5gaf-=G%4`vb~+7Cu0?Q%Xpm*83qjFbJu=II^#=+i|U6{r&O@G=qPdDu*z^LvWL
zodoHZ(dtNt&0L3)Cra4;)%_tqGKvD==|Aww9WA=rUNX|_(3v?3?jU6|=r2v-dgrv}
zqP^(U4NRx@8d?;Nf;^zHrOkHi<c<D5oaLxPQqtMoeCXt`a7&e@?@`&p{f-57qv0sj
zS;m))4407OeEzaafpPSvoS{b+|Fb5~GUG#!ac$UR0?s#A2>j4%@9?;L%9Ij{!Ug!T
zQPZJw%5I=q2<|geKF6?w3e}8hytd6S`6V}`Ho(CN!oq)>;Tm5>Njuj6*yk4(2|F8{
za6tKTp2gXYu0*=-HrZ){s@AH6%+{-yc?=I<4V6|CVX5|#v~!{zrtuwyO?)!zAfl7X
z)L?ttWD_{Xb<oNxxcqRCz1(cqtGE+wf4LCZ4Yo-f=#Km74czD1(;`#+R{c<`IiT<d
z){T_avA%8C7!dTx!SDCu5F5be@JB8?igl-1X?mB3xDftI{aN)sr{(?oakxO@Zp0G*
zah&w;(8AUozoy>jn$;&fkksVsI9$=<QtHamRENexYk#ScHsdy9jcLph6ZVRbyBE}d
zuWb+}H6)n7{Yf+Zet+9N$a_m&(gtZ%`?%Bf1G+V7fSjbWKPe%p)CMJa6`Y#7X5(Ax
zIb%Wz85r$h4xch78xn@N38)gi;4^=tU}sH^8x{4`O{atJDfWI5Y0PlpM(^q7iIkZz
zSe&(l*O2~f)vD@o9O*U4q(`4&0j$PN*0vb^?!Ix&&W~u={iMKdr`8fO9fcT?4sD*h
zZ$JKOfAdY`+nt~MxMDWp8Wz@!0C+23_`$+_jNM_VR%1)uPda3;55A?V$<dW+Y(eSd
ziIXBlto~YxaFNM#EEJ{vm2)V69}Ycvw<Sw$qvb-D@*!H3a9pF*YxWs?l9V_gH`Qld
zS#!!Vid5yj;zxeY6y$ZjdtZ$((>+8j3sc?Sx|)-!33W^+Djw|(p}|=BKJ=?qzc<|%
zgb*^Ve|T`4j19K#ny~=2Hu_d~jQG3^v%|&vK4DIvkdB)|2m?#%j=N=l6-?X>YB^yq
zrV+a?#8OH2pWgVr+6f%3$u8%acE?%X+v(#smPctmnh4UDzp-S*FI4?Kl9G~mF_c6b
z9IDTd(U^9<BQaW^R0+{mYSB-_IYws&Zn6^(^&p#7Iwhggj&rr<CSfAHdodvY<KgkH
z%S`>%UwJ&-pIC_MtDXU(2soRE?#xH8$1z4vT|0EHa<HwyXD6H2T%zH?#34QEhAkkj
zV&}o8f>3Cndnk7#9mqNiPU)Zw<9|WGhZ+ehMej=3nR!Xzj%acBWhi7MXa}%C1!0#!
zq17_wFs2vWYrD@n!%bOgvK-;azNq2wo*lD0B>9b0HWV_%FA=Q}akzgi{j-gi?oJPa
zJtPhOVo~1?^;~E3JDK;SN+@H`>|l`~?i9?vH2mzRhT|)80Hlf)6YwR*_+XQlJreVj
z4DJZXI`Lbz_ru5dJs^bZ)WUbKJmE?zPTI&iO6VcPeD`Rd(6KfLWh<gIe>~&Jw)lh5
z)>SC_Ev+z0kE?n1Tj7{h{Tm__lRMs+LI6`mgI;pZf<pVH?gnesJeXQemQ-!iTj-~g
z^mxUep-SZpuICI<W?0dv7A=`eBFjx3vN7=(J&vlsxnGAQUr5Dbe32x+?fDpdE+BD4
z9T)x3B4+bLGmThfW^kDH)H6;%AUEHW7FB$R{mYw6;LFwNd=aunK=C8CB5~n2A00b2
z(T_B3^*=?<pHY#^;eL^>NVw%CgQy;hiF`z-8+rV;LIMpog?Bt)ud(qte8_5%hdDN<
z{;A`qe`L2iXPpVgz00G{ysxOd<9Q_6jM(dKJ8bKEb=dJ+!K%>v&o6j$EA}h_zJbEX
z*2^4s!R#b@aaz^)gvQ%<MYM&7X$R345(`Kvk1D+1ccsnR8DKOp{JJ{`12!b;^{1xS
z$fK2e)R`cwGWosilM{(xm1;Mi8g<#rc$F{j3CE_Uor!NV*Oj&q4>N@vwN#m%K!o=2
zFb=8_4nt}#z7kAcHO!M5r|J@1NNnL1O1|*l;yLxwv{^4!{cC~;6ewZ`THA~*PqrAz
zbur@-@EL(ju*I0|w4P#pCuIR1T}~>FT~x7@shVjV@uXl%$k#=~Eq*MUmgP2zoHIe!
zceGycr`%pq99#Wnom}iS>LgNvE#Xq)4s(<Ewx0HbPZE&5*n$1)Pw}SzA;ti}JvyAs
zl|Gb$Kj=#T`U-hCs4D&D$LT(EimFi9*)GuRArS`OJET{kdPChjzIBIEO3VnhRVUsg
z?Gu?UZ9u-n&re8hI<@)b4b6U$KZ@;$iXtQ)1?y3<X>!24Gckv|-6<u&&$$cf6oIi&
z-1US>E3OR1%@%xv4@a)G*I&)2l5at6P>v<rF@`e}3H<1_+HyeOZPV^NaM*?;$7HHI
z{RZKlX8f}}Qq#67o*kX1kE`Cy|8%Y8yghB1Y8Ji!7*JXwG3*dL|30OZBjNaFY&loc
z*0hxB*N5rJw9ZzP4TCL0*{YCPAv?J#^n+d-U2dPIti%-WF>#7llX~dWwmq@w;`lYb
zwqAUcp{NfthHWZv)gnm!z9qCYV3~X)Y9_*Xpd}z59AZp>bz%-~2P72jrpj|goD`e8
zj8=+2Z7IH4H?=6>+iDGECJjj@snn~sVjdw_a-y{SOTTFOIlU28IcQ|dXM}EIpt_D>
z9vseWG**Lg`mG&^pH#4j{LunLZTKR~QY5MhaoylAT}K*aYc<Llp}`jUsxctPZ4ETs
zA~ygFMnd#B%v?PToo)G9clJ*!XiB&Z<#=bW3Jy{!9$?i54^tDoW==COWw=R@y{d)f
z5bF*N@CyPZg}2knUZ(agf>0U$dQZ;!=5RL(-ah-cUpfxaP%E?|Bs(%^jRQAMb3y8$
zB>CiW>M|Y`3PaX4jE8xz`jNM;buYP=Wd^im;ZW^r?O{Fb5A1BM&S&4)wJYP*LmHV%
zY}i}2fAG#C;pAE4kHFI`fVaM|y<RQqTw{U)DcN;2Kz41DIIR6##%VS?BF7(RDp*GO
z6h2rJ*#FqeK_pXgFdJXjnLP%m?wYvc>!_52km-PBBE9$Czuv5^D!?VGzpfOOq_-jg
zk2GXV_@ctq0fhrHQ-I@UqSd2B0NnKQq-o1fw24?>!sx*e5jS?$IxXlQe!rbfX0F3a
z#wJVMB^qd@)%-+zthdOZ$_mh)D#6=gRa9WEcE8fO*gC}!vzdPV5Pt--XSM4bNU*f5
zvITt7Bwr@K%2dCbQMvkkAOR}PAi7Bdcl`tl0Kp;aRkZ1XegnoJ#(&;d<Y-_c!pg*W
z_0?*TRq<RkTdClMr$({qKJO!!G#o^@W>z}TzfubSTpzM>EXxxCN#UAjycd_mZ%AFI
z-v!t&SEwwXVukj}tKo1QST~Ol4*}{JM*7XBvtTF=m?8(zKWC5M8a05ME|@8;TzY!<
zqCff7^$kdJWRHQujZ$kZmeckBs}}&eAAJd{liV{sDxU@6zuA|Uy1y(oHS?36A&O^V
z9acZBX=t;laTxqrg3*Gv=`lC)INfo|Anq>Zx`~~_oxrk4vU2%m=p+fjwh)Y2Z4z8>
z>?}uDnvOmw117@Q>bnoO5>m{b4v!{p^5GRd%002>9R#yD-&v^_w>LNC4<7w3QV1=a
z(=$3~y?^5^$1cB3mj3%7F@La@YJE%^eJFH;kcXyTtWg<9)0}NZWH1_fF8ibB?w@0h
z>hdg&h-SMWYSd6Zu~GV?HD&bR{IdAv;1Bx($(6=K{dMzE`%(o+1kj;w+7szb|NY2y
zVBt9kA9w*+U;c4;7pzy7Rz5xwik^>H0R?Ak7r)+(KCHGmNQvCs_gK&@H4Bi9a{EeX
z&bhJJ{fOaXsp1P95cjE{9qCX|M-*rUvkk3`&U7sMz4#a)wNlhp9@T8IA?YQTuo3_y
ziz@v?U5+D~uB_2gllwZ!^|SZ^IjHYtCHZf_yOqh*HxI-G*AFu&lF41`rC?8Ye7x}5
zI74HJT*B=&z~ix@7d4Kl7RW@G#OxKx4V;{FNYe<-#t@5+M#gk!vkRT;o!tL{RBNT@
zP^)13Wo`UHF<0a2%?h)dwQTub$u^Z{;o6UVqxEe|q~_9#UyqLrMCEAQq#if2;#M!*
zdn6)$n&+NB<V)m$<BQfIOKVp4>m5uV{<^&He+U$cB^}G~kXKN+%ICz=y+?=}mM#6(
zb4mff&h?qD5|9cxZ6#gw^;c*cZC99EJVwH6x-Z`CjF9K*$y4?H*G$LuFO6rv60R=|
z;EI2~%XG0}MT7n(YB4h~9dJhQVr7O~5-6o90h%?7@SVHlSR#7;zz$!>)XTvg4|2Cj
zl-BNJPq_i{)$7d`-ux+Zs%S)=7bil#*HqA#$i}OCun@sG!wTi)t1%&sZH1fuFmL?W
zF3h^-pI^94DZI7yvQtqkdlB%g*%(dqCeus-Mo7TgpPtfGaNq5)Od_yoLh;y{d{0Rg
zqAlNKv$VjqIoW2HN^z-Nv^B4g%vjCZF#kgaSNh<)sg#1_G;UkZ2Cgm`9nSZp=5<z_
zLxocCgBL4(KB&QP&7pogkxPJ#=_Inf;p{DS@1HCz{FqyBUmkeNy;kBJlFXlhEVO2;
zXx}_vaNE3Sm0)>2Y^<0<?Xu$}eUr!&QRQ47s6A4qU6aCDK6mLhshN^<svGHK!?nKO
zSS2nz1TC#D6W`>tRstq+hSO>v;Xd+-8^aGz?qlC=<<))(6PKLMKM76Lx%yCZY4LZb
z^nUFoqb<p*&Rp|urx6o@ezY$$qhDidDRuufb_A<z0LzShDb=rskQOi~)_2iF9`qV!
z_)m&;z&y((t6KBx7GtblTTFG$W$20Q#bHei$7z-?$-D;F@CAv4g|WTi%Zbw4aHoWW
zn|dRcXJ$9Pt0f<eI6`w84tfc&Dzn8#*$mBszMYdMPC5>yPkEaBtFQkfOZ&Dx@Ae#9
z^7n~XXBR0iXa1=flOoK*@)R>%_^Lj6F2GDY=Fv{BzuEY!2x`jW7|Dy=_D4A6Woq_1
z)AY_UPUsWPDFuIuuWP)X@c!Y@^>2_L-_SlO<+t5Y{^MbwcPFPvM!X>hH-hcKIGkF+
zvUv&5q8JqE8uQ3CM%UyAkVQpeb4SpNG=j0RqxqBtZhhz^;^nMaT^w{%xD&D`Vi9QE
zPA8qM!x>WPxCn;OcCWQi3l=Kv*j%1L@wPs+1Gf*1U93k3aebe54HPtUwojin`lrCV
z|8u;_wtWjAl|dT_pxO*ToUpM7MV5|TiR-s4+hoEIuP|filC-OuL#RKYZZw24g&bw}
zVqn<>{0q6}zQt~3<K%{X&Gsy`w?k-!R08X})A*Z1HFWK~qSZo9tdLS$LGn^K%enjS
zSmQOQEUJ{RtJF#JqlwEkMuCfb)>9M=WVX)ixZuy}2n**+wbcPOPy|&}PN#&gMhg=0
zM`_O+u(hFUwYtRb27;tW@?dR|!E7!5XiI~&-n_~x0$l-Z>)rlpQ}5UDz^=~K%dg8j
zf3yh!#JYc)b@03%&DB8h!c2}9ZJg4K<gus=iwW0iMT8}D38}TCE4N?GD6NJF%;@2H
zA3e$d&gY9MdjIevkEYHZn0$=v55kAEkg#>V@fPij58cABRJWmf+_x|mn91%z6@_or
zLvK42pZC7BqxF9Zz5O$MtJALPHSTeohoy@Q>VD_NJC2@Z`+3i*<SJ9|*gdbnRKl)C
zvDqrc-OKut{ait1#0@iO)?(xGoJ)8T<I_CPX_YHt&&hwgCQBfum!6YLv8#W$2;Diz
z(>XQO96<0MoDPwOW~(G0q|?!1s^R<oUZvAT-LtZqZ$?mT>N;Vw)5>U+Nf3Rf!5XK|
zedyqJMtm}|2}U~Lyi+@-`T9%p`mI_>Y5zC2`V4m4r2)Qw!l8c|`G4fDPDD^y1uU~$
zEwsIc)~RMI@+`B^_I)5<N8^M@mg4<y6HzjEIAwG3HVre`5HFa~mE-J{$+~YsYx5}D
zTLR(X!bzf{24P${shwxscJ;0?MG_uvYb)PH-5JNC>pg;%Ri&3Q!NG1t$fo@9;wvd`
zjARdz<&93@M2c+7+K#DW$jgZMD>o;}zSW3DLH#HIWUYThaRUjYuBm1y486?IF#YgI
z2ZW8TYVlql1;YIuK4iTio)c8KJ9J1rkj2^9c%^2mP4`x;5uKowbsfl-sa12fsU(;c
zuDVwxZa%AkCBqQ3FmuhW@pVgGry!BM91Ba%&?l8mdEItM`|Wl`xn|%Qem;C#>BhM$
zKdqLMOM;|NEza?LLk3nmP!on&#XOUETxeHVxYxd7tg#|@cAK))*LI>fmCF$nj=|t_
zVtQ_q?`2Gr{CJsIem`{n3Hn6J<h;`mlzQ8;XR~lgw6eD+IIy<MPFU{M7X~2_GF6(F
z_r)~m@Sud6>^sZzwQ$SKN`iw2Gl6DZ$W!vToiC>un8zT?(57&{$2A2Bno~ScT3)v%
zr%Q*j589$g6U)M|J{d+M66vScH`UD*(0~q_K2OC%X1v^T0*k905fYcIdzGSO#P3OW
zW(NbS8AFqffukS9Co6pKAmmx|)ZOh(;WJ;xn#<}^yB>`0#tN&UHb0t7I<4;k@5T)+
zY(W^EcbX5z%G2|_ZxmghD3h^r0(wlTSDI{O!;L#UH8~r#7c7jvsU6c?G5~RFW6I0m
z{zGz(o3w*VDg7JSh+;_{a&v0<;eaE4=ajiC^GP`VhLR_P%t@b=l2LPU%M6b`Ci<J^
z)b2ZyCon4VH{7Pc&{mJ1Lo;ql5W1DRl7k7Qy`eN9vVDhB2F8C>*6q_g5+1(nkW!f&
z5=Aqw(gPdy{DNBmT<P~GoA!`IiVY0->?nSy@=W%x|HZQTrxE`<ZvIv)^R)cxH(E6U
z5C2_~1Gh*HlNq1H#5HfI$Mww6^FLKuk3XA#_5Hi&x?2P&CmfUgt;sfGUYMHvi5nrp
zAQ{mcBin;Csqqf4w@-t1ixvZa{CZ&5+%~T92K{YrcrIu?1AyP%Jx?7yh=Vt~r_Ka-
zJZvE+z7L%6xK6sGzHuDishh5}NInIv=M(HBZphq**;S`|LPqd@U6g^Kp?YamN$-Qt
zVJ%zvdV!Q9bze-D-0b1rw!Z37<5lV(Vng6e(*h|21(|6(-0GozW*+TMAOI7F>|OYC
z<og{nQ9U=gH#|1VoLFb%DjVzdBeo?~LwdD92bAPM^hgv5kst#<YN1{N>38gO#_&(V
z-e9I)Ezq&56+x4`9edRM-vfaIQ!XSB4f$MicYH*#euGWU{!jSqJCR?+7fRT@h|0qb
z<ma-J;y7#L#@X=g-!oP=*1l!;F11#Bc|Mhxc}yRz_ag1gi;GOptszrkad$jG!_W&-
z$45ZFao2nN8Hl<nPJ-L{!crYGJP2st)68X2@*2Y*#ElVls3Rc)9ueSOBG|TVNznBB
z%vk59VI$vvs`PhD0|0_xiPv?pP5k!GPKVPamen^WWMyT6qw`6T#O0@d&@KqBlq@HA
z4@oA4*W*u$3`^`jvIR~E2&`3hf1VfMygASnE9A1rij?k=tDb1R(Rgh-B`Z;&S$LXX
zvW)y*5cNF6svqWdq}nMdSy5SI;-pGDIBwg8Ys2%@Q$sSzwV#Ny`1QJ@7BT|v{j+zj
zFzcRqF*e9|FCOU7_UrGNUK9l*CX$MvV%WpGkIl^V%V)qCD9Hc=u~GCbO%UL}iAbn&
zY%z6Hr`ECaira-gj$WF*@(r^eK*$!U#IM|Qsnl|Ddymof@}2mwno=^V`Qbeh_<KG{
z!NH3X-;5Pd^&Zw5kR0(rGGbDalU=wO^m}$qp;dL2<_*$=@P+vF7l@f~(C0YPCqw7h
z%lLZv|1|Mh;tY)4Dglan!CJwK0K!<fkXGBH2I%EpsVZ$g^0tX)W75dvkJ{^VF_N0E
z)^4d1F2zB!c~rt}j%iSek_Djy#m8LKzrH(96AHR7U~9tj+I?s~9*WPOnPf25FRJ?3
zix6;fQo-w?aS0@`uI@9voOKl&)=$Hp`o^h4W0xf3Oofy)^SOI=Sy_uqZY`)-_6-af
zBgzUmNXbE8PZU1PdMUPeus0Qy%%$;zgDe+CSCa$P*HtED44aGrYx#PI?_f=fdcUV>
zKc=di$guCLh5K<6(lYX5iR;?z+x$zur+d?b#<nJ}H^gS~Dd-n_)Hu$->SJldl@X)@
zH87Cq{q+)&J%Vs)`c@Xn%<RDz2RX{?jA!?I!(|ulfPkGm1|Dav`~!vAUN@_-UhAS=
z@x~wi;s@^6cyQDa>5bRf>T>!8k3$KQ))7ns8gn$rK@<?zJfY|-!NH7qkL0WhdQNxw
zW*<EAGcaQCsYAo-hOU_E)4kT*!fKOsvg&^cdM4d8u0NjXeecQ`*GyTK%Ftb7Oyd}B
z7UWwxZggv@Vx_*@o7d9y=7jM6vL;Rgtv2Kj5XE4=D&WoLY&D-m$6av=I+&A!dr!$$
zR93<x9g&qz4|9=#7p(7Oj{dk^Xm6WDS2`>U)#mANo9B#Gt&Xn-PL))%uam=fzhURk
z!`aRXyhzZXmM)>LUGh~?RmJU72>f3qa`yK7@|NH6^VEhX-0H+h1;}^TcbY#-N9TF`
zm<a6XmPrWpF*k?~adk}rAF&~(tlRea>{@(u&NT|Fd%jbr|KYW&A_mI5&(6;p2_tQe
z{vW{NU$K|37ffwDisW3MMMk2N&*py&OXk+}Ui5AAU??0o)Wxq3pF76XD!1K^LR=bp
zT~jA}Vz`H28Cn)uME2Zs3AHO@ZjIuEk#;{^CsI-UmJ%C0^k09N^ndBW@S3&1JP86(
zKQbRbLNQPUluU8?$5a#Hu(7VQy%@D1Nix>usZmPV93|!V$2sLPi4PrQ;n0w#x@;xZ
zsS#m$T5TO~K;~E!R-yb(4Xm{znzrEskYrs4bFj4$EFH#@qKwU)grCk$RhY(GeBR$F
zJIVK}y8bHob){AWK{Vot0WD<hNOYIfx9L(We494fBM2wewW-%xn6;v}-`MiLp~jH%
z<P+v<l#0G{uN&D`)jmd~^4q;XJS8aq*h+Up*TA}_*%~37k~k{x^+V$N2-mUding%`
zcoBcNB-CPNZRg7-sePJi?oO?1M4f&um%lU_mB&?3jTB;HN&l9G9m=Q5A!JIN*bqXt
z)My<vRwjFc_7mC4vS0lL46$PS*8%+dP^d<^+C6+38!bcV(%Zp6^6gRicMkit-Ppoo
zpnD_km647!<TUYCg_ZI~7CQGWWo|YPWBG;WdFY=)iGt=|p_1QJ&1?n<h!h+)zb@B1
z7!cra?yGR=?Zut(8-1oWo6;PYXr?D7`vPQa%wO<w6V9_lDG?5t1n^k7Tx*~tXJoJN
zaRYuLkqg%s^-hG_7^HPP?fAe7Qtr7%DuF=8s-NE6WpEhggp7-->btnR6^cpaR?TxI
zXE|8#*k-eJD@loJgglgC?M?y?f*z$*=hi1cNaSseJtyv15Q@Ary<Kiw1)Tyy(hF*9
zbte}DIC0$_hdXI5G`7{N%G}JDm_M$7qI35fS=PQfH2J-GPbwY#R_`cNC}lL2`+nvN
z@WDo9ird6mhI7P{we8IC>K?$eAAeqbtWxy8l$3l$)nWs65ba6c3Tna1nk50Bg{M;n
zje#L+U0Dj$b?Tb)^n8kHOR$)>C0{h{2||0uZrRSt!FS_=rN^(ggHYe#S`Cyb>nd7P
zcj2wYcu&=+{MTB(mp5^R8db&ft2jErt!V)F?78Y<6&d5b_SU;kk25*c!#8xDKF8`9
zvIuC|n5C1p#E*IB#n?ITF1@4@>}5Ab0(hZVhII-n5(cFn@uYf8Z+Qrav`Sq48G2!-
z{_s63b$t|I)mm_4_x2N0J+k-_`CXR(^gTLczW@AxNXWVSr&-m^>jTGb+8s7PZF-VS
z)*AIA7mad!bRgTjeWT}s<XO%Fn7EDcVHhuXdFl6PYHuA5s(kZw{KiJW;UVLVH~P!9
zWC0Ucj$=?J-x6Sb<Ixpaa)HI}RhWd$d6u)rOx(7b_aPYrGUZ$4mpvUg?ZuC|i^KiB
zDeVEt`q2lLWIHo^+?A6X(+4~kP7E1))R`C(Jvu|9C0QzOSJ#zTTiWS37^S}AwdmE9
zE9<zEF+A3FA-e6|?2MJiP<4fM&r<oFA(?}Bf9ee@hA#=tw^PvDDlJD<Rq!G+R<p=l
z$TC{#b$EDm23vK-ju;~cAeQp^i>qG11X`v9lA#s8t2_e1d>v=O-8Ee}TcUu*&NI~J
zjOJ9ZtDUXtUTDL=89EI%vXN#Av#H8Zl=CMAR|=QY!NFDD##wm7uiieXs^VgFU}@{b
zw%8}j9Pu4n;zUaV<6uzrv@PVD%)^wpe*n_|`f*Quu5+x&BN$P8Tuw4k=_IlvV~OBR
z4lvT0d&<8VOZ0Vk^=?9fQ&mi&qfYj0KjApVE<PzfRLZHJMEjNZU@P0vnx&dn*y(aw
z)UTb@(ZSM_jF_--00xx63a+ajCqf=3v#}%ity8dHgeX?lGn6?$8}nv{*GM3Jw3cBp
z>*zx9EU9X9#WQ1Ob2wPvg2PWX(_r2JQTilG_!tDOgAAUcnpsU~&)?>RAtvGOmW1-C
z^X}B=aQ2k1=Op`Nb3x5<)>O)OpaS~aXK%9NSKQ_6WU9)L4M9Hewb>U1&#(lElXbU`
zPJo*>8m=1-=e4toXFB8e(LuK#EjY*66-e^>_w{MMj6}CNj>W5LFGLO1@H>T09n*9p
zl1W&Aa?=cOZTvd15;j<$*clydorC&E4Dr8xoM&d2oXW%^eEm#b$mSKC7N{2f&MbU#
zlLyb%%=gFB&<iz8m258=a{Tb^_~v`|t5yKG0GT)E?gGXb*ds6{uj6#@`MNa87PdHI
zam&_D<}Mk6=A@Y_fs%ZuK+va^Q81-<{0>~+t`=!#^J@bVKEjE&uyQegOP*`vF6u8>
zUPOS2f4Q7moO}SNQk3Kb4emPx9?V@Kz+>w0*;`1zBzWF0^CVv4tz`C+pV{sAjyqX|
zE05bfcu^;GFTm4Jc^s2W`6r?DpEvV=X(zSfEpzaPvMN1pdcbV|->LSM!nfil{&uzz
z+>e}f0a>KdQf}D*JC$6a8!{$i$p)nl;Zs&|BdrAw7HEp@l|(<nkJj%=p=sL>vuqlh
z3l$p;ej-}N6MkvLET@$k{)uZ@`>v##nIr6;cC)cACOk&m-rClMOHV@k!{#qQ5G6D?
z{4nU*kiX!!B%U3Z#TgB&-E6F2FT8pp`1=RF9!32jQpuNv{{eXZ`!#Yam|xllS;nrO
z*K$U&f5pf@i>s?r*CI;avsuu+HKrtSzv!mqXIzI_(gk;811|8_WNR-+fePEiP3N^6
z1JE)(GN<dr)`5WKkCr^`4nVRH?Wk8Fx*qZM2TPoCo@l5Grpt?Z$sgH!7lf{3n~++2
zKuFE!GoU7$-)#YonJMTYi@B@aPxaW!aBJx*LE3128Fbb$k|Juw?YKzbo73rA-+nDC
z0sX4092#{TJnC%-f!M^}eCn2ZKJE^n5mk1EcC*SWe_!5c3QM9W?}to3{`brM=PIA|
zw7u>pzhlg(5zNF2OfoYAItSKeDNEw(%nDAn2nF@%d{-VQs_bO)%fT)y-Ikd!7R=_4
zwVs*1qV{X8<986;Vv<uzpRd(w?wNFEhkA9868-ftGif+G<oFRRYN#Zu!0?YX`h8t;
zhMH(q;KL(t!OWcV9LBn86D2Yg)d<ueUYMl`X3k6e*a>1iJKt7sSp9)4`#{4n1!Mv3
z&;1e9x8TtFJ6s)x4d;v9$nqGvGxJ=vJmahMZ0_i4vSQxQ`a)RnaHw0B3REK`g}RxY
z=3uXq^sm6^f3;r!R>G?h@DUD+e@LZvd*2%nac8^dz15_V4Q2xtGPP#U3X#!lJtnUq
zhkX4Un}OCn+pKmKxZBFo$|L*B>&F)qt|pI<%T^6(PLK8TxmKLRlhb-qs=K8G({CF=
zV*Zrad8*nBCd%~>42Q1Oq3m4szX;{!?n10nywFC9Z%D?17}dUXUS!lyg<Ic_qPyCu
zmEIykxb80F_)KR=(8s;2|0N*s-`=48s&viKb>SF!eyNxf%xn+Z4c>A8GMbgq_S$fI
z@w$%76dvFfiJqsV<$JHN+DRst&+OMW0<5bBa293vU^+#eonz}1=$Fsde8<6AAiX)n
ziaeXn7J0p-0z*QwR$Hf9;8Y)R#48=Q7y(`;sjnAJh)Z^c`Z?NW#3X#}uBZsIE$Ypb
zIc`}vQ1`^dxJJybBJ#{e{<bZy^d201W<&TgyJGccZ-V06>A$m?{|0pb$136ud;Meg
z<{kN)f6FGd;4L$<tfLW4_sV8R+pD*Z$?bT%%S;JPS#6Zor7Diw9{bThGg-~E&zz3A
zOUEMmV8J$M&Bh_aUC*=#32z!%^TfIa1_AC!cwA0Nv`5;_HM?1TG$M`-EP0p1_xDvQ
z%Drlme>PBmlnYc~w!;j4WL&ay+bM~RmVmNWkoA6d{l8`7KUU{Nm|3b9TvTBxr+>6+
zy@YQau6SE7BXG|~*WOvTRYR+>CG7!yDy7#$Jm?CNcv%9=qC2JxBK%}_x%g1*X1$X!
z<6Fd(qV-~XRPm}>wUbjtd&8Ys9*f`VGiy<`Em5kw{|SHp%Wh5TMx5j|n(7|h{M&5J
zJ>}VK_c7PW;vyx6KdnwlmBCAMse2lIeFKaGe$3Q0YB&17ZTjD*k<0tHaby0Vcl=H5
zoDVHm!||NNwF91f!aH4xsHuySxJT<+e7c)A2Bh~2npc&QnSSx>s7AOSyw^`;x4j{u
zDC@#KjUZh{Y@4RCdZ(JkjAKTTH=*kx*yz^o?4?TU$PkZL2%xuT{V9MW=2`jx?&)*4
zSVN%i#pz>d{g18v-(v2s|F%7#<lDE?$KI}9%fZE3X8^QrYSfu6k`P`GcMRzqSga`!
zA?F5c_~<E?nOL<<{hTF##GcA%dJL%NLmNGLQs09di{il+aLCe9>aQRh@8GG2Ts=m$
zrbyYUJY~PxskGp(onMhNCkZ&IHSdun4zEhzx*HQqPbM@wL9UY@6eyC?*?>nao}?-x
z4tJ?i^`Oo>s?VRCU9b<jn6>&}&f~v}wX+fvGo~fckB#|s*f4V(B>*1iRsfWSHaO1A
zLYAtBd;|0flC37}Xt9Ifek9V=MY*CLVOAVu2Og>y9+T=Eyp)W>xg-V=OFe679|`g?
zHw8auxEAm|ITTGlX3Jms3>-VPypH%8a<>`#MLs?tiL2MBuC1v|dvm|iHA;YWACo$P
zrS_h}na;fY1h>GN3h-t`|G)N{_(^N|nhIUkEj#M?F@Lk%+Po1-0uO1(7iihD8pfKD
zq_Wrl6tG@7UZ}{l))gI!vq^I1Znb$?lF$#!tdC!|#<){cBGF_;$(F|2jxQF!Taai|
zWzsK?1fj1k8$kTUucsja1B0U9GRxY8R?vlYGa0>}-mp6@F*CGQf0N<CA;a3(3*6Wk
z+oHPe<CZ*=!@1sDH!>7&DV?^B5uu?xdOifcJo2IT_rCVx8SD=LC^k8z6a|MWa{XTo
z%HK#M|3n1gPUJo1<2vjUOcPk2rz!O>a;P{ZMVr_B<<a7|aFeh+P4L6#8S#4uR;lST
z>RvE0G5;%r6y0?B_%~6<Msur7`qIy)0isGkr$GSsFFmW9uaSISjf9|&U*A_6uAH3U
z$>?76DXnO*wwjo#Y-ow}-l)DUb5CqyP&m<REC%lCB{OAv`K2kn5zm_TL9x1q(Z6+_
z?a!oCMPKDK44Qlo^;)+f#KZqz6v+Qs9fmEL%ods@yLOIA)OZ$IGhS(BJ(mYcLn8)Z
zN(zd$NZDXjkjHrNXhT3AQ&mgRO)p1|M=>Xx({tQRH#{7<Y*X06uScQN)-Y}Le}1q4
z#FyWtKz;fx&<!$ltp?&>hOeFU9r#JDzHup~8gS~cx@+~sWftr$c8(R0Rls95n}ra`
zPK`3(6tVKJq2@2XAZr~L(H{TvPyMI*9#=FH?fhLuF8(k-R+9+#2;sCcN%JgEc0FL9
zhf-O1fc2g1%fu>C)rr@9hDK+)%6%NLvr7{yMNVV8<H`~PLGN|efF+Y-RqROEZIe!a
z(B66gAOP-dx0rns;*>xc(1X2;R{6P#%yE$WB>#c<LlR&Fy<^(*D%p2P-@E4eg~nO)
z48^<MUozIJYTIU0ZgqzB-N|Cn`XBJ=zty<;meySJCuhU><FK!ZsQH4V$y$<!o&+gD
zYGy)g?-`nx114`A5Ne$xV3<=_H`OFzr1+RTiCJ>76{)Hu+~Dr=3UimigLAQO*F6+(
z$P9d;y#nJ17;h-1dYh2_NZm%8&>HJ^4uvcm*k3FzQwxq3TT^aG@qY8G*+=9f==^`T
zBLDW4&I!!1uaLUJ!VgbGoRsz(eI5e$>~^*Dnu)BN%~qaHUed-z7e6iYgUm{FHsjJq
zui-BAL)*q+`cG2=KX!fB2r|rGERN9-Kt=|>wr!SeOsbC^q<QJ%eW>3*nt46ZLp-vp
z@TSB|3Vz9{?QYv83i|6A_dQ+p0jLGN2ysk5XsuNzWrx@StzTHd@WT8sTvS`Dp90wk
zpg^S-a~O<*jRF+1gmZ9x1u|H;(boEf3|QW@@ue?bGs%0#|Ia;Fk<xmZ2QxGL8@D_Z
zHCWJtczXKACD{8MTiw>Kz|@qnLfI*uZCklQb>~o0VtNj9=_A#O^gj0uaEgaSk78|5
zF&AM<z*+cnl4lag%InI?O#q+UI{4;*`OYu>+JyK)go0#K*5|n*IL-03n$Fa+u2g4J
zW8GlrOIMrnuETXhawTC%$j1GRQVMQxO(pKxOar>mnc)lt&Q7<=2N%G#kEYcKC1!uJ
zJURh^*ymGL_NHz<J!u_eRkB*K_P@{QKUd$GwO>9=1H1Tl|K7~{2kYkFvi3i(=Aw93
zJYUbUO~|DDZHNC^ZvDR<;(!11ocP}$<fRTQ{r~UDw_24P=NjWDq;*jL^FRJCKR)9A
zc~QUrkG;2ki1SJIg})&X+}+*X-5r9v2X}V{2?T<>1t++>YtX@+!3TGDhs*B0ckkJA
z&U^O#1NO(~nd#~NR8?11SJzY#Qsbx3OZ<no|Gkzq(fBaY6%M#hA^&o<{ox;~m}hCK
zg6d!6D*wP`>OMNf{6D!Ze_tGlA|#AHaQLSM=l`&K{x^(3ZOji`<$6E;{|8U{|G@Yt
zo4UcL$i-0}{;zSBec;kwW4nR>?@9C@=pBas;T1U7{nq&(-Le0TOY`Fh20TjnZS=2M
zn+fy5+C;>08?ygTN%_AY)Y5(6(riX}75kU0-IDlVZKj=-=Etq@f57nH>8LP`4_viv
z<_FmSnzjEH*?)`d-|n;jEwcaCvj5hyANe-_*0TS04F2sHko?=175TSg@NdW9BUbYN
z=lB))J_&aJOTxKwt)7I0#6-ORXN1Xl=YrK&P7PBZ=Aw?&9m?~2O7BDOqWk{-ehhSU
zy(p>ZK`=lvBI0|=cNmzNmAC!&(LZ$zH5iy<ov;%pWA?5VdlQ}Oj}cNL<hiI*FjkN@
zbP(<~HbA#-4hXzY(ck~^{=Ys3%18c5>sPk}dqS`nEc-9ot_?52K6+aq9D>oteLGIf
zr-A=zkPRwCdP|igx4fsu|C#MSec>OEPPHgu@-hPo%x^J_V)}^`Rl00il`SgEZFtz0
zB-F(6@1E#Y)BTNsabO$Be+<e+Eop?Y+G+!V`PQ8O$EAOY{u3g;aX7iKeR1geaPtVe
zQ&}#hzn?KaG}&yCMGtnQ%}&gzhU3fl`rzt`q3Iy>Q@?&!d%$*%@;^-WcWR;i4Uw#;
z_`T%6K%^{=!h@9&wTCFkYOk7uH<Q1$HZ(FeIC3i;mz=2e68u*9ck=(^_m$~UOVZ2C
zDm%>9tp5d;?^3w}kabsAn>%J=ca$VQuNOU--bdst>LI~gMhS1p^UQvV|Gc_fm%q`;
zrR7ldU-5VpFYrWweMnFIz*t(%+pM1awxGabb6h6_MV5QloHCUUYuZ9FyQzf<p8B6B
z5wRB)L0@RxRXp_nCl>vUYX1QFzeSD-4Z>ouV4`TCzP|~p{P;?hZFnzdA#{tFxJs(O
zv<`N^?r3Cq92jAMdVqEidGIT#t}ZX8Ua}!AK3UH~$WQe@Ve=r#4L@FX&T(Vr{1+mO
zP56nla+JL42l>|JX!<ud$2A;AyajPX|0Sj_`S!56D7y|;OZ*N&1Lyg;zwD3&Ln@(_
zQc?(+p!(hSD4om=ZHjMT|Cu*|a^xSA%ZYn_f>+IN9P7V+v_y*s>_A?bOr)e~<jnn7
z@c%>$^)H{|m3cG1vRqa-H2Cg2e+8T~uezIi5(uY3B6C|ZEV(r_oD>pa!=L9~u*2A$
zAu4fWw_GznnZY#?Mldx-Ig`=r>FMKZA5{LweH*wOC6cwg`k20W-!X)fFe3Zc8lH0z
zQer#)7_JTrkRskpQN-VbZF0A#G?{so3hBFA{?ifL*#2yMs};G6=TDYN?_c`If9C$6
zD|SV1%oTk4>aK&l#=e^qBkeE*>wx}kVtBK+2X;bmrKi;!y=9#>`}PT=m#o0UU!7nq
zymUx7^^5C>_fBzvYsQxv2D4kzvus_?kmpmanoge(^T^Yk4C9G02%Lp5p<j#d`t#UM
zqQ;5}A2$nESHa=%a9RIEq5mp8#E>I(O@>Y65Y1h67||b1sSfwZM;af3PEnoQxi$|=
zKNEg2UOS^vJ_@<_2Zt!-5XP1)y26C>L=pumOqUoNNB|3SldC0vT2#**g_YY^8~T0y
zhd}A<sCp3beKES?2&{bNAo<uVf1sp3#CjgGCa$NHIUqEXF*MNE#yjC81b$OP!jWjq
zI!$13kjGhzmLHjy-t+fc^JJ+2FCdoebe?!~=M<@Knl4i3NI8IkMo+e4Qa$wN)fQa%
zPK(+-7YlWk^x(`XOCn13{gy$B?+f?Gkhpq{r@kvOK6wGM?P_JUlpR;qFPV5;5^En>
zsG}o_hW*xN69$p12F4U6O98SJZHMRKnVzBnE?$)<k;4N}iuUQfrpwVKv-y>j)o;xp
zvbgxP(4kqENrj7h*B_z$m{@Lf*nx`$b`pbT<(^fQIsCr(w&pP|qO-XFJ<)Te$VfCl
zT6de`lF_ax5(yE-0>FlWZf*BfN(<mEkqczBr3_@LeDB#O9sm=e0-o0*7X2lEB4|dQ
zi?FAW9AZ<Ub#W_N+Ija6*GDlNiYh|fu>EfR*qtbS0WXC~H1pg4XL0mtAY&v3Hk#er
zSpw!YSK0hovRk*);pda8v_9BVBlf`s&2Q@F+}t#1OQW8-isygrOpoXc8E~3no1_l!
z=rnuXI(QE$@fdkAaPvSE`8a&HMV;0VamL=Gt=;Y&5a%!maYfzie-MrMMkVUdK$$Yf
z0*(2mY1Ul9WkzY7)lo8=E<x{HPO*@9bpdFYP;Asz#YS6$N`vF4<}aw#F(-n)LWpg<
zc>0#Eq2SZZMH(k=9u`;fwQWyMn$O#|wf#A!=(ip}vYW#;5;iuW!5{GJ*aWBPd>}?}
z)|eP!7rgs}B4mgq*`G;Falm|NdHLWtk;Buk|F~`cMvDzq5|oUz=;oN2wnSG?(^C3!
zGnFZaT}|wFyLGgxyoSoej}d<<`9O}LfBpjJU$GWqX|}sHjjXk8_Z6<q?Xpj}>M$@z
zGnB5}_UTb-Y)QRhoj5u}+MNM=pk@e?qhO);wTJcB<&ccP4G7NcLeTH2>63QTHjggn
zotU1HWBaXHBvAbS?LcJ(m`uUcpl%O>-qqxl8h<02iP`ouc%`pB*Z2BH3XBIfA953T
zg)-Ie?hV81lMR}u^3>XyrQ}&?!y2_{yVL|t+#PMeEzLzGI@AtXjI0qm)LJy9HaD2+
z8)wY2F<9y-`Lh`W0x5f24O~ZA-!!piq`UzIjhUu`-o^vlP-*p6oas6eLHHVr>$=)@
ziK+ZoF`4RqY~C|2`o+3GzB7oBo7UH!%kWJUDaQw%ZCqb7$YFbXyVvkjpaYf6e_Q5K
zFLM<Kg(wc7XybbjJ)7VHgt$Zd8*%ge6Z-DyUMld92!vucJ5QNwA~o^?{)@8VfWU?d
zWbmPc-`0K7R}}iXM>aExK%H4kZKA_>T<~GQwNb1@<c}8n5h9w$oVYq*u98q@FT)SL
zb_T&#vd2fJ)RUVz%{K#!{%;RX(}ogK)SKm)qAlTAv!LK+L4ESA1XkBaQUSp8Ut=pM
zMfxF1?>6Lvf<oAz+c<&2{`RbGO11#Wc*)0*p~h92n!nHZ_caENki;o$_t}d|*E-!Q
zzI0hLD`rM8@3<E!Wkf9ywHC6?AF+KXFA+-AsroiK2(3JvZGvBtqwIZKhPZ6TA2xRe
zjrv8A!iTaHXVFsaTUnG8H}$?0Th6{%&}D7bifkdpv`k(`1r{bHL!~*Z;R4iz5O_!s
z=eLO~Kc{_3nXAJRWYHja1hKVljth_0lHR#xTqmltGW^&ju8g;MGIO-&tcHW2*w)EX
z=!ZA2y6!i&><WFfMemnLC6B@;9kg&;&zqBtn4)NX+X+ei-t5fD5nvNK?$FR4S=aZ5
z3Dbb99m27%(igIcWG}~nqs==sFx6Vaf8H#)kPzxn%=-5wiJ5()tbdc*Kr%=?Z`Q7x
zH1&v<;1(u6a8WDw-B=qu{++~TbEC1<^bG#+KKtQBJG2At8tVP)xl+X{Ud%c-abLkE
zVFcXrQbs!yLa}Ucz#Wj(_hQOv?D?9-umez2x0u4>qlJW6qc-9~!?;G)=d{r8UWzl{
zgzgP!?rZh4%<bZWD6zj+yw@UE&Okw_d$Mw4NO4&G5pwKl>Zna&-pZSDQH=({#Sc9R
z_CqubP)`Ik9(Emd84|%*Q5CkG<<~ZGhV4LLZx(7)*I~CnanDk_cA<dL0@~=pS*zl+
z%}WDM_o<689|aSCE$Rnjs}ZK|;YiDb?Y;Dy?VP{_t~83Pd!rlp%dgth_G`!<G1VY7
z6V%)@V?XK-{5k;37N^IG*hmT85y|&$hPg#pWVXVLt3fsjU^OY_2|)2uG)T%nN$4Xf
zW)DkH2OzsSo(K96Y0@vG2Q1Bl`zQuK3uh!lQ+Lt3!(_&Rn6Ap4Gn9^_^{akQTJEuf
zwHmoA>Hk|#rx7CX<izf$^32F&<Ceve+e`*qw%bT_9gVPyJFV_JzlzX?sA*WZ_)2*<
z!rl@lS|skeV_!`+<K;7cqSHP8=*1MEe2V-J^l3VGP3h8=EBv-L^Qsf%c*Z>Nod_q!
zHq>3@84L09I{jP0VDV;A_}WPdJ5mwc@SjF6!TY=^z#wmDr60@=6tT!zCUgAkB6gJ;
z@prYAx@01ob<86o+YiT-^Hbp0gqd}wk0$Khm3`T!(=6-)P)Aew4hGyK!?cTq+{?vh
zEpYFEttOYwpL4;-(|%Jh`VJr7^b^{eiX;LY?wHx02rOxv{gPO|qZko>aEtSp2fg{g
zR{Nik+)(?S|62=yUzj`+YXi$umad#7c21QsUb~r>S3w70S-qwuqmMlbY|3hH5(Q7I
zRpb-UyEr0q{=ufN4w$2XtUE+HzX<>8pEMhvb{f2zDL_XvC70cqB)m-<Zyzq5Fe$I;
zLFLYni386cde(<-?ROd;gZ8egz0z&$V!7(*N=R*ns%37jfN%vPN%TqG5gt?Zt!nWF
zkvKHcWf*Wz+#6%?%;NGu;&zDPrLsI1{a<t-U4+ON%$xnON7TpwqoAZD3`h@z{yt>E
z;7_m5S`Bnr?`ULGQ!9kDxJyaIJH3&$<?`}p4oo=H<(OYsz_-lw$lH2tusOFc606T3
zm(`bbO6~VeGdeeH=A029;@5AS5HEN%c`MDop~3Ig&}1ePzdbF5Z^**eVkkUxXx};p
zzULbnRN|;$DK>8Ezb)~YO=cCE6(~9v$Gtux@EG~QA0gl(CTjYYA~q}EtB@<(qLj%0
z=!K?=?wNxXM@@{2a?&+q&YQpW46sxG=^y(w?Yn1An}IiVB`0<ve;H~^)ZdJkJNPSU
zo-9PK+~>x*BU!uf!bUkU8hBoN=6DV+XA%w8;!(2d!j=|1?$v1ds3QS*)~YhbNT(DZ
zgRFP}3BhA6tdk80=P~Nuyy)3g@>jj1BBNhi02|R(Oo#fY)8UVCvVJh-p=s)uPqo>?
zScxH7(la=~<%)`O%9zHf@hfUshu^i;NQ(sQ6Ook8nwoqruE*b`W%yzwEOV8BK33P<
zQ2}d<Yp{U|Yfc*#u&uamIZ|U1ll(a%eC@42jvYDfX2dHm1rF`!Q63%_)-UF{T}$$l
zV!h#0Hv$1gTX(eFAH6LNbpPSy{+~0T@;FREQK9kE&Edn&b}S<&Kd<TNp2CCcK+Z2g
z0N0=qYgM}5+`It|q^}HB@l+WvCq1?h5{GIkQ~P;q6E)Xq>|kp5*&t9UX5h#9d&o0b
zC)}IaK)yCv@`!}Bp4itDbF~Df>u6061&t`y5&Il?@X7vSaokU3VZ7G;=8$5f^uT<o
zy^E3{=d;0_jtv>alFrc6q3xV3Z)g+*Zjr0Sm&|~SAHM5*PG_r~RCl`fvL66SjA3>X
zk%xyz#E1}~%-0#V!!MY2gfn@to%5u1Ea`e%T3Xp=YzbRbEmr=FqTe3+-8DM;t7TiC
zcX~4j`R_6D<uG2csT!oVdI!+tDdrR|=w9(4QMJY}o$BT(6*nQJw7k@x!bkQS>yGeW
z`|r)h%{I$^j>1=N3Qd_&UaH}Sq#h0*791uAi37f<T2C}LoIE{9iv6k8*^oT?TIulf
z`i#%u)!61BWVKUzl)9Q%I~{)Og2ttVLp2?!km;K-sr+MZh&*LJb1_wh?U7mgFPRE!
zb3%I^<fI|pt-p-MURo%2?26kjWc%U*_&)9RCyCu}@Q!-Mt?q5BDhGsL4SoV>u6?Mr
zPBWN(s@-q4+H}3L`uv#vwM~8;2&%e#)+hLS$EKj?y`YB$SnrgFh052}DgGLwZKHQT
zeYbEx!%7<ncu2U966&Ll^Sc*1&~<AE*vCnX-oCJpw~S>kszMztGJx`n-IKKIWlx%O
z6H79QK=(0#^%xrI_OZqFXdJWr>pti|A8YkVASDi(%}24uUXE;e+FQyvOQzNJ?HCLJ
zukHxA0x-8n&aVY((bBmJi9Y&!d)xXpqE>v+0D(-<Lf1!t#CA!v<MjeY;PMi$gU1#f
z>}3^Pw)w~m+E#z@W&rG4ybNnE4smXq@1cLxaw-xS=~7i?+fZ*t@;zE`Z&+Ms%5maK
zK&Z{z)k1tqHI=&SCEmnyhO~wP(-wL<P()unAxmc=MS1#tQggAP+Co!x5{mvJg|R+&
zUlCc9bnXMNHV=b`d6q2IHKRtmQ!XBb57fsyDa;I)Ho1|dkj$yGQ_A{i=r=)0e(g(F
z$Bvt>5x;<tI5LVoT`}B>Bx{S^;_a1Nm5RNJ?~@<ysJ7E|1*F%DC8yyb;Ep+po|M&X
zPr_$wbou3fG3&yh+XYn;mWtv7@;mC1O>b!m61Z{`ILmws4{6`h&#hGwCwO>NXt=O8
zOD=y|>)%mdy<BH@x{|0+4RYPQuDyO<7-80ycL^Kg;n^y{gKlrI*C`(qSJ2m*6}LzT
zZ)~Uk>K0v~P5;Q^5|q!7bm~X4tDSB><A^4v{iQD`+^m-Aa&tsDacsIVtOP4aU?F?O
z;SmWR64w+yjrUTGy~+eL<?4rjbe7&T31FN^Qhz@r^P3%XTWpn9u1-N!c~ae=v9-~b
z=z+Q^ip=m(pJE%NFh;T{yn+p)&OmLI9QCfIo^TZ}!p>Jr_`$A;zC-qKH^PaBg-=9o
z<eC>R>V>ts%AnK*UO%hP72#w3(~w>AzTNyB1sM$>2dkZr-aNWa@V36#h%>s@5m+~x
zSl{wE`L6YBa%bUt*P7RLx2wfHmH%0bZCpu<gdKl|MpCxQ6pbv}GRq5>n5#7fqF`c%
zR2j4zzJ1gGqO~{zgr{N>oS-VYvPsuGh=6#--xSp~nxyi6ommsVTwTbRvUZ0N-`h2j
zwqo%2zqEO&V}R*w=jgjItfW_OHdB<H6cZ5q?Bm^%7q4|`S=pOxFbIYAk-qH<{z8Bh
zt70qTUYexWaq&mO?R+Y4j6SE`1RJYrO^G*@QjZX?tK)G8@#4N5hgVsX+yo;&CwX{(
zj7%w>#m=3Udfa;H%Nh5YXxd>&e0jwh=1(K7v!3iYnttj+WnKX#*xQhrD^lmRT6^}W
zGwd*0^rxQL&X$blx+-A2Yc0h{&bt#T8;!Jkur?<DZ;|&RFTds6v2z*tnBBBy8|1y$
zMy)lY9>0_CF<v(*@#a`U1pau{V=v;vpD3|vpiLpVUt{sD`?oYKS|hTfl=x!zg|vek
z#u(-`B}dfgVw=YCH*cw_rde%nKi!SywAXx7n0{6^%(2q^S$vKY*(11|oT`z-{5f#-
z#4eK;FJ?&a8Kv`_M3E3lax2`bAcsFxKVeoeQ?JFRr7s%J*}e0q)V@y&kq6p2AA+zI
z?~2mGh1i>SX~+U6acoYV0@zP>sm<eIzVj`+Sa_n<E6>jZt*y!^!m)_pyHTp5)mk&Q
z?)_OfmHlfZ?Pl`yyo(a8SSAQ8_3*hqIF>QCZ?nC0%OO?od7Zis^K*N?8juSnnn%G@
zUW;OC?YYc8)al&aJl3cQrgFs<huZoRvXJnGE0=?^=MBtF^SCB`K=JkS)h6?3ysr?U
z0~6LHMC>{rQPq{=T#%b_*GuC@Z+Uy9g`$JYeW>eHZQNARmptCFNeWmacG(@YdN_af
zacc^1$;3ExGIU}hNwLM_U!qdjnWOv@;YBU2HL_b1BC8SQ6#Mp-Ip!!B5UXH>58rUM
zciH!s1II(&eimPT!`J?`gtE}jX0743bo4IPA61EWQ|58YFasHMcZo^D+t>vV57B_>
zkz!9$D)x&RRl{qXF?#2zAlhSr!7g8s=!b>hQ2~VBe5h+HfZY6UXlRN2yBV8`S0H{1
z?{-m4b_dGitSq^*AIu3qN^rG|Vk(xZ*w9I}<@=b{Y82osQ{NBqIQ{QoAQ31O9#<}0
zX&!0!Ce@~-Evh$%y=}zz6a@mb5#AXyt`PKRBxzF3B!2Ul5nik9pSM%Z(t0gg60U2C
zrajUnjR8#2YI}LDksO<Yy#5&=1r2_W2wpcC{X8O=7_l#$dkRHlh3YH4d6;W;-lidG
zr>zlhNWbfB#Z>9=d;od2z!XeIL$dd!{%aWXtO<Scn=^2VgMPW=G(g}8A@A^3wj#qP
zf|?#+n}Q}t=S{*SgbP5HB-0BP>F{)6{`~o6Dn0<4eV88=*AMH}r*V6x<iNY^EAh<Z
z*bt{XtX-g#O_olYt{ZOjp8?4~X_^X!l-}LP`Xpp!4P3XEBjg(k{qR-#=RZG=>JgtX
ztBZBEeY|#l3O(H2zj`^l2>aGq5f9$khs5UK{qVLGbH3g93oSUB-jG$kpW1Tcrqzw3
z`dl<`I$zcOg%D1bVWBHaZ+kREFValhSGt+a?3;K0?fLP&=?W=c^Uihq$YZ3DDKdwH
za;FP&Vmba1SP-%G?R2xl?$fFjLHxGp)nSaZRkHT0=4AM=wb$k~rS?~`>?rK&=qpvu
z;+@%VT%;LnjcS=nVbb3l`NddkKFZ768gca)P4CnE7!*793}xLa7(<n))AV4g%^5B|
zl=IME;!Wtp>cz!!(NpG-6^uOL3%l+(mb#OOEQ$hX$;pwy);)@teX^ix;YqUw_Fv6s
zve7CTb@OBB1jUoP9QNMP#(H}6)PPTAbW?@)nZ^tQvrIu=yvmAaD}-CqH;$RX=R@4H
z=G^O*5iQ_}(Qyrm+f5ByC$nqK{tzNcm8{;HD5eXCy-2LJwm;2k&=2k>-=!+`ddscV
zytwQ{W|r0B=iOx{hIQw-XtlP)Jgz&1ooFx;-}%w^Co+NkJqFIjT3i!f2&KL*vJ$M-
zA?{U=l`Is%rhA@7DdtP%j$-C-EVSm^Cnvu#x(JZUt#ICN@~9^qv(kMmMdhqKdff+p
zF5mWEZ}0|a%Z}YE;h>PDfmN~6qfALo*ASE0?Nf|r#6VsP+ib*JFk;)B?h*hxhm|tu
zf=)`O0aNob61E4$Uh9-~Sw4sv+8)w}!!I3~Yd5D=g^wM+$VA3PItPew%OT8(Z>0y^
z3K}cQHVcr3EGQ<VelF1AI1WCk5*dRm3|G^R9gpcSze9r-rhd0zadoj!ltqVC9F7%w
z&JOsQE(eI|^^HkyXdu&K?m{_7q9Q#U&w~Z_h!@NM5TRKMbVO1p1hKj!l~ly#P^X8d
znAw@jF+2~R2hG})*%7c`;q0H-pYEcLPL6#zf$c%ofxcwuo&4T;(MAUG^nZ^bsiOpL
zOp*KQJdV?t{-)aRdh}<){%o=8xsU|clw1F|V=-P}8F@{(SSPjx!`4ee3Z0^)JOcYZ
zlwr1ml~QE#b8$@nqVXEtnl=-r5cYd3zVO(3uqmoD!YHXLJatU$<Iw211V7rSnj=Aw
zUKOrP;c=djdO^>9w|-0|Am*CyKsBZplYDrt6_wHPDKWU?y-6jREN~#}FaZ1lr9zF)
zYv@|3JkmcjJPhTz&IU87&^}?+rMi-+PS$Z=%A9Pwu&2<;dOu1%)gb#SH*b!yJm}tg
zT|HT>p-7bSdLy9QTtYDYZO*du)q5K2rOu4I?aPzN8Mac=mst0BlV>sYfa_Vlmzr<v
zsxuD+c$VSy8IibsUm&+0trzmV>wgZW!M9}%biFiKK@0g^BSjJEa+a%-ZWBK)F!)~W
zbyk#*LY+2O>60rH!obi9{;K+O4B-Cbt;?yCIA@}>lrN3^@cgVLt=1M3`CSCv(X*kb
z{bFONU#=Mx2b!97IE*|`A?RPza_i}hmPT1@f-N#!!W=(P?5I05W2iKvjkI0rB4RW#
zpw}BWH=4+jnEdWwZ=I}I&M^YTJFEin`^VOJZB$Zbx@g)2?nttgS6^!+*=K;eNT*Gc
zkfy=LecQwVy|K-3t?{saOH}I@#qspi>GqK<x!?R3&_)@iq}%2~RKKx!b1;IYR}xd6
z)$h>F#cNfS#&dn|At=+nw9SOk@=JF(N4Q0YLs#xkr~H6=zskHRZ%fpkic}3!{rYVe
z!*M&baOEQ4UWuWRR%J--ao*J|&m<DR-H_*+u2FwL>6aH^M~6{X2&+8umF1~Ark=SA
z?C^`P7A>7?fR5?04~u!2bW*X@52K>&y(#%b8&Qbts>}}WWn8}o4`{l@TA~N-hG->Q
zjVNTLTwTzsBIN>i$7~QI2=5Ps)zz}3qVDSy{oHUgjYuekAlfeax+&eeT<^RsC+KOC
zb-H&J751y%>!MzgXM%Hf-V}Oce}P!q?~a<A8k*MG(&<Q<CB^~#^MY~3+4os7xN<$L
zF~>j9<1=*b`VPLVNvtN;nnqCe&PziE(0HcAU!!bK410pIJ5I9Bh{zm!;<DeTnW`Ye
zYaRSitej24IPLg-{5f{xBisj}$6r+AOU6YMaLUJkEy*e+w9q8<i>1U;p{2p*P#iBg
zr?XQ4W%#~Iw(rRb^y}IMHe5wsb=+&j23FhLeb(Fwdk%0d#QDhX-6sB)iKoJ{BYKsZ
zjz}BZ<rt1@UwPOEn~=ubDND65f8mEcPEg<9%|w|du(~n#i?ziw9@4q~dRi+#mc4G_
zRI&?Y;iZHnqO8y{@0L6<u8xY#Wuk>jK|j?6n`w1ZO?GJ!C*`LptNPDew4fH@c(e2A
z1k>H|47pMA-IIA*d!1r^^0!-WtqO~meb6}0dpg#z0{-DZ0jHwR`}q$MHtO}#IS1S6
z4xJFDHl@ik8_|s6aFfy<PHYG<-GJvlW;x^CK!9VlGCp7Z-$S+~bP!}Ru6a{MLb6Bn
zQXbT@qLr44cx1FE0)J3Apo5e)hu>eqnv2$hjt?Aa^JK%?u`S1ddj>a7{k+B6BKJqj
z5S7-$jGArGL3t>$Vh`i7shic&qF#?__|rwnN|G5WrloVt@Gio->0#2ik6AMGCy;G2
z2CbwxdY*&>!0PqzPn|1TdHxeUG`{81sYND~4vtb%6tW4Ut!0Ir89!t;Tq=8POt#c&
zt)*cc+I+sv)0tbVU=3l97T74JI1)esW4>~nHoW$AL_gfLyEi;d0_RPAE*`BagUv*Z
zi!Z1R_^Os%Fg8)^FZ!rsHQ~EF+LXO$k|6>WR>Vtblw(;>JT%f;nqYp=!+tfczKN0A
z*dpDKXqE2n^1IAAoV}bGWhSTp5%EG0z4MB6vItw<^<p$vS)RPc3??kqm3Oy}o1SR?
z6f;PnQJQB!S{p`9zt`jV@bJmr>J;y%ilDAW>4YF~V#<pGx^soIQdKY&V0<Yw6RbJ2
z29a*M-L;*WuQH^>YsAvI8@82-QgSy_d*IbSy%I6>q`tM|pnd)qK&Sll7yGtOM$Q9r
zwC`$JrQ&gfP)0LYOcx$^lA^$reMIiv8^{xNxUPb_kZ1QxCO=1<;!evRezT+gNnlL-
zzHm`!UyJde@ZiiAI+YEZ40qID(<58GqLR%Y=956`S<QI{N7AW=!e(uh1p(ju_iPM9
z-vb+6*ZmaQ(4>c*`c%8ZkKlnT-Ol?C?Cay0mY<M;p|rW7fVG>_twZ=iLBg<vV-lkr
z(HlNtJZ~U>@<%09AIPHdU(qW$kAGFE!#FH8kV5D^qUC@N#23a4mK#N8CbAZ9=bL4C
zT-3dTZms1Vp|#e4-LMTlXOR0gS0FU!Cj^Iw1!$fcW(2LcmzT?V>xhPw6|$7&Xx*7n
z)b}=}4hWr6>#)T}=x6`u&!o1X&5ej;7@g)%Z+<?;JLLD}0x7LNyAC#7!^cN*)Z1v6
zx97H^)dvLhB`3XY)p*P8y`W{>DAF#$0~}DooS+v4+_dGSsEo2sd)o}7r`ov>jMWl`
zc$??<mJ^5RIW)elSVlnY=^qt3Uf$W2Kz5>5X9Qn-Z+<6lj=@Ji!DN`N<dyp?xShoM
zws;VzlmGF0dD--~iDWl~Cj?G<l<Qo4Lme1)jH!oA#=1#>sI-<ux_8_6?6i8(Ys8N<
zvmy?IxtpcIGX?F4Q)yxuIVOG~h?+6vzq|>u<70YQAKRy&T1O@xdsT}zx1igOJz9|5
zv`Fk+oIHP9lo_bio`+`j?+iUxde$r)O*Hk2?Rv3~R=peE*Y*PT|HhebW(Q|>;`s43
zsT^;8{*8)&>Y`TbVRME2%$}IOx{bHYttRL>mnBd%2j-pSG*Hw>GYSq0)!jkbqRPZt
zAo;CdYQ_@#6G7ZxJ}cqDgqC|fBHQc>ZbjbKrq3(NPFzb$3+r7%3&Z3RZW#k`U|Dw@
z;h~0qTCQ(wQFISsw12`-hqq`|7sro=F;U2{5{9C9-}vLA;9NL9P(R#Mp;x<0dC=5&
z<=3h=uVp5;Kfd0u<H^ciysH%3)sW+{-*!jsIB8(jmJyKs&D|G`@2Nds8d?d??$P(K
z0QDh*q{g8S%lB^nUPhKTR`eCHeOOE+_1RIH%2CA9CPC=_hU4YDb22V#z_}&qQZF}&
z_2D^ZkA7?S_w!i`&4Gop7(jpx4%kbMijE(uu6R@!Np$8uT_9`PaOoI+ce<QQYQUA8
zqL>vEI7hxt*D93u?U~CSLg^!aU`1c?;p6?bWlSU}$af+WCX(zs%2$x=lXKj1EvgE2
zd`gNGqLbDGr)`8o7XNS4Z{O)0v(|ltNm*fg+(a?`1<+(=_h}}FLp}YN8;XugtK48U
zvbRJTI9+FFON%Ci<PrjQm+`%@jc)cYe8iR%WfoOr((IT91xs(JZav{nzJK?4a~o8B
z<ME~v64hbTVNSyP=u7^hQ;aa^3|H7rZ>@A?2$;t#k;c|7r0m%|sNLova)z)(N1J~w
zl^>0uipIOlZW8+z>S487vZWarWH}d}SO#ukzZ@DH3x<ahMSK)$u-gm;Rkny!eCxE_
zn!NItWMxu8n8;D0^ge=Q9&EHxyf?1Y<&t(4HgX@k(~Tt%R757dQrykgh}@Gg)@f~$
zFl-k9x=gye5Pb5k+Np-B)G_iqOX&@&YeAoR%$iK4oADeh4*14m3iYHef|4@GdmwD|
z6c6czP~39E;JgB}>a#}jz;&s~R9+ZekD<+I?%P!zpc`xOthBKi;P=B(*b9b{4kuvR
zg;4Qex)7@Cmoss-IM_rN)@<MSK5*OX_H{k(S7!LVB@^%jnJ5qdRoQN4g%b3B<lK2#
z4Xk1+=|*8{B;WpK4Y9MuEB{Veg_&9{n9JrhUoJm>#PHH&!tKyulglcQY}m5O&Zd}}
zQQC_O3Fuj_tJJ>SR*!qwdgiowynY+?{I(kAFhJLGyBw%)qa7rxb+^0dxj9@Xyr5H)
zbJ>d_AoR)~EImk?8|_#tq;Ufb`O}f-H}%K~fhj5inV}^7%HeZ=7BbMyEXu}M#P{Wk
z$<f>j3JQvHe3=^<@B5#rGyjJu(2R8wtj1v7qjJAa2frn?1K|QHqLrVY5GDNDXWt7p
zQ^QFwUxe_UtNqTqZ!j@3%AxI09JQXh#PlM69-5HOP`^h%lh3K7IZCKK_@`eta3QFe
zFrOmx!^%N=%b7(}wOk9`9&b4^+cpxlD;Ky!V4?<4(&|}d$<NeMVqymPZX1r2MFbQd
zt7xvY>5zXE0sy4hUL3VKwMzSE5(_)892}L^x!hi~@?<20=V+a%oB8k3m1e6Nc0eP8
zc{)}{gtFUH*igI`8Ktzq9Ap_j=I<_}8uDL+#1~=?0>d?Ks?-?$N;i{XNFUHE(^ujz
zUe=rCon9me-c2s|rP@x4Px{i1>FygaI7TYP%@aHh)*1iO67m;*x;cks_K7a3QkT)4
z<@22TNLhM*;@~J~UY$v3Av)*q34V&?$BG$We+%n`$09cYGD(~Z2Rl$SS9@<7S<M~I
zj3RgGv=LSNU)1-v*6AZv0zDx^Oy`T=o9NMNJNF1&_*_#QdyB6%jn`FvqHLtsu@Erq
z`hpPt=vJ+C6*XRO-y4Q1Wq0an+(Mu4j-99KF7FGrJG%)(hudx(4;<H;N~4Tk8J0KM
z0W5F%>Cr2F@mV3I^j3WaNGhm4Rido}LykSR!-vsF+~t|;O1*78E@lU7f1I|ef;r85
zziEt@orYeVVZ$>li29$D{qDqYW?UangJ1YYj7ik;%`-Pg=SJlC2a)>3gZVBOfe^+7
zo5cy<3b84)8~r?`x7d~U%X7UEs%OH8HyL)ps@#@px7$0SCJDy6K|MW8wf_)6>9M3k
z-g9cV6}hsac%2_s?=G~osb8$~6Jw37JRQ?vC1Yn*$ScQuaSAr^Sf0eDz~`?0;(}fs
zNKa641l!WOsi4I)mnnj<Gdt7S;5-Q#2qaL225S|*t*l)s!)LW-KwU#Q8c&1mq6Yce
zutA?v5@KypftK9D?8h~4DS%75pwq1#zput(Mmi9g-0%|jCDNccOW;Ylk%Z*w(+c@a
z4Wqzk>nPJKX+|B=N{Jn6*`~p+RV-7Zra@O@zs%56s$&7rvy{cL>0sA%%4NIu%Q1RX
zfP%uQ!|CcY<)0tKXynZ*rt{6bFD&r5CK>uXL-^^hVnKxk1#vq(lb1#&3Nt>Dc$&9I
z^W>YGP{YSoBin$a?Ch$exY%Fu1&Y>_%beNHOvy~w71wyc!l>e6%Th5tT5^d@ij^xd
z7jJD;KdJ1fD1@#fu5&&mIl~_~iPtmKJ>pmLYcq~LeHp<C-%OcdJ%aL+FjouT$qLv;
zmfAf4xB3%6%}TJy%%d7ok6U@4r`v1MsSE|_)3W?zvh1F&DVzU^%<a~M*E{=d$1)gj
z+`;zvbh!^zRvAj%GWw+6cm^CCFnt{1L{WW{xS^FyS+Nx{x#MtHMR|R?>37$Pm7Y8^
zh%kRX0~)b<zMWHqL}29a@S^oP4<0k;rd7xKG#LVp#qE~5<WYyEpwLVkjn=chc7@-!
z*pJF!K*V0Bwjld0*R+xSX*Mu!e$?Dbv6d$${Av?%v8l-={By1GESGx#MfD|@ytj8_
zh&GXs8_HSdwPwO^N>0L5CZiB$%U3JXs%S-*V{wT0Xsx$sq)+zq)7>DuKh^N5WG6J#
zm0!#J8!aD!Y3u7Tfv4dMkY91QBhK^uM8Ucy?{7eMUMdXVX80qNnWye|Zz<!fo?542
zV)|OMB|1qQ_)>aJD6vJbEj3kJ+)6j2PT*<#-Ao%tsS_EaHqL9(el_aK!FuzD=JqFR
z^EkAc2Ryg4?Y*`72)=1|;Yr63Z3{9gi?Gzye<F>;kKakPB^J#LMLN@>3h#X1FlNy2
zKmZ&FeU-6nwfr#Uhdr!|Ic7H-pZr*Cc9a`zXWLlhX=Hs{kvo0d23lDAE@sNlH(D)=
zj6ZRf9(#lO@R@~;ercWa#C-P(kSSJP$^Slu2~RAnYrQ7y&H=I$`Z~j%Ra-i#5!T}W
zK59-;spMK~kvDK>-;$t-2$Eg4l7q*zCa*DI7kIr=;*nhV?U=~$e$d!nS<lzOye_Ro
zk1Uq0zaCE6+VAE6nqSC+Lr`hrBhRomLg8%gU=Uz_L{lEsKO=<U0{6y<x0Qoz5K?UT
z6zC@u{y7Q}T&}nKq^}7`ebUEZ>}9LJ+C1JaHOX4Jv;3>FZ_l5#a)fot{<!g8ufh+D
zX(b%L>PV^l<*&Bk`+*M1mCFDnLr3OzAx(Ldp~Twgd#+SHEq{4Ip0h=NPn+RF{W*sb
zg1rd(D&+v|M&t59GoiS+xLlC-+1WfX{X$vksE?;e>Ympvb(>tYVu88-u(y*Ab^sX@
zl%sDmCU-27gd!GWwA7>8<s57zh_HwE=kl}Dn}dKSKW6zzS^osDvtT>@3qq#Av57J;
z;&xckJs?v2GoXQT;s#Vwm9mX@i5wY0pWFiOdkofvVeKBm;a7z%#~fZ=PLH|JWyIRS
zg4{_bYID|xomswnVgh+FUt2|D=SOrQHVIZOJJz0M)Z0OywOKed0@<r|C*@KUbAeOm
zsVrE|fm(mnHg);CF*j7@UfB$%6AF_V!n?U3d~$Al12xc&m|waQzek<;qC><(F#Cvv
z4VOehsu5;h*hQfyA)gAQ1Wo2VV=$IhtcGKi3obY3ktPYZ&9Jo_L>5^cXz(rR>pBKE
zbRD)LJAan|*YpTpj%Fz3);4QdFUb?!+Y^7!0Q=&0R&~O4cVAH{5+e-u6w*6gDWf>}
z?ISi66Kty%s^ihWJYk1xJ9kgiD#-Q?5!ws7K|aT3D{D5J-=0dG=m^|_>=;%W7E}!x
zB4|xn<8yB*iRY4d!V%7VeW<pqmomSm;kTSMd*D|kRZ}KG?z)~aP#Z%L$S38%FHO|V
zWK;AV%orj*Qn~&XcwRQ{oUYJ8@28@mPhAIykvU!b(sx{8%Rq|ZkyL&EHK4$=tKsL<
zApOpCs*bC*IR;nNm+TUlcg%u)lSn!WCy>nJyqdxs@#+s3vRClra~o&KLx5Kp_V`&H
zF08Q7^O_3r^v)TXF@JLC_QFuE(_*X3+K2|&I+NVNeS%mU_m*^zcmy)k@s{5eGgQL!
zi^uRjF;v(^w%KaCTEt`?hL%<Z*Cr;}wfD7pJKHN!*V^P=bg>La%2#5#+7!7=ss)KK
zjak)`Bh!Rfxwk!uni%ZQLO~{#e*ppCuPOaG6I;xbe<W!nTA-;>4ca*0IYxBkpHQ35
zE-8GZb5SutX6mXQ$7$9-q5<_x+(-Wu&QgktI9MxI;R>J1NJyZi5?m34LFH-0nynv!
z^uDwPUAwIERco*|>Vdt7&9Z=PjK_W}KD*77Z|+~7ji3QBX4_`ISc8aGBBE}$LTcgR
z;V{U^T4t%!$fSAr>~FHb7(!HR>`=90MOui(6m3iwNk}|@e}5}*N2@plEvfxSQP>h8
zk}Fi!gpZq0k5f69x`$Ait9~GG`{r;q?k<WVlJwAWrOEzry0QBrCOTTu%&ee|>$qu$
zPq)b|3MWI`If0si;VbGf$X@o1$)K&I3zmqLl{Mmf6Xi*vU@Ow0Qn_X&9Oq52nSl2d
z#Y84=zKk<zUqvEk45RLg-x^sZh06UMActNR)MB)SxhbLy^L9OgAZf^N)5zz)Dzh80
zSLrF_9M+M2J?eO5;8&17Y^&m;eX+v!ax+SnnZFywP!TPecgy~*Ge;zHO@qQVM7b-$
z`8!eDJCdX_oOf$FuwQHmbZRGI#({)NO<J`*Nfp-s9zLGXJl8Q^^R;ZUUym+u-RH3u
ziq!&p5mSGp0PHjcdK!a+vep2|TT`3L0<SybEjGQ{?&?n$hpF|}*7R7Q!ge!k`qkus
z%03~R`L}yvrF_7`#6X!*yK#BlL!$-;>gbjr067<t(QEzjLcW4zlbJl^N>#Pu%P8{Y
zAAACr-~j2`h{-A894{Fih!u;q7VmblgXChw@<dBn9!JVy=9Izejz^-(NpX1A_^#m<
zpLESbknCBHU0b@5#LTjC9SVYc8ZC`W!2B|zcC$0PL(3VRfYZ4;n%G?>j;YDyMi#OV
zkjdl^e#w)`TGMS8jl11?tJW*$Th}MXKA`E*4n7YP_B<o#$S8mx`dWr>UqwaSDX9x9
zx=WI?=(4SUMBrqI^+h0nRSE$&41f!5h(TV}ZUJ$3VF-5FZdCVmQh~hp6rNDSx~t0t
zmHMEfT=mv%pr>xdYy%j`u;34E(wcER*90mct?gGvH-GSLUp^0-4qA@Z`rG1ER>zjN
z%r!lZJR2<I<T13&_gRSXgybYsR>_xlclh)Ad>E1PtV{zIUuEhjycr$Tr%jb`?KYcp
zb6Ne-6HTc=3SCvnEI#>abV?|lGmKUJC=A~n3^K+3?X~wim2rhue-`kg32xCEKhbR`
zhqVXmnW)A}Twxpgd-x6?05IbXSy;3Ux&PPx;k=yTJ0C_0r-!q+9(WRrk$zW{tf=NM
zU6wBKc=2!vGhyH4TvbavtwJ3qwX3bdmFzCt>|nrZwIR~j#ge2{zFeeYI9O1h>1e;!
zgvsxDmbZ|cFSF$<_Cx_!TB$c1ZJ8s4!)8`fm(Dek+=#ggdhfV^5EBzC36}PS!-~wC
z<FTG`_qv&u9;Gw%Qp5=MJXsAtU1=ug;gP7!uWP}IZxwZ|`di<HnBON_li7mF5cbBR
zXEG((>;8KEEm2Dpat2Pj$y#Xgz*TA8h5Y+Kq0@|+>>j9<gVzGG{@b*Ejt)Tpy_Ea@
za{9Z1@mE`CK_=^&AtV-ip`Sl~JO64XgGr>ux=&FdIs@X6?JN#cxLGazMW~Bu<eI8=
zcka{Wx?({=(t+a2C5c7u%bex(U2`5C=WUA1e42%d<|T<TC({fpD!4rT@aNJy(>^Qp
z>0T#+acg<Wj!AphDGI)!wxMgzuLAY-dMRwPo&UUg?Qh8moO$wv?k1b*er451-g`J|
zsS#CM4hF^P(SOv`s)A#c$bD~mS8$SXkhtmQ%$%~;&ugWpe0!5&`25iTvLZw<v4fY1
z&JVVu{x6L_#RE>UhK_nOobEu-_<==7GMQO>ldUW=tODF>-)L~YSprWtcVYv$nH%B9
z7x%u7cfrFN&Ki6BPd}hHdU)42Qj4?27k`fPVEf?-3*1ngWj><vA+&Q$t{-KEqR6e9
z^-^x9$5o3`$PAp`XJTfIQdGs}MD!I{f`PwlXfDVVwn(AD93~yFMXW9ECn9b(E19G{
zI9RFM#bc#tGP<D>{Tug<?Ng-+u6kZ&H+uTkp2i3_%-P-2@m+VIakkWx4afKl?)=|l
z{XK##fN5RxinO$pz=7lYBzb*ia1Fk-dKq_oF4lzI$U_fh2XBfysfAFr;NjMrnr;R<
z7G-tKIJ4jRpMCY2gExbViOi3#Ko(@Qen`)~Wb%|%3+G6h9XZ1f;5Hu9_jvTM7<uK%
zR(t-D#&&I2epmki|LB6q3%*`N(3M#lBj_r!WpQmXRzVEZ%Q&Q@AnNZ%KwJ<6i_DmF
zU2@6{*(m?6c;WS=$%AR-m3FkuV8;8i%w?ZFIAll2fO!b6HIpI*#Ps9y#_b!&4iRs7
z(uz^B1q`u-j%a;HTBJ?#PES**QqY)daG?Ao(vTnlY$UzYVOT=flD$rFk0PR#c~%mF
zvuF0?u^JY5MuW7)yOPg9ACk>=jK_`bGq9vSE@R+R%J^uG4`fA?%U4Pv=&c0R1m`gt
z)7KRq$ISXS`xE??x~n1H9}lcYsV)goLU6+g!`&?uO1egtq&2r8s9QP#t7b+OHRNPu
z7}kuhs4nyAW67oo{aX+H;ph=Vr&GUw7(`mjPviwimXHrOjpq?a0>qTz)94GTcetNO
zp2g^7R}|7YXs4^Rxuqo~OJAe_s^xK0Vmy%|qlRoxW_Hm6s_U2AKul}<27CDySwqw)
z*g(by*h^-tb?I0ljqZ>?O<GbTxIA+Fr^}6r;sND1`(^GFwA~v8RRw@^U+_LjLUDE1
zqmV_%CB#QA77kzBU^Gz`c)J<=s=nz6P^v7CyT71LSU_9ZnGWTNuY07@-gkEs4#9eK
z?0)Cvieup%5|bX06E-KKS?KiL)s>{^x;=WICTxi%t2r?Uz5FWoYSKw1fn83U?e%AV
z02BHE%a@8VD`ca|pF6^`=><)HN5?-EvQAq;S|a;TXpVnnwkhDXrmF8ijUoKt!!RP}
z_+4Q6%2w!61Kerg)zM1W56=AN)1!CqRF2rIOwZp)1YTc8y&gj>r}OnJkBjCWjH?CH
z`l++z2W=f$EoOhCgaB%TUQ`NL;mk|ljxvZv7=AIH*E3Ev6*8J;t<ExlGae&T$k^!c
zm=ywa<GVnu=x7NS=h*8c6CZ_vjk7*NVLk|=2`x15#%B;dRU1LpBhhr_-yVB_Mw_vi
z<x2}>1H>yn5&rc;Tcqrb2sU4;4ZEQ(EAI0>4K*f{XvBwbD62!OV`FXhhiaWFOog)O
zLca^>{cLPh`O(oQ%((g$DDshc)Pbs-ycIJu<k-2^LY|~%-d423!fQT*a;#Q(7Fl>4
z3(v(~<%$QggDb;uHqhPG*<z|B;Vctd?U)gu#c|g%r>|P#wnLO89Q4VSId-c(BT}-x
zWbQIjViU%R1+&LEyc0Gvl*kz|Y3c+F@>L&Gfhn#oW}=|HQXCecZybQ8*r63oT@&67
zPpF;`<A>HFGrx@2E{Hn6ROko%)vV7y#8(ck4u`n_IK|kWY0)06OgLJTt$Sb1sXygG
zv}y$8$Azp3bcdxF_^U_!+E_tJ%;1#ThM-3+uh>1?Qx$3UIK}h5y=AalY(cdw-LOHC
z3kiW|7{=qUiTdaY^wGwaxOZO5K*P#j^;dNe|J9nH0u(5X-O}WYxx1`Ul_(iBKQgV%
zW<qorJ4T8B%rWqAsr7ByG`7?~X+G1-bh*ov+T(mx^smTZ%PMuVqp4bP9m#p#OUT>n
z)z*jf7Dz%NeH`ixVy83~?WYna)_g-hpP<8a;#k2e7THT#8$Sxy^uC_Zc)3ZAAnW_H
zHI{%LUxC}5#NDP!_9Tfv#lNY4PJK6dY})?i;dh>ZdqN<aci9&zY$v^v0{+OJ%)Mww
zKSw+_5-l4&pZ)lgM;tKKcD+X}Gt!&&EWg{iZS{0dyf{uWb&!0}qbM~iQW%`N(u4P_
zCnH#i*~cVG0cDhNgyn&WkO$4axmbl2mIpO)h~cVhm?dsfJMWXnH7MnWJ3PY<-1F!q
z#jl;wu&&)$*%0$LDt;69*Vh^Q7QZc#!8b-#vMSkB=HKxBY^&PB5vU7ZbjD#pKNUHN
z12+*3P*VuP?P4SkZ_rlui)>jRz5VltA&}13WDUOij!r6vUue5AAWcPh2#3%A0Opl8
zV1#nIA$R_C_&sluT~L;zB<^O0uwh8}I-MBo(fnQWhH5k41WP);p(~uV@}BL@B>*k#
z+VbVIf1!3eUqmLh;IYEvf&EyfFZTq2DGnQ++E~Dyu$Ng+ES*qO{;l??1p%We^Lp%P
zv+|h*?!tK*PZSuf<Km*+h&FOdlHynKx@%`|Z<8G!W8=A>c@bIVfP8h@y~$r|4U?Jv
z!nJm$8CipKOa$lx(@m|_F2C)Es^rWnPCog&c33s==563Q6{MG<|J(=_=F&l6rXe@#
za7KRfn`~5Xsb{tw-{OR8Rq)XATfv(ydw+`eBeX7~L4H0|h-L2c@nqoB9L&~sZ7u*@
zTm3}CsvbO+Q8XHV*lv<V-~^rizU5Gp9{lbIYWV~@hbvlT(itGchC*C%+mS$s$>QsK
z<f?4<4j8<x{zaB+@5i~@OjSkiOzrZ6JpS%Xziq1M8A6AHmhC$_?-HE?3_9DRv*&MH
zH12MQO-LiWG?X%&IbT}!t%Av~%>ror>2KcJ+3ySY-+9wCXUi1%vrGx8oL9y@veM^5
zOFv!bOzt?+e%)Gj!lp}PJ<QNxyA&tEnq?vfkhs3-j^bjeA<C~uQ7n*j!^>C~V@1aW
z&=Gk9m8JuPkCWL1_9arI5`u>oG#B9_BCnLVpqqyZ7!vS&J>YbcD%+B|1mpJAh@!s`
z<*n!^0r{#sw|5B>Ek17U_f==DVJ9Bg*rqw3EbMkWYMHSXt#P3uh+XpV5<_0%QW#`m
zK&~dqr3?~`An!rml}b@7d9W!khPDobIy2d%U>azt*ZqR!xopx((qHQ?gn9v^!T=hx
zUv8k)RIli#tQK|C%Sq~J0?mN-?VqW<V8WSChbj8nkU_g<MpO|dha}#G>n77oyQi(D
z%}n&KNp&VVhTZ;tl7o6V-I@2PudR6JejN3`3L8&v;kdT89<;q6+uUovj@Vx>J`1Mo
zG{&_!PF?ZU|EX;6^mX|vqwCzqXtv%EdcJ8X23{SZtmX2LuS&X)m%)T+tCJlX973nI
zmhBL2Jz***l77la%uqXEYOmN|Y$F_|p1go(p3G%o*AOg@NlEsRuyUmmLKRL>_q!vO
z1c?>iYF(h+uZU4;6%bm>pZ!L;V(xxp5~5h5W6h0Lw*fHJXM@&F?Co%!qtPgvw^Uyx
zb>E?<d)r;^=kuKEZ72O-VkdXXnP1DD7!qt>O+fb%4NXdH*DL+f2}>t;Y9pf$6b>8F
z4L+w*3<hthy#qWJot!MSvGs9Vk91sdtdr?3qqie$YvHarvI`0#?M;=|I(vad>O?c8
zTQD$P#oH5`A-LC(I@@8lrP@Nz%~w4E*&P$c<!YA)P8S!jihN%i&>R#3o6OI23@48w
zl~b5HOc$U{CwsPc&6@#+`aVlkq`}?K?&SNdZsJN>2g%Vo484tl(Bv2Y3ur)>zv=8%
z98ZeoD!F{#2ls7{L4B$wJYBJNKax{(&@HfOp+)#rV|(M<xf>9BHWeGUhcec6?Q|Dr
zdryqt?dVbJiTkdgW%Hl3gpX&8^I7QHu_byk4(I5hUC_NtYj|G-Yv*LHwboHN;Qg2H
zEd1uy^K`@YjI;RjZ(DG~!~w-f8>9bvTXPT)+x=KtT1Rtz&Y)aylMj`eJ{6xsSyuG&
z5z9jy=gHqjWME5T3Lc|{%g~07jaBlkijJn@*MDEeuk+?1Gd&H?RLFDTe7m$Ho}QL3
zTZT1j*W#9&Z^j$1zoym{rR6W{xXjgmU$O)*yznAILxSnGjWO}K4smfSouEicO2W@`
z=3vG06?o$D$MNj5&oays!=javBp#m#po^_j_^lAd+tQMJ`wt#E#Jc~5HEY(WY;{;q
zS^biA$vR@PdCL~ue8UZx@%9X5jV+T57s^H&E2Ur;yX&aZ`k{}PL}1d808Ia8GtQn%
zr|_GuNJ~lbWy{FS!6%Gq?e67DZ)OcJd2}a=jf%fVQrwM(7&i^;06r;Ly!9x(GP$Ei
zKvOwYZdyVTomyHXzuT9aS7#_fK&+<!+#aS5$lUEn)ET6tWzw3i_`82N{4|vg<!<ti
zW1$bdne+%OT8k9bSoOW*CmDunb5P;8Q`WCt1Daz@f5!*`M|;rPuQ6OX@6Y0TNs_GV
ze*9xQCXesO7`LTA(~j1E`Q(%DTRu+2C1b&wy}0js$CO7e_Vq8{e24@l<J|K;%E!76
z$C!I-XkRZjHZ{FH33s`C#)n8F*(=|8&qfU}?x|^N&S2elnV0xAX`ucJ$!y;5n6u|_
zgz>S1NM`RlLNc2QhvBE=v3|!P%0ftH%UX_Pwh)C~+O|-Zsv)#kZ9(hT&uGcplGfhU
zk%YV+_1J^>^&iip4(D%Y=`Cl&m*3)s*I#tFdU3s-L=U>M#w;y~R9_U2O7mP67Ihk{
zcODkYCMtZ_!F7z?-N?HhrW3mau1}&EEBTMj`*6=SBT6(e^S8CwNMclx*1Ru0JNjes
zdrvz|?3dQ35)D=0fXtEeI8HlTDL$4}d`OQ#T4}eyAwCCUbsS8O$U7O^dsN>pc8l!_
zIgSH#pDuV&IEe8ByQ^5#b~Bf8P`*_%Z*>F%aY^65b2~(zqCzLXLmL?<TVjg47(8J?
zJK<Ey2oesCkHfH@9nr5#TlHMaZ?VL_bzDhB=q(3nTqc|#y$iMEcdi#L!7qnY`fA~F
zxVj5;4JHaboMe(-;NHkyGbReN6v1qqq|n+#4bwWES|RrkC)XT!RpR7Nh-c-3n{JoP
zsT4BH;@FWS<C%?6O(tEEOdKpl)69C%6Tz6717V$!y<lqSr3iA9hr2}^hl>ge6DgAu
zR$L%N6BjzpTzu!yTBR5{&B;JM;}a@YRETaP$wI{Xw{cu=s|^7|C@*)~ERPCrSHY%u
z8+?vr@Vl|EHv)aBWT9@YJT&Desjo6g`X(Qj6Y<$txjUI2=K0KVvTK`+{>m9gA9ve2
zD)U=DCPorriA;bb$#D_f*b*Hg01=W_Txpl)y4$F)GFx9?Usbo16%x%zvM*$>oHydI
ziI7U%dE1iKMqF+mS_)P)Njt^|R)k6Np%n}a!r8VyIWE@Vl8kbKV_D^-K6x&Lh!<Dw
z#`9}i_vQ3kNHvK&EJVNUGi809f`zzNmUzS!O`{pBSIWQn_9u)bn_gx+LVW(ZV2R4}
z_#^jL>K@2-eDKLvinO**K*|+}cL%=R#!JoNM&ADt81TGkly;+=Z6T$qn#A3>SSlIq
zSe`M<C}SM6*M^)ESLe2Qte;WabJq0;u4~qB#@%;JW(@1{7d^J5wI~)zTgG@OZ5iKi
z>vm*c-naO(iFo&;FY(@6FBKR5+GZ~JT^?y|T-(Q=&%$B4B)3OetNw8wAnl8Fs3==V
zzonl>d82tbx0!C+<ypJqwwu)9@Z*uxfZoh<B`u?6+;F8PsJK6su{MJwfr-zLb=}fd
z3tGjGA2&+XCu7#!dB1VL<Z~nS$)o2@B^h}h#5;hK%!6!jm1p#}TPLY!P%4%9)<!a1
zzc=-1!g$G=xHfR9$X4(^$A$6Yl?VB3%hPR1mbrilBL)Skn7_vH^0uN09ek9FHl|)(
z+gQe!@(LB47njWJ4#NmAL<z?UAub$E(%++WQ9k|6Uki(f=JRfn?{Nv@V$vD6UN^8p
zmg9vLaYvr>pwLqYK61wTuyXC4P3z985ZBqX6kV}qKl|u}Yp&>1h*=vd4fN^W2HW;V
zV#&%qBvAXQF{oGvYWa_QCXb}TL><ihaV?VQ_AMkl9-;i3yLcN~($fEyYX;!%TSq7h
z^b2*V634x1<ND}r6G!~UtNLNif=$>L6j`kv04~&Z{N3x7(aTj0VMXIuS(wPYMyTea
z$y5dl&BWsrklsk^>#~x3Tg4st0}nog{d@ON*y&a#F>BN%-)0wDkH~lT=bz>vC@2Wu
zelrWbdzZA>Fv{55$~X1X&pyYCFTSW0RLdfTt;@^z)``Tnd}rT$^9@=w2jlb4KjU|^
z;%gD>dX0TqZ>;+3FFf+dqli8c<q#%PR^dV!h>Wkl{`w1!Q2BWFH{U4t;bBLPDso-c
zF}L1wtI?F+Rsw-52eif0Uu?kYJyDoKE9cVENyfDPXx=Vb3N<7V?TH&k1QyP{?3EGn
z`g)Sr@?OkdPNjv(1JI&r*`7h7Vv@1x@F|9RX^dgrTiL5tA1eic0o~d#90Fw^a-MUZ
zxP%l$olZda4#i37-4fqgCfH0e_mT5HlqFt>bT~W)Z+*X(-LVkLCWbq?VMOO@C$8Jk
zLreD#&9R5`Ff)xo<oY8{Ct><0i}A_7rxa7Xyb#w}<Ll)8ZbQ;Tlt2$1JI%Q?-^s;W
zaO)L)8EVzO<>IBfrfjbHzN42LV{K?6!8NpJJI1JObE&$d+pPcsF`;3I+Phzoy?K<K
z`O*`MPdmr%ohTTKt8n=n)6YUo%34jx>~LD09wd>yJ1i1MqhfHFf2%g{FYMWADjDAV
z?~iDSO$FZqH{;^!I)Ij~KJ++SnG`UvA3PZ%GLv&7nWF`-UWf6|Ji%iY?@MLXDRGjY
z|MVBU`t>3^QVvT=;d8wB{w@FHd9?PmFQ=HdnqlP5omV{3u%6;|poNmvP8`w`bCzyY
zW4X8szk`<8*5MwK>A6>kYaUdx6E|{QA3b}Q!=!at*LE?_iPx2zM-RZEkVtwMu7f<T
zsTZ!@St@ZY7U;S3oGfI$@K{qP48ADcy@CY!qSYj>iFat^X?}ms&<m|!v9`C8gia-Y
zC!E`;KAn~IySt}{l9_Efz)+y<x2>0%O?yLG2fx>R4~BFPpdyxMu{vw(562}qwe<=w
zK);RP>t$6fKIonOiUyPXbE%f>IV8>uc`Ngn>d^%Q1$<^&(V9fLv7$o!V);oB6~urv
zapEt3g>4y~rOOBvBbzQCve)ees8y*_!DLiP_>BI^32Lu8QTQTmA`0b?l2_`cwRnm8
z<(yHMrDpx_1wue>lll|Lxi2p4tdldfY?^sHp<4@Uty#%=p_2&#es$s7ARVU@osgT8
zr5co$jV{X8_-!1!(7iJ17qVIe>p}=iyf#VV(pqA$i6BOVKt_^$@>|?<3+Y<SQozVm
zg{`#LrcE0hrt3Qqwn%%DWWg@K#R4NRFi;(nKFBdSXCx^vW!koFtIiq69o<S@VnHqS
zdU<)NeGwLkTRzze@oJr<Eu)VH(QT9yoTQux!KAEpl6vHv;4CE7<(!r-U!^Rq1p@;s
zd)F(N6L`i2z8x(}iL;k~gqF=A`{C4<f}S1&N>npy#1KWyz41S4snXJ?toDRtm3Y8%
z&!2eg{xSteB^u&^m4B^QF{-VfKkIxF@iQ7K002M$Nkl<Zj=-IMe7kfCgg@grOOm+8
z5?lD(Ifm8Xf|ngs*DQ;SC)tO7eR`C5#;ohC$i7^=kTf#NG-=#OJ!fJ`otT)cj+KUu
z^>KNhBsFc?gszWVk;D)gsdS5PA=gWeR8lsTll()6LokMl1mz{8Ep_jixl@M#^yuD2
z#rhUfTjF^irq#2Yw~&0rYm|E^*6x4N)%aCcjxB!D?yr)4mTXeLT&oYqv>m(l;ZNRM
zd+iBn?9-$%uD^ObdiCrEFOHXqw6c=;(|_<ePoq|_Z8;G%{ru}9j2SglxnY<3t;bZO
zo1*YC<(5gfn%7Z}E~X^DwzzEHv3nn5Ywl7cy~@kJ-gnnzTIX8E>Ik|cKYl!_*f^-J
ziGA(Yw-+9``!*^WHC1DfJb(KS1YzTr9XN6{Ts<Fh-Eyz`_33GJMQ^Ja0eQY$cz>te
zHwM1+3?wngh0qD3FmlPty_mCbJN6xl#D}llMK{U~%WYli<Nua9gNz{;DareKHZc`T
zR#R(TB~dKlxXx$1fww2FpU_*y(XMQFWW4I!z8QuN3@jL93R<B?;-EeOm918l3#E`T
zC@hAqz&&x*xFRA;aJ=>ULD;f890!A=5kiH4E_4-oIVE{s?!09f#*OHP-L$mdzWXTl
z2Swrdi8DAAn~V=;{Y7PgQ~2KtQ<RIl>b`byiNA>MDksyWy{)2zC=LwmABbNTZ`H(g
zbpv06>Z`-|ES<_DABLY)F|x0xm5;q7^C@wCefu4EB8-;Kj*`aKb;<KC*4YOS9Kc<7
z--Ee7|HN3&-K?{ex-awfFTVT||Ni&?DQlLpEWAsr#4>Mk!taZJqf@zDd_Q|OyuB-~
zjA>oJ%o#Rq+Kl_|yI(2yRt1-Rd-mYYyYIrhUw*+o+)9Wh@c?Bg1zWeP_Z>&TznM42
z^lpt=e+HFFT<-`vMeDZn@NMXU2PgG$r~y~Xm~I-?NeK<U_-!wqy<;#Mc$V&cV9}-{
zNQqCylXP2Nzl?kiHU2lt)S-1#wBkH0;&dYMCV|Z<*5TtZ#fWQ}!yRCFLOHJz$DE<P
zsy0U{3udv#H|J6Dn{zEAxmG~w&?r;hR*XBIx_vC}`R@-L|2SxK{U~$1-!~qjb)qL;
zd+fSGrN8p(x;)RD6hZyq4MXwNJM)Q6K8e6YEZuMbx6x}%O-dVfAEDdmlZDR=pCH^f
zY3Sel{?(*ARCZtDBchN^Ie?20hfE^Fmbp!3ldM6-g|hsOF0itdxXsr*{T@OR&m(}=
zxI?<N$05dmKE|-GCr%u~#tnP8(4-uQ-X@yS0=u(+GxX@t3O(AlrpJet2x#G3X3T7(
zuTC6Sj9A1XM@gdrRB}wLL&UW#cH`N<c)n|;CUQS+v>wkTcVsW6QZ_Sp)pl|?r1?0P
zn#@?umU$XTR1d+=e=^)HuXEFWzB5N6(T`TYM%r~a97iK#EM>|4j2YMqBl_@Le2J6D
z5FE9(HoAJ#*#3Cur)5YL<4k@(mTf(N*UqP*fmd-7H<^~nOE&FO*DUe0MRCD+W{kQV
z-HKs^fwd!LhWF_R4|@0$*ZAkzx6QmKw#2ovoEK#Ux_TFv?Ex*CvX6e)7aWNr(PtFl
z-KSHV!sb$VJ-cZ^E@ZfPdLG-qd*Qj-zN3lj%G^PjJn_LSHF2;Jm)|C<*m34e#yF)l
zwYV9lUm8VFi&lrY+s=zVTWVEyPZ$=>v>pNDc_?X~WaMPCkW`oM*~ZmYc`QP|I=$Li
zi0iUG3VcaS61$HJ2s7gyD9NEwhs5<R0fEJ`QY^i7y08eyU6eohWEr<XtgmI?Ah3Bi
zILI4kdEz1``Su(Al%{3hwl~s$IexK{kk&%f1_T5sBHH-4h=4jFAwjtX7eS6S(INz-
z<P*1eO`A5Y_O3Q)&;VjBE$v&o-j<lF@$vC07Pb(-(!MPrEY~7^67sk*Ny<z65`WrS
zh$9$C3~OoAmb{jBq@0lc?c2A9r)SXwK*}9Cazwdfmwh7%zJj4B2VBm?^^#>P@%|@Y
ztNUb-lm-UHw<KbS$0PUOiODx#SNv4Lubj1iAn&^m;ljmxHpgP8>+gW;l!r>>9Witu
zO#k-}#xnj;S<95wt`MIKep{wUlBcGTyk;L9Nokw}&HnKh#XH-}FZGNVHW2rdG;G(_
zzgQM=alLfeD*QZeF_jv`0zDs(Ja89oxNc&xd{zA+c(!iY91juG-n5{tOH1~2DoTj^
z>My?fUb)&X3mdtHzP)?kUXr8%ZQIbhP8~(KuUfMK-_N1zPJYAfj#eDh#MQea#I;<b
zkX20>N9plL?!(AogNqRxGVW}p#quX~D-{zHSAv|C@)WCJ;qS{ZZY(VZ#r0+dk|-BQ
z4Pr^$zI|I-F^|NXbnPwE09!s#@aRk>gjZg827b+a3tI_*Kl+m(7GnCPSKmh5nX@cg
zG>Xgqc|I@Vb>Ly%iwL^ODmrVaM($M`|5kYP!Mh6UZA4AvmM#1+oX>OeJyWr6<5q0g
zv>jbL2V&fq5rz3qos6-o-zOTw;!PmH>iLuS>0(VSv9WjWJ%|;It?uM(!s8F$TUejo
zxiAR0aNc<B6}{1{X>kqn+eqd;_J7~ef;t}WeX$I0zc`hiPs$#LL|`edo=?Rd#{R=m
z6fl|{p4+6H&rr{|7i03-l8EJ9xxA~bTQtPIQ%0(1Jc(g^X8p8|Zcjlg=3aRIar<L;
zdG#9S#Ui91=i>i-=qlq-wQc8;7!?7GJ3TqATz8<57#BfM0)-+H^(QMkAHUP;`f?+#
z4G{epTl!jBo6DcLU)!<w7(So<7oVYMtlAiiKEH1#!9Co_UTxc83iv~g#^9!Fow069
zaIx`Gp7j=f4e=}6J{6mY7R~Gvn$^}BUC|{&K$c%(1@<MaDB5wo-<?E=ZIXHaGtWF*
zCTXpdAUQWMlB}`qxz~2)d?18zod5aEGgvr(z9MIhGWNDI-w-$B;vQVNV6?M#7h+wk
zpDanf<ea;f(!RJCUqRR9ufOpo-kC9@<buy$C+r-PIYeY+6rOzQNyNp*DvKrCY?4Q<
zhm>&F(l1VE6)v{IZowt!;6Xgjc-HgjhFv^a=~O)kJaSDB-1zbmghnNx6Rn+X)7-y;
z@dDk6FMNFx+BYvg7qHE1`&;I8H;?H=8;M+evt$qcaqB?4bA>D_h|FFUOj5uTcTebQ
zTaEr*4g#K3IPTr4H6mlrSv)ZE9p4`sjVTlQ6_z}ca30~&@%)yF)P|%I1x<r`v@JYe
zn|H<y?2Jcm9)Zv1Z&GBrT#K0sIJ1{*R|=OeKYFdh^UdnU!9s%YLvjg|yk8TBbVJX;
ztt7c<na=-a{k9o5jOmTKoL|<2B<^{?p(VJ;JtfK9x+mS(kLqhN{J7YCw6<Wq*lpL=
z_9Rr7K}iVldxpy9$y7e~rXqTEq?2b7F!hDc5JC&wHf{Z|aK@9gu=eLVC<mv{CNV@p
z4BauG#Ga4{9OU-I>9bh3?I6~z->s~c#m%^%PXh!pW_H(hEzzfA8xq>B;7=n(pN0(G
zYIL5RJ#{dcWOWk1R}y4~>s2*pa*!M0v)rUneRwiFnv}Jd#NxUhV?NihGiQ_hYjy`A
zR9s85PtEA@=jx%o3ac5}yE8*PwB{I3MS-kr{Ip_AG2(h}SR{=p=~<7Yw~Y4_26dNN
zUtxJ;CxKpt1Uu{NmUA*U6NR8Af{Bs2s#{K`6ByEN?#itki)pZBTOZr3OT4Ze(u3hf
zipHcaZCheQuZ~#0aUV=PA4&J<8~26MZF=8gHEi4)g5!*tuCAG~z8gmMBfjp%@;LN^
zYi^0Z{>!Ru>f2$jpYH6(_`y9;m)-^?b!h2J*Y(Y@`|xps2gb4v#(md~ENt!^jbpY4
z9aHn^uI-vLj<}DK-45q<?W61S?6eFjAcVr2xIRpU*@zf&i@g8!XlXsH*QFT;3ooJ?
zcQkQbjbIj5MQwX0YA=dQaU+@J<1T}mSSrht%t6QG?E$A2KFHo3W;tj5gv;A*{kxzd
zA*S;<p-lf^K61mNVHWb*TIgpCoP4oWn|zs#qy~_JL9R~kK)#^m45A#koW#W$2Mi%Y
zWiV3LAQf8GYt(PqvK3ja>vTa8kh^O984VRaG6g%f=Z$<9Ys*Jm5$n<qNrnBh{8axM
z=d2E0&|R54v~Al~5yjRG$%nTI<DQ8iM+jSM;#%^_d2tzjA&6_KUt(R0089F3w5{GZ
zo(m5TFC?suV^T))O21mQY9&7_oJ8;?WOb)bj5Ss;E*O;vG41c~uZU}D!?s;1+p%Lu
zMGhKeWGm&`v}uF=bbDx9PJ~Q?;e~LyZrz7VwZC}jpURcC+&^o)O7_R%PWv&EcDLPn
zL&?)TlCydVa%X>lJYtr`u~uju*Wc%mz@~-#)JIB`89rnn{yY6?y!zG$NTY>tS?vpX
zz3BJl%#)9QJpQ0V<n@M4+i;o-2t#OL-2ov(ubDVj#d)@UjwGL0?A}L0yDu$Vm#<ud
zp53}QXf15rO4<AJtTm#$^|xM|&iJ`43o}bx<nd!iGFEY8JoA6A6p+t_rwez4NEJ)*
zw_csjccO0L@n*h0xNGXoYC`<U7vC0=KZO|zc7&XFB&mKf=H-mnUO>mdfD%OnKOt@<
zhV`@m_bM&c<4W91(Re?4EP@Im;dHg`m=>(GEAOUQH&3`?w6cizqLuUB{f7`75>|pB
zm;4RrBI_k8L|Bv7)*bHJxg(x_>JdzT>8--?$v7{4T(@B>?!9xe1#{bhp3ih57j;7T
zw-0D*6f4|%d)LRLYp+y)g$E0E(&!f7-fBq2K|T~*jJP(+ig$>?1N*8!<CxxFSOjDY
z%A{*9+tjK>W88D=aJ=;HVys~Znlp4u>1RW3+m;dGox~|#wkAkf&L)z0^`Rwqc`211
zh4vqgRM`gfZeL#Z66fVTkiOk|-JrsA65o3M(!E%<{vhrm>Dkh+YQ&>hGF5kqC#IWs
zgeuFS6EWxT#>dNTnxHsD=J5Omtl4xJkK8$q;VMd!YBq&x^-mF`3>(l9UFhEQw#Poj
z$(R&uV2tkzL0tdw*FLyY=<&;<Z6pLs-d|DbkPt7+R`16{cZ{jkxc(hGFKZjl^3<iv
z+{~HdL}^LRCsH{01!FJ1*QE{It>)UIoI2~9Z?O8WRdy|{WzH;ff*w73piAe@%8FUm
z35VzsTogp*9kwN~ooJ=BdE-WW{>2yg_w&z})-84X3>9Nv`tQp~J)g?<T}$N6rSfTG
zD!C0FG#H&bcj7##E{;b=V8@P~YMmr~vsHei+CFB@nuRf=M`Pl|D@)Yr$N^E{efyoa
z5yaTm_AIS)C@l4$o5Z0*hM-rkUJ94k*cj}lW%a>>hm^9dt#>TJWz&WYm^pJMp8MCo
z9O;J6;{XEV`un3jmAgLweLp^aa%`~@OST-tTR-i<0+QCf+E<Qz5Fz#L<GYc#-iHt8
z?ZPv+4J_7%{8+vt0*69l@xTqe(Y|HF66f`S@(~!?GXTG>KV-?sc|lP4DT*n%_AD6N
zLLy>tE|nU4GH;-8z7>_iIx>vl<&vJeXFTHQGI+t714ZODEw8_!f>}Dp?>8U6{(^XC
zDGNVYZy!AohdclCt^I7gH`DK*NEg7(?4Pfuqq4U0@^r(4Hx0+Mw|=EyFBc(^XH*RG
z8^`vprdFy}%^te3-?Bek&3P>~(NotA!>FuUj-0z{&Dt$(Hs|4^pVwmEs@)}I1~O~t
z(#}uaH@k)Z#s_^nw1*o#tL1WDByy$XxL9nQ^&PH#;khcKaw<L%H$V3gc90Zo$(Yvj
z-+D@s)>5$>3B<N^6E1%f23tI_WpeHldp4QR`DyG8i=elODDKA~GA14ywjIKT^|Z_u
z?uBG_v&P<NPq*SiWcTjqj}Ekk-SN$jd{6R8`7op!Wr9v#P2k+fv2T-*#xowDj7cO{
zWw<RhaoX8{A!zNT@Ba`b9IicB!Igu0u#ZJbV?yxWcEw<>rH_-7a=`++&z^qo^@@C#
zx=x)*DyoumyZ{ob_R?ej`xfhW{y%$H0UpKGMQ@xyumlna1WSP6?(PmPE-fuB)TldE
z>il);UZ4tXDYTRd?(Xgcf`kwvgb>ev&f82fyF0ttKoV^3m&we&c{X?6JUdr*!ZmF!
zP^{vi{^2l7{@=%wpj=TfFe8N}4*+x?{me&3Rl$Tj6LZxvvS-#?c#hoqa1B2a+UE1A
zO%0-T<5@7vZ3|q_TCh&t6AxHp1$H;GXYQKcxIp7j;%a*_*B>?M14J-b7oeVjzz28y
zG)0Q}msXij0fpx6H^Lk^HRRVr<DDAw6igs(+ZQ3nP6LF-yQG<LO;~{f1s}}(7aG->
zvH<Vlp^>@s?KL|NN(Rh_vyC>)wiKW;yAJ*pOpCe>7*U{)z>bC=s|E~X(9c9ipqY(O
zWuXDOW8=aNonB@%Itjb>jR1uf+tdt=FyrWHN#B25Nj)pI2`vheSOFHru@KdFdIYn}
zLW3IQSuIH1XaYRj|ACf;)fs*bJOa_tCZtKR+j>j#jDI8x6Sy4k+A1=Buq;k;QI1K4
z%K@@lJ6MupJ<eJ)qV_ed9`SsoNUNWAW>svfqunctI&uKx_u<Th9f9c+Cr&8MRlTM$
z!ASa;=Gh3GyWILyJCp!4{jO=iHS?s_a$H=T%3dGN0B%?jTqYoODa7ZXwzQ+?VV$`7
zqckq?+!`^gEPS`{z7Y`&=ccS}I{qf8dOgzYKjzESkH1#$&Z-Z#eOR<XZUjt(#^=ak
zgKY1r-)RfyiUqXJkPNg}61TonFyZm}^G6GKJ?37!ez;wm7V_e=0Iy$vSDCLi0-052
z%n#u8?*Q7lAbs?~yX-a}wRk-9c@{5QY3XG2H*ihBH?&4onEf8_upMbh8o?wskHR3<
zqj%4)R-m=M9Gbaq)uO4aShZd~yZ#=p**A=VPi8%6t&cOHZ%_Gc&R=rk<Z0_MmXYv^
zs7dY$v78Baj4=eQ_33G9{ek=Lkmn}<&#FJr;;>b0*u2dp;F@KYg*Nl^Pd_Xzn_F)(
zEQMCH(RfaN_V3dJ8qmR7k%s4Xty@T=hIN(sa(>?$vCW(~bxIDwq<2Z0__pczc|R8B
z;~#zMC1r7-TbB+(ApuuNS%$ED7FN;OKYsYh*YIa~SO)d)CAGpr(XMR@wn}0CxKbP_
z4&PjC18K0_lg!1H3q;WI{V)EO<kT$r?za{4=-vJDO!edMtK=k1lzKwTzDv70dB!)2
zz|C7dQTzJeB|Bis?V^15%W|3Y;7$75SH(320%GE@@P*0L5d&c&PA}Yw8uF`%feG~4
zf3F9B5@gM$1Jbu!u0;M-@eqYn34g4CYGdOt@-$tSq^fqvKC+;QzTM};xA^482E$aj
zWAkj&W-dqjSs}L!ZExqBtf1;*=2Ng7U{8>aucvG=n4rYm*%R&N6a=YGp&hwlE`@H!
zo)E^Z^BvF@Sp`ju?&T~Si0uC&BO~Rr&%eO;X}=Xip+%bep8Vnqu&&ZAM|+y-2wwmB
z=RA4!_1EPv{5R8wqMp()SDZe5hKw0K+M!@8*A}y9&z6nr*W0D%bGo6NQKLu6q^F;@
zGCR)lqhM-#`n2it2DGYaMM)2y^IM`@ppQTPSh{xYqSgZXy5)7vc?A7^|NQezW&YeY
zekS<Rwq09!Y4VHGv7@!s8J7FUAAid8FTN;8jvP_z7kzs2<(qG($*57IB&2e#fTKQ+
zd7VEF_+YQ=$x$ujzV{Z&<HMW7EURTRmSFO0->;RQCl8dm)$BE~^HU1J>)ZM@mKp!-
zkgw)#m40o)q+)<&FDn&ui2u#nD1If2$)iJ><|kV-u8srEV9~}0{tc2dxK9E<&yi=9
zx!NG?6OuOVIi|E&35HOcmGP;meZ3F`+unM3h?)=lws^Y^yiS9b-)6W)N|E=U7+$m*
z#^`I~6H{c-1^^mHgueD}Xo?pFQR}*IyZTb6+7bYGQL4x<X0MR`nBx=_ICVMCL2c`d
z><v%)U#}TbPWrShoc)G8>xuWa6K;`q9y)ComETT0tgo76&w-$@8WPsNt?c;gAMwUM
zqNlfytomk#l&e-n+T1eIK7orNClzS@K0xa|QUM_BFH;_p=5=d0O<jyiIM8h6L7&j1
z6j+`)9u*_>L%d`EG1+?XxH2;y0nOHx>vzdYXlD!RO#G)rMvC}Xs3(DG$r7A)L5?T;
z%Bq*YmM@>aLut%9EgbEq6hKS=(v~sKX<UPw`yx)tqV>B~Jzy_aZt30DF4nN_tz^oK
zc@hhYWRcJ;pSygMj33lR#Y>|?BuHENvTrn^(-8DIb0QkZe>PBzaT=Y0@~K=7V7`{#
zDD5SOIoi{~x6l0^qcVHr{Z+15ULpy|<J~OUxEsFDV{?3V^LpV%xC5gZGCX_x=5?fg
zXq8K@qFP~-7e>T-a=mG@R_NNip1k$lUy_A%M@~kg?>H{48`M-eEJEEfVCtL#0$p3w
z%gMDx-B1ZGUlwclSd7m{A$S!hLH?HY#j?$K|Kt@w?alzjJsrHmoXoDF3;Wg5HB|Yl
zwyJ0#nI~f;D?Ukq!x`ul(zhPQ2c!G=(J@JB3-FT#e<8Mw7_%qi$75s10vRK;nq0c+
zF)MrwSuycB=`233b@||faLcyq?*=Iyk7^YpZ9M>WIIuUOEcmc?uToC3ptX{|4HG9v
z9R?F?^<^9YVkQ+n2M7~rZ<ozNyVgqPZ`&~KUQwL2@>x&TkHj!uYxULQ@Lo|7H(neO
zH(mVw{e_wkdf=LyDO#8#px05Ju7+2vSW)?MV_EvLZ2@cMN3fbEw=Y+F*%>dWNx{Qt
zQ`aa=drsbSl+IasTKa44OhEGk0A_qo&Yn->3(Xl1A7X4`HtoOPC2iVN$wXcnXsz*s
ztyY28fBh_}Xv5yO+=mZI`?4&t+O}#A)6S2}<X7L3^ANOfRv(rz7pBb_hv2oNc1{1(
z`wtvOJbua6#L`FLSsVer*0Brk7d3oaG_x!&9F@|vQ3F}AdcECkz87jA*A2H!673=&
zfX^3RD@;zFqSkq?5QkA*M`0`{1VC_)uALl3u?lbBrX@^u*Mvs&ekBBAbrX$&@5B}I
zg73>`pM1~`v`&Xq?9Sc$AfayGA5x;TH#D<dp>Cbq%a(1J^xF}98pG6AI5f-W{k_oc
zF546RWq<b7ck=zuv!!X{`p`CS4Rh@cl$O3-g6x3mFfq;r3SPM#rC>9jWzi&fU6>fB
zC4$y1n@V*od`&8P4%l>0IThH!O;B_mTugb}N^9rDvc3p~u;q|`E#;@V8)Vw771Fp~
zHR;+ucM)eDTVxIV8h`oyQj`NX2CZw!rD$B+=E>hxDwUHFgIdWKKdg}7{@N%v^==_`
zV3P1ssi1``C=Boi%zsm3fxziw_ZkEIoCY0&HqZ7wC*f~-jr8i=5MzLY#daPKcd6;}
z$o7(M?}SCFnSZR4C-3Wre%;qF4{G9b>?#I#O6->ll4zFQ5B~8wwQ;^_I;dAu`E2?E
zS-yIY^zQ0>>?+9mnR#6r2RKT417IG7@BgMif2oLdGuuu)HoBGpkp3p(ltjUYfR}^z
zl!p1%B53e*1#r!;jC1_I|NcjgMI03`Zg%R3bF;=xn#fN-d@r?X)zaPJl+R)v>Nfal
zZr-A~j2(NMvT&sLP0jgt1T>@n{^uXLYr+Jp^x6-)f0;Gw7rQwm@4KUOc;LYY<n`BH
zvlEPE`4wPl`?1F!l`v>XjUPWAbIl8C&adT7?U*&|*2%_=8>L;lc3QAYXAMwKh>x?=
zw&pyqcb`7;&DUQUTEC*pq*0?rO5M73;WPXe*|T>K!0X)o4f5sK(WCMg%$eVF_uVel
z$BdLW2S#+REpPq2L8kn)UOsuG2j<8E-;1Z@n<sl+HfXJt?t}fa0c}I&li6F;{7(!2
z9lovCZ8|EG#<c^eR!$2x&-vs)by&jTvG)kAJY`BOw5t!oG`I#_Z-IqJ^<tSXUT}vd
zj@L{1Ec&X6?gQZB-N%MWacCrdH-AHp_AY&G{{hQ1A~R*`lOv&_S%h2XoSrxsQyA`S
zjbD(Ar2r@0&d_J{6Yu}7c(eV$Nm;OVkM!%{SU|5J>)<G_Jx5N<e{1$Y*uVm~p@WcH
z`?tRGf)z&Z4PjImFv*?+N&(^V2PI-71Vv(CW*Z<Q_b28&|Ek1Ao|LZl-z`4atH}>3
zP3xzo%G!;4q&!S|&wS&-96&HX`7ka`P={5nppO3SEkaTkq0AQv<-^0LV`RZ!OJv^M
z|0E`(n8bN{%W-#KIqq3fHhBO{q*No!Rd<VcO}=<`ymW6_-)Zr-0@F?-+uWSFV67y?
zpXUNw8iiMrb}%VzOVlp!3$xlk%v*{6#U1_KO1Zs%N2SeM68@G+?0Puoib=Sje%t;V
z*0qJyt6EWL)}6x9M*+D0xpJ#gUZk*DK@zplYr&`UTMv$c5;1CK^9j+UIyL7@8?5=t
z!+K!<_KoGUng0Rr@{!YU(e&Ti9da9<m-H_L4o*j%127EJ>{;nDx_3LPIYxe-gY~2@
z^fx9Bx8z|^PnrlXoy}t{?`CzXOQj%xi3A9p2K-^g)_tm-(qh+A2wd=GREAqAy*Y52
zr*Bm+OnyJLPND$5Z-A-s{vDdinRD^7Vee72IZwPBQQF*EIoHFv2uI)Rfn6>>12<J6
ziIViz&*I&yr6m8f08$TWO41`I2TTIoN?A5lGS4PDo|xn$$i7I0#wR5s2ey;U)3MNk
zhM53%i0ol-&VX;V95U!Q6qwBjxaR@W7U`Q0WcN-vsd6tMso?=rK$&qDBz3_im^R0N
z;b0)fOb?pbNtU!+wU|_>93Y3a!?qxXvpHN1Z^5>Xw)MIi>S)V9Y9ZT##atZFY#%pO
z9BCTc=9Ayw1P-fJt0sH*?sb}!8%L}U<MP_1E60Iff1f8bA?*ri&G-8NK<lA{2N+hE
zpmn+f&{|8t?}gxXvH3FrUc+qonETN0TMG(Nvv@MW>(>BY!vv%e;H>4x4bng6|EJ<S
z{J>p?K9f&Joq=|2ylQh=I-c3~{IG~<o(Ev8Pg7Xe<ZVD6W|;f~U_Hda*hJu(&*F>%
z0{kr<tFuT>?r~AptSS83VB+iwag((T1QB-c#TacvoTO9hiR7#ga0g$ycWDpswwX;1
z7_On6bmmMn!tHxu@{^|T^Yo#OHgNLPX$V>vpvcJiz`?^ZcFb@o?mHdU7Lx4R5c@F-
z0#H+G`O0<bC{wzmw18%KukM`zwl}tWKKjOPP(NI@?bxFPJZv9~ZJNi9oqJ@*uD$Zr
zw?9a|y0ziYyR-7cUJ9GrCKU|_E<#H9A~dS`o~api5_48+1AqVPYE-~Ynm4L0fdQ_+
zkNa-#A)B@zmHkJ~%9F4BBzKPP3{BxiQnhmVoCi7saOl^+*2`A_t)V0$p;i3lxrcAc
zxnB?$w+!tdf5IH}sWXZ4*^Gtq{wrhc+Nmq$kT2xP`Yi_?bxqh)aFaS=!45%K+j8h%
znLeBeCim}dZDBzlms1Dc-?r-n%&Z=l1BXvZILv70m-q}3$InU{n9Y94mT+6uy?j32
zvuz%({-zoChn|}uQ85=$r)YT`;B~_~xk4-4Fq}Pqy=>Wa4E%=gHh}d7NtXOKDDCyU
zOjMRF<0qY<5k#v1CU9MlZ=rDV^1_&SAN(SYfCi490<Z~n6E>Wx0?2-T?-7YXe`jPm
zoCehP!~9<~Hadd<jBq{i-@*lU!!N(5GG)rh|E5f_1FiMta(+Pb;U9c3Rc^j{u*Aj3
zV_BcOSxBv!#Y>jR1OUsna|8;<9tE(y4St_Ju(_!xZd7*b(E}RIlkGrjeLQMe59r??
z8rhTN=}D8U%I2!%!ubSQ3e(*A1+Hn8Y5DRMN~=>}9>3RWSo6I1-n)jNwHB8=XxOlU
zy!+1EGJM2Hj6V=kVP}_36c|{xY`FsT_59NAo9Fy<pcsI>DdRiH;1}k~SooN3T{l>s
z9cQoQmH(WrQv~_@$xA3ld!OeLF3NM?tdbhl%gbZKTUbXnf8~n<)S7Kx2m22ZXO-Di
zf*IQnL;~~(6<QJ52XIiWCGjo!`glNNF?U?eS3y^KWNH__4l6o70BOJaeKmj^3Myk}
zqrhwUQTLrAZ$2^@;7M*@TWF62URQ)moKe_E)^%Yy#c{S(t>WcbGswxz#fw2*8p)@#
zmdar&TwxCX1*{+SfhM+5{anoQvZKxTbFCz&aSaNNpxx3M!;mXz!kogg{UB6J6YjPk
zH8Ja|m+4;UvKe+2XHcPbO&RvqtMcOmPe>{R;yf_F;^Ju0ho8xTRcjS^UAJ3j1(xRZ
z7$*TJ4}1CpS-WAk1i*UiPp{pt{2k|&E<g9_ueds_IK?epER`1jAwyzMNGj%viSFKV
z8eK<5Xl?Oo+7ea@PQcvrIXQClr1YBjhD>>2ggiXDpYjErpYrmGOB2&GVG5fTf4O!%
z1I3fUPr&UU7h*c1*6%TBkJJ5Z7OvOAv@}grw{2JpX3qopX<G;lb_+~ZkM3nVo@dGZ
zBYNp_6j``_hy1yGvsGAu{XqTwp?EnE!0e?E&4qtCXGsnpep|9Z#`f!otg>J#eFIL|
zSI0h9a4;=3T!tVq=BWXtipzj}*0WrhlG3_fsQh|jE$}%HSq7imPu$vHnWo<k&3<-m
zZR&?f0QSf9)U#E6`5o_@fcZjb{S&y}0d4K5=s1-o1i~6k!>j9)T=Un~E0$~iaf;{x
zY&rm=Qr$3-PyKhjr2MrO9Rq*`8wovT;hycAAPZoaHW4?<D<_OI19T$3@M+@Rxv8YV
zH+J&Z^OaNu<It&m`tD-@v}2TSYSkg39IgM!$tU~5vq3dUS+o&$PSeo9tvfX>kq4w{
zGEVHY)ZZv0Ci8(*2R30c<1e6>Mo*1}A-vAp7H9r0AP$h_`h(f>x9#g;7^iYqiX7EZ
zU(f8R=9Q|VVY!-lY<`p6L?ZFbO%87O6edyIn`XFUp@~S4+=y7e^5x5`%`Kz*W?12K
zAUQcj_U=0%AAR<1o}e{rLt%&e?z&9|59(`Jxy%E*ByIYKhMLy43D_c&F~Rcv=T}J&
zt|Fe@2iZn}S?ngzynakxoibHvwQB9yE+PU5^3D5ukq{6x(gZ&jiwTe+LGvcCJba^;
zF}z!pfz(>eYZKDh7|XP7c0H)|6L9#wT*|NJZ#9?<w;P*T3G_#aiAmW4HkLlhsBT=G
z4D8p_=oU<To|i-eFqx4rUc4y3!jgbXr20sMIv^<e>^TVvEGJ_|4hDeyV|IP*b7lL~
zG}$R6v0(9XZ0awC_H{$K9e%Q#z|^}&1l8}p<5qd}>B*>P3ep+($y#5XjP|`{+fJnw
z{=?6|$;jb1$-urnU7K@hg_`Glae(iWfc5>8z6JP!g)!O!ft0iGO`V>}-}D{rCr>{x
z5E^l=FGFbN`SZ8#lvm#S9RT=l`Doe_nE`X%l^}^+9A+zNvj5bXI5``ejQVCt(*_~(
z>QlqfPZew*6@Yq-8Qf0Z{_G!FylRiE-nd`dwXm0Jcg8eYJRoBht=OZU#eN3I)R=?7
zpWu(5-Y-osUw4J(LfhKS!%ISfOG}H!HJm2t)Fxa$`1)Tte<5AwE!=Vmz%|?;;Tax!
ze!7#5TzBcywSm0&_z<UcDTtc^{w3w%yL!v3@6DCXJ5R{1_kSWav3Xbt8-;9>Cr-u6
z?)@jQ>E$gwI@FPy`xGqLu?>C}j~qKA@4bAhOOJEQkoI!R{U6KDy^&Hs9DTiu<ZL{5
zF$*(VJ~+S;rY-!@4aFvq5fQ>@SgM4Kf*H!G@C{4zjJCx2!#RvsCop#;aLqaX?%jLr
z09y75-FtMGZe6?BW?}c6V`1}V&1BG^K}rkR9X_Zv;$|fMJ)eThj^N;6Em)l?Xt@hz
z)BTa{yvYZ1)w?H5P;P{*5*hrU<)_hOM$0sqCEvSmua(L049rn??%Ji+OtxhhWTk@G
zuA`%)vAG+8y*q1xOm~0*Lx&8Onqgs%lIMhzFEk_G4JOv-{QkR@DRi1*+z<2W6qa-5
zHYz7G^TH(#^luX?1ECQ;@%@Fe`lHc>qLpephkt4MYB?BjPUgKe6dSt6)-_zJyctPx
zpbh2=-~WwGd_`HZm0H%IgOSm4Iwk@06^ohhT2%t2N)ZF5sv<@7kD&BR_w-j9#vlH?
zRB0V@K1uMJX2UB0On%}v<8@U*R*?W`C0OWq0{*wJ7V^V9o4%u;`(&Qt#|G><uiAD<
zN?f5mUur1MS-MrtlgT5R;vS88U0HxfSB&u926t-;K)>D<tHQNhaNX`*Wb79o$Zt=*
zAZIZb@TS{B9FbeM%Z$4olxk4UXfga|sRQeEe&vh>H)#HO_|sEm4M6KM<x0yBuTGS1
zIy1`!!Jp_O5wdOGeAzO4o<!|Gh;;?rs{%0g#Qe(dLag+F0RD)#pO=8zwdDflevxM^
zX2!R|U-t4%dliWK!0_G$Y8hbBhMltKV59|*#(XOBRE$I(k1~E-&atrm=D3=Q`hEZR
zDg|8AjJgj@m8U>?ky_h-t=b|ljK4`r7T1rl_8Vd;5KgzOW9{d^#udn4DA$@95G3-z
zH=bW=4bZo7?W(eA*AazhE!(mWf&|gXFH@F7yIEm$5Gozmz6s22m(FkYR`FR+?mxLY
z@6{4_ZBbW#{SUx3+R(<mN8wW7g3woWJhkWXj%ZHYmfAP2CBD!yzX*YamD^xJ5oIsm
zvRCL*hwp+K*=4gWE!(EOR(}_P$k?^Y8YlI}2Ane2?Ieo21&KX3KWJCkfY$0BIs`Y^
zrq2S%n+3A~Y?O*9j%D=CYxg~|!45NYiqN=oA~Ov%t~1ZZN$ScS>UI`3@w8+rz~-Mj
zwN&DuCCxHz38u*1p&{WOQXcgK5bcOqZQRd<KO9QBQCgc)D<%~#2l&RwW$j>TTA2RK
z@GC|7dhl91v^s0QuT=dUmFsA*;oz=;A&$~sZaB-J`EQnEyGUo=4CBCbxhBFdx1Kk8
z`b21j{Ig)Gn)KwgiOlxYrd2bCpf!1!v1gkU6MOUEj`+-P2Q5L;cWrSLMVW>k1(43^
zAdsD!&tPos8QnX_Cj;Wx;){vw)nq2tpqf`t5MEk-tu^>SJDcy?h`9OesGneb_7y=8
zEC>s&W|Ul+Ds|R&HO=Kb&*-a+DJ#dP{VgMhKqz7WCh{;Th3~-`u}&<L=gnJp%43sW
zlAnH=W0j;q{W|i@6AxnJ8FLgD7BS=#{kk8IJSA_x|CzEzKn-w{3Z4UOcglwovOaM-
zPL4nu_bBFvC!*r;4zi?KgUWLI@V4^9#}gH(nBN)%V$<-wmu`{QCk=(K;_?!okb;GJ
zlx*4&DLeL^hF``csZljRp1QA(eEIeSXk#0LO?j28gkktB5Q;IPVFOx87)+C5rYxUK
zTL7Tm$voL61NNP@;Agoe>R&4~2xlIFhDY7nmBkk(tz98%n=f3xLwWUK{qhnDsD#bu
z4z0q~GcH`d6P7h#{mDhr-ZS4dO>f7aPcl5tUr13x3@&D?qLDw$*Aed><>d)2^8r1Z
z$v5xcB^_GUP+I<b4n)bqmHTAT%Kfqz?W7beU5y*wMy5PB3jE3c28g4w*<bzg$69II
zyp{w9!McYFL=*FMar_1g0r??rtdsvH6%7XngtqoCWvEH5)qwu4TS0506a{s(K$|nS
zl47y;=Xi7m7E|aS+SdO(HU4^Z@8R<MrRH<z&YiGf>ZQg^tw@fuk;jk2jQBP!DCf+!
zdh+B6$)F`T9Wk6BceCcrbfL~K10ZbMs#Qy@`?+bEO9alNVbO)cM6MF~LQ679Nf+#%
zpCEaM4jo<1N`dH|I(4#(&#xvLWyC{6-J}BK02urJ16^P?HBnv$pj_DW=c<G9!K_X4
zDEw>pYh9zTi@PE6Z&Eu1#uG6n;@jn3-d^}JMwg&8ue0DcSP}O5S~skLy>;jF_-mbE
z9A78h(OVwBwL7*p`K*@lmV$&I0L)&v4(3~nfGi-0qY<SDDqv6qwV5lIhvpyJ0{QaA
zuPZRu;^<cN^dkf<;x9nSgAEn$tgN56jO^9C=qqAV;+5iuO*O9BpS3!)mAhw7m!^XU
zs&!*J_MzM{2OuzfWZedt`RF8>+Ha`*{KzEP`p0~Ui8!7O)(I>w#mR_CAIK{BGA<2Y
z%QIe?D81TTh8Ar$7gHh7e{%O;Sv})N`TF*I<iml(<?lD%mFUAq0Id6{y|6S`cyWhv
zQHOiS%eWuDQJ^)Gmw>=W?W(~ts8e%!al#P!bLx{aVNlls-#?oHli%#S`8^Z^1{G2{
zP(q-9XmnJm5~NIPE8%}=w$E9%NlwGebc4_krTt5jWp3a%f$(qsSeng+tE(@{u2h~U
znh_t?wWU(7WZ#|;o1jdD(`5MhI14cB34V>}**ed>3M)d#CUvBENeJAaerMqJmwv04
zL-QIN%~Aq_I2{a3zSpV}EcI$sRshRhxGUOqI8uS#mcChp8rjzyxvuJPy*>HmJP$QQ
z(6FqCU@bSeY{QMdaVAB_;#s2-ROc|@V#9}^vp@rb=C~D5Ovx3iB9IaD!p2_~HiAeV
z!2$50T~e~nU$m6X`=~?O%AHbdXlKcc#>ePj(~arfVILnmL%g)3EKEW?>sAMN4apPR
zXR?cp07znb)U1qe1?DLrsbf;%b3kias#%~otLB2=4A<i1RU6Lv;w}O>Qy7GCDrzLM
zxs>(=_+VR@K>=DWJoC_!@T|XQko1ZMar1_UEitUHHw#(D>C>mdXLn^v&`}*2r&K9?
zzb0J~4wQg@ar*ZCXTcJSu#Q1{(JIExxHW4x%B<i1Fw|&fJom7=k_;_GnwYdT>7(VN
zKWpERXCq9+9MHxs+vVq9=i+0tZ_9Q`P%h0f8xc1QjfmMMB|+<#W0{S1j8Xg>J`697
z0uTA2qzf0(Z&F0Tp6DN#8|p@V6=PFczX~+AeSP&(vbp(i!`oSZ=ImZ>E{?Cb*~2nO
z?9cAM=MI?c4wvtK{8gC{=ZL@slcV}G9l_?*luY^L^XUNZipfm_dNWv2mrm`}^L-6X
z?p13yD9ukLIOn9jtMV|lTC!}l(*B<O>|;4~)bcgYm&XB`Uv2^OlDVUNZhmYB6`}on
zxxy}N`!_+WqG#tuasVbQx9vPCahOM!g5+_7I#s21Xa(hGIJe~dU%mG92uX%erXzob
z^ke+>J9kME=kREAUJxpEm016`C*P{tw=3Tn%~rZXWlQ_Xj1TWq-&+s>epl#)M{kw~
z?(BuSz={VpS6rnoFmwIa_m3gEQSYSB#SNP_#$$*+mj3!)n1sJ|SVw5<Q_w}5IqG9L
zzpe+c|C1@><nYlm$_zTS4%p_xVRF3=%%K;w2EJCCiQ{_atKD()|C1?qz;XoIkc(6q
z+T-)Se^Sa7f$4P@t90E*bH}<UAP*r2w$hRqk4yV_%LQm|7?}$vqf)Xzmt<^krYWt6
zT=sD+^!6z(l`COyz(r!8S}vd*()lQ1G<~v^<cqR=#d7J}w~szpjky=$kDL9Lo>)H3
zl;`C(MW06X=@o<YLD|=C5Lmfr*+ezC>nMm?;OvX^#CmxuOH-~3A+cV85Zp5&nrWw1
z8IuZ@1C`5{lFuIPA;TvBAuYly%1s>##}D$}V=*%RfB#6&meu9eJ3AEYL)^f*RffP-
zEw~5V3accXOCN`6+Y@JEp?Mt%u4MNW2yfRo?1tgeqX4cGp^f<2uLOh$&O+-Cial>n
zUmzWu)`B~wf)jqbR+vgZvSSCdme;>optP=+!{uZH%&WXGpGl)7p(3IfSo!?vpY?bz
zmNMPo(Dml7O{5acLYs8$bHG2Ol8pK29cgmY09pR!w{isgXz38P^TeJM_ZJf2#%|-R
z-)z0(V_eZ1H#%_|t1%ljwr#tyZM(6Xrm@l3wrx*rv*9EY`{umoJ@<aOf53d0wfBD3
zT0ff2Gc^q1q|nkWmC{lYuqP=g6^@=(!4-OaF?@Q=LuVxwD$Ze9mL7Oz`0B%PTmY1n
zKll<U=p?OpvC2gKaGvZs@{JOY@zE+g(-p(<yheE@JGU8iL!R?=TbFmsujGe)2A481
zKtxK)jiws@9;#Ij;5z#+DKy2GI_8oAxb2*{M!-TF;aUh|+X8hE(IhpguVnm}-Sb4U
z&Buw$`#i<JJ~*Qzi`<0dcnIj9m_hcn$dPZ~PxJ_MDjVIOo+?{inM!o%=bDNQ9=;VK
z`xo5Cbjo~DldSwcpqF?DakSWK;BGyiYji}PstVLmr<LBc>8nTYE^pJko5**s_c$hV
zIFi{s?PK0G=)0W#w^!gg@3h)#FN7swMD^aoQO~r;4iNRchQeO1R$=I1U;Y_ru6eB!
z@H@Y{=Ryju5!i)~kM-k@&03TAZ;)Dl;RrH;e0J3U<&Au&*&H+~RmkO6$U(=`H<#TZ
z>x4S4{$Xklj~LwC2Ik0bZE(VngvbK+H-d|`*4bHqy|KZQqWblF5Wm)gr`y}x`gl5Z
zow*B2tR4OM?*+Z6azyMIp7L*~jU1%p2NaQsS~GKU%n13(1jYFJ`?_p1?L3~+AsAf)
z9Yf$!AJ&Zew%0fq7X`xGM4_vM^s%81y@Q)m6rhfZ4oj-9ySuKdLFfON4&@HtVw#>$
z&T<!3CQ^Iu9`ws>jYslO*-`#&Y|LPCso@%=VPRtKFUWIGSiD&&GJS`ib15RzGEt3p
z?OkrceZAsL`Q&F(%SZx>)_$`A81d%vN&Gk!+*8TJQ5q|1q&A25yw^h#Sl*y0(f=WJ
z9<`_;B_>p6iw!o2DRp$>);iyCdS5TLBuC;|-kIoSn4NEzWZr_-Lxe6$?N;YXTV^$7
z$)eaWFfl78Mb;_Vq0YIT9-Tt<(;5N?m=lxdznqchGZoiZ$ORPM6+gqbN;>g;uyO{q
z38i;)vmaq%>|`z-y<yPP8dhhGZ{Hg@pv2Uq70aj3<+H!L&$heS(b?}f`dmCK)LWg%
zuD1K74)(EgACtrgTgpA9gDHL0=UpJ0MmZ3w4Acz*yc5ntv2Oc^=*%|5r(9kKY0RXQ
z2~F{y?a0SRrBlu6md5A4+U`JghNxh%on!C_3#Uq{C_4l4qjzlMr9?0}I&ShOAW#`}
z7QBH4;PQ}nL5^P)eX^?lr4*cG-Uq3&SP$sLVIuEN0)zW5zx;%)?nvD~Ys{;Q&Ar)W
zvJGhwjm?RxF~W#NX<=INH~$<%!ZB~iI8T=CC6|f4K&Q6`jHe&6Q0yYnpqyaVo|I{4
z)lPhg_pOhxMfkfqY)Y`VlC;;W5U1}?kMr5+sZ*XufWo-GT&Z0FjT9xeqN<eQtWILm
zcC-Tgc@X<&pCE!4*oF)6S_gkM1b^YYc}lQSA3D;0%x<MQmnNO}zL@OrcHXfFTm;5A
zQ0{L%>WYrP$glxcU}R=%F}NRxgm&^rHH(Gj;f}JV>?ecwm|)6umTIVUQL{u-@MbDx
zQt!|Ze!N%lq=qwB3dz@VK}&m+&$7*QZn^)X2+R^gpr8!wf_nLT3$fMdut*U3l?TXM
zuZj5d^PbkqDm%i&qFw&Qv`H+Go>~RM@U_`0N_;3k_BfmJ+1t?36K}o89U|4l!n24+
zxy8rw%LuRE$M41Z>(uSTopFu?T%v-lHn?8XOJj42?9@$N&#{^ajKjv79<P2`b&&Yu
zv){2o`ty*bD&g;+g39;TFd8Ba`<Jpi&}>+@ws=-OWimL2-!>vB2EueOHum*HB~qfU
zza#`ZW#}J$Rz*QMsIYw8hkI~5gv>W=cBYj5Q0edR2!8_islr@NF=i@jV6o?rEB>9j
z$;_XVE&`h`7)-@}R5x3pih0#@U;VA~TNf)>`?+PQ%LO^RtDL`qjKlRKH|u&Cq0Q8Y
z`jF0h?<yO->v_Y%!$@`An<OcC6EBa?1$f{i$e3?7g3B`Ip_-oXAmIt(VRWJnDTN-N
z=)X`*MvxT!UCSEjxjXuUyJ6oAIQ#ZF31w3)+|8%+@AhDctAdmqF*eUF`oe&pDwILE
zixcp#1_Lg2r6>;Jmx#e>EE8dsFCaxFgHkE#;F)#Q(&smLG+s&$vFkFiM5j@5T%fxZ
z<i{rHSdMD=7`Qvahl0Q~cz(Lu6)w8}{*&C4QO{9HyBD~R^5LP1K_KuLN3$rrvqVJV
z@f_LEx&)!ChkVmgED(*Fu*|-D;qw5v77mfs?IIv$^VLhGA)_?NAx$62r+`W%lWqMh
zm)6YBa1$gDV9ye;fsWF=agx!86(V8&njJaK9;LHE;V;G$sw7c@e4)|a+B)#ZWY|Bc
zgv&C9msJVWe5WppYNOw9IT-~MZPI&Y+r5DZ)y(C5+=0b}TUg*MyW#;<R|w`<VgT1F
zn!7a6!H*-F8A&OT%TWusaZR0G(>%=It}a-AR}7X)&f*RGMqjpNcp48CSew}(oCet{
z>Tr)*;fIE)a4LL!>Yf)+#OoR4)}44=4t}xQt)S`usRq_X+r2aXK-u80jm?<pewa<Q
z0atVptc&6)Ud+Y{erWua>Jy4(@P5Fjx3;p>o-f`bXX7N8cKjq)baRaN+$Y4(uBrG2
zkTaWUXFknw==NVonlbl6bKd<){H_=~WRg-UO~OcN)Y_8<jWKEh1Pu&(aYw<=4=2?U
zTtF0VVYFmOm|MCF&Hg8oVC@;|D;sItGw*8iDH%uQIG9}4_L?DAi;PY)8X+1IG{6oo
z`fQbtiDGMx&$DrvT1T<*lg@k0bY&tcHmu~EISc(WFI4Z5NM3}#&;1xaU{$yIJZ>~G
z4oiZ(1tu$f+gqBaMz2969O=|n)6UPz<wm;y+f8pCdAUOMcB}e5NT`q=@@kzeKLCf3
zBgmF~IL(*}P97zVuvLV6Cz{8t)bPt=PE1IEjZeN|*C84+3D!R*LA@ISW&Gh%^SWz{
z*gFA@k?@1aI)=c@Zf0P?5QPqUWK>9PN6eh6n%Q#oUsfmK0R7T^Ii1c`sDQ;Xnz6k@
zdvfVumwOU!($Bka3M?}HnfC~f{uAFpl+Y2y)(XG;)E3r~O>+2lW{aIlcSRGk<+0mj
z9+&-A=ve68M!4GFxinw~c14ym2z=Sa{VA*YBO4b4oQw|xp``LIB1q0qUHaA|c^fkx
z&6I&jUUf9#uR1z^XOvxV&orx)`a%fLi8_Ff>+N0WPqUt5tW@H2-E(_P6+}9aYC-@A
zQ3|*l1{xTx)BeO6iqvU-1(3^!q*`8x!h4?GhLw%grftV|>&w`C9X)}5SD>S+F>N~4
z^>(H8pW`ONWZ1SAeqr3bUFBEPL<=HS$9@wU<6D#ylrAHRGJ*u;GTB>TsdQzY3w2rk
zhPfi8ewH%N>q>D-<-oUA8|suJ89K){)VvEZ*UP>sxa=~3bhJ&jKrPN>mm%aLbnS`3
z5N5x-IUS4~d}H~U#~Hoxg^t`9SW?*5f#<2s1xonoK#J{<$BO5y+$IO+h+92)Vxqjp
zK-p=M-s&IiIlPb5MaFXcGdtN|xUVDrlRMK><`3QQc8t%X&zliRWhXHJ*^h_b?}wQ$
zncFxOzCfrAzoU?x!})D&IXEluQV=$?-#WEO=?(YruSLH55X)M~e?~oFoeDt`;VM8s
zNI2bpj7X;`FnyncjdygJY0KVSGo3y{(z~R?K!STaS-@Phtb$y4qlFmXG=PoI%&mr#
z)p870z4`EO0kKz$`3{1WbZQF9eA~~EyAA*Es=Vbt@k>kuI$9h^YufCMd;XUNuqiao
zC>pDQe6UIKj@`Z{BVGT!OqHfd3UTD-0sBgZnDBrpUZoBL+0)RjV?*o`AuikK@lJAi
z8}n-)fr}K>byq_yMb|?n7<3Lh_V<=3g5d~OOVCGxG-!O8EeOi(C;K{08~|LZBjO9&
zm~2wep=~n`LN%(j?6OIyd-+A-_sGZA>sj`~e66BABkI<BRgn7S3s*G1&5uBjdzCS6
z$S*R%!+_=nj09yEX*aY#OFv<=$<j{^I6eDM!cj(`h?JhI3sPEF(=F-*uRiViRbjg-
zbSrzB1aqmxyXp|Fsi_MAOJ%4N2}0w86#YayKEuuM6E|mPHR?<JIJj_+yiEft#a0|V
zHQOqL3|Tc%5v+*8KO9E07~;&Y<oARVNgASco+d*IfpQa>#%q3GPiP;ddR&{0(E5yJ
z|KSuTy^keQCv$Rel;0J~n4_;O{mSH}$UrJl;!Uh3gtO{S+aBUm6VlXSYPOfEz#HHZ
zc|M53OM|u?ON$R_(&e$N$p$peF3t7$DYeDS*c#kq(nz{Cc0uUGc9HZnGO<y&RfY|p
zOw{y1F@qu3n0tgr2E%%D{kyQNF61Bc=-Dl87b_u{o%q0$_92saPW<w@z+}e1YoafV
zj?7EW_SGMJz8@-APmd#=0cN-GFP@GQL1A(lqkRKX>X~(2Bbp=ze3OhToWhxD>)|(C
zToGqWWi%1wF~-#<L?QdV<%?&ZLO9qZE`&!-VPWl)mF^~9#@_SSVTJW1n(Hn=mi;=@
zNMs10=r20QC;>-lMT~PxOq}1SC6)ZGTE{vu5-`bhM6s994PnWali+<Cfd!OK$joEp
z(tFVBJ`jYFhUh-H<^|(BXqsZd)v&i_Y5VG(?*C1PDim6q0XPPYa8n%w?9`^tueJUS
zcqzjhX44W5qIs7ERPZormMV*UMphp{AHA&+=6LtPG*7;qF|~oVeDv!}u~2tjQn&pj
zvMY-?>areNwOY%yT@?V!SrM=>u(HhZMWD@&W!dka$!9r+0dESIxLk}%GwH5XpJ9Y!
zh5=I%;YoL#IO1YOI(PWqN5)e!H=6$3y>~O3v3E)6Odm7?B$<>q+k^-`=}!&NCc0aA
zRvIWi&M3kQH(*pSVvP>7QL8-<*E0y#IJxl07m`S&!t@WM!wH@c1hz+qmFrDui~c+N
zyq`rbwcpRE%Gi5E3<CEs^E%z2=F_?v4%XW$D5!`LNBQpw5Hb{QG%w@C;;~9==?&UA
zSTBn`VD$f_Z>x_&1hfj<x}Hh9>jA8~%%5!V9xj0{^ia5P_l3OF*-67=Ga43~ERH){
zG{;g1Gnj)aH*BvHPf09i`_Rz=+?EizNBnU&=%`GN*EA>8MZu7ir52^3>^7vy&OeZ9
zD2M8}G?bG>E?5HFwTV;ieIdN2VdS{>TjBXNzgYd4zrFALs%r*(H^tt;-!*-1Y{(Ge
znf~<I0y)uy(@~ch%Mws_9xK}9!g`ySl2E&SJv!Md)bxwxGZAe)EEnGooUMXCaJ|DP
z$B&FXBv@jPa3MI10k3VDvRKtWfGI>N@|EN0WcI90bqzH7`-odZ-bT8%E$7P)t2yz_
zc7djJA@Va_=6+>5Wu4-LLQn>lQDu-`prs<b#!otO>j1kzAYzB8kGra9%51whxA3PV
zztDr>=dV*UXYLX1D$&gN1FT{HREf5+x*$04CYjJ@3!$Z%&hswXxsE*%DSzsGdij1A
zMmlw}740BEtTQs49u2l-GgeyOF3%@^1vfm#Tp;I6lO;SK<BxIw#GvXS<3Kh_70Ayd
z!7gT&6Cs(qyu#a8VNm85rGvN{3dRu(w8|GIk1Y?{GpMU9tXiY7j<X9*MBC_(P^#iJ
z7+?=0pjh9S$O{qQx&$O|(j9SnfgBVly`Osk@#HgxJA$tUKK+V4e7@9PeQ^5Eno_x6
zkT0A(sANt6U6@7*{L5K?fpI7a?Iya_Id;_<vm#zirB&)G{Qb21c(k(5Tc&#`Wmq7C
zDGIjx&LIkJ&Yn$WO^;ioo0Hjo)sL)&BMD=tzyWAD^K5UYSISj`o0f{g!;wd)mI^RM
zU{(BkH}=u_WTE_kSVa!DVTGmt&BC1F^y*STh9J|kKmlwhx|h6Roq-i7Oyt%R2M^C$
zlX0RTQb@5ASE>eP`k34QwG~=etZxL`0AatWHS1Uy(3^$Z^U`;GN>naWt70R6%Uaj_
za$bRFGv_){O6F15*^VlT+l>ojAl8p*{rILluk-u|4WtWd*l3Yyv)GjMeu{p`O+SUS
zLM|K4glHA={Rri!ZXwkCy!kQ_a5}copua(Lvd0icCp`#0ie~?`%by4BBfY}BJ---!
z2PzB$S{WjTdZQ)naOS%|B>F`w21>jzBVv^p4wQPbP~Ph!8mViQGrk7Ol%vF9y}Q1P
zc0jJQOuksgyM>WF{B(CchV(#hfcn?LcRHzE77kP7k7*F4)ay^aru0|GSQW~@^v|zg
z)W5uq5NQT-!`m-ugIQ5cI=D_wYW_U&X~BFG8rAAYbGl8;N91RHod!QIon?XwYr!G*
z14{QdCwk)PZzZ#IECc^JGcxlyZMp`!R!GkDBL|)ZH83|=9^_;YIM2sQ$xuN5g%!JL
z=dDb2c5s8uv_mY|KU`K*ae=US3$(|@SpLxzSFG3E^HWF6db_);QojNJ+I9JB7K!~@
ztIU@FF_HJ-VJh;?93+j~-Ifn%EM-bJsYilAFW@M{coqEBWjXZA^G?NmJH4+ur}cu?
zz3`cyHLCe?QsyWDr$Z9gR@%4El(3Awu7U{Zz%gK|;ZN-tsLuO(ymf)zZ4Cxp?!Ohh
z<I6~%f1%iS1<t}CSfB5n=H7PG!Se_2Gl24=pRny|KIA55(32sq1X1`K#>wNSml>%r
zV?m^?(q*~@`IVc_ST`V~^P%Hv5qN|q2dysBs16G-FQzN8sKiFwo^f76*a5A?#D|TL
zW*~=R2GU39dyo4?VeQ%9-*u1cZG>D5py0J7qReoa`77Em{l@eU_2D2hkO`~zDltzQ
z!enSG&Ak>UHXXI@#&0=^So)Ls-1bJ~FL8xwt}f^nz4MGza<EHpSwLr@@Ee8<zymbn
zJ`7G^cN$zuFX1O!wx&|iE`KPiT5J;cm>fZwX7c1&c0k)93NQ<gF!`V$sn)QTsb-zK
z=2-%91B|PG_eS$m0;;WiTC5LjW@TM}ipOfyWBVtR&A6N&;PDWok{*r&2KqbCy7L4M
zEUe5w-E$i6(8ArA#oa)NECur3NhLI>2I{MFR%H()WR#?(D@;GvV+-a3TZr%wh{2`d
zPb$q@1@_)Ul(}cA6qdOPch8dXyvs6I&^S3ObC?lPF2wT|^j&Dm@QVS|fw@Q;c`V6_
zHr?DV&u9<(mI|e_6faXH9{puE8WE5LcIrV~>u>GXB=+$?cSsVx<llgBA)Zl7=gRg$
z_DyOx?HKT>(bHD&ub&|nfIlei`|tW_mHF&dett2FR8AL_ln2hh4h4Z-k@;=No_X1E
z2x}|az~aW$paY4^zQaLKl@G<8VH$MN7bjl{hq!T%ngb@{CI+JxzA3O%-GnwDulCyh
z@+jutdQcZvlx0-Lo^}bXurpvROFpmRx(h^A1e4v4MKyRW;_YrlN=&vy!lCfzrOTy+
z;-gjB-9MY7;umV5H84C+2fbX7QpiXAX_D%@lGJ?>FPFOcf%qN_uade{h477(v2!3%
z^{JaXOm5gg=<3$I1Y4(>FQZDDl%gOHPD<?t<sW10@Sy3_-Scc?i&XT|ph$Fdbkz<I
zd%nQ12k+Rwf~W${S4ZKp#^@82Cr#yTG{fZV>_5UlTV1+uOW7d=s_a8@SG@G}m9^y-
zM`z#QU~ez-sQUiHG`n8m8-C}g7W#PRTbv$_!L!f|)_e#m4{*=C4tgvu(!!p6lZt%!
z=Yy6?Qp7P^cst?PNf#N}*lfJ%QDw@ii;9QjI)SRtsiq)n4<dNG+^CD5e7weyhL9|C
z7A1*Dw9L?p84`b(UYDd*F!E=@pB=n$3H&qfpGK*o61O6L_i!%oovG(4G>I|U7D7BK
zKp^Mof=Z;}@hNpX$=Os&cH3YR(0v5?h+`y~m9cbtObF+1v)!IeePB-{{j!8qRgKzR
zXlwH$B1|J$?5fLt@i;GgAu#)OjgR#t5{}TufNct_es+*~A|&E^&ffbtjAqnon4Yps
zo0R4ylKu12oyYeUm+E<IuEbI^FFDvoU99oDhGj<41E<bT?xs~|{@5E&z&l+!+CS^{
z_BJFER%LvyN=vJZtn_*M_Sucle(5l4iGkC;Ns|9wtv|=)qH{0i%{gi1UtQw?N0cDj
z5PX-Xzc3&3H%h~!_5Zl9M_>yGhK2sjVMH|XbspeBZs}+kte~AE7>15YqKB!%^%6c>
z=J>^2gqYD^H1TP{k#2i#`xs7P=J@V8i%p$*JsSDmRJ9ylcy6m5(Kor+0S9$B9k(hg
z*;FQ8Y^cG9#hGwpm4+5&O?Hf$wSq8)TRtCtlM!qOt1^j&@_4ov_)XzZT5lT-z_D1N
zH;U|)!Ff!Eu^BAykN3B`z9V-s=Um+0vvlA?Dz%XQH>m7vhMLVq6=LGGNhijmQiK}~
zk|e&8S17~PuV%xA-Ky1<)~jRaH3;9b-jwOi>8dxqkya0Frc{_aSh1Y@^9EfC&|i`r
zcL#W)=A>2+!Z+)mXB3$bzO{NZ&8<LG9Y#EzQ81!I_U@>u%+yD}n6JNMuDG@|Dxv&k
zKddGYjmpPYoM_EB-D}KWzETLT7@+bam0P~V;!0~Pz7m(jZw!Zc|LhYK8Ad+M7w$1>
z2S$to25m}(`|r!|wd+dqCqoSTQaKO%X2GX}@lOr|l|87n#bchR$zu;%_ZR|K{4uX&
z1YHiEw-<&2Tq3G@xMN>vAE^E80Jpyn$H&DAo=f-L<g>)Hd8jRSBKTl}KNcPtQ6;8i
z4_}j;YpIj%+-?t6-oc}3-8`;_n+zRt9%RqAhWQ4Su@!@sz^LpV*IND<UgHP>YTBlW
zoBS84+k}n?51@=hccRu&OCo5hSu0bMF~vdk<IfV!hS{m9sj(`N|DrYq@n)24rT5Ig
ztqU`!WjnQF>3xVVIy&l|#56b%UBo%nzp}@oNw{(9Qnj)*CwTk!RY$8rYp?>$YK>J}
zFov8QaRcqGuJT;-P;JP;mviD?xt_If1r3;==NB*T0=ktGzKW$gk68WI_nGWiW4jO?
zJ__GM!u~ncqoWZt21f=Gg_jKM1TZm%%gw^LrGJbx-Nfu$FiJ_dzMWm=`9_;s`YD}C
zekf#ZnYDu2rsUcZ^zT+<)oQ)IC+1lpoHI-!BKsnRsh5TPqha}zxuUOuL$(<#C8!S3
zrQTjglX#CE$ic8@(J+uU{H+<&Z$C{{Lu7VzL4FBvUeS)2Z0wLpSzf9)8I}Ux?iWm#
z>SS@XwuxS_0}^64_s{D*%N08hsToVq0{ReD5nCHd#I2~gO$y#Tc=&qCe9!l~5fe!*
zLaO$&oCrO!cLZD$#SH6(8d}I@235Zyp-rd%svTvN*-s7izz#ZWHH&5YoCJVR5tU@W
z;JgZ~r+6}aP@z6n3%=Xo%lp26L2EC56#GuaD5!XSO2OW8+sXQKX*UFp8`?U0j}M`s
z=cKM6f+pu*vvX@NpZoJ4e!&y-lh3w}#sQTw6By~GpR@t<a45v_$;{N<_9KiGVGxS+
z&?h0h_fN}aV7A!2fF}4cg%|%6LS9HC!p!$boVml6wj1;Jo+vEbt!8yZ^YCsYN}ub^
ztgw_jP{8B}72hPcGmNR2o$h)wD534<U0v|d+h?kMs@`TwvPgfkTLIBpxd&yEIaw25
z{nmR5WO@STKsZ>k909T8CM;}mBC`7t-mD!2jzJ_sQNdLQpm;$L;%ek&RrXrwLCRC%
zTk9txZZV4`|GvAGsic(7YVxjVOS*ei*gOzIGR13MBs_XuJJEBW{U)6_99`t|;J|wP
zu)22%m3L*grx)k?@}ZFaEee|<LDvC~+<v{62=Ud4S~SyZDx(R!|C#pMDLDmYcO0W5
z$nW~*0v`ObTJK(Qd$Oq;a8}+~%<Rxot^Xg>RK4AaDqXa-U<u@1UUawFH3pubT^hUN
z7NJFtoGjLoa)oAfk~TB7zte|*h1*(4yJ^f^kD-wDLTw(lSvF!iltJz8%NEpTp~kNs
z{<{-a?Pdm5L9bf6#_}u>HKm?(d}_BorQvIx0x{97UUpZNeGIH7ndVbCu$w^-M)A!#
zl7ocVuvq9AqgK|rYt~I`nnHQL!JX)uNx%Q}T2WN`WD|tlj|GY$MATZivNxa>$6v0t
z``W_n@03IOWj`L>Yl;J-51FJj%#9NkOR}qxzoIwzc*jBEU|nb#io5Yo<XTrhcOr=)
zP&>?ck@a)uaoE>ME>x)+cM%**Kk=|y`X=K{n|x>r+E>j9A~j-_N3+zNXCzn>f$BbY
z$bj&6A;quO{^TX{Agk&k>9kjHkb{~ptkSx=zX~Rt@mzuIt_`QU&O7^h`RP;w(c#$*
zr!_*E?p+uoxJmAYb`jJAc^p1@t~Yv1)6QzDvB+aJ_#HZhVVst_!9=y(L`2A2{y(GG
z|G0kd+u&|XIOA-GrBJ_MYW8|Eyf^uB2A$bHU9@R=KGzq+dSxF37s_UPr=kB2g~H#F
zb4r`%dT*BBcd7Zp%C@0C7ih8W+qgX$LF(ZTHq)#6eC*HdU;`CQuFz@In}`V5n5)UY
z{v~nlGf6;tE=SW1LI(UJk-*O%3WZJWwMY(}*Pe^z>gw@?N##GvzVUgS*Z!Wkr8rXQ
z@qhc49#;W56@bDs)1s+)$IyNEUSe~j@?lVgvHJ$T@Z-3UD&oM`Dacr~f4_o9#@XEs
z)TlOqn|b!Ly6z`QN6c6nh(L;a0pWSdT!d!SpK;p7QzAfzx-}u!F@RSZKmgwh!p4oE
zk?EhS;J}Rfb&vBD$DYix$>eo2_stIqA*=9Msz#vr$bs9QWak`Ph(*??*|b>R=4^oJ
zxLvNqqyFcn<dfsBi<U>Ne@!JSjQM9oK^2P4vMUtKq9(?8XKknM{pr86YXeUZ4{1#o
zkGi1Ov3g@z2%!99943182>lqcaSfjHX6KjaE)0U|%mMe!(lbYL+4C{1(887~X761l
z$o~(&ixkRdpt45`&P7b1k8`_wFD)~kMISut%5elVZd_@VeZqe~b9t1F6~djLG)ToL
zBO2ADYN!$>rfTc>EysxAw01r=boehX&uB3}4vm9dL>EbEEE<N3cj!6FQ<)4=@>ad9
z6ptN#J5MW}*_<uaq`KQK|7O}#ouP+$zfNFQOz5;@|E-?e*Yha>DgR>&(Q0u=Q~83#
zUe4vXS{eG<$~phb_xkEKL1E_GHuU^1`;>HTCUz<7{Jaq(R%W5#eP(N;g*qF(eD=>%
znWpYJFdI-}Ajrqj=G&Al+*oJ}j&#(Q?Xijd^@$6=Q-a{x22H}a<8G~L=)+@qJo?QH
z4TcQajH2%-3y8?!)^ivb?w9;HPCR<n+Z61_&p+E&>K|HP>%AhNlb>*m+<yHC@z$x9
ziuoIp+_a85w(9l#(p<>{pOgZ=iO0FT1fdOn|GuSM;02nUpF7(s(|8f&c`Ar0VzyAo
z<kuLVXFdL!?+mp*UitAE#w$@t;(k%YT;5>q8CtpJfeRX_*Esayfe&c%1AUB^4U_9S
zFCv&9c4&f>g#(Ni6ebegeJ&<GNlK+C72pO)ssqU8R}A!W&k`K%^oWOou8L5~KnS|6
z`lkE|yq08a2<9dtT`VC`CH&3&;ZX<Amhqn_sYwGTztbLQD6jF2iaRc5z@ZFGFm*;x
z&%XKlrGLbo=dfl?C0;ve=LmNsNN7D$VCYAZWJ`hg>MmiYHHscaqQoLZW>b=A{)crX
z61}%DSp86{VV|9*S{I2mlb-&(6}v=SmFl%^#?e#5u9LMV@6~+(*z7|(NvUQReNDER
z^3_sX81Y7u=eElJKT<Oe%x<WV{6~+hO)lyD;6`?>#ev(fBHP+d>qYtnaX|*6i`Ah>
zYeoO|XD3(b3KB$)a-jjlM0f;S00)<dv@caxx<i$3M*LwhyvTlkZfWWFNBgc)fTG_!
zeX7pFtz(r+*=Oe}Zd1bvNQWHqdI8JQ%P6;r4!{uO&8UNNOXd~rpOCS<VdeHXRqByS
zb8M{0)6-}-gztS9pr-!foEG3w%#JE(@=rM3>)|35T8E9mpDR|#7gauZgsw`n02FqO
zBqF+1OD6lf1Am$7gyThR-D_zJ2MArOPu4v!-uLw&9_~_Al(V|fnG)rtH-cJG8StE9
z-vL0-4OyNPgNk5SPBGA(!Xd=U?ONWCr;f|<r<lQ*(euA_ZFsA2?(19WM}8yT<dtay
z1n^)uPhR<`y5^prGYQ8HAitFlTUPzJm~1B0Ba{9&uuk!x-T1#K%T8FGlXMt>C-~ZQ
zdaNOzi^ZE}V>i2rX=w;m12F}bvWnv=jbtS6GnZ*hj!7XUg2Y>VDgw8dI%As3c`Cn3
zmk?qp<q7(YQ|CB7=y#=Vi^0@aL@|J-;dXE<mGWkpnw0nHSjoW?PBxrjE`#NbJieh8
z@iBpX0bJ|d-Hu)p`;wAcac5qy?VG?&`#<iPe8t+zXRpT2>o*<m+kqkQMdt;$|H8gV
z=?s4b*D2AxLus6DUSlHTFZGUVeI386tu2jyzX5#SKO0}u6!9w4j6T7JUBJ<d9;&d@
z!n&BwLWPGuYpAWt{oj&ls?n1)fpUg&xWy$)y;R}-tXy}ewgfO|r5<O68n=&Vvu(;j
zk{b`M_fi_@(c(21BB%HQhhU`rTzQ>$)dE+8QW@6G?z<4vN<xAAUrqdVe!aK1J5WWv
zU$iYvJ;kmlYmA;;=o(Q4+cpU#LWO~b`R<4{yr1}Axk2I*^DaA|ZK#*uREIfG>`DR$
z*PiB6_fSqvOOjMbr}SNG?48Dq3O7I&wy?3FvnVJFpf&)MxZ?&!MWnF5AO>^;d>}~a
z0v6Mp4gfRXN2>CB9u?9_4Uh*aWvBl9PCnqFqp)98<Zam-wYkoWh5#Zb3_wTug}b4F
znvoCoZ~|N?D~0zbp-Ql%6^;1FA=V0J>ky<h%v~GO3fO~e1IH11!~gm|1Bd0MtV}kD
z1QCC43rTN~A^X3l*H9wnQfNfTj`Qt*2gk_t{+?kyrwv54mg`$?vV@{%6~^hwW*VD+
zuX|ju;E_Ii7}nogJe`}&u8^M!U~~>tBO4E}QTxV0HlaAi{02JT;a8$%>KsT=o<^lT
z2`bKfyKJ~AuvTY!FtPIYT4s#XkXlN-+s*5-z}6u&z=3hT3CSdYF8K1un>naDpW$r#
zxkG>VSJr;7GaWgX4{dO$v^*S7zR<$J!1(rAa9eJG?^G%Pb(2rx@D?7U4{ISD@0{xy
z%lOY<z-*k4gr-XULg0jM2jwD8@@^p6(8|xtU|BA;Mnk6xCr`yDsYXx$0;n<oJJ#j7
z=`ohulwwGeRuBMl)V1w<Y^q%cTio}Zc}T)f@R#YAumWCBI|k29XT-ID@Ol2m*QN#)
zmCoaH!Flcu(0R5-b_4hHrS-{^0xI!}<~P7mb(puU?u7+I_H6o_vh<<RBTN8>MQv4>
zT2}JKd3HCUP#Wo>&l}0wDMH#uchbW+arRv$CNUL||0&MK);^bLt6ITT8$xJ>0(oXB
zwW|8*{Tz}3g7{HVv_doJ<5mS@$57)5Hs}3~NtSdlAxA~F8EzlxiP)FrI1g<&`p9%M
zjohO2f4g%W)KFcDU+GUunc=mE?Rl2Hfv^cmv=I^#cs&8YOgpu&F5fqVGDTb=;w8=L
zQ3A)#yd@|-sFegNGh2e{w<E28Fe08pJqhDP7$a?j{aLo4Za<f+8M4;7(i}`*4nwE6
zyYIcPH^-O<43FSxfEZ5?^Eb;*vK7N(HyZDoDXzm}5vNRxxvz)9ktPP<(?=B6a@$t^
zn<-kn+t<&nG&doMewRUxlPEEJs$#r8R#NT_|FFhN!11$!v{&zOPa&etY5Yf^y}j0-
zQXQ7u(Utro(26TYtA%o?ZAK&`k_}|T_F3+j&$(G6ZO9pkAlEXxUPb*vZeYS(jUQB-
zr4`(lpd$xkv}Ic?57E5G<Fy8zyBnAvG((m{KH%;2i}(%5k%5uhjiPl*nQH}mB0hcB
z<6^fro8vxA@I$$NitpE0J*A1X>EX!NLo=fF--hkFF`7;7(nm!Tgxq+3ip1WQ;h|wa
z;yg)u6m%A<c3nQBu>Q01%W!b#no`Rnu3tCRgd2FSPnam~dXQIjCUO6j#s3NWmSwg(
zYVJ&h-4_|_OY83lxnZxNyU2n_#fF*ltT(p=LN}kw%+veMCh(;p%`L55Mz=v%)*l?F
z_v!$_UY_@N<i#d9gqy!Wo+|^-balahngRQP#a9)IkAuiMuM7{yi6}9u@{!`&0T1N{
zc(vN(LeN+r|Le)d+ng^HSRE;iL9yoMS|)!4HRYmCT!EQM@hQ-ekl)F0te%Oi5#@V~
zaoJMy7|ZfFGtl%X-B0`-HD<&DnAf*?mhnOh)HNHAY=jMT+8d!dLa=scD~B3Li=GwG
z1a~KKSMqU%!i(nw0=X0)+g8N@S|7fD23i;7;gONuS3TJwOpE8iCPZzs`5#q_nZ}<e
zBZ8U5)0$-R9@D7==I+K!J*C4D+Dx&KL&os@@zgQTC#SMdYAlp)%3r+5<nsF~okj9i
zGyOYN06W02BcSXgZR=lpqi@ZKYperN-KA?H*Vgk4N@wxv@h97TFl0#*f@ikegpfk7
zOIbrMq$zU7LhhL2GaSpneD8u82RN;9JsOK;3(%xuWSf<7l6RKWSugIfd%i8svG6Cc
z8?j40NEB|6RFdC^!kne>H>8g+ZXWa(e+jn(S@(1;K18}SM#r5KV2v6-Jv)lY6$mw4
zuvTYg-nqJ%4$Ov^3ksYOp($%zcKS7Tvfa5p`Sg9e4LT2T;fS^0Z{P<vxsEiC5BeBA
zxjcVAU4tUH`0aVSpNozwnltNtEgm(^CbE&56CnDO`+j}AD%-HN_#jIh*8}x@mvjwC
z`x2U%lsgcmH1<2$)_mz)$eeF-AQ9+&glIrRa?agiwWXEqdtd;g!v@rqLQck3Aefjm
zJf_WAV>h3ESFk7EIsZiuT1ED+q8lwh-C|(K;PAj&MB@5CjCJ5x5J3`DQC*6Erz9m0
zstA~4g}&*rcAA8Zdusz9gCc}(Eq1k;$2sQhwE-w=^aN&oI!rwztfcn-`GPy`ZZ{9+
z>eFst_d@YjM!R}C&cXC7T>j-6+q&XfZ*g~O3)^b3Nn>;mmSd860tbQDY-?oB+i+8#
z`gueob;?7-7NM`~gO_x4TXm-I0P;Y;JF3S{K}I!ytrGg1*S8+Bx$3;G-up$>V}deb
za-Sw;on<|BCk-NawT0{-$~50>7HivIZ=YMU{1}$<M0t+=XFnrZOkY}3$gbvnue=)V
zwp``?4Lc<`&v|Lh<-VpG*q_N70+!gx47w|~i?DZ9c?GxsXgX+~k*8viCQuKT2eQKG
z)}VJ~$k~4XUiOvp{i$EH#Pi<2w&3Stm5Rz|-<$|WozkS6UMg$~gBD^|BQ*0{vNIz{
zL}&49a(;lVl%P%3-qFoS=@vubrx_=!`Bc_rX^3K(M>y4xuH`au>;Wm6cX5)X-+elr
zMFB<6kZ@)ob9p)O>(IQBvD@xc4#izf(;n;%OZay}>_02&>P>kTZWemMSpkpa*FJ|R
z*OwdOrS^ZjxkNW)`vVyF*DyFwH$%q1Po3TVl^UF?!EJ1Xz$O@KQmFWtdZhJvh3ZD$
z{5~y7fw{Je7iZ!MwQ`|dQqIbj{bkDjobgPp!uB%CG8@QYEjOb-G!H&1OxbCZk?4nL
zrX}{RO2M|!uk_gFU3g-$fsmwZpU^xrDH3sFIl<NtEl&w6YkzHSIXAg=pE(`Z)%hEJ
zdeIr4>Y18}N14a5R%<Mlm+oN0G{4*=;Tk)&s&AFRM@kCFq&(DzEYu<owviury&FU(
zC$H6DdpbTowlqh{x=v4JOWbBBQ&ih+?&X*-pTY<1{v?gwtt8;+Y&te0!ix~R&qDX^
zZ9mP4IvFLm9DK%9o=Y8hl<ag$0p2;)_@ttgcVXHo=A{UQLcr@RBiGQ>DERB73whk}
z;4tVzpuLjcz2AWbzYDB`830uvM&TJ9x`?R5criGc<9mCvPpEA7;b$@-AF*{jwiLx@
z&YrelBt5(ywA_eCRbjw3F@`FQR!3FuVkX>uJSgBDxtqXvPh0L!RPD^^T&c0ZSFT#$
zKqTQc${9|jkY22P7Dpv3Ja?=4IYmsKlECw*=evH0V9wy9*}g26^b^0`;gCZ$-7!4(
zvkj4@>TSrxwOJnE$gTN!Qt(*0!*s&Y#y*#v>n>36F4xU!Th#)X&cx>dN?yQ<IpLXI
zc+kMuaomT!!@@nE#BqIKB!Ykk#Xp8Zn;DOVCTwj$f64~@9EutB(Eky$nB}@%&Pcv%
z*NT6RMg}SJ7_nA3BZ2y*^3ppOyNLyW<7#Il60{W&iU^V}SeTFEYOC8`ZnfJr_Gf@@
z^JN?+wNjbF{RWPM%$SEhB&g8Aiy?39&4Ky23IxQBI#k}-ADuO=$D+DfuG+4Y|7F-3
z7kbW&I!ik~IvRUYq46dDa!0C1aC4}q(iEASie>D7kfDPfO45+;PMyuyLlvzip6oOV
zcJfPFb0Yvgnx3KqoYMuZvjhKi_e-IAg$DTf%yBe043UxsUC@Fhd$#iX@io5lpMwov
z#4`?^3+uVUI{t^r2A6q-5HkT^lb-vZ&N7Pmfj*bKf{14~Fo-x~URIuo+&E-K#){?W
z=5QP$%nbB3Y-c8Q?@8uyI7c1f74w}gQ(a2k{I4@1bIta;SBD(htybvS$PTktc3D!1
zOlxaW*Dz-!HgeB!Nr_});k_G=iD<&r@tJwv?&{9DY6WP!#&e59L98o@b}BWr-INQ6
zZ;;2=5;Ep;*g%FH7#tTOc#5TJi&<LG7Oftm=<KJ+%j8RFYADI3$x07{oe7?Tb+DeU
zQJX61V**a;xh&^1cd`+gc+m7JDEp1>0TcW!c=<e}iF04fycDuf_(N3{_Tro2>+CpO
zj-DS1LuAGAc<pce(DuU;1KE|0MaW4n*_~X?Oi2kaQN}sv=dj;7Jp{D7+%{=zyF0JB
z-)Y>?R^q`P7XshoCJRc2s@$Aq(i`T`&WEIBV0DE6Z}CQCIv5r$0<@2EufJ01i-+25
z+=R8qtW{sPAZGq-(ct-fLx3hNzIVTt3YQtKhL?G=tnv~YXT%gVAUw<oi_{xy0ilDc
zr+zSCl6=UkzUw-&e4QSVZ~acWo&J3*PLQqaiW@C8i>Bv1CiUwGgt+Lhpeji|t#Xfx
z-9z%-_ba(|LB|_KOD40?v#0_}N@M^G2lcE&SKJona95@VIHh`~w_2GsAaSNfGdppB
z1uyPri?orA`}}m}XJlZFTh#^mh3(hM!S5m`A#kWVPccV@$-PK^<0o&O=xK|b&R#ja
zbuv_N2pLdx8a0VZJ!y=VrY1$kZ@m9u`&=5O^=WK(F@l%wGu9=o+eSOxZ<Wn|umAHG
ziBS2z$1FT37D0aCwty$IH~g)XyAm53HYE{%u|hW5Mm-rex}YAIqx7R!utyC8xB%G<
z!?3?&%14x$*a?)Vr{-juNTRv;u3O>ca6ddmt%beZI&dqvf?I^$N+%w02rbn#rG;`q
z`Fp?*GFz=cD#u@j({96ry0P!L^(=;EhLfaR;uCHm6e5d2C4nLJ$L=1V`}72%1@BH|
zyq2T=5Tvedh=PXS))Ll51mUKxO%Fi;3{mPF`SH;sJqgH46m#MS2gUF7hJNpMiN@vX
zETVO;)0S7hCW#4Rv<f<(Hi}D)<aP<eqlj0r2(sN&!ILahV{GrAlv+jdJF7PJKsQo$
z+v>2w2O6H2lO-1-pfosoCKp)n{}*@oga>mOlHBW#57PytDDX`4>LC;0<o*>^5VKEh
zq0~^lr^0)n!5cJKRK0TgG*^bF+^T3<s=Cr1Od&hQm?j5@Bm*3_yZs84KwIMqXcfcW
zy_MQ*hkADWy|DIzMynz4p-14W-2p84I)_bO?Pyk)W6fV7J>Z~2^M5mBxO`+3AjrDv
z3XLjAqizw1^Aw6SGO`*{_$Fi$PQtsp(6}oeJZK}o#q!1chuMqpyQ3~l>hTv)cc!H^
zz3u01lhyK)q~3qvr&bj&)iGIZ#Mg=pxaR?N3<gE_(-jq16vEucM9X3e=>HF6`H$%D
ze-wxf$^mL*dpSvw6<un(TLnb`o1w3R1Jg-IEKM#`39O(I-J5P=>(2(t4869rZ#X5>
z;ZLB5JQX5RyohfQzI@(9N!5x|Sh2Js3Elg(L-gD?MMRc1Vu!}VL(K@MTup6xi^*T6
zY`LVdimO(Py-}!j=Eo1bJS;BN>&mKScCT5=ob`J#Ji_ldp^}YAw5uKUJC_{Qlm%4-
zw(-^*J6j=yUBo&R<ia$V3fGo%ZsY#eBF(h_u5l~fr8D=_pD!<2G$hv9(%Gb^)aq{n
zH`dZQA5q19vC{i8y{%>LVYM!s!D$nSJj89stKlRQti3<eCnmKM@MxOaXl&K|)XXwq
z_S(v#0~QrhqoeB7NaJN}Vh$du#>IC1TF6-r2k|yB+g!BjGUe8$^8SQO{%BAoYwmu)
zRV64WVnYn0aE@aeuC6I1a-={^YYYp|js+bb`;`v5ynqzUcEil0oJN>i_Nu2`#w^Er
z^M8h!Do<~+B4U~`ks6P3_b+bBOUW~D{Ug1Y7cntV{Aph*k`5bR@YrXCj2c`O-Df7b
z;&fJ|#!_r2AUu^pP4Y8L5_uC><<WMd)wx{<&rmfaedG}1Yz<wQ1=UjA)&NU+|MiCw
zVTT_X{4qjZrQ%=SvXG43&+-~4b<C2DqpP-wl)Kb9C-X!S9GdDR+hLafdzk(|WY~Y<
zx6gq)?%exD_2(h2`(i2*8jtxj!XX#$VX8THuLkP9O>R>G99aTfAQ$=GpWnOaIx2Ih
zgcA@%$cfH<NYR!pb2CW$r)ki=Yh9{=UYtnD3q4%9#LY9glo`7L4p}fSa>J4Hdj<``
zUgC>!f9xX|fF^0Zq*veM|D51}0s9Tpy6h!LCYd5IlMv0p=P5>M4Glh$Um73Rw1<M&
zeRakqcw(o*hotz;Ob;YZeiXDemYvcK?IQ9qnHW63dQ~;4X4`{~lXJYc4|<y>Asni&
zN15!`>Nia+^tQLxEP|1RZJ?tG_*5@@jsIV2Ko1|zWIxet?o6mpG+e?b=LmP8Tr;lv
z;;byBE&KQHL`#DY?N1MXczBOFCuT5@cL}Gvxo|9%?7T9a0OM}(WLlFb3UMT)@_zJy
z9M#5}0<HS@3W+*Cr8lr+8Mdkc?45&yCsPkZM2zHE9VW|M5fQST_P;LRv0O+ybKZ7d
z5W-69S6jdzvMB=f<@^Kht;RQaKpED_3@<;hHRjv-770yGK+g}I{0$bdHJ;xoHCKLc
zN@ERb{(U|{Tl7?!<~ud@B3@rlHk<Mm%Cu<=z4gaN*^VpYqj#=#nf`PqEVdd?6h~gg
z_Z^xeZrD@_5u7b-bHMCNsv{)9QvU7O8AD}@qCuqZtL?|g$}!Va*V7T=@dg(Xh8<$-
zCN{WTpu;nOUNPQ+=PBdaT4bN5=KnbUf5YQ{ugs@}cyR;*v$ea>6B4sX24G*8`;`Q$
zp&yXMF=fUDx%N{z^Uye80vTWGl9^gEahG_}7=sNDf9>*@9D8iW#H22{-7j+pB%mzx
zcfEcoL<!#oa!!@GrAzdh>FEF9h=FU2r?ss1^1+25KG^xKDhj_F5$A$t_CR#51v)xL
zmKH)dP3^K3w7I~~44bZYE(rlO!a>du#NSZ(D;0m!nQEG_3-r%G9jctVG2M4oPt1l6
z{AR@zGIs3|lk2JUw&aZczFuF_&>zrA9;LXxRr%WJSG2Q`W;S-Zi>xu+*!|}pBg6eW
z)4ppW#Ay^<rb6M+yzI13+VDSv&*VE&`rl+7MTJvUn>QjB(jabaNd19SA2>mM^%BMv
z5I<OGhPTp?PBC!s#;K+E^CU7)wZJBKToCH!4z=f57amK<*)ZU13d)19QqO(EkFp<%
z+5S$X8<<{W{fB&Jdxee%1DcLF!JCq9w~yMbWLKQ+$)H;OmzaqjIZqvk<qMsqlwOys
zsZ@n1%}dq9rdPnSoCYXCtz?f8uT@b&E;V&4o#C_7^$S1%=g0D?TiLM}AQ~rk_vt>W
zEk)|l7gdN$tlR&Cu}p6^?KoA(z_^Idk4od7h+kW4HS?*xHwZ&(U|Y8+_*@`BB;jg`
zARVbh`}#<W54B5jEg3OZ-$>=3i`f!WkF1>?(q!n_J+pFfL~6@IxpqVEI4c|L|L>T2
z-V0>ebMq50?Gr*4HuMazyT^rqzv>egUVC`YEPN~&7;GR0M6fejyNjPgkT&{I1~`Xc
zM_BGVU6<m0yEN|GGoVBRu=YAZu3p(P&|DpUQGfuUAP;pI%2{cj<q876YLArx(t&bo
zvT08qFpX4KB5Fsg;&E#Nqe9e%T#(l*No>BH#Sk;uVxzJuoCqfxlTxhufKJ!B%+Cf}
zi25v2HsG@uNwNNM2^xbTL)C@jGp<}1tOu5_32-C(J4yAQWP9p7`@vt$gB~%=7s?g`
z_5<~xbt;a4%ulUo+(eLt0{*zb{)mNbg%y$GrC)|&7PCX+7oFq!!Os6KNUynbFOGv-
z>0jL%4|Dh1^95mI!foAB7`gn~IBMtz8`I0`A!{va@nXn7TU|`5CJn~}hyc|RW1_3c
zkPr%7QZHd`Av~7SX!deV_3oUmIb$LL(!tZ9?!V$fm$PV`l$56SwdNx#kH3UU{Lenn
znGHKm0tbal)eG1x_bF+4MaBkH`S_8ISjzC&vntF&j#Or^MQgdB&VGp(iJ%xu=u!98
zV@xdlgIt1hzQ$PqCOVN}=EN0(y8xfHH0=!I#ae^L0*a95p5oSkTWts0N*Q-3EB$7-
z>SEdnGe1=)B_(<#Iw1(`?6fWPWLj<d<F2bWWq7jQZB$VxH1JgMN-JVLN^nOBaL`(W
z$1*zAvi>RC6$;09sfsUl+yE{Y{4{8G=l{Ji7d_`>auu3POUYz&j4OINuWJPyg<p>4
zMtc2M)vLcS{}9mAyXN1`3h&Tm^e|a{RY}i91Ji=<844}qoYufelL}?`@gGfwlF%EL
zU@gOiR>uS=sJoqj!=?}>-SU6m?tiTi|MyfRkD}!)93iR`mMH|@2?UN#C$!@mv2dva
z%xr}nu>%kHfSa^iL=1>^mAHZ9nGbY<IyvUp*sjFPx>DfaC9xB2pe{JKnUm9kwXQ8b
zIqRE)|IkpEuQj3hkBx-@u5#CDA;?*NHv&1H2SPn%L_KRKD0{bv6K7BQ(M8p)RW!3>
z)FW$Q2ZJ^RDmnolu}Bpa(8AMMkydU|HmuB4F615n=Vk5Q=+Yyf(zP3bK_2RlK7$5S
z1)kwV$nrD)Q)W_jc5#0!wbumK*?y6SQNnHMAvMQ@c4LU@JR2n=jhKD#>5Xb+Q*pB2
z0NSkv`c%Mw$TurOT!B3c_otK8d38^qe_-#Im(vbVtAqoQz>g@K>>5%y_<QbeQ9Q;{
zo>NZ$S+#0T6fY<LngXOr_Y+%nrR>Ip!UBbL6d7juz?bCj#iAbuo$5b5>)<SjJ4$kh
zm@6iRPZNxeUDYgP)YDL?M|A{qlkA|Jc&}p&WTP_n`zG6L4|YBy90Kljb0!lrUz+1|
z!y8kdqMFD1zjjc3W7WC*d2Z`{aJe;CFH`yo8W1X!39*?gu{BM<d5~PKzrhtgqgbuu
zQ{D3NfDU{>1i#}1Oh`FyWZ%%sX<WwZzr;?cJ)kCOyDpEXT%e2g-d$36r&y8@7Y6!6
z_RU)`q|){sz5}=X0MhE&FC-rdo8yf1T_HdE5Po+_oYxIv6hzh);&fSml+tifwYAAO
z$ea9R8NzkMD7cn1UDi^rBhCL#+U2Qb^rHAC1WYGN(@)7)_1FYeG_biIB|Z2~L%L48
zW*;x+7um8u4TgjW`&AItbFo=6EItG{_oP&B<SH2kWNrmqQgnU17Ti5&9X_vjsd22N
z<g_>?-#NEdw>~}^UGv#zPNg6V`AR#!fn#G&L_6{x3P|{iF3Lk5`u-pG-ZQGnbPF4{
zAu0kkKtPH*DlMQWpman;L5lQVL^`2|UIYeF1W{=sQj}f;0TLjDfCwnPCWH_I(n&&3
z0tAvbXU>^%=Dc&(tnbJ7@BKm6v*1~<?|tum?Q8FQvu|V)DeRq;>E5hg&6XVexP3kD
zx?(6@%i6o+t6g~WH&UQdUP0QrU^V~Qh%EvuXl3cFfBThdN8By#<xcMP_cIEj_RDIU
z8%=EP)7^7wGE+(SSvemuHlUpnE-HHKL5kZ%nl1d%<>zE}Pn+%eUPcx0LP=4MTxUbs
z)+pn)UYy`9BV@zA4`OZOi*TFrlQw0Xi`<=)f&;@5=r7PjzHr6@)s_BPCXJLcV8(B=
zT$Y1Wt%i;^jblu&l6VhF@kTsk*o1`4a97l-Tz(tv_Y^5g&9}kHR6oHc@`O5-WrYH=
zUH4%~TnWLezSYya1mj6udc&>(+7P|wKGo7^*ogEKIvtX9;LtJQXTN=@E}R|XjwObL
z<H1V0x*5DF!yJ~mvQOvWH)T;-&gz;R=k1~vfer{qS@Y-jv^(yVafA+3PJKn!q;3L^
znP6Em12UbpVa=~mD;}+~%x-q$BSQ<duPn{Gj~s&n!KALZf)(yxxx+15u_P0lpvTRw
zmRHrT+dlMgf4N0UbNC{7_60_xB5KY_0GfFr<s?TsLu=c9b72o0hs0w()h1cW<e=3h
zcjornpoA^gSU~EdBb)u$^MiW}rJx6l&%!}Up*qPX=z>(8g^jZt1W_v#PtSNxB92!a
zKI+?~miy{N(Av1vQ^q_s@QAtJW47F|5$Ot*?I<>Q@q<n*sWs;4VydaF={E)d;6e{r
z6P6s{ezSHaibH%#t^a<Ri~s87;nD8suk4Av+#@=RgGDyO?C5M*9bOr4O+^+*AjC*z
z{#Qi=R%|~u?+S67)vltmTbn%E?<Y1iF;I>hQewoyLD-Pu4C-c!*Y1!;>9UO^XA_+m
z$jKT-m@AV#dT<#VNm;T>%?32JcZ^cz52Cx`&nWF~@$XX7r5;DXv$;sTqm#<JpA4f<
zI`>D=M~sVD9PWb6i~;?!l1<YcH*=LPW3kSFm^%)U)jMW*Ig8`|wqVw3r2$%=SA@th
zVu;BWtIDQdqH?b7sb?JA9y>m3QR>mvnJQE=7PP68UgFi?WrtJQB_HOtQn8Fn;C~V|
z1=yV>M-Ck1E~X64XSeRWAaxGL3FYGV7tg941x?Fuf|U%z5xCo7NjQB%Bz3pis@^|*
ztr~xCibly)VPJQIHQulwTMrGSNA51jdvB?-3yfI~J4+7fHHSN1XAE)iL!E-*?SYXY
zVBH$KJr-wy(_wTWTUs*Re<7uX)ZpqD1Ki(@RQ{~RPuQkLhHFJubq0Dijp%lNkrfQd
z^Q^A!RsnV})~jm{Ehwx+prXm*{@pOJezh^8u@^;Ff|pG3;1Tj`6-1NI$*-04a~d~l
zr>JYmwsb@x68b<>??NixI-3f4mcv7aI{3Y10ehL(TkTky5-DoNnBbi&HkX>FK1N6d
z7PVD2S61puA-3KzJ1Ht?LSg!P$~*Z(6lYlrvx?r3Rk>~QGDaJ16`oq%Ajy94$SKtW
zzkiI`JZm9SuEJ&7Yd(cbESFoitjFb4g}mvzna370zza82{`SoiZqohZTgXFC_lNwK
zC3lwZ!UwV*%Zog8&KPh^V*y_IqK?x_g-X2K+M5&GpWSKjsm4~8A*c}r9ds|)&ci3&
zZM5YXA6mJMC+cQV;25!H>&NKO_qbI%Zc8wHfKsYzE>WHwm$ulD+G{asR%n!*7``GW
zT3b71HtybkaC{J~8ZpT{`Z3*h3FW<r>=V$@yZRye6(_ht$8}zwpxptfz%r>A`YcB*
z7eMlH|BsXo3b-fGAxSBe^41xCP8NClU6<)`k-cZ=WB!+-w562s9Y-#F(qt%UT)QCm
zkU8`o9yIwfVA>itnq&iN*iRNhxr6)1E#}dI+dad&gD*RU$d!|U0US|*$*SXGok2%8
z?{_OwrZx$FX-j>0t0J|-rBISnH&0O&PX9f0FnrhI0DSyVkC$1=nqkOXrw4R(3y4$%
zTDEy2Ho#lTlVs6Op>szn!aseA3Bj7KJUwN$;)%(=NL%e-(BU14%itPaC5dkv<Q>Hq
z{536)eI8pON*j0bb9c-T5n@xl3+tM$u8Ja#*_j_Qvm;6U%8~Vs&Wghm>7t^qfuvzW
z!-e7w&(~xLKhBNarV=9)stSEMAc_#A$hXybcrRSSg+!AZPPnFXo|h>@Xn4=&rS8&*
zFVS0q^^{c~knG41gVqxYOB<l<K;2ROSNVNiDHEx!(ws_n4D+5-M{^=}UE!aE&un?k
z6ox2mt)hwWQsWlph}I9i8#mbEBj=ZgCAL3~FlZ=S|A8?zd8|YnM;Go<6GrSwFT_D%
zNV+)EJ<5yozTCJk1G-+L6471~f;ZPZ%E`GB>KeE}=0DQZkd=Ol`mDyeA$0S=r1;JB
z#V%@2v6%S9)n2a)X7EYR=g@gxx@Jb_;8|a_<r8dAOfjeQGNHGQ<ZL?Nj|ZX1PrJ*T
zoqLZ0!0!4Zlg#)(LkbK`FxDh2_eQ4kNSGCCPezuM%6VSr;MZ#HYr8};I$DL41kI(I
zE*C$PJ;L{29ujpRiXhNJ41w6bb{Qt-S6pY_DOOu!V$i4Uw^}YUxtn{YA5mx*=8A+&
z_ks<AyOFSko>XTsB99*N?!&cUdd<YgipcqNFQq!N0$lh^r3ma9+)+0)r3OJ}!Bq*-
z%_k&@6EYeZj{JaPo6jTBj<SM*1lv!<zC-v5SRWiFI)y$d{_0ZW^n1ZwWH33z*Y)d0
z<xJNvq{4#=D|G(ZxQaPB7Z~iF?NwyhbTmmvd`~e5h}FGX-h@D`YhvdKoY-q`gWi6@
z)LvyzSX9arH+oD&hJ`|d2O9J9?`deTq@E-Oj!8++ci&sV03fnQC|NC04)<MOIIgCp
z%xev<zyUP{*2h!QQ<vL>n#Q89E{v;WV(C}6mVnwPSKX|`ULQC_(`@mI5SNld@u>Ku
z?PBUjCM&mzQsiY~LB%1oNt?*A^4GPwF3rK-8!8g7f{5Sf%U<0kW9-G7+~7gAG<&Nt
zMUkb0eOmB`pui7QQ5EI7{?({U4DpVlP}NPC%HDhWeh+`^Hrl3<w#@mln3^qn(X6h2
zCW$%J(T^VttJ=Za$3`Y`?WJ<}q*<;)uebr)#>=Af0WPE{_sHh8kC*)y>px+g#DeB{
zmzp^uDNqyy7HR~goOd=Kh-_GBnDXP47789)%VMnn>cRvRbK{yRI}@k)i?V~>H1Fb&
zt~HLILNh>8h?N4UA+*^_#c;QZB`G|?ca_oK>}RasN8{>lqA&7zj>eT6?Oa(9<Vq2x
z4xjw8@%t=*)u~BaJggR%A3Xbs^~k{>Jg3wQ-C6vmjVERy9QghkXBZgj8hnitrFYxK
ztV)W0*-=h2#jLJ0#sj`H-?4w<xwY7ZYuJT#(43dxH>tMG=F^Mz%r<zq+I}F#c_K-@
z0ijYB*qSIoE94gl5vMC%JM(IF&8pjLLuH2Ox0MQ>82dzx>AU!EF7+FsYN7ME2!JLS
z`%8*SRSHB>Imj}SbPq5)gV}kH-f_CJEiIGAj@^~lUonJForaXVd71NrnLmC6JAP=v
zsb~KrW;gFLt3}4@eSRryj1RX5bZ+k}Mmq&-LA2+2FX3>p{#~l21a<gk=~-DM^>XCt
zF3Fy4>FKcf7gC;cO%^gaPOZy!k39HIdueYlRpvSjYRVT}x5V(op#u1^E|`Uo7o}J=
zl_gBY_kmp2t$H&?)d9-Zs7V_3P)$bK1}<V7I%mHcGOQd)pGJz&8`thI+&|{5xc5Y^
zfZ2+eL;7;|Hy?~d$dRZZkDT|DHz9Z1LZN*xD8Uq?veMFbDlW&G_*vpB44$jF2<Wv)
zjZ@0ISQ_PRRu@knI!3H~7Kx+`jqOkdyY&X8JVT9&1m%>!arEg?g<b6lP6t=-ivw)D
z@R^7#|DLFj7U|bgif~ZCw2Dx~9g!WPV?J9<TP$H5hZcNBjtB~<QkndS)MD(!z%5uR
zmD)m_d#Q4Kp5b}G7~{tqc0SFcavrwECEj(1Y1As!uxe#{Oy|!0{)s;0jk5t8R*t;W
zUOkqBZm&(9Bsn@hQC>!L4Y&2F1QZ4HZiXq0L(({_s>=~4sSMgyD6nVcjHRAyS;X$1
z<ZKi%fxNSo%)ucjPC7cv&!Bn9Zz{!^?W|6)KQOflyL;+=9~jS^0D5B&#ku+x|8=cI
z{2HqeaKfmpv#9BGQ$S3;V|t*t$m!K6VsVycg@qw-ZiKdA%9C<+)utho)&RO_Lj)b{
zZ&Yq5g|9$1*6T|0Icj~^Sg|os_k)amYA#u{NEam)N%y0Letv>xyTSSgnP(cHziX6I
z&hnVXZA4Q5;Xt*u@Ona2G~uo}yd|0J>ltwR7oO7|8J!C?ErG?i(*ZmbVsx5EjKI2M
zrlA&iQbb4{{^K>j5q+k=cyC^>WDhQ6x+4cR!I)O+FBM5vwbWG%!_vJ1T17*~M7V6j
z3*%L8n``jo6pzgwk5AyuUwjc2pCIrt6Q3@+7+MwN&C;oRD;X<k5sYZlFJXBU=0OzR
z&cdJSr)k?gO?uxPW)v9iV-Uz*_Cmz!socGLpS?s~Na0lchrKqbwt1$hg<DK-L08!p
zx{;KHH{>g4qz0dL6;~=k4A2*h8%$$R(1(1#uYC>5vVm#Y;o834X7i4wCLTlTJ$yrO
z*2=*#%M^?|mq2cES(3;3Z{pH!Sn?*U6F03x=r>H1;c6<h{mW{qkk&r-LfuTT<!aE~
zh3p$Xacb`PX0Qt0_u$kXe1a1G!5QBd;uovDm0^4Ipf6`_Uy#E~$Ab%6dbBe(u1=}s
z>dEIK8;(tN*2r|CjeYlS&?G%0y^`{7bl5}Wa{n(8Q|r8qILJ%vYg2f;=K#4H5}slB
zTF8rb76qnn)dS$|9q~3E8Oz9lGA{{sC}xtg|3W{k`ZDWcDw-R+jjM?P?V8~j?iiF{
z0(Bny0E+l7i}Y^wTN(4P1V?T)B;{1Z<%H=d3vQoIyFzkAh3~lvY0?_f=a-_(B1BXo
zntRKXsbzMNxFKq5Oz*ncqk;gxwUTQW^1UYalBR;BY@mJ8kVyUnM^eYIt!??F$z)he
z>hYXAp&1duSO*K&m4=!3>>Z1`taAKb1mf7~{TO`g)9wZoDKh8G_Ra;hQ@Du!6Pf(A
z8AjVTz|_yra=1C`e0N!Qbyz5&5gx{|PqL%+uqCR}u4qLfQXJf*71=P;&!-qPpu4c3
z{%6{(+QRa!J_3$p&GxY#Q_O3ToH)YU4;XHf<uG68O0H<445wvz9Q1&ToP%t%XE$8n
zyYt`%Ke*R&cFKOp56lV<+-~c*PVsQ|8XV;IplD{M)v-C~Q1w!m0p0K`C$j1!o{bGU
z7;jW>TJ)3l>t3m-1#jF}_#_cOcPX`EVWaF`gJ|r+T);)Zv2T8mNfUP@g4*Uc^6ZJg
z__a#S^jpiV8s?+L_r800ULfO^%#_E7o4dOfwqn=_i)lq=uW9fLB-O{p!Gya>@k8}V
z!V>|NawCnwFAW}7|FF}6s3VHUmS!YFWj*X^vvGQ(LaSl&-=0!DFlzf{TZkMSPXhS@
zWc4e(Zu?u?{Y8G;#@pv<m~;Tl?GY?&vuzh6BsjW2-|Uw~`9I<>btjXhtco2xnq~y*
zYyvo@ue2b7y+anKCe@~HsYJd_G!K84SVycT#SpKK@K-9yi5Tv~8R2)Cy=Mc7XzMpQ
z#&PDs%gBAd($wx|OVjYFAOYD3v+HS~4E&4nAzhW7Q{e<n>Y_Lvp`Ak;<5er!beB{x
z$1`>gb5}{BSSJI^D`|_o+}jm>QfHnft1vbc!^xq6%WOHfJG=ZcJ55z`h3Lx%#nb7|
zjuj~sW1-s*&vFVhgEGGGWV5Ukz&9~dlQ~{tOP6rI1ocK5-IaT9GXE7m)c?&6v75Yt
zsYq3uZaiVj)9%n3!d8?B;}1y6zHPus4}I=vp@#zCcdME=lOvbk7TLglJpN?oK{))J
za-+Z8vxyRhqf?#9G^C<y#wd;)tCtO&`|elNV_S<>*~hi*5=}j8q?*kIB4^JBAB8$+
z+eS?L?CuB&4hsaTxVHrxv4rQq?Lhh;CTc47&!?pWZ&K;uQ_&7FKCMXlKC<kKXF@T|
zuijn7_x=D#h2A{*W!^?v@HUWf>>g=i{zaE>FVt`4Wk8cl1<T(3bKs#<Ms8IqjA@rg
znvWFk<}fBejax(7ywU|SJga}o7**}A9co>w!euD;&1cjl?`#+Ax9E1cDbv-j6!rJA
zgzc|H!XZ=UV;T#BWgA|R`QDxdra*nCCBBlej<99Rf~u^wqmJ#C^KQF@nNr52ttAV>
zvZ7`3Y33QR{U0=AUyYR~_p$x^;UVqvqgG^VM|X(_o;<iFM}XhwS%+2QC1<IVlQ3$V
zb}BkzmQ|}+9%+mm&kS@PhUh%x%DVY*4!^VA=QL9jE?o8LVgZd?wghgCZS?MemS+k8
zL#)FsY{Q=*KD^8RZa;H%JYmP(5guZFI|qzkElsj$k>~DOJTLB`9Bfn)lIEM#^O~5!
zB)7lSpae8qZdzo1INNI01?)UaNtk^?Q(nb`_$pGM&g~)3y+%B>$ac`Aw@rB_hz8wk
z9~}Nr_uI79C4~>@o8IBr9r8|q;tO*#lZm1_QjW9HLb|9Gj9QKA7?G@g7AV1eyu+QM
z%+p7}G=XGK>Nl{w*&IfDmDkzPpl8sYhUXuV9mA}=q~6-p>nXoxP9Fp^#wp)#<|w_l
zt+$;-8_Q0DVk>H@YN2|Z&5rZPAnwZYsXFTS?!fdhA2APQ#P`0UE-gPU@LtVc5IPpy
zFI987+wP7fa@7)>$v+RUIGe&J<lyj;CchsPnv1c81)ki*S|C4l?7o9O+6SuZY_C3B
ztzp>AWrpr-PwdPj=kzfd=o+@c7GqF`2b+O~5(Nt1W$gvy<&>7sTdqGN6TEtcf)}!Q
z5(HFS22=%n2U_J9KKl80iC!y%Kp+nOwQSVS9dysgt=QOB+bJLZ6MY;R`&q!qILvC5
zN)Zvy5t)L;n_km9&$qXq290zG<7L1lQz86=NwxT3y@0Vq)YC{!dg%C*D<6jWR3NbX
zoJ$Qj1A5@3xSt7OM-J@*0wMT_76y310(tsWl)Tg4vnl-ZFR@K3oGM7+ZCGLbZMQbx
zUq~G}P6c~-ZTOvt#TGFE+0rB}w%vNW;5wT+vMIBThT(}GLo&s<P}sFGp>wZQzDV+z
ztf9dfL-PDl9p&W%AXsFi%kL#hW)@^>hs{`{lQfIQL!A67hQ!erv*EOt_B>8cIuqDB
zAi4-?J&Z+vfG6(K;gIh_3LdSo?*$@0&{}l4Aq2Mo<z$vzKL-yUtHElvD+>GySkv>6
zo?nOfB@OpstNLzz?{fWvs=OY<x953KCrn`0e4b^37?vxRJU|B97DRPjhILim&U(xc
zdBM!g>_%X4mG^6s?ZU#k?deE?#CAa2)iNK%CHc^VL<j59-Otk$ce@k6(~<$7BZ}9t
zvWpq3)~?^7379YFo&G?I)hNP>K9)7#I`;)-bD$pKs&K+3WUBVFU6^CPmM2zm%?k5w
zE{oAq^N}qRdrsE2Ff^7uc%(4+`FiCCYKf9_j`Bb>4VbBi&zcW+^Fk;in5i>i+*~E_
zDX)a-sYjB;;l;zdT+PO=Y9^tfo#HBft2WrA9V{iVWq~ZL0%RQD_Oe3;S;dV7EgY$U
z39y0<Za8g;=mhVI!XM||o12@Xz>)iSHf-9|XfyX3lF`IBZt5AT>zD2^@|OF&a)`fe
zd-;!F88)V9Spo~&C7R1o@cwky)GFUAQ!KlMsP%qcr;)=!oPwvksnAE_jwIYOP*GYt
zXRkGHcb7G3=pNPtOUY$<#aN%UO5__+pSiy^*vh&!#LaeBFVe40ncUVHt#w|`%BNP@
ze|)>*)vn6H8~o8kIVz$wG&>92R>44Lhra({DB5dQZ^OAqF(+4*a^o3W-{onGhok3y
zP`s9!<}&?<8H%6tjSp|H7<VphrS)%!OF|;S5kSITXUfiQM{4I{kHh_Wl^?vnm+VyS
zR8A0gOTR=Atu%4|0qPYH0^kKP%9>W11BDY1COPR?U1USgB=}vD1_BP44Wx&zjWb!y
zF7v9#{-$#~eIJryNCmByEFZ$71gxw?txWy*yp8vkLJG=fZGFct6cqMzO4r{^6ZrhA
zV@l{|4G#k^Rah6O))n{(vD8qogNb6<LrXHs(jzA^V223Pjm;x7W|~j<T+j~vyoQBs
zG#*&jx9|P^H-9&--?GjD%#ppOqRcG9YrwbhRS#MfY}eAts{E6B+)Bl0&19g6z7ysv
zPqTA*nmJcuKV$27IBnSh)RAxHEr(oACAgD)*w*Ite(XD4w4MKMUbeM~)En+!4b@py
z34WwczKDDj+7DtszTb}<mT?Kv3*Vo)<{`jwNo7?hGE5gNWmS}^vMI>EikIhST(V^F
zNGWL9()Mc_e^^opv2rF$Y3}&5MzA&eE4nHn_0P9f3qt{!w0d>crR7b|Ua$$9i4g%|
zyI-WdMxv32q$JT}_J~{gwgt_;y*}d}qhb+jNU37m9M1|!hY~hy<8d2#zDuXeLgrPb
za>y4HdHvAOy<?m>RUg4m&j4`u)SX-u_6FJpY&z(LUPs%aBbGJAc3Vo~1>|g|m6f9H
zD${5BP$LV`{geWQ2`N+Q2{|D#n8Lfw!7l>j&a`z9vDQ4}m;M7%C&g&THBX(#FGV+S
zs@o{9)yG6Lubh*r@eNc?%Sr`yStym1C&JS(MtLJ_HGq00G(o%0ri=xz4(!^f%Z>^Z
zFmI`;2u$%IA+sZ@J1psSDzq%D4R_O*j%sBb^UL1lrIrIQLMpmoJL*5t@ZXr{ZxiLK
zz{nawfUZRM!R5A^%8nbE-G^Meygk4I%uHWu-Y8#B+Z<^8(K+ZqTUtmvw*G@S8bT~+
zm#2+oIsd{S<Na<$4u&KOsOa0oD|ndWb|^KGea9dLIr~eFCwAX4a;F-c9bZO}V~0m7
zBr1i>-oQg)PxejKR53MQ5tp8W;Z=zjj0%Lpe%#BVw!=ZIBe7~z@{Aq!bRn)swDF4m
z#oFWQx2pP~Vmt&nnZ=wm;C?0}FeD-%`}7|baQSD<@*d3R6og%eyQxOFLW=*X7Pf4U
z$C>syr=-%jEos$YF_2SaqD|MM;#M;I$#V&ES#>uy_asqoaH&?lz2;){si&cc3gVP5
z+<`FWy4e~(o+7~6XiA}UEWSLsbS-k$5?XojygL^5wrUFG0&l77HP^p;19_o8OTC5J
zS;Wb}_2oR-ttHud@ptb2dhtfq9mpeumSxUtz-vTd`p@XXd}^s=xfCF1i@I6`BNV(p
z9QFF*8=zlBah2&~8X|%b$|3RbT+<2`@k{+mvQ7cHCbHSBpjOi(T_el37zL?dtW6Qb
zDnuTCvAG7QzJ&7ab@}8d!zgkzV2}u_IQs#4q+leSBGXCO!7wR4vMJv$N#Ijn$DA17
z%2S^5*&r{Ct{DsG*2OO-j;(8};lv}>K{HCbyFf#7oK#K&c_x!V)v)&eA(D8$e*YAg
zrSlJ#KM<frnMc@rKj7Oix3K5SSr*<7$`zHT4(}aNo_(pqP^ks_WXx!W48NacRq{kG
z(SUiSJ7`p^dF{UT)Og21N`K&J00n?c>y?#->1L40J5JqN0d-CVJe3ANzVoWxufu)$
zzy`gRoTk>Sfl1|0Evc~!cSt4~T9}L1&CWdWLYGHo*Xa;G9@@#RqfXi`WHOxQ@ifAl
zL<a##<}Vx&pYnbo%aYSd<2%;hq<a2}TmJ$tf3v83>xGxQpmJGs!Jd4}ZSf@~`$x98
z^|1>|)bSHp-eXe%)R}%x#3wer^96J^6w9Uz_If};dxJX*5*}F?VxdZ#6_CIN#A?VY
z;!FXCyX8`o1^98uP74UTa{_-2_-4gltSrW(hX^G_pS@3bce8_##MxBYUH;3CYw~mV
z<sL4JTHl)H&F{(MeSM*SWTH{~5~@?XqzeaxI`6!g#c(2L#t(O~sLg%L&DH|fp21EA
zcxytF;<C&UqH=nf$uSdOVf34p`0{d=Q8x9dPvr=#26t`x0Wf?TrqydDVvdYXTTNDq
zYideHatcyh>7Y>SgtPUYNd<X&o9b>0K_+xu7p~}xWn^5~yYQ6IF=uA75Yr85w0l(W
zdeQ5khwYo^e*uVp9o`Dqw8VS?VmVho77`@0i--2exoNy-(qyb%AM(msDL)KUQnH0j
zY-+)y2!H^<@-z?kvR>L_r3JKJ2`0@Q(ozRYvCa#z8$Tl;-w2K`CvLAmnb}$S<I5og
zAGYgqV{4!t;d&_{=(`0V7NOTa+^QaGag@9W6=F%RYW6Zm>oqkQ+3u3gDS2Qpun8G!
zIU7(y;-_AHXKd7sgTLeQSpOk7qryieu=X-=vNOMrh3GM{)Yy^hZr=TXvL4-35N@>?
ztr~E>n#7qYV@cp0ip@I#%PQ#&mAcbhlO}~D;nrR_Xk+Q(;q>GQKd0WS1w3Gr;?ky3
z--?CtcDP+;nh!}W+@k1gu^0y-KBjSLa)*69MZ);1Jud~5mMQFUf#BU_hRbu!x(T^g
zbh1vGn&5z2dVNFS{vRiGzaiG*mqP(FJNr_SsPW#!*4`y$ldTc7soM&~Eqn*^5Zii7
zwy3<+#%Sg_8ReMFIYQz@e&XFkFRwigx9lZgP-i<SrkvfSl@Om2{$$Znx$*14C|R_u
z>tG{aR@h>$NjGshk+qZ)W=BQz1FAOgWIhx!wCcea#a;)5;I14ymjdvD0+nRLOKd*Y
zX)(4@!3q)@$`fhGrBVkFsXu$*q3t$YIk>Mi?O`*}rOBEc*Z&h~DOm>|cfjlg#9%k_
zHUk~!Z|bt;we74|1~#swEkdrj%b6btHI4GLG!=nvzU!G5oI4SWJQ0mDcTD+!w5rbQ
zAXUj>mia{ArcJtD9}+L-N%ldgFLLaujd(WR-h3wA{aF%)<i54|^2$S*qPAAB<KtWT
z$-oN)>g!_t9BF#jVoTiH%0E}Y|7EFFS*qUOyDDviry2&O^Oamk=`}XiwHLoRd4fNT
zJMot_rYG`~NPawml?sB{j^^l_TSWv|;#q44Lde>VMkBdnqIOcb17xTr!~WgwPXD;>
zwVMIq(|})qN3^vr)BFuPw4TxAH+pa8JNMXvZTG@IG%0SkfhEyHbuu3)1Wvb+H>G<X
zB;`mVB5u)ytyv0dR+KdE{znz&nQG@b;S*%p*V@Gs9SM>(H_G=phQg+Hf#uiCP-)Z~
zd4ZU{{3oVr_EPW>C^4DkYfUC0pmBF=-og<uDIj(%$O{L?9V_;-ZphjSI___=z_n<6
zW^j}>aM<T?{8l%wj23pCLHw$fSn}ifuYI9LOLzW2?BD!Y)s*{~B-N~4MMxoJ<82$v
zASh#1iaxt8e0u_#0&{kvuDRQyICuv&d2*!hM@Ih83Gda4OXB;;b_VZ>K_zn#i=q&X
zEStt@=AYvn-Mn0TtQX?3pwbsgCKdP{P>J2NtjZJae2Ql9cPQbs;8$h8A^rWu#_z2Y
z{br);-(|TerV-R~`i?En*d8|hZa)}B^-`ru1B+dazQ%pc`?;C^52CC`vRB@OPp)R2
zu$+>&GS2Asm^9y6`J(ELwXsyaehwxKc5HOwujtvX{19HkL6*QSSqa|aIMf--i|0O%
zH{My%clD8UvED}D4x_xTFwaPrd9mvPIlmBVH^at09eEy*wDjDeks#i=HL#}Td4Ex#
zHLmYJy(xb{-kR)@cR=YJalrNcx0@Ws#aTXia$cVLj8Y)_lXB$r5gE%L?RpHa`&Dxv
zo;xXB2Ysr%LmjA45``H|L);B?08xalq5AvevH|T))KSMaSYJKARGL@0sS2C}RTJu3
z*1#rZoaOyZc)yc&ZCQ<fW*5fzfN^W4Og{CnM}`^KzoyJP{Is1G`dMbol_jbdXq*6v
z8=^XfFw^HpvtL?!n=S@qu6uniXn1B9@Cp~aeC;@$6wC@Z_fljca=|gl=t<z0-8@qK
zxRX5$bHDzpu0#NYBwGVxpwc)o=dn+%VEx0GLT*grnf-E!AjU>9?v;L!rn)os?Cz^S
zbn*L9lfs$X^SNsAu;dcrjG-y0CO!s0I@VpTOs#+0xc?SYIyqE~RF?1TfUo3T3>V+1
zDUM6$hDj_n7EYW;-+&8(;#tzs731Zdwl)w@vo+#@<+J^{JMR9JUq6YBx64$3$G0aO
zfKAKbJ-T_Xtqr2gc=M+XtFr#aIym02yuHl4^&;@CC#*gRF{9@CiX1E)SOoKdri}KD
ztjf>GyI;wIWCqw+h#upk-5vva2>@x$z@og+;wMe4gut|@$pCnh=3k-sH#{C&J=FWs
zsBAr7Zsb;RX(!afS%{Uk$)lsMW1ZYgiE{?6)@+hrvT=Bgt_r5vlhnsXQ45FA^yxS~
zp`!O!0>W1N(v6V{>T?)rsUGu+_A=OF@`4b`ZJ&fFk&r?qsrbBebN{dvYpdiq6Nn_Q
z*J3TDhJV)LC`td~u1kU{pf8jtck#G$N@4j%Pi;p?D+3<DJPk0+vFy2rgh23h1-wP`
zBWFT*^WIkhMeMFLzgys%efLu~KHAHBWkgpt3uj=@=d3zAA~81yzWlWh^`tcf5Bsci
z?g$=JxP9{XDES9zKBa00JF;OK`Xm;ee<IDw(i-d@xm{ce+*=5<G{u%f-l5dvvVz9!
zvVyk`?vCOMU4M*4@Qvf24~n}eb-g-QZ9m(T5Y)`9w}6}N=9-F-jgM>*<cz&p0cAN0
zZ`!V{N(H}^g27B2bzI&yzu;qVeHLWiS<vrUs)Wits>&TxCaLPY($)q*FK!UV8+~nl
zI->visAv(lxFy4)#RtT(7$g7b;|){(<B)Ph@SCw_JI$k8JuG$mzvfP{flw=2#?5>0
zoYi1YE#efl8@#QSC&n!pwHT%I&BOD~msp}U_L?x(5<HOg=)2*K4@=Vaze#=jzuBSi
zGu0yQ_wXmlDG%It_dw$=4yIm^1LQ7pUC4Ux=<ce5&A?kBxHT8o71=`n;Hn&LG-{Dt
zFd8yjpy5X8iUt5Y&|u}zR{Lc~^c!V+2L~9i3~(UZQ)?+0t6Mr{F(Ox~sGB}+Hm0)E
zt-ZU=DLB{>eIR&R-o&eXxRXJ<m~}nkndkPvV62d9#}{_3Tfb~|MU%&^V1Vn{IZL5J
zN;Yu`1aSZU*71G6t|n6aX%q$`UPh?3_siohpZI8-;QW(Ka$G&NPM`1})<KY#ZVHrp
z2{bC$994|iYdw+5C62I5O<k{Ke^+>uAS-TZnA5Ec4c_yU!J3VUhUNoyJ)(e|o^kQ<
zMq%rs@d7vGMZmZ)yb+sCk>5+S;8%*&cJ|w>NHhIA$~)fe971*K=^hSudjs_zL;F_W
z)Pc4`&OD5zQ49u`G>P7K`cLWY5w5U(u@v<s_`Uf=!b*jWHQwE6uBCcxIsqB+lY!Bf
z_9eXa)c?*Sy$%T~(!b-REQJH3cBy3p2I9#5i3CSVMDt3J2bAuTv|wXvZ$5I_f3rvr
zYW1p$TLNDUHtX=4u<@E0$z9@}pb(cm3p83IP7+6T;xJ<<%>)wO4QZ3%uC|7?)%OyY
zvT??Yg8^n`mpO@G$dC6=WLL;6vdRHSo4MW1yc%**9Dbk+RObdII9ir}rG6~n-)gC0
z^QWHm1NE(629d64&WfFYdQ3A4dq#UB!yg`&u*-hvxYa~%Xsrws@Ks%{*i5y!vr%)E
z9qq>U4ZrjC0>J9AKmrnneJ5jMS+8r01ZnAiY4W0;QMwaeq6Nqd+p7Zd;wl23%l%9i
zX*$Bi+$p_Io#vcQB$$o=2D>xLSbTl!(UqL6lC3W?T^{34S{=G8q4hrAuZprnzV^!-
zEETu|-dLA@rV3V!y>&Rp3nghsY&r~&>?Jv;SpTbM=L|MI1oKeso0gYzro<Q!gPCK1
z>ORkKj(sxe(0YvyU+&aGG(VU=NSVWSR8kIv4mz$rUcYi}m3hsQ@cLS}me-g|E8(bP
znR_Z3qWe7s5^!lMa4tkP0Clq)nA4<E(e;*gg1GrjP2HF>*L_axdsff()1UF|XJ~0@
z=BXltr{N~@pYR9DdtPY@|0vJt<m?gB0amIrA`#{t3}LmuI+}&1fnHiCZ3OcX`QD_T
zOqvrvHt+1QNxafLoHn380kJ{=76@d!Ap5eqo`n+bpa{ooM@OtQSGx}gEH?W7qVb38
zg*oJ#->OBezG@|1n{^kB*Npx(=_*_|wRV4Tu=kzDxGN}o%$_!-w)rE-h@D$lZ%n4r
zH*__9nU91}F-`N?FBMR}`Ug#+88dtB1m~$OULm8;t=0nl5RgRf15l}@_3vi^=O^;L
z`{RY%LNqhk54BU~6wcLDGq3Mrv5B5I^zEXMLMRWQ#^;r^I7(bGAKWX{UD1iIyOx=L
z#|s-~pk}@W71|X18<_s9gN(iXS1Z%^rrxF2pdn?GTIXxntU^qAlD2?B@x;m@GTeM$
zcQ~NqQ=eDg>T|LLX{r*v7baK_{r(Ae?4)xzmcFK71l%zpeD|8oQVw+<ci*8ysl0y*
zNIyf<U!I}_juEGgCImbR*mO7!4Xg3yn^mD(vkva;dsz#Js??48!K3Dd58&LO0?o&R
z(*l$YL*x7qZJfvSk?N}4e;0TE6p0Q*-92~+9n07BU#Zf+UzxJL^rosbtRK;Au<l~k
zdEwT;{b06|J8y?l&}r`a_!9!R^PR7!kzduAy3XbbF5?il#{7h^ySzC?fpUo|xq;Vv
zJ6}go>bUp@%B<oNh-V*#wx+qeJX)STt{x34y2-1==;*Rja`3>_vG<1fkH52{&B>Ys
z0Ily9>GBCCsE{07kk>;C067MVo^3>oexPO&|A1CRTiShqg}Fe2w)GPSBcVG1s`}ps
zI+r5sQDT|dBQN9FtG~q91FR7mH|HxKk5OdMcQ?9zJ<CA^w?e^E%;|iDv+|BVs_c3Q
zqBqmrdj`bV33$Y)Y;epU9#X|PvVota*m90Z|CP}H{sz$}%H?)Bk8|a~EAA#rZXK+c
zCD<7-SzI?tZlzVR>!jBSZ-C@fN_*>cT5Ids)nIlzP5@g={wAuBhAe5aXx{u;&?d@N
z=Vfg~2-=_j7b)~5C?ee%Y?8(D0+D*Ied|}2N@hMce)uadn+F;-2c5=RyAhC^C31lC
zlR8wz^+LLRQrM>IPMehE$Up>6Cd6A$i9T_+qO6ilnG&7>f0`#48u9iLK%IbqdS`WM
z{b-eq4D3H?oYHQ5F3D~E1E5t}c>~AT+tH9QpPFRP4iAr=i2GT5V4i1e2cUdjvnZ+k
z)dl`-6?RF-gS_h_`=!aHBACX$7c4-{lPjN^jU6qE1e6QGV&bp-_ocE84E-dl6g-WD
z$PmX(fRp+#d#wi{n}s01Q`O2hS(NDn7(dlx;*~L;iqn?pZm?aOJp45Vm~kVf?AF8~
z^g`CH`{AJh-CzTC5A#+0w<sll`tiKlr^b@sh&M+@<lLbwdD`)L%U*=-jnU4M&rL1_
z$bPBTXeTt4^IHz6=U7(2h_^uGnlL+*oXsTtS;53(SwV|ab{Tn{oFjKS69<G=BXw%h
zJx4;5gm|B3d0AhS`ssBPIMdblBaSWpzT-bB(!ZWUHCwcOLtRN<(sZ4p&rI*Q+VJiD
zNWSm>IdzmiB$b`gkDK%*=_u^KKkQF6^oud>wW`vcIcjWvG(efvQVT;7sxBuW73H-f
z!?zQ2M*<_@%~`4Z%UeInxk7-0A=Zk)9`ws>F6oxn5swxkmOR5((mJE3>Ycq8EC+Zj
z1Dk^zyuEA$aVnb&P?E!fP@!}Az3t7k*a+tQVW7&q(jPY462UF(jmY6VkomVDVCQur
zXdYBH@7RWY<W}{FkdzZyylaxS2#4v^-6*;Ybat(a-^(<-$}#%Ganz&(ZqtqO9Bc*5
z*e<A0dlUG^tToMYgY57?461d3J5UnpJNeznq`#^0?!JUsme)nX44FA5oPk)f61Y_g
zrW2C-T_37tZ0v=3%{7d4d{=(rp+Y9uKxGVR6%zeTKVxsGDHG=>$&RF--}=)NQ7$S9
zAzprj3iT;LTn2%9OGRFLk&=_nk{l6;aMFhvBlMdAKw$IJR>C4GcrUhpp`e2;lQmoK
z<Mn$g4FpGW&AUm#3z8m>Ejzk@O%gOHNWOM**a;iZ9K6?*&eD|x^PSW*hf+D|y9V?;
z;bYBi>J>-HB?FrMckT(nr}P1~NMH|e)vLMRk_W=0CIrmHvJfntHhMfbyZ(ga^4Y2c
zuMYyurk!JdA66y{nVsUCChCS-Fw2Y;(@CoeS|PLZ3TSpX)T=up8|hn<omJO0uWcc^
z`lflk`s}%`=P4eKJaMH-wo1<JR{>Drpv<lr`PHw3A>pkd0@9!RQ9Jt@e$o{z?(VmM
zlYC0#W$Ex$$^v<OLdJ5z+>Y=8k#CXdp1JjsjT0Sa$p|;(IhqsKX1~AYHN3UFSN8$#
zACnl2%Wt!QG$2&BGt=e}-a3hV0K=v#KphEShR|@AMRDnJ*c6rD+l7!pAK;%u;NfGQ
zPFH_=2GK7nS43YnQ^46WyRIMBcl2lR>ZfNhb06@DpcYD9EMa8iy}qNy%MMNg*OB5)
zerwOxNwQ2LpHgvLm$q-l7&~_@<$#a*L&=!&!)QN%nU%#kc1TfMwxsny8W{7rbzh9F
zaJD1U#!k%#d?&kBIN)pa;7lBOqLCEbT?P=&8aO6?WirpQdf6jW8>qxOuGma(=YD2m
zX<cN))t(k?UGfAAl@qF=^%n4_K5#0RQYzQOLYho1Oek(_7Mv@VpPH4rEXdbfXKebb
z=M-<N^qo{x$ovaN9<vJk=%A+e=f(XUTpoy$6jd!*lMEiixc-qX{JmM6Qq7$?cB+2m
zc4u_<PsRSPCu@UeEueHoBJtKAF8}v<bxfFv=>K1&oodt;3<%)+Uqb$|%EjA<j#Mi2
zRp(y&lZ*bnxT@*rguQWBI29BU|Cf-z7d?*oUtn?)fd#XHv;V9wZRUyK%DcF%tiL5N
ze@ObT$R+=piR!QYBo>eVv%apdF!lAltxxyzA5#7Ox|k(;P3AUq-r?Rl^3VEu$J7^m
z*50}3?|tJ>;zqr`tUA>=bAc@+`k(dnkf|^0jjEdAf8K`9)68x7pP~Iv0vtTjn9tPL
z|1;bFXSR#~&ukAI`u`1WPm$h*_WmQzw7~BRXX=XAl~{b|5>GLMYh9#Cjf0Ut4zr4m
z=V^U<Fl$`{UPTF9^xpSWf)IKB0pYCg31@1F4v)F0EepnP)vDH!0<UHmH9}3^4oSyq
zX~SRKTfpfYLT6FO2L1jCgRe(<KYkm3l(Y5oSvDpXe`QUo{0Gn+NM&id7gShjlJQ*L
z@dIB>T#B)5rm+a0VST;PTZnY5b~l&gMMC3cMkVw2ebbC<{-X%+Kk%(3;E;}=Q|VG_
ziJKMNVhNC$0)*y*#7f?b9-~T6Pbvmgcz=adzq-O^$3rIOnK9JF?)blpf1cRW`?a6r
z@~I1Q3NAC{nl<u|z>CNJL37Ml`dZb^uMS1u%8ZvAEy3KZ+~h?8P7~f}fB_5RXTkf7
z+*rUzf#b1UN%vAj%8c;t48ymcsig9(hcs>BCZudnz(0dOOiX`s>#&&Ch!~faICuJ!
z8?VeXAtomjD<1@BTuSANH}N;DFE{&SX7;#DO(2PRW|L;@CtKpuc6Ibo@M48$+71<R
zpx_Ht`g+;lQvW}}e3a7Vfu^UGd_^iHZxSs92r6TO>Rbec2X9vp5-!&EQOosrPqRfi
zNpLD;L2iHvsU?{5*$x@4a+^Y8=@l)YRoPdzq+()K9+%K<kADAzx+aH%TCqrnyC(k_
zH$$ykI?6rY??gMAYI-Cr-{3CxM2-ZaqEX6Yb?GO)un-dq#RkvVcWh2#6;)!d6}mfG
z3m<?ii}u&ETgY^M=gj|M41a{nwbw@sGErg;0p=_A;2L(Bu3K^<$yy@kUYk)sZ!fhw
zo-o}qYN85j&`v%sN2`j9+N5DCS-vQ>=n&2yN;$F09G(8B()iy;{}#z3?+B*FK9l}3
zfFR=%ayOTjtSH}cQFn%UHfZg_*E~{PXMW8IQIGzIuO7=4LE?=m24QPvsR?(6Aka>6
zD<jA&z{Yy>8R3}_+lMs*|Kw^xmWO(~=S!#OG)L+eW!j~W7u4_;5bZCUvl2Fi>(Y<*
zD=o-QAM}WP8qGrqKJ}GD)Ty9#c&Yr7@nIw1fpsyZ*<ZvJCij8lSFx0j%-F8v{Qbi}
zu~$1zwtP7-x)Dn^9DIuSVDQjUJxvpMdf8<}FbznGTFM9BydYI~a`_3Zr=uJy;&L@Z
zUv@CR+|jWUXhkgVcQ!{0)>483N9H$|mf|CpK^W6>ODG3U>c>$8*S{=<3ox(oCv*He
z3KbR3^oBh|eBtuQdtI?7t;<0YpMPb|rg0dTGhDdH%z0hl*7y_?v2#+#J75%H5UQiG
zPZlk1T%-FPq&?E~<n>S)kI1;$*&~%_zbEh+@TQ_FACuS^2Gn8YCOzt_#5!i0j2n*$
z{3m-FP&;x)e4)gZa;`f~w<P~7Bu`uHoRD8aqfk)vlkn%Ph@Qd^F;o7VyBY?D6;BU(
zf_bwOQ$;Ln3e1w|Vtlu3=Fav#F05wGpm8epp$uzD<rF-SJ@?c{(@CjhC`M1e$<{MV
z;3*YuBqzc(S->Hu@P8i%{zXF7Yp1{E*U>9iOI`LAWqy5S6tK-Jrm*E7t1X^V2{sx2
z;i@GOY1~Ul7s1wQTx9bMU0Ms<{8Vu^?XiHUm|^~B@Q-lKvY_o>+F1q9rSN&#(4V{6
z?x_@*_L)eWd+aoIaTzq7F0lwK=y0W3F;jBoYtAu$`TKwP*-uZe5|4UrZkQ7E!^AP}
z!sm?{Dbw-=)hn_RCNn*8BenjGmzLy(N-atrT|K`rZp$juvRUn(Iptta!#=p};b0Lr
z^07y3(!E}bL(#avLGQ818N)XIq_Tiz>v(hX^>;Tb4ZklwZ2hoZ?ZqVLtCn8SpV`<?
z5&rT7KlSa_h)WwDSu*6)Sr#@#OJSA`2)50ZiGDdoM$Ar!VTgB~Mj=pxax6}ra!i~U
z<rYdhO-d-RDq>DX0Lfma;Vn=QNP*ey&s9=Jckaz;HsK*vNk+aizM~A9i41zQm>Sny
zR+X5lgM}T65Fyz)c4CIh!{M&~EZec#(^b#>NZkyB+H$y7M^^Ft+)ZQJgb-J$B~Hvv
z;PtOuNt!XwK++DM7JqogpEDJ?05}mnnsE;yts`@yfbU#gZos7s1F=-7FjS+O#0dK3
z$!4svbO5ALz%LfUeG<PlrDt@@uC^<wu~?@l2oC#~&Vcr)KTU=Bn+;@Y4-R<49Kh$>
zEziYyp==i|^ccq$Xm-`J=AEE7hx6P)!jI4I4P;k9P52-#_B&s?UL+kmgjQCrOJ}97
zSH!5gls+sO2WeGi6%@`%iCfl)9n%jzg*`m>$>YJ5!Qv6W1Tj5?u030rSB5V<u^{|;
zh2MZDHG>!5z=Wef&u@JH1CF8se_0!INOIlTm<wUh@ZgP&jlfz-?tkZexixH0Z;HNB
z(KM!ZAxd*7rUfKq0($qg$T2hLymm2OP<*^*PNDbX1_OR>k?IKDQz^PKo{wcyx5lUA
z%^Fr81bb<eap>3f-;UJ|F*^_n<#XEC9<RNp^YYiB@3rYgCJ%op^=P*v-=66{(cV2$
zyUoQ<*gemG7#lra8-7W3DkSH6&FQ})cNF5Ho!r2}N-myX+&SytUPDxmDg!rLz;FSX
z9j^t-sEF0@#>LOC{I{p$SPS&1_HtwA{!Kju?&nS?b{f%$tA1*PH;(r;C2ls}uGE(j
zpHW&8V>1gh9R6}G)r=B->AOYum)9aV=}Hj&(cl>{SGADxEXYrqd2Pwtcp>l7C*k+8
zW*COaOnIbOvWt=5mqWOvgm~s%4!-rJeLJO`Z-5yXQH@4p(D~%RP|agjNNmz|THIfZ
zqKS=VIrGf!HXzWQ4godJ)vOLz&pdBtqrS7g`!CR5)H)=z^G$OrPG!%YAlW(Nqx0d`
z!*rXFl0Jo1MRs24rv&Gw|C}V?BnO&)umeLDX<hl5DeJkQTTe4kKJA*>rI45DqV`vi
znNIjJmqNAUuS}fFCt#zjoPsu26@}{i?l24C(xvK@7v>!Ss6!)z5v*m~Z>ZviwiV+g
zNXP=&4YK)&(<c4C!%)lRk(n*2Bi&~A_HcbTdy3SAeaKzS|F)<xohTvPO^wLlDHff~
zx_jwiw0?`)kQq<O?;ii&D1VNqUsEN+DHvxEsSdpy%_ga@uCBhYcpGu=w?H)|a3Y`I
z9hk6$$LgbAS`Ca36)3q_-=c@53i-tHIqm7ZMAOmI1%%P}5LJJXGbx)KmMwRj<RZ9u
zEP2<5CN6vOj=weQdD5Tu5SVoDYI(|+F3&38>HZ1gV-J4x?$OvT+EMJ<2=S=+&1`dJ
z9r8_~THl!?75M4tFzr`o>m#(SdJ_J!Ny1e~78I~@SZXCx3=_+S(Rg7@kaZpH_^^)y
zK>zLTEjDM1D=ST;U9UWH{m&u~Mm3t!OT72p*rc&z!`$B`L|=*rw0sv+*bXlZU5A~h
z2>!0jX0ESr;YhxcowZxkvdhGw5k~~q@*S;D%B<!O*L_YHb386CepKLiB_4a{>6mtu
z<pH>;qxpDmU1{XHsTR7zbna!2X>tAC8*ILzYx1rKNsYWzkvc~mo>Qy0;VzngI&k<m
zXsa%q4kD)RSil34pYmKud9>8y&_O7!PSGPNYQD+7^skqijHA5Y@8!YVI`)2BN0y;*
zoofN#f}-b@F8~*TuJQdY?yhoqRoqo;*Qi;fNYgZX_X{Wl&mG2rK_+Q%C-glB*x?xZ
zsYZ#%3%_^3`wBlqdn~z>PaGQ6=#mARJRS9K=R>nxX$7oQJnhhWZuzV+VtjRk^i`^a
z_=2;{b6F}C0xU9gO?6r}rY<9vBZjVdgdZ0-^_0^arj#-<4AIry^<VU!QrqHZA~<pM
zdUpk?LlQpUbD8UvK{HTKp+@;Hs9|TzHiD!VKXEh3Q@jlvoO5~GmDFVLGWBM87}ZF|
z^6Q;*)k9O<wUVNu&@=db34Yow(Z_jP%L}Q{>e87T-Cv;|9%Y9;)lDp&Kx5Js;+n-S
zBj#FOHsxbO&M8_Uv+|B=ZfvjP3`!$+l1gvygvfBp8%%RaVNMD3rln??1Wg+ia46uT
zQSAl}iI;F8B=>icbxNM21cSKDtiLied8F#~>^kRc#~Lw7z|i#NqyF@)C!D|enf%9=
z4>InxzH?n1Xes$$f3wpTxIs(b>JT}T$yr%sif*bwK|YU-sy}IpTMXhmGMlw|&8skd
zEb6i={tq9%y^D(zm)-DiL_IQeX+N80j2P^S<NQ2#==WIwaU&@dO_X4^G4M+J@Iwu^
zc~QiaP3)^iQQ2igJXfk&yLb0EvpQbW%LBA1E8IycBM!VueR&S+Y;S35;v-YyL%(?<
z-Kf|V!yj<AuJ;kvomr8i@&UyEt9&>%d?)~*Zpl|Zn`sSRYXQZwREh)3rT=rD9%?bQ
zO8_%3BR=c@ueleW;sgz>t#R3tM+m<+Rh)g`S<BxJ;q5NXF7V!T60dT+=++I@je8;{
z6#4oCw{k^LqfJRUYvpBG6VRiKcgpeXp-BeXf{%+^yIxuDcy}jpbW~A%%9%<5(WkqE
zgcTeQ5(m193jFgvx9PH@X4Jg<Cw0db64|o#>+7x7)y`JjYhc%LRP&a)Hd(t}Fmb0i
zmCegym#@=9idi6?d{tlmU)5Z@{u5)M!y1>ffj~l?T<`Z<<G`8MRHd?>@!vMmDz~}W
zhJdOVCc*HPHT}w#$yarT*ySd2mxhbP9eAq$10%v~eJs*CAUBI!O2;0^0YXR5l#-YO
zsgtnY`qeg<xg-gWQ8S|4s?ecnZaWUz<uj&XU!Ptv)iu6loaZF8IevH`O-TUy4AfNo
zK%>3A^|IT+Mg2GC0>-GWI62E_7Vg=m#rg0NhXk$onbweD?sHNQqMOu18^s@Ah2aWM
zH<|zHwl`bIr3JJp*nT$X_I_Re`XpHa?}r-5X1XH9=>2~Y!~uJj=UYps6EvJ2y8}zS
zhQ@X31fXF5-#WB%H&6V!=UEah>)dxCoJldz)t!Q6$-43nRcM(0+Na;&Mc)Z}`5)Z%
zGR|EmU$Z#j#yrQ}{~@eT3=|73S^3<0R>H2Srz36B1%rY*(O?bVAQkFLogyi22Ick*
zAKs;92^^`&i|+tR5L~P<Lz7);oCbxp$kgr1TF8_}!z1E@TOu;qb!6rhl>?zI_rzJV
z#d%i#4_n_DUfH^I+Z}Xl+qTV)jgFHQ+ji2iZ9C~W>DcMmwyhQ0x!Gr*eeU`0cYm(u
znLpO7w`SF-QRA%=vWPL~sCLtsKL1|0gCDL*z&p%?U)x!kR&F-=M@qF?=?zD|V}9M}
zeI2!iMC2d$`zNsIeZUl2;ntZurU278&@JD~;L1uZm3Yw=KeExVxpGtBKks}&tn8{S
zcFLz@IsH2K)o;X4yDv6j&?@g0hhO{P5&ZkRZVeP9j+^@Q103GYt0-Hu>%HOUZivs-
zWb*tPDk>^EJwLPlPL6u=kcE~UWj4>>Pka_i60Mi=f2@n)AdUPksg!9(TU2V=FiceE
zSp8jHU{U{ouiZ%5VBkHdMd;p2jBa*MYvn6qs3;mfLI$H6!VK4#?IB8=n?(mCDN08x
z*R3>S*I#dp-!4sz(&-qM3SMRWvHnd9>pDnPir2cRuQGom8BMi;w^V(eG3ZkiBvZ%>
zu-P9=DVtlsIG&cjV|Y_0D~MJqtZ<NLtceEsWX!=a+yDO4|EWKh1VCcidDT9g2Y1uC
zO-q?&U6}GzlC|tCJA_?K-?lcAxIIo0vpmP#*4>s%pU=f7Ya@_QQC0=SqbmQEo~%~t
zC0*B@)h;pFty2HOKsrv-qTJ6%1!zqb4F70xPStbI`+ccqYc%3U_@x%rXfmwoBh6s6
zJ*N}Y!+MI=lS(y~nU#J}Qlex>CrhbP^D1{OjJU6s79>GDbT<^4X-&1}2#4LV>kfb{
z`e<S)Q7%`=^p&}wJ^Wy;<HxL9;n*S@o^7C^ZS(l`qlq--?o6kF-zcMzQkzL|d%k)}
zigzwcrPwCGx6G~yKn>u~y_+TY(I1uqKKodE_}L}8;F4bWJ8=I8Wn7|z&#=oaH=Vo5
zY}Sge*p@c6?HL<Jf~%1`2)tT+uTkx~tg#v=r_-tx^7Va1OLC@}51bCT?KB*;ybe^v
zW@(mZgxB=cQSbDrL$6x-l>m<wr>o;x#{}ocN4%v$%nEiNBDgxU@KS)F<DLOve=QMs
zGglDy+UW~xz3hQ;et$arYs|AwT9EhOY*wz0UtfB^MdnlDZ}eRF{_vzSjRHGL8XlT@
zQyAJoX<AI3LZ{v2wWmDU2oWnUV7`>`Y_gDbtwlL9ekOw$-RDNWQJ)FcmEYz?Ea)|j
zKO0lPQjC>MmC>E(&rLPfap;Dub2M^!tPRylaM&qlUSe}ansX?hn;x63dqEfDW~gc8
zZEjXrd8}2226pbzsVWtSlN4_v2UR?_pu+F*QDqJXvp>5m9j3S%F#nTH>|RA|-hMzh
zF@ez>{as3TVugWrI`TPkUu6l{3|<=$0mYu&aGLM0Q&bb8lXdA{InP0HI~MumVe-t2
zmV{wBmdl^cXpuXh#dYPQvi`#&%?8*wcRX$kbXsI#X|Ql^;P<==d!DA4Xn1U{jQBhF
zbpsGDGt>Y+Py1U}Sr=a76xnY1^eubHS<VX@CyUjafF<JczrTXq1SlhxvNVULd?_k4
zs&QkPk`7YW^I~J)k(H%cp~kwTC!3Z!u4X7MBSZC&nXomuVg7;-+doGY;4O9R)u!UB
zU@w~!PrR7cAM~D*SCNxAwT{DyDKHOu_2b7tcv0HMGa{fiOrv#CPe8Z3Pd=ZiRmO&}
z{^<R^vu4`WxbnHu<bzJis_G?`z((m`)gbjFy@pTk0QTDsN6b-ZFTxa;sjOGZDO%hA
zxJHMkv2`KeRh;p0FG7!arUnsD1xK)?wV3H#7%gDd?4goaN)<P^(~xodT2bWTaUhH{
z>5-LwQsI@!fxzz>+}L1(<7##AikAL#!0c&?f#-j{-6;1tXKF6=hUe0{{WNFusZcJ5
zQ}YmC_Fn7DAi`gP)vtqEL07jr1mA<w?e=0**EHL`n7-py3D5WSikgOIwS3iN|KBM4
zhu;9#Y@+&YI(Wk8<*obAX%qPE<A@4*TiO$#{gvO-mLu1XtY#hZ4w8S?G33Y|js&k#
z<>r8zSi4Ja!7pT8RaoZCr@#T0JkG3b)|c0g4Zo@m`7bZmuM6r-`3p98Os=@JvuX9$
zOuZgURchof{Y3i1>eKG%w;=wKRUO0T#Qb#<;C8$p^!)6*UX;e3yymGUH>F{UYvU_H
z7vc9m)8i?bU+aL0_wHn}`56X+rvJjA2G#{n*F6#3sWI6G^a@e|1TY>2zE<Qk@}N?^
znu-PE*nYI$XWD;`X2BXBWJ8GEWp2W-akS88xZ-}rCb?z3z%=N2HQ?WV*om&*;nDE^
zI4JNf=4y8kxaK$s$6SlRb6T^diMzOae}|;+`7f)zNayNLM1*T5JJms?(ama>77AZ6
zmp2<9TDxeb)E?TE4FlBC8)XrC#?bBLR=M<>TNap0B2GI^wU(I6`4F>ym+8cbL<yq>
zB-X}n5aGOWXGnpUWFYa#%Y<0rHS&8FCEE7-uka5vgrBXkD4GU!ycV0n_^c)?HEvi{
zj4jlSEfDP4Ld^Es<7lmFwwAIJyfm8PysVT5Oy8$|`X!&(@{{V^TH;)#uZOTe+nqie
z8`<3YK#1qvUB70%QKcCERM*WJBHw=lN<TT0Ex$t!jLH#Xnbl#RB4f49=OF1u7)^|G
zg}ITym^$XI_s6}KK{I9RfDQFlRfG;!arrUzUtFV$r31y_`vcZlExFB=e{9Aof6>k_
zQxKHl&<m)9z#n>*OeHP;Z8d=2LWRclV2tmpA)fP`V(aVmXoLOkfcGX+W9Z*F+y@ad
z$q8m+(SNqlsAnMko~eiiEgW5=rj_YapO_9zlAWWOsjU8;aiCGL46=w$djG{4aVT0%
z-R`qYGWFlGpObP8K}MgWKe3#gXJ41{)cV|42%|Z8da%D@j84!**aBYZtrHiTgv3CQ
zdwa0&0)PDVM0<-HCt5?-<5#JPGh<GUnnx_pj~NmQWA8g3PXgjv;vnf?x$yzZECNv4
z7g&RG4d@@B4Ib7FolhN}PMZO<Dgbol+9;$$*I=uSncTsgY|*V&Wjsah|Dr}eXPZ7g
zM1r?`sY6h+HtuBLd?WmnB~IF0NMx;hy)5g#Dkpr_O6e@*$HfRad2I6y>4rspht-<Z
z&KW2E5!hs+_EGpt(kS8xGokN%uAXmW|F+fyn8(9;Wf%&D#mQW0S?Lwbk!>{_2Z!ZI
zBGsRONMMuckkY>lpCwPYQ4phq|Joov=HF9#q7oj%rMbOhGotBSRiCsQ9YUAT>QQRY
zAzto2Jj-4Uo=zu$2WOnPQvYKbV2HNPr~@ZCI?;%{1%+cmjjw6CJhP5UUG<x=Im=3O
zjRy0<V>Dhb@+8vO^@Y2qar?mONA1jp6s^w+$cifZ+ChVQ<L{yl)bONF%Aib_o$OIS
zm5uVJ+<XPX1Y0kFtK!$3{~9{#!&XZr4{n5OXTbCykI%ia0eG<;>v#+Wmp%1QtydYY
znkIJ^Do%B6YS8H)T%Nb398Tj9@=CntYO(({p5gOlyR$J;Ey=q0P`J%p@^&gn20DwP
z5?98Xq^(YYU26~RRTqG&UNP7yYu(NtrnpR+re?QVniwp9cLOg~!JluB3trCK<fW5o
zr}-Y%3VdI83DTJigOe~CWU88$DcPRcx7Ghf<I0eaOLN2Xqn~Xhnd$7efcSO%Hh8gU
z%{B>aSAPSQ?}-{J)_#^SV5<f0`>;s_S48tcv~^sGkjRV~){wuB(y_wvyh_s-Cw~jN
z6eJ-&*q3~lVx>TpwXMx4u&ktP&apMAByCY6me<f*W~sEUu*_&;#aAtR5P^|nP@H3K
zM^Uxlg$(zctKSq#zU`2t8g0Hu-os~<S5NHF+KiK0T)iC!TI)0dMh~KY+&AiZrp4nd
z{@R`|Ez9(ZtNeK{2c}tt+@7FPV`YjYu|iPOwegn8@33zBxs+CO^z+V}8D1@nxb1{a
z9>-zjIc=M$z<*N)pLy5~?N@s6!5SJg(32B?o^mwYhi3f3CWqA|6gB48vOgF@C$qf#
z^3o#f-#Gt1=qE(l8<WKxrH=30oyU30;qC3LJg^m5H9HhX@*s+_^zCmPk|jl0`zSll
z`yP`!ISl|!ibB^iTmD^12f5bn0ATDe+v0>#Se526l_BM`<D1<GM7{70&3Oj@v!4i_
z-COEGrrKGtbUBRv2oH`mr!iUFC{7)xFoU0FRL6Y3wq`WRzu`Q3SX5gRPZoeme`YFl
zo#LP8)In@Ngg=g+OONf4n4ThAcWV97v{6@6DdT3IWbB5ZW-t!2>QVqcZ=GtLfe+-M
z<*Xi``%!)zB>o}bx%d^azrkhlcTkbqS0TmIj(&7Q1}f~?NT%Y4ja6xPci-z7Pnnyd
z^i)j4$Xe2SWX}sUAw<;U4tf}vk0%oyFtEbUO-1FS6BA=}j>YSqlMh)4LG~6hWm!Yt
zUKRVCLA!Z!ejn^>T&}b^9-H$i*U{P+b2P}Ht0X~RSE|>#=&EGQaR>br79`3=RDh%e
zLj{5U`mdi;&5(9_2CSG{!U!j=#pn7$V?7#ha+o!Uq=oakWeru%<!@;JJWH6c7H!0e
z&15!3D3itvzS?NrYuoh}ESW(5XHq_>)K0Ik^WV>I^@s|#XR@SBhLGJub|gFMU7Cwk
z1NFu&TT+ee?JaOSlI6R}DhQyZqj2|+SrN=Zp)90tJyLme$dQUA=u^yyN}wggNZbje
z91^zXo|y;A*?TlXt&!)jC3Un#cG3xaRu|F!gkA4qo1d*9KOcbVM`_9!@Z{WuHZ{Oa
zd?U*DoC#RL0avsn%wAs;>{)|4K<A?bJ@@Y^Xv@Wrw{iIzXy<BUt_DvN8R4I1Y($q)
zzTaXu>C_vejkE|ic$vvSM8*aa)5VCV`%!~jW%4b_b?8cE+JVU^ZrqX`f1yIAY)Q0I
z>A%hRk5%D>1>H>Io)^SkUTKJ;M2O0&O4Cws#@FQGES2kSPBa5obpLY#QPDq}nmEX1
zQh)0DJc*Lj3cTGa!xHdk>OcKh8x@0S3qx(TID@ayuImWHjpBy=*Kyf#gFLmd7d)DQ
zIiZg@<Zw!TOE<Dx#ZSB<7m}r4QP+f(?lU>+$mr#2rWrQ)J*911)aUx1*Kg-=@9&tA
z$2hCCLiAvrN9$%krb{^<7Sj3F@Y57ia$l)g;e7R1K_UkX<K?P`E;LJk8ZdK=(wraj
z`6r-8Y)sr7P)1Hi-`B85L(SIR0BoQPRsR}5mSs3+fV{thR23jSxJ^kl7D8si0p%1H
zcNU18z#~I<re8)o9TF>VvW{I#@bofw^Lk5@dv7Thb!nL+e&(J{NT`|<5<|LGCEnRL
zgVwfnEN5<)MpQ;+Ux&Dgq<F=No1{LQZISW6oi-Qo7YE3=H=iGI{5);}>MSxp7fsWI
z@xDgIUZ=c!TbM|u)W6+37m+gs5fTQbmn4SQKZ!=o!eNJ@^O2a__1ue|O|fAgSFn40
zzW-c@a;1)%>aVZDuQ>Qi+HoeFuQ_RrpJAi__qKWxr?V-D1!N?c!>L%nqI(GY6weCu
z1HRf6Ogc>vD|-A=yx*Em-PVF3Ncfmvx@mo10s8Sp56H;RoF$QqV`Iv@b4bGwOMS})
zd6HUb9T0OSJlG73%{DqQ!A%!htEV(dUdD**6{;G>>ewxA4+G0U*Cya&1{inPHON)t
z0gDEtleZv+RFZ2jnFCAaHM72<mh9g2SY?~w#d$C1GF6QdaeJsW+x4TBMr3WJQU{Vk
zpL0hZPZLY{`FmrQV)NPihyG+IW^>DrNK^0P1uaU9PMh98EN8`yH2*gY{NumSejY@O
zt@55=j|uJQ@}EV{y$IP{OXlS+Rk}=OavYdTZj5-=r~=l1;>V>c;Z`5id8hZwP87#R
zcQTz;joCEu+ULwCNa*+)3~HStNZ6xQaFP%m5i%L$K-WB7n3G2|5Lx7}ouF9(-;WC_
zX4{kKkUSL)hC^!IEmMWLW5E^@a!n$S$3#5TZ1p0Kvl+o22!&)9kCE~E>RKs1*o+67
zdJLxmDA_z1<x9!0bcC<D1vWvn4w%#tCW7~en3rg|u=Oi#7vSXh&lJmCMfDvxq3_@P
zm55qkt{a*cg_SIkx1G)xLZXa1Kw~?OX%aOZCFEvT(?6utyF3Xz933~;)}=Kl;36M$
z^7ZY2>>@lm;M;9_HSu7F9_0y02hr>4d*-ig@ZqL9Ma98ChfXWWk6-6t1c+#8Agbt#
zujL>cxV4W-DmO6wUM_IqNDTW+3;$=({oC3n{y(G8FWXA@>R%*y4H8()DgYzRhH_@d
z7eV(#<94E((U>pmj!UkrNYYKX!uS*<C-EYhuak7Fjcj(wLdc`%Ys}r974OvloZ=R)
zSa)wPsG7EY(0y1;%;0b1QC5wsJK!P-gM3g2XNdf|zzi#mBr+Yd6bX-EghG@un*|%Y
zzAm;x&abVxzCS$S=!fOp6pdQCvTfzm($BdLoNUF^BdBb%ENs=}u$C1uIQvUQ@RFKb
zr1usHC-K!Hyh@W!rwzY;JYUWi4V3jN`pBv$3b-&`oBdi*dTLxvITp2yw-ckzf&<g`
zxD(%4U?cT<k{Oi<AoJqT_Js4-%845a=PBfK=o{rW1%foYV1Aht;vlj<*N<OkkD{gj
zjZWkWWOal0#kL8^5!@J<)Nd^A6-HxdNr86n080GC2}j9gkO+>#P^RK*?9bBj#E;{k
z+3s{t1gFp%{~&&l<+7IwE10Kjc4<Rj#_9Y?{}##@NxypB)4bfO8lY#hjN;yw^2L0R
zN{4=90XN_|sAn!6j4kY@eI3}qj_q90<?M->K~+#6to=Pa&I;1wP;bLv!%JH2bYS))
zEl-j(6^o;)ih;tND^%MJl_D+QFn+q$`;yD8uuSql;pacew!0<trwrv=qYVRpb{+aE
z@vee4VIV67sL@QMA>0{4l;Im@POEF%r^C;F<<MEdI7@#S9?&cyP&@CN_&#{r#|FH;
zy6f)G`fiz}d-m%efAVwu>bJF}w`M<xw{p1kCm>b8m)~SEJL+@fdF)a?Pl}W*x?ja9
zFYoIugyzF9wV3;(g%-G#25Y~cCj54;1y0Q}Gt6(;Ng+-5M!l4O060`hM{v!JMNrlE
zH*_>iCBMK3F2)D)chTwOfWs%Fjf*aMiG8vV@^~hZ(1m?fZ<x657zCq(7!Z!Ju@u>n
zMY&~F3!+X|)#FWw(>_(?YjXqOuFm(QVLHxL<C5qLNfDstK@}l^s&Ejb>vIYSDEMb5
z(TE(N#lNFgm7x^{<I7vUgcGqiLpGKdu@IbG2Dc6l%5aqWHEE!lJ@LJ9Y6r2(68ct}
zM;QhjV>Y$>v(eOyjSqJwAT|XxJ=i&p7~OZwc<mP1&|Ii%Pz_dMU}}x}(f1DaIUc!)
zy*&kV8D!c`z--!m#>~sUd^{X9skiAP*dwFjB!emQ8K?2MnnktQ;DkhYI8)OwCg=y9
z`#j1P78{#;4OVghf;}&q%d9+gFb){BL#UKZd&OoHYgp4@AGe|grwX)IThWW|tD(Ne
z_lX{xlRAKIt+mqKj%$I0F|9=9)ET2`w<74B9!12WAbwLmQIxV?-HOkWx%R;44Os(w
z=tNPdIWt9_`8haXnwUV3!DY!GJV=JnZTMJ6x%7mRnQ$LhcyJ>EX}$pzHR_6}^kU3(
znap8D-)5tmB)|YQ9y$&?Tie&f{O&>Gxr;#=wi@m=67m`4NK34%{4Q~vaICmWH8Z4+
z&}@9oj<=>ipgFN*hf129C50;vByEV%&KFE$Rb3^0@Ew}0{0~E?*@y}oRs4PuSCxO+
z_tH@r%Xp3<<GFmL5wPTF4oeWa^VS*S%Sy_M!D}l(v-j=<AeOEFzYg!``^C?Ev~fGT
z$#i9Wr5`7KpkAe@zJj-62|F4s1AfQ7E)1~rj=4eyt9E-4DBapvrFido)o91d=loR)
zuZlPGwe5MQ3NnwQ>AzCBF4ECV5zOArUV^;AXGbTM%aI(N0T8*gr0(IXF|pXIbXN7v
zFLKQ4_ITM3;Oi2z)f+b$8=oAeN(VY!q?{^-F127^seQFj1$#=x$OLJ%j%ra>n*PSZ
z=dll|Zz}IjYDi8m-7|xFzfKvWWhzf8E_5x+{A)<nX{G(P{t|bsi3p{iv@u9~M#-C^
zdij2y%6%!tra$q|J82$u@ss$C-ROzaUi|sutyhDGMU@r9sI~`f9DcIg&_Y~bGJ7^v
zP8ArBV>lxRJK83~-9TUCIU0`AI0I2YFNuNOtva(gJ;iMWnFjUE2SkryY<8S9$RyMi
zR4JN84LaIxt*JoYt7yiOi$|jBlI912|3&cli#;VVdiC4O1~fQcRM=rHF2$tyOikR(
zdjOMqy8%mQ&NFf_U`CooCEEKVufTXPJ2qAIVkNn9&3`ca1ruCYAzS96<2{zR5>JnH
z$Mw%2&>=uA?AI6psH|zohxhMsJRg`Hg%R6cywJ(LZCf)%4DG>yh)6C1^9Sv`2CzmQ
zX?=@jxbu!`VS5YOOaqRo*uvBp!IrAJxDFTST^(JB6rYD&N*@kdqzL^H*+3LHg1~Nc
zHd`hM$`3oliP51LK(-=Tp;*R4u;<|wCEbYlz3xRGD_wkHLy~=+^dn}$$QMcLYW;`a
z;dz{6n2iu+A@<>8C4PRmDRAdN%R|4l#o$b5m_OoQZ!VV))?p9m?=2UQy}_Pt4zD>i
z|EyJcljN`5Mkw_;qi_vgZ)G>x(Ey$=Xg5+}zf9uPvk&V#5x2g&ETg^vW%jV4EBhZE
zhw;}q^%3bx%Tho~W%(=!{&&H&)g!p&CWTI+!nJ*T-V-J)r#Gz}1PTrR$|X(60?7<T
z*#-A1p-wzO5ia$kua;CqgkmcrC<93Zq*bjA5w;MbVaS)`;bw0y6CUcAOTG1t(WGL{
z4@4Si)Cda~=E>m$gFLF4@r$Z>>AwTKEdJNJ<;c(BcosSJ^{lc|+D3ilH?LOLKUt?i
zt?*WG<@p?Tv0^3$m!WdvGx6#u!O6bGFV5JPWny}w^`GK!#ZRXN!o5h&lTm=aHOZd|
z+zGNLlRJL&XnVq!iWk}e6Vw|kZ+|Q+^)bcwOxnnm9M;`Jm<JLWOXJ;1?ht8-^u&4h
z$|IobTbc3GBRJF1P!)9t53&~$Et0?Wy(T!<_G)DRp<aF20||3cr=v7Tn~4&ox{1r2
z;h#U)bw?bFfQhwb+^M}QZ6+Ox)bp@fB*!9ixsY^dA*VVYF~;g=tWWoH{7{d2g5kJE
zu1y%?KEozFfRkV?*K>FI9#CCO*PV*=HZo*Hwa|TUw%Xt=(%BXatU^DlSv37_WApX>
z<=$2OarMQ{rBRoL4bPJ<e8EMd%NzmU;{+5ru^qm_b2!ZCm9P1&xVC{#_)JY2ObQxC
z^6`BbqFWJFOgygrOwe}E?=}?xVrarFxv6ASl{t8~u5Yu6QydMN8?)vQva#!dO>SEG
z_-!DJq1UtTZAd*HCZ&%L+g-=1$jMFeE3KzVFLqao!!F9P_Zx0_Jc(Y^su<UW87CU^
zfU_jh1>ogQzBQt+g~kcrZwkUWphZ;o2-ZZMSS^2R3i(V!zjlf-ln46jS)pt2OYDn2
z^tu1(xTAuX6UDC8>~33P&ptQFtJvDmtJvPzI;0jYu*j=94Y>X-%73FjDMgjafuo|p
zss&Et=Aid>$<U5@{w7ULx;<mYrol6uy@<rdf6#!UvS}+w&Br(-V1aPHFB36dbAtfc
zQjWiW&VXue4^3+OY#W-xmF06&T{AZhBC4Q9q9B8l*(@%xjsbgJT|xZ+ulnKy1&Pt-
zg9JW^8j?XB+*bzJjptS!4ciBmlw)bQql-`!8uF0!H4*SfgAH^pwCfpx$PF;vzQ-j>
zJoS>{u+$p8@xOR1(}QBezc##4?H>|WX0`C%T|pg9<u7At7PHfUO)!AX%qYdB{qZc+
zc5r<V)KgGsVT-Cu%}fl;#Erw_OZwbtv3;of@j^|;g=Jof#`i}q32+l15dota6g1zi
z4B99bNU#%Viq<DjHv$U}<yWHiHjjslIoV8@6DWa3RcI_NH&OnSLY>=^tM>Htr2p9l
zUJ)nnQQ`hTioHpVO9N$)cO4;KjNZe++VMyD=4PNq4TWcaMw3z)cv+!jC2`;(wRch~
z!NM<spo_dQufudJ-dA(L%53yImiIY(V+s*UTapxC{M!lAfsjKR{$l3i_u$qRmC)~e
z6x;~rWTe(s;MiEWBV{2F!1AQ0MvlGPE@9y_sRn&D@)v($z1e^pt;P;s`;JhDy}^n#
zfEC4%R`Zct#WxogQQSu-k-jPiWhEwJ$ED50FKxdO!tUVDxo_e0I;rL2>U&2XhMq-m
z3`lADpiV_7FkX=|VqV(ZvNtZf^rHgNQ<*H&I^HzBxuvXKbJH!%IyT)6XzL!jk;Z2=
zBmgbACSw3qzh-`CGw#R7=H1_MpeNQwB=+S9_|bo=_~I(c1D(aqb9}tHbIOXpO0T*G
z?RJW93c0{<_VQv9E$4V!G2=J4<*#{wmMLhA62<ynB3ex|rH44)hofQJpY5GM$LOa_
z13iKdoX_GhLo&xlx4CYm++xxr6H&zV#$<^o$l|xnC3HE#JJke0tEF=U7e*$xk(;t6
zX^|Jb0Q)JZJzQ6Wl4pxsYwa|dtMEaVi<WsTExCzeoMfulJDERx?U-BQIE#rv3{_Xv
zJNJilDS2Dtz+AtDn;}}G1X%J5H!RJ6<8XuRAfUH~h))GXztVMCW^b&xa7U&?M20v<
z+*1yZicO5P?5#Pahq#2<ep!2lUqGxGh^-K)At-W!7X|qaqAnRuv(vgZuXvMBTxB_M
zn7K(^ct;S?v@Hr@t~1P{c`9k;^@4siP=|`%J0^)o;6VfS=$_~LfEd^SB?dDPZ;7@O
zl}l*rlDhWTsg}~u<-4N-`X8F@pIZEo6676MdCdC`{xm9FTn%|~W5-=bRBB?Zf8o+l
zy_N>K+(mqwkgv-6={7qKN?S1&*<mHlC|Z$USZ`DuvP75$&Zgt)*bv3;E%rQ`Z@x)5
zcbJgzY?R?@|1d|D6CXu#{KNZ!FBq0UQQ!N!kCkQ@{KJM14K&R#;->qBcc$f|ZAUnN
zjV*yqmp$=sW)@(QF8RpjlNoeXmfQD^>&3+5H;zqCxW)AQ>h?<K4EEHkRc$b^Z>k$V
z$~PH!@4(nAw6yj+Z<1-hFP`(2tre?MgMoeBxA%V6g%H;KxL9p@+^W-n%r%ht6cw4R
zmvL!=aeP-V*8>B8CT8qL;NLh;CyA?*<PADI4#VGw?w{@S4IS$LHt>#CO;)zGv+n0(
zcHvEZVU9IY89G8OY{bqN5`<8qJnKvco9b;&IBzp@HrL&+LLs3J1C-2HNN?D>7QDT!
z&dHM#%Y*xVNTKJ8{u0hk(?|xooQQDGT8l7TA5>eZ5vQ7h0o3GVhva*;xOa*ir5ng~
zzv9fE^dY|q2YopCY@MyC_^9Q;o2uInzls#1ai7qW+UvZ%n>tDxC~+J2#UPvTOn|<)
z3{=nRhDd|r&GZfJoJdY?BNm+oJ#IF<Fkau#*%c{T1ogSPnnehDJ-;iXsV?4y^!aq%
zo(v-4C3IKFEwz0>Vh1#%TYVNFbcGULb{iDj9`hT%hi<$tedEs7`72$?kf~;|YNy+N
zO4#Dbr|S_tByo1dMvX5&iH6oYzfMp@&nTx=rIvq?9?R@-k-}~mUI-s##^jZ3hXp;f
zavf9tmH>DH-XXo{eWAHBOmf{ei8qQPvt;+Tg{7GYXt+7a7$=-(gp7dWjL-EyH&`&`
zY<#9Nw9R9b0+C0&QVz%pD0ex-fIm{*EHUDK&@Tv+)Nl@@yXHX)hlw-te%pjO+?v2P
z&uTMiMc2|2fz_7qAgPDorN^%(if!>?cRHr}VV(=Z410<1&1T%cUT>;%|GR%G;u=%J
z2>~CL;$bBS=@(hOtWT2z%U9WV^^CQz;iQkhlRPnJpD{+NGq39xaFNXHmHINOVw0&l
zJMoVe=rKt`oP?{-t*0ub9bJ6nG<+l@GYjW<`v!~^Y#73BMCx5wPD<CrrIAJ++&!C#
z>Wgd>dgbwA|95t?b3wecvmYRfRMSz0-U>X;t*T_I7sv5>{K*2}N3Oqp@Lj!im+>B7
zR?vfdT}@7o)XroWkjRcyQqWX@L$1Amz#xNP+CNZLir*khG?j>0qvUYq*LH5kdfm!B
z(@K=emRO(MYCQ9%E6-mQ=d`FTP)jsk8rzgt*no<Tf)}Ml4hYFH`2g5nw+a``A$%gx
zEq~$MQ0_V85VslUf+KM0o?!gW{4Yec;R(vjtdd3qwg_~c&JvEk$gA&WqLNqFU@j^@
zHTtcawUHT4R*KC=+_WTHa}%@utY)q8)^U>R>5a6anc?f#A*VwlGMI`T`6Mm5$O))#
zWvq6C-#jH1Ff?M-1z)ejCAC+Fk7Y)lERv@G(DbQ<RCTf?EGU`4xPq=E?DNyRKfzm^
zZ99q0XkI(tGUZj4;F}+lR;4k7fISN&?Vu=^W)p|w-;$DV^K73W!I4+)tM7TVTbv>W
z%BKx~uP0*S(rHue-^zE0w_FyR$ZwPaNxB)ewXXoqmXky~SP@E|M+6s>DB3sQ_;DOR
zjx;Y(NCGp+qkCu=HfE>tRo<}?8J}MMI6g9}ziSWK>p(H1kBP^k16ROuXefy9fC!S*
zvnXk=KMr0ArcSF4^=wu_&q>HhOrM-uikidr?!q9A#=LPh)OaCdgqnIV=b*T>e1fHI
zK#~xC(bIylXR+ten21o11ZW`?u(HLEa@$q%)zad?po%LjcS%4W`Vv*hvPL#84+S;g
zKsCK{D4ndLEaUi!ZY;Q?y6A=K_>95&DV*RyciBIzkoCA$1{e=&ax0~D%Lr?2c2a12
z+%KN|#^wGpy1|!@%Du)L`sXqsN<p_lR1j&gLG%Q7Ro<pb#qxf)G{w|xV@^}Xa>Fd$
zl&w<6OF~t5rVf-IXBH>mrHSMUHy2OHMGhZcoLtes5U24hQb^kX-Qjm#Tw-F_81A?1
zLjd<#46Oox5JQHK_sUk&1wld5W}5ExA*M_j*Z*6we+}y9itIk=P;@)m-DIjT)zWsh
z$KbWw7v%Nbh0K%~p9wt$WeN+Gk`ecC>0~bj9v+8(wu>;A+wiypS$RTYWY}bh?CufG
zXs<OTfG;*(XRPwT;QDqZ2}|)eVSvCxpW({n0AV#wK)p--nc0s-;%Yp-U$&nPo+}5L
zdL;Dr7{D<{HMzW@A)!lmz`ARMgO$NOR^XET$yTY+^HHN*AG;4dR_{!&H@;7!kCe@|
z50k;Op=2?~X+$%+Eb(x=p#PTAbuThBWZO(%bYNQ@sYXAKfZki-^+?Mze!_bn*mv?k
zmi8nK8*^E$(R+yzdt7Cw6_J{kqAom9RJ|WoKZ;UtQ4xK7&U9!+$*(0kR@X*8ce{k^
z!<C-0Ly`wxscA>L0z8|Az&yXN%+1{-Gu~E2mv!bN@9;T*Mcd|*x{6^-ArOe2AIREu
z88I67^7MKi8P`;3M1q`OU=swu#xHg*ZbCfx7-FgrLOcO|7l|=ZW7}Qi`221+H}W}d
zNt^F6WlPU(ZbJdEt9fXQ=!FG|g{pCwpwkKi)B}_8ax46+zL(W5P^$$1oTGO0!kH8m
zNMP>y<*73oUuRn$CkqnRXqU+Jv`*xg?rw<o*Re>~>Kk*Y3rqZ<jdh%$Nb{_YN7JY|
z_9=<NzMuiIT?!%b`5L7#AfGLKn8(0KaZ>{@b(-;UX`Y#Om`YQ5?5hQ?mWy1O#%Du;
ztvhqHyx-xI(IIhre#$YEG}wnLUZj;8GA{W3La5ErDKMbBf|HjwUO$`_dIaY6_6A*{
z?Tg8j`4|=@T{5p-YQjaqB%gVtVx0?~-ZXgJSKoe`e{@!Jp@92*XX8lUo4->052yKe
zWAZN)lxYg-1O+Fe3Ha)x(}~wbzZ_mJrW^QFy%voqWjb;=6TzKnDqfggj`-+_NJ&y%
z^7fOcH+Zo{3BugER-v^aZsyP13l1p)K!pW6qTNPjW@TDV6u=YvIqvCekf1dZCpJp8
zyB5dILokkUa#ognPDG`(MpR3Vo5-r9ng_l^)e^99&&6<l+s3KqHtwsY%#o5*kn6n#
zC)xHea|7n$j6|||T-cQ_rFnb90Oso9>PMbQ?$5w97yM=PFmh_9IUJf6mfM(sF$^2w
zAD|N^BSX2F55s$>+P>ezNj*X~@3t~RIg!=GG!Y19V3aq!uOq3yML*BG;vsF_xjw$+
zOc~rly49P*1(ZN|Rb$6Qnr~XR+Yh|DGrFEa2KchLMPnfVYt2$)f=P1lXX!ng2uFJ(
z9JV?BSROgsF{j7YJ*z48vR~`6V_LNg0j}_%qpb?GA4HlTI^b>i=tIanU<_4l{pp?{
z_gii-HAe6!0lTI92)0UR{QJ5sD#PN+V!U4lrZz6k1Tc%c?@==;h0mh}V!Hx1wn@k7
z@;e1*R{HfsN_A--wO=Tqjs(?FVu-x&e~d)W)}*iAu?Ajt9-w1pfSU^%(`kYA^_!Er
zODZ7pio?=?GP+EFgo~(8-Gzm)Ds@=}XmY~&1$U9j(I{1NW<U<X5c4wQ$Glo{J6zwW
zC&35R6CQtd%g&pPG3jyE1CoV3L_{g{N9IJ;?-zXn#ru>&v>|PR0}6^FQg{gN+lBVC
z>2mgpD>IF<g51@rHp)YRBU``zI`Pj`N=ri6OAiIEI2K|L<*80=Xt+JQ+HG1VU~Cp{
ztO4}M+7JG<^B){;myCGn>>A{zNHM&JL4m7``CWd1mpb@R14J8bwGeS?2*_hQ_;Dfu
zu-&Ku+|=7x%{(_sE-vyEJIhiO=k47s6ISA#sq8g?LfuvV<;+li1+`F^99J5Jg(f}g
z<6~3N*_RJ@Go*jC0>Ky?8wfzsp9lkMtKQz)S!bpbwsCZ@eEPea%Zc4zTuJR^*e&V%
zqSP{2MsXIyg$gDuo;@#1y&(M;Q+m_QnHqyw?_J3aUlGV4YEOT?d!5Ob6d4<=R@qIs
zF~PHaEO&I^i94UX$T;koA8TmJ06QS^xjJjEZjYv$<RN6<j<7ZY=u4oLq@%(GcN()K
zH0!=LzJfS;z6y<}_Bc)n?%Avc#|MOLB_>7PYOqY7zK3iXIRsx6w#8m|J?M?d1PAHc
zO0(0>Lt4g6FUDnd#^X=$4{2PXzy^^fh#q7FqsZ01#Gzxrr`*47t$3qqb1S0f<ix+i
zswWgEW}-*rSCA%ZayAyCwnPumaTmahUK_pw5i!$){zx!uCX!pZYYeS7Kt8>we#N??
zq|RUC>nO^4oD|6(FLkz+O57Z82h@<`eqR}rZ`S3G;*i>|VCi<=bMyH5Fv~~_DG<7b
zHhNS_H$d|3k!dX|A>vJQlnXg3aa_w>&p`BYTjJ40>DvmMj$3$h6;zg^ofe4kb+)@X
z_l_KCZxmjNPsi%+A5Ss;zNxCj2kM?3G|$VMi;P-Jaa4z+7QOttv}v=zk~YaY^0@EF
z%&m=*fG4^3+C}T=?t&-uaqOB#_j#XxCt3voFssw-K{O2Fuq+yv+E~*BH{~M6^l?fm
z>K=sx2qi1gB`pIX#!{fF2a5LcvXbO~gaU^+AUkkMimg{UX->SGcFJcekerCWXx!-v
z?>d^+r9kfn?&|rq3LADgfzp7kkA;lK>l=Bz{>&s>Q9urwx=J3jA9+ak#+x{!nV@Uv
zbCJxrl$RwGIMrkm>A+=D^B)hFNYdm6x76^aij)){FK^x{-XQ|Naj&Pd5~MX#I9#UW
z5{PX+OQkJ~8c*_thAJgwJ_Kin-q2X(K0R_ivYeIFVC`hMc)2^-BG#=g-lfns9?!X^
z(7O0)Ejk0UoTTu{Y2p4M;*%q&_B+2m%Br^~^n4u`gqYC03fKGt=feZB_uYK@61-B0
zypLM94%L`7>7)ddi=v@lyPSylqYe^36Y9@rO3^ysPQl033$Nc#Tz83L@lpf_aR`lH
z=nK!6;66G#*r3i#5^Vdznl^)!ZQyHc1%4;f`adM8{n-K{Y-XH)U#X{J+&hd#Y$US1
z%b}bY^_y(Ykd)B{De#5-o%{ir@(8JJ9;l$8M{=kQV);nEhrjgY@Ic?rW)^26Z~d5W
z{p-O3hKjFXJ0QF+A<oy0gf<bh310w`0(p&nU<-Xl6Mt$V@f*WDPp@&eTXO>M;+3oZ
zslL(JJ91GXZ_|wN>Zws+>7<@Vly2p`N@jL&WR5SX1^^7Dn&VKvSJKRNwlQhyiq!;t
zzxQcu(bK)hONqPvS*(mMyqQ0y$}e*bg!k?ur`Fk&RSfRD7-^w5#O1q(34Q0I2bND-
z6O^KO{E<qfA;sVq1lL36_*VFf(C|>Z4GHcaii%-N4HW1B4Cm_hZO=vjPO@;qKi`PP
zO`j)USqpiR^3fvA#A&(-U1I$uBo1o>vd(`58V#h6<QnTs4+LHtK$tbK!LZkH<ST@d
zB+~|RrcEvA1!K@X|M-svru)fX&_)Q!0lLD99s-|PvfF;_=>*TAQ?F`e`4;gPo%@@7
z{K6TJi+@bDZPXUVtf1rJz4iRLvKYY8;3XYuQLQb<@Vhl{W&c!{t8QCLB#Exd=9RsD
zD34D?nd}O;C_oJ+YUN*$KxFI0{ySmLGbz!5k<G*DL!6Y*`8VGIm|kspL*e`V0oEo}
z!YUX_QGOa~geQ;qWtG^-hJnDD6aL^H$MEK4*I7?xlUTv1#R<jD4LtkJEEnSPg~9ng
zmqZ}zr|1U;-MV5iE@>q9XY&bXm-Beiccc~Kns5gxBqTI(2ky>r>wA@ZhrqdoJe>?Z
znUCwubR>xethrz@q1^e@ZO%>?5~=tEq^Fze%abK|F$9FvbcjEP!9XgkcTgw;90zo|
z{6ohtYY?!3j=;vtjwc_Z*V`hU;*9J?xzz<?J|0lOu5Hk#4B}Dt9mAoCR&2PD%&zeu
zKbCvl1tep;5XE39agf-v7x!qrJ@?@<U}F~Hn;OFrEbMT1%Y1x~7wTbgLcN@*B!K*O
zCv4o8J$_0RDn)@h4;ix{ee*e%{u0**REFq`RmQ=!Lg;)(V=UzpeBiVq<a#|;w##Ad
zrhm)(ycCtkN`}^|q-oNGYJR0W>DQN39_P%MY5i>lAdPU={Zs5uD-}k*Nz-IPT}Fy+
zKaK5c^dkz^qy9+%rl$r$=a=7C=ZWBt&gbZT6otK3<Qn6g^YHO2SL>J|W)0|uPbYlg
z%{H)%Dp>yti$5VH7TrAo-#k+J><SAJM<af38@UX)Xg;OU{zMo)b`etxjBAG{5A%*2
zmhBxM`uY>|3ww#NZYLW=3-0ay^@F{bUntd(FiZi9rlYUG$o!96J)Q@B-8LVX{20|t
z#J#*c-%Y^LO3Pb|@0UBPJXog>^?H!2E6I|isd6<n*we+A`a6rDf3W~&)R3p)+<uvv
zUIVQ84v;w^))_BsN#B(R^AI1kkT_BP<-%%_UC;zR_d(>&p5zLhFDKINcPAezZ5d(!
zf%jjH9pu-o?AZ&#n!fMju9!?7G7WcJ?0ZJ0EIto#OHHOthV$l|;qClz$PL1u!JQD~
z*hwFNZYuP18Onz~GHB#+8bFS>hB>R)!@5Ww_HeYBK0=;kECLwpYtL-_6}wk56my7S
z|NDEWrD9|OgyAx&OHha9qoU|C+v!`AwdQXF7I<9liDxI+Me~-eFiW0GhqE8vZ=p?2
z{UXmbYoY}@{G^p_t~OB^*`1X;Rr|h@SFL5hnhTRna^s9&Mq542*VI|wV3~Yt)B6iJ
zHNb$$t;AG+5`jsv!<UO0>ibS3L_<vi@$C^6N}|ogryJNK9i8o&4u~<l)}F)0kfVlI
zw2t2_XH3-y7vC+eS1((=khNRyLI$_Ww`ZIQubUV`&bq}?DGBI_&?5r94vb1OEp_DB
zoo*Mz9B8E;j}n2BZOD%$fs=^tV3L{l28zC0zKS^PH)%#iyv^CQRqM~X-xlRSoBiS%
z9GNkdJ>jn#tI^K2BFxWZLWpNYze~z&3M*x1FN>-czCH4VlfbW%4PX_l#^ib7B5%9b
z4%%5wc*#C)L^)>AiNo`}P~<_)<%R-pwn))udu<jsC~wcP!d-Q{kTkf!-41u3Qn^e~
zY}aU0btu8l^&hI}foiNf_QT_6eG~o9<KnSNN#bePJnl20D!=moC5Gw-4-)jKMI)dP
zEy&5>2-o4g@)W|BS|*|%+3n-w(NLALEaJwAEpFNt^dbL-wa%n@0opqp-OfTVrXy)&
zIs>zs1=d6g<Efxg&o6h_Y6kx;VfhN0{P=8mqLXB^1^)WtCRuMjeH^zf8@rDaDPoYI
zwPP4%QQ(7{00Kd@mc;91so&xz8K+vc@2<sXyX^iiHQSw-pwU*RX8qG#A=#$w_qL$<
zHr^yfBD*{i1w41}MhW1mZKm5m>Vld!SLZ@uLuOaod7Caz6dX}C+sre|071p47P&sz
zfR~r=s+XZ_A-z5OljCKDQP^N*%LHkVB(f7CBy1nL(VDq(#^rmjfSp$O0a;fCW|O#m
z&jdu;D+?hoBpxj(3PBo|w0gOjmFa9*PXKG;S+>TQ7)^hS<65c9QiuEYm@u^Mj|nr+
zmUJRL-4m63w;!JnD;d3sdl=kuJT;I22zRFKnp5zn-#{C6L4A?;;c=uFGoXYKHDRW1
z6`crbN`b)JYE^3v-lHBG9v?jS7H<yOU6~=OP(joTVi#xHPb3@WRk*-EyXwwb?B3ke
z(sF$!z|#z5{cd@{qoVrM;lzoa61r<owSqhQ=Mr=6sT(KteSS1?^MSUjK+^^m%#8tq
zxwxS&gD=_pCc=ZrybqDIS~CJ{pzZ#A34c6f6^-JRLOUWb5%uW)e0E2##QTBn^}GXv
zUZ?f5361i4?v9Mr<pr}?xJz0cp(i6NdJ;LgweVu#28)AWs0TCRxcPbeEhTSj3LQ0&
z#l=O5Hj;ZIz-wxWvzoln>8V9@kZ8nFBGiIJfY@AD<k%*woFlTb?N8k*St4dhJx7jW
z5$tkmmm{Bp>c>|dG3e@XrEeBaJFnD+nlac2lhbPE*$YKKV5G9%`NCb49JF@~a5A?Y
zU##tHkn&YDGIL^vE{Y3MiQFPE-)k4o67ourf=J^KVpcs7^SnLGy5@7jA%9Vri}Fqk
z>w554FknqVy}c|ko|Vi$UWatq?TBD&>qOnw@OuV2V_~)zV0GdTMi=QFZWLfL^;M1Q
zHtNv*TWl;?iL@l6k-msbGMyE8$9X~c8k_Lb30_2+Ht2M#2B%LbI!rahIwF(ti_OvA
z^rtS`Dm5buj3uW(V9m=e6|fAt=3P4wA67sa{8_CDODKiZ1fa^uTd8?yuR;WKyHrf%
zkOp)hOTGO#(!RzsBBYn)<I0f3_jKP$2jwcjzGc5tu8x-f^jMObT?yP|D=3IPjX}u(
zlks-@V^KVMGnu(RG+@}_KLgs|uV0Qad7UsPzL{qUABz)Jvam2)SXGH>`tmnScymRy
zAAlWwjvXypxyH+0tnBTXDxWOn3^x6wwOECRfET6{J$MNeS<^Sq2f*t788#q|TP)Xz
z;$H=5cThn0bS5#bsotR?fV9#)S4Y!g?ua|@G;l6i9@?;h{01dkbS8R|A9NU%C@Sme
zN{>@Q0C{Pr=uA51EcsjvtcBBj&m}ZNh@=6}Oezgdj?__mi7tbU`wQI~jxFxQcVrxI
zPv&%cQ*qPwwZ)P@<0{~p8ITgE_i}i`S1*xGa+bM8Q{jgnH{d?m9q}n`S!Sq)zaId;
zkEBrEn#t5^M{bpJ?IxrEuY*SFrqPjIig<fq$49@=KudCK<2mGDfP81j1-~znjZKL`
zjhfca6$MMM9e5GF!D<-B^Zr4--1Nf22@W|y6C>7eEE_V5foeNTB-lw8>+Qa$>h894
zLE?G0Tk(7aVZ-3t>aOCZyED79b%^4v<jbxLb0fM(@ZzjQ+>%HRQ)vD5I`H9b&#eVl
z$hDW4z&~AFyqLZ#-ree`C4Ee)clxLUqWP>*zu&lyZv*K`zv_H$>?wVh&DW?N4hD{$
zPrZP3BO*dSOv$C=HgAmEj1j(IoNh+9%Z@6b;*`3Z!kE)tRY<RQP-*#~XnWqzM^_jA
zyxwA=^z3x}WSNn#*To*!99Y&ovWfF!uA6W24G6c9xn)}6vnz!g+OH|X%MyTH*?u(d
z7g%7<X3C8Kx+NsDc8*x#+X^B=gqNan7sHXcB2KqU&P;RX!^@iFjp+7=eu45I9y}Cu
z^M!T9@euoTi}mjS`zZf^W&wQlgwizi^R3@}rNM(B;+&ZKQ#&1DdmP^J3I8okHPI?Q
zaXYIqSRcvrOTFPEJ<V4i(sW%|i;&8o<)xy!Yb=?b^vP|(qiNm4;Elxjt73{fA!+&z
z`N*D?S6I?y)a}ZjE}TlKA*M<NSGXOG)tAsc4DOOk=JfsS11<E%HK-JwEU%O;^N~#2
znB(AIJqixo@dlcgBe7Q!iBt#wVdf68Kq^7JrW!?AJ{g#yE$|o}Zzz8+zKOa5g>e<g
zOixHickd6prtN8poSs7hUs#ykb2byHg2U0}PleH0JMbN%zCV2kINY=moSnJlREV(Q
z1EagpGy7au-Bp>y+gz}Ay_pRFG>}&ZX)`c*Yj)x`{Po`2n2_~%tOi%jx!>?AvbGlT
z?<_=`#x_URY>bWB99DbASbtUh9y4`6*^+UbGCos)aTd<K<|*OFznNE2Z#g}==b80j
z!9(A0Khxiv1$Ex&fy2(j9af)r<$wHU;&lV<pYf7Y8v6Upl)E#T=xZwb9hpq$jX-F$
z<kZQt<(Uoddv(m!Q^;iZQ{kbdhMz^=2IQ$$13uIXyy5DpfZbK)qUC<5ky|yyd0#RI
zzo0(gE4<GYoCp60sy0aapRM!gT!;(tp2LrTS;hEOE_aEKdz>Y5nV*B9ic@W#-4Df{
zapDN4r(}-$1~MlgVV*jPF+EW<v0a9EV|bnl2HHD@u7wybzwYgUqn8Y)zDcNiGeVRI
zMXE4T5t4wL<zEGUfhdxe6c`-ztHaKNUopsXau4t2_Ab^zw04-Nr<CCmu6MspjAJ)j
zoz6gH8&dQf5&SZyl9ScvwTWLygun=MAgb)JZAZdS6m~7#y}=;1zvD(_@$H@xg2dxn
ztA*kkE?s)vwH+cc&q+v*CZybHh0RQW`6Npshq!LF@H>$d!`wa+1Zti3@u#k^&m;Be
zRLMo_cPJ0U_wGbg=lc7>FzUjxJ;XFwfZHRR*{pGnCE|@?z+1VwvqRY*3hI%kyf6a&
zl=bLLk6uU+wdi~M8Z9~a6N$xW*M-b+bZ=;3CBK~)6^)Dzx(BsnDYL$(gj<Av0(QG-
zl33yRrzaGM+KxUDlAJRJ4%-;7fo-HPI}H9c9yB0RHytFN1pVjutC92y)K?({ARl<-
zJTbrwx3u*t%@=9k^1zggIL2^#F`-}?3)JSpJUZ=(YH7^W6>1a0Ks2@I_pDR>0(u(m
zz$82^ZGS>GQ3;&Yr2W#5n}3VnbHhjwPY=U30i9?u-rN|K-(N51?t0T7k;;3OAzAT3
zDp%`;WLRGU=1GaFgNFE&xs|z0M|9`!pFK3y=*!8kpUFM;+piB+jr(eFfgk)?N-d$#
zUHt{p62Le^`pTNlDgg^=z+!`TL)Yi)PSqnm-aRC7BIQ5}+`GPdbxe)LY<R$S9c`a~
zbM^3SZRT8*(wCO7NGst}#>P1bzvp2=lB~9wy6hkdn?;XGuXo>f2#GaE!=O#V#`(&^
zr_m5b{EimF9=C5(ktm+8apSYwPlP-=@Qrd8N}qRsog&V*)?j(W8avNxOM&*>XOHgn
zao%_9;&5JlycpKq(d(njcel7rS7;3?4z5Z6PkxjWbfy%;h4@etYkaMELBr!K?XN@Q
zUZh$Z7?%m1Nc45?JY(B7gKF1|g(Htex}}$=Ma_0#K#ot?xx4^KLK6op>o-`RTOv~L
z8@!{+FarQWTGy+8J_^N_^BLID`WmLK2jtjU8=>dCFsk*cAaa_vD$yZ#<&)h?9UOQY
zNAK_;LGhG}9Xt@s@J5x<?d8w#Jl?m(vZwO+9uU+L@_b4%d{KMFngNk+haF0pM2M#8
zw${+K3_p>kI!-L``kxR*e{?+W>vTVO`H}-(;+SqI5(^9cK+>j^MSFk;8Cx3B^>W$`
z-)g(Y3raZASh?#(7kO-E3j6m@!$lrjtBZT%<DzPa{ts90z+GqjHjOrR)7VyHn@t*H
zH@0otw%yo`ZS0thjmF+FcAT92AH3&1=M&s(-D~|W%*-_dHRIMxOY!&i_AD<)jehs}
zY<q26=~jyAgT<J!^t}!e&ytrV@mEWNOUupB#g3tw50zGx3*t|Wb!-c?{b)5tAKc|W
zxc8o1%1Yg*o4OH!>Kd}Sk5R*_v%46=L1KWohY9*u@j-)uv?3|_iJ(wsibQg5cx|T0
z93(-0>5(`Z<MkR`9lpX&-|lB`J&J^!oLJL|Gzw16>b1RZ(R?^KIFHXy<oTLkjH4gP
zSXcs*=wdNII;Zh*afWZMAS`ch@1185&4Q*|%}wAT`_f=Gud`)WqPnMYEHEc0ht+Wx
zx@}&xs<g@XMZxA@OZE7VFx+4r_nblN@@~%0Xh2Sr^$BKR5Nmqe@C;cnfl|!4HVot^
z2Hd?;aJhRH2L%(_)+hJ{iuU0RP8!6@By>FexH#oAdO<rnTuD1p$3=sJhprM83^D1I
z!Y0FaXb-6D{ZfRG<;-russT^?uvknnTF?Y$>>*>R|72VQs-Y-zNS~zUlncu$XR$&V
zE~OlaJMajOHmi4kEz<oT@#X?vpq_1TJ$Nr1lnpTb>?g<Y8@Ih;5m7O{p9$&}+?d>3
zjx=6(XQBRrPafS9f3oYv$`l^!h;O!2CmdgEqPpH`q^%`83HR@3{CrOZ(p*vsKJVOI
z#Yq_%V+YIBgO7fkYSpd*FW_x9J4oUMZqYo2pv*g^=B(uE1P?2+Dv^%dbdOz3%m@wK
zp<y4bSlxYb-PK2J&$*A>yLlhzf41@)5C1-?GC5r7DSaG+P&xK-&u6aDmX6%$kgs2S
z`vS@sSQIIDHWy5)w!uAODQo~~^z!z2droiNOmK2)k8V8|k3WqsA2G(6(Exg+EJp^Z
zx03?POrk-`c@)#LD;`rit617p#hGPgf@$@}-nzgchrMXjyd{C^W!sGrv-LV-b?6K4
zGa~d!=0pv<uJ%d=A}vMYSC|c_OKT~Goosy2v*i*Seq!79V){k3_59+yD(Sb4dPb(6
zCqA3!3!JJ)AAIVrCjo&IO~&#eDyUx~wY#Hujpgyam~f?X;>$F|VPg{*UZ|+hLJq=8
zez^$i!}64IvId$!;-wW_4?qUIbJ<kNJveweWPN+d9^PrWR^<Afh-Iznd!ZzUr`hq7
z-GZrEzQvXr=B$BOS2NjQb0>~VHZVP7=|(E0ZL#H5C*|-UpNBhZb3GJ!a=SQjno6a%
z$$<vrU@%r?jDsbvWxW^8q5Ig#%W~cHKo;?ssN06ML7m~K%Z4?(Eibt&9eaMQdW5!y
z!SdYerysy5%hr)Rd~UD6+kmunSE}>yHL{K9#Sr;w<f_wtE?b;vec|DAzjpLRjCMAX
zHtB1%iSAt|`3St%CYKW7?^|CdVhr~E1luL=j+YSs@xxIxi3c2-o?A3^C8H;?eZxu)
zdJIvaq&6ev<;20GEUQjY%)cdyS%^W%ztijFn*6rJ7cfUKIrDcb`I-Wf<t&LwOVRTw
zbY+rfsd9;E5Z%j@gnx=4kBFg>z53oH^5>YoHJEmH4dT`Foe_2B6%r}e_X37EQlP(5
zb}qBQ9tWMDh2>i}Zmr=)s<GqHLAoVW*{M^m<)?g0VPJP?qD}i>72}XrkNf53@k;$b
zX2l;zZ#z3Cz2}FC(b3T=N8mj7>9_GV%p!?69$)2lPP~{G@arvegR%-t4+mlbF4%p`
z*SwvuS%0I&7cN#7d9c9zoL#lPtm*nF6WE&>Oe(IU4)F_0y_*m(npCTMbBhf*9S4p)
zMQ_oF(YHOCS!qT3nUR86_QcTl4@<hE9_1!hDhHh=35e%A?)G6U^7#5sMLHcL<Lf|V
z0)7pW;-Q2TroJ3M#+{#7qrhyZRU^f{ZO#!dmr(IzNzYuUC7r59v!bHFVR@a}|DSrb
z_bIkFy7pC_Kj1lGSyT&nGpwD#|FSFr(>IddXJz5Cnjt-EjR)v{TdU;3k+L4CNANs9
zvr!vthWd8ztM)B{HyVY2Yr8DZPt?SOGL1pA-&6FmQ7N6S$?;o4`#)Z!rF_|ur7~Lg
ziAK57aE@jUzoSEJQhu5mn<=Sm$&d_8NjNgF4iA80vs_dvYPkg`(vBJ3>qnrU3)A(|
z(wLWi(&jF&mwW~Rb?el?^sH-^F}?r(9IU$7-V|P2UDpyZY{*f)ZVXu`tMn*{JR-%*
zH+c|37$<3)Y8XE0=giwx;+{b5lZ#7v=1MF;?qEakh}^Uz0pkG<b>x?3bDP8-borjt
z9fFD3SXaCWbeF~UwiH?4q||W7-256bxygH>YleWMTyP|7bjdfQbQ=@Ss^k&+VJwJA
zAEL#j7wyrL+ERgV?MrWzb!jc6f-b*#q0hUb@HMY?IDGp1(^-fuKd^vZ=Lcu8vV2R*
zt%Wi$2N1MD1Q6zGudis&pE%+7Lq?}4boAN#TJ^fGt@AX8!7?Wzsq|7=6Tr!hI)cL>
zy0r5>X<0viV&I{FbsE*{0>fpQNDB3<1o#z31&=9+3CyD;^agDw7S;>8sh_#+Hb+F6
z-yisgF;Zx-jGc0}4xv4Vfcw<NP%!6EFQD(qoNAVsGDXdMo+8B+J$C(P`+#1#1YPSh
zcae=-)(_)=UiNP`GYSa4RR|}U<+31gGf7veZ}Z>iyEBB%$R3B&zn=(>x*7zJ|Gbdo
z7)8(cuWLQDB;`LemE{hh@VoPT;O%G-d{$|__S|gphK!5nPq5-Q5V-S(m2tjX|HRO=
z*qHr^0dbJW7xVY8fX^Lp=<%jA_0Ejb`1PQh#YzM9?(!eL?HWhz)oxOpf{hN{r+}yb
z)HU3JgDbPxm7Jen?K6oyLX8B5l72%oj->kT`OYSgt6FS(Vk#yPT1LT+4E-^PfeE}+
zEGS3)QnGz`Dkoc|uy!;CH)1q79Q=6jAg=-BMITZdk8}nSc^f6><Va3b3^)2bm$3k6
zK(1v1vl+8R9=eaDf`Z+!JZCv%s|ES_UszdLUk8mgUxjDNmEpP``)}L8Yn9fc`ft=^
z`9wL@rw@F-vS|FyS={!PERKvsa@V;v&bzYq|JGd1DQn<R@FTtwt?gayM9f!`Rz>45
zmvcM5fodCR^Y*q3cD?>}ph!te?-+O#ad{*`B+0}Ei%=T3F~})M*0}h2ObU!2)fH2s
zFWJ*b7#Ry0H_eDhgLoZ%5xOe@vRx9qUOz~_%243m@@Y$1;+?Qb|A9X-@lC=;LaI(9
zL4XgI4J1ZFV&@S#G)NC|*I<oJ!@YcVm&M<uEkx7Hj=wNp1@SR6=NW!SOib4onGh#{
zTQ_TK``z59V+=5{5_uK#H)-e_Pat4(!=S*d=c@rEdDk>7HWd-lwz4M4uZ@Fb*UknJ
zP|Pu^6+o6dx_MyC@#aa{)lA=nj$)nmGz){&X=`a%Y$n2)(f!yi;1sw~hJ~Kb(~k;y
zVztAq86?%;dZjm8L!lsdvgq8#lpeXJ{$CWY&J6rQc$Ztk(h-J(yYLk$pX*7~kL=zQ
z*6d=SGV!ZVzfAM}tE9+tXs7$-@P7{OJ#P=m1O#;W|IEzHG=M%{g~v<1p+!l74_=d2
zY+oVQSzFxm72jJE^4ac|mmGg&133D#`1!#YMIKNi1qUN;_Fu1t!hXtZBZ?_64|*Ze
zCcoUYAbZS0R1j0GSW}i`{OmbRw9CD-XcBG=hv_tsW<yZ*k}lQy4470TA8v7K4(DjR
zR01uHSE-t$#SHOMkjGXTziX+hOb~UtAqIP(%$H!GC62J}ETyy5=`SnGlpc&HI@1i^
z3^Trr!&_*!I=e&H01w2ccHC!%Lk}7(qHiuH%m}wj=v(s>oZpl7@lQ6JV_O*ff6wO^
z7>ECwbk>Ym1uk6-%DZ<5)#$dNg8taA_0SV`K9J0g&s=YSkJl8EJ8SJd4{o0nO}9Il
z^#&&&x}tpH;u<o!7<B8g07Akrd0iirPYKTi+$pt^X>eEBKGKD+7vWn=b(k@mPad#G
zI64j=y2~n?x^FcQ5S{?jGV_7}XRf|#Z3}Nc$9Jp0P_2e)I*{mKNrQWJVWWj2_w>ns
zE|~&>T=@G<?;0_4R}?ER7nm-d`$BEaCVqNbJ?c8J&KmT^)7dbCBg%Z)4cnFK0}bZf
z8{uDr*QvzvAMQeEBPx>BbtDlk9qWjNqxwh1zQ@FgAnd86&#X-JCbY*`*qWEDvW=uN
z$0KAK&O3{vp~Sdk<IH<LCw#L<Is)hHY_w1y_qZ`To(DXRpnKxB)2Fe4?}d&{Ak#<`
z=UC$LVQMRDC{O(6l7Ej-u~x@V<xV_nU?4ng0tAs!d0UccPp;6aGWk%{yi!pM%3LQd
za)`2;E{BAth5{zSded`P=pgR13{^)t=^%|FTX$=_KLy)V0u+2*A8Yb$E++1iPE6s<
zl<`h;xS5)s8ueZzhs>)(>Ax`b9Tskn&J9d_tOQGtkrk+PxaQb9IhjcQL5ab-h>U>A
zu(}$p*FD_b*@t!4!Vm8HTZj}NFUl4_^*ll{QF*<aP$R65y9&*WGUx7b<fIt;KYj!r
zOJAM<-?awR_=pdhE$e)@uC4~!-r_pHVZGT4+}<TOkF8j|ybh%=f0{h5SsW~ullCMm
zDLofuIQ`{J#3L{NG!08nZi_RyPQ7LvI%L##e0_9mvRQ3VSj0&f)$H-1x^3=tE&*^`
zTs^<&c$P!HfX_z|hSAEcwNDqWkZg{GLz_x*B>0EZI|Au&*zDv=(Gqar(u%r&&m!@m
z7P~3Z?Q3vKkNl|BFDEAYYxkJCxmqIndi~0ul(6iwGpwHJn9@w!#~x@)_V4*Av}wci
zbnh%sMT?%8ktrlDWJ^M}g_@|wb+<_n{|F!<fxTr4D#vfgf@-#)q$*(<49$s1?`z_&
zhSv^n5oxU<VznBiI$j*8xl_zrd$<rDT=^M^H7ZDkrwt8PAXyTpuX->-Lv4Jy8H{y7
zKqfBe0R&$zM@wieqQ;mMio`jdKQl%MVvi&cXf_oy%)+bM+XI@AEo++pLzOl~JycbA
zQBs!~9M!y;o~N_T&kfFX%&`Xn;|U`T|CYJ_P7Erli^lsHo=={WG?(ISGSdYb(t|Hh
zkJ$OrU}ZA#(dbVRdjv?x`sS7t+B|y2&zv7~3YwI&LghBQOv}Kp&T@nkBHDn<ig%p*
zVw?atM9-WFp>?;j1NIB(Ii7q36qI4m$AjEz{((v)R(cX1jRjRL)tGn2Qpea>xf>M3
z1vLiSdy|&!dKe-{OcsaPzJZH{zg50oeQ^|UzrEcPq+pvcEvqCuXGbKx>xXksO1;_D
z%%CyRCg&8l_o>!q$*RoJCmYvpg6|ja?l2y#i?$B+A6lb-N2jQh*LE{}$bE~5i839H
zbD`Uh2+Wu6w9~rcJKZQJ3e%Y*nGA-cGpo6yXA~4`JWhA-h@De6!;j*bir(v@MiXoA
zfrO&}+E0g7;`cvC`MsV|BD$lXEPJ5XZIciQ>U~my*u?L*9e#g(IZ%VlV$gpiY2S#5
zV#TAecW{OuH#$<2%2J0TV@(&l(opw;n1lq#u`dzPhH;m6r7&myO5R3+3r3ne<fA#-
zJdQdXJLz-6@zH78`0}-98=vmqg$OZJS|#N{vOjhd<YM;;#orL?aB8><yJ<;K-KZRW
z8#+MiZ9DSA+f$Ca_;K1_4{#m6cRjZ!{MIVBExPXUMb@o_5;VScT{Y}qq_{f26XvzU
zGONVw1pmBoE*>UVkRFwNF=YyH4q5+t<NW8)C;K7MR~)g+cl{gknRPrBL*}5x?l4Fq
znLUDm-WY2Pq-yP+`c|_Axju4TOi4CDBaf>JG^!Y*LWIQkoa9+Rll&Rj8@BbKQHC~t
z`TAgZ-c~WDb+kATe%Dt<IY{F1Dj4^Hq^VA;=LzDU{9%7{Z+xn=8Ty+n*$QG_`1$K<
z$`}8Oz4V>_1lS~<KVKXU*ipAzKf1yPKJ8*sown0ASf$sgZ!vT1JBYo^G2;H_#?S3t
zuiSzqk3Al$uR!y%>m$v|G(5sD$ZXh5qEq^nc(92Q@3h(4Il?5X*2}%Vy&`R35|Nt@
zURC-WL%Vh97b!DsH|(f+Wy1-n9qv8Ak)zOvs4<=p{r;)zu1|DwG=Z_Tx3b>`i3ISA
zm(xp9@1Z~?#kE)6!BGLrF3SzKt;lBJ8_P@v+$&Ea*sTyEbGdoBw^>_V^6N!j{2vnk
zA9HIc`_1JwbUG7zsQt;#`Ee;zPXnfxfY8v>(SJ1^nBBv%ZZ>m0+|0Vc!aaGxhqj`h
z_t)rnBb`N%zfMQC!UG1Ab6X8MvDKe*)M*s`cw>^&S2fx^XukfW+^(4DFdW^!$iABF
zn|9{fS#BqAyt6>0v6D4C9PB8|id=G=p*D<_pwZzhC9(U>0}sVywPk_odi{*IQY-j;
zZy7DGO>s4gBU@+o4?<k$!#90QvvQ2JUeIP`CV9~Hts*J?)w&iK$dtoxc(y`+sxFn|
zoXX~Q=VtPW(o0ozO)aKPI9$#An0)Ed?Q}a>%I)Ftd*$(Yf5O2ZQ}(pR-ixW#>&9-~
z^m0Cs=X}Qofr=#Ktw!>)v#Z8hmoo}}$6j9}fSsqEGsJ0AtF>EccRTQ_PCNM0m%@R{
ztNo#GSq~kp;$bDwk`_KqjieYatBRhDzVvs9_k&rHiW=%kmNeAT0l8^BxCor>{n1ds
zbI*<1jNr!c$e1>bD1~YCN-Kg~6suY^^$8@lnPArX7EC4ba554b%7d?sf(S!uA-Jan
z9KYK2-m%f0FJ6Z-q|3969b-Hx>y1~iJ(*Gfw3HOeE2|ql&dg@-9bX2*?na1O6^dOQ
z{>A2-y|SUou9VX!2A+;l?(eNkNG?RRZHmu5KKTO=My)=m$<|6;zxRARLnVc)%H9(e
zy6&6n`%Js<Fe#8@SwnGEKY%QPx%ATh0Ja5B;%6yUYw@`yC`c#NTih?nmOfhCrVe-+
zG!V2>S(CRamu!kt?@Akf6sFux#OKW$b$IP+!Gm~ju?@l5p~z2Y(}d{ZQJg6`7%jhE
zY6qAbaV&mbnHq-~&E4;DifWwxp`}WC^nXfy@VMap@*PT~Y)Iz?Jn@jwz>cL4cnQrV
zlQc-SPX0LlnC^)>XfkHd2bh_0t+SIf9x+LfqmVT5{8ZJEMW_-ETwhi;IvL6HAIVS@
zbGXb&Mq6WR^KmR=G&eEZNnIA6i$NJTHZ1=Ams`IpIv9@PdJJd&8!=jmZHuv61uof~
ze_kuJVYSw7&ANTBs=A)mQBRsj9J59nVWZhc;Bm&M5amKe&<TrYYSL~pt3MnTmO?rO
z(6e(oUqc}|@vhT^?teuA4Fm#FVZoOJ2>BFUTXR<f+$2^Q3awkW#csL&zqy7I<$eFh
z0!IWxOk~vhgx}|5a(R(BQ$h>)<kq}CosJ@^1$ZkybpMTe=l{IOjmvvDcY9E$!q5B*
zn$qsRLbZ~`dWoS*ryV^r(+z4L2{Y~%TvtNFY&>}MVNW*_y+a70ioZUQ5pPT&IFe)Z
z{d{MqB5{SBhH-96)B)~>!m-BJ^2@pG+&UQ&Q&hU03Jb;oEneqB*jZ0|H(b>Cu)lej
zYRs8sq|L|ZMar$+FQb3djWIER5;r}GuBB->fX$?#V0Qd=Ho}1*;QUt}m1QGWSi^{(
z8>V5h@p4OjRFcuiDZ3w%aLIwOAHmrCT50aT^_|FYnw@T1VfKl!Yxh-+_GhI*?^Tnz
zJdT<gQz6^YKV{d!?3<6%Z}{!nJz)gYF-u(y>Q$>Hy3T7Jjo7xd`Vy%V*`P$nw_;G3
z@!yDdEH|_=f$)Oh79B(DbH64lybw!e{r>$<y&VCG1~*jsy!LFmOzPnRvzYB<E0-as
ze-Vl>!iA#0@;JbA(bw-;@iUb#82AJ%7p&_a3rKl$8G|vEBVWq_i8wmM?DeODkL0gX
zH(pET!n<)t7xbO5uVP{>Y5Vs<+(La*TR38Xqnto)zzj<gZ(ZE7J_<$->(@6Pq!Jg{
z)rVOe3*Ske5IxSc@kEvUqP>8R;z$S37LF!mU3KS!=yX;iB_~vi=|p0a5sZLDT*wjD
zp4890I}fuHui>mTRIS$Pho|o{(h^ITzygm;;)3#mB4}P(d#fAoKHtB%pW==FjOCPM
zo=m4A<8Su^$onjA?yLvHy8JC-58)S~*1SgvRgXUzypP!Tlh5pj+Gh%~0d#prsFKD@
zH1o3s_U<k_l2rVVQEl(xHPD{^<IuJ~qI|=;v*Gn*UG+yI48(q?+6?;6-_m1`1CR2$
zl(atv{&GG#t<innqnK4$LREutp;RyM{Hnycysl0hPX6S29Otqg|D!moB1+V-jZAm_
zh+f;7+2N6IQ0_Wb`Mhi9!CG#Mx}~k$_nKE)4KfyAZS^4RdD#rd6+gM|km*aqQW^=}
zYf@Wl_C^g$_V}PtB9<mdTouA_{8woXa5-Ot@dlp>c4vmTpdIvsWOSHynlwQ9C8eda
z&d#ma)~|Q{(_Lnt4#s!=f9ozso!^#EIWt&xI@rxsytB~)hwfV9sK#EkG~hehIt<S$
zC_D<RN!hA~G?MJa_j4V(Hn=eJn$Ds~=6A15pB?uVYfDp-`moU9THf%s_~y-qAfg4o
z0z%K@NN$Wi1vaIjX%W{u9=ZFj{M~q%J+l!YB|fxh_m<eX$1FUMS5U!DZu&0D%b|Ck
z;mFjRO(^o<f%5(Kl*0J9oA+qo0X=vB<WRHTV6zlDuhAUOSN;7k6JOh;Ch?ne0Qr-$
z^83()+Fop{usHSJzJ=b!0ehYflYVhRrSN2S_?GmkzW+JThU+?t3gXQyc;OUBRGlql
zTE#Sz=AqI^TeWhq=uvDKoe!6|Jk@rj`zBYZ7OcNcO5ABASLbj|x8c2PvcYkJZ_8HV
zRC5Cy8u%BtLrXZ6u)R0kGvtQe#TJkfnb|OSBrAW4*?9u-aoCxc>=4T^P*PlKeL`th
zD97&Jr&Og4#EKX2;IAjvRx?#anzQRGW@AzYz9*`kyd2-l1&7?6GpR@HsSw2q#-)F}
zKrkMoZnu|_m;kE|S4`u`-7Yrc<AvVTCPriwtb=`lQAZM^ZoECF^vhO9;#2Wbm+1hf
zzd3v@LqH4U>(OBMnVCAc3(rT-$%Osh0(gA+Ra|0fys6ZiRAw>8|E-|&BHJbE`tx6l
z>297yDXS%Qy)@@}>QBG_CWI_(YV^D{dwqYo%m6SqWMpJeyT{<HL718z%!s)#8W~~y
z$xvw~Ycen*fm#DL;)bz`MK^0ly}|KjA8Ck75h-RS@oLb}S^u^SsPQzH?XnJ&N1wL!
z7iRv<KL(rn+dr~16vxNEf<xjxT;{X1OReQyGaBz5-9)V$V~Z8{vUwxf*T?_$u0}+i
zTodbAn7Q@i>^wzGV{Hv8?~O%yFJ0r%JEgMN&?q~rL&b+XI`J~07!#h4pF5nXKXry?
z5gWo}s~a?GHN3e>PYd~Bh-yO{4A)NZZsjQ%tF?cn^mESBBXXR!-K)2@hc2Dg5j9;T
zY-v7R8gHJ>oZCm;@+KPb!zu|YQTpV;omM{jX+E+(rBLYo=W?b?D;jqD$EmQ`c5R2_
zWFA)+)&TbHuZVZzvct=1rL75<UZr4L?W8V0G{CEM6m=1k6oXz1-ao$9StHkp`qr<u
zQ|**w^sc`veh60fqb$e$EEtlZXytVoZJuWJy<25Io++iew$<G-V$SffQ-LS;JCNFU
zinHnC23k7pq$}X7yS)~ujCP(yde=wj4X>NO6$rjC^&C7moExUcg=iam0s9IP4RLHo
zWi|%X*HX-a4+*w6=~ULIsThzC!%0Nn1A|9*UtOhtQdW`mU3I%~=ybT$h>)gZZS=wz
zH-35C^nyb?w#*SLQI1SK=XykML&Zae+Oy$TLcmhoO|KrT_e4_s>-|?$ulOmV;MirI
za`;|IytIsjKd{8!KPWpy1zK-`$e6fCnc{}%z@)^~U@c3(_pkZ0?eqo|1`4N21I4da
znmW<y*>4Fw!5Xuk4Py1i<{WxHzSwECYh`2nJ-6d1ZQ$|u75`(NT%T*n;jV~$*F@sn
ziz&8gO>n(N)VH~Q)M-Jk+?NOIrk0#?(n#ljjozUDc;n?s!n<5<qRf~2PU5&3hN{Vi
zYr?7^ARw$rTpqfDmaQGF>CaRyMsfwc>-<0bbQa10?alZ4DPvEcsy_>&hYFecL`q&X
z_rePK*cPS)=GmFF0&t&riIS|89VHcws+RXNp#CeNB;fATsmsy$K*5ui%XV#Wh6n+^
z)W+U6N$M|?eowz1_Vny$2nk4BM^@7FV#BknV&6WJfQdM;CZ$HH{2e|b{e<~?3>QFu
zqRs2z>0?dUVv1SVUKyEVk8XS?AlScqDd+(G;QTUQt-VXG&fJwl80*{N!|%z6>O)Ng
zS1YP>$vFBW_nQ^XvNb_75fAi^Yktw98-ldGu#EBnepI4Z<?gQ{yj`4)f&DO5@l^+-
z>`kRj?oL}zTw8Epx|GWzyKTYp^(`|n7E?<=P4Z4vy{SnZmg^xQq!N(ys#<coq~2_=
z3aXd@W{y0Qpr^vXkW;9}#Z*@$B!l+f(b0v|>nlq?1k{oCt_LaH7ZZwhvIbus=gYTl
ziLs{-*P4~Bnp;yVkj@f-AJ@yjTQB_eyt38a7BL#ij8!Y72R_X$TT;~1n_QmoUmqrE
ztAtw>&Rdp$WP1WjlyXlOxl1k3?@|BxYd2rUR;)2zw{j~kZET*gxBheVamHbo@1ooE
zxYtym%lS=N{;8gwcs_psig3|<a$|PIibkXf$yxEWJz0^X6q48Dz`WfrZwmoqv4fuM
z<7Lr?0l-?+ZtjhOBf+^&b9H&M&7Yvd9?Hh~?^yqbbK>xYt{|^vHktDpSZhWeONy$j
zQ5|lV|2P$Xh`;I$gnHbI7iRg4=r~)c57zBwe-NC0D^g;_gdVhh{_QBna{U`E7BRxN
z%8aJD*))wQY~esq-qeEu(PSzcgXKm!&qdkWT`oW6;bgDmImL{`csygUSlIW>PZ<t%
zWAynZTKwYq?)<)7vCh%(QC`x^JD)^vk4F*x{EI<~aI=&?>Mf4A0i*N_=r{H3x~9<X
zF?o5%UNRV;N0{O^G|9`wIy}q1Ehcw)$?A60F9|T5E9$kJGFbFcVs0Ova8d~UOPAk@
zuIQDkT)l=7Pg^Zj&;LmX6KLR0nUDZ0%CgMudO3$3edrUgn|z*Tt$V0X6}j-ffoE?i
zL44EMv)s9#>H^xIVBI<0!LJEQ#O<g|Kz6gSdU4;_uS~#9k66{83PDw+c#b7hNo#%!
z*lc?pd#OvgmE$*K4oQ6&GMIvjf@>)b$o<E1bztte9iEri<BZDoHN^PH`jY%Eiq*n#
z)dJUof$V4~wqfIjmRz{N{LCWpRmeK>o|iQypWt9r*Zcl+IZq|tzQC%OFj`qKrd(r3
z4P$?#s3kyO%(2T3n3)}*KTG%cj!rN1O5${4Ni+w<Ve)+?#x2j@kqSjNe|XbYPMq>w
z9NF?F?wNcg%v#_^`}1sBl+O!-9~I*kVEdZq+m0+Ntzmw&U!-5l(sjEwc|4f0FmGPn
z$4~MeDPXnVsr*$3eG5%%mt7^f9+$HLPS5v(Z#bq3NR{0E^Zr3pQe@XqPala{J2BC3
zf0oAKc&31k45PLt_+xt0^~<4)+YU{`gJ?toX+-zKrZ?6<s2nqGqW8iQEuyB15-mPj
z)lTrHx4jXyn#pJa75|5`-3`^2T+h#68}q~NKzHEL=H=!?&ou9J0hWp)#Bz^ck4CGV
z>y)ShP+61bpZd-~#g?+~^(Sr`<EEVO-syT7Rlem5*cRsIsxO_Df@Ctny!ty2#UbtK
zM_d$s|8$GfF#Zc}_r5wZ_P4os_g~p`Cd12~H@V5hy;S-iF|^*quAqfk`f5MM2v~)L
z3Qb&E>Ul(Pylmp*trde{9{bJ@#6RD?T%x3OKqfs7a;WXG!K~Mhg;6AawAxU@5;{Y#
zf3ilmWn(rT@f=}NZHQQ-Pllkgr{V}~`+SffRWo1pg3M+a&jn}0qO-NYQx3(Z;Pzq<
zEsfjIusaRGGr?bR3)`~E<Y*%}5gJ^+47|j)XU=`KL4N@P@>?Gk=+y(pyg%o^lph{Q
zeYBFFT*i4Y>RTNJ#hm!1rs9q$13#$v!HJtXravZx57Y+Twd#$qeJ!TbSHJ&wJ`@t<
z->fC0JAW5)M;m#vcH3$PS`H{29ISizEw!=NyA8M<3|-wKkYHx~5c9t1@J$|;RZOuv
zT{`zZ=@v*e;78$#**8!9gg~k&O!p<mrFCcD{;uAp1PyF@iMJN{^kiPFyNF-2=(oGE
zYc<=%)a0yXFtpumxn^*9!YVy#Pj-^bABAD1cC5Q!s9PMCDZFdcdn~(DTvMh4&<X&R
z>aEmnti$b``iR=jG1@Jc^O)+wswjW(e{tqpZL=;1aCtE1-#<$03*E54+uf*}%?Dcx
zRKqm+InPW;9h4SDAnFO<r1w$q2F}d<y0ETN?mZ>qcblo>d{4{{=R5A=BsKU{;HMC)
zq8TjfI4pPZntihV-x)4v6_z~HSrr$5HrQ#mK>t0c%{Qt&Xx5$SSUVU3b?1|dw4|!`
zfeseqXXYlh-moFIr7zS?$R%)Z%9tC{Q%Edo^=W{YqNp`8JDj9P`$LIPXyQv_!ZaU{
za6yxc1dCIabux{xbp-`O9p#OVUjMi#6Rv9~X98u;tZDa;RUuKf2;%bZwH+*1=``jt
z=`!+mUhu{nyv<WAG*H!`lf<iJvyQGxFqwk<DZ$OATvANlyphW_6ea^^8*AMp*i8=7
zhu~;-!3iimAJ`gX+A>CUFmXRP)1N$ZiovfMzew=NrU*u>%n{&mv9HPFe#W5pVnRu$
zR#~j8D_9DPX#ZYVQ9FbGjV{B!v{v*Z9XK-X8lLz&EsTp1^6KSp>$ZLmrbN7cKs^Qy
ztDtzn!_0+LJ=<<+@l4Q%fNLG1mNmVkV9zDK#lj!5Qr`y@913bR3LOkYc<mJOX#aX@
z4sg2ZkR<elP?SJG>^-9;%n6WBaKt*9ZKF-Y;QaUfaAQm{qI{d%-;7TVc6&UPl!EG3
zL95#=XIHmcSLQ<UlS^DwP>M$?2+T=j&G@y7igQW1H$Pt-vFo}&ibHB{E&Ee>gt!02
zlbU<(D<$O)uaa0bN^~Eb9@e#dX2litjLnbfha2c>EYja1?7^C|jlYNGe99TL|6;$v
zt9@)ML(#ty7;%8w(Tm@+@*i&1;|-6Sl_PeY_Z^%V-ldQwsd~PkxH6z@x#)Gq;5n0q
zI@Zd6$>7~%cOtD1AMpoQOV4QevH#wZcT_S?-z8TOcx;?&Jf4Ey2Q3sdCoX+>OOHrh
z`bZpCHbVTEgf|t+y|6o;<QWm$@<>$KhJx2?kqEsxNFhfDY3JKK5rBs$uI9A{5!=3F
zU$6OVnsQbj`O$gJ)6-)*tu@3N9L5Vz@2p~KsK2|250jt62hgTCjU(kd<u=T@%0_eg
z^DVK4J^Co+Y(-l<r}pGzpJDl0w$h}mLmnee1ha~$E9>h$`{GfIO)rPvxr=tvEN4gn
z*BH$v-&%!n3~!hIt>MA&sjVGmoIffRX!M$c#x*F|TE%76=RKt2cZEBz9PvHnGWjMb
zEvhq%1OSv_Id|omc}>;KP8UP`K45!)kcs6~ZE0MSc87~}sA!kWK@>;sl}e37j?5{J
zDpUom%I915sNy8uZXWc&p3%DX@%V-j2aiONoL*3W(S7&%xc^?39jt~>mo^JoFd8Qw
z9l=qA8z0=5dbTLe{2g6KGua=zsi3#eZGPRWhF~4n0s0BTqTq)---C>=w)-?1&0-O?
zgg47{b8;#m{`0|HDU=l_lD$|~F~zx7WHWy-1th|-m8>WJqVWCL_Z1H;$*aIU4c2#p
zjUFe?vIv+5F*=vakW!3yxMf&ZG{8t3^l<3c=W}N0*+xy|Cz2L;PLG-0gNOoW5ik4!
z7AO00|7|1-o@XMW87+$=RA!w(6y#7GfLdRL;9AkVlv03o;L=I<R*)rd?|4eg!)%b=
zVn&PG>~$a)<X_@n`7th%8{!B*(`1Wk%;VPy85o0h-kX83beCqW0VMZ(c1vhLcp2^l
z8r`-iBQm!2C_p!1&&8rjxS0z?PCc)p-gOQJqzKc{EYh6>(!C4MXbyy-{Azci%I2eD
zwM8lU!z~^Yh!@uV{-7Mv->g1;!Q*h1p9p714gU0Dy1If-VaVhT7A+LT=n2J`^0h$n
ztzSTe|6|lbuJqKa+isMqz%OIU)tTd1BZ*96<)&@br-M@L%!(z?SCrP&1a2S~f32B-
zuE?C8WCM89e=C)M#QFXlUGsMVGsIlOXyM7=tPJOtCe??vEIV41NN;f%BGIzEEjuMZ
z#_g-c@qS<&6V*3^qYxFZ4A{BG=kP1iv*`H#dle<k+f~sCSRRa9CrT_2NMmC-J}pD9
z4MqXmqsZ2rF05&gZe}D;R+0$5qwaXt#^;7VR=V{NV@QbUldL=g11M<<FvXfk7r)HS
znJLs1gLGs<6i=Da)$JgRIiTm-9-h?paNh?#{3J5vX?RI@x|%@<l=eJy@^^-0z%IkN
zk15AcEb<CaII1sR7ZR;|$s3{{vv9yXU*{0p+SGL!PhnKX*X3&o!ooQscgkD;G{7P+
zGIps&flPnL@>OKaZ%~q$kLdKa``h$=P8jg+Rw_zc>aRQYrg0(2_?zpK6g^*2pn{2z
zFS;IV29d4wSPOI6VL$)Hr8r{808VN~s4?O@BK!TWnS4(1gNd-6@>_4Ujr-js;#zzi
zgv%n;5fQVaJtrO-zQr}i&8+vnRp-Ze=U+i^x=@j3`r124sVA%c7}V&q=w-7?ZG%zH
z*OBOLjOY(YE_)w*JW<^iA3$&xFREqYs5)M(7Q}-X|9<pFXN>;VY`}Zj7=HI`G$G(_
zK9w6DaHUG8I&(r7xB0BEurnbOG!tyCFMOlD15GPWuxR={68uM%T_jSM(Q(N>EM!*s
z`3slL=Fl&qovO+g?c|nsy+`J?>os&f2ed6Gp~v&Gu`VXTl*=su9;)OV7WjBB*LQX)
z)XRRgt3Ijhy~s*rG2lNB77z#9;c|K1OXGcTL)FFULF)Hz9A_DP1Q~Ox_rqFV9sf$J
z2PgEXIbewyd?~rjr#K(_{1jp(vb0X#Ad!4!`5AJexT_|M%c=UGO7owoL1eKvIUhnD
zRUJ9&@v9&1Eu$YHH<p;EPP1QZc6oY*3NgSROvWYmL1ah+Rd2JgY#{-cfS<EI3LB|3
zmFdlbB7q`yoVeH4l#N_v$8>c*(6+kz#2YIZ0(IS0bGIgvO_re-RIpA5K7)NZd&8M@
zFcKT64_AO){+jo*Bb8GN#Sh!HZL5f}d}H7_JpaB2rE~UZ@@S?7AbD<cuOSOX=TKb|
z^WvG|!41Q1R=YQ`&Sg?s*0(yFmeqpyM{ki}o(9zf8OC5R9BBCPe(T^m6=hm2NW6NP
zVmqN|@9OzIzqxCFXrAI%?<xC{z0u7><+VyYtjB#x%i4fSeBKw72&&FXYbJ|Ac2m7F
zDm)A=tUdfsV`6q{-zO||3}kNZ{{De__e-I>llcjcgR=!=$@oO-*RCI#w(Bi>i+#b!
zbMjQ##XEhG$uzNMFOMm#ziX%uYat>7_lIMV)N#y_*9c-7BVQF~KzKTJn61sC==R{T
zt~~r?IY_kzTy0FO=sn5l-Q*}!*0D-9l(az$S`6Wo%E0j!NHz0aja?$S4p)egq+JI=
z#>kYN;$KE`)-XDzF_`sgO)YgHM1lLz!<q_3j$(hrVa@)bh~ZeP4=HzYoD2a<sSEJ~
zt(|e8$`s&?{tXjFN!IKTGF-U;?QnTrST@?)7Sh=^OQh5{envJ{66L7_H~i1Nr7SY~
z5};kGq<ghUn$m)p1RiX)ImPLNLOusgHIXBuyM$$I2&Xb1RtbKD;4Lo_#qi7HFWGZ|
zmlj4AqfwEZhBf^(27OuM0G1rDBxVU{J*sHqjgPP*TaekBYRX=N&?aKt>qdZH#@)RO
zA*o0$O2YVX*5Ie*hXs|;yG8L2Uu6WgLv)?4_$LP*z7~7S3S96Re+&`yl&Nn4eN3>K
zF-)ZL+c?r&?A-6v!c@D)())Vb5j~&xMVF9JN&FZ=N)AzotS$c|lz)fvzI$z<<TkG>
z?z7#p{5eX6m0|bRd*=@{2Cc*%j9wiRoSUZjX2(#=6Y6%ona=4k*#t;Jk~!Bqe_B`6
zy{TF<-NJ=9Y%DhFzUKOu#zXqpuh;9F`$108+?pOuw?``;WmyR<Yi{1%5X?)JD}@Dg
zV;CD&7XI%mt$-yCfpxoFi6aY!+b!Gr=%Tq=vzhT)s%1H}tQKA_gskPvzo%l;FTXLd
z7FjY}uF?JMO9XWeJEDX<4!ss0bu`+tr)U3Xu9uz>alBZJdEFJ1BjLwl7k>X5Jc9h=
zA2y0p>BYEUMUU~;@Ws=FgUJ)0lI3)tYr_I3RlvF08hx2g`LpY<LN$~I%p@4j^(Qi#
zTQcI6xrq*9%gKAH<f+o5_78W1O(n{a!M-@>6Rta~l!MK@7Rp3hhs~5#o`zeNA0YGD
zC(EFlii`yj)?{8Qi9`#LFGmD1`p?7bK!de-kbx=#MoaOj+#byN;b9DIUN^KhzjYVC
z*;nwlCO0=0=<0T1X+Q;sz%mf;M8dVZCCVhhlY^X~A-yJKH}r>azH*-Q@$*8u$3b<T
z!`;ts_Jp1)DviY2C{i(I*T`JdqUBnZX7a|GR`{ITm-=Y2_+0ymOc5oDQ@@+7Im4-i
zmy@V<R&ZRXFv3yiwb|K}xOwC4>D8)!(g_CNi+|yY1XX`-90uY<@859aunLM%FlV$v
z2=Ixa_i%TxX$5?p>^bS63B;K&eDtvK5$Knksoz?&N;s9^uY6r@37_?NLlRr33>W`d
zY~*uuLcy_5aSB%73$$0Sra|F&To?PMiL3C@jwP>L0xjzw>B$IPtFvcjr()01S-6$4
znYD`srxux+NKc#r4})#96tO`|YX>A$Gg`y8XFAer?Y**XeiT2wt@uXLVyHIDed!-l
z#$Yg95iNWZ<yCB-{i_*<*fn1|!H&q?w+GqB!N7(<YQ0runt1llm7<kw;ZWcIJrUhr
z=hQM<hns-wJYZ%Ziv~K!a)ZNfgWoe~Nwl*kgqb!ov*S&g#0eGuZSz}~&@pdOelwT{
zIw4OE>We$R4DM%!+`YEr`rj)owj?w$SiFFx-|zcRT>Gvyt0_F%9edcpiTT^0--YAm
zqW!wBomNos_kN&-dJ2erW|}xPi$kioOxo+ii7CoqyjsnoD5HpcU4^O1K(P^rr*Y16
zlY5Q-?pyzvYz4ldq~zB-N!M<mNEqi_to6u4)F!tpFveiQH(g{;NVyAL>Ex;$;9qsr
zHSHV*EiGCtaA@RVN}yWhe6+v@1qIEtIT|Aq@nNC^wZO6$;u{+qqxs6OdcS{xD*mUH
z1ylrvmqI|)ZatksdAz*rmR)ZdADuGt8P`*rx~;3{i2)#2xTpltnl5p%{U3Q1X0AS%
z%9}Zjq+ic~&|22RC2zDZY&$r$2Is|>b>}Zz-!RZ%^x5|A^X`P9X7~=>F#X;==)Ph!
z`5m6CtITL=CsN0$Zm)$HMUiXvmNW(~vwEyB*lhWR>1nu)M@Rn}(qhv)c%1dctDQwM
zrePRd$~oGm%x~5k39>DoL;Cz*<B!KVLQg%9@NbvR1d46Am=bH_@H(A%p$Rkm5p6`#
z>xz!l+{5=BlTUO6eM5rT*K55k@PAIDwb9%S%yp67pbb<!**NR${=s^s>2k4oB5~{>
zX{XYq#_nSYNgGKE%gw@xU6?YkrEhk7;oAoZgLC-Z$>`|DJ%p60zMh4}W_c9*mwhod
zrNX`6=UU1bT>p$Y@1AuFf$cFIcpk1Ow=}aLjg!Ntx7+B$G4IFnav*osZ1!MgG^8P3
zK53)RU0mxIKnn8RP1Q|et<z_^2wS|b*Z~FHv`GL?Fok4y;bqbrTEyhM=*StRwdJ<~
zM#kncT{iM0e72C_u1y<DLl<?6u*M0gs#N49Uc1Do%*X;Mv)tfJH7Uq1#C)R6zTFc5
zKMGYk_1^UXSZBp22mjhNy6+XnktjA6)=A|7?U#Nmir3GaX3$D1Z%2z$eFG!XRE)$s
zUNoUley-c>e{>tQ^cJ0OLiDu831p~^E(VQ0`i#RgC6qj9^SLa$wOcKWD7m?%y9!`j
z7Qt6(I+0KR>UEI>WSJeDON5N6P`FsZR}oT#{El27r92K0S1<Qt>pXQ{&EpJ56U`Zh
z$*o0&uINzH`u#()ec449?PzOHWiR)nFy6*FH>Euha37%HT+XZUG-u*z^Zt5_GC5V%
z`Two!pOcsGd_bAG<6rHrv!1}^`BEQSJvr;^rX|Q~)BfNs4~(J_-=w1rBqTlh{QH3S
z(dl_D5l}l4&QYg-@)7CcUrA9Y^u1T``T7Hg8bZl@{tqahPi-WR%kej_PZwNs2SE3q
z)c@;a!LtGUVxVsMY0%nTOD^|4Dnm~57t6t_rV>QWyWAHq!tqwyDE?XZx`)eyHow=|
zwXFof>Pkt4?bAqS=$+`ITR5><&;3e=1;k4l=da`#TTKr=y$8<fS8I=G7W0+CEp!XU
z82G<tB{8DfmrWq5<MR;v6WskJEj%~~&uux{AdH#Kc31sSW=q6~{=T|Wq*V(+;}pPg
z&y}CKkCz^V)W4k+7+V+rR8vw?Ld0g;3Wt#e?{0)@-}M;rajQk6C47_XpG|A3U*ggb
zcp!RAXU0u!%*vuzYc7wKNtM~VJE@GIJ_w`(<ihUm?bSOiTE!4>=fZyT>Lpk-w6>1^
zc)Nt6%C%Col;pxJEGoLa@4ztVoth#BgWbdzT^0DHrN<&74L4Q=em=JvRv}4BjtIz@
zn!28?{D^$<KAIv5ZXhEoSn=N`HJ-(X1H=*Whke2^cnirS-bE59JIYHVQn`jncaGP)
z;seNqth%eM-Q6-Y)_(|Zm7u2^3Pvn^QN7Qi3!A%td9VvLdr%^&w#|gltL!)p&`6p8
z(7Kj8Ts~wk6fXDm_VnxxGi_zFyB<lm?+v!~)vkt=$mtG(D3RR;0QoYo(PG+Xxf7?J
z5glx8ECkY-V(b}n^CNYnXfbzxJ9X*JpFBB5>*c*9?fqJcl2HBKB_Q-E$0{G(T!bTl
z8S=hv?{RQ*8!rMEWl{AW2My69Q<DN;dXdsujTSs$s?O(&C}Q1De^``|q)6%5w8u3N
zKWlbJk^S%#CizT%sumge*87;g^+A2^@#pIb-Ez&Z!3V>Qo7bP?G8+f4$bNBzf|1Y^
zWEg{4p4+leH=tosp#EizMw1I?4ZB?{(=VHX!LRA<2UJ5mcC>rj2@l<Xt|FLip^@9%
z#y`nnPu>cJiIFX%?K@OPq{J~VcjM7-D08<m7V{0X)0Uc$J3shtlef;tlmm~qTD^Pt
zrYn6p0sn7@!IXeHpjT7r1>wL6gkG@>%+USj;>=w>oC`y;c-w2b<XDXLlabpwkr1(#
z-&qbrR?W>!rtvM6W_w%+SZ=U8S*7tGE+?FM?)MCCX~L*j{8JFHRCAZDD!wS9hyUkS
z0pu32bN8eyPvSFoK1C3QHg8Ldua;|3;D3>uUz}I^GbsBf2LI~?5D=V9vH9x3llI#1
z4TMwEC*Lq6_%TI_$($1{%j?}y1X`~xkO)_EFyY}u&71oOEyxmty@t1cw9$WL*U69V
z?uIa6%o6opwjC)XIqFMvtlxS%q)@3kw0C;}c>Ta?>p@i)Suz95Bl}*WhKT<hm`dvn
zh395r_vm|m#2oP+=rq18xXrkpkl^86H8}vi(G%oa#)R|jH-rD|+WS!Ge7R9J54yfC
zHeGSuCJcm<<oVWx5ueL3%r9SXUuHvDR&27(^s&2#B&j{<&Km?$aBy%l@R`$yQ)!V^
z^(5r13sYGbi6f$+|H4T8U3yHq&2bNQUI7>Gla_=JJxv+YFBFeH6t76i&o^kYTNm2G
z;eM*-uwTLizTY?}f|*e>LZh*b_YNod3<CXVZn7SZh9?U{{W0#Em#3GWE<oB}<+C{Z
zo?Qtt?INrqXqsrZy(aPJLMBXNrSgQjZx@qG*MJ{2ENgLCByHVZ;2BJ1VWa{Gexr!y
zoT|D8c^EIfk=>VG`0S{?x1=vEa3~Rj1H6e3u1NS=ZCAwq`X>>Xza`l1UIa-gwKBJ1
z1yZ%z?HM`IMa>3mQ{i}1F@X#C9aqq@0t~OISpP2Ls8*d{n?7P~kj<ozX|#rkZvXZj
zKS&zIUG{n2$4rb(FuJ?4%TPjo%aQrM95i@11*n16XIBcT_p;Pktnj3u@QjyK+LLho
zm8jPuI#z|cW-T&@K{LHOSnm9NO5FX8k8`%fnP8z(mniqu5BRgIE7iHsT{gj)slHs8
z@TCMI)I-lW5k$_!^rPLZX(Dn~48FU=)R|it7I`-QOV!iZ&_rL9dov51#m^<W)Ypl5
zZ(t$y*cku^hla!MSJpHg#J^_bN)JuA<bLfbWj%rbwvq8vx{Sp<S}0oI;2P(<HnlVv
zuOrC}N1<F6aW~4zQvhP*X3_C%b^9g<*1-$b6mu*C;!^)we$;;hhyR6C_Tqns_xM+C
z>fF}WvCrGjQDc8|Ylc1JHGH;(EKq$<;OBeHLPaZl_56l5!16M3^1O)BBN-A-!A3Hz
z1yOzU+K5YMVVf_+Nu~`&OiXOCTtftlj2rM-TiLp%-@NsIrPlmkD^)4%;N!<C*>sW3
zP=f}GFH?{A2YuJwW+nU|bzO4}(@Gph?@EO|)a~d_ciLb*rFQks(|GZf4i^l%#oD-Z
z$!^34bYj?p1?YMFUxC*19keQOHn`y%QT^V=AN`EOG|d$GWoWCNa^`{DdUi#{{;aO4
zqR9r85G|ZA2g#xH2r|nRYS_&8++Sg31U~&7rnYlfZyor_i9ftMadJH?5CjAp^k9vR
z(Ce%(dH=p!6O`xJ_ifH1UP}mMH+4>eG`OVhx#e%^Y7Yf}<HLXbI=nx&<#g5Fc|M+~
zvmsIJ*WK~>m|SnMRa2d~;Vqkl@=ma9pRqW4I2rkwc^HnfSB+INI5dRzcP!6zTWypv
zTi74M^>ksX)w=*mADG+Q94NzH?+W6I;BKj`tW@CN&1v%sUSR$6)8Q`^(OT;~t^H~A
zuSENmx-kp>W~(KUYAsIr&sk)0M#M&j4;wCQ+hm2vXQ7IXtNCH%HUGO@_csJ?0$ED5
zKlGCxOmDKD1|sFV-!Bcqlc9@8^x%hwNBJX?=IIS`e|aU(bvLxakt@w%R$Mh1WoCG>
z6CQam`e?Qbbl2vva_vJ-X;IQP?eHzb{$N!f+QUawZ9Cj#+|KdvY;CBMw1)#!MIngG
z(f~Z7%sqtA$G6gZq`cp;d`PtItut#ov*<UyUtHlCJsM^j37$~sEaiZc8Jq-Mmn!c*
z6P_5ocF{kW@57VvY>=NjCNI5hbOg8op}ATgxd~$)53DQoAQ3-#+<g$#tqcSgiR5ss
zN>EXYNg4|yHsW5`p#2np9HBodFlTBsaxbl@wF6faOM>o6odx>T^lTw{5dZJQA1Mi?
zlo$vjm2b55&92lLoRu2GHfzlf4uNGBXC28Bh0^7zQ{EEqz->tPa<%^k)cy;pGqV(d
z(gV%_nV~m0smQqPN%u?3b#CV-VX8T+Gm-S*as?~Pm5u=@7U!jqiJ?n7ds>Rf$XQ&r
zQGRdF1(NZ^-vHMCAB?Ur@GwWK&?gl!Vux4_!M>KpXe|x;Jj>NOdX15wNQX*)aW(&&
zdAA1H=^xdb84>jEeW9#ZW`*o_Sa1I7Np(9tv$?%Vps3~D#pag@HKM`HMs?@z`unw%
z?kOW^Xg$vwDnde(lX%nQo8E$G5AQpjIb4i8k6IcD;5?GwflUAEn8Fi1XfN^zwyC#E
zQ*5@$@r$7Od;-(;FI7d(gmBnTZ|}-4a1GKmPxBCi8tT0jT((-q?_KRHJ-)ZT5dRMV
z13~=0t@9Y7(X7Zw#xf<9_QMkSP43yV*R1Y4v~N$(Oi!a_#k>8$!9xalM~)md9$TE}
zqE)Mw@b)&3PG!fAT?h#YF=$_01G{(cmP1cwq=1@ah6bD&qq=9MQdn~-6s_m)#rW-~
z(A~=(yV_Skhte*nPO6*@4Qt>XKX06*ckA=V1JV1)8T@nm4n3~ZbK_RCFDrVl`a9So
zh;eMwNyRT<Ht934p7;Fz$Il`rF$1&9)kHr6&<b5NJ?3+SAfN!(;^Cb}BPbDxA1q#;
zovfT#r#OC&xQgX5x8R~(FQ);+0|`lCOvKw&7G#gJNIc>%CVq&LB|H`HS5w}*S@%p*
zvs3R^uieC~U*==Th%pEaHNA;jSiOm1Pa;`WjEmgAv&K=7PvdRz(qoKu@wm=U#4|WQ
zuWV@}nIRYqgqNup2^3c3I^^YTAZh&Fr1H0AS}Y)O1+A@%&X!Zo%UxxTS-(B~z6)|r
zG-ZvV;A~hty^Ty;zf1MIssU94ss>aIs2aGh8W8Y${*vV=QL<=_h_wf;(1Xu5jA^Z1
z#@Mld*B_4k*jQGPrKzBGk*V^rYA7He0At3C;Xc70Y}>XCBS(%@LF>or46Bn-HK1xh
z)qtu2b2Z@YTL#tY*MKLTc%B52y|Gedy%lP3l_9TiXT0ck=?d5XV^u4S8@vCmIfT_4
zkHDGnsCAsOxT~1P*w#<=Y)A@b3;ex!4{Qi<7~j&QT3Fe=xEtEE)}>C+C@efY7XSNf
z5hA1GNzqyrZw&6J$<q0hP2AF@-0}7s9dY2TAc70VPkBOOGL9U-j3Y;{AT0VWs#YkD
zwykQQT-g#j?V0mM<{c9gk83w?6Jtq(8>w(T-JR%hW~&pcSjyd|hoJ!cPEPj9rugY|
z*RgZ=IRsseRw&e4wWx&F&8pLj%Qz?!pO8dqbX#teXKmZc=K8gp(Fh5<4HtTSRbgCi
z8&c_-5*g>dLl?0BP!JO1Qs7tK1D)H~BJHzzAfTysALaB470RP><w`ht@>HJFIC0`6
zru{IT@ikB3xo4k7MQ&C+RN=+Gefth{>C*Y(41?G6j-9(OY}k-|`!7|>3vJr8!jh%S
zmEXE{1^Hd9C0%<b#=_Q?)vMR=H^NMPVN##oz4TK}eqT<SZ~>|HK@g$872sNxd((gw
z??vU^9MRsd1Xct`<L8q%vEsrljHq7$uhuOON8W?iMnvM9;9Izuz)%kSuAEf@()wb@
z)8xnd+rAivXOIgcTQXCUaXHf(FQ2}M2^Gs>eCf*Yw{gIXsB1{z!YGq~rU0uhme%yP
zCg8{(EC|fWS?^)xijMZ~_>pn4e@VKHK?J?6NzrVM$Y!B2NhbY`Lix<TA7|;@v9&@c
z{rAEZ^q4(JTIySzZq{$fm^W-zS|*O448#}T{D>)&#unO}*qB}gNEYT5Q(_8SH&xzL
zy0jyUY)$wGQ{@VhZpzRhiLuuUE8^vB>^#W$i&;L{Yg5LN7tmjn=SJ~R=NwhJ{~CzD
zn}U=A8h|?U9&W9-hP}hJtAqk_cHp{LFHel%lDT%ap&^Bp-gTQK*ErV!_C}bR;<%1a
z=GdIb=wWAL{9diEp;*?5O-xemVjc_yWUO8NI(dDM;F>y_Mk2B%cXzgjeIDy??RHYR
zc8}AG5wD%EeQZphntain=r*r1nzHgKeM;XhWIoZ?wRSn}V|fzGJX5mgDS9(k-zn@X
z-pqLxWK7!Q%j;PBm+Qox?_8Sqro75)=Oxf^Es58qlf4aWi_pkNyASpF@z8(<yw1<_
zx-hfVfYu#4cPUJDnB#d}sD@cd1+5FKxyQ3!Ng8iR+-ji$Z`G<5S$&)z&xxwDQZ=Az
zK-GY%0aXL422>5)M-7;_vvD5>@VGQBm_k_i9n6@ufg8Mr8>9j_d3rjdbK9CYdNLSi
zFNVOuj_S|FB63tAZKb4VqHVJ(@NhF+<jRKDocTL&@zPDWa%1SJ?)42e#c~?sfhEC&
zssUwDhqU7*OXzwt3D~}R{V{y^^IDueAA$^SddcRE#N~FMxdHEu=!Tb`ZKd6u$zu(5
z`Cp6oVc)*<2#t&<C~d_J4r>DWHYi_?bhIVhV5`L7W(-c!Ti(1LgTH?trs!kx_p7o0
z;3WtpvWRS;&G~0Lnl%W(H=jRGk5YHVyFC1MEDjyHh%LJU5yv>5fBrN~@$g!-Vm}ry
z+lSNVZ*lXQu{gOESGT$^zMA+P!3JF*S^)LA3wGeQIn-lNC^rxpACkb1ot-si{<ayz
zU+=)!;tvS`HL2kTlVMBG&Ss>sI(6!dqDN!B%BFmDbTkgpGx*TK!}#^r-|_zY?_%hX
zx0G{Xtj>K;<CwPZ*okBUA2NprFPS(<eT0Wcpk&GX4L<tx?u{k%N-z|m3*XLo^zk&_
za8k16H?e8+79JbkFiMy9;tO#L!)A=6+_-TQJE$YWb}Uejl<xrnRgBb9lM12%4IuSY
zKpCvK6sFLA-^#GU1m3SYRxE{p;?5|=>ob7>gaTR{0j!n&c;7Bg@Sy~$nFHf^PvnNY
zwg@;K%X`-Q7lLt&_rY-lumkL!F!9z!1lbbAVtsege<$&GL8t>(1gHBDv~FhSf^R}E
zVQyR)ESOiDv9-+=cQ<2YGjaV!2#u6d6`E*s+q&0s8a)@b9f=970i6rdhQx0kJ9;>2
zprdi@#2IWPE%?#nrx6-{TQM#$2ApPOq+{FmeWdQ*k4_!Td!CvzCZX+C;w?uTmUghW
zhci8pwPQ#dmNG9z?mB+HK6x5}nb%ToDWkO9ylg|mA{6ksFy~i1+=W#@f6k&Ky9fMC
zaLDO1%djDL=HNQR7A}kn&xvX$7xLJG272YWZZmG&SI9=yY9=)B?yQwqx#J`(Y;#|q
z<XV;WV}{&og%v|fG8sBz{<s&>p<X4eRN$9&2k_lro6x^=LwrB9w@zNfodon4_d7~^
zx?tHSFXyc9cb^Hu>t8NF@3wXD)vLNjW;e;$(Rs|T^zu)^nn|xAARnXC-DiR^Yt>%t
zJQaipK3|04$=kyjT^m%vh`uf1?`@nBMrWYeUlQ5pFY69s;kIKqPlMG2GEd2SL}|9y
zrX~%xx-~|VfO5vF=A`X7c?E;MUVu!3`1(4`DI^DcH~Qz}7tyAcaYM;-*TT__dZ=5;
z2mg$DAzvVV*!1Ptb|?@FKOKZ-)qHij(*GiW^XxSTuxQ6|T;Mzt`5s}-d3y6awdHtZ
zo((Je=vUMEzWL}yy!p*SWD@wzbDn%>TsHm{G^_5XQ~zGSjQ({EmaIR7@o)CTYh4=^
zsJStJtjE#~hcJ2MQ+U2z-2&BB3qN@pxF6v4y@J-7vmoF#IcaE2__-$Oo<CL4`kqzz
zSTx|^;D8n_THu*yo<Zl%ol&Mtna5&Y>LgSR+)oW$x^hEdsj41OUS}Us-l@-=3!)>H
z7hIejb%t<*q0=I6$Dmr(@(g2T+DS>L(tY?M`yYpo2GW=@6-^t}&@oU?;+pkrREiQR
zRNfV2KC*7~b+UxJZSH&9_4qX0N~F=JmJPDJB0BI@qnf)79S|&VIx-n4TpMcIv12>_
zu2!(O(#0Ll$D|-OMatz)meo+w-V%P!x$iELc8X)W#=fms*x$txmE5%0VzaAfjV(=i
zPmm;O*4xn%rDbqh5^*;jVToy|;hFayY+!UUT)4j_ZRpFbgf!eq$v^<-tS|AY8Mt^S
z6}3EU6emFa{P4SJ2#!lffSWbj*VRcX&x6eMMnXDlEG<xpdaLM?d(FrW+nGojXVRcp
zd;dYsL0O)wQhIaM?_2$@YCzS%{nvo*9q0b{_t^Fzo<2+eJ%FI#Fm6cN>F!7JrY9b6
zmFdM>y0iyo{Ye0d8`<2UF|H4mx8_5rMPuV2(w*45ShMjcH)83H!Hw&hHGGY?qm}AT
zuje^GzX3<m&T2%!+i3)>r~SAdmh}Ga(7Gy$bEE0Np-TuOI2=!U+6iASQ8rfwz0g)u
z&FE(|y$>dSwG2yE9zYt$-?e>BF4l|V%$ch=a_kE3-p#<tb2pHF>MEaj7PQ*WfR(ic
zCVeyj$GCC!{wE6%P5Rq<wS7^On@6`JW3gv{An8dnuzTMb#tfg231j-=eSRK0a|4zZ
z^fY5$SsxGVX1U4Vuf_4R*J;gaugoPqnHx%+(_wm%e>h<Q{+cy}^yyi1kKv}^`{Ng2
z)#`(aH+bJ(_2A**O3z^$6a?NN0F#0#Kdgo$DcWCtuC>wmKJJOVdi5Zc{aIz2LPskx
zmv7z-K`a3>L(o&=sYc?Xk3LpF+lil!FEaIrm!HI})?P&9)wrBHq1rA=?{5K*Jv`i%
z#b#c8OP(x9FI>1_7I59NWpj9!Da|-<hP2&RuU^IB!$%=r*&>Gy9mbU_SCnI&fB0K!
z*RCCk7dMY)Wz(k3h+=G6V<P8V=yFvl8V$6i$F&bxCT=9qlbY8>2^#Ns^ZqpLTsXRT
zyI^kZ${2L=BChiqCy%i~rxwL5Q8D{|efe$@pBcQd>I*@V3|<5C2wGf7ynrcH%43dS
zZF~`W85=Vb;cRJzyL|eHuk`?HH#D+w#>AWFvHWh7GA7qd3skntGLF!1+SqsBa)ui)
za?Rho8H&IQmr<`y4P({rOPUquW=!siuFQ}-j5AEt={~DjPksYp?3JH*db*=SyB5k{
z$gME!*>@O=m#*P6<f2kl^r9{Db<Mi1MG9Oyk#^pzWO3XKp@9U~7#b1qwp!In_<X_#
zI_aAHf_{qUuu-77pbcr2_w^}5-OH<@mQYETE(hZl!E5hQCABj0KNLfR<0npQt0Air
z&v8lW$P{Z!{w)`-gbmkb#Y+(UyU6=??x|6pWo3EapE~&y#SlRMIw@#xQs0pqrRu%A
zpqeXOc{fLUcyXO<k?nCR_Ym>gy>dMQ_Vn7V<ilsN*7?q)ai}fVqWaRcVHJ%1Z7t?*
zI)V?MZi6z#U2}?fxjVzx%N@&@x1V_}Ym`^=ekb4<cqIf=M)b`ot2tP*>lDtMyo`=*
z>!K3Z-ljx6Za<l`0aO0jiVS*>m-Q>9fUV+ne2#PYVcsVEz2z9D4ex^&%m62&<L}~`
zN%OF2`w7@{4t4##QIhW)G4y&q%lDyQm+r*xYY$-D%boBk|4d0@GS_Y)k+3H_Q)P}{
z@AjEfO^i}(O)!5*A@TOU&i9w=H;&<d)S<pnKu$y12ccxGx^XLt?<9uLVC}Mzv5Dya
z8AIdjJ`G12;MU<hOSn2h=6~jD7-lWnhTqog!}vj6G4A>H+EsOq#cNsS=}53!i~~%~
zQ!<2=t0+Z`8|G{}jOg$f{Iq%>UhY`mz}r8s{%-T#=_bb>Yo^C|UTvv)Plg5ryq>ps
z8D1RlRGu-f&Cy0tf!3P7-WWPuIV=kBS_Q2&T75D<WsjnE?b_(yzdw4@D7{9F2lp=Y
zWNKWU_2Z_2zZY-Btlw8+>*^n9j9V^edKWHTN9Vqy@czhW@$Rr^bJFGAXUM2;82T;(
zyEgm;E|_xi9^`?HW5bpM@GVyw<$X&l>}1;g2gjwM)ry-)paX+A6llc9p%}*3al~uY
zvey37PTs-PK<*dOkxJQ|kb-2*t7@NuQ_uJih)ze>wV}9`%$E)gOCI~K>28IeTDYTm
zsob!l!*)etOPIW0YrTxv*yClN49sbl<yf;L<Sxb@x`RF8T>EmrN&2)*PexxqM||1H
zjevNrKBt|G!PIjcn_w66(nBHci+5`{VqASU?#XARtqe}Yh=Xz1+}Q)ow8o=U$o?lV
zhT#6H+jzf*1IE^K*UnOohwQqI-JwZ1^Ry4V^uXdX1fH9(ion>~j`*~phe6#XR}wJn
z;9YF$=z<PD4mtU2uO{G=Be6IVCFAEg;CQXlQ#e-#OsMaQY6QX)h)eX{5Q@u*GMB9R
zC=FQI;H%m;7+u#br#<yR)qtvjhob@GJHHQSERS@rclbiTZ1o}7*^~BF>p7uJiub=X
zJ{Ouatfa&uJW1d+Pf9wLcO>;(?OMJDIuRPY3m0zSdT2B^9IO;da<7txH-7z!@=S4a
z%>^hV-s7BqcH+Aq*TIV(fKxty0qt7V<TKR**KXX#2cOJWVrYxE^mjjPpaoeKlp*MD
zOrj#=@b;K_*tq2cN_n|q+E*{4ZOg1hds0#=7B1g|&nGWeESxgA5mT*78GJgrpF+=<
z#0T-9;j@s&O{4dRcf-gxyV6q4TA@GPx%Vtaj{cKfT3|0dB9^W^gx_Wk!KYs>#>%ya
z2`t;v>o*?5KAej-O{?I`2~VT4e;LHmdt||)otQOe8@<u2aq8?f(vP1-_b&Ar=Q|aX
zzWo;~7-PJ=ZwbtpG7ydG`zw9i;l|V#->x8y`z{&<*kB5E)0pS13OA^pG-=tg1>{d7
zcaxG378=U9Ogr$y^dE8Q(j`rn)@LTZ|9%FVG;V}$-MVU}7yht(c|WXOv&w8wKUqL(
zyp#*yt5!|^aGoU%`X?Wc)k-vdDE&J)=vsuDD(&In0f}`z_pf<eTk764#B*!iy7kZi
z*K5|SRlK<j+G4fdy?YuY8%q%IY5DShnWfv8ly!G^L*KrAj8#+93aJ4P(rI?AP!fNf
z4OJHV<y;)#L<`f!*JAMdl_)H0Sss7Zs)RwO2wt-f@u1cbTG9p(B#9v)7sdOd0m!E*
z%xfi+ECUC5TAw{}9$#1X#n)v5;2&`vB`j%`%5twcl|lt8JB$el#JZ&DEGlt6KgwF$
z!`nLdWk=Jbsn7C$<rwn89`R%VkZWBda=k8Evg&~X*UoyD*UDHpq%eY>=CCdV1z+R4
z3%zqmrJgGme6H9Rn6bf|tOo<0>W-e>I$-Vkt@v*GuQbGp)zOjs?oOQvL?R8s9JCC`
za{Dg$kx)ce_AgJ5?=w1;1>6xsf;)F&QKPzX12~-u58+GAngNQJaH`_@o;3iurozG_
zv2oK5418%ofkt)w#95p;6=?W7;WbgWR<#1PRaAxR)U1l#dk*O|CvyzCc7xaHZ8U9C
zPbW|P^556M*YtG$>Q&wK&7RZ2X#MtdG^$b#TPDA5FbVNy)|cwOrO<<6aF!9&UA*%o
z-g&BJP7x{nX7?rpt`B40mZJ)A9U2{nfA^dw6>WKR)brjJ(0Sh0qsqqJkRDBqdLl^U
za?5^i)+)@HzZqr9d13OKJ<y|ZRfQ&7@^3}O;untRb9#cm{KY)pu`=;uyS%T_FA0Ee
z+IAcr+tkL)k^OW$wFT^5fA~Bm(o;7-Ds9C$k@u};RsFEx%U5MMCZ(8W)6Pf<QMDzX
zo#}j!_+iNoRP*&xbZMlhk|d#<s4zYY49}5vor%=hb{YqCYhMr3X*f`!RPOtbc*w6i
z7>LjQT%WIpw`QKL=!w2+;y~>#436`fr$J#e5*BPfuDml<ui=Mdf!82j?)ukKUd<@f
z;?$L~>p8D_YTlErfeQZq_Y7VaHE69lBSisTSFK*1`zedz=AQpvjHX}p^U>9S81rkS
zT)A=#vDKaX1ka*Tqef)YG5>u^(}wz~YCzQh(5Y=BvX+toXXg<e;Cl16L)<Usp4g`S
z81dGC9CoRz*TWdz@;qL6wj<xEAKvdSk)d|vy{=aEiY8BEQicV(m$gB^@>%<X;yL_l
zU_6HGk0)qKmP>-XbO=VV^i+J+&;_1$0&vqin}43RPRg1~*6-rIUC+Y`!)uWJj^D+@
z_}kNocxg*Gj`sI~n{5^_E?(KCY%?&aVJ_I3NzlBqb5{Nu0=fOS#y}j7Kd<M6mfm(q
z=2pm>tBII#E)j=sM__%|5~xDYWf5g;8L9ZPfh$VTYg^i19+Zd)r&Cbfoo!Xj!WU#2
zeb>SkqxME(Yp>Gu7SHNWX(YRy_xIjLjD>^Ib>!LT6l}PWgd{5mEF}m%v_`H$Z%!Ko
z_CG(7h{~=uc%gDGt|9rNBW>dVN&D-3EC%k51E+||4V=)#%Z`kP=~#E|E@qxj;vQKv
z=6A?)kV+=jU)!}9-mc}6)t0o$Ni`~N=>08itD<T^)xd+*fC^j}fxd+z`|yciT%pyy
zJ*lSkMQ)^g*H=K*sHixE+=?(wsmikQ04z&dY>7>+FVfd>dMIadvp1udHOiKDg_A?U
zZm81!g-f?EmB6emY0W#ft*!iN`YKCrhtVT@;-$BKL@I&hxYz`&Tz3dVU+tnPlJl8N
zAoz#b8?b51ae~A)82CcV9MD<{3HA0fPc=s%>AUAI-mQ3v2VaXMz;8p(loEJm&<{wa
z^}c^aZ@e+Eg92JhJ=u6_N{^Jbt!rU9DZm9tUb*flULV+w-cz1gaXmQ?d6*7*u_ea7
z*H@}3;^tzH;cs@s)*WYXAt(&RSmw~N%jnXvE_Uq;!~)WZyExl%TuljDSJG5tngiZ?
zwH-EWJH<K2Bc7X4fBmxypO1Scr_7UjV9%E^KR-VV8svvgojc;0r=Q0OB?dN?t0e-C
z3b5U&Q%BR+xK{c64vTT2<>~49z`f<P$0ZcA?)Pa!q7OmmzX)9GYLUJ3+TOfn3z-U%
z2v}!i(S$QiU;GFpHgDEcKiTN_ix)4EieDE*(G(PK#3@t0RW@(s{-7=5_5SWVZ$sj8
zt5Prx^r%z{f1SF9Gy=#~OSm9`0K=`sRNRIYUf2_irLD^2m+Jlsczp}jdf+vwjGP=0
zMo+YOg?={gjdh889YQbU!Hj!7vYa=DlG>SUNXpAWEEoC$T5m}vn9BAP;cu&sy^FS8
zX}*&afL1(t4<0$0{f3ZLMPjaQ*t`oJ+qXpfwz&o``8IeD1xnK+S0Bt5pd~2y2F_o&
zf?5O(O-Wo_0=;u36oT%xF3J#3Y9(f{smghz3kCF3eYz^Y=T9e1Qw-HK#iggaNy!AX
z?-UtuEp4}M)dUL`uhevt^O?Z-=~KS@8Plgu%%Y{v$@y;_1XQUAUtXh^uUykRkD2)M
zuf=H9yb&r?@HK0&aSW^Q!^}S*p-=Qh;siHqQeQv$fqZY-ydh@&I-m4$V)>pc=^W>j
zX+LB3k6)p<XD$GiTS7hhuWBGGF0k&s*ivq^6xp;fhpj;~x7WKi#<I;vF=xvW4DH!e
ziD|9LYgMZPs#o*FQi7%5lGb_E{&R@FMP{4l+8e|d-g71xNBI{}r7SwvudHdp;B(Q=
z6PU4RJA4RQulwqC)S$Puwv_g8!6z?vM3u57@$%>M@b-*<3C5SD2XvN)hIUyw7VlvJ
z-7RU9_4VsLbU<s#_hh<wZFeF4xTBqU?<ob<q4Z-+toXZ6<A0-Ou0R#OYqY6tdQ;I@
zeeE=f1HN?kNm$d9eDWJTbU<q<C!W{hz1+1?0F40hrbyJZTU7JZw3pxKUB0XRy8a+4
z@Z8QM)pz?*KPhze1p%(}>qE`IzZ!UR_z1l4%0Q!?xYGEvD5Nt%xF*e-8*R!lJ9LrJ
z%zP3Byxy>L58`8E^C@XO+lFf^PfycK62@zpmnz_O@2C3HnM`;6aO1{x3>iEScVc4l
zY^a>CADr$XcJa4A&O{2=hQ_2gV<fBc-_k(cx^<Pc{fjR$1W1Pt${xjkYlP}(R1G|O
z8mLvX61?dkwRYn^?!{*9>B^q>>h*iLw$8xmGna8LFc>wn0pi_zPmmEb16@0sw+D8=
zG$QZzvPYel$X<1$lC~HWkQ@H9O&NRCS{9Dg1dBD^$(EUE7|1du?8N23P|~^A;_qUG
zK~-|w5!&lfN0T9-ucjRtQ*kau2W$1GX)T;|-f!-Tf)~N@4P6LMYf*A{^sz^0A3O9R
zs6AqL1lD#d4XbSKa?RL;Zd{}fy_L)TeG_Z070Usxm5kJ69JphNALv2;KRu7?xdf5d
z+~Vjg<auXkZ187b9MUW-;ZZDwz<vt7(OqDp&7IC#3Y<@egEx0ap#pW$RNu2(YhULw
zX~jqEk3}_t(QCStL^-Ei*sDu9N1l&lg+5)3F=anV^jp!{Qb)haoYa3*4X7IUPiR2b
z6y`rMq(^TMvRNuImnDw1Y(^LpcUK4a`nciR&1lkaClHu5UQix3>3qV~tyL~3uAO#!
z8O*yF98G3fl8>`LS8k+ObAzTJA{62C7wtwY>AkJ^GT5_gU7hN(F;|6g+T2NxEQ<o-
z8wBP_^UdQNuLNa-&Jr<q;<0Y?aX2{H5csu6yOxHYO)|c2o$IkJ3&o>5nss&_xJ-}k
z3pjo<7}oScY|}gduH59(7E4-cHL6`s(Us^mmSDq4dbZ?@frag8WoIk~w}nL}1i%&@
ziO9o9=Kc5LJt#(S)q(&|+txLeJk3ASN6F%DC_x&)44F5<^JAy4Q`foHwVJX|@@E-u
zZ_<K)sGLhfJ!InOv17(*tqlvCm6EQvnP66>A}nM8ZWUqMO`0@Dc|Sj+EndESg>>y_
zaN_t01O){dDPw_-9Xk+|G8T8(u=v`w>j;G3HOyd2M8s|U^6T&T<(J=-c;&zHSo1gY
zr&-vsbEnaIYGU3RXiZj!08i4&(!04DnJum{_D{TkYXq6kC1#-iu3$J?SmRfDVWaVY
zh0unJ@MdJmGJcGcdzHou$wK3#wHVJTr1dY$TQHur1@Bod?CT2aMDc!SNzXLqU{=Ic
zY|SKTI6bp9V!=gvJ%@sAcG{4Nt7rEPM#hq<+$TT%;s@;BYuq?O({E9IRt>1cdyw7|
zhB^{(c=m69D>1iC^|ff}YFrMwt_PU1G#?iMm5c)%J9Ztw_|K*)F_=xYt4Xg#FK5w}
z&Vqn#i(!w16)8$-Sict8S*o!sU0uja*pXvrFy@oV4837IJb?B%A|fI&@r&<y|30mq
zeBX}67^-*AP6qAE{Z_{N;kYkx>B?1ueEo#D_ykP&e465QE_2ovp-gYms4nlZWwnzZ
z*l{KQ3TRNTmXUKq<H3u;cyILQG-MQ-_q;_s=O=wVLy3`|cS$w(VQ3()TXSCHX-6=%
zPIW(=AS=)whSkv)JFc4tcWQvhklR>DPuT_ZYIfq<^qJPRwTmgoe{4LA41%kJJ2ixZ
z34l0}z~yIuZ&u2GGo+URXsz9-^zjxwz@tbD{mo+2&wc@PV@Q>4!%aJ(2R9`GVCUy?
zZK`fz(q+w9l>qHK{aPWB2B-t4EJQF_stPMI$2;*!u%*GRfYhc$@@xzEK4z+|qE6p>
z{1VOwURMkWTG4C2HNEy%(}Vvq&#@|xh6bFu)-TO?xyJs?WqQhQrqYhF_9e~5)zJGX
zzxK5AoeA!mYCFI5`vqES#^+V4l)=~<Hg1Bs3zu-8CGU0ehP7)jYWPqcLq$!y=6uTB
zOwC^mhgJD+X<+>L@mRHLl@h~R4C_@<HK1zX-ZkK0Z;Q_D8sW(C^SI8~)gtFFUc)hl
zRD0=}PRcv=nhkq%I$FDNFAaa4(Xd{Yk-F?rFI}-6gNJ^B7hau!Ie)LkqGem?#l1wS
zvwPnOO!{Un-{E!FJS*1h!j$jllMT&un6cn%q1>;&K&P*3G(^sNhnGG7Yd6AZe0`Je
zakptSAENvvYn%geMyff;+K<+Ji%#PED))A5b?^WS+?9Bn6jOVT0)lHqJhzWTq@Y+@
z3fycgG|9?m_V%}Otlic!dNSWkx5roXp4JAfHI=*iI$~I5TWku;WZSuWMgkdY#+J^P
zp8a8rw_VJJv7hxocJ6byTcqHN6Y)4rYHy9)U>xnShhpJsnFb4bZ|h4m-JR!NOh7kp
zYkX423CH5BurtK)oMaZW!jJ&2Q`qnG+oN!mRP3f?&V@MKAw~PNCT=>QwWfR>qWj*C
zx)_SKezo<csiS_X8c;Rx@HC*i)A{hm@ksY9;O>E=R|z1C#g5)o_+1_zcC^BGcXLE&
zL<~1EbssL;H6%yc^D1t9F)Ym|ODr~m%W^DU1$nin-L~dfVmxo$aay6#rV$%#?d%a1
zoxsiFI6lzt@Bti+ZS?Ypp=X$4X~j6GiS(=x09SkDvQK{gQaElhKAe?2d~M`U_W>Y%
zim_D5;x3GzO)3;V5v;hG9Lk6MnvKWV9=)cSX32PvnlZ>{WK=w3UEac0QhaOrluiFT
z1lu*nS2BM$XIr>98Cq>gzd~QD5%J)bINXPi1u0{+x3xqj`;~sAKlux1T)dsT&mf$O
z8V!|u8W$)<4K!%R^eBJUt5*-MCN?qdk_uP6dsWF_1G16RsWXAVoU1&yjx54AZrFr%
z>o+P!F}XQeM`B<iajDG_`R)F@a+MLlQL8q8FS1C}|C16sc9`D0)@ngL)YE_+!RV&~
z$|$K-JRET;HW@-iq)2KC&L*Uz|L$NoTUg=O8WmBB_bCOu<~`Dd$6mZIULiQ3q-ovH
zl=|`<;eFS_)(%!YuFGf4=jFYzsZkAlQK>x7bupx{PAKo)uM{tfx|syFv#o+H7{}R_
z_<2QJU1Oo_k>&Z(sY7d2B0aSsP@1X4Paie<GtBsD4r7eoGH53*E*_h=?#A~se!u7Y
zYK>}@X`St5@ca|-a{I3R5Rd1ZA)z{BkpBOey9hI9&s9KVolJg_-&FIa4fK<7gE73n
z{`My<Td@Hz4}OOp!AlTMpx2aK4!TCVaz3ZE&lyjb74=@!v7&{VaL~)fX|=O-aR7tg
zd>{X=+JHzS0Coat-?#7FhhguG!{TLYG3?!svGm`y%6-7p{E8~QSGSHlhglR|+HJ}`
zDDeChyfJh%$Fh#kXG4!CVWSWuiMK|Kp|}4|j#u-;%5BM-MjS7_(630?KXO|&|457%
z<lGpGuPx&}LL-4!UVo3FO;#Gco+L!a{sTub{M}FR&*D}1;G-|_2ffiHWQ;10z6OL1
z;PtMJkjZ=WADavfex7bs8}42n7|)p0y9qq@X;Bm9O6r0r61RBezB6!VyyWNG)-kD+
zy=Q}Q=KKxR2q=fXq$tl%hWBa)2ga;kzV|dDXl!asoCxH)INISZS($#PH*ZmF@)Y!a
z#@M*z<w#clUohTu-;ND%_3}*&`eq^GsprCo3&RpQ(?~X+?+|}%G(1m*Y4btTZO>YF
z05&{Vnz-eIXcTdmp_YV-UKLdXss@Z}Kw?*$175FNwUY5yhZGvTHr~6Ms%k*hfU1Ef
zM+2QZG~wPr26pZ_rc8SM=KXxf2SyHg8jTuM!>YBrd5_IN#O-M8J$M@JTGxj&nd!uV
z?1OPLFy!5-IC<t0A|hij?z7n#_Wm@?{$qtw$H~zF-_KmEeAj3+DmoVLkDZ}_<;IxS
z?vfF7`Kle<&j?eP(!_yk#p<276`J+_9}^RYD24@#i%&!(+mb(H%wN`YrdoR~2_No{
z!u$IoF?44H25gRiOR;qHtB|!1BYWFv4C&T(WeBP)xQ2iwVR&u(ZOu@XPdh6Mg-Sc%
zpX;cz_y+1MxrzDLfmi))5#XN7_G8V)E+tq%$|-+KBQWz!7Td{Q0?tn9@n}cyYE#lj
zD9s5FAIb)<HDhbH>K1A&ypFo;yZ@F*l(WmgYgP4}XE={Bb?C6cPoWIg0Kxy;ABphf
zOpLF~JhA`^@UJb)38;r<*yB}tiN8|C1+E0YXPq^|(86)mU`*=2+j(+-3wUvB1O&j>
z7ID}R*&CXSGFHj-L^nNG?Rui$EupDd`WM|T4kdkhf_4%0Sk-{4frqF8LzM9$4(3tp
zTEJ9!u*pM9e<NQrAE7w0w6;-VXU5)%BXFy`pRFk;pK==)0NBUdv}JRe)~03Fty#N;
zypQF9A9(QwVi+$^ywzm<{a&3xlve|~%LoW2XQacLR$x-bg4SRDWjqPYlgc)GBVPKI
zec~vB;B@SR6Xz63@HgmsltNdiKMEUClG|`ziKNLD?`Mh0ca^cUt*8uZrvEy3J7)d4
zO97iTj$<+g@d~uIv_^881>EfI=?&+D6M?xNN>*$)Umfs?YfXUX{KXK&(>u$CVA9>Y
zsp$L4boQ5Bo8#x?xZp@*e*(6%T|RBgc^4<$dG2CBe_l`Ock!Y(>FLkaig@s<Vx9(i
z_3BAa=iiNNcw}PX!o@UX5o<!-1)j9*?_Uv(8B@y~IZ0agQwp`aIi>Z=@OQC){{h8Y
z+n&EcRSKp7iE};s=oNUAk>X$&V<B1dev*=HMN05GFu?{-@4AYGE&T91J+EIseHk}-
zx?LILz=JoV3#5GJO*6Ms{=Njr&ZJjaDc;Mvd%2@8!HueXDZ5FJ?%ys%Vdc#@Osr87
zj`o@O;QUp_z4pU&pXwNM;~chU#wm6ZzQyd|V^i?+>B#uZ?+hOd0jQG{ARzbJl;?v`
zRR2tRWTCIFPFis<uM!Gy7t8Pq5?5Miyu}k%D7jyJE;qzO?gY&{a^!q_bZv)Ozb)Xc
zn<%`N$T(K4-h{pT52JR?YD!-TG^ja$;Sw%h3eI_sX;o3i)VN_C((_i<PF6g(znDCO
zmenDA=4BuxG#uYdn~nMZEJy1WjnRUX)>SH3P@WS)0e$%BNlg9W7Xx5iUZi|{O3{GL
z^s`;3;{tqX-?}+EcWjjf;A*W8qzM5?uMkKdH{lzUE9;G_Rs2!L+e?Y#E#UjP^Owl%
za6wtWNZD9|`{O_Rj<jqSFzW5sxhcRG*!v{Ki@RgQkb(G!Ug7%pI2ntKPrTg!nCqjA
zyHu$XWMHvW0C+i%XU<)a$F?#@{UORV_H@7QXxg}*e%1s1-i(G2&kX3n>t%&<{b+YC
zbG${5Yw@NQgNmvF{wQ0<TcN6tjEbgpe-KWcmbl$nbChfNTV8we8+xDTrg(7=?dH_u
zM^FR(Th_v-WjA5v-ZQvP!`8C&s@8~430L%L&iJV-_M#X)sReA*m!-Q;BZd^>uRYxs
zWe7BzlD*>5O*V?Q^zs(KJU^*e+7s2vm%`!GK{y>8#(GW$Rh;P|^IDh2_~hr6_~f^>
z*hX-@ALrA&x}QQD?!^2837>6oG}JmBtZQ(j-G+?Ip7E>m6EW@(@8?<X^uwi_5!g-u
zUgCStdb?kKRSojp9J!g+r6E55Wexr}Yc)2LVe2Ux5j3mji;8?l5CFeG!g=1IcaN!g
z%Cn*(4H1gUOa0unaBSFn7M+_`Q)u92tlda!?(OG=KN&}TRR30xbE%4|0aXKr8W8YW
zVpzZO?0`IJ!v(w+!<(ryW-99x!<N((RRgL9R1K&aFr|U|b*sR`-I4FI2k>maj`)`#
zaf7-6DCgsaUR_(@-7zy1ujk-vx0L<lZk?JbO|9Fs5Azmn#_%`#;DdKwWY|s{oIM|m
z!9%{Fhja@6Qc<T?CG_du9CH_}$D4!uz^kN((*B*;M2vi^KkwPH5v(g)cN|8|Y84sI
z(kCaQQpK`ZM>_Mqy;`GI4S&T`JB=IRGEP$>K=RSssfq`(yw6$kdv#A+d|J<iAZ*s!
zOVYWqgG#Q}j1A2_dG39elg)K45v<qMhOw$yX4TcZin0B7O>8lwx=W5zmlT(_B;0ki
zY#Q)PvW#lz$7546;LP@&>1=08qz^FOc1&{C+Ey7G*VF-SRyY@(ilB5y^eB^tY0W%P
zk#yZU!f^{_`S`jPc<)3y=F{uh%f1-q-vHjL=78?>=pJ=Y*BDm7?OEqY>205ZUgfhw
z6Ah~5fS=CO5sGx*q7!Yo%L)xk+F@p6Ck#H2gps==@q616)Pe4tVj1q#jdQW*e9aNj
zujoa9aURnDgH9fFbW=sufU1H2h6eQRHvf%5JxZexiy_&lPa!pkDNg~p;R^sPAnjcO
zw|NT7O04KoCG{+rv?__mvWVQD#j8s;(`A#^9Fg&!zf9|-ge1jNxm;Nfd^_o7(sVoW
zK_|CqEy=#mOUqvL@<>Td*M-lRZz+S2df8QX?m2_59cpVeBJ`-FQwfR2t%w9T5~Oyd
z=R)mjWwCSD8HJ8pn)qyVFSO(aodD|w!hHTuK;MS6Fiy0_(*-FE%IhUaT?>NnS3~ab
zT&2Q_57{b}N@L1r&nt0B^rx+K#`%baS#f%b8_?0}wf`i3y%ib?p)WNh2G^3Qf@(S%
zG-!azl`G-gxj+MLTnP#?NK+E{qv_hU3-1-?0gwN#Sb0w#uHr#|;lf4Ku3gLcc+^yr
z8mL*)72W(x!;LRem*VI-DmNr;;Y-!IL>oM_I|z$fmdBi0{&<1lbty3p;fvHof>oJf
z#6#~<-i%<CmWo!qU-v8JiB=x2H144VZV0J_k6*)%&^RO#j1I6#M@uhfd_;ijIy-#h
z>}5>k%hoq#t7B5=Mf{s`2i0s{6i;pKp_qHzx@8j#d-G*{`@^p}_x{|vSzh}wyg%%v
zx2Q&B+Bx?H-gL$5xdCae11eY46e;j$|EIcR<(kb3-EvOb*{ujD7;!rao44;#QZ)VR
zm&?keaTE3X`rzmIoX%Yq3HUMVxA|n^5O2b)XH!=FOj=xr<DVs~6@Xrhn;Zz}CleSF
za4~Bwko{219*;cdx^`}-yl7@871@V`wfNurLlAs5E55g8teOrqbG~*xMDc{rdFClf
z$tsmohnX^R@xB&dc*+-J;ajeZrl}(T?9r_~&IVq@UkjES0d})=DDw)wb~E={%N}>m
znDs^_&+LZvYh&13uju7GoUg;)d<B7lmuc0{xa#~ZYm4T363TEfiqNd3H0u!EIm&Mf
zSiN=&jSbG@+por<a-|B|&8f$am<EJm``I>i@Wb4VSj2eI?~&eGTZH0UMAp&5e55TY
zG_?Gap4yfKR$rs%c3zU@HEoe6xUF4Z*eA*o2tUj|Z$+72>wWZWTgBM+$K|`RZQogi
zt%>f*@MEmPhLy{rXOpV@s|GhGGuA46=IuYn7>2J-(H=_Hv0V5x)H*nQBpNdAwmFhY
z<1V4^o;&(Ebo_uZ<(KY4wQ?o#cHfrfRM#o<@e3UkAo*t+5o{*K_GZQlmw8qokS_D=
zMNqgq=jzJ$n|y`y+;#Gb>3K5$*vxlOr=oYhO_@_*4#DXZK9^qYl6#G~^L)s4H+9ZN
ztUDNpr;Sk1o71*hM%BQRpn>W&YnlaKuU)x9QHKg%KM7aG!=GbHitd`=;g3iifvSPR
zYrxgTiC)wV_}+d9+jbtIVKoi!Ck{~>Y}=~7vIbkZc9-Ju>_W!P#`MA#S+ijuJX{^f
zdhx6RT1#5BDt-j3{b|5{O1>&G^!0vNPHOOZ|E$NT5zpbbzgDAeZGW~?LsKN@QykJ{
z4?r8VmP{FA*)m?Zb}fwTb7d7rth9VW8ZxXj-md9{|J7wEO0H#vC9gQ+Sj)bEM&xbR
zieT}acBKjQ8V5^L2-vnOV}peqz3};wXncG!1$Vh8u1hyg&u#Wmj*bh<I(uoF&-wIr
zr15u(1;XxTz|Yw@c5t1v-mtPonQR(uWo#KK99ua=CS{_<>Iix}TcCuAaVBw`!)v<Y
zpUWY5@8Df9oHqRJEbwuI61bTl&N#ZV_f`@PMrNRahXp?4yhKM~R9bQ|Tg<x<k53!A
z>sHAAHi+Kd7YJ;Bbpfb*>JA(=0Cl!ry&SBtmkuK_1nULB&QC<YO-fo}sS&;9S69tf
zHK1zX{%at2hxY#W_1N~1#to$md1z@pfQ(Jn*k-mtIkq7i=g#!bGbNU^00Q^WEx551
z8WxSa+}M)XAm)f{lHQ4<*B|S1*zk0BLG=KitazOHG>{yVOP3)JF&RUxYGqJ{z)Vbh
zJV6#Ktln@O?HN0|Lz|j8twlw};nz8vQH-6YrWeBpL%T5UV+DF>$0-~90)ChA_JCix
zEGw&=ibf6;`c)(8`6bDwUpx&A#G6~T<wakWE0iMDx~>&he(k9FSsIx4*WWanF!92~
zYpi6+{LiJT%Pb8@tXBb{&z(EZ1m%~NuWE#HxL3~}`IeIciHV6=vt}KS4BHmKEucz3
zHqd6eIk|5fJ$g(9u1%}RWR8Sec)gw<BxdPFQiUrwApu8oP6rS8{5Y4ycs+ZrVo?h}
z{21Ve<D{U9VW@&ja^K>;wLFb}I(fRGF9GXH1W7N%rr`Utx3TzYG=jw-imV>WO`Vp-
z1N=P@#~4CeZ^!Unp9z5-KD-!=c=q$DPZc<aT|ymu)82U|C!>`y=;fyskJxz&{>=uY
zb0(Bkir0(=FqH)DTuF~5z3kNQf58t^KcxYYdCxDc?v=w*^yVHl@->VZKZTyrDLVZr
zeX<_~>C@|9w;lSo@fkGmrT(NM)1}%C55J9N%hxLT(v-Gj%`Gc2iGX#I+z+!0Y0q2g
zh+&zZuMfI+X{+5tksg;|c#*F^{~zA@U?M@@8wMb-bSC4{8Jpgmv<D<Qk@I5DIP3RQ
zq@TBk4OT*vtXjL3<Kx0YPn0ohpMX5ZU5x7^@m!wx@hG@B8+wWAHT^)o$~Y!X_yF&J
zG>OIl7YRagy{IPznWL;V#q-=;!zI10)UW?B4Giww5I_C98-FrZwNP}6r?AMm>)}{K
zW6*%gKDf@c{>;_;(SsncMs}YL#xef2ZCp(OPc`YrJ{1E@{%(Y2$y|}JMZEdZJH+&c
zkA&6va6lU+zV@~gm$CWSMS{=QaE_aL!Iy4g@rFaF;_r>`hW19+hNeAIlrgbCKgOnh
zuC1=|tCGjD3z+WhY1*jQ)Vu@${&~~@JUd|yKAOE6ReinCsa|DM#f_#5Q`6Yz+LPM7
zF=U=wa^@M1D;I8H;f8~#=3fTWhWA0o{MzO`E0!sar&}2@1mrx$^T*5G+2}Y5ni$Ra
z+;cY_A(Mz7dJ|mNme;#A#*fQ(;TO`p58zm`-chwnsK=@XR1Ih}AmH_1i<V;D>Xn)#
zlb<J#9Yb9jC99%p;C^Yq!NCC?I&`4Xti%24LTy*ozysGn_s-3*c=<L={Q3_#F}z8K
zwhfgrl<_Wsmd$G8uYWcuai&`~s{>CO?~91RxIK*q?ey4hbl!JaPQ0$WbZCq@^tvul
z!kymRH}KQc_l$a53!|JV#xXZHXJt=W>D-Ke<dxNhz^N%fTDys4f~R?jCVMnN@Iw*d
z7<(caH9Qm0(@&Qj%~UI$ND=<i72xlSaTrt2I3WC6U>s}-qPHlmyH7;lFH~`}#*8M;
zcy@Op-Xi7sy!IsxJe>t!u;F_B`(~bKzA}tVgLYWi)>-iuzZz#avfl!^OU3t$v(YIy
zMjCh#@lv<4u*AHJ$#}1}tD-++dYRbBgWz>IMjc2%yHecK=f0Wdymu|@fYsqvSavxc
zgY_xRjWxzJ8M`VEPy=Z^_sKVCZNdFfQsu+eho1#~|KYo8LJ-?vTiSa?G7W{sm+!EG
zomq0P(SrDz6@M$1{3~V#29&{ZC9>@dGG$tO(z}%7(+2!x&+6_~UUzg%B8@$)iGSFY
zGGBq|Y(H7*CuEJ!u52OZZ+>@=ddaNakw<-wQsZ_206+jqL_t&*4|)7T`C*J=L^CYz
z`zZ9-X=xdJux8P2X!4AG%I1N1T^kc=%hkn>zeC1_=8y5}tvmE8i-T`j^EUqFek^pR
zvKb}ur4zZ~E>!0di?Kk$hSEuCQigPPV?OAE(G&im*LkX9B{g#N9Q5hY5ETeSN5|d8
z`i;kM{Pb1Evh={aL%Lwl3vF;YD4fqIq4}mo6=PFG#gGoy43Y6GLOlo)lEEjq{Fkq=
zyyy$`PLz2|?8pRC61wWqkL26V6J8*m1`4xg{eoY9{f(M1u6KKaYT`j$6e7Qc0$-a&
z(Xmz=0ubD9>CvM*rceKoUeQ_JRr&Q-y?QmXLFB#r_T%#9%Z5!-x81vS!|d5VWdn1@
z&!?thhA}n-O{V&prv}99I+j+ko}?VAL5rW%G%Lj-HkB)637x=b3`nIl>*aV0ys|eK
zbDR62UO6Y6qs6ayRf~6ZYfo3+D=n}&G!~<SLUAaH_b^!`X&5L{xcK2Ybw(PV_w&G-
z@F+xa(Vr=cB1#JHw;x}=fryk;j4WRfrlE*Dr;?K?zs(Ouy`@lJ&;EUZV#s0)bj+!s
zcOYfOBm0lJi}BWLFBE#yyi2Dx%1i54-^^5=Wd#|7%t1nYy!^sbiU+yg5Fx<$v~NDe
zkF(}r@4h37eoeNozgW(0OxkyIamFWO-iCn7_fz~U`r+s4lazSe+ji_@R}un*i|PW1
zMkl>{cEpG8z6lqO{eFuW&`cONN};}<JAawNyi%BP%Y0ilZ>Z>6&fR}We0)k{#&;7j
z`MaO7cKvo`XoZ_2J@-4d#)t2{b>FY2|C0V5=dskP;EfKXZeB-^>+L75pmT#PP*niO
zM8=o?X2Q$(d+Tv5+I$2juiQed3Z)fw%;9&r|G&|#QNBzLei9p->);DFZ=0pcWN!Xk
zBn3HnxeelK8*89AJ(~smls^dtbTQ;M)*m{LA6M=n%ToxR|6)G&em??L>G5n*XjoOn
zr-Txl+GLqRX7nSq_k>rwVcg7rF?jMqZ2NWyDiJ&`sECLBGxScDzZ9A0jR>qe6v%n*
z#aV`7dT!Eu?E7Iv-feT9)d-$T4DS0aD;WR$7ROkHam{D1J)n3HY5Ej{UJn`qY$phR
z{7MMwRxG2*Qa@D<s2VV$fvVN3L;h4zHSnM`plxg>>+@Q*_?#Uu0KI$nMs;3`lBSBP
zfyYGyjT=<wyNxTuTHZvvR<#wHaLwF$cW+5=>?|g?Zk?KHlF_hURnm(e!p1EJ(7khW
zC0%|u5j5TxgGX%{{zgCaer7a2{_Iy&@GZsnsoZw7%Q=zBD4O9tjY$~yS1Ob**m_C4
zh|Li@GK88V0_^_WsyNQ93difa8S|U;))idx-^=ezFt@qu9ejEslN8)G7*H`6@J{AF
z-;@)v_?KYs@XD4-zOk`9<L``v;mMeK$pVc|-oZPy`F>$Unvyd7_g3zVL7hop-ME3x
z)wonFxtfe;eQoe%W3T_ScNYLwmFXYHAEt+4=#cIf0SSvzR7~vd?yg;Hbywa0+U?ql
zwYDybi6R)F0@4Bk(lyio6HNYp&$(Q<GfWW!%<z46m>VZQXYS0s=Xaj>-4r@|x8FX#
z^i{uR+Nbl<?aa1CUx@AwFAk_@b3a{TU#0kYeq|gRo6qyql*M-AuUXbW)#FDu+q?~4
zbolg?wf5D*3>%_u#*;MksJEae*^Tq_t(*ls3|=u(S2^h1w>}qHp3b+gn`2i0FonQH
z=U_!{QQBOEzY{!c+cKI6znuK#=dZzF8a?RYb-V)Rsx7tt63)$~YPnHM^jTXy1+4pB
zUGWaod=Z~xbM!l6^%y<2@wJzxX{xJttEw%E+|&XO=4+X&f7J4Iv4(G#7oo1x@@nl@
zU^4D(79hCZY_UzZ{0(EAs&=DwHU1V?d4!kf<0$KgbA!UtWL4fMzxI5g*vY+HYp-zd
zKXQe~Yw+rZ9{V)0x898jez|8UbRMmfCe54n39qfh?^^A~F`IqaQMO>wnjkc;QKRTT
z2$2gb>dkSC_WQ(Rdt1W>HSAwcerl7a&bMrZEie7&8^xMhoA#q~`-XPi6-U|GryO9d
zT7<4bsipwGR?Q?`v8FCT6h8ZS*JW>I`{N(EJb&X-Q{BjT;P1B=EL>yD6!@2Azay8k
zC*B^d;B%U~v7WB4??#SNm!~ttP~0)7V!d>gd8<~fb_ms?MT_m+^DfXa3%#Kz>q7l+
zQ}5or?CPtoDC;amPp1Wa_W76Uig?LZRVw+yTDELyci(kKnYO*_K3#0@z4x}?bd`ST
zEfhG%m$6FqqYpm{x*6SKxiUKO#1nL3z*f9D*kU0(P*#CtjaU8Beob|$j{jQpK(UH<
zTA>So)MJOf`<DmVY5D_EP1V)jl~j9uaC&8*b~br+hTSrDiG8;`-4?3Lqu}M|m2=s8
z(-+xPbv?LolI>MB#@?NlqJ{cWL(RJyPf(?&cYWo}m%qKu|4v<|(0T99?G^66W|JoC
z4V#wh!&j8l{BMbWRZH>@Oa*_J<f!{w4|^k<-;X?b6ZxFqKI!-)HGXqrd*Yc_Y{JB;
z!Lt3NeBot>=L?pv-EZ2sp<Qvw**5qXy-6;j`8Ialr=val#C_&L@EaP}deY?SmZQMJ
zujmb3)mw1;!;1;;gIE9Fe(P20j;wR|sbarx+V_Y<<IBnQH}qv}O|(ys{_Af0<n!S+
z<hA#8k=VR!y?Hd->lQbDI;fVCwyIi+4yO}EwT7CjPV2O4DiKpqYK*BRF@&TjPOIpI
zmZGYr1Tlw1q^gFRYe)zZ^OVFC2@>I#bMC$G`n~tP|75LiWXZ~Yp69ba`?L46_qTqd
zz7=4bgR(-f?zTCs!y^&;pr<BR9T94YWHqt46&1lp|L`(sIslAry{~OEu_cO3PEM}O
zD8&!p7-NQGZAAY{k1o-RSCmG@YAjoe%u3Q>jdduL?u;#=^-9D2`dVf;DgJGRKUO|@
zrN<)FV7oz{_%=QH9>0gUdj~ur>xG$*PV`=UIX0)bHMD7RA0sTAaaVeZ_l0E+Kv2))
zt5m!*b+J6r`Bu;bYlpKHBH`DzQf?SXxLEjJ79y*y(a+~BmY*k4r8Ou{HbN{3(BYa`
zX#fu?O~V04T43u4S(CA!JJKWCxNsm)q^ClCvAL<$Pq`=&D7h++yM?Oz4hGm^^P3lg
z3=2HOqE#>o`V1A7#mK1$-gx{y*xbkX)Y`%oTEVePl$J{b2i7T+D-t}Gp3NH{;S3t6
zST0Wq?UH=Ez7Rz4mHPdg<ev@MSb|Gsa0UB(VgVC4f1&zZ{2c}FIKR+tCFh+<5b(Dj
zLzkhZOM9RbC-OvZu&!4y231<^Hx#i;fA~(gKat<F|Hv(uR*``stJZWiZ`W2icyT6-
z;$1^S@KQGALRw2R^Ujt92`w(3<MZLr8gn8Scy>KX$c9QZ56RU03!sl?Ykj8Y&voD2
zvVhOU1TZS-ay3CcABtIf;KOOo$>s9MkNG`YLPkYYJ?-H$Krk{#)SyS7f6(M1FvL>H
z^6W#UOqpHxQ8iZBcfXzQ?%DwjB(CO?9HQ_?PZGDVSWWHXO>(7o+&TYEtzBQ>Mdxks
zOQ6+X(#PEwDxFOe>$Zmsq3c@Lf|D*gK4B(!*{V~V799G;`A@4eHC<yP5!Nv-P5^g3
z$Ys=4Xb(ob5OPV}*}EXg-ZTDz1B5_tbx4h_3^)-`_F`dGqZUy*Mx@Oq5c#?(+1Vl-
z_o>^<bZ~16#?eDFxuYs_u>~#4`e9LB(NZg(eRSns&$2~X+ShTdu>A#I>)S`5LRmH!
zx=X3}IGnP|Nt4VG{b9KeWu&Z6pWF(t!yl~v=)^y<Dy})Oj$pkG*9iO&H(K$-l#VHC
zrAgcNnC*Dv$=--alzpf&7(UqFlbpY4#^chp6I;Pe%dzQyF$>=N>d-T03=c>ksuJT)
z^5Yfo-a58t+!~x@w3F-nn`VVBEk=M^_Evr+WymWsZE6%s`1QFW$28TEjmN#T+cU(g
zvd~|i-PCFF9ds0Gu?Iof&+=TemF!dVJIGIgpHA)Xkh3;EK$d$3zW`g3z9HY;!TM&M
z*|Bl0zN&q-+N#PId}fKaoIzKiNIPtV!4sGz(}3gKVwOKcYII>e`wkKn6fc?)51vQn
z#)D=J{t~&Y>tbeVN<P`8zD^M#KK(8sxe9*wcwXovy6k+#<N(?L9t<f#(SJ`Q7b_E?
z*TaX%@0bHq071jNbE($jXAOR46h2Xp!1L7!`g&H6Sh|qm2$p=|-W$I4?^057Q8f>`
zrgW^!!UVhkbwq1tUQWZCavRTCYbrKP;M$<gDoWp@F{?PpyeX_P@M>N7mqAM`HfXM-
zY-sZ&Z=zrU#>_L`&vOV|5L({4H}~i4!?iaqv$q_GqLk`Vw7>JQY3B>`lkS}=H`umK
z(TbNRoh+q}99Ol=i=0nTX{c(IL*6GYUpV!``G*{Diw(ilDa^N2{Z)YF=2SzVGql!h
zUv`9kQ@|pH8_nwJ&*<v{4f%CMdvvAe9dYN_^ej&Nyd#wrV49(!1a$%=Cu?#4<oQLH
zG8CF`I)R|FCEgu<Hl>c<O~g|<_hLYXQFekWG>;>Ba@OOjOYgIv?an^<wamDL{OF$0
zUl9$ETIG(N!q65F&0Y}?-d~s(Gyn8`{N|WG+T;d0H%oJS!#q_yJK3&Edp^q<SwnbP
zv!Y_a_fN9<4D(2^*KnFL*)1h%ZOMiDY*B&iDCuNY&56Vk_$lRw6#Lj1@p*3^Z23#z
z#wD5j)sWVt?8ghIb~f(Glrq;MH!=_gHkW>7s8LPEkZe_X+7SBrd55%a+32wIyQI+1
z^NqpxO43?75DUY|;kM=0c@*7}!@@}T^0`%}XDd~DRG!_lhGOD#<HZ<|ebr)6`tdQK
z%`{H;_w@yU*5(<R-I2#Kd{%%4Y;-^Lh6edM)G$5%$V6}d?%h^8yFR1<X1EF8%;C3N
zS2IS)ZnVf~ZzI<nrMCWZ|9+hN^z8fakwo*;5zl22e;WAFZzlfY2aUg}aCN^%;<q#%
zI}M;Y$!Fsel=W{iUh8Yjm+irQMz1SCi1Xv)dxZA`mnPh1TFErbK(Sr!fZtXB=q6!K
zB_F;Fai*U{tPcLkGnG0Q3St<(v+Rw|&a3}ccPHT@?0L=B{CV;67H*wF$=0fTZvYZJ
zfOC2mmpV7Xl$sc{*#xJO%8eXM?CKP1H&t{ADose331kQH6MpDs=(i}1y9UMx-<HC;
zJ@Pm7O!WM{$Qlh!1L&lr#1JyIE=WhGmbORweB^IHQr;d=?;K$HS2rcZ%O`tBZ7>yM
z7vP+{+jOGG!9#N_7}Y$#VM9|^Dsf1&;gB?^7&|QC;dp6JE$D&*eqmrIl>zcj>%*b$
z^IV{h^c;a>J>s%{*z6~NmIrP2tP!dF?x&Jj&Vivl6^SE`oqg#8Xm4XwdIWc%t`Q-5
zW7sTgNM%2Qi<<IFMRglP9Oq=RcYoX-%L&+BbWkhwi{D_lROYJ;&G1f9GqMNWdGm)V
zi@_ZPMhw13tR?=ifz*z^=T8NlrBV=M?2QD}vhfz{<x}as2hTkFwsba-3=SFg!;a;b
zCYtD*W8uC^4pHAYqNkA$kQp~ZUc_ZGfG&^V>V<AG$%}3>!M}oy*ndd*vlmGfnojj2
zJFDZ!=-+f4q>e6+R1;*wyi_7df_A1$6ax(DA&7Pl=*FC$5*Jtb(zeVOU-UmOXv>m~
zSas`F+6tHS$fJ+8AB|rk78L*uva>!hq$Z@sBDNPeKY;_Lp+Dc4oCog5qO$@dKpwvO
zZUsw;^k(=$CZX78bjkh+$=c1@xCW7Uj9Knv#~FHcI#HO=t-S1bzOcSj7Q-@VuBuU=
z^8KO8W|rTBFvqkOIQ`SrhIvomO1mHBp1>^Qr}s{IXj-fO;i)GjWhu6_XVZkgIOKia
zW;j=ZJhVsbSaH%?EbN|)MzT8i2bb`ro<Q{=VHSnLJq%PvTv|2Su>9j$QvPz|&LdjG
zPMMkB$kGnWkco!vh!W#V8KTwVlm-xwTrPvOD_{p88t~DJ@<}BD$q;#i^o&QjX(eI^
zISQ(SWsGkf&Xc9@A0&CBM3%pz*md4p8b0N5^P<@Sy&F}<lS-m~2SPd&##2`5bMuZK
zXB{xQX8lu6hnP9H?b78H4Z@E3b35ys>k-`p=tVtS4s<@kq}MIaiFo3MLrRZ!k2QRE
z<(LLC*|A1nhNsoFx8$W0xmxXN=gXtoG}H{btoc|KV-V+6>yI>bUSZ?BshxS0o{y9h
zrt~g@R%6Xy2u;A)9POOY{T(Iy>8rXYuZ$CibRGSNt7^LVL6*~28UDPUP1Jx|1~D{I
z{g-WbHL6ue@7(d7VL;DoOh-}0x-ofQ!Ub*BLB|x)iCGq7@N+7g-ruNynwtRZ2XIx0
zJCB+gP6xEg4WHMwdqd+t9*Dl9$Yk#(9gaRq*j8H#ZXn!z{M_s%%%;#GSST)@gdyJO
zqg46YK88kTSM5IOJPco7P$>p&*ueVLV-g&Z(=l@U83j*Dk*9R}2UhD$GaGvKcf+Hp
zua6S|a-5z%+u!N^8n#utlfy$1+3U!nE=2&P%Ey-tJ=SHa*S57RoGV$g@zBB|xNHFk
zbw=p?6kqDMyu?XpIENXK1VtY@AgA3chrHbgEs}DyE>~0`jvf%xju2jko)Ql{3URFU
zdZR!=fP_MS8Cc!veE8kSfhZ>D#ouOO+(k+wn~i0mU-i~k&Sw~<yVm4G$fYC!8+6T5
z+AIULsW{bt9gk@wC}^zo8Wr1=^cmsLdCcbc9rtq38CG8YMOl*5>eAm1g08gObPk|=
zH_Y4B7k#J*<waU&5E!`&2@Jdtnt6;}v1;AZ0we2-F^NH-5je}ruSMJMIZ><QSB6}+
zxqKjHabMRS_FG2x#~>wRP@c^%H|V<N)Z2?<jPLnkR~rjd3TC6mEn~z<DV==3;kDx#
z8hk*80kXX0o~f8ZT%<0Qut=`vFzp@aHeE4gRfVQn8;p0%)6g-(f>F%Hr)d@IxN)!<
z;Gt%_+zE_jDAA?S)uH0*ov{zRJ1*sh*B0DN$wgo*!x<8hzbe9V(!(8acJiw?Yux<O
zY@&3*J#VigcqmWvTlH}YS9`GtM<UyiW_j?j6e%v%zkJ!S+0RbuxGK2X2N?5h%qRq~
zDy3|H>=lx57nA@e?+&g%f@8Ss0i(M&*nd8ZL_gp>WG@@-*5BA17L`Z$_F$BcTwdxj
z{ZW$=+~4|K5TxZYYTd-K7fe!xr*5lQEX&YIrR2FpNARonUl*sijkcsN;8~}KvCs+j
z-|29<O`cy)c2VL;UG)ePI98Au(Fv2iM6At0()iJs;KZI93u3`sx5Hz!O_+*WSn5}|
zIdBKor%2RxorP03Fp1sx$iowF0l}|f6B7hRO)IrBO@Ft!!E)TzWSQM_v(}+()oBbq
z{EIgt38;UhG9Lzzez!PSRtXr!KG2yX;+OR10(piV{8oswy^I(u2!+Edk8W27ZoI}Y
z-UYNc@Vb?vRk5Px5)B5(EouBrtT}_!FJ2?($Bx2(&@dT`gQGF;V)Lq3mf!Z}kC5ij
zXq-n|Nx{fj&D{|v?(`*-6`zcUJ106V(-x4CTQ~6FMt=~L?&O$J{Rgjzh;=`N?0_3Y
z$~Zr)hL$R)>hOByYBpM^r6K|FjJwbnQEB~MSbgMIk)4jDE0aG=CqC9D|0ZxWWmfxr
z28KAIh9T_-Dvs@kl|75^H<qg*>TmdNY4P6(m;R!x-$h$MhQGWatTC81t~1i$vaEuj
znH&*2K;6{&xF*NXFBb%pXQ^gdh+9!B_}<BAJR+w&ey)hisI!-Pe-W5So~wwS>w3$!
zQ>x(NSDxCV>_aAhCdQ1{tpvT<2q4|;)-0S4>PW>L(F$IgZIzIg+}h$Q02Q4_Esc7%
z>dsVy2B9$TlG59Py+*FU!w0I~caEN^^?!X5IK39Yw6U6cK)!m!k+<dmsqIpYp${K$
zhf04RNwNwVH&!<}{8II$15zh;R>GWOA~ntZh=~vY?*aSL>v-30nNd`EJ!Z^UDvciI
z`61Yq??U~Oimb&M%F`~NtsT-_+ls?o9(U%EYls`|SjmrJjppY2gy!DRA3SvDmF032
zu3KI9=syQ)WR^${mFZgNon9*H-scX8B$zC11Epye*#+kmGdghF`!holNOM)v;HuL?
z1Jv!`X1(k5ouDujssQSwYfRrrONk$HDw5SSr^)y`1fE9I)CbvN;mn&QMi@g(y;&~@
zlabS^Ba~q=p6v+ySdcUb^B&WW{DkP4S(r?tj77SMK4{O3-s`Y)=kliRM*ErZ6m4OS
zzr=pbA?>ZI)#a9aEk65gk%36%Y@Y2#WICjHPFus1`jr3a1XMB(mpJ%hczyjU&^n-O
zI4Di(U}<E|6Yd`3c;_CJLdI8ePRQ;@2tao&rzN8_PqEY9&pu#U%;1zdeCaxVy#-Vp
z{`89&gVx+2tvX!au~bA<DMRY%59}a#R~(-2b8_d8Pi0e3DfHuOOHwGazii8v>Ctzt
zz(&Bk)z#dlB~VRQcD8F*`qt2sX5!}LagIFvu{oUG^~SJpB4^&nJkpgZNvaAU-*gq@
zW`1#2$VGm9WUkMO)mt5}TU#~g%yExbY!&Ua_#x|z_=5N-8xm{||AStXR~8<JTgNX?
zBs1hgKKVE`OAlXXo6JpIieK39tdWhZ-90xPlo8@KW;<9XdgAKU6OAz`ilw!kJFCg?
zmzf@(1`|?p5xabV&gEKaO^N*K*q=IE(-IwOL4rod#qf=#S#YJ)$a=@)mGaj2S}xp`
zrsM?2_t|QC6Pr^nBa(zW%)jlPV1S7@0{meB9J02T*P498_g<WG!_UEtPb>1`gw(g|
zI{Jph^B{MR5l<F*$P;<BMj85!Ia=DqtcQs)7dcR7M>E~Gjgg|^6P7enm=M?>O7_GS
z`=i-9kok%fuB2V_0q+QggPOP%{vj@Q><yeDzb>urdnCfI<0(G`M}_)#6lct?W(NRX
z^C8*F?)Wyh{!aJ$iIir!%Up7jk3Yz%#J>7>dT)d2O7(ftM9Uj_F6_s3QI#eYWiLvV
z<qB}Kj#;;SmddHBaQvfOy<_9$2F%Z1$l2kBe7}uy)D6Ir|8BVFb3Thm+pnhpesWtL
zVvFbIXX@4kNb&H>kNLdUDt?(bp1ze~NC)Ber8x~#>+gyCbTqN8rCN=p5$k$7Qw^F7
z-W4Qe3suPRc|@!YTFW+J+=J%oS)cXT2dq3?H=~BmrDnc_Wb@bX1l~_C9&O$m#(m~*
z7&7cM3<gVub7Qdypsy6Pb7Hl*<X<-ZCr2*HRfRlB5Ya28UGZK7H^wZjWH)`kvFUkA
z-W*>Oeq@+5>*hd==qQ41X9~2i^L*xDxQS2pt6vD8!;sx2sbil8Lb6>xJ;4NT@Er^J
z5euwrrlQus>zP&I)P`(2u-b}(AX%}4N?esMH}I-4zUc~U&cHi}3bXtZ{tc$`yM;ZW
zINOZ}vKenF&Bp6}n~M_63s56ujz1i<tR~Q!e)~so0v$XqY0ra&np<C}#w~W;sJIhH
z+Sx?Xc$Djd@G&7zSy?T+7g&fjCO^vzEvaKVUw`M9IS+4w!6}!7y><w`Tp30Ud<Gt<
zxJ^H3Dr0vFJW%x}c`L&7I3so7B~1goQPk6aRH!>|31SFf?{!#1#dN(qy@6Njs6E!5
zyIG$o3M@8W3ULhFxw3ZDKP}@H#h35_s@x))M5xeq8aD5a?ya)`vN8;aN{!Vjiubc$
zxQgz`4OPj%K)k8`KD}P&0XZ%=2iaFIJu<>Iu-+O~tJ2FcZr=$QB#Hcxj58J#r2Q26
z4qW#Rvyyay=rV5h{Ls+Y4U~D6QR3fzLp?D&xt$C$l^njaTzBW~qpZG$v<dPZ?}YI%
zgW=PQ4LYhfx2$R@ZOFS*>*uyj-8wR%lY2xy&?&{kI7)8eyM2o`5lwNY9dZ@%((1*2
z3CBfvFVfr4EI#1zT#_ksyPrEEp@Mj%KEP*J9ESp)Hg&*wrmez;kZJh{;(bQP#5=VE
zYzs8{%v4^1GN2{rlRmsCm3VuswHft=E1cMv>6i8?q>MROR+`mQu_`e;bVCJztzv)V
z%qD<@Cj5J47h8xq<>9RHOZfZ;VBgAFl}c_kn!?aBVb1r<RZ#UD79R&0p;4v&1oZl8
zs2zPSF2eUMVPx5zhr+W+CDKDebEjRC^|xN22*-e@aP8grH5!c2(j<5DbCAWODwm0;
zJX+ZkP9Hs&Q3FXTr!4yDRfPjEMwpGEbLb284nv7D(hmxjLL?7lM*7IxJ*fIvDdiyG
z#XB5hH{WIy6xNj&Q>B-<?%@%Gi_UbzG^n=ke5mZy598&#=)u<v5Y+(~Cp}3=dfJny
zhMS|BG+YSX13UWkU5m2<FXLP0jF!*9W&8veB3h4tk#J)kogY+j2QPpJ^YXGEvo)t;
zgyqrW+_~G`8Q7cm_O!k+mMh<iI_BS%P5gE@k=xMbUhOm3$DtvK$7W}X){YD6WaVye
z_&Jf6K%xAl<%Ag3QX}w_`Q=zd$d>T?VB}O~xy;9(=ssrZo5kVDIXl=Z<&`=M7LkmN
zHMPSVj$JOPaJKn6ylU!0aY(SbYRICFp<ZP6l~Kh{b>MWBY&8U2#^Q}obix(&oxuad
zhY>pV5<rXW-hu?eHV12SXM)jee0*C53_81JhblvC<~@?$Rcut&su*Xv+p%X*l+vQG
z-L>!6;9NF_xd0%7$UB44qJNMV2xTE7But1gd~H#}8eEL-G4b*QjDn<6=Z2=_UsaqH
zsgK`YHZ~~)DxEYKF_8H>Um3sPO41f-%&zA=Ki1mb%@%#gX7~TlA}xK7uOjurx0P%#
zpSKW+t9)`lV0iKh@>|Sf`Gl*L@7<o%;!Ke%6>iM*Rx0^{{(zAxz5lw($pI6o@>z<6
zd{PCd3WJH*j=6veua7$&q_+Grk!R<e&CF_8`W;f>QeQi(+>m{KP%;9}^t`s;+){!*
zIUHZA^~+zo$l0=oQuq_O_&sKrvkgT&noezGBZC{i`GAWf-YO%!N*1|-6hRIZ2R$#g
zs(Rz(KK@@$%!E(69q6-<Wr8tI)5mrH`kO936TC1Gd(q_LN-}MyPn=0>(hl*d=J}m*
zc>XQx%(&Y__+Vu=ZT;$1ZM#Nhg9sc%&vg!8(Q2wl(VCvW@%X{2)T`s>!0oxZUfp(;
zdd0QVv8@a5s`Vq4Sv{{TQS05N{o2)f4kNnLsGQraDU|q|kv3~AEW(3~Ka2?}OeK`x
z>T6|Fuc=h&JIBX$RPPsBSQV>*CBmoI_mg$T%iy;thQRM*8~%@S33U4HK|I$|{PxIC
zkxLPWn-Ib^)d9w);&QB@t8coHOZ_)0{Fc6}FD6xGntrJ5O_Hmk`k)hr+fHDPaMBf%
z%3?b}6k{VKa3;9EWZ+%_WOpr{u-0;r-d9wITk5GE7^E}OXtTrHv5I~xpG!FyVs_qA
zh0&C+0c$)A#x5~pm;<38lb4knJDM%}+rFQgQKBY*bSqE^sM<#qKC@sF!zQvC0W}c|
z_Y;7ZC+9-%^4IYS*p2d1o?7+3@S2)(kq9&}0f5t%(|3KzfllT-NuNu=M2<u<=x~3X
z#!_O$YtlOpBdT1Hi!SA%^@YF@8nt5R$Metlw_>OUTg(%VfBoWR{N!7sK_*=~L)9%R
ztQnS4xa~5!@{8DAg%F1Jxn&?fkxM$tX3%81t6bFL?oX7$E|{eb3aK;Srq#jNen!6B
zSFy8W2N1lm$1<x^!3hZ<_Hz!rggP`ZXq#rV6Zuj3`enGf$@FTGFIIScIY1t<P8@gq
z64Fm8wzG;ICPY8^D17*&%L{F%5lThmrx$JVul>NiCs<c*?rcdpR0Y0K_oh5f@N5EX
z#-6371cv!Gb$&z+y}x2r60+<xF_^N+Z#ypz8szR1!L#El1N&9tv-IHh85%eg`Y^NK
zJYHx_Euh>vT<>(w(kTCCAFSk7NBTX^q~#cMC6&#fE_)8uv%{4Ng~f=j1FeMa6^Uj9
z`}1y2+hE3-G4NX%KNT~($GacZdbk=TZgUG8%QPJ__LBlz-5*w^ZYu+kXU{)Ys+m%+
zg8*%H{sx&R`|I~^+5#tG-d5YZR~1WLgE4@rnJbXuchNumxeBAodOij`x1FaDN?EGx
zSCK_OZztKvK&%F*0VgBBGa_GOy_`Z2F4Tzh8BrAuuE~{j<iQzz=>T9b|3xHov?+WD
z#AUEbaA$WT8=sm0HwLKas1SLCOREPg?mKKbz5Xky(2V`9*W7gzUg4Ud%wR8-BtV>U
zA{fs~N!F6oH&U1r>iof0*~f~K#mN~Om>)|7G@UirRj`g_39|<f-DO85JcN05He(%I
zu^j}8dbxh4{m?>uFxDo|`kQ8v!;PwJZvDea#Nt<DMR2{NqfB7}Qigm_0LYhgOX8;>
z)^Cu-s0EBcx%uXVEAi68IqC@X?6+3u<jT4ZXkXrk8s~VA)SLuKdo=mlKVs(J$YEi*
zzd=U5J9R;q<$dYOpI^>C`&N2%#m6(*SFN=6!u(ywev4aRV0KRB>~n=w^%{I$j}>Sn
zvm-#}o&EgeVv$G+)O9qb<Z|X?vF~fqA00kwp0PJtvRTf#?=P9SRiXAE>ViUqdFDxJ
zVI9v*|J@UgS7kqu2R&61P@bud`g%sGVR*&f=h_buX1dOn#D%tBHNbDJ{hJK>Ds09S
zWBV3#O3DWjeeS+w$}VF4HX1}sl|{A~83xLpe%9vA#{KN<u7)TZ^p)1HSm4)iXJMmH
z#+dt|Pr%kDAP{wTW5@!|9Ftda0B3K$kj9*D>-{N<N#ZDUla!=~W~9WbSZ2!(pA#f$
z7Xc;mp_^_z4$m8v)orQqMJoOFAM!G+=|%!Gm7a8{B?Q6a%QIAvaFEoiAJ4zLn9ih}
zKzvym)5JWCtw9|fNZD9<^UkjbMU1=}PScbp-o}5udRJ0R8gk~WzVgAZk%yO2<u5oC
z0d(oZH7hp^c80SM3nm#vm%Q&wqb{$EmoHXD;p*^<lTD1#O76(?l_2I~M%++m-4#PD
zt?_9!psr3p3)3!gxCv<viPD1u?qPA@z^x!o6DCLve7Te3pQ~g*Ily6h?S@S}bU?kk
z0nlUDiSd1l-G3?>&mw-^sv^}Pdum`CewQdzK_yLxxAN<^Z(VZ-n6MZwyTmd#j#Tz2
zmnnii4<v4`DQ4)wlMpb;-V`Gl-OZEWXJs>o##d1F=eUFo{oc(5Z=DF4r_i%M9!a0C
zkL1DrhJ!KZcOWNdqG^_H-Z#jg=bAqqQ@ed#XO2;M-|_C|PfR@0P41Hvj14`l>h-#%
z+V5!A`(?b~R`Q^?2nLA-cqmuKE$Vo8-Vq+jq?uKdJ+JmRf<p3lxGP6Skd=!eU!5hV
zf8ExBo<&&G55PM1Fr$@Qu$vhbG7OoFzrEj?GS7pd(29*?lDQVx>sUQ^14WkVs2y*1
zS<u~;ArjRl$AdL<?JoWONu7hm4r%#&3xSP8Bz+Bau|4}?6}PgwJ(YR4=Tw28XY?$>
zE<BAf6e)#Vo(DDuwUcvh%Pu4J@9%lKkA2<#!uy%y_&mA8o?X_xCFj(@7JjN-YEHp+
z;lKBD(~w1F+UnMOn6U+e@OV^tlcK{;VolJULXpv!*~c?afDW<QN?ac0r^wIz_`RWk
zw0h6+bRK;tf;rOHJ7t(x7rlhr$KKXBFmne(cxWK~X^JGQ5KWg2{6I#vZ$v9*Tg|K%
zpZT2V_!a5VWm6l;eZd<6W3wI?IIq1D^vW0!NhVD6hmGrQwk>hK(YkAhJA7FayK0gX
za_Qyx9~RTso3MI<u6WSW?=<I_^2!_6uUo6jLaKr4y0-?$j8BtH!B3=|!3(UO4|Q!V
zrkhQIDTxp<I=L){vn(inS&}UNj`ePCw>B~FPdTq|*4olT+i98_JRr7#Di_qtS6=K&
z#n__n_pnoQN|&Bmn`RU#w@F=f@^ibST;>c+d4tpFnv{IzdF879UEbC}jMM@8J>a<R
zESbhkd&0VE3Y;Ts_<2NfmQPy$#oJ$0w184@4p2qyf1xWpj@3J}q1q5xzp5KMEqbCv
zq+Y|{r!0czkGYD;RS*h5oO_fM&696^P<f?4t*9M`V(XO=1a8KD?0wpr$@Quqdb7>|
zm;U1nZu9=~@^bAZRqfeEOfpl$mWYV#h4Nu4g&%~hF<Fk2A9{Df9q=&l3Qp1E=m@(k
zSxKhA>2Q1Bw&&A@zC6@#WO>f*u52_IpQ_y5E5C7F&A{o7Qmdz_S?P(>t=F%{-GhC3
z781l=s*2@w58UBQs`4m_&gK<>xsvkD;=_<npS(;V*3}+^5`N3!gC*}8C<CNglSPk<
zmPAa%pC`>Mm_K5gQU8ube~Ws!f19r(76m^0Z7v#|)2)5<UqR~UXU{+m7=D=Bk>bxc
z?cHvX^+H*{UlDm3q<NruF^B9UcU(FooORrRN+FHg3*Ab+?nt+CDRq4iYb8>u_raeK
zCOdM>5q)I9CP_qy+WtNIrN4Zuy`Im3t|njiD>haW;jXMM`lL<KUc<Go0PFqayoV|J
zhNXM^vs*gLSeS4p;7_5}JWie7nbHE0$z)os;oz_YBZ)_?ek@cZq~Ehx>6dkkbYiiO
z+aD584KUdHxfD8no)_3wO)4?z=+nGy6-w@Ka$guE>ExC?)QkxrvU0&i{zMZWx%SXo
z2rLIa`qd!Dk5Tl*o)$h6Cwn3~wF;4CB#6%yqHm#E-aHt%b8~sO;o5vrZUPELM_o$h
z4jzlnx~lM9zbx@zY*>)0%Tm4-NGRCd&%p67)l#9H;!}Ue_kw@oc<U^v{h(0um7H<=
zJE=CT-(s<-*W%c914-ZAV@G`<j5!>i?R&&7u0UZkG9v~e)Y)PEBok{h@$vS|nOd6<
zj@8YxGXW-n%%C~1kBYj_dj|ZJMw%7_$LD|(Pwb7moHEEwCD#vK-YH;S-h<I!8bg<W
z17QXZSf??`<32#^9Ngmt9~tgUfHXGT8B(meSTBOV)!ze^<fnbPHq=^2A1#k{FYMcj
zJ$pV{WMXgsiJ(i?NAW?o=~uxyYg`@L2wi^LSEgQN_n7^Arz$2zts+6?q?gRofo3d+
zU=*MKoOI#C>Ty9t5VrRxuKk(~(mct3<l6j!deagg8_<+*-+#8(zwPm#$EY#;Z}`1E
z_N6K_cc?%ZBKrHJ#d1SNjZV&+=*OAM>vQUi62Fy+W1xWW@OWz5Jy2P(atAO}T8*s?
zvDBG&ggk%(1D4S<-UjE4=%0<9k^b4TI7N8-_k*P&D{qoO<AF2H@#qA)cxu)g$?f7e
zkmVhz^gqyOCf4AjU-=?_cl{mUFzy#8dv333zq?@S+3&y+b;v`qpz6WR`Ed5*CS%hp
z*M^q@FDdG@#))MYtG)(N%nf=jL*LaeMo=U8eSIn~rWfzQ`o=3>8Cop5e*9a|DOB5w
zc$2(qSD1s|MbQK9*Y7y}LH}Ox9jTzuC0=>{KfC2WQPe-7)ENt{{e5$V3BG{hY8|=p
zi$#6fs9uY7xzm?44(TmRwwJ$D=vQ+Nt6CoxX{&Z|7pQ2|e32DdEo$)Kti69%Z^Lse
z&4}p3WN(U#v^8~%88t{arZq*C=B<9+UC!z+fp&0Hi<F%;)%nHYXH@5UE@-T?R$<fy
zehD;zThoZY^q1Gp4JOO<)AgQ7NyjP(?ZK3ZCneWhta1=SGJa4;4+!7z)Ixpg9{5nd
zyU}Kmq2czZTxO<!N7eHMpEtOC^`b({PI!|Gz3(2e%IkmrMC+$fuU;LN(eVAy^}#ZH
z)%Zez%f>(ZKq2>*f85^rtsEZ+09PS-LQ1nC=%EP|lPQI=<}CVV_uxmBgZkG@e8Ltb
z!<dhiLOcpho@SHqO3CQlaqXh7TG*w!^)QhIt7;7DG(ZzDMTQW<q<QHzgM0Ur9ZYSw
zhxM~8I!=f&%7weJ($y%m@$)W&vYm%7Nt^vD^_<L^xtZZa@@YSP4Ok%i-j6R+!57+3
zCZ?5~(~$`d^i23AsnG9nwPF|U>-7<TIN8>*ZtPXRD8EdfsRS`9?Xo_DT&qhSVIyT-
zvm-RRXFI_@ii0zC$x-}PMl)wp<~(CvG+P|iDIYd|RmVtj?*GVs|7d^zdExAj5|#`7
zmZUX%QfKn7^S)b8*?-y=y1O&$B}VHO6$Xj_wkhLU)Zy6L@^-u~>!JDE6VB!|h$LaN
zmXcoM@1>ose^<*s?mN9gEn6RSC+nla*G}6b-1dPGc^&xJvIqKZhSKiCje;wu$xrqE
z<eUM2`~-xc)k7a*_t|%A$4*I9DNR6q9X(zjQt0H2e-fCF`!gpc{?_|rbCEbTEYct%
z2aI4T*1*{Itk+~`fD@Du_~@nH##$hL3+KitY`Qp7;QXr62RL<mt2%$xzj;pC)*adH
z;K3K4_ezu=s7OJRD8fd_nr_+jp*qiX#pEt(yZbC1n&p}199*kpYUjDjizqI!{8Mk5
zn9E2EL6o5e-&PqcQf-LGpR!n2&Bne0w5u}4f@jV^MsUAuN)vwm+dKWojQ_%w!rHxp
zna>9`5MAl&C&Y__f^SF8_vUbBGXf|@1zA1?nK1jhrSZ2HOroKQCsn@-W`l%5`o7+)
zD@~3)|BtS5WiRfef@$LuryqALfev1C38F99p@XTY2TrH<u9ep7g_`-4*bNMZ-s@7V
z3QAo>_ug@lGeZZe6&`0NR6f=4PcTURywvVy{~UK&#e3KtVRm+NvD`je0S_EHC9P@8
z8y_reQ2B^EspOgZoyWEJ6o@-1hoB#BT{hSw|1OxA<;R%0q+CN6=nP60wwZ-e@rcLJ
z@_X+IETSsx$HW*TDI>XZRbr<~68&&+201UZfWZu~y*W%kKl6)#%YQb)Q4|9ZkQXnW
zaGv3&ORaQ<ljH^n+Zqk;F9;f<&0q+eOj(4j6l5g7T#wsR$t<=KFMH%{RwiUZw&%um
zetxT~s@%=-u^1DS!uu28Rpbw2i*ZqL|2Ja&!-1cDle-w$qqkuz7*tj)$dza8bpyQZ
z(V9DLVs4H-pR8LkdPjeI#Rxx>>QF4`U4aK;l4csFivL-!&k;h`(~8zY{R%sLd+OFC
zOaQ)my=>I<Ng<-#-PTf@gT>dha~>^>EDrYDaTQ9#o-u&52BkVVN32yf^pa^wYseoC
z==P=h_ZGL939m@uK9?&^oIkfpw(zcfcUs64$P77)YxA;0dRs|DJ7b}z_LFBK0_)ds
z##kFpwx<eKW+`;6<;rI>v%Y7H`}sGH@93}0w{o~aAWA0ov?rPG-ZrZ&S_M}9Wyu-a
zuwquQ#LFwqblL<%TRi#2o@mdQtN59vU7w>s#$_RzPG#79gYSY5uY(W(sb?O>PgXR=
z48k|&2=E%Q2G}e|(BJq!Uhm&d5T?0jw?1q666)`>ee3zHm;+*r{0#q>p+9<pj2(Q7
z!@aFVkt)|3#Tz6ft-mas#=SIkb-k%x9}{MG`tP7(PNX=y#K7l%U<&yFuvI@Pc=p)X
zl?A=>{Nz%Ux0*(crn~EG#}xr-h^mSk9UHBZb50=&Z``L}2HF~ftePutulzWXUehvr
z54({XPxlrnCCEGtIMlxFDy?`jKps`*v|2(Ec;7iU>o5WrRH@B>FB-0XvqUYbE!Un>
zrAtiQyAxGyN{{2T-$)ywEg@-fC==$7=e7i`R~4(ou5wSOo%T}v_MbZJ=XM;fPHu5{
z_2xKRvP97!|ADht|N3C5sgX|D%8o$VrrE0N5e+4~G)H%EdBuNLv$`3?M28w^<xCMg
z=1RZRkWvW2d+Jhmweb!jKO8(BTLXRhAo2g+k7bt*Zr1%KkQo@ddx*A=r`z6ex-@AY
zLVk{wJu;F}^Lou*8q>MFv!b-Bn#tb^Ao58Mt*lDiJ5phc@>Y96G7me{D<-2c1agja
z&%K*i0i~1d1anIKR>m7i^<gX*imzl^Xs>S2>;s-);%l+s&m?RBZJbl~sL7YTIB4k3
zKIQr8sI?~Vfj$#M-0|`D_J&cz=7dIOl4D!bovOSnuO;s8cnzX}M|^k@5*Htond@);
z*xn)tg7hTq#X=Y2cHMVB!JHpXvy`*fhwrgAXd5M&n}IH6U^#1y;5M^uT+bAJ9f~)4
z`m{pbzgS}F->#(Q&KZX(Ezc`oyV(QwN+wZ$H6b%4FQ@zLYxYuCgtDEWVE6J_hk|dK
zb(>oJ@92|4zg~3RW*rE?rvQP!rsQmmFU*Gt%{k4~=Qd4_KB!b*JU3c3=EP=Bgk_OH
zL2iJb<<*^UH*9`0uzYv9s@obgc}N6D?irYpeM!5NHT?UL(`JNn7I(8KLvH47-J!<|
zrc5*j=?O64e{}cswEKi8%*}OUevI$IE`c3}w9@n-R{7nPo0~&t>bt40-9S!HpO1Yh
zFOx#|Z4I#G$QwYzw8=W@XZk9{mziPhQLuiX@j{o^A(d(7=y_-I%JRjL64bg=(JD@c
zgz*j_M5wy~0}2!?|3U75ajAbHlVErFW<N_l71=Ba8q!&=xB86Tr&3$XtQlbleC@&L
zX%pqTANrf)F?$tR%9YFbWqtf?Iz&USA$Z}T4v$e8^Lj*n9pwcGrw<gl@wseK(uA8e
z{6oaBd)lyx3gNMwg{IFyn_SrR8`F+enSBQimn9qQ4EYQcKGGZwjM3ZKoFWz5-N;af
zq!Ko$@W$}(dTP&ALcK?7O2U2v3#M)I<_vbdcQ;!NuAX;uQ%_GlB`+T!_+UU;z(h*e
z>zy<~i|?#Rj$KFRz&-X~ut6YbxZm8s?^0&)+c40?#4Mc!b&g~Yat5r-W94W679<5f
z+u_n{)N5Ew^zh->Dr)`s6G-XA`h9Oe6affiKmDYT24P6x<afyAH+j&n5ZM>$7i(he
zN}MJ<vCF)*fT|tIoFzi!RmOA{CCTQFO3>g*A^M{D=*A%aJgC95ns@n=e^nSag4xk7
zU)7nHu58lZ*0hhUy>H#Pera~07Wnm9m6wjy88eWpQv~&v%-ZM-sVLIQfv9gX*e}Bf
z*xiVtKJysa7n13J4~Rxxig84Lk%ByMtcui#h|ViDt>i)>L#6^EHmtRyz|rioTr{hH
z$)D@8gtgo;A0dmRcH|1`n<@*OHv38mY@h?)pFlm`x?ghI&tCm%@?iOc{|4B9@V5Q;
z@kVrfzu>&XEE*$bKYCJ#yZit=t6L}L$ydE=92lQX7S5>-A@e5_R1N^iazmwHyvD)z
zg!_~Xm1O~E5Wh`d8p8_%M~S;Tcs*9<4eewsRi8V5KI7&SSBpEc;^~nM8#JWB!NkaV
zA3!^yZ+Ikx7Ky!bReZzo!|T^a_D>*pwhGvc;UKHDF*)0fh5k#MYrWjD5L)#}08wu!
zUsyikTH#IuZwVh0wp=?UE~D!DN16C;>%LWE`AD^cC6UFx092%ocJRE50B1x|^?DH-
zsx^eL>QKQL?`S9!zueI1wjJ{i8h*18oxbwHG+Rn~gyP9@ivMzaHheHB_*6rLfE|uH
zya7Q9dVurjnbZN@)70TEEF^}b9yC?ctoW*wQtINx9GM1EtHTQ??R$jXd?60G);Y`+
z%9aeeFN9MA`pjOqbUD=MZZli?rk7yu0M<B*-?_tEGEYs-dK7a2Ryij}=#7hHJM_wi
z6yuI<bUa+o>XXAQKhRknqNe6Z{a_;_$KoQjNT>zWvE(xT84r)S`E#jWI<*0p@VCJ6
z@>NLQco(;3DRrasb%kpp=|-ayGUR>Nr)q1d@*h63z^f+vsI(^?tFYC&cwhSDN5FrD
zlm9$MF;8qVu{6fBec}g2jOxD(H;n|cN~;N9!dqUQf{jNB`xO2>Epz?xUtzVU{pHre
z$rNfAHolWJ+a-$I<SaD`X7xx1Uy#fC`(%lszBWmA?raJW*;j?Q{q%@9T)k4bhZ->^
zXizMQQz9<m=+Q32tym6IKz=e4Wpzj>RA@7PeTh;H>2=4d%tebTADrE|Ek*O75x?_h
zz^U-+4Li?F_kjA<I3<UgPTj}O6^~!YhKxQjytNFRqzdZXPeowBc-WC=@H;i2G;)c9
zX5pegrD_3|X>|BRx4}Q>9A0{hlI=l990DrB-OH2hz~;l2Rj4mbz<~=3p-@;-xX8UN
zn|^f2QT7g`{Yx<yArkB0n^hUu41I6|s0~S%{nh^PNoFO<H&&sNfDV*58o{cfQtiM@
z$a|cq3d+3`WUHrrd=Wl>!uloFy0GM>zkT`MX0H=EdghE<QR(XFVLfc7?>-*WPaw4v
z2}IxeYA>gro$#qu4I{(V{vM?FPw5^CYI+zhzw6s7KW3sQ^2ab3`^m+sK&dP?0c0ED
z_~3|BE4t&!Zrv_7;{~Uy?FvcI+*oH~NZ@n!!1^yjJJt^jw3az#RkXC2a{C)(*myB{
zt-(|k=MoXRm&yHsl(Rk!N%rb!`)oc`@{mAL{V#SUc#HyH9?z+aub12F9KMhzRP(#8
ze`UxZ6YLGIu1<!_4oAF#ILC!_5nQ6Wu~+`&^iuFs{(^BC(_b@|lI8TY4>r0*yy{Bq
zYW*r6Puh7c+)S;mO~x|m$|%9kDgDlwBSh;sHZ!N}&C}Jc%;#_akTAb`4UcJ!p=Tp^
zw1&jx-A0Tc*<`#jAfBZEF2JbvjxtT21&t?5WyLN3D@1+|z7=K3{nYiY^-5<V1f?*w
znA3WjY~hD!sC6iBZ#<T9c{~$ehhJq^N;+ziPaE?6o&+s@YGp}UV{EE}CHZ<Ay_9|2
z2tgL9sI)ynv?Q`4;_%V~sFa+ZN|m!yQKlm1)q!v`v#N7K8brLpnz!85fIZ7g@LSnt
zq+Eyze2aaQGy3`{ss5fU-qOvcCj<LkpBMh=_Ee~h!K%5;4OnV51nfNJYCPPuk?Ahw
z$gcNmqqvmIU_P&B9X}>{4PL9u!P;rel0VvjCqn77&=Sa8KB>$y5)Hca^X<FI8288i
zZPf$pk2Mj%bR}^8X#N8SugW=0MKDx2D;I5#y7)sdbsAsfT~~*@oev&qA(fiungW9M
z{m<3RRC-;&vX(msXT8SVlgM0G{t`x}QX`}>YDvD-l$v<#!oq@~1!JN{(-}7LO)G+y
zEt+g1>y&CR>-@$rc(d+pgR%0ZwVHs+5GE6x36F(67F{j?S@AZhR{0MOWto|oQC%r*
zTidW{?Q;QASxLNquaQETAtC%R#Mg`Mb!@t0SOf~Gx(uy3cj?cE7oN5=dY{(gSG<%o
zO*)blH$(7kVOXAFtn-CxD3J~*thqFLr`EYPb-->U|0TW8KGB~_D9`We-?_ZWQ7$D)
zUHMNhfNt`u{tvS0n)&wLLCbG?BpjD7#_MTm{VF90_;av}6r7zBz)U;AX6)e2YGxG4
zLz{<!uG*+PKN}F~X|5OCfcstAueE4o1vwUk8VeTO<I2@+R;6ZsZ<*aw19F9LaEGPO
zAYP&>Tg^YAHAm`jN4~xczt&rot{=KD>s`S$ZQA5O52CoGKj|0i5VhG*)f8P3#n<s;
zXRNrMXl?qJA^w|Oy?X|_3)dW{)|;&S`0+z2b!sf8;Op1@$?$`YH*Gm(WX1Q9xGmP4
zFgT8kadsvgQN{{rF-1Fs^uk*m@CLcP+Byy^Fw2Zq{Su=yv)(h=oDg$?w}nr03jg``
z?b~Qx1E09Vg#?Z$c16DO$JB6Ur(DSbMpfJOFAO~R9{=qCzXXLk<3rd>|05BYS#Y0T
z@x1PipRF&`;!oKWX_Ogf#^b7p-iZeo$BundUa`c*s3Fw*>QDH>6iLK`oOG3bbW!Ak
zMFpI!zZSiVWY!-pnG}`>1xmta$(knq@>yS|mn|s(OsA9HL*AErk-p=<3(e3a<9Jh5
zO8d{PV?&dKVHMAk<q09Yd=7FW)2~0j2c$Xig3}f`H&h4G^%Rx*I@Fe(KQdh;T6`zr
z0BqMte#Br&ALwA)>`&mCcJzrVm<3dbI!hgs1YZc%Ro1WvC3Ar(IhkwA2pX;1Sl_8n
znOCe1u@-CTQTEpUN09!1aPD)xP(?+BFF!of!X_axQK|P1EObonCj1*+sRFXR!qRuX
z*~vjc00>O|+I{?77MwwaFN@*C0{Zh!uTj|mgY6^x+w@zYVc4pwDi=w)ZrYPp**m4(
z8(sT0{@h2C|99qA=gv2D4q0VD{*g-Yzz|7&12{g7#pY~9sN)oWm&45DDUJ`)Dxd%5
z16j6*mnaXs><_LJNq*<x+jRfs#|w3tINj_o?uysD%t;&JM<skBS8<oGnvZ-vY#tNk
zr3(gp;=@ik)Te?7@uZwG+)L`a=_`#%qTtsyEf*M;?~Xby$HnW|oDCu?E5#^5hU**j
z*ccY`yP{Lsdmx0-Om3()UN#^*A%X-oh;NdaoQ!1b%|w~NesF+21-Jg$9v2uYOKlGH
zVs7Xai(}hbo09!j!J=J2sRIJ`XdN;mh7C;@{(nsSw@xwiaj(;r%-5eqRMeMSMq)<W
zRx|le0C`Q1O5^KeGYn2FUDweaqwR1sSr*%Zz@kh*$8<h4r{Q;Zc*0Q}!8^90syf>U
z$4_bQ3!|YW_J+;XpoDnrjr!{}Y`$rRKgEDH8yGS>;k}vk4~4r`cj$MoKkWdR$irTk
zt5mAOe64nSEa2Cg+KDQVp79;WSCR!QwMhnZV{hqoZK!$Xm?5BfDzfkh5C~f3TiD9_
ztBOq1#RtNH3(QB*_qSPo-oTWt3u?bURZy~Kd^>Q8L%RFP_^mxG1xBeLHS8&`MN`Jl
zUf``|+|yX?3~M|&)SAuyKrNdc&J-m2FoR|`q5RaY^vatjCEyTc&SlZ4_z7xE<5|A;
z;^Khpu#k}*sax%TLd5aoEB*}*RbG#RW;+j|l5X6%p$RRL&FX9N0w4)t?57h?3JLaF
zr@b_NPb!c;2<F50nuew+gBe^pMiW!ZS!!~?`V+4FF0MkXZ-{TDaIR7JvF;`w7eC%F
z;K9JSu;$tjS*L;|v+e$Wt=!P5y>hn8Z6{{7`csXN`_V(cqM*~Tv?_VIwt#@k%XE?G
z&A+bcKjn{7e?RhM_*;H!ZF$C_KhU&oemJ(gKu*uu&7yTE*Bt5L2V_TZ4+#%h378C-
z&xS%qYTiU{&nCjwR(THL9!V{sJy$G%1U~+Kz@WN#NcKK$-`y{^d;b-L{g)|5u8ijd
z_hjkC<Za&U(N~6AD(RY4C(957w;%Vu6M&$+;e|?&Kiq=J?ABR6-0}vo(kRUQE~-+6
z;B)N+WVBtcs`S{eJFcM_dbZhGK^{?l$`P&Dt_)$8fU+uXt8M&81C0_ne=87dD4r5e
zjsz^a>>?O@GjyN45#z0C;%C>IN~-tLH}ttbyYUPyhl+@5y<*jJ=ZHyHNdK3d5(nbh
zlRWK7z2zEaNr*&NT0n^d)15hT90{GPQTkN(zYm5+lo3Er?ZYp{M1#M>XwlU(n&;0q
zCFyg$Btxys1<K0s2URzb2RD3&%ElYwNq&N{@hA)-dL%=5=<g7DEOaWdF)c(^-}T2|
zpM7*bxCrwlmiCUK(M=_S(V2#^SkPZY4h1q1uNj%G2j}FMmk{V{U%!5J!BY`DrfL7C
zm^@AAdU3KNXu^L`w6jh2KNQeK>fruJeOgeD9yPlYh<D)^KULgcKe94wO1DchOJok7
zr45%GZ<Mu&L54w#`nvAT(lXrP(1hgVa;qd-kD0;3EvV>wU)G7@stYDoO6G2dH)}N;
zUX}Q#U(nH{b!)M2>3j~smt<w0`k!{W42ZgDnm#1JRhOW-Zn4|m05`o)>}TAUilt?P
zXwZn(+$V_#Q;(LPACl-qe<Z+H=CYCcn`;a5h|NcILEI3ybM?Z7X2kZYu|sX(qq5V^
zsmMqk6KnVvL$X((5nh*%5C2=v76gxWQ!R;1LD(w3_bIJsWep^_BP(|s9=bCKavq-l
zl^au9uR>UVnAw^D$cL{sOT`?a#Lxv37>8e-x=|d-<1|(@Y)*=1sCmB?6o0oLlaTQH
z_2mwyMJ8bALYm{T&KrT_x6pzU*bBx5wElP@5z`tu5}^+mi_8?<i}x+l4GG`nZwUID
z6Cl9`&hvwd4gCL;+i(GiUTa~O>hE=^6L_5^azQQ-E$vB5`rz7nxqj#A=I^}84BAya
zx0o>=2Ea(bToM`_lFAG!%Zyw!<ZP|H;4JMT%3lbIZ5OK-PN3cYX#T_QQwxgY<4e#~
zsq>Eh>a$An+)cV*-dg!zhwWdFj6e1Okl6z9L}%u`mG9y}5p2W{D`^YI`j15il8hGL
znd@sEECmR}pK(=;2j@ORV9PNx1OB=xOBOcth$A?C`}fp>f(6@9@1zb_us$^l{RJb)
zT^{YsD~=DNxVYFwALFY$8qw(&Fg!dg)DSPfAuNahWuK25#T_p1(dS*=eEH&q>hkyP
zc16r#(#B{|&#>TIPellc_8Cx3aq-;~FoE@iC#pfu4c2>&8bO;{G<ljnpn<o(;3mg`
zUq-UJG=<--FO7}dO_Y}0zc3N@Z~q{05`yZI&)YU2NmyTd!O}}Hm&}oi<DlqL$7&Ml
z!Uc*W5SWdiPSrSp<?jGrh_qle#9RzM2N>h$i}#Q~QxAFTEUDrOLFGa<=B_C@RMW(7
zX<ITiXSK}D4P`6Y0odz{tXz5os|_p6%*=%7h1XOAc7sYve|I0>EEA*s2W|fU6deZG
zBTu6!z9Ha-@CP?UCI!NO-vNN!Y3sW@n{akz2%afP(T4@oNhQqZ5@y!`uqHpoP`Tl2
z#@XeSiKdwHlBS-pL6&OkX^G;deSeFml!mQYnT+f1FT>as@pBVTP=rYep2-R0413DU
zTyuFADX^U(6eifV(Vs_~JOijHSmA!TSMZa}NBIAby|)ajGfTRL1A(By-Q5YU!Gc3@
zcXxN!;O_43?jGFT-QC^cJJU0r?w+UTo$LEQf4J^rH+$FKr)uq0tLg-7;h4BSzg&->
z0t{Zo{r<uLUfQN=yMA`@u-3`{L`qFf4L-}+dgp#Cdcp_8Mg)Gs=?2?yx!n&Ix5tI!
z0LU5nyxtevRZQjM|4t;f$arnIC?1_>+8gfgEPcg&(Q8<HjVCUlUrGTROTw(aHDDWR
zwlg#7|9qw&aC>e>ojePhK0Y>7zj&>%kO39H*!4)xY&%A;+R8CX&rmZ%?NqK~Rzudi
zSaqaP<PgT+@{-MGw(hR->G}%xZ(~TD^v05DMO{I<%g`A2yPNCntYX7LZ;D0pAddTu
z^zm|orBq&PF9^r)0|sOVMuF{++ehtM#zU))uAX~3pBE#z$7?0q(M&~xF2Bv%>HH{P
zk^|<{?*qnkT+g^WU=W$hGS~guC<%64?+A4~lNEL5xa{uWOa1My$+dsgETBT>=I4Jh
zy+1NI7v-q_YEhB>b~~@>v)^`<^hchkQWvzaajh!7#mrbkyUl7)^y_w4f!Sm>^3hVk
z1`~uwrmgL@kIB)<XSUAkI#b04j6wM__v7ZdRsr`C)q?nD9LdUJ3`{Z-3Edr|{FTqn
zb8V)wrfa2ZUzS-ELVp8G|MutS7~I8W$vDULh?YDTN&R}cVnsTDWuZC<e%X%ThUsg?
zVYAVj$h#j~Aer<2{j(?JfIM;Bf$;tj#l&QBbNfN^J~hpJ1^8!}a}?JDU8Vhf^4PhX
zCec>+B`nxF+pC*K1glrvJnM>A*V^Kyb>~NnV^^&J>iF)Df9s6oNW9fPXWd_P!!GMa
zaH9Cm`rVrYTIj%}<1!jaZ5I5q`VO#gy^v&9L&Ne9NM+=Z`ieM8&Uo^@8M4Ep6WkQ#
zp)jArFgfBg${uTOlynZ*ra3ew(bM}$hvu)`l{>fUs6OXmQqAL5$W$&&lQz6yz1CPK
zi!aw&tG-+U58vHB-l#amx_r%#OX0D4xVpanYW!Jt*qoZmsQ^OHaDVCeJhZk2kiRLG
z4_hw2b;)ciI%G=BfoqZ7H@GYRaqR0`raG6uYxVc1a!8U&t4&@^F1N<lt6`e>^mGgR
zqnRr2k6<eAZ?Dwk<V8PDKc|HJX6v6{P%e1b>}!>IZEGI-3=V6Mcok>eS0&Oi!(@5n
z+iDh*HId*d6rIW<S*zldnULknGmPWegkp@6-9RVmU}%Rm4C3o}uWy!Gb&pAoFMA;y
zk=b3f4Rus&7M4`n<CRMy_T;_VwA*n^bM22UACu2Vqmd%=$6?H4z;=&4r(Yo3a*%@i
z3rV*yFf1H>=3?^o!8+@z=`}s&_zT;ep&IKxf&@4AXn|4137pV-FDw{+y^d|a>p$7O
z%V-c(2{XC$NE1y;)=WjP@L<q<KV5G_&ay}I`xlk-*VD19FHa1K_zA!Z!M5K8VjGbD
z(`}MT0#$e@oOje~=K&kZQ&pf>T(EYQYEb))Li(4~%dwVzU^5L<UVU>hOItj`MJ8jJ
zIF&O``mvYXHmX2wrop1VS;Koy1-cSNEp%TpBel@lFjq5RRt9{z^ieJ^shoy0fle~w
zy@+S&<9T_OJ#op8<%|$nrUn-hnEI-@Z;%yC5f$87S@qwuoMY!Qa(7+ua0IdpiJrsT
z%QBEu^bv&vBkDTVXcxj+?Qm5y+O<<0#%a`oWD-6D8Ur+nUo#;7t#kTg*L(B0se^W#
z%5%06A<4PUUi;^{h_NY%iLYvuXR3Uw;!dwrB%>Nrh&;`5tnJb{&t7ghzdJg0^Hch!
zxDi#4V^)N~UY}fCmSW+?8__EVD{r`Oq<m@4mv!SGJ5fsq`P0Xui%EUrb7MSi3Kryu
zt~(d(XkEKj8)T!8#fZC#iZcY9T0+9f7Y#t~1NqbUHwXXQgS91k`kEgJ>Pal*jfF((
zDvTAD>g-J9*JfhB${7B-a=*{!Ckil~hI*Okk(mAUY1~Xzb4C>&5>G!F)g4_Wp(<_r
zY2O5^5YT;b1u~XlGI7cCg_hXKKu~uBvXwKGBi)I&ZD?^NgPeC-N%2wf=xy!~L(K}X
zrEhp`*6CV(fu29CR?aC!M`a4r^S=zcv(?%l<28O!TvKnkc)B%N$hprjrB*r7Kg7{U
zxFm?&ATGWv1NeA7CX<69q+denKb-wvu9hj_N<J)>l-SDIV;58vv;i-F+@vUuzB_AZ
z%VyWytY(2tzqVVZEOa+JDrA3>X~ZrWIzbyRLo%|It}b%!l3(~HUoV-1Qs7)8uYIZ>
z>#BsEM{%sSdVg8bU<Ch7g*LxVNA5j+LR44vUY=#=*^CYh_CyH?AQ?SwQl%~njy{T~
zK3cSe)ztfpIR_=LA=J7TysQM<cx^{K_>;>HQ6m*T;_q^Gf2=~R^w0j<@X5rfNywbG
zuT~Yl3g2p-Gf%0D@|vMEv5L(kuu&nibdSxU%aPeh9Tg_p_&Xbl>)RlgK3>#KkIWrY
zn&#!tcU2<^S3Ik0>ejh|u1>0G!YP=dXhwi(0fYf?IXtTCK6|^VI4;LcYGLN5*A<Tf
z!{ZZn#n7<L7#Riq?&`(OW5a8#xa&iFUK`CEa*jFrf`@){(s9!?T-H%cCQ9p545AgS
z3RQKpMQM7g&9w_*cfR6(8z_GT&%#7tNPk3(<L}#&uKKl#68&UocNX1k#EkSMYwB5-
zPM`~rg{Ig@kWD@xmI~xAuqvEu6b|Zn56M6`*LEHEc>M+;lQ<*q;>#o1RN`j2JS%bJ
zmHLfFPA*)wj!J-+`E13vsTGhHaU@&^u*+^AUYv`eE0$?)8o~D3)6lQ$)Q5SmFD#oN
z4hs0^;dk~Ory7cO>rqfzhe5Aq%DT=OP?mRD+H`E+)yzcXw^{5jaht5yo_Q@S%5TqR
z0VM0DXh`tZlD{yn#s7Ql%zV5eq)%r@aS*^(y(z8kzO=TbSXn+68;s5Bu6=f@1vae6
zzWdVcj&shuk`W?PSId3AS&dUq@zmdfdH<EGgJypJ`jE5nODoQ~3y0(~d>%l5;GR-!
zlmuW+DpW^t?}w~tAYk|I?Td!3JUJ3+0u_Bk9u4~;g%j&Z!JT&PN#!*<?nd<I%rg|?
z<I7Q%>8K@X9^Sy}32VRf1qA#ZcKzcBS1<Fk1qwi@r-1e1NCfa5Z&ZbCOSuZv1&pu#
zxUAshSGacOJh;nbJFrkw4|k2LrRgq!!kK>6Y7T=p+Pr<Rac76k^3gX*;efvH_o1-L
z&|F5o(Ji;^d|4ZP?CXE!Dq8^oFCPwbxL36@8aaNtyOK><(hfM$HJ!Jd@FVbG6YW%B
zBJYG@&(!S%YYO-so&52=__wRrm=B%yY@KIgA!V8s{+yTN*C#(1z!sir&4x#Zq0Gza
z%{o@I)Co=3T`E?cQ5TqhKOO(tk^e+;zaAFy{|1A!EJn+TNEPc`Ex8xpoX-zYFfmP&
zFk3A>K<6PVz_1oNx3-wWNh_J4^i}@b9eK1c0W)p@Dytqrz{E7)PhFaSyFS^S%eMz~
zMT$3`Eg@dQvnQ1JhsySkRrtFrhj<>^AB}x%XSv7{tICx6oGlqVL&0@-6Nnj0Pcve!
zPnXx!6v3t#4jnl?D8Eqq@1FdxmXu-x1uv)Ew_e?A6BQS)!C;E6clI#g|F2`HSMaJ{
zDnl;WnO2ll-28RY{g0p1GK7RvI~O~JtSmn;@*tx4sjY+f|K5Lag>t~Tv2jOM)eL_>
z^gp?Io?b+}#&W0rP-?;dID+3_pojx(lsxv-6X^dEz5%s4mw*MMT%*$s`7c8SB-ccV
z0c=wd4C&W@W}g3Q<v$G-@PZr%AU*^;jx6Z>mxYGo$&>*EamC{9!Lfha?tk+TSrXu#
zx3C%N(f->8`iE%&82^8!{hw+7w`)QE=MVrQ{r@?H|M^+~*7#o-1Hgp;IfVZ?gnvM&
z|9{{MFMrFNUiEKz*gu28H~}p{y2@eQ{a%@umsfu=FRdC-A6;LI&*y&<YkwLV2Y|`Y
z`hU=|e*^S>gTn&<V4Rn34yUQ<=&A<xovp^<GD|Kvw^?fv{wrMmX+a67%v;96V21v$
zP;r3)fPwErw?{Mx{)%G%W?(I4A3AR>T#XiJ{|m6C=-?3(3zaXD$-6oyb5+U)Ao3s%
zj-Wr=>7SE8!jH6!FQ`!S6kUHE?ojq9-#wgETfN@rySKeT1E}2IGTP2RRF;q}dsxt@
z)`&2-eZwFEfs4`r1^Jeod2_A^1mzZ(jf)rf1GI8lE;bwwIEz9(`U8F>oD8_{P5Nr=
z>FM1w_jsx+vm;B&s(7_;<Oo0wGBlEOHL{Y#cFu7A6$L27OW-Gg7JMbR5(pKrD(}zZ
zy4c<L%Eh#^XUBrBrD_$xv;X#{Uk?u;K&s_F4PJ~NPs|QD+3k5pc&3+*LFbJ=w~jeE
ziK8r=G6}l?yJ80X=|BAI!<D84T0zI^H5%qc|M~t8An<VT)LEkb{B=kroE*~unyrHj
z5x5#fYR_mgLxeLucZsA<7HR+Y?i1!9(St<QYFN!}5-O|oB4^FvTc1II&BfviWNOo6
z7ag8OEZl}Vrj`{*8IFQY6&+Jd3>9gLs5AcWt$w!*iZ>1~5Buzs6B+lkg4>zBzabZ7
z&r}j?H9BsPDP+H}o~rpV4)B_w?!PSg`#hKNX)RV*`Ew>uDH;OTi;F3!G~2NVD&4to
zku15;seZV0{s0Z^)$wmdS>$jyQ9?pSHfOMIce>P>nVrpQY(xXpCM@mIGqSMg0<yKs
zsS)N9PGH<dLC4t8jpAd!*6r7l{Bs#myvMnK`wkk!ei+IQM7NFUeY_a-U<K8|Wu)qo
z4SDyuzI&k;j0@%U$CzKE5;41;Fzh&~2l+oYNf+elv2i8cQo6TnIb}X+Sob(>YdBK5
z<N&DM8*iq*!$W_PmXY}pfa`$<5X2jW6%-XgXufQrR_P5Q<RnoOJjbhAbZ!RVwvYN=
z0#vYm0Ie4U7`7c8pako2uD1S8Z!DFK^)NR^jElX?#B_UExm$J1#jp%Lh2}4t@-O3Z
zPyx{^q&?2^VB#KlsH<UO&M&5OKE-r0daF^wQ4OgPQ5)9375cWgm(99u-tm9-RtqQa
zBT4JB5B_Ik_xgE}VfOX9h=#U~E{f2=t#rJQh63p2Ycr1|$2;5aYUd|Rde?@3UC&?J
z0Vw2WL7-PwVbrHl2m=OTS)<)J1ExWvMTmQXp0u(WCw**)V*$xcpCizVCwj7r0AuXb
zE~Zb2Mdr%Kl2wrAQ-7M&2^V;yD!6GgN&0k2TTt3#YE3F_w{cRya`VLy<_^c0HDo!p
zR-{DtcnDF0E55pM_6BuwoPiT!7-GVv6dMb_e>%I(x?X=j-*=7`8Glrbih%2%r=+U3
z$}79D*in5JS+2pg;zS7Mj0MZ*DXVWPhmLmRql%8<oZp-%)6%;#zdFA$b}MZ?P4lnk
zoB!wON`!wPPC!3XJrw~=Rv@_lgFxz9DQ<c#?Du4a?KS=Po|0iw!Z?wih}kRjnE5X<
zTas+|2WpF!e_cy&Nf>WCFR0IKjxpEH0+^Nr;l{T~DmC<+WyC=Qm9oMGQ$(fC5!#$O
zIU>y0!z1@Hm#iG^A6j?v8OYZPJpHwsL_+b4rn$IZbZU_;>3iFvM{NXz7!pFTon0I3
z2?8l8DDY&L3DjB$Jdq6ms%_O76OA^PI=a(Uc@Bag*&?K;+v9+)t}f;W7Dh&ZIvj_V
z<ma-JS$8ksdHk9!^{A;UOJn0j9ahwWv)J!wCop3O^DTVT%k=-*4_1nUqj?iD6_8sn
zn6Lst@)8e~iZgCC%~k+@$uIq9avvoB3-QG#;zluy;-x@F;`^EYs5A@Nr#t+AGt3!b
z<c^rQprxCCqJ%8|Qc|F^(=;h8LJ-8$6hDli9YF(Azpc>;iiv53#(bdP@Vw615&O(6
zi`Hc}@x{|e;a*utF-i`irlM$dIVE3+?qKT$Z!oPBoLw@QhbW2sFoSw<s~UM|2useI
zuk7&EP^e#e?%VG<iDF8Tb*ADFH?bhCk&V)AgS306(^zBYCbIZ0cVZ+h1l8W(?mPNx
zji=$l1V3k`CyXd$XxNT28LS^!v99daR4I*|6s=1C><vNPuAP=(&}&hh3-3BiM*#}l
zIHOpJPk%36Y3R8%%*!|9qW+JOWMLD0Vj`ysDk<+S%+A(G7D1L`z4tZb-EVRB8kz<h
z8VNs6yh}3vYs#%Pw<$zykK#U>Y1I6j@?V3K+ZOl(>udB>ZN8=fj?k$|zyad7QzKp1
zHXsva*=-er_9D8pEi6lY{m*;U$^njCYCgmzbGoPg7(@qcZoL>bD?obF)(8t+fnbsh
z-omUum?(XeG!}gcVbH?1VKE}2Gf}?MYea=SP5WB0*iSid0o<VVrXT9ooT??0p0%PB
zcm=ig+{(&I-dw$~dQ=Obw2bK!G};776S-{u294(<R5KrAgR>PBDnU+OCv|o!<vjXk
z`3o~MbFx#toCC|-*oAfX38WCf^FY=a4gbm2cnN4x!VlQ!m}z7*S8dr-=G1rovSsCW
z{EbZ<KM_d0x{ll6zF8-&$()DrBbLIVm~V+)-#yvM2xE{UMGwe<$OSSuByG2Q`02GP
z;k>Cg^X&iRg#HozGpK-p$}neya;pNRD(=!9;t`uQ#w=!1n<jrqa7ZwWb<%Pa8CcvS
z@Og8GM;3sF+1nSETJX=I?zKv~$^*K2zlCmDp@dbja;ac<3k?_OZbr*rOrICN-tnLF
zrU!BEGiW3utgo)hE2{ey$rfw@YGPK!Qd~?c4`GZcZJwjQf9V7i>@JZU9UYmdxt`WV
zqBzl_nQb?ep$<iWAC3thp7ovfO(R>cu0C)bZ=O`_)oA!IzyD<p^+4Xq+^Z%%`)wXh
zr_3DYxK+Qw8o+ygp80sy7vB-L1?E6Fj=3ULlscnF8KFgq1?rNIO4>~G4bs#Qm3|~@
znqGQN#+W}e!x)rn|Kv~jbHzQfhImwnC!Z!SNmf%cA_A4Fr%M2;%4SzoPC9aFVZk@H
z)}HJ`7(W3SHTAcSc6U0r9&j-<ZXAGkD?5^jUp!CPBld3Pk5KI$4!5DhcPgE>+e-D_
zc7ubJ!@+_g|53GGpD<b+bHlQbzuE%3f1S+V&sBROz3rkYi;&LHnmnDFtk=|XoELlF
z8(n4PGWx@~o=hKf(@6L<9-E9x>43Q&mqrx~jbu`ITfnW5buL;bmi$>w4-+ILq}4EW
z1Dfr^(s2`M;97XGdhU(WSV}oSZavy++5TwY29O}GPCc!iXuIkL5CUKsHq4_TWv64o
zXy=u_sns1DX%$~pNjJA@ouRGi@A1Jq9A3Cg+0Xy0ry&Y1s>i7t$S2rS*37m)-k^>E
zbSg#$5XW&{Nm@2HUrp9(DLms$MLGI|n<gd9I4AGeCC`cK910=?`Y|GlNE44rm^7Fo
z0*%{6&oK~{*31lEk}2ev>+0n8#A=_I*8N;4OAS0+tTEg!Ygq3Sy583vj3%Brv0=RY
z3}@oVVLV%DY))!IWx}8VeV`b=*)6m&;RD;}4d#KPD;VLwZYki>BF|UtSNG2y;a$e#
zwOvYMRPQ8W9J#w7I)tu~^ht6eT*CHq^>Kct#oIDjv8r#E7!#?7M$eC3;n*;N%|t|6
z?Heg*94<(|O=uifeG-|Zz)c}~@+Fx0faH2_9=Me$j7}pUjFcl<{XVe9jnHj6GI=c&
zCKHvxwAwO4;OFxWZjYC3-0kM$a)yu_7Yw8%@cFd`f`Q?op(XxzfDUcr?fLwjf66>2
zvh9El<b<lBKXc`lAerf!ki%)6VFiiHoY|jy|3$WO)#n<a<2Y_Ee&E-}YJmlo2Z%u?
z+1nE*XIESZht3-v@5x)Ki++LHLb@1y%u__w(VReo3Q{dA>o8$<gSNZ~zM4paoFWJ)
zbT39BKgabj;?MSJ-J+Bh{fhjJ#|646A#Sj)?N9`?mTMd@*O#QB2DkmVG_+k7)>8<j
z>=Hut5cNgO)ZJ<aA*TV~YMrueR$Z>0?^h4WC@6X~T*G?d1F*v@(%cmyPNx%xV>$of
zX@2|CZZJgfM1L$4R209>4M81Xj;bu@tGC9cN5;+3p6v%Oa|UwvNqu)lFtF6-7WZ?l
zsk=~UgKcd<dVPiwnmh=(D3gWlYJOtXaQ8${PKg13s$+5RvhBh06rbL@OvH`K)-W<D
z?&<%osKrx->NE6R*d?IJUMxB_m|^^7<YP-3=)+w@N7!Ht>2}?`#_{E22xF5#D6ajC
zw7HG4va<1j%_-sOw#FXkVeV#`U5gFDj4CRb1DujApYrac#$uYE?Au50)f{T&t!5v8
z9ZR4Z!_odWPuJ)YukF1&ldEHfg~5F5H-Un?2iTRZKf$V>;e2?^ujYC<u5he}b~&pt
zyUxR)>-;;J-41FbW}?5`*#i<h9Gm*IOaOu=J}_ekRkkunR&0*3Tr`_47Y@S8REh&x
zk0f0;^WiuW@b`o-y=k6rPzEDzxc0a9Wv4+`GC7yT*-O?O#YI)GLFo=kCXeRqjy@RG
zDPeN>$CyWS?tQL%*AFq0TcHdR-E4-Qqlufj4SxiLg;RKNCql-<QQ`oiyr^<MgKplQ
zxiNuk9T*PzYw5^hfgxN$U3Son1R}eI05mXV?!$u*jx`c2ikW%UQ7JtysNMb80U_fY
zkhUmax{J|9eQhtZb&ry5`7NTb0iD0IT9SHn88?LZs5&{TL?UgFb=h@Aj$ir;+wweL
zk2Q4x2ZO@0vQxTr5v;k+TG5=g+UvAENtQJyLKGT}m!L%Oj+5%)5sf8Pf*t{07AB_6
ze}v%O(Q(zc$Bj-O>-{uG44g0T9&b%k6I`1BX|7eo>0}!2`<R18&~aLspu>+BH%3dk
zv!-W=a+IKy6@CKk@(1CKQS$Q?+;Px%Ta^fVim%_S%~5`xA1aseB6rt<XIV6Ss794<
z-xjIeny$3Ek7;u5@=f7A?(CgP2M1U4sTY3y1{A$lF@PYkGL)InuZ#CKFkREa+`r#t
zCCc=5*L|-`;QemAA4o8RZDJ^kLC!wO9>QY?ZghU+$#qA=A?&U5(J$6tpIOu7dlSkI
z{iFqd^QlY2)1g0BL|=sWiYhcAzFE8iFjB$YK>KO^S6cI>uZ<6?>s_6i4?mozJbr^j
z-jcvT)K>(OhI8vq&Sws)Nww1=LAhDCJ9A%3YB&-#qMm_MggNxk(#TSU?8<wZv#^UY
zn{euHm229n&W3$5h+v0u*2iDivLcB~jIWCA(1i8$3?;I5mJ0MOrl$FG$d*88+s)f|
z$|?!uYeota=EPO4V{y6L<$m0wMY}Q#+lB1m_vss53K(cL_S1)cvOH_wZV_J>At}sd
zJf#D#B@qbIcd={WYKdti9d;|(phzH(LD&xPGNLiDKwiy9HzSvT-TH)>-C1D!9U3(C
zaUvT=zO*R^%6dmhjqT&59}QmJox0Q3EI9qCUM-fq_a!3~*9zQ3eroA^DG#kUo$gYI
z;1cU`c4a-J?sub8Jsxz0d3l1fps1+3&J>s_j3zNZMAA8Nsp8cl=qD6@sXkh18A^2i
z39K-@=De;G|NH(>bltnm_NyUES6|&|5_@UsDExrB1c!D&6)*vg>q(X52>-*R5N=wA
zTEPBPZu@rlS1DQ9>8uWmJ3y@hpy=id<@K5q$7#d+Wmx@g1#pHio3&8wtj)Mxe}7_C
zN!~VicsQDu1h~iZps(*Yu;q|N{i^=0pZGPNUorGbYko&90N^J?2$yk-<K{lJiA!%+
z5^eD?-wobzO+x?f%Jd_rFVT>1{f(=&Vf}}HKMpS)Q$<4`v0??Ui#u^w##bv-oEq(4
zK=Go80FTZ%HJO>FEHxAzbAP*(vm>-dDAl)flM7sCn7#J{0>U$SM{Lp_6=(y3hKeEl
z0s?T%y3@z|HAmdNv9y5D;lxI7eHx9hF~INkZTKYuLoC|IGVHTUJKggNc`R|zc>2YB
zs^7-{e6m=xzp-N59{!!CeF5AGxr_a`qs~MOxRDrip`^?_QNdfddvYM}elm_ocxHQ~
z?dl*)fGR>_)4nhdNt1kD{pLkfB62`!`PA(x5eA}QPaMiED?jC+Sg*Jt$`fsfM^qb8
z8FGf$CHqn}B>rN(Dw?|GVw1CUYPBdufz!ww{tduk!)K&5*%kXe60RQE8l!6$lIrdD
zS}R9S9j$-<L=}=!&-Y*xzO^|=oN~68zEscmQ`=-U7JDPf$~29nNZahFh6!~!J~I7E
zG$Y9xv6Q*+IGLr#xCPBFS(sTbIXpQ6-3B>H6%2!0*80IDKF9eIb0#?*1~=}CBWFyI
ziXMqhlkFoaUS`0kr~oyVRLW%bCjnkd!7Q?mi$pGYbA&4NSb=w)UO+zZ>Qi3vAm6`L
ztHgARHTRl1Vp9bZh!Fqeih>(*MNMLzW+Xq$2|BQd3pzZNI0Hb0o?MZbi<@alOO_%k
z^W)=+4xEhVmHj22Mx9>B6|3&gWg&=5PG>)%;OCE6T)K+hm()|2`Od!V*6p5vco&D&
zH9n6=WMyS7Lf?>jJX)pNT}AB$4Og^&@N9pTmQ$S9K1!)!<uSbAUgzkJo=@8_Zu{i}
zFGTRA-Xos9-Z?<e5BD=KRzl#G8cspla_pw%F{M3+$GyDy=q{e$=P`0mf^l_EV<HH?
zcl?NHBo;pBD)Uff4;oJk6O7(CEWq&#AnB*JZX?b;asSsLctIInSlS5#n!gAQv77T6
zw`XV8a_g8-qBFrCJ7mpUhLA)2d!-mezFy*!k|DfNgcnE@EXM=ho5OgU(H>3!x`|p{
zHL9TSU_4;M+DV>#ibyfqSMy@tOpGqK)lP%rOlheIy_o9J26;p8X&T@92+J7?%as_A
zq2Y*qw_*$HSN%^e?FJ3_#9`r*m8eQ_X=3VAe&_-vf)p-Yp9fu;dJN?ubdXApb4jm`
z8^^|kYZ+88n!00P2qROG`;5xk4qD2S=r$4qx7^JwS;q;@M-tDJLVoz3vQAq`dpoK7
zQ-{qrh41=ZU4vE4rZEbrQIEm~_2Gy!tdcR6_n`7JDjxW@l=K{YShk`k{x@=mfM3C|
zkJLvao)(aSo~(<RYv`ko*pkCfEP$@_Z==X57x&0B?0Ef>4uKYCJsrMs;FM(#Dmv6z
zd0g*<1TVcj$-uR@UW*q!h~<M=CCDgOAyjzw(djvi*J+{(`pgg=5j=8cPGg!}6F%A-
zA=aR%QK)p*Bk%D{&npU}-+_;cBME4yKqMB7knDUK3?7!LRF){W>55m+G1{DS7ZyL=
zZfn6b0*DOec@tJ>6M`SjUtdALIC8Fz0Iz1g<Dr*^7SK6GPEIaT_h_u5?e=mGF{-N{
z*OR0DdG)-0Z8``gA#emh%-lL4ZkrRV`G*Mmp8hmmS-!Aa7Sc0uceGxfBXB^qbI7qF
zX>vJwb0yK)_E)*)V?T(5V7)#;RDnn1(t-QZapksRznoK8T-;k*WE+}6T%n3zP5KK#
zEog%~g@i<{R>A~ztozr&z93H`N(fF0B;eWXR9%>96R6tuTf(ciKzc8q2Q(-un`+o=
zxQ&JhYg~SwS$V^}X}V-7^LQjxE<XuvV?wuwT?va<eDkp)=$pNB)1)FFgf$rd!?`B$
z@F<9oQ|A{>he+Xt=7mvOAD41(_s&{wu1pqeG<pO~@90KWTl6b6^7l=(+(`*neC2|4
zkwxNFrrR;hZsv{>V`gE&?0Q;zK0n9vP^22qkx=*6H}#QTro%}|X9^Acf<Ewhykt(Q
zdB2*Z`MADg&Q(J0d^|mDxV#a#qv_T_46P%=cG^SrAL!aUoyY4{B6+FZ<|t(ZPq-_I
zqaX`rl-gc9yy8p4!3#n2V=TV7K1uRChVd>VzvH|J-?IIo-Be@;R{m%BA`4mHsBn^g
znM35Jv5{eR|32HRx>W}W4ht8FHCe{xvS-2T<i~URb0Rf)2@=a@JI{fFMZZCxI|1g#
ztD)YB?>cI}rOhrhb+s{UUA4<eX9QZwx#h=VUqHZv>?p=kS_{oAG*+yZT^LtramOOz
z6zFk~gWo?X#>LDRS;z$jEL|6OgrdLmZ`I)VQEWp;(!$(t>AW0N4fwJ~=(dOwwe@97
z1U`T^S#@sjW5H0z8b+$+cbJl_6@eDm4TT&L#O2pB2N_D@g-8Wnb5l!g*Ds<Y`@dWJ
zZv$DM>8t;It>u;+t^@tH1|^-aDij^X`l(fAr4quB6!e+X?m%kAZd$xCF<wWEd*kJ2
z^XJfk!>cQs8bb$H-utWooGw6>*?myV9H93LD+-m;1o`C`o3^Yh?^CJ{zdGu@X8U72
z#0?5r!_B?XlmH%{=tlMRMa_|N&Hh!2O-h<JKTup6L((X3;iKy>FTu*2aqkxs+8?bK
z@5c$9U%>~D*;}vgu&#C4-?L?<f3{)%1qbxwZJbZThPj2%yV_f?AMK%L@a?Tj0y1qM
zr5;Alvo~sMHYnHl$w;}910L2bz+l*c+s3#oYs644*Or9Nh2pihG&|t3+E^gira#{r
zc{mUpoxekI)HHGfT0F9ChBLUg4G0W3&rPgReRJ*pb*Rjs2gYfCb|GUOePYZUx+ww)
zH6BrZ=h~~t2zZBn18#4aXQ6AhBC9hqT8d8AMiRIE&IgIB?=SO?RF08#T%d-M@)9nT
zSzeFABDKKoJcMZm9&WXaEQG<<>>KmQqEb_sBH>hrFXTUn2x>tyA;*73cR#x^B7atB
z+uw0&8=5j+mUlN5$K47!|MJk!>YXh7PtMO9=bP7R_@ul+LLO)Kthk7*yg^6W+Ng+r
z%;d3z46^O_eQgd2?gC+ZAC;=m(Zfw<W5JZ4*zR<hEdo9xmaeU#3DyVB`-I2jY^2p3
z$3{oNM$E5u$Me0?yD_pO)%+A;(gtf-@(Au~kn)@LJ8Cu>>Mc2pRAu(NGeO^FLm3CH
zE*tz;@*+Bg&@&Br?uu;5FZEi|^1n&!vTX;b77%!+VaS-2fm<lLWZ%j|(+q20Flr8c
zz#gy5Em1WNdN9}>E1dql7r>`gZP{nt6v2TJR{<d~UL$DREKWr^ei<rMh!f~#!FVsj
zhscESfa=ucs2+#o?m^#agH#oVGbfSg=f*78xfntExgG?fCeM1=ZDot0G6ZRE9QZ!&
zO0pkq{s}p?v|Waw((X?sZcX&fK?}N+z=rA0`E=-CrkF8So6~;E#`=krsJI&Je}y0H
zc$%$n0Pm#SdRjEh5Mad`8|O{2ef8MwFb?^_ytc>Nb4%T@qEk<A_0&DueGtw#y&Q9c
z9q@9pUI(CeJ7AV$Gn?4<N~v@`jTAl^*B%a3anDEORni9E9v5G1b*$5l`w@OqyLp!|
z6q>w@7{#wBau%=IWRky4e0_7%l~^Gz*Ej5_uLruVV;r8G@KvG;cjE9y8ciQq3Hs!)
zYueSWOKW7+5pH8}{wLOV0u<OA;+b#^(Egc5!=wEIRn7YIy#!?T+qolw^=@zB^D2$B
z)7>Lk>xC*fG^Y2F<_ic;a>R*!_b9fB-uLe<p&m>HSV74t9zWh9I3sq3c8%P2JaiE_
zs%_9^hzdi>n{mFi?=aa)o-k^TY^j;AnDWjW47HGj-`4+*(0{@R@hmaPB-6QST-7Q6
z5TNl()JTquZG0I;w$xs0hP4`+*uI>VF3^b>&<bxtioR-7SYj3o5sY>r<XPKXY=H0x
z1}7rhZhU0v=*}IC33KC99z><bU~?0CwB2P$?hcZ(?qxz~Y(O<(5G&!=@PI6FbqN%L
zSX%y)Obc{S;Q3UOX0f8=r}_3|K!Jg@S;+@`MkaKZ{u}4BKmh`sCMGkR2s$5g5`(q1
zh|j!wR*LD;Pn0r?TKs)lX$Wz8vZzDWv(IUFQM|xI@*M_(oL9v?4>3n)vg_C_0r}FH
z(fqJr=U@rc+QQ+Ytd>aDkY>!#FA&^}6ybJQJx+adwB`k6vJBcvQ0MA+5J7cs6s}c`
zmh^hn#(&7rBhFG5jHGpFre2NYLczniki7JXk=RO$VLRkN$tS33UuHoS{n4N^4zfy?
zO^Hz*!c#~y`93j|r6E(hM(El?k8{cfl{xr(RHM?xfG5o^5x&ak<x@@S-XV^N>DHKD
z8ox7*`IJm<bJJjv#T9m#Hv{h+IAU>GNj?cXB2kV(v_8+N(3x(J#+iNF`G7RqOasWI
zRV2xwGD?h4U%`oTg8)b^>@A>Km=7R(;g}K|H=9!7aa%H>vH1AqThqLx#}y)=m^n+9
zp6xy9lnvGz))xLO4Ss$#NYg!d3%;SA$z9WN>#fwk3h)J}+hZSS2;6M3cm|`OED=Wd
zy2rHA+2C&ViuMWiOTWCLCsfSa^OwQhTy(pLuWwS&*j$zF7tHen74HeI8r&~GZCP(a
zb*0xTSn@l*qS_j`hwcu~W!C#AD-%t``Fj|hdYXAS91t{9Td#gnaMk|B!deh+P#&Al
zaRiKWfCVL;6FfffkvUsa1b19wGjhDaI=xxbM0>s<Q=GM9dlQC#yV~7{Z$FUXhv6~{
zG3JM1NBWGa7P{c$m~-_q4exnv&!1v6X0)ROV)2Ci$@3I=K<&Xl2FAzn0Nj_$lU7HP
z3mEs_`Cm}^w}b%`2=7XS)Ls`S09x-6%f?p9olnqn9u_$FFY!a2rMweqXfPpjr&>Y4
zVB5fPl56CDkRve77*SEt-Z3mYYM|_(-*np7d=Ey|aG9({p0=6!G-E}Cbw1E^Z*S+v
zXvR8rx?*c0Y{6l^HBcw9Efk@q=3ncu&9sTV7*cBm{DzmDoRTOP&wIzdGQdf6{xFTp
zJS}Vz6WaDKHG?;gN)Mq3+ocV<y?K+Ny&Ow|VVi46l}tgRr$htE)g)aeHM>-J6dC=k
zyE<EVuYZqT23fCu!V(kg^%7iYXg@LaAZfB~o_Q)qtV9YI)BgP4&qOiJ^MMv7#mM@y
z>O+Irz7o~*T4%;{-pw?_k-6nEG^zQJFS>yWG)&I&;h|M`UCRWEw^-L0xpy?2HpS;I
z2G41-8E1_<Qs)*pppuS9HL6Wopw8lYU86-bNuTF&cEb&-Yq#r)D{TvkW!_delJR5j
zeWjBZgmb>eLR2?9^~=?Ygk5u(45t{rCXZR;%rj$ip3miN=dcl=BI@}Vi8xofoeK6e
zjf%z3IaZrJ7+m_Hk28=(=vj54_GL<1C+?JF=<6sq=6B8z+RiPlr3Ril;s67x!<qKl
z#{spb435U*#JiwO1yi*+TyCr$kHeZ6Z{+LmXD#P_B^B*YK`G$llGEv)^x%MmaZKl}
zY>+$+vAzB0!koUEv9Q5t>K%T{2oqsi7A9uhy--+mE}!gdU@TX)dxcFHoGD!Hhs*bk
z*L$uW8qYgJIXSbPhw}6cPOb=g)y&E66Bh0Gs^j`kY7CHHwHqQ?T$g{r5ESn*_=~BL
z$P8Tzt8r<vYz6z(qJFLI1{$7s!}pM>VGkqllX#JeiJN<D;r5$U=PXKs%au4vgG3aB
z`6AQ|frATOxV9aeo5!u73#(njcp5O|0&KAH<hX;^k>fydS48r?PQ?0UKRdsY+lGH7
z2-4ty1~ZI#=_!G%BxHlP!~z1x{D~g<z0L<UG|`5S=QmD+4_WOt_KhLw&F<>+5n#JT
z8<!aN7#w+h&Ena4HgF@jg28ZICI+O4R^m^32Y88KjqOda_B?GD12D^(8YN~vrD1X~
z6d=T<7!*CTv4u2ceXu!uU1_?a)zb1*bG&7eLn7;;?s)usucjv4>GCP=n8NyX+{3@+
ztnuS65{&9Ur3v-OK*nE4je(nj6<87FeoVoUS<sq4ky9TMpo<XD=W-Q8^~2A@g&h^>
zVYK35@O~re;S6-&G8SJKy0k}?Xv+&U1=ZEcKgHK3>#EKpmgp^J)v1k+(hi`AvLnLc
zJ)a_=7}>ijI7^5vYEO&UI$U5IUJ(}CpRy5|WZXxnW?jOHu;td1nb*1cYE98VyZy}&
zJb42rH?W|V<@tu31du#I6k%lSl^V9$2^O4M3h-!ygeT$A5SVh7N!!thQH+K(u(z*G
zUW(S@w9()%Wt<88K}tD5Z(qU1(%ZOO6P*bw&?_+Gdxt;f=MyGw%~&_#LUk-vm&vGq
zpKn@#Wdem-haX_YvUH}_gV)vQbrp?JPaM%1cp42|CpPev!w=k$llrO^JFQiDN;F`T
ze#8=5sUr&rVienbqzFYU7a3}M>sEvClh#Qly|1yy$~kP}gZQ$Jk4P2pa^8$RykiYq
zzpZKvpm`(+K>p%dK+?^0KDCdk7R%yvvB69>n$C&Y@p@|6QdYORr|nubjSonlrIm};
zbMm=JTMr&>ZIRt&u~}RL&wh)gm!E9VL{}&;-O$VN-;RXMcz$7xwYoS9U#`r;h~T^e
z-jf+t=}wyaoY%BfsQJ!?MsN4w(`O2yb1r>G0i18Xh!Jq33FS)dD6nhyc8=K%+P{by
zE%*ayp!UE#;BQ0;)ZZ%JTYyFxH&0)?X^atG*l*e|6`VvEQB&QT9~obtjc3%{U`)!N
zfqqD~0c%!N!|0C9%w-;tDt4iQXU?y`2mO%kSRzrorKu2lNFyJ3*<j}nXYEUblf3jB
z>23vZFTWH<SilGdGB)I=#Kw<KM8xA#vD8@&ED~9XBcfO0^C|gKN%*<M{6!{#z|z6H
zA|_ywnO2Sr==+8+;3=DD1Q%za=#BgB&*CZ8i@*Ut2YNRUMof^$I<_|l+Lno9JZ)tu
zZN?V*mYnuqzT%OqC5?M$aDSMeX50(h>=tJi!geM?p;GIyg;-(W&<tEYZ-rus`Wf%6
zcKn-7e%9mRO<8E$6Xtg?z?RIFZqibeDZ~FbX(;T;Ei0uLL71FDJDZBy*^L~!Wb!r|
z2*CgNtn;wpO?ECD+^#PW==*|Yi~QXq;GnPAH5@T0kM?LWca##iHj6H-=~_{1aB5d+
z+#IoylJ2z)jm`<K-RTCOj1ro4r*6S)nso0z1Us*PO%A*L@TO*_T|B3W+}j{}n5mAg
zR88J{)|673FS|7;xhX~Mt;v=}+;(GZq4N<8HQ-sq?nxNVFd8BhjUKKa#}@AslCM-%
z7K$xu>CY3;5;()N=~|Du3PpGrZY#|brdrrh>1#Hi#<cQmx^Q&cc&*3ax<Yd!Ei)n&
zwtaZxQ>afy&50@-Y#0t-W4cgDq5C`#`=V&Tbx!*PM>J~@LgGvL&2@A5NTzj!hs?UW
zZkYCp6jlkU({{adQ>=}&Wbe%gHGo7B@0R>?e1o0&0`B_TbG}Q-s0DTz0lln6l6x1c
zB!Y^tBU*8xnW^T^S-VY}*I_9j7Gok5mhW$Y><wqsFyn=tQr(Nuz_?lwEXScB^DkOJ
z1q+YWjMC*$`QLTcmwyFU1N1aaWVff_=P`vB5-QKj16_aFW3hfZENHkaFDmLpR#f}8
zI}~h@bUJs=hEOGnX3n!R_=U>4Ek=Ie&OnHpJ?Q|4wi0W-b*J=8j_acz3wk;>8Q76K
zN$44UE3n(mCIW}mxh$Yv9*b<i2hf!aZYwO9uU8+Sap?ln`1-L!?i%X<B%<>^75ceu
z`N3_Q<R2Nj9EjED;$x|ApASy3JHAb83uSHi(ln6BSPh{ItJxw}cQgX}hrWaFI<C$i
zC3sdxc|<iaY&NC4;@1<FrD(dtTla<eMD%>fCq6xn2S_Pf=A9mfp7(`x{~(Pj5%KXh
zYjT4Ttd2PDB}DOccu2Y!fCiDs!FSyk!}ZVxsgQ?a@_1shRTg<}^9#$mSqrWBxRaT8
z`HUaY3!ENHK}v&vSPKUTIU?uLVAI~)QHwJvZVS%a_-8Vb?oD}|=7RU=(5*NupB7eB
zFxVgKO_%km<rA|eFc3jToYh(ELv`OKzKF5nr~;Z7(v8!4J)0fdI^RDuriyJp71cRh
z4QlqyazDWbdGXqIG&f^dw%?L$D}HylDl~e#?t)^y@EYH&m6c@LJW2THc|!$+f`xe_
zI3=UIT0f*{bVV$QzMpp}PS&3>ca5>GO7_GRI%+{=2ZLNTR?}4^QV>{;EFO<RI3&%(
z66y|z?Zf^=_Ja0vSZC!FE$^E_U(eep>60=^ZP9MBxD{1=JteA4Osv^t;fTkV0lL+F
zN$nx9lFMf>65Yw-x)UnI&;)*4_Fy9|&^I~<&l7H1e=Y4mU6;sb#IJe{f)=><5-3?o
zcS56Tc#&E449_YxLiAr{E}QHxsn%2#f($j1!_jLJGIh)mWllnvOr^v$YG8w=t6J2Z
zxsY%WW;B+H?p$LXO)71ZCt?ROy{vILTt36BJHqDj<tbH_f1(*z3i1?B|L7Tb3GZ*m
z7}Azs-o0MI3B)%PQuGanj>C&@jp4Nw#D2VOExP0E{dzXZ>E+Q_NnX0UIi|iPBJrYI
zz)eIGDjEl&BXz>aVmiro`hY65o_W<aLYs_=te2spBfxMiG2(HU*$P82ZHEU%yoXTK
zY-*7;N-sI7a4;gYxIlTBYsp!17^IOzQsu+Xm+x$nP$Ua*qh0h#R<LjZ!nC0^5*%2X
zUoC)1ps%a*o5-THYnINFmph%5Bu+`GG;(I)4H~f6gLN<Uc}ronrT|or0Mv0=B+*yv
zE0P5a*%3>mf=~#ZlgPC|DG@wjWEa@7M<a3HelzNAAI<KnuRvzvBk8q5nE=%)%eouG
z82d4mQH9B7#<?DW+snh{!=a>SvgdMY#U^iZP6QVQX{uG1fyb%kfunSPnH)|YGWO_s
z=`tI<)q1VN*vg`sIWq9mq;Xul+K-Rz>`IPaOEUb($XHkp1#PG+oCS&%#XW*1a_9F=
zZ7i-QX$^+kn_w9^&d}wzIxf0>F#Sn}8D5-1FsoE%&zMV4L-uEQ8yd?wzoZaUz^g($
zC5!F?y~5KsgyQLFW&7=pYXP4%*^!tO-JsgRNC`$bOVsC&hf1of4+%hUnoyZ?`m*Qa
z-uGRSbVtoQ{N6L}@!ii@Cho!JYcE5@GYqzFZVsXHHaClBs75pOo2F*Dwct1&X<W?H
zYf*o3(hqj<ky<N(%H)Bu0#-q>0yx9Ohq`wKxlNYJFL$C=;+^#neWskl-{yQrO$hn<
zfUjhJ*nFCzCUqd?Qa>bbg7%w33cE^U>$MUMvRu82=1EOnAt{6?a(!WqXmUKUCgtO)
z4h_I{2D#>X8UXKQnZ;H0ifU;@XO0_TAFT^o$-A^azvSHsgwQ_Q1-{%Eu(V6tNAfqW
z?IWkj6VX2NdV94dt_x|m>$)uKT5W!ES-C0E)P8eQ?jL5G?h|gT#ByA@H9P^dMn+@T
z3p_TB_-$U9%iukT)>KN>AeXF6em<aMz|;s{=wCo&aY?d8?nFa_O$w?E&?UAB`?vTp
zAWSAcK?N6eV)c~nGAX_Ov~_Ynk$O33m93l~Xvae}AE68Rj3j+(WLIO0h(7=ne^WEL
z_6aqOsmIO9Zp*W>xq2w{8WOPpg|P<WYS?Kbij;0+OIA405I*0blumA}a7LcpqytFz
zY=!hzEuM6=+Q4|LnQgPPdbZ9f)%TWAvLjrMY{>OCP|6jJh=p^>fd5fj({yL;q(t>u
zyEn}?`1|AhSh97rExEFNR+9xeJZYeaIf4ILkFuzNe<X3aNZ%o$a`J|&({A(U!r~%9
zXOYB5*ssP-QI_4-gI&mlW%!g5-|4xA&`0b7py2e{RiR#>A4Q{;auf~6Hs+N-^zyV5
zbXGGlGs?=gh3^5f`4?PYG$uyG4Cab@V7My;1WZk3sUH~>tY;J&52U^bkDk~?#~U)`
z5sVcR&dPhe_!GpUKP%;<zo$MhawW1s%)32fov;|-i3bkrvGhxE<s@E<m?j7rkLQsI
zs4(`gM9gQm6MHF%Lcul;cq5}_58YON14?z9f<WUZ=EWsVQQ5~M)lo|gv9*A?E5QWE
zvKhc{eAo)P?B{-~3KOgyCRTS`wP!3?H>^qIH#(kH2lUip=eX=al)yYW<6~Zd5x~2b
zUCJ_E>fyo%0d&p4wuo+Xb7V}d!>xas*ox*oqm$OMnVXpK?RdY1sPVM-P$+2S!oFX>
zM!Ii}T&$%Ef1lERc$0shrk+x{G-9eera##z`*Zet8U6y~@yAF4R~w*rf&aPpmg>20
zfeYcBwcpcHOgPW#`Z=%7qs+FZ!?n55;{CNz)9t5PG57A3>4dQq%SZApL>Qh$>V}l(
z!QEjg$MFnl>nmxc@kXgSAc4@Y077G%CMHJN;!5vI>uli<(fNrRxD%^}^C+vxW)(P3
zQOkwAoZ&YU>41l|lLELIK5>Gm1$s;i-!TjoTU>yG%VWMTq5(OqN@LKLl%%)FQ5~W%
z4n`o0<bz*<e{Otb!nkZ@yU6;IvkV<t0km?%GyxuUx)6^0$e?CRGd-|HeVT&g{mjY)
zD0kUgvmQXuF&ReYIP-0D<hqr1f3M5fE<7MeKKC}AP;i4~MwI=qho&xi?#gXzFv!XM
zDpouT*^f(1_PXH~lX;`b^F3v9e`}8KUo5!=L8i8hE28Tw{#5A7eL_j&7LlAjh1&Di
zf@pH0B4iPjiV)aCzB|54r;tSrdKoD%!LzEBpZGOV@uCz*vQDmNGB1~Fq*Z$AAE*3#
ziJXVeCKC-&Udc|CVLC*`m*#Miv4nmGD6YSuUB@eA(+xW6KZP88Vk)dd7Upv&%oocD
z$ctOaN%3`#Pj15zDAZoD>kk&jDr};)1=1wrMVJW@VL;t>gl+c)%Wy`tUz1_fFFM8g
z@{R8!Q;Z1DFhvqdk;w7Na6|yc?9_|{cXPa^AjZ*%Yx2^OM*DO?<E=s@NOP^HZcn}6
z(wISonerDVcP7H!rr23V55}odhty|Y(_x9IWt-;nQ0l$v+qL`>>I`B;HO7q)8OpS!
ztNb@%JVcOF`SPa7@kA~|(4Dxvoa^%A)Kg-Sn2l0W<DHVxLKsdJ@yr`J!Te1KTkm!&
zon3=EE3exW9gSRaaY0pz+d!rQIOt(4#~!#<g85gXci9;=OS9}d2VoRp`jrUIz{TN`
zi-3TP?i2mvFoTvIQn9d0^RC<+y2%ZZml(4grk<Bk$Gse`Uh^qoj{xJoW-_#{z$^Mx
zhpzToH`Sr~Bz{`F>*=XI1@}Uwl%>i1nG<D2a6mi1{BAstFQsVgc18gns;<)}Dwfnb
zU%6VF^^YC!K|+%(!YQe-e(G^$F%(o)6a`VH9Ar-hCXpSqCO&ibY(uPEH;~d@x53&&
zRkXd-Pu5CU!@_2ahX7JiknIj<A~Q1%BgRJWN1>l)BvO|Z9T9q5{>-?GgK*^O4=}Re
zkIv4OhhSMVBEYxDCft6r>7`Ew@4zbQphbx@^hBqAcm9;eUC?wMtx?!~!;w2zDrj<w
zA?Nw*=Hy&))3;7=H+5b3ur#RTKA}WZ-VP0s>@_^K61NqK<58OeO>bW})0l6}%4PUR
zb&rDrNN!tf{)O@nJ+`U-ml!(9BY5d};V9;~%){{zM$jZ>6Gn7mQ*xDkuBkp0=Tn6F
z8{=c5M>$}G@b6NCrpAj-MZ`qcIfqoBi8nVgi6&@0snD`f2Xe~8`jQQ$%MkF5_ixYA
z>aw%(Crro*C2eln0m}<=C3_y_U7sI+0wnEYqe6c6W5_X=rihAfHD-#CUE>h_xB(q+
zk6%t&&Otb5j|{9O2VBqE=7W~oN*=DZ^_t&ZUaYB9S{FWR{|c3bUm>hohh>%-`OvFe
zNMON4(T89r?QCXH`LY744U!hlPWWWkjV)T>A;Fpy?XZ)NS!6<({h|Y5;Z4e(*l+-q
zMjfx07-H{M#1KLz`uzYdrZmH-QQ|14UGBA<UD^bcB}U8R;!vb;+}HTk*Ey%7Z-c5X
zh>xhPOROTC|CFJkH7v}Yj)<7M;Lw5r3=Wb=bat}1!hY&X1`5N;dP<Dr=>$p4<D8JF
zdqxyy+K#ELFn61a|FsQ%)L&W*ix--jVXrTiEl4a0$B|n@!!;--;}De0@%|byTVPtN
z6x4f9FxF~ol10Yr!!WTLlWtUBRk_`l>Ul|1!=V($XnV~bd)KMDSEi2+N6=hEozpW!
zbMzmK>0($&zKmdAW6he8BW|k|UP~6-rkO%%e?buLNlCJ-$3+njHs3;=TCz;G-__p=
zB4R2WG>-Ha1X3jObu%6-Doh)XNZ14(F=R~5Vp2+G9-n~3+P-nFGm3<d2EU);cjDM(
zX%)uf4JVv{Vu)AR_G2;7ebX7@NwlJ$6XTD|rJX~^9mS<o=f+;#C=g2tSYj2Qs<Ge&
zir)Qff6(tf1-od72D4@eem{9PZPt?T61?Z8Vwfq)SQe-#QA1C9SE35Oqk3xo?p0E|
z`1UrZ5zwU7V_*FIV!As2Hh;RzFZ68j?55&WWnbO~VaK`3^;#BUJr7-`G1jaO?rr2<
zQ{-lA3kCKTPkqKxMqqmJec<hrIuUOSNfS(+<vRTh?gZE|MTs@;;Bk{Jiq;Y4IbADb
z^K0V)X2Iq}H@9K>^nC7nd&oZo+q3iPQ!eA_zcjW#2`&clBaKb?wGOO6L~uwGzWngq
zz$2wAv)ISsjACdHug0_x98QUebbit_ay}J!(DeNOsCozP%DQN4x1x&8if!ArQ58F>
z*fuM+lO5Z(ZQHh;9p~mf=icvY_ZRH7TU%|eF=p@m>Bw@O<5c>RMA~Xmk80XhS+}Yc
z;uQsO=L}q!5ID<3801pkdukb+(YzRo96AxfzWipYvF&8i^tr3{@kjnd{W%?Ouo-;l
zI=-F_|3@U7^7DJ@PE0U~>S|PIOc8YekoV%mFRpa~j-;sEj@ud03#lFsJnNAE0){oa
zMYUyW=yr?1cdavIz3zhlO~CbVx*3a_XT6nhyL>hH_IL`yKqg;LBgH?pV>ipwUK(1W
zHg-NgAgecez;9)|x>V_uM5j@(p>Z`rEESjORyE?KmUjTytjU3qea9K*)ZC_usB2+8
zx7N#3PE`J<5XYGzGUmc6po?B=QVA)(&-2+6S@l&&TL%mRNF+*cRpsTwG7R03v-#qO
zZ)=YK>t!PYHBs1YfD(<1Bvo3q{VUm#96x}w(ToN*9xl0Kub`*5&R^y|aCW6kO7(#&
zV#&UwzxZlVQE5?>0azQmxE8$_`R7UkWbxT;Y_>aTae$*EN-wj|{8TPe?k+`6{h6P6
zQVQ|8rd7`r<4Mj@Y{ttgio!ICL@HdrMv%VW!qDT{E8HO@_HBXQh8&#jeGhdj4(4PV
zmg|-x{-e1fE)?Nju&dO;{^&iSQ4={(h|yMi!OS)3od%W$F5PnN;9gDn`a~saV839d
zm=1S1wreckA2k2vDBjx|iqQfw5@o9X^D_NH<g0FL#nX)|m?bL!Y0^a|f5SUCCLC_^
zRn+*tD+|?g8*FOe^lw&6V_5dBsbr4~&gQv!Uuy1$qHMxV=Xi;d>_qc2-g}V!$$B{!
zkp+$A6`3gRZ%z@zLDN=jBHZfUt$a@-eRHIsg|g%**U4pOZSb60M#1~Tv>%wbGiC6W
z0c^t<f)&^nI7+p-2$q(6q-m`qS>P@zsFefgiFUhz1ofB|gU`9%wZIyFZ9-&5Sd{nh
znoLKgWowQ7QWEF8>EfcEekM-A%4gD-WWF&Oy8(X^`-@zol_OZCArHio&RYeO&CaW=
zb?lQ<c7?%>CcSmPo^SRolh?Cdy_pdXK1WQ+<)1%)(gxwVW9GrXNH$Q4zNzVt9L1iG
z8$M#64&3}-@Av98`a+g#ZOz{XyifKv1>JJngtWU5e2<ZVa)wQ<P7i0xj!OLYmoTy_
z*V><oV^ewz#7tGf<)x((8rO4l$%^HGcvcW%%n@_>bKa5O+Y=+izWtakW7g*4u3LWJ
zhK^~*?UrX*uiYKFau2o2ik|(L&d}b#w^OD6z3u$p{O&V2l4O;3^aD&6W%tKVWdd)%
zqf@jT8}$&V;~Bd7E#ZTk{LJ56_Ux#ZU5YQyds=R58b>6TXWya;;p0Jm=($Gll)MKQ
z5RPIDjU61gikMsP8#QMn@8>(CKdi8em9m$pA-z1f<qq2r5cJC98!_a|_wNQ1A;`D-
zHWc3l&8uG=OL-Ii|J?}x3lu*$Ann|UjRAypyJjqGCE@=PsL~D0)9CfXp6iJy5nN46
zIdIL-4fqMc%u@X*{F&O+>HuCk)tr?gC1ezzaLmPZP-LidGqr3Pu0U2-pdnLsU~H+(
z8kW|Z{y5KAd?%98xvXaQx<z4Re5!b#@ox&on)D&vu+Eg{7bv2@9U{qwRi%?^8nRG&
zzPvlJf7p@q(TO%d@y^n@)zMMma%_Pg>=+cw_Sol~giB$v_tjD0JX_n5Et>ysL~g5<
z#hm=(HCc-h80bEQ8w1Q-U>|Hgm4)SGcDpMrwmYLL)_=a)O&>J1{hx!@e+mgn<B}xz
zoJ@lxej0T|9~XMLUw{MSEebE19|FW;+(o{uOhLL-U;#<aacX>-W3P)suQ!A2d{(x7
zM+yK;raJx6Kk;Vv%aC+@o|Lwc#f2N)bWO-*oU~A?Zf>UWSKzspSBveQ?`#kLj7<8T
zcK*;*ED6i#$`3YYG4i2Ry`QB)TH|sN+1}pdV{b_YvWcEzkn;<MQ*jDh0ZSd9bzU;d
z@#@<vR!>|{p28Dg&|{o(niQ5b$9{zLypK?DR19L~_Dp$^B!YgdF*ipe&i6i36;L!u
zJR&H3<sUJvu~VrNKPP=uYAYCji{>r$ld#oD^?CjkPCvvfExc*4u-Q}Wnp+zZeSY*C
zS5)B>i8`k}{x6`(8YDN^INKkxi=!uiEg~&FLn9g90vR9iL?!l*`jB~@zQ0oQpt!Ta
zyFw!EZE8;TwM!(g<=~;8F;5T5pb5-KH`7`U`wBmq=Kz}cw7fXYgFY5YJdUXhNW2*+
zSrg?|;|2EVHr-uAt`SPf_KefAqf6e8Sy8fse+Z_eFn@&%!_q+J?rFN_h+joj1l;O#
z_WBmopMqG^bweDkXngj1J?lFW2S<boDfRKathBU)h2ACeSgIGIT+cKk;MK(id>NLk
z2|%x|Ph7j-bYhS?!WA^9%Bqi!OtgJJ9*e5(98X`*vwT9nm7zBib4c?mU(1G`zgJ$X
zb$?sl@5b*Fm~Sop^R%Yev)WICJ{B(w(FODc^;C#EK}WrSStOY$d(q*gDK^U;&C`)X
z>B3<xx{SV5M-#ecs@eZqEKa_3yu@+lG;{R|q3xqAU%wmc@A_Lj?VKJX|38iLzgSza
zU{M#s=pbwyq0r{t)bE0$NpY`U2};?4*qt|XVokpppKOVp@nRDGOZWoYb_hBNf{+n^
z$mXsNX{@YkQBh2t8@YQapLb+{IZDTs0!KRG;CA<rnK~p8JLOCAycN0cj2Z}vnazba
z>HE&c=wXY7FFt=Tp*8QTC%p3#HgsV@qQ)LFbOHax|3>04lM8j#i3my}(XXXFhh5C~
zf8uw7F~owoIYU(8kW##sa0_o#WEJl~<xon4lknt5H;+{o(n>vvKOJ%8a;+(b|A>(R
z@Q-XNvDzX+4zknA8}g3DJ}@}*UQ<v+rDywbZHR;A%wng7$uUnQ4@{f15T)W^k-}qe
z*$PXuq83I0pJ-=}0%Nfp(v??O_|H2^9W@Rp@_6o`^tir1JtRDEtf>$os;7{~H=1FB
zN_7SnWHl(?o37@Ca&mJ)zPk(90=%H_GsE|k;L8_JHR+G1?b2cKHccHQ?j`BHY<&La
zjl?X4cHlX@)rcVUIxp*r1o_Ls!pc2wEZ=X`d!PUt+1>TGWLe2|6Jk-FEk85$=h}Q+
zhv47{_&1)fORCYN@*mHaadvA`thGSB(<12@7>eseYsRfY3#v;hSh3otziWiLyx*`v
zoKldj{{Cq6U(>XS6QPum4u4s-zh8Yk=g>c`D|}8})8STIg@wt(V$>%bipHNDg!_<f
zZLX8V`?<W;R)*PVze_DkGE3P=JI)Q4?W^((MZs6jO>3%S^@%g$YdC%k9|ixdj7*Y%
zECk>CVqG;l{$7vE=~l?**NSccBQP(s=ypvDQFZ5V4c~TtIZZG#0<3MjSk5~cy;Tsb
z_RsP*L;Z<PVjZWOpL;#Tu-Dt}B2$8+rxDyH1{^F=AKP#ucg$s6ZWc3a;j!VCmube9
z>}SoI)TTAWx4uU%#u3|ZN-iGT)ua|xh?$mDC&)L2T6U2ncx8U}GNWwcv?@5ot|KKx
z7E;;7K#4I|fmonYNRgUKhU2g-PCPy!s_iqP90rcTw)nEn=lx}lx)0Z@^?cTbolIiT
z(+J~^zJ{Ix6XmlcFp2A=zPFda3+;TO9Tv3EPWcDhGF;m*DaL;9{z?9uU;L0WSp+tu
zZVZQQnB`Z<JJ3EWXS+EB_9S58;AwV)n*vFEiT=A0Qwn*bYN&o}K<0jQutNtjThd|d
zqZ1kcOR+@@c=ka#0l8T1Mhk33%1tG&NeY?L#@srZl{H`@9woQvdXt*66e3!W#}KUX
zL2u7q2GIizd`&Vm+x~$GYNlsLZ%BBXo~PQvIv-djvc{|+xxa?P4hzw-;H7psP&#;3
z%>D@S7TE)6ZIonNtie@ct<^15)0@x8340cQxYjazGDKvy=2}c@34iaBX<25zfK3n(
zjZ7P#(e@xZcGlz7M?=^&B@}-+76xnbr^FQgr;brSqj$^5KOa~l|L27tERu)gkHwNT
zA}C(k`Bv8RL&aGqPf?pEwY9zRCEc-=?Jt+X<HdQcW>_Ld<C7oB;+y%91XEi7jAHVU
zJYnhe#@oD+;8(WW9<1K?bB`HQ4jx}HGXnw<wwmQ?I2+qAeL5y&A2NDd?m=W})E`DE
zYSu-~82*oVU4uh1!|aWXZTr1p5=@E}S&)Q3I80%x3VcJUi!=Ot{6q50`g}<JhkJ%G
zJzKG)4$7)=qW}at!86?yDogVB<{nHI658oKt<^m%ol1QQ{R0w{nR5i%3D=#gtxs>P
z47!m#^DCic<y{i>X1^ZV_WAz__{KtKZ?%AolEEUUv%o@J%ZXfk{0ilJBJ7CSiilz*
zI=<Y5jI&2oeBZ}GB2TmazjqKGDE_1&{Ig4SIcbFre{kzrL*7x$@1M`cq@LIJBx)O6
z=y=h%e>M^jd#fxg&4JSC3B_viplv9F(WuJERkeuo^UL+R2L#L+cLaIjD#ywhIFl-l
z7^vEFkcjOzNm?m{2mA#T$ttbL7ZSlRa6Kv6eQWAgvBgw5cW=V#1nYjD{BBq(%bg*x
z!<iBLKk)+0V$JOe9#ul=;7RY{2D6Aa{!pLu^MM~tXUCw^sMWcw;79JX26k}nVXDE^
zQ~g*^Dw`=4^0{BLx6$Tpn{HD$)QPx=%{crP@dm#>PC_U*`kH93yTAB9UxD4}P7f`p
zru2N@)<BLFU+6vb>S&cMH!#yI9}j$~6O)4ko-y8|ziM9FR$7=l*Ak@iyvV~}x-MMu
zFLzQp<h)<|F}J?U+LmV|JI5P|-GgwGkGh_ZY@F{k{cT=8mS7t$!+5E8M_R$Z*KGU1
zH}1~$oFnwN*PQP98MGUq5{hJd&#M&z*7QTHW4s5Sy@&QoNMd?whj9ayNF<IVY8Q`X
zlIcmRmTNM!XGGlfdGY&0kv(_5qX!Tkk85>g5{>!8EvB#Y)BNM?CxhSZ7<Tv7K4GBo
zTkCteZo6a~-qU=P)CKPP+#gSxz9MZ7>kJ=G0z!S>8@irp9&s32t~zo$cvYQO#iZxU
zX?%g1D|bGAM<EA8M+ZHf(lZ+mx<*4gh~00Mo|etrFokHt!ZD$iOOTK}UEi#Y{c=iy
zIb*s5{FyDg<D$O8#>u$(SdRwj$p}UmV?%8tuC)0`jL_2LwBX=5G`6Dp4GB%akj<Zn
z64qz~KugdPO~MPmoXS1`%neJz?|Lo|HlGv|5=b>PY{?PNdc;@y>M&7hE<gJYbin0o
znuwkGWpVMEni`(8yCD?5{K`j~W|Be5Ct)&$R{}1M{X4L8IYBVI?zT@7I!xzCVBkH%
z?C*BLb|q1DCT^1t&G6c>=IJ<Vs}BeM)xH(fC0aF?1ae$?UaM#=MxjjmhJXJH9w&0!
zf!jL!8bF;xA##aDE++xr#3q#=l7T_Wc~T}^*e5Z#1u7=OM3&4I!_+3@lol$@`$})h
zp)8a(|7IpE`X?CP$3}Jrr^zq&ky?cT4{0M}ks!AR`?=$NUYefPq-460jb=Sk_F+&4
z5_*Fz&si&PYONSXd)QdJWGeeKx0IjG#Dn%BCOO`+!+3~EQBU%)9%pAbOhy>TARv!o
zRFU}XXpq+&DE*LVIcmsKmh^^9&RI0=$o)vd`zy&#Wot~TE)IbA=;Dw6Y`bm3IdAzM
zYD^kmnV|mufn$@O3+T9!m=k)z@HiGNp!F%E>u(#~M*BmWraQ(VfP!D~Lqp^Ua3-lM
zv|k2So*$Jm)53oTzrhabU1{A<vo=;1><lnC?7#O%hxOPE8n7pZ{dI~4VPX0x>8bWa
zo3X^iAX+|;(Ol~D>FJ!wbJN8v{&#EmkG!PMAx6mRL2L1Xq1)00Dn}wt^19BW#B0a+
z9qqfRT=af3hn0W!V<3D@(CfLcC&+!};K;q8;7I#%$yT+_pVWT#Z(|D4R=1AR_GTth
zd2&)f;vq0?<nw}~vY@dqTymMh3kf1PW$plZKY7W`VUn8l>)8Q~cAtx_28=fU|E7IU
z%)vp$Po(0C3f8?k*$Nn9uB)=6hEw<>q;QIF*&%WkDEERj(9Bc}rIqrK1Ep1EpgqDJ
zOdjH66Z=ljSoa=TrKIkPdi4Im#&9AYU%jCxDsy@-KHC~(Z{^!f=Se8dYKK^nu-%ls
zEDEy}FhfY?)+-4cb)cx8$pqMqvWlSHcs}!Y<hVflygpUm^zDG%R@NTtfAM>!nYNab
zLOezHX#MjD-uE45`5|VtNfGAPuq}$;)L=Io5LZ%e&Q)+-<fNk!uC>ZVpf(hLT&V&*
z5|z+B<!sLD0OI(`ov0G{pb7XXaCDO2-16-E-N@W>Rm`D(xz-r1JshPMcCsNyf!Wzw
zS)Gx<{*pHsQOgn5jdvIy1}<kz@66~7V;k=i>&fSHo0`wFC@8Ijqdaf~Xtb0BJzzYT
z7ec`m-~XN#YD2;AWU4#*T5#aIs=J=Bqu=&0Zx0jXFLo0%P6{r8NWc}ZliU!cKRSOI
znMJLuayem%7R)l5IIsH=Z+9i`Z>uS?SPQ-ot2^ex%y7CEry5HL#hHhbkX3(I^<RHI
zOT51aXUBJ(t9w<{%(dt?PtE3CZ?;F8WDE-1q?a3L<CH>o+V2m^U2+IeH_IJgndd6l
zD=ArK-3Pk3R5djiE=t$K`)TP?@UAEMF#I%uJdfTBYg#wz%4cl9;CrGYR$C+pSAVS8
zbTDS|waCZT<x7NoYjJCJ9LAEQ9^%xuu?jWVJv)51yLDhyoT7!L7`lQ=kDvG}5rB-6
zfLrff_b(4-?xfPTXkuKQH;SrWZKu#xz;L;E&|FuOJ69V3X4X9iHFT{MZSVOY{#Y)j
zt~Kq(%gQ4^PR)c;e?6fQO7evBom~%+<diX~MALjmH=qWgccP(#OWzg*T+*HY8Evge
zkC;erb$!CQraQnQAkAIH@gC2k%wKYsQc8?iEO4L1y;E1!>|>mO!#sO#D3SC1Nt-MJ
z&d$VIH2uP|PqvSlM|zfq=)JHR=gfBekn{I<+Tsx(ccY%S<}l{*ld5;b24-%$a#m^=
zF|fvlccQ#vxS+K5g3Nv64|Z7u+-1EZbap8Yy&fa%Rx5TdEi?2>qk62pBT;s!*)HXW
zF<;7|H=XK@;<_e`?Hu*A1&CK{1XQctOzaVO-|+j55<f-4g=|O*lqRBC-=;#stt?Bj
ziLW>;N-rs}^{Asd?i`#qBm2edBX}|l7QGRRDD1~MN$(yYS(@jf;a4x{iQd}kFRK#U
zp>viL{n1y=Diuk?>)uZLA>{p<uM?jW72#GnArLwhH0+FwF?;>#Kk&EA^|Ht0M$P}Z
zn^w5NxwG7@ba2@p<OBSkDiCT|1WsZ<ZZ1^5#CVej3YLp@EsuRx4-}8OvOQnBGej)}
z_b?&Z@z_F}GE1zfV}BM#OIilSX4k()p#Hfk{uql;;ovI~qFA=X+2xYRt*g^rF*-oH
z_-gGmR~{ys=it+y{=d_mC&>=oWsnc>6ZL$$_-LHRmbme|-^?ib8YOYgR?q*@cHa0|
zr(0xy#Xea72x-$u-af3s#^V)w;OZIvogyRP?%IV2Ykxgo@+^6kWmm5eZd4>%m}~rq
zj{2(BYc<pb9-KMqe7CgJ4xE`!om2xtG9+T>TwpF7+5<@q=Nm>?EGDQd3qwd|v#=FO
zbEdY}OE$f;ZK)?F(-ucALLx3`(a={yR>a42NXm#IM7!Gx6-yg*Xl~)<4*9IqLqyX`
z6qt?c^Glw@ymDEq(@_FUjMMRXbfw-7yvps!Vxh8H93bq4xLr<VS~!yoLUq9gP!%7k
zbTx%Za;FcgnFtIAySv|N7%NTfG{BF>_kfyOzc-dW;nS@?J<`!EXpC9ogDb7jYz9L{
z@~0%e-iLtY7?oxvu*im7`lB_Bb4417ZvOARNZ5VKuks)OM;tL-GKxXrW!R-|d4=hD
zaN2gDM2TcwJlmCQ?-J<yj=H4!o70`u{%B4O(4qR2!E4LS@{-^Ed3Js-<x#zwJJ868
z3=PO34%&Zcf7E}uJ51&G=KimZe&b^jF)0J5`{9h{J(C$ooi9D&8*+fOK39J=S*ikW
zPFFwICugEBrkm}wmy*$T*?UlM>NV{R+^g!|nq-d4F?=x=<`A4;2m6UIFhdj=C1sV?
zTvbxSdQb(!5ao#6`Dbx)JqhPgt;&i~kN<hhvU)FaUVab+61QgXS!{|E7CO=NDM&tl
z-SBye*BC~FP3DIzBb~J3)<e3JR{gCJHcVbYp8<+RYqAag*-*+_?nHydw#EmB)8(gb
z;UL|l0#}kmkwIgu7&LXHS6*Dr?KWQbzgD7HsjM-p`WzHF4VCPH_(o?aF`CjaY(`I1
z*J#AJVzC7Eu^6^La;Z$V5GD*&qAJ4PDJ)l`4q*)zZ$Nq?Ix1_cPxb};m7|U$*S@DA
z`}V3_=xSN5&@rq@jd|?w>~u_y`3@(-^+>)~^qmp=52hSnrXfVUfU{Q1mTji<6$MM`
zG{Pk-vmS}@a>fE*g2Xvom(YQdl}XaZF)_2(I_L)qA{}E{w*(8aUW*B>gj~$Vj*<Xa
z4&BUXzTn5gm(wk*m3E{L87XZQeueNPSj!G9<{E+g1KY?;3T*gXLSaDCfjLO?%9+^R
z{P=e5`@hPU%le87J2wLxM!w9l*?ac>v~5cJ9rN;ezWHFSYfk&d?v&$AkFpo2V#Xs#
zG7s%a?d``&`;Klqc-1?+u2jH-Y@Lckp>ctiJ!_cX5&r_wlq)EySG~4SEO|6!6oK)?
zm<aX)fTmVkS~UV*Y)J-e^E~blKx@bfyS;HCkx3z56=+{mSy-}4aN{`ZM{8}|svf&5
z_OBd%Ok5lFZT9Sl^@vU<W-wxzM40X)l_LOyUNV%C0iq(1_=v2c&A8L-9CNa+#fXc-
zRyM`mL^x7wfUTyj4Vs}|r*KvNt!^tCO8^%#np)&qIGJvO8vz~b(u3cyX5SSOiz~-G
z+U&nIt;QK@g(Ii3-?A7U@3;#O`2Gm}ZT5tcz;nzYsyJ^!>_1pGwbMKEp8T+Xi|kXd
zc}5`TzlGt?81F3SLOKZ&0;^gLF`$uAQylrQkrW($>5CI^F`r8@*uZ9Gb6*sVvAr!J
zYUOhr3OSl@?!fGFn%m`k9|4;<aHC21|1r6UcM3H+w*GUJnP&M7(R4vGKu`x2XD*s+
z@S&YNydGZH+CRZ9d|nLSaLfawKeeoi6<qrh?5U>KaD2dRyFX7J%LVLYSnBCp=R<I^
zJz=6!z6A77pdPx;L1Z*t!<Vj3)2wxa7jV>i?w_cqWIPeT5QEUN_O{f0HvVAE+L8vm
zpe|;~3=J87`?W8?p;BGtYcbKZEd@@8L{YQUKM7>DZ*h~^{t${*W|f2#b^a0;=Qp2E
zw=IvCQQjFxg~o|`LYx-UA2;8P_&pS#sE5zFONiSJoJF&`bC2JQCy9{6Fz3v5f{Q+L
zh5Bs#)DSH^?dKHJqzMf+V8JN=nhlxLO-SY{_DjOn)-dtQ`+b#;HS9>E*XN02vcEt?
zv71+Gnv;0YB5}$@V6EHqE6F*V7nFFkptn8JxXj)HyR4l(DOWr{oww{V)ls+oxCj|b
zk5AIJkxMMm@#VNp%y(2-p8r-<+h-NXXtqPhVH`4Sn(d2k+wqbd*KP%)&zv?cbESW`
zAFTKaXB5CBKnk5gr?=~IwUciwMrvc`qpB`556nDe;U=rv&SqOT+k+{llv9e(4o%oF
zQ=#Mg9S#6s@4Xe3DvXXN7Hz!Mn;M<Y6|Q_|e?2M5C;-P<z6iiApdXRhA3owh;JT|`
zhWwnm=9XAcP~|DzPS*1D$FV@FY{ZXp)ykn(y)W#l*Iq=;kLIs`e-{PT<!Ld7FI_i-
zx;Na5dpq)5HjA!*{~Ps|=xy4|nwKxEea)0%5c=}@kj*|HxPIp9rbWaQByADkrW25g
za-hk$dEfe`7MTE2Fx=@V0<>$t9&LU50c@6&|IL5<yn8eWz0xY(``~Y1{GFaSUz_(4
z-8v{CYt{UPA3+}wr0Icfu0D7sbO5=05|&aO91&s7x=hZf-k?YdnSfL@(njyatNtla
zMrw`GjHc{2U$o%572E7wldsd7=17ww4K*)xISMXj&sE49GQ*cM=TvS-e1JJO88)Rv
z_$9F@>aut1^6gf?bj@{%5392r*8}q3)Xtq>zgg$JSb>^T<bN@`f7vRRH`!JT!xeDd
zf+7QCdHUYdRR@K<S8_hFwyHzBIW!4LgtE!au@AbY0jLcFbtSu>%Z?ao23G;t@w*Gl
ze|)PEC!=Vx$7INp4z4-Jp0uOm0%4d+6sUp|X82Q$&9XrWq+wB@?o$&tAM_LdwT>7y
zN)`*u_*@L30J5)-F4}*2PBo*(wx1qG6f5?;1iOSqD@9j-<f~Lftio~_o?a>tZpMql
zO088H!wIQ`v1ZE~a?{-b#GngMGwv0ssua+sx4y}K4!wRFxI0IaRKzMS8Nn4p#!Q&Z
z75!uJgpB|x8pn#6yl&bUAs^+2U{u&zZ##^`d%I7uZgP5dt;Q<s@nuzmr4Sk=B^o++
zdhr8QdLauNnf11ce+~(Il*B~zyvV1MuMw2q^tuz0?dhRK8b8f9%klXQGp8~-2O4_w
z5i<Pp5e1MwUeO3Ov4oYF)Hv#*3%`>;L71&5D}mqV;EW2C6My)_Z))}|1vGyw;Cu`4
zih|Mf33MW@(a+d{YKr$wrsXTEc!GK<<jVJ_K--a4hH)^F%HEgfA%uEo848WsZ@Wij
zSUogiEcTB3l)Pvd!pm>X#4eYp>YJIRP1t#0&Spp)CoJO#x47u6-r0r{fk8VQeh7GX
zuj#6TK@_;bHJN_JI@WnkQguASD>VAQv0Z&vVM0b@tXE!6WSlM!(6diq`T`bSs3hi_
zFeDHwtPjUVY!gVIH%i+z0XzuT9YWuUaH+EZ3%~L}>gAJg*1)4<*%IOl16pYiJp9!F
z?GJW+iG;85B#O1xjDC2(?w!9Q(gX<esk&X7rXZjj49=$H{*3`+8Vhx*Aj}>9(S)o4
z?!pXCy0ai`o)+X)eFwHt??!A=?K>SAs04^i#7H%cKL~rYZZILGb$#k{DM8Gq>W>|=
zqTpLJl8h)01HGLs^duy<wzhzoo#Bv6v>>pCVIzwrAFW*`v^l<w3pFz*R!W^v{1r!l
z%hMSi0mQ0BwxKp0Q>A*ABay7ktzd<9mv5z}^ZPlQVJ<FG6x^qA`b8-KQ_gbk!U{^P
zB#`s#s4VJFYk?dXGrYJdF&t^lmeW*vFrmv;^U-pp07PQ9eQW;O&&&}aWRt-Kv$tNv
zs)4lP%%cXJ{q@`B?-23rH|UiS)Dbm(kNK3QZ-a&0hXYKWyND0l_9$~#tiX4GFEJee
zpv0s;;jC4-sDdOTBYQskWvaiYqF_%j>mi$V_mjSD2lN|V>0AH1QT<>vOo8i#{IfXQ
z2YbuYwus5OOJfJW<#w*T+VJSD9!muyUm}`$;I(YAyd+l1Qg#GLYTs$`XtD8<(w*vE
zxJdn50aMqGpZ;yim76qT*v|Km;NFqE*7KXv<<8qV{Iuz^mOp*Lq_f;piL@fVmCXkU
z<a;Kh|GIMZ{IPl^y8F)VMVhF<dBq8b!(PFIX#EGvTr0aHh;Nag`1G-nYtMs|ZBv13
zwbrop5A?;H|6;YB7CZoK+OAsP7$B)z-*e{4iH~D#CID<EH(yViH^ZUOz*2C~F625P
zh3@$Qt($*4RjcfYl0Za}n?ATJaS<eJ(dxlWI#!4~7-Y7#u0AaNNlqc3*>A)*mIoGR
zT_VAVcZ`MG;}3Zm(m5O>YVQ7P42J6o1I^%i*Jq)6WQNzBExgrCTNT!%T0P1j;?;12
zGJd2mNc$mqH&mgVh8F*qx`h_l@I^O8+1Z>M0u&AM|HlG2?sp~~GMZqBkc^*~*wY0p
zh9#o~XbbK-bw02f2_W_bA8W{q0u1TY$9#-$u?J=Hmq;67IT1OnzT%$O%tMU2Yr&Ls
zaha~~*J3nMEs!IcaN>%ZE4oHXV<cfcAK&<P;z<GNY3ee;1uT;#mh2?&{Xrn4Y!tJ+
zq2!yHFLTMLXt#L6SO8}8+swPmLv30qx>q+jybkzANhL>(1C~JS<JkN3vBLUROG9;T
zdl+VDK$K|<>%DOKMbSIaQg^nybZI4$<C#3kMCYIi@kkKYyrmRUy=0t<{}v^$wwp3m
zB<gk?7MIhHW9JQ#bm!xg#tb)<upA7Wno06~C<rVL=bDu2@_w1>>AWK^D99+f0k!BA
zNRqME8voRPBoG<p=35d`4ub{{^?zpn$KuKt=J)aEkra?EtZg5^^gZp_i)dO3bD7x*
zNS?P5$Yo5Vu@Tg7ePNZWs_qdW@<v2-J2ttOs<A^Np4#&3{N+lL)$iR<V|3YOuxH%T
zZZA{QkWy1>c#W9R?XrR|k_AMYtC*%I72F@u_g&j|BL3&Yi2TKq$5!!wmCc^3NI|>n
zTS}5$Eyt<RlCB5dM1e<YP#vV9!DH>vmBi&8_je-FWO^ZwevjwZWLwP0aFW3mRxqdx
zZ;<fm&XVzG6d8=%r}+DartmFV5xWh39eyLQ3tcE<fGXiVGEmqy4Oaedl;RM&t`#Cm
z7M#?%J`slRX-ElyGZqXdu;G6;{6(bL9Mu5(v3d>GxIr46rM+D-)Z4zZ<=GziViEt6
z+IKQ*S%yiTG29m6;=kCxGv!p@v{hQ9e4!H-RU{)us?Rfu;)s`h=It#WM~tm{q?S^F
z@=UZw`;Hp)OGQmsn658iXl}}a>9OQ!5NT@>EF%7%s-P~qGU*a;h7RL$j%n!ja^0j5
zZ~PWycniL~pW7uY_HJK+Ni2t<zFNgXX884bd;x;m1CJf<6|@{}8_??!2YY3BBD(Db
zAnX6)s@i+CVqiK>HI7{@3yzdw8N}fT<fB$;><^G78u(ia6X024z1Q(v^QxwZ&~^5N
ztydIJ$0E+}?owQrpWAwq>2xeuvs`)Y<>>$Y6}Vb4VzQjVodB?nU+8>%t-c}rC@nkx
zuzj^M!s%qt@sI!)D=1Y*VslYKmAhafpCvs?$s>O$PAwvDQm=7U0*RXQjQc+3#;^LC
zPVc|E-b|7=|GH}E3z0b*JT+=fcV2JZPq|KWjVCjdqqOLbeyTUSRG!91M#4qrVBAe;
z)K?^j4=qKY+P+BP73%P~-x<45QH^o!j@25~hM0`$;eSz&hY90A(zpMsa>UNU&90P|
zj^?mjva*OL`1U^74>C(c;rjDjx8zIDw0ZTf9UTaEx4~fe6__)I%P`<Yx!)hpZ(NbH
zRDIW?<2{_NqJlvpRFv<PO@|z&1H(4U<VcHoM&RsuCCfRHsg(scQEw#T4hpZ0G~sfm
zSeDl)0~WlJ>uUT|DSAarpVf~R{uZ@{lBfS=Pv0R%4S6=uKL)+FLyj&OwYi6v)_CX@
zDTHF33U0?|^Y`XY9(G>2%%wVqQneW!E}q9E6lAPR-6xBBl-^rgW9;{b^<W?N%kVJE
zKoe4y=8g{w*l>E0q<z14D=3~J;L)+-E@i_n;5sEsM<DiGLSd~I0H>omYw~<=KpMlY
z@v`>E@wTzuDj#-8F*wBbxyE>glBSq1VAF}3d1&C#Sepx>6I5Ai$L$^hXOHoZz1wS>
z2Cgg6{9KbWMO{-sYEHb&Y<VIir}uz3cC`s3@C7nE6${ves0p%x{E3#pD|EIxB6a>D
zqP}+K%z7Opb?*0!Yg6JF_hfL#H&{4$<KP(JB{dGLuhn#n1pPSh>;U(`13wg5kVdQv
z0W+}Raa5`0NFph;qX@`KuHbEkK)Q*7j34Q^H$gZ0<pS7aAT_HbTxYF&!uX3P0{$A<
zP;uyNSFgvk3GOz>g^>YXCc}j=dL4IMKhNsU{5GK{41|_i7%CFhVdfxiu-F-j?aG|V
z6+;EfD0Z9=U_B3;Nr<-MrP(F&BiLP8P1lR8Ua}f)i^8#&&&J*GJ``*)UxF%SCA-dX
zX&mmC!_gGgP|Js4s6P&i%@GU$b(Z%hsTa*O%^bP;ts<E7SpVfy4%d*({<sO;_8XGJ
zP93}`(i*~ox0EY@J+AIaul63B-%pnXTIogeX&o5=oBmQP;J%#3T@%yQy%Ji^H$e!U
z(cJEb*wD=1P4UHsP<%aMW6-JF3;!iWA*1}CpM-`XrL-egCM^9DTzET<sJYw<`{2;=
zNAgBeMbBdmI?CpNeG%~e@u{Jr)K|DwYsc<_coDF{V=I13K|q*+xr|jt9TE=gdvT}y
zd=M$ZGrg-}VL1Gi$_MX`xQ+Ogk+va5*Km7+v{sJ;X@ebcGG$!?jKSRD3od$|<s!Yx
zI7JIZvz2gL<O{`M@Q*2j5m!`#jA92YfmSkjA-Us)V4()1IFkwofI`D~@lXjO6N|+L
zC_Uq!|2bk12Fd?{Ubce;a%SVp|Jij#=4CsjaC6iYb2>aDK}W?A6oYo7OlkElHnzZS
z3J3+kErpOnS|@khT>w{BTtW#SAvKLP59kP0Trc5NkHm;xI+?Gb_*=ffS;wNcBxqL{
zC@hE57*e3d+OYXUd*y}Lf^xLPoC@BTMGuA%Y(hq?-uSh<XeNzX%?0#Gera25hFdZ!
zOYnwqt+=U&;FbShH^Q?3m}WgvQSIlMx|(CbDD!Rc5?btwL0TX`3a4@;?ciCG5imkL
z*(dy$fIxiuXvE8@xov>I!pA*9BWR^JxRuY_^L~*HN^-h>nX$f;u4-8t_<36)HDw8W
z5nkI4nC{hT`}B|HHb?HjA6Os*cM2QfKBiF>;Xv@@F;bgDM(-n98yAu<sa9p+)n=^J
zO<POomHh(#C3(rh!`NxevBzfsgnE-z>*%`N*ASoQli)JF4ph7ueyHJKcv31o%bqjR
zyVZPg0{P$?h_NE<M|<;z^yM5$QSW(9s-{V={Q!R~hQag|-%bD96-#=csG)V@GthB*
z)*>H*{7;&}>Pp$&Y4b_ZRysGX;&xv0a#0zmvC2sZ#RQN;QYKvpa`K7dFY(Ewa2O40
z&=edR*ZhmL16|c_aUE!rRaG23C**Cw4^tSS11Hcqso!_b820<>wTSBoXuC-qCOd-W
zXk<;woMv(hR}TSIk+sH`^s-s4_*yW~isYtD-GrLWmVQ&2*gL^Y$N8S?!L0ps_qz*A
zMdwH_=~vr8S|`ippYmg6ItvpncKhWMZnmqnh>pqD19y|2yZat^N8W~jrHs*gkAYnY
z#F5@=TfBgFhI4=VsQ#PaQ;D=6uBo_wG5g(#JFS^rjhh)|R;<->xJf=q&@OZTxZV#0
zgb@Nt7EX$O*%%akZ-{RtSzeNLj%r4-dUcpF0*~Cumn$*xskEDZ%U+O;N(%ft-=BDQ
z#i9piT}-01dqut{)%b?8(n?%-6fYCKV5_u)5ak^a_G}BA*ugYxp9M}tMN2Me{!w{;
zNuSP5@Dhlwl$kn-L{&6aQ?(Gef$R16kWmAOSZS+mJUX)JKX5$050P^GG=vm1tS@*u
z$^w-O|6sVr%&f9{x>5k2y009~ZztmbR+k`YtY+|4U4berAB5gxS>uA)cTNy%^QZJ6
z5ggLzdxur%BqWO%a)ZHOoz!+?^jDx|gr^NZ0bPAsb)X;T`az-d58(q7jm9IHC;x_R
zR3agNaEP-E&<s$N-E~QSPP0z0QxBG14(p{Kz!`8i5r9BgNP`E}^QX@8PPMPc>~l)i
z-Jn;?sF~hlrqV4X8@|YcRGt0eZ@@=%s!>9+PV4N2I?vIFo1MK*gFac8W(PLp({V17
z>tIy-B@F32sXMPzj>}^pc=fis5sJx_x|M9Qz}H0kLpCa-PxbyS$}l(t3Yw%M;eU_y
znL-uI{p#?~7wGy-&;|K6tP><|>kTTAe5gX#V$UZ{;r(w9xFu-ZD*H>5D84Udx1B)L
z`1>X8yyqsijgheN!05n?6kbE$_6KI%>raz*_aJV$A*o*)z3;_riJhh-#QnC54FGu!
z#CF5qh}64#K&?R)Wbl3>I66Mrg59F25*j<9*|AveHAbZx;c#}U)Pe?HfBG-T3=h>S
z;G&|{3_%H<&y2O}Gc*cX`rI!|A(`RX4+@>%pD0Ad(~_8%C0wAUk$xZHJ<Eb1RMl9N
z(f8mA(C>HL7UqoTVqU5BrtF`c2E0r7TmHuMrc!9h4&_dCmSv+MpfdhCN`MB^naf<o
zKbpE?;1k%2_h)$-vn^eljiKNXH?wunSaSxH%n7H{ivEMocpzSNf4Q;Aig#V}eS=?k
zF!x%?Y>g(X(0`)2Dl1z1FU}nl#mI!joPvAIW|M!=ns_qXi!ER8oF%8^m}`>2=ow-E
zK@(n1B3xTwyFLC#Nhs({G0BlWOJS|+4~ET-9d$G!fYwE@s0Mm9mNmS0t_Y%Cvj>HH
zF_R1S`8uEn2c@NG2L&oyUtd4duEtPjE)jFgvF#DE*6KLHV=!i*ihi$ZP^*8IH+EAT
z7>2`hrd{j>DGeQbJw7S+_H<d9UVK~a6*%_rG(#l9WIty-7U-4wuzK9`1hAY5E2`Ny
z*#7)@HXKVK<?01l((%bY%vYyBtKer8jgb&2%@@p5E772ffIn*zT3}SwanNiQ2Ucy?
z=+6qz!9RbuY%sY@VC9RsGeg*{)<Kl3RyVOyYv`}LSf2{CPmUV9_nEKKvQ#xnQDl34
zEcRCwJOL%W#OqJ<#!&!pzF+-__WO^^YC6G5LW|SU$2RZYrDu3NSzhw&Rc8ikPIbRB
zHBKM#Uq!%^pwZUMtp_m3Lv_%^nbXsH%_CmhpwN{?G(XENpgaJ=JL`jw=`{?E`g|J;
z(khGK$oUPrD9)BZ%wCOGO5N^E%d3UL5!_9%%Ckid>@^<?Zw9?iXqpt?pz=189JLsF
zv*k8qY~vQC*mlfHtp?H&Nq9{klIN4`)#*)R7lvicCD)16h?=Wvh8~3+t(^KmK;Ns^
zN>eoyYSDzmbrqMHsLI0Hf_Oo4P8<0orE-b+dXu?`v925acQ;G8TszQ+U&qB}Elvi&
zX<srE5<m<(OV$872poC-N8W==WMf@iWD}cy9RBg}R}N}vw5jy4bit0EE-B0+;93_G
z!*(-p*@V6isPdXN=`pu}A&U*^+-NR!{HGF`Ew+6~g0)VroYg9KY)`35#fr7_65#;#
zp|BBqo2ak*Vct6uvri2E`#+$Y2d+cajsI?m;*A-;6hBY4HVY$<$TnlGn0mW?`u(Ri
zlrE|wIXk(%wbf5^rp_4k8;9ZR^quM9MDRIx-~3n88aHz&XV@Kl!cN|!ITnvFj6Qzz
z6UI({d#mN<{gac10t_BPQ^j&k<kF4!+yvf$q9%J)=)wZiJ1?c~!#y#|4Ub6M_mGlx
z{GlJ+X+BeedVQc)@aG>9VoH|VHe;{vIwTX)tN&)Q9w}u)5bbPIknZRMegL}r)%G<Z
zf+mNhjk#tDWaa*2JjPQdP&>x=)0!6s5!2|dm<9gco6K9EfW}Ib4~!qM6kB~J$%@$a
zy-pcNjXqsY4$3fi`9<M0Ir=-~=!(P8w|bdD%y4J<d)dUiU7?q36M_+R0Lfg}gBd>u
zV<zlRYCvwy!uB~*LEFPgQadRk@x)lOhX>pAL?AsDjuC{J)52~Z6$Rg&Kz_OPb0nSr
z%$&P~P1|$e=+2`4R7fm?PYD5)cqIzti67Sxe$CDi%8PTB@PAmp7RP5Rvi@e8IrutJ
z#51&l4qkV_d@Xztrxn!*Pim38OcCU|ogLd%>x6m4@cBR3H<Kj%fkN7N@@D!K7e<ix
z{K2vFz5{>2R^VQ-izy;l&yNNCx_N{0`h@OybIA{NlpNd1z0r31#T={$?&>LHc4D;z
zN+!^O=P$1l$LmDumE94uV@oh}wf%vTbstP}2uN3CHV4&?IPUwL*CW|~11{l#pm1d(
ze@~>>f^LW3oB>Pk?d9HhV?oQ;<aN%o5YCmbbmo>_<$yxb|M`m8azG4>4;4c}`&y$d
ztWh24xR~+j>U`iA5u}Fvm{cs9yUx26k`l^eZMVQgQxlf<A3&i63(DZwK&E(Iz7AdI
z*$(#6(7D}qR=ELkP3sSL;bC=|&{v)CfYM<%4RuKpnOLQTxw?4CTjxR1Te}e1T)Cm^
zwp~DXv>l0bv>SrDU|i?VBn9aEV6K|OXm_!HFdNC=cqq<0+SxYa%#yX&BUdZ0S+K8T
z3EAV;l<J6})XF165Npfa-QA_}KuUNKmeJO`XmIS&$h{1UuNdwPW>ZJF!6m43Onf~=
zI&HX4Z~ko9sy9XS`5f0XB>S-7G2e6AnB>@tzV@jGWh9b2FwOE@G6N|Kb>m-hzp(B2
zzPUe4#iUa0Dh(O<JS;<#aVg0mba4S7l>Pe_f2Rp<_B(n5XBx~^tX4b1e#lc{&};q%
z`8LJa;5AvV%a5&^8`L1Txm-Cok(`yBr9+ceZ@2V5Gz#AC_J^eT#3~$sfV~=h_5~Fc
zB!Q=!PN%7SKR2MP$$J=K#MjjeRW!zC)2uAzUIpC%eRvp_q08)DV?8w4?k7v_Liykz
z8LJ*YcE7psM?t_4v@i20P0z*CwKryj`OwD?4#bo8UA-@#pJ3DcIM(Y8rA4VLZskYj
zmA3V?)`TykB8dI_hvI|)t7=^1A-<m5`;lqiXpoOLH6Lw`WeH3zju)SF$KB^p<rahE
zXQUm>0#Icb|GrPW^nBlA)sw{e*w23?4FSR+zvYBPg6v@sFz3+Rm|XB_2f`<LYF*y=
z(Vk00XtMjYfjV<rh32PJ^J;r#2Az}(d5`FTV_k{m1o-bZj}1DbIH4KWkE2UB$2sB(
z@|*MGzo_lr{Mw=zhw*}hwJC(Gmx;gb@8vB}-om-6ct<N*CuAId6|vhv&z`@Ovm5P>
z(P1sMvn|`U-S50%k#-ZJ&LV4A@1Cb=2!PQ8UTjj6!fm*skSWO(CqL|nG!v)~!*=XC
z^Y3IXDW2O>t`(SiPXjY7f!92Ogj*rJujBN&?hkXqFGEFC7m~;sQVpWW4n}*4=vZ61
zOd!UV7w0-~@oKS)X>|lId2KLC0S{ycxiIFzS~!3O$I%7Gt3`S+4(%mGjRp1MGM+aP
zO6$j7qkI#d%56!l4I!UlnS|3j1|8v=HJM3Za5Qnt#sgoaJ2?hx%&#{RD=pj!c@n>k
z3L%US1%kX}-gPO>V97VAw<OMP4S^-=Rk`gG{34h6?#ABCG{ni``8;{$a6CC2wmw&h
zD8YO3$C36zl1xA6pC2Kftz3lCf7}d^=!;C1)1D1}9YAs+<sCqTWX>ZVKV5~vfo^~-
z5wW!s$(1@ff(-jLl#7uRv#Z&4jEHuz>;3Oyy1)m4(&M~2|B!e@IrF;e+ZlyEzLXxO
zne)g8+z$PD$XZFGmLu1WvCz?l@#S#`kA|M4KS#lOD|PfuDUnEK8a|vqz*`B2rzzg)
zxpFp9E6g$%-mbZmK|8n<eXGJGEEhVzlbU$x4TJz6(HrdQe3<NqU`>a8>0AI!*}eqy
zos0()v(Z7=c60qIeBir%eU5p##P{#9<Z(44>*I(4J%=TJUS}34x`08>m~XRQfdl-l
z&Gl}>1G=~x$;yADMz%YaGBnuMWC#R|D3WF}rjd0Od2?Mrmq^^!xaOk`CNo<K8Y5&6
z(iWd=l`Yxv5cZ20q?HDIVhIW{86<_9FTi`drMw@k%Vm~CzCyq}UHd=BSZ;jZ5kN~H
zV?}@PxL-Jz2)qm9`dISvetCelnBbmmx@l?LZ!82aRc|3)iP-gXCn60rc|RaXLwT)J
zo+wee?~}E7Jow*d5#+b_oFQb6%?&He*|eZ{z0~ijZtz@*J-xG0zTB~hZhPZ(Q6=nV
zFiXLhg>Jd=)DgV?;nmnn&GE|>Y)Ji_P~CtCxu!9~-;kU<_hV;=Ng#fpYrYlsUlKWo
zYlwc0KptHm#FKn8k8An&d494?m!t~A4Cv9V7K&nvd6hZ6PUgQR{YTVqt&msxZQtP$
z4}FzMozhF5Iy$WKfvFt>4boR4$ZW-<QLF*8AYY;E;W;Io4})|~wMFpeT~TH+$QS5Y
z3oem`2=`&qT;W|9D-C)qT064clNw2y?t+=sCj46ML<?Rl?>(jrN}g2~PMibh3cjf<
z^N<`LdB&2BpOLG_A0?TYmB2}%AlzU#8jukpe53uoAoRPFipB-)CanNZ3hG7SZGL6z
zsDCqh4X4fBVNL@~pvB(t49G=M$#_{lN)2X|b;a|`i>6ysMJ9_Y96z|UbbVmKne+a@
zdFJz4mH--FEfprmCSt3iHc$5)rx&Z-lf#CL%A_dwTok&fr#gFDu7{+ynPStMJ2Tl3
z`FSgy=RUiAV7f&RK4sn5EU`_d5?wCVVA@<c@%f+INZJ?H{6rf{Kf9%?#nyKIi|VSt
z15seDHQQXJYZsm3K!m`m$>2L^O0Fr(SPk45VAD{4kz9X;blvcGghXUlDwa=|x3e?1
zvdKF=2}@u+svDn84R8(8X?NYf-TuNKWSzHye)_XD>s&U;SH6WlkDXm!R51Tg9I1_=
zNYtwE#qUjcX%xiJ^^Bqx(}|+{%9W)02>0q2Jzt~K?oy)H&Hr?{u2xlNF+B=7T3aNW
zZ<*m`qp6QqU-Ge4FA?+2;X(KnkfMtp(;pN9U-CNTd2Mu)BZSV5l_*wqD8)2+JQUbN
z1@gQI5f%8W-UOQ_<jiM20^1bV2<Ec1(wn)*8G?G~wS@1K?<t(g?I`V7U3eii$@lEE
z_;!8!9lNyQ?l)7Sv(?}_g~MRQ0>PSOj&{^hnRzWUiz^(6XnkK_9xByts(FR$W$NrF
zB#Gpwge&Goi7lp{1b-U$l3np??B<GGbF8j0*?k&czW5qcmD7xbOIXZjz$Km85f8*0
zOun1zoE5ER?Jpij8K@ug@F5!et*agiKiuEIphyYwhODT$8>fYKgVCS&%E?ln77fsj
z34YSq299k>5=)+vY|APTOr+#6yF*aONrsrw4PF&=TUwy3KPv);4qQehZboj4*g7fA
z`YH6LgkoHx;xY!|TJTp!)nvGOhh9Cb&;peCR?`y$XaPkC<Ov*BBa;UQ?uCyuQD$jF
z$>U&3;Rsnu8`SAWsoVpfHLDdWf?Gz9MIWi1ZY(CWE78iJT|}N#{?{mHhV>O9HccEc
z1&>&lhWFv;3RZkvb)O58dzu1H2{BK~utOp<?4q%HFEaGk1*i8ebXvJx!}8fjTb$=w
z)?IVK2!S}cypb~alk&=3c|PDH5--rkJbb@T2>HOzv=mtoN#lx`K<;i`ze|!phNAHP
ziJLQj(ec>|&mANj9CMG6w4;tch1^~r>lOdSwa|xj%YJa4v*mm|KrTpo3fSl`8UD0b
z^z(C1#w*=It5@oi#l5h_l=HEap8Y9qGJ*PPc0BacI($mrfM04OLkRW<vX2^gSn|5L
za0cG=?G$^h_#RKAd;bo`JGtDy|1Ks<JYh)v&fAJlsm5zdYQU!j-VjtPSIjVam$)YF
zmphk{36tgjOy;T_vk!eWvQ|e#NZ+mwXlUXvCIgmhuc@A?1~fXKjrONC$t8@5dGQ<g
z&$w-`e@>9)Y&QHsbh>h+;ze0wG_!X<e+=WSVaUEcdCS+OzVh55A0Kw%O~8&834S_8
z&}80K!TfdfP{~0qd^DidV5KcmHjrN(2nsubyTR6pwmzaL9jGQG8#R>B;H`EnVF!dG
za8ZJNyRv~M${?CCe<%4#pRNAguSWnA{KC?adYbL+q}<NU5CG%|xmg+_{B(-Otem%C
z*x`wqd|tvVbS26LTnB+yy;vJ+B)={-Q_uQUCxG7J;T8RAfD)Kf@%^M^rvC*?F_}%3
zIFTB#N3ad;bR9M1>l2PMU%o}UoA>1OozdHh;ak>0I6g*|eVv(6GP^5swz2trE8)KM
zwV$xkZUG_f<AABnWo=IW?r;x#x#6#Dp^wS`1iPX}FJ=d%Zvn8E6Q>MReu=?I&T~u3
zS^hr&zCc00hd*W>obzK{1WVIep!EX;tuHXznAVDS;<-ojsP(jX0wQZ?Ae_666L^WW
zmTDE1#z0CY>@B;)(fcwuJDp1g=;$>xID0(oi$5@?03DQu*vC8qXNPl+S*g(6*I`@u
zo|TTpIj{URY;F3Lr3-Cef$+P(l)OqZmTE&pLK~Vcj%GH4i*>Z0bv|rKG4saNrmFfu
zX#G5HPI=gX1~T;*%^3jq4S%V!-BslfSvwv0>~GY1p^!>GhtK~$oc+dB74?MgMfPt(
z@cBEq(E1Dj7|3h|&+X4az<*UCT2cVtOZUQFLZ5h2vpvorzkZRF2Jnpg8|)kp3eo*r
z5P1D3#-yV7u;U3%<#*wa;OsX1c#{23#FeRV1@W^o;hjo>XS<9RXpz?p8xQY9fDzpG
z5+lfz2+<NgT4|2;SKqGct4N>}*-2Trw9{COYde-eWhq`<_!?eaH36mE+~wuw5q%Qd
zf-a;vE&tku)bf@YQ?C)85?ZH#H<?1_L*c`CdHrjcxqBYQwHb?RyIq0{a?Zx{t3SlD
zyk)qk)eziz)-_1?q%lwCL6N7EW=pl<B1|nGHGsx5k5%==^V3NIjY0j?E%9Sqc%ph}
zLN;B-Kju8vf921W+@w)l>$&`tUh|!+aUIQJ$Aj^;sdTY1AZ=FxwhDzsMflBQk73*P
z?MCim;*^!aG$vWBY$b^0qbaTo+$f75ucIDTkyhzXR}ej7)lrdGU3n!wqWNuKX^GK1
zpEhGUE*v!q8#iypr=Namz#upMVUM5&w3AwIae%{WZ{1Y-;K76Va>^7#LrFN!8E`h5
zmE&mznvWbY9PfSbfr%qu*PAzQK|2D*CmICKmJemV%gf)aa9)mR>Oz8QWn;^LK@}n&
z_eB|9%cZ<++&IjiHxEnX_nZH|oH7+Xd-cZ6H(XyXmQGEDtYf7o$8{MWuNop8C#sXp
z+>2#xNT5Gfd8*T?2xafjsmT0WohP-A>1IQ4qBi*$byb18y!O!dbXrmdGU%K*8j}~-
z)Kn+UfTyKNlbAv*uBRF4E)4F|9=QkeY3-ztATwQru<yBI!XGTemb^T4Y?{HSA-wj*
z8cgGu+`N4o4(D<c8t2R~<2k53?5B2Yh=8YE%mn|dZVJ(eKvVk1rdF`>a1f-Fujnw%
zP&?TM>f++C--*SW4`Ri}J!sOqB@<EWni}LbS$3fX6&u%XWv9;oJ1rVIWLl4!=XUY}
z4sNKG`;mw?e^MwU>S&`<YMSMCGeo9BzO%DKHe^w5-XNXsN@#|A){3n-6iFq>P8mDj
z;}Csr=N3EZ^P5`UE{<?+U<E<p5bNNzbK{<`fG)uWGHZ*U+NW}?Hu6+2jydSa+z7~$
z-!SL&8EriwL@Vf+`pQgfw8Qxz>Swyxl;WVNc3}glZt_6VR8CGY?_(@^)d#kfH6&X`
zXqUX3m@S(v70E0+%%6+Dh<G;A-%Y+wA;38>K)QID24QX`pu3hRLFE?N9yHHPhnp4`
z9P}-%DIiKZhaKz>n(1!Suo(Hn&c<8QH_-hkTf*X;jKq;=tli}Li}3Hym*F8Q{57th
zV*18GD>>4v7ImWR%OLxT@H%aT^9HrTb*^eFFRhVL0{7bJY95=LtfL<BXPw2aICc{u
zL4gDX>Yf4#;JWT<ddhN?>7Sk3_gYLFr^#%ZL`l!6y&x(-?~vIs#p#(7ub4koRev<)
za_*);E~9=+6dc?MKNmqer3B^8@#tE9D3X)2O*{v8)s+te&hAc7eFIH&*y$(9kFOT3
zsx@#uclHu2m`f&@jDmJdUU%aq)YPs)TcUiyiSyG&f&HaXJUF|QroqXIaQ2WOM;g5C
z>By#pjKk902yz)mBkJ+R0W_m|mN9L##GiZ+5IALL2=fV?tK0#N92nU`X7^4$?=vk2
z(Io!M^h#I^l(K&WqDu2xrgf(63gd2WKzMaTGBUaAQ9-8Pm$MH^UJRhw_xdzbbSlHv
z!x2p05i*+`nsTgM)6Rt~*7r;a@jf_}V^n70=j^qFTHUklXr8K1mP9zyC=jNZ@clQB
zg=5J=r2PE_)Vt}nvL=Rh?LYxdcq2U%*-!q5KniyfA0rff_zrASCnG%SG85BIjp@P<
z-ZuWPYf7rTv!D784qiSC1rOW-_s7#96XMS1Es*wu`w>k^L+SnB#lc&yLW4Jt_~Nc9
z&&0Dcn*!02UG!;v4}~jV5d@+QHp6?Ke47nuO>N6fqwXhYJ&b}8&4VYAIr-1vytfga
z@*G@6`>5?e1Ak&R7-TultW3<_&P|@qg53zN`T~*e7s7Sn{jg;;HSywqqJ;<G$lXdm
zcjRfc0QhRB1r#mJg)?^x(^30`=^{*5VMY&@BC?O0SC7$5$y8Kx=k}!tub7NblTL6@
zBi-3yP`rw@K8C*f5RO9!sNJfzFxjf9aP3(SThpVhGE|vq=^@w;Y^J6u+eBt0RCKV)
zXeV<symTU*hj!7@j3uyg9nM2|<9s-~4y!s_X~FE>2pffj<-c>Pj(qf8eGi3G4wJ~%
z_)v^!(1-Beb(Dii@OpHbn(oL~UX*l7geoXtK(&}4kl!6NIEgEBF2Om?`r_lw|HXS-
zKgXWZT}I$6e~D2FpE((DCXk4O9YM_*i5276oZe)ak96=OI6%Rui-PM>H1T~?=PPhl
zS_^zYf7P!pdK>EsHc*&K;U$5wFoo4o&H_;iNd=mODL4*FC{CfX`0Ef&O1e0INCSwG
zsJ&yu7T-?%a@9XEaL?zst-}rY?YZ~Us#q}v`6+NxlgS9c6<;%p#tUCse$klkU<vIL
z;hRAL$xGZGYV6UErU{x{<G~W7m@bUpGE#2cS3E^E=Xx4XXB{<%bm`I=-~ZnCj7%n;
ze#p6l@%Fp#VAsxFEZ2iReYhF1v9*c+%{Sk|y!i{5r5P|*5>p>bkRLC>Jc!-bIY+$t
z8y{!pdHjz_#6Pjb)tRP_M?WP;wWr($qSmcjp?}{#)F_^1Os~o$w}9*UG?8A+cSv<@
z)UY8g7&`Rm>Zqyuon(-<_1d-TO!p;G+k4QUb10wE`)OsaaWQo0`Nq$)0K5D{1@yf=
z#rUK?QIONa{x*SSglRH-_ny7gwxW5nW;lPy5Hm*PGd4Fj57VYkElXc*!dgDiaESlP
zCy|--hko)C+;P`kI83w7qQ>}FUo|G)TeoTzqYv+z-4f+fp4_<r#oKPV#h8cp(;RhW
z8uJY>)*(B#?{IPjB~l+B1=!VMz2noJ5=(2<CV4LBI1kNAd#RyplSb<?f{hku?9_;s
zELbbJz{Gm>yfpPpIU>zDhoV&3<=QRqH%t)KUqYVAmW8>?sj2+c2AUS%k3&U%t<13V
zod^poriQke3OSgK(Q7gStQPqQ3dFcp+i2m4%p2um<Qu4XMFg1ag4Y1#s&0fJ_P+dL
z%v-Vs{W>;5{WO<Z^Uc6Fz$P;jb0L6Qd5cHp*mqE?SQ@y-Iso5iJDEcL(@PcAyTMb!
zqUPvgxzb=R^;7XHGt~gl;#us}{1x<TV;lT5Bkm>-F6%4~mZOd2^4%N`C=Zp*_>3#h
z)AB1W`m>1mv~J!@y)%Ng9fly{i5P=CkO<_U#<|uBTiDMwODkPflNP*Wx{SyF`sQLP
z&Vx?+sV4l-emHiB$$(8%w!2awW^ejC$^uoA2q=Hp#(y+5zoq>gC8+HoaGpXMdFfkP
z-w~=)RHoCZHSeOIa&0QS@ch2mPLu01mu;n{JvR%G27b2BL7}HMsIK365EG`Zq_v!E
zw51<xJNYkdJTT>oCTg?VYv`jgX#Sq{HgkTg>J|;-jfmZhUzQ!KyI&R|agv}wf&wQ%
zfn%>CCy;3($*G}0qsH~|!yk{sQ~!9K{$=tA46ng6Prr%VZod*@s%r`}r!T^*FHhjE
zE$%-fK;5^07hG}W@Yv*RrF6Ny$mui~&2oBS>$*Ao&8A5bnmXZPs}C$UeShS19E8>#
z&*6DR!MG;!KK;6yov+V7^EN-u{rK>`FSO3#Qu?^ADa@a{3@`uZL&I=tH`lc{j5cP%
zYf6)N_VrNU{mntF&5t69Glv9oKk4Ph_d2_g7W13DjwZw(nB^mo7D7jY*<udsBFJyu
zql<iaZWV<^WV9&%L=H{H3~ff{DIwpflEixV1X4dit?JG+izBVyZG{oMu|7yshh&o3
zhx7JDs09*WKUyEl@>hN*wdF<c4=+r@1vG<rs=|`oFupsb6x-!cmotTU@6K5c3~8F^
z(Bq-`2EKDQ(j>X4_~f}>+|Z#m9}>q?LL%<hO@RnaLLRu|MtHuO5B&7kNG}IkM=9LO
zy`KQ`*3C2%PE+AskC-y7uHK;?+>!p%yAesxLgT+aV|G{X9^3;V-t+e94_C|9)g?9e
zJzcsZ^>@^u{K1XLz3+B3dg~KzPPXO>0qQ{l>G;#HO`RK!f1jG3GO3ja|BDoe6z@i0
z)xB`}7S}OoO>mGtZ8wd&54oL2oIGevASy8V@4(ELX=*NjVgiz4U*r={3KV97<qZjQ
zf_zpQpx~5x3$CS~z`uJv+*kh=_7>gBbuq<5jHe$(Fea~Z4{JD$rz79ZRF;6LTy#$K
z$}<j(ipO&CkLLf%LFN!k4e%a>dpkFVzWEb`ulq9`?a!$kwc6_lwTQ#3r*lIp8_u9Z
z63sIQmwXI&ErDyb-l7GC@kV{2j@Kw|BnT{_n*!}_WRNV*P9b7=F#-23L#q|8NxRKO
zA(as(nw0!Qdg_}emX*0HK1$y}<%w)~Tc+pTvY>PInzU%uKZ@g4;#vfaF^#K4sEPul
zq4mG)p#+IdoQ-jNr<*XW$q-D~{wY4#IT5+RBBas8sGdugM*y|a^wAYN#7-Kshxi>q
zts{O&oL#>ku4;E7hBh3GJ-&nZ(U*_n^PO`T&rZOae6jl2N#<GDH$}q6`K5%?6nIK_
zt3H;PI4S|SiNyTX<?MfoMEQ$iZtf~9FZeCaYtR=rw!a2lk~*?3>T65TY%-U$oB8Ul
zgr$k_&7puc(`Tfo6QtutmC8W^ZcRAsHFGq5&ubmy!3e>5FBZ*;M-4lgnt$Nl`;eWT
z9gk^_IXO9a>Yx9_RDz%lV&JU&vd$ui`||klW=xh9pt4OJtb-||LT<V!D>xRZ+`afC
zbIZyrJ-zk{5lVKlK}|5#;9MLJ0&x2YxSl=BkaFe9Rn)pXh#50yS&<pBcaNT^mqv}m
z%0aX*Pyfo#YjGXm`u_g=9~#YW$+ztES-NV?8tmM)i}I~fqr@SAUt>Z?Kg}cyY@0QE
zw$Y%^%&6Hca&=V@7;lXyb(IY(SFSWZuccL9Idtya8GrtN|F7)!nzd^&b=p)j9IQWO
zk52ByjNgh`bnMvCSP6LkKQ9_<0Q>jv!^<zff?xgLFO3|~bbxsml<p)uBUZlN!;3Gv
zkQ)QqR;<h^ztRBZPxUy-rkwJseoY0CV<L$N@|ky7T#yC+3Ai;@im2%1tOdwP1zP4K
zS`+BzzoHU5HtyJiz4-+QQad)xb=znI6Ij(ko8<|kuq2F^CN03K4F^zEO7q&xBtYQV
zK_1XSu$zX*&5A1j(qdr6wW%9z1jw~MOPiUx%7#}XE=-V=?=ww=i>sDp9j(f;dNFnW
za-844D+YFMOn#93zm&JkVqb1pFk_QH$BLcvf??@cuHI#mhajodL9roih7_j_`fYZi
z_;7&j3}D^f68e`;;k-iaUTu<P1kp3aOP++9#&%iXFdOMaCvKW!A<pDv9He|NDB&oX
zPx*U~*UdCm#frHw-!g0yk5(NRPrMDU1<)#QdxY+G%%sd`qJhQyu<q9Ph}vW$jv(&E
zq9mi8MDVc<9uh~J4Ai%J&kZ2X-4J#=iN-pJewqW_G&!D3@Y+Gp+>l<T%vl!~`89ty
z2|c=`<2HLwZYV6q!nJ#_r-+*!7%{~0;^kI_5I0UwoVmfQ=i@Hwfflq_>*fXrl`HGu
zPJN${#+8H0S1@#`{4jBypg@8Gr;P$fb3&)B=tMy$LV@Nj8XLdSPyPKh`t&)7rC+VZ
zlk_Wn+Z|W&<L78bS%R7AQx@R$S3flqe`;#70fk3jc0PA3X+bzT2$b&7eFRd{>SO8L
zx47_X@vx(7-^&fCeRPT=cY5^d#9f%z;@PL)GXAOsUc0%=Zq!BR9*I=$Y{9(cc<BY2
zQzTO$!O(R#(4^Y%v&+RuoPLui@YMk$y)hu@aB4e0(4(R$aDml-80f*IZQL1qmKzP~
zNMm;yLCzP~(z^{Ai%d%RGFYtqGat=fe3Pj4&8SP`WSh}^S8dwVvm3Dgk@-G6zlOqW
z(s1Io5Pr~wn-8cVA{ndbY6P_wpLR+bp?>@0SAL_pFaOVtlkGUn&9AX<fKyWDnVP9+
z7JnCNDI-5qNpZ%u>);~~!cFby=v@yY`{7?zXlMRok5F^^GuVInE2Ld>V})l`T||98
z?7RJX*r>ss_0DH-HnKE%vY&Ys`B#m^em-mZ;g`n*o9lh&2IOs~=I5V&gS<z7f$ZNL
z5umA#2a5~ge)=!S{l$;a;E8`#Rh7iOGo1p(+YwoH7d)l&s7XLRvbqIZ_bF_OaM!#M
ztQ?Cj66B<$W5-1gps>qG`eCCGi=wBhf}6r41h}{yBZQ_t56pOx<_9ITvA|#nprXyY
zBHFqqqRBKzNT1i(xfYHcOW>wyVIKu8CH?@?4y-|F?Tkvm^)U*pgU6ctZK>IaWVfg&
zP+|S$Cp&s*4?I5l-=q*rruW>3a}oOJ_wd~LN||}<V^t%xd@|hrL!cp@vY?G^(WRfm
zHS~7aNSCiW1lP@k{UA++OIQ~R0SUpl`PR8-0~}2|#%~`vQXuAN)Q*<L3rv5PJ&>S`
z8sT<ozAHP)tD^iCijvMottb}J4AqqSWR@5|7LJr2<7G&jXv=QoQ(2dIq-u@<4mw+g
zH5XSCC2_kP1&knA9mKhyZF)1@-{lT!O^?Kj8{fqAJ@W`kQ_IT8l_}t)))sZ~2w+>A
zUPIKtX_M9tSLIxeF|973>2YrSrX+V${$_l!cL6uU5`q>pB|)#iWHSyVJmoEZgxEN?
zBnc03vkP^v%KBD9Y01Xp!3aB<$ipASr<k;FE<x<27~OOTZt8Fi`SQj_$gKfrG_M%X
z{41A7Jg>y@>!W~+0`q3nZr#3PhgAtJ5DfZjx=I6s_oP`@c5R+2PV8P;WF@lh-GA@B
z=-2Ov8wlkY4>{Ca&AIi~_$6k~os0kb?V|=LwTiduU`<f5b5pv`%NM3BOs%Zjv3i+w
zCihreWy*TFD8!Fzcbmtj;g86t8djbq&pr2C0`;##Cc8!ZKRxjTRuITljRkIu8FTs3
zwTm_N=m*DlXU^PtSi7Fu)+&wvi>O&V{?*q=A{G#%mGwv<k&qnn3Op=du>w=3PQ{g1
zTycB_#$tD9-yW^l_uFX-UPRfnWec`!-AaGe?T(pWh?`ig`W-WBts{um6&FY=sX(M#
zZob)=3E#ANv)SzR!ABp_|MeB9pUrhK9!!6jT;g|5{74#yVkYxXJd+bi$wtK6QAbTV
zky?C{(-_%<$i$aG|Foi&5(PeZ*pFQYifFBZ;4n3>j}@Za&?Nv<1TW#{ksSwoSh#Ex
zw(dGe{*D4b@$=f0NF}Tk0@)4frC`I(Tzs`T7X`(o#{9I*IY(_=(}TsxPIaMK{WMF#
zMF29_Z();W^QRbSU9&hlfnbWj2+_JwOpWUe+jHTghO@wCJKLl+V0T^#zFM;t-P>eS
zc1D1epBw>xTlo27Qns0`CLf{t3*eMiu%-qTNG`;@N4nG$VyrMRUqAU2UrCVK!KK)^
zTPDJ{VKcRYBP>|fTx8Ze!nV4o9h{Mt%sl{1%>SB#WO!WfyR?&|E<09l+>J)mOqWk@
z7lCkXc2bBPgxMPzn|w?M$sB3E)i;@g50nS7dSf-FQHD0;iLbHNDYZ*=j#8uB$4{-@
zc|`<^X|`OzI`5xsD5eSbR%sq?5a#$`XGCP;T|cw&fNhAQ{VYi3Nz+_-fi@PD=eLM#
z!%c1R-t8J;*TA+MKc&cDdjLLaWd}-qW>o4svxFwrC(l`p4$U)=+>>(ZEc&mOFqHkK
zjS3ol>OV7=R%%>T_bNq6TqY=xpuj1lK-H3-Q&?ak>yxKI(`F5E-;Zv_zn**@yLRly
z`n6l}#2;V8&9{s}$Fp)O6i`y)LqS0yf#bnw)HoZR&T7LQyjd0ERCm#|bq{RYxBw-G
zb|5*u1$14T(5-t1eD4QW8}Qmkb3X6A{W(%nk}>$aJ{6KL;O@1TUU=Uu`~t?Wx&9(t
zFd_k3S0K_i=t7HmA$Jvv*Avs!pc<3T-LoCguP(!igJA=fd&tB5<s1*rr>6C;Vwte1
z%TmtFyv#R1rKa7~!Hw59P<x$>>?WG)-bt<Jv$9SfF~vYzn%2PCH2MDA&?IBZJV`be
z6Cpu?1O>_{kpG8Y!~XKW5x(|TG<fvS)_n@7e2W*L_}$kLc;P7oY4$4Z?7_5l#t&$H
z*w+y9p7<TS3+Dj${|w1PhnK}nK7SZZd_N5Q?RQX|>|Z=u_N>Yi*1<dQToh$wA@a{Z
z5~wah%2n6HLyc|KA)9qQK>uT^dw#=4$b9(MRc4d8d1g{TAO8FH%%@IrON2Z*++9<B
zJ0(Ez+72S4^AbxkQGHbN#7?b?((bpz+hYs}*%|>xbxFiq+MSA1eF$%ugTR;17)@*O
zHUh39SuNnDX>D7_!8AE2KXuGO4=Lb^Y+HuV^cUbESS?N2Kn|@CopXKl1)QQMUe+^o
z)=+rHK5|qEHVS?s1-lUbY62Y8BG=p?FxR_pJ;E!$gnhtON2RHJBT8+L(2|Lg6Ph62
zPeQ&B|9A_wBTSRw&VDB*ps!rkiCzmdh<x?2(Wue(ggAeydL~hD87A;v6>x1!N=Ned
zo@RGin=#|%M=5v=%zh27FaJri<7_8s96fg&TxWlWeIFx4EV2Y>@K&vzC{627^Dc0Y
zdorGNjYlH@bR1i-TVp2wYY$_ZF?zct8t$B)gU9>+1T*%}!D|~Q;H!LsTpSeIq^(!C
zBvgtFcRDU<Jpwnj8;cgn4UGwKcC#`4nZ#T?)ZU5E&t?+A?V!fH1h~?UW*ki-(p1v9
zw2u_xLS1uF%^yp92#~k+l5h!L+x8I_>|crRw7&u)n+~FToOBAzCGMAowhCoLm6HhH
z2nq<ymFeoa^y{mE6dMXh1lt;1e1WYI!77_;tLm07-m)j=rhJdyNPobWU3$qe+b043
zhZ82?iNF0Fg@u+`W@`YJ%Ttqw%2cRuZf=;{R-IyL;&IJDj@?uJdD^H!1IlYETGX>L
zIQ@TRcDieqE|@lB27Q*6VB*9{tOVb7zJ#66ItyoY>Qq)KwZ5!ptc$W|$9p1$kJsLK
z!vdt^y-MPUr!hXHb+7zd5F??f0n2{x{SPqavdav(e!OL;Q?q~c#TVh<&pt=ci{{7k
z^DzFk*YL>Cer7ZSYs&8ks1h3sHASxFGmVKO`BqGnnwpAx?z$5{{n^hc$D=P=*7py8
z`lGQRpt@T=c14^r0#(f5TXZFgmc!4!MKwQNRpsg>lvj)wxv?OBTj-P8M)Tbpx9>Bq
z1*0CVfHeiaU)*r%;2Inyin?XnUhFwoi0%9HQ7lbj`Z^BDBsAB@Wb*YL8YI)ab|&7M
zJP-MFPZ^=R4haS2($Yl$Gs_#qIh~r}lJonZL3S#<^lzAy;-#V#*LDKw$>g_;f-UD0
zJ3-<w6{sTI#I|)`5niMfvE{pc$crTNyMTEU@GK_a`Nh0txMXm5)T2zhlwjz_UHh?h
zcdlk7@(nbjP4HAJfi}SETtK!gsMy&aGwo^);XgZp;!rS*Lx=LQ|G)t(UcVcgcORtI
zG<|UsboY>FDJd>M16pn<EIf>QJZ2Ew4N)6fV6&6Y#VyE{k@71zT!IfKO~sb&?YVIz
zi<VWzw^&SszJV0tV62mQP;G_0kKptu&&Ye27u}AQjni-?=_LzGN~U)>!a9p3iEsnp
zmi>n@VcHUY6VV-tE7@9aOztQChn}Tz<++`a)1n#MBrZr}NM-8CGt$-2&RnS-JkdB4
z$ZSP1^-vSsw8G`2JC_Rwbu*^g_ZBX|+O0+0G?9*wHmQ;>5vLEE_5d%uJ)7gwiF10i
zMN^~r#|FFPf*`?%$4H$xBq;EWqd)?<{>F7l?J6V?`u+!R#&iFA2OHLJqku3MfBVbJ
z7;)hsj2Lwe($cIC4{2Cman*?0Wm+kgJ`Or`AC9H7#+#%0_^uSEy2~CtJK<KEFn{5h
zcM;@nwAaUfjQR~S4REeBix#ZFi_g7BX4lfR7HB<uWbMrtR#$G~{;8vYK0V}zIHH}2
z<WJ}A3u8M?)3i*pn4(ixGH{rHxq|#^_b1R<k6R=4QxGsKv(t*xjGDV0>2o^~YNkLv
zYOJRaB+jFMWC?l$=3}GgG$-<OM^OYD4n-iX>OKveG-+z(qTbXmOs-Sr!PD87`Xxew
z0tpHnp+Lb?e}$hK&9-3|8_+tiWD$x!{Sd_;y#vQ40#J+^Y1$mN-8*q`><C0#v_jhG
zG4Px-1fKr%$49`pd?@+yGX($gD8d8IMbn@Ew)`V=p7pceVE4RPa6a)EN(Kx<^6(2Q
z#0k?Jpl{(^1m;XfA?sn=zSTSrunxihJdFT<!8YxYdim8zz2pjHJ^Cjc*teHv!+(Q<
zv~*<L{Xm8M5*KGS1$Ji+#^Ka{2yz2sO+gwG4NK-AGxCL5&ufaSXOumlUdJ}bNF``k
zCb_64m6jDQHq{AVDZxVM^QYmY`D-8JxwRlRYy-~?Pr%l=y)lWWoXf9Hj<(#K(X2B<
z|9ui6g5J(Czl1F%vziK=;@k4kR>^fwyhOV<6}C}7M5Lq$o`r8wTOy`Wu!$}QY5`nF
zHZKJBt~Z*@PSVBE>k@?5&qAaqpG8FBU-S{2eJ-PPiwxn{6r%e!AhdBF0cZ-~B(&@_
z1mOc)f!wX8-qEin!ZqX;T98PuSVKwWk~1sxt0JhM?Um>mbraG|t=)#-iiNN~bg`5O
zr-lMH`93sR^RG0usGa1b@O*gV^U*)659aP&gz=kS$CA9wNU|qmY>NwUMXS;1)u223
zSstq?UKcPeO)TX@Fe*%;XP5$7g%Bm74sMb$((Smp2m#g{I`WsOIVGz5F%a7%s`3w4
zYNiP>Upn1_mzqVJ{5$aI%0FSk)=9Xz{Z$y*Y>=so#<AIj!9%rkR+VKWZk{R%$bWD*
z0;q3jtzq}aMvGZj%)pVvJRTTF!yz8WI_JY!6!oe6T92c)Y8+6AC#`(U&(FtmFT8+P
zUVYt49xo<-Gzm2Rj#TAH9_4SULj1+o%6NSl#bjJ|-%f4cmPk!G7Ql3*lsY?$wMkWq
z2hm6Vy{8kn7C2)ns{_}fv(Fwt7kNiq&?tGF=B7=t{Ni~X&vEmXEm*%{y;UM<)xCQ+
z4CvRNAQ9(+c!<-E*D_~2d-iOsrm1d)wLE^cWT`QAZf+dMAB~S;7hHf>sA*nIuwDT0
z2OoZf7R_7WySLs_<CxJnje}FB^2bqbwNd;<1A_nnKmbWZK~&A$#>T3>RRq;_#PDI5
zG-)#D#3(h+SDJBV`ipaGwF#@pt2Vc)GMQeRbSD$X45jKxa57n+A!%tSobQITc7-pK
z%j8<@!IlH1)R^AR<xKvTxiw1~wKWBfpK!PoN$yly><qx;Ng{aZ;D$?rzuXK>=_CD7
z2YEjkZeKBm_w0b}1yM{}Nfa{d;{;#<tjq+-K_|MlZibsL9!1UCdYlpnRHr3z156QI
zOz&+#0Gs?nSQbV3&PlsD!o1xnSxC(cqZ^gH_WHM?;6RDloa&cvX#(f#wjIR$6`PPk
zcOMHEFUAKm*JJi70@rMJfEwCDd)?G54~duH&uCh6`>u^U=mcWxy@*J2gJqhr%!IO(
z5VU(Npqn2zVQw;YI$iLjW}txJcrsm;4D8kt4YR%2pD!(J@!RA*SZD2@ktVy7v^r35
z7?bDG!Vs_Hyo0sTMnq?PmZkePT8fc5bZJmKcuywPWg=bfO9r0R976{7Wt}WQTokp=
z=n<U#w>7r}6K1YL2|;&x(>8N$O?_I6XZ7rd(V<Z?veGjV=7s{BHocIJq8CwXLt6)V
zds*4geGW6TIYC+TQlr9@;!-CFj3+JWUL1pacg5ZVA-q?#7@Kx*)0Zm+0Vmz+aYL)y
zTOVupa1-ID^NCUTaS?&;taL8|wg5S_isMvti-$N}<JXB}f&vK&oL&l8UuUPc%tUE*
zM1joAblh|Q4KxG(3FggNW;B;4eKG?JxFc-P;NIxfyE7U$sn132#Jf4WVGcU=z6?4Z
zZ|F;(*Tsb;7*F5Y0;pel{(U_1o4c@i(+<4wYz(yKB6{_;3DEj@Y5R4>zp$kfZ*G*Z
z2iQu`TE2CE*u#xcG#j20^K*SF>K11k#GZ)3Dj!P8DgSh?4~zGQjd}9F4)T_NoH(z7
z0{cp$2I!ZJUM#Tp6lpqtyfuWM&-2lgqaB}Jnu7XOXz8D#qN`|i;#PtJ-yRASzVkW?
z|L;LW>SZFSaZ~KSnjrF$h2|5@-r+2G@BJx~FT50<p1n~rYdZXMX23V~a}@pi?+8Bg
zckJ^fBYDU$q>LO5H$iNh+k?D&Zl!tLrfB%Dm&<%om#2vQR5#+Y1Ka?YckgX*PMAt_
zYcz#6e>O_~I}xQbzC?0KF+aokvD~c}JojFWq`^b^8v@_5CGdUr2_lmxAoy2mZS#j-
z{b1|T1Ah9_4zVpVOP+ewb>)jpoS(@Qkl=wlo{=up)K12ckVm05f&pbKX@(9ae;sQI
zVjQXo;Vp|1*}9m58iBfu#P`H;>Ca%7|7KHA6$vS*wkM~<eKFt9!vr9*npY9&8*qzK
z9h(GVbn_Ej{2oaaH0A=M1$$@`kX)7errJQ`%8TCTW;2S~7&V%d2G@wY5DEC;S@VSf
z!JXT`LU`vYI9hhEHkVWVAh`5XxG9LD*pf0U2b@E`2dtY8$Mol^VH<@fp8)onnQ-?$
zwsvE@uH^+)x>pmG;%O4cuY&>vk|=x?QYcLwJI#D*Oh+jsPNRS7QO$><cf&52xcv*%
zPi~CiP0pd&ZtmKY;5aTAm6$!&!8Uga_h85`rL@PWJw+`mO)^HKTOT2c7yFrcD)w$j
zbZT+Z)zQFASbP{Y@16XCx>>$El7f{*>+tZ>Kj8BnXXC~;SD;J%PQ1^;jwR%;uBgQQ
zI;Vh_J~eyv=z)6m>Y=cxh~rBWB7d4cb!5<AGbVUXTGslfYyFGI)zHbH=G?Cyc?1oq
z*<4dtwR$xkef)8(UbB|@nA}adD$*)U$5<VBPq!-s6OngJoY)iHv#M*|h{rMatq0O%
zPD?w@z_p4t#p_M07%m+>8gISzHg@dZ&DV_YCF`2Oei%7?xJB=Z(c>8%b+@)Rg5-B6
zfA$$QoeNA|WN!Dqd+tI1zI|)VLZ-YQ`uWdI9|?&1&&w}!%ycJ+#X00SL%X(ZaqF$O
z;NQ<YOZJ-}6Z`NVPd;h<UEg}kIP~OLtQ<CO+=v%n8DEYj<vY4^?Al&44|RyfN9B$w
zm&LtzQLeXn3)ZfqX?6CI;>U(;xmYKEDmtuRzaD<NJMvS*+q`d;vQ-?>zhj3E=6$X7
z_=GM+NmCZ{PK2r`Kv0nzD9xJ6?k5z?4@AP%T*u^pS$wpULPI;Xplb|Vh@;7AC=~w7
zIjE4}btvScKjLJX2B#VDV5xDt;qsNDSDOYHOd0bFQ<hWP+C@OSfR>fGaf4;q$qi;_
zg>cEm=c7UW05`Z4Beh-@>*YjR1~+r+TZ2Hm_J~EuKiF6|$)Q5bH$o7%K~^$Gj~Ix2
znqt{KeK|FBsijL_%6_V6hq%f1{m<v2d*==Wi@m0uCFa|OAaJsa?IqvLJhYi5BvUSC
zZe{(*SLmKhT!&b8lst%er3l7~g^^%0Ea4`fOXHk6B}OAVjaua0TV~+e^E#kK!(<#P
zA#hFJhStLkpzL6tL26J(`NG+psXQ`&rZq#A*%6jDBv&;|;4>oBtd}&v8J97%*)L4G
z*=S{^sPGU%+L$W3=%3QDeG&eQNpkW?MT{3<9USaWLrW{?6aHIB3pd398+_DwH?&b%
z%8hl6ke)6owWzK-*Za+bMwp4CcB;?R1=`3Mq8W6D%YkH9nuZ{*8a4oJnl{4U{__!5
zZ=nr!X9+d!)9AX2KsGIDEn1%kA6;4PKd=|qjXe)3nGFq(R^8m9YE|`2_Y)yOfdmCk
zJq1o2(LD8aOO#Lx3M8i_;k)#2oztc{KK^hDii>^7rCF5;A5O(56goF*T;G7#+1Z(7
z;!@%9a%UE2^U6DY)6>(?tw)EWN|cs$^PJvC-H+c`v|uHcE?L7}Y(AP{ahrPd>eCtZ
z>s#6<LoVo#9Xs~W&-Oz4DLRDrX*OK<l4-6*W>T)Yb|gkpNS+Aa4hme*+=&}HIPu~d
znu+D?v60%e_ssC2YnC0C=D2WKYbSk`h^08aA<mP<kEgfvZ+2-iWm-N6=>ONs0GV*>
zZfBZxZ;^UxDR*jfFW<Vvd4dApehT>J&O*UM_n42gFinXSzwsiREpw1`=Z}mgv!_>I
z*eFbmhm;W)LBHrPA4hQGdX!H23^r;;mrVNtq0c@>fV;6B_0nN4$V1B0ufWwRC!X}E
zqqAjeq(1&v9J=!+?7j4SglVSC?JGejBin%BuD%12F>DlE?K<#7*7_(`CP`-xGQW(6
zeg!`@!;7UEKItQbS$7E>*%;*i^lmtt(FF6r!AF&wxN#;@fKmk$;>YUScwB45v9qhb
zph{0d%ReLB6{bE0H*isa73n+zPMQa=7PKy(o<80TXk8{1D*y1CbMBzFaDZkJZ4`o=
zK#dC{6|%%bRaP{ze+we(=TeYF@K<zcKM1x4Z43Y$Byg=Ra0cl&Zt29Y4Y-cdlw$a+
z$p(j%rhK?Dg^6u?Q)4;_zF8FFQwxV8FGLo9glNx;*riA2t126vxcRN3Ks2V&XXXwH
zUj<%ULz>z|<a;IH%=R>(Kk4g@pff@+I>gA*SdyZ&U8YSb@TE+Y$!K1b`6cn9#4ar#
z3WBw4@f%ovHw8x8DDaipaE<+Fm}yyt(fYPgqfGaBLqb&Lt@~C4J!2YPhY5oD7}vov
zq~#sqx%9|%{@>hLSdhB}_jVnJt6N`g^7~eG#u;2$X?kT6aF*}km8(`+E#;hVz-e9a
z+>F5(4KzM1fxiYq?4~)`@WcD=MaPaE%ipYUu5mZ{v(NFTzx)NcbhV=EcqT^#=DMfb
z6*4!s%(yLoTj$Ed+~Xf}<Tb~fMr`@}O53(=+oR+|CgW52hgC5xGJh%-l$Di*o;`bF
zCxL6h+A@DDKhQZjITa(uuS~n*7sr3hBh9Bwon|E@eOt6_fi|sMA2Xvt=L|G{(0A_K
zNt)WQX3ZMx+_?*Fa*mz0a^{lHjl1zi?B2Bt?|neaMzMaLI(<5pE?b7qozFtcmMt~?
zAiy<U{;)T1-HQGD5AcqNNIEf%%*>xm5WQ;?<p2Hl@7{(7AEK)v)zyr*c-r`}HaS%j
zCzhW;{NMe*KN?Nd2sLG6EoGwe1BdPAvp+oc7zPijE7>CRkG(@DIlQWA;J5#_0X|71
z`EE9P#RNCQB9^*es3JjMyB5?Mf}({=3CxmL4O0u&<>7jqM9`Rdg}7nT#kD%sO__9t
z3pZTaA4LRLznHUt=AY{``xFBP3xLj`C5FzeveB_+D>TeVp?_+E*#vb10-jmEfDfC^
zMWrrk8nc1|diBn^l9`x*vMfq=Zrcof&T55K8@3Vf+>fv;gYDAV@4(`<TQPU}dJO8?
zn(<2M_c|X1hYARo(oZwTe~|pOw4Q~G+r>K=MuHmM1hS*lJhzcC(S{h6X`|M(0$FV4
ztqr^uFq<qN)#1Y!-m5)s88ZYevfM~d_rNPF6Al8`rq1Lw$g>cFCZA4CVS5^L;1*7O
zZ^r}k)#?<VLv-7jM2&EQ#WDx3jV7`hV`DxtrS9hD)~GMvfcinwOH7pLZs+{~ty4Lp
zK4XPOX5pntPv#|F(g`1<&NLMH!^RIfhYvN}NgMX9*4H5EV`E)oQy}Rl8nP(;)HeRK
zRl%4==e@XJZ&geEn4RiGkB&`n`*ox6r>EY+o`4rFZaygHCJ4KSZDK#I+H(k{bJruS
zVFQ|#ZwN2%Yt7fMawt#7%Fhzl2?``AaOx?L0Ip9xjZb+Ann;IncUYIM?eN*8Sy;Sq
zC4FxM%-XewzBl&LZ-yp#P4rp_Oo$zHN4vn%GgI)`?|(>T6E3#L5$4WWiq$lm5~PV0
z)nn0uRrvJd>9}mn1-M}N*(Rs4SC7Kl)tj-G=FDf!T#WOE_QQP-++;MbhYqvi97mCf
zs9z%m#9aNhzZXdkKVDoLG|ZFyPOr#|VCg|Wo?R8dm^Lol*~Mkdiq}<D%Y(0;O1y-a
z6EQCu;;LV5gZw+ow3URiXKK)EzM;U{y?BUflA8~Q@&2ZeG4oFTpBhQPm>eff5uF+u
zsNa;ce&zd3Cd1<;p26flQ-@WeU8j%&erhfsxOps+30V91lJHdly7WZ$Bagw=wFjJy
zn^ar6KHl6NI>C{V319sNkZJGW>}l}&OA(^kwn$b3B=x9%BU(ITZ}(ovr)h5+cOeTN
z*GICab2lVkbQ!!uh96~uJf2QR0qO##1#G8WFcP_s|B{;G3(OqtWt-HthVOm`Pmf;Z
zlO@g*6gd495b(NVd0Zw*Aqica5#T&7)7N4?zTqKqcpsvEpW*%`7>Xp(#5w`FnnGw9
zO>9#up_K1_hyq9_&4g=yv9}w5NM=j8^0w2|BEKM(O@?#m_h3t_UsHCc<XLFlOgIX*
zaT6YeYvf_<JuW5_NbQzpXTjF04?LUcyPU#9#}=AA-?tHHQaxaFN-Fp*7#pXg-KQy2
z>&@fevR5!Rren7(@|Qg{Lf*1bUv^t!T`Pp?GdoOSrHumHs5Hnp?)AB0d}q?kcZ4=7
zZ5&SmvhD5&{QT~91U-n#k`O^6f!vO`%$UO9Fo9_cOJ|-E{!(ZyP&-~>V+zw09fp^h
z;D-XmSaN7J#zrrp06Za{F{Jqh{1Lz)t?Tyf+GEw~RhHnKv#A+5@xc?XC%-AngxhhU
zJ_n-iUHhG@ap@)1TYm8S{dn&A=kdaeFA+b{W(PA^tekbKvUpEgb!EP@<8iFPZk28B
zF@f&I!6P&K$MZ88S<mD3X52LTwr<v>dj2vVwJwhq5Mj|QUc1WqpLg!L_~g@1d7ClV
zw(i}#LT0fm2a}hHqiEITRykG0>lw3VVdJJvCcXN7;DEEKh;i(-P1^WZTz(mzeEMmV
zHjlokWj<V{*W+MH#gb~i@_0OW_@_UmUuzHEegAz<FVuP@Ae&cEfZ22AV%F?AWf_>R
zk2P1yCaQNTeekw!-MTE!@tzp^mC;aOQO)!`f5;G=bN1PoGk2~TX{J1;Iaa!3)yt$e
z4OE{-aER?UFM@fuG>#QCF03<+d8N_2R98Wop4Pj>qs|kdA_YiuI~AfrG?gs_pSFmf
z+QVGasHrTCU}@|6{gg>_EY}n?pQh2jvow{X%qTz*n<yjaLb`$B{br3Dp~qRRap{oe
zXisICr{AAPkdg6eHoVA@%{7!(Qi3IDKo>cq&goAJe@<lbN2}Wew^cWu6RNh$W|$Ug
z`mffzDSTG&F;{m2wz9OsO{}ya*@rUqy@x~ehdhyMJvX*#b0oEb-E=YY-|2JEwZ%xZ
z&S{SBeM+%t(PEU+5`bNRH5YmD2R6FNG#cH8*Ct^ktE3s~xVX*Q&n+;V^B>EVHnxYA
z&7$x4el*!VqF-B@(DoubGa2>LQ>o$2^rUB$8|dQN+hIR}T>|h*8(~8n<Wr*LJIw3K
zKW2?$+sw5&5)9@=ki3Zc#zp$-<tdf6GdI_H+}tc6VIBDoX@Ez_*SNWnPuRPjyO}PD
z*I*xqNIUahFo)_wZQg&mT`4W4H7E)i?Aq|Bt|4G8{z&~JpXDiZUy<zP#sK!AdF!nP
zTJ4HW13`O${Y$NTZXB>VN?ElC&C0uJVm%$_4(P<XUXEY>c@By~g-D{CDL?tGF#TO0
zCOuc}^x>tq7NK6)gG)!!l?=yp9JtoG+M#12Bq)%ez}HKG#Fx?6OaD48y>XKU7<bzh
z7)zhlt5<Ep3hoHoxnnO13ybM5#7A@8+yN)^BxdrpmNR4igUoQi?}m?l2#*)Ic9FRV
z@#9?#wI+Ejz(p+Ytv4p|e%NSJCntMx$>l@w+%xZ(Mdq`KGx7bquSF~RA4!C79|fe9
z^c#ZL!>E1z-0C1^?+w96|Iz}0<uh5@ykG7N;SXnfa9-2ur<ipGVa(YZ=FT?@aBfr2
z&iD=_#ksl|X?7Jyu#Vs>c^C7|Fl~333~9u~>p^Yd+03Je<qITL+$p<4XhL7tg=BsU
zdEZMOtY1U2Gvd$6g^#z=R}SxuZdK8gmxR}I*d|%+k>BM$4J35367HpmZRMX&XP7GQ
zZ2H^oQQv{3^s$}Gdzl_9zGCb1W+DB6A7(w8(KmQ~B0!&P3z_z|qv`DPn>mbzmz6W)
z($x8q1AG@*uIk#IZBzay8dPO{V?9JO>Z-rK0rXS71r`_c{Nk1ljO$df{WX=R{ATP9
zVNJeF2La8gsXdD3;|uv6u>7E<b<&IU5*=1E&RYIdp0vBa+!;orBpZ6xx07BGW5!dy
zYGiVL8fmKh;%Vb`5G`i0y!F)VSAWP?cE5%WbR%sl2R#=Ji%IVe^m8uD^`Da7D*G&6
zYb=T;^Eh5gDSXVKWdK@sGI2zgR%td|)Y4`AaPQ$b`Y*?@!2To_b$&A^)MuwFEa!Nd
z&$e(ADl$mRku=4x<2;T_(WEWkl^p&m2W<$@_?^6+yYLJ75x@qw&)`^N+tm+=@Xe=y
zgF>?Ue|-i;Q$B}%+ZF_99z48q2}-ZI2z%WGh1<4=vv+@Zd-o&A))(%aw#E#2XwNQ`
zPNk{v_uoQr=^~_3OS+WtQ!ct3u6_ei`0PL6*|!@9AG!~X-k4x~hE+w4D76}L8OM`%
z0KSGzsO9|t0t@D#_{+&~&6$Q=njm-e=!2B8*C2W5NI07|N0htcgWI>lzj!_Z%a<a!
zU^aYPHX)sL2=L?6nUQ5oi$~hDLpIHqtBr|}pupEnfk<&aY=`z5!I<XCus03f7G1vX
zY8_|!TGgr#(EvBC%otB$8HI;j8`RFI%%Im42v~>c8`{!ZpwOmKJGeRyG%0MU*>Lu_
z5cX-$qJ-<H=kOkc)=Y=1|CKf6SohB;*a|NEkb*i(%O~i~;AWUnRvigmJNsUS$fh}l
zukZv)I5$mz_mZE~eLbqlF-Y1(cCSUrs}E9OX8s=)(;*<U3pM<EjXvsG;>KyD0C`W-
zM`S`Z=5=Xq&`f8P_Z;CQ3WFomj;480MktZ+HsHd5R^3sW8YZ8+#t4#~)D%+&(zZ4}
zusJsE0uC8s2XlV(A8UX}812+nH7m){(GqvQHVU|C@_FdcA^3FCrv`*M0tlC{5=V~t
zSQKDqb)&|TK!v`2`r!V1?=|2_`4sW<^)$2m$G<#*1&bCLA-TD4GKmY#<1u5yBUX;?
zs*EE;CRPWf<<EMir$=H~l`D76Skja1*r`*E+Tbdyu7lYO#kNE#LkiQx^O4OQM@kwz
z=p1zK)(tDD?cS(SBaFFh%#nw&Gl8{gqgo|R;}Zdk4<A0vO#?6T-PLAqf+fkx7%^<v
zQDs$k<AR|>&8CBr;$kyzy!XNT7&&qT8Z~TKU6LB_YqP}9AASe|?x#+hM(}<jwGz1j
zjs0r%p8m#q-^@EeUz#)@H1KTt*Y0a*R$~D*JQO{R#<m$l))=fIIsq^j9{TZvxbvQS
zv76fQGLar&e}<(oc@p2A0_xOCfsWQ_=7pN9VBP`i)S?1oaK4uLW7Vgwf?b-IdY>4q
zaaLD3pBedNq<C=ah@LdLTxv|Xi&o}!jEz#YQe7ngy5_)0j7VxzrcBLWf}<$}D;s8J
zp+2pKHf`JxndvFW$aHgEbz)GLj>sUITSCURgzvnaV6>agjaxLzLcfk#NKYg9$_*e6
z0_NJ#!PS{TGD(%dy0DTOTh@J^@R=-<Y90}u+qy{>E*{tssnH^A*?Exfp4I>1U?Cc$
z(-ShsSeMrIk(TU6yEKC5#U+%3)66%)WETNwnXFg225e?=aS&HyNbX2<?V!-c%kT8o
z*<hXosJQ_qH8qVswB4x3%_nK8Nd#@_lbf2>r4*PoY8=7PK1H-7(}v~I^fduy`Kp%u
zi`z?C2<I*{R;#CVNET&8XQrj?8E(dtHn<;Y^*rd_F^6uDGI3syR%p{O2}N{`5zr<O
zk&EwvWc@0GJ*)!jB-HnYa9*GGNX@8+{@vRkvz`lka|`H>Nc<P;>2~3&!R;X>DG9P1
zlo1;ZYg0OD7~7oX5@^k~+qCJ>^s`k(59h{g3i3wK8f@lc%`tz$LLA7YUvkn@rrphK
zPa3+}=^GE^8utw9BLkAh=9u!JwJ9TZzfK5{$Rt651O;kM0lnRca0XFe{kpAKL(tmA
zT~pWHFq(^<*MQbOuDJSX*PFm=F{IiN_v$MXkb8i_J5JJ7F=Sd?7tUXSF5S4Oq$VFl
zDNW`|n?}>$_dam5F#)axPQlJaAwI$P?%M^8Xv%#5zC5g2xd8?Fhsk_U>mw1qwG<E#
zsNV_#mTzqgV#3xC<U5&`eW=+R!JX56cw=Oe0bt8FvVh?BpO*OX_NE{X5bQNP79$9-
z?w;kq|MhcYaFZib(`Va5%%7&o$w&&c`{68~F=Hd(?)BkGcyKn&<1-&=4bR*Y#%DW9
zjmEbGwEtRZfz#uMC1EIm_IMDp^3q!RzG0e+30#-wzb3Q1?@udbS@fOV%Y!}yN)<jK
z(EZB=e(Wfc-yX}HPc}6zu50havV&0rhz@Gvz=Vsf_k^z*9;ScimDEP>#@(R;iv>oi
zta=2*KViJ?1icUXB6wuJ9}@|(9wyKpXE)P`yk4H;#A5@z2DCJlIDtP!+!&xdRjz<y
zwM%*5*4~Am_wm$P+gg45quHgH%8dguH_mL#&e6f#Y|uH=f${5tMg!~JQOP)$bol4;
z0N!OEi^l5nA51Sr^AsE2yD%BQnD2u&0VI(qYPSG=_3it7rnCyn2;M$Iddqitu~a@8
z)P^k5{E}8q{Hh;!qDs>x7Ua+JhpgY0Ls7E<Vn4@(@=$0(6Hx!X%<{2)->g#XWFPix
zXoqOCn41Lz>av=+rd@~{=q}SAYq+sOxoaG0Bg8Z3dn=4flW`sY;f7^AzSxIXNXtVU
z3zU7EI&>oNeranLUgiD0r4jss{r2OY6?ZSI{@N_?&ZZLlcLz=1>Z6GD6a5F1rhlRD
z_ZF$*{1f4uOaYnF)-UOjD@-*b)ZFl|TaEDARY;yW4Ux4gQTRGdhrjj$IPGvU$$+Ct
zGnxorhYSjdBH0b#C7|qK`j+PrByP$LVjK&FFFa-ZW_hMeM!~=Sj?DXhTqPOw%+5H{
zSCG%^jC&c6>wIW8f$<q%!aMOJ1n12}n5N8k@B`7=t|KCQcLUU>PUjdr%sP2HbwToo
zi{U<hIGS`g3!byO!bUCd@*jUy7M{47pup**Kq>v4`Af|D=-?Q)r#FNxIpcKJ;H1jZ
z>u;l<xoAlN1w33pV?pnk0PDzstq9L~1CcdTXugo^0pCx*Ga2r#7Qh>iU-=kXJrnla
zO@>!UreH1HbvR8fq?JW;_8bl0>{sctu-MEkkwqUM+WS(@7e_>ZWsy(ONody^*tRY-
zb@XzL3wJyZj_g)trMWtvkCMz5)C}KY_!8Ul&k-5=J=jw$H%4U<Ptl24y9;D}b8sec
z(`~p(Hnwfs+1R#i+s-E0SkDvNwrzW3n;YB7&HG(_x9-1FQ&Tllzv<Jb`}Ap-U0vp~
zHIzbf0!aq|NOeWgtd)1~ldup}gLuLzsr;0e&{5Y*GIFW=&BsqAkbfmo)3MmxSj!*U
zx^qTckVi$HE)ym1pD(|Czg5SwO08!*EkE{o)8r|*VzAW;=FK9@t|hK&pV{!6<llbZ
zYm~+1Yi7CfB6nk>oh&ucYO^^6$z^b+1ZAjCRbFXUOe}c({#8}LWp8!<`7D8m6ew9%
zPux(N_SPZrE^rRuwte^|qU&x<Za96z+}9Zgku@SDTf|zVEi&S58e;)$*5x@=`piy3
zJ4V2<Wt)gQ2>3UX&SoEd!`NkNsto2;n5WAfZXfVIaFvkZgH(3!w+=Y!pbh(RkNw&2
zGNWmTOs%MvIc`N`z&m>N`FS(4OH4!#fNr$}0#&%Ro0hq&boP@lzeHk`K-DwBB=^~@
z6|!xS9JhM{40K%{)PA@oh<i#|MSzy7TKtuGIA1G*O>eObs+<HG?3>1Z6k+P`Fy(c|
zyJ$5O%f+om<1Ou`mE`mj+k+sLVv;$g$X*L@pIWZ&GP6dBVY#dpVa?V!G~x2^y$t@T
z^>R?%UOJCz|Jf68u7t-x$F^#QEGicgi8e~I<9*z8tZb8v3qfsiG|D+2bujYtrki`|
zDQE8`p8y<3Mjd_kIUxy!5xtP_^{;Dd;GwBClP6!9Z%ca~qMq(JSzFGTdAwi?N37Rq
zgSpEFIr6z1l3;9b=1tq8>d(xAmskS)*bUjDdcp7A=c9Ok!@7$|@1yVvGc$i`AAsWR
zqOiSuo$EmbqKPf}qk3@E)6sF$Ta5_;$jA#wBEzw`!OibGSu?lNW_V|=&&oV3xMtvr
zRg{pB$F!WMxn(r>j-<Yx)D&i>C#NY4jg~9cvQ97m{jBM}JT0%T+45;p9t`#VCq!Fh
z)s#(C#f&i2%ZPY-PE<?GzbC0{VjM=%`d5&_cn?)4?L-{HFL)%-_GtjE9o4R2QrA^T
zjLAN$maflldWO=FZ?00jenBog;gbGe{*-2CME)<sD()@Oy40HfhA^6mAqc|BLKO^#
zZW6bdA!q4*-1IdVnAzvpmWk`nDl%GL_~ivf?}8z$*R-R%KrZEMN4}`C+7J~s1^q4@
zjF$aZ_bwv2wU#nOc#laKFo|~&AMHYnqgRw`x}thr%W!uEZ0DO-oMiwmB{PJ=--03;
zoD$dmhB3wNF(^dgz4RoeE?8LdN#DGO6Tg^1@mymfq)&)aEEX&3X^@~P+?I(r0lMBu
z;%9vOd08B`^57u<%vVub4vaw#-o3;`viRxiEM%1D>o4C8vNU!R>hUsik4=}V!X;?J
z#xz7vz{117MKC1-zrFDYbQOG_=EO3U+%`yo4PKAS6om9v1fh=GOXatuoDQA01Niu)
z9DApM#^_O_l~i9NuWVmFA_hyGu(jH!>9GXr4*2?7m_~iv9T9*%)ad0Q5spJzHmQR?
zUPYs9#LuSuQw5zE|2iqK({Us0F%3W@%nHHxN){(9cKNb2oUm5hbRNMyFQPDot?hV5
zOc9&pQ|x3yalL0ndFvKsoAjUq>Urh)B<IbA4Wg!!%NxN<U37)2Z3;Ep_+W@GgRP(?
zFE9$CMPWHL>1RyJC@Crywy>p3=IAP);B!0pNz36|A*3r*p;r}?@Acwm!ci~Kwd=q`
z4Nc|X#~jQ)#wcoE&l_@rMF=Yy-4$2B$HLwvEZrnWiY5q3ykZZkDSy-1MSKA<^z0w0
zK|RwOp&YuQFh(4#^&cX9*SjP?#FbnD5ATkICMHdX+}0^7-xd&tfl83p@^}2tkX(jv
zRgH}mMs0`smcmy*PWjv$pkSe$4@BrBbJcw<d!JR0geua_*u$Ege&~McZjB;bIHD~6
z2~2c5CC&i^i*jR<n(h$b^b8bKLu%BZl<00it>;}#L1N6%PXQxDa@*oM50R->8)Tdp
zKSWnmOsZyY|MZZJ)?;Hpeea~+i^;bKAD&kgxZ1@~qgTd%k(#|4@MAN{(a`HrfEuQ@
zd%le~NYUbl)?`*f@iAiDi0>lXP3}-7f63qozuE!4k$chKkO>%`>QuDtdXqWV>q(-?
zYDZ@|yqK6m7SKPx(DOD_-lX7W<fnJSd<y<!+`8(RPCYZsZPTJpR8-q)$4T*R@e&z9
z$qRiwc;;WgPmoIVL}x(fb=66n%srob^j8lLza#u?E>9Uzdu=*7NzSIpw>pcV$(U5|
z%lYU4hmteV%TRVtH90<NNIThWez3zr&S($k3IyPWhJnnS7dD3790yTT@)l!Uvt52d
zAzaYkWy`17dw|Ar$eS0sbvj;qzqEGku-%$5XqygM1+6)-+N6vZ-v?NtrqlBx@Jx{p
z)Ed=2%)lGQAA*6mSsWEC^0$mxS${-5>x?7A{pdz5p7J}hh-iwcBP!)HgH4X{ppxIh
zM91r-;>iqv;Ck*a%1th=$NR&KMwz<k%NlNj`=dE+mZv;YjGnWlU0uAfRrw$J;@}`X
z#&Cbl!LKR%co}Qgl$s~`BnJ<LB-FN%qz9IqIB*Wx10dv+SO6yi2LX#BoV5=PB*}M^
zAg_#ycX7v|jl2;crCVW8o&GUG@a5Iz8BM<}9_&JzYVdO5XHuv(ksO*XMC-Zj&ncwu
zcfpR7fK^a-8xN<$D!>Zq0O^)fUL&X_?l%m&Pc0pU*N4d>npR&XN|P6<%B>07^jccx
zQw<)GmaJ3@lN7AE0O~ZZpcF)=5M}HehMC03mW{K7zutlvLo?5xgMT`ekwUFth01I6
zyOn$VDC|k<T3-lskn-N2+AeLVsGynC&nks|7V&)*u1xd3Z{`Iq28W#|XHnz1{UTMd
zECWDfuSq<1oDaVU%wrU3DKS5KP8ANo@t(n0q~auyVKa)+!HHu$`11#1%)lRSBcHKZ
zuwm`z{5ExjycERgSsxdcDl<*6mPmXJUQ7G=jrA@JW)4%TTgst_F|idw3lTeDuTUCH
zFF5zxXXPjk+zTI8&I)RH%pU11HLen#a3yslKh5P;Q(^>h4?nSVu2r0uss#{|8ZtVN
znAupbs^oG*79xi&2bQt=oNWwlbSZ34*;L2uGz`h|g2jl%h062^eh>0FiAk>rz9BJ%
z^RB}GN^VgL#v2=C%(RRFwb%8NqJxiUEa?z3QOXQ`T<8T2G!DA?P!^mKS#HWD0}Zo+
z?XIjU8&TzK0Zt+`_3d|r8`;jtU<us=ipG)KbA6$Z9G$#)hE#NhJ;=#XC<f@c=853l
z>5-pGGLNhFe?Z_JnJN3^&>*gQ7mv6%h*i!5cgDMbm8RU<UGRMJMeu5)sp?<&WMlM~
z?fDan%Nof|Z}ez2{@Y<yfIkJI00&ORCKDGlgbPz|5ST%06znPed?KR#Qj5hCDAFo@
zq)hdU8ViIZT=&92QKQE8x}hb{yy_zYQUKex`0_a?7hakrouZuZ_mbt$aC3z4BmC=8
zQrO$X(-RB99k(*%8p&qbTu>R6-K$C$liEKluWf}GY=%<9&TsYxDI4sx8cX;O<BM{5
zw<EB|XhPcfwD*64YTpt6WrCBqtQsMXvk`dZ*mFSh1AKEtbT-JGFa>`Ait$GDwkNOW
z<?IYluF05zN?&)F?YSS3phzhAy)=^ojAOuWr`_^x-9dKNfFP`y@q+lXWY;h}Fl?Ds
z>?d$&r@h_5J|s)zo|Vm6lB1A&+KSsl1Djj5RniRaB&)~py{KOYfdIEow>lY1<t#hn
zUn_~+zA(|UbLpx4i{#=)3Kw>O%YYDbFNPxvkqLh~M|^#Qq!J9!&UH?8>t0o^rWAuK
zd|J{HL0%mGmKwX=L}t6Je*~q$E@UY=f#Mua`NeF^*wA)f)?z@x01v<JP6U(`iixk-
z`kbJnCT!IYORlS$9`_if&m}2o%HRzSvwe&>PPe#D8G36?_!ZYlTGD`VrS#o4P1);?
zN9J4TOF7Q9r`g}SFV+<j&3pV?fj*O+8k0OTOYX@2v^loJXP6MSI#V<?!XoYHbV%1c
z3`1?(CP4%CscM|xGn0%t-Ge7ujc+^~9lCIWG7tfvyPpP@-HgM29|W~NSn3e2r5n&(
z^UQf6acBAd&i8F2<fs(wHvC^W?}{xEYfMdhnT*|KOihoYK<D$hZ0GyBZRx5Se>7NM
z3Fc=*l=G8<r~RT0N;Lydz1XhzOivGc_-b`T`W5|x;uS`0nnj$#F-FX$l#s0tGD!Yb
zw^?Xa3J6~&R?9xoP(lc*5u(p?;7)4opn;cr%`?5xcCP6(dt;Dngc_gsPir^FpD`iq
z;;(zBJj%vDHlt#3@Wqh(JPaG2TSS1^A4G3R0TbUi(WT^RE$hqOu1A_Z<$@Fk8Avvg
z5}lIjBC7kQU>5GNsmd;JJlH#I&;sx4;DHEC0O~wWlahJ-T{aF?;6=H)G~4c*>Q{t|
z9{*ZQ`Dlcsps6C_jY{7lt)adiDIM?<7li-mL@kJL^O4BU_yVg0OiT5BG~wGS@keZH
zxu}{e*H$SES|pEOa!;jwAR?wL!X;-1K<UH`b9GVD?fWt`Aim2gq+`wOVh8gQod8?I
zi_@>pA?Me{$QJ${qX#K*Zc;Uv&m^m0F=7WP=xi|gmhFhK#V#Zpcx-!x>2jmZWQNze
zEHj8{&SbLlzROB-_o13+D*5JFq6;xQ=9CF?r%Hqd>gIVl#ah5SIgR8Aw7%C*Ot+<7
z2L^hph(oNjWFdH|3|qT8#|Tm*gQUqUA$Ju|+0y|ISpRxLV9yZq`*RI5aAUdPSr3c<
zY5;TL^b}8^W54tZ#yY~wgrCROM_Of|({D5m!g@1avT5qqiw2bC4%dH~m&U<WStKgN
zm}ny_k{H?Ax)|DyNU<-CeBzTawnPU1sm#Qsq#kc`vZi6py6I%kp37u7u7inyH&F(>
z<pr4_tC(N1Dk9*nM>cCEgo#LsQ0)ZPacw4sr1V4V1_8=NhktUD{(3^L(T8Wc97#gs
zrE39<n(0I%cAy^>fkg@>kdky>&vOa8#p7KF<3<GIOX$Mw79PR=t3#65nKIl%)`CA|
zxrE=t*P{p}b}SI*uQIBwQ!jTx^}uVt={6mKEbRE%P&w)y2_)9m#xFshh0fH282`HX
zI3KT&;hlg!7^sx7jwyF`+OF;cw4t1-#4j~i(5Y8clqOt2dUbg`38lU6^R+oY2mWsK
zoMm+!(v-?FvwPlg&nGR^0*XMf{x&<w8V>`J6Pt%{YIJln31e}a#3g~+pTH+tm(-ot
zy<c9h`;8F5)+5)S06_{q0)J2XE-4ZJhY7M9QuM)tcf%Q0QqEc(7;>Z-!p`wW-I@M5
zngcve3ju0qp}VNYmUUxpF0av2YR9kjZ&kR>t7`U-fC;h}F84Gz*2Slm2Pyo-F*Y&=
zss3$fJDVTr<;8Jk2(JFX%S2?NktxCLMYhAP6T{^5u+uZkAXwat0o7-{DlU*_NfJ{w
z@TpuNo*6DwVCE+96x_+75s1^1V~ZljKOKT-`HCixCFrstrdKhS2*qW($3XX|k5JAJ
z*jmzN|3lu^0Guxwtl%j~?}Wb3Mm#@#ITT#wHJl=7QZUBOn&A!)lVNtd5nJ+L9*SeQ
zs+B=r>?Gs3RPGBpT!TS5HT;YNrtrmK{Y4Gi9A`Wuw%vZP$=HrQ&kqmV@?RHt<9S)=
zjNtE9m2x$T0Td9kPOyVc7Q$HEJCx^?PsVT?>s_EMV-1s4iOvgAiCd6BurI>HzMm02
zu52)G(o%rZ!~Enkd#i<mjaKfe$otnEy?C0eLEJVPCU&&du`0z{6ph4)1_Q+HQVlYr
zuQ9h_e|_Oj*dtq0CH~Vdl4gx;pvl*3N8$UfRMN$>PL0$#;APM68YbzFmCR4~g|YTK
z;$Q@vpz%L}?4U<yMqumd889k7p3N2n7!Jb(b&K@kq-~8cgAmdG0^{}N6#EK2%6`u+
zQAt`EaSuUQ%QzD2T8@fPmQpw)jfv5Ai!(K?e(=;a;X^dA4KeCE*F5XvUBko>XOq;>
zk06B0K4&m%vd|iFrslxRk70TCu@P7O*5sH(BYY3lsaRl9Nm^g(aEe`tB@9f$u$0XG
zz);9;rR<6Z4ZYzvRzDx{flVeZuY;ZEai_<e$!;=sSm={gw%4KjA(Gv0M}G9$Q^xGj
z7%6GaGm~(7Yx!`-1rEA3jYn>bgSU<4v)3C8Nyo%PGF8Do&>D~mSG_G^T=jn8$PBH5
zBfwL9$QzS>B5FHwurybE*1&Ss>-*xku2KJbLQ%I*-urV(x!A-ywut$wl4AA6d;rTD
zW0xKr!)0f>B1CM5N;K>)1cbf1!}XGk)nbO3V$@5@OK<8eaHKUklgt5jeyTuxv+y!T
z(TUS;i%#L!N3`um2qxbjLu{&$EHS3ie#gOntuQZyH$Jxc8$9qC7Xi8|Cz^BI)70hE
z^O4`?mIw%-PF2C;f~2{~RIFg0sZ{OQ+6`oUB7`dCmA%1{e)>{<@?c3ouE~K2q4R6*
zqE|^g>T2`_iV2djae|DoRKP+N_Pgh2y#jwj@7yW@${fF?FYe)s=VTot*b_IJ;e2VD
z?T}&Sj6dR9@4+89zKJ;G1%mE{eH<lUT_7|^5q#3Rr}#eJv`-TEnIN$&9P7TTld0iv
zVWv+rZgNN}^#VTVssmOg_6~T4wQmJWZ#BfAXeLd<ZM~tb8o$XvDk}np>UaxY6B39{
zcf$%%P8Lf&zh#$;7)TwJ9P|()OV{?LvP&D?#Gg=Tx|9p!F^;{Fb{Q=N)74ka|GaVl
zL}U&!_1CK#{4p}<YkT!}Z7dx28fB^2#h=pzCqjipFPCKMv2s<p#ENPHD%72v*TEZE
zt@}IcHq%itCUWC=(mw*IuVDQ+wfVN>P;062ScopFb<Ve<9OFNik+|cSPReB>K-ldq
z1`e3m0qcR`eh49UjqyL}wE)R%HIssS$wr)#tMo*H+_UXi+SL?RcA4zmK7qLvLGRxr
z!$&|CFCz$|1e+@l2glP^Jha#({oXWVF7^|okY>tn{^=0{Y40BOyI%w-SQyQMKuKho
z3L0h~kc_V*E5eq{pI;!813bYZeT%tvXweUd4vQo1ErV!baN|qi;k>8V@G=_gt8vV5
z2|$c$)IoosIAY1Xv}(Jchi}ZoQ*e}I<d|=G?0R2c^`}1)S>bCFL~PCc^#^q15#M_o
zc1bhUUKH!*S*04{RwGB40roKm3cVY9#Sl8p+3@8BzDF7De;gh5L$i-^kdii6X27Bg
zgHM(E81ityM0>kw^l^7rmWm-r+K%I;U)f&F<8h4v&6z|LwvQ8nJ`Zrov`x=$PLvgg
zg&ts!3%njLTLqSdDl#b^q#Q>u<T13e_^aul4-O;QDb%f=lGG2-EcL!vJpOnZ1dd$`
zrIe-Y;hpOkNp~(0X4BX)m&;ess!NMFr?e|(U26mfdMlT~0Dd1^EesRK+^Dib=ayOt
zlgKc0q5|4a^ki(&pF<po9#x=fb5UUH-U%#`8M$Z&n}($1v<uX%jW1R{^C4ll_rnZK
z-IHudJ5=Nw(a^b)0SE1S73;!Q8Wk|OjeYHIb+=AaFPKo2#_cg^I$<)4q8dtO%v(8b
zvYpqEQ=e%;I&ag5#;YCU;m|KfnnSQEK4^WxA6TOodcX84yBy%8)DSxKIlsI8eY$zX
zWN$v}{_(+KE$HRZCLjV+@F>s_8I&5Gv=rH>d+Ba>tCm$;ii>&&%d72aQI&!@RQO<6
zWp8yW$opNsbmOH*m5ihr1{4G72#M{?O$!clurUm{_T;j16>r^!V3-;in3<r*_^ILe
znk6l|id8X_8v+{qoBUGEnH$t~3oHE%u}fp6Wp)MbXqR$PgvGo{f%s{^n4rpkDutfb
zM27d`w`?=WBr~L_l*r>Cen)emD)0AVo5f0Uh4<4q;k$YB>)wTPL;?Bal&>rFL#WhG
z;l-E&0@WbZAPaN{rpxjXkpnOBA6J}Y#)>c^%``06X31kBtXXU9Y{%ldZb^;yZWtIg
zkmd8Z-wbdk03OtuT%r-XkReB2oNa75{7wmxnL7xyfy5*s`{jdgSbj&U1dZTXyl>C(
zhbSd#K0{I<T8q$qy{F6h$jAK8augF{fRaFC0Qc%|3C`nc%?uPcT-XS+@p2L{=Q2P3
zWAGVoatM}sU2<wpx^N&1r1Kpe_#tP6W+>>{E{s1U2>YBNUr=4QQXDZgPz<(_Oaxmi
zq+d2^Xpf4Q^d`>-7?0HHUhYg`^w}#U**?GeAjBQZ>P|Kl!=G4>gs895On6Hp1dm;?
zXk<L-lJ}Ni#XX%b<)~f4{@bZ(#k|Q=aM8nk4PC2+e+k+WVj3^#_v$ArqfCn87*g&`
zypFg5`Mt|jycdlO|Lcy9R-11DPxVj2g{Q56;6nB?N_d>5<bX~Nbt`GYU*5u{zoz0d
zzy-Xa8B$maNfnhsyLE{=P=P3DPFYDw_)El?(v_q%cwpQ1PowMCc`{&+WZ}B$4aG3y
zqk=he_n4E!Xd}Qyu)AvO_>n%)y)q*_`q>#)b|>u8As3E*yV>?njN;C?frwif+FL`6
zbcnoBOUvSHVe*=OGEO<88VFGDpDyBLjS_u@E!eZFu~veO0}`yhJ)*Gpl#`EQu+gy#
z;l@10C1rNmCEMrCns7*~ro7@&vh!FtgU7V3pcsdHt<3Bpv+zkJ9Fm5uq12+fVa3!J
z{v2Aq8w6zcrxkqKJ0xK8YbrW=MTk<Od|us!NM3S@TB@ddzb2Y9Cj@P$Ah8x`;_{G?
z^Jw-ea`h6P!qcKXBz=)DD7>ZwZC#(;h<7}K`F?X?ZY@b~GQm>Js_1*kurOSZW4CPQ
zUc%Z4|5o*Ru-k1@hFXHTEBAl4UJxQhiU`C960&vK1*_XT^(pkYdO~a4X~NH}MxlUF
zGI#RJn(w?29x7o^R(UV?ip*3~xcx4P#+Oayn)eAeJ0@|f#8{<&QGeeQ4~L}4o*~mz
z`DaY5Dg?8&X=CJC`@AzVfim+MBi$D`;}Rg-sR^_}ON)s5>YQqqnY^Y9whE7VE8!gE
zIrdxRe9i0268ONcD|tebF}~?0`GtEd4D@Q5Gc^wyTnc;l(!D?<$AUeJ_7BzU(2x%_
ze}b;v9I|nfpe|of(zld-bFWg2T2GB&0f3Ct?qL@?s#<Xsha}n*M;3f-8q2Y48A<Ei
zSdkrkXeA%tTB+tWWI{qLxYGM`@(X<Qy%h#*${fU+FH4@$Ofe-yLdLWP&_y;LUFC-K
z<<0fp?yZ^vtsl!-NG^V!YE^Xj)?M&w2D~aEc(LTRUGz7AERBr*ChY<@ZGvZg(El_7
zEP2ZCz{1aqCBlL&v=cZzIKEs16&8Ny8It26?gEs9)BLM+h_bNj8yXHbpSDuIPrJZ4
zSLY75(3DSN{_pg_5x=r*;&R)4Kcy^*#@mc>Pjn>YNh|R*FQ>_S8ZjZB<ebD*G7UsR
zgr02j2l*hD2k~$0pM?$~>ZOd|U7#aFnV|>Tn%X*<8cQ-P63&H2-WEk4rwQb)RxVU5
z-o?zY1!tRe@zX81GQq!E@Z-mn<lWdUIV?@>&vLQySANEW1CGbDV9$iM&%<VH_MK#l
zF!>o{UMIy8@epwlwO=ET_>XLIL#Mn|_3&_sW<%qgDiM(TIP$7iYB$FV-F6b&W}a(3
zurNY7Sb>l~F=_DMz?!gV;@e7NyfKYIhr?J{G!uVt)*T?vq-FH-PjG^Vvd^bFm9$Nb
zM5Pc?jKR<GFnL7R`}SsNEao-l7tE`5W|QklM4IV)yh^t8EyC86{<P)IFl&w(8S3mw
zZ00X;{HXd5!Lx(N7l4^|r*H5483Dq}18m%Ca_RBC|MWccN&ira!J&43@>>q7tu8Qi
zSShXEc<PP0J;KeAl5ZALKNbP*E{xH+@gr13N`+uzR&o)_e65_2ZH&sqI9OYsDRLv7
zXsuJ2aN@3&;0k>z?<esWkq)FVRZM!xJFhk6{@TRTo`GZApPEU~wbU2sVAJM;n0w=*
zvv!6PVtKL+NmjFS=6;um+dNli0X9}c=6i{%beIMueOdx_mI{8|7fdms+&ttF+x@VP
za|t?x;TqiX-I(RzoJm_(lVI{>7&R8h>>%f7lbIS*Md>$z8vLdgLKxMb1<5>8Bk8mM
z_zi*{DEC-YvJZ)#3PMA#iL<3b?;41T`w5oAyQ`miD|wq1Ne2)t%}`s)TRHwtOB6Fs
zF^AP3V&`jNF`eyy$OBI`af5u|U<C}Taw+_I^MY=8^YRGIs=}p*Au-mC{^ojI%<2yF
z`$aUcZ533Cgc(4A@ms!ox?iE`e9z%5;@)0|TV_KZ)X!?D=?As#rK;PJKPHrr9iN6R
zb|Jr=e?_1=nO`3t?U|wASYFR-s~To;SYmiRT_6V>QP=g_wj$twf`itutv%Hr<Uss|
z2DG(;*h`*|%wjSq2uq-L#lcJ|Y&-E*n93AC5WTBlZp`OdwJHEzGAFG8iC{7efBj%|
z*5Lu2X;+a`KGF}=%wa8(pNX79+~;xR^5LEVt2A3V0;nT_K2BE}uoYX%9c|whkH!y2
zKQRNw+*`M}J4GtXtc*^D5j$rSoAI`)CAG9)uS?jF0(`COHp}CH?$axV%>THzkKM;s
z*$j;RS3`zO{Uat;C|(4*sF^hc&}B6&p&*((Rw0@Ki98VoxuHys3Kf|6Yegc5!064(
zD;i?l<HxhJXot2Mbxh|P+|Pq@tWo^fP72ytn(<+gB2$oIZTMcSiG?Q1g7?;$bSgD7
zbA!bLp-cZFQv?As8NAvDx}24!vQb22pc;Dvb1+LvRy%H6{#cn6x#2%ZMEC$O0KT4=
zj<R9V<B{@q8g5>>4^_3{^Ah}9uT7J@jSx!?u_n8WUEDlE>OP8i7}eUjE>jUJbF%y@
zj`-8xacT+2Z6BV+V~cOK+mH8^^ZOLrLbS_u?6%t&dE;U*fEjIf``sQi%#15rTH%&C
z3SaC{z-&;`fN{on7pGyPa~tBNq8|O@+gH=xU$vFp$bbyBU6ixL7%uOZmPqn%dq~bG
zvYIc3jJVJKpX3Y{%+&KE^cZX)o1+iP;0E%I?$4p{v;yu7Eh%ZTSbhz}Zd3{+?wi!2
z&Ac(h1nV)XSD=OQE~Va05{6guVg1O63w}Vcm!0&%!)_P&zC7*v^f67&*fW(fPJM-J
zcz%y*(@G2@x425`+6p4xU3`MCE~Z#NjWNzXa`#MWd#YGc3=a_$5Aoo>3AGn8FJ^%@
zH*#UPD+87~3X#B8m#Pny&5n0Ei?XGQK>WL{ovu5Bt@vhX@RH?%o5Xr$>vXwQX)2XC
zta%UCh3*1ZGjU;$deR>Xxcjt(!&^<L|0H(rq>A^l$1f>zGLAvh^NIi43jhI$3zGg!
z>)PyN{=f?W1eWkjH4eKSO#4)aUY#W}!Qd1v0-*?AV_2T1P$zxfxn#|Dy|Uqedqps@
zy$9`>K;8Qy`t()?cw2Omsa|h{XV_^CGRCon5zIsCseijx2ymIAFSY(YcHPmzW0nBd
zRjequu<u<h@hoh=sgLxDdU{qbfn`3w>UjoeFUr$MDGDO492_T{Qj6t(ADxJarABZl
zCttiFd{<lu{L`sXE1Sm1<<75{Z7YF=5st5)hohw!mF=59d)D&uU?=5)o%~=aHEdyi
zR^UFVGbdI|Lh?D2k1Q;|@d({yo2coU`_P`qMmc}_r%eeqOh3&`fwnPRpPh$nANDi;
zFkidfm=oZT3vu8(N@l;N|7FnwwOpKgM%4F??YP@p&GzXg4QTpx8DULy8@J1!8}W46
z#N^3tPn?v*Ki(5m<GLqduvE31(@p{vIW2+zVY(~>O=(t|@O_o-a1;0G&7&5sHx%E%
zej+U3%CY|%DEH!wk_s`r7@9um-O9Bu{Z25ojfYAf67i+=R;P^hc2RD0qlkT_lAI?p
zB-)$dBwFcX_}lfwu@e{bgLhP?Nw<<HrBi~H2{TUWR!|_*_X!;E(zjmjw805JUu(<T
zM6baBOpVQ&QrXp6z+SE@G3|NqAnl^49=LfS<lnoa;_v_t9#<vvRhLs!#QQv0sQ3K}
z(FLsvF$S3I^+ny3Yt%FSRbG*gNKko^0moisVo)j{`W;A5vQl0WQ8k}r{N<IYJ(?UB
zR`0gUm5LmRZt>;Agowt3jhe6xZ@dD#JyVU!Oa8PX<kXi^*ZOm~uQ@$tNWCOU)4fLV
zz)AMe$tmEgo~!09V^-A5^csJaJe%+f;R5C#78ZttFTD>nCrE;oNL++V9tg@N`+?Ee
zmnU3f%N-e%oip8s^!E)>O={c1W7xZ!9rC`s7()9CwT1h33y=pNsKW=E3({QoVI2R~
zPWN(dBkMF1fKd}YsMHay41EqBs-VcHyk3f(XXb|f(~fl{>^M>TYnYBPJ2ZLF*xc|(
z+bvQX2K+Soh(A=YN?KE5N7#G7X8L~Ls4KZ;EBP84-$akkrcL03((t=@x4YZgmFXZx
zHRZVtz__wIMY3i>hjl5Fhr!RFJ%mR(+kJaE6=v#kI!MUZxiGAR)u^kbj~c9h38~}h
zQ4-pJ2xAcFM>_u{9GFZTiaOO!g`MiZV&4W();-VX?UBT%r_*?BdWG>lOtNZD#-)fr
zFeH|16%_08I-4q9W3!r%8|Zrq#o}@2ZzcSud`>(Ti9+4`OQ3Jg*YZpyJP^$~kK2Mf
z-QeuoYQ{8!D-jKy$%%T4#1O)8PSF#w7tEU~cV!8|28I)ag2dFeK5H6jC<Z|&bJVg%
zzdM0aD>GQF`l|{ojxHJX`=6e1W~ST1#j24NZ~;Jn`HGfH*bq69Ay4j)wvvbm52zYU
z!^kh3j$A8Ido<YQfJ3w^N##nr$-|>kp$YRR>fy~|<i?!~+NgHEh(*f^FA()A`HwcN
z2R}J70F3eG|BkTZVgo{qgZM^DPL=~2+INzR+hWqFOpF-qx@rZOKHPCbm5s-%4B`DT
z2O)9FlrvXThT>jCe}<3*zdBfA9Zz}wg%JR6n#M+psCk6z6mD9O<MZM{lX2M|u!)og
z6DwC%tvi_364(}7lq-}E^-H@k*D|7f2>K5Hj`B!UhJ@2nZtVb<2#_f?u^;@NtgH=Y
z+gl>_-iP8^&8Ag&Q(QqQ*84-HE-f6+^QE+8{KT&^E}P!$!D5%IeuB;}H_mbTp*Q#2
z2SczuFOC+%cP|dl&dSU*0vc*J1tNz+L<BAK1^6Zhv0B{YmCP^`78u84xJ*rQF$Z{b
z;<?R@sQ3afdzb80&K?=RBZLrKF6}Q8)86>T10F6|T@KV!&$^KYuY+s}C%rlj-?Cc6
zn5elc%S752xP0PD)QLD5ZBF79IFD&v#yQzI{c#gdhs_bv>emY>QY1KHK-;i*3Xz}D
zo5z{rgTC~0YJ3g1S$;{_|E6|IN6UdndD=ePWIC=I<}>R~CH*T8h=FSOxb467<xbpB
ziQ294;v8K?6k1~l6L8{`ExF_|gCm;I?b8}}M7`d7$Wwptb8$X=RF}7WTr-6_z=Mi;
z5QF*^IVpg9v^fu~a(00Y>uhIHptx|Ipr8McZs;Md9=ILQFCtdR&nfOA{09=lp+EQ&
zkBVR_um_U$?dWb6jT5Q|$ZUrn*?RQxLbxG=`|TY$KJLp6LU<)orx{c{E42{NHC2ES
z!lckBX!kEk6V7?Q(Y6gTiZR_VZ?r#HKM|%McbOgm7Ri3#ivHOcsR{{SqYMe!T5$x8
zV%f5r*J6on4Ckr)K=wWT%5+a4Id5<8hi?9w6xce7&HV#K+Bec2Z?`MS?GT=Ci|M8y
zrVM6?M^(&#$kt=>&8IC9maap=+u^f;<w2LiV9Fw=0`c>XNuJQT2P258Um1z1<i6(%
zjg3db6igM@aF`C4nK+O!xB*cHilH|Z4Odx!4!eX_1UoebMc>)!B(19mT%5t=X^n^~
zf1OkW1+*w$4Zbjs$biMTJ*}5u&0F~~srdeg42vDk$NIro&*TR3KU}lT&7?JmgX|`^
zOt7d8M#Tqyjy)^hx|EOJc1)Vi5dz1Fp3U?aGo1>#c3pB-es|be$I+M;VR;F1w2O7=
znpOz&J6V6CY;G_CQ?c0f$MUZNPI+6F=Jj2)6cONSXQIHLFpt#(L)yGuE3b(Pts!J%
z!Aor)&!A%x28RZmDtIH05k=OUrC!XkXsaFid_=izAbF*^m)IF9cvBrBSSH9R7K?1p
zqynXlRY__093MklwfK=t6p#5yVji@BHIQe2vgxi0q1s^k?Ix6Ev!)ds%sL`UZ6UL0
z(m&mkCVQ*(y`$KxFA|BzBOgXGcF;L1@FdeG1I`kHAEw-C6D3F#PRyl^H25}4x=f_D
zLlA5+8aT%VQlW210_^X;d2V){sJ-DOJV)L_TVNcTi6^>UO>8p$n&zMNirp)pRh9hh
zQno}ji-nxsw)~xMXsWnXB={PjNUf%`L|R?uG12%>Wpj-K=zp&Mq1Kz;ph*2op+cpU
z1AM#nf!o8~f|&&Hzk_jt<;ZjrgMC0A%SG$Cv0^mJi?J9a<^pbM741cr3}fTHu|_`g
z%wuqFcdi1UDm*N%e(`GNZ2kSNJAGOpg+HzkBYTU3uByTOjWojJT@|c_#Mo!m{>1?9
zMFKNCYQh?zq*IJ|WE4P<FN0*>yk5y^I>BNh+Y=^cn8&0H#_G0<^lDp-_cwc?{PZsq
zjjk>eMJJv)LL75lj5~b65F{-BW)>9*QvGOKxnloXCCxbwF^gx{Bn0xhB?he8FXAVa
zMh@{MRHaJ(h5uB-kin1@DqK`6s*5*}b5F~2iYI{~A)ZbHQ0Dgb1Z;&3Ldt|GCK3uX
za#pyF1d>!PC`{DM@_+Px2{QbfFB*jEp?)7f#6`23T9lVd=ZK#fkpq11bEdsbNAw!b
zAmiB1z>?48QVZYUm_E5059~Fx#1#lIihlQ!;+Ng*V<Y2vD_HsShi_8sG4%0d+ovtW
z-%CWqB7T9<&4b$Rf}Y9deK23Q2vmA%URRcCF5r>L((0=ygEl&zZ5o$9alnREy2R>M
z*Ef<>USeRG9j5n^-EIy$ecC)1=DvAWbn~Z|I3%>)9bSuVAEDsN-FtMGe%cSan^maN
zQ%IMk)Y2+Q)~zwD*-NOul3=`SiDVk2@pS<g7^FQpIkY{2odNz{*|%*|k8@}oAAAC{
z)5K4I=s=+91=jd)cG-8rdU)k?lQ|rf(_U6$XMGpo0y<Tq`5MR+9)nH4?&fsG@b1!`
z;w6;55GOSyj!e8d8Kje;iDhk9mPW4)eM(FN2TLA5AuI<JR`=D+tF;Ix3bigMc}FCB
zym$4m&W!y2j<cOH&5Lf)ifx(<2ASPL5~HCubmlh?X1m>pIOB(f3R~hey&9YlkUmXL
zCw2AS;v;Rcnarq_*m8bk5`t1FvccI9ni=B{lEW_>-4N%z`!Y%B5vE6wcP`W)<pUMv
zUWEHk!llCk1vqn0_i2c#yGG)qj-DCG&jvgOo83bXjNRNeMwRy3O-oHT1hiv6U*b^K
z_eqePm>ByE-KU2Wo_C^{!L5AjoRmLep2&_zA^>*}79_y#1QR<R>34b$seU!mp=4dP
z!&+h1q`pKk{1e8QKw+ZJFGh5#qOpD`i<1G?LPL4`SzR0O)E^G1nqXG=qZU%t<kPz|
zkro?QgKqz7Nou4xxLPJPD7rVVJ?w&4Z3)BmuFBxBo<gl`TRV()QpvY}0C;}FSa%qP
zd#WoWuUx<i5xnJjT_PVN#4ivvsHG8A`(4}EL3+ulEZOs&bH||vBMb!mwKJ}n=OZ#&
z<-~}-vg+q^7*Td2*eHiyZynGX`gL)^l(Ry&>Tz4!s-d8ix~9}pBdzDu+{@ap>PA)U
zZ6gd{S1iKnI~I_)8Uv}!Eiu$!9m(~&fx(U$VS?LKub~j>y%l}1!m^x3*1YXUzeK~H
zmm1ma`0(_H(be#PqHfzC`iCW6L=uw`WW!7|LP~_$wzb>(pdjl;gRgNL%5o&pc8~nV
z)utUs*@_(<1^1m#EXMK9Ta;XhLNYkm33yxQ9}kqCI(0Tfu!T%QPC|}Z={+(=!~(vO
z#AK|!vLN$AFg($fJh3Vi6N4$GegDI~4N(aITtxs3-GwV~ro}%_tb^55W}3lu#J5_M
z8n2E=l@H>w<CuWk{k`@5dcgE>hK-U^;`4D%=60-P-}G$BPTKJ9QfXOjlb3ioWuFlb
zBi&Jh6Sg62!!}ywv<*I!)t*L8Va3m+ct0)Px013=8o>8-&I;Iizkf>+4D~MfE{pwF
zz6tuxNdK!R>H*Pj@iEcGqbKA=YFNE?=ssO(=1I7RtCXwWd6JsW_`?bwIH`tGB(u!H
z;^>I@u%~)*SOy)$Dj3LU&@NxaQk|VnXBBfc7IHIBzCS93G#|=tIB86kvT2>t+4-Kk
ziumnPqmBS!Z+)ps(Jk;>{My(O!lJj4S&G%#6~NU|U#C|cbY(*z0ec^{38&q3WjC5)
zMzkt#B%A_^ic7I}Ky!c}bjNH8BLXJX(f4ox01-QtR2h-|HaI8y)GomSrV?ufcYH&O
z7-<I_$ZFZv1Gt6eGW0^nO{zf}E225{YToIg$J_9D$E^Mb^4cmHQrYer^CE+X@S7*g
zo_hb6O9oQIVr8)NVt9*W*gvrev8pw1$vcKdI}NkvL<bAZqxOH4D+k_|!3K~dIfE8f
z;5Vl_%f-<0`e3i<jCQ5)J>%d(tWq1+y{v4CIw=qjZ>{$CQ1W{z+Q$q0H3;)tWLi@B
ze~w6*73Tjn{q5e_Q$drqwdCj)BJs<`9R71OX7Fzp0BfUl*fYz<;j0ZRJF0`8yfAu+
zmzOXTVQ(E&A~c`+q|qqIXZf4|R~W_cz&sP8Srsm^4LqIfPM1WQ%EXQhA86}S@FSnp
zl_MD}8R_2~{U*U!0;Z_Xmt0LRmnz&I_|*QPerWw3;_JU#girKM&25H-)c%O(J6&To
z;xG2#3~cP5%+*7nbWFL;*z7fBL4MTZv%AZofj-CX?5p+_>w+5{?xSOITS=2M0?8r4
zZ-Y8r#e<)pPGvl|6iyrtc{rD=?ON{vKEg#6&I&<R*NUq0dC|{qv_rQeJ(B&kHVc$r
zeF9QTDJJAsz_0M*-43&>uV0ifL<2zkHkEU?Gg|C#Rl20?8?12D+#61iOC(5ydis)0
zsv>00T*8vHARncj6=wL?a8H!OdjpEXlntEq?WqO?p$-2#326-EzDBH(l}@0aGaV1U
zLK2TL{%b0i_7f{lb3BHQA<qW)9`+dFTkRmBCT-gZ+rYS}c5(?9^f?g1!eIBzJ&TL6
z8~zZT?^q;~{(%9;U}OhMaP*6y7=3fxwAJq>U?^it#Ws->sq^~foC^k4`lOfJ{;v6g
zaw+8B(dk}G?+}8@+2!GO{|57iq)8oGW6Q;_Y7L6c0<+>Vd$VTVwK2g=Ob{JgNQ$kG
z4vh$pV;Wrqfp^4UU55qi;+6R*YU20AF1LWOte_!2tr0TBkMhc_AI4wybrj183Z58o
zt)9y%7IX|WWtx;~u1)CW07IIbyVkHNC2hwz-aaIELkjq8_)8+KYs`vC?>$*28%K#>
zybB#1{%?!6V=To(a?Vy4QKtRXW%bX`JanRy10C{xU4(Q7SE^hPd$l0f2~dQI17Qdg
zd*m`Vd{0sc7-;sJ&A+a7M;iVhdb+*5m=T!dJmFsk)%NZSvbc5c5YeU4K)tL6VBK+;
zlLu9`m!u6vb4I$g)hfPv^Z;Qy+@h!y2(ACaX#aPF`A@*1p#Z}paM@Y^nc;@6B1*5O
zg_L?bpqqAmA)9*f2T@aY?*{Cy=#MrP0u)=#-aZ*Ow^G`p{aR;2VF@RIhAUgZqs{l@
zP_^y0yxZBSr<z=y`w|$zV)JU8bU8=dW%|q>Z1%paV)ir*9=;!iqQ7GlFyNW@yE(sp
zD9@g=DJL;8kS~}lo!cczD?sfhL~O8V!7w|_gyyv|8OwxlJ&QnujWxzHgq8IM@!|6Q
zhZeu3gbp(|u0GTuQJ*F(OCp+OJ2xpNuq3`soO&)rRsZCrfGWk5eC%EoX{4_$Y*rNA
z@C!3OKQp%)fh2{ZV}kYCN?7IJf}ld+)tw~$=WXSiZ}OB+^b=0RSs@D@f9~W$BY*S|
zI>$zVZglQI4Qf!qi~zY;4IkwvfghQ}i*dxv7>370Gr;i<AHej^Q>Uf?Z_~=is)Epa
z--6g(Z8M_}8Z+Hq-(sPYRJ`@hzPH+=fS|j9Z0@g8I2~O?RD;rg>l2HYT<O4{A{)OR
z3vWZl%b<^U!5i^#%#Ct)tpd0mu^5;;3;r^K*y&j8@=YBbGb_4t@*|V-6#za8N$3jK
z8rS;|urrR<v1vl*YdJu~I@vM#XbbQw&7qVV<2mEZ{7S8U#9wE;<R%QSOs89_gj<Ai
z@hy0UKr0*O<Z|al9o%xyU3uMLSXy9%b0+<PZjKxO*Tc8Rv!sV~OHCmB*HfV}k|qUs
zQDLQh^=ELJrO%a;@tHTg<aVGKL{j<AM1xVRra-4iOxrn;qsuxR>M}ed8d><p-7b&s
zLnBvmKoLga_mt_ukG;+_(mM4}f$ev^F`qZ{2<}>(C@G;O5s?L_E3P{Be^^?|2_ycN
z`NyAU6cN8#f52)874B$1OMIXncm^bmyxn0MXI!&w$$-T$)EE9nU3i~v9G2Q3^jE}L
ze#sG_m}cJ&y5?Zh=pr@1fO=O=;o0Fii1pFbJ);ytNIh&`AN+MqoEp*7rHI@%zlFR!
z*%Jk2qGEiG{SH;<LM#N+2Y$EjRTC4h8LcvTrX!rZW5tVcc=*t%XUyP3g;nC=zX^@i
z^TUKW8l9rwZ+1@K$GL(ns)mw5LihUPWjUt5rogS^={#X1$5%QH;i=Co{e~r~gqIiL
z$D@Qf<?jKaUE6HVc}JT+@%Me5=DK;lm0?{{L=e*znxgad`gj}t8y?$OZKtniQ>xw-
z{~h>qg-Nf+D04<rnN7$zcDAa8VReE+WE2!qxLuA^i6W+D6G_aqrz;h+-rk943!Wh7
zit#4}cEv)0MwycZ0t`?T_juP{_^9AHPO;Y${?{p_ESLK*fSjuYg^_O=)GDLjD|#1>
z$H1!{i^r035pz}|8l&HvK`D;!;v)Er!GPP;F(jz9&#g=QuIyb((-zN-kY9LhrI{7@
zx4s`mzGun^T>`H#uu>i-%Qu|MKnVf<caJYqxGkv-cglm<Qb2cXS)2X^+Z7~(Z>fg-
z)5%M}$g&O=sDXNMuV5fvmVk^dCSv?^hd-^8A5<TvhV1TOI5Be8`@tQ+<<4=;^G2)e
z<+W#E4&U!|2F2&TwpHJ+1IcPC#2sl@?XuGVVBf7$Z}CaC<+0P_^vITX1Li?7iGP-h
z%!LRYPgUe~%}P>5%{lo`N9CW?<Y8Q7&D+CXaGB#3Luv}Ymn8B5Kd)>zS#e~OyiA1E
z8vgpxUKpV{Tw|0y`^fH?+o1Dd&fwC8c4C{L?;B^hDZbiKO;z2^o?Io2!4&?`(z|e-
z;{0kXOBD1Hj@2ISeJ+XciNmr|*ja28jC#k)k8R5CbU9T&f_rNBbmr+Xw7jMOr1j=l
zCMyI$+D0UObL7guWJPmPHw=krt}s5TtzQ+_kG+r$Oypp8F07jWT2Qxpw}Y#wKT`X^
z_lD5HZ(|Py^=-5%Zx$>oBk%M0b)vS+Z;ICQotQuDF&*?e355_C*+}gcaYU!iLJaI`
zk`fU|MVqN2v7arm?Pj-P+rXX>s^pO;Ss?Ufd<NvrM)QE5I7q!La2saV)pkRSsM=2#
zfO+sJ0<e(dn!JQBa63!Ip6<@if3&xAv^ArMzyQ@j*V<KIjMNZT(mKKg07nigXg+sp
z81G*K;si5hJ<C--4)D2p=%pmxqDa1n15}n=1A)8Bt^XfkAi;Lw%!`n;V72hTA|h`3
z6|{t{W6;_zhlz=5D1}k`Dw_45*UkiLee#Uu@dU_oe4p{(&G*T18Aa}+^w^7}^IUm^
z|Ep>KKg+SVBE2{{MBTXJGIq~dv2D3BWi*jJ=hED(Un~hT{PCSSS76Mh&m+@1ZL*y?
z-HcnHDN+x_vx)Q+@mO(TGq(P>$&3kj&-2ZK7~f^8W1JwcyB(>y6Ib>skx&X|5aR{y
zhBD0xPPDhs<sd8&KNuE(e}NQR!p;%DoR`EthH#Y18yONFL*0T6OnnFT_$^6Jo|Tk)
ze=$aCzW}u45dc~N<!p(+rQ8_4J0EL~k0_kIELn0U1J2HAu*|pkk0%`4h|pXEadb=F
z2M5x<8C)}k-Vma5+NxG8I#ycWPvDk6(y`{9Mzi20gW%pe4<c<K1z9&8q_%ZQRFgO`
zHyRT1Pl|EXvi7^1Hd-O9m5k^3HK~lTTip{*Q7xQ+%56eBMNwtR!JKR4co2xaY30qe
z*7=IzE|~v#C*lqfuE0qBrzVX=RKU0Pv#@YfB~F0hrdE{obKPNg2?Q2p0aC2Oi+eGS
z4-NDVXFoqIQ)BF!=)oi?2D1C_q7=X8l;LH$$~z1AW6ami=73jvyW79f90DIz@27ue
z6bm~4i@N+DCL>rRB#644bflb=le;k9U8;a-w&rslhKff0@zP3|%f15)O1Xzq&&;_>
ze%k(!Mj+%XXKUm2u%ho`yu{<H@%V4a%C}yUi#Q!_dwvPzM)x1i-)#+_+Zt!rEx0jH
zoV6e=Et<2x76hoUbPcM0do`w6(t3C>F8=RcUPaRdsMZkEKfuVUE{J^2hA6V}=Xg}9
zP#}rZXDBob?L~me-Z}ggd3k!HA9iK1g2Lls^bVKhSIWNVe=(9+4|K~WqSVR9-a&4(
zOYu<Ni8aM1^p!W#jyTt}i6n@zMIfoF>GMuWo<wS+`PAl|>TVMgUo@QgE%{$G=CT3l
zM#Ri88Mpkn7ijTK!@n_*;`BEpimrlW%EfX7QlMv#AAoURQt50~bUD$uly6Ujfo?pS
ztvmBP&!n7V<}SEgL2@S!fl(RJNGs`KzSB(C=0=%bcw7qg>C~<7MuI)v&V-XT9P%<I
z2;YbK|4HoFz>*L^<#aXLWz_w_p8jpZq!V(yJO?`%acs~V_;O%VOuVx^&*_gp;j>pQ
z*Q_Ie(#=s-#csIXp-1*I2t>G!{kNxylH^iQPDpD*{d8)+AbMOM4nMBaAcOkg4mO$i
z;k0oVKe)4T8j?Q=%#qjPi4)HygD6cZ_SNP!A)|-kuL6H^l6txK`aT=qgS3KxX|jK+
z0T@I(ldW@-R>^&kE9TezJin8o$ibZaHDwJA@qGj6spBo#4ydVsUL=RNFTO#pLn+wl
zjI#RX!PsCGo{Aim+=gONkojwt<J-EMC`idKSkW91BvGr0?0(E*rz#~uz3|I=lwIEU
z@c}(g*|Mq6T{L~26s_Su$YW%1S`VGFms4<-TB*U7c{HndLOt&|_Pvr~wI(G^wmSuI
z`dwcx3M3|GC&Es=(uv}|CH`;h`A_1U%R-VHaW?L`IIeVb`HQzSB{Od8OZ@hB<JKU4
z*>{!4|Gbbcv*#!KThR~MsBNbi$q0pTS_vuFzyIxfCPINx_xPwj`oD#8Jzl8|9WR$r
zmLb09@s-b+eb$idJcWZ}6y&LD+UAltchAIxxH~2GXE(Am#W-1ga9L`P3|q~$Yq~y2
z#R!Ut^b(y?p+|7UjgX#k+9d>su@<_7H3O8{wP1APY0}>fJx^KjXFvf<BMRB^^g1o$
zpIuO7w0CHgAH3v6+HO``jb#fJP=t5o&?qaA6z8zg4-?Z_KBoK07l5VKJT`|75QlcQ
z;~Cw`ffYxOnQp&&`+W>4hc!_g_R^9_CI#A#ZAsVXQ6;S=>#BunJ(aon3}Yd#iMldl
z)gQixM@M6O=oNzzPzR6~YsR=#kb4Q^(=#)`!wC%;b@$|jnr*>!b~zEgi3oyJpR28|
zdZT^u)A<raIwi&b!QNK}#kFSbCP0wj5Fof~aQ8-n1q<%(?ruSXJAuaC-QC^Y-QC@}
zotZmx=bSn7)%?9xAGM1gT{PW$?X{kFt!G&P_Cd%fN(%w5+bojtc)B<DOE>By;@sBC
z-5QW4sjfXUWvL#qKE9Krz&CLUSpiDSL|r3#2F_4@-Erd?E^C?yzFVP8QPS1%j8?CA
z`Ad!IEc4~Q)xY5rsqk-ny5Eci?yoe~p(c$K4*Pt$e6)+6tHv{pDpV-yZ)t0JzwJ<S
zS*Qv87f+3kgGjq1^`qQYg!nG;UA`(xxs`Z{OgPJ7=35@pkKXi_g)3`B5831GkYE^+
z(vpL5*5-FygLy^_K`tM6YVudq)k~eb$n9VTZcKjb6d2LF-Vq<*(C{$z!)3_Z;3E9d
z&a2yL)ccpG{0~$7(X_$)?&PEgC;s>w2~37V(<V&%Ypq({DQQ;mhP@ov$-;Tr^0Buk
zOM|iF+$CyFaa42D-)Jaii2$$HG46l^a!ls@w(N+85X%#p_j38cJ`cGWsb#%h<VtXu
zM0G2S-;Qs=h?WVEODgRTY+a5{oO1N~Zb8)iDUeDb+DCx_KFASe`0aTwDf->iq?hNT
zP2`b-3@URyn#oDrSnItMnI}L-oq@r24>v2vrK<OkhdUaSUuLE_8HmXnEMBN0CFB)z
zwLi<W{=xK9d&PBcnRwlrJzHz7^-!>FZYp;y^5%%Ge`2d|n&)TJ5Oi~Sa{*@m*QAd6
zi4kJGp{u~U^sVh?IGQaMt3?UUS}St4RcBvj(|HQ+7T(D}a%P~eHeHBoY)R5q=N(Gd
zy;6(yt3{%-mnXA<k%6BJEU=obs5_}oWkA;(vsj~<M)QBshe1s5Zkeo|>G?Z(n&mlu
zV(VL^cL}l|4EGocH2F?GX>d}QMKMwYl8%3}-iPp-5ISN488qLAuY41B-fQK!5NShb
zYNnK8i#we;GxgE{U;@)#;M`G{&!K#QT3z^bp@Mj2B_qH78IZ@#ap*X~&S39$#r7@U
zE!(qY|Ex@rx?1PO9@x`<8OF!R@JqD%hlJyYk;p5FNOxwtbV&y5Eq*1)s68;TU%Mq!
z8{I?~pSg)fVtLR9z%O_$kM|h$Zn>N7$cIYmUWHxlkNlWPs06p^VLzH9KHGj*EYb9R
zG_O40%b6@uGpg<}7yPR0;jD>NZ#qxEVRW@i!)Vn3rc!NOVZK_a60WcRIlIQi!=}Wh
z0=Cp;on79zXe9Spu`ZzYRj7py#j=QhN%eQp%x{YFpXYpg89eB$d0Q;qS1}*^kfIuL
zVp-IDwF}&|UIfXz6xb_ynAU@3FY_-Ln2ZQPO#mzV`dnF<_{0gyve1G}r_%}s8HlR+
zXB!~#k<a{2z2JihO*C(yM0Fd)Ga7J;9&l{FJ6O&VQ3CaK9*{|5>1NdM^Wp<+WOv8j
zRLI`wg>Fw)>C)UU3zi-3j0@t_Nzc`FIWnYCj+3@C3Ny}u{CG(&!O<Am#DP}dM}ERW
z_d<+DrNd=kxx8H7u%YVTCO7B?z+9M>;UZLxX+(l|JAVJrOphtaK3l9tM=q7@16eOu
zYq5q<weggr)Bek){9B70A(HWGbHcgZ3Cmpm2lS%3(%26Km2BpV)a2o~F~!N|@zG;w
zWl(dtbz-4Yl%?~^K@kzY4-bpBI>6G&mrhIBDnlAE(LfAS2T&aIPkH`by!SP~j~cC`
zSRU~c)3u76Wsfxn6mTyc(1;dp@ti~5e>w5l*Py#^5A9lo#&692PVuzA!GV!&n4vUr
z@3y+Ae?l_kjnZgx9FV3zJMk_!fAyt}!?vo&%cc?2$jyMP(uyu3>5m+O6x$LG+t#9A
zX|WwKjW<oDoKyE8jc%uAE3ASLJ>6&VAJiZ0|CMoJD3wYVITmX0jd*pQX=^6V5}8`l
z;50%1`TmTogZo~;*=mX36amQp@<dd`uZiX7y1^)#oyHkUIh8jCwr~;|uMETi#1X=x
zzf#{EYJLbiot$rij`@b4ObnrGQ^H<nt5at=nW4dzV{!Jg%_;G9_eGm_e4QnChU>9{
zObi#+;*~u<ey?U`?hZ$TS|jXF%IG2p0`)!k$3Z!prrU#95oMZ!KmNr(v{#-FLLg{;
zG8l?(oR@elx@YKD&`vd3n8Ez{7k*ViJIp=N(+}cg4^Svg21UEJ2a)`jl*7SeNdRfc
z>m%YB*>xwZy)NGkuSA<D!v@KzjcaM#uylkFVnSTFjHizNb%dLe70Za{yS6QZ$%v`$
z1N(l-bGhX<D=Xr#G<_D{pB!e|8*;Ced!tF@Tuvu4yO~@Lt$e(=PX*NK^`an;`okq>
zqsevpJJ-Et!gjZyG>G_L(t!~GE8G2GmOndtXqdm9z3rkbgQ-75;{VMm@a2f4mp7PY
ziOI~XICQ3G@_+IDek&#=RcgM}pte4%%2{JYr+|XOuPVp1KoASRI#1v(#wTZ2V=<%{
zot#W05`h<U(yvC7gF7k5v;;S8j23S>8qMQ&$A2`#9pFqNW1yO`0=Kq6(gzz5$v$mV
z>&J*;m|}w$%O8R6nSa8Fwm1ok{z7dnO+&m6zCcnE+e`CN05ucBSAPsN$fRdTG5Et`
zT?%J)oF`AyP_&S^!A&B|eX(LU%TN%>X4a?ttbft=zuO7gNAC>5fNwxrjceuFP$g0_
zvfNM(Do0z89FC8&a(>H!iU7>+&TTv8=HZgB>GOdsaU*4GC{7wluW}D2W17PIB->u1
zmTDsDTf@hl96W~2N}&`Tt}Rac_x}P^zs2{~1RdG!(6nOvd~ajZIcyt(PV?#WH{wMc
z&qOr`f7L}iv2UPF&xHy*=NnzoQ8wM|S<o8b;s8-|C^F|+%XyNd*T+nq>!iie;0nkK
zGwZ!ge|*y>a8&+&x5o~GsS?&A{XN5>;|054(#O0Ww9y%Yv@nt91jAWOv)-m*!nB%=
z>Y%ynWHqKUk+7T&p8|#`Jsvek%vg(K4yp{hFl?3LCCW1UpEifMUS{``|McClyf66(
zI5;pvq+^D%j5Q2N1=f|M3gk1tWC;C+*l%C8?<mu_TiBH#rTU|b$c%<Wu&+qh<U=VK
z?iR$$VZWZ#adF!cH0=)*VLmlqPG_ncTYl0wd&0ilc`$H^-k~LvnOK;?jC<R%j2NCj
zq9inUNY->7FFMS-6_?o<DzS7}LD=!Di>Z8rf_)d*G4OnDCluRrkNL#S_`!nKBYgAQ
zm#2mYDDKM@ggWP76T1cR8IC_a>P%ujcw^scQe-pud+S^oYo<@f2@ILfqD#b+{>Tme
zUu_o=7!!E?G)k3&!a`!%m2h|~*oD>2&D-<N7clQ9jvGdFfTlA=f)7h{*mG8X17|Bj
z#`YN1J<CHwLjea@A3*$AoZ~o!v=9fg$|_Y$!gPs-$n6;y1(V^ZEJXbQ-NJBD-JgZ}
z_fUW!ou%|km%=3DZ`Pxde2RST?H4h2#1je401*kl7Z;t<EdJHN8m&D{@qJ1aU(e=e
z3RMgx^4+%W6Nd-c6@#b%E_BwJj)9t>ChkhwE^?<@$8XJ9_M37OTM(j}acTSI6zZqR
zvBU^CboHE?roE|4H=@Nr<xd#7CpUw4P0O}|OnK9RH1*%RwL7IE%sW|HYI5YFlY3%y
zler)yZ@CP2gE9EZTO2k=E!?EvKLucH4>am&CV0gvEGkwFP*QUfz-aZTjMql1Wy;Ab
zLgZf<+&40-YQstdCY@Zkw(MGJXL32`J96VmTQe?P+kH;-dAPc~q$f;4Oan^YpH&xG
z7-QL`ftH$CNp)6n+pS6HStgXSS7mS;4a&(b_Om8Ae6<{@Wz<B5ml-Mc-($S`j#ta0
zn(l5$VbNgd+VWffK+oDc*yl%v#_*oAj0Z6K)607gML9VRJYH|H`P0=FHPt$E!+SSj
z0FDJjFQbX<(|pj@zO?yxs}$TP5!6LU$a7ZehkZM`{c)*MTn=>;lPsdh1@8;eC*faj
zC*47ecC$G4hDJWPBCY78Rb8i%I!Xw!lreezsUA<;T@ipN2B$UBf`S<ZEsNWM3Dc!o
zJ@V?3I`P<TP``OuYN14~ctIky2A6|Rag6@Xp#!7yxc2?`@GwQc@V~@aG%W9ZCLc6a
zeHwKkfgf};0SJbow%?+=wS8X9{k0bqWLPCS6TDGp0}${O=w4>ziu_ZUrasl}==V*8
zqRLh&)@+iC1%LUP#<d6S-ZDaC#*hA5lO%3wxe>{I4jMf)5UXlK-{dr!L)joOYPP_z
zcQZ&8DNW$$1zWWl<!#t4_84b?5SlmulpKt^rc!)J_bSfzfo?6T5j>9fF3Rtw9I(Jx
z!pzAq=pn$|ojWt%d1phT{OO0!m`z}xuJ@8Er~Z>z(zJAWq1#z2M}sZJ5_t@51f3&8
z7YDk6k`e+m4!84nP4^2S1KVFcdD+?ddZzjeIPBJZsji2V_Z!19B<I8t+(Z^jwF-Op
zHLf?NE)P3Eg*8Fh$A`T%AQp>h{uOBPM9jrj-%v7#o!?DlJ1Kv1V9;$iF4v%fk_IV_
zS}j~$Ps&eNjKL(UR?OoeWvwR1^EFJ;=8InJO7n$E?ZtkQV>>8BJnAgruxPL6EqqYK
zGMR58X31tU`StPU2o&_+1XGhaOEjQUE2j@?FI?ALT~A4i`$EF|m=HM`4#yMXyPtap
z8{+BS?Po>!lP;C(_&%RldC8$ZaU)}3kpJFNjnr4({rC_6mWm#XAp4@24IuO}_&te+
zbYD-e80f?GXBHhQod<Le+-#(0>f#?l*}(M54+I+&@1s`BU5aoxqB9;Rt9!$un4e4d
zP0X{-=an&@1?Ie|tF^ls!Sp1439ijvT-f+io-u|kN)8Cr<>Q}hJQiIWN|Ki7cs$Ih
zo%J`uC+(a#tpO+MmPu;N=4hwiWKCi9FFNz?I)4Jhr7TnHH&G2veI6LiUb(9WBZP`s
z;{^eQ5(Q`TH6pAQt3)ZB=%Fz3m>HRW8)Ej4`}rrfe&<IX?{@gmLLVzs{PRkw-$;*K
ze9y9EXICvCMWJGEOoLX-lT`>(b5g@!b3rLqGWxmet~E*$+o=%a5fncS3P!B@4+P%J
z&hgkij$L+jyD-`-<73k2yA@m=`0ZhM>$xOHA|HHAo1A8r^7FquYWY%&{ywiobokXl
zg*Uf>+SaSd=fmf`5HgmMh~38>d7Qj7`XHma{;d?a$z%JWVu^>qy)2ARnh@5di+nUs
zT&h2{L`>E3`(9L%AzvG!rRU^Yp(900LF4;|(*RnM)mQQQ4yvk&h0ZgD3Z01L8)D~D
zDV!{!^_oqNCFZNmDk;*Ek_w4>!}XwrBQCoMM*3r^Jcr2z9=*1VjEwqor5cx0(trU_
z;$=J0tQ3Ydx|O%id?6Fvmf>=`K(50siVHNJ4Ne@pU`5zPt;RH`QYMC`dU?G1SJ3vw
z-I^C&-I{09vddmdlmd*{y;bXjuv98{!D1H#wb$-&g6-7^8^$*);bQd$*^RBPZtkOH
zD4ObtED>=B1IQWC9!6G+#ZTr;Qxz7wVDGA^{|SsB3im12=X*JRXy1H@2XwyL0fg6R
z)$V9aiCSHO=SA?BQu@k5#z>&rHLPNY!Y$stQ0v<U)IdPauU3sF(Xi=X+W2O(d*B;K
zz9r{i#Otg=7ItuB>mn-4cizUkuL&OntCDlZxf|w=qN;`Aci7r6Nv)o*a8;Ttv)rEt
zlrinjTAb@7q06YoOH&B4G3ZpNrsqdThoeoZ4EY?T4$?;m)=O|gb$84?rCK`+3|#G~
z&uuY1E-;7iX`}x=H2;nDJ3oJrl@7r!e3U<J<`~l73-_>H2^0L4P+ditICX#o<!;~8
zILaGUZS&%oQ!`sH_LAPO<#CZ^zS1nh>13hT9KV&*R!t^8BJjKS;`rEnq1>~GtW@$l
zRY$FrMpJXuDh;S{Ar^}7oz~<yEhWQG@<?M@cN7V0e80B?)|q0)`c@4TKKlTFt#sTc
z@X2HEvO}_Q_10`}jf87MtVfzuSo~f`iRbIW4|3l9kFbNP_T$7S+UT4#L8wMKur3s4
z_I3g0{v20Ez+I}5qb6<i=K9Eppo+BHMuP{PH0Gb?;D-zPpaF7>6)Tw8FN4II{m_iC
z+c(oZcn#&bl5xbNko<0Znlap@6Vib3B<cHX*6PUxVN>WN@7)Hrtts@+BR*sME$x|f
zhleU(n$_n0pJTLlxv)P{0NEHv8A_#bo8*Ih+EiW-Ea&iU22bTYO=Qrr@fZ6XFR>g2
zmN3s_fDcg3n~;zg{Qf`d74x_~Nu{Yp-61uf(rj9=$W(I#9g;46d9f|jA-tBE2SzVd
zb3X4(bh9m;WxhB)k1d=<(dHL<@ibjFy`V9_1nsa$;!M8VVJOFmp7F4jMhWSnS4;Cf
zVnsF_D^MAI37;v^akc^+#3<BDmB?6KZTlRhf1K8eg?yh#ak5lb=6*AyWQr0WbLe(=
z3Xd{31f)~4(BU4K6AD46tXi99H6++Dlj^RsTv0WF#piWbPIcad5&DtjXHlYBlPws4
zF!<@P(cws51@v=ySXA~ch0`J1NIDUch3rx#RVA09qD%_yQCG_(cgGWi`e0vwME%-R
zrW4d(1igz2)7H)|qrvfOtYGJT7a~C3nb~Z1n1bY-$7+>ZNHyt1XJPs6q7Q$k@u(Db
zDw2um^0_w*TY*zfs@A0^7>&_-oi8?2b-#O>drWk&qvstcbY=;e{6~Hx{|B@+T%NG0
zH|{>8p1O!`eLIWa2fr%Jdv~LCrZjL{%OqAyij_wD=!5d{i<fPa{WA4^)Fv@oTQp+q
zO6L|{71*|kwa_;N*mML@-$oYw?`<J|kUb-eVcolIhj7#40xCH=Gr#0!!e}7p(E*Sy
zfmy@LR0q>a{2ar&$ubEuyhHK^8zEa#^y{Lse6e+AE3(3x9yy^;&#)?0o=NVnGi<g)
zl5<5)=EV5Omm6bbQ$}O4atRBQ%_e#Wiw(rxti4qf3Zy@~kOn&4PVWFxQ8#96{oxvJ
zISL`U+!p;sx7WbG3Yhcfq5YUXp{Xg-te6l*f>OtJMs!gfz}x6)EXq8UZ`bB2O|`M;
zC5id5%M)_R{M4%l4Ja+bl7fbb_p8k=z-cRG@zaL0$0^$Q&&u?^-<KiYOfX8B;;_Lm
zd`a9JL_(YExwTFcVu)$@%~uyY3x@r_Od_+;-S>O}6>rLP8!$R&hO&U!PLaKxqJEiK
zH%rtZ6CDiL-5cbMOj76|#2-O>P(ll7p8cZO=nXOI4SxuDNzP+#Bu5PE`xHgdd68?v
zacL^8L};O=V2rF4&lb~5(CCGj`QmS~aJjvyE@Zw6*O2wo9I3o5VC}T5wuux*pG$1t
ztGKqEnq}D6H~%eNtJuAHfQEb})7=9=&nD8^N}`#n`iq>zf*c`8&PCD4d8dxO?lfOZ
zHa*mXQEQtep4zLT>5C|Dga|HOSwGMf2Hn7s2leZ*+R^hRWc7TRC7dIM&ZB0;INU9A
zWS@ff9(jHe)|gwBJL65T^8p{%o)L$8;{wf@uD8I7^WX<b!~WMZP$)avLyNimj;z!B
zAWerT)w2~NOf*vEspWDrnA@ezs#tw$I`Zje#^o}C<$=+WueiF<G)4q{yT6{svbw|g
z2%G&4rMy13Ku(eX6el5upoYSHHLZFhTOq{o{nUmSLK65M<D<m#L7deOhmWUOZDoTo
zHzFQ`eYbwNc!Ue1J;-d*V{sGbGcTBjSkSi5JpJ8ffrj~R!P)oSLz|<Fx{dXqMiCF%
zm~%-{(a7Op36=&)i?!7jH&`(ZQ@A1+N9Z0JmYh0^rCjC&vEfOGdd$hmN%>d0)_VcZ
zhaC)|dE3fnueaCBdA$f76ke4C*t}gv7_;bF``C+7t}}}0*$w(rr>f>jOBJZ0Tro?O
ziwr_rfwVp`F|o_Ew`UQH1;a$-G-$0fb3}toM@JGHNuR_MBhTpL?w>9osG*wXJcGbb
zj`D^bR94ha=dh2dCriypTsKrjnFEoKe{+ubNcn$k(-<rEY&=#hB9gTLi<L=5bcDL4
z4VB;*O2%zRfuQ9*-|XV2S)V@Lddz9D2{2Zan_O%fFrE&*4Vo+Sn=Dj*U@j~wYIR<!
zO{shjo55HG8j4(VXbaj<DfaSs<NIRA{*B}(@^~J@{YCiaV_3=n&H<XSE-dI)$%QZv
zG#&SfH9^iB2GxLjzQXcu>S_X?mxJ{89;9>tMExg-1O|^>_6qvvMP%acgK9y%%U#xm
z7NfLc)e6}#ngE0bd6Tq&X}nsu>wB04?gx<s9;e!DHY+%vw)EC=zkm9*|MUZY(~ck_
z>|-3W65j?@lIX$$2Y*ymrR_GT$-0>UDr*{HOFDHJE+W*nn;GgBKYCbBMP}4r#K358
zkPZrA=t7UYOL{$!?%@K)1_eB9A5;k6f3cNEg2-Dj%r~{fRePoo5P*mq%TmNPgdW+&
z!J&NxwsEdQ?D=KWPVn;5o^`RS@jxWh`^I9e7U~rcQMjdrfD^@1#F{aj5??*4Xfx3&
z`b{o;<_p_EYfEo;YZz-3vU_~HcEfc0=*U1<1;znuVRavv#cIpZkK;8b3e}1D>mP;(
zxhb|_7Hoq|WMr9Svr%a1@1^QOO;3NN`S%WJHTF_4o=^O2xP%Z@W22>H*K$GkBO6$&
z0M|3vEq8W#?ffhx-v+xGv%wdqg$KbfT%ATdB2AzL@JZX>x)+NaCc_+T3U4iVU;rl%
z#bIZyNf^1GG%w)SU>g0n$s&~~==(VF)8(>9F%bbVrIPU*7Od|5Rf(k#PR!?22%<!+
zjfZ>q_$x^#LA|8b;-zfmovrsavak?zJ6X-ky8=v!78$dbTpsF-XxmRfUtRv<U~aHx
zN4_hdw?&3acWR9V<A-<@C{qhLNPpsSB2S!o*Yl+(X)U0HFSoQ~cog-IJw8zD8=kjq
z6vZi2*dAs6)kuhX^aoiFYG?~I>(ypn_rql{uPKr>z;b+9^^O>y7rbmLEIO^#b|efz
zGC+70JCQ>l{S;;uFcNKX2{dpy^jz2T4_@Rjo<mL1)qjk=xFnC)Tzltr2oRWZ|EkyI
z&OjlBcZigQ4)q;U6Lzru>D2Yt60+(F%evWf(@;E{aOn@jM-pSLF-VyadW<C#g7z{&
zRXIELlyNOkidrguO5QnN#Wb<RFnyN=u)EE<#g572bgF)u`Nl)LRA;5Mm*Brm6J-2Y
zZO93UGKk1?9l3ZJ|Mj6iiWp61B*;EDOYt%RrG^?bf4R-YSTn#gSan>;Yx&pD%c#KI
zXG8j$ff2phsNG3S<*_ifRSL-@R)r_-R7Qi~1ZHz_pER;w71kYXkL5gcE-@=eQ?(ds
zqx5C!2$One0>GN{Xi?0g`2WpxfaaBa=EWo|_s#g!8?*n$r&*@nAkg|~*zEo?;&gd4
z_w4>fOW?}+LbfY9C?R&~D|KVzQTWpK;fB&TBJ}8!)hZ>=cy1wHtS8LNP@n<D5F*%v
z&+W-h)Y^-pYMq3V$A_m-f%hJPbH1<O2VvjG+X}WKsd;m=n&CnbQ<)q^y`L)f7t=cz
z0GJ-XzTx+43t{0;e&=Osi-xsTcN`#SxSs4Nv`S($C=7jb%q;B2A0N%NI_gq>H^*L*
zQT+qP8am9jxsW=}$o+iNdpt|T2}D``+7Sm1%vz{@SIDQP3VQvwWZG{)1es2WjYk}<
zWN*~g<>3&1e`JOSU;AlP>=0XfTAYaNhWJq-GI)+o*WzW5QH()XqlM{Y4T3}*JT@<1
zVx1^#%n!etIjz_1eod)f)%H#M=JZ&&HbW%ltj?E*=;M_86SxPL4ys>4K*eVkY3Uf`
zIfTw^R~7|Yv30h!dwJybH&;CziEs`b!=@TW>`@3kK;k>E39fCQ(GvY0#zguZ@e=96
zJhCB{In7Mb{Q^1k&lmCZWg2;n>VQLAlZna-Dp35@bNNzNWzdh-vUB<6(FX@W`F7Xo
zCQHg8GdWTF;Yu+dZIky`LUJ+zP-8;m@fx0OufcJ|aAvo~4IvnQcXS!AuKFyJc7b3_
zOmm5WL(=Clt<1~E@zIaurfu~e=fbf+g_dp25p6U~A^UO4t5v#CL0YjgLF;Nym*uq&
zAe#27F@8EUY}`_k4Lu!H*QsP58v5g4m&<5}BGZAnUp!)r2wNw*i4(dTUkB3tk~Xsb
zyF=Lfq5ADeh9FR#d`?~3<p997$uGAL6RX|*Hg^lb#Z@eoy8Yq(jkI(~1qr*!?Gs?U
z9hZd%N_EC1m;^z&7Qv9TK@9Ag5~=$J%&;Zx@Ysj)NaDFMjE`w*+BNr<UNRuYx+hEE
zLtNc?xrkyr$<2#J3L|$qha~D;8`Ons-!P6)dLnNkhmJFLb(@K76Rq`599cPLZ9BxR
zaAaMZZ|!}o2_zXcXx_UoN#!?{Mmo9P{%QH*AUIm)ZQ;n~X4Y#XDbzic56uqr95rP}
z{HI45CNGbN?(s->{jU8IavPxLB`Ti;tSJNudAz<%KP9EzB6eFpuJoSPn`|Z1zNHx=
z=-L;ucn2p)Md)!hT_$ZFJggZ74aEldJ7x2)5n>BsiE^-9h;tci=<n%6O|Xe$baG$t
z$B$dfb2U~~lggOy&w|gxaLof(3*@0A=_NrGl(<}u>veldo&^i<OwpP)L}F0K6^axU
zXemW=W%ce$n@&4@O1t>+r)h!WCSDT(Of{jBERH|_F$+KiVPFxRMpb(`QWZKvv&D5_
z4DePwc|qc#Gc|pG068c&Vl=1&IXGUf(<PufPckG1N3IK&+<j@3=8;?ZT{98MYN@tF
z#cxq6jh9E1NRF#TuP;0}TdT#jCfaMZSmk4c=}f3ia6*DZz768J*_8h&UvVdQci`8T
z4;bp{?jcM(LD!(|c`%aENptJnL8vknOha@33`aggpQpqUvd)sWG&%wOZ?FU5v3m7w
zV+$-D;$Sg7_7PQ~`r5sXZ<Zq>!lKOlwFpmCw{X!zauD!1i_tkMyhK41Cru(-%&0N}
z`j)M~u}u=`Cvz@`;~(H5)fRKX+~tdO>}IDAIpB|+(we->&eaOAdBaQ@ONNrzLpeaW
zIR7xL{MVV|sqf9m^JA=qbU*|=3I>}BS+tOj&;5B%7o(kbtK$5cOZ1RDi8k^ooHlwD
zaZ@ItbaF*Xz>fZ<QVncsi3lhJ_cm`EYjh;!$?s$3UjjB)p5vXz_mbj8NTve*l6@B-
zLSlf|A~Y!eB%mNEhT_TlaL#42)Y`Dj=vuFQaVhpTY4vu+B$TUIrm0}r@IocjJu37J
z(8T`Y1u`<ipb<Wc<GI%};u}WZ!`UwzW|f@P5r_kIYZ5VYS})3Y?%U|i5PkX8)02;5
zOIo^{?)ZTh?*-aMrt4FgZEBk@H=!=i_39`mn-x#wkwyf!yY702!rB@H2BOqi#9}+j
z93vgTnG=9Cnsf&=*kVJolH2QaBKoh(JD1Y^MjhQYLyBPv&pO6NV}j3IR-Uxdyj;Sv
zvW4x>Ca4dmZwp+g?fHdnZ*P@nFk+$tD9}$-l?4RB!3yd}sva|q(~3{3YGy}At4eRT
zEpsEaWsWvlsb6Lq6=)P`)`JP4E6;Rb-bY7zr0K^!d#y3qk+F3_fI-Cjop}$m+$FCq
z@ZOOz*|pu>ag|Zin;B>E_>tl>ImQ=j)kzlKmdK-L8BE7#OnzU>2797PC=zhVy#=@o
zvd0hQhAr)Q172pFWyUz&5<hNn2vfh=A8=lg@+w;CI^X09#g(ev_Rwii(eb`XgJv`m
z?4>$2K1B1HR<G^{hDE_}p7w+&9xNs~Ji*L0ImQ-<)GLvtat~e);Cn>8>t1NHQhG1b
zvlzmn&nnycF%DQHauQ%byO1Z4^GKV|R=!(=rIy<VJqCBSB5rT7SK-a_lTdRrRle1g
zj|=sy_&hN+%VE3Wn;PFJRzMv<s!I`Ir&+n2uhaD=*pdmg@4h}@A=mV=5ZVNRa5~ri
zrc_FSxMj`&Wp=%{6wMucVOITiUv<X96@RE9mE|Lpsn)oDg<&<5POGRfVpN+2VBH&n
zb@W%v^v%4=><VvlONB6N^F|7chwjTh$q5FlXn8w(j6%7vj}cR+wNr^nun)iz3%e|%
zo#(P>Y*9bWHCTEs@=SRO8%FcmX3?|gwoQrWbOHh5KI4EPIunc|9GQVgt>?hFFATuV
zcfP$By;FzfkJEXzR@)f1_mJ@L)MlZJ_v-jOHzsxnA7Q-}QEW9A_>T4=f0?*$9~?}N
zrI|<Wjo=-kZqnf3U?>}lpV=~ZZ`dK7=S9e6X=z(f`K)sXq)usx5sN@vr=44uKRiF8
zZZ!s$?b2;jY^=dzO`jR4bRmScu^$g_5`D|e*f?^@s;xyl$rwVXJ;h8&ZOO{xNX^hv
z*$xXPld<>F7yp@$krC`D*wj-^Es3wbmhDIy@M6Ek;jw6vHy|g?W3Y=jW(Pg0q;B)m
zWaza$IkFvTbst&=VFRlV>4dOQuf?hwe(Y?Bu5G#6moqFZZ1?>S>rB94g6Izj&_xvR
z?zNP7W1|3;mi;XqyvvAMt<Ul$t(o(hzfxxRr||OQID4yC-@cnX{Xy51GZObR`4a-i
zNwvwY!Ep_-M!tyC1aUN=Fv?T*mx&yC0*0*x<!#h)yl@y+0kW|0BU4zcS1LcY$J^jc
zz?5a<1*aaUwEGtI9xOW7=N6;1k$T*J<(>v`*I>sCPUm%52DQ2nFw(~=wxH?Kvxlp)
z2wH?r9MeR?2lj^xDGK7Y4IDqJEA*LvgUnJupc!rupF-<x*I_rvB-FL}V-w(QA&mth
zL8;Yc4>&xcxvi(Mul4*)h*k?6YSTEo1M_@J!#|W<b9^{>dlDXR;%p<BgYI~<8v|#2
zJBiq-`~nC#tUQikaqV(TTJ{)YBb9#o>hlf(@pSEF*T!YX>a3s59(P|E%^~0unrO2B
zG7f9=_qmYM!;N1Bn@qi)lig95Cry?gJumRm@utq6?JC0U;@$V$v=DX?r*ASgbXq_Y
z9f<Mr^70Z}PcR&zN5CPokKlckAC{93IO{?rIXsg&Ju;DJFka1qkV^?$+b$>DuF0zV
z+1fmOB~lMDs&u#<kI66R_EwqM@@-<UuHs#GKRrARUCktMi__NcOT>SWbM?m+q5o3v
zTft9l{qeR#;Pe^>3`v)efG8y{Nw>MMBBCZa!z#C+J_(4ELs<QSH*LoPS1*ZJxbNKf
zF+sG;p~Yl|${@%UUJqV33H@Mk9!WYN_hBwpkzBnhyjlrOtQaPse1R6%w+&70=)6`r
z$*r`sC6uMg2{i0|V=+%sg^Jcobz`cgz=KoT%WCPUr4gZS>?>`#<BA1gdTKPP9y|2}
zhlhiE-j1tA0H!W8cUy{BRcEfg!8;k;6Zf8pQNtV4GaQ@w!Y2c%(Qww)Gde4~p-=Ed
z7E_%omRp1#3I4PG>?<0FmZ7tFdp99jx;vg4bDWA+&nsDYN9WZdK68#u{`!7<^=K);
z0;pHhn{W7edjfCFev<c1epm15T%0YuOguxl8&@qB_dT;yBt7(Wo1f))-qNO#b{||V
zCYKsxB)#T+tu2I%oe7E6>(LZ8P8>FVVtjLOs6j5-SP4a%W3N4n1=%;#SxYVa5X44B
z7iqKfR?9dUHtiA~{vo=>&)cwy4G#|Hj@p_UJIYTudf{_gUM&){^u?nP84IqNSuL=%
z_3fX!)Ed*%2|j<rHPhB|TrlVu`}!GnwoP~`A4>W}EQW00<!;RiG%p2bbp>fu1|h=9
z*+6jFA3B128nMo?*3NyI{5=Z6dldF?h;vr-TS0jv{yjY2^p4N`rA=gSO&I|EGi*`z
z;Hk3=)`dG%1wRW#wxv-#U&to|9f;3k9g81p)4B4u@RPg%kvmA$O~XTY@vRqKDs#04
zVb!2|3abdNI*o#bTF#*ckv|$CA6S$``qh#KNknBt=tz-`*@lJroMCMjsP2?SE8Avk
zA;CY4;#2XLFhBBcU=i?w6qrIhFc9Q=RWBzgCP%jmOPfl1*4893WR!G^?DI6NG8Jlk
zQZ2LwEUJIm7o<zPDJJCG5@v2~<~liMx32mu9Vea4Oy(?zYj7UzsrJ(hdZV7ud@7!y
zYa0Af0~H0w<<`)frVkx&5>EsCSmGw#F;^xl086K>x_a0-Ug5MT7Pk?Pm3ulRljU%j
z*UI1hzQdMM!OA459s1?xs=0_uz0JI<BX(&j%@%7VOgqy9*^Tzg*^Y(nRVO+}5rN{I
z_3eQ861O3ZzH?=#+pPdvR)sKgbHW(TCu01=#m)#T`=L9sz9D8bdm|GHqv8}e25jB?
z?c>khfe+QeA;5yW+v{azyCj%e#3K040O{f23D!kYk(P96Sm^b75y-s_@D)f#)<-iq
z29UyBJ;I=UCDiO1S|sx;^q+Ux6DhGhx_6d?+^C|-#)TfACB||%VR-CJoQFo{Rhn~<
znr=I<nih1nOHsm;n?Sp2BOsXXnsv2WdYwS%4YQ9PeFB3|3ctyeyXklbeSmL5ar!{D
z*_+RfLl1j<Qik@r7t5JC=U5-xwB+2?1>P#fQ=;{qZkPJTUU=UriJq1TAt^(R3y_57
z8v2|xbM#oI#=X1Y8{!YST*vZ8RX*<7+Ey>w%GzNx+NUMIE^KV24of_#Pz-x*vef5@
z+?>r)`~>eR^>JDde~W6md|fo-HlwfHTj{L^tMj?-8tXB-7WuFN0Xw%<T>Jif!!XPk
zy3zT~8!6=bx^o*2gRUR4%7(@Bq&QtdGbjNFpqSL1Wo>4iO%TR?!h@V(Qy2Jb?(ScH
zrnll^7*K>wOUJ>XV%e~R{$f7)(B#7ahs|mZuq)PqKRb<!Lw7lAxf?e62plr_YEbM1
ze`b_a?pc!pd)RU_tLD?77w&jW(T6p!JheEg2O93ytG8GSerDQCp9;#eV9U6kE;rQ9
zL+^sK=u<V%5~>sP*BcW3&H4hh!BVs+1tAwy#vA5t+^VVs^1{A#KE9&RIsn&ZShFq9
z>Fcw0=bi8Z@Ej~Ej%4Osc?wMmkJpUu90Z^CIl<m!q|G~L^zdLJ-~Hjj!3X<P>XyLO
z8S_*870EzDJXu<x&N%)&4juSxw;E8nuB9y3IEB8}r%~#7<7|r=aZI8yRn%H4Bj~DR
zmVbiVk<^|4Uai<^WqFz?tWlHI@5q<@Z81^f80<xlM%qX~bS0If*aV*LY#c(SwXjl{
zXWXL^L8WRnFd{ZH^Z7fI9crevb?UF<<ti;qCMgLpWwv_$F5b59#}h0?OQ8X$XkB_c
zZF%Ha6Ik-E;&&O~ZMnKfS5%5b!?!7iCQrU5o;u7jE!J<giqXqe!=z(H<xgIkbt;0G
zJ{K#Eam<~v#49-hPGSDjo-#t`Ml+CjRc&0v6M+pB(lTe-&X;6PFE@NL^rS_25Qvtc
zgzAaKseKW{Qor8nibtUP%e^BXmzYqtTW6pdRtyD##%Kd4#usGB{MgNr#LDSO2aA7(
z=~eTJ_8E1VWIY3!P#k!6DR$PQfC|e2KRi-vU#_N5e6|M0Z3y)p1++(Nai6|~mv7vS
zN{rufRm0aICy*nC#$A2Xn-*U*nq3fngDzM}-#XZBC6pjh>APHC7$Ey~mqax@wsgRX
zH<5P;4hG;L?cnCqhP#34?9f{=#umWc0`uBlf;e+^_Txh39w@^?F506|MZw)O@aHu@
z4`4#wxjU>t%(d?B^+mW#P9!0ok;C5PS?udV9%bgor)}rlm$v9^{GrQzeKzjU6=iEb
z`r*X$%pJ#<UtVPH5!$NFxJ=2Pa;xl65L)6T<lSt!KNNkpQuYT}^!=ymp}-;K2rRZ?
zLMh^ICrQb$V&#fX=vE{v8CLguYdJaqV6EEMA(?QW$lh5_E+#woF)Q#h@yq)Ny(DcY
z8*cT(fiTy3zWUaO-6YA+XVxe4-1EQtm=L}01;eOf2*$XtLSe|m8RQid1~z*_cA~|f
zW%SlvUMEEGKLBEW^sT+UIxYPg5aA(l4i!AE>vRwj5(3_|b$k%unXT}TM<jD5)R8z}
z??q2tn3cmm($4bOW{Ucw?*FSPTuXux*1bOMk`S3p-{u}?gu@&$ixh48vE)G^VrH{x
zHP!y8QG(LXEeODXVAm*8JPC;SOieD0g*%(YL`o`iX&-j%OS<*5#KMW~^z>4&rLXG-
zwUqYKS`PEe2vK$8*k0_T>sw+%hQ;(8+oM9mxDS0FRHKYXdL_Li!xsS<zED4Uk0a+a
z`GR~nes<<#7KomJF|p`P*EHtB{*;nFr1%sVqMfX&82zM0?H9dbO58*Eq@p8s08V*F
z<M7YMRMum2pSjD>=3g(}QflER=4d>CdMcqwL?p1MQ2vU`JQW#X<ywag4tE)u<Whwa
zvnHrse}|+k@TnhsFP$F-_;uK1gmgmDdK8^S_OIQH$Oh}k9LGN59iI!=UQGfhZQFt$
zN12rucsx8S%}cHd0$c+^Wu#4>O-#pY+AUhN3PceuFa#dFC;50VkRs0#i}fZSwBgO5
z`y7XBPuZk`fplNMmw34k@H?KtMg*N(&xT2u!p9o{$%*M;tri7#ZYTZh#LK`!AnzeT
zHG8)`2@b%<veIO20q@aEqGb~q3wL!k%i&Z}qR%0aL-1Jy%xs3(SMTep85j4Zw`=~}
z-8ICPM;LeRtKhpM+fL%otL;cx78DlEYwc55Fq^EM8eZoeP#S~CtFFWG484$GHwAgA
zRG@4&%SrpyeGqwS<eB>(S1t~q9FLhhrDPwz0zIQLf@GuR^O1}bM&j}Ut2qMzdQKVm
z_!C1D+7;N<wfJ!k-|M*o`QXG2a4ZP;!Z+N@t?kRP;(Amf{8g*LHs`I%dlwO#br|~f
zGdLcY^XtQ&Z`XURz(vJ+k$y*Kdtz@w?x_^K_1qtTzsE^``TG&>{l__E+d^5{XqHgN
zaF{)hS&lqBd-*OA1jUFWs)QMEwdMU9wr0e|a|Xt96AJs{Iv#Dl!eWgo6)RS@Z^FWP
zPWP!RXq7GX)#9J0z~N~G=N7}m!x=K>`a*GMd#VcyOVxB*LkJNcA>KQ~U^r+}>Ga`H
z83!;4h@OCxibk*0#AJJMAd*dYHenMzbf5^>29P<Q@9IvR@O4&5B3Y;OQDE*P&6MEQ
zLg{{Zzf5_ViTeeeYw5@L(3<p=frFFdYd^<D`#Qkao^wKt6tSlGO>aN@0N={jhea#T
zjou*P^K}6ym?H(FjxcTspL{%8LN@!;a@Z86MMp9}elKl{Bp5-@t<Qb>$->KLR}cie
zg6=giXEhm_jDAYcbEAGWsZ*1jZ-Fb;UGSCG8v<K0aQt@*MrkaXz30Ncjq~Tb{{5kT
zyz7+kbo<r^83A~Vot0H^L}w{Zs|UlJ$1kCwa9bZ<c9X0U$Iy_H#l^)(nm72vE(i(z
z?tBq>>(|)d&I17b&kt9U8qNt_F*u>-f%elP8rYrgoqjOF`LtiYFr95th@{?#LJ`w<
z!4h_DG)QhdmP^xpiT+PW({4jPt`Nq4+I6r5{=*2_GWC|ZW7d^;L-UL@4WP#B{wDVz
zI_Kz^TUe;-vN-`X=(}C%IOf1e&Nvc0i<{Hb(C&;_^dW;sew{Pg8@Mz$GLOr~mSIz^
zMLYYryMXKLLVROC@925E%N%}D*=Ia-s;0KRp#Stfr@I^CVfle6C#OIXZ;GYC2^g1T
zy}u-1?)e-A>|Oy)l2Q?NG+QdS(B*P`hQ<w0T6Mh=T&Q%(gK1iwAE$=hql>C3Vk;6N
zH+zduscDe@?WwnjtaHE%wlyx2*M&(#Khg7@-E_x=lfVCxxXFV^*DNBL#;dWmgyP$k
z$mvkn-Pf07YX-hC^RbE^r&OoQ>v1X#D+<G+9f7bu&GXSDfz3)2!|LRyv}x3tX9?l>
zsSLLZRL3EyG2M3*Bh4EHq9mr{m!Rggu}@zXYC(fJ(Vs@`Zx@W9*6s-Nv+$j{=(Jjw
zxAhRpDXFNUkB^UO>70$07_uax*P*e)kq%f@tn6_F(|LD($-$|GV#D;Yc06H;C(d(}
zXf(~D+lc=;^7wyjz&I4l_*NrOO4YZNyhTr~z#n17tHrQvu4wb}HPtf+>(*^27`_qD
zp(s0>Y+xX!Z;(Z+Sv1w_R8r-7!zfyd{yWxTl0E#0><!-_Xr-EnttWr!^6ib;P6D|W
zP$A7CI-~2dXJ*mR+(z&@R7K90NnA_}#o6ZVrorVmKX5oRLj=^YDV0cI)l@87H0O>*
zMQQAv>j~S5k5}G$k7q9GgtUsy;Hp%%NC8xL3}20mn0l0<LO-L|6N0{iKMJWS^?w(>
z^mZ9Mb6Lw;R*}YO;gjsOfhpTqB*9@G?e=;#CaiDuy#w<ukBMn&#<y?=dMu6W&3>U;
zrzu>7MCu6+R5a#!kz<s^(&_b7)JMufBQ`g2dzhGI315UDL#Wx;Jm9Ajp~U+czgIoa
z?B|_1?)f;Li^OkvKB4AM)9;s^SPvyUFxdK*ri@N|!SGr8J6VG)?Rz=TQE^qH#Am#D
z<p>v0DqR@PKmX{HVaWxko^)7s?!30wbzYec3+Ai`$x0*~hw^u1o9y-aP#e4;@JYla
zzur7-9A6-gxI-Mf));kTums86B4r7DK_C>LQ9rQNYc_#Zu|(BivAc0W@mK44Cv3{d
zhr2w91l?Vq$bHcyRtrWt==IOoD1IJBoOjE1JD}1cW(&R6{WY@l9@wKmVjb!^b-OT{
z-MHT*)?Z)g5y<qXUqJJooM7heDk?;#tqFO(?mO~qABQQU6Jl2l%&}+D#D4`hZIOo_
zM{AW376WNCh^xj@XvGFn@Km{+j-$}hN~e%AZW?d(d_u7SvSiY@d3dT%upLiSruqlb
zGqReRCSSLsvO@G1DN(C6=W0w_4jF0$TFN(aQZmd8q|+G0Dl3_`Z<H`TcG|8v5A6su
z*Mj>2@2<vLDKXUDb3<|2mJLIePNFb&TMN|cmfeS9sXq1%0yMpp98c$I>O4<WgB1Di
z-|#%dE@7{>2aCp-G-W#m8><{|&~JQRzY3jI_4r@=m$&IpP(7IUxV9j#ru%rLCi6VW
z->8NF=QLG}zur$7CR2l|@ngJNUMhwg_Tdt&Cn=1oIy?fW&ROY}nxqrR22AoKe{~4?
zZiltCk>`!ZQ&f#68d`dexovqYlp;bbKc3C)k6dnRdz}7u$=aiktc&bSW10lZ$(}ko
z2k^xJ#ct0qeAL3mrp*C_{LpyJ#*r8A9K#X7tmNe6=*`yKy)~LEeLr~P1~#5+3*$o9
zAoUS`67)lmL>ehE##S|q*mxh#TU1zhtwReO@CPz3!q1VxA!RO?F2OG?FE7(xQPI({
zrB0R$acG{LoUk)nWLj+h6pxLK?HJ#q2nY;sP`$wQ&{<{eAjVE64AyG~ZDkANKJPR_
zAt_!6vnBh@j{NoI-75B@(_xL<3Wwz%QCGVS**h7u%>v0$q;*!rPFC|`-0m507p^SH
zW`gZbgxi%F{-j!ur8GOUCw>pJxfmOqVOIOq{l40e*CuSb!S5I_xA{=WST75oN=wml
zb+)JxQ`&Y6mNx$<J+(qkw_d3O?KA%$aPE%`=u3F}s}1?n$|nxO|8Q&ne#_pXWPBT(
z(PN`FI7uLXmsRP?lla^Pc@s55>}BZ%PJoe<vl;8OnhAm;c;$rlqW<N`+&}(qFauzp
z5BOwK#@sLb*Jk!_<`+%{@`^;ztk%qz^UNeIl`jw}S8faJ{Q^hT=4gKPK6=O&FXj3V
z`bt@kg)7KvHcA!VSpW}%PgLs!W@+6N3he>vrgkg^<tr$q`{^z;VKcqCt%h~xYv-^0
zCnkTC!~Z)6{`5K$CWJ1r-e;Vi3!?Gma%oVtG6|(YdbZ(qq7I+Fu;)bn!Q`@hi9y1q
ziPcOc1zN1y%+eywiNy$*H}(??+7f88u2A$8<TpKyhKpP&(Qm-eV$jx~r~UoUV%YSe
zCp@yM&jlnTxuU!6L?#xcGoFJrYhYp$qx2SNWo}BFo0~bt*AhWt;_?+u%0rAbC14hH
zlBQHn@z2BFf;}s<L^mu=BpeRuU;DZT9(T$H#u<!6<>jlH^XoM$+<b47%61Yav-?U$
zW}>5b<QG+sdbUjFEPS`OBVyy{@3(%Yz5GY_<&SO+1?V}D-y4ii${Tg>o+QQfXHS(y
zk)TB)_bwG>GB>B)ojywlOD7Y@?x-msg-3rz3nbLu`ng3@w8kL70CD=9DORaBMT8m8
zCJVV6!Jw~$ii`RMp2e;E=xTqcNOKtvE%~m$bTfxa8!;++bBEDj7bC1i?MWCnB2SXl
zY{>-NHpjm28)hMqYtQM8@Yk{uY8jaq`vtYiZ<CC+)vt^^sXBgEp**a-D(Y0hg6XR*
zTTGa|mT|7yUg;k2ah0-x3of#Xm5TnM+&z8_5ez(6Q2#K}Ki=lQ#)*Fes0*U@?;Cv~
z?hw}@HF<X@D-;(&2b&6r`f06^>y#;(t5Y0Qyie}9!tu8c?y6uNja|;)#RI%jjus1L
zi9dDua&oW?O=G)Enh%U(d*@CM8~GVe`|iV!9hjrlZtN`2E@m&0p#2KKQrphS-AlA}
zHm@`5KS$m&64&fthi7ZmY-v{0-}YPZv^qg|HdFd$Z=MO)Pb>T0Ei(8tAsS#`>1s}F
z6?n1eU*|N^n&RorNvkr+1WQWG+cRv@-+$1UrvJE$_}^Kz_m;?y&YKH8uVuH>?-!W5
z)=5PZ&O&vy%n-fVoA#k=h?s<}bylugUEUZW;(}&<<k{bTma5<;MUu?y^h5*nGyRV(
z88?{+zmF9Wc36CypkJNYS*ngOBz7)Jt3|GwnmOW7Xvy)=1o|V0@HpFsd>n5$6l;;i
zGQBH=PW^%na1*4uvy9HjGKz1lEA~{Q8mj#q#qBmO3a_(j?sw>yEgv+6w5>KfjG-j%
zLI2Fw%Va%HF(ndErXefKM8e8S%MRd@vK0F_0QpOt3CDk5KVBF)&%n=i6tPA>e|16h
zgQZ9VOBOqmNx)vO<PhPeTiCPt<KTIG@8h@)%s~D3&;VOAyOvU}6idJ3>1!O5afcFx
z@VHMmakuqqGh_?SCVEmxeM#6w%ybiUWJe4rvt`1{XSt8x#6-VMA+3W;@Om;&$0o2w
zS4Z?VekA79qozfhRZB9{-;3*^(jWUZoSZ>r4J<&yE%QK}zFn*-`-Uoov$}D?X&873
z6J%n@`x)vnC`^(bZ~nR#foKomztUbDB@p2k@x{~8lLCT3xvp+Hu<r|_<K+i_5XocW
zTl*gY?~nKw1=0(L?G)7Td~C4uRevBWPfpYOZu9?2tM;=X>U$mVd2Glx%aI3@GjD>N
zcR7syi?@2eN`XGr3(%E}|6koI$X36y{|=x%eb~AGi~s+(BW?c-f<pc7j%oPee_)IL
zfCzkksOi?Y8{~mq#Q%OM{^cwGdn5n*ME;;c|LMH>U)B0wWB;#!>wi(}{~P!Pl}X@5
z<VlCd_>%nf;gH2oaBIvw$olVoOC$MrHMK9GbYo-$;8of2c%G1jg+*3LiJX#>GCDSv
z^x@%wQLh&+0mQd`LPvL8LlXZ>Pe(L84G9zwN=uRC@&A$dLRx^}<mAlG&i<O}ajW9;
zbW&%q)f+asxJbpDHrD(d4-L&gb7Eow1=L;(s!NDbsWKcGVOyhSG8zp%hOxE(Yv;il
z0wVLfl2xR{_{bx%ae%^K+!x_@EiF1}-iK`Ni*D3Pha*LsrWN60sc+w+mzUKC`uii_
zgF}jni-)w=78ZV6kA)}126L2>3U}Xt)r#agtrrxIz*~`|8z%c}fwqeO4#Ihya5(<x
zHh|EzTyqxidJ(Y$Dr^Sc-Z~Ns6GPk!VQ7#Tr8vZeVzCSc&ceXJ_&Agjssm$V2ck*E
z^<SSKE?=L{u=%lf$kW^_pKt5hV(rFw*gB0yH~!jVXwLwJ-yX)z#*%*&HXj%d(GR5c
zquG)eE3c;|VZU3~7DmJ2&kwz9t%P%$ZYBCgM&jn?#RSys+(juc4urbIzwSGK!tQiH
z;WIDA$A8A;w9kLO1h)P9&6=wGeEMtgkLSUBuRop2i}3w>9mhYJioma6=hd_{G_lxL
z%`)h8n&y57;&d(Yo-apQL!gp;QeNKHzApcD4Gj&N-)!x6>(ePJaESE7hY!D7dIowh
zw3fFVjYa=_Q-A*!Q4$24(NKLAe-;U!7Rh&z!aA*a)PagzWIRCav_pc3JR~4B<06^B
zvI_vR%ytOZU$~vMD9OsoN>E&+y<X?_W;M}WZp8n)=KR)gf?w}`9(^a`{`1rKCMH;S
zY}&~IYDVv+d96CDjwLXOc-|CgiFtaydJ=XZ<|I{DGd~O}wGsgyCn6-cj^@gMTF;vp
zhAYQ{CISD!6-EjI`G+E#DjV^CvaLsw_kPfHbacP~e9!q}86Hp)6cvw~j;>5;HwLue
z=yzAKy2FeDn72Dfg(|f>d^Amx!v5L|Xulvqa^vPQTKl^!Jz{>09mVV}8WzwL#b9~N
z926DgE^Id>F8v?2zA`S#HvEz<r3Iv=5v03Ox<N`B3CW?mM;htw?v(EC?g3^<0YSPu
zc6eXk{qOE?zwilo2A=z>bDeYUzRjz*O03ImQBbptzIX(gVfmQ3@;8LhBBC`H2_-CB
z5`!m%r5)~gc60y!q79TUw)^vCnrYGN29JB1roZ|^KdQFzNJ>Z`Mq!%Kad4=1+^-yu
z+#h$cpvXK8QAG_MKrO%c_-^e|jY<FibtnV{I!;ji;<Uqhc?2|n2-;Ms9hJw&auw7V
zZ!0dfTU5;*l_U&+X+zH>Wa7p572L4f395!4P*S472qOZ0bfnXyv56@RDS;{MZo0tN
zS^V-ribRxry$))-k*Fo3Vt=2zD}bQOoHUlkqqK%AU~;iL?J%prg9>VD_+uw-#pnWI
z0z(xOVY=Rz^Jh>yfRUL3In(ps<Lu6RY7q3sfB*dd_v_RS3N$hti^Abwjt<aRtZVj2
zCC4MF+}qXPTE9)5p+P;QaomjmSlJ%DkOSwWoPQOxP?jO)ytiZty(*brVSc>EbDZpU
zt6zzRmO}eX^7rr}hvaDfWCpbagUR5)Ibb9-`fpUmD0J<d4}&UnOrfIom;AYt?il&z
z&6|t6Yzjj<YU&~AF=c=*IqK#X7Dii-JLEQ67G!Ea{{Q~zKw}xdu&(O-@s5`hn2m{5
z%utb-@T|+kxTa54K<rY+;vDV~ar-G?k17jA+}1XjVG+3HEHRvk?rC~F;GlKWch7Fp
zW0>MGO`YQM=glx#y6Jcm&A+J=7(%mt>0t^L&VGhg8faNzH=9VO!g#g1Vq$D$6s{~}
z)Q3Wq$fDo&<p`>`WnNubnKu>wzq9Ipy{tGTbo7dG)8PFFALYeBaH)VWk&o+rDBGz@
zXb5sa-#sKKs}{n4Pj7<UcQAA5X)<}op7Olt$?iTe>;OVHlb-JD9yFpq@c#?HWBh;-
ztc0!(6jq?s;Kf%Nqbws+?bFa{dHDyRT?FgK_VzZkR9PaNXU-L&|M#IpuMt2Rnj<<i
zkp3O9T~@Q3pGW&7iZV`4;ksWq<!Ld;gwprQ#r`LWJw`&aiI1!7zrZIiCtNGffePjh
zrpdg3Ro?;zi+Pj}lZc1Gm?uAf7TTilX;|Yb)u_G}#pv^CihQ$OY`W_{AG_P9uS#h-
zq<=pH2?rcjB_ktaM3S?lWKe!V!KLh)i{8N$6Q~8sW#iem(A3maZ92jOTL=98Q^lXE
z^S(52bEVo2Ts<}}H0{Qc>o@9Lo?wJVMiC@kNk9+BZVknMhXW}5G8dDJ07?FN6(r2S
zmM{;klg0b9;nC5)m0zK6yZN1jv8IJcZ9*-7#7;g{YlE0|SvaaPn0HpVb&$4915(eE
z|2{gB7`9wUPgBz@V1H}NL`Fg3FrhkIZ<!Nwg%VSLL3(_A9IEo1VSKE{{`+{|Y1DNU
z8^LIo5)ZK*+JXyk@5(ez!J4i2S=3?w=Y5+vjKkid!7HIB@tuj=xc^Q^G)$nCKKx5W
z+I`3FBKT!mRT16z^uSWGqDo%<-Ferd8m^&niHcmP{R@cs3k$)TJ5av!EIzNn$9(s#
z+dumy=no@P!cY)?=ZYQOW+$6MA@s=~_V5U_In7*MTr^Krf~r>CF2P3s5~yAnj1F)r
zq$-6mwP(n3KC;JxA>X7f=LYIO?>Nhamu?Cl(#k%w{h#&f23k0OC;S6^&|v{n+w0H3
zw!eo<8yAx422M>}4dk$XuIvHz+KVKAKkeGqJhewSY)6T(Xd$L}0~knH{(1YNB2+M6
z`B+#c-4x~*7KmAPSCexud(ax0pu1yCyY6#v*zfjfI>_Im48#rX0<~zDSOZXHhb|A9
zH__YcysBQy1`S@u(0M6-(O02mOe3OZ<kn{<=g@v<bS(*y66SBPD@HhJ9!oiZ58|Hm
zvFWW|X>|Ss$l&h2YneTyLf_S?qHFXxsIn@J?M$pl+PlJUE;%vb<0%{B{CZz^$k#Yw
z&wF^#!6Zd0ppmvCIN)R6QJV#S2<hlq3TPnoAgRPSO9${@XIxEqL6}Ec(|*)9Hn==B
zD46;1>ZF++R--5Fj5TgAQu5u`nSxfeR`pUi$gj8qtvcyUSC#^<9J8+g#LF*;vM4XK
zR`jZT{fC5hAqKZEvPjSJ^q^h>na$P*tqO~XF}76scxJ?xuh2YSotJIrI}Tjmy4(K^
zPVXcbP&zX`Ju6S1rIl3yv`K}Mz@4G}B2U?;Mg;|h8pj<)M-E0hx}t?oapb3dOaGm<
z3ktX&BQB$=X*?m+2$V_18e1CJt-bpRJXeXbPV(|q<Fv9OjQJ&RFIP&6WOdcTgHVqd
ze6F;mtappC?9lA590~brZe7StC(e$pGW?yu2u>De_DSCHRmChx3yfixA4AYAri1TS
zJhFB$A&Dcq@$!V}(V&87<&V=#NsenvGEH7vfeqW`j@j%*q*uN5w)+qt>kRm%+(?B6
zvWA98z%1C%>O7sBn<bs}tQ_FF-O#b3Enp@TPeCzhVRl^s*u{5FceX!}Zy5W{7X^T<
z005-c8Qk1!zn3=cbgYbb5Qz^2qI&<b`<z~&Iaib9e|Geb0WiV@lfEW7ZQjpt_Kltw
zKkU~CDl&?Y_6-&eO5*4`F2i-525;GS_Mw*s8y6v7|823>7qLR7i6L>c3b7X#P8L>H
zaW7UrP*W7wz6?m(#|b9Tif*G##v1~Hmkw1Py6JzH{y+38C#7ugJOlaB3KI?bWo$+=
zy~Qc&*+8Vx>F)hrcsVc@pTJfyTeE4jpfXRPFds7-)-tb5*>%l4N-mdLajkZ8f+e1!
z#G<CGdc%+F$G(+mwTG8xW5dYLaM(h97o!>2{huehI6uGVKFSBJrr+~67-iH;WGZm8
z)I~Bi-cEQD(PAP2#}pLm&m`TeKUmSn6-K&*MusIWMt(W{v(p8>;(l*y#nw{~m^G*`
zQ{MaFcmz3JIs}fCN34K-(wz@FGLlxnUab<sv9(M(znA&<b#z+mU9J|vzZQ>rY3tfr
z1tF`zhKYvqjPcsRi3-wJX9vO!8+*Ki){vhr4P#3|_0<4@sKbtmg^UOQi*9pmC15fV
zd2r$kzTSA>!P6S`okxMb#mt5!fq_+;_#c0*yC7;68C1$s`xPly-iBTiTWV-GwZR|q
zUEn1_PCcX4H8zZncDm^$c3hO4)(jvgJSD#yXl%UQ8W(A!QZL|g7@w?+zM-x>Um~C6
z+7W*Vm>7AOo=d2{%oN%x)i`X+x<5Z&BU1Eou(M-$E?&_p?}9c@hrT1eLeO9MBIS8?
zuoS$4{2y(lD^%3b)-i6m(Iaqf&woPLn>@nO;!PF=K=#n`b&j{$ZujI}kdQ*e=i|$v
z1gb&ln6NP^s(u7A-d@KAMOYMXCYA2HXYQ(`$Qw5f&}GH=S+z&d8rQAb`-5$+#;Aei
zJI^$Y<X-NJ<*!FM?pKcK+c?@}sZjzvP0R-x#POds<JwfJ74YZdOAQZa%Q0j^!Y(1Z
zwLeZ)Z5OCCc_z?uyuoZs6{PSD6Kzx8Rs`V8@nE6Y+T|k%lojI6y|$DzUfbZZUsvru
zQNh%Bd01UvJ=h?~Nj|u;^aU~nFxSc1o-i(|a3ts6ZEW4DJGAL+Z5eU}@sYIp&o^$n
zc+z{5nG1dp5QHPu&$@BZ46)tBurSd4P-9%>=BgQ1)uF<#t=8n?dGSc2k*gX<%^;+1
z6uc!fwo8G-<rCl}|CYiYaS2C(@BU=HR-@`=&}MwQgz{1h#L0k(clhp#3x2C7N3^F-
zZj`ou?ShmcXURJ?Nr`gC&T-2ZX)Lm5ep~uSf#ZKGQ5O(3zp!uwI&FPm)F_XJ+DDB*
zr!?xu6&DDQku>`akMF@aM{8gsmtaMf?~{AA(QW*DRg5XLAAiS_^*&k{zTJ6%PQFGb
zPiA~F<qiRQ+;~0L+2l+{Y&lt3EgG^btjRVNPuon3%spp%liVl;E14pLl?C@aSnVB)
z%JIIr_oS5NWz)7hy55nx{n@W{LBgaIrdKY9@s||}XO^emnvKui2d@<U@e}a~^i$2Q
z{hoL3n0DBgxUuMySZfM>3$;4&8)3z+jiWmEvRmV&I3;IwvTedA*$#0fmg%?`{6G(M
zTiJwK=av1VDlJtRU#CA*b}LKl))|hq_Y?ezItS#%ALc-0!DYtxHQYInqmc|iAeiJ}
z5d;lO-a1=Pub*e|OQhwMr2c?SLx_L9Yw>o@)r9c@<PHCXe?lM_;?i`e2gfV6`b+Ou
zX6F~d<5KvzFi`b4bLcdj;NtgAy1Q}q#rIEP;#RazE_FUF`p+OIX=l&iI@5fa-{tQ=
z-;6WTY=+0lyZ^*-m0o}QGS7#L))K#TK)i_vsg|kRp#oVkhodf~$@{{IdgRv~%lrl*
zmr*OHb<1}<X*67n+f7lS@c&kMUVD_j{{CTSsImE^@O^oFV&cdq)Vx1Q*CABvzNL}A
zuCZ|x%0{sUO}zjhRnUo5YHNN)XVd7PZ16H=l_0#suq{(v(w68-D;l3--GCa>xI|i)
zm&6`2h$fZVy#_WFS;PbpXX&)bq+SYXskdfoKqf22$R!y8=FzPpxAb3-V(7HR!lI-V
z6RhTONlKL4JA9jVg_icN!sG%E^B0@4g<K;sVHx+Jan|20-lFWO!{YSg<dR$?X?S!S
zVyYvT(PwFr6+j>H0U1^nj}ArIt?6SGenlxxgOY*A`-YMtni=37Dv#ux)dKg-uPHLJ
z^tr4jv-cQ__;;T?8$`??+?d?lOM(#YGe+&@&(ph!-W4_$&i5wT`mt{c>5w3G&W6M`
zYfDA5NDUKPb+x802cQY?qR#;+k=&fv(&}E6w2m0u02tMp1XVZuwExWq0Hn-<8kXXi
z_Li>Fb<FhAe6xkE39ZEr0V%Z#N7wAI?PDuhUbKgfJE1#yc(GG6%qni#63@(ag8|2{
zI(_f<nYkD=G@HF<^1s(3PqsOU%gC!&sCC`})h2W(d!No}39tP3dw6<uad^X^2mrZ6
zlXdD#%N&Ue{B~LA^9!OhdZi?%$U6-_3;KdNO-5qlxsETBvr{k8%k)nt*rh`2=UfP-
zGFh~hA9vi_K#kTDmZ8Ye*^bDw);o4`nVtyp#vslN0oO{w^Dz$Ex<6wn9NSR4B;nX^
zT1kJq^E)BnTlKqNYB#tB;cBJI=o>8(i0ic86xMEDPCTyHz0tSQ68lAs*3m8%JnTYZ
zM%ggMQ?BMR?cg~bPc!cf?3^EUq`rGtjc6ePX~a&}WE=w~cqP{*t}eko1JYA`hRpJV
zNq%@cKb>jQj>l?yGG;;GKpPdzt9>F%Q=32Ev{sc1_+x_*7S3(j%T>q<etix;t%~IS
z<t!C!(01k0SA4S-)ns;GZ(Qxo7iGU>grrnenYaMbl!*%)kbuY85}BZnj&pp-(m8Ar
z+lROu;6G~Vqy+;OJtXl6wDhJwV6s%jO0=e#xc*RDX2_-)aNp`>iAwlXVFhur5xdUt
z4m=WozGu=XCb5o{K@e@4S=1rO!WdEmZGsvb&~<hUU5d>B)#MYa4Hkf}xDykJ5SO3z
z!wo<{)6*wN?cgHi693g=gVkvI6$oU}`?yNAu2ri7Cim7CJZ4GwQuVBD4*cQFgPO!!
zc_yh8tX6lIuM%jLg}DGUr)GI1)!-r?Je<B$mT#4FSvk)a?;nt!)>|-dpID2sNj2j}
zy&!^4yzN1;=r5_e>y*xqt0L=h<GjY$ogBBK^;sS-01bE0HN?=zRY0;$17q@W7x8w4
z+;%H<VFhUP?-VUsg)0pmTXbxi`^)Xyp_Ep4s3Oc{D4ODIll=M1FXxJpUWG3UcTfUr
z4AdiMXJ&Dc9t!#vj#Fug-P;ZJc)aurFD*k7N?*yYfUz>LO}$!^Ot<!P;nvm&^JPut
zXNqriVFO|_Grfv*YEHL?N+nPjZq%jSL(G8Fy6@}SNO01%Z)uV9NA1*ygSwRNC!xLf
z*J;~XLy=e9&942Q2}_N2)U_y7?rwiww84dHV#oct!TGs>*r0ei@jIAUuZca)Y&#pt
zTl6$RsQ*mXJydN^3~!j>EzPb!nu;Xrh0aFNh^k%q94X2okiaIjIKxs?NcO2iU@Oo$
z;cUe>A(UT;7+b)jQHQ*kK1Q>WltxRY`eO{cZk|SVovDlP6)%(?(N<rf-*K*3a!`IA
zqRH2De?Hb7dRAY;$8KY2`Lncs5LDCP{xL6_)U4T&{M*Fl(G|dP!Xa&EnSWGF>x%#l
zQV`vLUI125`qa2%;*!Urw!l9vp2cSQ!b(Sl8N?pERAUDSN?f{|c(4OFYaZ2YKW$*2
zaB;~&HXZxVABb`GEoav#vfMCoFE%n6o`luvJPN>wK<z5F2s|?5r`#Lg&WB%hPu?o0
zM3cRSe}JT&ZrbdV8I*riCCOI}wSFLA_`{G-3%oWj|MXA0CJm$1-~RAp!F1D8=fYoN
zD*$;ASvyOZ?a1EqLQI8cE&C*J{~e207ysOr3I_2S#Jb536Zb!W;T2qyjH<HoyX#fY
zs&H}<{nXUdt?SO`cCY9AwWQM0QZX_(uz%4dlt}yj1&4&9dQvkNwAarMlg~rVJDHwC
zht+U@e^+`4g0P5nzWn4+JQ5xPL7CKkC3n)Be!3w5odP}zWCI73W;1<l=w`;UtVgL-
zQUuj1N`=S#5EHlZ+N^8%DKb^2PA+MM?lzF>6T$k2{9`#<LZf1`^?*jHao-o>IWprx
zl4MGMm?P(Rfw()?gd-XEypm|+=axEIa|dz9<$4U5hYX1f=%`s(q-Eb@y`?`&P`A^;
zsi3Y9VOrrO+A;moKt`a?B67P71ekyFy^oj;7m*ca03;xVd;zNmd+2%?bJ<48xy&^f
z<>x3k6AFOq98p(}G^b;#OWU+M9yLPW@VNEoLB-xHwppnZEWTfpz5e9O*B;rp9v<>w
zO0@>pSV2VMp*@G0clhuyoJ-DT-5_*I<DRtXt;ZD5L3VLjv&nF7fk+8KkoT6ni@cS<
zwU-AVt*raONI%cbZ}uyvRG}V65~nRX`CJ2xr1p2M9)6lmUcq&xP3^tgiIx%N*+~KK
zrli&C^fB+Q%o@KNKU7RJ9nHGzau?PsxQLudCU+tY+Dsh$>7%lX-eFY~e3#Dl@^UWJ
z;a-t>D!t4ALCDv^f&8HJld7UEf|3Z)3i6;l<6IhHHpS0Kh0@=iU`pPX#SM%|KEiB0
zQ*PT_4-MDS4VgaMqjtEHx((e8{}~tQWO~8jngTMbb^^Jox@n5&b$)w2IwBwW7yOq*
z<Pfmfz0S6V9oD?Bhe=(RDf;{S|AGDs71Fk#7GCkMSlX0-l3jJjDjkbg>bGl?dF)bb
zHlt$;a&f8qoW*4gDe$c1Q>+}IkNem6IsAtgRinOJKI<%#7BU-ReQ#wG>vU;IR$;_?
zb}iRrUzLJcuAh_Jutr{?-@j@8$n2Ttp`BF);8pdnbbee6n*Thb?0a(DP+V-_OQ9iY
zv9^o67WB9lt3n@}6-z0?XBuQ(Qjy?+S9PxX*;IT0#+SlZ5&<^ERk4~yZLAjVtERy7
z5=MQ7L4)Q~6QI4$;>*cFGQ#;g#7_a6SyBmqxC0P1ucek{L+5?+xX3BFp?e=bUiMb3
z$_#?oM6mq<GWuRTXiOo<1Myg{SQDK2G^rRKJGOPA^8M4GO_W$;L+8{JWN!Oh^T&v(
zXGSEy;$YdJ>e;XDBdx&LxPWRy;cv#NKM#Mw+~*upkh@DaER*m?W&n6cU*<=!b7Jt(
zb^iDiKX1e30e5;t{VN_?vv#}fm3p&tOpUeS8IZkAhBw%|)fsI6yo+V-dkk;Uc`Y1T
zVx`=zo%N-=YV2c6kFl0>OOSJlhqmVhW`GtUe8S1rkK6l=XHTQd@6#geH?6p)Ptm)X
zQ2#Xj1#f5*jy{f)mib*)p4x5>igpR_g)G6ooHY_}IAY;NYTHNC4j+qCvd4)}9w+V>
zagjdjGV(IfTd)kum?O~#-CFGCCYf82LkY_{@TQ{rLHXZ`MrPNZ%e)?b(MgU;a$vQp
z)_yiw+{%sC=p`>SElT0vkZYYpBsh6bqWKPYf^eao;=dJTHUVnizyMLyZc&yG*iDB;
zt44g)`&v({!wZURaY!+@9<cL0Ud$>C9dB)Itw)qMG;o(xRwgDeYNk%D+qRts#U~^X
zR_%BDBc~~E@GRSjMFkw|r(>l3MgM?49}Sy=kOs@>P*=-QRw6i+*UCghOq$XKn)a_3
z`0)~-b2kQETl8=WR+l7EuJ_GXTyxfP+JP>u96c=%+*)K8&9MiRcQDvfXx=%!)utJr
zD45ak0!V(9;XTlPGaWvjQCwg>nQjT<E5B%&&C=IHZdV{TvUX^cQ`aerZ%r-GUC5}g
zYm}`J{xDsPsajWJzK>glU2ZQm6gk*%NS@SbJEZy>DUwX`V?OAj-lDmtx<%osK2K(0
z@F*HKl+EEf#oxqS^80(&r=Nh-3X4}b1mL|l@f8szW_N!+^o|KLS|$40Sg9Q=hfBmw
z92bTz#l{`@X3y5`u&{|tlf`cD9=y6O)V*IIB|(a-=jfeV4sy|()fZa&8irsch!7pO
zOR!tf{s$R=_<Ed~fxgO8v$csc_g#P}Z$sV(_1@;jJ(80~xC*1Va?JeKAyKso*`liP
zb7kiPSB3aX({qJ8vrPIGZ>s17RRKRPJ_0u^hGPpNiQZEF4)mRGp*C!Ns$g}$!(EVS
z5O&H-LTfpnBp<ix>3w~(c5phPcxdKnoOUqF)@UJu${_ulx^Fb(LYy!)mfphtiS<OW
zV{!1Ioj=RFf|-M$uB>~HhJ_DX0U76s)(A%L-gbV!UgMr42qp(mK>s6L%ubjs;!Nrb
z9H6S5_UN8G9s?cyD2&Um7tF^&A-2%qEbX4ApXVW*s~&JP^@FF5yOPWCas88qfFSRt
zEB_3R(8^&#{Y*zyVoyCT^L5m}L}M1>m(3|KPbpiYJ=x&lwhZpr)N6`2akz{;=+yxJ
z5P^?Z^A^Tr%p1JQIF8sB((jL=2b{d|SgXNCkOItYIN|k+j830fH(!C#f0Z-P1S<tq
zQ`Tj&83&DJ2xvk#oFc=}QNT?6kEVgQ`tR{HI(TG^cx)<3>BQBaV`F2`84<svyqt5R
z%k>a!+GiPn8tlp)aCGc_?8)x<drS<c;`B*JRh7!w*?DGa%5cv}4=RDN1{zNM0|mY0
zbF5FIo!Xm_OVT){n2ozkR3I9=f`fEjyf&4#FKsI)r<;m(o7dsREVWo~l<T9->ig?O
z{b+x#2H*ByamEi!tC4;XDjGuxVMaH82*<CYZ%!+BRq(o+rLQc`egrHs%F8CgwI|?s
z6m;RcG(FiVd?n<rBC0PFZECHT;cny{`CX{JRJ^g}+Q4NIzxH-G@_H@q511$QW0U!;
zVK&wC$B)`=f<6{8<!ePoO`}v)H8`f?-*T~~;X&QwK7dbqXP7f0nk$Xmg|UBnM3j2v
zXs)2{m>OTzeX}^J`F_WzW6{(G7dT&SEWZM<DE~f&CpA_C${;p$8~a*hO9q!$SVoIj
zulmELvWa;pfY=^v!1NL3(dGyI``XF_%klGngbF-`A+)U!!4b*9N;(>q#)XKXxFk7-
z{NX$mWM@IBRlJ5`i`VnkMz`oOlSjwuvflewS97v5j%j?pSeR`1>C5!$NSl{H&5-M&
z*U||;f=otvFRh*b2sN%%t<ezg>*4$m5w*<BA#3PN5!QFWRReu=_H7_EZV%fyA1&SX
z5940~Wq+?<0DKyBP3E&2G859;MW(!MzSVL#%7%?iL)Z|0XJRCnx~I1Fyb%WQ$h14+
z6dMe2*B9Vq>4L1;)3K;3fabYJSQZXus_!7Mo0B^QBbtMl*Tg3~UvbSnH@QxipQWDH
zU;+?$hHZLze#jLnO6;TIRbW@b#t>bE8|lHG2mNk?n$p0?+8;g8JgsRxS1WZ!-JK}I
z;i8gIy--H6&0YRklS%o*1T1U%Ubf5~RV+-BN)1AYybZlA^Lf3|=DdPX<p;`?$`m`j
zQqws-z24C|@T|itu3yfJtdP9cBx|PnJ@hXwb@vK3;GB%xI{6<00}GVNL2GgYUHa3t
zx*p?)%zmjQ;_*4@L5FL@q9oBl0nZjH#Oc_!t21}p7~#^k6>W4^XL1X+t@+#oHrvrp
z!P_&$1|Gc(5@ON#czDB52_T>+Q3-0$Wk*%osk|fj9~L%*nqRD(fkW(L5oM`a<^etW
z1$(dplx$i@$(-XMAfZDpE^&lo3n4J6FWgP|H{7cVzn$e{?MN!GiP@|vpgh`9GUP*s
zRKxPB&oqu|u}HSQC6x?Yb!;J~bhyIg=gCJ(3%kbQN)SGt62!#gTjuR+i$N2eb2*JC
z{q*m6)T47ZUs8m>P%W2KPm>4~nU(De(cyh#yAj=9*L_qd)hrA%z7e$A%T-_LuJ%0%
zirM?w;?q!>0odO?4Y_#xUiR8kD6JGJ*2St6`L%uVZDEDqXPYtpAO4#c5JU|BpLr%m
zia~t@NyDoejlN`H?T8?yC-HXaDk5&_)>#n1vqj?mZNPR@eU1coF*1j^;g)gB=1iQ4
zxpuCH(*4{%SPiRDSQjSBY|$_mEAIK{*Cekm)7kro_W~j8KVIFlWgAGZma>dw;zyxP
zB<x+1(QUGS9)C-8;iDVTp=P{*#voK`SFwh3(UtYU6y~L&#}WltWz=DnjNkms#+EaU
z`5PN>A)BOV9S63yS!R3&*DBc%bBKTYy~mD#k&TtU??h_iR%~UepmNsKkWmcK)rg-h
zFc!c`ZpM>!ZsmEWdtcRrFK%JNPYHK;%z`zU2eo$75Ntv6o=oj20Oc=S3nzK(?*pHC
zaLiE6<Qpa18k#e1Gkodh6ng`VBdq2kCuI|wfI>giW@@o#!VoazvxAfFfW%+l|1<b>
zol^WfxWucu%W#&W5$8p)YV_#}BDl;ySBia4pqQwBTpp8PTaoxhaEVa5nn=I6iaKf%
z;0A<*mnj}H_WtJmcW-hB3%h*r{figWl%9x!DEG3)baV@f?Iq64eBQ)ffe!1Tp`nuH
zFZ!i)hmrRV1B7NnP4=7eK1!fIq*51j(p#tp#V|akpdn~5XpsAp!Y@fOed)K8(EH#N
zo-u`QvUT0J6TUGWh2qksIz0XFonflq>fm6-LlJkSdes<jC)OOIbo_ypO(KySr)AT(
zBo7^t8k+>{>vBSyYh%(wE2(ghbHKF$!5o?}C==2l29uR{V8*ahTrp&E9v*_0gUe8a
z9hFVsES(qhR5@kWsoqjCI|)GdK&M!)(;;sPmVQ$P_w_qk6bfHPDJBWVR4~cONTgh}
zQ#;6rQ=hZ#Jq7h<q`cy%*q|syPB2v)u$OimBXZD-Y>shi5eWg(WhTC>{f#UHGd$lI
zxEPrAR$%Obu{KW`m%R=UVm#Gj=+6q0WbkiK6(tiB@Js_2<gc!jL|PH#IXE52Jl)HF
z+v2Bv=B>}gUYLUOxUPB?RhX;Z7KHVqLnREM%JjqPz6kP@J=hUJY4>%zD;Fb=o<4mE
zhTA7AdY3~x3x!%QAM@Z!MjLH5?=&F}jDy?R*byXL2jZM|HmZ=pA`_a>kx`(pOMF96
zR=;Y$=XU#<2Pu&xFf}>2<D#~iRB*9h{QmK)s#AKA77%LysO?htEg2{SId3k`)i~1W
z)G1YGZKPPwTc55#-56rinEL_p$=z_TGNKkdX{>kK*0hq_inND%$lDqE2~4DSGK+i6
z4=cR&K-rJ7UMVActacd4mggijDac4Qe*PlhyZ5JB^>w_XV=;oD4tEH&k5>mVpF)Rj
z8t-fuJN}cgj|E|%r%r(;4`p0}$lGyFo#$f$9|0Ax*6Q|`eyax?>L|v~8`|V{hWv#H
z(530P_jl+zZ+IA0kkdh4%I<-GU%;~W)VMG6<@mm%M4dafigv$lHx)L#xPn<>ZlwMb
zmseYv^}g1YU#=Iplg8}grC}u4mHxQI0yohny*h}@YDet)`|a=)3zPicvC-}WDwO5(
z9%a#ZAUr09N@|2_LDzUy2b+>)bqa+kGd|v=Eq8-3f?)50lVa=!YHRk&{cM;{VbR{r
zMhE*vSRI>|W_1Upp2XlWY79X&zy-OvpI)vdfnIDEbBfmaD~!<tOnUe?rDsoKn}d*B
z_P`KonKk2DwF3cV@KV0jDJ5bc;WLGgOYup7&ZApGv7TDewN)n;RvBjgp-R=kvH8%c
zAUd*lafze|ah>_wGkHLfz~r$QAns}~$PW*1nppe~p87mPENG+w8bKdh@~0Onf5i%a
zDc+tkbngE;y&da3zWFwEA$&2}Z?Sm$<Th&dQ+bxKXyCr?V;iq{Rnf+Ki`b&fh2|Dp
z6P5P28IP7+ao^pWzHoh9t}wS7o<M4Rv(RE<RWIkl>6TwU%-G9rv^`1iI!0W*EUZjB
zziE|fpCJ`GL+V^{8>1UX=?Zb{b#ScsW}$QvTQLxAUyeK1)V1lgM|iWpXVmZDppp`c
zysx_TX86LgH2Z<Ywv@{#74If5*PCc$kxr><wP>~FCSxzUHte&hSLO3Z%!I`4S|NqA
z?n+uMm1$Z$rWVDjND-yHoGg{(51QfQ@wr_N&RlyHZ88$O<6O*3pSNS#Bc${Tdi26^
zo*vx;+4%*upf^>HX4WuRSWi2r<9Rt41)E6n$Z5%?y(yqY4E!P>3@^98VC8mV%AiYy
z;G!n_K}i|*{@^2)=Z4<lvw$Dd5#I(m*}FyUHG12m`M1+xpVYzTx0$i;s4g>gO2g*J
zE~hHhieh31zAeokB-(3v=+8QRm%Qws&#Cz1-kbH#@eObA?yJoY7BfSbX|k6TC{^y!
z-DuI~JalXtLHWOIrNTWn@AK6b+AAI0FT0W3wWlJv7mMGK&0y_GSn@z6RF9|}yXm-2
zHsv)d#Q?#drj2h(50$qApV-JbG-k6a4yzyu#@QA&9`aoa`IvHoURC0a^Kxlgvn^)o
z_D2k9-rpBf>{?u*(N{)qcV<6v@*{hJYoe*$#qKJ7knK)K&8}C1Xq7yQjg^!U?gzg2
z2bRu<8m+xo*T~E*yPs;E7OVH3-LAzp?%c9qul`xp2g?{f<sHk4xyK2=3ywN$j~nxl
zxeNF`yputl<a_*?(Bmwc8GZA&tAWz`eDQMEacY*{(n3-1>A#ek3l+Gg7~?<-c~~Qt
zlfqwLCs1>`k6ZScHv#A@`#n9MZlOHU4+eKLNhZ5fIUaEg10n0X$ogbhqe&mMD5C_=
zLQv^CzV;$DKVCr;o}g@4-{d?)uD^&)h&)^q_H$Ki1`%1Kc<F@@*1mdHqJVTIvCxE@
zx+@`9-P`ddz{4;*FCIm_{oHJR<8uyjana^lBOqoRP~osvx-Ho@fw}2UTB#l~&tkno
zopr>Rgzv31sqytJmqN@`aoR57O>!g;EEOn_RD4|)sA(lSq@%Nzomom#Q{JEmR^JMr
ze{jR|duKf$Iatb*N48mDWCYj1pgmaf8RhuPL+#g``9=dUr-@UAhq<k~%7{+cKHh$R
zG$8>Cm0GlrvmY}{Ue#9eqZmX7I34*XbYA@|Noj<O(>a*4jIjb!zxnPKt18qTb>RN`
zT8?46dmE3TrdQzYLT#k@;Bt;}a`HYwKBRt?JV|4SJV{+vfkQCa`RT5##?3RXT$Sx%
z-b%8>55&yVS*@y3R(z;&kKy*CEGA~yX@dOFGNC|#%ZoR*biubJw+m$fvIpK;jT$vs
zZICPhTv$HeWrqaGUY8OD2aD@N<~}G@l_Z%}5!sab97X_YazJ#<=uzRGn!-ZJ!s<Wf
zKQ4xQd#l$jpW8IYAI!^FM6K=O<MoU>de_J<d<(LRFd<Cw?}}1l+|MtsUwLjV8pD3{
z_#_(}J|Z1M>P!SjGE%c@#qLW(;>#dh28fYF&xmj?h&i0T%}R)4SQD~HW@hx0!h7&F
zvlCJ;;q7oTh{)gDxis1SekK1&LR8`tkBb53N{fiARA8o5)ta$x)Ys7Z@@K%VXmcIC
z(plWW<rIad&T<RrO89oL(y|?2!SUPcc{evhtLBI6I&2T7I~o_NDJxH`a)k%i3@TaK
zOIYB&`tH+G{>EPa(Y2lH>)~*`>1BJL$0$N`n!;YnsjHDVoJO;<x?OlOU^YB50NK}t
zNLwIZeSuEO1WA1M!XWv|^8CjP@BJc&l0=u}^D2T5V?Cx2cR|aCk80u68C>cPmHgM)
z-m}@RsOz2{-rntQoRr8<g-rTw;{E$S@{3d!JzO~lJGjsm1~d6QU-<YyD%ziBv-(;O
zzdn1jPIO*fVDl8C8<Or*dzYS%9Q*1|bezhci<{fl5r+cY;)!p@+rG8DiDV4vqj30J
z0O{TV?RZ8!!eA*cj(x5L+)w)nBIt#nw0uXeJt)8Ed(eCI1s4a08eNBB8d{QD<0v88
z&Zo<&g0?nYpR3v|!fE1tga4>BqP`R-8=0ToznWFMLBMlmPJXDD?M;e^=@I+e#B@r3
z*cFz4NfG6^1ilBon$B{i7pXqF6x-JrLDKWRi7HDP&G67I)~}Ja*AuK0&<S+A85?k}
zX)Dn;y8L!IRp<2HqBv>C^$oG@@=~Bg#=x>bAwqgC1^)n>u<=rb!Rr~gcE5KF9lAAA
zWFMFlv~64YTxA#-#FDvl>pj$nW3>BesC4%)4H2!SBd|~@$ZrjNV5>=uxW(;5LKv)D
z)W%kpzzjO6X<Hd*pHtE^-;1QNs`o45$&wn+erxf<4}e+CQG8XZ8TJY;(LR5UD|+7Q
zdTW_FRCS|H2JsBxkkr9CEs{@m&;m5#>^el)LgrG7X8e3Qq3CW8i0JDZ3?WRr?GMF?
zLOUn7woQz3<@s|-#>B*M7daA!<D3a{E~wAwVoBzHpU1)KEB%dOai6ycFMoKjAj08Z
z#C4?5GRh%*dac*ISxl&H&}3j(B#d|~RNZ-|uuUVead>hKLM4)(z@oy?@$qLr+T>ft
zrd!Uj(_L78zQdEXox^xj&kHgl5USK?|1D~_2G1?32=klU?&bmGC4;v&C7b?*jdg`8
zTDN0-ea(SMmlpmr<1Mpc%4rLyR^;Y{3u1;IpQpp75n_+lT|I*;>odx%zNDS|QyP;n
z1rDD&^iK%_NSKV=wEb$j22Ac=eQ&iYCE8l4(a!MiRJsUsu_a5PTR<P>*}+!0sR}f|
z+=`5;iXx?izw{ESsqYC%C`JW3x;y)qtH3_b9dz*z@XACsCs%iA)Fx?gkErf#KUTzr
zy)o<ZghY<0RK*1_Z8a1@+e=W$@rq_&#lCi$K{%OOU$cgkXNl_{TU^CA%=(VMoPP9l
z2~WK8*&O346tT|~6RP@}ncaDPiYqm`c<=LWI`qkTcN%TBn!+!5;<WWg023umx^!sI
zWvEjFpC&Y{e8gZ@d-Tzd{i6umV+0S$h6xj?8XumJ2%W(ptLE&v5Ez~l_(7c~J7e*v
z1pmK6N}xY>m+Ini+j-=&@AIQ%U8b;i3+khg<sAK{sdx4ridbew1_0DJ0M-(@>BdC(
z+IQT%Lk1m@G{-z3$fc9)f9u!oD};5X7DLTY>;xMDi<&Wpx8+}>(zaX~lS-0|Q~D~R
z62+IPF}wzuZ7sNSx(pasKfqtW{>=0`4_fBrWD=Wre-N%4WVh9*lx_*?)L#Ho``#R=
zro_gX^<{bBO>OX5ULYWgU$n~$Rm^gdTwp2+h@q+R8t8UNJ=u;*YAI_|n&G`>>nX}Z
zbB$sH+F>HxnZA2oDxbM5dv=}0NNH@8HBzHj2vvyY#S6|(>6zqa#?PF`vZl}wlbaXB
zON||?M$4X}Q!DOrHWSQoCN7{XXMSf~NuX=>WHocsyBC8trEOuMmvCTTKeE`>i|x}r
zBSO3L1W<L{KC51GIR3@n{5)$Js&Q;U6<z$N?jvg9C3&U_J&)o@LYe`DS#D%SvM#7F
zHTew{-S%GnZB<6iM=^&^r^mO1#S|r-m7mUQYFzNfSlRKqy~&m|_G9>ZYfV<sH`PoC
zn!WWyKhRsKPg-0KsMq#vy><VyCMnzUNtB6oQF)%hz&%ggrA*U37|o1oHgCJREO@j|
z5IwkZOcBKT%htL?6Vu!@`_SuWk@^$Qds+Q<pLD^N7Re*6CLQ9>s3hN`f<9*EUOtsO
z>Z(p-Xqp^q-tGyqC6!-b$V_$cfVf8|Xp_RFXA9t!F+XL}qIhuu4jFd2O9)!RyoW#z
z<>y{5iiYh5oh5uMg|W9^IqWIn0e_GlD=RJ7_hsmau+eHU;`cM%eMEN-Iuq+&_fw#X
zoxkcKtr6{w-rRtHzFR)oy&C3gCOnhCtFYIjNIBnI<1&W&=4o2prncbUHq{L4#+_6S
z+(O-&eqo?3Xb-YH-w&EqO8O%`<Zh9Jc?2g|n9%fG7hba-EFQ8p39-U{5SxA)z`ZjJ
zkP{TrT_9rm1@79z4|$4k9;1+lIKC}1<+XK7?hV*Jo_ZyYZhY}2OL02;p=FwhsQX$h
zO86nx`Idz6thY+WOA$ZSj1p)p3svT`^^Z@y3_|Nes0Z4OIvSG(bSFed5nJ<gmgkaN
zBRu5Ksjp{>*p=cDeAZDiC2*0m=x2Wm{>3UNfMBq;`#KO=KIHh}!RvP1*2fq$h2g4N
z-}<KrN;!ir@2GVeZ4zDtQqU_K*>53hPv1TTyJ`mCQrp#`x?M^#*wp-C;`*20PYHM8
z1Aq$P^Og@8NbZFgvEkZ8#q4;B7xrTBh@SPHMOkd>_orHKvbaEUYI2whmvfz(t6}UV
z8XG5jF~;9F1KrT0j-(bUV&?ZG9)V#$u=4rq3>c#kBb<>p52pCtLx*YQ`9=y~t5tjU
zYELI97U)T!*9|*8pnPGQ`U3z2u2rBD#d;`IdugndV~Q0A*YAnh@6C`D={GmU{#2~r
ztN1ugl$(eBb7Q4=K{+?9%x%s88>+2Y6i95!)TFNz97aWDASR)HikjnxEs1}eqO7r5
zN19)m1wWlC@)h&z@T=L}OYOkV1v2}&9_1L;;CJG0LuQokRASRenC`Q~Ej8c9X$DrZ
zF_26`%B|FS>W7yZrKG=lv?bn}v;@6{E*$&68i#T}!U=uKAt<CPCMZ@CioQ%8{6741
z8Y4J=aOB4Na_e=tyJm*mk(*~@Jh%B5J9IIoJ;J9rj#nlgK4GA#6f3Tf&L`mqz&jMz
z8t^xoLD~8snrq8WA41)Vx=_I7VQjg6S(Sl+uEw!df&o+ITmhU{Z1h_ic#e6=!+lPY
zXQ_Kj^(K&W>vSkp#b?`nH1z%mWGtttyZfEEVR()?Pb~=>M{bgVq3)j-<TF^8xmQs-
zzI$F984ukZ9u|7d3e2kAuG%iC?uJNQC!Oc%w^0gIH#rNUcDnq+udB6|tZmHM?n&tv
z@{=NU1$lL<*l7H4OIbW4a+eSG@wW+HkHi+t@4;1(!gnG);*Zs<?=0N%Mr0wT66HH~
zuVwCW>&fW=g!rfF8WJon!c6#N5Ur_E*1whebvZ2}w528eu)Y<~LtDQt*ctDFX!{~M
zF<G{+K6EzF)&xU{X;AY9r1$D(sVxq8Lx;(Vx$S?=7NZ?{I`M@?5Jk4R6}dTAT)%x(
zOy>WZ{`zK_Fjml_d{cY(me*VcuKlB5mV<{s<v%2Rpt1CNUh|LsA}g#*$sXjTn|!zC
z+r|a*2J=IZv)i2|oYYC%#=<j5v#87d-^nX|9bp|1aajeuQ-b<*FD0<P#B4m()JC2Z
zjmV4E&+YMfsTHHEG_2fv#`8EI*W>tI_RNP6QqS&&urR>NFYtcnUL{mY<BZK{bW9(X
zu&P53REB=1&~2yfQ7cqV#<E3&04*AA%bq(E=_!1!N6RU<ztbI|(#%mPm4gv_gK9jD
zTp~Sd+6EFK&P_W_SHVU8on=7^et4j7Dx=fjcNQ+tYj$cw>8@x|O&U={*0E7J21^nX
zEieTi2d(~9QsPifDVk~Iea$W}`fF~5`c9zM5lQm3FrDD%p7kVcplI-LJdq_0OnK+0
z7TcN`*i5d`f=8qj)h02|L(m45sm{laxeds;0$<?zGv6<r$)l7k<gTH*J=rCccWs@%
zelvA8J+8t%y>hTw-v$PTj~Rd(hSgj;Lg9mY1$bwThsdTZb*}9>mt}S3r%ROdy8*{<
zUyGMHG7qW3$6mXu(Yga`aOzwr=0YW>4`8B+V6t&PT6IZ{BhaC$)pi$)pgABYhoR$2
zi!sT%6>0n))R8O7vM{8px~MYe!a2x+x7w0%F(KYNqE`{7Cx!3gDQJ>BW8kW=SuK(|
z0E0cWvJT&|jD^FVj1g^+gRN*@WjmUgB)^Kf;s)+EeGqTC7?C`gf-(G!jmfbgzu(o8
zYdBdx8t098LW9oPN%$ZM_qx+CTV$1heQq~6-eDN|<X|1;6)LNak9K$?f*Y?K*Uj-n
z87Kyzs$!VgR<0r3P7Ry7pwG?)(i{+X8;vWgp($v_FYM8B5l5^-zb$aEclvqLl>JmJ
zFgmEKNuW_Y4nLyIo`t-6`DeqYxP*2X6q|x(j<){w^Utb)?m;IN(y&0wyau;aj2a9-
zdm}9xuQ<<<H|Ekxug?<Q4A39uoBVi|YDuWkr1Z|yCFc>-Fo?R6&H^5$n41bH5NNw_
z<-O|i3oi=cwJeI5VAj0*$^Cp~gyiyJm)(C3e0gmJo65~=Y<0R{%|;1==zR-B9I0*n
zEMb87p%@S!$V&KhQ{3@uC#WHt-E^FZT}?M!#6=EfveBns>Srn<$89MhI4L%_sv;MM
zOPiKEc#0IZQ|>5_PWHldA&j6y1mh;CF3%pN8D<;}S(vNAqaD!r!wP}K5p{Ph%~(Hr
zEe8h4?oj|`X{aaKj{;T<PKXAWJ7%=hA&3o+?Vapql5RoiwME$bl}s=P63#o!s}kyv
zFF2{ss`~vW<NP}@xFCersBdvtsqJ!Q{JJh}J0EGCS$T-a$e5v58AufU?iMJeqU%vn
zQE`BpbB>-*im=>Y9mYZLhaAqAiQ|_w3b_7+%4uw^sk26%$$IbpA@r458sjL<Jn85o
zs~O?`0W;gt(ahkm`a@2|?^mplxUw#21hT$F8U`vBy|73}XIqnF*+X;^lZaV9s<Rkr
zb+icbS_cP=t`5%Q*B#LO$b0OV5w*tc_TG%a+q=={Se$KveB`HhNtd>WReG?I3a|Yo
zi$c+0oqT=FMf~W#hgFCwsW5ek7bkGP+f7&z2v8QwD|;p1!kvfl_-41_oHcS#63&te
zJ~N7@s}>Pl>I?Be5iWw&7E7BZoQx;>sfa*yDo9$=iXs{ch|wOJfGI8Mlbao%>$~*B
z|G-r(Q>sAkHiJN!!&Hw|ha^6Z^^~dr1|c+}@^HZcF9#Z@iPxlYM$MNh)w27uE=G`s
znSz&Wq*76lJzi~3xcTy+0^i%686V=jvMeaA1ePvpqloU*ogv*WXtB+R45`|0CcQ<=
zh}HczQ~0eJM%>e1=@ZO&q*XlP_|6%TU;Z4@H>{`EukAEzK2n%CDi(Ov6se^=YZ!^9
zFz+2Rc4uCAp>JD-D+w<`{opHy5JNBtVHP6;1%8Vtp-Kpmew}HR!H9>WD4hC;CLTz8
zDHKO(>Xgi5@ZR*u(=t4&%~7Rox!ykOO)ne;(BO^Cl_uPms?xZele`KP_pL4-S0f6q
z@i@8>NC10ZSKI9gjLo-?dT&swBs8(H6kvy=Nx9a0g;ur)I<U`lKNrHiS)%1!rJQG-
zZYwL7bV>ROFBeoB{^|mV@AC8Y6$jw0DRNI126*$0^*nh)jzfF_lE`<Ei5%{3x1H5`
z6$E{Ovh3iC3!?PiEmb^>cXC?Un>qC|^fz9SoLu?==rAgoHl<{RYGJClbrnTYH*%7u
zA#Kj|nCxQr5~@oYpP91qw@1m5#4X%6s+x;`V{P{7qjGLTHMH>i1*;`iV33z|eFQ?q
zX-iG#=}Am9@exjE?Zlu!(IsjyqkoC%_Xr=Crp1*`O$`lZb-UT_tF7<jLQd-2e@ZTC
zGVD#WG(LYuV>DJb&%*e%x%uNlID<y@dOt@`@t35n9MmR{;OR?ZbTk&?mCowIBvjK_
zP**2^sR;xEe?{`iXqME~sSO32UVndu<?Czk^QXaTUAc;0#C1X?mS{ZkXg4}qdrah^
zd>xPMTyfk>NCzQA<{~tBwdxVEHcwr7letSGWEclYLdJ^;;eV`($5U-LZ$n5Oem@Yl
zmsm>b{Tt7Q((BYQqU!1i*5EoB;JjGSYwV}U0N#`=E~~_|Bs~=s3Y`XPHotu;>|NC4
zhPqsRd=|Nw9PdYJ!67lY$7SlSLG|SoeuIm>KYI?qMlwLUQ&-N%&#Z3%7Ug(6=J4Yu
z7}2V2P3y+d|9--QxGvUYsmINb>Y5Kih))%a>tbL9#X!l+i{fOL*C=h#+@hO&Z+`b(
zS~1mab|Sn(zDFGL$iHW%!u#z-Y$7UZ*m|K`N^7TJSV{tOyLTX8pR6}KM{htTk53;o
zYIyU$(*JOwX22u#<RY;awh~i<^fkW=j<R3<NahKKP9V}WO^@wq7B^X}lV6)Y4Bnd)
z`Ee=BUbt+RK1bSbAsmRhxP2}NJ$=U{^_6<_d}1<RN<_T^r5Kgra}Z?glW23kzR?B7
zkEQZYbg{z}_s}unD64{IO(a)o%`5OUw`;1hCBsmt8DAsR!{$*?kp0@9M&qW#?==yg
zp{Gx~<!TsG&wDWa)}}<<L{poK|5OfZR~t)j{HkNY|GOUbaw{IS?0bJWq*Y{Kr>#*n
zf)6shvW7IV>;;>sRpKD(Q(klEMF0H92N>-t*{bLj#Q7R0-{yx{jraA=^o$SYN|6q6
zok~#we|*s|M#?PXKUf;ozJ6Ef+JDD4y6q2#XReTPMz%jQII2!7ws~_N4rh<-C#T$s
zRM@!C6@EPhV@u=fu!J_nPvFO^0F}mXG<tq^3qf#i%ENe5NJD|>4?`W7oY`4xh(qF*
z+vW~qgJ9^Ak(~<?#!{!TkM=TW_D$0JVD)Nb+sJQ0lUBUgC5P7>9r&R;ijcJ(iJd1m
z(u3YR$5Yfl_4jW9N<Ad`UIzD%a^Tn|)ZEIL3N%yI+f+9oU4GFOf-XKeg$xX20bhfm
znKxTFs|ard1?IRCZCxrg@`qtchP{m#oI<OQKVlG3iA!PMA)QC2xu{t0K9!<RH_p>e
z=Z~F}>MIW3Fg7boOOyH1`4<a};gOM^pndJjNL;%D)2f>rw-@@G3H17c-`5K@ya*W!
z<ywqT$AY-<aCs-HT`5t7HfZ@`oGr{?rjiW*nbLqEyw`cRC%4o9XqD}4dNH)v+TNyk
zu&S3y+jfL&BFDw0a@?6n0Ky{|H$6bf!}M#uo$>`c%gM*g!2Q$B!O;}6OxTl$lP<oM
z$Z~_uj|n1FRANprE9hzRxp{ePlbQ6Yh4S62*}c7W2o;VF4%<*?c8;WoV@DGcYF*Ej
z(k=6jX*op<mKgodGI>P**5X}soNSkiMBB!%-*eF^JxdiyOu%vxixFT*$XULw*|J?K
zuVaAO-?)1TcHWQFUBe8ndXB%wLw&8SKe*WHq!J!@NDKwX?LzM~c`^Hi(xWOl*gmIE
z;n4T?9D9z|{A7(|@*z`T&#&r)tG<`Kmo-7D%lv9IyYC7b_-*8Yi=TP3`CWwrKPfD&
zPNED~dr@)&9r~!76&hA*R_U9vUIr0wSXIYqT8c%)RCzf^RC+7X_;ELGxHMwob&)rY
zawqy)(Nv7t8x=4dmL6JLQsfH{i3Fc|?+)wyiRiW<a9jf=Pu&iW>t9Q~IA`TLhmc4`
zaDKXlPjObpdh!%W6Up;<c2Nt$D%>e(Fujt}WtdVTEg_W?#gh8I7*g?|)4F+ElY&iV
zB6tUML3lfUm80?X4Q7CtT#`B6rg9#E0=$(t$|qh{_0vE;GfTU;<ix|uM-?4aaI79i
z+$60s>86O;;=8vCMR)-@SBIIp=Fhc;>k@Cr(G9`N6tHM^bOt!*i>azqG+eAYfbXk>
zzSaTAV^WPB0-j%8(zT~B=vkvyVG4~OI4k-clFRfnv=tM~=Vaish*h40eMVKBZ3d7c
zX@BOD?-N!ZISd>6^3`UEjNPbyCptuE(nEH&i>zzMc$!(Hrt0yc9wVkLS1>h|*CU&y
zf;ZJt42`9_hn3oD&&igr+#^QfIF3eo!=tf<ZyY|p^W8}lQc~dc?Mw6@@I|<LRJcN)
z+d~nqX3B5z;2|tua;;2NYDPynST&w(OiO%%I)VaUMONZxsJCF_l~^9hc6+5Sxk-77
zvRT|T$|UZN*LQ8Sic)PGbIdPmMxHrymmb=nj7eF#Af9U2ln}2!K5cqgA$DhGvJJ2-
zew(k5q~w?%oQisKYH&v|tj=o84I0veJ<hcI>!X`9gY6H*;oN*8kU=owhhT77NpqfU
zH{;eHim%s#9?O29loY}m^J$&f)YQ^dK!ZSqh54f@Q-?2<E{!%vi{-Usnp9s+CYgN?
zm2D(!{V$@<fx8YaTGu;n8rx{x*j8iPc4IcS8{1}M+t!Y;W2><m>+bKIamM`-Ypglv
zi|627D*W+B$+8YVL#*9;i~Kv4LblPuu)2;;CcW-Mce~;9UKA508JWnIw?jR+#)o#w
z;r5_2Av>EMOja(W*Qyf^Lna~%M<z-Ht91IecL<X=Ptf4^XLd4Ql9--Ob9cN==KHdv
z-~3~Bb+zKW$)q$bOA;Lj7T$~fpj&D??Q<J)ErMfsey4)Cg<2`B>r^?xN_`9BCD!rC
zSKnHXXbH;yd3ksg@jYy!&WSE8WtVG!QnuB<BvaFS_n_Bg>O-052O@%P-Ab!P;Qx;1
z;D1~d{+druMTP9-<YYj9W6`5@+#&P)X!>gUH~zA@xw&-%5#a`#ju!js$mSKeL&o}B
zK_e^fqjv`MELB#)k{qqouVfBBS+k3puX@d6J6c@ruND2QZnO2S^&)h_dYBiqH@-y~
ziq_8B7@}>U8JOiQYv-Ac=?8;ly55otbBTr4X|+|>vG_iC8VIiRb^${DmDXO0R7_b6
z>`0E4Bggvn?R_%t*DO~aZOCqGr@`lB;UHeXQQeb*<sZmQEt`spVI2I9B*!=-oFBKS
z?kE{}M3p|tiH#6Ct!Y>FU+bYiz8tCx%RY_|(XXmP$lJuOUlV<6le7)OWeXIxu0h8<
zxTBPl!r10#jV<+z<~!|#zQ((W|LVjZqf_(2p5MJ_ngk_p&V||slfEj}1IXdXr6O7W
zf)K#ThR}bNW1w+H&Q=qqGLUN9B<HqUVLP=4@i|65pu<rdWt~YuRKxp4ukyBNwx1Yz
z9_46An%6LwdVtAQpsAkY!VWrr(v+;LFB40o-*KasP(V~-jdD^ALD-z3_pJhU_2>MG
z#&=Y>5d$*~!bze*f7jyH#-Ug4iN8`L5qFPDnLX^S_R{NL8tBF|dt~F6$56cx;Z6b=
zo~#e;69NTupW4S`=XFZH%ej@SH#gLF$&A20_uG0fOnNLs7B4?><ojWW{Eb=@1!@Qg
zQxsM;qrQp5R^??Z>Q>A3J&JZ{T}ibw)8b;|$(X51mz>j=ix321bJ~qm{_=3689T-k
zXXRNmMEA*Gt^OzvYHg6%+)$?0fvFvKF<ve2H-wG(v~EHZ`o~uF$DD7copLEy5@IrF
z0jHrObVN$9#}ZD)ph8717~K-i<rdcgr5(QSMU_vb{`tqkmY|kClcB`{9kFd6t-s6H
z{UKs__OwG8*<C>^$34+z>d^1Vx$5MTzuHU|!e%xIhHU;FFj5G5DO&}-rf3?dXAVH?
zyPB-ea)p%F^NikqdEE^^Wc7Xd#TJm2kwN+S@z^^BXf*fAJ+t!caoVRcpUBE@EP1}R
z_?NSRI7kwKKZAhNQ*boXWzj>CgL4du!eP?TnXK*YK_wWvo62t`Ry>wedRx*oOahC4
zOc(66@NM9%4pYT5`&3~PckA*UIf{VK)mArP@{`>E8JL&C*&4agpZDN``EQfBRIogl
zo!Yt%LNBjOahj&}zd4mU2O8afEoLf}u>Bvs<0jS*UQ>4~2a1W5TaJLUt*2ixy#KqL
z?mQ&}s4F*?x-I9(wChb^CUecxeKpVmh}kTnZ#4zHA8Z>*=yW*>;^;O<v72kXgW907
zg%qk>w*>l^cbxX512U0>4eFHE0$gpV$C*YXqMV=N_<xi)bSMkH^XRvnGYqe}ok8MP
z4svK824hyXFlq@uI0s*i3UlG%UNI#}hDC;lf>z*l-tDonNI&;K*T0!Fe{+yWW`>u!
zE4Q!HIXF6{@inU9sEOn9Gng0ZP`8=k&2h4m+7XNIo9#2&y^^xhvuwLn)rY~@VJG)H
z4CuE=<+;9V$L@2h3s2J%xE|B<P0zrmmeho_I7cC6ACYFdno2orPhp;^4g{UEmDjHY
zeIy*qi$Ml{<rLL#Bi>f!0;mjG59)1FP5|Y}Hg+|!vao$;!@YLb_rnC2q#;V68zCBY
z6p@5cAP<J`kyJvakXIeklmQIGA_1{Wv$Qpbn)rugWkk^1YeZ;)c(E5x0<|IKoBHB)
zdLphSN^J4h!iJhB4v-~ysZa>?Cs<O0yz%jZUj@sH&}I^~W{BDy&wgyR1<Kwyxx^b6
zR0AB;eUpm&M>+brOA?|xLSH%t{W%U`CunL@&@J`(GCA#9^7+Yw1rEFK_WDv&(bB;?
z&=@ie+nI^<24X`|U9(_CjR)+0;hJ9*J8nkFB769s&}ZP~t)AX~`z{l`Cor?i8Ah*m
zELcbf@V1dZ{Og~PpG6+2tJxMWVA!h_JDfE?8!uQaKtX8XE#oop^Dn~Qpt+k|W3f7D
z495!QmaVC7HCGGymLZ6q^C(Rw`)64qp9lw4srABwCu!C86J&dhae-6b;Y_7Xvd`1u
zL!(wFn~-}qX{2gYF=RfIm=CtnVYG}=7BBQs8vMgIgkeo_N$6W+)csp74tjva6y4$b
zB4jj7cAa^?II;+ou9GQra0h*0YEbariW*f+oo)X3_D~_4=bm5-?QhXvK~FR76~JfW
zYUo9oQcoMACXIKLwXELW-lym1?@8MmG*ho#k|YZqCw7pN;1iG=!EC0}mk;M8uBp)D
z<KxQ0M!Q6~V-ShYb>N0Ga4M3-fi;GbC8y^k3wz|t%e)ufpsR2u_Gqs6B@avpo4`J^
z;&gKc^?t$^ezr?+2_FTHp-%!2!F18K&s{ZVK8Ji@9I=4T%e}19)%@kAcXEbv_j!AJ
z`z?4j23EIDJ7`+uurO4{5b~%@X+B{)=Z;KWzP~;$6#c6(y%Hn!S|VqqH3eU4!RM~?
z>_H>Xt2MJ|-KMzx`b2nn>U)Voz3+w-t9VeZ!T+}i_r>?kf#37Ue;Vn3sbTAeO2y=R
zek;a{o&Mn4P513_$}~MPMgE%*c?`2`DOEOJQc_{d$Y=}$h5U=TMbKS?p!2O9(lywi
z%3+0!fY2CgqL(*>uGCkv1UVZ)5z9*tO<j@<EbUhBt6qnyx2Dukm(o3Owya5-6-Ie>
zb1l{5rBhJAb2=aeMNlxMV(AA}$<oJ1;uG8fD5*Yn_wXhe`8cHkfEa*f-SB|exf<YO
zVN~3xe+Jhu&vIsy#$iS}z+(Igms7@Ity$iWb}R)y9j({?+Ogzt#YpFkig4(v_8!uE
zCS_n}Q58M^h$UiY<0wex+0dD#>2Hp@q4eLR9U@GZ-Te$yl6;CM1n~F^mjoh*;@^c^
z(KYb_1Oe(EVB>!XYn+L5T*u>-((5?UH6c0g$K^BkN3sGyo&d@Dg8JUhHaSky4Gjok
zzO6>nG?vs0y7o5gzF$d@t8>?VZQ5aQg`W?WFg=erO9P+0*5V86RQQB{AzgOy9jor>
z!chJmh}h<`2ku}>&@7g~!OS6YBW%#GVsWT^skR<5c1u>|?!hrQy_>DtWLwT2*P)mk
z)#itowC+iT-)tGcoy@vIj5y{=K}yt^WVJLef%OguCw~Lll#Z#3sp<jU?YzX|eKHNo
z0ESd0pC?P9ht8Z6os6}ULnw3-@rqq(ozV!O07pS6dDCR`olc+^D-_{ky9P3YVb8V>
z&wW=8>Q2OcbKu9;Ewv#Np!ejKqp8Yu$_DOQD1>#9R$d;IBXkQ|gIoXcJF8M`fc}?)
ze#xmK?jORn^-AN*uHM?C$E^C4F7*!`E4k1FLl@b)8wdjHw(0O!+t&9v&i8z$sh<_<
zUjk3%_!{&opp9hYFX(ozGCwxmHAUERW(>teeYF+tedrJKweb9r$peoD%$!`p8DP3d
zngC*jhp}FIHi~_2CW5BZpKsIYbg`E&jjcqFk6%kD>yZdJGn*qbnBI8}{hkk5jR!mq
z^D?iXewdEzZ@5oqyG+{G;=1khLAZEX<74pmfE&p$m1M!R^TuuePhW6cM&%u15L9c(
z1g@t92yd{Y%W6Bt%J;5q6w6PYv~GFn+EFb3IGI<ODqIbKfI4_NtCid`wl;mN?fsz5
z^L=Pvo#EVf*y_?w$+;)DTWt_ul3Xr3lDIpo?Nw0VyI^<C^PlE9CIsWlN^ZgJ95l1~
zwJ68`{^{DuR|#>ok|3|ND7X|Fwmv*jd&x)N68z6)I3GtgAHav}f^uY>=xiH!wnb|K
z{-8^;$zQ@BLDu;9|Etpkd<)zchRpPA2)*rtLZMmM$qrG-U=_ERO#cQQVU=qO-t%w4
z%uYHI4cSzBWxgpKMm_N9LRT}e&-L?z`R7*>U2va^vS4f5-I~EX4$(b1^-8v_3pKC1
zD+)gQn1O}_zbNErH*{OwF=R^l(Fm_)npI!3jt8ezYlbFFeS9hQ5SmbH8*wk0^i)l9
z<;)44%g-Ug$NTdW$PQz)PQ<ABYUqBYbj)<Cgv>{LqhXrfV&Yzj)ii_ex(}G+%XDn!
zTkY_(Iw)51_Styo+~X9?G^9$G_<}kgoi3eK;HJ`2fe7b?cr<)VkHqo4klGr^{e1YC
zoB60tmrzi3`FHs~d|0b`3#i7;S)YfHmMc=BpFEU9k_t%o%2avp05v`c3bl`92ABem
z(>lyojhM-w6}{%Tdu1-v@G#fth&#@8ajlbI;e%}AV8_dA6113*n&t!beSLnKSn)QE
z9i3a-ZiI)iy|}|LLlP38UBZ@ir#$riY=Y#O`oX};i%HW(TigB%P;y4moVddvtfB$M
ziVGP<31QD}9?Kl<icpbRGFFUNg{q|`APrEe5mutA-y5f=%P*OF)w!2{J~7$L0g*|`
zcm`u?ENn0rAd3ymYj>frv!zIIVKhs$Bum$YS5BT|&|DEts$+|N@xfW<oUrj`qagAt
ziy&g<h<VM!KSEGi9@@_4c|H|lumN_VLe8HBu&MH1lv#OpeoMM3@N2*XbV%%kf2OQt
z=c{W5S2ycel5+2(aTAmLCC`c~xQNYKY;%?9)XG#Q#R%?7R<ui<%<dft2#qgJbS^oR
zxAVG>Mvc+|VTk&u+v((mkNmjVAyW4XvzmmggV-<aSH9gz4Ze<_)@lpphP6cc@hhCR
zu>=N$qeJ#b%5(*F3?qxm8GZPhuK*zw`<z`Tl^NX@;RvUfSJc(th3ZSfPFcvhjW5;B
zlt^0ton$97IU3~)xE-1~+$m|QF}87n2DrB0;MhpznbTb?Zw;IN$~S6oE!eI$ED<Um
z7m&c~U~p4Q)u@Vy1a16UkiY%6peQSkl;fRG6wJg?ed5i_+5mQag>VG%xE%EjYr^(D
zAbnN)$scsJI0R;!Y238fZ<fZs4YvE~>FI%g``xOA^VOfg)1}I#BbVE69yytr78q?7
z_&x5?gC`(y#{cUA=*UAf11wVys&bxMg1_KCc&5Onp<h<z$H;uh$lj@hEaSYZ($d)A
z*NU|BV&;G(@&4b{2&^vsqYzel0&B6V_=_8%)B5|1rT^iEQnc9>Y<a9G+qz9hk^ghJ
z@1C8({g-Ct|9INJt@VGe+!RZ}SYA*R(ieRHr`oLhmS*Ue;CXkHLHENi8+YBi7`_SY
z8CEITwZ8&i<FY*QsKcPWvY;)sl8*YiyY-T#H9)m!l|d!58x>_EZ3J|IynQ*jn$%Z5
zE=t3Byr&1vVtpSW6WY_WK{i2E_yqOB7klN{n&p8YNai3R#u}^?K51p~46~!FWEU#Q
ziHkIpb36I5vbb*$os`UdLQ0oZ9!OJX_vXWU=}oE@V#8}WTfaSgqyYN)g?YxrZ^s+f
z0pS5j*X{W&6;Sb=N^L3W#23^qWbesg%_JKNZKP-t9y%g95H){I+fug)pC}mzS8Bfo
zxD%ywIMa_kJ<TlP8|~~N2h73Qa6w$w>|Gr(5`X#C-<CkiEk7GJsOCxIQ^ZE%JNYN>
zGo57ys~(qoJ27q`h$qmZgc#zZXz*9#8f~I&>5OH_nV8)Hd_s?u*zcy4#GOuj>_V8x
zV;u(Nj|epAwsAZbG$E4M^vb#;5gd#SW(i^YfK~eT;1DmgA&Z_Wme?^HL*wcL(}m=Y
zaMo691M;uj6i4bZ7=4d(%6=36oPwLP$kZsP6U@wGs&C1#?HW4zj5;Bp-UZ{^7rKD%
zzU~x7XM2$qiR~?;;bb|S=wlBJGnyJyldqawx~p0_R)>}%YaBeo=n*f&&(8p#CW>l3
zMCs~23CP6+M@;izGZ}K-&iRFS>O&}2ftiei9}Vo4QVnvXd|;T|*|D4X#HJ`&!S2<v
zj$HNMhLSot1KEjWkxUD&o86?ykSeOdUmfx$AQ{{zQNcD&$`lre9%qAJbw25dD0V)J
z@mxQWvr?t>@%?#|0NXfiNS5t8=1%Z238`y{{Pvno8NaI8&8emL<NPs>Wzx$z$J2Bk
zd7V^Cy={e@koo;EoxXnkie*`8!`vu<NW?3~bZZ=!&4z=Gow<fO6wt%?t)P~*(Jm+c
zvbhXGUrnuek1a1doAAuit|lDO$}o0i1vldNZ-8TV1wB1@W?mvEW1n41?5{H(#B`iu
zK9Xe4(AlN{rIEIPKGac(E2Gx3?eRwVz{n-q9&FiHp!_+k6*_Z(O%dgPPYf*hK*tMD
zv@{iI$^hpSsN4TSEhrS`)U|#;mUf}et~=!VYX84eGx%d`h34k*Jt;|BG)pb*G4R-h
zc^d4%P10rrHKW&3%V$#5_Po$eM(Q)sY1SlzE2qUW*ugL`9UHN1r}{+Wjoln@k9KYM
zLuR)3Wji`9ZrR=mLdpXn&q--1*WJSN+8V@mDPSb@TOfuOB=U5P^B@eo^Tc&L2uq7z
zn4_=B25HjlqmENo@Z-C@rAZe|rW8-;{p}oz6|22tU7Kjqx#dEO@9^!Lx3^rB;p|q_
zeNwmso?lbrMY<3bRPLpM^D17w^YkK(I^zqc9rUaUpt13F0cd{KgRY&Ij^a3g#0r%d
z7`n7kXrbM}4}-r-=5A;euuO!o(zdWpIXfdHd`ZkA@sZS~4#GSftaWH#CA4*@^>!#H
z5k#By#tisnKKnaV(HbDZF^d=*^!@s-sQX~AO&AY*2SzEQG#M=c-eV|_7qGyybgitf
zO09h@nvGIdU)ZBuw<#GF+y;#bZ?CkR3ZM;~k;q1?vStoktJ%T%B7Y+FBohl}y>c_5
z-0zo?HIQjTQh)$%FmL$OI1uULcvkZn9@?bz)+mTh3+Vd}@DpijU#3Jc6wOes)hS?)
zrlooPZ?_VoJODP#w1-5217+FR2ms8UCiNvWx3Xf`qkeVNy#YgZz%=xyMs@#_u;K|6
zXtjA(M1o8*VBP9azQNJEo}alnZqKxx`2ONU<$3=Wk%w|kbqm6=x@iy(sOC?R_U#*E
zWSW6SYF4qKjp;u2$=TUA)BN@~gg3d)%t?zZ;>^8KB-jO!cV&NhK{X%1Tg}toeVZ7T
zkB0M7R@7XkV8h#Glp`rD!GQfBU-PnFYEIm?e(H{vlK{a9j3~N;^(qtd((KF$9cRg8
zpz5$H>j6{%z$0$ii%6t^#ZTKOy{nEdhqihzpg|>y7v|!;{^Ep{a!3)jNMN2;7;EIY
zQ>Mb4K`tV#fZwO7qYHnIlPMZL7~WfzNZ!>5lOeX{n@DY^c^27|ucvLg?f0_y;IT9U
zj?Cveq|*jy!qLg3qkpZ%PQ&O<mKA+2BWHVP46%bGsK;!%$*JadC3E8hF_YriXR?b=
z2yuGZ*R(vQgDhNOgiMQH=ukmJ&m{t4TsqK>_ioedi+-HFY1ndQ5mR~QS!#l740~F+
ziGO~=F=DlS)ARr1N#P;SB@Gh0+w<XU`JL}=XIGg*2Dv&u{+iO<+;T&-XyICeqLoNJ
z`AJYL-PZ#$k^27!;tc3amVn6(1Q(W<-woli1H)9_<HOaiCdLk#3j4;)j{K;HSqaH7
z)%b`zOVL*zG)whNq8L=8<$Sc1#?d{m9cSh*hdG4OZ|y_b*QbA8WTfPD4ONe_Bpnl7
z03Y2N$ufRA)yaok!U$#JZ=(7XUS(;Bd1$18Xw?`u<YN-}e@R@_%L&&szL$93t7vbK
z8bC1aksQ(96m^;Zwffi#%6ZT)5ZGz;F>cwE-q=N?=U$VSnEaXyfl+t1so-^{2v=MM
zh#Dhh^8eZXECjVyBKKAz=I6=xN8iI8Qlijj;kbGG{&iSrp2AM^Okp0z0TCUi#&P9G
zZ%-B-pC2vfXxCc;cf{X64EgC$PAc&RmAO!PvWn=`yl|YFQlB?1haX4XuO(edS;d}3
z`ft0#ACk$we%3K<yPmJ1|7-<f%akPcz$l|6&}OGQ!n=u+id^4+wD8mH3z<CIGs#Sr
zsE7#VBt2`xg+*{4Te(vwPpOeKTWKL22g2Uk(g7?q{FkBg1S2!~4yuohJ<XooF4jus
zog3us&Ytg%TgNMPvAEE|^Lj32fRp7H_8v0Pb$>~%^OY~@dvP}jk_OwMp}KuvJTxDF
zs*UM9`Q)<nygyd|snpTlq>(N^^gwxz3E_+Z&cOlsDxq+$MIbkd9VoCB7%Y{t^W=tL
z1EPL%4ybuB06x^7VJ!xDmh!3G<g0^yK3pom$|f-8m|CltT>%L`TMzy3*%`{U7yV?I
zL(;z&9V?BsKh$Z63I}VP`KrJWuN#^4HG69}R0sN>dW$oL_UAazNG9$9EdW0HRPG9Z
zT&akS2Lb0JVP8G2kt;c==+19|G|^%lV{t~9EV;F3*hzoZrz51?3WUzeTeB3=2sm(z
z#B8chZ3FP&<UEECJeN{*e-`yAmir3NTw+6K4y-pNR*Q0xWibqFX^YkK>W>_$d7~W<
zD8140!FWttltyEBFK3u)MmJ-C+GXO!1qeh?ng+s`IZP}HJ4)=GCf4dk5z<#C-TJx$
z2D^(5^TW@XY6gk6C~=wgppcF{8b_L?lH;N#BOjC8F=Tfaf&;k7z9szxVadp7ck8jv
zY98k3sWEL6SH#zX>uP_8Cv5~eurx2aqw<H#HKX@cF-P+7LfatIBt0OBgRqN%!S{vZ
z7k7n0W_ErVv;`VG46IWIuSUL>*pm^JyyRMnl>#Ktbz5d;k$p&TZ7MS^2)W7@;sQ-`
zL<ghU=+8WGJKPiF{CGJ87|AfMdN;B&VjEJZA?4f2sGlvK%@)QJUtI@#SCs=BTAR=_
zmxodH&nyB}nz~DP#2e^aaqHRJE1jKMAsbb`@Nue<ZcRG+w=?78A65{z)fUzfPX;G6
z3(@3e{NiB_pjtkr3>nAhRGBkThUKu|y<;A;hZnY(vT}%&efccLzsl`VJDbm@V#vBg
z9c5vP82tBhh@&mfWHq^6Ih#`mPKe=tFF>Jhwf3TI^3o?xMGW{*@UvhG<9&}=5_}(6
zOyrfZq1({d+B_8h0KM5BT8(e>3UY;~rD<wOu;jT+EAC})EiY@rA`@EnxHshC_T4*y
zAip9+6$469J7n#daWDulLgh6ssGGD`c|4>iux!>2;$kD!n8Y=2C#J{MKQ7b5`1DN*
zi+B%|<Mhp~PEIEDf&@J4CVK=09ob2W+4;|<O%=1Vv-zc^<x?&qI?blm?yW}^2q%T)
zGFYWyQ3wNI`Ylsx1-h#C7fs=mbaakg4-RrWJw@`h_wCoZEvA55Hg2&@Z?BY21B@k|
zS8dJu$5u>v(|b!GV>nBV*z;|FVUWWbq)SSKH7OXmu9N|7hzj*WUB)@2FY$mXZ($~a
zP}VrzO>U5EtY=%bCcdPcy@hX}u0koEF^t>P^ltg6=BxW}E}jUn-K9|y{$e$QEpk7A
zW_zy+9b6Y2)QK`r45xlFNA~ujP~-C{lltbeuwwtn^~#kDjC{PEJnYy}WC7GnrAhiU
zT;4%Gzh$uV)Qvv~$KyF}vA>)Px8L4)oN!HZ?yPA4n25qx0~_KJ$d^jk1Ff?7-?Pn*
zV$fJ0KeRggM>c{v?99xvTwZ4%N#qhfFnkVD)8JOrYwry)rTijWUfBRg>$VWftK&<X
zw6d@B{aqQgjq*EQql%Ob`=g*K#MbVx{`Q=<^N$$2^@a0watOWFnQrb*g%20~ME;Kj
znt-<l-^w)?D}sR-!P<=m#WtsW@*OXMHPavXI2K}%FKxsuj_>_N-}f*M$hdkO%>W+j
znYfyxB9Pg2_F#ii2K0XMzeHI^7APQnoS;Mlzq1d&)1DX}pIg=2!@2{l!04TypPyYt
zpZ*cJb|{$jyMn*&e|<!eF5ucM<MZ-ne_u$uv$9~2`=)E!ZIXP<)w+GBJh-}giHc-@
z0DQkq1mH-xt1rn2p>W;w4NP50hZB5N?R;9CD`4gC*K{MI9ra$LKTNrAI5s-Dg4A;f
zOdoWRcL~WP5#YzZD$TbWk2ZH5wC*B!i31pFJ_B7vA<W>$KfCK?D|VQYfy?Szzu&#Y
z!?Fdb)X)o~Z``<s9VpIV(0@PPt7a}UQQ0T32ADwCBJ#3OD1}bV3H6J4Xs8$I;a-tH
zA|eE~)ck?W2V*DV8YA<U-smPH94494qVa>2ruM#G&qKYpSsW2O(f7Dd1JH;dNO_^~
z4wh@jA)MeJdC=N^2qSQ~u;i)Ri9w+?r^`XjVmy9@(7OOKX$1ToG6F2;_|VE&XBLY+
z$4SJFMdoeN+cI`MuM;};dE7Sw_b8BzoUC$Ar~(x*OGyBBK?jlP@Dpkp`qU>W>96Um
zhZlRW)a~!D{7z*R$^O|{fSomc<W!S8_FH8~20=3At!Z>yP80=F&4U6}ax*@&)O3`%
zdBFU5!`FQF{&SQ^BMh8vEX%K4eN8WaX;3^}6$pLoy=9PXLmYy&oisvjsnajvV*b@9
zyiEfd)Ei9Ev^zjWe$%hykUnO5`Ds-Q6)H30;vtS@5k4|ywTQQmDn7r6*L($1BmO*d
z9AV6J;=e(%?Yi#lmy4i6i05lHAD0i+o<^vUW4{%Na>iQH-JuskoO??%<T6OtTii6$
zrYkgcwEt*)Z|P^*Lw@#w5n7GWdUF&hVp-C#!<?;sn*3to5*Q-$b@I^%u-%Fn8Ro`S
z2i+uOQTT`B=h!bqGu>JHy}}e+b`CNn1t8t<t1_)Ur(*<YWh8?Eo?R2dP|DK~e|Cgn
zR2xajz}-E_MB)@~kN48EeA_XK8+07C@cU0O<u2mQ<Qw)C0yk&cs)_gHz`w^DKA0VE
zi}xCB!!sYdP!j1a-tAo82A_|Q50Sfr7N9r+=JbRP{_*>_Z*`F7@rivmf!KtSC`B+N
zduzqPDm}(oYL{!&NIcg>01QUtI`Jz)bQ*);*NB;+xjhb_d~V`z04sev&Mi1%PZ5ya
zO0nPv=<%Zch-^U@!7u?0fWuH;Sg2y(d1oLkEv-({3Gf%vzP%&>5Es!G5j}K)5$V_)
z?>~7Qck&~TkzClLwzX;CefRB3bsO!w-)@*tg!ilWhkbz|8`^ujKXwEpEadp)8M};P
zLqbhjHMxEZLis=4;AlU*$2Hi@5C<%+yu;1&{f2@=8CUGBGWs179y%U!V|o~LH<J*t
zKU(hb@bL`8|M4(u`m{o_1^t!pyTXp>pvYlz1jfN0!nAle$Jmzb+R%E7fby2^EgEbM
zGONqW39HXaJm+KFzdh8oF)rD(@me}fsN|VGxFj7@Eg6wl3S~D#pLWcg3UW?HF(CNT
zl@i6st+=;`94FG<`2^+p9@DyZk**?l@ih40bR496fWoEsQ}TQZs{}f@r$QxNO4-2y
zWf>{185sk~`^3^xuhaPF(T#92)1jOvL0|KOSef?Q4aWDcA>_F36C0QJ<C163DlSjM
zp3)5$uNoKYv9|*oiY<PcReo#^h?>H+bl61~B{7CL6S7=X(&Ea;+fAnpRL@6-8M8Xm
zDeIqHN>ql<zYbgcO+z<?Tr1z4ZsLn_)4FeY4ff2bm&c&~*d<Z>5mWB^#!VimU`&pD
zyslAbzZ>^VwgW$I781tD$jfCk=%+D$0w9jJ2^jj71)%y!q%)$MHq!w8yI>j*2RzD`
zFYzDm%~M|pshzXU&&D>s@Pn_`QdZW-M@L^r+d+LC3o3lC;L^$@o7!#-m3phB<Nq=c
zd|(Bv&aVPEF|mVl&Su{7pLT8yMehmr%K*;&wT#)v;)PLU0{}>A;z=ws2Ko_GD(RIq
zHDw9&VG^snU3ve`c0C^wu(z?Iwtb6y0eXYUB(Ruur?ld;6blgx6hJ3l9EWFxHCfOJ
z!@^B2$H`55)rL0b1PU4OCv6ce4r3dh`lF52RPUb}0V5e-(2(Fn=G;Dl`@kFTaWZp~
zt`hkbg8)t3G|7_HvwHhYxMX4l(+BEMJq6YbyJkZwLei;=Hqg7pLsc+me38{KSNGPK
zZ`Lfjffi49RPe3&qw80Xmv)D0&EWCk>y`az$8ctGH+tCB1!2{rhmpXMN0AnZVf)V^
zZcjST)_->ob8FK#8d6347Zd@CL5R1m37S;mW5d4hI}SQ*lGS8;B~yW%4-Hk$mLLsj
zKY1v*Ctmo6u1M-WachkQMfPng>kY8RRDk`%=7apE7DHH}dK)qT$Q-bo8R(3ZY~EY+
zTnfuy0j^TF4vM17ma7!}Qh#4hsj!aLTuLrLWNru#U}Y7dGN94>6vk=yk&2fKEJ?WX
zpF+o}a^=10p%w3r{@R=wMVgaU**GMk`+`cJ%aJt_Q!_1(9$beY`Iry|_dF=aDHl3;
z-r9Z#Yb3+AdnsJ0t}mBE1aaBQ_&Y7!ADD<$z9cp3H7LgGKM~;pHO~VKH4Q$Tt~W+U
zLUNZ(c0l2TQxl2W&`-g5B4Yn786wOwu`XZ9$2xat!hoo+a0#$G7T))8?f(V<5|#wS
zB~}zoUSd?`rsZlVn)ZxHGfZek`DpFw1Z>{#^b7hjPx_JseV%S)N(AC!3eWtk_(EMJ
z<gz~k>z76@kbT(DCg{xQBf)n|6|m>kMq7~K`SL_%KY4pou9Zl#wptBUG|Sn2aQ6>#
z)lfUwgC0fL_Na)=W?MC^6Ea`n+D)^kd?p$(?7I^+12XH*zR{ab{-4RT6VI&xm!#k7
zuMLxVw<QtRmFNTJqRQ>bCZ-`6{Z5W#xDHCk=#~(U7b|)t8x>lkBdFSh6$>Tb?%<p%
zx55zkv#PF!zmXbbMg|WVKW*+2&Ne;;xZ-tq*~ylxoGtSni;*rPw5d8d)wfrxYYtWf
zA(7cD8yY&qj?yS7j3VEfb>GMA(<2-&{5o+xtG@H}6kAjlaubt_+aDUe{7(E+#460q
zaSqG_`>a+hHd%0U3SdI{2mEXBtaz5@sUI{e0dO-Igq8Wrfg?SZavas@|H$52CEk~o
zrc(|*bK#p%?YRg7$+|Yf&g*Pdjv`loO{X9={J;&zpVR*q)Y#Nndq;|^ry5LTM}&H3
zDK;}PUM?^+%ny0q`A9LITkLzgiem`uc4vxjoXIp-bau%u+4f~Z!09H3Hd0<{dEO_l
z$gO;_us)oa0wy{1=u^pYT{M+Dk6yG2XSL;JZ0{=2lA=ruula;lvoCRQ$nfzA2#EH)
z9O<?nRR9k`Sq>qv_3s!o6Cd4=LQ0<KwEpfl=+DP&imMYfKx0Z~q34mTE86xmKZ^7`
z4l85aIwLq-^X$Y-DiZ&PP(9b-66g13&8WCIy9Ek8vy$xdgcR3YuoKAB^SQ<T{-q8*
z9o;#=s;o%FNLO|4#@jaM50_j11AFKY%=%OVgl{&!_<ByE4chmg6MPn0zn0M-=2w7*
zGn_WDjGo-$R0fzqKbDoPqx6%(_yl~<Um&-}2S?4s&o>K3PMSsF;C5^i-Rk_O*;t`B
zvZK;0Wd*HOvcExxZjEOL7*xa1B5T!&-;TE4^67d{3?Ka~>Bpa@rJ-}$+AZu}(q;`}
zZr4i4+9FujS`?dr_m*SEZ3cqg(;oHg%dwOzqHtxTS*6#f2sPV~zgwlPmj`ymJiO>T
z#hF9n+*6@R|87#Niv)Q}Bsd~;*1h<#jBX^Cr%Mgb|D(AjCMF($Ywgtn9>Mj)=4(w>
zUjdJSq2#GW`T4?}_8T-pZ`Z+9x~&SQMt0%+;0nY)lcBo7``Fref5M<U7y?D;sd`g&
zDXwtRAlhn{1gmj%jHK}<%-1$L*C*ptL=+;d>vyV9pt;eV2O(J^uJ(@vhOd3~i~8Vn
zj-&#@mW9)&+`=sMf2ygjQxLf0$Y`qvdkPpT4u<Y`m)B7ZLZ+9Mb!qsm#Y26r$Dm`M
zf5Ql0BbIat?8V)AIVu)e?dp_rj<hZ=tdQn6*5F|8QxChpPrG40Bf{E)pADoExF_^_
zkcz2o{3ABLmLq<<c}hQ=UFzz7&Xmw}E)&tn%2ji05I1FVh~?TK(5~h-5NGv6o8b+H
zQzk#JQ!Ko2i(JHbWD~*MJp|tQ32xJn9;ReP4xl`If0G(`4KwY12#Dt-z2dE)3-syI
z%fBF^!dgOVAkeuxL;sc)MB7|OjDNtmFe)*Y?_qdzQyeGS&E68{1HxiH>|v@#6zoPg
zRI&)gOdBMmSBe4{Mvnq4<-?+_R5llKj1uhx5vxZxIxtJkxGrhqp|sUi^deshCv!bG
zKYA`%xu+#19#G-|=}SxN2BRC%e0kOS)CdO8d5dN8Ph`h21auA?zTs`6-wr*BaD9?U
z2n8V45<)r=dC@3#;HyF3jkjh8?lpvS9_$?Bzjlq15YePMu+mZDyeIO3Vg>ZFAxcHE
zkck4wN3=p<;T0-u{2o1G$Up00uIZ#OfaeeDX+Qch``Do}Ar!dwb;YjHci%qPp|H*J
z);18!I!lyI=zWS#O{H{MWA5BjxR@@fdj}NVkzUrB0bTENaIryF7XTC-OCax9bCHOd
zg-8G`4j=OYt?7V3^-6yS#p%=SD$6a$%f0izg&(tpmn1vi2K(p+M%8cDGBm$?Vj)?J
z#=R6m{NkgWz_6%ZZ}A3ss->sVx6}Lp{fJ$^wWg><EM0>51?Ry%Vf66%G%_+!0rw!0
z@wyb)Z*Y5J4xc$mBmSFyeg<pp+~`Z`nvA2(+;={G!IuR0(a@&}l5jC;4l@P1HE4Zb
zKtN1=!`ROZSmm9@HGO+LRAeI^?;h0eKCzt@4^E!jUoz0}C|)NjDcTWBSB#rWQ1SQG
zuRmo;Gg+_Uo=9N)4@KX)3!X0+jFm2p2#jw$Khd9Z1vv;|kL0(}>`AV}BzPmt=gtN>
zxe%yks6$@HFB@c5wN>C}yRfOY&mjC-X?o9otPrl?^mK&xUEMPuU<Gt#r0tcks2v-4
z)cF4;pbCIWdBgv<D5W*bg4Itigr)sH7qUsx_na3#)EDlZV||85WO<9Ug}=<*NC^Rl
zW5_R?>E+PN5;)T{41YoP_N#m*4<hD_(CKSdY#ZY|Iwy9Lm=^b>kEG7qe!dT$QPlet
zF*Yvl3M`b7?q;ul?>zry6>~P-0-1w_MQNnWc_=|27+iz-#Wurlxn>j0g)H2H3S-Ng
zwWk%)xxKjL2|NXd0n`m{Ns$v_5SoU>xlyjZ-fV&*j$_g#bL0qsIrkHJlcSLge3bhI
z$0a_8u#~Wu2iA7jpu`{HF-%JL4+4qx0@AnJZ(1g~ZD8Y|Vq@$|^;s;wVOHFpF?Z+A
zaLeIP&IyC(-G)JK>Z)sKIKxLr9D;1_3r}R(K@G&Cd}2;gQB#I25O@$Ndwe>_%a1Ph
zG3xS}%G9G%vG<p$73wS!)y+$AYwJ`BHG`BNIOf5d{&E(n-e7*__PikA4(7Y$EE^lV
zAjx_%N59!F>BiL_i!0@kI$2nx^4~v+x@GllMNE4PS2SZE>N7}tKaTJGfV!veaNS5~
zQzicF{dohy^JUBSeT_hP6?|rP=3+>n4{2j$BG&%nY|H($P2d3RxF<L)c>Samg>7Yu
zYp%jHedEdQ`p3=RZ~<MkFN7xzv25Lu&(QX-7?I}l7IG>yK){C_?!G^Em-yvZenz`j
zhs-w1&4XEWrn#pw<qK=ioMbq+esLF4A(dZ}&#iyCg|ZoyI?&<ja3cDQY|?o2G$a!g
zH@U$GO~LRT`J-f}r#TKWi<tBWz|jh}m$Vz1syynjGp&*0F`z}1jRG`fe>e5-zU!}j
z00{daWb^Mr4N}s0S{Q8}Xlo@yA)Xher*GLelZm}{OKY1qQOoD^o4JnHbdEB%9<sj|
z0ekaISG!_(LIsdOy?g8B?nfhG`teySH4s5}bak_JZDQwKm;BqPly0lqO5UhlYOroy
zynW$J)*m8eKi<3|Px|qiG`IrK_m#LJ3cunDBn_g&8TZ{21osveyDjC7T*N=oRwu%L
z^0p^ZqCW6V7xG$(N4bf&(RaAg{EB$o_6{;Jv<%p?jay=?(5O!rvyXfgg1T99FMfXs
zo5|);-2w-?5BFaxYkO2MKEPH1-7T;~>F_kXb9p=|=_lmMM#(gbzK>7We8a~e$w&U1
zjcal^n!VJH6opdA6ppMc(40xr)pd{h?l02SKfMlLim`UMs8?%B!m9!t_9YXD*u*D;
zk2*%(9=(jBOP6W%?NNP$9tX%bVO^`zRhs@>(URM1IzH&vqd63<u^~bn8;qlG;!(wi
zYQT4<3jkn`Y9G#X4Y~$l*YG8YWZ@wA&}u@mu%!cn3fuX^*jG@Z+uMAk&9U80!4NNd
z$H4%V>*uB@b@JRjGuG?-nk~n&ndbyVp~I%;269`ehK9Xyv(K$+hfgHQW0lD`%J*O2
zQ2iX^KYfzvi{PNg?9z}hg&c4^U(YaCGxY+GcPWl@@#)?56_nQ1oeMVKAGzEijVxZU
zPcZhVb^&T1H(U17{K6#=z$Fs6gA6F8ZQ08X=ltluB)_NlTL`Fp<7(GZsU7^f4OLv~
z3Z#w~bmTUe;<*GB6O`Y54_jnm$Ks)dCd-?VP&zEjV@Bms@;=fT^FA|?RZooq-16&p
z4MC|PMWRVyBcnl$0JQ;`NbF=}w23F-M*+X}js9B1)J@EjLjA>xs2mO+Ce2M<sb-wu
z(eq4Cvb8-X6w3TE0oV8u6r9;<eE&n;>*hXp7D&G%>E;q#uM`LwEhuT>zYAkzE#{LA
z8N<8pNyDYsP%tOKoMMXhGoX@uZ@-|x?=A2xXX)^<W5%tVy+r+Pacf~|w#LuXq5hnv
zT$5_ByRJP#BMmR^9X*RxUKHbxs|tSgV(=2+hqZxA#Go<+XmQ@0Opm+=YHz-{@dcGT
zzqTJqWD~jyuNEesLzjmkL&=73i)bds_scwKiTwO%cG5Kw0YQ!3skxp#yNW)sM}kDC
z7yQGQ=G~$`w$w@_7dX6-7&Z8(qm0O!wW8@LWli%XDRrVPt0uY{o)=H<v6H#C*xSxO
z)qJOiCd7V!>1O(1D^FISNA_rXzECTc)W@nt*Q&B}a)_HG#VK?ZwXwPom~FPkV-<N7
zz8m5aRNO8m)z|RW0hznAUwGmI0l%0c5RS_;v`~)r5#t3R9|x}u6aqh;9yFm7JP-EG
zd2(*L^x^}oer+$Cg~%Q@dTFRRzOu3eMf`iIBc`_@tpdnN7+?gytFM=ha_3!yzUj?<
z!IQLlV+$B)Ano=`9v;3iw!3WfdGU>+I90Qm((x@FG~>Qoh&Vw!)q{-fftq~^kMJJm
z<X~))+l3`~zc&U{b(;>|CyQHe%q71WI$aZx#iBJ9JMB;H#eYUg^{(H<$D!<Wfa`^=
zw<dU~*O891fN7s)N4?|{h&Y;>Tv#KXH()}-QmIYmL@xg`rusdL=?HCWNe3Zj>g@<w
z`$;JQ<yY*GaTk=TbLUuKJA<@8&PX&~H8mfVB*<Z%({dzOO96BPo|O}AYT9+CMi~&)
zb#*KEF4tQX6?hJlIR$GNQlQ4EQ6|!Q-)^H2Nd)zlQe6D)QU6viivAsppv!$j=}G<9
z`=*~MGqK267;9blc*uV%y8e4)9$>duD)37AT+vxF^WG}GnCH;%A@aK8HuR@*7iHs-
z`ed8w2gmL7{B70_yz06rEQ$Xb_c?dju+JF-*IE^<Vb9(kx^|;|aFs#hbd|hFb)vuE
zCkftcZC*6qX1#^pcR9k3LkAR~SGqPe6FLr(UY(2W#CHtyqkmQacQ_i7g++>T2uJz6
z?W|VY6cYar`3Fjj<XPT1)+Z#Nf8)KfEW9Z~8{CRF4aX>FQhg6yE<g97Om0FYOhDuu
z45Av<+6W{^;%$mk`&7?<&ok(&)FPMd2qf5if5B@SYsMvrOqK2E9YqlK>AjAq+)h#(
zA>;G@L$bI@?rwEN%XZY@I#>|PuTL?LSNw%)$ke^H*F;H0h4vMNFnQ@3XS!(v&=-dS
zr{DQ_9vbnGXA8NU+Kw^QV|E$J`Z}q7bZqXq4dPiqyeRc_)qPVau8R~&r(EPKAZFzD
zg-#h@cVojwA*$zVWQ!!iBR9V3PfSw*2%Tkzx#n3W6l_2BpbNB_223V;GrSR}9wZwT
z)Z97il%CD=98?`_mWXw;-EuKm$Y>kr0trb3Q)&O`V^K|)Zg!7W9R1U^h1cOTMfG2Z
zdOxgs_ML=M@3v(T&7rg?OUrhVEcSUb%dC3lHYC;+oM+4%WnXwZt$zKbaf-_~Ap3DU
z&KyBF>(<)tpI#X)$foqp*ujLI3F@RUC)m=*==QvVz+)b77404K;(!0GIAI*oQ)JcQ
zJBumwys$DQVw9TP(XH}O&Gf0?TysC(Z;aqWxIxck86W<~AxW}_<U17A%1?%H0_a>`
zF6BfGh`Og7tDE5PHF`{TQSaD~nk!%bzgK$b<ej;R050WNu<zZju-G-r9GE&D0*^6y
ze$P^Mm~aeNx1zAULO6&_#M`8*l!37VelTNT(s>HW1-RSf%e%qCBscY2D%J8|i{Jb+
zyl3?0CO&-u5N-}->^K|TJSUWZLqqyDlluATZ}<@Ca*(kE?XJ)Y^8;#VK0ZC2|KiO|
z76=2);nIy`Vy=@Hc8awBSnUtwf7hj16~AYW+9dtnKHb37tQV9|RC!t<*~F_7Y%2*i
zn2VH)Yle41Q}X~UiJ*U}%XlwL9DYwf+{%)XwZe|_fEJW8Lt1qal#y@9YsCa&1IA`H
zIA8%b!r{WzGTzYsCZvrYv;13Wf%Ry&e@btWEmaNq$wwwr{KU8@v4BF`TMT&5EA-Xa
z-@ZY>^nt6Mv{u|4d)|fH-)^4eSzUj9VMkUbup~&N#~J&s2<f%fjXlm(k#v@)70knN
zIEN?*9oCOX4<F0L1;s9JmGJt}sGqCDtTKX(tYQ>vV*4U0|IpgQLqWnYBhrIQ=Q0>*
zAn{J5^adc_{oq`@OZ@|yNNSA>d*I=svx;sri%-`ho9Rx2ZqM)0Vk31T5*=0!z=C}0
z`Xcu2V^qj|EOUte`Sqg6jE4Q2HvyKZ(<@dRgZCw{w!F~H|9<v4Iyx%FFxL;=dA`qq
zk~s$oT1L35bxxxSIO%!*-PXghGb3$3ptT^{X=~^vyxJKFlvLyMz#}~|30h+@6-}Md
zbC1~&{P|$KfX}7QML{{s>}RDI;p12)vooUOV@w(Ns@kTq_eDutRPjb(|6TpP^OV35
zL-IkMH=xXQ4*%aMW6(9bhGmo7$|!T-DMk~rSVr^+hamZ>djGM^#<eeg1$|J30g_cb
z^i}Y^o;(wryltmQb$Wfj#F=~g*rIejle#{BqXx^E5*_-dbytQkf{Qui+fQ!t3Y7qy
zYwKrT__cUKin{&5wS`d8cXPR7PLf9YqSQDI$&wfawDgH0)`q7+$8N-ArXO|;S0HxK
zvX?WE9}koX)4ab9{a1R8sM?cR-zI}Sqe1Xx!RYx5HaWIjaT3wt&{<3IP`FW!P@GrB
zKjzXMo{y%4xRR>L=Wm&CQ&em{6VtScm0fBS4HDe)>y=$i!8}Yr=XCpKp1RGl4juC)
zszj$b14xC-J>FX<t2LiW55A*5cD{8KPI9B_mVW<=HpDi0*+C~rPG;U=Lpp|~)p?Z^
zDh2Yql++ipUn1{ocLEaMe#LOOoooF^qr*G?_WK=-71M;6`S&_0zu4Z+GXcpG)$aXh
z4_No5;N;Yx8dy4=NTEvx>Od|G5v!=G9)5mYT}_B%*JF1KQT7yW!4)6-JSVGC!5=q`
zCXU3Bpj27_DdA)?yjC@94H&@DP*(d5Zg><FRr4c>hicu{@|YufZ<9R*&+~f8sk`?d
z!LWm@YhEtuZM!}<dygrHoIO{1hNLV0_e+`?)$JMNk3mUE_-)gMx6Q@$9#WY`tXZ!h
za1a9C?;pm1($Wk%O}|*=PR3{SxXQyitQF+nf%o><d)LJGr>J>7O{RL3ZF7qS3~KUv
zQPY*#eHTs3v{N|GurOF)MxzBG=W0rtAzANRiwd##E#6%rK41MZHe;gZ=PT0^gf?6L
z_?={)hYmPHHY5dYFdByk)3|+SP9F<8Kq1*iL)&GwKeuf6+giHippvX*@=?A0`gMsS
zmuM@Bsk>s*z|ikx7&LBY&^Y)vTpo7Wz%_L06|q?R_W1MO@cAP=kHvu8W0O7#f`5VV
zB|k9yO3nR3TBTv`Ov-Pgyp6D@%b{<mklb)TdKqnw{?!+S|LbhW<i@zF+t0~#s@AFA
z@zU{MkQcT|IQ@!`{i5G*A39|ZfVI8qOcPaje}MAKGiNlNoVz#mEEBCBS;G4RrO*{J
z#vj{-Q4M(IS%J`jQ4KOXtvDa7gS=c!=MW^^s<qklBtj3*XyC0-DR9~qG{u%YL+u@D
zn5>OR+Dv%WdYW<}J%7&~-<asWtol%asa}F~dnmN9z7%0}6Nk`f{&1muartflk=64(
zxf&QbwaR0H{n*Dvj%j3z+~l+u(*KbXHwr948D2x`>h+xQD}cApd5L*Up!O%KZu)c2
ztULM}8pYG=9@o=j#BH_rN28_itkW*h_-!20zAfd6o0f1}*8G5PQt708a#O`WcbJ-c
zAH@)Ij(sbWkAf0%v%gkrVO6mc`pO%mirL+w$hOu?j8`=Fis4uNY#Se<Hhjy^3%#6E
z)1$~&TKgJtHiU6P@Q$p!UjHodIINSwUUcyb<UF>Yv6)?`2=Xr?@qP!e{ot&Za^Uo0
zvf$%}i(PDZwT-MF`@Ju?-xbJnoJ|f+WUKJaTiM4@4V<_~b?$&u8kLjH%1K$IZj#u}
zRnG*=VbJuHbv|4^^_`JJ{oIKtn7KY1`8VO)@ITU%N-Q*`;|<SXDSP}&099;vmF6d>
za^Orw5kM;TNAP?0VbDSmC2Nx_)cW_qU`hzVI+yyJj7t(-{IkP_yalt3S<*4_YK3)7
zu!0l%*1>~w#|)W@ycdbJM=Hc+dD0y8g*{kb3S))JB>T6Kb#uEEjZdJ?ykB~S-*riK
zBC<QpfixgF>C4FD5N$i0+|OlWeifL;<Hd;@2WLobJ!(oU#@k2n&VxXL@QL&v*GV3j
zVQch2nt;CXj9KR+eq2W>1?3d;8}rR+5zm*Y%|ydU7%5voz8v8W255Ofx~t$&A#XMN
zQv(X-dw99Jb-CsbL12NvR*<-CA{5RsiYOBFps_s;iu3@a0Hz`1m*qj6pH7}caxMGV
zw~Xb#;?smSnH0A#VHoUC*hVWdsh+og!W}fGJj=Lc))A`VSrKm#l&)me&FTfSVh0*h
z?j^nDBS|gX@c!<X-}bI=-<?~=pzK7|V9MO7K-S5y_lR48?b_L2#Jb2QMymLlYG`??
zp8*?|u~XG$@qCA%dmwIkkMesifW!qt@jA(?s=mZz3OZ%U?*RmNL$~vqsI&IZ+;=~t
z@gQ)Rq|Ei3V3D$xH}3Z5llEk5t4_}FE2a#|5_`CUr#9;Pb2y!*>sLK46M+gs86uBB
z9nSzw1?5+kG8pguMT)@9zgZ-!@zit(_`?YAbp2ANJT{+goDtKQe@Qyk4CV^OFhGVL
z)AvME-W*JI{jko<`^vz~`~tV|8EpQs$SxzYF1)P7m9#R<FE%46AX7?0svdIV(u{;9
zuc(oMH%$~(Q-z$~y-M-9(cG^2nPzq!a6ED(WNdx$$V16omA<K=LKT5nXn<kxG@AC`
zOhDlcQsljR{Ocbv0Q*lKJl*|CFKY-YW{t|GcKW0A<P-x*brjX@ibj^hsV6V~?k+Ff
zIEwDvm-eq}iUUD-w<@caTuX*Id46gl%*&$tl?Y*qkz$P1$6nR)uEV};Os=sjrMxY~
z9E@+W^Mm)iTExw`rQBW2>fwN14S@?WrZ^2hhLqs%i5=mVK8Ri~AdKpmdPE9fm$srh
z=S5g~HkBZ4%gwPPx+7vp>GeTeG0Xk`1Fk?(zkQMNBy=jS(iD2SILBS9ebY>doAOqO
z?D;=wC_e}#B#cucFBI1ikKnF#Ydu_UNB`cOIp+Y3O_Wl&CnbQZw{rrv+1UWy@jmN6
zmWFfD{QP_cT+>z74L95%FTC(CDJm*51He>9OioT#g}1~=2;#=X#K;v_T%p>XUOPQc
z{hCKxVYb5Ujc1*f^Y-hmn<p>5^s-X;)4^*3*PM^*eEF`h4*W5U8_Wsr`}%6*yRl=(
zD7Rje7kQ8vmx_!ereCpQg-n_>QMm+DfxYO@;YanosG`<YDPO-$Qty2NraT+ePGie{
zm@i4-nWss~kE_(KR~J8D@t!bJ>V8}!9<!&3lZS^m*46>+)`)`+`h3WK_BHYBA15JK
zoNt|7x39UAqPI{JXYqE>jA^H-ojBA2)=d{~+PqDHq_%udzw_P_`Q_IY>cu5{PKvJr
z08andT$JmK80WG}E&_->N*;Ll38gixbsF#&`r^wnY}gPPYNr;v-idUSXE;n(Q9GJi
z*PFL&Q=Jk!n{NP|cLrR)xA;S~6N$XlAV7`qFe%3FT54TC^5A_PWot8>9U%dK`nJ|P
zOeb{mcQ2T)fN*;jE(w-D`PhRpdC~;<CEu$y3fSwjyE9qYEc8hlSX;>*)TWVU56ghN
zd<j}-?#;FfxMudJT|Gs1EZ-&j)@cKU?i8uMx?WDZW~z)jW4PVPQ@5uHW#K~eS4WMo
zLtn}JsAX_MF#htn3}yP-R$)JRDotkG5Fu_@m^&8q)$<u`vc2;0xK2T9%LMjni>K|X
zl<mt)%t_t7o#g&6hj$8E>j~Vj$ojxn!)3w1^-^7?4KVj_tdQL+OFIQjbsqK}<3_Xv
zF-!A%&)C6U8FVFHZAFtjF?)yn?Tg_Zww=<vdU^DW?UKH;?V@^D@;Q>Yiyt^-P9D(5
z6{h>)rlyppNJZX%=n&f02=jg8<iwIdaB#5Pe*5h*fBt+0Cf{+#9rFD1&&!l4Q&dGb
z#?y%!($7BoEOu2dR$%qCY10%4t>3rj$O$}6i_@IgkRe0N6JzGXAHj5v`7HCbhp!RG
zkbtEGzTG8~%zc(nrX3U^GL%19m+S+D&`9hNg6quVmYN%D<-Z@NU@@@m_yQ9o{MR&Q
zTj*013xTcx9?v^HvW=E7E9L|sf*RP=o-V7x!aUNg-T@8lf@+uz_qO?dac;aPwM>!}
zC=P#h(43h%%3rRU9WVagt^gvqm@Tpuntac`vkTYE$wB$ge&+7cQVTVAFZfMnIT?}W
z%wPIoFU(eJAQUyj1ALri((oX;^o%&^50k7OZqAC&K4@kCuqI1>-kh(@R8uQBJ>Oa0
zffjLOh?j%|=<aD^&(;T~WL2eD7yPKv-V8Z#BW-zAqb&S<Km1hVTL%!Abw#iYi1UWL
zV`XT(pHOSNzP?!w!tW`4cQ0F;E9p7ZEbn-ofeMe-1Uy(Kj|!D5&We{I;Oh%h<J6-5
zZF8Rd_oKa#B^D7;hNQH5*_KoUfIAY;SYxytX5l~mEeB=ff+5T32-D)eFi#!@zuUo(
zA2u}LnPleID1Kk9%z)N@BTR+&l`|&wF&Cx0vO!+`WG}Szp+Us@;u-q;xX6Xmqh!{E
za2Ws}-I%5+86~Gz$Y<CHu@a`qOW-4x_07k|ihsPbTVA+#ocO|TcW<I@`+8RIh$e{z
z=gyH(C@cK5W2eO6yL;oAGsO>!$i0cGu#WWo=B;Rp+B~C8-LR;=YS$jg!a{#!C+^m4
zS%u&lfk7QhpbCB0rouY8b5)*X)@nYUxlM>a5;vI;ltwSk?f~O=d{3|mhwE-^rUaka
ze>f7I21rPtTC=o({*XYl0uJdbnH)5}O)4(<p}W(xtsS9ljXo6N)Vi+4p8?*U;)y=V
zk`6z<BX{O)nL<0))9FVQ&m0YD%;%=~JeV2bu8Pni>Da*Y(8?_Mj7GU2hbT;2Up*^E
z1_Z*b7loseY238rZ8_`tGcM27T^#jm#RGwQJl@yuk+*)X9~ovzdOC(#p0jBBwU$R~
zxh%u=bH!6_7GpY7z8X_?OYu;7s6Z9g%28cv`Kbn?U8_?~DCV-5L1io1DXuE6CMS)9
z1z()1Bqo0P18n;w^|Z{H2I)~k6NqhMZDwnEt;?3F6qzcf4yOF{Z1pI{@$eFG^o|q9
zVGpX!kd&iCQ>8RzFB1nhY%UC%Bo4LNQos3+;?h_ErAoY7*f8mt{hl<qhl=ySyHp!I
zd=9_g-7w%Fx86Em-hA^dwRu_xXnE9$?-A#t{EZql5;)8-{gyH-e$PF3tMt^i*6<n)
z<YgqL)qk{XHgDdwX3$q>;_U3A;^;VsBl=@**tRC};oO<`nU7W`jlXqVVjmhmt>rVV
zi<Mi=L4yVf%T5jHRJcG<^9sC()D*WQJzrW&x!{5eplFGSl!*v{&!2ybsy}^v^L^8Q
zjKZ|MwpAaU$BBGADS_+esv1eT?`dhSs4|1r&M>FaR91mO0w$iAJh>;tNPwIwwcoE4
z_fy7*$AG?4w{4GvUw?@t-*UeiY*;>c)vcqUq(1nZ_)H!r?lG;C63ckTxqT9tHFKIW
z3%w(8mwEyDQt#cD+)e}d5KKb6`ObSP4mq>axbm_~<o>_jt6pCFa?@h<jgOZXpMO?v
zz2lF{6gFS{DwtDz@xQOiEB}2#`TFS!zmDQ^$IvxbU9Nm->z&PZ)RRCi7Y}U_>;AJD
zoX1Xl_%Eo(riOBuHD_n~-S<C2)BAFXkB_sJ+-BI44?l&8^!;YpQPcYN`8Uc<H_Wpc
z|8Rk`&pt!=GwA3@fD1ZDlbRZuWcP|B8G8C)yRvcfbd!1iyi#7i@pVbvk#1KS%Sd%q
zbu#|^F>>j{7h#dmG^(|X(82kx->}=z{^%WW={)#o%e(Uu9NQSB`StS6zuPX}hV^xq
zyhGeTAu!ioFW>z)6I#Bpzk5N;3+ol|t7q&N<|60c8?9gV=yBa=1#S4vxhqNn`*dyP
zo#w+Y_;ZYW@NkNm7x{Lk>1&Q`=Sfi(7f)Ns!^ifwq&0?dRu3Tf@8>7VUp^it6V4BA
zBap((T6q*c)U%VZ_<sy&%d#R#THUJociF>n*kIHkOCHkN(WbA9BzZ%b44Bxtm5gIh
zd?U+FB)|#j+_`g=#`ViDzbu<KZ<Z^syizW@=pyBtIy^jFXhwYN)~)i{XP>Ef93yh#
z`@|DZw3~=`lAZasJ|Q*-7$=4VdQbxJ&Lm9$t_iO0&J{_*C-gI{QTyQcr<o$BAKtK3
z50<1mtN;=KyzZ1R2Bl_hZC$e@9V(N706Z;+v8Rm<m#<f5%1)TC<{S^J3i9o$Oj&T*
zAk0hKb1ww&`0J)z3~jOCgb1!KPIAtaC>cEvYU>u1SFC}WV5&kr0ai!B@2EBXur>#I
zm#Mr6b@&1NzU`8J0DaqzlUU}#alYy|epsM9^Y$Leff@AX#u`}%&H42^3*_9>qLkLR
zbsk4@O&NGAfK?X;U9U9@eMS5FxXPA2g>nEIdD<oc4EeEWc-Ev)x$~;Q5^CWq9Bx8o
z%-}%vn>Z|3{td8x*TE_c{Iyj;O>E{n1c!#g*YvY6jqYZl4Ic<C=Zj~?NK$GkwCwW~
zK;F_&CqHh?kyFQnY2bEwtt|X9*#xX>YYzmiAH8KHe4bl86I3=T%u}wJ6EA~se}8(p
zTsSS(Tn1{5@7P}~>#<3)xe09#^Y9>F7nwh|zs$8Toz4U<u))vr4ow&V|J$k9WRWbD
zRoVu{q?A(m27u7jv*XOXd-VzA)FI)X7aE5S!m_<xcIs61>oCm;eq;UKeR2>R-1NSo
zO?A*;l-AVB@~zwDhBMF!8`Pr`$cLqehc*{U3ch<D*kPjpXsi+wP^R5ZehXUw)V2Wh
zBe2{Ipqf6Yc~0QD3GI^*Hv4M+O#c<2t$jaQ`$@KX&g`1esGX7C1$^*TP+&K4ADR-T
zyACcHoeB$jOv!GK<35tcPo1hTeGO8-uia@MPY>6ROxIBwhFj;ubO)f3KL6WP*_n$z
z3vnh!y2?LhMag8ijUd_Lr-xbQr}NgstdEKr{^;>|t)J`s^fKy4J&u0gGQNJU$I*|L
zX?d<+Tk~Li>o7g8_Hg*r^=Hi37KC8t&-j)x^fDn>BkTLBCGn;n!GfpFQ(N&Erdp3h
z3R66`Y12J(N^@bf=qlVQ%z}||+64)410OdaawAtuZGqIUohNS9dthZBn_>n(APydV
z#I?Byu4Xi*Xfo1(jeRY-KVrupT#-5)UvMAiv48v9ee&zCzhREMMGB$JN59VcdqHqE
zIyzdWPMxBBJQF;&B;Nb$U;iqrSFeKU@KwqZ8Ljj3z0lL1if{hp$&+p3DsgZn!uj{Q
zb?X#{LKEaehYqpKLY=cb7hZUQEM5Ao$|E#1RA$dU+x$LTCKxhgFhJ(>;ErpfGSN-*
z<ni(GmQmDsKe)bS-RZV#{rU}(kpU$k%=LBPo@)a>K0erBK3HbVm>~%X3GH}Li~PR(
z{)%;jEwXdxPNDWS<;VI{R+A=8lDN3IcIkRUK--qk8){@n9r8b3Ds`K8nSENbb9E01
zlAzn?Ny+EmOUvFAsoK6rVjumZB>%Ebf-gBoYH%I>#2=+{%PweJ*Ngj<aZ<5<t65E-
z+0xv&OKzd$yz;KZKl3*`N@7ImNCJ+SP{!k>OWe8JJm4sSA98E@;G<8K9|ZMbM~6OQ
z_)xj`o&`2L73;LNuEWAY<*`TZmz!_B3mr+d>PXhD->7_HPns~^R+4Uo^^K1ccL3Pc
zG|L2{KkZuVn5AE7TeKLytu4(!EV%7v$<E1@MQ^>U%E(Ud!;e3c`|tact>o>(3OH<h
z0ssI&07*naRI&5+!%sdl%a(xu6m+sT-*|nyxJE$lmw*#?h3)!vkDT|%bHs!89qpjd
zxDZ*eXuiC5>mm%0Ors?`(lypM%A||N%MJg!#_qhxmS)2DZ#V!G&nM+)%MQu*T$IoL
zo+fowt(zq#UK9-f&`}D=({uU!*>pMU)<~hB<=>O`(_}N}QYIRIUa+`o4x;oR^RWR7
z%S#?fF<*CdGHy<gO(1#yM$N>xo^Hxzq55f;j#DoWZ3A3e^X(Ed8<CsfBa9mfEGc2E
zkLBfYdCtj|CB6NZgK$5<5n1O%Guqvuapwlf!L1c$f8aC&|Lojda?g8%<<u*}6wBO0
zHS!2RYfB&MG)F)Ex^Ve<QBHTV?NS<!XO=v4$eboL-d$$h6d`QSOW}8(e!5AEA54)a
zeroRu#hlPMIj$u@(_fE0_L$mm`NkV>s0r+=uf8ga7A;bfPj1*GSgl)L;$-x>=blsk
zv5)JD8HF<>fWDn>m30Nca&+q@$Hs6)m!5^A6E!gi4YZO16V#vsOSSZ!haXKgf4Dqb
zR&LFgJFgrp)1hUpYvKm^y2>~Js{2yPqz-DnG}TQodEU7R3Pf(@teqW%S?|qBuzUqo
z8RkC%Zbk;iwcn6rZMDq$`$=x~0D#ve7Dz=6%L+X|Xm4M6MwDCx?cKxCl#yX_^9A+t
z&&B&NFU5mHnU}52l1U>%Bs|EzIl<xhx4DPKz;>`B!nnp>3$xnWpsn7}OpU|_l;1^$
z#Cgd**9=i+v)k~oxEMJgQ0}~Zpgj1>PAMqHY77w}E3({Nz+3LF4$wf4Z39|cCYdxm
zMAqymko;mS7Narm1~^=cO}@S`&AxgY!wDIop<}%w02>9bJ1?OvXl=<1=i^`=oj*&e
zg<0~ie$7yAgaUBKriJrie%>0iwoJqGOJ>E%|Fd@;fKgRh`%CY=kc9LQdPh)DumFmR
z3MlH@*WTOOcJ1AD-CtdM*Im1UpeUk(s3;)4*N}wt-h2DMb7$`4Wtd4OkRZYwn7sGy
zyX~EK-)-M1>AAJ|cu@wcK(MOO1&deaGCU4m%;T^nwv+gXa6EhUmGE%4{hVT<ATB(N
zd}!A`Z!lOdl+Ay4D<y$Oj<quH60*KuR$Wb#<%OuAB7>M7k7M~UR35NL#<D!PiL3#1
ziC<V=flQh`6%FYCT5n8<j6k^A1gO3I90{NI+X@1s@>T8I?oH|2@CD;sHsz%K-XC+M
z9!FvvffU`@Cy2m%8?7-AJBB;^eF2wc3?#m+z-j>|1f*_efE!;^D?20wYRg-bNX$o?
zpc*r&MdigcM<9XlKz`2w6k73PbElUC_%h6k?N_XRNPKDU^~}4PeMvEWCS*}a!H;py
zj57)2O3p%RpH%u;0|_u=i;UsMbo$(QU#B;Q8P8cm>?*Fs?711(OxG3tB0MlFH4rz9
zi$*jBmDsyV*S`-L^RRa=iS#{v)EQY{>u~*@>-+ks@5|8`X7AiOp7pgco!k@JCye};
zXWw2@k6oqp)I9e@40)1P)~Gb>`_l!8H~Tp;<G&)e8kJlF9uw_@P+zSb$~GY5JU{(S
zMfKQQ-iV`Ujiiqs8w&H5woyuVAfhp<ni$7Li68E3(m0hm2>>l->!tUzya#i;kRFyL
zs2de*U%RRB7r;KPe~>T3O|cu8Us_cyzX0yr2m0_^kaj7#ney99MU_HYsZ9#-MHnR;
zO?P<DPTNxnoqsGGXf5A@){TEht6LB}#=d}-!e8K3w-wd(tsDbsWns_%;4$zPVgZ`7
zrli8&R#zYLn>^<`GHC-_vi-wPLOF(>efBJzamI9I4!g3lib8@7%2IA{a0tS}LOX3E
zK0e-<I&~_JKmG&)-$lywxG4XLpX`W;h)$nZz+EYK+O$(tTkT9cSuB*vPk>=jJhazt
zJw3e$a?eoK(1h34V7rmwV(Itz<BwNAF$G>hcdfN_ZzAdZ{rw=k5dgk3^5OhJz<K$}
z+Mub4LZ^Js!UL)2;Y#e;jv(SU-O9;n!GGr{DLDP)GR<q)m4V7%SE2uh|3UF53sCUU
ze0aui!S=vS2s&;oJo|*hJ2@U@)TmB=>z~N|zc*>BVc!X&m<%ua{2Rpn?J9T>40qE0
zbD)GDv#}cv4D7fOFD9<VAGRJKWwCL?wO2W09=x+!jTkl*GtN960*dQ_V8Pdm(38M*
zy_lNUJ36e4_2R}@zn#0%uxRlT)geiT**7K%r%gLqnfMm4T1<Y+<Yd7&i<BmJTwLcH
z6q5H(KmV%Cm22Kf`PW^2g-yvMV_lr*hbITb_iw?TLTp;H8RO16%HfBMOp3tmAKj>Y
zWILK@%9SS~Zg5|?i)v0c==XW6QBhP0Pai#D??z&0REIfGoK=r+U(K<^mj%%Uk0v92
zh#!WX6wEea0t~CUIVdK(#b@-vq>}88n#EEtdn7>Lyr=ityKM;%j`XkzTz4poi-5Sn
zwgrnU8QD3BN%duWr{A$g(}*TK?kN&;)-PYZ9b$r9Q*e|gMxJbcV@=2VqmS0>)^A^K
zZ+E>_x0nIGxZ?TL4#2ed9)I@2t!S)k#engF>Rwr{Y)YF*-re6m9s)5He=vY+(a7Jk
zmbP}ybpAt0$|t*+xIg>v33%uB-RjP!#bwyIqzuDPq8(l*{V6#hCc~e2;t5KYABWdp
ze_d%_%OX~jEU5eU?~mEDXXA=1u25#u4z&#kU03JP$pL#D;POoNQ4eVn&EC-i0uU3{
zahz}AMr?gU6KbhhCnmr57Ko2w?kCVhbWU+Ap8H@6{JmXiiaWyce5X$6i|?1`Bb%Ga
z0(#~X<XlM&=99<wwZxGV@iDz(Qz5nFxUo!edI49b938Wd&*~0|I=R@BQ%yg~RQ*${
zX96F76u27OM*-T#^|W;s0<H~63PitHA8bk!aK0Iv_Ee#$tgh#QYcj7=X$tEaQFD+W
zjA&tJ=G9;;wb->ub}_wv>GWg*!(Gq&2hgPUnG<91&Vnqo*wCEsxUVak?R;lL+uxZr
zPZFa8XtLW2g=GXaxgl6uNzHHy2@3tZu8@CC^=B0^tX~L@A7c`HQxP2e(?G4L^*co~
zpRJ7TFFM={Co-P~0_5hsf4e!&I6fA0mSl3nQtIJC)9MXK<NF_ML+HV7CV#dYY^VO*
z^qo3tghMr#y^&jAdlSpI?^FPpaLSuw>gzY(fPt~5Z*e}A6Q2BRE<r|7KLG_9Jn<&)
zM+61o={aBEOKMQ%QX@?A5(?v9)D)U8z3=>U74WPA&3|56@D1KuvJ@_CFA`hgestFz
z3iN)8$^zf7qd+9VYq=-gVH7v0&p3K4?mh1ugwU18{!lp;7k*f`K{_N;axU8X_l-s{
zH@<)0#knHuB53IEw6M3E3J<9<?Pf>!r#xqxjvVk~-*r-~H<lBWRT|a2Eq#sr2pJN@
zHYql_@x6%eYGo>%?YKpRn%H-U+3!Xo+H~H4AnwJ)(Op5JzdND`E(Qo_%`lns34rJ#
zZIEfzP|Bl`WIs;?cI0(S1b3#<dwjBYC_jB|OlR-h2+$bQ$+^9I*5T@!&*w|<_0(j4
z{B43ij;2M5cnWuU@wtqhoUsghb<*Lw9P2bXT;l0_I!xDFuH{@`>$p1HScbhg)_F=i
zYqHKy-`C;lElN7p=h2^YF>h-HeSL>uQbHguo1a19EslMo!|>P%aVX|^dHI)_$gOO_
zkC!A7xNZlg1adB<3HBSm&BW@=di>{%SX?$V1SR#&xaY@QY|X8LC&w3=VKh-FubJb_
zNy!0th~q~y+sl78XCSY>2_6Kz+*lW}2GB%&BiYXzm-p{0)q@}3-^#)=Jh>?g1B3nW
z+_)49Pnh=bvglgxAFFo5hi&!6qX!_&n`1Q_q2wbC@#P&^_^qH4*9?roUlJmfXpNha
zBJ0?GHGiDPjvQ#o{t_My(!X;Lsb~dUefuhl3S_quZmIJKf?J=7X!IWp|N5<{=RT9L
zR|J{d;%1^{+rQva{wrD|PlbEGtN2Rz-5o+CYh1A~Ccph5PfriT#Kfpychie@v!mpQ
z>siUGGZ5TXSprha??6cA$!=~vEMR&d73#&b-9rv~u_6Z}>sCr~)TL#jX47_MBHUP}
z0M4~PtwQ=;Phh}@FCc98X|$~1f!JHGP|>{NqS0Jdj-==AN9O(ihql#QR35Hu^sNR-
zPA)aznpXtzp&fR{6k`$lb?&IY;Qh+{5Kfd%+y5IjZc>XysisJf3>=W!p~hRc?mz*h
zU`-PXR}30B07s8=9CYiDnbpPFGtb25bLT59XUTu{nzhQ5Lst?oR@vCSTt)K^7M}*3
z6~shG*#+D`{P<JUQ-V@RCOosxo~a~qL@MX>Q%}aHpMRl1?MedJpYZ&S+itR3uD<cx
z@|F6OdAef6@L}|QIK&cmFiyHNU>uD49Li;g-?xK7OW*qiqfZ^#El}A%)F1M5l#)g!
zI2zRwue!7v%RX5F(F{Kr<Yq^b|6r8;N0;%*BN-4aR85mE=%YYuxxVO$WZZqsT73&&
zKAVNpZ|aM{@UAx%Er|~Bq?sFiM)DFd^j;zjG)wmG9^6>qM(6&LC;eTEwE-bxzMbVa
z=gCYgekV`$`?7eH%?LTFMKh1$r#WLIeI2Qd%l4cu{Z|S+Jomq@?KvHuXHm&Mc;c5)
z9ZZ39{f-x}*iH)qZWe#!^7I}AB<`L&6k|>e!}{+_dz~RIRM+^>U{=2GKwnI{uzf>(
z+I2A$7}<-`922nq!M%GheEFDOua~oc-s6Dyo}M*p7Nx(ZVb`u**s^5{%4mg0B&rh<
z5|n>00k~yBtiJu;tBNycCkKvL4!F2^Qfsy!CQ!@R7Kt`<en}nH?<mEKG}By46W^jG
zozG$6oiEeq-#FN80vkw<4WRGjaLikpO%PHx%3JW({N0#LEi>6<(zJ)#)(d{lC(ue$
zkkl>~;9SgJyPN$e>v)bH_vBO&U}e1&dcnO;3hO`q_GSXZ?I4D&yu@Wsvpiq1320}9
zDO~MAligJqloY(*^|4i!0D!ys+{Vp*FV3w5JTIYt>Am?>&F6qD?yLHSxg#|`kid;h
zfI25XA9su%7=r&T%p^|NRebRQNZ;QCQ0!4KO?LZm9wlpJa)a;9gL?v|`RB%6Wv2SG
z&fYXLe$?RoQ&ebbZlQKOYe1e*?+wLe4S4>eZK|I)^2b&k<q35h8Xi`@&A03-KfHl!
z^D~Q9vr(hk7Jnp66a<!S*^1qTMG8z&@z}0J>sbTWo4DV!EjO1&22AsFHa2`f*KEtT
z?ZC>kG?NM}-;bseYKDFK>k6#jla4PRxEIl(?Sg{>6mQR^RUWOFkORE2=sWyx@ls^a
z(wz!7=OsDjQ9<MN?-pZ6b`Czi`)~AB-~G+!pEhkq4)-Ue4Jl&%PdjQ1QmEwc`|dpq
zGPT*dCR$P8-c#zC(@pW5e|v-jeq0+)r3<NlaeG1m*OVV1#-sq`Ru=+z1h1vbmo8bf
zuv@52EjE)}8L5s!v)(izo*YD>sR$no4)#Pa`x6CnGY_*%VRJntK)!ks-*poO71(9-
z+!XB2KGK7rq?9H7f@QAA`}*1vrZQ;{H(%~^(COrct#b(zl6>{K3NVMMbKZ*y_Q8WE
z#ItT(N3(tc+8Xndc)D)JaGj^Vr}8uW(dBk_-w@q$Y8~Hrt@D(~`rLTml1Uf$JFC0S
zPvoHXR5mG%>spRkmHZ8f4#x1lf%tMq4L;gZiBO+hWL39dZ&fQ~Yv$dJWmuk7jnh&?
zkR0lZXK5K_VOk|t7PO#&FaN1u^01j;^|<&TyuYptNs(T-e^NX(w_UN8THF6zR!9x~
zO5A;H3;a0-4haiEG{>QDvI?=crV+zK{BeArFpLTJ!-mod{4XsZ7bQjWeNoAee?+@q
zKU*`e^VzRd5a_Pwc_Ho8`*8p4<6Sw(t!v=-(1tDLb)4I{;+yOe?5!}lXdM>lhqF>*
zjI4B?+u?v~%xMIym!j1@7#^XcXcjyY&8fG+C2J0vytpnLaz9+?%i3e?i)h~YEL=*~
z!qtcKk%Y_P;^7bXt#?!4jx&U!b#MtE54Vuvc1m&vJ2`OZbKu~h0EY-4TG@$Kty~Wm
z9!*1Sd&OiBX=JBf_0vjZ{^Oqns2{WpqB@p)NH7`;is3t`AA;U|0%17?DEwd^>UO4g
z0K(lkAXl&1)FZ&PXwizF=gx@Z0vBK&m~zqyG?B2cd9lvY9?W}spzf+nF&FW~KmSet
z%~dL@NE#kBdL&Z&rF2NTea8;NqyiX!%vc3*cZjr~7vk?aB{>P(=r>3P=)$66<Zv<4
zKegX}=GdNWZ&o(m|L_ybz%SF#2@{Ulr@cvvJ?R-(xabE3Obh=aBEpq6xX$e_l+yij
z!9rzXLM90FzFdF{&Y!J-a2?0`C?>~u?@5<gsJg{$F>dS_hc*VR^V;8Q;pr+01BfXN
zJpr<nm)w(0gYlF|PE3rq73GKs^Y)j;ta-uGOHV8s&}nPZ@%#KWm@xb3UMgk(vR(Z4
zPbf?;JV@HsF7WbemuB6+8vi*{<?gD*l6Uhhm3QNU{Je*fEZ5>sckG#A5dXSD0-Vly
zaxeb+?*s1_EroP(;uqX;+ESX^Q3Jb+<lVY$EK9)VO8OWM40BvUQy11iv!b=-=1nmM
z8uQh0Y#qg?xb`b<>%Oh9>9@w>gp2l1a@AIThih0kH6$MYc{CoJvL1UkbE8~{Lt<@(
zS!;!tUVg6l$Ctwosz&oq|I0_tcF~wK(O(`(;=HE4?-#T9;_v#^Yj>&$J6BZV_b-e0
z@zJf*JCA=34#-?cOoxveH44Lr4_EUdne)l~R|Gem<m7;p1HI3I2AadPN1~w{A4P33
z{se@0{GDwCt~qFMosv$#IIFNmfxJ5Xna9Ut@$dO)tZ3rU(uNFb6R+7;ig5&{g=(o0
z^&7RYtLxdn%UXwFlgCo?B)t3JmCBHRDykc4Zc&~=)^}|LMO&yrZ64orq)v`X)0uS<
z60p6HTD+oVY;9V(lPtQtCH>kug8%Z&T;LJRXeCcg!mm>ID*C#XwVx|}I!d^=m*f4P
zKeZGiI7iGW+V?$LW8Xer0!rJju7K%AAp_y3m|~PRqRgqAlZv)@Y#;lUf9#haIHn*^
z@+0w?tEhp$ZfCo-71Is-f3=dW#AxPwDy8r3!=0r4cyhdSlvG@W8k+gcghun-s%;zZ
z^ZW(wr6`R-a~o9+Mb3qIY-?`94r(vI_|?~V{L+g|;ruIFizbB9RF>5Bv5!7gU!x_G
z@kH`uUO#W$hL3+<hAU2<V!Z3n`Ml-7ci0|qq0q<4qem%|>H;FwQIlLqv~j<rcKP+E
zQ}fO_r*Me3jzsd}{Csez7ls597%Zcr4cE=8Ik9hPrpp6&qR--s){TI*yzVBdSvS+7
zLa2`mj_E^_u~FVgpoX;%1@onk>d+-IZ588|(kyT$HCxlFnvg^7*kW4$s9|`cWXPWj
z<9Twf=)=C~c=knS#RU^Cm;TL^p;<?JpT|-6_X%weuy@~9xUDdqlRT3Er-}i5Let(}
zhbOg!Nk00%KG)YeKjYC}xz^!2FJriMzSeP!_jNjb)OE1F?kZg0m*dYF<+yuc2DVi5
zT~SEt!_~!z@-kTip`toX%e&y0tU9DuHq-40eW-Ix+F3?nIktm|aRGe8KmhmuJw3fC
z#86A2fHn+>^g&FJH*%{Q+1A(|8LN`gQFYu)EvynS83?5|wZ9UOaKWjuq3X5lD6d31
z<BbXN#q7jzrF~sZOEa4(2=^tW^N#Nm%AA;3NZJ+Ye>-!qfNuOmi&sGPht}@K`0zkP
zanCY<1xYXH=31x96!Z4}UY;rkAC~PT$2i~?Is$DI=aY{#!p@(N`@mb#*8c|bT}-~4
z@^KC92aJ9RZf&#(L=)lCm$c;lz?)*Rp@qVcVs%0I&i7K61y^qh4jf{>uT4+l{Oja^
zeGWK2^Ru7FpXN?$O&!V?|E9ZLOTFcrccZQR=*wm!jQ*^>V!7!nvkn^*J24p^HhnVk
zUwt2iAI?V$J9?8rAWL@Yq$!IMmpwrLeg9)ir_^b795jN00x@;+iJeCKBX5d^vG|R)
zN8;OH?fQ+%Z}Y}YTP+h8(K^5Gnk)D57m!bFYfUng_Df0Dmj`_G-~=)uJ`US=>{Rt=
zY-~hvNhyKr&a>{!groQ>T(x>Fo_gjv<mDGwo=^O0pLWVrn;C!j(PspuO+1sy(af`^
zBRsravRj_{<S8dA@LCqs6&02E?DKiJ<)&+G<|l#G)io$9<?Px_GVe`Dh|`w`<tUTE
z#Y=v|k{^HWvOM9b0QC3Vd0Urp57!8O!8Bbc9hG^`*jvvj8V(DdTZlmu2OuOWxVMVw
zR=#^TXW_?pe};!w_W{Fh6{I7R&x+sr`UpCH^guc_blMx)<jd)UivAXjbm3#kSoIZw
zYjd=PH1B-o?Qsa}dvL)1*g+jP2shDG^7#)Y8LMbH_o3gx|CStQxz;DkKQF>7*X+=j
zc8{@xd?CP~CMoOK*^&Cv;iH&yHj?Dq(<C74g^wrWj9U(FUw$CZA!Cn`-pCpcFKVVc
zLhs#`cED!4gZp^m(WRsC$SE5rv||#K(`EZm^ZJ2>BQS8n!4n2-qBVh!AI{KKK;lS$
zOgLv>?LYZ0zn}?xE!6Pg{d+L>j4;j>Wsq>vpP2(P-|1`~`DcDMXWg9~I9xa&;B$D8
zJ56beNoTITY6vcHgV#u@@c|f|6huGC#RS1j%9e8$XIX&jGR9x<OE$L~sBO(PyyQ2W
zT2$_$GPj>30?a1J%#?uwESR@F;KI5X7sI}XtA`g#2wXQSaMiJK**ZU4*Bf`1(d4>P
zGiJS95x_Bj<N%YYL>a@BoEC>NStrUrJ=e6AmvxwI%Bx{eCKuMjA%l!$4P+bd**_Pc
z-BdRnN6J&0kvgCoN2@BC-V-;Mt*dS?ON(_VR-&Fh4$BB|9PU(0;rkWbn^7RKjfa&s
z)Ssq91AIHKU2H_P8`L6ufE<gx+})MA?+W^#6u;DJZ&0UVoNw2x!@X1%@U`PpQrfU0
zYQp+)4IAX^3(?qa5RGgp!%PxSK;_G)PVUw`E}xp#qH%4>i_e}I8HrK-lHp4WEhCeY
zu$;V7_gS83iErJTh2cqwx@hO|068G$m4moFF*C^zYf5BK*uw-E60GJeg4Xmh%MPN6
z{jdl+g|RC=J<$&*(`0r+fIGoP_JYz(3?F?hg;2JZ)?!;lGj`JqbT)zN3a$_8xsNJf
zq<rNq9CyUjvuJrM6XEPA8(CH!0q!YLfe74Bt6S0*ZLKAYVLFZu)7PD`(ilglGsZWD
z$+^TAGKL%D>vLniI?UeDn3uhD#yGkj#xQ$%TZh}r-xzNM1+%U>E)F*>$|m3l+%-7?
zGx`VOvoA9ElACee=svh{OeEglT!w3DDts`796p+nqy!Yi_jLt@-#pygaMQRj{Fq&X
z1RqyiKHLN_ugs$m6M5LcwzMq0LKunFbX`KJ8^i><E3JNEaXmGrMRU52TGy^@qaqL?
zVe-s_f&*}Dq(4gNJH3onE-dp1)2qRxm@u?*zx;z;dDQlI!8s|>7*7A!$@FJ!j)DxD
zInS#p#Uz@7z95PC3dzva)wT0a=NxcxZx?24t>Qji=0|i_%Xb=a5?rHCkyopTLl)Y~
zh+AZTPGI|Hnh-w$&He6xOXVuKhn)=9puuR%dke0mkD@K`CLosgoG+tu<~#2mQXDw4
zfNO$}HM{u1TfG9+xz^UB@a6xZ`nNTReef1|^)+>_R#DYuGeJL(U``57JO=f<GEtYE
zhgJfzEoPJ9ve0TVn|8L9zB^%h;O;67hj>awAG!+`F<T~@<L6y?ncYXJrk|c-`VkV+
z1HKy<4R_vt3&v36+M2{qw1qh6`f@?glN1op(fn~;oh-ENlhk?rhD~_r(I@wb)XYi1
zo(xLWZKaudE?Cri<!B+AG=ID0h7Mq~PMnds7hiw7i0ch$u`WuRe)g;x`nKhG0sSm}
zOH)(z_4Udu%NO&%B+z>{A|v<R@Rmf4Y^U;*35$u^&{HPRb&if49c|kZ&tg{Hk&F++
zI04~oi<bA*dk-kSu8a1T;PXf4;ffb7`NKER>$w2?`2NpPTUkfo`XCKg<g?;;@3Hxi
z%vnrMFZ-lj7|4~9+X9f4<QV!{*4#go<}9a$V(mgo*^-(l3I6G$8MyA%0qVN9|0Is^
zM*z)7R+X5>yKT!VkiNbONh1UHDOwg@0j#eziLYWuX!zt1EO{s267`!Gv-b<Q{`x=J
zmOSJ{@^yrKsX63LK-!vGC|pocAjalQ#62<md+k{C?ccLwpw7Ryj@An%c~_1C*Pb-t
z-4i4l^p7kViAPV{i0!{}AxQEMb;pB?MxftOfjv=#!+CuFMlOmnxUpcS3m;2Tn+N(@
zAH`(-c@)<8&!yW`Q0B%O{PJ-jCjF(4j^aEzIpE}glLJl;^b!Zely`K5H~rOe9Hu|$
zmR64rTDVfIm_<E(Vl39~C_{CfXxp_><GL7m#dYWt>do*{njWX<JJn`g-Mug%F+iCZ
zKaeW>dsF+0bGI4}t^$T9P&@pz<6_|NL$_w_i#~^nps{WYjHAZ0I8*FFdh3gwT-WLa
z4d-aSo;YfVNef-HoPB-Vl%Gaf^yC!QqOKDyc<XA_5#T8(rd2&YYj5qxx)U%Ep43ad
z_!#12+T>T<x;9;3yD2}RrkaY6>sYp_3#KKieX4H0F0}#OpIUf_HE};AgGvk2?t94Q
z+(_QLEBB4=IcFyBK4;ecvw{e~sbfYf-_!BjODUvZ>IXmg2w$&S(+(1|tX!J<E+BZE
z6xGSxi+No>^+en-{WRq_Tk3z?>u+IA+Mf2j_*{GP^C6&ocg%$^UcUjw)zr{8Hx>~*
znN0uRer6zj+EJsioaW0lu-(LX0+2g_>-}#U`=&JYW#^`N<E`!0$ff*}JHcep*mvVm
z`q6rtfc7ECJR#a07xnkYurMD2ebPtq+n0Bz7DH@Lw9d7T?i$vz56h+Hi7Eo!jcgmO
z1iqU^1($7!*2gI*!#})s5pbNHP#e#s7L61#8W#}&fA&wRR?OQ7y2|%qO>$p{+lp%|
z%$SGjYt4D+ysVEpzA>(Z*@|Z@Pp6ZkuA?zb;u_=Y{O!e;aASO(mriFq>U;WVyl2cq
z!Zk^_@mil-hnv#3(O0zxhO^I?p(N6~4bxKt=_48_q@@(0{vlrQ<Cs`n%Y7J@6X)lF
zaE_0B3jW%#v7iQb{ZxeekBcNg%{d9(Tg=;4MW5I86ojLYKdq*mJ1h)0jE#`wxbMeo
z>?+DfY=CKBB#&6dgKKu9jN@@7?aq6%tnUiS@mp~vHNV~Q@Te4AJs=i->{r$liBN`=
z+Wx%B0Y20OZ=-4OT;)^!gh;8s7lkM4kj*-a$@~)bX)+htLv8M@<yCx6PsI4P1F_7~
zNe*(Lt%hz;mR|&qrYzN^w`G2emLU(rJ>@b>4lQMy;F>cRZLzcA5)uLT0XM35&!(wJ
zwLRhxs!du_IZvD%_;YaJARz&V1~XhJeds?p6%CuYXfjuch8L8XZ=ZoF2t9KO!e^WU
zIq&e58>-(%0J4=61Ha>shVR(nXv!}_LuM{2S8qVsvQ=m!m?{$y*8`CBG!#d$G7#bm
zSWRw>Hw@=8FiM=r2-CvPaO;pd6<}4s*v?1~@~&LK^@$9}-)_ALr%sz{Gl@3`M+>n>
z)|$zdume1)s^Vh6K;mz(Gm^SzWo09qzKU#-+}F=11BN?&sNZ(;b$q^)ZAHgNpM0*g
zqvfXf_d1J<o+ulOqalL_;)F@Z;=3PyR3LW=1uN#v`x4h(eTB_@J=s>|Cxe(wWMl$V
zS=p{tXfshS1p4{;ngH|mIgrg##P?c!T&&G|M@nD_ecp7`qv*XS_vFUcs;|}{W<V59
zzj<2k71Tw}ZJZ2$`p8`DT9tN?OoW>%BzzWrA8GW#eS2g@Vj<DIzWZoy(l|gryp@L;
zcgF9do$Wx<ZbuQb$tPYKjW7Q%OQn_d`zth?{P<6!DBRF9V5Y9r(K9087qSaAWhRh(
z>(3SV{>@xWy^<!D9Hb@h<zd73?Hbpfl!%__Ai#JTeO22kOE!J)9J?BO)|$pBF&DYu
zxh!1s^1%@vaPZOpG|_x1eJ()t6K8G0-c8kb_=k~57}im6t+yXW%*lWDSY~@M9G`6o
zW9SIUqdV5`;|J3zR1pCYK5~+i15OS&IpE|#?{PptiQ-ZUGs^^oYbZI|^20od8dK3>
zyRnPDqJ^qy3i_L0b8z0&L@ZpEqvrKu>4-{77(XmR`Jz@4_w!F*nh6aK@IqOY_^NJ3
zG5t%5`SHGy)POR~@9bsIswgwcqGT-_)S^vjbX8$5oulDzeX$#Bw(I$jn0%(ItP$Zf
z?`>Mo2L*T%Y!9IJdW8b?a|-HFPLrnsz;{LReAxxn)J7D))qFR-NGSyL*K8NzP5S&K
z>aaVz<9>moMT?nn7rrMG5J7%WOLF9Z5G2Mp20+WZk^KZQ6D}me5=R0V_fxE&SLVlM
zv@EyZM1IDGhT`SxuTk38l25RoA0GJ2d04z|1K+pVvH}}iBemSCxwmDP_(6?HNyal*
z{MBNfTeP6>o;?faKJ`q8WRh<kf87DD=dD=TArC<_rqlJCrYR$b<MEF_gSp21rdqLJ
z)oR>HVBZlR;W{7Zv1<+_2GKg?5P#gYvKpQQ{RDcHwpdH>xqqMwt{)PBQxkk4xMPRU
zl%H%)brZg$#_XEnMwIepi6ze#g4Hcd%Pf>etQ;E&P|MgOKy?!XL>Ni_HF5l*H!DPN
z{(o*tghA<mfo76#MuAp3fJft=qz+bqmW{Y_-#Wc9Oy9Fkufuiz#x%xr>v&yV>->z*
zWlUo{*Kv(U<9&T@eY6!{$I<ufT^s8qaV4HHOx_`bkX=E5m-{V|Y%?Kr1yxYpz&?cn
zaTGrI_sRmy-Clu}xiz#3?S@tPbvSSC9t;Whzyp)wu&t17jr+Nk)W&b)7!d61j=ZX7
z0?_Wrui>~t!KlyH<xxm3kL}R~8>?9$LFiaN6a3@Pcqc}OpoC-CPle?uWLMVL&y(i9
z0}w&smvyC;cz0(mE=Y-jm)R1G5s#43#x(Nq`PkH?J&HN;6-DLvGN)XPM`FY;ks8>W
z%Br!vuo7{s<I`gYApBriQPeN$FgUhk%~PPY2to>ZQcb^Q_uFt!ynw3nbZy#@1^4(f
z(HcFKey=G^)l!SLilwv&&OOb5@zMx^K9{2{h`57y6gvRE@P>4>c_z~gxdBr-40z>y
zgu{~qM-*`F?(d7BlO~|_)34Rrl*OXo$iWCX<#_lGND<*RlzsmTia%L^n7giq2TgCQ
zkKFw3+ZvnDO39&`4O_AIAOBJywRG6heY9~FA_J1CD`97n(AS*mzz&Z;`~ZdxrD+G|
z-5H4=rr_X1A8fJp9C|R<&leaFh}mbKh4X11IwqPOs~w7>iET|Xc&6=1*Ov!;Y@$YT
z=H4s|-#sbx5FTbSW!72dww})zUz>mwf8956QFP)76OFem=ULgg`0AT)&E>nm-_IYj
zxzMmj7hiZjmMs05zRfs!VO#lp&U~DE_DqD?sI$MnA3{PyP+Df%JdklLEv@HxXRnUN
zaN)zH7hQn!&pq41W8;16^AP%%KaxoS*J4Y`B!t=PtPp?K3!eLynuq>4{-Wc0y*yh5
z%ztJ9mVLbZpn}#?k2do8P%)Eki!Z$qwqr#FR*U)X{Ulj*32=7X`-As0hwdetac7(Y
zkn1W<AH=Sem3Zd-EvkHPKkf>2LY3SYSYV|AZ7;gyb@tyA@L!sx*7Vx-JK;-{o{pFd
zUp|*6$*<m_ld9wNTVfFuAsegvPqION%_{?N&-gW_eRF2D=(Su-A)uZz+OZAK{V)CS
zOeydBz6HZjme+_rNu3L+^#%vNejyv>`FeYiC9u0+^jZ!scqG|oao@g@+aWw}R~;79
zI>5<S#dHXFUN|}6<baa{P7d@S2iP~KWmTh~oNImp2BJ;m>+OO-YC~C*_{Bc;*uL01
zcc=1=EdHXG(C@W?$!m91Q6Y-vH{Cpu7){f$$3$Bv@AmcJln_LQ`p}<tBj*MLO&Z;>
zjsR;cfnPD@*%f8xR}<`R#K``kJqv<zUHa;rUHI_ZbTpblR$2Q;QuF+;XQo)TTB5^!
zFgQ5~>vvUi2&7qDs%(9*FrC1)KLPoD`D=WhwCpP8pGlq?#!+L09y}jMAp_Bvm#rBA
z`B!f(rf@<vQVD2xhdc>14e!V2-A$mL+HrZswKOeXg#HOZ4rL>L(|2Z8u<ba<eTNw4
zNF?r0j809lfYv%mWN;9IC~#jd))nQ&J3z9}c>J)T7SLKJNuW7z@kuP&wEIgL1qJwx
zR{WIeon*rADT(`H;|C42WH~e;L1|a7-pw_Pl*Qk6Dp;)9m4*padT`Q#b3iu*83!&K
z6pSBp>#;DK;31cytrRRdE5QeMjS4^#wP7iz!lt9!4wXiz<+>oF8jG@PQA(v+X)ofh
zTDg2+JTc)cK(+KS5=MKCJU1VC-40xf$>&zwIV6(6pogUl0U6tOrcKY<UVi(v^u<DQ
z&v<T%Ax+D)$E~mJ9j)UT%d);_eJy!Pn2@b>I=()th~_^!zV$tQZGDtvI?R}l4ws|O
zQ{OknmvdtreXYavwJ{H4nCV>FK^rp4=nj$qL<qs;Ku;HJD{4YD9a@V}Q-5ma2N2M%
zpj8ji&rb~YMpO`eSX01@0oYwqhe*2Gpxgr+NHc<{c(S*m7O7!g$SI=>61Kk!DLgkg
z!cUn1-?5bIM%QL)Tnqfu27g+o7#kUaK0aQ^t#8EM+A@p|^T+9N5eks5p|W01T@$|8
zTZB_$`>56=<r>K-Ds6f$7mSSvp=t3Jn$Y*eM7kXm(0hAj4IWsVhK$-K)HbuiPT<<e
zMu*PZnx(C@M^&Q-uiGk?!zJw!zHeHM3%U|6$<OdzQE*}R4REQR%Snnm+TxyuOY{}e
z@ZsVU$M((B!pA91`2-O7B-$eWCbul4=eCvr=ZTX8hX4nTDB#+a9dqC~I=QEb3K#mT
zkq@*!J0CeO{TD6NuJx3UnExVgyBx(Ieu<DXrobyYk`oCT_$l#5|G_&ixg7!HMk4Hj
zvrzc{T(l6(mV2B%SYD@0ECP=jZn-{?lhDv$o8aSt<oQRWmBDNrr9f}I?&=Pc0SRBf
zaTA_>?j^ENx?qtw_P~XIITzylxicC#V1Okr>3~<PT#Xy9ZBgd8J4@%tz4VMs>`Bj1
z`AZ!|o4qp-UFRuU*}+`gSd;h-2@K@KH8FwO(Z^vDHKYUl?JsaY`D_lV=m$wAJp%I1
zJnMAjbK5#!eLZr-a7;PrL@fO72PIKkSX6|$^S{DXmtU%*SdLzlqDx6iGy(n0U&>hV
z`zo3{Yf_r~mWaK4A^^HAV7-@%{ZnQyK&oG`AF4}aGpD_;>8*!RlTY@KpFB7R%?-_%
ze8ma9Rn$KDws2Ex-qT;<yVn*&0Q7@N(*Fyek2H<z5ANM_z-<2IqXNvjCmsny?e}X3
zT!=Y?kSH%)^+JD(sbmTMeO|HBLcZkb6pWp|uk{gWS3iGPi1+`IhSHq&jZGT&K<d#H
zQnN#6-WH3M^NO(JH<MM07A_#4Id==zUs;Ot9!NxZ+`cBSN^=|V$s-wT!yUoJ#1Vct
zmuY%}`i~1#+SuQ{ZYo2DPXX2cxqP%*koQCxM_L}9+-dqFK<gU%u@j%w)+FzC_I-))
zpca;q>L^ja^ndxrAayP#`%k<is>dx4>{F04z{vq82b>%@f;eE>fHrBnt=nFLN8j8?
zEmzqsCUFuG6XAonDBG?&W}FnK0NVx%7R#>VUV_7~6U1#+<{Dk$?M@T2BO(wIc;Ff@
zzFuw^KQs)Rca@>0-j#dVO?c-0ZHSNdSDL)`G;(DPy(gy%&-`}_wxrkK;#1>s{wawF
zumQ%aXYS;W=9R2fL=*VejhuIVoq=t8D$u~qY!iuza5r3cPAbB=8L!FL+a1RY3&-b+
z_tFF@b+=lZ@YC9IjOdqzbEYK1%W5pt>59tg@!nTy*jpfG!gU$?ZqFZ2o7k7-?LsYG
z<;VtaR(>tsn!giI+%QUMtlKKFl)h@0t}ei|2{9COFs*HTXeH>JsqxsbXB#!<8c<i?
zhA)=vMLgTmNn>L=Y$O}=l=rnItsHm%XB}z?EbQ7_iR)$$uv@k<?cs2)l>lr#iz`8r
zaPGGp2>E-rw@ICkv}?6C)&UM4{cTw5bLGC0fRwW5)d7icFi(lLXw7<5HE=B>jX{X{
z4Npu|rpuL&ZU%``%P{UciCJ=UEMjD?m``)zPT*Q`?STIn+eC;b@X~RiG@V?FjOu0_
z7wx6Atb;tc(BylPS7q8!=AI?3NxZhL8apd!HIwg&7$aQRb*WK;@#H95%T&5n$7a4k
z0kP$xh5uT4L;1O8Jn@NrM!YZ1NerguE90<zseV}URBg=kn7R#{`e;*_uD|s?W4I}%
zWZMp4>vQXP`dZQ$&yCl*e0^VEOC0NZ=yW<<U&~QQr`36xLOYhN)9Sd!I6BNay>%R0
zd03~>ag1r@+`0_YwYd0f#ZFPNCj~1onwDH>ETon~2uTF3pPL%1OmHvDr1@{UCAnsF
z7-kQo4{P(1a7w>mjEQr_qI6nxq1JRfg(FVr8%9jn1$QjYV~e2$Sej(3tD_b-^Q>ce
z?0Aqs;d`E-fZ_MA-ia#*CBTdG7}1(`W9Dz}$Y$JT+&3}>7pFv`gub*JInGIMr6Q>_
zS{tE6MXA~Kq_oYFh9(3M49}wb)wg%$BhlZJfOjYYso5RLy=M>AepDv%$4TAffLrVt
zXv};czFt*IfJOEO8slfNPbjB<0{%q8rFy-}wXLxTE@j_~xdv)eQb4lwYqa*c!qj$J
zs`zcPPx7R4pce%a+(VS|W@}>sTs^3y*HQ{peZoe0qiQeRtfn)%lLNiRfrE<*^x9)=
zy=7FJQMWaU7Ap>=Xee5sKq>B}xLa|D;x561v`8sd+}$O(ySuvwx8Ux4>3Pq0?zwl|
zKN%!22HDSEGWXhR&B+sAi7{$<cOx6{TKJ*x85n+nJoR0(l+O97RK$q80e6NaudjB`
z?*|fv7Xf-g*}CJ~3j+^;wBoB?5*jOLSAM3V8YgBWdQ9r*W`lw#+%HFx<k#(Ei#T5v
z1!dAU>1ZOF)Z*9U^AjZu6k+KCX*kgN^)~9@It4C8(BxICi9EIBTiC?QOBfKTqBktc
z0-^ov0_uLW@mQMU+CU-T?uSs1vCk^?E<&fyuKV;#nByhUk$m%bycgDIrs}oGk@U9-
zAZ{S}k_I=b5_IN1)r01K_tJi<LvwkSyPcFkWL~SMWT!8*3mSy)qY9CQP!p88U&GLH
z3;_Bm*TzFQEYof<cfXqV(qXK$8pl`^|8)Vaf4#X^SbNiMxeb4~@oy5xxhAyK>$vIp
z-V^-0o6e*i=jT)E(`@{lQ`XdD%9k!4+KO)EDf37lf0#&3DzM+h{Sk22r)|!fSKNRO
z5au8%*)0#^&ttn^B>RpBkH1lU_9&GV9x7XDiALXIc7fs8f0bf#ZCcR3S^`sqw2sNI
zgM$qzW<<36vXxGAJnUv`IOCAb0)0Q8KjLdr2r|pjt4YG|Uo`L<a+IgGg5N2oR-$Er
z1lg_va41@1(aN%0uNN8;NgK^#An{=fZDo1(C!PJsIzLMld#)}7z4N{3!Qv+ou9q>w
zElHBE^Bz*PeyJQ&8Mb|hoI9ff3mxI8Ru!^tHn{^d_A8IG4(=go4HVg->2g69K$r@B
zpXsE4!ILXRzAo;xC@oJl?|ZVu*VEQQmOPgloaNla82eINHJ_0LU;O}SqKWMU|M-@K
z$OlGMM10@l>)HIv=+Zadk1|+e4V+^*%N~dSLD*!TWM_L#rj*D-+|HLZA*8pXsjyqn
zB)O+6iBl0UCq3H%l0CI$oi<l2C#U09d2V);pOT=@veIuL|Bz+ZtUyO~*@+lw1ohb`
zpAuVToK6US<l!$~BpOpK4O!t4NcndDmyFroS;Rrmv%}C`S-;JB33#ukiUnL~sbp46
zh}!4a-F-x7%qRbw1@ir;n$EE1INc0)%|@NM`o@!GNQ0kY4Yr7?G^+<%vvmW*^Xi2_
z%cCjDAB(#09JK}+86nZdZ*;j!gnPW}<&Br}ci%l^<)6y@EHwV2Q}e=@$WmTNvCPg`
z>8QaVV|l2PzI>;ESg7av+=$3F+vgm)KR$W~pQTxu<ZWjy=B!Gd{`!Mk-JYozO!QVo
zdkLXK)+Za(VChDjeh*Rqu{(R|F}hVaihiqW`29Dx{}m0w#evLz`fNw!Om2I*Ex8?!
zFJ&#T^%LD(udaicKIcm>pWT95h*R><*8LfR6EvJs<7R;D>soirbA=Ii7J~wr^8w^0
zq@FA1&xxo42~pmYCG@szjc9b#x9TGzK*_5${#0SV9pfgmu!aI#Z<*aoQLw*=Vk{&K
z2fR6N-{x<aaoku#+O2Amja_JHq9d3Yme@dlIR(U(U%qFuq|<$;pS(cNtlRnq0@kr-
zP;GNww6>gnCfpkGS<J4rKJP%@a~=NF$kOLgGYant|EI_IjG6k^0_*P*WR0C+ZMy-G
z2LRy=)KHDiYF{Q|8cOP2sKvy@!&dpJQcip3%6e3G(KP-gC+_*D_oGv})>YX(){gE;
zD8q&0RgCxeZ>hUKGWBTs-%Zc65vA{w!}jp<%deHYa*thInH@QJNfb9px3mqQUJr#(
z%J~PKsoT<NS6A?56pzH0UD}O^SR*-mfq9Pu0hc5`-TpYJ%FWg-%UI772OHwa&mApJ
zci2OtLSTUFcUziIP&eY0kZRFI7in;QA93ZFm8tW{FTk)^!x}x-+Bp~^#KX{u-QS0t
zoxXM9WOZ&o0Pd?=&C~4TqVe+V{-x(QGT@LKloD`FO056QsEWiGbBaT3K;%$&h&M89
z?`NdJ46Ax=H7zf#%$5O;0+tR9k#}l*aJduDxZ&DV89+NDVi5H6Gi8qfd={PaK3(i;
zfJlY#mm96*4`Vr^WDb}V==F}}Ilys#0;i&HstYvk`TC<0I@sTJyU?^qaDWA#31_(o
zk`w2t4lIUVo5PrCX6WTF$R8;wF(WdRSyh%jF-7bdE$7PoWnU~UFz)zusqBJe*8{zK
z&Ct+-t@gsUVP!_=_1_3(vqZyawpXUrm3#BG+huPbj%ieb-dP#XtEzl_IGv|{u9L*^
zK13_jZp7Lt_xAhwp$Iiy0Nw+sReXBsoktaQq<|aWN)ZF-V-zc`5F!G?-vooqV%jD1
zY-It9b-3V?qzr@KQ&UdEnJ2QsYuVyQ9~TifkX>32hlU-tz-~^sLxb;qA>NPVW=g|%
zg_Ai)W~_ck()0>l{TU<5z~~y*whKOq$%4pwOiQh1Vy)GM3d?F@p7?i0*#`v~*^2yv
zf218ZF8wD8GDgM4kw-zW=`%3G57OO~2le0_Ij*pnJOPAt17^+NvMdj>!R}6u0-SBV
zS00XzA%SkJtq(y6`#Y_C2NRoqQh)og!bEQRQh#Z)CG9sUGoA)>C*b{E4y`^F#CVR7
zIf>)5(Qm?ZrpO3BI_5pQs;gF>W1_7$XN+<EPl~ubl|MZztUih8FKx!>3s`I}{AAfx
zJ2;#r?;1LHIR;_+u+*V93Cj-6e~cShcWc4^){P#WIfJMn=UqDWM?WpTS>uHtW(a={
z2l9<P><@}>VA^B#(4aiYqeyyg4jS~YCtcPV;=`y<5fn}}^^1J?M&=wR97P(SmszMM
z)78g(lrxA4PwdF7dX=jxN4}?vzf8=;J2JjD#bAN3z7ue6mY7#F6Q<oINFi0WGP{ov
z+`ydi5pEvCzzTq3cHy;JSBd~7QvQ~Fb&sFKK@7kIyi!|x+8bqF_WZ;DF9c-!vQTY%
z`u@$v0CjI2^+1FDgqwt7l5Gd-+9V7L_#fDGwJKZJdE>6<^~(~KyZ3>7iQ`(rZexUs
zWTYXo`Js*0kJo};P6+QDRGY@$a-i>1lD41Nf!_;${C=NVr)&|N`967aw=R$hp5@D>
zmE*y$b_Pl}3mKfU4+EcQ=xWQ8qrO#uFs7Uu@^|xV{HE<k6}xqR?iD$T{65~ov`?la
zl;6N?mQ_;gzRroRi&?cMvMOyd+#C=>&FzZ?ZSHc3oJY2ec1VVP!JKrVr5*4dGmn#3
z;%?duq3ZC3MfXCFmHEH<&oc?T3gxMBGQzz&s6H59MW4S}MZC7u;k1`BeC2p{r@y@}
z5MF<0MWT@t4adrj^<ArJH1TdZ-CQwaknU;6oX!NFbD8^yQDa?&y)#Ih3BAHa{=-A>
zqluKvXYLoh3TJy{t^&0QK-F0`CaZ69&~F@ooG^<z7~1`_#i5_}mm@|Xh72RBc%^OZ
z^bOR+-mZ((*_Dcy%a3H?Wu@FI5Brp_bWvSy6u4_@C)%}C=wKjc;u9=bY5U-iVXFH&
zqtIU8orZ0E;&rWXqguRLFKqkkV}g?E)ZJpzlC&3@bd0`Iz|OLp;m`mmPWmldTEXf%
zoUkanvT~pf>V?OjMf-!jz#rZ?;CWyB!j8vgsE52#$}O}J!1Ns7li;(8q>&Qf)MFKh
zP(~a3oY`b1HW;^x3E$5}Mt&0Vnv)U|0LOY+QishY3(4+h@Y)+$`o>p=wSTyzFJPjD
z*Z-hhPz~gWXV{BYk6{Cl+l8^7hdG8FY_GunXxif-hb>)*d###9baVf)Ne-H0!6m8e
zR}vL3zMVoQ1$_SgQa904|C`Nb=lsbdsit@$jH>deQvmQ>i)wHGv0o4W!SL;>uXjrx
zQv2S_=E+ue<LPlA@>Q^)U-A;CjXBi}A^u_Tdy>xPgG9|IDUm91{$V!WLBI>rP#}#W
zQ6GMUjOuKu>2X~CEm!jG3q$=kG%ed4G$-75jJa{a8gAR0cn_`V?+Lperqy)KaFOqH
z*>dbZjs~D)WmYDE)4vhgj0Xa*SBP%KxhmuM`r_h>P+fQ|Ut#@hj-QsgB271M?30W`
zE#y7@j~BO_BzBIv1T$weP%TucmzjV1fa!~Fp(;;!%gbh}_1_=pmW(0lg5Oj4Im=rW
zHx(T|v;pcPhNvDjbj%s)Q9R=jC4Y?La#@pj(}e{kcziMXQ3OYH5>ol`(Ul?D7lKM;
zH4yL9Dkkl3zVd3HOi(UT9c;^>IP|%jQ;1}8Uhl`q#a|wxubgp0UY`(I`bz^tefHH?
z+YDNIs762C_w@FugeKz&3V!WZMs%n0MKH$TRQbFP>vx4_ZrGM2VvGq&T7D~$MnHK9
zPpMl^GB;4d7t}d#MjxiHcwMJf%&d#|q2c)`1)>WR)`oA=%Zq!|lmFf{Bs%nJ{DL4W
zbfK$)LeZ3a)3A$%W=;tJ@u9?V@Y>057#ZDRfPTx8e3*2BG})rO+>cay_e(fpL1V|D
z!haB@bMRggvlqn#Kmrx!wtVUB`=;LR5c7{xP%?re1##!Pz0K8SNdK@#TZC@%y9e03
zh~A!X*mx5H%6Ws653ZQ3hN@H;y@H$4vi1~GkjMJS@|KGe@CdDXi~P&c=H?9aVe>pg
zGV7I1@7E4rNfdqX`s-W-5VdB`a$5_a%Yl%+b3}Ud{^LIRde9|>Ob@lJ#0`%H7|MBH
zuKfOU+w&rf_`|RrQIBY^@{ug@^2a1&kpBPW0>~FPS&ECk^7Y_BGgnd)m0Q3F33(8N
zF7=;b3dd5JH;*XFB`;2wg?EZa(x<%3dRvm~TT?!gS^sDIY3sj>+6X8=VlKbV3#fp<
z{%qe1LfHuHd0z^M|5GcfdWJsU{elYr?OI6}5zTL6qyJCws@~#JJdhEj{n)hiHaYC&
z(I6Oi{a9D&6Q>Ydj*95EBQs6@PZLYyr>iX+#D@6ayD>sYWugd0v$ktjw!7N4Zt8^U
zc4d7Mc2cC4G@~Ie;f5Bz@l4vS#bc$uTBi7%m9l5ZAU&TN3)XlrYqmHm0+lYJ3>o|n
zRTI$p{x!A3r`iEqu;49dZwe>CpjagFu4icAL1jc)rfp5k=^?;#V>>73{00&K=;z!6
zaZtgVt&`!PiFsGb(O;oKJRW1UsBx`@QM!u46SSJmV6r$)q{Cd@ctQ#JdsuDbQ1SCu
z+m^$Tw~{8S;d?<Ju`=G=c|<ExvxULcqTB|?9>{ayu?4r8DiR7kejv?w`YVnJf(H)*
z=JYo2+?$P=A^vbG$!}<pHwIHAK13M?Xx>M;UufQ&V#;{E!~kxJpW^LlELJ~y$G4z*
zvz6V=`rIvT@R(VQANB9^krzvP*oPyGQFLBi7tdN$)N4><N&?lV6t=w$@H!4dA?in7
z-Na6CWH>(c^xVhg=Q$cPK?pZ$^~_A2);iWb#q7y<FNcN1OzMoq;?OvM1fIFG$Flsd
zO3Zq)Gv_Vo-x%>E;p`Y8*%1dl1&+L@8Ef+><?>-7?Uc$Xcwc^MG5S3Rl>NFQ&UaRY
zwAeQ=1agpe|4KvzYLgf6OG0br=Ibu1)KJja^J|5;^`Qd5L?M}mRMK+rFbpYg9SDFg
zC(e&|Oymm2f9=n01?A-6e6pj~Cb4`YfWRJnC8Ca8Yp&G}zhchAw}juRKclkSjJ^Ke
z*`|~`;?LFB_p91!6ZF@r!x-m%O~yZB=Qq2rk=>2d&@L9o>KwzKPJ@iSdn@<quVj9s
zJswT*jnM8t^R24QgkGfi^nV&wd!ufNC04#eS^tAYNs60*RA#uMEf;T*lg=pgP2K8f
z-mFb9vlP}<_g$e9vP5V_AKIjm6xv09g`w}rA8bhvJ&cz|2K-Yc_Qzi4q@n&t!XmaA
zvQs%q1?K^entu5EFks5GBa%=QuYU{wv(?5}YgR=yye~SZI!L1WJeB?d(0|j18ALb6
z=H17Ji9ZA8PQr+MK}F?JGiC5_&|dIgvhtDCIbOmt8pEJR!J<PUTLuXxa`K^?HmWc(
z+#9@h)Ey*rx0e1i)3^gBOK(Sz6lv+kMv31OCfg6PO6&|>XJ#=ypnQ}Zg8~>YKf$gk
zhQ7?j`H<sfG@+zlg@Qg##o{Y=%tJn@sKtmMvbMhj*|$H!Pb;KPnF6`(7lR_vep+3Q
zO&KZh9v~&7pC-}e)|8buyk+7gF3)j^mm)9ItB1$Xgd~~E*Xy+B=8Eaw&&!^zV6d`>
zOVQO;r8^Ttkn15%g5Dt8N3!a;jj~nChT9j(sQCD@i)iz!%gb&sx1MB?`*Ih0-39O8
z&uq#p`BT^1HgI`=g5OdMSob@{@s(#66_y^6#Yu8SVzk}ABT5za%6Lm-gC<`S8Uh?`
z=DloFF<td|l*h7;;8uD#dBzs*EUV#IafHgF7D&6(Ukjk%1@(9L#>5=Ii867i5V<ns
z?18vo5NQdwJ>skT*1n@wpa{w_4-=XC_>gvaeW)&dJ$o&slNJ$YP@0Xl$IMYFC#|5c
zwt9?;3}m`|4*T>?w##7SGU{uUC0%}NIE_$Bv-Qx!Cxm%OOQH#zM(cU-7n_I5x7`Xy
z4bs-13JS(i$neD=V<r`5Mrb~s!hn>7_?%A~x$*KeUg?uXLhn7CJ}NZupck1e`|FeU
zN9B;p1Jgl@2uQFSf^dP-hpP9+EUh6^6Ui%$19HUVewF4yCWmTl=qvNLo?9P5FPyBm
z{p6D1x<q;dG02iJi-75fzt-!1_ygai&E8i>S3!BNJ#B=1U4_nmpM2io24Y-IwgRcu
z$K76=mAg60OONKd+M1oORyqUrPm8!X)tlcgGZsUq?e?KFO&Ti>FOE(fgZ>m7aQ!6r
z^c_WE{A*Xs2M3wWACoHi_^iWo=x**eQ2MQi0ivS?I}5~vr8;niY^O(x9ZmK~&mjsS
z1tIqRU(E*}S7Puhy`7^>1^0$9yEl-kRkz4fBHbN*yk|dYYu>vQe4%EB>h`Ju7ZRFH
zPy#oKQY1FPtQKY|Uo9<7sp0XTGk5O}^`=yEtwS#=1!)*?Zl8c3yDZU{DJ-YA=WZLv
z!CcL~VfFlh6)lZsXFG42?*`L93m{CRRrK6sJtX|%i1j<0t`F<<!kCJ!14{EgyWlml
z!T03L=`JP5O?-7p<OVs>e<+ItoOt`x5-$_|%`q{mA};Ul;Z)N2VFRd})0-rqb3Yl&
zIm?LLFSe28MISC)nV`BaPB0!?dumncBv&?SOXTIHAWBa*e29Nb_@4dq+i?i*yK?U~
z(If)ak3~`H=G5g}?v3^5Ade}4d%%=F_D*9=#9J&5+|Sw-PK1w9H^5*|CwwQP6tuaI
zNMI)KalyK&Vyl2RJTLM~V=KgrWH|4jZQ_`&@ooJx^FD3Oc&E1IB5=2S$vP>8K$(sS
zyr{<nyl_ixG%73Sp^FGw&1c&j*{^Hm)=na*^GWrQe>&v5zv1+-4Q|hCPo<#Gdi{Ru
zm`D>&M+_m)8yh-2($~0mQ%8YmL|&F%%-jM=Ku4vY(j+2aF@vOgdnGY4m2^uBR=qI(
zITAOc;F^QI&x771i#U+ukc9eklmhQ3sl;t(|MKwp-~(Qd_?r!*Cip0C!A7FWlr2$d
zTl{Yy3u)(soUE7EPv!mjnif;yH$w?Eu#B3s3@PC+C?k3qHhe5L8(M<8Mmaf<PnaJ=
zRM6mAcIZ*ABaLh@<O#6&;!Pu9!ag_=Mvx=s9*pH`T)_G-Nr{4wXIfLTs<<mUmiD3i
z_(36x&=eH<!loyCMR-?rZagyk&NR_Ywm_kVQFKVyK`zD4S8H<JFMymIh<fHYn4{fU
zz~F73-WQfky6bVI@^Vqx84QO@iZ0_tA&_Dw;K=PGvNJioe?k}SN$t(y=at$&iC-by
zXjZ|=5A}zEa(&PGkjdloRC5I^GBK>Hw}<XHOe?D5_mN~_+X-kY`i|Vkuk%_R);G%*
zV0)!rj_O<H$8&CucLXJ41_*jqR<ktU#6Q1o#EM9h_6m73b&VxkwE#QzD6WQARXL(w
z5`wXa!$uLl<E`7$p<bWmC{E4;smBt1iuI~(+L;YPf+;{#*^NxU%zlU=jR|bkq!%X-
zNqXyL2$+6pd+^Lc$TaQnJq~xXn0w#}2wH&jG~9JvqPE%f-i9V5PuFh7jPDvQN02w1
zH<dVCE#$<`RkaSO&(9cA)TTn*9|dETs^r|MX~sSFY`KBMsZFh4$k13nG4|~ljMSq_
zxI0|#W<8#ER%CtY;8B1)S0L3x?y=*(y5CLGXOy_@DB`S1!Cx&h-y1l0+j77g6w_&5
z969g#2HJ9b03NF)Y%Q2XX7TbaXXf7V8=OB{wVcm2Yu5YZe13aykaV|)k4???VNEl3
z)cIU=ho+wwC+fX_VXV-=YP=-AGJ(evqLh3h0|P_HWmRN?N$&~-H*O^uPAeLrB1hFy
z`!f}ekiXDM!<SZwnGD{%+FVw4AzmZ@<=-|IMA%55a-zN%1ju~09-z(9^lA|u(s-tE
z=S}nXj;+P~WCAOaBx!qkV<`$F1I|sL-Lh2N`uo)Hc5`dK*Nt3IO*MFHq4~yHsoP_u
z$mnl|lAaoS@jmxLI7Y4C=Q=jK%o~DxWG}<rwDUuHkd3$zK8Z!y)bM=xF+|jM?L<)v
zK?o~(L1+${jaBla<qtT0SwJM+WDlFFxqz3=!ABpj{D!WK=Pa*XOmX~L1gro%A2%G5
z!zFG=PknU`j0z-#XRFTD_VrA2c`Nn~;OlYA7H`A96-pbOhR=4t_Mx2e(e@B_U~n5W
zX{YqzPwCf^!N_xI%_oJB6pnNZ*ESo)`QBGQJ1n+87Fl$HmX`bF?Xi1$74!DD&(54u
z6XM0jVnm}6Hx=t!T6DE2quDfLt^|c#5D5qfa(|^t{?{ST#-62Yg>hZkn-@d#-S%mq
zgOz(YG1;G3P5w-#lp?F*-5eJqgq7<zA-fkZVPXj1A}JkK22O`_FpllqgybkQbe=S^
zbGXiazpN6C$o}Dk6PqavN4@DuqVI7HrQJ&<D9;=z_Z=3&tW6>*%$t{p`KxR!vGNA(
z@8d)*bzz!raR-Fi(zG!9UEJMq{4KR%1FL_mKbn7{%XgXxss-C4lhSGeo1=hA<-&D|
z8qN@6Wdnd+G=3o#WdrLjSbU2=I$!oSA-6Te09vgW)?ZGFQRym4EHex(0!y96|HQ}r
zpwE+>+=nkI!MHA%CM2l%<GI-C04+W_L^S#rgRT(?hB&^Cw}i%Jx`3CM!`?L>`<Y2_
zz=w|$qP5r?KtD_(GJo&2$1<`Aa$>@}!vve7fzs@QYdR~fX~~cNPPiw31te}TdP@f#
zORGUyehWfv?oUC=LrC{4v|0l!1eCfRpBNR-0M$8XNT9eI?A~1EsF}~FKKv^Xw+%Tr
z3kSpQbQhbUm0^iVAvZJ8Zq8|iJ~o%v0ue<$9j)b)fw4YS7tw+Vl*Z0RQ6CRvpQRRJ
z_Wt_uNFd3-1K?jD)!iT89&|h1rxqOoNGMLiMLuvYDBw?kk6uIQRSbWhqmfK?m5V9=
z%5OkYT0Bd3n|X0&kAls-nER)L6O)dDtx?{rFSPlbMPq46#dYS;%<LToAxZb)eY*Y}
zg%!i3rvbA2V<cnVV%}me$GYJJzN`l-7I3ajdyq~ad=4oejQX5bJ|;VL$hX*3a0<;~
zOT&uE;XG*C&$$Na<p)5Wt{YiIgNK2xKqf1wPV8R|g9u<P8M~7eDo0gLWKIeed?EX=
zX(><ysh=Xp7m8PwHhfQtyCYt`-osSrf7&VFyQUMeO^H36TKL<vK}jhOaLbG!xU6YL
zIyDT%*&V*7gQVPLaiN(ey;e2q%CN6rrxM-c`f=gz37hreDRW@FGeWy0=T#k5s5f$M
z(0uc8S95ue(Kd0sr)g(q<~$kMg$?q>d_%w`0g-Tc0cXEnTVIMC%n+eFLbf0|@s@|A
zQF4=rMHIQVOmX{3ijy4}Nikd05K}0ldK%3-FJ4ov6S{p|>~KH*?w+z7b1Pye0#?TK
z|J4n@N==?Rg;lB2HI|B=;I{hL=Tg+6zzo=WZ!(in_?Bgs6zziK&{iP9kZo*JCZoeK
za%GK)u4}$jN3o+J8uS-97$lRVNE;Lx$sL5hUxVz90q3!I%)Ql=VyBT`K|5Z2Xtna#
zK|Mrtu^kg3+3qu_n|OceL;i(_P5)t<_3nOtXT{I9YDVM@?0TgwVjVc1lGn6ZPGmAT
zm7&&}>%-gFcz5~;U;yudG8PD^%q9R$uYjxC+2027&-oqJlPzl%M(kycFY2bdj{#ee
z-4mU^zNd-Hw$znjE%G+bDCma*?!zuhrzT~Sz9iwmR4H}rdKRn4sx)jOgU3Hu=&7g_
zAAS%>=P_!*<Jh+o=~ooM`h|j-dvJPu=rMI~17Uff*c(Z%R8-&GdP*vw=q0~hk(9T8
zV5Ns_(|24m`!<szzR5Lyn%w&CLnQ!X#hTutBB=20Jzb~?RMGo#z$W`*2dRwthUn~g
zOJ0AX8V~fl(<I75m0YUJB}_;CeR>MP^{XGdb!dHOyX}wvKy9xB4~Sxq5dPb9TTA~u
zA-$Ja&ktB!cG_3H)A&hCakjggo{&MbVww<ro3fu#kd^N*Y{zw;n(;nQ_5d1@!kZpE
zv90zaBe2~!ckZ^+JI^>m$=Z#=qVa<+4^UhLcjavN)G!L}@B(TZEmT?HtpSiq>)txD
z57NG*q*^;~n6tbF^2o=Ne+--wzJH09o^F87P*vchP2Il%&Nn6+|K>!HymSiaPiwV@
zuiY`12OXg%xWp#_DPR>TY?L29-<6d7Fj!k~yl#$^FDV7v6Fd%Qid4^Gbn7}?n6Cng
zyQK#i{)iL=Hiv*c$qge#3d9fE6&Im2e%7HDlXzucIJbpNadIYlsk%bu^Bo#_KKH5r
zISEEnViwlG^@+peDtm508*@nz!*{=#LAr+8Ej@<_pGuaokJ7ZIKhh&j5V#g%IUNn$
z_sKmB#~VnSBFZrF)b9oX_u2}xo-9IHZL^FK+6ikoW#6eRPhp6_M~n~uXmg&YUB%CL
zJ?i9(BqTZxzRZb<Rj>WSfPpsGn;gAAaS7m9wF}KYKv`OYRrOj!KI!pN&(1NY7$M^Q
zq7=@evI<VPd`7(^mt(*9aAmSeP9outes75<)tNh-FPhp<9fxS3yxi1{1=s%TD|*E$
z*OpvQz~YJGAqnaEq~h>jEzN<kBuE5~a0aW7-Ud>c{K|f1`;;X5pfafb&ninI&Q`E}
zzL$}Y1oQ4`k$Q5u>Gv|uL9CP;3UTM9E6+mzQKFfyXHNJ(D$lxXLUY)Axp!N4764>J
z?MQb|R{2wtLu)ncxbmX6(8sxRm(^?MeQR<>0~ldm{$-p4^}z+LTn02J0Cq!|NCs@c
zQiXpqX{i&McXaXL!{*iQuWjSXpFBU#OK|vLLNRv`KL4N_5iK$O&_W_`Z%y(0n2Oe~
zgF{d8`xKTNJn~dwMC7Y2V`p-U)yT4*JW-#^yre3`%{w9R2g1aBWT=EA;%L8WA|qD^
zB9*8<(@&_JTXz@PSoqQ~Zu8Y^;sT0BBwB%M9m<zs?TYFKcu7YgI_;^eFoo$YiXrnR
z4z<p>;J`OSH{^pe6nRV8*Q~cR=Fc!8TE=rzbu=T(po{jJ!FNJ?8eSM@&LYTj6S=U$
zi9zan@(2&&`#V2$CNFDJHX#AHf<E%(Im?}**P89L4kE%{w@%PzW@Uc=-$ut3sD*V?
z@X*w+`{ac-nKeJ1QL?s*7Y@n0_qv<&S)-gdEb_z-48HyyH^>$k&EaN!_C_hD59-Mw
ztUhG6@O$_ce0P`a!pDye>Mi}e6{lsIkwlIb=I=cpq|anRBySMMv+tq3u|M)X91c=P
zXSskY<QXD-wrtcalI@PvxVLAvsL3;BoYQPK{(6GXVK!1QZ%#?!U-N<z{=TVVCSmu?
zovH$zMbTSEr$j5}6Gu;_60)y4F?W<%xT6kUl5+kv%{huEE$<~R$S6L!y~>E)rZFYa
zl~b&*VxgOcI6FKP6;Q@yHVLA?eM<@V`2OYeo`}$aF_PA>SfKB;WNzIpdHfxhr<a%N
z#ITxW8HWQ^IXy)(HbO*DP|!Mz&}f%kp;|*XG8n_~`9AeN{`Lcmv!v^iU(Sh)c_!3n
zk@gsJ>Xj%>Kz-IJnX$nkv$gakO$f$((Q=XT+MT2LGHlGHduh@ip|{ZJ%nhZ@E1Bz{
z<_;f_3o-d)p6<ZzFy)AUHZRK(JCGLLGC#gM@+(ooEp}T^&-Xa4;pC8sOx|gsmsH3U
zHNNF?8W65ie;IfD70%+G!5F`&q*#)fvsB(T=#suK%8gza8@?G^*~-~A@N8cCKQko%
z)d@|8PWIPPwWe56_}Rr~r2%(1p1J#EwOMWUN12d|g=7hF@VB%yAdfc>B}t#+iWH=I
z#l<Zk+M*)pVj4&M=;iyx`yK-)Y~so&Ol52zjL{nJcLR-Pl-$>d;?9fXD2Od`EbqvQ
zwg9*VLE2-X@q0;oy=7;#A3ghe77&YNG&6~|=k?IBZ-Sm)W@s=%MH8v^?R9(v#U&Xn
zz_(PMe<JxZ$}Wx%+s5_`0oJ4>XPnHd2OwTGq1#Y8C9O^h3uZ^}!ga?lx36n=K5Q0t
zlizJmx_|cpT^-Sk`5n()3op8^aDvwiHWEgb2^>?S)7Tqbwxq*^1C8FGY*Jat6~u8H
zX}NO7U)1L$zXU+_(7p~OtOdc<tM@oak&+(<S%&z(e|HdU0bqQqWcl^8^cFu`JpIn0
zS3F;$R&IH?WZGP!L`F)CR=W6}t-v9V6UQ#oJfA%;cXR~3QJ*u&Hf`*E|D2$n0Z4HZ
z`q^veu;HPNrCYxtq5xW0Zkxv*4~Aw(_B~w|O>9gp_$@vQIE*f|p=Mr-IKrQI1fY7i
z!D`$qIR@!!p%%%3rzw=17`B>GdPC%zF>xst+8_BD*2vATb4>BIDR`L{F^y5h2hWs4
z4Q|$B(<0PyJ<rrg=GavbE7<DMO&Hc`w`JunX4<lN{(QY=d8nii0iPmis}XS1jY#hp
zdBQptltdpb&<$x-sU~Brv9oIa@Ex&~WE&!n#!Mr*7uFXbz&Nxtva++#`X%42FNvK0
z&JCS2>hy6pdDP#Yo%{91Ftww0(-8ah`~!}l%LmRNC*(}4@fBt`7CUOW`B_>2r<8k7
zw7>4ps03Al;=)CrqwALB36S#oxGbXA%-=@ZP`<+(^|d5YaB^CY-0Hx^*2RE6zqt{8
zjWqare><#nc2iKOTZazbi9;AaXW{pKnGJo@XthPfYk2ipOy861n|bP6ZmqmqoDj>B
zh#w^0d_jxtR&**l1_t)R2&bri4C|$So161mZ&Y?9dshG<Xcp9inY4J^lggmj{MuZ%
z03=w$RNJ^&%d(HpV4!d3JO<IEckST3GbT5KzUMU%S<Y=&bvYsV^!E8#fsEbmlfExh
z&x-8SuvI>Wo=u)AQbR{)Ss!QoisqZ2F?O<8lG9X3!%H$#RN{NchxFD^Q2OLy(s5oj
z)~n4J5svDH3ZQ7X!O`TiXROEH%7~+m>)1j1_0?OUjtD&jzItP~C5lhEbJvUPw$^{K
ziNB!Qw_FIU<+wKAp_UgGDgoJQF`@oLHhsUZ|GIt<h&Y@^@ol}LO6B&-pHi<^6Svlv
zCL3($|E{7km9`(Ow{mCX`1=G9*m8WIN|qcTi&Yf-Lr3X3@Q@$7C>f|t%x&j`ZV=cG
zd-VU^#OvU+uC(!0N($Ak?cR*mq0w@hF4gzteqdze=;rA_$%DdN@I`>^EYtLzq=>ag
zF-GJUORd|WFyq)@qLpM)+!LaJ_in6LJFKNBtCrWhI$BLlaDjRGwT&3uf%w_aj%4zJ
z{Dc461#FzZj+A<NxI3M$RCtUzD_ec`XQ-Pd7Nt$^sUPdsE9HDIJv6a%X2ImE>{+pG
zy4DPnCVlLQ^S)XON^AK6$xYhzk~gjLdHeUd8FcYwE^3c-n!SQu7(&3f^Y;h9Y&XC}
z&M56EjqSyvr4vLs?EJ-!SdW?|r=<0gUjDmj{QyP0+H>m*4akN*(q~|0)0+NHZil|^
zxYMO5ZXXbk!~map@oB$R<XBb4bZ2zxGr7e!+KWrbx_r%2nq{iXox{4Ob_FqqGu*A5
z9?qf3Pm`X!A!JeN&uMd9Kit0i95N+njAI=5dXLgIqe$Lu9w#B<%4q<EY(BB3#t%J)
znXk&df49MO=-L5^3>Og`-4ugOE-8Hk)9C*t!7$!ROnM)vx=9u5YXzJ>RJ2lpJ0VUR
zy0QKyxqY*p_&F>THTnbtenm7vU(LJTfSo7(nG_jw(F}PczCz5`{>fG|8ljGv?gTvK
zpnn60(h^gY*PkJ1HE2TLBOzi$9F=cry&@~}QMLXQk0<;#K4+A@)`Z=Oc8u_TJi_Sa
z!YzoOcOsO`0b$;3_s?SGUsbvy15pXCVs-m(qjPD6<71-MdV;zP;C*63_}9*&Gir-t
zc1r0aM(Cg$HU3;h1&;gTIm3#Y76O9Ik{{eJ05g9G(hBHyG=c54#Qtow9%4cduW$~u
zr6Rr$DEu&pW^Me9Od*MnxY{f|%UdVq1o+JT`EQ#Y+F|HTorTqKHSf2{Nsz;VIMMnn
z_nNxp;5(a@8u6sblj#CzV4GhZeqUtwR7S%2Vw<WZG7@+A?(sMs@O=9#=`=UVRwOTp
zc`6rXcQ8<K>kAK$lw~)E>QA+FHmZ?rgW0IP8ONUayVH{St{5Tk@)8exmPF@Hg1!wA
z+}r^l|ASKXQ<W@h?z`)0j?1+X;BO}NQe9i3hWHi)1cZJ3Co8L#4FACG<><V2rD}Tl
zBsEBao9)@<Wv|~$Q6k^HSgPmH_W(DlN~ZhZiJ1nAmSu#B9Ul#vaU6i#55&-u*a0%<
zhmYRXCX-n(GTUoB?8(PXM7cZ0C*c1Kdcu-4N($l^ZW%>Ijmz5H++3{QP+7A3kf7^C
zHWLB|W?a7I{rD#_ew{gF7|A~Qg)?l(X@ZpOH`+<KZex*zd=387uVs+ujRIS1v^75W
zAIP&6_p_3d+h3@aK~s5yyC>CrhWffzdF;u^%%Hz8J<7L6dPj`gG$g{J0<&zUNMoSU
z#Q^TR@b<EFxu-G+-`3lw3ZEPVMWj2lt+km%pXX1~<9ueH<SmGlKabR)(6OMhq&P+I
zv<abIT=%9`n;Gwn>B&IV+V;dwr1O-DM5j$xu$BL?h#~w?RJfTK%WmehmihhLP@lbm
zpVeSiHr<+r8SBj>&~A~6mJeMcO#pev<+9+eldnfo!my9+G2Tco-@;Vb^&DBVon9}F
z;W~yla5oUJhvt?h81wap3!~ioz3aI&y|(#IOI<fS^j<UA`ovV`wfxl+x8llJ2V=v1
zLhs9N$v8N&PXN}Lo|p=C0C5Cq^~0(n=hv0h4Sg<s?l)714xl5(@Q8xQn;|G;M%fhc
zCHJmr+8AGY+ni(?m*2_NwmD4*{x=*QAH68-J#4hjV_SL!?`jtj(1>4c#4gJMioa==
z;m?K68hY#88@}cVfmYJ&8lbpy?$?u~!SQ4&&`mAZW@pvI0&2YiwB^JU>n`ovD-sz*
z05)8mIh#>YvTT~9d-np8NbxGtFAHY(qDQ&;g=mGevu*l$+HNuOYK%s31Rh>9^iL>r
zOvuTPa(`?1TQQYcWxNSaL2A{ckhLlx|HUN+js%VqDt)8qJ`o=;FYYFwxtZqP>|``s
zjmooq-H?o&a0jQ5We0VE88}5Z+}i`Rfh0a}${H!-awD==#`&bU9m|NSsS#%L-UB!)
zgx_H8M+V&Sq|nund+2*`okucX$C`(Qgar8#VPNarzjK)UuCHHacmHrl#Zp!dwpc}v
zx%sOAPR*Byc>@x2Z<wla<G_cXsjhaeh#}3`yyT;14;@FPaYX#SBrYR^w`u%XEUzwk
zL1624&eYaU(?uI{elElA<9o?NHWl|HVZEq2T|6yuOZ_4-xLftvahx*cEv&HIVE$r@
z8hYHeJ_yioHXr;SaN9ZvpCoDe<A=jZ1BRBCR$)<*h=arDJqyQFQu^EXlq+BHGhgq=
zeb0uN$_qzNbnIIR=k>jgg`D*r&#9MSeX#fWPEdA}zPH{F2L+EkG52%Rio)S;YaS>s
zFE0&y>0S<#;zY;z<=h^7Bg_BNnO|PXrQ@kAG)`DXi~c4OyJHz!-tYFc-VJc3rgqTW
z5BZJ&f51#o6touV_fcHw4U!|AIPMvh6M1VLG5K=XK6!5+Bnr1LB6|QeJ3_Mq7bTa0
zky6dmo(he2hTsLNRk0;aiG!C7?=!48Fu3Sd-g?VDLX*1Ja_SW4@)ikaKzqHyE1cHI
zeu~xJ-K|`tr-{3RNo?$7;n?Thi7I`U2@s>Wj%6o4VLD}l6N{Gq(RtszHRy#~yVRl7
zzWg|Y=O(!B)QKDpy8%ZqQMMPGR<EPXgkxsNOJl0dM3O8`cDdGIe?pi4_6RsU-c+~B
z2j<s}8%2jkjuAB7vPKfdzqXi1dW}lk|K#P%)=m6A6nNgri8OBhQaVl3FyHRd-gym8
zTz_zpubL7>TE5oKu9GCU#!U54Xp?}0N5bJ5Mw+^526zu2Nr`!2ghucWv!o9TW*|SE
z!bCU-e?q-4>c7HU;LPprN3XDEDMyvj4h;HS3U>AcuX>IbH_U~aTfy#erI1&W36LYy
zJW>ydHu2>SGE|lwpTCr5aX~tT37+r^?nLJa-Uh}$^h+p|iEcPmmGI5AS}L?|sn1TL
zZ>jB+x~1;^SX*EER(|Pn%9bcP|Nc8w$#Kk5{GRK8;NmE0P3e>%l)70IQWqQh!#@AI
z1G^bc*SGxYRu*a@$;<t0Lf7p2;(3J)(-Q)fzc(`hytj9z_c_&;-5JA|y?vEEArE5q
zf%MqDcV#9+rSyYCK)r)>x4*u40cied57&VnU0?Z=eIh4M1s?RuQ@z;DZckQi2mXFg
zuQnScE3q8ow4PhYOVGE{skghnWFq({9Si-T%GlkK^b49U=(W^*+Lp3$`}vurD1I}I
z4_KA4N@{fN<pvX3u^EbGD7%!u;RBfRSaP*b)FKa$6#j}taK?O2F<Yv!ycwj=xa)8A
zx~-FCX{1T1t|CP+ukQdW`j|KqJs))yIcN;J_nhqOEI&M#9WH`Ybp;Xrvx5Ix2IUzO
z>b4O1BTT^6o3HmEDAasN!aq}}YTE3{WvqxxnwQ+1F?R+d>_KVyqUF8$CLa)F?z!JP
zyRUoq_;-oC{L~0GpEtk`iEO91bqkB~@~`8rsXG-)H+OXzY*|s<H=77uWe8UBZazBu
zopUONojyIEWm^-2-Nen-;AV_6o=4_t-{NozH%}^;{Y@Ki&^WU_bH!c0fBH4R+CKOG
z%D6RR`r*>2fm>Xx!7Tzx{hp6&R_z7a@NvFxKi6Iy8;fsre8bK$1NLuuM{^d)VFvki
zU6){jlm+5-D(H3AiDG-XJK8qux;K^vR!HM}IajHt%l&3pyFmfd@f}U$pYMwxhn7@0
zA7(jd0UjC)alJvs_dlQ6PYbrLomH2walO+#+E;wQ5Zrg4G!3`<!mD9i@XJ{98&czO
zu3#mdIvYzt&#MYp)aeVtBsD|Pn@uT`)`#sIA4}}`ajk;@!o9rV;GtffBh=@X&Y|^u
zsnn+Oo9ne{nIJXdzi<1`hyL@j9*bZu#LU0Lun7O}GfHlRIH+1pP}<?*QzTJSv9*R;
z#&DKjqgWuGtMV{Oozn^9aw9|MxMr!Ya>*>;8mM(zuzd+-481f(Z?*^PNYHTDy!?vw
zPk()}WgcT(%_{%RLRHf6bLez(wM7y}sl?@>`U<mmVf+f34&JjsbNy*uA{Co@y&gG}
zYF?~(6BeR0nslAn8sBoAXS1+ku?|s%Q>%=6rE4m=6rY7`J~I$n3&5;&_Gmr`mJAa2
zl=S%WUke$t_ow{*Je~2fU6P@u_WZA23gbhb=ASF{8kYose)!&X1D>YXi${E3pKp)H
z_)kZ0)4Nt08hE_+vx2>w9l+9{+f_I75<cyIGYa$~HTai3;nzitu#(z)*e}e*L*I?I
zSeLb_cL+OfiuqEg<(BZZXjPlN?6r5<)C&4uj&ir^EZ5mSo2%(rYmxD?<M7NQ7ps4?
z$)T{=;0GP(=d7l1Vf61lVXp2q5#0vEeEC0~@XxCQ&~bEwt<K)Vo<EN)65r}n=R2Ru
z9u@3{Kf8t^#bo-;#pxK0z;qM=uU60(<3FIgD;i<d&=Yy^-t($0^pEY0*sb~TS!cI;
znYLE5>5Tl%4(YV*WP&W%;as!%d=Ogd5;s-nd+YP~sv%?=qT{CZK|{;wasR_RB7-$o
zJGryC**um{rlbirRU06Q1)!*}J<)4hAmnzUO;l(_L0+MHV?MlMba+L3{{$IUwA{C=
zUDDky-x;Lnwmrs%f(FvMmq#2De_s7gM2-2s=EeM+jib%nJd3vH*8Z4QMDAvxL54hR
zg)3y#g#<7xA*|DO%CJCMj*gD@*2uwF(T+_*{DIt|&9eIdEw8^hUVdD!mx*J1xeVET
z3px=aoNq!&bDPK%nah(Uf^gDbdRsxkXFP>N>U)`)qS15x>N_=Mvfy6X_)o;1GOwEb
zV=3I@!E?nj<!UAI*hSX!WygDZt)8_8Igc;_ECuYHt*tH4=koF50W(U6G7N%0AvHrg
z?nOJguR_kg|7&L`|8gTdSu@(}CK19mXF$~-y>_z{m=VwAJVGWrwxd125w%eV8da<V
z9b>6p*J%4OjYPO*RTihps<Bv_mc%&{PFjPtr;A_O;uT&eYf%(y88Mk2Cmxz3_jIc5
z!k^C#!qawcOEjB*%0Fa`+e|rUFb3D1_#T>(ztOjHB#Nu%OPH&polaOBc)hIRm;6)W
zU1tAF;;qB-&~g(p6qE-9x|IbQe5$*f;{<o^T-#pXTCY+;&4bit=Pv$dE070@GqIMe
z=L?moMJ>ot8CvWnvjgotz&JNBaS2qe`|)JS+1?z<$h!N>y_Dh_H8k*$Rp2xt3RVqF
z=j*}Y?zkRO2U8iJtp6}Cl>C_Pb;4^m_LiRKMtt{=b>JLKTsXS*dQRsZKK|Bt(RlH$
z`fPO{EV&#-@Sjwe4#(d+GsLz(9F`o<>Dbr8@K8UXoNGT>hj7m-rtFzXt=Q$L)Qm53
zZf>o?F(+dSrq5K<7f$+kX-SW~fIc=$r<>KbOU`B-Ab8e+=rLC(@Vx2%&t?7}=lS=O
zc`ID!wbm>1_8V9n_9j%SJ;Tmmt<qa|ZB`H9wQnsVD#Sh#v?$R{IomB8diXg-l<2jy
zR?O8hu?wo|AaO0T=S)^}Ue?e#mfbwgv|ep>JTSZMa&3Ubdw*OMdhRDEnaZr<JYHHb
zd$Lw|Sehk@W!d~?kGwwJH&St6Kk5Kgw-!A5%%qu{8Rx3D5PSous%0tlxw@dYc}++3
z0ox!iE3+wc?PM6-jmtN2)Qn5`q_^z??b`M>PMShvndEALb~wE6{&{~t5`kt3iI!+2
zx9yTk7ytK#63V-m$J2V40^*c*tyNrG#<$fdYRM(Y%MG`|{Yty9FW|5^?FJ^4Xakdm
z{wbd!w5sp1Ls(+a-o|M;)dSl<Gy9hFAh1FE8v46W7NEx)`Ha&a*k3esCB?+<YV&*_
z4(LhQ&B6?Fnw7G?wf*Evl)N+m&Z@N`=z?3WFAd_>VOU9f1|3Q`#MGi+V-fY*Vz<iY
z1p^<`CB15Kg60vAyr*F<3diLWg6}UTlh>2oPS;wwY?nBe8=dK)+9+SW{|m1DuY3dN
z9|H?_8L`3Puuc5>LO4M(wR&=5Z1VbKcfvZQCdQgkJ+!2|JjB@WXaDmpm5a!{koQ=R
zy{?**2S9WDZr%o9Sgupi8MxD2DmB<uhS!u1ez60C)7r;su?&pC<*f@t4WB)SFTg&n
zP}wr;o!yoN6+MSmH}hy_S8Fvz?@h?fC<<iJ^m%E)pkU>6DK~#fgeaA%WsVD+vATdf
z%g^;>Q=%Jp{d1g18`AHolgCG>JA2KiflCop@~D>L&^Q?B|6c~^heXg7|53Tiq)@G{
zIN$4P-v|bwhTw9aVen`RQ3tv0L0go$NzL>Lrv8)CwOWwjv-Gb*W>5>hFIERz6)UJy
zKEXzgw7Y{aLGG~N1T+X2{4szdEV(Ol_?8~~p6b%42@A{){$md21<mWZk#lF1Y24-x
z*#{oL5`9$?yZOW6LHq0T=|S9DaV!O&OJVB<!Zm|xzB@)O`}{;}QCd^b&$E%^@n4@?
zq@t+myl(Ae<>YDt0+4=Mv-Sc0KQ4qy>A>i3utwoqEVvl)Z+(AZ72hihE6nmj^QetR
zXcqa#Qee43Luc))LiOQj$$f1eAxHUxYdF}Ixr^0>xl;|OM^)CqoYK0YCJk^MSF4I9
zlv#+Zj@K#GnfC1EE-C%mX#sFLg;YcI8*OU@fuqhh7XXRnW$c&XIo2f@({26rrJVPs
zDCzDTn1b_;kkt8<|K_@<ac)4(mAkpPGS0zS-CAW<eMIYEH?#^fl=~j4B2{*?-AL>d
zov`x$Kg&TvC?>F*FH3t>ne8FvviTf12;;-9w_UD7ZoVH!519?(M+IN!7K8i4N%wwf
z2lf}Is#la$XlB4vM(tp+iW9kMsVqyz)V5@Eu2vm3=}(-*RQBusyQv-wP=6|z0$Bmn
zG_0|}o#4+p)^7cMeGk~RZ`v=%_`%-y7j3hRUB~O*S^kab7|(oHx!M>h|1p&RSGY<o
zgCm?TVCsAMXZ=EWO2fz26h}s+MD}%QDfoKBXQ{}|p68&we5B<n;}i_=7T9p(J&|^K
zU2Xu4j*wSg?SXS;?a|^&M8oUM7hTrx-20m2vMw!+t=PG0d0^#o5czVAO*Q6IhR@BI
z=$7r=UAZ3gj6>Fid308$r2(V;l)|l<`Dxtvas5F0B=GSwz|XDW;0wTe-jdWlN*TB8
zuqkS`v9zuv607>`w?hdeD;>0{9bMwdUqUqWjQrzV(p@uv-%a_Hmix_rg0R0I!Y(Y@
zKU}A%@q*XYY^z=`658+PmSIPO8~Ac<?+lB=V)waHEuj&+iA?{vPg-9<+|MH_7GU)a
zZ6ciyuKTE%9ikb+i(4_zWN&R((`Ly!g{%(1#ZJgeofCDioA3!njj|)1aQn5E#qvRN
za`H3^8yF_*{DlJI1UjS&F^$Y=FQNAU!+S%CZg0t&up@E*cd+>fY2o}!k$gM?sY(Rh
zDgM2uG{7KWH5hq#+uEn!dMwZH{c$vSxB%42(m$&`&7?Q$^5~!N(A`cZxQfyT_0hD4
zog_+ur}6O(oRjtNtm-!i)^p&HlAtw<Q>-)#+$h}q+A@C36T@r0OhM}>ZuK%xt>&9a
z{Wed2=HuU-`%3fU&9>LB&6z)Np-x`(5IbFKGmT=6!E)2I^{(4^4en||{+lJJTll%P
zHgrY4nX_(K?`s*bKA~CWXkdT(@jUO0e~6r#-h=HwfE|@k36{^*t-lN3t^c~Y-^+N!
zQHSc6wBG(9WIC#y*T3Aj_Jc=!TAJ-VM{jgHTG-IBvrc!~S&1e+ao$(rbcUhg=gXvD
zhndN|F0ZPBV}oo-do!HH2rY83{toVYN}a5_G3Wh>4-i*f{-STXBK5@;swyf^By;5a
zu5!Zq<DkdmdZ8Be3Gj7CDB;$g9Ul(%f1f1&*O?+0q2PAtRl2~K`k$3_*wZTQRcX)d
zyaKBtgB9yysTp{i-l}o3-i=c+K@s+5wh44zu4RbmB%7~G-InDvIQ1&kQ^9whakR%v
zrvQ7s#!<88J>S&EE-DN$?V*kDL@tt=S=oU{Xa3z>4`ubGY8mluPD^<Qb$fe)H)GxS
zHn7W0&(e|}JMK*ItgXg!y$p;o8LFtqdHrSHRd-w-1WIOpI8`z5NK2019cvsPYObvh
zjT>GJh2WMgX$v_S{|8<;P{FH_a9hm`z~r*!4afz}M&1J?m|E0;m5detYC%f@X!u9*
zI(Fw!DeG`^TS%>S0=7Hh8*{>$Aa%~*bYbTK@+RhzeqP>PG3O$24|Ut2U&TigTLcmY
zZmYnXNlCzBi^rvRqCur`?^SJw`FOhRxZrIn_I|C;Ex3NoQ};K35AgqI&Pwn<p-Pw(
z)c=rAWyj91<>d%We%q6P(@*zO7nYEa;zU9@){NG!>jxKW`zBy{?M-c8K>wOBmqDU<
zzu2N8)O7*!7=Fbn(N7R^zU=apd~f}HDS~`cAG50!VEqWrdI!-Kg&AG>_5bPaOrx60
zvOTUS1;(bL0@5X5ENMZZ5Ze}1hA=2a2{I@ofdm4G3^D~oK!z{}EGfWJ5Cy>j5FsRC
z3P}i~fFdfRB#<!4JcB^OB=Anqw_YjlL-*Ithr6<JZ|*&J-+lJ}?f*IJo_rMD&YHW>
z&rCP<=4M!LTkpTa4i?=b9(1F;sote1O<H^swgbCRy)=fz7!dTWVtd|SgadU+&;?E8
zpSZdpE^03{pJNeGD$*K$d2ul99!dadp!h!TzA|R=p>uixEcA0rOtI?*$xcI+H+p%0
zt_3{x)mT&?2Nk~X;!?&WC3KBw{P1Mmbj7Ntc^JhGtK8=JAs3+pqWgn(Qcy9sp>wdB
zdiZ-Ltimbic&%|eeVjmJ`u>2pbPr>pi}~f+Gt{iM`xrycSPUDD<7Cymwn>f{eK&Bv
zX?Z*^!-}^1X-53PemyTS!Ux@3B-OmNW;MbfnWMqMyF}?<b*GF!_F+qUM@-C#JQ_@>
zpFK+KKHkHy_Q-R??>nmYv!E4M_m~+?f}$K999$-LP_rY3JyWqtR>qcf%+PSX@YTi2
zjVZQQ$oldK2_RPp-i^R!`p-0;U^DoHmv|~d<<hgLBt=96ncfcxozWeosFra7P_UQb
zm)iM^;p(+-(O1yD9*Q5L!+q|*ivpMbJS@~AoI&a}XES(UDR9-^QfiKmSpfbOu762p
zCE5IuJ69!WX!KGhv6#%7)SKCBGi!M(J>%gSQ9)?z3zjhREfAVaQFY^#FZQ?rVB{&+
z+TwW#BSXVE1WN!OTwG;$4BeGM(!osFJbvo{fRA4<m(bz-81Dp`3R;{_3t^r#AM!<d
zw6ysTZvURu0cZ$?JNSAlbi6d*Oxn&_IzXFH8-?x2L&qvUpXCS3z>EDBRYU?#d<iUP
zlEPTI*vgP{kM2^{8ZyK|(hx;NzK{M<))lHb8%8<7T6c{rzxrlhY;0^%E`qq?_Yn-d
z<PQ$E%>>^4n3=t#H`4gNS!?KOW>!`w*zew7`#Z8CIADT9B|;Y^XD8(3<)fR`74l^w
zze&{sLu14GQYu9SMm~GgjD9{(EW|#nea!4_@vHgeQ#UGo)#)tq^!iZMS1rXt2uy3{
z_XN#!B2YGtxXLyDE`L-9kw;wRO^igkPGxShB2~95mVt$n;q-J3fAaf>{~Dp6l~{4L
znFMNrEKOPT8P(M9SHH-Syy3RB9|kOZuEjUwi_>YWf}XN~qBxnetref=j8ftFz?Ii1
zTf8ICndZ5XKC?e<X1Z@S_3_)Pef<(UM8L0Sb_+uoh6AbT%xd(4=ddD0PWj!M|8Z#d
z<7x9njaf#>^Ht|Bj&(;i{Bg+2oAi*CYnX~)2Fa>O{_Q!5zWv88*NU`rF4%$!H$J{Z
z8duhmYmswB!+*M1d;L>_TEN^@0bPQs@xgE?kXY8>fJ{T%Mo>rdd;pu)$MhH_A2;s3
ze@_JMpz%O%!F$vil7=`!&bMY~E6(RdlW|}!34gvxd)21c$v5tR>STYcN{LnwzeC(q
z)4rSOPxI_9%?f=1S)F^ULW`Hx;5^ZdbZ>d4Z)Myxzv)NcY;=Cu&Z0L~RZ9sT9plIo
zRm$o*iI$XW@lw8WmT&}SQcx_HGr}@1W$uE;=D!&IjXB4EOKQ6lfBd&IBc6Xs>j{!s
zz1((Ty6bYe-xS*X$ee#KeuVe}EE^oy^^@eF;%Ik?M{4hLeE-0Jak%FchdKq`;Ybad
zX1qqpERMCk&P7qCBRzyGL7>WSNK#sTf+E$ndxB|wo&1k6u#{oMlh5EzR(0qB^TU2?
zJa)Fopt_Y=QrdDs1yXWh>C*r{Yf%Ii{3JX0@9-<NqP;6+#;Iy7hL5wLUenIb%~X{D
zZ_ed<urdswp>WcO?B8q>8gp19r@L?5v+;Sa$^yk<WjYQ0oPwb>BFWZcP2w`*D*+(=
zhdc&_K6kVwgJfS5{WLY;k1bv=Dg&Y?US^l6z$HfRhv{`SZqPCiFJzquVk&S<wV8rM
zxd0}Am~!j|FE18BuM)7-I2O6e)>IwYG~F44oe-(xlRKOcM#KZl1SxF(>NK<I*|QKU
z&vQRd1AS*ICp&$vx}+qh&@!AFU4bNelL#cg0Z5kQ--!KaH@n60_P6A6KAH#w9NQg1
zorR7ifjn2(s2WJeO;3GZybP$05N0v`aT|};&GKcVhByZEik2)6_W-0zN(DPv^U3Lb
z%JLzbrU`nS^l8pZ!ful91dCq-)n2;81g^-Lj2UjzQYwQ+PiKMp0DcB_l!8AMdinBQ
zT+TYspjOv>yB!-6-}Ghfv0-OdVUh+@Fv@kd!Jq0DCLoP9&R=reU&-f?MHM@fA$541
zu8<t%+4s*)@?j|4$v@Hp51w<toCtU->%EaB`=$q-vV7^V<}Y0|ZQKpV9n6(=Pvl|p
z&5*K-<XwUe-1o~KGx}#iNc~rn<^TmbRVjIrxX-FOk8_1sdb5k}U6E_$L6<)5>5ePs
zAp^Z+yPoS|3AB|C8tyUF&B4JzSzi7XO#;e_W%<3&*sGcSS{-Za*6l84!v#}G@$Ej%
zh0Ggct-z8;VTmFieNWhqTB~E{j|H=qRZ%A@ORkN&iFqX4x;2}?Gia``PM{`4To-OO
zg)e7HDk0xRLt&m%T-qZ0r$7_J5PA_<2CDrzf2y0w#*7^_v8W?2|MX2)-1M%noucIB
zAVGe7<SZ2UDPTILm+LGC_itx2ZFs)7p#@F*qwSO@u1MZSCu^u<Vu`6vH>)??8Bnsg
zz$Aw@L`R)7a-(9zq=#!<5HRAnB}})@ySWkbl*IC7sk3rRSp2-`VKFh^BF@bDris80
zcQNg)xqir-<mHH`)ROiP45<NzU${gBNjHD&k5FV48FHPv8`aKpNYHWuJcf!koOq{z
zAbKI<l=8`z1<&3;I@?AwY$}7<3P9_-TMFMsdbwj4T(D>4_b{vuuCwT29gTd=+&0b0
zNB$jkaP8Rrl%^f-!mPdaf9^*dZAJ4u?PYPE&UFn3E7wN@<~#<~HVc);Ct)FK0ESox
zLqB`lUef|2h?T=sbfi)kLiIZ1`cNLWJdg!=2ln>~#ytY7wCqtdOC71pXe70O065T?
zH3caY7Z-0+qOCOs%*I4aFnmT*6e}bj5M>#9Yxm+tM_rVx3Nw54f6Oja@3eM5_kWcB
z<y-jtxv;*K&r5KhWI?R-7J}eIH_V{0E2emlQsoDQ&8<Je-uQ3#+hZAH5M!00binep
zZn9OKdH@RHCnK>LAw(aY7IGcyE_5_<;}qvW<0HK+w?VM&h8j!tZW6*nl(M<o(5xaz
zm8A7V>5c9Ht>XBS=tf59#=3dB3F#G^Vmm{j{2F&)>}UQl=v9#|J?jikSd`qv#c7=u
z2oo}rW}=#QOFY*4b0d#e9i9A|(EXiHwPKWwH)PM2@`rKuk_Y$Of1Iuqk~)v6G2xM>
z#7Ly;<+m&Avb;<L=MJC$O-DCBmoQ(mq$)Hv*_D0q!(GjjCtE4LYjsk#mLNbDX`VWJ
zwo~3XWsv)bc#?^tV3GND)xRt{@A%#gL=PPTd1iQKlX7%^2v1Ah=iZ~R+kJhIP~tr%
zFC}Y0A6F!FkdNf7s!*a8i8VDZZ7}U#QV+AyKvyDtN)pSv!2T6kc;)vpb>;c56sx1Q
zv&4hzBoJVMnzJ-M7Bo}Nrr6u$S{#EJ64U6cN-S;f!rk$0+jhENIInBrnT7`=PW)CQ
zm1SQBST`8g1Yga&=Hd0VLjSAhdYuHN!@aS5F#!@J2w(m&yCa$)?eQ_$$bES-sRYmS
zGtx>N)|apwQL8tB<|b6cG#rLRVLHcwT*ehs|3GaJQ?$BzDo(d?pfaLMU0@D@h{0VL
zdYjA@jFa3`Fy*(iAsGO`M%m@aBr2<_r})}E91hF4?kIiA<qkmN2hHj*Jd7dn{ezw>
z0=yEkKJDePdD^|dT`-}_f?6d|>M8}udunU+x_ze=I6bxh*ljewx0sm7Dorl!6Gu2Q
z@$nx1f6`=kX<yO5EmhJlc^gxQ_C`{@C7I2_?ODXclKL07g0Hi!|JNc>5EuMJZSDEg
ze>y}Pr>VDt^mq--hpE8de5yytb7^_z2L|h{09pxh#(pU&XJLqhXj;VUznbz(OwhS6
zw3BVsn*U|&Oewth!zpeuy=fn9x^U-W!CU-4j!e!s$#(fSOWcTIWTI8P41U!|Uv%Fd
zt9;OnU{s6#MDItISzl7ZRy+tju6bCK*fy*A)yW3Wg(oITw8d~DiLGeu8M(uUPX@ba
zxvw4n3Y*__<U%DKuQZ!u{4L#E5Iw&+a!<YZ_J*#xsH6*9g_11?|7WL-n9#I$yu9Ut
z-(4T{z)7G0JELx|ec!Y#M{eIacRs97-PH1-#Fn;g`ST$rcov;WSG50)7HIB0L~j&F
z%Ikk?oByW)d+NyojLzzS{u?bY1!->nztglEyH(P=4Srv@+t>y^7xYcellAN({s;C^
BJ6`|*

literal 0
HcmV?d00001

diff --git a/docs/static_site/src/assets/img/mxfusion.png b/docs/static_site/src/assets/img/mxfusion.png
new file mode 100644
index 0000000000000000000000000000000000000000..00de0eeae510aa02fe5d1dda3d2cf95e23a9e467
GIT binary patch
literal 21013
zcmZ^~1yCJPvoMHU;Nll|xVXE!yA#~qg1ftWa0^awcMtCF1PLy|J?Q3p@9o>&s{N<x
z%$(`z(=uIMJ>6$IT1813837*w0s;bARz^bY-*xQYcm%@!JEzc$i9<j@dfSMLtH_Fr
zgH>D{Ep6;9ARuVI`6nsTOQ_<G1~QQ`Pltq?JKwe9n=TDWWy#VYB2WA}n&D)g*zDNy
z4Yy-RttlEB`4yu4b3{yszEoy>KpM=o@IWZOUn<}}bH_i+JKJ~sCW?sPDyX*vi#O?N
z>W}7c=O@POEm9l<(PErgyT#7I5jCXsfycaxS?;%7)cmpiNpQFbzZw>zw(GFyA+Q^;
z8{s{xS$$tggeT{v<4iY75VOxK^y0;i9*dBV@W*l^6T24!CEJD?I(xw}F3>mSWh85z
z_s*60Atfe^laHapP?Ly>^(Cr+U4&V6pm~wfHxpZ|c><of;djN6^@^dJrJ9ZM6&f}A
zOOQn??1PG)FEx!(;A>i)g=gU%9>VHv%QIDOwb-<M?@Bv#Fcyzc8}jHg4c5d$vaFXt
z*o1R=XIP^w_d43ddpQ}@@Kf*S5`gm0b?eb<op0}FEA#Z;d{)}s$(Ptr0$iPte#Etn
z2+MgCg&yenK`WphekSohTfjTX=(s{aFp>XfKzfx5d;GJEj*Ysuo3^3?pP8dQlZm;b
zsRfglz0*Hz2nYc$zJF7D3pW$6m%W{XE1#Dj*?(d1{hR*>%uELUFBG@0f@Io?DqwL(
z7Yi^a69*FunGgaP3>I)Px8zfkkouqH|Lz3Itliw4_?VeJJw2H`*_j+&te9DOd3l*x
z*qGVa82@1~x_UdfnRqcexRU=jk^h^HgoUe_i;a_;jiUqjKXgq@9o^jo$;kf0=zov@
zmeb9~^8exF;QBvm{Zo+nKTnuhnOK<rm-fG<0{?;XDZAKM{Nwx|`a-M%|AqPgf&C93
z0p|ZO|9=Vd-<tjx^q;Ci2m;Li``UyM6b7huARt5_WF<t^y&y0A;e+%gvjQKs1O)}3
z(#8*F4upaz!g3@;RCNFxqAIEa=?}W^u@g)&L(9ts23Vf%tFP(gkP<MMSc9bi88E3Q
z)u_28+tU`iKgs7#U8m7P)|6XcUVz}@;vL<o_k-b<(X!Tqzmfk#8mM!Tv_M=WG6)5d
z2#OEj20Q`H{ht8Av`CPHn6<u28iM%o_`T1PCfcobIfadl3%^}DkiHUrMT&=sAIT%m
zncrn0sJVnJ=04%{uE922B4W#_q1vza93yPdRnT-?JK&a>MhjfSM1psv!DNvZNBcs<
zFtsZ)Mi>ojQk7sZLF1GhM3V>OHb#UFV502lv6v4o5+X=X?W!y>ka<ZJ9XavXiS|s*
zm)A>>qsfPTB-Mu{QH8WWXOYp+qbKY|JJLWalix~v!$Q9u(ez1PGoo{nZQ^37o=W#h
zEh$Tc4A2xc)Qa%lOQUb^N`w3G05Ldd#X4aM*yJu)q{ZvFY!byLCM*CXD)BX%YYt%F
zyfqTO{YcQT<*kk#Y<|Vh^QQGFA=lvfP?oxB>&LYiyFr-Ym0hwj|21n?B>vc#Nd3pT
z`kkXEyS4g$M;6?@mI=Ge1Hllx^~PbZ!!#{?0)p`MQ_Q1~g7rOkqz#Fmwhyq-k4eIN
zQDH?1DirD<c)0K1>I=b`2pw;E_3Q_TU1Gs9CM+Pj5eT>N!EbxkxI1L?=q2<BzGS74
z%I!1<(YWH`XlNe$s~A`&adaPp#_4@3A^AHzA*s~9XrU;dGwl+A4EG9|d+a7dCL+7k
z*H2R^MEI{aRePoZz3hsnUPbv0c$Fnz{2DwD8PogFGnDx#70($zxYNDQC8I%>v8H`Z
zpCd)IN#Ho#L}eJmFuYI6Yr(ia$Tu-BtuyJ&f>_C{`ls1|uczENHC68qkykyZ5>#VF
z>in8rw9ilBI4B)?pVJAFz9jZJJN~9#h00+#5K)Nps1Fc!_GATSMB+ho(6$FbU<jmv
zus$jzN+Gk*BwABw32(X`WmGjpF~Vn^q)>Qe5lx{Quk0I#k;N!#DUSRxrIBcF%svL<
zK=hpyH&rq!6Ca^>pAGURzB{-bizM{g?zp!ZOgX~goNMAq6<JJQg1f6RH#ygXFWz|H
z@bi`77L|?B9d6ASYVR=;oFlQ6j1AFEgh6H03<5-=^Ym|aQiG*!O9r3ua6$nx(K$1B
z3h<{T6T$5iHUWQklmiYvg>E)I8G;PM`<d(zrv}FQ$NR^q%d}ABTG54{558y$_xpD8
z#z~L%T8&PSK+Kly?FIae|BjwmCh&LH?Y?Q?df@ZMQO?7PY_xamdNCND_y_;RbCLR^
z-PI}0sSn2t)7GAJEOU;ZW|#Dk+Q6Q>l`sKI6&{ODmR;s=J6JRnDapI4(9CnBr#If0
z!$4(EPFaY_r~%iv2d$mIgT7bqYxx1K0jlBkX(0s<LFMp6aCQSfHDG_A26LmIAtXtE
zb)-9?P_j{&MZssx_gd2yeesMZ4H8OwCxUH(FsIEl!6FKDMXajdd@pZ5t~yQSvJCu@
zGA=Q?)xmE2_h6&TzX=-?zebX3_YcDGSmw|Q#S3+3h>v#%BxC|4SbhixEBwyh^a{~n
z4_a?!ro(LVh^hsP0*&o(lO1)ZZvw(5re~AEZ;RAqNR4is7E0o;Vu$7_)(Rn%^Vv`1
zaO-Mnnxh$~=(1MA7d5^^1z7;^^L+((1_A*t8$+R;C>t9sk@bo59Xb>txk)wIBYvzk
zn)uZZ^Dp+|8q(@HyAnfx{47q09(AK$p+R~SGHg^uc0F+w@Q}9@QY_aXECO!EUjWtw
zKJG(M*xD7^?lKkiV@VJRUsYjk@q@PW2vHD^4lrB3G{*(#vgN+I9=Zl*Gd<*C%u-v|
z7TmZG529!SA2uU?Ihb}d#8p9rUzU&GKx!g#EST1xFi>|GlN8lDa{vI;Lk_B~t$O0d
z5|Ip3F&q|T$T3Q9gygm-wl(lO;_$H6r5kWe8QEh$whSBgCMG@=teDLwNM&dastSFA
z(YhArxU+Hb>4(>n`f2xamxIgp>GGaqNCI=QaG1&4^k$0aze!Q;lMD|!mhGfnHRxMl
z`lPj&z29)aU&Ue2=j3VQXT>>Vdd|R6g2%tuJVw#HO`W$B5I^qO3wS3^VbEc1a<m3f
z2-9tT^9*zw)av~_kCKr!EF%s9h#{884oXeRG8%vr;u4=0^U(z-JOg4sO+zo;O4y;F
z+;{}ugp-}(sqs2egf07qY(^I;D5OyW{AdErar?$u?2@2p)V82<%v4m+=a?~66jFE0
zb@38U&7AvdCOATFLaxb~J|@o1RHg6O!x~JPu*pBzM#Q750HdO4k>2MQQ{2`ZWS&W)
z0}UVwD1JU@>CDW6kHL$eEoQfvKRYc4MbkOlD5WWfOX5i?)-b^`Kv$<+_O66wpCQWn
z0!oCRqg~iLKnaUEnZC_%X&zYodxgVWLYNEu8*o<S=`-pMMjjhtcavcZx@onenKQ;i
z|A|#5x~IAOEHPVSR#`DioXp5Eo<Ti(fMoDD<>6Vc3E{n(d$R{l?4MTBy62>L{Y@^T
zc)AV8AGoWgsr0SUd=?aenWzxV?|yp*e5btcT(&zRk}{7u5|%IDl6HWq)?|}@p?OIs
zf_rK4BpJ9Q>#B!Cx^ar;=PB>+#$D~ZE0T`yXLX-9Y1)#@pWj`8=m}xWrg!L_l<r?4
zSx`dqpbh6q{RYqpsFnidajmUjHSB`$3UTDZB^hQ*rA%suyAXxpU}NF`IsVVxS-as`
z%Yj)M29K1ttb$yWWjm8S)PNMc-MFv)i!nTLiPYi9uyIi2u=x9v98_OGw!4E=P3!h-
z_yV{E#7*lw=B=56QAd(fCNz1kQ?MyGp-YiNhAHC*;%EWI{Rh&1ee-s=SqiW$#p<Mq
z0AyJCpSTHtLD=%-JFB=b1u5&rFByh(U3vqSO_YG;M<)vO_%%B(6e%(o4H&P{j9zOF
zH3=gC!Zemw)CtlIta}41ii%6>BCT@6*Hxy!CezuRx8A>hLWVBeax&<*&&{pCZZ<z;
zGdj)Xc|;73$lZ_rwQTxZ6}a};7DMv3-()({DAqqNyO%gc`asfQ>$HzMwtc@*Kr<4s
zj`MMq9pC2nJUv^Y<nIti8wrM3u}~Y%D%KOboQiXIxVwhw&&7j7eI3^8YnH3l=H5xH
zbsUAb8v1>~mZJ!b>m{z@aA$mtVy5Da4BNEIP=uRFMS`m?%(o1erSsFc?YU+|hQ*Wv
zHcslxPr~Ru%m`lVhTrBAfHG>b)+C~RYSYL^zagTLtRyERBO|~u8yOZBmUiO7?`TFI
zR;Z9rSv-~7EEH&7Ti>#yQDXHj_&$Ts&^&10&+TV!US5-de&-NZ*%j7}F^wH0Lvo}N
zTi$bQZvO!H>xd`)2HWku4QHF%#!T7}u<GFB#cbx;pH)oZ$CNTc3l)BHkd(ooGz2Pp
zZ9%0dw`PL776TfNVZ%VUD6mq`vjZ5M(b^X<iXr?JM6a0&pVwgWS>a5vt^mV&V9n3R
zAoTa{>n8VBYXh<WRw329wzCVYXshFn1DAKK{4ahVTfUr^<EJMiW@76n29opZT6nL%
z*89k|$oSw9oUn=!uq`&9q?5?|lvrLU-OWu$HhagV3kd4BB%#sA+N{Gqz#6vVi~vcF
za!Fl-lGFr{B_B}ww_I=Y?;UriW4rx6Az0d|vbkb_8d<dHSMiZ_!kf+Rx6lhm-WR`7
zVSk%_qcgZeJXTUd+$k=V&o`Z$6eJr`8b>f3p8WV>Wv-5lsca`E@xmvBZd{-e44$Ch
z0BQZ};oz#^)0SCLHbwyf2ADF9j!+Aaa1@K$<0;lN(8B)UEN6vr35r+yK8y^wEO92c
z%Jl2${^rwjd1SV)j|8|YhE?vR-BcnnMdvge6hf*n9)YPLHz7Dve#2iXZ-e6e<J(m0
z^inO<woj^T5X8%`>!w%YPbm^5$d$CNur|f5GttVQ`qr2|YXE(J`}p?L9<3UftJMk(
z3@U)AjF8~#?`VRL-oxi7xFJ^y3yCO67*i$LZwF0-LUu^II47an5{;Es*`C<Ql@4OO
zDVaj%pz!A;h5ThpUJs9gk92y_$;nB%-`|_!Cd()&v|CfyFgNA+{Zmz?5(kCWwoUKD
z^vbN#x(eAcE+tL0u%>~ntB-BHtF7lUxPE9JdP`mjdrk>Y>p&1vCeCpLrICm#b*Wm=
zxDK$e6^nLH78WH-d}$+-tddwj7R2M-em5eh>=^}96=OEVJ1R~$x(r>S@hgwid3qH4
z$<XD&`!kPDwsTiUJn8kax9<gEu<v7?mVy2cc2`(9I&3-Uxl@$gBr7RLfzD>ijUWv-
zA1EDmfDj_4hd$^QcqwAMGlQX5Ze|75L=>k2QEUn$nlWJh?b_7$al^CLAMP2Y0=2-R
zYU#I4=ONy9VE-z&<LgA;<N9Qbre=+YiwP(oNtyIq!nHJMDkJ8xOz&Fz#*6}+>xgAN
z4omt|$QyKWcII;7+gtGiN{%X5f_3hTW<*L}=^RDpqxTTjRn}_P(Zb>E4+V|{U^bbI
zoBYx<tn*VFzmdDJdS9)=*)sr+mDzY!j4a|uX@wo5F(VqF>o-*~C3#l+1p0%ujab3c
z((kjuME7X2T!Ld-Ro>#tu|c4F4b!=!f%9ODvZXv<1zt2vz@S&du2JiBEp7KW>mr%o
zZ)R&$IeRmWpJtn?<#ASS!8+Wyoul8Q^H{!uc$};EkBE`(5i-PYe^ek8fk%-H8VmO!
zS$DLNdP~pRDi-3n^;3AiPd!^i1o}J85Uwf;JpK6-mWEdDsnA(05l0W?NLGL-A2vPD
zzvnx};z5KW<d=SG>CV9KUTBzA<E>CLMgQJI?7aK^4rO)K43SY@2?NkEf9+^gj-H_C
zK_~}*|8oMU%u#7$osOTD$vB@1SEZoB7Dl?hJH)b;j{&OZy!_*WCAJFL-N6S3o0mLp
z!g4&xoO_plh91wXIRd=xc=B_+D7bc{HMEMfNqe3s#Bu?Xu$i$gyV`+Fk7RzKX+e6W
zY=H^6;M~gHGvq<m{M(w#-95@YEFBXciu1!1bzXM;t7zz)YwTHqbw?d`t<>ZI@7r-|
zd*%1rHP<F48uSlCuylm!FI={vq!5K3w9pNxqwG;idn`+na2Uz)#S(};9f*`zJWWuS
zDpI&Hs(~jjOz=^9ZMeY}u?Ztwboe++>EJ{^IUF=&fa+%}LWAk_z)`9VTT~KS%E#~T
zzI{Ajc!*q#_c!azCl+gv;P_!#H5PcIWb>T=me9^aJSspLJe$y#hP&BXq)oLB?>X^O
zy{9hRq~co=<)YQ=USv;>f5EPkmO%(k_yS$^c0x*Q9xF0bHR7B&#Apn3(6Hbx(W*pd
zDIfPW&vhmrtNVPqS#S(ESntv_)OMCK<z#y@zY;+r!-QkP^AT1Ga3^|<5{gDqC3I_;
zi|Z-ZzQUAjd5r3rYlQRE?Djir+2*I=qZ*eOQaGhR-$mlr<c<{QOv_=@6Aa<{=Kv>q
z@@x^9q35%hDZ~|+x!vC|EVte!B3mkk*E-?|L=C+TPRguAuF`!8Z91M~L}A!3Lmn^h
zVw9qu>(Qg#uX2d=-2yJeTM(;AYHMh>j5xd6q2c14f=;JUPNjF=YWS5t3YAl9JfcMU
z_~K`^k@qu`H7y>-)+0o$7Jp(bt)(+9tBCO=0>3C$Ky;$DxK8BFdc2Q>gZVu#oBixA
zTDx$*YmxEfR2>NdA*L`T5<Ucls<8u{$N8;%h5ZI&x-|2JujV@?-R&#5{Ji|qs0B4D
z$S6dVD|1DDWM0cYDX{@)_ImCvcD@grAulv|CRc;c1$CQlI3p$CF%9ME^&^|M!kkIR
zYA+aZM1D}9-t7l9@&Qznp!swdeK$>q^(sU2U)Ctw9K7x}b|7e%5T2d7aFd`vN7$F<
zN%=y|eI-gW`Z~%?aIFGljCO?sDw-8!(%~Woi(Zn&R1Lql(e>;tBnmPCcyCSb><^uL
z@LFugtMO+M+qy5Y9eck1{rGj+eN<FgS=p>mpS#>m!9J~XGvh-m^hYr;OCRV)bkc}?
zaVNhUcjnK2-$lY9u{=PgidQ+c0b2-#cPnkf%M>e3a`77U6?-P!44MgRgx!_R2s_i*
z#@An_dzbuSoBwlv#T}1ST9;Fk%xsGlDFLx2mGHi-6~uFS>EvUaG^sDjKUp!F%(g<a
z%uwTuzzW@-Y>~kgB5*eaX;pW<#xQK1njO#2kdw;eoIeCPzQix&H*A@tj%T+PpD0J>
zq*|K*)*)6oH76m)4i_3L9`mY7mvKRam{fEQPvKs#q?%r=&@$a@MjQI|hv`Pu0ohV#
zx>1Irv0b64#YV>Nc~#PMJmznpT=h?{w|%0+2onSh_Y8WF(-SI|y!e63mAqul+|9uk
zYqz@kwFYI$n7HB+LE{J?*aq-G1ySjZtS`{ZsL%E&KllO9{@i@XGNo(PU`FKUpT~5U
z`blKVY9v|T$o{4NfiQV;Vo6CDIO-x&8}g{p<GLb9KudyEgg0;mQq|B#@&?*=(Sn>9
zBG<#WNflLa78f8kuYw0}nLs7<h4;}A&<Tn&(zX>&rt8JtwL!&A{AEO$SXXI-)<Ddk
z&oL(Ael%bzKLU3)E>%wJL*YcT`_NCgH+J>%dLa7q7%{!Ujn}^7RNWL-6W=?sXMBSV
zjZ>LG^o37wIaTk=S%GCq<Bb5N<>*Y=vQARNI?+bG;_5ga0uYM7oiC&Wv72G!sS`Ra
z8GrY$E2ph^IbxRg57sQ_OeZM)4IrDq?;6dnkR+quGgjr~Wcq2Es=Bt5+767TDhoBX
z!Q@s{Cs#op$guDG8&9fwsl?2JKtRtf!ejASF9oqEZMr7gg(ktpU(2o1L~zfCt)@zv
zHcDuUtH&G;_KdeCtJz3g9Il&K=*rxpE2}w~aeN_IiS-YY78sNuRb_6lR_a0~Z^(|0
z+C3UZA2n@4EJLi9GuLdj)Ncs}o>FP-Q6MQHSyxu#q^@i4#~rSWC62yWSXPRNP+0WK
zV4>u2NP`@5g_;z3UQweEa=9v4vLq<oBIupI>flW<4%Y#(XrwsxaREwigkH=NX<nBL
z30IoW>%>CNwla^xui+^fXuARyDQ;xqgt7%-;$3-R_}{>7?~ic057L*Lc#PCU;cj1*
zL{dKjF|Eh_9vq9Oy&mXXU3wQA=V|mBt<Pdyz)s|>j?*_8#g~qeDpU3ZBSi9eu|+A6
zQGTBszK9Am+Yo7rlK<x(o`=Iyg=|{$OM4FV%b@B5Fl3Wv>o9G;JqujpCQBb80**XA
zZi~VpAjafTuCPT{NIUcz!~&@0?N1-O@qy!pe!S;nP%E7$8nQWdkeYqmxE(nwOX!GR
z&jk0gec?->eM5XeMVoKBT#!mqv1%Upt-hS9eTbGuA5}b88Qr6vq^-^zgl6PkzbiJ1
zDBKb9+YonVA-hA^Pkhr0IHuZ!u*i8)YRx6US+)1f4z;OIDRzH>bC>s?%tL$;qe(!A
ze0gKVV>@xC+DG+@YjqUbI#6AeRgeGA7j`fzT-1Xaz!Oz&OK&aOQ`u`J1GlqS5*FhP
z*0AMl5;B?5Kx&_9{e8_F)=FV1QlF^i0datAoo)8A<YFApUng*gF*)PfM}brGCV4O5
ziuz`8Ml#%dVKl|Wy@k^(KvS{9X4V@Ltx=V&)D}Es<vp-rXgoi3fKrQ{B5t6#e?S)s
zNsmk@-jLj?s9yR7k9LXt4EPgxq!Eo{M<e*<J<?;0YUtSd1^R>IS!hW4aIEWwj{iup
zEE$|srrhx=^h}0ArWhpu6~?5vmdnpj?#Ds}LE5C9QY_UZJ5w@%8?p?$C=S7cX9<;<
z0yhGo+@to*_U4#;bSUJugVkZJfEKdMKyWlAWz&%c&h&n_H=Pfyq8u}yKv_t30hh78
zSe1<Iv;0I^mdR`=*?foj@t8Qo?<UqL?1e(N*oT{-M#{~!d)j7sh$?N_gsMw1LYQls
z@)Qzp=qv<o*WJ)~fm1T+;R<Vq=h6R0`Mtd=c~kf`OX>7$Z{mGo+~BjEz#;D~!46j0
zL0LDo5XPP68<JJ_P<I-~1UF?Zy0Yv(v})X>|FFgm=GPc2JfAc-e8G5Zf^ZKfIY~8(
zI4?DJXm~FB4T|G&by+rK<=7$JRy({}tD+OG?Q-?f$?dq{9aFBa1L``tM-W;ii{#9b
zL<iN-viMftop|Jub=ljl(Ff;qKB*kC{OQe`$FkVWKiV1YxU&PzbBDaf_Y%{UXr;ZW
zMpWPA@+Q7S^Ec{K3a}ddC6IjO4`9YNXkV`<n4aIrs?P|{lQ3#bS>lTm<b~x-imIed
zc2c{bqo*e=izfxfH<Y@#Bq#27ESoL>M9;A?qLi0N*VXxoOeL(N!QZ03N~ny~#WHgW
z02f^K77bO+TcY;rWm7W;iU2vF;Wa2aQ9)<##!Wa{u{50k(xe<=xa{CbwSw@_u1mC$
zGAy(3ShNlS#0X?mtjrsZ-BBBORZ|S6cQ?PXSX9>VmjhX%7v6LyF34BX1~W_+Ln>4}
zRf&mV6W}SFiluO`=k9s}ibHhfuyd0%VVxmmXESKJICnmG*fy~Sj4mw2W!H>pjrmFx
zzYwNRQWN!UXF)89Tw1>&9%-Gv`eN){s+&AF-il)rwLE&Bi{%u0jE%93VX_@cpKi4D
zU;V}$iA)KnVC7I*5<w6FY`p*d`t;EP$whSniF#;UN7i1pv^0Syq+}p2jPUS2=ybLk
zIV6+e$9;(jP5v2EcP<PavyNy|*vgsV1IVd_zE-PID$%yrrs6>GHVZoh6I+d+3>n8r
zG;_d|%Vt2Gl!W^<!i`W1bj#Q;Qb6|I&1bV(tiZUt!;IP>MbV1$uq+%bE^ffe$3w&c
zsmsDwyQ0?LQc~NvY%3&gT2TQfE3lO9Cr^W0p<oa~Y!(7$aR>m>CsO6i*lzse#zUw%
zUNRZ?MRAWXvrG$ga;4XzBRa(ut1r5jnv=~Xhjdtpfe`t1nR3od5dtvbHz-Q$odlJk
zz8{UX7e=!$S+T}W%ESmb{qska-73GL-3~+}<wq+1BfM!Xe(-d`c9WenM4}jtNVNF`
zfP4#t8^F3WtW!5gZ(Uvj11AnUhzZ-q6afvt77s-Cf+#|8!HCNcwF9P2C3Qz}2xC|j
zn5`-VBC$Rv-sZ5uDm?1@4==3Vdn7&*(<k5x!hXqm50i`WYPW}q#q1UPOXtlhs`(I+
z-whE{--oU7jY}ze$6I*4^ALXPw0p{&wb`g89+aLHR)9eWl4lXGerqa<#r!hx5TeEq
z5`PTdS*voeL;jT~S`=g$WHVUsduFh6(12oh%&f##_u5v0l*G<IZjr`&<beL}-0L79
zAOvw!P$*fxp^}DOr!l6GggjJLwSFu+uUuFsk9J^TgQgiJ(F{h`EJQ|C3-6r44asF?
zcX<ZA)fUJd4PKH-qX(i%q$tW)o35-toBmCJlki?;QiZy0$3>_NU*q*O!RY~g5reFZ
z`6GTaI22i_mXlcqyzt84KlUn%ArDmh1wCdr;w%D{O)mYnAJIkdQ7MqEY)T>(f1xgy
zsl<raWGpKLM{A78oluWZOb|}Q?@>IX9Pw4Eh6l^L(XWulXZ>}kVWS21vh(Yi-}cXi
z{si2Ra=za91>Sj8eIuS<XV}slH8Yu6rQzTrw<$k0Sd}hb3kobW1<gs8>E7!&Ue^OW
z3%AQPlwxpefUyJsPX|RpQK^`d1Z1?;sqcp{Dta)MA=wZ%?v9X~cHC|&p0r>$7JLP4
zc&a$z*XkX=o@-~rC!eqX)l_gLso!H6A;pYk#`B`V<M6y8cLDet)_xB%&((-QC3FYL
zsKUB+3h1ZPP8UGJX>>Jc_eQH5wox>c|0#)PnoY=$=5Jros_9?aj1xNIOCr}!hKj2Q
zokxMA8Y+Aj6Yxy*V%Z>k|8Am5#g#UT8J)KJypbS2(r+wcSb;;znPlt6LMUvr?l1c7
zh<+n$<NJj?qA0`QQXxV9pva81)EBuHTrnZTcCwmOm0{&@a&P&@DRw7(QS}lzqSAf%
z-4Y+Cau_$HM~~%YD)-(F^w6itJK)jpNwM4$H2U3eWeh94a}p>6IC`A$xpfN8B)VT-
zfQ?%N6^r>WC>afkK~B78TZE8It&~PD>?}?E!8N85V+CZ#(U*DkxLE?sfWAHvMSqRq
zhN#R^83lG)er=5iUToSzRr4ecw}cPkA~+_?;luB0>;i?=^Aq1NajE!j!D*ps+1E+M
zY3#Rpy7XgXL$o{d;DvNQV{jRS=)|TuAWyP&nhP-ygQLx=QhpFY<rZcfekLqzv}TW`
zv029vLpZ=Y@mYye0ad0V8%mK&+u5$=RtO#Htu~C_<X;$j*PflfRe0mdHX4_wO}UB*
zJis`OSGgbrkgNH(fZPvF>yiZ)%U1umKe%^T%nS4?5>p&9L7Y)vfkbx+F1J9~dTce}
zo%eDXl`#e<ZlZH&`7Q8s<_XA9NoLi&=X)<u+0|focXv_x+Ar6pAF7sV=F3SY<&g)X
zuq;+EWX(+l(OviZdpOg-hCKNUr)jASQd%2)p;hRp2U|^Iy~;G-;OMOqGAH@;V3R!k
zWN5~we$EpP_B)PL)8Xqjc}X4SE-Z>AH7D{9p@m%_!jbD<2#47ZV>8UOhaPi8Q1okJ
z24usXA_D_?)Ll?8NB%+{k8V;cOHHB(Ql)!zQ%#5y6DG$X;2cykEk#8Z<-Qdnp!hKd
z<)-PUU_KPdBtNMQ#X4#ZnHY@?s(B?B?Vjv%TbQc;F6$<@R`5(H$6+na#Ig8BiSm0~
zZ(MeAF^Uj*47s4TX9UPZ6hVqXn9Zltp};W}ki*mI)gqd*zaVG7MSK<axc`3pV<QtF
z50CdcUW;OqGDm60KblbLFQW@YXE;EBDoekHTf(|AU;!yrRX1c$tnXv2ezuW(4OZ;=
z&EruPHj9i-530nczq;|uXCM6;suoD1yg~cz2ytU|L6D`nz^sjc9fpc;!#5Sf;MQer
zHTbB$Tz`L)*@H5%%A!1b{iOQV6gZ%~;HkN0&FoSK1zD(~Z4ffghIdm_Fys>^ZLRT>
zGacRE*abJd41+$3xrjC-t~~pr5(?_Q=Mu|+T411e4k;PuEi&etC~^2|iw3hNk&0@V
zrRu-%TU3<X<PE9?$?ETkVZetk($UMd4(v8_4OLfB#JW?(J+So4T{?+_UbMG$ti;@Q
z%tZ?7MtcGvA`}^G*COh&JJNxBkReMmwESG(+?Br0!G`YQMwhCz)7e%|q430h)6^Mn
zWU6%{EQ?|xvI-+>#DRE(gT7moEgN0NhFbb@X?rD{zo(lLPPUEN#1K1*Fz9?R+|X9}
zvhZbNg~r>#23a?w3Uh?dRE@#nH}$k(s{z`b?MMk?<m9#(#Au^dgAjB^+?)dx9GR2&
z%0P+x3Um`Pr2Ol+p$ttB;kgLbb3cOYg}uPn<5{v<Zj*lzvjQco;a4PNmJB~-4V|v?
zxpb~EOY(n&bjRKx4FZH1|E>~M*UZ3?TzEuw3p%2h)I*(Q0z^ba$myayiQrpA)8<xY
z?u?BtJhx+r^%&P9?@dO#+8}St#i^w7FI3);Z=?Kxe-X1uOG5(8s6{s0I_nm5>zGFr
z4glCt6>pMI+=JC9=LeghgL``md8Y&a;Dn>l!wLcz*LGyi_EK{C*eU2@5xWVv9a8bj
zWEZV*Ggn05!^Li}D0=YglY)%+iH9_eFF9PIk5Cxc;XtEqVZy<22?eWL)YM=_i5au>
z3b;7<;O&I|2m5L90U>Ux{^aj$mUg^t@VAM1c?IRZ;^T|5MV6E_i~x85R0Ea-vRRsn
z0m^g|2^Sov3ybi^Y^=l}CfbkeV3i;GH$g-aNhWO)AcwhA2Xye<@=D+!ILg2klCx1m
z9pbwc)aQv*c4w)@Y`ILKO=>d;z{jC#_`5=?Lt%wYsHrJQMTDQYq;Y|jg43s=Nv_z5
zR<_qmQ08n{8Oye;zTC$1z|%6-xaIqKZwxynvJHj7UkuS;Stnfdu}<$p*2B4>dWP3U
zGR~E19cbh*YO^T~e5@3D8JT}H+dQ#2d`K`%lqIy~;4jTNG#N7A`&}tQ4GeAX)#Sg^
zVP*4pyw%zl0X18!AmFU_nvIF_I8}Hn#CFmlgXLBHSo6Y@QtrY?HI&K?(Z4Vj7NX=*
z452om2B;Io^0r`PlHH+@qc-aL9Vfmnb?2*e6ocAIM5Za|uBeCZ$s*9Wf}WwhJjm^-
z&(;#~a|eD}dt^(D-cG}MBe5IiE0BaUj&@Rp&fE#u64C<la0;0WE;c)hZ5OQZs2KsG
z{i?2<FG*T!5Jk2`#irB-_Eu$69)qxGAG?0B*Mz9R&=`B+QhK1dr*O$BD@Mua7wl9r
zsI2$0URt4R%Ft)*iGrDBb6%L%3HE)T`q{O-S7H43BgQL`Cmt6*n`MHpPsCaFqe_T{
zMq6kC0xnHnWXG+`6s2oA=1tOm*uO7wz*DI4(;}7Mg-!W>@i)TE07scDEOrO*LxYgp
zY2bH4fy?~j2sjhv6r_EGg`GXLOHyUUku(ul=51!7-ghNuBMnYI^dxmfA6r!~uDjnq
zDJlyUUwi3=9&$_@h*SF0g1Yc)O{TIR-0$`0y8T7kk7)U{&7{&G&)L~)X9+e5KrMgt
z(X3sR*F<ScEp150dY-lk{6Ec%a12o`9XRQMdn%Fn_n~b@1n!=tdz38+tJ%Xp2@eGi
z*%QFf(*CAv`*reoMv`IveVF--VgeWmKO#{2#RUygQ&1De9CCwwtLHO|jnFdkd=j6*
z-8-`-_V$cDpL2-um5pLuKyR!{7TJWKpv>yw@xdT%|Ly8B9#*R8?`Z|i>E@bqnx@z$
z8{;ac2O5U@qMI8xV1~|;PGfG*k>zBcYPj*i!Y`1l9KW;b8ee3Ky-}m^XIM-+?BP*i
z%+-SZ&V-A>=w7_EL>b6C_3ehead(O4b2*(3#xCAGa?>C&LIjgd<2kG43}M!pB6hj-
z6@-Cg!ct3aOc;X&{gUic^!S5IUngxn0Bx-c(ZLLl4Ye!^1!O2@W)Lk07XGl4v<gy3
zWCnF~Enm4)yfuj(ddp+S?}&aO{4tlsiqkjksm)h{G9(!VJd-niq$*58idr^jqlh#{
z9`BM#O^A>j^>BP#v~ObtcSc(w6B+Cbf%@bBYBODv3-F8dOd1sFx!Yz|tWZMsGods!
z478qk1yXg*6(vroa=&SmW!ZxAkVV?r*SP)qX;c?JnqU0)zDfs@UpDokuuVn_m8q(8
zmRN02Dmp=t@p#09AUBY^Xay(=XmTpQQKsnL-``K!Zmc4^qm^em$(rJg8;3hNfM9<X
z;Dm}YSJ6*um$T))l*NNBV>>WhhF~fYpdl!tVeD=_ZMs9$JNdNXDsz%lGi<5NAoW+E
z>48if#Yk@TvtI@&g}-EbOh4=e3iN^Uh}xnZg8|N!1(h1Xj5uU?BBstx`nOkmIF2uI
zUV%UA&;C5t@;*e0n!u^jX(eICOMJ?NqKMapiW(WxDCOygwAk$#YyeF?wSV!9C7T9{
z@$=|qR>lpiRx^gAu4vvn?+3}(OC*J{Nk5q0<2RV6oSt2!Kh}9ybCTl;!y-HHMV?HN
zT)f26G=@na(?s#5sv-iXe|D=x`zIm<RRM0S8H#}KhCx>o&cBApTh9^Iiih?xhnO`D
z{4`HZpU_+aj*zB1RXHZ9hZBNQH&F{O$VmnSX}$tyO;Xn)tw^@{Z|tPM=ZKneYNp0g
z*=XFj%6u#QV|@t~dWppz3kkbIleUilg~n{3|8Kk9Z1!k6OLk5Ti(Av{pGx#B!9xQD
zgkM{gXri5Y^l%#(k1#eaLhGZ9of=kyd6~|`Q>$6rEm&B1-;$t;wjBr?j=r5Bp@lsZ
z#Lm`2U*CFW?znlvLh09#y!Gch?6tE7+aY`jNo$-0L4JD6R63{*zgxNFXp>McHRl$u
ztxW9KqKm6>q3gI(1y%Acl*Gr4)3u@zXcbtIHz#6Y;8Mg+`xKBpD-+fBKAM256Pr3O
zsUV=Xwzk6KBl#HAQTHe)y^}BxU5uhTJ>Ixo1J+$>i>w%V>;e5mq!+pHYO@dxe6IRx
z_-G4WFeaD7e=h36UY4h%tj<hDsgaw#hYGBmEYGVxxPO6wk&3R$mzU|93OsHHws|Eg
z5h<dmC}D@Sz7O5m5ft-H_^DhWnF>EpmW;mdBZ#9TqJImL`XP;h$W+1TCDn2RJLhYD
zz#IF}o3M2+8t89o*eUD_I<dXzy2tdGyGS#hGb2>H9bow>6XUHc)?qJM*nT}giQyB&
z(%|y17})c3`v<<L3VNwFMMM2riGil6r1V|J5_Yd#LycR-m|$2+VcQIAbT3nWkp0?2
z@nJ>aA~Dp(^!J=sV~-78w5E#ch(OBqbI4F3U;p(xXOpN$tS{fpHbPhSIAo{{B)1!A
zhNzH~1UL>wGrcX`O`vQ1<z1LzLRd1Tl5J!|lfQjz7k<r*ylg~-WH}l6QT&~N_HU|(
z7p=VfvKC=lfq-?1$xP!JT%+FLLB~Jev%oe>oMOWw#beH>glhITm+#p5-d$82zA{4-
z4UYuMbkQVBRy#m48*h$U_{G~E^qpI`_l5fc{M(~TUYYnjEE_-UHpoMco@$6hjS!dJ
z%@s$zx4^)DVV3p+ve9HQVi(<nA8lYfM4gTp^CtVeJ!9WXW~#b>siuKm*E&QSs~ct(
zyCgGBo8!-*AO>;nnL|d5)MnzG0XFH}Dra-Eja3~-dlEYD-UCa>3OICz=zilRoVroF
zCsn6R(3<wH?_B)O!I3}Ab@)(4?`S<~o>1uSk@_pz<=rhx>;p+Iw{CS@ul>K@MiUW<
z4iVCP;6+ud=>oci3Y$8No?ra1@H9k}bJ3xAOkYR7u(0d0tdcRNl%tKiqTjVe3{TO=
zyiKNS*xKUoScFSU$PkETe&msCeh|jh3-WGagJ4?LlF|<lDOL)YdF8LA7bbWo={i3z
z`?(tJKlSSLJa?lS*#jsWu@PUtl5S%a{h%zT#&bV|ZXv+~LFes;<7tV_f@uM|IbGY*
z^f4=HX_iv5@CD4{1+)BpZ^6%0b_;Bcc;%Mb-@gda`@cSM?>Mzre3sT~MTFz&Ha^P5
z)@$vKQU_2GP6$8jfoGgwdOcrPu&VwrS0>Uc=cgD*k<fATef}PuiIYfT#co=J2~zEU
zfGaA10^Z`ZIiVu`Nv>Y}Ilm1}JiD?q#~Vvg(+wAf<U@x;^38nCin+#{LF3*oG7(W!
zyMq5xo1okGPk4r=k^22%gy4G-q}$Q>cjzLtKJsf4|DGMA&!gL_52}y-!5nT!P4ahQ
zEj=p+x((5kBJ*=KUb25VvU-k_XsOoyL+4H|T|BW+01(LyWy)@Il{TgXmN|)B1BVr1
zjNV(6o@{kGCpt%qYSn_mCm!|9_oMycYKPCK{%`vBV<GBq%jvTizSWGQTEq9nw(ior
z7-t>#Wkfj?WHa6$WJUNmq(<r?uB0NBmyaKl71SK-lkM3)%ELTc?}LBv+a3|LSmTGB
z@5?U9l}yd&=zr4kO};OP{5EeDBnPO{1za4-`|N%?yp{RoB^}2Nb6x~g5Jc>$4H`|X
znk6%AqAxDccXoC<B}40xcP<h_5iuHE*}+)jOwpnhnz;LsEhl$_wrBf#->x@WzE*?_
zR5)x>tdy+q6<+_T>vPjKM65A|mZO43pFcpdA8aK&#rE+1R-2Lrp<L75fIRR>z~h}o
zdX{nQu5PY`^(4yjaHpm9DIs}ZEuD1>gu10sgQe90!37`c9IJ!LixUp>epo5>u0Vgi
z8aytQEBa?BC<|#L$Jjd(x<#{FGAt>qCm4C#e9bJns`(!RAt1h%3{2rmL9}Ezy8}aM
z^rUYihfMbUP_aBMW-9j&NQOBSox~hUz+ArdhDsA(ILSA%gJQ8fgclsufh4w#hChZ4
z#<v_^7&*B(K(^Pn#^X)xbVVN5gi^r<|KgWYC2TGh(hzKe8bL=A&g2hPa-du5!<N(D
z&mgDTpRlAL3d+C)hZOwoxz5a{g0a4UcQH>XtT`HIL$J)&Kr%E(6oYK_(lhBSIj&ix
z?r_BqVw|KH>>9T+Gyn~r9pB1FfE>=Whc>G!>A`WgR7*)foiaoB58DN9AePDjObCya
zrl-oO0Y0=0uf}k;k~8z+T|C@|Nfl13n|m1^z$@f>tyi<Gn<CQ3#g>pI%va}Kq*9XJ
zN}%#TJNn~@fc56Trn&hoKB0pq*Q5}7F+!v$&eA==xR-_uzF`%|{NCc)Lu*9cv&8N-
zroX7(lHvG|(0f}lAav%jvty=;@At@JFofR0;Cf<`?;T4m)4nvA{H{q*qU}ZlSVIkS
zWZ8y<AR?lk9L%7T*(uVw<#pHTXscHVQNdK5iq<?RQ3`LEkNMaf>OWwk?zMK^Y`2MS
zs;uKA9H1D>R8RG7v}mp_Inm@GR|VlXtKpv5_Tkd_<sTXDGP&Z&s!)-|n*`MHuX&J0
zvX4LHM$FLKM*|9eF`w;dG{_^$-&Vw^6XbCN*{p1atCCf<evz>-QnXZOZsR95vtnIv
z{9zIY8W%MX3D0!~Nh6fV3u?(GXHLYuCrL1A3+|WUX^Ji$*hZ%O2)86}f;q0UEnMcD
zm|v!0YD7t`bsDTU(aJfwBGJu?_fc#UfP;gxBJyuBu%#QLUr`6koC8Q-kbe%q?y`3P
znz}`k;t7ub(uEhVM*dP!WKpNKf20cK;7g#Rp0SPPc~ogTvA^=wYdrDE<O8}jqv(6=
z%^Rw!h!b>IG>B*aC^hMSOe&8sDJa_L#5j}7&eZk}1p`)dzsd!0E#are`N-|dN6lgl
zsf{3u*YS8)zlZXXB>ye=YJct*i2B=xR<-T3KY(oK!~r$R`%`75$HTsc`cBt6L)?&b
z?9E*x(N+3;8QvGZ>f_wqazbgc=jTGrjhVx(@J<41084+SGNElx?h44}S+N!ja72*4
zu)R&Zo2mP7jw$~aw6uVY){+;7+j!^^-XxrUKrIm4nLvo|bDHMvrF&iGHSt(6)a#31
z3v$=55*Wz=4OX4>>S%<|V0zA<f$D;J4439%hgc!{p624rTu71y{Dg~>dyhT{qwgTA
zEDb{M@-H0Ro;~D}Zh}1U09YSiX5NqEXCiQr;1hQ;?|9(#O!{^vObRdk(UmfGAltw#
zf+&(6(zkW3R67EpaEyrbCB=P)Q6hI=`*Yysjd+<*^&ROxNyPLhztGw?AA{Wk%iK>5
zeHicIfZJxkaJ76)D81IqB}w4vx@?vYrbXFi^&#Kqz=V;SYy#V0Dv<<0R2@HMzXcd+
z`1QwvsrL$9#webdDJv(jLl+%{86q}yEsttLC{uqhnv8q~;)<3~YOHkr9g1kWp~@@U
z#e-U1B-Q<wQ;TmX1@BDBmT(^~a?QEt-DHWQ&MVnR7Y{SPAuBRrhIvBsia7A2_GQJ%
zmIu-}QA?wQX#2e*!F5F5g;nZnWWhpoOg{|wX+`0^jV%MSkO?Ask}O;NeAIkQV{kU+
zS3OS~E({uw#k}8mVb1&Wap#2l3+p8+y%DW0CL}=zME_|Xp;QYLA66DzLsj(z;Fx14
z{MJQK%p5H;y_y_lhV3ZGp-^;fZp}OIEIvFbpwXU!Ry%t}eN6n6&|dnkMUl`pD5QeY
z*)0wl@cL`f6e!x!xn!WSD7IlbP|-#=Wl{I>5oT?Frk@h(pVp+Y_W^gm&gH{0tZfiO
zyN0<;nHbee+Sh*FcqVMV=%E{vO@XY*?w4$t6dR1V!r+&7^eDU2h@r-76Kg|57Bkxv
z{a)u5YbNc+MvpaxKKT0T?|vYRKzf$cl#F0+n~g&mDD!ImP-XeSz}lyOTZJpsp>E*r
zzTWQqckb=5l)8I#Nk`wjR)Q*q&_>Mq_;HZ?#5|o(n?1d&EKq!Kkrf)*M3_rN9oJfg
zfd+m14tuq`ytg?zJ}Uz#1~R;UT>soYd>VOZRD({tXHkS(Uf~;dHL?Lw<7YD|f0s)X
z1?JkGzQ~Zr1<OMtxA$`$Rw}|0or$#L>n|;L4Rz9MHXqSZ++mAqI_f_FYV%+>*9qu;
zCi0jw+0^vPwh!lJBm{~}R@#uj(AQwSX#KIa+fV}?3#*Gr;ztpTKvsOM{ET}?^-x#p
z?3PSICP;T@&{ba%c7r$yDo|1skUeZ~kRfPfFHpRHb9}5Dx{yh$SWtL!>iZ@9bX|*m
zPQL3^gLzdM3PhDb4iYtT`<NoBl4s>@xWyK2r|-J_?%LK2CopgxcGiMF@(l=uUg#Ds
z-hFQ!yY{X&=u1_m_d}_IXzSgP@4AUgRnq~7RMrqOs)A68NJG`Sz~uC{gSjL|C~Ma{
z77REm)vDV-NN4q!00~6kOT+Lj%qh9;7?A{AOoB;o@uh5ylNppIZxWO_VBx~eTCs{;
zunriCRIs)eaWP#jv0|5Tm~G|LKg}amnKIfOrVr(Rts3b4@;OfwY{5FliV`6y(WL{0
zSIMmFcdb2%RPC3=29N}}{6Zwd71@wDMUrj(#>2hAEtau!iW^wX!xj>A-LOY%!6ML*
zYko*;LY^;&XC}aEBQlGevG@BaL)v3JB?wKfPMyd0IoqV5aP|U&z)|m><yJLAMRI#R
zDtQ$s#`dS5M8N$xHt=l{S_6CArC>&tJ2uL^mLtk2z!py1LmL>zlMEO9FG>6a#^Yaq
z9hNr^TrGB(jqY>|V?^d)Fkruv?m(ICmI6Tq9+tW%&O-Db*o+8zA6mLUsXM_Qb;Ise
zI;6?l!u%;0wa&Z1EQFP<N-f&*QX@`6@E*KP+9-OFWX}I?cG(k8f$mZlfxmQ8^?8M(
z_xW5CnsZdch}O%L#-|&7sDwyE$;E>lh|do9mmkpq+Vkomb^$T>U3mTBl&$vGCY5Vf
zwG0N@XFNwM0A&S7(ij3;S>*GFB5ecW!_EndjMdnJ2q6;3{Xpf%LX~ZA-k-7lABu>8
zj=k)=gxWrfmHs@~^XlTzF`Bb~)ld;qxFF@yk#PDWApv+!nXWOLU~p~)mxJMZFDcE)
z4LA)Sdeuf;O*x4WDl^q9FqY)l&B;)D)9aVTXfpL!{M<iuz(bBh`*Yf%mL{?5)v+Z1
z;*a$X)(I6vho$6hB7q|uM6UEek`&FE$_xr9`;9ovS~ADilVNU;f$}U40)BhlytS>A
zKV?4f%qRM8CX|j6dFT$h$SG|Oz2;ZhZA(JieWTQjU1ihN%F#^I({gl=bz)>^b$Vuk
zNjWV7ZJLV+qHAw$fk$V08)4L@sZV%$h@kh_M^M!I4kv=}In`jm?N*16k5hBHGCyF~
zr1I-F=V#2(cOGFuX#8@O>=Y%m{RCJ_&YkAuxQc0MdjU>&<+zkg$0A`?CAPlg2<1aK
z=ES|$X4GhTVg5gn<H^UmYt36wT~dA@Opz?_(!-KUGMe?*`Iic%k?{D#`Lb;`-;_;>
zqdyeRIJcW^C#3~GM*C^>qIH_Pd1&P1E<EioUf)(n^lr^d%)XYDh7Nq+l^X05T%r`n
zv0TAMK&%Z<m#V7}F6^2j4Z@4$vk%Uzw>~GvjK#_LJj;l@^kEL8<gLl&Y201(;gD#P
zp5EF1n7i!ruQBn?n4L60S6qqvCX)B#3N4p=M}vR%;WhrV`FR&L;BiDel|qSATj49f
z#L9%yxjEO`Xx*<!90_I57C{?vLK=tj`#edMiV@KaA?u_SD1Jmpa>-l@qBm>mn>CO5
zfI?!IX50@Mpzs%(I4fLb*C7ycSUif^+HcPAVlHtSe6}#M@DUXrJCx}sKC#!~k85?)
zp}U5@2d>lqbHC{Se@CgBsIjgcro(#Uvd`OiacygcphY`H8KxYl6heXZaXQvnvj_Mg
z1~CwQg|c=`^RrMNPJ!iDeLS$Z)Ne0<>u>A=0@B;93-?RKIc>ho<gaz-JyG4+WVr%X
zJ(An5B22#9Xo>~eokK*AtEJ6*Vr6{af{$?&FlD7@+)-j6dziB~zP-wXXESYx-!8Y)
zYwFH(C=`6YIz|63ipg*G%Z#glu)o(tvr(wmL89Q?Myz?#AjG~Xf*Ux60X7w~IR8UO
zniIW7ABTL`ala=b&+6$8`;Bf%(3$Sm`}S4O!@3d2HXLIW%;5mj`JKrYf;jH?NTXgp
zhlFFF*C?N#mHkoI9<Ya`l@O_Z*i1~kUelvm{*SGRy@%ua^Xn7tG6`QXuioBgLYUtM
zuRcyN3Map6b%x<Z;Cng3)amjUJF(VQKS<z<2JsbgfTh_>n(v86+&3=;1A?3QOb>@O
z>ik~*tQxc&G*ar@Q!O77g}mK-JGR`sdmKfbrDb&Mz}^V!6Gj^P=>`*v-&u57lF;Ic
zkdcD*`CFLzz8Mpra^b%FEd224=k<3~{jU26y;?gV>Rmv<7(HP9CZlILzxzdN+V!}T
zp;fKv`f14i=17B{+R079Np;?7ilL%qC@h+h0)^-*ar1Stjj8?SF+LtcKW%aNaC*+h
z+PeN-{vEo;xO*~&>F2~e%z=tC^1Y4BNnUO+1lq7#oL*$ZEs4-3$y>hgwzlhdOUAvD
z6wGXLo`MuiGu_>)k?&>M)_|U7<2K4&kqF9MYiCY22<(Usu(4~+8G1V-o9r`q9qke5
zV#llbmn3<c{%N8odEM>TJE{r2*h)w;f4NUlPGQY$rcbHr>*J=#ZTF;L7--4=JLCEJ
zrE7EfnBx#FY;O}z=;=xN2tK2mC`L?a>ktw!_bt`oFBrB~&xqpVek@w~{qf^iQ_*em
zUzYmz^7&E!;Uw<pf1}$4L<u)Lo}k8$@$p^{g^)-T-83;W(q<rj$-v1Q4$FL`vZYGP
zSpW_!?<;@4zSLRjAvy;mCW+%q&j1L0HuOUwu897x0P_$G?+Fb@MB!qqINsD>^3!iW
zamz&3aY`e|6ZgL`@94h11qDk$r(zROclikci6owgm{sW#fB5X~A8p^heZm;_DUBe1
zy8qP=Q-wq_<kS+<UEV?<3Il)k8D`NM7_#*?i47Cg&?$=`TeogC?Rld8Q?7d4uBuo^
zoG7U2<xDsPqEy8Mkx&X<(dQo7`<02Rz?4Oh-owWE0Fu${G>S+7kkhw4CTh$~S~;8u
zT?=et@9)~z?cCW~FcB4*vIw%{kz=<O<02N$D%1?sU7kY#6Cfu->T3m{ZNKMlyDpfB
z7Eeh8q4etb?*7jdF1Cm;Z&=k=kBJy7lU{~Ase;aS35$6B>Bjd>M1`g#g8b+!zpDz0
zh|^L?eI1>=nuvjt8)X=<ibRB~)+&ztuJd+Glvy9YSuY?PfIYaYWkV5W5h=aW4|rq)
z#5tKK#l4&mZ53wGCj!PtfAxHIwktX%5#;EjU3V5$+a!54UFa+FS(M~RehZ7H!h6Z@
z(})L)hH%%R)hN#-w;u1fOh=Fr5P0&*23x!EtaH?wfgm_JJ|$6nM5B_tQuUk84Cd5Y
zW)ikioaTlqIDhoZcZAU?apg-M{~<4e3oFEl-R&RBb~7hzM^dYXoO<oRV)EoB3#1t#
zsF%ny=33>RJNa;Bpzy(pgI^S3drkzzP=8SPsoN@POImiW<3xi101nYfL_t(AK#pe;
zg+&%oFyDgs1`&XoIH^u~*GUYwa;H&R)C6rLnN?I_6UTqkdkuB7(C%agD>5o|$|1<X
zT}|)cJ==0~Fi)jdTnWsut-@=QyI=K)Zs8L%gi|cNu}aj;FA)VU8&Ja_hTT5V(KsMZ
zJklc$@9q=jfJ@~CPPwvDe~ITR^36#;#2XRznP$=4-7lW|(<`-TW7dx4PALSTc=vk4
zmX8<DvWXCYB$X5MYy=QF<dIX{%+zqOU#xpmt+@T2E5y?C<{qoAE&r^o!2En+Q{jME
zCJbJ$w{p1OcU{*#eP2Dar?F)F)_vmTCr*huGhD)6Wx;9^KXROJFajT9#$ESZNsk}u
zycErQFk4NWQV8<QW4nu6g{NdiRXNU^Q+qdCV40kJNmh}h>f7y=MAWB$@<ws_8_(Nb
zJ+u7%sL@G~5+jMG7JUC6CXOF0T|Iy0rPr+f=?j0}bN<iY`@Hb91x4BU1tQRwe{dqn
z8NozS3_j%ep4RsP23bRpDK*3nA8A};CUGIv+DDERfN2Uli5xIi(6xJ5R9{pmzP#fW
z@s8WCx)#WB6%eCw6y!MFT7*UL+fy~8e9cX_y<y{*U%53n^NM28`P#5tjhJ^{IrM+n
z3j`^&iH66!-n4aV_Rfe*DFiwCO6zT9<UeS}ha7C4I7wC&8%6JKkC=6NiTIbF+#FfG
ze(^jE`ls+yJrgi4e$T92TVH<Hk8W<5cU76_L7rCRoli)@uBQZ(f{BxzgJOQo!K!Qx
zg(-s|+%-M?aM!zw=79g8ddZZjdGfB~z@@^aw=D!4rY7<6Z(T1IES<Fk1HCbM%CSR{
z);+I&=E{%%*Y(F7AVWM&K`{LoRjN^K?7VX&gp-O1d<dm_#~WJKWvgCOW|%$w^h=c;
z!egY|pPNPb;y4=`(a}wvZt;N!Hi{K%7H+}-Z^*QL$iu(%y!q?izjb4zrEgeTFw8X1
zoJvo8kMkkN_O@@%R^6ryf;8@KyBLywDeKRdnNX5eK=_W=hQx+j=ZVX%K5siZ`Vp-<
z8%WZ3$yKXweZy_@MLT#KO2=4cJ~*^Zl367<Fn8qn_FE|7%tpJW41yfq*>*eC+w<vg
z6gN33jTyjo%Wv4UT9j0{-ieOabOaZd6+V2`rVIT2P!eaxc_SJJI#B(+V5vnk?&>%0
z-P=?$Z4rd02ai73b$wy2MMOwmWo53&26)HLlj;%{@E(IO*t}>(saU*X_BJ3#=h!L6
z7lJ&)^~+~{dHDquV&FBOFuTaGYl?&8tC$9<$iJCjn0yEm%*jK|%QeXIoGhlq3Q}9L
zu)0flu}6?UBm$>%8b^bXX0do<ji{JW`K5DuN23p=mCnbOykQ13V11IYmA8Col=q}&
zLp;Z?0OF0t1E=1Sjk-(;1ZmjcdJz{5s!rsKxRRs}+Y*2jX2#r7;dEM$WTS(bXJKJs
zOYOXJpa%-%pvof=IZ5s!oKrvYa@z;80lz7MAV+pJzn9vZvUAbLdEo$gO5-?Qiz=LK
z5^ihCn)4UB?2<Aid6h3Bazcwjhl1JQL!Ruf`upFT9a=*$=MjYaAIF~TxxT<@$(z-K
zotQX@n@m~n2CyP51FIG>GLIb3$XLKvk$m(Gw9nSMX<6@?Oe_4%GY6_VdxwRsHox{i
zNM1>mbO3005LfzPJ0w$BB}rfC_fd)!TRrjyNkDhY6_Gbp0r?vbp1LFn48|3d%}yCt
zg+?Z7+H(vyJ7U)qs^9q{!D1U@2x0(xX%Jnl1N@W~F(oq&c|!}k+Xv+Nk-XDmBgu_b
z(FsT%-P5VLS<;+GkfVD$ZZ2Tgl0nbfu^0}c33i;u(Ydjcah;;8y>ETCYA`S`{H|lK
zc0l{pnpau~gcm7Bg3@6!ixbau^8l5mV_MV9wC-T;jvjxe>sBWi^nBR?R$#=LJOq2Q
zB@680z~i0bWJBwgZ0rUjPfPR3wtw35n>JCl2o~^pkc!&SdE(d-3Oj6b&7$Mvka+p&
zL%iZ!Gndl{^5RnmN>3ddGCJz9L5O3PiHxmg5SXlt(6iJ?<z0$iTr<AouLsuo2mJH3
zu1+`*e#?%h_ZvMZ!&+>PYeH*X$|U9oIFj_{2}~+3Ha0dd(Xuu<jUbK3nwD|!SY4*P
zG%w1TJm?OK*$axq<6HKM1BV)aqa}%eyle;eop|WCpW7qm%y)_4K>UgbEk{re<P{A;
z-~qKAD@gHU`&&0^S*4stkcNG2Z{aQ}C6xK19&uLZ#a9t)5zQ||#e?_#b!Bho;1|w{
zGtL*1YXd!l-+bWPf3;D1Wi5gg7#LkcLHX$<qmKQMo!z%-*}$AWa(ncd&JU5X4Ck?N
zY6>6xI2K!dL7{l@!6xzR#ZTQkG~{tRosMs2qPXZ8^8e%g-~01Np8v%OG4K2mc`0T-
z@g5_QfEE`EU<#`F`GI;&4rag(>q_P>XIGFD+fUd}9vrA}E(L<nKto<JAZK?RSI7X!
zpSi1w#kMcLDt`XWC;n@&clamhXzSTAQ@<gNaj<{*?q7Z9$?x6&`JH0!%3=`$8JEwF
z@Jq0`D4zP&^Oah)H)mInU5yR1hQWW({w^<a%tsS8iJi!O5l#dg&<}m#RY+v|-}kOh
zTzvxu{JRj3M{iS(a-Yw)pke>XUqA4jzpnqw_l}6UD~jd7iI7Im*{LuQ3gh=H78lvf
zhQ{`#c(jc_w=*eo7C{cZcIqM*_>Y*N`%H!N)yD-g5=85ODRWmBi5)*aDPI5aFG_Cu
zpS4e1v~hWGsNZv+v%v8HB;HMkHH;rnN?2g}rz{*07qy@2y652^|8?b~AKfY3kmt->
zjg<ppWagRg$N@M=M?RI7-y2`+B%}V<_~(osIm@!$w&bh7bsq{`U%0|5Z#kVVoh&B=
z^oj+NJ6{*hIkdV(H59rp{McMke_pMqtSJ$N#W9^cs;G3e_lt%-r^GA&eNwanZ|2mv
zgrmkHf`eFaxQmvvJ)F_FJtr|&V4m&wcZ5W+J96wXaqq%&B8}riPJ$0)h2b*8zlP?m
zEH;KQ3(AfjN8_e)5_Y)BZjdCVw>>C2TRdVA2ZOB8I-<EzI?DZdnG9Fjs@eiDsXz=|
znJn*2$#arwtiI)eu!cST;;Y-QwQStDF=IwOCrPZ|{p4?j*w`|faat^dnK0k^kI_1t
zD<(HbeO_2QfpO|NYm4P-Kv69bfQ|@6OXr+Gct^ai3zihIw=!*3+Yx{x9}=^;7!hr|
zhN#=tJt9iluQ`bz`*$CiRf4%vs+Z*C8kaVl$Cm(Yr^as#QOKb19S9IN*d205L@<n{
z1{fj{17XwJq4_b3i&k;)Q2QLfB4Y%}$%XjHUprTont1<5ev5Zi4`zudK#Vv7@JeL<
z=!ih_`!vT52@I^B(&D1iCXPPdwMu@=@J~)6$npJymlrI-{evJerVEpqRzDIMbc8qu
zWRwI-i;D{&$!Z_C^qdIe_>hwb;uE1OjWB319hviS^r4<40Rj|m7(s&iMdY%C&*S#y
zB!XCkalSnJq0f@z?%3&?kgEvyL!iMoTMHQEB!WOe@n{$Zs;8?oMvIP}u6Y<BcH-SJ
z*f2b*1q^Z$L28yd5BW}n<Wi}VSz2`S^vFY!R}g!1>{QQn9?+uIIf)<(H&j0{5cEr{
zdDt}6qMdpk(*Xh`tzf%gAOLHXZ>oG;izeqJf~>e~&NIE(Bc|m+eFf2?o2N-0<XJJI
z`=N2La^3vrwP<xtvcXi@#$ALxFbGz{qm<kz)5V%vba(#dA$75E2MUi3cu$4;e;3_S
z4h?ATqGsfBas^?-uD)mKhnt(+I7f}+wooC^5hSC2&i}+Dubd{)c4}B$ao3^`Yk?dD
zT23MeD|YGIZhi!k2)DN*C@tsZn^hCGQFq!vK&~FpSEKeb6gk}+H{Gh)9V<DDAZyne
zf;Zo{_L>IV1I|NJ`VykeV9M8AxO$vu92A?rvhEs=9j)4#vk1ZlzW<Xqyma|>b>D7&
zc~Cg2VX`K!f2398^?ar{1SrG7m~d2?McXTGvF557-@fDS8(z|~RXL3yZ0NVv{`04b
zh5gB{mxkqe2SrIwOCacZO-%?yVa&rZ!y<ZK_6UbCKUsD8CqJciOL9_*HL{VAYME{{
z{M!RQ5!_UJQIYV1)+F7T$95b>f<S*yfB?6~sAb`(w21Z{ZX6#oZTnsH-doh1+fT?w
zl{e>C5VnF1|4sA@H`lE%{lfko-SPmDwFvgS$(q2)y+rOO@~6A}f`HPm@xEoB^ST56
z4(;d>Rp%Fd0myN4HjsmWoDwTYVvm3O^AFeG|K*pTDZ_;bWy>6=)ln$rqV*0b?NWf6
zm{9jmEeLRA;>WWtM(9^5XI^+}u=jvhbP0d!yFasf!zaG}u7+$jY|0@Bff3yrHQn{t
z_usbT{*%9eO+qKH;Ihv$%L^*0>MThwo&L(^xXFIrAPYzta1ShmbN2qGki5?^B%;F?
z-duOvf4%)9zcp-5Z3-#VicU!cQ5sD|`R@bUF5dgclUFtG7+gOrg6kq8S^*Tv-3=b4
z^7=C=1V{v+FVT;NZdmt!W#)xNFRi|2=F|VY`j!_b0x^`zO=$#C8j<*<MU-vZwiyo`
z7`8YZWfo^RY>fs?Hj@b1BgQCH*-UmQL>J)Nc?S@w0Nj27t|xPX=ZB&~6z@j7M{#MO
zp#XCjFAppLzBw?R74VyU=Qr?(`0?UDgkgVbIyjy~iozJZz<p;zK0SEi7xiG-_Qb<H
zfGg~FMLp<>VPS}PaAMvAW*6@fyc_Wz_24#m<YR`-ny}9pF?hqm>@!%y{$Z0T;Pv(f
zmo0M!H*MM!0dEoKJ|+jid50jA1IAdXM9vZAC%!9w<;kduUwJCYN4~?Xf1_dU+__5l
zSx<xsgpD?kMt)P?l_&F2{K_-&n=EIt2dS)t{zr#^4gnnkIs|kG=n&8$phG~1fDQp2
z0y+eA2<Q;dA)rG*hky<N9RfN8bO`7W&>^5hK!<=10UZJp1%dw$4JQvVK(ob+00000
LNkvXXu0mjfubX)X

literal 0
HcmV?d00001

diff --git a/docs/static_site/src/assets/img/mxnet-background-compressed.jpeg b/docs/static_site/src/assets/img/mxnet-background-compressed.jpeg
new file mode 100644
index 0000000000000000000000000000000000000000..78d664228490e20906a0320c2392e2a91b078409
GIT binary patch
literal 86747
zcmbTdcT`hR^EMi~ARy8Nq)8_TQi6ouq=lM7M--3}kP>=T0Rf40LJJUT=pelaC@Q@Z
zLJ<L_OP8ingv<MWzwf(i-M{X=bCPprt?WHH&&ghynP+BS&0hTgFhI2Rv;ia}0D$iG
z0k~QM#AyX0od5tsLjVu}0NemjkT3$st~HYD13<z7p!ly206<9Y{vRDiBJw|DNCAKt
zB!K*X##min|0CCy|1<l4u4MTn|F^|_(*Hf0!Y7~Xf9s?j|Cx5R3jmwAc=~$!xOjT;
z%1B58z#4jn<o~gLE&tU8|En?(x#GSi0_w>(Ur}f7{MC4I)djeHgS3Ofhm3>|Kzf^m
z>^8|&H-P8*E+|O;EB~E#eIg+xBd4IGx<O5I^LjuN1Avr-jEt0=jDq4nUqcdh{W*aA
zHU;B7DRoLFs2vp_idi~5x!?xBM%^bC*u*aZ8GG*tY8uu%Z0sC@Lc$`VK#=T1Ik3Ef
z<|8d_9bG*L9ARo^ZeeNV@XXQ4*#+t9<Ll=i5Ev93`8p~(=1nX*B^8sFo{^b_Ei5X=
zmz0*3SJXE&HZ`}jw!Q!SrMsuMuYX{WFiD)6o|*kRx3s*ny0*UYb93w9@aXvD_n*I~
zXaB)<{XG8{tn2gtLiYc_b^96@DLFYAIn{q~k&p)b2l#DrihEL&jOtJ-I}{V2bodQs
zjpTy5Pt^P}uwN|p-V-#e0-z<qga1JLFS7r4z#{&CA^ShT{%>600Ga^O{|Xr?DH#PB
z85sp7#kEl0p!|>Apt<p1q4^)7{jbpfM{fNexw<w&a&3d0oSf?V#c-4QCd2<{xmvhh
zmKIlY09rDV>%~NN8=wj}T|_7HVR#^yMAb1{=p_;EE$S`85{0K?#$ax|SkY@GKy}Ro
z>5Tfz)O#dW3~Y+OGQU1USjU59?|~CX*mn&VwAHsE-G_-q)gT6u1${dUKQM!67IWk2
zYb8nM9~TG&ytPfQ8`s!2Vvc7oF&0&3VO=j3yRGJ(@{|CJW?RpQKr%keK;gSiiov^Z
zJ9=70!YX$fmk78Ze@!62u8)P=P-07CO>jYtA2f^>5y+IQmeA5n)o(QsV}e4T!6G-+
z+Vn(9_D=Bq`DUycsN%x#Ya5d>vRuqnVoVN%Bu1bT)}))`zOkspkr6FWyz68+2LN)_
zj}=-nD)T>sfBuJ@O^-?FKvYNvl~iyZm^-||-gkic-)6{hde_I}pwIkbDu{nii}q;S
zjL#w(Tu!>e0*)h=->Eh#Wv)n@3OcFOeSPQ(W!M1@mNv;?{UX5sTt7bmg$E6Q@xsib
z^36jI;qYj-5tAeOMGVfaFTd0wm(UhCNCl}nbgInKQ8$nlEk4iSczC}m5)7(Z$*)>Z
zMgEvHSj{a=8wm>!)zx8cZ{aB8#0F+~GLI`bj$AxNDE2Ss2gwjBgGyq_MX+KIMpEI~
z;pjfg@_4EB6eRPn87EA}(>MfDMZPMc`%fiGj=8PI_17lf6(FnsPo=()&xn&}pH=oS
zS9t?9;Q=FLbt>p?0+2HAX1HnS6~M|pKZ>gDewCz@;rHYo1X6upZ$0V^VY7wEeS3eF
z9Ur%Wha%JJnyb3?HZ`>%xyhb^wlUat&Mbga!$k={i1grO)2I(rURvj`IX)%-%tXFy
z5rrPadN2G$`pOo>JWoNYZ<;n>-wKWi<y4q=8Yb$g3+UmbjMc2sY9-Z=UMZxi=j6sO
zh(S`;rcCu^{7K(uzoU8b<yPiXFu&>s%lKakaqLg3-rWwT&c$a9kCm~Zj8rV^4n9;6
z*W21zPKGD%y{m!g&EJ0+CvlH=o!i)BC*nez=C*N~uCP<J=kut4ZuAyO;f+4FIgbg>
z!yTG1R2aP_rjo%`%g&m~a@9EwJL(o-RG6F4Z_h8FlRAy<BgOmzwx^=$9PY*?w{Dw+
zf3JqzQ4TC6LF+XXDBi9(ho`;pKz{2Km4l9iQTUIzfx}TfekPwQI>pi&QAuuP5x$Oq
zlafI87}?$tHpfky9-?a%s!x_UBF%?88CX!w?DK?b8U($R9c-rxcAA*W;oKmHttbPb
zI}6E}mFXg>VADAJ2=&4rC4<=(D76vGE)YSA@OFN}X!UL<GQhuFk?<p+2*a5)Lag}W
zcUMcIE5zi^qosP$Qas-~yrKg=CWzfdOB)6G^nSSnJ*Q_3ag-OVydB6Q?Kh(9?r3Wa
z3D*7+=cD1Vp;Ypo_PH!DeC|D+oOWBgS2_3L!mAMv+d!v1g7}e|NpLc3-m_9(Fm`dE
z@%=G)b{XL5k!EM9*0ts4Y&;?1>GA0HgUr7Cnyrncns;;=a~)|OGU3?K5<UihOqBwn
zV%i9izncDACnJmWAP!qC-b<pqHFAPUug9QTIC-pw9O)+U?&Y8&)*AB^v!rtvU}LYm
zl0@w%h0;@cq<3!U=JPkKaw<~~Qn!uo=LaL495=D@P$j~%2tm$pm<aTwA`jW8Zs-=H
z3@)DS%)zZks}+qv7aNp%`BSxh40TyZPus{!RjuOY>y%7QW_KafLaB}+0&gjum5oI4
zv6sL%X=LwYl}u#vvcn;tk@w&}xfQ@ejS6*s5QRlAg=xyF^9?-^5gO@X&AO;%^f3*d
zqLcc8LbEj|1xd|oAH8QIYVmTR#}(P^86ZtTekds>tmI=``D=ajW~cqMe66;Y7V@yC
zWgPyxc*RI@0RIQqzVzfZ(^mQb=*Q)+XC&j$WzUF=;81bFR#ge38_OSdeo4eTvZmgQ
zBL6}2?SMPg5mnkRpr%XtQJ}gnttfVNGlYl0ti;ka^uUzkPj$$5_(7=!;n`#jw>(3U
z=qGaDDy4T;XKu@v;S~5I;~QkCxtCU_n?L5fhMrW|iJe<P3b$T@SAHFSwik}hZh!mz
zy>#DnaldTf=iDgXD7Ev#_wtq_yQw03eN7Vky*2-G?^Yb?{Wk5Df)74$V=Uv#*fdia
zE(%Z9b^k|Q@?$(iH#r|^qaB%Q)u5a1b;IF&Bk{6RWpUW@k)uz#Z1#O`{d-9{sh22I
zH*tCo7h*;V---P8A{y>_PrkosFr${TaLl1_yA=c5!T9I%|6#U#g~?w|rTgi!(J9H*
zN^n+2ww028IJUSiu>_%$6Vnb8S!0QnW72O$1c5rR*-Z9oKI&lRK_rM*H<hGS`NqcP
zVsn$wojJ$o^2EiyZ0pGR_n{MYSAZZ@6|r0L%PozFJ7a!@b>IFKvGOUWFZ1AJ{vN6V
z4gDnF?ahUKl=!%DDD{Q@FOQY<hbfMR_t7_7loO#l$nPD;ED-T+2kpeG@zW0&PB>Zc
zWWCtAx#q?B^-V@@8x+2!>CW!l{8YhVvz#m;@RbT{6^X*ny9jn;(QkuFqnVio70_xv
z`f3qz6FR|wKzV%Grnl>JW05n__IMGMMsM_&@E#GN>}1F-JS&st&yqb*7ASU2V4z5-
zXaG;Qtzl<(F^O5uyoG`oxwggfv@hZ~gPjVN<IbQckOl26SCxqTfl?~)wpP@jexfVH
z=?K@j+x4gLb8RE+K)*-mHLp-cKs6h8V|3bUMZWZ+YkzNHQ?&W5f){!1;3p))HZ>85
z{N->avR;>dS=JTET@c}psJKv2kI+^idjX$S?Hc<o4B!K)+=LhU=w(72G?q&d!qt$v
zb5jh&`Tka1f+X-5wYr?lH&6{;`oSorzA^L89=rr*akp1Vvj?p@Rlf%91o??!rX;mp
z{o<I+$e;S<FTH5w&my*df8a903+lBG<}jfyp0#ehSmiLB-8M1>e=6ps@fR@MXnJU{
zf~M}#CeUjm`HC1D`gKV2rE9n0JXZ0|D^ys+M!()}O2c)$ISn%VP7?{WtjN@CYl>4^
z&I?&KZL&y{THYhX)503avz2NMYh2UKRo^o91m(@yMdqRGIznNPv==Z76FKm^w9)~a
zVB0&G_LdKSMCxsbIC7Q#s?tcL{v9wsajxcoeWJ=G22!Dyf<5Clq@$kDif-M&z8UFJ
zvmiseJzyVoeU9XCK|TLdB#_WVxkAX}84jvg)2`uQEi$<yT<vM8Eh#NVO*KsQd`9!R
z_PD5@(!4_`{t6(b^uUTP8P#9Di)cvnu?pZHw0Q{(*$eck6xd04xHTTPyIjvrNrm0c
zp&E5lu%PtOA3yQ>Thi#Pa?b%4WSEfFc+W&ciq=$MGzQ(4RH3C?5Zec7T0815PmxLj
zDn*#aKYjqi4r+-&lE=~8YX=?$_qiX{bJ8!jJ}&T58wD=@FoC5BoH%F8>pe+s#Qk<^
zFle~=r@Mi*SPr_ylKcF{bB@K9L_syx5a!>+!)*z}+hko`D9cn{_Jzle!zguzjr%Ww
z%ljoay1C^G^kjV?%o}5agL`OozH&VVm19{#FwyDxamDz_=4aigq<4_ldwZ_azk<=R
zwc4&63Pr-i8jC+AL6OtN&xG;Xyuw(<hh|%kTUu{giG~jK%O=$&fkD;DqoQAnu)YpL
zF2*m`bVp#84Fj?U1ON7pJ6+nUybjRyTFpLj2}oAB>EUb%WglFRxxDajSONYgn!N`)
ze@m9Hj`=HN@ms)ClztxEfSyi*1n;02z>2lzLbBg&(uw8?!J4%p`AmEWxVudV!7D&C
zE<#&Xj``Ck*74uvy~d<P2@k38E;_fBk=F2(Dgs|L-EQQFzk+|};}@o*V)Qq<J*aEG
zLx|$y+K#Y*V5s8P*&{I`92=LLY_+fJl7TYODXVd^4r_4WbZXBvwvBKxfYuOIbA$f%
zOrNS~j!*lxVgIy9ui8#jNE|5Vbx|27Xv>iY<$g%}dA5r6u~3AVNGQ$>rDNMICH?H#
zeECW`4-BUUpQo7HZQ{hn-eveq{m$tlIDTFIX=lq0YoCFC4dt)+>Hj|8q~di8eDOmD
z@j`Q{Wt@gC`}glN^9`!s!w0%HQq`(|_21dbFsJk*4kG$kMiTm$tXxA!EPi`)i+2AI
zfuM`=FOyqVtSf^~#A4m+YTJ6nULVcyJb*Nae`*Wc)2o2##MK`kzw^GmW~MU?OV|r(
zdD#(wTPTm#5v~!9BzFd-IqLv5FH6GFjuI$cYH1NRS?(~TI<qBBcP8`g1WZi$r;?c6
z>%T2k0R);oVTubmz$7Q$Rr%^hZ?JtzEM~)<;pfvwU*v>}(|z9-R7l%6zuq>Dl^<B0
z{OBO4*Am3kF($mYX)q?sy{^Pz_4aOAp>eQ(;Cj)#%X~15DVBHD=W$=eiKb(P`UCab
z<p0E8i??jK;R%I-F<44!_L7xhth11W$L)3NQopSiIe(d!xv-mcl@OXFsS)b~^X?GG
z2F(3@z^&km<H?REWt%1-sSj$KxGAphZ%?=kr37N!Xc-hZ%|8!Ch$>u8<yG|cJ~!}!
zytY5B_T+Cq*oZ5badufcom36E5F5Zd+J3d_KZj@KS|oK%rM(A#iJSRm_fs3g%wMm{
z(>GOZ+wjhE(ZKA`TF_|H^efsY2c3jb1v&pJaWr;q0Q2!QmIQ&86qx>$+b)VK(oWrJ
z@EtHpgKWbC%T(}3d?#ZpMGg!}r`3OS_}XUkQ!HNx-qV4b?|XzcohwmMm2zVq;;KfQ
z?w%)yWYkDJsxjfgn0hT1$;P+FF?NTEUb=B>-hR~5R`~g=CC7PZ`B#?Y4b?vlI&%V4
zXserB!!upG`me-9Y}!i0@Ugq6lY&dDNWwvamb3V$F+Q9}OaXq_xHj3h0&AzGtYv;j
zBxpTHf?;C#fwO@A+Pz()Lk9nn`#%*pUgFv|m<bwxx(`{ifBVUbWEtb{hASi6Z(<**
zIv*FGOL1-W?))jGJdstPCMLso_P6!f-nV24wLI5n66+1)GSWG$4B~FwNk~&F*nI+T
z&scJfy)gOyD>=|3Fx8j{z{(0!SawN^{<+l(S6$KxeRKuL%Y3P@;V#>0^?AYl`MzT`
z^C*kOD>+V_^1z7Y72tK=lPf?z<_hrVYYn?yW%FF##-+0#r=N(r=GVo+Z|^R(E}gF?
zPI3Q9H9s~<yv$pPghwKL;p;51E%e82EEI~UQj~Wp6iQj@fwzCIl0y%aTx@J(?;&+b
z<xA+?#F&h?>3Jgqf{B)(_V|0=DJMnPx2RGvP7}>oLFR?tS3YWOI<QDIM1<iw6p-IX
zyI&`=w^pXFoisW6z6A6*e>v+vF$UbuJ8@xKzZEVmQhc4v`e^AGQ-Q{-#F?{t*qQfe
z13+s6aFLSCD*gAltqA5HVHDCfdSzt+vLX&xib4J3!dI7#52;7=AI<tJ8;bPE#Ub9t
zIdBrzm>44Gaw=Xl{wnP>$Pe$K^w5pd4w=Hl9T-0nuC_vqF>^Knlli#QJU}Z#_ynoN
znm$$?W=>f*1|23&2fVHBjcvHVeM;BaxD!_-x4VtnZ2oa&olZhw9^(q9=_>%fZhfj^
zwo7UbPc@wiF7oOzJ(k}`vA<D57!Hikdo@|&d#I5P7HYXDY=#%-6pPo?18|Q>nh0yh
zR4;|OGf%wvmq@G}&0S8UfKr{XP*##LXr~6q`s1X?LAT&9jLtljAC0tEB;KDtBMxML
z&gOm>Y0i9L8J@6&?py~*x)}I9wdC{zy(SHkg^w^t+e%?Qbceo5rQljmD(-i0OxMUi
zZjVR&8R2hE8INa9AELqhaKgR%s4QwLM22R5>pcUzzk!F;r37snC8+M3#{EDUZGFoc
z4MX=TaT1@apErr=$}zY;<i?=m&yomxE9@mnK4=fA=^VopKA#A1srCiY<4co@RO@nD
zJjeL#D^eLNNh!+u>Q8Ldw`910#=s7b9S_YwKE9Il3j-;fTSmG=LTkm4_veZkc~)Co
zDMEmKbxJ>08(hWYyL!>|R8jiSr^m3$>eFF+gO-JT4!xVa{vX)~ndT`jZ)+jRj-zxp
z<~L;1#0P|OIXBoVk3hfad+So0k#%FybB0c`z49aQbK|iTq~2?<79Bg+mB10->^E`D
zN;6b+{ZH9A783sa2^<lwTXm33dG9if%x3q&uFL9~#&9>D?%!Y=v9lR#<-{RHe)>MG
zb=S|zTKA_#jU)1q0tqMtJvDxW_RRQMylks*r%}sIoZB7Y7Ulzf8jwsn*WF59<t;CD
z(mFm2)5wzqBX2qh+fpv8hoEMfXf?I2lG9nvN!l*#^Usn2V*Ob^MNDoSm$!)S>*~l`
zrF#_b+C}kjH!<Doll2m{OG46|jSI?vWgbA1pKYwO$aLeqb=F|tHh1NJpbR$)iYt1=
z>Nz($m2}M)n?BaAlPS8gG#pyfe$3?@UOO9+YiOgPOx=r{Dy|PWpF;Y-TVO#tLgNlJ
zbp#=Y=OPu@KrSug-%mTWERKk@8?sD`yqx3N@<Cg=9$lTHOI_QRghes0G~DE19~o%R
zSBFm2!=sb%fV4<%KTg~MsL0vmqQk8KYw80sB%GzI5cec|^PS~E)2G2XrouUgthvd)
zKfgcIzyIZ<RT&{Aa5?`$>NUBA;HoW77TWu_=}o6)#I|kt@QJ9sJml%^o+q#xa8;qV
z6gC**po9~F)kQ(fr<jJA?#*{910}nr)}AGM1B)jz54*fvo@oS3#P{RNB56#%aKhS(
zVg*^aSBRIUN+_jVZ3=1I?06H4U6x=G(xvQFW3}WOFw5f&a(75*sbXsSn+Aaa9w=hb
z>^im8(A)FT(;A8g#ZocnA3<RoO?(3Z!GsN222U&7#+#U=ID%*oFPPa`iDUq8XVK)w
zj(aNA;}3iUjq@d~2Ym~BC2JT2CL4lJrW(2=-eGxwR52Z|mYqJ;9kXh2=cQ{Y4c;(Q
zn~@)5DXR(o!z@gkYa&m3<Hs;z6I3}q!O|b<9BJlBm3<p1)1{hbw-Fbqx96q}^wK+)
z`*pWk<MJuH_U2sq7oQ6WLtDD!<i#ilQT3iJjf`1EM>g3Y0n0ZQqwBH|-E^%X`ml<T
z>XzB0F{wG!%sA9JgvnH6#yZ=h+}-0&L|^H2+gEXF!SO>Q*E_`GApK^`5Sj*-9pg`6
zm1E1>kSK`j8f*OcQXhBYFbSdgFwsQMB>~)}GWGCb!o9E}33S?SNn-vykZuLc&&(xS
zTDxU}noCU}=1jKUT10;9l`o49nGEl9HPb*cdI!Vee3f05doi4cF6rNx2KAB@2^*l{
zgukVviYUu#J~9$QtrUU8Wc7%p+CLpteKXn7cX?2z84TM4?dhZMQ$IN;et4d>Z5n=3
zN;VH2?&*)c=qnlyjuDE(h7Q}rToF!jXpa@`eXDC?_m#X3RF@v<!T{A`2K?vU25Rp_
zHo^_=>$NLyYSN(562_wT=tv>ltxjd~2|?yztjS`d*o_z0%&U_+zM$JoV60Qb6`cVt
zXNna8?Ocz{K;>sJKEaD?b)dalILH5!w@Qn;2wXpSLFQNfE09~&(r5~M12j;<Yg4ZP
zoG^eVCp?3;Gkd4noi1m9MtU(mBdo$0KSfzK<#J+Dm101(uC$@7xv(sfe@ylfM32@~
zs07gm$#ST?$-W62wVdWNUBPBK$d^xZV%xadgwd|ToZH3b39QP%xo$0+gzwj`$EQ~W
zs&v+xD$kVMgZ#o!PVqqjU0cDLwlhl8wAaWi0Vr3ah(;nvk=^d=<?~D4!fXH%y+6jx
zT|UNSafBA1JNeV9Q9?`8`Dtwx`_g6G2GgKDi{wugrRzUTeN=)?>g`hx6^ir<`=55e
zvOdO4SFp?D7jJri#{Xsc+^4F3G)x77^NRJDI)Qs0ifF9mu5bZ`(@@yR7m8DQPxvET
zJv?dT93w?|SE{u>${Guomt>(9OEzp=eoMrv@z0eWk<oJW(_44y)-wc;_W>z!+VUl<
zrp7WVCj#!NW03T?+=sD)?cxezq5rmO*>$@rA(SK~$s@pOz7JhR2}DMlr>4z-1fUWH
zmx#-NL9E@W4>pPz{;86p7TC7kL%(qIZ9u!IPb#kIo?Qzz^oyr#9zR(Qhnr^2&CC~j
zCuBnjnD3dm#?LuNkPXDs_iv~ju$`o=;IidJpIy?Hd*KLr`BHZSDHejpqkM7sOp)@h
zre+PosV-yHobR{1;5Nh2$gnMP45NBQ3dFL`D9)#c_IQf9(DjjAj_(LkhhDy7db>$o
zHdKzeDk!aF|Czezx2}p?GEC@pA^EDTJfu)E{>HqS7rjC%%#*|oG?XF*^ALdRJS+QY
zu%GkFmB!|&Q%bZA^u@H1q@hdIPVS)?joj?lYQTB4sF3$eag9pQ_q@@DuU~r&ZIZo<
za+b@Q5tE*EzA)YRavA*|$twWROu^*hdPN3LuH9tH@2Zqc0e;X1mt%Hq5{|7llm_Nt
z3Iuj-V4Xk?5hllM0WE13k5ybRW|CZQAmPIa@IED&iB4$0HzKKKBOOIq+z1Gx^R;r4
zqbMB=`rCTg)97yf^U|L*p))G@NlXWK)U<WMVdBzBjny>VGuk#EW&Nes{9)3Miaj3a
zKexAxTdMw485!mUqDS`DFLXl6HaYCSwgyg-(o6LO3RUpO&yrmeSE}qbi@gp(VCC?4
zs+}Dn@$<MQFhu&byRd(-#aVLq3>;5y@vggdh9*)VZc1MEzE{pPzr1j&eg{B`(EiP7
z;**w#YgoOx5*8{EoHOmZ=MYty+-;sZT}l)*5@{~T`2{YY=Di`D3N6lD&bbb*Zmgx9
z-I2;c^sws;${=?2l6ynU^*DI@xXQp%OsoF#v0qL+(y+S&J$?bnViIaB0|pP<xQ`Qd
zxU@jR9xf#wMSfHPB4;KjgZWz!XzDlz`LgPDFl=~ZqcGL@wr+@h7X6o%<RW=4F+B^2
zG`$Yq#2Ptor)ASz``!}ple##0#PUnXmd12_x(X!Yi3hPa!q>f;ooxEU`d^-3K9B>t
zlqXw20STFL1}cwR!CNX)7fj>6VfXfT0Pz!goFx5Jkv*ZSfAi=#&8P1ENqkrENgi{!
zzbnWx;5K(QdvTUPm+7=htT(8O!!@4I;zVu1ZxoHh<O?!$R%DOEZXb9x?h1WcLk1ro
zmqaGyisA)$f^<t(LoN|ngu?esl-CueGIg@@QV*^&q7iu`LhX0yGc+}dM++oZt;m-<
zQ2T6ZTO_W!<CsgO=@DHw@I;AbbEi-jLwQ~yuX6l=KaT~TqT<+V=SfLLK21lQg^!7w
zt>ul__vQaou4FX-E=(1!p(Df?-QrLr0Iq&5UnSlxS)Dn%0-EmA781<NQ!G;cm}Wm5
zEt;$Rx6)lWLqXLc$%SYV-w+^7namvaH5g;u!eW27FplNSRDBYQTNG+ddXyJ0+%^HO
z4C&K%V92>n1u3M@pepuH-rw3V^^oYPfBUSzzmKbeB2$tgWYV)xsca%cuJI*bYWKvS
z<;)xWXu{l$dlhlDX{YI!!hp&0FgAgDZSU&8ru`?R^aB6HB;_{r+vn2D$|v3~aB&!=
zB-{celEZ4)oZ7m&y*o@>_l4ERhDGMr(r;Hk9Ws8lJ;*sFME^R5+`iHNL;PQF#QD_N
z0{GJ>%G`FQT|Adi9K-SM{B&G8&3v!_xhb_>I!{L%cVVi|BVeyJ1#?&6avU~gN9DR&
z>9hqkFuXO7)J!%g%uQA*dvRnu#$JZhMuMvIbD)yfKU53%1#FM1Nk^3Pbr-3Oej`Fd
zjZ#W~JrNRGxaNE{dHR;WQ-t@p>`>&){CB|xwLL^Mi!Z~El&6&teNy7V+(v~bt-U~7
zC7>w=^|WM!FmlzTwOb;iF8S(O%qAoddQp*lDi2WG#<iadV2Raq%1t(bVnaW#-G`b)
zgMJWQgkrCKp-jMat@4__{Sr*rThG!Gid_?$M|uM}v5$L`+Ngc7RxzkjQk1%c00vIN
zznGhx;38PyQrwTv*9<?ws}MiM9l^>34hRtz6?8_iSQhF^RPQ1tTYdx9RkvJS?9c>!
z6nGswlR^6O%xe1!*K6*ZN<Y$Z{m7~Ewc=1|ur;DkD+gsxAn9|h;{@-TT~sZn_vM;1
znkU@n_)zysM2kiW_D{Z`VXPe5tZj0;N86k$u-vj%#^m(XxX65PDGNXCNm=L~G5hbp
zh}2=d+BcFO?MY7i$^mg`kg=7~I`OAX$6?&FY2OrjRL>(qo>evfs$XHBACOh>h(d9g
zebh}cOxXx%RX4L^Xt=`N_(Q#FcQG0#*k?uin7cQFMh1&?{Pd^dt)z$OU179@2EL{^
zu8Ssr$0r#kU3-DNVbV$^lslR!@Md(K6J<Jx^MU8ApzR}qC)yexzt$4Q)+Z-40fi<~
zfT4o=<Gp?%w}V1a#_?W3<zV~|xY0Vjr~PM=8N7`)X@%%rWKB2iWZQtqe8;GiNn?2H
zre&fpGw1ILp`DFt;kIg}yH1VuPu)jMk8W9&Z;%n!v6E$meFzbYbW8SDSpZ+YObQbB
zbYFKnN{nxDiGb@kFon~)X){P+kv5|xQQur@!JN|(5mYR;v?OqOz7!>Nb=&QM2OcM>
z-sjO2R_R&;T-&l~IX_GihLu50V!ily8xsn??m-3n$mCCjeFke`@z&0R%hl;!Rl47u
z<;x9c$*>$?SG#WkS_3%_vXM%s0`#tWH8+3Vf{e-wgRJ$rW}mA{tWKm@V_Q3>L=t4M
z9XTXekZcuy3-bt3_lxXpJH5(Owx9CfNNcQhjVRq@{C%+4J2VTM#R`~z-|hzen3C6F
zFTdT|zmEN<t-#%Au^iDLMuv_5E6;p%m;<-Qrq=Mw1tSK!%BZX|nMOc;d<Mj*+I*Ub
z43Nzb2Ez@!^VE=CzE~_ZRWDu)Ft7;+(sv>&2_;aR$ZS>}f>B%13||#ZhMO{c_yF`y
zj+29Z0P5r7yIGmW6I}nG$t(>Ly<S_4`K?4`7sfu6DN6tR0Nc}AqL^?UeD5!I+3Hor
z#W5{*iglflxwVYXh5tE7-G!JkJNSFrN^GY+?qmcb-G$&C_j;tkMqU>o=HCLY0A%9T
z$EaXUvbYV^f3Bx)-5eUofch@AQ#SPSr<G0fj|~BJ6PZ6y7Ah9Yb{_5tmKtSU%b0<1
zY{&rHl6={~*ka9qc3)1@RFV=wj;54(igBYr*2Q5uVwQItIWobwmI{6@$1>SX%@b(O
zE%<40>hHdQ4N0<A^1A)6!}PLmtp=j=Q^P|ZaU128^)?-m`v_Gb@2{%yJooIDMEDB7
z^(tI8No(Yiu1<yXJ<wt<+4mHPs(>nR=}ECpjXmnIwAN5aw%jj!*=$V6m+RhOzxX+6
zAC_k3aWhcoyDFiW&Nb(t$A@wYK8cPSy0=BNQ7IBWkg0T=Wy(Rn+GeO>BTKgF_jI+1
zP%mB`v<y|qvk4Xc`+JcoN?aILS4O5B_=klMiuilPgZ5o-s24bmjr%w6^g);|A;)xE
zKQd2zG)%@;vEQra<20x8-o}KA;ILgpAluZx?{01$Gr}Q<1bM$tLWwOp<Ld<-Z^4-_
zWOY!oJCE~}X(TZo@loxsPM#wQb*)?ZYRfpGUV*0bi-*<UYD>aWB);%KdTGBD@D(#b
zRO1N3v--vH59%KLsw;jd{qm;suL?Im$;<oyT%jQ%tJq*arYKV)s<D+j=Aa}<hUDm8
zx!J#l!BjK550=Fiznj;=c*i#BG_ChMC06MUC|+KAN-d<}Lsf72%Y$m6j^^elI}f&F
zmK*8Q>PPMa`{=UX!5O948O++4$WQzE!7|)>eLWR%dW~q7sxlE5V(L(;{oQNADPW?S
zg3F+H$IX^MjcTs)19qi2>jHT3(8t||L0eSdbEgIPy4oM~wa*6Kmh6Z8blQAbm>q!h
z;s<NIz)fwc(p<7_FDi9NTJ<_a#?`TNFV}b5-_w@x<4VzPeJ*aG8Ly-iR2s&p^h)L-
zi~9Y*O>6xI<CgiL=P#T-<Q)X;^i}>f*Uz(V@>CocV%dJBaJ>IS_;F*k*WlI{J6>9I
z2{P$|H!;fWqFkrFhUm|x2q_bk|A$W9uUWGlG3f;YE~6oc5X-~`&%xr`1BtDDziPhm
z$K%~NL?uzM<Ye@)?w<PKqrP9&Y=os+rIL!e9vJoW!NV?vcUI+N2PpIcYk)g}=?wk^
z(t!%6zUUE?(qv*Oa_JWMji*C@@Qv3qqJ2`Wc+LY=Jsb}6(bHc4Zc%zMv@dgnO%_O&
z*ZF219a9#}d)8zCy1O#q(scz0&NlSDw_NU*!uJ;H*p>7w#O!r|<VctN?}`=8R-PBj
zELVVVSWO&Sdghnt86ku-O;CO33LtIyd?_PI(oMly@y&;yi^`QSMHjoZ6ARtrPn8F`
zyvggXbq_8`;+1JX)Gsy79lu=lwp@*nL>lc)&Hsz}B2&6AE8Tqs@TR>2Jgku3<mR!p
zBYNZn-}Jo#ELU)^AM|PMVNYc+6n&i{4&Tu~hhH$o{PTVP_8)&N)i$=>8M&0*LQNN4
zt`h8?emn1rhpSCT!UvblfYU2La^D`{$msi5x90ZRUza$t$3GR)Z}G}5mtGE79N7k1
zO7f0-P}Tg?O1zASNXbiP#5SPJ!y5ltNl7kjQoej|+*;l0c`SbVgpOLL===QbD?mc{
zW3G(fVFS=R5mk4US6ka(j)Tkn@DnzpEbzbozFWVD&taXpT!A)S0a~YaVvG69?0$?0
zkX`l+Tmj}9uK+uaSAYUJy(_@S0M!fs(rwt~lOB$9SDvGzU(Q#=Q;!^aQ8M~t)cnlv
zrm-I(^z$N=scR?2AWMpvi9AR`j)KXuv8d3BnmlM8O>$h$^az9SZ3Ep1lQ5J)l|C~e
zYY8T-!bS4U1PZfqV9_V}HbrH{^CJIguEOLHplT6lk$lqJ_t{#CQ86B%UsEj1Ha5lQ
zQ4(6}srv2HxQI2O!Xhd{&Mnt;!<bdV-bo1|NsIg^n3GrpB5Jr2G<>kM&Y(AG)Z6BK
zuX7L+pB1MuP<mnu*v<aFI;YYUF^(+Kj6b?YfY*<`)XgKv4@D8UT-l!~3+(AtD`!=a
zMq1)Rx8_4pN7(n2loUE_E1PioppuDCFmquo{>a5D5%wPSR+3bxQoAu_Y5CCLS5z&K
zUj#bh+&N(DV>-MvCX@%aSjvNI>q$%D8hd^?$X6(~uj2$cZyT-S`t<pbJjfo9kx!)Q
z;~`uQLhxXq*poxS)iOmBTXkp`RB>oT#R4rIfleAl3OVhY+G>u&?ueW03O2y}KT|X9
zclF4O2qnGi)TT5Nd0w21e8LqBALDhH&Me0@Il|JCG1x~3`2Y?~IFZ9;6CGTpjFd?*
z(2CDFBPKpC!kySz2(!=<;!x@-Iob6xt1y)`jvQ?53lCs6V+?cd=v;6m$GvbXcauBh
z@Te}~>WA->cPtBADN{7VbEe3A5>7ZX*tc9-f%NM!b6!BIwiWECG;Zqd!{D52=ev|P
zfXCppOeORRMky7hVBM1+HJfv#r*+Enn-$k9qEGNcJb2-P(5Uz?oxt*>C07daT9)xS
zD^3r-<fvdY&8p;FEvL5|<eI9n*7surSHgPw$XIluths_L2EJ$m8#Cm=mG$ovGs>&7
zqUl|&u2b*ys+%6FR!I^Hrq95#DnH~)(qjxV9vSD*M8H~0*kuR>{wpAI(XB^>WGE=R
zmBq}=Ubn#dsJKOON>MDGgoW(DXkcHR{f7y-l9ku?#7D_A@YPLYr>-*^`k<|Ti?!G?
z&NGt{B2h86*jJnURWgJv5_;VSIEqYju%)Jyg3IwvKf7fGQMV1A1Oq=0haU!&c^eu9
zUbu1xI5Y{peU<NkeF=azB6NiC5yVtKV8lF}fiY;kT6kzwbT+FUpjISYt%Ga)^Bf%+
zfS0y^*Ty>(T3Ahb8)b@E!7HNn)urQ)1?~qE9|+q)J6hSwJ_(mv8EN(AnJFrd06jd@
zw3PXT$)~0eP+VpSftsjqm)r#yqD{4({59=1qH2OZ;dRntvnT1=xB2*G|JIjf@fVAu
zFWdOT`|QyF)W<(iyLbhnl$yTs3uyzmKBOJVUjZ0QK5J=I{7rlr)e2|95;4E_2D3Xa
z{?r}ip<hUUqI~<C?t90#SDi=uyHD=?O|%U&+S9X0cedAM+0J9{5ICcH!wtT>>K%;p
z7VE~Tind4}DlWGl#tE{)Hi|!pdjxT6X4;D_FDO}0(+@lt9{uj6`;AL~?J+m?%`;;3
z9)%LlmGz_g=LokMos50uLZjy03lBqsT5b6eC)u6qN%ZTnd*{PQ7w*rV{vNFHiix9z
za!Kz{d9K}~#?^`f^q9<P7C%|~e5u14SN<?D{~nX?TcQCkm}`bC0wtoNv1VKAS?ydw
zWuNf#$oVGnSfx*!X#X7|`8wt{)`~3FA;8rJ9g}BuO-;U86B>0XyDqz#7{<zRc)jdO
za8*Ksg;g-rMgG*}zg9j-x&aX`#iFNB@<l|Xs_!p1?LGecmbOJX6CbKp^i%wQ4v%-8
zY#UXa^5<EhJR$TYv1MhE$wqX^Bc!!wRvud9Ng~3SOi42A;=~WF*e2=^retd)cGlK<
zK}!A;CrL*;irD#S4<>J<|95XC%05lgmJYAw&h>JM_A`dl-b1+e3LxjfI3j})*lm@~
z@mh^iQtaWD!G}*{AQ><mmX_ZC7H90U4-xcJ6}$cSx(?`Jdx7wt-CL8mnH^5hv}T?8
zL&~d9_6^&OUbGzLC%*-MOc#&u04NndR1SWDSWZ;EvqSXLde`)Lh??7ipDWn>J5T+`
zH@;$zS*>rz=z6?mO6PSBCww4lC2A9DAFRyPgZ>4W5j<W@+4=q&yk+{_JQcTw>RrNX
zz?#M!B4uLNGJiT0ke>zz=QVPx9+*Xy26`Xc+Gq##&ceRn#18&Ep5nipY0tXHCokY+
z;3a9yVr+Vl5i1*CWb!dxt&{Ab&YGzm@!tC<e8*nFJi$k;4l!nrskMyubjDFv0Jf>o
zN{woWp0;Rl>I_u7Lig0+VnlMQfxe7t#bnV!&;7h#FNJ^JC$ZX@ZgwFj@(FbKEbo?4
zl&Dzo^Ovsk{-!+DH!HqB5J5QLvZ`|bZX1&aFQ<JsGoP}%C^-(EI?lNR_y>Hld$V_2
z^q*J`Z)%#XlW2B9fquIbj{c@a4@Zj4yyT~}k4k241HQ%U=KR}cm{p%h-N9Rw*^ee0
zed~-vi@kSVGC}|8U2GoqUN@L9=Fp~|ju)-^^-hob6Y-xDzLxA!T>+|L#WJeYcX>Xi
zjvIdS=l!;6L>KqU3rIk=#7}7?`k2k=zIexbL2>6$GMx{*sBP7MkUMDT(o<glLt!-p
zWfq<7&Lb_U8n5_|@^%_fY7;WNUT&LnX7hF8zQX})k!NYQw9`|0mNV$D$sZqMd<`Y!
z*IRB0yy6bpQFc4Z`>cR(&V6U;%J@u+DWaPm^`ugT<^UI4<?>6mlR!xW7H+0^5wc7v
z#zKQa+-+Fw+j&8=_BHIesm@!ykx<brocLe!rCc8fZhnsM;bdeB;p0Ok#~+o(blZf@
zjR)L%us$;MKgEN0iN40SkW3@uggbLyasls#o-n?2zfhl%I2p2UK0|mvJl`%?%GM=v
zN1L7Z{ln1u^oqTGRk4h4R@goC{pdqHxbF9SkJ+?iG!1T=2)e{MxpWYAOrn#xay5RC
zoCvj4J@CtJf3T~l$(>gG%BO5GPeMYbJBVUU<t<0f8G2Q<F;p}^^Se&d6NW937jEsV
zqE4@!*CQqs^S<I$&fRcRP3~};4vy%iAi-Tie8pk2|ARjc&?v3nCKNB;ijD>gIxbws
zEP=c$5W8|hYkyv5oQxlT|Em{D$1|Y3{xOL6eDcwB^FiB*MTw9WcX&d`#1(*A=auTm
zWz~xtb?@$77;oq-zf{PpR6YHBc|?qN5%hHAxgfuqI<-YV1>@)4&Yxb=)@}vY{&bbR
zZr#HQEPlYWjqw#;*X1?V1U}_EA`3PH$fhHnOI#N~d~}Akby0I~Z<ur)7%gAd?k5I?
zVhEAwr=C{+T86L3e=Ih(N6GM^HR^=aH?<@=VSD+SM`&puy<{zbDnDLaM~nP01!}ke
zp(CwrgknV)U<BAJnN&3zN>@Ld@mO4%H@IOsD+-d%i{LBK9oRN!<VQC#lmEv1Bm}-?
zVZ4t*w_f++5hFR5ODqxsOM*_M<aiVA`?0(Lk8#3NWUv#_z*{O!sJfnU-^vJ}cfklG
za5-`~SNkismz1G64*4X(mDM3Z%J7~RQ#7wrF(Uw&mnju#I$Wv*^Csf8*Yk4cD)lAL
zmdh*aR%GvP;Ki`o5e7jSLF=V93{s|PQsL%HYB%PQa^72@^w*p0{N!-1hjBFT0Vic&
zwWVFF5(MFJ*@TH6I5K$sonZ^IDmKSqF)iUtstp!o<jT7N9^qf9apesvpFb(0u%AdJ
zW1hIT4`o8D>22!8sx=EPza~#cNAdZqOyOxQY>_1n(6Tq}@BJ%9yivORV=s_^xiFEY
zQA@@2LrEa-s+{cIhg4Q2j{Kc;$u#B<X(PE@U)LWYK1?J~m)ZVsN^v6o8j;@2<D6ob
zAW@nM$VoO$Fc!vEkIq^6wM(#fijo~An*uzhc>D3sTsI59V~eWq9w7!wJVGyuK;E&?
z2cCWE(ETy1*9{~QqB1xwRti!x*BERd4Eb4FVZx)xOjF|z=!6~2g}k&BW~)|Id`dug
z3|CA+&F#O=HuSThx0mam=7|)}Ett@Y*~)$Gdk%;F46RY*5>=*Yo{&71Lf0ud!dV{g
zney<X-^u=ZaV&rW`e5EUC|LUo2XxvmNNJ)MQ#N(`tTx`=6~JK#${jp1FAj`HU9+4<
z;Qb{RefWpLHCTz$SGoDqot*_3FEJYOe|yHV3$qoOU0f)Gqq>u@oUlnDLs3iRZ5)pH
zdA*ZOn>2SpERATU+?}wVcHA};-a9Zjyn#M|^|89y&kc?6zj8BEmNGUHwb-dljPsSx
zk7-huSc791-q*Xdu_P*_w1T{`CRO+f8EShl^F)p}a5j!60HcCQRqU(~y<{$^o2!0<
zq~-4<*}YAwayLQD>`wou3U1w9^MW{f9}W5K#_^gj%tdUpVrJiskqIcPO1!(R+H&-J
zoV&qu{(>QZu`FkndY;vEdy`~}mSfv|Fm$9~0#cY>#^6O=e)9){qH7z<>I}BU$k^(4
zYNZ%?iEfoLk7oxTBF`hxX5zme#Q^-)D?5tvw+)@0z*cE|h}A<wDNAun4(*YF%8A|z
zpbCZdBoGDZez;aB!3s6W(yx6~A+hsia<1`tHP-xFVtJ{(GX=}==uwx|(BBZN7}KQC
z<Yv_NlVJ686QtRk^jFU>J6$Wdw9^HYg7qW4F2tryRR+9&9o8rRBg8yDYc=q>_1o&Z
zv&XF=u`;xG8qbO`h?e&sPq~K<9`rQ?wwSr?nM!*pQ&k5gwMS^?o{m*NWQVP*1Ru+0
zpl+q!V>{Jb?o>b&L1-9e54pC8n2Pq1_LljtDvu)bOR~W{N%B>av+|sX&p|r(MqFnX
zx%HQv!fW~WS~g6Ya*D=@`e7nVZIz;B<x9kCzS(i!(A3sZBcT(K_!;|zr6MCqHS}dC
zY_~bJ9vfPG?*Bo+-dPwg?k0;72rY6~n+rKHqxw}QY91-F!6h2NkyR~mr(!h(LCgN)
zF!`^E3pPaRD{~W@NXTUCNvYkepP~RU2;@BdI?CQ=rCP#=P}pCJ(zPjWFAt!J5yCK`
zsl9|owZ&|Ee@Gr?r!+<1S~Aw-ct~5ymfu^=?dYI!_y7pgS@1}MSG!B&*IEWfgTvft
zQ;IA7SPU%&U+-1@(cDz;k(9(!sX|iSZq2(a^npGPQpr{pZsyIkY?D_peq|U^{H>c&
zF5DbQc2MGMhI(zYRxewHD%y00X1*-84XrKTVt&cuU|FdY&(E0@jN7PIG$W2yhR)n5
z0qYFVzMPNq<$=k|IxT2CB3a?7=FaA%VT`jWJ<$Jkc*-=m#E=K+me7+3!FGBz*r;aQ
zSZPxRcKfIQ-r{zm)p<vXTZ3bsHqDQrscUZBPEa<65EFVA7eWWJg}>v?GGP2`Ew;Jm
zW<G<2=4bXB#|0LpG^yVox#^W1)X$@~f)QSjy}l3+*=#d+BLV&zEeW?h`Z$L>4$wrF
zqz(Eni%oii$5pXD>vGJwS!|A#w@R6JQY3>B4rtmp9a4S7<;aR&e0r&D^6dAHx8qj;
z$V3R6AGuquRP<#v&*`QGR137hdH~AXE0=xeR_OMY7rV%RlYT8Cv9J2(7<{z5OzTKg
z<fF=m5;$pbairS=lVG$9dE9y@sW|J5nPa4NN5toCp8Ey$)H`$zwwnq@X;SF3HT?4=
z8nULkd6UKW4GKZ8IgSoz_73Ia!bi7E>F%9M8#j60+@-(u_fLZ~kJ<j|<gGWB;D`nO
zD}XA5s~vkDoVZkS>CSi8&H4{-5MAW_FEJ!2r1yNMOjX9hvwBO~H%a6-X=S{|&z5~h
zC#vh74;qZv(BnJHelKT#|NQtd@06zMH}<}&%8zdUZHvLJ3!KlkG4IP8q~$q-uV;GX
zPVX4uOSLX5?&tkM8Mr%sT+30-b<$@I_HzAbR7zRAmB5%iJp=lN=<s~6@sGmPt-WPw
z4Xl!86G5prM!>f;W4DZjNZQ(QT_ISB_Z$_7<#*2{)BSswyMIwtyU%S3s$qY=lFBV@
znZ_d;2@D?pcE9|h`WuWeW99hsRybXmdtuLv-tno1`i+xxr_a1+Ip>H$2=Gv9J@>Kb
z&-SR%;>bU&p)H2rmmWWfe0Sj^-GSpy*J_M<h1;@JdaG+(l`1u>y1?$~IwbX1@yq7<
z?<Xu<sMNzpwp<d@il9u!`IUdUWg+LwZYuoKO6qGRrQmZAmE>G^#9a&JqA1C?iLjd?
zGn{yZiV3TLm#YEL&y)Dd;8V{(SAZun6DKb@H-8<75B+U1F<aMb(JHxj1+c##E8JJ<
zt~ui#)cdZ+jhh#z81K5fhvmL!K#pd)+<{6kN?raO>wLx?a&)qN-Oxx#U}Tt{c{#lh
zV^*2!*(P!~$kp>D)NoAcNIwtqvl*KS>O0V39_fwmjVFJ3HGOJ?7J0WD`S;<c>*^Zm
zHEmQfq1MPvk91YA!IFO2>{>3IM}CVEdOdNyXOC723f0Hh6dh9I%X$=16|Ffsu+>S;
z;K^t@^4Z6|7#=GlWh9;0HQNOxL}*FCg7l=3n!&`7c#=+}_ghTHe|9@;G~K&Z4wOnO
z2MjDnJR`%_mZwlCfPK)D(Q{b?R`t4Q5ij75NzTEDrLHvaSW;41l-txs&lap)2@!k#
zW2&`av&Z%}#d<k^e>Hjs%6feAnhf0%Hy-9{^u)DS;HiK4crcudK4da&v$Q;}W(X|_
zWCah%vi=@p?h8EuSG46Iw{*mgOqIUm&|~5j$}{_hjVLq&h<kDOW7~a<5ErhXFWhaF
zyy2$L407M3%gi*XdbkeK&C>+Mp>4H|%4yxU&B>p71B*yI&fwE)YCWL1_3YVtfj$=U
zhNt!w1KxMQ6^u5~O2lTQG5un3eSi0D%arC|!jN}9%5wMN9`a$LSrn!6o07fjxW#h}
z)K={8o$h^Oxg<g92!Tlo1D@%10ZfwPb(`vnm^JTWt7@dd%az*Qar9G;x?Ea}&RMXv
z2!^->I_Kf7qH2rea85I2;w#!<7&mw7bAg=ja0GUx8lLiF1zq+`xLT2W>b^u&Z=MqT
zQ4ClXAqz-otI+-V+vVf;8sv|SaKzMUWt|`1fxWi}6Gz`D$&LAkZzpcw31&`^Q*=e>
z`zIYQ*N9h?f1zn3U8z>l8d#~mv61V)fTp9V>tZp}rQsHH1;6E{k_9yvvGb*`-Z#xn
z2)do<0qjy!z)o3U=(WtSvNw|vPp0K_(ciRPPm0KcNA8Cp-mLn-6fk>L0VacS*Bc3D
z#5k_mtz`XX=%<na{~lwICAaEtw{_x2@g)iG!2E$^Y*)Isq#?+b2M!tFpRf=I$r-#d
z(;^lm04o=z*f_DTJsjG~+9vgH4pj<~1z2SU)4W@^$7{>4r;RwWT0hAswVG)p8a$%?
zT@yOYTHu!8W3-|4t7B_;$8p*_bL3gaC2C_vHla#DF%eSB`2gb5%LbnO3!Sml4F_*q
zC5?p7P_|}=oM@1l5w@gu3o3E8ViaOKNZY||71z}@FN-tNx9v;9nieIF1AN&!{x@%|
zaOp3FZyHtYKg<#0ZcOOY%Q(;mzRKCCHmc_?N!9S3yZ;mi3^b>rtfW@kHMTq$p@hG6
z?F%Sjp`lbXJ-tg+%F=JS1<V}Pavdt~9r&~!orrxGnWxenob|YrYdhKL-W!%8zd&bl
zGE=^$6eB87cH6F<Xb3o@NBZVN;9!}$nboLnA0#R{W|6Iczj99WdB0BzYDCVte4sQP
zV28~5x{sy1&1)hek#B0A$0aXo(Jp&O6zb_cIciLrKPIn443g!e+u#zBU8@)WbQiKh
zCUDF$t$CyI&4Yv60~NuJWoXq#@an$_(-Z44aRZST{{JXs=MT0ArpgDn;{Ivu?=tSd
zF;Aw;Jx<>f0Y6ZX8=Nqs{u4VrE$_lrnM|I*>y2T8T5YdMz9-x4=~TN|+K_vSpZQ+_
zXe1IswRoDiCT!O9G>Pyap7<vnE%%lFo}2jxc#EupN@WGT)sWv_ZJHzoTj8wl4C?AC
zadLQ{ZLqK+)yG0%olQ(yc%1Kf%}b8j_fdl<UuZ^GrgP{;)}_)!Iwxweg*X8i@blu_
z$D!oEGb(y`atACjbBLO%Zah<5a3k?^k^4iL%j!2(YN)}SFraB`?kz@+XJNK(OUZtX
z+fnCn9F<L-pS=(AzF^a2*)bQhu8r@DpIsi@xNxm{y<qyjEj{r33y+n-87F%Fj+ykY
zZl5ht!`$Mc)vhrV-2O#<M$cU~yYg?9hK+?(1H$XT1`C@9;DzIIBv!kF2iA=2if`;q
zK%Dn~&b*7Z;+Q+<c<(n5Z7e=ExS(9&uFVy{QT91?#9{S!O~kZmd$Z)gpH~}s)8*py
z90#izjZb8l{3I@=^aKpaq~%O6P3QjJUHX`y!uP!?F|skpkBn96t2bZ7{VTu=^HcTx
zM#s9jx^8>q<l9`GXYIIXo|_5;6Z{>a-?}R#@hU;L2IYkb?e6~<S7#a3^dGnXAqawm
zNFyQL%_IjR(mlGR*$|{tLXj?M88C8;8i;fW(hZJI>F$umd%yesKl(q|gYE2`ot?9N
zzt`t|UDxaF&a{$ENkT*y?Dz+#X(Cd(=gH#Zyi0v%rOS6ktu9;39PvM?ViI2X-l{}9
zODV=2JnO~DU*TxxrF@+782r6}{*<f-KtJsJB~kj4Y*(^;bN6snDGkE-8QnN2#ITik
z5WU*5mKU84#OI)xq=0Q)xVr0|xVj~8Pc?7<15~oeYSqB~>bdXWNQ9U5$kHNVwHS8e
zvVs3DsYwS}<S&}RuWjk73AeJACyx$(@4I+CQ>Lk}Pt(7#GyJdyJLdokwTVnz&w|S=
zyjA<kWJ+jQO)CO#4b)>#>vk;NDn?|(;i3tUiT(WFYqGt?=D!?qWoI4uFecH2uCHn5
zU%oiq9)CyKh4IyfiA<Rcg*cIafJfzQv0(=Ra1<Rzf9CzzYg>1FaIm=^|HP33mOHOl
z%v|v*G}bymS?K%&So|d=`v*v9DD{3|J>JL*-ao=O79l%rJwLM$WY|lQzt)ZO;j^KH
zfRkcVJ_G}QT(ssakAUo={=9P?y&v;o?6aL#DsrT~U;FT5yuwc1eaToGbb9MsR?i@D
z!XkD<cf1od`yQ1aLh{bLI;b}4Rb}a}2+uCnB^9P-F{<({VNm>@$FUJ~E?%Zs*2b4~
z-PSR9CiUgmyC`ZCN@ZZ;{tqx7kb?QTAB)zE>Lou!y!(2Ov>?Npo5FpcQ?~lUCJt9M
z1pjZTnB5Qj8(J05_v=WFx^!SG-_63j7z26zsuWLVhgAQ|*TGn?PjGaCd*jnTILC*5
z^0=9P)zAwCcX`UDn%~k&KNdSNzp1~r`17evAPQKbIPS_~sXWGKujb_=V`3Ub=T9kK
zngz2^!c^bWMFKUrRv?|r(UwfTHgV<6KU=0I)4p3L>`Q(LhSq#okroapxhPkMnJ*^L
z<UzjgX=9S(af6F5S@Cqx2r+}ezJCB?s_IIeiwFI92^u^gFm7WhL%eBxL#R*uf)GUQ
z$ujfc)x?J}@}4nXrQwWUi1oi3a>LnbOWOMK;V+5Bgi6-JDdJAa7bC}H&*od}C5BdW
z;9S)Tpc=HPS<i2cr|&)8Y7<+ZfG|xKsJ*-RfnqjsGg)DnmIaRj8;+YXpp2MA^xQ#l
zROjH0dJlqixZ=rXUJ|Pv>q)el2U}+^IRRRYYpysq%^m~*xH~nEw}>-0#rK#RP66w$
z$2IF7-_?#TW%mvg?7pTSR^Mf<bs%doPrHwo98`d>1Wig^Q#ws4uCdI^j?|Uwf+r7&
zqn78Xni6r%3$QnI?=*+M#04&VGRH+whS~2w@U=Y5G|Q!qi#EQQ?+9*o^Ti!g^86y=
zy}*+){ZXfMw@$5)xw~iwKScDF?bP{)Y_wd`xNMp7FuZ|c<b*6Wl?~~UPc7px9F^3j
zTshr*Cq?_*=t`;ch-u12DXNxqYHGkh?a6!tY#(q&x@ZLmt~ECK6Xm7#tfMa7T?sdb
z%q6^M5{J^wCeJ86oaJ`Jxb@ahSWpnK?D@Au<VuH5rUmWepttK8ANh@Imw4?+V<ecs
z-gg;OuQ6AWk}q+GG?Ld4bbh)hcnNbRf08WPSbRxq-V;*wZ~9v4zi^i#G339gAPIwz
znhtua6(AGsa>$88%g;lcpL~MIHOs^3S=um8@xuHN`lfW(BAwUeQJlp)^Kn>K6PO9$
za7Sz?W{!xP1prcr)7j8NFdZH5=npiHQl3L*$SdTn5>-X!zViE0NGd{ggujmc=bFX<
zSzU8fe~0tJQw_wO41bI%TlMgO#p8o&s$L88Snvd0l}M{=vPM(mw^Ni5f+Bac;spUf
zTl$u9r5KaucP#A)t8R{O{u4w3qJJ`m$!E%qTw5XKnt}o2DWSuPL)6ROh`dq;8T~$F
zB@26=Fi`{fXLYwXCJCuO%r8s~0bcz@YA-Dmzz}zLNHtW?KM9VjVE}nb=Uvp&VguAu
z_UKimm9kPRsUovMn1Y}5mtQHmnM7u2Lx3gSCjIHT*w{-&Umr6}g4wyIa(El*v}d;l
z`1;}%sk`mW#4QwLAdUSyMwhy=0orpQ-dFSs(E+VR#lS&Xz-ocDc>~hVw_D{<F~wRb
z4Roe1Od($O+6KkK{JqGruqjrN-$_x_0$-xkwPLCE9mVM28%3b`xk78C5<=t))P5Du
z<g+1@huoKUOW`f<&$xRtdz^COS3))sVl?y+vjJa24@IYPLV~WQEI3vP?Q6f%=U+8=
z=zXF1!j%3Afw-wQ_6M51`AN@UuJ4bzf=2GIN+>0tWqsW^FkBvy13r4I0WqCdVFBQ&
z5_4Zr#3+lzdg!i1Kqu;|xeHv&gy%yVOB}+a^*-28809TYIx5FHz1kRfP6Q@PawX=J
zG+@jy+n#b9i>38UKBO2a`|1uo9YOd{vU%F^@?{U=YD->}o(>7h6`Rk%eJM1JDU>)w
zQ8xa1E{dnNC*VhJ`s9c2<5L}Jm2B6nht!9=&7YkN8`RRB8QQ+%Z0ywEv(-&w>()*Z
zmOu%Ig=^@$K0!9ta@FVnypV^*Gi%KGBy*wFX&YbmlEEU&?`SJtNE2`wr4y;BIiTJI
z*{J%G6Yzo!_}(nMo4AKYzZ$<N!+niMPOV8B_41HJ6<g@2`>k--c!LFg{#v~peI#z_
zbvPnpf*<<*@iAq5(fL}QVCGUe4<!Y`+s8)O*yJ!~?`_pQ|B3eKVok1}_K(3ro`;r1
zp~`5iMB{Mp?HWJSADFsK#jqoiW1~!NhPXC{?u($5riPb^cRFyEk>6u3*@2xWjka$n
zk}{J%&g-xDypGYt4__B2!5{skmc`v5wwYIA)cKb)Cqm!gfiKm;yzV^d6OlAe_e`m%
zRK<tgCBw#~KHrSD4h|Np46L*31J3jr3|vz6q%b+fb?*|#8wJI#PbXT&62)?OmE=Fy
z5IjN+$;oi3sm)YthmZ9Obg;pidcQ(_o&IG*NEPogDe)6VAqsOX0mjG;v&6-(ON(qx
zRNGV`JaK*u2EuJ@jU`7%n5qcXp@3=;V=a|pgS0?(+|pY8nI;|xDkIX(_o|uRn{`j=
z>_7+eQY^!Hn2rCyih~xnRttC71TMZ9F-5|L-dbwGSLGJc9doSHBj;nGzB>i|L)Q_R
z5wk^_hz3m9qBz@vU8r8?u6Pk#I8tHJHA{mi#u%i~igz1q2s{dgUG2bK^KXqRVzk<U
zP(cMg{x|P2euI))mS^E=_kc`_bw*Pbu>xN$MHH93;B8?7LXntrSjJXOp{A)({!}@b
zwhorEcRBG&mSBL<L=sK}b{x=qd8v4_JH~iXth5Klui3dd*qRPC`}_J5fkO%_qn0N3
zH>bxMceeaC|6y!uE9@gj@t*&EMdrI-bKY}u^l`4o<><P3=dP`KibF<R;u7T5&uLw4
zyZSn-t)VW~XO$kTs4&Ys2@qhjQAnQ-GN8T4q>I3))AIYN$=lght(x<MS*lJke|3Qp
zp<7)f2S*A0-VUa${fDGRAp9qJ&GFB$=e=|<YvS&}>i+<NGncFn->^UFiRMJh5Ko+M
zwbW0(^mSf6V@rIou7{mAdKpL(G<kM^c>pQcX&Pxcf&2qFk9n%;Wo$*Y1&t4Kq?4<n
zPOH9d(Z$-pYp>Y3?5{tkN)(}|<{2k5d~O<(2C`K@mVym_9y$L54Ck0b(_WdtGrf?z
zq~%kWq%H1cu_cPs?*bz$dj}V>SLji9b#oL`=_U7Y!?%H^x~&9{gmi8jtlqLD95I&c
zS(Kef{T-eBs8y)7@(+Lu5qA8(RKPmzF%Z7Y<7&Vbm!X%~vpP#3?z91tGD>9`C>j&T
z_IWL3xZr9?X#M?si~H1Vvo2>d$aJ-mc10)^Qce$)(L3TZxTm@!o0)UgoMXEGRC>ju
zMlV-$I;C}c?LilDvS-$l=X4@qM9AOr!NEV`r4mC+ViAdR)JEk>tRa5zD*M~njYp<S
zRU>r~q3_E<$H9oe=?Mu>@EuA2j?EUw0Vpo>;qp$X=?{@K7(bQoeFgQU#v8POY*aFP
z@&hLLm5_PpIIb-9K8=d;YCm<9UbX+{{TGMbvIp|~e*pEp`wYs!RHgHZFnmN~KiD^O
zJGEu<AK>rCH9g54PflcMb3*usftzxswsT6L@g2$9w|Lv*rNR&|cC_x@r_#EIm*Icz
z70Q$z)?;(}qQ_%;(n}sn4krRE)Mb+=9&T=cA2KldCa0z7Xye0VhVWHDm-ZQdVjzR$
zVRcsUe(LLApz@GWoGSmL@V5_8p2nC}tw^T!=zjnmxr1QY3;$Ne?i>omy*OR4?2&nO
zLVvZ=QCri)$plKHckxTxspXltWt~aoQU=w`cf5;IL&G#XrK2?t**}&im5`X#%cy89
z;KZ4LnaX<oS$sjmRi(F$Okj!#;d(?}oolctd4Ou$;AQYH=^NdMS5X-0o-VCm7k8y-
zaPuq}rM6K&zRfc|znYe{MrA=eZqIlyV;ZckOVVYD+t}UvSu~tsN*Q@({Fu$8>6S4X
zWK_!M^s4P(YLsoBbJ_NR++F4$0B@<)s>SBGZW-HaS<Nz-b&%>IlnL2e`SIB|M%iDN
z+QHLL``iB7OHT}v89oHIb>8*s?bO@J{;C{1)fm2GPocX%tlb+7IK~>WMFY2$?nPRN
z5^lG3V;)8cRd`k-Rn7%fFwgY{>86f@-3kSvmJk8mJ~@_ph7=)Uf>*?WPE*5e?Ubt^
zo35{0MYlZm{{UmqmL{=Z5UN*jp~;snOWt42NGj7huctl;M;NIciu$>v>oY(61Dr}^
zItTTF>os_Kcvz#T=Wz?3Ec&<r+KEYSt|BhAhGf6|Ej2-g*P1h{nf3nNGncEIT{OYw
z6?+TE36OJssQOCBP|)g0v@#l@bm)6$Q=gG<A@J~d^Jm|E!bgsk=0K$`+W5>>qn0;c
zBgsv>ai&f>d0b>Xl*A0*J&K_Jgaco)aL(?1`o<`-d)BiWle<g&(<%MxjL=LlF<n3`
zZ7EQ;vgKMu=Fy!)pzN*T1L@PM5B1(+Z`pV-r~5$6rAXw!tSzjCXf=mwR~OP`p4sC}
z*XzMMaDr$iE?s*OgE;~TiUr~UZ}Yc@T5Rqf2f04X!ZsW8d@~itM4jKLy`WbeQH%NJ
zJeq=5W}Tr~bX^xd33<JiH9dHIM7t_9`Lkj<-TC|<K;&Fg^mvdY)hdlk&)ra^jL-C7
z-lprr!5z)<u}V?qwF~fNvpuDH_=Dnpg4>zA1A~A5hi{=ta?8iCh}hbvw_@n`%rWxl
z%uokgV*{N%H!d05gWVC=;3l5WbBQ65d4}Jy0f9oA+wKw|r*m3g*7D8y;A#1jxifGZ
zbM0`~OHR3tN&s<4fUYs#mY#KvG()Yk(Q5N;jh}GQ>c9`Bdc+$|VxG5W`iKZ07L-Es
zi#JGjqNSAONmA<NXsm9kS8nWm2t0J&0tP^n!OuG13Xv=3nvc^G;%8j|pJ=ax*L62J
zQZTq3{&e=F=!b<S*)RK2Zn<KpSJ4JkT>sZutd%Z2jYr^SHfR$&elJ(U+}RXfvgQ{c
zvG-mR=YOR`kMTSv$0%@e9R)1c5<QP$QG%MNQPJ2frd-`re9-PdcLS!YJ`Yc+a35(>
zP+kTGi4y~uD>~nICY$KAi*rOf=?+!KimXP|Iw}y48>C^uQ|*|3^%r77iehZx6l--n
zioi#ux$_3t&J;?i`gQ7>xB=txZt)lT8~uF;%B+3s6AQz--W)jY_=WmJ<0u1O5g1Ht
zExsm6p`j*Bf{wUmicLuq()FnyGYHtxN+srAf4pFyM|L(Wse+FIW2I@MhUt)A+-8eU
z(VEEk3-~4VvL4Y(YmL{MTB@2#&eD<!`RnIL2G;7t+*ywIO$J#u)WfVA25H@UZr|-I
zX}$|`hrAB}Z{dL;Z7hRX{D;E=HGl@7qOmF5xAbtU5;_cDb1b?WEC`!^bK7XwZQmbD
zhb}~<v*#stx^kPx-{|e{OFSd(c`WpUL^OKdP1Bv)mR5Pbc!;bW`LQ6{W#6-SNgo><
zC1qBa$sQXw$%QR#U~wuzAw1$wpfgsYW7p9|P;u%)T~w{S1^_?FR@72;a*YKq_@7g}
zYVSn7DNF7z`TlxObv13Bkj7(2Zyh?$;{J6~w~wz|3Z@I&mH$BVb+dLYl)Am-Ju0M~
zx!$@CyOxMfyv16x=e@VR#j7)A&sHR>q3H*L;t}o_*|~7YVFH`|bN3$-Rkj0?2H7d{
zzN6%)%b&UH=cJLVNeUw#u&Un&0PF*8l~6r0odiaSEeZ>#^Eq$0V?dT*$H)dx7vU9!
z&O7~IqGw{?u7OO~VUz}q`${nGE@x_%=#rOQ1CvWi77A~c1-mr{kvt+YY7sp=;sZlL
z&}tc<XWVJTD_Rp5j=zlB@G%7hvZYc*ZgjYKuWFjdB;VUxBmMPG4`W{MiGG>i!&KtC
z;17C^RdYQ2#)8BUwkGOsTP@dKabzIT_bZ19&!o?entr8y%|Go?d-UwPN1Jlpg!?FQ
zUBl$6*ABURTDPI>brqs&&31hC;(yBx;_cbI8N`czk7~AXlv{*iA8w|Ildm|5AsB5j
zgJ@o-QHFIQer~<5f}^VLGs^yxd(Obcx`WLaGH|VEJ#*v@SQ-C)qIpmDN=XqTYe1{3
zh{1;znW@1D4djk)P(&gx8kQE3)nfWxq69}>l3*?k>%~Dga~K^hM5Jub(1a$25dIBG
z%?b=G)ae!p3ixduSRQTZ;O|<EcjRGEkEH~IPndzqo1Voe_a09Sx)kIVisT;YWY*Ac
zZ&Ljn<a!=O#nKvZ*%?_g%YJ{Y%Z@87WjLbvAn3cxQ-GG%U@J@Dnl&QMUHx9E1?T5F
zbl}wcvZk;2cRSF0Ep=R1*#}F%ss|Kc*gy9(Z+%D~UwY~<a&<7~s&1G^1`iv!MU^c5
z;|PRb?&|BHNSEG-GQR&ndVqMrW70i6VIJ-d4Q*Cd;lO~QSYX&T(R)%%ib6AK(e@hr
zM<#ae@g*$agHJjRUmttUfg-+j7~aP87sh|ou+t<~pF$t}9K_1*9qQaD&o;PQZjU=7
z>qmhUFIeI-i^O%z<fW9m=aV$_$Hz{OMd?)}tJ%zRy>esEiy?B%2-X1U!mMcXo{ScB
z366)ZwV8@fwAs3?o-et(I6YU<?^kSiKyUQ={E)Ca>c+s4RX3x5H6~FKsyejo^+bB|
zY;7arv>dcFi7GM=XLR_wH4Zd+nBPA=VsvYjqD599*>Kkc90U?|G4_`Pc?}|O5+nDY
zb7f*&F~^2-Sub%hlu{xl)5N~OC5M}f#Ey1O9?*bs^L3>AwDRP+C$0ZZR$l-)j`-^g
zx)~+A75G5y4Uu~$H!e}dyN39WTQpc?7vr!TWM6aJ*)?WuQJ3`%V|%>E<_#d&5&b~4
zYtlV1H~0Fi&u=C)_s}?j0I!6(sb4gK*jGPzwV>gwqVBHFAePqbMooY_G+v3lG25nl
zF1`$Fd_PeXB?bsgo9C{;(-ImT+S&~!7=>wUyA6189P{uW-8?jBOAoHd5|nioD+62Q
zBu(ZdO!Z`v&E8S6H1%G?MyQt9ufkYnG(=5;5|AY@y{GGtDZ#CKo6_T6X-)|rX#xLD
zPx?n|W=S@ih}5v8qo(F>LA#42>YoOiJ(Z`^Ky_z^UTx2Q`8dKE#_|LB2&Uv&-6U{L
zqnZNqm*WX@7A>1XLqw8XVVmBImq4VVP+I2Y!F}NUFvzC+a1MH<)o}#GAUferG&06V
z4geCFHJWzD(~+0nxjpWk%!fZt9otAe`55%Nrz7nHq03vFw~{2x{aUwwq(jdtWMBVw
zIN@(YUb#%9rt89`TsyO4m^#~jlX);AoUXRaLc18a+FHlwB|UlhcnyEd&Lwa^1AXKF
z2PmWMAOD<X^|-F`97N!zQ?!0hb#YZG>LG~CV9ve%1Ok;6f~%1I?Q}u<$wfUu5G{Fh
z`5%pqt~V!e9`py@uC9Qybw%f{9*A*u`;N}xup$zmO@g3nb@47XHhyl4J_IuSSMld?
zb)!%6KU3@RU(HN>4HTomoFA0b>CQzLZoy=8@Xu9aZ)W92_tYDE(-wEqdM~ZBWx0b=
zuC-oGWu&$S5JPTTJ+!gmkXax{Koq-@n<kcj&Qu<TaF^XZV1z@Afx{f=*y>jAbCC^5
z3nBqX_{B4;1p-&Atn)(lh<N1YL+b87z!MpsJ=rH8O{C?D-Uv8X*gP|z7kU4@i&)NJ
z9Z!BBxtEpB0l41CFme2PU*dwMT*%kNYo|$He-$$yC804SF#v8itMc#U6#2B({R4#b
zWSv2pC`&+Bx{-xO`3bJ{gqbropmu4G_>jA&CnT#Rkun^<`D@*9Rxgnu95)4qj}1&_
zsc|CxfFu>^B$efN#N+^}3j{UxqZYG*a6w<LrWBU4>+L|?z@ChFyT@7-;1l!bDj)aN
zO}#LcFg#4?4|x7cMx`qg`EoK6DUDldOm6Ex{#KRBBvW<vNRtJaN#5?dxO*xtYPOdB
zDsnx6WvL{q<QGaBdnz{xXcg_vovr4givu1vCdgt)$x+Ru$KGkbnRs`DPLAf~dG12)
zLO#Ix-3Ws#V#sb7cdR`4se7fK%p4mXA8oLYlVy#GHDbh(V(V)Y&c1>j&1HTK{xyyK
zxJRA!wAvo(2%2q9$FwLx4QiHFe&y9HTn&w=hEUM<CQ^@nr$Y!PD&j?IiF00(MrC6F
zru$oVB7db<XQlfr5iIpF(GWdVsesFUYMMra5lB^G)Cc#EEyL$QrTaQI@fUgIa)D<W
zaQ%;_+zPnj-A1QP`lt|EJTsV~^tHiz=7d(Y5U%Eq*a>^HbMktj>i0jM%=hhRV>rJw
z#DdUjxClB);SIbgp?}oGd?lD57af51%UDIRl+9AozK6ji0-gAx@PO8@E{dYF*=*kS
zw^-*|W2maWtDk6Wl66_wa3ZrGz<4GDiAL}Y)Y`M|*JIDWMAujSR{ar`^fj@64G3Yv
zhSx%vw>HbL0L9!zOsSNza#Q6!fW0}S(P=GNiQKSgypL|?n>R`b!^9JGnVOK!DQqNd
zeJxof6Wm#64&P$gC@*1MfPP75P1eCz*-`VDLVVd!M5{Kt`cK$3mRAb3LiIE0U<%Tp
znE76R6x%Ng$};IYeY2#1aUe2cckmCyU%CTYIuSDFxEyt<zs=Hz<6*pWmV@$=)Xx;B
z^Um+3<j?=&GLUQYS(dw|ti`h;(ufN)*(i$WlW6aLK6XMWEbOiLVm|f#c$fL^dPU*5
zWP*u=&RA(bO0n0)cD07ND$Y-uHf=;YHZfNJj&mEr5cvLW<v?_q3t4JOnu2C)$?fOM
za;av7`gm=v2ohh<TavOxPm_n;DJ#je)noH&3um16ia}~@c3zFo?N!_u0RH*MMl<WO
zzF5;@w>}??=8w{ow2e-Qi{jyaY5QWS8`_XYdM;5PdE&U4L%+=$1FLwRB;#)kP_$W{
zh*Dmh8O2EY5dAI<9oAHp!p;%cuSZh^#_Li3B7SZ0@;Rh5_e?Hc{<nrjeFUSgwli08
zUgBGSSanu_-inr$PE(eBQSXbAp$kqm4rx7Sfk&CbUsXm;DiW&$#+BZ`L%9uI7r$UV
z8HOrYp9T=j){T=BQ^}l*4sFGJA<TSA!_TI`g1z)}<VxcBZznj4UqGAE=9joQb!CS0
z!Izh>a1Cqyr^A9+2Ui+C=K5N|3oa@MM^cT!ZivRgbbHUBdYV;=<)8jJa(8N1PWhNj
z=NocC-OrMnKiMWkCqM7L-PT)eP3Q`y{s8<&D}B7+F?~H9w_tgXh&t3V+3Np3S)#j9
zemE?i@Z!x+jyWq9yJ<C=;rB{%Foz_p>o5}6?I3F?`?v3vR@;6$x*U&yd(n;16MQ+2
z=uUT;RAZzNeP<RpfbzHkIt^NTJLSyhOz2Pof)=%+Oc&nhajoT5GS^SvA%5|4zZmu$
zh}hydDVISpxC9jmb*%aYM$EEK2#~95*w*ebC_c99cRj6ghe{_e)n9-Hj5vF^MAR4R
z&CYacXD>?0wc{qAq|2at5n{tOe<+OT2z8RxE8d#)%XVw9(CNJx7MD1L7?~BOoQuDM
zd^(%@+)0xc-BZe_nQF2AvALDYc|kY<nx>P=oIEa{2w{<OyTgc$i~~hMafA)3P}LC+
zH^0ARS)M0M=L(j@5WN`|R=qgzD0Ujc6+eRYK7#tTr)=l%j+OJ)_v-~KCI7A3fVJdD
zb;j|gcRUJp$WKzM+^48S&719o0ZrEe<kh75%>+#!ZcbC--xUo7%#8~?VH(D-!Sr9Q
zdXzl(W{l@)9}<^26UIsU(qkz*6$GWl)IPGFf06W}n^qx=DmMN4#}`24-YA=DT-)db
z!3jkMyS*9P>>6`7G|gABakO>J`7EoW<AU0op*+NL!(%19cgo%yujMh)<M!w|a(C`X
zmnfD%j7aKuxPYPDf)6#vgV81gotL!O{uA%cH2b!p<^5Ic9xCd;%AWlKbzZx2*@MIj
zhBAzyeWw74-ZjJW{ff#(tIxt}$ATg`xfONe9d5>N1U|}1&UN`=HQJo(+(XrlXiSaU
z-+vT<^6CFPKMGHc(8R~a-sZMwsvh?J<GA3ud(@`YQjZs2gTe(ip8&_|ISTP3va4p|
zqu^QVlTZHvUMz|dA+P*N3unLT*LjG>{P~#ecTP>wIVhUkH?Fy<@}f+rA4om=8ZvjA
zs)khwbQt;vAZfnqkHV9R_vjy$jj9NJGiGWaV!mYdl!$#0($ws=z%sfR5Bvv6OKLlQ
zdDP!twC(?7A~#)sV)K?b#=c$46W2JiIQ;R0glW9Q)5$883a5X7c?N~l!bhE|-v{Pq
z^A<{ST3PB1Q~v?tMlC|4hX)>ZB~6`Ad$;a=?wZaur<zw;Cz&Hu(7BR9QMTQv10NTA
zn<>AU!0XIrPs^rUtmF|--2-&~FlQp}8dJxoq@Zl*3OULp?DQ{)>Q_=WiNXnET!tH8
z_V@Akrg>CNUuTT9%C+%q9*!%pNL{PCJ7T#Lth#+Hcy;rxd^&@OiEbyq>|VA9k51*{
zC6&vzQZyA~TC9dlMO_y10@{*>{FGFi#Wz1rbT{IfgDsCW8uS#NS;`stfUm2?q`A5w
zOX^IKH}J>raK$exnx%|Rk5i8PerPO)Nr~S<8U0-)_EgLcHdryNu2as@R)RNGY@Fgn
zxa9lMkcCX*#s95pn#E5n$Q4*j{s_^}z%^vZuihWgJ407KSMEm!FxVN*DROvVSivkO
zEF}KwHVFIQf2?jXc{i>UFhvw+k$t*&uKXpftJP8gZaVWPqae8*&Hm!zT(iPot&m@(
z^b@&bYEd!}c|8eC_i6@?`K^LFTRE-f5?`_Q_i#HR=7OLVM0_|6MNS2weVc`hK%2ra
zVkv{qGKzYWF7A2=Yo*t|rF<_0fVo71A&w9NGRItE2n79hz=uzfS}&Vm$XkP7LyQ^o
z(Wf~WG!?^tX}``Zb<X@~z<-bb#YVxf<hsS_#<>QhCTd-BRbmNYn3QRM8EJ(-x-ko3
zg<}iL*2P6A^q29FNP`>uB@4MoU25%^d!aRnYZT5_BSd%<>T(w1?Y$I1oWgRC%6~aG
z7o$Y$pKA7|TT<#PzsWJFOZ0$GGIxi9AIbG_F?jASP|En0oKk&y{n8x?gUHnKWufXD
zR+rIKcp==+6(M=YZW?|Gv&koS`Wl5y-)nYWjBS=ChhLDd#_hD%l=q?(H@+)y3l6I*
z(G<?iS94tlgo*Z$+tN0X)5fJstd<C;3~hQOn6NAmYpG_e@}LX6=UgB2h~!Ca$DDj0
zws@>OY^8{4E32S@t_n|elWLi*75$LdH<R90GZV$_&maOU9Q2P<l{fR29?KZFFHgN6
z2Z*nR$8qU8(?;=A$)sj_xysV!UN%ruyurY4MMdi3Nlbr0>`VD?e?U2sLC|Jpp*KpE
zs&SFC;rqIu+KWj|6qh|wtf6_LFv+3`pO<@mNbk+^8V(l)J)782h1dd~eHOY{0>?rc
zj%Y6yxpwPx(RO7CATt*Sdz?Rh+5wqo!CdqFqY~;0W~_SHpW2^B$Au@FTM-$$9wzij
z%2|}<+2GZeg~a*Q>k~HmBfGyETF?5oq|qBnsbPbK8$-R<;fkOzvv_@M`aL%D1kkRh
zcrOYc-mR*<{j!Ct2fC+O-Z%;z;p*5ZrExLM6-Rn}TAqN1Bf4)CpWp#UwA|4`!+LOQ
zmHtwD#bGJ4zk)Mt6z(e@Pc^x)N-gYt%Lrx)%Q5_2Ui4P&TZV6^0NIXAdnlozgsAgH
z@g<*>#Ik+iWEt@sm%L4=YUzb!cQX8(l#5l6Xu(jvXh%cjQAc$_B*&0b!|z2bGk62C
zS^DE31*iO?z?`p(@!vw7^cy|HxfioplJ8|w4}VgR7o6f$#dXpQyZ@9(WJ%OhnlENF
zIonyoRD@aGrrY(95k?UpOHCMDidDuEm?<I7q~YoD7G0rWM-BKWOR?=Km}l%VWaW5D
z3Qt8rkZ0qpQ?Zcegs`a^p@@a{#|3HY)^4X8W6{$}eov@T95wNi_%4$u2E>N^MtiQy
zuA5PMIyMAnqg2;N+R(s@wpqxfmhe&Ot54U{5}>J76=(!Fi|4r(`*-*<t;5v2?@!aV
zXT-kk3>!rs%W4vNSh#^W&nzYIaAW9?aA1<@4c4trGHSiCD-jH};HRk{xt4Dy8(E$c
zG7c0Sm3+?6Jz#y=M6v{@4)RLm)gWC%yiAQ)lqmANHwXG=J|-<~o>%mD)1O-Z6pAiq
z-S!>fY@|zy!hXFso~V6F!a`BD$wdcqnY8K^Wd|BxC_2787xgfNFOn=SW1LMYq-c#@
zPoeP>L~b~vudW#OA9AB+Z$<@<H4Hb_3gXej#YsIXYG%MUFC*L*XTiZjkrY?^3#IT9
zMY-!f6<^O;hw}v-Od+{U@jm_ncb~!0KeOB1EbA{r?c}z<FW=^Xi~@~BxrXrX!*v{D
z{MDwt5~jK@?ZnT8&T@2j6eAM8O$T?x?;}fhU*Khooy*-j#o6=wh_RmOnk@nsdK9IJ
zAIpE*Qz@v#57#Sj$+sP^j;{$Nk3jLz%m3i=fW=4CQgQLSVXE&1|G`)>S6eLdgUKfn
zh~Y<7uXeSebpN6B2)ZT$^yErrw?xMpHM04SG2tkOl)JdG8=aEbZ;Yc_4O>~>z=Xs^
zb6{MDr75tcm$#tJUphgSluFZL(s^RDlg>UX;eI#CSEFY5DQ3*2AUCRm4*xxeV)ImM
zgtW(pOfZ@#EOkATJc&Q&M9sL9-9wEE{HS@M#t8r))Ua4*y`d^w(}(Kr>CN3>;>o6)
zpE0>iF`8f0+{OP)B<$RP*LN2agzG>a?qD<Yoy_|Zq_2sJw%IQFsnP3Ej;uhQHy@tw
zz~0*b>6}^~Xral;8I;{xYPh{Oxzu;R1V5JyJ238A59DmqeVjf}_LJ?;ZP|w_w%g|U
zvTMT2qzt;V{<Y)Mupiv~{>d;`32`8|$!>zC=!AaKSc+$VMY{ZNzC*rD^Jz9Gb6VUP
zV_dt&uU?-Q7%GCJnqID-b+aet&)t7@dSDhH{#yPP+M?n2a*v$jJ7X`00=-1H!=A~;
zNV8|M+2imRUZ=VBk<QUeOlVPt?0)I0by#}YkC_gz`^WFsZJ0Wu1p*b&MMb;+y*Q4K
z7loy!rrb7Z+T*`=JG5v%${W~FE)>EQ0T1}SRHO6b$IPT0o2`nuSg5D+M}^fmS=H9n
zu15GlGauqd29o0e&>U1~$m>orxYT^Wpg49%RN)jo@8Z>CPCE~OYo_79_VDPr^ewFO
z*&iOY%3bkUkfx92wB3nJTd{Av*DQO1>lx0&s!$i?T#uk&#}LK*tTP_7lcY!B)dvwq
z?y1Fv7#uwKAC%J*_X>;N?6@4!6w+ir*=!+fj7=(8)(nTf)Uw!&h51PSCv3@nYafa(
z_fjbHs{F?y!yu=g6-4_=6k#C3n#_)Ym?b?65d6)8)Z_t(aZc-j2?xS<PHf1{B=e^k
ztjy8xV(o}V7*lTrGC<X0qeOPOpZVtQe;y0cpurF#gt-z@g4R@tAt|1PV=z`k5e9li
zAzj#jAv)q_CMqzwCtw6cK|YM^ZDy4wk@olxPH&1R9|CMWKk}Ne0ae=U=34or$LyLO
za?)XOOBbvzdh4|1Bq&)-7+e+ihbnCm9(xtV0NqyfSoFJlu7!;f#ku_7rAZ2RYE$K(
zxm=&@DGU|x(9F_rFL3{kb3C0!7yg-J!SK8Oe+luQjNn!vk8YHn#E@S6^^~JV`k+kW
zKyPuk=~mO|`PkKp-uEha6qM3)Gbsk~#NR0VrQF>Nt!r6eZFEeU5V*#BOY0<){p-j+
zptB9&SaR}vBeiJrBbc{1n{jJ*lpu(E3_wtp<FA-vL$g`b%bP#X=hBN=6@cptv>cLx
zg1U69Q<;yxz1kA}#eP;pn?EF1lD`K}nbm};ZX^M2#?8Y8AvDLn#pW?|x??(#3vf4d
zisBF!g{UK72bN6dulI99;xA7wQ*W)REzUuS9&ty`2EWHry1ayj7p+jVqWF%gZ(nHw
zcX2<6d!Mss&DNEKy+n0aQ%N=2I8=zaxaDdYrhZ<$GV3_-SW+s##UXk?#CGDtEAe5%
z{hQizNwy}<%_spu8I#S`Z@B$HA7AOs_;B}Mtb;`>e>R5|ST-~@^aS*gIo5E95t=%n
z6^|x_l}z9K(w#f;YZa?5wvhNPWafM=qUwD!8HTXzTVnI(^{~0J-l+l)8F$p<ADlE4
zo&?rCfNRJ$zBoBd`X;%#Vd^EpXjU+VOS#00%vuMt9;7&Oj8orG`0P`+7<1x8$xH2L
z@fs>S#?QI1QO2=!>yus<82W;$J&?whPS?VWHtHL}r<pbO)L0hP^#dUn5!DE~=t2^y
z(gkOD$}q=MzNfBrT0jqa+R{?DWMy0KSJ9;1oQvD0e!`NIG(`m594Ybm<=3q2sl&x9
zzvkVF>#^I5svmKMc+>B|qvH}KOZLDO0otWA)D)7va1Bb~tVp#0JtKRdXOCNZ?%UqE
z>~huOBpz;?N}!b{t`Hd22--!zE=Vh}Co7LM<e;H|xXtK(k5RV6&P8r|s2Vb!`Jh0O
zD{C3LJ{0j{jjQ#ddj={Le_Az~>Asb5%Gm6-MabA4!b-BXWK*4dn>+YC-kXO5Xc!tP
zhVDR9`{#RdF9Z!N8Y>dI+epv1-^1o!k+2g3E-xz6QM7-IFR64FI`lf!_;pbH5u-l8
zdu@-%81z;f{g4T662-vbfCDkj0Tpl7lOv-?5bBKt_MiCz8d_gF?1uRvFa80by)V(d
zL1LSV6@f(ncUK;6r|-V%_s}$Co)*9E*m*&@d^MD|w|SyxJk(6Q@heHIy5G9hWwk;K
zB1w_A+DAl#$=aQ}R=#1pQ#-*$aKk`;dsxhMM^%vP23x*IX?Usfm#0YWo@6xKz(G<~
zO*fax))Hl}f&JEpSguIUYvXH$FY29v!o~1Y-Grs0yV&=kJ&tSAdef9c8D2Zv+{Myv
zD)d5-vV1RZi|``TbUFqo{4_xK(;7-h75@-|7+}EPEXOCGV%o|>a@lP|j0TLn#^zs&
z;lVzkem9n?xef$Dvt2@8aj`Jkv3(DpjaV#J?Q0=5kBDdytMsi{H@*rQQ0tf4r(w+Y
zd|<xPSvz`a60_)_45OB+q9f=qf<etHRAjolCpO$SocZg#uUiw?iKR}I+)PNw9qOb!
zrgEJOST>lOa7&9`Yd&^tUr7lt2vFiq$nZEXLA@Z9%Zo1m@O-lU8vndB?d1BXx~ihH
zQ-;3j#G(>6(YARBj8|!LPkfxP?}V&NreDfqA`=d+7spE|3N|@8Q$HdJ)$!VJ%FfW#
ziRs~z$Jfe0-7x0Vxy8xMU=lN?oE^6$CL0%ON5GcIOimJxiJR%VJBO>_;*T;@8{=z4
z%>7>r{{q(N%i15heMW2~DpkYTfsm5%SvUn9d5L~fW#M?Wh28taZgHVQ=;#c8|384?
z4H~0BmKc}sooresBRmZkwG5~?iynqs9+vNR*3?@bwi)Au&5eBC(wNAPs@8}Owka^i
zNke@8>_2#x%cDp(xjc4MOyt#Dj_S{HcMRX28lL4Zy}h@lADZLQx@F&L=96~3#Jft1
zpqkMl)4GmroyeW4{42V+uA_DzBXD<4-e_sfz@6^Foz7l|8^y)Ar?jE@(Dl_TB}mWx
zXIG1zz@|_=!-c3A2~NJ_=uq20ZE%(4|Gr6sv^qyB0f8^IFW&=(4#Vt+FJ^2WMq>wM
z?R1i~5IR4Prz6j|6}lg?fh|GhUq~h}WQ?lmPI}s7v%RJ$(+IPWFT{d@dq*UczE3-v
zMnb(D0<1{VSgeVE{p$Y6;MObp-M6hw+|sG~oS5c#jWn*k#auFq&rjD}=ANW|kdOWF
zJREy4_8)+ZFenwx<9PG(4+DE9^{8$R654!1?1h%rDuvKOW^u@bD?nH*k6PB;tc~DW
zCb!YLn`V7?tG=qKruydTb`SOk4~;%Ss#QY0+p;C)vGIQ%nqq0SUzd9OXL}YaK20O&
zbF-NN<4Pb3`|xrqbMG{yV9))&QFnX64AQNfY~e3Y1eyEK{s!1d*|oTB`<Js^<+QyH
zKtpcy)6(r2Bc;_Ct~PLHXC0gtNqa6I_PPC&FPjLqF8DOhjG6-u<nH<{V{HhcC7C&w
zIrn8{Fx@=)&)`%Q_i;4Ha=XoN+^%B!BHR1cvQ_L7p-8SouKcPSN50Ul^0@e}fBnpu
z8Zg0vu`1*BomfzB=9txCt{+KIN|-KMI&q+oT8buX>^lF&<8Ph-FPR)T-kSNJcy(_w
z2%Pjw5}dxqs0ed4I`!BxV=U&SIxb~+@)>u!U=lO1<)dKK&hg<^Xjq62{__!Q5B>e)
zZL6tv+TW9yQ7@KZE|6N^e8R4q_Jvd5mWSEs;00y^6WlUAzIu94U$5JL=Om_`Gn_n#
z_XQOYrO<*0Dob5b;ay1Eto=~Z8y1?9t*DHTSbz$)j~53i<ykG?peH;ra$N$^Uxcwh
zRy5a7lUzhMY@%Z%5SuZ{1L`CgA@v5Iyg5!PCZ*BEjUA-m^qJt@6dlp+iYb9_X>?;}
zR6K$Q6DBKr#2ru(vRXQ!%Ao)}OtRI{!C)TZ4eEGOzS4m)i$@@G!&HtM7&8VNW=GTO
z4}HV>UldbA$`$EmgppPxC=3OHmxJ`0ycV<d5Haxoc0frK{+2HI0rAmwEaZ7^pOSTf
z8Q1WtfmwdsFL4v~q<j01agc$zLfs4~$|y96l}sARSK`MfbS1cTcH@XSR}0RlrV@k#
zAv2w+IT$DyLppAVh*sr6-RO!?v&IVz$*gCv%&?w!;5Br4IeVc|yuXfzm_EfTGPRAY
zWrNS1!zR&x;3<u<h)87wQHl8Q`(I8Wg518#*c2!nmR2h1SOcNdl{#P<(T|HVe3Ig_
z;;eVz#udZvR|d-M+ajuZ9=qJX-egVMbHL9pCDwQ%U*IP0l4MGVA}-zLiu$I%3aYFn
zDkW(Fg`IEoW3k6!K5P?JU~v^`bdAxGi4)fRSMFCKpN3iOF5=WjIXI$|?I+h@sVSj5
z+I_c@L88|#&O@^=7*!(~zv;QF<1KFH8$R%!Da1$qnwcRK>K5(8Z{Mtb+%C(fnGm}7
zCc%d4P0x?D53O>nnE_+viumP&8S!iJDJ?@K;Fc(4!&-C?To>K~9uz8X+}JD`*s2Qe
zeBP4lSz7VX3Kb9oNseoQAddd72y6X8pH;YiP)%qUIobpLC0xo8R4hz!19myk8CU>2
zLd<6j8r$1TpZA$v<c+%Ne_MmqF6<#x_@c1dyNy1%&z36V{{rTbb`luUo0Armjn9==
zYC@a7DoA;$Ii#|=Yp**{NIx$gfyTy@3hC!&*@mivB1Da=d2bsuxl68L-|#_Flc_IW
zv!@lAl}eEp#bKO!JRo0Js5k||`7oZDfV0xXH9a@m+DVC?bz#&KJue_wOZEFfFNNvj
zcnD0_T(OuXzSe(<1BVP9<JgFEGgXXzn)huQ<@{{unFO7_eUBgN*{&}zNAmfCXHmQf
zxVf5hXyKg~kgX`2buBRoa?)X<zu@%SGn2YY?pP!mzjy8ai>SDtDBjZ~AwA08fseF*
z)neX~60Wg2oL9<Bj4AeF=M^HnJl&ShOj6nUS(upQ=0)#I@l<2`$Q^*@TYc^Mz4>`u
zWDJ^ks;(~?G{9#e^loaKzpIcxFpN7vZ;BTZ9ON_4AtNj%HFI8v3t{c#ht><$`?IEV
zD0%EYU&c7erS2HJaIID+W?q#LiNbu3sfx5jme1lVTO}7|GHzO#2)(pT&WIr=+&!Jq
zfuiCbIITX-gjnR<3<c2@K~LjUg@ABKB5X^}FLQdSvL_Q%>f)`$U!Y|QHF{l^#=*Cz
z;nxv6>1VG;Hx;04E8zl9G1^7SXBw#S9thwp>**~->&Q(N*xB-t>nL0OUw0Y95B{yW
zVs%^64!@x1ck7ml%2yAaD(`gt&8S~$8pr7(q|lebUp^AYs-j0a0u383F>47n$s<R-
zaG~i%ILk?@H{#`}#$<_v79G|J$#KkOy_l*rGfrd6-whxr<#S0D6V<rZ@yG{SPpx4c
zX)bEVo|5RO3NPY9ek|z7C`gJvm+T=~dM7KQLeV-Dx)D1SORKT=?4DQ=u$QP(K#*!h
zzaF#_G{Q*uQEGWK0%kXfYH{}QdEU?3fRm(z3W#r8IpFDnncdhUT#~wlDVRb)fjk?P
zvz@E8pRYJHj;Evf--`X+CY6&uz`m-s=wBm=lX-lcojK@J@|KMS`njF(Z{-%@7FI%<
z&8uYhp9$tojA&_4#)cXva+tIDbT_-c4!JtoJxVCQd=v?5i#U*T%uTI(=ZdvX(h2NP
z_#oYYAq7?D|K!?*Gn`n)RMZCi1Ee2(?B<Sj80Yw^b>8hqv6&9~{>L`o_F!iEoB>xi
zm9z24&f{9^#W%1%lE9y**|PThjt~bOb$c*9W>As2a#i7yBp|>RFL7i{6zFez`Wn+{
zZ(eHpL!v>1*f*pqOe)96t0VFnbS9S8P_2spahp&pgLNl7UK3|Pujx2h_>R~uD}dns
z>CY3HnEbhjrfi!h*)ple)15~#aGG(^YFg#kUx5DJ!SfwU(4FnouhLyAuu{=dB4ETD
z?w=JKx?`DaZ;Z<#(*1C=%XvS}xjMw47NXN>0#4UNeSMh7d0zfeWi^NW7j~iTsn9g~
zCz$q^<+a`PQpJ+&-x?wbXSCRG@-%SZRK|dsB@}iq`}nhwzfJ*OzS2hR-o1ORx`N%2
z%3@}OadJ&CigU`Y=dI0Ixikh}ad%Ns2ov^|3?6t-WFLIJDJyAt5Ku8t8>a?CGTj^G
zoWd|b$?3q)=wK&{+aTletQ-^bN$HLol<WqOxGZocVIck1;Fu(%cf;8D5*bwEfj7DH
z55QG$U*a>1fk;>$sEuCl7|;GKkeOg-=wKYMd_cFzo=%L1-DAK?X}#y=M;);_s=1y_
zGrw=0`}cTgWu`GWFc57qE=#SrL@zTUKQK0ca(H5^JnkC(G^#q8#b!@C+{`w*$^ol2
z+ghjB;0CPX&8`!x>+Bdve@wFaP;mU?#&qXW%|=Xb^n*n(_YFz!KR{i_?i_1VaK!$Q
zh$g~^q5X2#X*tk(Uoi&x>vZ|!%=vjm0Fq|d_glq_U(@Af6JhHKYTT>59q|l$T>5Tb
zns|cj4peUfHBU`%ixzqJjT0IrN;7tJ&=)&Xt%#?r%?!cZs$Ch~?3%ldOujBPxV;=G
zENgq$vgB^>R|b?pjCG+VCxmLCt~L-V^dEpyQce7TG5^Bld_KVR1ED+vvaG9mH;L%)
zlrvgD#h&qo;O64*27g7xhYGuv)>I_Sd*Se2Wgiqmj|Zi=fjdZ59N;<n9IdxB|7_;p
z7?#&<o2+D;@S;&4A5t_rZ(n&6|4GjkuuRMy$((mImD3DT!1MpC+Lsm`<0a*VH6Rlo
z;Ij?dX)bjNfE*n%SJmPSyYJDyb4VAzmwX@>%Z0<LE9}jA+Ix!|PKeq^#JdqCqpP%9
z8+xLBbxNCUyd9{kAHIjRD@C)Uf1QP%kMOnmyqDjydN{L1OR?4d)OvxbNS0<Xm;1&$
zqs=#uE)TAZKgKSqZh3wN_|m}!#6d)gL*_B?A{a5JLy2m1Eck7q4T6(ZFCb28NG4Uf
z_$S!>Kkk)$14B&#=BD5E?HL40NUDlYUcnEBn)ehe1#<Zi;8(r;U}6>>`wZd{ZCxep
za2aC?8JK!hKZIPWTmA6WzLaydTgke0$vcopdqHX{Y?RYUfH_$-w-s$Zu{=qH5mDYw
z0~!sw)%qCA%Vpwd!K`rMW(@lBHS&<$kv$?5v&?$C#Iql>jw>XPTT<IiEfo`Ra}mwO
zFd{IkH_fBF8p$2d|E<L2dXmf@5(vpGapyYxFHK{s%UO_OR+vGq=ZRTL8b!>CS+ylG
zEaHaQWtAE>U_?bMMPOMYn_GH~dFPhxt!OX?9mXbC@`wzaPq(aJ6EW9T=Ms#kS*oCr
zfG}28!3?J)*7esTve3?1UP1<3g%Sq_&k4qiJSOy0>pAG$z<!fLith7C9?`#}NAs9#
za%*WeQP=N2Vx)=m?#$G=f;dW1(%_3heq=ZaMIH@>SmUNaB~uIuc`gNr+dGA`G(n8c
zr^vm+USifeYm(}nNa)*GW#+O+l8G?oJ%nc0K=;p7Yyf_@bpH&Y-do$vj20bfRb`y1
z6#Nkl{n)=KoZANSa(8Q1b9(WjrVS`-@(1!+7jgn-S5B#IyAkhtlR>yTm+n$gQx=)A
zh&qX?Th)<OaVx**iTat8o{YVJvW~>4|8tP|_@OCL4s<N!m9P^72hEK^WXduYI!h<j
z5tLYL|H2#IP~N}bE;sxJ{aizuvqyufF_z1H*It7O02{VxP6L(+(lA%Eo1K?b?TtA`
zem_t=fuWKLqivLx^WGln4P(D#p(#3~fLA*&pXruID&REnQDQsHWwH>p=rK5s0$<1+
zrtFK4`_T4EgCDcQpCu=QQ`=XwhyBXm7EqUA*D6MPz_JL>R5v|rcbLdgLX0M#avU*n
zXmN5DHVt7FHjXw5S`neug4=cCNh^Q_o+>XCt=vl2{g`K=+D%{OvglJ&OxDA&#oC}p
z8WR8M$NXHTZwC}m>XTtLZUg1-p$KIyYUE>p1LAE@rV`s>52+HsKG)&bo-%d4CR8JZ
zroiQHa#E^+BI6N-^Dw&32F3=kp+?$7kQR)&zgAh$QnlUBbj^=)`AU-Z1MY5$30~@z
zsA}%lv<O<wHJgipjn){xb^9GRX)G{6FX}S|J76^83$$6zB8|;c9R+mL@IRLQ(q9DL
zI9Zr-%}SJ)@U;}25g2Yeq+4x|PeKQ1Y&2#}lc3{k)ZGf*c;Ab#9!vO#T>zgd<7sNB
zJr`n@pU*A&R+wEe>U8C|VpVCO<f2Fw*Yoq!bP(yzl)vR1)jGFi0`xd1f`K!k^ODO=
zK%MY`V)sG#sz*TdAE0{fneRFr@2rx9x=Of=(cw7D7ylgvLBjJ&shvLqy(1=b{5kIZ
ziK(nXij684GJ>rT*izvKm#36{BmUFkr)f9dl@Pus?RF2V9t*Xg@jm}?<}ic=h>LI8
zUc%~=$?MVtgp4jaE5(pksg&0S;L2Syjip2f`|OmM#k5)KvYzBfAxSkQe|aR812YTN
zv86Rp(!luPwqrz(7&R9$z^Ee?xG&~hm8hn3@7RB!fLXDd|Bd?7F#m~0o{<+hAv_c)
zJU?<z#c0B-262&B>o!-TAr^bajh^X}xfC_(F^c3b(mZURIKfX1RYWq^PCfP97Sa+?
zVq^{Btol+V_M2t=f!tTF#i~NiPJ9l(pP_)|&gon;cved;nAmTRw!8?mbdTXXHm^9u
zt{lUgDGN9j!FZZWwKp8Ww>sN9D>ek`aG&&elHu`_%E(opZviI3;Q5mCj~w@j{pOo0
z9-eJZo{iUACuA#&m0xOicoT+lkF|=l5N=!_Adikqd6ZVgx}N@_*aI&fV)OcY)5xOy
zGMXE{#$o<zf$=Z0<+wgfToZib0E1nVwST7X`)K$%JQ?vwP~EP{I4HMzSZu?xPWU$2
zRPDXm!1q+`*5-d$I?JFaAFmCAlpvvmG^lingfxgqOLt2M?9$yJQqr;H(y=THEJ#VG
zG*U}<w{)lP^ZU>Hg<%FhFbwRT=bZb#E`i6~c~1LQE5;RJ+EA@2_wXt1FbO5E`WR6n
zKOKazXeLQJ+r*`sub=9<Nnpaxtz6{Wql@%fRiVxkZgR7pQB@U=@X3qWi)a1YjrEC}
z>HUM14mh-X%i;gf#?GTk>mE5*Zdc6f+ZQY!`5QDnt#xp4$#n7!IT-3ueyUHS?%wJ|
z_gmd#i)$`VlX-Z#BZ=MoB~0J<n5fTheH16kJ1>7fsFJu54%vvinRbU=myds}^QStL
ztkqNg-92sgc$;IO(WsC))68ih_}9Ysh<3UH@>nNeG38c3xM(5}EBh$X_23iP(KgOq
z@gYa4fq>2hkzKI=C=j(q`^JzL8q>TC9zT?v91woCgWU0#9CqTW;4WJps!J%iq?LT3
zl{o)M6a7-J!M~i&r!~$|$ssoG&O&p%+R-J3noxN6ku2G}cwzA$TA2eibT+taY~d{g
z(N0T*Rhff=K|YSxlAYD0cajW`0}z3IwM3EEtQyJtnw$Pp#lOe7A4hm%M*8mk3G^oI
z{R%FV=sDakC2AFqlv^uB9T-Fdo0}tN$G4O}j^#p9FmPHpjCPQWP$Z7~#1?=?6S2vj
z)A_Da;&ny#e2Cw(RKQIEuxV$Gd&6=$#b{9D=-mLcvg<T9KjS~Qq*9U&4KJIEKU>2U
zAg!(<3*Q1{VoKeBu`6I*UIo+u+n3G%c`!r24KdcP;!gN7O&o{>M!P+-<bv6UB1iG?
z!?lYYWj^heJL3*5$)@Odaomd{n7s<sKeXnYDnkJH(3g)A6;4#qZ;*^(b>5xITe*N?
zy20}12coD`V$l{vBFpuxF_7{>bDv_gni|lxWffR{(iP>TbJNPL^nK{ylt&{h%>L$^
zJvrA)&=&H7yJFoi#=gfqs6pwA4}W&Wj%c7zM{ozCC7D~`ipEn(fC2H>vR74ceMvWY
zPCs*yF{GL=vy!DjqDH`a`ytRoE^WMPrw#Zcgaux<-Rc&d8W|ZGt1?Y)#PBEn+V&8(
zk|_L#mg!a;SAj%(e3O_QC&gGJrGhh+A;W?3A3(jO_-BIx`m1$;8Lv;sq||JNOut4W
z&OUMLc>53HJ+4Mzn(un|1r?|!ae0ZXRixl2?I*rhf_#C#j@0P4?xYYQzGsT;ac7{@
z#Yb{Jw=|QF)5J7&zzwBeGGd4s>D*-Z?0PEkp!(O1b0|%Y!6s8u|7%G3A*hA_U2Y3Z
zPBZ_73O0f0n|xr`bW!M|!<1hz#LE9H$)EHaYE(HK$4rIT2Z;P)ncJ@gk0^#E5sRpC
zYnaGT@(*pLIx)k6k*4F;-`0VdTIP%M+^;;zSEVtB&T9pEjQ4qE0q+b>T?%zxn^V&z
zZE^CBLYPyT3zk)eK3dC!1F-GBB6Z-EGBJrpOP_zm--ev^p(m5708rSZ5(C3n`vht3
zTCBOZ66IxK-;i`YD%QKC-UWhxxs~c%+;IW7)Nls;qyL>3zXby&WQ5bW6u&|?Yd=5+
zOQI8oA>PwbPi19Jc$21|7XoWA)Hl^9p)naqL;1cQZ17mpRswmF=vL|g-GyzAqVRbM
z&t>$#shcpNO5L>(h=vS$kFpJ;bS%f65%AQpZ~jz+ez?4sNNnPy%ZmYKr1!wuu&Lek
zob$tEt$H*%DnyNXR|8{Y5EbS;Y!3mVH)89%pBG4dw3OejEA~oqIBW!U0s}_7-{L;I
zoTz-svQ2x3fC9C@X*yN4k#&m>uj{lxHsgZpToDe7IBS)F7OA>h9fiP?L%@@mMo6@+
zp588|)=TARzAQlLrzI;jp~d;fNAs&}dJ%;6NcGNT`^is(k6B>;BuEF$eY64rM~puU
zGwYV-FXUDzc~O0^uck<!r~;ZtB-wBzC^7p`!5Drb6gDC)5~8)TVL~&dO|^;z;$R+Z
z(B?N%StZ2S>%ni6mXd=(`mNS2Z?i@WJejo&`PEj=G@3JY6dKh)bUVB&*2f5@)eOtT
z{vj=^A^uh`0sO>J(y>{UoR66mwI$UGP)4Q*0Wu9aIK?axCNy4C8?4qmI=a!VrqEtG
zW_Zq}KF@5(Tyv*Y9ob@R_XA%d8G}u8$fS5vc@PSr(DG!jV9xGBj|>;}(PByu%J10|
zq*IZ90()5{`KElvpe7oZvOJ&TlX~Bf?z`TIUUrARwP-GKF6B7aUubJh)ESJHjCg(D
zM~LaL2q3DolrmK{9u1V>jK8^YDRp0d@&a*H6A>@<Zlb<bL>l9PYb?+68F6;ht0xtU
z9h-tf`iIMlTgkCMCbvI|5?(0k3U{9ADAE-{pTs&#)o@1cI{c9W*B2wY1wBAKGaDx|
zy=;OFZ1H%Zg)E6+oCGcRk|HZGr?CiA1o!(`c1UyFa`1;?Z+L7ynF6hPWNdeo-sQ!%
zz#_;g7w|mRcTuPti^r_J!t-ZWrRLX2aud<zxcWdr7KG>tQXsQ9a6avBoWe#F=V_o1
zJ^OR|Qx9}jN=bMv4zB*=S_>{!MA2{=)YX~0ENmsmm0p6`|E4f*vp@`-5i6P&(Xwn~
zgXuyCDpNw>p+O6!0|KHKzX&W%_eg~14pl(oilpvCuER(Ca7X^vt2~kSf1EvB4}G<-
z?T0-j!CVKID|My@Ri}kY{ds%t+K508#*h+16%G;5;BiitZi<B<Efx=L!INwlD$ibT
zwVM_MXop!S3(M`zfL;=menNAC?;Pi}h&lMbwxv85++rMKsf;Nq)6qd3K{-&Ut5GgP
ziCz10m~10@5*GV<6pz?itn%GXw05kHBs;V!1tMsKhXOH}6yj?!-;&bMj3EeXmOFn;
zt3KXo7azQ)mD|?JP-i*h((!G`Xk}NpbT|3C@Qm0;(_oAA@wL&7r$vgn&5g{vGOE3y
zunEm;DVE*Hva8ddH?ngD9lJ8vmA|FjSU^?|l(!Qp$LTpZ$tDUV#W4(+DhlP1ScYET
zE;q(DtbZW-%IaP!Of3;FKHb%2XsXYickNq<O)ocDTOV-^K8;OoG56F*NTk6Weyml5
ztAB?qZHwP4UkFs^?VRqD8<PJ0F7eg1U7Jj{vvXgwRf$?=k+JY>2F;sgR9Crt?>*EW
zdTlEs*`&hFjFWCSzQcyB5$K*yo}^M;3jBWbC}SL`ANH3!wTK|)eJ>tb&#7pRy3q&)
z%c!R7T-n^bf-kS3VY&Vc{v{1?^n$0?QavwFO}1+*tuD`b-%2U37I3PDhB1*j^))5w
z4ZThCjxfgfzHw0#*{@`f1M?S^KrzD3=Cv~o=n*@v{DZiSM=LxkW!OD;fnWL8Y`~p1
z3(xE^4pv)dr78|2(R;)vO8i@4UiK!Z*cT*8!I;1-f0zw(wDIoQ2|vMDc*(6G%wU?I
z0%<>PFb2CbNf7RAJNelAs6QY|9{|+So3mBk&HXy3^*H<30D2`;|J}LD7vvpjGoo!a
zvtB?b2Tm>ev0f39WPMP4H0hRlBQdgZUv*_q(L*4@X7X~GgkpJ8g`;e$SrXjne4-jg
zv9MD%|5Z*nyL;qQ#slP@*aEaWRsR=q6=>jGQzL@tR}oY?x=)|fzfRd>Iz7@-rLPEl
zY_XMk8CR@y8AXZ7du5V-8@WRC8HJ$45BV;7MW(Q95mv5`<Eebw#?`vsHr*`hVc)%g
zyG^=ruwS=p#C&$8sy^SgX`B--rtNDJ@Y`j{&HB(`Pdun}dR=ych0X0dCCz=A*Y*r5
z<w`Cmyld)rKMF|=t2S;S#H9K#Il6caK76rdz>_Vp2yNJw`;i<^YBn3j#_@N=Jk-aX
zY~@e_0*zwS2oo<B*aY5*3HDX{#COO-fqg=fuU5_L`Tv#-1ESh`I*fhrt~mZud<)J6
zybudYfpMG5U68|VR{Of+;G_R8YvwhyB-3#kju5{`z-D0~EW|<^K+oIgCzjW~YJG!m
zgTa~#a(u>v?5V|6YPyh}B%}M{p;8Xm<X!gpP}jqf&t#OLZN_}s0ha{`p0o-OWA4!|
z3RJdGRk~)5n036cmKrPlLQ!q<QhF)lpp@lK)=|U8k#eDV#)9NpoRV^%wl;92ZK)%k
z3{y%fYlc>mUG}E%3yk|0cego@wa&1Jqs)!7X_kt0<raq;>-2#Wk-_W0TiuE)zs8Gk
zxz`??dWpjdjM)k-6WK-ImTD;&D8xat*ojr*97GFj(mww!QL_3gf#1enxH~1-+?g3h
z>#F7e_PBi3YlQNBmNY%~mk?HJIAgJyP_=ycmgSx!x4Dt(SA)Bk<m4y6;+FSt8ZD6*
zA^{2h<^RwIb)G!+A9QCp$wx8O{Gx8+Rw$}no;Y?E5H1-oZkgO~Zu_J;S&sH(fvzPW
zd1U)ZVYaG-RjSxK;7$HbesAf=#1Ji+1y&sXKUA!hb!^Na7cjfW9)NWx*jFJ?2ZVq_
zm@}!)a0fCpM$I}ZEOWc9$Pk*VZqZksK!`d?Q&6)?1gH}jzW<}hWqxd{GgS2*DqP92
z__prz6bXH1<h(_Ko<_9%Z=eFKr(ORSV<{N=gT@W+D3Bqi%X%zNy7E{{;k=X-@1s#0
z;mo+i)*cy-oyvlWx+43}OhILQei&-5rY!oV0H4jKfs5FhU&{>`;S`-DYAGaY8c6ZF
zGD3v}$;en4H4Sj{jTgqN)xs_i>d)L)G`B2k;-BcP;MLN23G;7!U^k0X<aAYmo6DeS
z0emlK?GEfT6q*qKrEsQ$(Gu$t2kXK&XQ%{9+-Cdj-k*Qd$2JpRe6(i@7RPf-#(J}{
zK(C(WxBVhI3#;izy}Q<&P4*(w>yM5e$INI%3HxXGRCe8FYDAz>U{1=}WqK5MeY1j&
z&T6nEuH!_OO=}w{$RV{^B+iE$z1$}n9U^EZ(Er3mM#$QJoO?Y@3grg#d^(Yft(8pe
zKun3nifG)}32&3qxs@2F^M3Ub-z}xI7y^sE(GY?^?Yz$eQQi5z`4LB>rSVS6;B(fu
zjuPJaYIpvb-3jGAD?W!|RROVgClJMyGE=g`U)v5ZL(1K>%Mz<7TF~G>N9ZOtL*Up+
z8}#6Ugh6Bc^ijlG!B<8X_P<dLd&21Rkx{hHh<sUBzZNj0PXS3T9XXWldj+!?EwK7H
z(o3ejnXk(bS@c)gK(0VBok89ogy489t>yyyAfO{B$3D}rT}h)Q6XgOTrApAr%jZmZ
zp#jPh)lOs!aYdy~bm95nK`4c2P!)>FT1W<A`D{YMce>zjjCAaOMblh07Khne`qfCT
zCj{}yoY==OiBd|Nzs4G5_q_92Oy8yM1J8Aqh=bcd;0$)7t143SC+iGx@t5#0H_Cim
zh}^JX=1VZZEwQ6u4({tV+hlY7G@FJQoqAu&JP}rZ6NaS?y&r04F54Xw8-K#~ytrPf
zfz~A-lcMl+CR@0?P3E|JDv$UV3Vrv;FjWHISLl9P2+h@~s#{7axkZ(mf*lo%pjf1P
zhiKnsjMl*(YQZM(QOU#SYS6?yFQ<Ea%fbS4EAsd{)B1J5vVI$Ch9`2Ot^kyAm=XOF
zzN5aQFlIyVWEXbGuwjrk^fRz=HITDpePgi)YS_x-1(H_OiVC}*ceG|5oy6J3el8BF
zFD;gMNewF#;A#Dh5x*wGSnQ|h+=@QYVzR44<+FBXF)qQ54shjr-f8PES#=0J;Sg@8
z1^D!%Xt*Ul<PwTLh3}W<Sp<qoiiL9Rqn)+5xx*LfvlKcPN_au#?>eED#59(kB|N`f
z{Z_dq60+;d^W2{86by2cY8$Fmh`3F2W*3p(MXvgeuhi)QDEA8wJw3^k5|z19-hnpO
z)e@Z!s#Kj%uAeK3Ou++*y78Nh+8tJZ=L`KNL#uzd$bu8baX-@8^<g#PKd@^>dfb)X
zObzrzjeUTVYj5*srokfrMsHT9J*;8rDUm)hKz)sSTK1Zj@%_GSg~;snHU1h!tb$FG
zy~=c&(_)2+@vG`Mw}KvYRM)I->LcVB1Kcr8;W8tedIgmI@NIq+m)V;-eyU69QQ(fk
ze<mrnK3A%-af_<VFCeR8+{j7W-Umj-eRV+Fwcsi<;7krYv4I*eM?p5-W;g%aX~(MT
z`E$$DXg>4N2@S!PJ)`+5_XaXLT$rTG_g3GKV%j$@ZG!wtw9PF%_QL9PLyB(luIH!l
z-lFK}g&ZxOgX5fhyj!>1t(eQmlMcMpgxE>#jYL%nvH?Df#7y*wgv<c#13IcxKk`EV
z)?LB<t6;Ym*(sw5T``Zd;y8|58MYRjsFE1|#+Fpqw24jAr?Z0v`mOGUao4x;?my^n
ztl-mA^<pd5LKrO)-js)5y%iD``>n)M7*B1%zvZl-tes;;yO5sbAfK6`LY$7bGMaqq
zfL#kPyt&;}%uBkwo-9R<&!KuwISiffziq6~H<WQ47(b`BC46#H?{?4jNEKbXP%ZdJ
z%YtR%qO@hMaxf}-TTv5{zti?7wOcpQc#pN}{Snu@*xv$w5X0a@gkVPT$g2&ExPjs@
z3WU#|=gQ8hMzqF`NaW{8138)RskKa`=uH5-=>=GVass;mPR5kf%c(&yIzr;VvuP5I
zyooJ<?$pK+>|Wh|UgAC0_&ILQa4^L=zddezOYGB9O%sw@d1F{)`%UZu>5pSN+5EQD
zaKDje>sgcEH+jOcXr7Zk<nNyKFwG9SH0-KUoD-?N=!e9uEd$B@!2SDdgDZ!nBnQky
z4wSn+#^)2ARM)kCXy^t}9yO|h+GFvlwoFH^w%L}|EXA9vV$U_P{Dvs0F|&z;X&<^p
zv?pcU+q7p;YJ2j8Q}}R97b2K@J99PMM?Uz}@v+7EPA@B0CoIQpN>J%Fi-ptgx~bZR
z?&$oDq=FN!h_VZRX;ljv6?qy`2U-8pf;hc#0fEz6p%CkdQ%RA&se>oUtv?9jz5yW2
z5o_4{Mw-EAip|=e%I=WEJ?p<q-`q4>=1muCjvosQsX98_Y;s$$#hK(}y{wzov2UFF
zm_55Pt!%$0#7)vav5HB@cNM;=Fq3l`X}WA0F<?pWgZ`v()=l);Chu-y$cPZlKeXG@
ztfuj|WmQ&joAh16q{t0NX-buXDi8ncO*MZ2#`y6m!z)zquGG|;a=g!2ys|72l9e&@
zE#on3B{y?ymetjjIr<fOs>D)GjLm-PAV-V@(S<sl^Ed;{kmU|X3Fpzo`o1Tyvw=4N
z7STWAL*v6YT-btC1&!I?R(<%L3mcZ5`?BidbF2LzsM0*i-JO+q7=POJFrmH}2YY`p
zx{na*7FL*j{p+QmPbf-nM)oUIC3w^)=L1Jk!vq&UwYOUqu@u+*_c}p~3;7YV8EBN+
z#NYEMoiQ<qo8<&&4~g^e#){nX&lb`6=yR}E1f36@Q+m4?Nf_0Z@jyNl+fJ{lsHWt8
zpq>DM2CVNY$SD)a<V|RL0uVTxP3GTCCn_Vgd~cH86(lJW6+j)<6(-L9TZChdorz*f
zC3c}-h!IT{?AR6_x8yH^OF4tDbOtQV{TtP()7IEa)RN^!cWPNvNXO6R9}n`q6D{uk
z2o0I-UD0VN*}<{e;*mO8MJc|!B7I$FJ3?eV#zxc;b0!73w|GS()Nga=jTQwjeA>D~
z5{0l~MD4}6L(IyG;#1}NL{b|k+NU7t7b;z+Br9XVYByoo72QuZ>4Oy%X*7;O#IJ;4
zi~F)T$k%zfw7~j)OoLkkIOR1><zr`3cmn!D0t21sk0<)VWO-ITcbc*S(~Y)})2+&K
zq4+6;n0XcVJTZ|Ef+6)8y%#{a0ai?T)l<0#oAffwtFkgrem-nvwM`Y|f45U;0L|dE
zNPk$1dX|*)BtDT{9QewF2C(kKxE&9+%IlMuSUr#}tcR$pABlHkHK7`S&K&%SLVKZ%
zRq0qU224X%Gb^n31r9T^*g#6Eq_UsQ2of~Jd}Q#Wb*#T6PV#Hs&lu)GRUU(46y&^E
zkk}ipWwQOTF#aCNt%)kgPxp}gH+sVQXad%JwJQ}<EtjMmXvapswO>8)-f}DRL-*@N
zwUo8eG-3)F4Nu`7_Q5(#bZD^@i`WFMTfu?_tJx6yo5OB_<PsZ;1+TnnkYb;AbRdyH
zsZDM<id|?O1MLDa@nbG)L;|Vu)za9AC?#7f(>y;WNLYYDr66c3c^EsZ$6|0_J!ZZR
z$XXE@qz)N5vGQ-)xDo<`HHHLR)n~M=yjZuz%(bi-Gv%3@of0nugMACQIq|z(i>NTo
zMgE^uS^>_1-5O{wbln{4-qrN7(lWksV_ai&zjL*s^eso_Rze&Ow44duS&_y(FUt=a
zrRDJ9S*CXWb%`MHllmez^{9r_*CTe?F1eHCaMDn8U=^}m0y9f6+=l3eHU^=}em{T8
z?w_e)w}JR!pZ@qUVH`Vmwq|9jd;r1zWh?A?$tAl5LxMc1C)sO?UAX`@;|x~<ZQ6IL
z%<&V)^EL2TR+Wtboym|FM*`KPG<Yc^s;;AM$d^F{D@a^A6_%G7RbSr`pLgrjhg%Dj
zS5m<}e{vHURO4y;uQSH8zq;Qh?Po-Tf)l}%yTvw&`WZppLb9T$lJypaUm-tSUO$8M
zlFfVhFfG7tJ<HWeVr?}#ul`nqiJ51ngky^<x-FRdYv(UZ+f=^!MvAU;uUR(t3|of5
znMQYbf9=M8?Q}iqn~1J>@%1nLK3@4hKJEu5a+lz34Z9r}RM#zwRkPY!$?asLL2Rq}
zADUi0<Ie+VnEls6dapA*$e*+KdecbDgjmBA#mt66jYJnBF)c3EQe_Ho`6vkrL9awk
z%<x*Attrk`O4g(%oN}$CZ^kI-NRfay6-I!zK7xU?>4Mu_lx<L&MNVo4sq;}CDG_+g
z;g+z*aIDH0xqAm_@9rDCzZ|5XQA-al$)kUlGL`=hqN{{O8a+@zGvzk~yo^36+9p<U
zRIk&uI1ENrw<o%et_zhS99m4b1dxtk!e{4y*{Wevp#pj5*6ohr9<9t1Ylc7ZZDXJA
zDf{=IkgQaM*GLlJrU=bcS$`9m-tPVF_ygs|d!nyO+Z))~QrAOY^L47FPyqUl#X-U$
zC$A!5=qJToTCrj@64TRiw#ZZtS4Sw#q_6_@m?EKZVo;P*qHIroT+JO2PS%s2tiTQ4
zs`cb96&=<)>=(W$*AK#%zNkQc_n0_2rjY4_EKJ*8hvT_w&pnJR82Q_ZiWk$>D$_+f
z9UDJWcILZ~O#Y~s)2wh0YT(bk9T!knGFw@PVM3-d0GN(TC_o$21DYwAqz6kibF2h7
z8yXXCY8=EsF(;ZvvkuRO3h>PbCmekq2JHww&$Z0{4omFM{kEV;@^jBN*CTe0=6vp*
zy5YxPCuWuH8&fhv{n^IF0osMx;Tas0S&eT5J(B;#O^oCAcN~4jm)dIlhelwdlgcx+
zChk|`LmQ0FER?uqab*v@>)QZB$m5IYY&N#jnY~h>@i@N6<``9c5DzHj=XHk855@7;
z#rKnKo6Wf^1HT`=St?w|=g%cq0NHFfQG}c0wc@?E3u<xio1)Yn-+0jO`0>5%S**CH
z?g>%kL2XpXvyG~!tEv|~A96edaPol;;$d&+NW|mwNk0a7_X7-1316}78TzRE#&pL?
zt6B8#)wN^K$$f_(w-VR`JbGnr|Ilg-Q0)CJf<At2<fnC8@ZKW;(XdMBCujB=+COvb
zYh5J1zi#+oV0)Ax>52PS^uhK*@bdTd?-gU|-|L;vjpRI?wnf9)+kV{YZZYh5)G@dx
zx6?RD9REY3y7bZ{i9mjju2e+bt1?6se{q_PkzZY0`Q=-93)^x1fIbgB5R<^v;WeTY
z+<4R;TCkamsY+qnVm)j=zbsD!T)R7lhMQki!poKYsXS78N45kkM8G_rO+pQ(u6$SK
z%B(x=-0!1N-vLN6*k`JWF&|2V1gJ{S@1Y*6>Khu5fF_-&uTb1&p#t_k4*a}9S(!z5
zlAl0h-emM}qjG{X=TDed>#3lslN1eaymspAAhKQEMmop|wQ5FJD^9f`(Q|_WRm4tw
zaMdRhlOq)s1(nnJIH=erZ~aJotf&=oW{>>V_@T8FW;AH+YyB#Pa@9Uj?#=mEhpqqy
zO3df-V)dDld|Saasp}iVkpje5R^@HT$diHcyrgKs#g28lo@6OZ;7GBb>fs^zk|9HD
zlq<jTB)?S2yXfN+=@Rzn+U`h8%my?oL+9V8Cz*#hedwG*V&CN$Xo7=h0)HGuE)zv;
zw@en+$!fE?98^y3T(NmS+np9W=@9P)c<Mj7kxNsnE07X8@m{1fDN5>%68#boMa?4y
zdvFvR;#Wo2RkVOC`U4wE1FPSTJmL-n$0oJC+eKwe!xfF7Vk1%z8P1eHbU3WPN2e96
z*Ssg59$B1eHqZvXFe(R5_T6~%nHR@NdvE=X4r5}&OEH+Le{Ak~T-=uup2@V;4u+hm
zd788(n{klY51!T)uS`o>tZ_8iaW1#2?@yoQUkYVYwQzw3(>(&W%&jx&T9eyyX9}wX
zE_$DL^o@M9bs$5D$-J3=^S3^LZ!8>98t`TeK8_^+Jgz5SEaB%Qe-1RemWnqj(h&ku
zcUAs(G$sWBx1sYY%Wh7=qk$Lul0q97d6R4QiEJN5=?K8PRN_6A!g?dgn9^=lb&FCD
z%8G$}6AoGIo(^NuZdu%CIt`~>J!IaVj!jgzko^NggA^pl+GdU=eE#Kj1e`v=>Qc<F
z78{N<e8{*Xm&WG6ygRtp6hs_FPji!o^&iZ|rr!NSTM0DCtl1f;NmeZBw`<ego^`vJ
z`{36q>VY5+Qp*Om`oW|B(H4t=3DAZm31FM;0VP$E0g)scO&2yBGQjI>xt1Yk!iE)r
zVdlDPd>MnGZ=))x;OHc|!jfD5a%#5x(7YxthiBzMbQ%t3`eK7jJ#24~96v9r6XRT~
z_^seRz0tOk`K#3F)i=Yun~b>MEdc3XvC`l5G728=KPpeAWpP$GBKJ%>5!am_q6D@2
zt8vI*<~Se`Y2#>M+7snL&bk_V?$5>wFD~O&7FJ<PQg<dkgohafN2<vwax+K##*o^E
z$pFG%na}}+U<HLOjYI4x5-T#Fp;jw0*|NDzdAVBL7uqJA^Ryi7=U)(4$mh#k)=Q_~
zZmH~6f8T$VsKJOHN9*g)b#5;kz9V+v{~H<30(`(6<Mw9*2ul2FZ~P(+Lg;}Q8ZNNJ
zP0w_L5$u0(iG^XtCM`s-6>3=tJ^5~$7L=BW4y_|K+ANV@e^SOCNobJlONhJMt;oav
z#u=415(N3YRc-lq20#}9j6bc!?Wzgtpp9%BtI@oL^dhbUd1?kSa-6&Zp<NBf0cDv_
z$o+0fr~Dlq#s!-NP&!?WrO%i%KyNzr_z|R*{9C7{kJPPfHVr+&bk0-q&#gA~E1?}*
z37wl*_O959i<01!j(wf<9gR5kK@!PYg-Z_;Wdox(XY$mp&axc`)a=Lx7Di{CF81J8
z+L_-)EXYWHK1sCVF17pe%AJg)eh8EAJ6Z`bjtLm@K9qmf9gZi?4l$|}gBD%UxXvLc
zBZO%*8|IasEzH=)ijIfQ^au}`dzXnqBevW7#YVwDK|8iJ^Kk|sf4D$2q}pw(EX;#U
zkl)LfYS4ujeOCv1fL87sAdaB&duvd->&g<MoXX98MOT|S9(_^r_MN#is#Gj%M+vbz
z)@dlMdLS(7s^R@tbc!P2{N!DJ&j@}@u<#*pETbx>eCD1@H$f=m7WECO$Q}uS{)`D4
zdSi`Yg@fe;UcsR8EaQjJvb)!Rc_-DO5#=Z?)2FU~xEfJxVC8VHNy<mZ_TE@TXCcGk
zFxg6!(lA$S@<F6`DBfXPV95}akRL0m=3W06#a|<r>Hl3@k_UPBjFEzqqo}1aM)OV8
zo3Ec^Cs2D%N<8jnc|JTpT+q6eg=13PttqvfwHzwm7oq1dBno*e>QcJ8AhK#lR7C-g
zjmE-etg`Ns(Fz`IT*2>E@8?sZv3xoObXlOwOO=C6?W3w1Kd6oX!h~ayD&ZsrW~JVq
zo0AGH;Osyj(&UMzNf(2JYU(e8IA?}5p!?23yV~l50@T^boJ`5HC8s$Ww(mU|V|^#T
z1sK4EH+<C+S|}KMBJ{L&g(Q6tkw$mgGmG7P@;u@MlEVA?W<L-w%|#}J(nXA;y3vab
z7U;p+>8g~7;9yi@1KlYrKNFZ&Tm~HfbSp(vT{MJt7eQKy%TX2Dct0zE#3x`oCtKJF
zc-q*izVXq>T}&qowwmsi_sixN&o(riak1R%I(m-$O7mjJDO(mrCP!CmF)+isRVG$S
zx3-|e)jjGOx}uqg=QnN%QG|!AX#2O*xHBUJI=9UU{(!q)Gz<#g6jzD1q_R)6JpUxR
z4B1uU8n<T=mC;H3`r~UU6=+U#2kWWU@%`c7dhR);&^fQIS_8S+dXjF}N*eh#b;IS~
zIh|oqUE>O+&kYy6bJ+GoH5LrLOB652;*AQUwN`=+E~k}P(Js#O`rJYF5;kdFyoqHz
zGOQA{)vk8TMnPwIU9S-A3sk6`U6{Hu5%3BLBxeLkRMgLN*V6098jCtBUpaWg`if84
zKu-f?xa5=|ABgfZnx<kV(DqpKgY?*hhq#!XKY%e;be3}L`tP=AE-y<w-fyj5C7f35
z8>H4fS>$Q)(eT}IS_8j-$n<326ZCrcV@+3Ih8#dM`0Q}gKkRR7_~H1nbj{ytS!S$2
zpSzrI@Yl?GjO=!z>?6e}ezlKVe|x@$!u#P!tt4GG*lHX>|C-^hhJJT!h6QDPYqRsI
zVSBUn;>>+<%lusJ#k0~JDwFwLD%vLfokknWns1pm&X6;O*N<#Qr`yK|hSMoOZ9mdK
zxn|(C(RsTy;uat~!A&042thIJ;$52fK=UeWZ%xDh{zF@(Kc1qJ>YApw>zyByGrO7Q
z|2lG{0Gc^<*4|{#OiJD!uR03|Fy$_*C^ntB_L#rp`%Guqd(`IQuUhMpvh->SNeQcN
zNqqC*QAt^*$<Rbeb7&>Zn5|454&HHU>vA`9+0it)L>?|{Y+UJ*CsLMgfQ+szpg|O+
z4l$AR>fN$LFr33f_<#p1Q@-GVK#UAyEZ35aInL1ZS^1gal%3@`>e<Z4uvc_^CXR*h
z^gwdT`Lh6R#F|1i<2&o1ep1nyV`nca*RGH6Yiq|p_M_ZND_eV<LN15pHucAMM513c
zvL*#qdEb}fo2jgJT+rwww<3!TR|>m=W*f)|;G>6^WUI$2k8N9;2`0>4C2Dc%ky63N
zoB?mXt8XsPJJ1zCohnB!XdYtCo@Flz-;$^-rnT0w0)cP>Z>0JkbwkLt!|m(FC)Mb?
z9cB6!*%%RO@CkFsxNCm0m+DG#7Y;y;cy1OAxzRYn36iSgU>dgR?^&9p7e`3heK}Lz
zP~g-UbmY|KQ5+q@C`)L3C#U-nukq)rQZ^bMVVne$jwsWpv9FjqCKB3=C83c7t~ML7
zQM8xXs-H3_R~x;iRL6H!bY}Ke5^BCbT?xZ^^_@owC*GINXIj8cl2=(&p!$J6p_aY5
zQNT@ppP#rNbF9Wi_D^g%j8b0(@27(l?NhPXi^>A<Gmzly!WLzK=3iCS;U>+%uO}ix
zD^pBLsuXX2thOY4?*_(2PF=2c!^|&o@7m+dOf#v@3^NQfI1qLf`M$nCC<+tm=XUld
zp8LE2(qco#BFMwCvECmrT(~lnsy66nLni>QOih;jqho%))z~CRHI+71b>d9;KF3u0
zdYajjfnvcd^;Mj-p{U!Q>nghf9QR)7wtkPT2?6v`X+2)1zVhqME!wJev&ASjVQ;%l
zYqb_{&57Y-uO)r6$(2xD)C1KH-kavQn{{HqDJN1LuC}ioQpCj%lV){Ir1*!%ecrf~
z-xxCudD`79tcB5LhuWFr08<~mr7;AJx}0Y7Jr0b09iuacV<}H|ahaYco{8EMa*1{`
zmgm2mnv<}Es*s}38a5Q)OMxNn{6_>iZ$C$Gsy*zA&&0N=B=_x$thC0s-I3l&<PhP~
zE6+SKm(44Gyc|qpeC0zY%R0H3>2^8*)iSVkCr7reJbu#UQ3cO4OH&g{_7_*lV%wGl
zn^rZda;V3F#76*ACQ?Zv2;@|xgo`%v3L`;f&c;)YX7zA7rumj8?Y{thr8|64zmhnw
z{qc2QiQihNmCy6U#Abaivga&-yZm}=;C=XbozFO*Qi2?7ndF-!dd1kt1Q&<{Y=Ipg
zYIN2&^fZ;E;aDa$$DiqMj?0U0ftsq*wHUt?&~M-tad2SUpeqA647BvJ*Zx_xaB_5H
z0xDkgFWQZK=n^7b>br?%zwKdn_u(QRe8iP^qlA+{<AnJSu^4u#UMA6KXs7{~a3Okw
zucV>oKvK+A4R|Ov-(=}4WI+KjHfgGl&WxB>upvHo)>mgx9i}dK(o7%JXnZ1`6M!}T
ztV7>eX88W=NUauP<&|QMQ3R5*5%*1rNnpu6xRZ{(W%gn0vdDWPwKeh?3exh4V#PcY
zxv$upHy}xx<ei0ZAz~ae!-Xh8l!Er<L3&sVAmBf(ctgVmxaF>>Dgx~Pbr`_-UZ5jj
zVC(;6sqP7`FQ8>@r_tEJ^QCdku*`@2d6EZu59p%#I>#+LqY~V=)wPyn1Fp$TJyi70
zR0wwE_hT5t*CIcUCCHaPQ=|Ys`B0_+v>uL9r}FZnUb`f&FGc%K=);C+ijCm^P-h_1
z3ZJfqfTwVyg#$6Fj_l(UA%Ta&xHDVaP~X&NK2$#xG0dwU$FggpxK*%>N0iNGtGA9y
z-&s(S$)Xw3T~uRQP&=zU4C5Vqio<G}FynRJHDG>CZy^6=a0@0%6IoT;_bZeF1F4nn
zBBC=f)$Lt2Ya!YqgIlRyk_~6`#KnZ>d#yE6Pnsl$v!iR}`@f`uts{(n(kg2SC$yC4
z(w4}F3w1NJh;pRZucF~c{_?n_$de-#AQ$?|QPo^hzmBvVzNSF3nUY2D9l9PUgFVf@
z@VroVhAI-;D(M}`$40UiWHjjDc?}6-PLyiQk&JqcfCJWq_|a7H*!I%xsJPAN=n5|p
zA1j3rFU5TE(zOVKp~^JX#bJfUdkY{4Dvk6)fY)J|wQ@-KS8RqNUWirGj0rub<rCXK
zuN$Ao<06wLGSI9s_CJ-KOIXu}WWxT$MyWT%FtEcT&-P1E<sR7=0f$Ec-U35^HZz0^
zH0wCqi;+rlnoc~pC5fNU{I`lDH>@%fQUP0Zxvyd-L&@u>g66-txa9CBsSHb^>kKAp
zIO1_j<hQz}{Tj&`!ZBi5<GG1^5;oDD=PD1mD@Rw<(znJ#3I_zM%Ra5|8vUIXz12Lj
z=4~ZC(GtdnMqE%Bi9|bGktX^~R6ZQCQ>Abll<oPFH)*pqPL81vLsuo{7OF0Itog+Y
z{{y1!{Uwy)%@i?*Jn39WxN0cR_<+kbmm)AEhr749?=$G$a7G@iHX=#~365O23tZg?
z@!0HMd492$XxSeQ;Mi-l{loB!fuo<{q=LNW#J@Y5iP%ai_S;$mt!Wpp>C2laVgSW+
z#<oIg>#H+NF^Mz?aVmeQI2{o7_2KeHkL80cCnb{|A&`c}B^kJ;v2L-G!IN#h*nwM2
z`PCFy@-9tUP>O?41C`N`zM>%!+1AJ8_(K%!&{9^pjEI9HRJt&^$gWORF?uZfoq_@D
zu0<tx@WkrG?F-|ll1#4N8@{^ofJ=T~lRKQ5&2BirgvrlmTs!jDij$0z(8JkQ-tNap
zn>|D7lsem2zuh0*>fYyT_uVa)J|!kx?LcN2!XQt#@g)sZSZEDYh@jZ&ScbMvAn{W0
z*8DIf4bQHg1D?dx9=ld-q_)=R9vfeeO&^jQ%=MpyE35NAvaLTTN!`rl|BKp2`J41g
z0NdII6J?V@yrGpKJKo1&@}OF)Ln%fG&%G#5LCxA15DPAeg9I(`WlZF6AoSeB^dIsY
zW|=`7+}r4lT32=EtCzvRfTw@=18e@9$aXFnM<s;;AqUj3Y7^PA_^?6ck%O^lw9r>6
zO={uV&ydN^dZ>3S$=B_>U_zHF_bUfbQ!vH)%YKnueLa!f=8{LK43)I?QoWtp=ga|i
zO9Ya5*AhqVQyN$a_F@*^c1%qTNe6Mi8|?=BJszp&Lxs@%#`5Ostr&0OBAQL%Sz=zA
z9etOLVscm2Lh}OaL06d`s)z6o!otkWo4WHuF83TK0PI+n*Tr!Lwd`^G_L`~U#dWFb
zIFh<^>AA%vkmRN(*ZaNjVs|HJkv?LpxjA<E!3RvxAg*xeg9?iCyPYnem|bXoPt2cO
zaOU4#*uyq^nVUIjv@@Zo(MwTSfOc&2V(i*oP?@o95iP*o&=B&c&Fo<m%hrZ%bB;g6
z!uf$CwJDHyOYQ=!u;cAU{+`+8AotJ72ccc{jqzX$q^7uvIzTTB(AkIui+9EXT8Ej@
zV71oTR~@40JKhEhQC%+2i60jfTTULa8`Qfovw@9)u<aM1$`&ylay*<H=&@`S|48#F
zrbXiAvWt^$weJM4lYS%{JeFgSG^oa!>~6yOZWTuxw0>Gx9!*DFC>bsZnSOk_dr*#?
zL3SzAiObQa6$_#-3yjTQ=kLm;dQvU0IJZl#>WCc?fW<^I^r;&tDDQ<}{tCB4@_{dN
zh5&pe=4SQ2tcus8HcDwV9q%TEoJ}PAgLt4wSAA7;HDJE^p)Y=taI0<5H<!YlbRJUs
zSC>hCH~u%KZtib{!S%o#0Y?`JqsRnn<?J4zQ*}^hhucz1qGsPtZ;SWLV&?aE2TG@z
zrTD=!6GBT<lgS3(p-V${cY(&bnx+6h9~?(^&V|+8M3(q|ALzFC7K;;s$EOb%oS?Pv
zA>ab)728|+nCvj9Rds?@=0pFZ@bvHvwY{TZVr#;HPx3kAi4wfq+00oo>>}j!_jeL8
z#FgfPC+XC3Mf!$T1(O7bLq)VaR(#smf4@T3d7IQ43Oz&_av->1E4||jK9g$y(14yi
zzFN7jI#_YvT#Yu3(SoxJ4F~B#UfxKU0`-%7U|#Xt9`zK^{CzC_L;<-JErTX1KH^ht
z4@b@`t-XCNm};Z4DSef5SAqi*YWnMS-TcYu;BA;>bR*M_2Fgt<g#iJ!-_(=oXDQ1b
zym$a}SsO#N<}q<M^rgF;39)fpkJP}of97g_V+{O?`ms)nM%Q`)8AUlfhAY$X!`|8&
z21Ehqtl%wSZ<>=x<KELE&vUrL1w5eFJ=|I>K~u_IpwtQ%kKQ3Hw-0s&gKIF6PNq-J
zdBq9EK8gh|xZY~_^AGW9nevWZ3ZFmb*GKv&Fxs~=v&vm5i`F%4t8yuvY4}wf3%up`
z-Tz#vy!)`ZeO{I|BSgPyOqTNl9`($lNc)1;R;{~WpZ)V!9qH@$0ZAEW4Ex$3kb#J&
z9C>}FdOhA;<LFzi*d1qfU-vQ{3j4CB4Bv^2M$;D+YuGtD7M$!*VHd@fO6|AfTZgtl
z0PUBbB)k<?DrFbUAS|iaL9<@#^AGKJ6N3fZ=sH%L!U&5Ikc#(aK-Xy5i4&L2iKv&O
zbnY1ayxAEjF2q$=O$=~*I8+VWeD`OW`pqEy7vi>zFM=X{6OvYvsb0jX-~E?UT-b`_
z^wD8ljC2Br0Ye*8SH_!^{65$S_^C5cl)@#=JsUzF5Q%h<L$c=T{5RIA$rpBsI>R|=
z{!8~Il6-1v0pS7-$pQ&D>8w~mN4LU@u1Hm+bi%%h&W6Q=R-RB6zWrQ&17%Fht~mo`
zVcO<XBw6|<3`7CQqm6=;FAA|?*~t;;LvP7m%5b}WB>vP?yr}LwKFUY)mj94BLOS+4
zT)r;eFw&0)>n2XX30;oenoAL3hfDFK2;)h?a%?j7XSD4g`AtxjUz|Y&BcQ9+Oxz&}
z-Boj>RptOY;!EntpEh0WDkP|<j3|D-9UE{vCHXI1r#_&{2GG#=qMVD4B$A+5FZJP;
zlV7^VgLw|IUb{em<^ztvQ%6lH=MN3IJH>)yty#gdIRKYz&$l_zRbTb3Pbp_p^^neT
z_D#isXimG96M{kJ>yTc^=KebhycW!0e||DEOULflMmm2;rto+VC`*6#@kLMc9Xpej
zJ7O<2fD;U@1=Jbwg88@N?e-P7lXmwlJ}i`S(^bRkW4ufFjGTMHzq2>$&?Xrwh=Q7!
zn^K(_-OB1DYLs|-4G>$FlXMSM?>}{Tx+BPVybnI>noP1f$?z!{)gY6LM8CLdN2l#-
z7iOOGr)S#y>I7jE`b_EKFlX_l2vGTGH0+|_1L0pZUT9+4Sv%OwsM={Z_-*of7j6rY
zIp-c}Fw%~YraP2yl*zTI{J^0YGBEVqku<3J^%qC6Ma3X_Ox=QISF;4ofA6oy-$g7V
zC2WgC5v_vAA~vqs92(BQShg~jq)&RGwODy7@@r+u6uaMr^0?s{hNSaVyqk6H15;i)
zi$nJH?Y&5~`guys@jYXz3iBt0RNhG?D^zgCjcu~0@pS{($^==8$roP>*d%?SW%7XC
zG|S$BK3;t1g}39Gd!A^1{@SmnW?Wa{*cDGhzI@SoqZXo-sCs&$YC+lXo`L5{Nk(mW
z)=Y^Y$xk6o{Zj|AT9yrWRA*7kI_N@t9c{|)DtbX%ETO%4VKlZ;Pmd+{&qgBfr_6+A
zj$F>0&D9!x*b6KAb_xQs*%|lYkSRLU+FeN`D{7vZCplOsfaPRJbpXN&sTyzZljNDd
ztduL-_4#PueNf$m!U7gS-O{P9*)WwKP{u)DZFrh24|~<G?)H7{&G=%@KoJN8lD;S9
z{@zP`?`b~odmKeSb+MtAaI&Hwm9g0Gv_0Ggz|WGO(SWE{@G8Xn;$FEIA98<>K}=Pj
zs<I#oG(c)?Ew7l;5*MI~tR>0^7FB(0fnkY|xs3W}^ZUk_1Zi4#x?%yl$UzA+Q<##l
zv+d&NA>ejsv~*#BG>NHxkkKwGcT`=KQo)RsQ&%o%1uZ56!2vHCy8r?OS1|MEilqZR
zu}iV3maG}a#Xcurs#KC#BsuvA2Iu#1Tl^RDwk}%}D(M*bqEjT;F_Ds(T1HDTGW<X-
zdjV9FIjNta=jCW%+?fuT2Md8`zd_l};$7hpMd36($eUE5t@K;%K~W2*`-hI{76D!s
zCNdAxaU@{f0-|Mi`B}v#n3{Ocfk$=rT6d9o=~{`K6(WP%qf3{ESb?|E2An3sCq;Sh
zqgL;lA5w(K0wz7sd!bYxbc7;SRZv3{Uo0f7K+w?+)Mf4F*V{`v^<<r1om$q#oEP~w
z(TL}qL!)@NQLC!o7>*BPC0M<Zq#P?T8+}c<?U9gp0m^Y<cg@qej}qRSzwzH3nO0u}
z&Iqm^g_Sd$yi6|v>&{t0E5x^Qcw)UB3L=!eof}JD)PJLJt+&LtY7-9Ny#F%!$N`RG
zZ=drBwVN;dOLF)Pdot0^33YH2*hF#j+}-NrXTzdcztETErpxSQrafP;`@~i}(*yPq
zxanRuJ?;GmM-oame^O<~{F@=CEsvqehr!cnT{|8sxs6Z^Fh&g_$54<&E&WJNz1#}u
z+Vvh^62nwe@q37tc^o!r0hy&3F_5~R@HP5}_AEd)zBF#tv2QA~qEZJq1(n?2(htPU
z0Ifq5=#o&Q`?VdBPw)_MHx6*y$zn`?-njAe@%8u_%pbiMv1IrUEr0jJEw^pNu($Zl
z{eIabcYc%#JEO`se}KtEwI<Ug8aP!HxJ17$ZF71$po~zNubL#&GU^6uSR{|P^y^BO
zY2hb`dys4SHsbHBRpd9LuD~AwZbt&RtHoQ!T`T=pRDx7r7JM)OF?7x`+*=T*&>`T*
z@~TqS^S|5*u-j2CM<RL4e`u}L_v|v&ZM&Bs)rac9<ARJo;jHC_-UmwGy|9a@0`k3I
zRtGKvUJpE<U1)||P0r&(-L=-4cjDgfn*@Ab1&8_7gaXG)6%LR2uDKAS&!6^0d3yIa
zctBK@V)~y0SFn?8e1^TMQXjARC+j|MmHoVs{4stPa~8OIxB8gjV4wL9jrbqhkD|J8
zi+XA9RFA~%YeTg4{eNgvW$L;6S!J8|ZMPSUKG@B%bfRqP;m*AmaDbeSBoxGRdL*;;
zeh<BuUD$05O=I)4>rkZ*=eUVn9(VV(mt7JkH*k4k@%M_pmxS;eP@+}>#!K9+O0@P<
z)<^RB`!j<!OOc3Y2%EC-rh>NtHT*fM*DTB&B$Tp~oCnp>$>ZmN<CJRz?0dGa`AZ)m
zj=#w$^+_iE{hf{clS8gN-_1W=#LRT~`U-vF%oKoeRsKWk7-BG}Y@ELTg!NFSyxD=D
z`|h*bfvJ<NB%U7zViz^b9OFn5@>N!Tf=s;0XS&1c-aEW=&4f<PVZOC@$!P?`;+=lr
zlm$Pe?d`=aeJE9EZA_th%m=&b+MbzPN*cGmN9nV2a_!0T3sCUTsceqRyD_=z>3qM>
z$AVEcan#^Bv8f8)IhFkvm=<PYFLDglERH=R`#M1`t^V(G_Dx-J&$^Y}C$8;iskCTI
zwT$+MK+8%;3)>w)XVv(l7fvs4VCiUi&JOUaVlGuU9$)*Evv<!Iuq-e24{a9Fz|^o`
zQnQ3I^$}W}XbSaII?h=ucv!~WlY5*YdgLym$9d!uWat!$07xS2l9w>c6aJ}FffxzL
z(1o*y&gi@8+<$1D+1IuI&_2c%0OooKAZOQdJWgFJG*Va<mkB(G%-J~SR2HoH?v|;&
z@z=?>czMXf{<wGTV_@0w<Y{0FRonx#RPgsdG#WUuO)A@kHvOG|U14@FV<o@NnW>Mk
z-S)0xnbxKXN;GoEu)~B?I(*z%{=yRYF2TnaM8t2Gsu&)z+tziK-OL{H!T{-a<=q%T
zQJcU0sl4CB?uhAdoz9v3q~<^p+IA&{WR?Z+<8oCHwb+sGtwA1NWEr`~c<p!whH^Pt
zL{@bwoXLVDcJsTZ!<kgGhd=B;jCb5~V9OUp{8FICVL_w27@yPg3LE`K!+XnTeOgf@
zX?+x~?zP?ROaNY>Fng8E@G7UL;az>PI-DIc#$bn~SZgaALX`oj`iJ)2i#hbTpx1;k
zlzF^nmP)W46G4g7Ca`q<4^3_=fd1NQ`QyYZo|^jIg>&Iu)4Dmq&>Gq&Jscd*<R@Qz
z7#@Q}-qc;fu8m<{@Q&)elw)*vyTWp`9w0w@*4)%3wD!&!#%l0ZDW}l7m0~!XY|YpN
zByGq=o_?Ka$U+(%+2mZbG_QD)7BM5gRzd+YsjS;HEi$^+Q{gh<6!bwIl2J0!@VtqR
zGswSDkVZKQ7dZp)+I-`QrWk2Op}a~V$l+D4;5q=(b>QzFP*%jHWvA_Jp)MxW@b8J4
zB)oM3W3DMBsNkUTVZ&@M29)YiBv7k$*&_p$jrv&l6FF4_D;cbBg=q~c-^r<^F5sdV
z019%0l#K5S8$cVi%mYz=ikC0iN`H+$qbWse)3D3Bquh%`TQM&%9(H0sSHoiPWKJLf
zIWRi&eQA?6cv{jrF{#EjAMWErqnVNL-dUlIOuq=QI)tQB2R(IGM&gEI51uY!?SLWj
zriitYZJg|YnupG)Ea$?4N@f`@nFKk0vH=xB5#YxRXLRScBy1ihXEQUTp$b^lcHLE3
z4vtigXirbGg1jkMgz~c52rB@M7p@r?5i|q0E7iGxIiHa2Q(9r@fD&X^Ru#tQPrX+|
zs0ondWWPu;x)rN!LxE?55};cvaU~h@>YS4sKzP+;Qk8^2pubvc#zLhP*_^PwOO>2T
z`^rM?@V7F*(y|R~;};ueL<G1_Jo4`fV9kHG&E|8~rc$4%6@}1X8VWKs4}Un)N51D(
zRs17}FFMvxcs0b4RdXE4cq%{CC#LZmGL<M~DZmyf)j?VgUeKuGWZ;&hg1`mzb?bbp
zL|k?MxGKD8T5;Ak-4Z^EOus!A;zxy?7tYO~L`e1Mvi7Z2#tp2n!kJ5%n%>dq+Qa5U
zF~ptaH+$EGUmwOZCuV$jWmSFXcJ!7J@9<4HBQbmOxjZ_pO;p2KjVq<=+&1a2uVKzX
zny~Bzoru?R0wfA?O*%df&9fB0)R*G8d^OUAhdz#D?S4q*6R^+2w5oYQ=!9+i9`&`C
z1v>w$<0gjLJ1w1mvHiom3%I6O#%95&Cn7d^WXQWoXe}h#=_IrE=WH6r2)MH$^^cvp
zv*<>2HaPvi{N#39c%HpJX?dxE4_75r#rI4PmHlpET02*X=5teHEbEzIPk&F;=E5QJ
z+-Y=!%f(<g^G&}AkL|d<K&e*k-0v;2?YYO?#Fq>R=4wr!MS6h{7j1^VBmZGKh0hjT
zYSgLmU!2(gkEOE!YUBT+J<^tzBBi*uxVJ!XD^T1Wg0(<_1a}CMQlPkd&=TC;rG?@I
zcm3h+u6_G|@6EuFFgu%MlkDE_z2|(66ecxl7-|t@iiSOV*V4^itM$6^0wu>*iAC3J
z<13ihkBLwUCz|`c6~Gu3%@-viw3W@wFty(o!H_wX3FvLFSV2>xwHmRqGS>NqlA3>}
zia7$t^S=Lw2;XBpFcu@zc_n^Z(%pDBMJ|4to0>>mIKWcCmL#gfO4;&~IORW`$!L2P
z8EmoL%__ya^z$@t{H6LFIj$S(;@TRLTdgu<e+(|PjmcP+Ipg?~7cfMrIZx$>iShDV
zZ&H`*U5%RO<RGI?-UCrPNq>NpD|aL}GmS+2*EF9Y2CWmx09m`mqzLtIjE~8yvLy_s
zy_O*-3cSPiK^>5g#J5of!+7vP@gCIQFZgOK;I3G8<}SO_bsW4kyPq2X6W1d93cT#u
zb#bDHI@!0iLi-UhR;B}p+^Te8eiCLI7tBRwt}H0~`~XtA;CDp?t3uP1eoAEdIZM7F
zM#9KLcz}Tc5e~KQN|>u|+mI3X586FdJRDeo3r4`ZqC*f>v~y2nlX+Tw<~9t#W!`sy
z01iH|YbU;09ztIuDA;j=duvP{etGC@jl9!+`**^yCa(M~<XqaNmkeZA)$=l&M8c!a
z8+YZq=m#3SiuENDDmN1DB`6pEIKwCSaq*Ba{7)pGJCtI%t)x%*F<9*f3V+@8pfj#l
zW-lI2UmwY31+jiO^71>@QglSRoQT2jhub(?rB8mKs=oeL@~Ade$N3FO3-}qLr_-|t
zL4yPhGAFb@Z3ogzlq{~tqov!|RMjRmE<=wg;GwnFvR4UZH&1d&a-v%D@BRN)(}V<j
z6CRK<wQvgjrjeL-oY(tVr<5l~Zkzuac&)<4PjwT6ryXlQDcErx?%6PZhjmU*kLk_Y
zhExL8T}kw$kKMYRxy1Z3{p2zFA?L-0%Tu3YM;^2AagOOs%q@g-;i-MSR?QzM#72*%
zLHE8S^#9N}`6%xVztL)JiTEW}ci`9J(xLHH(A>Z#td>d6RSFeuI0(ws)=gB*^A@C2
z=NTZTaf%U7o+!mZ&W9WZ1}VIcO}Ek68tS`K-ot~V=g>7sf9-Vm-Feeaz^5At`li-X
z_nMNXXXdKoB$Ma|*HNFYDi%k+1M8<?AIX#8&g5khgZC3}JIUThspovY7ry;wYBd<6
zm(jtxC-|qL%ewSI;y)XUwo~QTh0xCq4dXR|G|{mAK2!N%&auV{#T%v5nYH8~wJ1ME
zt~!pSY5q5VsMAZD`;U+E&PvC=u4=qj|6BS^b<rZT?zGi3M!RQJk=G-9{86p^zf1;g
z_9kb)!?KynqWi?cy?SApplM{Wa?*5tRc-a)d+d-9o*{+*a9}g%hZR1GHy$1p(1vD@
zE)_Z(!?eNFUvY*dOJ{NnrF{q4-}C%&Yh8!MN(xS`4&6@EdS=wQI=pSfW73j*IXQJj
z*k_mAMLpQMm!|x88|?%I`-nHhIZmDupVx}BodhU+2(DOx<_5b?^R6>ZyC2Ct)Wjpp
zg<}-D0&i43NhL}jG!HIDFK!CL%)=ZbsgvD`T2}}}-(ej+k?p*`j03Wa57vNYT~PSy
z>a`!0r?s!<Zby3E=6#ePQ{?Z<iY9$*;|>y^@E&bnHBDf%MC6CX8<$mw8{*u<hphs5
z=g7qA*@M_g9H4}=>RTCxo(=hrYBXxy1<|aeB=HpQ!9s2_1CBgtJHs!=6`oww{a6({
zJpMe9XdNPpo}6cr?TONwE=%-<B?fP*P1ul2nLye1T2F)4rAwSrEkBy}64sLcpgvvD
zBJ<QcYQ8Wsm@6ewR6&ojx0VM(7%#~3PimV#bf*i>pItdSoSAOGqFPS%{t_@)C&8rR
z;K7OwPfg_(4;rTi5)3di{43WMjels>GxTLj?Ys0(JC7<BIA-O7o}9^B&a2kUDlVJ`
zJEkYKq9$tiW*tYn)4-FlC|p^yiU{qUw+p_n*WG`JYs8EO3q*s*WH__dARM^l5EkY$
zIY;22egV?6AHtswYw2$p<$y)RKeULK+ZqyqAEO`R8rwQ!8cVJs-QeCr%PN8UvJyAg
z|Ip~G#YIoO{-LF${X<)6{D*dffSitmM;oiE-ET=eF*Tm7rIS$@UGF^sBLs`Dx|2Y;
z`Mu&yxMp3AIO{e{>6ZCM3qL|s9k%D3b-(?<p3;}Q_?&r9bMWKJ`QHC~fP8VzQ`Y?=
zC7_N5-o1}E1%Ktrksr1sI-5{WKN19w*c`Sb4h|$9?oFKqMndb>XEf5D?tqo47Jew-
z$Sv^9p7??rDOek}^d<pWGu?eGuz-2>qISt44>)HOJ}pj*EcXK^7hN%8*2u4{aA1Q{
zw@B^F*7&D8<h`B-ds7ADFuctGbhv1afmo4hFHyVITa2##hxW~MUWzvGE^#<~ZH^&X
zuH$`RU=nx7#CZGBY42hDNm|z(P6L?f*JGYH9W5J6uSj&!;qA=oso5-6cEP4ET1EU&
zqX<^$Uk(}h{Tbs&wVc75c*?-+lM99f)5Q5>^YT-x2)AhBn_%@*;>)hkicN?-p6sX^
zW(9OxW^-GwF(K=2XJuXc$!HrO5Btgp%CaZ2({CFyw#Wo{Im1u@w6Z?>36yC&GLp*t
z_yB)m%RWcT7VPFv{&EGe4X3f_IG<L#2a4@=xp`(R8rQ7rE43%Kl5H`T?J}y^z2rqr
zUCz)=G0XU;Cy9S(#B!<cw}=1#Z5@9TEG?~GJ5X83y7`S+GJ|fv?_aeR^VZw=qrw08
zSh-4rzz2_WllE%tA6id^4AQt=AW^N%x;sazk+=PLJnO@?DqBi3yNAmglV!~4+QENl
za9WAsGCBUD_kW#-eZ}3M^6USMbKHtZKA`mC|B{5pVvzK%O380s4a2(%9K&XeoL?I^
z<6rml6%m&Wc|wlA!EZ>3hX%`+`rxk5;AK|*9xeiR{bWxd7f8{E=els><i2CzkeF57
zH(*H^Auc5XJ1CD@3D-QiOyq3joEoVB=F67PdF*mzujoZ-6%kSwFFkQ$$02ZViXyB|
z0K%F2h~sIvjuqgef|M!l_6s3L|02I0c58l>@QA#iRC!^Fzw@?is0QN|7X5e!--WM-
z{sf38X~tPLPvIRm@pdw)o&ab2nlwF)bMJIJJ^F7?F!5vCf?=+2`G@GL#`(8h269Ln
zcR<^I5?eZCq92wdRFte+<&co>YZI~PBD-$tG3=u>u<JT{<g*~;NvXnJW=DI2B|g=C
z{O7;*T`k6;-D?K=)8efotH<=2F8Vw>;n9=lpIm_^O%Tx2$jY-Q&vP!=vYUeNS<?El
z<_8_Hm!+~phJ?kWY%MW=;DS{&Nkk*G2fYY0$uvhZVS}#U_{uuZ8~A7wS@!Z9S|@v6
z;8Mn%SW|Q>6$_omP7<grvS)vbh!WP8B@5X_h+BpeH6uys((xT_6w2tuz86L!U>eR-
zp4FLq0&Atd(UnXoc9hv5;zRnyXs?UB6~!+JFel}+O==m^Z_G670h-%UP|=Tvs_JJS
zWyn!nUqQBp5`Fn)Rm)A?)(~OQxSZ2C-;$DlXo27ZS^St;i33Ecikmo`ATmFHTr}S$
z6r4e&yyQtd1*Lf1{odV(HqY$Vj5lCDa6~GThcIm8<>&{+Gzo$69H7VlS{WlwMWmWz
zffE#vU0iJx^bD^~;pjEa2gN{|jWN<+V>klNmhOH9VV|5doMfHG15vjM4cG$M!AuFX
zU=OUf>XVN>z}yGNTeEqOOX|yVZFYeAPqE$KVO+raM@IS>q0*DrCNp5L2*4>T8Z~0|
zhJ*T4<+J%;u)X=Md6ef}xxugq@t_EILWwvQ0sAw)a44(>5>-<GX~)#<S33KPV+NWf
zc4_;|-^656O(?jf(bKc5Zb<?Yh$Nqo&{_!<9yGp6Bk`I#oz_Lg+&>=3-2Hr{Mw4K&
z6-E*5@!wT3k5Uio@ZVDn^Xc#m&~9GiZ-sYy(h%s&XV<0jlO*N&LlNkbBT<EuuApBH
zk#dSro*&UMDr9*h#*KKgf9H|-oRx5yQ-PCjtF(GwtTzywF%7(hE@e*piRg6CPM3Kx
z%!U@oMO1d{CiQE-eeNnn$}Cn~yT_v$XO)FxRM8ZGBYII*0(piNuWj*_@(UhY=?t2q
zV9vJk+8(vW6tNz937hXHao_z6QueHr35zBpDh<cHB-sz<I_2ho)vlmr*0Wg65p}3-
zZ^7UQO^DX~iOXE%cHH2RYnbhQS^5o2s7M<~d0RdilU>wPO%E{Szl%qEVH34DIFYBE
zEFxC4+{<bEHg0w=uhr_lmrs3|{Cg?9j@_dW)kpp(XG{H$p%R4vPx%pBnlk#v)Qi;g
zGbo8AbAVdPVZl!Y1BCUTDA;sR&B1NLxjx%LowI=DLe$ShAP044FQ>I7fhdH~;JV5b
z!{l3Eh1HGT>dRJXSj@<^=Yxv3!(aSBuzPet-Cvu!dJFUj8j({IU!U;|;jr`uLB~ey
zvjPqo7A~FZn$?(rMa$BK!D>lEilk&a;(qnUVvIj}zbd3UqnOgy{kElXJ41dG^=BAn
z)@d%k%jSYY=+`&*RgSz=2{cDH>-4X!Am!eao^;LoX}s(-=*77CSw~u1d%k$2#AMw}
zLpXQXT3{6LKZO5*v8<yfGUYIe4&Tyhax|SH!_EWGC4RiQjQtO|2T7aZfZ2Rk9RE-x
zWa!Lp_LaCq2zl+I&0t$^Q!?rLdWkQ>!@5t-?4u2ieoe=-^<Al0BJMZP-2?(9s`RxA
zy0x;`B$${Ii6RG@EXA{0e`PLNYo70GZzNRx-ql-CYjiogC@<;q!)=-3(qvRWczmvu
z6j^N*mZvH@ZbfQGZn+d-IQE%-v#A2PdBa{f2cf$}Qicq3?;bxwvx4B3$6*q0JQh9S
zs)pXc_3Q(w+9nd-{86Ete5=uoR7l0(mFC~+;vzPlmb0ew+!_oI!fx{sA@}z%y<#8j
z8-Y{&RnEnaHv_+AY#imown)z3nF&YEl8OJ-i#ZZvhLo3z!Q8$SyfWsk78FovL>ZI^
z_|ruoLQrI@jStYxwz4JQ+9lhN1T?^%`5iWJAtTh~5KiI^>3CURo1EZ3PpnzbK~0H<
zzv6Ap&z;%MJ?YgOu;&x6vdju9Qe8{s9-a$Oco?giR%o;(Ok8+8L`jJ4^7Q7p6-b@5
zwG_6Vit^yEk<^b5!7=2C&7U~C%LS<QH0aAk)4n}=P*5FcJ2}Cz9iEnK&gIwIogt-r
zSzZags_b=7D<^I>N7X0A%2=l+%Ss=1{AE<d-!09TFVV}6yROgWRO|V`+gNR%bhsR<
zoYI$DlP+y8n*J^<EE-oD>ThI3OTOhj2aMe>T2QpVO@v#Fr|+Jhvu=<E_H?8NK?6Go
zTHW5wH*XHTg#TR3#TL)hEHlSd)0oV)oUE|tn!QFq82+J+rc4fKdc#dqGrnp3S?+&W
zxuq@x)YNajmlc~p5SrKbx=8HUOKmO}ZMDD$M(FI6kJFczdmM-M4wO4DwY7}C9ca*D
zQaszP#3(%OeHO1p*8iAOIhIV<a-V#=cmZm-QG#@LKeZHplsM~oZ3B{sDD8>a|A&TW
z7kZBsz}VU=*D=NT^(L=5=ZXBmW<;QHo2bQqhpp|$z6&y5$>w_DtZJ9_lugM5q=tP1
zS9$l<NZ&UH=<Z@<EJ!5sn1;g%Ekgd#hA>q}R~z)=7W*G=(#77+WY}MWUeJPxUvdRF
zsVpI_)K5u6`GQzW_huUQV4+9(34Y2Oeu>}f>-*-Sh_EJEMz*K(mgE(NCA+~9x_#|l
zVslkizI0~fBZH&xAaGM%l*JM)j?jyyq4?$61BcdXy0qBY3D}XNZddK?YLmiuO^wE{
zD$jgvU}w`JL5nF%vv)J%0|VV;UxPr$qw|qEe16CV^RV%W!`A6@BOhdp7&NgI--&5u
zmD*<P;*gSn6E#ieFNbysGC5cua;id73KS*U6&OP`!woI^?c2W7Y}eVVl|h#ALgaGS
zwsmP)vuHWQcLcYWt1hG8_S#TF?!>k3hlLUUEzR{?G63;s3T<QCtCfTm5Z!U=7NCTV
z<@<YXq<Ssz7F6A*U}<m2+-@`B^9f!FbQ?^&MBLRX7S+}$V?62Ji1(Wn%w~JgW_S&U
z=Pum@>s5_%L~*$(g_O~WIZjln05yokTR8TCAM3t*^LA7nFEB>v6Hu|3;7D*Kseiq}
zB$k?(_4M2*QD^(5D>zF}hDAzuqTuq;Rq%b#KQ!^gQBN1sWXZ;KVc!DPb~U!M*{me%
zyMU<S+)cx(t8xbhe}qxIvE7y6o~BlKM7@<##g0N=t;f7Y2W0$M+?w%-_W@7j<BqRQ
zI+hswuTj3j1L<8vSZeJ;O|aGj2Rzhwuf72}n5&HaF>YGzQZp*h`+mF~L8T8Hk9Z8S
zs*qcPBlfTFEHsptdc~{<J&;<XfbW}^DW)pt&w#v&8;tFs{=-sJ`R6##%kt?N0)u>6
z{<4?nH6Q%tnoukKTPE^LPQ9Dje|{~3qLy<fcvzV{q)5V~Lq`-9d02(7k&`8_WthZv
zt=-Xb!dPD#A&e*ISpZX2-Xd#)Itg`iWKWt>H~QB<C>EF%QNrbGLi>x{MW|R$vZ#i$
zm)r)R^2oOuwU`Pyv}d@lii5Lpv4R@tmUdOK=n<3QvW@WeKhLe@au)J&BAbJlXE!tX
z5qmD_K$0h8-caKl!S;;5v7n0hl*?Jsb@8|jzZ1M<kXCZM1kpoloZb<P5eVj-0QjTt
zCBWn1pZ$dr!{Kbh@kWh)bjBT8+6-+i@gljRciQK2l^GnL8{CB}L4%U#&uNFV;|*(6
z=`?3A%0F*hZttU*<V7`5b3X>nTNf-Jnl~p-$zI}@St)q9a7hoyi`8g*@UfUL2d-!p
zXOa-yXuRLp82`bo5d}ktCnQ8RDFo1@t}mxklx&P26(u_xy{0TQ?~yEYX6--cDKM(c
z4c!!h7!HnwjEHa>S%39EU@w}ocvX-<eZn4#71l)KZb<9YegG&@vKhwWBc`p818Oo-
z6v=Kt;-U(Rt$X1$zLr8G?5R%g9EfH-hN5rfjNGEgfwYx$#XV1^QqINon+{=Q<EVbD
zf_$o$nk$s^A)tbfZKXmw)&Omyfr2U`lY(h)S$1j94Z%CQ)l@nWKx?Ei1aO^xjop6F
z#AEfqoUVLUjTfo;x`=lK#7f>u&wtjQFp_7<d0faRy!2~d$3-AGj_ZqA1IQbc^r_n=
zU5{`3{f>+A$j>XUkdcW1O|RRCJZzOLl?T2XIQ3dDsOrci!rQVv&LOCe<Du)#e|iEZ
zz*kD`r=UR}CPsTvvhjF(r{A|8_Or!(P<hFYUq|PnNTB}bd6k;;v^4>L`r31*NcU>1
zXd}HrSQ$bVweX_-{-&nbAZ6Q)p3FY+M7+ZhENha=cTG<1rH%B;N^v5=CgQ~dQ;H2+
zev$~>Dg-GnsTeNxu>3wYnGrDivZ&~%g@^tkc@VDHQ0tDwDC~f0v8>ne;xFo~VHu4-
z&-+@OhtuGs$h)f-LX;-MWk`JJ69x~WEMA_<on;l$7t3KgLVA{%fMHt7(v?I_M|>d3
zmgUGrpw&~X#zKLaO27`AxW)(L2<8I7ZB_baQo3aEgb|jzi64Sg>P#^Q2t{Ry>G-$k
z6CfxF&}e-+ACF#!Y1PoONdSb;sZW?;rHApg+7i}iW=R63ZKiWpw>L#~g&?`YV}lxd
zEaD>In#T3qihC)>d!lJfvOVuh7kQyU^F%ta;GqUpRgvv+1S~`h(6NJ%n0d<Knb6h&
zXA-`PG8U_|K9o8R3F2Gd?_cQ?!Tj5*&0%)e#Umc=O(P#Gq~)y~z?2`OOyZk`3Ea^Z
zjA^2Z5Acm<(o>^&5&IX0OO4FqQRea7oqQ^1nKMLS#o3HmF46^|3ehPG0(-YZU6TJA
zL>kLIbX%yt+Xy>twig6Q;pNGf8yZ$6axM@~*Az6ZGfa8uHwblG_A?9imzDc|!(rp(
zK(%mqmiYUVqs)Jfz+%a3{uDV2TYa9Orz=t<qk(Q1^n%WaF7sSojwGi3suapy+SOD|
zgO(rQ)d2yK<+)|=iw~Kg{5}&N*qP?m6SPgmodN!c(qK+q$!<_L-&dHE>M-uR-OSvn
zkh1VMBjZ`z5eRTqVdVSHi!yohc^$nnJxgsgJhmZCHlN-&Yr(z#T5qfGq87_!ZYk!^
zaL$Ki+?M%oCfrKq`C8r*MOb9oY`?O98P2fQl-9J7*lx+x#P+2~(Qm9w)s|ZLAu0mO
zhc&u;RJT)*-XAZ|XRTJ|l#XyeaAoG3jhafFP+)KmFOl4Xel7kB<8xxOG(Y<RotJZj
z6=Gy@C!EdOxVzKNCv9&sXaybCcn}ZuVr*|SO!ckTtA3)5WKVbL)G{<$F`)BH<rAJi
z9(ga<So)z(j%<%x;j!f9x)bQw1)*=y5qRV(Ml-#y)$nt*UC#{*bGk#Toxn%<r1-{H
zsWU>JI@rlHZeC^R(2Z&=4*QL)itijSRlbA3ppq^uZ_$6WueBS7eVi(uc$}!Z@S%+O
zB~8k*X1f43GM%SiT~4)SY@R^saBo~ti-g@sS%2o?=|k)GZ8}w2BW?&OWej=ClS^ny
zVuSm}ecmPB*hg@WL#+5HiiX9tqn5UEUOsQ@6Oyrqjh?eflt90xlWL1Mz(5mJ<@=PG
z({^MYmGnvpLLEH+9`h&h9xZ;WTEWisDN1lD!sXAdsW4wZT9)d@-HaY`M|fzTc(dyt
zn&rGgXKQwH{8LkJm&6VmyQC`Dhr%jBK3VaxVU!*GZ2wF`?!yz8CtQ2X&h@mi$U^t`
z#*a_*)C#?MzK2L9bDDT-&?SMPmqI0a1P%@kT=+q@?j|7jfiL=rq29)$^dshQ6P=u?
z4}Oxk9Gb=5jKj(GRsT)mDnrtYu6(Pc18*u?Bcnw`!wHg0+*EmH`*sCewO9$idS_(x
z#x%l6t0*MgL$}#9@s2icr4}#kzeB@S{tnv6>Sm*lsY<LEpDi{OX^*YWcTFQTjeNL+
zYVxGW)x3Y7@%sf}v~~4J)b@t?ee9A5y7X4^749)_W_M-C?e^mCL#4<6dFD}(a%qZ}
z8_IbU-W11AXXpXYpkCu4seeD|96FY(>AjNdDKBelp%v+Fnx>_V*o+3fRWh#F?-<;2
zqc^MyF;|^*z5fKWts#GyMO1KA6e;I3o;vA>Nuj^_ht}(4V){e)>d7^=qPVrbWBbV}
zRE;js6!fhE5f1l_8uxu?Vl_OI^dIf*&cO<*xZ;6s4E@Y>Aq$Wb?9Z@ve^5;@0J4T&
zgW1MPZs<$JNfMh){sQ^(DLx-uUx?l;EH~L^fgRncFyO+3w3QYA&?F?Dxn`Y2m{`$3
ztuAvNa8060Q}1R(nNnQcX+sObTe3eg!tZw`wrH#W!iDc6f1Vtk?ADx@=z^*5`aHFI
z%Uss(PFzy~ex#o5CFSZz8gD;WAp+e@`o#OeBYuZkJINdgIv_mqAKL89<Cxkym=v6M
z6S!u6r>l3>^9xY!xZGgt@pj3*K{h|gQ8V4Z2f1%2;MKqr+s@i!h1Y**sg%%Ar9d4!
zcO;SuOU#Rg45#`@Y$orq;$`?qY{nJ=y}I(2?+|!>!H&9yV>^8{+vAV(R@wLiJ@Zn_
za=4w~Uc&KV4y-q;C$JY~daEIQkWBw%Q1@}c$UJ4?tW4rC@R+;+*MuvGl4i0g=#U-G
zN}S%nZ6-tzB8q*-zB=<;%yf-P!Zygg;yMK7`3#)vEFTuu8=EPtx5s>dI>qdA_oD$M
z%Sq%!=A3`}Fj|eS$#sLUgs~4+*#2}8L_~5(Z-lE2Y!8cbJ!W$G(Ljz%j30X5w>)$5
zH)Y#@qHAo4J^au1o#1*l@m=J-ijVvWsy8T0rM}T1-%)PB8}Sg+?8G<})kYplOeGOf
zVTwOuY_Qn6z}%@h#8B975w{P>``z{+9VQ6_Wa+N^-QIb*u!7EQ3clf%^<LUDcF6dk
zJ$(1+{E=ePd~bLq$jitnB-V`T0L^L!vtr#OwYU%TA*h)v?qh>EOIw<5ngiBBJ$z@n
zc;%orXagwJvsBp;;m!#_)lfpIJ;o5qa;!0C%!Jv8a)U9I5LHqWYTy3n_a<vqJh5-A
zhjpd+<KI=Jt|%be`00!CHB?MfX`Ju%QMsnAy5dWqrRST0GzY@+swNz69mO@h{U2>I
zJF1d;q-YZ)+=87HA0r&=HsRm&)=Wdy9CMjLDHmJu?X1WqI%~b5M8e4#W6vk!H1}I4
zqv`aZqZ!(+c<^0o+Lu>Zt=8?qY2ijw=7eeg>!n$ZhshOuy$_-c;t6|I#dN1WMa&|Y
zEu<hoVLXZ2O89uJxH=%=n7gEqn4P$f;9|#HWLacQefFP=E`435bddDTsgzk_(#EF%
zB{0D*ufU39ou3#R@gJAgXt3>8CL2%}FR9(~edR2-Sfv-ecQR&C5^+jro8*DkSNTrF
zA4+%Ve!c8h`Y$w#B-XL_I*_G|tk(P6mMjM}uz5j%fJqFc?tNb*ei4Ydt58cbLcqBc
zRixRo=&Jr)>aH%wcUb<STqY&HpYt-Cn--8Sa@2`gEC|1HYcdRp!B<A0;-1b-Xr$0D
ziiH5mdhW(>%Ga;dvE1(-*9=c{`^4Bpx5DHXVp7m|RZ)`h^3IUgF;&3yTrVdhs80>J
zAt|du0toSlU#PH{v5>Oz5x)V#fPLR>GL-NuHScjBYd3gLM<t#cm%v>qtOx32Z0Dv|
z9?3~3qsN<amM?3{+Se(b_`d7wC@gLdu0rLLJ=(ZbWMZc?TD3Ex=&vc%m+MGQ70ERJ
zw`Up`IYoEogoKYoEkTl5V56-VLBb8q4ILN@X$^;kMi#6>2K`|0ebMWvDU&eFn3`7a
z@t?(WpiNsmYeIi>cpI1GwfL0&w*`N&dMD|#OAxtst;`R?>uB(9ZA(wRqkxnCZ*&zN
z;tiSv8hbjLDd(D9)0aNd$?`H`vW!^LnTh+s>kJ<p8b;`&(q}qfl`0U-mqH(eAA&a-
zwp!<Jjj`jl;wDS^**|u5#U-?@@Gkp&UGJTKO;<IyvfRq6-DVivCmNt*ImwvTv{hKO
z6lYVP$kHiV1*S?kIZu=W*R-Yp*OkN%S-S=<Jp%TH9+=?8OR+$Mx^2sJ#!~eSSEh8v
zuZyEfT}TUsZWdVY`K(q@|3%J$8jdZnWD}W|Z=&9lbk5Hh0ut8jfg@z~I;bFYrVTf4
zr}}D706U*iOKA%`<M~0V3tdE5+16MqjrHc|dmd*QCP9?U7N#P&(5EzBd=j3?CN>GE
z;hzUTwA~`<EA#EM8}d|hDi1Wmcxs!#LMgQWW^O;AF8J(87lleC-!o%WNR3R?!Oa(+
zh)R^CC08P_UAakx_}-QapX-e!l7?9kEhWa9=>O)^lotuBMF9XZ32C*ILjEu#jSed8
z&@>#Ui^4x6U`6G_4Q7VSi_RM=ZCPa}^_qCqM-_SuvzvVl(u=}D?QfA>KuyT;V&tr>
zL<lVY{OfO?#p&+GL2UY}!7#;XVfl)=H%ClW_FOn+ao!VK@ldm>4d2@7-mIUUJpk@4
z4^8LA_7*6bYrPk0@3I^h4-l<x)zeNdigKJXp%`pDif63(Hhrn1C625M`%EIS5fll-
zwNp`8!5&Oae(ao?p<64z3tf7ASrS^PCm{w<2|6H8{FymI<NOUt?sRv;e>y3#d0&W}
zRDaC2zrH9_f6I5#n+F!XyBv2njOBRGtrE9t{^D1hhiyng7A$*IaeGT}1gZyEyYqZ4
zyLtOXzvCrV*bAYZK}D3FX*J#Q)lo>lO+r&oO=4^HBSUiS^sPgS_+lAr0c9rta|bmi
z3g318ibB~}^h^j)XVb!xR375e1*QK+sz7k7Ch{WqC_<YP6gd@|v$K1}>s{l33ge!{
z_OC#iwv3Ed3o<kKxoX-Lu9{D_&z#Y`(Q(hzmEa|~C=@*ICeKHJv~%jE2dS)>Y7gv|
z)s`hBCzeaV^Qrp0_({$3Qu%_bA{x}H%9igL2MH~g;g!-^>k@eem8V?H_<ig~Nk~r}
z;MLi8C~iMoVX?_~^G{uhHXA5Iy9c~$<}UF`L@N&=uPV4E-AFSv)z>rY;%0aS(2_==
zGh?knr8ZRxzBUwHU-3lu7k(0edoN?#fdTF}OaW*@oSY>oN6_nGwPV)&{Pj@rFCx+V
z#_p-VmZ9}X&6xbG{PeA(jfC)>fMa>z1lq#lEZdWwfS|F}r<F-bvSuJ7G)Lf(yIu$)
z77>W~IPW&FXe7Mv$K4tSk=RUUczAGCN1~I?qRT8S+}*~8dpon69{a^U8kf!Pg6@sk
zR(L$k7DnPEI*L{bUuNc$;bYR2DwI+in#R3i5VnnWabX_hyyyT$hy;!-(MpDOx1Ta@
z-V%I#Ghguftz$=&@RVC)nE7-O-y_`gL=|S>bK92Z2IqXbo*4s+%WA}?hB_PcZ^Xy>
z)s8A=vg;~YuOw2cSoJ?ET5!BVre-d(^%xR%I|Gg+soXbmWW7#1GO(9z$$E89^OYie
zh72!uN&Q8oqd%fgMz1~W5AlvPmfYDFdE_q3z%3WP1#Vcy_)1d$*@53pnwG$4Zqq#<
zI_9~oeU$7+n*G$C@TDs<r+fdhSvGXn?NCLH+<4EBy4dlh=f9dn;`cq`!}i_e?NaGZ
z@nUdv^;CX*KX(wkAImcHGKKnh)Hk=%jp%4>(YCx_&#Hjhu6E8O3N8yc9!`tR3_ejx
zhpg8T=M_Tqgk9oRS_3bf9&J82im}*U?W*!dgN*-YdsK>5sfT>Yi3sCiHR2!^tNJ^r
zZlSgQPQGAt%=sHNd6m-;2m9OlT%nL@mVzOd)uaf(cq$Z?so@OaQG@ZY^c4(I{Q`7S
zslNt?+)07#wm1Dk^KBvs-?|cI=fw|sHlmgDJ%X6jXva!gh&mhZszo0q+;p#DwEbfR
zn>Am}KiB$0W*QS%FDmmhwta^@FD?pwBW21X)&s()b6N!WBnY7M9RdwF|Ijp5%&b5G
z7yv{XOkbQq_6N+?ITfx%*#<sakgMF<d(uB#{fAZ_|1lD-i*-``dt>I;11J`RGFLxM
z*l+%TJ&W>5-Bei!c%Jj993A^~C<Cb@u~UJ{oQkN|wx8x5Mj_y9#=mmGD*3`MZAk|h
z;B8D-BR(_FHL}0>8#h#LrliL$_1_`<<V?YZ><lf~uqtaLa3(K_|KG7w4&x%Bp^lxF
zAD%XmEU#bQ<20G+Z`_ihdi1r4!kW(e>G4B@!|EB3jcA-cTlztG!mWnGWWL{uRc=#F
zTKj%RpD|dgemnhg#N23Vsx_%P!B5VN^_<{m`R38cnrvoF7H^-olR`-!d8`-@v?nRZ
z?mEWLB{MOxP<%h1CS7|)nuQB`c*RD+ntQIOuVs5t%1QM7NRTQETA4J_^jk{2RkMi)
zVlS!i-A2W=AAiLWIbSZ9|2w^L(OORQ)X`WOz5n<2uI}6U)zf<3kuq0FyH#qm2R3rP
z*ZOz-PP?jC+;@w4C!c@OB=Uag`vcvBI5IDbL{As>ml8gesjX{obTr3&sUP)aHO8u`
z5L=3h;Vf(X4~>nLk6oMATkxV>zeQk$F?t#yf575QF&ICsx^;p0(?h513k}~RRm;l4
zoG-M$%v)s96b7^u?$U$2=}nxMpmg_q3(X%a$_okZzB~OysYL(liY8Ee12E>PL>Air
zBbzJVj~{i*$e;r;ews%#b<c^)#Wj7qrsEw8t=GcC6S5q8Am}u8q(tW9;bDL0Xlm_=
z@o)uQ^GO)~S0N@?r~`y~c5_((KX$zH+{{AvD7Lc(hWJo47y)mwfYqjEkcvJ3W?8z&
z*}St}N&H#+oHV!q3giJMzteWrQl3eO2R;B%Vdk5RxQb+xpe8zhfQmq5PfT(Q_*VS&
z<J~Bup;kodbjF<-S>OPW{WZXVUnr8iER+N_fu4stv+m{fVIV^(WUtCtMn7PN8TIfn
z#oVmD8i7@_iT<bkcDRrSHtJNnF^JDJ7H1=_^yBvRcyYf_%=vg)BaSu@{GuAvz<^?_
zpFn(hcabYJ%z935<I1<5>_}GB1=9t+wX@5wmEH<oTq{x>itUlH9LO`AuSn{<F3e(-
zFKXOvA@U*uV1x?n*M-ZhO}xcj=W?pG{{F}?qqWK=qF=xBH+_q8!*Y*A2))`TUy-&+
zy?|-1Pp3(3etxn(RNA8HDS2qGJrZ$Ik}}c|Oo_TU6}ciWt_2bgtGHe^GYf8L1wKtF
zc_9IfEtgug%&DLlWZ6+{U*xm^0}JNCI3^pNA_kF$Q4BA#I(t(PGLC^-yOg#lP>aj8
z4h~=s(kcLGdW$#zf`Pj%WsSj+mD7xjxDaVwaX?IeLr)me!kZgewHsE>unyrX;$<<h
z$!N0WrYq*~gu`WxIQ<bN9E;|B%B{gwg{jegtmhe{*`ggn`J}`ILir>_@jGrTZ<7iK
zulNYW6Xq?ezMpN3vu+d3J6~qkN4~^@%{Gt$se%ng(@}>xMZEogMOp|J#XV8qj1<CZ
z=r?N&;<=gU6Ne-C-nT@XT++!?227WXNr@P}ZpytX-hWRGb(Aa^GH20;m?}aT@M_PT
zRM=zXArVBITJ#~4&WqY|^kv{GM*DTYUusOncf?|JbBFCOZWIb{a`45MMGN=j3w`uX
zdhR<9(+cYeN23xi3jU~3L4;MKuMCrPoYFV^d+VJ3p;5NKgWT0n+HjVZ^wTfYd1{|d
zEi+DRUaMj^!qlEqNfkdLcem$zhfI{!aXOgCOH}A$dogPAT0I7>$TxIt1`eEuoJ_98
zhGBvVViqX9rYP>2c%8UigEMr&(l`&=GUE_CBadU5;hEqFjq-*#bN?`_EcPEtj_jiK
zFUJ1xeWBu-hpb3vj42rsA5k)F6<SfVK^B-wCXww4w!uHUB|HluvLX|pDz3}iWj0Zm
zb|?V@g&yM^+8_-UF>HPX<uoS;w$QjOtFkeO&>cH0aGTtyFnC~h)^%-Aj?TWDY<HMj
z<&pR5@k@-iUx8$ym^V}d>Y<bF6b&H`QE55zZHplS;v>xg_v751InlcB)Hkk$#49`o
znfJ`qgaG1YkDA&p7(wG-dK0HWC|A$Zi^Z$_SI*((Ws+4V^Jw}VCU|Y3Q=A(2v2gt_
zEDnf<Xsi7xLN=1yfVrIz@W72$(4s5bwB~DN8pQcwL%%dzqN&FJ8A%nq1v|p(F@Ldl
zx@OzK0_F(z?wILY9tjZT$1WGuDf)0qJqC%*`nIMN0m~?aof~+}XN5dixxH!6D8~_%
zBf}8!Byx{pM1SM^!dFG3dK@SAu|YHb;A+Y1{_x<r__@Ig+Nxd1mEqUk!7hm=(E76m
zAuF8$-Q4o~3)7iz=N}&PyCjF)^_x3{UPuqfU6jMT%9&G8j+-0yrK&1Ai=Te4egEqO
zrWori6t`R01AIiJvHXs#=-c!G8pj|95dUzL=9;kr?ed1s&QY8)b`O`g=39I*7UdpK
zS5*JIFv(f(uwR@dIdL5yiE%tW(E3EfT9cicsH59Rc{bBU`!9@*Z5iVzCwU;(=7UYq
zk6Fj~vL@M2#O9i{zDzsdzD|7$4F+C3UHyfl%IgDPTrJPF<hU0qb`TqqUpD{HUQTDP
zs#kdn0sW(*1Yj8b56w(9vlsDUqN&%Qhv#b0p|(S3tNGdl@;s?j!w=<k-s#!DO}nmB
zhf5%p7Aca*EF#faxoKEJzC3=!AEmF4EWy)`%BkaUBmKVliu>B6RZCM+RJ5oAB6EDr
zi_gp`|A$&~U&d`lmwz7NHTsp|f5x*-%P>PPepUXg(QzwFN@5Cx?LEPmyo>CZ{2obq
z0S;@L%zH9&qKVg(rEm}ZpLN<>qFK;^Be`D%e!*Pi;ME&oTZQ;tO*g!3E$8KgV)8U>
z9!NfB$1uC>{D@$j$>VeJ`B}O1KAuS<6Sg0*S!t8%Q9@G*Ssbsno0Qx-FE5BA8KT+k
z%{Aiw#(6PCH01HIQ;cWla1vz+<k1S`G1>p$Tkjd%f1w?HM;vc^UPK37xd)nU|GhqT
zeQ>tDI9~dT?*lpjf^vU%Jx48$&CTcEhc20i%6Y{8C<L^PA>|8ee3P7Ox?{HTHWGM$
z@DkCf@#hk}%78(5O1lkPl8eS|G8XV=n9jgs|2hTSYAD|}(j;^UI2pRW1Nb@P-`dzd
zxe+Z*BE9aBAhcTT5(mdeLRxSo=r(%!ofdDReAygvS=WLhwU_26`sudo-yOL2tx$9s
zoZt>4TIi{_n9#G+RJ>1ocQW@vbGweaA-j>=Sv{?-E0k<R>FZ5Vq*yqXTtp7`6~F$V
z?gi`WV=Qz;rWA(7#7Z{sDQ?&`M%~EG<HQOW{sXvw_sSFWTs?otgf6;$XzyjfyP+bY
zjM5wH#OF;f;NUtF#e%lKiy}&x3T7YYJ&XaSW!CHbRQh#qv-125jP%MysL(Outz43-
z$W`g|By+MLAojkRQPbK=uu*Zaa7F(T;)wRh#@tb_s)Q4*YLn(3`*a?!qOo{c;EeP)
zg``E7ScS}-^2sXQ7d&xxDCX5_+7XqzO21YWp_Bbz;*{ESTxu<B4ES;J#_!J^hP23V
zjj2>t{%<=w;&+_D(2G(Ajq|-A(i&-<jp@d<X8n#rISkERg)&kASi<`2yRR$6E69ih
zv$RB#gAyzgJ3BYVvcQTgFM{zPPRAM>8nKf64BcpENT3roO2XKm9|uWA5n-{M_*!FS
zLJ|m<vpzb<aZ{HdC;SX`%8gSF%QI&W>d|1>^iADSCTp^s6ZTEcY>mta12S^sXcv`%
zFg&1s1=yXK3Qg9FwPBTc!RxXr0_^gJ<3+$;0qdKz3@O%IyX6Gn6ifsJT6ZPX#421A
zdYmHMk66zEB#{+Re0>cfA5pD63P?x>N`4LA7{fgNm#WTFIjz3z;+8HtMRsp4bTHAJ
zG><T{tuPCSWo_cGwWsJ2pv6>so0tI1GRu~lj3K~e8oH07j8tlxQw<Aw0BSitnswQA
zEf-Zy^~wK$&@<f0CZborD~Gsar^}_8HcN-LawD&3p@cjcB1rRh6Z%tbK2LlsLi@U<
zPG@fh1TdhP2TowD7e#}#r`Tx=E;$KFLze4Gt?SF#05`2Co_`YnW-e(Qfl@(6La)){
z#}nRo@tHayor+1{n{O8UKz@g2uB!q~1xmtv@G`4FG%SGr0U-d>+-9Cj0`3WfRkTn;
z7Lu~~lkub+f2V5|qX&#qsHI%x=7(-DlOM}EDO3LLmcShiW4Is=S`>pgi$T)x^SHWp
zH5{uY{*B#&m(|;VQ}VnvUfrPADn8M1ZTzs!Jx1?I#EG5<zg5$sBd*5Tb^86ZR}<P=
zE$8Ml7ir({E3Vg4rkqX@ItpLWUG%O4G?ca9BM}>&cD%&FQ2$~Go*FU9tF5Sl5)7(3
z@?KaxS?)WE5`>W@bV!<0=Snv(ucZn_%3B6PIv+@|<g0x6s*89bgR<zG6&=m>nmlB7
z<yXMt&a6|36s=_X6<ej0N)sulFMw?n79t=zQ5?@_?I*)xOT#A28d6O~;VGV|?C6ZE
z7`eqiwXaaki1Fp8?mXMwUph3{M2=1n5M=_2SyU!~VRJbv+TBqzWfd<<b<>%>!5*0`
z&u{u-Vc%5pajrTFi$T}z!j5LeJPb_pjZEvRA!^IEfR?2=YA!EH84>90KE*tF&Dz-=
zqUVGHdtm2Iys<@_GRSf<Ury|60EK_7vciF`3k*2vyzAlB6j^0k=fugcpi77_D1N5~
zvO)+FGlO-dCKBHI7V0qEYyZaa9<SoDCPLTZ+7cmhhH`ltS!5|r)iosaQ<0q3UK3K$
z(Ir`m`^zyrvc*@8=;|^_QkPmekr%(|hL@X^XXSso*6P1D4^<MPX*|Gkc4W_P9=}-P
z_CGbABXoNjsrlv|om=ffCBDAp@KsHXz{Gf7>1)AXgm@fm!SJo<*er_maiimeKKcsk
zRgohnDmJ;@ay;>bKd8p5QhSVlqdN<CFrt<{apbVIpwww&ZYI~c)(;S{4cp>_6GNNp
z3dbh~E5}DSIm$=}0>mqZcMTtlUeo4%U0Njnwlqcv*cB8r2h|W4$7Er|w7d-$MVl?l
zTNkTja+yGvf97(r47X)bzRD28=mN9O6@!wRh@&BEm(=stoT~T{T6-5k!kwnV;<H3y
zvQ-(%r6jM!>#VS^yV2b*+(8oiV0ue>{Gr@o<Gh^G|61F3RIE7vVN!n^RBKu+j8u>T
zvrDOTCd2NuwKd$uHI)No<;vug5Kv_w<jo-<m5D(3bXMMJM78aSB6zG_UWC5_g-*l*
z!FmP~9?ayWr3j<D$@5gju-F~vQa`3@9&`6H2pO(FYLb1d3YaC)Q_*USn6z5O^>t_4
zM%xGPsxPb<>!XOLa(@ba{W8#1NZ)aDucuWwXy=Wwf9K<;&D&@g5={4M<f(5vQM`#*
zPwBJQ0O2n#)9R};-*#W|qaF}xwbUFfJItN0t$9H+d@u&*DW~#dydxa@^9sQL{~}Fo
znl0Oy=9G1h$*hXC5*Jv)RB>ckP1;zSH2mFJ0afN>q<Z_r%c1(4I~}9(pR5J_T7Fwh
zTbH>U5IpU}uRmCuMu?0qZ0-Keg;l2?#s;^U1a-J`?`nSoFnE(xwR){R$Dsd!p6XV9
z7)iqE?|fGg#=>`As#C-jM677fE}yi!Utw(LvkMz0#l#f6R@`Q&(3rE;Z){z3m-Wjp
zI>KB==B~H#<`X68#B!a=rHHX{vGnCR-zorPkrbg_lssK*Gp9)C+^hC@cQsYEZ?y@T
zo<&bxzj2&j<t``jx_xQR@aXDbS}}za8nb-#w4b(<|G8rkV(0Hy#>*w1*yUB-*Q4~4
zM|q<*0#;KsNO&p4x8w8&FZn;c=-g$xc5L56*VC;20{E{5qh@J}E8+w`d*uQBlm)f@
zI}h%kSNDY~%A5uEv&5DBct29?%}sK38+bfJC4z`Zy88$D*?v!~jSRWS&j(7sbM`I9
zEX-Op@!btR&b4N&Yjy>vdnpQ|>*IQhcyvoy#I~Oke)82+TeFg#`B-p@U3{1D&gtB&
zvhi;GyZ)g@90Y3Qs9~7)s!)l+|BK1vz22B9t{=<DD!=?<7Kihv%Ze<!vgu{fdLA#n
z3@^`dfn<W;SaIxZlSoZ2UNF^IxSP2!-msY3SZIg!J!@$?`<vNbRhx~Kkd8Nur=06X
zee#F<JvQB6+cE{v`rI?Q4RJ%`GhK}IMT;Y`U^ZGly#%2ZgH+1L#N~luCquk3lEoAH
zpC5W0ZHTYC-eY(BvP}vH<UdR0+jSwlgvjZ0SI>`-r~*>}{q0Ql?5I6*m;flwu$yHA
z&wEtci4y|)Vh?IT8OQxeP7t~(R?fpCBaj(xROOjf>4|sB9k?62_{FRAx^eu-0c=ME
zz{v#4nAR0!(7$?67^C)8a=up{qV{FCF7wAe=^OV%06Y(LXY2mlobRMgL@kIkrF3=n
z*GWiDg?L#Nrr$z&<hlL13}cQ}jKs8JJ9$pdaT*#{FVeBEktsrQ7Mk132!4cgn9z^E
zX;qSbf`x?zL*kmBJcwyp_0%$A9-q5>bP>jftu*>p@{6&AFaFoV-iXBVV7MD4(^mA%
zrm0PcI?}Z3jO@%oQ5G%gGNLHI15r}I@XrX!&=Oq6C08b?N-dO+$IwJHE(_t6{{(vJ
zCgNClbD5{Ezv@+Ft@0L6^|{ec!t~6GSeXW_HZX1&7?}hxP*Tv?m=kc;e#L#^QP`@P
z*R{PO;<r)_fK_{Oy-vqe-^Svjr7mKnhQ6{@X&j{l@QMCs@j&&YuJOqk_}i%cZY}*K
zkoN-6de2x!+Aylzh45kZ<?JltFX_rSG7~0*_B6SUE(+6}o|$D)B$f2rtFSL_B}^*w
z>te-rGvT^gj>h&CJA(mRz`#!~j$RZwF{u8aP8&lIlsZF#7>%b3Qwb*er2?8YVkz)f
zB_q}&<)*<EWp^*Y98rvCitKZypH%~UN&+y+EO78IE@h2wQ7Vso4J~I1#%CS?Q~XB~
z(6LGbYQpS(BvYpU0Q}$hP2IwH_Ml|}xllJB($<mv?lh}g17gjsifb5V7hFNC6g&OH
z6ZI-^)y2Z7{3Ba!^7BUGlyCNvnO}1@IC#JS5OS;$Hi_PV&3aBcpcWApjX<;2EKbT<
zqY7|AH#SayzM+No)CCAK=w29VSc5Z7gv%~I*ATOYZ5-oKVoui|*%ND@qg)6Tsdt%7
zOcjn;{*d$GWC#|acYoe63(w5e&D92?u@jI)bX`uD1@?lu53G&~*usdsY&4v0OvGd;
zP_GuQMSN}&gVxrNGHT_$ZuuC$<!<;^$<^q<B(W{$Z|ObCMNtGcBl-1X0~s?B)3A>%
z+<l#}Q8N>o&yHyzqwdSA609Z7!U2scb7y#VKbq?)XvDFuW3)v@%*-ds9-T7!Vhf{`
z7)>kmB0edJF0r!$oY(&~Ge3i0$-*^XmOR}o{DLn~Q#RKe`?;J5TEdB9R3iAx5E>PB
z?j6;M$z|UQH6UH@I!_(TWHRXY)rc}6rvt0XqOKU9VXLLiX?`?FU;RYRaECsbl?P>t
z=3r?M{@XEk(!f?K0Bm|BEA*f$o(SVK<|MQX!kX2NAn#vsc?X!9a`3-B?6frXodRAu
zeA7X|g2aBC^koz|p<{UpfY!_yEDU>|C97PTw!9hfkO-3WS_{o-i0kSajbxiUs|H!r
z4Rwtxj10-$vj>&Kw$_j7eAfppHsbfxT#jrCPxSox;1ny5nSbqMHd8Jz_cd5^E0`;P
z$u2+Y!MU;+Z%wwziDdQk&1gL@4h@s5U%rnTSbV!(NNuy?_Tf7sDfk-`(#koRN4AL#
zeWcBkx*OG=${vKFRQfOngqw*oWqe-ZH(}12XBoiTH{_1d?}CsuP2owjkZm(B7n?wN
zm;4T~lF@dMcT=zI85?8zd5_E&C!T$GRB`aOu9dCq5;-5yyZIn_i}pn`0QmApPbb4@
zE)_Lqm;NR@A{`4GD$1BHFZ0vAgV~vYMl74y;J)nU<*&L$j(!1bbd*dJ#DkO6xGOPk
zO#O(+-V{t|-1&D7SagUaPe*oUX8i9C(DbGZE@Kg*wsyzUl-VwW?t){-kCblxd~3S~
z8UqC1{)I)G2^SsMP|^1IcdM?vrpsewSm%t)(Cx47N7@)fc4rKD)layt&^8_njs=^m
zGmetIb=rd-#e3U`e)t8g_Hu8)ZDxJBw(K|ZwD0P4b5)v_!d1$}RNWdqh#;AlGwt1R
ziSZ6o%Im1u|AkW1-BokDDF;L@+Z$u~aa<m(BX!UPqoU4C5$yG@W~vHW#rouLo{oOC
z3v~5(J3Gg&mx&%xl#_a(lYYtr`Yh;5)nw%$Ib*`9No@9wk&R{#eKgynXJHW^@z7is
zm5L&d_I%rwX#MiC%S#blE29!QR=mcFdcZX1@2FrMrz9lRB?9lO;J)!cL2P&ZXYyI}
zhP%I48s(&cVnLmC?|CR@(k1Rq1vmTb;ygbY`F&Ax$kW+$sI~ZsvtP>VqWHyg^W?3z
z*YU#zC9_4GT|>UbAIVP8%lN{$N3rA4T)RmR><;6`=()cnHe)9O_OlRTxbt-`B-Z!2
zNZh`{!RI9QsVJyJbx#}Vc_HqG`7hMxnWygvh1h-#1UnT}2qUYFq>AE9ceh(^TYiy5
zFkhm${|L42L~+Syd|VT<db?pBgqdEoDl48}75ZRPbOQdH`955$39u-LYC_)zj6P<#
zQ2aytojcF=?SuP{11?zs^XT+xHz4yP*x~<&1_I-@>b{GLq{BULSZ5d5G_qM9dOX#k
z@hWHXPD@(hI2f1jE%Fdad163VAAh#D!x1%$t72E8cQXefby$k48rVodEs?x57a4*?
z1_2=tV~yil3V-_Z+yi5oj=IY1=D=R?wisu*O&tzSk1TP?-#cvH`+q2EHO_j9t_Al|
z{r7@69j*Nz|GLZ~25@9};~;Wv^!XXY#1?|-VY4+^&S<bGc2Z){TQ6seOk5hXG9?5*
zsKR_%pyFiO<cQtn{Y<$HiR;|^^#Cat*=k+^JAFSEeSq#-TaxGtk?#+C85oICsah&f
zdpS7eiRy$_EEO_#x@jPmIga-%UT0|7;LFS$XroeV$cG~PM2S`-_E1;zfreoq3*1;9
zB|<^SoauNIp)whan2npGtt6H_q?Xls@zVkO^c$Gvi}q?`ZXTT1$vS%+n~U2zC(Pr2
zdUPX<FqUlK_y17#-eFC3!MZp_KoPJ7qy!K|NI;Mh1Vp-&009!3R4Gz|g7hj)1T=I)
z4Lu};A__{!f`uwkI!Y5MK}0~Q(){k?^E~IAd!KWk`}_T|_l9IAJA3Ulvu56zd58Gg
zftZKSH}7(AJ3n&okY-E0wwAI3*#3T8HViW#u2K8|qbZ7|AoKICS(KiKS{h5H@mC3l
zx!V5Hrw#d_(~7j=Y|l@jXh|HcLRdfCo!(F<{B~i+^^5Uy10bFk<f9MSEWVgv;jF+K
zE`izn#pq|Xb+r9+ibs9KiOdx1OJ(uwHdo5K*wkl|Ix;RnJms$x+yjkA27Y)1_PASW
z1!3keKLqMpnN0kIT0w@8vO}rEGP0(HuL-kCS>{A|d#NZ=d~FAYQ_o>JBs3A@rn*P%
zhv8$-2b;Sw^&8hhKrx$xYTcp>Ka=@VceyYBm2j(5ZV@QPDN<XhJEhnEvMjzx9}jV!
zN_eQ3LQwU<{~|*%O@61omcKQ7OOVlWq6v)+l~|L<4N~+iIh-y@Ch>8bz2*P5G(bo{
zE5q@{y4fo)c`U_IKBAw_vHiSwy(E{1jm^g6xT!(Knaa<d`95}qc`gerW#TRp=jUZ|
za+Q2bxv$UpSIT`_=4lU;e-$qzvv35zl=Abjsr5j66@LuwO`*7Kpdqd)bfa`xtae`E
za+@1Nja!OmU}LTGdCnfY3LdJz?Pd+<it9qYiMVVCqTaG^>mpKH8j5ZjXnLfls6;ud
zG>eOgaQ~tQJ<hXTT)257m^5{zPfD|6y%2t_u>N`%yFzerL~rA(vDD^e^Q*5>vVHP;
zjZM4#_$9OFV>d1*Wd6wgXYe;|5|TJP%j?Xk$ud)YOgHpTqp#b~d9xL_kKDGyqEx%)
zRIW<NMYACEi+W*V+>Q~vH5TKhw?C5)-7b#-#))gf!>X*|4)NgYuFo1-?_yGaQzddg
zWW}2vchpvy7XsbqC8DFml^*N7xq{Kn{K1!|Py5>4Ca){zEt>fyges>fT=3E9<kQ-l
z@-iPRq6y9m<jW%U^aEMB>SBuDQOB|!HJk#Um|Dx|2=nD-2@`QDMKVjMEX@hlGYw^O
zcgKVV3~$VXer4X$Ou7WjP)BB+c23*^<G>?h(P4ROj{8HFC-PZ?`aY(waf+tOuD9E_
z`1V8d4;ed!>#yHQ3G%Ses)7)lofTiNU<bNm+ckrED)$~LT^;njV2YYZg|hZNdy35q
z=G2$wiF!P%g_~Ts2mg6$k}>>PMaHAYe$En#x>}YRzXN8}PPH#!8``u6sj17R8y~H^
zez`?oMNa>G_I+!5Txm=+amBDOYS(hS!BxV>-E$fZ^(kkin`R3vmnYS|7+CRqqT?7m
zu0*%NbHwfZ`SK@4$v&z90(MfNcP?)E+v%zVHEK-<`{xo)g#<6Tc#igFSA~fOs1B(<
zb8+w?cFuPvh7<4d7`MQ`M!dj|5?-c{rXvW>c0Gwbyx+g({M6ekhtMxy<E0fJ<1!ih
z^f+=pwuKQ?&MUe;DNZu@UbOjJ!1!r?=6BusuuE^2_eZ-S%NOPOjqcPPEo%x?lB~}Q
z+CTbhcjVEoPU&f<NU1MVR&?io^2qD#Sq-_N8Wr&R;m1?egrct>VN8E3vOCzKQ%kl8
zZAf1R2zB<HXMJ+P$==<23U%s@OE)n4M@IZ_-7+Y+A_|vth>JMRw)G@q+4I!d<+G9E
z95KxmY7KXUb6sjv+s>;=4aV61etxA2_4XD4lc_h9IK48sbSvWMf!IhuE5C$s;rBo0
zdYODmDu6lamCbP7-V1A5u;fc!UmewY1Cw7yFDkDIy?l{=6Y7{lXyfLjMz`%jHn04`
zWQM@aEE>kPFM|@jf9S5NWr~eI+Izjr+R=r0Zb>5fg<FVrj^BQ@@Nhp?&=a5-g*V8~
zzm|2Gi=@LXR9)Mi*Qhvme$^fO5!t+Jp$75M{rBb9EwyTIcTJ>jt1s_ZsB+;M>!ln4
zHNod+TM1I1_AA)7B4XAa-NpEqk<_YmTQ6T50sog$>z3;*E{B)zi#Nw^22AFudOoS_
zr`ZUuj3}sPL_m~Id|zsQ)-rxTFc|?{86(n)%X-FS=$qY^6;H32hB3JljbE>4U={?2
zNBwT+@WmurYH?5_K3^PXFaKO7q_PkjB6H?o14=2M9ca?PrSZ@Br=NujOS`xzitC<B
zA*;!;ev)!o>7jZa1g>~^#qMxZXo61SVZTSs8?I~;D)o<^txaZV^?n+C(7F0;IGewj
z<sy<q68F?s@S=LPsx5gfnL1I;DYg!l!>^8Bcwj4nNgLg@?E2N1p}}7@v@bDZ<sX6x
zE=o(LBun7=kV9qD$>N#eneG;0=QpH{=mdtlgNJ|2|J>|rp)1!KEq)>~s4Bhqbd0t-
zpQgKD?=t%j9nScX^@~`_O38VAm)Pdwjq~?=#49W6ZwcC^%v?E5oMq`9t4}#G{MEQ#
ziG8Yue=C2pLagO1hmAZ7^UKBCcOUa6+{dIb+R=9?l@q=jO_-M@8Cyp$XRJNP3@yit
zIW=eLejn;}tDwlYJ`pYz$}XqHo+n=ZrWOroV!c%Ba^>xZ9%H<ij{Gv28%PgKsb($q
zP2T_&YldtM0&LAiq_N2!o8*u^70Rl}KCce?x24F6W-9ZFq5x&^*JDci1y8AJ1nh&Q
zZlgiqhMdBxtJ?OU^G~u^kXM|!yoZ_^2wBEB6GV#2DQ+s_C&zVI5G?JgH?ch^;O1*R
zA6TV)0Pwsho{$m_^9}BV_?7skh~OICyu1Yc!e8$WozxsX&1BEwXCsH=&$qGUi{~bP
zaI`PA{}IF=6xh&p-uT5JXM9t-0Thpte4fky6s2%(yVB6#z5J4_AxIViDH!O0iIKP4
ztcDAUWpkLDrY-zQHrOE16KJERl+aTX7B?}sBTiV4Sg^@0ndEk2pif(56`W&+Fe{L~
z*iYk8(?d*Am4-{nm2-Kqq9NtmIK~s!d|I_){9%3k8S_Tq0=W!?n(KYZJEL(toB?jh
zhIGTR&pEIf{2J|siDGdHpQikg%-rT;&!8DIl^4cK=H|?YT6JeBBbkG;TYhovXOqqL
z-pUI^vX1rCY*wx^Tn;lQ&faK|d+Od@Va;N%D_ktbuOl9k_S`y*1GwCvO0(}eD>Ti5
zAB>-lJw6=zO$iBLl=8PY6QrW`-L8w*9|m2%MS2>qI8*!RtCNPukBd%p$>LriJV{}a
z?8-K%PcsdJ2ENUoe5_kIaTEF_@eD8Y`Nah#(r{m&B+dn1y(-0ACc0+S_48KV>0Iu|
z9&2o_FS6fRhh7UR=l5w3iQNzPmLMkC2Tz_se#o>mvf~h#kt7XEGu@gj0d1SZii~xF
z9^^oL77eta9iwM{mID3{9*F39oi;}#BD+^nU}H~Jcy~DL&GG0(O!BoSCKb`hdsonD
z24<f}#1NZ{)XVq)iKu-XGZ<RNsdNS-IE811z+uE<-3axNwy8b~Av>OrFQ8fs(f64e
zz{(IMA)*BztQ3Tura6KueGRE=)fytb3u8G@9J{0bSncm^GA;KdK7@3u;u69SeaAzA
zz~p2tE;)1u<Yv2<H~LXQ{tIJ9RwB3vcF%7l`nuhOj}cuVT$<_XN%-f}pNMi(g%DQv
zP?L^(e1g`Os~XDT+#f@mAvwa?GE^(&>)#(0VL88D;qUe7Vf={-@qE!CeXF?P<(Ll2
zMR5{wcRpDEjJa@wYE0b9B@sW{vk#^E$32QdY0kfnbPKfYM}TZbs$P$Q+|5xg!DHQ0
zt^u=tXRV8#=~3>s;g@U+`nlyR0s>l9mI|8kp}V6cB@H*?-9JX~3Fo%N`Fwey-q}Q<
zg|j*PhIfu6{n<3=RatDM8ec(dX-|SAm=|Hgwm8Mj%BD$#^^^*nt?*t|T&I@KENK3A
z<_;oPo+Erw%P9@C2`Pn4zQKE!lw8N3AIO#f#Pz^lt-1cs(C-ov3H?!#MAs@V#mf1n
zCrh`!M{<iJpE|=mP#smS`GSSmvO8UdMjo=HxxuG0>PjUw*Mhf6N|ei%g+tVivROT)
z3ZiNLkWJ;A4d%+Z?&~$&IGnBuyU<lLwuw#R?kR2$ImN4{uP1SC?~RVQK^|kBx~`gG
z&fo%l483&auZc*@ok_8N#by%`xP*RvR8Y>8l;g{lu&Lu0)PEw~B$!{E&ZpDb)d)`C
zsV`;b=*e<gT;+2YNuqF5_|iF6&rIUQ_S8~N#tJvd!pz&LRYm&bmN*xdDyLY`jO`Ph
z#%~KlheGRmo?mojxyk1(5_bgEU?lT$E&7kuhRxsIo2x7=E+%s5dW;Q<U>l`0bp<a8
z?NzGyS#f2RE%|j#*`8Wk2@5-c@wlU~XHS#7GJ3%KLDmY;bS5!djep@+t!s2+53Uex
z_1cSky{7u$=W4r+4N8?;UT@C-=rW|hZYJU04`qgk)oQ;#@ZZlaJ@g;1mV1kSQE`qd
z?RTKjSM^1ExeNc$jSCIQbKZ~=HM<AlKP~g7d|yd*ZbXtasO_8nMi0KYIKexc%u%Fp
zMZT2x1D*o+>_6CwOsf{CR_QzW{UmwLIkEBmxn1c$^#P>=b0%4qg06U|uHx)B?nvt=
z<=X5uXR{WzP5m~+svTUBUsx5+?6Cgue9=wOl(X&Iw%i<aIv6P^AHTfx0BPDkx?s8?
zOpb$jT7GPrZ)GhUTl3_XgY*KR=%|wECQG83uZM*KC6Ih84%ic^1*FI}W9EY865C<>
zB)gy+bN|rwmiYKxc<<7L?lU`w8{~CwS}<%fNZ#X{!>?WCr4rztMemz+k`B0?61J~k
zjZRFaOpdOw;bg_7%{CBCL|W+@tg9@o6EY^HeEKFDJ<#9F_^r(KFnXgG&eJX@nx1@9
z`84pl2+0|z;dZQV*QSQ@axh+i1k;`CtRK3$LIE)GS~JBn7+b@0#l{lT%*ins;gBdA
z<vQ}}263)cvMc^7Ok_A`de(2<RK$^)j&jySY39D8ctOHgKrbP@XHuu63HI$v;I%u?
zlYZ#Vk1ky%-(fFKUg=NxY<thq`Ms3t#+^R{g)d84hDO-3x%XapY!yVZK5B_5#k=@3
zuPMn$?>#Nww4ZJHrR64Xh{$+B+}U}uZl}7cXW{)La2?lx@8<as{$1>8;hPXiDfJVA
z0rOVI;%(b(N-RH@&FAp$O3SFOm)GBa9p*cEOHQN^t;Af`5|n>)L%xT3Cx-W%=grf)
zjR=K_H!r!QJ<vMPsE#?tXd^3<{qy03Z@7ZtqEWp5@{dr+GHl|dl*UrK(Np-U6~c}G
zml^5ZH>tP|ZTSp&w~M`3@`q;TgZURHy8CZA>BOQe-H$F>JnsuNZFG(6gClv!R-rh#
zG+h6=AX<scV+D>n{Lp|r`w8~9N5+QhvK{!sBu6nZeI&++6T?x0NjHssROZ&~ZEsS&
zmn9Y+FI-4jZ@)8s?pfa*O803G*$b7ke?mI<x+CqXY_~>vx>nH<_XpE6^0O&Bf3Dn(
za~Ozj7rKjXNhp94(fUSM<>BKQ;@2!wAEf$r<R<=9Qq64igaoGT3>?g;brtti8=2Ge
zN-jE1gpMGd)~&esjgrrvW%u37EB+>aF{ZncCYs6hVxj5GqWU(ut5i5!=|xP6wc=)}
zgpNgW<hdH3CFB>JZ8a3*yLOzVsrq<`7M(KgqaxyW(fhl*gN7fxX%+~w-4d?bMd(4b
zy06@hxzbQdj!AEMt1@Q|R$OBsHF3y~pHZXz2(6$F9c@nSW5%rGBA6g{biKUZU`<qh
z$MR^PBlxO$U2FHw_aq^RlGJ^Df@@DtbssH_X!$|=CS2T?{klz-Jztt8K{}K9<*8L{
zvdOwRKfiIW1X5I&AN!7ZmLHoeZ#|%+UdlptudjS%1n24>#hW_c@H|btMjd$E$<;qV
z(feh}G>Z#V`fk~*(!f&hKaxz(`To48qxH!8^kzw+k!)Vt;+W*_Sl4lX#V6XvC)I`9
z9PLlAuS{*;+S6rJu1bE%D;MNe6xTjl+$>zDT<vFJP;71#Mti=t<$xro@2MDDcQ!R+
zj)^u>P}){JgOn?o(1qsQz)952v{a9|L1qzimfI1Wm8$p?%$afiqzQQJ_shO;KYrYi
z9Y}L!ggfO}2;&qX&A#hP>^&VObkp5#3}_G|aoH*|*a!72E9p|m)7v-&)(RT0!T{~c
zi?mzbpn`?B;w3&@%5hJ4>bv=cWF~XFmEc9olbl<KemNwUb0TcJ!oag~{C9U{`iD?P
z!sA=e?|8gaw<6j+F)tZ~nL>6`g`7fplby>Dr==DKlHR^Bc^}%p9@_7FkQNq9`eGI&
zO=dF=wQZ{qUfH$VG(9qS-)b(uFGo?hGbbFzW?ZjxmY+$lR0TT`m%5))eN}d&d<!QN
z)2^IbXUNTHJf-5%fPyJO2Qgx)wa+;=3f((7K?f~Hh#!ma#bfl}#Z6|R<{<3!yNsk2
z`(Al(a0H`NDXQ<fj+u-9ai_VNlQt!xqx?n7Hq{wZ-7&R+*Hy7)4dah`skmg)9eQ+b
z^rBaCFcmR*W+aKtIcrm%)*@E&sPitZDi^De*q1!!uY{CCqNe-XR#9D}8QeF+hFEY#
z0WxtO{;p|4-Q-D>%Ld7sZ=fSfuqE5O41JiJOar;WOn?|y$3q;(f${7}>L?*b&8z)0
z{M$2y)3(qfB)Bs3z@;}5YHIzj(v86>C0*3xfKTt5@g;$3F5e48=a1Ho+!RIvJ7O<A
zjss=G$WSF*__F@8vAz`iemq0fU=gD4*pj&u@zK4+nQUx@u*nTVUQ*Ay%EUL1s%uS~
zJM~BF8@buqF!oWE=I{yun|6EK^3Jq1)@qG9dc9s|ZJm!x?NZJS(jpUASoPxr=&9D>
z@8?7-9M3CN-rTH`@qRvRYnpr?uTR%*F_}(BN?q|tztAbKFocB>16ZKLr11XPfr{3z
zKBhJfB~<17_2g_JI!64!6C0a{86Ul;>{<fEUe%N@Pg|P$_}*<gvwp|Xd}Y;o{$BKa
zN4_{ml-blw5Pm-8%Rzu(WW*?+V`7N6*rd@HQ`0U~a+a{Y*pRy0=5_iVK;d$CEj$+U
zG}_C{M{W1P>k#8T#>Uyca1V*9+)Gjynj)SSvTW<nv8qIIQ^I-dyX`b?*GE!z4koE*
zXZ7wOmJonWgnodCs_Dd!a{B(#Zh3nq^1Wi`4bzdQ!vbFq&LAf0Ya#Us;R=Yl9n|MA
zc;s;7q-tEAB1$RoSZ`syg-vGE(dFNFtO>UE<HxcOoHjQr*2F%K*?#=$>Z~j?(J3<E
zHH&it&2)V8Ij~=9zx0Tiu1Hw$+wVfw$c{c|w=eL;FLgb7j-&6aKHKolj8s;A!Otxs
zDWKc37C#&j47#olnPIS&(QG60Ue13ubOTjOt6k>9;TWW_kBC%%%?gD%+4+(qZ2$Bg
z$Y6}wiQ8hqcB#h4>|nU$s}CNnOHFDXX=QBYyYbU8Wh8ei{PNNt6)!ef!RZ{I4K}rE
zRfnylyFQIGuRi{iQ&6bp8>^ncZfC{Fb!Y+@C&EQdQ?b&Sz3VEv_ZAOaqJ1J_S7Fc_
zu1K7%IoP;U_6xaD#wGYNpHH5Poi42%uxDuVa0(*gr&4{>=Ky22z%bo*g@y2B#lw<e
z;dj*TX|wLr#obF7=C*};Ik?%6W2eg(F<i$-s*v6+jD+&FX9w_!Ow<djfX6azl|Oz=
zdNf#PhhduQu;kiHFHomf9NyNOV|s&EtxrAfgP%cv=4<F}$$X0SJ+<}VspR!v`D>11
zHFv(Mr0}!*;gwv^&7Jht;6As_@?~_YNbUy@Xt#6_nR%ndvC8H$F=x+~Q9+#Li~T*v
zjn0odgBwclx1W1JJ=32P`$Y>&WonAm$B_+bRyM5*ID(i^t=^A{<5hB{%{MMzP0?R4
zGRxekZne^?Fdee+{Nbt~nG9?0jN7(}k?=y?r{y&{<<U;)dlrlUPMBVSjAJrm_a#m<
z=<25$O%MB>+aRB2^Dl-P5WPcxVRkC3UX}BxWy|N%*fy$B0^BPB+*~)ZVBJ}!-_qDS
z*V_b>_;So~iMB;r&X&THp8k)wPQfB~m!t%=_G53ReHp3v7$!N{+lUD$$o&vGrgU({
zM2cP!Vds@t{*`=GWP538^w-4<U?#Gk*hc9+g@42DFc-Xko@VSEjgn09Xze81)jzX3
zQE=|vt&a#LgjrR2)l)vjBhJgGe<ka9kg5GmPAo@HFjRlh7rP@Rz!_;xb^jc`XiJc3
zUC5e^$S6&&`w)MG7o%;OHt4tm0&)Hr+9^>m3-}>^^#5_>05ur{*t$B~<3GEsd?w72
zx?Vr}J$}o4S_i%k9gHpLxy{<Zt{_^L>nmsOt1UvEUU_)vTd#oz){~r$JD?zf3KV2Q
z$qC?z=e8sb*>SvGbMG$mDh)WR;W%WfWroWuZz_z@G1jsq2C)=s%L`AR7N3DFBca0{
z!gK6A+$tVx#k(UW_OtiI9VN<_G-yL*Q|+`^z6NpT7{)n7zkjh~3&E!;rIs}3T8c0W
z@AB3@HNm7vRkbonwD5bpzBXw1{l3(TS)1igvk2n;2Gt=mEp%OH4gTp>g>K37(RiN9
z^pDR29(@&B2_}gFk{uSPxM3^$%AdL+e7%}Tzi+iu$q^_oR<UBvcGx<v;8YNI&y{VW
zw84{+d>s!x%k7NeBxv2Gh2y_1Org)9oARY%J@xnw<B>d9|MRUPz+x&TX3O-c2^W@M
zU#P$6bEdoMJR>ehHh;NhlRng>m#$m>jB0=Wuukf$diFG4&-CugD`&~t?Q)CAO>(bS
ziRVLKSXX(6F`d~xxv69aHk`P6R3K#<-K&**GO_T5xW>uv@S*2uU!|N9thQ{nXcnj`
z#nFEhF1-kk?Hq_+%aU8wSq&|75vYspUNwB;`d+(TEZ5u@rJ^mWYQS|QDzLQId4NdF
z?4G|io&=NU07;_^^I}UVc`lUDRTC&NTebdF;5T+ek-q0;ccN4{Hi*>|g1cb3jHxoW
z_P%W{YVgn>Ixy@#m9b1CQQr|#JRF=gl*&>c9dQ`a9U+O9ULD+ovro87E^2V{h{=mS
zh?q5K9RWQ)gLoO<wZ`&r9$t~Y=ii!V0MJN-g>>Y%g>HAd7IIZr{=gwpfP7ODV&bE|
z0x^;MWE%Ga)AaaU7mcD0T}L|<vKw$g289iWa7^QboBd3j!uqYDL#Dm0?#-_Syqpya
zo0rvT#Hr)xSB$tb%FR~r{mOTYEMfkY<;S6g84KA;=8IaQ=$8+^oqu6&S4^w2om|04
zuwPG>GZsnfCen_xKvodVVu%4hq#k@?c^=9E9|yJSXE@Mpb2E?TqLYX1=p{{C9sRU>
z7hc3wt`qW4GF^LqN&bwQVoCb&=ku3mKkARvMl<l`J;{=K+saEhBJ++ov$~RO9){QR
zD#UWXqA=D#>^nG3)8`+&f8W@Yx-f69XwFYpDcMF<)mm@l_XtB+1ZJW4nwnK<MKb-6
z68__IX}4S(aFHRG1&oXLst!Dq^&-OrR^~W7&RK_8&lnj%qpd?jQ|jU(9vq`3Om(X0
zzGwMTK0Yxy)%9*hUU9!NarA14k2ecX`9ia7tLGkR_;wmfZy=LTq)4MJbT|R~JXc9g
z!rP<ayQZ&<c7I)IfUAA$1BsA?ZIoG6!^(xkcL&-SZ)Yb;{n_tss8_KYtnk((zNEAA
z{CX(SS$&SsK|>}gH~*qy@$3k}!K&eT>oc`)gR&<K_g|&WRLd_H0CHYb!<ey^(2ns8
zCB_Q0bzPWB*pkiUZVdClCMk*W46bm^9>nbk0uQ$Yq<oEwC6(UM;ihV87NYD^Fs%=-
z-G1X9{}Z`>X{n1OW%ao6&#T9=S5!0azRdJVtor<_a{t#K-87V^>?$_>i)T+judbND
z(vZoYUzOc=twVvI-42qk5Y$f>Z0;%ht7ThzvwaCnAk<0C8ngC#m?vJ6?5;Rbe^w@A
z?`UoLc16%Bs`^dya%;_CD68jFGrozsLHbp5Yl}!nbid}hd=es_K{cZp(gSn39`RMr
z8+AE@ji9H5{Ay3Do^zH54Ia{U-weZ{YnIL;;hp@kSH@^z%J*9u6w~F>CQkL;Q;zAw
zRcXvQ3|#gfw0+`iv^B+$P;SL$ZKO9o+LP+*tVT8VvHGn1GSju^mnZ*Bfx*}W`_tmt
z^Ga-LW2RmEFT|BwEuR(jxU%!n^*2GUx|N=o3qJuFmn|dQP2UN5vVJ4^;C-q=ZA|Dd
z%auM9m#)XVqz8Pr?8nr*>+%N-7pjk|-gJZ};`D0+1he7%Ng9ANGr97cwEOE)QP2e<
z=PuD<zyVNVp*j(MtgjOTs>5twlkM&NZ&f#xho?}*zR^AMoec7ajx>}Ro*Kh;R@d3&
zM>byS)!1faxIfu@OQPv*>ps`3g6!U2D9%ffO1HU9o?Jw!#w{NO9X8MsF?jS`g#1OB
z=W9m?OO}?6wk~{@czQs{%LW8<0ac9(G1!LtUv)9!nZ1j*i(I1iC#kM0jPl~T!lfT}
z3Mbj=CcNE01Xl{%LTRKkmwa$o!WH8ZH_lTJc+uj88_S;;4ei&?!QPT}f`YiK+ys-v
z&@tKx=B&y|QtK9m9v<gUEo*on(+<mLZmTRx!<sG5hLnbsr7h~PpFPeo<?+L~S!2~K
z5SJxa*GIZG>wm+!9gUw;;=FxQTXXuPyIfpi)${)6eHKZ?OzIEi;L_l-WypPc?RF>B
z67sp3gDdIO0>Sa7;>l3cH~9mVv-MVHA4Sl9N=Tsu0go|JB{C~~X8~e6Uto{|nf|0y
zzU|<LMiB8bCb#Qu&JZt+%U|!FD#ogOlTSMRwnxRIxd=Nw9EeXOMAeGPTl32h6NumN
zaaA(CkAmgX2zT9>6Eek-dClbndB;_=0jDd&nfd<0aeh}kZ(r-LSu{E8a&@C>KE~`~
zapEcc`8O2b3qFa-9s{bum4#^N6XtB1X}-y)dB~Pjk$t_@=nJJA-tCvH*@pYCLw}jJ
zsoouuBKv9^98*CZBYgBce!3`Q#8{<D)0Iy=2lMH;I=cT9`@5NHT>&M;W{UeZ#SPkL
z_Wi~+HL~Vfl(<;-({+=a&&lq$&l@)-FCZ9f$ojSPqXdwE0^wB>KKCeeqvD8zX*tDT
zE47pn7dcW|mQ|vccjhMMrWv!LDO)aaK1{Y>Uf|b~d4fQ0*&jqR&aAtaSSlwlEYPph
zZ;<oYOtFWSV^tA|vm>E>A4xNKn2>lGq6Z2L4TIar<%<AYI32TO0D?CF*gBq(EljWh
z&0F-0{nTOUT;;h<!a24WE@nzj|1~dARWRV_-bV5HWJgP)B@vT|39&I?QXH0f5WhNh
z3)$Bz-rcAi*H!`?tl6hWf~j=0685ozRwxHHb;dwPU7l;*p!F(&sRxO!Inv(>$t&m8
zx^jrZz-9Z;sEZm~rQX7sosRqHcF+|ONSKoGKcl~uZv|19w#)bXku=Lq&yTZ9vf;#R
zT%}!#zWGI6#0vEOUWTb*OuY_PT325wju^ixiudEl_(Di_Z`wiL{{XK(uVC#!HTLw{
zmfgV`%_&P-ap=xsgirg1TKwu0be;Xc<Yep0wH8IY-&-+rUgg}oG(aLPe$rd1{PhBY
zw2SsD(F98|vdLNAGC=QWyMa@NZhxAg)0IJA@yEwb_jiW6Uo{Cq<lnb4q$k``P%MVh
z!C!=bPfUm2{2@Mp77>?{V%e+u@~iBk&v-e(G3yWKpLz=`dri9!NYc3I#F%6}^(y{V
z&cZhpxX|7v{hg5_Fa9}t9n|GL!W#M1z|CgH7q%|K@{Pt7+ZCrjXh?0_vWjM&V~@%5
zu;U0a%_X1oOnpKf-N9(VWsHT?-bP#b>V534y7r^CQ<GL9Mb=MBH?FwuPcDll*k>fC
zxo?#U<8gGp%}H-LhJ8&gsJ|bn@G;dA<#QytU{ug_<cbTn{B7Q$Lr#QG15&%O5VZOm
zkP~A8x-Uo7pta9Y=^gh?$eG*k^rcrgPAuxzriWDgWI8LCkrr@=l*;rfNh<r`N#;Lv
z%z`82wYu%jG?_-{ec8m6y>F4RF4^W=<<H;vCDT*<ia<rkd&=gxGiGLl_Bp6G{*LR7
zT;mxD8M5S?4f4IP1lP$s5<1~2EZ?t~<Ad(V{6lBZz-IibbM4i9-#|0T5w`MV@z0H)
z`XQTSs?DB=Uu0+g_l3RmyGgsh%t57_U&B{6w~5F!&<@nSiK=pD2nBL;b4SUdM(#AJ
zcOdk(s_%KOY+Mj_&_PKIlDE@ASwUHC-x=2&3^O)4Z<Ks#42`?rel$QZO*hm9rby|v
zi-+>|*QH50@tXI)!M7Ycp*N@Y#(4Y2tCEG0XR>e(z8K^Dhl4Z=(Qy&kIypG<<g!E9
zsZYaAWZ2hM(}#ZsV&-?Acn%g>Ip3hY-%TN#>P)<>%TIiH;qd`Vd1&4F^d*mO=t%YR
z=vkS<TT<FGt?(b%iR<B)PQl9N`kVUwh;PdmQ4?6zZOEV{!&ZS;qxJoh-G3f6EPPZ?
zSeMz4)$;QmZ8Wu4vnnWBU)(j%^K~3tl}edPJ?`JRclXsC@!5JJy+KZo_`2^h1&q9L
zQvj$e4_Bi2p?e;!oYFzga+a?&Nbk7&$pvhckGdqPIww-aG#0H$Sr;busYtowey=X|
zDv6|n;fBOR*jR5*SOW@g;V4$`K>l@97pbe5_u|7B9vb3>;@JXqpawx`P<tOB?NLt3
zgZp2sQAB=W8k0`{6KnU>&;ig)6qSy@=6cn?jMEVlM=NM@CY;e;M_ADCVj~oXvK%u3
zrpq^E5O>mIOvB9OnNT*`fr=2@4y>1X>PbIBit6SgNdrd0qpf6R3{;}iDGS9dN|MP`
zls~ss!mG4(3R7l|n77}Qje^mX=4H^zLop1c6D*<5`pRbOw(VuzYbqX>Gj+Ah$22`u
z-YO#O$ItTKq-h41Wgj0YzLcDKEF#<kFP|hFm{pavQAPu72YDUGju$GG;`HIRfoE~P
zg+7L49xAl0Uq$bVnX_ufN;oVurw*2uRkW32eZQhzrw3~bIWzJ6DT4}jZ{!*CbZ~`M
z<1g07j`}H1e;A?3?23$=mE{vZJ?6qvjOrDn2{i-cmnP!9ff)MD>M98tQCV43fcLin
zB)3ZnVrRK|ZbpvtY)^_@;h4J8BAPfGCDHPt=F?G^MO$*)+Vw<qufJpM%l_+S8vMbP
zy}mwUg)7#%X*fRRnYdU+@|E07DBdtc9g1ltqS6;2T%5+eVq^s$il4S#oeM)L)^DJg
znC3wck(HP8ArbFA;z2a8BDERWXh7_lfM1%9yT}+XFX|01jn+QfFekIqMq&rq_xPN6
z5Y+>{U_VHqcOZEcb@!+H@*I1v)^PGTVUKl_s&3bxB;v&NS^u3UXF*X6h@Zpg$CE@+
zz<Gu+#debxMgac?j6O)9{#>MWf-qi~;8g1j$OZ17gm2^ty-UX>XNzXbTrFM4d131?
ze$ILrKf@33q5H0cG(8~CGkNvpZ_&PWpkn^Fm;;%PHADU2Q$$(O+@<j44Xqo)L24-z
zqrR^|K)w2C)Lu>V?%+E==lK}H2c>8~V$MP_0<>)S=p;V!zI3~xn(2B#ni?0y)wu45
z#VI`x^zAS2+RU05A65K@4tX{{ui7O@Nx8Oj{fb$?e9J~ZeT=8-%&DO4H3&S`=vus*
z{zW&7?5nmmZY6cUU>B(!5{b`)Nx3;3n|6;g9uJ;BW#ZMiw9boA^3D--9f!p7KSNDL
zt*Pm!xi&&Q5oI>c`;Z&GE3H4YTjV}+%HDQF=7e4h2%XSPWj*mBv#UZ`5=UhVo+){!
zKFd>GakU<*o6=ERmS5$l4(a_i<a=T=GB8#6*@>*Gy!ekEcYS+$Swc~zrCAnI2|mdz
z@-C7C6CsJR3uR$u7pN`Fe%Wedbl5}w1UWx5zjA#$f63=epIX!Kt7ty0q8DcSBVN_W
zEWvlEv$ZXh3q#FY8V9>eh+8n{6snp`yN^f8cEJNqid$8E#h8*cG}+LT7axDoYHX6o
z7o^z9NgF)va(g|-CcEWpp~}NFLfb!dw^Tw(bMl|DU%{?F@R;v@=cD^goaj=Z`vT>w
zk^N+nn;dSeX<&9On8bqcS<+GYzLUU(gO%o_78lJd;$WmtT*LPtnm>T)sld-WB&Apv
zYBV_nQcg>{Y5;dw6uC5#iuUb{12h`&jNApt%8|S{xPo64%v^F5mqAA%OFXQtd^G9d
zKv%{igXTF#_T{}@W6M!iB1IjHMx)Iz_XJa{t&?%AA|%OVnUP#=I=G3B_=G~X347%p
zey@5RzRzw-f}-!p=)<dIce0JM4{qyBzIwqkp~iW2;NYGm)3Tix#BVM9M_2`|oQ5ws
z;;l>OI{&%;=c~~_CuS7WADMS>2VUh>U~{cz*YqrrqHEIl+^M!biq_|P<F_VfBcmd?
zVBG7c|MkP@yO+z6)2W>!WUAg)w)Erad)EZt#d{x!=XD*Oxo3(QW}P7DyQ|hQ!1JyH
z<z1{$4GL^ag;P^?VD#_Lzgt2WT3l1YD4()EbDwa&+~#px=jq~a$zgyzp?rJhSsYzk
zh3CQ^V#;kwhtEz~wy<=3{0o^q*DT(=W&iHO+3-i)X4w^~qWtW-S2dX}9yY#(P4b1N
zXsuik{6Kl|(AnMQv7AOiU|oNXiaUgKQoryhX%SW6G;k(4G%|l+Xrw=!_+7%UKga7I
zI)7i|c_a>lw9C3<oOuW9+@E%}^B=lU`^YSbt7iC*TUEJUVS4XUil-wo&))3^lpF4!
zS9fv2RX>HtJGYuUZ#2DFHyOW1;!8*tHr@{&^6edbxfc9G1@=(Z_G6~!5b{Duez(xv
z;0Xj;Qy#4cczIYHh&b4BCSa*t2mdj=)`pl!Fe%DF#^A-ZK(PdVHR;!W=zVPmvXW_g
z8k7?oMBn6p$NJ^j10}|KzrIxKmb}x&i32pZqGV59EhmX{y82I4vgl`8j_al{%JWb4
zhQny8PKp)lpT4@)bs#G>&(*&Qejjrn!Ssb770x9<BA@!ifInXYbP<h}BN;ueQCx{j
zp_cd73GY}5jfsmgLO#mzd&uq-4@f)zrt(?jHi3RiQg46O-L31zq246K!^Pe&>gbYn
zo88%9O}^<jLrt@JW{AK*C}~6NM8A8MKiF=*uFZhnEYW-a`DwRe`NYDa$<AA{44FAz
zIg|Hx_BqT7)$-CJ^7+uj;O8uj74cxQi^B;}!souuSI^*or{r4;st0<;KG&_>rn;p<
zaonE8CBpeCVUalE-88B1jp;8^didImVp^CxdMgO6Tx-M>lC(cv`+7k>V%=<A*LvYX
z_;TTp?k#?+Ko0ctva3CM9pWm~C8W0L&=y29HL*WUj~fH?giyv(H_CDy(@<gqdD^Ou
zp8`Q)Gm4lAYPUe7+!JDZQ5l%^Wdq4|wigvmJFtK@g@C`1Ld2gZOCssDfl0y?O9ab+
zbu<aCn#to~h)1I2v`>M0uZ}$Jvx2bYj@0}CwD#K!@SHVzAZS2=19pt_hsNoD?7W>6
zK|NSA@-=f-)!^IEKCNg;2z98nTrA_z;&F|tsf$2N0?ak~(?9*cVe;L30}2ZolKYr5
zzX^|T`V9?+tlk(}(OnZ0a@Q*kQhQid7k9|RjcCr<Ik`stH0V=Umv`9uUDV{|(9JDk
zdW9a-do&)I5~`K|%Q%O7zaNhp8)`jK4ehefoEu2SGK3O>?Kf<*9p7lydq@;KAC|4s
zVM2XAeJ_kq9n9@OApQ0uJi&BaFDQ0Hz-2iv@W)?d)}hzNc6v#UrsVDw>Fe=0asN3#
zQdjWxl+WwxGm2Ffnah0(UeI`@?;a(}N!ME(Zr~mYORg*Q#59w`ZwI~UQQ)rp;7xqA
zpSl*|j7GfWbD9p_E@uuctdniFnaG@TySlMKI^vJ$QR9`uJeZ9}HVvT12+r=$Jpa_3
z*}po3X;|dm8*12Rx#RIDly>j*c1|;bSvVaRB>whon!(FbBB<BGgNBz^V8=Pc#N<5`
zCS;~6hTofA_$D02;clB>t<&FCDq?Bn=@Cosi1c$U@h#@fS_yeC@WeTd2k|pz$wDmU
zy_2?u>`58JRm{usj2h1428B#IMn)lJye{SG3o0mOrA70_w#;c0Rg8zYBrRzfJWqE3
zR1W1)m17@<kjsMzb~GS1l83a9`B1cvZwg6d0f<uSD-g};NNt`WC$jmW;Z!}AP)y;y
z@P}Et_g5eGNUu;J>!G6m(4{n4pbx&T)$HqXqCe5&JW_Q+F{n=|Hw&w|_7kv?vt!<m
zBI9mktNj#e*Fq?80ZQ6ZiPfQqBWA(OWL($-xw$U&Kda{ZNy;^KSs(t8gkE5Woj=xR
zM3S^XW4;JgHLZ5x@sQlTi&dBQ)B;|eN9o<2+&D*!KXK-ECaXfpOe*x(03pqS3uolM
zfbgRa{cw|YBwO%1Nr`1I<#YVzR_U#=UUUbqT-OiX@=ca{O!d*CWwa!qh_w^kefC|g
z`41#5R`2td(l7g6Pb@i}iRo76%7?0bY5OVo9B_M<079yD4?C6SFnX1cs=&^kxS8pq
zygM0`1Ip4aquB?U!c5m$_?<g+Uu$U!!z~dRew^}Qy3MversHp1ova!<@}Gr9?k=Wv
ztT$bo9oiDK&cddpYMj`u1EjtwAX_f)^D#fAf_uf|rw@kz)Z3mh&{lOyM_L^R%kc$>
zEz2RJ6+8p&EeMpHSxCE-pCJpVW&#Kx{a+?ix`?kjUk!;-;Qcgk5CN`38OE&;#$bpu
zq6lZ|c&OJ)KCCs3No)5UL)G281!YZh`K(p@-WvM76U)BR$BY$y#pFYni|yxM<ef`<
zO?IaK0W3Zdv<0O{9}%_GYad%H%FebQ-Kgk8TZm41h-MXEM4jt^=T+W(=`4_OzZQ^7
z^yAH}^@3FEN^!0z9t2xBq!*NF({Gr{x_1||^cw=3H|ntCK~kq;@kM0vOq7pi8qSu{
zD)#}iyW+CG9@OmS@HH$4F&D2hYj%BCc`yj%^qVjw#-w2$+6I;Ts(kC(E_q1m=K0(L
zXcUG*AZHeJ-mRs_fjOECIvM11&rotMLU(z+KU~4w?8oa$$I}SLWJHm*QJo)M9VVI}
z1FEU954F{p@mR!)s$*Rfg{jk8HG@UE2mVQAu`W*}TvFEneC`A-IUJjdXfDlRaT@`>
zYC);1{%p-YLEkHc^pR}MtM#o6e80xugZhcXAB>_?{r9HncJ;l$z0>W&a}hWDK-v>H
z@juT<#JTz))j@PV4c)JP+HD|Po>R)(8!qFEXEZSJLQD@Uh`wtE0kIH}4C`GB3O4ym
znC^DHT=kiZ+#MF^UDtbh<)qm(E}OyL`RiVC=i-eaYEjq7Enk(4Rwi9g%Dhs+pL394
z5L^CC7s3L+)B_KKl>>-DbONrxio-Fy8*k@(fz4?bQq8ahbhoV7NDaeoMAIh4yQY;2
zyBLa&Hae0uRwu$3B{CgQk(j{xHN+!{bn0H!c4dRE=}495(fOD2(GrYJep949gI7Jp
z_(k(fp0#)PHHK64Lo#6SlVd7jML$V(EKHwLGZJaW1^>{sC@~bL>!$91$<)<1a=%^A
z$8iL6W)Uo19sjOa{n~ATvj+J%7pX8)!{e~PH^$W(ge<KGnI3Fu?Mk2|DEVVO4`~7`
zXOL5}12L6b)^VqS2^9oZy&(GG!2UN45^6pb&aUhPr3V#!Rs8MGd%EbBuC3qKl*q#^
zxtF1Wtvr4NSJCa<qnX%nUVlof%$E9$Fl!cg4!64gY>ijCwAzx&SF4$38(UBw0{H^>
zU|$%??p3YZiOEdgv5(7}=XJ{o_cK~Iu19b=&dZBs4XrBnFVK%8E^|4vN-ygi{Vr}?
z$1#&(^fDvkt^K<ju@{VKol^xbg%vsVEzd+~TZ?~OInY)VK@9;0x%?z)MM~C2#Z}TD
zt}=w~GfYDZzBkt@8FjgAO>hVka+;%V1sgDtA`0Afl1$SdUaStk25~ydL>)1hHWu}%
z<mn?DgR1*>_g9TqnkLlju|ZZ{syC$t&3CfbzKa)zyI%}154;(6p+zbWi(83$7=I%h
zzY|b7{$M&$Pz%j09t3~AxoQ#dS%}_^0Z<6sda^Krw|9JhVc{Us?m+@?rZEqTz)Uum
zuXm#39i5%*b$&RoVC~w2E>-To-5`#g9^1HdHO_cro;y?81w9cN9BEDQEfKw-3dr1f
zZ<`_vMG{soDSi|*E~%2$N!Q8=L9d@%QL+5_9oC#~TOBB_6Neq3Q{AKg9Yir)_VZrm
zT?lj3t(i8RMLHne{Ka1fKGwB}U{+%_JjjwQo(Wt^nPzDm9mx$E1$+Q~R<@dPW9QQ&
z_Bj50(XV<Q-Hu3g{}9?=OLDxa7S6Yrk1^$c2xtpRiBW*kSz*ZEgiGX$&TcZ8^?BdE
z0CBqxvXOqF(dv&3K_f}ry=)v~MiD?{Ik|Z#Os_kwrMRH7A&VG#&5Bh_6w_H@Lv35L
zC>&Ne2l=sHBJEZ>7i0qGDP4%Qy<46Ke1j`Pe9yXy*SkxE)>1ANX7uh38qgcMc2m*%
z=hu)k1?W2f+-E0(>}3GwM$KO!5~tj!uoM60F%uLBQczNznGKbXdX@w8_ngB;7m?5F
zx^86Jw66VHXon2y?b9x^s+5z&7^1jvu7EY!wCTqc9P0g&o-NwD<~uyEov;1uaiDyj
zW8#1T2;O>=xUN-m-P)F*4?ikr#M6E`Uk-<g7T%!DjwAX%>St=3Z^(MRp*NyCz>fPF
zy)LG9rC`g_Ors0yCeYp(iBv2l+B9>9D4akL)oL2>Li{il_rf|J0t*))oepLAdvIU)
z{l^|pK9L^E!WHNZC+fVcZF?`Od2fO$+_k!rSZFUuYa;ypWt{Q-hEuR9Skv`z2z4fD
zCL#8Jd;xxZ7wtVSLM;8#F1oJW2^!zYg)4**XZ~Z%4v}W;QK~Koc=UTey8r>B2D`nE
z1ccPYyq2V4TVm}$gZux`k^W@VS#ApLXX)u>$9+8rkErKiqp-gQ)HV2EMJqK%V_#3t
zIJADx`%y~u1>t)DfI-E=&SB6R><I+AO%iz-$E+N!;o(9jcbP?>#c@Z<8{U48y=ruX
zaKmHyMh9r-)dk`~AhBk1*t1qL=%m{@4rGP?BVE~j>cJP|C%@4&@kFgJzgzzZWOBfa
z-ad2wJo)qb#y1v?jj~xNE3BevM-yCA=|6Y2CHhK;jE%i7`tH2^@DD6xZR5!2*o?WA
zcUjTe5VPb%SL+-R2cxS<bud%EIn+t9KC*Yzm>O-YBHnbBM*fKCBM7Tt;WprI0Nz8y
z(E-E#f7{^Gzi=)Px<BweZ5ef{frezf{=?j7BbhlHkT-rt)2XfFBZU_tR{_zA@1q0;
zH^FwJ^)I!Dbc+Q1nR^!>P6W{Xfe@!trkSg}5P3-)#?7kKK*^9_`1NbBOJ1QJk&Nbz
zyqbOnIZCIT4?tUj<0pFZxkjHU?&2y8P<mdfF0Z6c>@4q8ko9^l|MucEo&nma{A0bU
zqpvFMr9t#U2qr9!b@&;@rk#}sFY>B_g?n*uXH?|*&s%OrFZ3+CG9NIm6n@e7_~;jZ
zR?{}?;egDEweEGsr2`3EYeH9Z`o)c_ZhdPK<gBeJk*Spv{E#`q94+@TaA6&qKfvxZ
zXW1Dc8~ePpC3W>V#6wcR9GvVp?7B$*FUzV@6zq^}^==#Fuyr`}za5Z8`gdPDV%4d9
zC_S!ZCvHG|mBD7h1V4vq&bt{iLZB|1WG&#lQ08wl@Bn#B2@v(ej0wViPSp)y;iMT5
zzzH<g(6jkf=8Ef=9nuy<WT17W4<9}Vx%@x&h0RNbl!|uf8~4;x2JO1qTDj2DHE{(c
za#_8Zo41}P1Z|x2(gf7fuCu_<+8|N!KlY~4fT%+kuy(l(WFlA5F}NUWz6(VA`g5Fg
zik#wvtS<N5(4kp=@s^<;<Ite*j>70geI%sMN6*Mz$3oyJ$-{k{JQFAMa`;RlZ(60O
z5^-Dp;TblwYxzT@UnY3!dE)sB(x4xe)17oLuc1Jjky{5Yt`B3l0w34D9Chd*f-ST2
z&|ieUJ@gfm9sa-Wv!#8LCqTwd9lBT5^q(Oh$3&dOZt5RBL%0mV8YVt1c&8_XIp1s(
z1aaX#fg6iPGZ9+ul$wvduQFvb-=%_4z{7!<z!RmBZDh%mOzc|`$6$fF+~eD!yMxzk
zO>i-nK7>Uk$$u`Q`^n<rE<i!l*^=%7@Jsqow*LE8asIo<m9&Vjh%=IL0!Z-GG&Fz+
z(*NCZR@F~mg7$SHa@%I|G>+NDDyzWj0M)6M`w44((~<PlLs8|Aok@lR8P}dk$H6#E
z$l$=3VGoB9qkUr`nB+R9M^}9YGHN@pfT+Z;{m3UR@ZFY&kMQ`R)BP{F!gV;BAQl8+
zx`(re%|z8O#sK_5yB&7hn+YKr@4t+5tdJ|L=b0Y7eT0|{tN0h1IfQ5aYcq|a(w#dp
zRM|vkgw*v*;^3S75Zk!;6^2}Gp>w8916+X1b{n0hKku6&hTzB)_^yfNbh3L>>WoJb
zqeURa;8!#MHO!2r!7-@%GERzd8#FC-0WQd2f1~NdzihXMo(RTkqwHS`?huSfh+6K*
zul_XTKfk2iM!+K`X0ii>Z(n+WSf?JL5s*-moB>9HA!urvI8)FeWlZK#%{UB<+A@j_
zBo^r(X8cRh;}>;|$$W=-9dz9rrJ_jymgx6B>q9B&Dz9jpNqC4itN1ul%t8oOMA$wQ
z?inf`qT8m#@l@T>Dnwu0qRQb>U5H|*2d$}6@VC(@7D((1cPGvis2Lm9DoHwjz*tNB
z6eZeZDA7aM8?MCHP7lf1EDg94D{86<`1K%Q9=;Rp*I|-`H0LlgiGrhD)FI)XBxEOt
zeGE3fWJFAMW$=UysK7T&NUDxGh|CKRzY{sh=_KJC4TvAdjN2GqY#<4sAlcGy|F5_8
z_l$(7o4aUmzW~V5Zj%j%f%^pz4sjX`3wWam#HPGKZtwp7q|p(9%%ePt6;X`jNc5*a
z2I<$jWZ}I{oV*P%*CPRCE^fZEK6*BVaP|vYu31p-7(yibV70dTEJ2D3at}tMJh(Hr
zRk4-m%n*02@_Jb@9+MB7>lgYYtvq}eKT_B(86aR=n9quusvCpbmV5@S24w$z<g$m(
z=HEQBWCsx{CqKwk$pKE>$4~`crKm&K?(i70X!NbA%ngyK2Q@X+Dlc!8S5$xw!b>(V
zUXYuZrdS{tO<;B!&D9)|0FZ!^kapCDuu2(fg7vWYzf0hM{4E4dgz*AFxOWen!SFwA
z*;DDbBb|pFKp7_LKw5bJYx*CGf@xGS>H+;mg_%<Z8)i`UHjFfBD7mDkf4;k2x@Hw6
zxrqAUE_u!8j=muvHaZj`4SgXXi^_-J@}D>-AR&jC{2x0hpnQR3gPq`^+#Y*pg5BX1
zw1$a9Xd?i6b|{4!w*SND{(enq##Tji<>tBDyzUQ$1H3#8hak-e9>pyBc0gC8Fu2XC
zq~{|t0SAeTZ&Ia;A-b+P)G@;KK`a*$(s6YrqewrgqV!r!fTD`7w($SU(ES%gI(>)q
z1pomx2SZ+EXmySWtSm6&=)e#EKR34j)IT5xZhyx##<&|~l++(w`@Em?1_!G+`S&w0
z+jaFb@7mbRT9eGT_$wF%;vUny&JLwp92r88oYKizhx#}UJYl;3X%io4BnxjAZ&Lc~
zu7Rm=cuhLE8m~3FyI4vo)WjH4_jaWEhyL(GVE#ZL(@4aaTpXnDdO86K`F<;aBJ6ai
z6)xk_qiR9gzqP&$?y+M^7ikhWlJC^OQhunO<}gG4003f^gqZSGU)I!meK=`RsP~7E
zLn7htoR!6{|8>p(ElFBAWC^FyyOV%M``?dn@(U(J8?am#1~M*(4)Vu?d&3KBXhZxd
z-Ve~oGy|YQ!Sgm`h+fo&EongCMzu@wl-=wTMs*N?EWWbV|5u~aBMW^DkjWd5F`E6_
zke);R{{Lrl1e4Gej3(1HFkGzvmrvfa7O|k*V+Tx78ti8VXeBHo`P{4^5d>)nI45VK
zeN(6i0#LB}ta@P1dqPa40mQqiKEQ=j_jOk)zDw*!b#G8i{+-<aE-3f@Cz<;nUL(B)
z<1P(r18D9)qx*Y@OLx>CgTFtrtkD|FF7z2P^LY_XPq2??D2VG9(?F5M5Sb#$A9N98
zSP@JNA&?*F2l&y$sqjB--qWrGETce1-6sSXgLAB~(ie1)+}FkVrbwz88hP3abiVW$
ztdKc;SLjfBk2i6!i~3PzTKbMb%{L@K#TeQ|@9*UOJL%(J=)LkcQ$n;R%UGltiNr>g
z4bpW}=y8S?HFaohz5mypGm??$zGENYCGx)dI-o?z<{D8Iu1;0s|5h<Ck)tkSpv`&h
zG6baCz<3W;Vg5k&M)6|{q_+0+!I;x9$F9zSGdLGc_Skr?4M3SjOl|)+2ux>7ki{NG
zgvp1|--5Qknh8!fJymz8i~k$0*JKJS{x8t)Fw+9E6ZJO|P$bRqAC?qjBQIm<pgXx2
zEZerUQVIjSg6=zvSe^hhBXSv8`!4V>)0$HO_Lps5DeCX>ZW|Jq!n)d04u5rik@Vjp
zlCfk5flv%bMqMt%*6`odgz~(@vHm+&a@vf*UJ*JMP<)yT&jWIubo`JRKo7|IR5W$|
z3-~2F2<quzn*kq=s>4}GT^`_6Ir~@O;1d9;l_LNL5j=dQm_sYb_RXQ`2pDK%0RWcv
zHyj~#s6K&<2;NA*z1D`fA#5*#2d*5O@=xFYKI=4PvF;_(hHy$b#Z>nyfjmK;2!Nn5
zdiP!Px+nvurYdCdbbbbg{MZe`86e4+|Ilmyr4LO)(R#*;WpQ!Ib-O5HR@`f_79CE{
zRNDP$AyOZ+`)dkE=ACOcebz8yaTM{d3HiUqxc1tF(&d7)%(}Lm1_ls9f?>fIV8ZU3
z!gG0B7sJu$iFv>xK$Ha(b_6j)%0;i`O_>W+fP2WQE{Cr-)xY=J@7K(|948)kWjqN!
z$1a;WSHmYmlk?Fme-q$1^C?ZsWfNybX~rbwiX?DFY3z6>0m~Bv?ZWDwAi-G-nQC+Q
zT-*}4CG@U%i8=5*HhnKMpg+`(+yG`3PeowK#E6EOn!A$IEtd!S8ZI-QWx4#K|BRZ$
zmdn`^Z%qT9{`YY2UQM>@OBX#inrT=pGb^4|Dx${OdvbB%d%q>hCO_K?clTOW1#Sso
z)5wtq&Qq^c!B!%`#59+r&GiIYqjuTz#PZ~O)8-reY?}le|9w15ljE#7ci&yNS9!oC
zdTxyi-U2JXw<3&Peuv8BfkPTgfObuo01?a;jS^k>{~~G{0v6EBmnK#PU1IDizfcZ}
z=m|~Dm%20<o7dV+&RDwa#%*9aoPbeK%&O`tRkObAli7_eT_G1i<gpdcGUx((uoLE7
zEt-0Y<=MPBJs~BFZCABjwACvNc%SbT`}U=4XQ9f98=o6JB6}5N&oWE^=K0MSDV#jp
z5jlj;`WgRU!iXMV_>)wJQd_;0`%;Fqxo(D`XTl9;y8<tQ?Ftt?1ng3R`hX0>05ku;
F2>|JE`mF!}

literal 0
HcmV?d00001

diff --git a/docs/static_site/src/assets/img/mxnet_logo.png b/docs/static_site/src/assets/img/mxnet_logo.png
new file mode 100644
index 0000000000000000000000000000000000000000..be8ad9ab9ccdfcc03b8f32e1d66e661fd97ae5fb
GIT binary patch
literal 22390
zcmY(q19&FE(kL8l%#Cf^wr$%s-#8mP+1R#iY&N#-<c*Dule>G)cfb4pQ_swFR~Nc!
zx_g?DN(zz)usE<FARq|RQer9~AfO3<!;#RCf1h9|4zzz?5SGI7!XO|G@o?|P-~Psl
zOr=!hK|s7IKtKXRKtNvqq5@AqK-`%?K+cRnKzPzYKro%MJC*tVCcrpJX}f}eFp&Pc
zLA^=@J^pIYwpP<}(~_6tF?F<OFg9~EF=z0yclt{W0>bCT^EYU3?q*EnWpC%;%HzdP
z@*fJGzu|vqMiQd`kht0MlW55+5s5mwm=m!xurV-^2*45%5%Ia0S@5WcN&FA{-xxoM
zm7ALr4<n<erze9aD}$qpB_lI8H#Z{_3nL2){a*@tSAc_?u@}9AE9rlQ{9id@=B}nL
z)=qBLjt)fs<Qkhey1Vg{ko;5hzt?~B>1J*5|1>$c{*PII4P^Y6!pO|P#Q49m|FZJ^
z!}2J)SeyUV{7=3BGv9wG|9`mu(Zk31Px=2RGyk>pKiI#f3c&I){;%5vVC4oVwLw6H
zK%~Wl)x1D2bD+A-#2dbRT&E|Fy4%fAz=;^f!BI^h!M_jU5el##9Ut%y_L&U#|E7~+
z;S*Q>DJG1Zh?hDQG{rsgb-l5`zQO7_kkP^VG~MIo=OF)ds;aNAzooylm+k7)?g0Jv
z=kmP323rT=3FUOIXv9FaoE&W?*Nkx-e8zA86o9!Un<fqZ54Lo`haekdH8{9pR{O8V
zY!*}6(rECHhp;cyKl!zjQO3idi~vcZW2dAa6bNJpZl(xs+ECxI9nR{zl7Aylfo$QD
z;xT4p4LHLhAtPcUqKGVLuIfhYjW}PRIiNdW)S}j6FkmvEGN22P>y@yyQPY8>M6wPP
zco~!&(u^iB{Dk}mo#l|7+2Dymt>~gb3(#RcmFs&(dng0HLz+U8Lb>3x#YjPp8)e$1
zo<nYo_kssTLZT0u?sUz5!hGzfv(#CMWf58bgM3-5d|9ppqlk}c14y2}mg0xR`8(H;
z5tw)s0)|23WbhbXD_yyzGo+lk`bLlmP(&c{-cG0|^mS6TExLC?^(a!^<c4iW_9^Fo
z#j&#+4L1Ob;S|7M%);TahnY%xq@G>&ZbdZkChtu9$5v(f?`K=Ezr4@w6vCN2L~e$-
z2kT`qtEC*!KQolvSIcKbohWXkp>BjJsiknFa8A5&%2(1kYD#}O8znQ>9zkT!=an@t
zgw!E7&PRZDZi$?U8q^n^Ss)H&!%oNCnHoi(D-KktmHAwTote2b4w)m4JCrf~H*Y2E
zv&s@2N#D(sF1!1A5%pdB{i1I0_98`U5$sZeP&h>Go_JUXQHXGfX;IBsca1(J_7p=`
zMy)S)C7j6k-D6SZ64?I1EIHfkcSJGB#XO{R(4|Ku>+9G7PdIG=R=Tsqm4*urg`smq
zcPq=Sdki!eZHL%sr5WmJrDqU0Ilb__&_AfM$zQ;ah_N8VR+@%q$_Gc!CVmgRktzUX
z_0Ff|ip?&z517hug0eKZVHEGBe@}VXC4`G}HxZ+j`|A-Y94mGNw@mQF#0LpAi3MIL
z{X80=uiEo_KAFW8kpk&9By3xkT_7p5<@Y)nFPC$Y(}T9fYAwNH@_)R4xycdS+QAcz
zizf|OlKuM(S7zoPk3;H^qJFiz#9wA3rZJArDhpS18k7ut>AnI$h>Hmy5(qxTv0?rd
zI~^Hte4G5=)$4-#C0>Ho`v(K@FaS2pU2=YS3r`(qr8=8_8MrcQM&4T0_o{)q(Zq|N
zweQP0`>iQuy8jXI6Ou<09REltU0SjV(cra9>iLI~Urd={M#y=YZD6pG`VZ7^4ygWW
z-d*9*TH$(>|IPG?90HqPDPz%!=%{dqy|$ki`ezmLF^c2fAk;)oMbb1KF8(gxEP5xm
z!L#P+wJ0p0$c3{L%_IGwY!l+Ym9~U!R!x*lG;POQIXfQ9U-mhS2wKs=m?`UOPz0b)
zZ=@TIE}cj841}FYLDeSL-#7DmLC*Xi-kidqQf_2y?(E=z-#X5@)vpCN$i%F+@J03u
zRGIldAhlxiW!Iw|?R!My4jfLCZ4Cs((*Ccpdf@UIAFQIo^)TpLPZ&#4*TQ>zTJF0-
zKkpO1vw(GqSf0cww(C*m^ulZvnhrTL{69VfPa+npTjLi%<(#?5Hr+7{ZL2AcL8+IA
zr;z0dBL3arI&hv<vCf&{2dXq8wmI29Pn{?su(`oijRJ14Xc!^ECUfl>cPNYVI@yuW
z?<QP`xK1BJG`7y<u#^6N%CjY*-*{4q_s_i$-ipXk{O@d{wQZp?$I<TI4cVhm#L*l1
zZmr3ge+&GL+g#v9kRT<(Be{Xy^%@S;`6rGS^KT^$dttkFv_7?t9Tcv?uv(7N$j(ei
zldVr7g~J}Rh>b#=U^l`4R6P)cT{{S_q|E#+L^2Mos4t<C`)~T!OK7Fw+nJd~7W=D_
zzMA-f@k;a9&uVhsJXIF%rSN~-Sw-+hDBrywNJ~FNN$@J3q4(q)G4I97@;NByYiml3
z%9%{C=(p)c03h9Y3EAPA;;I2wf_UFL{rlF9l6d+7B42J8#;UT4lmD#MQ2=iQ=iBbn
z+?)G;B^Y|d<&|#j)P7YrE08nTH=Ry780Y0v%Y?dkDfA;@TZsCTI%OF5NQjSix}<o=
z!loZ5r}e#f-K2#F35jVo$XC+FVlUG3UtONC?)ONwkZMd~l^O}=7Mjx4a_3i}{T&i$
zAT>0j@(w10Np_sLAS(H_&u4dZgWfWB>O$fMc`k)duSmoAuK~Vi?>GGdLHxl*98pv6
z6Sa)w&W((IY>1XzkXiHWDS8X7@f1aIBPINSBlOe^dgIqJ)nI2#Ef=qN?Do??2{P9K
zhsh@fAtKSOD_r3$*J}Af$SlE$z}Lk02W+p}XfDn+9X1VhK~fM$e`Nj2i)hQ->a0eE
zJx_<R%B?hbPJR$*!DMDzY2&y~5_qEU_!lV^`VIb`un2rA{_x+zoy6Wh`GfqdK;%=M
z!LjP#W<eBziu7+Y#<?6g%v}ZJiuGGm{jzh9eda>Q<QLjk2pJ%UiQj(dhQgXtln41z
zrVJ3gK2G4EwCGvufti3s_V<6-+zdHs9D}5??*2P;`a(#Jr3xwc0uZ$Sg--S>XmNnl
zOjbCFT_~#_7Ia!O3tx1DI#+|(fc%uwI{(c^$aD*Dcv4BW%fYLJ6*~Z%sL#D5;W)C7
z;&5lK(H)&|@PtJe&N|Q)X}Z*^DSFZ4W2xaeTx~3}S&5Wf`6nb!HD~})t@AWDm7x%n
z)cxsaWsqP7Uc@*n(s1~j7kB8IoeP1ANHn-Yh^M<2`)l13joOZkuJ*j>kK&-Ka?qyJ
z88x5<d)E&Qtu+@jyJyd4JAHmdvDUmeaO)^ZT3V@2<#@<_j@OX&8*OXaDgyRWK51}$
z32=_J<CshsGu3!(8Xjb9yw=*)O0ZrcJTfy!e*0i47<~vU+VvR6-S@KD&o=L&)^LWu
z4)$CLw8Q&)tfg#aFmeoK*5P5h4XFmIk-nr~++VGr!90}`8PO+wWS(o<<Bqc?3ppk-
zl|ckaRdwViaDK}v0(HfxQ`YTO&bg3@3^FZnCHKm-74j?G_tFSx(6&!oRx77;Z@Jz5
zsw%5xT~Xx2;w)txM&e^R#`IGaa(Q{W2fJ6K$)*zPrAzWl+8O9C^;TLAf(YVr12PSN
zGNXkzp60}#dE2wYLc&)eufHAGEZ8odw$Ye{YCi@?7n=63<|{X==3fY161|M48df8W
znL<`RYlzk^S-A6lN@+U#sI>j$tB_UpV`9m0pG$yk3zk;)d+EbmY?9X(AAy_e_6qlF
z`eQn2bl*y|!x8!^82BBh-!LNa3auyP^(8zzL3+iK)=SVClto+LS0sb5sG!EynWW?k
z-3EC)`CSA3)s8r3f+w5hhr3PVir6!8JxY_{wkUq#f%qhQB4+i}qU3i#P$p5D`_JRb
zK-Uo89~7#W>|V$ml=UPvkTn#BOe*w6rA)bgZ?YT|@EIG8YWjmS*)m&S5#?*+k!N-a
zzcDM^a%B#kUYv*(spT=_Q17%|TTcK3i+Et2>kOOKzUOFvJ8wk3a?c4)aSy?!w@FOn
zXg}Miy@UF^aB0qh8lBvZ_@2{n0_Cw>HB$nnmanOowo@8YU7`Q1URjy@8yW=)y(pTL
zv!pz?zU9)WU?Lsf6;gMtG#{H<aF4JY;Vqmmw792e(<im{LYD|ejV{q^YIJ;mTogg^
zYf-7(9tm{H702z>eC*Y8sa8+_BK%Xrb<#V^6A&3^mqRaWnPyn&M^i#vcV#-u1pks!
zI*<LN8Uh7t2}=osgxt8%Z`lj_I!>`?vq!RrIv_b@*)WTX_xxLSWjpZHUsi}L3EN}(
z%Ycec`x%V*(PC(hK#0>-Z5bc74t<-3-%fcHJH)fTbNXEG)IIolF09!+h?Om@bH)Q*
zanzrwcxj(+bbtn~%PcNrLT+~0Um9zEa6%9eD&&g@J}jVbJQ2h>>(el;Q<vflHhXxa
zKa0+TRyDg#!xzI~OQr8^x!h!l%YLlwWr;}<G7k{ok0V;~A3@!z;8x&WmpKHdx$bgP
z2qow3yn31WMVnpY`#vJdgHuUIU+%z$MFnSO@oYK<$T1sBtFgV>(^|dvOHBZ^dsJ=S
z0tt9lK3Y-V{wPf{nU;C-R^w86!kUCXs}X?=APwXYL6Qx{lu2dRu%JHb3{UEJPLCV)
zslFj2mPlUXR4BsGqS*Hd4M1*07>~jGXbVA2#A`<PTjpicS6q5L1qD>MV9my2%h#0&
z8Tn1lUu1496M|U_v@13k!ZJob3>5sS<r4=dt=yn$C_+#~$)(OFgE*noe#LWd^4=<c
z#v@LGs%k1E4|+R^85^#P70y-pu_hF_%Rw9^3G=cPzE2O%eW25h-eNqV&}MUh*k<zE
zqs{w3q0Qo)p$#xX&}NAQ>6drD)m4VO-q(i9TU>`LSlE}vWD<zWUv0SklDw}i8%hm6
zg3KGI@AId#CCj5d1=pqB`(B~V;oPCkZ-u}T_;|js5R11s5{pmM5%%PHK(=xtY&UiU
z(wSaXvVye_Vf2c$7kRduMdm0Cj{Nv7d*mr32PQAE8dHkmys2|?CIq{iE5Tx*g65jD
zG->}w@>Oi;Kt<;hV}W0Id3tlt%A{|6GTnX6WO~884E7vn)DgnK@lZ3d17yKqjqi7!
za7Xxicjw3(lQ9v(0V{U%a|(uLJw4#@Tm8PN_{j7aZzyUJ->q0A5AaPzELZ?!AbohA
zj*X|*G&t6vm-@Z;p|QxIhbph}I7g7fD=Y!O;B+83BplWfrpE3ptb6FPxVX{Coryo0
zoX+)tc2Z^hSCu>XFY3XC3Kcr}O+@S-+iTsc)as2Z$j8f5GhAFSxy81t%>%?-rq!P3
zi~~MJc;|{pZe2bDU1N{<1ux$<3eTMZgeync*7m%bwSt#&3KAEbms(_5=^CTix-_4$
z(cY`Wir}r=8LZQ|be_q_^CVl;ajLe$NbMnK);o&(LUr9u(P+-j0J`-P2S1yvF6ZEj
zCl6}6YLESHfjGS$veiI?t4ZdME$M}@_G8)FU<$;?>0k~Jt8kuQ>)B1n7K~|du5uHs
zn-~tviA+-|Weykk-kO5MRnac>9Gk1Pvyf`s>pJs(wh*~zN*N#kO&DjQlh{a2_K&Dy
zlG#-*|3BB7u%@Er6L{s0=_tS+dJRmhIZx=aL#p?>yc}9c#ou%i;oizfB5r*8&*Xcy
z>dSpqg4;z2HMp-9KIgQpYr^J7makEH1=)G1CT5^jjP4K>uJEI<2*gh{Us(8^rPI#-
zU1f*e3i-r-oj1554|mF^`+2Y*MFUZyN$6BFpZMVKpEYt4W@Vcy?zBndd2#~%XX#y)
zpR)W|Nzs$(&vcbk6N2a2jtIW(h=^i8D@V6j{QG>ZV!O_MA@F$5Kd;hnk;2Lw5ogdV
zV2Lv0Kjr_zuHFF(_`IiggGV-@jQHF5=W$-pE<3t<^7RgXT+O*(9Zxiy){D9?$Zd}`
z#NuymAfPLHL%3vNp&{kz4**=4R_eC)YSWxOpK9EXr*QVC1w4N6n$0sAN@!4$Y!Msy
zhu)n0M7Zh<*XSagTH7Vis%n_AA3J>g<D?q?=<!-BRXZe4wG{@@*C^kOAzjv|)wuLw
z@@Y|nuF>I0z&zixcLJlsjDU=OcbwhPH~J}x;>H$(QkBpYk{vp$JmFv-Ib2*!h=nAc
z1(|GY6pYw05rp7<*u>RL;o0H4G@tm}OG}F$>B(LXMTbweSB9+=6;CCI&W5+vTF{Zs
z-LJG(klrjB+z5Vyt5%bM7WIP!E(vD+5Q|th`W>}NkoH1}vhdC+)7Ks+{DXaOBP5VJ
z)P<Mwfwo=;SK07fbrF|e#c=_O*y;ndM=0RO4dmFP|JX65^@)V@`AlaH%<j}6=*?x-
z>?;^>aU=JJHR9aL8Yn`W^_CR{CkZoY5U1(&1DR>)P+cOH0!gC_Z*~TswYW`}mK44y
zofX>_6#gCL<Go1{iBqNN`U6B$=HUmRXc}ht=(1}I0jb()r_;0K?OaA4F{9<_`V^61
zb>k^YzHtyY**&yIh%bDA@O{Z?$)Wh}K^>L9)2FWMW)I<N0FbrCPATtJ=QSN7r$FYx
zGO_I7s=3oN>30GuAJtY~_jrCvfpB>lR$h<))3WGLr<9?2qFKF~Z7jr`u<8{)3B33y
z{(AFV;Gr$$>Z}8Ii_<`0iQi{f$^!7ng|Z4-x|>#B^D?693H(|0IscNlS{%0X`KiqA
zqYn1G4ifCCmzts-6}d&q_(I3ntf9HD-G2s6@7&4pZWE+M;5F#dqG>pc3t05+kPKT2
z<8u*|wl+lR&By{9XCG0PLAt4IakwEz3d?w}er4KDt9r+jjSHYErW?1%DTg<^QBb`N
zM*r$aP-pLlsE93k^mGx#GFe|*--j8xMi!vbBiW>a;PTBQ;gfkDKyK?)?geVHAtgs|
zCk9b=`IEm$NHf|!8tdA+lbobCv4M0PcstKz!!{=WlpIqB1^jaH`!yf^JC=$WG9Bb1
z!=;W+RfE_LsRni0_?rqswbhAC;`t|pM(;j3FM%*akv)R*!JatoCZ~h;1w&5NS_k}t
z7dJEfgZJ1&Iyby|5tvn1whl5lWfQ}wpa!1=;`IT+R_{p9-4omvkcShn;GEQhs{Grh
z2!R*;?pie?Tecpg<7E&1syi#KvrDt;8vX)^h{iamO`J(C20`+OF*PABQtLf<C-kV9
zfYjj@(3pLekK(swf9}zFtan@7b^pk^*#H%bp<b((?HJonR6p&Nx$jxw+61et<nk5-
zq!H>h2~_mqsk;>wUMyC_o_#njgJ+rr^>t*%+Sz~Dw}mHYzD<uXALG<^9xk+XGrmjT
zwln%o=r?4JW?tuw-Nw=62+x&!SOVM2m_6MRnBn9PO{K8rMvuEN9PWmUeeinLNYfwe
zVeI3Wexv*IfNZQ-+-(TGMrs4$h|dY94?_W^P}huR2`TyF+!`_EJS*hnLSoErBfq!}
zbjdRNyCS;378fgWe^v7IEIG@r7<6CJ`-b{35_(w;8a?-PyFUElNG?_t8|0`YuTGas
z4tm1Mt5pf#1AZ0jEy7Ej9YSqF&q5M7=DF4$3v4?n0IGcTG?U);?%IRqk{Wxw2DU}@
zz!nv_N=RQxQjHuU9s&jTyM#NQpJi}mvum>FPicw&m-WxqAL&JP>aVH|sBkaz6`m-4
z{mW2RQ{#^kx^FsAXL@D^b-7nlEA;NBZuuXM;qeHp{w)f-p}%i*t?!_5-KD-3HZVF|
zziMk7?Nz6+mL>Tu^RgKAA^H+&RRD9HgO5`&;v_n-v*9T%TJjPBylS*RC0nnYXTE-e
zaavjldhR|G(_xkb!J^X*@ugX07?g1I5y2D9n<UarPhK303=X);e^)?nY;F};B={}G
zO)>Kk<=U?(`K?`&yLiZ`G0~&r28EcF{du<9G^8n%4;}q}#T?WLY0VT$c#Sq>B7`=y
z8=OfKdJuS|;?hTP?XB-G%_Sajn@*KY!KcI}715h7F7da^#U3=aJ7d1+PpEX|2+M4-
z_^nriuqyBREJ#9Fnuw9lCMDWirR*4PhoM`Gs=<aOdHg5B;7Z;j6`u;XKc(0RmhG9I
zg2zK(%!wiRlAn&>r%EV5$Uq<Krvd>DD1+v^$(&xz&BQ<(>>N5Dj4Y@(GvxqkJlx>1
zMCfQ>EiI@yc!(klw_*{Y&fBUVIRNJs0~K?A;#8<j2Fx!t653@>5qgdsm8cdWVuO9p
z0vbU|2%8edQpcpjDW_F^vy}hJzj~8l2nNVb@#h+tJ~oq<Qe4UuYpWAQ_%tzRMC22>
zvy9)hRl>>WM%XEOpiLVG4)x?RVV?6C7O%OYh0Z4D#s$Z^&J}jqId!p9YFaYhz$Un#
zd@K#fwTzg<0}OfYv=464iSj!y3XP?+E~7;b;?*;1H!bA^T=IF2p=ND@>Nvxzt(z?6
z+@qUikP$=Dit%$HeiNA1SXQZ^cK~T0PDqRH?!1jiC#X!BBITicy{d`cN;3#Z5WLY0
zM^oflfk-t@b@@AotXoh5kk}vlsVf}cM-BRr&z5>LgFLUPRU;rPrLNSZSu~&%_)Dyp
zH>6GjKyDc<jG-!<+jr8J*%v`lIFhgbw?9qCJ}X@`oh<)eH-H=Rg*J`CfawUC8m=TD
zKL;sh*}0ydWf%{W>{wrmgwZSW>_FV4{fu^^yP_TF+q+RN*RcI0t{)&^*6iTm+hKt<
zY~QH0LL`ygiGZwtN<bXK_*w!V=Ae<!XPf3JRZ1w!(d(9A{%lGAdm*xnJ3dmTT|~-C
zVtpa`S(sxz?UkR1PxX$OdSD_^!VmQ1<mOZ7shdEQKjK^ii2~!8%D#8t3$1WR#ZPvO
z;Pq_f@Hc1<5y1rdt2aa@N6=V4ZcOYGOomuSc9i$c9rbYPhGfVwOo4Ez4M<MQZ5i4K
zbE8)vWn8d-kSY!Z9yuPZ*b0$fhCm80xU#v@Bm+4J<uO<t8f`yiMzh}o)<s7XWjkve
zUX7&EQ>S{CM@OhP$@wyHV-taPk+KEIB7Te|OIB#T?5(+*L!F?v4RG3ap36J#{Ob0s
z$>M8_cQlXTgY<4x8wz^B<8DTGVfhpiWQbvf#@3%E)PSjQ_vOVHoG0(iA}w=G$lg2P
z0qHw3q}}vU$jlW)5=NR&%WASK^4YmQ?!Z+)7JVRFap4XP9p~(ySQ4f0n4zELNEck?
ztdj_0N|0N4`!f$171-&rJ9O#7N{FVri+9L)#v_7yeWc{*R6VYBXXJVYF(kZNfi`P6
z;ud$ti@+9E{r0HFdI<U5d0eSo;n!CF74wE~{7ut9fd~&Demh2Db$81CWls3>yxI+m
zfSXE@>YYFI9d=G1u?_)skLpu`>(7Ml{5kEdltTv$e`znj4i>kW^B?tiS_UcB;H$W#
zC5{@o0fC*Kp#z4n@o2*=CoHJ5>UO}OgLkXZp=WkUP{T8um3#EBq8mtJ$Or1Nkoy~=
zj4;|CO&|3)<RWfrRdyD%aVpzu1Lq;d=(zmkOHe<P?!l+Mci+JOPQt}BEve3N1QL|P
zdB3IIr0h4S@s0lWVR>lyneTJ>jc#!7nX=dIIy&lUg+HcsX~9h>7mF(TkMQ;k9Jkif
zqmq`gpm3P^xGD<SglZ5GqwNrUC?{rl_L2-()sOLkxfoOiu?i6UT-aF6XRz-O&?doC
zl;y~Tf9C#7yIRIQIKS=jhd<e^pAzVG|MEMp1ztU|RHB-ZX-2`}&={FzjED3-jn}V_
zAJ}rm2h_SZNj$n&rSxjRk5p8=Mmp_r=$}juAY2}7bDBo*TB!wi25y&})MR{R(syl6
zMRRu1KC+?455;M&tCEZetIe}KDM4G(C_r88_<Gg&EGe6Ag(T{PQbn@HkEA)cIX+J%
zUHwAn+T7Ct@R5awZ6+lr)-(b5M5;$o_(=F%1G6{^^9T@6E+_Px9m3xV5C`*C+b3di
z7dPy`e_#Mh(x`PXPI7d`n26mL`uG}$@p)~VSCZBd!8JJ?BXOB;qm%Q31-@J|t+1T{
zN}UdGk|<-Q5olOa7J`OzAG~OvYF_zF92T9`uog~+^a5oOl*7}(`}WzHTTRF>XiqHP
zG4o~am|c13W-~VROxUWg1LSY-laVrvMR=NTiqBsuV9ZY%c_KLK1aE^eI}77Z&QVJI
zlx}fDCl#nko6Ajrza|PcM#nBbMPa7^N`z<dgYH>%=h2~><(uT2bl*BhFos+9Uo1m+
z&_2760Zro+Ds`+yg2F3Y$Vu9Be}rd?m|u!X<h-e7)+ese!zG$TIH#5Loe4hByeakM
z^e%N?y!I!uN6XMGfnjvbfXxvTjb$h)pR~Jo8D9su2WI`T_7iJ3FSKAqQHm|2A&}(4
zi7o0c-UwY9sPOdbF7d<Wk^p9PV2ujiBoYbjgH_4YVOamXi>7!V4PEh}!T#@xn7IkZ
zzf(t9e2enW2YgQ--?FyQxKT7n&@<3)w@b7+rh{4IueaL!{e-6vuvrGP>+HT?!gK!s
z*kzsa?W&_fwYLelCiB{?J8^25BVAOfCYIN;x(PVHiqdYKJ7^!xNK=gZ&?QARa-p>L
z4M*H4+hcFlJ;^!|jkXv8Ej*{MkGzka(>1`6tZl6m#?8(h2&Uy4dpwVjJ<m{zHVcx%
zI+aCVfjjgCm5p{+^y{y$bC<H4m;^f4!!nZa>>b>KHU-pB4`>n)@ibpL*InJVml~Xa
z2cqlMeebjhLyC{T>lIu_c_CO$963@s!#JBFibejru`O3$^0%5a*X$zC#Fg0W;<RAk
zgYbkCzP-#3V!J)GG+iX9ClGkGDjYZ8h?@k=I7sb*RAYV;&6vo_0x^IgY-+a_kpz7G
z7s(RQPhRwGTC-(E*G#*p)NkiY7oko1w+%wp-lcI3q%W<tjA}10YqqnLYgDM-$i2yq
zP<O{sao9&$q0F%iRbUSwRmc=<DFl;&#TfOXu3iB=X2k?t2`>G9uNXqQ2i?q{?%eTs
zxQ2|`Qiiw2kH29<Jj^8Id36C%7$B#!3o}liZQnlB{Al2K=Gs8VjQoE1kXx^t(c~Bb
zux&9MMWq2Eudq^UT>y&>NwCKYWR1&38Y<2Mb%)y<M^)1s?i&J5-@v!sThx-uT!+M%
zbFR7Brm7%9@o~X~CoTxht&UZ{i)r=Do+oEoAfMGEHkP%;lM9Jvw_V}e=_yo~YJA3_
zMGXE0<Rh&c!3`EqeZ0@*9O{-kV-6kh19Z(ItIh?syIMD%Q+(ojUc^o9H@Rl_s1nZ6
znJ$amE?GvJeLjNJCM<G)(vbV+6whwNSx2ueH_th1s25LdC!Rkhg_ik*(+U)0Cjl4e
ztXpMB*hd=W0;6I`O(<5)L&W>__#>ZNq`00F84Vs4xJH)Zy-ZF8m#o3Bpp|B=GyT4L
zx16w=u+9YV>D7V6`B`%41{D6Jy9hhg@M6)4x8OpzGc<&()`2`Kn$KCkUW%=z07P#J
z6SSEYK`ZW5mhx1C4~8{zNj_oFg5=F>B(FhGj}&pgy>iC6G~4C8jbffq&KjX02XJ9R
zKWlE0uQb~A*=`^ND9vO;7)~E6>T&W7gDdujTCwtTw*-(G``ppni8hRg?X{=1pyoIA
zWHC7_`~~47wa-oYWWx5_R9)tF2)+m1#*L^}_%4T4H^(DDG;GTN$1;}*1X5Q9ewtU-
zwvx*R6c7TtVNiqxfhOO;@(-gX`P7M2{QGKtUu;VJX2X7Hi(jggu|(Bj!~v`&7z{7y
z+C4n|bZrMFrd&N_ErmZ8FL9KZMy|GImf7dEDPkWI2>PY^9NbfOp#-CB=rRd5S$foU
z9y%*}$$t(>#<^k&xQ6@*^yn)1S(e!#VKV;EWB72teC;;zIKR($rFBs|d+=LwRGE+N
zm`magOxA2t(C697GZb%KS~A8R95_iPA)S%n+;(Wv@({O{v{_8-w<&TO`Ko@tHJY?B
ztkU693AeI)L`K=k34BYI6NGjYC!p<NR?1+1wXxhMB6*DmYZb~E+Ae#}7`PO%(B(?6
z7HTrW%B6ize2&cVB;mLjzeBOV;1i%5xCdu`FGq60%8QS@{yOka=)k}pTgDdBEwGH?
z^KM*Yw{9kOKe+iKTe|dw_1l7;Ent$p-d(_b2%=26w()|V)lcot<$AAohX(5a{TaRT
zChJ!*GlAAk!4%|3zI`&Xo@LKxd!9)lbyx+W@fk1Hq!3X{a~q!f8Bt^z3dQuoBG8dM
z{Z`YG;}<%mHJQ8_-mLk@Nby5s#tC1fYRUJyNkryW(#OgVVgI~(pAn9uBYvk@?00c^
z7Li_^o5e{q$A!nSpl$Knx^>fLHH@h7B?239leZ`WNFNW$MM(|2hD7ph6lpU{nw(ug
ztU4IIHcSG5l*L-SS!k!`by^;7s{`pQM$?i2t|ikL804zkWZm}m>!RVRsV*0|gQgjN
z<MOL3S+01`4pc*dkl!kO^3q@jt6emT4(f2cCdTC@ZM0!8!Nth9W7B@#RnBD$ik6E>
z=^1n*tW!jUNy$G;;`Utpby{fJsG`*7Acn6z=jujhk29p1e7d4quG130^!-``W11br
z6(?jA)K}}Bfdov^3-wwoVk18jYj%E=$en`i*tu(;vPvMn!l0UKamt%Xb#jv$%--=9
zv=)6H5wP_$9c4A1Ff*~0M7unk+iM*IV8uEhV-)u8kD`wW&3az={oo`w&$qy}WDB_@
zjW+yG(;oqG_k&Fo9oc9tx7c!bkz#(m(QQg4kNP8rv@iNuy-yNC>a02MK_l#C+lRvi
zex4nyD*Afh86+MKW9t3$*<8`qNXsRwosr=+8kQiI>4!Q*m!RJT(NpVfX8%$>jSl4;
zlx^3^AN#JYFTUQySd*`L5_Xef`}OXPkHe7LD}73N&yNQ;oN3sg%CVBTgSI#1_l|`_
zp+hQws9(gHioCABnyTfVP0sBFyrY4}grqike>11XQyi7EpsNZNJPxKm&?$qZQ4=Yj
zM)&kN1Xj>F&rERJi%Heil#MR7@tbADH4>PkD-OobNzSZp)$jKHL*GxjWRP{Xqiv@T
z2=p85TT~uR9COjbv1fJ8&0h$VhKGn<YxJZ4ILs6{&^5J*S7LUNGR8V+^~L^_k@%w0
zw?p)-b|8nbl}9s-Ovzh)=RR~?kc@uL+=Qa}XpaFzX|cUGF8`T=+qI<L8;^m?nWxF1
ztY%lC1uwFc3@Apdm7K7uQ*xb2HqK!@z8$iRbt3eNjA<5di%7J9tb?2+OQqu9+Wqv~
zR5pbgPqv1Ww#e)Ww8=CjvqNu0;53MbXc95{#-szRn{8bh8i{qb>~X4OBxX#zviw2x
znLG#nXq~?8(5Au8A<vMuW64}Vz5wa>W}DX2>39lcNmB?f0s2}HRrut8`wfIj+&9GR
zV}!~rHg_k%LeRz5D;D;q30Aj`S<PpM$%#Hs4N!?jpNJ0)<VJv#KA0Q3MmC<M%~i)}
zY$nMk>^uhB%Tmu<s!YSbps8@EZw-*a!+>A_<X9fc=&ta?w6KW0=?Hqo&U3%zZ&luw
zBA!6DXXMQuEzd~Xu-&wC%{=GRE*Mv^?7uO>LZqC?-defo)LdpU3jRqA4_yr7O7Mjd
z_^B`84Y?t~SIaA_U>4UJKAkHt`q7mw`85hayQ}jJ%~OWD6H}EOw?E0e$3Pr)tpMJ;
z?$8llCpvxhXFc7!8-0%b4(n*^v8vR-JzM%!k#el|iI``XS2L0@G<g5Lt8y2Jv4;U%
zqYqz2tUvp;ujJ2EJFmIPDK!mG+(+0Sl~)!ch@#Wdh=_4_01_xmKEr&YzIngI{@zWh
zl$~+P81iiRw`kFf^+*ExB*mJyegV=6xJrG>&Nvhw?~uThMm$7nZGjq0vLpsAZY=A=
z>%1n)uF0(`OYeP4txJ*^Zx9GfiqG}&>=L>Cl|)QEbZRs}(A3l}n>X91c#W;}!8ASn
zwy9-r>TRj=?>zd`f1VvK)yr!Lg=-?)4UjMPa%VMA88`iohE%tpv9)t=8k>T3fc2(7
zm|r2FH^glRacoWLwPW+f*yRDob{hwAxJxrRKJ#jvLX6<cv}x9m1WE1*)0<I{4G)bh
z;t+HKE@g7&6-}E=?2~GSv;LXko%qll>{Ras-f<}bZ?ExDtbz%H8A}SN#$@4e0mN&i
zQFQ?!Dl1r+<LNcezyV>A|2jj|Ee#pP-qVSoIps0%OJ&PJt;dQst3q|DQ}}(5K@gyY
zkEIQ8zqhv>CQaGV7+KOFL^J=$XoAsD2`mBD;=I%LG`yx`u4>TGE(3!K^S<9T+jf5=
zz$ZwbJy5$FRPxsQ1qrPtoo2tr>b@)Qz?eQQ?C<mL#VPv*2R_<v3bda1fM1^Q{Y3Ww
zpjG*DuXEvdgh?~>-c76!F!DL8cs0}?VLp?za;x84d|~y}Q^8SYxcPrqPL9cxN!cnS
zKebWJ&Zj#NZOwLSxgR~A%^&DzWgX7Xqc_W_i&{3jc#X<^hi}b`+#$L>NYwQb)V0=x
z%>c<*s8^D{!khbuh_LDkzR#!Og6$l&AE<){RcC!O1~AAD8QQ+Z21T7idbUIX7YvfE
zX(LV1$_SnG-Mz%v6bHS7EQ^%Qhw9n%(m(foO7=~YrTQQnkH(bSM(fyz$;M3Mx3qP@
z#V41aW{Lh}ERBfZEA$zDWgU>u9NKA~b{0(U9$dZ{<4%qlvEntluy%MQ<xti#@Yy8u
zP6r@xa#rFHrQ%j2aUF$OT}3w7;n>Dgz}{ujNYJ>O{L1k#D4)8R+G;U*wAf1$(bYBs
zIw%~6jyq9jm_b`mKi43KkI;O}J>vUA<DWYISut>R$j6kw_;*Zt7VL@Q<+cuA^Ai`A
z7iYYN!@}-zm1(!ZA<$dL@DYaGf7G-p`DHULuARexj2&%o$IfCIJ!=WH!-7LC9noUA
zAHs<UM5cB8CFC_A27y?s<0F6e@6O7sjSkAyw7G*eN55?@PN^?%jN@NEh_QjaE(D+Q
z9+ycOW+~5^da8?y@uyuH2alg4xVfC$jwS4BwKsQWrI1!JH2XFiApKlJe#E5}ybPj7
z3TC~KxC)0l#ES13zv_mwaI#_?F_2o}yiQD`kO^94gg>_in$#)2!hdl(qasvCohkJ*
zo47r*jUf~qj)PYT+*XGd$OfKUBp+mGzAEE3`dQaurt76NrpM(2vhyg~0;(}xiO_Vd
zsj>uM%0%ddt{9+>gjPF2Zf;VKP|v=YdvvriP}QEHFBPBDJ_L%=?&30kO^1N6b3${8
zVxPPyo45}*)8z5reyvOE+F;`G4)=8haa|e!#D<<q@7B~(auA7|Kz}N!$28j;JJ**r
zq~(3HI8e(S&x?%M>Dl*59sN`+2d|h^fLN0gy;i}?@7rgaL_xVLS)wI9o+F1UJyV9)
z(A3cVnvh?bYlVuo#&x%UbwKO$ex4<?S3!+UZ>+W}6z;m;sz61%lX7l)4H|3KH^-xe
zNcc7HClX0`Xw1SC=X2J?*It-!e1Om@59Nx{`<<p{fd^g{-{2~1B<rAYwGMCu8h35W
z?vfhM(XimXoM^R#5jKwa@G`ulT=dqFglM`?H&LRl+ynKRD?As!eEyRB(cr6+B6Z0f
zg*5;IE<W<)6LvQG$4pA}6#`t;h!N*g+&Q~{CZyG-zPG%%F)({HxOxyf&_Zj?0T4a@
z66@tdDuSQ9tltkm&7yu1iCC%D#j62+Xy!7pOtZVej!FM!>j$x}I-$aT4SAU?u7IoQ
z;T9-H;d5VX%Vde$T!cHxSq8WB(fBA#2Wq$ijJ-gb#JWJt!y$kiPvviV?neEYLe3uq
z?zfG-2mJj+$T1Mmo-p7f77H=M@MY>(B?p7Rz?*CmI*i{L1M)}?MY%tr{C>>xz(tGr
z<20NLtW%gh{|~&9$V8L4^E99ThI~$VWg663j=16r{AGkzo)uK|dQ&ArMNtaAqJ#7d
zm;@BkK@z#4k2Mu%^nKv88D8nDOWY9N`usYrWH|5|A1B&x7&3UB-gH4Y?Az^O=Gx3`
z*$mVPud<Mb>-8A!YcRNRmn}O==z;{$pw|AOvb;cKWoA-`_6iQ+dKQPO+U^}yIVwQi
zIalnZJt4>vu|S7DAzGsW9hx?NhZR}6EXD@D^X4P0O*;B~fP$^r8sorjsX)*)8Aeyx
zax_fX_?ttOlXw>f(=J!wA;Ju#=!g9<!}6!b4xw#Yp#7kAl2U5VqmFhjp8TI`b3p64
z1agr+-?$|KndoK&XXZpI5%`QoYMwjwC(Mv`WH$XE@uCgY=x<c*3sTo<lPPf(&yb^5
z|8uL!vkadwK@ONDb27#vwBMVRMvV<`qP|)6P4u!;odZFE9To0VYqaL|f5Iu^G{>91
z4DQ*j{p<oVsE^o<nn)0BvzFn1t!~VJ^w317%vAM2ti_0To9q$lTq4gn()Ja0wE2M7
zU16}*$m>We+sLxww5(I$u1?+n+`84<!V;p(LQ-LCYB6XkY(b`jvGJ-BD8T%BA-B-r
z6MsZQj%)Gmth=hAtaN&#H*&P$GDr!-JbZf<TOj621chz;JIEIM)+;oc`YkxRR~sQ=
z!K#m(plT?3#Pj2;BQi#bUEtLJ+$MjyS1Q0rqEGJ`V^5D5s*<l@Wizn|%mze4(A^aH
z3!9+-tcx`t@<5M@AUHWA8(~>P#i^hep|5hqn^P&lyJf4iV}q<&QtBM_axam(4xT^0
zV4n|5>KMkpuM49ns2d9^W;lM(w#Mr&8S~iZ)6j{A|JQ;dIM%_)5~c=?-ZFaSP9$os
zH@)(<Gn-_ed-M{Xb0_0UZJ8Ce=NDqI2nCZMN-p+#c#vXfy7azOqZ;CI5b9hS%LixE
zbKjY#s)UqgLVmPOphwNO>OOci3Ifn0)TcpPpsH-fWdRU~^y-}kVc{re2zV{|Wpue}
z)Ou;YQfSiBrh_o4XlQk&!s9}~6_E$~cliQI*p^rqom4h8*g5;#F$bQEK`o0xZP4jg
zUFU!XJHdB$VQPf(k43vrypXR_5UsvZef2MM7GvG}&kgIPCbB3Ub|a`nc)t7~A7TWv
zl%yKwK0t%bWqd4kFR@-GEstLT3~H3#RCM;pOiI*!zjXS2zQ?Wzu@K?Z*(?**sGz=A
z=r~UJeZHJN<%teXJ`zZa|Lojd!x?m5<BjjUA}U+|uG!0=u)-6hE8cPs&M7UgzN<z@
z7<8k)o}KiT(s1Ivk-KxojavaXl+G~TN*%hOj#mjX5q}k3tNkM#a8Jn3$%Ul6eqx}L
z2#~5yRm?F^aEwn%WV<t_T(&cbBQ!R-%3(R3n4xsJDl=uVMZ*}774n`_1_<djqMZ+`
zAaNnU^2#2`3K^OPA92Y^_}Xhw*@Fu4gqxzK(m}V<dftUlME(>rzc<&^AZev9J%p&K
z1Z_c2{S8U=(N7dDPMjw%MiCm*-EH$8qX~<f4+@D9>?I2Bh2AhazbvlJ^U$~itP;mY
zz{VXmGsWtMo<$}{=Z-gwy2cG{xbI&z-E>6nl?FO0<p#mNbe?Jth>L9Gsx;FmVNysY
zJ6GPkiTKRbSMer>HN(7~kn|t4TjVt^=Cro;vIp~oecF9w6(fQ_ntn+EJl&1pS&ZZv
ztZp33*tCPsXQM9G;fpw69;NJSmS%gAxsmYtfddO69R1X&$G>0?t_K#_R#(FLbVZ*-
z=4UYOs`>aD2_hES<e7a_9%VZ@C{-p*2h3z=0XY3Zs{!cE{;KjgwolqNp1sfTT3^%p
z?z45Tta;1A1icjNv;zu>WpXX@hUN~oL_j>^y8~q5eg4UySt%iK+9e?k?lqkEjk7!T
zatGYG4KlS+y*mv2(bpe4Ecr=PH8vwOWAtQE;rx{whSFT|D{xcL{X5wseQ1K};PxrN
zIrjcC>1T&!`l!B@vcqhImqw7d0jn9*y7-^c@6X9mOPp|d8im%Lrfn+bm1BjiCg`tb
z`4j7eHTHQ0zNn(x3QJjML8N;<WWTH3VfH!M%PX^_{J&~Cl;YosbT9@@``OD)Ocsa~
zrj+$%;a0L~(vG88>}J>O7(e5kfUc<>rg1_A-0>@sY#s#FWLf||8s8qCLzRglTO=7#
z2J$yao`8a9OBc^~kF-nI@Zv^LU5z8LXRia@*zdg<ipCSxL<IRyP^Z=L8mP-B&xR!7
z-}ciDTi!1_>->Qenp|Sf?t5vWWiavI@6rKnpc_aA-)#m1O%mE>`wO>lsT9K9i}L3(
zgS{cDDHVf^hCzz(o<hM2t1)rNTJLswB{ZQ8$zyV$!XNA;r_g&9;!JvhbMBxgxnJIO
z-!nS&i8`sH`Y4fqEyRdR(O}QcOd0Fb5ZDf$<Amzp+Pm9U{Y1ZHh%DH;)V1c50-cT1
zUe6XVzWfjw`Xci8UxG2z=fq8qP@PsD_=<ov#q}t7-69DcebB!;)lnAI-fuJ~N|n8J
zC0~scA*dQAx=);_vO+ehNC>9s#ZD>-64O9XlMub|7frvw<d|U~TnnfpHyK^p<M_c*
zws1d6=2zgLB*}WrtR&||P=~gkUXBI6kv(EE<V+B+OQ4?yF?ZOvP5T20whUSk!-Q-%
ztAttmGF^EuAhBD;_d5R~p*CMuD7DigGHQ|NP*9v#kxNv{u#nI^6tgN8kepQp$1CF*
zL%F-u(f`D2cS*EY0IEs-fGB2M1@_8fI_TvCNX(z8Azp2*x2Ky>64N~|X=tbqdv`ah
z2=XDiOc1fbta5d2t|Ck)Rf@Q8m(iLARL}Ca)(|xFEmhL%Op7`x*h%J)n&}p493b%{
z#xQ=}wZ0nfrx~y%v*@686KmkoC;GN6QjwyP1U>~>ylFwbmn$~dm<;^prK&>^&par+
z-Uq2zndzY9nSRDGj=}i_o;d#N)hXB$^0Mbw>~>H&7@k@~4hWkR^68{2%N8)*^0kIk
zmZBG#z1$;X;v9LvgLV>YQq@okSFBZAtrW+KXm#AQp5=yQmo}tCgMbe>6!ap9Wf!&a
z5>l8V4CrpqY*$IPgmw0^3*AF&1be6!qkwtYIZcXj)PT&9TcP)HqRy$*U+o<{1@A*q
z@vJ@7TnZGh_dB<X7()8(;Mb$``DZ(z>hDHXx%wszhPX@6y_`LRBCvxUD*qv0yEfPl
z)-aHdh1-uCsfc0gx9erRCVvNG1bEm+G`+?CVz{3cH8vWZb~J%mE!<G+lTjBR`Eqzz
zhrghal*6h*>D*0^gMO7R*nhvvh0>LD8_n!X#T_?JeM-3NlOO#O;VjFyqR6*1GvN(V
z%!NntozgNlnU@xu;y`n5;c2XH-{f*Y%{Wi?(rq`kX4ZGcTa*7>sfdk-fv|v%KHc!E
zVR@Fct4=P9T!R7^&Bi*2cDA_FW9XbJJm8Vl7t=sD5r}O;?kF_PeQ6KK<&5}a1g-2u
zCnt5)-@kYfP1jy-7~z|8ZaMdmtzD%fXZc0lAOLD@>q;~k17abaK?IMZ0V$dSdVI@b
ze8Gn$gk_Q1Q)aunNu~3=a>1885|}|I`_)JF3({segPL!a=mT#p4%LOiL}AuKQ^tWt
zE54b@*0^Qf5~v$hyWMy;wITu8!fY?UQ9s^IhHPeU`^xT@ND2Dl30f_3QnkDzZ38XF
zGH@Lbq8jqRh%1X>SlK)gDn!~^H;J?Qh1PJc2Q&5Y7{zAJQq*)k-MkTm_9uU!s?xnO
zv74q3?1!KGwF$c_-Aq@;H?))d-2oZ%^Km>WP~i$V$b#9gbKTHv>iVG@mA{6ZNLrN7
z%<X;@Sz#;vaY-p`34SJOO4GfSMHduwy6t9lG0V>g0+j3vlH@&KCdYh4aDvLM)YrDb
zo3%1;K%l^BnUn`<UFpdH4c7}2S?0?E4Pn2|OxMf`W2f?aH7;ML6IS{lF!wJJ2UipY
zjEbKv(Ync*#G#VoKjq>GE92?mpK1*D8e55Czz-%`8H6%gS~W4eX~S9QdPx~Bq_@0&
zbp4=%cTGn3_t9d0xuZdrY#VJe4E$(_DE?z*fMLxQWoc~oeKDOL?wl|7xF#=iGYev)
z?trh#o3!~OJE7wcLL@l2&Dt3c9xEGU_>U0qrRaw2uh~jj6<554!0sTMyUD_m^G1Pu
z-`n3x#t(vZ{TA-d9ZTsW;O}y`ZyCsC?V`l-&-?E<VEf`*A7>KI+uyjjm?XmJcDo{=
zxwC;Eko_xynD&!(X3_QotYDh2!6}9nL`A}mGw>l;=N@C&>zM+Q8=q+j{u0>Q@Jx@!
ze~@nK;`0~<><s67wcW2-F~1TFr2>Korw>eFKY3?~vu>Y&r#QH8;8)S9_NyN8;9lRf
zs%H_~Y=OM}>1d(MdB64ute2;4EAky7)GeC!zo5m6M>0fR{sii$;CH!tM9kY5w^};N
z**97+c9EGIb?A0;LVh|^lix#hOhtv9C6m^0Z=I9>gd;bfSI-ajI?nxIpp^BC3L%Sj
zzw26pa)Qndcoq=WJ3#;`uyUW^MZ{&AY<BogKxvjh(t8`s3D|w4B@P6o|11u^rF7J$
zN+lmH>hCL;5`bh-j8d|wJ)r&xnt<tj$S><v<3&*DeFcfmI19Q`SOHCbM}#_l&<$zC
zYaW&0Qgw%lAjBy)Qmr?QGT_)Xen!fM|6OB|^u>fKRsCsj*%9{^Y?~Uz9;0`0=m|gc
zIO<8x4E{apF``=gJPxb*TjN&svp5;V==`O;XLT~*^~&mYjoj?}K0Zu$tZ@f2PQem5
zlu=T-u}q)VOYir)d4=97^g<*-XC<$oJ33T)K!G(x`sV$Rov~4R$ybq2Qc#l+(kMl7
z?#*MVqVod>tE4j1aNeCGZghWYWg8R;O`dp!&=o(8!O|tdFktHl|7yAghq1>eYR;cD
zZ3bC90WJEFeXc6p$7Kt#&CjxyurhRrlcbjP3<xWy;jp=vyGpZ76aJm1LE`t{eFHxt
znj6gc4S7}~R+@LtPEpUE3-r96mr@yyYEZ6cfe91mg?NTri(f%YV<L)A0q35bfA`P3
z6i}VvRO&EzX2)T1L^#55Rv(y3V-aPmW3lW)!WdG&2cIyaDqyS!G8+V?AW7Y`;@z&H
z+_L0iM47(`SNje%Cd9KD0z#3hvO^1u&eY+QEbnHI716dyw4Wcw(!T>!@i8d^BKbxu
zH{L9u8Bqehq|rl#hDxUs4+G@;uD|DfVZ5%@d1WEiWKZ(Rj?whaXhaI4HmVfz>U2bD
ze6yOHlOxtmEraBiZ8cPP2m<+_wsG+lc7CP33_Bg2-#!v6jrdgjc`6tB;7kGAE1!bF
zyU%ee<oMWMAne*X?}*x)_XalngR>hpal))i4*0IMBD5xu+@-%zcTB!rm2zI2urNM3
ze&V)d+U+$PFdM!@u5>~fWir4^)QZ#6JJi$hDU$I)kUL;4)2(hL@e8e>Sfsxmw_hxD
zt>j?)$ua#}z-dK>(^$!|O`({#U`(|+c2-t0+6W!dYDK_v!?vtzEgWwbhFlXGJpvNk
z049GMiZBsK<>Mt9t7rK78}DiDZKZQ8*rzK+g}+8W4)^r?ki0sle!E{Cudx>4Q#2?3
zAw?X{Nwj2bw%N^S%ARBL3w5z(u2OtBW{qdK;Y)GI_VMs&Lk)&1F15MVi!#5wkMlj}
zwTmH_Z^+vqHB@JiAGRWQ#DvHwK_R`e;-W&SxJc@s$vCdnrV)HfDnGIQ4A?wUJ_E1f
zSt0_aR14yqtje2d$R<{T#Z{8xO)O;loN@DS%HPQ_bArmcM{zYnx!k>aZlVW1MQAnW
zG6Ro7`X1k2q6{hDvz%&?U!L>M75^RuA1gDvCL4|3*1B6z9{ziJAs+oRi)p3eJdL?_
z!*BmdcdtjpY@JMxgnJ-HL{nYJrHMSO2#aD+Dx1nTCn21K3hv`a3y3#F4Q^ADb94eL
z$N#C}zN6Xx{y%_I#GbK7DzrvWik4W7J)$T@QPifjYSpflqK(?CLhZe4i%p3gdv7Hj
z_O9~Nem}qe-{+q9`*q&;o_p?nJs%gWTTUs2tt6Ei7Pc|yz>{*<ccP}kcfy$BR|MU@
z{yBcRd7iXKg^@QE3+~Sl>`W#fVH~DP?bA+oqM_^UtG>ugH_H~CO1W%uW@gq#AcM_`
zzxG*O2pckrDufS8kUbJ)Vaf5K8DYY$^>*SE?By@cfSv2;PUT$x#y^rh6=PB3VHAHC
z1wO9BlMx}4=1Eh}5z*7tx8<*8e;>OkCi=B>*U;if2QH<F&Tb$F2EV@+fX<3HR(QT~
znnQ@-`d9K}&k<=?>!r~oj6n=iMS~RWFn{mOrYU?;diOc*;^YSo@ZLT&qO6uYh2FH?
z9W7p^B#?Za0__gi=ow$wz(RTH;@7r{&+1YZdT$%uv^t##lP|a!{&EH0nI`kD$$FUv
z6R1wU|E#;@&a@pB$G0V~1tkfm!56ZqbVTUx&q{eB8G}B}_Y6oVjnvicFkwc~?S_xR
z-)=#QXeRM_Xof=rjN!Z+psy$HajRU3BgM>(G0Ir~Hnftu3xlQqS|BMk3IL!M$!ILO
z2(;q1VxI|AAU4#<mxzCMr-Y?0vI((7#S|Cb)U)y<KkQ|w^spX#xa#Y`{yDx4qM`XR
zjt^pPrn%F*5L|!)ipp2K(C5<RE6o_yRNI<Kt_w}I(18=i_nw56shcUO(yOMxy6r$=
zr7cO?LCq?&gbBdfNnS=hsSC{&yg@C(e=o-+8)%(}R1DK3Y$sJ9IJ-|(`dLra9YoIX
z$mrOAVq?lvQQqHauzIWj)Jq!M<~;~0qU02aSV{?6;8!AU7rzPmYepSI*MAdC;#!u<
z1-MuawCSD;ShAClf#dn_J`zpERLs|OImKr!H`-Oq3$CO6|4^N-R1fX=xj$@ZWb<kS
z8~eQIbp>fs##FNEtccQTZO@M{w)oUbf17q+JP$8@Et~=!jJhq?PMM&{I&qU_f;PCQ
z7gz%Px5{Omqv}TM;H^<{PCM^~cD}1lHJJ;|e)pyN`72#j$PN74&B5~WCN#sQ?d&E|
z<~#VOmzH~xHvU}$ZJZ74vsq)MxeFyBzhFgGCu_xW_4sOEor9nA<~xp^3?jA#Rgm#k
zG8A}m>M?*ZNy+=k&}7-2GgC$2*LdyY-SYdhPuc<#HT(ArS)YhBk?b}$sm$mf1y1Fy
zzKeDnF@fdLb-*I{Q-EX8tFtU_IvOU}SI5+kHXK+K;8ZR88Ln7pCg6vEsM4*ZTej^%
zn;6qy*Bp6#Zpiu16Fde_TOOW%;SsqPGwTs-#;k<rmT)Vc&Dem$!;dD2`Z7q+ci>6V
zH6naJ_c_hEy>R^a#uHs-W|o~#hm{YVD^Lm1_cWuQvuRU^if}PaTn>=2@E&L*3iwVd
z!kFK9gseVn5iq7(J5l}$yrqDo?V;`JJ?h`c{e^se{c>jh2^McVT58;oiRApb_Bbm1
z%!>T+?YY^dH(7D6#R;F4nWnU3(ipDbh@N)YBWo>M-UFaTM`t@=;8??)3w5VPT-g)+
zgU@2|#MEzby4-x_UP?1&Xrk0-hY`BMVd3AG^7!^xuZyzNQ)lDqpvaq>$?&8-EhYbC
z^`9e)1*E)%43XpfC+Fc*r}Ga2Y9~XvKh%HGY|VRB*YqV&ws*1WF@;x&$FNMdOPsf3
zLQ1V9^&Mq*`<Tg&r_is&bvs@zZ8-k@=ir~4fLG5J4nDE$%!;Xg^Hh@K^x`wT4oLNz
zmKDhJw229QO61?dL%oe-gx~A0nmS4qxXje^=&6Yp>coZqZa5zsVY<2-9@e@Mmd?Ip
zUpsyOW&G{deNG9l?x1IKtVk{<QWv^gl@B;~0umIYsCptDY-yE05g&An#IQYOU?GVO
z)iNS2gy3-;-EZ#_(Q?sdPsO)Z(Q3k5oZZl=xdR)4ki!hbY>&SWi$V6Sd~;;1a{}yn
z*YI#RaS=tcqd9x1mFy?YZ6Z}W8-Y(8)}i7Pr>R)9`F=9rX;0@g3AX+?b}ca5#0Pe7
zBg<($FoKo{j^A{bx{3Gg;AaoYm*#S~H{>I6rP^w8ZI)x6VjcC({l7!|yCPA%N=!8+
z$MVYXkjdWfOh6;qqO{u0Y5ieS8Z3_DvbEk>FbPkBZy)8_MQ`h&yDC?&t1{uVusMZ{
z$%(J}1kJ4f!F~T>AKyKC-MIUzO~yfUa(O(Pd%lBva#!u%;knPMY@$s*=)tn-USMQ;
zJbP6z1+K+NyXC7E^lhW;SHABZzA!eH76{TsM;7mLLaAh{HyHa~J5QbW-7D)Z?#Sne
za~OA?HkV=(tEp$>TyHhFOyVoSJdIM_57@|Fy6KU}gHFbdF~tc^jT_A;*7^PhovK2U
z=)$k^%U?}?J9r0D6DVNJsTZ*>f<~MJ>tB(V+(#aE*Yr)A%~}R-t{H6z=HG7=6zpvl
z8-ru!p13h4r#D`G|I*hd|8T1w&fw;JXt7#=sMDAT8qgNxD`}8{cRlZj&Qg5#{Oxuo
zz7<FXF1Ir)(yFwtt#R)PYOE?r&2s{Q)F<Yh-85Qyo@Q_YwYse(&V?p~sGp~$a(8&Z
zn5DA>JpTA-XC4fGO~ZSh;}LPQlcw3EXZi*Z>q@l9m}n+`h#gAocpt3k)ZRcJR|oz=
zXgl@8nlRW1VuvuGCD_;157NibJCO=m9)q!4XRdE~c8gEV8NVU}s*t(iHF+5wW)fO{
z9<sH{N~X1>Mtud{`i~4iW3z-Tz7%M)$RRzf0w~+<z*s_5ptD?vI%}saj`IpkXquhx
zFxO<9>9chB)Hc>T!qI6Yl}P|NuUe~167>}T$Q`;R<$ZTs%}4`danKW5N^e@V)bKDp
zDceQq6GlbaNJhZCL|e6{3S(_!G@E4R&S&MYtZGFP@c2)P&{_1&N%veGD*HT<WxwHZ
zsneRcy#_vabQLff_Wgy_7R9DN=7<e}<iB>M+y97{IKb5&ZPl|}auyFc{h}}rQbAgt
zs|yfM4A6Y4ARhs`fKE?0XEHZ<@Z48;!0-esL~d#b{ZR09Q{V+g^zqL|ZGgZuRh4a2
z-8UK@sFz%OIEB)!yhQ(`LM41z*?l<)&GUSLVINK9*=$krnM5_!nik(R`qYztT6?`>
zo)x^e5EIu2oBb+CM?9(V;S^%{`mH~>DNx?)Q-f8f++@6$&aFuu7}$M-g@Ie6$vK-K
z2&Yog%kLsaDJw8@^`wRc%ciwgRR=l{S*;v@iqDwXfytBP3oh=Ur9MSg^qNjG5lwPW
z)Y^}f8E+O2(fdjK^`Af(VCW?8R7q_r1~1_5Y@<s}-htaPo>aeObgO|;l&U;m=sRH6
z+k=Q-Ib_R4KX&<LRQu|ttih*NFs*(GpLM^@wLYz70xNGA)3v~5YX8<H;Z{d~=*g~*
zPHGi!^>*PO^;>Ae2v9!B2OC3O<riuDDytIRzkT_z4>oCpy*Qh;K8JL4T|Pg3%}H;c
zu8L)MoWOrzM}GPeuyYz-`kPsvTTvsqBM&_!#aRJBMG1~hQQGlPSBi4Mb2b%u#0lQw
zviCN7$LO4sWhPd^NyHwDwjmha*%@SQ50y(~U!!sJwE{<QT|SQ)muZ?c#ZBtVh)j%z
zwM@Qhkl<jD($JvQoMDUK&4p7;Ew)4qFKUhA;1Ho^*k|ST`q~3bO&&9)*%BE5;v;6c
zZwoVvZXEd27RM$PN=|<sIl57Qbn>nTApK4CTrx)aiKU=k0!pY@>RibC*rv@U_lIBN
zp_!>(8xSGPC}${Xsy3flp&|O+OZi!T^BWQ$`ZH~ai`WE&IzvWo#y+4^4oGM!k{`QH
zi$G6Af-;0<RQd>r<HKYIMRaaj5n@t#?|MdjXC%4iN6X9XJlPJu=vXcviaC3=IQr*u
zK8x=gcW0^s^KL=nck0p9@bSRka8HpIBb9_Hdmf4yf<}1=OHn~(F@AK_FlcPPEcv<B
z#)>4leT{!0VIisvVigR6Z088oy%bT};@SSGghL(z#lBA-O!~D&Z<ySj%`aR4dGH3$
zG26AT1ZbW~gWGey5OJ7IGiUz3T%@{qvaDA{^mQnieJs@|M2NJHW5fEZ*Bd)BBdzEj
zQ6i}e{Fx%1;>A}Ce0zK5)H-IS7fkDt%gq!|1EB1m@5*R;SGvA)4RNiB=`ABBVPRV$
z<?GgQW?EyPB1tB}GQ2wm?VIP0hI)&2`lo`6^ax0^eQMt3EY#H`!84Zu-PkA7Sqe9K
z3w_6SgH^z0!IBS63-l}caK)CbL^bP5#Tn+WvVYC!OPuXz-*>yO9BYMc7SExQHnet-
zsub$7D#|%hgSgQX08%$NjPptp{k=>zr?+4Avy}Jx5F7X)lT|<0f2TF88<!%}B+dyx
z*}89b2~xAWPVn%~noE#ON;|qmlAfQFYx6tnXDPHVvNWA?yZ8kS1NlUUufV;fwkR{y
zvz?~+W8<o!0SxMasb|ivZ&!I`u9;HTPqyBW`!+V&dR=IOjvXgpwNS0uFD*1)!tHf<
zn-KIJS%fq$Ta7x^v)o^jGN>t$J?(B3fyNVn%sIVHtEr?>-=O~TB8J7G$AnHwuXR{Z
z8g5mH^D63K5jhMK)@2iZ!uDoc-kcE%d^`rrd5{_CJlxF#W*&PF4AvEmpjMgGZlX`}
z(8xQe)ioX^x)4G3SaIQ=T!2eS>OA;)s#SfNibO!_(&pQV`7CmX3w<^H9h!&C{=$VK
z2s@$>@D_V6H-2pH-g{*(J>n0E$*S|des|MrPpLx>U-%4kbKXcI^;0mBcali-cm#@!
zKmvqjl;Bn%`819>)G<yD%_J`|Y2J`fuC?IWoP$`SotL=$-;Gu%%+VBtm1p9&QS!^*
zW*Ro&HFk1ZU<lI|{R^7aWB?qY(<-7Y%-6N*2zG@4F`CLhPuzq{DMJ%RHCQu;9FIFp
zaxp$Nx<Bq1{gcZ2%VCAlwuM)O=3rvD1erOfW(f^{)`#FSIp@R$-u~c9WlX%GgXm}v
zt7v4J_{|u6&9~(HUf4@oZ(_Tz=`3BJAv|!hFwC{bt-1G;#H&mSY1Oi+sNV4+rrahT
zN^yAt1`aj8=rY0CG|)8e;^t125*ikHmS^uS{^D16TVe~8K9&b>2<y4_Nqnzn4Avtg
zI3C6x!BKeFxIK?RBjf9;HRa3Lb`tE-%sumk!U+Vcx98~5>T^xmyqJINl_`Od0jHx)
z6<J4QsV+he1WBx`Hg5KWp0@ko-6p@u-D{RaqqN3c{*pk_4`MLEVWQ~OSp18Ya$zzD
z$LJYGbu|rna{Hh|N)yvSBPTDX82)s>8rb?B+M5D>01Wn77e_n2<=c>kpdtqu=N_u_
zfFcVO#b3;SkSGzqO8<6J8Dgy;_n+AYW-Y`7>ts{JULPS8<kY<4U6CSCTK+8Mb`<gM
z?q1)H)s(FempL8xUtcKAJ~K&3(A<XQPHW4E*+rMeiu56#-!BKRANts$M>Y6%dX56E
zypq&&WCi~}eim!tY;f4vpMnsZlrxz<UWHFBuSRw7fnNq$SCIWn53eKU-d#s&{?1oC
z9E50w{ROsa?KAHYbFFt<l#Y~~8PZ>S@rkpP8rLjK8l%E)HkA`R%OX?n3&PnoS^gnr
z_Jx3#as-iZY7?Wt9~%4SxTQ1%v)I`uc|CD?;DaEsUiH0H+~)xc)MKO9^RDUteK(fX
zKAAwCS?np<x!II{ry*%PFqTdV&vL87Yb|X9_7e$7SNDVw<Z{IUGKN|0Pa*Pv56Kk2
z{EQ0Vsc#uY*=!Av!SGRb%I@KHYRDRLMaKTahRmm&!|n%vm8vmVGY1KptE=ac_1n-r
zR<IVKT6hCgR{UVv<Ne3;wr`~}bwsKe84WLU^MMt)!MVZJto9KE!G0S`XT(R6u>**`
z-o8ARs)SKaSn@Xgq8X3ou%d?AKQL~e)jowl{-`g~(Gc~k^n+reH(GoM87ewQo2lsc
z^fYa3KeaR6VG~Ogm0$X|nx3yRv#1F9LxB4#TKm)x9Ga6uxWNK%+$v8V{D2VkCR5sj
zWbbwNH7D0rtQox$?LRb3>N&x$K(Z24cSq%UnLv*=aPc%O2eE$@(D;)-pbanz{?%PK
zsR4FLao@2R=60gaM*ic?ILiOxW-qyVgR%1hp#|Hm-1J+wb!E*-a(C@Ek92SnQ`lcz
z-99p0ren^RaAWtciQc(>e_v9a;4v1~RbOezKh%y{sTw-S+-XrdD}!~Newj&9R}ta6
zLpDm<RCT?z@7ZI{iVAn4=4nwTgJb`e?&CB}&ye%A=Z4ayp})G`<x?Va#^DX)psH>W
zuM`>jxsY}v;*TA}Qs4NlNTF2o)%`yziEOmF?%u3EGDG5{6AWsla2EONcj6B3uZ=tQ
zJK+tCDIM02u5o7vV9&ZQRO3W;D!r=1+#E@8@W0|1s}gX?txta^D7f34GT%0RjA^>c
z-t&<cdPx3Os#MLJi?Ww?`gphL3Vr0xaS;>P&<h<K$5v&B{Hu{raWK^z&U|8u9Ax&}
z5VhA=UEphzFjc(*!cRLt)dpYBkOx2Y3EpGt+=UBE)8`kI>L}cZQMtiMldu9PjW}0t
z$IGyS$iZ06iDTlhEs|bxeD=tpF=@wvq2z_L&$^yQ`Ido#=x4qwZ6x{n#Q*&?2dvvq
zi7n^&SJkK{TIxxcxllafE0v4V90(zBGpPaCx;A(n@0UNp!FlIE*rTNN6xCZLqC^hO
zWdEb!?0qO}5@_!|%xucwqC7Ay8%K;p);IuI&aIGYQbO;$Zt}Ysq#n^+m%9lio05$2
zg}&8^c>_lbrbn@Vr(5PL`^-G{h$2luV(Nu6%TtQ`0I!G;AVM-e`G&3hO4A0!VyW(W
zG4cSZz<?#rEuBk^Rdan|IZ5>v*Ly7<L;aBdu*2jr7+i`*oNxWTqa*HTY_Ltwsyt4N
zp(7H-C1W6|@r<HQq3UR+Xu@P`=81PNV+vD+Na^6@`vv~XoIT%3i;P8)-mD+!_!__e
z^uuk)UlG#l*`E;Z4z(}6zzT6!QB>j}V+N=H&DmuPZ&QCTG-(DuAt!1as|QuJ@GxQM
zP`_04{<7zVkv6md-Ki+kyxcN9kkGyJch);(lE2K{<WMUe7jakp7O-DT$hyc@yrQ%E
zq>~##OkP0NOF~S>MrzWHbSgsJFq)_2yFfWe<3q@x;@#9kCH}bok&r|WMsKQxGgO(~
zu+@j5oXSJ=5%x1zth^{e6kR7N*=s&-gd{sV{4Ln-UY*p7#IDqCFK`JNdnYTHDzw*;
ziJS?s$De3|hA4o%!PEjE11+NJ*rfl~y^>e$6t9!ZRbBb^8TER;AYjq!B3r><z>k8k
zTP;9$u1ojQYAVh>u1Bz?8re<#*uZ~4XfvP*LBQ{v+n1&DsKlxn>DN8ZPN~&XAqs(}
zSk8`l{?DR(gAHPt-qHR1{+L9o9i{R7RFW#%yT{^Vh<mbRi=`3H_P@ub+pmdF)IV14
TU!|e`>bC02T1w>#FGBtg^F;L5

literal 0
HcmV?d00001

diff --git a/docs/static_site/src/assets/img/mxnet_m.png b/docs/static_site/src/assets/img/mxnet_m.png
new file mode 100644
index 0000000000000000000000000000000000000000..d25535659f1a923696f8fdadec0b805e3960a9f1
GIT binary patch
literal 3463
zcmY*ccUaR&*Zn1-h%^Ba2rYyXijYu+(4~g1(qdMrp(r&8QbeU21p%qDtQ6@W2-15%
zq=SewjYyOxV8JLz_XT&q{oZ$;XXf5HbLO0T=fAmQV`ajA9C{o80CtS2;rYX;bU2ld
zF&(axZn%?&fx%PXQXc?nl34d#7!UU_H`DW$01zex01+_&uyY7Sd<1|X6acI`0|5E~
z0PqF;)pAbj@PH-26iWmE1@WT^3MtaP1^^(ex2+?|(b59#=I^WE;_mN?R|xS9IAjBW
zRtWkq^~IB1U?ILfenfPLHvBgOeV8A?NI2{_g>+RL?r3QPGw>(iVX6u$3MjbFaTpAy
zMR4~(pEorA=lbDJ8}3CS1)!10;NW0|U}Xh=f+tc@Lqh|JQbH;z$saQ0iJ^WZmk@bB
zqWIq;|EFV!C%O^514!Qfey}557gzrvk~SQEWc2U&yH1j~$NxL|5&voHutDU}2~tr3
zh5T3h@T%4kinb<r;}4yW^mP=qel!0E`^QHMd1U@y#r)mrZ}hNLo#R@_f1gd~xJ5St
z3jnMSFoycJUW_9bu|BrTygId^ZM2#iMYUoYufFhkSR|BemM0U)>m~B2Zq>lf5T|M;
zDs7oso)^dWX584eTy`;^Q{Rq0!Ch7kh5o6YR;Z#k!=0ku69S69$LF)|m*lzfbN^?~
z+E9p(hY(C-y!qF)&0l*%?aCrql0EZ*NJs66QXIpMw$}^DDPS2;j*L;Hov7y2(*6_x
z*$VgSQ#?idS#q&typEza!5qtlH*}I*t)~Q0pw1d7QBwq1ubUxx^ew|>uo{pAu|8|h
zY6eMOG?Bd9(KH*h%!<1sJ8ZbiLYs@YM0tG{!7mg2l(fkZgeG}{&bAwx8LdG4AUWU`
zvAex7_qfu05C(}d!hN&rB2a2X`OwSVEbS<pr7F<GG~^eAWWdi3<s$|cJ0dtkWuCE{
zj5+^Vez}k7vw1}f$}{ZT9J>%Rw--==7Mvf*W#h)5pGHnPPYC1ckVWSO&MkqQ7-dmm
z!Q-Ix3Q2sc=SSF^P!%XXY*ZRD2&XB+B4za29B5NNoun5ta$1y~QQPFFKnU2hiCXLI
zowY-HF&fDBqW)o4=tzW3a6D|)e}<4%5v-i<qx$O9MCH}*s8xXm<zD+K7ApvYme9tD
zFwn+`o=BM1*tm{*7qKLw#T(*V200M%qBLN$txB+_{CgZWVu88S2QLapRJufE9M|ZF
zGxv>|p9o2u?%ison8CzpCm!W+>%}QoPN{?ih_!0V%}_igd)UISTu_#%x;-*`rL{Pt
zctRPL$D<{_*DDQO%DWTg-D+9DW$ONl^ZkeV`$1@zd(9e;(dkxnu@^ksu4$pHqL3j?
z9Phg4COLn-)I>VdHKMi)C+A<M{FMy`8^b^!qqy!2Y#G|RqPo9+H@mj9P&h6vX}J(@
z=%#e4VJloM*UU1{jvAJqT02e-%q@^S6~9ke+khex&yaR6wwg%rubWAr1-PMHv61?n
z4k9<4BZmi$t6SAw;~30Z5~S<*Ro8LG;Dy48l+HS7bCEKcG~RT^UKaGTldeBlGFxvi
zkS^qgkNJJO7l~vJpN!3V>Wz)mYAPHXYl)%&ud>e4uF(|o>IJ(B;1i)mOKC>Izv{ah
zziuepH)=}Q$+x-T0#EFdKrCctlzhqD-o_G$(x-3fzE*gV+seV5+^sXl{5fGUo6+Og
zwm9<{WEw*B;-_I+e~94$s6)Htdf|%HQ_1^Dpufli=A3*(KhI=jzYfwUs3mYgr(EpA
zf{Z&7Ines+;XGWYM_Kx}bIxgusY>Cqbn@T&%uFA9zcx0?u~QW6Y;p73l>(tA6f9*i
zbt6_F@0;m9EoOn?hLUJdXCH!3JaBunle%c=k};tmmr^BI@bp(e{HI%@Pox6EEcN4t
zjVSN<6HHOh<cetXG3;<aH@xD4Z2VLFyo1@Wg=^1@*J)mCR7!)Wtx1T-iQ%g1_Xn<G
z9En!_bn(oEte?AuC*wGjyLke$GMCfkw4c$JgR2H3lb99|74*ec&XX8BOqDxa-b!V2
zFr)EJQF=2+`w&>9IfC4|@11Ka-=E(8M0F@W;KOy*=BGD;YEEBzAcqau`|(_-tfEu4
zQazJMaRBG!2;~axG&zWrXCXm_b?ukc7h9sW@9yY-v1m)K1ib^lwItLqiCn&mCl*f!
zh*e85wmb`b?Jy_xM1^x8x8od~s+NI~&i2e;Osc#reXB|Bw65+n!l)UvvOw|(Tbvy|
z=Y6XZbGe7@3l=A>xhh+TT4*zOnN1%Q?P~l^dXl!lIGlj0*ZsmeL@}Ki8)r~Pl(RKF
zW0q(&6{zn1Qc~i9L5U9JOQsSSZoDRsH-9S?(jWYcyC7{nZTIKiqQY|$*<~s`FSPo!
z1fjv&Sj<-_97|q$nNR)^oG@tJDU`C@V4pn_6<*$VJME%T$Axw!rsp2im@$q|9tC=k
zhD)V_xO4X4p_x*qC9p7em<0(!mFPEEG**|w@wpEssig}bno>h8FI$V;6nm^_Tq~Lw
zSoOelKd*v=(S3PIX+DM?$n19F7?Bu|4t7Fp+3@p6yLym8t(-wqGZ<>c$9_00QqDPL
zm^8Zs3ygr)5I;NOqUuw;M_G-PxRmzDh#d_<A@=(o>av|>50Q({o28~#!r@zr*$Jw(
zFE7u;^^{Q$@=}NEvZ^061nNpzq&WIczj9NjcfE<bRH3E|lat-L&w!ddRY9E;jmofJ
zU-Z~5yvC9wD_twB!an`1dgVvcPDxq$2qTFB6M7wXn+0~dzwfWzcK3%<oXW+V$oAL>
z0$uR~LuP?}pJ$koec2_p*|6s8-$+)hFhH{rQ&zWiUcA@d^@fW1y|={#&2)}}bj8}m
zoO7@Eh2Lj(Vkwsty-r=@&$x+xH0RQ3lQBCgu30m=v_>kN^!TuyYW$%@-;7bhIyKhk
z6-X;jh3N6fCtrMo;s?|JiLZz)QMfY1BnO1AZZDjWvQOb3QSELWw@CI$YsGWx)TcKH
znpEYDd$FD&^cU`I5qjcUs*JZrbQCAW?E0@BH)hwk$}qU_+<L9pRoB*uGS&APyWK<;
z(A2w%QL(t#9$_!&$DD(b?Hf*hJ~SsieB+M4ZwaoNPY*8KJbImrEtDs?IOA$`oa8ss
z5>@#9S<T)W>la3~%;DI8PpL0URf6dV?tXLi$@<x`L<jIXuRdZsigpzmKX^N{QhT7d
zfM<1CaYoYxO8#JMSeUaJ4ZGG*HofcJmmEH>3crrz#PBAZf$$FlCXkc9T{8P2cBWId
zBk6W_Q{KY8<`dsowFzJ75pClt5xeK#{$a`g61G)Edv`8=ZHH%442ye+lpDbnm~4ev
zbkt@tEg(lA<Ln*b>UYi6stl*DV@_<8bTyX3F4$)*KhL<y@|Ana?tn|3p4gGJ$7$7e
z8*~$tB&_vnabI}u1Rs-2GhcjvN2u!<5F-k`)1S}dOW&KSejFqIF$MK*GPo_&Xw*rl
zcZeYbB%JUNOkP#Y-tc_m?l9XF7-K+Mz^*^M!r&xRwLxC3XV6)iAoe|p|3f%6v%#TM
z9FZiVIwuhGRs5!vr3&<hz}HWtp4NP{Zq9zwv@-L{dCgW_@tc!+Jxo_0(I@Exf*jws
zUfX?NFf*#cu!K8-H7?mwm|6btjmtE_Z@6B*C{GJ}1L{ztzlTOA<bcz<*G6DEOcQCN
zHFSrCMayihJ>yTy4kxR4-lojAF%l}@-j3VYO#cWfYTil6`uK{IrB0Bbmj-UyG99^!
z#+G$!rQE6cVN$J-g%JgJGEsL|Ag@c``j|;KLRj`3O_n>n^s;}%&#N_lmIC-yYF#;n
zjIp;P)(spfF*0J>ei%Me)Y*Qvru`Vy#3|+%ys<0WpUP^;&6<pQyJ(L^MSA(?L}^k$
zC2B6iZO<3h2*1%~vO#37j}=`~qf9Z?XJxEB>Z&Mi_tMHq8tY7r29{jAGw?fKH=@-(
za<nSHyd%>~F;KXvY%cv1VENKk7xv|ID)!OZhJy;!_u$Q%`>T^CX{OA2ZK9<VPU=tS
zTeMTa>qYmJO-Xr|N(1uFlS@-`6}dEZ$V5@K1G~{}x9rUk$EYr!0g1EwGPGmKw1fNO
ztIf66jp3_%HPE{)ZmF9^woxw!j3%Z*)?i+K#o&W0zkJL_0etg?JGwpeU-E2)8ci3J
z!q9LWS>yrM>nl^suj}0qvw{b!`sIS;Y))6U^-h`A2y6q--cbKA>vi;e>W0Aszgr15
zjO&dQbBbY}a>6Hz(vsTjMqc*8$2R+=n&^7}7A1LWu?MW|)TRwl`8xBlci_EK$)ern
zkt=%4<j}PP#JW>5730y5XDZy7tE>~s=RT`s^bwbk&r8{j*b{hitRJ~AWn_~G-i@5q
z>$p#+X>-m~ZJ-eeIB?N^Sz0Z@kdb;f)Ovoy<+!tuQ(>&>vFJqqf(ro2BA>rE^$%J}
dGUD@=ZZ-0q^fTc;tE2A@#>mRB>a26re*h3qIeY*B

literal 0
HcmV?d00001

diff --git a/docs/static_site/src/assets/img/octocat.png b/docs/static_site/src/assets/img/octocat.png
new file mode 100644
index 0000000000000000000000000000000000000000..9c9e6f17915e23049489646f54e63db7a1344dbe
GIT binary patch
literal 32590
zcma&NWmr_-_dYxWNT+lUozk5{gOoH9(nxoQ4Bg$`CEXy>jYvtCbV;}5fAI7DUDxyS
z;o`-fb5`8zUa|K%1K}T(WYCa_kO2Synw+epDgXcr0svsr5#ga%z`e>w0DvE^oTQk#
z2k@we9S8s*c7rm<o*Ak(fT&Q3|6f1d^14Hv!f?a&wm{6)`H5!R<^Rub`?=T4U8lLL
zmboVC9Gj=KBO4`8-$5(B{knA{$a^}-{){YVsEQA*-2`nc#t186CNE@wL>u3GLt!Oi
z?;NXB2D_flZ;SYQ_=6)nPhS|K+`xxr;`dAVAE|6T5ax|VnPclP6V%za3^hoih|kHE
z;<)R{#S?&(8j5t^BW*sDudCXG$U!AI5{=~bf|cWrk9=LkWjVk_j2{6X5y&_6DuXt#
z-cZR=8F6RDD#yFlvi;K@ks21T9_&~k+X5X*CeU`HdjP721=`^#MgZ|&jrkr!nA1vY
znFb{qIps0{?OUFvJ&z$!^r)ES$aB(o+QekNDFlQRa@-t@%{|z|0lEnP6xTIP<+wJi
zn1jc|#Hi);>qx34u<NN$z_N_q5Lu!(KZoDm<AfUf^aEt4gZOX^K=Ng83PIG#{9tNZ
zf9AF>N9+{I&;5R~s-Kx-1t&w8+Z3@9MoB;kC*qpJ?F+y@3Zj%EtXiry8O>pRL^(4w
zOgX9o!jEF28+;0376b{jgXMR)ADQj?pfKhBP%>5HY8lf86^&-#YUSg8bR>p)Vh2;}
zhuVPLje*eX+Bmlq#KTQcODLFftLIGhKw<x4%wif#2BBaZa95t7xN16#<TT|l{H=Pq
zxTGpwWzb$6nw?`%5yzXbqOCYaxiDEB<O!-5DLSZW1~7F;$Wbs^k55={Tq7o##Ep0j
zC3P|lm^wQ2h;B865G&!(TyrJzh%P!ZPCWz=-$<tVG#m1Ron2ftG@T%o3PF~bf;yQ3
zOr0G%1Gem~BS4z6qmGWio{X9Sw;@uLqXH=pB(_0Q?h9nAm<d)Tf^I#^!(s)om<bVN
zpafblI!{O74wi(%G_nT*{{k0<xN1DC<n+=oMM*mwu5p}P2Hq_wG;K~&meC0!EAXZ~
z!(z~p0LTtbknU%&korm+BlGW$7kqifJu-0twm7LdUsQNF8;$wlh@N&CoxDl$8godn
z7Y8r&f++z$725g2S8Sf{dD+<+6GS;FXgVYc-z>6R5tK++<V8V7*zi;OXcu6I)_wG%
zm(MUi^|s%^h*JGoXMGHdJ%i5~l4!-nU*|iDepe@e*MmxcFF{2#6Xk&lwIKM7k{TD9
ztDiA8T2?*m1b9<<(gRW2WV&0jdJzw2$em!$dT2qmoCB)6rxuW`4apRO$Z%1{5~m_;
zv@(z=%EN$^mP|zg{X^PFD(u;8WO)6wd^q@<pS}l+#!E;VY$54|NYrBCb+_&Z(fezh
z5vr0JqXZRE!iN<##2tQUF%*&<i3;$nLyGD7=}~p!pWk%`Z`UWcKyqnJEeVW@&4dc=
zQ9jE<`_L1h-(_g+B91dAs}0cIauJF;jVmskaJ0|vEkbeYW|ehq9l}b8zyl=+Z15Hx
z?y@Ur44vn~Rcx}Y#7x9}{jThYg5F(EfTAMtYl9GapDw7V09sw)abMeMk7Dh@+_-?6
zLsK3AQK~mIA~j-3E`2}?@wx0lahz~d2(<h@5_lnTQdk)hFbDZM5O+$WQy!S;`B$;S
z3Q*k}nST#rMtv7R{}$TSjVSdWW&#4?O}I@_D?`-In04a0X`iA5>sayn+4lYWrXrYe
zY(xKvx(L)K>7fy#SZ^?Y53YnQe`7#_BKKl=N>;%9qnRzL^Qr+l0yV0YzG?j5AS|{0
zd{U1kP<QjKfjx8Tc~I1@vf(yzj+6}yL8(A0H;@p~Vb(m-Sh*#8##^V%rXqrc^N`G|
zU|Z>GX@ot4j7tRoVXo-Si_usCYE3b+sF>@Q2U{rJbOkO5(6!b<A4<1!;p^J){A%Nk
zVLbgR5Ef3G!q)?+&HmoK8vx`{BL4*7iI{%%C>%rx>d3xEN5u@=)HZ^bJ;m&Rg@r5N
zlTQOq3u>Hp1N`Z$am*oCEc1eFU{J(zFjQRd>YupXtGLoXaSv6fxVIiu9O?>EB2?~f
z2!Q}$z-l?uP&Me)JO=Fll=zL{Gobk45!u9`D#WjXV4S`n!fq@9s0uMA02LGJCtL8<
z55j-2%#GmB-a_w!)&3!=p8bc!`BlRCKMBcKB-;OwEXY93F3bD_&VvO7-aPeBeCJg>
z`JcG4BUId8?w|Nw2ODhBt>Ay_h}X5$zxBS#7|v%%<1S9eKiJzqe|XtH?AcIizK5WL
z)y9AELs4dQydeTrb-28;Va^Seu;zZ1DEQkBhAp~g{cj!gx@O1@41;D-x(GP!srfqt
z8vfl|Jiy)`Vo6d^_nbId-1yu*yb1zhZQKXgqK4W|v27p>`qW-HHw4xtuN(-o!a4`A
z6e_aD^o0OI5qZEw2nR%x<e+Xq54@~NT(#I05XNVT6cJ36`-{8G2m)w{bh`$e%y5>O
zK>*7r?$?0k*?;RU?2|*_qr^mB%4-RQguBlIsu@MfEI<4J0MT(y4}p4iLEv|=aCa3B
zutg4zQ(E(&djQZ2Zvv4Qj9iM_(TG-nfoX^^1uwgGRuJ0!3e!io7O1-TX&H0@0M5O&
zhPY_X@`mw&AwWvBxak#Rx$Hn$s6A_THE8S^I*+QSpk4s*i@E~lXUJ6uuECt}{y5Y~
zez8agN_W)&!WG6h6A0ibBD24B2S8HeKS>G;$M-%+7q9!90HPuqX7`>OFiq`IA0s~;
z2=myU#?<)^!%*^EN<XNY1k}wN;aKqS78;Y+0hP-D0C<nX8+l9&s7;m@*MjzgR=h6?
zVC>y<e&S?0v?UY_e**y%IJuMZ`%zE~Z3Il!r!Wv~ZfpSoFyzy~Y{5MlkWErBp9(6g
ztgsA#)D7%iV=r-nA*cA9{f*H8J~fH_Dp<IE{gvtQGeCD<^S`c3M3+PWNZ&K!5rED<
zJ+-DS_CW9DpU#m$n~7||H5-QvaMvg3iogaoOjn9R2dt=Ec~$HJ;1PYk68sw3075j@
zz6wISC)7pE1z@OmJ~Gb(m0icsCm;hXT<*L|UY+eC0Q7C#&Q~{mp^~t~h!}vZuWisC
z`Mr4_(hCddvOGS4wwW~#0V$xh(lrwWYW+LGvV#X;{j_RPAL=^PE({%@x9Iir4J@39
zhJqy}7hqir+IxmS?4X$RW^`7eH(JQCzwrwJ&LZdbSqMP2a*%yMx7^=<)>;X$K>`4_
z(mSt{f1cowF#tyyEw30RHsirlKuGvNsIwbBDyBA<p$EXQIP29Q3;HV{CIjalfIm4L
zItJj8UCSN1eu9-j0K^Rc(t`#HJ5baORcDBqx`Z-Dfg6MXNU{4G4IK|#&Vbgh&%4mT
z3|MwGFi8PvT0U+6+!Jd-S=Da&x5m)If%>RgdzBNdVS~cI7HEeOV54RP<um)=dgXP!
zzI31QN=)fAG^~|PHCI<C+J(GLXu7(;eS_Xv(RPGNoq7F}8r%v5sM~1^zlH%%3nl!q
zG3GV$P+BOFm0B{dVbEbiQ_m9p`!$SZxdK>#TEwTiSKpOM^iUi>7G9N=l%V*u`Q2U>
zO5aLB<BnW@Rk$31D#Q*QzcMY^3k9)f{7T<~$SdrTw14y|Lg`x&`t(X)&OlKRlqR*!
zSJZdruQ5EGzoKSN;CfBWK5<36A*9<Yybl&28aWHi`TB4_R3X3aUuX$@0)SfEtXGnR
zBN)O9VF7f52T<2%$=gsN;w_g~t7;cetF?UpLdL%|0(#$K8d@e)J3dyQEO7qN)O_92
z+HIEK7wTCqkJsC3EjKq2bzm!RSr<Ddy$sZN`V>&E?ZI`H=sC<0x=pox4qtUV##Q`w
zQE++n;=0V@Yqvg=*sEy!C*gAZW@&lLea*u6v~7U%D8AjsCl^8S{<n9<?eEj}1EOEY
ztX=DRf`6{mu06VIU`$rtAcDHphs24Mj&RLe47}UW{I<y=i3Pizs0MY90|=W1Xr_70
z2A5)1;Fgn9E!wm7PjcON`<G*mT4Sc4uK6yGO#}p;v2DGT0uKJp6-zCT79m#X^8Faa
zF40ly=hGE%ZIy~XvflC%HS9R;T(oOD<Pf}#`W1L~IhbCWW5fH&v5tW`>e#nS>f5Qt
zZ~HGZa1{z!=FyR#Gndu|qX?EWNPp`+{IUAvy>ejVJkd@eJV>-+%@CDi5x#{`eq6r#
zap8O@bk{x5VbBK?4C#(L)V6?pIUv25%ueC(Es79YPQWb<_`$Rv5cjDa-&nouP?({c
z*+4*MpmL{;t;yr!L#E*O*l5p95!?>OFM@w7c-4cfZ`Rj{Yg97pEd$9k;_V0<q;)M<
za4HPAe#{j~FYS);v4Xj#<^r039z5B&8MRrhxkQo+PLg#Vd!b;U6xzbI5EERA0e~=l
zPD7YJUfB)-!fr#+DTlp-OEdJi3XD0ERvzK?X7alpoUHOCsK3S{6%SwH(7e{sz1MfG
zk2B<1CTs4*eoJ!&7O~w7AM>^@*0keNa>15BzAhqG<cRiZ(*ETB31=Z~__>-FMeM=S
z37%$+E3$OL_NI1ChX_y476$fz0I-GvVC`BTjo+E6^vK5VHyHF{kD;*ten<6-bxTL2
zaqx}k&wdLYuzv}gc>%+W>~td*GH*_hBWx4SNFj{d+$7z1Km66*p<B5eo?h?o1!;06
zdy@(Ixbl04ZP!FNU#~K7$gYTxFhxpuRO0!5N(*_#e!<DhMYiE2{EvXp7z&3~=RS>+
zO=mhz5-?cp?$=R93d~z+m}&9<@^!{KgsD9)`p4zh^jFfhpcun9y<s=>2WJTPjS!XY
zGz=KTbw57`RDTKLGA$aJJ0GOzh~kbnm$n8QK7t<_C(%=wt)~1pgTGbXnQpPy{%EQb
z{u^Gx<6i3#Ps*ZeEHj!Ku{Sbm>izXFHAa*A;N_2qRhJj4aLU9|RmzAW;TfWD*2}xk
zD+oZivn~_Tz~7cntr1Bq@4RldCmJM*BaaUbalGp&etptS{%l{xRDa8pvNW{Jc-)ZP
zbhDG=_26Y-l6q%lALv`xJ}zt*?OWe5Dzc<wd1}wt;Le-0plgkyIPr2(Z{wXW(w9>%
z=R9DXP!3u#X#7x<jQI<;Y+tK;_O`nAtkN2RwYB;0=mm+luSVy*i0F!{5xIShs;+9L
z>cb(+J?Hw8b(8OUj+(7T&DOvmv-dZFS@cvE&F+(L!cv~$0&tL>kIxOiUba}k0N}Xm
zm|vb+YNLN^!#oe)Cb9solYKQqEe21jx0G(}UmvY(WaSP7Z*Skaj!prCfpTGk-28wK
zUB=coQvNgmWUI2{V^*C!aeYp-8$JDR9W!ayg|)i7z11jh+pTav1lB1=eCq0L><t%B
zTr%7f;l-CU$M6jhnY_)peQ8C3w%{Mqz|&RF)#PT8ci;Lri6>f(AERgAFrGXbEd1rJ
zojK&Jy~Wj@^Ci~)^sHjYuT!V~!_~%KEc3HAB2F+0mvadrqjxipVgvBzFhbAU**3r9
zVf84;oVN3*?RY$sU!~itoLx6pD|BhTISY>1;pO-$yz?^uWk??afDu|J@YyRm`=m+2
zS-v_DflPwy&)BT-nCJY%R^-vHEjr?GUh_*k*O8~arFMM^;(?akGI5c79}YFnw=1%%
zPdU?0rAxW*+;tv~o#@^rtL5$c#1-3ibX3ds%)9N;G;Wpc@sV!X>^W>G7~Hk|=km%K
zwNuw_HYf588RN%FaTRZFn7r<S))|{0rGi%KwbtQj9WCW!Yad?}!=`MK@J+c)&cuC7
z_S%+Qa9?stc;tS>+7~`N-my1U%EeagO*%6O?YA{C%No7+_PSbX42lMA_}!zgIRbCX
zzb5e@XdiPM6@F8G%=H7pVH$>+>_0Q5r1mQ{XY<=KiK{z&X{<^RM@Nh|3qXz$@T_<z
zR%ExKAjmJ2)!FdyINtTIJO=qgPMlYZ@t2y9H2r|HSgnkz-_^>+(Yq_dZWt{2&HntT
zcfxK`juL6~e&jbDa=`=xU=Tw@e6%WLJPx&{c;|8IZA$h4jknVCRF<yf6ANp%zf#|V
zp>9Fls6&(2#xl3|md7mt;pRe!WWxA)g%P@X8O0)|BS{-q<@$d-USiq-K)5gmRQ(Dz
zb7T1~8>_%N_iS_ki9=o>iq3sc0f!@zuk|lP5L5~dbA~x>$_6W+>C2xP$_N&KXlde@
z&Jy`_t4DJ%sh`l3&0hBQZ%2F(1aKacf2mGTsgSX3bAINdFKvx)2iq;}O4_z!BU(?(
zJ>A>ayYDQxmz77}4unBW1NtKeG?lCTJh}Gx?CY3`f(e*ZC}*KwRL`v!d6$7H4mdb~
zwh}NXJ+{eGF%&~TSO-6}Fb`(((>H*nGo4u#RgjCcwrtEE)H57riau$TtP}Ciwi1L%
z2AdD_volB(HsTlz(3=gU5<}44ru0Cq`f!^66FxZAvyP%+1p4OgJvH$!B#<z4_gxnc
zqE}8^G8N0);$lbxxHP4v?s3iT?QaX;o5IMJ!M^f9tw=m&N^v$Cp@&>-2fN;+;i-5d
zyy!eM#fuHb{E7$yK@6e8Ul9*bQ!`o$hM7G6WRU&alpKfG4dBc5Y5+6?g9yLk=bfm<
zG*^@G%q$DQ6rbUR?)4{-QyHXJvI)8kXL#oIocyZ~dg?16Z=m;OR$XR`-`#$`3-$Zg
zUson<R;%T7!OJl#K?WhQJN!7}fajq#ez05ym-XFJ^XaAm^d;r#Sf%Z9yUFrRBfA~+
zqhcynOW{q9fZvm_+U&K4hP(NfQ}1!-#;j2x!t*cbSnOt8gV8lea^##8d%4bd<0sgc
z?qN>=csNM88BsvI{btG+UY21knMzGce|DVy#Bz@8vFHI)+ZtMHE#XD4%#o#4@Z7jg
zJy&W%Ic+AYRA}6p92y%QIfa@n`p1kP!%H9cmbRL6%;$4jnziEF>cSLo%T0%46Dy?b
zXEL_N5dQ){x75PJ0SGVwWRh$b<KHn12=P~b+%PA4jfd7z4kSbPqo}rHvox_6?{!A>
zG9?N<>twBh;R!A_hxJlrnxEaAmYBC=NeTl^kIN`bJnM1%<|1lwx5mqhb2}U#ozWg=
zX9o2FhG0PJo1XoCA+I6Dq;o;#ng}<RlxZIa4s%Nes!-3Q0E*Jp_jA?o&tIcVwdf&r
zTs^K0V(5ZzBy48hDGrEd+KD8-cY8;umGnIJ<Q^ru+>JBzpV7BH`*&I1yM$1qQhRN>
zyHgJ4t?DBLbc!j>?|&OWnnn+(b?tv7iB7?EYda+!!Dizy@>iKd>6)~#YmD=I-WWq1
zT^9JgHMYrlRNy?(lg^R^1isRLi4_&2Jkwh<<eGXQze=ScZvJ(IvUET;kutQ;%thMS
zBn~xDj(1V7rr+FyW1EeXO3f@Iu4@3i^l5Zy|L+F1-AGFi=jzYNng4+DAptFgPK6%M
zT5o4aWm@DD;~cTx&08r1lY|(3fe9?~Pz4NL;LJDlGGK~|;Y>TY(;N}-*6jbn&+T7i
ziTV$cpCtGKvaEGs8`{_)!uNMC$mab{ZDN}UIg*nsa3w!F3q9j~-3AX1IbNqQYp!Lg
zfs06X5ND^^;T8*k4Fq`Phe@EDitIc`T+v%#_?cL7$n3OaOC3>3+9aD4h%p>ZtE-HS
zL|I+)_FN9n{m&)ej~mv99Hm~u-_+7aZ=jq2?s3l^CEPvXj*b+aE$Jm<3Y<F~*LlwZ
z!ck@dbsAAVoEniIQEzP5I{!>Ilsec*YVgb%O24^J#5jD)V#TLAfdRn6bWai#xhwG>
z6gmDHZ^w2YH4%Zsn=kARf`B)?P#zpGkeEsXL*0<CGp}h+Z^{rhs<lgBcGZP*uceIp
z|E1hQ5D7jg)Xg(3cBzcpDJ}M!-nfsf!?_~jTMNwL2Bz3djC@5|jgSvr`ubx6u?@CA
zTTT!ER!+SA&pkMO{3pWg$Su9l#P4Qsgi+w*NftKQM`%NdQ&u4v{;{O~*zc-6STC6$
zf`eycurKo4qxCQu-ym=P@)^-s1OfztQ4_B1z;5IBGbUaR2BUTx*c@b!LjyJSBYLT!
zK}2_NFkTWBh5Uv-)EcDyt`&8~FngR+t33N_aB%%S7CeRk%|w`3TF%cQG%dYR1314w
z2eUd1YrTS~64b>cH%Jpmz;5;UL#t{~;Za`)R|b{Rv6}CZKrF^P_fLLJWD}2yVSit9
z!LA({zSq^d9>Y%c5v!NZY_8fX;UoGMV)U*j`8qr#AGGG02x?%i!W9=DfL6G{sjX)!
zXcrdR=lzDwpMDNSYkfMLq9{=du-q#-L{$ab6vUZ&zj3(bI@obZsAHDPVmJsF6t)vP
z`6lMx0urVAPjy|QgDB3|xyu1zYg$--;Ds^^fA5~MG9n6>LAEg#g>BY6k#W=Vps;Oo
z<u3VD_eA9O4qp57-t*`Gf%ede-QB83nb=#xxJ~VTp6S>W?KrF~97(XWhzrReE-x)e
z@~U96XsrsagqJwWHIB~3fMK(Vi*|Fcr?`Kx$jcvZ%6+h6v@9x&wlT+6OksYv)BD&7
zYx%J9=D_Dh-+fWaOW@(R<bN#p*m`k+i2d%mpKxm#jg!yh2=VPg>j_d5>vkKdN42>k
zjeq;(+z3BRdXxw&Ac%TqUd-2QOR~_vh^FjvP_K{ytYs9}AFQqjW=YqSEuH;}_UB7T
z-9<wc{eohA{sM7r^(S_z`?thwOz8#a$L#g6n+AQK9r3}W*-yW)E`g|s0Aq)0^wOO6
zy)S3)hCfKRp2?;A)<%j`Tn@A0+UhFDvkDK$r5p5j-7<1hlTenfRtvK?d>n}*kL@RJ
zU6xPm6Vox<|5Lm&E6?!QHuShQ5i05zyX3?nZhmFk@w4}Fyf1wtsp)*4lTFRw*A)5x
zMcsAy6XJ8xi%sG|`23c>ue%3xW>BD<R%Fyx2xg;O#{Pq{8KGrzQz706uR{;^Wz(=x
zgX%%`I~<X%Dah^RRE=msehMveXO?(VUUjVgsR%-spL58k@s~`@e=%(u&L#row67cV
zTF9w<aTEw2ycqtL*1#yv3<^&$F4l#U@MM-Of==>n!7T)lu@>Ka3G8flmm~8|K6i?h
z$3&vu+lEzjDHiEs-YPm=ETwz#zp#S8|7#10!cSvEv#EN+!KoK3U(}0K*|-;Gz;*T=
z-Ex|;vgy~^DaNi}Zfh>#NEW;@Mhsk&aa3o1Oa$48?p5m?am@B4)oqutb$RMv>D;~>
zuA0}&4<$Gw54(MB7f0FINZ`l&MdFha*1!X!&EH>8T|UJ12XE=*=vjKoPc+ednGbNd
zrJ~-v@BiQdVQLy7Zz?Kn&D9G|NJ{^muxgxdI5M|NKm9JOop}A*>Dn&|)I02cZ|FdS
z@Ey%W0_Fa`EXQ7S{1{LkjlfW=?brXp<;O4w?rq79`)Nw)XKKkP%~7q!Y^$*yD-e0n
zl!<{>H1QIg)fu@}?Bk?O*m*Ip8+YBHA^IYN`RBEA(n3TIRvZS3Pxjzf<=WNlWe%qZ
z>36RQvt$(+%Nx4(ihg$a+sY1J%h+*EJe~bekokVrUv#?nc+Etw4c*<4FCT7cPu+4O
zZ&zHRN7?i6>d3kw01%%2eh-BCdD~Sh?%hJ?hnS)<s}k@|?cyCz0CpKezK{+6Un&z%
zj<-(PMqfuRtUD1}{BpiNtt2^dX-n`0>jV~%M(TauI;{Dv!VhmsG52qpY$^#8#8EN%
zIlWmdxr^SUGt)T0LB+NI&8IPv(JrXz{*hf#UFeupuC257x@lT6xz1rO30exCgqHTj
zhQrz!^kYPgBr8V(#EEl{1x~18QkDN|Vc_6vW~`dBNP6VEwmZFux%TE9X=L>$S8f{s
zpJRsVjo;Z)P^8CS2J{3c(s*JCrS@prg1IA?HDxuWO^jTGV&R&m4xKbtf{}>zOUI~m
zmm9*;3L=C#s{esj9&-3<1ODXsC^C=VIW7ES+vAH_!v~Pl((zDsH0JdvXHuIfdh*Gd
zw3&Ca%1!-Woa@mB3gsma*%e8%N?9@EYHWq3MJ_By>%9fu+`SHJI5Z>#h7bG-LqR%`
z7rsCAKW`&&lBsc{k4gU*j!eZUyy=tsX?uY6a(>k}k1w-v4#QMN$eHK#8bPO&?Pfmu
z;j31~qh-EAKJV0#h~1etjXi|=V93r4JHyLdZZ&HjA_c$W+Apsk#&9oaE9(!usrIA2
zZ#tk@oOnEKN>_L<+;_<LZPiuU?qvsCW2<{8?DDnl&t*S!JO}0)oU6fZv>Uk2BItRL
z-Jbj)8xh5@xumI{Mf5v4k4umH-t@zQ(kV?QKI;!m=RrdPYkFuX{lx(GM2+0@-#^&n
z*>yyyE8@J=4{aPytZx|Qt%KSAI5G<+<QkRhAzV;z3cYTOLYieZU=H4~k_3JEBH6K4
zQd`hhL7csKdW|7zi|5aQatl{77LBEmw3HH46x90>Cd~54t?|9_tfFTh4YnJX7mMJ0
zMK7JDGe>0ohecMXW-`vOXQR+Y$6?#x9skuY+38m+k?QuxO@nrWWBjfMTnu^?StUBs
z$Gdm#2{^Ymk=wP)bkDJZBowyJqa)jrrqvTN3DtZlDI6CxnTy*^(&b&%8MFoQF+4oo
zV!tO`L$_eb-VX=g^}6&4oLTc$KG=0C4QQWr{g1m=c@5Z$wMH3zu_jU18tC+H<4J{4
zakq?@2~cmf<o;WcK$<CT4lXdr-u)_ZPNS>Lncy-|hv62_I!i9>(c9s&|I2-toaX7u
zZ1VQ9M|lmsl`5s2QX`F<Pyxd^JK3P)BFf6Kn|+X+V*Fn#ZBc8W4PQ+BE`pw~ie*PD
zSJ7}Ft$oQ*Z+@nte=7!y`Y4V;Gg&E;o!eZ*aYamuS3ygsWKZ{n*e8WYxG2NqxV9hh
z6P{Ld^~L;$r!^U7mbUHEhZF_XBVmuj&i|bn{ywqClv{u)@qGr~(eB(a`c&pNMzmi@
z3Y~tGT#NHad(@DZelLqAd;gegNy)K2M5UsIV={tuQ9N~`RP#XZx9W5uv4SiBEd+;4
z9c0O7fAsvh5)K=h0oEs_Cce0E0u1O;A2U?}7fp84ivLiQ;F+;f<6V0BRzl&0ui&tO
zqkHZLB22qmYi<HAQN?(2!|I<D245r4FTcg!$>CamEiD`ByoTZT73xbU|3f_QUsRb{
ztV6O8eTV+fZWAL>+|_kMIh|$d-JAV^PzKDMN^L$C{!KdO=LkCCP_H;$YjdK%3B~0E
zBi{<Nmn&e_LWPzGxwvGp<7X}MhEsFU7r&5Mlof)xGVqzkmadAARO+zjg!<iICwc4l
zLJO2n47+}5R`m>+jzbk&G%|W=A}ZIK$;zYD2LWhIBdIp4(Xw;610s_Lm;4mOs0I)8
z?z6@j5D!@+*;~%vb8wU<o)I4|!5p)kpi|1ue`($Eg0itT_g9tLOejgABF5D%i;X|R
zdyj?#4Ly$aoQ>BVWeu~hD!@1Vk5OD@MR=b})c0_@bY&M-=xCs||0+lz^H3s8GX0b#
z@ZgOsxf>#$KS$@c3?uLm^-CiH7K7q{Zfc-2%~XV?{mh4Pxj`WTT|2r+BYH%pgI3mt
z`MWSSp^a7E6Z!XXztf_g5QOa7E2N38vy%D_!N(<e2F|V>x##NqK<3h4BQ2gM4Ey;Z
zx$g15$`$A;BYc_m$N$u5jzF?P|4TzAV>0UN{wl_;V!iE`vMC|8JoliKR3bbU+{)50
zt)(mV)HGY2(n=U<u99}c+MjX2z<SP&i#75sfB<=OB$WeiY-VoVaIg^Pa`$p&-*Ec2
zA-^TFaTGe8o%`<7TwPw?y+@;ZbnbV1&B}s})(B+9`&ZMnOSe-LdV(g+%LN*pn5s&<
zXo%0q2M>T;yD@=0_HgKmGSI9*SQKhVWQInQt}-ywaxl>S>stgJtoheHb@S5yJ^c2F
zw_R~e;qs0}k2QCqv?J38|3uDXXObv0xd@u|ZuRT~VZs*eqU9g{cm>cixp<X|Rxi5O
z^G3TyIBa;>pMNv8O_~O_*WlbM(f!9cOHS5O0)D&xBb=hiMs>M>d>tG~8ZojDm%2r&
zlPJE8wv^6JV@9w};d5ih)$`SmdN?f8|H1ieqOHLWnppEgKr1=L7G?htNm0ddbt9Wz
zOL~8#{~NxDM;B`C%m?58kbyPXNwk#pPO2SPrwg^VG3Ma)kRA$EKOM&M213U#NEa9O
zVnM$}mL0Y~j$D~&;LVOItqpt4L>kz88fM_FgP|o0jlGqea=eo$`O~h+G;^t1tE*}m
z{I)s&=D!C-X4L`6irY_ICl`1|0ZE-%4OM!R-ofk^t}&T7((ZSkJIYVazA)Tg`p2&2
zfvJ}Sgk2}9z7oW}#QpEdt`Pdcnx#V)9ahyRt?{cctNdV4^f|Yne^&}q3Ix{L8`l%d
zT_(|qb*SqW`3+^~8(hn5vhT<6xW?UaLXP;a?;xH(W{U;wjZ9@UGROPKw$U*~&9Z&v
zgoUZpzus2F#qus%`7u?aq&5o0xth$_520=|W_^9OO)578j|)C1?AU`dRAYJ}fN(qT
z*OAQ8J~z3x)WT1m(hRV@EknrtkO8NGe@~IxB>_PGouWkQYM{ysQc9mWjXei19z!am
z5WXnldpRx3`_T>(=!OEEgGiz@DW!gL$U%nR{>u=a3xFP^AOdmuV-CgbhORi<vew|q
zHz@kGA7Cj>u>^jTRX98wFl(*6J}^W4Nc!>vfR_B<6$AJ|bwwJ&$!5@;#IuSN|KJCu
z!KxN)C-(Lke6JZJ!Q4$sR8Dz1YKb~8ReLM?noz(c_&wE}Y<!yKtgLrM64LTB#-~eL
zu+<e05^3V3VB|&d$^=70=Px_34PFmz{;Ar=4VBDzD+&Nw>Y`<a;OUlk36$*4IzNk~
zGz-N>gWeqsv5urh?Tq|a=W=UY3p1ddFXQ?hL5-tEBv@9_L>P7&(DPv`=YLP?vy23B
zcN$$#1pKHHMJkV3a^Hg9c`y20&`4|mixceE+jkm0O|{>{s($k(woLXd(=Fp>4A(9U
z0H(8i{*M4r5)<$ZxbYxWxmb4WC{VvhL#Fc){4J<T$cD}{`Pab-D%?!ZhuU19Fx{p1
zExB!OQWZABKcP)`-#h^I-mo3r@yd6t)D4*HG;tnk#6~z83guA(YU7<?U;9ff0j^>%
zke_s0QBKsCVf71{O(k8`=D`_dmGa})^!m)$PWCT;qnos^Rgy<~O2;ZnHR2QW{(_>|
zoQ~v=4V}Fc3GoLmAc9hZ|Mzl-Uqt0MPS9h@uw7Gv{RsQ1`Q_MzDKALH>A$x)cNOf+
z90-Mb<ZW6pC*UUjZ<YhZ(1YQ&3*T_2O-DvfG2m}5@r5((YU8?DI-_c^PHu2+7fn0x
zulDo8zr377wU=wU-p6dNBor2D2&o8d2GbSEEkGwCHTkB$YXt3j_mYaln(6*=$|wZK
zR$tja3?V+EQN*A_M8XZ$srd1~KKT^KPmt|9c#uGCJ|&4RXsIzeW?w9~;IY)in*Mbf
zSFzi_jV0EH;kiSrcyZ#`^W(O}%g~xWK*rPFm&DIYzDCVm>M%fgb@kH<cdNu<>n9dX
zq!Y{)3gqHp{S^vXafi?%3hJQN>|4R~F@=!#Jnfm=e@}BbF9~aeA6)pmo_??&R;mrr
zHYuW$kI=))GoXRqryukDYB9<-=<1HD@JPh&pN&A*H&W<JaXAmb(OgN{E=Fs8FWe}G
zT;a<}M|{pwb7mdK8#y#<ESPaDlB+}h-OLGq6u3j0Gz}<Fr|(zS-GwJ`!k!~^Y)UX;
zC7!B%nyx6t(QHe%Tq^T!SPibZ86Xoe?0@)v9cr|xWcEqOs(oT_fH7GC9c0j}`W`my
z!n|bzJxSt?x)|E`XbcrW1J1jhKQJ#o9qjw?FH(@q5Ti-7PJh47<}(SCnZcKhW1ra%
zZ<O;%$Oe!1Vc2I^;yZpWWH>q1w5t8N_ceP6d1tlrmZ7qKlhq>eWT(Dn;K>v5=SckI
zc$LkINbc$;raR+1ugofu)c#|pBJnmST7<aKF3yQVx0Pro4;YOz%C$k6vS?|)v%M>P
z0+&D)#4|`ffqHl98EYQ*<0MIw${+XV$}ccG4&-)$W};C1jFF+|B%k_9Eds{(gN+cu
zpcD`)`jG_JI29H@rgNFU{?i!2G~Rh+tJ;tsdXszKTtRiMs!4BC(J1`9lFV49(VtuX
zGJWik_wu-@OuRA)w?T`C&f)0UmiM<${M3|4n}ySH5Kg{;CkI1rO%NuKAfRRn7=6z0
zNrpzK#9taV>>KdeUq*$Lyny{%f#{&I$d^HutbUDc@g#ezk9*iAL=9Pue>Sb^@bv?h
zc<OBXn$500IF_hlR9;@d>p5Om;%LsZ8oU*+u>JjcH#tl&*;jbYE%(PE!(RzRV6l_K
zo1t3R3N{QAcEIW+4tb#fS+9Xnx;SjuJu%}t?u?ks)sQ6ffF?<wt1QEa6eY_#A7w2u
zdZiR*3MHl*7UYz@4DbT8)K>!uDLbb4!n=?9;`7@OGXw<s%?xKu7*Gq3ZX*GQz9G~v
z$<5ehC~LAiDCq)=e)=h9;ZJ`R*z38E?g-uWaIWPi6~gizNATq*_h*yQJl2kUOC?F6
z@mFss=#Nn#W-w@w3Lx=p$fPcFhdT+=BwU2_h`bN=-Ps31l>&FBBlf?AqaZ;)l7t%v
z!H37GCP4}p0*cdu#P*}^J>JA_;%N^^LAfNI*q~~Z@>3Awqr^mDke1_%3|xp}8yk{D
zm0@*}R-5TxF7yAag6_U<U+c4C-~ex2&|tlne*WXEE_+jnW@vCYLi5^ozOw4i1I+=Y
z7BUk|S}fNFqu@q8j;5mw<uU({-BPSgyJBhE`qj_Vr6wq95;7rng!lS>ea9X+$^`;z
z(zcC~yjMLA3f+2eeg1*15OrDhhyjrxBV_nIQW0sI74$#1^k^XWLioci>nIrlbSlD4
zv3ca8!=1DUtUEkg6_lE-k1KoUE>-su3XN754klj+$A6AHIpdlp9BYZXJco{Ux%7P3
zz-$iIZ+B3lXU%v4a@`-ns=><AGi%s9hajAhe3DUY?9n0!DMuW1BVi3$V?{#13UIt5
zx>kX`1;d7=etV<5&Uw|(hk+2~lGSg{+)HpJ?d=nSe@_35E}#50G#-}yXIYJ4dIQ>@
zQBTLx>FUAdL*_6O3xmCk`btz7?XXy`^$4VOPlP&@ii%RDVJG73H;claQLVb^smco%
zoaLGao=`l*+M_9Zt_J<37J;Xj5SoP$&S6L^1Fj0<5P{3kP+1rU5%}oXJZVT~KZYG~
z$kDVtT?fO6yTpi1U=v$?ZriE|Gt4sM@wdz_$)p)^O1rC#YnUy3)6-PVz_+LO>|x4T
z<xkEfi<`da2Q%W+iPfGvH%!gt8AKxW4s^_BmEJ0Ea`Rx}jgd7ekVot~$%kG*Su2&m
zqV1J{y-7e|EdfIO!JttMA{+(axh&A?(?f8cNI=@|VrfoNQMMSoZQm%4djvmna~jIq
zHXHv(m4h3m<S)jtd}?~`OdG;W$@dRFURz%I-<=+2mky{D(LjF;8EV{lzZSA|_7lKS
zz#!RWkqU%K5kS6qz_(Zc9QD(Q0|r$kDN$4<*%zV+_k+F4HX9HEXB7F6*?(3`ZADv-
zahNEFw-0caj4{%8RB68u&&WH~Hw>hY$|>JGI;)!p9W(Fsy6L$TG24HvnOWN-HIa#=
ziy)+Leq<iJIkvL`9Kik9E7w)QMVrKLs3l})sm_ozW`NJ9$dMtm05Ie+uYujix<BFH
z%;*njj)@y^fi;qXi)YZh#1%L@4V0miW=4XzAKUfI-khrCT$=H_@^h^$`_XZkzZTfl
z941N;4X#`^Qsv(#MEzE|?QsW>M}H<KJz3lQ<!`iC|IvRIANgSPgj78Gti-o?*gGmG
zaAPR&ZNoL`IvH?*DIy`t+=nEK)!*Zfv>(x;f0wIO?ni}XoKV6o%3<yI*u-}(DOIJd
zSMYOblqY(7w2o@iWye&0v~yNu2~v>+Yb`}Re*phwV#bk%tfXd0@2#@B=Hr{?FX*fq
z6*k6zk0|mCyeqNpQ?Q8}4n{)#NX$|OJ|Uss*&%v?d=yydQSUIhggn=E%ds#Mg|7Rz
zoLv7n1)>+8Vk&F&uMpBW?`IKGvm@NdsW5WR&12+NR92)$xO7_Tc89(7%ne-TO$_2J
z?N>`CYMV-#c)7}GE3xacdLv(M?0~T*{W)r2kn(DM9_q{nva-$`z)sMlf}Wx}a71A$
zu2Ana>QD$B;z^R{vOBw@8^xEA9Umk4Tf53icW7uf@yYGGH4cqJaf+Sl>rHQyHuaH^
z{;bUvZcqub&oxi@ee4Ef_sJkXqsf=Qb;4%MV+jGl?SXg~)}w*_<j!X_Ydxg3@55Ca
zK(v2TXcvnHnUC+3&P}u3DtD|_LdfjrH2tRJ-cIDC!0hbKw!fz<#3%b&sL6t59)$B_
z`+0`RaZ`!V;hF`|iLZzX@~^c4mkXXl3z+1Nm=v7E(Y-YslD0YUHF?Z>ta>PtF0ze8
zJaDJZ+;*bzyqa|K=ZMj@Ni@%I<iGf=n2ZrRjxvQofTMW#bT+C`yosN>M(3tj)izH@
zq8?jj*s#NYYLhM$iNHx280;g>e@%Ysya``o&Z%BGvf*$05*_4fy>-vs9_TH<5-;o6
z#KB57JE)7>AF)6<PxKh&5{~|nrH3@5?NP-3$tVq_!ehK@rie_>e);n_K%Bv#I?Hl{
z3OVK9fdrj*!%YZsQ5K+ehHh2Ju+&ZN%LdnjnRs<P)BAn!Xw;J-W^{Zwjx+OxE;9rp
z$xO+{49RLasN>dTXTD701jA$*yF{^tk^Yp2Yd4^k%t}plyMJl6-e|o=Nr}gv;Q_Yv
zeeXe{s$R0^-=M18zmF=uih56B(xAaD8B}rByF%`ad>)Z*-LimE=ti@7TDwx31dmxf
zx=tn(o_fZfk%w|`NKI=b(p!s!#TN)?3ic6;9%W7Az$FEbKIcNGKoK7kC(TDb$(&pH
z3x7|}%YeZgOYH6SUVbNI#n%2@z7$dI$*1>%&1%GU*X`5FGGTO#PdWv9OD*y-aPQ)w
zGjXS5!uzwr=(4e^RUeU@kUQ<Ebk!zvKy%R%TFdq2$xKnqS;u~4J!ULtShsnQ308?Q
zj#nWk(U!^gs8R>=XXMvo=4j?h0Kyr}nuLUlm|s+%_+vl^UnF&y(;Dk9>5Tcb>=mZ9
z50$$Xg;mJu{+AUV62+ve=709aFxXXh{(iomfSae;;<_<J!!sm-FJL0tf)$E=aNw*=
zDB^<9U*oUDTHV1_O`(VyQOr>xJI1|D35w*4K(y)Afiog2p#t==0iO2}MP~--(%!I3
zg-SP#tx%EssVJ~~!MZuf57gpiN!jl{H9J*)GJB}CH$9DgqVbxu2zO_mURMEVaqig<
zaO9!p*f$vM-qI?GykOR>+s;S|-{o^YvI8WrAR~zVPC?!N!h8|(vW&rgF9ulQuG(Sj
z`_XQ91j!Dp%*9-gvfiWt&O6MiNn-3;7(oZ%$2eoBH*~ecU54x>hH#w{2weR#9=H=*
zDl6zeBu>OB`{9(;D?xkntZX&x%kA%-eyJ%$lP9>DMk@2nXz?(E9DJTi_pqALp5AZH
z{hS9Rus+?fus1oWRyz(d%-l?*T{@J!iQB1-=&|)6P~M{P36kCqJ7ptDti`OTgB`-c
zV4zLs3nkU|58n@*y8|e1k=H_NYA1ViUFCZW?S61LVxJf_Sco3&a~-x(n!m~&`tb*p
znhnd9z^?B4@EVE|g87=zHi>1Y`hmt#KKBJW;)8}Cbn*_%(n_Qe=vKczD6wXj0pZpG
zw~p*p+HLXBd29P(Q`7Kt_<Gbw0kJN+xc3QrAlGa<-)&e&bt?p!;E#jQHpR{tVm~NI
zvU2qi(}!}@9?4k(RBpF4xMcjs0$`#4PUNVD&SHWg&Q1tBMR<M2m?Qa78~%YwNlfAF
z{WW7kqe*dk7kcgj6|IKm3V*Im9&3<7YVw!sNS??7Ghu#mE)6b-p?jDq-5^1dh!ZvR
z+>bTk_A4t_0fju%&@<d`!|{7m#y<2r-T=0v9y5$<@{fGMXlXHDEF!cVMJqQ8p=snO
z3!9k4Ho=OJE%GuPpd_+phkX&Fo9U1Am>fdSnGH@e9$4rZ<oR4lABL2iea94SS~kHw
zA-AkG`nCF<euf;@Tw9yoa<ll|Lz%EqLZXj`LfMttLzHK=L5r=i4&d*?&_v9ah!}#1
zkJ^zI7?ov3RLC0yfhTu>o$NpQ&=k31ll8Rk2a#h%6b=Wp?!z=2V@VefTjevJgcN@<
z;qHfP67l%4zZ+%r<p!O6e$&ToO=hMM%T94eMCfnVo98bA<kWQBaz}cEpV8Bt347;u
zE*?3RGjT-Ry6+_pS=0yj_M<k^LaSQg993uwup-DOkxn<*SK#C=0p~O=>-;kV0OnpG
zuO>&eF?os+*SB>Oq42IARVhuZ15E77O!lSyUFI$E#t;l*od*r?7MNk87arbcVeTMm
zy09n4`MfhA^OI$sWd-?Zw<^m6aZc~hevpcGfPPqDD+u?24Z~LjU-?fuHX3Mc{j@A-
zkRV_u%*BzL3SE~C#aD$~!%?u(I6nleFuDHgHrjsN0wPI`e?wo5ax-A71$f)Sj4d9t
z+}f<wH-y&GT~|Dz`B{W7)@;RjTX3T2Yy+ZEss2%23ifJ%*+x1l^0N|U9f`ywj)sdv
zAv|Wg7hG;32HqB;6G$p9Og}Bs-y-C&6-_f-%}<7_A8sYo-p6@xLrZr|$xv!=k9cQ+
z+ga!>m?e1PKD(=nOkpC5ANmy~6{n(VArIm0BYJg~)zaaRb?y7?ki^0L*u{l#YW#o^
zBW5tGPeiCuG8`J{ki5*lnE)2yt)BKz2gA2s>-s`)Kip<2hJzRMf8PGS$^hCe&Tq+=
zQ^?;wqJcvqK9cUo1A5=-+zY*c@6T5gGP6Iv$7kv%68zBMKD0|49BWo<BQG3uI|+-u
z?qD=a4MdNU68s+K*P}`V*1Kl?9+Hz5yH8ndr8lSpmj}DnpIw`z0t2E&Bom#H&uG9L
z;qxw&{2c7?)6PFcq0V)b3P}CzhjQ)t91cB^Q9m3Ozs)|6_xqI`&%)+&olcj_M&n~z
zK*_vSCU{1)v@TOA!Q3y$AJ$KD)omRd{1y#F#N%a%P8o^G!%<7_e}{a!^-24a*b_D#
zh1>05<SdQRGIEX8c>U&h#P;rcM~(68iUho-kC_|4>%QtTwe%urogX?g_ulknCG{H%
z|A{Ab9=(fu>T}mu(0RH;O3_le-SG7Z2Omj%3xWC?XAHF3-xObH4bvcqq4ij!vs3s`
zLrHAx4)p!zsqdWPWteR`kW&&mda;d%vudXi_GAM84Hta#ykL-Pf>liVn_*E-OX%BX
zyyssyUE+Cy7n{vCf_$CgJw+t)I<GIkJ}32XS_Hj-NZ6%NY5e!Y-nV{)4ZCf{=Vm8C
zFT$a)2{HtT{0h$$v1jW)$?-ju<F6B#l()x3PQ6O>rV-q5n(C9avs<&08T?Bx$6u?N
zsZU33qeCfXOVoxVqS<T*(jtw#;{Qf}z3#;#!3X`5j8+_h`h~fHW6e*l(N}3AtBN{L
z7Vz?a8$&-}!(s+RV$xBYz&=M#IL5+bUTX60ZUlw)hNzm=k{Y|e`{Q$n&5KklZI^WQ
z3(K?X%zVJWP=kv0bbjl|x@(u)FuksnPu?jlIi@NoRb%|o_a(Q(JEorMp%d__$KA%e
z1nc#mbuIgwkBdwie=gj37(ei0FOOtgE^AnKGzAR)*miYa^?Hb%0DV3DT0OdWy5`K<
zvF@?n_<{qDxf5{G;5i|`Sl_qvbUA^rzYWX5+VWnx`_$#AlA|Zg8v7z*cjdMsS}S#$
zA5fg)WaeG8J(8o5ZRXxKqW`$hRCc34-v%%a#m0ooiNl}fgcNo1=JTP3TszE<|NPwC
z16TI5_8fT_&CWkff9A@Fd5t8Xw(u`gaICMhjNsLsq}qdb`CAb(G0n-aqbu&2;P6_s
zlV$$*_3-duM~^@t_1_NI9V0#)|InH^k`B;|HLXm60LZ-5PshW}7c&yyDXc-o*-UYs
zCmcR@)<Q>=lz%9Hh-Uh?$s)&Rbs7jN%rQ4+aj4Hal9<EFIL2H{w*x)ODI4T_=WQNG
zRA}}Y0W}teAyZUfhF&yd`Xml_iLr(eaEs05vwc=pOxio|q1(i0U%?+4cD7M|r-bv$
zO2H4q*}`?`qO>ZxZi?zi<XyDC5V9n5#aszY_Cil%jS!+jN6%PbWv8dbun3Ts*Tb{>
zJ_M>c5Jb=m9|73ggQ>h5!YC+do=A47s##dRDs+sZ6R9A#kXQP*yTK(?vB4pW2^a!^
z?q@*+t(HSEiA;ICIPV9PiNiVrP(bD>Q>%(E@>v6@SPZ<InjLyb8q%Wo0V(gprX|Go
z(SB@vBW-d)^?+n@zKK)L2Qml!0)@9C)lH%~N$5-}1kkgtXc-0eM(Vvy4#TN8G-cK3
z9rCL7lA=w-bCSRhYce^{`c(@c>Za2~2eE^YJYbd$>2XvHCGZMDK<4w6nr3YsyyI6=
z2v}27$(;$@iEpF#wAQUB!!}Fxyd|QNVP(DJK9d9xet=zrRb-cx5F4{g)3W}BPbS%4
zu613Jq-J(ih-?u-;e}z<jmigi5p@Ig^IHP5F~nYssva@`GH)?eF!+T>CMhWr*bc3V
zkU(iu;BRqj#DLQoWRPSMx@JIHdS}5{1iy)bmJ24<F>(&WNp&@2P;By%9E~x6f+N!1
z5F;$zY&=b@`A(5raY{_`YGRj-K@(Jiys<^?gGz+O4#7+xpsOYCAFKa7!rU%lUg8Cn
zDq@ZE59UxuB<H0-v5@Qsh@0}{_8Em>a%4KQU+(N7!EU#dOKe|!>sA*|?b2e7H=sr1
zTJQ4o%UTFyvDH8%kIg6fPfhc84-!-@a!c$N^;b_)K$!LN2bSt{<t7l$YJJYJUewEA
z?70YkKIHsK!&1N=Ap;f@3l<SeLKg2T$VOWzd?0S1>R{<U9RO47Y+9qvpNi%y(BZ<O
z3-5U;!k^BKGWZ9@#9@F5feIRJ!Kr#$Omfdp4a!n96BSdV9|ByS<@>Rg8p_qS=<<u@
zT1>vzCS9h!HR#`!l_beM2SUlARHf&YhlBxx!bG(4*;jW;b4};gtC?e%kqb%B@t*g4
zWT!3{L@IT^@0~9<QYbmGTG;P1``(u#Z%_fIAtjWQ@R*eMsBVF2`nQ^BJG&<Yc<<+|
z!Lco8^AD`gACFTh_Z(=g?7r^-noB(DTYppT%I1(PLV1;^Ahlrp2io0`CTwxGT3bh{
z#!SqDo7l@es^!jLxua@j2{*fPsI!w>Pt(N?@ATX~YJY>|7V@iFo$xPW$W<N|l&E;z
zqCjY!q_Tc?TRaj>4^9luicZJ32h;{b1iiNsc3Qn!7Z+NbAF0^h9gYsnK7SX*Yxyz-
zH<H0n$hZ+mMJ|a7l1xd24+%)i?iBx7)Jf*FteM6bi^=sCl|oSFIziCen3PPU8TIP@
zSz4D8;n;(*BSj+o0g8Uv^kvVP*lS@}Mz4!N1E^A$j;Z0ehr^M%S1;Qk&7>tj<hi|_
z@r#y-aW#{ut^6~^(qmF}GED>wev;PY$=9Ou^+&<&9m4r+$w&8^yvH-%utl?pEV;*_
z>(44?z&&I91s<5F)K1gWI!O7;--&q{6?7H?x;k4TS`Sp4py6^YJU36>phoKt63L42
z-u}|B!HjPcFTfTAlyEaSa`nT++$;O?6MSFEvU~idD|l@Ep{q_<72T-%g|Q|j{JG8R
zj-Ql2^tPwqxi`XkClX(C!}o?j2lAhxeY7*63Xlhr^9Hre_7DDtx1^sqRP14tIl}s(
zyWMh#OR}%C^bffs=!=y_N)w456nXrS@HoPY(h1o`p>DO`3vA`0C|xC;S;&_g*DYmv
z;jkKuhG!f&2+?5#;IJ%xwN`|J0A!j7`c7561Aoe9MOa}HYC&JC`M<4izMtS-`DKb1
zt8FcPRcKqn5dAMWxHz;M|FQy!LLDcUfwy?Z5#Jhly62d4Y<RA3<0>-6Jv!c5?5waS
zh-#s0tZ6x(iTvw9$q8>1AFz5pYXkpqVX@xAFr}K5*Vf$^oCHT(Vt${Qt+NEV?}K<Q
zZoY9YcB(ST?lWrpnOE<%UfW)Bb{!cJLR4X<c=YQdiye|le91mXhu;`MHA3h-G8vNq
zZ|c+KV4lBl#Qd!B)mja7oXd|_epL5l)elnrSD{#odrV=lDsnNE>CvEowhiVb92al*
z$|>@CZw2y?Yvup1v$u|_GU^@%@1?t?<AQ{A3DR6bQb1CWZt0S)OM`$QAR(o6cXxLv
z-Hmj2&x7y#{pMS1X3eY_{t=#g_CCAgc%Oapb$=OWo1@PPwxp+o{ji!>Ph~NCDOLIV
zE_<x;6RQ{EzX<iG&<bf9susheDn+t8`BAY*!+l4u;S0o_uzd}Mkb<wwAZ0&Uc;Na<
z`H9hy&8)@dOuQm*`Q?_}pn$rX$CXf^ZHnXDXLc0%N#RxwcbU?LcLz#Vl32D0#H!d`
z{M=p_zbvubH?yt8t_b+c_mA`HRr1g;mT}CCe(T8FjdaPC$24o9Az#|pQ6mE1h`oGv
ze8;9M11=#%W#yV7lI#!jJ>)MqQ8O;W>E&WczpHtr-=p!1$@~06#&1X#I&j`IRp05+
zw^QxMf)J+iJG&USr$ECroKWp#(E-~-!sB=7a~x99fak-^;PM`jZXPf-ZST`_^pW5|
z?=lgu;{McdZauC^6{dFKjf=V*5byC1V9DRlo=DOe$ev?$9*A08IqcF>q2w#j1@WYX
zX$d(Z#-FE+t0-!qVWEG^Tl}?-iM!BrIJi`*-#L&>S7z>G{GhI_9}UzE{mP<GGmh*r
ze|hwNmS-Ew4rZqKk9;;9(38KW?BTvvccZ&7YO;`~BJgG9U$O{4L!4e_DQ!>6VjpO@
zEm5^0T%)|E0lb-pX9M=>nyBlG9N~anQQiw))6j85Jep>GCIUlp&x7sN1*Xvp)#Csv
z=s!WKsA`~8e!_8QjQ(sl`JOQ9@TI4Od4KU@Ig1)=fMPLbbly&<2{iYwO1TN{ybpy2
zI_2R{E%@i$rA&AnzyKM6#5Oig6ug&1a<7Uz&X+hh)4BW>jdy;0FMn-)LN>3|P;Ck6
zM*myA>vYQ=C`SLgDD2q@w>=XvYN06#IT(j3#nz}he;U4(Blq{v#i}^(=x6K+W?tY5
z8gJGv9sD2`CMQ{3EP7-Sfz^|rK%P0=It8BxpFu|0^hfqpTIKkSqY%>D7fojO|5N}L
zwXi_ZNs~lgbF+LHorEXk5FN2Ur#LrJ$VP|ucRV4@CCB_gAR?F@V1EmUT9P}z*sPI$
zC0x<{$!&d|ul(neTl(Dc;HUe=T~T$#qRZHM&lz)ZP~wugmy-2KGL0BMGAXl)du<-O
zGt>8P{`F1*ca@)UgjR%qTmRGXE9UDqo9L`>$%Wwr!|BM)^x4_QnIEMBj;p{+czlMi
zB3%x>$Iu_lTT{r4lxj0k4raPP?@5G}rr3Y{M1F4^73r7yQRXg~CsHI++}$axv5+}E
zY)d$Z_!YbbR37D7?zqj^2L=o%{I*oFV?pS|hS}<imOY=@y_rTL{B%uFH4eWd3aDv}
zQZWd^<&t$D_D?=Kx@#jyz~EN|NSj5ZKim%3;X0iwqZ$9u?aLD5@^w>6nwXXdcCU`7
zv>}z<^O!vMG+8Jl*WzxyZO*CW@R#4g`{RoVNGizyP6t7jB2L(eK2s^1SW25Ifyt7f
z53}%lf(xc)=!Z&=p3}|;f!DFrX~hxmxyzNzlg36>|6(2)w#7j*uiB8h-+)-sxj(Y^
z#ZKP7#ZTv_Z&OKE9!-nov<!4Ygz-x>?&*g_a%Q6fFXkyV()Ua+<jdNulfEug*CJJ8
zW#<`8m?({MNIhNiFun7=JcJV{u(Dad{@RGcLQSpkWtq@5uMu6(Rf26{&3Tti=AkkB
z@NndUX<~74@%Po;a;2lWSYpx;?Mrj6QLVk&j|L-uheSSH#x^fEd%7qM2?80LWS$xy
z@VZj4WEQ?l_n$h1H|vHhT7#W+SX&14E)pM1+GRUTjHPu#^bKfYE*~RhKcO?wtWASx
z#>j})B8jcnud!3Ybk!CT-v=eaJj?OuX)M-X485gD2@Oq$50%ju!rh_+VH?p1bjXUU
z8Tc-Qiy9Pv4Q+~W-^OI1i9rKPn%J>~(DlhJgmd-<#Rk6WL0IrlH>8QFNb6wv?1zsl
zn+>AjdeuMpS){WZRX>xZeuj`}jUur$tWT+q0yz^+QIb|2KC~~AgX$WSi2mS7GaET)
z9rqqdUI5pfWXlMBqpcc=CIduMf${fc>)JcDHoBk<W2=;~t#TwnSDI6BN!L=rcdKDZ
z0;nWq(9LgGzA>Wt8#(R$F!XYQLPMG{qoXxT@`8_8xU#tbA-M4}&Af-k^AYpYH59Gs
zHVeK6s_FW{zwBG6O=l3z*V3;k2;)PGI#h;w>ptt*GSQ$xXl~rF<59Gu7JJ#;cbpne
zi>Y95T5GjFESq{GW23t+Tc!kf@hyKN$LKLx_?h7n)z?^uDs4=Qwkr8d6Kquz9A2wR
z<+j_}ZY+G3{?K-dy_}VQx7=pHw;X#pL+NDCycAF^PGbd(kLe)ahq>6VhK0U@g+?fK
z%{{tb-M^Fpf&c1w+R@q+wY@fJla;dwC_X0A2{Ko;?q&T}T8<EIPY|4mQsVw^yPmGQ
zs{K@KAubb;R^55#r@03FP5YQJu0NQW_HedcT=!`5c;BK<XbBDRYM`EcxVVTo-)6{y
zM-5IYmdkUWYA=nOL5>{<QPT5MWZOv7yD~_Y<*bhpL2fv9y$1C^`$)a~Ti2O}+CQDE
z7fU&YhQfu)cnX2S6AZQO>|(drYbq)A&CJ=|`*ZZX7N1<YzD&8!z$uPN5$=|~!0IHa
z!+q-L)kKV~sv$pGr>X<zuD!PoDCrBr$Mt<C1cN++%0Q$R?yfBiLROtB;5=88m1Uyk
z;81>|#LUcEeOkd)_-VX{iHWI5cfMw-L|1U{cT2PL{_l1sL6@q60l7Ujb<@dI0WAgR
zYUjg+SxYkO_u7Si-d+f|bvZdbd#=EX_1I0=N6p7RO;;0?O>eBUl~<d4Pfs1KmP;13
zF)xT4jbK>E__)fTZxhqF(;X^pw0z%+ii#*IxVU{CN3?`YOiVP*%yL3n2P-Noh57mU
z<CBy3`XzjPT5>@_K|y<rOiWyGV;*wvKdRupxu^KHdgC?{jfRE>w~Xs|gjLGE9Sgc;
zf<1=s-1Q9(dM2Ca`&)_N;wm$QvcFIMgS@2CtO+My#p*(3HD21({a4R5o#OS85E+Np
zxP5v$dO}(tb3psy&HeaTI1a)Gob8@=o~Y2!Jp33%(vIFBd&<}Q4y_pRD9MLSA-X4j
zUH5>u;2&>mYn~j;HFb4$^X~q(WfQG3(RvAeuL-a}G1EXpM<Z;1t0XBofhx;@acRD;
zP$xxA3y*(H$ddL|=xQu_{*uyXD#QM21SXH&F-a}Ryl4DqLBoQ4hy66<&!*e(d1ow}
z`8ylZjl97O4x0>4e~;faF(I~einnYYXl>SVBGCSd3XP$dteR3-)2C9gIRwR-`Uqe0
z%PP%F8k*g}EIg#EY)#Zole+iB2!tzvZse4;4@<~Fn2_G3^Y?XkAB?5zret5a94dfr
zgw^AbapOpd3g57)+3BQEz9MI5X0AN79~f*-{YBHgMOKUZ>OH&VE0L}GCZpShVE<F-
ziQAWz`qtGuwGJMIXC`CRFb<{LNf(f(36KI)9f(*ocK3D<;_e<_aKTvI4g+%Lt`Xr^
zJ`x$?h$d2e*PzX7n?=XGad+=&TOiA-EVeWFJvvo3<x_m%)m-dVSXk_&5Lw&IGCFTr
zMp0g?Yx`pP+tHql=|Zr`t8CG2GIq5SJuiH5H#^ZBDHK4-AY8;~W<yLL2@#z@F%yE8
z;BN8OUoZFleOq6=hNWCZ0dMH8JE^tH?j_wAL^lc%w)@vK`EiRrTp^96FdI^!;-<V^
zMGXD>Wd6b26Q`@mU@E6+AzA%bsl-UetH99bkO9}nP*%MUB6^ErK{YrVy#$XmyFf;%
z$qtK16XG7@eGDV#lf6lb^6>Ap`@`a$LD`mEZ2;M)_|)6RCyR`Z_HcM18iML^6nK>s
zjU#@2^EFxvjR;5sdHA#zq&3UcQhz(&hJ$-eSVFox%8F}iQOBcdy|>aVllSYitFA^v
zVkf$bq{&<wZ#i{le%iJdCaf?VEpfd%JbQCu_QOqo2^=x;qsz{<sHqA2W>lIt<=J*o
zbOTp7kf-_t?T0a_15P-HkbBy*DOU&9ayO5VXAk-cyYmW)tm!IAJ5Jx&)qH`wP7J}p
z()31Bl$898*Km#UYxSrM(oh2lR9jmvqrJo8vCn^tLW*5nozkP-F@9_HIxDAP4n2{d
zGlhhajj4guR+ok*Ls15enkCNo1xu)CXEh{{V1-vRM)>Qyx(NuvIzB1VSG@-q(qD`I
zdBdW-va;-;{iO#wIy&UGUw;3bO&kTy*nnCHWx4|=CP>~umKRg;K8m8?w&L~iRkeRe
zo>|vC5<mCLOgh1z#Th6q$bbZC0b9cr2fi~zegD;;I4~L`1v5M$jqKv`%fuZPCE=l0
z^hdsvYK)PY>nkMcrOHo*`dII+FC$QR==B|Z>2>OXIPL6DOTG$$PjBy-!DS;PhNd8f
zS1aS^&S#f;k+}VJcYRuvvTc<w8(l(XXmO!^mz7U~Yi6JPLYyysLyC{l5%4wySw5t_
z2Lpvfs!mXhpS)o?mqAyKOE=jvkB`~Wl?%U{QJH5?TNwM~I*}%VWYaB#mpQb7GH`ae
zctuhYPf5N^a<KGo$B$zy9cu(jaoeEo4&Z<-(Cczg|7nVM-gML;H!Gn%tkh&hRx?hr
ziDC?eP&qYjWW<u+o`z=UT`B~XXvU}3Lf4pF{PdWhDEm_I^1N)+XKH=0sHLUl#5<uQ
ztaKk4-=h7pS$R0PY_>h}f5E-5yjx7P>m?HiR9nT@l;3yR-k?wSZA3+W-C~C+1yCHf
z=A?-p>V~WuA0IE(Kwza{C?=-Fhx_ewwqmp-nsqDLFXYuV9C4&oS52o2N<O~#@OT4V
zXQdb}M}>WU@&f$W^mxn%^VQ*>WC^+HOWVUjvzP&K6TqK!m!TlmtVdRBvNR{T>|h$E
zKP8e;VK^W4+so3C86dthQb62IdBE*gDf(mYKueQBr%8i3f8+xW?{KkqK%)-VevQs$
z5UqPY<Vl_G-Sf0axVtn-jGFMef|!s!qak{Ke?JFptQ7_nNz#CNh><qGm%SPeH3l*E
zXqTCtjyz~q?Z^HcZMj=j?OeR0lv|};eGKF&bHhs|iAj#d=M5y-*rLJ!sxVt1wsBw#
z+;wDpCe$I9=mW8Y@O%J*i}i@}Rms`HI9t6)8M+4XG*Ala!sE+Iq8QTCF0k}X+n>Co
z$sob`gCV2jix>?>;U}CDUy3OL5|A;xlr7<~_Z<K+VMO<i7T2;P7{`uq16p^DgZl>~
z^b-;-QK5lyVk`_46KPsTd;P+Dg_fEvFpO5kIae`0DT(AYtnA&M#IqJM6r%%;+MdW+
z(6l@0Ac5!dYf3%KVu(6Avba9}VYDAjd%woxdN58I0E@4G<FWkVDhew@@eYBjLkNmi
zHIO|G96=6i>@@7BQBhXL-PMU^SY4Bll-z=aj)42yRWuPE;S$<FDlk_=Sw}>jg}$>g
z6Fk7#(Od*>Eg!W4-E{utDft98q&prrRKgz9KA5~c$}DQUBtu7c%6SM~vW$&Q@g@SJ
z-00}2C@LGpf83mSf6I`5DTGJUkD&}*r-DPKj)IWL*}0}u1bJTUY^3sE=sT#$z4D9{
zaV5}s*RREKf}R?mR_;U57h)eqO_M>2qpT|<+(3=ZHcJe#hAkVGtLcBRt0Y{mU$P#}
z#}+c}teG`D7%gcRvFD^FxkjO1BiaO7_;@4r&alubm)$^!Br#+tpnJ^4lY-c>e|EFf
z+jw4q&){IRR0Aa}=^F>G=<Ls$DMu$KGNR0M?1W%R$+z1y9iU$vQ=&Yg8*%6zgp2x@
zbJM(zce<n@(Gn|JZ%2Z{t`*eP)z?imsoJv{asS}4;2OfpfK-@w_sMaql$%P|lFYWw
zaNX0qO}4Tb3I8NBCMKp@qK~vnOhE6yf-DK1^YiUAG7Ry?4%@!R7a`hzl#j+FdXXOx
zcCBu+HL{lyla`R|NstmMW0j+bgBk$tz|my+d@!|0D0C$j3pP2u@BTICMzCp*R~}ZT
zMlJ9&n(n836^4eS8az#gNA#XyIfv!iIX!%zL#+^AN#yhi17BDlE5Po&L2;#6MMZ^A
zFg~uu@Q-U)C<inzY_q0GmAziX8pMBlnw)I8{^3Qg^geoG=yJ8)^hk=+vA7JKjt4$a
zVd4fw#~PNu*Vng`PW64;2dcQ3@usoaLyGG_q|-Izq+>pLqZ6rpBErHOIL!X&%5*DP
z_nMwY0999#n4Dl$RkO&N$I_p|^Q_aQJ9*i?-FS5@%6ceH>rc%Yr%OOA(n@sgP{F{!
z8BbnhK3yJ`W8eC#dz9FCi{+MwC%8UgAf5r;1R6DRhf%Tov~7*Ulb_##Yx0hUT1R%h
zPDd}|?Ch*>mBl!{%aLr0B&rU|e5a8_C4~I_waBw1U#ud(4Qv;qPxHH(h~gtew71A1
zJv)BuqnF0NRa9ZzJ9AN-0-g?JFc{-Hn5*H5_ht(LDp}0XP(CCS2}6-9Ml9I?Ckd`{
z*26X?H*+SwEumB^OQY#rF02w%ZUfd_it_8_A3Q^WRX{oZM|PWLjhI#kQ65ZXfQGjE
z@mr)lp@?5`u|x=#-t~$>?aXENKCqbshebV5J~OkiQ6e(XjFG-|4*)c>oj8;+FM6?K
z{@iA{2%E&%l;08WO=X&^UU>zK|3+kcd(mOd5)h=Kq45_ndLbpHtCb8fTG(LBXlY+p
z*5SkgkW{hq`I|5qN5z?v!peZZsa6TCm%{?{mwM0U99j7m31j(nj7wVA<EwzPau(~a
zPA9x9c!-FIdZ5*G(mp0wR#ibk0Y1=zeatjlprygQKvrtUGp+5+2^4NSuK3VeAsi(j
zFm@TgPE8&rkdE;6G4V?yO{(GUQf0y$^s2#j{N^O;cs+M;V04~X5u8kcHo`M|JWpo0
zI|1rtl8Me2x_ZAq4djK5Cu_4;5QqE}JzZI&MQ$#8Gtz^9jYyck1dlu)7xdv@e!gDH
z6*+(R6O@{*N!kX`)(`?@RG>G^Dj)YEgf=BbKXyv>taa@r*prQP)9wd<+YZ8dC5=k%
z_<rdp7JND8B_ORfVK;nc!(kr4m95*)s7kJy&2^uk5SW^pTGkl!44XVS4Gm)T&GkQB
zXI8mT6H53SMcF7?m>U-VMaL8$<SbrC_j*E5to00>JB*DG?Q$@eJ>dG$FLjml{wD~}
zsj!^-t9SvTDpAC9TNirXDW@sqe6X}QqIaqy3EluQpY?aJN$&#|`c0~<h@%Gt_Idv|
zwR+3A2_Ir~-7UDJJ*<pC1>?Wq<nZCOV#ziZXX)Op{U9#W_O(27!WzoT+Q>Aa@nnlx
zm-|OO<=SN=lT3zTl$%uPeNJi|XaN(ms-e9*atfS`IJ^uJB`iC%#J>GsZ7V}a8PcuY
zO5J%7IG>KfgL8#e8Dd+lD`_)1J?*J|r+0cvv{!ra_lo!KN2ASlyq@;33J_}r4}24c
zKYHcr9;eqHoKCk^hwI-DDSEFk3SP&Z6(6k_{_(#0^^(+1g8aABjF}%uMOXLW%5U>(
z`zI)78?=_+<yuC-LBK}v+$ZDacWs3U;F~0u-_4yf+hI;f>YR>k@ovp|+FbJrN-bas
z%DP_#uVP-iih5a2jMf3}&3lyQ>t+*(F7HXs&-%lqS+}LcZN2egu3(|e69Ti3PF_Zi
z_w`c+$x4Av$g$+Xf<W8{>UbHxWMiJ8qnQETwTw&VrTY=yMT@IEGZDr5BYoW3*3Go_
zOpnXwj(eP~xt);h;ni;`hvY9%Q2xQf?5Ot>KN<lvh7?+Hf;6;+6>Rn8pE`#EZx(X|
zk8mxZ$3soKUqk1Yyr%ysR}ZU{n)yaOm!f=Ia4MfKCBU?X;4W^;zfR--u~AyQ4>=w2
zH7xUA_-Ioue~;tVHlO)POTK{qU3|DA^d>R`UeX7A`eEUy_16LsSQ1S8646X7=f8~5
zI-bxDjrJ&svaLmP>}0e`E!d`uNi_MN$;^wa1XgOF3tJWx!h>m<nFmL`eOObd-~iJz
zXBz5N^ndQcalgq_w<mLm&U;QZ(RX&ZKe;Uyl7!_fmXH6H!V_uw_{~<>qh?!B&2^Um
z1;Rf&>vMR-N&xkG0UbCl1WZ`Q3W&EVQA!3ry!NrI)@z6#=H4o`HoEW~kC5}<7rjkE
z9jnzkuod(X6+A1?77IY0Kk;Cwaot<W>>eGhr}6{E0Nwqx&m)o+8pH<;l4fNn;!FvI
z;;LugebW-0?H_rI35mA0ZAQlUqLrLNXXHuFIeGcRod%g=q^4L(;gi<!rz{{eLqYw6
zgJ)LDdWcs~`-`e|w0kdlS9i;8sGh%gP-AnKGV|}>dD%@WhV`98;hTorX18>6{gQ(V
zX0+%vxD8ky`bAmZ92f$H6|Ze6vqu~v^@ZZTluJG10IQh5Frw1mutGryC=hXkJ~gn4
z0Zg(f3z(k@ERSsT<tH4^Rk(A7MUe6obdiX@_zq&^g^pM_!ayiE28-8b6N~Lt`Kt2R
z(Y@%=8So|-Imjq5;LNEjH{*~E%;j;ujb|>T4I?0j;4a&F`~&i8;@v&Og&6llFgo-U
zBgmY9Pb+*-aDXX#7+*u)E`EYDi32f;KudOVL0}w6Tlqojx1^n?3xXmC<KB4Q<>^c&
z5hy~R4_wwV2o6DW*_-+xN!bO~qy;;dgbm?C9#8<FP`7szk<`#M2-E`trSYML-o60X
z5nd-#uC%T+8)4yx+P;7Y<3rMMAP*S8`Z_nYd;Z-tI_L=D^W#z+h&MJ41Zs^-74f4x
zncMTS72f2&&LMl)H5#;&1ac32CJiddtQcYUcKGRspz9&9pyT?)v;{uv2J9#a54WN6
zPl8W8TqU7YZxy=k-OuE(q7l9EAPfnVkOS$sf6OJ;3A8XkY0$8uQQ@-8;Y_6e0cDfn
zns=LL9GV9pW3%x<gVCbXP@^vp`%a%XWp)W8>ZQs!d;?@tR!-<6vHB=NC@6^zszDA2
zp1$|y<T3BA`*^ie9wR#aLJfuz{S7XQ8xU0@Q9^g)k4KSj$}gp-iV^y<!ti}xLg#3p
z;;_%y&!9cZCmYu)6T*i|C;4t3DQ`?qH{g3C$=*;?iN1q<hQjW}VHg;Inufk)uC=(y
z-)F4+zIJS)Ka9dRUMD7;>;TBvi9GTH227zqnF+gE94v&WmvGX<m){CQA90q$u6ohe
z{pwy}eW^~sBc+A{%b)pCRrpeE)W}Dx`1NePpzbF9Fg)a8T-V`DR2SF0x7-10O=Z=a
zl_x+C6<~lOJlhry1WxkVkU(IX|Kb3_Wx@Y%&;Do8Kcj*`1t9+qfUN(20sm)571+an
zSOLMAn9gd!{=4h{dL{`YfWoT4&}WhXiUB`^fpGtag)#8|_6*^FE&4B?2LP-8^X&f%
zVCw$^ARx5=3Mh->|HS^^XB7YMfS&?DjNSFv!aEj9w;iLlkGXeihUkKiKjxR8#SFzp
z0<bd<W3qdxruJzTpi^F)pRT>s&~={FH*JYD7K@0qWc+~(0qhY9GpSF-+3T`(_?$vi
z+hx3lcoNSut#vgDm(@7+(X~0q{6WHtL4?vSVyioIEtt=Ia#G{2`dR-VVG@W{N%Kg0
z#e@%|57|Y9i5BH`J%WtIGCVNx4KNI&n-~8#lFPbt6dib(X-Nvw50nBsE_WNVwo>3h
z-twKUS?vv-e|K{h!pZIC<)kmrL;PJB0FM!Nv7T7RtVgjtG-h^VqXM{6_l>+4zaHtE
zUc+(^j4uUF02>A-))ZUas%yq4cX>op8_&8mA@QL&it6nqDYw9gL<5w{GJ8C8+G@0p
zf0CO=ME`8HS~<(T$m@R`EUk$pGT^qSLEuEH-maHx5+}X?U9t0Hxu<b$iH|l%dQuWF
zdt%D@F4L2YYsCqGW;jXM=R@X`BEC1gVeQJ`^N%o4L}=>zUpMW0wp}*-f1fXxt~on6
zTOU-g0u=a50Cowck%6_K<G?hm2|jcsH&T)VXi%b69=P;yx4KD{!5C^V(7W*UHIkFX
zJmkb@PO`l8!+N`^IYpRYF7OCHUnKXko_xzg2Q(cDVw_H2OLZ_U!hg|L0f<3zpC~Ha
zPFnXBV9*FMH!Z~2T_c=)1$jWg;1E#^P3S4jWcz3NSz11LqhC6WOn}|Mx7-4HB&|^m
zul|ujf2S@~x=joO5QqS<11@9}|0S>}xCeaFfg~C7kD^&0T&1t6nJ?6j0FNlM5MuGc
zaqj%*T0f4g9Fkol<1i6WI9N+Um82JOp0VOAk(u|*JkxRSTWz%Err60|{!fC(gKj$3
z<j;xWyOoL#d3qQrK>95U^ctE%o|yp$V6mTyV(31~_$!JfvPUy$mx~~Sdnvk^vYDQm
z-u=KST5t%E@cWDK`h{|(+g-YcEhqYh2bm2G)Pn^&wQN#pWVUQ}<$bSMS|{3lW)%KR
zLY28&3Tb+T$Ii@IVr;k;<jve^>HeZqqOR6d?}@AR{N_wes#=6>uatAH(Y!{G&HQ1>
zOn=v5RxzLPXpw8NdqJ?+s!^(EtFIT*u=mK)-Rzy~sFXUKOdDM2WKu#!ZdOkJZEOPi
z5>=UQwQ9NvIT)C(c={KeN!7au!Ckddt05WHBwGLzju46h`M$^FsQy@Adcf}0+Qv93
z@BPDJa}F2*31i0>LM!>)&16d-rB23+sR^QnsaZ`dsNW#7*QfeW2$(;;2?ar5AcQD~
z-59)$<=sxI`X545R`W?9yfTnVt1i)pO}nK`U$<LyF_WLS80%P25O{4echbOF5f#n$
zwo+SHfl?crSPF!y39{gRHLiXW9{-A@#T&wxDUhecfd!1bgrrp|=f?d!z8%%wu{@nI
zJ{5XqQf8F#thwdsxZPGg_5=kWp}geBTnR$CJ6wFZEI*i56TDGVbhjWZ(MbJrXZhPn
z1k=DZ0x+pm^Nhkd0WWZ>VV#JUr#Q4QY<(gZn5PJcA;%)s-7`3YPEPACCtbQne?sUY
z`)AY}Ac?gxOoE)>qKexM2`e@Jcuh}FKky@S3k3p3hge0W)iyX?k#|lB$%sZ3xvD4s
z{K^l2K>4esQ9E*Fr<Y3TBh+_!Y8-K&_josnq_7|?9ZAo+v-RgBLk?rW4IUV_>7_WV
z;j8X{tktj=K}*Nb_=BT{LdYM4_Zwv7$wn>`&WfGcD&2dePXDF=9s#GV5p6dSFHk?>
zTkN6&q4dHdF_`uz*kpqus@^MJpm<KYw$w0L?v!^47*h#KSHYjqkAvLDA9>5GgcCUP
zD8Exw{H7ivLJLE94J(>zs1!KA7K)|x6mIawYE^D-ig+f!4SSN14u$B6sF$QsVSWHm
zj~R#)Q{c1njSyffrw*Ldl*;;He-odHf2~3k2XQnLYL&pl!&^|m3t?*w2jLxqV#@Dr
z6Gn}Zh6YdnAUjBX4?F)4ui}x)zN-FxT233rjQW{1t*Cb8YZ|<8GG*|g6u<UsM8>Kj
zSr;6KlzdGBJf>wL|B<sb*(rzP?lKV9+i%!wxL)K1%o{^u*P~JecYPj6SvgH|jM~)o
zU5iN<3A@?FUnK{^F$(M`j{*P+AS`y~XRJcs|7*ub90l%*N9@)CX_cMWA7%%#J$Mbh
z*7wF&%3}{U9(wi=yh$y(&?bi~QT8xY5r&|h2fJ#n9vQ&w6Q>^RoXDU<$IjSyyO}fj
zEtCVC$bi|s$`*O=-<NzoV-*t<|JRY`6A?Arl1~h1?cL6o@93%w8o7sT_4lPO(~I@5
zcMJInd<Y60p5X$c!U5tkk9_1*RK}awL#SbVsyU96;?wfeoa4H_h~)v;IU?2lAJV(E
zF4jB#DFqs~9fW<JT-k9@!K~WykyXp{TnOA{ILkayq`mE-<+<M!SE@JYj8e`XuukRC
zum40B=#_9%2?1R0xN@7N(SMPCsmN0_*`S_xvp<sGlpf}$nyt4UJrOzSFp|n`3;gdy
ze>9!z^=>a;R!e;PKlZMJ1+r;RXO5I}RMf9lGkde@pVVF8LSMRkc-ObvyF2IU`MMYx
zW3V~A2iaO9-Bbwi6b~?GDH}CBe$AS@qEz}9p<#NRii;bU;~#)If-{f)D@R~dzwr;*
z%3G9vpOR}#tarzgAI@zSx)aeh=wKkt99phu=N0dI(*+D!R+ZN{_)T)oq?$>^`<zrz
zv(_etGH{mL4%aW+T#YNgPnkKxZH9!q-sesx93@LAfC2@WP(@jm&vMRFxI|q~DNr^G
zCgV0emn%+t--417ydl?beue!0;%ztI4lia=O1OJK%f4sz=iunNvY_kQgB8%s^|yus
zS}zWTgpjIjo>y02gP=iCbb^ZW++7MFt*V1Ag+U*ysb<PS+uYpQvHbVb->bn^=zf-s
zE)mt<xJf4qm)jTKDa!h0Dc!z6%5qbebu(CiAC<3`8nzK9;M1_ZyO>XVnFc4L7yl!&
zY2Bgpg0MuO(%05t-qE8KL(6q7Ls)u)I^5xM*$)Kwvk)(E35YdW>gm+HH>kApUgU?d
zK`P^4+?w=hluW5Smz`|)M&hG`Ds|sHx|c$0c0RJs^%VqA%{WOCK9&>|Ji!ApX(<xX
z!1D-<`Y}8P&o3+g?p>TuWx^KQIx0hHF5_nYKDYkDP7M}=(7hwy-tb&-HEv_bOZ$UH
zm%$pBKL9l*hOM^<>udm`944`Co})ja(W{x_!vZdG7F}$*=hs-fHHV4Qn=-b04t-;S
zG?k@0Nvu<5aML667^H@qQ9=tHb?87bw%k(UQ;a}uD(?h$Gnu(JvvWCUFR287cFiVW
z_I31(dxqXTWYoZ!B;TWo*SJ?!n+#1Ynd<4dP~)+$gXBWCM<0mZ*F0jV;-xNwF5vmS
zKf2EzeR5^;q!@Md()ec4-3Mtt`rz}EBEjxibai%H)g39TfL8rW>{Jq8F(B=X=T6+o
z;e0H__N7HdMfFRBO<GLq(TjegsCMPqDWBG9Ngx?L>O14EBI6o6v)NWQv>BL-zq(A1
zi(}CW$t)v6m?sW+#-oZ(htdor%d6%ICh9+m92M1f%Dx{uHAGhqAF^IBv+XJAjht~~
zXgP*Fc&A0-ef_aBYFk}nW;1di__1z1b-Rg*z?d8fn1?KS#Vpo0YqQG6@UeSPBj!9J
zpX=efam8a?l<TBThqdcbzG1HIjYn`$a#TaAPE_NQJJCgx+a2T1YscWYS0DN(?-(_5
z{EI|9_yvv-V@X1ui5twFER739E|{>|H}+R=XnMWN`R=yVON_qd*6*Fi!Anln3eott
zp6mk+u^%#T(|)+@?b(hk`5q2<iAvWO@OaruQgI6xTr@sTsF%C&I(POO9gNHwJQWcY
zD}0=fxV_uq@DMv+!p7nz3;@A(B^1bYAjeXT*4*J{ROB?P1Zc#haY`VS4vt__On)bn
zoJ#+qh{LFwU(hIfw|2mAc1P}F89%MPpGhi2-|F*FV{S7(msdSGfH_utAa%Ytu_yFC
z{-D)h^X<7;$O$o!`+Xub@bt<y*}LTvF!yK+OuBbf3Kg&|1ijK`K74gxZ!(Q-^s3a)
zOQ5JD_;XeHd78vGmxjke>!&&3h%U8*W&6U?1&Vf}5As^4;qrvmXNyS12Or671&vy&
zEv)7Z8#Y_=J`-1}{k0Y}H<kKaZ7B4t7b@&mVHk>CyMnHfYIaMqD&g^cS7s*}bc1q9
z1<qNP<CkM)^TlKIPHCUhomw&UTE%)qT&L)5sd=0R`L(?F<s_Gei<yrc-e^(P*(->p
z_a2yf0a++q#@mddo@M+^D!PSd6|cy~W#rV~;sb4}vK9mHj@#fx!I;v`&|<Ef!90d2
zZNb_ylTE453daAT2sus?OM{}B=JLDY5Vu`l88FQCw_t4_Y?3|vVa>`~x9OogqOn%~
zZ|9xqxhjJV93f_nbx-_gpr^mtmD;tyL%}D`#-!%9QEr1rrUY=7MmVpbzAca4R#0B`
zhdt&g^P;2sOnuYl^HCpD@1e)Ui+WV{X6DQ3(K!z`*M8tD?#eyJ&+g}-Cu?GO5a#=C
z!f~qm&b$>*JE_xIMN`GGqI7=Lj_kvBsqS0(I7brkzfBcyto4N*c@vR4?sbW-Pge&O
z|AT@$^spZqex-}i)ipeITFOUoYb@ufpu1w}Z)^DJq$W1Vr{oZSs}2Kl5d0Oxonr1o
z1tjq6LI9y-$7?VCK{pH_l2D11BK$FL?fQt*KU=o<<2$#>;2$iXpUG!~bw7UAQVB4<
z_VoFT`XP*Ka{)0rZsN68&7E~0-1)xE7Cf$ywS<tqkd9{vCkbF)B3{a<_nNL;=9tY-
zo6OYidX32)vrAFY3M0cSbhW+vA8_uza2TKQU)eaXriJgHp#)zM$3DARNL%`gko(fy
z^u|nkYLhu`ZAT?G$IRuF7u1gx&TlPw?GD&rGx?U>`ZLNaTOXb73*OCdH+p~M8{qF|
zB|aux=R|=(@9T=X=vsnj{vIV_P%~dPvzjv9HMsq@Ez<YpI``B8qT8!}#{5HZnQVaz
z(}O=}h-g%XD81Gky|({Vh9v6!A!brz=N0K4Vk7&8=d$z#J*$pF4=ZHI`+&$WPhwfV
zeC*D2gPY*)@zgMPU{fc9M93}XKaM4ogU~X(yy>amH}>G3d_Md5r2sS8qxmyO(Z@^5
zeXhXIWTMPbWFx*i9-|>aQQt$@k!#b9vHnANVje<Zbcf>OzJE8_ci(#ncG-T%7Yhn)
z+7~2v-)^;?&Qm5njg6V2><AeFb291;Z?>#Oj~X6KDX6^=bj<%lz48atdnXaps%-vg
zA)8M(X>YRMmWr`4Eb;uPiK*@#kuG|)K9aAu<k4*jzddN^8`Cu}QMVQBh-ar-4W{Cz
ze2zX*qlKccP~k2Eul10OrBR+d-9uQKCB6_aA3ApE<}1RZBjA>H0;yYmmT6MACH8f3
zp;-9T07dT83)%zP{QlBL{YN5ew}A5JK=UY+PfKi&i=)tgDM($VpE<pqReiBi&c-m?
z(<08Ycq28jYv2C&rL9D0{&Y<xD@kR=oUHZIQ6{R$(0_w@>URXB{Vb~KTE$ulk$L(G
z+g7g)9bRH}jbNq=vPN$u_2jDWv)T_xi#y394imQfd3}7(#_TJS%f`saQbuKR++Zy$
zBXeB8m8nJ6FL0ceETH4OEnzWHA2N?K<}$-ldnZz7!eK^kgz+4t^v~iDkf+k>b{%be
zvTsF{JS^=8-zQz4HXbSh?<5|iWSI|@&G#=hS8l|;Cb{V1bhWVtOt^eJffIH@^c-0q
zpbyHj_68>{;U)CJ0aML~_IRmGy7Z+FS9C^%OF9$j3RzBrlfK<&DoyX+x*nB2Y||Yo
zY8&sU4_(W6$N$68!m5hLlJbSeIUqebUmGK3!>ZDKI5@`Ps^*DtD`NQ1vS``b@69L1
z>Fr!axd*lYFIH`<$w_t4YR*|=O#5?H0JAI9XY!LjJo=Qk09owL;%DT4J7$;4h_B~!
z)L^Pxc4wrPFsjhs;$y#GxgpD_o*x|Us?{K8!<!ZYKaw|^_piRORaL=RVU{_m_?{tR
zShCa<i{WXYls1s;Oe;W^WNz1Z1BP4I_xLbpGV*;Q<4?16wp#bDEnMGefY(DP_3-2q
z&}4%Cs#>PUtUyQf{PUQVTjbOdv@dio---AtM_v2CHS)3Ro030FpjyniMjV)iY5a8h
zQ^R6%cJb0oXh8FB8K|(|M_}&w3E~d+qiV9!h={T27k)cWc?x~!anT9)eU|rBx*%;T
z^}aOw!wtvj$;+{#CpTGU4VsSR-$ad{w8JkQPt_y%a$7q2!mX%bpuYEb<cZZhw^#mE
z<U;&3v1?sjlKG1@x#y!EoV2cTnjE2$6tlyNL99o0Tg!2Z_!@Id5oN7gRcqPS#!ca%
z7$--q<6n&Hdy&BX7%R$j?ufS1RF`dfvT|}*^MQ3)hti*TJ~u?TZ5X^7v}uRzUsV3R
z{vI7a+j4!b{g^V7uf=Hbw)3>5akybx;=0UUfhr2KlK2B~pk~jluMzUSt@R?;i>1?2
z&`3;%p}nTOS@!4~*QWz3-W{y*;knhPAn(Jk$KJmxN@;3`nzSveci0&7-=aN?uu8fl
zHT4Ysb(()8L51CtFp+!8b@|+*imFpF`}uO$rZ{mLT8mnKqX^PonXfJ6wP3g5+Uoz^
zSVP!E{<Q@tbX@3+MmZn5YqPI1db1ryS5+Dui|?DC0ul13Taq!Fo#bB)cAYYltLp=e
z2oPX>;?!GJ;FbvwE}Xa<SbHs(?Be1I>>o1SZ6ut78iupPrhkn&ETEN_lH=>Ui!yCi
zmqPZGg3L1npY!5mXQ(u`?A7d*Y^Be7mRu&UN|gzTEbB+{FWZgmdJPe3`OBI>^~4NM
zRP-%K`|D;}-rt==>|2+z=0YEx=<??tFR$3A6v4-oqJH1;w9fiUE9v2sGU;?fD(POb
zW@y>|y_v1ow7OR3XPS5$`&#?;U4EwyjTzp7yK62)WVQme9wk711XAAOD0k3P<GG$F
zc>H+BfxW{*HR2hfB(i;VR4vfzsaS_JV~+x`GEm$*-niZ@_7i)obfeISs#d1GV8gU|
zlh@+u$gIzjs@B0d$F1|R*H1YPJvbl$#4#0w%dNoHQ8(iD$&jOg4B~b#fD=ULfF=H`
z_0m|rdwaayN$MfO0S{nhH9BYMJ=_QDVXyaaq24z3Z^UZ3mVE@?Ij_RLGrj`{Vs6ji
z9%=)3(E%QvUo5xM>%ZuS3h8+yb&Wj-az{uOTaNAVz4aq}U&JsN4dN#0o#ucG;Qe;2
zRs9jwE3tN59Te7q=q;kCcY5vI8~=KCngR|*eA~{_ALu=(0SBWgj=BxN+kgjm+19(f
zQBH5kKh8CDZ&d0WT5?$ePAJ6kbW{{bo`A+5M<IKHk<J|fE_BiHAgAe9llQ!B_LT=P
zVjh9IEEOFIAabQUVM2fo0-K=>{dxFZ(`6;*=}z<1V)73&0uI5H{^aWmiylEhrf#t%
zBa9ATL2D~Ut^D)GR#c%j=a;Qd0U&=b!OQ~A79euC^)hN<Gl9VyQiMlNwfY_Ot>vHZ
z>RC~lKM~WyR*o!_ZjIjePyx7pe)o_@MS+Oj7#X0s0f;UyFIilKxWQQv`&W7T!vBuj
z3ix+N<)EM&*F8lfvr);RBa2m<4)W{j0h3Z`mgi$7<!WUB2ROM3S>RnU;1Bj-+S~`o
zDW$TgLDLr=K%m=2ODb@1q2tC@2W(M^b+F$v{W=IZ3teFK^b^K-pru@?WKz{NT48{>
zF}Y$TM{NDtzp2ywv*pl-fYX%OkgvqW_I&21=cPLwd6YbX3JJgL>5<h$ze4znzygO-
zQt)lpDMA7uCG>-+hSh8yWjfg8rrp4ozmLZ?q!qucNCBbs0TSqwZ<cSz0i25BfaD%z
zSi?)%X%KMc1#!`(Pe$f;ZP~-Ub!lM4_bLji`sOLm2QMWD$S3%cC=jc!cNh>WC7t2X
zyIOOXD~uZUAQ0X(TYkPfHYEsH*HUPf7cOUn4>ozopQ%GPIo*3qGwl8~h!A~!{&hf2
z-;A7*7!dXfdO@qeF9gmvexFI88k8e<8;kK)H`vMu(R^j{&Kr1Qz#7PBkzYRwt?BxD
z=|64Z_c6{s#<R1Ey75!8QDgXk+LtHr3rLM6{q9A9d)pWWCqeI<M7nBh1RG~+>cedq
z{&P8V^16G?NRtp5_heq>^;6IIVy%^f(vBQbC|1&E;Q<JG^7r{#Wgcjq4^3LBF(<SN
z<hzcC@O)|TDnBKaK{VJ1wvnUq5=$7$q`(2ih<<M^&d@|mm8&Jo1*Ki||I+sw+F=4y
zBe#PQL)<OFXsdDvC(^)GusfPffxKbmb-%zJ6j^uzP^O5%*8uoJ4cx`?tH*F?3^<0E
zHZ^jTV+eG@ril~^#@pikg)AcTReM_wz^of0%CQ9M*M2%zl)VNq@(V43IH4U}2+ONR
z2>r6p_-~uSap7t8)4Q;v@j5YZ;A!Kw>j?a4;U!gG8=F)CWD`TmdlgV)oSVNsN`;uf
zWX96idUCfTs*(C|Iia8r;%}hwnTR57S}0hO06tlUb<+tkDd4(U0Q9&CBl>t);;oS1
zI*!cPH-u2y2O`(i59i$i@Fs%rS@$z2$f4j0wA5oBTS<tlJ1!*VGg_Lo`@1L?VAsta
zNfv<9@>jvq_Co$;&vM#v^a>S7GzLDT=74r^qpBa%k4WHvpQVfx2FA--lXyuELFz+L
zYb)eK7R&hdOl`Is{3Zv+9%l44R<x1C7|jbvcd#TcR3lUL%i7xj36%<@P}<bcyA%95
z8rA}^ij7<i7|#sY;p{ho{;D2%0OSiT2|yQ}o&@e^X+mDA0{;ihXklUqk_?&v4OLq2
zTa-(sX95~X?K1d0C|=2-oy&6qbiYFGGy2^BFcS@kGKQ^Z{2DcqgAyQP22o}fxEB=Q
z8|kGo+49P_<I?$mYrbxlD3m%dWbW#qbFug?$HldJu<4n@0`VcHQl`X^`jx1|CtE@5
zUj9)Fik@Hmr>mF?uqWz&plk0}{VdE0C{iv-sRnWBy-{N8pU(A~cB2lFWC-8@IrCve
z+x%*;spEOAX=lo_{w90vx`=Qx*H*aTN~lD!;`cu+iS|oUDPAMAWrPk~p{T1(HVzex
zr(n1q`c?R>g~VfclhI5PYi)j@`0iib8Rt_kcFIQ8>aNwD{){~Wg~;M!<H+L09x*4q
zZUN2`f{=4+HnFsgQR2FNujU9};iBMl41xGjYC?+Nh#HV-^)HVya0PP_MlweX-g?h}
zi_o^Gfs-A>MGmD6gYc)LZgC?Fu)mwwD@$lZUQU+D%CaXgrHdWoxnB~1r*(i2v<h_;
zcf>#1lY@I1bmbwGEAPxM%#}%+>*C#ccZuTbn!Wc)MnwoQly*mF;S0kFwT~p{gt#<@
zA&sEbJP+e42eg6%GvGJjq;ldiYd>y;m#MUQlfJvr<$>4HJA|05EI^}p_>bJP=)SJy
z)H|gWwGU(jo1WFPcS~t6S>R{zMO&b)6I%SzS~J|O8|h1og?xygglnxAW!fa!pl-ut
z{Bq^-4w^k~S&kDHyvDEN%XfPdt2b|^H)P%_o-alvJNU3s6h}Xs7A^f|FCJ?G2T-0z
zVdHb#tr&atP6sJ}NuM-u)Jt#luU<25Oi}H<o(YM9=L9Z9dbeS2-|z?eXqPHnC7CRI
z(rE08312iUMqW&V7jGB)mLuC3oSQ#PO-_%CWsF+zYdg|bQj+^MO%)S_YIL1Uq-Ah4
zZNv3w?MdH%99Jd=FDjrc`$2#Z1pE+h*a?TFQivgdKqx365CrlP1cE|=4-kMSI5@yB
kz|`LV>mUD?Jb!``-(Rjz!fLM%tOUuvRg^3dH}L;I0K&=R1^@s6

literal 0
HcmV?d00001

diff --git a/docs/static_site/src/assets/img/perl_logo.svg b/docs/static_site/src/assets/img/perl_logo.svg
new file mode 100644
index 000000000000..2ac676ee4bb7
--- /dev/null
+++ b/docs/static_site/src/assets/img/perl_logo.svg
@@ -0,0 +1 @@
+<svg width="2500" height="2500" viewBox="0 0 256 256" xmlns="http://www.w3.org/2000/svg" preserveAspectRatio="xMidYMid"><path d="M255.76 127.76c0 70.559-57.2 127.759-127.76 127.759S.24 198.319.24 127.759 57.44 0 128 0s127.76 57.2 127.76 127.76" fill="#3A3C5B"/><path d="M217 137.209c-4.304-27.163-32.802-44.758-54.023-58.028-9.27-5.797-24.3-13.348-26.244-25.54-.802-5.038-.773-10.243-.941-15.33-.067-1.998-.098-4.007-.276-6-.231-2.57-2.847.598-4.281-1.645-3.553-5.554-2.185 1.683-1.836 4.603.822 6.87 1.787 13.718 1.732 20.652-.107 13.251-2.242 25.936-5.546 38.704-7.738 29.899-13.842 60.533-6.187 91.129 1.586 6.34 3.65 12.594 6.363 18.544.82 1.8 2.39 6.821 4.646 7.423 8.126 2.168 14.27 2.796 20.76 8.84 4.03 3.753 7.051 1.703 11.817-.204 14.436-5.777 26.947-13.048 37.176-24.945 14.38-16.724 20.312-36.301 16.84-58.203m-15.268 27.35c-1.28 11.172-8.288 21.18-15.444 29.476-5.188 6.015-12.206 13.82-19.897 16.56-2.769.985.507-4.554.913-5.165 2.38-3.58 5.315-6.738 8.039-10.051 4.03-4.898 7.52-10.04 9.88-15.972 7.832-19.688 6.052-43.552-5.539-61.289-6.31-9.656-14.972-17.818-23.703-25.265-4.218-3.598-8.486-7-12.036-11.304-.82-.995-8.07-10.485-5.815-11.889.728-.453 16.573 16.214 18.297 17.515 6.552 4.947 13.29 9.522 19.357 15.093 8.21 7.543 16.642 15.968 21.346 26.226 5.155 11.24 5.994 23.911 4.602 36.064" fill="#FFF"/><path d="M123.294 3.443c2.379 1.587 2.907 10.836 2.907 19.293 0 8.458.793 44.93-1.85 54.709-2.642 9.779-8.721 20.615-15.329 30.13-6.607 9.514-14.271 29.6-14.007 42.02.264 12.424 7.4 32.773 12.95 41.23 5.571 8.49 15.029 20.322 12.95 22.995-3.7 4.757-18.764-11.63-26.693-21.143-7.929-9.515-15.858-28.81-16.121-43.873-.265-15.065 8.192-29.073 14.271-37.53 6.08-8.457 17.972-22.993 21.143-29.6 3.171-6.607 6.608-13.743 7.665-22.994 1.057-9.25 0-42.287 0-42.287s-.265-14.536 2.114-12.95" fill="#FFF"/><path d="M116.951 19.036c2.378 1.058 2.643 3.965 2.643 6.872s-.793 14.8-1.586 26.43c-.793 11.628-10.042 21.407-16.914 28.278C94.222 87.488 74.4 108.896 67 119.468c-7.4 10.571-11.088 24.496-10.307 36.207.792 11.893 3.7 23.522 14.8 35.944 11.1 12.422 18.765 18.236 24.579 21.144 5.815 2.905 11.63 5.02 10.308 7.664-1.322 2.642-6.873.792-12.951-1.321-6.079-2.116-26.693-10.571-38.587-24.05-11.892-13.48-17.97-31.452-17.442-48.632.527-17.178 5.286-24.314 13.214-35.414 7.929-11.1 29.865-27.486 36.736-31.714 6.872-4.23 16.122-11.365 20.616-17.18 4.492-5.814 6.87-10.572 6.606-18.5-.264-7.929.53-15.065.265-17.708-.265-2.642-.265-7.928 2.114-6.872M117.613 226.203c1.075-.043.396 4.614-1.502 6.925-1.898 2.31-5.157 3.7-5.553 2.77-.398-.93 2.353-1.465 4.373-3.713 1.855-2.064 1.364-5.929 2.682-5.982M139.003 226.08c-1.074-.044-.395 4.613 1.503 6.923 1.897 2.31 5.157 3.701 5.552 2.772.398-.93-2.353-1.465-4.373-3.713-1.855-2.063-1.364-5.93-2.682-5.981M129.495 231.133c0 4.067.207 7.364-.68 7.364-1.094 0-.682-3.297-.682-7.364s-.37-7.365.681-7.365c.97 0 .68 3.298.68 7.365" fill="#FFF"/></svg>
diff --git a/docs/static_site/src/assets/img/programming.svg b/docs/static_site/src/assets/img/programming.svg
new file mode 100644
index 000000000000..63fbe369a57a
--- /dev/null
+++ b/docs/static_site/src/assets/img/programming.svg
@@ -0,0 +1,55 @@
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" version="1.1" style="enable-background:new 0 0 504.123 504.123;" xml:space="preserve" width="504.123" height="504.123"><rect id="backgroundrect" width="100%" height="100%" x="0" y="0" fill="none" stroke="none"/>
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+<g class="currentLayer" style=""><title>Layer 1</title><circle style="fill:#FCBD5E;" cx="252.06199645996094" cy="252.06199645996094" r="252.06199645996094" id="svg_1"/><path style="fill:#FFFFFF;" d="M7.877,367.065h488.369l0,0c0,38.597-31.508,70.105-70.105,70.105H77.982  C39.385,437.169,7.877,406.449,7.877,367.065L7.877,367.065z" id="svg_2"/><path style="" d="M426.142,445.046H77.982C34.658,445.046,0,410.388,0,367.065c0-4.726,3.151-7.877,7.877-7.877  h488.369c4.726,0,7.877,3.151,7.877,7.877C504.123,410.388,469.465,445.046,426.142,445.046z M16.542,374.942  c3.938,30.72,29.932,54.351,61.44,54.351h348.16c31.508,0,57.502-23.631,61.44-54.351H16.542z" id="svg_3" class="" fill-opacity="1" fill="#4a90d6"/><path style="fill:#E1E6E8;" d="M55.138,70.105h393.846c13.391,0,23.631,10.24,23.631,23.631v273.329H31.508V93.735  C31.508,81.132,42.535,70.105,55.138,70.105z" id="svg_4"/><path style="" d="M472.615,374.942H31.508c-4.726,0-7.877-3.151-7.877-7.877V93.735  c0-17.329,14.178-31.508,31.508-31.508h393.846c17.329,0,31.508,14.178,31.508,31.508v273.329  C480.492,371.791,476.554,374.942,472.615,374.942z M39.385,359.188h425.354V93.735c0-8.665-7.089-15.754-15.754-15.754H55.138  c-8.665,0-15.754,7.089-15.754,15.754V359.188z" id="svg_5" class="" fill-opacity="1" fill="#4e90d3"/><rect x="63.20399856567383" y="102.21099853515625" style="fill:#FFFFFF;" width="378.0920104980469" height="233.94500732421875" id="svg_6"/><g id="svg_7">
+	<rect x="200.07400512695312" y="394.6340026855469" style="fill:#183651;" width="15.753999710083008" height="15.753999710083008" id="svg_8"/>
+	<rect x="244.18499755859375" y="394.6340026855469" style="fill:#183651;" width="15.753999710083008" height="15.753999710083008" id="svg_9"/>
+	<rect x="288.2950134277344" y="394.6340026855469" style="fill:#183651;" width="15.753999710083008" height="15.753999710083008" id="svg_10"/>
+	<polygon style="fill:#183651;" points="116.578,198.498 127.606,198.498 127.606,137.058 104.763,137.058 104.763,147.298    116.578,147.298  " id="svg_11"/>
+	<path style="fill:#183651;" d="M164.628,135.483c-8.665,0-14.966,3.151-18.905,8.665s-6.302,13.391-6.302,22.843   c0,9.452,2.363,17.329,6.302,22.843c3.938,5.514,10.24,8.665,18.905,8.665c8.665,0,14.966-3.151,18.905-8.665   s6.302-13.391,6.302-22.843s-2.363-17.329-6.302-22.843C179.594,138.634,173.292,135.483,164.628,135.483z M178.806,167.778   c0,6.302-0.788,11.815-3.151,15.754s-5.514,5.514-10.24,5.514c-4.726,0-7.877-1.575-10.24-5.514   c-2.363-3.938-3.151-9.452-3.151-15.754s0.788-11.815,3.151-15.754c2.363-3.938,5.514-5.514,10.24-5.514   c4.726,0,7.877,1.575,10.24,5.514C177.231,155.175,178.806,160.689,178.806,167.778z" id="svg_12"/>
+	<path style="fill:#183651;" d="M223.705,135.483c-8.665,0-14.966,3.151-18.905,8.665s-6.302,13.391-6.302,22.843   c0,9.452,2.363,17.329,6.302,22.843c3.938,5.514,10.24,8.665,18.905,8.665c8.665,0,14.966-3.151,18.905-8.665   s6.302-13.391,6.302-22.843s-2.363-17.329-6.302-22.843C238.671,138.634,232.369,135.483,223.705,135.483z M237.883,167.778   c0,6.302-0.788,11.815-3.151,15.754s-5.514,5.514-10.24,5.514c-4.726,0-7.877-1.575-10.24-5.514   c-2.363-3.938-3.151-9.452-3.151-15.754s0.788-11.815,3.151-15.754c2.363-3.938,5.514-5.514,10.24-5.514s7.877,1.575,10.24,5.514   C236.308,155.175,237.883,160.689,237.883,167.778z" id="svg_13"/>
+	<polygon style="fill:#183651;" points="255.212,147.298 267.028,147.298 267.028,198.498 278.055,198.498 278.055,137.058    255.212,137.058  " id="svg_14"/>
+	<path style="fill:#183651;" d="M315.077,135.483c-8.665,0-14.966,3.151-18.905,8.665c-3.938,5.514-6.302,13.391-6.302,22.843   c0,9.452,2.363,17.329,6.302,22.843c3.938,5.514,10.24,8.665,18.905,8.665c8.665,0,14.966-3.151,18.905-8.665   c3.938-5.514,6.302-13.391,6.302-22.843s-2.363-17.329-6.302-22.843C330.043,138.634,323.742,135.483,315.077,135.483z    M329.255,167.778c0,6.302-0.788,11.815-3.151,15.754c-2.363,3.938-5.514,5.514-10.24,5.514c-4.726,0-7.877-1.575-10.24-5.514   s-3.151-9.452-3.151-15.754s0.788-11.815,3.151-15.754c2.363-3.938,5.514-5.514,10.24-5.514c4.726,0,7.877,1.575,10.24,5.514   C327.68,155.175,329.255,160.689,329.255,167.778z" id="svg_15"/>
+	<path style="fill:#183651;" d="M374.154,199.286c8.665,0,14.966-3.151,18.905-8.665c3.938-5.514,6.302-13.391,6.302-22.843   c0-9.452-2.363-17.329-6.302-22.843c-3.938-5.514-10.24-8.665-18.905-8.665c-8.665,0-14.966,3.151-18.905,8.665   c-3.938,5.514-6.302,13.391-6.302,22.843c0,9.452,2.363,17.329,6.302,22.843C359.975,196.135,366.277,199.286,374.154,199.286z    M360.763,167.778c0-6.302,0.788-11.815,3.151-15.754c2.363-3.938,5.514-6.302,10.24-6.302s7.877,1.575,10.24,5.514   s3.151,9.452,3.151,15.754c0,6.302-0.788,11.815-3.151,15.754c-2.363,3.938-5.514,5.514-10.24,5.514s-7.877-1.575-10.24-5.514   C361.551,179.594,360.763,174.08,360.763,167.778z" id="svg_16"/>
+	<path style="fill:#183651;" d="M132.332,238.671c-8.665,0-14.966,3.151-18.905,8.665c-3.938,5.514-6.302,13.391-6.302,22.843   c0,9.452,2.363,17.329,6.302,22.843s10.24,8.665,18.905,8.665s14.966-3.151,18.905-8.665c3.938-5.514,6.302-13.391,6.302-22.843   c0-9.452-2.363-17.329-6.302-22.843C147.298,241.034,140.997,238.671,132.332,238.671z M145.723,270.178   c0,6.302-0.788,11.815-3.151,15.754c-2.363,3.938-5.514,5.514-10.24,5.514c-4.726,0-7.877-1.575-10.24-5.514   c-2.363-3.938-3.151-9.452-3.151-15.754c0-6.302,0.788-11.815,3.151-15.754s5.514-5.514,10.24-5.514   c4.726,0,7.877,1.575,10.24,5.514S145.723,263.877,145.723,270.178z" id="svg_17"/>
+	<polygon style="fill:#183651;" points="163.84,250.486 175.655,250.486 175.655,300.898 186.683,300.898 186.683,239.458    163.84,239.458  " id="svg_18"/>
+	<path style="fill:#183651;" d="M223.705,238.671c-8.665,0-14.966,3.151-18.905,8.665c-3.938,5.514-6.302,13.391-6.302,22.843   c0,9.452,2.363,17.329,6.302,22.843s10.24,8.665,18.905,8.665c8.665,0,14.966-3.151,18.905-8.665   c3.938-5.514,6.302-13.391,6.302-22.843c0-9.452-2.363-17.329-6.302-22.843C238.671,241.034,232.369,238.671,223.705,238.671z    M237.883,270.178c0,6.302-0.788,11.815-3.151,15.754c-2.363,3.938-5.514,5.514-10.24,5.514c-4.726,0-7.877-1.575-10.24-5.514   c-2.363-3.938-3.151-9.452-3.151-15.754c0-6.302,0.788-11.815,3.151-15.754c2.363-3.938,5.514-5.514,10.24-5.514   s7.877,1.575,10.24,5.514C236.308,258.363,237.883,263.877,237.883,270.178z" id="svg_19"/>
+	<polygon style="fill:#183651;" points="255.212,250.486 267.028,250.486 267.028,300.898 278.055,300.898 278.055,239.458    255.212,239.458  " id="svg_20"/>
+	<path style="fill:#183651;" d="M315.077,238.671c-8.665,0-14.966,3.151-18.905,8.665c-3.938,5.514-6.302,13.391-6.302,22.843   c0,9.452,2.363,17.329,6.302,22.843c3.938,5.514,10.24,8.665,18.905,8.665c8.665,0,14.966-3.151,18.905-8.665   c3.938-5.514,6.302-13.391,6.302-22.843c0-9.452-2.363-17.329-6.302-22.843C330.043,241.034,323.742,238.671,315.077,238.671z    M329.255,270.178c0,6.302-0.788,11.815-3.151,15.754c-2.363,3.938-5.514,5.514-10.24,5.514c-4.726,0-7.877-1.575-10.24-5.514   c-2.363-3.938-3.151-9.452-3.151-15.754c0-6.302,0.788-11.815,3.151-15.754c2.363-3.938,5.514-5.514,10.24-5.514   c4.726,0,7.877,1.575,10.24,5.514C327.68,258.363,329.255,263.877,329.255,270.178z" id="svg_21"/>
+	<path style="fill:#183651;" d="M374.154,238.671c-8.665,0-14.966,3.151-18.905,8.665c-3.938,5.514-6.302,13.391-6.302,22.843   c0,9.452,2.363,17.329,6.302,22.843c3.938,5.514,10.24,8.665,18.905,8.665s14.966-3.151,18.905-8.665   c3.938-5.514,6.302-13.391,6.302-22.843c0-9.452-2.363-17.329-6.302-22.843C389.12,241.034,382.818,238.671,374.154,238.671z    M388.332,270.178c0,6.302-0.788,11.815-3.151,15.754c-2.363,3.938-5.514,5.514-10.24,5.514s-7.877-1.575-10.24-5.514   c-2.363-3.938-3.151-9.452-3.151-15.754c0-6.302,0.788-11.815,3.151-15.754c2.363-3.938,5.514-5.514,10.24-5.514   s7.877,1.575,10.24,5.514C386.757,258.363,388.332,263.877,388.332,270.178z" id="svg_22"/>
+</g><g id="svg_23">
+</g><g id="svg_24">
+</g><g id="svg_25">
+</g><g id="svg_26">
+</g><g id="svg_27">
+</g><g id="svg_28">
+</g><g id="svg_29">
+</g><g id="svg_30">
+</g><g id="svg_31">
+</g><g id="svg_32">
+</g><g id="svg_33">
+</g><g id="svg_34">
+</g><g id="svg_35">
+</g><g id="svg_36">
+</g><g id="svg_37">
+</g></g></svg>
\ No newline at end of file
diff --git a/docs/static_site/src/assets/img/programmingv1.svg b/docs/static_site/src/assets/img/programmingv1.svg
new file mode 100644
index 000000000000..5204941ad335
--- /dev/null
+++ b/docs/static_site/src/assets/img/programmingv1.svg
@@ -0,0 +1,109 @@
+<?xml version="1.0" encoding="iso-8859-1"?>
+<!-- Generator: Adobe Illustrator 19.0.0, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<svg version="1.1" id="Capa_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 viewBox="0 0 504.123 504.123" style="enable-background:new 0 0 504.123 504.123;" xml:space="preserve">
+<circle style="fill:#FCBD5E;" cx="252.062" cy="252.062" r="252.062"/>
+<path style="fill:#FFFFFF;" d="M7.877,367.065h488.369l0,0c0,38.597-31.508,70.105-70.105,70.105H77.982
+	C39.385,437.169,7.877,406.449,7.877,367.065L7.877,367.065z"/>
+<path style="fill:#183651;" d="M426.142,445.046H77.982C34.658,445.046,0,410.388,0,367.065c0-4.726,3.151-7.877,7.877-7.877
+	h488.369c4.726,0,7.877,3.151,7.877,7.877C504.123,410.388,469.465,445.046,426.142,445.046z M16.542,374.942
+	c3.938,30.72,29.932,54.351,61.44,54.351h348.16c31.508,0,57.502-23.631,61.44-54.351H16.542z"/>
+<path style="fill:#E1E6E8;" d="M55.138,70.105h393.846c13.391,0,23.631,10.24,23.631,23.631v273.329H31.508V93.735
+	C31.508,81.132,42.535,70.105,55.138,70.105z"/>
+<path style="fill:#183651;" d="M472.615,374.942H31.508c-4.726,0-7.877-3.151-7.877-7.877V93.735
+	c0-17.329,14.178-31.508,31.508-31.508h393.846c17.329,0,31.508,14.178,31.508,31.508v273.329
+	C480.492,371.791,476.554,374.942,472.615,374.942z M39.385,359.188h425.354V93.735c0-8.665-7.089-15.754-15.754-15.754H55.138
+	c-8.665,0-15.754,7.089-15.754,15.754V359.188z"/>
+<rect x="63.204" y="102.211" style="fill:#FFFFFF;" width="378.092" height="233.945"/>
+<g>
+	<rect x="200.074" y="394.634" style="fill:#183651;" width="15.754" height="15.754"/>
+	<rect x="244.185" y="394.634" style="fill:#183651;" width="15.754" height="15.754"/>
+	<rect x="288.295" y="394.634" style="fill:#183651;" width="15.754" height="15.754"/>
+	<polygon style="fill:#183651;" points="116.578,198.498 127.606,198.498 127.606,137.058 104.763,137.058 104.763,147.298 
+		116.578,147.298 	"/>
+	<path style="fill:#183651;" d="M164.628,135.483c-8.665,0-14.966,3.151-18.905,8.665s-6.302,13.391-6.302,22.843
+		c0,9.452,2.363,17.329,6.302,22.843c3.938,5.514,10.24,8.665,18.905,8.665c8.665,0,14.966-3.151,18.905-8.665
+		s6.302-13.391,6.302-22.843s-2.363-17.329-6.302-22.843C179.594,138.634,173.292,135.483,164.628,135.483z M178.806,167.778
+		c0,6.302-0.788,11.815-3.151,15.754s-5.514,5.514-10.24,5.514c-4.726,0-7.877-1.575-10.24-5.514
+		c-2.363-3.938-3.151-9.452-3.151-15.754s0.788-11.815,3.151-15.754c2.363-3.938,5.514-5.514,10.24-5.514
+		c4.726,0,7.877,1.575,10.24,5.514C177.231,155.175,178.806,160.689,178.806,167.778z"/>
+	<path style="fill:#183651;" d="M223.705,135.483c-8.665,0-14.966,3.151-18.905,8.665s-6.302,13.391-6.302,22.843
+		c0,9.452,2.363,17.329,6.302,22.843c3.938,5.514,10.24,8.665,18.905,8.665c8.665,0,14.966-3.151,18.905-8.665
+		s6.302-13.391,6.302-22.843s-2.363-17.329-6.302-22.843C238.671,138.634,232.369,135.483,223.705,135.483z M237.883,167.778
+		c0,6.302-0.788,11.815-3.151,15.754s-5.514,5.514-10.24,5.514c-4.726,0-7.877-1.575-10.24-5.514
+		c-2.363-3.938-3.151-9.452-3.151-15.754s0.788-11.815,3.151-15.754c2.363-3.938,5.514-5.514,10.24-5.514s7.877,1.575,10.24,5.514
+		C236.308,155.175,237.883,160.689,237.883,167.778z"/>
+	<polygon style="fill:#183651;" points="255.212,147.298 267.028,147.298 267.028,198.498 278.055,198.498 278.055,137.058 
+		255.212,137.058 	"/>
+	<path style="fill:#183651;" d="M315.077,135.483c-8.665,0-14.966,3.151-18.905,8.665c-3.938,5.514-6.302,13.391-6.302,22.843
+		c0,9.452,2.363,17.329,6.302,22.843c3.938,5.514,10.24,8.665,18.905,8.665c8.665,0,14.966-3.151,18.905-8.665
+		c3.938-5.514,6.302-13.391,6.302-22.843s-2.363-17.329-6.302-22.843C330.043,138.634,323.742,135.483,315.077,135.483z
+		 M329.255,167.778c0,6.302-0.788,11.815-3.151,15.754c-2.363,3.938-5.514,5.514-10.24,5.514c-4.726,0-7.877-1.575-10.24-5.514
+		s-3.151-9.452-3.151-15.754s0.788-11.815,3.151-15.754c2.363-3.938,5.514-5.514,10.24-5.514c4.726,0,7.877,1.575,10.24,5.514
+		C327.68,155.175,329.255,160.689,329.255,167.778z"/>
+	<path style="fill:#183651;" d="M374.154,199.286c8.665,0,14.966-3.151,18.905-8.665c3.938-5.514,6.302-13.391,6.302-22.843
+		c0-9.452-2.363-17.329-6.302-22.843c-3.938-5.514-10.24-8.665-18.905-8.665c-8.665,0-14.966,3.151-18.905,8.665
+		c-3.938,5.514-6.302,13.391-6.302,22.843c0,9.452,2.363,17.329,6.302,22.843C359.975,196.135,366.277,199.286,374.154,199.286z
+		 M360.763,167.778c0-6.302,0.788-11.815,3.151-15.754c2.363-3.938,5.514-6.302,10.24-6.302s7.877,1.575,10.24,5.514
+		s3.151,9.452,3.151,15.754c0,6.302-0.788,11.815-3.151,15.754c-2.363,3.938-5.514,5.514-10.24,5.514s-7.877-1.575-10.24-5.514
+		C361.551,179.594,360.763,174.08,360.763,167.778z"/>
+	<path style="fill:#183651;" d="M132.332,238.671c-8.665,0-14.966,3.151-18.905,8.665c-3.938,5.514-6.302,13.391-6.302,22.843
+		c0,9.452,2.363,17.329,6.302,22.843s10.24,8.665,18.905,8.665s14.966-3.151,18.905-8.665c3.938-5.514,6.302-13.391,6.302-22.843
+		c0-9.452-2.363-17.329-6.302-22.843C147.298,241.034,140.997,238.671,132.332,238.671z M145.723,270.178
+		c0,6.302-0.788,11.815-3.151,15.754c-2.363,3.938-5.514,5.514-10.24,5.514c-4.726,0-7.877-1.575-10.24-5.514
+		c-2.363-3.938-3.151-9.452-3.151-15.754c0-6.302,0.788-11.815,3.151-15.754s5.514-5.514,10.24-5.514
+		c4.726,0,7.877,1.575,10.24,5.514S145.723,263.877,145.723,270.178z"/>
+	<polygon style="fill:#183651;" points="163.84,250.486 175.655,250.486 175.655,300.898 186.683,300.898 186.683,239.458 
+		163.84,239.458 	"/>
+	<path style="fill:#183651;" d="M223.705,238.671c-8.665,0-14.966,3.151-18.905,8.665c-3.938,5.514-6.302,13.391-6.302,22.843
+		c0,9.452,2.363,17.329,6.302,22.843s10.24,8.665,18.905,8.665c8.665,0,14.966-3.151,18.905-8.665
+		c3.938-5.514,6.302-13.391,6.302-22.843c0-9.452-2.363-17.329-6.302-22.843C238.671,241.034,232.369,238.671,223.705,238.671z
+		 M237.883,270.178c0,6.302-0.788,11.815-3.151,15.754c-2.363,3.938-5.514,5.514-10.24,5.514c-4.726,0-7.877-1.575-10.24-5.514
+		c-2.363-3.938-3.151-9.452-3.151-15.754c0-6.302,0.788-11.815,3.151-15.754c2.363-3.938,5.514-5.514,10.24-5.514
+		s7.877,1.575,10.24,5.514C236.308,258.363,237.883,263.877,237.883,270.178z"/>
+	<polygon style="fill:#183651;" points="255.212,250.486 267.028,250.486 267.028,300.898 278.055,300.898 278.055,239.458 
+		255.212,239.458 	"/>
+	<path style="fill:#183651;" d="M315.077,238.671c-8.665,0-14.966,3.151-18.905,8.665c-3.938,5.514-6.302,13.391-6.302,22.843
+		c0,9.452,2.363,17.329,6.302,22.843c3.938,5.514,10.24,8.665,18.905,8.665c8.665,0,14.966-3.151,18.905-8.665
+		c3.938-5.514,6.302-13.391,6.302-22.843c0-9.452-2.363-17.329-6.302-22.843C330.043,241.034,323.742,238.671,315.077,238.671z
+		 M329.255,270.178c0,6.302-0.788,11.815-3.151,15.754c-2.363,3.938-5.514,5.514-10.24,5.514c-4.726,0-7.877-1.575-10.24-5.514
+		c-2.363-3.938-3.151-9.452-3.151-15.754c0-6.302,0.788-11.815,3.151-15.754c2.363-3.938,5.514-5.514,10.24-5.514
+		c4.726,0,7.877,1.575,10.24,5.514C327.68,258.363,329.255,263.877,329.255,270.178z"/>
+	<path style="fill:#183651;" d="M374.154,238.671c-8.665,0-14.966,3.151-18.905,8.665c-3.938,5.514-6.302,13.391-6.302,22.843
+		c0,9.452,2.363,17.329,6.302,22.843c3.938,5.514,10.24,8.665,18.905,8.665s14.966-3.151,18.905-8.665
+		c3.938-5.514,6.302-13.391,6.302-22.843c0-9.452-2.363-17.329-6.302-22.843C389.12,241.034,382.818,238.671,374.154,238.671z
+		 M388.332,270.178c0,6.302-0.788,11.815-3.151,15.754c-2.363,3.938-5.514,5.514-10.24,5.514s-7.877-1.575-10.24-5.514
+		c-2.363-3.938-3.151-9.452-3.151-15.754c0-6.302,0.788-11.815,3.151-15.754c2.363-3.938,5.514-5.514,10.24-5.514
+		s7.877,1.575,10.24,5.514C386.757,258.363,388.332,263.877,388.332,270.178z"/>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+<g>
+</g>
+</svg>
diff --git a/docs/static_site/src/assets/img/python_logo.svg b/docs/static_site/src/assets/img/python_logo.svg
new file mode 100644
index 000000000000..116eaac31119
--- /dev/null
+++ b/docs/static_site/src/assets/img/python_logo.svg
@@ -0,0 +1,269 @@
+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<!-- Created with Inkscape (http://www.inkscape.org/) -->
+<svg
+   xmlns:dc="http://purl.org/dc/elements/1.1/"
+   xmlns:cc="http://web.resource.org/cc/"
+   xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
+   xmlns:svg="http://www.w3.org/2000/svg"
+   xmlns="http://www.w3.org/2000/svg"
+   xmlns:xlink="http://www.w3.org/1999/xlink"
+   xmlns:sodipodi="http://inkscape.sourceforge.net/DTD/sodipodi-0.dtd"
+   xmlns:inkscape="http://www.inkscape.org/namespaces/inkscape"
+   version="1.0"
+   width="388.84pt"
+   height="115.02pt"
+   id="svg2"
+   sodipodi:version="0.32"
+   inkscape:version="0.43"
+   sodipodi:docname="logo-python-generic.svg"
+   sodipodi:docbase="/home/sdeibel">
+  <metadata
+     id="metadata2193">
+    <rdf:RDF>
+      <cc:Work
+         rdf:about="">
+        <dc:format>image/svg+xml</dc:format>
+        <dc:type
+           rdf:resource="http://purl.org/dc/dcmitype/StillImage" />
+      </cc:Work>
+    </rdf:RDF>
+  </metadata>
+  <sodipodi:namedview
+     inkscape:window-height="543"
+     inkscape:window-width="791"
+     inkscape:pageshadow="2"
+     inkscape:pageopacity="0.0"
+     borderopacity="1.0"
+     bordercolor="#666666"
+     pagecolor="#ffffff"
+     id="base"
+     inkscape:zoom="1.4340089"
+     inkscape:cx="243.02499"
+     inkscape:cy="71.887497"
+     inkscape:window-x="0"
+     inkscape:window-y="0"
+     inkscape:current-layer="svg2" />
+  <defs
+     id="defs4">
+    <linearGradient
+       id="linearGradient2795">
+      <stop
+         style="stop-color:#b8b8b8;stop-opacity:0.49803922"
+         offset="0"
+         id="stop2797" />
+      <stop
+         style="stop-color:#7f7f7f;stop-opacity:0"
+         offset="1"
+         id="stop2799" />
+    </linearGradient>
+    <linearGradient
+       id="linearGradient2787">
+      <stop
+         style="stop-color:#7f7f7f;stop-opacity:0.5"
+         offset="0"
+         id="stop2789" />
+      <stop
+         style="stop-color:#7f7f7f;stop-opacity:0"
+         offset="1"
+         id="stop2791" />
+    </linearGradient>
+    <linearGradient
+       id="linearGradient3676">
+      <stop
+         style="stop-color:#b2b2b2;stop-opacity:0.5"
+         offset="0"
+         id="stop3678" />
+      <stop
+         style="stop-color:#b3b3b3;stop-opacity:0"
+         offset="1"
+         id="stop3680" />
+    </linearGradient>
+    <linearGradient
+       id="linearGradient3236">
+      <stop
+         style="stop-color:#f4f4f4;stop-opacity:1"
+         offset="0"
+         id="stop3244" />
+      <stop
+         style="stop-color:#ffffff;stop-opacity:1"
+         offset="1"
+         id="stop3240" />
+    </linearGradient>
+    <linearGradient
+       id="linearGradient4671">
+      <stop
+         style="stop-color:#ffd43b;stop-opacity:1"
+         offset="0"
+         id="stop4673" />
+      <stop
+         style="stop-color:#ffe873;stop-opacity:1"
+         offset="1"
+         id="stop4675" />
+    </linearGradient>
+    <linearGradient
+       id="linearGradient4689">
+      <stop
+         style="stop-color:#5a9fd4;stop-opacity:1"
+         offset="0"
+         id="stop4691" />
+      <stop
+         style="stop-color:#306998;stop-opacity:1"
+         offset="1"
+         id="stop4693" />
+    </linearGradient>
+    <linearGradient
+       x1="224.23996"
+       y1="144.75717"
+       x2="-65.308502"
+       y2="144.75717"
+       id="linearGradient2987"
+       xlink:href="#linearGradient4671"
+       gradientUnits="userSpaceOnUse"
+       gradientTransform="translate(100.2702,99.61116)" />
+    <linearGradient
+       x1="172.94208"
+       y1="77.475983"
+       x2="26.670298"
+       y2="76.313133"
+       id="linearGradient2990"
+       xlink:href="#linearGradient4689"
+       gradientUnits="userSpaceOnUse"
+       gradientTransform="translate(100.2702,99.61116)" />
+    <linearGradient
+       x1="172.94208"
+       y1="77.475983"
+       x2="26.670298"
+       y2="76.313133"
+       id="linearGradient2587"
+       xlink:href="#linearGradient4689"
+       gradientUnits="userSpaceOnUse"
+       gradientTransform="translate(100.2702,99.61116)" />
+    <linearGradient
+       x1="224.23996"
+       y1="144.75717"
+       x2="-65.308502"
+       y2="144.75717"
+       id="linearGradient2589"
+       xlink:href="#linearGradient4671"
+       gradientUnits="userSpaceOnUse"
+       gradientTransform="translate(100.2702,99.61116)" />
+    <linearGradient
+       x1="172.94208"
+       y1="77.475983"
+       x2="26.670298"
+       y2="76.313133"
+       id="linearGradient2248"
+       xlink:href="#linearGradient4689"
+       gradientUnits="userSpaceOnUse"
+       gradientTransform="translate(100.2702,99.61116)" />
+    <linearGradient
+       x1="224.23996"
+       y1="144.75717"
+       x2="-65.308502"
+       y2="144.75717"
+       id="linearGradient2250"
+       xlink:href="#linearGradient4671"
+       gradientUnits="userSpaceOnUse"
+       gradientTransform="translate(100.2702,99.61116)" />
+    <linearGradient
+       x1="224.23996"
+       y1="144.75717"
+       x2="-65.308502"
+       y2="144.75717"
+       id="linearGradient2255"
+       xlink:href="#linearGradient4671"
+       gradientUnits="userSpaceOnUse"
+       gradientTransform="matrix(0.562541,0,0,0.567972,-11.5974,-7.60954)" />
+    <linearGradient
+       x1="172.94208"
+       y1="76.176224"
+       x2="26.670298"
+       y2="76.313133"
+       id="linearGradient2258"
+       xlink:href="#linearGradient4689"
+       gradientUnits="userSpaceOnUse"
+       gradientTransform="matrix(0.562541,0,0,0.567972,-11.5974,-7.60954)" />
+    <radialGradient
+       cx="61.518883"
+       cy="132.28575"
+       r="29.036913"
+       fx="61.518883"
+       fy="132.28575"
+       id="radialGradient2801"
+       xlink:href="#linearGradient2795"
+       gradientUnits="userSpaceOnUse"
+       gradientTransform="matrix(1,0,0,0.177966,0,108.7434)" />
+    <linearGradient
+       x1="150.96111"
+       y1="192.35176"
+       x2="112.03144"
+       y2="137.27299"
+       id="linearGradient1475"
+       xlink:href="#linearGradient4671"
+       gradientUnits="userSpaceOnUse"
+       gradientTransform="matrix(0.562541,0,0,0.567972,-9.399749,-5.305317)" />
+    <linearGradient
+       x1="26.648937"
+       y1="20.603781"
+       x2="135.66525"
+       y2="114.39767"
+       id="linearGradient1478"
+       xlink:href="#linearGradient4689"
+       gradientUnits="userSpaceOnUse"
+       gradientTransform="matrix(0.562541,0,0,0.567972,-9.399749,-5.305317)" />
+    <radialGradient
+       cx="61.518883"
+       cy="132.28575"
+       r="29.036913"
+       fx="61.518883"
+       fy="132.28575"
+       id="radialGradient1480"
+       xlink:href="#linearGradient2795"
+       gradientUnits="userSpaceOnUse"
+       gradientTransform="matrix(2.382716e-8,-0.296405,1.43676,4.683673e-7,-128.544,150.5202)" />
+  </defs>
+  <g
+     id="g2303">
+    <path
+       id="path46"
+       style="fill:#646464;fill-opacity:1"
+       d="M 184.61344,61.929363 C 184.61344,47.367213 180.46118,39.891193 172.15666,39.481813 C 168.85239,39.325863 165.62611,39.852203 162.48754,41.070593 C 159.98254,41.967323 158.2963,42.854313 157.40931,43.751043 L 157.40931,78.509163 C 162.72147,81.842673 167.43907,83.392453 171.55234,83.148783 C 180.25649,82.573703 184.61344,75.507063 184.61344,61.929363 z M 194.85763,62.533683 C 194.85763,69.931723 193.12265,76.072393 189.63319,80.955683 C 185.7441,86.482283 180.35396,89.328433 173.46277,89.484393 C 168.26757,89.650093 162.91642,88.022323 157.40931,84.610843 L 157.40931,116.20116 L 148.50047,113.02361 L 148.50047,42.903043 C 149.96253,41.109583 151.84372,39.569543 154.12454,38.263433 C 159.42696,35.173603 165.86978,33.584823 173.45302,33.506853 L 173.57973,33.633563 C 180.50991,33.545833 185.85132,36.391993 189.60395,42.162263 C 193.10315,47.454933 194.85763,54.238913 194.85763,62.533683 z " />
+    <path
+       id="path48"
+       style="fill:#646464;fill-opacity:1"
+       d="M 249.30487,83.265743 C 249.30487,93.188283 248.31067,100.05998 246.32227,103.88084 C 244.32411,107.7017 240.52275,110.75254 234.90842,113.02361 C 230.35653,114.81707 225.43425,115.79178 220.15133,115.95748 L 218.67952,110.34316 C 224.05016,109.61213 227.83204,108.88109 230.02513,108.15006 C 234.34309,106.688 237.30621,104.44617 238.93397,101.44406 C 240.24008,98.997543 240.88339,94.328693 240.88339,87.418003 L 240.88339,85.098203 C 234.79146,87.866373 228.40711,89.240713 221.73036,89.240713 C 217.34417,89.240713 213.47457,87.866373 210.14107,85.098203 C 206.39818,82.086343 204.52674,78.265483 204.52674,73.635623 L 204.52674,36.557693 L 213.43558,33.506853 L 213.43558,70.828453 C 213.43558,74.815013 214.7222,77.885353 217.29543,80.039463 C 219.86866,82.193563 223.20217,83.226753 227.2862,83.148783 C 231.37023,83.061053 235.74667,81.482023 240.39603,78.392203 L 240.39603,34.851953 L 249.30487,34.851953 L 249.30487,83.265743 z " />
+    <path
+       id="path50"
+       style="fill:#646464;fill-opacity:1"
+       d="M 284.08249,88.997033 C 283.02006,89.084753 282.04535,89.123743 281.14862,89.123743 C 276.10937,89.123743 272.18129,87.924853 269.37413,85.517323 C 266.57671,83.109793 265.17314,79.786033 265.17314,75.546053 L 265.17314,40.456523 L 259.07146,40.456523 L 259.07146,34.851953 L 265.17314,34.851953 L 265.17314,19.968143 L 274.07223,16.800333 L 274.07223,34.851953 L 284.08249,34.851953 L 284.08249,40.456523 L 274.07223,40.456523 L 274.07223,75.302373 C 274.07223,78.645623 274.96896,81.014163 276.76243,82.398253 C 278.30247,83.538663 280.74899,84.191723 284.08249,84.357423 L 284.08249,88.997033 z " />
+    <path
+       id="path52"
+       style="fill:#646464;fill-opacity:1"
+       d="M 338.02288,88.266003 L 329.11404,88.266003 L 329.11404,53.878273 C 329.11404,50.379063 328.29528,47.367213 326.66753,44.852463 C 324.78634,42.006313 322.17411,40.583233 318.82112,40.583233 C 314.73708,40.583233 309.6296,42.737343 303.4987,47.045563 L 303.4987,88.266003 L 294.58985,88.266003 L 294.58985,6.0687929 L 303.4987,3.2616329 L 303.4987,40.700203 C 309.191,36.557693 315.40963,34.481563 322.16436,34.481563 C 326.88196,34.481563 330.70282,36.070333 333.62694,39.238143 C 336.56082,42.405943 338.02288,46.353513 338.02288,51.071103 L 338.02288,88.266003 L 338.02288,88.266003 z " />
+    <path
+       id="path54"
+       style="fill:#646464;fill-opacity:1"
+       d="M 385.37424,60.525783 C 385.37424,54.930953 384.31182,50.310833 382.19669,46.655673 C 379.68195,42.201253 375.77337,39.852203 370.49044,39.608523 C 360.72386,40.173863 355.85032,47.172273 355.85032,60.584263 C 355.85032,66.734683 356.86401,71.871393 358.91089,75.994413 C 361.52312,81.248093 365.44145,83.840823 370.66589,83.753103 C 380.47146,83.675123 385.37424,75.935933 385.37424,60.525783 z M 395.13109,60.584263 C 395.13109,68.547643 393.09395,75.175663 389.02941,80.468333 C 384.5555,86.394563 378.37584,89.367423 370.49044,89.367423 C 362.67328,89.367423 356.58135,86.394563 352.18541,80.468333 C 348.19885,75.175663 346.21044,68.547643 346.21044,60.584263 C 346.21044,53.098503 348.36455,46.801883 352.67276,41.674913 C 357.22466,36.236033 363.20937,33.506853 370.6074,33.506853 C 378.00545,33.506853 384.02914,36.236033 388.66877,41.674913 C 392.97697,46.801883 395.13109,53.098503 395.13109,60.584263 z " />
+    <path
+       id="path56"
+       style="fill:#646464;fill-opacity:1"
+       d="M 446.20583,88.266003 L 437.29699,88.266003 L 437.29699,51.928853 C 437.29699,47.942293 436.0981,44.832973 433.70032,42.591133 C 431.30253,40.359053 428.10549,39.277123 424.11893,39.364853 C 419.8887,39.442833 415.86314,40.826913 412.04229,43.507363 L 412.04229,88.266003 L 403.13345,88.266003 L 403.13345,42.405943 C 408.26042,38.672813 412.97801,36.236033 417.28621,35.095623 C 421.35076,34.033193 424.93769,33.506853 428.02752,33.506853 C 430.14264,33.506853 432.13104,33.711543 434.00248,34.120913 C 437.50169,34.929923 440.34783,36.430973 442.54093,38.633823 C 444.98744,41.070593 446.20583,43.994723 446.20583,47.415943 L 446.20583,88.266003 z " />
+    <path
+       id="path1948"
+       style="fill:url(#linearGradient1478);fill-opacity:1"
+       d="M 60.510156,6.3979729 C 55.926503,6.4192712 51.549217,6.8101906 47.697656,7.4917229 C 36.35144,9.4962267 34.291407,13.691825 34.291406,21.429223 L 34.291406,31.647973 L 61.103906,31.647973 L 61.103906,35.054223 L 34.291406,35.054223 L 24.228906,35.054223 C 16.436447,35.054223 9.6131468,39.73794 7.4789058,48.647973 C 5.0170858,58.860939 4.9078907,65.233996 7.4789058,75.897973 C 9.3848341,83.835825 13.936449,89.491721 21.728906,89.491723 L 30.947656,89.491723 L 30.947656,77.241723 C 30.947656,68.391821 38.6048,60.585475 47.697656,60.585473 L 74.478906,60.585473 C 81.933857,60.585473 87.885159,54.447309 87.885156,46.960473 L 87.885156,21.429223 C 87.885156,14.162884 81.755176,8.7044455 74.478906,7.4917229 C 69.872919,6.7249976 65.093809,6.3766746 60.510156,6.3979729 z M 46.010156,14.616723 C 48.779703,14.616723 51.041406,16.915369 51.041406,19.741723 C 51.041404,22.558059 48.779703,24.835473 46.010156,24.835473 C 43.23068,24.835472 40.978906,22.558058 40.978906,19.741723 C 40.978905,16.91537 43.23068,14.616723 46.010156,14.616723 z " />
+    <path
+       id="path1950"
+       style="fill:url(#linearGradient1475);fill-opacity:1"
+       d="M 91.228906,35.054223 L 91.228906,46.960473 C 91.228906,56.191228 83.403011,63.960472 74.478906,63.960473 L 47.697656,63.960473 C 40.361823,63.960473 34.291407,70.238956 34.291406,77.585473 L 34.291406,103.11672 C 34.291406,110.38306 40.609994,114.65704 47.697656,116.74172 C 56.184987,119.23733 64.323893,119.68835 74.478906,116.74172 C 81.229061,114.78733 87.885159,110.85411 87.885156,103.11672 L 87.885156,92.897973 L 61.103906,92.897973 L 61.103906,89.491723 L 87.885156,89.491723 L 101.29141,89.491723 C 109.08387,89.491723 111.98766,84.056315 114.69765,75.897973 C 117.49698,67.499087 117.37787,59.422197 114.69765,48.647973 C 112.77187,40.890532 109.09378,35.054223 101.29141,35.054223 L 91.228906,35.054223 z M 76.166406,99.710473 C 78.945884,99.710476 81.197656,101.98789 81.197656,104.80422 C 81.197654,107.63057 78.945881,109.92922 76.166406,109.92922 C 73.396856,109.92922 71.135156,107.63057 71.135156,104.80422 C 71.135158,101.98789 73.396853,99.710473 76.166406,99.710473 z " />
+    <path
+       id="text3004"
+       style="font-size:15.16445827px;font-style:normal;font-weight:normal;line-height:125%;fill:#646464;fill-opacity:1;stroke:none;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;font-family:Bitstream Vera Sans"
+       d="M 463.5544,26.909383 L 465.11635,26.909383 L 465.11635,17.113143 L 468.81648,17.113143 L 468.81648,15.945483 L 459.85427,15.945483 L 459.85427,17.113143 L 463.5544,17.113143 L 463.5544,26.909383 M 470.20142,26.909383 L 471.53589,26.909383 L 471.53589,17.962353 L 474.4323,26.908259 L 475.91799,26.908259 L 478.93615,17.992683 L 478.93615,26.909383 L 480.39194,26.909383 L 480.39194,15.945483 L 478.46605,15.945483 L 475.16774,25.33834 L 472.35477,15.945483 L 470.20142,15.945483 L 470.20142,26.909383" />
+    <path
+       id="path1894"
+       style="opacity:0.44382019;fill:url(#radialGradient1480);fill-opacity:1;fill-rule:nonzero;stroke:none;stroke-width:20;stroke-miterlimit:4;stroke-dasharray:none;stroke-opacity:1"
+       transform="matrix(0.73406,0,0,0.809524,16.24958,27.00935)"
+       d="M 110.46717 132.28575 A 48.948284 8.6066771 0 1 1  12.570599,132.28575 A 48.948284 8.6066771 0 1 1  110.46717 132.28575 z" />
+  </g>
+</svg>
diff --git a/docs/static_site/src/assets/img/scala_logo.svg b/docs/static_site/src/assets/img/scala_logo.svg
new file mode 100644
index 000000000000..9c075293ebea
--- /dev/null
+++ b/docs/static_site/src/assets/img/scala_logo.svg
@@ -0,0 +1 @@
+<svg height="60" width="120" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><linearGradient id="a"><stop offset="0" stop-color="#656565"/><stop offset="1" stop-color="#010101"/></linearGradient><linearGradient id="b" gradientUnits="userSpaceOnUse" x1="13.528" x2="88.264" xlink:href="#a" y1="-36.176" y2="-36.176"/><linearGradient id="c" gradientUnits="userSpaceOnUse" x1="13.528" x2="88.264" xlink:href="#a" y1="3.91" y2="3.91"/><linearGradient id="d"><stop offset="0" stop-color="#9f1c20"/><stop offset="1" stop-color="#ed2224"/></linearGradient><linearGradient id="e" gradientUnits="userSpaceOnUse" x1="13.528" x2="88.264" xlink:href="#d" y1="-55.974" y2="-55.974"/><linearGradient id="f" gradientUnits="userSpaceOnUse" x1="13.528" x2="88.264" xlink:href="#d" y1="-15.87" y2="-15.87"/><linearGradient id="g" gradientUnits="userSpaceOnUse" x1="13.528" x2="88.264" xlink:href="#d" y1="24.22" y2="24.22"/><g transform="translate(-1.427)"><g transform="matrix(.326431 0 0 .326431 4.558277 35.222891)"><path d="m13.4-31s75 7.5 75 20v-30s0-12.5-75-20z" fill="url(#b)"/><path d="m13.4 9s75 7.5 75 20v-30s0-12.5-75-20z" fill="url(#c)"/><path d="m88.4-81v30s0 12.5-75 20v-30s75-7.5 75-20" fill="url(#e)"/><path d="m13.4-21s75-7.5 75-20v30s0 12.5-75 20z" fill="url(#f)"/><path d="m13.4 49v-30s75-7.5 75-20v30s0 12.5-75 20" fill="url(#g)"/></g><path d="m42.866 39.882 1.004-4.434c1.192.335 2.3.816 3.472.962s2.385.063 3.556-.063c.67-.063 1.13-.586 1.213-1.318.063-.7-.293-1.213-.88-1.506l-2.236-1.023-2.97-1.422c-1.945-1.15-3.117-2.824-2.99-5.187.125-2.385 1.34-4.08 3.39-5.166 1.966-1.046 4.1-1.276 6.275-1.025 1.4.167 2.76.607 4.204.962l-1.17 4.434c-1.234-.377-2.405-.753-3.62-.88-.837-.084-1.757 0-2.552.25-1.192.377-1.34 1.694-.293 2.385.732.48 1.61.774 2.426 1.13 1.067.502 2.196.88 3.18 1.506 3.995 2.468 3.765 8.178-.376 10.417-2.154 1.15-4.476 1.422-6.86 1.213-1.6-.167-3.18-.502-4.77-1.234zm45.243.9h-4.853l-.397-1.34c-1.213 1.213-2.615 1.673-4.204 1.673-2.447 0-4.246-1.297-4.853-3.493-.628-2.3.356-4.623 2.426-5.836 1.694-.983 3.556-1.213 5.46-1.36.23-.02.44-.02.67-.042-.105-.88-.607-1.276-1.673-1.36-1.736-.146-3.347.23-5.02 1.067l-1.025-3.62c1.34-.377 2.594-.9 3.91-1.046 1.61-.167 3.284-.167 4.874.105 2.656.48 4.288 2.615 4.413 5.5.084 2.008.063 4.037.063 6.045.02 1.213.02 2.72.21 3.702zm-5.543-6.903c-1.025-.126-1.924.042-2.782.523-.586.335-.92.9-.774 1.548.105.418.377.94.732 1.15 1.088.628 2.615-.167 2.782-1.422.105-.586.042-1.17.042-1.8zm31.354 6.882h-4.853l-.397-1.34c-1.213 1.213-2.615 1.673-4.204 1.673-2.447 0-4.246-1.297-4.853-3.493-.628-2.3.356-4.623 2.426-5.836 1.694-.983 3.556-1.213 5.46-1.36.23-.02.44-.02.67-.042-.105-.88-.607-1.276-1.673-1.36-1.736-.146-3.347.23-5.02 1.067l-1.025-3.62c1.34-.377 2.594-.9 3.91-1.046 1.61-.167 3.284-.167 4.874.105 2.656.48 4.288 2.615 4.413 5.5.084 2.008.063 4.037.063 6.045 0 1.234.02 2.72.21 3.702zm-5.543-6.903c-1.025-.126-1.924.042-2.782.523-.586.335-.92.9-.774 1.548.105.418.377.94.732 1.15 1.088.628 2.615-.167 2.782-1.422.105-.565.042-1.17.042-1.8zm-37.022-4.038c-2.008-.397-3.89-.774-5.292 1.11-.88 1.192-.88 3.305 0 4.455 1.422 1.903 3.39 1.527 5.46 1.17.188 1.234.544 4.037.544 4.037s-.02 0-.25.084c-3.765 1.025-8.178.293-10.166-2.03-2.05-2.385-2.3-5.187-1.38-8.095.9-2.866 3.054-4.455 5.9-5.062a11.22 11.22 0 0 1 5.459.146 19.13 19.13 0 0 1 .46.126l-.732 4.058zm20.1-11.002h5.397v21.92h-5.397z" fill="#231f20"/></g></svg>
\ No newline at end of file
diff --git a/docs/static_site/src/assets/img/search.svg b/docs/static_site/src/assets/img/search.svg
new file mode 100644
index 000000000000..afbf046f954a
--- /dev/null
+++ b/docs/static_site/src/assets/img/search.svg
@@ -0,0 +1 @@
+<svg height="512pt" viewBox="0 0 512 512" width="512pt" xmlns="http://www.w3.org/2000/svg"><path d="m199.945312 359.898438h67.980469v39.988281h-67.980469zm-74.980468-4.996094c-13.804688 0-24.992188 11.1875-24.992188 24.992187 0 13.800781 11.1875 24.992188 24.992188 24.992188 13.804687 0 24.992187-11.191407 24.992187-24.992188 0-13.804687-11.1875-24.992187-24.992187-24.992187zm276.921875-121.96875h-201.941407v39.988281h201.941407zm-276.921875-4.996094c-13.804688 0-24.992188 11.1875-24.992188 24.992188 0 13.804687 11.1875 24.992187 24.992188 24.992187 13.804687 0 24.992187-11.1875 24.992187-24.992187 0-13.804688-11.1875-24.992188-24.992187-24.992188zm276.921875-114.96875h-201.941407v39.988281h201.941407zm-276.921875-5c-13.804688 0-24.992188 11.191406-24.992188 24.992188 0 13.804687 11.1875 24.996093 24.992188 24.996093 13.804687 0 24.992187-11.191406 24.992187-24.996093 0-13.800782-11.1875-24.992188-24.992187-24.992188zm-84.976563 343.90625v-391.890625c0-11.027344 8.96875-19.996094 19.996094-19.996094h391.890625c11.023438 0 19.992188 8.96875 19.992188 19.996094v225.9375h39.988281v-225.9375c0-33.074219-26.90625-59.984375-59.980469-59.984375h-391.890625c-33.074219 0-59.984375 26.910156-59.984375 59.984375v391.890625c0 33.074219 26.910156 59.980469 59.984375 59.980469h230.933594v-39.988281h-230.933594c-11.027344 0-19.996094-8.96875-19.996094-19.992188zm443.734375 60.125-39.820312-39.820312c-13.472656 8.050781-29.210938 12.683593-46.011719 12.683593-49.613281 0-89.976563-40.363281-89.976563-89.972656 0-49.613281 40.363282-89.976563 89.976563-89.976563 49.609375 0 89.972656 40.363282 89.972656 89.976563 0 18.355469-5.53125 35.441406-15.003906 49.691406l39.140625 39.140625zm-85.832031-67.125c27.5625 0 49.984375-22.421875 49.984375-49.984375s-22.421875-49.988281-49.984375-49.988281-49.988281 22.425781-49.988281 49.988281 22.425781 49.984375 49.988281 49.984375zm0 0"/></svg>
\ No newline at end of file
diff --git a/docs/static_site/src/assets/img/slack-logo-icon.svg b/docs/static_site/src/assets/img/slack-logo-icon.svg
new file mode 100755
index 000000000000..c37dc5eb49e3
--- /dev/null
+++ b/docs/static_site/src/assets/img/slack-logo-icon.svg
@@ -0,0 +1,33 @@
+<?xml version="1.0" encoding="utf-8"?>
+<!-- Generator: Adobe Illustrator 23.0.1, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 viewBox="0 0 270 270" style="enable-background:new 0 0 270 270;" xml:space="preserve">
+<style type="text/css">
+	.st0{fill:#E01E5A;}
+	.st1{fill:#36C5F0;}
+	.st2{fill:#2EB67D;}
+	.st3{fill:#ECB22E;}
+</style>
+<g>
+	<g>
+		<path class="st0" d="M99.4,151.2c0,7.1-5.8,12.9-12.9,12.9c-7.1,0-12.9-5.8-12.9-12.9c0-7.1,5.8-12.9,12.9-12.9h12.9V151.2z"/>
+		<path class="st0" d="M105.9,151.2c0-7.1,5.8-12.9,12.9-12.9s12.9,5.8,12.9,12.9v32.3c0,7.1-5.8,12.9-12.9,12.9
+			s-12.9-5.8-12.9-12.9V151.2z"/>
+	</g>
+	<g>
+		<path class="st1" d="M118.8,99.4c-7.1,0-12.9-5.8-12.9-12.9c0-7.1,5.8-12.9,12.9-12.9s12.9,5.8,12.9,12.9v12.9H118.8z"/>
+		<path class="st1" d="M118.8,105.9c7.1,0,12.9,5.8,12.9,12.9s-5.8,12.9-12.9,12.9H86.5c-7.1,0-12.9-5.8-12.9-12.9
+			s5.8-12.9,12.9-12.9H118.8z"/>
+	</g>
+	<g>
+		<path class="st2" d="M170.6,118.8c0-7.1,5.8-12.9,12.9-12.9c7.1,0,12.9,5.8,12.9,12.9s-5.8,12.9-12.9,12.9h-12.9V118.8z"/>
+		<path class="st2" d="M164.1,118.8c0,7.1-5.8,12.9-12.9,12.9c-7.1,0-12.9-5.8-12.9-12.9V86.5c0-7.1,5.8-12.9,12.9-12.9
+			c7.1,0,12.9,5.8,12.9,12.9V118.8z"/>
+	</g>
+	<g>
+		<path class="st3" d="M151.2,170.6c7.1,0,12.9,5.8,12.9,12.9c0,7.1-5.8,12.9-12.9,12.9c-7.1,0-12.9-5.8-12.9-12.9v-12.9H151.2z"/>
+		<path class="st3" d="M151.2,164.1c-7.1,0-12.9-5.8-12.9-12.9c0-7.1,5.8-12.9,12.9-12.9h32.3c7.1,0,12.9,5.8,12.9,12.9
+			c0,7.1-5.8,12.9-12.9,12.9H151.2z"/>
+	</g>
+</g>
+</svg>
diff --git a/docs/static_site/src/assets/img/tensorly_logo.png b/docs/static_site/src/assets/img/tensorly_logo.png
new file mode 100644
index 0000000000000000000000000000000000000000..3b912a1908eb3e25c5ff28b9ae481e15e373511f
GIT binary patch
literal 100093
zcmd43g;!fa*EbqMaM$8m+=9EiTcNlV2rk7b?ozD9-L+_e;t-(49f}pFxVytmpXYtQ
z@8195X05E8Gnq59efIojC(#;e@))Qjr~m)}Ls3CS3jlz-g}uFz5n(NgNp;My4>&h1
zc_~26IN3hz2E|1|-wgo3GXM96D;F=bg>}%keW&NHr>Y`k;q1t6Zt48Nirw4M1=bq?
z5b+j*T{>F1n^So^Iykurd5hBgr-u;i`d>2#4b^|TxZ8`;=&5Q@Njtk*QSq|#u!CvD
zP^qY>L|iQ)LRvC%|MNKPPL#&R-Q7iqgTu?qi`|Qx-PzTegHupYkOR!c!NtV}>%r#c
z<K%Ac&F17r``<zScN`fjHw#x=7k67{C#rwrntyQia2KVa`8U!3dHt78cU#E+oyp1V
zf7pUC$nozR4o-G3$Nw1{_Nd6eRv~p)TPxVi|Hc>N6!}lj|4-Zh%p=0_Z}R^aG5_WC
zKdmrU#ZW~!{*TziP*r*v^Z@_~fTE1#J8!t7PNWX<UmgRmnZv&7o$uf6b)u*Y@R`NL
z?aT56e~iXsP+R^mkay?IFfJaQq+WT9Z;{FQD=02_p~Wn7EDr+i3{AuaqR8NUQr}j5
z2n;OB`JKAx>vg)~yS6egzRT^kGd`Xx=my!z=C&UG(UekCETI_u6-bAmgqn=c$fTi_
z|Np;+DBv+2aReU%pY!T+m-qQ*Jvlk~@!@>!=^v-|<GJSMW@n0}CU*Px|E2ww4?$@|
zEbLF<uNZ<Nxp*=kNMPV=B)`-0osbSWw6?ES4%!{M{{Bg|*XwRg-Kb0bqYr+_%lP<s
z7YZKz-Ya76|CI@<M6g!NoBS_!k{^Fp>ij_`<va2N-9FA$8_tV@!DmG0>}DP~n9_P!
zv|ZS*$S;B&9wK&>R0&Ur>gCEzi9u;nbtJERA%WMWs##T1>nsQ+U1G^0FGEQThdFVC
zF2t$-D{wbPDu`(twG14{`BC_^qd}bhE>NPJWAFI56A?~W8AyDJX0Yj4tZY+D_2L}{
zG8RcXOT#rwhTF6l;@7nGF7db+&Ag5KI{1oFY#))p&RMsS^4^44ugmYgy||iQbuOeC
zW$hQ~2*8~3zA9w0%v~R?R6VaNwIl1!wC#U&UmgrBaj6ZqseV9}a=&Xo?RwM%ZK13S
z#Jtj;adnQgR`KBGlkeEBM|zEp<`f5P1~V8I#k==3#{_Aj-f_s9;Ra-Gb`-r1%g;g`
z<<nVc!X<Qp7Hr<vzu(Un5miyEjv#y|RA2&+AfTSh2`<!`hn^kw-Hyb7y-t=r{(8}l
zDwnYQ$NHeY;V?odY1qab{Apf9!v6Fi3y<}`*Jx7fAn;`0sLANK_`AV4J-Wo}n|8$q
zc1(W<xKnC9O95VdQ;(-I9v)KJ><?M*45YGk#tn>d!7B|c!ap-5jKa`EnJd5_)S@bp
zNR62^0bUT51X=rk$9DJdxK5xHHyQ2)*ruvIu^Io@9b#1fslNRg{Ra6uCn`H3Xes#2
z=ZR#q0hvyKABQ1oV)B}m+W0<e^KCJcO{d65?b2Q|k)XkzpS;?Eu|l@`5_5oS^KXEw
zvq08;x2srpQ8ZLXoDV_zF91?>ew%$jxI|3PlGR9RwcI)>3mmvH*6ap{5tK@qrj`E;
zN}^Z&O4nn$LuI=EU$>m(!e<Qk9pRDnl);uFx1X!J2lO7OY+3~6H@UI6IM|G?BNuvJ
z$e?NFc0f)v7vk<}QPZtJ2j4YUb)pXT%fF4;-QuMu`e^~>q;pYXwo)h>yc6j^hw1Z~
z2||w;B&ZV+Bv6CTe>UYoSt&ucxMwxBRLPGh4&2G+|D+LDEWxx)s<?vK3o&l<x<2?_
zMynSu=WXNvFl6?z7y%&<)REDJbcwyD(4}T3^VRUC7II(nsaS3SZ!)u*ff<1Lsa&Pq
z-7E#331MqUIDFDcgr{ZSAJmy#aX#tAK5<H)Y}Lz%z)C*IMHLQp;!2iUKc!pO&%43H
zw&RdAZOsEHCI2T(sr(ZHy=#-)PFGh~MtaJGyui_CY<=KmAWIDKM63$~54Z6}I{2$|
zl0Su_^Y8AL(as@csnWZE5IX^?y4$xCeot{79OlmW@Y7}3d5V7)tq?@HNeF}SL4o3M
z$^cLCU_RmlqD|or>v-C)6O5oR$ReO+rx9*q>>0!=cy;=Hgng-8kLWnA?Z5PpF>0XR
zl0_}BVt;sY?tZ=zL%wl~o+w=|!&&m=_d~`Y>VUMm;u2=vuc?_NVc&fLYP@amTj9Qy
zSjoa^0?muTIlgeu0XIt>Nab)OiN2}w@QuOjQjuwoo=jR`KzaT$e^U3+uA=vdj%p2%
zh0s(+)$&h4XSwyLi35xX548fT%j(bo;IBB3Q0xD?PtZRRNs?fcilTEreMH_*lGHDC
zudX%R!1;zSQLS&%yYiqRM&vE%hNW4|`{M0bc5_L<KaK8+>L?P!cFaTvWkd*@V>W|X
zixtAO@gvHp32@z3xW}6nWPHTn9n%G2MMy4F{=B-q65z}sX@erUFLIhE<WZR%@>7iX
zSM!tUzZksrPcHpaeEfsXo!4{To<H+2wnWI}9)(_-?4ag9<XT3^CVaEbIU7V=hBJJ5
zrRHvz$E!+=c$cSgZvsM^e&%coPKRf8=LOvbToJ4&pON_dGfX<{Q|Jg#W}46*yPyh3
zNUkL~fG<dkN;$Z-F{(-^qI{d?H30Z>>+6dWGJ^8l02!Muk#!1#)A&z>`>vgjf3j#h
zH_H$1b5;-jY!=Xt-4!$+e^iONLz3Ov+~a2qlxwI;pR7goJL6IS;G?Yd6uo=+O4l&v
z4;R+fq%jA~LHBOOMZTyY_bUl{x>4F1DxQ!)sY?x=3;^C>mroFvK+*W?1mBTj90vf~
z-Kh|u&Da{b`h1DuL7y`p7QLq5pD+YT!L*e4Z&6{uhSVjQryhgr`;gy+jDUI>kQ=H(
zNiG>xG)HxUAtqCH1a>=W<c+cGT3UtIb}6r^>$1Iq1jjZ=*R_qecz596Soan;xY!2U
zW^eb4?z-b{SsSrr9BZCIe;1(vNx?X@at?_DO8Ls4pM+5c89t|Ww}Oe%w1Q4qZt)~i
zraKw_sff@4lKze#a;I%BHxRNa^rBp6?FyC>c6-B}k{FFbbb)U-MlO`heXG)$UpnfJ
z7Wmy5FGz`FDohL2k{tP%VPU$_kdLfw6^WrCFUU^|{bw`&0b$=qkw0m7o?+s&ji>~g
zLZVwiA_jcmnBY{*v+uxkJS35sefVPr@R}eM&wCOU^t&!s@nCA}du=Mz<lfF-B%hUS
zOB+6_LGGR<9EnG*b>bLFHZZkkGTTAEDlPEs8r)t7R6Q^^>cHVC6mP7lC(e2?*m~<r
zt`i*a5&%R>zc1dW2p*uBnBDa+34+wc^axzo-9t|pz{jD9{I&Ki>(8nYvDEE(W*Da#
zR`3@GpDb2D`*|W};=$dqP;NYqTRa(q;-^16|ERGot}(%e+;xJ5bLGbc@2t(4?Z0)W
zr1y<4S8I5o$Zq5GvBb>iw8^%)mod7t3FRLA(RwPxByQJ#L~88MyTd8oQ+}FHulGg7
zU&5l^r&XBQ5A_u6c^@`7a{`mVEpFn(5zCoaNO1|gv7Y5EUe!i-Jvw$KMx++52GK)f
zo&*E#4_Jb8T}&WHTTD@6&-srg1QJmH<gfl#yvZl^O$&N*<m&;+kzZ9>7HAgWQ#^wL
zr*J>)h>L~Yo-5rr=}NY=i$>-U$&N7AtM^|&-}F>f<G-nq#bZ9`^VzH_RbEz4oYh;^
z<5q(EScIQA9{{o?-P~-xD6Gd1lSZL5@2I5;bP*30OX<*kL{Yd+!x}tf0r8Eym0ZJ^
z!J}8vFjA6*txxIkx?jz)nd<C*SJvAcT2~vl-`MBZ6COW*&z|Ju_9BWT$~(!)>w&(3
z=h=}W`kXrw$;WZtosyIMg$89<w{$zPCoZ6xxbdMsUkD2cW=SnQvgTkX9FSF<qJO3j
z@%fu`UE;*E+gm)waqe&UAOfN|cz@84-zDI4-VHA37H?3=?1X_TL0^cfyW1gOz-3H6
zkzPHQwFG*GHm%Gr!<et8-6mNG*qz_yE_aVRa>}RDHg-ZE#?)2u^W|>wh-LrlU6T<_
zHg)5MG6t>14=L+LQR2W1!E<AYErFB8_=<3UPNl)>`vsRIT#AI`oBCfq&+kc9rTh-!
ze3TM}n7B3dRsz>~sQ!aT#OtEp018a7fWSt{sEQiex*PK9D#uB`!mb0QK#bK2Dv^Li
zRDLY)7e1e{X}AJSVB<Teo3Ax2f|WlZe^pp&tHD1Bo#aFR>d7}ouHsDyZ_C9f61^7u
zy5dt1O7A>3jdLVy@!dD(H34WyFP1VD`o2XtfYn6GQ2tBl`tjvLEQQf83F!vF%cBz9
z#Pz7(o=m~5sS0(ie(*5I@@NhpBguF;?#UGj%COYN=mOYR9+_@gQoMajw0#JC2#Z?Q
zROR>z)RWt?b0O<2KFKrjg_ZSX&!A0m?lY|VJPCQ663|{RjU(7EvgIyV{N@+@ng8Kj
zHD3JlzhwMhWGSF;(Q4rj9_*pnTc-v0<;g^f=elEzGa&P}(E%)gDUG9;l42=6?xiM{
zNfX9>kywFm8eWBqyU9R^m|MvRWC8dp@3XD7FspE}(R`ee0jnueX1REj`<>sQUVl?k
zpzncEq;&aO$gF8867Ju*DGEzQvVe^#i-NiQ%ZPnRRLrxZIWLa=Lgo$UrGwRJggE@F
z7JbE@g@!iE(F}VQ3XdC<>*O%+FTEX2W1tZ674HGyp&uhGfE^aV|9lb;FyjDzTMNr*
zof$CYs3!xHQ7>_dUB%&MNA{lj2k%TWIuTL9hIxe+P92{B<PQYB{H#PThhO8{{)DRf
zv=@-)0a%T-zo{qG(9Gt?qR3v@%&os;RG2qQ_E8ht0SO-jF$gz;{IIqUq#5Q=u`%qM
zaBV!PBV56&R&<j{2AN~5MV81f%`(iRqvQ|w+&|1@4Mdz&Ce~LsZ6YQcw%9qo=^}ft
z26lXGivL)4{%dWF<y}NcD@M`w*KRxx`ylVEyTlA0Shg!|FF4s?wpD>;_m4x|OX|1y
zKyvw6-e2Fb)3@O{8j}$Fu)Lk{K~ga8ZZ85t+QL!dG}`d-CCX3btVQywF3%@6f1~)z
zTdUwV29yqe!G!F}gNZPu>cqHQ{|;ug6kLAmpeb;wDgWGsDb}iw1|sz)xg&Al8e?Hf
zDLR2blLRY4>8~RE)Mq6EgOr=Utjv*9dR{xKK-4YUd8wsA$-`IVHN2G{QjVpNZvjKa
z$;DkvNuHd(sKC=l&D{Ip;Jg;z54U&=?Z`iA<u1oTQbnxbxo~9#CaN)*epVO}7(wuU
zprIqmAP%Q64mP@ESE|DxP2`i--Q4)k99`bO;`mqZ$o8fCQ+hjlR28j{{{Cn!LRRm)
z)mq-VTL>yG8j^8tx>wOVH&KUe4lBakI0ILDe`nu*0{lD^+8;&j({fQpEHR{SptHgb
z=n+my0g#X*eQFqb75^!%3!L^<@M1zL^4Z}a$4;0c(kl!l-m6L6_4LGEQkU@g#4&Zg
z45nD+_*&5(#qG6WRL_P>YMV$gNq1o4M)`J@j#~@u*5IS%-oi5|8a0T&BSf~GvgHO(
zfjV9N1xw;K50&o~<p=tN?`fx3R#{$vfu9Rpz~Dc>s$+UzV<eb)eI1Vf5oB_xq^`qQ
zQ<@>neQi6s(9|V{UJxMI>;Ewy9Zj3$n{w2ykQ-L=pYn3Kq6#E<?a>ZGmL8W?Hzv53
zE+A1*u6hZ2Qi2QUHEt^-ZrCp09}eQ5D8jxiQ7YeB@%Ojh%=ci?5;KZq@m389e;R?%
z5d<6B;7P35uZnt%C7?_m*_L?)-$CLvbt=8s?sJOKfliSC96f<uv;A1rmM3(CIM`Yx
zi7{}0?YJP~3P*0ME;RQ+3guQwEr&(hx6alHE54iz>nX*3+&;ZPetzJW06HG^gl@%O
zb4jhKlhk<(kNQvVXzZ?tkEu48DN9oFQbYobaAJ~EBtp7KNgkDg(g+)g_Kv=Lk{NFI
zFu&VPgk;;O{cHwdV=*C!+`bkiX80);RY6eTrubC=p+3s5$h(j!1`er(E=`_->ET|{
zdPgGZ%qjaeiUuL7<;ePTy*vgAE%qcZu@<khhSi?Par(v@(p^KeI)h(K-GM{gc$yVq
z_4Rd8dJ|<(oli@9`y*sH+QVSFQE_72?t>Ig$M<BEmL88;g5<0~X>uzi7n>C))znVV
z-!<ZgJT-3wlW|o4BF3}s$lVzABJ|7HI$ah?6?1^@?*N8knwqFyWB(4D-j(rXG`i(|
zZ3B1?{PBae^=e*Ui}?B1d<#W4B0_bh-_pY=y`lp<#qbyd=(RZqlmvP&_Wq*BtP124
z=-!=Ke}fhet81O5BcF&%?;evbzB-LwbkLF=sZggIqfez!jLvX<tfcm{P*NJ9B9aUX
z$nONXvh=Ljwk}Td7;^2Wc^x$2-a$p^SxdJK;iDdv029yd_H<{a?7GD+?c26q$29Y{
zglriE5libvgBil0RF~#0P8nx<C6#4iUiOG&mT+ISu^|V4+!rIEZuT-Cj5-$XkQ$-S
z{UC98?I&XBu}ryXgo~+G33~eu`^E`wjLt|EuzN8fDLEOkHC1=9C%k!;Q4P8hKBhZM
zLysa=mf(4tR|TS{ESOP(3LCuKad+Un{Jl&8zybu^NAVnyCOs6um(TXmht>_Vprxe@
z-;Zw}Q&CZ!(^A07*GRO^g)Wh0y-Fh)k7hhdBWXjN%{k^y4=RV4RF7n04TOX3xQ)@n
zgx2%7f6*V$Z60cA76N7VvQgxs==uwK_V8npD|o$7o(t1I=uoD8m2y=Unli505b*8Q
z0sh!rqP|C$LaeCP1-fy-*)EB@5EbAfurcUcNs_`hr9od3^nE*C4$*us`jY_{>AR!v
zR0m(1lD|Z{;EBm{6Bv>ZVw_U*yIGHQCOvCr`0)AHlo%I+ZEdRzL4riuNNc9ARHFyz
zn@cL&B0H(L#u%<?`M)0?rmAXBvAF%BuCMdHaHQ-UaT;9(#VhSDS2MLBtMpeC7Z*Ec
zut~wG*F{u;u8180$A#*zLSI*2N8HBla#Dj8+MFaB?<2HxgL@w7XR*$kK)MTAiGK8r
z`(HG&(28_{D8uh8Lj2e^%9{mI<&9&&Cm}qr%sajUVI~H<mdEY%==k`z0Ff3fN#W&V
zxMjZOvPjc-#m+G%-x?y3%$N_d$VhU&p;0|NEW;a@YIqW_`QlGyLA<14JsQ~2oKob3
z@PYXA#lqcOS@jzh>l0NQyaz+s@i7Z>xa3+JI^@EGYw6cHDSS2BtKbsYW<@GLu3Wzg
z)Yhu8CN3TVE@sqY2~IPqH~DLuS+q*x#ulm`5o15^gX1mRs1)66Y=5#fkicPmq8Zcq
z=GCG<<Cn^VpU}M+xPlIxIIF@QZ35+lmkXt!|8?;IRdM$3B<FrAsX}2>{lw2iF5o$4
ztZ&r2k}fk;rfQ^UW<XC)W))nwGn(3CA4R=+z~J%zOR4s2!2G3SY{omV?J19L2oqpt
zEc~IZFd0~u5_DCi3tUlNUk*d4`edw$e_$iZOU+tHmF&`tSFVxMxHBFhbUjAB4cqNT
z>?Aw*JRUdSTFF0k<55ONKgp#hL%<%wC?BvC-W(pb*LA&^Pp7&C7#|+Cv2!-S%YW1m
zmX6|dOi`rZG}yNFLF(Eb?L4n84w&Z3q<C(qObH732=~JFaIP*xv3W3|2t(a)05wwj
zC=f;tlh+)JFn5#&C1r#g7MmMH>FsK{KAID}e1nKQf$(CAA9ddZ+y<BpGPey|&PifO
zM$L#IcL`KB>LJmio85Cct~f_hWO{NfhfhLhw=zjjq9M<_BHyLDQy=z?tB(~K`|$-i
zR%%-3VP1S1WuD4?3(@Yp9zi=!z3tb6xd!c1g=>k1Qm}u`SR_d|Do?7)U_uqBOH1OD
z_}0>Upm%8rl73;M=G0$!xuPl%{+9%J(~RxU%*#21NMp<O@u|I{dF)!OjQ;@<;#+g1
z2gG9IpM@4i(4A`u$4a=MDUb^NE2pa3wJ`d7Gt3~mWjgd<K7KWLLG}cZQlrZ~hDu6b
z!rC|PuCfYO*f@mV3{|Ml#&KIr8lbGI82V(+>7fjsuDu%sQME)u8R4K5>L&UqRa#~P
zTV)RVB59LVHq?`D7a1aFsNoleJZLm=p0m<PVTGu-=@%e2VeAtj1Eb7s(iI;A3^B;v
z@jl>9(7f8+?zw%3BvBU1e2nnnVFS1?Q0A0&HZa5`utQhx%k5D_+uIE%qCz2oP`(R0
z<rmdTd1V?yvtraKV&GL1ssmMppk{v@zU7)yz7|fK4x05Sxnim4+5GzP_n=-gqnMx*
znPybUYTc0ZUcf4+q`aVQ_FZlce(}&6vrky_w{ddTP|usItJ%h@MdlQ@jW1b2u5cW#
zBstxoP1k)WZ#H?A1A)%Gx*31n3dqUrO+k+z(LGl_lwS6~@UI@0P=Y<;JJHg!&Ny8}
zIA$}239#p5-@nyum{SP4;cEYZ$i-9-SfkM=cBq#5dw~0U$0<i6kHI*VkUX>0geBM%
zzfMaZ#k<E;Ny2tYEA%dRJ5;O#DD!m=wmP^`ihgp>^p!@{7^`O7p~?RdLQ2AkXavI^
zX0W3MG-xvpa${#zGVMDX4=w$l1w2<y`_%3p^(_LU)NT}q$7f{NH>y|qvxw6<nfkbk
zf<N10C%mh_ehINyp^PUkHuz;DuHgNSjgSOwe(n@~BlGWvRIE^kBk{c|ZO#k`w4>-r
zzZ!IWR*%y`+JxNlq7LX%<sS(v1fDV@pD-+7E$=2-vI1e*sx2oPoR9lvK0z{EnG2Hj
z*m66)O1j4Q^6x?E#g9y5?tJ4p>P4sWqG{8d#)r<Ct@Es5IE_U(M?&3V<akeU>6?^N
zgX&j%2HZyS?NP+(wbj^dpxB&a{11HSNg<YUcLJ3L^qrARW{=+H3V^^Vf#$8+FW$=a
z^Rb4jzecjHeD4;w0Wc_XKl7|3V!n%3G*2RPjNunmzqL1Py>w=Od-4vL41*FU`UKVK
zW|)?nGUBbK;#=KzQ+sm|iN0g@bn#2w7%>YVy~fV>e7L?AvRCEy5}I}pn%xJijMB#|
zr#EqE!$ElrM9iIf$t4-nBccKPe0x_$L(EAnlQy<U@u|dwA3XP&XbnQ+rn<sWjFFe*
z_R3DG>B<#cm0y1kIE{YSWBI_e3NXR-)-3R1wN*b`zV2bRpj&UPMaOPV4YFZm3D!Y1
z(gpIC;(txl0*oDflx{b|*20If3@NSSpRjgfB0>J-`kv@Jm4_}!k55szrB_paHWLqz
zpupswi{N8XFuK-38fIPQd}THBf7Tn;8z#QDM+qI^h;=D*FfLzb=5`}F`y~PzZN2$?
z#)<xg3yst5y{HW6pd=%+AnhgXL{uH_dQD2v^DITm_@|fy`y$ZmeHoeoiqS-lZLIbA
zbmAFO*F9Fkv_FC_O1gv};r+FWSSimX>yT1D7v$1tq{_eh<+|2)AfEh@?<~SXlpNa5
z_vcGf3wuKlvy;wBdaXf|9qZDcMG=4T7gf;8hh96iP%gCNwwB)~0z%WP$S02i-7jdX
zSWH;%Lfn%>NePx^05c_T(v9)8bii%~Q<7hJdMlC*A?d7$y{xsMndTV*#%BSy_iLDO
zWx7Y`IN3bxXyM#`y=NjL{501U_Z(+$6ODC4lo+tS>!Hwp8a!5MAx&!eA^Vvy{04V4
zgL_v$*OuFD(sb<A@GRV2AM}ozMGwVxymq3jFIRncx+#SNS58%9eBOuQdX(JC938f-
zga$A#ZD3T@iXVXvjwMp^RlQA=zlSwj`LV-UWGJhg&1>o5t=9K`XT0MJev84>JKahR
zB?Y|m|8ujA803ts5a(88#1HxeAEeaMHvrOqa;`HbWRb*b3B(B`zY~u<?YbCdvF^Oz
z$+Ade)#utp62I%|NMU*ZBAlvYtoBw`!o>70JC>u$R~^-hq#3pjQ-@hkt=kPUL|b!?
zEQ~uMlr_NDb%{k@U(a~Ad|7<B&1mBAg1p5M8I_PH9@J^k=IEJ;ibe6tC9oUI5z<3+
zI&l4oBcFXetF7)BI7l5S0``BnYVMCCo#+3!`SV3FiT?V$+j*`^|GB|>;>6lH)E^6b
z%x!YsMQZ7X!obLf=VBlHZ>?|R`U;ue_O@#D8h1q3RLOmRQa?}iT6dVcB90gOBmkf^
z@n0~H6lMAuct2uV6RfTtuKr4Xj~K4b6BBeQxS_Tc|2O@QV%0VomgfAWX<T+R=Ol{C
z?vAnl3;5w)aYolY^?9F1{?aeHxvM6QIwhNU@9#vlDpVbe7cR_Gx4VqSiB~EHIR!wP
z;yL{Hm5~(cKIeBoA3MK!N5k6d&zAP?>Rf-|MYBkb>4=mYqSBoV=Z~Q@4E;Nl^jM~r
zv&8SVGb*Oy{@kcvK;&HVVC_eLl4C1k8MYR6v=pkz(+|to+vmOip+9lvXF96+Z<nQ?
z6Kb74sF2ripb}QUh8=7?BxO;3gmZ4<nVT!)vJg**5CO1V5uAP7?<Eq7vU(~seb?eO
zWiAHvTfqv%;=VEc!r(YiZ~wP7QH^w`*s&qn88N26Yu1-O==ek{N1V4MsV3*!1BL&$
zY(<Yb)9V6u$K4#SRn6wLfXCGy6<7+i*4OBW<+wan5lOL>AGw|QWjM5uSBrw%=qx4X
zBZfDqLlabt?=mEthY!yL@9=YfKx&fTYB*U8KO{gOMFTmR!s=6M68=X%qc37?H;L><
zEh`teC;C<4p=z!+4x<-gLZ~8pcg3;*zvm3!D{}8&E^4f@KHfZr)UVyvVZthbUZ52k
z16#2d{f;mrRka*Lj_Dp=w_9^SU{!OMSO(^$1td1%0c%DU^gdMV%`OvlACAKS(1q6o
z=*Mi*uD*R7`>e2eA=gfKMgZ9CDPoK<Jl!qGE(KTcuX-0<Hsn1!7{04>i`_maby+8L
zoo(OxcP88FzCWG2J(6}?S2Oqga9Dd=H{a`BLwJ%ZMP((CtD3OVnCH3sqv3_mC|ATF
zQhB>^i!eNYtpRtCq|T=-$J4IplucsD&^8Ud7=KVIpAMdVLN8(ZD{4Rz*HlSUj0V{b
zhOrD(iW<EuJgaqvA-036F)54QW9T8KK%BG$)M4SL#O>wX<T;5Y$p1H77-K1#_(d9d
zbtdZY?x)3FBe$(}=#2gm`q-PFqp`a*Dve!TJ%*-%zwceQWqjZGZ5!?9dTQ<(4+_PO
zKT-iL>3R>uY}V&<KJDGKacrIb<oIP+z#+7F$GEwq_u=)?gMLgPfugI9fwL4KGXtef
z`tdYKn-F{dmqFa;ZAhD{{QH+pj+e~`Xa7S(Yh*YZ%09(?1N%IFXOp~+m$HvgyELh@
zixCblJSFPAFH+E|ZbKzF{|cn=e9~Y?0-!!hTY(3XZn*iy-!HhoZ=NnEnpfN=L}TdL
zNzGOk8PVWSC0&M~dPqm#;gmwEK+iL!s)v6dS&o)$dh-h5K|h!>^OGZvZ|!k90BWd;
z1z~UR41CP*P2mO|=*=NJn0YZ7+nReG_-ecRk=YG#ynmTXQG3$m`@-JfiUMExUTpm+
z#`9Xyh@_tVkrAqEIU+Po<<kw)WfG@IyjL7U@`<<gu}k3s@uW^D!p496{H5VXub_Vj
z)rs}vJ>NzCCUkMQMSy!Hi2y}?F<UB@$kodUqV1;_($PT;xc+7u_b_TL<|ZQLfge(H
zO!=}wStGiWZjme5a{1}I3qKb3OFpurT+q$P<v}G+t@P=Sc@tkp5UOMXeh2}JE)g1>
zBN#*bB{)IsVGpmk$%haaxERz4pL21gc|9{VoYZgYLj%igzr*<=HzvSN{Pf@(>orh=
z`3UW_&DUayx#udKnAV+nWlIK^Y4oL@+hkoTqY$6FU3Qm6v#nNGvh__Kn8o<q<Xa9+
zzG$_)KdOegU&b66(e5jnv}la}6akNe6D8sqC`o8U0piHex9n9Gx;z&9tUI`lHbJ8B
zfg`74<2PGvzU8RIqJZ`*bl+=oU869yqMfMujKEE0ySxFie9;!bl@e+)oo$R7G#!E}
zc{Ili^8HgL{=CnJX#OW&_++8KK84S-Cot+3^Lse$GQD<x@KZA(Jl@w*XB&RMbvwd1
zY-0v2%$HuV?b^THXAf-dXgSG=Qc!ZVzIqvV4#OWRsKKnD6;{y&I(Q4}x)DSJu1PVV
zKB9MXXut|f0ub%)0Mm$XM!d<=EES_MFvuVDe1x}`6b{YhS+LIYm&2xvms$``XTYeO
zXH1rUp1(Pt4Ap&>4oqySpfH)bstj(>oJNU+;kYJ(WMcPDGtrA-Knw0l`sBq)nmuo!
z9Z$f<BJvM%v#_M--4*Xrw;4{gVlImXKM&E;oB`MJ^mQTCQCqXVJFmX00ATM9Z~EnL
z<cH(@V3Q{P+19Xju2~}pNk@J+4@6(>ueM;A=Dg|awJD|h`L8$KrjBioC+)Q@JDK*W
zRNjPub*qlrL@+J+*z4_<mChOYsckAXB1t1AXp4FT&L>VwBJWG0Q+XXsIPA~Y7yoca
zyS6%{{KJwolLH@E0Z03}98;oH{xiIHgF_Gjl$A6e+lEpy;RZ7*LJRZnQ0-P;$}&F1
zNfr84(Sjm86KLt(f)oQrqhh{B2<|lDdFn4IRImPP@Hx;=g{h|v@o{guh0@hY0mX~N
z85DaABDpMTOh$ZEzC$QxpwRzTB8!tb(|H@gP1=S$cG_gHF0%%AYJlJ<oO1=ASwhQ0
z(PQb0S7#-}{WYg0tPRvbq|~T4HS{KVDl+t((tP%%KEeMe>R)9LAf@~D<x$)n#=)9O
zCnWe4c<}KJP>+G!bKZ?_q*LnQSMfJ-BsRF7mU^;8dE1#mXEu~BDKlGX3LK_#5NJyd
zj!K;o!_SgB3U~g+i%G|F;-rx_FhTUjTVCw=5Au~7KG1^N>>j$N7v(YX9<|XDx-i8M
zS@uEGhQSG641Xb0jl@Q~z^^0;aY;JpR9(O}@yDm(vOmK%AyPwLmpTG}cE7*##J(i)
zfve^xjGmj+OO;0P6sGXUg{4*9${Q!z#R#+xA2NT&K8t7wr}xi7ex!ic$wlA&X5w~S
zzgYc@0B(oJIg#Io<G#t;rCaC>c^k{kWHhVOzM(ExcN&HGzydMw@~vCqK_*~G2x|t#
zDi6+9Y9PCxVD@kwUCpSAZx1o#@i=-d*_J&&n7(YA2FM$~OC`<*rzUbqt;A6k=8`~#
z2&hio|Du%4$X~$}MwRfI=Xxv;=E-V84HhdP4le?sc+)<0PVOw?321lnun{bXXkqsT
z9gzw~Fv^lHHoeyP@zzalZT;1Un`zf4MYE!V*^w1I<O!l(z(+W&1ocKUK>omhG5G#c
z5uRKWot^Y;4v*QB<37<8VukKhPhd^Jdqzi)ABJ<tY(4SWdQt-oqHfCoSHYd9xd&Ug
zZ!8zvS3uHYIA$}l`;P(fNd7o0VQTZeK?63MzN-lH%_qiKAAX|YPUPfUIR4X~4BIhs
zqkdQn4~a}gtSL?sBds#T7r$t3blKI))ZG2IFF{V(pY)~`-VBTl?428$i~NDSat8@-
zA4{VHl_ry-h?cE~LO5OUInbhN0f~~_Ca>ZXRk&uJW0!vF(>KjQ-fMCMas<G~CYEdS
zt)8A{+%qTfx=jTH>Ny;wE<vQouX->>Pxs85%doHV>xj{U)kp>{=)hU1$KT<uZRbsZ
zKEBgbssyw0mgHv6L8+#gg&04YvrwBNB}Nd(#B380ojDT1+BLhlJGjgB3+nF?5sr0a
zHYon6Fns707v9JsF_j*-%B1^YaJ+9xK1vl&HM??b;N?$tibW(;v>zstw4>W_8CE`=
z9)#VQN_^o4F1>{lcYZ)I<wmQHV4Z^}?-b7QdtRM}9!_~dCnkYUyzPtVmr2K5nW`uS
z87@51+%IdN4y&~nZ~}j%3v-W&dHyu@IH)M=B4he9?kasZN4rdWru|$(83nNyabEp6
zsRNX6xEAky1$1ZM%Hb;E)b*|zAUwT_<jIJAV`Ri9L+IM-#s`|N<E2Th2cl}fcJhcP
zL~;*RWS6*Aj`HDc(Rfco>EENv^5^iZ$2%X>CWhx54q!D|uInSWiH5^s5%^@ZU#h89
zB<6XKA=g{VYOkf#Pv>^oZNguBF1C`GG^?OQ{QjU)$Y0^fscwHqs@fp)E)a$c2ikR@
z3#K)@*pURA0gH$htf8~<g&JdRx$gK*yrY?iLk=Gd=YrG`eh?J0L>X$EF-smnS(p^z
z%a~Qp>ht28<WGP8G#<y4*<eAzNyRl>1zR5yx76HbeZ6@;4GjF_9B`RcX211@bkwAK
zukdrt-+sZsc^?4Ew~Vkb;TScYOawC?Zo6nZem~-ZLoE<)qL!~m5xSnab24B_$sDYj
z*PA`0SGQm9xs$<%5>W+Q$9bsTg-+?@e27pLtdw2JX24(+pNbB+DTYJs5rCZTT3ol@
zhQBeP@<~ZWgGJwTK<t#96x!Li@M*T+)s(#bz7yD3doRlk;g>v{`WUghbPpY>SH||@
z8!gn^h_B-CE{(T~HpidMWPJO`Qo##3?Q!N>a)z{~s=egqZs6W$<Y8}$<C9P8w*7Az
zsA>C+@r>XUTz-$YdQB21lyspr@&MGjLGc9MoQdo9VZsIGHz4YQz0Q0VXXAZN?>GfB
z`KPWlBiEtGo&x{X4xi790$v#ME37$1vJbsncM{w!>#x-K@BMjc-&{^1_VTD@jl_ln
zG7$*?w0DZyjN<Vx;LypPa{}On4Ppn<M3cI#)gvL`H1Gq-DPQ)zGIbpTMpSLl=#M@u
z@h3W$&rElt)4q$%A5An`8kjiKp81pCCurxXlC@t0oMzi6^+&QWoYxT7^B9-w13bTj
z=vbB$-F(o>B5<R|f5%5V!HqtVZ8k6-*%Fy<N?^Fl@H*Kc9c@(o$_5-6V;6c+%Q$aY
zvFNdq9m`Fm1gx`coSd%yKwx@nJdo}_vdUM5v-0x-8rrjq@)1@`vo*(n>yJn=m^W;I
zJ(#DQyy*EjApUYrD>x942sm)M`&QF-P^z?}b<l-EGNeMjbiG#DOYV1!^V7&C6*p>0
zzgJ)=a|?*~#CM}_Ik_#GuvJZCMgTabPTwhEVJpR-=F=*td%`0Kg1RLkaS5uH{{41P
z{%!Qnl86!yD+}F%{*r!JMj)33qNso92K?xip`?Uj%b#=orrhd8IHAvL1gO>tPQ8B?
z2i`94lfvpQM==rMiTcm8xAx5#VyrhQ;q%I&afLRBoIcuU=0rb1p!T`>fvG)lsi=Mk
zya_dzby)<dEbB)18Nm#?VAwpqV>Yd3+GnHwrJb)D5r4%a{`jD4gox5FS4huTpQJ0c
zj|*(YCbhOYS{DTLqA)Dq96JX-s*Md+3jA3u_@vRDn%gS9HHipi_8^NLcxkSe)bT%K
zdwA{pjZIx!!x7I6xapR<wlX#qp-beL8#Kw#rFLOL(0Ug#(d1{A&U;GY{nYFqT6I*3
zf$YhCr2otxc*#HSf8v(cE-|qXzJ}UMJ{!lyksP!6^VA^NQ-5E_cBf|LVtlMoAI-Mu
z>7ND6Vwr=3i62|hBczG@SQurxDrvjT0Ub$|xi&X{%wxn6p#GzC9s!-x*QS}RyKL`1
zG}u5|gb&I|J$&%8*<}GjX;el(u`kc2<G_CdZTi1i&r<7_qUoC0erSVbjW<#?b~zO*
z;|_(7OnJ`!JQ#js<Is7E<4aS?`)CyOS99Wua3Hm01}Nyqq)=CfePGxn??XPTwk87f
zI#7$s)FIo)-w?RQzCk9lbn|p-;kF9x4M)4R=ocfwd&*Si=)4-x3wv55=Jpb1A6)fA
z$}JUJU9cAeHl;Ckz<_n*)GE)h<&AFvYvnmS)wdAl0{(Ox+F6fP9~_g0j@`2gQlPn+
z5KU->ooEIzWlZ87l}}zU&QsVV7e<VGn_R}WzY&|-?3SX?I>vf(3uxo#BzM7iE<r#6
zaqVq9QI3BufIiQ1^yH7<2AV0jfkE==X;(au{QZbn4x!NKBx;U^sDby+$xnVqe<UW;
zs2@IC5_pwZBlDm9>vceu1R6%x`wUwpJfoXZMXPS(g4R3(Oih}#$8Co-hgKW|g>Ov#
zz6=<Wd7TEva+<Tjaef1|KJlETTzqYA7*_Hor#{uXh_s$tZpQ3;LbJh^8C$kU6N~2H
z$~d%?Ha^l1J+oofGfm`an#B*F3KjzQb?2R_d{3bD%LXUS4GPnX7wFr&5sYwG*XBh9
zEf7<GLJE1tywEQ_`t+-KiW4O4{E(r~Q}@h$oxXkw#m_@6WBd-S=f)qYb0ivNMzqLK
z1&iL^@pqkL<OrSRbRCo?bW6Cot+kG``g-Lae$s|r-r^(&bcL026nY%h^<g?@Ta{M)
zIG|;;Qterj_p_0om9M;cg?tU-WJb$o)#dy~;O{50@4FfJ>z6!BD`4M`{f{Q4&u-xB
zZD6f*-Jvmg{ftR)(V1gx#V?Sq;^ZSA#TFiP{_ZSX^jxMSsDqxeqj>XYkY9g@T<6gp
z$bmXrEGIJ>sac_Vwx&OZw5Lp)+bhg>v*i7kr|^fYW|!fT3!4?8;leSC0UIc`m?o_m
zXT?wQJ>>9co|6nHWhkICQ!?)pAydnGD*v-322np9<r{3Z>5NZvp?1~n3Olu}zP&Er
zWM*gB#u@Q%)M=$%z;}aWSk*}RopTO<78uJLMjmX2fAVZg-!_zBgD!JjhnWTp!-GbY
z9~M=s)r$`7ZFaaTjNIDz%CSLfj_<NBZ2HF>!M<r)zso<YX}j#BLB!!lL}?cY0>$Jv
z=xG+#x}IR*z!t55bIgltcF8RQd+pP5DN?j-TYp0nvR-g4I9aZ%zQZ~1k<^S$rQoNq
z7BE{0?mjl??V5MTLx-9Sa(Aj$C6wVnCJ%=DRffiU#WY78NE;xDl%sWNqTXU#kz675
z@WB_fBcDC?kflxUYuE15<^Rn#Z%FNg$N1KC<i+U|h!u?24x)$4Rn)bHn=lG*9{^jH
zcq3)ABbI%d*vt$rS7R+kF_w}8&eL6~ie5g|_vRd0a#*~%)PkLBUXoeW{4x1h>0EYL
z#r4Q~)I2lhHREwBc<*b&eTjp=;zal}?zHq2?};)_`+AHK)ONV+t&i-Ul~E(`TNUyh
zdykL%8@3Bs?Z%pF!sY_aD;;Z}QJpNdTLE0)-g=cU>GQtc!x#}!6X>x7jgS>N<9VM$
zXMp)PVJ&u1?ch=9-vZMiJA7?GvYgn8rdbNWe*(&kB3D5-p;=69AM(KPTFqr9ZBG}q
zA}`UIBHxwaNJm2Gx`UZx!R>XU=P_)7!#Z}s;~-7WuXQY4CYmPoqvtT)kmAtzAXd^X
zi%*j*!X;3=_tF+}WMDdBgqdRnC0%Hp3~S00t*9OSoCQ`7#9FAhC45T_K&M>}{v&Ce
zGDf$Y!l01%*2SaDaAQ1(YZ%nacb&6qvhmQn_(l`a$@}GgH!aU=)hF+Vg6%-3GWBE}
zB-xrE#N_4n`_yokE+>8$qdufULMixJrLu-E1&bL}bxML3JZ^fK8*Wd;aXfj@zRlJZ
zj+Phjus`+2#M_({;zheq+p-y>oKYVMYG?kH?mRe+;U}@hph<0Z^vg!+HGR-GufTA5
z(2ZQU5jCi_Ki&G~75`M9-?ptC>eXStB`HfqgvA6(w!lYNHAJft&>}PlPxSIQS5r~Y
z^)At&u-)l`Tb8~9n}e0+Oni@ZG&9gT-{p(zG%Z4A5emJ`BgEsstMwmV`WcwkR=^9L
zUGsOrd4WQo-a?ii28SJyZ`ubbojxK3g}p74rJ{B&vGkuhR&beiGWB0kDQ#;*Xn&uN
zb3&2~#{WH&CIlBIWBvXUm9m&7in8SQUQw2Jzk~VgvxL1)K0U>(KdwEWc%7oZVD;}C
z8Xx8JnaZLC(Eom9&TO4aj>~!wsl3OIVFj(*bY-b6G2T;kD1A&WkDHT%vbu(LNvh`w
z2W_YLXdyQmLFF}K!_9U^GyETDNcKs~4e9E}{7Sh`g-GT{x$Kz_zsOS%_cbG{{al)f
zC~V3kexy^(o3M&)0@Z$g_wEfI8%inNR1RkqF9r8ynST`mlo_taKK7Uih8ql9lb!x4
zXZ1D!*6G3(VJAXfg=;wxQ`IV?B8E+Q7&36RmD17C<uM(8hNO8Exl$j9?~zj=3da*a
zTJP6APWGhEw7%I@6f~>UY{$+`GW%{|N*Ny15+mndI_`=)e=*aGkcNZ&RU4%lA<q!`
zPtPU1?OJVWLK`V2hAOZ97ne1m-sUb=gna6T%xYPO=&620$|`zZMXo)I)iRR|NUzX*
z$6-x-O=`ks@>QOgAZs~x9i>E`QqyftWDWoCufeRq#+=sE?6~}*ZGAf%-yPzaG51aB
z)qR^sI3|#u!-Ewyz_h{A>=s*rc=Bto*cUqjU@q^d2x^YKj(5(rz04*oc@?>-Y8W?=
z5svGrTDs{x^*=3L@%+Q~V`DsZOn}tg#QI}!2c!dsWcyF)d(@$CT^uEWs$YTF{(0s!
z;m~E>!G$Cmvt;13UkOxs=K5DdRF+7`8w9#=i8<Ac&fGiwyY+dDY8@&Y!AX;f+3U{)
z>CPdZ_XCg<#w*%|d7dB-cKW+Zwg8uW47*5cGbgkujv&DR3dCC~soMg?L`nOtq_e#I
zs^-q?>&Ev!^o8)O`XH&&?>|T5k=BdG{2mGoImnMtA)Xv(f#TnHm~5Uigtwyvf65;d
zqA9BO&S_T1D3032xs;x;i!gT`iZ)7>ZQR-MvE|*g?|*2~LcWsa@frQO#1GEU@0m&v
zf4QS&rK?KvkhslNCQeKX2N<{)9u2zk%86m%BuBn{hYN>hbJaZ};VV&3QUIAi+NCP6
z?E%OvaCpSf&M~X8d_HFa?`T*0^rN1XuG_LBkMG;nf&_+Mhyw<5?Pn^;KnpR}7wgvN
z>;4X$4qE~s>TN9tJ*?z(T~E13vRE<5{)0s=#}6gxsx>GrFYVk#kTb!0XFf3hYBDf)
zw-Q_9hppFoKS;m=drsI7AS-f%-hq0GSkfoG=ZXA@k=%*;XBx4hfyu>SRnBPUM#ZJ7
zv|IIcmABS@rr@GvX7WHr{Ls8#Y((3iF+T&ZB+sgNV>}gWNkhnC;kU4Y8YvHyUmYVV
z62E3Azzqgt7l0q5;C=;MG{<T9hcuNw(lh5%Sg9if5v8+4h9WL7J>X6XpMcYdFBoI~
z%~UFzA*3sd{SYu$<<xFT6&_UoAsMa;{yX;7nMq_S#$TUYMe0e|xom|7Axv&sh<}?b
zp>mDpBBydw6me2iEK)V2d`h8Juauwm3hJ{h_(RCE*E(QWVnDE2n(X)u2E(9kW@~6Z
z`4*e3VS-D~;b-Y2s)x(f$$P<zXBw%$>({^JsMlL{kQ*({X8OO1kvqQ*^mAG%P$Q%G
zy$mD=nNh4W7}N(bBz1V(JM4_NOpp4*EBlqWMCN-4Ba7ljxTB7q_1P)Bh)a4C1-Y%v
zmbem4GQhnbJ>`W6idT}}0{||%k;7GFoT1*7+)YIl-C}ZX<Sv#c-^VdMF#<@NC+sjv
ztPH*Dd;X@tva)Kc;!g=eq=-h5F3qU<%8H8N(kxbArSdVnFKSfH*o{iC^8xAV#Rr3o
zguu(_cRAoh*hvhu1bY%`lz*n4J~qXx2ebHC)u;<&r&+8LdoLLV8ZT)uhD#6v{kl`p
zcgC{jBR^<pP9hEu;rpNUAPrD2ya|3406kW+)OK8aH}+hVEmH;!z@(;Pth?z4Mvjj4
zaU0LkuyDf$m$wcUD?2<Lehzk^XSP+!e@!TU>B5By<33EiZmwx}T7RM-eVo+nU912A
z)^2_&{>?%>J+}6%Dto;uVzdB|{*XL|KEm)GLbYoRfT}HX>3}Tur9ZgheUKH8bnQpi
zJ_PMuOTNi$6u)CJW4ZKC)x3_+v()9%gxSLTDi05ftLbH@a&@nn-=%0t)8*jl)XUb(
zew8SRc{n+ilTOBRWTv@hdNz0Pj<laeZEio*2BUU;vJ7bSQH40ZUnNv*I6l;>w3E_A
zg&Vk@8-iaS<B`bsA|*wUAC`h*IPEMHh`L7E3KwJ7iST{4CN&Rgt1Bii5HK>KKjX1t
z!s_+U!3X;2BObX1>mciK?ZN83L_x3v+SJq9Qf%*#XFn!-MuKm5pVmv3q$O7x;eOw5
zq*HaxQjh3ibY=P)Z5c|ST#{_HZlwb>ycx#7;;kyGlpZ(MtBdJ~6H+Z12rn0*O1xv5
zL|hp1F1g*5J)}jBm!NhMUJrtwmH&f%mc=>t4ddr$y3g)p1}5iuo2I>Q=6I|7Xcunt
zIVVE3F3*>&isg7QdzCMTt#-z#Rx>g-qTNH98@t9;?iJP_->7=a&XEJOb3_Q92mgE~
z*g)?kwu#{flYUwV2TUBBO=z2CoHl9aj$Uw5Z>3~d`_xos@PLx03J-HywK2Fa;Y_}v
z-O3p7Tn6I<*)R23OskD-9`!Folt@=1q_7*YuN0^gJz&4d>$t&K5Ct*Tv_&b5zAJ-w
zXSM?Q47_KV5VNkT`CZxz{UYtQtigb;!6Wyw`KDVy#s}xfnQDQfA&d6+n`AZdAKX08
zae`-xyt)<~eDpo>%`pP}rF(7pF%hjOw+7&XoRZn$fq3Dz^(pbvFE30_DR*A`78d^s
zASTM3<E5r@0tU#v+@~YyCYwa6RBQ1%@3z8Vo{AhD-@j++LJ?;sP9T-=)-QInKuz@%
zMX#~^uo$3O93OH68r>{|{(guPDl!*}R+zhyAU!yuQVj!$%|Pv`iG?N!IjkU8u3C^Y
z-d^$+u0T4gkxcfPJfeKa7xsL+&kM0)_SCi)e=>Zl+qXNpEP*`NlcVI+$)$Te!%2^$
zohSsHjU{<b5H{ax5vx+Jrb4}DZf=qajBv1;TN_n5Hu#Wl7!VWCj1PZIY35)<I438d
zMNukTphX@UvNB@*SX0t;c}<9SqDx&r+<~`Cs6ytxk()w)rrEHAiIwcJ?veT>HE<Iz
z&s)i*>oVt*tS60$!L@Q*1<B}5t-GcDN^yi;0~sYrpTt~gD=k%zA=qkc!B{Pe+|sFH
z=)8ugj|sY#nsHv6LHs%nEOhwzx_pSTT*>VPnqUWu=;N5kFSd}B6fLPLc-FnwA#r%t
z3WJQ12MeJ*i~`>rpWS-Z;=a)yZRn6mF7MVn(C}uJRvR_k2wo&o$Qr@7-`An`y=SCF
z_&LjrI{el<wtdWPcl<bwLEVO1cUl;E1(OK`g8{xSYLsguN`?nTOWr=;u63uauKC}t
z<kfNC07OQ@r12u4Zg`Ho4I*2USEl`z_`h{uIK&LfIg#?fgy!e==dt;H(>&;Oy2IZ_
z!nxaB8@&xqq$cLxVED0*-ufZ8A=!RLVL{<TQL0X|^h8GWKQzjG&p)~yiF~_DEAgc4
zK(p`hT+&dU#4uHx6YnMff9qm%MpQ~wz4ngRx&j`R$tI<&?)Pd?D|pHgMABV593?dT
z;x-*S$xzeKD2YIYzuT(DZ@Hs1*^8boWPQRsKhAELGo^=Uj-Zizbb9{^J0u>U7rB4?
zp-yuG*=ugkxKvLVt!9Bzv+<HlTd}-U$@+=N$c|XL0@X%>-i0vdcfz-edPICvZiTZS
z+_&k`>b>NLdH7S*`(MMf$5KlAel$dUq$*$+N=M9Rh#o*DWexJnY_&FV!y1i!&&mA)
z*W1^`Dcvi|eMA*dBLMzAWN}5i@;Xcw(_hU<*6q=mmk0^hF^N^UyAGKF37z91AIV2|
zOJt@pRi#}tp}lBk$>s7`>y?~GgmvATzHz+UZ>)~iz7bj)0Iv&$w}VQ}_qayzIE=gS
zvEiF}nr^ZF_rCy?6uS}+!*=YPAJP^BH8HHcWgV*KqSZue|C}2azu13dPj-n+Tg>o1
zVkE<(7+W^xD$4!gnBnDie1`1`f_P^XB$P!gg;%Sh*b>R=&a}?WCz#Cz)Is`>hC9D}
z&qfVP%e5E%{b^P+XW7%6ZvW8KqDT47Osuus*J~EHo9uSTiN_WidP6i;qg>FT+xlg)
zN<Omgv2-GT168hVoSXH<n$n(Yy)xV2{{uHc$i5UjyQ<x&QWWg&RK@)z7U1&N&}%bS
zt#0H(mo|1sA4s|LN%oZhFCJ11c-PgYQV^_guQ*#H-(b5meSffVEKK<*%Q0cdn4>Ed
zYqvc38ij=Z9ji~PHU>EB^n~Gic1*V_u0g!OJ8|X?ukJ!&6weuwu5KY>slsf1GX6aN
zYu5fMp>`mqb*X3Wz}BwZQwZmyb^Q{d_5W4sY&4#ZfaO-tK!qgSHOrBp*@V#C2H|-I
z@%2~{zE8O;h-Y<nu!od}%s{pr1w4!Qq_wU@0eTxRLE|zHOF&jCPaD`|x=Ou{%77BQ
z*l`uwZ2{?1p_6vI>I8#!Q%f0;kF^nSMB}IepnUUrwgIKix5)5c%Q{*vur%av%QzR^
zTL!rZA}u>}XS~PSmUcIa9_?OH(PMnZ`d6ghP5TS38Xpz$Z5lEu3j$|DKf)r;+|_Sl
ziZ=X+F4a|<<tz08g!-;D`Xcgdhkq8Im0+5o`HY&l(=k(@>aDMjkQp8U`l2&qAkpiL
zRbdBSNfm{u7)GN`kI8R0ye{QgqRVm!v)12~$$NI-R1&ro@dFRtes>zMbk**q&`XMt
zg%=Yt<kN;brRT=ffhXF$>v<nvHb>3o&(y_>7e7XxyZr}5z(Z|h1$|N|0WM}PIP*BC
z+QHdvqQ-q*zrc~=5K;(83;ow~6c(GNwu%jW(%#FiCF#Kuml<=BhB3mOw2C;RPqvJ3
z2iL+0cW}1!bG1RFNm}Z!@OD`MbwZ=F6z&Oa1%vM3c#F=FpN<#fl^H&B<HLNb3U>)m
zgw_e<)z0(Fn2EYJ*TgU-zXYc3n6c-zCQx4^h+!H>qjc;8J`W(+e-6BQG;%{bU|V6F
z2|aMxj~U9b;PM<ax}LgA(Ak-}m|sk|Q<lM(yfPJLZo4c9f|n_aa$I<*54`R9M6lrb
z1s>i9j5|4N{c++mz06k;x|D*@M=`v-QUYvLbdi9!!oF%q*`jMzM?UTBS6P`pbW;^@
zV>9@KX>2jsp}WlTK(Y8%9CwV1n#NRiEK|K9TpBvoQelxH6J6OH&!sEJcAaMcGZ<Ml
zX-E|A^eK(^A?a!sdK_69#f<$78v8>~W;On)S}CLs!@Ehd>kc$5t*;-1aCj6`>`inu
zXJaLlue@Ra((o%XfQ0v*$>3~e4B)jF9pwL*RXpI#m?06EX226>Z5Qo9kdpB}tQq$r
z*ndf}wyuIHW+EYGXE{WDXPHdH84kD^oKVzUc=!lai3o;ypZ?(Y1Ny`>Ro;`3l>%eF
zl0{y-SHM>YfM?^=$Y<kPJ_tGB2nVYKg!Typ()HZb7b`(M^$nHXgg;V5D$atNfP=G$
zdS>qQxvXQ#i9;TVUO%+sE)S3mhXKw;IihP=>te&p%&CA%!o5qEB;{02%Ymt>!*lt>
zYy`;#H1u9D#Ft=RnsL?RtN;!P7~t#6S^6aET+Ao8u)cmT@l55=DNSeOy<x;t&A6%+
zK;0){ML0(JU^qWPxa(q`Je(Qu;A#d^8i3>-A=5i;u$@cEpw5CRp(a7&f@=g2`OGGt
zG-cMB#|n4oSC6n$2QafW`a~uM>CwOn<=3P2fQHc|DFhlL(ftE4#eh7M@KzxR(HR)0
z^Gto*mTf}tNmLAYr>5hElrdx-{X5p)w%>Yfn^^Z2Tyu}rB@y}7cwl4&bRIIo9ZP2?
z{JY#xr~XC5wzF=!AI$k2;xkBk3Wm78-oD2GH8U_^%JgG8J!MQoXzBd=FogQ$F!IH<
zlmk558Pz%K7VgXdwnwm^3M|tpP;OqU1uxe-7FTE*5)Q7sN+X|k=br`L#?ncD2=ja1
zkyrE-JYOgV^N8NOFIcI{48EH+o|ABw1UAY6@EXn_p^f^FEK~BS95mHzUxHpI5zYn1
zJ)}tiw!&Y^!C2C!dudW0ta+K}7RU3X<AzcWe8qr#Mp>W@)xWc8zg}5^K&K<Li87GX
z8d2BG9XzRid(}F(l5mHb8f&db;L6QxggdkzYG(OTxWk}^bIk2{8uvu{fJ}Ff6ILBm
zmHUhT)CzYqyGEnhox%7~%36tu`5Kr_&6WZ1w6N>G(iNe8Fv9&vgv!$>8+U`}1=PTF
zdcP+1MU1eb-c<!H^qEQd3n^{`G}7+;l?g@bNaAoYY<x_B@XVH3xS6|E782?`Xvxr2
z3h#R8y^gr>SVH(@1%#>{H?&HDtOM3+;5=@~^N@8Q;lHmKfR`B`LEK2oYg`Ax)*VO5
zk12DE6)AdE@ogouJopOdSY7HSP-6jDK36onWtO)Syby)p=UwmlnLD%fiTHCFNQW@)
zSICE`ROcVyS^=yJV6=980F3crHmKd4)G%p_1h#2daJHQ@?W@6jT`UhA&n#gN1j=0q
z)7!vNj5e1u>4Ad8q~t5&3ZvSUj(HMwdj%R@O(=$*<TMz~OhL08M<zRisH#vn-p!PR
zLS)JUk9y4VnyMK^B&%6I6SB?CT+4Ao(a;M0O5wObzSzDNZ+rw50$Ck)Bwc+ln69{#
zf<ZfO=rYTLkLY0gFj2Qd8E$z0c9$Gag%{=d)yH@N4M<Uc0B`X-r=V;=k$HhkTyvLM
z9)&TrMJ_Ldngyq#5D^jx^&Jr=Ux5&3AxJJq=;kWey^0#)P8mA3H4UcybBKOlPDHSs
zF~-=^z(){`2Cp*+r=sOu4}*D3?|Dp7(Y%?4xeH!^T@G9hgHe5ec6gNbxRE@zey;3F
zlW><Xbk1%_)>q?HpU#9VNpLf#Q-;9-{Oc0#R!J#<a5wd(-ns9Vr>!K=-t~N{wg3%{
zfl7*io=E{5k6+;BjkOhUWd08ce$qn!^_+IHX}KIXbj{o;NBCA1?kM9`c^z<?;-HmL
zwwqE|PKD6l4E?~ph-P^$Z-t!NfQe)$6E%&YA?Yf1cx{}65q|+eqV;v%^wSWN3~)8O
zkXERh-L$^`QeM<nXY^oKtgEX$>OKQus&)T1I>E)v+Ls6N)eJWP!xa@=m%<?H!AD`3
zyF;JbpwpeMm!oOa-zp2{gDcVJw5`ncT62#v@o3(9|AV(-!Z6x;%JM%PLwhu&Kh|5%
zR(+p#mJ+QcuEb?7`pcJju8X1WRz3JH^S@OH?2>@}Z^Nb>H%t)j>Pt?=Rrp)SSk65D
z?Yj*K&CFfnu0YQ6q-v9iR;!W}TG#Bn1K!>>YSbv#vsSA^P54yo@XQQfVdk!N!)Jhg
z6k$!X-%6NaUfvVSfUc|6&hwk|6zqZtbFyoS73-?JK}W-D-CyVGmm}D<?jO?dNH>B7
znq-0-EF&Rqg|YvbGIVO-Ou`vK>w%x4LIgY|P|Zw%c+4yhOj8dYHGJkSMM9|7sYnQh
zsYlRE3ru}wOW<3^L0ikcV{~sBB;$w(SBtk4Zq3r=Q{BN$Ka!Qm95<vrtnlxleT-rd
zNXv5E&|t~6#rpw&4u7mgK;c-t6h4`a69rF;33t(`P>Lz|YR?kEmU}TwgB0UOGRw*U
zA7kp$iR|edGiJ<tQ@BHbjhtnFje#jMod$ZI!d>UrwZ6VT1O73}*v-`lerwbou`}$n
zxa-XL2f^zQ@T#=#Z{Uil)Q+0-O$~Y<CU{H+pR-8Qsey{$gLgrr%>+-v-DlX5d`94s
z)^N#aH^9|9bGNvzT)EO%d{m!#^X7dOWwfuix3>>%ypKU^LT<5JgYV-z&V5s-PBosd
zjG3>HK~GzEfhgTp3MBQFtvM269`iTCR|;3uxzfaF$>%Eqj`$S8p<SmE%<`VwnWP@c
zeHA!j;z7aR2R>%=Am#dFiae8*kNksm+)%g|O1OJ`E>U@+KCSAsNjvt@7?iyZ3HK>e
zro1<WyIPCEvB4WOipMVWsHH`LqmP>CR=ZiuzWxuU{}=*nH~t3)U@f3y?|{1%?p0w^
z{s{0onQ(h72<G1U7(g!}05{5Hl3q!hZiR6h7LECIz|$#9SIMTnl`m3|ECCYS;<PaV
z&a<Yix#wh-S3TPQ@-P2#|7V_gW;<I~&CF0l$H?2XY9YdXEteu0B|r+n=+UFsj2%0+
zPZwKEoH)@}2v7#rY_rWaQVjHEyJ}#A&#jOw+RQyzhla=12Ps5MHi^;hFC_TR`?ciN
zcPnI(v&Rh&;qNZ^Tkuf@VGQ}Ek=`q2PG}rye;kOfB_2`;0{{I}1-xVOpd`qOVcXS$
z$r_4h#5JF+P@0=x{IgIBiANq;bYe4i`229v+jri1=cGQ@T<SKUw5kG+GS}0wb+jj!
znLC3_N4U-aTnEhcG8q06;zmG1?6k9Mp_9siyJ=WpSF9_8c03A(zUVdv)AyAq9nV!M
zK!a0*ydvBxFk+9vSIUS?Mhg(`$01CfN3dT<x|m8W&MFJSH6~<$-?TONJZA2+)8?;z
z?Q1iyxZ;X^M2tfZJ@o#6|M!2tb=k6I-T0o@UVE*F+Y@_Mty<Od=9_Q!a6NJ_OG`Z*
z<Lp_pW{vR-Yzcuj`r$}<^65N!RVP|W8L$e0lmS@?2>T{Xn9w(Q@?@hFNEwiIVD8+x
z11Jqu;kICgdWBbRRieFpSnvy9xG+w|lf;{}T&9`XBYsAA<o~hBymK*eqX_U`R3VsT
zpAl+#!8r1nkarbHQU-$QzGA?;QQ=a!nLBkBpEDCBLx|%1IA`wggyG^x+itt9+H6R=
zs)EVum>N`C*Q#sgj=<U*fu#?uXdU}UFiWd7S@^jNcknh0fie^8ikWyGg8ek|_Jh|&
zG<xjzvV_*xtT+*F!aEg{fwvKcHV>Q*0FV2?=NG)NmBUqhJN}S*tNvmpVoEXLuJZI2
z`5M0FZiPGbi{T+0syPI4$LBuxx$!sLaD&!w+fF?3#NYhqfBxrP^rJS-s`uGvpSP;P
z&~y4>yOe>YOPBUYA&@fg#v5-)A?W44`W`6*SO9#5fQ_ZS^xI+J-GP!YN}hMp6eUw~
z8_Na9pfDate2pvwC<p7GfByMdfA@EP_tOu3=tH?(f|LrO>X~Y4o9bX|%K_c}8F#`m
zik@d0AGG7rG}tgEv^L&Nn6#15Dh64P8){i#0Jw(}1{DH7ZpURBJe!n9Jfx7q9a=_}
zE1d!x!I&IiY{s57iJlEFrmA3qajs`D2$9sEWp!ZP$!zHu;4FZ_{~m$$bMhFW63D9~
z7L5?@5^55{PS^+x^RX2M+KV*wwS+R$rF9yhRcV+O7&uCK8b>%1i^k7jX7{IkHz^<Z
zsqr*0w|w|Ip3%00&qU){@<|B@_snG>c!!;O>Zzwa{q)nl66P0Pc;Ss-|N7S-ss%F!
z_2zl==9zW(wC`g9=z8g;my9y->Z`Bzu-@N;nb;@;Jd=gMR|c>U^r8^xoA>P~0&RE+
z2cwV#%e&x$3;qb@;v2!V7Klr@Ys|ESgO=*M#xXu^U$sdJ)t4HAFYTwXvxZQ!@KlcR
zCO#9quGpm*h-YZ5FThJdh>Zu}$WrGEd&Nl${Z|@1)u!cg+z^;fN7)rOEIMCmjYQ|n
z9pxQ1FnoY<`rZ}pbZlFBnZYv8-06Iym=$Y%eI};vn<41$6WY8U<skv2pDc|tDui{;
z@mOZa`yzDiLE-onc{0ra$S;M&XFh&uHAJA3xk{E-M=+3H%tNpX4e%2~3^KqF^X9pP
zep6=dwXV6p_~MH_XP$ZHSuel*@=WNm`9J>SKbCytBOj?ru!|<OIM(6^_Sj>OcjVK&
zPkXj4U%q@4R)WtnOTPo9WchQ?JvZ@3Kl;&8Y2GjMY@q<m*B@WBy-T>OKW81c4I?k(
zN&Heiv7a#B&ewDD{+Li>FL>P0S_2dY6@zNW4FyU9-d_$NZUw(Hr2N!NedHDYEY!>$
zx*DGEwF&5Nbtv2;kVhM4&6;K9+t!CeBo-BTv1&mWv);~w%)*!T^;yh}Pv_ls2IeI&
z_m^V@Y)uf6owMt$S!#|6wqIB3q@!h0IA+tpH==OVDkvn}eYmw^=j}sS$os<cWjdt>
zV_r@SQ6_iIZUDXIJc|X+ELc_zDFHqcwa@Yn?aV#PZutivd~n?P=bwKLX7h<CVEtEI
zamA{G4mwD)sl3t46`rgMFb-n{nm>R3D&os7y6B=0=2}Q&fQ6L5#uSkrDhjyYFImor
z33vKI2VJ^lgnP=GN!j-6LLDSz<=;&CzS_uG2k2)(Wx&LNXF}+b<+!1EP9?=a<@q?L
z#B)%yQGeadBHk8LO0WpYEH8drs5)gKXjQ>P=h>iH2!xW5Pc3|zvFzimuP=isYkfU*
zPCSYv@IwMP`n=BnzR4fKG8Kk#rs{&ip`&F>YlRKY>KIjNSP4Ef(p9o20_#=Gj#q=*
z3FP+~TBUc>Jo9iAJTX8c;Dhhc@hxK%!)d6qGk5w^s<M9h%U@3Y(?9*wbF{|I#<jQq
z>%ac%ih~b6_-Jqzb1Q-~8VIxYZk7}-(8fIk)Vs9zNEEud`b~o?@6?=RK{@1*L%yi8
z_uO;O$FXLZ(*-(esM7@;E9#pzZCW2E3)ak;Gp9enjGhVg;LII95@W>lZ}lZ7zOI7E
zpE|GW{XEjZLCSWlE*W5}4rVqk-kjtp!+)eQ`4dwF9BC81G+7N|d<ZyyMY<_F7*du~
zF(Ch79XIqhfP;f3rl7mk(XWQVkF+5({9tRA!pEvI7>E*_xu-G9`{r&0`U4E`TcA>!
zj-1&tke2#1pfStCRChv{p3MI@U=){vU*b(&DPh;O=8jdvUM2e&bowb}3eUO~h6wH&
z{Xt<Enl*Uk>6O?(R$`tmi*Ud4$}8u7^{Zb!lQtWH&{)BY{fb?8-E}qnQG+p+a*!wP
zwZg&_--cj69Kqg=FnsHhOD?$_h2<31+Go}Zv%&8L`-)%G;>C-*tzcK;!w)~atN$<|
z!pvx$?j5jq59oA(jv2}a?;e;jV}?0uh+?pgJ`!TKj1j+Ix@PVaEqs$DTm(0)M`?Ib
z<gI-F(uGy0)Hvr+I%TTf+Q4i#F;@i(pA^5h3c=CDwS4f0K$@QE=;<`_+I+G8!!ud1
zVhVxMc;??lpt#adSY*Wr^#lHAcEGbUcTF5a4&go=tF4u7p=U$oPgUVgMJ-_zR-C!h
zD5IHq%G^D@26J~+__Y!pe55$cMzHIHG*7@JZctjS@a%hZo?6%3wZTwV$?gF&yB!O{
z9Tp~`%;XwGxj0~1W(K-u+=T`o&+VGKb_VBCW)cv~xT~4v|Mg%0br<}}<m;R}ckWBq
zUVH7;2moW^2DX@DA%y!ax7;%2i(mX=8%qk^T%z;(4{%zu_u3(eOoN?nt|1E|p&9!r
zr<`(LFzxKK&%OnP#cU|l$pZP{QA7GtyA(5d@|jDpg73TH&}B1Y(;PLFLLg-TtBr()
zao_pQcXmMmSTlY4baT`Y>wpvmW0p_bcj<#M6Rm~c#IW9(ds9$_rV@VgZ_zdyf0~8g
zQ=xEu-O%nI@b?Swjurei^p6|zte)eBQU;9005~yzPMVQd;2xW~Bd0WTml82#T~*<Z
z0FvoKS9`99Scx-t%#5?W_4Uo(`uehrRBMQE2X{5_SnAPZ1u6B&h?+Y)aEwL3{nVYx
zJJqg|6-<A&;)aqwm3A}2-PRd;M1{DB!Q;lSxr2+Dt<V;U!daKoFiAgFHQa5t-JZPe
zy6b)nlMgav9m&l7LoDejnl2}J&;HVvzH~NYq74P*ZR&K%Ja*`NZ|Bni)1gvVE-Vkd
zr=51%c^v(l5(M<EZ++|D;QwG8UeNx^E3b?U9yR1-LFlMq;O+r)vH&H(Tq>fI2J)On
z2!b?mg!@U+L@aQdumY%0d{RdZb?1QY9tc_ou>42>7}p}gT?&cW>tgXLKqy(E4SSWG
zzHTTbKz>!FjP@CHFvkt~Zv}r3X})3*NY8rQP-D&V8GYq&JST3}Kl<pST^yTz6Qxln
zlI(=RqBEqzs=^&JmRWO$`Ahq8-pIkg)P*a@Lco_XFaL<pA53Yf1>^VE#nZl1Egv0i
z3>w-(^69MV(#Q<C+UseaOK?`Dp#{WDbQ1WS#sA^ndjHGznGL~HgFZD(`(9EAEIV^o
zN0(3u&D_2Bl*bMPFDd^3YfhqYr>)vP@rh4-klF5`Dj5^-159*(hG{n{2zBXB!*Q*r
z_L$!1f8!h9IFm6kY`^{XYy1B9zyDjUNn4n-akNhMQA>o3MKS3%33iqg79nUSBRJpP
zYp=bY`t5IjtK(Z09ZYjG>+b#XWPGDyX73?0|IQ^#miR{vQ6?nddmeb;f$g4p>Z$FO
zjg5&f!jz3NfLT@d)$0oD4o)TL@pS!EbMUjEqF60^qn=B7x8t%=W#kQ4cgyFlkT90T
z6C^KunL%1b+`&T=HnX9S&{qn)xK#`!tH$Ex4#9Uk_Sj=>3l~ZpY;oQO-xv5NO;UH8
zSpRp#Qz<9pYgt!Sh(KtwE&)Lmil{-?#UMM5JbOc^x-MJ+Mxyc%EHG`H*@JfKpGkhL
zi!Xs;H9|{Ey;~keZ?BSlhuOyE;N!e-MFR+&^PIUGfeK6s^>!GdOSmgfZe;??>0tT>
z7cu2v2HL5Eqi)CFci(+`DgA^KPPpyc-~RTU&?uPA#*-8j`+QJCF4fuTpZ@8ePSuja
z_S<j&{8d+7wJj!QT}<TTMH~C^7L9C1ppH7@j58K7<DSH9c=g2>UwrBH*I&P5qwuAS
zjoMQoA1nh4$nwvA_OnK~-*wkrm)mEeC$|xfK-izi#Y5vz0&erH1NLS@vs8f6(903Z
zS@u2Zbf=9EVyLmUwL=UdB58}J&$i4tcgYIymQOrxsCt3(4g|V&^k0Y%?6lKP<LJlJ
zStc9%hd=ybkK1m$?M*C9*OEue1oHFo`{Vx}UwD^BxZAZ1318nV&*0Lm_zcpfG9$Zz
z8P>z~P{V7ms`#WS153hQ2-A<ypvz=Q0MFyVS8GAJI&~QEt_s()Y#c}a=fS7qRkFa#
zRk(Zb+7LFwpvfErrUt2w+-N2#ZCRo2mv8tDb*`DaE~mlldzxlv;IR3^3opExS?`13
zl;p$|ol-yPg=VGy{onun9+-|#gud-|Nr5%wuSUyPJ)>zY^51A3FTC)=NSfq)+GZj`
z_nk{Gz4WpJ4me;HXY`v+clxZeOeub>y|06onu)K)Lh?+MhEf0)En4&;E8N-0_~S2s
z`OA++^9{oP!hQ0UJ}utd3RaIS&1EK|^byIf^;{4A+R;ZJ{h9zE+^29n^+kmHSF!*s
zQr@b<lo|PY1cU0NHFrZSX2o-uZJkEiM$F3>Gb4MY-byoAW&V#^2C{;cneHg?)zz&J
zGDH5EaKx)qI+ql(77)oVxH1DJIelU7Lmob9EgQ9N(sSZK$KKL->(_sj_M1Q^X|pkg
z$+N@ByEo5*LO(IC!Qw2iLSkU%ZiGniavGiuu-<;{AOG<m$Cz{wBsfxZ>m7yr%2&R!
z-{qHIew=Vc&^&z2HP_t8*s!y+&{SP@2-c~U;xciwdG25Yd#{ckQm4zPk2!y>we)7=
z%J`{fQ4&s;&LQ+Z^{G#N;5Wbd&4Ef}$>+uv5$r-o>&#|c>Q6zO+M$rb-Gw*(3YODV
zZ4W^~n#PT0RpCx2^k?pX7>-cipFw^!3~DK}P+iyRua&rqNS!L}s%GKNyCd0wek$R7
z8tvyW*86P9;F#KcZr@20QkG`CP8#xTIQX1Gs4vWg@Tmk_FnR8TS?Rr`XDAjrs-hip
zBbhnh%}hviS%=qJm0h1Yk+{CLwLFkMB@p*5Hl!IRni2^Kf6B0yfIgHp6Gve)aYBtI
z!-R=<L@%fL$AA3Ct61M%>VZ!TD5K?O?tk`Ye|88a>m!8pVTT=dAA1FEp{^|}z>|o}
z@wTxjS8ZIc+sm;%Z34VBcwfKTE$Q7xrNeKk33uTXqxEM#^O-|`_OqWICcMr$=bWF{
zdR1|Q>ZLjrO}L9E6;ZfrpFm9cgEuq(>wIqucLubbp{X<K@{MC5%00}Af5rb8(MYSw
zAuOas)Fj*yU=z{Av{Ry&<$)^@VlPCH5bWFpriBe11}HlQ7n<oRS*`Vq2cI-wn6n_Y
zP9!>4V`zT<N)d3IM|*uld7#P7;5$}kPVka&mp3yq%2a$A>9-Qb9M4rpZD6s=fDMBR
zf<CV~ojgkG29}xSktW4L_1e7McH60(HtoFT*111n?W>C;V(Tuu?6L>;-g|Fteo_1T
z3Ip%C7@!z=(`S&B8FRF=?zlga%XSXd!{d%S?ymp&pa1!dOvXV+z7W2mMeunm#3k4{
z-gX|x*4p%SJ2nMgLYurD#4TEn;Il@>QwsMGAL97o0YCZ4Pd+G|SYG%QilY?TR&pBO
zA)H$YxddFq16<=VA$v<Ci=Q|Z5?O@c&Ehpf*2M%<w5AMb3C6Jqiv0;Yn34R1S@9#$
zeD(f^;i(Za3wMOowybHN#`~*LIKBsCb?tI&)XZHthXgUgRG+k6NcsTyd>?_jA`5M(
zM=ru0n(PT}J_<Yu?b*;p8ww>rLxRS`t>T(H@2j3!F2GP3W;ZM4YFD#S1{hEh{`;y-
z33fJ|4Y1*CJ!jYtA}K^i<pFmo0~&z7z$Y#kI{l!{cfoXBhN1jTakO(g!tgk|dt5@~
z-~R32?oLxUxVMyWmlcKC?IJ#YGmc|>Z75rJp@@Y9yXr+dhP9S7m8~-meK^)HA|Tb*
zd_nouuYPp_7a$$2JjWk@{OxJmBm%RMe}ykyApf1`jYJyQk`+!t+2C^P0WWzX`4w~U
zqAvE6<Yje^!Ec3xs=^&XwFiUxlNMP7){`(%`!}}`m}-Pf!X4ZWgvlO-0C^6)u4lkF
znLFt<DGn(r7g8EvM7tnVPog|?l`LT`ohPO+q`)JT1-iT+x*U#T@gV*_aQY)~u}sBv
zMm+E1W$GYCDARX*+&EgbJ%5aFSK_nJKHIfm!Gh6#1~0;~t97$y&wiJ0E%)!Z<Bl74
z*=3iFY*6ds8|jm1>v3e$GQkPa_o$*2qo^_vRRH{@121pp&J2A!SVA}vp}4zn_}Irj
zcJtr<?cXMS5UHil<+ICt+_~qTdp_sR$MQ}3RqRfmfU+e;NmUCu6nyoeraJm=Md41H
z%MxXT&NsjL%{|%t_hBK(r*wb!-S2+)j;11;qA=mBY~Wn9S@4h&?u_A1`n!0;kabms
zJ2ig|hPnj*1Onq^n6>$YUfvX;(@VH(Ad7*-IZL=e-SGAiaM=st^*aRnt(D&KV5g~T
zxRfgYfGehA;Gzj03iJCQbP8T2TLnV=F)%ggYsFP`IUc&~i5dP@%uc@rCvBoLvo3H`
z)HVB3nH8C+UaZ$0bIdU>vlG557>+lMc=E|7<=bw)`R1zm=>wY({Hy29o400%9d=lc
zGBAK|Cq4=*(-Z*MWdz6@lTTWA3kv*-afF~YoSpjk<B#iNjLodK|Cq~a9;=FEAcNiu
z{2jFDz4zWbp5+k<_AxNJH`on-1%myvAmqf4e&y;r4aL8ja7RcAr%nI-&;PvZfB*M?
z8-<U35BGAnz^}dj-cbB%0B*<GR;{-NAcOFVcKB;DGYCc3Tx8<RUBVq6`EJdS3-Dr9
zAp&!Jnn7|6v(ovh4~;vVM$TR10j4pZ5bCIw)0Cp}VKDlHtd8JKPrF1wUS9|cMqUnF
zjUYy#o+yC|KGCaWYbi=F<qXr>60QDA9kroQSO0DU9zRB~n<GCSjsjq%nhk?MRc6B(
zw=Ol&_se0}?T<Y2$XI5LlQ{x6neU`e=19sU*5W7gtm@IiAmWOnz;ti{%2@e3?zkiR
z*!iDzW!-1K2J>_O&O7fs!1?OUC<Mc~?MzD-+BYB{vLtbHTHD7z{_)KS_IBvo#+mk4
zG5u}_jc1TIWFg?cQ4(kmal;VJ8Bc@GA@9EX?g>~N&gUHXXkCrV?*7Z5gVXW_{>D+2
zP(5sVBf~UidDho^F~4i`YWsl)9{Au7fB3^&T2!#h9&e7Ef~y_pjU-<#u!XY#fwQrY
z<N~xgKoW(!%<_z3jnN_NstR`m#(FN{P^UBkmj(_oyu2u5;UgpyEdwy|=`@Nirs+WG
z&?jV<r70iS>Xf<T4X$E#BM5h+qg;<Bm}v&*!rcyf4JhKNw0p|e2EMb&KN`CH0J^*s
zfNfv^&kAoBgH|3pqX@j(m?*T`j5+=-c}jRTO({Aych5cdOyY>v;V1$#e)qfIEn`>r
zWQ3DB4Uh&#Fb}t%=?1jk-~T?znjXx})W5fPJ;Ghf0qt0dHZe<l9@DSrX1}v7>wtex
zcKGeKBcT5R(u}}IftX~UlfJ7>CzOI9lX$9jw=lAXTm~}{C1nv7lF=wRE3dx#>dU#{
zL$eswPtK`)V7aq>XiKgp{8kh0+-TVQ=YRg^lc8DrzWeU`2z9x+RRz1q#aPwVv*M?@
z-}ctP=CE>N$Ow04?h@|m^F!9zMoX+97e#E4DU!};cFi-0ECWb<r<Mo6^_2&8{-|S+
z+fSpefVp1H0M-Vtw9a-)39wu=W)zJ{_yd8bS=?m^_N8f&sg#ep8(d)mGJ_>rFf0sD
z;W>-{b_1WTQ1l%zOnt>vFkh=3T?|@znkL+R<T6C&aW6Z53Ha-{-Z=Wp6_s9Rb66K<
z=ZRcaF`2dg$;|2}Nf|((Pp<S{(lbx6o5z@dFFxw1qqI>mlwGtXO`HAfGJt0Pr{I_1
z-^81A6fkWzwC+?Ju_5nk^P|%e5J|VzfvWw)!oKs)J11Ru;f0I1S#p%Vw#P?#F2{P2
zY6e4JbM+kyx*=gDvpi?;=W@htGQ6RE_uY4Yg1Y<^#Wvh14Z*5*ptP+WXSwj9+A&H0
zCVg60&lXJ~Q3WO6Vq=54bZFlU7fijkg*$}U!0bqOO<N-6J_A-OP>vC5bfhSJT?F%f
z7|US@^hY^ce<Rtgu-2<po$8=WUMH=4P{B;hRkDCI3J-!kl?9<HqGX_s20uHXD!l_9
z+r#{HQeY)?xsuSuqzycti7`uSifc|xC{%Q%vRdewn0HlNIj23q%=$I?G`KS>ZATFp
z%O*K36->fXprrz%48REdr2+|&lTJFx+1|97=(vW^ZwKgpGHK6|cAv$A<p(Ka+k5~2
zfjqrDH%kFFZbS0uLmE@<bN<JaYo2R77(q{{&uFe;yc#P2{mD;fXa8_!>?b1Fd--_A
zYn;u$9EC~wth0T?DImeYKDYTB9@59HFbtKY5A%HOYhOD{f}QUWyv;_sAEPui)Z!6B
zp!PquK1IOQ)RO*WCV{knCcaxciE?{Wvc$F2)Tx;}{pGza+(l*@Rl?EE+(oe59UUm9
zJW%d%1p7%as+|y^zh;K~yIKnNn4jt)lfSF6Q-ejA?h8ZtFmPUgaXyC9aRP9q*&6z!
z29%HySXTM4Ou(5FJoK@i<Cu*<gJ8d&_bmNF`N7M;gNB{VYLbMw@>DD@1axr!s1CUc
z8O``AEfsM0{c)JH_hT3T%8b>QpbX(loA@ATw{`L)m?NCT%`+S7PcY8AVzfpPAPiav
z{5bi?k#C0b5m(+e6qdG6fBMsH+Bd-df;P<8qxhK3`8*0*2+Y{vomspe+Gt1`UA;AC
z?l;|Z(+tkCpH2TC#v1!NX7De-sO`8>bz9a!M_J?wH46q`yHB8q!rdjnJ7f`J0+7Y}
z+p>Ks2{EIQB|QwJ>$9&`c*JH7fqeBu4SYJkQ63OR;}Gn+2xc4u_bQn0GqnN-hV7JT
z>+34Yze*Nd?gyt^=pZ}5@RV0@a}n;)*#Hz1a3MjO@Hm2U4@R)x%M!&e$m^VGdGDJU
zf(9u6lLTXu&`&^NP?J^Hsx$YzF@%1G#)MlXbM1vxYOjh=y}tYondH;BcFwKu{8xYV
zSEt#`T(<M*{rBHLz_-ym@xu`Ey1j4$&!r$p@EkxO?>o}gaTj;+zjmhkR)J_7JaZWj
z^z+a-;KHD(tg)ZV*c&l-?%bES4R8cwpfKy#(9!f==u-$~L=6tEhHrxVkk~CxeL5xF
z+53`q42cYdbE$+K#MnT?hJ<h4H4VV;!bGaO>rbbQbqw5#5Mo(pOCj`vutN`0{-OTP
zt7O&L1wx&LcuHthvspzX#MHdEy1bqFyP{zH0tMrqs?fFwbY-*fT!@O%G{hp40>7+T
zk`$DzaI)q%0-3gU(XSG$m>{RG#L_AOsPoz8pkNm)zFKF_=Lg|3&ic7upr6mGsz1Mi
zHE0!^?FQIMzX=omX7=8+bGl(i-myy*lZem3%Q9foh;DJzQGF_C6$r&CBdQq4QV^pH
zvvb>T+q7TcPyXaj&O^B!iK6i`yX3EA-SlI;XB5~iCAa*qtxsWuI~28Y!t$n;C$fj2
z*_=&Z3*k%u>5>(S(;?-|9rN>gX3`o!(vTZ6bB7ri<pIII6GiRI|0OUOeH*^+`+tF9
zJEiTUp~kNC2VC^^yR)cw>?+xM;3ky`Ogrd9xKe&Mg8fXw8O*YN#1h4mNiZDgYBz26
z(uFFnS+B};r8__kZc>FhG!!w$fPued7-TLgq!3tGnW5zH0w(#V*d^bC@5MylL*F)=
z`xMIa*;JJxlfHbSn=@z5R0Qi9P7%C;Qqada`Sn-;Hn2%=6H37*lz}#sf=<s58#iv;
zXzD!`fOc;|h$db^QUI(s!k_o1TO?Rk_!#>Iy1CO}#SJ&ya5ZJgI-rILb8q`XYlc?1
z7e*n0XXtpcls?OwT9dMItcHQJ<^BFsJ`p^EzNqW4)yIac3ki^<T0$UelGhBrQ8PT#
zabTYNF;h?b1+^rUqv@uBsYfQ<Q8Z>?L0AZ|jm&IZSIHWjft!ksQKS<7TbPy!==jWd
z1pC?K?`F1s8MF466W&cq7EHlYJGz*v7>!>Z@s@S;KjG@CLql<<Tq8he>#SwF1dl7N
zq4*0pLik<G+SddNqJMWRUAnZJ(*!-7B$z;+Gdaiq`xjn#VKpZXdN?T~ZxjK(pDy8S
zoYuQ_U9G%Ls}#aaH=zvlNg<$&6$tHhm_;|pIv|CB<$=vo1PD9u!?73)M~T&v+(Zi(
zrrgbuy*75mPeGCR0_hU&8hbjSpk)PZCe%^FH8u>Uk#6=BkTz%zyrg6oYRw&d4Sxgo
z!kOj4-O4V%&DkKXzBK?JjXm=-4S`51p#*_6h~zYb&wb_&e9cNvQvoo6hY_SdS3EDL
zPAOP~sp_k|Ft4gt$y&H%PYqYFt2iUv$zy~q3dSr?!B~qW<09yzBUM@5CUEWKUA3P}
zI90q`K5JnO&M(2(bSne2GXgn*e$}cRqB(6bD8k)UF2Zw@W-#6?MnX|{vOh;XpNdwB
zwfa-}z|5{nXQ%yta+;ur<AqWRdimf?55iZzR|<iw16T%(QovXm4!jQ1M#*zpAPU;~
zA3lIFVXe<^Pz2t*=%S1M55X>a+6{z<5!Vb%N}ui=m`$jSg#Mia#7Q#aUwJ~y1N=7@
zf@nJLh!*{XdM7srJTtBd?Ksa3b^`%f{uY}BH?Yh+Ks(Ly2=@_KJ_q2ZrErJJh6r?a
zg_&l*)mn(PkI4*R&5pHhw-F}#O0{=tcy92A!E|92sQNmaEe?Sp9RV)Yu9CGDf$Bi3
zmbxVaH1=VF$4=mJ3Teyfh*yc(qy!VqCbPV7Oft*oeYbqH{5=1Q*$$d;r|giRuBBqM
zV=b@c@lG|R!XoC(7gTHxIN$*7?st8pDTXJ^v`d!|aXVqp^5x5WG5z<*qY(5;A&@da
z9-|0osQ^Xa?C^&#_ySr$*%g23ym|B9p)dPoK+FC4^DJq%`iVqHS06D;0A6U70UPR>
z;*$gU{C;KIh){P9I2C|Ey8j$JstF9sD{@sDz@=eMyhHTB4~dyOeM~d=4Sc++T3a=Y
zrp*9sHL{9jc{4ucSx-afCfu2o?!c^6!s{)x^vfC4pCX<cd|N4XN-tgwwtqJ}b-n|E
zdb@?;3IlLc(a{CNk!Cb{l%8Gj`+&#e;Bf=_Y#ki0R888bnG?Ch6)gh~zN9iO(+Q@x
zVmzr1jmFhTl%Rl1S)dDF11shGxn-ljrh>&7cjD*up;Rz}nfUtFz#e<-@s4~jJt>Y-
z+m0g8y?F8BURefy^PAty=R5NcCcLjo#RMf)P@0ZGxsF0Pi)O4znXp3NDh5Uw0G{p}
zP<)!=go?-b33((i%)SBQM(9JgK46<T@7iUIF{EwanUn`7y-9BgM?DOG1NVA(P*dw-
zaaMbPcc^THyP3Ij-E=A3A&w=2Lf=dlqFBa14B&&Al^#h4e+t3=6B<FLe>Fs&g3@3G
zY||+jNQtm1S2G6PSwEAt7+r|*Xo@NsL7XSfz%q<Vf*mVE)sNz&z)@MO*<HOn#^Iq)
zIdiW<NN8ko&^}diR)0*NuOCSDVq!U{L&b7=9_t%HAdH`i&x3jEiDNR|%nV&O5=uz6
zEnK+pr3&z@(pn))x<{U%m9TYMB%*B(xx`Pbpbv&7PJV^Db6}<y58h08q2TdMZDN!G
zLf-<A5Z7}p6&SulyeUrg6t2xKx1P#@vo_NSXDhpWaBRwC;yz5ryDN?d|Am4^5`|r`
zJ`l*Rxr<OSZ8exTMO??ZKFUDa9pQB+g8g>hu|$?|uNS@x1tZ+mNnH;Sq?0fKp2ag{
zy4kR{)Rixq20Q~7oSkr^`K$d`hs2Vh^TQhS4?&w>S9>STb6`nfFlKo|-}GqT1^;s9
zZi$f)LPOI>p;cA^r}whHlYm9t$#$G20kU{H^HKL$-$?@0#B|{lo4GfmeCVO^FKa|9
ziv)d0o9Xr@{0Q${f!9)`6o9B=piP7}Ek<+R*T%qVihvXbYb~(+o22XxoW<WnV`sm}
z4zclXcsPrX#%Asa_u)+7>Y_>GYfyg)se}?jcmo1Zgpygl=9xPJeKbN;$GN7UonMFc
z^mwoeY6$)}Lb$6Ktrx}WOTFfS)9IAGT1x}Ws@@9XR3&behPqAK;MPm0(~-N$DwEDv
zX|_@Yt=QwB@U{#*m`MXS#zcY{S|v_9$;#lWLqkzy3>d~nf2q4{>M$rwvfgr=HFM9{
zkW|a`iHxr`KRf+a8(R&}p!CK3uQ~%Kzf}Ot@&NByp&wKVrUcSs{23T3$Egf}gGq<)
z`BxT`S9^PceN=qY;!U*m9d%Bexih&O27ev&lM{`mVocb%3S>}QEl0vvoh-9seSIdg
z(ldE+6GHT2nB{T@5UoKqOt`DTgJrTbun$WEx=Qv%*0rxC%+%oIHyxe<l8TPOvV(EY
za&R_jy6^ZB==4d&gL&$Vn{)0AU<q-Ta0&|lI*V%f>z3)L9yZ?aLIY*F=vS$HZDMSv
zkNA1?*Z<SR2<28Q&1P`o$JlH|1<>5zP_1IE@|-rXSGiUd?82FTBc3Fm>XZ!d#W!UB
zucQo&;rVpD_7Nzr>7RlAq4t-6x3hoc(bw!g;HBAgg(eQ3sp>=ira161ydIpLGj}GJ
zT7DVQE|ma*P!j5L67nGdX;Q2FAguO4xPKHvYuESHgwhsWZh&W*;ky7i(t<O0UEz8Z
zLiIp2ICGUO`D-!l*>_UW(ScJNbkS{mXHsuj4lYFizs-NUyWRClEonYH%5gDh4NO;h
zEoC+I9r~i7x$4nmhGGOBWm*APb3MVm=6CCS(J8bYFrE2Ybmp#pYsaCZDy@}7|7j?`
zz}JLBH?PU3aq5f`-aYW19@^Mf3h1XcE@eP??}b-d%u-2S$%dHFPf7#NOcN>KWOYW4
zP>z*d{`>IP;ncMh?xF|;(&huZei1YK_*9EWBt(xyh#mwn|A1h>nJkGK9%VH0k{oGp
z+88pN#yuIFc0jQIoSE`{akMj~1eJzNFJd*=4eP;))Z5<1XFHr|6dOR9fQK`i4Rs_X
zNfj~2Jj)B0p6@>^c#`UpZ)qMh`fsgp^+m-HuC%wUL#;4tdM`z}Av~jnwCK!TI2T5^
zgR90_A`gtxyb1r+7tIWu_6-#WuQdPZH%AiLng9Q(1pZ3~1eXP(aVY@WH!vHoxRw?|
z4j7_=o%tsz1?EB`<s*OAI|uB{J$8JL<<Bmbgj4O42q9gtKdEM%2tl*vPUEIN3P;`%
z2=-IxFx&Bd+I!GtmXCFWJZMVi7yx!J>n#_6+XT!qSJA-F<^Wroa%RDZP`794A4ci9
z2^hfvFAGLym=)?`g||YNlv3GwmRA9lx*u5%<f=#Az>@&b9){D=hbjudr1x7PojRmN
z!>N2w_omW3C9=ZFiEqmHo1IPL%=Y1CmtAXlk`q3Ek{sZ_e5(CP9xto5R4@v7Q}EVW
zU^;8C&l0yw1wmyXX)nP@@7>tU9l11&WnsY>(k_*7XHXjgX=5Gnsxx=;On`v;qT6VM
z=noO1&!?oK%26Zd?!ZNzWjA~u=5N<kvbB~e;hu_)UI87@=EGPH_Ce{n1Ixkfj)G-I
z&jQl{Vrvqe+zRg`CYU7jw*qOMn5{!<NtFlc%-l`6UO&nLGPdT<%eD?#;MAR$eo(F4
ziZo?hSA1SKg0%sVa4(Foqz#(AoKzcC!*7Wfnf*oK<aGZSGvYs4?i^@mylIkW1-%W;
zvH{PGr2x2Q-vIgaKeU|d33ig(K2H8Mx8HvICN4qh<ukAS@ZYiApP*#{olIJZf87hE
ztW+B#WGRMAB|sR|#w-spC456fj8+q&Wp<yFz6K%sJrsgBqIoL(uL{4yyNQpOCGX5b
znTh(DCF=^;)E8SM0^Rx`wIGIm`nud{g!55&ev0Drhpk{jvHzdF^MJRks_y@r-g{*V
z%)rnY>d^ZD(vhGbT_d6~CdR1IzZ#;68e=pj(IiF%M5RiRqKGsRhF+vgXBdV)3^Pn+
z3iJQ|);sIWIp>~w?|t{(H(`H1`|fjgS$plZ%Wv&+_6_&QW4?912;8Z|%!5Y?hbIl(
zvTfbC;ZrA$wKS;m)G3kb$;i(#3*=OtKqLk2uXT(^^7S%AypoR&-l_ai_I8}PQ>>nG
zT3*6NhMKvDv*2U}wCQ_8;9f_Ww3VeTO4%CxAgqQYZ9upb-5$`Gzccm`WUgBf$8kI2
z;6MH8Ps0~3T-b+qg<Xk_8sUP@7o@iTXvCiITngL)A-0EC+oy08N{usBTV^kNHx7s$
zhM=DVL@&ejJ>*idAb!)obNA(M7+4DZc})g($)wcGod&7T0QISx{#{P18P8=21)4t7
z&WcjWpF9QL1E`;^QNE@?Uw`@O%UR0^lj5VYOvYJ))n1nrt+q1)MvVSU$APFNJR%$G
z@W`)fzDgHM1Fe?u-0Vz{d%H32{Pd*3xF;^li|4v$?#*t8Y4n2eYB6E58p}rE=(E|I
z^nTP)N14_0hd=z`)4{2mnazKN*KydffvXjQp466LH`|l~cLq}JuSj4v;3F_y$R9oJ
z*azS`LQBoe)bC`*ek0Lq)vIhiJVb{da#sdCVhLAQ$sPsoJHpegxD!cJs=Os=N26$e
zJeMwV8IOEUQ6glc&j4SK5=ePgM^cAsib1BTiwQL7p@8E{$U^UG(9(3(p#7Y~89bW_
z>RJa`1pc8SKw$LQ_r4UTCCWfh7PZBe&fJk(Aep8(-7<h6v)p_M_fPgx<I#?rDuhNQ
zsWu=Q-X<EQ*P`_5*YC~TyI$DBk<>AXNlsrTpBl$r3vAbO(+xS~f|Bz?r*<J3$kUlZ
zaOYWv)K0*iT(H#a<nmnQ(=fQhi~eO!SIJIcU)!#d<^6;5&&#}?Fa{VYB$+_WipQp`
z9(;&+3jy{E0SV2Fi;Oydsk$j51aJMZn-Fg(^)m-tf0Osvb^yF>OZW$Ie@d7yZwd7E
z?^<$YM`Nc3&#nyc+G!*Qp7c*z2L7ar{_SQ|A@Fc(OP;=h>%il)whBq>kE#^|rKVg}
z^HsW78mCo1H}$2YZd`V`1Sn%_ygJIBn@<x0U9R;^B$|+YbrQ%>-;4JsaED)gWz^O)
z_mrB*1b48Y5C3<dacF}!r2qs?XB7v(!kIe~u|su${sd;EC-JV!SM)#QFE%1A8j--g
z6i-?Cz|$Ble-l11!IK(iL$uTK3>tkZT|j+D!2M_j^XC9|U6NB4(3-O64sTMtPJwTM
z^8(~Cm-k`Ga=@2;E+XJ3tgz7&U&>5$f6EjB{DvscAWVP1IYoZh*uQ)6uC`>#0FSmh
zAQx+^1cdn~FriWaEtj1S<gK1XI^a2=z;1np^3|8H>r>@2M*C>B%!^9w{CM3I#qaj;
zpzri3aHsESe5<s@+U<hw9P52j8e>N@cgDiPaW0mq2fn0&QnDQUkMe2`AjsQ<MP>KX
zAvJ*O&uFX*0IrJxFpH2(Tjd>EqF?vS9bosXWS7EI?7z>U-OMHbXt7df*(ewX!q0vz
z!QT%r*OI_NDVjFxSu`_O1F{lD{TYZy1LUXT*N?h+oI2AoeCl7m5@v&q1|FWHWKy4l
z_*#J|vkGBL=*0Cc00;ljg`S>AVkcvzAfzb+;!zz^N`cH_luNgE`PM*8A%I47+sG0$
z@<U!G804A@fGK$>T`Uc9E*5eMp+>!m@>SU|9V#o&HQC|kU|JK_9Uk!NTQjxHb_YY{
z^3>m_YA?84?DmKyobZ0v9vl^OkvVe*-1~6u={;}{WN5P{8l|~_`fdQ1{T=Dx!Go`&
zp{8m`#qy8G2<T`a6tEfK#A9T?p=|h7vUAx_(JVQsX{vFSQf9JqNez+G5T&cVe*H1d
z_AG<nNF%yEHC4W+c&!`U*;}7P13m>AtU$3i2ifcaj}xG2C(To`_QA1B25<5feVs&=
zEi~{gK3o|{x;i^CnmEIuJ1&S<v)w|tD+4j`2e{O!YgP%Mm8%RmxDzi1)X=JI;H_Im
zb}vI^hJyqG|9aDQQ99!XdG;i*lmcC8Z0jP~RUxafXoCWG#^zFEQ57MzTihrpW%8!M
zXvvZ#YXEc`lmzU|NN)x>Zlj@QrmGr2KH<s$&t*G6^cKAP0QTRKuY*;T1-qdx>d}m6
z_!PL?9yIPifL(k2QvvnMX+V$GlLv#amgP&;O_Dx}*9^d1e}wZIvbYde7eDwizbdO{
zfF>{9V5b>&shN8+&t|{FPo^?}Y&Ir*3-Y`I*FKYhZ_VHa;c5jSToFh-4<~#h;wd~-
z26*Sywh7eULPdb*@E{?1E*m*=<aouSPIYzVK!7f(JSsTN)N~N|NQ>7Fe$H48&teW)
zb&2v<Z^5;a5Xdr>-+JkGaXQJc?AX@Dtdu6-Q}^7{6p~(AmIu2^tvZ&LP#Z=p4Ilve
zV1#nK$^fw6CSY%NdFZHC84Myiuva*E5-)m{>~-v;+?~Z!K)ZB#v*@Jwi{~K>G$V8(
z9-fB5$Im%`FfS!;DKBUUj4N+X0O~sc?z+SOnIS`lTm}taQPE(cK}CaL%>WHp*~~|S
zWhn-`H<AtHQC=yhlmQ3hLhg`HR2fLF47hS&$^gW4sBrK`8*OBCCr+HWCGTH^hUJN8
zrb0lNcoG2FKU5h3et7RLFe;TVJg3G(<_`W|lx}sAqwxc~Y07-J@|Kf%<yFZXTCED|
zi>NgwwJN`6<Rc8xgf2!4zdl-)XX0<zM3(>nKmbWZK~(eJ%w3Xs?X}k?a3Akq^Jp3;
z0(v7Ru_nE!G6GVCz{Wr`T!;Cnyi!-mLR%nQNVjTH$X|_Kr}IBX+BGOWXQTYsjIt`N
zNPNk;(y~0V*cSzCHhDh)55Iw@aKNMNPA~L}<fR#z*K1qM+@riDpS@g^$v@jlMk#y`
zXp~k4AlPf}+_`(>9z<W5i+e9tN`NZ@a))6jz?Pl|kw*xGJnxDB?)aM`Fm>wGaOD91
z{Rp?M0<91b$DV25Qf}iT6CRBii7YQY;UUjy@!+%Flt1SuwOyx`p$+`huX_@|5}77N
zX|rI3wX2ar?T2uqJoaW1)Q9cl_h#;r%7h6MUVHuZ*RR!V6cG75V?fhEGLU|_EFZ0n
z<TD_)k$@e4|NGe2l5vvnRJ3R}edZ2bU6(tKf%#=X^&(`W!MPy=O?9NQ;I3t+!;#6Z
z)Xy!f8vG%dIrKD`h@J*_9}uI_4BRuqqVd|{*#Ro899_lVI?gfJp8T?9%XDz>@q&y5
zk^=A{w1?Ai=VPD4+A0CSy&2DLb|a4bW6BDCDyIQ`!Zkm5SSDovTFV2sP#J)x=UL>s
zjAQZ0t2BXF!mfMqHjv71*JaxfW%2C&tsz1!Ostu^eE?KJtG-dw!aN(zN;7vU@G(00
zY3AN{%$P9^9{`ULZo`+-V8=4XO?2)_ltD`!h4;(XQ&t4;;GW4BT9;1gAIE_98bGE~
z`lXgv)HvZOlWsly!pkhuZH`@qCipYtV*J-bE1CvgwHi={*S@S697nxu#a{3=?DIbm
zrQ^?{$FqSu&ou)ydHI#DZ9j9T96sx*DTkN0i`_E$YDCd2T~_UG0W!W9*UZZ=zkC=E
zn#pU`<hwYcDP<tsBA}YI0wC*tdLHL~sz=gliC;8)soz!xh~us+2YB)QDh6jPR^d8}
z5g++F`M^8fkdK3DdW6>ddR=&%Xgvqjb5uV}n^9J$)VdTJ7kc8%y=FWXu%oE=-gx7U
z-<!F6`=N5ZK?>ZvWaiF5<&}|2A28h;U_Tf@e+*?q_X3x0aFNC-&@7$K%b#$ghxl~k
zn?B!i-V;%)zaj^Ly9R+wL%0s_X<K5FlpJR5Pi02#oGEBv)*z?>Q8padk*DpaQI2*S
zykWD(INvSh6&YE+$h(H20uLo{5VELjog|6qU4SdAKd~^tmYZSp#~M1+Hw4%IOoKL)
zR0O0HNEwi8X7uRM<B0dE;5C}3lmJ;bTN7^tWp(91?h5jYmBS-<mkW>fDI9zz<=I^<
zmC0BAx+f_kDpNDY%rdfRW*jZc*Nn+amoBx5>8x3^-UD}+c0^DD`7W8cL)R*UyprG^
zyGoYj?^}plsY5de8lXelmY7IiuhDGz&lu>o0Z^}lhz42B;G1c3!JYhc-r!WqG8ACf
z<w5iFGG`F9nLF_%Gyj*eP=0vZU_-pNpSg=y_|#0*zj4khdeg!p;NA`Zr|-};^A_M*
z89S>sv$$VX2!ELI3Ua%SG^PmfoKy((rx`MPV*Mn#dGNE2)gf&G**j^E=OGII_6#5H
zCq*S}W0t7NKa|6Gq;7cZF?cC(?@7z@YVT5{8=Vj}x%Lamr5>nTym;{d7O>XPmTQ2&
zkYW#uP-f}ZibMxmr4B@=mQa%5+mvDlv_&hV&tMU~O7>TP*~4{a=8A*By)F{)G8kY#
z5qBdR$0an9C$yRXvC?uZnXx)j>dc+Jb=^~cD&abBpuesyb9hgKi8`ncwn+i_V3V3B
z>DtL;m7z;{q|MZuuhPZj9ii2wJSU0McJ!Cc^W2|y?2qQc*G4>i9D6p;x*#Y7f6@)1
ztsjA(ubAkfp)rv-=1)4jLgT9u0(=MksB6#gp{t5l;8sFt(pY9y(}mo1!vic!DfRx=
z4IEU<5`KMb>oGhBCDiA8H2}_KZ2cL#*Rb+i6IM+!?H}7Ku8>m~6qT)+dkDUT2`fZ^
zw$HAOK7&#8D%plUFY}puy}UEH>BhHHNUv*c&ttayl8T00GJ#m(^%&%!j#N`O>=kXo
z%;`kJ7X$3)Q>Ieq4WN}AG&K;1os}|l%WSwF*=UvqzmmIyOg&^Ory*ZMLfSzLy($wT
zLCUgxJ!C4Uq20vMJexx5UvAPL5BDU<{}=u&7x@V6qvr$CwhKlRM+$>%Sby-vD;iMe
zoeVYLPmuhg{;o7Qjk4uZHr;BFTaTK#QzrsRALqJNb`>*Eow@VXm(ARn!M_LYE>J4i
z_S|tLl&)Hq7vQL)_5;YK0c85am71$$ou?`;e7oXM1DZv2;AINDoTR}AP_4Pvmh_cr
z=H*E~l0$43%~^wOm^mFs*gUk(OK>~aNAm~;VF5i~%9ea%0L8#;d+x|6mS+jlEsp|^
zK9I-C(AA6}L(78ftLE$5AYIL3GwA1*4aLU6v?edYbXs3_VKi`kbwzy*$Oj-(2YOcu
zk_v(Tw$oP+(%Po?fuo@nyNZ}}H3|W|B$eDaQ>}T^)>k|5QL41?YGWJn?Qy5&;k^dH
zwdammJ!8g<ny^v==|Pc7gOUWL0S+s-OJ?qY+*UI~*aR>;oP4sblC3Oa@f{>8U1r4x
zQ0;U+>0bs^FUHkgXVeIYH7IJ}Yf7g@)-iBLvDgg-<8VOu(cmDV!b_Wgp8W?pG{kFc
z5GUOE&K$f;XV&!*yM<TeW=aLJYDNj0LZm7qwB4e7y<McnqwF4DHS3!A(0WIffS59%
zh-os0cijSMN&wFW_{7sT!me0-)}#rClmS;1YLo%eN%@Gb0AxLDWk8=%d0XSPX-0R;
zn=;)rid{>{<!F<muFa}B)RC`v#;Lu;hEG|wEH4b}in?u10h&amG|;h>XnXGDD_0$;
zD~tgT^tWR$&AUprp1dp1+yT`+0oC^fpn4UV)--S$Nl2GxTivLKxoDbzrgbXz*AGJ3
z*$4UG$*RE(#7iA`qv?~+@bAii<ZFK(lNdlf+&glTL_58E`I`nbWeOJo_p147i943Y
zX_d*hnQ>@WF1u!uZ3!*fs}up*x4i8lM}1<rEA~eG--t3oSq1jt$^h@-bKtL=2PgS6
zZp~Oo8Yu$?^tic-fZ{>V`w_31MhbM9c$oxR89<rrj1scwF*A2~^&25;#_Isw)s8lJ
zQ`4ZcN=zmD%vWyHXYQ&H_Ct;nZJwWFhWeW<Uh45z<5W7zQHFPY>UJ2Qx*MQ62T;9P
zNz?L$o;RRo0;4*AZ}#|)1sHZ<fB$#v^WT-0e;Ge`G%XcZAk!-eHu(G9@-01qUWWIS
zIIeNGbd`bjWAz`%FomrR!poJA)VAJkC8Ql^o|dPcq-e`(xjuC@K>lv1Usncv<{z#S
zKx;7J`rK731s+~Ab`nRRAtfNH2qcvQjX#>bYk9uyK^{J>oIQb8<Z1?9eT;X^@}LgJ
z)c%a8O__v+MBDVCrh#VhiNH)yRQ9Tf@Uo&;@~4vy158hbt`6KRMk9Z{5`l7&4TQb1
zW+frK9}VCG0Q(j!-~5{WnftTi%AfeIG}L_xKS>{Xm>2F$@~`_3Xq+zc^g+Z2Z+AV4
zT*Tiq_S})Vt+~N(l>aV$_SrUhrpYcZ@0vY#_-`HK5zflytA-Fs*0zt7$La!hUE!;n
zS7^kP0Vm|5aM%6GS^$z<4bXe26!1KebRH)<*6vN%-q4UTkOcos<p5gVk4e@t<9z!h
zrezt|n~~}!sIMAuX9C#|aMy~=25-FL3Ja=bc>quGntDSWH2o^sb<B{@Whwmq<l&lE
zAzx)9P~le5RC;Ea0n?!<8M+u|9H4q3;QmYz{RnVo(k-G94JYp*$YfvnF<ZZchWJ!8
zyq;%h`k<h}a2-7B!{8(-#cAg1Ly~?|-o-lQ(fNUv&fLk@_*dSI8fZ06qz)#E@}-Z&
zuE4D$i&iJ`Hqdf;(hfA9EDCH*xs&ZgfI}n!{U@=HVqe7iDu62l6G-QNHDe$&q=00C
zKVdON0b0!fWE<DWyT>fcQ*M`C4Y)JDzX$F#l$b$aCMYUdt_v>~r|Jub)wbf`oKAqh
z0kB`h;G<8wy5t5tHBRvtsrVSX0P0vO{uDg*cYx=j%)A<lkn!Rj2)AKy2e9<H-Kk`y
z&x-WP)clNWt9l!_3lNcmnG^y-dmk)Qg4@Q+267bwnYWB72S)O&JadQ1SQ=Pw8Q*ng
z)H=b|$zYJo+iQ)c+8Ej9r>5O#++0N6i|5yPe=wO#v^^6~W2^&xxFV478_&D14AhLt
z(2z0^1AN{yl>_omQwB`o&j^EDTN;;zVOEqwfji@>WU#@T(trdosG-QnZTM3-fPHU(
zU02CI&i?wZpy>@u6;hfn;+8c&d#$0(p*jD4{B3~!Ja}4MmM9&_`L3Z`mWJZN!z5NM
zPJ!ME?Xe@9W9d*$S?Y(T4}Oj+?nYVEzI%_jp)O$B2li+gy7Jiok2*<{U8Q`X(JkOf
z{TAihaM>yeqVdSiuea~UDRw;tfn_1TG!c!18iz`aMbxdvpQsX$_B@U_n*{M{#&BpT
z&m{QkD+QS?3D-(R7#{Fwfo=Aa3GPguG;>dW@ziGQH;0|lfCRX&2K4MxII?!()x$*}
z_C1cIVeEtkbt_<c3q-;}Cum@e(9h#Zs{*ky1E@0r)?>+6pWXcux9Uks19#}wy`&A0
z&p_EZfxKUXhYOMQMEq2ax@jhaV2Ta&1&BJmod%3r?oRVhkm~kVZ!12qw}c_us^yD}
zs^+V7u{2JrC5%Thq5fTV5#RNXv<ss2(uFi?BkKd&^hxcR`mn<yZOUSjEwuy5xapt5
zAqR~^Rh5CFm4UI)92FGeh`<dU%ArAOIj;YGa@s0E=m(EBsD%W&?!ob+CO>BGed&MV
zX@fU0pvx;Qd(f)@L3Jd}+|@{x)@QAGNy9MG>MGe2aHpa@WnCpJ0Jdf6Fk$l^*3b~c
zGE>p}vK&8~!R$dm_19$Mv-{FHs-p&xL%O9C6A@6~5g<PXI!_N7GUPJKuJRjA+@^xN
z25-trR;f$c%ClMdRS`kexQ;q4RYU}MVw5_G0d-a8yE8u3UN-w&C7>*ku8t=qRb`@X
zc=Tq`)w$MBA6~7^djGDUQsXJ%L1VY>2awG)@Vo<o$6~*Q)wmp85isQdTJERoh&RT{
z-)H<hb{c3YPhB;v*}py=3|9f<FTcR`a9=IPyOQy-xCY$8pFW(WdT-_)6(=BQf}&!~
zrDF8ST4``6y<a6eh=XF8SIGwSUGs{v(2Qpt3K8wz0Q-^fv@=RZ>VKeD$joE_U2jmO
zle%(&Ciws>7{7srk5DexrhqLnbp^x$(cLJoij(FajnfX#4m_ois+*a1enU1~Q3yR+
z_fW%2G;7*PPm!sy?fgdiO|&@e`0V}J`(r!u7~@IfnwkZ!fL1nF8SuoUQCZVg2J}gx
z`|IndsaPoonaTiRwmKjeYpVp%uTc&H9*GjJEZ2bhdoy>5Ct;Xft^x?PrexX_f)u!y
z(tw!pN3W9o34@z`dz#3?Umbw`y_xPBH2eW<3+S`E>1c3QvLf+NBN-rrdca-(u5~!d
z#ja_}1vIVm!((NIA)_p&G(gt}Z+D~7wI<sm_S{trY)ik%cR;57q{%KS$F%2I8hFnI
z*)#&p@GF}y?XFw+tD}srjP?kAk-zmX#Oq3Y9Yt<!67fuqKG`UpIc3xsroUA!_1CIF
zJ`Vax*A)V75lq4A0-~DLU1%sz&HAl9@cw@6vODg$qtAW!-PeEQ$dPX|<_}-AXpzR%
zVOSmD?h$PVJ}aBj;K6@EujU_he9v8)T+M-uwA@*C-U-FvD1`h%u%})0jbWFZmJfJa
z%iz2{&wZFFpU;f>`81v@`dP!Le6E!jCD&iZ9tR&=(a^3zxp*KYfq^^zWof36SnmaI
zMvpv2Z#-7o_;B3lvY7S(eXv}gxJ{qx1lhVvgS>1IKt?g3E<vzn@PJl{WX+F)f>K1Z
zdl)O?Dm&Z@)FZ%>wqyNS{gLPFQngozFrqTJS1khas8<=NQ3S|)I(8T|r3_5I=bn2;
zeEZwqj$+;E_{-{i)f<7;eI{pOHy9Hwa8aRb#!kcZrH-PdbKUZ$t5f#|MDGVouO)-R
zP8iTHqFuymO*ni^z*EaWYvAQ<cv7V{Y~a@#J`Jf*mc$tdu<JYeNo-AA%&h&XGzQf9
z<&WDGaEFx9V+Cavd2gDQCw>}jhLh(;xKYseXFoKfcdgiT=jfzWhHja;Q|@{IFqx*i
zWLK20^H~%RF|>toehbHyw%~UCwwLwCHsPs=jh29W5fjO9#<5IgfU#je;*TQExk~rx
zPk*}O%rnpI`|`^#kN)zPzkDM92zT`(k34c6LE{3e3C{xT24xBX$xDmC)-GAH<Xu40
zm*q2O?!>jLWHmblz^Y!ID&?UxU(R2i%gVuKI*m>o9jJSzS->9T=>tnH&&Xmp%Fd}Y
zxS;_1&ydUf9G*>K&`cYX6Rlbhc~2+bN2$Ymc-G#T_PX32!1FuEXAxoYlh*56vCla0
zGNZwNIo5$cd?xKOW{B=@3ITpqzd-2BCHq%0_kle-Rue;fS)Q^qoNtL4bQ3vWPl~$j
zDWJWc>6|CZ)6-7NduQ%x;NFw?Rg2N&&?lkRf><?Pv)N{wy}n?<g0<Uiw;kC2$i)`0
z0srIzQnK6(Y^&%{CN!Q?XV(Cdu9DU4bSxltQP%7<Njvf``l+-daY{c1+X6>3pgtq8
zg#XfMD@4>eflOHfrsD<d(0i9vgL44vH}h!2qjjb{ntAD?VbJKWFBIfQzIU??un1nX
zJ+T-sSu^ChGY<>#zcTO&9m1l2&pCuRL-2E@K(3SkX~FW>eppP%_^CV&_<lGhx)R`F
z=90c>8BieQOR_w6y>UQ>w@R5&c9O*9;fg{fLQRMaFGW;5LbXB`QSG|$9>p*1z{Vfy
z!ERaXI<Yj5GQ8^l+>82S^D=A>r5bQ|vYd6&X?g9nZ}27)kTOcHzsmEOdkp{sV$rK)
z1!Avd(8=_MzQA1?t~zc*gA5=x8=m%MFtvY}<NXAHscM-sd5r|9@w-+Y@n)%D{RKIT
zwa*VPWg3=&JF-Zn8OtE&g-$sbU_S*{pQz}Qp*L_;{xo1zo~ikfzUJ>a@gsVwQN0bq
zCKUiz1_b^i37@Pu*tCN&YGdJQ0M9Y-=UrPCIx{d7t5dVSdZM5%F%dv^KxrhOOyM;`
zv_#sn`R2w$_POCD_-INL>Rs))EqEzYAmnEK4!*5_mr2vzao|<sMUMk_WUaGXN%83*
zb6;lhWyauS+8*~_3Fr`3ICH0@v1NH?$Z1!}Ru%}AVhxGBbQ<3jiONI(DM0Q00QpR`
zv>TbN-Yy!@6m88in{7n5MwV;`Y=c~Mz3x03)Frs3mZynloge;F!L|(Zq#c1Wa~fqv
zrx<u1&u8-N^wI!I0GS5*WwMLLQI8TZ3v{JS<QmE<o_zV=6##eLe>`#A&y-4N?}?wR
zfL@0*lL|oiPF#N$Uv-@qAP*ZUkZYRk#73$iZ>5W+fmUkVuJ(*5C|OyRV$5ZcMWluP
zYOh_c*fp^{-v4?GxKnoV+~dHVZf7gZ(YmAE_SVe(J#ddoC3t3VSk2(3hN%H7oVg>g
zy_lgMLipo@2M@k7O|U8CQoX1EuN2X*4DRHuLu((xoeHR52Dm>K$tV?sBjNQv>hx<2
z+>yy{$mDQj@+gf-OWjpEw8cdhJ{VNui`9y80(Rv|-sixBK0Opo?NQ228^q;bSAL|c
z+XVbn`D8}z;%~94*Qb$@eI_H9cRkD1c>z}j?4lFi-MI4MfNw78!m|Q;hMs*#%d;tD
z$k4!@_cYl#?NTnZjaI41O^j3_-(3EXMY{m1>Y`nEDN|G4zj_SMw3!~cEDzslGxt6M
z_hG|^mHIrdOog^{oERV#G0MQ^V5`4<)yzG5mF#WI5Py$<?!ZtY4+D3|Xy#rNJr<7Q
zF%nSIGW3v)i*iJ}Qki2JWW2y<Olg1zeI>9D+Y)yolN(4@RVIqBeYwiKqEJkx0qc(X
zCFK1J24Y`=a`4un!n7H>hi8<sJ-m^LwuUq~F5&qptTE<pl1s|(w`$p00mALSxp5u*
zwX{D0n*J@Vk#KtWOD@Z%O`B%zl3N5v;CG1p0vp>tAYF2nBL-s)*%bx+TWZL+D8Cq=
zS&;+zX9c&?Uj?e#w_3?xEHB!I@45GwPvPKMW03klPcr(;SI4T5Z`y5dR#N+p7%}2K
za1W&tJRmR|;CPi*G>FS!bozT^X2%U*2IF~A0hR}JD*+pzraxXe89J)~wbVmv(C(@W
zax|bzEAP#!!7(TqJ8+QjdK8R1y<By;mBBXnD&Rwu^HpZ-7ve_m6p*seN5GP?v@F1k
zZMD?FbE}ew*FvNcGn7>_kPQzpA_oPf8G)OFzi$bc5<vT!NZ1(Ur+VHBEB~}xwKS+W
z@XOUIf)v%=aZM3mbxR6G%MInuEO@|-gL`hg?ov)t1g197lV-uzW~qjr1eO|ylL~7}
zRkW3t?(xup-568!;XZ(SpYh|z>r0^x-c$lmI`t|BO9^ESGt*hP>w?d^<~2)FREjGQ
z;j(`bZ7OX`fc*oceGO2%2v>t>n#n!Yo|d4DANh%Hf4}*T?SRWscpfd2HYbk#OIuEO
zEC^uVR{Q(Z(X)WZrSRi&^mtBBE#3!l50hJ2nTRHC@pD!g8f8Ot{S<$U)*ElUF%6F`
z_(oQr3jU7*sdoXrKt84j1b)6UpmwbL6%NK)f7@h}O?d1y(D(vRQDwlNO%Wh3d#7HP
z1&WGztjm-IW%A6;1D@LgP~k(LBD4iBB`d_2<$D^qM<)p-i`sqBXcn5eC&#~zrAwFE
z<Yk8)c6blmQ>te*K&p{x1oj<(^lS!<g(>kG@$#8_DR2kWwYPm3d^`a^SCDb0mPw_H
zb|r<TOhFiQH5hJ*ZR*W;G*BN5a@jxzyCQ=lkio;q;Hr!PPfLNCIjlntHiJkI)gYPX
zpNQ5LuPx_QIHHmx!%#8eS%b6&ip;@+bRQ*rA#tzdS#5gAOE0~&8?GM)yo2@={jxw`
zHn_qNo);j!lnL?R;P0-T4WRDrop|mqC8cI*lJuDd`U1<$a1S7_;z%C33tf1%_S}V8
zG?g^s?Qz%7A^>eAX!6ojS#%{acufU&Zde__it5@46DHU>ny%!b9cWhqI+mRG14K@T
zah;lfnoizqVAlXv7Tk3M-XW|Y?8)BtT`a|44_`$sP{3cQ%te*Nz^qxBmTWuVK{wx3
z{`@Z^kD5bzY4HH|{i%}!O31)w<}`fCC<Leo%^vZ8?g779Wr!4~>5ta75MP??@-mY$
zWuQq~we3aAmMvT431~la^xRo3?%V0ozhdVFAmgh6?&@>EiSj4CRu5tZf3<gky%`3z
zsrVhjOQ--up0!forl=pa%WPTT-Y&ej>@r>I$dq;JRrgF}#%r^Ho_e6JMj-**Syb(>
z8%hmh+Dy((XQvX-(IB@5ME(e%j$)7~HLI-3aWEihu*;jd`~B=&qlx{Nt$}+==A*$b
zk8VjaozVbQ?em|)o32Bh-$Y<n21U!{Qf>~TE_P!J;3gWafkY=~wZHJE(L{Bofje!6
z-c$b1U4`q8>t~g3Z02qrb^Mf_`O@FC94c$)1=17%&z}0#mk@(+9r&Z}zKW-j4JY%w
z0O@VegFhWg)uE%L5+E`i^f@Wv%CEZ-N{Uu@^I$S5`D6VKxv6jW6eEo2N&HH#8slpK
zoMrg|jFoF}Yr>RhLo=1Y9gXKZbn1Nol@n>8O#{iOP#IWE@LBZn*TpQl0dFFpuKU@a
zjHb_fhIi2xz-dq@nw{j4>R^w127BBmKvOey4T6<qnM<5N=;ndDzT-cZ!GBvCYu2C7
zB(u;;fP;J5>@zRFB(3g#&pH&?WqhHy`OLj)9*8|Y+b;|5RWc$Efn4f&0sQwBE$-HS
zns`^~nf9gjnmj8o9lsrjBV}SBaVGNYN<!Ga`IJZp0?#5`vwb@ofE!Z;NEd!usIWj$
z;jyWvM=i@!a-R_OB(T)sw3=CPQ){vL#InF$`t2J3@}>FNwim^)!<J!-L1`tOR<jp<
z3a2H(rW@$mo_R3%#OO0i9gSjfDxUoT_VWPtmtqkLUYs`3)X6NLf+VTIfclPr`!VqN
zG@nLY#zDHHp{*s<W*T)U9}Gge<k^p9U46$t4Pd{N#`>o)Fz11ET6XstdltBRcn+nu
z2<ncRyMue0?Ak&oC71{m+Mq8a&kJa~Z3pac0pk_K2}$CoS^t;h!D^N60l1ks0)GFx
zK+k?&KxYVI`}e#``G_e3&<_;=JrnL$=LpK27f^fcF*A2$&=dFEd*re_?V@IO&NwY_
zSG(#lZ3bTJHkH7g4rc<Rk?K>pE}6Mg5{)PVwitbuz^5{(oJ8KswYQC;5$;=-Ej?)s
z&_&yy%ck{sC>Q&pWXxtJ_W*qS23lIq*0<x8Wa;#BTxs_)n&hL;5A|R4<H`F1cz8Z1
zeJqGH;mjOf(?DDezDAjd5ow<nwUL&BEHZPXtD0{OuU#>7FH<!|`BFwNo0l~tOZy4I
z7Epf&`+x@$=8x|7BCmk~`1@hO<SK#v10iXp6zIC*n1L1bRVfj^8sWenx`Yi?-natI
zwx12dV~<isf(mJ9A{49C7QCn}kd4}L4Y+Gro(Z88*qR300@hldMGQQOa+hELSw&Mv
zkAZJd+KS=2s3fG%EZMJdozH1-UE``NL@OK8Ik;=7scL%NOME!MzAJLLg_VOpH~~Ob
zp9VG?7(*~tJ`9xlL!Q$#7?StT$n#~-zocv69Srbkg;#!QpkI=FOQWeQE#(z?+kk-l
z(_~kv@U*>d8C=S?dCHi}A8n{;=A&&B^-<XcS>#v&>qTE$hip}s4*rAbOLo&B{0<?^
zw*|%$XA`X6?USYm-M=TUPC$pipYm+02Bc9OJ?EB@@UAc#BiddGNfcZdVdG}Pzb#*B
z2RbP)-fF;|txOg>`-MtsTNa>+OjQDRI;;y!Gk5D?P2?p?DqI-=Q2lD#NA#H&k?QUo
zRJ`+Fsodo02l%#1+v7$SYW!7cM$4P_9D+N8-53G8A~9n>mjN%lkR>f)hF?f8BfQ7V
z+^I)I-Z;o+$RX96Jk7t3d}~0&X{Bb=g`SdI0r^sjO9?u;*)TycP-ltM_<8ivM~49P
zy(dqe{1z9CyoT}+rbpiT^CgS#p5(8-CXhb_I|=)T;N27f!lfL@-xY&U@g@v!GqEiK
zd|WYzeOf4g#=T~J=Pf>p6eDA<jeaES9s};wyH5&ztPe#Axc6uLZdP4}bldfz5+ETs
z*Loy47h+eGZ4Di&AC++=?kvE}uC^tkqQR+%7oWM8=AAO@u6iBdn-m=2dnzT9QlG7*
zq|5WtQhy(SUH`OqD$mORcKxed<~P-O#0c{}_bd=E0{;T?tqZ0uREG5k(1NUyzqLK`
zt|zw;QIyze)kCO}G!^pAEo6zAdnTI>)DJ)W@bL4`KY#1zpMQSbYp=aF3MGC3pDGOn
z<Ogs)=;BQ`-SnmX_uv2NkAM8*PtosUfJhyvA1|gnS{cwepogLvs*V^pWKHhG>!%d}
zT;C!{``hAi(2z2qGX!bp1swcstAKdE0#LJUK>jtrBL48#H{<GOJ;`X#y<2!z9ZJ!A
z;)y4Q-g)Pp<42Dk{mzzKZn+pGa3yD*)<l&+7bx=0o4K=4)b~AbcVR_cgstUr4Ne02
zuAI3G+_xc*5ot5!C{I<M;oHHzlu`o^!v(A~?x9)=r(w)b%P*i?ie_5+G(TwSTUh<G
z+ec``Z)lGlzq+;~L_eUXhOdD;1~x(dEeFgX-+G*^GSow>R2tf$0UqK@lU*K-8la>;
zYy9#EG%JiYP(-RQf(SsTZvFxAzMud6=lA>LAOE<;>#x5)e&ooJZ*9N*_D}D<_ukKK
zz4g{_VFm#F<9`49-_QKd|NPHmZoT!^$3OI;4?T43vB%D*|0VO)B|kxvju%Y>e?-n2
zuXG6bt<L7(88`YINu73Hz*hjwC7u4VcoJ6cY3Bt5?x8XeJTHgW38WRzn?=I4QqV2n
z2@kmjK5fMVw_8uk-+ue;6aM92{^dB%8)@q%G_2&Vm@;L`%P4}cY_Y`_uLJxGx7%*J
z{`hq;UawXgh#JPy{wR6xfqPUa{;ZRR?0>6stMLlVG#Dg1oJX(A1<>@Fjqc;_Lw*-B
zs6JB{q4Y$qQ5OO+Gv@IEcF4R7u%_Ns?^&qv^IS;N<Ck&i@V2xF?PtmVm$(|B8ZjC`
zbfP~t3yKg_c({BS@o&e+mMbF2-`ja9089uimIhjh;MTlG%8-?zGv;MYjmMyw)rL~z
z)2D3)cHe#XjXdLwGfr8sV8K{`dd>02AAjT5zy9^R`LxcIZci*AKje@@UbyI@i?+S$
zs;dqp-F9>5&b<bu;4zOAxUp~TAA9VvVfWvE{}^Wfefgq9v-Nke>rfz8GV5El<BmJN
zmAsuy`~JQ?5YSx5>cR@_O3L}B6oe#5M0r4_(enZ^C4hMTK2Fc{X?<a&KBjRcG|&*>
zxH6EsT|in7mx7;LKOy19AAC%U)lcJO%TaK-(-vN){J(nk*=I++_~MJBpMLu3sjt5J
z>I7C)2l2d_?C*Z}J0lwDTOR{ERtfyEuOEEy!A~t&vP9rs`5<;VZrd-GS6(U306YUn
z1ZY5mz;)`pQP2tVhQ9u)^b|a#-9Fc3V3$&2jTF9f%T34LBsiJBC|C;je^v{5jXpq6
zbejf@3^+SOb7N>O=breBaGRmoYe2R?j-k`|AdXjPS`L_D&eb0@xTTe$I;D~+1QLo$
zy;RM&W+0{=*7%iVqoHUO^3CN>fLDbP4bngT=}$L3>#Vc13TE=-`I3))>|@V}P7+`>
zL*IAbec#01%9zmm*T4St{{Q~(|Ng;w^X85EkN^0OIX>u<RJ7jprT(vd?Q8qrbI(0H
zP*-c2xxGVq71j^sq8C6gc(1+oy8rv%|NaddZM2b2`<ueyz|Km-2xfw7nE@^pefJlC
zd4}?+-)MZu9V0{oeso4amk9kF>%ea=VFG{qtPs~%1r+A9XCBQ+_O1Z9Yrs#s@Y6!#
z*mePaI%rszA+7(5UANBMH{N*T72a07AfNv9r}she+n@F!9;JY(uKSJ~H}36Gqecy9
zRp@QT@ZKl^<DPr&xoHa*E*v&v#tfQ9AIa?hgvkZDqy!M&Dm8qB=`&#MT{i{Xp*@Yt
zI1$>5p(%~I8Gsfm0<;a>aWnVaBPa9c1t;?hI|uZ{Yb9C67>m3x(Cq^c2SM`<yd~$R
z$bpWkJ$DThsTaVg48+l3DO+jqCVs0;w<#iljH;H`0L>H8w%b*{F+QE&q)S_o{7V~V
zRTBYTom=UJ!oK=1{^BpL(O|8?SNrK{K<eQ8qaXd~7C?RzX6_qbe);7GvGn@Jr#|(m
zCxgs2YhLHe^(^ntXD}blpZttr`9Hje1Aa~39M)UREZ)Wdjbi|QpKpEZTRSlLkJI4K
zo&OV<@ec+7-sR7I=I^xAPS4MtJ$v4vhaUQB>B8nmRR2>y(-<ck8vN|pT@Qfw0oYIx
zKrVi6z`rg~n5_&DZ?I(SY31Ul@gr0Mcn=i;{CzdRU2PHg*sioTrr~EY6TR*-;6pF9
z2^rPhKmF4`-F?LsSIqYE3gm|l9lDg2pnHx!`sjm+b~Y2mWz6&kQNKgAM}Ogk7amIc
zdk@@AiisaO<7xm;l^&hBCt_89h3@uL-ut0D7y7@XQjVq~Yxc6LQ?5y2jg$tF3dHcC
zKcP9C#`7qATuJJ(z$&`xR2pn54Sd9Rqw8efM<d;X!Ri*uuZ}v+0~>Xbtp=6YGG>$}
zVOCb+DNfmPM@h8uStDU=5bNs99gvYt!&xEYhH2DEpe*yb@mv;Vc)0oIo5!)7emwn3
zpP#PVYOAfD`G5cK|2?AFx_~{4h6ZpqU;N@1um8j+KJjtAvk!mtM?d<}XGpsi5RUaX
zRs$X+?nBJ*2cu9-`LF-_ufti24M84YUqQdNwte;0S5NxqfBxsgptE}7#EA=!Ip&y$
z0CfAGZ<hDRTzB1d+jHRW@IU<F4=-MS{q=wPtH1iIho(=TUN<lZoB$T_Yx(P4U`2aE
zEH?AhwZ1iSC;S~*(k)c^Wpdh}8kWXQR{#Y3Q6)gD13E7-S#hv67jMCfz}>;#CvLjf
zXk5hK!=(&l)*<nmW|FeuY=+Jc>3jrYd>5zpyPVm024H`SF;uc#t@g!ieVMEnVfCmF
zbx`-YrKT08MTAm8E-P>Lt5%^At8tljhuiR>IsjFFMspyQ^B^7X8uC_S5m;u<uCw?n
z@(&uW_^&B9z<LmkVjl*KJ6Vdq0a`U3EsH*ML#2Ud?+{th)BL0V1Ai@z@?HTwd^T;V
zAHD?cHgku8G<5M!YO{+pCfmqQysjVpTfi9dv-W@t(`1*)fzl<@IIYY?T@*muE^5=I
z%BGoZ85TJOKj7SR&)u0>vHo1CW9-<m3qJhe58th4`n#M`X4`GIT|9N_)cMT9rqC}2
z{@vgG-JSsV9QtdoWSO)I5De%~KK}UQ<FCB(%Gt_9D+c6m;LKkBCSU#PSC2pP$RqFg
z+~+>`0DH2rLso|$e)y}{c|ZBdPqx4G(n}A%_~MJFa&_;>|Nig)zLC9q8~aKzP9)!Q
z5I8Z;^#O?1!b=p$bL&J(KoZz;@)j+XQK0)qPCN^u9jdQK!9V&e@V9!yorcwRLd}>A
zjp%s+2mR264?T+~xecEekV2%kg94_1Ce=jK<VQPLi;|}O^nMB_->{xM4Q+>LeZ<HA
z2~z`d1*O*K2U0a1+-oYkAbm7gfTkJxjx^ri18BEK0!6<O+7VK*M-5793{l!qV7(qb
z?u?}<@j^o~a3^YIFh-WTPWFS)n8Cml{U3M-;}RT{ilVX`xKoEUgE;wX*<PK+uE;D!
zvHK#aEH!25N_HY3(r~`&LtV*h1GK$tx%|;aa>3mPQvi6oIdkUhOnLEow>LAZ=TAE6
zq!+|r1;FaijCt0qSx*Vv0rWn67e5UWbKpZ~b96(bNQTA%kppzE0Pg0G!oGsRf7#h*
zpFM+p`y<#Uxa{PUPoAql2VjdB{Gd=QA>QpQ)sMR4jyrbY)c$Ut{p@GgoOj-NH$>wV
zJxkfpem-{W;>C+6vb`|k?z`_kgWJ*u4<9~!DONMfS9acc=a&yW@W5A8R;QW9mxndO
zRiB5BRx519B1se3PQvd@?1jXAm3NJs0)5#~5y0R4h$B~rp*@c3hu6@kq8#W2vSvva
zJu6`0^263_iK=cTQx4H7pAg(pm{y?x8n}~A`Pd4H;?Vx&bO_w}%rca(d2YZzmB5{f
zTF1;?pTpVg6t}LKJF*`Du%CcCjZXe60QKRhjG-G|ViWam(z6bSSh;cg2YgIV0BfbJ
z;5RyRFP)bB2l9D`_98bTe|;J7tS1g=R#p}~eTwVKf(GWAX_E$E_|Uh(HDM{Vy4y<%
zW=oY22X|x?8?A~2L3|A~u{1u|6s2u;I5LZ)U`7T3&l#aMSmx4E`IxQuV@Z1(&X4GX
zvJ}U4yY04{0ecnD^0*w53I}CQ+mE{JvdgA&+Fr{dHHwCm3mNv_1wdv<zxdvJ@14YK
ze#!?v@PRo3c2*5D_xxQ!;4{1j1@0okx7OS9d_M>C!X@%f>gm6t&&t5wq}zsmK4_O+
zcIm~J0L!sgBbVM<{zp-n$e!>7o;SYcnroJ@{D0R$2Oadf${-L!q1f{?pZUy#I#iaP
zOR=xZLoutgJ?~@4*vB_bx&)xIq3LYm;cNInURw4Kl>qpNZWl-a(3epY$j1&7h6#fQ
z`~}da0Pt)F1fgm746rOe89#x$jAXTX%$PB1Bf81ZCZ7SB&;i{0vhotPqcC6teW(QP
zfea)#6CRZsodK5;5f!X&$4?>c2tfT8fcxB(gaY?^cuOHw<0a)p7c&e2*u}>forq@u
zdcG!RNe>#FYl)?KMh-)fgH8_*M-D$n4)deg!J}xFrWZ|{_Dnvar~Py-4UUDrv~t&+
zrSMo8@NX$-rtw<BujPMF>w_Y4iUD;AUeluFf@4~OX85^+l0}QUk6qr96%Sd|0BSPs
z&wlo^%{j5Gfwn`l?Mag+y#lzuruQndc8|-9Yb9TfET_*7VqhQ5#U@%Z?hQ!3OB|66
zO;AVc=Fgu$(mes|z9<{pvwAT1AOG<mAD|EDTj<PkP)4gGX4b1w3>GR+X4^y0Ip>@m
z0r#67{GGnLqN}#T41Ev>2akT_kw><s+z*oOTEOR6;thbmHx&PU?|Yx*@wlFKsSjrn
z-p6@@GjT8d<R?G*)TyVQdMGO&yYMO4^Ywtccoc1yp`P7{Pi*dzKYW^CX%=;zmWRHU
z>}5k`2%649(iN}C^8&sCAn@1oXn2{5JD9q#^8&Q#K9sYA_BC>kJ@z=BF#Dem;+@Fz
zGQPCvhXU1)^A<}ua5n_SP-ieU!Y;t3DgpR!5GLL$N)!np>*9Tzx$|5J-09>K0ko6J
z#=jl6MxM-;&as-fm1rkv<h<)1_+bnRXAq|!z^?0DZP3X{lN*E_T(d1DPa6Fw2C35^
z+L!!)O8zx+fTqB<X)|}~O!WFPgE$h}8W?xSnhEODm%#LIchbxa_;HsK-=3)#zlt-n
zhjvoz@?YiO43I6&D0O6IMi!;h1QBa^by`&kwJ?5eAszVg_~Ag?wrn>Ir7a<{b-g&O
z_6p0-_PLm5txBsf0io#0%`*Vw3P4Iqzs<(!H|le}k)`#$w6uWx82M|zojvo7rcImn
z!s(}<{-FE>Y<cByez+iIDa-i_1nx?=Xwf2pjPWctYM^PrPQC-b^rbKDFJK3l@9?F3
z%B=%krcfxZc#tdb<)r`H*Is+=Wk3G$k2n9$cfNBh=K@~kyY5W^rA3Tab+vT|b$zYC
z`G-L$Der8)`Q~qPsL?+6^YUwjL40|cBW3dOOk+n^8?`yjs&mf^NEYfpY;F8GZDC`8
z-Ns4e_$F;aiqdrKc&r-K1nxZ;i=sII+~L*CXZV$sk<#%x_G;`Gu^W`}$}3$c8o+7<
z=`;7z%kngA{r~y#bZAw=h0w|TE|f|W{jLIceG0Y{e7p@G=fH=6tRVv}A7Bc>n8qmu
z=TtIW$pJ#;ANwRG!bnrW9lDa|APuy1j=SLFA>K7mxz$X5JFMD-p55QRb>-bo=~7k=
zPU?iJzwjzf=!VLG{N3W;6$68K$VUSrG}BOS2fVrl+Gg_704*;=7Ku`2qs?Z{c%3ja
z>lySj?YUZ>99nzRFObQbF>AI-I<+;DcBa+xG&6VoX;nC00an8l7XYi-bN6&C%iF%J
zXo;`cvu8gN%$8GUv(9_e)#1Eoc~MvfJ@d>nL*yTgr)TjaUIFNB*rVTFs}-Cvctl~^
zUSPb@s+f%x(2OYvDzlUxuG@Y3si&U04RAkH>A2u$Dc^2~$Rxisqx7Qb=BAr&8qb-B
zJ@_<mEaS}pEnACs-SQ@di2dnTIFs-^Ul%;!^^&9kul5I9$TN55u5>cd{GE?xyPgNi
zmK~@gZ(D7G$2Xao-vQ6Mdqt}w7b@?6_=kTm%Qn$qwqrg?JNh2mGxriC1;Fhyio2ni
zu(Omc7(&ymT^&|?xTOG?hP^Wl_b7z^G=O$Fu4rT$!n*|ArC^Y$8pTopEBVyDkCkBF
zIxO<Yl_#CwFUMI({^ygwKr+=3CYpuHLTciKCqO<LyDQ!rV6NtQ1iZW&jYfV`c;AjP
z{08?852G__`OAQwj^ul0+AA|Z;*aEca`0|TWju!lx>^x1MF2MheLa&_Gl<X+`h|m-
z2t$*kO<5inxl%^61x2hQU!_|=>guJN4%9U(-y%BP7eAQ3)}MG(X(zzz8+DdQ_XcEE
z39vZ1zKA=*7fzo({Y_?)Z_b=KbCCekXPXi=HZ&|tFJ&AV;2r>#CKi%wES|#2S5Aim
zfY;e^#~oK`5Emc|tofWyZQTqr+%?`7+|OO-og4Kwl^@^azo1+3^ej3UF_x<Aif31P
zI1lgyd;43z@WKll>(Cv5WRoe;Ni)u;!hZFuUrpv9;A|9-P08~e4kA9v#XPUFGVnSp
z7fZzZU3c9zfy;h2x$(vucR>l*`G_NqxQ9z&9@gbPQTp^V8_(d|#<yr#^x5Sd_3Nr<
zm$9};l!w<Xz`g<{NUO>McdhWmSYYMn9oj=L4lO<ew*NX7xPiXpf>B19y`f2vMKgDO
zyFD8)*@sHGm&SV?Gz+!<NMjOzxeY6W#~N=sz>W+!#}Kd*zMsZ63)lrV-eEK437Brd
z@|RXFUIf@L#?4i5;L~Xe;QhI#TtyzpUf1ZJPCUtRE%mUN0oP_UiL_QP@D<y04**x7
zYyU)->@-mtO3S-}e$>U^lmXHzOnF-z{B;I^*G|K`co5hx<=GC<1n=hO&!o{H**Svq
z&<f)NI(e6x-4^h)zR(2`TNzAUBlhCr3AFS00zAelT=6iy0jR|Q&c!SVFX7bqXn<!V
zhsegTR6bfK-!Hh}f}!*c{r|hbbQJ^jG6v&C%zPIDNN;Yl%{FfWu>I&meMOVHSWCP$
zwB^*(@(uvn77!ow0qyy}44u?d;fj-H9JDy)u6<fF@Y&x%*!|p9d?_}syz<I8`LUd@
zOLKg@l$Tb)G%3<^su56ga()pr>(>SDq|XI+Evbti<kOc~_+BVb`>DK~_J5N1n{_xZ
znGt}$h?(qjC?OAh_q*Rc;O9U8`4OBE82jySfBTl`-afqQY?1)9W(-z-Pd@qNP(ZdX
z`B`72-gKx<r`A`b$%y(56)*8beOhBfZiU-2(K2w4!621UZ9(lzE(+sv<q6OF{yi#l
zPud$<>7tuTZNL-vL^l(RvI>LX9F(*?pEq;w!@lyd#MzF{b_0UHBa3Fzp9-?dAunlR
zH4Q5nzvvxs+n2^Ln?d0L+%amjMQJLA8;|gtO4k(&8sQUKxqx;dSi_^mfu;=z75TB~
zbe_pHX#JzyBFg>{?}tK91GCmVlNZ`JPj!PPv_<3Xz#0$w!9RSCJY@YrM^q8eKIc@d
z{Av_Q%AkF+)Kvk(LVxm4{j7$xl?}9t(JxN5bA34?1KL46MvLB%NoqxS_Jpf!xp5TN
z6&+8PbpQ0HKN+~YKLhG=fNlk|^yRYn&-XBQ87Ddb{2?so57%c->^G0$Gc?^JK82I<
z!|21dDx|?&mUQB^7yVfSx|0cAfvn<rnVHqSl?FIrf|LhW5&*Jc9xOfuVE&$TIj}Xr
z<MZjB?fFOINO8_pj66^N0OchP)6HVmz5@Aq-;9v5a|1TpM@bsUS%PDc`<9BpR|Gcz
z)OUG#qceLapzrmyMZsaeD>(Rfh(7gW#b7))0$!od098(8CuQBn3s2{?e)z*5Zp|GA
z2Xn}Ag%mP{Qx|<1WBQ;Bjo@~_$5^eoM*aeEl}qhjN&#N$0Ao!w!nWpd6hF&|C(%$F
zQ5>&Z+T$AkngSm5oqG*ts4Vc_mwev?_bdS;IDxtZs|u|daFvb*JP2U_5bhKz{!*5*
z9?zne<_~QLciZzxi&N6C7vOd{6}~GC=@uIMAJ}s`m9(iKT#{x@H2Af~qz04>B!Bxq
zdK%#UEQ$Yy@UTHA<3`_+op<OOn$dV!&vX`D=X(-7y@o<@A*&W!MdI4P7<pHn*&wMh
z@Ei^5s%M>j(CUfof}%X(OLgWm{sW1x6aV)^!?y|)roq(W5FQ)xi7R=gtq8<c1&E{f
znwz(Z0F5-I4ZO5Oz)6PayJhk%j069<0L3YJ14A#T0Kj?i0a|X<XwcTOe6W}ON;J%$
zbx@7{=wa-)4+em=+`YBR6m2_BKYGatao%MMz*e3-+-VlMnkACPlmwN>XNXzp*uP$>
z6a9+&;DZlt#SHmIzssIBq`vI^I+cXvAj6fr^Upr}Y=PD?`fq9)#pQrszmI(6BkxBp
zGn9@c_CI|2%U`}v!0u&b3{I_60jgFO{`Y_X_l6TsJaGsI0Jr(uzx~@IIWuq-D<&(D
ztH$3LP&y!^Y-!T5j`EGt7L4+ik*p;zcs9Ui46>C4eLe8YU;c6iD>su-+@|tL^)Oa+
zmavNOBwGRx=#ZcD+exYRmvB1-i4tQgkIG-Mv{iz_rGBe%OcTzUnLB0e4Y>DVJLo-d
zm!Q%N!gImNt(mz~0i&pRePTA42KF-=!;5Kj^8BbY2lpEKdFc{ied%P!!OvDy{xvl4
z2NWLAZYsD#);fSXLzD;Pa4?F+-t=kxAN|eIu%c)9beiFTwxTp1PD9ofzzNWp%bm)X
zsLrr9K;S3PQ>Tc5ADz;c3yD{kGU0i5l?z{z$Kt^15adwmc8>BROit9OY{}1RqALPY
z05rSY1S|iVc7~V)Na5_CLC^*(@<LR-x5U+8VwPtM<2}s~99J|*U&C-(!e;w*R(&qj
zSewX-3IB0qPDFh3%{L9)z1#vseNv`qq_x=YCX$A))NS~z2gTP-a*5mIL@X9jfF&^V
zOHRZC{ckNZRU7=Fn|n&$Ggbb|GdfG;{at~r;&`~ncbEQd`6*xR3j>x1@pq-a7g!QU
z{H6l6r+1gH3zm~dZ|=ii>VWEDQI{F}kti~zxKqYAzxmB?-a#5`k3}*TUCqP=@O)oC
zhZFw#-hNB&ZQqOA_-<kBsQE8<r%AaUL6MjXP!C6mnZRX1djr@bXk*s4*#@zL%2G1)
zr$L-p--~uJ3gvCyDW{w=2YI~B?SI?x*S<TWR7_)g;F{fc-`&n9M9UX_mds^Xm9Z}u
zJdE*u6=k-1!B2fTR3<$RW3l~#5NA8IviN_(#K5RbiD|Sxkm>)CV}l|cOUu9~L1z_U
zb~c@AVHuh!aa@6j(NBq&>IGi|0d`#uGl|A^F^%}CNJO3d3=if+{Of+CL0gA~uFJDG
zD+EXJz7u)>4j%5RgSZa@PO}sVwCL^VuzyA;d<yOPGI%gLyqil0vD0V}>l|eRs>Jg_
zGUi_@)7D24uX%iGfYI`A?q@a5Hua}=W;1@dyi0M2t_Tca0GZ(Nk=2R*ESLCFJVHf)
zX9NAfZ#dxs&GDp@zy8-7r}c*X%P19vao|5Uj@p<?hii2N;PuwbsY;0*L<-U<A8)r7
zl_JV-T@(Nqhjf3u>Zi0(8Q@`^&k{vfN`lW2Jv>Q}Rf@Gc?6AY*4?OU|OoiQc+ikl7
zn(MeS_fCMN4`Tu1=<M2gWz_Pu<j4MX>P(Zc4wlfj;8QQ%`>w5@b$lCty#sraudH_|
zLIU`sjymd<3of|e8U8zcB%jXhbIUEaJO+4c?6FBkGQF17wRbPeML+X6l|O<5dV2=)
z?_jmU_~yR=w&oh&Lpbm7EHuw|I(*7DiaOdz3V|%k{Iho5b=Mp6Wa4$J?HZ>e7n!bN
z#l#LLBM0?m$B!@?TLlADpPc)s0sb)32KG>?!>R1*D1i**hRZQ@-Yo(4k09K)XyA<i
zyU(tDi6zLcoI|4@4zPcehA@sw*UCW@*hNH*wJxP0hl3~(YsKJr^4GPxx_z%6V3!Px
zCUi>Ar0Jx)U_U@3egt5@6n>qrkRE;n+x4?agz>ng^UIM(R|g4PlKj`5Co<N`#v<%X
z*r#YiTIIQo=j*VSV}H&7be=NcS@yd;{t)|f?B&>-u#aN>@X~mmHMk$a(>F{206+jq
zL_t)HolN{mxSym>KaV|&*~_2tZie>{U=QPEU+P7lXl{Yk?OP+UO>L+2-CR(Hhf*@7
zUB>{mvn1<(Dc@@-L)yLz#~1x8*ZLSudlLQBGWmWtve!&r;O;ZKQke@pWdI;;^_ZdD
zYXFf{uvG-D8tAh!?xj~-P)E~guXnYd%|7@ixX4DAuh@zJ{)%EN07_>sksrXlA1Cb}
zJ>Y-?Qt#m4AmVTiDe4O(inva*??r$4mw&0Y8=+F}Q2?)W%%~qi@v#1nLUJ6bGr?Vy
zmBxAJ6#BEYuQm8t!Ds`E9(w2@f%f6L5s)SM-zbfi>E)uB?5yjA^(>_cC1@vB0QBWe
zq0vA8DU!-9mofGo>NgkM>A(6Ma1E1A$1f$_pp7c8l0l$0bEi>hKs0ct!gi;U52L~U
z2~fELf(><KXj}TKgBuM~C&N!Af4?N>&6L84cI9%X&`El!VGm%&ehPA#LW4`L7$oBt
zy;lNT>cIE*@1X(ftAyB~2Tw*1*YZWS8Wlnw6pojafY=H4FhUzWz+=mAsEYz4_3U?H
z7fJ<QKSU&5ymAB2LrAL=#vAj#eUMn&0=j~8D0QH%gqXo!{moYbqHefSKsxu=tYm$q
z#we9~(O_Aao_?MihyCIgnFU%Oga37WZ$29EX=qHy;68aMRPEPzW8dHQWtEP;xQgX`
zo4FV9?)e~7lLh<yA&^L6VYykdPBnnPfuul|8TD=av)sOlsC4wt0pIw>H+u2E^t<wj
z++e`)Dg8N3aG^|%Jo$8O(ZBuMzx__iLRw}lgKtVZ8b*0^R%RUs@SagTfvvzgElr*u
zbUHYt|CW~a*+(CP9sP@6{9^oRr=6BrZ%X6xM8<1<?_E5xt+14{04MLi|Nam7jev?z
zJkvg;1W3k;EB+a`2Qk}M`^_}o2l=GUlz^a)I#3)}QZJSkTB$R4lqJN{e>M5M2ku!C
zMxZVnEz9c))k6W*JrI25e^blK1Aof{`lV+sG;05bd=cPwK8?mYb21MqUrI@rfV<>C
ze*I{`x>k2yGVdzy@M)c@46gw1KoGyw!GYwxA9awox9=*wG-nM$ra?PT>SW|4ZMf}c
z?(nF=t7f+2W$6mn2Nuz;IDks2(pJ#?PPREcxL|?K4j8!O_cPIyq0Ut&QUrX9z|ILo
z-nCUQku>gab%h^M%t$T{^yU&b1S3vf>-tWuB6wKZz+DIoYAc=DrT+q0Z!1Lc)Xo6f
zaVY(+tfnWb&zItuq8FwnPkZ)n-1WkE@DAW^gTIG|2?`!Kyca47fWs;P$m9u~R5>|)
z{!aFGWZ&zin{L_z(6e!jF`<Lc^ZIhAY$gZbreAf{Rc`>wb5VxoacX_R^vIvaXxUr9
zA7u|&+N_`D?Xmoo?sf{3d;#QzvuDqKTJN5}o~s&UxsGq*U*J>5F^ngDIPE?Ga9^fn
z`zi_p<KN2j&p&@_$~u@C`Ymbc*+RJQh8u3!OFXI!l>d)9c$mktlqZD))WhUTzH$Ng
z6(~VA>EtyP+?jOQ%$+<Ozm&95qx{{9miTk4_ii<nL5_;h_wBySJB~&$1)<yDls2m*
z(06bz#UGt#8ye7Yq?^YeatUr;Bhf%xT4^X#CS)Q_Qv3M58Auww&Nj%?2g5wD)ja}I
zu6Ad3a3dY?_AGvpiPQ7OXdT1jBNGpgfxd^gnJaK<DWB27JQ!3`2los)#bh0&6^##F
zl}iJ9Y*0_c316wx0a#N6XoI@y)s+Hwwcjr=H^Yf>DHOh9pgydAVn83bN~Z&yyXqZ&
zEDU~i?Ql4&%Hp3iW^{Otndjpy%Wf+Vw&|8}P-~bzvFeOb|1W$*0ZeTmRu2vUn$>zP
zI<3#lPIonwHm=P1EKv!iB(Mi=%fHAVla_eYjQe}v``)d9{8GSpCQ8Bt`l(KzBi41j
z0I1=z9Q@mi8UAwiq@UqX+Z}#`o)kzgi)Z9YC7?Ks)8|v&pH-2?C<1mEt*CB1O`0ok
z<&!~E%oNUwOMkW{mQ9;BEhgi%w9h^FoXw_@+gesB2J#u-Ht&D``)xc*la&t7aZ3Lu
zEXPk}>{`nKy@%82CjF4(0OVlz9Ux~&_kNf5wmhOdrh+@NCYyf3l{AGmnN8mNKmyga
z7GR5j+{fBnWjHUhM#4&gXEXyE+-ih7goe5W{sYm{F2e7rXq-l$=>QJyg&<5r+6{gV
zr;;C~g0CcR2dPvxpsmWST<%n2%B*uM;4~g}jQSxOcj_KlR{AV|`1EpQfqVFuxis3}
z0POc=(F**H9v#Af#(D*VP{jC4R(_GF-TtUf6$8w!=Ew(o<>N9enP<(wNZpo9nja02
zct@643l)>+tOQ?G00Z?LoX4;Nai9I0A^=bNbB+=C3$=6xK%al%bkgP7+eZgWu3OT6
zw1prAK#EAHARwD%0NXNVqfZIkiT5rF=s@;dH;*pQif%mydeLNh3HkB>zvKj-?VwrF
z=WCuW>N>FcEKy-n5>Su|l?3sK96EFg{a^p}U+?C`{bOgHb=CpwlkWn(a62pjn-0$C
z3}zXB`#=BlKi_`8{r0<uljYZGrYtabKBBxK*NwFVE+EC|^DbR_Q>b)AX(XQ+N9~g}
z>eDJ+v^lsofSdCA0`9h2mBx$HA+Fkvt=4FzLW&T7fb)R-veF%W^wBTNDo&A+JSA6@
z#Nmpox|J(Fk+YRuad_9*UNbw_%pG}aVw%}WM6ZN7m^fEs=V4#MivRi87XmAP`RV+P
z#@s6TN%pceZ5qGIMuMnh5Yi+X-5Dg(OjXxKHWk<dW>h%YTySU5IEco*4;5NvOF$K0
zx!eI?F$XzJrBj?tgIP?cP{V0dD~!@NFotd#*y@6y573ymLjG4U5Iho1nDz{BMh{m5
zqFt;KjYmGd=iaLC+@sl7eOB90Ia+3-prS#yV#cxf)IpUFUoorf=nT4223}2zfxiaK
zJqbI2=WkJ7%}@;Zd3QzN2&Gk9!cWW9Q-V0**C#F~zMFpMx4-@ET4r>8$aerUu02UN
zjQ+ZuIF+s)_JN(H=yCiB%T6bpaKa-x-7QwI;Xvr|s(!u(ja2i>E3eor(d!E(VMSzy
zn3~2tT@C_GEwA(0*RP?o154cxawA+nKx%WwQf<MQA-<JEhwnc4!4D3&`s%B%OoF1*
z1V|@%I=LuY3nI(X(V)`LUPky0$6V^I7voH+wo3gPxYI7It+NvGBHIP4)0X6;OsUS)
zpQOx0{gcncv#%Gp70<RwoL^+5Tk)h^O1agTAX@|Ol%pSUed%7&>oF>utY+<#u^VHP
zMtMuw&<cnY!E><xiY;R#gQOf(9+PQ6K<y+MCkTo=l|hcqxjlkiOGCSx=UH)bO?}he
zc-YVqlr?KTh6>+-3cH@wgF6%9d9O{Gxzq6VM>ros7BA7^FM^*xC(|?_&f_IB47#Q$
z;Ku5R_kQeU>4KnXl=~9O|70eScz_Rr-aw-b#$!U(I*7lwtE<MH#($Ubivk&TC=E!B
zm?*7A=u}NhTM5gMlh)!;Mh#4|(L<>Q?ttE(XeT#0ji8MRsBA@m!FmY)rZ$9G*$h5~
znx=RxBah&l=v_D4Y_q|n4QH<kBfS()oBi_~ZU^9Js{+W&!T`MO_%v-AC$aBPI<-~e
zhh<6vfbt=2;pc0j6*a0~0V#QfvqXU}GeiRs509qNGqdtO{H^TxK=H6(`lvM)0T?sK
zKHP8rDt}G-82j4y@j2KeX5pK#(lCinwQN~ii8wXB9m>(Fi!Z+TS|bwtsQ#oZaB)ox
zNCEdnWtEO7z38(m4wOMkon&kMi>3KqMF6e%#54XWXJ1x0ma>QbqWqn%p3{uNi-29}
zBy$;*#nJW<{Al%qtpuwV+JfUn%_NKQxj%6WS6!mCQ=Zk<uECDSj>Dda{W4a{mJbTz
zLCf}PJCflJSQ$NsZPk3@KMFqxtChGHu&-mquhZqRnu!5NnWP-bxd>nzP9xii`2EYI
zsWXl$$SW%(L8I$WMV){<oknpv74~QtSmJ>OAi4tKB4KAzqwF(j#K$slJk9dcWkeKB
z4N#TknVCtjy+Aobg@I?C#y=T%Bl5j~{GZQBp9~Uzc6fyjRdb*&h^#naet}=xIh0=~
zY+E{Wj{+k~t=djmqYhmqep$Np#LF98M6VQ|w7b}VLw%_IYo;QrrBQ)H$C+oIc@O)^
zA7m!I1s9d{`=9^$pPRX?<ZjxZ?vc0q1c+{{1MZSe?_;nV%a4B6AK7;Rh8=u^-NCHA
zga2gzOVaC$1t}EU&=!=&Rs--`#-~!7;)j><qR-c)B$PJLwpk*4+bj{D^e!dA=y-UN
z;Mc$Y^;ztP?+m#AP+%TyGb#hdPKRs%ACR9f%i+ogFSy`>P1&~C1CXDpNVKm$0RIjs
zGY=kf%rRQx4Gmi>g8^HewXi-6U+-4^%r7Jsd4P_!4aWBHT!4ZBZB3;5FxIM$Q;ot=
zI7|^zgz3|#&({G#Jx67r_j--$fVAuQd@cs=C{L>rpTQ}MnwdM}re^K}&PI**5tZ);
zSh*Kq)hATR8bhzgeg>;^D!y7!)b}P5_Y$ny++wU07Ae-M{}-{(W1qv`jh%}vG?l@K
zhAqLZp>fIeWsq2BFU+-EgqoSVIzA0apPG#U+|I8EZkB7nw`L2veW|7q6tM=d`)7X-
z!h^IpFS`!vRhDN~ei}>^hP(}Ip*@(gpNcz*2K{r&K0hmMnm^?=dW2cy<UKZf3eu=7
zEd|XqUW@!R5MjKN6-94KxWwgzG;sTKoo-00&h&L9qYOpq6rqqe2ZADE+H>YA)SLe1
zZ~kU12h&FLk7fHGaKHgCXl9EHS2EZd0A$h8ER{Z{_CNS@pZna&fa|*e;=`0feggDe
zvFdZ1=v{q8?E>1on(4)U^blss`b>9Etp7!^tq2f918CIRwZrr|OB|gg!fURQAb{hK
zUd95B{j}#F{R0*e3wV~&WBt(E5d5t|K2OMg^rIg=NEgT`F95thf7!Yp;C`|51OK{i
z)+SUuzbhqxv0x?J1r8v_cRfF%EdyUR-Z%{oCidbqx{n?19dlWd@6U{XsC%NkEG%<2
z!Q)qPli#@0KHfojvpsiY?32Noah)yPevFynq0lOMCgLXh#~i}7a;z;a8BI37ihWuy
z6^v%-|BF2ttM)8qX%coOHfp}W%Xgz5S<jWhhz4a12|=1mW9`qg7;CD5rYb1pj|M(k
z;D(<LYt(%r9e%CnLr)rNO(V!F1P#PLIlBWMZb-xx-H_izSmjqA1mLj`yy}lLPRDN$
z`JP83t>@65bx`Q37i$nt8|k(0-u8R$$f4y*2!mhPsmVW0cByrYJc%BU&uUXhD>YFU
zd5V`3qzmKF{#>EWB_~iih<_#f+Sk5z<!3+p*$>l(2Y%%%UpbB?<V)qR#ERp<;>6?!
zNZJ7}+;!ZPb{JQe4&XrEg)Hm8u5ikt=vqCzANk&)7l5fZyba_pJx}6wiu^E3IZgef
z>Hs4E8i$>Rm2%)<z+<N&&T?p&5<uSi+CTxlSZeOgXJVsxp8~%MUkc4|{9vn0f0&|K
zb$|YCZ4BT(KT$SUnynuK;GvwQ#Ax|mr|bDPd?@_ys7wLPF?=?sKVezyOXJMkwd5Nb
z#)D?eMvWeyEDEGeL85&2M=#`CIQs^02T`|npS}ojxNji}hmEr#qYXY#CnhhxzqjRo
z?^CoTT}f=94bNVmVZC^^@TUWa3;n%GuEzTpu@7Pe?&=p>qPG<vT-AB0zCygWuwTJG
zh5aE`N}SS4*-~H8L{c)HgDqt$gAtXznk5<$HvuAK{c~H>!9AdFl|`qWL<cy9d{)qy
z&!KZIDpk6aNYJoG7w}zHlheVHBj~KN=#*LiaHrLbnAV$;eHAZNENrf`8~1qDEas;)
zP%YKeW2});2JJ&Cy$?1~S~@6|<{ypI4$tc7r2)1X3{&qRnTpHuVY=>qh$<*)ywHBr
zR&#2i7SZtp#Ir{Zi0N?EkC=Ub5C9nuz#ska!w=udWiL+%)I7dsvjRP(lmGSCUq9g+
z-}uJ+^qoA*)@Sp1oh|!&eB9XJ834d?;tQZWnr_VeJ?{c!+b-|`-Zj1m<ipPh=^wgi
zNPa`22iI6N7{tN21Mxr5%SC#Pfj09e%}U0_@htxife)Qa8X4qiUlfQR<&UlT%U6|E
zW!FjvSGhjO=W)}Rp^wye;(RARMc__)wfA)_;~R(u?*d-U@~uO|Lye+Dn$m{y3Zw^k
z0`cqr3E*|(VNsVA3!AlzC(7~;SG+bGL_-CGo90Zp@$%~%cUC`K!N~*nz9`i;!;kXb
z;Ip^gdC(EA=zSvA&P3o=G2WJcMxy<(AHk}9{SWrPu`gh~oIDnr^&?hsT#KP)Bm}HR
z>N9s2Xh{mCquH`N+OrO>=}%BzLrc35w=5v1W<+||D9e^zC)4!@G+Wa+uBHS3F^w>E
z*AmCHp0w~f{npZ{_9CtR8R|R+745~;WyGHn;%Xq!Kp~qnYMeGuBm>&)U3_*TM~fIk
z_90qm5G`vMgwhm|kXBhb&4@?cmmyQg8-P+?06~tkXU~3vPrNSRfZZ(JLC)U$@$AFT
zVVV7Lu33Fce+VOB*DRLrpSR@u=NTNpo5J6nE@Qv_2Liqn=2f5=zkr`+pW*&5z`inT
ziI}PpO-&SZ^>9cYIoWaxC$s+we|_@T0cg*MzpHuFAwC0to<o=A>yeC$7MDDCCT!P0
zi8fH@-7<jQp`_o9cLROel_>{|ExIgb`N=1ryqF7MUe;Btj5|705HqX@Ea3pz8&Z-K
zK>Esc5hKWp12;;AzP%5@6QJ&3iM?>VWgk3TO<<f_0e^*Ql01}oXV%^iJkUxpVziYQ
z{Y&2ptZy3_77;G&h>=8=`Zm7Ve7Wq;C$qo8P7X5N0$1ax^A&jKI-=!iY;}V7Q?Zvu
zQn>;U@ydQI3vf{i-pSl;HlTT4s-UqncztU2G#Z-z1jY08IQ6(j16qY;Cyoy9>16sQ
zbiQ8#>`&xmD5asMGv>t44Wh$MB7%5a$zX9VGSDE<i1CKz17g}puQ)9Q%{mLDj?hv*
zHJ~`J8Vvop9ygd_)|0fMG|@7o5khGpN^#MO=%mq>pJv|Q`qsDR?6c24bASEoUvGQo
zop;XU^S0gT+gjq*g-qIZTB+F|e#@D~&f#0>N3xVD{ChKN>_@*Gs6%Q}4!-b(FYHVG
zgoncDzXJdqZ~GyTW$`F~(UF@_5>~Lc`xboq<UmnE^UO2POyGajPb0i9hX`-tJO4k@
zZ#5pc0x*`agLv1Ln)n%p9Ch2E!c8e4UmiO;_}0DS@y8$6)<g$?rm{crW)fz9R}9^E
zIMm#SgKicVWoV%DYaVs{xO_Q4I0hwVu<941^9t{BcyUP)Nv`O1Fw$*t%lPL!oA?!_
zD;qBU0Os%;cPTVFZ$P>2FL6ajap+eD?(klN6SS>9T`+T3*6J`XVO>sYJ11lR3!7YN
z!YkLPT}YO@W50sE94q6X@5)4{Z^3UNz$MdD@$b^;{Xm_Txw>TL4qYv6jHALHpu_)$
z_eupz^6l^NZ=JJ<Z^CteO^5f!GT59?Lz?R}(p=(6CY9xxMvK8oSLv#OysPp<w^3lX
z<v``70Z|<VnrYyu(Y@{W+$n}SP%#Rrz)dpI0hGF~l6MWSl7Dv?>efIjD{sP8uG~0E
zi!AEyi~83xE^B#Cme0UO0AjM^7<ku;t}LI?E!I-L^3qJ6{uj&72YY6Wz0Nq}jGfqz
z-(Gd1Wo)%?`i#zw=n|N|>|2lHn%BvEmiLn69<4{wPZ}l@_RV!LOVpO5$%7RH%@9+8
zT{N^@&Uj%U&33{p?uEY-UbJ0lOVrTOKCHm~E_j`b8ydpYAcnx7DFTFVk36;k-21ZX
zuz+x#^qvHu4Pkp=2%y^sWkR2jb+FGrg0lc;5Xb)SKrXcWOn^P>J+L}Rs6&IXa!7Xg
zuj7)P$pTHuPcEwj>!=H*5x8cy3-;W@WoTryj8&FqgS&mbko?9SKEwZU7mt|CnY+Lp
zc{O|Ht~@0d$?q!cR>AW&xS8@IEX{b|`#gbC6wbzayAPGqw1ioHb%7C$VKt4&Dv;-9
z&)j>_p$~`Qaai3GuY&~5G~Tc?%TA{pL1WO#bj>8R&z*YlN0ccwG<ArmUp>!s=*@#|
zh!@F!Og%I*(pW>G-ueU`RgEcWv>(`T8InYs{Z1Kl4}Kc|&CV*MCtz?xJvW+n)sF93
zx56#t4O#f0Qx`uDp!_xJVp)jHOCzKek|eMzp4UY{N0W`An!%>E39SaOxBoE?^}Wu_
zdjb3AFaQ1D|NXt%UX13cyafCJfR&&BVAs;Rlwio(r*P&kWrMzM&rwAICBhU7r4c{6
zaA!7K5qg(_hOYG$J^K%bPT$U7!X!@(=&~K$R<|`X=swKQ7s2!8j7K)OtK7uxU=My4
zhwctztWcxWcj$jmI%sR}TMEJm;!O&k?YniJLqk06-8$eeLy@pm0hFZ8#G~>d&kp|B
zW*wK&7*8(qkVxdgzj2orz^(&~KE_63)&D47WZ0&Wf)pC+YBlv}`{=~cS)$CDJBn9d
z6f}z)5@_Co+QSvtFJi^4+SW(0|A{SYL}N7e%h(TL!?we7T_zLY@~F{pbQ_y5Z#Q%1
zuD?sw{`OYTSBI~QzdYU}e5cZ8;2z6>a2ho9&vH7IU+QoOGEoOirJ2W1G>p!wJMuq3
zp7#AbGm*a}yjftY#v?0wa?=1x4O0U~yBqN@qcZrjd9>n3Ku{r{8d*CyRG`t6s0`3W
zvMN=s5_EF;bpTC^<O8|DBnqC%xb)-R9Ng+)30!~B@}NFHWA;3NCE~#>FOTLkwlOS|
zj|O-~=}%+;t})uD=6y65>O3ZJ50ry-+zL0Ae{0)|{x(Qu!yO2%G324ceLC#c7y1*^
zRuROP=rDV=LuHCjKkm<+@&^O%7rD{^E$hd@IFT$)_^uTLoiz|0K>UfwesUP+6QB6R
z!<<Xd1jy?1?6c2a`~B~Ke@+-7@(|=}D*||jF8-znz>f|9PT|6yVU$_nGREt{2Or#*
zutRv)NpM#NG?Vw|WyoYX%8WqCjP3$pX}+o84v*H)skhi<hjz4zl_2qAD+|Q)?U<UG
zE`0W3yTIaha$5L1jToC?@4(h9^W*2n#~>*|S{V=qNTE{w2;3#h8h-Fgnn8H{9d<tU
z(qv#g@AZOFbmra^aHnDh0_-Q@PC{G1m`<@N9esK|h004qqqc^JTT^!OoJ=Fosr)4X
zyZ%|uwAMU&k+6oob7z3qi9z9L@_dxMuY|6a$kiyUqeb#*<k4s58Whq%yxI9R!fR$)
z+K8$0$VVT%m+3f}w5kM<AK5j&Vn7{N$+l^6$+rmHl~-XLK(})$)ilo56RIE^Q$)Ni
z=(}=R{<d^c^5Pd!7ie-%y7s+$&73*Y_Ch@`T~@=A^cFhlzMhlLCjq;jn3+%D%)sBW
z2mP$R<KAVLU0yo%)Kj0=%7Q>2`UdVg?8fJM^SNGj2ImX5=YP>34rqF%=eAcqqZ_Tf
zm}M^g%x6AxcaS#bNHIC=u)}V@_S$RrRh#7Of|h~jNHOv<c>>C-{8U~mbHv{t&Hc`I
zzB3yIXdlUkGHc)eY5quNa{?d5YJ%gd0;74Jij}MeFlKi!8Pci&uWT0YzWbgiB-`@t
z3P7j~K->Q+S!XPCps>>EZ<I6Tt<?+5jB)ZEcnoLb<S8sl1!93aD->nG-NE=IXzhjl
zDE19(mht}b5)rgVerga=TNOI=_<Hc3<)|#8u?tpd{{btb_R>&4s{uG8Tkt~zT17*0
zkZgEa9$LeLW$>YNmY*?r%%_1(r4pN>I3$W=d?#rGteXO`C(=M(4Nm22zpS(&o#@sj
zPa57Hl=o2NdLMaT3mtDT_T71DCW<4fqYDt#0XlnQJdK6Nz1s54<&##1E@PNFMs(|X
zRa}<uJk}dIy(>Ch#$;oJOUaXV+7A31SO#?xmRbNuUs6R`S+q)`6#(6H9=#S;34CSX
z;~)R{)5jir?Dv_S57vosKx7C9^+vL%KSr0P=%3-3oetwfyuLx-L223leR}rn*{@9J
zZ(0GkKE$&_Wqa+l*P~oMGl#8!{Vu%l!u_~CZRv+U{Nd-|!<2ORlY*^f?_T0nVC;PG
zRlsO&xjRhsIU{iUtXZ=b`RaoFqK4V~1H|73rD6<x4bkcYfV?0Yr|dKP_e#IB$7c*$
z`hy?*;8(Ex1^k|d-$y(w>LUBTe0M(y9zKG<$r}KDf-(t21^%)EWP2vQ>V?-%!>h&{
zQv!IluLt}+>WZGiQ9i<hd!hiJE0M2_(~={!bw<L*cZEj{V{BirMT$m|@jkEz5f@?0
ztlX+@H4%IQE2DDFz-q<gDr_E5OCZ9~Q?bute~Oi92-roSE-*rft{v$_dqV)sKsjE(
zuEL;+okN3qQ;bnb%A+CLqLVaU`S2Z<9bn&<PIMd%<T(zw{xVX&Wb(}SfNtekHjQq7
z2BQNg_wAJb#%KnjiF`snqhWPEo3a4SQ4B0&p+AhWnr2PerA@0rRW3uFyfl)`8@>(j
zjS#Am7J1poR1w^3vOw1BN4T2MuJb6FZy|5ARhNI)F<VU8>psQ{W%Rqs3(FDBLI?e1
z1*lPff2N=I)<2T5G(1#y!m~=S7==N5tGX^$%kpEma`i!G>vzeIW%)Zl^{Gz{X8HU;
zE!ltUV;@@%eVyp0>lvtr{E7CJS6(@V&jb%bso0n+c5mQQL8Ftkg&{ss8g4<6IEGK~
zh7*4u!2L=o8i1a_rIb<LKmPHLxARpC(k|yP<E^YftfO2Shf~2)#2GKvu>BF9e}Pxc
z#Q%tWo-jp;8iA&ko^|c81HasGdw}P$*i94%8%`Rb%PVW%tP+q`k9K~6Iv9za#rqQK
zUCO~2o;Akx=h^B;k!2{SX6|Lc-OD?ZWZ%P<k)^jmfwS;SX9L8`##oseVRU*M{wlxI
zu=`=Z5%_BiE|YH%J>97bI1rqJy95|kLV6b2zJQueg`Y%a=-`}|!K1?O5^#qbRfMdS
z9Z;VIxF1R0kI=BMq-+YSY)Gd{sw7X8n)lIx_N39>L<63a%oLi<p1EuPa8u|`Coh$A
z_n@=bL7gCfo%;Vc&#H^$(wg~^Uat0+vKlQxU1~gE*Rs1bGbfH*EMLlBC10XfitpJq
z-F5zR2$w1s0Oj(}A=R}(l*ML&$hNNjn#Y}H{sLgHH<ee*?^^Pg)t&Is^!gukX6iSw
zRKGwc>NVTeclj4waKUxVfVcY5kA8Fr&jYyzSF`jzIB=#D@YXJr2ipRZxYTEB!2b=D
zgP)&%`svS$exL+Ivx+_c^<V$><E$WzV|ji*ZnB&C&2N75`LBHCD-W^C&?jxLKAPBR
zpL5PRGx(ZdZ-r6LwR`Tl=R@#tFJt0Rc$K%UH0UACpo|Q6f6b&(c(PVJ)n>GWuG9K&
zc$zF1`DkJnDgmrc4C5@sQTT^)Vb(sF{ry?Qvs?8dw7x{VH5{T`%a%oNo!&<t1G)GA
zXyRzOTubG$uVGb}Z(*ZG+Z<D{H%C2lp1nU@L!7g)0(P05u*z?-UQ&!J5`RP3ZP+V<
zI1K~4=veIq1WP*rp_b(pzwVhkjYo&aj-wLh(I_s#P42&_;7y;o2ZdY%|NT%b_M;;o
zNTa?R<>GgQc_jtW$~*9{3Mng3ebajsz`ipb=vsjN-i(|?vzasLesrwq<oN`7E6<ma
zgK5t2ua0y8Rx_ekuzDYdorYDMux!NT)nw)1Z=c@a7hPI&<v_08*{wv)FqA>(5v0Xy
zi=i1LE73r9r9d!?Xm^2QP;Xgm$zSCv6e#>R9Z>U_N(-ai+W*Zn7U>UE(xTMuNB?jc
zxLZg|pZ0kq`m^x_Zyf;e6SD00&teJt$Xjl?WgW}@`s1y>xH{}NoTdGj&OP_spYZ+v
z0x1!qqcSOdmQlFk0_eAMo81uT@4~^lBe?qYZC4JYWGKDL<Fs5>dgqUPw)o)>fA~HD
zCv9UL+ZMOtcdOD6?)}{XNZvJuTkS${*WCoo`o^7pt;yXY_+2DkITJBTeVu1r7N(i|
z8dfas#_tcrQ#tj0{^7V2vA1A{@EPHz$aW0!8OYW_Z<h77hpuf8tsuDhcOE|LFM+!o
zuFC&Z!awg-JecwlM3UT&$NpXLo^%vn?dxOMVb~vGr5L$CkBu3xz)DmC!E11>YvxYF
z-VIPaoQir>vt=@`(Nv(V%2n%@ZEo-7QYIS6Hk4xu1J%s{yR@c8jVe<WQ^c=sy!$id
z&y4*_27-sOGDZeX19z<wkdOXNYA%Dqlkm_RkWyokU7(Ib*i39+c#{SiGx%$TCj2Ka
z;+s-{pD(LvDQZgauDCHndd>8sfS;)lK)a?rM2XREh|;-l8TIe#XUbrMJ@ToNRSiIN
zBe>V_l2VYuI1%tmN!}VS>O%cYv+GKg(Hiay94#xY0;s(L+)J1hcN}uaA$I^u8rwSf
z%uRp#lG@fulgp~Y)eg0lp#O?TxmbDAO*ieqAN8EZXLxtAEpVGI_DK>^I>4?Umjms^
zmj-(=$?DH1cZ*Lt>7-lEI_s<l6<>6TXV`$8bx=4-CVbbq1b@+Of?fEH`$*;EeULV&
z3v2|Y;#bOx#uKeFXoB&m{Mib*8>=(}_?zI*e({T6{2<EE9D+1)8IPTS)iQe2DC{Jx
zXyht3(L_AEkS^kR7px{_qO}<|nFn5B{D0v2zp%!0#Gl8;OugVPP?ykFBiKr_Ye428
zKxH2WxH|#W8zAFCiIqgO<v~U1^9IW=7NPzZ30*VTDS+#50N4BKC$&B$JX%A*)g>@l
z1CBoRI{}$(O5Q~`-if$q)>TLAIqL}T#m7&7i}@<@*UUl<Tgye7eftu-_RmsB75r5P
zt8kM`Sa`<`G7!&d*arN(4<)})F~DD%YV39{-a|utt>P@jinq`uZ=JT)*#IdLYJb{W
zvktEJ78q-*b<EtQ^mV&IcBy?W!dNLj+hOJo&(3RBUyBs0`yO2pqMhn~i4GkA1CT?|
zWunuYu|PLVRc6xDNP+n2Pk(yjHP>A8INJdGvHZLjSNm?t<u>#9obJUfx7_kA!0l~5
z6C85UMHkKFYTM2EByb$(i<T4q*ylh0`P=v2d+!i{M3=s$aZ=-N4Z!(|07^<fRv!#>
z6_-iFs%9&$j2j}|!9hnmDQ_?QAhXrTQR9Qa-4rNz6&4w8$lncR>8+I#6pyz9Kc#y<
zuwgVF)IVlm55Zo5l~Ki1X=9fV|KEZ%QrwQm-h)jVwcn3mwQT<bY%?(qa&-a+Le!^l
zs(86&!Cn6gd<+$_0}cLq8iM{NFVjHRD@pX5QgHFgzRd~H)2YfQn+5DDgm(hUm-=sW
zgzK{r`_zwoFQjokr*s8InrQe(4h3=3f(7i7`HtjwHG{zJMAGt=_S3X-F^zXMGBxT|
zfYubf(?Ji$T}ik=-z?AS5L#ZBn+p=e(SRVuK~}Pn6T0>i{J@I_k-^yHN<jE$9)yj>
z+Cdzr#k<VkkY4R1Q~-DufBqg@9U!fghPn-TSwW!Vz*2@*jjWnI0G44wap1p9)Ea)1
zi5UE=40g%n&3bn<EabOY88%4A!7?^e*V1}}bZyz4KCTn?FS9yu1xx?q0r8#qUVbvm
z*_)}oGF#WOwl#oh)26*}zySx`zyZLQxyycTM*cpj2*F)Sgp?J=FO&HN7hJFzcL_}N
z^i13axZ*3N#oM_nI3BNx8}Pi2z5Q^7K<VK@|KqN{9va%WCT)%zp+AQO`@vUll$<4@
zXYGTxqeFwBTzslc9{Q-BpTKU3l??a6$|RM9jqv+CR=}<?*2iHUn`68JD^a7;e9v7o
zxtb-|Br(O&j1_k}m2x>i^Jp?o@;w-|1qcG-WN_to=nSHgPsW`<;<qS+YjKrnr6qtz
z>$K1pxW}R>Z_2M5@5a!Xe?en^F%}{FMKo<t$c~a3B4z4WI{kL!_bcT7u)rN2CZTyR
zBGt|CshPUy>vpY~xWgH^=VG-drM)!OZP-SLYaIYbhwfDOHk&6-R1sKC*b3;W<7tU3
zckm)yWmKoD>3D?U9WxTF6aigwbQJD&Salj-A<%z%Pv%{#2C}g|dD18^yN!!@t^l}e
zN&)`1IspIba4|!AW5@@vSYZB3tU)-P{3Oi}Es<}G&nyla8LYEt*5U8qp4-0apxaeR
zoL?I!nw^Pe+cNqFp40C0k390oS2(3V;GTQ#8B1HXnJvrM`p32<Y6k$kwQtSB9jv1~
z(GP4KBW`%0?zGcRJB%{|Ls@Yp*(&{S{UpX1UHoHp002*6|K7%nW+*LDekhC0+~LdG
zC_IG!<4*oQE@>iI({JHP+mRjIQJ}t_W|U4!f%fcmxb0Q!v)FpfOC<Ug_Dexxt!!+8
zeF`hy#j7goOsvMTf5OVt40fH~_5X$gcNG!{*t8=ArUP|iFw+n|ga&X5?iecMd_eX2
z%*c@d&jVM2Z^+QN^?&H6K}+A!pT|Da5e(3El|73Q8klRiF}mbEl)O(T?;(``Jj$<z
zQ_7I0-qZ5h*Z%-{Z%N0!3}xzZGSsJUI*cOaX1@pn>X_nPK>jA-nr*9tXdhlcxB{yK
zY-)Tu$fCcT6!;Is9f+%y1ue6yp2E5c;_FDTUwEXpB4uDDVJpc;bhV_uoM*XOZZ8`6
z#DiwzzBQo#>6W5mhG&12s{w5ww0#A@R|cd2xH2$-=S_*LcT=LEZ7U48l8Y%QJlmxk
z$gwXok`9(Lx5Q8N=m!YJe=Kv}-PHIi4>$0q{Y`)EQr8E;oJ@T1$%%rX+TcJPGVHR`
z^0|etGZ1#|KO1Oo284BzDV0oZinB~>r}GUyc3xH@#Y+Qr#fM)T$Kcn-HO<;t1(?CX
zwAk`^ls14rfIs{B3*`xrzQ~^cVwTRA^U2<`Rlr_+DL?Yo)w;TZSa<h_%m2z#C-Ze?
zLC=x?Bc5w!?%cmmG5fDZDXj@3zNZn6{RLJt>fd4=+-v$TVOd6Fh{mv2f(TkE6tEwF
z6}W4B)EM~x*?SLgIg9JyUtOqo2^Cb3=pBoWK_Eo$7NUbO#&HtICH9xZaru(i{@eMd
z__34NaiNHg>CN<Rn!#W?NHifpfCQpk1W16s-*2=tyt{YX-Q9cduB~~Vd1u}!XU@!=
znRBMw|0+nFn){FO_-&lZV?@=0rO-vtPS9&;^9nr+gGw3+lQ|BzzA!ega(y-HU(OEP
zKvblW7)H79rTk#xBW14m_srq241$s2)Rgy9>40a%H^C+UKHF&8eMs9o>X(&@U(B8G
zwVZQL>-sc`3m|BTY7F?T54_*e5T51VH>(4K!`z<#sp!R&iOgygZj<@f-)Sz4r2Gkt
z7&A?I6YmZGb8cb%5H~xB0l*pn3<Uy(0R;$0)cwFEmJ`m2*;$6tRuTk=t1#e9fC7hU
zXa;VGd)~+#_pEYXfnnbR#?q`uv}f}MET#KcCfWS~g9#_jqbSQD0I&&aPJ}@mt+8Cf
z=%0VfF_zD0{)KI)5oQUnr$PnCFsj0@(^U@FO6errN(F3&kvrwqX}jf1<+~nt=t0`~
zxE>hQ#?3IhxH+z9)NHuI0MJSw+Xp)IYt}i>@cu75{s%JMKa`DhLzXOAGKA6kXl~RS
z0n_)%+aYeyTd-ik47D(c_aV3G{inAF7N$|^U%YZcher^5cuzme3W3j9@5V#W_FnnG
zhZF$Bk3L0S(T;(0G_XJ6KSUrX^s^5#e-2W9f!+(PT7-^!5GuBZ5M#|J4o>dpcOiO%
zoZosmGy0#x|7+-<6u9Sp8)L){wnNY;#8og&C8m-a>X5u9Fv>X=B6$zSb}C`h2&>A(
zVntYCH7Rf$4`V+L99DqCS>O<+;`z5iYld44nH)^L0s|mz@2HotOH`-TkbfJN#1P;K
zxRL?~_Y@pOyva0<^C0+Vh~wyVD)=}ee~K^?>j%`~a_VS*>{G$*YW^k2JS99DQ)GC-
zBtT?#nl`vem@+>{@Igr%Ii(;F7#^^qDISr@IWyr-S3Bzdq!nfbfG0B)p71sM8Uz!s
z)t*82@*RGAc&ndGR3Y;N+-2lS^QIfqcAtO}bXjGXT)5Dvv9}8kJ^vpZ2eL8gmrQTk
zS%p~v%ZT(XgIGqx_@9vbkLB_O{)6)E@@OaAXV75=z`T%dC>#8P$$#ivWB7VZ(rKTm
z3_$$DXgK3k)C#S136m<Cc+L@2Lnlx9bUQBP@XYv$Z)zAB_fEA!Q#HY-Tmx^QoYQW3
z&+fAU?=5vHpfLLWciwsDm}j4Tb{L!B#=!6gupAKk@>0=)yX*Ad{qA>X5`Pme(RtIC
z_~^G+atFW+7<hQcJpw?mVBkJ3wCkK8pqG8RC!xZ~KZa%G9+*2v;j(yGFt)Ar(LX@X
zL&s5uePPKw;#W=y_a5{z^ai<rGcnggFGe4X-VXguv`dkO?-UxYiMdnpL1F?a*InB&
zNu)O`V;+R@Dva%1%&Mg}U<O5;P!W;HqI<O_jB6^yax}!f6ao<kJNu}t)SB`h0TeMq
z!xi6*P|=nXD(t9hQLcAY#oP(kk39u0e~d@kJsZZph&+#n&?ke>a?FJw5IcA8k3NX7
z+fY7N&{;oN3_1Lr;cyDvJqpEwBsao&gf1{R;-#r??1AB7I(6?3dH<D^3zaky1%PNe
zBM_Jz@LlJ3G(Hg+dthGz?u#&;8L;6GCC{U@6UPRFHDuQYYvXaBNO<rF_YVjkW&<#z
zFkqVR5+*TT{__-J13=2tdQn(?Gna;m<%Nwu#Bus<I{FfHm=Qqe8-sl(wDBynv4peC
z+AVS6ZU+`{!wh8+P5i<?@uC^qB|+LN%Bwh%`9Uj&ffg!;$p_gff3YKO23)Gipbe<Y
znR_*el?W3Xd{N<&cH(>UwzHt53EJOk8E)>9pBJNFwI(C%J_rS4nH`vlE*cG*50CjU
zh#7R)VTT=zko_tz{Vkh1b?Vy)9eurK7&?z5{{Vyb)|WnU6))UnnY;bO*U*QdXQCs6
zm9C!5{A2BqiuAacShjKNd^XyCPbE{o1AU7>sF?1WzXpD5VsKP!C?pD6(?UuLroJ_m
zelUIuxzykyI-YESrf`)kIoNi7?SzL)alv6@aF`2>MKD({$SLgfoc1loU@Czw=}#J=
z5zy~XfN}20|LX`gXJd9)u*oDMVZ=t3an84zm)yRFF%WUsut&NRsHgS7<#qg@&cx`2
z+ab7ZK_l?~=!Y|YHCurv^Hth?3$rX7{CzMwjdY0{1%RwMHs|j8exY(BU_W5E_8k}-
zrb0**3Zyyjba!dM83C6B29Q?;f&S#%k@iT;C<sJJz$x+|2oO#fVbdUC!;Ao(SEvi7
z_*{21-K@aKZ&77q7vGol#q7ao#`uA`L#Jm0H+1@>V`nLY59JU|++(>|X2#W27|1JV
z!byXnJPJ&H*%&co$Y_kw<gtx1X(z<s28cY6AztB!{C8T1RG3zo|HxRH0hcmpuJNL-
z+^^CSxQa2j#%cMY0tqw}F3~BKacC)c8LnF8H2XQ{oOAU<4?T1z_uUUgk6_c@2$ldw
z`<fr^W+(%ga2p-)d$Cz?8*~ECNM9P?o{{|eF!{F_{lCo_e)sf@q0f`H_av~4b~p4m
z^c1u_&~{_nmiXUB-;b_UMdp4s01iRh#|U>CdMf%;=o8QmTsl?N%G_7xD$NcsF<&I4
zPt)P<NhkUMO!aCAx@KagB7#r=j0C@$l7O+t*VP_}xn4?~cEkxxE3>A&m%%}pP6zD`
z?l0zMEDD8+GSQ2ARm79LxD(&af+Jw;XTm66CjChe^u!X3{V>9AMjc&D9X?hx)lPOK
z{f|i7Mx?H!dXX0y*@l=dBfLib#5*uS++}p5bEa-Kq^z<IBGZqi=|-Wzk#XQ^z>wBF
zDj0+T%>FsJVR?W~BhVrp#!i&;Oyi-W5TL;5jDXYo3Uq3jzXF9loOp$XPua%~KzhM>
zjUt@z#7mtN?#Z692+w7VJV5}r_;0z4M{5Sfja|DFes|$v+Ab`QP$rtVDNvdB&7&Xx
z;$mJB64YI?<KA5=a*dk%n65;eGL?C>>x;S)hmNF0z1u(5jBk!VbR@|T)+zSww%cyG
z8iz%N0Gzt#%H6?S**om{=bs;r05F<c^1{=;w55scs+ajsW-mZtRR3?tIZD5_ANvM{
zT=Qx>mJu6K_j>E=PGGnsCw<z;{{lS{Js-Ug-RY_(21f;~vQvU!Erfn_x;+_z><44t
zj!y4Zn9OYrz&!MNq)|0<hq3Ph9(zGpg|DmSp+o2DMzyNp=0z^?LwQWo3w&Q?H}%<A
zKk2ng#Hr*AEE|_ZR#ZX6o<wjbzQ<GCiT^5bCQ~0y$G?XAb1@?By(yc|;I<Lvb0PYP
z=-1??jl<(S`(i!=?Oi|~N_9Ft2@{w;;nUIp_Q3EgC;Z(b5E-E1v&uS?nZ?a;GQRLA
zh3<A9m;;$U{G<Prsj~{E=N<uPg0v4q+ew6x4|HZ6H~e@E)8WHPMkpy=+&@KKn2v&g
z`vWvxd#}W_?4V+JtYy{z)t9{j*8=wyaESSr@kjd@{6_I_hx8eAP=wVqEpZHrD^5R+
z`)mB@rVeG6GsUSA?gXt{#WEDPR!|BfrRcU9@s~H$-G|kZG1_amm_`#?3iS-Vg=v3y
z%f~#u`?ea`iz9seE#L3Z-Y#b|-_yKL@0$NenEY@AflZm_AC6$KlrR&}Q82Wf7OF>b
z4w1eJ{j2@A4cvg9gO)}QLYw|c=q^z;F?R}3rf$Kn(xlM5b!{W2>#n5peL%McDK~Re
z!-T!7eGYi-L`QiWqvl)7r4LNu)|B^Ra1koDf^i<A9}M<xj7BwzlFXg*_ITfk<fA`~
zJ?zAXz^CFqj&gVfb5X<<cNzOU!cGUj3n-%n(XZJ}^5JH{W59C}wDlYEP_BcFOvtn9
zDQo08*)tX!ucPW91dvW*bd*=PQ4jwx@+Ey4U@z#(t2=uzaz7Y1@sPn6GPih(p2z(@
zO&B)=dZ&Qrixmde0Urf`ps;(2L0S~|w4n{W8hL~;q(wV@2g@R0hyTJ4__;q|D*9CH
zk?AKP3HMoO*E8Ke;V8r~?K()}1_1@X!1PJan-PN`pdI&AX8f0{Pl6U#q!CU^5Wgz~
ziQ-6f%(dQ;V8>FcKXkV-ciS#H5@|H^a)h9r_qY%A09p34|6Roze4CmGFB$2lKmF-C
zS6p$$P#FFeIUr*(r=X{yXJqtr><aFUP%cqkjF!2p<eBHApGJ2HE!4!|DA-jLA~>#@
zs-qK|fkbsaqmV{34Li6>%sqSa-8JCxdl9Rk6iRB!dm&sp%y3UWcs@u+vkhh_n8I+i
z`~%C5uN^$fdl)136Y-PboK8b{gRtvUFJr;M$6#KF>EYf7#(pI3Q>nA_2>VR*YjzX<
zIhnl3*k7RGo=+YcB{Z3JI}I<~Q7^Sa$*LTEXPpKhO#LMeL%7J}CgMm#Ng%-Psf6`0
z2ZMKBosoyx=)lm3r|D8aPzZ)kBAmTV`YsJP^W(a|mkKF(IPLB<xeU~_JVI!D2pyhb
z1CTYsq@9OL1DfIZAa<u5Wwb#NH$RinBi|(2YWEtrCRn&GILn|+eO`|L678U1J4%88
z|Lv|C#;xQ=O8mqzG5cr-eiV|DA8uhVL6|VIBd$z<qG}2LE={xqqLwd8d>h%`0?<Z%
z;5DHQ057(M*q;X)ktP%XpqZXrV$+xKE-gv_)`7E5>if{8R6Ti*=`7Cyw=5m>{Vez2
zC3FxAB$sA&hA;mxf*DHT%{B%j_KQ1#u?xE7Q9C_Reb``e1Qj@g^e=(2$0?OHzNNob
zTqU8QbnKi@W{<($FrVvTxZB`WQ<;`XOg!ztBeB@2d3V5xxP6pk>~GP*&47Ws?))PD
zFGg5&etlscNAtfP9s9Y2doKDly9t9_kGvcQo-cI*W3MIq?ye7wl8rsjL2oY=17Z+x
zKM=o9QU144<}Qz9N56xq3rD>{;qT@~<3uMrD;Zi~YQS)c-jS~zMHuzsrXb*b0&dds
zq8^0+g@Gstc-81w{1pnK0MKLV)Tz6Gx1$#Y02#O);rp2JE`bsJdn2T#=^Cz`qCQgR
zL>Or_oE$J9{~>%d!%yx5Tsyge=qL;Xld##Ngro5<cGa+gg!n{e9}*(&_PD!gG|mo$
z3y1hm0u*tYL7Ded$<2UJ1sv?htlL&mu|E&Y9ommO=2z1<!lL>*YnA90d+b+Z`;ea0
z*)aUpq#}$*iTwxkr_hetpF*d;9DAp#nivt1`!pKQ=fNx#44u`oPQucPDvBfNyF)zQ
z)qWn8_jI*5&>2XyMd4NpBex8&Go9rv2o<;I;yK-KBW{QSR_F}u6tRB{1MuwpiTr=S
zXm36QEWvI7@p=U81v>ecW5WFR!~Gck$5TIN6LwMbYjzXd-4J*jc)koHIhX&?V7nV{
z$~`a?>LGRr?N9>mJ#lm${YuKc=n@rc_CpEtH6v4>|G_kWx=#S7#PF~y6v${T58c~M
zf#c@LJTiSvmkQp}B(8f0r1k0O9*kg~;on)FC<wT|K8$c<Fttxce+b_DW12@hsd$7i
zF?x0)Z%F>!Wawxp2qNIuZudQYd5UzTXK7ZQr|`@B=g0KJI|!KB8In*w<iR}x#@QUL
zaFchT5&!1f{0HGkJ35q8G>IPs0&q7UovrNDTaCH5625uNJzJTyu|Cjrn!(EXNj#mI
zKkM6lO}3jm&{v~X?FfC1bK}1?F*umnON<B|y*z~JPVHiyPTknjFpEyh+tE>B$C2(Z
zaG4JtFLop`;S#>4yc5s${R8OWKSjFNlK#D!v?Z2W;by?BvC@GC;v>Jk(cz{&aBpM8
zekP{Gx)EhC9Q{20uS5X+_h(i`<~RlzGWNyMuh~s-cbC7oze4#o4P&?5DD$!n6Pnl+
z{M~GJT`9&c9O^?OD8ftQZG?|gc+sn!NjQZ78C_s@IeYd}A%%tsz?1>Xtn(C1c{|Eh
z#-BC}0`qnLot4-D)7gM)aeEsbX9V0MFo3)zcteK{osQcSfs>YHQtSw!CkMi;0RGad
zN0Op2P}tGp-A)Nt#w=4Vre@%HEczFOkx3W3YB-jSGXZJYgg8L}$1Th>U@F7}Aq9I7
ziulhH1pLq8BJO4B0RLjjG~<Y_n18dmdAGcSm1e`M6h5)vZ8s30zwQgYRzq{8VAtYq
zU2le-j&?aO4z9YaKx2WBcJ<0LgiKAIPe(B!7b{9l)dQ(6WV>m3I*1XB*pDZ=SGszo
z?b7N%<%LqJw}(}Cq`{@fSePlef>Gszc~I9(W7Slq1#rSIjVmyB%NZfcbNg?>*w4bW
zla#R!!|ypv*N=k2z78F+<uj79Ig?H=jplPfn%1Ch$LDZg3S&Qqe5TQ2GawB<p<#k&
zyOBG1Z-@JA%KJ9T{ho68MmUBk2CGzD>Q+G@O@Wh6si0vk9N3Qh6$DN(AQyi;NqlKX
zrl$blTDblL&_NOBFtqlx7EYXZ=^zu6#r`PqmJud<mw=<}xJSSvgu-{$L7^Z>Gn5V1
zpe#r0HY^Pgw%rjz+}w3<nx&X&1SYJSuNuzbX&I=aFfa=HWdG5rZzF&F2F5N<;+AGi
za=1o;Ao<}Q`xh%KA!&e0002M$Nkl<Zv}eqwSLD^x9`=(hp};jfFb3Z31QM2Rd()pt
z_g$@iM1s!V{kE$UHobZnk+*$?&X<Dil#x65DTSrRMF%>Lj>S>kN|;<tSGp3{jxP``
zcq^S&%RtilK1@A6#v-_^lCbJ2(=04U6#-8e`;p-J8hMuC*%59+2RsDE{w!gZW+Dy5
zO~yWqj^AnfG@8$ZX-X^kS(o}f0pym^5WW1T+Is1-{D4zn?%<in-1*-M{P)NI9?JdJ
zN(dns1~`T*gjFeSwf@o$_;{g^BlV!T|NAk@c0v5tgkBCL?6%O#WrUTM(*6M4Wp3f0
z{Eareie&_6uGajl`C3<r(P4LHAj}Zxjy+7-V=4^v1Fph^Ywtym5aMq6#FGQYaid_|
zENEKJGz`PEEl<L5)*~=n!bN7PcTq){ysL(b2Ff`C-~K0*1NQJHgJyKYbXj9U?mx|L
z@E;Um<7|cbb+5#}gm<%GyX6AhLwnCf?N$R^?8ll0pGt*6xduJ~-o-}lGI!~tE7c|>
zb^v-?bhqsPsF@L2h!kin$i#pfTzWFg0Gm!|GCR|cA<Z{(OfHQiI+`XhBhi|FJBzZ4
z@IG046y|y`_Hyg{!Yw_VDw|4sPdczMy3!GNg{~uZJM2wyA53{X4L(k(Mm2~vcA4W~
zn8z8IX*7?$*_FrlPOxrajxzRcX$+MLiFyb@DF4LV!COJ=5O98g4&=H@Fbe&NTj<o8
z|4FDY5ol-@H01t(%PIGV%O%VS0R{MP8iqXszk<emVK9z#(++vy$@oU*Ozo+xjEl0*
z2qRm{ERJ+NCK%lm1j5My@N)DhQw$ph2_y4d2RAq1t&iz4!c6Q~*PMsOv|qAKM`1vC
z;f6un?ciM=FfEyDo`iy1J0(0E6&{vj5B||C3=Dkx6Z0H2|4BH)ZBw*;!}_`dBh8lJ
z78LH%eJF<lGcBVKFO(T(#eQoI)SAEI8vDtbfpk?w$|H{4t@pfS;Tz8Kb|rIH5dJp$
zdbCID>^Qo#p_5+iBX=5=Q|5udELedxT0!hql%vrNsh}hH_vW|rbB&<p<b-QvU^{Xz
z3x_ZsfjSm5UhVoqsR+Qbv#42M%EE<FPbc4d=|)F-HLwF?$Ia2hLhyMbhPRW2$(+dl
zIxv_sFqcNZ7BI(>S8;z62BQEIMtMz&cjz@J>p(P=`*gI+C680?S5=EM;ZyHJ{PF}?
zEzl~$QNAf%@-Pz`+MRZMBkp!UWmU|xfyk>2#^Zah#z?@3)~spXWMYB&OQ+b=i+iLm
zlbfg;rVKGowPOuR<`S8HVtRy~jJt4d#$1f{zHvKu*WJCL%u@pj1olh9RWONtlrsUb
z%!O|smQDa*L7%=3;1%B-&<wKy_$vrhWOTVO&RB%m68^ESQQ+sMR|hK>=t4T?Rly~c
zNi=cK3Iv|22!aCfBF;F4_(@3OHwY`3f$?h+J{%>~k9^e9rm7nF0KX{|<reCfe22b0
zixmBLZ?2`Sg}GbiR-$Dw7VUtk938~)GYK~beF$3h2_dZc?~-rLjEF+CQ7Dasd%imf
zTbcVX82eE)j;CPk7n4r8!mSDo2qC}#c5LuP<JlQO7;YFGlH0gfY5iKbHJ52bLScrf
z_<7*@5@}DybZD{}9js5NK84+N*P!?xiu;NDc$V`Ff@Vkaxlqk%kPq*C7WcOhV9vs<
zr4#DP%%w(3nNOzdkD|Pvrra+9iR4!aA;PDyS*BSjUcLU{8bFYyvkeFEe=kh=&T8Ny
zfF;Ju>{Yd+5c6rLx`Otbw~??&nj#lQ<<LcXk{Gwd?7-JO0y4aHjE{XbdC{uUl9(Ln
z#c6NbgG^5xKC%qZSi@ZcK?nejrn3Sg(Bmi*`vn8x6{<?ygr6q};1`b&n#VZ(Zn|O6
zgIiL#DgC4wDgG@(@lq#+d$MONrmwJ)CkWsc{&gqbc>Waxbn}%(%Li)^1VY*17B>YZ
zK?!fuk7cF1`7omWSS<{ZXkD+!YZ$p>w&M@*52xkLTSt`iZXIoo{w#VY^o8i3qKnag
z3_l(JFQczR-;K8JidHJ_ovLbPL=<9_kTlCO$y#$j8g6VE2~j=>W4{V}u|ll{d?bL9
z`KR;qqL>o^-j69AZ-84`MM8D2xzmYm3S<8a9m`WNyxSqnzW|pFN&8(ovZpY^s6bpO
zpJBM2h_x5QdphOh^kGXVa37sx>}@&5-W?lWI#gDaZ)l|8>zns;i2Ne<3&~4|=n^<7
zUEm~pXIsRd^roVHX7*8N#tx_|1zjejX$zgvSqbw!@KU$CR7fN#=Z&D5qp;ou?ss6O
z{Q=nHt%8B!k;gH(P1PM8+IdD0u+U{(fw4uCHs@@ZOu%Vy1p$`_WRxBq3~vr#MnS++
zDl!yrAPj;J|K`DRl9`5?P5hk|P!Gq=nZgax3b7#_@T^9l6|Nf$i!$3a$r2)`Kqs6W
z;5W^ZV2?{E3IOfO2Ap!S10xbQg&iMP@-2$Ia>70Si&NJTaKx~P3I6h+$dmFy(1-Rx
zAQk71s7T}ABa$O<|7Gyf$eCyhEQY}v{xxOn3ZVbs52`TS8rLrPmSk>~(x%cq|7tL$
zRHwZfsu(fPhH%yc=zHANenlgJLkNKgfL}EKbONJD{{+(f7@@+Ad(|>cg@)d>;tCON
zN#}Ja={-iemx9M=Vd1s`IJ?F^71LuszUt-6YV+{^ILFu%)2}uCu8K!KM&%g$3Yg=3
z(k<F)brn9<rbF3>#yocHSQ)z;|IQ;XQBbTFj3e08DG)#Ps#OE1F(5QG9GVgLMU?wx
zq@D1n1Ts5vCvKN_=+OHFa91dC3FJob6?aF@X{L&DEt(CoY5$bR{Qbv0jC3*Wj3<+Z
z|Al>-=_EnG_Q$`>-`TS;;>GTgS(xs}bapN55kLrXCPF6ZjKgsJKE`c({?n)$oGr_E
zmw+>s@d%;9t~wkc%(eNd1YpuvU{Egu9!6;ePCl0$1)_MFkmaxg+QEvm8I#b4DJ&aj
zK`a}Eflw}l4|fM>X5F>prqI(V3fKx-rI>qfW?sdyBPtf$hPg=cIqPyP+CDy;7x{?&
zfI`4AXc_#&XjPZDl{LxMH-#JvQi4=!YLxOE`GGX#ZK<qhfUT6-Xcar;c6gJnJMxbw
z?e`e5pGl)?ci~1EWNhR`1%@(lf)}IpZDDu^k=`Hags%_|dOh?~I|~BY*d(s(ZD#(i
zAl}iKb~sME1?C_B?G&^d4jl;=YxgMd8<}J5@3#_Tx3pj?L2!f7DU_UpQ@iQcYDzUb
z@`c{IjJb~=KYlj&y+J-MpdPZFQ$@6)&%`;&dzaD?xH>~+r}-*rU&?=n(x+WmPj1$9
z)KyjyHr^$W7#<Pg41ofHBk)O>GQiLt3jii&2Q1s2{o@o&FAur_y8?-VfQ&Kh5y130
z;xHqCe|U}<)4oZ1b3Z|#5lr)KUb06B-xnrsQ4ol2y{h(3Twz%*p=_{YWw^=9farzv
zJ=X_^HE#B8p<FP-tN{Mm*?}R%QIIiASUSN>3P14_zt|@Q6Zc>eFZp4Q{3XRNX90%g
zRV+?Vr{!xHxm)HIM&uE(y*S$y?O4M;k;qT}mH(^Iztsa3VXXP<;%`liNE|7AiA*Ap
zINC{c`qiNNFcp3{1hX-X_%h-Tp(CA81Dvu^z&TU~c<1FGrZWjbI2QlqFj#kO+d!&T
zgm(vVhs%OAy<B8}8gF>b4mf?mZBukFh;lwKC3?%c51rh4=(+rwhkJ?Rj$#{=9Z!(5
z3GaWvgRoeGl2B9Q!JdQw;srbKJ+@>RJBl@}9805=_~jV;N;<Rozzz9o6#UMFLzxa4
zK79BHo|o*gWXY17NXs&+Nl|Ah9&nSrvn`bWhqVeN)LURM;s}f`LmHgDkm*>G^$yYV
z%|mCF6Yx8HmjL*LSpeb&#+NaN;%EOQlXfZRD9o&q+2t`f+Mlz8p`BuP6dz{<Y|m~M
zRM3x018&G0hE~Xkf&kVvOog}#7C{ie&Hg%^8qh??X@1ixHob1Yk~$B<*ymgJ&RD3^
z>;vxp<|)z^pLQDt2^(hzEUVFI%g%6liumFi%AwFCd^k#od)Pz3zegL3j@-qA!CD{s
zbIZ9SDrV}xi1ZWmL+F1&KZ>?3sBOrWiT10}3P^v0j%h_0ICZ_sV{RQup2Cb$l4g`{
zIszguEb#j0j+;$l3MD*31Jm1~5wP<T;zy#_jWkpKVS>|0?`Zs%KnUkzeoz>%ihnD*
z9if(sN18j+dCh^@-OWtF^`z?sAa0&>H_u~q2uZ-;e`JoayH0&3a3%ht3OH#50*t`?
z378#$jKqC1Zu<4j2m!*g^A>iP4FFb{5y-hkK_K|yRuz*2_u)Cl?)9dg!H*395KXTJ
zQa&euWngfOVr!v|lu7cQ8a?F_U?$H7TY#U0&1K7$-2nKKG*d3=?8qH~GvQw@P6PaU
zK{NA!GlbcOQ!#78y#aU>1wq+5Q}-60-X~BPrYE@1CCqBt>M!tL1`RpdR|jFBh&h(v
z`WNd%q&f2+1#r!`{ns74jN7Au_HXtf3IXv5p`&O8fFWq<!G15)A>jhznBfQ^rcBlS
z0^;EN1diZ?Fo0X=69|*X^a<Ba<-$1Y*>2_&zryh1Q1Yr_{OqG9p+otBPqC|p?X&Re
zc{FC*{Q2_-a!vYbo>y+;(zU)k1M9%Y<rjsgb_n2BO8p#<J#wB4F=Jd!)7%FA4fLky
zt<X)=uO;RNQXNuMdR%j_o4HfjzDs^A=13U(Sv2GqM23nIcFj1J$`H8AgoPhq(+D?$
z$sWP~bAtvA`VDxPZif}|gsa3_l_JgENP7>M-5oIYn@Qg_IvKmq89s(RF;4#DB`@wQ
zm$4_mVu_gt-~}lg_{D-GH^SQB;|}E@2;devA>3W79E~|uJKEU;jHD#+(7|YuruOhp
zIY)a>;v9;*YmR5&-mbyp*OZEKaO8MA$PM6p_cF|})=xdiQU<Yu2rpOF1FJ&Z$Q(m3
z@Rk|Wq_TOYjauiOt|<2dNyOI-y>8jEZy7~-?FQ{^3awuN(ywB7uZ87bO&$2uoy^tt
z76tM`)4K%pH(a7e;_(|s>4kzoY>Ns4j?|s*SG(pO_6gv$4*%NM1%@vW%5b+9ASx^+
zV49ZwTo3|?qtM|DL(NABiEn#wO}-l8%~K4;t{UDJ81`BA>+9Zr`|V?HzWL_9^szei
zc;=aB<_F7~sr%QktN(xW`8oGehIu^({VVj-=zGz3qwO<Cp(ms5i)HKzdQGEhW}H++
zV1N{)m7GQg9nBep6nCY^cgbIkFk%|~i>HO$5Cz--3)9Nqf==Wx2<8b!?3dt{$GJL^
zAdTTe8g*4=%2d&wg#9#vOn7*Pa&$-Trgox~$)nhvvI~lTHxs!Jz;(m3a7=!=AaxnJ
zI&`XBskW>FFGm1D5FlQ5I?|5D|FM|sYX|2-sRE~uP9{I0;~`9*5I|gK1!AYPrd1NC
zyA=8B%d*h1q(2;i>J0EcD2GexOuGxWD$;f2cnIO9RuQ&h5McF+fuNI4XYSC+r=f>!
zpodG*i(=ZXV1m0x4QGM(EtJ)hHQ?+BJTl`-(WONhd}x!T8`r|Kf`NMk!av~>?Gjel
z_JyGxLhqU$ZJvVpBKmIZVagtSWbS=1!*j&g9f)L?2eL;9ofVZ~o0k2lZ8<CvXveGS
zBZP!6R7I~E1|ONMGmLI>?eXhh|JtjaeV%yS9((NZ#SJ#t;OGY*e9*kwKaI=jWtf$e
zd9c4#!2cTBLCR)m`JcS%Li8o*rc+fhqGDK6fHHNJl{R3FwPRlz!5BBM5$i)3>!}b<
z9&@3>f&c-0*HaqC+(}Qu^Od#rA%M+E*G+6!>V~dafv1FAu}sPHKBT=PjQx5>L3fik
z*V1I{pc5E-Vr;ni^r;NyH9CrO2%G$BMzoHIx%58(m#_h+94K&io$)xr*eTpbTt_5P
z2ypE!Y`!6!Zh?8=A3<8*k<rJFE-nRx&Ip*n9XOE|W8At;VD@Gz`X~U6qn?~8NkS+1
zrVg@}gn%dg1ywJs2&d3QDA^0QEvcuAFz4z|d8Ksy?}&oAr#xrz?+A=)!;;qKw1+*x
z_g3(KxY~Hl5DxlX7kwD?^BB0_-UT>Wr?wN<p<hFXBuTqi($=Aah%gHPo^f_A3Iy&E
zn39X=fZ~V<B-bwO-$EM%Mr@0wEt3p5knfMcK|#Rhh)t`SBZM&-;in^n#1Dc1X*)7r
z!GADK3%9}nPP+|tmIX7St9T8u$C+oIdCL}CY?0b<Ot1<w+h)w?uhFq^#aCsyX2S)?
z?*3F{M5Pd<(wz2n3Z$kDbu{3(dF?GKz?aMHsPh!Cv&`L!ZKQ%eKHi*0a4+d!2jE6j
zjz>qzm1$3wAND8RnRJl9g|Xj99&Knc_78D)Dn2nP{->a2>@O4EeE`X?kp<Biu)y2t
z{G);G8T?bh-6aAUOwr`Xx9ftA{Ea8G4;>Hj;t{3^Ie*;)5W-;%N;<J~Ny`Gn3;(+5
zCw-}bA7~1<a;g{1QHD4f`cR;H$-u-9Gy;rraVz=(F9GNb<_^rAKw@WbzXqHi!ycGB
z|0&&yNEQUhj@*G+;M=%gaGy;1Iy!m?d~a(U<Sv0U2>cZYUuUN0@_Ol{6EL>^Ws>T`
zy9B%_>lpM(+Uqaze-nFblSv4OZ4>`EOBdT}==aQXu3g$VBg41dg*J%kpdhx@52Ynw
zsp5=4I5|K#-yIl=|0uLG!xA0NYxW4>Cj4}SkTe~UzlXbP;6Vt$Z3T?o>$pGSCBsJc
zosp@NR8Tdys|cVo%yNvVY<e`jn%CUvIA<^_@&-0HubqckD20WzaSx+)Dzxqmd)dq|
za-Pk<N6nsxv0t0@tH++hw^W(R*bj!_wuacki*kgwfowvDptLGuPmG8EX}BK)K8xAF
z<Wy7gs|UxfhzsuVI^2GA6sMEVz}U;BO&Dpw>AGb!(7d3~TJ~Ql*(t#&2xNtT(9z)6
zZW<DtA`BaO>|6rlCr!=3{Ply8?Too0>7EZR&ts1&G-B#S9BNjD`KuXFk%-{7C3)SO
zJl#P$zb_J`CMVi#U3so1QR*%3D3|81<iDC|O%aCjcBbb@kn!#CmTIpSAat`k<vj!3
zJtY*{D`skwv5-sAE^*Ux9brS8#4QO0xLLQ8F>RxvKgT}`0#<Yo1Te#fLHulwu3?8Z
zin-FX!AX^Ogb=u+@gIi!`{2I`_8<rlzUOn#J!jZnH{N(-;3fF|*{D*CsA2(7_>tK-
za<7{C(n)MiBXHX4X*RE2lz~$$&6q~j@QVS-xOwJrKL~j{+}@}17iawHwvs0aD3256
zfcjLdPibxjv%7?j>k+~ZA@3W5i$ao&JrN)O({VqFFwY|hT#)sv8_CWG;OON9lJTB<
z8GGJRO9PU(BPe>484bG-ybQ@Dqc9slxyI>7mjo0J?0nM3B>eLj82Ols4ve4hZM=py
z!UQ-HH=VfR5rqOb1Eu;iOgTllB(4Sgyfn+jtK<j%GtfR_^B`&5To@jDPw5u=Rr&|d
ze2J@4gpT|BhJ?cGyWlR(7mkiP4zUYk(61dpaudM)49dTnO><p{UHPP-nGMjpLmxMz
zpD7=@xG#Bjxv;9`I>LIACkWy>GYaL=w4F`L`KA3_{Kv#sU<phgSf2Br&i|X(?_IQL
z(aNWve!B1E$&**GOTOnfzxmCBX3m^>>&Yjd+%lVZV<zi0Yi8~gv`@Y|Rk@0WpDLgn
zLNM0tX~6pt=OISyS5PUbwu;3Aeqio|s~OJ>Tp8>k;Jqab^Gfh=>ar1VQxdrjsSsvw
z(w$4X8<OsYF!m?N|1kf-!8Iird+Nw^r#zk2c!u!q+)sWfq2{#=q?41e56E?L71!lE
zIV&<VI|A<&uecrw?)wmS2g=@k5U*elvjdo6jgq=^bkbwQh!HcXyP4WE%FZnb0@&l4
zWEhD=KLik`Clixx6my48Cg$kIyY8>b;FYZ8DW#hntF$1HcN%jCuRg#(3RL=o`>*+T
z#Mm+lTG)Z|ck`dm=Dyf6h=VA2kAQ9t2k!;Y=I^=!chYo8SD}A-5rI4p1;X{APYg^S
zkGMn^1#``5xI6fBm;b@+lJD{LuYWzkpiVx1;e{8*<8TYwHFx!6=wG7$q*Wm!syefD
zbo7O*Bn}yH#gR1(ZWe{VJC*5q?i+|xv7@1419Qi$2)_d2iNS*R#W2lBX^`7VI2Gb`
z$QyW$-m);glxH6KUZ3^#3+T8OKx|$CDg*ai>f`9dym51v?Ofn^ul&VXzYZbNJ%I=0
z82g|cV^7zDb`LroOO>+Ug%~pS>%jk>VzGG-{j<1FS-5cF#nAEd*yBh;GfxoU-!sd)
zJ9Cf}>1a>Pi&=nupha&li-JK|G9XMmIZ##r78vPxov)+bSD>4V_%9j<+B0}f3c76A
z3_}3|J)s-d*vIGSrs5J#LHut0rCglF*@ybO8=82iTaoEt;^6FQ5SJ2GfcvGG75n54
zCQ)~A%GSFCq=`Tm;F)03mUhB_Q{2vlwignv_nbL%Mx1^2*%R)&@4jvByz|b9jO2g5
zbm>y_H7=*Oz*Hbu%iJl1qKblDsRsqBDMPdI7bA|eiHwftMwsiJgsVycJ1}=Dux92C
zjK1%_`|eS|m`vK|gTumH;%;E88E%OT$lNUlADie+x<`|)Z@`~R$Mq}(=p{5W(B2pQ
zIN?$!%>QO+@6LXVuwJp5{7R(NOwN(9d)bK7lFOP2xk|WD?%ShhQ$Oy`F3Q-2LB0Zg
zQ#YOu4$Qa`Z2UbTkk%fFI+(aJ{n@#=o|G7YHcT-=z|BE<j2}GwkGqyV8g#J#(019i
zUh-C~eG;{@f(g_v8I%E!P_id^-;6T(4f%ShOwgwD0`F0@Wrgu)p!*w|4#5ftQKttI
zWE6DsE6gNtR*2RV-jw}B=zcHC{%&X|^y#>L#1+fSVCDl)J@wS_haZ0U*YCgo{{LzU
zgu4NW>?pHC$wSm}xBMz2Qm`IZ3k*L4$Pu$^&6`3%SHk2T$oN%bO|Yv8To?wN)>*M)
zg*!mUF>*eK2Kam?OyMXy<5ioL{D*Z{%4Psd5c5cPUD7?9bQb~9jzGrlqc2bJpO`mp
zZh|_L_>U0wGW=@pglogR_E?rN9rW=)7)BkRfw>gjGK#X_in8C&P6Fn4TLr{Jy%1gj
zu3F`pW!L-CnjdKyH!yc*11_W7f-r=;j{)Tg0))#>K@Q=6JY{YecQWTGI_odtP)raA
z;g~UVE%Pn3OBEfWG4Se0o<n+-KET|TyzEN(Tu<5CCR#@2=>BLBeiz&?#_Ujec6UZ8
z`|ZfXmeAZK;NpcYEu(T}KNp0T&~|R(KjF@1>2?0<um0+gZMWU_I&P`^Z;rqvzdtui
z&CH!bT*<n59J$Nfz3C`1cmHARM`BK)0>WBzM++Btfms5h!y|0sc<bG9n8RVPXTmh|
zMg_oDaHu)|#qvU0Va^rCu0S!0biHlwMZ%8Ze|;L%J2}Rls1dhqa61@$+yr$cVUu66
zG@8kcJb8QDap2@7FY_^5s*|AXT?TOj+5?pR^_9vNCKT$XgUr1hSl}c#a7av3`oYi5
zSQR&9Wy*dGac&}xw3ihGhVeg<a0&uJaTVuW1OV;kBfBZdqe|(t0OgVFxp1o+J#wcE
zy<_4)(z+k!c(+Kkkn+aG&w#H>P%XDdfVy_G+P;+iz2N^y3zd67m9jqwi~D-!lb6uv
z#ytGp-~HVo^uO<X=}TWaf&8^hUmEdmq**g_8#iv;s<+>MTjm~m3y?|-m;xUN^OwPm
z$&Fl}uMufSx89hPU{?bN!W~NbClD{*dRL6;cqYB7jeympX&A~PEC>O2B%`GHbXrSr
zcJ0+;ZEu0sQ~W1_!)+#R2N3Um2=%vFKkQAdEZZDoA4NVDkKL%!GKz9{XSq|VE`wZE
z4dR3i!N_}J$koIv7p6v7GM7VuyDp6VOv-<8xis+mzxpp<zWh@1`T_ZNSs*J2wA-Bw
ze50tO*LNpOn2-c_{Pxlf+=CE6xx`I_E+HiP#$HN!h3`B(-TZe=sX)!@4&Bco@6#!F
z??8CHS@A4^*C6n>>|a74zkz&|2-cj=(9KM6_F7+;aq{+OoPYlLJ6>_c6;r?b<uC6w
zZ{9p-sQ#=~&CH!rTSWn{hEVdR&gnR0tjALU17UDy&;Vp`^(f(2&D=?UBqR0{Opuv{
z)4{RSj%{ZJ1{$sr$_|qqzhSDC<p(!hErIyFt!fGc`6lK9(n&3ea&w1ml%B6`1P)yV
z1HAiz|FNVuhJ2n$8P{9KU4?o`Cs!v&Qa>`6sy7l5KGYxa8^+v$)#1ET_WM%@8&me4
z!>`2H6MRPBuabvXiI?08E%+BZrZku~HG!}r4G+$nVkjVV_`xCKtfoCU>RnFRUxeB5
z0w7X%DRAG4vfqhvx|Xtkrn~T{F<mcbI)b>ppn(g?N0Q)bgx96fpga3h`^@{mKW`a_
zyW_`y``h2%{h)&m`oRx>_`^H96tN~vtY+p;0k7sE4R<mIiQWz$!XGVTC$Fnv?59xy
zjgV?(?p5JQ`eRKWoIW6ZnNG>+O~O<Sw^H3fa6@wfMVJX7UC&%jz<n|u@#~b$!YmB%
z+!?nyxZOnucSF{%+3n;j-nTv$+|T0w<z~k@iyw93wVKD^w%Fw&>Y*ddB`|m5WI@(t
z2bT^B3WVRC^k+h2SD+Obt5$@s7#7$?k16rg?M6i2Kme56Pc;cEy{F?z+Hs3}26P{T
z+f?1rg%1ung8XaT!|4F%DC`yRA3O{94K&4OFUsDlpbr99m(cD7*OpQ+b_XBcy6CHn
z@3a!~(i-LOI{Px{?|S|d`ndP`;upVo=*AmweAjvBo%jDSf=_;b&X%f}W~l<Afb9rm
zlHPE%E{%0h8!3$StOgR#D$=PqawqMHthF3R`foB~_m(9)jdH5eRk7vbCO;bca2WfE
zIP`?*uAl)Oh25Q;1Hfq+xIdi&W1*Zrji2RoI~~L=S-)ntgL7YICuEH4<{0}+&5m;c
zeohCwN8mN^@9^8;&~)fgsJ{*|<N_S>exZ-5VS)RnNP8A|+)M}kU>=|<eyG1sdc-Qi
zXM;e??eGMig1wI!m6{z(Ajqf7I7i|z3H)y%t`8|BK>+`;_^ppF`rr`maq7RF5I|g)
z2cnSReFF_w;5rm7{x8wauO`igr_q@J!QI)GohkS0D62)CnO0}OhV~Bx?oit8FDQFw
zU6cCmcfWhkYp=cb@%O*~eJ}p%h_Ieu{3%-MW$s>NV*~f;O3!+}0%P?K^!FN7Sin+v
zD9hYQdn%0Gx7c6JrC*JtFLN&ow^AM;xRJ~hoJhEjNcRlj^@q93z^7t=5}n7`!E+XF
z-UQ`qQMXru-%S3Lt&H8>!4tt>;pxR@!mko8l=}$E-&v)%=^$Jy4Rl*6Ue=#D4QK9j
zy4fU3+M%h93G4oh-$Ofhlnhwlk%dpZnmH11Di8Y$($4;Z|DOE&e5+@YL%YDt21$16
z%98-$;vmd$yP*Ht@vHa2p{yWK6%A14E<Y%=e+Xl5wTq>kVHpknZX9)gNy($0<gY0e
zxNl7!wgCT2(0Q}LS6_YgHk@nU_>@ylIrwv*``pr|%s@9GRTp!oajs(2I$hi?bgq*<
zlS&99`9|djxMi6;9rlJWc5ifBMEZ`#s!d<%ujc$iaK32gj?DLSO91bX_9o^V2ER3i
z+BK~>((nwkYnIQVR}g<=$}jj~x;d#@youhmiaf>}#3s=>p98)xbS(z;r3PafN_n3E
zln>~f&%&%m_-mIa<q+Jct9GN<c7dyO0Jn;;2;&1nZvK0m&iUFZL5E;j_!0k(#E)Sq
zzlwOqu!SaV)cqyfLpN8U^90_)u>OOm_2WAOp(7_wv0%|H`^uTq|3L^KZV&`WcLHv4
zxgY?dOk!NyTpaO-wn)72kGucDPWZjBkB0V7%ei5+h9dvYm?)UM1^!pG2G*Zo3I1a!
ze}!6?Lh^R@vs5(d8{hcGUi<8`&reX-eG+S}%@U}YxfvPewj+ddn05%~_f$l~85A&O
z!bL~eFwt;NJ&k_>Bj<|<TlEGJ;&^RtP5CF@xEy0|Bi%FY5YX$B{!p051^D~wk{wML
zb>QxLrHuV;w8T0U)5}dTl5!LgJD8%T{@`tglNT{T=mZP-=uJ609qC$~jD1ljB-5E7
zz3bi)dr$B`6VvjmNl_LJAGt#tm6)bW3C@_j0Bw1>QHMs~SsJ76IutisNZ?SG!)C)~
z?b1GaU)=7Z{Qr>kYmhy@GtlnAp#u1pbN0cZAPCq_DWf0=;6DjmqkvFYFCS6{^Uy92
z1je5;ZIek5&<+gytfo-l>!}Jam-3~zs%9#h0&$Q4U-$PMfps~we+}iDP{ZDv^X!LD
znl$N=OD?(O9|@cM)>_t-KuyfesIR>8${-4UUzs`tv@K3W8St89YX4Gc3JmRFGe!I<
z(m#;K^e_zea>8_o;pG}&JGhlFMVOhuOo5yBmXod<%6w*U2Dpp{=8MFCkur&3deU+1
z$In>8{1*57avoA*-iQM@p_9=Bjvb8fH1l@!Q`Ul5fsO(|7+nxA{>RS7f0su-X%YN%
zL;*9tbQPylpYKTg?hZ@&dBM(c;1M6OQMjsEQ68Zzfm<_-jzlD#VLANWSzgm)D$q;n
z^`OC;5zoS@HV@Fgn-{%x(HVyutBu!5;mms!1oECc&9v8;bZDQH{T9@LyR)A_FIEV^
zjI#hG9~=UR>;BnxnSEy<?H!;#oU!XPL4dM%<L)M;a{+kgO=ri9HnS;z0lyInCgdY;
z;P|5-{b*kvh*^jAc2B)@S{XKT`BcKKX6C-`y6e9C=9_PRi-MiNHLI?NbeK_ALN4du
zZbKc+btf9$-c<U1oTI;na2@Wnas<(#{FCN-q<t)Lm%`Xxw{`l^TjxeWd@s=WvpdIo
z68=Ekx217iNe7a>olkrn-K$eaQ>2rz2(s6gM#dja*CmG%W&);yKz}2EkA_NtCv-BH
zam0~j{@G5)4k;<{a>TCGygqoHM;@L_o-JUZJmPV^cynIGO<$F=MVSOTqn;DPtQ4n`
zKV@7Bman8a<M1KnFRkU(PbIh+|5ETXK`L9JsSb_YDgR-_IR?nD)-vNz8KX`OVLkh5
z<7~=Z!Q>+TSC?p?mahVMoB>FC19St1|3w9L+*Q_V63nq@+hl6Hq^%&38nzM3DQI*W
z+Ur?=N8BgNg>6PZXkWp~zViz5ur!O~Ew|jV>Cb=u^Q}Jn+0P!!Wi^YkerxTYlt71=
z+b7Z1*dUta5RhwHK3f`&ffa^nz`qh(8oe?dz6YJgZlrw&jQ!?1;0xD=X{S|j)DN+_
zY3~>a?PVCdn+A=)Aq42FN;3BBS#KZ1a5`*Di1RXVeF%H4s@?Uar2v7GXOu(df+@2P
z8XTh;b|izZ|7m(52T$oYbTT<}UCg1tydT{M7!!3TKb<<-AdR|}_)`9^GmfR~=VQKB
zH_$Zz0&}M<q??AQVz8{o+^Ga-98TlkdZ|f42Pqz95%5nA9k4(%Rp*&Jxz2km;8(%e
zJA5jk188+tP-i8N2KejXe;N5HS8n>rggwIdY6XN8)Po>ExXAdUpdSA#JvbBvfx!54
zw2@{5fUDhWjOSv14*IF5kF1cL&he(WofYx+!bhOlyi*}u*F5yAU;XO7vu4dY0|tL#
z=V0)s0ktmXP6dQD94bLZ*C``+VUv!qd&1cFfoON2!FA{XnqpN2+%S~`>^|7bu0_$%
zr_<Sb%;JUIraj}z*oV`JJ&&1f)V**!6#vZ$e+k6-XtDG=!woz<n&_=fZ<21=BnVq<
zN3tEv5F$=TD*(L5|90eO`vPFjSlG#AM;Wox38wyZb}k=$!d>|30_8;=b4h1C%HV8p
z$j+wJl{~OQd^+f=8C?wtivK*~c|p(_)Ms5Y4$y7F6?o;CR9RGMcU6tt!8h*FaGm#5
zfXakgMu|UVX8D7DI4)`5)+nW*9@|GHn1wTTL1?Ev+AiZsf^oTj+j60@u7^QOE+Li_
z1iGn>7UG)zbM$ir4sFouRmo$Gr57Xh!+CP~`G5cSfB(`o*EngrR+TD&dYOA9e3@gX
zjocyNzU<0Bm`-(T82i=S0C#_>3{~q65yf>H1&5N%9mcj1jr|D1KQnOPKrcP{2)lQZ
z55ezQ{<Bj?GWO5l=Ivn@(QqEG7N4#TM;>I*g*%eP|8VdhXvapFrwAi+$WA-jxx`UM
z9Cef%ZG?`6IH6Osa|6Eia9Igy#N`6_2h^)v3ON+`8&Li(B`m5JU^YIu2IkmE=3WSk
z&iG)`bScEmi`m(rEGkvo!8tMasv{K%NV!(U+zEFe(WijF8%>uL0oBw_l>h!9=vjOP
ztM0tQv?DWi>A@j{uvy?g9o*f#D7`8OD6sqBkZm_C-C+hrcuj@y&_0Qm6$FCtZkx^d
z8!of~%uZ31|K0#ru=URVyngw={_DT?<dwIPfBUz8yU%8uZI<WPDaf=q(7KpA6|gc#
z9C38r@Zx|VrM}^Gf}>$Lm%!NbjziR%AaJuI_o84Rv`t~`pMkiaVk-V(0<DJNWbA|K
z#GVGm>oFqkVT~HM4ZzcTk7bOlq#|SV-sTH4_}iFm@D-;WEd%Hwj4%iv8GqIsN!d-t
z&oJINg^`pa{K)8IXOj1CC!?l}qfnV5YoSgKz;AQtQo-u!JkJLF0JGs88)cA~FZR7a
zb1Ta6V$yoH0VsP_Yyv~M)QmrH5}pZQfd%}%plNGxzZBh2!KxssPWc7*?V%snc`v7|
z7I#V_od6V^!-GSV|1`jF0RDx0d{U~=Rv+>iX9402Ky0J_4<qnIOv4oJ;gNo#AP^Wo
z;o=^itRP^2(P2RV{5J-kM<#!Zegzmw{r>mApU!A~yQ7aj`Y*rpo$s_--<tB#m6p1g
zI~5T+SqMh6;nVWKbq#qFVY9w|K1{P=m72rZ3m%G=0qg5q!YmGj$R35UUuLBcXA|58
zfs>3qO_$>CIq-P|37gOOZ#ZKy{Z3Sou}8-EL@jAiCLMXz%$Xj`GRy>Ej~z_>Ul(}M
zEsPoo8_|;9;z%QygdK<5WXk?tOz-j^N(bGcO-K=VDHGU%IRd*PqbmhNch(J4TlvaY
zzH-uk{KtPRUT?ki`puXzV;nTT5Sr`HhSiNY=F>KH>eRN4Hrl9-<)A!Cs2u#PAM~1V
zt%tb-r|ifb|IMj`J;D7}(s?+GLo3*+Q(tX#XZ$_j|HoFqIg2Csd+)mI)DMFH16jYU
zojUT$aAzdm!pwTLv*WKI5ZhyFtH6lD!3ZLGC2tVc1wa`_IJT8IYZpvtqu8vSelGtF
z1CAH%JOTdqMIf4N*dEAq{DC{}xZ@?i{`Id<=dJdv_gZ_25-7^pN`!6aOoha$k(Jns
zt^2f#Q>_E=`ui3;;guv;HIlLCaL7)}=b;6o-j>cP+#wBPzY@=05M5;K3&3r8jDWkF
zpWM9VIdE^^YRHHy!tVmp7sd`IPvHhA@PE7u(o|5Ac2-hmsVvK+>#@PW=yRRTAmYc}
z$#y;pfSL+xJCL6pwC6e+r~C5+0pf<v1~ZO2^shM5Abeo%q*1<CH1QIO<)vOcZlNHU
z)C(`XupYX{op;`;+XgwE{IzgV())u44{jSebZFb~;ltJMjv6(pZS2^wZR5v}Z<{!A
zVjIFi+q7xZRv-u`DX1P?vj~)fLpe{z-+d+zQJ=SE<F$ev+((j!Bf#}p`k5PA0p|i7
z2Y~;PxGiPb^0(xtz`M3z;3x=$Jv>67Zc73pTzGIOwod{Ep#T_B5EzBu6x{tU_TZ4P
z3BI~Nc{${J-}~Nh>f(K9{H?q1zIzI@s9wVJb<#21T2(~})WzJXEH`kOj3X)6bsD0m
z;DL04$6=13LhqrncWMaW3=r^6)AAYT9*o$xhq3PmVZ~#1y})BLa99WZBw^D!9Pa%f
zx}$NM2%fHyKA(lv0`_=ZZ)?iq67u|Llj2<Rd@^;l1NAVSviC~g-$9ev-N><niRMu9
z7uOzxFiM@qET@qMWf7JGfE$DYU<Z>Wp|G!nG-~Q$z_sV$haY~(f&~j^fa^y*O?mki
zTWsMaJ9D0U?zxvAdE}92Uw{4e!5lFi%yY_vc$jH$TU%SWYn{!c>!_D4TQ)i(T*_pW
z|87nYco&pyn?W(^*<!oxwtH#v<jHN*r%!Lg^p1w;UDgE0q5&x*J8gyA#iVs5|7GDc
zn<x0Xv)?xSF7$s9W)h5=4c8L**oF_suRr+vzIhz9lnCBA&ZH|ZSPorR6xIoAJ5o3g
zj309*|I^S5fZ^RBW3IjS+VL;G_+l^m<AgB(FY)7`;5D?<_TNEYi(adWlt5j~9byV=
z?sO2Eq2nu(Mu(iJ*dcUczQsO((aIUXT{qXU*ts;U;Xy;O(@SLlA?-{<nFE2|&FJTP
z((?$|X5i%du#7!jf`WTI?lX@3`%q5HtkJ~^A+N4cyT<5oy@u>%h_JiDq)xnZdSBA=
z_Oshfle`2*Tq%9(Kdn6yHp~KO0xt*x)VWM_s&1qcXoP8Ue%Y-wefn&Ky6v&1JpsUr
z>89BLaRFZHY)?G##5rsT+LExV|LLFp>HIHz;R{|vd=PM+VZ?PlW;^-@j~-mJxeiZS
z4qmiq(O@o*8oXr5lEDfAOz$fQ3_=hHLIBg3u9^1KRKNOa_7F#p9v5DC;cPU?tWn_i
zv4X&mAw$}R4I36_1V)Y=**1Fg=r)!G+IYjgZOW7>&Ir5<p2e8EXYrj~lQGtG$xg(j
zn@ne|=90EM`+r5fyx&cD)SN8%?@pa7Ec)JqqwJ<v;3<O+4-P@!&Qz@rE#5#L?uy9Y
zbI(0J5O&u)>ZqfR-G2M+e|ptbS2=^~(%ZT@Q+q{0f-rx!DlxWNN&un?3{n%_w-y-L
zI8^iqM(iix)(ggdI+gu8Zleq%e2C%`F)kaiHhUbo2fShIyU|hYA(J3&U#;p(nlg6H
zCrHB1J1G9+&1Y`%b62nuWlJgY>YL!7qEWed=%JQE9%QMPP0<Ha_V-f<H*^$#@}AZn
zjY}Dn1VLCg77ga*DFulcFxG{n>*yg01Mw0eM<O0!!bnP*ZTs<$f86W(>#ui>vd2IF
z^FKfF`Okk|p><b&-vIw>bT6i;pB}8ozpuuBS+OwDjpd=9%m560{`uzzFJ8QO@XIg1
zJb2;4g^q%DF$_$58F;UDHh^|E06cp!J1|5|*a)U76FBj`0Y{APY;HNmh=8^inVnAE
zdr45*99vZZG(jNhZv%9AhzdG+rU_8$5DMV#n-7N(X(4rRcQ!KpaX;4C57}_T4R5^i
z$}9hY`pIil*|2Nv#Y><r=1#?|q|oE~X<f`6Sm7ZS&|Jwn_ZbjtnEnQSV#3%P#@sV-
z`!G{5+m03jyBWrQ2Y&tN(4C5R<o-BB=o)QQKIM8mKf?(>pRh}!N2{3Nzc2aPjxu&v
zZ7Xgb+JL(8TGK~iEWhpSawi?>?gQxL9n)fJ{6*6JU6FL1t{;HT?gtpmbojJ?`?r6a
zVfb%;^P6oj_Gy&=rv9VGQ6FDkV@*4e)3N|{6gDAgqQhPR!oG)YH~-DF_-Z!ay{pDQ
z4BKJ!10r+3^wLY`^X7FBQep1BVf=#-1_m?T8D;|zUYrdW<cxr`0dZLXp`mbA&P$qH
zV;@gBoI&}t#E21mlY)M>;%8@Y_X*UNV~o&`muDRg{tIZscchH9j-{7v7<7GU?=s9h
zdj5xh_=iIgLf`w=x4z{${k4p}hI*}wxx@5UL0Bg12)?ibs2UwQH}7jdj!JwR#_oOX
ziJ4*#vjYIDOL!<mh6Wvk2GZV^hBAXr>UyT)?<T$e{L9!~Yk!P9y3s1CfplulEDV7l
z=VRv2d&W>rY?9x7$kz_!|7z;4<gVIg%>wl?g}U(-z=hOVyH24q@g~jK0m;ORDgC9F
z6_l?d`yT)EKmW4<BlW$s&zUpluCIOVYqwJeC-LjGvQEVtC(Zz5&EdFDBEK#Zgkyu`
z8*5N@c9GGir2*0mO9RwzS{fiu!Zm|`58ByBTW-1K+v*(5qK2~pyYIgH@!aD*%>d_}
zcizvze^1*8>3QU{<z}tG-Eut${QpQD+*35;P3%aS`=H8^p!gp6Uy2#&vWc<IM-3X<
z9TzW}nva?7V|!e5(M8)Mh_27beeYwBJ$CKXdw$VX{Z|)rXQYtMxszf|%$G`@0%P}D
z*VpK9;(hHY12n|I+%fCIH37_7==9=@zzi65xEKc<5@V<FKSr94kb<(4C60{U3trB^
zTpB!D=tGAv2XuF&Q}Yp<`&$V0fGYJdkvedVcQJK#5&vB((L)~rk1WG1SB9)_Px}7%
zzdsK=d(NCW^Wn43I_qZW{W#)y-~H*BK>#QRLq9w79Fxx;M+$ob@Kzugi|J7=-Ga1D
zc&tn#3>XlF;~uQ9A42*bfeJ!E6b1+ugaP0r>A|?dIOdpRe4t|*^zspxh@7#<9(%|b
zcO~xixIJOGR#M>ZRIrTkW!mvo<hhm554h5f55zJG8u~S6%Pogc2hLpW1`XYUUX-nX
zXP<p`9HaI<4mjX|AN}jU{%hDrll5O~uf7E8VwhA?=s4&s+#unG8#|V~4x$_?5a|p?
z2EOXEm`=zW(S(zyaEr{nCgC9u*e5?d5hxD!7vg#ZA?1G3@i|CG>~?C8Vo!~R{~@@a
zNbr7i2&WUa<<{ru)Mtag(~#HFDc@(_TTG43F~--ZgKoL699jvS0NPl?<;xV{%{SjX
zhMTmG1?N7j8$QjmlvfbXUCrKpb{1w;jNK%N7j`0JbvLq3B^6=U!86PNK(~WwUnBY7
z!wA&9%S`E6Q~V3_A<YNIPuMmt4H<mfZMRLutp^(b?_#IFXQ}t+_kQrmTO&10vn%rC
z5v-%Y^J5reD{cVvc;`&&&`p__kdKy|IRs}<hd3So95i$XW>UEK;ThM%XfO9(aKQ!N
z`^|5Dll<0NI$i>GF?Sf`N*JS^Mq<F&t77gj_Hg?f;h*+eS5OZl=^|N(BacQg_g<_w
z9!4j%8ELJeqkRP&2GU?Qg}87IBlfhmkGrp1$=G{4VkhhyMR06tCm75g<Rgx@?rmyh
ziy#N?BguoaCT~&)&hE4m0*gfJ45!H4%fkNSAOAS?lv7SQ&ZBGWuz%_D%P;>8%*5r8
zO~BvhH(w|Vpbk%9%b>khIJ|ET4vyvnbEnN+2p(}ZO2$4Aowq*#9K(zN)1qVl^FRO7
z8G#<({`R+foP6@h-ahBA%IB6h!3=_cChaKf4TyHqk3vEclJV<S1<nfeqhTtiVWy?O
zZbhmCiBmVun(Rl}{vJAdqytf#C8&Ef6mZ?Zs&Eq8Q$&vCs{=V1(({Ku{Gso7RK-VP
z+_h%=5~z#0v-@@x8*hXfI$MaZAC*$EF*BOjhDvtLE8hN=dW}L9ftLh_LcbdRd&Ai0
z(m*z(le-Wc9EC*2E@yWtJ~0mLJ`5vc_Z108?5((QXm|4A6xfZeING94oJN(gzfYZ=
zt!aI9hoTHaX9P}#Bi@7#m(dJhD*Jfa#ZY&r!z9n)p^F`f6E-kHLr<l^U0|W?60YE%
zP0_r_;M!d)%$ny!&ftF*y6Lg&uDjmPXzEVFeGcuVLpS4|W(RO{mLOg$tYA=hL5ZDo
zxSA9*2?LZvvV&I?4x%7W(8emt;nU#h4U89(-YboT-X+im(HG4lrI%<2*IPHOrl^Bm
z!P#3pJvwatC&i}Et<E^(j7`{VdiY5voz#l;lk(Cmmb#cbnFx%qBMg|){*DmoK2+}g
zOvV2e7-gxnBd{wJY9AQ;k#w@tm~A+p2C@+5?ym7km~Pr|Dn4~C*c~mNh`Sq~=7UF&
zVk)H3RNi)I<aZWjeG_@GgKjnD6rPM-N&ifYZXInw1F`&)B|2zw=DhD=haGmz(xppB
zb9VflbIv*EY>u$`7Qvp>ftTz&kPY7zcG6DCfPdMxX2ARIyKnf{zy9@Op#89%!1U;i
zINJ`agZbnmoEY%Gn4;adu{O*GkY1Pt00+%Mx}(coD{OxfNBmhcPdEUN4qYn@P8;Rw
z;5P7ooHSZZ#g`6yk@pM9Ps@#JX~&bPgFT>$JE((avlYPI4I@~>V!!E}pRupw>a5>d
zd+jAq7jvhgKIXWLgcg}%*{O3F`|flavmnqrIdW#FQBjdbn68JQ%fc=P2pE02)Z=Ji
ztOsL17smc9ZbQ(kAv$+oPa*!^Xgid5aQEE4ZmxB@w&?Wa0JQ!q&=osL!gQ-5PrKwg
zv~Y7<w<1$R;*`I42^>#U&r$oPc{2!Cq24LicFY~YvDd)|AAAHO_es$9`@j0tug=<L
zn{C!558n5EA2fe^4aHD*6zCw~N;#ILTh7L>bHWKH9Lu%O1G&WJ74`}E0FSQ(x_Rp~
zkSWX9MV7qEpu+_v{;Pno#*+UPFZa2m{Q<;HLV@o1kItDcA0$Pdw7a~J6#&wy0pO(5
z0@RfoVLdkKo#nSBAT4BJ@Zo-$Dd1mGxN6dDd8dNw?2DnnTeCP)zrFwZumAcpT%r8L
zcfb4Hzq{g!E3)2e?RAtuUCiBdsT>(#7{OyNc^r<lzrAT>J3*k~EO`fT4$Pf2YGUr-
z6z-_UT$iJ6XG2_zfHjo=jbZe6()lij@CM*E1UEaHQRwM8{5%fl8}jj<@{nadjv!-a
zVuzPH6T$~O9ojqCqtIi#E>o5>dD@FS-PT=9rJl@EZvCj2V}UW0`f@p>DUQQXmVr5f
zUtabo>mGCG&OP{vC!X*{zz^AocJ8cMvlLF|g8LJ=U6U7~3xB{ZG4~QoHeiGB+nYQb
z>GihUU;p;GDDL(4$eYjSo=c!cZ{D&H+g&S6TE$Aoi-E%a06c>*P+$(_KVBr%UjVk*
zjQW^-jG#U|m6NwOfH1KgYo@(6nYi6*CEuHZKs=AXs9TY#F>%WMP@?vwZZF3CWGaat
z?tj?(@y8z@_^oe!>j+MibW49(W44+;THVZ@9Im8Mgi4_S6=T3XVeALe*tWHkgTem3
zhEj1P-C<mb<9*!TQ9qh=&m#R7>EMQg%ZBLpaes_|Uowj*jYCR4fuCiJRL;a)LEd`P
zz{2z*|6ydINnG6&2prWHJ%)sPscA-M2g0a>FbV?-3;7%ahguX3X-9C{8~AsS-&<>e
z(VcO?yAO466gZ5fGnmi+((c43n>M&a=AIa){#+}&7f(|9RO{-0{KtR1aNfLm?^6$-
z!|O%p=u+yVA$6O@rCK|5o^Xx$7siFYgK)wzq^StJD}VO0pPjqOCYx*u+CIN|75eGI
zI9>ej+vc&oluepEhL_C9(@^w*1RjQ)%OUn3w%^g1Q?*kzg?)+xiR^4Z-f}?1L6fox
zgAT%Xnj($S;OI+fkI|Oz>9mA85pb?Q+E4+Ja?6{2<DsODxCdhE<Bvc7v)upid?%vu
zX9>EHxI0`35Ma5HyBpZtnZ7BV++}newO(^-8pQ;MlK6s`kIx*BIgDx2`4HC<h|61@
zyxnXCI6Y3}Bt`N+9(Ni0a&SEhGwdoR{zs(kZF%9wJm6q0t_T}98Vv#7VB!^<W*kCn
zr}&d1T|0lL`vcP_Uf$>exbMV{1-~#$k|Jj;r0ILq?(0)58UO%507*naRA;yoJa5i;
zwTjiX#Unu0%Pr0(A1^l(;*@<m=Kj^Mes#xq1KeN!<zHUG#T&2Ep`8rgZ_y!l%aOfO
zWd=S)g$y<^&5C#4vt#);d+)us8%g&Cj(7I|p%lDkbcg=ER`^ZKUBPb@G-TU<p%|`&
zag-kg^(43xKe*vP5gqpm_@GG^jHCRlAdsB_&^^rVk-lbL#4M0fcG7kB>PY;34Xq{T
zs&nNxhw{9ea#)znEBjN1&{p>U^rt_4_W%Cx|DOMgU;HBLz1H4<5~!QGQ)w$9`eGw@
zH?YmaZ#vBKLKyoKb>O=SI8`%u!VYG{?&C89VeDtnI9?;|Q83XN=r;VtB{ew0O~U;+
z+?LV-o`YG_j%<k7c<C4tb~hnu<SD}SCcW5c#Lguw3lK0ny6}8`007PrhGm`YRDwW2
z9$cUF{OiN$dkNDEog&4^O>FwQ)nVkpM=;KVu9|KN5}ZQuQ5Rt}{DU9-U<)?Q?W5xn
zM;vhzjPWUi6G!YHkq000==Nz|=qHWbBmN;1xew>px90Bd-Tmbuo2x$exzD{vJ)8vY
zkJA=z%!X|hJ9*v?I`Z9wE2xK}jNQEC>gNN@aBoGi^2z1dGE^WB0syeXOkROGHuqmB
zxC=tTJ+)cFg{8Y_l718n!hpgE<iV+Im!*D*e@TJfu+6B*TUeF?w<YxdH}RiEv*+LZ
z&EFix%Y`rg_kaKQUy;8&&uqZ8_S#FJZsty<$!wgWT1_Pd28>&O7`q$TCc@O+!1he-
zD27I88dWiOI^LnAeFEY8<rw=i>@q&5&fm!~_C!p$O~&n5!n`75?}~;5E-nQ)MU^*A
z#eX|R*f<+dII@VsfO`bIsx<6(z`iAUQVT1L9xy}4LYmG9WM=_lhtus$2!?VXxNSyf
z>?W?KMZO7Dq??|MG-mwtr$4m=?lF7z?0cAAyf?R`;|+$VLPwTuw=3u%F?Y&0;veuj
z{q)nP{ru-YKggf`_uu~x-q3!QdiXrQuh3?@bvYp*Si^_5JPqwx@Oz=92WtRPEQ;kO
zgHG297Yn2t=@ts^L5Qd5qd;I=&AV1O1505%(vABFo~8b_vfb(qj35+f?}~nkbZS=M
zEnN+uE%*#=-UbM0ZV%pQAH%7P9XHrugKqD8YR+VnM{ERhk5Z-U;y$=C0OsmC_bAr6
z&!ln}H=qnY1YSkvPNN=9+9#4;PmbB0PNQ%mmJ;<ei0y6spX5JvaQsh2AA{RV3L%73
zh-fNBBW^bvkVTMuZ9^Uo#?2kscVOCyM8+Ry1vEX2JVk%v6xO@KnH@;l3qZP|L&=$O
z(_l?gjKD3cU?_WopHI(Tj&`}EDHXT{+IjNHCnw!<%Pli0&tBVZyX~WApMCZX<l!ig
z9!DKC?L|k>c%uCqZ@h8rcfRu-kA{XkoO<f1caaBY9ah2En`$|sT4I2PTy{Hx@Xyij
zTwiUxMur22ss(r2R=8G}ItYv(JQUz3Vs3?g1MPL#3hMe-n!$^s|G0-Iv|ngep&*aB
z0;^LM_|7EFO=$}klE*i*i9PVZ1Ji!=qaW>X<dH{ynceY^W&PIL8(RW}oqUI(Lp)BI
z8&Bpw2<Cc{W^UB{N{0a!k5F;s4nd7%D*i;Ge*|L>ugXDKUYF@nw>L2t;Fmf#{vF95
zjoS-!dUj?_$JmP(#V4-dzZLn2&z}A^=OZIt@=2)nHu74y*66Xo?3CkV!q}lW>PU+8
zw5KUX>|qvwu&E;fP81TVlE{I%JMle5aSgQe$COwTTF6_VAC9}NCwh!Jp5?gMWw;+i
z9;TDEef3jY-S>4ug1eOz4GzVj#U&J{I4x2fiWMmC7M$YJ;_f_Xixv&;PI33*P+Y(1
zXWoC|<^GbHWQKds-DjUIYpsngfcxgRIB-w^*&XEg_NsGWjSD#lpwHoToy199JA&#)
zJFva;>q%O6L@B|n2G+wpeFjTOyBsxVx9uGxtAiU?yf#+Y6tvb{?x1FTjMg|aa|C1(
zIl4y3jCEQ%vLQPyx(%RZD`!TMs<8~f5}kAPnl!2Cxq~#5^<&Fqjn_Z{RkhvlZ9q&b
zRqbXB5(KzlUHq7kn*S=d5?AXT=Jd!CZPWK(!4d`Zw<K2|xUZ&Y=gjM%@9AQq8W<`I
zD6%3$a#om+_-J3r<!sXa9C&>rfN(T<!Fl}UKj)gk8t$vrwUhq3LXsPM1#vvaZ2a+5
znO?&W)Zz4-b`3M~nWVkf7#iy<@VQh$5w&BBpMn%|v+;4TG^%Q9II^rQkZ&U-qaJQw
z{N${!l*_^z6SMr>Pak_If$Y{){0ccv@Jh}mT0{HDvf8}_Y);$Nzvuldyi?JzLeB5B
z)i2N(ee6U1Z9cZ?l=3olT$X;(4&<!y;@kZjaVd<D#3UcREGKtie`?QD%;`w(zk;uy
zWGqh=Yv%)Mi%<IrF010G7j0j6$-Hf3<Ng$jJ2*#rIIUoeF8eb`j)bV2S(R{f7JGMN
z&pN+T@50MTeI)mW%d&TmVQrdGdW*5;s@Mwi+ou#6DKd9`nULsS8w@Yf{GBOic}r=^
z#>9W_sD=Z$t_7`}^lqyUcJy{4%&}EU1vP2XLH<;MLwfDo{`4C+pY~8FkmVC$^AEn2
z@4^_2(t{eJ9wmiq$}r}2aeL_HN5LgSuTC9DR!60TS(a#Wn(>qNk_9NBYWsuEx9MiM
zfq?a&6fp_hFuz4tmfn+BJw2T3-O@_2BGpGxK_~(O7$fTzP4y;Sp>?=4;w`v4^Sz|C
zcEn6d9+n&3U+tn27ClI$t~t{g^VsPl(!_pA+8_U`mu6j4YH@oBhf!SU8_uP9wUM;4
zP&9rxtlgbo+m{`7E)CNnQSqua>WWT1n9Ne`(^r?9FE$IB-Ywf+Q`4fRQ0f%9Du;4o
zEPU-mHXiV|Gf;O`KV5V%Ja{$YGA|$O<@eIxTm|nW)7`Z7MTUzQxi7mO_#&qhr}hj5
zh8mR#{{rtLWyOglUiuPw_~HCuFEFJToEwplHnW_IVTHlkKwM@<cxKmB9$Y;-_;i`@
zRo)FTvxgFgYsZu^f*l3&iJqHXg+3mAzVs~<zEg2ZN*X47ZPT60mE{l-^L?CAij+#y
zq^9re9_j*miD5xE@rKlWE-#>N>|M89b?YV7QVLNtg1JGoUr2Cp7S%5Q9ewyv`72*!
zO}P)ngM{i_2eSQ2w!^ohow?x+p8BR1zkx-h79B>dqplJWxHszM6u;ZyoO@$wS#+Pp
zg2iykME%p<L3_FZGZ%JkF!M#0>~RM3N*K13sPh9M*-=JA7oLa`JnO39@Jr9pIuSH&
zA@xBhs1Dn+kvfyPU*OHg%dP${t?^iUe0`aFPu9)frCO60bH;UjK0&7{x?%Kf&?NAU
z{$gfKx}EzQldHZF<uoh~u^d+c@`YH5KFq{tDM+$S{u9+z@wm;0pm#Zh!;d<zMx6^Q
zX$oTs;yth^JUl7(jR_7q=3?m;Rs-<+VyaaGs`dYRD!(1vfZcF7h(vM6c9L*)Rc%8=
zGU3<X*fLVWh6bcGxvy-6w0{OYyX>z6&T|}0#l8)L)+5|lx^X)JPorooB!Z~A^4`>J
zgA}?1V|pOa-#+SO?8&oy#YWXE(OWV8tYyWI$FqV-r}#Yg&O-M}UgtZvIlEcjU%0g!
z@eF@{8i<=U^tR*HTnkF(G7?YWYYj5F+ov5jp$T61yNQ0E{EnDXTcp#CA0~;I2YiY4
z?wf}@ZuZs*Q2<ozDC}z0DLOmQxx?Jn`t4tlW!W!}0+3)k0nYVXtz><??Q2@dz1<OA
zJsjle9C~bg{wR0u;EtWMgeP;_&@RlK941kVJ<fnPZc4Dix(jWI)G83zH^FgvMC}wz
z+=H6|UW<ooqYA9`cBvmmtx^Vfu-a{@Jj}=-XVvu?`(d^w{nEsU)+$|}#Y%~>!E?MW
z2y%WsA2hnx^3mjb1Um6*_NWVQ_46z@!Q}g;7R7TU{72JqB|!l9+L2&^h<qrIot@Ea
z32)?04%;c)fZETqv;h@rekOdmIMp#4BdjkR%o*_K_vg8=`Z!VjIfSTuA7~<m>1PeV
zksmfyDx-`VTJ98lLT;9Z3TQuMhrc7j0*h;%)1I~-?$rc)28hZZe3%6}dXLXv>LFP+
z#^3;2QG21A-`Yl0QF=sR59Ke&uHvHc%ys6AiYDK{p|D@`aCiL|#;@K>=8X8F%pzNm
zqiZjx)#a#ML2@@)c@-H^U1h^K8Op-A+1ve$v(}-PN@6|%;1vdZ%UV-v#C*+~za;ev
zJ$~`os*|_B6}qp<RyH%f*g#iRztl<sap2Zc`j;kaLHetT#(}d-lt2eWk@UykJBxvh
zHvVkiV9WIGuV$^O1S~5GOpin<Dj6UykY9Wwd>~U{<aEOygnKBD)vCquQ5{c2(SD9E
zfWmM)N0D%Df7pHtd3%!C;aWTG$KGS7x9@n}_ATgK9ba#^voy@+elAL>7$b5!c#pPx
zd^_2B`psnJjQ*+1E-={H2!26v!ZK@ksKpxslH;L)Ij^i{Tc8sFYT0l|GI1RWx~A_H
zFDRN1f4(CJ61HUq_If)}9YS3)a4`ZA2UYFV79brgNECG*6sBX(cMy~#roW7*GiuVX
z?AH9uEO9$PgK1;=z*+n0-dd<vCAC@0hs5(Hg?~B06W}fY6o#IgFpLE@P@18;7cXD;
zKoqF`6sXT3B4bozTMWH8SvV{07-skrkL4dSHBnso+{tGZg^zCFO2&w2+3&sV6BoHW
zw$gG;iQb{=EvhsNP>VHj@_Qtg{M~$QhmFY674yRhZJW(czY0@yfA_0q;3@051CNJ5
z0;}5xo~Zno7<wML2x^f-{!mkNY2BhtxfgJyhVa2rwEn%;b%z&4froz)6CB6F$FX9~
z2Xn#^Q%}|};2w@IppC3JfMTm+6`_h1kIm@C=}yk#C1AU8M_@<f3l8!_Tfh)X5GhIu
zA!OqxKyH<vTYUGdiW$Gp><FEsZmZJiFHlM-*y-;PP~b$1oo4fOdLFeKh?!>FQQ>UF
zqN|GB$7(8F6Mse35!F*CExOBbg6B|u-nsqp%FMg3eKq4!&*mhJsKjfbibS{^seKL-
z4vdz)H?72<dekkgu50QVch8kvgxta8Re4BhQZwXATN*b|Ck+!q@YiqQgW?3s%Pya0
zEgwJjsALr-HslX<O+~qpS~Nwo&V0036OCvR{CabuhW~fqUBj_Q6(h}72j6ry<1zGn
z?a0})dFR%hAL05ecu8XkBmvaQxeJ86P$W`OKr6yW)(rV9nv~<XN71JF4oG4m1BPU`
zIwIZ9!6wEH!Fghbib7+dtX_%hW*h-7z=X9bgdfh$J>}mc)D@01Y^vx2<p)%kt(?sS
z_Q{(|@ijO|Tva@KeI#%hbIR#ai(jJRbCvg&3aYS7siNktjUUBzA#J0`FPi9eq!9g5
za2>7m7Kkh+?>xq54!Fiok#|QG?5?@V{YO~-TUmO23?2ShEw>qs>|+M3v{jCJw9^)8
zlDF5g)uKd8a&ci(+<ze=&yF+u%EJ4K^xPqTIz<MtuQ9sk;hlW_G{m!oVJv_xOmNeb
z`1BWYTLB7~*p1JD#m#f5XdQ!Dv2vrRd2?H)Pc8B@9%U4z3iS&Zm%ExhN3oOL1_psW
zJ-#-n#oE7$0-mv%wf)R|rlbDQ95!}I<P$~5lJ>;7=>FQY=c?@HNXet->phN;A=mo&
zANET0FBQWsDT`t|8DzTVGf!&}1Dj$EjIl3%!rN!Jo5YWlKN$X;a>5N3^%y|{=UpfG
zc(iNPNTuH+5rz1hmb{*7f&=+=kP=W4z5P&bn#f?+RADfOs1u1LFpvOXCn!Q-0b+43
zmlUEP2mus7k+RGbM~g>jL1>cyk|ldtfWlYIKeJzu#Qs=WFfZljrX6gvS5fo9_i{Q>
zD*NVZ$Ql>c>Ijjd)?fG<)Jza+6Wy>TJzyaUdwsH%sOl%gwXu-R$q~?$Dq<7FX7ZEH
zj<I>Z#3X|1Y!S352hGY2O2gy%dV<D_x8$mqq>LjV$DAGdV^wJ`>087=`s{XSwB<Lu
zf4=M!z*CTVs&09zL_#i=1F3iHn_H}$TfCY>=4y^6e=RjQk_xZ{4>BIvB7}qSr=HUu
zip_yfeW&f5cl3wpnp~g#f)aj#X<>;Mftf96iKiyI2>(OR^uihGlb<I5NQ7NyD-p*K
z$;%Q-7_}^I^)orLIr*d9&Z;lq_tkXJtc<so7;B6_-Y({ruZR4fWYGc_&vPA{KS}>e
zFI`b@^NY=__WGAy-Gpabp?S^Fyq?!zX0rw8soc@Hf)VF#6q^!ZL`$jFv&6f{Usy+k
zi5dM9S;x$PHG&Oi<;xeJGZkFCT=skJc<>ETy@W68TBeAPg25%&$!_&wS(SlVS%<+4
z<fs~GN88H#Ey~J`v4sX1QNIX6oy4DxpYVri8XRoZ5`AO?5ZheIK@a#usOvw_3Qwsm
zLFY+e07VGOGKCn$c%<kss4{TFXL68&6>a$AnpEm2SlP%!Q*b~}))sgHiWnbo*jp#q
zUsE`bcQ<%_+}bGc<e(DBzV>pxy><V;vX6{<{y#ltMwYBS*#11|yW>R1L|oTbB2H~M
z-pC#{cR?+H*KWrV?IExE30{1eqRnGg=$cUU$0??9y%y}n3)W5>rWbzHlbpC|)m1Ip
z!*<=(4CUyXD~%PO{!U*$G2P?ygq^C)_C6#(v7$6`emvowh`$wP0d^eNe=Ojgsy%{)
ztMdVp=5#wO1~OR%c(R3mzZQX?2AqM|mK2ltHmL*LPLdO|03`gKD}YI?8XyAG8|dJU
z20g?bkMWn0RbE_)txhbBybZ3ZEe^;TDekWk;1mYkdJjIV?{rnait<t?bH=;{dRhs*
zdPbr#M8gg&!u*iLsjN57JHt(P&;M2HQk0nfOC;wR+avz&IF2lPcizA4exuEKdX<+r
zhl<kl58WWNr`$yyjk&5*_p(U%)s&;vx;g__)tTL2gCAikAs!~d9sc=p#BkdT6ifRB
zVZ88n#)rzc7q+Coj(Th(>B|+q-RthPOkscMwvT{L2c_3{zVjY8es??%&+<V?(UviL
zVkC?HR%^88hd1F&1pLOsu@h@Cig|g3qUMG?m{J~^+1IEOOlyGkt9dm5gm+kDn+rej
z{r7^$x6R#pT_yxd6K-cjHOwKgrM0qnh=ewG{@$A<Tm6-UP6PMlI$AP%yP?ZoX8$7_
zBky{1-b?0~68g0!lISX~hR?o{gGSk-j~;F{&%vWPOqh$CE-ok{Fxc!5&tqzW1CUSb
z!e!s1!eI=uJ=00p9rK_n%_Y02-aR7w1v>a&ZM{`-fLyxpLWe-3iSSQ(>;zpvra7}c
zasMJF!av%_d#7&0s;I`H6!b;ZqVDT(u#-;_p3d@%?_Lg9Ti|`@N)GdKZQ4)Hn829g
zP9+u4ZBZ%;8nY^(`|7|J)(FGk<G|gEr4Ao`bsz~8tJPa%tIiXvH&?cQX<Kx)nb3K*
zw!Kqhv~~B=Pw(9X)5b(AMSq`x?qREv^_bYTXvjT;bJHYcOyj9y&f=%;UE+^fBv+GH
z+i}LXuBh;Bewlfn4^q_?=u(-TC)JcTaqx&hg0Fg4$gY_Ic3p2Uxd?S*?rG<k$Bj8d
z*3OHXPEVr!DTTnH`0UU5zM@xixg;xZcRV-d)Pt?1*BEzbj#9A5U0EG2UTIQ1Prk4?
z<cy@0zHK@?eCvYrcc(N`zLZi1f}Ksl&q;>`CD;N$FQLI-TZPt;X50||&F~Z7Ve|2?
zDq;w}lzDtRQxexR&m}|VC90I5@%mXo5$?3-(k8<CYWy0SLEZ<3?!+$+vowvIRDuVy
z)~^H>E?Jjv3oL0Sog%y-iZ5x<Hjuo*b;F`8+Z>UomotpzHU9DEb_4uVPTw^cJ40~u
z2d!>9tXx*^KCn5T476T^!KC#jN)7SMp6RPU9UkwyZgHA5sLO6IjIB4?WBo8t%J*dV
zk|u7Cg%i%ah;*gqp)8sV%n$Aq8OSH#rfk227aQlq1;oFvC&nDkex08>1Hm+KQGh>G
zVvEWhRy7Ekt%jY?hk7+e{j9J2sIraBQDjz%I6peI2Zg9K&&4-kqytK0i}fn$V33D<
zO&U2<14(}eyrNDw1#=Hk&?d7X!Jc&(QW4(oNp&FAvj+I9p7IKffQj38kR9Xili66?
zn{g9K>YH=`v+XJB4BcTxiB~*+RsUu5?GX3hl<O+L%d%yAWUS>Vy4f@}DsrM<pY@UF
zZ$*r=REkK*+IyW0yn}wnMniEysr)69B0nDR1q*HsO|*LN3ie-~hfgt*>$@Rz&W@{b
zXq#sRUhX{a%K1+t+!GQl4yAuKM}^JBuURs0EMKLWC_raDPbtWyDHXswRTP6Y_a_Jf
z`%ZmyL7I&uX0f%E=?KwLlwGRQu+@avur%`d$$n2;)!w!=(MI-!hAbW7kIuJo=&>wE
zAT9U!Sc%tUZXe915`FrWLg_v|lGo66<+Ocj8Ql>YRK-+B8_24;!7@w%u1Sobs98ut
z5$xA)w_)7_HeCf|&p}_?`_Q^<0oAgJGr7T@IHFi`%|b3|t1z7<T=v#Y6LE|Eu%3t9
zFzU4PfA#$0U(UNvN7rrFt#U4UWocz#%3m`TI_|ev-J&mL9~-`41SxQpqgdH@jxnx;
z2DgWMsE$MKrnvd@0~QHry0XXx{CKZC8<oT6ka*tz<nxDu!kHVx@IZW{fE$`4`s!F&
zGB)qOHkO34xLa?OSrb-2F-POYGQj86WT=j$pRUlV(o14j-;j8a0rM$+C-yxOC~*1C
zKF-fJv-}BVk`imQ-()y7f{AmYc&uQQ<$iUg<4zf{wer*68}Oz0eL<TUh%3k-nFa1-
zhcg}L;@XT^hlIRkB!2zWH{GY%?McCharuj%G9<9*1zj$yfMgb6^r*7m9Md&(zQ!_}
z*YDwG5edB3vR|}O@M0@(l&E_Sjas4w7zH2n_qUEkkg~-vjJ{5}yTt+#^b0?PVe>=F
z<$f@4^o<Er$L5_Hv<t^xWLeKd08SF#q1@aapM70>dgUojJe4b(%!N6v=l0sm9(O6-
z>(-!**KKG;7dq@}5{`_-k#522v*4v8&aDS(TGKbAU$C+!(}Ukt)*exeFZa&bQH;M$
z!#$<(c^$!uPOEY)*RfpRLjOG4%|R#8Rju0n9>StOGc<}O9ZEAI*tL6EDgI{ZH=kEf
z4y&bF7QOGa=%){=S&m}go^CRcxRY(^^ZA5}q>=IL|5|B>sW!^IMyO2phA5`wh%H-}
zSe|v4L^Th>GwTUu53N6a_Q3dK^~#tixJde0pG^zxmI5hydNPTbEknIgF0IXlSx_fE
z$Q#XqsQM2tSkzW!4pf?aZyX^|XKE16cPkj@GYl_6YH)JZnTjJ;uMy^lWjUou%WSap
z>r{hS8gV7xt)t#A=U%rd{3MW{9+Un@!8|V-u>1QGb(aRRhrT0A)jnteEB;3}rOehF
zJGQX2-yo*;+$#B~Y=`wqxD|?&#G)%X!OK$F?e9apZn5fjP$lZW;wLSC-L?YLSdE^}
zldnaQDD{;}p6I%XrW`PN&k|K#^WW)6Ud7`iBrKTz%8aMaar%for3;&23-%;1!s8})
z=!k|0`Qt~h@B?9xjONUL7sAPs7|JXu?j!j}8qlmzvV8ZU5X_qYIw1!~5sg{DkTKv)
z`ifCZBt9ks+2RCmhI>Q)h$<}`XcEOUUFUGN6wN8alk>8_IJrdTSsFAeg%k?_vm*X@
z*$=Tonv;(N*>;D!zE8g!+LjC+=zD)bx%X%EZEvN-rFr`mn%}y`2=CFHTk0|eK@xx^
zSx{t+$%w0GV)-72JK>xe3pk;K;&VZ7CxEvj6GZ@Tz`nZdm}-6%96#L|g5Br8E3!I&
z6eopuyYi16p0xZfj`xE3ivp3U*zKp0<@ih4sQ%|Hv^x=5B;2GkC}qZ^Vn;CC>2=hj
zt#APutq0?bGPDHWG5JST#98(I@Q&j{ENwYOreTy{#ixW=!mZ8cI(MdVT~b}@u>KeF
zZ*>x3D43CSD8d}k7mYY&DA9CuudrUT<Q2O%$z{47ekryq9(&m=w%<reB@LE5e9=wi
zxTH>EYbCAz11L$L`-#V7m1O6io6fv$Wgtl48qikwE#&&&iudJ8nYYcahT~RO8&Owr
zj4xSG<xmBkBfoM36q9AsZ@N>5Y!YZZm;gI7CW6V$W1_Xz&$t&gh8GPTO8Pxlhgvp0
zDyO@M94aQUlweE1&>gXl7;D6oQNb4qIdfjPnbWrv1{ua7HOvLc#h%Lil4(0W1vb~n
z%nBms$O7O_iHU=C3Si$f6<Fv-*{1*sysq$#we`fINBAD8>?_>8bUsFTVS0b6M>mjH
zKy*1O@%-?W@=vSJM4pVszz57INdsREJRF8k+bWwhd76K&!~THPO>;;+%%XOC<M_>0
zw_MQ9Eri1|#$9qBOjf?1>8Df+Y&=2QX7v-v%mu!K>OcFVrby5AsT2O%%T!6j2{!N`
z4A3$dEFc2JsMPlLd|Q>&SFwGY4soQropi%2k=mo-k%FD`gu=y%_0&%viL?p7saoY^
zD_qhG<V0HGCwI3ydH_P51GaG-SO$U;gAD+*o{ixQk@YEPl#XTHg7Czfz%~YaU;5I#
z`p6O53f{cFc1GOsB_fe_plDKX1vg)nqfH?oiLigIu)cezV`w?Gp{Yi=iVxZI5a!UU
z7fp+gN79p;Ts7LiFsw)lQIXdO$&)2&y<B=23gb0~(spN#nqvN?B{y99#LkUb_`{Uu
zU1~4E74u%W9&2Jp9mahJyn3jyJG9q@o$#vY+xNfdu5`O7r+><Cv(?W|K!W<2E#N-2
zwYgwv3yYG9&4plo4h2SIZM-zKR|Q0CmZn5_y^j}Q&OOVYPu~^SHMJP+GZ1A8AKoW&
zHXimh#;tnud{jYO*sxvO!^3fg_bBWbgojD~f_FEXGe$3R7VRn!#S9HkReeJLuBfL(
zOZ~gW7db?*?%lh0L-V!PnpeoC;!K9)U!KYNDY!L&C$i)*H0^%db3BZp3ziq&Jdw!f
zNL>`vt##}Yhof~!=Wt1TI0flnh+Oy<;EQzzFt0PkuUbgs*6Gz~Q-F4^q<y-1rRfSU
zwT8{bf`bjp-tVF~)B^B$K8D~Nn%%W%i%dR!b)YP$2y~)k=*^*iWQY<_W-v`KB?3HI
ze?Zysm|XkCq^oSjG>*T`E$_ALdQnDp>vpKC*8LR>*|IUndMS}>)OsLY_^yQEi1zsr
zWYvs1jGmp0{9doN?@w)CY=8-8wXI9uB;)%89?o4k=BeUZ;(<AE9G5gsd#=rFB?meV
zgu6{OTio55d)O{_mXXd%(pKi8?25oNfiaz6Lq#r5(2)CUfBJWkAOSw;q3gt={Gpb-
zt*p>3>x!!7N6Dy>rEke>3Op#$PzKewFU-PZaH%_i4%*o?RX|PRi^AGy2no+eUX%=l
zkH;Ts90;-K%86!^6&PVc^s?`tt38=C*glF@D#G1ANoL4ROR|&&l=9hWY`+3N8Rf#f
z`x(n1-*DyCGTw-SVzi=S32S>4dv8TZ-zvUoQ_}lH95$IOZjr%N(fi-~z1iBZ`hDC2
zu@}Ih|4#cF{U`s?VBam&*<24GMUlP~E8YCSxT}YTp4|H+{LfHTzGxjI3ci28a;}eK
z$$GdZxO(Z5mf5|tlzi;QwcWm^8@0H(KqTGUECgC|i2dgH3J}KbFEgi!BACH&h%k9s
zL6g#0YghHLJ6eg!lqJHGCejUS@Ou$a|EItN3bheM*MXrA$2{(srxmBU3?`V8c`4~u
zmqSh*AAWxzS$fx=hKj>e)X2`ote*OQ4I<+kv8O#2A2H#~iNy7Rt&2O|7B)#MGlL{a
zUr^Te6<&BAz4u_lLKnX7utDnUJM>Yx>#L+z?@udu*yt4!zg_x6LbtzgOuO00L~!M}
zg%6D&Gnsu=${3VOuihv4lz9L#*){n3rwgo}G&dSTAo;0f)*I!9G0%;spXvbeic_!%
zC2WDK6y#fyq6qiKO=;{^KMO+8o;dGo)16Nkpzt2nY6Mtp)eWj5zqkLuqGo@9C<Qtw
zt$$3wAPdSs8#pbeb?BF$;l$7nk|{nmcw4B6ivHN6o7X4UuI7NSQbvyv3_U|)$^Qsj
zPZm%kX;<wVp%h={);`kT-th)7qhPw=yqx?>{EeC^`GH^SP&GmEVIl3F*J6Q`;0lsH
z<g1$)SxsY-qm<uL23Due1cxyhcM#SXZfEfYT#dKs+DRyZ@OW5Mct1|-c_X;cV|L2r
zi{d{DPGA2g%?P84+RJ|Kna2PRSGv9>63EryC*M-rGW{}WJT!krpceI4lHr`2we!oO
zRBQ%TRP5i9Y9!5K&{5Efcg6K)hEH7(*)*H@)@6*AZ|VOKFGn38+=GtJ6GyNy+U<O^
z))6$h>;guYfXx+8nbgK=sHmR5BoLuJWcxL<^%>>byqq!6s{X6G_#f?X$=~^3qX4L3
zwx}75$jU#pgFYrDNB4j;hi_VB2az;kLMlM5(o8krSsJ<hZJnMhkAUT`p<&+UPL$me
zo9|Jg(vMrb$#FVtvxID_Q7J8XSEAQnR+pnQF@v0Sm`!PnJ;H7Z6JcuB#&N9(=-o8Y
znhs$A{WelDrKQ00?~cvMuiY%8w!8hZ_`at}QXGch#*_J)Vv0-|VYfdPbz~a7?}V0Y
z`Vceo2W7u#m=4c>RG#0b!Y#);GM1QRA1R~=P~|q^)gsnxFulxRKEgi)i{mfOl?orr
zRM75z-x4HzLF9CW%zWS{cqmIz%=zY@q{oQ3d8tC#@7~1^kmZFfes6l0^Udr1AG`hK
zEWuzePLuLxTMs6)HA1NBZW>pK1_RTn<%PDwF6VaQV*KaUQP-te8jrPrr+3MmBM(T7
z*PB!1aBt@iu0-gXcCGRlj+Sc{MD4(@>3i&A{SD@@U7m?AyTXI(7O26#_?b3nC-O@B
zrt-KL-ifeJO<gk3lA23Kj^xDi<&$#1XoYv)#jfBZHUwS%$S15cnW2BjE@u=Ck6sz%
zU3NUVHL_quD!9W593<7mgeT64ofkIIC^K|gVN-pkzwGzw?SE!GU;J>(amrenv|uLT
zAZUZZk}*Lo!8j4$6gw&Ic!lEscwi*5?7r$N_D>Ar!2fUhYPp6d%~B6si<lJ8gLSy@
zud4B-JKi6mMwSeG;HvDdF7cBfu@+vY4bF80^xrmcInf7j>ZX3~S$PdT9F3^9j*YZR
zo=QEnlp-)c-1`KGnF^4yv}%8sKI~2u?*G2&joBXg&x4ors)^LBxG#+Bn!ypJ;Jy4m
zEtc;=lH^ne<la2q03L8lzRl*_6r1~U{W5_z#vs`aAmoyU-qopG(_ZMm_~kBZ1W!n<
z{%306;wF&U7EW(J_rjG>Z-4Rn-Qh}WvqHPyW989!ZWkLeubQzM?B@E@72>;s`M+2w
zjA!N?XO9V1(PiTI_M<$yP|$QE&9<)ZhQxt5mCcLN#2_k2xy_Oi+P%w~!}sc;eg(a*
zC<@JFOp(VbXKrf_*VJlIP`!qsO!gg~;7(kU;+!WFXhu;R^OPdm?%iHj>C4#!6!2JK
zLJc^V)1g04lvq%jAd~W7T90ixi^0?{o7pv~dP@7_M0)|Xu55ZYq1)Pj-c!l~xR&CJ
zUf0X+MhBQ8kWwHmGFSmZ#k_%FQ?5)ao=kZygj=LK-ZgJyCC%D9(_4RfuJ|}Y((^BZ
zZ@gsC5|3G#M!Er%SKJi0Eo3I{Zq-w0z5%=Jr^q<Q$=Hz>AjSE3!h08&kvHs2gz*ZO
zax%nFK}*i=>7~bDohc{k6K@%Ky6-lW@R={hm7z2qD=tZw5h1V@Q!8}hGbpey)tBaM
zmjXEdkVnLViqGsqAy{BItqcB1=HM2k_IxK#!-?vlpk#zvDJZIx&VQt@bs&^6<>xk*
zCDv1Ls|MmGApQLvy8uajJuMo|6nPeTk+hip#-^S}K%fEROhw(z?dtd+V(Q`_b}A6b
zPdU`{A(f~a2pTQIq|cmIf-Ke0Nf)Y1^q7*o=Fu_BbNHMR2q(m-q8vo+`(c<DL&T?8
zIGzw79^@}<<AO$bYk69=$+`+o!jD09=atA;{y52`t=VViVdtH2AV_7(J1eFkaFp2=
zc(==UtFo;-$rl~9O!NT-5hwSE9o`(CW>i2PKFVi0$023U=$&*E*5^+5lG4xftz@$?
z(|IrF-3&3J(6fI4w%QbTcMZSIRBG-~Aa`V4JkE>cY;w9eU2hP(-AcR`!OW?0`3ecg
zYa+s?I$@H2Jm)lMTGH3iNg3@x84*O`r=n6Z6P{GZkjHxZ8q06D;KCQ|Z*(wIab5TM
z_h(fttr^Y~cqS?;s*I<nrwFUJJSe=DI+$6xJNtRzdyonEQ0(|1H6uPaNIJ#ab5)6a
z>y5%3rgZ6mCe_O~RGLcze;Z+~(sPWQzcID=ZbEC&4PbL);8>}S5frrhMMt=>OKuy0
zow(#2xHkSmT065@UW_+I{L1Vu35UGhrR3$te~Rh-MLVAtOi}IVc<KXpQhMDEQHsM`
zzwhf>!+-TXmN5JfeFYTPT!)5<m~QD!FvU(XrHN$VTvU8n^8s_WfZ2ySVaD{otNov!
zW_g*pe30f{7#V9c8HRn|;*4CEA}8P}(*KNnY;?%I*dyYa*|tK2lIc5iRDUSb8ZjQb
zkI+Pl9;O;JyVVuwU-d>)IFAoB1Y-8@8ltHy!KlKY172`^q=c)_;h#ys-hWPokKEqg
z${Z~=BrhuR09XIU#C)8d$d|=JR2@m1FykFH&lcoDuv_x+%0INx-n7^kq9ol6Ra*%>
z$45vGhf04WO19uL5%PX0CqTeX@RaBdBMq1i5zywa3AoEQw=_&qPGUEzg^9=k_sY#+
z1I|df2`1sG2XkYMWKA{MT?|ThZJd+$I*f?+C@_3l9&l6ja<)meP3aYIZBNegR$;vK
z+bEIS>3oI<?dB$*@mKmog!b-?o;_8seoL(Hkr@lI4Y%0e4&Q%%$`FOTz)VjrLzPnD
zn^c^s$>siUT-qn=G10$D)VFvZvV%c$yKirL0H2&sm2A#O;!z5mjSSZ`c=`D7p}=q+
zphM=pkiAFjdq4shipnBQUQMjNASqlg_)MaO%X?A7hDJ}gma6cJ%L1{Je?veF)dTWK
z8u0?!ej6A2L=j>zMHP^tYwl`7kCQ=tPE4z03YnC{wE;^%(ms&lL@aG0r)V>S9(DCr
zrkSv<69qEBfr{vMyfL<D(w~0zN}G3fJ}bV*JXIZ9(<?Hxc2T0yWJCyBE2rZCLuUib
z)y+HAQ&(TaK$sJ32W*PE&m5gk59e#f0voRbs_`GDvY#&e%x8N1o4vRRj&8n|%h-!w
z5&r1(;zQF9vTwU(vO{9j?S!-iX_#`vz!GGE*`TwrBI1VLcopnp^BkhM%EN-FsHh-$
zps5o4J2)KEYN}{NMMZdl)f%vP<?=Q&1+Y%XBp&?3s@`_~r+BN^<>zk1_{vFQcoL?X
zIAyKVYjE~YSt^9UND`gK<|hVYtc&bd_)1z-PzG&{h7ULwLhsjtr-?oou21@>pak&}
zWw@cl{6YLx{v(rkk7+j<%Ks7{r(QYZQ+=@c!Y4sHI_)CiN1oL8T8?IE!Vz<N1BNMt
z<BcysG<f7?TK|!$bTxxt@AR-U7%WaOX<TL~q>HMgch93&7&P-oZtC~plE2=o)UC02
z)P`7PR|!E%lr{{vb@KRT?~&x*sehdILnL)P&lu@9j%cqEz_qj%lGQcxJ>b{h834(@
zE~loZ@INX5h~S_Ea1PyEgU8v%akl@HC&-75%lLVBl1VvDhY1D}`ffE1jK`#BM`f|@
zB*@Rlq_uth>544!hHpX-jW%Ye$mbUb%z*!v?nnB(fU>WF0L~pV6MLpeYtxc^=?<B7
zkP?$o#eZ1w+<(Sn3X&hbh4S7d^NbewlP-!FEQkzvWb@Eb#GFk>SAOTse*NA1=j~Ts
zpnA}1Nrh?@;dsi9NE2s`BK#*Q9>Rp-ZB(LmY1hUnx~G=?=~@SI97zSjIy%P7)lD3u
zYx{2x55cxpWU|8^9@U4$PbLKIEEW4oMa(7-$)T%TGc^7WjvYxHdfniyA9rJtV&iCe
ze*Dafzb${aCI5{Y3uF}(;NVbJLP#E>Tn+I<<G_bK+z#$4Dlt(YfuCkM7+*WY+qmdd
zOzXj^0|70%mvWQ*`ZjNJ?bO!yL$z^}*7{OV)@p|Xv~Ua2n3!+l+9FWWUk=yYYA>lO
z`to4Xz;N!>o@sb=TVXD#)kDApTS2@jCRI>kO>kx#<1-2!!3n*>?Q6_t<y2&qJBWqd
zJ~0+J=GKbg$bKb07E?^rzs~5K1XzvY%tCG;9VWQFUT@CQq{^Kp)AM)7rPcgk1B8Rm
z!8o#=P(O-HScD>YM@P|n$a_~=LIbRJ*|c(!mg6L2yJtY*QZHFfjKL}V{!b4{?~>RD
z!jOJb7~dPt{}U6cNHm_1!s-gJrtf|T>q-Em0C-Tm<1qR;5hWmD<Pf9qK^U5M5~da6
zIjS)(;H4P~#78T*#5>|YBS9;N7xYgZVwrlJ{#zoc)h>BDodv*ktoJh1y~WJc58nm`
z|5SL_23+Kr*1HTYZt7FeX^;vkT}bRTFN6vLAzL4>tlP3(^8WnnRC>O{d_3^EOKy3c
z)WpJaa2@EBrf2q9{FISNoIfkb#yR$WLPjg)R|9xe9)M^Ndk5Pz9DWNq`FWm<thw{2
zO3w>QX-=nE>La&6P*wABQ{+EgTy54}DcoK-^YK|Uo23XB;dA<p*F*kf6{Li9vEI(+
z0jYca+W(~W8mk(=Uyj!}1@Im)FAwa-sFmi8`zj2LeqDz|eXi;;AuU6mv6@U4#t&>>
zsJCm@RDwsLzAP|BiHV!BB;s8J5A@YzpZmXiIQm3eCn+fJ0?A_Nd<E#DJk?JOWl!r$
zl)0h!oq)blsnkXva4p4DKKoxG5#}AM0}d#Qnjr4wnwM{&xE@vZ#4jx3n=|~{fp0nf
zT5@8q9#j{kinU@2#a>Ku+H2heZQlSkwUwI9kB_caiM23KsYZ-+149}E#18)(q27tF
z7#8{Scz@l#gOtL{y6D8_TFOHgpSH#E*f4DI4M|j_b--zB3hyR2!ymWp2t+-a=S|^q
z1%RqJx+UcJfk@S^r;%<j0#ZQNB!)s<ZPXh;>x%G$0Q2z(h!Mx|w-6M<Ln)u;-`!t{
znIDkCjUwX>_C`FfSf!OIW$6e8Y{(=9F}@JqK+YOGnXv8Xd86RxOI0rUEyf$)vu&E~
zn=}?sbzX_VV56X{wzXr=@cwhfXOrzmSeX=?Zybi+XKrTD5v|z0>w7=8O<N}G!s|Z~
zP%Ql+^zh#DGxCyeJglLH|FyGE)=MkwF<_R9ATwQI<OzW9hw5rVWgB%xFLVe0$?LU5
z88Mb{P3IM-mKex<tT2~`eCF~e3gkKQi?ui4mK*pJ(@JhQ<c7i(FbOy!ARx}M3yh|P
z$UzY3DF6*xD68+?#cilf#RDVe94@h1!7}iWXiny(9r3jB48J6wki!@<1dUYyzV%dQ
znP{V9Lg?TnlIM*HeCzEFHAM-0cDN6g(t?^Ug-^_9vz=xVKRM<Z!F-l8?~G8yccxTM
znC|XB+=>~;le(#{jZYvB{ySg&`atIU;Z=g84<D#gJoWwK`~U$r>#XK&G~zrN5Z;Sa
zc{Vvtdt>u+?sAH1F)xa%{o$Oe1n<p?bNIaEs~w{Ram$yM`^Qq4knnvkMV1Zw4{y*?
zJYJ6%sA?{~eTdUx=N=#rlPnkKJ#M{T(L5u36a@2NpDn)QMsuA%r;~-Xp`0T_h9OS4
zJw=#g00Tl0ClJVu7gUWIXR@XRfq#$(nc@ZA{J6L-yu}*d&wXPqobY*rpo^fg%$U2|
z$Ql8dvkPPjk+<rD4Od6Cln=;h8lsmBZ!FAzK((8Sxx)t~sp>NyBF^cJ<GV7o^_dQ9
zLsYz);nMZ^Q}UUNhtpyqZZ9S8K*5iw8t)3Oua1|!8F7>hV(dpCNbO;r>CoN@h`B96
zS0#_l>W^#RkH`$dAo@2T?lT9*7|7GelxCx^yOF>R0!BB2hDmB*=3vED0ZKtX`vrCl
zhfU&I&}4m3`vn~uE=l2ih>|YoC3Ca#_!rurc~XTuzX98MMg2s~5twnC(27?vFqrsO
zCKcTiH|mT9><V24yuX6Nv`zl%o?|jBRbOPJ08#rG<#vM+coRX4tER|E8!84j7kvE8
zi8DK?5h3K5{#0ndunP=p`BQ<fO}@Dr<@b1}ytmSN&+dk%TyP{xh)-V<x+~^bC4{>~
zrPdw7^&f`363L}HX2DpdNW%!Szt;_}O;!fHf>X+rf{ejiZ>mcffaUNFO0)rO01n(1
zWlLDc4e0cO#1&HD8yH<uOnbBS;l#xyP=n8<s~%<ul-YCjgP*TMH@;xD#$)JM!zb0m
zM>PZ=0$vRYpb5<EDJ7l#Hpq4gQoff@aN?ke`*No+DLA3@OG00aOL3VG_?9KP{Cs-P
z7B0yOSC8je6G5CCg5$nT<jci&ob;_E8+!dws{a$=d6oOZ1QEGyqhjWthM@+1yAb-<
zfIx!nC|&91G9n^kvbtrY*CywT6kx)S7ZgYI38~VoKM7Y^iWI_0r~<HI5)cDG(Akb&
zZNQ)7QqAJ5Ax|(a8OO)hnNA-=PSXYbH-998JuMhfgf(=ACA;Je7<ag2A6)Sv(ZWy<
z&N|7rpw;BaCAOOqegDC<CG*ZosYLJ)EylgAh9xpNmlj9kL=Qbm5O+|XnTNVBC9B<6
zK@YL>)I=XnyL=rrrZ$rFzP#MP^d=Fh+gzwxvXK+frue3m(?f~&6%&R9tqS;wsb(8c
zo5R8OA6f>d4mzk)DhYEO56A<Q2VKP<Px4D<(joP&iaV>jpcp0fnB@7e^ImUgsVnpo
z0|+Yl8K{8MDVgWQh8bQIDr9V4@P0q=ry7WART!mFH2~h<j_5rdqa4Cc8iE%}Aqs4r
z%b(WwXA1qJH)N@+MHQP#LTH4O$k=}>Ot5}W+{@l?_<@8|KUQ>w6Vh|GBl);rf=&F`
zuMn1>Nzp_dt3Hfm)*@EkjeL2VEa8U~C_3TLZ>$Cc!Ae2r1kVlMl>t|P`GP`W93nnA
z`5K%7$PJv)h7l^k2yp^kAq+;q?_LGmWu=A)Cp_XWbX0lo-*rjO-M^hH9Q)q9`r)_6
zSMcLEHzE9;Dz0i#RCXh#y_(<WFO7xJb?!isZtUf_+9<L@-#My@e}s8H9yP4C1wI+R
z2z~v4nO#K-Iqhx@eO=IHT$^{iaLv&tiJS<77p^>PLXZXeYl0G01-N*YB_a?+ngrYo
zd4t3Nz8}bwD4+^X$AhXAI{7COQ*b4|GXX`OE(d~p7y6#UL<*5Y0)QO4brg^~B6&+e
z;rdu-9@uo}ek8Eu%78-RYJF7pQF|G1j2nV4=IK_PD7=CGNR8~oIlnqWHI>(V@Fl@*
zBdKped3*Uf<W%^Yd<B6AXI4Kc?dJWekUA|*@Wz_ePdSR5$Z?3}Ai4|chW6SVRkPZp
z3vE*vVhz)y#5DK6{H?8lUi+4OJk{H}yLay!@@JlS!8dG*RMo3lGyvl2uN;9qVUh&a
z2W&Z*&w^n=5ni--2IxHca-{5MV|n__c_{!pRtA&RNr_RSxDpAai=!7Eb7B|?NJr(D
zGl=3koa;vxbf9X$e7MF*tRo9=`Hj#pH7e!p)_RZ=9+s{40hwgeJT6`QnHuxkj23X3
zsL9^_pD~~OGiIZ7?{F!I@p-HePG`FI{pG=IB&Jp0ppgi;0IOe?5z3S!gadbkfIGy<
zugK?0)bsNpT-#TB6Pu^z$p-v1M!*m%VHcJtL7Yh)?0a&uvj*9KAK$!9QSuhS=5dx7
z;^5gsjGPr7Sr`sLy$$6Pj&3gCOg;!=IOKnYI8@q9|1hZYzT`YUIY2TrdLd$tz_f&K
zB=e0eiB*Y$>;$zZH|u2030Bb1Xwp)yR9Fl(zwO)+pXF!^^TY724f8*P&OAEK6_>=}
zE=3+&O4Wb<iD8Mjfl2lr;L7RznSspJJd~LFNbPMS8~Y!~l_Z|#LZ-SERRDTi*hM?O
zH5|DgyI>G_UU(O@)xsCGJ6!ykY+|<VI|fU%;!5qZ0#US}E8qmO!PN^JQ+T)ZqR023
zj)NG5R~HAgor%lMdcM7i%VdrP^EqILzR&QRO?PX7yRlk4L#3vHAT<3*qX@>iGMqxH
zM6KmbCyGX`>%FbDkB`2-Vrc(?K}spSN|S@C>i-27N)zgT^=c1F$-bPBqZzPtr#j%?
zLzcCNdS=^G017BhJb1&BE?f%m7POsCa5&+3Qqi}#E@(a;920r`77)c97^lEp<v|e+
zq4cvI79lFHhWwl95d^-+Ayd3Gti#@PTbW<&pwKa(eMlj|r)&X5P>i_K4-F?GLqte<
zE<oz2B;S<`sLIpTYxI0K+qI{M{!gmia2wOR&h=Np{STvG%m#@!SVF--!r8E|_ynpq
z+z=+)O>T@66QPW?-;(=)6mThMIKC4|JRbP9nITMah6$#J>RM2ZEJfK8vl9^`Cc4mX
z+Hmf-*mHni<56HNrrk7e36gL=IZ>HknQIzr-z#o1pb(JLGp@DypfW^%;6JT5FZ6zM
z$}U4G5!)n00WYMj*2ibRkAo>~3y%)1*NlnZZ>HO~{d)e#@00X@*reGN!7i!t=;T@Z
z>fNcnFR{lHr-Q?t+eeAEPRWwJ8R8nJMy&*kc>p88pJ*|y2I}I$4$Dw6u}5Qvg6cC!
z_$!$8uuYB+YK|;R-jcXWZR_)Cn8Zb<nD-U0{&!9|u(~g%7XpOx-a@}GKq<@P%_z@`
z;bJ=d+(2&7`MTk}61;^;WlN$t&PkMzF?3zAIWDX^791MG%_^KNHo8Z0AEyOE$D<t6
zCAuC(>KaKH!24!@>~kaU_V@b2^MF~71`mJ``91{|su^De;_21fT5~l6Ab=WWH6_4p
zQUX|>3f5-w87W)0Qnp&5*Fax_#&4AjK>k~_W7G6C&S{{CIc4|}LVQqx$bp2;IROGV
zU(c5_>&|xKw?D*t3yZ9dL{Q|zG;C;$>(S1$;)%dS0q1+su9QcRwnePpo%ud;tIqs6
zANMe`{IS@_07DVn(6#?okjiP5xg1(ZeXm9?>!k>B{h5EeFVD1b;VUYBT^|e=0pspC
z0h9E+DGUIc$j`U=dM6_Ds<N&id~rdt&Uh5s?0{If#SPuq$6ro`3@8aW@>Fr5?{nh+
znYIH)tr1=jN;+VZn?+Fh)MhE;MJN^8+4Ck9Ylc*6VyP-|xQ=ZuP+jwP4|wjof5vC{
z{znDaQhaC2Y^?i&6pp1@uXQILrS94%0=p)#&IX3a*!}}j5Q!#GyD#zIte7D&6Eaz!
za8oFMP9FY4T_3y;7Tt%Kf(gdWMe0PoBp`-k<?!qV2zWm~UjO|UMj|{>c){Oh5y*aM
z+%!=5lsWn}srt>e2@_NeU7pC;4P$N@_>&OMqCk{GdUqB>!moic$f`^m8YHHnqY=Qq
zq3z>d7%p-t&<qZ2;nTY-X?VDOS9dCQlhNs_zZ^g8lFe<>skTvwFLp6LWOALMqyYec
zFBPOE-+#Jk>4WzmbI=#W;+K#TU&tPy|D%|{8sRvtxa%27N|W2sf|4h0w`ANIImQox
zVoW;J{y;*j5Mcy2upnC3fJPFf+9(RP#x1`O_g$a@tPAV|s92-M0A1iye2(2WM)P)o
zKj=evq;0VY52FZk>u*>UIS4LEbM8NmP&5PD$TV@oZ@LI{NCSA@7U<WZV%$6IyT9^s
zyG5)LR$?@Bq^;-dr2W&EI!iY4yG>rV6hGl1_?I)A*#5ioK3Jg%ObL*;CCiPXtm|9f
z^)IN)p+eyX2nD=`RR$rQ0rI{b%RnGx`b<T~paAIhr@`r<Mt&sipmJYwq~!<7?Tn;f
zOAze)qQL#FnS;)-VJqbi3eg;J<;x76YzQ9Rang9$Z=kFU%=fTW(J?&e^EyuVS=Y^&
zGr_fL7wM+oV1GP)AHCT8U;uY3lA8YK!mWqfRy^AS$0O5w137_Uk7tcX4FwIVLzMY0
zs<JVpF-3<R=CetO=BSwChOuQG1_<V$*g@Q2!AQrcwL2?Vv+2ZqXDbct;}~G-{6KEN
zJ+ef$2c_L)16U(ev~CxetfZF<gkR0c1EE=@LK=cCcgR(5W0QqdC$Xv}YfYPAczk@p
zczB2KMV7IjcC-Dn-0qJWo+FtL&EHyAaBgWI*EQXWol=8T%+@SE`*V3cI38XaGF1g2
zLmT}9z`9*v0=VBMNB^M6;XQKwSQG#_h$lGV9DNr|0rZIs!Qb&V1)hS8Ex!~}v*MUY
z1Pl71oEDs}4(Rt)44#R)pCeCg*(fTJt9^w;Cb4aoGNf`V=D(wp==J@-Wx-*9fEFwo
z5?J0|)AZf*cmEU@0ZtlvAJy@f`(ck8Gz6_hVpcDL_43KBI$Cj3;7m6KRa{Ik4FC^N
znu@N#ECd<S@Sq?I=)zLaQ6#Dx<{3*;54GK-SoJ;aK$mm)@@-JUT|KN7GI|Ny=O&Zm
z&;MZ)&R1Q-bvJDWjRLAJb<Lf6#sXu3$=q===H?Db+@|Qn#*}CynIZz%MnF_2Nbt=n
zH*gL`Crpx~pTY>}1o@Vf<fhi-FT(@;=Mtf^04C$&Wm-555SesFEFoWA#5nkE7TynW
zGR#g^gC-Z<o=d~Q0^}GZ&5D21pOA~;)~!N>LoWMYbVc`w^e+*=xEt~b^w<DFWc`CW
z49|ZZX(Ver4n8<AIMOmq(4h)2>L|D?l`R#Txzm^+&W1y-R023S6>v<|!)Khl(&~NE
zemSGJD@}z)8+a0Vrmq_Qe8c!Isb7biIovMrf>1+sNiq=s3>oB#QPVsPnjw%E)CBUs
z(;L}~m`<1i{S}f?OF-^a=#zbr$okXX9L0~p)vt0Lz4BmWPTh@DUBT@<oWk=gY`0MR
ztgKV`5cV1M-)kftISjafoI>Q^;_M-E555YIUM3<_jTE>~0g#IJnAusYS;ae1a0UI}
z8vnm5fT0&&Pus54!t?cDuWaXkPy6qQk<T0miDNV15)+dwxZR)z{r|h_NI}o|gY$Qf
U5?tPS0OX&7jEZ#STjRk010^3<C;$Ke

literal 0
HcmV?d00001

diff --git a/docs/static_site/src/assets/img/textbook.svg b/docs/static_site/src/assets/img/textbook.svg
new file mode 100644
index 000000000000..551f6c8ab428
--- /dev/null
+++ b/docs/static_site/src/assets/img/textbook.svg
@@ -0,0 +1 @@
+<svg height="464pt" viewBox="-28 0 464 464" width="464pt" xmlns="http://www.w3.org/2000/svg"><path d="m376 344h32v120h-32zm0 0" fill="#277baa"/><path d="m40 0h16v464h-16c-22.089844 0-40-17.910156-40-40v-384c0-22.089844 17.910156-40 40-40zm0 0" fill="#277baa"/><path d="m376 464h-336c-22.089844 0-40-17.910156-40-40s17.910156-40 40-40h336zm0 0" fill="#ebebeb"/><path d="m56 0h352v384h-352zm0 0" fill="#4799cf"/><path d="m344 328h-224c-4.417969 0-8-3.582031-8-8v-224c0-4.417969 3.582031-8 8-8h224c4.417969 0 8 3.582031 8 8v224c0 4.417969-3.582031 8-8 8zm-216-16h208v-208h-208zm0 0" fill="#a8a8a8"/><path d="m104 80h32v32h-32zm0 0" fill="#4799cf"/><path d="m104 304h32v32h-32zm0 0" fill="#4799cf"/><path d="m328 80h32v32h-32zm0 0" fill="#4799cf"/><path d="m328 304h32v32h-32zm0 0" fill="#4799cf"/><path d="m128 104h208v208h-208zm0 0" fill="#c0f5f9"/><g fill="#a8a8a8"><path d="m304 408c-4.417969 0-8 3.582031-8 8s3.582031 8 8 8h72v-16zm0 0"/><path d="m96 424h-56c-4.417969 0-8-3.582031-8-8s3.582031-8 8-8h56c4.417969 0 8 3.582031 8 8s-3.582031 8-8 8zm0 0"/><path d="m160 424h-32c-4.417969 0-8-3.582031-8-8s3.582031-8 8-8h32c4.417969 0 8 3.582031 8 8s-3.582031 8-8 8zm0 0"/><path d="m136 120h-32c-4.417969 0-8-3.582031-8-8v-32c0-4.417969 3.582031-8 8-8h32c4.417969 0 8 3.582031 8 8v32c0 4.417969-3.582031 8-8 8zm-24-16h16v-16h-16zm0 0"/><path d="m136 344h-32c-4.417969 0-8-3.582031-8-8v-32c0-4.417969 3.582031-8 8-8h32c4.417969 0 8 3.582031 8 8v32c0 4.417969-3.582031 8-8 8zm-24-16h16v-16h-16zm0 0"/><path d="m360 120h-32c-4.417969 0-8-3.582031-8-8v-32c0-4.417969 3.582031-8 8-8h32c4.417969 0 8 3.582031 8 8v32c0 4.417969-3.582031 8-8 8zm-24-16h16v-16h-16zm0 0"/><path d="m360 344h-32c-4.417969 0-8-3.582031-8-8v-32c0-4.417969 3.582031-8 8-8h32c4.417969 0 8 3.582031 8 8v32c0 4.417969-3.582031 8-8 8zm-24-16h16v-16h-16zm0 0"/></g><path d="m232 144-48 72 24 72h48l24-72zm0 0" fill="#ebebeb"/><path d="m228 150-44 66 24 72h40l24-72zm0 0" fill="#f7f7f7"/><path d="m248 216c0 8.835938-7.164062 16-16 16s-16-7.164062-16-16 7.164062-16 16-16 16 7.164062 16 16zm0 0" fill="#a8a8a8"/><path d="m232 296c-4.417969 0-8-3.582031-8-8v-64c0-4.417969 3.582031-8 8-8s8 3.582031 8 8v64c0 4.417969-3.582031 8-8 8zm0 0" fill="#a8a8a8"/><path d="m192 128h16v16h-16zm0 0" fill="#8aced8"/><path d="m160 128h16v16h-16zm0 0" fill="#8aced8"/><path d="m224 128h80v16h-80zm0 0" fill="#8aced8"/><path d="m208 288h48v24h-48zm0 0" fill="#4799cf"/><path d="m208 288h40v24h-40zm0 0" fill="#6cbae5"/></svg>
\ No newline at end of file
diff --git a/docs/static_site/src/assets/img/tvm.png b/docs/static_site/src/assets/img/tvm.png
new file mode 100644
index 0000000000000000000000000000000000000000..4fb0bacd6a837610e3521cdb0da3e2c8ad008db7
GIT binary patch
literal 4808
zcmZ`-XH-*Lw@r}VdoKZzP6$m}LY0<Fhs1z_6iMhck={ZCq)Qc~DMiIlgG#ydVnb1i
z(xgbQf+B<>JiPb5`@Q$ZcxQ}#_TF>PHRoDupI_&Wm4z`AJwH7F0AMmTxngrZd!LV+
zwAAPCxV85a=MyDH&s+}xXvknVaiKcjgWODP%mILK2>>9L0011FL$Rv>01g5GtUCh$
zn)d(zo`9lOYv}m_U4V&wFaW^7@p}LPg+;so0C3FH4uwUTn`yfF`+{9=`n$S=Lwy6z
z*#H1ERP%i4>yC8+h5GvV1#5=F#QtJvp09ty3SywYC|GZp7|PrVWZ)m<4pIfHfFWYq
z^dJxj8gvt_X>-NspYZb?Ow0p|4bW6j2nh)RhbV*ngD?t;8X6i35G4gACHZrPd~lc_
z)+JQlFZkl$BLA&(#XZ<9$TI-z>F)>nt?T0IkHf;m#C{w7`~00J))W07C%@o-$~rGl
z;rEGxA{e6Zul9K;^f#($;O`p{<Q^P+uCJ{K{fqg(u>bh{&9@BlbU)AZcRv5}|0ni;
z`BonOSpV~`1$nxe`eEIJ&dvUbR{F>7|7ZN81y%T6`2T3c-(CI-J#Vu%JyhY}E7PW@
z7>_;wLRcM5ujtu@0=FD}QymTYqK8^;w0&`erz8zqQPA&Qc#bl)vNcD*N67{pt-Kt`
zcZ}FoJYK~1#ZF5|QLpOj>XTjY^E4F*@!fbzYh;%QZ5lQoTxG{^D|OGyU@dy4ZsEtE
zM)1MrOhm`_OkHp*Nh4%=upn%u_@E#R^S-DcgekDC9SKiJZA|75^G&lG6eV;@C0rNF
zBi=6Ucb^{$z<5WEQ&<itvTZ<4@+(}oa<5%(uYL3Gn&)eQO7xDiJ#rFKXds&;l9Q{?
z&#;8R53jHM&>zytfX8%*vz#hQ_N#`7cBc(T4A4J`MYc%U^ibt!F5B?kRRJr@EBydY
zgzCd9?=KnO&$zSPuD1+Qe*+9smZ8PGS^7Rk8^azbYd&I^TWcrtqcIu9?5Tb@@i?6`
zrN47;Z%<H+51-Ju$;__%8LfNu*5cgDewj0iBV<kXTDDkj5FG+U#}RcWs@f&t0-5#)
zZ_R*f9W{@U1Cq#D5Pnusk?MVOWiB|4Vi{mqL9}^-m8U#bCMYoWT9tcRq3PVAcTRK&
zXPx!2d=C+&OrvjyM>-wQ5)5#VYeAaQ=+{V}Ekjcb$radZvnCt#V@`QE7EUNLqb1^J
zngn!z5h5?9%eT9GQ7-Ah9tX`Bk%ODl3BbWMdm56Gl{N?#wQ=*77c(|UocqGn98RoT
zi;AStx((k<DC+q)^Vb~@th}fwY}`1*M}I!CTUX>Uf!~uS>In1*>aOb*(9FkBTxD|!
zO~71ih_HxR0<UQW7Y~|o>57AOt@9|W0lt~>Z=wloje$L+#`TsT3$#^~F#y>5N-d^_
zI@z!xn^lV9Iora7xy4}-HdNC<_-LiYMoJJrxnb~frTIfm^$>|Y)GEuz(g9AI8YNdE
z+ANuA;Xh@_v$~CvbHUWN`OK~fy{uJvb4tTMh|Wu`$9z+&gs6$PUjQTz4zNohjOTqC
z<YvE`b0#9A+;jHKEuLx(7y>-g45otF6Z-}(tHNHip|it01zK`A6-W6$ar0?RdUwiJ
zuQiKYaB!^s{>SK74F5f+9Ob#3$_1}viN^fL2T~S83mc5bun(w<{T$v1)$j98%4N~6
zw%*KDD|7=M>Xuj9Ixa5?hEpo{HdPRnxjF^*`(J1aQ(#Dq`I%oCkw4vq$fzYUmW##e
z&xc#d2-%HXxx?L}+qejMjw$+b`k8O;Dl#?5N=t5(<E&a9wOeDCL!n%&_JI=0k>w*a
zo(`Du?Zpg3*^jZb5GhgT!20#miD{eraQ&{JzA~Q7JegeZyHdIOR=Zd4TxI#31ABol
zi3H0pQ@amp?3v!)Psh`R%0Jcf=fe$U6UrB9HTk?CYex$9&hiJu?T<lmMe52a1WO3}
zLuw%vj+t(D3gm*6OE2RsEN;4iqL5|ijW{JSVXyQJ0mf^rMVm{#9JRAE3({t%$g2&u
z!tp>q7o2YP-O}d{U)hl*lfx)(JRL)BQ(bTM)9fyLN=8#nm=+-plB2T3l9ULx%a0<P
zc{2Cwc3K3GTY~g&rczg=u5np7NF4(ctLl<XXi=Nu%}KUROv<gNv$r>^M4RMj*&yj6
z%pa>Wq+)3JQ#e>Tp6$}JOz$;w<iCs==2a3gBHLHE)eXaZ6@_qh1vSSI?YpgdJ!<Bd
zPV&C)zO*3iovFtx^8r0b!mNRMN=lIE$LiSKR2Teen5f85NXtmWL312N;h|f>boDP<
z<|~mR+Co1LTW=DXCJ{{l&i80LF(G2?hfwvno(QVb@TNU2$XYYC_-C=&$$M;Q=~l$}
zrB8=7`R|1im8vq++(x6CPj%Uo3?SqMPkfhtZ-vi@#0#+$vs7w&WMwZ4WhHQd=)*$@
z(Xx<q%6W%A{<vV*sIs3iyz7*RxKgZMY_#I8xeR<4B#|9Pv()}hJ;y*k*7`E9RFx$w
z%Ie~#Jr~9p?9J1y5!)acxOp#5hg&qFq5@!=&#@+l#dm!@kbBjY!(yCOB&(=vv%ogS
z{!sb@QXQ7wKr{jCIcxlUzDU6}S+5$<wV#z!o)?KhJB3~2o2~vd0xxOz5KyPOgBhUC
zb&kLt4#-Are$A%LlU8?NRFh)q2)m4CitMI?y-vCiMEP{1H6k9b{E-A#?{9(Ak6e}J
zZ|e_R@yc|~cfW}GHh-OCA{jb|vv>5lCF2oMwR-^<EXnVx{oL5!yN{us_(HrNkvI7)
zt8ny-$lbh_wXDUXx}3WWQ#mr?Lm3@~rg*rSTkEk{_$Q1agoL@I=UZ{pkNpuyIppoD
zxJE{SgNIIBEx^vEyA8WO5rVd){;1HfnVB6T8)tco)-IU$J5{r=i-9n&2%%AZZr$FL
zH-|1(v_(6PEzg?pjV(CHE<vX^|I7Q<b`J<1_g0iyYaR(!>Aehz7%0t|IrVnwqF?43
zr6`XU#)!mrBduz6D$S_5&}U2&f2PXs*6Ep@>62j|T;tI)L-6jtq8snTH$JOnKdV5Y
z#*=tn?v_|Q8aY5DukmTLoRTRR{6sf#cBG5W%DTb0JPjZ&l-goDK5*)Jrj|=;Z{(rU
zrDEkv@v2U>L3XVD2H%?Y(Sb&IOO6`51h1F%M|<DFqac2>u)59GlLL6w<STnxpn%s-
z`+V}lRKb(13Y(!WBD0L`vYOC)&8$TLt%SAL;2fiM&Ewur0+M~c0oR{Ba>-@vb4W;C
zsoF1(W9-!(XYd**#02kNO}RBRygH+{ue$f_ku$^V%n4H!ttIufGN9Jd1L<m$pXE-P
zzU=EWetIVOd}~-{b<H~&*@D0l=>gWY53c+_+5fm74GvHCNQr*+^r?)zOuspIW#B-K
zL=dm#)r#DPd&7e@N`J07y<RcDzW6HpY1nIzE~5dkL4xrmwH?n(FM8x0C{#_$Z6luN
zL1gJ<iffC|lP*Pv2p$hxwGTc`qZo`y_w#aTTS>os$NSL^s*<bLn^2Tl8PIKf+g&SG
zS?`z@8=WH1-b+756*#}bY;3Fd(rs>lvdm65mi*)w`=ZkJd#9w;HD#B(+Z$GQEET$(
zt+%-G{fQ}CKVjr$KAS=fkt~Na(<Es${qvDG&kniS(xcxZIC%xTuU>oHCs-T(_znp$
z80I0j5%&@OcqMqN;0t40xJ?J|oHv@A`wRUy6HBk+NdJY3a&~WIj+d;D0J7q?rL6?$
z;eAH_3ZV?}iNs1M*;&V776I61{!_<y0FdUCmy{n$fgtcrs4C&U8;!0BAFf_iw0!kB
zmTTHT-JW+6^JagJNy<D9)4f8j0cj+AdsBUr{4qEiGN*_VYM-H&qmy9^*B)Vb>qlB>
z^bTO<O0ZpMQbtcHb~{h3X`a0`B1MQ@JWSi4>3}UL^nez*rN#9xRmbt_gWcGUlqSL^
z1bTAQAKYGcTTpKMI$EN;<T8~1Ud4}uc>?DGd&I4W6-94?ZS&;9^W7bZsM3H>5XYZM
zET}@yFQi*eM`}dfO=Bu+L}6Ka18LT<VV=ey*WKC1@CD*mYs^;~?v@qVgsqZZGkVd_
zq07*u8cdkNlRgfj4Erb9_Y4t(8`d?M{-BcF>40gz@;8Z!7q3u_r9x&FkVy#P8jDCa
z989H|ds1%2=P=pAA_gM2avE<bD7kxPigdkHoZ64MCD8tfjAiW!q>%<wyk$x@Z@Z2)
z2$+ZWDAZz&);Obetp2c_Gw<*JmH2j97+jBG69@Dy36*3*EQ&Rsg*!W!&2y)o;7@KQ
zp2^4dNH|{RAEPCMnzUj9km>1`6W&R8zM)Pxy!oZbm<4Qq4eQtZ<KyEYQI7{ytx}@j
z7ZCb5ygiwNoG70`(KHL;+m5n4MVPJ)A9i-^r@nI*YB-YW0Ipo)=J^OM!VKZ(>@s>@
zC(6_6q`5)7PFcFcGrgq3nchqKUU1Inodr%-AM~))4Kqf@A)tn@Mp&QLyyu(-uH%=Q
z0)*pRct4kdpr_d!;2+~6VnQ8kiN;02Ql$;>SDH?Sa^cH2$AoX78}j^}Xu8k<-UE>J
zrK2W=;<4Q^$-b(~WrkEuho3Nm-;14fS1&SKO`G?;u*`B5#<58!2;I2tc(AQ9hS98B
zAT{iWJjwy`W+eLo<x7nAT=+DyHX{o?nNX}b{AzPCj7ly1+J<g*FP|BiP}(T)IIeJZ
zHz$EnX;;L@3l_ECHBrk2Jf@_2hE;UX7;0zVpeFD<sZC~4VLYaM1r23O>WdnrudS9u
zy-NN@_6=#JNi214D(**e@lKDg$5pnh5xO+3t7`ode>tme({ZEGzL=Skx~BH=an4mb
zq@-7@;?prVH_7U;CZ#rdV&#~OsIGBAtAe?6@t_b?HzcbKozp%|DirNU5APJnh|?06
zO2!5+lgwmODJ?P-WmCn!7E9=PL1uLAT6ACoL@ykN0BYO*3-#9bdt<&y<Q)=`I*Pzi
zZd~HUI!n^DP8$gw`@;4jV;t7K-dC+P4ol++iodbG5m$Ll?STII6z<{Rt<Mef-%q;2
z8A|1v)$pDwu}nOinbup~x?zdMSnb|TG5BOr@55E{O!!7OfBg>7YAf^h8MVv_dt|kQ
zFGo3-lCy!;I*V3O3FB3OUBc<U`}`ouwP9>aS`foj*(e|wjksS)<<o5abK~?0@NL**
z0!@+MJS=iMZLUMlrrfR&*TBc6TRQbIn&FI(JnhIuWDd!gY#(Z5eq!JO{A6!saiG~&
z++8@OB7VK$*&jjzow#m%{ay?^SqpvJfE5%H*m{-?=)OKo6^@m@u8|ode5xjpjdr)2
za7{i~tZ)^f&*@c)I^2LW<9tp&QKj4wg>Jtx?+!iDCV1k|BHM@l@#6yHy?NL;?t|9O
zIM`lW*=a|tQmExl4j2#>6S9K5)^dO@xn0oeR-e+xR6avLTk7zr6d5p`2ZdG=tCn)*
zGx^({LXMl31@R-WP0-izUSzKh4UgV)`rBb7Kk_#UhV9)yYuz-VkmcoNbWqDQ8=&$Y
zg>QA|P7ji%C-8c%k+#u*Fy42%&HX#&7>4}X)RB2g*uML2WvW>zLH^1DkHs2JfXIIz
zKkk--DKzO}6kCVV_>NuFZL7LOl{F_Gf9R1gw|exeKCOX{x~F-pjUz(%!bDAWEDj7m
zTbJ8ZV|u$Y_v_;zaO}b3au5n;vG%y~NRXjCei4kguh1H^sV~)iwGz`WQ&2qcntk5y
zt5w=Z^V&3*2z5TPm61e-4)a?NtukU)ooh?28T2JF+|0JVnVex!h#PaScA6*&RJqYN
za;!x&i)S0QbTPtRIYO=%w?{6O!Cf>|oabVm=x3@(6-RI9mh`WwOn!z+J#|?%e_}>2
z5vvrmPFC<O)0OMIZxZ&V;{LS~cEeQ>-;eSv0+ttYX+T8(KUgeK!-f35j{5S1s<A>s
zLU?}}PQ$OPSmP%+`A~823|f_3ZW{dJb-5-~oJJ05abaMm<g?lX+bEg`PXmfG%8Bi~
zv*Zfp_cEl0qAu0+jvwAA-Q!$eAQNae6`M3)7C-&M1nu)c;6q&~AvgU%_Qo~YrBVA;
z#}3jgci>{CYGBc=?R(Ra{U<TzOPnwfS76tJof!7l+)T%xx#7S67nvGbT&dM}#{U;t
CO}C2x

literal 0
HcmV?d00001

diff --git a/docs/static_site/src/assets/img/video-tutorial.svg b/docs/static_site/src/assets/img/video-tutorial.svg
new file mode 100644
index 000000000000..002ae58e718f
--- /dev/null
+++ b/docs/static_site/src/assets/img/video-tutorial.svg
@@ -0,0 +1,2 @@
+<?xml version="1.0"?>
+<svg xmlns="http://www.w3.org/2000/svg" id="Layer_1" data-name="Layer 1" viewBox="0 0 128 128" width="512" height="512"><title>VIDEO TUTORIAL</title><path d="M117,32.506h-5.049a19.87,19.87,0,0,0-2.493-6.012l3.572-3.573a1.749,1.749,0,0,0,0-2.475L109.18,16.6a1.749,1.749,0,0,0-2.475,0l-3.572,3.571a19.886,19.886,0,0,0-6.013-2.493V12.626a1.75,1.75,0,0,0-1.75-1.75H89.925a1.75,1.75,0,0,0-1.75,1.75v5.049a19.876,19.876,0,0,0-6.013,2.493L78.59,16.6a1.749,1.749,0,0,0-2.475,0l-3.851,3.85a1.749,1.749,0,0,0,0,2.475l3.572,3.573a19.841,19.841,0,0,0-2.493,6.012H68.295a1.75,1.75,0,0,0-1.75,1.75v.3H11A1.75,1.75,0,0,0,9.25,36.3v65.8a1.75,1.75,0,0,0,1.75,1.75h98.844a1.75,1.75,0,0,0,1.75-1.75V54.947l1.437-1.437a1.749,1.749,0,0,0,0-2.475l-3.572-3.572a19.876,19.876,0,0,0,2.493-6.013H117a1.75,1.75,0,0,0,1.75-1.75V34.256A1.75,1.75,0,0,0,117,32.506ZM12.75,100.354v-62.3H66.545V39.7a1.75,1.75,0,0,0,1.75,1.75h5.049a19.847,19.847,0,0,0,2.493,6.013l-3.572,3.572a1.749,1.749,0,0,0,0,2.475l3.851,3.851a1.749,1.749,0,0,0,2.475,0l3.572-3.572a19.847,19.847,0,0,0,6.013,2.493v5.049a1.75,1.75,0,0,0,1.75,1.75H95.37a1.75,1.75,0,0,0,1.75-1.75V56.282a19.841,19.841,0,0,0,6.012-2.493l3.573,3.572a1.738,1.738,0,0,0,1.389.5v42.495Zm102.5-62.4h-4.744a1.751,1.751,0,0,0-1.73,1.488,16.292,16.292,0,0,1-2.986,7.2,1.75,1.75,0,0,0,.172,2.275l3.357,3.357-.7.7-.015.015-.657.657-3.357-3.357a1.749,1.749,0,0,0-2.275-.172,16.3,16.3,0,0,1-7.2,2.986,1.75,1.75,0,0,0-1.488,1.73v4.744H91.675V54.837a1.75,1.75,0,0,0-1.488-1.73,16.289,16.289,0,0,1-7.2-2.986,1.752,1.752,0,0,0-2.275.172l-3.357,3.357-1.376-1.376,3.357-3.357a1.75,1.75,0,0,0,.172-2.275,16.289,16.289,0,0,1-2.986-7.2,1.75,1.75,0,0,0-1.73-1.488H70.045V36.006h4.744a1.75,1.75,0,0,0,1.73-1.488,16.3,16.3,0,0,1,2.986-7.2,1.751,1.751,0,0,0-.172-2.275l-3.357-3.357,1.376-1.375,3.357,3.356a1.751,1.751,0,0,0,2.275.172,16.292,16.292,0,0,1,7.2-2.986,1.751,1.751,0,0,0,1.488-1.73V14.376H93.62V19.12a1.751,1.751,0,0,0,1.488,1.73,16.3,16.3,0,0,1,7.2,2.986,1.75,1.75,0,0,0,2.275-.172l3.357-3.356,1.376,1.375-3.357,3.357a1.751,1.751,0,0,0-.172,2.275,16.3,16.3,0,0,1,2.986,7.2,1.751,1.751,0,0,0,1.73,1.488h4.744Z"/><path d="M92.647,26.417a10.562,10.562,0,1,0,10.562,10.562A10.574,10.574,0,0,0,92.647,26.417Zm0,17.624a7.062,7.062,0,1,1,7.062-7.062A7.07,7.07,0,0,1,92.647,44.041Z"/><path d="M73.385,67.722,49.32,52.611a1.751,1.751,0,0,0-2.681,1.482V84.314A1.751,1.751,0,0,0,49.32,85.8l24.064-15.11a1.751,1.751,0,0,0,0-2.965ZM50.14,81.149V57.259L69.163,69.2Z"/><path d="M109.844,109.8H36.992a5.324,5.324,0,0,0-5.09-3.825H21.838a5.323,5.323,0,0,0-5.089,3.825H11a1.75,1.75,0,0,0,0,3.5h5.749a5.323,5.323,0,0,0,5.089,3.825H31.9a5.324,5.324,0,0,0,5.09-3.825h72.852a1.75,1.75,0,0,0,0-3.5Zm-76.108,1.992a1.835,1.835,0,0,1-1.833,1.833H21.838a1.835,1.835,0,0,1-1.833-1.833v-.484a1.835,1.835,0,0,1,1.833-1.833H31.9a1.835,1.835,0,0,1,1.833,1.833Z"/></svg>
diff --git a/docs/static_site/src/assets/img/visual.svg b/docs/static_site/src/assets/img/visual.svg
new file mode 100644
index 000000000000..6b9c0946b4df
--- /dev/null
+++ b/docs/static_site/src/assets/img/visual.svg
@@ -0,0 +1 @@
+<svg height="464pt" viewBox="0 0 464 464" width="464pt" xmlns="http://www.w3.org/2000/svg"><path d="m0 0h464v384h-464zm0 0" fill="#5a6470"/><path d="m32 32h400v320h-400zm0 0" fill="#d0bafa"/><path d="m171.3125 296h-115.3125v-16h108.6875l37.65625-37.65625 11.3125 11.3125zm0 0" fill="#fef000"/><path d="m56 312h32v16h-32zm0 0" fill="#9362f5"/><path d="m104 312h16v16h-16zm0 0" fill="#9362f5"/><path d="m136 312h48v16h-48zm0 0" fill="#9362f5"/><path d="m277.65625 149.65625-11.3125-11.3125 74.34375-74.34375h67.3125v16h-60.6875zm0 0" fill="#fef000"/><path d="m112 416h240v48h-240zm0 0" fill="#5a6470"/><path d="m208 384h48v32h-48zm0 0" fill="#aabece"/><path d="m376 192s-61.710938 64-144 64-144-64-144-64 54.855469-64 144-64 144 64 144 64zm0 0" fill="#f4f4e6"/><g fill="#9362f5"><path d="m72 104h-16v-48h48v16h-32zm0 0"/><path d="m408 328h-48v-16h32v-32h16zm0 0"/><path d="m352 96h48v16h-48zm0 0"/><path d="m352 128h32v16h-32zm0 0"/><path d="m392 248h16v16h-16zm0 0"/></g><path d="m296 192c0 35.347656-28.652344 64-64 64s-64-28.652344-64-64 28.652344-64 64-64 64 28.652344 64 64zm0 0" fill="#e1462b"/><path d="m264 192c0 17.671875-14.328125 32-32 32s-32-14.328125-32-32 14.328125-32 32-32 32 14.328125 32 32zm0 0" fill="#f4f4e6"/></svg>
\ No newline at end of file
diff --git a/docs/static_site/src/assets/img/xfer.png b/docs/static_site/src/assets/img/xfer.png
new file mode 100644
index 0000000000000000000000000000000000000000..3c93662dad6f8accafc8b3ef66c2543c1f2b966e
GIT binary patch
literal 8932
zcmY*fcT^K!uqJc}(xekWN+duC(m|>q(z`&A-c+Q6(o2FMU?_?d5d`TX2n3OYzEVP!
zrqW9U>Aff@?dA9WdhhJnySw+?viIA$Gdtf*l9`F_6?z_eGBUC&`g*r5$jCqjr2Af4
zYEqec{k<#cLiym9@hvhkY#R8?g^Kh{?P{odo9yzx<6Ud{D^d-ezn*On85u(OzY`Q%
zsu4`8q<O4wtV6R%%R#Rwo)OSqKt^_rO#k++yP?zDZ$nzxyQg~EJE?SDmc{ak@l<{J
z_eidxi^C0GGxr{wI|;sOX9VXOm^O5@Oy)_E=XpAs_@%4bJn6ZCH<#wq_sADQ{sV0>
zgZ8ub#QC7$;NhvDq21HJvuU&;4<0-Si8)o39ad$c6^vun`acC0w)k)~h9l*eVeJAV
z<o03D^@uDSWJs=tDn(g4^N%nLQr+h@tn@XF0+(RH`3cYzkE}a7Y?;37^<27LEH<DR
z8Un>D)G-Q&N&-%SW<Ua%R9cKxa(!Z(_e3(?iy{q_;Cc6UC!L4Z_cx1)zn2oTQQ7EM
zC`t4WR6a(jp7RFRB*zKe4YAbF8>w8gzUKxvycWJvXC(`pP+ljqBQxgV8}-*({}}%h
z5S|a8z4orr0Tn}W+{Ho>Ia5Tvf9xwt3?puIY3Rdr3fJ?Ws(7Hz(KE6;n~p$rK-W0o
zpvBkWtL>{+^a9x_VzB_4Q6vpFaN|7Ne-61Fh#sI;<<!j+Qf#?D>%ExxDj;a|6tOZv
zYSbp#&zSYA6zB_!@CVofy5!s%wQ;qf<epox4_w2X8EVZ-)hD&sri&<3Y3^9p?&5k_
zn%6lvOhuLweFM6dfdpt`3EaT^Qfu8QSrYB_VdT<oDe+cQN_@a4V+fY&2CvfR<W)m0
zu?-v8RjJ-gtBn`|Vh24fYHj*CgmM<7`n5>A{Z}~VO=$$RRIe{kCYI}I$WvZ2Blg$~
zf)$+Fk=dZOcZTC|PUXlGiuBhQRdQUBgZ%KFL{(|-7P$h@dB5tG7}12NJgaK0=*&*c
z1qy+iR9v}9z_jM4xIpZ2v;>ir$cr|1@P<ElbX%vjX}-Cfxo<AnLxLFY#{B@k%R9j4
z7RHg7?BVc^c1(^z$po|mwo4C)OA?NzQ+<ini(P@~w9=O{(M-1vb_UC3(QvE|HDJ=Q
z_S9#!WJ^IF5bUF3y9|9BmDEWAoayD7P?&-i+mE6xMsFlxSHK+};}i3f<CHK-b$Jfm
zJ0wpYwYn%CsTEqMs&j%!Gvb_O-)jyRGIz5X__;i$8Hew`paXUX0$k2_9cd!(ElW@H
zapl)n@$^0Kg4S};MQVCVzhpHKEHpw!87|eGXLP*&!#*Z6d<fbL&g^-wa-&JY5O4Ca
zR~{XLN<7ex{uK^xoCo?n&UWw_t_*vjAcE&tCGoC2&r&XCUHgQYg4Rc03bY03&rw;Q
zrqA_(urdz!syhUh*3G%|RAI(1P;>R_*z!yru?=gOhSX9>Hj~n@I2CuL3o!Z{f?(4<
z>R7(qveqUoAU;}i0DGjx0ct>uggRIc|I{ML@G6mIqc6B^GqfmaOL6a4i0J|u9gOP5
zMz*Vo!q(K|u*uhRM2hOo@l%B?PeL8ER#a>y8=9e|)fXF|#bMy|(veB2<StI0<wdkm
zt^F5*H>YzbpIzp*fLQW!^z2aXTfqtOBvu1+Ty+R|&w{%>#u9?{Inc3BW}mvK<EPAX
zye$dZ`LLQ$qz$wAgiQwhqsU$|pf`lY$JKRZixGM_v%UxibTP5oKH#VmI$tQDAt{uA
z({M<@BVpBRNj{~LKr8;<7OTOinfpt%htvoucjcDEnf#OMH@+PgOZgi9g3Y>WDJLst
zv3UH62!CgHDd6njd`FnFsE0)j#SK^}%Uyc8$<C=DZcD@a<K#olo_8vOb~-a9qoyv;
zDDcpDJ#t9i;%jrX8;;&TlEQ=Yrj+Q*3=-JWzkNgc`1fM7pE&|`_`fyvQa`r|GV1Y2
z2XG{tQgrcRV&=w=BBK(kP1CliJdund_4qmmxSFWdk{Tf%_IAEd3QC8<)-PHIX*YfZ
z)q&5)V+P}1n3a9LnY^s*|G7aT5!B`f-=#rzN`Yc%w|d3_0$-W%2DygG40*(w6$@+s
zUN)UuBR#$-HcI0=a>rD{B&o3*Ony4bhi~Pd+fgctpk5N5u+xLR@~y$<-9f&ruy{x{
zq>yE&1&;aaU$P&UAoP)2H_t*UC^IQwj>D!07DqeDAF5rG``&g}JRuvSw5UluRz3=0
z+;eaXgS)w`F&cra=^x(bsa3zx%1<fV>HqG6a1aK;Qtn9-?{JumhJ3<8$|lUef7Z#V
z7e6oXJC^EHnk_PfNdidcFwzj4jhX^RQNGTy5*9R5@vKqpauhGm3OV&re+dV**eY`S
z6&*YuJLlCD7jr~^Q?l?Ilw^Q(=uDaS4uZ(qzNf|iq~RPh8#mC2cXbZZPWG@!0eff=
z)tqxsWrQV;#(Nx3+DWLRuA*eoDk;lkt*J#jlKtJt?2m+lIIMR9SmctbI(DIaKE)R_
zeP$|);2nKDoB9@vZX?ub>{-;YI`g0Z{;AdcU}Tn$5B_QTUtrUItDu<(2q6<ZDzyF=
zKn`_ym<iO_n9JqqG%A5*_b(-u8kqi2XYIwP4L7@eOF0*3R%HU$fhA6k0W`??GsuqH
zZZtC0Hnr0P=tO^2UG~CdpV_-d7Gx8ADjj%mict!P(^2?k07p@%maUub>OTMsq=Sr&
zZ)833+MCV>pj&fFd)m^#<ZR^wp36c9e%yz)tF_#v9$lA^Q1(#96J7*(-23wCrZF>X
z|Jd!NaN;Cze@G?^^GHXeRlb#GuLoF(M%Un86^quY_{$;0`ZjDCK-JT~C}YlX;a@nl
zw5H-8M#j!0<Y$?pe@FJs-NVA{=yGws6xKJg)|DS)>9TL1q{aPk7SEI{R46yS{pbDr
zZ;Q&%kt=o_O6r1II;))As!R#I14oeR5avA_Vjq2y2wVC4w=Z;TAA&1+Z{+MXjZ5$h
zBv9e<ZT5X16rffJpxnhrJMMjVfNz?^^i0F*ajPKaI@snJ*ws@w*S~O7-Yj|RpXN(S
zJTN(^bKpGNanDrkrIwbhz1L;|V2n2QVTi0uR0)nO2<|67y)lHm$k_j#0xVw{krfxo
zplX24#BU2ZM{8Od&$|}uMF3I3@1Ozt5?Zg*DvffW6zHD6;sk42I**s#W5{^|wEBum
zC?vr8OO=N|Her)S0e8(Z@|i%a2JsP$&YVGVggFC_u%sJ6$nl==-1tnJRf@Z=Aq?Nw
zk*y6R{M57y^QxO3F5?J_gZHyR1VSUFh;ffF?ymg%6LAk{ZptM1zmqruan`}SzP+Ku
zJvgy)4kbx$%JPC$TmBco%dzF#K`i#nln_ym`!nv&@<K<9W5hw*tYe~!?p6>4E463f
z9K;0jvN7B{|7_0<OP5AGaU4IBTl({+b+3&!5;b5XFMtGPoA_P3!G&YD0Ig_ocvuTv
z)ji`;l6hC5;LPN-g5#OMg7=)AWlebo#<@d$t|8{8=?k+HXFMMBBGz@3k2=3<*E|CQ
zF;1p!B=vLkjSHYb2Of#;L5^45rElyyx+S%6TtlTWF@RigpY2ZZ|FSH2xKaSxB=!f=
zzU00)D7J8=KrfW}A5TD+skm7Bg?;mbYw5+w9wv-m4(+Ja(>?;htonkOI%L+eE`UN^
zzfZ9VdL<5f?SnpUnQHDWSRGj%1&+y6+c}iXP0L7v!+`c)rR}H#|0cItY+$wHT}rf*
zJ-NZ};f)ZYs|_RWmv64K)xj{LS;lzg+|T5>c3&sYIIcuGO;9=#GVkaa@`IHwfBZnX
z6?86tU?TzI(uQzi7gm8iT_HTKy(`PE?|1INLBi7CH#KYb9kApyZYd^`(ak;>fN#Vy
zkEaU%8LDh5k=m`bbG9Ubwh##E7ex2Oye~J`gJHH~osuj0bM6hJ%)^c(Q;|0*1pT_D
z>VDzZ>H~@U1?!${to^QbnU-iLDg{~9R3#0X><EDl+4=%ExYMr|V*O*{zSD+2TVvZQ
zPwZgVy0-nA5$#iSpDof?U1fx!qkkv*Ii~qcWR<z>D$0vIRpa{8v`o_W2nucoG4{S|
zl$B-RY?2`9q*Pgjke8IuHFnnirjS;W%<B{X0xPLW!q4L9NhTJA5c8uQ<{Eg*F76~a
zd`Az)dkdaY%B=){y2AezXlhNYjkW&@aBojskBhT#Uy0=%pp1;lBYW1xlJJ<hEwF0F
z0ri&Rbgu-fTG1>X7bg=`ITj>HnSBSQGEvM#v!Shk;aT{`$F8N7Q|=s1Hp?|RNK{%Z
zFGXyLbf6hUR6*OG${sYVMZpVRS8n-Mhdz|9vv%fl3#;rpd_uEAG_WpN4sw1%#Ly2L
zr;1d0`?e;~Jn3B4*rqu86QekDX(Z}h&;kQx#`dPQqRS~#{m6&N8MonI{1z8>=-Bj8
zS5uZT_s$SMz8u!E)wUedHgrQ}9Mi@Ip0&hFSRTOk;Cs@(UJr5|b!;<qK3|`S-=TBH
z04vXU2kbCc4P&ZP0=OS*sYj;q4E(X;sT)BZf_od7Cpm!<dWX|z7ylG{e3l14<p1pf
z+=;^g6qpsCop1z3WS*3<hpH6^%rQ)UPxJD-G-+7exE{sOf-!u5Ky!J_+AmJG@(e!)
z7Bo`G`r?qcddoQ*6(o1c`MW^5Y1VlD(LH*c2ev)twIX>N1%t%M$B4;eF{yt<aT7<h
zA=-v6n$CWsyU@a$rXszfWVF-~FLO<Ul2K|2k2?!eDz9v0dil!A6E5}BA}_v{^!xKz
zbam=KIUsQH!q@n80(_jEh>=8fd(8_lKE2(KLJHL0{lieex^_lUs;5EsU)m}KJDa}$
zA$}=9rQzS*wLBk9!{<wF4lBC_y9MQ|18n*&PsApg$q=DVMB|PR;5LdXuPqxzd2{#{
zMcW&qsiCQxEPKLJl%3bf_&MFA@RCA1^;vgw(T_5!N$Sw+tB2kc$)#svg%YT51}9v8
zzsfnZ0^uzy4v%d!est_k=G^*ELjc3mR5H>|VMf$KVO7|{0VT+L+mOq4{CB%CZSYvZ
z4nCgXMSs(=bTD&aTe26;9+v%^!Dg^2j}gu}IrxTfkm^^Ha7q6RPHdrxe7l8--=>)r
z!=!th)ps&dwT+Jg`M*Bh%pWg0BU60x>Ah-ACn~@lw+6-3Y3?WjA+isB??NO`@aIW;
zd)40p;T+>@3_$$)mf2qP4)v^ct@`i#qtd+*;R7vxr79wYRjAYL?!dgurf2saX)h&8
z%7%gjWV0gbDMUH{Ev;ORSH8U9C$qPDcLDG0u-#4DAOCCdW9>y;otCR7ToUNxmOH+&
z_e#?%5aDkTv2eTWi0mRY*QOe3(%-w;_uQB@Mx7U5_~z|PGwTs5#jJ?{iEzQHTcN@4
zuml;$5NOaF|H@k7NA;KO#Y&qTGyg90Z!yZ3_UqVjN$kd6GE5fi#oqJugywp0xw@oM
z1P|7>+Twq#PXsi3-(S><$m)`0VC?i^LuwQsN@8Y8>uf-TKtuc2Q^Od4H)sYshoQ4I
zBtQ+pXD6>ThP?lzA;!MMm5;aq&tsZ3*b>Xg?`lko$xju5yT}67__yLxR$;2AX77;p
z@ibP$>;>di)<3_QP>)mHUxlYPF9y#IikWl$TjbCjJ4nK{)h)6h&wStO)Qb4loKvG$
zqquN>YChLaJ^+mX3;O&-gE+}RkoE~RgNli%>T!7dK1prY&;0e+Eyn?Un9*#vc$av9
zI(!Q{D|>z%xT^Wm`*4Dp=jmP>PJE#Q+MzdK&Q0ercdf80Ri5s~7jS<y`^{~nTVv%r
zL<mk?K)^r`vS@EVIkCoY8YEUd{#PE<<Zp4uk}lKzx^MHY98q}JW3cn<W^hkD$M5d1
z-PM!~9ngo;r3{Jhk0KMUeo#tPD%R5a3*9U3YSal%?&v-*{Va04FECiu$YC=k?9;NH
z=A{XnL6xI&Q5tn?Z&e*eZ__oWv+>{6sT@lHTM7L-jdlyu{j!|%jrSxP*f0MjlD@sA
z^DpX{0yZ>%B;rIYv;A`<@ZJZNYx!IR4|{>Qs$mGL95zLZDHkj;+rDbN0l7KMK1~<$
zD#qe@4!+*k^WpH2r3f4WJ{twj=Pf_bvi5H@EpQFkcRU?YS)w6~33<tIn$Y=k4_BB8
z>>7LeJSjesVGEO?u_<p8K;XP&tzhSZh45z&9aC~6hu$cjVMrYcLRNu{o(moW37%|_
zB|1qg?c*QD;t;eTe%f62L)0f7c70^H%F4*u6gxf5quZ(a5d#$P(fU>1R|k`yKd2J?
zez|Mv!MsMPj$M`Wy}|Bk-tc43xFENJjGj4ts?~8-PtIiPiUTf1_P!`)DrQ5sq<z~#
zS!c%OZ8*#F=tOgzXH~O?Rbl(3UYNbc+#@;<7OEifsghUWUC8-w<R-pVnoT@<hwq2@
zH^#{%8ftuTadl>0EKw+J*@qi3i?D~g^=o;VdfA%6h)lb#_1=<<p5LR}S!NviB440#
zYdN=qBq$z0S2T1Qyt5AQoRyQSo~#CR9v{IX__xeG?vqEoO0JUeRLdP4VrdaY&!h5R
z9pFnJH$yXY7S(5|&*6WqAfpb}9xLAI2lyCk>~N4JzoIG5PG9;%YDiiLRa=hsp&clf
zd*GYF_gpd0&ii*319hx#GI>kvrDXI(SIkHj!eeLV;%0W=avqaQ7>LLg>jK!BapG?S
z*mAgyjYw<gHl6pIr3&jr;h`Ui4+taF&GYL`%dGv6nrEW|ROAm<CY4-KE8?;)pQ)Q2
zznUAsDjyh!k2byT_x{F1hKvV$Xw^_RSvSs+WZ~W`d|+{c&0f7iDpz46X_eeY-$+nc
z=c=Hf;*moO1eQ5>D+8FJ`}6-$d{ccqUsB`2+HnUMuM>Z|Hl;c7FW(oZ{qt5PyPm)N
zyWt8~#pr}eb<wd=Jq)kkX7E5!6<#mg&^Ly-X|>h=D4?sfNw~r@<94s~UYRcr-v4~j
zCNG;{l?--aM6_P=fP5g_y7pdG_LcPDWl<nfyj@oGiew!On(-sBxn}rX#koI5^O!P}
z@;u;4uIb1);W_<sA+X@J)|&etY*qCGt#3k_wL^JAdy&(NiTZKdtjg-C_#tGYjmJSW
zfQ{nqeNrPKr)#V1D;n8-r0V8*7&BDFHTAY1aaZ9gMJiv_&sS#8b%?9ikD;n6Bf^A(
zi9Zf%1Q`}QqV_8b@Mjup$#F=LI(NHMw2%$s-@KHV&zzUxdVkEvl-4dzg0|pZ51Myo
zh7Brfr6pF##+_8cS<s65Op)8naRL{6*cQ0NCY2>vRpnn;r*AjrVtXkpk|E^s`!|g*
zo%e<zaTW9FBzf7pqf;q0MVPwp9FbFQ!9PL4RZV&;`U|hNT^o{DFg=01A0p^l-Vv*l
zFLy*94L@#n&%eUl|5YxM)nGqUUYX&<(b+PMcc4H8^W=#9-?hDa#Ku&Nu)@fkL=uy}
zQ<az0m4Y~n$nF$;)Lg|COJZ#B&uycO6zGs<252WmIN5#=k+ae*b7g_j`UmrWBBu+X
z^}aeeNI^JwSW(^PG}$P$m#2+tWkY<2%A#YaATiC)%(SM~gr;beq-t}*mhlhiT$-t0
zv&eX%>|AW{W)rjQ{#zUXPA-nM{|;MkFyl423l#u!^Azj(aELf@C-wtDHC(!1eB{V+
zRFD};`)a`qGX==}2RnMaAcfaKU45T;^0F#%4p4`%>oR1Y&WZWNdBjeWaeZ_lH7t@3
zv!njH?E?>@a99vv;YXCXx;q3tAu7cT;{-)AuDBUjQ$;G)R!9>LR?S8gvUF*gIByE2
zB5i^)-yTR2Qvd}MO8#$V1~A_4H)l|npPFQ=3HSWzTxL|B4)F5n%JqG)hadVw6SSwO
zvyCIu#GQsGP}=^>TG!YA7{ER?TxX(cVD#PqxrJSq*jLEHp}`cnJmg!Vm35ZBRD=g2
z7RTg%KdrkZy36qJy9J<{r4<Jz!jD)^e(25>-1UeD*YrN2qHZGN3Ky>+zOO31bDAzg
z8Jf*H*+60qGg$tqKcN`!f<DYApQTf!Fajh;K9_lia67YK%CaE^Cb=dpa3)$g&3fUt
zZgL@&Ya0u=2U_3*GAF_6$-)SVJ5+L?A3*|Iv5@lSHl*YJI5C>LL7CR3y7)@AK@EGh
z!#Pyy%`4P!fSuUtQo7bLB{jLk&*00drFa|-LZm`}H!>V^tAukPdftQ1rRlPNcyORQ
zMR@)?yg`;w`aD9mT%Ba)9cHERlp;S$aye^%Z2sEMe~O8wb>VgPMk%F%f)O3g4^w55
zBV-;fk+~hR^mPdhy2Qm}xrpBJ;3o#WwUXk!R@r*6Z<vwmaZ*H8Z@AkbYe8JCn)bi%
z&f5@(Zt_TnNx2bu>h~LZFz5@Xr0LK2I;^KBAv>|xGV*Mkpiq~i5rH(-%$~inRpRo(
zOhInmK!tKrwYGvLWmzDTHN@>N)6dF7i7po1CnwH{@nMuUoe}xZNW{g9ad!w7^79tK
zDcx8zv~O{z@!3%r+n29aj_*YaMSMXDB9T~Al9e(PX(d8p!R@p^<=i|!)?3|&3$V1|
zLNy@)pI5{U7oG4ApSJ%fIxLwHEi~E;l+q3joHa$~)+Oh_4{subB4Z8$0=j4$7oZEf
zd?*LnN;*gVi;a&iB%3<?=4iIyR***TH5D@$&62C|w=v4RQvbqqPl}7!0V;pxJx9=~
z5<8yB=FD4nc0P#I#SX40+S1Z_zTMsN0se0ZB}$$<t(ZEgB#dR4lN`INoi?WD_i<Y%
z^jSF^&ztQNMX^x6Dm$M2BBJ@Wx$m15K?Ba$q5<)RV=@BSDCbC)mE*|F-VpNKzr9H7
z-ghcqf3cp%e;F=?pL$hrbfT}Kwo&Y;<-nV!m;&mP$r#u!^s?&<1=_FtJNMGZ*>*#i
zm##<}q0}7wlhIAN_Fm8{zK;febon|b;(wBW&-Z5O$`UyHF92G<WuNCWm2a;{Et~?C
z_`y4-r%ZjTfav!jdr#6A3*Ih6fQ<+q&iVtYZtJ#y*XHJ2`-_7LOLQSY{Qivx)L~$s
zoUSy`%3{aY`~M>L1?XJ<z+UN1SWl#p>#!0N`V_$X@sH16v*_BJAaB)W1~g}+BF29I
z5VSl?Be4NW*sUv(ER%!oB;Gc7^tQ;pbSP(biDKN9<Yjg>`6B+&4<V~((5DKFiVdMc
zQ)o33S3?=S(9{th7gu-7-uH6BZ^^Y^3%`K8u)e#m7x$;dAhZLcHvI{#50AIsrDis`
ztZOIpW*zyS4O}P46yB+hkndOp)XA+!R7*+I?>aGxrYBH67Xj9ISoM>a)rfgO88vzN
z02PVV*<9G>PsAv1@{N3lziY?Vf1mt(++no{7JEQ)K%p%<S^tf2Vfgy&awnk6BH;F}
zQc=z2`u1gY#V5DJ;Kl$ea0HD)$Zg3a_hzUM{1Y9Ro!n-nzGO?QFG2!*SFT<sC;1~r
zIJlD?d=HER7YbAd+9!ka&e>i+n=#&dfkmiUl`bAGCN2jYdJ8TcAP|s;z8~-14e0W^
z;L)1=j&$7GXiOAlsC|X)&qweWk)k^wU<~LhnGsjyRpQ6bA#pR;m`06`j>Bdfu*|^<
zS<>?ARMKp(N!~mcu~~DoXvYRJdIcA9F$_2*f35#s$=n^>szN)Z!+baP9jE`*Jzo+H
z-;8<vN-0v5;$_PWlJLEbcm4Fj&web9Ihb6*@6tKg|5FGh|FK5vXqL(-jKkv;Lv%_m
z1Tgq37q$oDwQsSJ>7PAv(DKSxvj>R_ziSg&bQwCf#pzwjut)l;0s~|rWX-`Rt#--F
zX8%j)8nkn1La+k9sn{6Ca|eM?`P51A0boUG@ijwLUvu(D-j`j-GNN`A`5K>Y-mlf@
zn(nW+cAF9xyna-|Hs*_r2KS=`@E^&~^TCmwNJmC2ymf?;O#K@CuNMY;^uC;?GtJoa
za@0=tT?IEDuJd-^&P`dXrue-bMn*7w0r)Cv9h(oh1$4b&etFb$@p_M8>QuB`U}F)y
zp&Hkyxn}kIhu3v?^P6a{MDS+CW3tl-;2uFnv=o25&tXb?6_rx))BhbSVUy1pnOcq?
zMW%k=gl&u!86nLk%-=ewSN_yW>#tj3js2rX9i>!A*4=UNxE<7{9U3es$TLxtEd1=M
z<Aq$tJVB;%F#1wzF9DFoe?v09U#027l!APz+6umTM!Ugv*PpOG@iY(LF$@@FAcg=~
zfFHl%CmQk7z^(C2y<1x23R_<e&>OxGzPc*zM)Yn)6q(bBQ?clJgQ_f7T-qs8yFRk@
z@J%;UbGobZ*ko4U4Neg*%Mh$n^H1W{!gnGszQH0gQal170TBNv{%4hrI_BA0>r}1<
zGE6GpkO&h<yV<At_zP>uJ}*q!y@c1dt<GYw3p)J%lt-Gcqh`L#M`9aV$QIaY$T<}l
zTOTuuNXJ}#{cGW`)kp&UP2UJ!L&H|PS4nHAwt%ir`16_81EoyT93iARid1TYlM*6n
z^X{hBo-C+QWs9}wC3klFD`<6pxz%Q{v<j@m_2e4Bbe3pKITQKuZzf9jnAaOBT2l;1
z%tY?+44mC$q7h2PGlzohWzt(rKiZhH<-F+5v+6&YD_23xX21vmkbR|PDX!&i2g3Mg
z`6*#i%n*;Ng|CuAdYG|K_T23%@<dfXPWLMHP8!^Vv@~Mc`L}qUO=1&Uf8YvZHl31b
zdu;L#NrH@|%l0@QVayetKryqF?sAuY`ya~_i41|P{bs@H!xbv7@-RZ6L8}JCHeGPa
zvbjBbzrl8U7Adr33Og^kEH?`TecWD5^j)qF?_oJd|E9lT5+PFE?CUu!w&AIe#fz3l
z^|+hU(|J7Sq{lsX*m!=V03=$x+P=<V7z~Z1F@_D3yYY%Z*4lTl?JG(1><fj-OvP~)
zdN4IfK*Hnf;#)wMfkTpqP;Z#pY;ez`pt6EyFD2HA*+=lbyB<?yWaL)=<pQ`x1uo{I
z?>Ekq82D+!?{~sH`?JU$Nuj}YqsJ|J(N{x1kOHiqu&VQGO!@~)_u&aY4W`<}F&1)z
zD$MTGw~q+h(=Z!<DYBg|tA}~ZX}c*iHBqW;LP?tp_El?Jcj~V<*PWaf3{#m1smlCh
zbyL<Zme?37I+7YRvTv#jRHm^h4N%>?x%VbysX!`GkKEo5{^@t(<3|qpm>EL2uJb2x
z#PyBoBJT&{wHBGxKiS=;uK8{1!!Ir(_Gc;ZtXz3T72uR*Q66*i5Dt&A4zi&ti_6?E
z9)uy`$GSpOzfVN0dv<9QL>ywBY%_|Y<7M{KXUS*n;xC+h;Zl0vKS;gKA5K|b#ZL|d
z3JKM}7T%aCGOE=;IJuPK`zJrx))gHxt=_h;9&3g9R941W$i;1@VC%`6Epya5iqV$5
zr3~6B_p-kd0tISSZ)6QU{jnU5A=JWJq0&q^-IHgKQ|}ay5VxddUrwhRDITfVEwV`7
z*YHBNE0P&WoW)bfXi5lu<VzPP4eAEPtO0lZ@e+S^7x+^&DPyBK-RwDmxxd6C2T8Q0
zzzL~~zU%V@qyc8Pu)BgPpY<0TPq4=~?-;^#I_W(U7~}+a2f7JkV4Kf-K7Jm-4K|_b
zzBYprfI6Wr9eeg+j{c?Ic(p`6KE#h}U}t{w;Y+jU9UARcwQ%chDkxJIRmJg_kKscA
zRdozyix*sA{m*w?{JKq_4P_RIypj{wdK{{MGidnsk{Y;a_y1;e{Qm?GwzNwcgi|pY
VYxx8pDgT5_U&rKjwU%@2{{W}6=JNmm

literal 0
HcmV?d00001

diff --git a/docs/static_site/src/assets/js/clipboard.js b/docs/static_site/src/assets/js/clipboard.js
new file mode 100644
index 000000000000..b47e3ec60c39
--- /dev/null
+++ b/docs/static_site/src/assets/js/clipboard.js
@@ -0,0 +1,818 @@
+/*!
+ * clipboard.js v1.6.1
+ * https://zenorocha.github.io/clipboard.js
+ *
+ * Licensed MIT © Zeno Rocha
+ */
+(function (f) {
+    if (typeof exports === "object" && typeof module !== "undefined") {
+        module.exports = f()
+    } else if (typeof define === "function" && define.amd) {
+        define([], f)
+    } else {
+        var g;
+        if (typeof window !== "undefined") {
+            g = window
+        } else if (typeof global !== "undefined") {
+            g = global
+        } else if (typeof self !== "undefined") {
+            g = self
+        } else {
+            g = this
+        }
+        g.Clipboard = f()
+    }
+})(function () {
+    var define, module, exports;
+    return (function e(t, n, r) {
+        function s(o, u) {
+            if (!n[o]) {
+                if (!t[o]) {
+                    var a = typeof require == "function" && require;
+                    if (!u && a) return a(o, !0);
+                    if (i) return i(o, !0);
+                    var f = new Error("Cannot find module '" + o + "'");
+                    throw f.code = "MODULE_NOT_FOUND", f
+                }
+                var l = n[o] = {exports: {}};
+                t[o][0].call(l.exports, function (e) {
+                    var n = t[o][1][e];
+                    return s(n ? n : e)
+                }, l, l.exports, e, t, n, r)
+            }
+            return n[o].exports
+        }
+
+        var i = typeof require == "function" && require;
+        for (var o = 0; o < r.length; o++) s(r[o]);
+        return s
+    })({
+        1: [function (require, module, exports) {
+            var DOCUMENT_NODE_TYPE = 9;
+
+            /**
+             * A polyfill for Element.matches()
+             */
+            if (typeof Element !== 'undefined' && !Element.prototype.matches) {
+                var proto = Element.prototype;
+
+                proto.matches = proto.matchesSelector ||
+                    proto.mozMatchesSelector ||
+                    proto.msMatchesSelector ||
+                    proto.oMatchesSelector ||
+                    proto.webkitMatchesSelector;
+            }
+
+            /**
+             * Finds the closest parent that matches a selector.
+             *
+             * @param {Element} element
+             * @param {String} selector
+             * @return {Function}
+             */
+            function closest(element, selector) {
+                while (element && element.nodeType !== DOCUMENT_NODE_TYPE) {
+                    if (element.matches(selector)) return element;
+                    element = element.parentNode;
+                }
+            }
+
+            module.exports = closest;
+
+        }, {}], 2: [function (require, module, exports) {
+            var closest = require('./closest');
+
+            /**
+             * Delegates event to a selector.
+             *
+             * @param {Element} element
+             * @param {String} selector
+             * @param {String} type
+             * @param {Function} callback
+             * @param {Boolean} useCapture
+             * @return {Object}
+             */
+            function delegate(element, selector, type, callback, useCapture) {
+                var listenerFn = listener.apply(this, arguments);
+
+                element.addEventListener(type, listenerFn, useCapture);
+
+                return {
+                    destroy: function () {
+                        element.removeEventListener(type, listenerFn, useCapture);
+                    }
+                }
+            }
+
+            /**
+             * Finds closest match and invokes callback.
+             *
+             * @param {Element} element
+             * @param {String} selector
+             * @param {String} type
+             * @param {Function} callback
+             * @return {Function}
+             */
+            function listener(element, selector, type, callback) {
+                return function (e) {
+                    e.delegateTarget = closest(e.target, selector);
+
+                    if (e.delegateTarget) {
+                        callback.call(element, e);
+                    }
+                }
+            }
+
+            module.exports = delegate;
+
+        }, {"./closest": 1}], 3: [function (require, module, exports) {
+            /**
+             * Check if argument is a HTML element.
+             *
+             * @param {Object} value
+             * @return {Boolean}
+             */
+            exports.node = function (value) {
+                return value !== undefined
+                    && value instanceof HTMLElement
+                    && value.nodeType === 1;
+            };
+
+            /**
+             * Check if argument is a list of HTML elements.
+             *
+             * @param {Object} value
+             * @return {Boolean}
+             */
+            exports.nodeList = function (value) {
+                var type = Object.prototype.toString.call(value);
+
+                return value !== undefined
+                    && (type === '[object NodeList]' || type === '[object HTMLCollection]')
+                    && ('length' in value)
+                    && (value.length === 0 || exports.node(value[0]));
+            };
+
+            /**
+             * Check if argument is a string.
+             *
+             * @param {Object} value
+             * @return {Boolean}
+             */
+            exports.string = function (value) {
+                return typeof value === 'string'
+                    || value instanceof String;
+            };
+
+            /**
+             * Check if argument is a function.
+             *
+             * @param {Object} value
+             * @return {Boolean}
+             */
+            exports.fn = function (value) {
+                var type = Object.prototype.toString.call(value);
+
+                return type === '[object Function]';
+            };
+
+        }, {}], 4: [function (require, module, exports) {
+            var is = require('./is');
+            var delegate = require('delegate');
+
+            /**
+             * Validates all params and calls the right
+             * listener function based on its target type.
+             *
+             * @param {String|HTMLElement|HTMLCollection|NodeList} target
+             * @param {String} type
+             * @param {Function} callback
+             * @return {Object}
+             */
+            function listen(target, type, callback) {
+                if (!target && !type && !callback) {
+                    throw new Error('Missing required arguments');
+                }
+
+                if (!is.string(type)) {
+                    throw new TypeError('Second argument must be a String');
+                }
+
+                if (!is.fn(callback)) {
+                    throw new TypeError('Third argument must be a Function');
+                }
+
+                if (is.node(target)) {
+                    return listenNode(target, type, callback);
+                } else if (is.nodeList(target)) {
+                    return listenNodeList(target, type, callback);
+                } else if (is.string(target)) {
+                    return listenSelector(target, type, callback);
+                } else {
+                    throw new TypeError('First argument must be a String, HTMLElement, HTMLCollection, or NodeList');
+                }
+            }
+
+            /**
+             * Adds an event listener to a HTML element
+             * and returns a remove listener function.
+             *
+             * @param {HTMLElement} node
+             * @param {String} type
+             * @param {Function} callback
+             * @return {Object}
+             */
+            function listenNode(node, type, callback) {
+                node.addEventListener(type, callback);
+
+                return {
+                    destroy: function () {
+                        node.removeEventListener(type, callback);
+                    }
+                }
+            }
+
+            /**
+             * Add an event listener to a list of HTML elements
+             * and returns a remove listener function.
+             *
+             * @param {NodeList|HTMLCollection} nodeList
+             * @param {String} type
+             * @param {Function} callback
+             * @return {Object}
+             */
+            function listenNodeList(nodeList, type, callback) {
+                Array.prototype.forEach.call(nodeList, function (node) {
+                    node.addEventListener(type, callback);
+                });
+
+                return {
+                    destroy: function () {
+                        Array.prototype.forEach.call(nodeList, function (node) {
+                            node.removeEventListener(type, callback);
+                        });
+                    }
+                }
+            }
+
+            /**
+             * Add an event listener to a selector
+             * and returns a remove listener function.
+             *
+             * @param {String} selector
+             * @param {String} type
+             * @param {Function} callback
+             * @return {Object}
+             */
+            function listenSelector(selector, type, callback) {
+                return delegate(document.body, selector, type, callback);
+            }
+
+            module.exports = listen;
+
+        }, {"./is": 3, "delegate": 2}], 5: [function (require, module, exports) {
+            function select(element) {
+                var selectedText;
+
+                if (element.nodeName === 'SELECT') {
+                    element.focus();
+
+                    selectedText = element.value;
+                } else if (element.nodeName === 'INPUT' || element.nodeName === 'TEXTAREA') {
+                    var isReadOnly = element.hasAttribute('readonly');
+
+                    if (!isReadOnly) {
+                        element.setAttribute('readonly', '');
+                    }
+
+                    element.select();
+                    element.setSelectionRange(0, element.value.length);
+
+                    if (!isReadOnly) {
+                        element.removeAttribute('readonly');
+                    }
+
+                    selectedText = element.value;
+                } else {
+                    if (element.hasAttribute('contenteditable')) {
+                        element.focus();
+                    }
+
+                    var selection = window.getSelection();
+                    var range = document.createRange();
+
+                    range.selectNodeContents(element);
+                    selection.removeAllRanges();
+                    selection.addRange(range);
+
+                    selectedText = selection.toString();
+                }
+
+                return selectedText;
+            }
+
+            module.exports = select;
+
+        }, {}], 6: [function (require, module, exports) {
+            function E() {
+                // Keep this empty so it's easier to inherit from
+                // (via https://github.com/lipsmack from https://github.com/scottcorgan/tiny-emitter/issues/3)
+            }
+
+            E.prototype = {
+                on: function (name, callback, ctx) {
+                    var e = this.e || (this.e = {});
+
+                    (e[name] || (e[name] = [])).push({
+                        fn: callback,
+                        ctx: ctx
+                    });
+
+                    return this;
+                },
+
+                once: function (name, callback, ctx) {
+                    var self = this;
+
+                    function listener() {
+                        self.off(name, listener);
+                        callback.apply(ctx, arguments);
+                    };
+
+                    listener._ = callback
+                    return this.on(name, listener, ctx);
+                },
+
+                emit: function (name) {
+                    var data = [].slice.call(arguments, 1);
+                    var evtArr = ((this.e || (this.e = {}))[name] || []).slice();
+                    var i = 0;
+                    var len = evtArr.length;
+
+                    for (i; i < len; i++) {
+                        evtArr[i].fn.apply(evtArr[i].ctx, data);
+                    }
+
+                    return this;
+                },
+
+                off: function (name, callback) {
+                    var e = this.e || (this.e = {});
+                    var evts = e[name];
+                    var liveEvents = [];
+
+                    if (evts && callback) {
+                        for (var i = 0, len = evts.length; i < len; i++) {
+                            if (evts[i].fn !== callback && evts[i].fn._ !== callback)
+                                liveEvents.push(evts[i]);
+                        }
+                    }
+
+                    // Remove event from queue to prevent memory leak
+                    // Suggested by https://github.com/lazd
+                    // Ref: https://github.com/scottcorgan/tiny-emitter/commit/c6ebfaa9bc973b33d110a84a307742b7cf94c953#commitcomment-5024910
+
+                    (liveEvents.length)
+                        ? e[name] = liveEvents
+                        : delete e[name];
+
+                    return this;
+                }
+            };
+
+            module.exports = E;
+
+        }, {}], 7: [function (require, module, exports) {
+            (function (global, factory) {
+                if (typeof define === "function" && define.amd) {
+                    define(['module', 'select'], factory);
+                } else if (typeof exports !== "undefined") {
+                    factory(module, require('select'));
+                } else {
+                    var mod = {
+                        exports: {}
+                    };
+                    factory(mod, global.select);
+                    global.clipboardAction = mod.exports;
+                }
+            })(this, function (module, _select) {
+                'use strict';
+
+                var _select2 = _interopRequireDefault(_select);
+
+                function _interopRequireDefault(obj) {
+                    return obj && obj.__esModule ? obj : {
+                        default: obj
+                    };
+                }
+
+                var _typeof = typeof Symbol === "function" && typeof Symbol.iterator === "symbol" ? function (obj) {
+                    return typeof obj;
+                } : function (obj) {
+                    return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj;
+                };
+
+                function _classCallCheck(instance, Constructor) {
+                    if (!(instance instanceof Constructor)) {
+                        throw new TypeError("Cannot call a class as a function");
+                    }
+                }
+
+                var _createClass = function () {
+                    function defineProperties(target, props) {
+                        for (var i = 0; i < props.length; i++) {
+                            var descriptor = props[i];
+                            descriptor.enumerable = descriptor.enumerable || false;
+                            descriptor.configurable = true;
+                            if ("value" in descriptor) descriptor.writable = true;
+                            Object.defineProperty(target, descriptor.key, descriptor);
+                        }
+                    }
+
+                    return function (Constructor, protoProps, staticProps) {
+                        if (protoProps) defineProperties(Constructor.prototype, protoProps);
+                        if (staticProps) defineProperties(Constructor, staticProps);
+                        return Constructor;
+                    };
+                }();
+
+                var ClipboardAction = function () {
+                    /**
+                     * @param {Object} options
+                     */
+                    function ClipboardAction(options) {
+                        _classCallCheck(this, ClipboardAction);
+
+                        this.resolveOptions(options);
+                        this.initSelection();
+                    }
+
+                    /**
+                     * Defines base properties passed from constructor.
+                     * @param {Object} options
+                     */
+
+
+                    _createClass(ClipboardAction, [{
+                        key: 'resolveOptions',
+                        value: function resolveOptions() {
+                            var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};
+
+                            this.action = options.action;
+                            this.emitter = options.emitter;
+                            this.target = options.target;
+                            this.text = options.text;
+                            this.trigger = options.trigger;
+
+                            this.selectedText = '';
+                        }
+                    }, {
+                        key: 'initSelection',
+                        value: function initSelection() {
+                            if (this.text) {
+                                this.selectFake();
+                            } else if (this.target) {
+                                this.selectTarget();
+                            }
+                        }
+                    }, {
+                        key: 'selectFake',
+                        value: function selectFake() {
+                            var _this = this;
+
+                            var isRTL = document.documentElement.getAttribute('dir') == 'rtl';
+
+                            this.removeFake();
+
+                            this.fakeHandlerCallback = function () {
+                                return _this.removeFake();
+                            };
+                            this.fakeHandler = document.body.addEventListener('click', this.fakeHandlerCallback) || true;
+
+                            this.fakeElem = document.createElement('textarea');
+                            // Prevent zooming on iOS
+                            this.fakeElem.style.fontSize = '12pt';
+                            // Reset box model
+                            this.fakeElem.style.border = '0';
+                            this.fakeElem.style.padding = '0';
+                            this.fakeElem.style.margin = '0';
+                            // Move element out of screen horizontally
+                            this.fakeElem.style.position = 'absolute';
+                            this.fakeElem.style[isRTL ? 'right' : 'left'] = '-9999px';
+                            // Move element to the same position vertically
+                            var yPosition = window.pageYOffset || document.documentElement.scrollTop;
+                            this.fakeElem.style.top = yPosition + 'px';
+
+                            this.fakeElem.setAttribute('readonly', '');
+                            this.fakeElem.value = this.text;
+
+                            document.body.appendChild(this.fakeElem);
+
+                            this.selectedText = (0, _select2.default)(this.fakeElem);
+                            this.copyText();
+                        }
+                    }, {
+                        key: 'removeFake',
+                        value: function removeFake() {
+                            if (this.fakeHandler) {
+                                document.body.removeEventListener('click', this.fakeHandlerCallback);
+                                this.fakeHandler = null;
+                                this.fakeHandlerCallback = null;
+                            }
+
+                            if (this.fakeElem) {
+                                document.body.removeChild(this.fakeElem);
+                                this.fakeElem = null;
+                            }
+                        }
+                    }, {
+                        key: 'selectTarget',
+                        value: function selectTarget() {
+                            this.selectedText = (0, _select2.default)(this.target);
+                            this.copyText();
+                        }
+                    }, {
+                        key: 'copyText',
+                        value: function copyText() {
+                            var succeeded = void 0;
+
+                            try {
+                                succeeded = document.execCommand(this.action);
+                            } catch (err) {
+                                succeeded = false;
+                            }
+
+                            this.handleResult(succeeded);
+                        }
+                    }, {
+                        key: 'handleResult',
+                        value: function handleResult(succeeded) {
+                            this.emitter.emit(succeeded ? 'success' : 'error', {
+                                action: this.action,
+                                text: this.selectedText,
+                                trigger: this.trigger,
+                                clearSelection: this.clearSelection.bind(this)
+                            });
+                        }
+                    }, {
+                        key: 'clearSelection',
+                        value: function clearSelection() {
+                            if (this.target) {
+                                this.target.blur();
+                            }
+
+                            window.getSelection().removeAllRanges();
+                        }
+                    }, {
+                        key: 'destroy',
+                        value: function destroy() {
+                            this.removeFake();
+                        }
+                    }, {
+                        key: 'action',
+                        set: function set() {
+                            var action = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 'copy';
+
+                            this._action = action;
+
+                            if (this._action !== 'copy' && this._action !== 'cut') {
+                                throw new Error('Invalid "action" value, use either "copy" or "cut"');
+                            }
+                        },
+                        get: function get() {
+                            return this._action;
+                        }
+                    }, {
+                        key: 'target',
+                        set: function set(target) {
+                            if (target !== undefined) {
+                                if (target && (typeof target === 'undefined' ? 'undefined' : _typeof(target)) === 'object' && target.nodeType === 1) {
+                                    if (this.action === 'copy' && target.hasAttribute('disabled')) {
+                                        throw new Error('Invalid "target" attribute. Please use "readonly" instead of "disabled" attribute');
+                                    }
+
+                                    if (this.action === 'cut' && (target.hasAttribute('readonly') || target.hasAttribute('disabled'))) {
+                                        throw new Error('Invalid "target" attribute. You can\'t cut text from elements with "readonly" or "disabled" attributes');
+                                    }
+
+                                    this._target = target;
+                                } else {
+                                    throw new Error('Invalid "target" value, use a valid Element');
+                                }
+                            }
+                        },
+                        get: function get() {
+                            return this._target;
+                        }
+                    }]);
+
+                    return ClipboardAction;
+                }();
+
+                module.exports = ClipboardAction;
+            });
+
+        }, {"select": 5}], 8: [function (require, module, exports) {
+            (function (global, factory) {
+                if (typeof define === "function" && define.amd) {
+                    define(['module', './clipboard-action', 'tiny-emitter', 'good-listener'], factory);
+                } else if (typeof exports !== "undefined") {
+                    factory(module, require('./clipboard-action'), require('tiny-emitter'), require('good-listener'));
+                } else {
+                    var mod = {
+                        exports: {}
+                    };
+                    factory(mod, global.clipboardAction, global.tinyEmitter, global.goodListener);
+                    global.clipboard = mod.exports;
+                }
+            })(this, function (module, _clipboardAction, _tinyEmitter, _goodListener) {
+                'use strict';
+
+                var _clipboardAction2 = _interopRequireDefault(_clipboardAction);
+
+                var _tinyEmitter2 = _interopRequireDefault(_tinyEmitter);
+
+                var _goodListener2 = _interopRequireDefault(_goodListener);
+
+                function _interopRequireDefault(obj) {
+                    return obj && obj.__esModule ? obj : {
+                        default: obj
+                    };
+                }
+
+                function _classCallCheck(instance, Constructor) {
+                    if (!(instance instanceof Constructor)) {
+                        throw new TypeError("Cannot call a class as a function");
+                    }
+                }
+
+                var _createClass = function () {
+                    function defineProperties(target, props) {
+                        for (var i = 0; i < props.length; i++) {
+                            var descriptor = props[i];
+                            descriptor.enumerable = descriptor.enumerable || false;
+                            descriptor.configurable = true;
+                            if ("value" in descriptor) descriptor.writable = true;
+                            Object.defineProperty(target, descriptor.key, descriptor);
+                        }
+                    }
+
+                    return function (Constructor, protoProps, staticProps) {
+                        if (protoProps) defineProperties(Constructor.prototype, protoProps);
+                        if (staticProps) defineProperties(Constructor, staticProps);
+                        return Constructor;
+                    };
+                }();
+
+                function _possibleConstructorReturn(self, call) {
+                    if (!self) {
+                        throw new ReferenceError("this hasn't been initialised - super() hasn't been called");
+                    }
+
+                    return call && (typeof call === "object" || typeof call === "function") ? call : self;
+                }
+
+                function _inherits(subClass, superClass) {
+                    if (typeof superClass !== "function" && superClass !== null) {
+                        throw new TypeError("Super expression must either be null or a function, not " + typeof superClass);
+                    }
+
+                    subClass.prototype = Object.create(superClass && superClass.prototype, {
+                        constructor: {
+                            value: subClass,
+                            enumerable: false,
+                            writable: true,
+                            configurable: true
+                        }
+                    });
+                    if (superClass) Object.setPrototypeOf ? Object.setPrototypeOf(subClass, superClass) : subClass.__proto__ = superClass;
+                }
+
+                var Clipboard = function (_Emitter) {
+                    _inherits(Clipboard, _Emitter);
+
+                    /**
+                     * @param {String|HTMLElement|HTMLCollection|NodeList} trigger
+                     * @param {Object} options
+                     */
+                    function Clipboard(trigger, options) {
+                        _classCallCheck(this, Clipboard);
+
+                        var _this = _possibleConstructorReturn(this, (Clipboard.__proto__ || Object.getPrototypeOf(Clipboard)).call(this));
+
+                        _this.resolveOptions(options);
+                        _this.listenClick(trigger);
+                        return _this;
+                    }
+
+                    /**
+                     * Defines if attributes would be resolved using internal setter functions
+                     * or custom functions that were passed in the constructor.
+                     * @param {Object} options
+                     */
+
+
+                    _createClass(Clipboard, [{
+                        key: 'resolveOptions',
+                        value: function resolveOptions() {
+                            var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};
+
+                            this.action = typeof options.action === 'function' ? options.action : this.defaultAction;
+                            this.target = typeof options.target === 'function' ? options.target : this.defaultTarget;
+                            this.text = typeof options.text === 'function' ? options.text : this.defaultText;
+                        }
+                    }, {
+                        key: 'listenClick',
+                        value: function listenClick(trigger) {
+                            var _this2 = this;
+
+                            this.listener = (0, _goodListener2.default)(trigger, 'click', function (e) {
+                                return _this2.onClick(e);
+                            });
+                        }
+                    }, {
+                        key: 'onClick',
+                        value: function onClick(e) {
+                            var trigger = e.delegateTarget || e.currentTarget;
+
+                            if (this.clipboardAction) {
+                                this.clipboardAction = null;
+                            }
+
+                            this.clipboardAction = new _clipboardAction2.default({
+                                action: this.action(trigger),
+                                target: this.target(trigger),
+                                text: this.text(trigger),
+                                trigger: trigger,
+                                emitter: this
+                            });
+                        }
+                    }, {
+                        key: 'defaultAction',
+                        value: function defaultAction(trigger) {
+                            return getAttributeValue('action', trigger);
+                        }
+                    }, {
+                        key: 'defaultTarget',
+                        value: function defaultTarget(trigger) {
+                            var selector = getAttributeValue('target', trigger);
+
+                            if (selector) {
+                                return document.querySelector(selector);
+                            }
+                        }
+                    }, {
+                        key: 'defaultText',
+                        value: function defaultText(trigger) {
+                            return getAttributeValue('text', trigger);
+                        }
+                    }, {
+                        key: 'destroy',
+                        value: function destroy() {
+                            this.listener.destroy();
+
+                            if (this.clipboardAction) {
+                                this.clipboardAction.destroy();
+                                this.clipboardAction = null;
+                            }
+                        }
+                    }], [{
+                        key: 'isSupported',
+                        value: function isSupported() {
+                            var action = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : ['copy', 'cut'];
+
+                            var actions = typeof action === 'string' ? [action] : action;
+                            var support = !!document.queryCommandSupported;
+
+                            actions.forEach(function (action) {
+                                support = support && !!document.queryCommandSupported(action);
+                            });
+
+                            return support;
+                        }
+                    }]);
+
+                    return Clipboard;
+                }(_tinyEmitter2.default);
+
+                /**
+                 * Helper function to retrieve attribute value.
+                 * @param {String} suffix
+                 * @param {Element} element
+                 */
+                function getAttributeValue(suffix, element) {
+                    var attribute = 'data-clipboard-' + suffix;
+
+                    if (!element.hasAttribute(attribute)) {
+                        return;
+                    }
+
+                    return element.getAttribute(attribute);
+                }
+
+                module.exports = Clipboard;
+            });
+
+        }, {"./clipboard-action": 7, "good-listener": 4, "tiny-emitter": 6}]
+    }, {}, [8])(8)
+});
\ No newline at end of file
diff --git a/docs/static_site/src/assets/js/copycode.js b/docs/static_site/src/assets/js/copycode.js
new file mode 100644
index 000000000000..3e7ce5d4b0f8
--- /dev/null
+++ b/docs/static_site/src/assets/js/copycode.js
@@ -0,0 +1,97 @@
+/*!
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/* Copy code to clipboard */
+
+
+function addBtn() {
+    copyBtn = '<button type="button" class="copy-btn" data-placement="bottom" title="Copy to clipboard">copy</button>'
+    codeBlock = $('figure,highlight, div.highlight');
+    codeBlock.css('position', 'relative')
+    codeBlock.prepend(copyBtn);
+    codeBlock.hover(
+        function () {
+            $(this).children().first().show();
+        }, function () {
+            $(this).children().first().hide();
+        }
+    );
+
+};
+
+function html2clipboard(content) {
+    var tmpEl = document.createElement("div");
+    tmpEl.style.opacity = 0;
+    tmpEl.style.position = "absolute";
+    tmpEl.style.pointerEvents = "none";
+    tmpEl.style.zIndex = -1;
+
+    tmpEl.innerHTML = content;
+    document.body.appendChild(tmpEl);
+
+    var range = document.createRange();
+    range.selectNode(tmpEl);
+    window.getSelection().addRange(range);
+    document.execCommand("copy");
+    document.body.removeChild(tmpEl);
+}
+
+$(document).ready(function () {
+    addBtn()
+
+    clipboard = new Clipboard('.copy-btn', {
+        target: function (trigger) {
+            return trigger.parentNode.querySelector('code');
+        }
+    });
+
+    clipboard.on('success', function (e) {
+        //Deal with codes with leading gap
+        var btnClass = e.trigger.classList;
+        var lang = btnClass[btnClass.length - 1];
+        var lines = e.text.split('\n');
+        var hasGap = false;
+        var continueSign = '...';
+
+        e.clearSelection();
+
+        for (var i = 0; i < lines.length; ++i) {
+            lines[i] = lines[i].replace(/^\s+|\s+$/g, "");
+            if (!hasGap && lines[i].startsWith(LANG_GP[lang])) hasGap = true;
+        }
+
+        if (hasGap) {
+            var content = '';
+            for (var i = 0; i < lines.length; ++i) {
+                if (lines[i].startsWith(LANG_GP[lang]) || ((lang == 'python' || lang == 'default') &&
+                    lines[i].startsWith(continueSign))) {
+                    content = content.concat(lines[i].substring(LANG_GP[lang].length, lines[i].length) + '<br />');
+                } else if (lines[i].length == 0) content = content.concat('<br />');
+            }
+            content = content.substring(0, content.length - 6);
+            html2clipboard(content);
+        }
+    });
+
+    clipboard.on('error', function (e) {
+        $(e.trigger).attr('title', 'Copy failed. Try again.')
+            .tooltip('fixTitle')
+            .tooltip('show');
+    });
+});
diff --git a/docs/static_site/src/assets/js/options.js b/docs/static_site/src/assets/js/options.js
new file mode 100644
index 000000000000..59913a0c7ec7
--- /dev/null
+++ b/docs/static_site/src/assets/js/options.js
@@ -0,0 +1,120 @@
+/*!
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file 
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/* Installation page display functions for install selector */
+
+$(document).ready(function () {
+    function label(lbl) {
+        lbl = lbl.replace(/[ .]/g, '-').toLowerCase();
+
+        return lbl;
+    }
+
+    function urlSearchParams(searchString) {
+        let urlDict = new Map();
+        let searchParams = searchString.substring(1).split("&");
+        searchParams.forEach(function (element) {
+            kvPair = element.split("=");
+            urlDict.set(kvPair[0], kvPair[1]);
+        });
+        return urlDict;
+    }
+
+    function is_a_match(elem, text) {
+
+        if (label(elem.text()).includes(label(text))) {
+            elem.addClass(('active'))
+        }
+    }
+
+    function setSelects() {
+        let urlParams = urlSearchParams(window.location.search);
+        if (urlParams.get('version'))
+            versionSelect = urlParams.get('version');
+        $('.current-version').html( versionSelect + ' <span class="caret"></span></button>' );
+        if (urlParams.get('platform'))
+            platformSelect = label(urlParams.get('platform'));
+        if (urlParams.get('language'))
+            languageSelect = label(urlParams.get('language'));
+        if (urlParams.get('processor'))
+            processorSelect = label(urlParams.get('processor'));
+        if (urlParams.get('environ'))
+            environSelect = label(urlParams.get('environ'));
+
+        $('li.versions').removeClass('active');
+        $('li.versions').each(function(){is_a_match($(this), versionSelect)});
+        $('button.opt').removeClass('active');
+        $('button.opt').each(function(){is_a_match($(this), platformSelect)});
+        $('button.opt').each(function(){is_a_match($(this), languageSelect)});
+        $('button.opt').each(function(){is_a_match($(this), processorSelect)});
+        $('button.opt').each(function(){is_a_match($(this), environSelect)});
+
+        showContent();
+        if (window.location.href.indexOf("/get_started/") >= 0) {
+            history.pushState(null, null, '?version=' + versionSelect + '&platform=' + platformSelect + '&language=' + languageSelect + '&environ=' + environSelect + '&processor=' + processorSelect);
+        }
+    }
+
+    function showContent() {
+        $('.opt-group .opt').each(function () {
+            $('.' + label($(this).text())).hide();
+        });
+        $('.opt-group .active').each(function () {
+            $('.' + label($(this).text())).show();
+        });
+    }
+
+    showContent();
+    setSelects();
+
+    function setContent() {
+        var el = $(this);
+        let urlParams = urlSearchParams(window.location.search);
+        el.siblings().removeClass('active');
+        el.addClass('active');
+        if ($(this).hasClass("versions")) {
+            $('.current-version').html($(this).text());
+            if (window.location.search.indexOf("version") < 0) {
+                if (window.location.search.length > 0) {
+                    var url = 'index.html' + window.location.search.concat('&version=' + $(this).text());
+                } else {
+                    var url = 'index.html?version=' + $(this).text();
+                }
+                history.pushState(null, null, url);
+            } else {
+                history.pushState(null, null, 'index.html' + window.location.search.replace(urlParams.get('version'), $(this).text()));
+            }
+        } else if ($(this).hasClass("platforms")) {
+            history.pushState(null, null, 'index.html' + window.location.search.replace('='+urlParams.get('platform'), '='+label($(this).text())));
+        } else if ($(this).hasClass("languages")) {
+            history.pushState(null, null, 'index.html' + window.location.search.replace('='+urlParams.get('language'), '='+label($(this).text())));
+        } else if ($(this).hasClass("processors")) {
+            history.pushState(null, null, 'index.html' + window.location.search.replace('='+urlParams.get('processor'), '='+label($(this).text())));
+        } else if ($(this).hasClass("environs")) {
+            history.pushState(null, null, 'index.html' + window.location.search.replace('='+urlParams.get('environ'), '='+label($(this).text())));
+        }
+
+        showContent();
+    }
+
+    $('.opt-group').on('click', '.opt', setContent);
+    $('.install-widget').css("visibility", "visible");
+    $('.install-content').css("visibility", "visible");
+
+});
diff --git a/docs/static_site/src/assets/main.scss b/docs/static_site/src/assets/main.scss
new file mode 100644
index 000000000000..3ab5c7091e68
--- /dev/null
+++ b/docs/static_site/src/assets/main.scss
@@ -0,0 +1,8 @@
+---
+---
+body {
+  background-image: url("{{'/assets/img/mxnet-background-compressed.jpeg' | relative_url}}");
+}
+
+
+@import "minima";
diff --git a/docs/static_site/src/assets/minima-social-icons.svg b/docs/static_site/src/assets/minima-social-icons.svg
new file mode 100644
index 000000000000..fa7399fe2509
--- /dev/null
+++ b/docs/static_site/src/assets/minima-social-icons.svg
@@ -0,0 +1,33 @@
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
+
+<symbol id="dribbble" fill-rule="evenodd" clip-rule="evenodd" stroke-linejoin="round" stroke-miterlimit="1.414"><path d="M8 16c-4.41 0-8-3.59-8-8s3.59-8 8-8 8 3.59 8 8-3.59 8-8 8zm6.747-6.905c-.234-.074-2.115-.635-4.257-.292.894 2.456 1.258 4.456 1.328 4.872 1.533-1.037 2.624-2.68 2.93-4.58zM10.67 14.3c-.102-.6-.5-2.688-1.46-5.18l-.044.014C5.312 10.477 3.93 13.15 3.806 13.4c1.158.905 2.614 1.444 4.194 1.444.947 0 1.85-.194 2.67-.543zm-7.747-1.72c.155-.266 2.03-3.37 5.555-4.51.09-.03.18-.056.27-.08-.173-.39-.36-.778-.555-1.16-3.413 1.02-6.723.977-7.023.97l-.003.208c0 1.755.665 3.358 1.756 4.57zM1.31 6.61c.307.005 3.122.017 6.318-.832-1.132-2.012-2.353-3.705-2.533-3.952-1.912.902-3.34 2.664-3.784 4.785zM6.4 1.368c.188.253 1.43 1.943 2.548 4 2.43-.91 3.46-2.293 3.582-2.468C11.323 1.827 9.736 1.176 8 1.176c-.55 0-1.087.066-1.6.19zm6.89 2.322c-.145.194-1.29 1.662-3.816 2.694.16.325.31.656.453.99.05.117.1.235.147.352 2.274-.286 4.533.172 4.758.22-.015-1.613-.59-3.094-1.543-4.257z"/></symbol>
+
+<symbol id="facebook" fill-rule="evenodd" clip-rule="evenodd" stroke-linejoin="round" stroke-miterlimit="1.414"><path d="M15.117 0H.883C.395 0 0 .395 0 .883v14.234c0 .488.395.883.883.883h7.663V9.804H6.46V7.39h2.086V5.607c0-2.066 1.262-3.19 3.106-3.19.883 0 1.642.064 1.863.094v2.16h-1.28c-1 0-1.195.48-1.195 1.18v1.54h2.39l-.31 2.42h-2.08V16h4.077c.488 0 .883-.395.883-.883V.883C16 .395 15.605 0 15.117 0" fill-rule="nonzero"/></symbol>
+
+<symbol id="flickr" fill-rule="evenodd" clip-rule="evenodd" stroke-linejoin="round" stroke-miterlimit="1.414"><path d="M0 8c0 2.05 1.662 3.71 3.71 3.71 2.05 0 3.713-1.66 3.713-3.71S5.76 4.29 3.71 4.29C1.663 4.29 0 5.95 0 8zm8.577 0c0 2.05 1.662 3.71 3.712 3.71C14.33 11.71 16 10.05 16 8s-1.662-3.71-3.71-3.71c-2.05 0-3.713 1.66-3.713 3.71z"/></symbol>
+
+<symbol id="github" fill-rule="evenodd" clip-rule="evenodd" stroke-linejoin="round" stroke-miterlimit="1.414"><path d="M8 0C3.58 0 0 3.582 0 8c0 3.535 2.292 6.533 5.47 7.59.4.075.547-.172.547-.385 0-.19-.007-.693-.01-1.36-2.226.483-2.695-1.073-2.695-1.073-.364-.924-.89-1.17-.89-1.17-.725-.496.056-.486.056-.486.803.056 1.225.824 1.225.824.714 1.223 1.873.87 2.33.665.072-.517.278-.87.507-1.07-1.777-.2-3.644-.888-3.644-3.953 0-.873.31-1.587.823-2.147-.09-.202-.36-1.015.07-2.117 0 0 .67-.215 2.2.82.64-.178 1.32-.266 2-.27.68.004 1.36.092 2 .27 1.52-1.035 2.19-.82 2.19-.82.43 1.102.16 1.915.08 2.117.51.56.82 1.274.82 2.147 0 3.073-1.87 3.75-3.65 3.947.28.24.54.73.54 1.48 0 1.07-.01 1.93-.01 2.19 0 .21.14.46.55.38C13.71 14.53 16 11.53 16 8c0-4.418-3.582-8-8-8"/></symbol>
+
+<symbol id="googleplus" fill-rule="evenodd" clip-rule="evenodd" stroke-linejoin="round" stroke-miterlimit="1.414"><path d="M5.09 7.273v1.745h2.89c-.116.75-.873 2.197-2.887 2.197-1.737 0-3.155-1.44-3.155-3.215S3.353 4.785 5.09 4.785c.99 0 1.652.422 2.03.786l1.382-1.33c-.887-.83-2.037-1.33-3.41-1.33C2.275 2.91 0 5.19 0 8s2.276 5.09 5.09 5.09c2.94 0 4.888-2.065 4.888-4.974 0-.334-.036-.59-.08-.843H5.09zm10.91 0h-1.455V5.818H13.09v1.455h-1.454v1.454h1.455v1.455h1.46V8.727H16"/></symbol>
+
+<symbol id="instagram" fill-rule="evenodd" clip-rule="evenodd" stroke-linejoin="round" stroke-miterlimit="1.414"><path d="M8 0C5.827 0 5.555.01 4.702.048 3.85.088 3.27.222 2.76.42c-.526.204-.973.478-1.417.923-.445.444-.72.89-.923 1.417-.198.51-.333 1.09-.372 1.942C.008 5.555 0 5.827 0 8s.01 2.445.048 3.298c.04.852.174 1.433.372 1.942.204.526.478.973.923 1.417.444.445.89.72 1.417.923.51.198 1.09.333 1.942.372.853.04 1.125.048 3.298.048s2.445-.01 3.298-.048c.852-.04 1.433-.174 1.942-.372.526-.204.973-.478 1.417-.923.445-.444.72-.89.923-1.417.198-.51.333-1.09.372-1.942.04-.853.048-1.125.048-3.298s-.01-2.445-.048-3.298c-.04-.852-.174-1.433-.372-1.942-.204-.526-.478-.973-.923-1.417-.444-.445-.89-.72-1.417-.923-.51-.198-1.09-.333-1.942-.372C10.445.008 10.173 0 8 0zm0 1.44c2.136 0 2.39.01 3.233.048.78.036 1.203.166 1.485.276.374.145.64.318.92.598.28.28.453.546.598.92.11.282.24.705.276 1.485.038.844.047 1.097.047 3.233s-.01 2.39-.05 3.233c-.04.78-.17 1.203-.28 1.485-.15.374-.32.64-.6.92-.28.28-.55.453-.92.598-.28.11-.71.24-1.49.276-.85.038-1.1.047-3.24.047s-2.39-.01-3.24-.05c-.78-.04-1.21-.17-1.49-.28-.38-.15-.64-.32-.92-.6-.28-.28-.46-.55-.6-.92-.11-.28-.24-.71-.28-1.49-.03-.84-.04-1.1-.04-3.23s.01-2.39.04-3.24c.04-.78.17-1.21.28-1.49.14-.38.32-.64.6-.92.28-.28.54-.46.92-.6.28-.11.7-.24 1.48-.28.85-.03 1.1-.04 3.24-.04zm0 2.452c-2.27 0-4.108 1.84-4.108 4.108 0 2.27 1.84 4.108 4.108 4.108 2.27 0 4.108-1.84 4.108-4.108 0-2.27-1.84-4.108-4.108-4.108zm0 6.775c-1.473 0-2.667-1.194-2.667-2.667 0-1.473 1.194-2.667 2.667-2.667 1.473 0 2.667 1.194 2.667 2.667 0 1.473-1.194 2.667-2.667 2.667zm5.23-6.937c0 .53-.43.96-.96.96s-.96-.43-.96-.96.43-.96.96-.96.96.43.96.96z"/></symbol>
+
+<symbol id="linkedin" fill-rule="evenodd" clip-rule="evenodd" stroke-linejoin="round" stroke-miterlimit="1.414"><path d="M13.632 13.635h-2.37V9.922c0-.886-.018-2.025-1.234-2.025-1.235 0-1.424.964-1.424 1.96v3.778h-2.37V6H8.51v1.04h.03c.318-.6 1.092-1.233 2.247-1.233 2.4 0 2.845 1.58 2.845 3.637v4.188zM3.558 4.955c-.762 0-1.376-.617-1.376-1.377 0-.758.614-1.375 1.376-1.375.76 0 1.376.617 1.376 1.375 0 .76-.617 1.377-1.376 1.377zm1.188 8.68H2.37V6h2.376v7.635zM14.816 0H1.18C.528 0 0 .516 0 1.153v13.694C0 15.484.528 16 1.18 16h13.635c.652 0 1.185-.516 1.185-1.153V1.153C16 .516 15.467 0 14.815 0z" fill-rule="nonzero"/></symbol>
+
+<symbol id="pinterest" fill-rule="evenodd" clip-rule="evenodd" stroke-linejoin="round" stroke-miterlimit="1.414"><path d="M8 0C3.582 0 0 3.582 0 8c0 3.39 2.108 6.285 5.084 7.45-.07-.633-.133-1.604.028-2.295.146-.625.938-3.977.938-3.977s-.24-.48-.24-1.188c0-1.11.646-1.943 1.448-1.943.683 0 1.012.513 1.012 1.127 0 .687-.436 1.713-.662 2.664-.19.797.4 1.445 1.185 1.445 1.42 0 2.514-1.498 2.514-3.662 0-1.91-1.376-3.25-3.342-3.25-2.276 0-3.61 1.71-3.61 3.47 0 .69.263 1.43.593 1.83.066.08.075.15.057.23-.06.25-.196.8-.223.91-.035.15-.115.18-.268.11C3.516 10.46 2.89 9 2.89 7.82c0-2.52 1.834-4.84 5.287-4.84 2.774 0 4.932 1.98 4.932 4.62 0 2.76-1.74 4.98-4.16 4.98-.81 0-1.57-.42-1.84-.92l-.5 1.9c-.18.698-.67 1.57-1 2.1.75.23 1.54.357 2.37.357 4.41 0 8-3.58 8-8s-3.59-8-8-8z" fill-rule="nonzero"/></symbol>
+
+<symbol id="rss" fill-rule="evenodd" clip-rule="evenodd" stroke-linejoin="round" stroke-miterlimit="1.414"><path d="M12.8 16C12.8 8.978 7.022 3.2 0 3.2V0c8.777 0 16 7.223 16 16h-3.2zM2.194 11.61c1.21 0 2.195.985 2.195 2.196 0 1.21-.99 2.194-2.2 2.194C.98 16 0 15.017 0 13.806c0-1.21.983-2.195 2.194-2.195zM10.606 16h-3.11c0-4.113-3.383-7.497-7.496-7.497v-3.11c5.818 0 10.606 4.79 10.606 10.607z"/></symbol>
+
+<symbol id="stackoverflow" class="svg-icon" xmlns="http://www.w3.org/2000/svg" fill-rule="evenodd" clip-rule="evenodd" stroke-linejoin="round" stroke-miterlimit="1.414"><path d="M12.658 14.577v-4.27h1.423V16H1.23v-5.693h1.42v4.27h10.006zm-8.583-1.423h7.16V11.73h-7.16v1.424zm.173-3.235l6.987 1.46.3-1.38L4.55 8.54l-.302 1.38zm.906-3.37l6.47 3.02.602-1.3-6.47-3.02-.602 1.29zm1.81-3.19l5.478 4.57.906-1.08L7.87 2.28l-.9 1.078zM10.502 0L9.338.863l4.27 5.735 1.164-.862L10.5 0z"/></symbol>
+
+<symbol id="twitter" fill-rule="evenodd" clip-rule="evenodd" stroke-linejoin="round" stroke-miterlimit="1.414"><path d="M16 3.038c-.59.26-1.22.437-1.885.517.677-.407 1.198-1.05 1.443-1.816-.634.37-1.337.64-2.085.79-.598-.64-1.45-1.04-2.396-1.04-1.812 0-3.282 1.47-3.282 3.28 0 .26.03.51.085.75-2.728-.13-5.147-1.44-6.766-3.42C.83 2.58.67 3.14.67 3.75c0 1.14.58 2.143 1.46 2.732-.538-.017-1.045-.165-1.487-.41v.04c0 1.59 1.13 2.918 2.633 3.22-.276.074-.566.114-.865.114-.21 0-.41-.02-.61-.058.42 1.304 1.63 2.253 3.07 2.28-1.12.88-2.54 1.404-4.07 1.404-.26 0-.52-.015-.78-.045 1.46.93 3.18 1.474 5.04 1.474 6.04 0 9.34-5 9.34-9.33 0-.14 0-.28-.01-.42.64-.46 1.2-1.04 1.64-1.7z" fill-rule="nonzero"/></symbol>
+
+<symbol id="youtube" fill-rule="evenodd" clip-rule="evenodd" stroke-linejoin="round" stroke-miterlimit="1.414"><path d="M0 7.345c0-1.294.16-2.59.16-2.59s.156-1.1.636-1.587c.608-.637 1.408-.617 1.764-.684C3.84 2.36 8 2.324 8 2.324s3.362.004 5.6.166c.314.038.996.04 1.604.678.48.486.636 1.588.636 1.588S16 6.05 16 7.346v1.258c0 1.296-.16 2.59-.16 2.59s-.156 1.102-.636 1.588c-.608.638-1.29.64-1.604.678-2.238.162-5.6.166-5.6.166s-4.16-.037-5.44-.16c-.356-.067-1.156-.047-1.764-.684-.48-.487-.636-1.587-.636-1.587S0 9.9 0 8.605v-1.26zm6.348 2.73V5.58l4.323 2.255-4.32 2.24z"/></symbol>
+
+<symbol id="mastodon" fill-rule="evenodd" clip-rule="evenodd" stroke-linejoin="round" stroke-miterlimit="1.414">
+  <path transform="scale(0.07)" d="M211.80734 139.0875c-3.18125 16.36625-28.4925 34.2775-57.5625 37.74875-15.15875 1.80875-30.08375 3.47125-45.99875 2.74125-26.0275-1.1925-46.565-6.2125-46.565-6.2125 0 2.53375.15625 4.94625.46875 7.2025 3.38375 25.68625 25.47 27.225 46.39125 27.9425 21.11625.7225 39.91875-5.20625 39.91875-5.20625l.8675 19.09s-14.77 7.93125-41.08125 9.39c-14.50875.7975-32.52375-.365-53.50625-5.91875C9.23234 213.82 1.40609 165.31125.20859 116.09125c-.365-14.61375-.14-28.39375-.14-39.91875 0-50.33 32.97625-65.0825 32.97625-65.0825C49.67234 3.45375 78.20359.2425 107.86484 0h.72875c29.66125.2425 58.21125 3.45375 74.8375 11.09 0 0 32.975 14.7525 32.975 65.0825 0 0 .41375 37.13375-4.59875 62.915"/>
+  <path transform="scale(0.07)" fill="#FFF" d="M177.50984 80.077v60.94125h-24.14375v-59.15c0-12.46875-5.24625-18.7975-15.74-18.7975-11.6025 0-17.4175 7.5075-17.4175 22.3525v32.37625H96.20734V85.42325c0-14.845-5.81625-22.3525-17.41875-22.3525-10.49375 0-15.74 6.32875-15.74 18.7975v59.15H38.90484V80.077c0-12.455 3.17125-22.3525 9.54125-29.675 6.56875-7.3225 15.17125-11.07625 25.85-11.07625 12.355 0 21.71125 4.74875 27.8975 14.2475l6.01375 10.08125 6.015-10.08125c6.185-9.49875 15.54125-14.2475 27.8975-14.2475 10.6775 0 19.28 3.75375 25.85 11.07625 6.36875 7.3225 9.54 17.22 9.54 29.675"/>
+</symbol>
+
+
+</svg>
diff --git a/docs/static_site/src/index.html b/docs/static_site/src/index.html
new file mode 100644
index 000000000000..9f5460c6ff9a
--- /dev/null
+++ b/docs/static_site/src/index.html
@@ -0,0 +1,54 @@
+---
+# Feel free to add content and custom Front Matter to this file.
+# To modify the layout, see https://jekyllrb.com/docs/themes/#overriding-theme-defaults
+
+layout: home
+
+key_features:
+- title: Hybrid Front-End
+  text: A hybrid front-end seamlessly transitions between Gluon eager imperative mode and symbolic mode to provide both flexibility and speed.
+  icon: /assets/img/circuit.svg
+- title: Distributed Training
+  text: Scalable distributed training and performance optimization in research and production is enabled by the dual Parameter Server and Horovod support.
+  icon: /assets/img/algorithm.svg
+- title: 8 Language Bindings
+  text: Deep integration into Python and support for Scala, Julia, Clojure, Java, C++, R and Perl.
+  icon: /assets/img/programming.svg
+- title: Tools &amp; Libraries
+  text: A thriving ecosystem of tools and libraries extends MXNet and enable use-cases in computer vision, NLP, time series and more.
+  icon: /assets/img/chip.svg
+
+ecosystem:
+- title: D2L.ai
+  text: An interactive deep learning book with code, math, and discussions. Used at Berkeley, University of Washington and more.
+  icon: /assets/img/textbook.svg
+  link: https://d2l.ai
+- title: GluonCV
+  text: GluonCV is a computer vision toolkit with rich model zoo. From object detection to pose estimation.
+  icon: /assets/img/visual.svg
+  link: https://gluon-cv.mxnet.io
+- title: GluonNLP
+  text: GluonNLP provides state-of-the-art deep learning models in NLP. For engineers and researchers to fast prototype research ideas and products.
+  icon: /assets/img/artificial-intelligence.svg
+  link: https://gluon-nlp.mxnet.io/
+- title: GluonTS
+  text: Gluon Time Series (GluonTS) is the Gluon toolkit for probabilistic time series modeling, focusing on deep learning-based models.
+  icon: /assets/img/line-graph.svg
+  link: https://gluon-ts.mxnet.io/
+
+
+community:
+- title: GitHub
+  text: Report bugs, request features, discuss issues, and more.
+  icon: /assets/img/octocat.png
+  link: https://github.com/apache/incubator-mxnet
+- title: Discuss Forum
+  text: Browse and join discussions on deep learning with MXNet and Gluon.
+  icon: /assets/img/mxnet_m.png
+  link: https://discuss.mxnet.io/
+- title: Slack
+  text: Discuss advanced topics. Request access by mail dev@mxnet.apache.org
+  icon: /assets/img/slack-logo-icon.svg
+  link: mailto:dev@mxnet.apache.org
+
+---
\ No newline at end of file
diff --git a/docs/static_site/src/pages/api/api.html b/docs/static_site/src/pages/api/api.html
new file mode 100644
index 000000000000..098efda5e605
--- /dev/null
+++ b/docs/static_site/src/pages/api/api.html
@@ -0,0 +1,191 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page
+title: Docs
+subtitle: Documentation for the supported language bindings
+action: Get Started
+action_url: /get_started
+permalink: /api/
+tag: main_docs
+
+faq_categories:
+- Deployment Environments
+- Model
+- Speed
+- Security
+- Extend and Contribute to MXNet
+
+docs:
+- title: Python
+  guide_link: /api/python.html
+  api_link: /api/python/docs/api
+  tutorial_link: /api/python/docs/tutorials
+  icon: /assets/img/python_logo.svg
+  tag: python
+- title: Scala
+  guide_link: /api/scala.html
+  api_link: /api/scala/docs/api
+  tutorial_link: /api/scala/docs/tutorials
+  description:
+  icon: /assets/img/scala_logo.svg
+  tag: scala
+- title: Java
+  guide_link: /api/java.html
+  api_link: /api/java/docs/api
+  tutorial_link: /api/java/docs/tutorials
+  description:
+  icon: /assets/img/java_logo.svg
+  tag: java
+- title: Clojure
+  guide_link: /api/clojure
+  api_link: /api/clojure/docs/api
+  tutorial_link: /api/clojure/docs/tutorials
+  description:
+  icon: /assets/img/clojure_logo.svg
+  tag: clojure
+- title: C/C++
+  guide_link: /api/cpp
+  api_link: /api/cpp/docs/api
+  tutorial_link: /api/cpp/docs/tutorials
+  description:
+  icon: /assets/img/cpp_logo.svg
+  tag: cpp
+- title: Julia
+  guide_link: /api/julia
+  api_link: /api/julia/docs/api
+  tutorial_link: https://github.com/apache/incubator-mxnet/tree/master/julia/examples
+  description:
+  icon: /assets/img/julia_logo.svg
+  tag: julia
+- title: R
+  guide_link: /api/r
+  api_link: https://s3.amazonaws.com/mxnet-prod/docs/R/mxnet-r-reference-manual.pdf
+  tutorial_link: /api/r/docs/tutorials
+  description:
+  icon: /assets/img/R_logo.svg
+  tag: r
+- title: Perl
+  guide_link: /api/perl
+  api_link: https://metacpan.org/release/AI-MXNet
+  tutorial_link: /api/perl/docs/tutorials
+  description:
+  icon: /assets/img/perl_logo.svg
+  tag: perl
+---
+{%- for doc in page.docs -%}
+  {%- if doc.tag == 'python' -%}
+    <h2>Python API</h2>
+    <div class="row docs-hero">
+      <div class="col-4 docs-hero-left">
+        <div class="docs-card">
+            <div class="docs-logo-container">
+                <img class="docs-logo-image" src="{{doc.icon | relative_url}}">
+            </div>
+            <div class="docs-action-btn">
+              <a href="{{doc.guide_link | relative_url}}"> <img src="{{'assets/img/compass.svg' | relative_url}}" class="docs-logo-docs">{{doc.title}} Guide  <span class="span-accented">›</span></a>
+            </div>
+            <div class="docs-action-btn">
+              <a href="{{doc.tutorial_link | relative_url}}"> <img src="{{'assets/img/video-tutorial.svg' | relative_url}}" class="docs-logo-docs">{{doc.title}} Tutorials  <span class="span-accented">›</span></a>
+            </div>
+            <div class="docs-action-btn">
+              <a href="{{doc.api_link | relative_url}}"> <img src="{{'assets/img/api.svg' | relative_url}}" class="docs-logo-docs">{{doc.title}} API Reference  <span class="span-accented">›</span></a>
+            </div>
+        </div>
+      </div>
+
+    <div class="col-8 docs-hero-right">
+        <h4>Python-first API</h4>
+        <p>MXNet provides a comprehensive and flexible Python API to serve a broad community of developers with different levels of experience and wide ranging requirements. Current efforts are focused on the
+        <a href="{{doc.api_link | relative_url}}"></a>Gluon API. Gluon provides a clear, concise, and simple API for deep learning. It makes it easy to prototype, build, and train deep learning models without sacrificing training speed.</p>
+        <p>You can checkout the <a href="{{'/ecosystem'|relative_url}}">rich ecosystem</a> built around Apache MXNet Gluon, including <a href="https://d2l.ai">D2L.ai</a>, <a href="https://gluon-cv.mxnet.io">GluonCV</a>,
+        <a href="https://gluon-nlp.mxnet.io">GluonNLP</a> and <a href="https://gluon-ts.mxnet.io">GluonTS</a>.</p>
+        <p>While most of the usability improvement around training are focused on the python API, the performance of MXNet is accessible through a variety of different language bindings, checkout their respective API and guides below!</p>
+      </div>
+
+    </div>
+  {%- endif -%}
+{%- endfor -%}
+<h2>Other Bindings</h2>
+<div class="row">
+{%- for doc in page.docs -%}
+  {%- if doc.tag != 'python' -%}
+  <div class="col-4">
+      <div class="docs-card">
+          <div class="docs-logo-container">
+              <img class="docs-logo-image" src="{{doc.icon | relative_url}}">
+          </div>
+          <div class="docs-action-btn">
+            <a href="{{doc.guide_link | relative_url}}"> <img src="{{'assets/img/compass.svg' | relative_url}}" class="docs-logo-docs">{{doc.title}} Guide  <span class="span-accented">›</span></a>
+          </div>
+          <div class="docs-action-btn">
+            <a href="{{doc.tutorial_link | relative_url}}"> <img src="{{'assets/img/video-tutorial.svg' | relative_url}}" class="docs-logo-docs">{{doc.title}} Tutorials  <span class="span-accented">›</span></a>
+          </div>
+          <div class="docs-action-btn">
+            <a href="{{doc.api_link | relative_url}}"> <img src="{{'assets/img/api.svg' | relative_url}}" class="docs-logo-docs">{{doc.title}} API Reference  <span class="span-accented">›</span></a>
+          </div>
+      </div>
+  </div>
+  {%- endif -%}
+{%- endfor -%}
+</div>
+</div> <!-- closing outer wrapper -->
+<div class="docs-architecture">
+    <div class="wrapper">
+        <h2>MXNet Architecture</h2>
+        <p>
+        Building a high-performance deep learning library
+        requires many systems-level design decisions.
+        In this design note, we share the rationale
+        for the specific choices made when designing _MXNet_.
+        We imagine that these insights may be useful
+        to both deep learning practitioners
+        and builders of other deep learning systems.
+        </p>
+        <h4>Deep Learning System Design Concepts</h4>
+        <p>
+        The following pages address general design concepts for deep learning systems.
+        Mainly, they focus on the following 3 areas:
+        abstraction, optimization, and trade-offs between efficiency and flexibility.
+        Additionally, we provide an overview of the complete MXNet system.
+        </p>
+        <ul>
+        {%- for p in site.pages -%}
+          {%- if p.category == 'architecture' -%}
+            <li><a href="{{p.url | relative_url}}">{{p.title}}</a></li>
+          {%- endif -%}
+        {%- endfor -%}
+        </ul>
+    </div>
+</div>
+<div class="docs-faq">
+    <div class="wrapper">
+        <h2>FAQ</h2>
+        <ul>
+        {%- for faq_c in page.faq_categories -%}
+            <h3>{{faq_c}}</h3>
+            {%- for p in site.pages -%}
+                {%- if p.faq_c == faq_c -%}
+                  <li><a href="{{p.url | relative_url}}">{{p.question}}</a></li>
+                {%- endif -%}
+            {%- endfor -%}
+            <br>
+        {%- endfor -%}
+        </ul>
+    </div>
+</div>
+<div> <!-- reopening to close wrapper -->
diff --git a/docs/static_site/src/pages/api/architecture/exception_handling.md b/docs/static_site/src/pages/api/architecture/exception_handling.md
new file mode 100644
index 000000000000..b985b27b285e
--- /dev/null
+++ b/docs/static_site/src/pages/api/architecture/exception_handling.md
@@ -0,0 +1,130 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_category
+title:  Exception Handling in MXNet
+category: architecture
+permalink: /api/architecture/exception_handling
+---
+
+# Exception Handling in MXNet
+
+This tutorial explains the exception handling support in MXNet,
+and provides examples on how to throw and handle exceptions when in a multithreaded context.
+Although, the examples are in Python, they can be easily extended to MXNet
+language bindings.
+
+MXNet exceptions can be thrown from two areas:
+- MXNet main thread. For eg. Infershape and InferType.
+- Spawned threads:
+    * By dependency engine for operator execution in parallel
+    * By the iterators, during the data loading, text parsing phase etc.
+
+In the first case, the exception is thrown and can be handled in the main thread.
+In the second case, the exception is thrown in a spawned thread, caught and transported to the
+main thread, where it is rethrown. This tutorial will give more explanation and examples on how
+to handle exceptions for the second case.
+
+## Prerequisites
+
+To complete this tutorial, we need:
+- MXNet [7b24137](https://github.com/apache/incubator-mxnet/commit/7b24137ed45df605defa4ce72ec91554f6e445f0). See Instructions in [Setup and Installation](http://mxnet.io/install/index.html).
+
+## Exception Handling for Iterators
+
+The below example shows how to handle exceptions for iterators. In this example,
+we populate files for data and labels with fewer number of labels compared to the
+number of samples. This should throw an exception.
+
+CSVIter uses PrefetcherIter for loading and parsing data.
+The PrefetcherIter spawns a producer thread in the background which prefetches
+the data while the main thread consumes the data. The exception is thrown in the spawned
+producer thread during the prefetching, when the label is not found corresponding to a specific sample.
+
+The exception is transported to the main thread, where it is rethrown when Next is
+called as part of the following line: `for batch in iter(data_train)`.
+
+In general, Exception may be rethrown as part of `Next` and `BeforeFirst` calls which correspond to `reset()` and `next()` methods in `MXDataIter` for Python language bindings.
+
+```python
+import os
+import mxnet as mx
+
+cwd = os.getcwd()
+data_path = os.path.join(cwd, "data.csv")
+label_path = os.path.join(cwd, "label.csv")
+
+with open(data_path, "w") as fout:
+    for i in range(8):
+        fout.write("1,2,3,4,5,6,7,8,9,10\n")
+
+with open(label_path, "w") as fout:
+    for i in range(7):
+        fout.write("label"+str(i))
+
+try:
+    data_train = mx.io.CSVIter(data_csv=data_path, label_csv=label_path, data_shape=(1, 10),
+                               batch_size=4)
+
+    for batch in iter(data_train):
+        print(data_train.getdata().asnumpy())
+except mx.base.MXNetError as ex:
+    print("Exception handled")
+    print(ex)
+```
+
+### Limitation
+
+There is a race condition when your last `next()` call doesnt reach the batch in your dataset where exception occurs. Exception may or may not be thrown in this case depending on which thread wins the race. To avoid this situation, you should try and iterate through your full dataset if you think it can throw exceptions which need to be handled.
+
+
+## Exception Handling for Operators
+
+The below example shows how to handle exceptions for operators in the imperative mode.
+
+For the operator case, the dependency engine spawns a number of threads if it is running in the `ThreadedEnginePool` or `ThreadedEnginePerDevice` mode. The final operator is executed in one of the spawned threads.
+
+If an operator throws an exception during execution, this exception is propagated
+down the dependency chain. Once there is a synchronizing call i.e. WaitToRead for a variable in the dependency chain, the propagated exception is rethrown.
+
+In the below example, I illustrate how an exception that occured in the first line is propagated down the dependency chain, and finally is rethrown when we make a synchronizing call to WaitToRead.
+
+```python
+import mxnet as mx
+a = mx.nd.random.normal(0, 1, (2, 2))
+b = mx.nd.random.normal(0, 2, (2, 2))
+c = mx.nd.dot(a, b)
+d = mx.nd.random.normal(0, -1, (2, 2))
+e = mx.nd.dot(c, d)
+e.wait_to_read()
+```
+
+Although the above exception occurs when executing the operation which writes to the variable d in one of the child threads, it is thrown only when the synchronization happens as part of the line: `e.wait_to_read()`.
+
+Let us take another example. In the following case, we write to two variables and then `wait_to_read` for both. This example shows that any particular exception will not be thrown more than once.
+
+```python
+import mxnet as mx
+a = mx.nd.random.normal(0, 1, (2, 2))
+b = mx.nd.random.normal(0, -1, (2, 2))
+c, d  = mx.nd.dot(a, b)
+try:
+    c.asnumpy()
+except mx.base.MXNetError as ex:
+    print("Exception handled")
+d.asnumpy()
+```
diff --git a/docs/static_site/src/pages/api/architecture/note_data_loading.md b/docs/static_site/src/pages/api/architecture/note_data_loading.md
new file mode 100644
index 000000000000..63d90024a80c
--- /dev/null
+++ b/docs/static_site/src/pages/api/architecture/note_data_loading.md
@@ -0,0 +1,267 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_category
+title:  Efficient Data Loaders
+category: architecture
+permalink: /api/architecture/note_data_loading
+---
+
+# Designing Efficient Data Loaders for Deep Learning
+
+Data loading is an important component of any machine learning system.
+When we work with tiny datasets, we can get away with loading an entire dataset into GPU memory.
+With larger datasets, we must store examples in main memory.
+And when datasets grow too large to fit into main memory,
+data loading can become performance-critical.
+In designing a data loader,
+we aim to achieve more efficient data loading,
+to spend less effort on data preparation,
+and to present a clean and flexible interface.
+
+We organize this design note as follows:
+
+* **IO Design Insight:**  Guiding principles in data loading design.
+* **Data Format:** Our solution using dmlc-core's binary recordIO implementation.
+* **Data Loading:** Our method to reduce IO cost by utilizing the threaded iterator provided by dmlc-core.
+* **Interface Design:** Our approach to facilitate writing MXNet data iterators in just a few lines of Python.
+* **Future Extension:** Prospective ideas for making data loading more flexible.
+
+Our analysis will motivate several requirements that an effective IO system should fulfill.
+
+***List of Key Requirements***
+- Small file size.
+- Parallel (distributed) packing of data.
+- Fast data loading and online augmentation.
+- Quick reads from arbitrary parts of the dataset in the distributed setting.
+
+## Design Insight
+To design an IO system, we must address two kinds of tasks:
+data preparation and data loading.
+Data preparation is usually performed offline,
+whereas data loading influences the online performance.
+In this section, we will introduce our insight of IO design involving the two phases.
+
+### Data Preparation
+Data preparation describes the process of packing data
+into a desired format for later processing.
+When working with large datasets like ImageNet, this process can be time-consuming.
+In these cases, there are several heuristics we ought to follow:
+
+- Pack the dataset into small numbers of files. A dataset may contain millions of data instances. Packed data distributes easily from machine to machine.
+- Do the packing once. We don't want to repack data every time run-time settings, like the number of machines, are changed.
+- Process the packing in parallel to save time.
+- Be able to access arbitrary parts of the data easily. This is crucial for distributed machine learning when data parallelism is introduced. Things may get tricky when the data has been packed into several physical data files. The desired behavior could be: the packed data can be logically separated into arbitrary numbers of partitions, no matter how many physical data files there are. For example, if we pack 1000 images into 4 physical files, then each file contains 250 images. If we then use 10 machines to train a DNN, we should be able to load approximately 100 images per machine. Some machines may need images from different physical files.
+
+### Data Loading
+The next step to consider is how to load the packed data into RAM.
+Our goal is to load the data as quickly as possible.
+There are several heuristics we try to follow:
+- **Read continuously:** We can read faster when reading from contiguous locations on disk.
+- **Reduce the bytes to be loaded:** We can achieve this by storing data in a compact way, e.g. saving images in JPEG format.
+- **Load and train in different threads:** This avoids computational bottlenecks while loading data.
+- **Save RAM:** Judiciously decide whether to load entire files into RAM.
+
+## Data Format
+
+Since the training of deep neural network often involves large amounts of data,
+the format we choose should be both efficient and convenient.
+To achieve our goals, we need to pack binary data into a splittable format.
+In MXNet, we rely on the binary recordIO format implemented in dmlc-core.
+
+### Binary Record
+
+![baserecordio](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/io/baserecordio.jpg)
+In MXNet's binary RecordIO, we store each data instance as a record.
+**kMagic** is a *magic number* indicating the start of a record.
+**Lrecord** encodes length and a continue flag.
+In lrecord,
+- cflag == 0: this is a complete record
+- cflag == 1: start of a multiple-records
+- cflag == 2: middle of multiple-records
+- cflag == 3: end of multiple-records
+
+**Data** is the space to save data content.
+**Pad** is simply a padding space to make record align to 4 bytes.
+
+After we pack the data, each file contains multiple records.
+Then, loading can be continuous.
+This avoids the low performance that can result
+from reading random locations on disk.
+
+One advantage of storing data via records
+is that each record can vary in length.
+This allows us to save data compactly
+when good compression algorithms are available for our data.
+For example, we can use JPEG format to save image data.
+The packed data will be much smaller
+compared with storing uncompressed RGB values for each pixel.
+
+Take ImageNet_1K dataset as an example.
+If we store the data as 3 * 256 * 256 array of raw RGB values,
+the dataset would occupy more than **200G**.
+But after compressing the images using JPEG,
+they only occupy about **35G** of disk space.
+This significantly reduces the cost owing to reading from disk.
+
+Here's an example of binary recordIO:
+![baserecordio](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/io/ImageRecordIO.jpg)
+We first resize the image into 256 * 256,
+then compress into JPEG format.
+After that, we save a header that indicates the index and label
+for that image to be used when constructing the *Data* field for that record.
+We then pack several images together into a file.
+You may want to also review the [example using im2rec.py to create a RecordIO dataset](https://mxnet.incubator.apache.org/tutorials/basic/data.html#loading-data-using-image-iterators).
+
+### Access Arbitrary Parts Of Data
+
+One desirable property for a data loader might be:
+The packed data can be logically sliced into an arbitrary number of partitions,
+no matter how many physical packed data files there are.
+Since binary recordIO can easily locate
+the start and end of a record using the Magic Number,
+we can achieve the above goal using the InputSplit
+functionality provided by dmlc-core.
+
+InputSplit takes the following parameters:
+- FileSystem *filesys*: dmlc-core wrapper around the IO operations for different file systems, like hdfs, s3, local. User shouldn't need to worry about the difference between file systems anymore.
+- Char *uri*: The URI of files. Note that it could be a list of files because we may pack the data into several physical parts. File URIs are separated by ';'.
+- Unsigned *nsplit*: The number of logical splits. *nsplit* could be different from the number of physical files.
+- Unsigned *rank*: Which split to load in this process.
+
+The splitting process is demonstrated below:
+- Determine the size of each partition.
+
+![beforepartition](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/io/beforepartition.jpg)
+
+- Approximately partition the records according to file size. Note that the boundary of each part may be located in the middle of a record.
+
+![approxipartition](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/io/approximatepartition.jpg)
+
+-  Set the beginning of partitions in such a way as to avoid splitting records across partitions.
+
+![afterpartition](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/io/afterpartition.jpg)
+
+By conducting the above operations,
+we now identify the records belong to each part,
+and the physical data files needed by each logical part.
+InputSplit greatly simplifies data parallelism,
+where each process only reads part of the data.
+
+Since our partitioning scheme does not depend on the number of physical data files,
+we can process a huge dataset like ImageNet_22K in parallel fashion as illustrated below.
+We don't need to consider distributed loading issue at the preparation time,
+just select the most efficient physical file number
+according to the dataset size and computing resources available.
+![parallelprepare](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/io/parallelprepare.jpg)
+
+## Data Loading and Preprocessing
+
+When the speed of loading and preprocessing can't keep up
+with the speed of training or evaluation,
+IO can bottleneck the speed of the whole system.
+In this section, we will introduce a few tricks
+to achieve greater efficiency when loading
+and preprocessing data packed in binary recordIO format.
+When applied to the ImageNet dataset, our approach achieves
+the IO speed of **3000** images/sec **with a normal HDD**.
+
+### Loading and preprocessing on the fly
+
+When training deep neural networks,
+we sometimes must load and preprocess the data
+while simultaneously training for the following reasons:
+- When the whole size of the dataset exceeds available RAM size, we can't load it in advance;
+- Sometimes, to make models robust to things like translations, rotations, and small amounts of color shift of noise, we introduce randomness into the training process. In these cases we must re-preprocess the data each time we revisit an example.
+
+In service of efficiency, we also address multi-threading techniques. Taking Imagenet training as an example, after loading a bunch of image records, we can start multiple threads to simultaneously perform image decoding and image augmentation. We depict this process in the following illustration:
+![process](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/io/process.jpg)
+
+### Hide IO Cost Using Threadediter
+
+One way to lower IO cost is to pre-fetch the data for next batch on one thread,
+while the main thread performs the forward and backward passes for training.
+To support more complicated training schemes,
+MXNet provides a more general IO processing pipeline
+using *threadediter* provided by dmlc-core.
+The key of *threadediter* is to start a stand-alone thread that acts as a data provider,
+while the main thread acts as a data consumer as illustrated below.
+
+The threadediter maintains a buffer of a certain size
+and automatically fills the buffer when it's not full.
+And after the consumer finishes consuming part of the data in the buffer,
+threadediter will reuse the space to save the next part of data.
+![threadediter](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/io/threadediter.png)
+
+## MXNet IO Python Interface
+We make the IO object as an iterator in numpy.
+By achieving that, the user can easily access the data
+using a for-loop or calling next() function.
+Defining a data iterator is very similar to defining a symbolic operator in MXNet.
+
+The following example code demonstrates a Cifar data iterator.
+
+```python
+dataiter = mx.io.ImageRecordIter(
+    # Dataset Parameter, indicating the data file, please check the data is already there
+    path_imgrec="data/cifar/train.rec",
+    # Dataset Parameter, indicating the image size after preprocessing
+    data_shape=(3,28,28),
+    # Batch Parameter, tells how many images in a batch
+    batch_size=100,
+    # Augmentation Parameter, when offers mean_img, each image will subtract the mean value at each pixel
+    mean_img="data/cifar/cifar10_mean.bin",
+    # Augmentation Parameter, randomly crop a patch of the data_shape from the original image
+    rand_crop=True,
+    # Augmentation Parameter, randomly mirror the image horizontally
+    rand_mirror=True,
+    # Augmentation Parameter, randomly shuffle the data
+    shuffle=False,
+    # Backend Parameter, preprocessing thread number
+    preprocess_threads=4,
+    # Backend Parameter, prefetch buffer size
+    prefetch_buffer=1,
+    # Optional, the device context which data loader optimized for, could be 'gpu' or 'cpu'
+    ctx="gpu",
+    # The out data type, could be 'float32' 'int8' or 'uint8'
+    dtype="float32")
+```
+
+Generally, to create a data iterator, you need to provide five kinds of parameters:
+
+* **Dataset Param:** Information needed to access the dataset, e.g. file path, input shape.
+* **Batch Param:** Specifies how to form a batch, e.g. batch size.
+* **Augmentation Param:** Which augmentation operations (e.g. crop, mirror) should be taken on an input image.
+* **Backend Param:** Controls the behavior of the backend threads to hide data loading cost.
+* **Auxiliary Param:** Provides options to help with debugging.
+
+Usually, **Dataset Param** and **Batch Param** MUST be given,
+otherwise the data batch can't be created.
+Other parameters can be given as needed.
+Ideally, we should separate the MX Data IO into modules,
+some of which might be useful to expose to users, for example:
+
+* **Efficient prefetcher:** allows the user to write a data loader that reads their customized binary format that automatically gets multi-threaded prefetcher support.
+* **Data transformer:** image random cropping, mirroring, etc. Allows the users to use those tools, or plug in their own customized transformers (maybe they want to add some specific kind of coherent random noise to data, etc.)
+
+## Future Extensions
+
+In the future, there are some extensions to our data IO
+that we might consider adding.
+Specifically, we might add specialized support
+for applications including image segmentation, object localization, and speech recognition.
+More detail will be provided when such applications have been running on MXNet.
diff --git a/docs/static_site/src/pages/api/architecture/note_engine.md b/docs/static_site/src/pages/api/architecture/note_engine.md
new file mode 100644
index 000000000000..cb626649e5ff
--- /dev/null
+++ b/docs/static_site/src/pages/api/architecture/note_engine.md
@@ -0,0 +1,391 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_category
+title:  Dependency Engine
+category: architecture
+permalink: /api/architecture/note_engine
+---
+
+# Dependency Engine for Deep Learning
+
+We always want deep learning libraries
+to run faster and scale to larger datasets.
+One natural approach is to see if we can benefit
+from throwing more hardware at the problem,
+as by using multiple GPUs simultaneously.
+
+Library designers then ask:
+How can we *parallelize* computation across devices?
+And, more importantly, how can we *synchronize* computation
+when we introduce multi-threading?
+A runtime dependency engine is a generic solution to these problems.
+
+In this document, we examine approaches for using
+runtime dependency scheduling to accelerate deep learning.
+We aim to explain how runtime dependency scheduling
+can both speed up and simplify multi-device deep learning.
+We also explore potential designs for a generic dependency engine
+that could be both library- and operation-independent.
+
+Most of the discussion of on this page draws inspiration
+from the MXNet dependency engine.
+The dependency tracking algorithm we discuss
+was primarily developed by [Yutian Li](https://github.com/hotpxl)
+and [Mingjie Wang](https://github.com/jermainewang).
+
+## Dependency Scheduling
+
+Although most users want to take advantage of parallel computation,
+most of us are more familiar with serial programs.
+So one natural question is: how can we write serial programs
+and build a library to automatically parallelize our programs
+in an asynchronous way?
+
+For example, in the following code, we can run `B = A + 1`
+and `C = A + 2` in any order, or in parallel:
+
+```python
+    A = 2
+    B = A + 1
+    C = A + 2
+    D = B * C
+```
+
+However, it's quite hard to code the sequence manually
+because the last operation, `D = B * C`, needs to wait
+for both of the preceding operations to complete before it starts.
+The following dependency graph/data flow graph illustrates this.
+
+![Dep Simple](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/engine/dep_simple.png)
+
+
+A dependency engine is a library that takes a sequence of operations
+and schedules them according to the dependency pattern,  potentially in parallel.
+So in this example, a dependency library
+could run ```B = A + 1``` and ```C = A + 2``` in parallel,
+and run ```D = B * C``` after those operations complete.
+
+## Problems in Dependency Scheduling
+
+A dependency engine relieves the burden of writing concurrent programs.
+However, as operations become parallelized,
+new dependency tracking problems arise.
+In this section, we discuss those problems.
+
+### Data Flow Dependency
+Data flow dependency describes how the outcome of one computation
+can be used in other computations.
+Every dependency engine has to solve the data flow dependency problem.
+
+![Dep Simple](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/engine/dep_simple.png)
+
+Because we discussed this issue in the preceding section,
+we include the same figure here. Libraries that have
+data flow tracking engines include Minerva and Purine2.
+
+### Memory Recycling
+When should we recycle the memory that we allocated to the arrays?
+In serial processing, this is easy to determine.
+We simply recycle the memory after the variable goes out of scope.
+However, as the following figure shows, this is a bit harder in parallel processing.
+
+![Dep Del](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/engine/dep_del.png)
+
+In this example, because both computations need to use values from A,
+we can't recycle the memory until both complete.
+The engine must schedule the memory recycling operations according to the dependencies,
+and ensure that they are executed after both ```B = A + 1``` and ```C = A + 2``` complete.
+
+
+### Random Number Generation
+Random number generators, which are commonly used in machine learning,
+pose interesting challenges for dependency engines.
+Consider the following example:
+
+![Dep Rand](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/engine/dep_rand.png)
+
+In this example, we are generating random numbers in a sequence.
+Although it seems that the two random number generations can be parallelized,
+this is usually not the case. A pseudo-random number generator (PRNG)
+is not thread-safe because it might cause some internal state
+to mutate when generating a new number.
+Even if the PRNG is thread-safe,
+it is preferable to serialize number generation,
+so we can get reproducible random numbers.
+
+## Case Study: A Dependency Engine for a Multi-GPU Neural Network
+
+In the last section, we discussed the problems
+we might face in designing a dependency engine.
+Before thinking about how to design a generic engine to solve those problems,
+let's consider how a dependency engine can help in multi-GPU training of a neural network.
+The following pseudocode Python program illustrates
+training one batch on a  two-layer neural network.
+
+```python
+    # Example of one iteration Two GPU neural Net
+    data = next_batch()
+    data[gpu0].copyfrom(data[0:50])
+    data[gpu1].copyfrom(data[50:100])
+    # forward, backprop on GPU 0
+    fc1[gpu0] = FullcForward(data[gpu0], fc1_weight[gpu0])
+    fc2[gpu0] = FullcForward(fc1[gpu0], fc2_weight[gpu0])
+    fc2_ograd[gpu0] = LossGrad(fc2[gpu0], label[0:50])
+    fc1_ograd[gpu0], fc2_wgrad[gpu0] =
+      FullcBackward(fc2_ograd[gpu0] , fc2_weight[gpu0])
+      _, fc1_wgrad[gpu0] = FullcBackward(fc1_ograd[gpu0] , fc1_weight[gpu0])
+    # forward, backprop on GPU 1
+    fc1[gpu1] = FullcForward(data[gpu1], fc1_weight[gpu1])
+    fc2[gpu1] = FullcForward(fc1[gpu1], fc2_weight[gpu1])
+    fc2_ograd[gpu1] = LossGrad(fc2[gpu1], label[50:100])
+    fc1_ograd[gpu1], fc2_wgrad[gpu1] =
+         FullcBackward(fc2_ograd[gpu1] , fc2_weight[gpu1])
+         _, fc1_wgrad[gpu1] = FullcBackward(fc1_ograd[gpu1] , fc1_weight[gpu1])
+    # aggregate gradient and update
+    fc1_wgrad[cpu]  = fc1_wgrad[gpu0] + fc1_wgrad[gpu1]
+    fc2_wgrad[cpu]  = fc2_wgrad[gpu0] + fc2_wgrad[gpu1]
+    fc1_weight[cpu] -= lr *  fc1_wgrad[cpu]
+    fc2_weight[cpu] -= lr *  fc2_wgrad[cpu]
+    fc1_weight[cpu].copyto(fc1_weight[gpu0] , fc1_weight[gpu1])
+    fc2_weight[cpu].copyto(fc2_weight[gpu0] , fc2_weight[gpu1])
+```
+In this program, the data 0 to 50  is copied to GPU 0,
+and the data 50 to 100 is copied to GPU 1.
+The calculated gradients are aggregated in the CPU,
+which then performs a simple SGD update,
+and copies the updated weight back to each GPU.
+This is a common way to write a parallel program in a serial manner.
+The following dependency graph shows how it can be parallelized:
+
+![Dep Net](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/engine/dep_net.png)
+
+***Notes:***
+
+- The gradient can be copied to the CPU as soon as we get the gradient of a layer.
+- The weight can be copied back soon as the weight is updated.
+- In the forward pass, we have a dependency on ```fc1_weight[cpu].copyto(fc1_weight[gpu0] , fc1_weight[gpu1])```
+  from the previous iteration.
+- There is a delay in computation between the last backward pass to layer k and the next forward call to layer k. We can synchronize the weight of layer k *in parallel* with other computation during this delay.
+
+This approach to optimization is used by multi-GPU deep learning libraries, such as CXXNet.
+The point is to overlap weight synchronization (communication) with computation.
+However, it's not easy to do that, because the copy operation needs to be triggered
+as soon as the backward pass of the layer completes,
+which then triggers the reduction, updates, etc.
+
+A dependency engine can schedule these operations and perform multi-threading
+and dependency tracking.
+
+## Designing a Generic Dependency Engine
+
+We hope that you're convinced that a dependency engine is useful
+for scaling deep learning programs to multiple devices.
+Now let's discuss how we can design and implement
+a generic interface for a dependency engine.
+This solution isn't the only possible design for a dependency engine.
+It's an example that we think is useful in most cases.
+
+Our goal is to create a dependency engine that is *generic* and *lightweight*.
+Ideally, we'd like the engine that easily plugs into existing deep learning code,
+and that can scale up to multiple machines with minor modifications.
+To do that, we need to focus only on dependency tracking,
+not on assumptions about what users can or can't do.
+
+Here's a summary of goals for the engine:
+
+- The engine should not be aware of what operations it performs, so that users can perform any operations they define.
+- It should not be restricted in what type of objects it can schedule.
+	- We should be able to schedule dependencies on GPU and CPU memory.
+	- We should be able to track dependencies on the random number generator, etc.
+- The engine should not allocate resources. It should only track dependencies. Users can allocate their own memory, PRNG, etc.
+
+The following Python snippet provides an engine interface that might help us reach our goal. Note that a real implementation will be closer to the metal, typically in C++.
+
+```python
+    class DepEngine(object):
+	    def new_variable():
+		    """Return a new variable tag
+		    Returns
+		    -------
+		    vtag : Variable Tag
+		        The token of the engine to represent dependencies.
+		    """
+		    pass
+
+	    def push(exec_func, read_vars, mutate_vars):
+		    """Push the operation to the engine.
+
+		    Parameters
+		    ----------
+		    exec_func : callable
+			    The real operation to be performed.
+
+		    read_vars : list of Variable Tags
+			    The list of variables this operation will read from.
+
+		    mutate_vars : list of Variable Tags
+			    The list of variables this operation will mutate.
+		    """
+		    pass
+```
+
+Because we can't make assumptions about what objects we are scheduling, we ask the user to allocate a
+_virtual tag_ that is associated with each object to represent what we need to schedule.
+So, at the beginning, the user can allocate the variable tag,
+and attach it to each of the objects that we want to schedule.
+
+![Dep Net](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/engine/tag_var.png)
+
+The user then calls `push` to tell the engine about the function to execute.
+The user also needs to specify the dependencies of the operation,
+using `read_vars` and `write_vars`:
+
+- `read_vars` are variable tags for objects that the operation will _read from_, without changing their internal state.
+- `mutate_vars` are variable tags for objects whose internal states the operation will mutate.
+
+![Push Op](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/engine/push_var.png)
+
+The preceding figure shows how to push operation `B = A + 1` to the dependency engine. `B.data` and
+`A.data` are the allocated space. Note that the engine is *only aware of variable tags*.
+Any execution function can be processed.
+This interface is generic for the operations and resources we want to schedule.
+
+For fun, let's look at how the engine internals work with the tags by considering the following code snippet:
+
+```
+    B = A + 1
+    C = A + 2
+    A = C * 2
+    D = A + 3
+```
+
+The first line reads variable `A` and mutates variable `B`. The second line reads variable `A` and mutates variable `C`. And so on.
+
+The engine maintains a queue for each variable, as the following animation shows for each of the four lines. Green blocks represents a read action, while red blocks represent mutations.
+
+![Dependency Queue](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/engine/dep_queue.gif)
+
+Upon building this queue, the engine sees that the first two green blocks at the beginning of `A`'s queue could actually be run in parallel because they are both read actions and won't conflict with each other. The following graph illustrates this point.
+
+![Dependency Parallelism](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/engine/dep_parallel.png)
+
+One cool thing about all this scheduling is that it's not confined to numerical calculations.
+Because everything that is scheduled is only a tag, the engine could schedule everything!
+
+The following figure gives a complete push sequence of the programs we mentioned in previous sections.
+
+![Push Seq](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/engine/push_seq.png)
+
+### Porting Existing Code to the Dependency Engine
+Because the generic interface doesn't control things like memory allocation and which operation to execute,
+most existing code can be scheduled by the dependency engine in two steps:
+
+
+1. Allocate the variable tags associated with resources like memory blob, PRNGS.
+2. Call `push` with the execution function as the original code to execute, and put the variable tags of
+  corresponding resources correctly in `read_vars` and `mutate_vars`.
+
+## Implementing the Generic Dependency Engine
+
+We have described the generic engine interface and
+how it can be used to schedule various operations.
+In this section, we provide a high-level discussion
+of how to implement such an engine.
+
+The general idea is as follows:
+
+- Use a queue to track all of the pending dependencies on each variable tag.
+- Use a counter on each operation to track how many dependencies are yet to be fulfilled.
+- When operations are completed, update the state of the queue and dependency counters to schedule new operations.
+
+The following figure illustrates the scheduling algorithm
+and might give you a better sense of what is going on in the engine.
+
+![Dep Tracking](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/engine/engine_queue_step.png)
+
+Below, we show another example involving random number generators.
+
+![Dep Rand](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/engine/engine_queue_rand.png)
+
+As you can see, the purpose of the algorithm is to update pending queues
+of operations and to make the right state transition when an operation has completed.
+More care should be taken to make sure the state transitions
+are done in a way that's safe for threads.
+
+### Separate Dependency Tracking with Running Policy
+If you're reading carefully, you might have noticed
+that the preceding section shows only the algorithm
+for deciding when an operation can be executed.
+We didn't show how to actually run an operation.
+In practice, there can be many different policies.
+For example, we can either use a global thread-pool to run all operations,
+or use a specific thread to run operations on each device.
+
+This running policy is usually independent of dependency tracking,
+and can be separated out as either an independent module
+or a virtual interface of base-dependency tracking modules.
+Developing an elegant runtime policy that is fair
+to all operations and schedules is an interesting systems problem itself.
+
+## Discussion
+
+The design that we discussed in this article
+isn't the only solution to the dependency tracking problem.
+It's just one example of how we might approach this.
+To be sure, some of these design choices are debatable.
+We'll discuss some of them in this section.
+
+### Dynamic vs. Static
+The dependency engine interface discussed in this topic is somewhat dynamic
+in the sense that the user can push operations one by one,
+instead of declaring the entire dependency graph (static).
+Dynamic scheduling can require more overhead
+than static declarations, in terms of data structure.
+However, it also enables more flexibility, such as supporting auto parallelism
+for imperative programs or a mixture of imperative and symbolic programs.
+You can also add some level of predeclared operations
+to the interface to enable data structure reuse.
+
+### Mutation vs. Immutable
+The generic engine interface presented in this page
+supports explicit scheduling for mutation.
+In a typical data flow engine, the data are usually immutable.
+Working with immutable data has a lot of benefits.
+For example, immutable data is generally more suitable for parallelization,
+and facilitates better fault tolerance in a distributed setting (by way of re-computation).
+
+However, immutability presents several challenges:
+
+- It's harder to schedule resource contention problems, as arise when dealing with random numbers and deletion.
+- The engine usually needs to manage resources (memory, random number) to avoid conflicts. It's harder to plug in user-allocated space, etc.
+- Preallocated static memory isn't available, again because the usual pattern is to write to a preallocated layer space, which is not supported if data is immutable.
+
+Allowing mutation mitigates these issues.
+
+
+## Source Code of the Generic Dependency Engine
+[MXNet](https://github.com/dmlc/mxnet) provides an implementation
+of the generic dependency engine described in this page.
+You can find more details in [this topic](http://mxnet.io/architecture/note_engine.html).
+We welcome your contributions.
+
+## Next Steps
+
+* [Squeeze the Memory Consumption of Deep Learning](http://mxnet.io/architecture/note_memory.html)
+* [Efficient Data Loading Module for Deep Learning](http://mxnet.io/architecture/note_data_loading.html)
+* [Survey of RNN Interface](http://mxnet.io/architecture/rnn_interface.html)
diff --git a/docs/static_site/src/pages/api/architecture/note_memory.md b/docs/static_site/src/pages/api/architecture/note_memory.md
new file mode 100644
index 000000000000..771d6a5ff012
--- /dev/null
+++ b/docs/static_site/src/pages/api/architecture/note_memory.md
@@ -0,0 +1,351 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_category
+title:  Memory Consumption
+category: architecture
+permalink: /api/architecture/note_memory
+---
+
+# Optimizing Memory Consumption in Deep Learning
+
+Over the last ten years, a constant trend in deep learning
+is towards deeper and larger networks.
+Despite rapid advances in hardware performance,
+cutting-edge deep learning models continue to push the limits of GPU RAM.
+So even today, it's always desirable to find ways
+to train larger models while consuming less memory.
+Doing so enables us to train faster, using larger batch sizes,
+and consequently achieving a higher GPU utilization rate.
+
+In this document, we explore techniques for optimizing
+memory allocation for deep neural networks.
+We discuss a few candidate solutions.
+While our proposals are by no means exhaustive,
+these solutions are instructive and allow us to
+introduce the major design issues at play.
+
+## Computation Graph
+
+First, let's revisit the idea of the computation graph.
+A computation graph describes the (data flow) dependencies
+between the operations in the deep network.
+The operations performed in the graph
+can be either fine-grained or coarse-grained.
+The following figure shows two examples of computation graphs.
+
+![Comp Graph Example](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/memory/comp_graph_example.png)
+
+The concept of a computation graph is explicitly encoded in packages like Theano and CGT.
+In other libraries, computation graphs appear implicitly as network configuration files.
+The major difference in these libraries comes down to how they calculate gradients.
+There are mainly two ways: performing back-propagation on the _same_ graph
+or explicitly representing a _backwards path_ to calculate the required gradients.
+
+![Backward Graph](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/memory/back_graph.png)
+
+Libraries like Caffe, CXXNet, and Torch take the former approach,
+performing back-prop on the original graph.
+Libraries like Theano and CGT take the latter approach,
+explicitly representing the backward path.
+In this discussion, we adopt the *explicit backward path* approach
+because it has several advantages for optimization.
+
+However, we should emphasize that choosing the explicit backward path approach doesn't restrict us
+to symbolic libraries, such as Theano and CGT. We can also use the explicit backward path for gradient calculation of
+layer-based (which ties forward and backward together) libraries. The following graph shows how to do this.
+Basically, we introduce a backward node that links to the forward node of the graph and calls the ```layer.backward```
+in the backward operations.
+
+![Backward Layer](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/memory/explicit_back_layer.png)
+
+This discussion applies to almost all existing deep learning libraries.
+(There are differences between libraries,  e.g., higher-order differentiation, which is beyond the scope of this topic.)
+
+Why is the explicit backward path better? Let's explain it with two examples.
+The first reason is that the explicit backward path
+clearly describes the dependency between computations.
+Consider the following case, where we want to get
+the gradient of A and B. As we can see clearly from the graph,
+the computation of the ```d(C)``` gradient doesn't depend on F.
+This means that we can free the memory of ```F```
+right after the forward computation is done.
+Similarly, the memory of ```C``` can be recycled.
+
+![Backward Prune](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/memory/back_dep_prune.png)
+
+Another advantage of the explicit backward path
+is the ability to have a different backward path,
+instead of a mirror of forward one.
+A common example is the split connection case,
+as shown in the following figure.
+
+![Backward Agg](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/memory/back_agg_grad.png)
+
+In this example, the output of B is referenced by two operations.
+If we want to do the gradient calculation in the same
+network, we need to introduce an explicit split layer.
+This means we need to do the split for the forward pass, too.
+In this figure, the forward pass doesn't contain a split layer,
+but the graph will automatically insert a gradient
+aggregation node before passing the gradient back to B.
+This helps us to save the memory cost of allocating the output of the split layer,
+and the operation cost of replicating the data in the forward pass.
+
+If we adopt the explicit backward approach,
+there's no difference between the forward pass and the backward pass.
+We simply step through the computation graph in chronological order
+and carry out computations.
+This makes the explicit backward approach easy to analyze.
+We just need to answer the question:
+how do we allocate memory for each output node of a computation graph?
+
+
+## What Can Be Optimized?
+
+As you can see, the computation graph is a useful way
+to discuss memory allocation optimization techniques.
+Already, we've shown how you can save some memory
+by using the explicit backward graph.
+Now let's explore further optimizations,
+and see how we might determine reasonable baselines for benchmarking.
+
+Assume that we want to build a neural network with `n` layers.
+Typically, when implementing a neural network,
+we need to allocate node space for both the output of each layer
+and the gradient values used during back-propagation.
+This means we need roughly `2 n` memory cells.
+We face the same requirement when using the explicit backward graph approach
+because the number of nodes in a backward pass
+is roughly the same as in a forward pass.
+
+### In-place Operations
+One of the simplest techniques we can employ
+is _in-place memory sharing_ across operations.
+For neural networks, we can usually apply this technique
+for the operations corresponding to activation functions.
+Consider the following case, where we want
+to compute the value of three chained sigmoid functions.
+
+![Inplace op](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/memory/alloc_inline.png)
+
+Because we can compute sigmoid ```in-place```,
+using the same memory for input and output,
+we can compute an arbitrary-length chain
+of sigmoid functions using constant memory.
+
+Note: it's easy to make mistakes when implementing in-place optimization.
+Consider the following case, where the value of B is used not only by C, but also by F.
+
+![In-place trap](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/memory/alloc_inline_trap.png)
+
+We can't perform in-place optimization because the value of B
+is still needed after ```C=sigmoid(B)``` is computed.
+An algorithm that simply does in-place optimization
+for every sigmoid operation might fall into such trap,
+so we need to be careful about when we can use it.
+
+### Standard Memory Sharing
+In-place operations are not the only places where we can share memory.
+In the following example, because the value of B is no longer needed
+after we compute E, we can reuse B's memory to hold the result of E.
+
+![Normal Sharing](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/memory/alloc_normal.png)
+
+*Memory sharing doesn't necessarily require the same data shape*.
+Note that in the preceding example, the shapes of `B` and `E` can differ.
+To handle such a situation, we can allocate a memory region
+of size equal to the maximum of that required by `B` and `E` and share it between them.
+
+### Example of Real Neural Network Allocation
+Of course, these are only toy examples and they address only the computation of the forward pass.
+But the same ideas apply to real neural networks.
+The following figure shows an allocation plan for a two-layer perceptron.
+
+![Net Alloc](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/memory/alloc_mlp.png)
+
+In this example:
+
+- In-place optimization is applied when computing ```act1```, ```d(fc1)```, ```out``` and ```d(fc2)```.
+- Memory is shared between ```d(act1)``` and ```d(A)```.
+
+## Memory Allocation Algorithm
+
+So far, we've discussed general techniques for optimizing memory allocation.
+We've seen that there are traps to avoid,
+as demonstrated in the case of in-place memory optimization.
+So, how can we allocate memory correctly?
+This is not a new problem.
+For example, it is very similar
+to the problem with register allocation in compilers.
+There might be techniques that we can borrow.
+We're not attempting to give a comprehensive review of techniques here,
+but rather to introduce some simple
+but useful tricks to attack the problem.
+
+The key problem is that we need to place resources
+so that they don't conflict with each other.
+More specifically, each variable has a *life time*
+between the time it gets computed until the last time it is used.
+In the case of the multi-layer perceptron,
+the *life time* of ```fc1``` ends after ```act1``` get computed.
+
+![Net Alloc](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/memory/alloc_mlp.png)
+
+The principle is *to allow memory sharing only between variables whose lifetimes don't overlap*.
+There are multiple ways to do this.
+You can construct the conflicting graph
+with each variable as a node and link the edge
+between variables with overlapping lifespans,
+and then run a graph-coloring algorithm.
+This likely has ```$O(n^2)$``` complexity,
+where ```n``` is the number of nodes in the graph.
+This might be too costly.
+
+Let's consider another simple heuristic.
+The idea is to simulate the procedure of traversing the graph,
+and keep a count of future operations that depends on the node.
+
+![Alloc](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/memory/alloc_step.png)
+
+- An in-place optimization can be performed when only the current operation depends on the source (i.e., ```count==1```).
+- Memory can be recycled into the box on the upper right corner when the ```count``` goes to 0.
+- When we need new memory, we can either get it from the box or allocate a new one.
+
+***Note:*** During the simulation, no memory is allocated.
+Instead, we keep a record of how much memory each node needs,
+and allocate the maximum of the shared parts in the final memory plan.
+
+## Static vs. Dynamic Allocation
+
+The preceding strategy exactly simulates
+the dynamic memory allocation procedure
+in imperative languages, such as Python.
+The ```count``` is the reference counter for each memory object,
+and the object gets garbage collected
+when the reference counter goes to 0.
+In that sense,
+we are simulating dynamic memory allocation once
+to create a static allocation plan.
+Can we simply use an imperative language
+that dynamically allocates and deallocates memory?
+
+The major difference is that static allocation is only done once,
+so we can afford to use more complicated algorithms.
+For example, we can search for memory sizes
+that are similar to the required memory block.
+The Allocation can also be made graph aware.
+We'll talk about that in the next section.
+Dynamic allocation puts more pressure
+on fast memory allocation and garbage collection.
+
+There is also one takeaway for users
+who want to rely on dynamic memory allocations:
+*do not unnecessarily reference objects*.
+For example, if we organize all of the nodes in a list
+and store then in a Net object,
+these nodes will never get dereferenced, and we gain no space.
+Unfortunately, this is a common way to organize code.
+
+
+## Memory Allocation for Parallel Operations
+
+In the previous section, we discussed
+how we can *simulate* running the procedure
+for a computation graph to get a static allocation plan.
+However, optimizing for parallel computation presents other challenges
+because resource sharing and parallelization are on the two ends of a balance.
+Let's look at the following two allocation plans for the same graph:
+
+![Parallel Alloc](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/memory/parallel_alloc.png)
+
+Both allocation plans are valid
+if we run the computation serially,
+from ```A[1]``` to ```A[8]```.
+However, the allocation plan on the left
+introduces additional dependencies,
+which means we can't run computation of ```A[2]``` and ```A[5]``` in parallel.
+The plan on the right can.
+To parallelize computation, we need to take greater care.
+
+### Be Correct and Safe First
+Being correct is our first principle.
+This means to execute in a way that takes implicit dependency
+memory sharing into consideration.
+You can do this by adding the implicit dependency edge to the execution graph.
+Or, even simpler, if the execution engine is mutation aware,
+as described in [our discussion of dependency engine design](note_engine),
+push the operation in sequence
+and write to the same variable tag
+that represents the same memory region.
+
+Always produce a safe memory allocation plan.
+This means never allocate the same memory
+to nodes that can be parallelized.
+This might not be ideal when memory reduction is more desirable,
+and we don't gain too much when we can get benefit
+from multiple computing streams simultaneously executing on the same GPU.
+
+### Try to Allow More Parallelization
+Now we can safely perform some optimizations.
+The general idea is to try and encourage memory sharing between nodes that can't be parallelized.
+You can do this by creating an ancestor relationship
+graph and querying it during allocation,
+which costs approximately ```$O(n^2)$``` in time to construct.
+We can also use a heuristic here,
+for example, color the path in the graph.
+As shown in the following figure,
+when you try to find the longest paths in the graph,
+color them the same color and continue.
+
+![Path Color](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/memory/graph_color.png)
+
+After you get the color of the node,
+you allow sharing (or encourage sharing)
+only between nodes of the same color.
+This is a stricter version of the ancestor relationship,
+but it costs only `$O(n)$` of time
+if you search for only the first `k` path.
+
+This is by no means the only solution.
+More sophisticated approaches might exist:
+
+## How Much Can you Save?
+
+We've discussed the techniques and algorithms you can use
+to squeeze memory usage for deep learning.
+How much can you really save by using these techniques?
+
+On coarse-grained operation graphs
+that are already optimized for big operations,
+you can reduce memory consumption roughly *by half*.
+You can reduce memory usage even more
+if you are optimizing a fine-grained computation network
+used by symbolic libraries, such as Theano. Most of the ideas in this article inspired the design of _MXNet_.
+
+Also, you will notice that memory cost, for forward pass only execution, is extremely low compared to running both forward and backward pass. This is simply because there's  more memory reuse if you run only the forward pass.
+
+So here are two takeaways:
+
+- Use a computation graph to allocate memory.
+- For deep learning models, prediction consumes much less memory than training.
+
+
+## Next Steps
+
+* [Efficient Data Loading Module for Deep Learning](http://mxnet.io/architecture/note_data_loading.html)
+* [Survey of RNN Interface](http://mxnet.io/architecture/rnn_interface.html)
diff --git a/docs/static_site/src/pages/api/architecture/overview.md b/docs/static_site/src/pages/api/architecture/overview.md
new file mode 100644
index 000000000000..d2ad51c87776
--- /dev/null
+++ b/docs/static_site/src/pages/api/architecture/overview.md
@@ -0,0 +1,874 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_category
+title:  MXNet System Architecture
+category: architecture
+permalink: /api/architecture/overview
+---
+
+# MXNet System Architecture
+
+![System Overview](https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/mxnet/system/overview.png)
+
+This figure shows the major modules and components of the MXNet system and their interaction. The modules are:
+
+- Runtime Dependency Engine: Schedules and executes the
+  operations according to their read/write dependency.
+- Storage Allocator: Efficiently allocates and recycles memory blocks
+  on host (CPU) and devices (GPUs).
+- Resource Manager: Manages global resources, such as the random number generator
+  and temporal space.
+- NDArray: Dynamic, asynchronous n-dimensional arrays,
+  which provide flexible imperative programs for MXNet.
+- Symbolic Execution: Static symbolic graph executor,
+  which provides efficient symbolic graph execution and optimization.
+- Operator: Operators that define static forward and gradient
+  calculation (backprop).
+- SimpleOp: Operators that extend NDArray operators and symbolic operators
+  in a unified fashion.
+- Symbol Construction: Symbolic construction, which provides a way to construct
+  a computation graph (net configuration).
+- KVStore: Key-value store interface for efficient parameter synchronization.
+- Data Loading(IO): Efficient distributed data loading and augmentation.
+
+# MXNet System Components
+
+## Execution Engine
+
+You can use MXNet's engine not only for deep learning,
+but for any domain-specific problem.
+It's designed to solve a general problem:
+execute a bunch of functions following their dependencies.
+Execution of any two functions with dependencies should be serialized.
+To boost performance, functions with no dependencies *can* be executed in parallel.
+For a general discussion of this topic,
+see our [notes on the dependency engine](note_engine.md).
+
+### Interface
+
+The following API is the core interface for the execution engine:
+
+```c++
+    virtual void PushSync(Fn exec_fun, Context exec_ctx,
+                          std::vector<VarHandle> const& const_vars,
+                          std::vector<VarHandle> const& mutate_vars) = 0;
+```
+This API allows you to push a function (`exec_fun`),
+along with its context information and dependencies, to the engine.
+`exec_ctx` is the context information in which the `exec_fun` should be executed,
+`const_vars` denotes the variables that the function reads from,
+and `mutate_vars` are the variables to be modified.
+The engine provides the following guarantee:
+
+>*The execution of any two functions
+that modify a common variable
+is serialized in their push order.*
+
+### Function
+
+The function type of the engine is:
+
+```c++
+    using Fn = std::function<void(RunContext)>;
+```
+`RunContext` contains runtime information, which is determined by the engine:
+
+```c++
+    struct RunContext {
+        // stream pointer which could be safely cast to
+        // cudaStream_t* type
+	    void *stream;
+    };
+```
+Alternatively, you could use `mxnet::engine::DAGEngine::Fn`, which has the same type definition.
+
+All of the functions are executed by the engine's internal threads.
+In such a model, it's usually not a good idea to push *blocking* functions
+to the engine (usually for dealing with I/O tasks like disk, web service, UI, etc.)
+because it will occupy the execution thread and reduce total throughput.
+In that case, we provide another *asynchronous* function type:
+
+```c++
+    using Callback = std::function<void()>;
+    using AsyncFn = std::function<void(RunContext, Callback)>;
+```
+In the `AsyncFn` function, you can pass the heavy part to your own threads
+and safely exit the body of the function.
+The engine doesn't consider the function finished
+until the `Callback` function is called.
+
+### Context
+
+You can specify the `Context` of the function to be executed within.
+This usually includes whether the function should be run on a CPU or a GPU,
+and if you specify a GPU, which GPU to use.
+`Context` is different from `RunContext`.
+`Context` contains device type (GPU/CPU) and device id,
+ while `RunContext` contains information that can be decided only during runtime,
+ for example, on which stream the function should be executed.
+
+### VarHandle
+
+`VarHandle` is used to specify the dependencies of functions.
+The MXNet engine is designed to be decoupled from other MXNet modules.
+So `VarHandle` is like an engine-provided token you use
+to represent the external resources the functions can use or modify.
+It's designed to be lightweight, so creating,
+deleting, or copying a variable incurs little overhead.
+Upon pushing the functions, you need to specify the variables
+that will be used (immutable) in the `const_vars` vector,
+and the variables that will be modified (mutable) in the `mutate_vars` vector.
+The engine uses one rule for resolving the dependencies among functions:
+
+>*The execution of any two functions when one of them modifies at least one common variable is serialized in their push order.*
+
+For example, if `Fn1` and `Fn2` both mutate `V2` then `Fn2`
+is guaranteed to be executed after `Fn1`
+if `Fn2` is pushed after `Fn1`.
+On the other hand, if `Fn1` and `Fn2` both use `V2`,
+their actual execution order could be random.
+
+This design allows the engine to schedule *state-mutating* operations in a manner
+that minimizes calls to allocate new memory.
+For example, the weight update function in DNN
+can now use the `+=` operator
+to update the weights in place,
+rather than generating a new weight array each time.
+
+To create a variable, use the `NewVar()` API.
+To delete a variable, use the `PushDelete` API.
+
+### Push and Wait
+
+*All `Push` APIs are asynchronous.* The API call returns immediately
+regardless of whether the pushed `Fn` is finished or not.
+This allows the engine to start computing at the same time
+as the user thread is pushing functions.
+`Push` APIs are not thread-safe.
+To be specific, only one thread should make engine API calls at a time.
+
+If you want to wait for a specific `Fn` to finish,
+include a callback function in the closure,
+and call the function at the end of your `Fn`.
+
+If you want to wait for all `Fn`s
+that involve (use or mutate) a certain variable to finish,
+use the `WaitForVar(var)` API.
+
+If you want to wait for all pushed `Fn`s to finish,
+use the `WaitForAll()` API.
+
+### Save Object Creation Cost
+
+In some cases, you need to push several functions to the engine for a long period of time.
+If the computation of these functions is light,
+the overhead of copying lambdas and creating use/mutate variable lists becomes relatively high.
+We provide an API to create an `OprHandle` beforehand:
+
+```c++
+    virtual OprHandle NewOperator(AsyncFn fn,
+                                  std::vector<VarHandle> const& const_vars,
+                                  std::vector<VarHandle> const& mutate_vars) = 0;
+```
+You can keep pushing the `OprHandle` without repeatedly creating them:
+
+```c++
+    virtual void Push(OprHandle op, Context exec_ctx) = 0;
+```
+To delete it, call the `DeleteOperator(OprHandle op)` API.
+Ensure that the operator has finished computing before calling this API.
+
+
+## Operators in MXNet
+
+In MXNet, an operator is a class that contains both actual computation logic
+and auxiliary information that can aid the system in performing optimizations,
+like in-place updates and auto-derivatives.
+To understand the remainder of the document,
+we recommend that you familiarize yourself with the `mshadow` library,
+because all operators compute on the tensor-like structure `mshadow::TBlob`
+provided by the system during runtime.
+
+MXNet's operator interface allows you to:
+
+* Reduce memory allocation cost by specifying in-place updates.
+* Hide some internal arguments from Python to make it cleaner.
+* Define the relationships among input tensors and output tensors,
+which allows the system to perform shape checking for you.
+* Acquire additional temporary spaces from the system
+to perform computation (e.g., calling `cudnn` routines).
+
+### Operator Interface
+
+`Forward` is the core operator interface:
+
+```c++
+    virtual void Forward(const OpContext &ctx,
+                         const std::vector<TBlob> &in_data,
+                         const std::vector<OpReqType> &req,
+                         const std::vector<TBlob> &out_data,
+                         const std::vector<TBlob> &aux_states) = 0;
+```
+The `OpContext` structure is:
+
+```c++
+           struct OpContext {
+             int is_train;
+             RunContext run_ctx;
+             std::vector<Resource> requested;
+           }
+```
+It describes whether the operator is in the train or test phase,
+which device the operator should be run on (in `run_ctx`),
+and requested resources (covered in the following sections).
+
+- `in_data` and `out_data` represent the input and output tensors, respectively.
+All of the tensor spaces have been allocated by the system.
+- `req` denotes how the computation results are written into the `out_data`.
+In other words, `req.size() == out_data.size()` and `req[i]`
+correspond to the write type of `out_data[i]`.
+
+- The `OpReqType` is defined as:
+
+```c++
+           enum OpReqType {
+             kNullOp,
+             kWriteTo,
+             kWriteInplace,
+             kAddTo
+           };
+```
+  Normally, the types of all `out_data` should be `kWriteTo`,
+  meaning that the provided `out_data` tensor is a *raw* memory block,
+  so the operator should write results directly into it.
+  In some cases, for example when calculating the `gradient` tensor,
+  it would be great if we could accumulate the result,
+  rather than directly overwrite the tensor contents
+  so that  no extra space needs to be created each time.
+  In such a case, the corresponding `req` type is set as `kAddTo`,
+  indicating that a `+=` should be called.
+
+- `aux_states` is intentionally designed for auxiliary tensors used to help computation. Currently, it is useless.
+
+Aside from the `Forward` operator, you could optionally implement the `Backward` interface:
+
+```c++
+    virtual void Backward(const OpContext &ctx,
+                          const std::vector<TBlob> &out_grad,
+                          const std::vector<TBlob> &in_data,
+                          const std::vector<TBlob> &out_data,
+                          const std::vector<OpReqType> &req,
+                          const std::vector<TBlob> &in_grad,
+                          const std::vector<TBlob> &aux_states);
+```
+This interface follows the same design principle as the `Forward` interface,
+except that `out_grad`, `in_data`, and `out_data` are given,
+and the operator computes `in_grad` as the results.
+ The naming strategy is similar to Torch's convention,
+ and can be summarized in following figure:
+
+[input/output semantics figure]
+
+Some operators might not require all of the following:
+`out_grad`, `in_data` and `out_data`.
+You can specify these dependencies with the `DeclareBackwardDependency` interface in `OperatorProperty`.
+
+### Operator Property
+
+One convolution might have several implementations,
+and you might want to switch among them to achieve the best performance.
+Therefore, we separate the operator *semantic* interfaces
+from the implementation interface (`Operator` class)
+into the `OperatorProperty` class.
+The `OperatorProperty` interface consists of:
+
+* **InferShape:**
+
+```c++
+           virtual bool InferShape(mxnet::ShapeVector *in_shape,
+                                   mxnet::ShapeVector *out_shape,
+                                   mxnet::ShapeVector *aux_shape) const = 0;
+```
+
+This interface has two purposes:
+* Tell the system the size of each input and output tensor,
+  so it can allocate space for them before the `Forward` and `Backward` call.
+* Perform a size check to make sure that there isn't an obvious error before running.
+  The shape in `in_shape` is set by the system
+  (from the `out_shape` of the previous operators).
+  It returns `false` when there is not enough information
+  to infer shapes or throws an error when the shape is inconsistent.
+
+* **Request Resources:** Operations like `cudnnConvolutionForward` need a work space for computation.
+If the system can manage that, it could then perform optimizations,
+like reuse the space, and so on.
+MXNet defines two interfaces to achieve this:
+
+```c++
+           virtual std::vector<ResourceRequest> ForwardResource(
+               const mxnet::ShapeVector &in_shape) const;
+           virtual std::vector<ResourceRequest> BackwardResource(
+               const mxnet::ShapeVector &in_shape) const;
+```
+  The `ResourceRequest` structure (in `resource.h`) currently contains only a type flag:
+
+```c++
+           struct ResourceRequest {
+             enum Type {
+               kRandom,  // get a mshadow::Random<xpu> object
+               kTempSpace,  // request temporary space
+             };
+             Type type;
+           };
+```
+  If `ForwardResource` and `BackwardResource` return non-empty arrays,
+  the system offers the corresponding resources through the `ctx` parameter
+  in the `Forward` and `Backward` interface of `Operator`.
+  Basically, to access those resources, simply write:
+
+```c++
+           auto tmp_space_res = ctx.requested[kTempSpace].get_space(some_shape, some_stream);
+           auto rand_res = ctx.requested[kRandom].get_random(some_stream);
+```
+  For an example, see `src/operator/cudnn_convolution-inl.h`.
+
+* **Backward dependency:** Let's look at two different operator signatures
+(we name all of the arguments for demonstration purposes):
+
+```c++
+           void FullyConnectedForward(TBlob weight, TBlob in_data, TBlob out_data);
+           void FullyConnectedBackward(TBlob weight, TBlob in_data, TBlob out_grad, TBlob in_grad);
+
+           void PoolingForward(TBlob in_data, TBlob out_data);
+           void PoolingBackward(TBlob in_data, TBlob out_data, TBlob out_grad, TBlob in_grad);
+```
+  Note that `out_data` in `FullyConnectedForward`
+  is not used by `FullyConnectedBackward`,
+  while `PoolingBackward` requires all of the arguments of `PoolingForward`.
+  Therefore, for `FullyConnectedForward`,
+  the `out_data` tensor once consumed could be safely freed
+  because the backward function will not need it.
+  This provides a chance for the system to collect some tensors
+  as garbage as soon as possible.
+  To specify this situation, we provide an interface:
+
+```c++
+          virtual std::vector<int> DeclareBackwardDependency(
+               const std::vector<int> &out_grad,
+               const std::vector<int> &in_data,
+               const std::vector<int> &out_data) const;
+```
+  The `int` element of the argument vector is an ID
+  to distinguish different arrays.
+  Let's see how this interface specifies different dependencies
+  for `FullyConnected` and `Pooling`:
+
+ ```c++
+           std::vector<int> FullyConnectedProperty::DeclareBackwardDependency(
+               const std::vector<int> &out_grad,
+               const std::vector<int> &in_data,
+               const std::vector<int> &out_data) const {
+             return {out_grad[0], in_data[0]};  // NOTE: out_data[0] is NOT included
+           }
+           std::vector<int> PoolingProperty::DeclareBackwardDependency(
+               const std::vector<int> &out_grad,
+               const std::vector<int> &in_data,
+               const std::vector<int> &out_data) const {
+             return {out_grad[0], in_data[0], out_data[0]};
+           }
+```
+
+* **In place Option:** To further save the cost of memory allocation,
+you can use in-place updates.
+They are appropriate for element-wise operations
+when the input tensor and output tensor have the same shape.
+You specify and in-place update with the following interface:
+
+```c++
+           virtual std::vector<std::pair<int, void*>>    ElewiseOpProperty::ForwardInplaceOption(
+               const std::vector<int> &in_data,
+               const std::vector<void*> &out_data) const {
+             return { {in_data[0], out_data[0]} };
+           }
+           virtual std::vector<std::pair<int, void*>> ElewiseOpProperty::BackwardInplaceOption(
+               const std::vector<int> &out_grad,
+               const std::vector<int> &in_data,
+               const std::vector<int> &out_data,
+               const std::vector<void*> &in_grad) const {
+             return { {out_grad[0], in_grad[0]} }
+           }
+```
+  This tells the system that the `in_data[0]` and `out_data[0]` tensors could share the same memory spaces during `Forward`, and so do `out_grad[0]` and `in_grad[0]` during `Backward`.
+
+  >**Important:** Even if you use the preceding specification, it's *not* guaranteed that the input and output tensors will share the same space. In fact, this is only a suggestion for the system, which makes the final decision. However, in either case, the decision is completely transparent to you, so the actual `Forward` and `Backward` implementation does not need to consider that.
+
+* **Expose Operator to Python:** Because of the restrictions of C++, you need user to implement following interfaces:
+
+```c++
+           // initial the property class from a list of key-value string pairs
+           virtual void Init(const vector<pair<string, string>> &kwargs) = 0;
+           // return the parameters in a key-value string map
+           virtual map<string, string> GetParams() const = 0;
+           // return the name of arguments (for generating signature in python)
+           virtual vector<string> ListArguments() const;
+           // return the name of output values
+           virtual vector<string> ListOutputs() const;
+           // return the name of auxiliary states
+           virtual vector<string> ListAuxiliaryStates() const;
+           // return the number of output values
+           virtual int NumOutputs() const;
+           // return the number of visible outputs
+           virtual int NumVisibleOutputs() const;
+```
+
+### Create an Operator from the Operator Property
+
+ `OperatorProperty` includes all *semantic* attributes of an operation. It's also responsible for creating the `Operator` pointer for actual computation.
+
+#### Create Operator
+Implement the following interface in `OperatorProperty`:
+
+```c++
+    virtual Operator* CreateOperator(Context ctx) const = 0;
+```
+For example:
+
+```c++
+    class ConvolutionOp {
+     public:
+      void Forward( ... ) { ... }
+      void Backward( ... ) { ... }
+    };
+    class ConvolutionOpProperty : public OperatorProperty {
+     public:
+      Operator* CreateOperator(Context ctx) const {
+        return new ConvolutionOp;
+      }
+    };
+```
+
+#### Parametrize Operator
+When implementing a convolution operator, you need to know the kernel size,
+the stride size, padding size, and so on.
+These parameters should be passed to the operator
+before any `Forward` or `Backward` interface is called.
+To do so, you could define a `ConvolutionParam` structure, as follows:
+
+```c++
+    #include <dmlc/parameter.h>
+    struct ConvolutionParam : public dmlc::Parameter<ConvolutionParam> {
+      mxnet::TShape kernel, stride, pad;
+      uint32_t num_filter, num_group, workspace;
+      bool no_bias;
+    };
+```
+Put it in `ConvolutionOpProperty`, and pass it to the operator class during construction:
+
+```c++
+    class ConvolutionOp {
+     public:
+      ConvolutionOp(ConvolutionParam p): param_(p) {}
+      void Forward( ... ) { ... }
+      void Backward( ... ) { ... }
+     private:
+      ConvolutionParam param_;
+    };
+    class ConvolutionOpProperty : public OperatorProperty {
+     public:
+      void Init(const vector<pair<string, string>& kwargs) {
+        // initialize param_ using kwargs
+      }
+      Operator* CreateOperator(Context ctx) const {
+        return new ConvolutionOp(param_);
+      }
+     private:
+      ConvolutionParam param_;
+    };
+```
+
+#### Register the Operator Property Class and the Parameter Class to MXNet
+Use the following macros to register the parameter structure and the operator property class to MXNet:
+
+```c++
+    DMLC_REGISTER_PARAMETER(ConvolutionParam);
+    MXNET_REGISTER_OP_PROPERTY(Convolution, ConvolutionOpProperty);
+```
+The first argument is the name string, the second is the property class name.
+
+### Interface Summary
+
+We've almost covered the entire interface required to define a new operator. Let's do a recap:
+
+* Use the `Operator` interface to write your computation logic (`Forward` and `Backward`).
+* Use the `OperatorProperty` interface to:
+  - Pass the parameter to the operator class (you can use the `Init` interface).
+  - Create an operator using the `CreateOperator` interface.
+  - Correctly implement the operator description interface, such as the names of arguments, etc.
+  - Correctly implement the `InferShape` interface to set the output tensor shape.
+  - [Optional] If additional resources are needed, check `ForwardResource` and `BackwardResource`.
+  - [Optional] If `Backward` doesn't need all of the input and output of `Forward`, check `DeclareBackwardDependency`.
+  - [Optional] If in-place update is supported, check `ForwardInplaceOption` and `BackwardInplaceOption`.
+* Register the `OperatorProperty` class and the parameter class.
+
+## Unifying the NDArray Operator and Symbolic Operator
+NDArray operations are similar to symbolic operations,
+except that sometimes you can't write in place to the operands
+without a complete dependency graph.
+However, the logic underlying NDArray and symbolic operations are almost identical.
+*SimpleOp*, a new unified operator API,
+unifies different invoking processes
+and returns to the fundamental elements of operators.
+Because most mathematical operators attend to one or two operands,
+and more operands make dependency-related optimization useful,
+the unified operator is specifically designed for unary and binary operations.
+
+Consider the elements of an operation.
+Ideally, you need only functions and derivatives
+to describe an operation.
+Let's restrict that to the space of unary and binary operations.
+How do we classify all operations to maximize the possibility
+of in-place write optimization?
+Note that you can separate functions by the number of operands.
+Derivatives are a bit more complex.
+To construct a dependency graph, you need to know whether output value,
+input data, or neither are needed alongside head gradient.
+Gradient functions in the unified API are differentiated
+by the types of operands it takes for calculation.
+
+Before you learn more about the SimpleOp interface,
+ we recommend that you review the
+ [mshadow library guide](https://github.com/dmlc/mshadow/tree/master/guide)
+ because  calculations will be done in the `mshadow::TBlob` structure.
+
+In the following example, we'll create an operator
+functioning as a smooth l1 loss,
+which is a mixture of l1 loss and l2 loss. The loss itself can be written as:
+
+```
+    loss = outside_weight .* f(inside_weight .* (data - label))
+    grad = outside_weight .* inside_weight .* f'(inside_weight .* (data - label))
+```
+ `.*` stands for element-wise multiplication, and `f`, `f'` is the smooth l1 loss function,
+which we are assuming is in `mshadow` for now.
+At first glance, it's impossible to implement
+this particular loss as a unary or binary operator.
+But we have automatic differentiation in symbolic execution.
+That simplifies the loss to `f` and `f'` directly.
+This loss is no more complex than a `sin` or an `abs` function,
+and can certainly be implemented as a unary operator.
+
+## SimpleOp: The Unified Operator API
+### Define Shapes
+The `mshadow` library requires explicit memory allocation.
+As a consequence, all data shapes
+must be provided before any calculation occurs.
+ Before we proceed with defining functions and gradient,
+let's check input data shape consistency and provide output shape.
+
+```cpp
+    typedef mxnet::TShape (*UnaryShapeFunction)(const mxnet::TShape& src,
+                                         const EnvArguments& env);
+    typedef mxnet::TShape (*BinaryShapeFunction)(const mxnet::TShape& lhs,
+                                          const mxnet::TShape& rhs,
+                                          const EnvArguments& env);
+```
+You can use `mshadow::TShape` to check input data shape and designate output data shape.
+If you don't define this function, the default output shape is the same as the input shape.
+In the case of a binary operator, the shape of `lhs` and `rhs` is checked as the same by default.
+
+You can also use shape functions to check if any additional arguments and resources are present.
+Refer to the additional usages of `EnvArguments` to accomplish this.
+
+Before we start on our smooth l1 loss example, we define a `XPU` to `cpu` or `gpu` in the header
+`smooth_l1_unary-inl.h` implementation so that we reuse the same code in `smooth_l1_unary.cc` and
+`smooth_l1_unary.cu`.
+
+```cpp
+    #include <mxnet/operator_util.h>
+    #if defined(__CUDACC__)
+    #define XPU gpu
+    #else
+    #define XPU cpu
+    #endif
+```
+In our smooth l1 loss example, it's okay to use the default behavior whereby the output has the same shape as the source.
+Written explicitly, it is:
+
+```cpp
+    inline mxnet::TShape SmoothL1Shape_(const mxnet::TShape& src,
+                                 const EnvArguments& env) {
+      return mxnet::TShape(src);
+    }
+```
+
+### Define Functions
+Create a unary or binary function with one output: `mshadow::TBlob`.
+
+```cpp
+    typedef void (*UnaryFunction)(const TBlob& src,
+                                  const EnvArguments& env,
+                                  TBlob* ret,
+                                  OpReqType req,
+                                  RunContext ctx);
+    typedef void (*BinaryFunction)(const TBlob& lhs,
+                                   const TBlob& rhs,
+                                   const EnvArguments& env,
+                                   TBlob* ret,
+                                   OpReqType req,
+                                   RunContext ctx);
+```
+* Functions are differentiated by the types of input arguments.
+* `RunContext ctx` contains information needed during runtime for execution.
+
+```cpp
+        struct RunContext {
+          void *stream;  // the stream of the device, can be NULL or Stream<gpu>* in GPU mode
+          template<typename xpu> inline mshadow::Stream<xpu>* get_stream() // get mshadow stream from Context
+        }  // namespace mxnet
+```
+  `mshadow::stream<xpu> *s = ctx.get_stream<xpu>();` is an example of obtaining a stream from `ctx`.
+* `OpReqType req` denotes how computation results are written into `ret`.
+
+```cpp
+        enum OpReqType {
+          kNullOp,  // no operation, do not write anything
+          kWriteTo,  // write gradient to provided space
+          kWriteInplace,  // perform an in-place write
+          kAddTo  // add to the provided space
+        };
+```
+  A macro is defined in `operator_util.h` for a simplified use of `OpReqType`.
+  `ASSIGN_DISPATCH(out, req, exp)` checks `req` and performs an assignment.
+
+In our smooth l1 loss example, we use `UnaryFunction` to define the function of this operator.
+
+```cpp
+    template<typename xpu>
+    void SmoothL1Forward_(const TBlob& src,
+                          const EnvArguments& env,
+                          TBlob *ret,
+                          OpReqType req,
+                          RunContext ctx) {
+      using namespace mshadow;
+      using namespace mshadow::expr;
+      mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+      real_t sigma2 = env.scalar * env.scalar;
+      MSHADOW_TYPE_SWITCH(ret->type_flag_, DType, {
+        mshadow::Tensor<xpu, 2, DType> out = ret->get<xpu, 2, DType>(s);
+        mshadow::Tensor<xpu, 2, DType> in = src.get<xpu, 2, DType>(s);
+        ASSIGN_DISPATCH(out, req,
+                        F<mshadow_op::smooth_l1_loss>(in, ScalarExp<DType>(sigma2)));
+      });
+    }
+```
+After obtaining `mshadow::Stream` from `RunContext`, we get `mshadow::Tensor` from `mshadow::TBlob`.
+`mshadow::F` is a shortcut to initiate a `mshadow` expression. The macro `MSHADOW_TYPE_SWITCH(type, DType, ...)`
+handles details on different types, and the macro `ASSIGN_DISPATCH(out, req, exp)` checks `OpReqType` and
+performs actions accordingly. `sigma2` is a special parameter in this loss, which we will cover later.
+
+### Define Gradients (Optional)
+Create a gradient function with various types of inputs.
+
+```cpp
+    // depending only on out_grad
+    typedef void (*UnaryGradFunctionT0)(const OutputGrad& out_grad,
+                                        const EnvArguments& env,
+                                        TBlob* in_grad,
+                                        OpReqType req,
+                                        RunContext ctx);
+    // depending only on out_value
+    typedef void (*UnaryGradFunctionT1)(const OutputGrad& out_grad,
+                                        const OutputValue& out_value,
+                                        const EnvArguments& env,
+                                        TBlob* in_grad,
+                                        OpReqType req,
+                                         RunContext ctx);
+    // depending only on in_data
+    typedef void (*UnaryGradFunctionT2)(const OutputGrad& out_grad,
+                                        const Input0& in_data0,
+                                        const EnvArguments& env,
+                                        TBlob* in_grad,
+                                        OpReqType req,
+                                        RunContext ctx);
+```
+Gradient functions of binary operators have similar structures, except that `Input`, `TBlob`, and `OpReqType`
+are doubled.
+
+`GradFunctionArgument`
+
+  `Input0`, `Input`, `OutputValue`, and `OutputGrad` all share the structure of `GradFunctionArgument`,
+  which is defined as:
+
+  ```cpp
+      struct GradFunctionArgument {
+          TBlob data;
+      }
+  ```
+
+In our smooth l1 loss example, note that it's an `f'(x)`,
+which utilizes input for the gradient calculation,
+so the `UnaryGradFunctionT2` is suitable.
+To enable the chain rule of the gradient,
+we also need to multiply `out_grad` from the top to the result of `in_grad`.
+
+```cpp
+    template<typename xpu>
+    void SmoothL1BackwardUseIn_(const OutputGrad& out_grad,
+                                const Input0& in_data0,
+                                const EnvArguments& env,
+                                TBlob *in_grad,
+                                OpReqType req,
+                                RunContext ctx) {
+      using namespace mshadow;
+      using namespace mshadow::expr;
+      mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+      real_t sigma2 = env.scalar * env.scalar;
+      MSHADOW_TYPE_SWITCH(in_grad->type_flag_, DType, {
+        mshadow::Tensor<xpu, 2, DType> src = in_data0.data.get<xpu, 2, DType>(s);
+        mshadow::Tensor<xpu, 2, DType> ograd = out_grad.data.get<xpu, 2, DType>(s);
+        mshadow::Tensor<xpu, 2, DType> igrad = in_grad->get<xpu, 2, DType>(s);
+         ASSIGN_DISPATCH(igrad, req,
+                        ograd * F<mshadow_op::smooth_l1_gradient>(src, ScalarExp<DType>(sigma2)));
+      });
+    }
+```
+
+### Register SimpleOp to MXNet
+After creating the shape, function, and gradient, restore them into both an NDArray operator and
+a symbolic operator. To simplify this process, use the registration macro defined in `operator_util.h`.
+
+```cpp
+    MXNET_REGISTER_SIMPLE_OP(Name, DEV)
+    .set_shape_function(Shape)
+    .set_function(DEV::kDevMask, Function<XPU>, SimpleOpInplaceOption)
+    .set_gradient(DEV::kDevMask, Gradient<XPU>, SimpleOpInplaceOption)
+    .describe("description");
+```
+`SimpleOpInplaceOption` is defined as:
+
+```cpp
+    enum SimpleOpInplaceOption {
+      kNoInplace,  // do not allow inplace in arguments
+      kInplaceInOut,  // allow inplace in with out (unary)
+      kInplaceOutIn,  // allow inplace out_grad with in_grad (unary)
+      kInplaceLhsOut,  // allow inplace left operand with out (binary)
+      kInplaceOutLhs  // allow inplace out_grad with lhs_grad (binary)
+    };
+```
+
+In our example, we have a gradient function that relies on input data, so the function can't be written in
+place. The output gradient has no purpose after gradient computation, so the gradient can be written in place.
+
+```cpp
+    MXNET_REGISTER_SIMPLE_OP(smooth_l1, XPU)
+    .set_function(XPU::kDevMask, SmoothL1Forward_<XPU>, kNoInplace)
+    .set_gradient(XPU::kDevMask, SmoothL1BackwardUseIn_<XPU>, kInplaceOutIn)
+    .set_enable_scalar(true)
+    .describe("Calculate Smooth L1 Loss(lhs, scalar)");
+```
+Remember from the discussion of shape functions that a default behavior without `set_shape_function` forces the inputs
+(if they're binary) to be the same shape and yield the same shape for output. We'll discuss `set_enable_scalar` later.
+
+### NDArray Operator Summary
+* Create a shape function for determining the output shape.
+* Create a function as the forward routine by choosing a suitable function type.
+* Create a gradient as the backward routine by choosing a suitable gradient type.
+* Register the operator using the registration process.
+
+## Additional Information on SimpleOp
+### Using SimpleOp on EnvArguments
+Some operations might need a scalar as input, such as a  gradient scale, a set of keyword arguments
+controlling behavior, or a temporary space to speed up calculations.`EnvArguments` provides additional arguments and resources to make calculations more scalable
+and efficient.
+
+```cpp
+    struct EnvArguments {
+      real_t scalar;  // scalar argument, if enabled
+      std::vector<std::pair<std::string, std::string> > kwargs;  // keyword arguments
+      std::vector<Resource> resource;  // pointer to the resources requested
+    };
+```
+
+More registration parameters are required to enable these additional features. To prevent confusion on parameters, `scalar` and `kwargs`
+can't be present at the same time. To enable `scalar`, use
+`set_enable_scalar(bool enable_scalar)` in registration. Then, in forward functions and gradients, the `scalar` can be accessed from `env.scalar` as in the function parameter `EnvArguments env`.
+
+To enable `kwargs`, use `set_enable_kwargs(bool enable_kwargs)` in registration. Then, in forward
+functions and gradients, additional arguments are contained in `env.kwarg`, which is defined as
+`std::vector<std::pair<std::string, std::string> >`. Use the DMLC parameter structure to
+simplify parsing keyword arguments. For more details, see the [guide on parameter structure](https://github.com/dmlc/dmlc-core/blob/master/doc/parameter.md).
+
+Additional resources like `mshadow::Random<xpu>` and temporary memory space can also be requested and
+accessed from `EnvArguments.resource`. The registration routine is `set_resource_request(ResourceRequest req)`
+or `set_resource_request(const std::vector<ResourceRequest>)`, where `mxnet::ResourceRequest` is defined as:
+
+```cpp
+    struct ResourceRequest {
+      enum Type {  // Resource type, indicating what the pointer type is
+        kRandom,  // mshadow::Random<xpu> object
+        kTempSpace  // A dynamic temp space that can be arbitrary size
+      };
+      Type type;  // type of resources
+    };
+```
+Registration will request the declared resource requests from `mxnet::ResourceManager`, and place resources
+in `std::vector<Resource> resource` in `EnvArguments`. To access resources, use the following:
+
+```cpp
+    auto tmp_space_res = env.resources[0].get_space(some_shape, some_stream);
+    auto rand_res = env.resources[0].get_random(some_stream);
+```
+For an example, see `src/operator/loss_binary_op-inl.h`.
+
+In our smooth l1 loss example, a scalar input is needed to mark the turning point of a loss function. Therefore,
+in the registration process, we use `set_enable_scalar(true)`, and use `env.scalar` in function and gradient
+declarations.
+
+### Crafting a Tensor Operation
+Because computation utilizes the `mshadow` library and we sometimes don't have functions readily available, we
+can craft tensor operations in operator implementations. If you define such functions as element-wise, you
+can implement them as a `mxnet::op::mshadow_op`. `src/operator/mshadow_op.h` that contains a lot of `mshadow_op`,
+for example. `mshadow_op` are expression mappers. They deal with the scalar case of desired functions. For details, see
+[mshadow expression API guide](https://github.com/dmlc/mshadow/tree/master/doc).
+
+If an operation can't be done in an element-wise way, like the softmax loss and gradient, then you need to create a new tensor operation. You need to create as `mshadow` function and as `mshadow::cuda`
+function directly. For details, see the `mshadow` library. For an example, see `src/operator/roi_pooling.cc`.
+
+In our smooth l1 loss example, we create two mappers, namely the scalar cases of smooth l1 loss and gradient.
+
+```cpp
+    namespace mshadow_op {
+    struct smooth_l1_loss {
+      // a is x, b is sigma2
+      MSHADOW_XINLINE static real_t Map(real_t a, real_t b) {
+        if (a > 1.0f / b) {
+          return a - 0.5f / b;
+        } else if (a < -1.0f / b) {
+          return -a - 0.5f / b;
+        } else {
+          return 0.5f * a * a * b;
+        }
+      }
+    };
+    }
+```
+The gradient, which can be found in `src/operator/smooth_l1_unary-inl.h`, is similar.
+
+### Beyond Two Operands
+The new unified API is designed to fulfill the fundamentals of an operation. For operators with more than two inputs,
+more than one output, or that need more features, see the original [Operator API](http://mxnet.io/architecture/overview.html#operators-in-mxnet).
diff --git a/docs/static_site/src/pages/api/architecture/program_model.md b/docs/static_site/src/pages/api/architecture/program_model.md
new file mode 100644
index 000000000000..be291d24b19e
--- /dev/null
+++ b/docs/static_site/src/pages/api/architecture/program_model.md
@@ -0,0 +1,629 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_category
+title:  Deep Learning Programming Paradigm
+category: architecture
+permalink: /api/architecture/program_model
+---
+
+# Deep Learning Programming Paradigm
+
+However much we might ultimately care about performance,
+we first need working code before we can start worrying about optimization.
+Writing clear, intuitive deep learning code can be challenging,
+and the first thing any practitioner must deal with is the language syntax itself.
+Complicating matters, of the many deep learning libraries out there,
+each has its own approach to programming style.
+
+In this document, we focus on two of the most important high-level design decisions:
+1. Whether to embrace the _symbolic_ or _imperative_ paradigm for mathematical computation.
+2. Whether to build networks with bigger (more abstract) or more atomic operations.
+
+Throughout, we'll focus on the programming models themselves.
+When programming style decisions may impact performance, we point this out,
+but we don't dwell on specific implementation details.
+
+
+## Symbolic vs. Imperative Programs
+
+If you are a Python or C++ programmer, then you're already familiar with imperative programs.
+Imperative-style programs perform computation as you run them.
+Most code you write in Python is imperative, as is the following NumPy snippet.
+
+```python
+    import numpy as np
+    a = np.ones(10)
+    b = np.ones(10) * 2
+    c = b * a
+    d = c + 1
+```
+When the program executes ```c = b * a```, it runs the actual numerical computation.
+
+Symbolic programs are a bit different. With symbolic-style programs,
+we first define a (potentially complex) function abstractly.
+When defining the function, no actual numerical computation takes place.
+We define the abstract function in terms of placeholder values.
+Then we can compile the function, and evaluate it given real inputs.
+In the following example, we rewrite the imperative program from above
+as a symbolic-style program:
+
+```python
+    A = Variable('A')
+    B = Variable('B')
+    C = B * A
+    D = C + Constant(1)
+    # compiles the function
+    f = compile(D)
+    d = f(A=np.ones(10), B=np.ones(10)*2)
+```
+As you can see, in the symbolic version, when ```C = B * A``` is executed, no computation occurs.
+Instead, this operation generates a _computation graph_ (also called a _symbolic graph_)
+that represents the computation.
+The following figure shows a computation graph to compute ```D```.
+
+![Comp Graph](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/prog_model/comp_graph.png)
+
+Most symbolic-style programs contain, either explicitly or implicitly, a *compile* step.
+This converts the computation graph into a function that we can later call.
+In the above example, numerical computation only occurs in the last line of code.
+The defining characteristic of symbolic programs is their clear separation
+between building the computation graph and executing it.
+For neural networks, we typically define the entire model as a single compute graph.
+
+Among other popular deep learning libraries, Torch, Chainer, and Minerva embrace the imperative style.
+Examples of symbolic-style deep learning libraries include Theano, CGT, and TensorFlow.
+We might also view libraries like CXXNet and Caffe, which rely on configuration files, as symbolic-style libraries.
+In this interpretation, we'd consider the content of the configuration file as defining the computation graph.
+
+Now that you understand the difference between these two programming models, let's compare the advantages of each.
+
+
+### Imperative Programs Tend to be More Flexible
+
+When you're using an imperative-style library from Python, you are writing in Python.
+Nearly anything that would be intuitive to write in Python, you could accelerate by calling down in the appropriate places to the imperative deep learning library.
+On the other hand, when you write a symbolic program, you may not have access to all the familiar Python constructs, like iteration.
+Consider the following imperative program, and think about how you can translate this into a symbolic program.
+
+```python
+    a = 2
+    b = a + 1
+    d = np.zeros(10)
+    for i in range(d):
+        d += np.zeros(10)
+```
+This wouldn't be so easy if the Python for-loop weren't supported by the symbolic API.
+When you write a symbolic program in Python, you're *not* writing in Python.
+Instead, you're writing in a domain-specific language (DSL) defined by the symbolic API.
+The symbolic APIs found in deep learning libraries
+are powerful DSLs that generate callable computation graphs for neural networks.
+<!-- In that sense, config-file input libraries are all symbolic. -->
+
+Intuitively, you might say that imperative programs
+are more *native* than symbolic programs.
+It's easier to use native language features.
+For example, it's straightforward to print out the values
+in the middle of computation or to use native control flow and loops
+at any point in the flow of computation.
+
+### Symbolic Programs Tend to be More Efficient
+
+As we've seen, imperative programs tend to be flexible
+and fit nicely into the programming flow of a host language.
+So you might wonder, why do so many deep learning libraries
+embrace the symbolic paradigm?
+The main reason is efficiency, both in terms of memory and speed.
+Let's revisit our toy example from before.
+
+```python
+    import numpy as np
+    a = np.ones(10)
+    b = np.ones(10) * 2
+    c = b * a
+    d = c + 1
+    ...
+```
+
+![Comp Graph](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/prog_model/comp_graph.png)
+
+Assume that each cell in the array occupies 8 bytes of memory.
+How much memory do you need to execute this program in the Python console?
+
+As an imperative program we need to allocate memory at each line.
+That leaves us allocating 4 arrays of size 10.
+So we'll need `4 * 10 * 8 = 320` bytes.
+On the other hand, if we built a computation graph,
+and knew in advance that we only needed `d`,
+we could reuse the memory originally allocated for intermediate values.
+For example, by performing computations in-place,
+we might recycle the bits allocated for ```b``` to store `c`.
+And we might recycle the bits allocated for `c` to store `d`.
+In the end we could cut our memory requirement in half,
+requiring just `2 * 10 * 8 = 160` bytes.
+
+Symbolic programs are more *restricted*.
+When we call `compile` on D, we tell the system
+that only the value of `d` is needed.
+The intermediate values of the computation,
+in this case ```c```, is then invisible to us.
+
+We benefit because the symbolic programs
+can then safely reuse the memory for in-place computation.
+But on the other hand, if we later decide that we need to access `c`, we're out of luck.
+So imperative programs are better prepared to encounter all possible demands.
+If we ran the imperative version of the code in a Python console,
+we could inspect any of the intermediate variables in the future.
+
+<!-- Of course, this is somewhat misleading, because garbage collection can occur in imperative programs and memory could then be reused.
+However, imperative programs do need to be "prepared to encounter all possible demands," and this limits the optimization you can perform. This is true for non-trivial cases, such
+as gradient calculation, which we discuss in next section. -->
+
+Symbolic programs can also perform another kind of optimization, called operation folding.
+Returning to our toy example, the multiplication and addition operations
+can be folded into one operation, as shown in the following graph.
+If the computation runs on a GPU processor,
+one GPU kernel will be executed, instead of two.
+In fact, this is one way we hand-craft operations
+in optimized libraries, such as CXXNet and Caffe.
+Operation folding improves computation efficiency.
+
+![Comp Graph Folded](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/prog_model/comp_graph_fold.png)
+
+Note, you can't perform operation folding in imperative programs,
+because the intermediate values might be referenced in the future.
+Operation folding is possible in symbolic programs
+because you get the entire computation graph,
+and a clear specification of which values will be needed and which are not.
+
+
+### Case Study: Backprop and AutoDiff
+
+In this section, we compare the two programming models
+on the problem of auto differentiation, or backpropagation.
+Differentiation is of vital importance in deep learning
+because it's the mechanism by which we train our models.
+In any deep learning model, we define a _loss function_.
+A _loss function_ measures how far the model is from the desired output.
+We then typically pass over training examples (pairs of inputs and ground-truth outputs).
+At each step we update the model's _parameters_ to minimize the loss.
+To determine the direction in which to update the parameters,
+we need to take the derivative of the loss function with respect to the parameters.
+
+In the past, whenever someone defined a new model,
+they had to work out the derivative calculations by hand.
+While the math is reasonably straightforward,
+for complex models, it can be time-consuming and tedious work.
+All modern deep learning libraries make the practitioner/researcher's job
+much easier, by automatically solving the problem of gradient calculation.
+
+Both imperative and symbolic programs can perform gradient calculation.
+So let's take a look at how you might perform automatic differentiation with each.
+
+Let's start with imperative programs.
+The following example Python code performs automatic differentiation using our toy example:
+
+```python
+    class array(object) :
+        """Simple Array object that support autodiff."""
+        def __init__(self, value, name=None):
+            self.value = value
+            if name:
+                self.grad = lambda g : {name : g}
+
+        def __add__(self, other):
+            assert isinstance(other, int)
+            ret = array(self.value + other)
+            ret.grad = lambda g : self.grad(g)
+            return ret
+
+        def __mul__(self, other):
+            assert isinstance(other, array)
+            ret = array(self.value * other.value)
+            def grad(g):
+                x = self.grad(g * other.value)
+                x.update(other.grad(g * self.value))
+                return x
+            ret.grad = grad
+            return ret
+
+    # some examples
+    a = array(1, 'a')
+    b = array(2, 'b')
+    c = b * a
+    d = c + 1
+    print d.value
+    print d.grad(1)
+    # Results
+    # 3
+    # {'a': 2, 'b': 1}
+```
+
+In this code, each array object contains a grad function (it is actually a closure).
+When you run ```d.grad```, it recursively invokes the grad function of its inputs,
+backprops the gradient value back, and
+returns the gradient value of each input.
+
+This might look a bit complicated, so let's consider
+the gradient calculation for symbolic programs.
+The following program performs symbolic gradient calculation for the same task.
+
+```python
+    A = Variable('A')
+    B = Variable('B')
+    C = B * A
+    D = C + Constant(1)
+    # get gradient node.
+    gA, gB = D.grad(wrt=[A, B])
+    # compiles the gradient function.
+    f = compile([gA, gB])
+    grad_a, grad_b = f(A=np.ones(10), B=np.ones(10)*2)
+```
+
+The grad function of ```D``` generates a backward computation graph,
+and returns a gradient node, ```gA, gB```,
+which correspond to the red nodes in the following figure.
+
+![Comp Graph Folded](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/prog_model/comp_graph_backward.png)
+
+The imperative program actually does the same thing as the symbolic program.
+It implicitly saves a backward computation graph in the grad closure.
+When you invoked ```d.grad```, you start from ```d(D)```,
+backtrack through the graph to compute the gradient, and collect the results.
+
+The gradient calculations in both symbolic
+and imperative programming follow the same pattern.
+What's the difference then?
+Recall the *be prepared to encounter all possible demands* requirement of imperative programs.
+If you are creating an array library that supports automatic differentiation,
+you have to keep the grad closure along with the computation.
+This means that none of the history variables can be
+garbage-collected because they are referenced by variable `d` by way of function closure.
+
+What if you want to compute only the value of `d`,
+and don't want the gradient value?
+In symbolic programming, you declare this with `f=compiled([D])`.
+This also declares the boundary of computation,
+telling the system that you want to compute only the forward pass.
+As a result, the system can free the memory of previous results,
+and share the memory between inputs and outputs.
+
+Imagine running a deep neural network with ```n``` layers.
+If you are running only the forward pass,
+not the backward(gradient) pass,
+you need to allocate only two copies of
+temporal space to store the values of the intermediate layers,
+instead of ```n``` copies of them.
+However, because imperative programs need to be prepared
+to encounter all possible demands of getting the gradient,
+they have to store the intermediate values,
+which requires ```n``` copies of temporal space.
+
+As you can see, the level of optimization depends
+on the restrictions on what you can do.
+Symbolic programs ask you to clearly specify
+these restrictions when you compile the graph.
+One the other hand, imperative programs
+must be prepared for a wider range of demands.
+Symbolic programs have a natural advantage
+because they know more about what you do and don't want.
+
+There are ways in which we can modify imperative programs
+to incorporate similar restrictions.
+For example, one solution to the preceding
+problem is to introduce a context variable.
+You can introduce a no-gradient context variable
+to turn gradient calculation off.
+
+```python
+    with context.NoGradient():
+        a = array(1, 'a')
+        b = array(2, 'b')
+        c = b * a
+        d = c + 1
+```
+
+<!-- This provides an imperative program with the ability to impose some restrictions, but reduces efficiency. -->
+
+However, this example still must be prepared to encounter all possible demands,
+which means that you can't perform the in-place calculation
+to reuse memory in the forward pass (a trick commonly used to reduce GPU memory usage).
+The techniques we've discussed generate an explicit backward pass.
+Some of the libraries such as Caffe and CXXNet perform backprop implicitly on the same graph.
+The approach we've discussed in this section also applies to them.
+
+Most configuration-file-based libraries,
+such as CXXNet and Caffe are designed
+to meet one or two generic requirements:
+get the activation of each layer,
+or get the gradient of all of the weights.
+These libraries have the same problem:
+the more generic operations the library has to support,
+the less optimization (memory sharing) you can do,
+based on the same data structure.
+
+As you can see, the trade-off between restriction
+and flexibility is the same for most cases.
+
+### Model Checkpoint
+
+It's important to able to save a model and load it back later.
+There are different ways to *save* your work.
+Normally, to save a neural network,
+you need to save two things: a net configuration
+for the structure of the neural network and the weights of the neural network.
+
+The ability to check the configuration is a plus for symbolic programs.
+Because the symbolic construction phase does not perform computation,
+you can directly serialize the computation graph, and load it back later.
+This solves the problem of saving the configuration
+without introducing an additional layer.
+
+```python
+    A = Variable('A')
+    B = Variable('B')
+    C = B * A
+    D = C + Constant(1)
+    D.save('mygraph')
+    ...
+    D2 = load('mygraph')
+    f = compile([D2])
+    # more operations
+    ...
+```
+
+Because an imperative program executes as it describes the computation,
+you have to save the code itself as the ```configuration```,
+or build another configuration layer on top of the imperative language.
+
+### Parameter Updates
+
+Most symbolic programs are data flow (computation) graphs.
+Data flow graphs describe computation.
+But it's not obvious how to use graphs to describe parameter updates.
+That's because parameter updates introduce mutation,
+which is not a data flow concept.
+Most symbolic programs introduce a special update statement
+to update persistent state in the programs.
+
+It's usually easier to write parameter updates in an imperative style,
+especially when you need multiple updates that relate to each other.
+For symbolic programs, the update statement is also executed as you call it.
+So in that sense, most symbolic deep learning libraries
+fall back on the imperative approach to perform updates,
+while using the symbolic approach to perform gradient calculation.
+
+### There Is No Strict Boundary
+
+In comparing the two programming styles,
+some of our arguments might not be strictly true,
+i.e., it's possible to make an imperative program
+more like a traditional symbolic program or vice versa.
+However, the two archetypes are useful abstractions,
+especially for understanding the differences between deep learning libraries.
+We might reasonably conclude that there is no clear boundary between programming styles.
+For example, you can create a just-in-time (JIT) compiler in Python
+to compile imperative Python programs,
+which provides some of the advantages of global
+information held in symbolic programs.
+
+
+## Big vs. Small Operations
+
+When designing a deep learning library, another important programming model decision
+is precisely what operations to support.
+In general, there are two families of operations supported by most deep learning libraries:
+
+- Big operations - typically for computing neural network layers (e.g. FullyConnected and BatchNormalize).
+- Small operations - mathematical functions like matrix multiplication and element-wise addition.
+
+Libraries like CXXNet and Caffe support layer-level operations.
+Libraries like Theano and Minerva support fine-grained operations.
+
+### Smaller Operations Can Be More Flexible
+It's quite natural to use smaller operations to compose bigger operations.
+For example, the sigmoid unit can simply be composed of division, addition and an exponentiation:
+
+```python
+    sigmoid(x) = 1.0 / (1.0 + exp(-x))
+```
+Using smaller operations as building blocks, you can express nearly anything you want.
+If you're more familiar with CXXNet- or Caffe-style layers,
+note that these operations don't differ from a layer, except that they are smaller.
+
+```python
+    SigmoidLayer(x) = EWiseDivisionLayer(1.0, AddScalarLayer(ExpLayer(-x), 1.0))
+```
+This expression composes three layers,
+with each defining its forward and backward (gradient) function.
+Using smaller operations gives you the advantage of building new layers quickly,
+because you only need to compose the components.
+
+### Big Operations Are More Efficient
+Directly composing sigmoid layers requires three layers of operation, instead of one.
+
+```python
+    SigmoidLayer(x) = EWiseDivisionLayer(1.0, AddScalarLayer(ExpLayer(-x), 1.0))
+```
+This code creates overhead for computation and memory (which could be optimized, with cost).
+
+Libraries like CXXNet and Caffe take a different approach.
+To support coarse-grained operations,
+such as BatchNormalization and the SigmoidLayer directly,
+in each layer, the calculation kernel is hand crafted
+with one or only some CUDA kernel launches.
+This makes these implementations more efficient.
+
+### Compilation and Optimization
+
+Can small operations be optimized? Of course, they can.
+Let's look at the system optimization part of the compilation engine.
+Two types of optimization can be performed on the computation graph:
+
+- Memory allocation optimization, to reuse the memory of the intermediate computations.
+- Operator fusion, to detect sub-graph patterns, such as the sigmoid, and fuse them into a bigger operation kernel.
+
+Memory allocation optimization isn't restricted to small operations graphs.
+You can use it with bigger operations graph, too.
+However, optimization might not be essential
+for bigger operation libraries like CXXNet and Caffe,
+because you can't find the compilation step in them.
+However, there's a (dumb) ```compilation step``` in these libraries,
+that basically translates the layers into a fixed forward,
+backprop execution plan, by running each operation one by one.
+
+For computation graphs with smaller operations,
+these optimizations are crucial to performance.
+Because the operations are small,
+there are many sub-graph patterns that can be matched.
+Also, because the final, generated operations
+might not be enumerable,
+an explicit recompilation of the kernels is required,
+as opposed to the fixed amount of precompiled kernels
+in the big operation libraries.
+This creates compilation overhead for the symbolic libraries
+that support small operations.
+Requiring compilation optimization also creates engineering overhead
+for the libraries that solely support smaller operations.
+
+As in the case of symbolic vs. imperative,
+the bigger operation libraries "cheat"
+by asking you to provide restrictions (to the common layer),
+so that you actually perform the sub-graph matching.
+This moves the compilation overhead to the real brain, which is usually not too bad.
+
+### Expression Template and Statically Typed Language
+You always have a need to write small operations and compose them.
+Libraries like Caffe use hand-crafted kernels to build these bigger blocks.
+Otherwise, you would have to compose smaller operations using Python.
+
+There's a third choice that works pretty well.
+This is called the expression template.
+Basically, you use template programming to
+generate generic kernels from an expression tree at compile time.
+For details, see [Expression Template Tutorial](https://github.com/dmlc/mshadow/blob/master/guide/exp-template/README.md).
+CXXNet makes extensive use of an expression template,
+which enables creating much shorter and more readable code that matches
+the performance of hand-crafted kernels.
+
+The difference between using an expression template and Python kernel generation
+is that expression evaluation is done at compile time for C++ with an existing type,
+so there is no additional runtime overhead.
+In principle, this is also possible with other statically typed languages that support templates,
+but we've seen this trick used only in C++.
+
+Expression template libraries create a middle ground between Python operations
+and hand-crafted big kernels by allowing C++ users to craft efficient big
+operations by composing smaller operations. It's an option worth considering.
+
+## Mix the Approaches
+
+Now that we've compared the programming models, which one should you choose?
+Before delving into that, we should emphasize that depending on the problems you're trying to solve,
+our comparison might not necessarily have a big impact.
+
+Remember [Amdahl's law](https://en.wikipedia.org/wiki/Amdahl%27s_law):
+If you are optimizing a non-performance-critical part of your problem,
+you won't get much of a performance gain.
+
+As you've seen, there usually is a trade-off between efficiency,
+flexibility, and engineering complexity.
+The more suitable programming style depends on the problem you are trying to solve.
+For example, imperative programs are better for parameter updates,
+and symbolic programs for gradient calculation.
+
+We advocate *mixing* the approaches.
+Sometimes the part that we want to be flexible
+isn't crucial to performance.
+In these cases, it's okay to leave some efficiency on the table
+to support more flexible interfaces.
+In machine learning, combining methods usually works better than using just one.
+
+If you can combine the programming models correctly,
+you can get better results than when using a single programming model.
+In this section, we discuss how to do so.
+
+### Symbolic and Imperative Programs
+There are two ways to mix symbolic and imperative programs:
+
+- Use imperative programs within symbolic programs as callbacks
+- Use symbolic programs as part of imperative programs
+
+We've observed that it's usually helpful to write parameter updates imperatively,
+and perform gradient calculations in symbolic programs.
+
+Symbolic libraries already mix programs because Python itself is imperative.
+For example, the following program mixes the symbolic approach with NumPy, which is imperative.
+
+```python
+    A = Variable('A')
+    B = Variable('B')
+    C = B * A
+    D = C + Constant(1)
+    # compiles the function
+    f = compile(D)
+    d = f(A=np.ones(10), B=np.ones(10)*2)
+    d = d + 1.0
+```
+The symbolic graphs are compiled into a function that can be executed imperatively.
+The internals are a black box to the user.
+This is exactly like writing C++ programs and exposing them to Python, which we commonly do.
+
+Because parameter memory resides on the GPU,
+you might not want to use NumPy as an imperative component.
+Supporting a GPU-compatible imperative library
+that interacts with symbolic compiled functions
+or provides a limited amount of updating syntax
+in the update statement in symbolic program execution
+might be a better choice.
+
+### Small and Big Operations
+
+There might be a good reason to combine small and big operations.
+Consider applications that perform tasks such as changing
+a loss function or adding a few customized layers to an existing structure.
+Usually, you can use big operations to compose existing
+components, and use smaller operations to build the new parts.
+
+Recall Amdahl's law. Often, the new components
+are not the cause of the computation bottleneck.
+Because the performance-critical part is already optimized by
+the bigger operations, it's okay to forego optimizing the additional small operations,
+or to do a limited amount of memory optimization instead
+of operation fusion and directly running them.
+
+### Choose Your Own Approach
+
+In this document, we compared multiple approaches
+to developing programming environments for deep learning.
+We compared both the usability and efficiency implications of each,
+finding that many of these trade-offs (like imperative vs symbolic aren't necessarily black and white).
+You can choose your approach, or combine the approaches
+to create more interesting and intelligent deep learning libraries.
+
+## Contribute to MXNet
+
+This document is part of our effort to provide [open-source system design notes](index.md)
+for deep learning libraries. If you're interested in contributing to _MXNet_ or its
+documentation, [fork us on GitHub](http://github.com/dmlc/mxnet).
+
+## Next Steps
+
+* [Dependency Engine for Deep Learning](http://mxnet.io/architecture/note_engine.html)
+* [Squeeze the Memory Consumption of Deep Learning](http://mxnet.io/architecture/note_memory.html)
+* [Efficient Data Loading Module for Deep Learning](http://mxnet.io/architecture/note_data_loading.html)
+* [Survey of RNN Interface](http://mxnet.io/architecture/rnn_interface.html)
diff --git a/docs/static_site/src/pages/api/clojure/docs/tutorials/index.md b/docs/static_site/src/pages/api/clojure/docs/tutorials/index.md
new file mode 100644
index 000000000000..a424c87b335a
--- /dev/null
+++ b/docs/static_site/src/pages/api/clojure/docs/tutorials/index.md
@@ -0,0 +1,23 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_landing_tutorials
+title:  Clojure Tutorials
+action: Get Started
+tag: clojure
+permalink: /api/clojure/docs/tutorials
+---
diff --git a/docs/static_site/src/pages/api/clojure/docs/tutorials/kvstore.md b/docs/static_site/src/pages/api/clojure/docs/tutorials/kvstore.md
new file mode 100644
index 000000000000..96eb03abdeb7
--- /dev/null
+++ b/docs/static_site/src/pages/api/clojure/docs/tutorials/kvstore.md
@@ -0,0 +1,109 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: KVStore API
+is_tutorial: true
+permalink: /api/clojure/docs/tutorials/kvstore
+tag: clojure
+---
+# KVStore API
+
+Topics:
+
+* [Basic Push and Pull](#basic-push-and-pull)
+* [List Key-Value Pairs](#list-key-value-pairs)
+* [API Reference](http://mxnet.incubator.apache.org/api/clojure/docs/org.apache.clojure-mxnet.kvstore.html)
+
+To follow along with this documentation, you can use this namespace to with the needed requires:
+
+```clojure
+(ns docs.kvstore
+  (:require [org.apache.clojure-mxnet.kvstore :as kvstore]
+            [org.apache.clojure-mxnet.ndarray :as ndarray]
+            [org.apache.clojure-mxnet.context :as context]))
+```
+
+## Basic Push and Pull
+
+Provides basic operation over multiple devices (GPUs) on a single device.
+
+### Initialization
+
+Let's consider a simple example. It initializes
+a (`int`, `NDArray`) pair into the store, and then pulls the value out.
+
+```clojure
+(def kv (kvstore/create "local")) ;; create a local kvstore
+(def shape [2 3])
+;;; init the kvstore with a vector of keys (strings) and ndarrays
+(kvstore/init kv ["3"] [(ndarray/* (ndarray/ones shape) 2)])
+(def a (ndarray/zeros shape))
+(kvstore/pull kv ["3"] [a])
+(ndarray/->vec a) ;=> [2.0 2.0 2.0 2.0 2.0 2.0]
+```
+
+### Push, Aggregation, and Updater
+
+For any key that's been initialized, you can push a new value with the same shape to the key, as follows:
+
+```clojure
+(kvstore/push kv ["3"] [(ndarray/* (ndarray/ones shape) 8)])
+(kvstore/pull kv ["3"] [a])
+(ndarray/->vec a);=>[8.0 8.0 8.0 8.0 8.0 8.0]
+```
+
+The data that you want to push can be stored on any device. Furthermore, you can push multiple
+values into the same key, where KVStore first sums all of these
+values, and then pushes the aggregated value, as follows (Here we use multiple cpus):
+
+```clojure
+(def cpus [(context/cpu 0) (context/cpu 1) (context/cpu 2)])
+(def b [(ndarray/ones shape {:ctx (nth cpus 0)})
+        (ndarray/ones shape {:ctx (nth cpus 1)})
+        (ndarray/ones shape {:ctx (nth cpus 2)})])
+(kvstore/push kv ["3" "3" "3"] b)
+(kvstore/pull kv "3" a)
+(ndarray/->vec a) ;=> [3.0 3.0 3.0 3.0 3.0 3.0]
+```
+
+
+### Pull
+
+You've already seen how to pull a single key-value pair. Similar to the way that you use the push command, you can
+pull the value into several devices with a single call.
+
+```clojure
+(def b [(ndarray/ones shape {:ctx (context/cpu 0)})
+        (ndarray/ones shape {:ctx (context/cpu 1)})])
+(kvstore/pull kv ["3" "3"] b)
+(map ndarray/->vec b) ;=> ([3.0 3.0 3.0 3.0 3.0 3.0] [3.0 3.0 3.0 3.0 3.0 3.0])
+```
+
+## List Key-Value Pairs
+
+All of the operations that we've discussed so far are performed on a single key. KVStore also provides
+the interface for generating a list of key-value pairs. For a single device, use the following:
+
+```clojure
+(def ks ["5" "7" "9"])
+(kvstore/init kv ks [(ndarray/ones shape) (ndarray/ones shape) (ndarray/ones shape)])
+(kvstore/push kv ks [(ndarray/ones shape) (ndarray/ones shape) (ndarray/ones shape)])
+(def b [(ndarray/zeros shape) (ndarray/zeros shape) (ndarray/zeros shape)])
+(kvstore/pull kv ks b)
+(map ndarray/->vec b);=> ([1.0 1.0 1.0 1.0 1.0 1.0] [1.0 1.0 1.0 1.0 1.0 1.0] [1.0 1.0 1.0 1.0 1.0 1.0])
+```
diff --git a/docs/static_site/src/pages/api/clojure/docs/tutorials/module.md b/docs/static_site/src/pages/api/clojure/docs/tutorials/module.md
new file mode 100644
index 000000000000..013e6ec44820
--- /dev/null
+++ b/docs/static_site/src/pages/api/clojure/docs/tutorials/module.md
@@ -0,0 +1,259 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: Module API
+is_tutorial: true
+tag: clojure
+permalink: /api/clojure/docs/tutorials/module
+---
+
+# Module API
+The module API provides an intermediate and high-level interface for performing computation with neural networks in MXNet. Module wraps a Symbol and one or more Executors. It has both a high level and intermediate level API.
+
+
+Topics:
+
+* [Prepare the Data](#prepare-the-data)
+* [List Key-Value Pairs](#list-key-value-pairs)
+* [Preparing a Module for Computation](#preparing-a-module-for-computation)
+* [Training and Predicting](#training-and-predicting)
+* [Saving and Loading](#saving-and-loading)
+* [API Reference](http://mxnet.incubator.apache.org/api/clojure/docs/org.apache.clojure-mxnet.module.html)
+
+
+To follow along with this documentation, you can use this namespace to with the needed requires:
+
+```clojure
+(ns docs.module
+  (:require [clojure.java.io :as io]
+            [clojure.java.shell :refer [sh]]
+            [org.apache.clojure-mxnet.eval-metric :as eval-metric]
+            [org.apache.clojure-mxnet.io :as mx-io]
+            [org.apache.clojure-mxnet.module :as m]
+            [org.apache.clojure-mxnet.symbol :as sym]
+            [org.apache.clojure-mxnet.ndarray :as ndarray]))
+```
+
+## Prepare the Data
+
+In this example, we are going to use the MNIST data set. If you have cloned the MXNet repo and `cd contrib/clojure-package`, we can run some helper scripts to download the data for us.
+
+```clojure
+(def data-dir "data/")
+
+(when-not (.exists (io/file (str data-dir "train-images-idx3-ubyte")))
+  (sh "../../scripts/get_mnist_data.sh"))
+```
+
+MXNet provides function in the `io` namespace to load the MNIST datasets into training and test data iterators that we can use with our module.
+
+```clojure
+(def train-data (mx-io/mnist-iter {:image (str data-dir "train-images-idx3-ubyte")
+                                   :label (str data-dir "train-labels-idx1-ubyte")
+                                   :label-name "softmax_label"
+                                   :input-shape [784]
+                                   :batch-size 10
+                                   :shuffle true
+                                   :flat true
+                                   :silent false
+                                   :seed 10}))
+
+(def test-data (mx-io/mnist-iter {:image (str data-dir "t10k-images-idx3-ubyte")
+                                  :label (str data-dir "t10k-labels-idx1-ubyte")
+                                  :input-shape [784]
+                                  :batch-size 10
+                                  :flat true
+                                  :silent false}))
+```
+
+
+## Preparing a Module for Computation
+
+To construct a module, we need to have a symbol as input. This symbol takes input data in the first layer and then has subsequent layers of fully connected and relu activation layers, ending up in a softmax layer for output.
+
+```clojure
+(let [data (sym/variable "data")
+      fc1 (sym/fully-connected "fc1" {:data data :num-hidden 128})
+      act1 (sym/activation "relu1" {:data fc1 :act-type "relu"})
+      fc2 (sym/fully-connected "fc2" {:data act1 :num-hidden 64})
+      act2 (sym/activation "relu2" {:data fc2 :act-type "relu"})
+      fc3 (sym/fully-connected "fc3" {:data act2 :num-hidden 10})
+      out (sym/softmax-output "softmax" {:data fc3})]
+  out)
+  ;=>#object[org.apache.mxnet.Symbol 0x1f43a406 "org.apache.mxnet.Symbol@1f43a406"]
+```
+
+You can also write this with the `as->` threading macro.
+
+```clojure
+(def out (as-> (sym/variable "data") data
+           (sym/fully-connected "fc1" {:data data :num-hidden 128})
+           (sym/activation "relu1" {:data data :act-type "relu"})
+           (sym/fully-connected "fc2" {:data data :num-hidden 64})
+           (sym/activation "relu2" {:data data :act-type "relu"})
+           (sym/fully-connected "fc3" {:data data :num-hidden 10})
+           (sym/softmax-output "softmax" {:data data})))
+;=> #'tutorial.module/out
+```
+
+
+By default, `context` is the CPU. If you need data parallelization, you can specify a GPU context or an array of GPU contexts like this `(m/module out {:contexts [(context/gpu)]})`
+
+Before you can compute with a module, you need to call `bind` to allocate the device memory and `init-params` or `set-params` to initialize the parameters. If you simply want to fit a module, you don’t need to call `bind` and `init-params` explicitly, because the `fit` function automatically calls them if they are needed.
+
+```clojure
+(let [mod (m/module out)]
+  (-> mod
+      (m/bind {:data-shapes (mx-io/provide-data train-data)
+               :label-shapes (mx-io/provide-label train-data)})
+      (m/init-params)))
+```
+
+Now you can compute with the module using functions like `forward`, `backward`, etc.
+
+## Training and Predicting
+
+Modules provide high-level APIs for training, predicting, and evaluating. To fit a module, call the `fit` function with some data iterators:
+
+```clojure
+(def mod (m/fit (m/module out) {:train-data train-data :eval-data test-data :num-epoch 1}))
+;; Epoch  0  Train- [accuracy 0.12521666]
+;; Epoch  0  Time cost- 8392
+;; Epoch  0  Validation-  [accuracy 0.2227]
+```
+
+You can pass in batch-end callbacks using batch-end-callback and epoch-end callbacks using epoch-end-callback in the `fit-params`. You can also set parameters using functions like in the fit-params like optimizer and eval-metric. To learn more about the fit-params, see the fit-param function options. To predict with a module, call `predict` with a DataIter:
+
+```clojure
+(def results (m/predict mod {:eval-data test-data}))
+(first results) ;=>#object[org.apache.mxnet.NDArray 0x3540b6d3 "org.apache.mxnet.NDArray@a48686ec"]
+
+(first (ndarray/->vec (first results))) ;=>0.08261358
+```
+
+The module collects and returns all of the prediction results. For more details about the format of the return values, see the documentation for the [`predict`](docs/org.apache.clojure-mxnet.module.html#var-fit-params) function.
+
+When prediction results might be too large to fit in memory, use the [`predict-every-batch`](docs/org.apache.clojure-mxnet.module.html#predict-every-batch) API.
+
+```clojure
+(let [preds (m/predict-every-batch mod {:eval-data test-data})]
+  (mx-io/reduce-batches test-data
+                        (fn [i batch]
+                          (println (str "pred is " (first (get preds i))))
+                          (println (str "label is " (mx-io/batch-label batch)))
+                          ;;; do something
+                          (inc i))))
+```
+
+If you need to evaluate on a test set and don’t need the prediction output, call the `score` function with a data iterator and an eval metric:
+
+```clojure
+(m/score mod {:eval-data test-data :eval-metric (eval-metric/accuracy)}) ;=>["accuracy" 0.2227]
+```
+
+This runs predictions on each batch in the provided data iterator and computes the evaluation score using the provided eval metric. The evaluation results are stored in `eval-metric` object itself so that you can query later.
+
+
+## Saving and Loading
+
+To save the module parameters in each training epoch, use the `save-checkpoint` function:
+
+```clojure
+(let [save-prefix "my-model"]
+  (doseq [epoch-num (range 3)]
+    (mx-io/do-batches train-data (fn [batch
+                                          ;; do something
+]))
+    (m/save-checkpoint mod {:prefix save-prefix :epoch epoch-num :save-opt-states true})))
+
+;; INFO  org.apache.mxnet.module.Module: Saved checkpoint to my-model-0000.params
+;; INFO  org.apache.mxnet.module.Module: Saved optimizer state to my-model-0000.states
+;; INFO  org.apache.mxnet.module.Module: Saved checkpoint to my-model-0001.params
+;; INFO  org.apache.mxnet.module.Module: Saved optimizer state to my-model-0001.states
+;; INFO  org.apache.mxnet.module.Module: Saved checkpoint to my-model-0002.params
+;; INFO  org.apache.mxnet.module.Module: Saved optimizer state to my-model-0002.states
+
+```
+
+To load the saved module parameters, call the `load-checkpoint` function:
+
+```clojure
+(def new-mod (m/load-checkpoint {:prefix "my-model" :epoch 1 :load-optimizer-states true}))
+
+new-mod ;=> #object[org.apache.mxnet.module.Module 0x5304d0f4 "org.apache.mxnet.module.Module@5304d0f4"]
+```
+
+To initialize parameters, Bind the symbols to construct executors first with `bind` function. Then, initialize the parameters and auxiliary states by calling `init-params` function.
+
+```clojure
+(-> new-mod
+    (m/bind {:data-shapes (mx-io/provide-data train-data) :label-shapes (mx-io/provide-label train-data)})
+    (m/init-params))
+```
+
+To get current parameters, use `params`
+
+```clojure
+
+(let [[arg-params aux-params] (m/params new-mod)]
+  {:arg-params arg-params
+   :aux-params aux-params})
+
+;; {:arg-params
+;;  {"fc3_bias"
+;;   #object[org.apache.mxnet.NDArray 0x39adc3b0 "org.apache.mxnet.NDArray@49caf426"],
+;;   "fc2_weight"
+;;   #object[org.apache.mxnet.NDArray 0x25baf623 "org.apache.mxnet.NDArray@a6c8f9ac"],
+;;   "fc1_bias"
+;;   #object[org.apache.mxnet.NDArray 0x6e089973 "org.apache.mxnet.NDArray@9f91d6eb"],
+;;   "fc3_weight"
+;;   #object[org.apache.mxnet.NDArray 0x756fd109 "org.apache.mxnet.NDArray@2dd0fe3c"],
+;;   "fc2_bias"
+;;   #object[org.apache.mxnet.NDArray 0x1dc69c8b "org.apache.mxnet.NDArray@d128f73d"],
+;;   "fc1_weight"
+;;   #object[org.apache.mxnet.NDArray 0x20abc769 "org.apache.mxnet.NDArray@b8e1c5e8"]},
+;;  :aux-params {}}
+
+```
+
+To assign parameter and aux state values, use the `set-params` function.
+
+```clojure
+(m/set-params new-mod {:arg-params (m/arg-params new-mod) :aux-params (m/aux-params new-mod)})
+;=> #object[org.apache.mxnet.module.Module 0x5304d0f4 "org.apache.mxnet.module.Module@5304d0f4"]
+```
+
+To resume training from a saved checkpoint, pass the loaded parameters to the `fit` function. This will prevent `fit` from initialzing randomly.
+
+Create fit-params and then use it to set `begin-epoch` so that `fit` knows to resume from a saved epoch.
+
+```clojure
+;; reset the training data before calling fit or you will get an error
+(mx-io/reset train-data)
+(mx-io/reset test-data)
+
+(m/fit new-mod {:train-data train-data :eval-data test-data :num-epoch 2
+                :fit-params (-> (m/fit-params {:begin-epoch 1}))})
+
+```
+
+
+## Next Steps
+* See [Symbolic API](symbol.md) for operations on NDArrays that assemble neural networks from layers.
+* See [NDArray API](ndarray.md) for vector/matrix/tensor operations.
+* See [KVStore API](kvstore.md) for multi-GPU and multi-host distributed training.
diff --git a/docs/static_site/src/pages/api/clojure/docs/tutorials/ndarray.md b/docs/static_site/src/pages/api/clojure/docs/tutorials/ndarray.md
new file mode 100644
index 000000000000..a989d7482ecf
--- /dev/null
+++ b/docs/static_site/src/pages/api/clojure/docs/tutorials/ndarray.md
@@ -0,0 +1,143 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: NDArray
+is_tutorial: true
+tag: clojure
+permalink: /api/clojure/docs/tutorials/ndarray
+---
+
+# NDArray API
+
+
+The NDArray API contains tensor operations similar to `numpy.ndarray`. The syntax is also similar, except for some additional calls for dealing with I/O and multiple devices.
+
+Topics:
+
+* [Create NDArray](#create-ndarray)
+* [NDArray Operations](#ndarray-operations)
+* [NDArray API Reference](http://mxnet.incubator.apache.org/api/clojure/docs/org.apache.clojure-mxnet.ndarray.html)
+
+To follow along with this documentation, you can use this namespace with the needed requires:
+
+```clojure
+(ns docs.ndarray
+  (:require [org.apache.clojure-mxnet.ndarray :as ndarray]
+            [org.apache.clojure-mxnet.context :as context]))
+```
+
+
+## Create NDArray
+
+Create `mxnet.ndarray` as follows:
+
+```clojure
+
+(def a (ndarray/zeros [100 50])) ;;all zero arrray of dimension 100 x 50
+(def b (ndarray/ones [256 32 128 1])) ;; all one array of dimension
+(def c (ndarray/array [1 2 3 4 5 6] [2 3])) ;; array with contents of a shape 2 x 3
+```
+
+There are also ways to convert a NDArray to a vec or get the shape or the NDArray as an object or vec as follows:
+
+```clojure
+(ndarray/->vec c) ;=> [1.0 2.0 3.0 4.0 5.0 6.0]
+(ndarray/shape c) ;=> #object[org.apache.mxnet.Shape 0x583c865 "(2,3)"]
+(ndarray/shape-vec c) ;=> [2 3]
+```
+
+
+## NDArray Operations
+
+There are some basic NDArray operations, like arithmetic and slice operations.
+
+### Arithmetic Operations
+
+```clojure
+(def a (ndarray/ones [1 5]))
+(def b (ndarray/ones [1 5]))
+(-> (ndarray/+ a b) (ndarray/->vec)) ;=>  [2.0 2.0 2.0 2.0 2.0]
+
+;; original ndarrays are unchanged
+(ndarray/->vec a) ;=> [1.0 1.0 1.0 1.0 1.0]
+(ndarray/->vec b) ;=> [1.0 1.0 1.0 1.0 1.0]
+
+;;inplace operators
+(ndarray/+= a b)
+(ndarray/->vec a) ;=>  [2.0 2.0 2.0 2.0 2.0]
+```
+
+Other arithmetic operations are similar.
+
+
+### Slice Operations
+
+```clojure
+(def a (ndarray/array [1 2 3 4 5 6] [3 2]))
+(def a1 (ndarray/slice a 1))
+(ndarray/shape-vec a1) ;=> [1 2]
+(ndarray/->vec a1) ;=> [3.0 4.0]
+
+(def a2 (ndarray/slice a 1 3))
+(ndarray/shape-vec a2) ;=>[2 2]
+(ndarray/->vec a2) ;=> [3.0 4.0 5.0 6.0]
+```
+
+### Dot Product
+
+```clojure
+(def arr1 (ndarray/array [1 2] [1 2]))
+(def arr2 (ndarray/array [3 4] [2 1]))
+(def res (ndarray/dot arr1 arr2))
+(ndarray/shape-vec res) ;=> [1 1]
+(ndarray/->vec res) ;=> [11.0]
+```
+
+### Save and Load NDArray
+
+You can use MXNet functions to save and load a list or dictionary of NDArrays from file systems, as follows:
+
+```clojure
+(ndarray/save "filename" {"arr1" arr1 "arr2" arr2})
+;; you can also do "s3://path" or "hdfs"
+```
+
+To load:
+
+```clojure
+(def from-file (ndarray/load "filename"))
+from-file
+;=>{"arr1" #object["org.apache.mxnet.NDArray@43d85753"], "arr2" #object["org.apache.mxnet.NDArray@5c93def4"]}
+```
+
+The good thing about using the `save` and `load` interface is that you can use the format across all `mxnet` language bindings. They also already support Amazon S3 and HDFS.
+
+### Multi-Device Support
+
+Device information is stored in the `mxnet.Context` structure. When creating NDArray in MXNet, you can use the context argument (the default is the CPU context) to create arrays on specific devices as follows:
+
+```clojure
+(def cpu-a (ndarray/zeros [100 200]))
+(ndarray/context cpu-a) ;=> #object[org.apache.mxnet.Context 0x3f376123 "cpu(0)"]
+
+(def gpu-b (ndarray/zeros [100 200] {:ctx (context/gpu 0)})) ;; to use with gpu
+
+```
+
+## Next Steps
+* See [KVStore API](kvstore.md) for multi-GPU and multi-host distributed training.
diff --git a/docs/static_site/src/pages/api/clojure/docs/tutorials/symbol.md b/docs/static_site/src/pages/api/clojure/docs/tutorials/symbol.md
new file mode 100644
index 000000000000..352b0406bd60
--- /dev/null
+++ b/docs/static_site/src/pages/api/clojure/docs/tutorials/symbol.md
@@ -0,0 +1,156 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: Symbolic API
+is_tutorial: true
+tag: clojure
+permalink: /api/clojure/docs/tutorials/symbol
+---
+
+# MXNet Clojure Symbolic API
+
+Topics:
+
+* [How to Compose Symbols](#how-to-compose-symbols)
+* [More Complicated Compositions](#more-complicated-compositions)
+* [Group Multiple Symbols](#group-multiple-symbols)
+* [Serialization](#serialization)
+* [Executing Symbols](#executing-symbols)
+* [Symbol API Reference](http://mxnet.incubator.apache.org/api/clojure/docs/org.apache.clojure-mxnet.symbol.html)
+
+
+We also highly encourage you to read [Symbolic Configuration and Execution in Pictures](symbol_in_pictures.md).
+
+To follow along with this documentation, you can use this namespace to with the following requirements:
+
+```clojure
+(ns docs.symbol
+  (:require [org.apache.clojure-mxnet.executor :as executor]
+            [org.apache.clojure-mxnet.ndarray :as ndarray]
+            [org.apache.clojure-mxnet.symbol :as sym]
+            [org.apache.clojure-mxnet.context :as context]))
+```
+
+
+## How to Compose Symbols
+
+The Symbolic API provides a way to configure computation graphs.
+You can configure the graphs either at the level of neural network layer operations or as fine-grained operations.
+
+The following example configures a two-layer neural network.
+
+```clojure
+(def data (sym/variable "data"))
+(def fc1 (sym/fully-connected "fc1" {:data data :num-hidden 128}))
+(def act1 (sym/activation "act1" {:data fc1 :act-type "relu"}))
+(def fc2 (sym/fully-connected "fc2" {:data act1 :num-hidden 64}))
+(def net (sym/softmax-output "out" {:data fc2}))
+```
+
+This can also be combined more dynamically with the `as->` Clojure threading form.
+
+```clojure
+(as-> (sym/variable "data") data
+  (sym/fully-connected "fc1" {:data data :num-hidden 128})
+  (sym/activation "act1" {:data data :act-type "relu"})
+  (sym/fully-connected "fc2" {:data data :num-hidden 64})
+  (sym/softmax-output "out" {:data data}))
+
+net ;=> #object[org.apache.mxnet.Symbol 0x5c78c8c2 "org.apache.mxnet.Symbol@5c78c8c2"]
+```
+
+The basic arithmetic operators (plus, minus, div, multiplication) work as expected.
+
+The following example creates a computation graph that adds two inputs together.
+
+```clojure
+(def a (sym/variable "a"))
+(def b (sym/variable "b"))
+(def c (sym/+ a b))
+```
+
+## More Complicated Compositions
+
+MXNet provides well-optimized symbols for layers commonly used in deep learning (see src/operator). We can also define new operators in Python. The following example first performs an element-wise add between two symbols, then feeds them to the fully connected operator:
+
+```clojure
+(def lhs (sym/variable "data1"))
+(def rhs (sym/variable "data2"))
+(def net (sym/fully-connected "fc1" {:data (sym/+ lhs rhs) :num-hidden 128}))
+(sym/list-arguments net) ;=> ["data1" "data2" "fc1_weight" "fc1_bias"]
+```
+
+## Group Multiple Symbols
+
+To construct neural networks with multiple loss layers, we can use `group` to group multiple symbols together. The following example groups two outputs:
+
+```clojure
+(def net (sym/variable "data"))
+(def fc1 (sym/fully-connected {:data net :num-hidden 128}))
+(def net2 (sym/activation {:data fc1 :act-type "relu"}))
+(def out1 (sym/softmax-output {:data net2}))
+(def out2 (sym/linear-regression-output {:data net2}))
+(def group (sym/group [out1 out2]))
+(sym/list-outputs group)
+;=> ["softmaxoutput0_output" "linearregressionoutput0_output"]
+```
+
+## Serialization
+You can use the [`save`](docs/org.apache.clojure-mxnet.symbol.html#var-save) and [`load`](docs/org.apache.clojure-mxnet.symbol.html#var-load) functions to serialize the Symbol objects. The advantage of using save and load functions is that it is language agnostic and cloud friendly. The symbol is saved in JSON format. You can also get a JSON string directly using mxnet.Symbol.toJson. Refer to API documentation for more details.
+
+ The following example shows how to save a symbol to a file, load it back, and compare two symbols using a JSON string. You can also save to S3 as well
+
+```clojure
+(def a (sym/variable "a"))
+(def b (sym/variable "b"))
+(def c (sym/+ a b))
+(sym/save c "symbol-c.json")
+(def c2 (sym/load "symbol-c.json"))
+(= (sym/to-json c) (sym/to-json c2)) ;=>true
+```
+
+
+## Executing Symbols
+
+To execute symbols, first we need to define the data that they should run on. We can do it by using the bind method, which accepts device context and a dict mapping free variable names to NDArrays as arguments and returns an executor. The executor provides forward method for evaluation and an attribute outputs to get all the results.
+
+```clojure
+(def a (sym/variable "a"))
+(def b (sym/variable "b"))
+(def c (sym/+ a b))
+
+(def ex (sym/bind c {"a" (ndarray/ones [2 2]) "b" (ndarray/ones [2 2])}))
+(-> (executor/forward ex)
+    (executor/outputs)
+    (first)
+    (ndarray/->vec));=>  [2.0 2.0 2.0 2.0]
+```
+
+We can evaluate the same symbol on GPU with different data.
+_To do this you must have the correct native library jar defined as a dependency_
+
+**Note In order to execute the following section on a cpu set gpu_device to (cpu)**
+
+
+```clojure
+(def ex (sym/bind c (context/gpu 0) {"a" (ndarray/ones [2 2]) "b" (ndarray/ones [2 2])}))
+```
+
+## Next Steps
+* See [NDArray API](ndarray.md) for vector/matrix/tensor operations.
+* See [KVStore API](kvstore.md) for multi-GPU and multi-host distributed training.
diff --git a/docs/static_site/src/pages/api/clojure/docs/tutorials/symbol_in_pictures.md b/docs/static_site/src/pages/api/clojure/docs/tutorials/symbol_in_pictures.md
new file mode 100644
index 000000000000..09c9cfce02f7
--- /dev/null
+++ b/docs/static_site/src/pages/api/clojure/docs/tutorials/symbol_in_pictures.md
@@ -0,0 +1,101 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: Symbolic API with Pictures
+is_tutorial: true
+tag: clojure
+permalink: /api/clojure/docs/tutorials/symbol_in_pictures
+---
+
+# Symbolic Configuration and Execution in Pictures
+
+This topic explains symbolic construction and execution in pictures.
+
+We recommend that you read the [Symbolic API](symbol.md) as another useful reference.
+
+## Compose Symbols
+
+Symbols are a description of the computation that you want to perform. The symbolic construction API generates the computation
+graph that describes the computation. The following picture shows how you compose symbols to describe basic computations.
+
+![Symbol Compose](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/compose_basic.png)
+
+- The ```mxnet.Symbol.Variable``` function creates argument nodes that represent input to the computation.
+- The symbol is overloaded with basic element-wise mathematical operations.
+
+## Configure Neural Networks
+
+In addition to supporting fine-grained operations, MXNet provides a way to perform big operations that are analogous to layers in neural networks.
+You can use operators to describe the configuration of a neural network.
+
+![Net Compose](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/compose_net.png)
+
+
+## Example of a Multi-Input Network
+
+The following example shows how to configure multiple input neural networks.
+
+![Multi Input](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/compose_multi_in.png)
+
+
+## Bind and Execute Symbol
+
+When you need to execute a symbol graph, you call the bind function to bind ```NDArrays``` to the argument nodes
+in order to obtain an ```Executor```.
+
+![Bind](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/bind_basic.png)
+
+To get the output results, given the bound NDArrays as input, you can call ```Executor.Forward```.
+
+![Forward](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/executor_forward.png)
+
+
+## Bind Multiple Outputs
+
+To group symbols, then bind them to get outputs of both, use ```mx.symbol.Group```.
+
+![MultiOut](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/executor_multi_out.png)
+
+Remember: Bind only what you need, so that the system can perform more optimizations.
+
+
+## Calculate the Gradient
+
+In the bind function, you can specify NDArrays that will hold gradients. Calling ```Executor.backward``` after ```Executor.forward``` gives you the corresponding gradients.
+
+![Gradient](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/executor_backward.png)
+
+
+## Simple Bind Interface for Neural Networks
+
+It can be tedious to pass the argument NDArrays to the bind function, especially when you are binding a big
+graph. ```Symbol.simple_bind``` provides a way to simplify
+the procedure. You need to specify only input data shapes. The function allocates the arguments, and binds
+the Executor for you.
+
+![SimpleBind](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/executor_simple_bind.png)
+
+## Auxiliary States
+
+Auxiliary states are just like arguments, except that you can't take the gradient of them. Although auxiliary states might not be part of the computation, they can be helpful for tracking. You can pass auxiliary states in the same way that you pass arguments.
+
+![SimpleBind](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/executor_aux_state.png)
+
+## Next Steps
+
+See [Symbolic API](symbol.md) and [Python Documentation](index.md).
diff --git a/docs/static_site/src/pages/api/clojure/index.md b/docs/static_site/src/pages/api/clojure/index.md
new file mode 100644
index 000000000000..af535c8614fa
--- /dev/null
+++ b/docs/static_site/src/pages/api/clojure/index.md
@@ -0,0 +1,47 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: Clojure Guide
+action: Get Started
+action_url: /get_started
+permalink: /api/clojure
+tag: clojure
+---
+
+# MXNet - Clojure API
+
+MXNet supports the Clojure programming language. The MXNet Clojure package brings flexible and efficient GPU
+computing and state-of-art deep learning to Clojure. It enables you to write seamless tensor/matrix computation with multiple GPUs in Clojure. It also lets you construct and customize the state-of-art deep learning models in Clojure, and apply them to tasks, such as image classification and data science challenges.
+
+
+## Tensor and Matrix Computations
+You can perform tensor or matrix computation in pure Clojure:
+
+```clojure
+(def arr (ndarray/ones [2 3]))
+
+arr ;=> #object[org.apache.mxnet.NDArray 0x597d72e "org.apache.mxnet.NDArray@e35c3ba9"]
+
+(ndarray/shape-vec arr) ;=>  [2 3]
+
+(-> (ndarray/* arr 2)
+    (ndarray/->vec)) ;=> [2.0 2.0 2.0 2.0 2.0 2.0]
+
+(ndarray/shape-vec (ndarray/* arr 2)) ;=> [2 3]
+
+```
diff --git a/docs/static_site/src/pages/api/cpp/docs/tutorials/basics.md b/docs/static_site/src/pages/api/cpp/docs/tutorials/basics.md
new file mode 100644
index 000000000000..aec71d013ee6
--- /dev/null
+++ b/docs/static_site/src/pages/api/cpp/docs/tutorials/basics.md
@@ -0,0 +1,222 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: Basics
+action: Get Started
+action_url: /get_started
+permalink: /api/cpp/docs/tutorials/basics
+is_tutorial: true
+tag: cpp
+---
+
+Basics
+======
+
+This tutorial provides basic usages of the C++ package through the classical handwritten digits
+identification database--[MNIST](http://yann.lecun.com/exdb/mnist/).
+
+The following contents assume that the working directory is `/path/to/mxnet/cpp-package/example`.
+
+Load Data
+--------
+Before going into codes, we need to fetch MNIST data. You can either use the script `/path/to/mxnet/cpp-package/example/get_data.sh`,
+or download mnist data by yourself from Lecun's [website](http://yann.lecun.com/exdb/mnist/)
+and decompress them into `data/mnist_data` folder.
+
+Except linking the MXNet shared library, the C++ package itself is a header-only package,
+which means all you need to do is to include the header files. Among the header files,
+`op.h` is special since it is generated dynamically. The generation should be done when
+[building the C++ package](http://mxnet.incubator.apache.org/versions/master/api/c++/index.html).
+It is important to note that you need to **copy the shared library** (`libmxnet.so` in Linux and MacOS,
+`libmxnet.dll` in Windows) from `/path/to/mxnet/lib` to the working directory.
+We do not recommend you to use pre-built binaries because MXNet is under heavy development,
+the operator definitions in `op.h` may be incompatible with the pre-built version.
+
+In order to use functionalities provides by the C++ package, first we include the general
+header file `MxNetCpp.h` and specify the namespaces.
+
+```c++
+#include "mxnet-cpp/MxNetCpp.h"
+
+using namespace std;
+using namespace mxnet::cpp;
+```
+
+Next we can use the data iter to load MNIST data (separated to training sets and validation sets).
+The digits in MNIST are 2-dimension arrays, so we should set `flat` to true to flatten the data.
+
+```c++
+auto train_iter = MXDataIter("MNISTIter")
+    .SetParam("image", "./data/mnist_data/train-images-idx3-ubyte")
+    .SetParam("label", "./data/mnist_data/train-labels-idx1-ubyte")
+    .SetParam("batch_size", batch_size)
+    .SetParam("flat", 1)
+    .CreateDataIter();
+auto val_iter = MXDataIter("MNISTIter")
+    .SetParam("image", "./data/mnist_data/t10k-images-idx3-ubyte")
+    .SetParam("label", "./data/mnist_data/t10k-labels-idx1-ubyte")
+    .SetParam("batch_size", batch_size)
+    .SetParam("flat", 1)
+    .CreateDataIter();
+```
+
+The data have been successfully loaded. We can now easily construct various models to identify
+the digits with the help of C++ package.
+
+
+Multilayer Perceptron
+---------------------
+If you are not familiar with multilayer perceptron, you can get some basic information
+[here](http://mxnet.io/tutorials/python/mnist.html#multilayer-perceptron). We only focus on
+the implementation in this tutorial.
+
+Constructing multilayer perceptron model is straightforward, assume we store the hidden size
+for each layer in `layers`, and each layer uses
+[ReLu](https://en.wikipedia.org/wiki/Rectifier_(neural_networks)) function as activation.
+
+```c++
+Symbol mlp(const vector<int> &layers) {
+  auto x = Symbol::Variable("X");
+  auto label = Symbol::Variable("label");
+
+  vector<Symbol> weights(layers.size());
+  vector<Symbol> biases(layers.size());
+  vector<Symbol> outputs(layers.size());
+
+  for (int i=0; i<layers.size(); ++i) {
+    weights[i] = Symbol::Variable("w" + to_string(i));
+    biases[i] = Symbol::Variable("b" + to_string(i));
+    Symbol fc = FullyConnected(
+      i == 0? x : outputs[i-1]
+      weights[i],
+      biases[i],
+      layers[i]
+    );
+    outputs[i] = i == layers.size()-1 ? fc : Activation(fc, ActivationActType::relu);
+  }
+
+  return SoftmaxOutput(outputs.back(), label);
+}
+```
+
+The above function defines a multilayer perceptron model where hidden sizes are specified
+by `layers`.
+
+We now create and initialize the parameters after the model is constructed. MXNet can help
+ you to infer shapes of most of the parameters. Basically only the shape of data and label
+ is needed.
+
+```c++
+std::map<string, NDArray> args;
+args["X"] = NDArray(Shape(batch_size, image_size*image_size), ctx);
+args["label"] = NDArray(Shape(batch_size), ctx);
+// Let MXNet infer shapes other parameters such as weights
+net.InferArgsMap(ctx, &args, args);
+
+// Initialize all parameters with uniform distribution U(-0.01, 0.01)
+auto initializer = Uniform(0.01);
+for (auto& arg : args) {
+  // arg.first is parameter name, and arg.second is the value
+  initializer(arg.first, &arg.second);
+}
+```
+
+The rest is to train the model with an optimizer.
+```c++
+// Create sgd optimizer
+Optimizer* opt = OptimizerRegistry::Find("sgd");
+opt->SetParam("rescale_grad", 1.0/batch_size);
+
+// Start training
+for (int iter = 0; iter < max_epoch; ++iter) {
+  train_iter.Reset();
+
+  while (train_iter.Next()) {
+    auto data_batch = train_iter.GetDataBatch();
+    // Set data and label
+    args["X"] = data_batch.data;
+    args["label"] = data_batch.label;
+
+    // Create executor by binding parameters to the model
+    auto *exec = net.SimpleBind(ctx, args);
+    // Compute gradients
+    exec->Forward(true);
+    exec->Backward();
+    // Update parameters
+    exec->UpdateAll(opt, learning_rate, weight_decay);
+    // Remember to free the memory
+    delete exec;
+  }
+}
+```
+
+We also want to see how our model performs. The C++ package provides convenient APIs for
+evaluating. Here we use accuracy as metric. The inference is almost the same as training,
+ except that we don't need gradients.
+
+```c++
+Accuracy acc;
+val_iter.Reset();
+while (val_iter.Next()) {
+  auto data_batch = val_iter.GetDataBatch();
+  args["X"] = data_batch.data;
+  args["label"] = data_batch.label;
+  auto *exec = net.SimpleBind(ctx, args);
+  // Forward pass is enough as no gradient is needed when evaluating
+  exec->Forward(false);
+  acc.Update(data_batch.label, exec->outputs[0]);
+  delete exec;
+}
+```
+
+You can find the complete code in `mlp_cpu.cpp`. Use `make mlp_cpu` to compile it,
+ and `./mlp_cpu` to run it. If it complains that the shared library `libmxnet.so` is not found
+ after typing `./mlp_cpu`, you will need to specify the path to the shared library in
+ the environment variable `LD_LIBRARY_PATH` in Linux and `DYLD_LIBRARY_PATH`
+ in MacOS. For example, if you are using MacOS, typing
+ `DYLD_LIBRARY_PATH+=. ./mlp_cpu` would solve the problem. It basically tells the system
+ to find the shared library under the current directory since we have just copied it here.
+
+GPU Support
+-----------
+It's worth noting that changing context from `Context::cpu()` to `Context::gpu()` is not enough,
+because the data read by data iter are stored in memory, we cannot assign it directly to the
+parameters. To bridge this gap, NDArray provides data synchronization functionalities between
+GPU and CPU. We will illustrate it by making the mlp code run on GPU.
+
+In the previous code, data are used like
+
+```c++
+args["X"] = data_batch.data;
+args["label"] = data_batch.label;
+```
+
+It will be problematic if other parameters are created in the context of GPU. We can use
+`NDArray::CopyTo` to solve this problem.
+
+```c++
+// Data provided by DataIter are stored in memory, should be copied to GPU first.
+data_batch.data.CopyTo(&args["X"]);
+data_batch.label.CopyTo(&args["label"]);
+// CopyTo is imperative, need to wait for it to complete.
+NDArray::WaitAll();
+```
+
+By replacing the former code to the latter one, we successfully port the code to GPU.
+You can find the complete code in `mlp_gpu.cpp`. Compilation is similar to the cpu version.
+Note that the shared library must be built with GPU support enabled.
diff --git a/docs/static_site/src/pages/api/cpp/docs/tutorials/index.md b/docs/static_site/src/pages/api/cpp/docs/tutorials/index.md
new file mode 100644
index 000000000000..82a5330044a8
--- /dev/null
+++ b/docs/static_site/src/pages/api/cpp/docs/tutorials/index.md
@@ -0,0 +1,22 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_landing_tutorials
+title: C++ Tutorials
+permalink: /api/cpp/docs/tutorials/
+tag: cpp
+---
diff --git a/docs/static_site/src/pages/api/cpp/docs/tutorials/mxnet_cpp_inference_tutorial.md b/docs/static_site/src/pages/api/cpp/docs/tutorials/mxnet_cpp_inference_tutorial.md
new file mode 100644
index 000000000000..72a56240523a
--- /dev/null
+++ b/docs/static_site/src/pages/api/cpp/docs/tutorials/mxnet_cpp_inference_tutorial.md
@@ -0,0 +1,293 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: C++ API inference tutorial
+action: Get Started
+action_url: /get_started
+permalink: /api/cpp/docs/tutorials/cpp_inference
+is_tutorial: true
+tag: cpp
+---
+
+# C++ API inference tutorial
+
+## Overview
+MXNet provides various useful tools and interfaces for deploying your model for inference. For example, you can use [MXNet Model Server](https://github.com/awslabs/mxnet-model-server) to start a service and host your trained model easily.
+Besides that, you can also use MXNet's different language APIs to integrate your model with your existing service. We provide [Python](https://mxnet.incubator.apache.org/api/python/module/module.html),    [Java](https://mxnet.incubator.apache.org/api/java/index.html), [Scala](https://mxnet.incubator.apache.org/api/scala/index.html), and [C++](https://mxnet.incubator.apache.org/api/c++/index.html) APIs.
+
+This tutorial is a continuation of the [Gluon end to end tutorial](https://mxnet.apache.org/versions/master/tutorials/gluon/gluon_from_experiment_to_deployment.html), we will focus on the MXNet C++ API. We have slightly modified the code in [C++ Inference Example](https://github.com/apache/incubator-mxnet/tree/master/cpp-package/example/inference) for our use case.
+
+## Prerequisites
+
+To complete this tutorial, you need:
+- Complete the training part of [Gluon end to end tutorial](https://mxnet.apache.org/versions/master/tutorials/gluon/gluon_from_experiment_to_deployment.html)
+- Learn the basics about [MXNet C++ API](https://github.com/apache/incubator-mxnet/tree/master/cpp-package)
+
+
+## Setup the MXNet C++ API
+To use the C++ API in MXNet, you need to build MXNet from source with C++ package. Please follow the [built from source guide](https://mxnet.incubator.apache.org/install/ubuntu_setup.html), and [C++ Package documentation](https://github.com/apache/incubator-mxnet/tree/master/cpp-package)
+to enable the C++ API.
+The summary of those two documents is that you need to build MXNet from source with `USE_CPP_PACKAGE` flag set to 1. For example: `make -j USE_CPP_PACKAGE=1`.
+
+## Load the model and run inference
+
+After you complete [the previous tutorial](https://mxnet.apache.org/versions/master/tutorials/gluon/gluon_from_experiment_to_deployment.html), you will get the following output files:
+1. Model Architecture stored in `flower-recognition-symbol.json`
+2. Model parameter values stored in `flower-recognition-0040.params` (`0040` is for 40 epochs we ran)
+3. Label names stored in `synset.txt`
+4. Mean and standard deviation values stored in `mean_std_224` for image normalization.
+
+
+Now we need to write the C++ code to load them and run prediction on a test image.
+The full code is available in the [C++ Inference Example](https://github.com/apache/incubator-mxnet/tree/master/cpp-package/example/inference), we will walk you through it and point out the necessary changes to make for our use case.
+
+
+
+### Write a predictor using the MXNet C++ API
+
+In general, the C++ inference code should follow the 4 steps below. We can do that using a Predictor class.
+1. Load the pre-trained model
+2. Load the parameters of pre-trained model
+3. Load the image to be classified in to NDArray and apply image transformation we did in training
+4. Run the forward pass and predict the class of the input image
+
+```c++
+class Predictor {
+ public:
+    Predictor() {}
+    Predictor(const std::string& model_json_file,
+              const std::string& model_params_file,
+              const Shape& input_shape,
+              bool gpu_context_type = false,
+              const std::string& synset_file = "",
+              const std::string& mean_image_file = "");
+    void PredictImage(const std::string& image_file);
+    ~Predictor();
+
+ private:
+    void LoadModel(const std::string& model_json_file);
+    void LoadParameters(const std::string& model_parameters_file);
+    void LoadSynset(const std::string& synset_file);
+    NDArray LoadInputImage(const std::string& image_file);
+    void LoadMeanImageData();
+    void LoadDefaultMeanImageData();
+    void NormalizeInput(const std::string& mean_image_file);
+    inline bool FileExists(const std::string& name) {
+        struct stat buffer;
+        return (stat(name.c_str(), &buffer) == 0);
+    }
+    NDArray mean_img;
+    std::map<std::string, NDArray> args_map;
+    std::map<std::string, NDArray> aux_map;
+    std::vector<std::string> output_labels;
+    Symbol net;
+    Executor *executor;
+    Shape input_shape;
+    NDArray mean_image_data;
+    NDArray std_dev_image_data;
+    Context global_ctx = Context::cpu();
+    std::string mean_image_file;
+};
+```
+
+### Load the model, synset file, and normalization values
+
+In the Predictor constructor, you need to provide paths to saved json and param files. After that, add the following methods `LoadModel` and `LoadParameters` to load the network and its parameters. This part is the same as [the example](https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/inference/inception_inference.cpp).
+
+Next, we need to load synset file, and normalization values. We have made the following change since our synset file contains flower names and we used both mean and standard deviation for image normalization.
+
+```c++
+/*
+ * The following function loads the synset file.
+ * This information will be used later to report the label of input image.
+ */
+void Predictor::LoadSynset(const std::string& synset_file) {
+  if (!FileExists(synset_file)) {
+    LG << "Synset file " << synset_file << " does not exist";
+    throw std::runtime_error("Synset file does not exist");
+  }
+  LG << "Loading the synset file.";
+  std::ifstream fi(synset_file.c_str());
+  if (!fi.is_open()) {
+    std::cerr << "Error opening synset file " << synset_file << std::endl;
+    throw std::runtime_error("Error in opening the synset file.");
+  }
+  std::string lemma;
+  while (getline(fi, lemma)) {
+    output_labels.push_back(lemma);
+  }
+  fi.close();
+}
+
+/*
+ * The following function loads the mean and standard deviation values.
+ * This data will be used for normalizing the image before running the forward
+ * pass.
+ * The output data has the same shape as that of the input image data.
+ */
+void Predictor::LoadMeanImageData() {
+  LG << "Load the mean image data that will be used to normalize "
+     << "the image before running forward pass.";
+  mean_image_data = NDArray(input_shape, global_ctx, false);
+  mean_image_data.SyncCopyFromCPU(
+        NDArray::LoadToMap(mean_image_file)["mean_img"].GetData(),
+        input_shape.Size());
+  NDArray::WaitAll();
+   std_dev_image_data = NDArray(input_shape, global_ctx, false);
+   std_dev_image_data.SyncCopyFromCPU(
+       NDArray::LoadToMap(mean_image_file)["std_img"].GetData(),
+       input_shape.Size());
+    NDArray::WaitAll();
+}
+```
+
+
+
+### Load input image
+
+Now let's add a method to load the input image we want to predict and converts it to NDArray for prediction.
+```c++
+NDArray Predictor::LoadInputImage(const std::string& image_file) {
+  if (!FileExists(image_file)) {
+    LG << "Image file " << image_file << " does not exist";
+    throw std::runtime_error("Image file does not exist");
+  }
+  LG << "Loading the image " << image_file << std::endl;
+  std::vector<float> array;
+  cv::Mat mat = cv::imread(image_file);
+  /*resize pictures to (224, 224) according to the pretrained model*/
+  int height = input_shape[2];
+  int width = input_shape[3];
+  int channels = input_shape[1];
+  cv::resize(mat, mat, cv::Size(height, width));
+  for (int c = 0; c < channels; ++c) {
+    for (int i = 0; i < height; ++i) {
+      for (int j = 0; j < width; ++j) {
+        array.push_back(static_cast<float>(mat.data[(i * height + j) * 3 + c]));
+      }
+    }
+  }
+  NDArray image_data = NDArray(input_shape, global_ctx, false);
+  image_data.SyncCopyFromCPU(array.data(), input_shape.Size());
+  NDArray::WaitAll();
+  return image_data;
+}
+```
+
+### Predict the image
+
+Finally, let's run the inference. It's basically using MXNet executor to do a forward pass. To run predictions on multiple images, you can load the images in a list of NDArrays and run prediction in batches. Note that the Predictor class may not be thread safe. Calling it in multi-threaded environments was not tested. To utilize multi-threaded prediction, you need to use the C predict API. Please follow the [C predict example](https://github.com/apache/incubator-mxnet/tree/master/example/image-classification/predict-cpp).
+
+An additional step is to normalize the image NDArrays values to `(0, 1)` and apply mean and standard deviation we just loaded.
+
+```c++
+/*
+ * The following function runs the forward pass on the model.
+ * The executor is created in the constructor.
+ *
+ */
+void Predictor::PredictImage(const std::string& image_file) {
+  // Load the input image
+  NDArray image_data = LoadInputImage(image_file);
+
+  // Normalize the image
+  image_data.Slice(0, 1) /= 255.0;
+  image_data -= mean_image_data;
+  image_data /= std_dev_image_data;
+
+  LG << "Running the forward pass on model to predict the image";
+  /*
+   * The executor->arg_arrays represent the arguments to the model.
+   *
+   * Copying the image_data that contains the NDArray of input image
+   * to the arg map of the executor. The input is stored with the key "data" in the map.
+   *
+   */
+  image_data.CopyTo(&(executor->arg_dict()["data"]));
+  NDArray::WaitAll();
+
+  // Run the forward pass.
+  executor->Forward(false);
+
+  // The output is available in executor->outputs.
+  auto array = executor->outputs[0].Copy(global_ctx);
+  NDArray::WaitAll();
+
+  /*
+   * Find out the maximum accuracy and the index associated with that accuracy.
+   * This is done by using the argmax operator on NDArray.
+   */
+  auto predicted = array.ArgmaxChannel();
+  NDArray::WaitAll();
+
+  int best_idx = predicted.At(0, 0);
+  float best_accuracy = array.At(0, best_idx);
+
+  if (output_labels.empty()) {
+    LG << "The model predicts the highest accuracy of " << best_accuracy << " at index "
+       << best_idx;
+  } else {
+    LG << "The model predicts the input image to be a [" << output_labels[best_idx]
+       << " ] with Accuracy = " << best_accuracy << std::endl;
+  }
+}
+```
+
+### Compile and run the inference code
+
+You can find the [full code for the inference example](https://github.com/apache/incubator-mxnet/tree/master/cpp-package/example/inference) in the `cpp-package` folder of the project
+, and to compile it use this [Makefile](https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/inference/Makefile).
+
+Make a copy of the example code, rename it to `flower_inference` and apply the changes we mentioned above. Now you will be able to compile and run inference. Run `make all`. Once this is complete, run inference with the following parameters. Remember to set your `LD_LIBRARY_PATH` to point to MXNet library if you have not done so.
+
+```bash
+make all
+export LD_LIBRARY_PATH=$LD_LIBRARY_PATH=:path/to/incubator-mxnet/lib
+./flower_inference --symbol flower-recognition-symbol.json --params flower-recognition-0040.params --synset synset.txt --mean mean_std_224.nd --image ./data/test/lotus/image_01832.jpg
+```
+
+Then it will predict your image:
+
+```bash
+[17:38:51] resnet.cpp:150: Loading the model from flower-recognition-symbol.json
+
+[17:38:51] resnet.cpp:163: Loading the model parameters from flower-recognition-0040.params
+
+[17:38:52] resnet.cpp:190: Loading the synset file.
+[17:38:52] resnet.cpp:211: Load the mean image data that will be used to normalize the image before running forward pass.
+[17:38:52] resnet.cpp:263: Loading the image ./data/test/lotus/image_01832.jpg
+
+[17:38:52] resnet.cpp:299: Running the forward pass on model to predict the image
+[17:38:52] resnet.cpp:331: The model predicts the input image to be a [lotus ] with Accuracy = 8.63046
+```
+
+
+
+## What's next
+
+Now you can explore more ways to run inference and deploy your models:
+1. [Java Inference examples](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer)
+2. [Scala Inference examples](https://mxnet.incubator.apache.org/tutorials/scala/)
+3. [ONNX model inference examples](https://mxnet.incubator.apache.org/tutorials/onnx/inference_on_onnx_model.html)
+4. [MXNet Model Server Examples](https://github.com/awslabs/mxnet-model-server/tree/master/examples)
+
+## References
+
+1. [Gluon end to end tutorial](https://mxnet.apache.org/versions/master/tutorials/gluon/gluon_from_experiment_to_deployment.html)
+2. [Gluon C++ inference example](https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/inference/)
+3. [Gluon C++ package](https://github.com/apache/incubator-mxnet/tree/master/cpp-package)
diff --git a/docs/static_site/src/pages/api/cpp/docs/tutorials/subgraphAPI.md b/docs/static_site/src/pages/api/cpp/docs/tutorials/subgraphAPI.md
new file mode 100644
index 000000000000..bd5450e0bf7a
--- /dev/null
+++ b/docs/static_site/src/pages/api/cpp/docs/tutorials/subgraphAPI.md
@@ -0,0 +1,175 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: Subgraph API
+action: Get Started
+action_url: /get_started
+permalink: /api/cpp/docs/tutorials/subgraph_api
+is_tutorial: true
+tag: cpp
+---
+
+## Subgraph API
+
+The subgraph API has been proposed and implemented as the default mechanism for integrating backend libraries to MXNet. The subgraph API is a very flexible interface. Although it was proposed as an integration mechanism, it has been used as a tool for manipulating NNVM graphs for graph-level optimizations, such as operator fusion.
+
+The subgraph API works as the following steps:
+
+* Search for particular patterns in a graph.
+* Group the operators/nodes with particular patterns into a subgraph and shrink the subgraph into a single node.
+* Replace the subgraph in the original graph with the subgraph node.
+
+The figure below illustrates the subgraph mechanism.
+
+![](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tutorials/subgraph/subgraph.png)
+
+The subgraph API allows the backend developers to customize the subgraph mechanism in two places:
+
+* Subgraph searching: define a subgraph selector to search for particular patterns in a computation graph.
+* Subgraph node creation: attach an operator to run the computation in the subgraph. We can potentially manipulate the subgraph here.
+
+
+The following is a demonstration of how the subgraph API can be applied to a simple task. Refer to the previous figure for an overview of the process. That is, replacing `Convolution` and `BatchNorm` with the conv_bn.
+
+The first step is to define a subgraph selector to find the required pattern. To find a pattern that has `Convolution` and `BatchNorm`, we can start the search on the node with `Convolution`. Then from the `Convolution` node, we search for `BatchNorm` along the outgoing edge.
+
+```c++
+class SgSelector : public SubgraphSelector {
+ public:
+  SgSelector() {
+    find_bn = false;
+  }
+  bool Select(const nnvm::Node &n) override {
+    // Here we start on the Convolution node to search for a subgraph.
+    return n.op() && n.op()->name == "Convolution";
+  }
+  bool SelectInput(const nnvm::Node &n, const nnvm::Node &new_node) override {
+    // We don't need to search on the incoming edge.
+    return false;
+  }
+  bool SelectOutput(const nnvm::Node &n, const nnvm::Node &new_node) override {
+    // We search on the outgoing edge. Once we find a BatchNorm node, we won't
+    // accept any more BatchNorm nodes.
+    if (new_node.op() && new_node.op()->name == "BatchNorm" && !find_bn) {
+      find_bn = true;
+      return true;
+    } else {
+      return false;
+    }
+  }
+  std::vector<nnvm::Node *> Filter(const std::vector<nnvm::Node *> &candidates) override {
+    // We might have found a Convolution node, but we might have failed to find a BatchNorm
+    // node that uses the output of the Convolution node. If we failed, we should skip
+    // the Convolution node as well.
+    if (find_bn)
+      return candidates;
+    else
+      return std::vector<nnvm::Node *>();
+  }
+ private:
+  bool find_bn;
+};
+```
+
+The second step is to define a subgraph property to use the subgraph selector above to customize the subgraph searching. By defining this class, we can also customize subgraph node creation. When customizing node creation, we can specify what operator to run the subgraph on the node. In this example, we use `CachedOp`, which itself is a graph executor, to run the subgraph with `Convolution` and `BatchNorm`. In practice, it's most likely that we use a single operator from a backend library to replace the two operators for execution.
+
+{% raw %}
+```c++
+class SgProperty : public SubgraphProperty {
+ public:
+  static SubgraphPropertyPtr Create() {
+    return std::make_shared<SgProperty>();
+  }
+  nnvm::NodePtr CreateSubgraphNode(
+      const nnvm::Symbol &sym, const int subgraph_id = 0) const override {
+    // We can use CachedOp to execute the subgraph.
+    nnvm::NodePtr n = nnvm::Node::Create();
+    n->attrs.op = Op::Get("_CachedOp");
+    n->attrs.name = "ConvBN" + std::to_string(subgraph_id);
+    n->attrs.subgraphs.push_back(std::make_shared<nnvm::Symbol>(sym));
+    std::vector<std::pair<std::string, std::string> > flags{{"static_alloc", "true"}};
+    n->attrs.parsed = CachedOpPtr(new CachedOp(sym, flags));
+    return n;
+  }
+  SubgraphSelectorPtr CreateSubgraphSelector() const override {
+    auto property = std::make_shared<CreateSubgraphSelector>();
+    property->SetAttr<std::string>("property_name", "subgraph example pass"); // Optional, better to have it.
+    property->SetAttr<bool>("inference_only", true); // Optional, only for inference_only pass.
+    return property;
+  }
+};
+```
+{% endraw %}
+`SetAttr` is optional and developer can define their own attributes to control property behavior.
+There're 2 built-in attributes that used by MXNet executor.
+
+`property_name`  : std::string, name of this property.
+
+`inference_only` : bool, apply this property only for inference. Property will be skiped when need_grad=True. Default `false` if this attribute isn't defined.
+
+After defining the subgraph property, we need to register it under a backend in .cc file.
+
+Firstly, we need to register the backend
+
+```C++
+MXNET_REGISTER_SUBGRAPH_BACKEND(SgTest);
+```
+
+Then register the property under it.
+
+```c++
+MXNET_REGISTER_SUBGRAPH_PROPERTY(SgTest, SgProperty);
+```
+
+It's possible to register multiple properties for same backend. In practice, we recommend to put each property definition into .h file, and register backend in single .cc file. Property will be executed according to the register order.
+
+```c++
+#include "SgProperty.h" // Define SgProperty class
+#include "SgProperty2.h" // Define SgProperty2 class
+#include "SgProperty3.h" // Define SgProperty3 class
+
+MXNET_REGISTER_SUBGRAPH_BACKEND(SgTest);
+MXNET_REGISTER_SUBGRAPH_PROPERTY(SgTest, SgProperty);  // Execution order 1.
+MXNET_REGISTER_SUBGRAPH_PROPERTY(SgTest, SgProperty2); // Execution order 2.
+MXNET_REGISTER_SUBGRAPH_PROPERTY(SgTest, SgProperty3); // Execution order 3.
+```
+
+After compiling this subgraph mechanism into MXNet, we can use the environment variable `MXNET_SUBGRAPH_BACKEND` to activate it during symbol bind.
+
+```bash
+export MXNET_SUBGRAPH_BACKEND=SgTest
+```
+
+Or you can use python symbol API `get_backend_symbol` to run all properties registered for this backend and get returned symbol.
+
+```python
+sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)
+sym = sym.get_backend_symbol('SgTest')
+```
+
+When `SgProperty` is activated, a message will be shown in terminal as
+
+```bash
+start to execute subgraph example pass.
+```
+
+This tutorial shows a simple example of how to use the subgraph API to search for patterns in an NNVM graph.
+Intested users can try different pattern matching rules (i.e., define their own `SubgraphSelector`) and
+attach different operators to execute the subgraphs.
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/static_site/src/pages/api/cpp/index.md b/docs/static_site/src/pages/api/cpp/index.md
new file mode 100644
index 000000000000..f3b65a366078
--- /dev/null
+++ b/docs/static_site/src/pages/api/cpp/index.md
@@ -0,0 +1,61 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: C++ Guide
+action: Get Started
+action_url: /get_started
+permalink: /api/cpp
+tag: cpp
+---
+
+# MXNet - C++ API
+
+The MXNet C++ Package provides C++ API bindings to the users of MXNet.  Currently, these bindings are not available as standalone package.
+The users of these bindings are required to build this package as mentioned below.
+
+## Building C++ Package
+
+The cpp-package directory contains the implementation of C++ API. As mentioned above, users are required to build this directory or package before using it.
+**The cpp-package is built while building the MXNet shared library, *libmxnet.so*.**
+
+### Steps to build the C++ package:
+1.  Building the MXNet C++ package requires building MXNet from source.
+2.  Clone the MXNet GitHub repository **recursively** to ensure the code in submodules is available for building MXNet.
+	```
+	git clone --recursive https://github.com/apache/incubator-mxnet mxnet
+	```
+
+3.  Install the [prerequisites](<https://mxnet.incubator.apache.org/install/build_from_source#prerequisites>), desired [BLAS libraries](<https://mxnet.incubator.apache.org/install/build_from_source#blas-library>) and optional [OpenCV, CUDA, and cuDNN](<https://mxnet.incubator.apache.org/install/build_from_source#optional>) for building MXNet from source.
+4.  There is a configuration file for make, [make/config.mk](<https://github.com/apache/incubator-mxnet/blob/master/make/config.mk>) that contains all the compilation options. You can edit this file and set the appropriate options prior to running the **make** command.
+5.  Please refer to  [platform specific build instructions](<https://mxnet.incubator.apache.org/install/build_from_source#build-instructions-by-operating-system>) and available [build configurations](https://mxnet.incubator.apache.org/install/build_from_source#build-configurations) for more details.
+5.  For enabling the build of C++ Package, set the **USE\_CPP\_PACKAGE = 1** in [make/config.mk](<https://github.com/apache/incubator-mxnet/blob/master/make/config.mk>). Optionally, the compilation flag can also be specified on **make** command line as follows.
+	```
+	make -j USE_CPP_PACKAGE=1
+	```
+
+## Usage
+
+In order to consume the C++ API please follow the steps below.
+
+1. Ensure that the MXNet shared library is built from source with the **USE\_CPP\_PACKAGE = 1**.
+2. Include the [MxNetCpp.h](<https://github.com/apache/incubator-mxnet/blob/master/cpp-package/include/mxnet-cpp/MxNetCpp.h>) in the program that is going to consume MXNet C++ API.
+	```c++
+	#include <mxnet-cpp/MxNetCpp.h>
+	```
+3. While building the program, ensure that the correct paths to the directories containing header files and MXNet shared library.
+4. The program links the MXNet shared library dynamically. Hence the library needs to be accessible to the program during runtime. This can be achieved by including the path to the shared library in the environment variable  **LD\_LIBRARY\_PATH** for Linux, Mac. and Ubuntu OS and **PATH** for Windows OS.
diff --git a/docs/static_site/src/pages/api/faq/add_op_in_backend.md b/docs/static_site/src/pages/api/faq/add_op_in_backend.md
new file mode 100644
index 000000000000..f633e57ce645
--- /dev/null
+++ b/docs/static_site/src/pages/api/faq/add_op_in_backend.md
@@ -0,0 +1,692 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_category
+title:  Exception Handling in MXNet
+category: faq
+faq_c: Extend and Contribute to MXNet
+question: How do I implement operators in MXNet backend?
+permalink: /api/faq/add_op_in_backend
+---
+
+# A Beginner's Guide to Implementing Operators in MXNet Backend
+
+## Introduction
+Operators are essential elements for constructing neural networks. They define mathematical formulas
+of transforming input data (tensors) to outputs. MXNet has a rich set of operators from simple ones,
+such as element-wise sum, to complicated ones, such as convolution, that is
+capable of constructing most of the popular neural networks. You may have noticed
+that many operators implemented in MXNet have their equivalent forms in Numpy, such as
+[repeat](https://docs.scipy.org/doc/numpy/reference/generated/numpy.repeat.html),
+[tile](https://docs.scipy.org/doc/numpy/reference/generated/numpy.tile.html),
+etc., and wonder why we could not simply use those Numpy operators in MXNet. One of the
+major reasons is that we need to support both CPU and GPU computing for the operators in MXNet,
+while Numpy operators do not possess GPU computing capability.
+In addition, we have performed plenty of
+optimizations for various components in MXNet, such as tensor data structure (`NDArray`),
+execution engine, computational graph and so on, for maximizing memory and runtime efficiency.
+An operator implemented under the MXNet operator framework would greatly
+leverage those optimizations for exhaustive performance enhancement.
+
+In this tutorial, we are going to practice implementing an operator using
+C++ in the MXNet backend. After finishing the implementation,
+we will add unit tests using Python for the operator we just implemented.
+
+## Implementation
+### An Operator Example
+Let's take the [quadratic function](https://en.wikipedia.org/wiki/Quadratic_function)
+as an example: `f(x) = ax^2+bx+c`. We want to implement an operator called `quadratic`
+taking `x`, which is a tensor, as an input and generating an output tensor `y`
+satisfying `y.shape=x.shape` and each element of `y` is calculated by feeding the
+corresponding element of `x` into the quadratic function `f`.
+Here variables `a`, `b`, and `c` are user input parameters.
+In frontend, the operator works like this:
+```python
+x = [[1, 2], [3, 4]]
+y = quadratic(data=x, a=1, b=2, c=3)
+y = [[6, 11], [18, 27]]
+```
+To implement this, we first create three files: `quadratic_op-inl.h`,
+`quadratic_op.cc`, and `quadratic_op.cu`. The header file's name
+is prefixed by the operator name and followed by `op` and `-inl`
+indicating that this is an operator implementation with inline
+functions shared by CPU and GPU computing. The CPU and GPU
+specific implementations reside in their own `.cc` and `.cu` files,
+respectively. We normally put pure tensor related operators
+(e.g. `tile`, `repeat`, etc.) under
+the directory `src/operator/tensor`, and neural network operators
+(e.g. `Convolution`, `Pooling`, etc.) under `src/operator/nn`.
+You may have noticed that many neural network operators including
+`Convolution` and `Pooling` are currently saved under `src/operator`.
+We plan to move them to `src/operator/nn` for better file organization
+and clearer hierarchy in the future.
+
+Next, we are going to
+1. Define the parameter struct
+for registering `a`, `b`, and `c` in `quadratic_op-inl.h`.
+2. Define type and shape inference functions in `quadratic_op-inl.h`.
+3. Define forward and backward functions in `quadratic_op-inl.h`.
+4. Register the operator using [nnvm](https://docs.tvm.ai/dev/nnvm_overview.html)
+in `quadratic_op.cc` and `quadratic_op.cu` for
+CPU and GPU computing, respectively.
+
+Now let's walk through the process step by step.
+
+### Parameter Registration
+We first define `struct QuadraticParam` as a placeholder for the
+parameters `a`, `b`, and `c` in `quadratic_op-inl.h`.
+The struct inherits from a base template
+struct named `dmlc::Parameter`, where the template argument is the derived struct
+`QuadraticParam`. This technique, which is called [curiously recurring template
+pattern](https://en.wikipedia.org/wiki/Curiously_recurring_template_pattern),
+achieves static polymorphism. It is similar to using a virtual function,
+but without the cost associated with dynamic polymorphism.
+
+```cpp
+struct QuadraticParam : public dmlc::Parameter<QuadraticParam> {
+  float a, b, c;
+  DMLC_DECLARE_PARAMETER(QuadraticParam) {
+    DMLC_DECLARE_FIELD(a)
+      .set_default(0.0)
+      .describe("Coefficient of the quadratic term in the quadratic function.");
+    DMLC_DECLARE_FIELD(b)
+      .set_default(0.0)
+      .describe("Coefficient of the linear term in the quadratic function.");
+    DMLC_DECLARE_FIELD(c)
+      .set_default(0.0)
+      .describe("Constant term in the quadratic function.");
+  }
+};
+```
+
+The function calls in the above parameter struct are self-explanatory by their names.
+Note that for each parameter, we set the default value to `0.0` such that users can
+skip passing 0-value parameters through the quadratic operator interface. You
+can choose not to define the default value for a parameter if it is required
+at runtime. Meanwhile, adding brief descriptions to the parameters enables
+the documentation engine to display them on
+[MXNet documentation web page](https://mxnet.incubator.apache.org/api/python/index.html).
+
+### Attribute Inference
+Attribute inference is the process of deducing the properties of `NDArray`s
+in neural networks from user provided information. Two most common attributes
+of an `NDArray` are data shape and data type.
+Let's take a look at the following example.
+Given an input `NDArray` called `data`, you invoke the `quadratic` operator
+like this: `output = mx.nd.quadratic(data, a=1, b=2, c=3)`. Before calculating
+the `output` values, its shape and data type are inferred from the input
+`data`'s shape and type following
+the rules you defined in order to allocate memory space for the output tensor.
+
+One important thing to note that inference functions should be capable of
+performing **mutual inference**, i.e.
+inferring one argument's attribute from another argument's attribute if
+possible according to the definition of the operator.
+This is very useful for a computational graph to deduce unknown attributes
+for a neural network in symbolic programming. Users can view the computational
+graph as a symbol with every element initialized for running data
+throughout the neural network, including memory allocation for each tensor,
+device placement for each operator, etc. Users normally just need
+to provide minimum necessary information, such as input data shapes, etc.,
+to the computational graph, and the graph will fill up the unknown attributes
+using the attribute inference functions defined in the operators building up
+the neural network.
+
+Let's consider the following example.
+```python
+>>> import mxnet as mx
+>>> a = mx.sym.Variable('a', shape=(2, 0))
+>>> b = mx.sym.Variable('b')
+>>> c = mx.sym.Variable('c', shape=(0, 3))
+>>> d = a * b + b * c
+>>> print d.infer_shape()
+([(2L, 3L), (2L, 3L), (2L, 3L)], [(2L, 3L)], [])
+```
+The last line of the above code snippet is a tuple of three lists returned
+by `d.infer_shape()`. The first list contains all the argument shapes
+of `a`, `b`, and `c`. The second contains the output shape of `d`. The
+third one represents the shapes of auxiliary states, which is not used
+in this case, and thus is empty.
+In this example, we only specified values for variable `a`'s first dimension
+and `c`'s second dimension. The `0` in shape `(2, 0)` indicates that the size
+of the second dimension is unknown, same meaning for shape `(0, 3)`.
+However, the symbol `d` still successfully inferred the shapes
+for all the variables and final output. This is a result of mutual
+inference. In MXNet, the whole process can be interpreted as this:
+1. `a` and `b` are combined via an element-wise multiplication operator,
+so the shapes of `a` and `b` are same and `b`'s first dimension size is `2`.
+2. `b` and `c` are combined via an element-wise multiplication operator too,
+so the shapes of `b` and `c` are same and `b`'s second dimension size is `3`.
+3. Now `b`'s shape is completely known, so `a` and `c` missing dimension sizes
+are known as well.
+4. `d` is a result from adding `a * b` and `b * c`, so d should also
+have the same shape as `b`.
+
+The above four steps illustrate how shape inference logic works in MXNet.
+It is actually implemented in the shape inference functions of the operators for
+element-wise multiplication and addition.
+
+For our `quadratic` operator, shape inference possesses quite similar logic.
+```cpp
+inline bool QuadraticOpShape(const nnvm::NodeAttrs& attrs,
+                             mxnet::ShapeVector* in_attrs,
+                             mxnet::ShapeVector* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
+  SHAPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
+  return out_attrs->at(0).ndim() != 0U && out_attrs->at(0).Size() != 0U;
+}
+```
+Here are a few things to note about the above function:
+
+1. `attrs` contains parameters `a`, `b`, and `c` from user input.
+It's not used here since we don't rely on that information for shape inference.
+2. `in_attrs` is a vector containing all input shapes. Since there is
+only one input argument for operator `quadratic`, we used macro `CHECK_EQ`
+to assert when the vector's size is wrong.
+3. `out_attrs` is a vector containing all output shapes. We also used
+`CHECK_EQ` to verify the size of the vector since there is only one output.
+4. We called macro `SHAPE_ASSIGN_CHECK` twice for mutual inference. One for
+inferring the output shape from the input shape, the other one is for inferring
+the input shape from the output shape.
+If there are any unequal non-zero values in the same
+dimension of two shapes, such as `(2, 3)` and `(3, 3)`, the macro would throw an
+exception with an error message for shape inference.
+5. At the end of the function body, we checked whether the output shape
+is completely known by testing whether the shape is not empty and
+the shape's size is greater than `0`. Note that in MXNet, an empty shape
+means that the shape is unknown, and
+a `0` in a shape means that the size of that dimension is unknown. In both
+situations, the missing shape information must
+be inferred from other shapes. If it cannot be inferred,
+the function should return `false` to notify the caller about shape inference failure.
+6. MXNet provides a convenience function implementing the logic of mutual inference
+for general element-wise operators with the following interface. Users can
+instantiate this function with `n_in=1` and `n_out=1` to replace the above
+function `QuadraticOpShape` in operator registration (explained later).
+The function `QuadraticOpShape` posted here is for the purpose of illustration only.
+```cpp
+template<int n_in, int n_out>
+inline bool ElemwiseShape(const nnvm::NodeAttrs& attrs,
+                          mxnet::ShapeVector *in_attrs,
+                          mxnet::ShapeVector *out_attrs);
+```
+
+The same logic goes for data type inference. We will leave the analysis of
+the following code sample to users. Note that `-1` means the data type
+is unknown and must be inferred from other input or output data types.
+```cpp
+inline bool QuadraticOpType(const nnvm::NodeAttrs& attrs,
+                            std::vector<int>* in_attrs,
+                            std::vector<int>* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
+  TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
+  return out_attrs->at(0) != -1;
+}
+```
+
+Again, MXNet provides the following convenience function for mutual
+type inference of element-wise operators. Users can use that
+in operator registration (explained later).
+```cpp
+template<int n_in, int n_out>
+inline bool ElemwiseType(const nnvm::NodeAttrs& attrs,
+                         std::vector<int>* in_attrs,
+                         std::vector<int>* out_attrs);
+```
+
+### Forward Function
+Forward function defines the operator's behavior in the forward pass
+of neural networks. For our `quadratic` operator, it simply implements
+the logic of running a tensor through the quadratic function by performing
+a few element-wise operations. The forward function's signature is fixed
+in MXNet as follows:
+```cpp
+void (const nnvm::NodeAttrs& attrs,
+      const OpContext& ctx,
+      const std::vector<TBlob>& inputs,
+      const std::vector<OpReqType>& req,
+      const std::vector<TBlob>& outputs);
+```
+We first paste the whole forward function code here
+and then go through it line by line.
+```cpp
+template<typename xpu>                                                        // 1
+void QuadraticOpForward(const nnvm::NodeAttrs& attrs,                         // 2
+                        const OpContext& ctx,                                 // 3
+                        const std::vector<TBlob>& inputs,                     // 4
+                        const std::vector<OpReqType>& req,                    // 5
+                        const std::vector<TBlob>& outputs) {                  // 6
+  CHECK_EQ(inputs.size(), 1U);                                                // 7
+  CHECK_EQ(outputs.size(), 1U);                                               // 8
+  CHECK_EQ(req.size(), 1U);                                                   // 9
+  mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();                            // 10
+  const TBlob& in_data = inputs[0];                                           // 11
+  const TBlob& out_data = outputs[0];                                         // 12
+  const QuadraticParam& param = nnvm::get<QuadraticParam>(attrs.parsed);      // 13
+  using namespace mxnet_op;                                                   // 14
+  MSHADOW_TYPE_SWITCH(out_data.type_flag_, DType, {                           // 15
+    MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {                               // 16
+      Kernel<quadratic_forward<req_type>, xpu>::Launch(                       // 17
+          s, out_data.Size(), out_data.dptr<DType>(), in_data.dptr<DType>(),  // 18
+          param.a, param.b, param.c);                                         // 19
+    });                                                                       // 20
+  });                                                                         // 21
+}                                                                             // 22
+```
+- Line 1: `xpu` stands for a generic device type so that the function can be instantiated
+for both CPU and GPU computing using concrete types `cpu` and `gpu`. The instantiation happens
+at the time when the operator is registered in `.cc` and `.cu` files.
+- Line 2: `attrs` is a node attribute containing the user input parameters `a`, `b`, and `c`.
+Here the node represents a placeholder for the operator in the whole computational graph for
+the neural network.
+- Line 3: `ctx` holds something called `stream` for
+serializing asynchronous executions. Let's consider
+this example for understanding the functionality of `stream`.
+We want to launch several GPU kernels with the same `stream` from CPU.
+Even though the launching operation is non-blocking, the `stream` guarantees
+that the kernels execute in the same order on GPU as they are launched from CPU.
+- Line 4: `inputs` is a vector of input tensors (only one input tensor
+for the `quadratic` operator).
+- Line 5: `req` is a vector of `OpReqType` values. Each value defines
+the way of writing calculated values to the output tensors.
+Therefore, the number of `req`s must be the same as the number of output tensors.
+MXNet currently supports three types of `req` in frontend: `null`, `write`, and `add`.
+`null` means skipping calculating the corresponding output tensor,
+`write` means overwriting the values in the output tensor with the ones
+calculated by this operator, and `add` means adding the calculated values
+to the existing ones in the output tensor. Note that `null` and `add` are usually
+seen in backward passes. The former is for skipping calculating
+the gradients of un-learnable parameters (such as index arrays),
+and the latter is for accumulating gradients throughout networks.
+- Line 6: `outputs` is a vector of output tensors (only one
+output tensor for the `quadratic` operator).
+- Lines 7-9: Verify that the size of each vector is expected.
+Otherwise, stop moving forward and print error message.
+- Line 10: Get the `stream` from the `ctx` for launching kernels.
+- Lines 11-12: Define the references of the input and output tensors
+for later coding convenience. Note that `TBlob` can be understood
+as a uniform data structure for tensors of various dimensions, such
+that tensors of different dimensions can be put in a homogeneous container,
+such as `std::vector` and `std::list`. You can still
+get tensors of desired dimensions from a `TBlob` object through
+the interface `get_with_shape`.
+- Line 13: Get user input parameters from the node attribute.
+- Lines 15-21: This is the place where the mathematical formula of the operator
+is implemented. The macros `MSHADOW_TYPE_SWITCH` and `MXNET_ASSIGN_REQ_SWITCH` enable
+the code block to work for all the supported data types and `req` types in MXNet.
+Inside the inner-most macro, we launch the kernel for calculating
+the output tensor such that each thread takes an element from
+the input tensor, feeds it into the quadratic function, and assigns
+the output element to the output tensor based on `req` type. Note that
+`Kernel::Launch` serves as a universal interface for launching
+parallel computation on both CPU and GPU. This allows most of
+the simple operators to share the same piece of code for CPU and GPU as
+parallelization approaches are often identical on both types of devices.
+The kernel function is defined as the following, where the function
+`Map` is executed by each thread for each input element. The `out_data.Size()`,
+in the `Kernel::Launch` function corresponds to the factor by which the
+workload will get parallelized among the different threads, which here
+corresponds to the size of the output array. To explain a little
+bit more on the two macros used in the kernel struct: (1) `MSHADOW_XINLINE` is
+a consolidated macro for inlining functions compiled by both CPU and GPU
+compilers. It enables CPU and GPU computing to share the same piece of code.
+(2) `KERNEL_ASSIGN` is a macro for unifying the statements of different `req`s
+into the same line of code. It's named `KERNEL_ASSIGN` because we call
+the code blocks running parallel computation kernels.
+On CPUs, the kernels are normally wrapped by the OpenMP `parallel` directive;
+while on GPUs, they are the kernel functions launched by CUDA library.
+
+```cpp
+template<int req>
+struct quadratic_forward {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int i, DType* out_data, const DType* in_data,
+                                  const float a, const float b, const float c) {
+    KERNEL_ASSIGN(out_data[i], req, in_data[i] * (a * in_data[i] + b) + c);
+  }
+};
+```
+
+### Backward Function
+Backward functions play the role of propagating derivatives of loss function
+with respect to the outputs of the last layer throughout the network to the first
+layer. The whole process is often known as backward propagation. We are not
+going to delineate the principle of backward propagation here since users can find
+great details covered in other resources, such as
+[CS231n](http://cs231n.github.io/optimization-2/) and
+[How the backgropagation algorithm works](http://neuralnetworksanddeeplearning.com/chap2.html).
+The problem we are going to solve here for the `quadratic` operator is that
+given a tensor representing the gradient of the loss function with respect
+to the output of the operator, calculate the gradient with respect to
+the input of the operator. There is no need to calculate the derivatives
+of loss function with respect to user input parameters `a`, `b`, and `c`
+since they are not learnable parameters in the network. To formulate the problem:
+given `dL/dy` and `y = a*x^2 + b*x + c`, where `L` represents the loss function and
+`y` stands for the output of the quadratic tensor, we need to solve for
+`dL/dx`. Using the chain-rule, it is obvious to find that
+```
+dL/dx = dL/dy * dy/dx = dL/dy * (2*a*x + b).
+```
+The above equation indicates that `dL/dx` depends on the gradient
+of the output tensor and value of the input tensor.
+The backward function's signature is the same as the forward function's.
+With the aforementioned information in mind,
+let's breakdown the following backward function line by line.
+```cpp
+template<typename xpu>                                                       // 1
+void QuadraticOpBackward(const nnvm::NodeAttrs& attrs,                       // 2
+                         const OpContext& ctx,                               // 3
+                         const std::vector<TBlob>& inputs,                   // 4
+                         const std::vector<OpReqType>& req,                  // 5
+                         const std::vector<TBlob>& outputs) {                // 6
+  CHECK_EQ(inputs.size(), 2U);                                               // 7
+  CHECK_EQ(outputs.size(), 1U);                                              // 8
+  CHECK_EQ(req.size(), 1U);                                                  // 9
+  mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();                           // 10
+  const TBlob& out_grad = inputs[0];                                         // 11
+  const TBlob& in_data = inputs[1];                                          // 12
+  const TBlob& in_grad = outputs[0];                                         // 13
+  const QuadraticParam& param = nnvm::get<QuadraticParam>(attrs.parsed);     // 14
+  using namespace mxnet_op;                                                  // 15
+  MSHADOW_TYPE_SWITCH(out_grad.type_flag_, DType, {                          // 16
+    MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {                              // 17
+      Kernel<quadratic_backward<req_type>, xpu>::Launch(                     // 18
+          s, in_grad.Size(), in_grad.dptr<DType>(), out_grad.dptr<DType>(),  // 19
+          in_data.dptr<DType>(), param.a, param.b);                          // 20
+    });                                                                      // 21
+  });                                                                        // 22
+}                                                                            // 23
+```
+- Lines 1-6: Backward function has the same signature as forward function.
+- Lines 7-9: Check the sizes of the function arguments. One thing to note
+that since the gradient of the input depends on both the gradient of the output and
+the input tensor itself, `inputs` must contain two `TBlob` objects.
+- Line 10: Get the `stream` of the context for serializing asynchronous executions.
+- Lines 11-13: Convenience reference variables for later use. We name `out_grad`
+as the gradient of the operator output, `in_data` as the input of the operator,
+and `in_grad` as the gradient of the operator input.
+- Line 14: Get the parameter object of `QuadraticParam`.
+- Lines 16-22: Same as in the forward function, this is where parallel
+computation for `in_grad` happens. The struct `quadratic_backward` implements
+the formula of calculating each element of `in_grad` by one thread as the following.
+
+```cpp
+template<int req>
+struct quadratic_backward {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int i, DType* in_grad, const DType* out_grad,
+                                  const DType* in_data, const float a, const float b) {
+    KERNEL_ASSIGN(in_grad[i], req, out_grad[i] * (2 * a * in_data[i] + b));
+  }
+};
+```
+
+### Operator Registration
+So far, we have implemented necessary data structure and functions for the operator `quadratic`.
+Now let's register them using `nnvm` to expose the operator `quadratic`
+to frontend. Users can consider the registration process as creating the operator object
+instance, saving it in the operator manager (a singleton),
+and setting attributes for the operator instance.
+
+The following code is from `quadratic_op.cc`, which is responsible
+for registering the operator working on CPU.
+
+{% raw %}
+
+```cpp
+DMLC_REGISTER_PARAMETER(QuadraticParam);                                           // 1
+
+NNVM_REGISTER_OP(quadratic)                                                        // 2
+.describe(R"code(This operators implements the quadratic function:                 // 3
+.. math::
+
+    f(x) = ax^2+bx+c
+
+where :math:`x` is an input tensor and all operations
+in the function are element-wise.
+
+Example:
+
+  .. code-block:: python
+     :emphasize-lines: 1,3
+     x = [[1, 2], [3, 4]]
+     y = quadratic(data=x, a=1, b=2, c=3)
+     y = [[6, 11], [18, 27]]
+
+)code" ADD_FILELINE)                                                               // 4
+.set_attr_parser(ParamParser<QuadraticParam>)                                      // 5
+.set_num_inputs(1)                                                                 // 6
+.set_num_outputs(1)                                                                // 7
+.set_attr<nnvm::FListInputNames>("FListInputNames",                                // 8
+  [](const NodeAttrs& attrs) {                                                     // 9
+    return std::vector<std::string>{"data"};                                       // 10
+  })                                                                               // 11
+.set_attr<nnvm::FInferShape>("FInferShape", QuadraticOpShape)                      // 12
+.set_attr<nnvm::FInferType>("FInferType", QuadraticOpType)                         // 13
+.set_attr<FCompute>("FCompute<cpu>", QuadraticOpForward<cpu>)                      // 14
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_quadratic"})  // 15
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",                                  // 16
+  [](const NodeAttrs& attrs) {                                                     // 17
+    return std::vector<std::pair<int, int> >{{0, 0}};                              // 18
+  })                                                                               // 19
+.add_argument("data", "NDArray-or-Symbol", "Input ndarray")                        // 20
+.add_arguments(QuadraticParam::__FIELDS__());                                      // 21
+
+NNVM_REGISTER_OP(_backward_quadratic)                                              // 22
+.set_attr_parser(ParamParser<QuadraticParam>)                                      // 23
+.set_num_inputs(2)                                                                 // 24
+.set_num_outputs(1)                                                                // 25
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)                                  // 26
+.set_attr<FCompute>("FCompute<cpu>", QuadraticOpBackward<cpu>);                    // 27
+```
+
+{% endraw %}
+
+- Line 1: Register the parameter struct.
+- Line 2: Register an operator named `quadratic` by creating an instance
+of `Op` type and save it in the operator manager and return a reference
+of the just created operator object.
+- Lines 3-4: Add description as an operator attribute
+including examples of the operator. The documentation engine will extract
+this description and display it on the documentation web page.
+`emphasize-lines` is optional.
+For more examples and troubleshooting with doc strings, refer to the [MXNet
+developer wiki's Documentation Guide](https://cwiki.apache.org/confluence/display/MXNET/Documentation+Guide).
+- Line 5: Set parameter struct parser for the operator. It is used for parsing
+the parameters `a`, `b`, and `c` input from frontend.
+- Line 6: Set the number of inputs for the operator.
+- Line 7: Set the number of outputs for the operator.
+- Lines 8-11: Defines a function generating a vector of names of
+the operator input arguments. This function is used to add missing
+arguments that users did not specify when creating a symbolic operator.
+For example, `quad_func=mx.sym.quadratic()` is still a valid symbol
+since we have added the attribute `FListInputNames` to the operator node
+in the computational graph. MXNet would
+add the missing argument with name `quadratic0_data`, where the prefix
+`quadratic0` is the operator name appended with an index and the postfix
+`data` comes from the return value of the user defined `FListInputName` function.
+Users still can generate an executor for the `quad_func` like the following:
+```python
+quad_exe = quad_func.simple_bind(ctx=mx.cpu(), quadratic0_data=(1,))
+```
+- Line 12: Register shape inference function.
+- Line 13: Register type inference function.
+- Line 14: Register forward function.
+- Line 15: Register the function for creating the node of the operator in
+a backward pass. Note that we used a convenience functor struct `ElemwiseGradUseIn`.
+As you can tell from the name, the registered functor creates the node for gradient computation
+with dependencies on the output gradient node and input node. Similarly, there are
+other three functors defined as `ElemwiseGradUseOut`, `ElemwiseGradUseInOut`,
+and `ElemwiseGradUseNone` for developers' convenience. In order to add
+this attribute, we also need to register a backward operator for `quadratic` with
+several basic attributes, as it can share attribute inference
+functions with the forward operator and is not exposed to frontend.
+- Lines 16-19: This registered function implies that which output tensor can reuse
+which input tensor's memory space instead of allocating a new memory space for the output.
+In the operator `quadratic`, there is only one input and output, and the output can reuse
+the input memory space, so we store a pair of zeros in the function return vector
+indicating that `inputs[0]`'s memory space can be reused by `outputs[0]`.
+Note that this function just provides a hint to the computational graph initializer.
+If there are other nodes depending on the input tensor, the memory space
+of the input tensor will not be overwritten by the output.
+- Line 20: Define the input argument name as `data` for the operator.
+- Line 21: Add user input parameters `a`, `b`, and `c` as the attributes of the operator.
+- Line 22: Register an operator named `_backward_quadratic` for backward pass
+of the operator `quadratic`. The underscore prefix in the operator name indicates
+that this is an operator not exposed to users. The convention
+of naming an internally used backward operator is prepending the prefix `_backward_`
+to the corresponding forward operator name.
+- Line 23: Set the parameter parser for the operator `_backward_quadratic`.
+- Line 24: Set the number of inputs.
+- Line 25: Set the number of outputs.
+- Line 26: Add `TIsBackward` attribute for the operator. The shape and type
+inference passes use this attribute to determine whether a node in the graph is a
+forward or backward node.
+- Line 27: Register backward function.
+
+So far, we have acquired an operator working on CPU in frontend.
+In order to register the operator working on GPUs, we just need to add the following
+code to `quadratic_op.cu`. Note that forward and backward functions
+are registered with attribute key `FCompute<gpu>`, rather than `FCompute<cpu>`.
+```cpp
+NNVM_REGISTER_OP(quadratic)
+.set_attr<FCompute>("FCompute<gpu>", QuadraticOpForward<gpu>);
+
+NNVM_REGISTER_OP(_backward_quadratic)
+.set_attr<FCompute>("FCompute<gpu>", QuadraticOpBackward<gpu>);
+```
+
+### Unit Test
+Now we have finished implementing the operator `quadratic` in MXNet backend.
+If you use python, when you type `import mxnet as mx`, two python
+functions for invoking your backend implementation are
+generated on the fly: one is for imperative programming
+registered as `mxnet.ndarray.quadratic` or `mx.nd.quadratic` for short;
+the other one is for symbolic programming registered under
+module `mxnet.symbol.quadratic` or `mx.sym.quadratic` for short.
+
+In order to unit test it in frontend, we need to add the following code
+to the python file `test_operator.py`. A typical operator implementation
+tests for both the `symbol` API and the `ndarray` API. The following test
+has both these tests. The imperative API test, tests for the `ndarray` API,
+`mx.nd.contrib.quadratic`. The `symbol` API test, tests for the complete
+functionality of the operator - the forward pass and the backward
+pass. To facilitate the testing of these functionalities we use three
+helper functions available in the `mxnet.test_utils` module:
+ - `check_symbolic_forward`
+ - `check_symbolic_backward`
+ - `check_numeric_gradient`
+
+```python
+def test_quadratic_function():
+    def f(x, a, b, c):
+        return a * x**2 + b * x + c
+
+    a = np.random.random_sample()
+    b = np.random.random_sample()
+    c = np.random.random_sample()
+    data = mx.symbol.Variable('data')
+    quad_sym = mx.sym.contrib.quadratic(data=data, a=a, b=b, c=c)
+    for dtype in [np.float16, np.float32, np.float64]:
+        for ndim in range(1, 6):
+            shape = rand_shape_nd(ndim, 5)
+            data_np = np.random.randn(*shape).astype(dtype)
+            expected = f(data_np, a, b, c)
+            backward_expected = 2 * a * data_np + b
+
+            # check imperative forward
+            output = mx.nd.contrib.quadratic(mx.nd.array(data_np), a=a, b=b, c=c)
+            assert_almost_equal(output.asnumpy(),expected,
+                                rtol=1e-2 if dtype is np.float16 else 1e-5,
+                                atol=1e-2 if dtype is np.float16 else 1e-5)
+            # check forward
+            check_symbolic_forward(quad_sym, [data_np], [expected],
+                                    rtol=1e-2 if dtype is np.float16 else 1e-5,
+                                    atol=1e-2 if dtype is np.float16 else 1e-5)
+            # check backward
+            check_symbolic_backward(quad_sym, [data_np], [np.ones(expected.shape)],
+                                        [backward_expected],
+                                        rtol=1e-2 if dtype is np.float16 else 1e-5,
+                                        atol=1e-2 if dtype is np.float16 else 1e-5)
+            # check backward using finite difference
+            check_numeric_gradient(quad_sym, [data_np], atol=0.001)
+```
+
+In the above test we create a `quadratic` symbol and feed it into the three
+utility functions. The `check_symbolic_forward` and `check_symbolic_backward`
+tests the computed values against the expected values that we pass
+as an argument to the function. The `check_numeric_gradient` utility function
+performs [gradient checking](http://ufldl.stanford.edu/tutorial/supervised/DebuggingGradientChecking/)
+to verify the implementation for the backward function of the operator.
+It will perform a perturbation on the input and calculate the response
+rate of the output using the
+[finite difference method](https://en.wikipedia.org/wiki/Finite_difference_method).
+Then it will compare the gradient from the backward pass with the values
+from the finite difference method. All three of these tests will be successful
+once the comparison satisfies user specified `rtol` and `atol` values. Here `rtol`
+and `atol` expand to relative tolerance and absolute tolerance respectively. They
+are used to specify how far the computed values can deviate from the expected values.
+They are defined as follows
+```
+abs(Expected_Value - Computed_Value) < RTOL * abs(Expected_Value) + ATOL
+```
+
+For example, if `rtol` is `1e-5` and `atol` is `1e-5` and the expected value is
+`1.5623145`, then the computed value should lie within the range of
+`(1.562288876855, 1.562340123145)` else the test will fail. Make sure you
+tune the `rtol` and `atol` values accordingly. Giving very low values for `rtol`
+and `atol` will likely make the test very flaky. It is recommended that you
+use the flakiness checker tool to check if the test you have written is flaky
+or not. You can run the flakiness checker tool for the above test with the
+following command -
+
+```bash
+python tools/flakiness_checker.py test_operator.test_quadratic_function
+```
+
+Please note that for `check_symbolic_forward` and `check_symbolic_backward` we pass
+both the operator symbols and expected results for comparison, for
+`check_numeric_gradient` we only pass the operator symbol, as the
+`check_numeric_gradient` computes the expected value using finite difference
+method. Which is why it is highly recommended to add `check_numeric_gradient`
+test for every operator with backward function implemented as it eliminates
+the possibility of passing incorrect expected results into `check_symbolic_backward`.
+
+
+## Summary
+In this tutorial, we practiced implementing the operator `quadratic` in MXNet backend
+and unit testing the implementation in frontend. More specifically, we added parameter
+struct for user-input parameters, walked through shape and type inference workflow,
+implemented forward and backward functions, and registered the operator
+using nnvm. Congratulations! You now know how to add operators.
+We welcome your contributions to MXNet.
+
+**Note**: Source code in the tutorial can be found in
+[quadratic_op-inl.h](https://github.com/apache/incubator-mxnet/blob/master/src/operator/contrib/quadratic_op-inl.h),
+[quadratic_op.cc](https://github.com/apache/incubator-mxnet/blob/master/src/operator/contrib/quadratic_op.cc),
+[quadratic_op.cu](https://github.com/apache/incubator-mxnet/blob/master/src/operator/contrib/quadratic_op.cu),
+and
+[test_operator.py](https://github.com/apache/incubator-mxnet/blob/master/tests/python/unittest/test_operator.py#L6514).
+
+## Additional Resources
+- [Use TensorInspector to Help Debug Operators](tensor_inspector_tutorial.md)
diff --git a/docs/static_site/src/pages/api/faq/caffe.md b/docs/static_site/src/pages/api/faq/caffe.md
new file mode 100644
index 000000000000..62b204e85dde
--- /dev/null
+++ b/docs/static_site/src/pages/api/faq/caffe.md
@@ -0,0 +1,208 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_category
+title:  Convert from Caffe to MXNet
+category: faq
+faq_c: Deployment Environments
+question: How to convert a Caffe model to MXNet?
+permalink: /api/faq/caffe
+---
+# How to | Convert from Caffe to MXNet
+
+Key topics covered include the following:
+
+- [Converting Caffe trained models to MXNet](#converting-caffe-trained-models-to-mxnet)
+- [Calling Caffe operators in MXNet](#calling-caffe-operators-in-mxnet)
+
+## Converting Caffe trained models to MXNet
+
+The converting tool is available at
+[tools/caffe_converter](https://github.com/dmlc/mxnet/tree/master/tools/caffe_converter). On
+the remaining of this section, we assume we are on the `tools/caffe_converter`
+directory.
+
+### How to build
+
+If Caffe's python package is installed, namely we can run `import caffe` in
+python, then we are ready to go.
+
+For example, we can used
+[AWS Deep Learning AMI](https://aws.amazon.com/marketplace/pp/B06VSPXKDX) with
+both Caffe and MXNet installed.
+
+Otherwise we can install the
+[Google protobuf](https://developers.google.com/protocol-buffers/?hl=en)
+compiler and its python binding. It is easier to install, but may be slower
+during running.
+
+1. Install the compiler:
+  - Linux: install `protobuf-compiler` e.g. `sudo apt-get install
+    protobuf-compiler` for Ubuntu and `sudo yum install protobuf-compiler` for
+     Redhat/Fedora.
+  - Windows: Download the win32 build of
+    [protobuf](https://github.com/google/protobuf/releases). Make sure to
+    download the version that corresponds to the version of the python binding
+    on the next step. Extract to any location then add that location to your
+    `PATH`
+  - Mac OS X: `brew install protobuf`
+
+2. Install the python binding by either `conda install -c conda-forge protobuf`
+   or `pip install protobuf`.
+
+3. Compile Caffe proto definition. Run `make` in Linux or Mac OS X, or
+   `make_win32.bat` in Windows
+
+### How to use
+
+There are three tools:
+
+- `convert_symbol.py` : convert Caffe model definition in protobuf into MXNet's
+  Symbol in JSON format.
+- `convert_model.py` : convert Caffe model parameters into MXNet's NDArray format
+- `convert_mean.py` : convert Caffe input mean file into MXNet's NDArray format
+
+In addition, there are two tools:
+- `convert_caffe_modelzoo.py` : download and convert models from Caffe model
+  zoo.
+- `test_converter.py` : test the converted models by checking the prediction
+  accuracy.
+
+## Calling Caffe operators in MXNet
+
+Besides converting Caffe models, MXNet supports calling most Caffe operators,
+including network layer, data layer, and loss function, directly. It is
+particularly useful if there are customized operators implemented in Caffe, then
+we do not need to re-implement them in MXNet.
+
+### How to install
+
+This feature requires Caffe. In particular, we need to re-compile Caffe before
+[PR #4527](https://github.com/BVLC/caffe/pull/4527) is merged into Caffe. There
+are the steps of how to rebuild Caffe:
+
+1. Download [Caffe](https://github.com/BVLC/caffe). E.g. `git clone
+   https://github.com/BVLC/caffe`
+2. Download the
+   [patch for the MXNet interface](https://github.com/BVLC/caffe/pull/4527.patch)
+   and apply to Caffe. E.g.
+   ```bash
+   cd caffe && wget https://github.com/BVLC/caffe/pull/4527.patch && git apply 4527.patch
+   ```
+3. Build and install Caffe by following the
+   [official guide](http://caffe.berkeleyvision.org/installation.html).
+
+Next we need to compile MXNet with Caffe supports
+
+1. Copy `make/config.mk` (for Linux) or `make/osx.mk`
+   (for Mac) into the MXNet root folder as `config.mk` if you have not done it yet
+2. Open the copied `config.mk` and uncomment these two lines
+   ```bash
+   CAFFE_PATH = $(HOME)/caffe
+   MXNET_PLUGINS += plugin/caffe/caffe.mk
+   ```
+   Modify `CAFFE_PATH` to your Caffe installation, if necessary.
+3. Then build with 8 threads `make clean && make -j8`.
+
+### How to use
+
+This Caffe plugin adds three components into MXNet:
+
+- `sym.CaffeOp` : Caffe neural network layer
+- `sym.CaffeLoss` : Caffe loss functions
+- `io.CaffeDataIter` : Caffe data layer
+
+#### Use `sym.CaffeOp`
+The following example shows the definition of a 10 classes multi-layer perceptron:
+
+```Python
+data = mx.sym.Variable('data')
+fc1  = mx.sym.CaffeOp(data_0=data, num_weight=2, name='fc1', prototxt="layer{type:\"InnerProduct\" inner_product_param{num_output: 128} }")
+act1 = mx.sym.CaffeOp(data_0=fc1, prototxt="layer{type:\"TanH\"}")
+fc2  = mx.sym.CaffeOp(data_0=act1, num_weight=2, name='fc2', prototxt="layer{type:\"InnerProduct\" inner_product_param{num_output: 64} }")
+act2 = mx.sym.CaffeOp(data_0=fc2, prototxt="layer{type:\"TanH\"}")
+fc3 = mx.sym.CaffeOp(data_0=act2, num_weight=2, name='fc3', prototxt="layer{type:\"InnerProduct\" inner_product_param{num_output: 10}}")
+mlp = mx.sym.SoftmaxOutput(data=fc3, name='softmax')
+```
+
+Let's break it down. First, `data = mx.sym.Variable('data')` defines a variable
+as a placeholder for input.  Then, it's fed through Caffe operators with `fc1 =
+mx.sym.CaffeOp(...)`. `CaffeOp` accepts several arguments:
+
+- The inputs to Caffe operators are named as `data_i` for *i=0, ..., num_data-1*
+- `num_data` is the number of inputs. In default it is 1, and therefore
+skipped in the above example.
+- `num_out` is the number of outputs. In default it is 1 and also skipped.
+- `num_weight` is the number of weights (`blobs_`).  Its default value is 0. We
+need to explicitly specify it for a non-zero value.
+- `prototxt` is the protobuf configuration string.
+
+#### Use `sym.CaffeLoss`
+
+Using Caffe loss is similar.
+We can replace the MXNet loss with Caffe loss.
+We can replace
+
+Replacing the last line of the above example with the following two lines we can
+call Caffe loss instead of MXNet loss.
+
+```Python
+label = mx.sym.Variable('softmax_label')
+mlp = mx.sym.CaffeLoss(data=fc3, label=label, grad_scale=1, name='softmax', prototxt="layer{type:\"SoftmaxWithLoss\"}")
+```
+
+Similar to `CaffeOp`, `CaffeLoss` has arguments `num_data` (2 in default) and
+`num_out` (1 in default). But there are two differences
+
+1. Inputs are `data` and `label`. And we need to explicitly create a variable
+   placeholder for label, which is implicitly done in MXNet loss.
+2. `grad_scale` is the weight of this loss.
+
+#### Use `io.CaffeDataIter`
+
+We can also wrap a Caffe data layer into MXNet's data iterator. Below is an
+example for creating a data iterator for MNIST
+
+```python
+train = mx.io.CaffeDataIter(
+    prototxt =
+    'layer { \
+        name: "mnist" \
+        type: "Data" \
+        top: "data" \
+        top: "label" \
+        include { \
+            phase: TEST \
+        } \
+        transform_param { \
+            scale: 0.00390625 \
+        } \
+        data_param { \
+            source: "caffe/examples/mnist/mnist_test_lmdb" \
+            batch_size: 100 \
+            backend: LMDB \
+        } \
+    }',
+    flat           = flat,
+    num_examples   = 60000,
+)
+```
+
+### Put it all together
+
+The complete example is available at
+[example/caffe](https://github.com/dmlc/mxnet/blob/master/example/caffe/)
diff --git a/docs/static_site/src/pages/api/faq/cloud.md b/docs/static_site/src/pages/api/faq/cloud.md
new file mode 100644
index 000000000000..1782b3fc33a7
--- /dev/null
+++ b/docs/static_site/src/pages/api/faq/cloud.md
@@ -0,0 +1,208 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_category
+title:  MXNet on the Cloud
+category: faq
+faq_c: Deployment Environments
+question: How to run MXNet on AWS?
+permalink: /api/faq/cloud
+---
+
+# MXNet on the Cloud
+
+Deep learning can require extremely powerful hardware, often for unpredictable durations of time.
+Moreover, _MXNet_ can benefit from both multiple GPUs and multiple machines.
+Accordingly, cloud computing, as offered by AWS and others,
+is especially well suited to training deep learning models.
+Using AWS, we can rapidly fire up multiple machines with multiple GPUs each at will
+and maintain the resources for precisely the amount of time needed.
+
+## Set Up an AWS GPU Cluster from Scratch
+
+In this document, we provide a step-by-step guide that will teach you
+how to set up an AWS cluster with _MXNet_. We show how to:
+
+- [Use Amazon S3 to host data](#use-amazon-s3-to-host-data)
+- [Set up an EC2 GPU instance with all dependencies installed](#set-up-an-ec2-gpu-instance)
+- [Build and run MXNet on a single computer](#build-and-run-mxnet-on-a-gpu-instance)
+- [Set up an EC2 GPU cluster for distributed training](#set-up-an-ec2-gpu-cluster-for-distributed-training)
+
+### Use Amazon S3 to Host Data
+
+Amazon S3 provides distributed data storage which proves especially convenient for hosting large datasets.
+To use S3, you need [AWS credentials](http://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSGettingStartedGuide/AWSCredentials.html),
+including an `ACCESS_KEY_ID` and a `SECRET_ACCESS_KEY`.
+
+To use _MXNet_ with S3, set the environment variables `AWS_ACCESS_KEY_ID` and
+`AWS_SECRET_ACCESS_KEY` by adding the following two lines in
+`~/.bashrc` (replacing the strings with the correct ones):
+
+```bash
+export AWS_ACCESS_KEY_ID=AKIAIOSFODNN7EXAMPLE
+export AWS_SECRET_ACCESS_KEY=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
+```
+
+There are several ways to upload data to S3. One simple way is to use
+[s3cmd](http://s3tools.org/s3cmd). For example:
+
+```bash
+wget http://data.mxnet.io/mxnet/data/mnist.zip
+unzip mnist.zip && s3cmd put t*-ubyte s3://dmlc/mnist/
+```
+
+### Use Pre-installed EC2 GPU Instance
+The [Deep Learning AMI](https://aws.amazon.com/marketplace/pp/B01M0AXXQB?qid=1475211685369&sr=0-1&ref_=srh_res_product_title) is an Amazon Linux image
+supported and maintained by Amazon Web Services for use on Amazon Elastic Compute Cloud (Amazon EC2).
+It contains [MXNet-v0.9.3 tag](https://github.com/dmlc/mxnet) and the necessary components to get going with deep learning,
+including Nvidia drivers, CUDA, cuDNN, Anaconda, Python2 and Python3.
+The AMI IDs are the following:
+
+* us-east-1: ami-e7c96af1
+* us-west-2: ami-dfb13ebf
+* eu-west-1: ami-6e5d6808
+
+Now you can launch _MXNet_ directly on an EC2 GPU instance.
+You can also use [Jupyter](http://jupyter.org) notebook on EC2 machine.
+Here is a [good tutorial](https://github.com/dmlc/mxnet-notebooks)
+on how to connect to a Jupyter notebook running on an EC2 instance.
+
+### Set Up an EC2 GPU Instance from Scratch
+
+_MXNet_ requires the following libraries:
+
+- C++ compiler with C++11 support, such as `gcc >= 4.8`
+- `CUDA` (`CUDNN` in optional) for GPU linear algebra
+- `BLAS` (cblas, open-blas, atblas, mkl, or others) for CPU linear algebra
+- `opencv` for image augmentations
+- `curl` and `openssl` for the ability to read/write to Amazon S3
+
+Installing `CUDA` on EC2 instances requires some effort. Caffe has a good
+[tutorial](https://github.com/BVLC/caffe/wiki/Install-Caffe-on-EC2-from-scratch-(Ubuntu,-CUDA-7,-cuDNN-3))
+on how to install CUDA 7.0 on Ubuntu 14.04.
+
+***Note:*** We tried CUDA 7.5 on Nov 7, 2015, but found it problematic.
+
+You can install the rest using the package manager. For example, on Ubuntu:
+
+```
+sudo apt-get update
+sudo apt-get install -y build-essential git libcurl4-openssl-dev libatlas-base-dev libopencv-dev python-numpy
+```
+
+The Amazon Machine Image (AMI) [ami-12fd8178](https://console.aws.amazon.com/ec2/v2/home?region=us-east-1#LaunchInstanceWizard:ami=ami-12fd8178) has the  packages listed above installed.
+
+
+### Build and Run MXNet on a GPU Instance
+
+The following commands build _MXNet_ with CUDA/CUDNN, Amazon S3, and distributed
+training.
+
+```bash
+git clone --recursive https://github.com/dmlc/mxnet
+cd mxnet; cp make/config.mk .
+echo "USE_CUDA=1" >>config.mk
+echo "USE_CUDA_PATH=/usr/local/cuda" >>config.mk
+echo "USE_CUDNN=1" >>config.mk
+echo "USE_BLAS=atlas" >> config.mk
+echo "USE_DIST_KVSTORE = 1" >>config.mk
+echo "USE_S3=1" >>config.mk
+make -j$(nproc)
+```
+
+To test whether everything is installed properly, we can try training a convolutional neural network (CNN) on the MNIST dataset using a GPU:
+
+```bash
+python example/image-classification/train_mnist.py
+```
+
+If you've placed the MNIST data on `s3://dmlc/mnist`, you can read the data stored on Amazon S3 directly with the following command:
+
+```bash
+sed -i.bak "s!data_dir = 'data'!data_dir = 's3://dmlc/mnist'!" example/image-classification/train_mnist.py
+```
+
+***Note:*** You can use `sudo ln /dev/null /dev/raw1394` to fix the opencv error `libdc1394 error: Failed to initialize libdc1394`.
+
+### Set Up an EC2 GPU Cluster for Distributed Training
+
+A cluster consists of multiple computers.
+You can use one computer with _MXNet_ installed as the root computer for submitting jobs,and then launch several
+slave computers to run the jobs. For example, launch multiple instances using an
+AMI, e.g.,
+[ami-12fd8178](https://console.aws.amazon.com/ec2/v2/home?region=us-east-1#LaunchInstanceWizard:ami=ami-12fd8178),
+with dependencies installed. There are two options:
+
+- Make all slaves' ports accessible (same for the root) by setting type: All TCP,
+   Source: Anywhere in Configure Security Group.
+
+- Use the same `pem` as the root computer to access all slave computers, and
+   then copy the `pem` file into the root computer's `~/.ssh/id_rsa`. If you do this, all slave computers can be accessed with SSH from the root.
+
+Now, run the CNN on multiple computers. Assume that we are on a working
+directory of the root computer, such as `~/train`, and MXNet is built as `~/mxnet`.
+
+1. Pack the _MXNet_ Python library into this working directory for easy
+  synchronization:
+
+  ```bash
+  cp -r ~/mxnet/python/mxnet .
+  cp ~/mxnet/lib/libmxnet.so mxnet/
+  ```
+
+  And then copy the training program:
+
+  ```bash
+  cp ~/mxnet/example/image-classification/*.py .
+  cp -r ~/mxnet/example/image-classification/common .
+  ```
+
+2. Prepare a host file with all slaves private IPs. For example, `cat hosts`:
+
+  ```bash
+  172.30.0.172
+  172.30.0.171
+  ```
+
+3. Assuming that there are two computers, train the CNN using two workers:
+
+  ```bash
+  ../../tools/launch.py -n 2 -H hosts --sync-dir /tmp/mxnet python train_mnist.py --kv-store dist_sync
+  ```
+
+***Note:*** Sometimes the jobs linger at the slave computers even though you've pressed `Ctrl-c`
+at the root node. To terminate them, use the following command:
+
+```bash
+cat hosts | xargs -I{} ssh -o StrictHostKeyChecking=no {} 'uname -a; pgrep python | xargs kill -9'
+```
+
+***Note:*** The preceding example is very simple to train and therefore isn't a good
+benchmark for distributed training. Consider using other [examples](https://github.com/dmlc/mxnet/tree/master/example/image-classification).
+
+### More Options
+#### Use Multiple Data Shards
+It is common to pack a dataset into multiple files, especially when working in a distributed environment.
+_MXNet_ supports direct loading from multiple data shards.
+Put all of the record files into a folder, and point the data path to the folder.
+
+#### Use YARN and SGE
+Although using SSH can be simple when you don't have a cluster scheduling framework,
+_MXNet_ is designed to be portable to various platforms.
+We provide scripts available in [tracker](https://github.com/dmlc/dmlc-core/tree/master/tracker)
+to allow running on other cluster frameworks, including Hadoop (YARN) and SGE.
+We welcome contributions from the community of examples of running _MXNet_ on your favorite distributed platform.
diff --git a/docs/static_site/src/pages/api/faq/distributed_training.md b/docs/static_site/src/pages/api/faq/distributed_training.md
new file mode 100644
index 000000000000..85e04d69cf30
--- /dev/null
+++ b/docs/static_site/src/pages/api/faq/distributed_training.md
@@ -0,0 +1,332 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_category
+title:  Distributed Training in MXNet
+category: faq
+faq_c: Deployment Environments
+question: How to do distributed training using MXNet on AWS?
+permalink: /api/faq/distributed_training
+---
+
+# Distributed Training in MXNet
+MXNet supports distributed training enabling us to leverage multiple machines for faster training.
+In this document, we describe how it works, how to launch a distributed training job and
+some environment variables which provide more control.
+
+## Types of Parallelism
+There are two ways in which we can distribute the workload of training a neural network across multiple devices (can be either GPU or CPU).
+The first way is *data parallelism*, which refers to the case where each device stores a complete copy of the model.
+Each device works with a different part of the dataset, and the devices collectively update a shared model.
+These devices can be located on a single machine or across multiple machines.
+In this document, we describe how to train a model with devices distributed across machines in a data parallel way.
+
+When models are so large that they don't fit into device memory, then a second way called *model parallelism* is useful.
+Here, different devices are assigned the task of learning different parts of the model.
+Currently, MXNet supports Model parallelism in a single machine only. Refer [Training with multiple GPUs using model parallelism](https://mxnet.incubator.apache.org/versions/master/faq/model_parallel_lstm.html) for more on this.
+
+## How Does Distributed Training Work?
+The following concepts are key to understanding distributed training in MXNet:
+### Types of Processes
+MXNet has three types of processes which communicate with each other to accomplish training of a model.
+- Worker: A worker node actually performs training on a batch of training samples.
+Before processing each batch, the workers pull weights from servers.
+The workers also send gradients to the servers after each batch.
+Depending on the workload for training a model, it might not be a good idea to run multiple worker processes on the same machine.
+- Server: There can be multiple servers which store the model's parameters, and communicate with workers.
+A server may or may not be co-located with the worker processes.
+- Scheduler: There is only one scheduler. The role of the scheduler is to set up the cluster. This includes waiting for messages that each node has come up and which port the node is listening on.
+The scheduler then lets all processes know about every other node in the cluster, so that they can communicate with each other.
+
+### KV Store
+MXNet provides a key-value store, which is a critical component used for multi-device training. The communication of parameters across devices on a single machine, as well as across multiple machines, is relayed through one or more servers with a key-value store for the parameters. Each value in this store is represented by a key and value, where each parameter array in the network is assigned a key, and value refers to the weights of that parameter array. Workers `push` gradients after processing a batch, and `pull` updated weights before processing a new batch.
+We can also pass in optimizers for the KVStore to use while updating each weight. Optimizers like Stochastic Gradient Descent define an update rule,
+essentially a mathematical formula to compute the new weight based on the old weight, gradient, and some parameters.
+
+If you are using a Gluon Trainer object or the Module API,
+it uses a kvstore object internally to aggregate gradients from multiple devices on the same machine as well as across different machines.
+
+Although the API remains the same whether or not multiple machines are being used,
+the notion of kvstore server exists only during distributed training.
+In this case, each `push` and `pull` involves communication with the kvstore servers. When there are multiple devices on a single machine, gradients from these devices are first aggregated on the machine and then sent to the servers.
+Note that we need to compile MXNet with the build flag `USE_DIST_KVSTORE=1` to use distributed training.
+
+The distributed mode of KVStore is enabled by calling `mxnet.kvstore.create` function
+with a string argument which contains the word `dist` as follows:
+> kv = mxnet.kvstore.create('dist_sync')
+
+Refer [KVStore API](https://mxnet.incubator.apache.org/versions/master/api/python/kvstore/kvstore.html) for more information about KVStore.
+
+### Distribution of Keys
+Each server doesn't necessarily store all the keys or parameter arrays.
+Parameters are distributed across different servers. The decision of which server stores a particular key is made at random.
+This distribution of keys across different servers is handled transparently by the KVStore.
+It ensures that when a key is pulled, that request is sent to the server which has the corresponding value.
+If the value of some key is very large, it may be sharded across different servers. This means that different servers hold different parts of the value.
+Again, this is handled transparently so that the worker does not have to do anything different.
+The threshold for this sharding can be controlled with the environment variable `MXNET_KVSTORE_BIGARRAY_BOUND`.
+See [environment variables](#environment-variables) for more details.
+
+### Split training data
+When running distributed training in data parallel mode, we want each machine to be working on different parts of the dataset.
+
+For data parallel training on a single worker,
+we can use `mxnet.gluon.utils.split_and_load` to split a batch of samples provided by the data iterator, and then load each part of the batch on the device which will process it.
+
+In the case of distributed training though, we would need to divide the dataset into `n` parts at the beginning, so that each worker gets a different part. Each worker can then use `split_and_load` to again divide that part of the dataset across different devices on a single machine.
+
+Typically, this split of data for each worker happens through the data iterator,
+on passing the number of parts and the index of parts to iterate over.
+Some iterators in MXNet that support this feature are [mxnet.io.MNISTIterator](https://mxnet.incubator.apache.org/versions/master/api/python/io/io.html#mxnet.io.MNISTIter) and [mxnet.io.ImageRecordIter](https://mxnet.incubator.apache.org/versions/master/api/python/io/io.html#mxnet.io.ImageRecordIter).
+If you are using a different iterator, you can look at how the above iterators implement this.
+We can use the kvstore object to get the number of workers (`kv.num_workers`) and rank of the current worker (`kv.rank`).
+These can be passed as arguments to the iterator.
+You can look at [example/gluon/image_classification.py](https://github.com/apache/incubator-mxnet/blob/master/example/gluon/image_classification.py)
+to see an example usage.
+
+### Updating weights
+KVStore server supports two modes, one which aggregates the gradients and updates the weights using those gradients, and second where the server only aggregates gradients. In the latter case, when a worker process pulls from kvstore, it gets the aggregated gradients. The worker then uses these gradients and applies the weights locally.
+
+When using Gluon there is an option to choose between these modes by passing `update_on_kvstore` variable when you create the [Trainer](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/gluon.html#mxnet.gluon.Trainer) object like this:
+
+```
+trainer = gluon.Trainer(net.collect_params(), optimizer='sgd',
+                        optimizer_params={'learning_rate': opt.lr,
+                                          'wd': opt.wd,
+                                          'momentum': opt.momentum,
+                                          'multi_precision': True},
+                        kvstore=kv,
+                        update_on_kvstore=True)
+```
+
+When using the symbolic interface, it performs the weight updates on the server without the user having to do anything special.
+
+### Different Modes of Distributed Training
+Distributed training itself is enabled when kvstore creation string contains the word `dist`.
+
+Different modes of distributed training can be enabled by using different types of kvstore.
+
+- `dist_sync`: In synchronous distributed training, all workers use the same synchronized set of model parameters at the start of every batch.
+This means that after each batch, the server waits to receive gradients from each worker before it updates the model parameters.
+This synchronization comes at a cost because the worker pulling parameters would have to wait till the server finishes this process.
+In this mode, if a worker crashes, then it halts the progress of all workers.
+
+- `dist_async`: In asynchronous distributed training, the server receives gradients from one worker and immediately updates its store, which it uses to respond to any future pulls.
+This means that a worker who finishes processing a batch can pull the current parameters from server and start the next batch,
+even if other workers haven't finished processing the earlier batch.
+This is faster than `dist_sync` because there is no cost of synchronization, but can take more epochs to converge.
+The update of weights is atomic, meaning no two updates happen on the same weight at the same time. However, the order  of updates is not guaranteed.
+In `async` mode, it is required to pass an optimizer because in the absence of an optimizer kvstore would replace the stored weights with received weights and this doesn't make sense for training in asynchronous mode. Hence, when using Gluon with `async` mode we need to set `update_on_kvstore` to `True`.
+
+- `dist_sync_device`: Same as `dist_sync` except that when there are multiple GPUs being used on each node,
+this mode aggregates gradients and updates weights on GPU while dist_sync does so on CPU memory.
+This is faster than `dist_sync` because it reduces expensive communication between GPU and CPU, but it increases memory usage on GPU.
+
+- `dist_async_device` : The analogue of `dist_sync_device` but in asynchronous mode.
+
+
+### Gradient Compression
+When communication is expensive, and the ratio of computation time to communication time is low, communication can become a bottleneck.
+In such cases, gradient compression can be used to reduce the cost of communication, thereby speeding up training.
+Refer [Gradient compression](https://mxnet.incubator.apache.org/versions/master/faq/gradient_compression.html) for more details.
+
+Note: For small models when the cost of computation is much lower than cost of communication,
+distributed training might actually be slower than training on a single machine because of the overhead of communication and synchronization.
+
+## How to Start Distributed Training?
+MXNet provides a script tools/launch.py to make it easy to launch a distributed training job. This supports various types of cluster resource managers like `ssh`, `mpirun`, `yarn` and `sge`.
+If you already have one of these clusters setup, you can skip the next section on setting up a cluster.
+If you want to use a type of cluster not mentioned above, skip ahead to Manually launching jobs section.
+
+### Setting up the Cluster
+An easy way to set up a cluster of EC2 instances for distributed deep learning is by using the [AWS CloudFormation template](https://github.com/awslabs/deeplearning-cfn).
+If you can not use the above, this section will help you manually set up a cluster of instances
+to enable you to use `ssh` for launching a distributed training job.
+Let us denote one machine as the `master` of the cluster through which we will launch and monitor the distributed training on all machines.
+
+If the machines in your cluster are a part of a cloud computing platform like AWS EC2, then your instances should be using key-based authentication already.
+Ensure that you create all instances using the same key, say `mxnet-key` and in the same security group.
+Next, we need to ensure that master has access to all other machines in the cluster through `ssh` by
+adding this key to [ssh-agent](https://en.wikipedia.org/wiki/Ssh-agent) and forwarding it to master when we log in. This will make `mxnet-key` the default key on master.
+
+```
+ssh-add .ssh/mxnet-key
+ssh -A user@MASTER_IP_ADDRESS
+```
+
+
+If your machines use passwords for authentication, see [here](https://help.ubuntu.com/community/SSH/OpenSSH/Keys) for instructions on setting up password-less authentication between machines.
+
+
+It is easier if all these machines have a shared file system so that they can access the training script. One way is to use [Amazon Elastic File System](https://aws.amazon.com/efs) to create your network file system.
+The options in the following command are the recommended options when mounting an AWS Elastic File System.
+
+```
+sudo mkdir efs && sudo mount -t nfs4 -o nfsvers=4.1,rsize=1048576,wsize=1048576,hard,timeo=600,retrans=2 NETWORK_FILE_SYSTEM_IP:/ efs
+```
+
+Tip: You might find it helpful to store large datasets on S3 for easy access from all machines in the cluster. Refer [Using data from S3 for training](https://mxnet.incubator.apache.org/versions/master/faq/s3_integration.html) for more information.
+
+### Using Launch.py
+MXNet provides a script [tools/launch.py](https://github.com/apache/incubator-mxnet/blob/master/tools/launch.py) to make it easy to launch distributed training on a cluster with `ssh`, `mpi`, `sge` or `yarn`.
+You can fetch this script by cloning the mxnet repository.
+
+```
+git clone --recursive https://github.com/apache/incubator-mxnet
+```
+
+#### Example
+Let us consider training a VGG11 model on the CIFAR10 dataset using [example/gluon/image_classification.py](https://github.com/apache/incubator-mxnet/blob/master/example/gluon/image_classification.py).
+```
+cd example/gluon/
+```
+On a single machine, we can run this script as follows:
+```
+python image_classification.py --dataset cifar10 --model vgg11 --epochs 1
+```
+
+For distributed training of this example, we would do the following:
+
+If the mxnet directory which contains the script `image_classification.py` is accessible to all machines in the cluster (for example if they are on a network file system), we can run:
+```
+../../tools/launch.py -n 3 -H hosts --launcher ssh python image_classification.py --dataset cifar10 --model vgg11 --epochs 1 --kvstore dist_sync
+```
+
+If the directory with the script is not accessible from the other machines in the cluster, then we can synchronize the current directory to all machines.
+```
+../../tools/launch.py -n 3 -H hosts --launcher ssh --sync-dst-dir /tmp/mxnet_job/ python image_classification.py --dataset cifar10 --model vgg11 --epochs 1 --kvstore dist_sync
+```
+
+> Tip: If you don't have a cluster ready and still want to try this out, pass the option `--launcher local` instead of `ssh`
+
+#### Options
+Here, launch.py is used to submit the distributed training job. It takes the following options:
+- `-n` denotes the number of worker nodes to be launched.
+- `-s` denotes the number of server nodes to be launched.
+If it is not specified, it is taken to be equal to the number of worker nodes.
+The script tries to cycle through the hosts file to launch the servers and workers.
+For example, if you have 5 hosts in the hosts file and you passed `n` as 3 (and nothing for `s`).
+The script will launch a total of 3 server processes,
+one each for the first three hosts and launch a total of 3 worker processes, one each for the fourth, fifth and first host.
+If the hosts file has exactly `n` number of worker nodes, it will launch a server process and a worker process on each of the `n` hosts.
+- `--launcher` denotes the mode of communication. The options are:
+    - `ssh` if machines can communicate through ssh without passwords. This is the default launcher mode.
+    - `mpi` if Open MPI is available
+    - `sge` for Sun Grid Engine
+    - `yarn` for Apache Yarn
+    - `local` for launching all processes on the same local machine. This can be used for debugging purposes.
+- `-H` requires the path of the hosts file
+  This file contains IPs of the machines in the cluster. These machines should be able to communicate with each other without using passwords.
+  This file is only applicable and required when the launcher mode is `ssh` or `mpi`.
+  An example of the contents of the hosts file would be:
+  ```
+  172.30.0.172
+  172.31.0.173
+  172.30.1.174
+  ```
+- `--sync-dst-dir` takes the path of a directory on all hosts to which the current working directory will be synchronized. This only supports `ssh` launcher mode.
+This is necessary when the working directory is not accessible to all machines in the cluster. Setting this option synchronizes the current directory using rsync before the job is launched.
+If you have not installed MXNet system-wide
+then you have to copy the folder `python/mxnet` and the file `lib/libmxnet.so` into the current directory before running `launch.py`.
+For example if you are in `example/gluon`, you can do this with `cp -r ../../python/mxnet ../../lib/libmxnet.so .`. This would work if your `lib` folder contains `libmxnet.so`, as would be the case when you use make. If you use CMake, this file would be in your `build` directory.
+
+- `python image_classification.py --dataset cifar10 --model vgg11 --epochs 1 --kvstore dist_sync`
+is the command for the training job on each machine. Note the use of `dist_sync` for the kvstore used in the script.
+
+#### Terminating Jobs
+If the training job crashes due to an error or if we try to terminate the launch script while training is running,
+jobs on all machines might not have terminated. In such a case, we would need to terminate them manually.
+If we are using `ssh` launcher, this can be done by running the following command where `hosts` is the path of the hostfile.
+```
+while read -u 10 host; do ssh -o "StrictHostKeyChecking no" $host "pkill -f python" ; done 10<hosts
+```
+
+### Manually Launching Jobs
+If for some reason, you do not want to use the script above to start distributed training, then this section will be helpful.
+MXNet uses environment variables to assign roles to different processes and to let different processes find the scheduler.
+The environment variables are required to be set correctly as follows for the training to start:
+- `DMLC_ROLE`: Specifies the role of the process. This can be `server`, `worker` or `scheduler`. Note that there should only be one `scheduler`.
+When `DMLC_ROLE` is set to `server` or `scheduler`, these processes start when mxnet is imported.
+- `DMLC_PS_ROOT_URI`: Specifies the IP of the scheduler
+- `DMLC_PS_ROOT_PORT`: Specifies the port that the scheduler listens to
+- `DMLC_NUM_SERVER`: Specifies how many server nodes are in the cluster
+- `DMLC_NUM_WORKER`: Specifies how many worker nodes are in the cluster
+
+Below is an example to start all jobs locally on Linux or Mac. Note that starting all jobs on the same machine is not a good idea.
+This is only to make the usage clear.
+
+```bash
+export COMMAND='python example/gluon/image_classification.py --dataset cifar10 --model vgg11 --epochs 1 --kvstore dist_sync'
+DMLC_ROLE=server DMLC_PS_ROOT_URI=127.0.0.1 DMLC_PS_ROOT_PORT=9092 DMLC_NUM_SERVER=2 DMLC_NUM_WORKER=2 $COMMAND &
+DMLC_ROLE=server DMLC_PS_ROOT_URI=127.0.0.1 DMLC_PS_ROOT_PORT=9092 DMLC_NUM_SERVER=2 DMLC_NUM_WORKER=2 $COMMAND &
+DMLC_ROLE=scheduler DMLC_PS_ROOT_URI=127.0.0.1 DMLC_PS_ROOT_PORT=9092 DMLC_NUM_SERVER=2 DMLC_NUM_WORKER=2 $COMMAND &
+DMLC_ROLE=worker DMLC_PS_ROOT_URI=127.0.0.1 DMLC_PS_ROOT_PORT=9092 DMLC_NUM_SERVER=2 DMLC_NUM_WORKER=2 $COMMAND &
+DMLC_ROLE=worker DMLC_PS_ROOT_URI=127.0.0.1 DMLC_PS_ROOT_PORT=9092 DMLC_NUM_SERVER=2 DMLC_NUM_WORKER=2 $COMMAND
+```
+
+For an in-depth discussion of how the scheduler sets up the cluster, you can go [here](https://blog.kovalevskyi.com/mxnet-distributed-training-explained-in-depth-part-1-b90c84bda725).
+
+## Environment Variables
+### For tuning performance
+ - `MXNET_KVSTORE_REDUCTION_NTHREADS`
+  Value type: Integer
+  Default value: 4
+  The number of CPU threads used for summing up big arrays on a single machine
+  This will also be used for `dist_sync` kvstore to sum up arrays from different contexts on a single machine.
+  This does not affect summing up of arrays from different machines on servers.
+  Summing up of arrays for `dist_sync_device` kvstore is also unaffected as that happens on GPUs.
+
+- `MXNET_KVSTORE_BIGARRAY_BOUND`
+  Value type: Integer
+  Default value: 1000000
+  The minimum size of a *big array*.
+  When the array size is bigger than this threshold, `MXNET_KVSTORE_REDUCTION_NTHREADS` threads are used for reduction.
+  This parameter is also used as a load balancer in kvstore.
+  It controls when to partition a single weight to all the servers.
+  If the size of a single weight matrix is less than this bound, then it is sent to a single randomly picked server; otherwise, it is partitioned to all the servers.
+
+- `MXNET_ENABLE_GPU_P2P` GPU Peer-to-Peer communication
+  Value type: 0(false) or 1(true)
+  Default value: 1
+  If true, MXNet tries to use GPU peer-to-peer communication, if available on your device. This is used only when kvstore has the type `device` in it.
+
+### Communication
+- `DMLC_INTERFACE` Using a particular network interface
+  Value type: Name of interface
+  Example: `eth0`
+  MXNet often chooses the first available network interface.
+  But for machines with multiple interfaces, we can specify which network interface to use for data communication using this environment variable.
+
+- `PS_VERBOSE` Logging communication
+  Value type: 1 or 2
+  Default value: (empty)
+    - `PS_VERBOSE=1` logs connection information like the IPs and ports of all nodes
+    - `PS_VERBOSE=2` logs all data communication information
+
+
+When the network is unreliable, messages being sent from one node to another might get lost.
+The training process can hang when a critical message is not successfully delivered.
+In such cases, an additional ACK can be sent for each message to track its delivery.
+This can be done by setting `PS_RESEND` and `PS_RESEND_TIMEOUT`
+- `PS_RESEND` Retransmission for unreliable network
+Value type: 0(false) or 1(true)
+Default value: 0
+Whether or not to enable retransmission of messages
+- `PS_RESEND_TIMEOUT` Timeout for ACK to be received
+Value type: Integer (in milliseconds)
+Default value: 1000
+If ACK is not received in `PS_RESEND_TIMEOUT` milliseconds, then the message will be resent.
diff --git a/docs/static_site/src/pages/api/faq/env_var.md b/docs/static_site/src/pages/api/faq/env_var.md
new file mode 100644
index 000000000000..2372a5478800
--- /dev/null
+++ b/docs/static_site/src/pages/api/faq/env_var.md
@@ -0,0 +1,354 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_category
+title:  Environment Variables
+category: faq
+faq_c: Deployment Environments
+question: What are MXNet environment variables?
+permalink: /api/faq/env_var
+---
+
+Environment Variables
+=====================
+MXNet has several settings that you can change with environment variables.
+Typically, you wouldn't need to change these settings, but they are listed here for reference.
+
+For example, you can set these environment variables in Linux or macOS as follows:
+```
+export MXNET_GPU_WORKER_NTHREADS=3
+```
+
+Or in powershell:
+```
+$env:MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
+```
+
+## Variables controlling the execution environment
+
+* MXNET_LIBRARY_PATH
+    Absolute path indicating where the mxnet dynamic library is to be located, this would be the absolute
+    path to `libmxnet.so` or `libmxnet.dll` depending on the platform. The logic for loading the
+    library is in `python/mxnet/libinfo.py`
+
+## Set the Number of Threads
+
+* MXNET_GPU_WORKER_NTHREADS
+  - Values: Int ```(default=2)```
+  - The maximum number of threads to use on each GPU. This parameter is used to parallelize the computation within a single GPU card.
+* MXNET_GPU_COPY_NTHREADS
+  - Values: Int ```(default=2)```
+  - The maximum number of concurrent threads that do the memory copy job on each GPU.
+* MXNET_CPU_WORKER_NTHREADS
+  - Values: Int ```(default=1)```
+  - The maximum number of scheduling threads on CPU. It specifies how many operators can be run in parallel. Note that most CPU operators are parallelized by OpenMP. To change the number of threads used by individual operators, please set `OMP_NUM_THREADS` instead.
+* MXNET_CPU_PRIORITY_NTHREADS
+  - Values: Int ```(default=4)```
+  - The number of threads given to prioritized CPU jobs.
+* MXNET_CPU_NNPACK_NTHREADS
+  - Values: Int ```(default=4)```
+  - The number of threads used for NNPACK. NNPACK package aims to provide high-performance implementations of some layers for multi-core CPUs. Checkout [NNPACK](http://mxnet.io/faq/nnpack.html) to know more about it.
+* MXNET_MP_WORKER_NTHREADS
+  - Values: Int ```(default=1)```
+  - The number of scheduling threads on CPU given to multiprocess workers. Enlarge this number allows more operators to run in parallel in individual workers but please consider reducing the overall `num_workers` to avoid thread contention (not available on Windows).
+* MXNET_MP_OPENCV_NUM_THREADS
+  - Values: Int ```(default=0)```
+  - The number of OpenCV execution threads given to multiprocess workers. OpenCV multithreading is disabled if `MXNET_MP_OPENCV_NUM_THREADS` < 1 (default). Enlarge this number may boost the performance of individual workers when executing underlying OpenCV functions but please consider reducing the overall `num_workers` to avoid thread contention (not available on Windows).
+
+## Memory Options
+
+* MXNET_EXEC_ENABLE_INPLACE
+  - Values: true or false ```(default=true)```
+  - Whether to enable in-place optimization in symbolic execution. Checkout [in-place optimization](http://mxnet.io/architecture/note_memory.html#in-place-operations) to know more about it.
+* NNVM_EXEC_MATCH_RANGE
+  - Values: Int ```(default=16)```
+  - The approximate matching scale in the symbolic execution memory allocator.
+  - Set this to 0 if you don't want to enable memory sharing between graph nodes(for debugging purposes).
+  - This variable has impact on the result of memory planning. So, MXNet sweep between [1, NNVM_EXEC_MATCH_RANGE], and selects the best value.
+* MXNET_EXEC_NUM_TEMP
+  - Values: Int ```(default=1)```
+  - The maximum number of temporary workspaces to allocate to each device. This controls space replicas and in turn reduces the memory usage.
+  - Setting this to a small number can save GPU memory. It will also likely decrease the level of parallelism, which is usually acceptable.
+  - MXNet internally uses graph coloring algorithm to [optimize memory consumption](http://mxnet.io/architecture/note_memory.html).
+  - This parameter is also used to get number of matching colors in graph and in turn how much parallelism one can get in each GPU. Color based match usually costs more memory but also enables more parallelism.
+* MXNET_GPU_MEM_POOL_RESERVE
+  - Values: Int ```(default=5)```
+  - The percentage of GPU memory to reserve for things other than the GPU array, such as kernel launch or cudnn handle space.
+  - If you see a strange out-of-memory error from the kernel launch, after multiple iterations, try setting this to a larger value.
+
+* MXNET_GPU_MEM_POOL_TYPE
+  - Values: String ```(default=Naive)```
+  - The type of memory pool.
+  - Choices:
+    - Naive: A simple memory pool that allocates memory for the exact requested size and cache memory buffers. If a buffered memory chunk matches the size of a new request, the chunk from the memory pool will be returned and reused.
+    - Round: A memory pool that always rounds the requested memory size and allocates memory of the rounded size. MXNET_GPU_MEM_POOL_ROUND_LINEAR_CUTOFF defines how to round up a memory size. Caching and allocating buffered memory works in the same way as the naive memory pool.
+    - Unpooled: No memory pool is used.
+
+* MXNET_GPU_MEM_POOL_ROUND_LINEAR_CUTOFF
+  - Values: Int ```(default=24)```
+  - The cutoff threshold that decides the rounding strategy. Let's denote the threshold as T. If the memory size is smaller than `2 ** T` (by default, it's 2 ** 24 = 16MB), it rounds to the smallest `2 ** n` that is larger than the requested memory size; if the memory size is larger than `2 ** T`, it rounds to the next k * 2 ** T.
+
+* MXNET_GPU_MEM_LARGE_ALLOC_ROUND_SIZE
+  - Values: Int ```(default=2097152)```
+  - When using the naive pool type, memory allocations larger than this threshhold are rounded up to a multiple of this value.
+  - The default was chosen to minimize global memory fragmentation within the GPU driver.  Set this to 1 to disable.
+
+## Engine Type
+
+* MXNET_ENGINE_TYPE
+  - Values: String ```(default=ThreadedEnginePerDevice)```
+  - The type of underlying execution engine of MXNet.
+  - Choices:
+    - NaiveEngine: A very simple engine that uses the master thread to do the computation synchronously. Setting this engine disables multi-threading. You can use this type for debugging in case of any error. Backtrace will give you the series of calls that lead to the error. Remember to set MXNET_ENGINE_TYPE back to empty after debugging.
+    - ThreadedEngine: A threaded engine that uses a global thread pool to schedule jobs.
+    - ThreadedEnginePerDevice: A threaded engine that allocates thread per GPU and executes jobs asynchronously.
+
+## Execution Options
+
+* MXNET_EXEC_BULK_EXEC_INFERENCE
+  - Values: 0(false) or 1(true) ```(default=1)```
+  - If set to `1`, during inference MXNet executes the entire computation graph in bulk mode, which reduces kernel launch gaps in between symbolic operators.
+* MXNET_EXEC_BULK_EXEC_TRAIN
+  - Values: 0(false) or 1(true) ```(default=1)```
+  - If set to `1`, during training MXNet executes the computation graph as several subgraphs in bulk mode.
+* MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN
+  - Values: Int ```(default=15)```
+  - The maximum number of nodes in the subgraph executed in bulk during training (not inference). Setting this to a larger number may reduce the degree of parallelism for multi-GPU training.
+* MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN_FWD
+  - Values: Int ```(default=<value of MXNET_EXEC_BULK_MAX_NODE_TRAIN>)```
+  - The maximum number of nodes in the subgraph executed in bulk during training (not inference) in the forward pass.
+* MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN_BWD
+  - Values: Int ```(default=<value of MXNET_EXEC_BULK_MAX_NODE_TRAIN>)```
+  - The maximum number of nodes in the subgraph executed in bulk during training (not inference) in the backward pass.
+
+## Control the Data Communication
+
+* MXNET_KVSTORE_REDUCTION_NTHREADS
+  - Values: Int ```(default=4)```
+  - The number of CPU threads used for summing up big arrays on a single machine
+  - This will also be used for `dist_sync` kvstore to sum up arrays from different contexts on a single machine.
+  - This does not affect summing up of arrays from different machines on servers.
+  - Summing up of arrays for `dist_sync_device` kvstore is also unaffected as that happens on GPUs.
+
+* MXNET_KVSTORE_BIGARRAY_BOUND
+  - Values: Int ```(default=1000000)```
+  - The minimum size of a "big array".
+  - When the array size is bigger than this threshold, MXNET_KVSTORE_REDUCTION_NTHREADS threads are used for reduction.
+  - This parameter is also used as a load balancer in kvstore. It controls when to partition a single weight to all the servers. If the size of a single weight is less than MXNET_KVSTORE_BIGARRAY_BOUND then, it is sent to a single randomly picked server otherwise it is partitioned to all the servers.
+
+* MXNET_KVSTORE_USETREE
+  - Values: 0(false) or 1(true) ```(default=0)```
+  - If true, MXNet tries to use tree reduction for Push and Pull communication.
+  - Otherwise, MXNet uses the default Push and Pull implementation.
+  - Tree reduction technology has been shown to be faster than the standard ```--kv-store device``` Push/Pull and ```--kv-store nccl``` Push/Pull for small batch sizes.
+
+* MXNET_KVSTORE_LOGTREE
+  - Values: 0(false) or 1(true) ```(default=0)```
+  - If true and MXNET_KVSTORE_USETREE is set to 1, MXNet will log the reduction trees that have been generated.
+
+* MXNET_KVSTORE_TREE_ARRAY_BOUND
+  - Values: Int ```(default=10000000)```
+  - The minimum size of a "big array".
+  - When the array size is bigger than this threshold and MXNET_KVSTORE_USETREE is set to 1, multiple trees are used to load balance the big gradient being communicated in order to better saturate link bandwidth.
+  - Note: This environmental variable only takes effect if Tree KVStore is being used (MXNET_KVSTORE_USETREE=1).
+
+* MXNET_KVSTORE_TREE_BACKTRACK
+  - Values: 0(false) or 1(true) ```(default=0)
+  - If true and MXNET_KVSTORE_USETREE is set to 1, MXNet tries to use backtracking to generate the trees required for tree reduction.
+  - If false and MXNET_KVSTORE_USETREE is set to 1, MXNet tries to use Kernighan-Lin heuristic to generate the trees required for tree reduction.
+
+* MXNET_KVSTORE_TREE_LINK_USAGE_PENALTY
+  - Values: Float ```(default=0.7)```
+  - The multiplicative penalty term to a link being used once.
+
+* MXNET_ENABLE_GPU_P2P
+  - Values: 0(false) or 1(true) ```(default=1)```
+  - If true, MXNet tries to use GPU peer-to-peer communication, if available on your device,
+    when kvstore's type is `device`.
+
+* MXNET_UPDATE_ON_KVSTORE
+  - Values: 0(false) or 1(true) ```(default=1)```
+  - If true, weight updates are performed during the communication step, if possible.
+
+## Memonger
+
+* MXNET_BACKWARD_DO_MIRROR
+  - Values: 0(false) or 1(true) ```(default=0)```
+  - MXNet uses mirroring concept to save memory. Normally backward pass needs some forward input and it is stored in memory but you can choose to release this saved input and recalculate it in backward pass when needed. This basically trades off the computation for memory consumption.
+  - This parameter decides whether to do `mirror` during training for saving device memory.
+  - When set to `1`, during forward propagation, graph executor will `mirror` some layer's feature map and drop others, but it will re-compute this dropped feature maps when needed.
+  - `MXNET_BACKWARD_DO_MIRROR=1` will save 30%~50% of device memory, but retains about 95% of running speed.
+  - One extension of `mirror` in MXNet is called [memonger technology](https://arxiv.org/abs/1604.06174), it will only use O(sqrt(N)) memory at 75% running speed. Checkout the code [here](https://github.com/dmlc/mxnet-memonger).
+
+## Control the profiler
+
+The following environments can be used to profile the application without changing code. Execution options may affect the granularity of profiling result. If you need profiling result of every operator, please set `MXNET_EXEC_BULK_EXEC_INFERENCE`, `MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN` and `MXNET_EXEC_BULK_EXEC_TRAIN` to 0.
+
+* MXNET_PROFILER_AUTOSTART
+  - Values: 0(false) or 1(true) ```(default=0)```
+	- Set to 1, MXNet starts the profiler automatically. The profiling result is stored into profile.json in the working directory.
+
+* MXNET_PROFILER_MODE
+  - Values: 0(false) or 1(true) ```(default=0)```
+	- If set to '0', profiler records the events of the symbolic operators.
+	- If set to '1', profiler records the events of all operators.
+
+## Interface between Python and the C API
+
+* MXNET_ENABLE_CYTHON
+  - Values: 0(false), 1(true) ```(default=1)```
+  - If set to 0, MXNet uses the ctypes to interface with the C API.
+  - If set to 1, MXNet tries to use the cython modules for the ndarray and symbol. If it fails, the ctypes is used or an error occurs depending on MXNET_ENFORCE_CYTHON.
+
+* MXNET_ENFORCE_CYTHON
+  - Values: 0(false) or 1(true) ```(default=0)```
+  - This has an effect only if MXNET_ENABLE_CYTHON is 1.
+  - If set to 0, MXNet fallbacks to the ctypes if importing the cython modules fails.
+  - If set to 1, MXNet raises an error if importing the cython modules fails.
+
+If cython modules are used, `mx.nd._internal.NDArrayBase` must be `mxnet._cy3.ndarray.NDArrayBase` for python 3 or `mxnet._cy2.ndarray.NDArrayBase` for python 2.
+If ctypes is used, it must be `mxnet._ctypes.ndarray.NDArrayBase`.
+
+## Other Environment Variables
+
+* MXNET_GPU_WORKER_NSTREAMS
+  - Values: 1, or 2 ```(default=1)```
+  - Determines the number of GPU streams available to operators for their functions.
+  - Setting this to 2 may yield a modest performance increase, since ops like the cuDNN convolution op can then calculate their data- and weight-gradients in parallel.
+  - Setting this to 2 may also increase a model's demand for GPU global memory.
+
+* MXNET_CUDNN_AUTOTUNE_DEFAULT
+  - Values: 0, 1, or 2 ```(default=1)```
+  - The default value of cudnn auto tuning for convolution layers.
+  - Value of 0 means there is no auto tuning to pick the convolution algo
+  - Performance tests are run to pick the convolution algo when value is 1 or 2
+  - Value of 1 chooses the best algo in a limited workspace
+  - Value of 2 chooses the fastest algo whose memory requirements may be larger than the default workspace threshold
+
+* MXNET_CUDA_ALLOW_TENSOR_CORE
+  - 0(false) or 1(true) ```(default=1)```
+	- If set to '0', disallows Tensor Core use in CUDA ops.
+	- If set to '1', allows Tensor Core use in CUDA ops.
+  - This variable can only be set once in a session.
+
+* MXNET_CUDA_TENSOR_OP_MATH_ALLOW_CONVERSION
+  - 0(false) or 1(true) ```(default=0)```
+	- If set to '0', disallows implicit type conversions to Float16 to use Tensor Cores
+	- If set to '1', allows CUDA ops like RNN and Convolution to use TensorCores even with Float32 input data by using implicit type casting to Float16. Only has an effect if `MXNET_CUDA_ALLOW_TENSOR_CORE` is `1`.
+
+* MXNET_CUDA_LIB_CHECKING
+  - 0(false) or 1(true) ```(default=1)```
+  - If set to '0', disallows various runtime checks of the cuda library version and associated warning messages.
+  - If set to '1', permits these checks (e.g. compile vs. link mismatch, old version no longer CI-tested)
+
+* MXNET_CUDNN_LIB_CHECKING
+  - 0(false) or 1(true) ```(default=1)```
+  - If set to '0', disallows various runtime checks of the cuDNN library version and associated warning messages.
+  - If set to '1', permits these checks (e.g. compile vs. link mismatch, old version no longer CI-tested)
+
+* MXNET_GLUON_REPO
+  - Values: String ```(default='https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/'```
+  - The repository url to be used for Gluon datasets and pre-trained models.
+
+* MXNET_HOME
+  - Data directory in the filesystem for storage, for example when downloading gluon models.
+  - Default in *nix is .mxnet APPDATA/mxnet in windows.
+
+* MXNET_MKLDNN_ENABLED
+  - Values: 0, 1 ```(default=1)```
+  - Flag to enable or disable MKLDNN accelerator. On by default.
+  - Only applies to mxnet that has been compiled with MKLDNN (```pip install mxnet-mkl``` or built from source with ```USE_MKLDNN=1```)
+
+* MXNET_MKLDNN_CACHE_NUM
+  - Values: Int ```(default=-1)```
+  - Flag to set num of elements that MKLDNN cache can hold. Default is -1 which means cache size is unbounded. Should only be set if your model has variable input shapes, as cache size may grow unbounded. The number represents the number of items in the cache and is proportional to the number of layers that use MKLDNN and different input shape.
+
+* MXNET_ENFORCE_DETERMINISM
+  - Values: 0(false) or 1(true) ```(default=0)```
+  - If set to true, MXNet will only use deterministic algorithms in forward and backward computation.
+  If no such algorithm exists given other constraints, MXNet will error out. This variable affects the choice
+  of CUDNN convolution algorithms. Please see [CUDNN developer guide](https://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html) for more details.
+
+* MXNET_CPU_PARALLEL_COPY_SIZE
+  - Values: Int ```(default=200000)```
+  - The minimum size to call parallel copy by OpenMP in CPU2CPU mode.
+  - When the array size is bigger than or equal to  this threshold, NDArray::Copy(from, to) is implemented by OpenMP with the Recommended OMP Thread Count.
+  - When the array size is less than this threshold, NDArray::Copy(from , to)) is implemented by memcpy in single thread.
+
+* MXNET_OPTIMIZER_AGGREGATION_SIZE
+  - Values: Int ```(default=4)```
+  - Maximum value is 60.
+  - This variable controls how many weights will be updated in a single call to optimizer (for optimizers that support aggregation, currently limited to SGD).
+
+* MXNET_CPU_TEMP_COPY
+  - Values: Int ```(default=4)```
+  - This variable controls how many temporary memory resources to create for all CPU context for use in operator.
+
+* MXNET_GPU_TEMP_COPY
+  - Values: Int ```(default=1)```
+  - This variable controls how many temporary memory resources to create for each GPU context for use in operator.
+
+* MXNET_CPU_PARALLEL_RAND_COPY
+  - Values: Int ```(default=1)```
+  - This variable controls how many parallel random number generator resources to create for all CPU context for use in operator.
+
+* MXNET_GPU_PARALLEL_RAND_COPY
+  - Values: Int ```(default=4)```
+  - This variable controls how many parallel random number generator resources to create for each GPU context for use in operator.
+
+* MXNET_GPU_CUDNN_DROPOUT_STATE_COPY
+  - Values: Int ```(default=4)```
+  - This variable controls how many CuDNN dropout state resources to create for each GPU context for use in operator.
+
+* MXNET_SUBGRAPH_BACKEND
+  - Values: String ```(default="MKLDNN")``` if MKLDNN is avaliable, otherwise ```(default="")```
+  - This variable controls the subgraph partitioning in MXNet.
+  - This variable is used to perform MKL-DNN FP32 operator fusion and quantization. Please refer to the [MKL-DNN operator list](../tutorials/mkldnn/operator_list.md) for how this variable is used and the list of fusion passes.
+  - Set ```MXNET_SUBGRAPH_BACKEND=NONE``` to disable subgraph backend.
+
+* MXNET_SAFE_ACCUMULATION
+  - Values: Values: 0(false) or 1(true) ```(default=0)```
+  - If this variable is set, the accumulation will enter the safe mode, meaning accumulation is done in a data type of higher precision than
+    the input data type, leading to more accurate accumulation results with a possible performance loss and backward compatibility loss.
+    For example, when the variable is set to 1(true), if the input data type is float16, then the accumulation will be done
+    with float32.
+  - Model accuracies do not necessarily improve with this environment variable turned on.
+
+Settings for Minimum Memory Usage
+---------------------------------
+- Make sure ```min(MXNET_EXEC_NUM_TEMP, MXNET_GPU_WORKER_NTHREADS) = 1```
+  - The default setting satisfies this.
+
+Settings for More GPU Parallelism
+---------------------------------
+- Set ```MXNET_GPU_WORKER_NTHREADS``` to a larger number (e.g., 2)
+  - To reduce memory usage, consider setting ```MXNET_EXEC_NUM_TEMP```.
+  - This might not speed things up, especially for image applications, because GPU is usually fully utilized even with serialized jobs.
+
+Settings for controlling OMP tuning
+---------------------------------
+- Set ```MXNET_USE_OPERATOR_TUNING=0``` to disable Operator tuning code which decides whether to use OMP or not for operator
+   - Values: String representation of MXNET_ENABLE_OPERATOR_TUNING environment variable
+   -            0=disable all
+   -            1=enable all
+   -            float32, float16, float32=list of types to enable, and disable those not listed
+   - refer : https://github.com/apache/incubator-mxnet/blob/master/src/operator/operator_tune-inl.h#L444
+
+- Set ```MXNET_USE_NUM_CORES_OPERATOR_TUNING``` to define num_cores to be used by operator tuning code.
+  - This reduces operator tuning overhead when there are multiple instances of mxnet running in the system and we know that
+    each mxnet will take only partial num_cores available with system.
+  - refer: https://github.com/apache/incubator-mxnet/pull/13602
diff --git a/docs/static_site/src/pages/api/faq/float16.md b/docs/static_site/src/pages/api/faq/float16.md
new file mode 100644
index 000000000000..d6b6210940eb
--- /dev/null
+++ b/docs/static_site/src/pages/api/faq/float16.md
@@ -0,0 +1,256 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_category
+title:  Float16
+category: faq
+faq_c: Speed
+question: How do I use mixed precision (float16) with MXNet or Gluon?
+permalink: /api/faq/floa16
+---
+
+# Mixed precision training using float16
+
+In this tutorial we will walk through how one can train deep learning neural networks with mixed precision on supported hardware. We will first see how to use float16 (both with Gluon and Symbolic APIs) and then some techniques on achieving good performance and accuracy.
+
+## Background
+
+The computational resources required for training deep neural networks have been lately increasing because of growing complexity and model size. Mixed precision training allows us to reduce the utilization of the resources by using lower precision arithmetic which is computationally less expensive and less costly in terms of space utilization. In this approach you can train using 16 bit floating point (half precision) while using 32 bit floating point (single precision) for output buffers of float16 computation. This allows one to achieve the same accuracy as training with single precision, while decreasing the required memory and training or inference time.
+
+The float16 data type is a 16 bit floating point representation according to the [IEEE 754 standard](https://ieeexplore.ieee.org/document/4610935). It has a dynamic range where the precision can go from 0.0000000596046 (highest, for values closest to 0) to 32 (lowest, for values in the range 32768-65536). Despite the inherent reduced precision when compared to single precision float (float32), using float16 has many advantages. The most obvious advantages are that you can reduce the size of the model by half allowing the training of larger models and using larger batch sizes. The reduced memory footprint also helps in reducing the pressure on memory bandwidth and lowering communication costs. On hardware with specialized support for float16 computation you can also greatly improve the speed of training and inference. The Volta range of Graphics Processing Units (GPUs) from Nvidia have [Tensor Cores](https://www.nvidia.com/en-us/data-center/tensorcore/) which perform efficient float16 computation. A tensor core allows accumulation of half precision products into single or half precision outputs. For the rest of this tutorial we assume that we are working with Nvidia's Tensor Cores on a Volta GPU.
+
+## Prerequisites
+
+- [Volta](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/) range of Nvidia GPUs (e.g. AWS P3 instance)
+- CUDA 9 or higher
+- cuDNN v7 or higher
+
+This tutorial also assumes understanding of how to train a network with float32 (the default). Please refer to [logistic regression tutorial](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/logistic_regression_explained.html) to get started with Apache MXNet and Gluon API. This tutorial focuses on the changes needed to switch from float32 to mixed precision and tips on achieving the best performance with mixed precision.
+
+## Using the Gluon API
+
+### Training or Inference
+
+With Gluon API, you need to take care of three things to convert a model to support computation with float16.
+
+1. Cast Gluon `Block`'s parameters and expected input type to float16 by calling the [cast](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.Block.cast) method of the `Block` representing the network.
+
+```python
+net.cast('float16')
+```
+
+2. Ensure the data input to the network is of float16 type. If your `DataLoader` or `Iterator` produces output in another datatype, then you would have to cast your data. There are different ways you can do this. The easiest would be to use the [astype](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.astype) method of NDArrays.
+
+```python
+data = data.astype('float16', copy=False)
+```
+
+If you are using images and DataLoader, you can also use a [Cast transform](https://mxnet.incubator.apache.org/api/python/gluon/data.html#mxnet.gluon.data.vision.transforms.Cast).
+
+3. It is preferable to use **multi_precision mode of optimizer** when training in float16. This mode of optimizer maintains a master copy of the weights in float32 even when the training (i.e. forward and backward pass) is in float16. This helps increase precision of the weight updates and can lead to faster convergence in some scenarios.
+
+```python
+optimizer = mx.optimizer.create('sgd', multi_precision=True, lr=0.01)
+```
+
+You can play around with mixed precision using the image classification [example](https://github.com/apache/incubator-mxnet/blob/master/example/gluon/image_classification.py). We suggest using the Caltech101 dataset option in that example and using a ResNet50V1 network so you can quickly see the performance improvement and how the accuracy is unaffected. Here's the starter command to run this example.
+
+```bash
+python image_classification.py --model resnet50_v1 --dataset caltech101 --gpus 0 --num-worker 30 --dtype float16
+```
+
+### Fine-tuning
+
+You can also fine-tune a model, which was originally trained in float32, to use float16. Below is an example of how to fine-tune a pretrained model from the Model Zoo. You would first need to fetch the pretrained network and then cast that network to float16.
+
+```python
+import numpy as np
+import mxnet as mx
+from mxnet.gluon.model_zoo.vision import get_model
+
+
+pretrained_net = get_model(name='resnet50_v2', ctx=mx.cpu(),
+                           pretrained=True, classes=1000)
+pretrained_net.cast('float16')
+```
+
+Then, if you have another Resnet50V2 model you want to fine-tune, you can just assign the features to that network and then cast it.
+
+```python
+net = get_model(name='resnet50_v2', ctx=mx.cpu(),
+                pretrained=False, classes=101)
+net.collect_params().initialize(mx.init.Xavier(magnitude=2.24), ctx=mx.cpu())
+net.features = pretrained_net.features
+net.cast('float16')
+```
+
+You can check the parameters of the model by calling [summary](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.Block.summary) with some fake data. Notice the provided `dtype=np.float16` in the line below. As it was mentioned earlier, we have to provide data as float16 as well.
+
+```python
+net.summary(mx.nd.uniform(shape=(1, 3, 224, 224), dtype=np.float16))
+```
+
+## Using the Symbolic API
+
+Training a network in float16 with the Symbolic API involves the following steps.
+
+1. Add a layer at the beginning of the network, to cast the data to float16. This will ensure that all the following layers compute in float16.
+2. It is advisable to cast the output of the layers before softmax to float32, so that the softmax computation is done in float32. This is because softmax involves large reductions and it helps to keep that in float32 for more precise answer.
+3. It is advisable to use the multi-precision mode of the optimizer for more precise weight updates. Here's how you would enable this mode when creating an optimizer.
+
+```python
+optimizer = mx.optimizer.create('sgd', multi_precision=True, lr=0.01)
+```
+
+For a full example, please refer to [resnet.py](https://github.com/apache/incubator-mxnet/blob/master/example/image-classification/symbols/resnet.py) file on GitHub. A small, relevant excerpt from that file is presented below.
+
+```python
+data = mx.sym.Variable(name="data")
+
+if dtype == 'float16':
+    data = mx.sym.Cast(data=data, dtype=np.float16)
+
+# ... the rest of the network
+net_out = net(data)
+
+if dtype == 'float16':
+    net_out = mx.sym.Cast(data=net_out, dtype=np.float32)
+
+output = mx.sym.SoftmaxOutput(data=net_out, name='softmax')
+```
+
+If you would like to train ResNet50 model on ImageNet using float16 precision, you can find the full script [here](https://github.com/apache/incubator-mxnet/tree/master/example/image-classificatiIfon/train_imagenet.py)
+
+If you don't have ImageNet dataset at your disposal, you can still run the script above using synthetic float16 data by providing the following command:
+
+```bash
+python train_imagenet.py --network resnet-v1 --num-layers 50 --benchmark 1 --gpus 0 --batch-size 256 --dtype float16
+```
+
+There's a similar example for float16 fine tuning [here](https://github.com/apache/incubator-mxnet/tree/master/example/image-classification/fine-tune.py) of selected models: Inception v3, Inception v4, ResNetV1, ResNet50, ResNext or VGG. The command below shows how to use that script to fine-tune a Resnet50 model trained on Imagenet for the Caltech 256 dataset using float16.
+
+```bash
+python fine-tune.py --network resnet --num-layers 50 --pretrained-model imagenet1k-resnet-50 --data-train ~/.mxnet/dataset/caltech-256/caltech256-train.rec --data-val ~/data/caltech-256/caltech256-val.rec --num-examples 15420 --num-classes 256 --gpus 0 --batch-size 64 --dtype float16
+```
+
+If you don't have the `Caltech256` dataset, you can download it using the script below, and convert it into .rec file format using [im2rec utility file](https://github.com/apache/incubator-mxnet/blob/master/tools/im2rec.py)
+
+```python
+import os
+from os.path import expanduser
+import tarfile
+import mxnet as mx
+
+
+data_folder = expanduser("~/.mxnet/datasets/")
+dataset_name = "256_ObjectCategories"
+archive_file = "{}.tar".format(dataset_name)
+archive_path = os.path.join(data_folder, archive_file)
+data_url = "http://www.vision.caltech.edu/Image_Datasets/Caltech256/"
+
+if not os.path.isfile(archive_path):
+    mx.test_utils.download("{}{}".format(data_url, archive_file),
+                           dirname=data_folder)
+    print('Extracting {} in {}...'.format(archive_file, data_folder))
+    tar = tarfile.open(archive_path)
+    tar.extractall(data_folder)
+    tar.close()
+    print('Data extracted.')
+```
+
+## Example training results
+
+Let us consider training a Resnet50V1 model on the ImageNet 2012 dataset. For this model, the GPU memory usage is close to the capacity of V100 GPU with a batch size of 128 when using float32. Using float16 allows the use of 256 batch size. Shared below are results using 8 V100 GPUs on a an [AWS p3.16xlarge](https://aws.amazon.com/ec2/instance-types/p3/#Amazon_EC2_P3_Instance_Product_Details) instance.
+
+Let us compare the three scenarios that arise here: float32 with 1024 batch size, float16 with 1024 batch size and float16 with 2048 batch size. These jobs trained for 90 epochs using a learning rate of 0.4 for 1024 batch size and 0.8 for 2048 batch size. This learning rate was decayed by a factor of 0.1 at the 30th, 60th and 80th epochs. The only changes made for the float16 jobs when compared to the float32 job were that the network and data were cast to float16, and the multi-precision mode was used for optimizer. The final accuracy at 90th epoch and the time to train are tabulated below for these three scenarios. The top-1 validation errors at the end of each epoch are also plotted below.
+
+Batch size | Data type | Top 1 Validation accuracy | Time to train | Speedup |
+--- | --- | --- | --- | --- |
+1024 | float32 | 76.18% | 11.8 hrs | 1 |
+1024 | float16 | 76.34% | 7.3 hrs | 1.62x |
+2048 | float16 | 76.29% | 6.5 hrs | 1.82x |
+
+![Training curves of Resnet50V1 on Imagenet 2012](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tutorials/mixed-precision/resnet50v1b_imagenet_fp16_fp32_training.png)
+
+The difference in accuracies above are within normal random variation, and there is no reason to expect float16 to have better accuracy than float32 in general. As the plot indicates, training behaves similarly for these cases, even though we didn't have to change any other hyperparameters. We can also see from the table that using float16 helps train faster through faster computation with float16 as well as allowing the use of larger batch sizes.
+
+## Things to keep in mind
+
+### For performance
+
+Typical performance gains seen for float16 typically range 1.6x-2x for convolutional networks like Resnet and even about 3x for networks with LSTMs. The performance gain you see can depend on certain things which this section will introduce.
+
+1. Nvidia Tensor Cores essentially perform the computation `D = A * B + C`, where A and B are half precision matrices, while C and D could be either half precision or full precision. The tensor cores are most efficient when dimensions of these matrices are multiples of 8. This means that Tensor Cores can not be used in all cases for fast float16 computation. When training models like Resnet50 on the Cifar10 dataset, the tensors involved are sometimes smaller, and Tensor Cores can not always be used. The computation in that case falls back to slower algorithms and using float16 turns out to be slower than float32 on a single GPU. Note that when using multiple GPUs, using float16 can still be faster than float32 because of reduction in communication costs.
+
+2. When you scale up the batch size ensure that IO and data pre-processing is not your bottleneck. If you see a slowdown this would be the first thing to check.
+
+3. It is advisable to use batch sizes that are multiples of 8 because of the above reason when training with float16. As always, batch sizes which are powers of 2 would be best when compared to those around it.
+
+4. You can check whether your program is using Tensor cores for fast float16 computation by profiling with `nvprof`. The operations with `s884cudnn` in their names represent the use of Tensor cores.
+
+5. When not limited by GPU memory, it can help to set the environment variable `MXNET_CUDNN_AUTOTUNE_DEFAULT` to `2`. This configures MXNet to run tuning tests and choose the fastest convolution algorithm whose memory requirements may exceed the default memory of CUDA workspace.
+
+6. Please note that float16 on CPU might not be supported for all operators, as in most cases float16 on CPU is much slower than float32.
+
+### For accuracy
+
+#### Multi precision mode
+
+When training in float16, it is advisable to still store the master copy of the weights in float32 for better accuracy. The higher precision of float32 helps overcome cases where gradient update can become 0 if represented in float16. This mode can be activated by setting the parameter `multi_precision` of optimizer params to `True` as in the above example. It has been found that this is not required for all networks to achieve the same accuracy as with float32, but nevertheless recommended. Note that for distributed training, this is currently slightly slower than without `multi_precision`, but still much faster than using float32 for training.
+
+#### Large reductions
+
+Since float16 has low precision for large numbers, it is best to leave layers which perform large reductions in float32. This includes BatchNorm and Softmax. Ensuring that Batchnorm performs reduction in float32 is handled by default in both Gluon and Module APIs. While Softmax is set to use float32 even during float16 training in Gluon, in the Module API it needs to be a cast to float32 before softmax as the above symbolic example code shows.
+
+#### Loss scaling
+
+For some networks just switching the training to float16 mode was not found to be enough to reach the same accuracy as when training with float32. This is because the activation gradients computed are too small and could not be represented in float16 representable range. Such networks can be made to achieve the accuracy reached by float32 with a couple of changes.
+
+Most of the float16 representable range is not used by activation gradients generally. So you can shift the gradients into float16 range by scaling up the loss by a factor `S`. By the chain rule, this scales up the loss before backward pass, and then you can scale back the gradients before updating the weights. This ensures that training in float16 can use the same hyperparameters as used during float32 training.
+
+Here's how you can configure the loss to be scaled up by 128 and rescale the gradient down before updating the weights.
+
+*Gluon API*
+
+```python
+loss = gluon.loss.SoftmaxCrossEntropyLoss(weight=128)
+optimizer = mx.optimizer.create('sgd',
+                                multi_precision=True,
+                                rescale_grad=1.0/128)
+```
+
+*Module API*
+
+```python
+mxnet.sym.SoftmaxOutput(other_args, grad_scale=128.0)
+optimizer = mx.optimizer.create('sgd',
+                                multi_precision=True,
+                                rescale_grad=1.0/128)
+```
+
+Networks like Multibox SSD, R-CNN, bigLSTM and Seq2seq were found to exhibit such behavior.
+You can choose a constant scaling factor while ensuring that the absolute value of gradient when multiplied by this factor remains in the range of float16. Generally powers of 2 like 64, 128, 256, 512 are chosen. Refer to the linked articles below for more details on this.
+
+## References
+
+1. [Training with Mixed Precision User Guide](http://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html)
+2. [Mixed Precision Training at ICLR 2018](https://arxiv.org/pdf/1710.03740.pdf)
+3. [Mixed-Precision Training of Deep Neural Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/)
+
+## Recommended Next Steps
+
+* Check out our video tutorial on [Using Mixed Precision with MXNet](https://www.youtube.com/watch?v=pR4KMh1lGC0)
diff --git a/docs/static_site/src/pages/api/faq/gradient_compression.md b/docs/static_site/src/pages/api/faq/gradient_compression.md
new file mode 100644
index 000000000000..74d35537508c
--- /dev/null
+++ b/docs/static_site/src/pages/api/faq/gradient_compression.md
@@ -0,0 +1,132 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_category
+title:  Gradient Compression
+category: Speed
+faq_c: Speed
+question: How do I use gradient compression with distributed training?
+permalink: /api/faq/gradient_compression
+---
+
+# Gradient Compression
+
+Gradient Compression reduces communication bandwidth, and in some scenarios, it can make training more scalable and efficient without significant loss in convergence rate or accuracy. Example implementations with GPUs, CPUs, and distributed training are provided in this document.
+
+
+## Benefits
+
+**Increased Speed**
+
+For architectures with fully connected layers, the gradient compression capability is observed to speedup training by about 2x, depending on the size of the model and the network bandwidth of the instance. Bigger models see larger speedup with gradient compression.
+
+**Minimal Accuracy Loss**
+
+Gradient compression uses the approach of delaying the synchronization of weight updates which are small. Although small weight updates might not be sent for that batch, this information is not discarded. Once the weight updates for this location accumulate to become a larger value, they will be propagated. Since there is no information loss, but only delayed updates, it does not lead to a significant loss in accuracy or convergence rate. In distributed training experiments[1], the accuracy loss observed due to gradient compression was as low as 1%
+
+
+## When to Use Gradient Compression
+
+When training models whose architectures include large fully connected components, it can be helpful to use gradient compression. For larger models, as well as recurrent neural networks, the communication cost becomes a major factor. Such models stand to benefit greatly with gradient compression.
+
+
+### GPU versus CPU
+
+The greatest benefits from gradient compression are realized when using multi-node (single or multi-GPU) distributed training. Training on CPU would provide a lower compute density per compute node as compared to the massive compute density per compute node on a GPU. Due to this, the required communication bandwidth for CPU-based nodes during training is not as high as for GPU-based nodes. Hence, the benefits of gradient compression are lower for CPU-based nodes as compared to GPU-based nodes.
+
+
+### Network Latency
+
+Benefits of gradient compression can be found when using distributed training with network connected nodes. Depending on the network latency between nodes and the model's size, these can contribute to slow performance such that gradient compression may provide speed improvements.
+
+You may not want to use gradient compression if you have low latency network communication.
+
+
+### Model Size
+
+Distributed training involves synchronization of weights after each batch. Larger models have much higher communication costs during training, hence such models stand to benefit much more from gradient compression.
+When running distributed training with gradient compression, the quantize and dequantize operations happen on CPU parallelized with OpenMP. For smaller models, when training on GPUs, it helps to set `OMP_NUM_THREADS=1` on each node, so that the overhead of launching OMP threads doesn't cause the compression and decompression to be slow.
+
+### Model Architecture
+
+The communication bandwidth requirements during training vary across various neural network architectures and hence the benefits of gradient compression vary accordingly.
+
+In networks which have significant fully connected components, since such layers have low compute cost on GPUs, communication becomes a bottleneck limiting the speed of distributed training. Gradient compression can help reduce the communication cost, and thus speed up training in such cases. We have observed speedup of about 2x on large fully connected neural networks. Models like AlexNet and VGG have large fully connected components as part of the network, hence stand to benefit from gradient compression. As with these models, Long Short-Term Memory architectures require more communication bandwidth, so they also exhibit speed improvements with gradient compression.
+
+Architectures like Convolutional Neural Networks on the other hand have a higher compute cost, in which case some communication can be parallelized with computation. Since communication is not the bottleneck in such networks, gradient compression doesn't help much.
+
+
+### Single Node Gradient Compression
+
+When the training is configured to use device to device communication on a single node with multiple GPUs, gradient compression can be used to reduce the cost of communication. This can provide about 20% speedup for large models using older generation architectures. However, speed benefits may be negligible on a machine with a newer generation architecture where GPUs can communicate at low latency.
+
+
+## Approach
+
+The idea behind gradient compression comes from two observations:
+
+First, when training large neural networks, the gradients of weights computed for a small mini-batch of training data are typically sparse. Only a small fraction of the weights have significant updates after each mini-batch. The synchronization of updates that are near zero can be safely delayed longer than the typical mini-batch size. This essentially means that the rate of weight-update can vary depending on the value of an individual weight.
+
+Secondly, gradients can be compressed significantly by considering only those gradient elements whose absolute values exceed a threshold, and then quantizing them to use lower bits per gradient value. By compressing the gradients, we can reduce communication bandwidth. The delayed gradient values, in the form of quantization error and values that don't meet the threshold, are aggregated into a gradient residual which is communicated when it reaches the threshold.
+
+## Technical Implementation
+
+### Two Bit Quantization
+
+Currently the supported type of quantization uses two bits for each gradient value. Any positive value greater than or equal to the threshold sets two bits as `11`, any negative value whose absolute value is greater or equal to the threshold sets two bits as `10`, and others are set to `00`. This enables us to store 16 quantized gradients as one float. The error in quantization, which is `original_value - quantized_value` is stored in the form of a gradient residual.
+
+### Types of Kvstore
+
+Supported types of `kvstore` are `device` and all distributed kvstores such as `dist_sync`, `dist_async`, and `dist_sync_device`. When `kvstore` is `device`, the communication between GPUs is compressed. Please note that this increases the memory usage of GPUs because of the additional residual stored. When using a distributed kvstore, worker-to-server communication is compressed. In this case, compression and decompression happen on the CPU, and gradient residuals will be stored on the CPU. Server-to-worker communication and device-to-device communication are not compressed to avoid multiple levels of compression.
+
+## Enabling the Gradient Compression in MXNet
+
+Gradient compression is a run-time configuration parameter to be enabled during training. Here are the MXNet APIs to enable gradient compression:
+
+**Gluon API**:
+
+```python
+trainer = gluon.Trainer(..., compression_params={'type’:'2bit', 'threshold':0.5})
+```
+A reference `gluon` implementation with a gradient compression option can be found in the [train.py script from a word-level language modeling RNN example](https://github.com/apache/incubator-mxnet/blob/master/example/gluon/word_language_model/train.py).
+
+**Module API**:
+
+```python
+mod = mx.mod.Module(..., compression_params={'type’:'2bit', 'threshold':0.5})
+```
+
+A `module` example is provided with [this guide for setting up MXNet with distributed training](https://mxnet.incubator.apache.org/versions/master/faq/multi_devices.html#distributed-training-with-multiple-machines). It comes with the option of turning on gradient compression as an argument to the [train_mnist.py script](https://github.com/apache/incubator-mxnet/blob/master/example/image-classification/train_mnist.py).
+
+### Configuration Details
+
+**Threshold**
+
+A default `threshold` value of `0.5` is good for most use cases, but to get the most benefit from gradient compression for a particular scenario, it can be beneficial to experiment. If the threshold is set to a very large value, say `10.0`, then the updates become too infrequent and the training will converge slower. Setting the threshold automatically is expected in a future release.
+
+**Quantization**
+
+This release supports 2-bit quantization for encoding of gradients to reduce the communication bandwidth during training. Future releases will support 1-bit quantization and other approaches for encoding of gradients based on experimental evidence of benefits and user demand.
+
+**Sparse Format**
+
+We believe that the density of data will need to be really low (i.e. around > 90% zeros) to reap benefits of the sparse format. However, this is an area of experimentation that will be explored in a future release.
+
+
+## References
+
+1. [Nikko Storm, Amazon.com, Scalable Distributed Training using commodity GPU cloud computing.](https://s3-us-west-2.amazonaws.com/amazon.jobs-public-documents/strom_interspeech2015.pdf)
diff --git a/docs/static_site/src/pages/api/faq/model_parallel_lstm.md b/docs/static_site/src/pages/api/faq/model_parallel_lstm.md
new file mode 100644
index 000000000000..37a617042ae4
--- /dev/null
+++ b/docs/static_site/src/pages/api/faq/model_parallel_lstm.md
@@ -0,0 +1,90 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_category
+title:  Model Parallel
+category: faq
+faq_c: Model
+question: How can I train using multiple GPUs with model parallelism?
+permalink: /api/faq/model_parallel_lstm
+---
+
+
+# Training with Multiple GPUs Using Model Parallelism
+Training deep learning models can be resource intensive.
+Even with a powerful GPU, some models can take days or weeks to train.
+Large long short-term memory (LSTM) recurrent neural networks
+can be especially slow to train,
+with each layer, at each time step, requiring eight matrix multiplications.
+Fortunately, given cloud services like AWS,
+machine learning practitioners often  have access
+to multiple machines and multiple GPUs.
+One key strength of _MXNet_ is its ability to leverage
+powerful heterogeneous hardware environments to achieve significant speedups.
+
+There are two primary ways that we can spread a workload across multiple devices.
+In a previous document, [we addressed data parallelism](./multi_devices.md),
+an approach in which samples within a batch are divided among the available devices.
+With data parallelism, each device stores a complete copy of the model.
+Here, we explore _model parallelism_, a different approach.
+Instead of splitting the batch among the devices, we partition the model itself.
+Most commonly, we achieve model parallelism by assigning the parameters (and computation)
+of different layers of the network to different devices.
+
+In particular, we will focus on LSTM recurrent networks.
+LSTMS are powerful sequence models, that have proven especially useful
+for [natural language translation](https://arxiv.org/pdf/1409.0473.pdf), [speech recognition](https://arxiv.org/abs/1512.02595),
+and working with [time series data](https://arxiv.org/abs/1511.03677).
+For a general high-level introduction to LSTMs,
+see the excellent [tutorial](http://colah.github.io/posts/2015-08-Understanding-LSTMs/) by Christopher Olah.
+
+
+## Model Parallelism: Using Multiple GPUs As a Pipeline
+Model parallelism in deep learning was first proposed
+for the _extraordinarily large_ convolutional layer in GoogleNet.
+From this implementation, we take the idea of placing each layer on a separate GPU.
+Using model parallelism in such a layer-wise fashion
+provides the benefit that no GPU has to maintain all of the model parameters in memory.
+
+<img width="517" alt="screen shot 2016-05-06 at 10 13 16 pm" src="https://cloud.githubusercontent.com/assets/5545640/15089697/d6f4fca0-13d7-11e6-9331-7f94fcc7b4c6.png">
+
+In the preceding figure, each LSTM layer is assigned to a different GPU.
+After GPU 1 finishes computing layer 1 for the first sentence, it passes its output to GPU 2.
+At the same time, GPU 1 fetches the next sentence and starts training.
+This differs significantly from data parallelism.
+Here, there is no contention to update the shared model at the end of each iteration,
+and most of the communication happens when passing intermediate results between GPUs.
+
+
+## Workload Partitioning
+
+Implementing model parallelism requires knowledge of the training task.
+Here are some general heuristics that we find useful:
+
+- To minimize communication time, place neighboring layers on the same GPUs.
+- Be careful to balance the workload between GPUs.
+- Remember that different kinds of layers have different computation-memory properties.
+
+<img width="449" alt="screen shot 2016-05-07 at 1 51 02 am" src="https://cloud.githubusercontent.com/assets/5545640/15090455/37a30ab0-13f6-11e6-863b-efe2b10ec2e6.png">
+
+Let's take a quick look at the two pipelines in the preceding diagram.
+They both have eight layers with a decoder and an encoder layer.
+Based on our first principle, it's unwise to place all neighboring layers on separate GPUs.
+We also want to balance the workload across GPUs.
+Although the LSTM layers consume less memory than the decoder/encoder layers, they consume more computation time because of the dependency of the unrolled LSTM.
+Thus, the partition on the left will be faster than the one on the right
+because the workload is more evenly distributed.
diff --git a/docs/static_site/src/pages/api/faq/new_op.md b/docs/static_site/src/pages/api/faq/new_op.md
new file mode 100644
index 000000000000..360e7050efb5
--- /dev/null
+++ b/docs/static_site/src/pages/api/faq/new_op.md
@@ -0,0 +1,400 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_category
+title:  Create New Operators
+category: faq
+faq_c: Extend and Contribute to MXNet
+question: How do I create new operators in MXNet with Python?
+permalink: /api/faq/new_op
+---
+
+# How to Create New Operators (Layers)
+
+This tutorials walks you through the process of creating new MXNet operators (or layers).
+We've done our best to provide high-speed operators for most common use cases.
+However, if you're engaged in research,
+there's a good chance you'll want to define custom layers,
+like a novel loss function. In these cases, you have two options:
+
+* Use CustomOp to write new operators using a front-end language (e.g., Python) that run on CPUs or GPUs.
+Depending on your implementation, this can range from very fast (if you only use operators under mx.nd) to very slow (if you copy out the data, using `.asnumpy()`).
+
+* Use C++/mshadow (CUDA). This provides the best performance, but can be difficult
+if you're not familiar with MXNet, mshadow, or Cuda.
+
+## CustomOp
+Implementing an operator in Python is simple.
+As an example, let's create a softmax operator.
+Start by subclassing `mxnet.operator.CustomOp`,
+and then override a few methods:
+
+```python
+import os
+import mxnet as mx
+import numpy as np
+
+class Softmax(mx.operator.CustomOp):
+    def forward(self, is_train, req, in_data, out_data, aux):
+        x = in_data[0].asnumpy()
+        y = np.exp(x - x.max(axis=1).reshape((x.shape[0], 1)))
+        y /= y.sum(axis=1).reshape((x.shape[0], 1))
+        self.assign(out_data[0], req[0], mx.nd.array(y))
+```
+
+We defined the computation for the forward pass of our operator.
+The forward function takes a list of input and a list of output NDArrays.
+For convenience, we called `.asnumpy()` on the first NDArray in input
+and convert it to a CPU-based NumPy array.
+This can be very slow. If you want the best performance,
+keep data in the NDArray format and use operators under mx.nd to do the computation.
+
+At the end, we used CustomOp.assign to assign the resulting array y to out_data[0]. It handles assignment based on the value of req, which can be 'write', 'add', or 'null'.
+
+Then do the same for the backward pass:
+
+```python
+def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
+    l = in_data[1].asnumpy().ravel().astype(np.int)
+    y = out_data[0].asnumpy()
+    y[np.arange(l.shape[0]), l] -= 1.0
+    self.assign(in_grad[0], req[0], mx.nd.array(y))
+```
+
+Softmax defines the computation of our custom operator,
+but you still need to define its input/output format
+by subclassing mx.operator.CustomOpProp.
+First, register the new operator with the name 'softmax':
+
+```python
+@mx.operator.register("softmax")
+class SoftmaxProp(mx.operator.CustomOpProp):
+```
+
+Then, call the base constructor with `need_top_grad=False`
+because softmax is a loss layer and you don't need gradient input from preceding layers:
+
+```python
+def __init__(self):
+    super(SoftmaxProp, self).__init__(need_top_grad=False)
+```
+
+Then declare the input and output:
+
+```python
+def list_arguments(self):
+    return ['data', 'label']
+
+def list_outputs(self):
+    return ['output']
+```
+
+Note that list_arguments declares both input and parameter.
+We recommend ordering them as follows:  `['input1', 'input2', ... , 'weight1', 'weight2', ...]`
+
+Next, provide `infer_shape` to declare the shape of the output/weight
+and check the consistency of the input shapes:
+
+```python
+def infer_shape(self, in_shape):
+    data_shape = in_shape[0]
+    label_shape = (in_shape[0][0],)
+    output_shape = in_shape[0]
+    return [data_shape, label_shape], [output_shape], []
+```
+The first axis of an input/output tensor corresponds to different examples within the batch.
+The label is a set of integers, one for each data entry,
+and the output has the same shape as the input.
+The `infer_shape` function should always return three lists in this order:
+inputs, outputs, and auxiliary states (which we don't have here),
+even if one of them is empty.
+
+Optionally, you can also define `infer_type` to declare the input and output data type of your operator. Supported types are `np.float32`, `np.float64`, `np.float16`, `np.uint8`, and `np.int32`.
+
+```python
+def infer_type(self, in_type):
+    dtype = in_type[0]
+    return [dtype, dtype], [dtype], []
+```
+
+Finally, define a create_operator function that will be called by the back end to create an instance of softmax:
+
+```python
+def create_operator(self, ctx, shapes, dtypes):
+    return Softmax()
+```
+
+To use the custom operator, create a mx.sym.Custom symbol with op_type as the registered name:
+
+```python
+mlp = mx.symbol.Custom(data=fc3, name='softmax', op_type='softmax')
+```
+
+Please see the full code for this example [here](https://github.com/dmlc/mxnet/blob/master/example/numpy-ops/custom_softmax.py).
+
+## C++
+With MXNet v0.9 (the NNVM refactor) or later, creating new operators has become easier.
+Operators are now registered with NNVM.
+The following code is an example on how to register an operator (checkout [src/operator/tensor](https://github.com/dmlc/mxnet/tree/master/src/operator/tensor) for more examples):
+
+```c++
+NNVM_REGISTER_OP(abs)
+.MXNET_DESCRIBE("Take absolute value of the src")
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<nnvm::FInferShape>("FInferShape", ElemwiseShape<1,1>);
+```
+
+The syntax is quite simple, we register the operator with a name,
+then set number of inputs and outputs.
+You can register attributes with any key (`FInferShape` for example) to any operator,
+without having to modify a central class interface definition.
+
+### Operator Attribute System
+
+One of the biggest improvements brought by NNVM is the operator attribute system.
+This is like traits for types in common languages like C++.
+We can register any attribute to any operator, with the syntax
+
+``` c++
+NNVM_REGISTER_OP(op-name)
+.set_attr<AttributeType>("AttributeKey", CorrespondingAttributeObject);
+```
+
+These attributes can be retrieved later for various purposes.
+For example, `FInferShape` is used for shape inference, `FCompute<cpu>` is used for carrying out actual computation on CPU.
+
+As long as all attributes registered with the same key have the same type,
+we can register any attributes to operators.
+The more attribute an operator provides,
+the more information the system can use for optimization.
+
+### List of basic attributes
+
+In this section, we will go through the basic attributes MXNet expect for all operators.
+You can find the definition for them in the following two files:
+
+- [nnvm/op_attr_types.h](https://github.com/dmlc/nnvm/blob/master/include/nnvm/op_attr_types.h)
+- [mxnet/op_attr_types.h](https://github.com/dmlc/mxnet/blob/master/include/mxnet/op_attr_types.h)
+
+#### Descriptions (Optional)
+
+`.describe(comment)` adds a comment to the operator. Use `.MXNET_DESCRIBE(comment)` to add the current file name and line number to comment.
+
+#### Attribute Parser (Optional)
+
+Set attribute parser with `.set_attr_parser(PARSER)` where PARSER is a function with prototype `void(nnvm::NodeAttr* attrs)`. This function should parse the key-word arguments in `attrs->dict` and store the result in `attrs->parsed`.
+
+Simple arguments can be parsed like
+```c++
+NNVM_REGISTER_OP(scalar_op)
+.set_attr_parser(
+  [](NodeAttrs* attrs) {
+    attrs->parsed = std::stod(attrs->dict["scalar"]);
+  })
+```
+
+The parsed arguments can then be accessed in other attribute functions with
+```c++
+double alpha = nnvm::get<double>(attrs.parsed);
+```
+
+More complex ops can use `dmlc::Parameters` and `ParamParser` (defined in operator_common.h) for parsing:
+
+```c++
+#include <dmlc/parameter.h>
+#include <operator_common.h>
+struct ActivationParam : public dmlc::Parameter<ActivationParam> {
+  // use int for enumeration
+  int act_type;
+  DMLC_DECLARE_PARAMETER(ActivationParam) {
+    DMLC_DECLARE_FIELD(act_type)
+    .add_enum("relu", activation::kReLU)
+    .add_enum("sigmoid", activation::kSigmoid)
+    .add_enum("tanh", activation::kTanh)
+    .add_enum("softrelu", activation::kSoftReLU)
+    .describe("Activation function to be applied.");
+  }
+};
+NNVM_REGISTER_OP(Activation)
+.set_attr_parser(ParamParser<ActivationParam>);
+// access with:
+// const ActivationParam& param = nnvm::get<ActivationParam>(attrs.parsed);
+```
+
+#### Inputs & Outputs
+
+Number of inputs/outputs can be set with `.set_num_inputs(n_in)` and `.set_num_outputs(n_out)`
+where n_in and n_out are integers.
+
+Alternatively, if the number of inputs/outputs is variable and depends on arguments,
+you can set `n_in`/`n_out` to functions with prototype `uint32_t(const nnvm::NodeAttrs& attrs)`
+that return the number of inputs/outputs based on parsed arguments.
+
+Outputs can be made invisible to other operators by registering `FNumVisibleOutputs`
+and returning an integer smaller than `n_out`.
+
+Inputs/outputs can be named by registering `FListInputNames` and `FListOutputNames` with prototype `std::vector<std::string>(const NodeAttrs& attrs)`.
+
+
+#### Argument Descriptions
+
+Set argument descriptions with `.add_argument(name, type, comment)`.
+This is necessary for operators to be properly called imperatively.
+
+First, add NDArray arguments `num_inputs` times with type "NDArray"
+or one time with type "NDArray[]" for ops with variable length inputs.
+
+Then add key-word arguments with proper type (float, string, etc).
+Operators that parse key-word arguments with `dmlc::Parameter`
+can add argument descriptions in bulk with `.add_arguments(ActivationParam::__FIELDS__())`
+(NDArray arguments still need to be manually added with type "NDArray").
+
+#### FInferShape or TIsBackward (for Backward Only Ops)
+
+Normally operators need to have `FInferShape` with prototype `bool(const nnvm::NodeAttrs& attrs, mxnet::ShapeVector *in_attrs, mxnet::ShapeVector *out_attrs)`. `FInferShape` fills unknown shapes (`shape.ndim() == 0`) in in_attrs/out_attrs based on known shapes in in_attrs/out_attrs. Use `ElemwiseShape<n_in, n_out>` for simple operators with uniform shapes.
+
+Operators that are only used for a backward pass can instead register `.set_attr<nnvm::TIsBackward>("TIsBackward", true)`
+and their shapes with be copied from the corresponding forward operators.
+
+#### FInferType
+
+Similar to `FInferShape`, `FInferType` fills unknown types (-1) based on known types. Use `ElemwiseType<n_in, n_out>` for simple operators with uniform types. Operators that registered `TIsBackward` don't need to register this.
+
+
+#### FInplaceOption (Optional)
+
+`FInplaceOption` with prototype `std::vector<std::pair<int, int> >(const NodeAttrs& attrs)`
+specifies which input/output pairs can be computed in-place
+and share memory with each other.
+Each pair (i, j) in the returned list means
+that the i-th input can share memory with the j-th output.
+
+
+#### FGradient (Optional for imperative use, required for symbolic use)
+
+If an operator has gradient, it can be described with `FGradient` with prototype
+
+```c++
+std::vector<nnvm::NodeEntry>(const nnvm::NodePtr& n,
+                             const std::vector<nnvm::NodeEntry>& ograds)
+```
+
+Use utility functions `ElemwiseGradUseIn{op_name}`, `ElemwiseGradUseOut{op_name}`, `ElemwiseGradUseNone{op_name}`  for ops that need corresponding forward op's input,
+output or nothing to calculating gradient.
+
+For more complicated patterns, use `MakeGradNode(op_name, n, heads, dict)` to create gradient entries,
+where heads are input entries to the backward op, composed from ograds and n->inputs.
+
+When assembling a return vector of `std::vector<nnvm::NodeEntry> ret;` a common pattern would be to
+either create nodes in place as in:
+
+```c++
+ret.emplace_back(MakeNode("zeros_like", n->attrs.name + "_xyz_backward",
+    {n->inputs[1]}, nullptr, &n))
+```
+
+Or create the node, modify and then move into NodeEntry's constructor if this node is not to be used
+again. This avoids uneccessary copies of the shared_ptr.
+
+```c++
+for (size_t i = 0; i < n->inputs.size(); ++i) {
+  nnvm::NodePtr node = nnvm::Node::Create();
+  node->attrs.op = copy_op;
+  node->inputs = {ograds[0]};
+  ret.emplace_back(std::move(node));
+}
+```
+
+The first case uses RVO and the second in place construction.
+
+#### FCompute\<xpu\>
+
+Simple operators can register FCompute<xpu> with `.set_attr<FCompute>("FCompute<cpu>", ...)` and `.set_attr<FCompute>("FCompute<gpu>", ...)` for both CPU and (optionally) GPU computation.
+
+FCompute has prototype
+
+```c++
+void(const nnvm::NodeAttrs& attrs,
+     const OpContext& ctx,
+     const std::vector<TBlob>& inputs,
+     const std::vector<OpReqType>& req,
+     const std::vector<TBlob>& outputs)
+```
+
+`req` has the same length as `outputs`.
+Each entry of `req` specifies
+how the corresponding `output` should be written to.
+`OpReqType` is defined as:
+
+```c++
+enum OpReqType {
+  kNullOp,
+  kWriteTo,
+  kWriteInplace,
+  kAddTo
+};
+```
+
+Normally, the `req` of all `outputs` should be `kWriteTo`,
+meaning that the provided `outputs` tensor is a *raw* memory block,
+so the operator should write results directly into it.
+In some cases, for example, when calculating the gradient tensor,
+it would be great if we could accumulate the result,
+rather than directly overwrite the tensor contents
+so that no extra space needs to be created each time.
+In such cases, the corresponding `req` is set to `kAddTo`,
+indicating that a `+=` should be used.
+
+### Example: abs operator
+
+{% raw %}
+
+```c++
+NNVM_REGISTER_OP(abs)
+.MXNET_DESCRIBE("Take absolute value of the src")
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<nnvm::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+[](const NodeAttrs& attrs){
+  return std::vector<std::pair<int, int> >{{0, 0}};
+})
+.set_attr<FCompute>("FCompute<cpu>", UnaryCompute<cpu, mshadow_op::abs>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_abs"});
+.add_argument("data", "NDArray", "Source input")
+
+NNVM_REGISTER_OP(_backward_abs)
+.set_num_inputs(2)
+.set_num_outputs(1)
+.set_attr<nnvm::FInferShape>("FInferShape", ElemwiseShape<2, 1>)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<2, 1>)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+[](const NodeAttrs& attrs){
+  return std::vector<std::pair<int, int> >{{0, 0}, {1, 0}};
+})
+.set_attr<FCompute>("FCompute<cpu>", BinaryCompute<cpu, backward_grad<mshadow_op::sign> >);
+```
+
+{% endraw %}
+
+### Legacy Operators
+
+For the legacy (pre 0.9) way of defining operators with C++, please see:
+- [Developer Guide - Operators](http://mxnet.io/architecture/overview.html#operators-in-mxnet)
+- [Developer Guide - SimpleOp](http://mxnet.io/architecture/overview.html#simpleop-the-unified-operator-api)
diff --git a/docs/static_site/src/pages/api/faq/nnpack.md b/docs/static_site/src/pages/api/faq/nnpack.md
new file mode 100644
index 000000000000..f03b08646f62
--- /dev/null
+++ b/docs/static_site/src/pages/api/faq/nnpack.md
@@ -0,0 +1,162 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_category
+title: NNPACK for Multi-Core CPU Support in MXNet
+category: faq
+faq_c: Speed
+question: Can I use nnpack to improve the CPU performance of MXNet?
+permalink: /api/faq/nnpack
+---
+
+### NNPACK for Multi-Core CPU Support in MXNet
+[NNPACK](https://github.com/Maratyszcza/NNPACK) is an acceleration package
+for neural network computations, which can run on x86-64, ARMv7, or ARM64 architecture CPUs.
+Using NNPACK, higher-level libraries like _MXNet_ can speed up
+the execution on multi-core CPU computers, including laptops and mobile devices.
+
+_MXNet_ supports NNPACK for forward propagation (inference only) in convolution, max-pooling, and fully-connected layers.
+In this document, we give a high level overview of how to use NNPACK with _MXNet_.
+
+
+### Conditions
+The underlying implementation of NNPACK utilizes several acceleration methods,
+including [fft](https://arxiv.org/abs/1312.5851) and [winograd](https://arxiv.org/abs/1509.09308).
+These algorithms work better on some special `batch size`, `kernel size`, and `stride` settings than on other,
+so depending on the context, not all convolution, max-pooling, or fully-connected layers can be powered by NNPACK.
+When favorable conditions for running NNPACKS are not met,
+_MXNet_ will fall back to the default implementation automatically.
+
+NNPACK only supports Linux and OS X systems. Windows is not supported at present.
+The following table explains under which conditions NNPACK will work.
+
+| operation      | conditions |
+|:---------      |:---------- |
+|convolution     |2d convolution `and` no-bias=False `and` dilate=(1,1) `and` num_group=1 `and` batch-size = 1 or batch-size > 1 && stride = (1,1);|
+|pooling         | max-pooling `and` kernel=(2,2) `and` stride=(2,2) `and` pooling_convention=full    |
+|fully-connected| without any restrictions |
+
+### Build/Install NNPACK with MXNet
+
+If the trained model meets some conditions of using NNPACK,
+you can build MXNet with NNPACK support.
+Follow these simple steps:
+* Build NNPACK shared library with the following commands. _MXNet_ will link NNPACK dynamically.
+
+Note: The following NNPACK installation instructions have been tested on Ubuntu 14.04 and 16.04.
+
+```bash
+# Install Pip
+$ sudo apt-get update
+$ sudo apt-get install -y python-pip
+$ sudo pip install --upgrade pip
+
+# Install Peach
+$ git clone https://github.com/Maratyszcza/PeachPy.git
+$ cd PeachPy
+$ sudo pip install --upgrade -r requirements.txt
+$ python setup.py generate
+$ sudo pip install --upgrade .
+
+# Install Ninja Build System
+$ sudo apt-get install ninja-build
+$ pip install ninja-syntax
+
+# Build NNPack shared library
+$ cd ~
+$ git clone --recursive https://github.com/Maratyszcza/NNPACK.git
+$ cd NNPACK
+# Latest NNPACK do not support building NNPACK as shared library using --enable-shared flag
+# Reset to commit that supports it.
+$ git reset --hard 9c6747d7b80051b40e6f92d6828e2ed997529cd2
+$ git submodule init && git submodule update --recursive
+$ python ./configure.py --enable-shared
+$ ninja
+$ cd ~
+
+```
+
+* Set lib path of NNPACK as the environment variable, e.g. `export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$YOUR_NNPACK_INSTALL_PATH/lib`
+* Add the include file of NNPACK and its third-party to  `ADD_CFLAGS` in config.mk, e.g. `ADD_CFLAGS = -I$(YOUR_NNPACK_INSTALL_PATH)/include/ -I$(YOUR_NNPACK_INSTALL_PATH)/third-party/pthreadpool/include/`
+* Set `USE_NNPACK = 1` in config.mk.
+* Build MXNet from source following the [install guide](http://mxnet.io/install/index.html).
+
+### NNPACK Performance
+
+Though not all convolutional, pooling, and fully-connected layers can make full use of NNPACK,
+for some popular models it provides significant speedups. These include the most popular image recognition networks: Alexnet, VGG, and Inception-bn.
+
+To benchmark NNPACK, we use `example/image-classification/benchmark_score.py`(changed with  more range of batch-size). We use CPU e5-2670, MXNET_CPU_NNPACK_NTHREADS=4.
+
+build MXNet without NNPACK, the log is:
+```
+INFO:root:network: alexnet
+INFO:root:device: cpu(0)
+INFO:root:batch size  1, image/sec: 6.389429
+INFO:root:batch size  2, image/sec: 7.961457
+INFO:root:batch size  4, image/sec: 8.950112
+INFO:root:batch size  8, image/sec: 9.578176
+INFO:root:batch size 16, image/sec: 9.701248
+INFO:root:batch size 32, image/sec: 9.839940
+INFO:root:batch size 64, image/sec: 10.075369
+INFO:root:batch size 128, image/sec: 10.053556
+INFO:root:batch size 256, image/sec: 9.972228
+INFO:root:network: vgg
+INFO:root:device: cpu(0)
+INFO:root:batch size  1, image/sec: 1.223822
+INFO:root:batch size  2, image/sec: 1.322814
+INFO:root:batch size  4, image/sec: 1.383586
+INFO:root:batch size  8, image/sec: 1.402376
+INFO:root:batch size 16, image/sec: 1.415972
+INFO:root:batch size 32, image/sec: 1.428377
+INFO:root:batch size 64, image/sec: 1.443987
+INFO:root:batch size 128, image/sec: 1.427531
+INFO:root:batch size 256, image/sec: 1.435279
+```
+
+build MXNet with NNPACK, log is:
+
+```
+INFO:root:network: alexnet
+INFO:root:device: cpu(0)
+INFO:root:batch size  1, image/sec: 19.027215
+INFO:root:batch size  2, image/sec: 12.879975
+INFO:root:batch size  4, image/sec: 17.424076
+INFO:root:batch size  8, image/sec: 21.283966
+INFO:root:batch size 16, image/sec: 24.469325
+INFO:root:batch size 32, image/sec: 25.910348
+INFO:root:batch size 64, image/sec: 27.441672
+INFO:root:batch size 128, image/sec: 28.009156
+INFO:root:batch size 256, image/sec: 28.918950
+INFO:root:network: vgg
+INFO:root:device: cpu(0)
+INFO:root:batch size  1, image/sec: 3.980907
+INFO:root:batch size  2, image/sec: 2.392069
+INFO:root:batch size  4, image/sec: 3.610553
+INFO:root:batch size  8, image/sec: 4.994450
+INFO:root:batch size 16, image/sec: 6.396612
+INFO:root:batch size 32, image/sec: 7.614288
+INFO:root:batch size 64, image/sec: 8.826084
+INFO:root:batch size 128, image/sec: 9.193653
+INFO:root:batch size 256, image/sec: 9.991472
+```
+
+The results show that NNPACK can confer a speedup of about 2X~7X as compared to the original _MXNet_ CPU implementation.
+
+### Tips
+
+NNPACK aims to provide high-performance implementations of some layers for multi-core CPUs, so you can easily set the thread number by changing the environmental variable `MXNET_CPU_NNPACK_NTHREADS`. However, we found that the performance is not proportional to the number of threads, and suggest using 4~8 threads when using NNPACK.
diff --git a/docs/static_site/src/pages/api/faq/perf.md b/docs/static_site/src/pages/api/faq/perf.md
new file mode 100644
index 000000000000..21eefb6c3fc7
--- /dev/null
+++ b/docs/static_site/src/pages/api/faq/perf.md
@@ -0,0 +1,315 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_category
+title: Some Tips for Improving MXNet Performance
+category: faq
+faq_c: Speed
+question: What are the best setup and data-handling tips and tricks for improving speed?
+permalink: /api/faq/perf
+---
+
+
+# Some Tips for Improving MXNet Performance
+Even after fixing the training or deployment environment and parallelization scheme,
+a number of configuration settings and data-handling choices can impact the _MXNet_ performance.
+In this document, we address some tips for improving _MXNet_ performance.
+
+Performance is mainly affected by the following 4 factors:
+
+1. Implementation of operators (Convolution, Pooling, ..)
+   - [Intel CPU](#intel-cpu)
+   - [Nvidia GPU](#nvidia-gpu)
+2. Input data loading and augmentation
+   - [Input Data](#input-data)
+3. Workloads (computation graph) optimization and scheduling
+   - [Profiler](#profiler)
+4. Communication for multi-devices training
+   - [Multiple Devices](#multiple-devices)
+
+## Intel CPU
+
+When using Intel Xeon CPUs for training and inference, the `mxnet-mkl` package is recommended. Adding `--pre` installs a nightly build from master. Without it you will install the latest patched release of MXNet:
+
+```
+$ pip install mxnet-mkl [--pre]
+```
+
+Or build MXNet from source code with `USE_MKLDNN=1`. For Linux users, `USE_MKLDNN=1` will be turned on by default.
+
+We also find that setting the following environment variables can help:
+
+
+| Variable  | Description |
+| :-------- | :---------- |
+| `OMP_NUM_THREADS`            | Suggested value: `vCPUs / 2` in which `vCPUs` is the number of virtual CPUs. For more information, please see the guide for [setting the number of threads using an OpenMP environment variable](https://software.intel.com/en-us/mkl-windows-developer-guide-setting-the-number-of-threads-using-an-openmp-environment-variable) |
+| `KMP_AFFINITY`               | Suggested value: `granularity=fine,compact,1,0`.  For more information, please see the guide for [Thread Affinity Interface (Linux* and Windows*)](https://software.intel.com/en-us/node/522691). |
+| `MXNET_SUBGRAPH_BACKEND` | Set to MKLDNN to enable the [subgraph feature](https://cwiki.apache.org/confluence/display/MXNET/MXNet+Graph+Optimization+and+Quantization+based+on+subgraph+and+MKL-DNN) for better performance. For more information please see [Build/Install MXNet with MKL-DNN](https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/mkldnn/MKLDNN_README.md)|
+
+Note that _MXNet_ treats all CPUs on a single machine as a single device.
+So whether you specify `cpu(0)` or `cpu()`, _MXNet_ will use all CPU cores on the machine.
+
+### Scoring results
+The following table shows performance of [MXNet-1.2.0.rc1](https://github.com/apache/incubator-mxnet/releases/download/1.2.0.rc1/apache-mxnet-src-1.2.0.rc1-incubating.tar.gz),
+namely number of images that can be predicted per second.
+We used [example/image-classification/benchmark_score.py](https://github.com/dmlc/mxnet/blob/master/example/image-classification/benchmark_score.py)
+to measure the performance on different AWS EC2 machines.
+
+AWS EC2 C5.18xlarge:
+
+
+| Batch | Alexnet | VGG 16    | Inception-BN | Inception-v3 | Resnet 50 | Resnet 152 |
+|-------|---------|--------|--------------|--------------|-----------|------------|
+| 1     | 390.53  | 81.57  | 124.13       | 62.26        | 76.22     | 32.92      |
+| 2     | 596.45  | 100.84 | 206.58       | 93.36        | 119.55    | 46.80      |
+| 4     | 710.77  | 119.04 | 275.55       | 127.86       | 148.62    | 59.36      |
+| 8     | 921.40  | 120.38 | 380.82       | 157.11       | 167.95    | 70.78      |
+| 16    | 1018.43 | 115.30 | 411.67       | 168.71       | 178.54    | 75.13      |
+| 32    | 1290.31 | 107.19 | 483.34       | 179.38       | 193.47    | 85.86      |
+
+
+
+AWS EC2 C5.9xlarge:
+
+
+| Batch | Alexnet | VGG 16   | Inception-BN | Inception-v3 | Resnet 50 | Resnet 152 |
+|-------|---------|-------|--------------|--------------|-----------|------------|
+| 1     | 257.77  | 50.61 | 130.99       | 66.95        | 75.38     | 32.33      |
+| 2     | 410.60  | 63.02 | 195.14       | 87.84        | 102.67    | 41.57      |
+| 4     | 462.59  | 62.64 | 263.15       | 109.87       | 127.15    | 50.69      |
+| 8     | 573.79  | 63.95 | 309.99       | 121.36       | 140.84    | 59.01      |
+| 16    | 709.47  | 67.79 | 350.19       | 128.26       | 147.41    | 64.15      |
+| 32    | 831.46  | 69.58 | 354.91       | 129.92       | 149.18    | 64.25      |
+
+
+AWS EC2 C5.4xlarge:
+
+| Batch | Alexnet | VGG 16   | Inception-BN | Inception-v3 | Resnet 50 | Resnet 152 |
+|-------|---------|-------|--------------|--------------|-----------|------------|
+| 1     | 214.15  | 29.32 | 114.97       | 47.96        | 61.01     | 23.92      |
+| 2     | 310.04  | 34.81 | 150.09       | 60.89        | 71.16     | 27.92      |
+| 4     | 330.69  | 34.56 | 186.63       | 74.15        | 86.86     | 34.37      |
+| 8     | 378.88  | 35.46 | 204.89       | 77.05        | 91.10     | 36.93      |
+| 16    | 424.00  | 36.49 | 211.55       | 78.39        | 91.23     | 37.34      |
+| 32    | 481.95  | 37.23 | 213.71       | 78.23        | 91.68     | 37.26      |
+
+
+AWS EC2 C5.2xlarge:
+
+| Batch | Alexnet | VGG 16   | Inception-BN | Inception-v3 | Resnet 50 | Resnet 152 |
+|-------|---------|-------|--------------|--------------|-----------|------------|
+| 1     | 131.01  | 15.67 | 78.75        | 31.12        | 37.30     | 14.75      |
+| 2     | 182.29  | 18.01 | 98.59        | 39.13        | 45.98     | 17.84      |
+| 4     | 189.31  | 18.25 | 110.26       | 41.35        | 49.21     | 19.32      |
+| 8     | 211.75  | 18.57 | 115.46       | 42.53        | 49.98     | 19.81      |
+| 16    | 236.06  | 19.11 | 117.18       | 42.59        | 50.20     | 19.92      |
+| 32    | 261.13  | 19.46 | 116.20       | 42.72        | 49.95     | 19.80      |
+
+
+AWS EC2 C5.xlarge:
+
+| Batch | Alexnet | VGG 16  | Inception-BN | Inception-v3 | Resnet 50 | Resnet 152 |
+|-------|---------|------|--------------|--------------|-----------|------------|
+| 1     | 36.64   | 3.93 | 27.06        | 10.09        | 12.98     | 5.06       |
+| 2     | 49.21   | 4.49 | 29.67        | 10.80        | 12.94     | 5.14       |
+| 4     | 50.12   | 4.50 | 30.31        | 10.83        | 13.17     | 5.19       |
+| 8     | 54.71   | 4.58 | 30.22        | 10.89        | 13.19     | 5.20       |
+| 16    | 60.23   | 4.70 | 30.20        | 10.91        | 13.23     | 5.19       |
+| 32    | 66.37   | 4.76 | 30.10        | 10.90        | 13.22     | 5.15       |
+
+
+## Other CPU
+
+If using CPUs (not just Intel CPUs -- ARMs also), NNPACK can improve the running performance with 2x~7x, please check [nnpack.md](./nnpack.md) for details.
+
+## Nvidia GPU
+
+`cuDNN` typically accelerates _MXNet_ performance on NVIDIA GPUs significantly,
+especially for convolution layers.
+We suggest always checking to make sure that a recent cuDNN version is used.
+
+Setting the environment `export MXNET_CUDNN_AUTOTUNE_DEFAULT=1` sometimes also helps.
+
+We show results when using various GPUs including K80 (EC2 p2.2xlarge), M60 (EC2 g3.4xlarge),
+and V100 (EC2 p3.2xlarge).
+
+### Scoring results
+
+Based on
+[example/image-classification/benchmark_score.py](https://github.com/dmlc/mxnet/blob/master/example/image-classification/benchmark_score.py)
+and  [MXNet-1.2.0.rc1](https://github.com/apache/incubator-mxnet/releases/download/1.2.0.rc1/apache-mxnet-src-1.2.0.rc1-incubating.tar.gz), with cuDNN 7.0.5
+
+- K80 (single GPU)
+
+| Batch | Alexnet | VGG 16    | Inception-BN | Inception-v3 | Resnet 50 | Resnet 152 |
+|-------|---------|--------|--------------|--------------|-----------|------------|
+| 1     | 243.93  | 43.59  | 68.62        | 35.52        | 67.41     | 23.65      |
+| 2     | 338.16  | 49.14  | 113.41       | 56.29        | 93.35     | 33.88      |
+| 4     | 478.92  | 53.44  | 159.61       | 74.43        | 119.18    | 45.23      |
+| 8     | 683.52  | 70.50  | 190.49       | 86.23        | 131.32    | 50.54      |
+| 16    | 1004.66 | 109.01 | 254.20       | 105.70       | 155.40    | 62.55      |
+| 32    | 1238.55 | 114.98 | 285.49       | 116.79       | 159.42    | 64.99      |
+| 64 | 1346.72 | 123.56 | 308.73 | 122.21 | 167.58 | 70.21 |
+| 128 | 1416.91 | OOM | 320.98 | 123.11 | 171.55 | 71.85 |
+| 256 | 1462.97 | OOM | 329.16 | 127.53 | 153.01 | 57.23 |
+
+- M60
+
+| Batch | Alexnet | VGG 16    | Inception-BN | Inception-v3 | Resnet 50 | Resnet 152 |
+|-------|---------|--------|--------------|--------------|-----------|------------|
+| 1     | 243.49  | 59.95  | 101.97       | 48.30        | 95.46     | 39.29      |
+| 2     | 491.04  | 69.14  | 170.35       | 80.27        | 142.61    | 60.17      |
+| 4     | 711.54  | 78.94  | 257.89       | 123.09       | 182.36    | 76.51      |
+| 8     | 1077.73 | 109.34 | 343.42       | 152.82       | 208.74    | 87.27      |
+| 16    | 1447.21 | 144.93 | 390.25       | 166.32       | 220.73    | 92.41      |
+| 32    | 1797.66 | 151.86 | 416.69       | 176.56       | 230.19    | 97.03      |
+| 64 | 1779.38 | 150.18 | 427.51 | 183.47 | 239.12 | 101.59 |
+| 128 | 1787.36 | OOM | 439.04 | 185.29 | 243.31 | 103.39 |
+| 256 | 1899.10 | OOM | 450.22 | 183.42 | 242.36 | 100.98 |
+
+
+- V100
+
+| Batch | Alexnet | VGG 16    | Inception-BN | Inception-v3 | Resnet 50 | Resnet 152 |
+|-------|---------|--------|--------------|--------------|-----------|------------|
+| 1     | 659.51  | 205.16 | 157.37 | 87.71 | 162.15    | 61.38      |
+| 2     | 1248.21 | 265.40 | 297.34 | 159.24 | 293.74    | 116.30     |
+| 4     | 2122.41 | 333.97 | 520.91 | 279.84 | 479.14    | 195.17     |
+| 8     | 3894.30 | 420.26 | 898.09 | 455.03 | 699.39    | 294.19     |
+| 16    | 5815.58 | 654.16 | 1430.97 | 672.54 | 947.45    | 398.79     |
+| 32    | 7906.09 | 708.43 | 1847.26 | 814.59 | 1076.81   | 451.82     |
+| 64 | 9486.26 | 701.59 | 2134.89 | 899.01 | 1168.37 | 480.44 |
+| 128 | 10177.84 | 703.30 | 2318.32 | 904.33 | 1233.15 | 511.79 |
+| 256 | 10990.46 | 473.62 | 2425.28 | 960.20 | 1155.07 | 449.35 |
+
+Below is the performance result on V100 using float 16.
+
+| Batch | VGG 16  | Inception-BN | Inception-v3 | Resnet 50 | Resnet 152 |
+| ----- | ------- | ------------ | ------------ | --------- | ---------- |
+| 1     | 276.29  | 155.53       | 150.99       | 270.89    | 96.79      |
+| 2     | 476.91  | 296.45       | 282.02       | 493.99    | 176.88     |
+| 4     | 711.92  | 525.05       | 492.45       | 851.15    | 321.52     |
+| 8     | 1047.11 | 900.26       | 807.94       | 1282.36   | 517.66     |
+| 16    | 1299.88 | 1441.41      | 1192.21      | 1722.97   | 724.57     |
+| 32    | 1486.63 | 1854.30      | 1512.08      | 2085.51   | 887.34     |
+| 64    | 1219.65 | 2138.61      | 1687.35      | 2341.67   | 1002.90    |
+| 128   | 1169.81 | 2317.39      | 1818.26      | 2355.04   | 1046.98    |
+| 256   | 764.16  | 2425.16      | 1653.74      | 1991.88   | 976.73     |
+
+### Training results
+
+Based on
+[example/image-classification/train_imagenet.py](https://github.com/dmlc/mxnet/blob/master/example/image-classification/train_imagenet.py)
+and  [MXNet-1.2.0.rc1](https://github.com/apache/incubator-mxnet/releases/download/1.2.0.rc1/apache-mxnet-src-1.2.0.rc1-incubating.tar.gz), with CUDNN 7.0.5. The benchmark script is available at
+[here](https://github.com/mli/mxnet-benchmark/blob/master/run_vary_batch.sh),
+where the batch size for Alexnet is increased by 16x.
+
+- K80 (single GPU)
+
+| Batch | Alexnet(\*16) | Inception-v3 | Resnet 50 |
+| --- | --- | --- | --- |
+|   1 | 300.30 | 10.48 | 15.61 |
+|   2 | 406.08 | 16.00 | 23.88 |
+|   4 | 461.01 | 22.10 | 32.26 |
+|   8 | 484.00 | 26.80 | 39.42 |
+|  16 | 490.45 | 31.62 | 46.69 |
+|  32 | 414.72 | 33.78 | 49.48 |
+
+- M60
+
+| Batch | Alexnet(\*16) | Inception-v3 | Resnet 50 |
+| --- | --- | --- | --- |
+|   1 | 380.96 | 14.06 | 20.55 |
+|   2 | 530.53 | 21.90 | 32.65 |
+|   4 | 600.17 | 31.96 | 45.57 |
+|   8 | 633.60 | 40.58 | 54.92 |
+|  16 | 639.37 | 46.88 | 64.44 |
+|  32 | 576.54 | 50.05 | 68.34 |
+
+- V100
+
+| Batch | Alexnet(\*16) | Inception-v3 | Resnet 50 |
+| --- | --- | --- | --- |
+|   1 | 1629.52 | 21.83 | 34.54 |
+|   2 | 2359.73 | 40.11 | 65.01 |
+|   4 | 2687.89 | 72.79 | 113.49 |
+|   8 | 2919.02 | 118.43 | 174.81 |
+|  16 | 2994.32 | 173.15 | 251.22 |
+|  32 | 2585.61 | 214.48 | 298.51 |
+| 64 | 1984.21 | 247.43 | 343.19 |
+| 128 | OOM | 253.68 | 363.69 |
+
+## Multiple Devices
+
+If more than one GPU or machine are used, MXNet uses `kvstore` to communicate data.
+It's critical to use the proper type of `kvstore` to get the best performance.
+Refer to [multi_device.md](http://mxnet.io/faq/multi_devices.html) for more
+details.
+
+Besides, we can use [tools/bandwidth](https://github.com/dmlc/mxnet/tree/master/tools/bandwidth)
+to find the communication cost per batch.
+Ideally, the communication cost should be less than the time to compute a batch.
+To reduce the communication cost, we can consider:
+
+- Exploring different `--kv-store` options.
+- Increasing the batch size to improve the computation to communication ratio.
+
+## Input Data
+
+To make sure you're handling input data in a reasonable way consider the following:
+
+* Data format: If you are using the `rec` format, then everything should be fine.
+* Decoding: By default, _MXNet_ uses 4 CPU threads for decoding images.
+This is often sufficient to decode more than 1K images per second.
+If you are using a low-end CPU or your GPUs are very powerful, you can increase the number of threads.
+* Storage location. Any local or distributed file system (HDFS, Amazon S3) should be fine.
+If multiple devices read the data from the shared network file system (NFS) at the same time, problems might occur.
+* Use a large batch size. We often choose the largest one that fits into GPU memory.
+A value that's too large can slow down convergence.
+For example, the safe batch size for CIFAR 10 is approximately 200, while for ImageNet 1K, the batch size can exceed 1K.
+
+## Profiler
+
+As of v0.9.1 (with the NNVM merge), _MXNet_ has a built-in profiler
+that gives detailed information about execution time at the symbol level.
+This feature complements general profiling tools like _nvprof_ and _gprof_
+by summarizing at the operator level, instead of a function, kernel, or instruction level.
+
+The profiler can be turned on with an [environment variable](http://mxnet.io/faq/env_var.html#control-the-profiler)
+for an entire program run, or programmatically for just part of a run.
+See [example/profiler](https://github.com/dmlc/mxnet/tree/master/example/profiler)
+for complete examples of how to use the profiler in code, but briefly, the Python code looks like:
+
+```python
+    mx.profiler.set_config(profile_all=True, filename='profile_output.json')
+    mx.profiler.set_state('run')
+
+    # Code to be profiled goes here...
+
+    mx.profiler.set_state('stop')
+```
+
+The `mode` parameter can be set to
+
+* `symbolic` to only include symbolic operations
+* `all` to include all operations
+
+After the program finishes, navigate to your browser's tracing (Example - chrome://tracing in a Chrome browser) and load the `profile_output.json` file output by the profiler to inspect the results.
+
+![MLP Profile](https://cloud.githubusercontent.com/assets/17693755/18035938/0a43484a-6d93-11e6-80d4-241c6ca552ea.png)
+
+Note that the output file can grow extremely large, so this approach is not recommended for general use.
diff --git a/docs/static_site/src/pages/api/faq/recordio.md b/docs/static_site/src/pages/api/faq/recordio.md
new file mode 100644
index 000000000000..93389a1da400
--- /dev/null
+++ b/docs/static_site/src/pages/api/faq/recordio.md
@@ -0,0 +1,109 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_category
+title: Create a Dataset Using RecordIO
+category: faq
+faq_c: Speed
+question: How can I create a .rec dataset ?
+permalink: /api/faq/recordio
+---
+
+
+## Create a Dataset Using RecordIO
+
+RecordIO implements a file format for a sequence of records. We recommend storing images as records and packing them together. The benefits include:
+
+* Storing images in a compact format--e.g., JPEG, for records--greatly reduces the size of the dataset on the disk.
+* Packing data together allows continuous reading on the disk.
+* RecordIO has a simple way to partition, simplifying distributed setting. We provide an example later.
+
+We provide two tools for creating a RecordIO dataset.
+
+* [im2rec.cc](https://github.com/dmlc/mxnet/blob/master/tools/im2rec.cc) - implements the tool using the C++ API.
+* [im2rec.py](https://github.com/apache/incubator-mxnet/blob/master/tools/im2rec.py) - implements the tool using the Python API.
+
+Both provide the same output: a RecordIO dataset.
+You may want to also review the [example using real-world data with im2rec.py.](https://mxnet.incubator.apache.org/tutorials/basic/data.html#loading-data-using-image-iterators)
+
+### Prerequisites
+
+Download the data. You don't need to resize the images manually. You can use ```im2rec``` to resize them automatically. For details, see the "Extension: Using Multiple Labels for a Single Image," later in this topic.
+
+### Step 1. Make an Image List File
+
+* Note that the im2rec.py provides a param `--list` to generate the list for you, but im2rec.cc doesn't support it.
+
+After you download the data, you need to make an image list file.  The format is:
+
+```
+integer_image_index \t label_index \t path_to_image
+```
+Typically, the program takes the list of names of all of the images, shuffles them, then separates them into two lists: a training filename list and a testing filename list. Write the list in the right format.
+This is an example file:
+
+```bash
+95099  464.000000     n04467665_17283.JPEG
+10025081        412.000000     ILSVRC2010_val_00025082.JPEG
+74181   789.000000     n01915811_2739.JPEG
+10035553        859.000000     ILSVRC2010_val_00035554.JPEG
+10048727        929.000000     ILSVRC2010_val_00048728.JPEG
+94028   924.000000     n01980166_4956.JPEG
+1080682 650.000000     n11807979_571.JPEG
+972457  633.000000     n07723039_1627.JPEG
+7534    11.000000      n01630670_4486.JPEG
+1191261 249.000000     n12407079_5106.JPEG
+```
+
+### Step 2. Create the Binary File
+
+To generate a binary image, use `im2rec` in the tool folder. `im2rec` takes the path of the `image list file` you generated, the `root path` of the images, and the `output file path` as input. This process usually takes several hours, so be patient.
+
+Sample command:
+
+```bash
+./bin/im2rec image.lst image_root_dir output.bin resize=256
+```
+For more details, run ```./bin/im2rec```.
+
+### Extension: Multiple Labels for a Single Image
+
+The `im2rec` tool and `mx.io.ImageRecordIter` have multi-label support for a single image.
+For example, if you have four labels for a single image, you can use the following procedure to use the RecordIO tools.
+
+1. Write the image list files as follows:
+
+```
+integer_image_index \t label_1 \t label_2 \t   label_3 \t label_4 \t path_to_image
+```
+
+2. Run `im2rec`, adding a 'label_width=4' to the command argument, for example:
+
+```bash
+./bin/im2rec image.lst image_root_dir output.bin resize=256 label_width=4
+```
+
+3. In the iterator generation code, set `label_width=4` and `path_imglist=<<The PATH TO YOUR image.lst>>`, for example:
+
+```python
+dataiter = mx.io.ImageRecordIter(
+  path_imgrec="data/cifar/train.rec",
+  data_shape=(3,28,28),
+  path_imglist="data/cifar/image.lst",
+  label_width=4
+)
+```
diff --git a/docs/static_site/src/pages/api/faq/s3_integration.md b/docs/static_site/src/pages/api/faq/s3_integration.md
new file mode 100644
index 000000000000..7c144c267c0a
--- /dev/null
+++ b/docs/static_site/src/pages/api/faq/s3_integration.md
@@ -0,0 +1,125 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_category
+title: Use data from S3 for training
+category: faq
+faq_c: Deployment Environments
+question: How to use data from S3 for training?
+permalink: /api/faq/s3_integration
+---
+
+
+# Use data from S3 for training
+
+AWS S3 is a cloud-based object storage service that allows storage and retrieval of large amounts of data at a very low cost. This makes it an attractive option to store large training datasets. MXNet is deeply integrated with S3 for this purpose.
+
+An S3 protocol URL (like `s3://bucket-name/training-data`) can be provided as a parameter for any data iterator that takes a file path as input. For example,
+
+```
+data_iter = mx.io.ImageRecordIter(
+    path_imgrec="s3://bucket-name/training-data/caltech_train.rec",
+    data_shape=(3, 227, 227),
+    batch_size=4,
+    resize=256)
+```
+Following are detailed instructions on how to use data from S3 for training.
+
+## Step 1: Build MXNet with S3 integration enabled
+
+Follow instructions [here](http://mxnet.io/install/index.html) to install MXNet from source with the following additional steps to enable S3 integration.
+
+1. Install `libcurl4-openssl-dev` and `libssl-dev` before building MXNet. These packages are required to read/write from AWS S3.
+2. Append `USE_S3=1` to `config.mk` before building MXNet.
+    ```
+    echo "USE_S3=1" >> config.mk
+    ```
+
+## Step 2: Configure S3 authentication tokens
+
+MXNet requires the S3 environment variables `AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY` to be set. [Here](https://aws.amazon.com/blogs/security/wheres-my-secret-access-key/) are instructions to get the access keys from AWS console.
+
+```
+export AWS_ACCESS_KEY_ID=<your-access-key-id>
+AWS_SECRET_ACCESS_KEY=<your-secret-access-key>
+```
+
+## Step 3: Upload data to S3
+
+There are several ways to upload data to S3. One easy way is to use the AWS command line utility. For example, the following `sync` command will recursively copy contents from a local directory to a directory in S3.
+
+```
+aws s3 sync ./training-data s3://bucket-name/training-data
+```
+
+## Step 4: Train with data from S3
+
+Once the data is in S3, it is very straightforward to use it from MXNet. Any data iterator that can read/write data from a local drive can also read/write data from S3.
+
+Let's modify an existing example code in MXNet repository to read data from S3 instead of local disk. [`mxnet/tests/python/train/test_conv.py`](https://github.com/dmlc/mxnet/blob/master/tests/python/train/test_conv.py) trains a convolutional network using MNIST data from local disk. We'll do the following change to read the data from S3 instead.
+
+```
+~/mxnet$ sed -i -- 's/data\//s3:\/\/bucket-name\/training-data\//g' ./tests/python/train/test_conv.py
+
+~/mxnet$ git diff ./tests/python/train/test_conv.py
+diff --git a/tests/python/train/test_conv.py b/tests/python/train/test_conv.py
+index 039790e..66a60ce 100644
+--- a/tests/python/train/test_conv.py
++++ b/tests/python/train/test_conv.py
+@@ -39,14 +39,14 @@ def get_iters():
+
+     batch_size = 100
+     train_dataiter = mx.io.MNISTIter(
+-            image="data/train-images-idx3-ubyte",
+-            label="data/train-labels-idx1-ubyte",
++            image="s3://bucket-name/training-data/train-images-idx3-ubyte",
++            label="s3://bucket-name/training-data/train-labels-idx1-ubyte",
+             data_shape=(1, 28, 28),
+             label_name='sm_label',
+             batch_size=batch_size, shuffle=True, flat=False, silent=False, seed=10)
+     val_dataiter = mx.io.MNISTIter(
+-            image="data/t10k-images-idx3-ubyte",
+-            label="data/t10k-labels-idx1-ubyte",
++            image="s3://bucket-name/training-data/t10k-images-idx3-ubyte",
++            label="s3://bucket-name/training-data/t10k-labels-idx1-ubyte",
+             data_shape=(1, 28, 28),
+             label_name='sm_label',
+             batch_size=batch_size, shuffle=True, flat=False, silent=False)
+```
+
+After the above change `test_conv.py` will fetch data from S3 instead of the local disk.
+
+```
+python ./tests/python/train/test_conv.py
+[21:59:19] src/io/s3_filesys.cc:878: No AWS Region set, using default region us-east-1
+[21:59:21] src/io/iter_mnist.cc:94: MNISTIter: load 60000 images, shuffle=1, shape=(100,1,28,28)
+[21:59:21] src/io/iter_mnist.cc:94: MNISTIter: load 10000 images, shuffle=1, shape=(100,1,28,28)
+INFO:root:Start training with [cpu(0)]
+Start training with [cpu(0)]
+INFO:root:Epoch[0] Resetting Data Iterator
+Epoch[0] Resetting Data Iterator
+INFO:root:Epoch[0] Time cost=11.277
+Epoch[0] Time cost=11.277
+INFO:root:Epoch[0] Validation-accuracy=0.955100
+Epoch[0] Validation-accuracy=0.955100
+INFO:root:Finish fit...
+Finish fit...
+INFO:root:Finish predict...
+Finish predict...
+INFO:root:final accuracy = 0.955100
+final accuracy = 0.955100
+```
diff --git a/docs/static_site/src/pages/api/faq/security.md b/docs/static_site/src/pages/api/faq/security.md
new file mode 100644
index 000000000000..6a47244ea9ef
--- /dev/null
+++ b/docs/static_site/src/pages/api/faq/security.md
@@ -0,0 +1,49 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_category
+title: MXNet Security Best Practices
+category: faq
+faq_c: Security
+question: How to run MXNet securely?
+permalink: /api/faq/security
+---
+
+# MXNet Security Best Practices
+
+MXNet framework has no built-in security protections. It assumes that the MXNet entities involved in model training and inferencing (hosting) are fully trusted. It also assumes that their communications cannot be eavesdropped or tampered with. MXNet consumers shall ensure that the above assumptions are met.
+
+In particular the following threat-vectors exist when training using MXNet:
+
+* When running distributed training using MXNet there is no built-in support for authenticating cluster nodes participating in the training job.
+* Data exchange between cluster nodes happens is in plain-text.
+* Using `kvstore.set_optimizer` one can use a custom optimizer to combine gradients. This optimizer code is sent to the server nodes as a pickle file. A server does not perform any further validation of the pickle file and simply executes the code trusting the sender (worker).
+* Since there is no authentication between nodes, a malicious actor running on the same network can launch a Denial of Service (DoS) attack by sending data that can overwhelm/crash a scheduler or other server nodes.
+
+It is highly recommended that the following best practices be followed when using MXNet:
+
+* Run MXNet with least privilege, i.e. not as root.
+* Run MXNet training jobs inside a secure and isolated environment. If you are using a cloud provider like Amazon AWS, running your training job inside a [private VPC](https://aws.amazon.com/vpc/) is a good way to accomplish this. Additionally, configure your network security settings so as to only allow connections that the cluster nodes require.
+* Make sure no unauthorized actors have physical or remote access to the nodes participating in MXNet training.
+* During training, one can configure MXNet to periodically save model checkpoints. To protect these model checkpoints from unauthorized access, make sure the checkpoints are written out to an encrypted storage volume, and have a provision to delete checkpoints that are no longer needed.
+* When sharing trained models, or when receiving trained models from other parties, ensure that model artifacts are authenticated and integrity protected using cryptographic signatures, thus ensuring that the data received comes from trusted sources and has not been maliciously (or accidentally) modified in transit.
+* By default, mx.random uses a static and fixed seed value. The random utilities in MXNet should therefore never be used to implement any type of security critical functionality where cryptographically secure pseudorandom number generation is required.
+
+# Deployment Considerations
+The following are not MXNet framework specific threats but are applicable to Machine Learning models in general.
+
+* When deploying high-value, proprietary models for inference, care should be taken to prevent an adversary from stealing the model. The research paper [Stealing Machine Learning Models via Prediction APIs](https://arxiv.org/pdf/1609.02943.pdf) outlines experiments performed to show how an attacker can use a prediction API to leak the ML model or construct a nearly identical replica. A simple way to thwart such an attack is to not expose the prediction probabilities to a high degree of precision in the API response.
diff --git a/docs/static_site/src/pages/api/faq/smart_device.md b/docs/static_site/src/pages/api/faq/smart_device.md
new file mode 100644
index 000000000000..5ed9ebc31bec
--- /dev/null
+++ b/docs/static_site/src/pages/api/faq/smart_device.md
@@ -0,0 +1,120 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_category
+title: Deep Learning at the Edge
+category: faq
+faq_c: Deployment Environments
+question: How to run MXNet securely?
+permalink: /api/faq/smart_device
+---
+
+# Deep Learning in a Single File for Smart Devices
+
+Deep learning (DL) systems are complex and often depend on a number of libraries.
+Porting a DL library to different platforms can be painful, especially for smart devices.
+One simple solution to this problem is to provide a light interface to the library, complete with all required code in a single file with minimal dependencies.
+In this document, we explain how to amalgamate all necessary code into a single file,
+and demonstrate the approach with an example in which we run image recognition on a mobile device.
+
+## Amalgamation: Making the Whole System a Single File
+
+We come to the idea of of amalgamation following the example of SQLite,
+which pack all the code needed to run a simple database into a single source file.
+All that's necessary to create the library is to compile that single file.
+This simplifies the problem of porting to various platforms.
+
+Thanks to [Jack Deng](https://github.com/jdeng),
+MXNet provides an [amalgamation](https://github.com/dmlc/mxnet/tree/master/amalgamation) script
+that compiles all code needed for prediction based on trained DL models into a single `.cc` file,
+containing approximately 30K lines of code. This code only depends on the BLAS library.
+Moreover, we've also created an even more minimal version,
+with the BLAS dependency removed.
+You can compile the single file into JavaScript by using [emscripten](https://github.com/kripken/emscripten).
+
+The compiled library can be used by any other programming language.
+The `.h` file contains a light prediction API.
+Porting to another language with a C foreign function interface requires little effort.
+
+For examples, see the following examples on GitHub:
+
+- Go: [https://github.com/jdeng/gomxnet](https://github.com/jdeng/gomxnet)
+- Java: [https://github.com/dmlc/mxnet/tree/master/amalgamation/jni](https://github.com/dmlc/mxnet/tree/master/amalgamation/jni)
+- Python: [https://github.com/dmlc/mxnet/tree/master/amalgamation/python](https://github.com/dmlc/mxnet/tree/master/amalgamation/python)
+
+
+If you plan to amalgamate your system, there are a few guidelines you ought to observe when building the project:
+
+- Minimize dependence on other libraries.
+- Use namespace to encapsulate the types and operators.
+- Avoid running commands such as ```using namespace xyz``` on the global scope.
+- Avoid cyclic include dependencies.
+
+
+## Image Recognition Demo on Mobile Devices
+
+With amalgamation, deploying the system on smart devices (such as Android or iOS) is simple. But there are two additional considerations:
+
+- The model should be small enough to fit into the device's memory.
+- The model shouldn't be too expensive to run given the relatively low computational power of these devices.
+
+Let's use image recognition as an example.
+We start with the state-of-the-art inception model.
+We train it on an ImageNet dataset,
+using multiple servers with GTX 980 cards.
+The resulting model fits into memory,
+but it's too expensive to run.
+We remove some layers, but now the results are poor.
+
+Finally, we show an Android example, thanks to Leliana, [https://github.com/Leliana/WhatsThis](https://github.com/Leliana/WhatsThis) to demonstrate how to run on Android.
+
+<img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/apk/subinception.png" height="488" width="274">
+
+
+By using amalgamation, we can easily port the prediction library to mobile devices, with nearly no dependency.
+After compiling the library for smart platforms, the last thing we must do is to call C-API in the target language (Java/Swift).
+
+<img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/apk/poolnet.png" height="488" width="274">
+
+Besides this pre-trained Inception-BatchNorm network, we've provided two pre-trained models.
+
+We tested our model on a Nexus 5:
+
+
+|                  | Top-1 Validation on ILSVRC2012      | Time  | App Size  | Runtime Temp Memory Req |
+| ---------------- | ----------------------------------- | ----- | --- | ------------ |
+| FastPoorNet      | around 52%, similar to 2011 winner  | 1s    | <10MB    |  <5MB               |
+| Sub InceptionBN  | around 64%, similar to 2013 winner  | 2.7s  | <40MB    |  <10MB              |
+| InceptionBN      | around 70%                          | 4s-5s | <60MB    | 10MB               |
+
+These models are for demonstration only.
+They aren't fine-tuned for mobile devices,
+and there is definitely room for improvement.
+We believe that making a lightweight, portable,
+and fast deep learning library is fun and interesting,
+and hope you enjoy using the library.
+
+## Source Code
+[https://github.com/Leliana/WhatsThis](https://github.com/Leliana/WhatsThis)
+
+
+## Demo APK Download
+
+- [FastPoorNet](https://github.com/dmlc/web-data/blob/master/mxnet/apk/fastpoornet.apk?raw=true)
+
+
+- [SubInception](https://github.com/dmlc/web-data/blob/master/mxnet/apk/subinception.apk?raw=true)
diff --git a/docs/static_site/src/pages/api/faq/visualize_graph.md b/docs/static_site/src/pages/api/faq/visualize_graph.md
new file mode 100644
index 000000000000..e520bc71d640
--- /dev/null
+++ b/docs/static_site/src/pages/api/faq/visualize_graph.md
@@ -0,0 +1,88 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_category
+title: Visualize Neural Networks
+category: faq
+faq_c: Model
+question: How do I visualize neural networks as computation graphs?
+permalink: /api/faq/visualize_graph
+---
+# How to visualize Neural Networks as computation graph
+
+Here, we'll demonstrate how to use ```mx.viz.plot_network```
+for visualizing your neural networks. ```mx.viz.plot_network```
+represents the neural network as a computation graph consisting of nodes and edges.
+The visualizations make clear which nodes correspond to inputs,
+where the computation starts,
+and which correspond to output nodes,
+from which the result can be read.
+
+## Prerequisites
+You need the [Jupyter Notebook](http://jupyter.readthedocs.io/en/latest/)
+and [Graphviz](http://www.graphviz.org/) libraries to visualize the network.
+Please make sure you have followed [installation instructions](http://mxnet.io/install/index.html)
+in setting up above dependencies along with setting up MXNet.
+
+## Visualize the sample Neural Network
+
+```mx.viz.plot_network``` takes [Symbol](http://mxnet.io/api/python/symbol/symbol.html), with your Network definition, and optional node_attrs, parameters for the shape of the node in the graph,  as input and generates a computation graph.
+
+We will now try to visualize a sample Neural Network for linear matrix factorization:
+- Start Jupyter notebook server
+```bash
+  $ jupyter notebook
+```
+- Access Jupyter notebook in your browser - http://localhost:8888/.
+- Create a new notebook - "File -> New Notebook -> Python 2"
+- Copy and run below code to visualize a simple network.
+
+```python
+import mxnet as mx
+user = mx.symbol.Variable('user')
+item = mx.symbol.Variable('item')
+score = mx.symbol.Variable('score')
+
+# Set dummy dimensions
+k = 64
+max_user = 100
+max_item = 50
+
+# user feature lookup
+user = mx.symbol.Embedding(data = user, input_dim = max_user, output_dim = k)
+
+# item feature lookup
+item = mx.symbol.Embedding(data = item, input_dim = max_item, output_dim = k)
+
+# predict by the inner product, which is elementwise product and then sum
+net = user * item
+net = mx.symbol.sum_axis(data = net, axis = 1)
+net = mx.symbol.Flatten(data = net)
+
+# loss layer
+net = mx.symbol.LinearRegressionOutput(data = net, label = score)
+
+# Visualize your network
+mx.viz.plot_network(net)
+```
+You should see computation graph something like the following image:
+<img src=https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/SampleNetworkVisualization.png
+width=400/>
+
+# References
+* [Example MXNet Matrix Factorization](https://github.com/dmlc/mxnet/blob/master/example/recommenders/demo1-MF.ipynb)
+* [Visualizing CNN Architecture of MXNet Tutorials](http://josephpcohen.com/w/visualizing-cnn-architectures-side-by-side-with-mxnet/)
diff --git a/docs/static_site/src/pages/api/faq/why_mxnet.md b/docs/static_site/src/pages/api/faq/why_mxnet.md
new file mode 100644
index 000000000000..779458ed0c9c
--- /dev/null
+++ b/docs/static_site/src/pages/api/faq/why_mxnet.md
@@ -0,0 +1,207 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_category
+title: Why MXNet came to be?
+category: faq
+faq_c: Extend and Contribute to MXNet
+question: Why was MXNet developed in the first place ?
+permalink: /api/faq/why_mxnet
+---
+
+# Why was MXNet developed in the first place ?
+
+Probably, if you've stumbled upon this page, you've heard of _deep learning_.
+Deep learning denotes the modern incarnation of neural networks,
+and it's the technology behind recent breakthroughs
+in self-driving cars, machine translation, speech recognition and more.
+While widespread interest in deep learning took off in 2012,
+deep learning has become an indispensable tool for countless industries.
+
+![alt text](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/get-started/image-classification.png)
+
+It might not come as a surprise that researchers
+have investigated neural networks for decades.
+Warren McCulloch and Walter Pitts
+suggested the forerunner of today's artificial neurons back in 1943.
+Each neuron is connected to other neurons along _edges_, analogous to the synapses that connect real neurons.
+And associated with each edge is a _weight_ that indicates whether the connection is excitatory or inhibitatory and the strength of the connection.
+
+![alt_text](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/get-started/artificial-neuron-2.png)
+
+In the 1980s, the modern version of neural networks took shape.
+Researchers arranged artificial neurons into _layers_.
+Neurons in any layer get input from the neurons in the layers below them.
+And, in turn, their output feeds into the neurons in the layer above.
+Typically, the lowest layer represents the _input_ to a neural network.
+After computing the values of each layer, the _output_ values are read out from the topmost layer.
+The behavior of the network is determined by the setting of the weights.
+And the process of _learning_ in neural networks
+is precisely the process of searching for good settings of these _weights_.
+
+All that we need is an algorithm that tells us how to perform this search.
+And since David Rumelhart and colleagues
+introduced the _backpropagation_ learning algorithm to train neural networks,
+nearly all the major ideas have been in place.
+Still, for many years neural networks took a backseat
+to classical statistical methods like logistic regression and support vector machines (SVMs).
+So you might reasonably ask, what's changed to garner such interest?
+
+## Scale and Computation
+The two biggest factors driving innovation in deep learning now are data and computation.
+With distributed cloud computing and parallelism across GPU cores,
+we can train models millions of times faster than researchers could in the 1980s.
+The availability of large, high-quality datasets is another factor driving the field forward.
+In the 1990s, the best datasets in computer vision had thousands of low-resolution images and ground truth assignments to a small number of classes.
+Today, researchers cut their teeth on ImageNet, a massive dataset containing millions of high-resolution images from a thousand distinct classes.
+The falling price of storage and high network bandwidth
+make it affordable to work with big data at will.
+
+In this new world, with bigger datasets and abundant computation,
+neural networks dominate on most pattern recognition problems.
+Over the last five years, neural networks have come to dominate on nearly every problem in computer vision,
+replacing classical models and hand-engineered features.
+Similarly, nearly every production speech recognition system now relies on neural networks,
+where replacing the hidden Markov models that previously held sway.
+
+![alt text](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/get-started/nvidia-gpus.jpg)
+
+While GPUs and clusters present a huge opportunity for accelerating neural network training,
+adapting traditional machine learning code
+to take advantage of these resources can be challenging.
+The familiar scientific computing stacks (Matlab, R, or NumPy & SciPy)
+give no straight-forward way to exploit these distributed resources.
+
+Acceleration libraries like _MXNet_ offer powerful tools
+to help developers exploit the full capabilities of GPUs and cloud computing.
+While these tools are generally useful and applicable to any mathematical computation, _MXNet_ places a special emphasis on speeding up the development and deployment of large-scale deep neural networks. In particular, we offer the following capabilities:
+* __Device Placement:__ With _MXNet_, it's easy to specify where each data structures should live.
+* __Multi-GPU training__: _MXNet_ makes it easy to scale computation with number of available GPUs.
+* __Automatic differentiation__: _MXNet_ automates the derivative calculations that once bogged down neural network research.
+* __Optimized Predefined Layers__: While you can code up your own layers in _MXNet_, the predefined layers are optimized for speed, outperforming competing libraries.
+
+## Deep Nets on Fast Computers
+While MXNet can accelerate any numerical computation,
+we developed the library with neural networks in mind.
+However you plan to use MXNet, neural networks make for a powerful motivating example to display MXNet's capabilities.
+
+Neural networks are just functions for transforming input arrays `X` into output arrays `Y`.
+In the case of image classification, `X` might represent the pixel values of an image, and `Y` might represent the corresponding probabilities that the image belongs to each of `10` classes.
+For language translation, `X` and `Y` both might denote sequences of words. We'll revisit the way you might represent sequences in subsequent tutorials - so for now it's safe to think of `X` and `Y` as fixed length vectors.
+
+To perform this mapping, neural networks stack _layers_ of computation. Each layer consists of a linear function followed by a nonlinear transformation. In _MXNet_ we might express this as:
+```python
+hidden_linear = mx.sym.dot(X, W)
+hidden_activation = mx.sym.tanh(hidden_linear)
+```
+The linear transformations consist of multiplication by parameter arrays (`W` above).
+When we talk about learning we mean finding the right set of values for `W`.
+With just one layer, we can implement the familiar family of linear models,
+including linear and logistic regression, linear support vector machines (SVMs), and the perceptron algorithm.
+With more layers and a few clever constraints, we can implement all of today's state-of-the-art deep learning techniques.
+
+Of course, tens or hundreds of matrix multiplications can be computationally taxing.
+Generally, these linear operations are the computational bottleneck.
+Fortunately, linear operators can be parallelized trivially across the thousands of cores on a GPU.
+But low-level GPU programming requires specialized skills that are not common even among leading researchers in the ML community. Moreover, even for CUDA experts, implementing a new neural network architecture shouldn't require weeks of programming to implement low-level linear algebra operations. That's where _MXNet_ comes in.
+*  _MXNet_ provides optimized numerical computation for GPUs and distributed ecosystems, from the comfort of high-level environments like Python and R
+* _MXNet_ automates common workflows, so standard neural networks can be expressed concisely in just a few lines of code
+
+Now let's take a closer look at the computational demands of neural networks
+and give a sense of how _MXNet_ helps us to write better, faster, code.
+Say we have a neural network trained to recognize spam from the content of emails.
+The emails may be streaming from an online service (at inference time),
+or from a large offline dataset __D__ (at training time).
+In either case, the dataset typically must be managed by the CPU.
+
+![alt text](https://raw.githubusercontent.com/kevinthesun/web-data/master/mxnet/get-started/architecture.png)
+
+To compute the transformation of a neural network quickly, we need both the parameters and data points to make it into GPU memory. For any example _X_, the parameters _W_ are the same. Moreover the size of the model tends to dwarf the size of an individual example. So we might arrive at the natural insight that parameters should always live on the GPU, even if the dataset itself must live on the CPU or stream in. This prevents IO from becoming the bottleneck during training or inference.
+
+Fortunately, _MXNet_ makes this kind of assignment easy.
+```python
+import mxnet.ndarray as nd
+
+X  = nd.zeros((10000, 40000), mx.cpu(0))           #Allocate an array to store 1000 datapoints (of 40k dimensions) that lives on the CPU
+W1 = nd.zeros(shape=(40000, 1024), mx.gpu(0))      #Allocate a 40k x 1024 weight matrix on GPU for the 1st layer of the net
+W2 = nd.zeros(shape=(1024, 10), mx.gpu(0))         #Allocate a 1024 x 1024 weight matrix on GPU for the 2nd layer of the net
+```
+
+<!-- * __Talk about how mxnet also makes it easy to assign a context (on which device the computation happens__ -->
+Similarly, _MXNet_ makes it easy to specify the computing device
+
+```python
+with mx.Context(mx.gpu()):          # Absent this statement, by default, MXNet will execute on CPU
+    h = nd.tanh(nd.dot(X, W1))
+    y = nd.sigmoid(nd.dot(h1, W2))
+```
+
+Thus, with only a high-level understanding of how our numerical computation maps onto an execution environment, _MXNet_ allows us to exert fine-grained control when needed.
+
+## Nuts and Bolts
+
+MXNet supports two styles of programming: _imperative programming_ (supported by the _NDArray_ API) and _symbolic programming_ (supported by the _Symbol_ API). In short, imperative programming is the style that you're likely to be most familiar with. Here if A and B are variables denoting matrices, then `C = A + B` is a piece of code that _when executed_ sums the values referenced by `A` and `B` and stores their sum `C` in a new variable. Symbolic programming, on the other hand, allows functions to be defined abstractly through computation graphs. In the symbolic style, we first express complex functions in terms of placeholder values. Then, we can execute these functions by _binding them_ to real values.
+
+
+### Imperative Programming with _NDArray_
+If you're familiar with NumPy, then the mechanics of _NDArray_ should be old hat. Like the corresponding `numpy.ndarray`, `mxnet.ndarray` (`mxnet.nd` for short) allows us to represent and manipulate multi-dimensional, homogenous arrays of fixed-size components. Converting between the two is effortless:
+
+```python
+# Create a numpy array from an mxnet NDArray
+A_np = np.array([[0,1,2,3,4],[5,6,7,8,9]])
+A_nd = nd.array(A)
+
+# Convert back to a numpy array
+A2_np = A_nd.asnumpy()
+```
+
+Other deep learning libraries tend to rely on NumPy exclusively for imperative programming and the syntax.
+So you might reasonably wonder, why do we need to bother with _NDArray_?
+Put simply, other libraries only reap the advantages of GPU computing when executing symbolic functions. By using _NDArray_, _MXNet_ users can specify device context and run on GPUs. In other words, _MXNet_ gives you access to the high-speed computation for imperative operations that Tensorflow and Theano only give for symbolic operations.
+
+
+```python
+X = mx.nd.array([[1,2],[3,4]])
+Y = mx.nd.array([[5,6],[7,8]])
+result = X + Y
+```
+
+
+### Symbolic Programming in _MXNet_
+
+In addition to providing fast math operations through NDArray, _MXNet_ provides an interface for defining operations abstractly via a computation graph.
+With `mxnet.symbol`, we define operations abstractly in terms of place holders. For example, in the following code `a` and `b` stand in for real values that will be supplied at run time.
+When we call `c = a+b`, no numerical computation is performed. This operation simply builds a graph that defines the relationship between `a`, `b` and `c`. In order to perform a real calculation, we need to bind `c` to real values.
+
+```python
+a = mx.sym.Variable('a')
+b = mx.sym.Variable('b')
+c = a + b
+executor = c.bind(mx.cpu(), {'a': X, 'b': Y})
+result = executor.forward()
+```
+
+Symbolic computation is useful for several reasons. First, because we define a full computation graph before executing it, _MXNet_ can perform sophisticated optimizations to eliminate unnecessary or repeated work. This tends to give better performance than imperative programming. Second, because we store the relationships between different variables in the computation graph, _MXNet_ can then perform efficient auto-differentiation.
+
+**However** Symbolic programming is error-prone and very slow to iterate with, as the graph needs to be computed before it is processed.
+
+### Gluon for briding the gap between the two
+
+[MXNet Gluon]({{'/api/python'|relative_url}}) aims to bridge the gap between the imperative nature of MXNet and its symbolic capabilities and keep the advantages of both through [hybridization](http://d2l.ai/chapter_computational-performance/hybridize.html).
+
+## Conclusions
+Given its combination of high performance, clean code, access to a high-level API, and low-level control, _MXNet_ stands out as a unique choice among deep learning frameworks.
diff --git a/docs/static_site/src/pages/api/java/docs/tutorials/index.md b/docs/static_site/src/pages/api/java/docs/tutorials/index.md
new file mode 100644
index 000000000000..00806e7911c9
--- /dev/null
+++ b/docs/static_site/src/pages/api/java/docs/tutorials/index.md
@@ -0,0 +1,22 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_landing_tutorials
+title: Java Tutorials
+permalink: /api/java/docs/tutorials
+tag: java
+---
diff --git a/docs/static_site/src/pages/api/java/docs/tutorials/mxnet_java_on_intellij.md b/docs/static_site/src/pages/api/java/docs/tutorials/mxnet_java_on_intellij.md
new file mode 100644
index 000000000000..4bfc8ab419bf
--- /dev/null
+++ b/docs/static_site/src/pages/api/java/docs/tutorials/mxnet_java_on_intellij.md
@@ -0,0 +1,188 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: Java with IntelliJ
+permalink: /api/java/docs/tutorials/mxnet_java_on_intellij
+is_tutorial: true
+tag: java
+---
+
+
+# Run MXNet Java Examples Using the IntelliJ IDE (macOS)
+
+This tutorial guides you through setting up a simple Java project in IntelliJ IDE on macOS and demonstrates usage of the MXNet Java APIs.
+
+## Prerequisites:
+To use this tutorial you need the following pre-requisites:
+
+- [Java 8 JDK](http://www.oracle.com/technetwork/java/javase/downloads/index.html)
+- [Maven](https://maven.apache.org/install.html)
+- [OpenCV](https://opencv.org/)
+- [IntelliJ IDEA](https://www.jetbrains.com/idea/) (One can download the community edition from [here](https://www.jetbrains.com/idea/download))
+
+### MacOS Prerequisites
+
+Run the following commands to install the prerequisites on MacOS.
+```
+/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
+brew update
+brew tap caskroom/versions
+brew cask install java8
+brew install maven
+brew install opencv
+```
+
+### Ubuntu Prerequisites
+
+Run the following commands to install the prerequisites on Ubuntu.
+
+```
+sudo apt-get install openjdk-8-jdk maven
+```
+
+
+## Set Up Your Project
+
+**Step 1.** Install and setup [IntelliJ IDEA](https://www.jetbrains.com/idea/)
+
+**Step 2.** Create a new Project:
+
+![intellij welcome](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/scala/intellij-welcome.png)
+
+From the IntelliJ welcome screen, select "Create New Project".
+
+Choose the Maven project type.
+
+Select the checkbox for `Create from archetype`, then choose `org.apache.maven.archetypes:maven-archetype-quickstart` from the list below. More on this can be found on a Maven tutorial : [Maven in 5 Minutes](https://maven.apache.org/guides/getting-started/maven-in-five-minutes.html).
+
+![maven project type - archetype](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tutorials/java/project-archetype.png)
+
+click `Next`.
+
+![project metadata](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tutorials/java/intellij-project-metadata.png)
+
+Set the project's metadata. For this tutorial, use the following:
+
+**GroupId**
+```
+mxnet
+```
+**ArtifactId**
+```
+javaMXNet
+```
+**Version**
+```
+1.0-SNAPSHOT
+```
+
+![project properties](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tutorials/java/intellij-project-properties.png)
+
+Review the project's properties. The settings can be left as their default.
+
+![project location](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tutorials/java/intellij-project-location.png)
+
+Set the project's location. The rest of the settings can be left as their default.
+
+![project 1](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tutorials/java/intellij-project-pom.png)
+
+After clicking Finish, you will be presented with the project's first view.
+The project's `pom.xml` will be open for editing.
+
+**Step 3.** The Java packages are currently available on Maven. Add the following under the `dependencies` tag :
+
+```html
+<dependency>
+  <groupId>org.apache.mxnet</groupId>
+  <artifactId>mxnet-full_2.11-osx-x86_64-cpu</artifactId>
+  <version>1.4.0</version>
+</dependency>
+```
+The official Java Packages have been released as part of MXNet 1.4 and are available on the [MXNet Maven package repository](https://search.maven.org/#search%7Cga%7C1%7Cg%3A%22org.apache.mxnet%22).
+
+Note :
+- Change the osx-x86_64 to linux-x86_64 if your platform is linux.
+- Change cpu into gpu if you have a gpu backed machine and want to use gpu.
+
+
+**Step 4.** Import dependencies with Maven:
+
+  - Note the prompt in the lower right corner that states "Maven projects need to be imported". If this is not visible, click on the little greed balloon that appears in the lower right corner.
+
+![import_dependencies](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tutorials/java/project-import-changes.png)
+
+Click "Import Changes" in this prompt.
+
+**Step 5.** Build the project:
+- To build the project, from the menu choose Build, and then choose Build Project.
+
+**Step 6.** Navigate to the App.java class in the project and paste the code in `main` method from HelloWorld.java from [Java Demo project](https://github.com/apache/incubator-mxnet/blob/java-api/scala-package/mxnet-demo/java-demo/src/main/java/mxnet/HelloWorld.java) on MXNet repository, overwriting the original hello world code.
+You can also grab the entire [Java Demo project](https://github.com/apache/incubator-mxnet/tree/java-api/scala-package/mxnet-demo/java-demo) and run it by following the instructions on the [README](https://github.com/apache/incubator-mxnet/blob/java-api/scala-package/mxnet-demo/java-demo/README.md).
+
+**Step 7.** Now run the App.java.
+
+The result should be something similar to this:
+
+```
+Hello World!
+(1,2)
+Process finished with exit code 0
+```
+
+### Troubleshooting
+
+If you get an error, check the dependencies at the beginning of this tutorial. For example, you might see the following in the middle of the error messages, where `x.x` would the version it's looking for.
+
+```
+...
+Library not loaded: /usr/local/opt/opencv/lib/libopencv_calib3d.x.x.dylib
+...
+```
+
+This can be resolved be installing OpenCV.
+
+### Command Line Build Option
+
+- You can also compile the project by using the following command at the command line. Change directories to this project's root folder then run the following:
+
+```bash
+mvn clean install dependency:copy-dependencies
+```
+If the command succeeds, you should see a lot of info and some warning messages, followed by:
+
+```bash
+[INFO] ------------------------------------------------------------------------
+[INFO] BUILD SUCCESS
+[INFO] ------------------------------------------------------------------------
+[INFO] Total time: 3.475 s
+[INFO] Finished at: 2018-11-08T05:06:31-08:00
+[INFO] ------------------------------------------------------------------------
+```
+The build generates a new jar file in the `target` folder called `javaMXNet-1.0-SNAPSHOT.jar`.
+
+To run the App.java use the following command from the project's root folder and you should see the same output as we got when the project was run from IntelliJ.
+```bash
+java -cp "target/javaMXNet-1.0-SNAPSHOT.jar:target/dependency/*" mxnet.App
+```
+
+## Next Steps
+For more information about MXNet Java resources, see the following:
+
+* [Java Inference API](/api/java/index.html)
+* [Java Inference Examples](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer)
+* [MXNet Tutorials Index](http://mxnet.io/tutorials/index.html)
diff --git a/docs/static_site/src/pages/api/java/docs/tutorials/ssd_inference.md b/docs/static_site/src/pages/api/java/docs/tutorials/ssd_inference.md
new file mode 100644
index 000000000000..3f30402628c0
--- /dev/null
+++ b/docs/static_site/src/pages/api/java/docs/tutorials/ssd_inference.md
@@ -0,0 +1,210 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: SSD Inference
+permalink: /api/java/docs/tutorials/ssd_inference
+is_tutorial: true
+tag: java
+---
+
+# Multi Object Detection using pre-trained SSD Model via Java Inference APIs
+
+This tutorial shows how to use MXNet Java Inference APIs to run inference on a pre-trained Single Shot Detector (SSD) Model.
+
+The SSD model is trained on the Pascal VOC 2012 dataset. The network is a SSD model built on Resnet50 as the base network to extract image features. The model is trained to detect the following entities (classes): ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']. For more details about the model, you can refer to the [MXNet SSD example](https://github.com/apache/incubator-mxnet/tree/master/example/ssd).
+
+## Prerequisites
+
+To complete this tutorial, you need the following:
+* [MXNet Java Setup on IntelliJ IDEA](mxnet_java_on_intellij.md) (Optional)
+* [wget](https://www.gnu.org/software/wget/) To download model artifacts
+* SSD Model artifacts
+    * Use the following script to get the SSD Model files :
+```bash
+data_path=/tmp/resnet50_ssd
+mkdir -p "$data_path"
+wget https://s3.amazonaws.com/model-server/models/resnet50_ssd/resnet50_ssd_model-symbol.json -P $data_path
+wget https://s3.amazonaws.com/model-server/models/resnet50_ssd/resnet50_ssd_model-0000.params -P $data_path
+wget https://s3.amazonaws.com/model-server/models/resnet50_ssd/synset.txt -P $data_path
+```
+* Test images  : A few sample images to run inference on.
+    * Use the following script to download sample images :
+```bash
+image_path=/tmp/resnet50_ssd/images
+mkdir -p "$image_path"
+cd $image_path
+wget https://cloud.githubusercontent.com/assets/3307514/20012567/cbb60336-a27d-11e6-93ff-cbc3f09f5c9e.jpg -O dog.jpg
+wget https://cloud.githubusercontent.com/assets/3307514/20012563/cbb41382-a27d-11e6-92a9-18dab4fd1ad3.jpg -O person.jpg
+```
+
+Alternately, you can get the entire SSD Model artifacts + images in one single script from the MXNet Repository by running [get_ssd_data.sh script](https://github.com/apache/incubator-mxnet/blob/master/scala-package/examples/scripts/infer/objectdetector/get_ssd_data.sh)
+
+## Time to code!
+1\. Following the [MXNet Java Setup on IntelliJ IDEA](mxnet_java_on_intellij.md) tutorial, in the same project `JavaMXNet`, create a new empty class called : `ObjectDetectionTutorial.java`.
+
+2\. In the `main` function of `ObjectDetectionTutorial.java` define the downloaded model path and the image data paths. This is the same path where we downloaded the model artifacts and images in a previous step.
+
+```java
+String modelPathPrefix = "/tmp/resnet50_ssd/resnet50_ssd_model";
+String inputImagePath = "/tmp/resnet50_ssd/images/dog.jpg";
+```
+
+3\. We can run the inference code in this example on either CPU or GPU (if you have a GPU backed machine) by choosing the appropriate context.
+
+```java
+
+List<Context> context = getContext();
+...
+
+private static List<Context> getContext() {
+List<Context> ctx = new ArrayList<>();
+ctx.add(Context.cpu()); // Choosing CPU Context here
+
+return ctx;
+}
+```
+
+4\. To provide an input to the model, define the input shape to the model and the Input Data Descriptor (DataDesc) as shown below :
+
+```java
+Shape inputShape = new Shape(new int[] {1, 3, 512, 512});
+List<DataDesc> inputDescriptors = new ArrayList<DataDesc>();
+inputDescriptors.add(new DataDesc("data", inputShape, DType.Float32(), "NCHW"));
+```
+
+The input shape can be interpreted as follows : The input has a batch size of 1, with 3 RGB channels in the image, and the height and width of the image is 512 each.
+
+5\. To run an actual inference on the given image, add the following lines to the `ObjectDetectionTutorial.java` class :
+
+```java
+BufferedImage img = ObjectDetector.loadImageFromFile(inputImagePath);
+ObjectDetector objDet = new ObjectDetector(modelPathPrefix, inputDescriptors, context, 0);
+List<List<ObjectDetectorOutput>> output = objDet.imageObjectDetect(img, 3); // Top 3 objects detected will be returned
+```
+
+6\. Let's piece all of the above steps together by showing the final contents of the `ObjectDetectionTutorial.java`.
+
+```java
+package mxnet;
+
+import org.apache.mxnet.infer.javaapi.ObjectDetector;
+import org.apache.mxnet.infer.javaapi.ObjectDetectorOutput;
+import org.apache.mxnet.javaapi.Context;
+import org.apache.mxnet.javaapi.DType;
+import org.apache.mxnet.javaapi.DataDesc;
+import org.apache.mxnet.javaapi.Shape;
+
+import java.awt.image.BufferedImage;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+
+public class ObjectDetectionTutorial {
+
+    public static void main(String[] args) {
+
+        String modelPathPrefix = "/tmp/resnet50_ssd/resnet50_ssd_model";
+
+        String inputImagePath = "/tmp/resnet50_ssd/images/dog.jpg";
+
+        List<Context> context = getContext();
+
+        Shape inputShape = new Shape(new int[] {1, 3, 512, 512});
+
+        List<DataDesc> inputDescriptors = new ArrayList<DataDesc>();
+        inputDescriptors.add(new DataDesc("data", inputShape, DType.Float32(), "NCHW"));
+
+        BufferedImage img = ObjectDetector.loadImageFromFile(inputImagePath);
+        ObjectDetector objDet = new ObjectDetector(modelPathPrefix, inputDescriptors, context, 0);
+        List<List<ObjectDetectorOutput>> output = objDet.imageObjectDetect(img, 3);
+
+        printOutput(output, inputShape);
+    }
+
+
+    private static List<Context> getContext() {
+        List<Context> ctx = new ArrayList<>();
+        ctx.add(Context.cpu());
+
+        return ctx;
+    }
+
+    private static void printOutput(List<List<ObjectDetectorOutput>> output, Shape inputShape) {
+
+        StringBuilder outputStr = new StringBuilder();
+
+        int width = inputShape.get(3);
+        int height = inputShape.get(2);
+
+        for (List<ObjectDetectorOutput> ele : output) {
+            for (ObjectDetectorOutput i : ele) {
+                outputStr.append("Class: " + i.getClassName() + "\n");
+                outputStr.append("Probabilties: " + i.getProbability() + "\n");
+
+                List<Float> coord = Arrays.asList(i.getXMin() * width,
+                        i.getXMax() * height, i.getYMin() * width, i.getYMax() * height);
+                StringBuilder sb = new StringBuilder();
+                for (float c: coord) {
+                    sb.append(", ").append(c);
+                }
+                outputStr.append("Coord:" + sb.substring(2)+ "\n");
+            }
+        }
+        System.out.println(outputStr);
+
+    }
+}
+```
+
+7\. To compile and run this code, change directories to this project's root folder, then run the following:
+```bash
+mvn clean install dependency:copy-dependencies
+```
+
+The build generates a new jar file in the `target` folder called `javaMXNet-1.0-SNAPSHOT.jar`.
+
+To run the ObjectDetectionTutorial.java use the following command from the project's root folder.
+```bash
+java -cp "target/javaMXNet-1.0-SNAPSHOT.jar:target/dependency/*" mxnet.ObjectDetectionTutorial
+```
+
+You should see a similar output being generated for the dog image that we used:
+```bash
+Class: car
+Probabilties: 0.99847263
+Coord:312.21335, 72.02908, 456.01443, 150.66176
+Class: bicycle
+Probabilties: 0.9047381
+Coord:155.9581, 149.96365, 383.83694, 418.94516
+Class: dog
+Probabilties: 0.82268167
+Coord:83.82356, 179.14001, 206.63783, 476.78754
+```
+
+![dog_1](https://cloud.githubusercontent.com/assets/3307514/20012567/cbb60336-a27d-11e6-93ff-cbc3f09f5c9e.jpg)
+
+The results returned by the inference call translate into the regions in the image where the model detected objects.
+
+![dog_2](https://cloud.githubusercontent.com/assets/3307514/19171063/91ec2792-8be0-11e6-983c-773bd6868fa8.png)
+
+## Next Steps
+For more information about MXNet Java resources, see the following:
+
+* [Java Inference API](/api/java/index.md)
+* [Java Inference Examples](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer)
+* [MXNet Tutorials Index](/tutorials/index.md)
diff --git a/docs/static_site/src/pages/api/java/index.md b/docs/static_site/src/pages/api/java/index.md
new file mode 100644
index 000000000000..56d779b30703
--- /dev/null
+++ b/docs/static_site/src/pages/api/java/index.md
@@ -0,0 +1,41 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: Java Guide
+action: Get Started
+action_url: /get_started
+permalink: /api/java
+tag: java
+---
+
+
+# MXNet - Java Inference API
+
+MXNet supports Java for performing inference on a trained model. The MXNet Java Inference API is an extension of the [Scala Infer API](../../api/scala/infer.html) which provides model loading and inference functionality.
+
+The goal of the MXNet Java package is to provide an efficient and easy to use inference API.
+The MXNet Java package makes it easy to quickly deploy an existing model into a production level Java ecosystem.
+
+## Installation
+* [MXNet Java Inference API setup instructions](../../install/java_setup.md)
+
+## Tutorials
+See the [tutorial page](../../tutorials/index.html#java-tutorials) for detailed tutorials and examples using the Java Inference API.
+
+## Java Inference API Reference
+The [Java Infer API javadocs](docs/index.html#org.apache.mxnet.infer.package) provides detailed API information.
diff --git a/docs/static_site/src/pages/api/julia/index.md b/docs/static_site/src/pages/api/julia/index.md
new file mode 100644
index 000000000000..10c08da3ec90
--- /dev/null
+++ b/docs/static_site/src/pages/api/julia/index.md
@@ -0,0 +1,39 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: Julia Guide
+action: Get Started
+action_url: /get_started
+permalink: /api/julia
+tag: julia
+---
+
+
+# MXNet - Julia API
+
+
+MXNet supports the Julia programming language. The MXNet Julia package brings flexible and efficient GPU
+computing and the state-of-art deep learning to Julia.
+
+- It enables you to write seamless tensor/matrix computation with multiple GPUs in Julia.
+- It also enables you to construct and customize the state-of-art deep learning models in Julia,
+  and apply them to tasks such as image classification and data science challenges.
+
+## Installation
+* [Ubuntu installation guide]({{'/get_started' | relative_url}})
+* Mac / Windows guides are not available (contributions welcome!)
diff --git a/docs/static_site/src/pages/api/perl/docs/tutorials/index.md b/docs/static_site/src/pages/api/perl/docs/tutorials/index.md
new file mode 100644
index 000000000000..e98a3e4b4613
--- /dev/null
+++ b/docs/static_site/src/pages/api/perl/docs/tutorials/index.md
@@ -0,0 +1,23 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_landing_tutorials
+title:  Perl Tutorials
+action: Get Started
+tag: perl
+permalink: /api/perl/docs/tutorials
+---
diff --git a/docs/static_site/src/pages/api/perl/docs/tutorials/io.md b/docs/static_site/src/pages/api/perl/docs/tutorials/io.md
new file mode 100644
index 000000000000..b9ce200c5c2e
--- /dev/null
+++ b/docs/static_site/src/pages/api/perl/docs/tutorials/io.md
@@ -0,0 +1,136 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: Data Loading API
+is_tutorial: true
+tag: perl
+permalink: /api/perl/docs/tutorials/io
+---
+
+# Data Loading API
+
+## Overview
+
+A data iterator reads data batch by batch.
+
+```perl
+pdl> $data = mx->nd->ones([100,10])
+pdl> $nd_iter = mx->io->NDArrayIter($data, batch_size=>25)
+pdl> for my $batch (@{ $nd_iter }) { print $batch->data->[0],"\n" }
+<AI::MXNet::NDArray 25x10 @cpu(0)>
+<AI::MXNet::NDArray 25x10 @cpu(0)>
+<AI::MXNet::NDArray 25x10 @cpu(0)>
+<AI::MXNet::NDArray 25x10 @cpu(0)>
+```
+
+If `$nd_iter->reset()` is called, then reads the data again from beginning.
+
+In addition, an iterator provides information about the batch, including the
+shapes and name.
+
+```perl
+pdl> $nd_iter = mx->io->NDArrayIter(data=>{data => mx->nd->ones([100,10])}, label=>{softmax_label => mx->nd->ones([100])}, batch_size=>25)
+pdl> print($nd_iter->provide_data->[0],"\n")
+DataDesc[data,25x10,float32,NCHW]
+pdl> print($nd_iter->provide_label->[0],"\n")
+DataDesc[softmax_label,25,float32,NCHW]
+```
+
+So this iterator can be used to train a symbol whose input data variable has
+name `data` and input label variable has name `softmax_label`.
+
+
+```perl
+pdl> $data  = mx->sym->Variable('data')
+pdl> $label = mx->sym->Variable('softmax_label')
+pdl> $fullc = mx->sym->FullyConnected(data=>$data, num_hidden=>1)
+pdl> $loss  = mx->sym->SoftmaxOutput(data=>$data, label=>$label)
+pdl> $mod   = mx->mod->Module($loss)
+pdl> print($mod->data_names->[0])
+data
+pdl> print($mod->label_names->[0])
+softmax_label
+pdl> $mod->bind(data_shapes=>$nd_iter->provide_data, label_shapes=>$nd_iter->provide_label)
+```
+
+Then we can call `$mod->fit($nd_iter, num_epoch=>2)` to train `loss` by 2 epochs.
+
+## Predefined Data iterators
+
+```perl
+mx->io->NDArrayIter
+mx->io->CSVIter
+mx->io->ImageRecordIter
+mx->io->ImageRecordInt8Iter
+mx->io->ImageRecordUInt8Iter
+mx->io->MNISTIter
+mx->recordio->MXRecordIO
+mx->recordio->MXIndexedRecordIO
+mx->image->ImageIter
+```
+
+## Helper classes and functions
+
+Data structures and other iterators provided in the `AI::MXNet::IO` package.
+
+```perl
+AI::MXNet::DataDesc
+AI::MXNet::DataBatch
+AI::MXNet::DataIter
+AI::MXNet::ResizeIter
+AI::MXNet::MXDataIter
+```
+
+A list of image modification functions provided by `AI::MXNet::Image`.
+
+```perl
+mx->image->imdecode
+mx->image->scale_down
+mx->image->resize_short
+mx->image->fixed_crop
+mx->image->random_crop
+mx->image->center_crop
+mx->image->color_normalize
+mx->image->random_size_crop
+mx->image->ResizeAug
+mx->image->RandomCropAug
+mx->image->RandomSizedCropAug
+mx->image->CenterCropAug
+mx->image->RandomOrderAug
+mx->image->ColorJitterAug
+mx->image->LightingAug
+mx->image->ColorNormalizeAug
+mx->image->HorizontalFlipAug
+mx->image->CastAug
+mx->image->CreateAugmenter
+```
+
+Functions to read and write RecordIO files.
+
+```perl
+mx->recordio->pack
+mx->recordio->unpack
+mx->recordio->unpack_img
+```
+
+## Develop a new iterator
+
+Writing a new data iterator in Perl is straightforward. Most MXNet
+training/inference programs accept an object with ``provide_data``
+and ``provide_label`` properties.
+Please refer to AI-MXNet/examples for the examples of custom iterators.
diff --git a/docs/static_site/src/pages/api/perl/docs/tutorials/kvstore.md b/docs/static_site/src/pages/api/perl/docs/tutorials/kvstore.md
new file mode 100644
index 000000000000..ea0343f7162f
--- /dev/null
+++ b/docs/static_site/src/pages/api/perl/docs/tutorials/kvstore.md
@@ -0,0 +1,134 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: KVStore API
+is_tutorial: true
+tag: perl
+permalink: /api/perl/docs/tutorials/kvstore
+---
+
+
+# KVStore API
+
+Topics:
+* [Basic Push and Pull](#basic-push-and-pull)
+* [List Key-Value Pairs](#list-key-value-pairs)
+
+## Basic Push and Pull
+
+Provides basic operation over multiple devices (GPUs) on a single device.
+
+### Initialization
+
+Let's consider a simple example. It initializes
+a (int, NDArray) pair into the store, and then pulls the value out.
+
+```perl
+pdl> $kv = mx->kv->create('local')
+pdl> $shape = [2,3]
+pdl> $kv->init(3, mx->nd->ones($shape)*2)
+pdl> $a = mx->nd->zeros($shape)
+pdl> $kv->pull(3, out => $a)
+pdl> print $a->aspdl
+[
+ [2 2 2]
+ [2 2 2]
+]
+```
+
+### Push, Aggregation, and Updater
+
+For any key that's been initialized, you can push a new value with the same shape to the key, as follows:
+
+```perl
+pdl> $kv->push(3, mx->nd->ones($shape)*8)
+pdl> $a = mx->nd->zeros($shape)
+pdl> $kv->pull(3, out => $a)
+pdl> print $a->aspdl
+[
+ [8 8 8]
+ [8 8 8]
+]
+```
+
+The data that you want to push can be stored on any device. Furthermore, you can push multiple
+values into the same key, where KVStore first sums all of these
+values, and then you pull the aggregated value, as follows:
+
+```perl
+pdl> $kv->push(3, [mx->nd->ones($shape, ctx=>mx->cpu(0)), mx->nd->ones($shape, ctx=>mx->cpu(1))])
+pdl> $kv->pull(3, out => $a)
+pdl> print $a->aspdl
+[
+ [2 2 2]
+ [2 2 2]
+]
+```
+
+For each push command, KVStore applies the pushed value to the value stored by an
+`updater`. The default updater is `ASSIGN`. You can replace the default to
+control how data is merged.
+
+```perl
+pdl> $updater = sub { my ($key, $input, $stored) = @_; print "update on key: $key\n"; $stored += $input * 3; }
+pdl> $kv->_set_updater($updater)
+pdl> $kv->push(3, [mx->nd->ones($shape, ctx=>mx->cpu(0)), mx->nd->ones($shape, ctx=>mx->cpu(1))])
+update on key: 3
+pdl> $kv->pull(3, out => $a)
+pdl> print $a->aspdl
+[
+ [8 8 8]
+ [8 8 8]
+]
+```
+
+### Pull
+
+You've already seen how to pull a single key-value pair. Similar to the way that you use the push command, you can
+pull the value into several devices with a single call.
+
+```perl
+pdl> $b = [mx->nd->zeros($shape, ctx=>mx->cpu(0)), mx->nd->zeros($shape, ctx=>mx->cpu(1))]
+pdl> $kv->pull(3, out => $b)
+pdl> print $b->[1]->aspdl
+[
+ [8 8 8]
+ [8 8 8]
+]
+```
+
+## List Key-Value Pairs
+
+All of the operations that we've discussed so far are performed on a single key. KVStore also provides
+the interface for generating a list of key-value pairs. For a single device, use the following:
+
+```perl
+pdl> $keys = [5,7,9]
+pdl> $kv->init($keys, [map { mx->nd->ones($shape) } 0..@$keys-1])
+pdl> $kv->push($keys, [map { mx->nd->ones($shape) } 0..@$keys-1])
+update on key: 5
+update on key: 7
+update on key: 9
+pdl> $b = [map { mx->nd->ones($shape) } 0..@$keys-1]
+pdl> $kv->pull($keys, out => $b)
+pdl> print $b->[1]->aspdl
+[
+ [4 4 4]
+ [4 4 4]
+]
+```
diff --git a/docs/static_site/src/pages/api/perl/docs/tutorials/module.md b/docs/static_site/src/pages/api/perl/docs/tutorials/module.md
new file mode 100644
index 000000000000..deb25ad92348
--- /dev/null
+++ b/docs/static_site/src/pages/api/perl/docs/tutorials/module.md
@@ -0,0 +1,70 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: Module API
+is_tutorial: true
+tag: perl
+permalink: /api/perl/docs/tutorials/module
+---
+
+
+# Module API
+
+## Overview
+
+The module API, defined in the `module` (or simply `mod`) package (`AI::MXNet::Module` under the hood), provides an
+intermediate and high-level interface for performing computation with a
+`AI::MXNet::Symbol` or just `mx->sym`. One can roughly think a module is a machine which can execute a
+program defined by a `Symbol`.
+
+The class `AI::MXNet::Module` is a commonly used module, which accepts a `AI::MXNet::Symbol` as
+the input:
+
+```perl
+pdl> $data = mx->symbol->Variable('data')
+pdl> $fc1  = mx->symbol->FullyConnected($data, name=>'fc1', num_hidden=>128)
+pdl> $act1 = mx->symbol->Activation($fc1, name=>'relu1', act_type=>"relu")
+pdl> $fc2  = mx->symbol->FullyConnected($act1, name=>'fc2', num_hidden=>10)
+pdl> $out  = mx->symbol->SoftmaxOutput($fc2, name => 'softmax')
+pdl> $mod  = mx->mod->Module($out)  # create a module by given a Symbol
+```
+
+Assume there is a valid MXNet data iterator `data`. We can initialize the
+module:
+
+```perl
+pdl> $mod->bind(data_shapes=>$data->provide_data,
+         label_shapes=>$data->provide_label)  # create memory by given input shapes
+pdl> $mod->init_params()  # initial parameters with the default random initializer
+```
+
+Now the module is able to compute. We can call high-level API to train and
+predict:
+
+```perl
+pdl> $mod->fit($data, num_epoch=>10, ...)  # train
+pdl> $mod->predict($new_data)  # predict on new data
+```
+
+or use intermediate APIs to perform step-by-step computations
+
+```perl
+pdl> $mod->forward($data_batch, is_train => 1)  # forward on the provided data batch
+pdl> $mod->backward()  # backward to calculate the gradients
+pdl> $mod->update()  # update parameters using the default optimizer
+```
diff --git a/docs/static_site/src/pages/api/perl/docs/tutorials/ndarray.md b/docs/static_site/src/pages/api/perl/docs/tutorials/ndarray.md
new file mode 100644
index 000000000000..18ae72300c07
--- /dev/null
+++ b/docs/static_site/src/pages/api/perl/docs/tutorials/ndarray.md
@@ -0,0 +1,66 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: NDArray API
+is_tutorial: true
+tag: perl
+permalink: /api/perl/docs/tutorials/ndarray
+---
+
+# NDArray API
+
+## Overview
+
+A `AI::MXNet::NDArray` is a multidimensional container of items of the same type and
+size. Various methods for data manipulation and computation are provided.
+
+```perl
+pdl> $x = mx->nd->array([[1, 2, 3], [4, 5, 6]])
+pdl> print $x->aspdl->shape
+[3, 2]
+pdl> $y = $x + mx->nd->ones($x->shape)*3
+pdl> print $y->aspdl
+[
+ [4 5 6]
+ [7 8 9]
+]
+pdl> $z = $y->as_in_context(mx->gpu(0))
+pdl> print $z,"\n"
+<AI::MXNet::NDArray 2x3 @gpu(0)>
+```
+
+A detailed tutorial is available at
+[http://mxnet.io/tutorials/basic/ndarray.html](http://mxnet.io/tutorials/basic/ndarray.html).
+
+Note: AI::MXNet::NDarray is similar to numpy.ndarray in some aspects. But the difference is not negligible. For example
+
+- AI::MXNet::NDArray->T does real data transpose to return new a copied array, instead
+     of returning a view of the input array.
+- AI::MXNet::NDArray->dot performs dot between the last axis of the first input array
+     and the first axis of the second input, while numpy.dot uses the second
+     last axis of the input array.
+
+In additional, NDArray supports GPU computation and various neural
+network layers.
+
+AI::MXNet::NDarray also provides almost same routines as AI::MXNet::symbol. Most
+routines between these two packages share the same C++ operator source
+codes. But AI::MXNet::NDarray differs from AI::MXNet::Symbol in several aspects:
+
+- AI::MXNet::NDArray adopts imperative programming, namely sentences are executed
+     step-by-step so that the results can be obtained immediately.
diff --git a/docs/static_site/src/pages/api/perl/docs/tutorials/symbol.md b/docs/static_site/src/pages/api/perl/docs/tutorials/symbol.md
new file mode 100644
index 000000000000..94d416a2d0b2
--- /dev/null
+++ b/docs/static_site/src/pages/api/perl/docs/tutorials/symbol.md
@@ -0,0 +1,151 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: Symbol API
+is_tutorial: true
+tag: perl
+permalink: /api/perl/docs/tutorials/symbol
+---
+
+# MXNet Perl Symbolic API
+
+Topics:
+
+* [How to Compose Symbols](#how-to-compose-symbols) introduces operator overloading of symbols.
+* [Symbol Attributes](#symbol-attributes) describes how to attach attributes to symbols.
+* [Serialization](#serialization) explains how to save and load symbols.
+* [Executing Symbols](#executing-symbols) explains how to evaluate the symbols with data.
+* [Multiple Outputs](#multiple-outputs) explains how to configure multiple outputs.
+
+## How to Compose Symbols
+
+The symbolic API provides a way to configure computation graphs.
+You can configure the graphs either at the level of neural network layer operations or as fine-grained operations.
+
+The following example configures a two-layer neural network.
+
+```perl
+pdl> use AI::MXNet qw(mx)
+pdl> $data = mx->symbol->Variable("data")
+pdl> $fc1  = mx->symbol->FullyConnected(data => $data, name => "fc1", num_hidden => 128)
+pdl> $act1 = mx->symbol->Activation(data => $fc1, name => "relu1", act_type => "relu")
+pdl> $fc2 =  mx->symbol->FullyConnected(data => $act1, name => "fc2", num_hidden => 64)
+pdl> $net =  mx->symbol->SoftmaxOutput(data => $fc2, name => "out")
+```
+
+The basic arithmetic operators (plus, minus, div, multiplication) are overloaded for
+*element-wise operations* of symbols.
+
+The following example creates a computation graph that adds two inputs together.
+
+```perl
+pdl> use AI::MXNet qw(mx)
+pdl> $a =  mx->symbol->Variable("a")
+pdl> $b =  mx->symbol->Variable("b")
+pdl> $c = $a + $b
+```
+
+## Symbol Attributes
+
+You can add an attribute to a symbol by providing an attribute hash when you create a symbol.
+
+```perl
+$data =  mx->symbol->Variable("data", attr => { mood => "angry" })
+$op   =  mx->symbol->Convolution(data => $data, kernel => [1, 1], num_filter => 1, attr => { mood => "so so" })
+```
+
+For proper communication with the C++ backend, both the key and values of the attribute dictionary should be strings. To retrieve the attributes, use `->attr($key)`:
+
+```
+    $data->attr("mood")
+```
+
+To attach attributes, you can use ```AI::MXNet::AttrScope```. ```AI::MXNet::AttrScopeAttrScope``` automatically adds
+the specified attributes to all of the symbols created within that scope.
+The user can also inherit this object to change naming behavior. For example:
+
+```perl
+use AI::MXNet qw(mx);
+use Test::More tests => 3;
+my ($data, $gdata);
+{
+    local($mx::AttrScope) = mx->AttrScope(group=>4, data=>'great');
+    $data = mx->sym->Variable("data", attr => { dtype => "data", group => "1" });
+    $gdata = mx->sym->Variable("data2");
+}
+ok($gdata->attr("group") == 4);
+ok($data->attr("group") == 1);
+
+my $exceedScopeData = mx->sym->Variable("data3");
+ok((not defined $exceedScopeData->attr("group")), "No group attr in global attr scope");
+```
+
+## Serialization
+
+There are two ways to save and load the symbols. You can use the `mx->symbol->save` and `mxnet->symbol->load` functions to serialize the ```AI::MXNet::Symbol``` objects.
+The advantage of using `save` and `load` functions is that it is language agnostic and cloud friendly.
+The symbol is saved in JSON format. You can also get a JSON string directly using `$symbol->tojson`.
+
+The following example shows how to save a symbol to an S3 bucket, load it back, and compare two symbols using a JSON string.
+
+```perl
+pdl> use AI::MXNet qw(mx)
+pdl> $a = mx->sym->Variable("a")
+pdl> $b = mx->sym->Variable("b")
+pdl> $c = $a + $b
+pdl> $c->save("s3://my-bucket/symbol-c.json")
+pdl> $c2 = $c->load("s3://my-bucket/symbol-c.json")
+pdl> ok($c->tojson eq $c2->tojson)
+ok 1
+```
+
+## Executing Symbols
+
+After you have assembled a set of symbols into a computation graph, the MXNet engine can evaluate them.
+If you are training a neural network, this is typically
+handled by the high-level [AI::MXNet::Module package](module.md) and the [`fit()`] function.
+
+For neural networks used in "feed-forward", "prediction", or "inference" mode (all terms for the same
+thing: running a trained network), the input arguments are the
+input data, and the weights of the neural network that were learned during training.
+
+To manually execute a set of symbols, you need to create an [`AI::MXNet::Executor`] object,
+which is typically constructed by calling the [`simple_bind(<parameters>)`] method on a AI::MXNet::Symbol.
+
+## Multiple Outputs
+
+To group the symbols together, use the [AI::MXNet::Symbol->Group](#mxnet.symbol.Group) function.
+
+```perl
+pdl> use AI::MXNet qw(mx)
+pdl> use Data::Dumper
+pdl> $data  = mx->sym->Variable("data")
+pdl> $fc1   = mx->sym->FullyConnected($data, name => "fc1", num_hidden => 128)
+pdl> $act1  = mx->sym->Activation($fc1, name => "relu1", act_type => "relu")
+pdl> $fc2   = mx->sym->FullyConnected($act1, name => "fc2", num_hidden => 64)
+pdl> $net   = mx->sym->SoftmaxOutput($fc2, name => "softmax")
+pdl> $group = mx->sym->Group([$fc1, $net])
+pdl> print Dumper($group->list_outputs())
+$VAR1 = [
+    'fc1_output',
+    'softmax_output'
+];
+```
+
+After you get the ```Group```, you can bind on ```group``` instead.
+The resulting executor will have two outputs, one for fc1_output and one for softmax_output.
diff --git a/docs/static_site/src/pages/api/perl/index.md b/docs/static_site/src/pages/api/perl/index.md
new file mode 100644
index 000000000000..79bc22dcb0c9
--- /dev/null
+++ b/docs/static_site/src/pages/api/perl/index.md
@@ -0,0 +1,91 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: Perl Guide
+action: Get Started
+action_url: /get_started
+permalink: /api/perl
+tag: perl
+---
+
+# MXNet - Perl API
+
+MXNet supports the Perl programming language. The MXNet Perl package brings flexible and efficient GPU
+computing and state-of-art deep learning to Perl. It enables you to write seamless tensor/matrix computation with multiple GPUs in Perl.
+It also lets you construct and customize the state-of-art deep learning models in Perl,
+  and apply them to tasks, such as image classification and data science challenges.
+
+One important thing to internalize is that Perl interface is written to be as close as possible to the Python's API,
+so most if not all of Python's documentation and examples should just work in Perl after making few
+changes in order to make the code a bit more Perlish. In nutshell just add $ sigils and replace . = \n with -> => ; and in 99% of cases
+that's all that is needed there.
+In addition please refer to [excellent metacpan doc interface](https://metacpan.org/release/AI-MXNet) and to very detailed
+[MXNet Python API Documentation]({{'/api/python' | relative_url}}).
+
+AI::MXNet supports new imperative PyTorch like Gluon MXNet interface. Please get acquainted with this new interface
+at [Dive into Deep Learning](https://www.d2l.ai/).
+
+For specific Perl Gluon usage please refer to Perl examples and tests directories on github, but be assured that the Python and Perl usage
+are extremely close in order to make the use of the Python Gluon docs and examples as easy as possible.
+
+AI::MXNet is seamlessly glued with [PDL](https://metacpan.org/release/PDL), the C++ level state can be easily initialized from PDL and the results can be
+transferred to PDL objects in order to allow you to use all the glory and power of the PDL!
+
+Here is how you can perform tensor or matrix computation in Perl with AI::MXNet and PDL:
+
+```perl
+pdl> use AI::MXNet qw(mx); # creates 'mx' module on the fly with the interface close to the Python's API
+
+pdl> print $arr = mx->nd->ones([2, 3])
+<AI::MXNet::NDArray 2x3 @cpu(0)>
+
+pdl> print Data::Dumper::Dumper($arr->shape)
+$VAR1 = [
+          2,
+          3
+        ];
+
+pdl> print (($arr*2)->aspdl) ## converts AI::MXNet::NDArray object to PDL object
+
+[
+ [2 2 2]
+ [2 2 2]
+]
+
+pdl> print $arr = mx->nd->array([[1,2],[3,4]]) ## init the NDArray from Perl array ref given in PDL::pdl constructor format
+<AI::MXNet::NDArray 2x2 @cpu(0)>
+pdl> print $arr->aspdl
+
+[
+ [1 2]
+ [3 4]
+]
+
+## init the NDArray from PDL but be aware that PDL methods expect the dimensions order in column major format
+## AI::MXNet::NDArray is row major
+pdl> print mx->nd->array(sequence(2,3))->aspdl ## 3 rows, 2 columns
+
+[
+ [0 1]
+ [2 3]
+ [4 5]
+]
+```
+
+Export/import to/from sparse MXNet tensors are supported via [PDL::CCS](https://metacpan.org/release/PDL-CCS).
+Please check out the examples directory for the examples on how to use the sparse matrices.
diff --git a/docs/static_site/src/pages/api/python/index.md b/docs/static_site/src/pages/api/python/index.md
new file mode 100644
index 000000000000..bbd82e5c384f
--- /dev/null
+++ b/docs/static_site/src/pages/api/python/index.md
@@ -0,0 +1,44 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: Python Guide
+action: Get Started
+action_url: /get_started
+permalink: /api/python
+tag: python
+---
+
+## MXNet - Python API
+
+MXNet provides a comprehensive and flexible Python API to serve a broad community of developers with different levels of experience and wide ranging requirements. In this section, we provide an in-depth discussion of the functionality provided by various MXNet Python packages.
+
+
+MXNet’s Python API has two primary high-level packages*: the Gluon API and Module API. We recommend that new users start with the Gluon API as it’s more flexible and easier to debug. Underlying these high-level packages are the core packages of NDArray and Symbol.
+
+
+NDArray works with arrays in an imperative fashion, i.e. you define how arrays will be transformed to get to an end result. Symbol works with arrays in a declarative fashion, i.e. you define the end result that is required (via a symbolic graph) and the MXNet engine will use various optimizations to determine the steps required to obtain this. With NDArray you have a great deal of flexibility when composing operations (as you can use Python control flow), and you can easily step through your code and inspect the values of arrays, which helps with debugging. Unfortunately, this comes at a performance cost when compared to Symbol, which can perform optimizations on the symbolic graph.
+
+
+Module API is backed by Symbol, so, although it’s very performant, it’s also a little more restrictive. With the Gluon API, you can get the best of both worlds. You can develop and test your model imperatively using NDArray, a then switch to Symbol for faster model training and inference (if Symbol equivalents exist for your operations).
+
+
+Code examples are placed throughout the API documentation and these can be run after importing MXNet as follows:
+
+```python
+>>> import mxnet as mx
+```
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/callback_function.md b/docs/static_site/src/pages/api/r/docs/tutorials/callback_function.md
new file mode 100644
index 000000000000..e124483a3c5f
--- /dev/null
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/callback_function.md
@@ -0,0 +1,278 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: Callback Function
+is_tutorial: true
+tag: r
+permalink: /api/r/docs/tutorials/callback_function
+---
+
+Callback Function
+======================================
+
+This tutorial provides guidelines for using and writing callback functions,
+which can very useful in model training.
+
+Model Training Example
+----------
+
+Let's begin with a small example. We can build and train a model with the following code:
+
+
+ ```r
+    library(mxnet)
+    data(BostonHousing, package="mlbench")
+    train.ind = seq(1, 506, 3)
+    train.x = data.matrix(BostonHousing[train.ind, -14])
+    train.y = BostonHousing[train.ind, 14]
+    test.x = data.matrix(BostonHousing[-train.ind, -14])
+    test.y = BostonHousing[-train.ind, 14]
+    data <- mx.symbol.Variable("data")
+    fc1 <- mx.symbol.FullyConnected(data, num_hidden=1)
+    lro <- mx.symbol.LinearRegressionOutput(fc1)
+    mx.set.seed(0)
+    model <- mx.model.FeedForward.create(
+      lro, X=train.x, y=train.y,
+      eval.data=list(data=test.x, label=test.y),
+      ctx=mx.cpu(), num.round=10, array.batch.size=20,
+      learning.rate=2e-6, momentum=0.9, eval.metric=mx.metric.rmse)
+ ```
+
+ ```
+    ## Auto detect layout of input matrix, use row major..
+    ## Start training with 1 devices
+    ## [1] Train-rmse=16.063282524034
+    ## [1] Validation-rmse=10.1766446093622
+    ## [2] Train-rmse=12.2792375712573
+    ## [2] Validation-rmse=12.4331776190813
+    ## [3] Train-rmse=11.1984634005885
+    ## [3] Validation-rmse=10.3303041888193
+    ## [4] Train-rmse=10.2645236892904
+    ## [4] Validation-rmse=8.42760407903415
+    ## [5] Train-rmse=9.49711005504284
+    ## [5] Validation-rmse=8.44557808483234
+    ## [6] Train-rmse=9.07733734175182
+    ## [6] Validation-rmse=8.33225500266177
+    ## [7] Train-rmse=9.07884450847991
+    ## [7] Validation-rmse=8.38827833418459
+    ## [8] Train-rmse=9.10463850277417
+    ## [8] Validation-rmse=8.37394452365264
+    ## [9] Train-rmse=9.03977049028532
+    ## [9] Validation-rmse=8.25927979725672
+    ## [10] Train-rmse=8.96870685004475
+    ## [10] Validation-rmse=8.19509291481822
+ ```
+
+We also provide two optional parameters, `batch.end.callback` and `epoch.end.callback`, which can provide great flexibility in model training.
+
+How to Use Callback Functions
+---------
+
+This package provides two callback functions:
+
+- `mx.callback.save.checkpoint` saves a checkpoint to files during each period iteration.
+
+```r
+         model <- mx.model.FeedForward.create(
+           lro, X=train.x, y=train.y,
+           eval.data=list(data=test.x, label=test.y),
+           ctx=mx.cpu(), num.round=10, array.batch.size=20,
+           learning.rate=2e-6, momentum=0.9, eval.metric=mx.metric.rmse,
+           epoch.end.callback = mx.callback.save.checkpoint("boston"))
+```
+
+```
+          ## Auto detect layout of input matrix, use row major..
+          ## Start training with 1 devices
+          ## [1] Train-rmse=19.1621424021617
+          ## [1] Validation-rmse=20.721515592165
+          ## Model checkpoint saved to boston-0001.params
+          ## [2] Train-rmse=13.5127391952367
+          ## [2] Validation-rmse=14.1822123675007
+          ## Model checkpoint saved to boston-0002.params
+```
+
+
+- `mx.callback.log.train.metric` logs a training metric each period. You can use it either as a `batch.end.callback` or an
+`epoch.end.callback`.
+
+
+```r
+         model <- mx.model.FeedForward.create(
+           lro, X=train.x, y=train.y,
+           eval.data=list(data=test.x, label=test.y),
+           ctx=mx.cpu(), num.round=10, array.batch.size=20,
+           learning.rate=2e-6, momentum=0.9, eval.metric=mx.metric.rmse,
+           batch.end.callback = mx.callback.log.train.metric(5))
+ ```
+
+```
+         ## Auto detect layout of input matrix, use row major..
+         ## Start training with 1 devices
+         ## Batch [5] Train-rmse=17.6514558545416
+         ## [1] Train-rmse=15.2879610219001
+         ## [1] Validation-rmse=12.3332062820921
+         ## Batch [5] Train-rmse=11.939392828565
+         ## [2] Train-rmse=11.4382242547217
+         ## [2] Validation-rmse=9.91176550103181
+         ............
+```
+
+You also can save the training and evaluation errors for later use by passing a reference class:
+
+
+ ```r
+    logger <- mx.metric.logger$new()
+    model <- mx.model.FeedForward.create(
+      lro, X=train.x, y=train.y,
+      eval.data=list(data=test.x, label=test.y),
+      ctx=mx.cpu(), num.round=10, array.batch.size=20,
+      learning.rate=2e-6, momentum=0.9, eval.metric=mx.metric.rmse,
+      epoch.end.callback = mx.callback.log.train.metric(5, logger))
+ ```
+
+ ```
+    ## Auto detect layout of input matrix, use row major..
+    ## Start training with 1 devices
+    ## [1] Train-rmse=19.1083228733256
+    ## [1] Validation-rmse=12.7150687428974
+    ## [2] Train-rmse=15.7684378116157
+    ## [2] Validation-rmse=14.8105319420491
+    ............
+ ```
+
+ ```r
+    head(logger$train)
+ ```
+
+ ```
+    ## [1] 19.108323 15.768438 13.531470 11.386050  9.555477  9.351324
+ ```
+
+ ```r
+    head(logger$eval)
+ ```
+
+ ```
+    ## [1] 12.715069 14.810532 15.840361 10.898733  9.349706  9.363087
+ ```
+
+How to Write Your Own Callback Functions
+----------
+
+You can find the source code for the two callback functions on [GitHub](https://github.com/dmlc/mxnet/blob/master/R-package/R/callback.R) and use it as a template:
+
+Basically, all callback functions follow the following structure:
+
+
+ ```r
+    mx.callback.fun <- function() {
+      function(iteration, nbatch, env) {
+      }
+    }
+ ```
+
+The following `mx.callback.save.checkpoint` function is stateless. It gets the model from the environment and saves it:.
+
+
+ ```r
+    mx.callback.save.checkpoint <- function(prefix, period=1) {
+      function(iteration, nbatch, env) {
+      if (iteration %% period == 0) {
+      mx.model.save(env$model, prefix, iteration)
+      cat(sprintf("Model checkpoint saved to %s-%04d.params\n", prefix, iteration))
+    }
+    return(TRUE)
+      }
+    }
+ ```
+
+The `mx.callback.log.train.metric` is a little more complex. It holds a reference class and updates it during the training
+process:
+
+
+ ```r
+    mx.callback.log.train.metric <- function(period, logger=NULL) {
+      function(iteration, nbatch, env) {
+    if (nbatch %% period == 0 && !is.null(env$metric)) {
+      result <- env$metric$get(env$train.metric)
+      if (nbatch != 0)
+        cat(paste0("Batch [", nbatch, "] Train-", result$name, "=", result$value, "\n"))
+      if (!is.null(logger)) {
+        if (class(logger) != "mx.metric.logger") {
+          stop("Invalid mx.metric.logger.")
+        }
+        logger$train <- c(logger$train, result$value)
+        if (!is.null(env$eval.metric)) {
+          result <- env$metric$get(env$eval.metric)
+          if (nbatch != 0)
+            cat(paste0("Batch [", nbatch, "] Validation-", result$name, "=", result$value, "\n"))
+          logger$eval <- c(logger$eval, result$value)
+        }
+      }
+    }
+    return(TRUE)
+      }
+    }
+ ```
+
+Now you might be curious why both callback functions `return(TRUE)`.
+
+Can we `return(FALSE)`?
+
+Yes! You can stop the training early with `return(FALSE)`. See the following examples.
+
+
+  ```r
+     mx.callback.early.stop <- function(eval.metric) {
+      function(iteration, nbatch, env) {
+    if (!is.null(env$metric)) {
+      if (!is.null(eval.metric)) {
+        result <- env$metric$get(env$eval.metric)
+        if (result$value < eval.metric) {
+          return(FALSE)
+        }
+      }
+    }
+    return(TRUE)
+      }
+    }
+    model <- mx.model.FeedForward.create(
+      lro, X=train.x, y=train.y,
+      eval.data=list(data=test.x, label=test.y),
+      ctx=mx.cpu(), num.round=10, array.batch.size=20,
+      learning.rate=2e-6, momentum=0.9, eval.metric=mx.metric.rmse,
+      epoch.end.callback = mx.callback.early.stop(10))
+ ```
+
+ ```
+    ## Auto detect layout of input matrix, use row major..
+    ## Start training with 1 devices
+    ## [1] Train-rmse=18.5897984387033
+    ## [1] Validation-rmse=13.5555213820571
+    ## [2] Train-rmse=12.5867564040256
+    ## [2] Validation-rmse=9.76304967080928
+ ```
+
+When the validation metric dips below the threshold we set, the training process stops.
+
+## Next Steps
+* [Neural Networks with MXNet in Five Minutes](http://mxnet.io/tutorials/r/fiveMinutesNeuralNetwork.html)
+* [Classify Real-World Images with a Pretrained Model](http://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
+* [Handwritten Digits Classification Competition](http://mxnet.io/tutorials/r/mnistCompetition.html)
+* [Character Language Model Using RNN](http://mxnet.io/tutorials/r/charRnnModel.html)
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/char_rnn_model.md b/docs/static_site/src/pages/api/r/docs/tutorials/char_rnn_model.md
new file mode 100644
index 000000000000..2c516608a1d2
--- /dev/null
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/char_rnn_model.md
@@ -0,0 +1,317 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: Char RNN Model
+is_tutorial: true
+tag: r
+permalink: /api/r/docs/tutorials/char_rnn_model
+---
+
+
+# Character-level Language Model using RNN
+
+This tutorial will demonstrate creating a language model using a character level RNN model using MXNet-R package. You will need the following R packages to run this tutorial -
+ - readr
+ - stringr
+ - stringi
+ - mxnet
+
+We will use the [tinyshakespeare](https://github.com/dmlc/web-data/tree/master/mxnet/tinyshakespeare) dataset to build this model.
+
+
+```R
+library("readr")
+library("stringr")
+library("stringi")
+library("mxnet")
+```
+
+## Preprocess and prepare the data
+
+Download the data:
+
+
+```R
+download.data <- function(data_dir) {
+    dir.create(data_dir, showWarnings = FALSE)
+    if (!file.exists(paste0(data_dir,'input.txt'))) {
+        download.file(url='https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tinyshakespeare/input.txt',
+                      destfile=paste0(data_dir,'input.txt'), method='wget')
+    }
+}
+```
+
+Next we transform the test into feature vectors that is fed into the RNN model. The `make_data` function reads the dataset, cleans it of any non-alphanumeric characters, splits it into individual characters and groups it into sequences of length `seq.len`.
+
+
+```R
+make_data <- function(path, seq.len = 32, dic=NULL) {
+
+  text_vec <- read_file(file = path)
+  text_vec <- stri_enc_toascii(str = text_vec)
+  text_vec <- str_replace_all(string = text_vec, pattern = "[^[:print:]]", replacement = "")
+  text_vec <- strsplit(text_vec, '') %>% unlist
+
+  if (is.null(dic)) {
+    char_keep <- sort(unique(text_vec))
+  } else char_keep <- names(dic)[!dic == 0]
+
+  # Remove terms not part of dictionary
+  text_vec <- text_vec[text_vec %in% char_keep]
+
+  # Build dictionary
+  dic <- 1:length(char_keep)
+  names(dic) <- char_keep
+
+  # reverse dictionary
+  rev_dic <- names(dic)
+  names(rev_dic) <- dic
+
+  # Adjust by -1 to have a 1-lag for labels
+  num.seq <- (length(text_vec) - 1) %/% seq.len
+
+  features <- dic[text_vec[1:(seq.len * num.seq)]]
+  labels <- dic[text_vec[1:(seq.len*num.seq) + 1]]
+
+  features_array <- array(features, dim = c(seq.len, num.seq))
+  labels_array <- array(labels, dim = c(seq.len, num.seq))
+
+  return (list(features_array = features_array, labels_array = labels_array, dic = dic, rev_dic = rev_dic))
+}
+
+
+seq.len <- 100
+data_prep <- make_data(path = "input.txt", seq.len = seq.len, dic=NULL)
+```
+
+Fetch the features and labels for training the model, and split the data into training and evaluation in 9:1 ratio.
+
+
+```R
+X <- data_prep$features_array
+Y <- data_prep$labels_array
+dic <- data_prep$dic
+rev_dic <- data_prep$rev_dic
+vocab <- length(dic)
+
+samples <- tail(dim(X), 1)
+train.val.fraction <- 0.9
+
+X.train.data <- X[, 1:as.integer(samples * train.val.fraction)]
+X.val.data <- X[, -(1:as.integer(samples * train.val.fraction))]
+
+X.train.label <- Y[, 1:as.integer(samples * train.val.fraction)]
+X.val.label <- Y[, -(1:as.integer(samples * train.val.fraction))]
+
+train_buckets <- list("100" = list(data = X.train.data, label = X.train.label))
+eval_buckets <- list("100" = list(data = X.val.data, label = X.val.label))
+
+train_buckets <- list(buckets = train_buckets, dic = dic, rev_dic = rev_dic)
+eval_buckets <- list(buckets = eval_buckets, dic = dic, rev_dic = rev_dic)
+```
+
+Create iterators for training and evaluation datasets.
+
+
+```R
+vocab <- length(eval_buckets$dic)
+
+batch.size <- 32
+
+train.data <- mx.io.bucket.iter(buckets = train_buckets$buckets, batch.size = batch.size,
+                                data.mask.element = 0, shuffle = TRUE)
+
+eval.data <- mx.io.bucket.iter(buckets = eval_buckets$buckets, batch.size = batch.size,
+                               data.mask.element = 0, shuffle = FALSE)
+```
+
+## Train the Model
+
+
+This model is a multi-layer RNN for sampling from character-level language models. It has a one-to-one model configuration since for each character, we want to predict the next one. For a sequence of length 100, there are also 100 labels, corresponding the same sequence of characters but offset by a position of +1. The parameters output_last_state is set to TRUE in order to access the state of the RNN cells when performing inference.
+
+
+```R
+rnn_graph_one_one <- rnn.graph(num_rnn_layer = 3,
+                               num_hidden = 96,
+                               input_size = vocab,
+                               num_embed = 64,
+                               num_decode = vocab,
+                               dropout = 0.2,
+                               ignore_label = 0,
+                               cell_type = "lstm",
+                               masking = F,
+                               output_last_state = T,
+                               loss_output = "softmax",
+                               config = "one-to-one")
+
+graph.viz(rnn_graph_one_one, type = "graph", direction = "LR",
+          graph.height.px = 180, shape=c(100, 64))
+
+devices <- mx.cpu()
+
+initializer <- mx.init.Xavier(rnd_type = "gaussian", factor_type = "avg", magnitude = 3)
+
+optimizer <- mx.opt.create("adadelta", rho = 0.9, eps = 1e-5, wd = 1e-8,
+                           clip_gradient = 5, rescale.grad = 1/batch.size)
+
+logger <- mx.metric.logger()
+epoch.end.callback <- mx.callback.log.train.metric(period = 1, logger = logger)
+batch.end.callback <- mx.callback.log.train.metric(period = 50)
+
+mx.metric.custom_nd <- function(name, feval) {
+  init <- function() {
+    c(0, 0)
+  }
+  update <- function(label, pred, state) {
+    m <- feval(label, pred)
+    state <- c(state[[1]] + 1, state[[2]] + m)
+    return(state)
+  }
+  get <- function(state) {
+    list(name=name, value = (state[[2]] / state[[1]]))
+  }
+  ret <- (list(init = init, update = update, get = get))
+  class(ret) <- "mx.metric"
+  return(ret)
+}
+
+mx.metric.Perplexity <- mx.metric.custom_nd("Perplexity", function(label, pred) {
+  label <- mx.nd.reshape(label, shape = -1)
+  label_probs <- as.array(mx.nd.choose.element.0index(pred, label))
+  batch <- length(label_probs)
+  NLL <- -sum(log(pmax(1e-15, as.array(label_probs)))) / batch
+  Perplexity <- exp(NLL)
+  return(Perplexity)
+})
+
+model <- mx.model.buckets(symbol = rnn_graph_one_one,
+                          train.data = train.data, eval.data = eval.data,
+                          num.round = 20, ctx = devices, verbose = TRUE,
+                          metric = mx.metric.Perplexity,
+                          initializer = initializer, optimizer = optimizer,
+                          batch.end.callback = NULL,
+                          epoch.end.callback = epoch.end.callback)
+
+mx.model.save(model, prefix = "one_to_one_seq_model", iteration = 20)
+```
+
+    Start training with 1 devices
+    [1] Train-Perplexity=13.7040474322178
+    [1] Validation-Perplexity=7.94617194460922
+    [2] Train-Perplexity=6.57039815554525
+    [2] Validation-Perplexity=6.60806110658011
+    [3] Train-Perplexity=5.65360504501481
+    [3] Validation-Perplexity=6.18932770630876
+    [4] Train-Perplexity=5.32547285727298
+    [4] Validation-Perplexity=6.02198756798859
+    [5] Train-Perplexity=5.14373631472579
+    [5] Validation-Perplexity=5.8095658243407
+    [6] Train-Perplexity=5.03077673487379
+    [6] Validation-Perplexity=5.72582993567431
+    [7] Train-Perplexity=4.94453383291536
+    [7] Validation-Perplexity=5.6445258528126
+    [8] Train-Perplexity=4.88635290100261
+    [8] Validation-Perplexity=5.6730024536433
+    [9] Train-Perplexity=4.84205646230548
+    [9] Validation-Perplexity=5.50960780230982
+    [10] Train-Perplexity=4.80441673535513
+    [10] Validation-Perplexity=5.57002263750006
+    [11] Train-Perplexity=4.77763413242626
+    [11] Validation-Perplexity=5.55152143269169
+    [12] Train-Perplexity=4.74937775290777
+    [12] Validation-Perplexity=5.44968305351486
+    [13] Train-Perplexity=4.72824849541467
+    [13] Validation-Perplexity=5.50889348298234
+    [14] Train-Perplexity=4.70980846981694
+    [14] Validation-Perplexity=5.51473225859859
+    [15] Train-Perplexity=4.69685776886122
+    [15] Validation-Perplexity=5.45391985233811
+    [16] Train-Perplexity=4.67837107034824
+    [16] Validation-Perplexity=5.46636764997829
+    [17] Train-Perplexity=4.66866961934873
+    [17] Validation-Perplexity=5.44267086113492
+    [18] Train-Perplexity=4.65611469144194
+    [18] Validation-Perplexity=5.4290169469462
+    [19] Train-Perplexity=4.64614689879405
+    [19] Validation-Perplexity=5.44221549833917
+    [20] Train-Perplexity=4.63764001963654
+    [20] Validation-Perplexity=5.42114250842862
+
+
+## Inference on the Model
+
+We now use the saved model to do inference and sample text character by character that will look like the original training data.
+
+
+```R
+set.seed(0)
+model <- mx.model.load(prefix = "one_to_one_seq_model", iteration = 20)
+
+internals <- model$symbol$get.internals()
+sym_state <- internals$get.output(which(internals$outputs %in% "RNN_state"))
+sym_state_cell <- internals$get.output(which(internals$outputs %in% "RNN_state_cell"))
+sym_output <- internals$get.output(which(internals$outputs %in% "loss_output"))
+symbol <- mx.symbol.Group(sym_output, sym_state, sym_state_cell)
+
+infer_raw <- c("Thou ")
+infer_split <- dic[strsplit(infer_raw, '') %>% unlist]
+infer_length <- length(infer_split)
+
+infer.data <- mx.io.arrayiter(data = matrix(infer_split), label = matrix(infer_split),
+                              batch.size = 1, shuffle = FALSE)
+
+infer <- mx.infer.rnn.one(infer.data = infer.data,
+                          symbol = symbol,
+                          arg.params = model$arg.params,
+                          aux.params = model$aux.params,
+                          input.params = NULL,
+                          ctx = devices)
+
+pred_prob <- as.numeric(as.array(mx.nd.slice.axis(
+    infer$loss_output, axis = 0, begin = infer_length-1, end = infer_length)))
+pred <- sample(length(pred_prob), prob = pred_prob, size = 1) - 1
+predict <- c(predict, pred)
+
+for (i in 1:200) {
+
+  infer.data <- mx.io.arrayiter(data = as.matrix(pred), label = as.matrix(pred),
+                                batch.size = 1, shuffle = FALSE)
+
+  infer <- mx.infer.rnn.one(infer.data = infer.data,
+                            symbol = symbol,
+                            arg.params = model$arg.params,
+                            aux.params = model$aux.params,
+                            input.params = list(rnn.state = infer[[2]],
+                                                rnn.state.cell = infer[[3]]),
+                            ctx = devices)
+
+  pred_prob <- as.numeric(as.array(infer$loss_output))
+  pred <- sample(length(pred_prob), prob = pred_prob, size = 1, replace = T) - 1
+  predict <- c(predict, pred)
+}
+
+predict_txt <- paste0(rev_dic[as.character(predict)], collapse = "")
+predict_txt_tot <- paste0(infer_raw, predict_txt, collapse = "")
+print(predict_txt_tot)
+```
+
+    [1] "Thou NAknowledge thee my Comfort and his late she.FRIAR LAURENCE:Nothing a groats waterd forth. The lend he thank that;When she I am brother draw London: and not hear that know.BENVOLIO:How along, makes your "
+
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/classify_real_image_with_pretrained_model.md b/docs/static_site/src/pages/api/r/docs/tutorials/classify_real_image_with_pretrained_model.md
new file mode 100644
index 000000000000..228ecb848a2d
--- /dev/null
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/classify_real_image_with_pretrained_model.md
@@ -0,0 +1,208 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: Classify Images with a PreTrained Model
+is_tutorial: true
+tag: r
+permalink: /api/r/docs/tutorials/classify_real_image_with_pretrained_model
+---
+
+Classify Images with a PreTrained Model
+=================================================
+MXNet is a flexible and efficient deep learning framework. One of the interesting things that a deep learning
+algorithm can do is classify real world images.
+
+In this tutorial, we show how to use a pre-trained Inception-BatchNorm network to predict the class of an
+image. For information about the network architecture, see  [1].
+
+The pre-trained Inception-BatchNorm network is able to be downloaded from [this link](http://data.mxnet.io/mxnet/data/Inception.zip)
+This model gives the recent state-of-art prediction accuracy on image net dataset.
+
+Load the MXNet Package
+---------------
+To get started, load the mxnet package:
+
+ ```r
+    require(mxnet)
+ ```
+
+ ```
+    ## Loading required package: mxnet
+    ## Loading required package: methods
+ ```
+
+Now load the imager package to load and preprocess the images in R:
+
+
+ ```r
+    require(imager)
+ ```
+
+ ```
+    ## Loading required package: imager
+    ## Loading required package: plyr
+    ## Loading required package: magrittr
+    ## Loading required package: stringr
+    ## Loading required package: png
+    ## Loading required package: jpeg
+    ##
+    ## Attaching package: 'imager'
+    ##
+    ## The following object is masked from 'package:magrittr':
+    ##
+    ##     add
+    ##
+    ## The following object is masked from 'package:plyr':
+    ##
+    ##     liply
+    ##
+    ## The following objects are masked from 'package:stats':
+    ##
+    ##     convolve, spectrum
+    ##
+    ## The following object is masked from 'package:graphics':
+    ##
+    ##     frame
+    ##
+    ## The following object is masked from 'package:base':
+    ##
+    ##     save.image
+ ```
+
+Load the PreTrained Model
+-------------------------
+Make sure you unzip the pre-trained model in the current folder. Use the model
+loading function to load the model into R:
+
+ ```r
+    model = mx.model.load("Inception/Inception_BN", iteration=39)
+ ```
+
+Load in the mean image, which is used for preprocessing using:
+
+
+ ```r
+    mean.img = as.array(mx.nd.load("Inception/mean_224.nd")[["mean_img"]])
+ ```
+
+Load and Preprocess the Image
+-----------------------------
+Now, we are ready to classify a real image. In this example, we simply take the parrots image
+from the imager package. You can use another image, if   you prefer.
+
+Load and plot the image:
+
+
+```r
+    im <- load.image(system.file("extdata/parrots.png", package="imager"))
+    plot(im)
+ ```
+
+![plot of chunk unnamed-chunk-5](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/knitr/classifyRealImageWithPretrainedModel-unnamed-chunk-5-1.png)
+
+Before feeding the image to the deep network, we need to perform some preprocessing
+to make the image meet the deep network input requirements. Preprocessing
+includes cropping  and subtracting the mean.
+Because MXNet is deeply integrated with R, we can do all the processing in an R function:
+
+
+ ```r
+    preproc.image <- function(im, mean.image) {
+      # crop the image
+      shape <- dim(im)
+      short.edge <- min(shape[1:2])
+      xx <- floor((shape[1] - short.edge) / 2)
+      yy <- floor((shape[2] - short.edge) / 2)
+      cropped <- crop.borders(im, xx, yy)
+      # resize to 224 x 224, needed by input of the model.
+      resized <- resize(cropped, 224, 224)
+      # convert to array (x, y, channel)
+      arr <- as.array(resized) * 255
+      dim(arr) <- c(224, 224, 3)
+      # subtract the mean
+      normed <- arr - mean.img
+      # Reshape to format needed by mxnet (width, height, channel, num)
+      dim(normed) <- c(224, 224, 3, 1)
+      return(normed)
+    }
+ ```
+
+Use the defined preprocessing function to get the normalized image:
+
+
+ ```r
+    normed <- preproc.image(im, mean.img)
+ ```
+
+Classify the Image
+------------------
+Now we are ready to classify the image! Use the ```predict``` function
+to get the probability over classes:
+
+
+ ```r
+    prob <- predict(model, X=normed)
+    dim(prob)
+ ```
+
+ ```
+    ## [1] 1000    1
+ ```
+
+As you can see, ```prob``` is a 1 times 1000 array, which gives the probability
+over the 1000 image classes of the input.
+
+Use the ```max.col``` on the transpose of ```prob``` to get the class index:
+
+ ```r
+    max.idx <- max.col(t(prob))
+    max.idx
+ ```
+
+ ```
+    ## [1] 89
+ ```
+
+The index doesn't make much sense, so let's see what it really means.
+Read the names of the classes from the following file:
+
+
+ ```r
+    synsets <- readLines("Inception/synset.txt")
+ ```
+
+Let's see what the image really is:
+
+
+ ```r
+    print(paste0("Predicted Top-class: ", synsets  [[max.idx]]))
+ ```
+
+ ```
+    ## [1] "Predicted Top-class: n01818515 macaw"
+ ```
+
+It's a macaw!
+
+Reference
+---------
+[1] Ioffe, Sergey, and Christian Szegedy. "Batch normalization: Accelerating deep network training by reducing internal covariate shift." arXiv preprint arXiv:1502.03167 (2015).
+
+## Next Steps
+* [Handwritten Digits Classification Competition](http://mxnet.io/tutorials/r/mnistCompetition.html)
+* [Character Language Model using RNN](http://mxnet.io/tutorials/r/charRnnModel.html)
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/custom_iterator.md b/docs/static_site/src/pages/api/r/docs/tutorials/custom_iterator.md
new file mode 100644
index 000000000000..5ecfb47b7ce9
--- /dev/null
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/custom_iterator.md
@@ -0,0 +1,227 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: Custom Iterator Tutorial
+is_tutorial: true
+tag: r
+permalink: /api/r/docs/tutorials/custom_iterator
+---
+
+
+Custom Iterator Tutorial
+========================
+
+This tutorial provides a guideline on how to use and write custom iterators, which can very useful when having a dataset that does not fit into memory.
+
+Getting the data
+----------
+The data we are going to use is the [MNIST dataset](http://yann.lecun.com/exdb/mnist/) in CSV format, the data can be found in this [web](http://pjreddie.com/projects/mnist-in-csv/).
+
+To download the data:
+
+```bash
+wget http://pjreddie.com/media/files/mnist_train.csv
+wget http://pjreddie.com/media/files/mnist_test.csv
+```
+
+You'll get two files, `mnist_train.csv` that contains 60.000 examples of hand written numbers and `mxnist_test.csv` that contains 10.000 examples. The first element of each line in the CSV is the label, which is a number between 0 and 9. The rest of the line are 784 numbers between 0 and 255, corresponding to the levels of grey of a matrix of 28x28. Therefore, each line contains an image of 28x28 pixels of a hand written number and its true label.
+
+Custom CSV Iterator
+----------
+Next we are going to create a custom CSV Iterator based on the [C++ CSVIterator class](https://github.com/dmlc/mxnet/blob/master/src/io/iter_csv.cc).
+
+For that we are going to use the R function `mx.io.CSVIter` as a base class. This class has as parameters `data.csv, data.shape, batch.size` and two main functions, `iter.next()` that calls the iterator in the next batch of data and `value()` that returns the train data and the label.
+
+The R Custom Iterator needs to inherit from the C++ data iterator class, for that we used the class `Rcpp_MXArrayDataIter` extracted with RCPP. Also, it needs to have the same parameters: `data.csv, data.shape, batch.size`. Apart from that, we can also add the field `iter`, which is the CSV Iterator that we are going to expand.
+
+```r
+CustomCSVIter <- setRefClass("CustomCSVIter",
+								fields=c("iter", "data.csv", "data.shape", "batch.size"),
+								contains = "Rcpp_MXArrayDataIter",
+								#...
+                            )
+```
+
+The next step is to initialize the class. For that we call the base `mx.io.CSVIter` and fill the rest of the fields.
+
+```r
+CustomCSVIter <- setRefClass("CustomCSVIter",
+								fields=c("iter", "data.csv", "data.shape", "batch.size"),
+								contains = "Rcpp_MXArrayDataIter",
+								methods=list(
+	                             	initialize=function(iter, data.csv, data.shape, batch.size){
+										feature_len <- data.shape*data.shape + 1
+										csv_iter <- mx.io.CSVIter(data.csv=data.csv, data.shape=c(feature_len), batch.size=batch.size)
+										.self$iter <- csv_iter
+										.self$data.csv <- data.csv
+										.self$data.shape <- data.shape
+										.self$batch.size <- batch.size
+										.self
+	                               	},
+                             	#...
+                             	)
+                            )
+```
+
+So far there is no difference between the original class and the custom class. Let's implement the function `value()`. In this case what we are going to do is transform the data that comes from the original class as an array of 785 numbers into a matrix of 28x28 and a label. We will also normalize the training data to be between 0 and 1.
+
+```r
+CustomCSVIter <- setRefClass("CustomCSVIter",
+								fields=c("iter", "data.csv", "data.shape", "batch.size"),
+								contains = "Rcpp_MXArrayDataIter",
+								methods=list(
+	                             	initialize=function(iter, data.csv, data.shape, batch.size){
+										feature_len <- data.shape*data.shape + 1
+										csv_iter <- mx.io.CSVIter(data.csv=data.csv, data.shape=c(feature_len), batch.size=batch.size)
+										.self$iter <- csv_iter
+										.self$data.csv <- data.csv
+										.self$data.shape <- data.shape
+										.self$batch.size <- batch.size
+										.self
+	                               	},
+									value=function(){
+										val <- as.array(.self$iter$value()$data)
+										val.x <- val[-1,]
+										val.y <- val[1,]
+										val.x <- val.x/255
+										dim(val.x) <- c(data.shape, data.shape, 1, ncol(val.x))
+										val.x <- mx.nd.array(val.x)
+										val.y <- mx.nd.array(val.y)
+										list(data=val.x, label=val.y)
+									},
+                             	#...
+                             	)
+                            )
+```
+Finally we are going to add the rest of the functions needed for the training to work correctly. The final `CustomCSVIter` looks like this:
+
+```r
+CustomCSVIter <- setRefClass("CustomCSVIter",
+								fields=c("iter", "data.csv", "data.shape", "batch.size"),
+								contains = "Rcpp_MXArrayDataIter",
+								methods=list(
+	                             	initialize=function(iter, data.csv, data.shape, batch.size){
+										feature_len <- data.shape*data.shape + 1
+										csv_iter <- mx.io.CSVIter(data.csv=data.csv, data.shape=c(feature_len), batch.size=batch.size)
+										.self$iter <- csv_iter
+										.self$data.csv <- data.csv
+										.self$data.shape <- data.shape
+										.self$batch.size <- batch.size
+										.self
+	                               	},
+									value=function(){
+										val <- as.array(.self$iter$value()$data)
+										val.x <- val[-1,]
+										val.y <- val[1,]
+										val.x <- val.x/255
+										dim(val.x) <- c(data.shape, data.shape, 1, ncol(val.x))
+										val.x <- mx.nd.array(val.x)
+										val.y <- mx.nd.array(val.y)
+										list(data=val.x, label=val.y)
+									},
+									iter.next=function(){
+										.self$iter$iter.next()
+									},
+									reset=function(){
+										.self$iter$reset()
+									},
+									num.pad=function(){
+										.self$iter$num.pad()
+									},
+									finalize=function(){
+										.self$iter$finalize()
+									}
+                             	)
+                            )
+```
+
+To call the class we can just do:
+
+```r
+batch.size <- 100
+train.iter <- CustomCSVIter$new(iter = NULL, data.csv = "mnist_train.csv", data.shape = 28, batch.size = batch.size)
+```
+
+CNN Model
+----------
+
+For this tutorial we are going to use the known LeNet architecture:
+
+```r
+lenet.model <- function(){
+  data <- mx.symbol.Variable('data')
+  conv1 <- mx.symbol.Convolution(data=data, kernel=c(5,5), num_filter=20) #first conv
+  tanh1 <- mx.symbol.Activation(data=conv1, act_type="tanh")
+  pool1 <- mx.symbol.Pooling(data=tanh1, pool_type="max", kernel=c(2,2), stride=c(2,2))
+  conv2 <- mx.symbol.Convolution(data=pool1, kernel=c(5,5), num_filter=50)# second conv
+  tanh2 <- mx.symbol.Activation(data=conv2, act_type="tanh")
+  pool2 <- mx.symbol.Pooling(data=tanh2, pool_type="max", kernel=c(2,2), stride=c(2,2))
+  flatten <- mx.symbol.Flatten(data=pool2)
+  fc1 <- mx.symbol.FullyConnected(data=flatten, num_hidden=100) # first fullc
+  tanh3 <- mx.symbol.Activation(data=fc1, act_type="tanh")
+  fc2 <- mx.symbol.FullyConnected(data=tanh3, num_hidden=10) # second fullc
+  network <- mx.symbol.SoftmaxOutput(data=fc2) # loss
+  network
+}
+network <- lenet.model()
+```
+
+Training with the Custom Iterator
+----------
+Finally, we can directly add the custom iterator as the training data source.
+
+```r
+model <- mx.model.FeedForward.create(symbol=network,
+                                     X=train.iter,
+                                     ctx=mx.gpu(0),
+                                     num.round=10,
+                                     array.batch.size=batch.size,
+                                     learning.rate=0.1,
+                                     momentum=0.9,
+                                     eval.metric=mx.metric.accuracy,
+                                     wd=0.00001,
+                                     batch.end.callback=mx.callback.log.speedometer(batch.size, frequency = 100)
+                                     )
+```
+
+The last 2 iterations with a K80 GPU looks like this:
+
+```bash
+[8] Train-accuracy=0.998866666666667
+Batch [100] Speed: 15413.0104454713 samples/sec Train-accuracy=0.999
+Batch [200] Speed: 16629.3412459049 samples/sec Train-accuracy=0.99935
+Batch [300] Speed: 18412.6900509319 samples/sec Train-accuracy=0.9995
+Batch [400] Speed: 16757.2882328335 samples/sec Train-accuracy=0.999425
+Batch [500] Speed: 17116.6529207406 samples/sec Train-accuracy=0.99946
+Batch [600] Speed: 19627.589505195 samples/sec Train-accuracy=0.99945
+[9] Train-accuracy=0.9991
+Batch [100] Speed: 18971.5745536982 samples/sec Train-accuracy=0.9992
+Batch [200] Speed: 15554.8822435383 samples/sec Train-accuracy=0.99955
+Batch [300] Speed: 18327.6950115053 samples/sec Train-accuracy=0.9997
+Batch [400] Speed: 17103.0705411788 samples/sec Train-accuracy=0.9997
+Batch [500] Speed: 15104.8656902394 samples/sec Train-accuracy=0.99974
+Batch [600] Speed: 13818.7899518255 samples/sec Train-accuracy=0.99975
+[10] Train-accuracy=0.99975
+```
+
+Conclusion
+----------
+
+We have shown how to create a custom CSV Iterator by extending the class `mx.io.CSVIter`. In our class, we iteratively read from a CSV file a batch of data that will be transformed and then processed in the stochastic gradient descent optimization. That way, we are able to manage CSV files that are bigger than the memory of the machine we are using.
+
+Based of this custom iterator, we can also create data loaders that internally transform or expand the data, allowing to manage files of any size.
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/custom_loss_function.md b/docs/static_site/src/pages/api/r/docs/tutorials/custom_loss_function.md
new file mode 100644
index 000000000000..dbf1280462a9
--- /dev/null
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/custom_loss_function.md
@@ -0,0 +1,231 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: Custom Loss Function
+is_tutorial: true
+tag: r
+permalink: /api/r/docs/tutorials/custom_loss_function
+---
+
+
+Customized loss function
+======================================
+
+This tutorial provides guidelines for using customized loss function in network construction.
+
+Model Training Example
+----------------------
+
+Let's begin with a small regression example. We can build and train a regression model with the following code:
+
+``` r
+data(BostonHousing, package = "mlbench")
+BostonHousing[, sapply(BostonHousing, is.factor)] <-
+  as.numeric(as.character(BostonHousing[, sapply(BostonHousing, is.factor)]))
+BostonHousing <- data.frame(scale(BostonHousing))
+
+test.ind = seq(1, 506, 5)    # 1 pt in 5 used for testing
+train.x = data.matrix(BostonHousing[-test.ind,-14])
+train.y = BostonHousing[-test.ind, 14]
+test.x = data.matrix(BostonHousing[--test.ind,-14])
+test.y = BostonHousing[--test.ind, 14]
+
+require(mxnet)
+```
+
+    ## Loading required package: mxnet
+
+``` r
+data <- mx.symbol.Variable("data")
+label <- mx.symbol.Variable("label")
+fc1 <- mx.symbol.FullyConnected(data, num_hidden = 14, name = "fc1")
+tanh1 <- mx.symbol.Activation(fc1, act_type = "tanh", name = "tanh1")
+fc2 <- mx.symbol.FullyConnected(tanh1, num_hidden = 1, name = "fc2")
+lro <- mx.symbol.LinearRegressionOutput(fc2, name = "lro")
+
+mx.set.seed(0)
+model <- mx.model.FeedForward.create(lro, X = train.x, y = train.y,
+                                     ctx = mx.cpu(),
+                                     num.round = 5,
+                                     array.batch.size = 60,
+                                     optimizer = "rmsprop",
+                                     verbose = TRUE,
+                                     array.layout = "rowmajor",
+                                     batch.end.callback = NULL,
+                                     epoch.end.callback = NULL)
+```
+
+    ## Start training with 1 devices
+
+``` r
+pred <- predict(model, test.x)
+```
+
+    ## Warning in mx.model.select.layout.predict(X, model): Auto detect layout of input matrix, use rowmajor..
+
+``` r
+sum((test.y - pred[1,])^2) / length(test.y)
+```
+
+    ## [1] 0.2485236
+
+Besides the `LinearRegressionOutput`, we also provide `LogisticRegressionOutput` and `MAERegressionOutput`. However, this might not be enough for real-world models. You can provide your own loss function by using `mx.symbol.MakeLoss` when constructing the network.
+
+How to Use Your Own Loss Function
+---------------------------------
+
+We still use our previous example, but this time we use `mx.symbol.MakeLoss` to minimize the `(pred-label)^2`
+
+``` r
+data <- mx.symbol.Variable("data")
+label <- mx.symbol.Variable("label")
+fc1 <- mx.symbol.FullyConnected(data, num_hidden = 14, name = "fc1")
+tanh1 <- mx.symbol.Activation(fc1, act_type = "tanh", name = "tanh1")
+fc2 <- mx.symbol.FullyConnected(tanh1, num_hidden = 1, name = "fc2")
+lro2 <- mx.symbol.MakeLoss(mx.symbol.square(mx.symbol.Reshape(fc2, shape = 0) - label), name="lro2")
+```
+
+Then we can train the network just as usual.
+
+``` r
+mx.set.seed(0)
+model2 <- mx.model.FeedForward.create(lro2, X = train.x, y = train.y,
+                                      ctx = mx.cpu(),
+                                      num.round = 5,
+                                      array.batch.size = 60,
+                                      optimizer = "rmsprop",
+                                      verbose = TRUE,
+                                      array.layout = "rowmajor",
+                                      batch.end.callback = NULL,
+                                      epoch.end.callback = NULL)
+```
+
+    ## Start training with 1 devices
+
+We should get very similar results because we are actually minimizing the same loss function. However, the result is quite different.
+
+``` r
+pred2 <- predict(model2, test.x)
+```
+
+    ## Warning in mx.model.select.layout.predict(X, model): Auto detect layout of input matrix, use rowmajor..
+
+``` r
+sum((test.y - pred2)^2) / length(test.y)
+```
+
+    ## [1] 1.234584
+
+This is because output of `mx.symbol.MakeLoss` is the gradient of loss with respect to the input data. We can get the real prediction as below.
+
+``` r
+internals = internals(model2$symbol)
+fc_symbol = internals[[match("fc2_output", outputs(internals))]]
+
+model3 <- list(symbol = fc_symbol,
+               arg.params = model2$arg.params,
+               aux.params = model2$aux.params)
+
+class(model3) <- "MXFeedForwardModel"
+
+pred3 <- predict(model3, test.x)
+```
+
+    ## Warning in mx.model.select.layout.predict(X, model): Auto detect layout of input matrix, use rowmajor..
+
+``` r
+sum((test.y - pred3[1,])^2) / length(test.y)
+```
+
+    ## [1] 0.248294
+
+We have provided many operations on the symbols. An example of `|pred-label|` can be found below.
+
+``` r
+lro_abs <- mx.symbol.MakeLoss(mx.symbol.abs(mx.symbol.Reshape(fc2, shape = 0) - label))
+mx.set.seed(0)
+model4 <- mx.model.FeedForward.create(lro_abs, X = train.x, y = train.y,
+                                      ctx = mx.cpu(),
+                                      num.round = 20,
+                                      array.batch.size = 60,
+                                      optimizer = "sgd",
+                                      learning.rate = 0.001,
+                                      verbose = TRUE,
+                                      array.layout = "rowmajor",
+                                      batch.end.callback = NULL,
+                                      epoch.end.callback = NULL)
+```
+
+    ## Start training with 1 devices
+
+``` r
+internals = internals(model4$symbol)
+fc_symbol = internals[[match("fc2_output", outputs(internals))]]
+
+model5 <- list(symbol = fc_symbol,
+               arg.params = model4$arg.params,
+               aux.params = model4$aux.params)
+
+class(model5) <- "MXFeedForwardModel"
+
+pred5 <- predict(model5, test.x)
+```
+
+    ## Warning in mx.model.select.layout.predict(X, model): Auto detect layout of input matrix, use rowmajor..
+
+``` r
+sum(abs(test.y - pred5[1,])) / length(test.y)
+```
+
+    ## [1] 0.7056902
+
+``` r
+lro_mae <- mx.symbol.MAERegressionOutput(fc2, name = "lro")
+mx.set.seed(0)
+model6 <- mx.model.FeedForward.create(lro_mae, X = train.x, y = train.y,
+                                      ctx = mx.cpu(),
+                                      num.round = 20,
+                                      array.batch.size = 60,
+                                      optimizer = "sgd",
+                                      learning.rate = 0.001,
+                                      verbose = TRUE,
+                                      array.layout = "rowmajor",
+                                      batch.end.callback = NULL,
+                                      epoch.end.callback = NULL)
+```
+
+    ## Start training with 1 devices
+
+``` r
+pred6 <- predict(model6, test.x)
+```
+
+    ## Warning in mx.model.select.layout.predict(X, model): Auto detect layout of input matrix, use rowmajor..
+
+``` r
+sum(abs(test.y - pred6[1,])) / length(test.y)
+```
+
+    ## [1] 0.7056902
+
+
+## Next Steps
+* [Neural Networks with MXNet in Five Minutes](http://mxnet.io/tutorials/r/fiveMinutesNeuralNetwork.html)
+* [Classify Real-World Images with a PreTrained Model](http://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
+* [Handwritten Digits Classification Competition](http://mxnet.io/tutorials/r/mnistCompetition.html)
+* [Character Language Model Using RNN](http://mxnet.io/tutorials/r/charRnnModel.html)
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/five_minutes_neural_network.md b/docs/static_site/src/pages/api/r/docs/tutorials/five_minutes_neural_network.md
new file mode 100644
index 000000000000..429240e2eda3
--- /dev/null
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/five_minutes_neural_network.md
@@ -0,0 +1,341 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: Five Minutes Neural Network
+is_tutorial: true
+tag: r
+permalink: /api/r/docs/tutorials/five_minutes_neural_network
+---
+
+Develop a Neural Network with MXNet in Five Minutes
+=============================================
+
+This tutorial is designed for new users of the `mxnet` package for R. It shows how to construct a neural network to do regression in 5 minutes. It shows how to perform classification and regression tasks, respectively. The data we use is in the `mlbench` package. Instructions to install R and MXNet's R package in different environments can be found [here](http://mxnet.incubator.apache.org/install/index.html?platform=Linux&language=R&processor=CPU).
+
+## Classification
+
+ ```
+    ## Loading required package: mlbench
+ ```
+ ```r
+    if (!require(mlbench)) {
+      install.packages('mlbench')
+    }
+ ```
+
+ ```
+    ## Loading required package: mxnet
+ ```
+
+ ```r
+    require(mxnet)
+ ```
+
+ ```
+    ## Loading required datasets
+ ```
+
+ ```r
+    data(Sonar, package="mlbench")
+
+    Sonar[,61] = as.numeric(Sonar[,61])-1
+    train.ind = c(1:50, 100:150)
+    train.x = data.matrix(Sonar[train.ind, 1:60])
+    train.y = Sonar[train.ind, 61]
+    test.x = data.matrix(Sonar[-train.ind, 1:60])
+    test.y = Sonar[-train.ind, 61]
+ ```
+
+We are going to use a multi-layer perceptron as our classifier. In `mxnet`, we have a function called `mx.mlp` for building a general multi-layer neural network to do classification or regression.
+
+`mx.mlp` requires the following parameters:
+
+- Training data and label
+- Number of hidden nodes in each hidden layer
+- Number of nodes in the output layer
+- Type of the activation
+- Type of the output loss
+- The device to train (GPU or CPU)
+- Other parameters for `mx.model.FeedForward.create`
+
+The following code shows an example usage of `mx.mlp`:
+
+
+ ```r
+    mx.set.seed(0)
+    model <- mx.mlp(train.x, train.y, hidden_node=10, out_node=2, out_activation="softmax",
+                num.round=20, array.batch.size=15, learning.rate=0.07, momentum=0.9,
+                eval.metric=mx.metric.accuracy)
+ ```
+
+ ```
+    ## Auto detect layout of input matrix, use rowmajor..
+    ## Start training with 1 devices
+    ## [1] Train-accuracy=0.488888888888889
+    ## [2] Train-accuracy=0.514285714285714
+    ## [3] Train-accuracy=0.514285714285714
+    ## [4] Train-accuracy=0.514285714285714
+    ## [5] Train-accuracy=0.514285714285714
+    ## [6] Train-accuracy=0.523809523809524
+    ## [7] Train-accuracy=0.619047619047619
+    ## [8] Train-accuracy=0.695238095238095
+    ## [9] Train-accuracy=0.695238095238095
+    ## [10] Train-accuracy=0.761904761904762
+    ## [11] Train-accuracy=0.828571428571429
+    ## [12] Train-accuracy=0.771428571428571
+    ## [13] Train-accuracy=0.742857142857143
+    ## [14] Train-accuracy=0.733333333333333
+    ## [15] Train-accuracy=0.771428571428571
+    ## [16] Train-accuracy=0.847619047619048
+    ## [17] Train-accuracy=0.857142857142857
+    ## [18] Train-accuracy=0.838095238095238
+    ## [19] Train-accuracy=0.838095238095238
+    ## [20] Train-accuracy=0.838095238095238
+ ```
+
+Note that `mx.set.seed` controls the random process in `mxnet`. You can see the accuracy in each round during training. It's also easy to make predictions and evaluate.
+
+To get an idea of what is happening, view the computation graph from R:
+
+ ```r
+    graph.viz(model$symbol)
+ ```
+
+[<img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/knitr/graph.computation.png">](https://github.com/dmlc/mxnet)
+
+ ```r
+    preds = predict(model, test.x)
+ ```
+
+ ```
+    ## Auto detect layout of input matrix, use rowmajor.
+ ```
+
+ ```r
+    pred.label = max.col(t(preds))-1
+    table(pred.label, test.y)
+ ```
+
+ ```
+    ##           test.y
+    ## pred.label  0  1
+    ##          0 24 14
+    ##          1 36 33
+ ```
+
+Note for that for multi-class predictions, mxnet outputs `nclass` x `nexamples`, with each row corresponding to the probability of the class.
+
+## Regression
+
+Again, let us preprocess the data:
+
+
+ ```r
+    data(BostonHousing, package="mlbench")
+
+    train.ind = seq(1, 506, 3)
+    train.x = data.matrix(BostonHousing[train.ind, -14])
+    train.y = BostonHousing[train.ind, 14]
+    test.x = data.matrix(BostonHousing[-train.ind, -14])
+    test.y = BostonHousing[-train.ind, 14]
+ ```
+
+Although we can use `mx.mlp` again to do regression by changing the `out_activation`, this time we are going to introduce a flexible way to configure neural networks in `mxnet`. Configuration is done by the "Symbol" system in `mxnet`. The Symbol system takes care of the links among nodes, activation, dropout ratio, etc. Configure a multi-layer neural network as follows:
+
+
+ ```r
+    # Define the input data
+    data <- mx.symbol.Variable("data")
+    # A fully connected hidden layer
+    # data: input source
+    # num_hidden: number of neurons in this hidden layer
+    fc1 <- mx.symbol.FullyConnected(data, num_hidden=1)
+
+    # Use linear regression for the output layer
+    lro <- mx.symbol.LinearRegressionOutput(fc1)
+ ```
+
+What matters for a regression task is mainly the last function. It enables the new network to optimize for squared loss. Now let's train on this simple data set. In this configuration, we dropped the hidden layer so that the input layer is directly connected to the output layer.
+
+Next, make prediction with this structure and other parameters with `mx.model.FeedForward.create`:
+
+
+ ```r
+    mx.set.seed(0)
+    model <- mx.model.FeedForward.create(lro, X=train.x, y=train.y,
+                                         ctx=mx.cpu(),     num.round=50, array.batch.size=20,
+                                         learning.rate=2e-6, momentum=0.9,  eval.metric=mx.metric.rmse)
+ ```
+
+ ```
+    ## Auto detect layout of input matrix, use rowmajor.
+    ## Start training with 1 devices
+    ## [1] Train-rmse=16.063282524034
+    ## [2] Train-rmse=12.2792375712573
+    ## [3] Train-rmse=11.1984634005885
+    ## [4] Train-rmse=10.2645236892904
+    ## [5] Train-rmse=9.49711005504284
+    ## [6] Train-rmse=9.07733734175182
+    ## [7] Train-rmse=9.07884450847991
+    ## [8] Train-rmse=9.10463850277417
+    ## [9] Train-rmse=9.03977049028532
+    ## [10] Train-rmse=8.96870685004475
+    ## [11] Train-rmse=8.93113287361574
+    ## [12] Train-rmse=8.89937257821847
+    ## [13] Train-rmse=8.87182096922953
+    ## [14] Train-rmse=8.84476075083586
+    ## [15] Train-rmse=8.81464673014974
+    ## [16] Train-rmse=8.78672567900196
+    ## [17] Train-rmse=8.76265872846474
+    ## [18] Train-rmse=8.73946101419974
+    ## [19] Train-rmse=8.71651926303267
+    ## [20] Train-rmse=8.69457600919277
+    ## [21] Train-rmse=8.67354928674563
+    ## [22] Train-rmse=8.65328755392436
+    ## [23] Train-rmse=8.63378039680078
+    ## [24] Train-rmse=8.61488162586984
+    ## [25] Train-rmse=8.5965105183022
+    ## [26] Train-rmse=8.57868133563275
+    ## [27] Train-rmse=8.56135851937663
+    ## [28] Train-rmse=8.5444819772098
+    ## [29] Train-rmse=8.52802114610432
+    ## [30] Train-rmse=8.5119504512622
+    ## [31] Train-rmse=8.49624261719241
+    ## [32] Train-rmse=8.48087453238701
+    ## [33] Train-rmse=8.46582689119887
+    ## [34] Train-rmse=8.45107881002491
+    ## [35] Train-rmse=8.43661331401712
+    ## [36] Train-rmse=8.42241575909639
+    ## [37] Train-rmse=8.40847217331365
+    ## [38] Train-rmse=8.39476931796395
+    ## [39] Train-rmse=8.38129658373974
+    ## [40] Train-rmse=8.36804269059018
+    ## [41] Train-rmse=8.35499817678397
+    ## [42] Train-rmse=8.34215505742154
+    ## [43] Train-rmse=8.32950441908131
+    ## [44] Train-rmse=8.31703985777311
+    ## [45] Train-rmse=8.30475363906755
+    ## [46] Train-rmse=8.29264031506106
+    ## [47] Train-rmse=8.28069372820073
+    ## [48] Train-rmse=8.26890902770415
+    ## [49] Train-rmse=8.25728089053853
+    ## [50] Train-rmse=8.24580511500735
+ ```
+
+It's also easy to make a prediction and evaluate it:
+
+
+ ```r
+    preds = predict(model, test.x)
+ ```
+
+ ```
+    ## Auto detect layout of input matrix, use rowmajor..
+ ```
+
+ ```r
+    sqrt(mean((preds-test.y)^2))
+ ```
+
+ ```
+    ## [1] 7.800502
+ ```
+
+Currently, we have four predefined metrics: "accuracy", "rmse", "mae", and "rmsle". MXNet provides the interface for defining your own metrics:
+
+
+ ```r
+    demo.metric.mae <- mx.metric.custom("mae", function(label, pred) {
+      pred <- mx.nd.reshape(pred, shape = 0)
+      res <- mx.nd.mean(mx.nd.abs(label-pred))
+      return(res)
+    })
+ ```
+
+This is an example of the mean absolute error metric. Simply plug it into the training function:
+
+
+ ```r
+    mx.set.seed(0)
+    model <- mx.model.FeedForward.create(lro, X=train.x, y=train.y,
+                                         ctx=mx.cpu(),    num.round=50, array.batch.size=20,
+                                         learning.rate=2e-6, momentum=0.9, eval.metric=demo.metric.mae)
+ ```
+
+ ```
+    ## Auto detect layout of input matrix, use rowmajor.
+    ## Start training with 1 devices
+    ## [1] Train-mae=14.953625731998
+    ## [2] Train-mae=11.4802955521478
+    ## [3] Train-mae=8.50700579749213
+    ## [4] Train-mae=7.30591265360514
+    ## [5] Train-mae=7.38049803839789
+    ## [6] Train-mae=7.36036252975464
+    ## [7] Train-mae=7.06519222259521
+    ## [8] Train-mae=6.9962231847975
+    ## [9] Train-mae=6.96296903822157
+    ## [10] Train-mae=6.9046172036065
+    ## [11] Train-mae=6.87867620256212
+    ## [12] Train-mae=6.85872554779053
+    ## [13] Train-mae=6.81936407089233
+    ## [14] Train-mae=6.79135354359945
+    ## [15] Train-mae=6.77438741260105
+    ## [16] Train-mae=6.75365140702989
+    ## [17] Train-mae=6.73369296391805
+    ## [18] Train-mae=6.71600982877943
+    ## [19] Train-mae=6.69932826360067
+    ## [20] Train-mae=6.6852519777086
+    ## [21] Train-mae=6.67343420452542
+    ## [22] Train-mae=6.66315894656711
+    ## [23] Train-mae=6.65314838621351
+    ## [24] Train-mae=6.64388704299927
+    ## [25] Train-mae=6.63480265935262
+    ## [26] Train-mae=6.62583245171441
+    ## [27] Train-mae=6.61697626113892
+    ## [28] Train-mae=6.60842116673787
+    ## [29] Train-mae=6.60040124257406
+    ## [30] Train-mae=6.59264140658908
+    ## [31] Train-mae=6.58551020092434
+    ## [32] Train-mae=6.57864215638902
+    ## [33] Train-mae=6.57178926467896
+    ## [34] Train-mae=6.56495311525133
+    ## [35] Train-mae=6.55813185373942
+    ## [36] Train-mae=6.5513252152337
+    ## [37] Train-mae=6.54453214009603
+    ## [38] Train-mae=6.53775374094645
+    ## [39] Train-mae=6.53098879920112
+    ## [40] Train-mae=6.52423816257053
+    ## [41] Train-mae=6.51764053768582
+    ## [42] Train-mae=6.51121346155802
+    ## [43] Train-mae=6.5047902001275
+    ## [44] Train-mae=6.49837123023139
+    ## [45] Train-mae=6.49216641320123
+    ## [46] Train-mae=6.48598252402412
+    ## [47] Train-mae=6.4798010720147
+    ## [48] Train-mae=6.47362396452162
+    ## [49] Train-mae=6.46745183732775
+    ## [50] Train-mae=6.46128723356459
+ ```
+
+Congratulations! You've learned the basics for using MXNet in R. To learn how to use MXNet's advanced features, see the other tutorials.
+
+
+## Next Steps
+* [Classify Real-World Images with Pre-trained Model](http://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
+* [Handwritten Digits Classification Competition](http://mxnet.io/tutorials/r/mnistCompetition.html)
+* [Character Language Model using RNN](http://mxnet.io/tutorials/r/charRnnModel.html)
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/index.md b/docs/static_site/src/pages/api/r/docs/tutorials/index.md
new file mode 100644
index 000000000000..0e8293dd4b97
--- /dev/null
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/index.md
@@ -0,0 +1,23 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_landing_tutorials
+title:  R Tutorials
+action: Get Started
+tag: r
+permalink: /api/r/docs/tutorials
+---
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/mnist_competition.md b/docs/static_site/src/pages/api/r/docs/tutorials/mnist_competition.md
new file mode 100644
index 000000000000..d8aec3782524
--- /dev/null
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/mnist_competition.md
@@ -0,0 +1,363 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: MNIST Competition
+is_tutorial: true
+tag: r
+permalink: /api/r/docs/tutorials/mnist_competition
+---
+
+Handwritten Digits Classification Competition
+=============================================
+
+[MNIST](http://yann.lecun.com/exdb/mnist/) is a handwritten digits image data set created by Yann LeCun. Every digit is represented by a 28 x 28 pixel image. It's become a standard data set for testing classifiers on simple image input. A neural network is a strong model for image classification tasks. There's a [long-term hosted competition](https://www.kaggle.com/c/digit-recognizer) on Kaggle using this data set.
+This tutorial shows how to use [MXNet](https://github.com/dmlc/mxnet/tree/master/R-package) to compete in this challenge.
+
+## Loading the Data
+
+First, let's download the data from [Kaggle](https://www.kaggle.com/c/digit-recognizer/data) and put it in the `data/` folder in your working directory.
+
+Now we can read it in R and convert it to matrices:
+
+
+ ```r
+    require(mxnet)
+ ```
+
+ ```
+    ## Loading required package: mxnet
+    ## Loading required package: methods
+ ```
+
+ ```r
+    train <- read.csv('data/train.csv', header=TRUE)
+    test <- read.csv('data/test.csv', header=TRUE)
+    train <- data.matrix(train)
+    test <- data.matrix(test)
+
+    train.x <- train[,-1]
+    train.y <- train[,1]
+ ```
+
+Every image is represented as a single row in train/test. The greyscale of each image falls in the range [0, 255]. Linearly transform it into [0,1] by using the following command:
+
+
+ ```r
+    train.x <- t(train.x/255)
+    test <- t(test/255)
+ ```
+Transpose the input matrix to npixel x nexamples, which is the major format for columns accepted by MXNet (and the convention of R).
+
+In the label section, the number of each digit is fairly evenly distributed:
+
+
+ ```r
+    table(train.y)
+ ```
+
+ ```
+    ## train.y
+    ##    0    1    2    3    4    5    6    7    8    9
+    ## 4132 4684 4177 4351 4072 3795 4137 4401 4063 4188
+```
+
+## Configuring the Network
+
+Now that we have the data, let's configure the structure of our network:
+
+
+ ```r
+    data <- mx.symbol.Variable("data")
+    fc1 <- mx.symbol.FullyConnected(data, name="fc1", num_hidden=128)
+    act1 <- mx.symbol.Activation(fc1, name="relu1", act_type="relu")
+    fc2 <- mx.symbol.FullyConnected(act1, name="fc2", num_hidden=64)
+    act2 <- mx.symbol.Activation(fc2, name="relu2", act_type="relu")
+    fc3 <- mx.symbol.FullyConnected(act2, name="fc3", num_hidden=10)
+    softmax <- mx.symbol.SoftmaxOutput(fc3, name="sm")
+ ```
+
+1. In `mxnet`, we use the data type `symbol` to configure the network. `data <- mx.symbol.Variable("data")` uses `data` to represent the input data, i.e., the input layer.
+2. We set the first hidden layer with `fc1 <- mx.symbol.FullyConnected(data, name="fc1", num_hidden=128)`. This layer has `data` as the input, its name, and the number of hidden neurons.
+3. Activation is set with `act1 <- mx.symbol.Activation(fc1, name="relu1", act_type="relu")`. The activation function takes the output from the first hidden layer, `fc1`.
+4. The second hidden layer takes the result from `act1` as input, with its name as "fc2" and the number of hidden neurons as 64.
+5. The second activation is almost the same as `act1`, except we have a different input source and name.
+6. This generates the output layer. Because there are only 10 digits, we set the number of neurons to 10.
+7. Finally, we set the activation to softmax to get a probabilistic prediction.
+
+## Training
+
+We are almost ready for the training process. Before we start the computation, let's decide which device to use:
+
+
+ ```r
+    devices <- mx.cpu()
+ ```
+
+We assign CPU to `mxnet`. Now, you can run the following command to train the neural network! Note that `mx.set.seed` is the function that controls the random process in `mxnet`:
+
+
+ ```r
+    mx.set.seed(0)
+    model <- mx.model.FeedForward.create(softmax, X=train.x, y=train.y,
+                                         ctx=devices, num.round=10, array.batch.size=100,
+                                         learning.rate=0.07, momentum=0.9,  eval.metric=mx.metric.accuracy,
+                                         initializer=mx.init.uniform(0.07),
+                                            epoch.end.callback=mx.callback.log.train.metric(100))
+ ```
+
+ ```
+    ## Start training with 1 devices
+    ## Batch [100] Train-accuracy=0.6563
+    ## Batch [200] Train-accuracy=0.777999999999999
+    ## Batch [300] Train-accuracy=0.827466666666665
+    ## Batch [400] Train-accuracy=0.855499999999999
+    ## [1] Train-accuracy=0.859832935560859
+    ## Batch [100] Train-accuracy=0.9529
+    ## Batch [200] Train-accuracy=0.953049999999999
+    ## Batch [300] Train-accuracy=0.955866666666666
+    ## Batch [400] Train-accuracy=0.957525000000001
+    ## [2] Train-accuracy=0.958309523809525
+    ## Batch [100] Train-accuracy=0.968
+    ## Batch [200] Train-accuracy=0.9677
+    ## Batch [300] Train-accuracy=0.9696
+    ## Batch [400] Train-accuracy=0.970650000000002
+    ## [3] Train-accuracy=0.970809523809526
+    ## Batch [100] Train-accuracy=0.973
+    ## Batch [200] Train-accuracy=0.974249999999999
+    ## Batch [300] Train-accuracy=0.976
+    ## Batch [400] Train-accuracy=0.977100000000003
+    ## [4] Train-accuracy=0.977452380952384
+    ## Batch [100] Train-accuracy=0.9834
+    ## Batch [200] Train-accuracy=0.981949999999999
+    ## Batch [300] Train-accuracy=0.981900000000001
+    ## Batch [400] Train-accuracy=0.982600000000003
+    ## [5] Train-accuracy=0.983000000000003
+    ## Batch [100] Train-accuracy=0.983399999999999
+    ## Batch [200] Train-accuracy=0.98405
+    ## Batch [300] Train-accuracy=0.985000000000001
+    ## Batch [400] Train-accuracy=0.985725000000003
+    ## [6] Train-accuracy=0.985952380952384
+    ## Batch [100] Train-accuracy=0.988999999999999
+    ## Batch [200] Train-accuracy=0.9876
+    ## Batch [300] Train-accuracy=0.988100000000001
+    ## Batch [400] Train-accuracy=0.988750000000003
+    ## [7] Train-accuracy=0.988880952380955
+    ## Batch [100] Train-accuracy=0.991999999999999
+    ## Batch [200] Train-accuracy=0.9912
+    ## Batch [300] Train-accuracy=0.990066666666668
+    ## Batch [400] Train-accuracy=0.990275000000003
+    ## [8] Train-accuracy=0.990452380952384
+    ## Batch [100] Train-accuracy=0.9937
+    ## Batch [200] Train-accuracy=0.99235
+    ## Batch [300] Train-accuracy=0.991966666666668
+    ## Batch [400] Train-accuracy=0.991425000000003
+    ## [9] Train-accuracy=0.991500000000003
+    ## Batch [100] Train-accuracy=0.9942
+    ## Batch [200] Train-accuracy=0.99245
+    ## Batch [300] Train-accuracy=0.992433333333334
+    ## Batch [400] Train-accuracy=0.992275000000002
+    ## [10] Train-accuracy=0.992380952380955
+ ```
+
+## Making a Prediction and Submitting to the Competition
+
+To make a prediction, type:
+
+
+ ```r
+    preds <- predict(model, test)
+    dim(preds)
+ ```
+
+ ```
+    ## [1]    10 28000
+ ```
+
+It is a matrix with 28000 rows and 10 cols, containing the desired classification probabilities from the output layer. To extract the maximum label for each row, use `max.col`:
+
+
+ ```r
+    pred.label <- max.col(t(preds)) - 1
+    table(pred.label)
+ ```
+
+ ```
+    ## pred.label
+    ##    0    1    2    3    4    5    6    7    8    9
+    ## 2818 3195 2744 2767 2683 2596 2798 2790 2784 2825
+ ```
+
+With a little extra effort to modify the .csv format, our submission is ready for the competition!
+
+
+ ```r
+    submission <- data.frame(ImageId=1:ncol(test), Label=pred.label)
+    write.csv(submission, file='submission.csv', row.names=FALSE,  quote=FALSE)
+ ```
+
+## LeNet
+
+Now let's use a new network structure: [LeNet](http://yann.lecun.com/exdb/lenet/). It has been proposed by Yann LeCun for recognizing handwritten digits. We'll demonstrate how to construct and train a LeNet in `mxnet`.
+
+First, we construct the network:
+
+
+```r
+# input
+data <- mx.symbol.Variable('data')
+# first conv
+conv1 <- mx.symbol.Convolution(data=data, kernel=c(5,5), num_filter=20)
+tanh1 <- mx.symbol.Activation(data=conv1, act_type="tanh")
+pool1 <- mx.symbol.Pooling(data=tanh1, pool_type="max",
+                          kernel=c(2,2), stride=c(2,2))
+# second conv
+conv2 <- mx.symbol.Convolution(data=pool1, kernel=c(5,5), num_filter=50)
+tanh2 <- mx.symbol.Activation(data=conv2, act_type="tanh")
+pool2 <- mx.symbol.Pooling(data=tanh2, pool_type="max",
+                          kernel=c(2,2), stride=c(2,2))
+# first fullc
+flatten <- mx.symbol.Flatten(data=pool2)
+fc1 <- mx.symbol.FullyConnected(data=flatten, num_hidden=500)
+tanh3 <- mx.symbol.Activation(data=fc1, act_type="tanh")
+# second fullc
+fc2 <- mx.symbol.FullyConnected(data=tanh3, num_hidden=10)
+# loss
+lenet <- mx.symbol.SoftmaxOutput(data=fc2)
+```
+
+Then let's reshape the matrices into arrays:
+
+
+```r
+train.array <- train.x
+dim(train.array) <- c(28, 28, 1, ncol(train.x))
+test.array <- test
+dim(test.array) <- c(28, 28, 1, ncol(test))
+```
+
+We want to compare training speed on different devices, so define the devices:
+
+
+```r
+n.gpu <- 1
+device.cpu <- mx.cpu()
+device.gpu <- lapply(0:(n.gpu-1), function(i) {
+  mx.gpu(i)
+})
+```
+
+We can pass a list of devices to ask MXNet to train on multiple GPUs (you can do this for CPUs,
+but because internal computation of CPUs is already multi-threaded, there is less gain than with using GPUs).
+
+Start by training on the CPU first. Because this takes a bit time, we run it for just one iteration.
+
+
+ ```r
+    mx.set.seed(0)
+    tic <- proc.time()
+    model <- mx.model.FeedForward.create(lenet, X=train.array, y=train.y,
+                                     ctx=device.cpu, num.round=1, array.batch.size=100,
+                                     learning.rate=0.05, momentum=0.9, wd=0.00001,
+                                     eval.metric=mx.metric.accuracy,
+                                       epoch.end.callback=mx.callback.log.train.metric(100))
+ ```
+
+ ```
+    ## Start training with 1 devices
+    ## Batch [100] Train-accuracy=0.1066
+    ## Batch [200] Train-accuracy=0.16495
+    ## Batch [300] Train-accuracy=0.401766666666667
+    ## Batch [400] Train-accuracy=0.537675
+    ## [1] Train-accuracy=0.557136038186157
+ ```
+
+ ```r
+    print(proc.time() - tic)
+ ```
+
+ ```
+    ##    user  system elapsed
+    ## 130.030 204.976  83.821
+ ```
+
+Train on a GPU:
+
+
+ ```r
+    mx.set.seed(0)
+    tic <- proc.time()
+    model <- mx.model.FeedForward.create(lenet, X=train.array, y=train.y,
+                                     ctx=device.gpu, num.round=5, array.batch.size=100,
+                                     learning.rate=0.05, momentum=0.9, wd=0.00001,
+                                     eval.metric=mx.metric.accuracy,
+                                       epoch.end.callback=mx.callback.log.train.metric(100))
+ ```
+
+ ```
+    ## Start training with 1 devices
+    ## Batch [100] Train-accuracy=0.1066
+    ## Batch [200] Train-accuracy=0.1596
+    ## Batch [300] Train-accuracy=0.3983
+    ## Batch [400] Train-accuracy=0.533975
+    ## [1] Train-accuracy=0.553532219570405
+    ## Batch [100] Train-accuracy=0.958
+    ## Batch [200] Train-accuracy=0.96155
+    ## Batch [300] Train-accuracy=0.966100000000001
+    ## Batch [400] Train-accuracy=0.968550000000003
+    ## [2] Train-accuracy=0.969071428571432
+    ## Batch [100] Train-accuracy=0.977
+    ## Batch [200] Train-accuracy=0.97715
+    ## Batch [300] Train-accuracy=0.979566666666668
+    ## Batch [400] Train-accuracy=0.980900000000003
+    ## [3] Train-accuracy=0.981309523809527
+    ## Batch [100] Train-accuracy=0.9853
+    ## Batch [200] Train-accuracy=0.985899999999999
+    ## Batch [300] Train-accuracy=0.986966666666668
+    ## Batch [400] Train-accuracy=0.988150000000002
+    ## [4] Train-accuracy=0.988452380952384
+    ## Batch [100] Train-accuracy=0.990199999999999
+    ## Batch [200] Train-accuracy=0.98995
+    ## Batch [300] Train-accuracy=0.990600000000001
+    ## Batch [400] Train-accuracy=0.991325000000002
+    ## [5] Train-accuracy=0.991523809523812
+ ```
+
+ ```r
+    print(proc.time() - tic)
+ ```
+
+ ```
+    ##    user  system elapsed
+    ##   9.288   1.680   6.889
+ ```
+
+By using a GPU processor, we significantly speed up training!
+Now, we can submit the result to Kaggle to see the improvement of our ranking!
+
+
+ ```r
+    preds <- predict(model, test.array)
+    pred.label <- max.col(t(preds)) - 1
+    submission <- data.frame(ImageId=1:ncol(test), Label=pred.label)
+    write.csv(submission, file='submission.csv', row.names=FALSE, quote=FALSE)
+ ```
+
+![](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/knitr/mnistCompetition-kaggle-submission.png)
+
+##  Next Steps
+* [Character Language Model using RNN](http://mxnet.io/tutorials/r/charRnnModel.html)
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/multi_dim_lstm.md b/docs/static_site/src/pages/api/r/docs/tutorials/multi_dim_lstm.md
new file mode 100644
index 000000000000..46c6f22ba849
--- /dev/null
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/multi_dim_lstm.md
@@ -0,0 +1,327 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: LSTM Time Serie
+is_tutorial: true
+tag: r
+permalink: /api/r/docs/tutorials/multi_dim_lstm
+---
+
+
+LSTM Time Series Example
+========================
+
+This tutorial shows how to use an LSTM model with multivariate data, and generate predictions from it. For demonstration purposes, we used an open source [pollution data](https://archive.ics.uci.edu/ml/datasets/Beijing+PM2.5+Data).
+The tutorial is an illustration of how to use LSTM models with MXNet-R. We are forecasting the air pollution with data recorded at the US embassy in Beijing, China for five years.
+
+Dataset Attribution:
+"PM2.5 data of US Embassy in Beijing"
+We want to predict pollution levels(PM2.5 concentration) in the city given the above dataset.
+
+```r
+Dataset description:
+No: row number
+year: year of data in this row
+month: month of data in this row
+day: day of data in this row
+hour: hour of data in this row
+pm2.5: PM2.5 concentration
+DEWP: Dew Point
+TEMP: Temperature
+PRES: Pressure
+cbwd: Combined wind direction
+Iws: Cumulated wind speed
+Is: Cumulated hours of snow
+Ir: Cumulated hours of rain
+```
+
+We use past PM2.5 concentration, dew point, temperature, pressure, wind speed, snow and rain to predict
+PM2.5 concentration levels.
+
+Load and pre-process the data
+---------
+The first step is to load in the data and preprocess it. It is assumed that the data has been downloaded in a .csv file: data.csv from the [pollution dataset](https://archive.ics.uci.edu/ml/datasets/Beijing+PM2.5+Data)
+
+ ```r
+## Loading required packages
+library("readr")
+library("dplyr")
+library("mxnet")
+library("abind")
+ ```
+
+
+
+ ```r
+## Preprocessing steps
+Data <- read.csv(file = "/Users/khedia/Downloads/data.csv",
+                 header = TRUE,
+                 sep = ",")
+
+## Extracting specific features from the dataset as variables for time series We extract
+## pollution, temperature, pressue, windspeed, snowfall and rainfall information from dataset
+df <- data.frame(Data$pm2.5,
+                 Data$DEWP,
+                 Data$TEMP,
+                 Data$PRES,
+                 Data$Iws,
+                 Data$Is,
+                 Data$Ir)
+df[is.na(df)] <- 0
+
+## Now we normalise each of the feature set to a range(0,1)
+df <- matrix(as.matrix(df),
+             ncol = ncol(df),
+             dimnames = NULL)
+
+rangenorm <- function(x) {
+    (x - min(x))/(max(x) - min(x))
+}
+df <- apply(df, 2, rangenorm)
+df <- t(df)
+  ```
+For using multidimesional data with MXNet-R, we need to convert training data to the form
+(n_dim x seq_len x num_samples). For one-to-one RNN flavours labels should be of the form (seq_len x num_samples) while for many-to-one flavour, the labels should be of the form (1 x num_samples). Please note that MXNet-R currently supports only these two flavours of RNN.
+We have used n_dim = 7, seq_len = 100,  and num_samples = 430 because the dataset has 430 samples, each the length of 100 timestamps, we have seven time series as input features so each input has dimesnion of seven at each time step.
+
+
+```r
+n_dim <- 7
+seq_len <- 100
+num_samples <- 430
+
+## extract only required data from dataset
+trX <- df[1:n_dim, 25:(24 + (seq_len * num_samples))]
+
+## the label data(next PM2.5 concentration) should be one time step
+## ahead of the current PM2.5 concentration
+trY <- df[1, 26:(25 + (seq_len * num_samples))]
+
+## reshape the matrices in the format acceptable by MXNetR RNNs
+trainX <- trX
+dim(trainX) <- c(n_dim, seq_len, num_samples)
+trainY <- trY
+dim(trainY) <- c(seq_len, num_samples)
+```
+
+
+
+Defining and training the network
+---------
+
+```r
+batch.size <- 32
+
+# take first 300 samples for training - remaining 100 for evaluation
+train_ids <- 1:300
+eval_ids <- 301:400
+
+## The number of samples used for training and evaluation is arbitrary.  I have kept aside few
+## samples for testing purposes create dataiterators
+train.data <- mx.io.arrayiter(data = trainX[, , train_ids, drop = F],
+                              label = trainY[, train_ids],
+                              batch.size = batch.size, shuffle = TRUE)
+
+eval.data <- mx.io.arrayiter(data = trainX[, , eval_ids, drop = F],
+                             label = trainY[, eval_ids],
+                             batch.size = batch.size, shuffle = FALSE)
+
+## Create the symbol for RNN
+symbol <- rnn.graph(num_rnn_layer = 1,
+                    num_hidden = 5,
+                    input_size = NULL,
+                    num_embed = NULL,
+                    num_decode = 1,
+                    masking = F,
+                    loss_output = "linear",
+                    dropout = 0.2,
+                    ignore_label = -1,
+                    cell_type = "lstm",
+                    output_last_state = T,
+                    config = "one-to-one")
+
+
+
+mx.metric.mse.seq <- mx.metric.custom("MSE", function(label, pred) {
+    label = mx.nd.reshape(label, shape = -1)
+    pred = mx.nd.reshape(pred, shape = -1)
+    res <- mx.nd.mean(mx.nd.square(label - pred))
+    return(as.array(res))
+})
+
+
+
+ctx <- mx.cpu()
+
+initializer <- mx.init.Xavier(rnd_type = "gaussian",
+                              factor_type = "avg",
+                              magnitude = 3)
+
+optimizer <- mx.opt.create("adadelta",
+                           rho = 0.9,
+                           eps = 1e-05,
+                           wd = 1e-06,
+                           clip_gradient = 1,
+                           rescale.grad = 1/batch.size)
+
+logger <- mx.metric.logger()
+epoch.end.callback <- mx.callback.log.train.metric(period = 10,
+                                                   logger = logger)
+
+## train the network
+system.time(model <- mx.model.buckets(symbol = symbol,
+                                      train.data = train.data,
+                                      eval.data = eval.data,
+                                      num.round = 100,
+                                      ctx = ctx,
+                                      verbose = TRUE,
+                                      metric = mx.metric.mse.seq,
+                                      initializer = initializer,
+                                      optimizer = optimizer,
+                                      batch.end.callback = NULL,
+                                      epoch.end.callback = epoch.end.callback))
+```
+Output:
+```
+Start training with 1 devices
+[1] Train-MSE=0.197570244409144
+[1] Validation-MSE=0.0153861071448773
+[2] Train-MSE=0.0152517843060195
+[2] Validation-MSE=0.0128299412317574
+[3] Train-MSE=0.0124418652616441
+[3] Validation-MSE=0.010827143676579
+[4] Train-MSE=0.0105128229130059
+[4] Validation-MSE=0.00940261723008007
+[5] Train-MSE=0.00914482437074184
+[5] Validation-MSE=0.00830172537826002
+[6] Train-MSE=0.00813581114634871
+[6] Validation-MSE=0.00747016374953091
+[7] Train-MSE=0.00735094994306564
+[7] Validation-MSE=0.00679832429159433
+[8] Train-MSE=0.00672049634158611
+[8] Validation-MSE=0.00623159145470709
+[9] Train-MSE=0.00620287149213254
+[9] Validation-MSE=0.00577476259786636
+[10] Train-MSE=0.00577280316501856
+[10] Validation-MSE=0.00539038667920977
+..........
+..........
+[91] Train-MSE=0.00177705133100972
+[91] Validation-MSE=0.00154715491225943
+[92] Train-MSE=0.00177639147732407
+[92] Validation-MSE=0.00154592350008897
+[93] Train-MSE=0.00177577760769054
+[93] Validation-MSE=0.00154474508599378
+[94] Train-MSE=0.0017752077546902
+[94] Validation-MSE=0.0015436161775142
+[95] Train-MSE=0.00177468206966296
+[95] Validation-MSE=0.00154253660002723
+[96] Train-MSE=0.00177419915562496
+[96] Validation-MSE=0.00154150440357625
+[97] Train-MSE=0.0017737578949891
+[97] Validation-MSE=0.00154051734716631
+[98] Train-MSE=0.00177335749613121
+[98] Validation-MSE=0.00153957353904843
+[99] Train-MSE=0.00177299699280411
+[99] Validation-MSE=0.00153867155313492
+[100] Train-MSE=0.00177267640829086
+[100] Validation-MSE=0.00153781197150238
+
+   user  system elapsed
+ 21.937   1.914  13.402
+```
+We can see how mean squared error varies with epochs below.
+
+![png](https://github.com/dmlc/web-data/blob/master/mxnet/doc/tutorials/r/images/loss.png?raw=true)<!--notebook-skip-line-->
+
+Inference on the network
+---------
+Now we have trained the network. Let's use it for inference.
+
+```r
+## We extract the state symbols for RNN
+internals <- model$symbol$get.internals()
+sym_state <- internals$get.output(which(internals$outputs %in% "RNN_state"))
+sym_state_cell <- internals$get.output(which(internals$outputs %in% "RNN_state_cell"))
+sym_output <- internals$get.output(which(internals$outputs %in% "loss_output"))
+symbol <- mx.symbol.Group(sym_output, sym_state, sym_state_cell)
+
+## We will predict 100 timestamps for 401st sample (first sample from the test samples)
+pred_length <- 100
+predicted <- numeric()
+
+## We pass the 400th sample through the network to get the weights and use it for predicting next
+## 100 time stamps.
+data <- mx.nd.array(trainX[, , 400, drop = F])
+label <- mx.nd.array(trainY[, 400, drop = F])
+
+
+## We create dataiterators for the input, please note that the label is required to create
+## iterator and will not be used in the inference. You can use dummy values too in the label.
+infer.data <- mx.io.arrayiter(data = data,
+                              label = label,
+                              batch.size = 1,
+                              shuffle = FALSE)
+
+infer <- mx.infer.rnn.one(infer.data = infer.data,
+                          symbol = symbol,
+                          arg.params = model$arg.params,
+                          aux.params = model$aux.params,
+                          input.params = NULL,
+                          ctx = ctx)
+## Once we get the weights for the above time series, we try to predict the next 100 steps for
+## this time series, which is technically our 401st time series.
+
+actual <- trainY[, 401]
+
+## Now we iterate one by one to generate each of the next timestamp pollution values
+
+for (i in 1:pred_length) {
+
+    data <- mx.nd.array(trainX[, i, 401, drop = F])
+    label <- mx.nd.array(trainY[i, 401, drop = F])
+    infer.data <- mx.io.arrayiter(data = data,
+                                  label = label,
+                                  batch.size = 1,
+                                  shuffle = FALSE)
+    ## note that we use rnn state values from previous iterations here
+    infer <- mx.infer.rnn.one(infer.data = infer.data,
+                              symbol = symbol,
+                              ctx = ctx,
+                              arg.params = model$arg.params,
+                              aux.params = model$aux.params,
+                              input.params = list(rnn.state = infer[[2]],
+                                                  rnn.state.cell = infer[[3]]))
+
+    pred <- infer[[1]]
+    predicted <- c(predicted, as.numeric(as.array(pred)))
+
+}
+
+```
+Now predicted contains the predicted 100 values. We use ggplot to plot the actual and predicted values as shown below.
+
+![png](https://github.com/dmlc/web-data/blob/master/mxnet/doc/tutorials/r/images/sample_401.png?raw=true)<!--notebook-skip-line-->
+
+We also repeated the above experiments to generate the next 100 samples to 301st time series and we got the following results.
+
+![png](https://github.com/dmlc/web-data/blob/master/mxnet/doc/tutorials/r/images/sample_301.png?raw=true)<!--notebook-skip-line-->
+
+The above tutorial is just for demonstration purposes and has not been tuned extensively for accuracy.
+
+For more tutorials on MXNet-R, head on to [MXNet-R tutorials](https://mxnet.incubator.apache.org/tutorials/r/index.html)
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/ndarray.md b/docs/static_site/src/pages/api/r/docs/tutorials/ndarray.md
new file mode 100644
index 000000000000..20098a04e341
--- /dev/null
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/ndarray.md
@@ -0,0 +1,231 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: NDArray
+is_tutorial: true
+tag: r
+permalink: /api/r/docs/tutorials/ndarray
+---
+
+
+# NDArray: Vectorized Tensor Computations on CPUs and GPUs
+
+`NDArray` is the basic vectorized operation unit in MXNet for matrix and tensor computations.
+Users can perform usual calculations as on an R"s array, but with two additional features:
+
+
+
+- Multiple devices: All operations can be run on various devices including
+CPUs and GPUs.
+
+
+- Automatic parallelization: All operations are automatically executed in
+   parallel with each other.
+
+## Create and Initialize
+
+Let"s create `NDArray` on either a GPU or a CPU:
+
+
+```r
+require(mxnet)
+```
+
+```
+## Loading required package: mxnet
+## Loading required package: methods
+```
+
+```r
+a <- mx.nd.zeros(c(2, 3)) # create a 2-by-3 matrix on cpu
+b <- mx.nd.zeros(c(2, 3), mx.cpu()) # create a 2-by-3 matrix on cpu
+# c <- mx.nd.zeros(c(2, 3), mx.gpu(0)) # create a 2-by-3 matrix on gpu 0, if you have CUDA enabled.
+```
+
+Typically for CUDA-enabled devices, the device id of a GPU starts from 0.
+That's why we passed in 0 to the GPU id.
+
+We can initialize an `NDArray` object in various ways:
+
+
+```r
+a <- mx.nd.ones(c(4, 4))
+b <- mx.rnorm(c(4, 5))
+c <- mx.nd.array(1:5)
+```
+
+To check the numbers in an `NDArray`, we can simply run:
+
+
+```r
+a <- mx.nd.ones(c(2, 3))
+b <- as.array(a)
+class(b)
+```
+
+```
+## [1] "matrix"
+```
+
+```r
+b
+```
+
+```
+##      [,1] [,2] [,3]
+## [1,]    1    1    1
+## [2,]    1    1    1
+```
+
+## Performing Basic Operations
+
+### Elemental-wise Operations
+
+You can perform elemental-wise operations on `NDArray` objects, as follows:
+
+
+```r
+a <- mx.nd.ones(c(2, 4)) * 2
+b <- mx.nd.ones(c(2, 4)) / 8
+as.array(a)
+```
+
+```
+##      [,1] [,2] [,3] [,4]
+## [1,]    2    2    2    2
+## [2,]    2    2    2    2
+```
+
+```r
+as.array(b)
+```
+
+```
+##       [,1]  [,2]  [,3]  [,4]
+## [1,] 0.125 0.125 0.125 0.125
+## [2,] 0.125 0.125 0.125 0.125
+```
+
+```r
+c <- a + b
+as.array(c)
+```
+
+```
+##       [,1]  [,2]  [,3]  [,4]
+## [1,] 2.125 2.125 2.125 2.125
+## [2,] 2.125 2.125 2.125 2.125
+```
+
+```r
+d <- c / a - 5
+as.array(d)
+```
+
+```
+##         [,1]    [,2]    [,3]    [,4]
+## [1,] -3.9375 -3.9375 -3.9375 -3.9375
+## [2,] -3.9375 -3.9375 -3.9375 -3.9375
+```
+
+If two `NDArray`s are located on different devices, we need to explicitly move them to the same one. For instance:
+
+
+```r
+a <- mx.nd.ones(c(2, 3)) * 2
+b <- mx.nd.ones(c(2, 3), mx.gpu()) / 8
+c <- mx.nd.copyto(a, mx.gpu()) * b
+as.array(c)
+```
+
+### Loading and Saving
+
+You can save a list of `NDArray` object to your disk with `mx.nd.save`:
+
+
+```r
+a <- mx.nd.ones(c(2, 3))
+mx.nd.save(list(a), "temp.ndarray")
+```
+
+You can load it back easily:
+
+
+```r
+a <- mx.nd.load("temp.ndarray")
+as.array(a[[1]])
+```
+
+```
+##      [,1] [,2] [,3]
+## [1,]    1    1    1
+## [2,]    1    1    1
+```
+
+We can directly save data to and load it from a distributed file system, such as Amazon S3 and HDFS:
+
+
+```r
+mx.nd.save(list(a), "s3://mybucket/mydata.bin")
+mx.nd.save(list(a), "hdfs///users/myname/mydata.bin")
+```
+
+## Automatic Parallelization
+
+`NDArray` can automatically execute operations in parallel. Automatic parallelization is useful when
+using multiple resources, such as CPU cards, GPU cards, and CPU-to-GPU memory bandwidth.
+
+For example, if we write `a <- a + 1` followed by `b <- b + 1`, and `a` is on a CPU and
+`b` is on a GPU, executing them in parallel improves
+efficiency. Furthermore, because copying data between CPUs and GPUs are also expensive, running in parallel with other computations further increases efficiency.
+
+It's hard to find the code that can be executed in parallel by eye. In the
+following example, `a <- a + 1` and `c <- c * 3` can be executed in parallel, but `a <- a + 1` and
+`b <- b * 3` should be in sequential.
+
+
+```r
+a <- mx.nd.ones(c(2,3))
+b <- a
+c <- mx.nd.copyto(a, mx.cpu())
+a <- a + 1
+b <- b * 3
+c <- c * 3
+```
+
+Luckily, MXNet can automatically resolve the dependencies and
+execute operations in parallel accurately. This allows us to write our program assuming there is only a single thread. MXNet will
+automatically dispatch the program to multiple devices.
+
+MXNet achieves this with lazy evaluation. Each operation is issued to an
+internal engine, and then returned. For example, if we run `a <- a + 1`, it
+returns immediately after pushing the plus operator to the engine. This
+asynchronous processing allows us to push more operators to the engine. It determines
+the read and write dependencies and the best way to execute them in
+parallel.
+
+The actual computations are finished, allowing us to copy the results someplace else, such as `as.array(a)` or `mx.nd.save(a, "temp.dat")`. To write highly parallelized codes, we only need to postpone when we need
+the results.
+
+## Next Steps
+* [Symbol](http://mxnet.io/tutorials/r/symbol.html)
+* [Write and use callback functions](http://mxnet.io/tutorials/r/CallbackFunction.html)
+* [Neural Networks with MXNet in Five Minutes](http://mxnet.io/tutorials/r/fiveMinutesNeuralNetwork.html)
+* [Classify Real-World Images with Pre-trained Model](http://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
+* [Handwritten Digits Classification Competition](http://mxnet.io/tutorials/r/mnistCompetition.html)
+* [Character Language Model using RNN](http://mxnet.io/tutorials/r/charRnnModel.html)
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/symbol.md b/docs/static_site/src/pages/api/r/docs/tutorials/symbol.md
new file mode 100644
index 000000000000..c7c7b968b630
--- /dev/null
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/symbol.md
@@ -0,0 +1,154 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: NDArray
+is_tutorial: true
+tag: r
+permalink: /api/r/docs/tutorials/symbol
+---
+
+# Symbol and Automatic Differentiation
+
+The computational unit `NDArray` requires a way to construct neural networks. MXNet provides a symbolic interface, named Symbol, to do this. Symbol combines both flexibility and efficiency.
+
+## Basic Composition of Symbols
+
+The following code creates a two-layer perceptron network:
+
+
+```r
+require(mxnet)
+net <- mx.symbol.Variable("data")
+net <- mx.symbol.FullyConnected(data=net, name="fc1", num_hidden=128)
+net <- mx.symbol.Activation(data=net, name="relu1", act_type="relu")
+net <- mx.symbol.FullyConnected(data=net, name="fc2", num_hidden=64)
+net <- mx.symbol.Softmax(data=net, name="out")
+class(net)
+```
+
+```
+## [1] "Rcpp_MXSymbol"
+## attr(,"package")
+## [1] "mxnet"
+```
+
+Each symbol takes a (unique) string name. *Variable* often defines the inputs,
+or free variables. Other symbols take a symbol as the input (*data*),
+and may accept other hyper parameters, such as the number of hidden neurons (*num_hidden*)
+or the activation type (*act_type*).
+
+A symbol can be viewed as a function that takes several arguments, whose
+names are automatically generated and can be retrieved with the following command:
+
+
+```r
+arguments(net)
+```
+
+```
+## [1] "data"       "fc1_weight" "fc1_bias"   "fc2_weight" "fc2_bias"
+## [6] "out_label"
+```
+
+The arguments are the parameters need by each symbol:
+
+- *data*: Input data needed by the variable *data*
+- *fc1_weight* and *fc1_bias*: The weight and bias for the first fully connected layer, *fc1*
+- *fc2_weight* and *fc2_bias*: The weight and bias for the second fully connected layer, *fc2*
+- *out_label*: The label needed by the loss
+
+We can also specify the automatically generated names explicitly:
+
+
+```r
+data <- mx.symbol.Variable("data")
+w <- mx.symbol.Variable("myweight")
+net <- mx.symbol.FullyConnected(data=data, weight=w, name="fc1", num_hidden=128)
+arguments(net)
+```
+
+```
+## [1] "data"     "myweight" "fc1_bias"
+```
+
+## More Complicated Composition of Symbols
+
+MXNet provides well-optimized symbols for
+commonly used layers in deep learning. You can also define new operators
+in Python. The following example first performs an element-wise add between two
+symbols, then feeds them to the fully connected operator:
+
+
+```r
+lhs <- mx.symbol.Variable("data1")
+rhs <- mx.symbol.Variable("data2")
+net <- mx.symbol.FullyConnected(data=lhs + rhs, name="fc1", num_hidden=128)
+arguments(net)
+```
+
+```
+## [1] "data1"      "data2"      "fc1_weight" "fc1_bias"
+```
+
+We can construct a symbol more flexibly than by using the single
+forward composition, for example:
+
+
+```r
+net <- mx.symbol.Variable("data")
+net <- mx.symbol.FullyConnected(data=net, name="fc1", num_hidden=128)
+net2 <- mx.symbol.Variable("data2")
+net2 <- mx.symbol.FullyConnected(data=net2, name="net2", num_hidden=128)
+composed.net <- mx.apply(net, data=net2, name="compose")
+arguments(composed.net)
+```
+
+```
+## [1] "data2"       "net2_weight" "net2_bias"   "fc1_weight"  "fc1_bias"
+```
+
+In the example, *net* is used as a function to apply to an existing symbol
+*net*. The resulting *composed.net* will replace the original argument *data* with
+*net2* instead.
+
+## Training a Neural Net
+
+The [model API](https://github.com/apache/incubator-mxnet/blob/master/R-package/R/model.R) is a thin wrapper around the symbolic executors to support neural net training.
+
+We encourage you to read [Symbolic Configuration and Execution in Pictures for python package](../../api/python/symbol_in_pictures/symbol_in_pictures.md)for a detailed explanation of concepts in pictures.
+
+## How Efficient Is the Symbolic API?
+
+The Symbolic API brings the efficient C++
+operations in powerful toolkits, such as CXXNet and Caffe, together with the
+flexible dynamic NDArray operations. All of the memory and computation resources are
+allocated statically during bind operations, to maximize runtime performance and memory
+utilization.
+
+The coarse-grained operators are equivalent to CXXNet layers, which are
+extremely efficient.  We also provide fine-grained operators for more flexible
+composition. Because MXNet does more in-place memory allocation, it can
+be more memory efficient than CXXNet and gets to the same runtime with
+greater flexibility.
+
+## Next Steps
+* [Write and use callback functions](http://mxnet.io/tutorials/r/CallbackFunction.html)
+* [Neural Networks with MXNet in Five Minutes](http://mxnet.io/tutorials/r/fiveMinutesNeuralNetwork.html)
+* [Classify Real-World Images with Pre-trained Model](http://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
+* [Handwritten Digits Classification Competition](http://mxnet.io/tutorials/r/mnistCompetition.html)
+* [Character Language Model using RNN](http://mxnet.io/tutorials/r/charRnnModel.html)
diff --git a/docs/static_site/src/pages/api/r/index.md b/docs/static_site/src/pages/api/r/index.md
new file mode 100644
index 000000000000..8afdb29fde69
--- /dev/null
+++ b/docs/static_site/src/pages/api/r/index.md
@@ -0,0 +1,52 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: R Guide
+action: Get Started
+action_url: /get_started
+permalink: /api/r
+tag: r
+---
+
+
+# MXNet - R API
+
+See the [MXNet R Reference Manual](https://s3.amazonaws.com/mxnet-prod/docs/R/mxnet-r-reference-manual.pdf).
+
+MXNet supports the R programming language. The MXNet R package brings flexible and efficient GPU
+computing and state-of-art deep learning to R. It enables you to write seamless tensor/matrix computation with multiple GPUs in R. It also lets you construct and customize the state-of-art deep learning models in R,
+  and apply them to tasks, such as image classification and data science challenges.
+
+You can perform tensor or matrix computation in R:
+
+```r
+   > require(mxnet)
+   Loading required package: mxnet
+   > a <- mx.nd.ones(c(2,3))
+   > a
+        [,1] [,2] [,3]
+   [1,]    1    1    1
+   [2,]    1    1    1
+   > a + 1
+        [,1] [,2] [,3]
+   [1,]    2    2    2
+   [2,]    2    2    2
+```
+## Resources
+
+* [MXNet R Reference Manual](https://s3.amazonaws.com/mxnet-prod/docs/R/mxnet-r-reference-manual.pdf)
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/char_lstm.md b/docs/static_site/src/pages/api/scala/docs/tutorials/char_lstm.md
new file mode 100644
index 000000000000..6125ad77f5b1
--- /dev/null
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/char_lstm.md
@@ -0,0 +1,533 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: Char-LSTM
+is_tutorial: true
+tag: scala
+permalink: /api/scala/docs/tutorials/char_lstm
+---
+
+# Developing a Character-level Language model
+
+This tutorial shows how to train a character-level language model with a multilayer recurrent neural network (RNN) using Scala. This model takes one text file as input and trains an RNN that learns to predict the next character in the sequence. In this tutorial, you train a multilayer LSTM (Long Short-Term Memory) network that generates relevant text using Barack Obama's speech patterns.
+
+There are many documents that explain LSTM concepts. If you aren't familiar with LSTM, refer to the following before you proceed:
+- Christopher Olah's [Understanding LSTM blog post](http://colah.github.io/posts/2015-08-Understanding-LSTMs/)
+- [Training a LSTM char-rnn in Julia to Generate Random Sentences](http://dmlc.ml/mxnet/2015/11/15/char-lstm-in-julia.html)
+- [Bucketing in MXNet in Python](https://github.com/dmlc/mxnet-notebooks/blob/master/python/tutorials/char_lstm.ipynb)
+- [Bucketing in MXNet](http://mxnet.io/faq/bucketing.html)
+
+## How to Use This Tutorial
+
+There are three ways to use this tutorial:
+
+1) Run it by copying the provided code snippets and pasting them into the Scala command line, making the appropriate changes to the input file path.
+
+2) Reuse the code by making changes to relevant parameters and running it from command line.
+
+3) [Run the source code directly](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/scala/org/apache/mxnetexamples/rnn) by running the [provided scripts](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/scripts/rnn).
+
+To run the scripts:
+- Build and train the model with the [run_train_charrnn.sh script](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/scripts/rnn/run_train_charrnn.sh). Edit the script as follows:
+
+Edit the CLASS_PATH variable in the script to include your operating system-specific folder (e.g., linux-x86_64-cpu/linux-x86_64-gpu/osx-x86_64-cpu) in the path. Run the script with the following command:
+
+```bash
+
+    bash run_train_charrnn.sh <which GPU card to use; -1 means CPU> <input data path> <location to save the model>
+
+    e.g.,
+    bash run_train_charrnn.sh -1 ./datas/obama.txt ./models/obama
+
+```
+
+- Run inference with the [run_test_charrnn.sh script](https://github.com/dmlc/mxnet/blob/master/scala-package/examples/scripts/rnn/run_test_charrnn.sh). Edit the script as follows:
+
+Edit the CLASS_PATH variable in the script to include your operating system-specific folder (e.g., linux-x86_64-cpu/linux-x86_64-gpu/osx-x86_64-cpu) in the path. Run the script with the following command:
+
+```bash
+
+    bash run_test_charrnn.sh <input data path> <trained model from previous script>
+
+    e.g.,
+    bash run_test_charrnn.sh ./datas/obama.txt ./models/obama
+```
+
+In this tutorial, you will accomplish the following:
+
+-	Build an LSTM network that learns speech patterns from Barack Obama's speeches at the character level. At each time interval, the input is a character.
+-	Clean up the dataset.
+-	Train a model.
+-	Fit the model.
+-	Build the inference model.
+
+## Prerequisites
+
+To complete this tutorial, setup and run the scala interpreter by following the [instructions](https://mxnet.incubator.apache.org/install/scala_setup.html#interpreter).
+
+## Download the Data
+
+First, download the data, which contains Barack Obama's speeches. The data is stored in a file called obama.txt and is available on [mxnet.io](http://data.mxnet.io/data/char_lstm.zip)
+
+To download the data which contains Barack Obama's speeches:
+
+1) Download the dataset with the following command:
+
+    ```bash
+        wget http://data.mxnet.io/data/char_lstm.zip
+    ```
+
+2) Unzip the dataset with the following command:
+
+    ```bash
+        unzip char_lstm.zip -d char_lstm/
+    ```
+
+3) The downloaded data contains President Obama's speeches. You can have sneak peek at the dataset with the following command:
+
+    ```bash
+        head -10 obama.txt
+    ```
+
+Output:
+```
+        Call to Renewal Keynote Address Call to Renewal Pt 1Call to Renewal Part 2 TOPIC: Our Past, Our Future & Vision for America June
+        28, 2006 Call to Renewal' Keynote Address Complete Text Good morning. I appreciate the opportunity to speak here at the Call to R
+        enewal's Building a Covenant for a New America conference. I've had the opportunity to take a look at your Covenant for a New Ame
+        rica. It is filled with outstanding policies and prescriptions for much of what ails this country. So I'd like to congratulate yo
+        u all on the thoughtful presentations you've given so far about poverty and justice in America, and for putting fire under the fe
+        et of the political leadership here in Washington.But today I'd like to talk about the connection between religion and politics a
+        nd perhaps offer some thoughts about how we can sort through some of the often bitter arguments that we've been seeing over the l
+        ast several years.I do so because, as you all know, we can affirm the importance of poverty in the Bible; and we can raise up and
+         pass out this Covenant for a New America. We can talk to the press, and we can discuss the religious call to address poverty and
+         environmental stewardship all we want, but it won't have an impact unless we tackle head-on the mutual suspicion that sometimes
+```
+
+## Prepare the Data
+
+To preprocess the dataset, define the following utility functions:
+
+* `readContent` - Reads data from the data file.
+* `buildVocab` - Maps each character to a unique Integer ID, i.e., a build a vocabulary
+* `text2Id` - Encodes each sentence with an Integer ID.
+
+Then, use these utility functions to generate vocabulary from the input text file (obama.txt).
+
+To prepare the data:
+
+1) Read the dataset with the following function:
+
+    ```scala
+        scala> import scala.io.Source
+
+        import scala.io.Source
+
+        scala> // Read file
+        scala> def readContent(path: String): String = Source.fromFile(path).mkString
+
+        readContent: (path: String)String
+
+    ```
+
+2) Build a vocabulary with the following function:
+
+    ```scala
+        scala> // Build  a vocabulary of what char we have in the content
+        scala> def buildVocab(path: String): Map[String, Int] = {
+                val content = readContent(path).split("\n")
+                var idx = 1 // 0 is left for zero padding
+                var theVocab = Map[String, Int]()
+                for (line <- content) {
+                 for (char <- line) {
+                   val key = s"$char"
+                   if (!theVocab.contains(key)) {
+                     theVocab = theVocab + (key -> idx)
+                     idx += 1
+                   }
+                 }
+                }
+                theVocab
+               }
+
+               buildVocab: (path: String)Map[String,Int]
+    ```
+
+3) To assign each character a unique numerical ID, use the following function:
+
+    ```scala
+        scala> def text2Id(sentence: String, theVocab: Map[String, Int]): Array[Int] = {
+                val words = for (char <- sentence) yield theVocab(s"$char")
+                words.toArray
+              }
+
+              text2Id: (sentence: String, theVocab: Map[String,Int])Array[Int]
+    ```
+
+4) Now, build a character vocabulary from the dataset (obama.txt). Change the input filepath (dataPath) to reflect your settings.
+
+    ```scala
+        scala> // Give your system path to the "obama.txt" we have downloaded using previous steps.
+        scala> val dataPath = "obama.txt"
+        dataPath: String = obama.txt
+
+        scala> val vocab = buildVocab(dataPath)
+
+        scala> vocab.size
+        res23: Int = 82
+    ```
+
+
+## Build a Multi-layer LSTM model
+
+Now, create a multi-layer LSTM model.
+
+To create the model:
+
+1) Load the helper files (`Lstm.scala`, `BucketIo.scala` and `RnnModel.scala`).
+`Lstm.scala` contains the definition of the LSTM cell. `BucketIo.scala` creates a sentence iterator. `RnnModel.scala` is used for model inference. The helper files are available on the [MXNet site](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/scala/org/apache/mxnetexamples/rnn).
+To load them, at the Scala command prompt type:
+
+    ```scala
+        scala> :load ../../../scala-package/examples/src/main/scala/org/apache/mxnet/examples/rnn/Lstm.scala
+        scala> :load ../../../scala-package/examples/src/main/scala/org/apache/mxnet/examples/rnn/BucketIo.scala
+        scala> :load ../../../scala-package/examples/src/main/scala/org/apache/mxnet/examples/rnn/RnnModel.scala
+    ```
+
+2) Set the LSTM hyperparameters as follows:
+
+    ```scala
+        scala> // We can support various input lengths.
+        scala> // For this problem, we cut each input sentence to a length of 129 characters.
+        scala> // So we only need a fixed length bucket length.
+        scala> val buckets = Array(129)
+        buckets: Array[Int] = Array(129)
+
+        scala> // hidden unit in LSTM cell
+        scala> val numHidden = 512
+        numHidden: Int = 512
+
+        scala> // The embedding dimension, which maps a char to a 256 dim vector
+        scala> val numEmbed = 256
+        numEmbed: Int = 256
+
+        scala> // The number of lstm layers
+        scala> val numLstmLayer = 3
+        numLstmLayer: Int = 3
+
+        scala> // The batch size for training
+        scala> val batchSize = 32
+        batchSize: Int = 32
+    ```
+
+3) Now, construct the LSTM network as a symbolic computation graph. Type the following to create a graph in which the model is unrolled for a fixed length explicitly in time.
+
+    ```scala
+        scala> // generate symbol for a length
+        scala> def symGen(seqLen: Int): Symbol = {
+            Lstm.lstmUnroll(numLstmLayer, seqLen, vocab.size + 1,
+                        numHidden = numHidden, numEmbed = numEmbed,
+                        numLabel = vocab.size + 1, dropout = 0.2f)
+          }
+        symGen: (seqLen: Int)org.apache.mxnet.Symbol
+
+        scala> // create the network symbol
+        scala> val symbol = symGen(buckets(0))
+        symbol: org.apache.mxnet.Symbol = org.apache.mxnet.Symbol@3a589eed
+
+    ```
+
+4) To train the model, initialize states for the LSTM and create a data iterator, which groups the data into buckets.
+Note: The BucketSentenceIter data iterator supports various length examples; however, we use only the fixed length version in this tutorial.
+
+    ```scala
+
+        scala> // initialize states for LSTM
+        scala> val initC = for (l <- 0 until numLstmLayer) yield (s"l${l}_init_c", (batchSize, numHidden))
+
+        initC: scala.collection.immutable.IndexedSeq[(String, (Int, Int))] = Vector((l0_init_c,(32,512)),
+        (l1_init_c,(32,512)), (l2_init_c,(32,512)))
+
+        scala> val initH = for (l <- 0 until numLstmLayer) yield (s"l${l}_init_h", (batchSize, numHidden))
+
+        initH: scala.collection.immutable.IndexedSeq[(String, (Int, Int))] = Vector((l0_init_h,(32,512)),
+        (l1_init_h,(32,512)), (l2_init_h,(32,512)))
+
+        scala> val initStates = initC ++ initH
+
+        initStates: scala.collection.immutable.IndexedSeq[(String, (Int, Int))] =
+        Vector((l0_init_c,(32,512)), (l1_init_c,(32,512)), (l2_init_c,(32,512)), (l0_init_h,(32,512)),
+        (l1_init_h,(32,512)), (l2_init_h,(32,512)))
+
+        scala> val dataTrain = new BucketIo.BucketSentenceIter(dataPath, vocab, buckets,
+                                              batchSize, initStates, seperateChar = "\n",
+                                              text2Id = text2Id, readContent = readContent)
+
+        dataTrain: BucketIo.BucketSentenceIter = non-empty iterator
+
+    ```
+
+5) You can set more than 100 epochs, but for this tutorial, specify 75 epochs. Each epoch can take as long as 4 minutes on a GPU. In this tutorial, you will use the [ADAM optimizer](http://mxnet.io/api/scala/docs/index.html#org.apache.mxnet.optimizer.Adam):
+
+    ```scala
+        scala> import org.apache.mxnet._
+        import org.apache.mxnet._
+
+        scala> import org.apache.mxnet.Callback.Speedometer
+        import org.apache.mxnet.Callback.Speedometer
+
+        scala> import org.apache.mxnet.optimizer.Adam
+        import org.apache.mxnet.optimizer.Adam
+
+        scala> // and we will see result by training 75 epochs
+        scala> val numEpoch = 75
+        numEpoch: Int = 75
+
+        scala> // learning rate
+        scala> val learningRate = 0.001f
+        learningRate: Float = 0.001
+
+    ```
+
+6) Define the perplexity utility function for the evaluation metric which is used to calculate the negative log-likelihood during training.
+
+    ```scala
+        scala> def perplexity(label: NDArray, pred: NDArray): Float = {
+                val shape = label.shape
+                val size = shape(0) * shape(1)
+                val labelT = {
+                  val tmp = label.toArray.grouped(shape(1)).toArray
+                  val result = Array.fill[Float](size)(0f)
+                  var idx = 0
+                  for (i <- 0 until shape(1)) {
+                    for (j <- 0 until shape(0)) {
+                      result(idx) = tmp(j)(i)
+                      idx += 1
+                    }
+                  }
+                  result
+                }
+                var loss = 0f
+                val predArray = pred.toArray.grouped(pred.shape(1)).toArray
+                for (i <- 0 until pred.shape(0)) {
+                  loss += -Math.log(Math.max(1e-10, predArray(i)(labelT(i).toInt)).toFloat).toFloat
+                }
+                loss / size
+                }
+
+        perplexity: (label: org.apache.mxnet.NDArray, pred: org.apache.mxnet.NDArray)Float
+
+        scala> def doCheckpoint(prefix: String): EpochEndCallback = new EpochEndCallback {
+                    override def invoke(epoch: Int, symbol: Symbol,
+                                        argParams: Map[String, NDArray],
+                                        auxStates: Map[String, NDArray]): Unit = {
+                      Model.saveCheckpoint(prefix, epoch + 1, symbol, argParams, auxStates)
+                    }
+                }
+
+        doCheckpoint: (prefix: String)org.apache.mxnet.EpochEndCallback
+
+    ```
+
+7) Define the initializer that is required for creating a model, as follows:
+
+    ```scala
+        scala> val initializer = new Xavier(factorType = "in", magnitude = 2.34f)
+
+        initializer: org.apache.mxnet.Xavier = org.apache.mxnet.Xavier@54e8f10a
+
+    ```
+
+8) Now, you have implemented all the supporting infrastructures for the char-lstm model. To train the model, use the standard [MXNet high-level API](http://mxnet.io/api/scala/docs/index.html#org.apache.mxnet.FeedForward). You can train the model on a single GPU or CPU from multiple GPUs or CPUs by changing ```scala .setContext(Array(Context.gpu(0),Context.gpu(1),Context.gpu(2),Context.gpu(3)))``` to ```scala .setContext(Array(Context.gpu(0)))```:
+
+    ```scala
+        scala> val model = FeedForward.newBuilder(symbol)
+                .setContext(Array(Context.gpu(0),Context.gpu(1),Context.gpu(2),Context.gpu(3)))
+                .setNumEpoch(numEpoch)
+                .setOptimizer(new Adam(learningRate = learningRate, wd = 0.00001f))
+                .setInitializer(initializer)
+                .setTrainData(dataTrain)
+                .setEvalMetric(new CustomMetric(perplexity, name = "perplexity"))
+                .setBatchEndCallback(new Speedometer(batchSize, 20))
+                .setEpochEndCallback(doCheckpoint("obama"))
+                .build()
+
+        model: org.apache.mxnet.FeedForward = org.apache.mxnet.FeedForward@4926f6c7
+    ```
+
+Now, you have an LSTM model and you've trained it. Use this model to create the inference.
+
+## Build the Inference Model
+
+You can now sample sentences from the trained model. The sampler works as follows:
+- Takes some fixed character set (e.g., "The United States") and feeds it into the LSTM as the starting input.
+- The LSTM produces an output distribution over the vocabulary and a state in the first time step then, samples a character from the output distribution and fixes it as the second character.
+- In the next time step, feeds the previously sampled character as input.
+- Continues running until it has sampled enough characters. Note we are running mini-batches, so several sentences could be sampled simultaneously.
+
+To build the inference model, define the following utility functions that help MXNet make inferences:
+
+* `makeRevertVocab` - Reverts the key value in the dictionary for easy access to characters while predicting
+* `makeInput` -  Uses a given character as input
+* `cdf`, `choice` - `cdf` is a helper function for the `choice` function, which is used to create random samples
+* `makeOutput` - Directs the model to use either random output or fixed output by choosing the option with the greatest probability.
+
+    ```scala
+        scala> import scala.util.Random
+
+        scala> // helper structure for prediction
+        scala> def makeRevertVocab(vocab: Map[String, Int]): Map[Int, String] = {
+                  var dic = Map[Int, String]()
+                  vocab.foreach { case (k, v) =>
+                    dic = dic + (v -> k)
+                  }
+                  dic
+                }
+
+      makeRevertVocab: (vocab: Map[String,Int])Map[Int,String]
+
+      scala> // make input from char
+      scala> def makeInput(char: Char, vocab: Map[String, Int], arr: NDArray): Unit = {
+              val idx = vocab(s"$char")
+              val tmp = NDArray.zeros(1)
+              tmp.set(idx)
+              arr.set(tmp)
+            }
+
+      makeInput: (char: Char, vocab: Map[String,Int], arr: org.apache.mxnet.NDArray)Unit
+
+      scala> // helper function for random sample
+      scala> def cdf(weights: Array[Float]): Array[Float] = {
+                val total = weights.sum
+                var result = Array[Float]()
+                var cumsum = 0f
+                for (w <- weights) {
+                  cumsum += w
+                  result = result :+ (cumsum / total)
+                }
+                result
+              }
+
+      cdf: (weights: Array[Float])Array[Float]
+
+      scala> def choice(population: Array[String], weights: Array[Float]): String = {
+              assert(population.length == weights.length)
+              val cdfVals = cdf(weights)
+              val x = Random.nextFloat()
+              var idx = 0
+              var found = false
+              for (i <- 0 until cdfVals.length) {
+                if (cdfVals(i) >= x && !found) {
+                  idx = i
+                  found = true
+                }
+              }
+              population(idx)
+            }
+
+      choice: (population: Array[String], weights: Array[Float])String
+
+      scala> // we can use random output or fixed output by choosing largest probability
+      scala> def makeOutput(prob: Array[Float], vocab: Map[Int, String],
+                              sample: Boolean = false, temperature: Float = 1f): String = {
+                 var idx = -1
+                 val char = if (sample == false) {
+                   idx = ((-1f, -1) /: prob.zipWithIndex) { (max, elem) =>
+                     if (max._1 < elem._1) elem else max
+                   }._2
+                   if (vocab.contains(idx)) vocab(idx)
+                   else ""
+                 } else {
+                   val fixDict = Array("") ++ (1 until vocab.size + 1).map(i => vocab(i))
+                   var scaleProb = prob.map(x => if (x < 1e-6) 1e-6 else if (x > 1 - 1e-6) 1 - 1e-6 else x)
+                   var rescale = scaleProb.map(x => Math.exp(Math.log(x) / temperature).toFloat)
+                   val sum = rescale.sum.toFloat
+                   rescale = rescale.map(_ / sum)
+                   choice(fixDict, rescale)
+                 }
+                 char
+               }
+
+      makeOutput: (prob: Array[Float], vocab: Map[Int,String], sample: Boolean, temperature: Float)String
+
+    ```
+
+1) Build the inference model:
+
+    ```scala
+        scala> // load from check-point
+        scala> val (_, argParams, _) = Model.loadCheckpoint("obama", 75)
+
+        scala> // build an inference model
+        scala> val model = new RnnModel.LSTMInferenceModel(numLstmLayer, vocab.size + 1, \
+                                   numHidden = numHidden, numEmbed = numEmbed, \
+                                   numLabel = vocab.size + 1, argParams = argParams, \
+                                   ctx = Context.cpu(), dropout = 0.2f)
+
+        model: RnnModel.LSTMInferenceModel = RnnModel$LSTMInferenceModel@2f0c0319
+    ```
+
+2) Now you can generate a sequence of 1200 characters (you can select any number of characters you want) starting with "The United States" as follows:
+
+    ```scala
+
+        scala> val seqLength = 1200
+        seqLength: Int = 1200
+
+        scala> val inputNdarray = NDArray.zeros(1)
+        inputNdarray: org.apache.mxnet.NDArray = org.apache.mxnet.NDArray@9c231a24
+
+        scala> val revertVocab = makeRevertVocab(vocab)
+
+        scala> // Feel free to change the starter sentence
+
+        scala> var output = "The United States"
+        output: String = The United States
+
+        scala> val randomSample = true
+        randomSample: Boolean = true
+
+        scala> var newSentence = true
+        newSentence: Boolean = true
+
+        scala> val ignoreLength = output.length()
+        ignoreLength: Int = 17
+
+        scala> for (i <- 0 until seqLength) {
+                if (i <= ignoreLength - 1) makeInput(output(i), vocab, inputNdarray)
+                else makeInput(output.takeRight(1)(0), vocab, inputNdarray)
+                val prob = model.forward(inputNdarray, newSentence)
+                newSentence = false
+                val nextChar = makeOutput(prob, revertVocab, randomSample)
+                if (nextChar == "") newSentence = true
+                if (i >= ignoreLength) output = output ++ nextChar
+              }
+
+        scala> output
+
+        res7: String = The United States who have been blessed no companies would be proud that the challenges we face, it's not as directly untelle are in my daughters - you can afford -- life-saving march care and poor information and receiving battle against other speeces and lead its people. After champions of 2006, and because Africa in America, separate has been conferenced by children ation of discrimination, we remember all of this, succeeded in any other feelings of a palently better political process - at lliims being disability payment. All across all different mights of a more just a few global personal morality and industrialized ready to succeed.One can afford when the earliest days of a pension you can add to the system be confructive despair. They have starting in the demand for...
+
+    ```
+
+
+You can see the output generated from Obama's speeches. All of the line breaks, punctuation, and uppercase and lowercase letters were produced by the sampler (no post-processing was performed).
+
+
+## Next Steps
+* [Scala API](http://mxnet.io/api/scala/)
+* [More Scala Examples](https://github.com/dmlc/mxnet/tree/master/scala-package/examples/)
+* [MXNet tutorials index](http://mxnet.io/tutorials/index.html)
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/index.md b/docs/static_site/src/pages/api/scala/docs/tutorials/index.md
new file mode 100644
index 000000000000..f9b6e56c9d4f
--- /dev/null
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/index.md
@@ -0,0 +1,22 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_landing_tutorials
+title: Scala Tutorials
+permalink: /api/scala/docs/tutorials
+tag: scala
+---
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/infer.md b/docs/static_site/src/pages/api/scala/docs/tutorials/infer.md
new file mode 100644
index 000000000000..daa996196393
--- /dev/null
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/infer.md
@@ -0,0 +1,66 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: Infer API
+is_tutorial: true
+tag: scala
+permalink: /api/scala/docs/tutorials/infer
+---
+
+# Infer API
+The MXNet Scala Infer API provides you with model loading and inference functionality using the MXNet Scala package.
+
+
+## Prerequisites
+To use the Infer API you must first install the MXNet Scala package. Instructions for this are provided in the following variations:
+* [Tutorial for setting up a project in the IntelliJ IDE](../../tutorials/scala/mxnet_scala_on_intellij.html)
+* [Installing the MXNet Scala Package for macOS](../../install/ubuntu_setup.html#install-the-mxnet-package-for-scala)
+* [Installing the MXNet Scala for Linux](../../install/ubuntu_setup.html#install-the-mxnet-package-for-scala)
+
+
+## Inference
+The Scala Infer API includes both single image and batch modes. Here is an example of running inference on a single image by using the `ImageClassifier` class. A complete [image classification example](https://github.com/apache/incubator-mxnet/blob/master/scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/imageclassifier/ImageClassifierExample.scala) using ResNet-152 is provided in the [Scala package's example folder](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/scala/org/apache/mxnetexamples). This example also demonstrates inference with batches of images.
+
+```scala
+def runInferenceOnSingleImage(modelPathPrefix: String, inputImagePath: String,
+                              context: Array[Context]):
+IndexedSeq[IndexedSeq[(String, Float)]] = {
+  val dType = DType.Float32
+  val inputShape = Shape(1, 3, 224, 224)
+
+  val inputDescriptor = IndexedSeq(DataDesc("data", inputShape, dType, "NCHW"))
+
+  // Create object of ImageClassifier class
+  val imgClassifier: ImageClassifier = new
+      ImageClassifier(modelPathPrefix, inputDescriptor, context)
+
+  // Loading single image from file and getting BufferedImage
+  val img = ImageClassifier.loadImageFromFile(inputImagePath)
+
+  // Running inference on single image
+  val output = imgClassifier.classifyImage(img, Some(5))
+
+  output
+}
+```
+
+
+## Related Resources
+* [Infer API Scaladocs](docs/index.html#org.apache.mxnet.infer.package)
+* [Single Shot Detector Inference Example](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/objectdetector)
+* [Image Classification Example](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/imageclassifier)
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/io.md b/docs/static_site/src/pages/api/scala/docs/tutorials/io.md
new file mode 100644
index 000000000000..f9e9b462c4a2
--- /dev/null
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/io.md
@@ -0,0 +1,189 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: Data Loading API
+permalink: /api/scala/docs/tutorials/io
+is_tutorial: true
+tag: scala
+---
+
+# MXNet Scala Data Loading API
+This topic introduces the data input method for MXNet. MXNet uses an iterator to provide data to the neural network.  Iterators do some preprocessing and generate batches for the neural network.
+
+MXNet provides basic iterators for MNIST and RecordIO images. To hide the cost of I/O, MXNet uses a prefetch strategy that enables parallelism for the learning process and data fetching. Data is automatically fetched by an independent thread.
+
+Topics:
+
+* [Data Iterator Parameters](#parameters-for-data-iterator) clarifies the different usages for dataiter parameters.
+* [Create a Data Iterator](#create-a-data-iterator) introduces how to create a data iterator in MXNet for Scala.
+* [How to Get Data](#how-to-get-data) introduces the data resource and data preparation tools.
+* [IO API Reference](http://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.IO$) explains the IO API.
+
+
+## Data Iterator Parameters
+
+To create a data iterator, you typically need to provide five parameters:
+
+* **Dataset Param** provides basic information about the dataset, e.g., file path, input shape.
+* **Batch Param** provides information required to form a batch, e.g., batch size.
+* **Augmentation Param** tells MXNet which augmentation operations (e.g., crop or mirror) to perform on an input image.
+* **Backend Param** controls the behavior of the back-end threads to hide the cost of data loading.
+* **Auxiliary Param** provides options for checking and debugging.
+
+You *must* provide the **Dataset Param** and **Batch Param**, otherwise MXNet can't create the data batch. Provide other parameters as required by your algorithm and performance needs. We provide a detailed explanation and examples of the options later.
+
+## Create a Data Iterator
+
+The IO API provides a simple way to create a data iterator in Scala.
+The following example code shows how to create a CIFAR data iterator.
+
+```scala
+     val dataiter = IO.ImageRecordIter(Map(
+            // Utility Parameter
+            // Optional
+            // Name of the data, should match the name of the data input of the network
+            // data_name='data',
+            // Utility Parameter
+            // Optional
+            // Name of the label, should match the name of the label parameter of the network
+            // Usually, if the loss layer is named 'foo', then the label input has the name
+            // 'foo_label', unless overwritten
+            // label_name='softmax_label',
+            // Dataset Parameter
+            // Impulsary
+            // indicating the data file, please check the data is already there
+            "path_imgrec" -> "data/cifar/train.rec",
+            // Dataset Parameter
+            // Impulsary
+            // indicating the image size after preprocessing
+            "data_shape" -> "(3,28,28)",
+            // Batch Parameter
+            // Impulsary
+            // tells how many images in a batch
+            "batch_size" -> "100",
+            // Augmentation Parameter
+            // Optional
+            // when offers mean_img, each image will subtract the mean value at each pixel
+            "mean_img" -> "data/cifar/cifar10_mean.bin",
+            // Augmentation Parameter
+            // Optional
+            // randomly crop a patch of the data_shape from the original image
+           "rand_crop" -> "True",
+            // Augmentation Parameter
+            // Optional
+            // randomly mirror the image horizontally
+            "rand_mirror" -> "True",
+            // Augmentation Parameter
+            // Optional
+            // randomly shuffle the data
+            "shuffle" -> "False",
+            // Backend Parameter
+            // Optional
+            // Preprocessing thread number
+            "preprocess_threads" -> "4",
+            // Backend Parameter
+            // Optional
+            // Prefetch buffer size
+            "prefetch_buffer" = "1"))
+```
+
+First, explicitly specify the kind of data (MNIST, ImageRecord, etc.) to fetch. Then, provide the options for the dataset, batching, image augmentation, multi-tread processing,  and prefetching operations. The code automatically validates the parameters. If a required parameter is missing, MXNet returns an error.
+
+## How to Get Data
+
+
+We provide [scripts](https://github.com/apache/incubator-mxnet/tree/master/scala-package/core/scripts) to download MNIST data and CIFAR10 ImageRecord data. If you want to create your own dataset, we recommend using the Image RecordIO data format.
+
+## Create a Dataset Using RecordIO
+
+RecordIO implements a file format for a sequence of records. We recommend storing images as records and packing them together. The benefits include:
+
+* Storing images in a compact format--e.g., JPEG, for records--greatly reduces the size of the dataset on the disk.
+* Packing data together allows continuous reading on the disk.
+* RecordIO has a simple way to partition, simplifying distributed setting. We provide an example later.
+
+We provide the [im2rec tool](https://github.com/apache/incubator-mxnet/blob/master/tools/im2rec.cc) so you can create an Image RecordIO dataset by yourself. The following walkthrough shows you how.
+
+### Prerequisites
+Download the data. You don't need to resize the images manually. You can use `im2rec` to resize them automatically. For details, see "Extension: Using Multiple Labels for a Single Image," later in this topic.
+
+### Step 1. Make an Image List File
+After you download the data, you need to make an image list file.  The format is:
+
+```
+    integer_image_index \t label_index \t path_to_image
+```
+Typically, the program takes the list of names of all of the images, shuffles them, then separates them into two lists: a training filename list and a testing filename list. Write the list in the right format.
+
+This is an example file:
+
+```bash
+    95099  464     n04467665_17283.JPEG
+    10025081        412     ILSVRC2010_val_00025082.JPEG
+    74181   789     n01915811_2739.JPEG
+    10035553        859     ILSVRC2010_val_00035554.JPEG
+    10048727        929     ILSVRC2010_val_00048728.JPEG
+    94028   924     n01980166_4956.JPEG
+    1080682 650     n11807979_571.JPEG
+    972457  633     n07723039_1627.JPEG
+    7534    11      n01630670_4486.JPEG
+    1191261 249     n12407079_5106.JPEG
+
+```
+
+### Step 2. Create the Binary File
+To generate a binary image, use `im2rec` in the tool folder. `im2rec` takes the path of the `_image list file_` you generated, the `_root path_` of the images, and the `_output file path_` as input. This process usually takes several hours, so be patient.
+
+A sample command:
+
+```bash
+    ./bin/im2rec image.lst image_root_dir output.bin resize=256
+```
+For more details, run ```./bin/im2rec```.
+
+### Extension: Multiple Labels for a Single Image
+
+The `im2rec` tool and `IO.ImageRecordIter` have multi-label support for a single image.
+For example, if you have four labels for a single image, you can use the following procedure to use the RecordIO tools.
+
+1. Write the image list files as follows:
+
+     ```
+         integer_image_index \t label_1 \t label_2 \t   label_3 \t label_4 \t path_to_image
+     ```
+
+2. Run `im2rec`, adding a 'label_width=4' to the command argument, for example:
+
+     ```bash
+         ./bin/im2rec image.lst image_root_dir output.bin resize=256 label_width=4
+     ```
+
+3. In the iterator generation code, set `label_width=4` and `path_imglist=<<The PATH TO YOUR image.lst>>`, for example:
+
+     ```scala
+         val dataiter = IO.ImageRecordIter(Map(
+           "path_imgrec" -> "data/cifar/train.rec",
+           "data_shape" -> "(3,28,28)",
+           "path_imglist" -> "data/cifar/image.lst",
+           "label_width" -> "4"
+         ))
+     ```
+
+## Next Steps
+* [NDArray API](ndarray.md) for vector/matrix/tensor operations
+* [KVStore API](kvstore.md) for multi-GPU and multi-host distributed training
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/kvstore.md b/docs/static_site/src/pages/api/scala/docs/tutorials/kvstore.md
new file mode 100644
index 000000000000..08cb4cb50869
--- /dev/null
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/kvstore.md
@@ -0,0 +1,129 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: KVStore API
+permalink: /api/scala/docs/tutorials/kvstore
+is_tutorial: true
+tag: scala
+---
+
+# KVStore API
+
+Topics:
+* [Basic Push and Pull](#basic-push-and-pull)
+* [List Key-Value Pairs](#list-key-value-pairs)
+* [API Reference](http://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.KVStore)
+
+
+## Basic Push and Pull
+
+Provides basic operation over multiple devices (GPUs) on a single device.
+
+### Initialization
+
+Let's consider a simple example. It initializes
+a (`int`, `NDArray`) pair into the store, and then pulls the value out.
+
+```scala
+val kv = KVStore.create("local") // create a local kv store.
+val shape = Shape(2,3)
+kv.init(3, NDArray.ones(shape)*2)
+val a = NDArray.zeros(shape)
+kv.pull(3, out = a)
+a.toArray
+// Array[Float] = Array(2.0, 2.0, 2.0, 2.0, 2.0, 2.0)
+```
+
+### Push, Aggregation, and Updater
+
+For any key that's been initialized, you can push a new value with the same shape to the key, as follows:
+
+```scala
+kv.push(3, NDArray.ones(shape)*8)
+kv.pull(3, out = a) // pull out the value
+a.toArray
+// Array[Float] = Array(8.0, 8.0, 8.0, 8.0, 8.0, 8.0)
+```
+
+The data that you want to push can be stored on any device. Furthermore, you can push multiple
+values into the same key, where KVStore first sums all of these
+values, and then pushes the aggregated value, as follows:
+
+```scala
+val gpus = Array(Context.gpu(0), Context.gpu(1), Context.gpu(2), Context.gpu(3))
+val b = Array(NDArray.ones(shape, gpus(0)), NDArray.ones(shape, gpus(1)), \
+NDArray.ones(shape, gpus(2)), NDArray.ones(shape, gpus(3)))
+kv.push(3, b)
+kv.pull(3, out = a)
+a.toArray
+// Array[Float] = Array(4.0, 4.0, 4.0, 4.0, 4.0, 4.0)
+```
+
+For each push command, KVStore applies the pushed value to the value stored by an
+`updater`. The default updater is `ASSIGN`. You can replace the default to
+control how data is merged.
+
+```scala
+val updater = new MXKVStoreUpdater {
+          override def update(key: Int, input: NDArray, stored: NDArray): Unit = {
+            println(s"update on key $key")
+            stored += input * 2
+          }
+          override def dispose(): Unit = {}
+       }
+kv.setUpdater(updater)
+kv.pull(3, a)
+a.toArray
+// Array[Float] = Array(4.0, 4.0, 4.0, 4.0, 4.0, 4.0)
+kv.push(3, NDArray.ones(shape))
+// update on key 3
+kv.pull(3, a)
+a.toArray
+// Array[Float] = Array(6.0, 6.0, 6.0, 6.0, 6.0, 6.0)
+```
+
+### Pull
+
+You've already seen how to pull a single key-value pair. Similar to the way that you use the push command, you can
+pull the value into several devices with a single call.
+
+```scala
+val b = Array(NDArray.ones(shape, gpus(0)), NDArray.ones(shape, gpus(1)),\
+NDArray.ones(shape, gpus(2)), NDArray.ones(shape, gpus(3)))
+kv.pull(3, outs = b)
+b(1).toArray
+// Array[Float] = Array(6.0, 6.0, 6.0, 6.0, 6.0, 6.0)
+```
+
+## List Key-Value Pairs
+
+All of the operations that we've discussed so far are performed on a single key. KVStore also provides
+the interface for generating a list of key-value pairs. For a single device, use the following:
+
+```scala
+val keys = Array(5, 7, 9)
+kv.init(keys, Array.fill(keys.length)(NDArray.ones(shape)))
+kv.push(keys, Array.fill(keys.length)(NDArray.ones(shape)))
+// update on key: 5
+// update on key: 7
+// update on key: 9
+val b = Array.fill(keys.length)(NDArray.zeros(shape))
+kv.pull(keys, outs = b)
+b(1).toArray
+// Array[Float] = Array(3.0, 3.0, 3.0, 3.0, 3.0, 3.0)
+```
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/mnist.md b/docs/static_site/src/pages/api/scala/docs/tutorials/mnist.md
new file mode 100644
index 000000000000..fbc6786b8e10
--- /dev/null
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/mnist.md
@@ -0,0 +1,141 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: MNIST Example
+is_tutorial: true
+tag: scala
+permalink: /api/scala/docs/tutorials/mnist
+---
+
+# Handwritten Digit Recognition
+
+This Scala tutorial guides you through a classic computer vision application: identifying hand written digits.
+
+Let's train a 3-layer network (i.e multilayer perceptron network) on the MNIST dataset to classify handwritten digits.
+
+## Prerequisites
+To complete this tutorial, we need:
+
+- to compile the latest MXNet version. See the MXNet installation instructions for your operating system in [Setup and Installation](http://mxnet.io/install/index.html).
+- to compile the Scala API. See Scala API build instructions in [Build](https://github.com/dmlc/mxnet/tree/master/scala-package).
+
+## Define the Network
+
+First, define the neural network's architecture using the Symbol API:
+
+```scala
+import org.apache.mxnet._
+import org.apache.mxnet.optimizer.SGD
+
+// model definition
+val data = Symbol.Variable("data")
+val fc1 = Symbol.api.FullyConnected(Some(data), num_hidden = 128, name = "fc1")
+val act1 = Symbol.api.Activation(Some(fc1), "relu", "relu1")
+val fc2 = Symbol.api.FullyConnected(Some(act1), num_hidden = 64, name = "fc2")
+val act2 = Symbol.api.Activation(Some(fc2), "relu", "relu2")
+val fc3 = Symbol.api.FullyConnected(Some(act2), num_hidden = 10, name = "fc3")
+val mlp = Symbol.api.SoftmaxOutput(Some(fc3), name = "sm")
+```
+
+## Load the Data
+
+Then, load the training and validation data using DataIterators.
+
+You can download the MNIST data using the [get_mnist_data script](https://github.com/dmlc/mxnet/blob/master/scala-package/core/scripts/get_mnist_data.sh). We've already written a DataIterator for the MNIST dataset:
+
+```scala
+// load MNIST dataset
+val trainDataIter = IO.MNISTIter(Map(
+  "image" -> "data/train-images-idx3-ubyte",
+  "label" -> "data/train-labels-idx1-ubyte",
+  "data_shape" -> "(1, 28, 28)",
+  "label_name" -> "sm_label",
+  "batch_size" -> "50",
+  "shuffle" -> "1",
+  "flat" -> "0",
+  "silent" -> "0",
+  "seed" -> "10"))
+
+val valDataIter = IO.MNISTIter(Map(
+  "image" -> "data/t10k-images-idx3-ubyte",
+  "label" -> "data/t10k-labels-idx1-ubyte",
+  "data_shape" -> "(1, 28, 28)",
+  "label_name" -> "sm_label",
+  "batch_size" -> "50",
+  "shuffle" -> "1",
+  "flat" -> "0", "silent" -> "0"))
+```
+
+## Train the model
+
+We can use the FeedForward builder to train our network:
+
+```scala
+// setup model and fit the training data
+val model = FeedForward.newBuilder(mlp)
+      .setContext(Context.cpu())
+      .setNumEpoch(10)
+      .setOptimizer(new SGD(learningRate = 0.1f, momentum = 0.9f, wd = 0.0001f))
+      .setTrainData(trainDataIter)
+      .setEvalData(valDataIter)
+      .build()
+```
+
+## Make predictions
+
+Finally, let's make predictions against the validation dataset and compare the predicted labels with the real labels.
+
+```scala
+val probArrays = model.predict(valDataIter)
+// in this case, we do not have multiple outputs
+require(probArrays.length == 1)
+val prob = probArrays(0)
+
+// get real labels
+import scala.collection.mutable.ListBuffer
+valDataIter.reset()
+val labels = ListBuffer.empty[NDArray]
+while (valDataIter.hasNext) {
+  val evalData = valDataIter.next()
+  labels += evalData.label(0).copy()
+}
+val y = NDArray.concatenate(labels)
+
+// get predicted labels
+val predictedY = NDArray.argmax_channel(prob)
+require(y.shape == predictedY.shape)
+
+// calculate accuracy
+var numCorrect = 0
+var numTotal = 0
+for ((labelElem, predElem) <- y.toArray zip predictedY.toArray) {
+  if (labelElem == predElem) {
+    numCorrect += 1
+  }
+  numTotal += 1
+}
+val acc = numCorrect.toFloat / numTotal
+println(s"Final accuracy = $acc")
+```
+
+Check out more MXNet Scala examples below.
+
+## Next Steps
+* [Scala API](http://mxnet.io/api/scala/)
+* [More Scala Examples](https://github.com/dmlc/mxnet/tree/master/scala-package/examples/)
+* [MXNet tutorials index](http://mxnet.io/tutorials/index.html)
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/model.md b/docs/static_site/src/pages/api/scala/docs/tutorials/model.md
new file mode 100644
index 000000000000..80fd24f0bca5
--- /dev/null
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/model.md
@@ -0,0 +1,142 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: Model API *Deprecated*
+permalink: /api/scala/docs/tutorials/model
+is_tutorial: true
+tag: scala
+---
+
+# MXNet Scala Model API
+
+The model API provides a simplified way to train neural networks using common best practices.
+It's a thin wrapper built on top of the [ndarray](ndarray.md) and [symbolic](symbol.md)
+modules that make neural network training easy.
+
+Topics:
+
+* [Train a Model](#train-a-model)
+* [Save the Model](#save-the-model)
+* [Periodic Checkpoint](#periodic-checkpointing)
+* [Multiple Devices](#use-multiple-devices)
+* [Model API Reference](#http://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.Model)
+
+## Train the Model
+
+To train a model, perform two steps: configure the model using the symbol parameter,
+then call ```model.Feedforward.create``` to create the model.
+The following example creates a two-layer neural network.
+
+```scala
+    // configure a two layer neuralnetwork
+    val data = Symbol.Variable("data")
+    val fc1 = Symbol.api.FullyConnected(data, num_hidden = 128, name = "fc1")
+    val act1 = Symbol.api.Activation(Some(fc1), "relu", "relu1")
+    val fc2 = Symbol.api.FullyConnected(Some(act1), num_hidden = 64, name = "fc2")
+    val softmax = Symbol.api.SoftmaxOutput(Some(fc2), name = "sm")
+
+    // Construct the FeedForward model and fit on the input training data
+    val model = FeedForward.newBuilder(softmax)
+      .setContext(Context.cpu())
+      .setNumEpoch(num_epoch)
+      .setOptimizer(new SGD(learningRate = 0.01f, momentum = 0.9f, wd = 0.0001f))
+      .setTrainData(trainDataIter)
+      .setEvalData(valDataIter)
+      .build()
+```
+You can also use the `scikit-learn-style` construct and `fit` function to create a model.
+
+```scala
+    // create a model using sklearn-style two-step way
+    val model = new FeedForward(softmax,
+                                numEpoch = numEpochs,
+                                argParams = argParams,
+                                auxParams = auxParams,
+                                beginEpoch = beginEpoch,
+                                epochSize = epochSize)
+
+  model.fit(trainData = train)
+```
+For more information, see [API Reference](http://mxnet.incubator.apache.org/api/scala/docs/index.html).
+
+## Save the Model
+
+After the job is done, save your work.
+We also provide `save` and `load` functions. You can use the `load` function to load a model checkpoint from a file.
+
+```scala
+    // checkpoint the model data into file,
+    // save a model to modelPrefix-symbol.json and modelPrefix-0100.params
+    val modelPrefix: String = "checkpt"
+    val num_epoch = 100
+    Model.saveCheckpoint(modelPrefix, epoch + 1, symbol, argParams, auxStates)
+
+    // load model back
+    val model_loaded = FeedForward.load(modelPrefix, num_epoch)
+```
+The advantage of these two `save` and `load` functions is that they are language agnostic.
+You should be able to save and load directly into cloud storage, such as Amazon S3 and HDFS.
+
+##  Periodic Checkpointing
+
+We recommend checkpointing your model after each iteration.
+To do this, use ```EpochEndCallback``` to add a ```Model.saveCheckpoint(<parameters>)``` checkpoint callback to the function after each iteration .
+
+```scala
+    // modelPrefix-symbol.json will be saved for symbol.
+    // modelPrefix-epoch.params will be saved for parameters.
+    // Checkpoint the model into file. Can specify parameters.
+    // For more information, check API doc.
+    val modelPrefix: String = "checkpt"
+    val checkpoint: EpochEndCallback =
+    if (modelPrefix == null) null
+    else new EpochEndCallback {
+      override def invoke(epoch: Int, symbol: Symbol,
+                         argParams: Map[String, NDArray],
+                         auxStates: Map[String, NDArray]): Unit = {
+       Model.saveCheckpoint(modelPrefix, epoch + 1, symbol, argParams, auxParams)
+            }
+           }
+
+    // Load model checkpoint from file. Returns symbol, argParams, auxParams.
+    val (_, argParams, _) = Model.loadCheckpoint(modelPrefix, num_epoch)
+
+```
+You can load the model checkpoint later using ```Model.loadCheckpoint(modelPrefix, num_epoch)```.
+
+## Use Multiple Devices
+
+Set ```ctx``` to the list of devices that you want to train on. You can create a list of devices in any way you want.
+
+```scala
+    val devices = Array(Context.gpu(0), Context.gpu(1))
+
+    val model = new FeedForward(ctx = devices,
+             symbol = network,
+             numEpoch = numEpochs,
+             optimizer = optimizer,
+             epochSize = epochSize,
+             ...)
+```
+Training occurs in parallel on the GPUs that you specify.
+
+## Next Steps
+* See [Symbolic API](symbol.md) for operations on NDArrays that assemble neural networks from layers.
+* See [IO Data Loading API](io.md) for parsing and loading data.
+* See [NDArray API](ndarray.md) for vector/matrix/tensor operations.
+* See [KVStore API](kvstore.md) for multi-GPU and multi-host distributed training.
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/module.md b/docs/static_site/src/pages/api/scala/docs/tutorials/module.md
new file mode 100644
index 000000000000..286baf58e1f0
--- /dev/null
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/module.md
@@ -0,0 +1,162 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: Module API
+permalink: /api/scala/docs/tutorials/module
+is_tutorial: true
+tag: scala
+---
+
+# Module API
+The module API provides an intermediate and high-level interface for performing computation with neural networks in MXNet. A *module* is an instance of subclasses of the `BaseModule`. The most widely used module class is called `Module`. Module wraps a `Symbol` and one or more `Executors`. For a full list of functions, see `BaseModule`.
+A subclass of modules might have extra interface functions. This topic provides some examples of common use cases. All of the module APIs are in the `Module` namespace.
+
+## Preparing a Module for Computation
+
+To construct a module, refer to the constructors for the module class. For example, the `Module` class accepts a `Symbol` as input:
+
+```scala
+    import org.apache.mxnet._
+    import org.apache.mxnet.module.{FitParams, Module}
+
+    // construct a simple MLP
+    val data = Symbol.Variable("data")
+    val fc1 = Symbol.api.FullyConnected(Some(data), num_hidden = 128, name = "fc1")
+    val act1 = Symbol.api.Activation(Some(fc1), "relu", "relu1")
+    val fc2 = Symbol.api.FullyConnected(Some(act1), num_hidden = 64, name = "fc2")
+    val act2 = Symbol.api.Activation(Some(fc2), "relu", "relu2")
+    val fc3 = Symbol.api.FullyConnected(Some(act2), num_hidden = 10, name = "fc3")
+    val out = Symbol.api.SoftmaxOutput(fc3, name = "softmax")
+
+    // construct the module
+    val mod = new Module(out)
+```
+
+By default, `context` is the CPU. If you need data parallelization, you can specify a GPU context or an array of GPU contexts.
+
+Before you can compute with a module, you need to call `bind()` to allocate the device memory and `initParams()` or `SetParams()` to initialize the parameters.
+If you simply want to fit a module, you don't need to call `bind()` and `initParams()` explicitly, because the fit() function automatically calls them if they are needed.
+
+```scala
+    mod.bind(dataShapes = train_dataiter.provideData, labelShapes = Some(train_dataiter.provideLabel))
+    mod.initParams()
+```
+
+Now you can compute with the module using functions like `forward()`, `backward()`, etc.
+
+## Training, Predicting, and Evaluating
+
+Modules provide high-level APIs for training, predicting, and evaluating. To fit a module, call the `fit()` function with some `DataIter`s:
+
+```scala
+    import org.apache.mxnet.optimizer.SGD
+    val mod = new Module(softmax)
+
+    mod.fit(train_dataiter, evalData = scala.Option(eval_dataiter), \
+    numEpoch = n_epoch, fitParams = new FitParams()\
+    .setOptimizer(new SGD(learningRate = 0.1f, momentum = 0.9f, wd = 0.0001f)))
+```
+
+The interface is very similar to the old `FeedForward` class. You can pass in batch-end callbacks using `setBatchEndCallback` and epoch-end callbacks using `setEpochEndCallback`. You can also set parameters using methods like `setOptimizer` and `setEvalMetric`. To learn more about the `FitParams()`, see the [API page](http://mxnet.io/api/scala/docs/index.html#org.apache.mxnet.module.FitParams). To predict with a module, call `predict()` with a `DataIter`:
+
+```scala
+    mod.predict(val_dataiter)
+```
+
+The module collects and returns all of the prediction results. For more details about the format of the return values, see the documentation for the [`predict()` function](http://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.module.BaseModule).
+
+When prediction results might be too large to fit in memory, use the `predictEveryBatch` API:
+
+```scala
+    val preds = mod.predictEveryBatch(val_dataiter)
+    val_dataiter.reset()
+    var i = 0
+    while (val_dataiter.hasNext) {
+       val batch = val_dataiter.next()
+       val predLabel: Array[Int] = NDArray.argmax_channel(preds(i)(0)).toArray.map(_.toInt)
+       val label = batch.label(0).toArray.map(_.toInt)
+       //do something...
+       i += 1
+    }
+```
+
+If you need to evaluate on a test set and don't need the prediction output, call the `score()` function with a `DataIter` and an `EvalMetric`:
+
+```scala
+    mod.score(val_dataiter, metric)
+```
+
+This runs predictions on each batch in the provided `DataIter` and computes the evaluation score using the provided `EvalMetric`. The evaluation results are stored in `metric` so that you can query later.
+
+## Saving and Loading Module Parameters
+
+To save the module parameters in each training epoch, use a `checkpoint` callback:
+
+```scala
+    val modelPrefix: String = "mymodel"
+
+    for (epoch <- 0 until 5) {
+      while(train_dataiter.hasNext){
+          // forward backward pass
+         //do something...
+       }
+        val checkpoint = mod.saveCheckpoint(modelPrefix, epoch, saveOptStates = true)
+
+    }
+```
+
+To load the saved module parameters, call the `loadCheckpoint` function:
+
+```scala
+    val mod = Module.loadCheckpoint(modelPrefix, loadModelEpoch, loadOptimizerStates = true)
+```
+
+To initialize parameters, Bind the symbols to construct executors first with `bind` method. Then, initialize the parameters and auxiliary states by calling `initParams()` method.
+
+```scala
+    mod.bind(dataShapes = train_dataiter.provideData, labelShapes = Some(train_dataiter.provideLabel))
+    mod.initParams()
+```
+
+To get current parameters, use `getParams` method.
+
+```scala
+    val (argParams, auxParams) = mod.getParams
+```
+
+To assign parameter and aux state values, use `setParams` method.
+
+```scala
+    mod.setParams(argParams, auxParams)
+```
+
+To resume training from a saved checkpoint, instead of calling `setParams()`, directly call `fit()`, passing the loaded parameters, so that `fit()` knows to start from those parameters instead of initializing randomly:
+
+```scala
+    mod.fit(..., fitParams=new FitParams().setArgParams(argParams).\
+    setAuxParams(auxParams).setBeginEpoch(beginEpoch))
+```
+
+Create an object of the `FitParams()` class, and then use it to call the `setBeginEpoch()` method to pass `beginEpoch` so that `fit()` knows to resume from a saved epoch.
+
+## Next Steps
+* See [Model API](model.md) for an alternative simple high-level interface for training neural networks.
+* See [Symbolic API](symbol.md) for operations on NDArrays that assemble neural networks from layers.
+* See [IO Data Loading API](io.md) for parsing and loading data.
+* See [NDArray API](ndarray.md) for vector/matrix/tensor operations.
+* See [KVStore API](kvstore.md) for multi-GPU and multi-host distributed training.
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/mxnet_scala_on_intellij.md b/docs/static_site/src/pages/api/scala/docs/tutorials/mxnet_scala_on_intellij.md
new file mode 100644
index 000000000000..252c0a044246
--- /dev/null
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/mxnet_scala_on_intellij.md
@@ -0,0 +1,456 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: Scala on IntelliJ
+is_tutorial: true
+tag: scala
+permalink: /api/scala/docs/tutorials/mxnet_scala_on_intellij
+---
+
+# Run MXNet Scala Examples Using the IntelliJ IDE (macOS)
+
+This tutorial guides you through setting up a Scala project in the IntelliJ IDE on macOS, and shows how to use the MXNet package from your application.
+
+## Prerequisites:
+To use this tutorial you need the following software:
+
+- [Java 8 JDK](http://www.oracle.com/technetwork/java/javase/downloads/index.html)
+- [Maven](https://maven.apache.org/install.html)
+- [Scala](https://www.scala-lang.org/download/) - comes with IntelliJ, so you don't need to install it separately
+- [OpenCV](https://opencv.org/)
+- [IntelliJ IDE](https://www.jetbrains.com/idea/)
+
+The following instructions are provided for macOS and Ubuntu 16.04. Windows support is not yet available.
+
+### Mac Prerequisites Setup
+
+**Step 1.** Install brew:
+```
+/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
+```
+
+Or, if you already have brew, update it:
+```
+brew update
+```
+
+**Step 2.** Install Java 8 JDK:
+```
+brew tap caskroom/versions
+brew cask install java8
+```
+
+**Step 3.** Install maven:
+```
+brew install maven
+```
+
+**Step 4.** Install OpenCV:
+```
+brew install opencv
+```
+
+
+### Ubuntu Prerequisites Setup
+
+**Step 1.**: Download the MXNet source.
+
+```bash
+git clone --recursive https://github.com/apache/incubator-mxnet.git mxnet
+cd mxnet
+```
+
+**Step 2.**: Run the dependency installation scripts.
+
+```bash
+sudo ./ci/docker/install/ubuntu_core.sh
+sudo ./ci/docker/install/ubuntu_scala.sh
+```
+
+
+## Access the Maven Package for MXNet
+
+To view the latest Maven packages, you can check the Maven repository for the `org.apache.mxnet` packages.
+
+*  [MXNet Maven Packages](https://search.maven.org/#search%7Cga%7C1%7Cg%3A%22org.apache.mxnet%22)
+
+In the tutorial below, you will install the latest package at the time of this writing. You may find a different artifact for your specific operating system, or a newer one by reviewing what is currently available in the Maven repo.
+
+The configuration you should update is in the pom file's dependency for MXNet:
+
+```html
+<dependency>
+  <groupId>org.apache.mxnet</groupId>
+  <artifactId>mxnet-full_2.11-osx-x86_64-cpu</artifactId>
+</dependency>
+```
+
+## Set Up Your Project
+
+Now that you've installed your prerequisites, you are ready to setup IntelliJ and your first MXNet-Scala project!
+
+**Step 1.** Install and setup IntelliJ:
+    - When prompted for what to features to enable during IntelliJ's first startup, make sure you select Scala.
+
+    - Install the plugin for IntelliJ IDE by following these steps:
+   On **Menu**, choose **Preferences**, choose **Plugins**, type **Scala**, and then choose **Install**. For further plugin help and instructions, refer to [Scala plugin setup for IDE](https://www.jetbrains.com/help/idea/scala.html).
+
+**Step 2.** Create a new project:
+
+![intellij welcome](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/scala/intellij-welcome.png)
+
+From the IntelliJ welcome screen, select "Create New Project".
+
+![maven project type](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/scala/intellij-project-type.png)
+
+Choose the Maven project type.
+
+![maven project type - archetype](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/scala/intellij-project-type-archetype-check.png)
+
+Select the checkbox for `Create from archetype`.
+
+![maven project type - archetype](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/scala/intellij-project-type-archetype-add.png)
+
+Click the `Add Archetype` button, and add the following information to each field.
+
+**GroupId**
+```
+net.alchim31.maven
+```
+**ArtifactId**
+```
+scala-archetype-simple
+```
+**Version**
+```
+1.6
+```
+**Repository**
+```
+https://mvnrepository.com/artifact/net.alchim31.maven/scala-archetype-simple
+```
+
+![maven project type - archetype](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/scala/intellij-project-type-archetype-add-confirm.png)
+
+Click `Ok` to add the archetype, make sure it is selected from the list, and then click `Next`.
+
+![project metadata](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/scala/intellij-project-metadata.png)
+
+Set the project's metadata. For this tutorial, use the following:
+
+**GroupId**
+```
+mxnet
+```
+**ArtifactId**
+```
+scalaMXNet
+```
+**Version**
+```
+1.0-SNAPSHOT
+```
+
+![project properties](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/scala/intellij-project-properties.png)
+
+Review the project's properties. The settings can be left as their default.
+
+![project location](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/scala/intellij-project-location.png)
+
+Set the project's location. The rest of the settings can be left as their default.
+
+![project 1](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/scala/intellij-project-1.png)
+
+After clicking Finish, you will be presented with the project's first view.
+The project's `pom.xml` will be open for editing.
+
+**Step 3.** Replace the pom file's content with the following code. Changes include:
+  - Project properties: `scala.version`, upgrading from `2.11.5` to `2.11.8`
+  - Project dependencies: adding the MXNet package from Maven and updating the dependency for JUnitRunner (specs2-junit_) and logging
+  - Build options: removing '-make:transitive'
+
+
+```html
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">
+  <modelVersion>4.0.0</modelVersion>
+  <groupId>mxnet</groupId>
+  <artifactId>scalaMXNet</artifactId>
+  <version>1.0-SNAPSHOT</version>
+  <name>${project.artifactId}</name>
+  <description>My wonderful scala app</description>
+  <inceptionYear>2018</inceptionYear>
+  <licenses>
+    <license>
+      <name>Apache2</name>
+      <url>http://mxnet.apache.org</url>
+      <distribution>repo</distribution>
+    </license>
+  </licenses>
+  <profiles>
+    <profile>
+      <id>osx-x86_64-cpu</id>
+      <properties>
+        <platform>osx-x86_64-cpu</platform>
+      </properties>
+    </profile>
+    <profile>
+      <id>linux-x86_64-cpu</id>
+      <properties>
+        <platform>linux-x86_64-cpu</platform>
+      </properties>
+    </profile>
+    <profile>
+      <id>linux-x86_64-gpu</id>
+      <properties>
+        <platform>linux-x86_64-gpu</platform>
+      </properties>
+    </profile>
+  </profiles>
+  <properties>
+    <maven.compiler.source>1.6</maven.compiler.source>
+    <maven.compiler.target>1.6</maven.compiler.target>
+    <encoding>UTF-8</encoding>
+    <scala.version>2.11.8</scala.version>
+    <scala.compat.version>2.11</scala.compat.version>
+    <slf4jVersion>1.7.7</slf4jVersion>
+    <platform>osx-x86_64-cpu</platform>
+  </properties>
+
+  <dependencies>
+    <dependency>
+      <groupId>org.apache.mxnet</groupId>
+      <artifactId>mxnet-full_2.11-osx-x86_64-cpu</artifactId>
+      <version>[1.4.0,)</version>
+    </dependency>
+    <dependency>
+      <groupId>org.scala-lang</groupId>
+      <artifactId>scala-library</artifactId>
+      <version>${scala.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>args4j</groupId>
+      <artifactId>args4j</artifactId>
+      <version>2.0.29</version>
+    </dependency>
+
+    <!-- Test -->
+    <dependency>
+      <groupId>junit</groupId>
+      <artifactId>junit</artifactId>
+      <version>4.11</version>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.specs2</groupId>
+      <artifactId>specs2-junit_${scala.compat.version}</artifactId>
+      <version>2.4.16</version>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.scalatest</groupId>
+      <artifactId>scalatest_${scala.compat.version}</artifactId>
+      <version>2.2.4</version>
+      <scope>test</scope>
+    </dependency>
+
+    <!-- Logging -->
+    <dependency>
+      <groupId>org.slf4j</groupId>
+      <artifactId>slf4j-api</artifactId>
+      <version>${slf4jVersion}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.slf4j</groupId>
+      <artifactId>slf4j-log4j12</artifactId>
+      <version>${slf4jVersion}</version>
+    </dependency>
+  </dependencies>
+
+  <build>
+    <sourceDirectory>src/main/scala</sourceDirectory>
+    <testSourceDirectory>src/test/scala</testSourceDirectory>
+    <plugins>
+      <plugin>
+        <!-- see http://davidb.github.com/scala-maven-plugin -->
+        <groupId>net.alchim31.maven</groupId>
+        <artifactId>scala-maven-plugin</artifactId>
+        <version>3.2.0</version>
+        <executions>
+          <execution>
+            <goals>
+              <goal>compile</goal>
+              <goal>testCompile</goal>
+            </goals>
+            <configuration>
+              <args>
+                <!--arg>-make:transitive</arg-->
+                <arg>-dependencyfile</arg>
+                <arg>${project.build.directory}/.scala_dependencies</arg>
+              </args>
+            </configuration>
+          </execution>
+        </executions>
+      </plugin>
+      <plugin>
+        <groupId>org.apache.maven.plugins</groupId>
+        <artifactId>maven-surefire-plugin</artifactId>
+        <version>2.18.1</version>
+        <configuration>
+          <useFile>false</useFile>
+          <disableXmlReport>true</disableXmlReport>
+          <!-- If you have classpath issue like NoDefClassError,... -->
+          <!-- useManifestOnlyJar>false</useManifestOnlyJar -->
+          <includes>
+            <include>**/*Test.*</include>
+            <include>**/*Suite.*</include>
+          </includes>
+        </configuration>
+      </plugin>
+    </plugins>
+  </build>
+</project>
+```
+
+**Step 4.** Import dependencies with Maven:
+
+  - Note the prompt in the lower right corner that states "Maven projects need to be imported". If this is not visible, click on the little greed balloon that appears in the lower right corner.
+
+![import dependencies](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/scala/intellij-project-import-changes.gif)
+
+Click "Import Changes" in this prompt.
+
+**Step 5.** Setup log4j configuration
+
+Create a folder `src/main/resources` and a new file in it `src/main/resources/log4j.properties` with the contents:
+
+```
+log4j.rootLogger = info, stdout
+
+log4j.appender.stdout = org.apache.log4j.ConsoleAppender
+log4j.appender.stdout.Target = System.out
+log4j.appender.stdout.layout = org.apache.log4j.PatternLayout
+log4j.appender.stdout.layout.ConversionPattern=%d{yyyy-MM-dd HH:mm:ss,SSS} [%t] [%c] [%p] - %m%n
+```
+
+**Step 6.** Build the project:
+- To build the project, from the menu choose Build, and then choose Build Project.
+* Note that if you run into ```bad option: '-make:transitive'```, please remove ```<arg>-make:transitive</arg>``` from `pom.xml` and `<parameter value="-make:transitive" />` from  `.idea/scala_compiler`.
+
+**Step 7.** Run the Hello World App:
+
+![hello world app](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/scala/intellij-project-hello-world-app.png)
+
+Navigate to the App included with the project.
+
+![run hello world](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/scala/intellij-project-hello-world-run.png)
+
+Run the App by clicking the green arrow, and verify the Hello World output
+
+**Step 8.** Run Sample MXNet Code in the App:
+
+![run hello mxnet](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/scala/intellij-project-hello-mxnet.png)
+
+Paste the following code in the App, overwriting the original hello world code. Then click the green arrow to run it.
+
+```scala
+object App extends App {
+  import org.apache.mxnet._
+
+  private val a = NDArray.ones(2, 3)
+  println("Testing MXNet by generating an 2x3 NDArray...")
+  println("Shape is: ")
+  println(a.shape)
+}
+```
+
+The result should be this output:
+
+```
+Testing MXNet by generating an 2x3 NDArray...
+Shape is:
+(2,3)
+
+Process finished with exit code 0
+```
+
+
+### Troubleshooting
+
+If you get an error, check the dependencies at the beginning of this tutorial. For example, you might see the following in the middle of the error messages, where `x.x` would the version it's looking for.
+
+```
+...
+Library not loaded: /usr/local/opt/opencv/lib/libopencv_calib3d.x.x.dylib
+...
+```
+
+This can be resolved be installing OpenCV.
+
+
+### Using MXNet from source
+
+If you chose to "Build from Source" when following the [install instructions](https://mxnet.incubator.apache.org/install/index.html) (or the detailed [build from source instructions](https://mxnet.incubator.apache.org/install/build_from_source.html#installing-mxnet-language-bindings)), you can use your custom build instead of the build from maven.  Use your build by editing the `pom.xml` file and replacing the `org.apache.mxnet` dependency with the following:
+
+```
+      <groupId>org.apache.mxnet</groupId>
+      <artifactId>mxnet-core_${scala.version}-${platform}-sources</artifactId>
+      <scope>system</scope>
+      <systemPath>/PathToMXNetSource/incubator-mxnet/scala-package/assembly/osx-x86_64-cpu/target/mxnet-full_${scala.version}-osx-x86_64-cpu-1.6.0-SNAPSHOT-sources.jar</systemPath>
+    </dependency>
+
+    <dependency>
+      <groupId>org.apache.mxnet</groupId>
+      <artifactId>mxnet-full_${scala.version}-${platform}</artifactId>
+      <scope>system</scope>
+      <systemPath>/PathToMXNetSource/incubator-mxnet/scala-package/assembly/osx-x86_64-cpu/target/mxnet-full_${scala.version}-osx-x86_64-cpu-1.6.0-SNAPSHOT.jar</systemPath>
+    </dependency>
+```
+
+Note that you have to edit both of the `systemPath` properties to point to your generated jar files.
+
+
+### Command Line Build Option
+
+- You can also compile the project by using the following command at the command line. Change directories to this project's folder then run the following:
+
+```bash
+mvn clean package -e -P osx-x86_64-cpu
+```
+The `-P <platform>` parameter tells the build which platform to target.
+The `-e` will give you more details if the build fails. If it succeeds, you should see a lot of info and some warning messages, followed by:
+
+```bash
+[INFO] ------------------------------------------------------------------------
+[INFO] BUILD SUCCESS
+[INFO] ------------------------------------------------------------------------
+[INFO] Total time: 1.186 s
+[INFO] Finished at: 2018-03-06T15:17:36-08:00
+[INFO] Final Memory: 11M/155M
+[INFO] ------------------------------------------------------------------------
+```
+The build generates a new jar file in the `target` folder called `scalaInference-1.0-SNAPSHOT.jar`.
+
+
+
+## Next Steps
+For more information about MXNet Scala resources, see the following:
+
+* [Scala API](http://mxnet.io/api/scala/)
+* [Scala Inference API](https://mxnet.incubator.apache.org/api/scala/infer.html)
+* [Scala Examples](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/)
+* [MXNet Tutorials Index](http://mxnet.io/tutorials/index.html)
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/ndarray.md b/docs/static_site/src/pages/api/scala/docs/tutorials/ndarray.md
new file mode 100644
index 000000000000..04fbadf8367e
--- /dev/null
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/ndarray.md
@@ -0,0 +1,181 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: NDArray
+permalink: /api/scala/docs/tutorials/ndarray
+is_tutorial: true
+tag: scala
+---
+
+# NDArray API
+
+
+The NDArray package (`mxnet.ndarray`) contains tensor operations similar to `numpy.ndarray`. The syntax is also similar, except for some additional calls for dealing with I/O and multiple devices.
+
+Topics:
+
+* [Create NDArray](#create-ndarray)
+* [NDArray Operations](#ndarray-operations)
+* [NDArray API Reference](http://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.NDArray)
+
+## Create NDArray
+
+Create `mxnet.ndarray` as follows:
+
+```scala
+import org.apache.mxnet._
+// all-zero array of dimension 100x50
+val a = NDArray.zeros(100, 50)
+// all-one array of dimension 256x32x128x1
+val b = NDArray.ones(256, 32, 128, 1)
+// initialize array with contents, you can specify dimensions of array using Shape parameter while creating array.
+val c = NDArray.array(Array(1, 2, 3, 4, 5, 6), shape = Shape(2, 3))
+```
+This is similar to the way you use `numpy`.
+## NDArray Operations
+
+We provide some basic ndarray operations, like arithmetic and slice operations.
+
+### Arithmetic Operations
+
+```scala
+import org.apache.mxnet._
+val a = NDArray.zeros(100, 50)
+a.shape
+// org.apache.mxnet.Shape = (100,50)
+val b = NDArray.ones(100, 50)
+// c and d will be calculated in parallel here!
+val c = a + b
+val d = a - b
+// inplace operation, b's contents will be modified, but c and d won't be affected.
+b += d
+```
+
+### Multiplication/Division Operations
+
+```scala
+import org.apache.mxnet._
+// Multiplication
+val ndones = NDArray.ones(2, 1)
+val ndtwos = ndones * 2
+ndtwos.toArray
+// Array[Float] = Array(2.0, 2.0)
+(ndones * ndones).toArray
+// Array[Float] = Array(1.0, 1.0)
+(ndtwos * ndtwos).toArray
+// Array[Float] = Array(4.0, 4.0)
+ndtwos *= ndtwos // inplace
+ndtwos.toArray
+// Array[Float] = Array(4.0, 4.0)
+
+//Division
+val ndones = NDArray.ones(2, 1)
+val ndzeros = ndones - 1f
+val ndhalves = ndones / 2
+ndhalves.toArray
+// Array[Float] = Array(0.5, 0.5)
+(ndhalves / ndhalves).toArray
+// Array[Float] = Array(1.0, 1.0)
+(ndones / ndones).toArray
+// Array[Float] = Array(1.0, 1.0)
+(ndzeros / ndones).toArray
+// Array[Float] = Array(0.0, 0.0)
+ndhalves /= ndhalves
+ndhalves.toArray
+// Array[Float] = Array(1.0, 1.0)
+```
+
+### Slice Operations
+
+```scala
+import org.apache.mxnet._
+val a = NDArray.array(Array(1f, 2f, 3f, 4f, 5f, 6f), shape = Shape(3, 2))
+val a1 = a.slice(1)
+assert(a1.shape === Shape(1, 2))
+assert(a1.toArray === Array(3f, 4f))
+
+val a2 = arr.slice(1, 3)
+assert(a2.shape === Shape(2, 2))
+assert(a2.toArray === Array(3f, 4f, 5f, 6f))
+```
+
+### Dot Product
+
+```scala
+import org.apache.mxnet._
+val arr1 = NDArray.array(Array(1f, 2f), shape = Shape(1, 2))
+val arr2 = NDArray.array(Array(3f, 4f), shape = Shape(2, 1))
+val res = NDArray.dot(arr1, arr2)
+res.shape
+// org.apache.mxnet.Shape = (1,1)
+res.toArray
+// Array[Float] = Array(11.0)
+```
+
+### Save and Load NDArray
+
+You can use MXNet functions to save and load a list or dictionary of NDArrays from file systems, as follows:
+
+```scala
+import org.apache.mxnet._
+val a = NDArray.zeros(100, 200)
+val b = NDArray.zeros(100, 200)
+// save list of NDArrays
+NDArray.save("/path/to/array/file", Array(a, b))
+// save dictionary of NDArrays to AWS S3
+NDArray.save("s3://path/to/s3/array", Map("A" -> a, "B" -> b))
+// save list of NDArrays to hdfs.
+NDArray.save("hdfs://path/to/hdfs/array", Array(a, b))
+val from_file = NDArray.load("/path/to/array/file")
+val from_s3 = NDArray.load("s3://path/to/s3/array")
+val from_hdfs = NDArray.load("hdfs://path/to/hdfs/array")
+```
+The good thing about using the `save` and `load` interface is that you can use the format across all `mxnet` language bindings. They also already support Amazon S3 and HDFS.
+
+### Multi-Device Support
+
+Device information is stored in the `mxnet.Context` structure. When creating NDArray in MXNet, you can use the context argument (the default is the CPU context) to create arrays on specific devices as follows:
+
+```scala
+import org.apache.mxnet._
+val cpu_a = NDArray.zeros(100, 200)
+cpu_a.context
+// org.apache.mxnet.Context = cpu(0)
+val ctx = Context.gpu(0)
+val gpu_b = NDArray.zeros(Shape(100, 200), ctx)
+gpu_b.context
+// org.apache.mxnet.Context = gpu(0)
+```
+
+Currently, we *do not* allow operations among arrays from different contexts. To manually enable this, use the `copyto` member function to copy the content to different devices, and continue computation:
+
+```scala
+import org.apache.mxnet._
+val x = NDArray.zeros(100, 200)
+val ctx = Context.gpu(0)
+val y = NDArray.zeros(Shape(100, 200), ctx)
+val z = x + y
+// mxnet.base.MXNetError: [13:29:12] src/ndarray/ndarray.cc:33:
+// Check failed: lhs.ctx() == rhs.ctx() operands context mismatch
+val cpu_y = NDArray.zeros(100, 200)
+y.copyto(cpu_y)
+val z = x + cpu_y
+```
+
+## Next Steps
+* See [KVStore API](kvstore.md) for multi-GPU and multi-host distributed training.
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/symbol.md b/docs/static_site/src/pages/api/scala/docs/tutorials/symbol.md
new file mode 100644
index 000000000000..d193200cb2cf
--- /dev/null
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/symbol.md
@@ -0,0 +1,155 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: Symbol API
+permalink: /api/scala/docs/tutorials/symbol
+is_tutorial: true
+tag: scala
+---
+
+# MXNet Scala Symbolic API
+
+Topics:
+
+* [How to Compose Symbols](#overloaded-operators) introduces operator overloading of symbols.
+* [Symbol Attributes](#symbol-attributes) describes how to attach attributes to symbols.
+* [Serialization](#serialization) explains how to save and load symbols.
+* [Executing Symbols](#executing-symbols) explains how to evaluate the symbols with data.
+* [Execution API Reference](http://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.Executor) documents the execution APIs.
+* [Multiple Outputs](#multiple-outputs) explains how to configure multiple outputs.
+* [Symbol Creation API Reference](http://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.Symbol) documents functions.
+
+We also highly encourage you to read [Symbolic Configuration and Execution in Pictures](symbol_in_pictures.md).
+
+## How to Compose Symbols
+
+The symbolic API provides a way to configure computation graphs.
+You can configure the graphs either at the level of neural network layer operations or as fine-grained operations.
+
+The following example configures a two-layer neural network.
+
+```scala
+    import org.apache.mxnet._
+    val data = Symbol.Variable("data")
+    val fc1 = Symbol.api.FullyConnected(Some(data), num_hidden = 128, name = "fc1")
+    val act1 = Symbol.api.Activation(Some(fc1), "relu", "relu1")
+    val fc2 = Symbol.api.FullyConnected(Some(act1), num_hidden = 64, name = "fc2")
+    val net = Symbol.api.SoftmaxOutput(Some(fc2), name = "out")
+    :type net
+    // org.apache.mxnet.Symbol
+```
+
+The basic arithmetic operators (plus, minus, div, multiplication) are overloaded for
+*element-wise operations* of symbols.
+
+The following example creates a computation graph that adds two inputs together.
+
+```scala
+    import org.apache.mxnet._
+    val a = Symbol.Variable("a")
+    val b = Symbol.Variable("b")
+    val c = a + b
+```
+
+## Symbol Attributes
+
+You can add an attribute to a symbol by providing an attribute dictionary when you create a symbol.
+
+```scala
+    val data = Symbol.Variable("data", Map("mood"-> "angry"))
+    val op = Symbol.api.Convolution(Some(data), kernel = Shape(1, 1), num_filter = 1, attr = Map("mood" -> "so so"))
+```
+For proper communication with the C++ backend, both the key and values of the attribute dictionary should be strings. To retrieve the attributes, use `attr(key)`:
+
+```
+    data.attr("mood")
+    // Option[String] = Some(angry)
+```
+
+To attach attributes, you can use ```AttrScope```. ```AttrScope``` automatically adds the specified attributes to all of the symbols created within that scope. The user can also inherit this object to change naming behavior. For example:
+
+```scala
+    val (data, gdata) =
+    AttrScope(Map("group" -> "4", "data" -> "great")).withScope {
+      val data = Symbol.Variable("data", attr = Map("dtype" -> "data", "group" -> "1"))
+      val gdata = Symbol.Variable("data2")
+      (data, gdata)
+    }
+    assert(gdata.attr("group").get === "4")
+    assert(data.attr("group").get === "1")
+
+    val exceedScopeData = Symbol.Variable("data3")
+    assert(exceedScopeData.attr("group") === None, "No group attr in global attr scope")
+```
+
+## Serialization
+
+There are two ways to save and load the symbols. You can use the `mxnet.Symbol.save` and `mxnet.Symbol.load` functions to serialize the ```Symbol``` objects.
+The advantage of using `save` and `load` functions is that it is language agnostic and cloud friendly.
+The symbol is saved in JSON format. You can also get a JSON string directly using `mxnet.Symbol.toJson`.
+Refer to [API documentation](http://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.Symbol) for more details.
+
+The following example shows how to save a symbol to an S3 bucket, load it back, and compare two symbols using a JSON string.
+
+```scala
+    import org.apache.mxnet._
+    val a = Symbol.Variable("a")
+    val b = Symbol.Variable("b")
+    val c = a + b
+    c.save("s3://my-bucket/symbol-c.json")
+    val c2 = Symbol.load("s3://my-bucket/symbol-c.json")
+    c.toJson == c2.toJson
+    // Boolean = true
+```
+
+## Executing Symbols
+
+After you have assembled a set of symbols into a computation graph, the MXNet engine can evaluate them.
+If you are training a neural network, this is typically
+handled by the high-level [Model class](model.md) and the [`fit()`] function.
+
+For neural networks used in "feed-forward", "prediction", or "inference" mode (all terms for the same
+thing: running a trained network), the input arguments are the
+input data, and the weights of the neural network that were learned during training.
+
+To manually execute a set of symbols, you need to create an [`Executor`] object,
+which is typically constructed by calling the [`simpleBind(<parameters>)`] method on a symbol.
+
+## Multiple Outputs
+
+To group the symbols together, use the [mxnet.symbol.Group](#mxnet.symbol.Group) function.
+
+```scala
+    import org.apache.mxnet._
+    val data = Symbol.Variable("data")
+    val fc1 = Symbol.api.FullyConnected(Some(data), num_hidden = 128, name = "fc1")
+    val act1 = Symbol.api.Activation(Some(fc1), "relu", "relu1")
+    val fc2 = Symbol.api.FullyConnected(Some(act1), num_hidden = 64, name = "fc2")
+    val net = Symbol.api.SoftmaxOutput(Some(fc2), name = "out")
+    val group = Symbol.Group(fc1, net)
+    group.listOutputs()
+    // IndexedSeq[String] = ArrayBuffer(fc1_output, out_output)
+```
+
+After you get the ```group```, you can bind on ```group``` instead.
+The resulting executor will have two outputs, one for fc1_output and one for softmax_output.
+
+## Next Steps
+* See [IO Data Loading API](io.md) for parsing and loading data.
+* See [NDArray API](ndarray.md) for vector/matrix/tensor operations.
+* See [KVStore API](kvstore.md) for multi-GPU and multi-host distributed training.
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/symbol_in_pictures.md b/docs/static_site/src/pages/api/scala/docs/tutorials/symbol_in_pictures.md
new file mode 100644
index 000000000000..fa6124849085
--- /dev/null
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/symbol_in_pictures.md
@@ -0,0 +1,100 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: Symbol in Pictures
+permalink: /api/scala/docs/tutorials/symbol_in_pictures
+is_tutorial: true
+tag: scala
+---
+
+# Symbolic Configuration and Execution in Pictures
+
+This topic explains symbolic construction and execution in pictures.
+We recommend that you also read [Symbolic API](symbol.md).
+
+## Compose Symbols
+
+Symbols are a description of the computation that you want to perform. The symbolic construction API generates the computation
+graph that describes the computation. The following picture shows how you compose symbols to describe basic computations.
+
+![Symbol Compose](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/compose_basic.png)
+
+- The ```mxnet.Symbol.Variable``` function creates argument nodes that represent input to the computation.
+- The symbol is overloaded with basic element-wise mathematical operations.
+
+## Configure Neural Networks
+
+In addition to supporting fine-grained operations, MXNet provides a way to perform big operations that are analogous to layers in neural networks.
+You can use operators to describe the configuration of a neural network.
+
+![Net Compose](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/compose_net.png)
+
+
+## Example of a Multi-Input Network
+
+The following example shows how to configure multiple input neural networks.
+
+![Multi Input](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/compose_multi_in.png)
+
+
+## Bind and Execute Symbol
+
+When you need to execute a symbol graph, you call the bind function to bind ```NDArrays``` to the argument nodes
+in order to obtain an ```Executor```.
+
+![Bind](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/bind_basic.png)
+
+To get the output results, given the bound NDArrays as input, you can call ```Executor.Forward```.
+
+![Forward](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/executor_forward.png)
+
+
+## Bind Multiple Outputs
+
+To group symbols, then bind them to get outputs of both, use ```mx.symbol.Group```.
+
+![MultiOut](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/executor_multi_out.png)
+
+Remember: Bind only what you need, so that the system can perform more optimizations.
+
+
+## Calculate the Gradient
+
+In the bind function, you can specify NDArrays that will hold gradients. Calling ```Executor.backward``` after ```Executor.forward``` gives you the corresponding gradients.
+
+![Gradient](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/executor_backward.png)
+
+
+## Simple Bind Interface for Neural Networks
+
+It can be tedious to pass the argument NDArrays to the bind function, especially when you are binding a big
+graph. ```Symbol.simple_bind``` provides a way to simplify
+the procedure. You need to specify only input data shapes. The function allocates the arguments, and binds
+the Executor for you.
+
+![SimpleBind](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/executor_simple_bind.png)
+
+## Auxiliary States
+
+Auxiliary states are just like arguments, except that you can't take the gradient of them. Although auxiliary states might not be part of the computation, they can be helpful for tracking. You can pass auxiliary states in the same way that you pass arguments.
+
+![SimpleBind](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/executor_aux_state.png)
+
+## Next Steps
+
+See [Symbolic API](symbol.md) and [Python Documentation](index.md).
diff --git a/docs/static_site/src/pages/api/scala/index.md b/docs/static_site/src/pages/api/scala/index.md
new file mode 100644
index 000000000000..62f6a58adb8b
--- /dev/null
+++ b/docs/static_site/src/pages/api/scala/index.md
@@ -0,0 +1,59 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page_api
+title: Scala Guide
+action: Get Started
+action_url: /get_started
+permalink: /api/scala
+tag: scala
+---
+
+
+# MXNet - Scala API
+
+MXNet supports the Scala programming language. The MXNet Scala package brings flexible and efficient GPU
+computing and state-of-art deep learning to Scala. It enables you to write seamless tensor/matrix computation with multiple GPUs in Scala. It also lets you construct and customize the state-of-art deep learning models in Scala, and apply them to tasks, such as image classification and data science challenges.
+
+
+
+## Image Classification with the Scala Infer API
+The Infer API can be used for single and batch image classification. More information can be found at the following locations:
+
+## Tensor and Matrix Computations
+You can perform tensor or matrix computation in pure Scala:
+
+```scala
+   import org.apache.mxnet._
+
+   val arr = NDArray.ones(2, 3)
+   // arr: org.apache.mxnet.NDArray = org.apache.mxnet.NDArray@f5e74790
+
+   arr.shape
+   // org.apache.mxnet.Shape = (2,3)
+
+   (arr * 2).toArray
+   // Array[Float] = Array(2.0, 2.0, 2.0, 2.0, 2.0, 2.0)
+
+   (arr * 2).shape
+   // org.apache.mxnet.Shape = (2,3)
+```
+
+## Related Resources
+
+* [Neural Style in Scala on MXNet](https://github.com/apache/incubator-mxnet/blob/master/scala-package/examples/src/main/scala/org/apache/mxnetexamples/neuralstyle/NeuralStyle.scala)
+* [More Scala Examples](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/scala/org/apache/mxnetexamples)
diff --git a/docs/static_site/src/pages/blog.html b/docs/static_site/src/pages/blog.html
new file mode 100644
index 000000000000..1f797c9e10ad
--- /dev/null
+++ b/docs/static_site/src/pages/blog.html
@@ -0,0 +1,38 @@
+---
+layout: page
+title: Blog
+subtitle: Latest news on MXNet and its ecosystem
+permalink: /blog/
+action: See All Blog Posts
+action_url: https://medium.com/apache-mxnet
+---
+
+<div class="row">
+    <div class="col-8">
+    </div>
+    <div class="col-4">
+
+    </div>
+</div>
+
+<div id="medium-widget"></div>
+<script>
+
+    MediumWidget.Init({renderTo: '#medium-widget',
+        params: {
+            "resource": "https://medium.com/apache-mxnet",
+            "postsPerLine": 3,
+            "limit": 9,
+            "picture": "big",
+            "fields": ["description", "claps", "publishAt"],
+            "ratio": "landscape"
+        }
+    })
+
+
+</script>
+
+<div class="blog-more">
+<a style="margin-top:20px" href="https://medium.com/apache-mxnet" class="btn btn-action">See All Blog Posts<span class="span-accented">›</span></a>
+
+</div>
diff --git a/docs/static_site/src/pages/community/contribute.md b/docs/static_site/src/pages/community/contribute.md
new file mode 100644
index 000000000000..a48e1fe13e58
--- /dev/null
+++ b/docs/static_site/src/pages/community/contribute.md
@@ -0,0 +1,192 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page
+title: Contribute
+subtitle: Contribute to the Apache MXNet project
+action: Get Started
+action_url: /get_started
+permalink: /community/contribute
+---
+
+# Contributing to MXNet
+
+Apache MXNet (incubating) is a community led, open source deep learning project. We welcome new members and look forward to your contributions. Here you will find how to get started and links to detailed information on MXNet best practices and processes.
+
+
+## Getting Started
+
+The following actions are recommended steps for you to get started with contributing to MXNet.
+
+| Action | Purpose |
+|---|---|
+| [Create a forum account](#forum) | Asking & answering MXNet usage questions |
+| [Join the dev comm channels](#mxnet-dev-communications) | Discussions about the direction of MXNet |
+| [Follow MXNet on Social Media](#social-media) | Get updates about new features and events |
+| [Create a JIRA account](#jira) | Tracking tasks & prioritizing issues |
+| [Check out the MXNet wiki](#confluence-wiki) | The wiki has detailed contributor information |
+| [Setup MXNet for development](#setup-mxnet-for-development) | Your very own fork for creating pull requests |
+| [Your first contribution](#your-first-contribution) | Complete a first contribution task |
+
+
+### FAQ
+
+* I found a bug. How do I report it?
+    * [Bug submission info](#file-a-bug-report)
+* I have a minor bug fix or docs update I'd like to submit. What do I do?
+    * [Minor fixes process](#minor-fixes)
+* I would like to submit a pull request for a significant update. What is the process?
+    * [Pull request process](#formal-pull-request-process)
+* I want to propose a new feature. What is the process for this?
+    * [New feature process](#new-feature-process)
+* What's coming next with MXNet, and how can I help?
+    * [Roadmap info](#roadmap)
+
+
+## MXNet Dev Communications
+
+### Forum
+
+If you need help with using MXNet, have questions about applying it to a particular kind of problem, or have a discussion topic, please use the discussion forum:
+* [discuss.mxnet.io](https://discuss.mxnet.io) <i class="fas fa-external-link-alt"></i>
+
+### Mailing Lists
+
+Please join either or both of the MXNet mailing lists:
+
+**For MXNet Users, join the USER mailing list**:
+
+- [MXNet Apache USER mailing list](https://lists.apache.org/list.html?user@mxnet.apache.org) (user@mxnet.apache.org): To subscribe, send an email to <a href="mailto:user-subscribe@mxnet.apache.org">user-subscribe@mxnet.apache.org</a> <i class="far fa-envelope"></i>
+
+**For Contributors to MXNet, join the DEV mailing list**:
+- [MXNet Apache DEV mailing list](https://lists.apache.org/list.html?dev@mxnet.apache.org) (dev@mxnet.apache.org): To subscribe, send an email to <a href="mailto:dev-subscribe@mxnet.apache.org">dev-subscribe@mxnet.apache.org</a> <i class="far fa-envelope"></i>
+
+
+* [archive](https://lists.apache.org/list.html?dev@mxnet.apache.org) <i class="fas fa-external-link-alt"></i>
+
+### Slack
+
+To join the MXNet slack channel send request to the contributor mailing list.
+ * <a href="mailto:dev@mxnet.apache.org?subject=Requesting%20slack%20access">email</a> <i class="far fa-envelope"></i>
+
+
+### Social Media
+
+Keep connected with the latest MXNet news and updates.
+
+<p>
+<a href="https://medium.com/apache-mxnet"><img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/social/medium_black.svg?sanitize=true" height="30px"/> Contributor and guest blogs about MXNet</a>
+</p>
+<p>
+<a href="https://reddit.com/r/mxnet"><img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/social/reddit_blue.svg?sanitize=true" height="30px" alt="reddit"/> Discuss MXNet on r/mxnet</a>
+</p>
+<p>
+<a href="https://twitter.com/apachemxnet"><img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/social/twitter.svg?sanitize=true" height="30px"/> Apache MXNet on Twitter</a>
+</p>
+<p>
+<a href="https://www.youtube.com/apachemxnet"><img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/social/youtube_red.svg?sanitize=true" height="30px"/> MXNet YouTube channel</a>
+</p>
+
+
+## JIRA
+
+MXNet uses Apache's JIRA to track issues and larger projects. Anyone can review open issues, but in order create issues or view JIRA boards, you must create an account.
+
+* [Open JIRA Issues](https://issues.apache.org/jira/projects/MXNET/issues)
+* [JIRA boards](https://issues.apache.org/jira/secure/RapidBoard.jspa) <i class="fas fa-lock"></i>
+
+
+## Confluence Wiki
+
+The [MXNet Confluence Wiki](https://cwiki.apache.org/confluence/display/MXNET/Apache+MXNet+Home) has detailed development environment setup info, design proposals, release process info, and more. This is generally where contributor information is maintained.
+
+* [MXNet Confluence Wiki](https://cwiki.apache.org/confluence/display/MXNET/Apache+MXNet+Home) <i class="fas fa-external-link-alt"></i>
+
+
+## Setup MXNet for Development
+
+The process for setting up MXNet for development depends on several factors, and is constantly being improved and expanded for more development languages. Setup information is on the MXNet Confluence Wiki.
+
+* [MXNet Confluence Wiki: Development](https://cwiki.apache.org/confluence/display/MXNET/Development) <i class="fas fa-external-link-alt"></i>
+
+
+## Your First Contribution
+
+**Step 1**: Visit the project on GitHub and review the [calls for contribution](https://github.com/apache/incubator-mxnet/labels/Call%20for%20Contribution). Click the GitHub button:
+<a class="github-button" href="https://github.com/apache/incubator-mxnet/labels/Call%20for%20Contribution" data-size="large" data-show-count="true" aria-label="Issue apache/incubator-mxnet on GitHub">Call for Contribution</a>
+
+**Step 2**: Tackle a smaller issue or improve documentation to get familiar with the process. As part of your pull request, add your name to [CONTRIBUTORS.md](https://github.com/apache/incubator-mxnet/blob/master/CONTRIBUTORS.md).
+
+**Step 3**: Follow the [formal pull request (PR) process](#formal-pull-request-process) to submit your PR for review.
+
+**Important**: keep an eye on your pull request, respond to comments and change requests, and rebase or resubmit your PR if fails the Jenkins continuous integration tests. Ask for help in the [forum or slack channel](#mxnet-dev-communications) if you get stuck.
+
+
+## File a bug report
+
+Please let us know if you experienced a problem with MXNet. Please provide detailed information about the problem you encountered and, if possible, add a description that helps to reproduce the problem. You have two alternatives for filing a bug report:
+<p><a href="http://issues.apache.org/jira/browse/MXNet"><i class="fas fa-bug"></i> JIRA</a></p>
+<p><a href="https://github.com/apache/incubator-mxnet/issues"><i class="fab fa-github"></i> GitHub</a></p>
+
+
+## Minor Fixes
+
+If you have found an issue and would like to contribute a bug fix or documentation update, follow these guidelines:
+
+* If it is trivial, just create a [pull request](https://github.com/apache/incubator-mxnet/pulls).
+* If it is non-trivial, you should follow the [formal pull request process](#formal-pull-request-process) described in the next section.
+
+
+## Formal Pull Request Process
+
+Any new features of improvements that are non-trivial should follow the complete flow of:
+
+1. [Review the contribution standards](https://cwiki.apache.org/confluence/display/MXNET/Development+Process) for your type of submission.
+1. [Create a JIRA issue](https://issues.apache.org/jira/secure/CreateIssue!default.jspa).
+1. [Create the PR on GitHub](https://github.com/apache/incubator-mxnet/pulls) and add the JIRA issue ID to the PR's title.
+
+Further details on this process can be found on the [Wiki](https://cwiki.apache.org/confluence/display/MXNET/Development).
+
+
+## New Feature Process
+
+Our community is constantly looking for feedback to improve Apache MXNet. If you have an idea how to improve MXNet or have a new feature in mind that would be beneficial for MXNet users, please open an issue in [MXNet’s JIRA](http://issues.apache.org/jira/browse/MXNet). The improvement or new feature should be described in appropriate detail and include the scope and its requirements if possible. Detailed information is important for a few reasons:<br/>
+- It ensures your requirements are met when the improvement or feature is implemented.<br/>
+- It helps to estimate the effort and to design a solution that addresses your needs. <br/>
+- It allows for constructive discussions that might arise around this issue.
+
+Detailed information is also required, if you plan to contribute the improvement or feature you proposed yourself. Please read the [contributions](http://mxnet.io/community/contribute.html) guide in this case as well.
+
+
+## Roadmap
+
+MXNet is evolving fast. To see what's next and what the community is currently working on, check out the Roadmap issues on GitHub and the JIRA Boards:
+
+<a class="github-button" href="https://github.com/apache/incubator-mxnet/labels/Roadmap" data-size="large" data-show-count="true" aria-label="Issue apache/incubator-mxnet on GitHub">Roadmap</a>
+<br/>
+[JIRA boards](https://issues.apache.org/jira/secure/RapidBoard.jspa) <i class="fas fa-lock"></i>
+
+
+<script defer src="https://use.fontawesome.com/releases/v5.0.12/js/all.js" integrity="sha384-Voup2lBiiyZYkRto2XWqbzxHXwzcm4A5RfdfG6466bu5LqjwwrjXCMBQBLMWh7qR" crossorigin="anonymous"></script>
+<script async defer src="https://buttons.github.io/buttons.js"></script>
+<script src="https://apis.google.com/js/platform.js"></script>
+
+
+## Contributors
+MXNet has been developed by and is used by a group of active community members. Contribute to improving it!
+
+<i class="fab fa-github"></i> [Contributors and Committers](https://github.com/apache/incubator-mxnet/blob/master/CONTRIBUTORS.md)
diff --git a/docs/static_site/src/pages/ecosystem.html b/docs/static_site/src/pages/ecosystem.html
new file mode 100644
index 000000000000..75ca48817413
--- /dev/null
+++ b/docs/static_site/src/pages/ecosystem.html
@@ -0,0 +1,133 @@
+---
+layout: page
+title: Ecosystem
+subtitle: Explore a rich ecosystem of libraries, tools, and more to support research and development of Deep Learning application across many fields and domains of application.
+action: Get Started
+action_url: /get_started
+permalink: /ecosystem/
+
+ecosystem_toolkits:
+- title: GluonCV
+  text: GluonCV is a computer vision toolkit with rich model zoo. From object detection to pose estimation.
+  icon: /assets/img/visual.svg
+  link: https://gluon-cv.mxnet.io
+- title: GluonNLP
+  text: GluonNLP provides state-of-the-art deep learning models in NLP. For engineers and researchers to fast prototype research ideas and products.
+  icon: /assets/img/artificial-intelligence.svg
+  link: https://gluon-nlp.mxnet.io/
+- title: GluonTS
+  text: Gluon Time Series (GluonTS) is the Gluon toolkit for probabilistic time series modeling, focusing on deep learning-based models.
+  icon: /assets/img/line-graph.svg
+  link: https://gluon-ts.mxnet.io/
+
+ecosystem_other:
+- title: Coach RL
+  text: Coach is a python reinforcement learning framework containing implementation of many state-of-the-art algorithms, it supports MXNet as a back-end
+  icon: /assets/img/coach_logo.png
+  link: https://github.com/NervanaSystems/coach
+- title: Deep Graph Library
+  text: DGL is a Python package dedicated to deep learning on graphs supporting MXNet as a backend.
+  link: https://www.dgl.ai/
+- title: GluonFR
+  text: Community-driven toolkit for Face Recognition and Face Detection
+  link: https://gluon-face.readthedocs.io/en/latest/
+- title: InsightFace
+  text: State-of-the-art face detection and face recognition repository, including ArcFace loss and RetinaFace implementation
+  link: https://github.com/deepinsight/insightface
+- title: Keras-MXNet
+  text: Keras-MXNet provides a backend support for the widely used high level API Keras.
+  link: https://github.com/awslabs/keras-apache-mxnet
+  icon: /assets/img/keras.png
+- title: MXBoard
+  text: MXBoard provides a set of APIs for logging MXNet data for visualization in TensorBoard.
+  link: https://github.com/awslabs/mxboard
+- title: MXFusion
+  text: MXFusion is a modular deep probabilistic programming library. It lets you use state-of-the-art inference techniques for specialized probabilistic models.
+  icon: /assets/img/mxfusion.png
+  link: https://mxfusion.readthedocs.io/en/master/index.html
+- title: MXNet Model Server
+  text: Model Server for Apache MXNet (MMS) is a flexible and easy to use tool for serving deep learning models exported from MXNet or the Open Neural Network Exchange (ONNX).
+  link: https://github.com/awslabs/mxnet-model-server
+- title: Sockeye
+  text: Sockeye is a sequence-to-sequence framework for Neural Machine Translation based on Apache MXNet Incubating. It implements state-of-the-art encoder-decoder architectures.
+  link: https://awslabs.github.io/sockeye/
+- title: TensorLy
+  text: TensorLy is a high level API for tensor methods and deep tensorized neural networks in Python that aims to make tensor learning simple.
+  icon: /assets/img/tensorly_logo.png
+  link: http://tensorly.org/stable/home.html
+- title: TVM
+  text: TVM is an open deep learning compiler stack for CPUs, GPUs, and specialized accelerators. It supports a number of framework including MXNet.
+  link: https://tvm.ai/about
+  icon: /assets/img/tvm.png
+- title: XFer
+  text: Xfer is a library that allows quick and easy transfer of knowledge stored in deep neural networks implemented in MXNet.
+  link: https://xfer.readthedocs.io/en/master/
+  icon: /assets/img/xfer.png
+
+---
+<div class="ecosystem-page">
+    <div class="row">
+        <h2>D2L.ai</h2>
+        <div class="row">
+            <div class="col-4">
+                <a href="http://d2l.ai/"><img src="{{'/assets/img/front.jpg' | relative_url}}"></a>
+            </div>
+            <div class="col-8">
+                <p>A <a href="https://d2l.ai">deep learning book</a> with interactive jupyter notebooks, math formula,
+                    and a dedicated forum for discussions.</p>
+                <p>It offers an interactive learning experience with mathematics, figures, code, text, and discussions,
+                    where concepts and techniques are illustrated and implemented with experiments on real data
+                    sets.</p>
+                <p>Each section is an executable Jupyter notebook. You can modify the code and tune hyperparameters to
+                    get instant feedback to accumulate practical experiences in deep learning.</p>
+                <p>The book is authored by <a href="https://www.astonzhang.com/">Aston Zhang</a>, Amazon Applied
+                    Scientist UIUC Ph.D., <a href="http://zacklipton.com/">Zack C. Lipton</a>, CMU Assistant Professor
+                    UCSD Ph.D.,
+                    <a href="https://scholar.google.com/citations?user=Z_WrhK8AAAAJ&hl=en">Mu Li</a> Amazon Principal
+                    Scientist CMU Ph.D. and <a href="https://alex.smola.org/">Alex J. Smola</a> Amazon VP/Distinguished
+                    Scientist TU Berlin Ph.D.
+                <p>D2L is used as a textbook or a reference book at Carnegie Mellon University, Georgia Institute of
+                    Technology, the University of California Berkeley and many more university</p>
+            </div>
+        </div>
+    </div>
+    <br><br>
+    <h2>Toolkits</h2>
+    <div class="row">
+        {%- for feature in page.ecosystem_toolkits -%}
+        <div class="col-4">
+            <div class="card">
+                <a href="{{feature.link}}">
+                    <div class="card-text">
+                        <div class="card-header-title">
+                            <h4>{{feature.title}}</h4>
+                            <img src="{{feature.icon | relative_url}}">
+                        </div>
+                        <p class="card-summary">{{feature.text}}</p>
+                    </div>
+                </a>
+            </div>
+        </div>
+        {%- endfor -%}
+    </div>
+    <br><br>
+    <h2>Ecosystem</h2>
+    <div class="row">
+        {%- for feature in page.ecosystem_other -%}
+        <div class="col-3">
+            <div class="card">
+                <a href="{{feature.link}}">
+                    <div class="card-text">
+                        <div class="card-header-title">
+                            <h4>{{feature.title}}</h4>
+                            <img src="{{feature.icon | relative_url}}">
+                        </div>
+                        <p class="card-summary">{{feature.text}}</p>
+                    </div>
+                </a>
+            </div>
+        </div>
+        {%- endfor -%}
+    </div>
+    <br><br>
+</div>
\ No newline at end of file
diff --git a/docs/static_site/src/pages/features.html b/docs/static_site/src/pages/features.html
new file mode 100644
index 000000000000..6865bc90e4c5
--- /dev/null
+++ b/docs/static_site/src/pages/features.html
@@ -0,0 +1,106 @@
+---
+layout: page
+title: Features
+subtitle: Whether you are looking for a flexible library to quickly develop cutting-edge deep learning research or a robust framework to push production workload, MXNet caters to all needs.
+permalink: /features/
+action: Get Started
+action_url: /get_started
+---
+
+<!-- HYBRID FRONTEND -->
+<div class="row">
+    <div class="col-12">
+        <h3 class="feature-title">Hybrid Front-End</h3>
+    </div>
+    <div class="col-5">
+        <p class="feature-paragraph">The Gluon Python API lets you use MXNet in a fully imperative manner. It also
+            allows you to simply switch to
+            symbolic mode by calling the <a
+                    href="{{'/api/python/docs/tutorials/packages/gluon/hybridize' | relative_url}}">hybridize</a>
+            functionality. The symbolic execution provides faster and more optimized
+            execution as well as the ability to export the network for inference in different language bindings like
+            java or C++.
+        </p>
+    </div>
+    <div class="col-1">
+
+    </div>
+    <div class="col-6 code-block">
+        {% highlight python %}
+net = model_zoo.vision.resnet50_v2(pretrained=True)
+net.hybridize()
+
+dummy_input = mx.nd.ones(shape=(1,3,224,224))
+net(dummy_input)
+
+net.export("symbolic_resnet50")
+        {% endhighlight %}
+    </div>
+</div>
+
+<!-- DISTRIBUTED TRAINING -->
+<br>
+<br>
+<div class="row ">
+    <div class="col-7"></div>
+    <div class="col-5">
+        <h3 class="feature-title">Distributed Training</h3>
+    </div>
+
+    <div class="col-6 code-block">
+        {% highlight python %}
+import horovod.mxnet as hvd
+
+# Horovod: initialize Horovod
+hvd.init()
+
+# Horovod: pin a GPU to be used to local rank
+context = mx.gpu(hvd.local_rank())
+        {% endhighlight %}
+    </div>
+
+    <div class="col-1"></div>
+    <div class="col-5">
+        <p class="feature-paragraph">MXNet allows you to make the most out of your hardware, whether it is multi-gpu or
+            multi-host training with near-linear scaling efficiency. MXNet recently introduced support for
+            <a href="https://medium.com/apache-mxnet/distributed-training-using-apache-mxnet-with-horovod-44f98bf0e7b7">Horovod</a>,
+            the distributed learning framework developed by Uber.
+        </p>
+    </div>
+</div>
+
+
+<!-- 8 Language Bindings -->
+<br><br>
+<div class="row">
+    <div class="col-12">
+        <h3 class="feature-title">8 Language Bindings</h3>
+    </div>
+    <div class="col-5">
+        <p class="feature-paragraph">Deep integration into Python and support for Scala, Julia, Clojure, Java, C++, R
+            and Perl.
+            Combined with the hybridization feature, this allows a very smooth transition from Python training to
+            deployment
+            in the language of your choice to shorten the time to production.
+        </p>
+    </div>
+    <div class="col-1">
+
+    </div>
+    <div class="col-6 code-block">
+        {% highlight java %}
+import org.apache.mxnet.javaapi.*;
+...
+List
+<DataDesc> inputDesc = new ArrayList<>();
+Shape inputShape = new Shape(new int[]{1, 3, 224, 224});
+inputDesc.add(new DataDesc("data", inputShape, DType.Float32(), "NCHW"));
+Predictor predictor = new Predictor(inst.modelPathPrefix, inputDesc, context,0);
+...
+float[][] result = predictor.predict(new float[][]{img.toArray()});
+            {% endhighlight %}
+    </div>
+</div>
+
+
+</div>
\ No newline at end of file
diff --git a/docs/static_site/src/pages/get_started/build_from_source.md b/docs/static_site/src/pages/get_started/build_from_source.md
new file mode 100644
index 000000000000..af07c38e105a
--- /dev/null
+++ b/docs/static_site/src/pages/get_started/build_from_source.md
@@ -0,0 +1,329 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page
+title: Building From Source
+action: Get Started
+action_url: /get_started
+permalink: /get_started/build_from_source
+---
+
+
+# Build MXNet from Source
+
+This document explains how to build MXNet from source code.
+
+**For Java/Scala/Clojure, please follow [this guide instead](./scala_setup.md)**
+
+## Overview
+
+Building from source follows this general two-step flow of building the shared library, then installing your preferred language binding. Use the following links to jump to the different sections of this guide.
+
+1. Build the MXNet shared library, `libmxnet.so`.
+    * [Clone the repository](#clone-the-mxnet-project)
+    * [Prerequisites](#prerequisites)
+        * [Math library selection](#math-library-selection)
+        * [Install GPU software](#install-gpu-software)
+        * [Install optional software](#install-optional-software)
+    * [Adjust your build configuration](#build-configurations)
+    * [Build MXNet](#build-mxnet)
+        * [with NCCL](#build-mxnet-with-nccl) (optional)
+        * [for C++](#build-mxnet-with-c++) (optional)
+        * [Usage Examples](#usage-examples)
+            * [systems with GPUs and Intel CPUs](#recommended-for-Systems-with-NVIDIA-GPUs-and-Intel-CPUs)
+            * [GPUs with non-Intel CPUs](#recommended-for-Systems-with-Intel-CPUs)
+            * [Intel CPUs](#recommended-for-Systems-with-Intel-CPUs)
+            * [non-Intel CPUs](#recommended-for-Systems-with-non-Intel-CPUs)
+2. [Install the language API binding(s)](#installing-mxnet-language-bindings) you would like to use for MXNet.
+MXNet's newest and most popular API is Gluon. Gluon is built into the Python binding. If Python isn't your preference, you still have more options. MXNet supports several other language APIs:
+    - [Python (includes Gluon)](../api/python/index.md)
+    - [C++](../api/cpp/index.md)
+    - [Clojure](../api/clojure/index.md)
+    - [Java](../api/java/index.md)
+    - [Julia](../api/julia/index.md)
+    - [Perl](../api/perl/index.md)
+    - [R](../api/r/index.md)
+    - [Scala](../api/scala/index.md)
+
+<hr>
+
+## Build Instructions by Operating System
+
+Detailed instructions are provided per operating system. Each of these guides also covers how to install the specific [Language Bindings](#installing-mxnet-language-bindings) you require.
+You may jump to those, but it is recommended that you continue reading to understand more general "build from source" options.
+
+* [Amazon Linux / CentOS / RHEL](centos_setup.md)
+* [macOS](osx_setup.md)
+* [Devices](https://mxnet.incubator.apache.org/versions/master/install/index.html?platform=Devices&language=Python&processor=CPU)
+* [Ubuntu](ubuntu_setup.md)
+* [Windows](windows_setup.md)
+
+
+<hr>
+
+## Clone the MXNet Project
+
+1. Clone or fork the MXNet project.
+```bash
+git clone --recursive https://github.com/apache/incubator-mxnet mxnet
+cd mxnet
+```
+
+<hr>
+
+## Prerequisites
+
+The following sections will help you decide which specific prerequisites you need to install.
+
+#### Math Library Selection
+It is useful to consider your math library selection prior to your other prerequisites.
+MXNet relies on the
+[BLAS](https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) (Basic
+Linear Algebra Subprograms) library for numerical computations.
+Those can be extended with [LAPACK (Linear Algebra Package)](https://github.com/Reference-LAPACK/lapack), an additional set of mathematical functions.
+
+MXNet supports multiple mathematical backends for computations on the CPU:
+* [Apple Accelerate](https://developer.apple.com/documentation/accelerate)
+* [ATLAS](http://math-atlas.sourceforge.net/)
+* [MKL](https://software.intel.com/en-us/intel-mkl) (MKL, MKLML)
+* [MKL-DNN](https://github.com/intel/mkl-dnn)
+* [OpenBLAS](http://www.openblas.net/)
+
+The default order of choice for the libraries if found follows the path from the most
+(recommended) to less performant backends.
+The following lists show this order by library and `cmake` switch.
+
+For desktop platforms (x86_64):
+
+1. MKL-DNN (submodule) | `USE_MKLDNN`
+2. MKL | `USE_MKL_IF_AVAILABLE`
+3. MKLML (downloaded) | `USE_MKLML`
+4. Apple Accelerate | `USE_APPLE_ACCELERATE_IF_AVAILABLE` | Mac only
+5. OpenBLAS | `BLAS` | Options: Atlas, Open, MKL, Apple
+
+Note: If `USE_MKL_IF_AVAILABLE` is set to False then MKLML and MKL-DNN will be disabled as well for configuration
+backwards compatibility.
+
+For embedded platforms (all other and if cross compiled):
+
+1. OpenBLAS | `BLAS` | Options: Atlas, Open, MKL, Apple
+
+You can set the BLAS library explicitly by setting the BLAS variable to:
+
+* Atlas
+* Open
+* MKL
+* Apple
+
+See the [cmake/ChooseBLAS.cmake](https://github.com/apache/incubator-mxnet/blob/master/cmake/ChooseBlas.cmake) file for the options.
+
+[Intel's MKL (Math Kernel Library)](https://software.intel.com/en-us/mkl) is one of the most powerful math libraries
+
+It has following flavors:
+
+* MKL is a complete math library, containing all the functionality found in ATLAS, OpenBlas and LAPACK. It is free under
+community support licensing (https://software.intel.com/en-us/articles/free-mkl),
+but needs to be downloaded and installed manually.
+
+* MKLML is a subset of MKL. It contains a smaller number of functions to reduce the
+size of the download and reduce the number of dynamic libraries user needs.
+
+<!-- [Removed until #11148 is merged.] This is the most effective option since it can be downloaded and installed automatically
+by the cmake script (see cmake/DownloadMKLML.cmake).-->
+
+* MKL-DNN is a separate open-source library, it can be used separately from MKL or MKLML. It is
+shipped as a subrepo with MXNet source code (see 3rdparty/mkldnn or the [MKL-DNN project](https://github.com/intel/mkl-dnn))
+
+Since the full MKL library is almost always faster than any other BLAS library it's turned on by default,
+however it needs to be downloaded and installed manually before doing `cmake` configuration.
+Register and download on the [Intel performance libraries website](https://software.intel.com/en-us/performance-libraries).
+You can also install MKL through [YUM](https://software.intel.com/en-us/articles/installing-intel-free-libs-and-python-yum-repo)
+or [APT](https://software.intel.com/en-us/articles/installing-intel-free-libs-and-python-apt-repo) Repository.
+
+Note: MKL is supported only for desktop builds and the framework itself supports the following
+hardware:
+
+* Intel® Xeon Phi™ processor
+* Intel® Xeon® processor
+* Intel® Core™ processor family
+* Intel Atom® processor
+
+If you have a different processor you can still try to use MKL, but performance results are
+unpredictable.
+
+
+#### Install GPU Software
+
+If you want to run MXNet with GPUs, you must install [NVDIA CUDA and cuDNN](https://developer.nvidia.com/cuda-downloads).
+
+
+#### Install Optional Software
+
+These might be optional, but they're typically desirable as the extend or enhance MXNet's functionality.
+
+* [OpenCV](http://opencv.org/) - Image Loading and Augmentation. Each operating system has different packages and build from source options for OpenCV. Refer to your OS's link in the [Build Instructions by Operating System](#build-instructions-by-operating-system) section for further instructions.
+* [NCCL](https://developer.nvidia.com/nccl) - NVIDIA's Collective Communications Library. Instructions for installing NCCL are found in the following [Build MXNet with NCCL](#build-mxnet-with-nccl) section.
+
+More information on turning these features on or off are found in the following [build configurations](#build-configurations) section.
+
+
+<hr>
+
+## Build Configurations
+
+There is a configuration file for make,
+[`make/config.mk`](https://github.com/apache/incubator-mxnet/blob/master/make/config.mk), that contains all the compilation options. You can edit it and then run `make` or `cmake`. `cmake` is recommended for building MXNet (and is required to build with MKLDNN), however you may use `make` instead. For building with Java/Scala/Clojure, only `make` is supported.
+
+**NOTE:** When certain set of build flags are set, MXNet archive increases to more than 4 GB. Since MXNet uses archive internally archive runs into a bug ("File Truncated": [bugreport](https://sourceware.org/bugzilla/show_bug.cgi?id=14625)) for archives greater than 4 GB. Please use ar version 2.27 or greater to overcome this bug. Please see https://github.com/apache/incubator-mxnet/issues/15084 for more details.
+
+<hr>
+
+## Build MXNet
+
+### Build MXNet with NCCL
+- Download and install the latest NCCL library from NVIDIA.
+- Note the directory path in which NCCL libraries and header files are installed.
+- Ensure that the installation directory contains ```lib``` and ```include``` folders.
+- Ensure that the prerequisites for using NCCL such as Cuda libraries are met.
+- Append the ```config.mk``` file with following, in addition to the CUDA related options.
+- USE_NCCL=1
+- USE_NCCL_PATH=path-to-nccl-installation-folder
+
+``` bash
+echo "USE_NCCL=1" >> make/config.mk
+echo "USE_NCCP_PATH=path-to-nccl-installation-folder" >> make/config.mk
+cp make/config.mk .
+```
+- Run make command
+``` bash
+make -j"$(nproc)"
+```
+
+#### Validating NCCL
+- Follow the steps to install MXNet Python binding.
+- Comment the following line in ```test_nccl.py``` file at ```incubator-mxnet/tests/python/gpu/test_nccl.py```
+``` bash
+@unittest.skip("Test requires NCCL library installed and enabled during build")
+```
+- Run test_nccl.py script as follows. The test should complete. It does not produce any output.
+``` bash
+nosetests --verbose tests/python/gpu/test_nccl.py
+```
+
+**Recommendation to get the best performance out of NCCL:**
+It is recommended to set environment variable NCCL_LAUNCH_MODE to PARALLEL when using NCCL version 2.1 or newer.
+
+<hr>
+
+### Build MXNet with C++
+
+* To enable C++ package, just add `USE_CPP_PACKAGE=1` when you run `make` or `cmake` (see examples).
+
+<hr>
+
+### Usage Examples
+
+For example, you can specify using all cores on Linux as follows:
+
+```bash
+mkdir build && cd build
+cmake -GNinja ..
+ninja -v
+```
+
+
+#### Recommended for Systems with NVIDIA GPUs and Intel CPUs
+* Build MXNet with `cmake` and install with MKL DNN, GPU, and OpenCV support:
+
+```bash
+mkdir build && cd build
+cmake -DUSE_CUDA=1 -DUSE_CUDA_PATH=/usr/local/cuda -DUSE_CUDNN=1 -DUSE_MKLDNN=1 -GNinja ..
+ninja -v
+```
+
+#### Recommended for Systems with NVIDIA GPUs
+* Build with both OpenBLAS, GPU, and OpenCV support:
+
+```bash
+mkdir build && cd build
+cmake -DBLAS=open -DUSE_CUDA=1 -DUSE_CUDA_PATH=/usr/local/cuda -DUSE_CUDNN=1 -GNinja ..
+ninja -v
+```
+
+#### Recommended for Systems with Intel CPUs
+* Build MXNet with `cmake` and install with MKL DNN, and OpenCV support:
+
+```bash
+mkdir build && cd build
+cmake -DUSE_CUDA=0 -DUSE_MKLDNN=1 -GNinja ..
+ninja -v
+```
+
+#### Recommended for Systems with non-Intel CPUs
+* Build MXNet with `cmake` and install with OpenBLAS and OpenCV support:
+
+```bash
+mkdir build && cd build
+cmake -DUSE_CUDA=0 -DBLAS=open -GNinja ..
+ninja -v
+```
+
+#### Other Examples
+
+* Build without using OpenCV:
+
+```bash
+mkdir build && cd build
+cmake -DUSE_OPENCV=0 -GNinja ..
+ninja -v
+```
+
+* Build on **macOS** with the default BLAS library (Apple Accelerate) and Clang installed with `xcode` (OPENMP is disabled because it is not supported by the Apple version of Clang):
+
+```bash
+mkdir build && cd build
+cmake -DBLAS=apple -DUSE_OPENCV=0 -DUSE_OPENMP=0 -GNinja ..
+ninja -v
+```
+
+* To use OpenMP on **macOS** you need to install the Clang compiler, `llvm` (the one provided by Apple does not support OpenMP):
+
+```bash
+brew install llvm
+mkdir build && cd build
+cmake -DBLAS=apple -DUSE_OPENMP=1 -GNinja ..
+ninja -v
+```
+
+<hr>
+
+## Installing MXNet Language Bindings
+After building MXNet's shared library, you can install other language bindings.
+
+**NOTE:** The C++ API binding must be built when you build MXNet from source. See [Build MXNet with C++](#build-mxnet-with-c++).
+
+The following table provides links to each language binding by operating system:
+| | [Ubuntu](ubuntu_setup.html) | [macOS](osx_setup.html) | [Windows](windows_setup.html) |
+| --- | ----  | --- | ------- |
+| Python | [Ubuntu guide](ubuntu_setup.html#install-mxnet-for-python) | [OSX guide](osx_setup.html) | [Windows guide](windows_setup.html#install-mxnet-for-python) |
+| C++ | [C++ guide](c_plus_plus.html) | [C++ guide](c_plus_plus.html) | [C++ guide](c_plus_plus.html) |
+| Clojure | [Clojure guide](https://github.com/apache/incubator-mxnet/tree/master/contrib/clojure-package) | [Clojure guide](https://github.com/apache/incubator-mxnet/tree/master/contrib/clojure-package) | n/a |
+| Julia | [Ubuntu guide](ubuntu_setup.html#install-the-mxnet-package-for-julia) | [OSX guide](osx_setup.html#install-the-mxnet-package-for-julia) | [Windows guide](windows_setup.html#install-the-mxnet-package-for-julia) |
+| Perl | [Ubuntu guide](ubuntu_setup.html#install-the-mxnet-package-for-perl) | [OSX guide](osx_setup.html#install-the-mxnet-package-for-perl) | n/a |
+| R | [Ubuntu guide](ubuntu_setup.html#install-the-mxnet-package-for-r) | [OSX guide](osx_setup.html#install-the-mxnet-package-for-r) | [Windows guide](windows_setup.html#install-the-mxnet-package-for-r) |
+| Scala | [Scala guide](scala_setup.html) | [Scala guide](scala_setup.html) | n/a |
+| Java | [Java guide](java_setup.html) | [Java Guide](java_setup.html) | n/a |
diff --git a/docs/static_site/src/pages/get_started/c_plus_plus.md b/docs/static_site/src/pages/get_started/c_plus_plus.md
new file mode 100644
index 000000000000..edfb51b4ecf4
--- /dev/null
+++ b/docs/static_site/src/pages/get_started/c_plus_plus.md
@@ -0,0 +1,55 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page
+title: C++ Setup
+action: Get Started
+action_url: /get_started
+permalink: /get_started/cpp_setup
+---
+
+
+## Build the C++ package
+The C++ package has the same prerequisites as the MXNet library.
+
+To enable C++ package, just add `USE_CPP_PACKAGE=1` in the [build from source](build_from_source.md) options when building the MXNet shared library.
+
+For example to build MXNet with GPU support and the C++ package, OpenCV, and OpenBLAS, from the project root you would run:
+
+```bash
+cmake -DUSE_CUDA=1 -DUSE_CUDA_PATH=/usr/local/cuda -DUSE_CUDNN=1 -DUSE_MKLDNN=1 -DUSE_CPP_PACKAGE=1 -GNinja ..
+ninja -v
+```
+
+You may also want to add the MXNet shared library to your `LD_LIBRARY_PATH`:
+
+```bash
+export LD_LIBRARY_PATH=~/incubator-mxnet/lib
+```
+
+Setting the `LD_LIBRARY_PATH` is required to run the examples mentioned in the following section.
+
+## C++ Example Code
+You can find C++ code examples in the `cpp-package/example` folder of the MXNet project. Refer to the [cpp-package's README](https://github.com/apache/incubator-mxnet/tree/master/cpp-package) for instructions on building the examples.
+
+## Tutorials
+
+* [MXNet C++ API Basics](../api/cpp/docs/tutorials/basics)
+
+## Related Topics
+
+* [Image Classification using MXNet's C Predict API](https://github.com/apache/incubator-mxnet/tree/master/example/image-classification/predict-cpp)
diff --git a/docs/static_site/src/pages/get_started/centos_setup.md b/docs/static_site/src/pages/get_started/centos_setup.md
new file mode 100644
index 000000000000..d3d203ffb7e6
--- /dev/null
+++ b/docs/static_site/src/pages/get_started/centos_setup.md
@@ -0,0 +1,115 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page
+title: CentOS setup
+action: Get Started
+action_url: /get_started
+permalink: /get_started/centos_setup
+---
+
+
+# Installing MXNet on CentOS and other non-Ubuntu Linux systems
+
+Step 1. Install build tools and git on `CentOS >= 7` and `Fedora >= 19`:
+
+```bash
+sudo yum groupinstall -y "Development Tools" && sudo yum install -y git
+```
+
+Step 2. Install Atlas:
+
+```bash
+sudo yum install atlas-devel
+```
+
+Installing both `git` and `cmake` or `make` by following instructions on the websites is
+straightforward. Here we provide the instructions to build `gcc-4.8` from source codes.
+
+Step 3. Install the 32-bit `libc` with one of the following system-specific commands:
+
+```bash
+sudo apt-get install libc6-dev-i386 # In Ubuntu
+sudo yum install glibc-devel.i686   # In RHEL (Red Hat Linux)
+sudo yum install glibc-devel.i386   # In CentOS 5.8
+sudo yum install glibc-devel.i686   # In CentOS 6/7
+```
+
+Step 4. Download and extract the `gcc` source code with the prerequisites:
+
+```bash
+wget http://mirrors.concertpass.com/gcc/releases/gcc-4.8.5/gcc-4.8.5.tar.gz
+tar -zxf gcc-4.8.5.tar.gz
+cd gcc-4.8.5
+./contrib/download_prerequisites
+```
+
+Step 5. Build `gcc` by using 10 threads and then install to `/usr/local`
+
+```bash
+mkdir release && cd release
+../configure --prefix=/usr/local --enable-languages=c,c++
+make -j10
+sudo make install
+```
+
+Step 6. Add the lib path to your configure file such as `~/.bashrc`:
+
+```bash
+export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/lib64
+```
+
+Step 7. Build [OpenBLAS from source](https://github.com/xianyi/OpenBLAS#installation-from-source).
+
+Step 8. Build OpenCV
+
+To build OpenCV from source code, you need the [cmake](https://cmake.org) library.
+
+* If you don't have cmake or if your version of cmake is earlier than 3.6.1, run the following commands to install a newer version of cmake:
+
+```bash
+wget https://cmake.org/files/v3.6/cmake-3.6.1-Linux-x86_64.tar.gz
+tar -zxvf cmake-3.6.1-Linux-x86_64.tar.gz
+alias cmake="cmake-3.6.1-Linux-x86_64/bin/cmake"
+```
+
+* To download and extract the OpenCV source code, run the following commands:
+
+```bash
+wget https://codeload.github.com/opencv/opencv/zip/2.4.13
+unzip 2.4.13
+cd opencv-2.4.13
+mkdir release
+cd release/
+```
+
+* Build OpenCV. The following commands build OpenCV with 10 threads. We
+disabled GPU support, which might significantly slow down an MXNet program
+running on a GPU processor. It also disables 1394 which might generate a
+warning. Then install it on `/usr/local`.
+
+```bash
+cmake -D BUILD_opencv_gpu=OFF -D WITH_CUDA=OFF -D WITH_1394=OFF -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local ..
+make -j10
+sudo make install
+```
+
+* Add the lib path to your configuration such as `~/.bashrc`.
+
+```bash
+export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/usr/local/lib/pkgconfig/
+```
diff --git a/docs/static_site/src/pages/get_started/download.md b/docs/static_site/src/pages/get_started/download.md
new file mode 100644
index 000000000000..ec26e2227ef3
--- /dev/null
+++ b/docs/static_site/src/pages/get_started/download.md
@@ -0,0 +1,83 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page
+title: Download Source Files
+action: Get Started
+action_url: /get_started
+permalink: /get_started/download
+---
+
+
+# Source Download
+
+These source archives are generated from tagged releases. Updates and patches will not have been applied. For any updates refer to the corresponding branches in the [GitHub repository](https://github.com/apache/incubator-mxnet). Choose your flavor of download from the following links:
+
+| Version | Source                                                                                                      | PGP                                                                                                             | SHA                                                                                                                |
+|---------|-------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------|
+| 1.5.0   | [Download](https://apache.org/dist/incubator/mxnet/1.5.0/apache-mxnet-src-1.5.0-incubating.tar.gz)                 | [Download](https://apache.org/dist/incubator/mxnet/1.5.0/apache-mxnet-src-1.5.0-incubating.tar.gz.asc)    |  [Download](https://apache.org/dist/incubator/mxnet/1.5.0/apache-mxnet-src-1.5.0-incubating.tar.gz.sha512)     |
+| 1.4.1   | [Download](https://www.apache.org/dyn/closer.cgi/incubator/mxnet/1.4.1/apache-mxnet-src-1.4.1-incubating.tar.gz)   | [Download](https://apache.org/dist/incubator/mxnet/1.4.1/apache-mxnet-src-1.4.1-incubating.tar.gz.asc)    | [Download](https://apache.org/dist/incubator/mxnet/1.4.1/apache-mxnet-src-1.4.1-incubating.tar.gz.sha512)      |
+| 1.4.0   | [Download](https://www.apache.org/dyn/closer.cgi/incubator/mxnet/1.4.0/apache-mxnet-src-1.4.0-incubating.tar.gz)   | [Download](https://apache.org/dist/incubator/mxnet/1.4.0/apache-mxnet-src-1.4.0-incubating.tar.gz.asc)    | [Download](https://apache.org/dist/incubator/mxnet/1.4.0/apache-mxnet-src-1.4.0-incubating.tar.gz.sha512)      |
+| 1.3.1   | [Download](https://www.apache.org/dyn/closer.cgi/incubator/mxnet/1.3.1/apache-mxnet-src-1.3.1-incubating.tar.gz)   | [Download](https://apache.org/dist/incubator/mxnet/1.3.1/apache-mxnet-src-1.3.1-incubating.tar.gz.asc)    | [Download](https://apache.org/dist/incubator/mxnet/1.3.1/apache-mxnet-src-1.3.1-incubating.tar.gz.sha512)      |
+| 1.3.0   | [Download](https://archive.apache.org/dist/incubator/mxnet/1.3.0/apache-mxnet-src-1.3.0-incubating.tar.gz)   | [Download](https://archive.apache.org/dist/incubator/mxnet/1.3.0/apache-mxnet-src-1.3.0-incubating.tar.gz.asc)    | [Download](https://archive.apache.org/dist/incubator/mxnet/1.3.0/apache-mxnet-src-1.3.0-incubating.tar.gz.sha512)      |
+| 1.2.1   | [Download](https://archive.apache.org/dist/incubator/mxnet/1.2.1/apache-mxnet-src-1.2.1-incubating.tar.gz)   | [Download](https://archive.apache.org/dist/incubator/mxnet/1.2.1/apache-mxnet-src-1.2.1-incubating.tar.gz.asc)    | [Download](https://archive.apache.org/dist/incubator/mxnet/1.2.1/apache-mxnet-src-1.2.1-incubating.tar.gz.sha512)      |
+| 1.2.0   | [Download](https://archive.apache.org/dist/incubator/mxnet/1.2.0/apache-mxnet-src-1.2.0-incubating.tar.gz)   | [Download](https://archive.apache.org/dist/incubator/mxnet/1.2.0/apache-mxnet-src-1.2.0-incubating.tar.gz.asc)    | [Download](https://archive.apache.org/dist/incubator/mxnet/1.2.0/apache-mxnet-src-1.2.0-incubating.tar.gz.sha512)      |
+| 1.1.0   | [Download](https://archive.apache.org/dist/incubator/mxnet/1.1.0/apache-mxnet-src-1.1.0-incubating.tar.gz)      | [Download](https://archive.apache.org/dist/incubator/mxnet/1.1.0/apache-mxnet-src-1.1.0-incubating.tar.gz.asc)      | [Download](https://archive.apache.org/dist/incubator/mxnet/1.1.0/apache-mxnet-src-1.1.0-incubating.tar.gz.sha512)     |
+| 1.0.0   | [Download](https://archive.apache.org/dist/incubator/mxnet/1.0.0/apache-mxnet-src-1.0.0-incubating.tar.gz)   | [Download](https://archive.apache.org/dist/incubator/mxnet/1.0.0/apache-mxnet-src-1.0.0-incubating.tar.gz.asc)   | [Download](https://archive.apache.org/dist/incubator/mxnet/1.0.0/apache-mxnet-src-1.0.0-incubating.tar.gz.sha512)   |
+| 0.12.1  | [Download](https://archive.apache.org/dist/incubator/mxnet/0.12.1/apache-mxnet-src-0.12.1-incubating.tar.gz) | [Download](https://archive.apache.org/dist/incubator/mxnet/0.12.1/apache-mxnet-src-0.12.1-incubating.tar.gz.asc) | [Download](https://archive.apache.org/dist/incubator/mxnet/0.12.1/apache-mxnet-src-0.12.1-incubating.tar.gz.sha512) |
+| 0.12.0  | [Download](https://archive.apache.org/dist/incubator/mxnet/0.12.0/apache-mxnet-src-0.12.0-incubating.tar.gz) | [Download](https://archive.apache.org/dist/incubator/mxnet/0.12.0/apache-mxnet-src-0.12.0-incubating.tar.gz.asc) | [Download](https://archive.apache.org/dist/incubator/mxnet/0.12.0/apache-mxnet-src-0.12.0-incubating.tar.gz.sha512) |
+| 0.11.0  | [Download](https://archive.apache.org/dist/incubator/mxnet/0.11.0/apache-mxnet-src-0.11.0-incubating.tar.gz) | [Download](https://archive.apache.org/dist/incubator/mxnet/0.11.0/apache-mxnet-src-0.11.0-incubating.tar.gz.asc) | [Download](https://archive.apache.org/dist/incubator/mxnet/0.11.0/apache-mxnet-src-0.11.0-incubating.tar.gz.sha512) |
+
+## Verify the Integrity of the Files
+It is essential that you verify the integrity of the downloaded file using the PGP signature (.asc file) or a hash (.md5 or .sha* file). Please read [Verifying Apache Software Foundation Releases](https://www.apache.org/info/verification.html) for more information on why you should verify our releases.
+
+The PGP signature can be verified using PGP or GPG. First download the KEYS as well as the .asc signature file for the relevant distribution. Make sure you get these files from the main distribution site, rather than from a mirror. Then verify the signatures using one of the following alternatives:
+
+```bash
+% gpg --import KEYS
+% gpg --verify downloaded_file.asc downloaded_file
+```
+
+```bash
+% pgpk -a KEYS
+% pgpv downloaded_file.asc
+```
+
+```bash
+% pgp -ka KEYS
+% pgp downloaded_file.asc
+```
+
+Alternatively, you can verify the hash on the file.
+
+Hashes can be calculated using GPG:
+
+```bash
+% gpg --print-md SHA1 downloaded_file
+```
+
+The output should be compared with the contents of the SHA1 file. Similarly for other hashes (SHA256 MD5 etc) which may be provided.
+
+Windows 7 and later systems should all now have `certUtil`:
+
+```bash
+% certUtil -hashfile pathToFileToCheck
+```
+
+HashAlgorithm choices: MD2 MD4 MD5 SHA1 SHA256 SHA384 SHA512
+
+Unix-like systems (and macOS) will have a utility called `md5`, `md5sum` or `shasum`.
diff --git a/docs/static_site/src/pages/get_started/index.html b/docs/static_site/src/pages/get_started/index.html
new file mode 100644
index 000000000000..1abf8b9e06d4
--- /dev/null
+++ b/docs/static_site/src/pages/get_started/index.html
@@ -0,0 +1,33 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page
+title: Get Started
+action: MXNet Tutorials
+action_url: /api/python/docs/tutorials
+permalink: /get_started/index.html
+---
+
+{% include /get_started/get_started.html %}
+
+</div>
+<div class="get-started-from-source">
+<div class="wrapper">
+    <h2>Download from source</h2>
+    <p>The signed source code for Apache MXNet (incubating) is available for download <a href="download">here</a></p>
+</div>
+</div>
diff --git a/docs/static_site/src/pages/get_started/java_setup.md b/docs/static_site/src/pages/get_started/java_setup.md
new file mode 100644
index 000000000000..52edde8cb383
--- /dev/null
+++ b/docs/static_site/src/pages/get_started/java_setup.md
@@ -0,0 +1,135 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page
+title: Java Setup
+action: Get Started
+action_url: /get_started
+permalink: /get_started/java_setup
+---
+
+# Setup the MXNet Package for Java
+
+The following instructions are provided for macOS and Ubuntu. Windows is not yet available.
+
+**Note:** If you use IntelliJ or a similar IDE, you may want to follow the [MXNet-Java on IntelliJ tutorial](../tutorials/java/mxnet_java_on_intellij.md) instead of these instructions.
+
+<hr>
+
+## Maven
+
+### Setup Instructions
+
+**Step 1.** Install dependencies:
+
+**macOS Steps**
+
+```bash
+brew update
+brew tap caskroom/versions
+brew cask install java8
+brew install maven
+```
+
+**Ubuntu Steps**
+
+Please run the following lines:
+
+```bash
+sudo apt-get install openjdk-8-jdk maven
+```
+
+**Step 2.** Run the demo MXNet-Java project.
+
+Go to the [MXNet-Java demo project's README](https://github.com/apache/incubator-mxnet/tree/master/scala-package/mxnet-demo/java-demo) and follow the directions to test the MXNet-Java package installation.
+
+#### Maven Repository
+
+MXNet-Java can be easily included in your Maven managed project. The Java packages are currently available on Maven. Add the dependency which corresponds to your platform to the `dependencies` tag :
+
+**Linux CPU**
+```html
+<dependency>
+  <groupId>org.apache.mxnet</groupId>
+  <artifactId>mxnet-full_2.11-linux-x86_64-cpu</artifactId>
+  <version>1.4.0</version>
+</dependency>
+```
+
+**Linux GPU**
+```html
+<dependency>
+  <groupId>org.apache.mxnet</groupId>
+  <artifactId>mxnet-full_2.11-linux-x86_64-gpu</artifactId>
+  <version>1.4.0</version>
+</dependency>
+```
+
+**macOS CPU**
+```html
+<dependency>
+  <groupId>org.apache.mxnet</groupId>
+  <artifactId>mxnet-full_2.11-osx-x86_64-cpu</artifactId>
+  <version>1.4.0</version>
+</dependency>
+```
+
+The official Java Packages have been released as part of MXNet 1.4 and are available on the [MXNet Maven package repository](https://search.maven.org/#search%7Cga%7C1%7Cg%3A%22org.apache.mxnet%22).
+<hr>
+
+### Eclipse IDE Support
+You can convert your existing Maven project to a project that can run in Eclipse by:
+```
+mvn eclipse:eclipse
+```
+This can be done once you have your maven project properly configured.
+
+## Source
+
+The previously mentioned setup with Maven is recommended. Otherwise, the following instructions for macOS and Ubuntu are provided for reference only:
+
+**If you have already built mxnet from source using `cmake`, run `make clean` and then follow the appropriate guide below***
+
+| OS | Step 1 | Step 2 |
+|---|---|---|
+|macOS | [Shared Library for macOS](osx_setup.html#build-the-shared-library) | [Scala Package for macOS](osx_setup.html#install-the-mxnet-package-for-scala) |
+| Ubuntu | [Shared Library for Ubuntu](ubuntu_setup.html#installing-mxnet-on-ubuntu) | [Scala Package for Ubuntu](ubuntu_setup.html#install-the-mxnet-package-for-scala) |
+| Windows | <a class="github-button" href="https://github.com/apache/incubator-mxnet/issues/10549" data-size="large" data-show-count="true" aria-label="Issue apache/incubator-mxnet on GitHub"> | <a class="github-button" href="https://github.com/apache/incubator-mxnet/issues/10549" data-size="large" data-show-count="true" aria-label="Issue apache/incubator-mxnet on GitHub">Call for Contribution</a> |
+
+
+#### Build Java from an Existing MXNet Installation
+If you have already built MXNet **from source** and are looking to setup Java from that point, you may simply run the following from the MXNet `scala-package` folder:
+
+```
+mvn install
+```
+This will install both the Java Inference API and the required MXNet-Scala package.
+<hr>
+
+## Documentation
+
+Javadocs are generated as part of the docs build pipeline. You can find them published in the [Java API](../api/java/index.md) section of the website or by going to the [scaladocs output](https://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.package) directly.
+
+To build the docs yourself, follow the [developer build docs instructions](https://github.com/apache/incubator-mxnet/tree/master/docs/build_version_doc#developer-instructions).
+
+<hr>
+
+## Resources
+
+* [Java API](../api/java/)
+* [javadocs](https://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.package)
+* [MXNet-Java Tutorials](../tutorials/java/)
diff --git a/docs/static_site/src/pages/get_started/jetson_setup.md b/docs/static_site/src/pages/get_started/jetson_setup.md
new file mode 100644
index 000000000000..1bb52d30e137
--- /dev/null
+++ b/docs/static_site/src/pages/get_started/jetson_setup.md
@@ -0,0 +1,243 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page
+title: Jetson Setup
+action: Get Started
+action_url: /get_started
+permalink: /get_started/jetson_setup
+---
+
+
+# Install MXNet on a Jetson
+
+MXNet supports the Ubuntu Arch64 based operating system so you can run MXNet on NVIDIA Jetson Devices, such as the [TX2](http://www.nvidia.com/object/embedded-systems-dev-kits-modules.html) or [Nano](https://developer.nvidia.com/embedded/learn/get-started-jetson-nano-devkit).
+
+These instructions will walk through how to build MXNet and install MXNet's Python language binding.
+
+For the purposes of this install guide we will assume that CUDA is already installed on your Jetson device. The disk image provided by NVIDIA's getting started guides will have the Jetson toolkit preinstalled, and this also includes CUDA. You should double check what versions are installed and which version you plan to use.
+
+After installing the prerequisites, you have several options for installing MXNet:
+1. Use a Jetson MXNet pip wheel for Python development and use a precompiled Jetson MXNet binary.
+3. Build MXNet from source
+   * On a faster Linux computer using cross-compilation
+   * On the Jetson itself (very slow and not recommended)
+
+
+## Prerequisites
+To build from source or to use the Python wheel, you must install the following dependencies on your Jetson.
+Cross-compiling will require dependencies installed on that machine as well.
+
+### Python Dependencies
+
+To use the Python API you need the following dependencies:
+
+```bash
+sudo apt update
+sudo apt -y install \
+                        build-essential \
+                        git \
+                        graphviz \
+                        libatlas-base-dev \
+                        libopencv-dev \
+                        python-pip
+
+sudo pip install --upgrade \
+                        pip \
+                        setuptools
+
+sudo pip install \
+                        graphviz==0.8.4 \
+                        jupyter \
+                        numpy==1.15.2
+```
+
+If you plan to cross-compile you will need to install these dependencies on that computer as well.
+If you get an error about something being busy, you can restart the Nano and this error will go away. You can then continue installation of the prerequisites.
+
+### Download the source & setup some environment variables:
+
+These steps are optional, but some of the following instructions expect MXNet source files and the `MXNET_HOME` environment variable. Also, CUDA commands will not work out of the box without updating your path.
+
+Clone the MXNet source code repository using the following `git` command in your home directory:
+
+```bash
+git clone --recursive https://github.com/apache/incubator-mxnet.git mxnet
+```
+
+Setup your environment variables for MXNet and CUDA in your `.profile` file in your home directory.
+Add the following to the file.
+
+```bash
+export PATH=/usr/local/cuda/bin:$PATH
+export MXNET_HOME=$HOME/mxnet/
+export PYTHONPATH=$MXNET_HOME/python:$PYTHONPATH
+```
+
+You can then apply this change immediately with the following:
+```bash
+source .profile
+```
+
+**Note:** Change the `~/.profile` steps according to how you prefer to use your shell. Otherwise, your environment variables will be gone after you logout.
+
+### Configure CUDA
+
+You can check to see what version of CUDA is running with `nvcc`.
+
+```bash
+nvcc --version
+```
+
+To switch CUDA versions on a device or computer that has more than one version installed, use the following and replace the symbolic link to the version you want. This one uses CUDA 10.0, which is preinstalled on the Nano.
+
+```bash
+sudo rm /usr/local/cuda
+sudo ln -s /usr/local/cuda-10.0 /usr/local/cuda
+```
+
+**Note:** When cross-compiling, change the CUDA version on the host computer you're using to match the version you're running on your Jetson device.
+**Note:** CUDA 10.1 is recommended but doesn't ship with the Nano's SD card image. You may want to go through CUDA upgrade steps first.
+
+
+## Option 1. Install MXNet for Python
+
+To use a prepared Python wheel, download it to your Jetson, and run it.
+* [MXNet 1.4.0 - Python 3](https://s3.us-east-2.amazonaws.com/mxnet-public/install/jetson/1.4.0/mxnet-1.4.0-cp36-cp36m-linux_aarch64.whl)
+* [MXNet 1.4.0 - Python 2](https://s3.us-east-2.amazonaws.com/mxnet-public/install/jetson/1.4.0/mxnet-1.4.0-cp27-cp27mu-linux_aarch64.whl)
+
+
+It should download the required dependencies, but if you have issues,
+install the dependencies in the prerequisites section, then run the pip wheel.
+
+```bash
+sudo pip install mxnet-1.4.0-cp27-cp27mu-linux_aarch64.whl
+```
+
+Now use a pre-compiled binary you can download it from S3 which is a patch v1.4.1:
+* https://s3.us-east-2.amazonaws.com/mxnet-public/install/jetson/1.4.1/libmxnet.so
+
+Place this file in `$MXNET_HOME/lib`.
+
+To use this with the MXNet Python binding, you must match the source directory's checked out version with the binary's source version, then install it with pip.
+
+```bash
+cd $MXNET_HOME
+git checkout v1.4.x
+git submodule update --init --recursive
+cd python
+sudo pip install -e .
+```
+
+Refer to the following Conclusion and Next Steps section to test your installation.
+
+## Option 2. Build MXNet from Source
+
+Installing MXNet from source is a two-step process:
+
+1. Build the shared library from the MXNet C++ source code.
+2. Install the supported language-specific packages for MXNet.
+
+You can use a Docker method or you can build from source manually.
+
+### Docker
+
+You must have installed Docker and be able to run `docker` without `sudo`.
+Follow these [setup instructions to get to this point](https://docs.docker.com/install/linux/#manage-docker-as-a-non-root-user).
+Then run the following to execute cross-compilation via Docker.
+
+```bash
+$MXNET_HOME/ci/build.py -p jetson
+```
+
+### Manual
+
+**Step 1** Build the Shared Library
+
+(Skip this sub-step for compiling on the Jetson device directly.)
+Edit the Makefile to install the MXNet with CUDA bindings to leverage the GPU on the Jetson:
+
+```bash
+cp $MXNET_HOME/make/crosscompile.jetson.mk config.mk
+```
+
+Now edit `config.mk` to make some additional changes for the Nano. Update the following settings:
+
+1. Update the CUDA path. `USE_CUDA_PATH = /usr/local/cuda`
+2. Add `-gencode arch=compute-63, code=sm_62` to the `CUDA_ARCH` setting.
+3. Update the NVCC settings. `NVCCFLAGS := -m64`
+4. (optional, but recommended) Turn on OpenCV. `USE_OPENCV = 1`
+
+Now edit the Mshadow Makefile to ensure MXNet builds with Pascal's hardware level low precision acceleration by editing `3rdparty/mshadow/make/mshadow.mk`.
+The last line has `MSHADOW_USE_PASCAL` set to `0`. Change this to `1` to enable it.
+
+```bash
+MSHADOW_CFLAGS += -DMSHADOW_USE_PASCAL=1
+```
+
+Now you can build the complete MXNet library with the following command:
+
+```bash
+cd $MXNET_HOME
+make -j $(nproc)
+```
+
+Executing this command creates a file called `libmxnet.so` in the `mxnet/lib` directory.
+
+**Step 2** Install MXNet Python Bindings (optional)
+
+To install Python bindings run the following commands in the MXNet directory:
+
+```bash
+cd $MXNET_HOME/python
+sudo pip install -e .
+```
+
+Note that the `-e` flag is optional. It is equivalent to `--editable` and means that if you edit the source files, these changes will be reflected in the package installed.
+
+**Step 3** Install the MXNet Java & Scala Bindings (optional)
+
+Change directories to `scala-package` and run `mvn install`.
+
+```bash
+cd $MXNET_HOME/scala-package
+mvn install
+```
+
+This creates the required `.jar` file to use in your Java or Scala projects.
+
+## Conclusion and Next Steps
+
+You are now ready to run MXNet on your NVIDIA Jetson TX2 or Nano device.
+You can verify your MXNet Python installation with the following:
+
+```python
+import mxnet
+mxnet.__version__
+```
+
+You can also verify MXNet can use your GPU with the following test:
+
+```python
+import mxnet as mx
+a = mx.nd.ones((2, 3), mx.gpu())
+b = a * 2 + 1
+b.asnumpy()
+```
+
+If everything is working, it will report the version number.
+For assistance, head over to the [MXNet Forum](https://discuss.mxnet.io/).
diff --git a/docs/static_site/src/pages/get_started/osx_setup.md b/docs/static_site/src/pages/get_started/osx_setup.md
new file mode 100644
index 000000000000..26a63a1d13a3
--- /dev/null
+++ b/docs/static_site/src/pages/get_started/osx_setup.md
@@ -0,0 +1,275 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page
+title: OSX Setup
+action: Get Started
+action_url: /get_started
+permalink: /get_started/osx_setup
+---
+
+# Installing MXNet from source on OS X (Mac)
+
+**NOTE:** For pre-built MXNet with Python, please refer to the [new install guide]({{'/get_started'|relative_url}}).
+
+Installing MXNet is a two-step process:
+
+1. Build the shared library from the MXNet C++ source code.
+2. Install the supported language-specific packages for MXNet.
+
+**Note:** To change the compilation options for your build, edit the ```make/config.mk``` file and submit a build request with the ```make``` command.
+
+## Prepare Environment for GPU Installation
+
+This section is optional. Skip to next section if you don't plan to use GPUs. If you plan to build with GPU, you need to set up the environment for CUDA and cuDNN.
+
+First, download and install [CUDA 8 toolkit](https://developer.nvidia.com/cuda-toolkit).
+
+Once you have the CUDA Toolkit installed you will need to set up the required environment variables by adding the following to your ~/.bash_profile file:
+
+```bash
+    export CUDA_HOME=/usr/local/cuda
+    export DYLD_LIBRARY_PATH="$CUDA_HOME/lib:$DYLD_LIBRARY_PATH"
+    export PATH="$CUDA_HOME/bin:$PATH"
+```
+
+Reload ~/.bash_profile file and install dependencies:
+```bash
+    . ~/.bash_profile
+    brew install coreutils
+    brew tap caskroom/cask
+```
+
+Then download [cuDNN 5](https://developer.nvidia.com/cudnn).
+
+Unzip the file and change to the cudnn root directory. Move the header files and libraries to your local CUDA Toolkit folder:
+
+```bash
+    $ sudo mv include/cudnn.h /Developer/NVIDIA/CUDA-8.0/include/
+    $ sudo mv lib/libcudnn* /Developer/NVIDIA/CUDA-8.0/lib
+    $ sudo ln -s /Developer/NVIDIA/CUDA-8.0/lib/libcudnn* /usr/local/cuda/lib/
+```
+
+Now we can start to build MXNet.
+
+## Build the Shared Library
+
+### Install MXNet dependencies
+Install the dependencies, required for MXNet, with the following commands:
+- [Homebrew](http://brew.sh/)
+- OpenBLAS and homebrew/core (for linear algebraic operations)
+- OpenCV (for computer vision operations)
+
+```bash
+	# Paste this command in Mac terminal to install Homebrew
+	/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
+
+	# Insert the Homebrew directory at the top of your PATH environment variable
+	export PATH=/usr/local/bin:/usr/local/sbin:$PATH
+```
+
+```bash
+	brew update
+	brew install pkg-config
+	brew install graphviz
+	brew install openblas
+	brew tap homebrew/core
+	brew install opencv@3
+
+	# If building with MKLDNN
+	brew install llvm
+
+	# Get pip
+	easy_install pip
+	# For visualization of network graphs
+	pip install graphviz==0.8.4
+	# Jupyter notebook
+	pip install jupyter
+```
+
+### Build MXNet Shared Library
+After you have installed the dependencies, pull the MXNet source code from Git and build MXNet to produce an MXNet library called ```libmxnet.so```. You can clone the repository as described in the following code block, or you may try the [download links](download.md) for your desired MXNet version.
+
+The file called ```osx.mk``` has the configuration required for building MXNet on OS X. First copy ```make/osx.mk``` into ```config.mk```, which is used by the ```make``` command:
+
+```bash
+    git clone --recursive https://github.com/apache/incubator-mxnet ~/mxnet
+    cd ~/mxnet
+    cp make/osx.mk ./config.mk
+    echo "USE_BLAS = openblas" >> ./config.mk
+    echo "ADD_CFLAGS += -I/usr/local/opt/openblas/include" >> ./config.mk
+    echo "ADD_LDFLAGS += -L/usr/local/opt/openblas/lib" >> ./config.mk
+    echo "ADD_LDFLAGS += -L/usr/local/lib/graphviz/" >> ./config.mk
+    make -j$(sysctl -n hw.ncpu)
+```
+
+To build with MKLDNN
+
+```bash
+echo "CC=$(brew --prefix llvm)/bin/clang" >> ./config.mk
+echo "CXX=$(brew --prefix llvm)/bin/clang++" >> ./config.mk
+echo "USE_OPENCV=1" >> ./config.mk
+echo "USE_OPENMP=1" >> ./config.mk
+echo "USE_MKLDNN=1" >> ./config.mk
+echo "USE_BLAS=apple" >> ./config.mk
+LIBRARY_PATH=$(brew --prefix llvm)/lib/ make -j $(sysctl -n hw.ncpu)
+```
+
+If building with ```GPU``` support, add the following configuration to config.mk and build:
+```bash
+    echo "USE_CUDA = 1" >> ./config.mk
+    echo "USE_CUDA_PATH = /usr/local/cuda" >> ./config.mk
+    echo "USE_CUDNN = 1" >> ./config.mk
+    make -j$(sysctl -n hw.ncpu)
+```
+**Note:** To change build parameters, edit ```config.mk```.
+
+
+&nbsp;
+
+We have installed MXNet core library. Next, we will install MXNet interface package for the programming language of your choice:
+- [Python](#install-mxnet-for-python)
+- [R](#install-the-mxnet-package-for-r)
+- [Julia](#install-the-mxnet-package-for-julia)
+- [Scala](#install-the-mxnet-package-for-scala)
+- [Perl](#install-the-mxnet-package-for-perl)
+
+## Install MXNet for Python
+To install the MXNet Python binding navigate to the root of the MXNet folder then run the following:
+
+```bash
+$ cd python
+$ pip install -e .
+```
+
+Note that the `-e` flag is optional. It is equivalent to `--editable` and means that if you edit the source files, these changes will be reflected in the package installed.
+
+## Install the MXNet Package for R
+You have 2 options:
+1. Building MXNet with the Prebuilt Binary Package
+2. Building MXNet from Source Code
+
+### Building MXNet with the Prebuilt Binary Package
+Install OpenCV and OpenBLAS.
+
+```bash
+brew install opencv
+brew install openblas@0.3.1
+```
+
+Add a soft link to the OpenBLAS installation. This example links the 0.3.1 version:
+
+```bash
+ln -sf /usr/local/opt/openblas/lib/libopenblasp-r0.3.* /usr/local/opt/openblas/lib/libopenblasp-r0.3.1.dylib
+```
+
+Note: packages for 3.6.x are not yet available.
+
+Install 3.5.x of R from [CRAN](https://cran.r-project.org/bin/macosx/). The latest is [v3.5.3](https://cran.r-project.org/bin/macosx/R-3.5.3.pkg).
+
+For OS X (Mac) users, MXNet provides a prebuilt binary package for CPUs. The prebuilt package is updated weekly. You can install the package directly in the R console using the following commands:
+
+```r
+  cran <- getOption("repos")
+  cran["dmlc"] <- "https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/R/CRAN/"
+  options(repos = cran)
+  install.packages("mxnet")
+```
+
+### Building MXNet from Source Code
+
+Run the following commands to install the MXNet dependencies and build the MXNet R package.
+
+```r
+    Rscript -e "install.packages('devtools', repo = 'https://cran.rstudio.com')"
+```
+```bash
+    cd R-package
+    Rscript -e "library(devtools); library(methods); options(repos=c(CRAN='https://cran.rstudio.com')); install_deps(dependencies = TRUE)"
+    cd ..
+    make rpkg
+```
+
+## Install the MXNet Package for Julia
+The MXNet package for Julia is hosted in a separate repository, MXNet.jl, which is available on [GitHub](https://github.com/dmlc/MXNet.jl). To use Julia binding it with an existing libmxnet installation, set the ```MXNET_HOME``` environment variable by running the following command:
+
+```bash
+	export MXNET_HOME=/<path to>/libmxnet
+```
+
+The path to the existing libmxnet installation should be the root directory of libmxnet. In other words, you should be able to find the ```libmxnet.so``` file at ```$MXNET_HOME/lib```. For example, if the root directory of libmxnet is ```~```, you would run the following command:
+
+```bash
+	export MXNET_HOME=/~/libmxnet
+```
+
+You might want to add this command to your ```~/.bashrc``` file. If you do, you can install the Julia package in the Julia console using the following command:
+
+```julia
+	Pkg.add("MXNet")
+```
+
+For more details about installing and using MXNet with Julia, see the [MXNet Julia documentation](http://dmlc.ml/MXNet.jl/latest/user-guide/install/).
+
+
+## Install the MXNet Package for Scala
+
+To use the MXNet-Scala package, you can acquire the Maven package as a dependency.
+
+Further information is in the [MXNet-Scala Setup Instructions](./scala_setup.md).
+
+If you use IntelliJ or a similar IDE, you may want to follow the [MXNet-Scala on IntelliJ tutorial](../tutorials/scala/mxnet_scala_on_intellij.md) instead.
+
+
+## Install the MXNet Package for Perl
+Before you build MXNet for Perl from source code, you must complete [building the shared library](#build-the-shared-library).
+After you build the shared library, run the following command from the MXNet source root directory to build the MXNet Perl package:
+
+```bash
+    brew install swig
+    sudo sh -c 'curl -L https://cpanmin.us | perl - App::cpanminus'
+    sudo cpanm -q -n PDL Mouse Function::Parameters Hash::Ordered PDL::CCS
+
+    MXNET_HOME=${PWD}
+    export PERL5LIB=${HOME}/perl5/lib/perl5
+
+    cd ${MXNET_HOME}/perl-package/AI-MXNetCAPI/
+    perl Makefile.PL INSTALL_BASE=${HOME}/perl5
+    make
+    install_name_tool -change lib/libmxnet.so \
+        ${MXNET_HOME}/lib/libmxnet.so \
+        blib/arch/auto/AI/MXNetCAPI/MXNetCAPI.bundle
+    make install
+
+    cd ${MXNET_HOME}/perl-package/AI-NNVMCAPI/
+    perl Makefile.PL INSTALL_BASE=${HOME}/perl5
+    make
+    install_name_tool -change lib/libmxnet.so \
+            ${MXNET_HOME}/lib/libmxnet.so \
+            blib/arch/auto/AI/NNVMCAPI/NNVMCAPI.bundle
+    make install
+
+    cd ${MXNET_HOME}/perl-package/AI-MXNet/
+    perl Makefile.PL INSTALL_BASE=${HOME}/perl5
+    make install
+```
+
+## Next Steps
+
+* [Tutorials](../tutorials/index.md)
+* [How To](../faq/index.md)
+* [Architecture](../architecture/index.md)
diff --git a/docs/static_site/src/pages/get_started/scala_setup.md b/docs/static_site/src/pages/get_started/scala_setup.md
new file mode 100644
index 000000000000..0dc47aad4db6
--- /dev/null
+++ b/docs/static_site/src/pages/get_started/scala_setup.md
@@ -0,0 +1,165 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page
+title: Scala Setup
+action: Get Started
+action_url: /get_started
+permalink: /get_started/scala_setup
+---
+
+
+# Setup the MXNet Package for Scala
+
+The following instructions are provided for macOS and Ubuntu. Windows is not yet available.
+
+**Note:** If you use IntelliJ or a similar IDE, you may want to follow the [MXNet-Scala on IntelliJ tutorial](../tutorials/scala/mxnet_scala_on_intellij.md) instead of these instructions.
+**Note:** Currently, we only support scala 2.11
+
+<hr>
+
+## Maven
+
+### Setup Instructions
+
+**Step 1.** Install dependencies:
+
+**macOS Steps**
+
+```bash
+brew update
+brew tap caskroom/versions
+brew cask install java8
+brew install opencv
+brew install maven
+```
+
+**Ubuntu Steps**
+
+These scripts will install Maven and its dependencies.
+
+```bash
+wget https://raw.githubusercontent.com/apache/incubator-mxnet/master/ci/docker/install/ubuntu_core.sh
+wget https://raw.githubusercontent.com/apache/incubator-mxnet/master/ci/docker/install/ubuntu_scala.sh
+chmod +x ubuntu_core.sh
+chmod +x ubuntu_scala.sh
+sudo ./ubuntu_core.sh
+sudo ./ubuntu_scala.sh
+```
+
+**Step 2.** Run the demo MXNet-Scala project.
+
+Go to the [MXNet-Scala demo project's README](https://github.com/apache/incubator-mxnet/tree/master/scala-package/mxnet-demo) and follow the directions to test the MXNet-Scala package installation.
+
+#### Maven Repository
+
+Package information can be found in the Maven Repository:
+https://mvnrepository.com/artifact/org.apache.mxnet
+
+**Linux CPU**
+```html
+<!-- https://mvnrepository.com/artifact/org.apache.mxnet/mxnet-full_2.11-linux-x86_64-cpu -->
+<dependency>
+    <groupId>org.apache.mxnet</groupId>
+    <artifactId>mxnet-full_2.11-linux-x86_64-cpu</artifactId>
+</dependency>
+```
+
+**Linux GPU**
+```html
+<!-- https://mvnrepository.com/artifact/org.apache.mxnet/mxnet-full_2.11-linux-x86_64-gpu -->
+<dependency>
+    <groupId>org.apache.mxnet</groupId>
+    <artifactId>mxnet-full_2.11-linux-x86_64-gpu</artifactId>
+</dependency>
+```
+
+**macOS CPU**
+```html
+<!-- https://mvnrepository.com/artifact/org.apache.mxnet/mxnet-full_2.11-osx-x86_64-cpu -->
+<dependency>
+    <groupId>org.apache.mxnet</groupId>
+    <artifactId>mxnet-full_2.11-osx-x86_64-cpu</artifactId>
+</dependency>
+```
+
+**NOTE:** You may specify the version you wish to use by adding the version number to the `dependency` block. For example, to use v1.2.0 you would add `<version>1.2.0</version>`. Otherwise Maven will use the latest version available.
+
+<hr>
+
+## Source
+
+The previously mentioned setup with Maven is recommended. Otherwise, the following instructions for macOS, Ubuntu, and Windows are provided for reference only:
+
+**If you have already built mxnet from source using `cmake`, run `make clean` and then follow the appropriate guide below***
+
+| OS | Step 1 | Step 2 |
+|---|---|---|
+|macOS | [Shared Library for macOS](osx_setup.html#build-the-shared-library) | [Scala Package for macOS](osx_setup.html#install-the-mxnet-package-for-scala) |
+| Ubuntu | [Shared Library for Ubuntu](ubuntu_setup.html#installing-mxnet-on-ubuntu) | [Scala Package for Ubuntu](ubuntu_setup.html#install-the-mxnet-package-for-scala) |
+| Windows | <a class="github-button" href="https://github.com/apache/incubator-mxnet/issues/10549" data-size="large" data-show-count="true" aria-label="Issue apache/incubator-mxnet on GitHub"> | <a class="github-button" href="https://github.com/apache/incubator-mxnet/issues/10549" data-size="large" data-show-count="true" aria-label="Issue apache/incubator-mxnet on GitHub">Call for Contribution</a> |
+
+
+#### Build Scala from an Existing MXNet Installation
+If you have already built MXNet **from source** and are looking to setup Scala from that point, you may simply run the following from the MXNet `scala-package` folder:
+
+```
+mvn install
+```
+
+<hr>
+
+## Interpreter
+
+To run the scala interpreter, first download and install scala 2.11.x (run `scala -version` to make sure you have the right version installed.**
+
+### Installing the Interpreter
+
+**Ubuntu***
+
+```
+sudo apt-get install scala
+```
+
+**macOS***
+
+```
+brew install scala@2.11
+```
+
+Then, add scala to your path by following the instructions output by homebrew.
+
+### Running the Interpreter
+
+To run the interpreter, download the appropriate mxnet jar from [the maven repository](https://search.maven.org/search?q=g:org.apache.mxnet) or build from source following the instructions above.
+
+Then, run `scala -cp {path/to/mxnet-full_2.11-os-version.jar}` to start it.
+If you receive a "NumberFormatException" when running the interpreter, run `export TERM=xterm-color` before starting the interpreter.
+
+## Documentation
+
+Scaladocs are generated as part of the docs build pipeline. You can find them published in the [Scala API](../api/scala/index.md) section of the website or by going to the [scaladocs output](https://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.package) directly.
+
+To build the docs yourself, follow the [developer build docs instructions](https://github.com/apache/incubator-mxnet/tree/master/docs/build_version_doc#developer-instructions).
+
+<hr>
+
+## Resources
+
+* [Scala API](../api/scala/)
+* [scaladocs](https://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.package)
+* [MXNet-Scala Tutorials](../tutorials/scala/)
diff --git a/docs/static_site/src/pages/get_started/ubuntu_setup.md b/docs/static_site/src/pages/get_started/ubuntu_setup.md
new file mode 100644
index 000000000000..5fc0e237d086
--- /dev/null
+++ b/docs/static_site/src/pages/get_started/ubuntu_setup.md
@@ -0,0 +1,561 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page
+title: Ubuntu Setup
+action: Get Started
+action_url: /get_started
+permalink: /get_started/ubuntu_setup
+---
+
+# Installing MXNet on Ubuntu
+
+The following installation instructions are for installing MXNet on computers running **Ubuntu 16.04**. Support for later versions of Ubuntu is [not yet available](#contributions).
+<hr>
+
+## Contents
+
+* [CUDA Dependencies](#cuda-dependencies)
+* [Quick Installation](#quick-installation)
+    * [Python](#install-mxnet-for-python)
+    * [pip Packages](#pip-package-availability)
+* [Build from Source](#build-mxnet-from-source)
+* [Installing Language Packages](#installing-language-packages-for-mxnet)
+    * [R](#install-the-mxnet-package-for-r)
+    * [Julia](#install-the-mxnet-package-for-julia)
+    * [Scala](#install-the-mxnet-package-for-scala)
+    * [Java](#install-the-mxnet-package-for-java)
+    * [Perl](#install-the-mxnet-package-for-perl)
+  * [Contributions](#contributions)
+  * [Next Steps](#next-steps)
+
+<hr>
+
+## CUDA Dependencies
+
+If you plan to build with GPU, you need to set up the environment for CUDA and cuDNN.
+
+First, download and install [CUDA toolkit](https://developer.nvidia.com/cuda-toolkit). CUDA 9.2 is recommended.
+
+Then download [cuDNN 7.1.4](https://developer.nvidia.com/cudnn).
+
+Unzip the file and change to the cuDNN root directory. Move the header and libraries to your local CUDA Toolkit folder:
+
+```bash
+    tar xvzf cudnn-9.2-linux-x64-v7.1
+    sudo cp -P cuda/include/cudnn.h /usr/local/cuda/include
+    sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda/lib64
+    sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
+    sudo ldconfig
+```
+
+<hr>
+
+
+## Quick Installation
+
+### Install MXNet for Python
+
+#### Dependencies
+
+The following scripts will install Ubuntu 16.04 dependencies for MXNet Python development.
+
+```bash
+wget https://raw.githubusercontent.com/apache/incubator-mxnet/master/ci/docker/install/ubuntu_core.sh
+wget https://raw.githubusercontent.com/apache/incubator-mxnet/master/ci/docker/install/ubuntu_python.sh
+sudo ./ubuntu_core.sh
+sudo ./ubuntu_python.sh
+```
+
+Using the latest MXNet with CUDA 9.2 package is recommended for the fastest training speeds with MXNet.
+
+**Recommended for training:**
+```bash
+pip install mxnet-cu92
+```
+
+**Recommended for inference:**
+```bash
+pip install mxnet-cu92mkl
+```
+
+Alternatively, you can use the table below to select the package that suits your purpose.
+
+| MXNet Version | Basic | CUDA | MKL-DNN | CUDA/MKL-DNN |
+|-|-|-|-|-|
+| Latest | mxnet | mxnet-cu92 | mxnet-mkl | mxnet-cu92mkl |
+
+
+#### pip Package Availability
+
+The following table presents the pip packages that are recommended for each version of MXNet.
+
+![pip package table](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/install/pip-packages-1.4.0.png)
+
+To install an older version of MXNet with one of the packages in the previous table add `==` with the version you require. For example for version 1.1.0 of MXNet with CUDA 8, you would use `pip install mxnet-cu80==1.1.0`.
+
+<hr>
+
+
+## Build MXNet from Source
+
+You can build MXNet from source, and then you have the option of installing language-specific bindings, such as Scala, Java, Julia, R or Perl. This is a two-step process:
+
+1. Build the shared library from the MXNet C++ source code.
+2. (optional) Install the supported language-specific packages for MXNet. Be sure to check that section first, as some scripts may be available to handle all of the dependencies, MXNet build, and language bindings for you. Here they are again for quick access:
+
+* [R](#install-the-mxnet-package-for-r)
+* [Julia](#install-the-mxnet-package-for-julia)
+* [Scala](#install-the-mxnet-package-for-scala)
+* [Java](#install-the-mxnet-package-for-java)
+* [Perl](#install-the-mxnet-package-for-perl)
+
+**Note:** To change the compilation options for your build, edit the ```make/config.mk``` file prior to building MXNet. More information on this is mentioned in the different language package instructions.
+
+### Build the Shared Library
+
+#### Quick MXNet Build
+You can quickly build MXNet from source with the following script found in the `/docs/install` folder:
+
+```bash
+cd docs/install
+./install_mxnet_ubuntu_python.sh
+```
+
+Or you can go through a manual process described next.
+
+#### Manual MXNet Installation
+
+It is recommended that you review the general [build from source](build_from_source) instructions before continuing.
+
+On Ubuntu versions 16.04 or later, you need the following dependencies:
+
+**Step 1:** Install prerequisite packages.
+```bash
+    sudo apt-get update
+    sudo apt-get install -y build-essential git ninja-build ccache
+```
+
+**For Ubuntu 18.04 and CUDA builds you need to update CMake**
+
+```bash
+#!/usr/bin/env bash
+set -exuo pipefail
+sudo apt remove --purge --auto-remove cmake
+
+# Update CMAKE for correct cuda autotedetection: https://github.com/clab/dynet/issues/1457
+version=3.14
+build=0
+mkdir -p ~/tmp
+cd ~/tmp
+wget https://cmake.org/files/v$version/cmake-$version.$build.tar.gz
+tar -xzvf cmake-$version.$build.tar.gz
+cd cmake-$version.$build/
+./bootstrap
+make -j$(nproc)
+sudo make install
+```
+
+
+**Step 2:** Install a Math Library.
+
+Details on the different math libraries are found in the build from source guide's [Math Library Selection](build_from_source#math-library-selection) section.
+
+For OpenBLAS use:
+
+```bash
+    sudo apt-get install -y libopenblas-dev
+```
+
+For other libraries, visit the [Math Library Selection](build_from_source#math-library-selection) section.
+
+**Step 3:** Install OpenCV.
+
+*MXNet* uses [OpenCV](http://opencv.org/) for efficient image loading and augmentation operations.
+
+```bash
+    sudo apt-get install -y libopencv-dev
+```
+
+**Step 4:** Download MXNet sources and build MXNet core shared library.
+
+If building on CPU and using OpenBLAS:
+
+Clone the repository:
+
+```bash
+    git clone --recursive https://github.com/apache/incubator-mxnet.git
+    cd incubator-mxnet
+```
+
+Build with CMake and ninja, without GPU and without MKL.
+
+```bash
+    rm -rf build
+    mkdir -p build && cd build
+    cmake -GNinja \
+        -DUSE_CUDA=OFF \
+        -DUSE_MKL_IF_AVAILABLE=OFF \
+        -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache \
+        -DCMAKE_C_COMPILER_LAUNCHER=ccache \
+        -DCMAKE_CXX_COMPILER_LAUNCHER=ccache \
+    ..
+    ninja
+```
+
+If building on CPU and using MKL and MKL-DNN (make sure MKL is installed according to [Math Library Selection](build_from_source#math-library-selection) and [MKL-DNN README](https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/mkldnn/MKLDNN_README.md)):
+
+```bash
+    rm -rf build
+    mkdir -p build && cd build
+    cmake -GNinja \
+        -DUSE_CUDA=OFF \
+        -DUSE_MKL_IF_AVAILABLE=ON \
+        -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache \
+        -DCMAKE_C_COMPILER_LAUNCHER=ccache \
+        -DCMAKE_CXX_COMPILER_LAUNCHER=ccache \
+    ..
+    ninja
+```
+
+If building on GPU (make sure you have installed the [CUDA dependencies first](#cuda-dependencies)):
+Cuda 10.1 in Ubuntu 18.04 builds fine but is not currently tested in CI.
+
+```bash
+    rm -rf build
+    mkdir -p build && cd build
+    cmake -GNinja \
+        -DUSE_CUDA=ON \
+        -DUSE_MKL_IF_AVAILABLE=OFF \
+        -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache \
+        -DCMAKE_C_COMPILER_LAUNCHER=ccache \
+        -DCMAKE_CXX_COMPILER_LAUNCHER=ccache \
+    ..
+    ninja
+```
+
+*Note* - You can explore and use more compilation options as they are delcared in the top of `CMakeLists.txt` and also review common [usage examples](build_from_source#usage-examples).
+Optionally, you can also use a higher level, scripted version of the above with an editable CMake options file by doing the
+following:
+
+```bash
+cp cmake/cmake_options.yml .
+# Edit cmake_options.yml in the MXNet root to your taste
+$EDITOR cmake_options.yml
+# Launch a local CMake build
+./dev_menu.py build
+```
+
+Building from source creates a library called ```libmxnet.so``` in the `build` folder in your MXNet project root.
+
+After building the MXNet library, you may install language bindings.
+
+<hr>
+
+
+## Installing Language Packages for MXNet
+
+After you have installed the MXNet core library. You may install MXNet interface packages for the programming language of your choice:
+- [Python](#install-mxnet-for-python)
+- [C++](#install-the-mxnet-package-for-c&plus;&plus;)
+- [Clojure](#install-the-mxnet-package-for-clojure)
+- [Julia](#install-the-mxnet-package-for-julia)
+- [Perl](#install-the-mxnet-package-for-perl)
+- [R](#install-the-mxnet-package-for-r)
+- [Scala](#install-the-mxnet-package-for-scala)
+- [Java](#install-the-mxnet-package-for-java)
+
+<hr>
+
+### Install MXNet for Python
+
+To install the MXNet Python binding navigate to the root of the MXNet folder then run the following:
+
+```bash
+$ cd python
+$ pip install -e .
+```
+
+Note that the `-e` flag is optional. It is equivalent to `--editable` and means that if you edit the source files, these changes will be reflected in the package installed.
+
+#### Optional Python Packages
+
+You may optionally install ```graphviz``` library that is used for visualizing network graphs you build on MXNet. You may also install [Jupyter Notebook](http://jupyter.readthedocs.io/) which is used for running MXNet tutorials and examples.
+
+```bash
+sudo pip install graphviz==0.8.4 \
+                 jupyter
+```
+<hr>
+
+
+### Install the MXNet Package for C++
+
+Refer to the [C++ Package setup guide](c_plus_plus).
+<hr>
+
+
+### Install the MXNet Package for Clojure
+
+Refer to the [Clojure setup guide](https://github.com/apache/incubator-mxnet/tree/master/contrib/clojure-package).
+<hr>
+
+
+### Install the MXNet Package for Julia
+
+#### Install Julia
+The package available through `apt-get` is old and not compatible with the latest version of MXNet.
+Fetch the latest version (1.0.3 at the time of this writing).
+
+```bash
+wget -qO julia-10.tar.gz https://julialang-s3.julialang.org/bin/linux/x64/1.0/julia-1.0.3-linux-x86_64.tar.gz
+```
+
+Place the extracted files somewhere like a julia folder in your home dir.
+
+```bash
+mkdir ~/julia
+mv julia-10.tar.gz ~/julia
+cd ~/julia
+tar xvf julia-10.tar.gz
+```
+
+Test Julia.
+```bash
+cd julia-1.0.3/bin
+julia -e 'using InteractiveUtils; versioninfo()'
+```
+
+If you're still getting the old version, remove it.
+```bash
+sudo apt remove julia
+```
+
+Update your PATH to have Julia's new location. Add this to your `.zshrc`, `.bashrc`, `.profile` or `.bash_profile`.
+```bash
+export PATH=~/julia/julia-1.0.3/bin:$PATH
+```
+
+Validate your PATH.
+```bash
+echo $PATH
+```
+
+Validate Julia works and is the expected version.
+```bash
+julia -e 'using InteractiveUtils; versioninfo()'
+```
+
+#### Setup Your MXNet-Julia Environment
+
+**For each of the following environment variables, add the commands to your `.zshrc`, `.bashrc`, `.profile` or `.bash_profile` to make them persist.**
+
+Create a `julia-depot` folder and environment variable.
+```bash
+mkdir julia-depot
+export JULIA_DEPOT_PATH=$HOME/julia/julia-depot
+```
+
+To use the Julia binding with an existing `libmxnet` installation, set the `MXNET_HOME` environment variable to the MXNet source root. For example:
+```bash
+export MXNET_HOME=$HOME/incubator-mxnet
+```
+
+Now set the `LD_LIBRARY_PATH` environment variable to where `libmxnet.so` is found. If you can't find it, you might have skipped the building MXNet step. Go back and [build MXNet](#build-the-shared-library) first. For example:
+```bash
+export LD_LIBRARY_PATH=$HOME/incubator-mxnet/lib:$LD_LIBRARY_PATH
+```
+
+Verify the location of `libjemalloc.so` and set the `LD_PRELOAD` environment variable.
+```bash
+export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libjemalloc.so:$LD_PRELOAD
+```
+
+With all of these updates, here's an example of what you might want to have in your `.zshrc`, `.bashrc`, `.profile` or `.bash_profile`.
+
+```
+export PATH=$HOME/bin:$HOME/.local/bin:$HOME/julia/julia-1.0.3/bin:$PATH
+export JULIA_DEPOT_PATH=$HOME/julia/julia-depot
+export MXNET_HOME=$HOME/incubator-mxnet
+export LD_LIBRARY_PATH=$HOME/incubator-mxnet/lib:$LD_LIBRARY_PATH
+export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libjemalloc.so:$LD_PRELOAD
+```
+
+Install MXNet with Julia:
+
+```bash
+julia --color=yes --project=./ -e \
+	  'using Pkg; \
+	   Pkg.develop(PackageSpec(name="MXNet", path = joinpath(ENV["MXNET_HOME"], "julia")))'
+```
+
+For more details about installing and using MXNet with Julia, see the [MXNet Julia documentation](../api/julia/site/).
+<hr>
+
+
+### Install the MXNet Package for Perl
+
+Before you build MXNet for Perl from source code, you must complete [building the shared library](#build-the-shared-library). After you build the shared library, run the following command from the MXNet source root directory to build the MXNet Perl package:
+
+```bash
+    sudo apt-get install libmouse-perl pdl cpanminus swig libgraphviz-perl
+    cpanm -q -L "${HOME}/perl5" Function::Parameters Hash::Ordered PDL::CCS
+
+    MXNET_HOME=${PWD}
+    export LD_LIBRARY_PATH=${MXNET_HOME}/lib
+    export PERL5LIB=${HOME}/perl5/lib/perl5
+
+    cd ${MXNET_HOME}/perl-package/AI-MXNetCAPI/
+    perl Makefile.PL INSTALL_BASE=${HOME}/perl5
+    make install
+
+    cd ${MXNET_HOME}/perl-package/AI-NNVMCAPI/
+    perl Makefile.PL INSTALL_BASE=${HOME}/perl5
+    make install
+
+    cd ${MXNET_HOME}/perl-package/AI-MXNet/
+    perl Makefile.PL INSTALL_BASE=${HOME}/perl5
+    make install
+```
+<hr>
+
+
+### Install the MXNet Package for R
+
+Building *MXNet* from source is a 2 step process.
+1. Build the *MXNet* core shared library, `libmxnet.so`, from source.
+2. Build the R bindings.
+
+#### Quick MXNet-R Installation
+You can quickly build MXNet-R with the following two scripts found in the `/docs/install` folder:
+
+```bash
+git clone --recursive https://github.com/apache/incubator-mxnet.git mxnet
+cd mxnet/docs/install
+./install_mxnet_ubuntu_python.sh
+./install_mxnet_ubuntu_r.sh
+```
+
+Or you can go through a manual process described next.
+
+#### Manual MXNet-R Installation
+
+**Minimum Requirements**
+1. [GCC 4.8](https://gcc.gnu.org/gcc-4.8/) or later to compile C++ 11.
+2. [GNU Make](https://www.gnu.org/software/make/)
+
+<br/>
+
+**Build the MXNet core shared library**
+
+**Step 1** Install build tools and git.
+```bash
+$ sudo apt-get update
+$ sudo apt-get install -y build-essential git
+```
+
+**Step 2** Install OpenBLAS.
+
+*MXNet* uses [BLAS](https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) and [LAPACK](https://en.wikipedia.org/wiki/LAPACK) libraries for accelerated numerical computations on CPU machine. There are several flavors of BLAS/LAPACK libraries - [OpenBLAS](http://www.openblas.net/), [ATLAS](http://math-atlas.sourceforge.net/) and [MKL](https://software.intel.com/en-us/intel-mkl). In this step we install OpenBLAS. You can choose to install ATLAS or MKL.
+```bash
+$ sudo apt-get install -y libopenblas-dev liblapack-dev
+```
+
+**Step 3** Install OpenCV.
+
+*MXNet* uses [OpenCV](http://opencv.org/) for efficient image loading and augmentation operations.
+```bash
+$ sudo apt-get install -y libopencv-dev
+```
+
+**Step 4** Download MXNet sources and build MXNet core shared library. You can clone the repository as described in the following code block, or you may try the [download links](download) for your desired MXNet version.
+
+```bash
+$ git clone --recursive https://github.com/apache/incubator-mxnet
+$ cd incubator-mxnet
+$ echo "USE_OPENCV = 1" >> ./config.mk
+$ echo "USE_BLAS = openblas" >> ./config.mk
+$ make -j $(nproc)
+```
+
+*Note* - USE_OPENCV and USE_BLAS are make file flags to set compilation options to use OpenCV and BLAS library. You can explore and use more compilation options in `make/config.mk`.
+
+<br/>
+
+**Step 5** Make and install the MXNet-R bindings.
+
+```bash
+$ make rpkg
+```
+#### Verify MXNet-R Installation
+
+You can verify your MXNet-R installation as follows:
+
+```bash
+sudo -i R
+```
+
+At the R prompt enter the following:
+
+```r
+library(mxnet)
+a <- mx.nd.ones(c(2,3), ctx = mx.cpu())
+b <- a * 2 + 1
+b
+```
+
+You should see the following output:
+
+```
+     [,1] [,2] [,3]
+[1,]    3    3    3
+[2,]    3    3    3
+> quit()
+```
+<hr>
+
+
+### Install the MXNet Package for Scala
+
+To use the MXNet-Scala package, you can acquire the Maven package as a dependency.
+
+Further information is in the [MXNet-Scala Setup Instructions](scala_setup).
+
+If you use IntelliJ or a similar IDE, you may want to follow the [MXNet-Scala on IntelliJ tutorial](../api/scala/docs/tutorials/mxnet_scala_on_intellij) instead.
+<hr>
+
+### Install the MXNet Package for Java
+
+To use the MXNet-Java package, you can acquire the Maven package as a dependency.
+
+Further information is in the [MXNet-Java Setup Instructions](java_setup).
+
+If you use IntelliJ or a similar IDE, you may want to follow the [MXNet-Java on IntelliJ tutorial](../api/java/docs/tutorials/mxnet_java_on_intellij) instead.
+<hr>
+
+## Contributions
+
+You are more than welcome to contribute easy installation scripts for other operating systems and programming languages. See the [community contributions page](../community/contribute) for further information.
+
+## Next Steps
+
+* [Tutorials](../api/python/docs/tutorials)
+* [How To](../api)
+* [Architecture](../api)
+
+
+<link rel="stylesheet" href="https://use.fontawesome.com/releases/v5.1.0/css/all.css" integrity="sha384-lKuwvrZot6UHsBSfcMvOkWwlCMgc0TaWr+30HWe3a4ltaBwTZhyTEggF5tJv8tbt" crossorigin="anonymous">
diff --git a/docs/static_site/src/pages/get_started/validate_mxnet.md b/docs/static_site/src/pages/get_started/validate_mxnet.md
new file mode 100644
index 000000000000..8cdb05170903
--- /dev/null
+++ b/docs/static_site/src/pages/get_started/validate_mxnet.md
@@ -0,0 +1,226 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page
+title: Validate MXNet
+action: Get Started
+action_url: /get_started
+permalink: /get_started/validate_mxnet
+---
+
+# Validate Your MXNet Installation
+
+- [Python](#python)
+- [Python with GPU](#python-with-gpu)
+- [Verify GPU training](#verify-gpu-training)
+- [Virtualenv](#virtualenv)
+- [Docker with CPU](#docker-with-cpu)
+- [Docker with GPU](#docker-with-gpu)
+- [Cloud](#cloud)
+- [C++](#alternative-language-bindings)
+- [Clojure](#clojure)
+- [Julia](#julia)
+- [Perl](#perl)
+- [R](#r)
+- [Scala](#scala)
+
+
+## Python
+
+Start the python terminal.
+
+```bash
+$ python
+```
+
+Run a short *MXNet* python program to create a 2X3 matrix of ones, multiply each element in the matrix by 2 followed by adding 1. We expect the output to be a 2X3 matrix with all elements being 3.
+
+```python
+>>> import mxnet as mx
+>>> a = mx.nd.ones((2, 3))
+>>> b = a * 2 + 1
+>>> b.asnumpy()
+array([[ 3.,  3.,  3.],
+       [ 3.,  3.,  3.]], dtype=float32)
+```
+
+
+## Python with GPU
+
+This is similar to the previous example, but this time we use *mx.gpu()*, to set *MXNet* context to be GPUs.
+
+```python
+>>> import mxnet as mx
+>>> a = mx.nd.ones((2, 3), mx.gpu())
+>>> b = a * 2 + 1
+>>> b.asnumpy()
+array([[ 3.,  3.,  3.],
+       [ 3.,  3.,  3.]], dtype=float32)
+```
+
+
+## Verify GPU Training
+
+From the MXNet root directory run: `python example/image-classification/train_mnist.py --network lenet --gpus 0` to test GPU training.
+
+
+## Virtualenv
+
+Activate the virtualenv environment created for *MXNet*.
+
+```bash
+$ source ~/mxnet/bin/activate
+```
+
+After activating the environment, you should see the prompt as below.
+
+```bash
+(mxnet)$
+```
+
+Start the python terminal.
+
+```bash
+$ python
+```
+
+Run the previous Python example.
+
+
+## Docker with CPU
+
+Launch a Docker container with `mxnet/python` image and run example *MXNet* python program on the terminal.
+
+```bash
+$ docker run -it mxnet/python bash # Use sudo if you skip Step 2 in the installation instruction
+
+# Start a python terminal
+root@4919c4f58cac:/# python
+```
+
+Run the previous Python example.
+
+
+## Docker with GPU
+
+Launch a NVIDIA Docker container with `mxnet/python:gpu` image and run example *MXNet* python program on the terminal.
+
+```bash
+$ nvidia-docker run -it mxnet/python:gpu bash # Use sudo if you skip Step 2 in the installation instruction
+
+# Start a python terminal
+root@4919c4f58cac:/# python
+```
+
+Run the previous Python example and run the previous GPU examples.
+
+
+## Cloud
+
+Login to the cloud instance you launched, with pre-installed *MXNet*, following the guide by corresponding cloud provider.
+
+Start the python terminal.
+
+```bash
+$ python
+```
+
+Run the previous Python example, and for GPU instances run the previous GPU example.
+
+
+## Alternative Language Bindings
+
+### C++
+
+Please contribute an example!
+
+
+### Clojure
+
+Please contribute an example!
+
+
+### Julia
+
+Please contribute an example!
+
+
+### Perl
+
+Start the pdl2 terminal.
+
+```bash
+$ pdl2
+```
+
+Run a short *MXNet* Perl program to create a 2X3 matrix of ones, multiply each element in the matrix by 2 followed by adding 1. We expect the output to be a 2X3 matrix with all elements being 3.
+
+```perl
+pdl> use AI::MXNet qw(mx)
+pdl> $a = mx->nd->ones([2, 3])
+pdl> $b = $a * 2 + 1
+pdl> print $b->aspdl
+
+[
+ [3 3 3]
+ [3 3 3]
+]
+```
+
+### R
+
+Run a short *MXNet* R program to create a 2X3 matrix of ones, multiply each element in the matrix by 2 followed by adding 1. We expect the output to be a 2X3 matrix with all elements being 3.
+
+```r
+library(mxnet)
+a <- mx.nd.ones(c(2,3), ctx = mx.cpu())
+b <- a * 2 + 1
+b
+```
+
+You should see the following output:
+
+```r
+[,1] [,2] [,3]
+[1,]    3    3    3
+[2,]    3    3    3
+```
+
+
+#### R with GPU
+
+This is similar to the previous example, but this time we use *mx.gpu()*, to set *MXNet* context to be GPUs.
+
+```r
+library(mxnet)
+a <- mx.nd.ones(c(2,3), ctx = mx.gpu())
+b <- a * 2 + 1
+b
+```
+
+You should see the following output:
+
+```r
+[,1] [,2] [,3]
+[1,]    3    3    3
+[2,]    3    3    3
+```
+
+
+### Scala
+
+Run the <a href="https://github.com/apache/incubator-mxnet/tree/master/scala-package/mxnet-demo">MXNet-Scala demo project</a> to validate your Maven package installation.
diff --git a/docs/static_site/src/pages/get_started/windows_setup.md b/docs/static_site/src/pages/get_started/windows_setup.md
new file mode 100644
index 000000000000..a7a3aabfa75b
--- /dev/null
+++ b/docs/static_site/src/pages/get_started/windows_setup.md
@@ -0,0 +1,490 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+---
+layout: page
+title: Windows Setup
+action: Get Started
+action_url: /get_started
+permalink: /get_started/windows_setup
+---
+
+# Installing MXNet on Windows
+
+The following describes how to install with pip for computers with CPUs, Intel CPUs, and NVIDIA GPUs. Further along in the document you can learn how to build MXNet from source on Windows, or how to install packages that support different language APIs to MXNet.
+
+- [Prerequisites](#prerequisites)
+- [Install MXNet with Python](#install-mxnet-with-python)
+    - [Install with CPUs](#install-with-cpus)
+    - [Install with Intel CPUs](#install-with-intel-cpus)
+    - [Install with GPUs](#install-with-gpus)
+    - [Notes on the Python Packages](#notes-on-the-python-packages)
+- [Build from Source](#build-from-source)
+- Install MXNet with a Programming Language API
+    - [Python](#install-the-mxnet-package-for-python)
+    - [R](#install-the-mxnet-package-for-r)
+    - [Julia](#install-the-mxnet-package-for-julia)
+
+
+## Prerequisites
+
+### Minimum System Requirements
+
+* Windows 7<sup><a href="#fn1" id="ref1">1</a></sup>, 10, Server 2012 R2, or Server 2016
+* Visual Studio 2015 or 2017 (any type)
+* Python 2.7 or 3.6
+* pip
+
+<sup id="fn1">1. There are [known issues](https://github.com/apache/incubator-mxnet/issues?utf8=%E2%9C%93&q=is%3Aissue+windows7+label%3AWindows+) with Windows 7. <a href="#ref1" title="Return to source text.">↩</a></sup>
+
+### Recommended System Requirements
+
+* Windows 10, Server 2012 R2, or Server 2016
+* Visual Studio 2017 (any type)
+* At least one [NVIDIA CUDA-enabled GPU](https://developer.nvidia.com/cuda-gpus)
+* MKL-enabled CPU: Intel® Xeon® processor, Intel® Core™ processor family, Intel Atom® processor, or Intel® Xeon Phi™ processor
+* Python 2.7 or 3.6
+* pip
+
+
+## Install MXNet with Python
+
+The easiest way to install MXNet on Windows is by using a [Python pip package](https://pip.pypa.io/en/stable/installing/).
+
+**Note**: Windows pip packages typically release a few days after a new version MXNet is released. Make sure you verify which version gets installed.
+
+### Install with CPUs
+
+Install MXNet with CPU support with Python:
+
+```bash
+pip install mxnet
+```
+
+Now [validate your MXNet installation with Python](validate_mxnet.md).
+
+### Install with Intel CPUs
+
+MXNet has experimental support for Intel [MKL](https://software.intel.com/en-us/mkl) and [MKL-DNN](https://github.com/intel/mkl-dnn). When using supported Intel hardware, inference and training can be vastly faster when using MXNet with [MKL](https://software.intel.com/en-us/mkl) or [MKL-DNN](https://github.com/intel/mkl-dnn).
+
+The following steps will setup MXNet with MKL. MKL-DNN can be enabled only when building from source.
+1. Download and install [Intel MKL](https://software.intel.com/en-us/mkl/choose-download/windows) (registration required).
+1. Install MXNet with MKL support with Python:
+
+```bash
+pip install mxnet-mkl
+```
+
+Now [validate your MXNet installation with Python](validate_mxnet.md).
+
+### Install with NVIDIA GPUs
+
+When using supported NVIDIA GPU hardware, inference and training can be vastly faster with [NVIDIA CUDA](https://developer.nvidia.com/cuda-toolkit) and [cuDNN](https://developer.nvidia.com/cudnn). You have two options for installing MXNet with CUDA support with a Python package.
+- [Install with CUDA support](#install-with-cuda-support)
+- [Install with CUDA and MKL support](#install-with-cuda-and-mkl-support)
+
+#### Install with CUDA Support
+
+The following steps will setup MXNet with CUDA. cuDNN can be enabled only when building from source.
+1. Install [Microsoft Visual Studio 2017](https://www.visualstudio.com/downloads/) or [Microsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/).
+1. Download and install [NVIDIA CUDA](https://developer.nvidia.com/cuda-downloads?target_os=Windows&target_arch=x86_64&target_version=10&target_type=exelocal). CUDA versions 9.2 or 9.0 are recommended. Some [issues with CUDA 9.1](https://github.com/apache/incubator-mxnet/labels/CUDA) have been identified in the past.
+1. Download and install [NVIDIA_CUDA_DNN](https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#install-windows)
+1. Install MXNet with CUDA support with pip:
+
+```bash
+pip install mxnet-cu92
+```
+
+Once you have installed a version of MXNet, [validate your MXNet installation with Python](validate_mxnet.md).
+
+#### Install with CUDA and MKL Support
+
+You can also use a combination of CPU/GPU enhancements provided by Intel and NVIDIA.
+
+The following steps will setup MXNet with CUDA and MKL.
+1. Install [Microsoft Visual Studio 2017](https://www.visualstudio.com/downloads/) or [Microsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/).
+1. Download and install [Intel MKL](https://software.intel.com/en-us/mkl/choose-download/windows) (registration required).
+1. Download and install [NVIDIA CUDA](https://developer.nvidia.com/cuda-downloads?target_os=Windows&target_arch=x86_64&target_version=10&target_type=exelocal).
+1. Download and install [NVIDIA_CUDA_DNN](https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#install-windows)
+1. Install MXNet with MKL support with pip:
+
+```bash
+pip install mxnet-cu92mkl
+```
+
+Once you have installed a version of MXNet, [validate your MXNet installation with Python](validate_mxnet.md).
+
+### Notes on the Python Packages
+To get further enhancements for deep neural networks, you may want to enable MKL-DNN and/or cuDNN. Each of these require you to [build from source](#build-from-source) and to enable the build flags for each.
+
+Check the chart below for other options or refer to [PyPI for other MXNet pip packages](https://pypi.org/project/mxnet/).
+
+![pip packages](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/install/pip-packages.png)
+
+
+## Build from Source
+
+**IMPORTANT: It is recommended that you review the [build from source guide](build_from_source.md) first.** It describes many of the build options that come with MXNet in more detail. You may decide to install additional dependencies and modify your build flags after reviewing this material.
+
+We provide two primary options to build and install MXNet yourself using [Microsoft Visual Studio 2017](https://www.visualstudio.com/downloads/) or [Microsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/).
+
+**NOTE:** Visual Studio 2017's compiler is `vc15`. This is not to be confused with Visual Studio 2015's compiler, `vc14`.
+
+You also have the option to install MXNet with MKL or MKL-DNN. In this case it is recommended that you refer to the [MKLDNN_README](https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/mkldnn/MKLDNN_README.md).
+
+**Option 1: Build with Microsoft Visual Studio 2017 (VS2017)**
+
+To build and install MXNet yourself using [VS2017](https://www.visualstudio.com/downloads/), you need the following dependencies. You may try a newer version of a particular dependency, but please open a pull request or [issue](https://github.com/apache/incubator-mxnet/issues/new) to update this guide if a newer version is validated.
+
+1. Install or update VS2017.
+    - If [VS2017](https://www.visualstudio.com/downloads/) is not already installed, download and install it. You can download and install the free community edition.
+    - When prompted about installing Git, go ahead and install it.
+    - If VS2017 is already installed you will want to update it. Proceed to the next step to modify your installation. You will be given the opportunity to update VS2017 as well
+1. Follow the [instructions for opening the Visual Studio Installer](https://docs.microsoft.com/en-us/visualstudio/install/modify-visual-studio) to modify `Individual components`.
+1. Once in the Visual Studio Installer application, update as needed, then look for and check `VC++ 2017 version 15.4 v14.11 toolset`, and click `Modify`.
+1. Change the version of the Visual studio 2017 to v14.11 using the following command (by default the VS2017 is installed in the following path):
+```
+"C:\Program Files (x86)\Microsoft Visual Studio\2017\Community\VC\Auxiliary\Build\vcvars64.bat" -vcvars_ver=14.11
+```
+1. Download and install [CMake](https://cmake.org/download) if it is not already installed. [CMake v3.12.2](https://cmake.org/files/v3.12/cmake-3.12.2-win64-x64.msi) has been tested with MXNet.
+1. Download and run the  [OpenCV](https://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.4.1/opencv-3.4.1-vc14_vc15.exe/download) package. There are more recent versions of OpenCV, so please create an issue/PR to update this info if you validate one of these later versions.
+1. This will unzip several files. You can place them in another directory if you wish. We will use `C:\utils`(```mkdir C:\utils```) as our default path.
+1. Set the environment variable `OpenCV_DIR` to point to the OpenCV build directory that you just unzipped. Start ```cmd``` and type `set OpenCV_DIR=C:\utils\opencv\build`.
+1. If you don’t have the Intel Math Kernel Library (MKL) installed, you can install it and follow the [MKLDNN_README](https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/mkldnn/MKLDNN_README.md) from here, or you can use OpenBLAS. These instructions will assume you're using OpenBLAS.
+1. Download the [OpenBlas](https://sourceforge.net/projects/openblas/files/v0.2.19/OpenBLAS-v0.2.19-Win64-int32.zip/download) package. Later versions of OpenBLAS are available, but you would need to build from source. v0.2.19 is the most recent version that ships with binaries. Contributions of more recent binaries would be appreciated.
+1. Unzip the file, rename it to ```OpenBLAS``` and put it under `C:\utils`. You can place the unzipped files and folders in another directory if you wish.
+1. Set the environment variable `OpenBLAS_HOME` to point to the OpenBLAS directory that contains the `include` and `lib` directories and type `set OpenBLAS_HOME=C:\utils\OpenBLAS` on the command prompt(```cmd```).
+1. Download and install [CUDA](https://developer.nvidia.com/cuda-downloads?target_os=Windows&target_arch=x86_64&target_version=10&target_type=exelocal). If you already had CUDA, then installed VS2017, you should reinstall CUDA now so that you get the CUDA toolkit components for VS2017 integration. Note that the latest CUDA version supported by MXNet is [9.2](https://developer.nvidia.com/cuda-92-download-archive). You might also want to find other CUDA verion on the [Legacy Releases](https://developer.nvidia.com/cuda-toolkit-archive).
+1. Download and install cuDNN. To get access to the download link, register as an NVIDIA community user. Then follow the [link](http://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#install-windows) to install the cuDNN and put those libraries into ```C:\cuda```.
+1. Download and install [git](https://git-for-windows.github.io/) if you haven't already.
+
+After you have installed all of the required dependencies, build the MXNet source code:
+
+1. Start ```cmd``` in windows.
+2. Download the MXNet source code from GitHub by using following command:
+```
+cd C:\
+git clone https://github.com/apache/incubator-mxnet.git --recursive
+```
+3. Verify that the `DCUDNN_INCLUDE` and `DCUDNN_LIBRARY` environment variables are pointing to the `include` folder and `cudnn.lib` file of your CUDA installed location, and `C:\incubator-mxnet` is the location of the source code you just cloned in the previous step.
+4. Create a build dir using the following command and go to the directory, for example:
+```
+mkdir C:\incubator-mxnet\build
+cd C:\incubator-mxnet\build
+```
+5. Compile the MXNet source code with `cmake` by using following command:
+```
+cmake -G "Visual Studio 15 2017 Win64" -T cuda=9.2,host=x64 -DUSE_CUDA=1 -DUSE_CUDNN=1 -DUSE_NVRTC=1 -DUSE_OPENCV=1 -DUSE_OPENMP=1 -DUSE_BLAS=open -DUSE_LAPACK=1 -DUSE_DIST_KVSTORE=0 -DCUDA_ARCH_LIST=Common -DCUDA_TOOLSET=9.2 -DCUDNN_INCLUDE=C:\cuda\include -DCUDNN_LIBRARY=C:\cuda\lib\x64\cudnn.lib "C:\incubator-mxnet"
+```
+* Make sure you set the environment variables correctly (OpenBLAS_HOME, OpenCV_DIR) and change the version of the Visual studio 2017 to v14.11 before enter above command.
+6. After the CMake successfully completed, compile the MXNet source code by using following command:
+```
+msbuild mxnet.sln /p:Configuration=Release;Platform=x64 /maxcpucount
+```
+
+
+**Option 2: Build with Visual Studio 2015**
+
+To build and install MXNet yourself using [Microsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/), you need the following dependencies. You may try a newer version of a particular dependency, but please open a pull request or [issue](https://github.com/apache/incubator-mxnet/issues/new) to update this guide if a newer version is validated.
+
+1. If [Microsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/) is not already installed, download and install it. You can download and install the free community edition. At least Update 3 of Microsoft Visual Studio 2015 is required to build MXNet from source. Upgrade via it's ```Tools -> Extensions and Updates... | Product Updates``` menu.
+2. Download and install [CMake](https://cmake.org/) if it is not already installed.
+3. Download and install [OpenCV](http://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.0.0/opencv-3.0.0.exe/download).
+4. Unzip the OpenCV package.
+5. Set the environment variable ```OpenCV_DIR``` to point to the ```OpenCV build directory``` (```C:\opencv\build\x64\vc14``` for example). Also, you need to add the OpenCV bin directory (```C:\opencv\build\x64\vc14\bin``` for example) to the ``PATH`` variable.
+6. If you don't have the Intel Math Kernel Library (MKL) installed, download and install [OpenBlas](http://sourceforge.net/projects/openblas/files/v0.2.14/).
+7. Set the environment variable ```OpenBLAS_HOME``` to point to the ```OpenBLAS``` directory that contains the ```include``` and ```lib``` directories. Typically, you can find the directory in ```C:\Program files (x86)\OpenBLAS\```.
+8. Download and install [CUDA](https://developer.nvidia.com/cuda-downloads?target_os=Windows&target_arch=x86_64) and [cuDNN](https://developer.nvidia.com/cudnn). To get access to the download link, register as an NVIDIA community user.
+9. Set the environment variable ```CUDACXX``` to point to the ```CUDA Compiler```(```C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.1\bin\nvcc.exe``` for example).
+10. Set the environment variable ```CUDNN_ROOT``` to point to the ```cuDNN``` directory that contains the ```include```,  ```lib``` and ```bin``` directories (```C:\Downloads\cudnn-9.1-windows7-x64-v7\cuda``` for example).
+
+After you have installed all of the required dependencies, build the MXNet source code:
+
+1. Download the MXNet source code from [GitHub](https://github.com/apache/incubator-mxnet) (make sure you also download third parties submodules e.g. ```git clone --recurse-submodules```).
+2. Use [CMake](https://cmake.org/) to create a Visual Studio solution in ```./build```.
+3. In Visual Studio, open the solution file,```.sln```, and compile it.
+These commands produce a library called ```mxnet.dll``` in the ```./build/Release/``` or ```./build/Debug``` folder.
+
+&nbsp;
+Next, we install ```graphviz``` library that we use for visualizing network graphs you build on MXNet. We will also install [Jupyter Notebook](http://jupyter.readthedocs.io/)  used for running MXNet tutorials and examples.
+- Install ```graphviz``` by downloading MSI installer from [Graphviz Download Page](https://graphviz.gitlab.io/_pages/Download/Download_windows.html).
+**Note** Make sure to add graphviz executable path to PATH environment variable. Refer [here for more details](http://stackoverflow.com/questions/35064304/runtimeerror-make-sure-the-graphviz-executables-are-on-your-systems-path-aft)
+- Install ```Jupyter``` by installing [Anaconda for Python 2.7](https://www.anaconda.com/download/)
+**Note** Do not install Anaconda for Python 3.5. MXNet has a few compatibility issues with Python 3.5.
+
+We have installed MXNet core library. Next, we will install MXNet interface package for programming language of your choice:
+- [Python](#install-the-mxnet-package-for-python)
+- [R](#install-mxnet-package-for-r)
+- [Julia](#install-the-mxnet-package-for-julia)
+- **Scala** is not yet available for Windows
+
+## Install the MXNet Package for Python
+
+These steps are required after building from source. If you already installed MXNet by using pip, you do not need to do these steps to use MXNet with Python.
+
+1. Install ```Python``` using windows installer available [here](https://www.python.org/downloads/release/python-2712/).
+2. Install ```Numpy``` using windows installer available [here](https://scipy.org/index.html).
+3. Start ```cmd``` and create a folder named ```common```(```mkdir C:\common```)
+4. Download the [mingw64_dll.zip](https://sourceforge.net/projects/openblas/files/v0.2.12/mingw64_dll.zip/download), unzip and copy three libraries (.dll files) that openblas.dll depends on to ```C:\common```.
+5. Copy the required .dll file to ```C:\common``` and make sure following libraries (.dll files) in the folder.
+```
+libgcc_s_seh-1.dll (in mingw64_dll)
+libgfortran-3.dll (in mingw64_dll)
+libquadmath-0.dll (in mingw64_dll)
+libopenblas.dll (in OpenBlas folder you download)
+opencv_world341.dll (in OpenCV folder you download)
+```
+6. Add ```C:\common``` to Environment Variables.
+ * Type ```control sysdm.cpl``` on ```cmp```
+ * Select the **Advanced tab** and click **Environment Variables**
+ * Double click the **Path** and click **New**
+ * Add ```C:\common``` and click OK
+7. Use setup.py to install the package.
+```bash
+    # Assuming you are in root mxnet source code folder
+    cd python
+    python setup.py install
+```
+
+Done! We have installed MXNet with Python interface.
+
+You can continue with using MXNet-Python, or if you want to try a different language API for MXNet, keep reading. Otherwise, jump ahead to [next steps](#next-steps).
+
+## Install the MXNet Package for R
+MXNet for R is available for both CPUs and GPUs.
+
+### Installing MXNet-R on a Computer with a CPU Processor
+
+To install MXNet on a computer with a CPU processor, choose from two options:
+
+* Use the prebuilt binary package
+* Build the library from source code
+
+#### Installing MXNet-R with the Prebuilt Binary Package(CPU)
+For Windows users, MXNet provides prebuilt binary packages.
+You can install the package directly in the R console.
+
+Note: packages for 3.6.x are not yet available.
+Install 3.5.x of R from [CRAN](https://cran.r-project.org/bin/windows/base/old/).
+
+For CPU-only package:
+
+```r
+  cran <- getOption("repos")
+  cran["dmlc"] <- "https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/R/CRAN/"
+  options(repos = cran)
+  install.packages("mxnet")
+```
+
+#### Building MXNet-R from Source Code(CPU)
+1. Clone the MXNet github repo.
+```sh
+git clone --recursive https://github.com/apache/incubator-mxnet
+```
+The `--recursive` is to clone all the submodules used by MXNet. You will be editing the ```"/mxnet/R-package"``` folder.
+2. Download prebuilt GPU-enabled MXNet libraries for Windows from [Windows release](https://github.com/yajiedesign/mxnet/releases). You will need `mxnet_x64_vc14_cpu.7z` and `prebuildbase_win10_x64_vc14.7z` where X stands for your CUDA toolkit version
+3. Create a folder called ```R-package/inst/libs/x64```. MXNet supports only 64-bit operating systems, so you need the x64 folder.
+4. Copy the following shared libraries (.dll files) into the ```R-package/inst/libs/x64``` folder.
+```
+libgcc_s_seh-1.dll
+libgfortran-3.dll
+libmxnet.dll
+libmxnet.lib
+libopenblas.dll
+libquadmath-0.dll
+mxnet.dll
+unzip.exe
+unzip32.dll
+vcomp140.dll
+wget.exe
+```
+These dlls can be found in `prebuildbase_win10_x64_vc14/3rdparty`, `mxnet_x64_vc14_cpu/build`, `mxnet_x64_vc14_cpu/lib`.
+5. Copy the header files from `dmlc`, `mxnet`, `mxshadow` and `nnvm` from `mxnet_x64_vc14_cpu/include and mxnet_x64_vc14_cpu/nvnm/include` into `./R-package/inst/include`. It should look like
+```
+./R-package/inst
+└── include
+    ├── dmlc
+    ├── mxnet
+    ├── mshadow
+    └── nnvm
+```
+
+6. Make sure that R executable is added to your ```PATH``` in the environment variables. Running the ```where R``` command at the command prompt should return the location.
+7. Also make sure that Rtools is installed and the executable is added to your ```PATH``` in the environment variables.
+8. Temporary patch - im2rec currently results in crashes during the build. Remove the im2rec.h and im2rec.cc files in R-package/src/ from cloned repository and comment out the two im2rec lines in [R-package/src/mxnet.cc](https://github.com/apache/incubator-mxnet/blob/master/R-package/src/mxnet.cc) as shown below.
+```
+#include "./kvstore.h"
+#include "./export.h"
+//#include "./im2rec.h"
+......
+......
+  DataIterCreateFunction::InitRcppModule();
+  KVStore::InitRcppModule();
+  Exporter::InitRcppModule();
+//IM2REC::InitRcppModule();
+}
+```
+
+9. Now open the Windows CMD with admin rights and change the directory to the `mxnet` folder(cloned repository). Then use the following commands to build R package:
+
+```bash
+echo import(Rcpp) > R-package\NAMESPACE
+echo import(methods) >> R-package\NAMESPACE
+Rscript -e "install.packages('devtools', repos = 'https://cloud.r-project.org')"
+cd R-package
+Rscript -e "library(devtools); library(methods); options(repos=c(CRAN='https://cloud.r-project.org')); install_deps(dependencies = TRUE)"
+cd ..
+
+R CMD INSTALL --no-multiarch R-package
+
+Rscript -e "require(mxnet); mxnet:::mxnet.export('R-package')"
+rm R-package/NAMESPACE
+Rscript -e "require(devtools); install_version('roxygen2', version = '5.0.1', repos = 'https://cloud.r-project.org/', quiet = TRUE)"
+Rscript -e "require(roxygen2); roxygen2::roxygenise('R-package')"
+
+R CMD INSTALL --build --no-multiarch R-package
+```
+
+### Installing MXNet-R on a Computer with a GPU Processor
+To install MXNet on a computer with a GPU processor, choose from two options:
+
+* Use the prebuilt binary package
+* Build the library from source code
+
+However, a few dependencies remain for both options.  You will need the following:
+* Install [Nvidia-drivers](http://www.nvidia.com/Download/index.aspx?lang=en-us) if not installed. Latest driver based on your system configuration is recommended.
+
+* Install [Microsoft Visual Studio](https://visualstudio.microsoft.com/downloads/) (VS2015 or VS2017 is required by CUDA)
+
+* Install  [NVidia CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit)(cu92 is recommended though we support cu80, cu90, cu91 and cu92)
+
+* Download and install [CuDNN](https://developer.nvidia.com/cudnn) (to provide a Deep Neural Network library). Latest version recommended.
+
+Note: A pre-requisite to above softwares is [Nvidia-drivers](http://www.nvidia.com/Download/index.aspx?lang=en-us) which we assume is installed.
+
+#### Installing MXNet-R with the Prebuilt Binary Package(GPU)
+For Windows users, MXNet provides prebuilt binary packages.
+You can install the package directly in the R console after you have the above software installed.
+
+Note: packages for 3.6.x are not yet available.
+Install 3.5.x of R from [CRAN](https://cran.r-project.org/bin/windows/base/old/).
+
+For GPU package:
+
+```r
+  cran <- getOption("repos")
+  cran["dmlc"] <- "https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/R/CRAN/GPU/cu92"
+  options(repos = cran)
+  install.packages("mxnet")
+```
+Change cu92 to cu80, cu90 or cu91 based on your CUDA toolkit version. Currently, MXNet supports these versions of CUDA.
+
+#### Building MXNet-R from Source Code(GPU)
+After you have installed above software, continue with the following steps to build MXNet-R:
+1. Clone the MXNet github repo.
+```sh
+git clone --recursive https://github.com/apache/incubator-mxnet
+```
+The `--recursive` is to clone all the submodules used by MXNet. You will be editing the ```"/mxnet/R-package"``` folder.
+2. Download prebuilt GPU-enabled MXNet libraries for Windows from https://github.com/yajiedesign/mxnet/releases. You will need `mxnet_x64_vc14_gpu_cuX.7z` and `prebuildbase_win10_x64_vc14.7z` where X stands for your CUDA toolkit version
+3. Create a folder called ```R-package/inst/libs/x64```. MXNet supports only 64-bit operating systems, so you need the x64 folder.
+4. Copy the following shared libraries (.dll files) into the ```R-package/inst/libs/x64``` folder:
+```
+libgcc_s_seh-1.dll
+libgfortran-3.dll
+libmxnet.dll
+libmxnet.lib
+libopenblas.dll
+libquadmath-0.dll
+mxnet.dll
+unzip.exe
+unzip32.dll
+vcomp140.dll
+wget.exe
+```
+These dlls can be found in `prebuildbase_win10_x64_vc14/3rdparty`, `mxnet_x64_vc14_gpu_cuX/build`, `mxnet_x64_vc14_gpu_cuX/lib`.
+5. Copy the header files from `dmlc`, `mxnet`, `mxshadow` and `nnvm` from `mxnet_x64_vc14_gpuX/include` and `mxnet_x64_vc14_gpuX/nvnm/include` into `./R-package/inst/include`. It should look like:
+```
+./R-package/inst
+└── include
+    ├── dmlc
+    ├── mxnet
+    ├── mshadow
+    └── nnvm
+```
+6. Make sure that R executable is added to your ```PATH``` in the environment variables. Running the ```where R``` command at the command prompt should return the location.
+7. Also make sure that Rtools is installed and the executable is added to your ```PATH``` in the environment variables.
+8. Temporary patch - im2rec currently results in crashes during the build. Remove the im2rec.h and im2rec.cc files in R-package/src/ from cloned repository and comment out the two im2rec lines in [R-package/src/mxnet.cc](https://github.com/apache/incubator-mxnet/blob/master/R-package/src/mxnet.cc) as shown below.
+```bash
+#include "./kvstore.h"
+#include "./export.h"
+//#include "./im2rec.h"
+......
+......
+  DataIterCreateFunction::InitRcppModule();
+  KVStore::InitRcppModule();
+  Exporter::InitRcppModule();
+//IM2REC::InitRcppModule();
+}
+```
+9. Now open the Windows CMD with admin rights and change the directory to the `mxnet` folder(cloned repository). Then use the following commands
+to build R package:
+
+```bat
+echo import(Rcpp) > R-package\NAMESPACE
+echo import(methods) >> R-package\NAMESPACE
+Rscript -e "install.packages('devtools', repos = 'https://cloud.r-project.org')"
+cd R-package
+Rscript -e "library(devtools); library(methods); options(repos=c(CRAN='https://cloud.r-project.org')); install_deps(dependencies = TRUE)"
+cd ..
+
+R CMD INSTALL --no-multiarch R-package
+
+Rscript -e "require(mxnet); mxnet:::mxnet.export('R-package')"
+rm R-package/NAMESPACE
+Rscript -e "require(devtools); install_version('roxygen2', version = '5.0.1', repos = 'https://cloud.r-project.org/', quiet = TRUE)"
+Rscript -e "require(roxygen2); roxygen2::roxygenise('R-package')"
+
+R CMD INSTALL --build --no-multiarch R-package
+```
+
+**Note:** To maximize its portability, the MXNet library is built with the Rcpp end. Computers running Windows need [MSVC](https://en.wikipedia.org/wiki/Visual_C%2B%2B) (Microsoft Visual C++) to handle CUDA toolchain compatibilities.
+
+## Install the MXNet Package for Julia
+The MXNet package for Julia is hosted in a separate repository, MXNet.jl, which is available on [GitHub](https://github.com/dmlc/MXNet.jl). To use Julia binding it with an existing libmxnet installation, set the ```MXNET_HOME``` environment variable by running the following command:
+
+```bash
+export MXNET_HOME=/<path to>/libmxnet
+```
+
+The path to the existing libmxnet installation should be the root directory of libmxnet. In other words, you should be able to find the ```libmxnet.so``` file at ```$MXNET_HOME/lib```. For example, if the root directory of libmxnet is ```~```, you would run the following command:
+
+```bash
+export MXNET_HOME=/~/libmxnet
+```
+
+You might want to add this command to your ```~/.bashrc``` file. If you do, you can install the Julia package in the Julia console using the following command:
+
+```julia
+Pkg.add("MXNet")
+```
+
+For more details about installing and using MXNet with Julia, see the [MXNet Julia documentation](/api/julia/site/).
+
+
+## Installing the MXNet Package for Scala
+
+MXNet-Scala is not yet available for Windows.
diff --git a/tests/nightly/apache_rat_license_check/rat-excludes b/tests/nightly/apache_rat_license_check/rat-excludes
index c08a2816045f..4297fa6bdec4 100755
--- a/tests/nightly/apache_rat_license_check/rat-excludes
+++ b/tests/nightly/apache_rat_license_check/rat-excludes
@@ -1,8 +1,34 @@
+# Generated files
+.*html
+.buildinfo
+Gemfile.lock
+Manifest.toml
+searchindex.js
+_build/*
+_static/*
+_api/*
+build/*
+latex/*
+target/*
+site/*
+xml/*
+
+# Separate licenses, refer to LICENSE in each folder or header
+
+# Sphinx themes
+themes/*
+
+# Jekyll & other licenses in static_site/*
+clipboard.js
+
+# Includes
+_includes/*
+
+# Other
 \..*
 .*css
 \\.*
 .*ipynb
-.*html
 .*json
 .*txt
 3rdparty/*
@@ -15,7 +41,6 @@ trunk/*
 .*cfg
 .*config
 __init__.py
-build/*
 .*\\.t
 MANIFEST
 Changes
diff --git a/tools/license_header.py b/tools/license_header.py
index c8add72288e7..2e3a03451369 100755
--- a/tools/license_header.py
+++ b/tools/license_header.py
@@ -71,6 +71,12 @@
                # 3rdparty headerfiles under different licenses
                'include/mkldnn',
 
+               # Docs Sphinx themes under different licenses
+               'docs/python_docs/themes',
+
+                # Docs Jekyll website under different licenses
+               'docs/static_site',
+  
                # Code shared with project by author - see file for details
                'src/operator/special_functions-inl.h',
 
@@ -91,6 +97,7 @@
                'docs/_static/searchtools_custom.js',
                'docs/_static/js/clipboard.js',
                'docs/_static/js/clipboard.min.js',
+               'docs/static_site/src/assets/js/clipboard.js',
 
                # Licensed under 2-Clause BSD in header
                'example/ssd/dataset/pycocotools/coco.py',

From e98dbe776201424cff93f87e67ff3bddb87d45e5 Mon Sep 17 00:00:00 2001
From: Doron Singer <48903991+doronsinger@users.noreply.github.com>
Date: Fri, 6 Sep 2019 17:53:04 +0300
Subject: [PATCH 290/813] Speed up group executor (#16069)

* Speed up group executor

Current implementation is O(n^2), this implementation is O(n)

* Speed up group executor

Current implementation is O(n^2), this implementation is O(n)

* CI
---
 python/mxnet/module/executor_group.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/python/mxnet/module/executor_group.py b/python/mxnet/module/executor_group.py
index 637acce317cc..d47665d6d509 100755
--- a/python/mxnet/module/executor_group.py
+++ b/python/mxnet/module/executor_group.py
@@ -273,9 +273,9 @@ def __init__(self, symbol, contexts, workload, data_shapes, label_shapes, param_
         self.data_layouts = None
         self.label_layouts = None
         self.output_names = self.symbol.list_outputs()
-        self.output_layouts = [DataDesc.get_batch_axis(self.symbol[name].attr('__layout__'))
-                               for name in self.output_names]
-        self.num_outputs = len(self.symbol.list_outputs())
+        self.num_outputs = len(self.output_names)
+        self.output_layouts = [DataDesc.get_batch_axis(self.symbol[index].attr('__layout__'))
+                               for index in range(self.num_outputs)]
 
         self.bind_exec(data_shapes, label_shapes, shared_group)
 

From d85a2d08ad4419a15b521d4653cdd30957257d76 Mon Sep 17 00:00:00 2001
From: Haibin Lin <linhaibin.eric@gmail.com>
Date: Fri, 6 Sep 2019 10:39:01 -0700
Subject: [PATCH 291/813] [DOC] Fix doc for nn.Embedding, nn.Dense and
 nd.Embedding (#15869)

* Update basic_layers.py

* Update indexing_op.cc
---
 python/mxnet/gluon/nn/basic_layers.py | 17 ++++++++++-------
 src/operator/tensor/indexing_op.cc    |  5 +++--
 2 files changed, 13 insertions(+), 9 deletions(-)

diff --git a/python/mxnet/gluon/nn/basic_layers.py b/python/mxnet/gluon/nn/basic_layers.py
index fb0b62e8a74f..61f9ea07fbec 100644
--- a/python/mxnet/gluon/nn/basic_layers.py
+++ b/python/mxnet/gluon/nn/basic_layers.py
@@ -151,8 +151,9 @@ class Dense(HybridBlock):
     created by the layer, and `bias` is a bias vector created by the layer
     (only applicable if `use_bias` is `True`).
 
-    Note: the input must be a tensor with rank 2. Use `flatten` to convert it
-    to rank 2 manually if necessary.
+    .. note::
+        the input must be a tensor with rank 2. Use `flatten` to convert it
+        to rank 2 manually if necessary.
 
     Parameters
     ----------
@@ -379,11 +380,13 @@ class Embedding(HybridBlock):
     r"""Turns non-negative integers (indexes/tokens) into dense vectors
     of fixed size. eg. [4, 20] -> [[0.25, 0.1], [0.6, -0.2]]
 
-    Note: if `sparse_grad` is set to True, the gradient w.r.t weight will be
-    sparse. Only a subset of optimizers support sparse gradients, including SGD, AdaGrad
-    and Adam. By default lazy updates is turned on, which may perform differently
-    from standard updates. For more details, please check the Optimization API at:
-    https://mxnet.incubator.apache.org/api/python/optimization/optimization.html
+    .. note::
+        if `sparse_grad` is set to True, the gradient w.r.t weight will be
+        sparse. Only a subset of optimizers support sparse gradients, including SGD,
+        AdaGrad and Adam. By default lazy updates is turned on, which may perform
+        differently from standard updates. For more details, please check the
+        Optimization API at:
+        https://mxnet.incubator.apache.org/api/python/optimization/optimization.html
 
     Parameters
     ----------
diff --git a/src/operator/tensor/indexing_op.cc b/src/operator/tensor/indexing_op.cc
index 147205505e24..7a770163da17 100644
--- a/src/operator/tensor/indexing_op.cc
+++ b/src/operator/tensor/indexing_op.cc
@@ -490,8 +490,9 @@ All the input values should be integers in the range [0, input_dim).
 If the input_dim is ip0 and output_dim is op0, then shape of the embedding weight matrix must be
 (ip0, op0).
 
-By default, if any index mentioned is too large, it is replaced by the index that addresses
-the last vector in an embedding matrix.
+When "sparse_grad" is False, if any index mentioned is too large, it is replaced by the index that
+addresses the last vector in an embedding matrix.
+When "sparse_grad" is True, an error will be raised if invalid indices are found.
 
 Examples::
 

From 255dff0ea56007780ef5d4e96123f9525f9a723b Mon Sep 17 00:00:00 2001
From: kshitij12345 <kshitijkalambarkar@gmail.com>
Date: Fri, 6 Sep 2019 23:49:58 +0530
Subject: [PATCH 292/813] [MXNET-978] Higher Order Gradient Support `arctan`,
 `arctanh`, `radians`. (#15531)

* support arc{tan/tanh} for higher order grad

* add relevant tests

* add new abstraction for Node operations

* support radians for higher order grad

* add test for radians

* changes

* use NodeOp for arctan.
* update few comments.
* update few variable names.

* rename grad_x to x_grad

* update comments

* move node_op_util.h to src/nnvm

* address comments

* rename NodeOp to NodeOpGen.
* rename Op to op.

* fix file description
---
 src/nnvm/node_op_util.h                       | 76 +++++++++++++++++++
 src/operator/tensor/elemwise_unary_op_trig.cc | 63 ++++++++++++++-
 .../python/unittest/test_higher_order_grad.py | 46 +++++++++++
 3 files changed, 182 insertions(+), 3 deletions(-)
 create mode 100644 src/nnvm/node_op_util.h

diff --git a/src/nnvm/node_op_util.h b/src/nnvm/node_op_util.h
new file mode 100644
index 000000000000..8d5916aafff9
--- /dev/null
+++ b/src/nnvm/node_op_util.h
@@ -0,0 +1,76 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file node_op_util.h
+ * \brief abstraction for commonly used nnvm::Node operations.
+ */
+#ifndef MXNET_NNVM_NODE_OP_UTIL_H_
+#define MXNET_NNVM_NODE_OP_UTIL_H_
+#include <mxnet/base.h>
+#include <string>
+#include <unordered_map>
+#include "../operator/elemwise_op_common.h"
+
+namespace mxnet {
+namespace util {
+
+class NodeOpGen {
+ private:
+    const nnvm::NodePtr &dependent_node;
+
+ public:
+    explicit NodeOpGen(const nnvm::NodePtr &dependent_node) : dependent_node{dependent_node} {}
+
+    nnvm::NodeEntry mul(const nnvm::NodeEntry &lhs, const nnvm::NodeEntry &rhs) {
+        return nnvm::NodeEntry{mxnet::op::MakeNode("elemwise_mul",
+                                                   dependent_node->attrs.name + "_mul",
+                                                   {lhs, rhs}, nullptr, &dependent_node)};
+    }
+
+    nnvm::NodeEntry mul(const nnvm::NodeEntry &x, double scalar) {
+        const std::unordered_map<std::string, std::string> scalar_dict =
+            {{"scalar", std::to_string(scalar)}};
+        return nnvm::NodeEntry{mxnet::op::MakeNode("_mul_scalar",
+                                                   dependent_node->attrs.name + "_mul_scalar",
+                                                   {x}, &scalar_dict, &dependent_node)};
+    }
+
+    nnvm::NodeEntry mul(double scalar, const nnvm::NodeEntry &x) {
+        return NodeOpGen::mul(x, scalar);
+    }
+
+    nnvm::NodeEntry div(const nnvm::NodeEntry &lhs, const nnvm::NodeEntry &rhs) {
+        return nnvm::NodeEntry{mxnet::op::MakeNode("elemwise_div",
+                                                   dependent_node->attrs.name + "_div",
+                                                   {lhs, rhs}, nullptr, &dependent_node)};
+    }
+
+    nnvm::NodeEntry square(const nnvm::NodeEntry &x) {
+        return nnvm::NodeEntry{mxnet::op::MakeNode("square",
+                                                   dependent_node->attrs.name + "_square",
+                                                   {x}, nullptr, &dependent_node)};
+    }
+};
+
+}  // namespace util
+}  // namespace mxnet
+
+#endif  // MXNET_NNVM_NODE_OP_UTIL_H_
diff --git a/src/operator/tensor/elemwise_unary_op_trig.cc b/src/operator/tensor/elemwise_unary_op_trig.cc
index ff210040d552..bd0c8973ae07 100644
--- a/src/operator/tensor/elemwise_unary_op_trig.cc
+++ b/src/operator/tensor/elemwise_unary_op_trig.cc
@@ -24,6 +24,7 @@
 #include <mxnet/base.h>
 #include "elemwise_unary_op.h"
 #include "./elemwise_binary_op-inl.h"
+#include "../../nnvm/node_op_util.h"
 
 namespace mxnet {
 namespace op {
@@ -227,7 +228,35 @@ The storage type of ``arctan`` output depends upon the input storage type:
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{ "_backward_arctan" });
 
 MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_arctan,
-                                                  unary_bwd<mshadow_op::arctan_grad>);
+                                                  unary_bwd<mshadow_op::arctan_grad>)
+.set_attr<nnvm::FGradient>("FGradient",
+    [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
+      // ograds[0]: head_grad_grads (dL/dxgrad)
+      // inputs[0]: dL/dy
+      // inputs[1]: x (ElemwiseGradUseIn)
+      // n: dL/dy * f'(x)
+      // f(x) = arctanh(x)
+      // dydx = f'(x) = 1/(1+x^2)
+      // f''(x) = f'(x) * f'(x) * -2 * x = (-2 * x) / (1 + x^2)^2
+      // return:
+      //     0: dL/dy_grad * dy/dx
+      //     1: dL/dy_grad * dL/dy * f''(x)
+      auto dldy = n->inputs[0];
+      auto x = n->inputs[1];
+      auto dldy_mul_dydx = nnvm::NodeEntry{n};
+      auto op = mxnet::util::NodeOpGen{n};
+
+      auto x_grad = op.div(dldy_mul_dydx, dldy);
+      auto x_grad_square = op.square(x_grad);
+      auto x_grad_square_mul_x = op.mul(x_grad_square, x);
+      auto x_grad_square_mul_2_x = op.mul(-2.0, x_grad_square_mul_x);
+      auto grad_grad_x = op.mul(dldy, x_grad_square_mul_2_x);
+
+      std::vector<nnvm::NodeEntry> ret;
+      ret.emplace_back(op.mul(ograds[0], x_grad));
+      ret.emplace_back(op.mul(ograds[0], grad_grad_x));
+      return ret;
+    });
 
 // degrees
 MXNET_OPERATOR_REGISTER_UNARY_WITH_RSP_CSR(degrees, cpu, mshadow_op::degrees)
@@ -265,7 +294,8 @@ The storage type of ``radians`` output depends upon the input storage type:
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{ "_backward_radians" });
 
 MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_radians,
-                                                  unary_bwd<mshadow_op::radians_grad>);
+                                                  unary_bwd<mshadow_op::radians_grad>)
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes);
 
 // sinh
 MXNET_OPERATOR_REGISTER_UNARY_WITH_RSP_CSR(sinh, cpu, mshadow_op::sinh)
@@ -391,8 +421,35 @@ The storage type of ``arctanh`` output depends upon the input storage type:
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{ "_backward_arctanh" });
 
 MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_arctanh,
-                                                  unary_bwd<mshadow_op::arctanh_grad>);
+                                                  unary_bwd<mshadow_op::arctanh_grad>)
+.set_attr<nnvm::FGradient>("FGradient",
+    [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
+      // ograds[0]: head_grad_grads (dL/dxgrad)
+      // inputs[0]: dL/dy
+      // inputs[1]: x (ElemwiseGradUseIn)
+      // n: dL/dy * dy/dx
+      // f(x) = arctanh(x)
+      // dy/dx = f'(x) = 1/(1-x^2)
+      // f''(x) = f'(x) * f'(x) * 2 * x = (2 * x) / (1 - x^2)^2
+      // return:
+      //     0: dL/dy_grad * dy/dx
+      //     1: dL/dy_grad * dL/dy * f''(x)
+      auto dldy = n->inputs[0];
+      auto x = n->inputs[1];
+      auto dldy_mul_dydx = nnvm::NodeEntry{n};
+      auto op = mxnet::util::NodeOpGen{n};
+
+      auto x_grad = op.div(dldy_mul_dydx, dldy);
+      auto x_grad_square = op.square(x_grad);
+      auto x_grad_square_mul_x = op.mul(x_grad_square, x);
+      auto x_grad_square_mul_2_x = op.mul(2.0, x_grad_square_mul_x);
+      auto grad_grad_x = op.mul(dldy, x_grad_square_mul_2_x);
 
+      std::vector<nnvm::NodeEntry> ret;
+      ret.emplace_back(op.mul(ograds[0], x_grad));
+      ret.emplace_back(op.mul(ograds[0], grad_grad_x));
+      return ret;
+    });
 
 }  // namespace op
 }  // namespace mxnet
diff --git a/tests/python/unittest/test_higher_order_grad.py b/tests/python/unittest/test_higher_order_grad.py
index 429070de5896..c70c747411b8 100644
--- a/tests/python/unittest/test_higher_order_grad.py
+++ b/tests/python/unittest/test_higher_order_grad.py
@@ -17,6 +17,7 @@
 
 
 import math
+import random
 from mxnet import nd, autograd
 from mxnet.test_utils import assert_almost_equal, random_arrays, rand_shape_nd
 from common import with_seed
@@ -85,6 +86,51 @@ def grad_grad_op(x):
             array, tanh, grad_grad_op, rtol=1e-6, atol=1e-6)
 
 
+@with_seed()
+def test_arctan():
+    def arctan(x):
+        return nd.arctan(x)
+
+    def grad_grad_op(x):
+        return (-2 * x)/((1 + x**2)**2)
+
+    for dim in range(1, 5):
+        shape = rand_shape_nd(dim)
+        array = random_arrays(shape)
+        # Domain of arctan is all real numbers.
+        # Scale std_dev
+        array *= random.randint(500, 10000)
+        check_second_order_unary(array, arctan, grad_grad_op)
+
+
+@with_seed()
+def test_arctanh():
+    def arctanh(x):
+        return nd.arctanh(x)
+
+    def grad_grad_op(x):
+        return (2 * x)/((1 - x**2)**2)
+
+    for dim in range(1, 5):
+        shape = rand_shape_nd(dim)
+        array = random_arrays(shape)
+        check_second_order_unary(array, arctanh, grad_grad_op)
+
+
+@with_seed()
+def test_radians():
+    def radians(x):
+        return nd.radians(x)
+
+    def grad_grad_op(x):
+        return nd.zeros_like(x)
+
+    for dim in range(1, 5):
+        shape = rand_shape_nd(dim)
+        array = random_arrays(shape)
+        check_second_order_unary(array, radians, grad_grad_op)
+
+
 @with_seed()
 def test_relu():
     def relu(x):

From 259f6bbe8b3b742fd561b377356be5562571072d Mon Sep 17 00:00:00 2001
From: Pedro Larroy <pedro.larroy.lists@gmail.com>
Date: Fri, 6 Sep 2019 12:56:34 -0700
Subject: [PATCH 293/813] Revert accidental change to CMakelists (#16040)

---
 CMakeLists.txt | 12 +++++-------
 1 file changed, 5 insertions(+), 7 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index b6faf639a70e..f441e9b0bd3b 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -24,7 +24,6 @@ mxnet_option(USE_OLDCMAKECUDA     "Build with old cmake cuda" OFF)
 mxnet_option(USE_NCCL             "Use NVidia NCCL with CUDA" OFF)
 mxnet_option(USE_OPENCV           "Build with OpenCV support" ON)
 mxnet_option(USE_OPENMP           "Build with Openmp support" ON)
-mxnet_option(USE_OPENMP_BUNDLED_LLVM "Build with bundled llvm openmp from 3rdparty" OFF)
 mxnet_option(USE_CUDNN            "Build with cudnn support"  ON) # one could set CUDNN_ROOT for search path
 mxnet_option(USE_SSE              "Build with x86 SSE instruction support" ON IF NOT ARM)
 mxnet_option(USE_F16C             "Build with x86 F16C instruction support" ON) # autodetects support if ON
@@ -436,11 +435,11 @@ if(USE_OPENMP)
   find_package(OpenMP REQUIRED)
   # This should build on Windows, but there's some problem and I don't have a Windows box, so
   # could a Windows user please fix?
-  if(USE_OPENMP_BUNDLED_LLVM AND EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/3rdparty/openmp/CMakeLists.txt
-      AND SYSTEM_ARCHITECTURE STREQUAL "x86_64"
-      AND NOT MSVC
-      AND NOT CMAKE_CROSSCOMPILING)
-    message("Using bundlded LLVM OpenMP")
+  if(EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/3rdparty/openmp/CMakeLists.txt
+     AND SYSTEM_ARCHITECTURE STREQUAL "x86_64"
+     AND NOT MSVC
+     AND NOT CMAKE_CROSSCOMPILING)
+
     # Intel/llvm OpenMP: https://github.com/llvm-mirror/openmp
     set(OPENMP_STANDALONE_BUILD TRUE)
     set(LIBOMP_ENABLE_SHARED TRUE)
@@ -454,7 +453,6 @@ if(USE_OPENMP)
     set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
     add_definitions(-DMXNET_USE_OPENMP=1)
   else()
-    message("Using platform provided OpenMP")
     if(OPENMP_FOUND)
       set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}")
       set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")

From 6de684825130c28dfa75f2a707aeaed64a4340e5 Mon Sep 17 00:00:00 2001
From: Anirudh Subramanian <anirudh2290@ufl.edu>
Date: Fri, 6 Sep 2019 13:14:25 -0700
Subject: [PATCH 294/813] Not to search for coverage files when none exist
 (#16107)

---
 tests/nightly/JenkinsfileForBinaries | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/nightly/JenkinsfileForBinaries b/tests/nightly/JenkinsfileForBinaries
index 725c76969e23..8582643e0163 100755
--- a/tests/nightly/JenkinsfileForBinaries
+++ b/tests/nightly/JenkinsfileForBinaries
@@ -45,7 +45,7 @@ core_logic: {
         ws('workspace/build-cpu-int64') {
           utils.init_git()
           utils.docker_run('ubuntu_nightly_cpu', 'build_ubuntu_cpu_large_tensor', false)
-          utils.pack_lib('cpu_int64', mx_cmake_lib, true)
+          utils.pack_lib('cpu_int64', mx_cmake_lib)
         }
       }
     },
@@ -54,7 +54,7 @@ core_logic: {
         ws('workspace/build-gpu-int64') {
           utils.init_git()
           utils.docker_run('ubuntu_nightly_gpu', 'build_ubuntu_gpu_large_tensor', true)
-          utils.pack_lib('gpu_int64', mx_cmake_lib, true)
+          utils.pack_lib('gpu_int64', mx_cmake_lib)
         }
       }
     }

From c928392a2d865dd3d452f0a3b9d7a320476c02fa Mon Sep 17 00:00:00 2001
From: kshitij12345 <kshitijkalambarkar@gmail.com>
Date: Sat, 7 Sep 2019 04:40:44 +0530
Subject: [PATCH 295/813] [MXNET-978] Higher Order Gradient Support `sqrt`,
 `cbrt`. (#15474)

* support sqrt, cbrt for higher order grad

* add relevant tests

* remove unnecessary variable
---
 src/operator/tensor/elemwise_unary_op_pow.cc  | 71 ++++++++++++++++++-
 .../python/unittest/test_higher_order_grad.py | 40 +++++++++++
 2 files changed, 109 insertions(+), 2 deletions(-)

diff --git a/src/operator/tensor/elemwise_unary_op_pow.cc b/src/operator/tensor/elemwise_unary_op_pow.cc
index f22dabc7201a..486fe268b0cf 100644
--- a/src/operator/tensor/elemwise_unary_op_pow.cc
+++ b/src/operator/tensor/elemwise_unary_op_pow.cc
@@ -143,7 +143,38 @@ The storage type of ``sqrt`` output depends upon the input storage type:
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{"_backward_sqrt"});
 
 MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_sqrt,
-                                                  unary_bwd<mshadow_op::square_root_grad>);
+                                                  unary_bwd<mshadow_op::square_root_grad>)
+.set_attr<nnvm::FGradient>("FGradient",
+  [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
+      // NodeEntry{n} : y_grad * f'(x)
+      // n->inputs[0] : y_grad
+      // n->inputs[1] : f(x) = x^1/2
+      // ograds[0] : head_grads
+      // f'(x) = 1/(2*x^1/2)
+      // f''(x) = f'(x) * -1/(2*x) = -1/(4 * x^3/2)
+      const std::unordered_map<std::string, std::string> mul_args = {{"scalar", "0.5"}};
+      auto x = MakeNode("square", n->attrs.name + "_cube_x", {n->inputs[1]}, nullptr, &n);
+      auto r_x = MakeNode("reciprocal", n->attrs.name + "_reciprocal_x",
+                                    {nnvm::NodeEntry{x}}, nullptr, &n);
+      auto neg_r_x = MakeNode("negative", n->attrs.name + "_neg_reciprocal_x",
+                                    {nnvm::NodeEntry{r_x}}, nullptr, &n);
+      auto half_neg_r_cube_x = MakeNode("_mul_scalar", n->attrs.name + "_half_neg_reciprocal_x",
+                                    {nnvm::NodeEntry{neg_r_x}}, &mul_args, &n);
+      auto grad_grad_mid = MakeNode("elemwise_mul", n->attrs.name + "_grad_grad_mid",
+                                    {nnvm::NodeEntry{half_neg_r_cube_x}, n->inputs[0]},
+                                    nullptr, &n);
+      auto dydx = MakeNode("elemwise_div", n->attrs.name + "_grad_div",
+                           {nnvm::NodeEntry{n}, n->inputs[0]}, nullptr, &n);
+
+      // when building gradient graph, the backward node of n->inputs[1] will be
+      // added to the graph again, therefore f`(x) will be multiplied
+      std::vector<nnvm::NodeEntry> ret;
+      ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "backward_grad_grad",
+                                {ograds[0], nnvm::NodeEntry{dydx}}, nullptr, &n));
+      ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "backward_grad_grad_in",
+                                {ograds[0], nnvm::NodeEntry{grad_grad_mid}}, nullptr, &n));
+      return ret;
+  });
 
 // rsqrt
 MXNET_OPERATOR_REGISTER_UNARY_WITH_SPARSE_DR(rsqrt, cpu, mshadow_op::reciprocal_square_root)
@@ -186,7 +217,43 @@ The storage type of ``cbrt`` output depends upon the input storage type:
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{"_backward_cbrt"});
 
 MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_cbrt,
-                                                  unary_bwd<mshadow_op::cube_root_grad>);
+                                                  unary_bwd<mshadow_op::cube_root_grad>)
+.set_attr<nnvm::FGradient>("FGradient",
+  [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
+      // NodeEntry{n} : y_grad * f'(x)
+      // n->inputs[0] : y_grad
+      // n->inputs[1] : f(x) = x^1/3
+      // ograds[0] : head_grads
+      // f'(x) = 1/(3*x^2/3)
+      // f''(x) = f'(x) * -2/(3*x) = -2/(9 * x^5/3)
+      const std::unordered_map<std::string, std::string> three = {{"scalar", "3.0"}};
+      const std::unordered_map<std::string, std::string> two = {{"scalar", "2.0"}};
+      auto x = MakeNode("_power_scalar", n->attrs.name + "_x", {n->inputs[1]}, &three, &n);
+      auto three_x = MakeNode("_mul_scalar", n->attrs.name + "_three_x",
+                                    {nnvm::NodeEntry{x}}, &three, &n);
+      auto r_three_x = MakeNode("reciprocal", n->attrs.name + "_reciprocal_three_x",
+                                    {nnvm::NodeEntry{three_x}}, nullptr, &n);
+      auto neg_r_three_x = MakeNode("negative", n->attrs.name + "_neg_reciprocal_three_x",
+                                    {nnvm::NodeEntry{r_three_x}}, nullptr, &n);
+      auto two_third_neg_r_x = MakeNode("_mul_scalar",
+                                        n->attrs.name + "_two_third_neg_reciprocal_x",
+                                        {nnvm::NodeEntry{neg_r_three_x}}, &two, &n);
+      auto grad_grad_mid = MakeNode("elemwise_mul", n->attrs.name + "_grad_grad_mid",
+                                    {nnvm::NodeEntry{two_third_neg_r_x}, n->inputs[0]},
+                                    nullptr, &n);
+      auto dydx = MakeNode("elemwise_div", n->attrs.name + "_grad_div",
+                           {nnvm::NodeEntry{n}, n->inputs[0]}, nullptr, &n);
+
+      // when building gradient graph, the backward node of n->inputs[1] will be
+      // added to the graph again, therefore f`(x) will be multiplied
+      std::vector<nnvm::NodeEntry> ret;
+      ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "backward_grad_grad",
+                                {ograds[0], nnvm::NodeEntry{dydx}}, nullptr, &n));
+      ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "backward_grad_grad_in",
+                                {ograds[0], nnvm::NodeEntry{grad_grad_mid}}, nullptr, &n));
+      return ret;
+  });
+
 
 // rcbrt
 MXNET_OPERATOR_REGISTER_UNARY(rcbrt)
diff --git a/tests/python/unittest/test_higher_order_grad.py b/tests/python/unittest/test_higher_order_grad.py
index c70c747411b8..64c429a94c49 100644
--- a/tests/python/unittest/test_higher_order_grad.py
+++ b/tests/python/unittest/test_higher_order_grad.py
@@ -231,6 +231,46 @@ def grad_grad_op(x):
         check_second_order_unary(array, sigmoid, grad_grad_op)
 
 
+@with_seed()
+def test_sqrt():
+    def sqrt(x):
+        return nd.sqrt(x)
+
+    def grad_grad_op(x):
+        return -1/(4 * sqrt(x**3))
+
+    sigma = random.randint(25, 100)
+    mu = random.randint(500, 1000)
+
+    for dim in range(1, 5):
+        shape = rand_shape_nd(dim)
+        array = random_arrays(shape)
+        array = sigma * array + mu
+        # Only positive numbers
+        assert((array > 0).all())
+        check_second_order_unary(array, sqrt, grad_grad_op)
+
+
+@with_seed()
+def test_cbrt():
+    def cbrt(x):
+        return nd.cbrt(x)
+
+    def grad_grad_op(x):
+        return -2/(9 * cbrt(x**5))
+
+    sigma = random.randint(25, 100)
+    mu = random.randint(500, 1000)
+
+    for dim in range(1, 5):
+        shape = rand_shape_nd(dim)
+        array = random_arrays(shape)
+        array = sigma * array + mu
+        # Only positive numbers
+        assert((array > 0).all())
+        check_second_order_unary(array, cbrt, grad_grad_op)
+
+
 def check_second_order_unary(x, op, grad_grad_op, rtol=None, atol=None):
     x = nd.array(x)
     grad_grad_x = grad_grad_op(x)

From 24f0a10e2c41dc43b99089f8312986e6e241c390 Mon Sep 17 00:00:00 2001
From: kshitij12345 <kshitijkalambarkar@gmail.com>
Date: Sat, 7 Sep 2019 04:46:22 +0530
Subject: [PATCH 296/813] [MXNET-978] Higher Order Gradient Support `clip`,
 `dropout`. (#15746)

* support clip, dropout for higher order grad

* add relevant tests

* retrigger CI
---
 src/operator/nn/dropout.cc                    |  4 ++-
 src/operator/tensor/matrix_op.cc              |  3 +-
 .../python/unittest/test_higher_order_grad.py | 30 +++++++++++++++++++
 3 files changed, 35 insertions(+), 2 deletions(-)

diff --git a/src/operator/nn/dropout.cc b/src/operator/nn/dropout.cc
index c4ad4250853e..745bba142b6e 100644
--- a/src/operator/nn/dropout.cc
+++ b/src/operator/nn/dropout.cc
@@ -171,6 +171,7 @@ Example::
 .add_arguments(DropoutParam::__FIELDS__());
 
 NNVM_REGISTER_OP(_backward_Dropout)
+.set_num_inputs(2)
 .set_num_outputs(1)
 .set_attr<bool>("TIsLayerOpBackward", true)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
@@ -178,7 +179,8 @@ NNVM_REGISTER_OP(_backward_Dropout)
 .set_attr<nnvm::FInplaceOption>("FInplaceOption", [](const NodeAttrs& attrs){
   return std::vector<std::pair<int, int> >{{0, 0}};
 })
-.set_attr<FStatefulCompute>("FStatefulCompute<cpu>", DropoutGradCompute<cpu>);
+.set_attr<FStatefulCompute>("FStatefulCompute<cpu>", DropoutGradCompute<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes);
 
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/tensor/matrix_op.cc b/src/operator/tensor/matrix_op.cc
index 9697744d03d3..c60402488b65 100644
--- a/src/operator/tensor/matrix_op.cc
+++ b/src/operator/tensor/matrix_op.cc
@@ -779,7 +779,8 @@ NNVM_REGISTER_OP(_backward_clip)
 .set_num_outputs(1)
 .set_attr_parser(ParamParser<ClipParam>)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
-.set_attr<FCompute>("FCompute<cpu>", ClipGrad_<cpu>);
+.set_attr<FCompute>("FCompute<cpu>", ClipGrad_<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes);
 
 NNVM_REGISTER_OP(repeat)
 .add_alias("_np_repeat")
diff --git a/tests/python/unittest/test_higher_order_grad.py b/tests/python/unittest/test_higher_order_grad.py
index 64c429a94c49..a758775a09ba 100644
--- a/tests/python/unittest/test_higher_order_grad.py
+++ b/tests/python/unittest/test_higher_order_grad.py
@@ -215,6 +215,36 @@ def grad_grad_op(x):
         check_second_order_unary(array, abs, grad_grad_op)
 
 
+@with_seed()
+def test_clip():
+    def clip(x):
+        a_min, a_max = sorted([random.random(), random.random()])
+
+        return nd.clip(x, a_min, a_max)
+
+    def grad_grad_op(x):
+        return nd.zeros_like(x)
+
+    for dim in range(1, 5):
+        shape = rand_shape_nd(dim)
+        array = random_arrays(shape)
+        check_second_order_unary(array, clip, grad_grad_op)
+
+
+@with_seed()
+def test_dropout():
+    def dropout(x):
+        return nd.Dropout(x)
+
+    def grad_grad_op(x):
+        return nd.zeros_like(x)
+
+    for dim in range(1, 5):
+        shape = rand_shape_nd(dim)
+        array = random_arrays(shape)
+        check_second_order_unary(array, dropout, grad_grad_op)
+
+
 def test_sigmoid():
     def sigmoid(x):
         return nd.sigmoid(x)

From 4f8bc3a5c27c54e71f71d517998feddb2ae376e3 Mon Sep 17 00:00:00 2001
From: Lin Yuan <apeforest@gmail.com>
Date: Fri, 6 Sep 2019 22:33:51 -0700
Subject: [PATCH 297/813] Fix unary operator ceil/floor/trunc when data type is
 integer (#14251)

* fix integer precision loss due to casting to float

* add test

* update comment
---
 src/operator/math_functions-inl.h     | 15 ++++++++++-----
 tests/python/unittest/test_ndarray.py | 19 +++++++++++++++++++
 2 files changed, 29 insertions(+), 5 deletions(-)

diff --git a/src/operator/math_functions-inl.h b/src/operator/math_functions-inl.h
index be5bbe20d036..45d74a62d8bd 100644
--- a/src/operator/math_functions-inl.h
+++ b/src/operator/math_functions-inl.h
@@ -35,19 +35,24 @@ namespace op {
 namespace math {
 
 // Wrappers for math.h unary and binary functions
-// - For DType != double: math::name(a) does computation in float
+// - For DType == float: math::name(a) does computation in float
 //   and returns float
-// - For DType == double: math::name(a) does computation in double
+// - For DType == double or DType == integer: math::name(a) does computation in double
 //   and returns double
 
 #define MXNET_UNARY_MATH_FUNC(name) \
-template<typename DType> MSHADOW_XINLINE \
-float name(DType a) { \
-  return ::name##f(static_cast<float>(a)); \
+MSHADOW_XINLINE \
+float name(float a) { \
+  return ::name##f(a); \
 } \
 MSHADOW_XINLINE \
 double name(double a) { \
   return ::name(a); \
+} \
+template<typename DType> MSHADOW_XINLINE \
+typename std::enable_if<std::is_integral<DType>::value, double>::type \
+name(DType a) { \
+  return ::name(static_cast<double>(a)); \
 }
 
 #define MXNET_BINARY_MATH_FUNC(name) \
diff --git a/tests/python/unittest/test_ndarray.py b/tests/python/unittest/test_ndarray.py
index a05b3eae0e44..7091abf7308a 100644
--- a/tests/python/unittest/test_ndarray.py
+++ b/tests/python/unittest/test_ndarray.py
@@ -1952,6 +1952,25 @@ def test_op(op, num_inputs, mutated_inputs, **kwargs):
             {'rescale_grad': 0.1, 'lr': 0.01, 'wd': 1e-3})
 
 
+def test_large_int_rounding():
+    large_integer = 50000001
+
+    a = mx.nd.array([large_integer], dtype='int32')
+    assert np.all(a == large_integer)
+
+    a = mx.nd.array([large_integer], dtype='int32').floor()
+    assert np.all(a == large_integer)
+
+    a = mx.nd.array([large_integer], dtype='int32').round()
+    assert np.all(a == large_integer)
+
+    a = mx.nd.array([large_integer], dtype='int32').ceil()
+    assert np.all(a == large_integer)
+
+    a = mx.nd.array([large_integer], dtype='int32').trunc()
+    assert np.all(a == large_integer)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 3ff1d8794c432142999199df77963ace161ff493 Mon Sep 17 00:00:00 2001
From: Per Goncalves da Silva <perdasilva@gmail.com>
Date: Sun, 1 Sep 2019 18:58:35 +0200
Subject: [PATCH 298/813] Fixes openblas installation for static build

---
 tools/dependencies/openblas.sh | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/tools/dependencies/openblas.sh b/tools/dependencies/openblas.sh
index 1201c8947568..05a500da3147 100755
--- a/tools/dependencies/openblas.sh
+++ b/tools/dependencies/openblas.sh
@@ -18,9 +18,8 @@
 # under the License.
 
 # This script builds the static library of openblas that can be used as dependency of mxnet.
-set +e # This script throws an error but otherwise works
-set -x
-OPENBLAS_VERSION=0.3.5
+set -ex
+OPENBLAS_VERSION=0.3.7
 if [[ ! -e $DEPS_PATH/lib/libopenblas.a ]]; then
     # download and build openblas
     >&2 echo "Building openblas..."
@@ -32,8 +31,13 @@ if [[ ! -e $DEPS_PATH/lib/libopenblas.a ]]; then
     pushd .
     cd $DEPS_PATH/OpenBLAS-$OPENBLAS_VERSION
 
-    $MAKE DYNAMIC_ARCH=1 NO_SHARED=1 USE_OPENMP=1
+    # Adding NO_DYNAMIC=1 flag causes make install to fail
+    $MAKE DYNAMIC_ARCH=1 USE_OPENMP=1
     $MAKE PREFIX=$DEPS_PATH install
+
+    # Manually removing .so to avoid linking against it
+    rm $DEPS_PATH/lib/libopenblasp-r${OPENBLAS_VERSION}.so
+
     popd
     ln -s libopenblas.a $DEPS_PATH/lib/libcblas.a
     ln -s libopenblas.a $DEPS_PATH/lib/liblapack.a

From 27a9782e7afee7ed1be3ee3b5fe3061b0c299d98 Mon Sep 17 00:00:00 2001
From: Haibin Lin <linhaibin.eric@gmail.com>
Date: Sat, 7 Sep 2019 20:53:22 -0700
Subject: [PATCH 299/813] [Dataset] Add take, filter, sample API to dataset
 (#16078)

* first commit

* fix lint

* add more test  for transformed data

* address CR

* Update dataset.py

* use sampler to implement

* Update dataset.py
---
 python/mxnet/gluon/data/dataset.py       | 89 ++++++++++++++++++++++++
 python/mxnet/gluon/data/sampler.py       | 23 +++++-
 tests/python/unittest/test_gluon_data.py | 39 +++++++++++
 3 files changed, 150 insertions(+), 1 deletion(-)

diff --git a/python/mxnet/gluon/data/dataset.py b/python/mxnet/gluon/data/dataset.py
index 28d19c9fe37c..a5399f259435 100644
--- a/python/mxnet/gluon/data/dataset.py
+++ b/python/mxnet/gluon/data/dataset.py
@@ -40,6 +40,70 @@ def __getitem__(self, idx):
     def __len__(self):
         raise NotImplementedError
 
+    def filter(self, fn):
+        """Returns a new dataset with samples filtered by the
+        filter function `fn`.
+
+        Note that if the Dataset is the result of a lazily transformed one with
+        transform(lazy=False), the filter is eagerly applied to the transformed
+        samples without materializing the transformed result. That is, the
+        transformation will be applied again whenever a sample is retrieved after
+        filter().
+
+        Parameters
+        ----------
+        fn : callable
+            A filter function that takes a sample as input and
+            returns a boolean. Samples that return False are discarded.
+
+        Returns
+        -------
+        Dataset
+            The filtered dataset.
+        """
+        from . import FilterSampler
+        return _SampledDataset(self, FilterSampler(fn, self))
+
+    def take(self, count):
+        """Returns a new dataset with at most `count` number of samples in it.
+
+        Parameters
+        ----------
+        count : int or None
+            A integer representing the number of elements of this dataset that
+            should be taken to form the new dataset. If count is None, or if count
+            is greater than the size of this dataset, the new dataset will contain
+            all elements of this dataset.
+
+        Returns
+        -------
+        Dataset
+            The result dataset.
+        """
+        if count is None or count > len(self):
+            count = len(self)
+        from . import SequentialSampler
+        return _SampledDataset(self, SequentialSampler(count))
+
+    def sample(self, sampler):
+        """Returns a new dataset with elements sampled by the sampler.
+
+        Parameters
+        ----------
+        sampler : Sampler
+            A Sampler that returns the indices of sampled elements.
+
+        Returns
+        -------
+        Dataset
+            The result dataset.
+        """
+        from . import Sampler
+        if not isinstance(sampler, Sampler):
+            raise TypeError('Invalid sampler type: %s. Expected gluon.data.Sampler instead.'%
+                            type(sampler))
+        return _SampledDataset(self, sampler)
+
     def transform(self, fn, lazy=True):
         """Returns a new dataset with each sample transformed by the
         transformer function `fn`.
@@ -135,6 +199,31 @@ def __call__(self, x, *args):
             return (self._fn(x),) + args
         return self._fn(x)
 
+class _FilteredDataset(Dataset):
+    """Dataset with a filter applied"""
+    def __init__(self, dataset, fn):
+        self._dataset = dataset
+        self._indices = [i for i, sample in enumerate(dataset) if fn(sample)]
+
+    def __len__(self):
+        return len(self._indices)
+
+    def __getitem__(self, idx):
+        return self._dataset[self._indices[idx]]
+
+class _SampledDataset(Dataset):
+    """Dataset with elements chosen by a sampler"""
+    def __init__(self, dataset, sampler):
+        self._dataset = dataset
+        self._sampler = sampler
+        self._indices = list(iter(sampler))
+
+    def __len__(self):
+        return len(self._sampler)
+
+    def __getitem__(self, idx):
+        return self._dataset[self._indices[idx]]
+
 class ArrayDataset(Dataset):
     """A dataset that combines multiple dataset-like objects, e.g.
     Datasets, lists, arrays, etc.
diff --git a/python/mxnet/gluon/data/sampler.py b/python/mxnet/gluon/data/sampler.py
index 2f827c83bc4d..980c63134fac 100644
--- a/python/mxnet/gluon/data/sampler.py
+++ b/python/mxnet/gluon/data/sampler.py
@@ -18,7 +18,7 @@
 # coding: utf-8
 # pylint: disable=
 """Dataset sampler."""
-__all__ = ['Sampler', 'SequentialSampler', 'RandomSampler', 'BatchSampler']
+__all__ = ['Sampler', 'SequentialSampler', 'RandomSampler', 'FilterSampler', 'BatchSampler']
 
 import numpy as np
 
@@ -72,6 +72,27 @@ def __iter__(self):
     def __len__(self):
         return self._length
 
+class FilterSampler(Sampler):
+    """Samples elements from a Dataset for which `fn` returns True.
+
+    Parameters
+    ----------
+    fn : callable
+        A callable function that takes a sample and returns a boolean
+    dataset : Dataset
+        The dataset to filter.
+    """
+    def __init__(self, fn, dataset):
+        self._fn = fn
+        self._dataset = dataset
+        self._indices = [i for i, sample in enumerate(dataset) if fn(sample)]
+
+    def __iter__(self):
+        return iter(self._indices)
+
+    def __len__(self):
+        return len(self._indices)
+
 
 class BatchSampler(Sampler):
     """Wraps over another `Sampler` and return mini-batches of samples.
diff --git a/tests/python/unittest/test_gluon_data.py b/tests/python/unittest/test_gluon_data.py
index 58e241b528ea..62bf7c3db869 100644
--- a/tests/python/unittest/test_gluon_data.py
+++ b/tests/python/unittest/test_gluon_data.py
@@ -283,6 +283,45 @@ def test_dataloader_context():
 def batchify(a):
     return a
 
+def test_dataset_filter():
+    length = 100
+    a = mx.gluon.data.SimpleDataset([i for i in range(length)])
+    a_filtered = a.filter(lambda x: x % 10 == 0)
+    assert(len(a_filtered) == 10)
+    for idx, sample in enumerate(a_filtered):
+        assert sample % 10 == 0
+    a_xform_filtered = a.transform(lambda x: x + 1).filter(lambda x: x % 10 == 0)
+    assert(len(a_xform_filtered) == 10)
+    # the filtered data is already transformed
+    for idx, sample in enumerate(a_xform_filtered):
+        assert sample % 10 == 0
+
+def test_dataset_take():
+    length = 100
+    a = mx.gluon.data.SimpleDataset([i for i in range(length)])
+    a_take_full = a.take(1000)
+    assert len(a_take_full) == length
+    a_take_full = a.take(None)
+    assert len(a_take_full) == length
+    count = 10
+    a_take_10 = a.take(count)
+    assert len(a_take_10) == count
+    expected_total = sum([i for i in range(count)])
+    total = 0
+    for idx, sample in enumerate(a_take_10):
+        assert sample < count
+        total += sample
+    assert total == expected_total
+
+    a_xform_take_10 = a.transform(lambda x: x * 10).take(count)
+    assert len(a_xform_take_10) == count
+    expected_total = sum([i * 10 for i in range(count)])
+    total = 0
+    for idx, sample in enumerate(a_xform_take_10):
+        assert sample < count * 10
+        total += sample
+    assert total == expected_total
+
 def test_dataloader_scope():
     """
     Bug: Gluon DataLoader terminates the process pool early while

From 75ee1e9f434383eff9b250f32b10c89e721f66d3 Mon Sep 17 00:00:00 2001
From: Iblis Lin <iblis@hs.ntnu.edu.tw>
Date: Sun, 8 Sep 2019 12:30:57 +0800
Subject: [PATCH 300/813] julia: rename build env var `MXNET_HOME` to
 `MXNET_ROOT` (#15568)

* julia: rename build env var `MXNET_HOME` to `MXNET_ROOT`

- Add MXNET_LIBRARY_PATH support

Ref: https://github.com/apache/incubator-mxnet/pull/15561

* backward compatibility
---
 ci/docker/runtime_functions.sh       |  8 +++----
 ci/windows/test_jl07_cpu.ps1         |  2 +-
 ci/windows/test_jl10_cpu.ps1         |  2 +-
 julia/NEWS.md                        |  7 ++++++
 julia/deps/build.jl                  | 33 +++++++++++++++++++++++-----
 julia/docs/src/user-guide/install.md | 20 ++++++++++-------
 julia/src/base.jl                    | 30 ++++++++++++++++++++-----
 7 files changed, 77 insertions(+), 25 deletions(-)

diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index 689d525a5cce..ac962527d7f7 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -1088,7 +1088,7 @@ unittest_ubuntu_gpu_R() {
 unittest_ubuntu_cpu_julia() {
     set -ex
     export PATH="$1/bin:$PATH"
-    export MXNET_HOME='/work/mxnet'
+    export MXNET_ROOT='/work/mxnet'
     export JULIA_DEPOT_PATH='/work/julia-depot'
     export INTEGRATION_TEST=1
 
@@ -1098,7 +1098,7 @@ unittest_ubuntu_cpu_julia() {
     export LD_PRELOAD='/usr/lib/x86_64-linux-gnu/libjemalloc.so'
     export LD_LIBRARY_PATH=/work/mxnet/lib:$LD_LIBRARY_PATH
 
-    # use the prebuilt binary from $MXNET_HOME/lib
+    # use the prebuilt binary from $MXNET_ROOT/lib
     julia --project=./julia -e 'using Pkg; Pkg.build("MXNet")'
 
     # run the script `julia/test/runtests.jl`
@@ -1253,7 +1253,7 @@ build_docs() {
 
     # Setup environment for Julia docs
     export PATH="/work/julia10/bin:$PATH"
-    export MXNET_HOME='/work/mxnet'
+    export MXNET_ROOT='/work/mxnet'
     export JULIA_DEPOT_PATH='/work/julia-depot'
 
     julia -e 'using InteractiveUtils; versioninfo()'
@@ -1465,7 +1465,7 @@ deploy_docs() {
 
     # Setup for Julia docs
     export PATH="/work/julia10/bin:$PATH"
-    export MXNET_HOME='/work/mxnet'
+    export MXNET_ROOT='/work/mxnet'
     export JULIA_DEPOT_PATH='/work/julia-depot'
 
     julia -e 'using InteractiveUtils; versioninfo()'
diff --git a/ci/windows/test_jl07_cpu.ps1 b/ci/windows/test_jl07_cpu.ps1
index 6cd34ef209de..d3b77b1cbc1d 100644
--- a/ci/windows/test_jl07_cpu.ps1
+++ b/ci/windows/test_jl07_cpu.ps1
@@ -20,7 +20,7 @@
 # set default output encoding to utf8
 $PSDefaultParameterValues['Out-File:Encoding'] = 'utf8'
 
-$env:MXNET_HOME = [System.IO.Path]::GetFullPath('.\windows_package')
+$env:MXNET_ROOT = [System.IO.Path]::GetFullPath('.\windows_package')
 $env:JULIA_URL = "https://julialang-s3.julialang.org/bin/winnt/x64/0.7/julia-0.7.0-win64.exe"
 $env:JULIA_DEPOT_PATH = [System.IO.Path]::GetFullPath('.\julia-depot')
 
diff --git a/ci/windows/test_jl10_cpu.ps1 b/ci/windows/test_jl10_cpu.ps1
index 96c419066354..2dbda4886c42 100644
--- a/ci/windows/test_jl10_cpu.ps1
+++ b/ci/windows/test_jl10_cpu.ps1
@@ -20,7 +20,7 @@
 # set default output encoding to utf8
 $PSDefaultParameterValues['Out-File:Encoding'] = 'utf8'
 
-$env:MXNET_HOME = [System.IO.Path]::GetFullPath('.\windows_package')
+$env:MXNET_ROOT = [System.IO.Path]::GetFullPath('.\windows_package')
 $env:JULIA_URL = "https://julialang-s3.julialang.org/bin/winnt/x64/1.0/julia-1.0.3-win64.exe"
 $env:JULIA_DEPOT_PATH = [System.IO.Path]::GetFullPath('.\julia-depot')
 
diff --git a/julia/NEWS.md b/julia/NEWS.md
index e678b97176d1..9935d6885ca5 100644
--- a/julia/NEWS.md
+++ b/julia/NEWS.md
@@ -17,6 +17,13 @@
 
 # v1.6.0
 
+* Rename environment variable `MXNET_HOME` to `MXNET_ROOT` (#15568).
+
+* Environment variable `MXNET_LIBRARY_PATH` supports (#15568).
+
+  ```shell
+  $ MXNET_LIBRARY_PATH=/path/to/libmxnet.so julia
+  ```
 
 # v1.5.0
 
diff --git a/julia/deps/build.jl b/julia/deps/build.jl
index a87343d9dab5..dfd3f78379b0 100644
--- a/julia/deps/build.jl
+++ b/julia/deps/build.jl
@@ -26,14 +26,35 @@ libmxnet_detected = false
 libmxnet_curr_ver = get(ENV, "MXNET_COMMIT", "master")
 curr_win = "20190608"  # v1.5.0
 
+# TODO: remove MXNET_HOME backward compatibility in v1.7
 if haskey(ENV, "MXNET_HOME")
-  MXNET_HOME = ENV["MXNET_HOME"]
-  @info("MXNET_HOME environment detected: $MXNET_HOME")
+  @warn "The environment variable `MXNET_HOME` has been renamed, please use `MXNET_ROOT` instead."
+end
+
+# TODO: remove MXNET_HOME backward compatibility in v2.0
+MXNET_ROOT = get(ENV, "MXNET_ROOT", get(ENV, "MXNET_HOME", ""))
+search_locations = if !isempty(MXNET_ROOT)
+  !isabspath(MXNET_ROOT) && error("MXNET_ROOT should be a absolute path")
+  @info "env var: MXNET_ROOT -> $MXNET_ROOT"
+  [joinpath(MXNET_ROOT, "lib"), MXNET_ROOT]
+else
+  []
+end
+
+MXNET_LIBRARY_PATH = get(ENV, "MXNET_LIBRARY_PATH", "")
+println(typeof(MXNET_LIBRARY_PATH))
+# In case of macOS, if user build libmxnet from source and set the MXNET_ROOT,
+# the output is still named as `libmxnet.so`.
+search_names = ["libmxnet.$(Libdl.dlext)", "libmxnet.so"]
+if !isempty(MXNET_LIBRARY_PATH)
+  !isabspath(MXNET_LIBRARY_PATH) && error("MXNET_LIBRARY_PATH should be a absolute path")
+  @info "env var: MXNET_LIBRARY_PATH -> $MXNET_LIBRARY_PATH"
+  pushfirst!(search_names, MXNET_LIBRARY_PATH)
+end
+
+if (!isempty(MXNET_ROOT)) || (!isempty(MXNET_LIBRARY_PATH))
   @info("Trying to load existing libmxnet...")
-  # In case of macOS, if user build libmxnet from source and set the MXNET_HOME,
-  # the output is still named as `libmxnet.so`.
-  lib = Libdl.find_library(["libmxnet.$(Libdl.dlext)", "libmxnet.so"],
-                           [joinpath(MXNET_HOME, "lib"), MXNET_HOME])
+  lib = Libdl.find_library(search_names, search_locations)
   if !isempty(lib)
     @info("Existing libmxnet detected at $lib, skip building...")
     libmxnet_detected = true
diff --git a/julia/docs/src/user-guide/install.md b/julia/docs/src/user-guide/install.md
index d1a90a808507..d9d5574b025d 100644
--- a/julia/docs/src/user-guide/install.md
+++ b/julia/docs/src/user-guide/install.md
@@ -40,9 +40,13 @@ libmxnet.
 
 There are several environment variables that change this behaviour.
 
-- `MXNET_HOME`: If you already have a pre-installed version of mxnet
-  you can use `MXNET_HOME` to point the build-process in the right direction.
-- `CUDA_HOME`: If the automatic cuda detection fails you can also set `CUDA_HOME`
+- `MXNET_ROOT`: If you already have a pre-installed version of mxnet
+  you can use `MXNET_ROOT` to point the build-process in the right direction.
+  Note that you should set this variable as a absolute path.
+- `MXNET_HOME`: This variable is replaced by `MXNET_ROOT`. It has been renamed
+  as of v1.6.
+- `MXNET_LIBRARY_PATH`: The absolute path of the libmxnet share object.
+- `CUDA_HOME`: If the automatic CUDA detection fails you can also set `CUDA_HOME`
   to override the process.
 - `MXNET_COMMIT`: To control which version of libmxnet will be compiled,
   you can use the`MXNET_COMMIT` variable to point to either a version tag
@@ -80,18 +84,18 @@ to work with a separate, maybe customized libmxnet.
 
 To build libmxnet, please refer to [the installation guide of
 libmxnet](https://mxnet.incubator.apache.org/install/index.html). After
-successfully installing libmxnet, set the `MXNET_HOME` *environment
+successfully installing libmxnet, set the `MXNET_ROOT` *environment
 variable* to the location of libmxnet. In other words, the compiled
-`libmxnet.so` should be found in `$MXNET_HOME/lib`.
+`libmxnet.so` should be found in `$MXNET_ROOT/lib`.
 
 > **note**
 >
-> The constant `MXNET_HOME` is pre-compiled in MXNet.jl package cache.
+> The constant `MXNET_ROOT` is pre-compiled in MXNet.jl package cache.
 > If you updated the environment variable after installing MXNet.jl,
 > make sure to update the pre-compilation cache by
 > `Base.compilecache("MXNet")`.
 
-When the `MXNET_HOME` environment variable is detected and the
+When the `MXNET_ROOT` environment variable is detected and the
 corresponding `libmxnet.so` could be loaded successfully, MXNet.jl will
 skip automatic building during installation and use the specified
 libmxnet instead.
@@ -99,7 +103,7 @@ libmxnet instead.
 Basically, MXNet.jl will search `libmxnet.so` or `libmxnet.dll` in the
 following paths (and in that order):
 
--   `$MXNET_HOME/lib`: customized libmxnet builds
+-   `$MXNET_ROOT/lib`: customized libmxnet builds
 -   `Pkg.dir("MXNet", "deps", "usr", "lib")`: automatic builds
 -   Any system wide library search path
 
diff --git a/julia/src/base.jl b/julia/src/base.jl
index 683146402620..7a2bde0907c1 100644
--- a/julia/src/base.jl
+++ b/julia/src/base.jl
@@ -47,11 +47,31 @@ const grad_req_map = Dict{Symbol,GRAD_REQ}(
 ################################################################################
 # Initialization and library API entrance
 ################################################################################
-const MXNET_LIB = Libdl.find_library(["libmxnet.$(Libdl.dlext)", "libmxnet.so"],  # see build.jl
-                                     [joinpath(get(ENV, "MXNET_HOME", ""), "lib"),
-                                      get(ENV, "MXNET_HOME", ""),
-                                      joinpath(@__DIR__, "..",
-                                               "deps", "usr", "lib")])
+function _get_search_names()
+  MXNET_LIBRARY_PATH = get(ENV, "MXNET_LIBRARY_PATH", "")
+  A = ["libmxnet.$(Libdl.dlext)", "libmxnet.so"]  # see build.jl
+  if !isempty(MXNET_LIBRARY_PATH)
+    !isabspath(MXNET_LIBRARY_PATH) && error("MXNET_LIBRARY_PATH should be a absolute path")
+    pushfirst!(A, MXNET_LIBRARY_PATH)
+  end
+  A
+end
+
+function _get_search_dirs()
+  # TODO: remove MXNET_HOME backward compatibility in v1.7
+  if haskey(ENV, "MXNET_HOME")
+    @warn "The environment variable `MXNET_HOME` has been renamed, please use `MXNET_ROOT` instead."
+  end
+  A = [joinpath(@__DIR__, "..", "deps", "usr", "lib")]
+  MXNET_ROOT = get(ENV, "MXNET_ROOT", get(ENV, "MXNET_HOME", ""))
+  if !isempty(MXNET_ROOT)
+    !isabspath(MXNET_ROOT) && error("MXNET_ROOT should be a absolute path")
+    prepend!(A, [joinpath(MXNET_ROOT, "lib"), MXNET_ROOT])
+  end
+  A
+end
+
+const MXNET_LIB = Libdl.find_library(_get_search_names(), _get_search_dirs())
 const LIB_VERSION = Ref{Cint}(0)
 
 if isempty(MXNET_LIB)

From 5806200e949beb8fd48c2fed13cf5bbd4baef351 Mon Sep 17 00:00:00 2001
From: Yuan Tang <terrytangyuan@gmail.com>
Date: Sun, 8 Sep 2019 05:44:15 -0400
Subject: [PATCH 301/813] [DOC] Consistent capitalization: mxnet -> MXNet,
 scala -> Scala (#16041)

---
 CONTRIBUTORS.md | 26 +++++++++++++-------------
 1 file changed, 13 insertions(+), 13 deletions(-)

diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md
index 86c20cbc3d87..8eb540d8b759 100644
--- a/CONTRIBUTORS.md
+++ b/CONTRIBUTORS.md
@@ -26,37 +26,37 @@ Committers are people who have made substantial contribution to the project and
 The committers are the granted write access to the project.
 
 * [Bing Xu](https://github.com/antinucleon)
-  - Bing is the initiator and major contributor of operators and ndarray modules of mxnet.
+  - Bing is the initiator and major contributor of operators and ndarray modules of MXNet.
 * [Tianjun Xiao](https://github.com/sneakerkg)
   - Tianqjun is the master behind the fast data loading and preprocessing.
 * [Yutian Li](https://github.com/hotpxl)
-  - Yutian is the ninja behind the dependency and storage engine of mxnet.
+  - Yutian is the ninja behind the dependency and storage engine of MXNet.
 * [Mu Li](https://github.com/mli)
-  - Mu is the contributor of the distributed key-value store in mxnet.
+  - Mu is the contributor of the distributed key-value store in MXNet.
 * [Tianqi Chen](https://github.com/tqchen)
-  - Tianqi is one of the initiator of the mxnet project.
+  - Tianqi is one of the initiator of the MXNet project.
 * [Min Lin](https://github.com/mavenlin)
-  - Min is the guy behind the symbolic magics of mxnet.
+  - Min is the guy behind the symbolic magics of MXNet.
 * [Naiyan Wang](https://github.com/winstywang)
-  - Naiyan is the creator of static symbolic graph module of mxnet.
+  - Naiyan is the creator of static symbolic graph module of MXNet.
 * [Mingjie Wang](https://github.com/jermainewang)
   - Mingjie is the initiator, and contributes the design of the dependency engine.
 * [Chuntao Hong](https://github.com/hjk41)
   - Chuntao is the initiator and provides the initial design of engine.
 * [Chiyuan Zhang](https://github.com/pluskid)
-  - Chiyuan is the creator of MXNet Julia Package.
+  - Chiyuan is the creator of MXNet Julia package.
 * [Junyuan Xie](https://github.com/piiswrong)
 * [Haibin Lin](https://github.com/eric-haibin-lin)
 * [Qiang Kou](https://github.com/thirdwing)
   - KK is a R ninja, he makes mxnet available for R users.
 * [Tong He](https://github.com/hetong007)
-  - Tong is the major maintainer of MXNetR, he designs the mxnet interface and wrote many of the tutorials on R.
+  - Tong is the major maintainer of MXNet R package, he designs the MXNet interface and wrote many of the tutorials on R.
 * [Yizhi Liu](https://github.com/yzhliu)
   - Yizhi is the main creator on mxnet scala project to make deep learning available for JVM stacks.
 * [Zixuan Huang](https://github.com/yanqingmen)
-  - Zixuan is one of major maintainers of mxnet scala package.
+  - Zixuan is one of major maintainers of MXNet Scala package.
 * [Yuan Tang](https://github.com/terrytangyuan)
-  - Yuan is one of major maintainers of mxnet scala package.
+  - Yuan is one of major maintainers of MXNet Scala package.
 * [Chris Olivier](https://github.com/cjolivier01)
 * [Sergey Kolychev](https://github.com/sergeykolychev)
   - Sergey is original author and current maintainer of Perl5 interface.
@@ -89,9 +89,9 @@ List of Contributors
 * [Full List of Contributors](https://github.com/apache/incubator-mxnet/graphs/contributors)
   - To contributors: please add your name to the list when you submit a patch to the project:)
 * [Feng Wang](https://github.com/happynear)
-  - Feng makes mxnet compatible with Windows Visual Studio.
+  - Feng makes MXNet compatible with Windows Visual Studio.
 * [Jack Deng](https://github.com/jdeng)
-  - Jack created the amalgamation script and Go bind for mxnet.
+  - Jack created the amalgamation script and Go bind for MXNet.
 * [Li Dong](https://github.com/donglixp)
 * [Piji Li](https://github.com/lipiji)
 * [Hu Shiwen](https://github.com/yajiedesign)
@@ -103,7 +103,7 @@ List of Contributors
 * [Nan Xiao](https://github.com/road2stat)
 * [Wei Wu](https://github.com/tornadomeet)
 * [Michaël Benesty](https://github.com/pommedeterresautee)
-  -Michaël contributes the R visualization module of mxnet
+  -Michaël contributes the R visualization module of MXNet
 * [Kublai Jing](https://github.com/Kublai-Jing)
 * [chenjx1005](https://github.com/chenjx1005)
 * [ry](https://github.com/ry)

From 1c67928ad387c09731e5d062827439414b6d4851 Mon Sep 17 00:00:00 2001
From: Anand J <anandj@cs.toronto.edu>
Date: Sun, 8 Sep 2019 18:32:49 -0400
Subject: [PATCH 302/813] [MXNET-1294] Add KVSTORE PushPull API (#15559)

* Switch to latest ps-lite

* Add PushPull API

* Add PushPull test cases
---
 3rdparty/ps-lite                          |  2 +-
 include/mxnet/c_api.h                     | 42 ++++++++++++
 include/mxnet/kvstore.h                   | 27 ++++++++
 python/mxnet/kvstore.py                   | 81 +++++++++++++++++++++++
 src/c_api/c_api.cc                        | 52 +++++++++++++++
 src/kvstore/kvstore_dist.h                | 75 +++++++++++++++++++++
 src/kvstore/kvstore_dist_server.h         | 22 ++++--
 src/kvstore/kvstore_local.h               | 31 +++++++++
 tests/nightly/dist_device_sync_kvstore.py | 30 ++++++---
 9 files changed, 345 insertions(+), 17 deletions(-)

diff --git a/3rdparty/ps-lite b/3rdparty/ps-lite
index 2c8ed25384fc..60b826e4422f 160000
--- a/3rdparty/ps-lite
+++ b/3rdparty/ps-lite
@@ -1 +1 @@
-Subproject commit 2c8ed25384fc0ea00a70f081f8308170146d8f25
+Subproject commit 60b826e4422fee0df00b892c66ffffea11e5da3f
diff --git a/include/mxnet/c_api.h b/include/mxnet/c_api.h
index e5719cb36447..2fa2af5ebcf2 100644
--- a/include/mxnet/c_api.h
+++ b/include/mxnet/c_api.h
@@ -2649,6 +2649,48 @@ MXNET_DLL int MXKVStorePullRowSparseEx(KVStoreHandle handle,
                                        const NDArrayHandle* row_ids,
                                        int priority);
 
+/*!
+ * \brief push and pull a list of (key, value) pairs from the kvstore
+ * \param handle handle to the kvstore
+ * \param vnum the number of key-value pairs corresponding to vkeys
+ * \param vkeys the list of keys for the values to be pushed
+ * \param onum the number of key-value pairs corresponding to okeys
+ * \param okeys the list of keys for the values to be pulled
+ * \param vals the list of values
+ * \param outs the list of outputs
+ * \param priority the priority of the action
+ * \return 0 when success, -1 when failure happens
+ */
+MXNET_DLL int MXKVStorePushPull(KVStoreHandle handle,
+                                mx_uint vnum,
+                                const int* vkeys,
+                                mx_uint onum,
+                                const int* okeys,
+                                NDArrayHandle* vals,
+                                NDArrayHandle* outs,
+                                int priority);
+/*!
+ * \brief push and pull a list of (key, value) pairs from the kvstore,
+ * where each key is a string
+ * \param handle handle to the kvstore
+ * \param vnum the number of key-value pairs corresponding to vkeys
+ * \param vkeys the list of keys for the values to be pushed
+ * \param onum the number of key-value pairs corresponding to okeys
+ * \param okeys the list of keys for the values to be pulled
+ * \param vals the list of values
+ * \param outs the list of outputs
+ * \param priority the priority of the action
+ * \return 0 when success, -1 when failure happens
+ */
+MXNET_DLL int MXKVStorePushPullEx(KVStoreHandle handle,
+                                  mx_uint vnum,
+                                  const char** vkeys,
+                                  mx_uint onum,
+                                  const char** okeys,
+                                  NDArrayHandle* vals,
+                                  NDArrayHandle* outs,
+                                  int priority);
+
 /*!
  * \brief user-defined updater for the kvstore
  * It's this updater's responsibility to delete \a recv and \a local
diff --git a/include/mxnet/kvstore.h b/include/mxnet/kvstore.h
index a73d96356132..c5e8a8219989 100644
--- a/include/mxnet/kvstore.h
+++ b/include/mxnet/kvstore.h
@@ -198,6 +198,33 @@ class KVStore {
                     const std::vector<NDArray*>& values,
                     int priority = 0, bool ignore_sparse = true) = 0;
 
+  /*!
+   * \brief push and pull a list of key-value pairs from the store
+   * \param vkeys the list of keys to be pushed
+   * \param okeys the list of keys to be pulled. Should be the same set of keys in vkeys.
+   * \param values the list of values to be pushed
+   * \param outs the list of buffers for the pulled data, they should be preallocated
+   * \param priority Priority of the action.
+   */
+  virtual void PushPull(const std::vector<int>& vkeys,
+                        const std::vector<int>& okeys,
+                        const std::vector<NDArray>& values,
+                        const std::vector<NDArray*>& outs,
+                        int priority = 0) = 0;
+
+  /*!
+   * \brief push and pull a list of key-value pairs from the store
+   * \param vkeys the list of keys to be pushed in string format
+   * \param okeys the list of keys to be pulled in string format. Should be the same set of keys in vkeys.
+   * \param values the list of values to be pushed
+   * \param outs the list of buffers for the pulled data, they should be preallocated
+   * \param priority Priority of the action.
+   */
+  virtual void PushPull(const std::vector<std::string>& str_vkeys,
+                        const std::vector<std::string>& str_okeys,
+                        const std::vector<NDArray>& values,
+                        const std::vector<NDArray*>& outs,
+                        int priority = 0) = 0;
   /*!
    * \brief pull a list of key-value pairs from the store.
    *        The NDArray pulled back will be in row_sparse storage with only the
diff --git a/python/mxnet/kvstore.py b/python/mxnet/kvstore.py
index a54817501391..5d332ff45ecb 100644
--- a/python/mxnet/kvstore.py
+++ b/python/mxnet/kvstore.py
@@ -311,6 +311,87 @@ def pull(self, key, out=None, priority=0, ignore_sparse=True):
                                                     cvals, ctypes.c_int(priority),
                                                     ctypes.c_bool(ignore_sparse)))
 
+    def pushpull(self, key, value, out=None, priority=0):
+        """ Performs push and pull a single value or a sequence of values from the store.
+
+        This function is coalesced form of push and pull operations. This function returns
+        immediately after adding an operator to the engine. Subsequent attempts to read
+        from the `out` variable will be blocked until the pull operation completes.
+
+        `value` is pushed to the kvstore server for the specified keys and the updated
+        values are pulled from the server to `out`. If `out` is not specified the pulled
+        values are written to `value`. The returned values are guaranteed to be the latest
+        values in the store.
+
+        pushpull with `RowSparseNDArray` is not supported for dist kvstore.
+
+        Parameters
+        ----------
+        key : str, int, or sequence of str or int
+            Keys.
+
+        value : NDArray, RowSparseNDArray, list of NDArray or RowSparseNDArray,
+                or list of list of NDArray or RowSparseNDArray
+            Values corresponding to the keys.
+
+        out: NDArray or list of NDArray or list of list of NDArray
+            Values corresponding to the keys.
+
+        priority : int, optional
+            The priority of the pull operation.
+            Higher priority pull operations are likely to be executed before
+            other pull actions.
+
+        Examples
+        --------
+        >>> # push a single key-value pair
+        >>> kv.pushpull('3', mx.nd.ones(shape)*8, out=a)
+        >>> print a.asnumpy()
+        [[ 8.  8.  8.]
+        [ 8.  8.  8.]]
+
+        >>> # aggregate the value and the push
+        >>> gpus = [mx.gpu(i) for i in range(4)]
+        >>> b = [mx.nd.ones(shape, gpu) for gpu in gpus]
+        >>> kv.pushpull('3', b, out=a)
+        >>> print a.asnumpy()
+        [[ 4.  4.  4.]
+        [ 4.  4.  4.]]
+
+        >>> # push a list of keys.
+        >>> # single device
+        >>> keys = ['4', '5', '6']
+        >>> b = [mx.nd.zeros(shape)]*len(keys)
+        >>> kv.push(keys, [mx.nd.ones(shape)]*len(keys), out=b)
+        >>> print b[1].asnumpy()
+        [[ 1.  1.  1.]
+        [ 1.  1.  1.]]
+
+        >>> # multiple devices:
+        >>> keys = ['7', '8', '9']
+        >>> b = [[mx.nd.ones(shape, gpu) for gpu in gpus]] * len(keys)
+        >>> kv.pushpull(keys, b)
+        >>> print b[1][1].asnumpy()
+        [[ 4.  4.  4.]
+        [ 4.  4.  4.]]
+        """
+
+        cvkeys, cvals, use_str_keys = _ctype_key_value(key, value)
+        if out is not None:
+            cokeys, couts, _ = _ctype_key_value(key, out)
+        else:
+            cokeys = cvkeys
+            couts = cvals
+
+        if use_str_keys:
+            check_call(_LIB.MXKVStorePushPullEx(
+                self.handle, mx_uint(len(cvkeys)), cvkeys, mx_uint(len(cokeys)), cokeys,
+                cvals, couts, ctypes.c_int(priority)))
+        else:
+            check_call(_LIB.MXKVStorePushPull(
+                self.handle, mx_uint(len(cvkeys)), cvkeys, mx_uint(len(cokeys)), cokeys,
+                cvals, couts, ctypes.c_int(priority)))
+
     def row_sparse_pull(self, key, out=None, priority=0, row_ids=None):
         """ Pulls a single RowSparseNDArray value or a sequence of RowSparseNDArray values \
         from the store with specified row_ids. When there is only one row_id, KVStoreRowSparsePull \
diff --git a/src/c_api/c_api.cc b/src/c_api/c_api.cc
index f3e0ba8f5c26..70ce869b0ef7 100644
--- a/src/c_api/c_api.cc
+++ b/src/c_api/c_api.cc
@@ -1064,6 +1064,58 @@ int MXKVStorePullEx(KVStoreHandle handle,
   API_END();
 }
 
+int MXKVStorePushPull(KVStoreHandle handle,
+                      mx_uint vnum,
+                      const int* vkeys,
+                      mx_uint onum,
+                      const int* okeys,
+                      NDArrayHandle* vals,
+                      NDArrayHandle* outs,
+                      int priority) {
+  API_BEGIN();
+  std::vector<int> v_vkeys(vnum);
+  std::vector<int> v_okeys(onum);
+  std::vector<NDArray> v_vals(vnum);
+  std::vector<NDArray*> v_outs(onum);
+  for (mx_uint i = 0; i < vnum; ++i) {
+    v_vkeys[i] = vkeys[i];
+    v_vals[i] = *static_cast<NDArray*>(vals[i]);
+  }
+  for (mx_uint i = 0; i < onum; ++i) {
+    v_okeys[i] = okeys[i];
+    v_outs[i] = static_cast<NDArray*>(outs[i]);
+  }
+  static_cast<KVStore*>(handle)->PushPull(v_vkeys, v_okeys, v_vals, v_outs,
+    priority);
+  API_END();
+}
+
+int MXKVStorePushPullEx(KVStoreHandle handle,
+                        mx_uint vnum,
+                        const char** vkeys,
+                        mx_uint onum,
+                        const char** okeys,
+                        NDArrayHandle* vals,
+                        NDArrayHandle* outs,
+                        int priority) {
+  API_BEGIN();
+  std::vector<std::string> v_vkeys(vnum);
+  std::vector<std::string> v_okeys(onum);
+  std::vector<NDArray> v_vals(vnum);
+  std::vector<NDArray*> v_outs(onum);
+  for (mx_uint i = 0; i < vnum; ++i) {
+    v_vkeys[i] = vkeys[i];
+    v_vals[i] = *static_cast<NDArray*>(vals[i]);
+  }
+  for (mx_uint i = 0; i < onum; ++i) {
+    v_okeys[i] = okeys[i];
+    v_outs[i] = static_cast<NDArray*>(outs[i]);
+  }
+  static_cast<KVStore*>(handle)->PushPull(v_vkeys, v_okeys, v_vals, v_outs,
+    priority);
+  API_END();
+}
+
 int MXKVStorePullWithSparse(KVStoreHandle handle,
                             uint32_t num,
                             const int* keys,
diff --git a/src/kvstore/kvstore_dist.h b/src/kvstore/kvstore_dist.h
index 5ac28cb20c8d..f2df582db3b1 100644
--- a/src/kvstore/kvstore_dist.h
+++ b/src/kvstore/kvstore_dist.h
@@ -206,6 +206,81 @@ class KVStoreDist : public KVStoreLocal {
     }
   }
 
+  void PushPullImpl(const std::vector<int>& vkeys,
+                    const std::vector<int>& okeys,
+                    const std::vector<NDArray>& values,
+                    const std::vector<NDArray*>& outputs,
+                    int priority) override {
+    std::vector<int> uniq_vkeys;
+    std::vector<int> uniq_okeys;
+    std::vector<std::vector<NDArray>> grouped_vals;
+    std::vector<std::vector<NDArray*>> grouped_outs;
+
+    GroupKVPairsPush(vkeys, values, &uniq_vkeys, &grouped_vals, false);
+    GroupKVPairsPull(okeys, outputs, &uniq_okeys, &grouped_outs, true);
+    CHECK_EQ(uniq_vkeys.size(), uniq_okeys.size())
+             << "List of push and pull keys are different";
+
+    for (size_t i = 0; i < uniq_vkeys.size(); ++i) {
+      CHECK_EQ(uniq_vkeys[i], uniq_okeys[i])
+             << "Mismatch in push and pull key";
+      int key = uniq_vkeys[i];
+      const auto& vals = grouped_vals[i];
+      const auto& outs = grouped_outs[i];
+
+      NDArray merged = comm_->Reduce(key, vals, priority);
+
+      const auto push_stype = merged.storage_type();
+      const auto pull_stype = outs[0]->storage_type();
+      CHECK_EQ(push_stype, kDefaultStorage)
+               << "Expected push_stype of value to be kDefaultStorage";
+      CHECK_EQ(pull_stype, kDefaultStorage)
+               << "Expected pull_stype of value to be kDefaultStorage";
+
+      const int push_dtype = merged.dtype();
+      const int pull_dtype = outs[0]->dtype();
+      CHECK_EQ(push_dtype, pull_dtype) << "Output buffer dtype is different";
+
+      auto &comm_buf = comm_buf_[key];
+      if (merged.ctx().dev_mask() == cpu::kDevMask) {
+        comm_buf = merged;  // avoid memory copy
+      } else {
+        if (comm_buf.is_none()) {
+          comm_buf = NDArray(outs[0]->shape(), pinned_ctx_, true, pull_dtype);
+        }
+        CopyFromTo(merged, &comm_buf);
+      }
+
+      CHECK(gradient_compression_->get_type() == CompressionType::kNone)
+               << "Compression not supported with PushPull";
+      auto pushpull = [this, key, comm_buf](
+          RunContext rctx, Engine::CallbackOnComplete cb) {
+        size_t size = comm_buf.shape().Size();
+        const int dtype = comm_buf.dtype();
+        const int num_bytes = mshadow::mshadow_sizeof(dtype);
+        const int cmd = GetCommandType(RequestType::kDefaultPushPull, dtype);
+
+        PSKV& pskv = EncodeDefaultKey(key, size, num_bytes);
+        char* data = static_cast<char*>(comm_buf.data().dptr_);
+        auto vals = new ps::SArray<char>(data, size * num_bytes, false);
+
+        CHECK_NOTNULL(ps_worker_)->ZPushPull(
+          pskv.keys, *vals, vals, &pskv.lens, cmd, [vals, cb](){ delete vals; cb(); });
+      };
+
+      CHECK_NOTNULL(Engine::Get())->PushAsync(
+          pushpull,
+          pinned_ctx_,
+          {},
+          {comm_buf.var()},
+          FnProperty::kNormal,
+          priority,
+          "KVStoreDistDefaultStoragePushPull");
+
+      comm_->Broadcast(key, comm_buf, outs, priority);
+    }
+  }
+
   void PushImpl(const std::vector<int>& keys,
                 const std::vector<NDArray>& values,
                 int priority) override {
diff --git a/src/kvstore/kvstore_dist_server.h b/src/kvstore/kvstore_dist_server.h
index 0cb1a11e3fcc..65ded79743e4 100644
--- a/src/kvstore/kvstore_dist_server.h
+++ b/src/kvstore/kvstore_dist_server.h
@@ -344,7 +344,8 @@ class KVStoreDistServer {
   }
 
   inline void ApplyUpdates(const DataHandleType type, const int key,
-                           UpdateBuf *update_buf, ps::KVServer<char>* server) {
+                           const ps::KVPairs<char>& req_data, UpdateBuf *update_buf,
+                           ps::KVServer<char>* server) {
     if (!sync_mode_ || update_buf->request.size() == (size_t) ps::NumWorkers()) {
       // let the main thread to execute updater_, which is necessary for python
       auto& stored = has_multi_precision_copy(type) ? store_realt_[key] : store_[key];
@@ -364,7 +365,16 @@ class KVStoreDistServer {
         LOG(INFO) << "sent response to " << update_buf->request.size() << " workers";
       }
       for (const auto& req : update_buf->request) {
-        server->Response(req);
+        /**
+         * Request can be for either push, pull or pushpull
+         * If pull flag is set, respond immediately with the updated values
+         * Otherwise, only send the notification
+         */
+        if (req.pull) {
+          DefaultStorageResponse(type, key, req, req_data, server);
+        } else {
+          server->Response(req);
+        }
       }
       update_buf->request.clear();
       if (has_multi_precision_copy(type)) CopyFromTo(stored, store_[key]);
@@ -532,7 +542,7 @@ class KVStoreDistServer {
                                        true, merged_dtype);
             }  // else nothing to aggregate
             updates.request.push_back(req_meta);
-            ApplyUpdates(type, master_key, &updates, server);
+            ApplyUpdates(type, master_key, req_data, &updates, server);
           } else {
             server->Response(req_meta);
           }
@@ -570,7 +580,7 @@ class KVStoreDistServer {
             AccumulateRowSparseGrads(type, recved, &updates);
           }
           updates.request.push_back(req_meta);
-          ApplyUpdates(type, master_key, &updates, server);
+          ApplyUpdates(type, master_key, req_data, &updates, server);
         }
       }
     } else {
@@ -649,7 +659,7 @@ class KVStoreDistServer {
           merged.merged += decomp_buf;
         }
         merged.request.push_back(req_meta);
-        ApplyUpdates(type, key, &merged, server);
+        ApplyUpdates(type, key, req_data, &merged, server);
       } else {
         // async push
         gradient_compression_->Dequantize(recved, &decomp_buf, 0);
@@ -732,7 +742,7 @@ class KVStoreDistServer {
           }
         }
         updates.request.push_back(req_meta);
-        ApplyUpdates(type, key, &updates, server);
+        ApplyUpdates(type, key, req_data, &updates, server);
       }
     } else {
       DefaultStorageResponse(type, key, req_meta, req_data, server);
diff --git a/src/kvstore/kvstore_local.h b/src/kvstore/kvstore_local.h
index 5c3c80838a36..ad70bc15ea0a 100644
--- a/src/kvstore/kvstore_local.h
+++ b/src/kvstore/kvstore_local.h
@@ -129,6 +129,15 @@ class KVStoreLocal : public KVStore {
     PullImpl(keys, values, priority, ignore_sparse);
   }
 
+  void PushPull(const std::vector<int>& vkeys,
+                const std::vector<int>& okeys,
+                const std::vector<NDArray>& values,
+                const std::vector<NDArray*>& outs,
+                int priority) override {
+    SetKeyType(kIntKey);
+    PushPullImpl(vkeys, okeys, values, outs, priority);
+  }
+
   void PullRowSparse(const std::vector<int>& keys,
                      const std::vector<std::pair<NDArray*, NDArray>>& val_rowids,
                      int priority = 0) override {
@@ -155,6 +164,19 @@ class KVStoreLocal : public KVStore {
     PullImpl(keys, values, priority, ignore_sparse);
   }
 
+  void PushPull(const std::vector<std::string>& str_vkeys,
+                const std::vector<std::string>& str_okeys,
+                const std::vector<NDArray>& values,
+                const std::vector<NDArray*>& outs,
+                int priority) override {
+    SetKeyType(kStringKey);
+    std::vector<int> vkeys(str_vkeys.size());
+    std::vector<int> okeys(str_okeys.size());
+    LookupKeys(str_vkeys, &vkeys);
+    LookupKeys(str_okeys, &okeys);
+    PushPullImpl(vkeys, okeys, values, outs, priority);
+  }
+
   void PullRowSparse(const std::vector<std::string>& str_keys,
                      const std::vector<std::pair<NDArray*, NDArray>>& val_rowids,
                      int priority = 0) override {
@@ -269,6 +291,15 @@ class KVStoreLocal : public KVStore {
     CHECK_EQ(key_type_, key_type) << "Mixed key types are not allowed";
   }
 
+  virtual void PushPullImpl(const std::vector<int>& vkeys,
+                            const std::vector<int>& okeys,
+                            const std::vector<NDArray>& values,
+                            const std::vector<NDArray*>& outs,
+                            int priority) {
+    PushImpl(vkeys, values, priority);
+    PullImpl(okeys, outs, priority, true);
+  }
+
   /**
    * \brief group values on keys for push
    */
diff --git a/tests/nightly/dist_device_sync_kvstore.py b/tests/nightly/dist_device_sync_kvstore.py
index 7fd0333aea79..dc2c7bc35747 100644
--- a/tests/nightly/dist_device_sync_kvstore.py
+++ b/tests/nightly/dist_device_sync_kvstore.py
@@ -55,23 +55,33 @@ def init_kv():
 def test_sync_push_pull():
     kv, my_rank, nworker = init_kv()
     num_gpus = 2
-    def check_default_keys(kv, my_rank, nworker):
-        nrepeat = 3
+    def check_default_keys(kv, my_rank, nworker, nrepeat=3, offset=0, use_pushpull=False):
         # checks pull after push in loop, because behavior during
         # consecutive pushes doesn't offer any guarantees
-        for i in range(nrepeat):
+        for i in range(offset, nrepeat):
             scale = my_rank + 1
-            kv.push('3', [mx.nd.ones(shape, ctx=mx.gpu(j)) * scale for j in range(num_gpus)])
-            kv.push('99', [mx.nd.ones(big_shape, ctx=mx.gpu(j)) * scale for j in range(num_gpus)])
             num = (nworker + 1) * nworker * rate * num_gpus / 2 * (i + 1) + 1
+
+            arr = [mx.nd.ones(shape, ctx=mx.gpu(j)) * scale for j in range(num_gpus)]
             val = mx.nd.zeros(shape)
-            kv.pull('3', out=val)
+            if use_pushpull:
+                kv.pushpull('3', arr, out=val)
+            else:
+                kv.push('3', arr)
+                kv.pull('3', out=val)
             check_diff_to_scalar(val, num)
-            val2 = mx.nd.zeros(big_shape)
-            kv.pull('99', out=val2)
-            check_diff_to_scalar(val2, num)
 
-    check_default_keys(kv, my_rank, nworker)
+            big_arr = [mx.nd.ones(big_shape, ctx=mx.gpu(j)) * scale for j in range(num_gpus)]
+            big_val = mx.nd.zeros(big_shape)
+            if use_pushpull:
+                kv.pushpull('99', big_arr, out=big_val)
+            else:
+                kv.push('99', big_arr)
+                kv.pull('99', out=big_val)
+            check_diff_to_scalar(big_val, num)
+
+    check_default_keys(kv, my_rank, nworker, nrepeat=3, offset=0, use_pushpull=False)
+    check_default_keys(kv, my_rank, nworker, nrepeat=3, offset=3, use_pushpull=True)
     print('worker ' + str(my_rank) + ' is done')
 
 def test_sync_init():

From 9a9c5f8fcce4c8875e9b6aabef11bcec57fceb96 Mon Sep 17 00:00:00 2001
From: Alexander Chalk <contact@alexchalk.net>
Date: Mon, 9 Sep 2019 08:59:22 -0400
Subject: [PATCH 303/813] Fix failing tests (#16117)

---
 .../test/org/apache/clojure_mxnet/operator_test.clj       | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/contrib/clojure-package/test/org/apache/clojure_mxnet/operator_test.clj b/contrib/clojure-package/test/org/apache/clojure_mxnet/operator_test.clj
index 5e1b127d18bd..c4edb19d7e3d 100644
--- a/contrib/clojure-package/test/org/apache/clojure_mxnet/operator_test.clj
+++ b/contrib/clojure-package/test/org/apache/clojure_mxnet/operator_test.clj
@@ -407,8 +407,8 @@
                  (-> (executor/forward exec-test) (executor/outputs) (first))))))
 
 (deftest test-maximum
-  (let [data1 (sym/variable "data")
-        data2 (sym/variable "data")
+  (let [data1 (sym/variable "data1")
+        data2 (sym/variable "data2")
         shape-vec [3 4]
         data-tmp1 (random/uniform 0 100 shape-vec)
         data-tmp2 (random/uniform 0 100 shape-vec)
@@ -424,8 +424,8 @@
                  out))))
 
 (deftest test-minimun
-  (let [data1 (sym/variable "data")
-        data2 (sym/variable "data")
+  (let [data1 (sym/variable "data1")
+        data2 (sym/variable "data2")
         shape-vec [3 4]
         data-tmp1 (random/uniform 0 100 shape-vec)
         data-tmp2 (random/uniform 0 100 shape-vec)

From e260f13499a6c84737a97aae09e5921ad8fdd4fd Mon Sep 17 00:00:00 2001
From: Xi Wang <xidulu@gmail.com>
Date: Mon, 9 Sep 2019 22:58:29 +0800
Subject: [PATCH 304/813] [Numpy] Random.choice implemented (#16089)

* imperative choice done

* unit test done

* expose take to np internal

* style fixed

* style fixed

* style problems fixed

* remove out parameter and fix style

* fix syntax error
---
 python/mxnet/ndarray/numpy/random.py      |  78 ++++++-
 python/mxnet/numpy/random.py              |  57 ++++-
 python/mxnet/symbol/numpy/random.py       |  77 +++++++
 src/operator/numpy/random/np_choice_op.cc |  81 +++++++
 src/operator/numpy/random/np_choice_op.cu |  46 ++++
 src/operator/numpy/random/np_choice_op.h  | 244 ++++++++++++++++++++++
 src/operator/tensor/indexing_op.cc        |   1 +
 tests/python/unittest/test_numpy_op.py    |  97 +++++++++
 8 files changed, 679 insertions(+), 2 deletions(-)
 create mode 100644 src/operator/numpy/random/np_choice_op.cc
 create mode 100644 src/operator/numpy/random/np_choice_op.cu
 create mode 100644 src/operator/numpy/random/np_choice_op.h

diff --git a/python/mxnet/ndarray/numpy/random.py b/python/mxnet/ndarray/numpy/random.py
index 9ea2ef0f5405..9372beaf1e92 100644
--- a/python/mxnet/ndarray/numpy/random.py
+++ b/python/mxnet/ndarray/numpy/random.py
@@ -24,7 +24,7 @@
 from ...base import numeric_types
 
 
-__all__ = ['randint', 'uniform', 'normal']
+__all__ = ['randint', 'uniform', 'normal', "choice"]
 
 
 def randint(low, high=None, size=None, dtype=None, **kwargs):
@@ -243,3 +243,79 @@ def multinomial(n, pvals, size=None):
         if any(isinstance(i, list) for i in pvals):
             raise ValueError('object too deep for desired array')
         return _npi.multinomial(n=n, pvals=pvals, size=size)
+
+
+def choice(a, size=None, replace=True, p=None, **kwargs):
+    """Generates a random sample from a given 1-D array
+
+    Parameters
+    -----------
+    a : 1-D array-like or int
+        If an ndarray, a random sample is generated from its elements.
+        If an int, the random sample is generated as if a were np.arange(a)
+    size : int or tuple of ints, optional
+        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k`` samples are drawn.  Default is None, in which case a
+        single value is returned.
+    replace : boolean, optional
+        Whether the sample is with or without replacement
+    p : 1-D array-like, optional
+        The probabilities associated with each entry in a.
+        If not given the sample assumes a uniform distribution over all
+        entries in a.
+    ctx : Context, optional
+        Device context of output. Default is current context.
+
+    Returns
+    --------
+    samples : ndarray
+        The generated random samples
+
+    Examples
+    ---------
+    Generate a uniform random sample from np.arange(5) of size 3:
+
+    >>> np.random.choice(5, 3)
+    array([0, 3, 4])
+    >>> #This is equivalent to np.random.randint(0,5,3)
+
+    Generate a non-uniform random sample from np.arange(5) of size 3:
+
+    >>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])
+    array([3, 3, 0])
+
+    Generate a uniform random sample from np.arange(5) of size 3 without
+    replacement:
+
+    >>> np.random.choice(5, 3, replace=False)
+    array([3,1,0])
+    >>> #This is equivalent to np.random.permutation(np.arange(5))[:3]
+
+    Generate a non-uniform random sample from np.arange(5) of size
+    3 without replacement:
+
+    >>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])
+    array([2, 3, 0])
+    """
+    from ...numpy import ndarray as np_ndarray
+    ctx = kwargs.pop('ctx', None)
+    if ctx is None:
+        ctx = current_context()
+    out = kwargs.pop('out', None)
+    if size == ():
+        size = None
+    if isinstance(a, np_ndarray):
+        ctx = None
+        if p is None:
+            indices = _npi.choice(a, a=None, size=size,
+                                  replace=replace, ctx=ctx, weighted=False)
+            return _npi.take(a, indices)
+        else:
+            indices = _npi.choice(a, p, a=None, size=size,
+                                  replace=replace, ctx=ctx, weighted=True)
+            return _npi.take(a, indices)
+    else:
+        if p is None:
+            return _npi.choice(a=a, size=size, replace=replace, ctx=ctx, weighted=False, out=out)
+        else:
+            return _npi.choice(p, a=a, size=size, replace=replace, ctx=ctx, weighted=True, out=out)
diff --git a/python/mxnet/numpy/random.py b/python/mxnet/numpy/random.py
index bd534f0b6d97..aace767c8d55 100644
--- a/python/mxnet/numpy/random.py
+++ b/python/mxnet/numpy/random.py
@@ -21,7 +21,7 @@
 from ..ndarray import numpy as _mx_nd_np
 
 
-__all__ = ["randint", "uniform", "normal"]
+__all__ = ["randint", "uniform", "normal", "choice"]
 
 
 def randint(low, high=None, size=None, dtype=None, **kwargs):
@@ -180,3 +180,58 @@ def multinomial(n, pvals, size=None, **kwargs):
     array([32, 68])
     """
     return _mx_nd_np.random.multinomial(n, pvals, size, **kwargs)
+
+
+def choice(a, size=None, replace=True, p=None, **kwargs):
+    """Generates a random sample from a given 1-D array
+
+    Parameters
+    -----------
+    a : 1-D array-like or int
+        If an ndarray, a random sample is generated from its elements.
+        If an int, the random sample is generated as if a were np.arange(a)
+    size : int or tuple of ints, optional
+        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k`` samples are drawn.  Default is None, in which case a
+        single value is returned.
+    replace : boolean, optional
+        Whether the sample is with or without replacement
+    p : 1-D array-like, optional
+        The probabilities associated with each entry in a.
+        If not given the sample assumes a uniform distribution over all
+        entries in a.
+    ctx : Context, optional
+        Device context of output. Default is current context.
+
+    Returns
+    --------
+    samples : ndarray
+        The generated random samples
+
+    Examples
+    ---------
+    Generate a uniform random sample from np.arange(5) of size 3:
+
+    >>> np.random.choice(5, 3)
+    array([0, 3, 4])
+    >>> #This is equivalent to np.random.randint(0,5,3)
+
+    Generate a non-uniform random sample from np.arange(5) of size 3:
+
+    >>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])
+    array([3, 3, 0])
+
+    Generate a uniform random sample from np.arange(5) of size 3 without
+    replacement:
+
+    >>> np.random.choice(5, 3, replace=False)
+    array([3,1,0])
+    >>> #This is equivalent to np.random.permutation(np.arange(5))[:3]
+
+    Generate a non-uniform random sample from np.arange(5) of size
+    3 without replacement:
+
+    >>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])
+    array([2, 3, 0])
+    """
+    return _mx_nd_np.random.choice(a, size, replace, p, **kwargs)
diff --git a/python/mxnet/symbol/numpy/random.py b/python/mxnet/symbol/numpy/random.py
index c5b8e1dc4906..523983bac20a 100644
--- a/python/mxnet/symbol/numpy/random.py
+++ b/python/mxnet/symbol/numpy/random.py
@@ -190,3 +190,80 @@ def normal(loc=0.0, scale=1.0, size=None, **kwargs):
         raise NotImplementedError('np.random.normal only supports loc and scale of '
                                   'numeric types for now')
     return _npi.random_normal(loc, scale, shape=size, dtype=dtype, ctx=ctx, out=out, **kwargs)
+
+
+def choice(a, size=None, replace=True, p=None, **kwargs):
+    """Generates a random sample from a given 1-D array
+
+    Parameters
+    -----------
+    a : 1-D array-like or int
+        If an ndarray, a random sample is generated from its elements.
+        If an int, the random sample is generated as if a were np.arange(a)
+    size : int or tuple of ints, optional
+        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k`` samples are drawn.  Default is None, in which case a
+        single value is returned.
+    replace : boolean, optional
+        Whether the sample is with or without replacement
+    p : 1-D array-like, optional
+        The probabilities associated with each entry in a.
+        If not given the sample assumes a uniform distribution over all
+        entries in a.
+    ctx : Context, optional
+        Device context of output. Default is current context.
+
+    Returns
+    --------
+    samples : _Symbol
+        The generated random samples
+
+    Examples
+    ---------
+    Generate a uniform random sample from np.arange(5) of size 3:
+
+    >>> np.random.choice(5, 3)
+    array([0, 3, 4])
+    >>> #This is equivalent to np.random.randint(0,5,3)
+
+    Generate a non-uniform random sample from np.arange(5) of size 3:
+
+    >>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])
+    array([3, 3, 0])
+
+    Generate a uniform random sample from np.arange(5) of size 3 without
+    replacement:
+
+    >>> np.random.choice(5, 3, replace=False)
+    array([3,1,0])
+    >>> #This is equivalent to np.random.permutation(np.arange(5))[:3]
+
+    Generate a non-uniform random sample from np.arange(5) of size
+    3 without replacement:
+
+    >>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])
+    array([2, 3, 0])
+    """
+    from ._symbol import _Symbol as np_symbol
+    ctx = kwargs.pop('ctx', None)
+    if ctx is None:
+        ctx = current_context()
+    out = kwargs.pop('out', None)
+    if size == ():
+        size = None
+
+    if isinstance(a, np_symbol):
+        ctx = None
+        if p is None:
+            indices = _npi.choice(a, a=None, size=size,
+                                  replace=replace, ctx=ctx, weighted=False)
+            return _npi.take(a, indices)
+        else:
+            indices = _npi.choice(a, p, a=None, size=size,
+                                  replace=replace, ctx=ctx, weighted=True)
+            return _npi.take(a, indices)
+    else:
+        if p is None:
+            return _npi.choice(a=a, size=size, replace=replace, ctx=ctx, weighted=False, out=out)
+        else:
+            return _npi.choice(p, a=a, size=size, replace=replace, ctx=ctx, weighted=True, out=out)
diff --git a/src/operator/numpy/random/np_choice_op.cc b/src/operator/numpy/random/np_choice_op.cc
new file mode 100644
index 000000000000..328d7e264861
--- /dev/null
+++ b/src/operator/numpy/random/np_choice_op.cc
@@ -0,0 +1,81 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_choice_op.cc
+ * \brief Operator for random subset sampling
+ */
+
+#include "./np_choice_op.h"
+#include <algorithm>
+
+namespace mxnet {
+namespace op {
+
+template <>
+void _sort<cpu>(float* key, int64_t* data, index_t length) {
+  std::sort(data, data + length,
+            [key](int64_t const& i, int64_t const& j) -> bool {
+              return key[i] > key[j];
+            });
+}
+
+DMLC_REGISTER_PARAMETER(NumpyChoiceParam);
+
+NNVM_REGISTER_OP(_npi_choice)
+.describe("random choice")
+.set_num_inputs(
+  [](const nnvm::NodeAttrs& attrs) {
+    int num_input = 0;
+    const NumpyChoiceParam& param = nnvm::get<NumpyChoiceParam>(attrs.parsed);
+    if (param.weighted) num_input += 1;
+    if (!param.a.has_value()) num_input += 1;
+    return num_input;
+})
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>(
+    "FListInputNames",
+    [](const NodeAttrs& attrs) {
+      int num_input = 0;
+      const NumpyChoiceParam& param =
+          nnvm::get<NumpyChoiceParam>(attrs.parsed);
+      if (param.weighted) num_input += 1;
+      if (!param.a.has_value()) num_input += 1;
+      if (num_input == 0) return std::vector<std::string>();
+      if (num_input == 1) return std::vector<std::string>{"input1"};
+      return std::vector<std::string>{"input1", "input2"};
+})
+.set_attr_parser(ParamParser<NumpyChoiceParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyChoiceOpShape)
+.set_attr<nnvm::FInferType>("FInferType", NumpyChoiceOpType)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const nnvm::NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{
+        ResourceRequest::kRandom,
+        ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", NumpyChoiceForward<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.add_argument("input1", "NDArray-or-Symbol", "Source input")
+.add_argument("input2", "NDArray-or-Symbol", "Source input")
+.add_arguments(NumpyChoiceParam::__FIELDS__());
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/random/np_choice_op.cu b/src/operator/numpy/random/np_choice_op.cu
new file mode 100644
index 000000000000..0f42a2e76df0
--- /dev/null
+++ b/src/operator/numpy/random/np_choice_op.cu
@@ -0,0 +1,46 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_choice_op.cu
+ * \brief Operator for random subset sampling
+ */
+
+#include <thrust/execution_policy.h>
+#include <thrust/sort.h>
+#include <thrust/swap.h>
+#include "./np_choice_op.h"
+
+namespace mxnet {
+namespace op {
+
+template <>
+void _sort<gpu>(float* key, int64_t* data, index_t length) {
+  thrust::device_ptr<float> dev_key(key);
+  thrust::device_ptr<int64_t> dev_data(data);
+  thrust::sort_by_key(dev_key, dev_key + length, dev_data,
+                      thrust::greater<float>());
+}
+
+NNVM_REGISTER_OP(_npi_choice)
+.set_attr<FCompute>("FCompute<gpu>", NumpyChoiceForward<gpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/random/np_choice_op.h b/src/operator/numpy/random/np_choice_op.h
new file mode 100644
index 000000000000..335cc2741759
--- /dev/null
+++ b/src/operator/numpy/random/np_choice_op.h
@@ -0,0 +1,244 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_choice_op.h
+ * \brief Operator for random subset sampling
+ */
+
+#ifndef MXNET_OPERATOR_NUMPY_RANDOM_NP_CHOICE_OP_H_
+#define MXNET_OPERATOR_NUMPY_RANDOM_NP_CHOICE_OP_H_
+
+#include <mshadow/base.h>
+#include <mxnet/operator_util.h>
+#include <algorithm>
+#include <string>
+#include <vector>
+#include "../../elemwise_op_common.h"
+#include "../../mshadow_op.h"
+#include "../../mxnet_op.h"
+#include "../../operator_common.h"
+#include "../../tensor/elemwise_binary_broadcast_op.h"
+
+namespace mxnet {
+namespace op {
+
+struct NumpyChoiceParam : public dmlc::Parameter<NumpyChoiceParam> {
+  dmlc::optional<int64_t> a;
+  std::string ctx;
+  dmlc::optional<mxnet::Tuple<int64_t>> size;
+  bool replace;
+  bool weighted;
+  DMLC_DECLARE_PARAMETER(NumpyChoiceParam) {
+    DMLC_DECLARE_FIELD(a);
+    DMLC_DECLARE_FIELD(size);
+    DMLC_DECLARE_FIELD(ctx).set_default("cpu");
+    DMLC_DECLARE_FIELD(replace).set_default(true);
+    DMLC_DECLARE_FIELD(weighted).set_default(false);
+  }
+};
+
+inline bool NumpyChoiceOpType(const nnvm::NodeAttrs &attrs,
+                              std::vector<int> *in_attrs,
+                              std::vector<int> *out_attrs) {
+  (*out_attrs)[0] = mshadow::kInt64;
+  return true;
+}
+
+inline bool NumpyChoiceOpShape(const nnvm::NodeAttrs &attrs,
+                               std::vector<TShape> *in_attrs,
+                               std::vector<TShape> *out_attrs) {
+  const NumpyChoiceParam &param = nnvm::get<NumpyChoiceParam>(attrs.parsed);
+  if (param.size.has_value()) {
+    // Size declared.
+    std::vector<dim_t> oshape_vec;
+    const mxnet::Tuple<int64_t> &size = param.size.value();
+    for (int i = 0; i < size.ndim(); ++i) {
+      oshape_vec.emplace_back(size[i]);
+    }
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, TShape(oshape_vec));
+  } else {
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, TShape(0, -1))
+  }
+  return true;
+}
+
+template <typename xpu>
+void _sort(float *key, int64_t *data, index_t length);
+
+namespace mxnet_op {
+
+// Uniform sample without replacement.
+struct generate_samples {
+  MSHADOW_XINLINE static void Map(index_t i, int64_t k, unsigned *rands) {
+    rands[i] = rands[i] % (i + k + 1);
+  }
+};
+
+template <typename xpu>
+struct generate_reservoir {
+  MSHADOW_XINLINE static void Map(index_t dummy_index, int64_t *indices,
+                                  unsigned *samples, int64_t nb_iterations,
+                                  int64_t k) {
+    for (int64_t i = 0; i < nb_iterations; i++) {
+      int64_t z = samples[i];
+      if (z < k) {
+        int64_t t = indices[z];
+        indices[z] = indices[i + k];
+        indices[i + k] = t;
+      }
+    }
+  }
+};
+
+// Uniform sample with replacement.
+struct random_indices {
+  MSHADOW_XINLINE static void Map(index_t i, unsigned *samples, int64_t *outs,
+                                  int64_t k) {
+    outs[i] = samples[i] % k;
+  }
+};
+
+// Weighted sample without replacement.
+// Use perturbed Gumbel variates as keys.
+struct generate_keys {
+  MSHADOW_XINLINE static void Map(index_t i, float *uniforms, float *weights) {
+    uniforms[i] = -logf(-logf(uniforms[i])) + logf(weights[i]);
+  }
+};
+
+// Weighted sample with replacement.
+struct categorical_sampling {
+  MSHADOW_XINLINE static void Map(index_t i, float *weights, size_t length,
+                                  float *uniforms, int64_t *outs) {
+    outs[i] = 0;
+    float acc = 0.0;
+    float threshold = uniforms[i];
+    for (size_t k = 0; k < length; k++) {
+      acc += weights[k];
+      if (acc < threshold) {
+        outs[i] += 1;
+      }
+    }
+  }
+};
+
+}  // namespace mxnet_op
+
+template <typename xpu>
+void NumpyChoiceForward(const nnvm::NodeAttrs &attrs, const OpContext &ctx,
+                        const std::vector<TBlob> &inputs,
+                        const std::vector<OpReqType> &req,
+                        const std::vector<TBlob> &outputs) {
+  using namespace mshadow;
+  using namespace mxnet_op;
+  const NumpyChoiceParam &param = nnvm::get<NumpyChoiceParam>(attrs.parsed);
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  bool replace = param.replace;
+  bool weighted = param.weighted;
+  int64_t input_size = 0;
+  int weight_index = 0;
+  if (param.a.has_value()) {
+    input_size = param.a.value();
+  } else {
+    input_size = inputs[0].Size();
+    weight_index += 1;
+  }
+  int64_t output_size = outputs[0].Size();
+  if (weighted) {
+    Random<xpu, float> *prnd = ctx.requested[0].get_random<xpu, float>(s);
+    int64_t random_tensor_size = replace ? output_size : input_size;
+    int64_t indices_size = replace ? 0 : input_size;
+    Tensor<xpu, 1, char> workspace =
+        ctx.requested[1].get_space_typed<xpu, 1, char>(
+            Shape1(indices_size * sizeof(int64_t) +
+                   (random_tensor_size * sizeof(float) / 7 + 1) * 8),
+            s);
+    // slice workspace
+    char *workspace_ptr = workspace.dptr_;
+    Tensor<xpu, 1, float> random_numbers =
+        Tensor<xpu, 1, float>(reinterpret_cast<float *>(workspace_ptr),
+                              Shape1(random_tensor_size), s);
+    prnd->SampleUniform(&random_numbers, 0, 1);
+    workspace_ptr += ((random_tensor_size * sizeof(float) / 7 + 1) * 8);
+    if (replace) {
+      Kernel<categorical_sampling, xpu>::Launch(
+          s, output_size, inputs[weight_index].dptr<float>(), input_size,
+          random_numbers.dptr_, outputs[0].dptr<int64_t>());
+    } else {
+      Tensor<xpu, 1, int64_t> indices = Tensor<xpu, 1, int64_t>(
+          reinterpret_cast<int64_t *>(workspace_ptr), Shape1(indices_size), s);
+      indices = expr::range((int64_t)0, input_size);
+      Kernel<generate_keys, xpu>::Launch(s, input_size, random_numbers.dptr_,
+                                         inputs[weight_index].dptr<float>());
+      _sort<xpu>(random_numbers.dptr_, indices.dptr_, input_size);
+      Copy(outputs[0].FlatTo1D<xpu, int64_t>(s), indices.Slice(0, output_size), s);
+    }
+  } else {
+    Random<xpu, unsigned> *prnd = ctx.requested[0].get_random<xpu, unsigned>(s);
+    int64_t random_tensor_size =
+        (replace ? output_size
+                 : std::min(output_size, input_size - output_size));
+    int64_t indices_size = replace ? 0 : input_size;
+    Tensor<xpu, 1, char> workspace =
+        ctx.requested[1].get_space_typed<xpu, 1, char>(
+            Shape1(indices_size * sizeof(int64_t) +
+                   (random_tensor_size * sizeof(unsigned) / 7 + 1) * 8),
+            s);
+    // slice workspace
+    char *workspace_ptr = workspace.dptr_;
+    Tensor<xpu, 1, unsigned> random_numbers =
+        Tensor<xpu, 1, unsigned>(reinterpret_cast<unsigned *>(workspace_ptr),
+                                 Shape1(random_tensor_size), s);
+    prnd->GetRandInt(random_numbers);
+    workspace_ptr += ((random_tensor_size * sizeof(unsigned) / 7 + 1) * 8);
+    if (replace) {
+      Kernel<random_indices, xpu>::Launch(s, output_size, random_numbers.dptr_,
+                                          outputs[0].dptr<int64_t>(),
+                                          input_size);
+    } else {
+      Tensor<xpu, 1, int64_t> indices = Tensor<xpu, 1, int64_t>(
+          reinterpret_cast<int64_t *>(workspace_ptr), Shape1(indices_size), s);
+      indices = expr::range((int64_t)0, input_size);
+      int64_t nb_iterations = random_tensor_size;
+      int64_t split = input_size - nb_iterations;
+      Kernel<generate_samples, xpu>::Launch(s, random_tensor_size, split,
+                                            random_numbers.dptr_);
+      // Reservoir sampling.
+      Kernel<generate_reservoir<xpu>, xpu>::Launch(
+          s, 1, indices.dptr_, random_numbers.dptr_, nb_iterations, split);
+      index_t begin;
+      index_t end;
+      if (2 * output_size < input_size) {
+        begin = input_size - output_size;
+        end = input_size;
+      } else {
+        begin = 0;
+        end = output_size;
+      }
+      Copy(outputs[0].FlatTo1D<xpu, int64_t>(s), indices.Slice(begin, end), s);
+    }
+  }
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_NUMPY_RANDOM_NP_CHOICE_OP_H_
diff --git a/src/operator/tensor/indexing_op.cc b/src/operator/tensor/indexing_op.cc
index 7a770163da17..463e9f98820e 100644
--- a/src/operator/tensor/indexing_op.cc
+++ b/src/operator/tensor/indexing_op.cc
@@ -654,6 +654,7 @@ NNVM_REGISTER_OP(_backward_SparseEmbedding)
 .set_attr<FComputeEx>("FComputeEx<cpu>", SparseEmbeddingOpBackwardEx<cpu>);
 
 NNVM_REGISTER_OP(take)
+.add_alias("_npi_take")
 .describe(R"code(Takes elements from an input array along the given axis.
 
 This function slices the input array along a particular axis with the provided indices.
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 51aa9054451c..1c9ebbb32866 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -1647,6 +1647,103 @@ def hybrid_forward(self, F, a):
                         assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
 
 
+@with_seed()
+@use_np
+def test_np_choice():
+    class TestUniformChoice(HybridBlock):
+        def __init__(self, sample_size, replace):
+            super(TestUniformChoice, self).__init__()
+            self.sample_size = sample_size
+            self.replace = replace
+
+        def hybrid_forward(self, F, a):
+            return F.np.random.choice(a=a, size=self.sample_size, replace=self.replace, p=None)
+
+    class TestWeightedChoice(HybridBlock):
+        def __init__(self, sample_size, replace):
+            super(TestWeightedChoice, self).__init__()
+            self.sample_size = sample_size
+            self.replace = replace
+        
+        def hybrid_forward(self, F, a, p):
+            op = getattr(F.np.random, "choice", None)
+            return F.np.random.choice(a, self.sample_size, self.replace, p)
+
+    def test_sample_with_replacement(sampler, num_classes, shape, weight=None):
+        samples = sampler(num_classes, shape, replace=True, p=weight).asnumpy()
+        generated_density = _np.histogram(samples, _np.arange(num_classes + 1), density=True)[0]
+        expected_density = (weight.asnumpy() if weight is not None else
+                            _np.array([1 / num_classes] * num_classes))
+        # test almost equal
+        assert_almost_equal(generated_density, expected_density, rtol=1e-1, atol=1e-1)
+        # test shape
+        assert (samples.shape == shape)
+
+    def test_sample_without_replacement(sampler, num_classes, shape, num_trials, weight=None):
+        samples = sampler(num_classes, shape, replace=False, p=weight).asnumpy()
+        # Check shape and uniqueness
+        assert samples.shape == shape
+        assert len(_np.unique(samples)) == samples.size
+        # Check distribution
+        bins = _np.zeros((num_classes))
+        expected_freq = (weight.asnumpy() if weight is not None else
+                         _np.array([1 / num_classes] * num_classes))
+        for i in range(num_trials):
+            out = sampler(num_classes, 1, replace=False, p=weight).item()
+            bins[out] += 1
+        bins /= num_trials
+        assert_almost_equal(bins, expected_freq, rtol=1e-1, atol=1e-1)
+
+    def test_indexing_mode(sampler, set_size, samples_size, replace, weight=None):
+        a = np.arange(set_size)
+        if weight is not None:
+            samples = sampler(a, weight)
+        else:
+            samples = sampler(a)
+        assert len(samples) == samples_size
+        if not replace:
+            assert len(_np.unique(samples)) == samples_size
+        
+    num_classes = 10
+    num_samples = 10 ** 8
+    # Density tests are commented out due to their huge time comsumption.
+    # Tests passed locally.
+    # shape_list1 = [
+    #     (10 ** 8, 1),
+    #     (10 ** 5, 10 ** 3),
+    #     (10 ** 2, 10 ** 3, 10 ** 3)
+    # ]
+    # for shape in shape_list1:
+    #     test_sample_with_replacement(np.random.choice, num_classes, shape)
+    #     weight = np.array(_np.random.dirichlet([1.0] * num_classes))
+    #     test_sample_with_replacement(np.random.choice, num_classes, shape, weight)
+    
+    # Tests passed locally,
+    # commented out for the same reason as above.
+    # shape_list2 = [
+    #     (6, 1),
+    #     (2, 3),
+    #     (1, 2, 3),
+    #     (2, 2),
+    # ]
+    # for shape in shape_list2:
+    #     test_sample_without_replacement(np.random.choice, num_classes, shape, 10 ** 5)
+    #     weight = np.array(_np.random.dirichlet([1.0] * num_classes))
+    #     test_sample_without_replacement(np.random.choice, num_classes, shape, 10 ** 5, weight)
+    
+    # Test hypridize mode:
+    for hybridize in [True, False]:
+        for replace in [True, False]:
+            test_choice = TestUniformChoice(num_classes // 2, replace)
+            test_choice_weighted = TestWeightedChoice(num_classes // 2, replace)
+            if hybridize:
+                test_choice.hybridize()
+                test_choice_weighted.hybridize()
+            weight = np.array(_np.random.dirichlet([1.0] * num_classes))
+            test_indexing_mode(test_choice, num_classes, num_classes // 2, replace, None)
+            test_indexing_mode(test_choice_weighted, num_classes, num_classes // 2, replace, weight)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 9675a2d5dbefc5fb8faf59126f74a0d31bfcbd02 Mon Sep 17 00:00:00 2001
From: Zach Kimberg <zachary@kimberg.com>
Date: Mon, 9 Sep 2019 14:09:32 -0700
Subject: [PATCH 305/813] Update python dependencies (#16105)

---
 tools/pip/setup.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tools/pip/setup.py b/tools/pip/setup.py
index 3905a69fb94d..f0a3e37fd3c3 100644
--- a/tools/pip/setup.py
+++ b/tools/pip/setup.py
@@ -52,8 +52,8 @@ def has_ext_modules(self):
 
 
 DEPENDENCIES = [
-    'numpy>1.16.0,<2.0.0',
-    'requests>=2.20.0',
+    'numpy<2.0.0,>1.16.0',
+    'requests>=2.20.0,<3',
     'graphviz<0.9.0,>=0.8.1'
 ]
 

From e87995db6f8ac7e8d4424001e1db5296398562f6 Mon Sep 17 00:00:00 2001
From: Rohit Kumar Srivastava <srivastava.141@osu.edu>
Date: Tue, 10 Sep 2019 21:25:33 -0700
Subject: [PATCH 306/813] Reducing memory footprint of one_hot for Large Array
 Testing (#16136)

* reducing memory footprint of one_hot for Large Tensor

* removing one_hot from large_vector and changing large_array test to incorporate large vector part of the test
---
 tests/nightly/test_large_array.py  | 10 +++++-----
 tests/nightly/test_large_vector.py |  9 ---------
 2 files changed, 5 insertions(+), 14 deletions(-)

diff --git a/tests/nightly/test_large_array.py b/tests/nightly/test_large_array.py
index 7622b76a3120..99856f770d5c 100644
--- a/tests/nightly/test_large_array.py
+++ b/tests/nightly/test_large_array.py
@@ -25,6 +25,7 @@
 
 # dimension constants
 MEDIUM_X = 10000
+VLARGE_X = 4300000000
 LARGE_X = 100000000
 SMALL_X = 100
 SMALL_Y = 50
@@ -1197,12 +1198,11 @@ def test_slice_axis():
 
 
 def test_one_hot():
-    a = nd.array(np.zeros(SMALL_Y))
-    a[0] = 1
-    a[-1] = 1
-    b = nd.one_hot(a, LARGE_X)
+    #default dtype of ndarray is float32 which cannot index elements over 2^32
+    a = nd.array([1, (VLARGE_X - 1)], dtype=np.int64)
+    b = nd.one_hot(a, VLARGE_X)
     b[0][1] == 1
-    b[-1][1] == 1
+    b[1][-1] == 1
 
 
 def test_full():
diff --git a/tests/nightly/test_large_vector.py b/tests/nightly/test_large_vector.py
index b59a4462d922..e3407792f2da 100644
--- a/tests/nightly/test_large_vector.py
+++ b/tests/nightly/test_large_vector.py
@@ -714,15 +714,6 @@ def test_full():
     assert a[-1] == 3
 
 
-def test_one_hot():
-    a = nd.zeros(10)
-    a[0] = 1
-    a[-1] = 1
-    b = nd.one_hot(a, LARGE_X)
-    assert b[0][1] == 1
-    assert b[-1][1] == 1
-
-
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From c12c978aa0b1096e0153fbdc2355628ed6271c07 Mon Sep 17 00:00:00 2001
From: reminisce <wujun.nju@gmail.com>
Date: Wed, 11 Sep 2019 00:27:34 -0700
Subject: [PATCH 307/813] Fix sample.normal shape inference

---
 src/operator/random/multisample_op.h   |  2 +-
 tests/python/unittest/test_operator.py | 13 +++++++++++++
 2 files changed, 14 insertions(+), 1 deletion(-)

diff --git a/src/operator/random/multisample_op.h b/src/operator/random/multisample_op.h
index 7d5e256297ad..bbd58e23c210 100644
--- a/src/operator/random/multisample_op.h
+++ b/src/operator/random/multisample_op.h
@@ -42,7 +42,7 @@ struct MultiSampleParam : public dmlc::Parameter<MultiSampleParam> {
   int dtype;
   DMLC_DECLARE_PARAMETER(MultiSampleParam) {
     DMLC_DECLARE_FIELD(shape)
-      .set_default(mxnet::TShape())
+      .set_default(mxnet::TShape(0, 1))
       .describe("Shape to be sampled from each random distribution.");
     DMLC_DECLARE_FIELD(dtype)
     .add_enum("None", -1)
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index bcf618f2784a..40763fabef19 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -9239,6 +9239,19 @@ def test_transpose_infer_shape_mixed():
     assert(y[0].shape == (2,3))
 
 
+@with_seed()
+def test_sample_normal_default_shape():
+    # Test case from https://github.com/apache/incubator-mxnet/issues/16135
+    s = mx.nd.sample_normal(mu=mx.nd.array([10.0]), sigma=mx.nd.array([0.5]))
+    assert s.shape == (1,)
+    s = mx.nd.sample_normal(mu=mx.nd.array([10.0]), sigma=mx.nd.array([0.5]), shape=())
+    assert s.shape == (1,)
+    s = mx.nd.sample_normal(mu=mx.nd.array([10.0]), sigma=mx.nd.array([0.5]), shape=1)
+    assert s.shape == (1, 1)
+    s = mx.nd.sample_normal(mu=mx.nd.array([10.0]), sigma=mx.nd.array([0.5]), shape=(1,))
+    assert s.shape == (1, 1)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 85fe0b439adf587024457a5346e8e84929195985 Mon Sep 17 00:00:00 2001
From: Anirudh <anirudhkrec@gmail.com>
Date: Wed, 11 Sep 2019 15:06:47 -0700
Subject: [PATCH 308/813] Fixes NAG optimizer #15543 (#16053)

* fix update rules

* readable updates in unit test

* mom update
---
 src/operator/optimizer_op-inl.h         | 50 ++++++++++++-------------
 tests/python/unittest/test_optimizer.py | 14 ++-----
 2 files changed, 27 insertions(+), 37 deletions(-)

diff --git a/src/operator/optimizer_op-inl.h b/src/operator/optimizer_op-inl.h
index 3c03f8061d87..d22bd644d21f 100644
--- a/src/operator/optimizer_op-inl.h
+++ b/src/operator/optimizer_op-inl.h
@@ -1066,21 +1066,18 @@ struct NAGMomKernel {
     const DType param_lr, const DType param_wd,
     const DType param_rescale_grad, const OpReqType req) {
     if (param_clip_gradient >= 0.0f) {
-      mom_data[i] = param_momentum*mom_data[i]
-                    + mshadow_op::clip::Map(param_rescale_grad*grad_data[i],
-                                            param_clip_gradient)
-                    + (param_wd*weight_data[i]);
-      KERNEL_ASSIGN(out_data[i], req, weight_data[i]
-                    - param_lr*(param_momentum*mom_data[i]
-                    + mshadow_op::clip::Map(param_rescale_grad*grad_data[i],
-                                            param_clip_gradient)));
+      mom_data[i] = param_momentum*mom_data[i];
+      KERNEL_ASSIGN(out_data[i], req, weight_data[i]-mom_data[i]+(param_momentum+1)
+              *(mom_data[i]-(param_lr*(mshadow_op::clip::Map(param_rescale_grad
+                              *grad_data[i], param_clip_gradient)+(param_wd*weight_data[i])))));
+      mom_data[i] = mom_data[i] - (param_lr*((mshadow_op::clip::Map(param_rescale_grad*grad_data[i],
+                          param_clip_gradient))+(param_wd*weight_data[i])));
     } else {
-      mom_data[i] = param_momentum*mom_data[i]
-                    + param_rescale_grad*grad_data[i]
-                    + (param_wd*weight_data[i]);
-      KERNEL_ASSIGN(out_data[i], req, weight_data[i]
-                    - param_lr*(param_momentum*mom_data[i]
-                    + param_rescale_grad*grad_data[i]));
+      mom_data[i] = param_momentum*mom_data[i];
+      KERNEL_ASSIGN(out_data[i], req, weight_data[i]-mom_data[i]+(param_momentum+1)
+              *(mom_data[i]-(param_lr*(param_rescale_grad*grad_data[i]+param_wd*weight_data[i]))));
+      mom_data[i] = mom_data[i] - param_lr*((param_rescale_grad*grad_data[i])
+              +(param_wd*weight_data[i]));
     }
   }
 };
@@ -1119,22 +1116,21 @@ struct MP_NAGMomKernel {
     const OpReqType req) {
     float w = weight32[i];
     if (param_clip_gradient >= 0.0f) {
-      mom_data[i] = param_momentum*mom_data[i]
-                    + mshadow_op::clip::Map(param_rescale_grad
-                    *static_cast<float>(grad_data[i]), param_clip_gradient)
-                    + (param_wd*w);
-      w = w - param_lr*(param_momentum*mom_data[i]
-                        + mshadow_op::clip::Map(param_rescale_grad
-                        *static_cast<float>(grad_data[i]),
-                        param_clip_gradient));
+      mom_data[i] = param_momentum*mom_data[i];
+      w = w-mom_data[i]+(param_momentum+1)*(mom_data[i]-param_lr
+              *(mshadow_op::clip::Map(param_rescale_grad*static_cast<float>(grad_data[i]),
+                          param_clip_gradient)+(param_wd*w)));
+      mom_data[i] = mom_data[i] - param_lr
+          *((mshadow_op::clip::Map(param_rescale_grad*static_cast<float>(grad_data[i]),
+                          param_clip_gradient))+(param_wd*w));
       weight32[i] = w;
       KERNEL_ASSIGN(out_data[i], req, w);
     } else {
-      mom_data[i] = param_momentum*mom_data[i]
-                    + param_rescale_grad*static_cast<float>(grad_data[i])
-                    + (param_wd*w);
-      w = w - param_lr*(param_momentum*mom_data[i]
-       + param_rescale_grad*static_cast<float>(grad_data[i]));
+      mom_data[i] = param_momentum*mom_data[i];
+      w = w-mom_data[i]+(param_momentum+1)*(mom_data[i]-param_lr
+              *(param_rescale_grad*static_cast<float>(grad_data[i])+(param_wd*w)));
+      mom_data[i] = mom_data[i] - param_lr
+          *((param_rescale_grad*static_cast<float>(grad_data[i]))+(param_wd*w));
       weight32[i] = w;
       KERNEL_ASSIGN(out_data[i], req, w);
     }
diff --git a/tests/python/unittest/test_optimizer.py b/tests/python/unittest/test_optimizer.py
index 3e6cdd0997ce..cea469960f64 100644
--- a/tests/python/unittest/test_optimizer.py
+++ b/tests/python/unittest/test_optimizer.py
@@ -384,11 +384,8 @@ def update(self, index, weight, grad, state):
                 weight[:] += -lr * (grad + wd * weight)
             else:
               mom = state
-              mom[:] *= self.momentum
-              mom[:] += grad
-              mom[:] += wd * weight
-              grad[:] += self.momentum * mom
-              weight[:] -= lr * grad
+              weight[:] += (self.momentum**2 * mom) - lr*(self.momentum + 1)*(grad + wd*weight)
+              mom[:] = (self.momentum*mom) - lr*(grad + wd*weight)
         else:
             grad32 = array(grad, ctx=grad.context, dtype=np.float32)
             grad32 = grad32 * self.rescale_grad
@@ -399,11 +396,8 @@ def update(self, index, weight, grad, state):
             if self.momentum == 0.0:
                 weight32[:] += -lr * (grad32 + wd * weight32)
             else:
-                mom[:] *= self.momentum
-                mom[:] += grad32
-                mom[:] += wd * weight32
-                grad32[:] += self.momentum * mom
-                weight32[:] -= lr * grad32
+                weight32[:] += (self.momentum**2 * mom) - lr*(self.momentum+1)*(grad32 + wd*weight32)
+                mom[:] = (self.momentum*mom) - lr*(grad32 + wd*weight32)
             tmp = weight32.astype(weight.dtype)
             tmp.copyto(weight)
 

From eb037a85c9bd05a55ff37813758b8ced7ea11733 Mon Sep 17 00:00:00 2001
From: kshitij12345 <kshitijkalambarkar@gmail.com>
Date: Thu, 12 Sep 2019 04:05:20 +0530
Subject: [PATCH 309/813] [MXNET-978] Higher Order Gradient Support `sinh`,
 `cosh`. (#15412)

* add higher order support for sinh cosh

* add relevant tests

* update comments

* retrigger CI
---
 src/operator/tensor/elemwise_unary_op_trig.cc | 51 ++++++++++++++++++-
 .../python/unittest/test_higher_order_grad.py | 29 +++++++++++
 2 files changed, 78 insertions(+), 2 deletions(-)

diff --git a/src/operator/tensor/elemwise_unary_op_trig.cc b/src/operator/tensor/elemwise_unary_op_trig.cc
index bd0c8973ae07..7546f5096ccd 100644
--- a/src/operator/tensor/elemwise_unary_op_trig.cc
+++ b/src/operator/tensor/elemwise_unary_op_trig.cc
@@ -313,7 +313,30 @@ The storage type of ``sinh`` output depends upon the input storage type:
 )code" ADD_FILELINE)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{ "_backward_sinh" });
 
-MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_sinh, unary_bwd<mshadow_op::sinh_grad>);
+MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_sinh, unary_bwd<mshadow_op::sinh_grad>)
+.set_attr<nnvm::FGradient>("FGradient",
+    [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
+      // ograds[0]: head_grad_grads (dL/dxgrad)
+      // inputs[0]: dL/dy
+      // inputs[1]: x (ElemwiseUseIn)
+      // f(x) = sinh(x)
+      // f'(x) = cosh(x)
+      // f''(x) = sinh(x)
+      auto dydx = MakeNode("cosh", n->attrs.name + "_dydx",
+                             {n->inputs[1]}, nullptr, &n);
+      auto d2ydx2 = MakeNode("sinh", n->attrs.name + "_grad_grad_mid", {n->inputs[1]}, nullptr, &n);
+
+      auto grad_grad_mid = MakeNode("elemwise_mul", n->attrs.name + "backward_grad_grad_mid",
+                                    {n->inputs[0], nnvm::NodeEntry{d2ydx2}}, nullptr, &n);
+
+      std::vector<nnvm::NodeEntry> ret;
+
+      ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "_backward_grad_grad",
+                                {ograds[0], nnvm::NodeEntry{dydx}}, nullptr, &n));
+      ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "_backward_grad_grad_in",
+                                {ograds[0], nnvm::NodeEntry{grad_grad_mid}}, nullptr, &n));
+      return ret;
+    });
 
 // cosh
 MXNET_OPERATOR_REGISTER_UNARY_WITH_SPARSE_DR(cosh, cpu, mshadow_op::cosh)
@@ -328,7 +351,31 @@ The storage type of ``cosh`` output is always dense
 )code" ADD_FILELINE)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{ "_backward_cosh" });
 
-MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU(_backward_cosh, unary_bwd<mshadow_op::cosh_grad>);
+MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU(_backward_cosh, unary_bwd<mshadow_op::cosh_grad>)
+.set_attr<nnvm::FGradient>("FGradient",
+    [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
+      // ograds[0]: head_grad_grads (dL/dxgrad)
+      // inputs[0]: dL/dy
+      // inputs[1]: x (ElemwiseUseIn)
+      // f(x) = cosh(x)
+      // f'(x) = sinh(x)
+      // f''(x) = cosh(x)
+      auto dydx = MakeNode("sinh", n->attrs.name + "_dydx",
+                             {n->inputs[1]}, nullptr, &n);
+      auto d2ydx2 = MakeNode("cosh", n->attrs.name + "_grad_grad_mid", {n->inputs[1]}, nullptr, &n);
+
+      auto grad_grad_mid = MakeNode("elemwise_mul", n->attrs.name + "backward_grad_grad_mid",
+                                    {n->inputs[0], nnvm::NodeEntry{d2ydx2}}, nullptr, &n);
+
+      std::vector<nnvm::NodeEntry> ret;
+
+      ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "_backward_grad_grad",
+                                {ograds[0], nnvm::NodeEntry{dydx}}, nullptr, &n));
+      ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "_backward_grad_grad_in",
+                                {ograds[0], nnvm::NodeEntry{grad_grad_mid}}, nullptr, &n));
+      return ret;
+    });
+
 
 // tanh
 MXNET_OPERATOR_REGISTER_UNARY_WITH_RSP_CSR(tanh, cpu, mshadow_op::tanh)
diff --git a/tests/python/unittest/test_higher_order_grad.py b/tests/python/unittest/test_higher_order_grad.py
index a758775a09ba..8e6ac3d0cf52 100644
--- a/tests/python/unittest/test_higher_order_grad.py
+++ b/tests/python/unittest/test_higher_order_grad.py
@@ -68,6 +68,34 @@ def grad_grad_op(x):
         check_second_order_unary(array, tan, grad_grad_op)
 
 
+@with_seed()
+def test_sinh():
+    def sinh(x):
+        return nd.sinh(x)
+
+    def grad_grad_op(x):
+        return sinh(x)
+
+    for dim in range(1, 5):
+        shape = rand_shape_nd(dim)
+        array = random_arrays(shape)
+        check_second_order_unary(array, sinh, grad_grad_op)
+
+
+@with_seed()
+def test_cosh():
+    def cosh(x):
+        return nd.cosh(x)
+
+    def grad_grad_op(x):
+        return cosh(x)
+
+    for dim in range(1, 5):
+        shape = rand_shape_nd(dim)
+        array = random_arrays(shape)
+        check_second_order_unary(array, cosh, grad_grad_op)
+
+
 @with_seed()
 def test_tanh():
     def tanh(x):
@@ -245,6 +273,7 @@ def grad_grad_op(x):
         check_second_order_unary(array, dropout, grad_grad_op)
 
 
+@with_seed()
 def test_sigmoid():
     def sigmoid(x):
         return nd.sigmoid(x)

From 61e8347a88565c7660c8e28535cc230457df45d9 Mon Sep 17 00:00:00 2001
From: Aaron Markham <aaron.s.markham@gmail.com>
Date: Wed, 11 Sep 2019 20:33:53 -0700
Subject: [PATCH 310/813] Revert "julia: rename build env var `MXNET_HOME` to
 `MXNET_ROOT` (#15568)" (#16147)

This reverts commit 75ee1e9f434383eff9b250f32b10c89e721f66d3.
---
 ci/docker/runtime_functions.sh       |  8 +++----
 ci/windows/test_jl07_cpu.ps1         |  2 +-
 ci/windows/test_jl10_cpu.ps1         |  2 +-
 julia/NEWS.md                        |  7 ------
 julia/deps/build.jl                  | 33 +++++-----------------------
 julia/docs/src/user-guide/install.md | 20 +++++++----------
 julia/src/base.jl                    | 30 +++++--------------------
 7 files changed, 25 insertions(+), 77 deletions(-)

diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index ac962527d7f7..689d525a5cce 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -1088,7 +1088,7 @@ unittest_ubuntu_gpu_R() {
 unittest_ubuntu_cpu_julia() {
     set -ex
     export PATH="$1/bin:$PATH"
-    export MXNET_ROOT='/work/mxnet'
+    export MXNET_HOME='/work/mxnet'
     export JULIA_DEPOT_PATH='/work/julia-depot'
     export INTEGRATION_TEST=1
 
@@ -1098,7 +1098,7 @@ unittest_ubuntu_cpu_julia() {
     export LD_PRELOAD='/usr/lib/x86_64-linux-gnu/libjemalloc.so'
     export LD_LIBRARY_PATH=/work/mxnet/lib:$LD_LIBRARY_PATH
 
-    # use the prebuilt binary from $MXNET_ROOT/lib
+    # use the prebuilt binary from $MXNET_HOME/lib
     julia --project=./julia -e 'using Pkg; Pkg.build("MXNet")'
 
     # run the script `julia/test/runtests.jl`
@@ -1253,7 +1253,7 @@ build_docs() {
 
     # Setup environment for Julia docs
     export PATH="/work/julia10/bin:$PATH"
-    export MXNET_ROOT='/work/mxnet'
+    export MXNET_HOME='/work/mxnet'
     export JULIA_DEPOT_PATH='/work/julia-depot'
 
     julia -e 'using InteractiveUtils; versioninfo()'
@@ -1465,7 +1465,7 @@ deploy_docs() {
 
     # Setup for Julia docs
     export PATH="/work/julia10/bin:$PATH"
-    export MXNET_ROOT='/work/mxnet'
+    export MXNET_HOME='/work/mxnet'
     export JULIA_DEPOT_PATH='/work/julia-depot'
 
     julia -e 'using InteractiveUtils; versioninfo()'
diff --git a/ci/windows/test_jl07_cpu.ps1 b/ci/windows/test_jl07_cpu.ps1
index d3b77b1cbc1d..6cd34ef209de 100644
--- a/ci/windows/test_jl07_cpu.ps1
+++ b/ci/windows/test_jl07_cpu.ps1
@@ -20,7 +20,7 @@
 # set default output encoding to utf8
 $PSDefaultParameterValues['Out-File:Encoding'] = 'utf8'
 
-$env:MXNET_ROOT = [System.IO.Path]::GetFullPath('.\windows_package')
+$env:MXNET_HOME = [System.IO.Path]::GetFullPath('.\windows_package')
 $env:JULIA_URL = "https://julialang-s3.julialang.org/bin/winnt/x64/0.7/julia-0.7.0-win64.exe"
 $env:JULIA_DEPOT_PATH = [System.IO.Path]::GetFullPath('.\julia-depot')
 
diff --git a/ci/windows/test_jl10_cpu.ps1 b/ci/windows/test_jl10_cpu.ps1
index 2dbda4886c42..96c419066354 100644
--- a/ci/windows/test_jl10_cpu.ps1
+++ b/ci/windows/test_jl10_cpu.ps1
@@ -20,7 +20,7 @@
 # set default output encoding to utf8
 $PSDefaultParameterValues['Out-File:Encoding'] = 'utf8'
 
-$env:MXNET_ROOT = [System.IO.Path]::GetFullPath('.\windows_package')
+$env:MXNET_HOME = [System.IO.Path]::GetFullPath('.\windows_package')
 $env:JULIA_URL = "https://julialang-s3.julialang.org/bin/winnt/x64/1.0/julia-1.0.3-win64.exe"
 $env:JULIA_DEPOT_PATH = [System.IO.Path]::GetFullPath('.\julia-depot')
 
diff --git a/julia/NEWS.md b/julia/NEWS.md
index 9935d6885ca5..e678b97176d1 100644
--- a/julia/NEWS.md
+++ b/julia/NEWS.md
@@ -17,13 +17,6 @@
 
 # v1.6.0
 
-* Rename environment variable `MXNET_HOME` to `MXNET_ROOT` (#15568).
-
-* Environment variable `MXNET_LIBRARY_PATH` supports (#15568).
-
-  ```shell
-  $ MXNET_LIBRARY_PATH=/path/to/libmxnet.so julia
-  ```
 
 # v1.5.0
 
diff --git a/julia/deps/build.jl b/julia/deps/build.jl
index dfd3f78379b0..a87343d9dab5 100644
--- a/julia/deps/build.jl
+++ b/julia/deps/build.jl
@@ -26,35 +26,14 @@ libmxnet_detected = false
 libmxnet_curr_ver = get(ENV, "MXNET_COMMIT", "master")
 curr_win = "20190608"  # v1.5.0
 
-# TODO: remove MXNET_HOME backward compatibility in v1.7
 if haskey(ENV, "MXNET_HOME")
-  @warn "The environment variable `MXNET_HOME` has been renamed, please use `MXNET_ROOT` instead."
-end
-
-# TODO: remove MXNET_HOME backward compatibility in v2.0
-MXNET_ROOT = get(ENV, "MXNET_ROOT", get(ENV, "MXNET_HOME", ""))
-search_locations = if !isempty(MXNET_ROOT)
-  !isabspath(MXNET_ROOT) && error("MXNET_ROOT should be a absolute path")
-  @info "env var: MXNET_ROOT -> $MXNET_ROOT"
-  [joinpath(MXNET_ROOT, "lib"), MXNET_ROOT]
-else
-  []
-end
-
-MXNET_LIBRARY_PATH = get(ENV, "MXNET_LIBRARY_PATH", "")
-println(typeof(MXNET_LIBRARY_PATH))
-# In case of macOS, if user build libmxnet from source and set the MXNET_ROOT,
-# the output is still named as `libmxnet.so`.
-search_names = ["libmxnet.$(Libdl.dlext)", "libmxnet.so"]
-if !isempty(MXNET_LIBRARY_PATH)
-  !isabspath(MXNET_LIBRARY_PATH) && error("MXNET_LIBRARY_PATH should be a absolute path")
-  @info "env var: MXNET_LIBRARY_PATH -> $MXNET_LIBRARY_PATH"
-  pushfirst!(search_names, MXNET_LIBRARY_PATH)
-end
-
-if (!isempty(MXNET_ROOT)) || (!isempty(MXNET_LIBRARY_PATH))
+  MXNET_HOME = ENV["MXNET_HOME"]
+  @info("MXNET_HOME environment detected: $MXNET_HOME")
   @info("Trying to load existing libmxnet...")
-  lib = Libdl.find_library(search_names, search_locations)
+  # In case of macOS, if user build libmxnet from source and set the MXNET_HOME,
+  # the output is still named as `libmxnet.so`.
+  lib = Libdl.find_library(["libmxnet.$(Libdl.dlext)", "libmxnet.so"],
+                           [joinpath(MXNET_HOME, "lib"), MXNET_HOME])
   if !isempty(lib)
     @info("Existing libmxnet detected at $lib, skip building...")
     libmxnet_detected = true
diff --git a/julia/docs/src/user-guide/install.md b/julia/docs/src/user-guide/install.md
index d9d5574b025d..d1a90a808507 100644
--- a/julia/docs/src/user-guide/install.md
+++ b/julia/docs/src/user-guide/install.md
@@ -40,13 +40,9 @@ libmxnet.
 
 There are several environment variables that change this behaviour.
 
-- `MXNET_ROOT`: If you already have a pre-installed version of mxnet
-  you can use `MXNET_ROOT` to point the build-process in the right direction.
-  Note that you should set this variable as a absolute path.
-- `MXNET_HOME`: This variable is replaced by `MXNET_ROOT`. It has been renamed
-  as of v1.6.
-- `MXNET_LIBRARY_PATH`: The absolute path of the libmxnet share object.
-- `CUDA_HOME`: If the automatic CUDA detection fails you can also set `CUDA_HOME`
+- `MXNET_HOME`: If you already have a pre-installed version of mxnet
+  you can use `MXNET_HOME` to point the build-process in the right direction.
+- `CUDA_HOME`: If the automatic cuda detection fails you can also set `CUDA_HOME`
   to override the process.
 - `MXNET_COMMIT`: To control which version of libmxnet will be compiled,
   you can use the`MXNET_COMMIT` variable to point to either a version tag
@@ -84,18 +80,18 @@ to work with a separate, maybe customized libmxnet.
 
 To build libmxnet, please refer to [the installation guide of
 libmxnet](https://mxnet.incubator.apache.org/install/index.html). After
-successfully installing libmxnet, set the `MXNET_ROOT` *environment
+successfully installing libmxnet, set the `MXNET_HOME` *environment
 variable* to the location of libmxnet. In other words, the compiled
-`libmxnet.so` should be found in `$MXNET_ROOT/lib`.
+`libmxnet.so` should be found in `$MXNET_HOME/lib`.
 
 > **note**
 >
-> The constant `MXNET_ROOT` is pre-compiled in MXNet.jl package cache.
+> The constant `MXNET_HOME` is pre-compiled in MXNet.jl package cache.
 > If you updated the environment variable after installing MXNet.jl,
 > make sure to update the pre-compilation cache by
 > `Base.compilecache("MXNet")`.
 
-When the `MXNET_ROOT` environment variable is detected and the
+When the `MXNET_HOME` environment variable is detected and the
 corresponding `libmxnet.so` could be loaded successfully, MXNet.jl will
 skip automatic building during installation and use the specified
 libmxnet instead.
@@ -103,7 +99,7 @@ libmxnet instead.
 Basically, MXNet.jl will search `libmxnet.so` or `libmxnet.dll` in the
 following paths (and in that order):
 
--   `$MXNET_ROOT/lib`: customized libmxnet builds
+-   `$MXNET_HOME/lib`: customized libmxnet builds
 -   `Pkg.dir("MXNet", "deps", "usr", "lib")`: automatic builds
 -   Any system wide library search path
 
diff --git a/julia/src/base.jl b/julia/src/base.jl
index 7a2bde0907c1..683146402620 100644
--- a/julia/src/base.jl
+++ b/julia/src/base.jl
@@ -47,31 +47,11 @@ const grad_req_map = Dict{Symbol,GRAD_REQ}(
 ################################################################################
 # Initialization and library API entrance
 ################################################################################
-function _get_search_names()
-  MXNET_LIBRARY_PATH = get(ENV, "MXNET_LIBRARY_PATH", "")
-  A = ["libmxnet.$(Libdl.dlext)", "libmxnet.so"]  # see build.jl
-  if !isempty(MXNET_LIBRARY_PATH)
-    !isabspath(MXNET_LIBRARY_PATH) && error("MXNET_LIBRARY_PATH should be a absolute path")
-    pushfirst!(A, MXNET_LIBRARY_PATH)
-  end
-  A
-end
-
-function _get_search_dirs()
-  # TODO: remove MXNET_HOME backward compatibility in v1.7
-  if haskey(ENV, "MXNET_HOME")
-    @warn "The environment variable `MXNET_HOME` has been renamed, please use `MXNET_ROOT` instead."
-  end
-  A = [joinpath(@__DIR__, "..", "deps", "usr", "lib")]
-  MXNET_ROOT = get(ENV, "MXNET_ROOT", get(ENV, "MXNET_HOME", ""))
-  if !isempty(MXNET_ROOT)
-    !isabspath(MXNET_ROOT) && error("MXNET_ROOT should be a absolute path")
-    prepend!(A, [joinpath(MXNET_ROOT, "lib"), MXNET_ROOT])
-  end
-  A
-end
-
-const MXNET_LIB = Libdl.find_library(_get_search_names(), _get_search_dirs())
+const MXNET_LIB = Libdl.find_library(["libmxnet.$(Libdl.dlext)", "libmxnet.so"],  # see build.jl
+                                     [joinpath(get(ENV, "MXNET_HOME", ""), "lib"),
+                                      get(ENV, "MXNET_HOME", ""),
+                                      joinpath(@__DIR__, "..",
+                                               "deps", "usr", "lib")])
 const LIB_VERSION = Ref{Cint}(0)
 
 if isempty(MXNET_LIB)

From 5b42065c129e5ce80ad41a7839f21d25821f5667 Mon Sep 17 00:00:00 2001
From: Vikas-kum <vikumar@amazon.com>
Date: Wed, 11 Sep 2019 21:24:26 -0700
Subject: [PATCH 311/813] Fixing build for gluon estimator test, including
 libtvm in pack libs (#16148)

* Fixing build for gluon estimator test, including libtvm in pack libs

* Fixing html build for nightly
---
 docs/mxdoc.py                        | 8 +++++++-
 tests/nightly/JenkinsfileForBinaries | 4 ++--
 2 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/docs/mxdoc.py b/docs/mxdoc.py
index f2bb9714de0c..237b36a96606 100644
--- a/docs/mxdoc.py
+++ b/docs/mxdoc.py
@@ -468,7 +468,13 @@ def copy_artifacts(app):
 def setup(app):
     # If MXNET_DOCS_BUILD_MXNET is set something different than 1
     # Skip the build step
-    if os.getenv('MXNET_DOCS_BUILD_MXNET') == '1'or _MXNET_DOCS_BUILD_MXNET:
+    env_build_mxnet = os.getenv('MXNET_DOCS_BUILD_MXNET', default=None)
+    should_build_mxnet = _MXNET_DOCS_BUILD_MXNET
+    if env_build_mxnet is not None:
+        print("env MXNET_DOCS_BUILD_MXNET is set to " + str(env_build_mxnet))
+        should_build_mxnet = (env_build_mxnet == '1')
+
+    if should_build_mxnet:
         print("Building MXNet!")
         app.connect("builder-inited", build_mxnet)
     if _DOXYGEN_DOCS:
diff --git a/tests/nightly/JenkinsfileForBinaries b/tests/nightly/JenkinsfileForBinaries
index 8582643e0163..2bd78ac2afb6 100755
--- a/tests/nightly/JenkinsfileForBinaries
+++ b/tests/nightly/JenkinsfileForBinaries
@@ -18,8 +18,8 @@
 //
 //This is a Jenkinsfile for nightly tests. The format and some functions have been picked up from the top-level Jenkinsfile
 
-mx_lib = 'lib/libmxnet.so, lib/libmxnet.a, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a'
-mx_cmake_lib = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so'
+mx_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a'
+mx_cmake_lib = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so'
 
 node('utility') {
   // Loading the utilities requires a node context unfortunately

From 287e3b52524ef791a0429a741084fe2db28b0c44 Mon Sep 17 00:00:00 2001
From: "Shi, guangyong" <35016185+gyshi@users.noreply.github.com>
Date: Thu, 12 Sep 2019 14:02:24 +0800
Subject: [PATCH 312/813] Numpy add numpy op indices (#15837)

* add numpy op indices

* re-trigger ci

* fix cpplint
---
 python/mxnet/ndarray/numpy/_op.py      |  73 ++++++++++++++++-
 python/mxnet/numpy/multiarray.py       |  67 +++++++++++++++-
 python/mxnet/symbol/numpy/_symbol.py   |  72 ++++++++++++++++-
 src/operator/numpy/np_init_op.cc       |  33 ++++++++
 src/operator/numpy/np_init_op.cu       |   4 +
 src/operator/numpy/np_init_op.h        | 107 +++++++++++++++++++++++++
 tests/python/unittest/test_numpy_op.py |  54 ++++++++++++-
 7 files changed, 402 insertions(+), 8 deletions(-)
 create mode 100644 src/operator/numpy/np_init_op.h

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index 964d533b2387..671345c9a546 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -33,7 +33,7 @@
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'mean',
-           'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var']
+           'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -1888,7 +1888,7 @@ def tile(A, reps):
     """
     return _unary_func_helper(A, _npi.tile, _np.tile, reps=reps)
 
-
+# pylint: disable=redefined-outer-name
 @set_module('mxnet.ndarray.numpy')
 def split(ary, indices_or_sections, axis=0):
     """Split an array into multiple sub-arrays.
@@ -1936,6 +1936,7 @@ def split(ary, indices_or_sections, axis=0):
     if not isinstance(ret, list):
         return [ret]
     return ret
+# pylint: enable=redefined-outer-name
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -2363,3 +2364,71 @@ def var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):  # pylint:
     0.2025
     """
     return _npi.var(a, axis=axis, dtype=dtype, ddof=ddof, keepdims=keepdims, out=out)
+
+
+# pylint: disable=redefined-outer-name
+@set_module('mxnet.ndarray.numpy')
+def indices(dimensions, dtype=_np.int32, ctx=None):
+    """Return an array representing the indices of a grid.
+
+    Compute an array where the subarrays contain index values 0,1,...
+    varying only along the corresponding axis.
+
+    Parameters
+    ----------
+    dimensions : sequence of ints
+        The shape of the grid.
+    dtype : data-type, optional
+        The desired data-type for the array. Default is `float32`.
+    ctx : device context, optional
+        Device context on which the memory is allocated. Default is
+        `mxnet.context.current_context()`.
+
+    Returns
+    -------
+    grid : ndarray
+        The array of grid indices,
+        ``grid.shape = (len(dimensions),) + tuple(dimensions)``.
+
+    Notes
+    -----
+    The output shape is obtained by prepending the number of dimensions
+    in front of the tuple of dimensions, i.e. if `dimensions` is a tuple
+    ``(r0, ..., rN-1)`` of length ``N``, the output shape is
+    ``(N,r0,...,rN-1)``.
+
+    The subarrays ``grid[k]`` contains the N-D array of indices along the
+    ``k-th`` axis. Explicitly::
+
+        grid[k,i0,i1,...,iN-1] = ik
+
+    Examples
+    --------
+    >>> grid = np.indices((2, 3))
+    >>> grid.shape
+    (2, 2, 3)
+    >>> grid[0]        # row indices
+    array([[0, 0, 0],
+           [1, 1, 1]])
+    >>> grid[1]        # column indices
+    array([[0, 0, 0],
+           [1, 1, 1]], dtype=int32)
+
+    The indices can be used as an index into an array.
+
+    >>> x = np.arange(20).reshape(5, 4)
+    >>> row, col = np.indices((2, 3))
+    >>> x[row, col]
+    array([[0., 1., 2.],
+           [4., 5., 6.]])
+
+    Note that it would be more straightforward in the above example to
+    extract the required elements directly with ``x[:2, :3]``.
+    """
+    if isinstance(dimensions, (tuple, list)):
+        if ctx is None:
+            ctx = current_context()
+        return _npi.indices(dimensions=dimensions, dtype=dtype, ctx=ctx)
+    else:
+        raise ValueError("The dimensions must be sequence of ints")
+# pylint: enable=redefined-outer-name
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 7d6e81a2d4a5..4eddb0380c09 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -52,7 +52,7 @@
            'degrees', 'log2', 'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative',
            'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh',
            'tensordot', 'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate',
-           'stack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var']
+           'stack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -1520,6 +1520,7 @@ def _full(self, value):
         """
         return _mx_nd_np.full(self.shape, value, ctx=self.context, dtype=self.dtype, out=self)
 
+    # pylint: disable=redefined-outer-name
     def _scatter_set_nd(self, value_nd, indices):
         """
         This is added as an ndarray class method in order to support polymorphism in NDArray and numpy.ndarray indexing
@@ -1527,6 +1528,7 @@ def _scatter_set_nd(self, value_nd, indices):
         return _npi.scatter_set_nd(
             lhs=self, rhs=value_nd, indices=indices, shape=self.shape, out=self
         )
+    # pylint: enable=redefined-outer-name
 
     @property
     def shape(self):
@@ -3808,3 +3810,66 @@ def var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=None):
     0.2025
     """
     return _npi.var(a, axis=axis, dtype=dtype, ddof=ddof, keepdims=keepdims, out=out)
+
+
+# pylint: disable=redefined-outer-name
+@set_module('mxnet.numpy')
+def indices(dimensions, dtype=_np.int32, ctx=None):
+    """Return an array representing the indices of a grid.
+
+    Compute an array where the subarrays contain index values 0,1,...
+    varying only along the corresponding axis.
+
+    Parameters
+    ----------
+    dimensions : sequence of ints
+        The shape of the grid.
+    dtype : data-type, optional
+        The desired data-type for the array. Default is `float32`.
+    ctx : device context, optional
+        Device context on which the memory is allocated. Default is
+        `mxnet.context.current_context()`.
+
+    Returns
+    -------
+    grid : ndarray
+        The array of grid indices,
+        ``grid.shape = (len(dimensions),) + tuple(dimensions)``.
+
+    Notes
+    -----
+    The output shape is obtained by prepending the number of dimensions
+    in front of the tuple of dimensions, i.e. if `dimensions` is a tuple
+    ``(r0, ..., rN-1)`` of length ``N``, the output shape is
+    ``(N,r0,...,rN-1)``.
+
+    The subarrays ``grid[k]`` contains the N-D array of indices along the
+    ``k-th`` axis. Explicitly::
+
+        grid[k,i0,i1,...,iN-1] = ik
+
+    Examples
+    --------
+    >>> grid = np.indices((2, 3))
+    >>> grid.shape
+    (2, 2, 3)
+    >>> grid[0]        # row indices
+    array([[0, 0, 0],
+           [1, 1, 1]])
+    >>> grid[1]        # column indices
+    array([[0, 0, 0],
+           [1, 1, 1]], dtype=int32)
+
+    The indices can be used as an index into an array.
+
+    >>> x = np.arange(20).reshape(5, 4)
+    >>> row, col = np.indices((2, 3))
+    >>> x[row, col]
+    array([[0., 1., 2.],
+           [4., 5., 6.]])
+
+    Note that it would be more straightforward in the above example to
+    extract the required elements directly with ``x[:2, :3]``.
+    """
+    return _mx_nd_np.indices(dimensions=dimensions, dtype=dtype, ctx=ctx)
+# pylint: enable=redefined-outer-name
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index ec4f6a4dd741..0841c0e4d2cc 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -35,7 +35,7 @@
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'mean',
-           'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var']
+           'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices']
 
 
 def _num_outputs(sym):
@@ -2305,6 +2305,7 @@ def arange(start, stop=None, step=1, dtype=None, ctx=None):
     return _npi.arange(start=start, stop=stop, step=step, dtype=dtype, ctx=ctx)
 
 
+# pylint: disable=redefined-outer-name
 @set_module('mxnet.symbol.numpy')
 def split(ary, indices_or_sections, axis=0):
     """Split an array into multiple sub-arrays.
@@ -2345,6 +2346,7 @@ def split(ary, indices_or_sections, axis=0):
         raise ValueError('indices_or_sections must either int or tuple of ints')
     ret = _npi.split(ary, indices, axis, False, sections)
     return ret
+# pylint: enable=redefined-outer-name
 
 
 @set_module('mxnet.symbol.numpy')
@@ -2678,4 +2680,72 @@ def var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):  # pylint:
     return _npi.var(a, axis=axis, dtype=dtype, ddof=ddof, keepdims=keepdims, out=out)
 
 
+# pylint: disable=redefined-outer-name
+@set_module('mxnet.symbol.numpy')
+def indices(dimensions, dtype=_np.int32, ctx=None):
+    """Return an array representing the indices of a grid.
+
+    Compute an array where the subarrays contain index values 0,1,...
+    varying only along the corresponding axis.
+
+    Parameters
+    ----------
+    dimensions : sequence of ints
+        The shape of the grid.
+    dtype : data-type, optional
+        The desired data-type for the array. Default is `float32`.
+    ctx : device context, optional
+        Device context on which the memory is allocated. Default is
+        `mxnet.context.current_context()`.
+
+    Returns
+    -------
+    grid : _Symbol
+        The array of grid indices,
+        ``grid.shape = (len(dimensions),) + tuple(dimensions)``.
+
+    Notes
+    -----
+    The output shape is obtained by prepending the number of dimensions
+    in front of the tuple of dimensions, i.e. if `dimensions` is a tuple
+    ``(r0, ..., rN-1)`` of length ``N``, the output shape is
+    ``(N,r0,...,rN-1)``.
+
+    The subarrays ``grid[k]`` contains the N-D array of indices along the
+    ``k-th`` axis. Explicitly::
+
+        grid[k,i0,i1,...,iN-1] = ik
+
+    Examples
+    --------
+    >>> grid = np.indices((2, 3))
+    >>> grid.shape
+    (2, 2, 3)
+    >>> grid[0]        # row indices
+    array([[0, 0, 0],
+           [1, 1, 1]])
+    >>> grid[1]        # column indices
+    array([[0, 0, 0],
+           [1, 1, 1]], dtype=int32)
+
+    The indices can be used as an index into an array.
+
+    >>> x = np.arange(20).reshape(5, 4)
+    >>> row, col = np.indices((2, 3))
+    >>> x[row, col]
+    array([[0., 1., 2.],
+           [4., 5., 6.]])
+
+    Note that it would be more straightforward in the above example to
+    extract the required elements directly with ``x[:2, :3]``.
+    """
+    if isinstance(dimensions, (tuple, list)):
+        if ctx is None:
+            ctx = current_context()
+        return _npi.indices(dimensions=dimensions, dtype=dtype, ctx=ctx)
+    else:
+        raise ValueError("The dimensions must be sequence of ints")
+# pylint: enable=redefined-outer-name
+
+
 _set_np_symbol_class(_Symbol)
diff --git a/src/operator/numpy/np_init_op.cc b/src/operator/numpy/np_init_op.cc
index b85a92f97683..4f031bdaa050 100644
--- a/src/operator/numpy/np_init_op.cc
+++ b/src/operator/numpy/np_init_op.cc
@@ -24,10 +24,33 @@
  */
 #include "../tensor/init_op.h"
 #include "../tensor/elemwise_unary_op.h"
+#include "./np_init_op.h"
 
 namespace mxnet {
 namespace op {
 
+DMLC_REGISTER_PARAMETER(IndicesOpParam);
+
+inline bool NumpyIndicesShape(const nnvm::NodeAttrs& attrs,
+                              mxnet::ShapeVector* in_shapes,
+                              mxnet::ShapeVector* out_shapes) {
+  const IndicesOpParam& param = nnvm::get<IndicesOpParam>(attrs.parsed);
+  CHECK_EQ(in_shapes->size(), 0U);
+  CHECK_EQ(out_shapes->size(), 1U);
+  CHECK_GE(param.dimensions.ndim(), 0)
+    << "_npi_indices dimensions the number of dim must not be less than 0";
+  mxnet::TShape param_dim = param.dimensions;
+  if (!shape_is_known(param_dim)) return false;
+  const int indim = param.dimensions.ndim();
+  mxnet::TShape ret(indim + 1, -1);
+  ret[0] = indim;
+  for (int i = 1; i < indim + 1; ++i) {
+    ret[i] = param.dimensions[i-1];
+  }
+  SHAPE_ASSIGN_CHECK(*out_shapes, 0, ret);
+  return shape_is_known(out_shapes->at(0));
+}
+
 NNVM_REGISTER_OP(_npi_zeros)
 .set_num_inputs(0)
 .set_num_outputs(1)
@@ -110,5 +133,15 @@ NNVM_REGISTER_OP(_npi_arange)
 .set_attr<FCompute>("FCompute<cpu>", RangeCompute<cpu, RangeParam>)
 .add_arguments(RangeParam::__FIELDS__());
 
+NNVM_REGISTER_OP(_npi_indices)
+.describe("Return an array representing the indices of a grid.")
+.set_num_inputs(0)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<IndicesOpParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyIndicesShape)
+.set_attr<nnvm::FInferType>("FInferType", InitType<IndicesOpParam>)
+.set_attr<FCompute>("FCompute<cpu>", IndicesCompute<cpu>)
+.add_arguments(IndicesOpParam::__FIELDS__());
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/np_init_op.cu b/src/operator/numpy/np_init_op.cu
index fe631f388a19..49f1051735d8 100644
--- a/src/operator/numpy/np_init_op.cu
+++ b/src/operator/numpy/np_init_op.cu
@@ -24,6 +24,7 @@
  */
 
 #include "../tensor/init_op.h"
+#include "./np_init_op.h"
 
 namespace mxnet {
 namespace op {
@@ -43,5 +44,8 @@ NNVM_REGISTER_OP(_np_ones_like)
 NNVM_REGISTER_OP(_npi_arange)
 .set_attr<FCompute>("FCompute<gpu>", RangeCompute<gpu, RangeParam>);
 
+NNVM_REGISTER_OP(_npi_indices)
+.set_attr<FCompute>("FCompute<gpu>", IndicesCompute<gpu>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/np_init_op.h b/src/operator/numpy/np_init_op.h
new file mode 100644
index 000000000000..5c41820b57f8
--- /dev/null
+++ b/src/operator/numpy/np_init_op.h
@@ -0,0 +1,107 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file np_init_op.h
+ * \brief CPU Implementation of numpy init op
+ */
+#ifndef MXNET_OPERATOR_NUMPY_NP_INIT_OP_H_
+#define MXNET_OPERATOR_NUMPY_NP_INIT_OP_H_
+
+#include <vector>
+#include <string>
+#include "../tensor/init_op.h"
+#include "../tensor/elemwise_unary_op.h"
+
+
+namespace mxnet {
+namespace op {
+
+struct IndicesOpParam : public dmlc::Parameter<IndicesOpParam> {
+  mxnet::TShape dimensions;
+  int dtype;
+  std::string ctx;
+  DMLC_DECLARE_PARAMETER(IndicesOpParam) {
+    DMLC_DECLARE_FIELD(dimensions)
+    .describe("The shape of the grid.");
+    DMLC_DECLARE_FIELD(dtype).set_default(mshadow::kInt32)
+      MXNET_ADD_ALL_TYPES
+      .describe("Target data type.");
+    DMLC_DECLARE_FIELD(ctx)
+    .set_default("")
+    .describe("Context of output, in format [cpu|gpu|cpu_pinned](n)."
+              "Only used for imperative calls.");
+  }
+};
+
+template<int req>
+struct indices_fwd {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(index_t i, DType* out,
+                                  const nnvm::dim_t value,
+                                  const nnvm::dim_t N,
+                                  const nnvm::dim_t dim_i,
+                                  const nnvm::dim_t j,
+                                  const nnvm::dim_t k,
+                                  const nnvm::dim_t t) {
+    KERNEL_ASSIGN(out[dim_i*N+N/(t*value)*j+i+k*N/t], req, static_cast<DType>(j));
+  }
+};
+
+template<typename xpu>
+void IndicesCompute(const nnvm::NodeAttrs& attrs,
+                    const OpContext& ctx,
+                    const std::vector<TBlob>& inputs,
+                    const std::vector<OpReqType>& req,
+                    const std::vector<TBlob>& outputs) {
+  using namespace mxnet_op;
+  CHECK_EQ(inputs.size(), 0U);
+  CHECK_EQ(outputs.size(), 1U);
+  CHECK_EQ(req.size(), 1U);
+  const IndicesOpParam& param = nnvm::get<IndicesOpParam>(attrs.parsed);
+  const TBlob& out_data = outputs[0];
+  mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+  dim_t indim = param.dimensions.ndim();
+  dim_t t = 1;
+  dim_t N = out_data.Size()/indim;
+  dim_t value = 0;
+  if (out_data.Size() == 0) return;
+  if (req[0] != kNullOp) {
+    MSHADOW_TYPE_SWITCH(out_data.type_flag_, DType, {
+      MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
+          for (int i = 0; i < indim; ++i) {
+            value = param.dimensions[i];
+            for (int k = 0; k < t; ++k) {
+              for (int j = 0; j < param.dimensions[i]; ++j) {
+                Kernel<indices_fwd<req_type>, xpu>::Launch(s, N/(param.dimensions[i] * t),
+                    out_data.dptr<DType>(), value, N, i, j, k, t);
+              }
+            }
+            t = t * param.dimensions[i];
+          }
+      });
+    });
+  }
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_NUMPY_NP_INIT_OP_H_
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 1c9ebbb32866..0f7355b7ef13 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -30,6 +30,7 @@
 import collections
 import scipy.stats as ss
 from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf, retry
+import platform
 
 
 @with_seed()
@@ -1664,7 +1665,7 @@ def __init__(self, sample_size, replace):
             super(TestWeightedChoice, self).__init__()
             self.sample_size = sample_size
             self.replace = replace
-        
+
         def hybrid_forward(self, F, a, p):
             op = getattr(F.np.random, "choice", None)
             return F.np.random.choice(a, self.sample_size, self.replace, p)
@@ -1703,7 +1704,7 @@ def test_indexing_mode(sampler, set_size, samples_size, replace, weight=None):
         assert len(samples) == samples_size
         if not replace:
             assert len(_np.unique(samples)) == samples_size
-        
+
     num_classes = 10
     num_samples = 10 ** 8
     # Density tests are commented out due to their huge time comsumption.
@@ -1717,7 +1718,7 @@ def test_indexing_mode(sampler, set_size, samples_size, replace, weight=None):
     #     test_sample_with_replacement(np.random.choice, num_classes, shape)
     #     weight = np.array(_np.random.dirichlet([1.0] * num_classes))
     #     test_sample_with_replacement(np.random.choice, num_classes, shape, weight)
-    
+
     # Tests passed locally,
     # commented out for the same reason as above.
     # shape_list2 = [
@@ -1730,7 +1731,7 @@ def test_indexing_mode(sampler, set_size, samples_size, replace, weight=None):
     #     test_sample_without_replacement(np.random.choice, num_classes, shape, 10 ** 5)
     #     weight = np.array(_np.random.dirichlet([1.0] * num_classes))
     #     test_sample_without_replacement(np.random.choice, num_classes, shape, 10 ** 5, weight)
-    
+
     # Test hypridize mode:
     for hybridize in [True, False]:
         for replace in [True, False]:
@@ -1744,6 +1745,51 @@ def test_indexing_mode(sampler, set_size, samples_size, replace, weight=None):
             test_indexing_mode(test_choice_weighted, num_classes, num_classes // 2, replace, weight)
 
 
+@with_seed()
+@use_np
+def test_np_indices():
+    dtypes = ['int32', 'int64', 'float16', 'float32', 'float64']
+    shapes = [
+        (0,),
+        (3,),
+        (2, 3, 4),
+        (2, 0, 4),
+        (1, 1, 1, 1),
+        (1, 0, 0, 1),
+        (2, 3, 4, 5, 6, 7)
+    ]
+    if platform.system() == 'Windows':
+        shapes = shapes[1:]  #beacuse in numpy windows version, indces not support dimensions is empty tuple.
+    for dtype in dtypes:
+        for shape in shapes:
+            np_out = _np.indices(dimensions=shape, dtype=dtype)
+            mx_out = np.indices(dimensions=shape, dtype=dtype)
+            same(mx_out.asnumpy(), np_out)
+            assert mx_out.shape == np_out.shape
+
+    @use_np
+    class TestIndices(HybridBlock):
+        def __init__(self, dimensions=None, dtype=None):
+            super(TestIndices, self).__init__()
+            self._dimensions = dimensions
+            self._dtype = dtype
+
+        def hybrid_forward(self, F, x):
+            return x + F.np.indices(dimensions=self._dimensions, dtype=self._dtype)
+
+    for dtype in dtypes:
+        for shape in shapes:
+            x = np.zeros(shape=(), dtype=dtype)
+            for hybridize in [False, True]:
+                net = TestIndices(dimensions=shape, dtype=dtype)
+                np_out = _np.indices(dimensions=shape, dtype=dtype)
+                if hybridize:
+                    net.hybridize()
+                mx_out = net(x)
+                same(mx_out.asnumpy(), np_out)
+                assert mx_out.shape == np_out.shape
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From d9364ba20f6dfe0c0049b91daece3032ddc2c53e Mon Sep 17 00:00:00 2001
From: Iblis Lin <iblis@hs.ntnu.edu.tw>
Date: Thu, 12 Sep 2019 21:41:53 +0800
Subject: [PATCH 313/813] julia: fix `mx.forward` kwargs checking (#16138)

close https://github.com/dmlc/MXNet.jl/issues/431
---
 julia/src/executor.jl       |  2 +-
 julia/test/unittest/bind.jl | 15 +++++++++++++++
 2 files changed, 16 insertions(+), 1 deletion(-)

diff --git a/julia/src/executor.jl b/julia/src/executor.jl
index e565617976ce..37f2dde615b8 100644
--- a/julia/src/executor.jl
+++ b/julia/src/executor.jl
@@ -176,7 +176,7 @@ end
 
 function forward(self::Executor; is_train::Bool = false, kwargs...)
   for (k,v) in kwargs
-    @assert(k ∈ self.arg_dict, "Unknown argument $k")
+    @assert(k ∈ keys(self.arg_dict), "Unknown argument $k")
     @assert(isa(v, NDArray), "Keyword argument $k must be an NDArray")
     copy!(self.arg_dict[k], v)
   end
diff --git a/julia/test/unittest/bind.jl b/julia/test/unittest/bind.jl
index 0ae0ab427b99..a221733cded1 100644
--- a/julia/test/unittest/bind.jl
+++ b/julia/test/unittest/bind.jl
@@ -84,11 +84,26 @@ function test_arithmetic()
   end
 end
 
+function test_forward()
+  # forward with data keyword argument
+  x = @var x
+  y = x .+ 42
+
+  A = 1:5
+  B = A .+ 42
+
+  e = bind(y, args = Dict(:x => NDArray(24:28)))
+  z = forward(e, x = NDArray(A))[1]
+
+  @test copy(z) == collect(B)
+end
+
 ################################################################################
 # Run tests
 ################################################################################
 @testset "Bind Test" begin
   test_arithmetic()
+  test_forward()
 end
 
 end

From b4b7bfb20705c8ac5e86caf7b69e709c77b2cc06 Mon Sep 17 00:00:00 2001
From: Haibin Lin <linhaibin.eric@gmail.com>
Date: Thu, 12 Sep 2019 07:31:32 -0700
Subject: [PATCH 314/813] Update env_var.md (#16145)

---
 docs/faq/env_var.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/faq/env_var.md b/docs/faq/env_var.md
index b33a104fd315..ca5292c3a294 100644
--- a/docs/faq/env_var.md
+++ b/docs/faq/env_var.md
@@ -127,10 +127,10 @@ $env:MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
   - Values: Int ```(default=15)```
   - The maximum number of nodes in the subgraph executed in bulk during training (not inference). Setting this to a larger number may reduce the degree of parallelism for multi-GPU training.
 * MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN_FWD
-  - Values: Int ```(default=<value of MXNET_EXEC_BULK_MAX_NODE_TRAIN>)```
+  - Values: Int ```(default=<value of MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN>)```
   - The maximum number of nodes in the subgraph executed in bulk during training (not inference) in the forward pass.
 * MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN_BWD
-  - Values: Int ```(default=<value of MXNET_EXEC_BULK_MAX_NODE_TRAIN>)```
+  - Values: Int ```(default=<value of MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN>)```
   - The maximum number of nodes in the subgraph executed in bulk during training (not inference) in the backward pass.
 
 ## Control the Data Communication

From c5383f788c2f2eec21ff97261b2ac0f5cc902684 Mon Sep 17 00:00:00 2001
From: perdasilva <perdasilva@gmail.com>
Date: Thu, 12 Sep 2019 23:00:46 +0200
Subject: [PATCH 315/813] CD Fixes (#16127)

* Ignore load lib test in CD jobs

* Removes cu80 and adds cu101 support to CD builds

* Disable cython in CD python tests

* Updates CD documentation to reflect variant changes
---
 cd/Jenkinsfile_cd_pipeline                    | 2 +-
 cd/README.md                                  | 4 ++--
 ci/docker/runtime_functions.sh                | 2 ++
 python/mxnet/test_utils.py                    | 5 +++++
 tests/python/unittest/test_library_loading.py | 3 ++-
 5 files changed, 12 insertions(+), 4 deletions(-)

diff --git a/cd/Jenkinsfile_cd_pipeline b/cd/Jenkinsfile_cd_pipeline
index 85b06cb09bb0..d7872204fcb7 100644
--- a/cd/Jenkinsfile_cd_pipeline
+++ b/cd/Jenkinsfile_cd_pipeline
@@ -30,7 +30,7 @@ pipeline {
 
   parameters {
     // Release parameters
-    string(defaultValue: "cpu,mkl,cu80,cu80mkl,cu90,cu90mkl,cu92,cu92mkl,cu100,cu100mkl", description: "Comma separated list of variants", name: "MXNET_VARIANTS")
+    string(defaultValue: "cpu,mkl,cu90,cu90mkl,cu92,cu92mkl,cu100,cu100mkl,cu101,cu101mkl", description: "Comma separated list of variants", name: "MXNET_VARIANTS")
     booleanParam(defaultValue: false, description: 'Whether this is a release build or not', name: "RELEASE_BUILD")
   }
 
diff --git a/cd/README.md b/cd/README.md
index fd978c1d819e..a66fd0ce5533 100644
--- a/cd/README.md
+++ b/cd/README.md
@@ -31,14 +31,14 @@ Currently, 10 variants are supported:
 
 * *cpu*: CPU
 * *mkl*: CPU w/ MKL
-* *cu80*: CUDA 8.0
-* *cu80mkl*: CUDA 8.0 w/ MKL-DNN
 * *cu90*: CUDA 9.0
 * *cu90mkl*: CUDA 9.0 w/ MKL-DNN
 * *cu92*: CUDA 9.2
 * *cu92mkl*: CUDA 9.2 w/ MKL-DNN
 * *cu100*: CUDA 10
 * *cu100mkl*: CUDA 10 w/ MKL-DNN
+* *cu101*: CUDA 10
+* *cu101mkl*: CUDA 10.1 w/ MKL-DNN
 
 *For more on variants, see [here](https://github.com/apache/incubator-mxnet/issues/8671)*
 
diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index 689d525a5cce..67cc98c73620 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -834,6 +834,8 @@ cd_unittest_ubuntu() {
     export PYTHONPATH=./python/
     export MXNET_MKLDNN_DEBUG=1  # Ignored if not present
     export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
+    export MXNET_ENABLE_CYTHON=0
+    export CD_JOB=1 # signal this is a CD run so any unecessary tests can be skipped
 
     local mxnet_variant=${1:?"This function requires a mxnet variant as the first argument"}
     local python_cmd=${2:?"This function requires a python command as the first argument"}
diff --git a/python/mxnet/test_utils.py b/python/mxnet/test_utils.py
index 4533729e2c15..abdf57039c3a 100644
--- a/python/mxnet/test_utils.py
+++ b/python/mxnet/test_utils.py
@@ -2220,3 +2220,8 @@ def collapse_sum_like(a, shape):
             assert s == 1
             axes.append(i+ndim_diff)
     return np.sum(a, axis=tuple(axes)).reshape(shape)
+
+
+def is_cd_run():
+    """Checks if the test is running as part of a Continuous Delivery run"""
+    return os.environ.get("CD_JOB", 0) == "1"
diff --git a/tests/python/unittest/test_library_loading.py b/tests/python/unittest/test_library_loading.py
index 596d124c89d6..29b99dacdbe1 100644
--- a/tests/python/unittest/test_library_loading.py
+++ b/tests/python/unittest/test_library_loading.py
@@ -22,12 +22,13 @@
 import unittest
 import mxnet as mx
 from mxnet.base import MXNetError
-from mxnet.test_utils import download
+from mxnet.test_utils import download, is_cd_run
 
 def check_platform():
     return platform.machine() not in ['x86_64', 'AMD64']
 
 @unittest.skipIf(check_platform(), "not all machine types supported")
+@unittest.skipIf(is_cd_run(), "continuous delivery run - ignoring test")
 def test_library_loading():
     if (os.name=='posix'):
         lib = 'libsample_lib.so'

From ccd24a8a55275fe3522bd203dab25f08bc24e24a Mon Sep 17 00:00:00 2001
From: YixinBao <yixin.bao@intel.com>
Date: Fri, 13 Sep 2019 07:03:48 +0800
Subject: [PATCH 316/813] avoid test relu at the origin due to discontinuous
 gradient (#16133)

* avoid test relu at the origin due to discontinuous gradient

* retrigger CI
---
 tests/python/mkl/test_mkldnn.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/tests/python/mkl/test_mkldnn.py b/tests/python/mkl/test_mkldnn.py
index 3e623b59977d..2ffe3eaa233d 100644
--- a/tests/python/mkl/test_mkldnn.py
+++ b/tests/python/mkl/test_mkldnn.py
@@ -337,13 +337,17 @@ def check_pooling_training(stype):
 def test_activation():
     def check_activation_training(stype):
         for shape in [(2, 3, 3), (2, 3, 2, 2)]:
+            eps = 1e-5
             data_tmp = np.random.normal(-0.1, 1, size=shape)
+            # Avoid finite difference method inaccuracies due to discontinuous gradient at the origin.
+            # Here we replace small problematic inputs with 1.0.  Repro issue with seed 851486559.
+            data_tmp[abs(data_tmp) < eps] = 1.0
 
             data = mx.symbol.Variable('data', stype=stype)
             in_location = [mx.nd.array(data_tmp).tostype(stype)]
 
             test = mx.symbol.Activation(data, act_type="relu")
-            check_numeric_gradient(test, in_location, numeric_eps=1e-5, rtol=0.16, atol=1e-4)
+            check_numeric_gradient(test, in_location, numeric_eps=eps, rtol=0.16, atol=1e-4)
 
     stypes = ['row_sparse', 'default']
     for stype in stypes:

From e9e267ef711261f20528d443f38eb7b9e991057c Mon Sep 17 00:00:00 2001
From: reminisce <wujun.nju@gmail.com>
Date: Sat, 14 Sep 2019 09:33:08 -0700
Subject: [PATCH 317/813] Fix remaining errors reported by D2L (#16157)

* Fix

* Fix ssd

* Add test for np.empty

* Add np.linalg.norm

* Fix indexing bug

* Improve doc
---
 python/mxnet/_numpy_op_doc.py               | 353 +++++++++++++++++++-
 python/mxnet/gluon/loss.py                  |  10 +-
 python/mxnet/ndarray/numpy/linalg.py        |  48 ++-
 python/mxnet/numpy/linalg.py                |  42 ++-
 python/mxnet/numpy/multiarray.py            | 102 ++++--
 python/mxnet/symbol/numpy/_symbol.py        |  20 +-
 python/mxnet/symbol/numpy/linalg.py         |  48 ++-
 tests/python/unittest/test_numpy_gluon.py   |  38 ++-
 tests/python/unittest/test_numpy_ndarray.py |  71 +++-
 tests/python/unittest/test_numpy_op.py      |  81 ++++-
 10 files changed, 751 insertions(+), 62 deletions(-)

diff --git a/python/mxnet/_numpy_op_doc.py b/python/mxnet/_numpy_op_doc.py
index 4e5ea74c2771..c95752a00cb6 100644
--- a/python/mxnet/_numpy_op_doc.py
+++ b/python/mxnet/_numpy_op_doc.py
@@ -21,7 +21,10 @@
 
 
 def _np_ones_like(a):
-    """Return an array of ones with the same shape and type as a given array.
+    """
+    ones_like(a)
+
+    Return an array of ones with the same shape and type as a given array.
 
     Parameters
     ----------
@@ -38,7 +41,10 @@ def _np_ones_like(a):
 
 
 def _np_zeros_like(a):
-    """Return an array of zeros with the same shape and type as a given array.
+    """
+    zeros_like(a)
+
+    Return an array of zeros with the same shape and type as a given array.
 
     Parameters
     ----------
@@ -55,7 +61,10 @@ def _np_zeros_like(a):
 
 
 def _np_cumsum(a, axis=None, dtype=None, out=None):
-    """Return the cumulative sum of the elements along a given axis.
+    """
+    cumsum(a, axis=None, dtype=None, out=None)
+
+    Return the cumulative sum of the elements along a given axis.
 
     Parameters
     ----------
@@ -103,3 +112,341 @@ def _np_cumsum(a, axis=None, dtype=None, out=None):
 
     """
     pass
+
+
+def _np_repeat(a, repeats, axis=None):
+    """
+    repeat(a, repeats, axis=None)
+
+    Repeat elements of an array.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input array.
+    repeats : int
+        The number of repetitions for each element.
+    axis : int, optional
+        The axis along which to repeat values.  By default, use the
+        flattened input array, and return a flat output array.
+
+    Returns
+    -------
+    repeated_array : ndarray
+        Output array which has the same shape as `a`, except along
+        the given axis.
+
+    Notes
+    -----
+    Unlike the official NumPy ``repeat`` operator, this operator currently
+    does not support array of ints for the parameter `repeats`.
+
+    Examples
+    --------
+    >>> x = np.arange(4).reshape(2, 2)
+    >>> x
+    array([[0., 1.],
+           [2., 3.]])
+    >>> np.repeat(x, repeats=3)
+    array([0., 0., 0., 1., 1., 1., 2., 2., 2., 3., 3., 3.])
+    >>> np.repeat(x, repeats=3, axis=0)
+    array([[0., 1.],
+           [0., 1.],
+           [0., 1.],
+           [2., 3.],
+           [2., 3.],
+           [2., 3.]])
+    >>> np.repeat(x, repeats=3, axis=1)
+    array([[0., 0., 0., 1., 1., 1.],
+           [2., 2., 2., 3., 3., 3.]])
+    """
+    pass
+
+
+def _np_transpose(a, axes=None):
+    """
+    transpose(a, axes=None)
+
+    Permute the dimensions of an array.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input array.
+    axes : list of ints, optional
+        By default, reverse the dimensions,
+        otherwise permute the axes according to the values given.
+
+    Returns
+    -------
+    p : ndarray
+        a with its axes permuted.
+
+    Notes
+    -----
+    This function differs from the original `numpy.transpose
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.transpose.html>`_ in
+    the following way(s):
+
+    - only ndarray is accepted as valid input, python iterables are not supported
+    - the operator always returns an `ndarray` that does not share the memory with the input
+
+    Examples
+    --------
+    >>> x = np.arange(4).reshape((2,2))
+    >>> x
+    array([[0., 1.],
+           [2., 3.]])
+    >>> np.transpose(x)
+    array([[0., 2.],
+           [1., 3.]])
+    >>> x = np.ones((1, 2, 3))
+    >>> np.transpose(x, (1, 0, 2)).shape
+    (2, 1, 3)
+    """
+    pass
+
+
+def _np_dot(a, b, out=None):
+    """dot(a, b, out=None)
+
+    Dot product of two arrays. Specifically,
+
+    - If both `a` and `b` are 1-D arrays, it is inner product of vectors
+
+    - If both `a` and `b` are 2-D arrays, it is matrix multiplication,
+
+    - If either `a` or `b` is 0-D (scalar), it is equivalent to :func:`multiply`
+      and using ``np.multiply(a, b)`` or ``a * b`` is preferred.
+
+    - If `a` is an N-D array and `b` is a 1-D array, it is a sum product over
+      the last axis of `a` and `b`.
+
+    - If `a` is an N-D array and `b` is a 2-D array, it is a
+      sum product over the last axis of `a` and the second-to-last axis of `b`::
+
+        dot(a, b)[i,j,k] = sum(a[i,j,:] * b[:,k])
+
+    Parameters
+    ----------
+    a : ndarray
+        First argument.
+    b : ndarray
+        Second argument.
+
+    out : ndarray, optional
+        Output argument. It must have the same shape and type as the expected output.
+
+    Returns
+    -------
+    output : ndarray
+        Returns the dot product of `a` and `b`.  If `a` and `b` are both
+        scalars or both 1-D arrays then a scalar is returned; otherwise
+        an array is returned.
+        If `out` is given, then it is returned
+
+    Examples
+    --------
+    >>> a = np.array(3)
+    >>> b = np.array(4)
+    >>> np.dot(a, b)
+    array(12.)
+
+    For 2-D arrays it is the matrix product:
+
+    >>> a = np.array([[1, 0], [0, 1]])
+    >>> b = np.array([[4, 1], [2, 2]])
+    >>> np.dot(a, b)
+    array([[4., 1.],
+           [2., 2.]])
+
+    >>> a = np.arange(3*4*5*6).reshape((3,4,5,6))
+    >>> b = np.arange(5*6)[::-1].reshape((6,5))
+    >>> np.dot(a, b)[2,3,2,2]
+    array(29884.)
+    >>> np.sum(a[2,3,2,:] * b[:,2])
+    array(29884.)
+    """
+    pass
+
+
+def _np_sum(a, axis=0, dtype=None, keepdims=None, initial=None, out=None):
+    r"""
+    sum(a, axis=None, dtype=None, keepdims=_Null, initial=_Null, out=None)
+
+    Sum of array elements over a given axis.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input data.
+    axis : None or int, optional
+        Axis or axes along which a sum is performed.  The default,
+        axis=None, will sum all of the elements of the input array.  If
+        axis is negative it counts from the last to the first axis.
+    dtype : dtype, optional
+        The type of the returned array and of the accumulator in which the
+        elements are summed. The default type is float32.
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left
+        in the result as dimensions with size one. With this option,
+        the result will broadcast correctly against the input array.
+
+        If the default value is passed, then `keepdims` will not be
+        passed through to the `sum` method of sub-classes of
+        `ndarray`, however any non-default value will be.  If the
+        sub-classes `sum` method does not implement `keepdims` any
+        exceptions will be raised.
+    initial: Currently only supports None as input, optional
+        Starting value for the sum.
+        Currently not implemented. Please use ``None`` as input or skip this argument.
+    out : ndarray or None, optional
+        Alternative output array in which to place the result. It must have
+        the same shape and dtype as the expected output.
+
+    Returns
+    -------
+    sum_along_axis : ndarray
+        An ndarray with the same shape as `a`, with the specified
+        axis removed. If an output array is specified, a reference to
+        `out` is returned.
+
+    Notes
+    -----
+    - Input type does not support Python native iterables.
+    - "out" param: cannot perform auto type change. out ndarray's dtype must be the same as the expected output.
+    - "initial" param is not supported yet. Please use None as input.
+    - Arithmetic is modular when using integer types, and no error is raised on overflow.
+    - The sum of an empty array is the neutral element 0:
+
+    >>> a = np.empty(1)
+    >>> np.sum(a)
+    array(0.)
+
+    This function differs from the original `numpy.sum
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.sum.html>`_ in
+    the following aspects:
+
+    - Input type does not support Python native iterables(list, tuple, ...).
+    - "out" param: cannot perform auto type cast. out ndarray's dtype must be the same as the expected output.
+    - "initial" param is not supported yet. Please use ``None`` as input or skip it.
+
+    Examples
+    --------
+    >>> a = np.array([0.5, 1.5])
+    >>> np.sum(a)
+    array(2.)
+    >>> a = np.array([0.5, 0.7, 0.2, 1.5])
+    >>> np.sum(a, dtype=np.int32)
+    array(2, dtype=int32)
+    >>> a = np.array([[0, 1], [0, 5]])
+    >>> np.sum(a)
+    array(6.)
+    >>> np.sum(a, axis=0)
+    array([0., 6.])
+    >>> np.sum(a, axis=1)
+    array([1., 5.])
+
+    With output ndarray:
+
+    >>> a = np.array([[0, 1], [0, 5]])
+    >>> b = np.ones((2,), dtype=np.float32)
+    >>> np.sum(a, axis = 0, out=b)
+    array([0., 6.])
+    >>> b
+    array([0., 6.])
+
+    If the accumulator is too small, overflow occurs:
+
+    >>> np.ones(128, dtype=np.int8).sum(dtype=np.int8)
+    array(-128, dtype=int8)
+    """
+    pass
+
+
+def _np_copy(a, out=None):
+    """
+    copy(a, out=None)
+
+    Return an array copy of the given object.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input data.
+    out : ndarray or None, optional
+        Alternative output array in which to place the result. It must have
+        the same shape and dtype as the expected output.
+
+    Returns
+    -------
+    arr : ndarray
+        Array interpretation of `a`.
+
+    Notes
+    -------
+    This function differs from the original `numpy.copy
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.copy.html>`_ in
+    the following aspects:
+
+    - Input type does not support Python native iterables(list, tuple, ...).
+    - ``out`` param: cannot perform auto broadcasting. ``out`` ndarray's shape must be the same as the expected output.
+    - ``out`` param: cannot perform auto type cast. ``out`` ndarray's dtype must be the same as the expected output.
+    - Does not support "order" parameter.
+
+    Examples
+    --------
+    Create an array x, with a reference y and a copy z:
+
+    >>> x = np.array([1, 2, 3])
+    >>> y = x
+    >>> z = np.copy(x)
+
+    Note that, when ``x`` is modified, ``y`` is also modified, but not ``z``:
+
+    >>> x[0] = 10
+    >>> x[0] == y[0]
+    array([1.])
+    >>> x[0] == z[0]
+    array([0.])
+    """
+    pass
+
+
+def _np_reshape(a, newshape, order='C', out=None):
+    """
+    reshape(a, newshape, order='C')
+
+    Gives a new shape to an array without changing its data.
+    This function always returns a copy of the input array if
+    ``out`` is not provided.
+
+    Parameters
+    ----------
+    a : ndarray
+        Array to be reshaped.
+    newshape : int or tuple of ints
+        The new shape should be compatible with the original shape. If
+        an integer, then the result will be a 1-D array of that length.
+        One shape dimension can be -1. In this case, the value is
+        inferred from the length of the array and remaining dimensions.
+    order : {'C'}, optional
+        Read the elements of `a` using this index order, and place the
+        elements into the reshaped array using this index order.  'C'
+        means to read / write the elements using C-like index order,
+        with the last axis index changing fastest, back to the first
+        axis index changing slowest. Other order types such as 'F'/'A'
+        may be added in the future.
+
+    Returns
+    -------
+    reshaped_array : ndarray
+        It will be always a copy of the original array. This behavior is different
+        from the official NumPy ``reshape`` operator where views of the original array may be
+        generated.
+
+    See Also
+    --------
+    ndarray.reshape : Equivalent method.
+    """
+    pass
diff --git a/python/mxnet/gluon/loss.py b/python/mxnet/gluon/loss.py
index 4cdb361eb146..45c3dee08139 100644
--- a/python/mxnet/gluon/loss.py
+++ b/python/mxnet/gluon/loss.py
@@ -189,9 +189,15 @@ def __init__(self, weight=None, batch_axis=0, **kwargs):
 
     def hybrid_forward(self, F, pred, label, sample_weight=None):
         label = _reshape_like(F, label, pred)
-        loss = F.abs(label - pred)
+        loss = F.np.abs(label - pred) if is_np_array() else F.abs(label - pred)
         loss = _apply_weighting(F, loss, self._weight, sample_weight)
-        return F.mean(loss, axis=self._batch_axis, exclude=True)
+        if is_np_array():
+            if F is ndarray:
+                return F.np.mean(loss, axis=tuple(range(1, loss.ndim)))
+            else:
+                return F.npx.batch_flatten(loss).mean(axis=1)
+        else:
+            return F.mean(loss, axis=self._batch_axis, exclude=True)
 
 
 class SigmoidBinaryCrossEntropyLoss(Loss):
diff --git a/python/mxnet/ndarray/numpy/linalg.py b/python/mxnet/ndarray/numpy/linalg.py
index 0222bb45d148..36f3f21a7588 100644
--- a/python/mxnet/ndarray/numpy/linalg.py
+++ b/python/mxnet/ndarray/numpy/linalg.py
@@ -18,5 +18,51 @@
 """Namespace for operators used in Gluon dispatched by F=ndarray."""
 
 from __future__ import absolute_import
+from . import _op as _mx_nd_np
 
-__all__ = []
+__all__ = ['norm']
+
+
+def norm(x, ord=None, axis=None, keepdims=False):
+    r"""Matrix or vector norm.
+
+    This function can only support Frobenius norm for now.
+    The Frobenius norm is given by [1]_:
+
+        :math:`||A||_F = [\sum_{i,j} abs(a_{i,j})^2]^{1/2}`
+
+    Parameters
+    ----------
+    x : ndarray
+        Input array.
+    ord : {'fro'}, optional
+        Order of the norm.
+    axis : {int, 2-tuple of ints, None}, optional
+        If `axis` is an integer, it specifies the axis of `x` along which to
+        compute the vector norms.  If `axis` is a 2-tuple, it specifies the
+        axes that hold 2-D matrices, and the matrix norms of these matrices
+        are computed.  If `axis` is None, the norm of the whole ndarray is
+        returned.
+
+    keepdims : bool, optional
+        If this is set to True, the axes which are normed over are left in the
+        result as dimensions with size one.  With this option the result will
+        broadcast correctly against the original `x`.
+
+    Returns
+    -------
+    n : float or ndarray
+        Norm of the matrix or vector(s).
+
+    References
+    ----------
+    .. [1] G. H. Golub and C. F. Van Loan, *Matrix Computations*,
+           Baltimore, MD, Johns Hopkins University Press, 1985, pg. 15
+    """
+    if ord is not None and ord != 'fro':
+        raise ValueError('only support Frobenius norm for now, received ord={}'.format(str(ord)))
+    if isinstance(axis, tuple) and len(axis) > 2:
+        raise ValueError('Improper number of dimensions to norm')
+    if ord == 'fro' and x.ndim > 2 and axis is None:
+        raise ValueError('Improper number of dimensions to norm')
+    return _mx_nd_np.sqrt(_mx_nd_np.sum(x * x, axis=axis, keepdims=keepdims))
diff --git a/python/mxnet/numpy/linalg.py b/python/mxnet/numpy/linalg.py
index c4109378e146..9758af47233d 100644
--- a/python/mxnet/numpy/linalg.py
+++ b/python/mxnet/numpy/linalg.py
@@ -18,5 +18,45 @@
 """Namespace for ops used in imperative programming."""
 
 from __future__ import absolute_import
+from ..ndarray import numpy as _mx_nd_np
 
-__all__ = []
+__all__ = ['norm']
+
+
+def norm(x, ord=None, axis=None, keepdims=False):
+    r"""Matrix or vector norm.
+
+    This function can only support Frobenius norm for now.
+    The Frobenius norm is given by [1]_:
+
+        :math:`||A||_F = [\sum_{i,j} abs(a_{i,j})^2]^{1/2}`
+
+    Parameters
+    ----------
+    x : ndarray
+        Input array.
+    ord : {'fro'}, optional
+        Order of the norm.
+    axis : {int, 2-tuple of ints, None}, optional
+        If `axis` is an integer, it specifies the axis of `x` along which to
+        compute the vector norms.  If `axis` is a 2-tuple, it specifies the
+        axes that hold 2-D matrices, and the matrix norms of these matrices
+        are computed.  If `axis` is None, the norm of the whole ndarray is
+        returned.
+
+    keepdims : bool, optional
+        If this is set to True, the axes which are normed over are left in the
+        result as dimensions with size one.  With this option the result will
+        broadcast correctly against the original `x`.
+
+    Returns
+    -------
+    n : float or ndarray
+        Norm of the matrix or vector(s).
+
+    References
+    ----------
+    .. [1] G. H. Golub and C. F. Van Loan, *Matrix Computations*,
+           Baltimore, MD, Johns Hopkins University Press, 1985, pg. 15
+    """
+    return _mx_nd_np.linalg.norm(x, ord, axis, keepdims)
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 4eddb0380c09..1f8aa92f9851 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -59,6 +59,7 @@
 _NDARRAY_BASIC_INDEXING = 0
 _NDARRAY_ADVANCED_INDEXING = 1
 
+
 # This function is copied from ndarray.py since pylint
 # keeps giving false alarm error of undefined-all-variable
 def _new_alloc_handle(shape, ctx, delay_alloc, dtype=mx_real_t):
@@ -311,14 +312,14 @@ def _prepare_value_nd(self, value, bcast_shape, squeeze_axes=None):
         Note: mxnet.numpy.ndarray not support NDArray as assigned value.
         """
         if isinstance(value, numeric_types):
-            value_nd = full(bcast_shape, value, ctx=self.context, dtype=self.dtype)
+            value_nd = full(bcast_shape, value, ctx=self.ctx, dtype=self.dtype)
         elif isinstance(value, self.__class__):
-            value_nd = value.as_in_context(self.context)
+            value_nd = value.as_in_ctx(self.ctx)
             if value_nd.dtype != self.dtype:
                 value_nd = value_nd.astype(self.dtype)
         else:
             try:
-                value_nd = array(value, ctx=self.context, dtype=self.dtype)
+                value_nd = array(value, ctx=self.ctx, dtype=self.dtype)
             except:
                 raise TypeError('mxnet.np.ndarray does not support assignment with non-array-like '
                                 'object {} of type {}'.format(value, type(value)))
@@ -329,6 +330,19 @@ def _prepare_value_nd(self, value, bcast_shape, squeeze_axes=None):
             squeeze_axes = tuple([ax for ax in squeeze_axes if ax < len(value_nd.shape)])
             value_nd = value_nd.squeeze(axis=tuple(squeeze_axes))
 
+        # handle the cases like the following
+        # a = np.zeros((3, 3)), b = np.ones((1, 1, 1, 1, 3)), a[0] = b
+        # b cannot broadcast directly to a[0].shape unless its leading 1-size axes are trimmed
+        if value_nd.ndim > len(bcast_shape):
+            squeeze_axes = []
+            for i in range(value_nd.ndim - len(bcast_shape)):
+                if value_nd.shape[i] == 1:
+                    squeeze_axes.append(i)
+                else:
+                    break
+            if squeeze_axes:
+                value_nd = value_nd.squeeze(squeeze_axes)
+
         if value_nd.shape != bcast_shape:
             if value_nd.size == 0:
                 value_nd = value_nd.reshape(bcast_shape)
@@ -336,7 +350,6 @@ def _prepare_value_nd(self, value, bcast_shape, squeeze_axes=None):
                 value_nd = value_nd.broadcast_to(bcast_shape)
         return value_nd
 
-
     def __add__(self, other):
         """x.__add__(y) <=> x + y"""
         return add(self, other)
@@ -727,14 +740,14 @@ def copyto(self, other):
 
         Examples
         --------
-        >>> x = np.ones((2,3))
-        >>> y = np.zeros((2,3), ctx=mx.gpu(0))
+        >>> x = np.ones((2, 3))
+        >>> y = np.zeros((2, 3), ctx=npx.gpu(0))
         >>> z = x.copyto(y)
         >>> z is y
         True
-        >>> y.asnumpy()
+        >>> y
         array([[ 1.,  1.,  1.],
-               [ 1.,  1.,  1.]], dtype=float32)
+               [ 1.,  1.,  1.]])
         """
         if isinstance(other, ndarray):
             if other.handle is self.handle:
@@ -756,6 +769,11 @@ def argmax(self, axis=None, out=None):  # pylint: disable=arguments-differ
         return argmax(self, axis, out)
 
     def as_in_context(self, context):
+        """This function has been deprecated. Please refer to ``ndarray.as_in_ctx``."""
+        warnings.warn('ndarray.context has been renamed to ndarray.ctx', DeprecationWarning)
+        return self.as_nd_ndarray().as_in_context(context).as_np_ndarray()
+
+    def as_in_ctx(self, ctx):
         """Returns an array on the target device with the same value as this array.
 
         If the target context is the same as ``self.context``, then ``self`` is
@@ -771,15 +789,58 @@ def as_in_context(self, context):
         ndarray
             The target array.
         """
-        if self.context == context:
+        if self.ctx == ctx:
             return self
-        return self.copyto(context)
+        return self.copyto(ctx)
+
+    @property
+    def ctx(self):
+        """Device context of the array.
+
+        Examples
+        --------
+        >>> x = np.array([1, 2, 3, 4])
+        >>> x.ctx
+        cpu(0)
+        >>> type(x.ctx)
+        <class 'mxnet.context.Context'>
+        >>> y = np.zeros((2, 3), npx.gpu(0))
+        >>> y.ctx
+        gpu(0)
+        """
+        dev_typeid = ctypes.c_int()
+        dev_id = ctypes.c_int()
+        check_call(_LIB.MXNDArrayGetContext(
+            self.handle, ctypes.byref(dev_typeid), ctypes.byref(dev_id)))
+        return Context(Context.devtype2str[dev_typeid.value], dev_id.value)
+
+    @property
+    def context(self):
+        """This function has been deprecated. Please refer to ``ndarray.ctx``."""
+        warnings.warn('ndarray.context has been renamed to ndarray.ctx', DeprecationWarning)
+        return self.as_nd_ndarray().context
 
     def copy(self, order='C'):  # pylint: disable=arguments-differ
+        """Return a coyp of the array, keeping the same context.
+
+        Parameters
+        ----------
+        order : str
+            The memory layout of the copy. Currently, only c-contiguous memory
+            layout is supported.
+
+        Examples
+        --------
+        >>> x = np.ones((2, 3))
+        >>> y = x.copy()
+        >>> y
+        array([[ 1.,  1.,  1.],
+               [ 1.,  1.,  1.]])
+        """
         if order != 'C':
             raise NotImplementedError('ndarray.copy only supports order=\'C\', while '
                                       'received {}'.format(str(order)))
-        return super(ndarray, self).copy().as_np_ndarray()
+        return self.copyto(self.ctx)
 
     def dot(self, b, out=None):
         """Dot product of two arrays.
@@ -787,7 +848,7 @@ def dot(self, b, out=None):
         return _mx_np_op.dot(self, b, out=out)
 
     def reshape(self, *args, **kwargs):  # pylint: disable=arguments-differ
-        """Returns an array containing the same data with a new shape.
+        """Returns a copy of the array with a new shape.
 
         Notes
         -----
@@ -854,7 +915,7 @@ def broadcast_axes(self, *args, **kwargs):
 
     def repeat(self, repeats, axis=None):  # pylint: disable=arguments-differ
         """Repeat elements of an array."""
-        raise NotImplementedError
+        return _mx_np_op.repeat(self, repeats=repeats, axis=axis)
 
     def pad(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`pad`.
@@ -1182,22 +1243,22 @@ def var(self, axis=None, dtype=None, out=None, ddof=0, keepdims=None):  # pylint
 
     def cumsum(self, axis=None, dtype=None, out=None):
         """Return the cumulative sum of the elements along the given axis."""
-        raise NotImplementedError
+        return _mx_np_op.cumsum(self, axis=axis, dtype=dtype, out=out)
 
     def tolist(self):
         return self.asnumpy().tolist()
 
     def max(self, axis=None, out=None, keepdims=False):  # pylint: disable=arguments-differ
         """Return the maximum along a given axis."""
-        raise NotImplementedError
+        return _mx_np_op.max(self, axis=axis, keepdims=keepdims, out=out)
 
-    def min(self, *args, **kwargs):
+    def min(self, axis=None, out=None, keepdims=False):  # pylint: disable=arguments-differ
         """Convenience fluent method for :py:func:`min`.
 
         The arguments are the same as for :py:func:`min`, with
         this array as data.
         """
-        raise NotImplementedError
+        return _mx_np_op.min(self, axis=axis, keepdims=keepdims, out=out)
 
     def norm(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`norm`.
@@ -1549,7 +1610,7 @@ def tostype(self, stype):
 
 
 @set_module('mxnet.numpy')
-def empty(shape, dtype=float, order='C', ctx=None):
+def empty(shape, dtype=_np.float32, order='C', ctx=None):
     """Return a new array of given shape and type, without initializing entries.
 
     Parameters
@@ -1573,7 +1634,8 @@ def empty(shape, dtype=float, order='C', ctx=None):
         Array of uninitialized (arbitrary) data of the given shape, dtype, and order.
     """
     if order != 'C':
-        raise NotImplementedError
+        raise NotImplementedError('`empty` only supports order equal to `C`, while received {}'
+                                  .format(str(order)))
     if ctx is None:
         ctx = current_context()
     if dtype is None:
@@ -1609,7 +1671,7 @@ def array(object, dtype=None, ctx=None):
     if isinstance(object, ndarray):
         dtype = object.dtype if dtype is None else dtype
     else:
-        dtype = mx_real_t if dtype is None else dtype
+        dtype = _np.float32 if dtype is None else dtype
         if not isinstance(object, (ndarray, _np.ndarray)):
             try:
                 object = _np.array(object, dtype=dtype)
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 0841c0e4d2cc..077008aba119 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -223,11 +223,11 @@ def dot(self, b, out=None):
         return _mx_np_op.dot(self, b, out=out)
 
     def reshape(self, *args, **kwargs):  # pylint: disable=arguments-differ
-        """Returns an array containing the same data with a new shape.
+        """Returns a copy of the array with a new shape.
 
         Notes
         -----
-        Unlike the free function `numpy.reshape`, this method on `ndarray` allows
+        Unlike the free function `mxnet.numpy.reshape`, this method on `ndarray` allows
         the elements of the shape parameter to be passed in as separate arguments.
         For example, ``a.reshape(10, 11)`` is equivalent to
         ``a.reshape((10, 11))``.
@@ -289,7 +289,7 @@ def broadcast_axes(self, *args, **kwargs):
 
     def repeat(self, repeats, axis=None):  # pylint: disable=arguments-differ
         """Repeat elements of an array."""
-        raise NotImplementedError
+        return _mx_np_op.repeat(self, repeats=repeats, axis=axis)
 
     def pad(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`pad`.
@@ -543,19 +543,15 @@ def var(self, axis=None, dtype=None, out=None, ddof=0, keepdims=None):  # pylint
 
     def cumsum(self, axis=None, dtype=None, out=None):
         """Return the cumulative sum of the elements along the given axis."""
-        raise NotImplementedError
+        return _mx_np_op.cumsum(self, axis=axis, dtype=dtype, out=out)
 
     def max(self, axis=None, out=None, keepdims=False):  # pylint: disable=arguments-differ
         """Return the maximum along a given axis."""
-        raise NotImplementedError
-
-    def min(self, *args, **kwargs):
-        """Convenience fluent method for :py:func:`min`.
+        return _mx_np_op.max(self, axis=axis, keepdims=keepdims, out=out)
 
-        The arguments are the same as for :py:func:`min`, with
-        this array as data.
-        """
-        raise NotImplementedError
+    def min(self, axis=None, out=None, keepdims=False):  # pylint: disable=arguments-differ
+        """Return the minimum along a given axis."""
+        return _mx_np_op.min(self, axis=axis, keepdims=keepdims, out=out)
 
     def norm(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`norm`.
diff --git a/python/mxnet/symbol/numpy/linalg.py b/python/mxnet/symbol/numpy/linalg.py
index 28cfd0f3806a..d1918ef8b903 100644
--- a/python/mxnet/symbol/numpy/linalg.py
+++ b/python/mxnet/symbol/numpy/linalg.py
@@ -18,5 +18,51 @@
 """Namespace for operators used in Gluon dispatched by F=symbol."""
 
 from __future__ import absolute_import
+from . import _symbol
+from . import _op as _mx_sym_np
 
-__all__ = []
+__all__ = ['norm']
+
+
+def norm(x, ord=None, axis=None, keepdims=False):
+    r"""Matrix or vector norm.
+
+    This function can only support Frobenius norm for now.
+    The Frobenius norm is given by [1]_:
+
+        :math:`||A||_F = [\sum_{i,j} abs(a_{i,j})^2]^{1/2}`
+
+    Parameters
+    ----------
+    x : ndarray
+        Input array.
+    ord : {'fro'}, optional
+        Order of the norm.
+    axis : {int, 2-tuple of ints, None}, optional
+        If `axis` is an integer, it specifies the axis of `x` along which to
+        compute the vector norms.  If `axis` is a 2-tuple, it specifies the
+        axes that hold 2-D matrices, and the matrix norms of these matrices
+        are computed.  If `axis` is None, the norm of the whole ndarray is
+        returned.
+
+    keepdims : bool, optional
+        If this is set to True, the axes which are normed over are left in the
+        result as dimensions with size one.  With this option the result will
+        broadcast correctly against the original `x`.
+
+    Returns
+    -------
+    n : float or ndarray
+        Norm of the matrix or vector(s).
+
+    References
+    ----------
+    .. [1] G. H. Golub and C. F. Van Loan, *Matrix Computations*,
+           Baltimore, MD, Johns Hopkins University Press, 1985, pg. 15
+    """
+    if ord is not None and ord != 'fro':
+        raise ValueError('only support Frobenius norm for now, received ord={}'.format(str(ord)))
+    if isinstance(axis, tuple) and len(axis) > 2:
+        raise ValueError('Improper number of dimensions to norm')
+    # TODO(junwu): When ord = 'fro', axis = None, and x.ndim > 2, raise exception
+    return _symbol.sqrt(_mx_sym_np.sum(x * x, axis=axis, keepdims=keepdims))
diff --git a/tests/python/unittest/test_numpy_gluon.py b/tests/python/unittest/test_numpy_gluon.py
index e96f829a8580..62ea38fc0c13 100644
--- a/tests/python/unittest/test_numpy_gluon.py
+++ b/tests/python/unittest/test_numpy_gluon.py
@@ -19,9 +19,11 @@
 from __future__ import absolute_import
 from __future__ import division
 
+import numpy as _np
 import mxnet as mx
 from mxnet import gluon, autograd, np
-from mxnet.test_utils import use_np
+from mxnet.test_utils import use_np, assert_almost_equal
+from common import with_seed
 
 
 def test_create_np_param():
@@ -108,6 +110,40 @@ def hybrid_forward(self, F, pred, label):
         trainer.step(1)
 
 
+@with_seed()
+@use_np
+def test_np_loss_ndarray():
+    # Ported from test_loss.test_loss_ndarray
+    output = np.array([1, 2, 3, 4])
+    label = np.array([1, 3, 5, 7])
+    weighting = np.array([0.5, 1, 0.5, 1])
+
+    loss = gluon.loss.L1Loss()
+    assert np.sum(loss(output, label)) == 6.
+    loss = gluon.loss.L1Loss(weight=0.5)
+    assert np.sum(loss(output, label)) == 3.
+    loss = gluon.loss.L1Loss()
+    assert np.sum(loss(output, label, weighting)) == 5.
+
+    loss = gluon.loss.L2Loss()
+    assert np.sum(loss(output, label)) == 7.
+    loss = gluon.loss.L2Loss(weight=0.25)
+    assert np.sum(loss(output, label)) == 1.75
+    loss = gluon.loss.L2Loss()
+    assert np.sum(loss(output, label, weighting)) == 6
+
+    output = np.array([[0, 2], [1, 4]])
+    label = np.array([0, 1])
+    weighting = np.array([[0.5], [1.0]])
+
+    loss = gluon.loss.SoftmaxCrossEntropyLoss()
+    L = loss(output, label).asnumpy()
+    assert_almost_equal(L, _np.array([2.12692809,  0.04858733]), use_broadcast=False)
+
+    L = loss(output, label, weighting).asnumpy()
+    assert_almost_equal(L, _np.array([1.06346405,  0.04858733]), use_broadcast=False)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()
diff --git a/tests/python/unittest/test_numpy_ndarray.py b/tests/python/unittest/test_numpy_ndarray.py
index 883060466836..bffa7a00dccb 100644
--- a/tests/python/unittest/test_numpy_ndarray.py
+++ b/tests/python/unittest/test_numpy_ndarray.py
@@ -26,12 +26,45 @@
 from mxnet.test_utils import same, assert_almost_equal, rand_shape_nd, rand_ndarray, retry, assert_exception, use_np
 from common import with_seed, TemporaryDirectory
 from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf
+from mxnet.ndarray.ndarray import py_slice
+from mxnet.base import integer_types
 import scipy.stats as ss
 
 
 @with_seed()
 @use_np
-def test_array_creation():
+def test_np_empty():
+    dtypes = [np.int8, np.int32, np.float16, np.float32, np.float64, None]
+    expected_dtypes = [np.int8, np.int32, np.float16, np.float32, np.float64, np.float32]
+    orders = ['C', 'F', 'A']
+    shapes = [
+        (),
+        0,
+        (0,),
+        (0, 0),
+        2,
+        (2,),
+        (3, 0),
+        (4, 5),
+        (1, 1, 1, 1),
+    ]
+    ctxes = [npx.current_context(), None]
+    for dtype, expected_dtype in zip(dtypes, expected_dtypes):
+        for shape in shapes:
+            for order in orders:
+                for ctx in ctxes:
+                    if order == 'C':
+                        ret = np.empty(shape, dtype, order, ctx)
+                        assert ret.dtype == expected_dtype
+                        assert ret.shape == shape if isinstance(shape, tuple) else (shape,)
+                        assert ret.ctx == npx.current_context()
+                    else:
+                        assert_exception(np.empty, NotImplementedError, shape, dtype, order, ctx)
+
+
+@with_seed()
+@use_np
+def test_np_array_creation():
     dtypes = [_np.int8, _np.int32, _np.float16, _np.float32, _np.float64, None]
     objects = [
         [],
@@ -54,7 +87,7 @@ def test_array_creation():
 
 @with_seed()
 @use_np
-def test_zeros():
+def test_np_zeros():
     # test np.zeros in Gluon
     class TestZeros(HybridBlock):
         def __init__(self, shape, dtype=None):
@@ -102,7 +135,7 @@ def check_zero_array_creation(shape, dtype):
 
 @with_seed()
 @use_np
-def test_ones():
+def test_np_ones():
     # test np.ones in Gluon
     class TestOnes(HybridBlock):
         def __init__(self, shape, dtype=None):
@@ -149,7 +182,7 @@ def check_ones_array_creation(shape, dtype):
 
 
 @with_seed()
-def test_ndarray_binary_element_wise_ops():
+def test_np_ndarray_binary_element_wise_ops():
     np_op_map = {
         '+': _np.add,
         '*': _np.multiply,
@@ -304,7 +337,7 @@ def check_binary_op_result(shape1, shape2, op, dtype=None):
 
 
 @with_seed()
-def test_hybrid_block_multiple_outputs():
+def test_np_hybrid_block_multiple_outputs():
     @use_np
     class TestAllNumpyOutputs(HybridBlock):
         def hybrid_forward(self, F, x, *args, **kwargs):
@@ -338,7 +371,7 @@ def hybrid_forward(self, F, x, *args, **kwargs):
 
 @with_seed()
 @use_np
-def test_grad_ndarray_type():
+def test_np_grad_ndarray_type():
     data = np.array(2, dtype=_np.float32)
     data.attach_grad()
     assert type(data.grad) == np.ndarray
@@ -379,7 +412,7 @@ def test_np_ndarray_copy():
 def test_np_ndarray_indexing():
     def np_int(index, int_type=np.int32):
         """
-        Helper function for testing indexing that converts slices to slices of ints or None, and tuples to 
+        Helper function for testing indexing that converts slices to slices of ints or None, and tuples to
         tuples of ints or None.
         """
         def convert(num):
@@ -401,7 +434,7 @@ def convert(num):
         else:
             assert False
 
-    # Copied from test_ndarray.py. Under construction. 
+    # Copied from test_ndarray.py. Under construction.
     def test_getitem(np_array, index):
         np_index = index
         if type(index) == mx.nd.NDArray:  # use of NDArray is prohibited
@@ -439,6 +472,13 @@ def assert_same(np_array, np_index, mx_array, mx_index, mx_value, np_value=None)
 
             assert same(np_array, mx_array.asnumpy())
 
+        def _is_basic_index(index):
+            if isinstance(index, (integer_types, py_slice)):
+                return True
+            if isinstance(index, tuple) and all(isinstance(i, (integer_types, py_slice)) for i in index):
+                return True
+            return False
+
         np_index = index  # keep this native numpy type
         if isinstance(index, np.ndarray):
             np_index = index.asnumpy()
@@ -467,6 +507,13 @@ def assert_same(np_array, np_index, mx_array, mx_index, mx_value, np_value=None)
             assert_same(np_array, np_index, mx_array, index, np.array(np_value))
             # test native numpy array with broadcast
             assert_same(np_array, np_index, mx_array, index, np_value)
+
+            # test value shape are expanded to be longer than index array's shape
+            # this is currently only supported in basic indexing
+            if _is_basic_index(index):
+                expanded_value_shape = (1, 1, 1) + np_value.shape
+                assert_same(np_array, np_index, mx_array, index, np.array(np_value.reshape(expanded_value_shape)))
+                assert_same(np_array, np_index, mx_array, index, np_value.reshape(expanded_value_shape))
             # test list with broadcast
             assert_same(np_array, np_index, mx_array, index,
                         [_np.random.randint(low=-10000, high=0)] * indexed_array_shape[-1])
@@ -664,18 +711,18 @@ def test_setitem_autograd(np_array, index):
         test_setitem(np_array, index)
         test_getitem_autograd(np_array, index)
         test_setitem_autograd(np_array, index)
-    
+
     # Test indexing to zero-size tensors
     index_list = [
-        (slice(0, 0), slice(0, 0), 1, 2),  
-        (slice(0, 0), slice(0, 0), slice(0, 0), slice(0, 0)),  
+        (slice(0, 0), slice(0, 0), 1, 2),
+        (slice(0, 0), slice(0, 0), slice(0, 0), slice(0, 0)),
     ]
     for index in index_list:
         test_getitem(np_array, index)
         test_setitem(np_array, index)
         test_getitem_autograd(np_array, index)
         test_setitem_autograd(np_array, index)
-    
+
     # test zero-size tensors get and setitem
     shapes_indices = [
                         ((0), [slice(None, None, None)]),
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 0f7355b7ef13..c5b0907fb7a8 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -288,7 +288,7 @@ def __init__(self, axis=None, keepdims=False):
             self._keepdims = keepdims
 
         def hybrid_forward(self, F, a, *args, **kwargs):
-            return F.np.max(a, axis=self._axis, keepdims=self._keepdims)
+            return a.max(axis=self._axis, keepdims=self._keepdims)
 
     class TestMin(HybridBlock):
         def __init__(self, axis=None, keepdims=False):
@@ -297,7 +297,7 @@ def __init__(self, axis=None, keepdims=False):
             self._keepdims = keepdims
 
         def hybrid_forward(self, F, a, *args, **kwargs):
-            return F.np.min(a, axis=self._axis, keepdims=self._keepdims)
+            return a.min(axis=self._axis, keepdims=self._keepdims)
 
     def is_int(dtype):
         return 'int' == dtype
@@ -326,12 +326,8 @@ def get_grad(axis, func_name):
             raise ValueError('axis should be int or None or ()')
 
     def _test_np_exception(func, shape, dim):
-        x = _np.random.uniform(-1.0, 1.0, shape)
-        x = mx.nd.array(x).as_np_ndarray()
-        if func == 'max':
-            out = mx.np.max(x)
-        else:
-            out = mx.np.min(x)
+        x = np.random.uniform(-1.0, 1.0, shape)
+        out = getattr(x, func)()
         assert out.ndim == dim, 'dimension mismatch, output.ndim={}, dim={}'.format(output.ndim, dim)
 
     in_data_dim = random.choice([2, 3, 4])
@@ -1620,7 +1616,7 @@ def __init__(self, axis=None, dtype=None):
             self._dtype = dtype
 
         def hybrid_forward(self, F, a):
-            return F.np.cumsum(a, axis=self._axis, dtype=self._dtype)
+            return a.cumsum(axis=self._axis, dtype=self._dtype)
 
     shapes = [(2, 3, 4), (2, 0, 3), ()]
     for hybridize in [True, False]:
@@ -1790,6 +1786,73 @@ def hybrid_forward(self, F, x):
                 assert mx_out.shape == np_out.shape
 
 
+@with_seed()
+@use_np
+def test_np_repeat():
+    config = [
+        ((), 2, None),
+        ((), 0, None),
+        ((4, 2), 2, None),
+        ((4, 2), 2, 0),
+        ((4, 2), 2, 1),
+        ((4, 2), 2, -1),
+    ]
+
+    class TestRepeat(HybridBlock):
+        def __init__(self, repeats, axis=None):
+            super(TestRepeat, self).__init__()
+            self._repeats = repeats
+            self._axis = axis
+
+        def hybrid_forward(self, F, x):
+            return x.repeat(self._repeats, self._axis)
+
+    for shape, repeats, axis in config:
+        data_np = _np.random.randint(low=0, high=1000, size=shape)
+        data_mx = np.array(data_np, dtype=data_np.dtype)
+        ret_np = data_np.repeat(repeats, axis)
+        ret_mx = data_mx.repeat(repeats, axis)
+        assert same(ret_mx.asnumpy(), ret_np)
+
+        net = TestRepeat(repeats, axis)
+        for hybrid in [False, True]:
+            if hybrid:
+                net.hybridize()
+            ret_mx = net(data_mx)
+            assert same(ret_mx.asnumpy(), ret_np)
+
+
+@with_seed()
+@use_np
+def test_np_linalg_norm():
+    @use_np
+    class TestLinalgNorm(HybridBlock):
+        def __init__(self, ord=None, axis=None, keepdims=False):
+            super(TestLinalgNorm, self).__init__()
+            self._ord = ord
+            self._axis = axis
+            self._keepdims = keepdims
+
+        def hybrid_forward(self, F, x):
+            return F.np.linalg.norm(x, ord=self._ord, axis=self._axis, keepdims=self._keepdims)
+
+    a = np.arange(5 * 6 * 7 * 8).reshape((5, 6, 7, 8))
+    ords = [None, 'fro']
+    axes = [None, (0, 2), (1, 0), (1, 2)]
+    for ord in ords:
+        for axis in axes:
+            if ord == 'fro' and axis is None and a.ndim > 2:
+                continue
+            for keepdims in [False, True]:
+                for hybridize in [False, True]:
+                    net = TestLinalgNorm(ord, axis, keepdims)
+                    if hybridize:
+                        net.hybridize()
+                    mx_ret = net(a)
+                    np_ret = _np.linalg.norm(a.asnumpy(), ord=ord, axis=axis, keepdims=keepdims)
+                    assert_almost_equal(mx_ret.asnumpy(), np_ret, atol=1e-5, rtol=1e-4)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 90091b155d6f53c070e3c406f9edc69f38d02e96 Mon Sep 17 00:00:00 2001
From: Haozheng Fan <fhztc1997618@gmail.com>
Date: Mon, 16 Sep 2019 02:57:51 +0800
Subject: [PATCH 318/813] [Numpy] Numpy copysign (#15851)

* add numpy compatible copysign

* fix scalar op registration error

* add test
---
 python/mxnet/ndarray/numpy/_op.py             |  53 ++++++++-
 python/mxnet/numpy/multiarray.py              |  53 ++++++++-
 python/mxnet/symbol/numpy/_symbol.py          |  36 +++++-
 src/operator/mshadow_op.h                     |  10 ++
 .../numpy/np_elemwise_broadcast_op.cc         |  36 ++++++
 .../numpy/np_elemwise_broadcast_op.cu         |  21 ++++
 src/operator/operator_tune.cc                 |   5 +
 tests/python/unittest/test_numpy_op.py        | 105 ++++++++++++++++++
 8 files changed, 316 insertions(+), 3 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index 671345c9a546..b8e4f3f28b86 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -33,7 +33,7 @@
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'mean',
-           'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices']
+           'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -2432,3 +2432,54 @@ def indices(dimensions, dtype=_np.int32, ctx=None):
     else:
         raise ValueError("The dimensions must be sequence of ints")
 # pylint: enable=redefined-outer-name
+
+
+@set_module('mxnet.ndarray.numpy')
+def copysign(x1, x2, out=None):
+    r"""copysign(x1, x2, out=None)
+
+    Change the sign of x1 to that of x2, element-wise.
+
+    If `x2` is a scalar, its sign will be copied to all elements of `x1`.
+
+    Parameters
+    ----------
+    x1 : ndarray or scalar
+        Values to change the sign of.
+    x2 : ndarray or scalar
+        The sign of `x2` is copied to `x1`.
+    out : ndarray or None, optional
+        A location into which the result is stored. It must be of the
+        right shape and right type to hold the output. If not provided
+        or `None`,a freshly-allocated array is returned.
+
+    Returns
+    -------
+    out : ndarray or scalar
+        The values of `x1` with the sign of `x2`.
+        This is a scalar if both `x1` and `x2` are scalars.
+
+    Notes
+    -------
+    This function differs from the original `numpy.copysign
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.copysign.html>`_ in
+    the following aspects:
+
+    - ``where`` param is not supported.
+
+    Examples
+    --------
+    >>> np.copysign(1.3, -1)
+    -1.3
+    >>> 1/np.copysign(0, 1)
+    inf
+    >>> 1/np.copysign(0, -1)
+    -inf
+
+    >>> a = np.array([-1, 0, 1])
+    >>> np.copysign(a, -1.1)
+    array([-1., -0., -1.])
+    >>> np.copysign(a, np.arange(3)-1)
+    array([-1.,  0.,  1.])
+    """
+    return _ufunc_helper(x1, x2, _npi.copysign, _np.copysign, _npi.copysign_scalar, _npi.rcopysign_scalar, out)
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 1f8aa92f9851..632cfadf86a6 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -52,7 +52,7 @@
            'degrees', 'log2', 'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative',
            'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh',
            'tensordot', 'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate',
-           'stack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices']
+           'stack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -3935,3 +3935,54 @@ def indices(dimensions, dtype=_np.int32, ctx=None):
     """
     return _mx_nd_np.indices(dimensions=dimensions, dtype=dtype, ctx=ctx)
 # pylint: enable=redefined-outer-name
+
+
+@set_module('mxnet.numpy')
+def copysign(x1, x2, out=None):
+    r"""copysign(x1, x2, out=None)
+
+    Change the sign of x1 to that of x2, element-wise.
+
+    If `x2` is a scalar, its sign will be copied to all elements of `x1`.
+
+    Parameters
+    ----------
+    x1 : ndarray or scalar
+        Values to change the sign of.
+    x2 : ndarray or scalar
+        The sign of `x2` is copied to `x1`.
+    out : ndarray or None, optional
+        A location into which the result is stored. It must be of the
+        right shape and right type to hold the output. If not provided
+        or `None`,a freshly-allocated array is returned.
+
+    Returns
+    -------
+    out : ndarray or scalar
+        The values of `x1` with the sign of `x2`.
+        This is a scalar if both `x1` and `x2` are scalars.
+
+    Notes
+    -------
+    This function differs from the original `numpy.copysign
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.copysign.html>`_ in
+    the following aspects:
+
+    - ``where`` param is not supported.
+
+    Examples
+    --------
+    >>> np.copysign(1.3, -1)
+    -1.3
+    >>> 1/np.copysign(0, 1)
+    inf
+    >>> 1/np.copysign(0, -1)
+    -inf
+
+    >>> a = np.array([-1, 0, 1])
+    >>> np.copysign(a, -1.1)
+    array([-1., -0., -1.])
+    >>> np.copysign(a, np.arange(3)-1)
+    array([-1.,  0.,  1.])
+    """
+    return _mx_nd_np.copysign(x1, x2, out=out)
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 077008aba119..5a38f81f9102 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -35,7 +35,7 @@
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'mean',
-           'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices']
+           'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign']
 
 
 def _num_outputs(sym):
@@ -2744,4 +2744,38 @@ def indices(dimensions, dtype=_np.int32, ctx=None):
 # pylint: enable=redefined-outer-name
 
 
+@set_module('mxnet.symbol.numpy')
+def copysign(x1, x2, out=None):
+    r"""copysign(x1, x2, out=None)
+
+    Change the sign of x1 to that of x2, element-wise.
+
+    If `x2` is a scalar, its sign will be copied to all elements of `x1`.
+
+    Parameters
+    ----------
+    x1 : _Symbol or scalar
+        Values to change the sign of.
+    x2 : _Symbol or scalar
+        The sign of `x2` is copied to `x1`.
+    out : _Symbol or None
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+
+    Returns
+    -------
+    out : _Symbol
+        The values of `x1` with the sign of `x2`.
+        This is a scalar if both `x1` and `x2` are scalars.
+
+    Notes
+    -------
+    This function differs from the original `numpy.copysign
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.copysign.html>`_ in
+    the following aspects:
+
+    - ``where`` param is not supported.
+    """
+    return _ufunc_helper(x1, x2, _npi.copysign, _np.copysign, _npi.copysign_scalar, _npi.rcopysign_scalar, out)
+
+
 _set_np_symbol_class(_Symbol)
diff --git a/src/operator/mshadow_op.h b/src/operator/mshadow_op.h
index 616192e4af57..f3d24b2c119e 100644
--- a/src/operator/mshadow_op.h
+++ b/src/operator/mshadow_op.h
@@ -417,6 +417,16 @@ MXNET_BINARY_MATH_OP(rdiv, math::id(b) / math::id(a));
 
 MXNET_BINARY_MATH_OP(rdiv_grad, -math::id(b) / math::sqr(a));
 
+MXNET_BINARY_MATH_OP(copysign, (a >= 0 && b >= 0) || (a < 0 && b < 0) ? a : -a);
+
+MXNET_BINARY_MATH_OP(copysign_grad, (a >= 0 && b >= 0) || (a < 0 && b < 0) ? 1: -1);
+
+MXNET_BINARY_MATH_OP(copysign_rgrad, 0);
+
+MXNET_BINARY_MATH_OP(rcopysign, (b >= 0 && a >= 0) || (b < 0 && a < 0) ? b : -b);
+
+MXNET_BINARY_MATH_OP(rcopysign_grad, 0);
+
 struct mod : public mxnet_op::tunable {
   template<typename DType>
   MSHADOW_XINLINE static typename enable_if<!is_unsigned<DType>::value, DType>::type
diff --git a/src/operator/numpy/np_elemwise_broadcast_op.cc b/src/operator/numpy/np_elemwise_broadcast_op.cc
index 697657d84dd5..a9254e8a7d02 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op.cc
+++ b/src/operator/numpy/np_elemwise_broadcast_op.cc
@@ -76,6 +76,26 @@ MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_power)
 .set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, mshadow_op::power>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_broadcast_power"});
 
+MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_copysign)
+.describe(R"code()code" ADD_FILELINE)
+.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, mshadow_op::copysign>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_copysign"});
+
+NNVM_REGISTER_OP(_backward_npi_copysign)
+.set_num_inputs(3)
+.set_num_outputs(2)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs){
+    return std::vector<std::pair<int, int> >{{0, 1}};
+  })
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastBackwardUseIn<cpu, mshadow_op::copysign_grad,
+                                                                  mshadow_op::copysign_rgrad>);
+
 MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_add_scalar)
 .set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, op::mshadow_op::plus>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_copy"});
@@ -108,5 +128,21 @@ MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_rpower_scalar)
 .set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::rpower>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{"_backward_rpower_scalar"});
 
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_copysign_scalar)
+.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::copysign>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_copysign_scalar"});
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_rcopysign_scalar)
+.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::rcopysign>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_rcopysign_scalar"});
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_backward_npi_copysign_scalar)
+.set_attr<FCompute>("FCompute<cpu>",
+                    BinaryScalarOp::Backward<cpu, mshadow_op::copysign_grad>);
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_backward_npi_rcopysign_scalar)
+.set_attr<FCompute>("FCompute<cpu>",
+                    BinaryScalarOp::Backward<cpu, mshadow_op::rcopysign_grad>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/np_elemwise_broadcast_op.cu b/src/operator/numpy/np_elemwise_broadcast_op.cu
index ac8def2af2c2..ecf8e8531334 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op.cu
+++ b/src/operator/numpy/np_elemwise_broadcast_op.cu
@@ -42,6 +42,13 @@ NNVM_REGISTER_OP(_npi_mod)
 NNVM_REGISTER_OP(_npi_power)
 .set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, mshadow_op::power>);
 
+NNVM_REGISTER_OP(_npi_copysign)
+.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, mshadow_op::copysign>);
+
+NNVM_REGISTER_OP(_backward_npi_copysign)
+.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastBackwardUseIn<gpu, mshadow_op::copysign_grad,
+                                                                  mshadow_op::copysign_rgrad>);
+
 NNVM_REGISTER_OP(_npi_add_scalar)
 .set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, op::mshadow_op::plus>);
 
@@ -66,5 +73,19 @@ NNVM_REGISTER_OP(_npi_power_scalar)
 NNVM_REGISTER_OP(_npi_rpower_scalar)
 .set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::rpower>);
 
+NNVM_REGISTER_OP(_npi_copysign_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::copysign>);
+
+NNVM_REGISTER_OP(_npi_rcopysign_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::rcopysign>);
+
+NNVM_REGISTER_OP(_backward_npi_copysign_scalar)
+.set_attr<FCompute>("FCompute<gpu>",
+                    BinaryScalarOp::Backward<gpu, mshadow_op::copysign_grad>);
+
+NNVM_REGISTER_OP(_backward_npi_rcopysign_scalar)
+.set_attr<FCompute>("FCompute<gpu>",
+                    BinaryScalarOp::Backward<gpu, mshadow_op::rcopysign_grad>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/operator_tune.cc b/src/operator/operator_tune.cc
index 98ce14e7bf05..51595254cafc 100644
--- a/src/operator/operator_tune.cc
+++ b/src/operator/operator_tune.cc
@@ -328,6 +328,11 @@ IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::elu); // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::power_grad);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::rpower_grad);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::power_rgrad);  // NOLINT()
+IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::copysign);  // NOLINT()
+IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::rcopysign);  // NOLINT()
+IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::copysign_grad);  // NOLINT()
+IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::copysign_rgrad);  // NOLINT()
+IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::rcopysign_grad);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::xelu_grad); // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::gelu_grad); // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::prelu_grad); // NOLINT()
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index c5b0907fb7a8..1f2af8dfa4a9 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -1853,6 +1853,111 @@ def hybrid_forward(self, F, x):
                     assert_almost_equal(mx_ret.asnumpy(), np_ret, atol=1e-5, rtol=1e-4)
 
 
+@with_seed()
+@use_np
+def test_np_copysign():
+    class TestCopysign(HybridBlock):
+        def __init__(self):
+            super(TestCopysign, self).__init__()
+
+        def hybrid_forward(self, F, a1, a2):
+	            return F.np.copysign(a1, a2)
+
+    def get_grad(a1, a2):
+        sign = _np.logical_or(_np.logical_and(a1 < 0, a2 < 0),
+                              _np.logical_and(a1 >= 0, a2 >= 0))
+        sign = 2 * sign.astype(int) - 1
+        sign = sign.reshape(-1, *a1.shape)
+        sign = _np.sum(sign, axis=0)
+        return sign, _np.zeros_like(a2)
+    
+    def get_grad_left(a1, a2):
+        sign = _np.logical_or(_np.logical_and(a1 < 0, a2 < 0),
+                              _np.logical_and(a1 >= 0, a2 >= 0))
+        sign = 2 * sign.astype(int) - 1
+        sign = sign.reshape(a1.shape)
+        return sign
+    
+    def get_grad_right(a1, a2):
+        return _np.zeros_like(a2)
+        
+    shapes = [
+        (),
+        (1),
+        (2, 1),
+        (3, 2, 1),
+        (4, 3, 2, 1),
+        (2, 4, 3, 2, 1)
+    ]
+    types = ['float16', 'float32', 'float64', 'int8', 'int32', 'int64']
+    for a1shape in shapes:
+        for a2shape in shapes:
+            for hybridize in [True, False]:
+                for dtype in types:
+                    test_copysign = TestCopysign()
+                    if hybridize:
+                        test_copysign.hybridize()
+                    rtol = 1e-3
+                    atol = 1e-5
+                    a1_np = _np.array(_np.random.uniform(-1.0, 1.0, a1shape), dtype=dtype)
+                    a2_np = _np.array(_np.random.uniform(-1.0, 1.0, a2shape), dtype=dtype)
+                    a1 = np.array(a1_np, dtype=dtype)
+                    a2 = np.array(a2_np, dtype=dtype)
+                    a1.attach_grad()
+                    a2.attach_grad()
+                    expected_np = _np.copysign(a1_np, a2_np)
+                    with mx.autograd.record():
+                        mx_out = test_copysign(a1, a2)
+                    assert mx_out.shape == expected_np.shape
+                    assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol)
+
+                    # Test gradient
+                    mx_out.backward()
+                    a1_grad, a2_grad = get_grad(a1_np, a2_np)
+                    assert_almost_equal(a1.grad.asnumpy(), a1_grad, rtol=rtol, atol=atol)
+                    assert_almost_equal(a2.grad.asnumpy(), a2_grad, rtol=rtol, atol=atol)
+
+                    # Test imperative once again
+                    mx_out = np.copysign(a1, a2)
+                    expected_np = _np.copysign(a1_np, a2_np)
+                    assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol)
+    
+    types = ['float16', 'float32', 'float64']
+    for x_shape in shapes:
+        for dtype in types:
+            # Test left
+            x_np = _np.array(_np.random.uniform(-2.0, 2.0, x_shape), dtype=dtype)
+            scalar = _np.random.uniform(-2.0, 2.0)
+            x = np.array(x_np, dtype=dtype)
+            x.attach_grad()
+            expected_np = _np.copysign(x_np, scalar)
+            with mx.autograd.record():
+                mx_out = np.copysign(x, scalar)
+            assert mx_out.shape == expected_np.shape
+            assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol)
+            
+            # Test gradient
+            mx_out.backward()
+            x_grad = get_grad_left(x_np, scalar)
+            assert_almost_equal(x.grad.asnumpy(), x_grad, rtol=rtol, atol=atol)
+            
+            # Test right
+            x_np = _np.array(_np.random.uniform(-2.0, 2.0, x_shape), dtype=dtype)
+            scalar = _np.random.uniform(-2.0, 2.0)
+            x = np.array(x_np, dtype=dtype)
+            x.attach_grad()
+            expected_np = _np.copysign(scalar, x_np)
+            with mx.autograd.record():
+                mx_out = np.copysign(scalar, x)
+            assert mx_out.shape == expected_np.shape
+            assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol)
+            
+            # Test gradient
+            mx_out.backward()
+            x_grad = get_grad_right(scalar, x_np)
+            assert_almost_equal(x.grad.asnumpy(), x_grad, rtol=rtol, atol=atol)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 5c136c9a312c40d642c13c36367ab4157ad91f81 Mon Sep 17 00:00:00 2001
From: Zhennan Qin <zhennan.qin@intel.com>
Date: Mon, 16 Sep 2019 00:56:17 -0500
Subject: [PATCH 319/813] Update dmlc-core (#16149)

Change-Id: I472fb7bbffc16ed8c36494ab49838b08c59b2f12
---
 3rdparty/dmlc-core                                            | 2 +-
 amalgamation/amalgamation.py                                  | 3 ++-
 .../src/main/scala/org/apache/mxnet/utils/CToScalaUtils.scala | 4 ++--
 3 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/3rdparty/dmlc-core b/3rdparty/dmlc-core
index f1ff6cc117f4..9088d2ee02cd 160000
--- a/3rdparty/dmlc-core
+++ b/3rdparty/dmlc-core
@@ -1 +1 @@
-Subproject commit f1ff6cc117f4e95169a9f62be549c8fe3e15c20f
+Subproject commit 9088d2ee02cdfe393fd0569af4aefebe94f8b105
diff --git a/amalgamation/amalgamation.py b/amalgamation/amalgamation.py
index fef54aaecba4..5f825de77483 100644
--- a/amalgamation/amalgamation.py
+++ b/amalgamation/amalgamation.py
@@ -30,7 +30,8 @@
     'opencv2/opencv.hpp', 'sys/stat.h', 'sys/types.h', 'cuda.h', 'cuda_fp16.h', 'omp.h',
     'onnx/onnx.pb.h', 'execinfo.h', 'packet/sse-inl.h', 'emmintrin.h', 'thrust/device_vector.h',
     'cusolverDn.h', 'internal/concurrentqueue_internal_debug.h', 'relacy/relacy_std.hpp',
-    'relacy_shims.h', 'ittnotify.h', 'shared_mutex', 'nvToolsExt.h'
+    'relacy_shims.h', 'ittnotify.h', 'shared_mutex', 'nvToolsExt.h', 'dmlc/build_config.h',
+    'sys/isa_defs.h'
     ]
 
 minimum = int(sys.argv[6]) if len(sys.argv) > 5 else 0
diff --git a/scala-package/macros/src/main/scala/org/apache/mxnet/utils/CToScalaUtils.scala b/scala-package/macros/src/main/scala/org/apache/mxnet/utils/CToScalaUtils.scala
index c984d07143ef..8fb95bfeb83a 100644
--- a/scala-package/macros/src/main/scala/org/apache/mxnet/utils/CToScalaUtils.scala
+++ b/scala-package/macros/src/main/scala/org/apache/mxnet/utils/CToScalaUtils.scala
@@ -55,8 +55,8 @@ private[mxnet] object CToScalaUtils {
       case "double" | "doubleorNone" => types("double")
       case "string" => "String"
       case "boolean" | "booleanorNone" => types("bool")
-      case "tupleof<int>" | "tupleof<float>" | "tupleof<double>" | "tupleof<intorNone>" |
-           "tupleof<>" | "ptr" | "" => "Any"
+      case "tupleof<int>"| "tupleof<long>" | "tupleof<float>" | "tupleof<double>" |
+           "tupleof<intorNone>" | "tupleof<>" | "ptr" | "" => "Any"
       case default => throw new IllegalArgumentException(
         s"Invalid type for args: $default\nString argType: $argType\nargName: $argName")
     }

From 18d145c7f1c0f4af1470483fcdd85d5f761c894d Mon Sep 17 00:00:00 2001
From: Xingjian Shi <xshiab@connect.ust.hk>
Date: Sun, 15 Sep 2019 23:52:35 -0700
Subject: [PATCH 320/813] use 1E-4 in groupnorm test(#16169)

---
 tests/python/unittest/test_operator.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 40763fabef19..2e7cc3ce7504 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -1943,7 +1943,7 @@ def np_groupnorm_grad(ograd, data, gamma, beta, mean, std, num_groups, eps):
                                   num_groups=num_groups, eps=eps, output_mean_var=True)
         check_symbolic_forward(mx_sym, [mx_data, mx_gamma, mx_beta], [np_out, np_mean, np_std],
                                rtol=1e-2 if dtype == np.float16 else 1e-3,
-                               atol=5e-3 if dtype == np.float16 else 1e-5, dtype=dtype)
+                               atol=5e-3 if dtype == np.float16 else 1e-4, dtype=dtype)
         mx_sym = mx.sym.GroupNorm(data=data_sym, gamma=gamma_sym, beta=beta_sym,
                                   num_groups=num_groups, eps=eps, output_mean_var=False)
         np_ograd = np.random.uniform(-1.0, 1.0, dshape).astype(dtype)
@@ -1956,7 +1956,7 @@ def np_groupnorm_grad(ograd, data, gamma, beta, mean, std, num_groups, eps):
         check_symbolic_backward(mx_sym, [mx_data, mx_gamma, mx_beta], [mx.nd.array(np_ograd)],
                                 [np_data_grad, np_gamma_grad, np_beta_grad],
                                 rtol=1e-2 if dtype == np.float16 else 1e-3,
-                                atol=5e-2 if dtype == np.float16 else 1e-5, dtype=dtype)
+                                atol=5e-2 if dtype == np.float16 else 1e-4, dtype=dtype)
 
 
 @with_seed()

From 5ed5689b6a769708090cd4df47f8faf995cf4f4e Mon Sep 17 00:00:00 2001
From: tingying <18640449+tingying2020@users.noreply.github.com>
Date: Mon, 16 Sep 2019 21:29:55 +0800
Subject: [PATCH 321/813] numpy operator ravel, derive from reshape (#16016)

* it is the same as reshape(x, -1)

* register reshape with prefix _npi_

* fix format error

* edit examples in doc

* fix error in review

* add out in wrapper

* remove out

* test data type and add order

* change order check

* remove redundant test and add docstring about order
---
 python/mxnet/ndarray/numpy/_op.py      | 56 +++++++++++++++++++++++++-
 python/mxnet/numpy/multiarray.py       | 49 +++++++++++++++++++++-
 python/mxnet/symbol/numpy/_symbol.py   | 42 ++++++++++++++++++-
 src/operator/numpy/np_matrix_op.cc     |  1 +
 tests/python/unittest/test_numpy_op.py | 33 +++++++++++++++
 5 files changed, 178 insertions(+), 3 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index b8e4f3f28b86..163d90835e2a 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -33,7 +33,8 @@
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'mean',
-           'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign']
+           'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
+           'ravel']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -2483,3 +2484,56 @@ def copysign(x1, x2, out=None):
     array([-1.,  0.,  1.])
     """
     return _ufunc_helper(x1, x2, _npi.copysign, _np.copysign, _npi.copysign_scalar, _npi.rcopysign_scalar, out)
+
+
+@set_module('mxnet.ndarray.numpy')
+def ravel(x, order='C'):
+    r"""
+    ravel(x)
+
+    Return a contiguous flattened array.
+    A 1-D array, containing the elements of the input, is returned.  A copy is
+    made only if needed.
+
+    Parameters
+    ----------
+    x : ndarray
+        Input array.  The elements in `x` are read in row-major, C-style order and
+        packed as a 1-D array.
+    order : `C`, optional
+        Only support row-major, C-style order.
+
+    Returns
+    -------
+    y : ndarray
+        y is an array of the same subtype as `x`, with shape ``(x.size,)``.
+        Note that matrices are special cased for backward compatibility, if `x`
+        is a matrix, then y is a 1-D ndarray.
+
+    Notes
+    -----
+    This function differs from the original numpy.arange in the following aspects:
+        - Only support row-major, C-style order.
+
+    Examples
+    --------
+    It is equivalent to ``reshape(x, -1)``.
+
+    >>> x = np.array([[1, 2, 3], [4, 5, 6]])
+    >>> print(np.ravel(x))
+    [1. 2. 3. 4. 5. 6.]
+
+    >>> print(x.reshape(-1))
+    [1. 2. 3. 4. 5. 6.]
+
+    >>> print(np.ravel(x.T))
+    [1. 4. 2. 5. 3. 6.]
+    """
+    if order != 'C':
+        raise NotImplementedError('order {} is not supported'.format(order))
+    if isinstance(x, numeric_types):
+        return _np.reshape(x, -1)
+    elif isinstance(x, NDArray):
+        return _npi.reshape(x, -1)
+    else:
+        raise TypeError('type {} not supported'.format(str(type(x))))
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 632cfadf86a6..a43045235503 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -52,7 +52,8 @@
            'degrees', 'log2', 'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative',
            'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh',
            'tensordot', 'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate',
-           'stack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign']
+           'stack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
+           'ravel']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -3986,3 +3987,49 @@ def copysign(x1, x2, out=None):
     array([-1.,  0.,  1.])
     """
     return _mx_nd_np.copysign(x1, x2, out=out)
+
+
+@set_module('mxnet.numpy')
+def ravel(x, order='C'):
+    r"""
+    ravel(x)
+
+    Return a contiguous flattened array.
+    A 1-D array, containing the elements of the input, is returned.  A copy is
+    made only if needed.
+
+    Parameters
+    ----------
+    x : ndarray
+        Input array.  The elements in `x` are read in row-major, C-style order and
+        packed as a 1-D array.
+    order : `C`, optional
+        Only support row-major, C-style order.
+
+    Returns
+    -------
+    y : ndarray
+        y is an array of the same subtype as `x`, with shape ``(x.size,)``.
+        Note that matrices are special cased for backward compatibility, if `x`
+        is a matrix, then y is a 1-D ndarray.
+
+    Notes
+    -----
+    This function differs from the original numpy.arange in the following aspects:
+        - Only support row-major, C-style order.
+
+    Examples
+    --------
+    It is equivalent to ``reshape(x, -1)``.
+
+    >>> x = np.array([[1, 2, 3], [4, 5, 6]])
+    >>> print(np.ravel(x))
+    [1. 2. 3. 4. 5. 6.]
+
+    >>> print(x.reshape(-1))
+    [1. 2. 3. 4. 5. 6.]
+
+    >>> print(np.ravel(x.T))
+    [1. 4. 2. 5. 3. 6.]
+    """
+    return _mx_nd_np.ravel(x, order)
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 5a38f81f9102..12c333b253ba 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -35,7 +35,8 @@
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'mean',
-           'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign']
+           'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
+           'ravel']
 
 
 def _num_outputs(sym):
@@ -2778,4 +2779,43 @@ def copysign(x1, x2, out=None):
     return _ufunc_helper(x1, x2, _npi.copysign, _np.copysign, _npi.copysign_scalar, _npi.rcopysign_scalar, out)
 
 
+@set_module('mxnet.symbol.numpy')
+def ravel(x, order='C'):
+    r"""
+    ravel(x)
+
+    Return a contiguous flattened array.
+    A 1-D array, containing the elements of the input, is returned.  A copy is
+    made only if needed.
+
+    Parameters
+    ----------
+    x : ndarray
+        Input array.  The elements in `x` are read in row-major, C-style order and
+        packed as a 1-D array.
+    order : `C`, optional
+        Only support row-major, C-style order.
+
+    Returns
+    -------
+    y : ndarray
+        y is an array of the same subtype as `x`, with shape ``(x.size,)``.
+        Note that matrices are special cased for backward compatibility, if `x`
+        is a matrix, then y is a 1-D ndarray.
+
+    Notes
+    -----
+    This function differs from the original numpy.arange in the following aspects:
+        - Only support row-major, C-style order.
+    """
+    if order != 'C':
+        raise NotImplementedError('order {} is not supported'.format(order))
+    if isinstance(x, numeric_types):
+        return _np.reshape(x, -1)
+    elif isinstance(x, _Symbol):
+        return _npi.reshape(x, -1)
+    else:
+        raise TypeError('type {} not supported'.format(str(type(x))))
+
+
 _set_np_symbol_class(_Symbol)
diff --git a/src/operator/numpy/np_matrix_op.cc b/src/operator/numpy/np_matrix_op.cc
index 5ad6c8908017..f88dd56065f4 100644
--- a/src/operator/numpy/np_matrix_op.cc
+++ b/src/operator/numpy/np_matrix_op.cc
@@ -163,6 +163,7 @@ bool NumpyReshapeShape(const nnvm::NodeAttrs& attrs,
 
 NNVM_REGISTER_OP(_np_reshape)
 .describe(R"code()code" ADD_FILELINE)
+.add_alias("_npi_reshape")
 .set_num_inputs(1)
 .set_num_outputs(1)
 .set_attr_parser(ParamParser<NumpyReshapeParam>)
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 1f2af8dfa4a9..ed4375939688 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -1320,6 +1320,39 @@ def hybrid_forward(self, F, a, *args):
                 assert same(mx_out.asnumpy(), np_out)
 
 
+@with_seed()
+@use_np
+def test_np_ravel():
+    class TestRavel(HybridBlock):
+        def __init__(self):
+            super(TestRavel, self).__init__()
+
+        def hybrid_forward(self, F, a):
+            return F.np.ravel(a)
+
+    types = ['float64', 'float32', 'float16', 'int64', 'int32', 'int8']
+    for oneType in types:
+        for hybridize in [True, False]:
+            for shape in [(), (2,), (2, 2), (1, 2, 3), (3, 0), (1, 0, 2)]:
+                test_ravel = TestRavel()
+                if hybridize:
+                    test_ravel.hybridize()
+                x = rand_ndarray(shape, dtype=oneType).as_np_ndarray()
+                x.attach_grad()
+                np_out = _np.ravel(x.asnumpy())
+                with mx.autograd.record():
+                    mx_out = test_ravel(x)
+                assert mx_out.shape == np_out.shape
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+                mx_out.backward()
+                np_backward = _np.ones(shape)
+                assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=1e-3, atol=1e-5)
+
+                mx_out = np.ravel(x)
+                np_out = _np.ravel(x.asnumpy())
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+
+
 @with_seed()
 @use_np
 def test_np_randint():

From 7fc1d84747f6787a43ac21937d55ec7dad3117d3 Mon Sep 17 00:00:00 2001
From: perdasilva <perdasilva@gmail.com>
Date: Mon, 16 Sep 2019 22:11:19 +0200
Subject: [PATCH 322/813] Adds dynamic libmxnet to CD pipeline (#16163)

* Adds dynamic libmxnet release pipeline

* Updates default variants on CD pipeline
---
 cd/Jenkinsfile_cd_pipeline                   |   8 +-
 cd/Jenkinsfile_release_job                   |   4 +-
 cd/mxnet_lib/dynamic/Jenkins_pipeline.groovy |  57 +++++++++++
 ci/docker/runtime_functions.sh               | 101 +++++++++++++++++++
 4 files changed, 167 insertions(+), 3 deletions(-)
 create mode 100644 cd/mxnet_lib/dynamic/Jenkins_pipeline.groovy

diff --git a/cd/Jenkinsfile_cd_pipeline b/cd/Jenkinsfile_cd_pipeline
index d7872204fcb7..4e4e6a7af3d4 100644
--- a/cd/Jenkinsfile_cd_pipeline
+++ b/cd/Jenkinsfile_cd_pipeline
@@ -48,10 +48,16 @@ pipeline {
         script {
           cd_utils.error_checked_parallel([
 
-            "Static libmxnet based Release": {
+            "Static libmxnet based release": {
               stage("Build") {
                 cd_utils.trigger_release_job("Build static libmxnet", "mxnet_lib/static", params.MXNET_VARIANTS)    
               }
+            },
+
+            "Dynamic libmxnet based release": {
+              stage("Build") {
+                cd_utils.trigger_release_job("Build dynamic libmxnet", "mxnet_lib/dynamic", params.MXNET_VARIANTS)    
+              }
             }
             
           ])
diff --git a/cd/Jenkinsfile_release_job b/cd/Jenkinsfile_release_job
index 92ea72fca9c4..6a8864f4dbb1 100644
--- a/cd/Jenkinsfile_release_job
+++ b/cd/Jenkinsfile_release_job
@@ -37,8 +37,8 @@ pipeline {
   parameters {
     // Release parameters
     string(defaultValue: "Generic release job", description: "Optional Job name", name: "RELEASE_JOB_NAME")
-    choice(choices: ["mxnet_lib/static"], description: "Pipeline to build", name: "RELEASE_JOB_TYPE")
-    string(defaultValue: "cpu,mkl,cu80,cu80mkl,cu90,cu90mkl,cu92,cu92mkl,cu100,cu100mkl", description: "Comma separated list of variants", name: "MXNET_VARIANTS")
+    choice(choices: ["mxnet_lib/static", "mxnet_lib/dynamic"], description: "Pipeline to build", name: "RELEASE_JOB_TYPE")
+    string(defaultValue: "cpu,mkl,cu90,cu90mkl,cu92,cu92mkl,cu100,cu100mkl,cu101,cu101mkl", description: "Comma separated list of variants", name: "MXNET_VARIANTS")
     booleanParam(defaultValue: false, description: 'Whether this is a release build or not', name: "RELEASE_BUILD")
   }
 
diff --git a/cd/mxnet_lib/dynamic/Jenkins_pipeline.groovy b/cd/mxnet_lib/dynamic/Jenkins_pipeline.groovy
new file mode 100644
index 000000000000..57812d2c3690
--- /dev/null
+++ b/cd/mxnet_lib/dynamic/Jenkins_pipeline.groovy
@@ -0,0 +1,57 @@
+// -*- mode: groovy -*-
+
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+//
+// Jenkins pipeline
+// See documents at https://jenkins.io/doc/book/pipeline/jenkinsfile/
+
+// NOTE: ci_utils is loaded by the originating Jenkins job, e.g. jenkins/Jenkinsfile_release_job
+
+// libmxnet location
+libmxnet = 'lib/libmxnet.so'
+
+// licenses
+licenses = 'licenses/*'
+
+// libmxnet dependencies
+mx_deps = ''
+mx_mkldnn_deps = 'lib/libiomp5.so, lib/libmkldnn.so.0, lib/libmklml_intel.so'
+
+// library type
+// either static or dynamic - depending on how it links to its dependencies
+libtype = 'dynamic'
+
+libmxnet_pipeline = load('cd/mxnet_lib/mxnet_lib_pipeline.groovy')
+
+// Builds the dynamic binary for the specified mxnet variant
+def build(mxnet_variant) {
+  node(NODE_LINUX_CPU) {
+    ws("workspace/mxnet_${libtype}/${mxnet_variant}/${env.BUILD_NUMBER}") {
+      def image = libmxnet_pipeline.get_environment(mxnet_variant)
+      ci_utils.init_git()
+      ci_utils.docker_run(image, "build_dynamic_libmxnet ${mxnet_variant}", false)
+      ci_utils.pack_lib("mxnet_${mxnet_variant}", libmxnet_pipeline.get_stash(mxnet_variant))
+    }
+  }
+}
+
+def get_pipeline(mxnet_variant) {
+  return libmxnet_pipeline.get_pipeline(mxnet_variant, this.&build)
+}
+
+return this
diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index 67cc98c73620..de753a56be7f 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -146,6 +146,107 @@ build_wheel() {
 # Build commands: Every platform in docker/Dockerfile.build.<platform> should have a corresponding
 # function here with the same suffix:
 
+gather_licenses() {
+    mkdir -p licenses
+
+    cp tools/dependencies/LICENSE.binary.dependencies licenses/
+    cp NOTICE licenses/
+    cp LICENSE licenses/
+    cp DISCLAIMER licenses/
+}
+
+build_ubuntu_cpu_release() {
+    set -ex
+
+    build_ccache_wrappers
+
+    make  \
+        DEV=0                         \
+        ENABLE_TESTCOVERAGE=0         \
+        USE_CPP_PACKAGE=0             \
+        USE_MKLDNN=0                  \
+        USE_BLAS=openblas             \
+        USE_SIGNAL_HANDLER=1          \
+        -j$(nproc)
+}
+
+build_ubuntu_cpu_mkldnn_release() {
+    set -ex
+
+    build_ccache_wrappers
+
+    make  \
+        DEV=0                         \
+        ENABLE_TESTCOVERAGE=0         \
+        USE_CPP_PACKAGE=0             \
+        USE_MKLDNN=1                  \
+        USE_BLAS=openblas             \
+        USE_SIGNAL_HANDLER=1          \
+        -j$(nproc)
+}
+
+build_ubuntu_gpu_release() {
+    set -ex
+    # unfortunately this build has problems in 3rdparty dependencies with ccache and make
+    # build_ccache_wrappers
+
+    make \
+        DEV=0                                     \
+        ENABLE_TESTCOVERAGE=0                     \
+        USE_BLAS=openblas                         \
+        USE_MKLDNN=0                              \
+        USE_CUDA=1                                \
+        USE_CUDA_PATH=/usr/local/cuda             \
+        USE_CUDNN=1                               \
+        USE_CPP_PACKAGE=0                         \
+        USE_DIST_KVSTORE=1                        \
+        USE_SIGNAL_HANDLER=1                      \
+        -j$(nproc)
+}
+
+build_ubuntu_gpu_mkldnn_release() {
+    set -ex
+    # unfortunately this build has problems in 3rdparty dependencies with ccache and make
+    # build_ccache_wrappers
+
+    make \
+        DEV=0                                     \
+        ENABLE_TESTCOVERAGE=0                     \
+        USE_BLAS=openblas                         \
+        USE_MKLDNN=1                              \
+        USE_CUDA=1                                \
+        USE_CUDA_PATH=/usr/local/cuda             \
+        USE_CUDNN=1                               \
+        USE_CPP_PACKAGE=0                         \
+        USE_DIST_KVSTORE=1                        \
+        USE_SIGNAL_HANDLER=1                      \
+        -j$(nproc)
+}
+
+# Compiles the dynamic mxnet library
+# Parameters:
+# $1 -> mxnet_variant: the mxnet variant to build, e.g. cpu, cu100, cu92mkl, etc.
+build_dynamic_libmxnet() {
+    set -ex
+    
+    local mxnet_variant=${1:?"This function requires a mxnet variant as the first argument"}
+
+    # relevant licenses will be placed in the licenses directory
+    gather_licenses
+
+    if [[ ${mxnet_variant} = "cpu" ]]; then
+        build_ubuntu_cpu_release
+    elif [[ ${mxnet_variant} = "mkl" ]]; then
+        build_ubuntu_cpu_mkldnn_release
+    elif [[ ${mxnet_variant} =~ cu[0-9]+$ ]]; then
+        build_ubuntu_gpu_release
+    elif [[ ${mxnet_variant} =~ cu[0-9]+mkl$ ]]; then
+        build_ubuntu_gpu_mkldnn_release
+    else
+        echo "Error: Unrecognized mxnet variant '${mxnet_variant}'"
+    fi
+}
+
 build_jetson() {
     set -ex
     pushd .

From 692f49f2b1b9df1bb226c586405226291c6095cf Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Mon, 16 Sep 2019 13:33:57 -0700
Subject: [PATCH 323/813] Sequence last fix (#16156)

* seq last fix

* index tensor to have int64

* fix dtypes

* revert unnecessary changes

* if seq len not passed, pass int64 dtype

* dtype comment

* use int32 or int64 as index dtype based on build flag

* Trigger notification

* Trigger notification

* lint fix
---
 src/operator/sequence_last-inl.h   | 12 ++++++------
 src/operator/sequence_last.cc      | 10 ++++++++--
 tests/nightly/test_large_vector.py |  4 +++-
 3 files changed, 17 insertions(+), 9 deletions(-)

diff --git a/src/operator/sequence_last-inl.h b/src/operator/sequence_last-inl.h
index 3c3c8b0cd49e..78ade5e9de06 100644
--- a/src/operator/sequence_last-inl.h
+++ b/src/operator/sequence_last-inl.h
@@ -101,8 +101,8 @@ class SequenceLastOp : public Operator {
     using namespace mshadow::expr;
 
     int axis = param_.axis;
-    int out_size = out.size(0) * out.size(1);
-    int max_seq_len = data.size(axis);
+    index_t out_size = out.size(0) * out.size(1);
+    index_t max_seq_len = data.size(axis);
     index_t offset1 = axis ? out.size(1) : out_size;
     index_t offset2 = axis ? (max_seq_len * out.size(1)) : out.size(1);
 
@@ -121,11 +121,11 @@ class SequenceLastOp : public Operator {
     using namespace mshadow::expr;
 
     auto axis = param_.axis;
-    int batch = out_grad.size(0);
-    int rest = out_grad.size(1);
-    int out_size = batch * rest;
+    index_t batch = out_grad.size(0);
+    index_t rest = out_grad.size(1);
+    index_t out_size = batch * rest;
 
-    int max_seq_len = in_grad.size(axis);
+    index_t max_seq_len = in_grad.size(axis);
     index_t offset1 = axis ? rest : out_size;
     index_t offset2 = axis ? (max_seq_len * rest) : rest;
 
diff --git a/src/operator/sequence_last.cc b/src/operator/sequence_last.cc
index 44869c518504..3a6cdbad6149 100644
--- a/src/operator/sequence_last.cc
+++ b/src/operator/sequence_last.cc
@@ -46,8 +46,14 @@ Operator *SequenceLastProp::CreateOperatorEx(Context ctx,
     DO_BIND_DISPATCH(CreateOp, param_, (*in_type)[0], (*in_type)[1]);
   }
 
-  // sequence_length not passed in, so fall back to using input array dtype for second argument
-  DO_BIND_DISPATCH(CreateOp, param_, (*in_type)[0], (*in_type)[0]);
+  // sequence_length not passed in, so fall back to using int32/int64 dtype for second argument
+  // second argument is the dtype of the sequence_length NDArray
+  // use int32 or int64 as index dtype based on build flag
+  #if MXNET_USE_INT64_TENSOR_SIZE == 1
+      DO_BIND_DISPATCH(CreateOp, param_, (*in_type)[0], mshadow::kInt64);
+  #else
+      DO_BIND_DISPATCH(CreateOp, param_, (*in_type)[0], mshadow::kInt32);
+  #endif
 }
 
 DMLC_REGISTER_PARAMETER(SequenceLastParam);
diff --git a/tests/nightly/test_large_vector.py b/tests/nightly/test_large_vector.py
index e3407792f2da..169f5244d784 100644
--- a/tests/nightly/test_large_vector.py
+++ b/tests/nightly/test_large_vector.py
@@ -356,7 +356,9 @@ def test_sequence_last():
     # test with sequence length
     # parameter sequence_length - NDArray with shape (batch_size)
     # (2,3) indicates 2nd sequence from batch 1 and 3rd sequence from batch 2
-    b = nd.SequenceLast(a, sequence_length=mx.nd.array([2, 3]),
+    # need to mention dtype = int64 for sequence_length ndarray to support large indices
+    # else it defaults to float32 and errors
+    b = nd.SequenceLast(a, sequence_length=mx.nd.array([2, 3], dtype="int64"),
                         use_sequence_length=True)
     # check if it takes 2nd sequence from the first batch
     assert b[0] == a[1][0]

From 2a55cd7d1801e6ed801057cac9018b707fe3e27f Mon Sep 17 00:00:00 2001
From: Vikas-kum <vikumar@amazon.com>
Date: Mon, 16 Sep 2019 14:52:10 -0700
Subject: [PATCH 324/813] fixing test for model compatibility checker (#16159)

* fixing test for model compatibility checker

* fake change
---
 .../model_backwards_compatibility_check/JenkinsfileForMBCC      | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/nightly/model_backwards_compatibility_check/JenkinsfileForMBCC b/tests/nightly/model_backwards_compatibility_check/JenkinsfileForMBCC
index 08f562189593..725261d904f2 100644
--- a/tests/nightly/model_backwards_compatibility_check/JenkinsfileForMBCC
+++ b/tests/nightly/model_backwards_compatibility_check/JenkinsfileForMBCC
@@ -18,7 +18,7 @@
 //
 //This is a Jenkinsfile for the model backwards compatibility checker. The format and some functions have been picked up from the top-level Jenkinsfile.
 
-mx_lib = 'lib/libmxnet.so, lib/libmxnet.a, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a'
+mx_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so,lib/libtvmop.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a'
 
 node('restricted-utility') {
   // Loading the utilities requires a node context unfortunately

From 3dacabeaa4dea080f91363701a4a928cc807ed54 Mon Sep 17 00:00:00 2001
From: Vikas-kum <vikumar@amazon.com>
Date: Mon, 16 Sep 2019 17:26:13 -0700
Subject: [PATCH 325/813] Tutorials nighly fix (#16179)

* Fixing tutorials test failures

* profier set state

* modifying Jenkins for fast run

* chanign comments

* more test comment for faster run

* changing comments from

* more comments

* profiler tutorial test fixes

* uncommenting all tutorial tests

* enabling all tests for nightly

* removing parallel keyword from tutorials test

* updating comments

* Added github issue link in the comment
---
 docs/tutorials/gluon/custom_layer.md  |  2 +-
 docs/tutorials/python/profiler.md     |  3 +++
 docs/tutorials/sparse/train_gluon.md  |  2 +-
 tests/nightly/JenkinsfileForBinaries  |  6 +++---
 tests/tutorials/test_tutorials.py     | 17 ++++++++++-------
 tests/utils/notebook_test/__init__.py |  2 +-
 6 files changed, 19 insertions(+), 13 deletions(-)

diff --git a/docs/tutorials/gluon/custom_layer.md b/docs/tutorials/gluon/custom_layer.md
index 40ba0823a9d5..d2d1f56160b3 100644
--- a/docs/tutorials/gluon/custom_layer.md
+++ b/docs/tutorials/gluon/custom_layer.md
@@ -156,7 +156,7 @@ class NormalizationHybridLayer(gluon.HybridBlock):
 
             self.scales = self.params.get('scales',
                                       shape=scales.shape,
-                                      init=mx.init.Constant(scales.asnumpy().tolist()), # Convert to regular list to make this object serializable
+                                      init=mx.init.Constant(scales.asnumpy()), # Convert to regular list to make this object serializable
                                       differentiable=False)
             
     def hybrid_forward(self, F, x, weights, scales):
diff --git a/docs/tutorials/python/profiler.md b/docs/tutorials/python/profiler.md
index 91a74e4f49cf..49d50f494042 100644
--- a/docs/tutorials/python/profiler.md
+++ b/docs/tutorials/python/profiler.md
@@ -57,6 +57,7 @@ from mxnet import profiler
 
 profiler.set_config(profile_all=True,
                     aggregate_stats=True,
+                    continuous_dump=True,
                     filename='profile_output.json')
 ```
 
@@ -195,6 +196,8 @@ print(profiler.dumps())
 You can also dump the information collected by the profiler into a `json` file using the `profiler.dump()` function and view it in a browser.
 
 ```python
+profiler.set_state('run')
+profiler.set_state('stop')
 profiler.dump(finished=False)
 ```
 
diff --git a/docs/tutorials/sparse/train_gluon.md b/docs/tutorials/sparse/train_gluon.md
index e9c5301aed84..f9f1a50f0925 100644
--- a/docs/tutorials/sparse/train_gluon.md
+++ b/docs/tutorials/sparse/train_gluon.md
@@ -151,7 +151,7 @@ Similar to using a [`DataLoader`](https://mxnet.incubator.apache.org/versions/ma
 
 
 ```python
-data_iter = mx.io.LibSVMIter(data_libsvm='test.libsvm', data_shape=(num_features,), label_shape=(1,), batch_size=10)
+data_iter = mx.io.LibSVMIter(data_libsvm=filepath, data_shape=(num_features,), label_shape=(1,), batch_size=10)
 for batch in data_iter:
     data = batch.data[0]
     print('data.stype: {}'.format(data.stype))
diff --git a/tests/nightly/JenkinsfileForBinaries b/tests/nightly/JenkinsfileForBinaries
index 2bd78ac2afb6..8be95adb9194 100755
--- a/tests/nightly/JenkinsfileForBinaries
+++ b/tests/nightly/JenkinsfileForBinaries
@@ -39,7 +39,7 @@ core_logic: {
           utils.pack_lib('gpu', mx_lib)
         }
       }
-    },
+    }/*,
     'CPU: USE_INT64_TENSOR_SIZE': {
       node(NODE_LINUX_CPU) {
         ws('workspace/build-cpu-int64') {
@@ -57,7 +57,7 @@ core_logic: {
           utils.pack_lib('gpu_int64', mx_cmake_lib)
         }
       }
-    }
+    }*/
   }
 
   stage('NightlyTests'){
@@ -73,7 +73,7 @@ core_logic: {
       node('mxnetlinux-gpu-p3-8xlarge') {
         ws('workspace/nt-KVStoreTest') {
           utils.unpack_and_init('gpu', mx_lib)
-          utils.docker_run('ubuntu_nightly_gpu', 'nightly_test_KVStore_singleNode', true) 
+          utils.docker_run('ubuntu_nightly_gpu', 'nightly_test_KVStore_singleNode', true)
         }
       }
     },
diff --git a/tests/tutorials/test_tutorials.py b/tests/tutorials/test_tutorials.py
index c2173a7dc071..1d2da0c1eedd 100644
--- a/tests/tutorials/test_tutorials.py
+++ b/tests/tutorials/test_tutorials.py
@@ -57,7 +57,6 @@ def _test_tutorial_nb(tutorial):
     """
     return run_notebook(tutorial, TUTORIAL_DIR, kernel=KERNEL, no_cache=NO_CACHE)
 
-
 def test_basic_ndarray():
    assert _test_tutorial_nb('basic/ndarray')
 
@@ -114,10 +113,11 @@ def test_gluon_save_load_params():
 
 def test_gluon_hybrid():
     assert _test_tutorial_nb('gluon/hybrid')
-
+# https://github.com/apache/incubator-mxnet/issues/16181
+"""
 def test_gluon_performance():
     assert _test_tutorial_nb('gluon/performance')
-    
+"""
 def test_gluon_pretrained_models():
     assert _test_tutorial_nb('gluon/pretrained_models')    
 
@@ -183,10 +183,11 @@ def test_module_to_gluon():
 
 def test_python_types_of_data_augmentation():
     assert _test_tutorial_nb('python/types_of_data_augmentation')
-
+#https://github.com/apache/incubator-mxnet/issues/16181
+"""
 def test_python_profiler():
     assert _test_tutorial_nb('python/profiler')
-
+"""
 def test_sparse_row_sparse():
     assert _test_tutorial_nb('sparse/row_sparse')
 
@@ -216,6 +217,8 @@ def test_control_flow():
 
 def test_amp():
     assert _test_tutorial_nb('amp/amp_tutorial')
-
+# https://github.com/apache/incubator-mxnet/issues/16181
+"""
 def test_mkldnn_quantization():
-    assert _test_tutorial_nb('mkldnn/mkldnn_quantization')
\ No newline at end of file
+    assert _test_tutorial_nb('mkldnn/mkldnn_quantization')
+"""
diff --git a/tests/utils/notebook_test/__init__.py b/tests/utils/notebook_test/__init__.py
index a32c5269b812..0a794cd13619 100644
--- a/tests/utils/notebook_test/__init__.py
+++ b/tests/utils/notebook_test/__init__.py
@@ -31,7 +31,7 @@
 
 
 IPYTHON_VERSION = 4  # Pin to ipython version 4.
-TIME_OUT = 10*60  # Maximum 10 mins/test. Reaching timeout causes test failure.
+TIME_OUT = 15*60  # Maximum 10 mins/test. Reaching timeout causes test failure.
 ATTEMPTS = 8
 KERNEL_ERROR_MSG = 'Kernel died before replying to kernel_info'
 

From b777a69c9a2b1a2557b2cbcb4acc7bb4d13d0007 Mon Sep 17 00:00:00 2001
From: Anirudh Subramanian <anirudh2290@ufl.edu>
Date: Wed, 18 Sep 2019 07:47:26 -0700
Subject: [PATCH 326/813] Add register_op_hook for gluon (#15839)

* Set monitor callback basic support

* Trigger CI

* Add base.pyi and ndarray.pyx

* Change not supported to experimental and check for both static_shape and static_alloc
---
 include/mxnet/c_api.h               | 12 +++++
 python/mxnet/_ctypes/ndarray.py     | 28 +++++++++++-
 python/mxnet/cython/base.pyi        |  8 ++++
 python/mxnet/cython/ndarray.pyx     | 16 ++++++-
 python/mxnet/gluon/block.py         | 37 ++++++++++++++++
 src/c_api/c_api_ndarray.cc          | 20 +++++++++
 src/common/utils.cc                 | 57 ++++++++++++++++++++++++
 src/common/utils.h                  |  9 ++++
 src/imperative/cached_op.cc         | 23 ++++++++--
 src/imperative/cached_op.h          |  8 ++++
 src/imperative/imperative_utils.cc  | 20 ++++++++-
 src/imperative/imperative_utils.h   |  9 +++-
 tests/python/unittest/test_gluon.py | 69 +++++++++++++++++++++++++++++
 13 files changed, 307 insertions(+), 9 deletions(-)

diff --git a/include/mxnet/c_api.h b/include/mxnet/c_api.h
index 2fa2af5ebcf2..46953b11714b 100644
--- a/include/mxnet/c_api.h
+++ b/include/mxnet/c_api.h
@@ -111,6 +111,11 @@ typedef void (*EngineFuncParamDeleter)(void*);
 typedef void (*ExecutorMonitorCallback)(const char*,
                                         NDArrayHandle,
                                         void*);
+/*! \brief Monitor callback called at operator level for cached op */
+typedef void (*CachedOpMonitorCallback)(const char*,
+                                        const char*,
+                                        NDArrayHandle);
+
 
 struct NativeOpInfo {
   void (*forward)(int, float**, int*, unsigned**, int*, void*);
@@ -1284,6 +1289,13 @@ MXNET_DLL int MXInvokeCachedOpEx(CachedOpHandle handle,
                                  NDArrayHandle **outputs,
                                  const int** out_stypes);
 
+/*!
+ * \brief cached op set monitor callback
+ */
+MXNET_DLL int MXCachedOpRegisterOpHook(NDArrayHandle handle,
+                                       CachedOpMonitorCallback callback,
+                                       bool monitor_all);
+
 //--------------------------------------------
 // Part 3: symbolic configuration generation
 //--------------------------------------------
diff --git a/python/mxnet/_ctypes/ndarray.py b/python/mxnet/_ctypes/ndarray.py
index b1a38c1d2621..0d5dade2f163 100644
--- a/python/mxnet/_ctypes/ndarray.py
+++ b/python/mxnet/_ctypes/ndarray.py
@@ -29,6 +29,13 @@
 from ..base import check_call
 
 
+def _monitor_callback_wrapper(callback):
+    """A wrapper for the user-defined handle."""
+    def callback_handle(name, opr_name, array, _):
+        """ ctypes function """
+        callback(name, opr_name, array)
+    return callback_handle
+
 class NDArrayBase(object):
     """Base data structure for ndarray"""
     __slots__ = ["handle", "writable"]
@@ -112,10 +119,11 @@ def _imperative_invoke(handle, ndargs, keys, vals, out, is_np_op):
 
 class CachedOp(object):
     """Cached operator handle."""
-    __slots__ = ["handle", "is_np_sym"]
+    __slots__ = ["handle", "is_np_sym", "_monitor_callback"]
 
     def __init__(self, sym, flags=()):
         self.handle = CachedOpHandle()
+        self._monitor_callback = None
 
         from ..symbol.numpy._symbol import _Symbol
         self.is_np_sym = bool(isinstance(sym, _Symbol))
@@ -170,3 +178,21 @@ def __call__(self, *args, **kwargs):
         else:
             return [create_ndarray_fn(ctypes.cast(output_vars[i], NDArrayHandle),
                                       stype=out_stypes[i]) for i in range(num_output.value)]
+
+    def _register_op_hook(self, callback, monitor_all=False):
+        """Install callback for monitor.
+
+        Parameters
+        ----------
+        callback : function
+            Takes a string for node_name, string for op_name and a NDArrayHandle.
+        monitor_all : bool, default False
+            If true, monitor both input _imperative_invoked output, otherwise monitor output only.
+        """
+        cb_type = ctypes.CFUNCTYPE(None, ctypes.c_char_p, ctypes.c_char_p, NDArrayHandle, ctypes.c_void_p)
+        if callback:
+            self._monitor_callback = cb_type(_monitor_callback_wrapper(callback))
+        check_call(_LIB.MXCachedOpRegisterOpHook(
+            self.handle,
+            self._monitor_callback,
+            ctypes.c_int(monitor_all)))
diff --git a/python/mxnet/cython/base.pyi b/python/mxnet/cython/base.pyi
index 548afc782763..0a35555aabb3 100644
--- a/python/mxnet/cython/base.pyi
+++ b/python/mxnet/cython/base.pyi
@@ -2,13 +2,18 @@ from ..base import MXNetError
 
 from libcpp.vector cimport vector
 from libcpp.string cimport string
+from libcpp cimport bool as _bool
 from cpython.version cimport PY_MAJOR_VERSION
 
 ctypedef void* SymbolHandle
 ctypedef void* NDArrayHandle
 ctypedef void* OpHandle
 ctypedef void* CachedOpHandle
+ctypedef void* MonitorCallbackHandle
 ctypedef unsigned nn_uint
+ctypedef void (*CachedOpMonitorCallback)(const char*,
+                                         const char*,
+                                         NDArrayHandle)
 
 cdef py_str(const char* x):
     if PY_MAJOR_VERSION < 3:
@@ -112,3 +117,6 @@ cdef extern from "mxnet/c_api.h":
                            int *num_outputs,
                            NDArrayHandle **outputs,
                            const int **out_stypes);
+    int MXCachedOpRegisterOpHook(NDArrayHandle handle,
+                                 CachedOpMonitorCallback callback,
+                                 _bool monitor_all);
diff --git a/python/mxnet/cython/ndarray.pyx b/python/mxnet/cython/ndarray.pyx
index 50791e9b9a86..74ad1c4c3d49 100644
--- a/python/mxnet/cython/ndarray.pyx
+++ b/python/mxnet/cython/ndarray.pyx
@@ -22,6 +22,7 @@ import ctypes as _ctypes
 import numpy as np
 from ..ndarray_doc import _build_doc
 from libc.stdint cimport uint32_t, int64_t
+from ..base import _LIB
 
 include "./base.pyi"
 
@@ -47,7 +48,6 @@ cdef class NDArrayBase:
                 return _ctypes.cast(<unsigned long long>self.chandle, _ctypes.c_void_p)
         def __set__(self, value):
             self._set_handle(value)
-
     property writable:
         def __get__(self):
             return bool(self.cwritable)
@@ -75,6 +75,10 @@ def _set_np_ndarray_class(cls):
     global _np_ndarray_cls
     _np_ndarray_cls = cls
 
+def _monitor_callback_wrapper(callback):
+    def callback_handle(name, opr_name, arr, _):
+        callback(name, opr_name, arr)
+    return callback_handle
 
 cdef NewArray(NDArrayHandle handle, int stype=-1, int is_np_array=0):
     """Create a new array given handle"""
@@ -103,6 +107,7 @@ cdef class CachedOp:
             self._set_handle(value)
 
     cdef int is_np_sym
+    cdef readonly object mhandle
 
     def __init__(self, sym, flags=()):
         cdef vector[string] s_flag_keys
@@ -169,6 +174,15 @@ cdef class CachedOp:
         else:
             return [NewArray(p_output_vars[i], p_output_stypes[i], self.is_np_sym) for i in range(num_output)]
 
+    def _register_op_hook(self, callback, monitor_all=False):
+        cb_type = _ctypes.CFUNCTYPE(None, _ctypes.c_char_p, _ctypes.c_char_p, _ctypes.c_void_p, _ctypes.c_void_p)
+        if callback:
+            self.mhandle = cb_type(_monitor_callback_wrapper(callback))
+        chandle = _ctypes.cast(<unsigned long long>self.chandle, _ctypes.c_void_p)
+        CALL(_LIB.MXCachedOpRegisterOpHook(chandle,
+                                           self.mhandle,
+                                           _ctypes.c_int(monitor_all)))
+
 
 def _imperative_invoke(handle, ndargs, keys, vals, out, is_np_op=0):
     """cython implementation of imperative invoke wrapper"""
diff --git a/python/mxnet/gluon/block.py b/python/mxnet/gluon/block.py
index 97e6e8b68453..fc08b4c6bd32 100644
--- a/python/mxnet/gluon/block.py
+++ b/python/mxnet/gluon/block.py
@@ -590,6 +590,19 @@ def forward(self, *args):
         # pylint: disable= invalid-name
         raise NotImplementedError
 
+    def register_op_hook(self, callback, monitor_all=False):
+        """Install callback monitor.
+
+        Parameters
+        ----------
+        callback : function
+            Takes a string and a NDArrayHandle.
+        monitor_all : bool, default False
+            If true, monitor both input and output, otherwise monitor output only.
+        """
+        for cld in self._children.values():
+            cld.register_op_hook(callback, monitor_all)
+
     def summary(self, *inputs):
         """Print the summary of the model's output and parameters.
 
@@ -754,6 +767,8 @@ def __init__(self, prefix=None, params=None):
         self._in_format = None
         self._active = False
         self._flags = []
+        self._callback = None
+        self._monitor_all = False
 
     def __setattr__(self, name, value):
         """Registers parameters."""
@@ -833,6 +848,12 @@ def _deferred_infer_shape(self, *args):
     def _call_cached_op(self, *args):
         if self._cached_op is None:
             self._build_cache(*args)
+        assert self._cached_op, "cached op is not None"
+        if self._callback:
+            self._cached_op._register_op_hook(self._callback, self._monitor_all)
+            if len(self._flags) >= 2 and (self._flags[1] or self._flags[0]):
+                warnings.warn("register_op_hook is experimental when static_alloc=True / static_shape=True "
+                              " and may not work correctly")
 
         args, fmt = _flatten(args, "input")
         assert fmt == self._in_format, "Invalid input format"
@@ -938,6 +959,22 @@ def export(self, path, epoch=0, remove_amp_cast=True):
         save_fn = _mx_npx.save if is_np_array() else ndarray.save
         save_fn('%s-%04d.params'%(path, epoch), arg_dict)
 
+    def register_op_hook(self, callback, monitor_all=False):
+        """Install op hook for block recursively.
+
+        Parameters
+        ----------
+        callback : function
+            Takes a string and a NDArrayHandle.
+        monitor_all : bool, default False
+            If true, monitor both input and output, otherwise monitor output only.
+        """
+        self._callback = callback
+        self._monitor_all = monitor_all
+        for cld in self._children.values():
+            cld._callback = callback
+            cld._monitor_all = monitor_all
+
     def forward(self, x, *args):
         """Defines the forward computation. Arguments can be either
         :py:class:`NDArray` or :py:class:`Symbol`."""
diff --git a/src/c_api/c_api_ndarray.cc b/src/c_api/c_api_ndarray.cc
index a80e171c8d36..5863f08762e6 100644
--- a/src/c_api/c_api_ndarray.cc
+++ b/src/c_api/c_api_ndarray.cc
@@ -378,3 +378,23 @@ int MXAutogradGetSymbol(NDArrayHandle handle, SymbolHandle *out) {
   *out = reinterpret_cast<SymbolHandle>(sym);
   API_END();
 }
+
+int MXCachedOpRegisterOpHook(NDArrayHandle handle,
+                             CachedOpMonitorCallback callback,
+                             bool monitor_all) {
+  API_BEGIN();
+  CachedOpMonitorCallback callback_temp = nullptr;
+  std::function<void(const char *, const char *, void*)> clbk;
+  if (callback) {
+    callback_temp = callback;
+    clbk = [callback_temp](const char *name, const char *opr_name,
+                           void *handle) {
+      callback_temp(name, opr_name, handle);
+    };
+  } else {
+      clbk = nullptr;
+  }
+  CachedOpPtr op = *static_cast<CachedOpPtr *>(handle);
+  op->RegisterOpHook(clbk, monitor_all);
+  API_END();
+}
diff --git a/src/common/utils.cc b/src/common/utils.cc
index 9fe46d94d036..032a324c96b0 100644
--- a/src/common/utils.cc
+++ b/src/common/utils.cc
@@ -51,5 +51,62 @@ void CastStorageDispatch<cpu>(const OpContext& ctx,
   mxnet::op::CastStorageComputeImpl<cpu>(ctx, input, output);
 }
 
+void ExecuteMonInputCallback(
+    const nnvm::IndexedGraph &idx, const std::vector<NDArray *> &state_arrays,
+    size_t nid, const std::function<void(const char *, const char *, void *)>
+                    &monitor_callback) {
+  static const auto &flist_inputs =
+      nnvm::Op::GetAttr<nnvm::FListInputNames>("FListInputNames");
+  std::vector<std::string> input_names;
+  const nnvm::IndexedGraph::Node &inode = idx[nid];
+  const nnvm::Node *node = inode.source;
+  if (flist_inputs.count(node->op())) {
+    input_names = flist_inputs[node->op()](node->attrs);
+  } else {
+    for (size_t i = 0; i < node->num_inputs(); ++i) {
+      input_names.emplace_back("input" + std::to_string(i));
+    }
+  }
+
+  for (size_t i = 0; i < node->num_inputs(); ++i) {
+    const nnvm::NodeEntry &input = node->inputs[i];
+    if (state_arrays[idx.entry_id(input)]->is_none()) {
+      continue;
+    }
+    NDArray *cpy = new NDArray(*state_arrays[idx.entry_id(input)]);
+    std::string name = inode.source->attrs.name + "_" + input_names[i];
+    monitor_callback(name.c_str(), inode.source->op()->name.c_str(),
+                     reinterpret_cast<void *>(cpy));
+  }
+}
+
+void ExecuteMonOutputCallback(
+    const nnvm::IndexedGraph &idx, const std::vector<NDArray *> &state_arrays,
+    size_t nid, const std::function<void(const char *, const char *, void *)>
+                    &monitor_callback) {
+  static const auto &flist_outputs =
+      nnvm::Op::GetAttr<nnvm::FListOutputNames>("FListOutputNames");
+  std::vector<std::string> output_names;
+  const nnvm::IndexedGraph::Node &inode = idx[nid];
+  const nnvm::Node *node = inode.source;
+  if (flist_outputs.count(node->op())) {
+    output_names = flist_outputs[node->op()](node->attrs);
+  } else {
+    for (size_t i = 0; i < node->num_outputs(); ++i) {
+      output_names.emplace_back(std::to_string(i));
+    }
+  }
+
+  for (size_t i = 0; i < node->num_outputs(); ++i) {
+    if (state_arrays[idx.entry_id(nid, i)]->is_none()) {
+      continue;
+    }
+    NDArray *cpy = new NDArray(*state_arrays[idx.entry_id(nid, i)]);
+    std::string name = inode.source->attrs.name + "_" + output_names[i];
+    monitor_callback(name.c_str(), inode.source->op()->name.c_str(),
+                     reinterpret_cast<void *>(cpy));
+  }
+}
+
 }  // namespace common
 }  // namespace mxnet
diff --git a/src/common/utils.h b/src/common/utils.h
index 6edfa3c6bb56..2bd6aac6f5d9 100644
--- a/src/common/utils.h
+++ b/src/common/utils.h
@@ -804,6 +804,15 @@ inline void ConvertToLegacyShape(mxnet::ShapeVector* shapes) {
     ConvertToLegacyShape(&(shapes->at(i)));
   }
 }
+void ExecuteMonInputCallback(
+    const nnvm::IndexedGraph &idx, const std::vector<NDArray *> &state_arrays,
+    size_t nid, const std::function<void(const char *, const char *, void *)>
+                    &monitor_callback);
+
+void ExecuteMonOutputCallback(
+    const nnvm::IndexedGraph &idx, const std::vector<NDArray *> &state_arrays,
+    size_t nid, const std::function<void(const char *, const char *, void *)>
+                    &monitor_callback);
 
 /*!
  * \brief This is function can return the output names of a NodeEntry.
diff --git a/src/imperative/cached_op.cc b/src/imperative/cached_op.cc
index efe38019cfda..6818d757ab79 100644
--- a/src/imperative/cached_op.cc
+++ b/src/imperative/cached_op.cc
@@ -697,6 +697,9 @@ void CachedOp::StaticRunOps(
         ndinputs.emplace_back(state_arrays[idx.entry_id(j)]);
         CHECK(!ndinputs.back()->is_none());
       }
+      if (monitor_callback_ && monitor_all_) {
+          mxnet::common::ExecuteMonInputCallback(idx, state_arrays, i, monitor_callback_);
+      }
       ndoutputs.clear();
       ndoutputs.reserve(num_outputs);
       req.clear();
@@ -708,6 +711,7 @@ void CachedOp::StaticRunOps(
         CHECK(req.back() == kNullOp || !ndoutputs.back()->is_none());
       }
       const DispatchMode dispatch_mode = dispatch_modes[i];
+
       if (createop.count(node.source->op())) {
         arg_shapes.clear();
         arg_dtypes.clear();
@@ -735,6 +739,9 @@ void CachedOp::StaticRunOps(
             default_ctx, node.source->attrs, ndinputs, ndoutputs, req,
             dispatch_mode);
       }
+      if (monitor_callback_) {
+          mxnet::common::ExecuteMonOutputCallback(idx, state_arrays, i, monitor_callback_);
+      }
     }
   }
 }
@@ -883,12 +890,12 @@ OpStatePtr CachedOp::DynamicForward(
     // So if it's not the inline mode, we disable recording.
     RunGraph(false, idx, arrays, 0, idx.num_nodes(), std::move(array_reqs),
             std::move(ref_count), &states, dispatch_modes,
-            recording && inlining_);
+            recording && inlining_, nullptr, monitor_callback_, monitor_all_);
   } else {
     mxnet::ShapeVector shapes = g.GetAttr<mxnet::ShapeVector>("shape");
     NaiveRunGraph(false, default_ctx, idx, arrays, 0, idx.num_nodes(),
                   std::move(array_reqs), std::move(ref_count), &states,
-                  dispatch_modes, recording && inlining_, &shapes);
+                  dispatch_modes, recording && inlining_, &shapes, monitor_callback_, monitor_all_);
     {
       auto state_ptr = GetCachedOpState(default_ctx);
       auto& state = state_ptr.get_state<CachedOpState>();
@@ -1028,7 +1035,7 @@ void CachedOp::DynamicBackward(
 
   RunGraph(retain_graph, idx, arrays, num_forward_nodes, idx.num_nodes(),
            std::move(array_reqs), std::move(ref_count), &states, dispatch_modes,
-           Imperative::Get()->is_recording());
+           Imperative::Get()->is_recording(), nullptr, monitor_callback_);
 
   if (retain_graph) {
     buff.resize(num_forward_entries);
@@ -1295,6 +1302,16 @@ void CachedOpBackward(const OpStatePtr& state_ptr,
       CopyFromTo(out_bufs[i], outputs[i]);
 }
 
+/*
+ * Register the callback to be called when the operator is executed
+ */
+void CachedOp::RegisterOpHook(const CachedOp::CachedOpMonCallback& callback,
+                              bool monitor_all) {
+    CHECK(callback) << "invalid callback";
+    monitor_callback_ = callback;
+    monitor_all_ = monitor_all;
+}
+
 OpStatePtr CreateCachedOpState(const NodeAttrs& attrs,
                                Context ctx,
                                const mxnet::ShapeVector& in_shapes,
diff --git a/src/imperative/cached_op.h b/src/imperative/cached_op.h
index c45f137b2d63..db049d59ed80 100644
--- a/src/imperative/cached_op.h
+++ b/src/imperative/cached_op.h
@@ -74,6 +74,9 @@ struct CachedOpConfig : public dmlc::Parameter<CachedOpConfig> {
 };
 
 class CachedOp {
+  using CachedOpMonCallback =
+      std::function<void(const char *, const char *, void *)>;
+
  public:
   CachedOp(
       const nnvm::Symbol& sym,
@@ -134,6 +137,8 @@ class CachedOp {
     sym.outputs = fwd_graph_.outputs;
     return sym;
   }
+  void RegisterOpHook(const CachedOp::CachedOpMonCallback& callback,
+                      bool monitor_all = false);
 
  private:
   struct GraphInfo;
@@ -203,6 +208,9 @@ class CachedOp {
   std::vector<bool> save_inputs_, save_outputs_;
   std::vector<OpReqType> bwd_output_reqs_;
 
+  std::function<void(const char*, const char*, NDArrayHandle)> monitor_callback_{nullptr};
+  bool monitor_all_{false};
+
   std::mutex mutex_;
   std::unordered_map<Context, std::vector<OpStatePtr> > cached_op_states_;
 };
diff --git a/src/imperative/imperative_utils.cc b/src/imperative/imperative_utils.cc
index 568d39fc8043..5491457b188f 100644
--- a/src/imperative/imperative_utils.cc
+++ b/src/imperative/imperative_utils.cc
@@ -137,7 +137,9 @@ void RunGraph(
     std::vector<OpStatePtr> *p_states,
     const DispatchModeVector &dispatch_modes,
     bool recording,
-    mxnet::ShapeVector *shapes) {
+    mxnet::ShapeVector *shapes,
+    const imperative::CachedOpMonCallback& callback,
+    const bool monitor_all) {
   CHECK(shapes == nullptr);
   for (size_t i = node_start; i < node_end; ++i) {
     const nnvm::IndexedGraph::Node& node = idx[i];
@@ -148,6 +150,9 @@ void RunGraph(
     std::vector<NDArray*> ndoutputs = NodeOutputs(idx, i, arrays);
     std::vector<OpReqType> req = NodeReq(idx, i, array_reqs);
     Context ctx = ndoutputs[0]->ctx();
+    if (callback && monitor_all) {
+        mxnet::common::ExecuteMonInputCallback(idx, arrays, i, callback);
+    }
     auto invoke = [&](const OpStatePtr &state) {
       const nnvm::IndexedGraph::Node& node = idx[i];
       DispatchMode dispatch_mode = dispatch_modes[i];
@@ -159,6 +164,9 @@ void RunGraph(
     };
     InvokeOperator(idx, i, retain_graph, arrays, ctx, p_states, ndinputs, ndoutputs,
                    &req, &ref_count, invoke);
+    if (callback) {
+        mxnet::common::ExecuteMonOutputCallback(idx, arrays, i, callback);
+    }
   }
 }
 
@@ -173,7 +181,9 @@ void NaiveRunGraph(
     std::vector<OpStatePtr> *p_states,
     const DispatchModeVector &dispatch_modes,
     bool recording,
-    mxnet::ShapeVector *shapes) {
+    mxnet::ShapeVector *shapes,
+    const imperative::CachedOpMonCallback& callback,
+    const bool monitor_all) {
   for (size_t i = node_start; i < node_end; ++i) {
     const nnvm::IndexedGraph::Node& node = idx[i];
     if (node.source->op() == nullptr) {
@@ -183,6 +193,9 @@ void NaiveRunGraph(
     std::vector<NDArray*> ndoutputs = NodeOutputs(idx, i, arrays);
     std::vector<OpReqType> req;
     Context ctx = GetContext(node.source->attrs, ndinputs, ndoutputs, default_ctx);
+    if (callback && monitor_all) {
+        mxnet::common::ExecuteMonInputCallback(idx, arrays, i, callback);
+    }
     auto invoke = [&](const OpStatePtr &state) {
       const nnvm::IndexedGraph::Node& node = idx[i];
       DispatchMode dispatch_mode = DispatchMode::kUndefined;
@@ -205,6 +218,9 @@ void NaiveRunGraph(
     };
     InvokeOperator(idx, i, retain_graph, arrays, ctx, p_states, ndinputs, ndoutputs,
                    &req, &ref_count, invoke);
+    if (callback) {
+        mxnet::common::ExecuteMonOutputCallback(idx, arrays, i, callback);
+    }
   }
 }
 
diff --git a/src/imperative/imperative_utils.h b/src/imperative/imperative_utils.h
index 21caafa124f9..c5932bb3bbfe 100644
--- a/src/imperative/imperative_utils.h
+++ b/src/imperative/imperative_utils.h
@@ -59,6 +59,7 @@ struct EngineOprSeg {
 };
 
 using MemoryPlanVector = std::vector<MemoryPlanInfo>;
+using CachedOpMonCallback = std::function<void(const char*, const char*, void*)>;
 
 inline Context GetContext(const nnvm::NodeAttrs& attrs,
                 const std::vector<NDArray*>& inputs,
@@ -1056,7 +1057,9 @@ void RunGraph(const bool retain_graph,
               std::vector<OpStatePtr> *p_states,
               const DispatchModeVector &dispatch_modes,
               bool recording,
-              mxnet::ShapeVector *shapes = nullptr);
+              mxnet::ShapeVector *shapes = nullptr,
+              const CachedOpMonCallback& callback = nullptr,
+              const bool monitor_all_ = false);
 
 void NaiveRunGraph(const bool retain_graph,
                    const Context& default_ctx,
@@ -1068,7 +1071,9 @@ void NaiveRunGraph(const bool retain_graph,
                    std::vector<OpStatePtr> *p_states,
                    const DispatchModeVector &dispatch_modes,
                    bool recording,
-                   mxnet::ShapeVector *shapes);
+                   mxnet::ShapeVector *shapes,
+                   const CachedOpMonCallback& callback = nullptr,
+                   const bool monitor_all_ = false);
 
 }  // namespace imperative
 }  // namespace mxnet
diff --git a/tests/python/unittest/test_gluon.py b/tests/python/unittest/test_gluon.py
index af30980b10ea..46e976432fa8 100644
--- a/tests/python/unittest/test_gluon.py
+++ b/tests/python/unittest/test_gluon.py
@@ -21,6 +21,7 @@
 import mxnet as mx
 from mxnet import gluon
 from mxnet.gluon import nn
+from mxnet.base import py_str
 from mxnet.test_utils import assert_almost_equal
 from mxnet.ndarray.ndarray import _STORAGE_TYPE_STR_TO_ID
 from common import (setup_module, with_seed, assertRaises, teardown,
@@ -1503,6 +1504,74 @@ def call_pre_hook(block, x):
     assert hook_call_count == 1
     assert pre_hook_call_count == 2
 
+@with_seed()
+def test_op_hook_output_names():
+    def check_name(block, expected_names, inputs=None, expected_opr_names=None, monitor_all=False):
+        opr_names = []
+        output_names = []
+
+        def mon_callback(node_name, opr_name, arr):
+            output_names.append(py_str(node_name))
+            opr_names.append(py_str(opr_name))
+
+        block.register_op_hook(mon_callback, monitor_all)
+        if not inputs:
+            block(mx.nd.ones((2, 3, 4)))
+        else:
+            block(inputs)
+
+        for output_name, expected_name in zip(output_names, expected_names):
+            print(output_name)
+            assert output_name == expected_name
+
+        if expected_opr_names:
+            for opr_name, expected_opr_name in zip(opr_names, expected_opr_names):
+                assert opr_name == expected_opr_name
+
+    # Test with Dense layer
+    model = mx.gluon.nn.HybridSequential(prefix="dense_")
+    with model.name_scope():
+        model.add(mx.gluon.nn.Dense(2))
+    model.initialize()
+    model.hybridize()
+    check_name(model, ["dense_dense0_fwd_output"])
+
+    # Test with Activation, FListInputNames not registered, input name will have _input appended
+    model = mx.gluon.nn.HybridSequential(prefix="relu_")
+    with model.name_scope():
+        model.add(mx.gluon.nn.Activation("relu"))
+    model.initialize()
+    model.hybridize()
+    check_name(model, ["relu_relu0_fwd_output"])
+
+    # Test with Pooling, monitor_all is set to True
+    model = mx.gluon.nn.HybridSequential("pool_")
+    with model.name_scope():
+        model.add(mx.gluon.nn.AvgPool1D())
+    model.initialize()
+    model.hybridize()
+    check_name(model, ['pool_pool0_fwd_data', 'pool_pool0_fwd_output'], expected_opr_names=["Pooling"],
+               monitor_all=True)
+
+    # stack two layers and test
+    model = mx.gluon.nn.HybridSequential("dense_")
+    with model.name_scope():
+        model.add(mx.gluon.nn.Dense(2))
+        model.add(mx.gluon.nn.Activation("relu"))
+    model.initialize()
+    model.hybridize()
+    check_name(model,
+               ['dense_dense0_fwd_data', 'dense_dense0_fwd_weight',
+                'dense_dense0_fwd_bias', 'dense_dense0_fwd_output',
+                'dense_relu0_fwd_input0', 'dense_relu0_fwd_output'], monitor_all=True)
+
+    # check with different hybridize modes
+    model.hybridize(static_alloc=True)
+    check_name(model,
+               ['dense_dense0_fwd_data', 'dense_dense0_fwd_weight',
+                'dense_dense0_fwd_bias', 'dense_dense0_fwd_output',
+                'dense_relu0_fwd_input0', 'dense_relu0_fwd_output'], monitor_all=True)
+
 
 @with_seed()
 def test_apply():

From 8cc344305ed9409f5364aa4cefdc7628e064b0ff Mon Sep 17 00:00:00 2001
From: Aaron Markham <aaron.s.markham@gmail.com>
Date: Wed, 18 Sep 2019 08:40:23 -0700
Subject: [PATCH 327/813] adding codeowners (#16165)

* add aaron to codeowners

* retrigger CI

* ci
---
 CODEOWNERS | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/CODEOWNERS b/CODEOWNERS
index 90744501ec71..6bf30b90602d 100644
--- a/CODEOWNERS
+++ b/CODEOWNERS
@@ -3,7 +3,7 @@
 # https://help.github.com/articles/about-codeowners/ and
 # https://github.com/blog/2392-introducing-code-owners
 #
-# Anybody can add themselves or a team as additional watcher or contributor 
+# Anybody can add themselves or a team as additional watcher or contributor
 # to get notified about changes in a specific package.
 # See https://help.github.com/articles/about-teams how to setup teams.
 
@@ -47,7 +47,7 @@ CMakeLists.txt    @szha @pllarroy
 
 # MXNet CI
 dev_menu.py         @pllarroy
-/ci/                @pllarroy @marcoabreu 
+/ci/                @pllarroy @marcoabreu @aaronmarkham
 /docker/            @marcoabreu
 /tests/ci_build/    @marcoabreu
 Jenkinsfile         @marcoabreu
@@ -59,13 +59,13 @@ Makefile          @szha
 prepare_mkl.sh    @szha
 
 # Docs
-/docs/            @szha @pllarroy
+/docs/            @szha @pllarroy @aaronmarkham
 
 # Submodules
 .gitmodules       @szha
 
 # Examples
-/example/         @szha @pllarroy
+/example/         @szha @pllarroy @aaronmarkham
 
 # Tools
 /tools/           @szha @pllarroy

From 956cfa3122e31a6957e964657422d625c1ef245d Mon Sep 17 00:00:00 2001
From: kshitij12345 <kshitijkalambarkar@gmail.com>
Date: Wed, 18 Sep 2019 23:05:28 +0530
Subject: [PATCH 328/813] assert_allclose -> rtol=1e-10 (#16198)

---
 tests/python/unittest/test_ndarray.py | 33 ++++++++++++++++++++-------
 1 file changed, 25 insertions(+), 8 deletions(-)

diff --git a/tests/python/unittest/test_ndarray.py b/tests/python/unittest/test_ndarray.py
index 7091abf7308a..7be59df6efda 100644
--- a/tests/python/unittest/test_ndarray.py
+++ b/tests/python/unittest/test_ndarray.py
@@ -21,6 +21,7 @@
 from itertools import permutations, combinations_with_replacement
 import os
 import pickle as pkl
+import functools
 from nose.tools import assert_raises, raises
 from common import with_seed, assertRaises, TemporaryDirectory
 from mxnet.test_utils import almost_equal
@@ -1887,14 +1888,16 @@ def check_save_load(save_is_np_shape, load_is_np_shape, shapes, save_throw_excep
     check_save_load(True, True, [(2, 0, 1), (0,), (), (), (0, 4), (), (3, 0, 0, 0), (2, 1), (0, 5, 0)], False, False)
 
 
-@with_seed()
-def test_update_ops_mutation():
-    def assert_mutate(x, y, op):
+def _test_update_ops_mutation_impl():
+    assert_allclose = functools.partial(
+                np.testing.assert_allclose, rtol=1e-10)
+
+    def assert_mutate(x, y):
             np.testing.assert_raises(
-                AssertionError, np.testing.assert_allclose, x, y)
+                AssertionError, assert_allclose, x, y)
 
-    def assert_unchanged(x, y, op):
-            np.testing.assert_allclose(x, y)
+    def assert_unchanged(x, y):
+            assert_allclose(x, y)
 
     def test_op(op, num_inputs, mutated_inputs, **kwargs):
         for dim in range(1, 7):
@@ -1919,9 +1922,9 @@ def test_op(op, num_inputs, mutated_inputs, **kwargs):
             for idx, (pre_array, post_array) in \
                     enumerate(zip(pre_arrays, post_arrays)):
                 if idx in mutated_inputs:
-                    assert_mutate(pre_array, post_array, op)
+                    assert_mutate(pre_array, post_array)
                 else:
-                    assert_unchanged(pre_array, post_array, op)
+                    assert_unchanged(pre_array, post_array)
 
     test_op(mx.nd.signsgd_update, 2, [0], **
             {'rescale_grad': 0.1, 'lr': 0.01, 'wd': 1e-3,
@@ -1952,6 +1955,20 @@ def test_op(op, num_inputs, mutated_inputs, **kwargs):
             {'rescale_grad': 0.1, 'lr': 0.01, 'wd': 1e-3})
 
 
+@with_seed()
+def test_update_ops_mutation():
+    _test_update_ops_mutation_impl()
+
+
+# Problem :
+# https://github.com/apache/incubator-mxnet/pull/15768#issuecomment-532046408
+@with_seed(412298777)
+def test_update_ops_mutation_failed_seed():
+    # The difference was -5.9604645e-08 which was
+    # lower than then `rtol` of 1e-07
+    _test_update_ops_mutation_impl()
+
+
 def test_large_int_rounding():
     large_integer = 50000001
 

From f75d0933efc566d98b083113fe58969a0ab4efbf Mon Sep 17 00:00:00 2001
From: Haibin Lin <linhaibin.eric@gmail.com>
Date: Wed, 18 Sep 2019 11:05:10 -0700
Subject: [PATCH 329/813] [Dataset] add shard API (#16175)

* add shard API

* more balanced

* fix a bug

* Update test_gluon_data.py
---
 python/mxnet/gluon/data/dataset.py       | 31 ++++++++++++++++++++++++
 python/mxnet/gluon/data/sampler.py       | 10 +++++---
 tests/python/unittest/test_gluon_data.py | 18 ++++++++++++++
 3 files changed, 55 insertions(+), 4 deletions(-)

diff --git a/python/mxnet/gluon/data/dataset.py b/python/mxnet/gluon/data/dataset.py
index a5399f259435..7f2c6342d595 100644
--- a/python/mxnet/gluon/data/dataset.py
+++ b/python/mxnet/gluon/data/dataset.py
@@ -64,6 +64,37 @@ def filter(self, fn):
         from . import FilterSampler
         return _SampledDataset(self, FilterSampler(fn, self))
 
+    def shard(self, num_shards, index):
+        """Returns a new dataset includes only 1/num_shards of this dataset.
+
+        For distributed training, be sure to shard before you randomize the dataset
+        (such as shuffle), if you want each worker to reach a unique subset.
+
+        Parameters
+        ----------
+        num_shards : int
+            A integer representing the number of data shards.
+        index : int
+            A integer representing the index of the current shard.
+
+        Returns
+        -------
+        Dataset
+            The result dataset.
+        """
+        assert index < num_shards, 'Shard index of out bound: %d out of %d'%(index, num_shards)
+        assert num_shards > 0, 'Number of shards must be greater than 0'
+        assert index >= 0, 'Index must be non-negative'
+        length = len(self)
+        shard_len = length // num_shards
+        rest = length % num_shards
+        # Compute the start index for this partition
+        start = shard_len * index + min(index, rest)
+        # Compute the end index for this partition
+        end = start + shard_len + (index < rest)
+        from . import SequentialSampler
+        return _SampledDataset(self, SequentialSampler(end - start, start))
+
     def take(self, count):
         """Returns a new dataset with at most `count` number of samples in it.
 
diff --git a/python/mxnet/gluon/data/sampler.py b/python/mxnet/gluon/data/sampler.py
index 980c63134fac..f53e636317df 100644
--- a/python/mxnet/gluon/data/sampler.py
+++ b/python/mxnet/gluon/data/sampler.py
@@ -36,23 +36,25 @@ def __len__(self):
 
 
 class SequentialSampler(Sampler):
-    """Samples elements from [0, length) sequentially.
+    """Samples elements from [start, start+length) sequentially.
 
     Parameters
     ----------
     length : int
         Length of the sequence.
+    start : int, default is 0
+        The start of the sequence index.
     """
-    def __init__(self, length):
+    def __init__(self, length, start=0):
         self._length = length
+        self._start = start
 
     def __iter__(self):
-        return iter(range(self._length))
+        return iter(range(self._start, self._start + self._length))
 
     def __len__(self):
         return self._length
 
-
 class RandomSampler(Sampler):
     """Samples elements from [0, length) randomly without replacement.
 
diff --git a/tests/python/unittest/test_gluon_data.py b/tests/python/unittest/test_gluon_data.py
index 62bf7c3db869..80bea2a511f2 100644
--- a/tests/python/unittest/test_gluon_data.py
+++ b/tests/python/unittest/test_gluon_data.py
@@ -296,6 +296,24 @@ def test_dataset_filter():
     for idx, sample in enumerate(a_xform_filtered):
         assert sample % 10 == 0
 
+def test_dataset_shard():
+    length = 9
+    a = mx.gluon.data.SimpleDataset([i for i in range(length)])
+    shard_0 = a.shard(4, 0)
+    shard_1 = a.shard(4, 1)
+    shard_2 = a.shard(4, 2)
+    shard_3 = a.shard(4, 3)
+    assert len(shard_0) + len(shard_1) + len(shard_2) + len(shard_3) == length
+    assert len(shard_0) == 3
+    assert len(shard_1) == 2
+    assert len(shard_2) == 2
+    assert len(shard_3) == 2
+    total = 0
+    for shard in [shard_0, shard_1, shard_2, shard_3]:
+        for idx, sample in enumerate(shard):
+            total += sample
+    assert total == sum(a)
+
 def test_dataset_take():
     length = 100
     a = mx.gluon.data.SimpleDataset([i for i in range(length)])

From 2a201ba29d568bb0e87fc027350d5b52b8593c12 Mon Sep 17 00:00:00 2001
From: reminisce <wujun.nju@gmail.com>
Date: Wed, 4 Sep 2019 16:36:50 -0700
Subject: [PATCH 330/813] Add __array_function__

Add test

Fix

Fix sanity

Fix build failure

Use array_function protocol only when np.version >= 1.17

Add unit tests for array ufunc protocol

Fix pylint

Reformat

Fix build

Fix

Refactor test suite for numpy interoperability
---
 python/mxnet/__init__.py                      |   4 +
 python/mxnet/numpy/multiarray.py              |  58 ++++-
 python/mxnet/numpy_dispatch_protocol.py       | 214 ++++++++++++++++++
 python/mxnet/symbol/numpy/_symbol.py          |   2 +-
 tests/python/gpu/test_operator_gpu.py         |   1 +
 .../unittest/test_numpy_interoperability.py   | 211 +++++++++++++++++
 tests/python/unittest/test_numpy_ndarray.py   |   2 +-
 tests/python/unittest/test_numpy_op.py        |  11 +-
 8 files changed, 488 insertions(+), 15 deletions(-)
 create mode 100644 python/mxnet/numpy_dispatch_protocol.py
 create mode 100644 tests/python/unittest/test_numpy_interoperability.py

diff --git a/python/mxnet/__init__.py b/python/mxnet/__init__.py
index e9c1229d7f2f..e1b1a95838c5 100644
--- a/python/mxnet/__init__.py
+++ b/python/mxnet/__init__.py
@@ -96,3 +96,7 @@
 # checks the __version__ attr of MXNet, which is not set on kvstore server due to the
 # fact that kvstore-server module is imported before the __version__ attr is set.
 from . import kvstore_server
+
+from .numpy_dispatch_protocol import _register_array_function, _register_array_ufunc
+_register_array_function()
+_register_array_ufunc()
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index a43045235503..d48ccf1f4b9c 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -108,6 +108,10 @@ def _get_index(idx):
         return idx
 
 
+_NUMPY_ARRAY_FUNCTION_DICT = {}
+_NUMPY_ARRAY_UFUNC_DICT = {}
+
+
 @set_module('mxnet.numpy')  # pylint: disable=invalid-name
 class ndarray(NDArray):
     """
@@ -117,6 +121,54 @@ class ndarray(NDArray):
     floating point number, or something else, etc.). Arrays should be constructed using
     `array`, `zeros` or `empty`. Currently, only c-contiguous arrays are supported.
     """
+
+    @staticmethod
+    def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):  # pylint: disable=bad-staticmethod-argument
+        """
+        Dispatch official NumPy unary/binary operator calls on mxnet.numpy.ndarray
+        to this function. The operators must comply with the ufunc definition in NumPy.
+        The following code is adapted from CuPy.
+        """
+        if 'out' in kwargs:
+            # need to unfold tuple argument in kwargs
+            out = kwargs['out']
+            if len(out) != 1:
+                raise ValueError('The `out` parameter must have exactly one ndarray')
+            kwargs['out'] = out[0]
+
+        if method == '__call__':
+            if ufunc.signature is not None:
+                # we don't support generalised-ufuncs (gufuncs)
+                return NotImplemented
+            name = ufunc.__name__
+            mx_ufunc = _NUMPY_ARRAY_UFUNC_DICT.get(name, None)
+            if mx_ufunc is None:
+                raise ValueError('mxnet.numpy operator `{}` has not been registered in '
+                                 'the _NUMPY_ARRAY_UFUNC_LIST. Please make sure the implementation'
+                                 ' is compatible with NumPy and then add it to the list.'
+                                 .format(name))
+            return mx_ufunc(*inputs, **kwargs)
+        else:
+            return NotImplemented
+
+    @staticmethod
+    def __array_function__(self, func, types, args, kwargs):  # pylint: disable=bad-staticmethod-argument
+        """
+        Dispatch official NumPy operators that comply with the array function protocol to
+        this function.
+        """
+        mx_np_func = _NUMPY_ARRAY_FUNCTION_DICT.get(func, None)
+        if mx_np_func is None:
+            raise ValueError('mxnet.numpy operator `{}` has not been registered in '
+                             'the _NUMPY_ARRAY_FUNCTION_LIST. Please make sure the '
+                             'implementation is compatible with NumPy and then add '
+                             'it to the list.'.format(func))
+        # Note: this allows subclasses that don't override
+        # __array_function__ to handle mxnet.numpy.ndarray objects
+        if not all(issubclass(t, ndarray) for t in types):
+            return NotImplemented
+        return mx_np_func(*args, **kwargs)
+
     def _get_np_basic_indexing(self, key):
         """
         This function indexes ``self`` with a tuple of `slice` objects only.
@@ -1238,7 +1290,7 @@ def std(self, axis=None, dtype=None, out=None, ddof=0, keepdims=False):  # pylin
         """Returns the standard deviation of the array elements along given axis."""
         return std(self, axis=axis, dtype=dtype, ddof=ddof, keepdims=keepdims, out=out)
 
-    def var(self, axis=None, dtype=None, out=None, ddof=0, keepdims=None):  # pylint: disable=arguments-differ
+    def var(self, axis=None, dtype=None, out=None, ddof=0, keepdims=False):  # pylint: disable=arguments-differ
         """Returns the variance of the array elements, along given axis."""
         return var(self, axis=axis, dtype=dtype, out=out, ddof=ddof, keepdims=keepdims)
 
@@ -3739,7 +3791,7 @@ def mean(a, axis=None, dtype=None, out=None, keepdims=False):  # pylint: disable
 
 
 @set_module('mxnet.numpy')
-def std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=None):
+def std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
     """
     Compute the standard deviation along the specified axis.
     Returns the standard deviation, a measure of the spread of a distribution,
@@ -3806,7 +3858,7 @@ def std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=None):
 
 
 @set_module('mxnet.numpy')
-def var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=None):
+def var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
     """
     Compute the variance along the specified axis.
     Returns the variance of the array elements, a measure of the spread of a
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
new file mode 100644
index 000000000000..ab010fbd593f
--- /dev/null
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -0,0 +1,214 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Utils for registering NumPy array function protocol for mxnet.numpy ops."""
+
+from __future__ import absolute_import
+import functools
+import numpy as _np
+from . import numpy as mx_np  # pylint: disable=reimported
+from .numpy.multiarray import _NUMPY_ARRAY_FUNCTION_DICT, _NUMPY_ARRAY_UFUNC_DICT
+
+
+def _find_duplicate(strs):
+    str_set = set()
+    for s in strs:
+        if s in str_set:
+            return s
+        else:
+            str_set.add(s)
+    return None
+
+
+def _implements(numpy_function):
+    """Register an __array_function__ implementation for MyArray objects."""
+    def decorator(func):
+        _NUMPY_ARRAY_FUNCTION_DICT[numpy_function] = func
+        return func
+    return decorator
+
+
+def with_array_function_protocol(func):
+    """A decorator for functions that expect array function protocol.
+    The decorated function only runs when NumPy version >= 1.17."""
+    from distutils.version import LooseVersion
+    cur_np_ver = LooseVersion(_np.__version__)
+    np_1_17_ver = LooseVersion('1.17')
+
+    @functools.wraps(func)
+    def _run_with_array_func_proto(*args, **kwargs):
+        if cur_np_ver >= np_1_17_ver:
+            try:
+                func(*args, **kwargs)
+            except:
+                raise RuntimeError('Running function {} with NumPy array function protocol failed'
+                                   .format(func.__name__))
+
+    return _run_with_array_func_proto
+
+
+def with_array_ufunc_protocol(func):
+    """A decorator for functions that expect array ufunc protocol.
+    The decorated function only runs when NumPy version >= 1.15."""
+    from distutils.version import LooseVersion
+    cur_np_ver = LooseVersion(_np.__version__)
+    np_1_15_ver = LooseVersion('1.15')
+
+    @functools.wraps(func)
+    def _run_with_array_ufunc_proto(*args, **kwargs):
+        if cur_np_ver >= np_1_15_ver:
+            try:
+                func(*args, **kwargs)
+            except:
+                raise RuntimeError('Running function {} with NumPy array ufunc protocol failed'
+                                   .format(func.__name__))
+
+    return _run_with_array_ufunc_proto
+
+
+_NUMPY_ARRAY_FUNCTION_LIST = [
+    'argmax',
+    'broadcast_arrays',
+    'broadcast_to',
+    'clip',
+    'concatenate',
+    'copy',
+    'cumsum',
+    'dot',
+    'expand_dims',
+    'fix',
+    'max',
+    'mean',
+    'min',
+    'ones_like',
+    'prod',
+    'repeat',
+    'reshape',
+    'split',
+    'squeeze',
+    'stack',
+    'std',
+    'sum',
+    'swapaxes',
+    'tensordot',
+    'tile',
+    'transpose',
+    'var',
+    'zeros_like',
+]
+
+
+@with_array_function_protocol
+def _register_array_function():
+    """Register __array_function__ protocol for mxnet.numpy operators so that
+    ``mxnet.numpy.ndarray`` can be fed into the official NumPy operators and
+    dispatched to MXNet implementation.
+
+    Notes
+    -----
+    According the __array_function__ protocol (see the following reference),
+    there are three kinds of operators that cannot be dispatched using this
+    protocol:
+    1. Universal functions, which already have their own protocol in the official
+    NumPy package.
+    2. Array creation functions.
+    3. Dispatch for methods of any kind, e.g., methods on np.random.RandomState objects.
+
+    References
+    ----------
+    https://numpy.org/neps/nep-0018-array-function-protocol.html
+    """
+    dup = _find_duplicate(_NUMPY_ARRAY_FUNCTION_LIST)
+    if dup is not None:
+        raise ValueError('Duplicate operator name {} in _NUMPY_ARRAY_FUNCTION_LIST'.format(dup))
+    for op_name in _NUMPY_ARRAY_FUNCTION_LIST:
+        strs = op_name.split('.')
+        if len(strs) == 1:
+            mx_np_op = getattr(mx_np, op_name)
+            onp_op = getattr(_np, op_name)
+            setattr(mx_np, op_name, _implements(onp_op)(mx_np_op))
+        elif len(strs) == 2:
+            mx_np_submodule = getattr(mx_np, strs[0])
+            mx_np_op = getattr(mx_np_submodule, strs[1])
+            onp_submodule = getattr(_np, strs[0])
+            onp_op = getattr(onp_submodule, strs[1])
+            setattr(mx_np_submodule, strs[1], _implements(onp_op)(mx_np_op))
+        else:
+            raise ValueError('Does not support registering __array_function__ protocol '
+                             'for operator {}'.format(op_name))
+
+
+# https://docs.scipy.org/doc/numpy/reference/ufuncs.html#available-ufuncs
+_NUMPY_ARRAY_UFUNC_LIST = [
+    'add',
+    'subtract',
+    'multiply',
+    # Uncomment divide when mxnet.numpy.true_divide is added
+    # 'divide',
+    'negative',
+    'power',
+    'mod',
+    'absolute',
+    'rint',
+    'sign',
+    'exp',
+    'log',
+    'log2',
+    'log10',
+    'expm1',
+    'sqrt',
+    'square',
+    'cbrt',
+    'reciprocal',
+    'remainder',
+    'sin',
+    'cos',
+    'tan',
+    'sinh',
+    'cosh',
+    'tanh',
+    'arcsin',
+    'arccos',
+    'arctan',
+    'arcsinh',
+    'arccosh',
+    'arctanh',
+    'maximum',
+    'minimum',
+    'ceil',
+    'trunc',
+    'floor',
+]
+
+
+@with_array_ufunc_protocol
+def _register_array_ufunc():
+    """Register NumPy array ufunc protocol.
+
+    References
+    ----------
+    https://numpy.org/neps/nep-0013-ufunc-overrides.html
+    """
+    dup = _find_duplicate(_NUMPY_ARRAY_UFUNC_LIST)
+    if dup is not None:
+        raise ValueError('Duplicate operator name {} in _NUMPY_ARRAY_UFUNC_LIST'.format(dup))
+    for op_name in _NUMPY_ARRAY_UFUNC_LIST:
+        try:
+            mx_np_op = getattr(mx_np, op_name)
+            _NUMPY_ARRAY_UFUNC_DICT[op_name] = mx_np_op
+        except AttributeError:
+            raise AttributeError('mxnet.numpy does not have operator named {}'.format(op_name))
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 12c333b253ba..962fee2d9375 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -538,7 +538,7 @@ def std(self, axis=None, dtype=None, out=None, ddof=0, keepdims=False):  # pylin
         """Returns the standard deviation of the array elements along given axis."""
         return std(self, axis=axis, dtype=dtype, ddof=ddof, keepdims=keepdims, out=out)
 
-    def var(self, axis=None, dtype=None, out=None, ddof=0, keepdims=None):  # pylint: disable=arguments-differ,too-many-arguments
+    def var(self, axis=None, dtype=None, out=None, ddof=0, keepdims=False):  # pylint: disable=arguments-differ,too-many-arguments
         """Returns the variance of the array elements, along given axis."""
         return var(self, axis=axis, dtype=dtype, out=out, ddof=ddof, keepdims=keepdims)
 
diff --git a/tests/python/gpu/test_operator_gpu.py b/tests/python/gpu/test_operator_gpu.py
index db550e4254b7..c4d96fb550c0 100644
--- a/tests/python/gpu/test_operator_gpu.py
+++ b/tests/python/gpu/test_operator_gpu.py
@@ -37,6 +37,7 @@
 from test_operator import *
 from test_numpy_ndarray import *
 from test_numpy_op import *
+from test_numpy_interoperability import *
 from test_optimizer import *
 from test_random import *
 from test_exc_handling import *
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
new file mode 100644
index 000000000000..f0c205f72dc4
--- /dev/null
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -0,0 +1,211 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# pylint: skip-file
+from __future__ import absolute_import
+import numpy as _np
+from mxnet import np
+from mxnet.test_utils import assert_almost_equal
+from mxnet.test_utils import use_np
+from common import assertRaises, with_seed
+from mxnet.numpy_dispatch_protocol import with_array_function_protocol, with_array_ufunc_protocol
+from mxnet.numpy_dispatch_protocol import _NUMPY_ARRAY_FUNCTION_LIST, _NUMPY_ARRAY_UFUNC_LIST
+
+
+class OpArgMngr(object):
+    """Operator argument manager for storing operator workloads."""
+    _args = {}
+
+    @staticmethod
+    def add_workload(name, *args, **kwargs):
+        if name not in OpArgMngr._args:
+            OpArgMngr._args[name] = []
+        OpArgMngr._args[name].append({'args': args, 'kwargs': kwargs})
+
+    @staticmethod
+    def get_workloads(name):
+        return OpArgMngr._args.get(name, None)
+
+
+@use_np
+def _prepare_workloads():
+    array_pool = {
+        '4x1': np.random.uniform(size=(4, 1)) + 2,
+        '1x2': np.random.uniform(size=(1, 2)) + 2,
+        '1x1x0': np.array([[[]]])
+    }
+
+    # workloads for array function protocol
+    OpArgMngr.add_workload('argmax', array_pool['4x1'])
+    OpArgMngr.add_workload('broadcast_arrays', array_pool['4x1'], array_pool['1x2'])
+    OpArgMngr.add_workload('broadcast_to', array_pool['4x1'], (4, 2))
+    OpArgMngr.add_workload('clip', array_pool['4x1'], 0.2, 0.8)
+    OpArgMngr.add_workload('concatenate', [array_pool['4x1'], array_pool['4x1']])
+    OpArgMngr.add_workload('concatenate', [array_pool['4x1'], array_pool['4x1']], axis=1)
+    OpArgMngr.add_workload('copy', array_pool['4x1'])
+    OpArgMngr.add_workload('cumsum', array_pool['4x1'])
+    OpArgMngr.add_workload('cumsum', array_pool['4x1'], axis=1)
+    OpArgMngr.add_workload('dot', array_pool['4x1'], array_pool['4x1'].T)
+    OpArgMngr.add_workload('expand_dims', array_pool['4x1'], -1)
+    OpArgMngr.add_workload('fix', array_pool['4x1'])
+    OpArgMngr.add_workload('max', array_pool['4x1'])
+    OpArgMngr.add_workload('min', array_pool['4x1'])
+    OpArgMngr.add_workload('mean', array_pool['4x1'])
+    OpArgMngr.add_workload('mean', array_pool['4x1'], axis=0, keepdims=True)
+    OpArgMngr.add_workload('ones_like', array_pool['4x1'])
+    OpArgMngr.add_workload('prod', array_pool['4x1'])
+    OpArgMngr.add_workload('repeat', array_pool['4x1'], 3)
+    OpArgMngr.add_workload('reshape', array_pool['4x1'], -1)
+    OpArgMngr.add_workload('split', array_pool['4x1'], 2)
+    OpArgMngr.add_workload('squeeze', array_pool['4x1'])
+    OpArgMngr.add_workload('stack', [array_pool['4x1']] * 2)
+    OpArgMngr.add_workload('std', array_pool['4x1'])
+    OpArgMngr.add_workload('sum', array_pool['4x1'])
+    OpArgMngr.add_workload('swapaxes', array_pool['4x1'], 0, 1)
+    OpArgMngr.add_workload('tensordot', array_pool['4x1'], array_pool['4x1'])
+    OpArgMngr.add_workload('tile', array_pool['4x1'], 2)
+    OpArgMngr.add_workload('tile', np.array([[[]]]), (3, 2, 5))
+    OpArgMngr.add_workload('transpose', array_pool['4x1'])
+    OpArgMngr.add_workload('var', array_pool['4x1'])
+    OpArgMngr.add_workload('zeros_like', array_pool['4x1'])
+
+    # workloads for array ufunc protocol
+    OpArgMngr.add_workload('add', array_pool['4x1'], array_pool['1x2'])
+    OpArgMngr.add_workload('add', array_pool['4x1'], 2)
+    OpArgMngr.add_workload('add', 2, array_pool['4x1'])
+    OpArgMngr.add_workload('add', array_pool['4x1'], array_pool['1x1x0'])
+    OpArgMngr.add_workload('subtract', array_pool['4x1'], array_pool['1x2'])
+    OpArgMngr.add_workload('subtract', array_pool['4x1'], 2)
+    OpArgMngr.add_workload('subtract', 2, array_pool['4x1'])
+    OpArgMngr.add_workload('subtract', array_pool['4x1'], array_pool['1x1x0'])
+    OpArgMngr.add_workload('multiply', array_pool['4x1'], array_pool['1x2'])
+    OpArgMngr.add_workload('multiply', array_pool['4x1'], 2)
+    OpArgMngr.add_workload('multiply', 2, array_pool['4x1'])
+    OpArgMngr.add_workload('multiply', array_pool['4x1'], array_pool['1x1x0'])
+    OpArgMngr.add_workload('power', array_pool['4x1'], array_pool['1x2'])
+    OpArgMngr.add_workload('power', array_pool['4x1'], 2)
+    OpArgMngr.add_workload('power', 2, array_pool['4x1'])
+    OpArgMngr.add_workload('power', array_pool['4x1'], array_pool['1x1x0'])
+    OpArgMngr.add_workload('mod', array_pool['4x1'], array_pool['1x2'])
+    OpArgMngr.add_workload('mod', array_pool['4x1'], 2)
+    OpArgMngr.add_workload('mod', 2, array_pool['4x1'])
+    OpArgMngr.add_workload('mod', array_pool['4x1'], array_pool['1x1x0'])
+    OpArgMngr.add_workload('remainder', array_pool['4x1'], array_pool['1x2'])
+    OpArgMngr.add_workload('remainder', array_pool['4x1'], 2)
+    OpArgMngr.add_workload('remainder', 2, array_pool['4x1'])
+    OpArgMngr.add_workload('remainder', array_pool['4x1'], array_pool['1x1x0'])
+    OpArgMngr.add_workload('maximum', array_pool['4x1'], array_pool['1x2'])
+    OpArgMngr.add_workload('maximum', array_pool['4x1'], 2)
+    OpArgMngr.add_workload('maximum', 2, array_pool['4x1'])
+    OpArgMngr.add_workload('maximum', array_pool['4x1'], array_pool['1x1x0'])
+    OpArgMngr.add_workload('minimum', array_pool['4x1'], array_pool['1x2'])
+    OpArgMngr.add_workload('minimum', array_pool['4x1'], 2)
+    OpArgMngr.add_workload('minimum', 2, array_pool['4x1'])
+    OpArgMngr.add_workload('minimum', array_pool['4x1'], array_pool['1x1x0'])
+    OpArgMngr.add_workload('negative', array_pool['4x1'])
+    OpArgMngr.add_workload('absolute', array_pool['4x1'])
+    OpArgMngr.add_workload('rint', array_pool['4x1'])
+    OpArgMngr.add_workload('sign', array_pool['4x1'])
+    OpArgMngr.add_workload('exp', array_pool['4x1'])
+    OpArgMngr.add_workload('log', array_pool['4x1'])
+    OpArgMngr.add_workload('log2', array_pool['4x1'])
+    OpArgMngr.add_workload('log10', array_pool['4x1'])
+    OpArgMngr.add_workload('expm1', array_pool['4x1'])
+    OpArgMngr.add_workload('sqrt', array_pool['4x1'])
+    OpArgMngr.add_workload('square', array_pool['4x1'])
+    OpArgMngr.add_workload('cbrt', array_pool['4x1'])
+    OpArgMngr.add_workload('reciprocal', array_pool['4x1'])
+    OpArgMngr.add_workload('sin', array_pool['4x1'])
+    OpArgMngr.add_workload('cos', array_pool['4x1'])
+    OpArgMngr.add_workload('tan', array_pool['4x1'])
+    OpArgMngr.add_workload('sinh', array_pool['4x1'])
+    OpArgMngr.add_workload('cosh', array_pool['4x1'])
+    OpArgMngr.add_workload('tanh', array_pool['4x1'])
+    OpArgMngr.add_workload('arcsin', array_pool['4x1'] - 2)
+    OpArgMngr.add_workload('arccos', array_pool['4x1'] - 2)
+    OpArgMngr.add_workload('arctan', array_pool['4x1'])
+    OpArgMngr.add_workload('arcsinh', array_pool['4x1'])
+    OpArgMngr.add_workload('arccosh', array_pool['4x1'])
+    OpArgMngr.add_workload('arctanh', array_pool['4x1'] - 2)
+    OpArgMngr.add_workload('ceil', array_pool['4x1'])
+    OpArgMngr.add_workload('trunc', array_pool['4x1'])
+    OpArgMngr.add_workload('floor', array_pool['4x1'])
+
+
+_prepare_workloads()
+
+
+def _get_numpy_op_output(onp_op, *args, **kwargs):
+    onp_args = [arg.asnumpy() if isinstance(arg, np.ndarray) else arg for arg in args]
+    onp_kwargs = {k: v.asnumpy() if isinstance(v, np.ndarray) else v for k, v in kwargs.items()}
+    for i, v in enumerate(onp_args):
+        if isinstance(v, (list, tuple)):
+            new_arrs = [a.asnumpy() if isinstance(a, np.ndarray) else a for a in v]
+            onp_args[i] = new_arrs
+
+    return onp_op(*onp_args, **onp_kwargs)
+
+
+def _check_interoperability_helper(op_name, *args, **kwargs):
+    strs = op_name.split('.')
+    if len(strs) == 1:
+        onp_op = getattr(_np, op_name)
+    elif len(strs) == 2:
+        onp_op = getattr(getattr(_np, strs[0]), strs[1])
+    else:
+        assert False
+    out = onp_op(*args, **kwargs)
+    expected_out = _get_numpy_op_output(onp_op, *args, **kwargs)
+    if isinstance(out, (tuple, list)):
+        assert type(out) == type(expected_out)
+        for arr in out:
+            assert isinstance(arr, np.ndarray)
+        for arr, expected_arr in zip(out, expected_out):
+            assert isinstance(arr, np.ndarray)
+            assert_almost_equal(arr.asnumpy(), expected_arr, rtol=1e-3, atol=1e-4, use_broadcast=False)
+    else:
+        assert isinstance(out, np.ndarray)
+        assert_almost_equal(out.asnumpy(), expected_out, rtol=1e-3, atol=1e-4, use_broadcast=False)
+
+
+def check_interoperability(op_list):
+    for name in op_list:
+        workloads = OpArgMngr.get_workloads(name)
+        assert workloads is not None, 'Workloads for operator `{}` has not been ' \
+                                      'added for checking interoperability with ' \
+                                      'the official NumPy.'.format(name)
+        for workload in workloads:
+            _check_interoperability_helper(name, *workload['args'], **workload['kwargs'])
+
+
+@with_seed()
+@use_np
+@with_array_function_protocol
+def test_np_array_function_protocol():
+    check_interoperability(_NUMPY_ARRAY_FUNCTION_LIST)
+
+
+@with_seed()
+@use_np
+@with_array_ufunc_protocol
+def test_np_array_ufunc_protocol():
+    check_interoperability(_NUMPY_ARRAY_UFUNC_LIST)
+
+
+if __name__ == '__main__':
+    import nose
+    nose.runmodule()
diff --git a/tests/python/unittest/test_numpy_ndarray.py b/tests/python/unittest/test_numpy_ndarray.py
index bffa7a00dccb..b60316724808 100644
--- a/tests/python/unittest/test_numpy_ndarray.py
+++ b/tests/python/unittest/test_numpy_ndarray.py
@@ -729,8 +729,8 @@ def test_setitem_autograd(np_array, index):
                         ((3, 0), [2, (slice(None, None, None)), (slice(None, None, None), None)]),
     ]
     for shape, indices in shapes_indices:
+        np_array = _np.zeros(shape)
         for index in indices:
-            np_array = np.zeros(shape)
             test_getitem(np_array, index)
             test_setitem(np_array, index)
             test_getitem_autograd(np_array, index)
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index ed4375939688..fde88f8f7dec 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -20,16 +20,14 @@
 import numpy as _np
 import mxnet as mx
 from mxnet import np, npx
-from mxnet.base import MXNetError
 from mxnet.gluon import HybridBlock
 from mxnet.base import MXNetError
 from mxnet.test_utils import same, assert_almost_equal, rand_shape_nd, rand_ndarray
 from mxnet.test_utils import check_numeric_gradient, use_np, collapse_sum_like
 from common import assertRaises, with_seed
 import random
-import collections
 import scipy.stats as ss
-from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf, retry
+from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf
 import platform
 
 
@@ -657,7 +655,6 @@ def hybrid_forward(self, F, a):
     for hybridize in [True, False]:
         for shape_pair in shape_pairs:
             shape1, shape2 = shape_pair
-            print(shape1, shape2)
             test_reshape = TestReshape(shape2)
             if hybridize:
                 test_reshape.hybridize()
@@ -742,7 +739,6 @@ def hybrid_forward(self, F, a, *args, **kwargs):
                             test_prod.hybridize()
                         x = np.array(_np.random.uniform(-2.0, 2.0, size=shape), dtype=itype)
                         x.attach_grad()
-                        print(x.grad.dtype)
                         expected_ret = _np.prod(x.asnumpy(), axis=axis, keepdims=keepdims)
                         expected_ret = expected_ret.astype(dtype)
                         with mx.autograd.record():
@@ -963,7 +959,6 @@ def __init__(self, func):
                 self._func = func
 
             def hybrid_forward(self, F, a, *args, **kwargs):
-                print(self._func)
                 return getattr(F.np, self._func)(a)
 
         np_func = getattr(_np, func)
@@ -1588,10 +1583,6 @@ def test_np_random():
     for shape in shapes:
         for dtype in dtypes:
             for op_name in op_names:
-                print('-------------------------------')
-                print(op_name)
-                print(shape)
-                print(dtype)
                 op = getattr(np.random, op_name, None)
                 assert op is not None
                 out = op(size=shape, dtype=dtype)

From 681d9a7c611fd7b701615c45e857ae712ea10676 Mon Sep 17 00:00:00 2001
From: reminisce <wujun.nju@gmail.com>
Date: Tue, 17 Sep 2019 13:21:28 -0700
Subject: [PATCH 331/813] Improved error mesages

---
 python/mxnet/numpy/multiarray.py        | 13 ++++++++-----
 python/mxnet/numpy_dispatch_protocol.py | 10 ++++++----
 2 files changed, 14 insertions(+), 9 deletions(-)

diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index d48ccf1f4b9c..52dc9fb7a685 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -144,8 +144,10 @@ def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):  # pylint: disable=
             mx_ufunc = _NUMPY_ARRAY_UFUNC_DICT.get(name, None)
             if mx_ufunc is None:
                 raise ValueError('mxnet.numpy operator `{}` has not been registered in '
-                                 'the _NUMPY_ARRAY_UFUNC_LIST. Please make sure the implementation'
-                                 ' is compatible with NumPy and then add it to the list.'
+                                 'the _NUMPY_ARRAY_UFUNC_LIST. Please make sure you are '
+                                 'using NumPy >= 1.15.0 and the operator implementation '
+                                 'is compatible with NumPy. Then add the operator name '
+                                 'to the list.'
                                  .format(name))
             return mx_ufunc(*inputs, **kwargs)
         else:
@@ -160,9 +162,10 @@ def __array_function__(self, func, types, args, kwargs):  # pylint: disable=bad-
         mx_np_func = _NUMPY_ARRAY_FUNCTION_DICT.get(func, None)
         if mx_np_func is None:
             raise ValueError('mxnet.numpy operator `{}` has not been registered in '
-                             'the _NUMPY_ARRAY_FUNCTION_LIST. Please make sure the '
-                             'implementation is compatible with NumPy and then add '
-                             'it to the list.'.format(func))
+                             'the _NUMPY_ARRAY_FUNCTION_LIST. Please make sure you are '
+                             'using NumPy >= 1.17.0 and the operator '
+                             'implementation is compatible with NumPy. Then add '
+                             'the operator name to the list.'.format(func))
         # Note: this allows subclasses that don't override
         # __array_function__ to handle mxnet.numpy.ndarray objects
         if not all(issubclass(t, ndarray) for t in types):
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index ab010fbd593f..93b81b1a868d 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -54,9 +54,10 @@ def _run_with_array_func_proto(*args, **kwargs):
         if cur_np_ver >= np_1_17_ver:
             try:
                 func(*args, **kwargs)
-            except:
+            except Exception as e:
                 raise RuntimeError('Running function {} with NumPy array function protocol failed'
-                                   .format(func.__name__))
+                                   ' with exception {}'
+                                   .format(func.__name__, str(e)))
 
     return _run_with_array_func_proto
 
@@ -73,9 +74,10 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
         if cur_np_ver >= np_1_15_ver:
             try:
                 func(*args, **kwargs)
-            except:
+            except Exception as e:
                 raise RuntimeError('Running function {} with NumPy array ufunc protocol failed'
-                                   .format(func.__name__))
+                                   ' with exception {}'
+                                   .format(func.__name__, str(e)))
 
     return _run_with_array_ufunc_proto
 

From 477e6f7a00426ddb4d0e6bfb160a2ab9c036029a Mon Sep 17 00:00:00 2001
From: reminisce <wujun.nju@gmail.com>
Date: Tue, 17 Sep 2019 22:30:49 -0700
Subject: [PATCH 332/813] Fix np.choice

---
 tests/python/unittest/test_numpy_op.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index fde88f8f7dec..1656716b4a35 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -1723,7 +1723,7 @@ def test_indexing_mode(sampler, set_size, samples_size, replace, weight=None):
             samples = sampler(a)
         assert len(samples) == samples_size
         if not replace:
-            assert len(_np.unique(samples)) == samples_size
+            assert len(_np.unique(samples.asnumpy())) == samples_size
 
     num_classes = 10
     num_samples = 10 ** 8

From 479ab46af4d0f92e199c910be7e04b1924680543 Mon Sep 17 00:00:00 2001
From: Sheng Zha <szha@users.noreply.github.com>
Date: Wed, 18 Sep 2019 13:30:21 -0700
Subject: [PATCH 333/813] [MEMORY] retry GPU memory allocation if fragmented
 (#16194)

---
 src/storage/pooled_storage_manager.h | 24 ++++++++++++++++++++----
 1 file changed, 20 insertions(+), 4 deletions(-)

diff --git a/src/storage/pooled_storage_manager.h b/src/storage/pooled_storage_manager.h
index 91eb536ec7bd..6e54ddd7e52a 100644
--- a/src/storage/pooled_storage_manager.h
+++ b/src/storage/pooled_storage_manager.h
@@ -153,8 +153,16 @@ void GPUPooledStorageManager::Alloc(Storage::Handle* handle) {
 
     void* ret = nullptr;
     cudaError_t e = cudaMalloc(&ret, size);
-    if (e != cudaSuccess && e != cudaErrorCudartUnloading) {
-      LOG(FATAL) << "cudaMalloc failed: " << cudaGetErrorString(e);
+    if (e != cudaSuccess) {
+      if (e == cudaErrorMemoryAllocation) {
+        ReleaseAll();
+        e = cudaMalloc(&ret, size);
+        if (e != cudaSuccess && e != cudaErrorCudartUnloading) {
+          LOG(FATAL) << "cudaMalloc retry failed: " << cudaGetErrorString(e);
+        }
+      } else if (e != cudaErrorCudartUnloading) {
+        LOG(FATAL) << "cudaMalloc failed: " << cudaGetErrorString(e);
+      }
     }
     used_memory_ += size;
     handle->dptr = ret;
@@ -328,8 +336,16 @@ void GPUPooledRoundedStorageManager::Alloc(Storage::Handle* handle) {
 
     void* ret = nullptr;
     cudaError_t e = cudaMalloc(&ret, size);
-    if (e != cudaSuccess && e != cudaErrorCudartUnloading) {
-      LOG(FATAL) << "cudaMalloc failed: " << cudaGetErrorString(e);
+    if (e != cudaSuccess) {
+      if (e == cudaErrorMemoryAllocation) {
+        ReleaseAll();
+        e = cudaMalloc(&ret, size);
+        if (e != cudaSuccess && e != cudaErrorCudartUnloading) {
+          LOG(FATAL) << "cudaMalloc retry failed: " << cudaGetErrorString(e);
+        }
+      } else if (e != cudaErrorCudartUnloading) {
+        LOG(FATAL) << "cudaMalloc failed: " << cudaGetErrorString(e);
+      }
     }
     used_memory_ += size;
     handle->dptr = ret;

From 995b477d940bcba9927b34cd7bda8cabc85628a5 Mon Sep 17 00:00:00 2001
From: Zach Kimberg <kimbergz@amazon.com>
Date: Wed, 18 Sep 2019 13:33:28 -0700
Subject: [PATCH 334/813] Fix README Build Status (#16183)

* Fix README Build Status

* Add labels to Jenkins Badges

* Add more line breaks
---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 849a54f3c60a..04b7bcf9e7fa 100644
--- a/README.md
+++ b/README.md
@@ -23,7 +23,7 @@ Apache MXNet (incubating) for Deep Learning
 =====
 | Master         | Docs          | License  |
 | :-------------:|:-------------:|:--------:|
-| [![Build Status](http://jenkins.mxnet-ci.amazon-ml.com/job/incubator-mxnet/job/master/badge/icon)](http://jenkins.mxnet-ci.amazon-ml.com/job/incubator-mxnet/job/master/)  | [![Documentation Status](http://jenkins.mxnet-ci.amazon-ml.com/job/restricted-website-build/badge/icon)](https://mxnet.incubator.apache.org/) | [![GitHub license](http://dmlc.github.io/img/apache2.svg)](./LICENSE) |
+[![CentOS CPU Build Status](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/centos-cpu/job/master/badge/icon?subject=build%20centos%20cpu)](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/centos-cpu/job/master/) [![CentOS GPU Build Status](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/centos-gpu/job/master/badge/icon?subject=build%20centos%20gpu)](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/centos-gpu/job/master/) [![Clang Build Status](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/clang/job/master/badge/icon?subject=build%20clang)](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/clang/job/master/) <br> [![Edge Build Status](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/edge/job/master/badge/icon?subject=build%20edge)](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/edge/job/master/) [![Miscellaneous Build Status](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/miscellaneous/job/master/badge/icon?subject=build%20miscellaneous)](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/miscellaneous/job/master/) [![Sanity Build Status](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/sanity/job/master/badge/icon?subject=build%20sanity)](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/sanity/job/master/) <br> [![Unix CPU Build Status](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/unix-cpu/job/master/badge/icon?subject=build%20unix%20cpu)](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/unix-cpu/job/master/) [![Unix GPU Build Status](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/unix-gpu/job/master/badge/icon?subject=build%20unix%20gpu)](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/unix-gpu/job/master/) [![Website Build Status](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/website/job/master/badge/icon?subject=build%20website)](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/website/job/master/) <br> [![Windows CPU Build Status](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/windows-cpu/job/master/badge/icon?subject=build%20windows%20cpu)](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/windows-cpu/job/master/) [![Windows GPU Build Status](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/windows-gpu/job/master/badge/icon?subject=build%20windows%20gpu)](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/windows-gpu/job/master/) | [![Documentation Status](http://jenkins.mxnet-ci.amazon-ml.com/job/restricted-website-build/badge/icon)](https://mxnet.incubator.apache.org/) | [![GitHub license](http://dmlc.github.io/img/apache2.svg)](./LICENSE) |
 
 ![banner](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/banner.png)
 

From 1e058a31bcf565a2a7c868e6726ab77527513ee7 Mon Sep 17 00:00:00 2001
From: Xingjian Shi <xshiab@connect.ust.hk>
Date: Wed, 18 Sep 2019 14:43:38 -0700
Subject: [PATCH 335/813] add exception check for numpy reshape (#16180)

---
 tests/python/unittest/test_exc_handling.py | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/tests/python/unittest/test_exc_handling.py b/tests/python/unittest/test_exc_handling.py
index 960a4e8d1364..f029d09b5f54 100644
--- a/tests/python/unittest/test_exc_handling.py
+++ b/tests/python/unittest/test_exc_handling.py
@@ -190,6 +190,15 @@ def check_resize():
     assert_raises(MXNetError, check_resize)
 
 
+@with_seed()
+def test_np_reshape_exception():
+    a = mx.np.ones((10, 10))
+    a.reshape((-1,)).asnumpy()  # Check no-raise
+    assert_raises(MXNetError, lambda: a.reshape((1,)))
+    assert_raises(MXNetError, lambda: mx.np.reshape(a, (1,)))
+    assert_raises(MXNetError, lambda: mx.np.reshape(a, (-1, 3)))
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 53b2b40273dbd7659a75470ddcb2a495862b3141 Mon Sep 17 00:00:00 2001
From: "Joshua Z. Zhang" <cheungchih@gmail.com>
Date: Wed, 18 Sep 2019 18:23:06 -0700
Subject: [PATCH 336/813] improve dataloader signals and messages (#16114)

* improve dataloader signals and messages

* address comments

* fix spawn tests on windows
---
 python/mxnet/gluon/data/dataloader.py    | 50 +++++++++++++++++++-----
 tests/python/unittest/test_gluon_data.py |  5 ++-
 2 files changed, 44 insertions(+), 11 deletions(-)

diff --git a/python/mxnet/gluon/data/dataloader.py b/python/mxnet/gluon/data/dataloader.py
index 9f0939ec7f37..68412267da6b 100644
--- a/python/mxnet/gluon/data/dataloader.py
+++ b/python/mxnet/gluon/data/dataloader.py
@@ -24,6 +24,7 @@
 import pickle
 import io
 import sys
+import signal
 import multiprocessing
 import multiprocessing.queues
 from multiprocessing.reduction import ForkingPickler
@@ -426,7 +427,8 @@ def _thread_worker_fn(samples, batchify_fn, dataset):
 class _MultiWorkerIter(object):
     """Internal multi-worker iterator for DataLoader."""
     def __init__(self, worker_pool, batchify_fn, batch_sampler, pin_memory=False,
-                 pin_device_id=0, worker_fn=_worker_fn, prefetch=0, dataset=None, data_loader=None):
+                 pin_device_id=0, worker_fn=_worker_fn, prefetch=0, dataset=None,
+                 data_loader=None, timeout=120):
         self._worker_pool = worker_pool
         self._batchify_fn = batchify_fn
         self._batch_sampler = batch_sampler
@@ -439,6 +441,7 @@ def __init__(self, worker_pool, batchify_fn, batch_sampler, pin_memory=False,
         self._pin_device_id = pin_device_id
         self._dataset = dataset
         self._data_loader = data_loader
+        self._timeout = timeout
         # pre-fetch
         for _ in range(prefetch):
             self._push_next()
@@ -465,12 +468,29 @@ def __next__(self):
         assert self._rcvd_idx < self._sent_idx, "rcvd_idx must be smaller than sent_idx"
         assert self._rcvd_idx in self._data_buffer, "fatal error with _push_next, rcvd_idx missing"
         ret = self._data_buffer.pop(self._rcvd_idx)
-        batch = pickle.loads(ret.get()) if self._dataset is None else ret.get()
-        if self._pin_memory:
-            batch = _as_in_context(batch, context.cpu_pinned(self._pin_device_id))
-        batch = batch[0] if len(batch) == 1 else batch
-        self._rcvd_idx += 1
-        return batch
+        try:
+            if self._dataset is None:
+                batch = pickle.loads(ret.get(self._timeout))
+            else:
+                batch = ret.get(self._timeout)
+            if self._pin_memory:
+                batch = _as_in_context(batch, context.cpu_pinned(self._pin_device_id))
+            batch = batch[0] if len(batch) == 1 else batch
+            self._rcvd_idx += 1
+            return batch
+        except multiprocessing.context.TimeoutError:
+            msg = '''Worker timed out after {} seconds. This might be caused by \n
+            - Slow transform. Please increase timeout to allow slower data loading in each worker.
+            '''.format(self._timeout)
+            if not isinstance(self._worker_pool, multiprocessing.pool.ThreadPool):
+                msg += '''- Insufficient shared_memory if `timeout` is large enough.
+            Please consider reduce `num_workers` or increase shared_memory in system.
+            '''
+            print(msg)
+            raise
+        except Exception:
+            self._worker_pool.terminate()
+            raise
 
     def next(self):
         return self.__next__()
@@ -537,16 +557,22 @@ def default_batchify_fn(data):
         If ``True``, use threading pool instead of multiprocessing pool. Using threadpool
         can avoid shared memory usage. If `DataLoader` is more IO bounded or GIL is not a killing
         problem, threadpool version may achieve better performance than multiprocessing.
-
+    timeout : int, default is 120
+        The timeout in seconds for each worker to fetch a batch data. Only modify this number
+        unless you are experiencing timeout and you know it's due to slow data loading.
+        Sometimes full `shared_memory` will cause all workers to hang and causes timeout. In these
+        cases please reduce `num_workers` or increase system `shared_memory` size instead.
     """
     def __init__(self, dataset, batch_size=None, shuffle=False, sampler=None,
                  last_batch=None, batch_sampler=None, batchify_fn=None,
                  num_workers=0, pin_memory=False, pin_device_id=0,
-                 prefetch=None, thread_pool=False):
+                 prefetch=None, thread_pool=False, timeout=120):
         self._dataset = dataset
         self._pin_memory = pin_memory
         self._pin_device_id = pin_device_id
         self._thread_pool = thread_pool
+        self._timeout = timeout
+        assert timeout > 0, "timeout must be positive, given {}".format(timeout)
 
         if batch_sampler is None:
             if batch_size is None:
@@ -577,9 +603,13 @@ def __init__(self, dataset, batch_size=None, shuffle=False, sampler=None,
                                                initializer=_thread_worker_initializer,
                                                initargs=(is_np_shape(), is_np_array()))
             else:
+                # set ignore keyboard interupt signal before forking processes
+                original_sigint_handler = signal.signal(signal.SIGINT, signal.SIG_IGN)
                 self._worker_pool = multiprocessing.Pool(
                     self._num_workers, initializer=_worker_initializer,
                     initargs=[self._dataset, is_np_shape(), is_np_array()])
+                # resume keyboard interupt signal in main process
+                signal.signal(signal.SIGINT, original_sigint_handler)
         if batchify_fn is None:
             if num_workers > 0:
                 self._batchify_fn = default_mp_batchify_fn
@@ -604,7 +634,7 @@ def same_process_iter():
                                 worker_fn=_thread_worker_fn if self._thread_pool else _worker_fn,
                                 prefetch=self._prefetch,
                                 dataset=self._dataset if self._thread_pool else None,
-                                data_loader=self)
+                                data_loader=self, timeout=self._timeout)
 
     def __len__(self):
         return len(self._batch_sampler)
diff --git a/tests/python/unittest/test_gluon_data.py b/tests/python/unittest/test_gluon_data.py
index 80bea2a511f2..f87228739c36 100644
--- a/tests/python/unittest/test_gluon_data.py
+++ b/tests/python/unittest/test_gluon_data.py
@@ -249,7 +249,10 @@ def test_multi_worker_forked_data_loader():
 @with_seed()
 def test_multi_worker_dataloader_release_pool():
     # will trigger too many open file if pool is not released properly
-    for _ in range(100):
+    if os.name == 'nt':
+        print('Skip for windows since spawn on windows is too expensive.')
+        return
+    for _ in range(10):
         A = np.random.rand(999, 2000)
         D = mx.gluon.data.DataLoader(A, batch_size=8, num_workers=8)
         the_iter = iter(D)

From 66c4207faec92b33550381da31ebe4e1dcda6c6c Mon Sep 17 00:00:00 2001
From: Xi Wang <xidulu@gmail.com>
Date: Thu, 19 Sep 2019 12:00:15 +0800
Subject: [PATCH 337/813] [Numpy] Numpy behavior normal distribution (#16109)

* normal implemented

* numpy behavior normal imlemented

* retrigger CI

* retrigger CI

* regrigger ci

* add normal parameter check

* add raise for normal

* remove dead code
---
 python/mxnet/ndarray/numpy/random.py       |  38 ++--
 python/mxnet/numpy/random.py               |  10 +-
 python/mxnet/symbol/numpy/random.py        |  39 ++--
 src/operator/numpy/random/dist_common.cc   |  43 ++++
 src/operator/numpy/random/dist_common.cu   |  43 ++++
 src/operator/numpy/random/dist_common.h    |   7 +
 src/operator/numpy/random/np_normal_op.cc  |  69 ++++++
 src/operator/numpy/random/np_normal_op.cu  |  35 ++++
 src/operator/numpy/random/np_normal_op.h   | 233 +++++++++++++++++++++
 src/operator/numpy/random/np_uniform_op.cc |   6 +
 tests/python/unittest/test_numpy_op.py     |   1 -
 11 files changed, 482 insertions(+), 42 deletions(-)
 create mode 100644 src/operator/numpy/random/dist_common.cc
 create mode 100644 src/operator/numpy/random/dist_common.cu
 create mode 100644 src/operator/numpy/random/np_normal_op.cc
 create mode 100644 src/operator/numpy/random/np_normal_op.cu
 create mode 100644 src/operator/numpy/random/np_normal_op.h

diff --git a/python/mxnet/ndarray/numpy/random.py b/python/mxnet/ndarray/numpy/random.py
index 9372beaf1e92..883c56e12393 100644
--- a/python/mxnet/ndarray/numpy/random.py
+++ b/python/mxnet/ndarray/numpy/random.py
@@ -21,7 +21,6 @@
 from ...context import current_context
 from . import _internal as _npi
 from ..ndarray import NDArray
-from ...base import numeric_types
 
 
 __all__ = ['randint', 'uniform', 'normal', "choice"]
@@ -145,7 +144,7 @@ def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
                             ctx=ctx, dtype=dtype, out=out)
 
 
-def normal(loc=0.0, scale=1.0, size=None, **kwargs):
+def normal(loc=0.0, scale=1.0, size=None, dtype=None, **kwargs):
     """Draw random samples from a normal (Gaussian) distribution.
 
     Samples are distributed according to a normal distribution parametrized
@@ -166,31 +165,36 @@ def normal(loc=0.0, scale=1.0, size=None, **kwargs):
         Data type of output samples. Default is 'float32'
     ctx : Context, optional
         Device context of output. Default is current context.
-    out : ``ndarray``, optional
-        Store output to an existing ``ndarray``.
 
     Returns
     -------
     out : ndarray
         Drawn samples from the parameterized normal distribution.
-
-    Notes
-    -----
-    This function currently does not support ``loc`` and ``scale`` as ndarrays.
     """
-    dtype = kwargs.pop('dtype', None)
+    from ...numpy import ndarray as np_ndarray
+    input_type = (isinstance(loc, np_ndarray), isinstance(scale, np_ndarray))
+    ctx = kwargs.pop('ctx', None)
+    out = kwargs.pop('out', None)
     if dtype is None:
         dtype = 'float32'
-    ctx = kwargs.pop('ctx', None)
     if ctx is None:
         ctx = current_context()
-    out = kwargs.pop('out', None)
-    if size is None and out is None:
-        size = ()
-    if (not isinstance(loc, numeric_types)) or (not isinstance(scale, numeric_types)):
-        raise NotImplementedError('np.random.normal only supports loc and scale of '
-                                  'numeric types for now')
-    return _npi.random_normal(loc, scale, shape=size, dtype=dtype, ctx=ctx, out=out, **kwargs)
+    if out is not None:
+        size = out.shape
+    if size == ():
+        size = None
+    if input_type == (True, True):
+        return _npi.normal(loc, scale, loc=None, scale=None, size=size,
+                           ctx=ctx, dtype=dtype, out=out)
+    elif input_type == (False, True):
+        return _npi.normal(scale, loc=loc, scale=None, size=size,
+                           ctx=ctx, dtype=dtype, out=out)
+    elif input_type == (True, False):
+        return _npi.normal(loc, loc=None, scale=scale, size=size,
+                           ctx=ctx, dtype=dtype, out=out)
+    else:
+        return _npi.normal(loc=loc, scale=scale, size=size,
+                           ctx=ctx, dtype=dtype, out=out)
 
 
 def multinomial(n, pvals, size=None):
diff --git a/python/mxnet/numpy/random.py b/python/mxnet/numpy/random.py
index aace767c8d55..f89b8cac9871 100644
--- a/python/mxnet/numpy/random.py
+++ b/python/mxnet/numpy/random.py
@@ -110,7 +110,7 @@ def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
     return _mx_nd_np.random.uniform(low, high, size=size, ctx=ctx, dtype=dtype, out=out)
 
 
-def normal(loc=0.0, scale=1.0, size=None, **kwargs):
+def normal(loc=0.0, scale=1.0, size=None, dtype=None, **kwargs):
     """Draw random samples from a normal (Gaussian) distribution.
 
     Samples are distributed according to a normal distribution parametrized
@@ -130,7 +130,7 @@ def normal(loc=0.0, scale=1.0, size=None, **kwargs):
     dtype : {'float16', 'float32', 'float64'}, optional
         Data type of output samples. Default is 'float32'
     ctx : Context, optional
-        Device context of output. Default is current context.
+        Device context of output, default is current context.
     out : ``ndarray``, optional
         Store output to an existing ``ndarray``.
 
@@ -138,12 +138,8 @@ def normal(loc=0.0, scale=1.0, size=None, **kwargs):
     -------
     out : ndarray
         Drawn samples from the parameterized normal distribution.
-
-    Notes
-    -----
-    This function currently does not support ``loc`` and ``scale`` as ndarrays.
     """
-    return _mx_nd_np.random.normal(loc, scale, size, **kwargs)
+    return _mx_nd_np.random.normal(loc, scale, size, dtype, **kwargs)
 
 
 def multinomial(n, pvals, size=None, **kwargs):
diff --git a/python/mxnet/symbol/numpy/random.py b/python/mxnet/symbol/numpy/random.py
index 523983bac20a..a5ccbc1d1995 100644
--- a/python/mxnet/symbol/numpy/random.py
+++ b/python/mxnet/symbol/numpy/random.py
@@ -20,7 +20,6 @@
 from __future__ import absolute_import
 from ...context import current_context
 from . import _internal as _npi
-from ...base import numeric_types
 
 
 __all__ = ['randint', 'uniform', 'normal']
@@ -144,7 +143,7 @@ def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
                             ctx=ctx, dtype=dtype, out=out)
 
 
-def normal(loc=0.0, scale=1.0, size=None, **kwargs):
+def normal(loc=0.0, scale=1.0, size=None, dtype=None, **kwargs):
     """Draw random samples from a normal (Gaussian) distribution.
 
     Samples are distributed according to a normal distribution parametrized
@@ -162,34 +161,40 @@ def normal(loc=0.0, scale=1.0, size=None, **kwargs):
         samples are drawn. If size is `None` (default), a scalar tensor containing
         a single value is returned if loc and scale are both scalars.
     dtype : {'float16', 'float32', 'float64'}, optional
-        Data type of output samples. Default is 'float32'
+        Data type of output samples. Default is 'float32'.
     ctx : Context, optional
         Device context of output. Default is current context.
-    out : ``ndarray``, optional
-        Store output to an existing ``ndarray``.
 
     Returns
     -------
     out : _Symbol (symbol representing `mxnet.numpy.ndarray` in computational graphs)
         Drawn samples from the parameterized normal distribution.
-
-    Notes
-    -----
-    This function currently does not support ``loc`` and ``scale`` as `_Symbol`s.
     """
-    dtype = kwargs.pop('dtype', None)
+    from ._symbol import _Symbol as np_symbol
+    input_type = (isinstance(loc, np_symbol), isinstance(scale, np_symbol))
+    ctx = kwargs.pop('ctx', None)
+    out = kwargs.pop('out', None)
     if dtype is None:
         dtype = 'float32'
-    ctx = kwargs.pop('ctx', None)
     if ctx is None:
         ctx = current_context()
     out = kwargs.pop('out', None)
-    if size is None and out is None:
-        size = ()
-    if (not isinstance(loc, numeric_types)) or (not isinstance(scale, numeric_types)):
-        raise NotImplementedError('np.random.normal only supports loc and scale of '
-                                  'numeric types for now')
-    return _npi.random_normal(loc, scale, shape=size, dtype=dtype, ctx=ctx, out=out, **kwargs)
+    if out is not None:
+        size = out.shape
+    if size == ():
+        size = None
+    if input_type == (True, True):
+        return _npi.normal(loc, scale, loc=None, scale=None, size=size,
+                           ctx=ctx, dtype=dtype, out=out)
+    elif input_type == (False, True):
+        return _npi.normal(scale, loc=loc, scale=None, size=size,
+                           ctx=ctx, dtype=dtype, out=out)
+    elif input_type == (True, False):
+        return _npi.normal(loc, loc=None, scale=scale, size=size,
+                           ctx=ctx, dtype=dtype, out=out)
+    else:
+        return _npi.normal(loc=loc, scale=scale, size=size,
+                           ctx=ctx, dtype=dtype, out=out)
 
 
 def choice(a, size=None, replace=True, p=None, **kwargs):
diff --git a/src/operator/numpy/random/dist_common.cc b/src/operator/numpy/random/dist_common.cc
new file mode 100644
index 000000000000..925565654a11
--- /dev/null
+++ b/src/operator/numpy/random/dist_common.cc
@@ -0,0 +1,43 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2015 by Contributors
+ * \file dist_common.cc
+ * \brief Function definition of common functions for distributions
+ * \with two parameters.
+ */
+
+#include "./dist_common.h"
+
+namespace mxnet {
+namespace op {
+
+template <>
+void _copy<cpu>(float *dst, float *src) {
+  *dst = *src;
+}
+
+template <>
+void _copy<cpu>(double *dst, double *src) {
+  *dst = *src;
+}
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/random/dist_common.cu b/src/operator/numpy/random/dist_common.cu
new file mode 100644
index 000000000000..7dde0123e099
--- /dev/null
+++ b/src/operator/numpy/random/dist_common.cu
@@ -0,0 +1,43 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2015 by Contributors
+ * \file dist_common.cuh
+ * \brief Function definition of common functions for distributions
+ * \with two parameters.
+ */
+
+#include "./dist_common.h"
+
+namespace mxnet {
+namespace op {
+
+template <>
+void _copy<gpu>(float *dst, float *src) {
+CUDA_CALL(cudaMemcpy(dst, src, sizeof(float), cudaMemcpyDeviceToHost));
+}
+
+template <>
+void _copy<gpu>(double *dst, double *src) {
+CUDA_CALL(cudaMemcpy(dst, src, sizeof(double), cudaMemcpyDeviceToHost));
+}
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/random/dist_common.h b/src/operator/numpy/random/dist_common.h
index 42d7507f7e74..a3c48f5b4d5c 100644
--- a/src/operator/numpy/random/dist_common.h
+++ b/src/operator/numpy/random/dist_common.h
@@ -41,6 +41,13 @@
 namespace mxnet {
 namespace op {
 
+template <typename xpu>
+void _copy(float *dst, float*src);
+
+template <typename xpu>
+void _copy(double *dst, double*src);
+
+
 inline int FillShape(const mxnet::TShape &lshape, const mxnet::TShape &rshape,
                      const mxnet::TShape &oshape, mxnet::TShape *new_lshape,
                      mxnet::TShape *new_rshape, mxnet::TShape *new_oshape) {
diff --git a/src/operator/numpy/random/np_normal_op.cc b/src/operator/numpy/random/np_normal_op.cc
new file mode 100644
index 000000000000..1a5df95ff9e8
--- /dev/null
+++ b/src/operator/numpy/random/np_normal_op.cc
@@ -0,0 +1,69 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_random_op.cc
+ * \brief Operator for numpy sampling from normal distributions.
+ */
+#include "./np_normal_op.h"
+
+namespace mxnet {
+namespace op {
+
+DMLC_REGISTER_PARAMETER(NumpyNormalParam);
+
+NNVM_REGISTER_OP(_npi_normal)
+.describe("Numpy behavior normal")
+.set_num_inputs(
+  [](const nnvm::NodeAttrs& attrs) {
+    const NumpyNormalParam& param = nnvm::get<NumpyNormalParam>(attrs.parsed);
+    int num_inputs = 2;
+    if (param.loc.has_value()) num_inputs -= 1;
+    if (param.scale.has_value()) num_inputs -= 1;
+    return num_inputs;
+  }
+)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    const NumpyNormalParam& param = nnvm::get<NumpyNormalParam>(attrs.parsed);
+    int num_inputs = 2;
+    if (param.loc.has_value()) num_inputs -= 1;
+    if (param.scale.has_value()) num_inputs -= 1;
+    if (num_inputs == 0) return std::vector<std::string>();
+    if (num_inputs == 1) return std::vector<std::string>{"input1"};
+    return std::vector<std::string>{"input1", "input2"};
+  })
+.set_attr_parser(ParamParser<NumpyNormalParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", TwoparamsDistOpShape<NumpyNormalParam>)
+.set_attr<nnvm::FInferType>("FInferType", NumpyNormalOpType)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const nnvm::NodeAttrs& attrs) {
+      return std::vector<ResourceRequest>{
+        ResourceRequest::kRandom, ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", NumpyNormalForward<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.add_argument("input1", "NDArray-or-Symbol", "Source input")
+.add_argument("input2", "NDArray-or-Symbol", "Source input")
+.add_arguments(NumpyNormalParam::__FIELDS__());
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/random/np_normal_op.cu b/src/operator/numpy/random/np_normal_op.cu
new file mode 100644
index 000000000000..6cdf9f9c4eae
--- /dev/null
+++ b/src/operator/numpy/random/np_normal_op.cu
@@ -0,0 +1,35 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_normal_op.cu
+ * \brief Operator for numpy sampling from normal distributions
+ */
+
+#include "./np_normal_op.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(_npi_normal)
+    .set_attr<FCompute>("FCompute<gpu>", NumpyNormalForward<gpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/random/np_normal_op.h b/src/operator/numpy/random/np_normal_op.h
new file mode 100644
index 000000000000..678f0ec2fb47
--- /dev/null
+++ b/src/operator/numpy/random/np_normal_op.h
@@ -0,0 +1,233 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_normal_op.h
+ * \brief Operator for numpy sampling from normal distributions
+ */
+#ifndef MXNET_OPERATOR_NUMPY_RANDOM_NP_NORMAL_OP_H_
+#define MXNET_OPERATOR_NUMPY_RANDOM_NP_NORMAL_OP_H_
+
+#include <mxnet/operator_util.h>
+#include <algorithm>
+#include <string>
+#include <vector>
+#include "../../elemwise_op_common.h"
+#include "../../mshadow_op.h"
+#include "../../mxnet_op.h"
+#include "../../operator_common.h"
+#include "../../tensor/elemwise_binary_broadcast_op.h"
+#include "./dist_common.h"
+
+namespace mxnet {
+namespace op {
+
+struct NumpyNormalParam : public dmlc::Parameter<NumpyNormalParam> {
+  dmlc::optional<float> loc;
+  dmlc::optional<float> scale;
+  std::string ctx;
+  int dtype;
+  dmlc::optional<mxnet::Tuple<int>> size;
+  DMLC_DECLARE_PARAMETER(NumpyNormalParam) {
+    DMLC_DECLARE_FIELD(loc);
+    DMLC_DECLARE_FIELD(scale);
+    DMLC_DECLARE_FIELD(size)
+        .set_default(dmlc::optional<mxnet::Tuple<int>>())
+        .describe(
+            "Output shape. If the given shape is, "
+            "e.g., (m, n, k), then m * n * k samples are drawn. "
+            "Default is None, in which case a single value is returned.");
+    DMLC_DECLARE_FIELD(ctx).set_default("cpu").describe(
+        "Context of output, in format [cpu|gpu|cpu_pinned](n)."
+        " Only used for imperative calls.");
+    DMLC_DECLARE_FIELD(dtype)
+        .add_enum("float32", mshadow::kFloat32)
+        .add_enum("float64", mshadow::kFloat64)
+        .add_enum("float16", mshadow::kFloat16)
+        .set_default(mshadow::kFloat32)
+        .describe(
+            "DType of the output in case this can't be inferred. "
+            "Defaults to float32 if not defined (dtype=None).");
+  }
+};
+
+inline bool NumpyNormalOpType(const nnvm::NodeAttrs &attrs,
+                               std::vector<int> *in_attrs,
+                               std::vector<int> *out_attrs) {
+  const NumpyNormalParam &param = nnvm::get<NumpyNormalParam>(attrs.parsed);
+  int otype = param.dtype;
+  if (otype != -1) {
+    (*out_attrs)[0] = otype;
+  } else {
+    (*out_attrs)[0] = mshadow::kFloat32;
+  }
+  return true;
+}
+
+namespace mxnet_op {
+template <int ndim, typename IType, typename OType>
+struct normal_kernel {
+  MSHADOW_XINLINE static void Map(index_t i, const Shape<ndim> &lstride,
+                                  const Shape<ndim> &hstride,
+                                  const Shape<ndim> &oshape, IType *loc,
+                                  IType *scale, float *normals, OType *out) {
+    Shape<ndim> coord = unravel(i, oshape);
+    auto lidx = static_cast<index_t>(dot(coord, lstride));
+    auto hidx = static_cast<index_t>(dot(coord, hstride));
+    IType loc_value = loc[lidx];
+    IType scale_value = scale[hidx];
+    out[i] = loc_value + normals[i] * scale_value;
+  }
+};
+
+template <int ndim, typename IType, typename OType>
+struct normal_one_scalar_kernel {
+  MSHADOW_XINLINE static void Map(index_t i, int scalar_pos,
+                                  const Shape<ndim> &stride,
+                                  const Shape<ndim> &oshape, IType *array,
+                                  float scalar, float *normals, OType *out) {
+    Shape<ndim> coord = unravel(i, oshape);
+    auto idx = static_cast<index_t>(dot(coord, stride));
+    IType loc_value;
+    IType scale_value;
+    if (scalar_pos == 0) {
+      loc_value = scalar;
+      scale_value = array[idx];
+    } else {
+      loc_value = array[idx];
+      scale_value = scalar;
+    }
+    out[i] = loc_value + normals[i] * scale_value;
+  }
+};
+
+template <typename OType>
+struct normal_two_scalar_kernel {
+  MSHADOW_XINLINE static void Map(index_t i, float loc, float scale,
+                                  float *normals, OType *out) {
+    out[i] = loc + normals[i] * scale;
+  }
+};
+
+template <typename IType>
+struct check_legal_scale_kernel {
+  MSHADOW_XINLINE static void Map(index_t i, IType *scalar, float* flag) {
+    if (scalar[i] < 0) {
+      flag[0] = -1.0;
+    }
+  }
+};
+
+}  // namespace mxnet_op
+
+template <typename xpu>
+void NumpyNormalForward(const nnvm::NodeAttrs &attrs,
+                         const OpContext &ctx,
+                         const std::vector<TBlob> &inputs,
+                         const std::vector<OpReqType> &req,
+                         const std::vector<TBlob> &outputs) {
+  using namespace mshadow;
+  using namespace mxnet_op;
+  const NumpyNormalParam &param = nnvm::get<NumpyNormalParam>(attrs.parsed);
+  CHECK_EQ(outputs.size(), 1);
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+
+  // Generate base random number.
+  Random<xpu, float> *prnd = ctx.requested[0].get_random<xpu, float>(s);
+  index_t output_len = outputs[0].Size();
+  Tensor<xpu, 1, float> workspace =
+      ctx.requested[1].get_space_typed<xpu, 1, float>(Shape1(output_len + 1), s);
+  Tensor<xpu, 1, float> normal_tensor = workspace.Slice(0, output_len);
+  Tensor<xpu, 1, float> indicator_device = workspace.Slice(output_len, output_len + 1);
+  float indicator_host = 1.0;
+  float *indicator_device_ptr = indicator_device.dptr_;
+  prnd->SampleGaussian(&normal_tensor, 0.0, 1.0);
+  mxnet::TShape new_lshape, new_hshape, new_oshape;
+
+  // [scalar scalar] case
+  if (inputs.size() == 0U) {
+    CHECK_GE(param.scale.value(), 0.0) << "ValueError: scale < 0";
+    MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, OType, {
+      Kernel<normal_two_scalar_kernel<OType>, xpu>::Launch(
+          s, outputs[0].Size(), param.loc.value(), param.scale.value(),
+          normal_tensor.dptr_, outputs[0].dptr<OType>());
+    });
+  } else if (inputs.size() == 1U) {
+    // [scalar tensor], [tensor scalar] case
+    int ndim = FillShape(inputs[0].shape_, inputs[0].shape_, outputs[0].shape_,
+                         &new_lshape, &new_lshape, &new_oshape);
+    int scalar_pos;
+    float scalar_value;
+    if (param.loc.has_value()) {
+      scalar_pos = 0;
+      scalar_value = param.loc.value();
+      MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, IType, {
+        Kernel<check_legal_scale_kernel<IType>, xpu>::Launch(
+            s, inputs[0].Size(), inputs[0].dptr<IType>(), indicator_device_ptr);
+      });
+      _copy<xpu>(&indicator_host, indicator_device_ptr);
+      CHECK_GE(indicator_host, 0.0) << "ValueError: scale < 0";
+    } else {
+      scalar_pos = 1;
+      scalar_value = param.scale.value();
+      CHECK_GE(scalar_value, 0.0) << "ValueError: scale < 0";
+    }
+    MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, IType, {
+      MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, OType, {
+        BROADCAST_NDIM_SWITCH(ndim, NDim, {
+          Shape<NDim> oshape = new_oshape.get<NDim>();
+          Shape<NDim> stride = calc_stride(new_lshape.get<NDim>());
+          Kernel<normal_one_scalar_kernel<NDim, IType, OType>, xpu>::Launch(
+              s, outputs[0].Size(), scalar_pos, stride, oshape,
+              inputs[0].dptr<IType>(), scalar_value, normal_tensor.dptr_,
+              outputs[0].dptr<OType>());
+        });
+      });
+    });
+  } else if (inputs.size() == 2U) {
+    // [tensor tensor] case
+    MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, IType, {
+      Kernel<check_legal_scale_kernel<IType>, xpu>::Launch(
+          s, inputs[1].Size(), inputs[1].dptr<IType>(), indicator_device_ptr);
+    });
+    _copy<xpu>(&indicator_host, indicator_device_ptr);
+    CHECK_GE(indicator_host, 0.0) << "ValueError: scale < 0";
+    int ndim = FillShape(inputs[0].shape_, inputs[1].shape_, outputs[0].shape_,
+                         &new_lshape, &new_hshape, &new_oshape);
+    MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, IType, {
+      MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, OType, {
+        BROADCAST_NDIM_SWITCH(ndim, NDim, {
+          Shape<NDim> oshape = new_oshape.get<NDim>();
+          Shape<NDim> lstride = calc_stride(new_lshape.get<NDim>());
+          Shape<NDim> hstride = calc_stride(new_hshape.get<NDim>());
+          Kernel<normal_kernel<NDim, IType, OType>, xpu>::Launch(
+              s, outputs[0].Size(), lstride, hstride, oshape,
+              inputs[0].dptr<IType>(), inputs[1].dptr<IType>(),
+              normal_tensor.dptr_, outputs[0].dptr<OType>());
+        });
+      });
+    });
+  }
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_NUMPY_RANDOM_NP_NORMAL_OP_H_
diff --git a/src/operator/numpy/random/np_uniform_op.cc b/src/operator/numpy/random/np_uniform_op.cc
index 394626d07596..7307b7744d5e 100644
--- a/src/operator/numpy/random/np_uniform_op.cc
+++ b/src/operator/numpy/random/np_uniform_op.cc
@@ -43,6 +43,12 @@ NNVM_REGISTER_OP(_npi_uniform)
 .set_num_outputs(1)
 .set_attr<nnvm::FListInputNames>("FListInputNames",
   [](const NodeAttrs& attrs) {
+    const NumpyUniformParam& param = nnvm::get<NumpyUniformParam>(attrs.parsed);
+    int num_inputs = 2;
+    if (param.low.has_value()) num_inputs -= 1;
+    if (param.high.has_value()) num_inputs -= 1;
+    if (num_inputs == 0) return std::vector<std::string>();
+    if (num_inputs == 1) return std::vector<std::string>{"input1"};
     return std::vector<std::string>{"input1", "input2"};
   })
 .set_attr_parser(ParamParser<NumpyUniformParam>)
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 1656716b4a35..07bd2864cfb8 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -1579,7 +1579,6 @@ def test_np_random():
     shapes = [(), (1,), (2, 3), (4, 0, 5), 6, (7, 8), None]
     dtypes = ['float16', 'float32', 'float64']
     op_names = ['uniform', 'normal']
-    op_names = ['normal']
     for shape in shapes:
         for dtype in dtypes:
             for op_name in op_names:

From b3da7d2334a7d6fddd46c9c737a63c289198e816 Mon Sep 17 00:00:00 2001
From: Jake Lee <gstu1130@gmail.com>
Date: Wed, 18 Sep 2019 21:02:02 -0700
Subject: [PATCH 338/813] fix multinomial bug on gpu (#16204)

---
 .../numpy/random/np_multinomial_op.cu         | 13 +++
 src/operator/numpy/random/np_multinomial_op.h | 26 ++++--
 tests/python/unittest/test_numpy_ndarray.py   | 90 ++++++++++++-------
 3 files changed, 88 insertions(+), 41 deletions(-)

diff --git a/src/operator/numpy/random/np_multinomial_op.cu b/src/operator/numpy/random/np_multinomial_op.cu
index a80926024735..bf05ede55ac7 100644
--- a/src/operator/numpy/random/np_multinomial_op.cu
+++ b/src/operator/numpy/random/np_multinomial_op.cu
@@ -27,6 +27,19 @@
 namespace mxnet {
 namespace op {
 
+template<typename DType>
+void CheckPvalGPU(DType* input, int prob_length) {
+  std::vector<DType> pvals_(prob_length);
+  CUDA_CALL(cudaMemcpy(&pvals_[0], input, sizeof(DType) * prob_length,
+    cudaMemcpyDeviceToHost));
+  DType sum = DType(0.0);
+  for (int i = 0; i < prob_length; ++i) {
+    sum += pvals_[i];
+    CHECK(sum <= DType(1.0))
+      << "sum(pvals[:-1]) > 1.0";
+  }
+}
+
 NNVM_REGISTER_OP(_npi_multinomial)
 .set_attr<FCompute>("FCompute<gpu>", NumpyMultinomialForward<gpu>);
 
diff --git a/src/operator/numpy/random/np_multinomial_op.h b/src/operator/numpy/random/np_multinomial_op.h
index 7115f2761202..2a54fba9b1cc 100644
--- a/src/operator/numpy/random/np_multinomial_op.h
+++ b/src/operator/numpy/random/np_multinomial_op.h
@@ -99,6 +99,19 @@ inline bool NumpyMultinomialOpType(const nnvm::NodeAttrs& attrs,
   return true;
 }
 
+template<typename DType>
+void CheckPvalGPU(DType* input, int prob_length);
+
+template<typename DType>
+void CheckPval(DType* input, int prob_length) {
+  DType sum = DType(0.0);
+  for (int i = 0; i < prob_length; ++i) {
+    sum += input[i];
+    CHECK_LE(sum, 1.0)
+      << "sum(pvals[:-1]) > 1.0";
+  }
+}
+
 struct multinomial_kernel {
   template<typename DType>
   MSHADOW_XINLINE static void Map(int i,
@@ -172,14 +185,11 @@ void NumpyMultinomialForward(const nnvm::NodeAttrs& attrs,
       s, num_output, num_exp, prob_length, pvals_, temp_tensor.dptr_, outputs[0].dptr<int64_t>());
   } else {
     MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, DType, {
-      // check if sum of input(pvals) > 1.0
-      DType sum = DType(0);
-      DType* input = inputs[0].dptr<DType>();
-      for (int i = 0; i < prob_length; ++i) {
-        sum += input[i];
-        CHECK_LE(sum, 1.0)
-          << "sum(pvals[:-1]) > 1.0";
-      }
+       if (std::is_same<xpu, cpu>::value) {
+         CheckPval<DType>(inputs[0].dptr<DType>(), prob_length);
+       } else {
+         CheckPvalGPU<DType>(inputs[0].dptr<DType>(), prob_length);
+       }
       Kernel<multinomial_kernel, xpu>::Launch(
         s, num_output, num_exp, prob_length,
         inputs[0].dptr<DType>(), temp_tensor.dptr_, outputs[0].dptr<int64_t>());
diff --git a/tests/python/unittest/test_numpy_ndarray.py b/tests/python/unittest/test_numpy_ndarray.py
index b60316724808..923ee53fc400 100644
--- a/tests/python/unittest/test_numpy_ndarray.py
+++ b/tests/python/unittest/test_numpy_ndarray.py
@@ -821,47 +821,71 @@ def test_np_multinomial():
     pvals_list = [[0.0, 0.1, 0.2, 0.3, 0.4], [0.4, 0.3, 0.2, 0.1, 0.0]]
     sizes = [None, (), (3,), (2, 5, 7), (4, 9)]
     experiements = 10000
-    for have_size in [False, True]:
-        for pvals in pvals_list:
-            if have_size:
-                for size in sizes:
-                    freq = mx.np.random.multinomial(experiements, pvals, size=size).asnumpy() / _np.float32(experiements)
-                    # for those cases that didn't need reshape
-                    if size in [None, ()]:
-                        mx.test_utils.assert_almost_equal(freq, pvals, rtol=0.20, atol=1e-1)
+    for pvals_mx_np_array in [False, True]:
+        for have_size in [False, True]:
+            for pvals in pvals_list:
+                if pvals_mx_np_array:
+                    pvals = mx.np.array(pvals)
+                if have_size:
+                    for size in sizes:
+                        freq = mx.np.random.multinomial(experiements, pvals, size=size).asnumpy() / _np.float32(experiements)
+                        # for those cases that didn't need reshape
+                        if size in [None, ()]:
+                            if type(pvals) == np.ndarray:
+                                mx.test_utils.assert_almost_equal(freq, pvals.asnumpy(), rtol=0.20, atol=1e-1)
+                            else:
+                                mx.test_utils.assert_almost_equal(freq, pvals, rtol=0.20, atol=1e-1)
+                        else:
+                            # check the shape
+                            assert freq.shape == size + (len(pvals),), 'freq.shape={}, size + (len(pvals))={}'.format(freq.shape, size + (len(pvals)))
+                            freq = freq.reshape((-1, len(pvals)))
+                            # check the value for each row
+                            for i in range(freq.shape[0]):
+                                if type(pvals) == np.ndarray:
+                                    mx.test_utils.assert_almost_equal(freq[i, :], pvals.asnumpy(), rtol=0.20, atol=1e-1)
+                                else:
+                                    mx.test_utils.assert_almost_equal(freq[i, :], pvals, rtol=0.20, atol=1e-1)
+                else:
+                    freq = mx.np.random.multinomial(experiements, pvals).asnumpy() / _np.float32(experiements)
+                    if type(pvals) == np.ndarray:
+                        mx.test_utils.assert_almost_equal(freq, pvals.asnumpy(), rtol=0.20, atol=1e-1)
                     else:
-                        # check the shape
-                        assert freq.shape == size + (len(pvals),), 'freq.shape={}, size + (len(pvals))={}'.format(freq.shape, size + (len(pvals)))
-                        freq = freq.reshape((-1, len(pvals)))
-                        # check the value for each row
-                        for i in range(freq.shape[0]):
-                            mx.test_utils.assert_almost_equal(freq[i, :], pvals, rtol=0.20, atol=1e-1)
-            else:
-                freq = mx.np.random.multinomial(experiements, pvals).asnumpy() / _np.float32(experiements)
-                mx.test_utils.assert_almost_equal(freq, pvals, rtol=0.20, atol=1e-1)
+                        mx.test_utils.assert_almost_equal(freq, pvals, rtol=0.20, atol=1e-1)
     # check the zero dimension
     sizes = [(0), (0, 2), (4, 0, 2), (3, 0, 1, 2, 0)]
-    for pvals in pvals_list:
-        for size in sizes:
-            freq = mx.np.random.multinomial(experiements, pvals, size=size).asnumpy()
-            assert freq.size == 0
+    for pvals_mx_np_array in [False, True]:
+        for pvals in pvals_list:
+            for size in sizes:
+                if pvals_mx_np_array:
+                    pvals = mx.np.array(pvals)
+                freq = mx.np.random.multinomial(experiements, pvals, size=size).asnumpy()
+                assert freq.size == 0
     # check [] as pvals
-    for pvals in [[], ()]:
-        freq = mx.np.random.multinomial(experiements, pvals).asnumpy()
-        assert freq.size == 0
-        for size in sizes:
-            freq = mx.np.random.multinomial(experiements, pvals, size=size).asnumpy()
+    for pvals_mx_np_array in [False, True]:
+        for pvals in [[], ()]:
+            if pvals_mx_np_array:
+                pvals = mx.np.array(pvals)
+            freq = mx.np.random.multinomial(experiements, pvals).asnumpy()
             assert freq.size == 0
+            for size in sizes:
+                freq = mx.np.random.multinomial(experiements, pvals, size=size).asnumpy()
+                assert freq.size == 0
     # test small experiment for github issue
     # https://github.com/apache/incubator-mxnet/issues/15383
     small_exp, total_exp = 20, 10000
-    for pvals in pvals_list:
-        x = np.random.multinomial(small_exp, pvals)
-        for i in range(total_exp // small_exp):
-            x = x + np.random.multinomial(20, pvals)
-    freq = (x.asnumpy() / _np.float32(total_exp)).reshape((-1, len(pvals)))
-    for i in range(freq.shape[0]):
-        mx.test_utils.assert_almost_equal(freq[i, :], pvals, rtol=0.20, atol=1e-1)
+    for pvals_mx_np_array in [False, True]:
+        for pvals in pvals_list:
+            if pvals_mx_np_array:
+                pvals = mx.np.array(pvals)
+            x = np.random.multinomial(small_exp, pvals)
+            for i in range(total_exp // small_exp):
+                x = x + np.random.multinomial(20, pvals)
+        freq = (x.asnumpy() / _np.float32(total_exp)).reshape((-1, len(pvals)))
+        for i in range(freq.shape[0]):
+            if type(pvals) == np.ndarray:
+                mx.test_utils.assert_almost_equal(freq[i, :], pvals.asnumpy(), rtol=0.20, atol=1e-1)
+            else:
+                mx.test_utils.assert_almost_equal(freq[i, :], pvals, rtol=0.20, atol=1e-1)
 
 
 if __name__ == '__main__':

From 5fe4d2ab61a25387415a6df30ec7f11f103bf041 Mon Sep 17 00:00:00 2001
From: Haibin Lin <linhaibin.eric@gmail.com>
Date: Wed, 18 Sep 2019 21:02:45 -0700
Subject: [PATCH 339/813] Update ndarray.py (#16205)

---
 python/mxnet/ndarray/ndarray.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/python/mxnet/ndarray/ndarray.py b/python/mxnet/ndarray/ndarray.py
index 59306e21e6f5..0b72865dc17a 100644
--- a/python/mxnet/ndarray/ndarray.py
+++ b/python/mxnet/ndarray/ndarray.py
@@ -3302,8 +3302,8 @@ def arange(start, stop=None, step=1.0, repeat=1, infer_range=None, ctx=None, dty
     repeat : int, optional
         Number of times to repeat each element. The default repeat count is 1.
     infer_range : boolean, optional
-        When set to True, infer the stop position from the start, step,
-        repeat, and output tensor size.
+        Infer the stop position from the start, step, repeat, and output tensor size.
+        Deprecated. Only False is supported.
     ctx : Context, optional
         Device context. Default context is the current default context.
     dtype : str or numpy.dtype, optional

From 5f9a68033f0cad7b777cc3988b819997c6f4c9eb Mon Sep 17 00:00:00 2001
From: Sheng Zha <szha@users.noreply.github.com>
Date: Wed, 18 Sep 2019 21:08:09 -0700
Subject: [PATCH 340/813] subscribe to build and CD changes (#16192)

---
 CODEOWNERS | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/CODEOWNERS b/CODEOWNERS
index 6bf30b90602d..f548c2ad30cc 100644
--- a/CODEOWNERS
+++ b/CODEOWNERS
@@ -41,19 +41,24 @@
 /src/             @pllarroy
 /plugin/          @pllarroy
 
-# CMake
+# Build system
 CMakeLists.txt    @szha @pllarroy
 /cmake/           @szha @pllarroy
+/make/            @szha
 
 # MXNet CI
 dev_menu.py         @pllarroy
 /ci/                @pllarroy @marcoabreu @aaronmarkham
+/ci/publish/        @szha
 /docker/            @marcoabreu
 /tests/ci_build/    @marcoabreu
 Jenkinsfile         @marcoabreu
 .travis.yml         @marcoabreu
 appveyor.yml        @marcoabreu
 
+# MXNet CD
+/cd/                @szha
+
 # Build logic
 Makefile          @szha
 prepare_mkl.sh    @szha

From a37a76c42bf790f288a06adfc81e2df325fe0c24 Mon Sep 17 00:00:00 2001
From: Zhennan Qin <zhennan.qin@intel.com>
Date: Thu, 19 Sep 2019 03:22:51 -0500
Subject: [PATCH 341/813] Float64 fallback for mkldnn subgraph and rnn op
 (#15853)

* Fix mkldnn subgraph with float64

* Fix ci

Change-Id: I0bb4e8d7a0a534aa661601887cc633cb9c4fcadf

* Fix test

Change-Id: I96c529abe7adb6def90a22f03b3432263ef12fda

* Update dmlc-core

Change-Id: I472fb7bbffc16ed8c36494ab49838b08c59b2f12

* pin to offical dmlc

Change-Id: I5a27dc83b892bf8fcb34bb089449d1d3b6e9beed

* Fix GPU CI

Change-Id: I285947e01bdb0651c2c7830ed4eb76931a09b754

* Fix GPU CI

Change-Id: I6f23b51d6bda44f6ae18766ebe390118740bb9c7
---
 src/operator/nn/mkldnn/mkldnn_base-inl.h      |  17 ++-
 src/operator/nn/mkldnn/mkldnn_base.cc         |  23 +++-
 src/operator/rnn.cc                           |  16 ++-
 src/operator/subgraph/build_subgraph.cc       |  33 +++++-
 .../subgraph/mkldnn/mkldnn_conv_property.h    |  10 +-
 .../mkldnn/mkldnn_fc_post_quantize_property.h |   2 +-
 .../subgraph/mkldnn/mkldnn_fc_property.h      |   8 +-
 ...kldnn_post_quantize_align_scale_property.h |   2 +-
 .../mkldnn/mkldnn_post_quantize_property.h    |   8 +-
 .../mkldnn/mkldnn_subgraph_base-inl.h         |  42 +++++++
 src/operator/subgraph/subgraph_property.h     | 110 ++++++++++++------
 src/operator/tensor/matrix_op-inl.h           |   2 +-
 tests/python/mkl/test_subgraph.py             |  21 ++++
 tests/python/unittest/test_operator.py        |  34 ++++++
 14 files changed, 266 insertions(+), 62 deletions(-)
 create mode 100644 src/operator/subgraph/mkldnn/mkldnn_subgraph_base-inl.h

diff --git a/src/operator/nn/mkldnn/mkldnn_base-inl.h b/src/operator/nn/mkldnn/mkldnn_base-inl.h
index 5db9d6e5defc..34f4a0b0b062 100644
--- a/src/operator/nn/mkldnn/mkldnn_base-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_base-inl.h
@@ -47,17 +47,18 @@
 #define MXNET_OPERATOR_NN_MKLDNN_MKLDNN_BASE_INL_H_
 
 #if MXNET_USE_MKLDNN == 1
+#include <algorithm>
 #include <iterator>
+#include <memory>
 #include <string>
 #include <unordered_map>
-#include <vector>
 #include <utility>
-#include <algorithm>
-#include <memory>
+#include <vector>
 #include "mkldnn.hpp"
+#include "mxnet/graph_attr_types.h"
 #include "mxnet/ndarray.h"
-#include "mxnet/resource.h"
 #include "mxnet/op_attr_types.h"
+#include "mxnet/resource.h"
 using namespace mkldnn;
 namespace mxnet {
 
@@ -132,6 +133,11 @@ static inline bool SupportMKLDNN(int dtype, const mxnet::TShape &shape) {
   return dtype == mshadow::kFloat32 && (ndim == 1 || ndim == 2 || ndim == 4);
 }
 
+static inline bool SupportMKLDNNRNN(const NDArray &input) {
+  int ndim = input.shape().ndim();
+  return (input.dtype() == mshadow::kFloat32) && (ndim == 3);
+}
+
 static inline bool SupportMKLDNNQuantize(int dtype) {
   return dtype == mshadow::kFloat32 || dtype == mshadow::kInt8 ||
          dtype == mshadow::kUint8;
@@ -569,7 +575,8 @@ class MKLDNNMemory {
   }
 };
 
-void FallBackCompute(FCompute fn, const nnvm::NodeAttrs &attrs,
+template <typename Compute, typename AttrState>
+void FallBackCompute(Compute fn, const AttrState &attrs,
                      const OpContext &ctx,
                      const std::vector<NDArray> &inputs,
                      const std::vector<OpReqType> &req,
diff --git a/src/operator/nn/mkldnn/mkldnn_base.cc b/src/operator/nn/mkldnn/mkldnn_base.cc
index a13337b122c3..862947eb726a 100644
--- a/src/operator/nn/mkldnn/mkldnn_base.cc
+++ b/src/operator/nn/mkldnn/mkldnn_base.cc
@@ -420,7 +420,8 @@ mkldnn::memory::primitive_desc GetPrimitiveDesc(mkldnn::memory::primitive_desc p
   return mkldnn::memory::primitive_desc(data_md, pd.get_engine());
 }
 
-void FallBackCompute(FCompute fn, const nnvm::NodeAttrs &attrs,
+template <typename Compute, typename AttrState>
+void FallBackCompute(Compute fn, const AttrState &attrs_states,
                      const OpContext &ctx,
                      const std::vector<NDArray> &inputs,
                      const std::vector<OpReqType> &req,
@@ -461,7 +462,7 @@ void FallBackCompute(FCompute fn, const nnvm::NodeAttrs &attrs,
     out_blobs[i] = output.data();
   }
 
-  fn(attrs, ctx, in_blobs, req, out_blobs);
+  fn(attrs_states, ctx, in_blobs, req, out_blobs);
   for (size_t i = 0; i < out_blobs.size(); i++) {
     if (req[i] == kAddTo && outputs[i].IsMKLDNNData())
       mxnet::common::CastNonDefaultStorage(temp_src, temp_dst, ctx, false);
@@ -518,6 +519,24 @@ static bool SimilarArray(const mxnet::NDArray &arr1, const mxnet::NDArray &arr2,
   return success.load();
 }
 
+template void FallBackCompute(void (*)(nnvm::NodeAttrs const &, OpContext const &,
+                                       std::vector<TBlob, std::allocator<TBlob> > const &,
+                                       std::vector<OpReqType, std::allocator<OpReqType> > const &,
+                                       std::vector<TBlob, std::allocator<TBlob> > const &),
+                              nnvm::NodeAttrs const &, OpContext const &,
+                              std::vector<NDArray, std::allocator<NDArray> > const &,
+                              std::vector<OpReqType, std::allocator<OpReqType> > const &,
+                              std::vector<NDArray, std::allocator<NDArray> > const &);
+
+template void FallBackCompute(void (*)(OpStatePtr const &, OpContext const &,
+                                       std::vector<TBlob, std::allocator<TBlob> > const &,
+                                       std::vector<OpReqType, std::allocator<OpReqType> > const &,
+                                       std::vector<TBlob, std::allocator<TBlob> > const &),
+                              OpStatePtr const &, OpContext const &,
+                              std::vector<NDArray, std::allocator<NDArray> > const &,
+                              std::vector<OpReqType, std::allocator<OpReqType> > const &,
+                              std::vector<NDArray, std::allocator<NDArray> > const &);
+
 void OpCheck::Init(const std::vector<mxnet::NDArray> &inputs_,
                    const std::vector<mxnet::NDArray> &outputs_) {
   auto ctx = inputs_[0].ctx();
diff --git a/src/operator/rnn.cc b/src/operator/rnn.cc
index fd016d6819b9..b2ac2f0cb615 100644
--- a/src/operator/rnn.cc
+++ b/src/operator/rnn.cc
@@ -633,6 +633,20 @@ static void RNNStatefulComputeCPU(const OpStatePtr& state_ptr,
     });
   });
 }
+
+static void RNNStatefulComputeExCPU(const OpStatePtr& state_ptr, const OpContext& ctx,
+                                    const std::vector<NDArray>& inputs,
+                                    const std::vector<OpReqType>& req,
+                                    const std::vector<NDArray>& outputs) {
+  if (SupportMKLDNNRNN(inputs[0])) {
+    RNNStatefulComputeCPU(state_ptr, ctx, inputs, req, outputs);
+    return;
+  }
+  int use_mkldnn_rnn = dmlc::GetEnv("MXNET_USE_MKLDNN_RNN", 1);
+  dmlc::SetEnv("MXNET_USE_MKLDNN_RNN", 0);
+  FallBackCompute(RNNStatefulCompute<cpu>, state_ptr, ctx, inputs, req, outputs);
+  dmlc::SetEnv("MXNET_USE_MKLDNN_RNN", use_mkldnn_rnn);
+}
 #endif
 
 NNVM_REGISTER_OP(RNN)
@@ -719,7 +733,7 @@ The definition of GRU here is slightly different from paper but compatible with
 .set_attr<FStatefulCompute>("FStatefulCompute<cpu>", RNNStatefulCompute<cpu>)
 #if MXNET_USE_MKLDNN == 1
 .set_attr<bool>("TIsMKLDNN", true)
-.set_attr<FStatefulComputeEx>("FStatefulComputeEx<cpu>", RNNStatefulComputeCPU)
+.set_attr<FStatefulComputeEx>("FStatefulComputeEx<cpu>", RNNStatefulComputeExCPU)
 #endif
 .set_attr<nnvm::FGradient>("FGradient", RNNGrad{"_backward_RNN"})
 .set_attr<FResourceRequestEx>("FResourceRequestEx", RNNResourceEx)
diff --git a/src/operator/subgraph/build_subgraph.cc b/src/operator/subgraph/build_subgraph.cc
index 8a33115feb8b..d43647ac83b9 100644
--- a/src/operator/subgraph/build_subgraph.cc
+++ b/src/operator/subgraph/build_subgraph.cc
@@ -24,7 +24,6 @@
  */
 #include <nnvm/graph.h>
 #include <nnvm/pass.h>
-#include <mxnet/op_attr_types.h>
 #include <unordered_set>
 #include <stack>
 #include <queue>
@@ -105,6 +104,28 @@ void ResetNodeLabels(const nnvm::Graph& g,
   subgraph_nodes->clear();
 }
 
+/*
+ * \brief Prepare NodeAttr for node. NodeAttr will be used in SubgraphSelectorV2.
+ */
+static const std::shared_ptr<NodeAttr> PrepareNodeAttr(const nnvm::Graph& g,
+                                                       const BiDirectedNode& node) {
+  const auto& indexed_graph = g.indexed_graph();
+  if (g.HasAttr("dtype") && g.HasAttr("shape") && g.HasAttr("dispatch_mode")) {
+    const auto& vdtype = g.GetAttr<nnvm::DTypeVector>("dtype");
+    const auto& vshape = g.GetAttr<mxnet::ShapeVector>("shape");
+    const auto& dispatch_modes = g.GetAttr<mxnet::DispatchModeVector>("dispatch_mode");
+    auto ret = std::make_shared<NodeAttr>();
+    ret->dispatch_mode = dispatch_modes[indexed_graph.node_id(node.node)];
+    for (const auto& e : node.node->inputs) {
+      ret->ishape.emplace_back(vshape[indexed_graph.entry_id(e)]);
+      ret->itype.emplace_back(vdtype[indexed_graph.entry_id(e)]);
+    }
+    return ret;
+  } else {
+    return nullptr;
+  }
+}
+
 /*!
  * \brief This function traverses the nodes in a computation graph from a starting
  * node following the input edges and output edges, and marks all nodes that
@@ -153,7 +174,7 @@ bool LabelSubgraph(const nnvm::Graph& g, SubgraphSelectorV2Ptr subgraph_selector
       CHECK_LT(nid, simple_nodes.size());
       const bool select_input =
           (snode->label == -1) && (!excluded_nodes || !excluded_nodes->count(snode)) &&
-          subgraph_selector->SelectInput(*cur_node, *snode);
+          subgraph_selector->SelectInput(*cur_node, *snode, PrepareNodeAttr(g, *snode));
       if (select_input) {
         // e.node is a subgraph node
         snode->label = label;
@@ -170,7 +191,7 @@ bool LabelSubgraph(const nnvm::Graph& g, SubgraphSelectorV2Ptr subgraph_selector
       CHECK_LT(nid, simple_nodes.size());
       const bool select_output =
           (snode->label == -1) && (!excluded_nodes || !excluded_nodes->count(snode)) &&
-          subgraph_selector->SelectOutput(*cur_node, *snode);
+          subgraph_selector->SelectOutput(*cur_node, *snode, PrepareNodeAttr(g, *snode));
       if (select_output) {
         // it->first is a subgraph node
         snode->label = label;
@@ -325,14 +346,16 @@ void SelectSubgraphNodes(nnvm::Graph* g, SubgraphSelectorV2Ptr subgraph_selector
                          std::vector<SubgraphSelectorV2Ptr>* subgraph_selectors,
                          const BiDirectedNode* node, const size_t snid, size_t* subgraph_id) {
   const auto& indexed_graph = g->indexed_graph();
+
   auto node_cmp = [&] (const BiDirectedNode* node1, const BiDirectedNode* node2) {
     return indexed_graph.node_id(node1->node) < indexed_graph.node_id(node2->node);
   };
-  if (simple_nodes[snid]->label == -1 && subgraph_selector->Select(*node)) {
+  if ((simple_nodes[snid]->label == -1) &&
+      subgraph_selector->Select(*node, PrepareNodeAttr(*g, *node))) {
     // pre-select nodes that can be grouped in a subgraph
     std::vector<BiDirectedNode*> preselected_nodes;
     PreSelectSubgraphNodes(*g, subgraph_selector, *subgraph_id, snid, simple_nodes,
-                           &preselected_nodes);
+                            &preselected_nodes);
 
     // filter out unqualified pre-selected nodes
     std::vector<BiDirectedNode*> filtered_nodes = subgraph_selector->Filter(preselected_nodes);
diff --git a/src/operator/subgraph/mkldnn/mkldnn_conv_property.h b/src/operator/subgraph/mkldnn/mkldnn_conv_property.h
index 42ea9ea67fcf..40b3f7c1d010 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_conv_property.h
+++ b/src/operator/subgraph/mkldnn/mkldnn_conv_property.h
@@ -28,7 +28,7 @@
 #include "../../nn/mkldnn/mkldnn_ops-inl.h"
 #include "../../tensor/matrix_op-inl.h"
 #include "../common.h"
-#include "../subgraph_property.h"
+#include "mkldnn_subgraph_base-inl.h"
 
 namespace mxnet {
 namespace op {
@@ -61,15 +61,15 @@ class SgMKLDNNConvSelector : public SubgraphSelector {
         disable_conv_sum_(dis_conv_sum),
         quantize_(quantize) {}
 
-  bool Select(const nnvm::Node &n) override {
+  bool Select(const nnvm::Node& n, const std::shared_ptr<NodeAttr>& node_attr) override {
     if (n.op() && n.op()->name == "Convolution") {
       const auto &param = nnvm::get<ConvolutionParam>(n.attrs.parsed);
-      if (param.kernel.ndim() == 2) {
+      if (param.kernel.ndim() == 2 && SupportMKLDNNAttr(node_attr)) {
         status_ = disable_all_ ? kSuccess : kStart;
         matched_list_.clear();
         matched_list_.push_back(&n);
         return true;
-      }
+        }
     }
     return false;
   }
@@ -161,7 +161,7 @@ class SgMKLDNNConvSelector : public SubgraphSelector {
     CHECK_GE(matched_list_.size(), 1);
     auto new_selector = SgMKLDNNConvSelector(disable_all_, disable_conv_bn_, disable_conv_act_,
                                              disable_conv_sum_, quantize_);
-    new_selector.Select(*matched_list_[0]);
+    new_selector.Select(*matched_list_[0], nullptr);
     *this = new_selector;
   }
 };
diff --git a/src/operator/subgraph/mkldnn/mkldnn_fc_post_quantize_property.h b/src/operator/subgraph/mkldnn/mkldnn_fc_post_quantize_property.h
index 8b5c08802986..f4f252bc92e9 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_fc_post_quantize_property.h
+++ b/src/operator/subgraph/mkldnn/mkldnn_fc_post_quantize_property.h
@@ -33,7 +33,7 @@
 #include "../../nn/fully_connected-inl.h"
 #include "../../quantization/requantize-inl.h"
 #include "../common.h"
-#include "../subgraph_property.h"
+#include "mkldnn_subgraph_base-inl.h"
 
 namespace mxnet {
 namespace op {
diff --git a/src/operator/subgraph/mkldnn/mkldnn_fc_property.h b/src/operator/subgraph/mkldnn/mkldnn_fc_property.h
index 17d30d272ad1..6dcd114d9ec4 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_fc_property.h
+++ b/src/operator/subgraph/mkldnn/mkldnn_fc_property.h
@@ -32,7 +32,7 @@
 #include <vector>
 #include "../common.h"
 #include "../../tensor/matrix_op-inl.h"
-#include "../subgraph_property.h"
+#include "mkldnn_subgraph_base-inl.h"
 #include "mkldnn_fc-inl.h"
 
 namespace mxnet {
@@ -58,8 +58,8 @@ class SgMKLDNNFCSelector : public SubgraphSelector {
       disable_fc_eltwise_(dis_fc_eltwise),
       quantized_(quantized) {}
 
-  bool Select(const nnvm::Node &n) override {
-    if (n.op() == Op::Get("FullyConnected")) {
+  bool Select(const nnvm::Node &n, const std::shared_ptr<NodeAttr>& node_attr) override {
+    if (n.op() == Op::Get("FullyConnected") && SupportMKLDNNAttr(node_attr)) {
       status_ = disable_fc_eltwise_ ? kSuccess : kStart;
       matched_list_.clear();
       matched_list_.push_back(&n);
@@ -150,7 +150,7 @@ class SgMKLDNNFCSelector : public SubgraphSelector {
   void Reset() override {
     CHECK_GE(matched_list_.size(), 1);
     auto new_selector = SgMKLDNNFCSelector(disable_fc_eltwise_, quantized_);
-    new_selector.Select(*matched_list_[0]);
+    new_selector.Select(*matched_list_[0], nullptr);
     *this = new_selector;
   }
 };
diff --git a/src/operator/subgraph/mkldnn/mkldnn_post_quantize_align_scale_property.h b/src/operator/subgraph/mkldnn/mkldnn_post_quantize_align_scale_property.h
index 5c5037e7a116..c05c2a8e4a6a 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_post_quantize_align_scale_property.h
+++ b/src/operator/subgraph/mkldnn/mkldnn_post_quantize_align_scale_property.h
@@ -24,7 +24,7 @@
 #include <string>
 #include <vector>
 #include "../common.h"
-#include "../subgraph_property.h"
+#include "mkldnn_subgraph_base-inl.h"
 
 namespace mxnet {
 namespace op {
diff --git a/src/operator/subgraph/mkldnn/mkldnn_post_quantize_property.h b/src/operator/subgraph/mkldnn/mkldnn_post_quantize_property.h
index e78b8d1bfa42..38b08968d8a5 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_post_quantize_property.h
+++ b/src/operator/subgraph/mkldnn/mkldnn_post_quantize_property.h
@@ -20,14 +20,14 @@
 #define MXNET_OPERATOR_SUBGRAPH_MKLDNN_MKLDNN_POST_QUANTIZE_PROPERTY_H_
 #if MXNET_USE_MKLDNN == 1
 
+#include <set>
 #include <string>
 #include <vector>
-#include <set>
-#include "../common.h"
-#include "../subgraph_property.h"
 #include "../../nn/mkldnn/mkldnn_convolution-inl.h"
-#include "mkldnn_conv-inl.h"
 #include "../../quantization/requantize-inl.h"
+#include "../common.h"
+#include "mkldnn_conv-inl.h"
+#include "mkldnn_subgraph_base-inl.h"
 
 namespace mxnet {
 namespace op {
diff --git a/src/operator/subgraph/mkldnn/mkldnn_subgraph_base-inl.h b/src/operator/subgraph/mkldnn/mkldnn_subgraph_base-inl.h
new file mode 100644
index 000000000000..4c8a7ab285b3
--- /dev/null
+++ b/src/operator/subgraph/mkldnn/mkldnn_subgraph_base-inl.h
@@ -0,0 +1,42 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+#ifndef MXNET_OPERATOR_SUBGRAPH_MKLDNN_MKLDNN_SUBGRAPH_BASE_INL_H_
+#define MXNET_OPERATOR_SUBGRAPH_MKLDNN_MKLDNN_SUBGRAPH_BASE_INL_H_
+#if MXNET_USE_MKLDNN == 1
+
+#include "../subgraph_property.h"
+
+namespace mxnet {
+namespace op {
+
+static inline bool SupportMKLDNNAttr(const std::shared_ptr<NodeAttr>& node_attr) {
+  if (node_attr) {
+    int ndim = node_attr->ishape[0].ndim();
+    return (node_attr->dispatch_mode == DispatchMode::kFComputeEx) &&
+           (node_attr->itype[0] == mshadow::kFloat32) && (ndim == 1 || ndim == 2 || ndim == 4);
+  } else {
+    return true;
+  }
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_USE_MKLDNN == 1
+#endif  // MXNET_OPERATOR_SUBGRAPH_MKLDNN_MKLDNN_SUBGRAPH_BASE_INL_H_
diff --git a/src/operator/subgraph/subgraph_property.h b/src/operator/subgraph/subgraph_property.h
index fd1dc2827ffd..bbc8f4076899 100644
--- a/src/operator/subgraph/subgraph_property.h
+++ b/src/operator/subgraph/subgraph_property.h
@@ -20,9 +20,11 @@
 #ifndef MXNET_OPERATOR_SUBGRAPH_SUBGRAPH_PROPERTY_H_
 #define MXNET_OPERATOR_SUBGRAPH_SUBGRAPH_PROPERTY_H_
 
-#include <nnvm/node.h>
 #include <dmlc/base.h>
 #include <dmlc/thread_local.h>
+#include <mxnet/graph_attr_types.h>
+#include <mxnet/op_attr_types.h>
+#include <nnvm/node.h>
 #include <unordered_map>
 #include <vector>
 #include <string>
@@ -54,6 +56,12 @@ struct BiDirectedNode {
   std::unordered_map<nnvm::Node*, std::vector<size_t>> outputs;
 };  // struct BiDirectedNode
 
+struct NodeAttr {
+  DispatchMode dispatch_mode;
+  ShapeVector ishape;
+  std::vector<int> itype;
+};
+
 /*
  * This provides criteria for the graph partitioning algorithm to select
  * nodes to subgraphs.
@@ -81,21 +89,41 @@ class SubgraphSelector {
   /*!
    * \brief Determines if to search for other nodes to form a subgraph from the seed_node.
    */
-  virtual bool Select(const nnvm::Node &seed_node) = 0;
+  virtual bool Select(const nnvm::Node& seed_node) {
+    LOG(FATAL) << "No Select is implemented.";
+    return false;
+  }
+  virtual bool Select(const nnvm::Node& seed_node, const std::shared_ptr<NodeAttr>& node_attr) {
+    return Select(seed_node);
+  }
   /*!
    * \brief Determines if to select input_node when traverse to the cur_node.
    * \param cur_node the node for determining whether its input_node should be selected
    * \param input_node the input node of the cur_node
    * \return true if input_node is selected
    */
-  virtual bool SelectInput(const nnvm::Node &cur_node, const nnvm::Node &input_node) = 0;
+  virtual bool SelectInput(const nnvm::Node& cur_node, const nnvm::Node& input_node) {
+    LOG(FATAL) << "No SelectInput is implemented.";
+    return false;
+  }
+  virtual bool SelectInput(const nnvm::Node& cur_node, const nnvm::Node& input_node,
+                           const std::shared_ptr<NodeAttr>& input_node_attr) {
+    return SelectInput(cur_node, input_node);
+  }
   /*!
    * \brief Determines if to select output_node when traverse to the cur_node.
    * \param cur_node the node for determining whether its output_node should be selected
    * \param output_node the output node of the cur_node
    * \return true if output_node is selected
    */
-  virtual bool SelectOutput(const nnvm::Node &cur_node, const nnvm::Node &output_node) = 0;
+  virtual bool SelectOutput(const nnvm::Node& cur_node, const nnvm::Node& output_node) {
+    LOG(FATAL) << "No SelectOutput is implemented.";
+    return false;
+  }
+  virtual bool SelectOutput(const nnvm::Node& cur_node, const nnvm::Node& output_node,
+                            const std::shared_ptr<NodeAttr>& output_node_attr) {
+    return SelectOutput(cur_node, output_node);
+  }
   /*!
    * \brief Post processes pre-selected subgraph nodes. Return a list of nodes that
    *        users want to keep in subgraph(s).
@@ -120,31 +148,48 @@ class SubgraphSelectorV2 {
   /*!
    * \brief Determines if to search for other nodes to form a subgraph from the seed_node.
    */
-  virtual bool Select(const BiDirectedNode& seed_node) = 0;
+  virtual bool Select(const BiDirectedNode& seed_node) {
+    LOG(FATAL) << "No Select is implemented.";
+    return false;
+  }
+  virtual bool Select(const BiDirectedNode& seed_node, const std::shared_ptr<NodeAttr>& node_attr) {
+    return Select(seed_node);
+  }
   /*!
    * \brief Determines if to select input_node when traverse to the cur_node.
    * \param cur_node the node for determining whether its input_node should be selected
    * \param input_node the input node of the cur_node
    * \return true if input_node is selected
    */
-  virtual bool SelectInput(const BiDirectedNode& cur_node,
-                           const BiDirectedNode& input_node) = 0;
+  virtual bool SelectInput(const BiDirectedNode& cur_node, const BiDirectedNode& input_node) {
+    LOG(FATAL) << "No SelectInput is implemented.";
+    return false;
+  }
+  virtual bool SelectInput(const BiDirectedNode& cur_node, const BiDirectedNode& input_node,
+                           const std::shared_ptr<NodeAttr>& input_node_attr) {
+    return SelectInput(cur_node, input_node);
+  }
   /*!
    * \brief Determines if to select output_node when traverse to the cur_node.
    * \param cur_node the node for determining whether its output_node should be selected
    * \param output_node the output node of the cur_node
    * \return true if output_node is selected
    */
-  virtual bool SelectOutput(const BiDirectedNode& cur_node,
-                            const BiDirectedNode& output_node) = 0;
+  virtual bool SelectOutput(const BiDirectedNode& cur_node, const BiDirectedNode& output_node) {
+    LOG(FATAL) << "No SelectOutput is implemented.";
+    return false;
+  }
+  virtual bool SelectOutput(const BiDirectedNode& cur_node, const BiDirectedNode& output_node,
+                            const std::shared_ptr<NodeAttr>& output_node_attr) {
+    return SelectOutput(cur_node, output_node);
+  }
   /*!
    * \brief Post processes pre-selected subgraph nodes. Return a list of nodes that
    *        users want to keep in subgraph(s).
    * \param candidates re-selected subgraph nodes to filt
    * \return a list of nodes to keep
    */
-  virtual std::vector<BiDirectedNode*> Filter(
-      const std::vector<BiDirectedNode*>& candidates) {
+  virtual std::vector<BiDirectedNode*> Filter(const std::vector<BiDirectedNode*>& candidates) {
     return candidates;
   }
 
@@ -163,22 +208,22 @@ class SubgraphSelectorV2Bridge : public SubgraphSelectorV2 {
 
   virtual ~SubgraphSelectorV2Bridge() {}
 
-  bool Select(const BiDirectedNode& seed_node) override {
-    return ss_ptr_->Select(*seed_node.node);
+  bool Select(const BiDirectedNode& seed_node,
+              const std::shared_ptr<NodeAttr>& node_attr) override {
+    return ss_ptr_->Select(*seed_node.node, node_attr);
   }
 
-  bool SelectInput(const BiDirectedNode& cur_node,
-                   const BiDirectedNode& input_node) override {
-    return ss_ptr_->SelectInput(*cur_node.node, *input_node.node);
+  bool SelectInput(const BiDirectedNode& cur_node, const BiDirectedNode& input_node,
+                   const std::shared_ptr<NodeAttr>& node_attr) override {
+    return ss_ptr_->SelectInput(*cur_node.node, *input_node.node, node_attr);
   }
 
-  bool SelectOutput(const BiDirectedNode& cur_node,
-                    const BiDirectedNode& output_node) override {
-    return ss_ptr_->SelectOutput(*cur_node.node, *output_node.node);
+  bool SelectOutput(const BiDirectedNode& cur_node, const BiDirectedNode& output_node,
+                    const std::shared_ptr<NodeAttr>& node_attr) override {
+    return ss_ptr_->SelectOutput(*cur_node.node, *output_node.node, node_attr);
   }
 
-  std::vector<BiDirectedNode*> Filter(
-      const std::vector<BiDirectedNode*>& candidates) override {
+  std::vector<BiDirectedNode*> Filter(const std::vector<BiDirectedNode*>& candidates) override {
     std::unordered_map<nnvm::Node*, BiDirectedNode*> node_2_snode_map;
     std::vector<nnvm::Node*> n_candidates;
     for (auto i : candidates) {
@@ -282,7 +327,7 @@ class SubgraphProperty {
    * \param subgraph_id subgraph id
    */
   virtual void AdjustSubgraphNode(const std::vector<nnvm::Node*>& subgraph_nodes,
-                                  const SubgraphSelectorV2Ptr &subgraph_selector,
+                                  const SubgraphSelectorV2Ptr& subgraph_selector,
                                   const int subgraph_id = 0) const {
     CHECK_EQ(GetPropertyType(), kAdjust);
     LOG(FATAL) << "Not implement AdjustSubgraphNode() for this subgraph property.";
@@ -315,7 +360,7 @@ class SubgraphProperty {
   /*!
    * \brief Set an attr with name in the attr map.
    */
-  template<typename T>
+  template <typename T>
   SubgraphProperty& SetAttr(const std::string& name, const T& value) {
     attrs_[name] = std::make_shared<dmlc::any>(value);
     return *this;
@@ -323,7 +368,7 @@ class SubgraphProperty {
   /*!
    * \brief Get the attr with the name.
    */
-  template<typename T>
+  template <typename T>
   const T& GetAttr(const std::string& name) const {
     auto it = attrs_.find(name);
     CHECK(it != attrs_.end()) << "Cannot find attribute " << name << " in SubgraphProperty";
@@ -361,7 +406,7 @@ class SubgraphPropertyEntry {
  public:
   explicit SubgraphPropertyEntry(std::shared_ptr<SubgraphProperty> entry) : entry_(entry) {}
 
-  template<typename T>
+  template <typename T>
   SubgraphPropertyEntry set_attr(const std::string& name, const T value) const {
     if (entry_) entry_->SetAttr<T>(name, value);
     return *this;
@@ -377,7 +422,7 @@ class SubgraphBackend {
   /*!
    * \brief Set an attr with name in the attr map.
    */
-  template<typename T>
+  template <typename T>
   SubgraphBackend& SetAttr(const std::string& name, const T& value) {
     attrs_[name] = std::make_shared<dmlc::any>(value);
     return *this;
@@ -385,7 +430,7 @@ class SubgraphBackend {
   /*!
    * \brief Get the attr with the name.
    */
-  template<typename T>
+  template <typename T>
   const T& GetAttr(const std::string& name) const {
     auto it = attrs_.find(name);
     CHECK(it != attrs_.end()) << "Cannot find attribute " << name << " in SubgraphProperty";
@@ -433,7 +478,7 @@ class SubgraphBackendEntry {
  public:
   explicit SubgraphBackendEntry(SubgraphBackendPtr entry) : entry_(entry) {}
 
-  template<typename T>
+  template <typename T>
   SubgraphBackendEntry set_attr(const std::string& name, const T value) const {
     entry_->SetAttr<T>(name, value);
     return *this;
@@ -454,8 +499,8 @@ class SubgraphBackendRegistry {
 
   SubgraphBackendPtr& GetSubgraphBackend(const std::string& name) {
     auto it = backend_map_.find(name);
-    CHECK(it != backend_map_.end()) << "SubgraphProperty " << name
-                                    << " is not found in SubgraphBackendRegistry";
+    CHECK(it != backend_map_.end())
+        << "SubgraphProperty " << name << " is not found in SubgraphBackendRegistry";
     return it->second;
   }
 
@@ -487,7 +532,7 @@ class SubgraphBackendRegistry {
 // This set is only used for the testing purpose.
 // key: property name, value: op name set
 typedef dmlc::ThreadLocalStore<std::unordered_map<std::string, std::unordered_set<std::string>>>
-  SubgraphPropertyOpNameSet;
+    SubgraphPropertyOpNameSet;
 
 #define DECLARE_PROPERTY_EX(NAME, SubgraphPropertyType, X) \
   static const DMLC_ATTRIBUTE_UNUSED auto __make_##SubgraphPropertyType##_##Name##_##X##__
@@ -498,8 +543,7 @@ typedef dmlc::ThreadLocalStore<std::unordered_map<std::string, std::unordered_se
   DECLARE_PROPERTY(Name, SubgraphPropertyType, __LINE__) =           \
       SubgraphBackendRegistry::Get()->__REGISTER_PROPERTY__(#Name, &SubgraphPropertyType::Create)
 
-#define DECLARE_BACKEND(Name) \
-  static const DMLC_ATTRIBUTE_UNUSED auto __make_##Name##__
+#define DECLARE_BACKEND(Name) static const DMLC_ATTRIBUTE_UNUSED auto __make_##Name##__
 
 #define MXNET_REGISTER_SUBGRAPH_BACKEND(Name) \
   DECLARE_BACKEND(Name) = SubgraphBackendRegistry::Get()->__REGISTER_BACKEND__(#Name)
diff --git a/src/operator/tensor/matrix_op-inl.h b/src/operator/tensor/matrix_op-inl.h
index 5fcb718eab71..5a2bd036c22b 100644
--- a/src/operator/tensor/matrix_op-inl.h
+++ b/src/operator/tensor/matrix_op-inl.h
@@ -2656,7 +2656,7 @@ inline bool SplitOpShape(const nnvm::NodeAttrs& attrs,
   mxnet::TShape ishape = in_attrs->at(split_enum::kData);
   if (!mxnet::ndim_is_known(dshape)) return false;
   if (param.axis >= 0) {
-    CHECK_LT(static_cast<size_t>(param.axis), dshape.ndim());
+    CHECK_LT(param.axis, dshape.ndim());
   } else {
     CHECK_LT(param.axis + dshape.ndim(), dshape.ndim());
   }
diff --git a/tests/python/mkl/test_subgraph.py b/tests/python/mkl/test_subgraph.py
index 00e2e824c6f5..8ba6d6183c86 100644
--- a/tests/python/mkl/test_subgraph.py
+++ b/tests/python/mkl/test_subgraph.py
@@ -844,6 +844,27 @@ def test_neg_fc_relu():
     syms, attrs, excluded_attrs = neg_fc_relu(no_bias, dshape, flatten)
     check_neg_fusion(syms, attrs, excluded_attrs, dshape, name='fc')
 
+def test_float64_fallback():
+    sym = mx.sym.FullyConnected(
+        mx.sym.Variable('in'),
+        mx.sym.Variable('w'),
+        mx.sym.Variable('b'),
+        num_hidden=2
+    )
+
+    dtype = 'float64'
+    ex = sym.bind(mx.cpu(),
+                  {
+        'in': mx.nd.array([[2, 3, 4]], dtype=dtype),
+        'w': mx.nd.array([[1, 2, 3], [4, 5, 6]], dtype=dtype),
+        'b': mx.nd.array([7, 8], dtype=dtype)
+    },
+        args_grad=None,
+        grad_req='write'
+    )
+    ex.forward()
+    ex.outputs[0].wait_to_read()
+
 if __name__ == "__main__":
   import nose
   nose.runmodule()
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 2e7cc3ce7504..77b8080422d9 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -310,6 +310,40 @@ def test_rnnrelu_dropout():
     out = exe.forward(is_train=True)
     out[0].wait_to_read()
 
+def test_RNN_float64():
+    if default_context().device_type == 'gpu':
+        return
+    sym = mx.sym.RNN(
+        mx.sym.Variable('in'),
+        mx.sym.Variable('par'),
+        mx.sym.Variable('s'),
+        state_size = (2),
+        num_layers = 1,
+        mode = 'rnn_tanh'
+    )
+
+    dtype = 'float64'
+    explicit_grad = {
+        'in': mx.nd.ones([2, 1, 2], dtype=dtype),
+        'par': mx.nd.ones([12], dtype=dtype),
+        's': mx.nd.ones([1, 1, 2], dtype=dtype)
+    }
+
+    args_grad = explicit_grad
+    grad_req = 'write'
+
+    ex = sym.bind(default_context(),
+        {
+            'in': mx.nd.ones([2, 1, 2], dtype=dtype),
+            'par': mx.nd.ones([12], dtype=dtype),
+            's': mx.nd.ones([1, 1, 2], dtype=dtype)
+        },
+        args_grad = args_grad,
+        grad_req = grad_req
+    )
+    ex.forward()
+    ex.outputs[0].wait_to_read()
+
 def np_softmax(x, axis=-1, temperature=1.0):
     x = x - np.max(x, axis=axis, keepdims=True)
     x = np.exp(x/temperature)

From f5d8fbfeec8e476872a02ac580afebc0e56c7147 Mon Sep 17 00:00:00 2001
From: Thomas Delteil <thomas.delteil1@gmail.com>
Date: Thu, 19 Sep 2019 11:25:19 -0700
Subject: [PATCH 342/813] New Website: Remove Old Content [2/3] (#15885)

* Adding new website files

Co-authored-by: Thomas Delteil <thomas.delteil1@gmail.com>
Co-authored-by: Aaron Markham <markhama@amazon.com>

* removing old content

Co-authored-by: Thomas Delteil <thomas.delteil1@gmail.com>
Co-authored-by: Aaron Markham <markhama@amazon.com>

* add license header

* fixing links and url + css

* fixing tutorials

* exclude python_docs folder when building prod site

This allows things to coexist until a following PR deletes this file.

* license header fixes

* exclude static site from license header check

* exclude static site and sphinx theme

* exclude static_site and cpp_docs from sphinx

* add licenses and excludes

* fix headers

* restore file for legacy builds

* empty commit to trigger CI

* update submodule to correct one

* delete website check

* add placeholder website check

* add placeholder website check, for reals this time

* trigger CI

* trigger PR
---
 ci/jenkins/Jenkinsfile_website                |    7 +-
 docs/Dockerfile                               |   63 -
 docs/Doxyfile                                 | 2370 -----------------
 docs/Makefile                                 |  214 --
 docs/README.md                                |  160 +-
 docs/_static/cn.svg                           |   20 -
 docs/_static/js/auto_module_index.js          |   46 -
 docs/_static/js/clipboard.js                  |  778 ------
 docs/_static/js/clipboard.min.js              |    7 -
 docs/_static/js/copycode.js                   |  110 -
 docs/_static/js/docversion.js                 |   47 -
 docs/_static/js/navbar.js                     |  117 -
 docs/_static/js/options.js                    |  118 -
 docs/_static/js/page.js                       |   93 -
 docs/_static/js/search.js                     |   39 -
 docs/_static/js/sidebar.js                    |  277 --
 docs/_static/mxnet-theme/footer.html          |   16 -
 docs/_static/mxnet-theme/index.html           |   73 -
 docs/_static/mxnet-theme/layout.html          |  201 --
 docs/_static/mxnet-theme/navbar.html          |  141 -
 docs/_static/mxnet-theme/theme.conf           |    2 -
 docs/_static/mxnet.css                        | 1474 ----------
 docs/_static/searchtools_custom.js            |  853 ------
 docs/_static/selectlang.js                    |   44 -
 .../tutorials/tensorrt/wavenet_optimized.png  |  Bin 275440 -> 0 bytes
 .../tensorrt/wavenet_unoptimized.png          |  Bin 614819 -> 0 bytes
 docs/_static/us.svg                           |  117 -
 docs/api/c++/index.md                         |   72 -
 docs/api/clojure/index.md                     |   61 -
 docs/api/clojure/kvstore.md                   |  103 -
 docs/api/clojure/module.md                    |  252 --
 docs/api/clojure/ndarray.md                   |  136 -
 docs/api/clojure/symbol.md                    |  149 --
 docs/api/clojure/symbol_in_pictures.md        |   94 -
 docs/api/index.md                             |   33 -
 docs/api/java/index.md                        |   32 -
 docs/api/julia/index.md                       |   34 -
 docs/api/perl/index.md                        |   91 -
 docs/api/perl/io.md                           |  129 -
 docs/api/perl/kvstore.md                      |  126 -
 docs/api/perl/module.md                       |   62 -
 docs/api/perl/ndarray.md                      |   59 -
 docs/api/perl/symbol.md                       |  144 -
 docs/api/python/autograd/autograd.md          |  176 --
 docs/api/python/callback/callback.md          |   44 -
 docs/api/python/contrib/contrib.md            |   29 -
 docs/api/python/contrib/onnx.md               |   78 -
 docs/api/python/contrib/svrg_optimization.md  |  103 -
 docs/api/python/contrib/text.md               |  418 ---
 docs/api/python/executor/executor.md          |   59 -
 docs/api/python/gluon/contrib.md              |  179 --
 docs/api/python/gluon/data.md                 |  155 --
 docs/api/python/gluon/gluon.md                |  189 --
 docs/api/python/gluon/loss.md                 |   59 -
 docs/api/python/gluon/model_zoo.md            |  257 --
 docs/api/python/gluon/nn.md                   |  114 -
 docs/api/python/gluon/rnn.md                  |   99 -
 docs/api/python/image/image.md                |  232 --
 docs/api/python/index.md                      |  219 --
 docs/api/python/io/io.md                      |  203 --
 docs/api/python/kvstore/kvstore.md            |  146 -
 docs/api/python/libinfo/libinfo.md            |  110 -
 docs/api/python/metric/metric.md              |   45 -
 docs/api/python/model.md                      |  175 --
 docs/api/python/module/module.md              |  227 --
 docs/api/python/ndarray/contrib.md            |  100 -
 docs/api/python/ndarray/linalg.md             |   75 -
 docs/api/python/ndarray/ndarray.md            |  745 ------
 docs/api/python/ndarray/random.md             |   77 -
 docs/api/python/ndarray/sparse.md             |  618 -----
 docs/api/python/optimization/contrib.md       |   69 -
 docs/api/python/optimization/optimization.md  |  183 --
 docs/api/python/profiler/profiler.md          |   82 -
 docs/api/python/rtc/rtc.md                    |   46 -
 docs/api/python/symbol/contrib.md             |   97 -
 docs/api/python/symbol/linalg.md              |   78 -
 docs/api/python/symbol/random.md              |   76 -
 docs/api/python/symbol/rnn.md                 |  378 ---
 docs/api/python/symbol/sparse.md              |  241 --
 docs/api/python/symbol/symbol.md              |  739 -----
 .../symbol_in_pictures/symbol_in_pictures.md  |   94 -
 docs/api/python/tools/test_utils.md           |   44 -
 docs/api/python/tools/visualization.md        |   44 -
 docs/api/r/index.md                           |   44 -
 docs/api/scala/index.md                       |   82 -
 docs/api/scala/infer.md                       |   59 -
 docs/api/scala/io.md                          |  182 --
 docs/api/scala/kvstore.md                     |  122 -
 docs/api/scala/model.md                       |  135 -
 docs/api/scala/module.md                      |  155 --
 docs/api/scala/ndarray.md                     |  174 --
 docs/api/scala/symbol.md                      |  148 -
 docs/api/scala/symbol_in_pictures.md          |   93 -
 docs/architecture/exception_handling.md       |  125 -
 docs/architecture/index.md                    |   46 -
 docs/architecture/note_data_loading.md        |  261 --
 docs/architecture/note_engine.md              |  385 ---
 docs/architecture/note_memory.md              |  345 ---
 docs/architecture/overview.md                 |  875 ------
 docs/architecture/program_model.md            |  623 -----
 docs/architecture/rnn_interface.md            |  117 -
 docs/build_version_doc/AddPackageLink.py      |   58 -
 docs/build_version_doc/AddVersion.py          |   91 -
 docs/build_version_doc/Dockerfile             |   65 -
 docs/build_version_doc/README.md              |  334 ---
 docs/build_version_doc/artifacts/.htaccess    |   24 -
 docs/build_version_doc/artifacts/404.html     |   50 -
 docs/build_version_doc/artifacts/api.html     |   50 -
 docs/build_version_doc/artifacts/mxnet.css    | 1479 ----------
 docs/build_version_doc/build_all_version.sh   |  185 --
 docs/build_version_doc/build_doc.sh           |  148 -
 docs/build_version_doc/build_site_tag.sh      |   65 -
 docs/build_version_doc/setup_docs_ubuntu.sh   |  151 --
 docs/build_version_doc/update_all_version.sh  |  106 -
 docs/community/contribute.md                  |  188 --
 docs/community/ecosystem.md                   |   88 -
 docs/community/index.md                       |   28 -
 docs/community/mxnet_channels.md              |   27 -
 docs/community/powered_by.md                  |   83 -
 docs/conf.py                                  |  217 --
 docs/faq/add_op_in_backend.md                 |  679 -----
 docs/faq/bucketing.md                         |  113 -
 docs/faq/caffe.md                             |  201 --
 docs/faq/cloud.md                             |  200 --
 docs/faq/develop_and_hack.md                  |   26 -
 docs/faq/distributed_training.md              |  324 ---
 docs/faq/env_var.md                           |  352 ---
 docs/faq/faq.md                               |   86 -
 docs/faq/finetune.md                          |  228 --
 docs/faq/float16.md                           |  248 --
 docs/faq/gradient_compression.md              |  124 -
 docs/faq/index.md                             |  103 -
 docs/faq/model_parallel_lstm.md               |   92 -
 docs/faq/multi_devices.md                     |  104 -
 docs/faq/new_op.md                            |  388 ---
 docs/faq/nnpack.md                            |  155 --
 docs/faq/perf.md                              |  302 ---
 docs/faq/recordio.md                          |  100 -
 docs/faq/s3_integration.md                    |  116 -
 docs/faq/security.md                          |   41 -
 docs/faq/smart_device.md                      |  112 -
 docs/faq/tensor_inspector_tutorial.md         |  168 --
 docs/faq/visualize_graph.md                   |   81 -
 docs/faq/why_mxnet.md                         |  216 --
 docs/gluon/index.md                           |  127 -
 docs/index.md                                 |   32 -
 docs/install/amazonlinux_setup.md             |   25 -
 docs/install/build_from_source.md             |  321 ---
 docs/install/c_plus_plus.md                   |   47 -
 docs/install/centos_setup.md                  |  107 -
 docs/install/download.md                      |   75 -
 docs/install/index.md                         | 1567 -----------
 docs/install/install-jetson.md                |  235 --
 docs/install/install_mxnet_ubuntu_python.sh   |   74 -
 docs/install/install_mxnet_ubuntu_r.sh        |   55 -
 docs/install/java_setup.md                    |  128 -
 docs/install/osx_setup.md                     |  268 --
 docs/install/requirements.txt                 |    8 -
 docs/install/scala_setup.md                   |  157 --
 docs/install/ubuntu_setup.md                  |  554 ----
 docs/install/validate_mxnet.md                |  219 --
 docs/install/windows_setup.md                 |  508 ----
 docs/model_zoo/index.md                       |  120 -
 docs/mxdoc.py                                 |  510 ----
 .../tutorials/performance/backend/amp.md}     |   32 +-
 .../tutorials/performance/backend/index.rst   |    6 +
 docs/settings.ini                             |   12 +-
 docs/static_site/src/pages/api/api.html       |   33 +-
 .../api/architecture/exception_handling.md    |   12 +-
 .../api/architecture/note_data_loading.md     |   12 +-
 .../src/pages/api/architecture/note_engine.md |   12 +-
 .../src/pages/api/architecture/note_memory.md |   12 +-
 .../src/pages/api/architecture/overview.md    |   12 +-
 .../pages/api/architecture/program_model.md   |   12 +-
 .../pages/api/clojure/docs/tutorials/index.md |   14 +-
 .../api/clojure/docs/tutorials/kvstore.md     |   14 +-
 .../api/clojure/docs/tutorials/module.md      |   14 +-
 .../api/clojure/docs/tutorials/ndarray.md     |   14 +-
 .../api/clojure/docs/tutorials/symbol.md      |   14 +-
 .../docs/tutorials/symbol_in_pictures.md      |   14 +-
 .../src/pages/api/clojure/index.md            |   16 +-
 .../pages/api/cpp/docs/tutorials/basics.md    |   18 +-
 .../src/pages/api/cpp/docs/tutorials/index.md |   12 +-
 .../tutorials/mxnet_cpp_inference_tutorial.md |   18 +-
 .../api/cpp/docs/tutorials/subgraphAPI.md     |   24 +-
 docs/static_site/src/pages/api/cpp/index.md   |   16 +-
 .../src/pages/api/faq/add_op_in_backend.md    |   16 +-
 docs/static_site/src/pages/api/faq/caffe.md   |   16 +-
 docs/static_site/src/pages/api/faq/cloud.md   |   16 +-
 .../src/pages/api/faq/distributed_training.md |   16 +-
 docs/static_site/src/pages/api/faq/env_var.md |   20 +-
 docs/static_site/src/pages/api/faq/float16.md |   16 +-
 .../src/pages/api/faq/gradient_compression.md |   16 +-
 .../src/pages/api/faq/model_parallel_lstm.md  |   16 +-
 docs/static_site/src/pages/api/faq/new_op.md  |   16 +-
 docs/static_site/src/pages/api/faq/nnpack.md  |   16 +-
 docs/static_site/src/pages/api/faq/perf.md    |   16 +-
 .../static_site/src/pages/api/faq/recordio.md |   16 +-
 .../src/pages/api/faq/s3_integration.md       |   16 +-
 .../static_site/src/pages/api/faq/security.md |   16 +-
 .../src/pages/api/faq/smart_device.md         |   16 +-
 .../src/pages/api/faq/visualize_graph.md      |   16 +-
 .../src/pages/api/faq/why_mxnet.md            |   16 +-
 .../pages/api/java/docs/tutorials/index.md    |   12 +-
 .../docs/tutorials/mxnet_java_on_intellij.md  |   14 +-
 .../api/java/docs/tutorials/ssd_inference.md  |   14 +-
 docs/static_site/src/pages/api/java/index.md  |   16 +-
 docs/static_site/src/pages/api/julia/index.md |   16 +-
 .../pages/api/perl/docs/tutorials/index.md    |   16 +-
 .../src/pages/api/perl/docs/tutorials/io.md   |   14 +-
 .../pages/api/perl/docs/tutorials/kvstore.md  |   14 +-
 .../pages/api/perl/docs/tutorials/module.md   |   14 +-
 .../pages/api/perl/docs/tutorials/ndarray.md  |   14 +-
 .../pages/api/perl/docs/tutorials/symbol.md   |   14 +-
 docs/static_site/src/pages/api/perl/index.md  |   16 +-
 .../static_site/src/pages/api/python/index.md |   16 +-
 .../api/r/docs/tutorials/callback_function.md |   14 +-
 .../api/r/docs/tutorials/char_rnn_model.md    |   14 +-
 ...assify_real_image_with_pretrained_model.md |   14 +-
 .../api/r/docs/tutorials/custom_iterator.md   |   14 +-
 .../r/docs/tutorials/custom_loss_function.md  |   14 +-
 .../tutorials/five_minutes_neural_network.md  |   14 +-
 .../src/pages/api/r/docs/tutorials/index.md   |   14 +-
 .../api/r/docs/tutorials/mnist_competition.md |   14 +-
 .../api/r/docs/tutorials/multi_dim_lstm.md    |   14 +-
 .../src/pages/api/r/docs/tutorials/ndarray.md |   14 +-
 .../src/pages/api/r/docs/tutorials/symbol.md  |   14 +-
 docs/static_site/src/pages/api/r/index.md     |   16 +-
 .../api/scala/docs/tutorials/char_lstm.md     |   14 +-
 .../pages/api/scala/docs/tutorials/index.md   |   12 +-
 .../pages/api/scala/docs/tutorials/infer.md   |   14 +-
 .../src/pages/api/scala/docs/tutorials/io.md  |   14 +-
 .../pages/api/scala/docs/tutorials/kvstore.md |   14 +-
 .../pages/api/scala/docs/tutorials/mnist.md   |   14 +-
 .../pages/api/scala/docs/tutorials/model.md   |   14 +-
 .../pages/api/scala/docs/tutorials/module.md  |   14 +-
 .../docs/tutorials/mxnet_scala_on_intellij.md |   14 +-
 .../pages/api/scala/docs/tutorials/ndarray.md |   14 +-
 .../pages/api/scala/docs/tutorials/symbol.md  |   14 +-
 .../docs/tutorials/symbol_in_pictures.md      |   14 +-
 docs/static_site/src/pages/api/scala/index.md |   16 +-
 .../src/pages/community/contribute.md         |   16 +-
 .../pages/get_started/build_from_source.md    |   16 +-
 .../src/pages/get_started/c_plus_plus.md      |   14 +-
 .../src/pages/get_started/centos_setup.md     |   14 +-
 .../src/pages/get_started/download.md         |   14 +-
 .../src/pages/get_started/index.html          |   14 +-
 .../src/pages/get_started/java_setup.md       |   14 +-
 .../src/pages/get_started/jetson_setup.md     |   14 +-
 .../src/pages/get_started/osx_setup.md        |   14 +-
 .../src/pages/get_started/scala_setup.md      |   14 +-
 .../src/pages/get_started/ubuntu_setup.md     |   14 +-
 .../src/pages/get_started/validate_mxnet.md   |   14 +-
 .../src/pages/get_started/windows_setup.md    |   14 +-
 docs/tutorials/amp/index.md                   |   25 -
 docs/tutorials/basic/data.md                  |  474 ----
 docs/tutorials/basic/index.md                 |   25 -
 docs/tutorials/basic/module.md                |  364 ---
 docs/tutorials/basic/ndarray.md               |  491 ----
 docs/tutorials/basic/ndarray_indexing.md      |  394 ---
 docs/tutorials/basic/reshape_transpose.md     |  214 --
 docs/tutorials/basic/symbol.md                |  460 ----
 docs/tutorials/c++/basics.md                  |  213 --
 docs/tutorials/c++/index.md                   |   25 -
 .../c++/mxnet_cpp_inference_tutorial.md       |  284 --
 docs/tutorials/c++/subgraphAPI.md             |  166 --
 .../control_flow/ControlFlowTutorial.md       |  405 ---
 docs/tutorials/control_flow/index.md          |   25 -
 docs/tutorials/embedded/index.md              |   25 -
 docs/tutorials/embedded/wine_detector.md      |  405 ---
 docs/tutorials/gluon/autograd.md              |   72 -
 docs/tutorials/gluon/custom_layer.md          |  265 --
 docs/tutorials/gluon/customop.md              |  238 --
 docs/tutorials/gluon/data_augmentation.md     |  235 --
 docs/tutorials/gluon/datasets.md              |  333 ---
 docs/tutorials/gluon/fit_api_tutorial.md      |  271 --
 docs/tutorials/gluon/gluon.md                 |  159 --
 .../gluon_from_experiment_to_deployment.md    |  330 ---
 .../tutorials/gluon/gotchas_numpy_in_mxnet.md |  185 --
 docs/tutorials/gluon/hybrid.md                |  266 --
 docs/tutorials/gluon/index.md                 |   25 -
 docs/tutorials/gluon/info_gan.md              |  456 ----
 docs/tutorials/gluon/learning_rate_finder.md  |  332 ---
 .../gluon/learning_rate_schedules.md          |  345 ---
 .../gluon/learning_rate_schedules_advanced.md |  325 ---
 .../gluon/logistic_regression_explained.md    |  255 --
 docs/tutorials/gluon/mnist.md                 |  460 ----
 docs/tutorials/gluon/multi_gpu.md             |  193 --
 docs/tutorials/gluon/naming.md                |  210 --
 docs/tutorials/gluon/ndarray.md               |  162 --
 docs/tutorials/gluon/performance.md           |  483 ----
 docs/tutorials/gluon/pretrained_models.md     |  392 ---
 docs/tutorials/gluon/save_load_params.md      |  281 --
 docs/tutorials/gluon/transforms.md            |  173 --
 docs/tutorials/index.md                       |  257 --
 docs/tutorials/java/index.md                  |   25 -
 docs/tutorials/java/mxnet_java_on_intellij.md |  180 --
 docs/tutorials/java/ssd_inference.md          |  203 --
 docs/tutorials/mkldnn/MKLDNN_README.md        |  291 --
 docs/tutorials/mkldnn/index.md                |   25 -
 docs/tutorials/mkldnn/mkldnn_quantization.md  |  259 --
 docs/tutorials/mkldnn/operator_list.md        |   90 -
 docs/tutorials/nlp/cnn.md                     |  468 ----
 docs/tutorials/nlp/index.md                   |   25 -
 docs/tutorials/onnx/export_mxnet_to_onnx.md   |  151 --
 docs/tutorials/onnx/fine_tuning_gluon.md      |  458 ----
 docs/tutorials/onnx/index.md                  |   25 -
 .../tutorials/onnx/inference_on_onnx_model.md |  256 --
 docs/tutorials/onnx/super_resolution.md       |  140 -
 docs/tutorials/python/data_augmentation.md    |  180 --
 .../python/data_augmentation_with_masks.md    |  261 --
 docs/tutorials/python/index.md                |   25 -
 docs/tutorials/python/kvstore.md              |  171 --
 docs/tutorials/python/linear-regression.md    |  215 --
 docs/tutorials/python/mnist.md                |  222 --
 docs/tutorials/python/module_to_gluon.md      |  365 ---
 docs/tutorials/python/predict_image.md        |  171 --
 .../python/types_of_data_augmentation.md      |  395 ---
 docs/tutorials/r/CallbackFunction.md          |  271 --
 docs/tutorials/r/CustomIterator.md            |  219 --
 docs/tutorials/r/CustomLossFunction.md        |  223 --
 docs/tutorials/r/MultidimLstm.md              |  319 ---
 docs/tutorials/r/charRnnModel.md              |  310 ---
 .../r/classifyRealImageWithPretrainedModel.md |  201 --
 docs/tutorials/r/fiveMinutesNeuralNetwork.md  |  334 ---
 docs/tutorials/r/index.md                     |   31 -
 docs/tutorials/r/mnistCompetition.md          |  356 ---
 docs/tutorials/r/ndarray.md                   |  223 --
 docs/tutorials/r/symbol.md                    |  147 -
 docs/tutorials/scala/char_lstm.md             |  526 ----
 docs/tutorials/scala/index.md                 |   34 -
 docs/tutorials/scala/mnist.md                 |  134 -
 .../scala/mxnet_scala_on_intellij.md          |  449 ----
 docs/tutorials/sparse/csr.md                  |  552 ----
 docs/tutorials/sparse/index.md                |   25 -
 docs/tutorials/sparse/row_sparse.md           |  581 ----
 docs/tutorials/sparse/train.md                |  338 ---
 docs/tutorials/speech_recognition/ctc.md      |   43 -
 docs/tutorials/speech_recognition/index.md    |   25 -
 docs/tutorials/tensorrt/index.md              |   25 -
 docs/tutorials/tensorrt/inference_with_trt.md |  143 -
 docs/tutorials/tensorrt/wavenet_optimized.svg |    1 -
 .../tensorrt/wavenet_unoptimized.svg          |   17 -
 docs/tutorials/unsupervised_learning/gan.md   |  418 ---
 docs/tutorials/unsupervised_learning/index.md |   25 -
 docs/tutorials/vision/cnn_visualization.md    |  275 --
 docs/tutorials/vision/index.md                |   25 -
 .../vision/large_scale_classification.md      |  282 --
 348 files changed, 803 insertions(+), 55565 deletions(-)
 delete mode 100644 docs/Dockerfile
 delete mode 100644 docs/Doxyfile
 delete mode 100644 docs/Makefile
 delete mode 100644 docs/_static/cn.svg
 delete mode 100644 docs/_static/js/auto_module_index.js
 delete mode 100644 docs/_static/js/clipboard.js
 delete mode 100755 docs/_static/js/clipboard.min.js
 delete mode 100644 docs/_static/js/copycode.js
 delete mode 100644 docs/_static/js/docversion.js
 delete mode 100644 docs/_static/js/navbar.js
 delete mode 100644 docs/_static/js/options.js
 delete mode 100644 docs/_static/js/page.js
 delete mode 100644 docs/_static/js/search.js
 delete mode 100644 docs/_static/js/sidebar.js
 delete mode 100644 docs/_static/mxnet-theme/footer.html
 delete mode 100644 docs/_static/mxnet-theme/index.html
 delete mode 100644 docs/_static/mxnet-theme/layout.html
 delete mode 100644 docs/_static/mxnet-theme/navbar.html
 delete mode 100644 docs/_static/mxnet-theme/theme.conf
 delete mode 100644 docs/_static/mxnet.css
 delete mode 100644 docs/_static/searchtools_custom.js
 delete mode 100644 docs/_static/selectlang.js
 delete mode 100644 docs/_static/tutorials/tensorrt/wavenet_optimized.png
 delete mode 100644 docs/_static/tutorials/tensorrt/wavenet_unoptimized.png
 delete mode 100644 docs/_static/us.svg
 delete mode 100644 docs/api/c++/index.md
 delete mode 100644 docs/api/clojure/index.md
 delete mode 100644 docs/api/clojure/kvstore.md
 delete mode 100644 docs/api/clojure/module.md
 delete mode 100644 docs/api/clojure/ndarray.md
 delete mode 100644 docs/api/clojure/symbol.md
 delete mode 100644 docs/api/clojure/symbol_in_pictures.md
 delete mode 100644 docs/api/index.md
 delete mode 100644 docs/api/java/index.md
 delete mode 100644 docs/api/julia/index.md
 delete mode 100644 docs/api/perl/index.md
 delete mode 100644 docs/api/perl/io.md
 delete mode 100644 docs/api/perl/kvstore.md
 delete mode 100644 docs/api/perl/module.md
 delete mode 100644 docs/api/perl/ndarray.md
 delete mode 100644 docs/api/perl/symbol.md
 delete mode 100644 docs/api/python/autograd/autograd.md
 delete mode 100644 docs/api/python/callback/callback.md
 delete mode 100644 docs/api/python/contrib/contrib.md
 delete mode 100644 docs/api/python/contrib/onnx.md
 delete mode 100644 docs/api/python/contrib/svrg_optimization.md
 delete mode 100644 docs/api/python/contrib/text.md
 delete mode 100644 docs/api/python/executor/executor.md
 delete mode 100644 docs/api/python/gluon/contrib.md
 delete mode 100644 docs/api/python/gluon/data.md
 delete mode 100644 docs/api/python/gluon/gluon.md
 delete mode 100644 docs/api/python/gluon/loss.md
 delete mode 100644 docs/api/python/gluon/model_zoo.md
 delete mode 100644 docs/api/python/gluon/nn.md
 delete mode 100644 docs/api/python/gluon/rnn.md
 delete mode 100644 docs/api/python/image/image.md
 delete mode 100644 docs/api/python/index.md
 delete mode 100644 docs/api/python/io/io.md
 delete mode 100644 docs/api/python/kvstore/kvstore.md
 delete mode 100644 docs/api/python/libinfo/libinfo.md
 delete mode 100644 docs/api/python/metric/metric.md
 delete mode 100644 docs/api/python/model.md
 delete mode 100644 docs/api/python/module/module.md
 delete mode 100644 docs/api/python/ndarray/contrib.md
 delete mode 100644 docs/api/python/ndarray/linalg.md
 delete mode 100644 docs/api/python/ndarray/ndarray.md
 delete mode 100644 docs/api/python/ndarray/random.md
 delete mode 100644 docs/api/python/ndarray/sparse.md
 delete mode 100644 docs/api/python/optimization/contrib.md
 delete mode 100644 docs/api/python/optimization/optimization.md
 delete mode 100644 docs/api/python/profiler/profiler.md
 delete mode 100644 docs/api/python/rtc/rtc.md
 delete mode 100644 docs/api/python/symbol/contrib.md
 delete mode 100644 docs/api/python/symbol/linalg.md
 delete mode 100644 docs/api/python/symbol/random.md
 delete mode 100644 docs/api/python/symbol/rnn.md
 delete mode 100644 docs/api/python/symbol/sparse.md
 delete mode 100644 docs/api/python/symbol/symbol.md
 delete mode 100644 docs/api/python/symbol_in_pictures/symbol_in_pictures.md
 delete mode 100644 docs/api/python/tools/test_utils.md
 delete mode 100644 docs/api/python/tools/visualization.md
 delete mode 100644 docs/api/r/index.md
 delete mode 100644 docs/api/scala/index.md
 delete mode 100644 docs/api/scala/infer.md
 delete mode 100644 docs/api/scala/io.md
 delete mode 100644 docs/api/scala/kvstore.md
 delete mode 100644 docs/api/scala/model.md
 delete mode 100644 docs/api/scala/module.md
 delete mode 100644 docs/api/scala/ndarray.md
 delete mode 100644 docs/api/scala/symbol.md
 delete mode 100644 docs/api/scala/symbol_in_pictures.md
 delete mode 100644 docs/architecture/exception_handling.md
 delete mode 100644 docs/architecture/index.md
 delete mode 100644 docs/architecture/note_data_loading.md
 delete mode 100644 docs/architecture/note_engine.md
 delete mode 100644 docs/architecture/note_memory.md
 delete mode 100644 docs/architecture/overview.md
 delete mode 100644 docs/architecture/program_model.md
 delete mode 100644 docs/architecture/rnn_interface.md
 delete mode 100644 docs/build_version_doc/AddPackageLink.py
 delete mode 100755 docs/build_version_doc/AddVersion.py
 delete mode 100755 docs/build_version_doc/Dockerfile
 delete mode 100644 docs/build_version_doc/README.md
 delete mode 100644 docs/build_version_doc/artifacts/.htaccess
 delete mode 100644 docs/build_version_doc/artifacts/404.html
 delete mode 100644 docs/build_version_doc/artifacts/api.html
 delete mode 100644 docs/build_version_doc/artifacts/mxnet.css
 delete mode 100755 docs/build_version_doc/build_all_version.sh
 delete mode 100755 docs/build_version_doc/build_doc.sh
 delete mode 100755 docs/build_version_doc/build_site_tag.sh
 delete mode 100755 docs/build_version_doc/setup_docs_ubuntu.sh
 delete mode 100755 docs/build_version_doc/update_all_version.sh
 delete mode 100644 docs/community/contribute.md
 delete mode 100644 docs/community/ecosystem.md
 delete mode 100644 docs/community/index.md
 delete mode 100644 docs/community/mxnet_channels.md
 delete mode 100644 docs/community/powered_by.md
 delete mode 100644 docs/conf.py
 delete mode 100644 docs/faq/add_op_in_backend.md
 delete mode 100644 docs/faq/bucketing.md
 delete mode 100644 docs/faq/caffe.md
 delete mode 100644 docs/faq/cloud.md
 delete mode 100644 docs/faq/develop_and_hack.md
 delete mode 100644 docs/faq/distributed_training.md
 delete mode 100644 docs/faq/env_var.md
 delete mode 100644 docs/faq/faq.md
 delete mode 100644 docs/faq/finetune.md
 delete mode 100644 docs/faq/float16.md
 delete mode 100644 docs/faq/gradient_compression.md
 delete mode 100644 docs/faq/index.md
 delete mode 100644 docs/faq/model_parallel_lstm.md
 delete mode 100644 docs/faq/multi_devices.md
 delete mode 100644 docs/faq/new_op.md
 delete mode 100644 docs/faq/nnpack.md
 delete mode 100644 docs/faq/perf.md
 delete mode 100644 docs/faq/recordio.md
 delete mode 100644 docs/faq/s3_integration.md
 delete mode 100644 docs/faq/security.md
 delete mode 100644 docs/faq/smart_device.md
 delete mode 100644 docs/faq/tensor_inspector_tutorial.md
 delete mode 100644 docs/faq/visualize_graph.md
 delete mode 100644 docs/faq/why_mxnet.md
 delete mode 100644 docs/gluon/index.md
 delete mode 100644 docs/index.md
 delete mode 100644 docs/install/amazonlinux_setup.md
 delete mode 100644 docs/install/build_from_source.md
 delete mode 100644 docs/install/c_plus_plus.md
 delete mode 100644 docs/install/centos_setup.md
 delete mode 100644 docs/install/download.md
 delete mode 100644 docs/install/index.md
 delete mode 100644 docs/install/install-jetson.md
 delete mode 100755 docs/install/install_mxnet_ubuntu_python.sh
 delete mode 100755 docs/install/install_mxnet_ubuntu_r.sh
 delete mode 100644 docs/install/java_setup.md
 delete mode 100644 docs/install/osx_setup.md
 delete mode 100644 docs/install/requirements.txt
 delete mode 100644 docs/install/scala_setup.md
 delete mode 100644 docs/install/ubuntu_setup.md
 delete mode 100644 docs/install/validate_mxnet.md
 delete mode 100644 docs/install/windows_setup.md
 delete mode 100644 docs/model_zoo/index.md
 delete mode 100644 docs/mxdoc.py
 rename docs/{tutorials/amp/amp_tutorial.md => python_docs/python/tutorials/performance/backend/amp.md} (94%)
 delete mode 100644 docs/tutorials/amp/index.md
 delete mode 100644 docs/tutorials/basic/data.md
 delete mode 100644 docs/tutorials/basic/index.md
 delete mode 100644 docs/tutorials/basic/module.md
 delete mode 100644 docs/tutorials/basic/ndarray.md
 delete mode 100644 docs/tutorials/basic/ndarray_indexing.md
 delete mode 100644 docs/tutorials/basic/reshape_transpose.md
 delete mode 100644 docs/tutorials/basic/symbol.md
 delete mode 100644 docs/tutorials/c++/basics.md
 delete mode 100644 docs/tutorials/c++/index.md
 delete mode 100644 docs/tutorials/c++/mxnet_cpp_inference_tutorial.md
 delete mode 100644 docs/tutorials/c++/subgraphAPI.md
 delete mode 100644 docs/tutorials/control_flow/ControlFlowTutorial.md
 delete mode 100644 docs/tutorials/control_flow/index.md
 delete mode 100644 docs/tutorials/embedded/index.md
 delete mode 100644 docs/tutorials/embedded/wine_detector.md
 delete mode 100644 docs/tutorials/gluon/autograd.md
 delete mode 100644 docs/tutorials/gluon/custom_layer.md
 delete mode 100644 docs/tutorials/gluon/customop.md
 delete mode 100644 docs/tutorials/gluon/data_augmentation.md
 delete mode 100644 docs/tutorials/gluon/datasets.md
 delete mode 100644 docs/tutorials/gluon/fit_api_tutorial.md
 delete mode 100644 docs/tutorials/gluon/gluon.md
 delete mode 100644 docs/tutorials/gluon/gluon_from_experiment_to_deployment.md
 delete mode 100644 docs/tutorials/gluon/gotchas_numpy_in_mxnet.md
 delete mode 100644 docs/tutorials/gluon/hybrid.md
 delete mode 100644 docs/tutorials/gluon/index.md
 delete mode 100644 docs/tutorials/gluon/info_gan.md
 delete mode 100644 docs/tutorials/gluon/learning_rate_finder.md
 delete mode 100644 docs/tutorials/gluon/learning_rate_schedules.md
 delete mode 100644 docs/tutorials/gluon/learning_rate_schedules_advanced.md
 delete mode 100644 docs/tutorials/gluon/logistic_regression_explained.md
 delete mode 100644 docs/tutorials/gluon/mnist.md
 delete mode 100644 docs/tutorials/gluon/multi_gpu.md
 delete mode 100644 docs/tutorials/gluon/naming.md
 delete mode 100644 docs/tutorials/gluon/ndarray.md
 delete mode 100644 docs/tutorials/gluon/performance.md
 delete mode 100644 docs/tutorials/gluon/pretrained_models.md
 delete mode 100644 docs/tutorials/gluon/save_load_params.md
 delete mode 100644 docs/tutorials/gluon/transforms.md
 delete mode 100644 docs/tutorials/index.md
 delete mode 100644 docs/tutorials/java/index.md
 delete mode 100644 docs/tutorials/java/mxnet_java_on_intellij.md
 delete mode 100644 docs/tutorials/java/ssd_inference.md
 delete mode 100644 docs/tutorials/mkldnn/MKLDNN_README.md
 delete mode 100644 docs/tutorials/mkldnn/index.md
 delete mode 100644 docs/tutorials/mkldnn/mkldnn_quantization.md
 delete mode 100644 docs/tutorials/mkldnn/operator_list.md
 delete mode 100644 docs/tutorials/nlp/cnn.md
 delete mode 100644 docs/tutorials/nlp/index.md
 delete mode 100644 docs/tutorials/onnx/export_mxnet_to_onnx.md
 delete mode 100644 docs/tutorials/onnx/fine_tuning_gluon.md
 delete mode 100644 docs/tutorials/onnx/index.md
 delete mode 100644 docs/tutorials/onnx/inference_on_onnx_model.md
 delete mode 100644 docs/tutorials/onnx/super_resolution.md
 delete mode 100644 docs/tutorials/python/data_augmentation.md
 delete mode 100644 docs/tutorials/python/data_augmentation_with_masks.md
 delete mode 100644 docs/tutorials/python/index.md
 delete mode 100644 docs/tutorials/python/kvstore.md
 delete mode 100644 docs/tutorials/python/linear-regression.md
 delete mode 100644 docs/tutorials/python/mnist.md
 delete mode 100644 docs/tutorials/python/module_to_gluon.md
 delete mode 100644 docs/tutorials/python/predict_image.md
 delete mode 100644 docs/tutorials/python/types_of_data_augmentation.md
 delete mode 100644 docs/tutorials/r/CallbackFunction.md
 delete mode 100644 docs/tutorials/r/CustomIterator.md
 delete mode 100644 docs/tutorials/r/CustomLossFunction.md
 delete mode 100644 docs/tutorials/r/MultidimLstm.md
 delete mode 100644 docs/tutorials/r/charRnnModel.md
 delete mode 100644 docs/tutorials/r/classifyRealImageWithPretrainedModel.md
 delete mode 100644 docs/tutorials/r/fiveMinutesNeuralNetwork.md
 delete mode 100644 docs/tutorials/r/index.md
 delete mode 100644 docs/tutorials/r/mnistCompetition.md
 delete mode 100644 docs/tutorials/r/ndarray.md
 delete mode 100644 docs/tutorials/r/symbol.md
 delete mode 100644 docs/tutorials/scala/char_lstm.md
 delete mode 100644 docs/tutorials/scala/index.md
 delete mode 100644 docs/tutorials/scala/mnist.md
 delete mode 100644 docs/tutorials/scala/mxnet_scala_on_intellij.md
 delete mode 100644 docs/tutorials/sparse/csr.md
 delete mode 100644 docs/tutorials/sparse/index.md
 delete mode 100644 docs/tutorials/sparse/row_sparse.md
 delete mode 100644 docs/tutorials/sparse/train.md
 delete mode 100644 docs/tutorials/speech_recognition/ctc.md
 delete mode 100644 docs/tutorials/speech_recognition/index.md
 delete mode 100644 docs/tutorials/tensorrt/index.md
 delete mode 100644 docs/tutorials/tensorrt/inference_with_trt.md
 delete mode 100644 docs/tutorials/tensorrt/wavenet_optimized.svg
 delete mode 100644 docs/tutorials/tensorrt/wavenet_unoptimized.svg
 delete mode 100644 docs/tutorials/unsupervised_learning/gan.md
 delete mode 100644 docs/tutorials/unsupervised_learning/index.md
 delete mode 100644 docs/tutorials/vision/cnn_visualization.md
 delete mode 100644 docs/tutorials/vision/index.md
 delete mode 100644 docs/tutorials/vision/large_scale_classification.md

diff --git a/ci/jenkins/Jenkinsfile_website b/ci/jenkins/Jenkinsfile_website
index 5e97696a2953..f6c853046b1c 100644
--- a/ci/jenkins/Jenkinsfile_website
+++ b/ci/jenkins/Jenkinsfile_website
@@ -33,13 +33,10 @@ utils.assign_node_labels(utility: 'utility', linux_cpu: 'mxnetlinux-cpu')
 
 utils.main_wrapper(
 core_logic: {
-  utils.parallel_stage('Build', [
-    custom_steps.compile_unix_cpu_openblas()
+  utils.parallel_stage('Placeholder', [
+    // Do nothing
   ])
 
-  utils.parallel_stage('Deploy', [
-    custom_steps.docs_website(),
-  ])
 }
 ,
 failure_handler: {
diff --git a/docs/Dockerfile b/docs/Dockerfile
deleted file mode 100644
index 61a1e5f915e2..000000000000
--- a/docs/Dockerfile
+++ /dev/null
@@ -1,63 +0,0 @@
-# -*- mode: dockerfile -*-
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-#
-#
-FROM ubuntu:14.04
-MAINTAINER Mu Li <muli@cs.cmu.edu>
-
-#
-# First, build MXNet binaries (ref mxnet/docker/cpu/Dockerfile)
-#
-
-RUN apt-get update && apt-get install -y build-essential git libopenblas-dev liblapack-dev libopencv-dev
-RUN git clone --recursive https://github.com/dmlc/mxnet/ && cd mxnet && \
-    cp make/config.mk . && \
-    echo "USE_BLAS=openblas" >>config.mk && \
-    make -j$(nproc)
-
-# python pakcage
-RUN apt-get install -y python-numpy wget unzip
-ENV PYTHONPATH /mxnet/python
-
-#
-# Now set up tools for doc build
-#
-
-RUN apt-get update && apt-get install -y \
-    doxygen \
-    build-essential \
-    git \
-    python-pip
-
-RUN pip install sphinx==1.3.5 CommonMark==0.5.4 breathe mock==1.0.1 recommonmark
-
-WORKDIR /opt/mxnet/docs
-
-# Fool it into thinking it's on a READTHEDOCS server, so it builds the
-# API reference
-ENV READTHEDOCS true
-
-ENTRYPOINT /opt/mxnet/docs/build-preview.sh
-
-EXPOSE 8008
-
-# Put this at the end so that you don't have to rebuild the earlier
-# layers when iterating on the docs themselves.
-ADD . /opt/mxnet/docs
-
diff --git a/docs/Doxyfile b/docs/Doxyfile
deleted file mode 100644
index bf6344d4f74a..000000000000
--- a/docs/Doxyfile
+++ /dev/null
@@ -1,2370 +0,0 @@
-# Doxyfile 1.8.8
-
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-# This file describes the settings to be used by the documentation system
-# doxygen (www.doxygen.org) for a project.
-#
-# All text after a double hash (##) is considered a comment and is placed in
-# front of the TAG it is preceding.
-#
-# All text after a single hash (#) is considered a comment and will be ignored.
-# The format is:
-# TAG = value [value, ...]
-# For lists, items can also be appended using:
-# TAG += value [value, ...]
-# Values that contain spaces should be placed between quotes (\" \").
-
-#---------------------------------------------------------------------------
-# Project related configuration options
-#---------------------------------------------------------------------------
-
-# This tag specifies the encoding used for all characters in the config file
-# that follow. The default is UTF-8 which is also the encoding used for all text
-# before the first occurrence of this tag. Doxygen uses libiconv (or the iconv
-# built into libc) for the transcoding. See http://www.gnu.org/software/libiconv
-# for the list of possible encodings.
-# The default value is: UTF-8.
-
-DOXYFILE_ENCODING      = UTF-8
-
-# The PROJECT_NAME tag is a single word (or a sequence of words surrounded by
-# double-quotes, unless you are using Doxywizard) that should identify the
-# project for which the documentation is generated. This name is used in the
-# title of most generated pages and in a few other places.
-# The default value is: My Project.
-
-PROJECT_NAME           = "mxnet"
-
-# The PROJECT_NUMBER tag can be used to enter a project or revision number. This
-# could be handy for archiving the generated documentation or if some version
-# control system is used.
-
-PROJECT_NUMBER         =
-
-# Using the PROJECT_BRIEF tag one can provide an optional one line description
-# for a project that appears at the top of each page and should give viewer a
-# quick idea about the purpose of the project. Keep the description short.
-
-PROJECT_BRIEF          =
-
-# With the PROJECT_LOGO tag one can specify an logo or icon that is included in
-# the documentation. The maximum height of the logo should not exceed 55 pixels
-# and the maximum width should not exceed 200 pixels. Doxygen will copy the logo
-# to the output directory.
-
-PROJECT_LOGO           =
-
-# The OUTPUT_DIRECTORY tag is used to specify the (relative or absolute) path
-# into which the generated documentation will be written. If a relative path is
-# entered, it will be relative to the location where doxygen was started. If
-# left blank the current directory will be used.
-
-OUTPUT_DIRECTORY       = docs/doxygen
-
-# If the CREATE_SUBDIRS tag is set to YES, then doxygen will create 4096 sub-
-# directories (in 2 levels) under the output directory of each output format and
-# will distribute the generated files over these directories. Enabling this
-# option can be useful when feeding doxygen a huge amount of source files, where
-# putting all generated files in the same directory would otherwise causes
-# performance problems for the file system.
-# The default value is: NO.
-
-CREATE_SUBDIRS         = NO
-
-# If the ALLOW_UNICODE_NAMES tag is set to YES, doxygen will allow non-ASCII
-# characters to appear in the names of generated files. If set to NO, non-ASCII
-# characters will be escaped, for example _xE3_x81_x84 will be used for Unicode
-# U+3044.
-# The default value is: NO.
-
-#ALLOW_UNICODE_NAMES    = NO
-
-# The OUTPUT_LANGUAGE tag is used to specify the language in which all
-# documentation generated by doxygen is written. Doxygen will use this
-# information to generate all constant output in the proper language.
-# Possible values are: Afrikaans, Arabic, Armenian, Brazilian, Catalan, Chinese,
-# Chinese-Traditional, Croatian, Czech, Danish, Dutch, English (United States),
-# Esperanto, Farsi (Persian), Finnish, French, German, Greek, Hungarian,
-# Indonesian, Italian, Japanese, Japanese-en (Japanese with English messages),
-# Korean, Korean-en (Korean with English messages), Latvian, Lithuanian,
-# Macedonian, Norwegian, Persian (Farsi), Polish, Portuguese, Romanian, Russian,
-# Serbian, Serbian-Cyrillic, Slovak, Slovene, Spanish, Swedish, Turkish,
-# Ukrainian and Vietnamese.
-# The default value is: English.
-
-OUTPUT_LANGUAGE        = English
-
-# If the BRIEF_MEMBER_DESC tag is set to YES doxygen will include brief member
-# descriptions after the members that are listed in the file and class
-# documentation (similar to Javadoc). Set to NO to disable this.
-# The default value is: YES.
-
-BRIEF_MEMBER_DESC      = YES
-
-# If the REPEAT_BRIEF tag is set to YES doxygen will prepend the brief
-# description of a member or function before the detailed description
-#
-# Note: If both HIDE_UNDOC_MEMBERS and BRIEF_MEMBER_DESC are set to NO, the
-# brief descriptions will be completely suppressed.
-# The default value is: YES.
-
-REPEAT_BRIEF           = YES
-
-# This tag implements a quasi-intelligent brief description abbreviator that is
-# used to form the text in various listings. Each string in this list, if found
-# as the leading text of the brief description, will be stripped from the text
-# and the result, after processing the whole list, is used as the annotated
-# text. Otherwise, the brief description is used as-is. If left blank, the
-# following values are used ($name is automatically replaced with the name of
-# the entity):The $name class, The $name widget, The $name file, is, provides,
-# specifies, contains, represents, a, an and the.
-
-ABBREVIATE_BRIEF       =
-
-# If the ALWAYS_DETAILED_SEC and REPEAT_BRIEF tags are both set to YES then
-# doxygen will generate a detailed section even if there is only a brief
-# description.
-# The default value is: NO.
-
-ALWAYS_DETAILED_SEC    = NO
-
-# If the INLINE_INHERITED_MEMB tag is set to YES, doxygen will show all
-# inherited members of a class in the documentation of that class as if those
-# members were ordinary class members. Constructors, destructors and assignment
-# operators of the base classes will not be shown.
-# The default value is: NO.
-
-INLINE_INHERITED_MEMB  = NO
-
-# If the FULL_PATH_NAMES tag is set to YES doxygen will prepend the full path
-# before files name in the file list and in the header files. If set to NO the
-# shortest path that makes the file name unique will be used
-# The default value is: YES.
-
-FULL_PATH_NAMES        = YES
-
-# The STRIP_FROM_PATH tag can be used to strip a user-defined part of the path.
-# Stripping is only done if one of the specified strings matches the left-hand
-# part of the path. The tag can be used to show relative paths in the file list.
-# If left blank the directory from which doxygen is run is used as the path to
-# strip.
-#
-# Note that you can specify absolute paths here, but also relative paths, which
-# will be relative from the directory where doxygen is started.
-# This tag requires that the tag FULL_PATH_NAMES is set to YES.
-
-STRIP_FROM_PATH        =
-
-# The STRIP_FROM_INC_PATH tag can be used to strip a user-defined part of the
-# path mentioned in the documentation of a class, which tells the reader which
-# header file to include in order to use a class. If left blank only the name of
-# the header file containing the class definition is used. Otherwise one should
-# specify the list of include paths that are normally passed to the compiler
-# using the -I flag.
-
-STRIP_FROM_INC_PATH    =
-
-# If the SHORT_NAMES tag is set to YES, doxygen will generate much shorter (but
-# less readable) file names. This can be useful is your file systems doesn't
-# support long names like on DOS, Mac, or CD-ROM.
-# The default value is: NO.
-
-SHORT_NAMES            = NO
-
-# If the JAVADOC_AUTOBRIEF tag is set to YES then doxygen will interpret the
-# first line (until the first dot) of a Javadoc-style comment as the brief
-# description. If set to NO, the Javadoc-style will behave just like regular Qt-
-# style comments (thus requiring an explicit @brief command for a brief
-# description.)
-# The default value is: NO.
-
-JAVADOC_AUTOBRIEF      = NO
-
-# If the QT_AUTOBRIEF tag is set to YES then doxygen will interpret the first
-# line (until the first dot) of a Qt-style comment as the brief description. If
-# set to NO, the Qt-style will behave just like regular Qt-style comments (thus
-# requiring an explicit \brief command for a brief description.)
-# The default value is: NO.
-
-QT_AUTOBRIEF           = NO
-
-# The MULTILINE_CPP_IS_BRIEF tag can be set to YES to make doxygen treat a
-# multi-line C++ special comment block (i.e. a block of //! or /// comments) as
-# a brief description. This used to be the default behavior. The new default is
-# to treat a multi-line C++ comment block as a detailed description. Set this
-# tag to YES if you prefer the old behavior instead.
-#
-# Note that setting this tag to YES also means that rational rose comments are
-# not recognized any more.
-# The default value is: NO.
-
-MULTILINE_CPP_IS_BRIEF = NO
-
-# If the INHERIT_DOCS tag is set to YES then an undocumented member inherits the
-# documentation from any documented member that it re-implements.
-# The default value is: YES.
-
-INHERIT_DOCS           = YES
-
-# If the SEPARATE_MEMBER_PAGES tag is set to YES, then doxygen will produce a
-# new page for each member. If set to NO, the documentation of a member will be
-# part of the file/class/namespace that contains it.
-# The default value is: NO.
-
-SEPARATE_MEMBER_PAGES  = NO
-
-# The TAB_SIZE tag can be used to set the number of spaces in a tab. Doxygen
-# uses this value to replace tabs by spaces in code fragments.
-# Minimum value: 1, maximum value: 16, default value: 4.
-
-TAB_SIZE               = 8
-
-# This tag can be used to specify a number of aliases that act as commands in
-# the documentation. An alias has the form:
-# name=value
-# For example adding
-# "sideeffect=@par Side Effects:\n"
-# will allow you to put the command \sideeffect (or @sideeffect) in the
-# documentation, which will result in a user-defined paragraph with heading
-# "Side Effects:". You can put \n's in the value part of an alias to insert
-# newlines.
-
-ALIASES                =
-
-# This tag can be used to specify a number of word-keyword mappings (TCL only).
-# A mapping has the form "name=value". For example adding "class=itcl::class"
-# will allow you to use the command class in the itcl::class meaning.
-
-TCL_SUBST              =
-
-# Set the OPTIMIZE_OUTPUT_FOR_C tag to YES if your project consists of C sources
-# only. Doxygen will then generate output that is more tailored for C. For
-# instance, some of the names that are used will be different. The list of all
-# members will be omitted, etc.
-# The default value is: NO.
-
-OPTIMIZE_OUTPUT_FOR_C  = NO
-
-# Set the OPTIMIZE_OUTPUT_JAVA tag to YES if your project consists of Java or
-# Python sources only. Doxygen will then generate output that is more tailored
-# for that language. For instance, namespaces will be presented as packages,
-# qualified scopes will look different, etc.
-# The default value is: NO.
-
-OPTIMIZE_OUTPUT_JAVA   = NO
-
-# Set the OPTIMIZE_FOR_FORTRAN tag to YES if your project consists of Fortran
-# sources. Doxygen will then generate output that is tailored for Fortran.
-# The default value is: NO.
-
-OPTIMIZE_FOR_FORTRAN   = NO
-
-# Set the OPTIMIZE_OUTPUT_VHDL tag to YES if your project consists of VHDL
-# sources. Doxygen will then generate output that is tailored for VHDL.
-# The default value is: NO.
-
-OPTIMIZE_OUTPUT_VHDL   = NO
-
-# Doxygen selects the parser to use depending on the extension of the files it
-# parses. With this tag you can assign which parser to use for a given
-# extension. Doxygen has a built-in mapping, but you can override or extend it
-# using this tag. The format is ext=language, where ext is a file extension, and
-# language is one of the parsers supported by doxygen: IDL, Java, Javascript,
-# C#, C, C++, D, PHP, Objective-C, Python, Fortran (fixed format Fortran:
-# FortranFixed, free formatted Fortran: FortranFree, unknown formatted Fortran:
-# Fortran. In the later case the parser tries to guess whether the code is fixed
-# or free formatted code, this is the default for Fortran type files), VHDL. For
-# instance to make doxygen treat .inc files as Fortran files (default is PHP),
-# and .f files as C (default is Fortran), use: inc=Fortran f=C.
-#
-# Note For files without extension you can use no_extension as a placeholder.
-#
-# Note that for custom extensions you also need to set FILE_PATTERNS otherwise
-# the files are not read by doxygen.
-
-EXTENSION_MAPPING      =
-
-# If the MARKDOWN_SUPPORT tag is enabled then doxygen pre-processes all comments
-# according to the Markdown format, which allows for more readable
-# documentation. See http://daringfireball.net/projects/markdown/ for details.
-# The output of markdown processing is further processed by doxygen, so you can
-# mix doxygen, HTML, and XML commands with Markdown formatting. Disable only in
-# case of backward compatibilities issues.
-# The default value is: YES.
-
-#MARKDOWN_SUPPORT       = YES
-
-# When enabled doxygen tries to link words that correspond to documented
-# classes, or namespaces to their corresponding documentation. Such a link can
-# be prevented in individual cases by by putting a % sign in front of the word
-# or globally by setting AUTOLINK_SUPPORT to NO.
-# The default value is: YES.
-
-#AUTOLINK_SUPPORT       = YES
-
-# If you use STL classes (i.e. std::string, std::vector, etc.) but do not want
-# to include (a tag file for) the STL sources as input, then you should set this
-# tag to YES in order to let doxygen match functions declarations and
-# definitions whose arguments contain STL classes (e.g. func(std::string);
-# versus func(std::string) {}). This also make the inheritance and collaboration
-# diagrams that involve STL classes more complete and accurate.
-# The default value is: NO.
-
-BUILTIN_STL_SUPPORT    = NO
-
-# If you use Microsoft's C++/CLI language, you should set this option to YES to
-# enable parsing support.
-# The default value is: NO.
-
-CPP_CLI_SUPPORT        = NO
-
-# Set the SIP_SUPPORT tag to YES if your project consists of sip (see:
-# http://www.riverbankcomputing.co.uk/software/sip/intro) sources only. Doxygen
-# will parse them like normal C++ but will assume all classes use public instead
-# of private inheritance when no explicit protection keyword is present.
-# The default value is: NO.
-
-SIP_SUPPORT            = NO
-
-# For Microsoft's IDL there are propget and propput attributes to indicate
-# getter and setter methods for a property. Setting this option to YES will make
-# doxygen to replace the get and set methods by a property in the documentation.
-# This will only work if the methods are indeed getting or setting a simple
-# type. If this is not the case, or you want to show the methods anyway, you
-# should set this option to NO.
-# The default value is: YES.
-
-IDL_PROPERTY_SUPPORT   = YES
-
-# If member grouping is used in the documentation and the DISTRIBUTE_GROUP_DOC
-# tag is set to YES, then doxygen will reuse the documentation of the first
-# member in the group (if any) for the other members of the group. By default
-# all members of a group must be documented explicitly.
-# The default value is: NO.
-
-DISTRIBUTE_GROUP_DOC   = NO
-
-# Set the SUBGROUPING tag to YES to allow class member groups of the same type
-# (for instance a group of public functions) to be put as a subgroup of that
-# type (e.g. under the Public Functions section). Set it to NO to prevent
-# subgrouping. Alternatively, this can be done per class using the
-# \nosubgrouping command.
-# The default value is: YES.
-
-SUBGROUPING            = YES
-
-# When the INLINE_GROUPED_CLASSES tag is set to YES, classes, structs and unions
-# are shown inside the group in which they are included (e.g. using \ingroup)
-# instead of on a separate page (for HTML and Man pages) or section (for LaTeX
-# and RTF).
-#
-# Note that this feature does not work in combination with
-# SEPARATE_MEMBER_PAGES.
-# The default value is: NO.
-
-INLINE_GROUPED_CLASSES = NO
-
-# When the INLINE_SIMPLE_STRUCTS tag is set to YES, structs, classes, and unions
-# with only public data fields or simple typedef fields will be shown inline in
-# the documentation of the scope in which they are defined (i.e. file,
-# namespace, or group documentation), provided this scope is documented. If set
-# to NO, structs, classes, and unions are shown on a separate page (for HTML and
-# Man pages) or section (for LaTeX and RTF).
-# The default value is: NO.
-
-INLINE_SIMPLE_STRUCTS  = NO
-
-# When TYPEDEF_HIDES_STRUCT tag is enabled, a typedef of a struct, union, or
-# enum is documented as struct, union, or enum with the name of the typedef. So
-# typedef struct TypeS {} TypeT, will appear in the documentation as a struct
-# with name TypeT. When disabled the typedef will appear as a member of a file,
-# namespace, or class. And the struct will be named TypeS. This can typically be
-# useful for C code in case the coding convention dictates that all compound
-# types are typedef'ed and only the typedef is referenced, never the tag name.
-# The default value is: NO.
-
-TYPEDEF_HIDES_STRUCT   = NO
-
-# The size of the symbol lookup cache can be set using LOOKUP_CACHE_SIZE. This
-# cache is used to resolve symbols given their name and scope. Since this can be
-# an expensive process and often the same symbol appears multiple times in the
-# code, doxygen keeps a cache of pre-resolved symbols. If the cache is too small
-# doxygen will become slower. If the cache is too large, memory is wasted. The
-# cache size is given by this formula: 2^(16+LOOKUP_CACHE_SIZE). The valid range
-# is 0..9, the default is 0, corresponding to a cache size of 2^16=65536
-# symbols. At the end of a run doxygen will report the cache usage and suggest
-# the optimal cache size from a speed point of view.
-# Minimum value: 0, maximum value: 9, default value: 0.
-
-LOOKUP_CACHE_SIZE      = 0
-
-#---------------------------------------------------------------------------
-# Build related configuration options
-#---------------------------------------------------------------------------
-
-# If the EXTRACT_ALL tag is set to YES doxygen will assume all entities in
-# documentation are documented, even if no documentation was available. Private
-# class members and static file members will be hidden unless the
-# EXTRACT_PRIVATE respectively EXTRACT_STATIC tags are set to YES.
-# Note: This will also disable the warnings about undocumented members that are
-# normally produced when WARNINGS is set to YES.
-# The default value is: NO.
-
-EXTRACT_ALL            = YES
-
-# If the EXTRACT_PRIVATE tag is set to YES all private members of a class will
-# be included in the documentation.
-# The default value is: NO.
-
-EXTRACT_PRIVATE        = NO
-
-# If the EXTRACT_PACKAGE tag is set to YES all members with package or internal
-# scope will be included in the documentation.
-# The default value is: NO.
-
-#EXTRACT_PACKAGE        = NO
-
-# If the EXTRACT_STATIC tag is set to YES all static members of a file will be
-# included in the documentation.
-# The default value is: NO.
-
-EXTRACT_STATIC         = NO
-
-# If the EXTRACT_LOCAL_CLASSES tag is set to YES classes (and structs) defined
-# locally in source files will be included in the documentation. If set to NO
-# only classes defined in header files are included. Does not have any effect
-# for Java sources.
-# The default value is: YES.
-
-EXTRACT_LOCAL_CLASSES  = YES
-
-# This flag is only useful for Objective-C code. When set to YES local methods,
-# which are defined in the implementation section but not in the interface are
-# included in the documentation. If set to NO only methods in the interface are
-# included.
-# The default value is: NO.
-
-EXTRACT_LOCAL_METHODS  = NO
-
-# If this flag is set to YES, the members of anonymous namespaces will be
-# extracted and appear in the documentation as a namespace called
-# 'anonymous_namespace{file}', where file will be replaced with the base name of
-# the file that contains the anonymous namespace. By default anonymous namespace
-# are hidden.
-# The default value is: NO.
-
-EXTRACT_ANON_NSPACES   = NO
-
-# If the HIDE_UNDOC_MEMBERS tag is set to YES, doxygen will hide all
-# undocumented members inside documented classes or files. If set to NO these
-# members will be included in the various overviews, but no documentation
-# section is generated. This option has no effect if EXTRACT_ALL is enabled.
-# The default value is: NO.
-
-HIDE_UNDOC_MEMBERS     = NO
-
-# If the HIDE_UNDOC_CLASSES tag is set to YES, doxygen will hide all
-# undocumented classes that are normally visible in the class hierarchy. If set
-# to NO these classes will be included in the various overviews. This option has
-# no effect if EXTRACT_ALL is enabled.
-# The default value is: NO.
-
-HIDE_UNDOC_CLASSES     = NO
-
-# If the HIDE_FRIEND_COMPOUNDS tag is set to YES, doxygen will hide all friend
-# (class|struct|union) declarations. If set to NO these declarations will be
-# included in the documentation.
-# The default value is: NO.
-
-HIDE_FRIEND_COMPOUNDS  = NO
-
-# If the HIDE_IN_BODY_DOCS tag is set to YES, doxygen will hide any
-# documentation blocks found inside the body of a function. If set to NO these
-# blocks will be appended to the function's detailed documentation block.
-# The default value is: NO.
-
-HIDE_IN_BODY_DOCS      = NO
-
-# The INTERNAL_DOCS tag determines if documentation that is typed after a
-# \internal command is included. If the tag is set to NO then the documentation
-# will be excluded. Set it to YES to include the internal documentation.
-# The default value is: NO.
-
-INTERNAL_DOCS          = NO
-
-# If the CASE_SENSE_NAMES tag is set to NO then doxygen will only generate file
-# names in lower-case letters. If set to YES upper-case letters are also
-# allowed. This is useful if you have classes or files whose names only differ
-# in case and if your file system supports case sensitive file names. Windows
-# and Mac users are advised to set this option to NO.
-# The default value is: system dependent.
-
-CASE_SENSE_NAMES       = YES
-
-# If the HIDE_SCOPE_NAMES tag is set to NO then doxygen will show members with
-# their full class and namespace scopes in the documentation. If set to YES the
-# scope will be hidden.
-# The default value is: NO.
-
-HIDE_SCOPE_NAMES       = NO
-
-# If the SHOW_INCLUDE_FILES tag is set to YES then doxygen will put a list of
-# the files that are included by a file in the documentation of that file.
-# The default value is: YES.
-
-SHOW_INCLUDE_FILES     = YES
-
-# If the SHOW_GROUPED_MEMB_INC tag is set to YES then Doxygen will add for each
-# grouped member an include statement to the documentation, telling the reader
-# which file to include in order to use the member.
-# The default value is: NO.
-
-#SHOW_GROUPED_MEMB_INC  = NO
-
-# If the FORCE_LOCAL_INCLUDES tag is set to YES then doxygen will list include
-# files with double quotes in the documentation rather than with sharp brackets.
-# The default value is: NO.
-
-FORCE_LOCAL_INCLUDES   = NO
-
-# If the INLINE_INFO tag is set to YES then a tag [inline] is inserted in the
-# documentation for inline members.
-# The default value is: YES.
-
-INLINE_INFO            = YES
-
-# If the SORT_MEMBER_DOCS tag is set to YES then doxygen will sort the
-# (detailed) documentation of file and class members alphabetically by member
-# name. If set to NO the members will appear in declaration order.
-# The default value is: YES.
-
-SORT_MEMBER_DOCS       = YES
-
-# If the SORT_BRIEF_DOCS tag is set to YES then doxygen will sort the brief
-# descriptions of file, namespace and class members alphabetically by member
-# name. If set to NO the members will appear in declaration order. Note that
-# this will also influence the order of the classes in the class list.
-# The default value is: NO.
-
-SORT_BRIEF_DOCS        = NO
-
-# If the SORT_MEMBERS_CTORS_1ST tag is set to YES then doxygen will sort the
-# (brief and detailed) documentation of class members so that constructors and
-# destructors are listed first. If set to NO the constructors will appear in the
-# respective orders defined by SORT_BRIEF_DOCS and SORT_MEMBER_DOCS.
-# Note: If SORT_BRIEF_DOCS is set to NO this option is ignored for sorting brief
-# member documentation.
-# Note: If SORT_MEMBER_DOCS is set to NO this option is ignored for sorting
-# detailed member documentation.
-# The default value is: NO.
-
-SORT_MEMBERS_CTORS_1ST = NO
-
-# If the SORT_GROUP_NAMES tag is set to YES then doxygen will sort the hierarchy
-# of group names into alphabetical order. If set to NO the group names will
-# appear in their defined order.
-# The default value is: NO.
-
-SORT_GROUP_NAMES       = NO
-
-# If the SORT_BY_SCOPE_NAME tag is set to YES, the class list will be sorted by
-# fully-qualified names, including namespaces. If set to NO, the class list will
-# be sorted only by class name, not including the namespace part.
-# Note: This option is not very useful if HIDE_SCOPE_NAMES is set to YES.
-# Note: This option applies only to the class list, not to the alphabetical
-# list.
-# The default value is: NO.
-
-SORT_BY_SCOPE_NAME     = NO
-
-# If the STRICT_PROTO_MATCHING option is enabled and doxygen fails to do proper
-# type resolution of all parameters of a function it will reject a match between
-# the prototype and the implementation of a member function even if there is
-# only one candidate or it is obvious which candidate to choose by doing a
-# simple string match. By disabling STRICT_PROTO_MATCHING doxygen will still
-# accept a match between prototype and implementation in such cases.
-# The default value is: NO.
-
-STRICT_PROTO_MATCHING  = NO
-
-# The GENERATE_TODOLIST tag can be used to enable ( YES) or disable ( NO) the
-# todo list. This list is created by putting \todo commands in the
-# documentation.
-# The default value is: YES.
-
-GENERATE_TODOLIST      = YES
-
-# The GENERATE_TESTLIST tag can be used to enable ( YES) or disable ( NO) the
-# test list. This list is created by putting \test commands in the
-# documentation.
-# The default value is: YES.
-
-GENERATE_TESTLIST      = YES
-
-# The GENERATE_BUGLIST tag can be used to enable ( YES) or disable ( NO) the bug
-# list. This list is created by putting \bug commands in the documentation.
-# The default value is: YES.
-
-GENERATE_BUGLIST       = YES
-
-# The GENERATE_DEPRECATEDLIST tag can be used to enable ( YES) or disable ( NO)
-# the deprecated list. This list is created by putting \deprecated commands in
-# the documentation.
-# The default value is: YES.
-
-GENERATE_DEPRECATEDLIST= YES
-
-# The ENABLED_SECTIONS tag can be used to enable conditional documentation
-# sections, marked by \if <section_label> ... \endif and \cond <section_label>
-# ... \endcond blocks.
-
-ENABLED_SECTIONS       =
-
-# The MAX_INITIALIZER_LINES tag determines the maximum number of lines that the
-# initial value of a variable or macro / define can have for it to appear in the
-# documentation. If the initializer consists of more lines than specified here
-# it will be hidden. Use a value of 0 to hide initializers completely. The
-# appearance of the value of individual variables and macros / defines can be
-# controlled using \showinitializer or \hideinitializer command in the
-# documentation regardless of this setting.
-# Minimum value: 0, maximum value: 10000, default value: 30.
-
-MAX_INITIALIZER_LINES  = 30
-
-# Set the SHOW_USED_FILES tag to NO to disable the list of files generated at
-# the bottom of the documentation of classes and structs. If set to YES the list
-# will mention the files that were used to generate the documentation.
-# The default value is: YES.
-
-SHOW_USED_FILES        = YES
-
-# Set the SHOW_FILES tag to NO to disable the generation of the Files page. This
-# will remove the Files entry from the Quick Index and from the Folder Tree View
-# (if specified).
-# The default value is: YES.
-
-SHOW_FILES             = YES
-
-# Set the SHOW_NAMESPACES tag to NO to disable the generation of the Namespaces
-# page. This will remove the Namespaces entry from the Quick Index and from the
-# Folder Tree View (if specified).
-# The default value is: YES.
-
-SHOW_NAMESPACES        = YES
-
-# The FILE_VERSION_FILTER tag can be used to specify a program or script that
-# doxygen should invoke to get the current version for each file (typically from
-# the version control system). Doxygen will invoke the program by executing (via
-# popen()) the command command input-file, where command is the value of the
-# FILE_VERSION_FILTER tag, and input-file is the name of an input file provided
-# by doxygen. Whatever the program writes to standard output is used as the file
-# version. For an example see the documentation.
-
-FILE_VERSION_FILTER    =
-
-# The LAYOUT_FILE tag can be used to specify a layout file which will be parsed
-# by doxygen. The layout file controls the global structure of the generated
-# output files in an output format independent way. To create the layout file
-# that represents doxygen's defaults, run doxygen with the -l option. You can
-# optionally specify a file name after the option, if omitted DoxygenLayout.xml
-# will be used as the name of the layout file.
-#
-# Note that if you run doxygen from a directory containing a file called
-# DoxygenLayout.xml, doxygen will parse it automatically even if the LAYOUT_FILE
-# tag is left empty.
-
-LAYOUT_FILE            =
-
-# The CITE_BIB_FILES tag can be used to specify one or more bib files containing
-# the reference definitions. This must be a list of .bib files. The .bib
-# extension is automatically appended if omitted. This requires the bibtex tool
-# to be installed. See also http://en.wikipedia.org/wiki/BibTeX for more info.
-# For LaTeX the style of the bibliography can be controlled using
-# LATEX_BIB_STYLE. To use this feature you need bibtex and perl available in the
-# search path. See also \cite for info how to create references.
-
-CITE_BIB_FILES         =
-
-#---------------------------------------------------------------------------
-# Configuration options related to warning and progress messages
-#---------------------------------------------------------------------------
-
-# The QUIET tag can be used to turn on/off the messages that are generated to
-# standard output by doxygen. If QUIET is set to YES this implies that the
-# messages are off.
-# The default value is: NO.
-
-QUIET                  = NO
-
-# The WARNINGS tag can be used to turn on/off the warning messages that are
-# generated to standard error ( stderr) by doxygen. If WARNINGS is set to YES
-# this implies that the warnings are on.
-#
-# Tip: Turn warnings on while writing the documentation.
-# The default value is: YES.
-
-WARNINGS               = YES
-
-# If the WARN_IF_UNDOCUMENTED tag is set to YES, then doxygen will generate
-# warnings for undocumented members. If EXTRACT_ALL is set to YES then this flag
-# will automatically be disabled.
-# The default value is: YES.
-
-WARN_IF_UNDOCUMENTED   = YES
-
-# If the WARN_IF_DOC_ERROR tag is set to YES, doxygen will generate warnings for
-# potential errors in the documentation, such as not documenting some parameters
-# in a documented function, or documenting parameters that don't exist or using
-# markup commands wrongly.
-# The default value is: YES.
-
-WARN_IF_DOC_ERROR      = YES
-
-# This WARN_NO_PARAMDOC option can be enabled to get warnings for functions that
-# are documented, but have no documentation for their parameters or return
-# value. If set to NO doxygen will only warn about wrong or incomplete parameter
-# documentation, but not about the absence of documentation.
-# The default value is: NO.
-
-WARN_NO_PARAMDOC       = YES
-
-# The WARN_FORMAT tag determines the format of the warning messages that doxygen
-# can produce. The string should contain the $file, $line, and $text tags, which
-# will be replaced by the file and line number from which the warning originated
-# and the warning text. Optionally the format may contain $version, which will
-# be replaced by the version of the file (if it could be obtained via
-# FILE_VERSION_FILTER)
-# The default value is: $file:$line: $text.
-
-WARN_FORMAT            = "$file:$line: $text"
-
-# The WARN_LOGFILE tag can be used to specify a file to which warning and error
-# messages should be written. If left blank the output is written to standard
-# error (stderr).
-
-WARN_LOGFILE           =
-
-#---------------------------------------------------------------------------
-# Configuration options related to the input files
-#---------------------------------------------------------------------------
-
-# The INPUT tag is used to specify the files and/or directories that contain
-# documented source files. You may enter file names like myfile.cpp or
-# directories like /usr/src/myproject. Separate the files or directories with
-# spaces.
-# Note: If this tag is empty the current directory is searched.
-
-INPUT                  = include src/common cpp-package/include/mxnet-cpp
-
-# This tag can be used to specify the character encoding of the source files
-# that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses
-# libiconv (or the iconv built into libc) for the transcoding. See the libiconv
-# documentation (see: http://www.gnu.org/software/libiconv) for the list of
-# possible encodings.
-# The default value is: UTF-8.
-
-INPUT_ENCODING         = UTF-8
-
-# If the value of the INPUT tag contains directories, you can use the
-# FILE_PATTERNS tag to specify one or more wildcard patterns (like *.cpp and
-# *.h) to filter out the source-files in the directories. If left blank the
-# following patterns are tested:*.c, *.cc, *.cxx, *.cpp, *.c++, *.java, *.ii,
-# *.ixx, *.ipp, *.i++, *.inl, *.idl, *.ddl, *.odl, *.h, *.hh, *.hxx, *.hpp,
-# *.h++, *.cs, *.d, *.php, *.php4, *.php5, *.phtml, *.inc, *.m, *.markdown,
-# *.md, *.mm, *.dox, *.py, *.f90, *.f, *.for, *.tcl, *.vhd, *.vhdl, *.ucf,
-# *.qsf, *.as and *.js.
-
-FILE_PATTERNS          = *.h
-
-# The RECURSIVE tag can be used to specify whether or not subdirectories should
-# be searched for input files as well.
-# The default value is: NO.
-
-RECURSIVE              = YES
-
-# The EXCLUDE tag can be used to specify files and/or directories that should be
-# excluded from the INPUT source files. This way you can easily exclude a
-# subdirectory from a directory tree whose root is specified with the INPUT tag.
-#
-# Note that relative paths are relative to the directory from which doxygen is
-# run.
-
-EXCLUDE                = 3rdparty
-
-# The EXCLUDE_SYMLINKS tag can be used to select whether or not files or
-# directories that are symbolic links (a Unix file system feature) are excluded
-# from the input.
-# The default value is: NO.
-
-EXCLUDE_SYMLINKS       = NO
-
-# If the value of the INPUT tag contains directories, you can use the
-# EXCLUDE_PATTERNS tag to specify one or more wildcard patterns to exclude
-# certain files from those directories.
-#
-# Note that the wildcards are matched against the file with absolute path, so to
-# exclude all test directories for example use the pattern */test/*
-
-EXCLUDE_PATTERNS       = */test/* \
-                         logging.h
-
-# The EXCLUDE_SYMBOLS tag can be used to specify one or more symbol names
-# (namespaces, classes, functions, etc.) that should be excluded from the
-# output. The symbol name can be a fully qualified name, a word, or if the
-# wildcard * is used, a substring. Examples: ANamespace, AClass,
-# AClass::ANamespace, ANamespace::*Test
-#
-# Note that the wildcards are matched against the file with absolute path, so to
-# exclude all test directories use the pattern */test/*
-
-EXCLUDE_SYMBOLS        =
-
-# The EXAMPLE_PATH tag can be used to specify one or more files or directories
-# that contain example code fragments that are included (see the \include
-# command).
-
-EXAMPLE_PATH           =
-
-# If the value of the EXAMPLE_PATH tag contains directories, you can use the
-# EXAMPLE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp and
-# *.h) to filter out the source-files in the directories. If left blank all
-# files are included.
-
-EXAMPLE_PATTERNS       =
-
-# If the EXAMPLE_RECURSIVE tag is set to YES then subdirectories will be
-# searched for input files to be used with the \include or \dontinclude commands
-# irrespective of the value of the RECURSIVE tag.
-# The default value is: NO.
-
-EXAMPLE_RECURSIVE      = NO
-
-# The IMAGE_PATH tag can be used to specify one or more files or directories
-# that contain images that are to be included in the documentation (see the
-# \image command).
-
-IMAGE_PATH             =
-
-# The INPUT_FILTER tag can be used to specify a program that doxygen should
-# invoke to filter for each input file. Doxygen will invoke the filter program
-# by executing (via popen()) the command:
-#
-# <filter> <input-file>
-#
-# where <filter> is the value of the INPUT_FILTER tag, and <input-file> is the
-# name of an input file. Doxygen will then use the output that the filter
-# program writes to standard output. If FILTER_PATTERNS is specified, this tag
-# will be ignored.
-#
-# Note that the filter must not add or remove lines; it is applied before the
-# code is scanned, but not when the output code is generated. If lines are added
-# or removed, the anchors will not be placed correctly.
-
-INPUT_FILTER           =
-
-# The FILTER_PATTERNS tag can be used to specify filters on a per file pattern
-# basis. Doxygen will compare the file name with each pattern and apply the
-# filter if there is a match. The filters are a list of the form: pattern=filter
-# (like *.cpp=my_cpp_filter). See INPUT_FILTER for further information on how
-# filters are used. If the FILTER_PATTERNS tag is empty or if none of the
-# patterns match the file name, INPUT_FILTER is applied.
-
-FILTER_PATTERNS        =
-
-# If the FILTER_SOURCE_FILES tag is set to YES, the input filter (if set using
-# INPUT_FILTER ) will also be used to filter the input files that are used for
-# producing the source files to browse (i.e. when SOURCE_BROWSER is set to YES).
-# The default value is: NO.
-
-FILTER_SOURCE_FILES    = NO
-
-# The FILTER_SOURCE_PATTERNS tag can be used to specify source filters per file
-# pattern. A pattern will override the setting for FILTER_PATTERN (if any) and
-# it is also possible to disable source filtering for a specific pattern using
-# *.ext= (so without naming a filter).
-# This tag requires that the tag FILTER_SOURCE_FILES is set to YES.
-
-FILTER_SOURCE_PATTERNS =
-
-# If the USE_MDFILE_AS_MAINPAGE tag refers to the name of a markdown file that
-# is part of the input, its contents will be placed on the main page
-# (index.html). This can be useful if you have a project on for instance GitHub
-# and want to reuse the introduction page also for the doxygen output.
-
-#USE_MDFILE_AS_MAINPAGE =
-
-#---------------------------------------------------------------------------
-# Configuration options related to source browsing
-#---------------------------------------------------------------------------
-
-# If the SOURCE_BROWSER tag is set to YES then a list of source files will be
-# generated. Documented entities will be cross-referenced with these sources.
-#
-# Note: To get rid of all source code in the generated output, make sure that
-# also VERBATIM_HEADERS is set to NO.
-# The default value is: NO.
-
-SOURCE_BROWSER         = NO
-
-# Setting the INLINE_SOURCES tag to YES will include the body of functions,
-# classes and enums directly into the documentation.
-# The default value is: NO.
-
-INLINE_SOURCES         = NO
-
-# Setting the STRIP_CODE_COMMENTS tag to YES will instruct doxygen to hide any
-# special comment blocks from generated source code fragments. Normal C, C++ and
-# Fortran comments will always remain visible.
-# The default value is: YES.
-
-STRIP_CODE_COMMENTS    = YES
-
-# If the REFERENCED_BY_RELATION tag is set to YES then for each documented
-# function all documented functions referencing it will be listed.
-# The default value is: NO.
-
-REFERENCED_BY_RELATION = NO
-
-# If the REFERENCES_RELATION tag is set to YES then for each documented function
-# all documented entities called/used by that function will be listed.
-# The default value is: NO.
-
-REFERENCES_RELATION    = NO
-
-# If the REFERENCES_LINK_SOURCE tag is set to YES and SOURCE_BROWSER tag is set
-# to YES, then the hyperlinks from functions in REFERENCES_RELATION and
-# REFERENCED_BY_RELATION lists will link to the source code. Otherwise they will
-# link to the documentation.
-# The default value is: YES.
-
-REFERENCES_LINK_SOURCE = YES
-
-# If SOURCE_TOOLTIPS is enabled (the default) then hovering a hyperlink in the
-# source code will show a tooltip with additional information such as prototype,
-# brief description and links to the definition and documentation. Since this
-# will make the HTML file larger and loading of large files a bit slower, you
-# can opt to disable this feature.
-# The default value is: YES.
-# This tag requires that the tag SOURCE_BROWSER is set to YES.
-
-#SOURCE_TOOLTIPS        = YES
-
-# If the USE_HTAGS tag is set to YES then the references to source code will
-# point to the HTML generated by the htags(1) tool instead of doxygen built-in
-# source browser. The htags tool is part of GNU's global source tagging system
-# (see http://www.gnu.org/software/global/global.html). You will need version
-# 4.8.6 or higher.
-#
-# To use it do the following:
-# - Install the latest version of global
-# - Enable SOURCE_BROWSER and USE_HTAGS in the config file
-# - Make sure the INPUT points to the root of the source tree
-# - Run doxygen as normal
-#
-# Doxygen will invoke htags (and that will in turn invoke gtags), so these
-# tools must be available from the command line (i.e. in the search path).
-#
-# The result: instead of the source browser generated by doxygen, the links to
-# source code will now point to the output of htags.
-# The default value is: NO.
-# This tag requires that the tag SOURCE_BROWSER is set to YES.
-
-USE_HTAGS              = NO
-
-# If the VERBATIM_HEADERS tag is set the YES then doxygen will generate a
-# verbatim copy of the header file for each class for which an include is
-# specified. Set to NO to disable this.
-# See also: Section \class.
-# The default value is: YES.
-
-VERBATIM_HEADERS       = YES
-
-# If the CLANG_ASSISTED_PARSING tag is set to YES, then doxygen will use the
-# clang parser (see: http://clang.llvm.org/) for more accurate parsing at the
-# cost of reduced performance. This can be particularly helpful with template
-# rich C++ code for which doxygen's built-in parser lacks the necessary type
-# information.
-# Note: The availability of this option depends on whether or not doxygen was
-# compiled with the --with-libclang option.
-# The default value is: NO.
-
-#CLANG_ASSISTED_PARSING = NO
-
-# If clang assisted parsing is enabled you can provide the compiler with command
-# line options that you would normally use when invoking the compiler. Note that
-# the include paths will already be set by doxygen for the files and directories
-# specified with INPUT and INCLUDE_PATH.
-# This tag requires that the tag CLANG_ASSISTED_PARSING is set to YES.
-
-#CLANG_OPTIONS          =
-
-#---------------------------------------------------------------------------
-# Configuration options related to the alphabetical class index
-#---------------------------------------------------------------------------
-
-# If the ALPHABETICAL_INDEX tag is set to YES, an alphabetical index of all
-# compounds will be generated. Enable this if the project contains a lot of
-# classes, structs, unions or interfaces.
-# The default value is: YES.
-
-ALPHABETICAL_INDEX     = YES
-
-# The COLS_IN_ALPHA_INDEX tag can be used to specify the number of columns in
-# which the alphabetical index list will be split.
-# Minimum value: 1, maximum value: 20, default value: 5.
-# This tag requires that the tag ALPHABETICAL_INDEX is set to YES.
-
-COLS_IN_ALPHA_INDEX    = 5
-
-# In case all classes in a project start with a common prefix, all classes will
-# be put under the same header in the alphabetical index. The IGNORE_PREFIX tag
-# can be used to specify a prefix (or a list of prefixes) that should be ignored
-# while generating the index headers.
-# This tag requires that the tag ALPHABETICAL_INDEX is set to YES.
-
-IGNORE_PREFIX          =
-
-#---------------------------------------------------------------------------
-# Configuration options related to the HTML output
-#---------------------------------------------------------------------------
-
-# If the GENERATE_HTML tag is set to YES doxygen will generate HTML output
-# The default value is: YES.
-
-GENERATE_HTML          = YES
-
-# The HTML_OUTPUT tag is used to specify where the HTML docs will be put. If a
-# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
-# it.
-# The default directory is: html.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_OUTPUT            = html
-
-# The HTML_FILE_EXTENSION tag can be used to specify the file extension for each
-# generated HTML page (for example: .htm, .php, .asp).
-# The default value is: .html.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_FILE_EXTENSION    = .html
-
-# The HTML_HEADER tag can be used to specify a user-defined HTML header file for
-# each generated HTML page. If the tag is left blank doxygen will generate a
-# standard header.
-#
-# To get valid HTML the header file that includes any scripts and style sheets
-# that doxygen needs, which is dependent on the configuration options used (e.g.
-# the setting GENERATE_TREEVIEW). It is highly recommended to start with a
-# default header using
-# doxygen -w html new_header.html new_footer.html new_stylesheet.css
-# YourConfigFile
-# and then modify the file new_header.html. See also section "Doxygen usage"
-# for information on how to generate the default header that doxygen normally
-# uses.
-# Note: The header is subject to change so you typically have to regenerate the
-# default header when upgrading to a newer version of doxygen. For a description
-# of the possible markers and block names see the documentation.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_HEADER            =
-
-# The HTML_FOOTER tag can be used to specify a user-defined HTML footer for each
-# generated HTML page. If the tag is left blank doxygen will generate a standard
-# footer. See HTML_HEADER for more information on how to generate a default
-# footer and what special commands can be used inside the footer. See also
-# section "Doxygen usage" for information on how to generate the default footer
-# that doxygen normally uses.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_FOOTER            =
-
-# The HTML_STYLESHEET tag can be used to specify a user-defined cascading style
-# sheet that is used by each HTML page. It can be used to fine-tune the look of
-# the HTML output. If left blank doxygen will generate a default style sheet.
-# See also section "Doxygen usage" for information on how to generate the style
-# sheet that doxygen normally uses.
-# Note: It is recommended to use HTML_EXTRA_STYLESHEET instead of this tag, as
-# it is more robust and this tag (HTML_STYLESHEET) will in the future become
-# obsolete.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_STYLESHEET        =
-
-# The HTML_EXTRA_STYLESHEET tag can be used to specify additional user-defined
-# cascading style sheets that are included after the standard style sheets
-# created by doxygen. Using this option one can overrule certain style aspects.
-# This is preferred over using HTML_STYLESHEET since it does not replace the
-# standard style sheet and is therefor more robust against future updates.
-# Doxygen will copy the style sheet files to the output directory.
-# Note: The order of the extra stylesheet files is of importance (e.g. the last
-# stylesheet in the list overrules the setting of the previous ones in the
-# list). For an example see the documentation.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-#HTML_EXTRA_STYLESHEET  =
-
-# The HTML_EXTRA_FILES tag can be used to specify one or more extra images or
-# other source files which should be copied to the HTML output directory. Note
-# that these files will be copied to the base HTML output directory. Use the
-# $relpath^ marker in the HTML_HEADER and/or HTML_FOOTER files to load these
-# files. In the HTML_STYLESHEET file, use the file name only. Also note that the
-# files will be copied as-is; there are no commands or markers available.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_EXTRA_FILES       =
-
-# The HTML_COLORSTYLE_HUE tag controls the color of the HTML output. Doxygen
-# will adjust the colors in the stylesheet and background images according to
-# this color. Hue is specified as an angle on a colorwheel, see
-# http://en.wikipedia.org/wiki/Hue for more information. For instance the value
-# 0 represents red, 60 is yellow, 120 is green, 180 is cyan, 240 is blue, 300
-# purple, and 360 is red again.
-# Minimum value: 0, maximum value: 359, default value: 220.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_COLORSTYLE_HUE    = 220
-
-# The HTML_COLORSTYLE_SAT tag controls the purity (or saturation) of the colors
-# in the HTML output. For a value of 0 the output will use grayscales only. A
-# value of 255 will produce the most vivid colors.
-# Minimum value: 0, maximum value: 255, default value: 100.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_COLORSTYLE_SAT    = 100
-
-# The HTML_COLORSTYLE_GAMMA tag controls the gamma correction applied to the
-# luminance component of the colors in the HTML output. Values below 100
-# gradually make the output lighter, whereas values above 100 make the output
-# darker. The value divided by 100 is the actual gamma applied, so 80 represents
-# a gamma of 0.8, The value 220 represents a gamma of 2.2, and 100 does not
-# change the gamma.
-# Minimum value: 40, maximum value: 240, default value: 80.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_COLORSTYLE_GAMMA  = 80
-
-# If the HTML_TIMESTAMP tag is set to YES then the footer of each generated HTML
-# page will contain the date and time when the page was generated. Setting this
-# to NO can help when comparing the output of multiple runs.
-# The default value is: YES.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_TIMESTAMP         = YES
-
-# If the HTML_DYNAMIC_SECTIONS tag is set to YES then the generated HTML
-# documentation will contain sections that can be hidden and shown after the
-# page has loaded.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_DYNAMIC_SECTIONS  = NO
-
-# With HTML_INDEX_NUM_ENTRIES one can control the preferred number of entries
-# shown in the various tree structured indices initially; the user can expand
-# and collapse entries dynamically later on. Doxygen will expand the tree to
-# such a level that at most the specified number of entries are visible (unless
-# a fully collapsed tree already exceeds this amount). So setting the number of
-# entries 1 will produce a full collapsed tree by default. 0 is a special value
-# representing an infinite number of entries and will result in a full expanded
-# tree by default.
-# Minimum value: 0, maximum value: 9999, default value: 100.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-#HTML_INDEX_NUM_ENTRIES = 100
-
-# If the GENERATE_DOCSET tag is set to YES, additional index files will be
-# generated that can be used as input for Apple's Xcode 3 integrated development
-# environment (see: http://developer.apple.com/tools/xcode/), introduced with
-# OSX 10.5 (Leopard). To create a documentation set, doxygen will generate a
-# Makefile in the HTML output directory. Running make will produce the docset in
-# that directory and running make install will install the docset in
-# ~/Library/Developer/Shared/Documentation/DocSets so that Xcode will find it at
-# startup. See http://developer.apple.com/tools/creatingdocsetswithdoxygen.html
-# for more information.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-GENERATE_DOCSET        = NO
-
-# This tag determines the name of the docset feed. A documentation feed provides
-# an umbrella under which multiple documentation sets from a single provider
-# (such as a company or product suite) can be grouped.
-# The default value is: Doxygen generated docs.
-# This tag requires that the tag GENERATE_DOCSET is set to YES.
-
-DOCSET_FEEDNAME        = "Doxygen generated docs"
-
-# This tag specifies a string that should uniquely identify the documentation
-# set bundle. This should be a reverse domain-name style string, e.g.
-# com.mycompany.MyDocSet. Doxygen will append .docset to the name.
-# The default value is: org.doxygen.Project.
-# This tag requires that the tag GENERATE_DOCSET is set to YES.
-
-DOCSET_BUNDLE_ID       = org.doxygen.Project
-
-# The DOCSET_PUBLISHER_ID tag specifies a string that should uniquely identify
-# the documentation publisher. This should be a reverse domain-name style
-# string, e.g. com.mycompany.MyDocSet.documentation.
-# The default value is: org.doxygen.Publisher.
-# This tag requires that the tag GENERATE_DOCSET is set to YES.
-
-DOCSET_PUBLISHER_ID    = org.doxygen.Publisher
-
-# The DOCSET_PUBLISHER_NAME tag identifies the documentation publisher.
-# The default value is: Publisher.
-# This tag requires that the tag GENERATE_DOCSET is set to YES.
-
-DOCSET_PUBLISHER_NAME  = Publisher
-
-# If the GENERATE_HTMLHELP tag is set to YES then doxygen generates three
-# additional HTML index files: index.hhp, index.hhc, and index.hhk. The
-# index.hhp is a project file that can be read by Microsoft's HTML Help Workshop
-# (see: http://www.microsoft.com/en-us/download/details.aspx?id=21138) on
-# Windows.
-#
-# The HTML Help Workshop contains a compiler that can convert all HTML output
-# generated by doxygen into a single compiled HTML file (.chm). Compiled HTML
-# files are now used as the Windows 98 help format, and will replace the old
-# Windows help format (.hlp) on all Windows platforms in the future. Compressed
-# HTML files also contain an index, a table of contents, and you can search for
-# words in the documentation. The HTML workshop also contains a viewer for
-# compressed HTML files.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-GENERATE_HTMLHELP      = NO
-
-# The CHM_FILE tag can be used to specify the file name of the resulting .chm
-# file. You can add a path in front of the file if the result should not be
-# written to the html output directory.
-# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
-
-CHM_FILE               =
-
-# The HHC_LOCATION tag can be used to specify the location (absolute path
-# including file name) of the HTML help compiler ( hhc.exe). If non-empty
-# doxygen will try to run the HTML help compiler on the generated index.hhp.
-# The file has to be specified with full path.
-# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
-
-HHC_LOCATION           =
-
-# The GENERATE_CHI flag controls if a separate .chi index file is generated (
-# YES) or that it should be included in the master .chm file ( NO).
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
-
-GENERATE_CHI           = NO
-
-# The CHM_INDEX_ENCODING is used to encode HtmlHelp index ( hhk), content ( hhc)
-# and project file content.
-# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
-
-CHM_INDEX_ENCODING     =
-
-# The BINARY_TOC flag controls whether a binary table of contents is generated (
-# YES) or a normal table of contents ( NO) in the .chm file. Furthermore it
-# enables the Previous and Next buttons.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
-
-BINARY_TOC             = NO
-
-# The TOC_EXPAND flag can be set to YES to add extra items for group members to
-# the table of contents of the HTML help documentation and to the tree view.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
-
-TOC_EXPAND             = NO
-
-# If the GENERATE_QHP tag is set to YES and both QHP_NAMESPACE and
-# QHP_VIRTUAL_FOLDER are set, an additional index file will be generated that
-# can be used as input for Qt's qhelpgenerator to generate a Qt Compressed Help
-# (.qch) of the generated HTML documentation.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-GENERATE_QHP           = NO
-
-# If the QHG_LOCATION tag is specified, the QCH_FILE tag can be used to specify
-# the file name of the resulting .qch file. The path specified is relative to
-# the HTML output folder.
-# This tag requires that the tag GENERATE_QHP is set to YES.
-
-QCH_FILE               =
-
-# The QHP_NAMESPACE tag specifies the namespace to use when generating Qt Help
-# Project output. For more information please see Qt Help Project / Namespace
-# (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#namespace).
-# The default value is: org.doxygen.Project.
-# This tag requires that the tag GENERATE_QHP is set to YES.
-
-QHP_NAMESPACE          = org.doxygen.Project
-
-# The QHP_VIRTUAL_FOLDER tag specifies the namespace to use when generating Qt
-# Help Project output. For more information please see Qt Help Project / Virtual
-# Folders (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#virtual-
-# folders).
-# The default value is: doc.
-# This tag requires that the tag GENERATE_QHP is set to YES.
-
-QHP_VIRTUAL_FOLDER     = doc
-
-# If the QHP_CUST_FILTER_NAME tag is set, it specifies the name of a custom
-# filter to add. For more information please see Qt Help Project / Custom
-# Filters (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#custom-
-# filters).
-# This tag requires that the tag GENERATE_QHP is set to YES.
-
-QHP_CUST_FILTER_NAME   =
-
-# The QHP_CUST_FILTER_ATTRS tag specifies the list of the attributes of the
-# custom filter to add. For more information please see Qt Help Project / Custom
-# Filters (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#custom-
-# filters).
-# This tag requires that the tag GENERATE_QHP is set to YES.
-
-QHP_CUST_FILTER_ATTRS  =
-
-# The QHP_SECT_FILTER_ATTRS tag specifies the list of the attributes this
-# project's filter section matches. Qt Help Project / Filter Attributes (see:
-# http://qt-project.org/doc/qt-4.8/qthelpproject.html#filter-attributes).
-# This tag requires that the tag GENERATE_QHP is set to YES.
-
-QHP_SECT_FILTER_ATTRS  =
-
-# The QHG_LOCATION tag can be used to specify the location of Qt's
-# qhelpgenerator. If non-empty doxygen will try to run qhelpgenerator on the
-# generated .qhp file.
-# This tag requires that the tag GENERATE_QHP is set to YES.
-
-QHG_LOCATION           =
-
-# If the GENERATE_ECLIPSEHELP tag is set to YES, additional index files will be
-# generated, together with the HTML files, they form an Eclipse help plugin. To
-# install this plugin and make it available under the help contents menu in
-# Eclipse, the contents of the directory containing the HTML and XML files needs
-# to be copied into the plugins directory of eclipse. The name of the directory
-# within the plugins directory should be the same as the ECLIPSE_DOC_ID value.
-# After copying Eclipse needs to be restarted before the help appears.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-GENERATE_ECLIPSEHELP   = NO
-
-# A unique identifier for the Eclipse help plugin. When installing the plugin
-# the directory name containing the HTML and XML files should also have this
-# name. Each documentation set should have its own identifier.
-# The default value is: org.doxygen.Project.
-# This tag requires that the tag GENERATE_ECLIPSEHELP is set to YES.
-
-ECLIPSE_DOC_ID         = org.doxygen.Project
-
-# If you want full control over the layout of the generated HTML pages it might
-# be necessary to disable the index and replace it with your own. The
-# DISABLE_INDEX tag can be used to turn on/off the condensed index (tabs) at top
-# of each HTML page. A value of NO enables the index and the value YES disables
-# it. Since the tabs in the index contain the same information as the navigation
-# tree, you can set this option to YES if you also set GENERATE_TREEVIEW to YES.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-DISABLE_INDEX          = NO
-
-# The GENERATE_TREEVIEW tag is used to specify whether a tree-like index
-# structure should be generated to display hierarchical information. If the tag
-# value is set to YES, a side panel will be generated containing a tree-like
-# index structure (just like the one that is generated for HTML Help). For this
-# to work a browser that supports JavaScript, DHTML, CSS and frames is required
-# (i.e. any modern browser). Windows users are probably better off using the
-# HTML help feature. Via custom stylesheets (see HTML_EXTRA_STYLESHEET) one can
-# further fine-tune the look of the index. As an example, the default style
-# sheet generated by doxygen has an example that shows how to put an image at
-# the root of the tree instead of the PROJECT_NAME. Since the tree basically has
-# the same information as the tab index, you could consider setting
-# DISABLE_INDEX to YES when enabling this option.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-GENERATE_TREEVIEW      = NO
-
-# The ENUM_VALUES_PER_LINE tag can be used to set the number of enum values that
-# doxygen will group on one line in the generated HTML documentation.
-#
-# Note that a value of 0 will completely suppress the enum values from appearing
-# in the overview section.
-# Minimum value: 0, maximum value: 20, default value: 4.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-ENUM_VALUES_PER_LINE   = 4
-
-# If the treeview is enabled (see GENERATE_TREEVIEW) then this tag can be used
-# to set the initial width (in pixels) of the frame in which the tree is shown.
-# Minimum value: 0, maximum value: 1500, default value: 250.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-TREEVIEW_WIDTH         = 250
-
-# When the EXT_LINKS_IN_WINDOW option is set to YES doxygen will open links to
-# external symbols imported via tag files in a separate window.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-EXT_LINKS_IN_WINDOW    = NO
-
-# Use this tag to change the font size of LaTeX formulas included as images in
-# the HTML documentation. When you change the font size after a successful
-# doxygen run you need to manually remove any form_*.png images from the HTML
-# output directory to force them to be regenerated.
-# Minimum value: 8, maximum value: 50, default value: 10.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-FORMULA_FONTSIZE       = 10
-
-# Use the FORMULA_TRANPARENT tag to determine whether or not the images
-# generated for formulas are transparent PNGs. Transparent PNGs are not
-# supported properly for IE 6.0, but are supported on all modern browsers.
-#
-# Note that when changing this option you need to delete any form_*.png files in
-# the HTML output directory before the changes have effect.
-# The default value is: YES.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-FORMULA_TRANSPARENT    = YES
-
-# Enable the USE_MATHJAX option to render LaTeX formulas using MathJax (see
-# http://www.mathjax.org) which uses client side Javascript for the rendering
-# instead of using prerendered bitmaps. Use this if you do not have LaTeX
-# installed or if you want to formulas look prettier in the HTML output. When
-# enabled you may also need to install MathJax separately and configure the path
-# to it using the MATHJAX_RELPATH option.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-USE_MATHJAX            = NO
-
-# When MathJax is enabled you can set the default output format to be used for
-# the MathJax output. See the MathJax site (see:
-# http://docs.mathjax.org/en/latest/output.html) for more details.
-# Possible values are: HTML-CSS (which is slower, but has the best
-# compatibility), NativeMML (i.e. MathML) and SVG.
-# The default value is: HTML-CSS.
-# This tag requires that the tag USE_MATHJAX is set to YES.
-
-#MATHJAX_FORMAT         = HTML-CSS
-
-# When MathJax is enabled you need to specify the location relative to the HTML
-# output directory using the MATHJAX_RELPATH option. The destination directory
-# should contain the MathJax.js script. For instance, if the mathjax directory
-# is located at the same level as the HTML output directory, then
-# MATHJAX_RELPATH should be ../mathjax. The default value points to the MathJax
-# Content Delivery Network so you can quickly see the result without installing
-# MathJax. However, it is strongly recommended to install a local copy of
-# MathJax from http://www.mathjax.org before deployment.
-# The default value is: http://cdn.mathjax.org/mathjax/latest.
-# This tag requires that the tag USE_MATHJAX is set to YES.
-
-MATHJAX_RELPATH        = http://www.mathjax.org/mathjax
-
-# The MATHJAX_EXTENSIONS tag can be used to specify one or more MathJax
-# extension names that should be enabled during MathJax rendering. For example
-# MATHJAX_EXTENSIONS = TeX/AMSmath TeX/AMSsymbols
-# This tag requires that the tag USE_MATHJAX is set to YES.
-
-MATHJAX_EXTENSIONS     =
-
-# The MATHJAX_CODEFILE tag can be used to specify a file with javascript pieces
-# of code that will be used on startup of the MathJax code. See the MathJax site
-# (see: http://docs.mathjax.org/en/latest/output.html) for more details. For an
-# example see the documentation.
-# This tag requires that the tag USE_MATHJAX is set to YES.
-
-#MATHJAX_CODEFILE       =
-
-# When the SEARCHENGINE tag is enabled doxygen will generate a search box for
-# the HTML output. The underlying search engine uses javascript and DHTML and
-# should work on any modern browser. Note that when using HTML help
-# (GENERATE_HTMLHELP), Qt help (GENERATE_QHP), or docsets (GENERATE_DOCSET)
-# there is already a search function so this one should typically be disabled.
-# For large projects the javascript based search engine can be slow, then
-# enabling SERVER_BASED_SEARCH may provide a better solution. It is possible to
-# search using the keyboard; to jump to the search box use <access key> + S
-# (what the <access key> is depends on the OS and browser, but it is typically
-# <CTRL>, <ALT>/<option>, or both). Inside the search box use the <cursor down
-# key> to jump into the search results window, the results can be navigated
-# using the <cursor keys>. Press <Enter> to select an item or <escape> to cancel
-# the search. The filter options can be selected when the cursor is inside the
-# search box by pressing <Shift>+<cursor down>. Also here use the <cursor keys>
-# to select a filter and <Enter> or <escape> to activate or cancel the filter
-# option.
-# The default value is: YES.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-SEARCHENGINE           = YES
-
-# When the SERVER_BASED_SEARCH tag is enabled the search engine will be
-# implemented using a web server instead of a web client using Javascript. There
-# are two flavors of web server based searching depending on the EXTERNAL_SEARCH
-# setting. When disabled, doxygen will generate a PHP script for searching and
-# an index file used by the script. When EXTERNAL_SEARCH is enabled the indexing
-# and searching needs to be provided by external tools. See the section
-# "External Indexing and Searching" for details.
-# The default value is: NO.
-# This tag requires that the tag SEARCHENGINE is set to YES.
-
-SERVER_BASED_SEARCH    = NO
-
-# When EXTERNAL_SEARCH tag is enabled doxygen will no longer generate the PHP
-# script for searching. Instead the search results are written to an XML file
-# which needs to be processed by an external indexer. Doxygen will invoke an
-# external search engine pointed to by the SEARCHENGINE_URL option to obtain the
-# search results.
-#
-# Doxygen ships with an example indexer ( doxyindexer) and search engine
-# (doxysearch.cgi) which are based on the open source search engine library
-# Xapian (see: http://xapian.org/).
-#
-# See the section "External Indexing and Searching" for details.
-# The default value is: NO.
-# This tag requires that the tag SEARCHENGINE is set to YES.
-
-#EXTERNAL_SEARCH        = NO
-
-# The SEARCHENGINE_URL should point to a search engine hosted by a web server
-# which will return the search results when EXTERNAL_SEARCH is enabled.
-#
-# Doxygen ships with an example indexer ( doxyindexer) and search engine
-# (doxysearch.cgi) which are based on the open source search engine library
-# Xapian (see: http://xapian.org/). See the section "External Indexing and
-# Searching" for details.
-# This tag requires that the tag SEARCHENGINE is set to YES.
-
-#SEARCHENGINE_URL       =
-
-# When SERVER_BASED_SEARCH and EXTERNAL_SEARCH are both enabled the unindexed
-# search data is written to a file for indexing by an external tool. With the
-# SEARCHDATA_FILE tag the name of this file can be specified.
-# The default file is: searchdata.xml.
-# This tag requires that the tag SEARCHENGINE is set to YES.
-
-#SEARCHDATA_FILE        = searchdata.xml
-
-# When SERVER_BASED_SEARCH and EXTERNAL_SEARCH are both enabled the
-# EXTERNAL_SEARCH_ID tag can be used as an identifier for the project. This is
-# useful in combination with EXTRA_SEARCH_MAPPINGS to search through multiple
-# projects and redirect the results back to the right project.
-# This tag requires that the tag SEARCHENGINE is set to YES.
-
-#EXTERNAL_SEARCH_ID     =
-
-# The EXTRA_SEARCH_MAPPINGS tag can be used to enable searching through doxygen
-# projects other than the one defined by this configuration file, but that are
-# all added to the same external search index. Each project needs to have a
-# unique id set via EXTERNAL_SEARCH_ID. The search mapping then maps the id of
-# to a relative location where the documentation can be found. The format is:
-# EXTRA_SEARCH_MAPPINGS = tagname1=loc1 tagname2=loc2 ...
-# This tag requires that the tag SEARCHENGINE is set to YES.
-
-#EXTRA_SEARCH_MAPPINGS  =
-
-#---------------------------------------------------------------------------
-# Configuration options related to the LaTeX output
-#---------------------------------------------------------------------------
-
-# If the GENERATE_LATEX tag is set to YES doxygen will generate LaTeX output.
-# The default value is: YES.
-
-GENERATE_LATEX         = YES
-
-# The LATEX_OUTPUT tag is used to specify where the LaTeX docs will be put. If a
-# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
-# it.
-# The default directory is: latex.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_OUTPUT           = latex
-
-# The LATEX_CMD_NAME tag can be used to specify the LaTeX command name to be
-# invoked.
-#
-# Note that when enabling USE_PDFLATEX this option is only used for generating
-# bitmaps for formulas in the HTML output, but not in the Makefile that is
-# written to the output directory.
-# The default file is: latex.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_CMD_NAME         = latex
-
-# The MAKEINDEX_CMD_NAME tag can be used to specify the command name to generate
-# index for LaTeX.
-# The default file is: makeindex.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-MAKEINDEX_CMD_NAME     = makeindex
-
-# If the COMPACT_LATEX tag is set to YES doxygen generates more compact LaTeX
-# documents. This may be useful for small projects and may help to save some
-# trees in general.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-COMPACT_LATEX          = NO
-
-# The PAPER_TYPE tag can be used to set the paper type that is used by the
-# printer.
-# Possible values are: a4 (210 x 297 mm), letter (8.5 x 11 inches), legal (8.5 x
-# 14 inches) and executive (7.25 x 10.5 inches).
-# The default value is: a4.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-PAPER_TYPE             = a4
-
-# The EXTRA_PACKAGES tag can be used to specify one or more LaTeX package names
-# that should be included in the LaTeX output. To get the times font for
-# instance you can specify
-# EXTRA_PACKAGES=times
-# If left blank no extra packages will be included.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-EXTRA_PACKAGES         =
-
-# The LATEX_HEADER tag can be used to specify a personal LaTeX header for the
-# generated LaTeX document. The header should contain everything until the first
-# chapter. If it is left blank doxygen will generate a standard header. See
-# section "Doxygen usage" for information on how to let doxygen write the
-# default header to a separate file.
-#
-# Note: Only use a user-defined header if you know what you are doing! The
-# following commands have a special meaning inside the header: $title,
-# $datetime, $date, $doxygenversion, $projectname, $projectnumber,
-# $projectbrief, $projectlogo. Doxygen will replace $title with the empy string,
-# for the replacement values of the other commands the user is refered to
-# HTML_HEADER.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_HEADER           =
-
-# The LATEX_FOOTER tag can be used to specify a personal LaTeX footer for the
-# generated LaTeX document. The footer should contain everything after the last
-# chapter. If it is left blank doxygen will generate a standard footer. See
-# LATEX_HEADER for more information on how to generate a default footer and what
-# special commands can be used inside the footer.
-#
-# Note: Only use a user-defined footer if you know what you are doing!
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_FOOTER           =
-
-# The LATEX_EXTRA_FILES tag can be used to specify one or more extra images or
-# other source files which should be copied to the LATEX_OUTPUT output
-# directory. Note that the files will be copied as-is; there are no commands or
-# markers available.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-#LATEX_EXTRA_FILES      =
-
-# If the PDF_HYPERLINKS tag is set to YES, the LaTeX that is generated is
-# prepared for conversion to PDF (using ps2pdf or pdflatex). The PDF file will
-# contain links (just like the HTML output) instead of page references. This
-# makes the output suitable for online browsing using a PDF viewer.
-# The default value is: YES.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-PDF_HYPERLINKS         = YES
-
-# If the USE_PDFLATEX tag is set to YES, doxygen will use pdflatex to generate
-# the PDF file directly from the LaTeX files. Set this option to YES to get a
-# higher quality PDF documentation.
-# The default value is: YES.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-USE_PDFLATEX           = YES
-
-# If the LATEX_BATCHMODE tag is set to YES, doxygen will add the \batchmode
-# command to the generated LaTeX files. This will instruct LaTeX to keep running
-# if errors occur, instead of asking the user for help. This option is also used
-# when generating formulas in HTML.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_BATCHMODE        = NO
-
-# If the LATEX_HIDE_INDICES tag is set to YES then doxygen will not include the
-# index chapters (such as File Index, Compound Index, etc.) in the output.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_HIDE_INDICES     = NO
-
-# If the LATEX_SOURCE_CODE tag is set to YES then doxygen will include source
-# code with syntax highlighting in the LaTeX output.
-#
-# Note that which sources are shown also depends on other settings such as
-# SOURCE_BROWSER.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_SOURCE_CODE      = NO
-
-# The LATEX_BIB_STYLE tag can be used to specify the style to use for the
-# bibliography, e.g. plainnat, or ieeetr. See
-# http://en.wikipedia.org/wiki/BibTeX and \cite for more info.
-# The default value is: plain.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_BIB_STYLE        = plain
-
-#---------------------------------------------------------------------------
-# Configuration options related to the RTF output
-#---------------------------------------------------------------------------
-
-# If the GENERATE_RTF tag is set to YES doxygen will generate RTF output. The
-# RTF output is optimized for Word 97 and may not look too pretty with other RTF
-# readers/editors.
-# The default value is: NO.
-
-GENERATE_RTF           = NO
-
-# The RTF_OUTPUT tag is used to specify where the RTF docs will be put. If a
-# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
-# it.
-# The default directory is: rtf.
-# This tag requires that the tag GENERATE_RTF is set to YES.
-
-RTF_OUTPUT             = rtf
-
-# If the COMPACT_RTF tag is set to YES doxygen generates more compact RTF
-# documents. This may be useful for small projects and may help to save some
-# trees in general.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_RTF is set to YES.
-
-COMPACT_RTF            = NO
-
-# If the RTF_HYPERLINKS tag is set to YES, the RTF that is generated will
-# contain hyperlink fields. The RTF file will contain links (just like the HTML
-# output) instead of page references. This makes the output suitable for online
-# browsing using Word or some other Word compatible readers that support those
-# fields.
-#
-# Note: WordPad (write) and others do not support links.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_RTF is set to YES.
-
-RTF_HYPERLINKS         = NO
-
-# Load stylesheet definitions from file. Syntax is similar to doxygen's config
-# file, i.e. a series of assignments. You only have to provide replacements,
-# missing definitions are set to their default value.
-#
-# See also section "Doxygen usage" for information on how to generate the
-# default style sheet that doxygen normally uses.
-# This tag requires that the tag GENERATE_RTF is set to YES.
-
-RTF_STYLESHEET_FILE    =
-
-# Set optional variables used in the generation of an RTF document. Syntax is
-# similar to doxygen's config file. A template extensions file can be generated
-# using doxygen -e rtf extensionFile.
-# This tag requires that the tag GENERATE_RTF is set to YES.
-
-RTF_EXTENSIONS_FILE    =
-
-#---------------------------------------------------------------------------
-# Configuration options related to the man page output
-#---------------------------------------------------------------------------
-
-# If the GENERATE_MAN tag is set to YES doxygen will generate man pages for
-# classes and files.
-# The default value is: NO.
-
-GENERATE_MAN           = NO
-
-# The MAN_OUTPUT tag is used to specify where the man pages will be put. If a
-# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
-# it. A directory man3 will be created inside the directory specified by
-# MAN_OUTPUT.
-# The default directory is: man.
-# This tag requires that the tag GENERATE_MAN is set to YES.
-
-MAN_OUTPUT             = man
-
-# The MAN_EXTENSION tag determines the extension that is added to the generated
-# man pages. In case the manual section does not start with a number, the number
-# 3 is prepended. The dot (.) at the beginning of the MAN_EXTENSION tag is
-# optional.
-# The default value is: .3.
-# This tag requires that the tag GENERATE_MAN is set to YES.
-
-MAN_EXTENSION          = .3
-
-# The MAN_SUBDIR tag determines the name of the directory created within
-# MAN_OUTPUT in which the man pages are placed. If defaults to man followed by
-# MAN_EXTENSION with the initial . removed.
-# This tag requires that the tag GENERATE_MAN is set to YES.
-
-#MAN_SUBDIR             =
-
-# If the MAN_LINKS tag is set to YES and doxygen generates man output, then it
-# will generate one additional man file for each entity documented in the real
-# man page(s). These additional files only source the real man page, but without
-# them the man command would be unable to find the correct page.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_MAN is set to YES.
-
-MAN_LINKS              = NO
-
-#---------------------------------------------------------------------------
-# Configuration options related to the XML output
-#---------------------------------------------------------------------------
-
-# If the GENERATE_XML tag is set to YES doxygen will generate an XML file that
-# captures the structure of the code including all documentation.
-# The default value is: NO.
-
-GENERATE_XML           = YES
-
-# The XML_OUTPUT tag is used to specify where the XML pages will be put. If a
-# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
-# it.
-# The default directory is: xml.
-# This tag requires that the tag GENERATE_XML is set to YES.
-
-XML_OUTPUT             = xml
-
-# If the XML_PROGRAMLISTING tag is set to YES doxygen will dump the program
-# listings (including syntax highlighting and cross-referencing information) to
-# the XML output. Note that enabling this will significantly increase the size
-# of the XML output.
-# The default value is: YES.
-# This tag requires that the tag GENERATE_XML is set to YES.
-
-XML_PROGRAMLISTING     = YES
-
-#---------------------------------------------------------------------------
-# Configuration options related to the DOCBOOK output
-#---------------------------------------------------------------------------
-
-# If the GENERATE_DOCBOOK tag is set to YES doxygen will generate Docbook files
-# that can be used to generate PDF.
-# The default value is: NO.
-
-#GENERATE_DOCBOOK       = NO
-
-# The DOCBOOK_OUTPUT tag is used to specify where the Docbook pages will be put.
-# If a relative path is entered the value of OUTPUT_DIRECTORY will be put in
-# front of it.
-# The default directory is: docbook.
-# This tag requires that the tag GENERATE_DOCBOOK is set to YES.
-
-#DOCBOOK_OUTPUT         = docbook
-
-# If the DOCBOOK_PROGRAMLISTING tag is set to YES doxygen will include the
-# program listings (including syntax highlighting and cross-referencing
-# information) to the DOCBOOK output. Note that enabling this will significantly
-# increase the size of the DOCBOOK output.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_DOCBOOK is set to YES.
-
-#DOCBOOK_PROGRAMLISTING = NO
-
-#---------------------------------------------------------------------------
-# Configuration options for the AutoGen Definitions output
-#---------------------------------------------------------------------------
-
-# If the GENERATE_AUTOGEN_DEF tag is set to YES doxygen will generate an AutoGen
-# Definitions (see http://autogen.sf.net) file that captures the structure of
-# the code including all documentation. Note that this feature is still
-# experimental and incomplete at the moment.
-# The default value is: NO.
-
-GENERATE_AUTOGEN_DEF   = NO
-
-#---------------------------------------------------------------------------
-# Configuration options related to the Perl module output
-#---------------------------------------------------------------------------
-
-# If the GENERATE_PERLMOD tag is set to YES doxygen will generate a Perl module
-# file that captures the structure of the code including all documentation.
-#
-# Note that this feature is still experimental and incomplete at the moment.
-# The default value is: NO.
-
-GENERATE_PERLMOD       = NO
-
-# If the PERLMOD_LATEX tag is set to YES doxygen will generate the necessary
-# Makefile rules, Perl scripts and LaTeX code to be able to generate PDF and DVI
-# output from the Perl module output.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_PERLMOD is set to YES.
-
-PERLMOD_LATEX          = NO
-
-# If the PERLMOD_PRETTY tag is set to YES the Perl module output will be nicely
-# formatted so it can be parsed by a human reader. This is useful if you want to
-# understand what is going on. On the other hand, if this tag is set to NO the
-# size of the Perl module output will be much smaller and Perl will parse it
-# just the same.
-# The default value is: YES.
-# This tag requires that the tag GENERATE_PERLMOD is set to YES.
-
-PERLMOD_PRETTY         = YES
-
-# The names of the make variables in the generated doxyrules.make file are
-# prefixed with the string contained in PERLMOD_MAKEVAR_PREFIX. This is useful
-# so different doxyrules.make files included by the same Makefile don't
-# overwrite each other's variables.
-# This tag requires that the tag GENERATE_PERLMOD is set to YES.
-
-PERLMOD_MAKEVAR_PREFIX =
-
-#---------------------------------------------------------------------------
-# Configuration options related to the preprocessor
-#---------------------------------------------------------------------------
-
-# If the ENABLE_PREPROCESSING tag is set to YES doxygen will evaluate all
-# C-preprocessor directives found in the sources and include files.
-# The default value is: YES.
-
-ENABLE_PREPROCESSING   = YES
-
-# If the MACRO_EXPANSION tag is set to YES doxygen will expand all macro names
-# in the source code. If set to NO only conditional compilation will be
-# performed. Macro expansion can be done in a controlled way by setting
-# EXPAND_ONLY_PREDEF to YES.
-# The default value is: NO.
-# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
-
-MACRO_EXPANSION        = NO
-
-# If the EXPAND_ONLY_PREDEF and MACRO_EXPANSION tags are both set to YES then
-# the macro expansion is limited to the macros specified with the PREDEFINED and
-# EXPAND_AS_DEFINED tags.
-# The default value is: NO.
-# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
-
-EXPAND_ONLY_PREDEF     = NO
-
-# If the SEARCH_INCLUDES tag is set to YES the includes files in the
-# INCLUDE_PATH will be searched if a #include is found.
-# The default value is: YES.
-# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
-
-SEARCH_INCLUDES        = YES
-
-# The INCLUDE_PATH tag can be used to specify one or more directories that
-# contain include files that are not input files but should be processed by the
-# preprocessor.
-# This tag requires that the tag SEARCH_INCLUDES is set to YES.
-
-INCLUDE_PATH           =
-
-# You can use the INCLUDE_FILE_PATTERNS tag to specify one or more wildcard
-# patterns (like *.h and *.hpp) to filter out the header-files in the
-# directories. If left blank, the patterns specified with FILE_PATTERNS will be
-# used.
-# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
-
-INCLUDE_FILE_PATTERNS  =
-
-# The PREDEFINED tag can be used to specify one or more macro names that are
-# defined before the preprocessor is started (similar to the -D option of e.g.
-# gcc). The argument of the tag is a list of macros of the form: name or
-# name=definition (no spaces). If the definition and the "=" are omitted, "=1"
-# is assumed. To prevent a macro definition from being undefined via #undef or
-# recursively expanded use the := operator instead of the = operator.
-# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
-
-PREDEFINED             = MXNET_USE_CUDA DMLC_USE_CXX11
-
-# If the MACRO_EXPANSION and EXPAND_ONLY_PREDEF tags are set to YES then this
-# tag can be used to specify a list of macro names that should be expanded. The
-# macro definition that is found in the sources will be used. Use the PREDEFINED
-# tag if you want to use a different macro definition that overrules the
-# definition found in the source code.
-# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
-
-EXPAND_AS_DEFINED      =
-
-# If the SKIP_FUNCTION_MACROS tag is set to YES then doxygen's preprocessor will
-# remove all references to function-like macros that are alone on a line, have
-# an all uppercase name, and do not end with a semicolon. Such function macros
-# are typically used for boiler-plate code, and will confuse the parser if not
-# removed.
-# The default value is: YES.
-# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
-
-SKIP_FUNCTION_MACROS   = YES
-
-#---------------------------------------------------------------------------
-# Configuration options related to external references
-#---------------------------------------------------------------------------
-
-# The TAGFILES tag can be used to specify one or more tag files. For each tag
-# file the location of the external documentation should be added. The format of
-# a tag file without this location is as follows:
-# TAGFILES = file1 file2 ...
-# Adding location for the tag files is done as follows:
-# TAGFILES = file1=loc1 "file2 = loc2" ...
-# where loc1 and loc2 can be relative or absolute paths or URLs. See the
-# section "Linking to external documentation" for more information about the use
-# of tag files.
-# Note: Each tag file must have a unique name (where the name does NOT include
-# the path). If a tag file is not located in the directory in which doxygen is
-# run, you must also specify the path to the tagfile here.
-
-TAGFILES               =
-
-# When a file name is specified after GENERATE_TAGFILE, doxygen will create a
-# tag file that is based on the input files it reads. See section "Linking to
-# external documentation" for more information about the usage of tag files.
-
-GENERATE_TAGFILE       =
-
-# If the ALLEXTERNALS tag is set to YES all external class will be listed in the
-# class index. If set to NO only the inherited external classes will be listed.
-# The default value is: NO.
-
-ALLEXTERNALS           = NO
-
-# If the EXTERNAL_GROUPS tag is set to YES all external groups will be listed in
-# the modules index. If set to NO, only the current project's groups will be
-# listed.
-# The default value is: YES.
-
-EXTERNAL_GROUPS        = YES
-
-# If the EXTERNAL_PAGES tag is set to YES all external pages will be listed in
-# the related pages index. If set to NO, only the current project's pages will
-# be listed.
-# The default value is: YES.
-
-#EXTERNAL_PAGES         = YES
-
-# The PERL_PATH should be the absolute path and name of the perl script
-# interpreter (i.e. the result of 'which perl').
-# The default file (with absolute path) is: /usr/bin/perl.
-
-PERL_PATH              = /usr/bin/perl
-
-#---------------------------------------------------------------------------
-# Configuration options related to the dot tool
-#---------------------------------------------------------------------------
-
-# If the CLASS_DIAGRAMS tag is set to YES doxygen will generate a class diagram
-# (in HTML and LaTeX) for classes with base or super classes. Setting the tag to
-# NO turns the diagrams off. Note that this option also works with HAVE_DOT
-# disabled, but it is recommended to install and use dot, since it yields more
-# powerful graphs.
-# The default value is: YES.
-
-CLASS_DIAGRAMS         = YES
-
-# You can define message sequence charts within doxygen comments using the \msc
-# command. Doxygen will then run the mscgen tool (see:
-# http://www.mcternan.me.uk/mscgen/)) to produce the chart and insert it in the
-# documentation. The MSCGEN_PATH tag allows you to specify the directory where
-# the mscgen tool resides. If left empty the tool is assumed to be found in the
-# default search path.
-
-MSCGEN_PATH            =
-
-# You can include diagrams made with dia in doxygen documentation. Doxygen will
-# then run dia to produce the diagram and insert it in the documentation. The
-# DIA_PATH tag allows you to specify the directory where the dia binary resides.
-# If left empty dia is assumed to be found in the default search path.
-
-#DIA_PATH               =
-
-# If set to YES, the inheritance and collaboration graphs will hide inheritance
-# and usage relations if the target is undocumented or is not a class.
-# The default value is: YES.
-
-HIDE_UNDOC_RELATIONS   = YES
-
-# If you set the HAVE_DOT tag to YES then doxygen will assume the dot tool is
-# available from the path. This tool is part of Graphviz (see:
-# http://www.graphviz.org/), a graph visualization toolkit from AT&T and Lucent
-# Bell Labs. The other options in this section have no effect if this option is
-# set to NO
-# The default value is: YES.
-
-HAVE_DOT               = YES
-
-# The DOT_NUM_THREADS specifies the number of dot invocations doxygen is allowed
-# to run in parallel. When set to 0 doxygen will base this on the number of
-# processors available in the system. You can set it explicitly to a value
-# larger than 0 to get control over the balance between CPU load and processing
-# speed.
-# Minimum value: 0, maximum value: 32, default value: 0.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-DOT_NUM_THREADS        = 0
-
-# When you want a differently looking font in the dot files that doxygen
-# generates you can specify the font name using DOT_FONTNAME. You need to make
-# sure dot is able to find the font, which can be done by putting it in a
-# standard location or by setting the DOTFONTPATH environment variable or by
-# setting DOT_FONTPATH to the directory containing the font.
-# The default value is: Helvetica.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-DOT_FONTNAME           = Helvetica
-
-# The DOT_FONTSIZE tag can be used to set the size (in points) of the font of
-# dot graphs.
-# Minimum value: 4, maximum value: 24, default value: 10.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-DOT_FONTSIZE           = 10
-
-# By default doxygen will tell dot to use the default font as specified with
-# DOT_FONTNAME. If you specify a different font using DOT_FONTNAME you can set
-# the path where dot can find it using this tag.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-DOT_FONTPATH           =
-
-# If the CLASS_GRAPH tag is set to YES then doxygen will generate a graph for
-# each documented class showing the direct and indirect inheritance relations.
-# Setting this tag to YES will force the CLASS_DIAGRAMS tag to NO.
-# The default value is: YES.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-CLASS_GRAPH            = YES
-
-# If the COLLABORATION_GRAPH tag is set to YES then doxygen will generate a
-# graph for each documented class showing the direct and indirect implementation
-# dependencies (inheritance, containment, and class references variables) of the
-# class with other documented classes.
-# The default value is: YES.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-COLLABORATION_GRAPH    = YES
-
-# If the GROUP_GRAPHS tag is set to YES then doxygen will generate a graph for
-# groups, showing the direct groups dependencies.
-# The default value is: YES.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-GROUP_GRAPHS           = YES
-
-# If the UML_LOOK tag is set to YES doxygen will generate inheritance and
-# collaboration diagrams in a style similar to the OMG's Unified Modeling
-# Language.
-# The default value is: NO.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-UML_LOOK               = YES
-
-# If the UML_LOOK tag is enabled, the fields and methods are shown inside the
-# class node. If there are many fields or methods and many nodes the graph may
-# become too big to be useful. The UML_LIMIT_NUM_FIELDS threshold limits the
-# number of items for each type to make the size more manageable. Set this to 0
-# for no limit. Note that the threshold may be exceeded by 50% before the limit
-# is enforced. So when you set the threshold to 10, up to 15 fields may appear,
-# but if the number exceeds 15, the total amount of fields shown is limited to
-# 10.
-# Minimum value: 0, maximum value: 100, default value: 10.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-#UML_LIMIT_NUM_FIELDS   = 10
-
-# If the TEMPLATE_RELATIONS tag is set to YES then the inheritance and
-# collaboration graphs will show the relations between templates and their
-# instances.
-# The default value is: NO.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-TEMPLATE_RELATIONS     = NO
-
-# If the INCLUDE_GRAPH, ENABLE_PREPROCESSING and SEARCH_INCLUDES tags are set to
-# YES then doxygen will generate a graph for each documented file showing the
-# direct and indirect include dependencies of the file with other documented
-# files.
-# The default value is: YES.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-INCLUDE_GRAPH          = YES
-
-# If the INCLUDED_BY_GRAPH, ENABLE_PREPROCESSING and SEARCH_INCLUDES tags are
-# set to YES then doxygen will generate a graph for each documented file showing
-# the direct and indirect include dependencies of the file with other documented
-# files.
-# The default value is: YES.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-INCLUDED_BY_GRAPH      = YES
-
-# If the CALL_GRAPH tag is set to YES then doxygen will generate a call
-# dependency graph for every global function or class method.
-#
-# Note that enabling this option will significantly increase the time of a run.
-# So in most cases it will be better to enable call graphs for selected
-# functions only using the \callgraph command.
-# The default value is: NO.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-CALL_GRAPH             = NO
-
-# If the CALLER_GRAPH tag is set to YES then doxygen will generate a caller
-# dependency graph for every global function or class method.
-#
-# Note that enabling this option will significantly increase the time of a run.
-# So in most cases it will be better to enable caller graphs for selected
-# functions only using the \callergraph command.
-# The default value is: NO.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-CALLER_GRAPH           = NO
-
-# If the GRAPHICAL_HIERARCHY tag is set to YES then doxygen will graphical
-# hierarchy of all classes instead of a textual one.
-# The default value is: YES.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-GRAPHICAL_HIERARCHY    = YES
-
-# If the DIRECTORY_GRAPH tag is set to YES then doxygen will show the
-# dependencies a directory has on other directories in a graphical way. The
-# dependency relations are determined by the #include relations between the
-# files in the directories.
-# The default value is: YES.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-DIRECTORY_GRAPH        = YES
-
-# The DOT_IMAGE_FORMAT tag can be used to set the image format of the images
-# generated by dot.
-# Note: If you choose svg you need to set HTML_FILE_EXTENSION to xhtml in order
-# to make the SVG files visible in IE 9+ (other browsers do not have this
-# requirement).
-# Possible values are: png, png:cairo, png:cairo:cairo, png:cairo:gd, png:gd,
-# png:gd:gd, jpg, jpg:cairo, jpg:cairo:gd, jpg:gd, jpg:gd:gd, gif, gif:cairo,
-# gif:cairo:gd, gif:gd, gif:gd:gd and svg.
-# The default value is: png.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-DOT_IMAGE_FORMAT       = png
-
-# If DOT_IMAGE_FORMAT is set to svg, then this option can be set to YES to
-# enable generation of interactive SVG images that allow zooming and panning.
-#
-# Note that this requires a modern browser other than Internet Explorer. Tested
-# and working are Firefox, Chrome, Safari, and Opera.
-# Note: For IE 9+ you need to set HTML_FILE_EXTENSION to xhtml in order to make
-# the SVG files visible. Older versions of IE do not have SVG support.
-# The default value is: NO.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-INTERACTIVE_SVG        = NO
-
-# The DOT_PATH tag can be used to specify the path where the dot tool can be
-# found. If left blank, it is assumed the dot tool can be found in the path.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-DOT_PATH               =
-
-# The DOTFILE_DIRS tag can be used to specify one or more directories that
-# contain dot files that are included in the documentation (see the \dotfile
-# command).
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-DOTFILE_DIRS           =
-
-# The MSCFILE_DIRS tag can be used to specify one or more directories that
-# contain msc files that are included in the documentation (see the \mscfile
-# command).
-
-MSCFILE_DIRS           =
-
-# The DIAFILE_DIRS tag can be used to specify one or more directories that
-# contain dia files that are included in the documentation (see the \diafile
-# command).
-
-#DIAFILE_DIRS           =
-
-# When using plantuml, the PLANTUML_JAR_PATH tag should be used to specify the
-# path where java can find the plantuml.jar file. If left blank, it is assumed
-# PlantUML is not used or called during a preprocessing step. Doxygen will
-# generate a warning when it encounters a \startuml command in this case and
-# will not generate output for the diagram.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-#PLANTUML_JAR_PATH      =
-
-# The DOT_GRAPH_MAX_NODES tag can be used to set the maximum number of nodes
-# that will be shown in the graph. If the number of nodes in a graph becomes
-# larger than this value, doxygen will truncate the graph, which is visualized
-# by representing a node as a red box. Note that doxygen if the number of direct
-# children of the root node in a graph is already larger than
-# DOT_GRAPH_MAX_NODES then the graph will not be shown at all. Also note that
-# the size of a graph can be further restricted by MAX_DOT_GRAPH_DEPTH.
-# Minimum value: 0, maximum value: 10000, default value: 50.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-DOT_GRAPH_MAX_NODES    = 50
-
-# The MAX_DOT_GRAPH_DEPTH tag can be used to set the maximum depth of the graphs
-# generated by dot. A depth value of 3 means that only nodes reachable from the
-# root by following a path via at most 3 edges will be shown. Nodes that lay
-# further from the root node will be omitted. Note that setting this option to 1
-# or 2 may greatly reduce the computation time needed for large code bases. Also
-# note that the size of a graph can be further restricted by
-# DOT_GRAPH_MAX_NODES. Using a depth of 0 means no depth restriction.
-# Minimum value: 0, maximum value: 1000, default value: 0.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-MAX_DOT_GRAPH_DEPTH    = 0
-
-# Set the DOT_TRANSPARENT tag to YES to generate images with a transparent
-# background. This is disabled by default, because dot on Windows does not seem
-# to support this out of the box.
-#
-# Warning: Depending on the platform used, enabling this option may lead to
-# badly anti-aliased labels on the edges of a graph (i.e. they become hard to
-# read).
-# The default value is: NO.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-DOT_TRANSPARENT        = NO
-
-# Set the DOT_MULTI_TARGETS tag to YES allow dot to generate multiple output
-# files in one run (i.e. multiple -o and -T options on the command line). This
-# makes dot run faster, but since only newer versions of dot (>1.8.10) support
-# this, this feature is disabled by default.
-# The default value is: NO.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-DOT_MULTI_TARGETS      = YES
-
-# If the GENERATE_LEGEND tag is set to YES doxygen will generate a legend page
-# explaining the meaning of the various boxes and arrows in the dot generated
-# graphs.
-# The default value is: YES.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-GENERATE_LEGEND        = YES
-
-# If the DOT_CLEANUP tag is set to YES doxygen will remove the intermediate dot
-# files that are used to generate the various graphs.
-# The default value is: YES.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-DOT_CLEANUP            = YES
diff --git a/docs/Makefile b/docs/Makefile
deleted file mode 100644
index 90603b0875fc..000000000000
--- a/docs/Makefile
+++ /dev/null
@@ -1,214 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-# Makefile for Sphinx documentation
-#
-
-# You can set these variables from the command line.
-SPHINXOPTS    =
-SPHINXBUILD   = sphinx-build
-PAPER         =
-BUILDDIR      = _build
-
-# User-friendly check for sphinx-build
-ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1)
-$(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/)
-endif
-
-# Internal variables.
-PAPEROPT_a4     = -D latex_paper_size=a4
-PAPEROPT_letter = -D latex_paper_size=letter
-ALLSPHINXOPTS   = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .
-# the i18n builder cannot share the environment and doctrees with the others
-I18NSPHINXOPTS  = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .
-
-.PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest coverage gettext
-
-help:
-	@echo "Please use \`make <target>' where <target> is one of"
-	@echo "  html       to make standalone HTML files"
-	@echo "  dirhtml    to make HTML files named index.html in directories"
-	@echo "  singlehtml to make a single large HTML file"
-	@echo "  pickle     to make pickle files"
-	@echo "  json       to make JSON files"
-	@echo "  htmlhelp   to make HTML files and a HTML help project"
-	@echo "  qthelp     to make HTML files and a qthelp project"
-	@echo "  applehelp  to make an Apple Help Book"
-	@echo "  devhelp    to make HTML files and a Devhelp project"
-	@echo "  epub       to make an epub"
-	@echo "  latex      to make LaTeX files, you can set PAPER=a4 or PAPER=letter"
-	@echo "  latexpdf   to make LaTeX files and run them through pdflatex"
-	@echo "  latexpdfja to make LaTeX files and run them through platex/dvipdfmx"
-	@echo "  text       to make text files"
-	@echo "  man        to make manual pages"
-	@echo "  texinfo    to make Texinfo files"
-	@echo "  info       to make Texinfo files and run them through makeinfo"
-	@echo "  gettext    to make PO message catalogs"
-	@echo "  changes    to make an overview of all changed/added/deprecated items"
-	@echo "  xml        to make Docutils-native XML files"
-	@echo "  pseudoxml  to make pseudoxml-XML files for display purposes"
-	@echo "  linkcheck  to check all external links for integrity"
-	@echo "  doctest    to run all doctests embedded in the documentation (if enabled)"
-	@echo "  coverage   to run coverage check of the documentation (if enabled)"
-
-clean:
-	rm -rf $(BUILDDIR)/*
-
-livehtml:
-	sphinx-autobuild --ignore "web-data/*" -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
-
-html:
-	export BUILD_VER=$(BUILD_VER)
-	@echo "Env var set for BUILD_VER: $(BUILD_VER)"
-	$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
-	@echo
-	@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
-
-dirhtml:
-	$(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml
-	@echo
-	@echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml."
-
-singlehtml:
-	$(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml
-	@echo
-	@echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml."
-
-pickle:
-	$(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle
-	@echo
-	@echo "Build finished; now you can process the pickle files."
-
-json:
-	$(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json
-	@echo
-	@echo "Build finished; now you can process the JSON files."
-
-htmlhelp:
-	$(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp
-	@echo
-	@echo "Build finished; now you can run HTML Help Workshop with the" \
-	      ".hhp project file in $(BUILDDIR)/htmlhelp."
-
-qthelp:
-	$(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp
-	@echo
-	@echo "Build finished; now you can run "qcollectiongenerator" with the" \
-	      ".qhcp project file in $(BUILDDIR)/qthelp, like this:"
-	@echo "# qcollectiongenerator $(BUILDDIR)/qthelp/zongyanwei.qhcp"
-	@echo "To view the help file:"
-	@echo "# assistant -collectionFile $(BUILDDIR)/qthelp/zongyanwei.qhc"
-
-applehelp:
-	$(SPHINXBUILD) -b applehelp $(ALLSPHINXOPTS) $(BUILDDIR)/applehelp
-	@echo
-	@echo "Build finished. The help book is in $(BUILDDIR)/applehelp."
-	@echo "N.B. You won't be able to view it unless you put it in" \
-	      "~/Library/Documentation/Help or install it in your application" \
-	      "bundle."
-
-devhelp:
-	$(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp
-	@echo
-	@echo "Build finished."
-	@echo "To view the help file:"
-	@echo "# mkdir -p $$HOME/.local/share/devhelp/zongyanwei"
-	@echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/zongyanwei"
-	@echo "# devhelp"
-
-epub:
-	$(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub
-	@echo
-	@echo "Build finished. The epub file is in $(BUILDDIR)/epub."
-
-latex:
-	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
-	@echo
-	@echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex."
-	@echo "Run \`make' in that directory to run these through (pdf)latex" \
-	      "(use \`make latexpdf' here to do that automatically)."
-
-latexpdf:
-	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
-	@echo "Running LaTeX files through pdflatex..."
-	$(MAKE) -C $(BUILDDIR)/latex all-pdf
-	@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
-
-latexpdfja:
-	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
-	@echo "Running LaTeX files through platex and dvipdfmx..."
-	$(MAKE) -C $(BUILDDIR)/latex all-pdf-ja
-	@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
-
-text:
-	$(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text
-	@echo
-	@echo "Build finished. The text files are in $(BUILDDIR)/text."
-
-man:
-	$(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man
-	@echo
-	@echo "Build finished. The manual pages are in $(BUILDDIR)/man."
-
-texinfo:
-	$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
-	@echo
-	@echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo."
-	@echo "Run \`make' in that directory to run these through makeinfo" \
-	      "(use \`make info' here to do that automatically)."
-
-info:
-	$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
-	@echo "Running Texinfo files through makeinfo..."
-	make -C $(BUILDDIR)/texinfo info
-	@echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo."
-
-gettext:
-	$(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale
-	@echo
-	@echo "Build finished. The message catalogs are in $(BUILDDIR)/locale."
-
-changes:
-	$(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes
-	@echo
-	@echo "The overview file is in $(BUILDDIR)/changes."
-
-linkcheck:
-	$(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck
-	@echo
-	@echo "Link check complete; look for any errors in the above output " \
-	      "or in $(BUILDDIR)/linkcheck/output.txt."
-
-doctest:
-	$(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest
-	@echo "Testing of doctests in the sources finished, look at the " \
-	      "results in $(BUILDDIR)/doctest/output.txt."
-
-coverage:
-	$(SPHINXBUILD) -b coverage $(ALLSPHINXOPTS) $(BUILDDIR)/coverage
-	@echo "Testing of coverage in the sources finished, look at the " \
-	      "results in $(BUILDDIR)/coverage/python.txt."
-
-xml:
-	$(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml
-	@echo
-	@echo "Build finished. The XML files are in $(BUILDDIR)/xml."
-
-pseudoxml:
-	$(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml
-	@echo
-	@echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml."
diff --git a/docs/README.md b/docs/README.md
index c0b40362b7e2..b98d15eecb49 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -20,44 +20,138 @@
 The website is hosted at http://mxnet.incubator.apache.org/.
 http://mxnet.io redirects to this site and advised to use links with http://mxnet.incubator.apache.org/ instead of http://mxnet.io/.
 
-MXNet Documentation Website is built with [Sphinx](http://www.sphinx-doc.org) and a variety of plugins including [pandoc](https://pandoc.org/), [recommonmark](https://github.com/rtfd/recommonmark), a custom Sphinx plugin ([mxdoc.py](https://github.com/apache/incubator-mxnet/blob/master/docs/mxdoc.py)).
+## Website & Documentation Contributions
 
+Detailed information on website development, continuous integration, and proposals for future projects can be found on the [MXNet Wiki](https://cwiki.apache.org/confluence/display/MXNET/Website).
+
+The website is built using Jekyll. You may run your own version of the static website by following the instructions on the wiki.
+
+Each language documentation is built in a modular way, so that if you are a contributor to Julia, for example, you only need Julia-related tools to build it. Each language API has a section on installation and building along with how to build the docs locally.
+
+You can also use the project's CI tools to emulate any changes with Docker. You can use these tools to install dependencies and run the parts of the build you want to test.
+
+If you plan to contribute changes to the documentation or website, please submit a pull request. Contributions are welcome!
+
+## Python Docs
+
+MXNet's Python documentation is built with [Sphinx](http://www.sphinx-doc.org) and a variety of plugins including [pandoc](https://pandoc.org/), and [recommonmark](https://github.com/rtfd/recommonmark).
+
+More information on the dependencies can be found in the [CI folder's installation scripts](https://github.com/apache/incubator-mxnet/tree/master/ci/docker/install/ubuntu_docs.sh).
+
+You can run just the Python docs by following the instructions in the Python API guide.
+
+## Other API Docs
+
+The docs are hosted on the website in each language API's section. You can find installation and build instructions there.
 
 ## How to Build the MXNet Website for Development and QA
 
-Using `make docs` from the MXNet root is the quickest way to generate the MXNet API docs and the website, as long as you already have all of the dependencies installed. This method automatically generates each API, [except the Perl and R APIs](#other-build-processes).
+`conda` or `miniconda` is recommended.
+* [Conda](https://www.anaconda.com/distribution/#download-section) (install to PATH)
+
+If you only need to make changes to tutorials or other pages that are not generated from one of the API source code folders, then you can use a basic Python pip or conda installation. But if you want edit the API source and have the reference API docs update, you also need to build MXNet from source. Refer to the build from source instructions for this requirement.
+
 
-**Easy docs setup for Ubuntu:** Run the following on Ubuntu 16.04 to install all MXNet and docs dependencies and to build MXNet from source. Then issue the `make docs` command from the source root to build the docs.
+### Ubuntu Setup
+
+As this is maintained for CI, Ubuntu is recommended. Refer to [ubuntu_doc.sh](https://github.com/apache/incubator-mxnet/tree/master/ci/docker/install/ubuntu_docs.sh) for the latest install script.
+
+### Caveat for Rendering Outputs
+
+Note that without a GPU you will not be able to generate the docs with the outputs in the tutorials.
+
+### GPU setup
+To run the full build, including tests of all tutorials,
+**you will need at least two GPUs**.
+Distributed training is a key feature of MXNet,
+so multiple GPUs are required for running through every tutorial.
+* [CUDA 9.2](https://developer.nvidia.com/cuda-downloads)
+
+### CPU-only setup
+In the `environment.yml` file:
+* Change `mxnet-cu92` to `mxnet`.
+
+### macOS setup
+In the `environment.yml` file:
+* Change `mxnet-cu92` to `mxnet`. (There is no CUDA package for mac anyway.)
+
+### Windows Setup
+If you have a GPU and have installed CUDA 9.2 you can leave the MXNet dependency alone.
+Otherwise, in the `environment.yml` file:
+* Change `mxnet-cu92` to `mxnet`.
+
+Install recommended software:
+* [git bash](https://gitforwindows.org/)
+* Be sure to install `Conda` in `PATH`
+* Install `make` from a `git bash` terminal with Admin rights
+    - [Install chocolatey](https://chocolatey.org/install)
+    - Use `choco to install make`
+* Restart terminals after installations to make sure PATH is set.
+    - The `choco`, `make`, and `conda` commands should work in `git bash`.
+
+### Conda environment setup
+Run the following commands from the project root (`new-docs`) to setup the environment.
 
 ```bash
-git clone --recursive https://github.com/apache/incubator-mxnet.git mxnet
-cd mxnet/docs/build_version_doc
-./setup_docs_ubuntu.sh
-cd ../../
-make docs USE_OPENMP=1 SPHINXOPTS=-W
+conda env create -f environment.yml
+source activate mxnet-docs
 ```
 
-OpenMP speeds things up and will work on Ubuntu if you used the `setup_docs_ubuntu.sh` script.
-The `-W` Sphinx option enforces "warnings as errors". This will help you debug your builds and get them through CI.
-**CI will not let a PR through if it breaks the website.** Refer to the [MXNet Developer wiki's documentation guide](https://cwiki.apache.org/confluence/display/MXNET/Documentation+Guide) for troubleshooting tips.
+## Build the docs
 
-For more information on each API's documentation dependencies, how to serve the docs, or how to build the full website with each legacy MXNet version, refer to the following links:
+* Change directories to `new-docs/python`.
 
-* [Dependencies](https://github.com/apache/incubator-mxnet/tree/master/docs/build_version_doc#dependencies) - required before you build the docs
-* [Developer Build Instructions](https://github.com/apache/incubator-mxnet/tree/master/docs/build_version_doc#developer-instructions) - build your local branch
-* [Full Site Build Instructions](https://github.com/apache/incubator-mxnet/tree/master/docs/build_version_doc#full-website-build) - build the latest commits to the official branches
+To build without GPUs and without testing the notebooks (faster):
 
+```bash
+make EVAL=0
+```
+
+To build with testing the notebooks (requires GPU):
+
+```bash
+make
+```
 
-## File Structure
+The build docs will be available at `build/_build/html`.
 
-* Static files such as **css**, **javascript** and **html** templates are under the `_static` folder:
-  - Javascript files are under `_static/js` folder
-  - Layout templates and landing page html file are under `_static/mxnet-theme` folder
-  - `_static/mxnet.css` contains all MXNet website styles
+Each build may take a few minutes even without evaluation. To accelerate it, we can use one of the following ways:
 
-* Page contents originate as markdown files. Sphinx converts markdown files to html through an `rst` intermediate format. Each content folder should contain an index file as landing page.
+1. open `build/conf.py`, add the folders you want to skip into `exclude_patterns`, such as `exclude_patterns = ['templates', 'api', 'develop', 'blog']`.
+2. move the files into a different folder, such as `mv api /tmp/`, and then `make clean`.
 
-* There are some utility scripts to help building website, such as `mxdoc.py` and `build_version_doc/`. They are used to manipulate website contents during building. Refer to [Developer Build Instructions](https://github.com/apache/incubator-mxnet/tree/master/docs/build_version_doc#developer-instructions) for more information.
+## Check results
+
+To run a server to see the website:
+
+1. Start a http server: `cd build/_build/html; python -m http.server`
+2. For viewing a remote machine, ssh to your machine with port forwarding: `ssh -L8000:localhost:8000 your_machine`
+3. Open http://localhost:8000 in your local machine
+
+## Run tutorials
+
+In addition to view the built html pages, you can run the Jupyter notebook from a remote machine.
+1. Install `notedown` plugin: `pip install https://github.com/mli/notedown/tarball/master` in remote server
+2. Start Jupyter notebook `jupyter notebook --NotebookApp.contents_manager_class='notedown.NotedownContentsManager'` in remote server
+3. ssh to your machine with port forwarding: `ssh -L8888:localhost:8888 your_machine`
+4. Open http://localhost:8888 in your local machine and run the md files directly
+
+Optionally, one can run the following to launch the notedown plugin automatically when starting jupyter notebook.
+1. Generate the jupyter configure file `~/.jupyter/jupyter_notebook_config.py` if it
+is not existing by run `jupyter notebook --generate-config`
+2. Add `c.NotebookApp.contents_manager_class = 'notedown.NotedownContentsManager'` to `~/.jupyter/jupyter_notebook_config.py`
+3. Simply run `jupyter notebook`
+
+## Troubleshooting
+Dependencies and the setup steps for this website are changing often. Here are some troubleshooting tips.
+
+* You might need to update the environment for the latest modules.
+```bash
+conda env update -f environment.yml
+```
+
+The `-W` Sphinx option enforces "warnings as errors". This will help you debug your builds and get them through CI.
+**CI will not let a PR through if it breaks the website.** Refer to the [MXNet Developer wiki's documentation guide](https://cwiki.apache.org/confluence/display/MXNET/Documentation+Guide) for troubleshooting tips.
 
 
 ## Production Website Deployment Process
@@ -65,31 +159,17 @@ For more information on each API's documentation dependencies, how to serve the
 [Apache Jenkins MXNet website building job](https://builds.apache.org/job/incubator-mxnet-build-site/) is used to build MXNet website.
 
 The Jenkins docs build job will fetch MXNet repository, build MXNet website and push all static files to [host repository](https://github.com/apache/incubator-mxnet-site.git).
-The host repo is hooked with [Apache gitbox](https://gitbox.apache.org/repos/asf?p=incubator-mxnet-site.git;a=summary) to host website.
-
-### Process for Running the Docs Build Job
-
-1. Login to [Jenkins](http://jenkins.mxnet-ci.amazon-ml.com/).
-1. View the pipeline currently called `restricted website build`.
-1. Click `Build with Parameters`.
-1. Use the defaults, or change the domain to be your staging server's IP/DNS web address.
-1. Wait about 20-30 minutes while it builds the full site.
-1. On your staging server, clone the [mxnet site repo](https://github.com/apache/incubator-mxnet-site.git).
-1. When you ran `restricted website build` it followed up with `restricted website publish` which pushed the changes to the incubator-mxnet-site repo.
-1. Make sure you git pull if you had already cloned the site repo before this first run-through.
-1. Copy the files to your webroot. For more info on this see the developer instructions for docs build.
-1. Preview the site on your staging server. Note, use the domain default before you try to use this for production, but using your own is helpful for QA'ing the site.
 
+The host repo is hooked with [Apache gitbox](https://gitbox.apache.org/repos/asf?p=incubator-mxnet-site.git;a=summary) to host website.
 
-## Build Versioning Website
+### Processes for Running the Docs Build Jobs
 
-**IMPORTANT**: Refer to [Full Site Build Instructions](https://github.com/apache/incubator-mxnet/tree/master/docs/build_version_doc#full-website-build) for a working site build with the versions dropdown in the UI.
+This information is maintained on the [MXNet Wiki](https://cwiki.apache.org/confluence/display/MXNET/Website).
 
 
-## Other Build Processes
+## Other Docs Build Processes
 
 * Perl API docs are maintained separately at [metacpan](https://metacpan.org/release/AI-MXNet).
-* R API docs building must be triggered manually. The function for generating these automatically was disabled in the nightly builds. You may run the R docs build process in a local docs build by uncommenting the [function call in mxdoc.py](https://github.com/apache/incubator-mxnet/blob/master/docs/mxdoc.py#L378).
 
 
 ## Troubleshooting
diff --git a/docs/_static/cn.svg b/docs/_static/cn.svg
deleted file mode 100644
index 515176d60f15..000000000000
--- a/docs/_static/cn.svg
+++ /dev/null
@@ -1,20 +0,0 @@
-<?xml version="1.0" encoding="UTF-8" standalone="no"?>
-<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="640" height="480" viewBox="-5 -5 12.8 9.6">
-  <title>
-    Flag of the People&apos;s Republic of China
-  </title>
-  <defs>
-    <path id="b" fill="#ffde00" d="M-.588.81L0-1 .588.81-.952-.31H.952z"/>
-    <clipPath id="a">
-      <path d="M-5-15.4h26.667v20H-5z"/>
-    </clipPath>
-  </defs>
-  <g transform="matrix(.48 0 0 .48 -2.6 2.39)" clip-path="url(#a)">
-    <path fill="#de2910" d="M-5-15.4h30v20H-5z"/>
-    <use height="20" width="30" transform="matrix(3 0 0 3 0 -10.4)" xlink:href="#b"/>
-    <use height="20" width="30" transform="rotate(-120.93 -1.29 -8.12)" xlink:href="#b"/>
-    <use height="20" width="30" transform="rotate(-98.11 -1.45 -8.74)" xlink:href="#b"/>
-    <use height="20" width="30" transform="rotate(-74.04 -2.07 -8.84)" xlink:href="#b"/>
-    <use height="20" width="30" transform="rotate(-51.32 -4.16 -8.4)" xlink:href="#b"/>
-  </g>
-</svg>
diff --git a/docs/_static/js/auto_module_index.js b/docs/_static/js/auto_module_index.js
deleted file mode 100644
index 8527a71edd95..000000000000
--- a/docs/_static/js/auto_module_index.js
+++ /dev/null
@@ -1,46 +0,0 @@
-/*!
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- */
-
-/* Customizations to the Sphinx auto module plugin output */
-function auto_index(module) {
-  $(document).ready(function () {
-    // find all classes or functions
-    var div_query = "div[class='section'][id='" + module + "']";
-    var class_query = div_query + " dl[class='class'] > dt";
-    var func_query = div_query + " dl[class='function'] > dt";
-    var targets = $(class_query + ',' + func_query);
-
-    var li_node = $("div.sphinxsidebarwrapper li a[href='#" + module + "']").parent();
-    var html = "<ul>";
-
-    for (var i = 0; i < targets.length; ++i) {
-	var id = $(targets[i]).attr('id');
-	if ( id ) {
-	    // remove 'mxnet.' prefix to make menus shorter
-	    var id_simple = id.replace(/^mxnet\./, '');
-	    html += "<li><a class='reference internal' href='#";
-	    html += id;
-	    html += "'>" + id_simple + "</a></li>";
-	}
-    }
-
-    html += "</ul>";
-    li_node.append(html);
-  });
-}
diff --git a/docs/_static/js/clipboard.js b/docs/_static/js/clipboard.js
deleted file mode 100644
index 75b6af35323d..000000000000
--- a/docs/_static/js/clipboard.js
+++ /dev/null
@@ -1,778 +0,0 @@
-/*!
- * clipboard.js v1.6.1
- * https://zenorocha.github.io/clipboard.js
- *
- * Licensed MIT © Zeno Rocha
- */
-(function(f){if(typeof exports==="object"&&typeof module!=="undefined"){module.exports=f()}else if(typeof define==="function"&&define.amd){define([],f)}else{var g;if(typeof window!=="undefined"){g=window}else if(typeof global!=="undefined"){g=global}else if(typeof self!=="undefined"){g=self}else{g=this}g.Clipboard = f()}})(function(){var define,module,exports;return (function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require=="function"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);var f=new Error("Cannot find module '"+o+"'");throw f.code="MODULE_NOT_FOUND",f}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require=="function"&&require;for(var o=0;o<r.length;o++)s(r[o]);return s})({1:[function(require,module,exports){
-var DOCUMENT_NODE_TYPE = 9;
-
-/**
- * A polyfill for Element.matches()
- */
-if (typeof Element !== 'undefined' && !Element.prototype.matches) {
-    var proto = Element.prototype;
-
-    proto.matches = proto.matchesSelector ||
-                    proto.mozMatchesSelector ||
-                    proto.msMatchesSelector ||
-                    proto.oMatchesSelector ||
-                    proto.webkitMatchesSelector;
-}
-
-/**
- * Finds the closest parent that matches a selector.
- *
- * @param {Element} element
- * @param {String} selector
- * @return {Function}
- */
-function closest (element, selector) {
-    while (element && element.nodeType !== DOCUMENT_NODE_TYPE) {
-        if (element.matches(selector)) return element;
-        element = element.parentNode;
-    }
-}
-
-module.exports = closest;
-
-},{}],2:[function(require,module,exports){
-var closest = require('./closest');
-
-/**
- * Delegates event to a selector.
- *
- * @param {Element} element
- * @param {String} selector
- * @param {String} type
- * @param {Function} callback
- * @param {Boolean} useCapture
- * @return {Object}
- */
-function delegate(element, selector, type, callback, useCapture) {
-    var listenerFn = listener.apply(this, arguments);
-
-    element.addEventListener(type, listenerFn, useCapture);
-
-    return {
-        destroy: function() {
-            element.removeEventListener(type, listenerFn, useCapture);
-        }
-    }
-}
-
-/**
- * Finds closest match and invokes callback.
- *
- * @param {Element} element
- * @param {String} selector
- * @param {String} type
- * @param {Function} callback
- * @return {Function}
- */
-function listener(element, selector, type, callback) {
-    return function(e) {
-        e.delegateTarget = closest(e.target, selector);
-
-        if (e.delegateTarget) {
-            callback.call(element, e);
-        }
-    }
-}
-
-module.exports = delegate;
-
-},{"./closest":1}],3:[function(require,module,exports){
-/**
- * Check if argument is a HTML element.
- *
- * @param {Object} value
- * @return {Boolean}
- */
-exports.node = function(value) {
-    return value !== undefined
-        && value instanceof HTMLElement
-        && value.nodeType === 1;
-};
-
-/**
- * Check if argument is a list of HTML elements.
- *
- * @param {Object} value
- * @return {Boolean}
- */
-exports.nodeList = function(value) {
-    var type = Object.prototype.toString.call(value);
-
-    return value !== undefined
-        && (type === '[object NodeList]' || type === '[object HTMLCollection]')
-        && ('length' in value)
-        && (value.length === 0 || exports.node(value[0]));
-};
-
-/**
- * Check if argument is a string.
- *
- * @param {Object} value
- * @return {Boolean}
- */
-exports.string = function(value) {
-    return typeof value === 'string'
-        || value instanceof String;
-};
-
-/**
- * Check if argument is a function.
- *
- * @param {Object} value
- * @return {Boolean}
- */
-exports.fn = function(value) {
-    var type = Object.prototype.toString.call(value);
-
-    return type === '[object Function]';
-};
-
-},{}],4:[function(require,module,exports){
-var is = require('./is');
-var delegate = require('delegate');
-
-/**
- * Validates all params and calls the right
- * listener function based on its target type.
- *
- * @param {String|HTMLElement|HTMLCollection|NodeList} target
- * @param {String} type
- * @param {Function} callback
- * @return {Object}
- */
-function listen(target, type, callback) {
-    if (!target && !type && !callback) {
-        throw new Error('Missing required arguments');
-    }
-
-    if (!is.string(type)) {
-        throw new TypeError('Second argument must be a String');
-    }
-
-    if (!is.fn(callback)) {
-        throw new TypeError('Third argument must be a Function');
-    }
-
-    if (is.node(target)) {
-        return listenNode(target, type, callback);
-    }
-    else if (is.nodeList(target)) {
-        return listenNodeList(target, type, callback);
-    }
-    else if (is.string(target)) {
-        return listenSelector(target, type, callback);
-    }
-    else {
-        throw new TypeError('First argument must be a String, HTMLElement, HTMLCollection, or NodeList');
-    }
-}
-
-/**
- * Adds an event listener to a HTML element
- * and returns a remove listener function.
- *
- * @param {HTMLElement} node
- * @param {String} type
- * @param {Function} callback
- * @return {Object}
- */
-function listenNode(node, type, callback) {
-    node.addEventListener(type, callback);
-
-    return {
-        destroy: function() {
-            node.removeEventListener(type, callback);
-        }
-    }
-}
-
-/**
- * Add an event listener to a list of HTML elements
- * and returns a remove listener function.
- *
- * @param {NodeList|HTMLCollection} nodeList
- * @param {String} type
- * @param {Function} callback
- * @return {Object}
- */
-function listenNodeList(nodeList, type, callback) {
-    Array.prototype.forEach.call(nodeList, function(node) {
-        node.addEventListener(type, callback);
-    });
-
-    return {
-        destroy: function() {
-            Array.prototype.forEach.call(nodeList, function(node) {
-                node.removeEventListener(type, callback);
-            });
-        }
-    }
-}
-
-/**
- * Add an event listener to a selector
- * and returns a remove listener function.
- *
- * @param {String} selector
- * @param {String} type
- * @param {Function} callback
- * @return {Object}
- */
-function listenSelector(selector, type, callback) {
-    return delegate(document.body, selector, type, callback);
-}
-
-module.exports = listen;
-
-},{"./is":3,"delegate":2}],5:[function(require,module,exports){
-function select(element) {
-    var selectedText;
-
-    if (element.nodeName === 'SELECT') {
-        element.focus();
-
-        selectedText = element.value;
-    }
-    else if (element.nodeName === 'INPUT' || element.nodeName === 'TEXTAREA') {
-        var isReadOnly = element.hasAttribute('readonly');
-
-        if (!isReadOnly) {
-            element.setAttribute('readonly', '');
-        }
-
-        element.select();
-        element.setSelectionRange(0, element.value.length);
-
-        if (!isReadOnly) {
-            element.removeAttribute('readonly');
-        }
-
-        selectedText = element.value;
-    }
-    else {
-        if (element.hasAttribute('contenteditable')) {
-            element.focus();
-        }
-
-        var selection = window.getSelection();
-        var range = document.createRange();
-
-        range.selectNodeContents(element);
-        selection.removeAllRanges();
-        selection.addRange(range);
-
-        selectedText = selection.toString();
-    }
-
-    return selectedText;
-}
-
-module.exports = select;
-
-},{}],6:[function(require,module,exports){
-function E () {
-  // Keep this empty so it's easier to inherit from
-  // (via https://github.com/lipsmack from https://github.com/scottcorgan/tiny-emitter/issues/3)
-}
-
-E.prototype = {
-  on: function (name, callback, ctx) {
-    var e = this.e || (this.e = {});
-
-    (e[name] || (e[name] = [])).push({
-      fn: callback,
-      ctx: ctx
-    });
-
-    return this;
-  },
-
-  once: function (name, callback, ctx) {
-    var self = this;
-    function listener () {
-      self.off(name, listener);
-      callback.apply(ctx, arguments);
-    };
-
-    listener._ = callback
-    return this.on(name, listener, ctx);
-  },
-
-  emit: function (name) {
-    var data = [].slice.call(arguments, 1);
-    var evtArr = ((this.e || (this.e = {}))[name] || []).slice();
-    var i = 0;
-    var len = evtArr.length;
-
-    for (i; i < len; i++) {
-      evtArr[i].fn.apply(evtArr[i].ctx, data);
-    }
-
-    return this;
-  },
-
-  off: function (name, callback) {
-    var e = this.e || (this.e = {});
-    var evts = e[name];
-    var liveEvents = [];
-
-    if (evts && callback) {
-      for (var i = 0, len = evts.length; i < len; i++) {
-        if (evts[i].fn !== callback && evts[i].fn._ !== callback)
-          liveEvents.push(evts[i]);
-      }
-    }
-
-    // Remove event from queue to prevent memory leak
-    // Suggested by https://github.com/lazd
-    // Ref: https://github.com/scottcorgan/tiny-emitter/commit/c6ebfaa9bc973b33d110a84a307742b7cf94c953#commitcomment-5024910
-
-    (liveEvents.length)
-      ? e[name] = liveEvents
-      : delete e[name];
-
-    return this;
-  }
-};
-
-module.exports = E;
-
-},{}],7:[function(require,module,exports){
-(function (global, factory) {
-    if (typeof define === "function" && define.amd) {
-        define(['module', 'select'], factory);
-    } else if (typeof exports !== "undefined") {
-        factory(module, require('select'));
-    } else {
-        var mod = {
-            exports: {}
-        };
-        factory(mod, global.select);
-        global.clipboardAction = mod.exports;
-    }
-})(this, function (module, _select) {
-    'use strict';
-
-    var _select2 = _interopRequireDefault(_select);
-
-    function _interopRequireDefault(obj) {
-        return obj && obj.__esModule ? obj : {
-            default: obj
-        };
-    }
-
-    var _typeof = typeof Symbol === "function" && typeof Symbol.iterator === "symbol" ? function (obj) {
-        return typeof obj;
-    } : function (obj) {
-        return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj;
-    };
-
-    function _classCallCheck(instance, Constructor) {
-        if (!(instance instanceof Constructor)) {
-            throw new TypeError("Cannot call a class as a function");
-        }
-    }
-
-    var _createClass = function () {
-        function defineProperties(target, props) {
-            for (var i = 0; i < props.length; i++) {
-                var descriptor = props[i];
-                descriptor.enumerable = descriptor.enumerable || false;
-                descriptor.configurable = true;
-                if ("value" in descriptor) descriptor.writable = true;
-                Object.defineProperty(target, descriptor.key, descriptor);
-            }
-        }
-
-        return function (Constructor, protoProps, staticProps) {
-            if (protoProps) defineProperties(Constructor.prototype, protoProps);
-            if (staticProps) defineProperties(Constructor, staticProps);
-            return Constructor;
-        };
-    }();
-
-    var ClipboardAction = function () {
-        /**
-         * @param {Object} options
-         */
-        function ClipboardAction(options) {
-            _classCallCheck(this, ClipboardAction);
-
-            this.resolveOptions(options);
-            this.initSelection();
-        }
-
-        /**
-         * Defines base properties passed from constructor.
-         * @param {Object} options
-         */
-
-
-        _createClass(ClipboardAction, [{
-            key: 'resolveOptions',
-            value: function resolveOptions() {
-                var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};
-
-                this.action = options.action;
-                this.emitter = options.emitter;
-                this.target = options.target;
-                this.text = options.text;
-                this.trigger = options.trigger;
-
-                this.selectedText = '';
-            }
-        }, {
-            key: 'initSelection',
-            value: function initSelection() {
-                if (this.text) {
-                    this.selectFake();
-                } else if (this.target) {
-                    this.selectTarget();
-                }
-            }
-        }, {
-            key: 'selectFake',
-            value: function selectFake() {
-                var _this = this;
-
-                var isRTL = document.documentElement.getAttribute('dir') == 'rtl';
-
-                this.removeFake();
-
-                this.fakeHandlerCallback = function () {
-                    return _this.removeFake();
-                };
-                this.fakeHandler = document.body.addEventListener('click', this.fakeHandlerCallback) || true;
-
-                this.fakeElem = document.createElement('textarea');
-                // Prevent zooming on iOS
-                this.fakeElem.style.fontSize = '12pt';
-                // Reset box model
-                this.fakeElem.style.border = '0';
-                this.fakeElem.style.padding = '0';
-                this.fakeElem.style.margin = '0';
-                // Move element out of screen horizontally
-                this.fakeElem.style.position = 'absolute';
-                this.fakeElem.style[isRTL ? 'right' : 'left'] = '-9999px';
-                // Move element to the same position vertically
-                var yPosition = window.pageYOffset || document.documentElement.scrollTop;
-                this.fakeElem.style.top = yPosition + 'px';
-
-                this.fakeElem.setAttribute('readonly', '');
-                this.fakeElem.value = this.text;
-
-                document.body.appendChild(this.fakeElem);
-
-                this.selectedText = (0, _select2.default)(this.fakeElem);
-                this.copyText();
-            }
-        }, {
-            key: 'removeFake',
-            value: function removeFake() {
-                if (this.fakeHandler) {
-                    document.body.removeEventListener('click', this.fakeHandlerCallback);
-                    this.fakeHandler = null;
-                    this.fakeHandlerCallback = null;
-                }
-
-                if (this.fakeElem) {
-                    document.body.removeChild(this.fakeElem);
-                    this.fakeElem = null;
-                }
-            }
-        }, {
-            key: 'selectTarget',
-            value: function selectTarget() {
-                this.selectedText = (0, _select2.default)(this.target);
-                this.copyText();
-            }
-        }, {
-            key: 'copyText',
-            value: function copyText() {
-                var succeeded = void 0;
-
-                try {
-                    succeeded = document.execCommand(this.action);
-                } catch (err) {
-                    succeeded = false;
-                }
-
-                this.handleResult(succeeded);
-            }
-        }, {
-            key: 'handleResult',
-            value: function handleResult(succeeded) {
-                this.emitter.emit(succeeded ? 'success' : 'error', {
-                    action: this.action,
-                    text: this.selectedText,
-                    trigger: this.trigger,
-                    clearSelection: this.clearSelection.bind(this)
-                });
-            }
-        }, {
-            key: 'clearSelection',
-            value: function clearSelection() {
-                if (this.target) {
-                    this.target.blur();
-                }
-
-                window.getSelection().removeAllRanges();
-            }
-        }, {
-            key: 'destroy',
-            value: function destroy() {
-                this.removeFake();
-            }
-        }, {
-            key: 'action',
-            set: function set() {
-                var action = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 'copy';
-
-                this._action = action;
-
-                if (this._action !== 'copy' && this._action !== 'cut') {
-                    throw new Error('Invalid "action" value, use either "copy" or "cut"');
-                }
-            },
-            get: function get() {
-                return this._action;
-            }
-        }, {
-            key: 'target',
-            set: function set(target) {
-                if (target !== undefined) {
-                    if (target && (typeof target === 'undefined' ? 'undefined' : _typeof(target)) === 'object' && target.nodeType === 1) {
-                        if (this.action === 'copy' && target.hasAttribute('disabled')) {
-                            throw new Error('Invalid "target" attribute. Please use "readonly" instead of "disabled" attribute');
-                        }
-
-                        if (this.action === 'cut' && (target.hasAttribute('readonly') || target.hasAttribute('disabled'))) {
-                            throw new Error('Invalid "target" attribute. You can\'t cut text from elements with "readonly" or "disabled" attributes');
-                        }
-
-                        this._target = target;
-                    } else {
-                        throw new Error('Invalid "target" value, use a valid Element');
-                    }
-                }
-            },
-            get: function get() {
-                return this._target;
-            }
-        }]);
-
-        return ClipboardAction;
-    }();
-
-    module.exports = ClipboardAction;
-});
-
-},{"select":5}],8:[function(require,module,exports){
-(function (global, factory) {
-    if (typeof define === "function" && define.amd) {
-        define(['module', './clipboard-action', 'tiny-emitter', 'good-listener'], factory);
-    } else if (typeof exports !== "undefined") {
-        factory(module, require('./clipboard-action'), require('tiny-emitter'), require('good-listener'));
-    } else {
-        var mod = {
-            exports: {}
-        };
-        factory(mod, global.clipboardAction, global.tinyEmitter, global.goodListener);
-        global.clipboard = mod.exports;
-    }
-})(this, function (module, _clipboardAction, _tinyEmitter, _goodListener) {
-    'use strict';
-
-    var _clipboardAction2 = _interopRequireDefault(_clipboardAction);
-
-    var _tinyEmitter2 = _interopRequireDefault(_tinyEmitter);
-
-    var _goodListener2 = _interopRequireDefault(_goodListener);
-
-    function _interopRequireDefault(obj) {
-        return obj && obj.__esModule ? obj : {
-            default: obj
-        };
-    }
-
-    function _classCallCheck(instance, Constructor) {
-        if (!(instance instanceof Constructor)) {
-            throw new TypeError("Cannot call a class as a function");
-        }
-    }
-
-    var _createClass = function () {
-        function defineProperties(target, props) {
-            for (var i = 0; i < props.length; i++) {
-                var descriptor = props[i];
-                descriptor.enumerable = descriptor.enumerable || false;
-                descriptor.configurable = true;
-                if ("value" in descriptor) descriptor.writable = true;
-                Object.defineProperty(target, descriptor.key, descriptor);
-            }
-        }
-
-        return function (Constructor, protoProps, staticProps) {
-            if (protoProps) defineProperties(Constructor.prototype, protoProps);
-            if (staticProps) defineProperties(Constructor, staticProps);
-            return Constructor;
-        };
-    }();
-
-    function _possibleConstructorReturn(self, call) {
-        if (!self) {
-            throw new ReferenceError("this hasn't been initialised - super() hasn't been called");
-        }
-
-        return call && (typeof call === "object" || typeof call === "function") ? call : self;
-    }
-
-    function _inherits(subClass, superClass) {
-        if (typeof superClass !== "function" && superClass !== null) {
-            throw new TypeError("Super expression must either be null or a function, not " + typeof superClass);
-        }
-
-        subClass.prototype = Object.create(superClass && superClass.prototype, {
-            constructor: {
-                value: subClass,
-                enumerable: false,
-                writable: true,
-                configurable: true
-            }
-        });
-        if (superClass) Object.setPrototypeOf ? Object.setPrototypeOf(subClass, superClass) : subClass.__proto__ = superClass;
-    }
-
-    var Clipboard = function (_Emitter) {
-        _inherits(Clipboard, _Emitter);
-
-        /**
-         * @param {String|HTMLElement|HTMLCollection|NodeList} trigger
-         * @param {Object} options
-         */
-        function Clipboard(trigger, options) {
-            _classCallCheck(this, Clipboard);
-
-            var _this = _possibleConstructorReturn(this, (Clipboard.__proto__ || Object.getPrototypeOf(Clipboard)).call(this));
-
-            _this.resolveOptions(options);
-            _this.listenClick(trigger);
-            return _this;
-        }
-
-        /**
-         * Defines if attributes would be resolved using internal setter functions
-         * or custom functions that were passed in the constructor.
-         * @param {Object} options
-         */
-
-
-        _createClass(Clipboard, [{
-            key: 'resolveOptions',
-            value: function resolveOptions() {
-                var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};
-
-                this.action = typeof options.action === 'function' ? options.action : this.defaultAction;
-                this.target = typeof options.target === 'function' ? options.target : this.defaultTarget;
-                this.text = typeof options.text === 'function' ? options.text : this.defaultText;
-            }
-        }, {
-            key: 'listenClick',
-            value: function listenClick(trigger) {
-                var _this2 = this;
-
-                this.listener = (0, _goodListener2.default)(trigger, 'click', function (e) {
-                    return _this2.onClick(e);
-                });
-            }
-        }, {
-            key: 'onClick',
-            value: function onClick(e) {
-                var trigger = e.delegateTarget || e.currentTarget;
-
-                if (this.clipboardAction) {
-                    this.clipboardAction = null;
-                }
-
-                this.clipboardAction = new _clipboardAction2.default({
-                    action: this.action(trigger),
-                    target: this.target(trigger),
-                    text: this.text(trigger),
-                    trigger: trigger,
-                    emitter: this
-                });
-            }
-        }, {
-            key: 'defaultAction',
-            value: function defaultAction(trigger) {
-                return getAttributeValue('action', trigger);
-            }
-        }, {
-            key: 'defaultTarget',
-            value: function defaultTarget(trigger) {
-                var selector = getAttributeValue('target', trigger);
-
-                if (selector) {
-                    return document.querySelector(selector);
-                }
-            }
-        }, {
-            key: 'defaultText',
-            value: function defaultText(trigger) {
-                return getAttributeValue('text', trigger);
-            }
-        }, {
-            key: 'destroy',
-            value: function destroy() {
-                this.listener.destroy();
-
-                if (this.clipboardAction) {
-                    this.clipboardAction.destroy();
-                    this.clipboardAction = null;
-                }
-            }
-        }], [{
-            key: 'isSupported',
-            value: function isSupported() {
-                var action = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : ['copy', 'cut'];
-
-                var actions = typeof action === 'string' ? [action] : action;
-                var support = !!document.queryCommandSupported;
-
-                actions.forEach(function (action) {
-                    support = support && !!document.queryCommandSupported(action);
-                });
-
-                return support;
-            }
-        }]);
-
-        return Clipboard;
-    }(_tinyEmitter2.default);
-
-    /**
-     * Helper function to retrieve attribute value.
-     * @param {String} suffix
-     * @param {Element} element
-     */
-    function getAttributeValue(suffix, element) {
-        var attribute = 'data-clipboard-' + suffix;
-
-        if (!element.hasAttribute(attribute)) {
-            return;
-        }
-
-        return element.getAttribute(attribute);
-    }
-
-    module.exports = Clipboard;
-});
-
-},{"./clipboard-action":7,"good-listener":4,"tiny-emitter":6}]},{},[8])(8)
-});
\ No newline at end of file
diff --git a/docs/_static/js/clipboard.min.js b/docs/_static/js/clipboard.min.js
deleted file mode 100755
index 1993676f9928..000000000000
--- a/docs/_static/js/clipboard.min.js
+++ /dev/null
@@ -1,7 +0,0 @@
-/*!
- * clipboard.js v1.6.1
- * https://zenorocha.github.io/clipboard.js
- *
- * Licensed MIT © Zeno Rocha
- */
-!function(e){if("object"==typeof exports&&"undefined"!=typeof module)module.exports=e();else if("function"==typeof define&&define.amd)define([],e);else{var t;t="undefined"!=typeof window?window:"undefined"!=typeof global?global:"undefined"!=typeof self?self:this,t.Clipboard=e()}}(function(){var e,t,n;return function e(t,n,o){function i(a,c){if(!n[a]){if(!t[a]){var l="function"==typeof require&&require;if(!c&&l)return l(a,!0);if(r)return r(a,!0);var u=new Error("Cannot find module '"+a+"'");throw u.code="MODULE_NOT_FOUND",u}var s=n[a]={exports:{}};t[a][0].call(s.exports,function(e){var n=t[a][1][e];return i(n?n:e)},s,s.exports,e,t,n,o)}return n[a].exports}for(var r="function"==typeof require&&require,a=0;a<o.length;a++)i(o[a]);return i}({1:[function(e,t,n){function o(e,t){for(;e&&e.nodeType!==i;){if(e.matches(t))return e;e=e.parentNode}}var i=9;if("undefined"!=typeof Element&&!Element.prototype.matches){var r=Element.prototype;r.matches=r.matchesSelector||r.mozMatchesSelector||r.msMatchesSelector||r.oMatchesSelector||r.webkitMatchesSelector}t.exports=o},{}],2:[function(e,t,n){function o(e,t,n,o,r){var a=i.apply(this,arguments);return e.addEventListener(n,a,r),{destroy:function(){e.removeEventListener(n,a,r)}}}function i(e,t,n,o){return function(n){n.delegateTarget=r(n.target,t),n.delegateTarget&&o.call(e,n)}}var r=e("./closest");t.exports=o},{"./closest":1}],3:[function(e,t,n){n.node=function(e){return void 0!==e&&e instanceof HTMLElement&&1===e.nodeType},n.nodeList=function(e){var t=Object.prototype.toString.call(e);return void 0!==e&&("[object NodeList]"===t||"[object HTMLCollection]"===t)&&"length"in e&&(0===e.length||n.node(e[0]))},n.string=function(e){return"string"==typeof e||e instanceof String},n.fn=function(e){var t=Object.prototype.toString.call(e);return"[object Function]"===t}},{}],4:[function(e,t,n){function o(e,t,n){if(!e&&!t&&!n)throw new Error("Missing required arguments");if(!c.string(t))throw new TypeError("Second argument must be a String");if(!c.fn(n))throw new TypeError("Third argument must be a Function");if(c.node(e))return i(e,t,n);if(c.nodeList(e))return r(e,t,n);if(c.string(e))return a(e,t,n);throw new TypeError("First argument must be a String, HTMLElement, HTMLCollection, or NodeList")}function i(e,t,n){return e.addEventListener(t,n),{destroy:function(){e.removeEventListener(t,n)}}}function r(e,t,n){return Array.prototype.forEach.call(e,function(e){e.addEventListener(t,n)}),{destroy:function(){Array.prototype.forEach.call(e,function(e){e.removeEventListener(t,n)})}}}function a(e,t,n){return l(document.body,e,t,n)}var c=e("./is"),l=e("delegate");t.exports=o},{"./is":3,delegate:2}],5:[function(e,t,n){function o(e){var t;if("SELECT"===e.nodeName)e.focus(),t=e.value;else if("INPUT"===e.nodeName||"TEXTAREA"===e.nodeName){var n=e.hasAttribute("readonly");n||e.setAttribute("readonly",""),e.select(),e.setSelectionRange(0,e.value.length),n||e.removeAttribute("readonly"),t=e.value}else{e.hasAttribute("contenteditable")&&e.focus();var o=window.getSelection(),i=document.createRange();i.selectNodeContents(e),o.removeAllRanges(),o.addRange(i),t=o.toString()}return t}t.exports=o},{}],6:[function(e,t,n){function o(){}o.prototype={on:function(e,t,n){var o=this.e||(this.e={});return(o[e]||(o[e]=[])).push({fn:t,ctx:n}),this},once:function(e,t,n){function o(){i.off(e,o),t.apply(n,arguments)}var i=this;return o._=t,this.on(e,o,n)},emit:function(e){var t=[].slice.call(arguments,1),n=((this.e||(this.e={}))[e]||[]).slice(),o=0,i=n.length;for(o;o<i;o++)n[o].fn.apply(n[o].ctx,t);return this},off:function(e,t){var n=this.e||(this.e={}),o=n[e],i=[];if(o&&t)for(var r=0,a=o.length;r<a;r++)o[r].fn!==t&&o[r].fn._!==t&&i.push(o[r]);return i.length?n[e]=i:delete n[e],this}},t.exports=o},{}],7:[function(t,n,o){!function(i,r){if("function"==typeof e&&e.amd)e(["module","select"],r);else if("undefined"!=typeof o)r(n,t("select"));else{var a={exports:{}};r(a,i.select),i.clipboardAction=a.exports}}(this,function(e,t){"use strict";function n(e){return e&&e.__esModule?e:{default:e}}function o(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}var i=n(t),r="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof Symbol&&e.constructor===Symbol&&e!==Symbol.prototype?"symbol":typeof e},a=function(){function e(e,t){for(var n=0;n<t.length;n++){var o=t[n];o.enumerable=o.enumerable||!1,o.configurable=!0,"value"in o&&(o.writable=!0),Object.defineProperty(e,o.key,o)}}return function(t,n,o){return n&&e(t.prototype,n),o&&e(t,o),t}}(),c=function(){function e(t){o(this,e),this.resolveOptions(t),this.initSelection()}return a(e,[{key:"resolveOptions",value:function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};this.action=t.action,this.emitter=t.emitter,this.target=t.target,this.text=t.text,this.trigger=t.trigger,this.selectedText=""}},{key:"initSelection",value:function e(){this.text?this.selectFake():this.target&&this.selectTarget()}},{key:"selectFake",value:function e(){var t=this,n="rtl"==document.documentElement.getAttribute("dir");this.removeFake(),this.fakeHandlerCallback=function(){return t.removeFake()},this.fakeHandler=document.body.addEventListener("click",this.fakeHandlerCallback)||!0,this.fakeElem=document.createElement("textarea"),this.fakeElem.style.fontSize="12pt",this.fakeElem.style.border="0",this.fakeElem.style.padding="0",this.fakeElem.style.margin="0",this.fakeElem.style.position="absolute",this.fakeElem.style[n?"right":"left"]="-9999px";var o=window.pageYOffset||document.documentElement.scrollTop;this.fakeElem.style.top=o+"px",this.fakeElem.setAttribute("readonly",""),this.fakeElem.value=this.text,document.body.appendChild(this.fakeElem),this.selectedText=(0,i.default)(this.fakeElem),this.copyText()}},{key:"removeFake",value:function e(){this.fakeHandler&&(document.body.removeEventListener("click",this.fakeHandlerCallback),this.fakeHandler=null,this.fakeHandlerCallback=null),this.fakeElem&&(document.body.removeChild(this.fakeElem),this.fakeElem=null)}},{key:"selectTarget",value:function e(){this.selectedText=(0,i.default)(this.target),this.copyText()}},{key:"copyText",value:function e(){var t=void 0;try{t=document.execCommand(this.action)}catch(e){t=!1}this.handleResult(t)}},{key:"handleResult",value:function e(t){this.emitter.emit(t?"success":"error",{action:this.action,text:this.selectedText,trigger:this.trigger,clearSelection:this.clearSelection.bind(this)})}},{key:"clearSelection",value:function e(){this.target&&this.target.blur(),window.getSelection().removeAllRanges()}},{key:"destroy",value:function e(){this.removeFake()}},{key:"action",set:function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"copy";if(this._action=t,"copy"!==this._action&&"cut"!==this._action)throw new Error('Invalid "action" value, use either "copy" or "cut"')},get:function e(){return this._action}},{key:"target",set:function e(t){if(void 0!==t){if(!t||"object"!==("undefined"==typeof t?"undefined":r(t))||1!==t.nodeType)throw new Error('Invalid "target" value, use a valid Element');if("copy"===this.action&&t.hasAttribute("disabled"))throw new Error('Invalid "target" attribute. Please use "readonly" instead of "disabled" attribute');if("cut"===this.action&&(t.hasAttribute("readonly")||t.hasAttribute("disabled")))throw new Error('Invalid "target" attribute. You can\'t cut text from elements with "readonly" or "disabled" attributes');this._target=t}},get:function e(){return this._target}}]),e}();e.exports=c})},{select:5}],8:[function(t,n,o){!function(i,r){if("function"==typeof e&&e.amd)e(["module","./clipboard-action","tiny-emitter","good-listener"],r);else if("undefined"!=typeof o)r(n,t("./clipboard-action"),t("tiny-emitter"),t("good-listener"));else{var a={exports:{}};r(a,i.clipboardAction,i.tinyEmitter,i.goodListener),i.clipboard=a.exports}}(this,function(e,t,n,o){"use strict";function i(e){return e&&e.__esModule?e:{default:e}}function r(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}function a(e,t){if(!e)throw new ReferenceError("this hasn't been initialised - super() hasn't been called");return!t||"object"!=typeof t&&"function"!=typeof t?e:t}function c(e,t){if("function"!=typeof t&&null!==t)throw new TypeError("Super expression must either be null or a function, not "+typeof t);e.prototype=Object.create(t&&t.prototype,{constructor:{value:e,enumerable:!1,writable:!0,configurable:!0}}),t&&(Object.setPrototypeOf?Object.setPrototypeOf(e,t):e.__proto__=t)}function l(e,t){var n="data-clipboard-"+e;if(t.hasAttribute(n))return t.getAttribute(n)}var u=i(t),s=i(n),f=i(o),d=function(){function e(e,t){for(var n=0;n<t.length;n++){var o=t[n];o.enumerable=o.enumerable||!1,o.configurable=!0,"value"in o&&(o.writable=!0),Object.defineProperty(e,o.key,o)}}return function(t,n,o){return n&&e(t.prototype,n),o&&e(t,o),t}}(),h=function(e){function t(e,n){r(this,t);var o=a(this,(t.__proto__||Object.getPrototypeOf(t)).call(this));return o.resolveOptions(n),o.listenClick(e),o}return c(t,e),d(t,[{key:"resolveOptions",value:function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};this.action="function"==typeof t.action?t.action:this.defaultAction,this.target="function"==typeof t.target?t.target:this.defaultTarget,this.text="function"==typeof t.text?t.text:this.defaultText}},{key:"listenClick",value:function e(t){var n=this;this.listener=(0,f.default)(t,"click",function(e){return n.onClick(e)})}},{key:"onClick",value:function e(t){var n=t.delegateTarget||t.currentTarget;this.clipboardAction&&(this.clipboardAction=null),this.clipboardAction=new u.default({action:this.action(n),target:this.target(n),text:this.text(n),trigger:n,emitter:this})}},{key:"defaultAction",value:function e(t){return l("action",t)}},{key:"defaultTarget",value:function e(t){var n=l("target",t);if(n)return document.querySelector(n)}},{key:"defaultText",value:function e(t){return l("text",t)}},{key:"destroy",value:function e(){this.listener.destroy(),this.clipboardAction&&(this.clipboardAction.destroy(),this.clipboardAction=null)}}],[{key:"isSupported",value:function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:["copy","cut"],n="string"==typeof t?[t]:t,o=!!document.queryCommandSupported;return n.forEach(function(e){o=o&&!!document.queryCommandSupported(e)}),o}}]),t}(s.default);e.exports=h})},{"./clipboard-action":7,"good-listener":4,"tiny-emitter":6}]},{},[8])(8)});
\ No newline at end of file
diff --git a/docs/_static/js/copycode.js b/docs/_static/js/copycode.js
deleted file mode 100644
index d42e99277ff8..000000000000
--- a/docs/_static/js/copycode.js
+++ /dev/null
@@ -1,110 +0,0 @@
-/*!
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- */
-
-/* Copy code to clipboard */
-LANG_GP = {'default':'>>> ', 'python':'>>> ' , 'scala':'scala>', 'julia':'julia> ', 'r':'> ', 'perl':'pdl>' , 'cpp':'', 'bash':'$ '};
-
-function addBtn() {
-    copyBtn = '<button type="button" class="btn btn-primary copy-btn" data-toggle="tooltip"' +
-              'data-placement="bottom" title="Copy to clipboard"><i class="fa fa-copy"></i></button>'
-    for (var lang in LANG_GP) {
-        codeBlock = $('div .highlight-' + lang);
-        codeBlock.css('position', 'relative')
-        codeBlock.prepend(copyBtn);
-        codeBlock.find('.copy-btn').addClass(lang);
-        codeBlock.hover(
-          function() {
-            $(this).children().first().show();
-          }, function() {
-            $(this).children().first().hide();
-          }
-        );
-    }
-};
-
-function html2clipboard(content) {
-    var tmpEl = document.createElement("div");
-    tmpEl.style.opacity = 0;
-    tmpEl.style.position = "absolute";
-    tmpEl.style.pointerEvents = "none";
-    tmpEl.style.zIndex = -1;
-
-    tmpEl.innerHTML = content;
-    document.body.appendChild(tmpEl);
-
-    var range = document.createRange();
-    range.selectNode(tmpEl);
-    window.getSelection().addRange(range);
-    document.execCommand("copy");
-    document.body.removeChild(tmpEl);
-}
-
-$(document).ready(function(){
-    addBtn()
-    $('[data-toggle="tooltip"]').tooltip();
-    $('.copy-btn').hover(
-      function() {}, function() {
-        $(this).attr('title', 'Copy to clipboard').tooltip('fixTitle');
-      }
-    );
-
-    clipboard = new Clipboard('.copy-btn', {
-        target: function(trigger) {
-            return trigger.parentNode.querySelector('.highlight');
-        }
-    });
-
-    clipboard.on('success', function(e) {
-        //Deal with codes with leading gap
-        var btnClass = e.trigger.classList;
-        var lang = btnClass[btnClass.length - 1];
-        var lines = e.text.split('\n');
-        var hasGap = false;
-        var continueSign = '...';
-
-        e.clearSelection();
-
-        for(var i = 0; i < lines.length; ++i) {
-            lines[i] = lines[i].replace(/^\s+|\s+$/g, "");
-            if(!hasGap && lines[i].startsWith(LANG_GP[lang])) hasGap = true;
-        }
-
-        if(hasGap) {
-            var content = '';
-            for(var i = 0; i < lines.length; ++i) {
-                if(lines[i].startsWith(LANG_GP[lang]) || ((lang == 'python' || lang == 'default') &&
-                                                          lines[i].startsWith(continueSign))) {
-                    content = content.concat(lines[i].substring(LANG_GP[lang].length, lines[i].length) + '<br />');
-                }
-                else if(lines[i].length == 0) content = content.concat('<br />');
-            }
-            content = content.substring(0, content.length - 6);
-            html2clipboard(content);
-        }
-        $(e.trigger).attr('title', 'Copied')
-             .tooltip('fixTitle')
-             .tooltip('show');
-    });
-
-    clipboard.on('error', function(e) {
-        $(e.trigger).attr('title', 'Copy failed. Try again.')
-             .tooltip('fixTitle')
-             .tooltip('show');
-    });
-});
diff --git a/docs/_static/js/docversion.js b/docs/_static/js/docversion.js
deleted file mode 100644
index 320179854448..000000000000
--- a/docs/_static/js/docversion.js
+++ /dev/null
@@ -1,47 +0,0 @@
-/*!
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- */
-
-/* Set the version of the website */
-function setVersion(anchor){
-        if (arguments.length==0) {
-            anchor = window.location.hash
-        };
-        let doc = window.location.pathname.match(/^\/versions\/[^\/]+\/([^*]+.*)$/);
-        if (doc) {
-            if (document.getElementById('dropdown-menu-position-anchor-version')) {
-                    versionNav = $('#dropdown-menu-position-anchor-version a.main-nav-link');
-                    $(versionNav).each( function( index, el ) {
-                            currLink = $( el ).attr('href');
-                            version = currLink.match(/\/versions\/([^\/]+)\//);
-                            if (version) {
-                                    versionedDoc = '/versions/' + version[1] + '/' + doc[1] + (anchor || '') + (window.location.search || '');
-                                    $( el ).attr('href', versionedDoc);
-                            }
-                    });
-            }
-        }
-}
-
-$(document).ready(function () {
-    setVersion();
-});
-
-$('a.reference.internal').click(function(){
-    setVersion($(this).attr("href"));
-});
diff --git a/docs/_static/js/navbar.js b/docs/_static/js/navbar.js
deleted file mode 100644
index 5dde7d8ff2e2..000000000000
--- a/docs/_static/js/navbar.js
+++ /dev/null
@@ -1,117 +0,0 @@
-/*!
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- */
-
-/* Custom navigation bar formatting */
-var searchBox = $("#search-input-wrap");
-var TITLE = ['/install/', '/gluon/', '/api/', '/docs/', '/community/' ];
-var DOC_TITLE = ['/faq/', '/tutorials/', '/architecture/', '/model_zoo/'];
-var APISubmenu, versionSubmenu, docSubmenu, communitySubmenu;
-$("#burgerMenu").children().each(function () {
-    if($(this).children().first().html() == 'API') APISubmenu = $(this).clone();
-    if($(this).children().first().html().indexOf('Versions') == 0) versionSubmenu = $(this).clone();
-    if($(this).children().first().html().indexOf('Community') == 0) communitySubmenu = $(this).clone();
-    if($(this).children().first().html() == 'Docs') docSubmenu= $(this).clone();
-});
-
-$('.burger-link').on('click', function(e) { e.stopPropagation() });
-$('.burger-link').on('touchstart', function(e) { e.stopPropagation() });
-
-function navbar() {
-    var leftOffset = 40;
-    var plusMenuList = [];
-    var plusIconLeft =$("#search-input-wrap").offset().left - leftOffset;
-    var isCovered = false;
-    $("#main-nav").children().each(function () {
-        var rightPos;
-        if($(this).is(':hidden')) {
-            $(this).show();
-            rightPos = $(this).offset().left + $(this).width();
-            $(this).hide;
-        }
-        else rightPos = $(this).offset().left + $(this).width();
-
-        if(isCovered) {
-            plusMenuList.push($(this).clone());
-            $(this).hide();
-        }
-        else if(rightPos > plusIconLeft) {
-            isCovered = true;
-            $(".plusIcon").first().show();
-            plusMenuList.push($(this).clone());
-            $(this).hide();
-        }
-        else $(this).show();
-    });
-
-    if(plusMenuList.length == 0) {
-        $(".plusIcon").first().hide();
-        return;
-    }
-    $("#plusMenu").empty();
-    for (var i = 0; i < plusMenuList.length; ++i) {
-        if(plusMenuList[i].attr('id') == 'dropdown-menu-position-anchor') {
-            $("#plusMenu").append(APISubmenu);
-        }
-        else if(plusMenuList[i].attr('id') == 'dropdown-menu-position-anchor-version') {
-            $("#plusMenu").append(versionSubmenu);
-        }
-        else if(plusMenuList[i].attr('id') == 'dropdown-menu-position-anchor-docs') {
-            $("#plusMenu").append(docSubmenu);
-        }
-        else if(plusMenuList[i].attr('id') == 'dropdown-menu-position-anchor-community') {
-            $("#plusMenu").append(communitySubmenu);
-        }
-        else {
-            $("#plusMenu").append("<li></li>");
-            plusMenuList[i].removeClass("main-nav-link");
-            $("#plusMenu").children().last().append(plusMenuList[i]);
-        }
-    }
-};
-
-/*Show bottom border of current tab*/
-function showTab() {
-    var url = window.location.href;
-    if(url.indexOf('/get_started/why_mxnet') != -1) return;
-    for(var i = 0; i < TITLE.length; ++i) {
-        if(url.indexOf(TITLE[i]) != -1) {
-            var tab = $($('#main-nav').children().eq(i));
-            if(!tab.is('a')) tab = tab.find('a').first();
-            tab.css('border-bottom', '3px solid');
-            return;
-        }
-    }
-     for(var i = 0; i < DOC_TITLE.length; ++i) {
-        if(url.indexOf(DOC_TITLE[i]) != -1) {
-            var tab = $($('#main-nav').children().eq(3));
-            if(!tab.is('a')) tab = tab.find('a').first();
-            tab.css('border-bottom', '3px solid');
-        }
-     }
-}
-
-$(document).ready(function () {
-    navbar();
-    showTab();
-    $(window).resize(function () {
-        navbar();
-        if($("body").prop("clientWidth") < 1000 || $('div.sphinxsidebar').css('visibility') == 'hidden') $('div.content').css('width', '100%');
-        else $('div.content').css('width', 'calc(100% - 300px)');
-    });
-});
diff --git a/docs/_static/js/options.js b/docs/_static/js/options.js
deleted file mode 100644
index d400d1ce2c9f..000000000000
--- a/docs/_static/js/options.js
+++ /dev/null
@@ -1,118 +0,0 @@
-
-/*!
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file 
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- */
-
-/* Installation page display functions for install selector */
-var versionSelect   = defaultVersion = 'v1.5.0';
-var platformSelect    = 'Linux';
-var languageSelect  = 'Python';
-var processorSelect = 'CPU';
-var environSelect   = 'Pip';
-
-$(document).ready(function () {
-    function label(lbl) {
-        return lbl.replace(/[ .]/g, '-').toLowerCase();
-    }
-
-    function urlSearchParams(searchString) {
-        let urlDict = new Map();
-        let searchParams = searchString.substring(1).split("&");
-        searchParams.forEach(function(element) {
-            kvPair = element.split("=");
-            urlDict.set(kvPair[0], kvPair[1]);
-        });
-        return urlDict;
-    }
-
-    function setSelects(){
-        let urlParams = urlSearchParams(window.location.search);
-        if (urlParams.get('version'))
-            versionSelect = urlParams.get('version');
-        $('li a:contains(' + versionSelect + ')').parent().siblings().removeClass('active');
-        $('li a:contains(' + versionSelect + ')').parent().addClass('active');
-        $('.current-version').html( versionSelect + ' <span class="caret"></span></button>' );
-        if (urlParams.get('platform'))
-            platformSelect = urlParams.get('platform');
-        $('button:contains(' + platformSelect + ')').siblings().removeClass('active');
-        $('button:contains(' + platformSelect + ')').addClass('active');
-        if (urlParams.get('language'))
-            languageSelect = urlParams.get('language');
-        $('button:contains(' + languageSelect + ')').siblings().removeClass('active');
-        $('button:contains(' + languageSelect + ')').addClass('active');
-        if (urlParams.get('processor'))
-            processorSelect = urlParams.get('processor');
-        $('button:contains(' + processorSelect + ')').siblings().removeClass('active');
-        $('button:contains(' + processorSelect + ')').addClass('active');
-        if (urlParams.get('environ'))
-            environSelect = urlParams.get('environ');
-        $('button:contains(' + environSelect + ')').siblings().removeClass('active');
-        $('button:contains(' + environSelect + ')').addClass('active');
-        showContent();
-        if (window.location.href.indexOf("/install/index.html") >= 0) {
-            if (versionSelect.indexOf(defaultVersion) >= 0) {
-                history.pushState(null, null, 'index.html?platform=' + platformSelect + '&language=' + languageSelect + '&processor=' + processorSelect);
-            } else {
-                history.pushState(null, null, 'index.html?version=' + versionSelect + '&platform=' + platformSelect + '&language=' + languageSelect + '&processor=' + processorSelect);
-            }
-        }
-    }
-
-    function showContent() {
-        $('.opt-group .opt').each(function(){
-            $('.'+label($(this).text())).hide();
-            $('.highlight-'+label($(this).text())).hide();
-        });
-        $('.opt-group .active').each(function(){
-            $('.'+label($(this).text())).show();
-            $('.highlight-'+label($(this).text())).show();
-        });
-    }
-    showContent();
-    setSelects();
-    function setContent() {
-        var el = $(this);
-        let urlParams = urlSearchParams(window.location.search);
-        el.siblings().removeClass('active');
-        el.addClass('active');
-        if ($(this).hasClass("versions")) {
-            $('.current-version').html( $(this).text() + ' <span class="caret"></span></button>' );
-            if ($(this).text().indexOf(defaultVersion) < 0) {
-                if (window.location.search.indexOf("version") < 0) {
-                    history.pushState(null, null, 'index.html' + window.location.search.concat( '&version=' + $(this).text() ));
-                } else {
-                    history.pushState(null, null, 'index.html' + window.location.search.replace( urlParams.get('version'), $(this).text() ));
-                }
-            } else if (window.location.search.indexOf("version") >= 0) {
-                  history.pushState(null, null, 'index.html' + window.location.search.replace( 'version', 'prev' ));
-              }
-        }
-        else if ($(this).hasClass("platforms")) {
-            history.pushState(null, null, 'index.html' + window.location.search.replace( urlParams.get('platform'), $(this).text() ));
-        }
-        else if ($(this).hasClass("languages")) {
-            history.pushState(null, null, 'index.html' + window.location.search.replace( urlParams.get('language'), $(this).text() ));
-        }
-        else if ($(this).hasClass("processors")) {
-            history.pushState(null, null, 'index.html' + window.location.search.replace( urlParams.get('processor'), $(this).text() ));
-        }
-        showContent();
-        //window.location.search = window.location.search.replace( urlParams.get('version'), $(this).text() );
-    }
-    $('.opt-group').on('click', '.opt', setContent);
-});
diff --git a/docs/_static/js/page.js b/docs/_static/js/page.js
deleted file mode 100644
index 425998d6d706..000000000000
--- a/docs/_static/js/page.js
+++ /dev/null
@@ -1,93 +0,0 @@
-/*!
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- */
-
-/* Generate url tracking for each page */
-var protocol = location.protocol.concat("//");
-var host = protocol.concat(window.location.host);
-var path = window.location.pathname;
-var pathArr = path.split('/');
-var icon = '<i class="fa fa-angle-right" aria-hidden="true"></i>';
-var urlTracker = "<ul><li><a href=" + host + ">MXNet</a>" + icon + "</li>";
-
-// Check whether this is another version
-var lastUrl = host;
-var versionIDX = -1;
-for (var i = 1; i < pathArr.length; ++i) {
-    lastUrl += '/' + pathArr[i];
-    if(pathArr[i] == 'versions') {
-        versionIDX = i;
-        lastUrl += '/' + pathArr[i + 1];
-        break;
-    }
-}
-if (versionIDX > 0) {
-    pathArr = pathArr.slice(versionIDX + 1, pathArr.length);
-    urlTracker = "<ul><li><a href=" + lastUrl + "/index.html>MXNet</a>" + icon + "</li>";
-}
-else lastUrl = host;
-
-for (var i = 1; i < pathArr.length; ++i) {
-    pathVal = pathArr[i]
-    if (pathVal == 'index.html' || pathVal.length == 0) continue;
-    if (pathVal.indexOf('#') != -1) pathVal = pathVal.substring(0, pathVal.indexOf('#'));
-    lastUrl += '/' + pathVal;
-    if (pathVal.endsWith('.html')) pathVal = pathVal.substring(0, pathVal.length - 5);
-    if (i == pathArr.length - 1 || pathArr[i + 1].length == 0 || pathArr[i + 1] == 'index.html') {
-        if ( pathVal == 'faq' ){
-             pathVal = "FAQ";
-        }
-        urlTracker += "<li>" + pathVal.replace(/_/g, ' ') + "</li>";
-    }
-    else {
-        // Check whether current folder has index.html.
-        // If it doesn't, disable the link.
-        $.ajax(lastUrl + '/index.html', {
-            type: "GET",
-            statusCode: {
-                403: function (response) {
-                    if (pathVal == 'api') urlTracker += "<li>API" + icon + "</li>";
-                    else urlTracker += "<li>" + pathVal.replace(/_/g, ' ') + icon + "</li>";
-                },
-                404: function (response) {
-                    if (pathVal == 'api') urlTracker += "<li>API" + icon + "</li>";
-                    else urlTracker += "<li>" + pathVal.replace(/_/g, ' ') + icon + "</li>";
-                }
-            },
-            success: function () {
-                item = pathVal == 'ndarray' ? "NDArray" : pathVal;
-                urlTracker += "<li><a href=" + lastUrl + '/index.html' + ">" + item.replace(/_/g, ' ') + "</a>" + icon + "</li>";
-            }
-        });
-    }
-}
-urlTracker += '</ul>';
-$('.page-tracker').append(urlTracker);
-
-/* Generate top download btn*/
-if ($('div.download-btn').length > 0) {
-    var topBtn = $('div.download-btn').clone();
-    topBtn.addClass('download-btn-top');
-    topBtn.insertAfter(".page-tracker");
-}
-
-/* Adjust footer position */
-var footerHeight = 252;
-if ($('div.content-block').height() > $(window).height() - footerHeight) {
-    $('div.footer').css('position', 'relative');
-}
diff --git a/docs/_static/js/search.js b/docs/_static/js/search.js
deleted file mode 100644
index 6a70b4ef5cbb..000000000000
--- a/docs/_static/js/search.js
+++ /dev/null
@@ -1,39 +0,0 @@
-/*!
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- */
-
-/* Display functionality for the search feature */
-$(document).ready(function () {
-    var searchForm = $("#search-input-wrap").children("form").first();
-    searchForm.append('<div class="form-group searchBtn"><input type="submit" class="form-control" value="Go"></div>');
-    searchForm.children("div").first().addClass("searchBox");
-    $(".searchBox").addClass("searchBoxNorm");
-
-    $('#searchIcon').click(function () {
-        if($('#search-input-wrap').is(':hidden')) {
-            $('#search-input-wrap').show();
-            $('#searchIcon span').removeClass('glyphicon-search');
-            $('#searchIcon span').addClass('glyphicon-remove-circle');
-        }
-        else {
-            $('#search-input-wrap').hide();
-            $('#searchIcon span').removeClass('glyphicon-remove-circle');
-            $('#searchIcon span').addClass('glyphicon-search');
-        }
-    });
-});
diff --git a/docs/_static/js/sidebar.js b/docs/_static/js/sidebar.js
deleted file mode 100644
index 65899d5ddd65..000000000000
--- a/docs/_static/js/sidebar.js
+++ /dev/null
@@ -1,277 +0,0 @@
-/*!
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- */
-
-/* Customizations to the sphinx theme */
-var LANG = ['python', 'c++', 'clojure', 'julia', 'perl', 'r', 'scala', 'java'];
-var TITLE_WITH_LANG = ['/get_started/', '/tutorials/', '/faq/', '/architecture/', '/community/'];
-for(var i = 0; i < LANG.length; ++i) {
-    TITLE_WITH_LANG.push('/api/' + LANG[i] + '/');
-}
-
-/*Check whether is API page*/
-var API_PAGE = ['python'];
-var isAPI = false;
-
-function render_left_helper(toc) {
-    var lefttoc = toc;
-
-    lefttoc.addClass('current');
-    $('.leftsidebar > .sphinxsidebarwrapper').children().remove();
-    $('.leftsidebar > .sphinxsidebarwrapper').append(lefttoc);
-
-    addToggle('.leftsidebar');
-
-    $('.leftsidebar li a').click(function () {
-        $('.leftsidebar li a').css('color', '#337ab7');
-        $(this).css('color', 'black');
-    });
-}
-
-/*Render content tree of different pages*/
-function render_lefttoc() {
-    var url = window.location.href, indexTrailing = 'index.html';
-    if (url.indexOf('/get_started/') != -1) {
-        var leftToc = "<ul><li class='leaf'><a href='install.html'>Installation</a></li><li class='leaf'><a href='why_mxnet.html'>Why MXNet</a></li></ul>";
-        render_left_helper($($.parseHTML(leftToc)), 'Get Started');
-        keepExpand();
-        $('.sphinxsidebar').css("visibility", "visible");
-        return;
-    }
-    // If current page is not index page
-    if (url.indexOf(indexTrailing) == -1) {
-        for(var i = 0; i < TITLE_WITH_LANG.length; ++i) {
-            var path = TITLE_WITH_LANG[i];
-            if (url.indexOf(path) != -1) {
-                urlElem = url.split('/');
-                version = '';
-                for (var j = 0; j < urlElem.length; ++j) {
-                    if(urlElem[j] == 'versions') {
-                        version = '/versions/' + urlElem[j + 1];
-                        break;
-                    }
-                }
-                var protocol = location.protocol.concat("//");
-                var urlPath = protocol + window.location.host + version +  path;
-                $.get(urlPath + indexTrailing, null, function(data) {
-                    var lastToc = $($.parseHTML(data)).find('.leftsidebar > .sphinxsidebarwrapper > ul.current > li.current > ul')
-                    render_left_helper(lastToc);
-                    var tocLink = $('.leftsidebar .sphinxsidebarwrapper .leaf a');
-                    var staticLink = 'http';
-                    tocLink.each(function () {
-                        if (!$(this).attr('href').startsWith(staticLink)) {
-                            $(this).attr('href', urlPath + $(this).attr('href'));
-                        }
-                    });
-                    keepExpand();
-                    $('.sphinxsidebar').css("visibility", "visible");
-                    if ($('div.sphinxsidebar').css('display') != 'none') $('.content').css('width', 'calc(100% - 300px)');
-                    else $('.content').css('width', '100%');
-                })
-            }
-        }
-    }
-    else {
-        var toc = $('.leftsidebar > .sphinxsidebarwrapper > ul.current > li.current > ul').clone();
-        render_left_helper(toc);
-        $('.sphinxsidebar').css("visibility", "visible");
-    }
-}
-
-/*Render contents inside page*/
-function render_righttoc() {
-    var url = window.location.href, indexTrailing = 'index.html';
-
-    var rightTocTitle = "Page Contents";
-    $("div.rightsidebar > div.sphinxsidebarwrapper > h3").children().remove();
-    $("div.rightsidebar > div.sphinxsidebarwrapper > h3").html(rightTocTitle);
-
-    addToggle('.rightsidebar');
-
-    $('.rightsidebar li a').click(function () {
-        $('.rightsidebar li a').css('color', '#337ab7');
-        $(this).css('color', 'black');
-    });
-
-    if (url.indexOf(indexTrailing) != -1 || isAPI) {
-        $('.rightsidebar').hide();
-    }
-}
-
-/*Highlight entry when scrolling*/
-function scroll_righttoc() {
-    var navbarHeight = 60;
-    var links = $('.rightsidebar a');
-    for(var i = 1; i < links.length; ++i) {
-        var divID = links.eq(i).attr('href');
-        if ($(divID).offset().top - $(window).scrollTop() > navbarHeight) {
-            $('.rightsidebar a').css('color', '#337ab7');
-            links.eq(i - 1).css('color', 'black');
-            if (!links.eq(i - 1).parent().hasClass('leaf')) {
-                links.eq(i - 1).parent().removeClass('closed');
-                links.eq(i - 1).parent().addClass('opened');
-                links.eq(i - 1).parent().find('ul').first().show();
-            }
-            break;
-        }
-    }
-}
-
-/*Decorate toc*/
-function addToggle(tocClass) {
-    var allEntry = $(tocClass + " div.sphinxsidebarwrapper li");
-    var subEntry = $(tocClass + " div.sphinxsidebarwrapper").children("ul").first().children("li");
-    if (subEntry.length == 1) {
-        subEntry.prepend("<span class='tocToggle' onclick='toggle(this)'></span>");
-        subEntry.addClass('opened');
-        allEntry = subEntry.find("li");
-        //subEntry.children("a").hide();
-        subEntry.children("ul").css("padding-left", "0");
-        //subEntry.parent().css("margin-left", "-10px");
-    }
-    allEntry.each(function () {
-        $(this).prepend("<span class='tocToggle' onclick='toggle(this)'></span>");
-        var childUL = $(this).find("ul");
-        if (childUL.length && childUL.first().children().length) {
-            $(this).addClass("closed");
-            $(this).find("ul").first().hide();
-        }
-        else $(this).addClass("leaf");
-        var anchor = $(this).children("a").first();
-        anchor.click(function () {
-            autoExpand(anchor);
-        });
-    });
-};
-
-/*Sidebar toc toggle button behavior*/
-function toggle(elem) {
-    if ($(elem).parent().hasClass("closed")) {
-        $(elem).parent().find("ul").first().show();
-        $(elem).parent().removeClass("closed").addClass("opened");
-    }
-    else if ($(elem).parent().hasClass("opened")) {
-        $(elem).parent().find("ul").first().hide();
-        $(elem).parent().removeClass("opened").addClass("closed");
-    }
-}
-
-/*Automatically expand child level while cilcking an entry*/
-function autoExpand(elem) {
-    if (elem.parent().hasClass("closed")) {
-        elem.parent().removeClass("closed").addClass("opened");
-        elem.parent().children("ul").first().show();
-    }
-    else {
-        elem.parent().removeClass("opened").addClass("closed");
-        elem.parent().children("ul").first().hide();
-    }
-}
-
-/*Keep toc expansion while redirecting*/
-function keepExpand() {
-    var url = window.location.href, currentEntry;
-    var entryList = $('.sphinxsidebar li');
-    for(var i = entryList.length - 1; i >= 0; --i) {
-        var entryURL = entryList.eq(i).find('a').first().attr('href');
-        if (entryURL != '#' && url.indexOf(entryURL) != -1) {
-            currentEntry = entryList.eq(i);
-            break;
-        }
-    }
-
-    //Merge right toc into left toc for API pages since they are quite long
-    if (isAPI) {
-        var rootEntry = currentEntry;
-        rootEntry.append($('.rightsidebar .sphinxsidebarwrapper > ul > li > ul').clone());
-        rootEntry.addClass('closed').removeClass('leaf');
-        var allEntry = $(".leftsidebar div.sphinxsidebarwrapper li.toctree-l2 li");
-        allEntry.each(function () {
-            var anchor = $(this).children("a").first();
-            anchor.click(function () {
-                autoExpand(anchor);
-            });
-        });
-        $('.sphinxsidebar li').each(function () {
-            if (url.endsWith($(this).find('a').first().attr('href'))) {
-                currentEntry = $(this);
-                return false;
-            }
-        });
-        $('.leftsidebar li a').click(function () {
-            $('.leftsidebar li a').css('color', '#337ab7');
-            $(this).css('color', 'black');
-        });
-    }
-    currentEntry.find('a').first().css('color', '#337ab7');
-    currentEntry.children("ul").first().show();
-    if (!currentEntry.hasClass('leaf')) currentEntry.removeClass("closed").addClass("opened");
-    while(currentEntry.parent().is('ul') && currentEntry.parent().parent().is('li')) {
-        currentEntry = currentEntry.parent().parent();
-        currentEntry.removeClass("closed").addClass("opened");
-        currentEntry.children("ul").first().show();
-    }
-}
-
-
-$(document).ready(function () {
-    var url = window.location.href, searchFlag = 'search.html';
-    var showRightToc = false;
-    try {
-        if (url.indexOf('/get_started/') == -1 && url.indexOf(searchFlag) == -1) {
-            for(var i = 0; i < API_PAGE.length; ++i) {
-                if (url.indexOf('/api/' + API_PAGE[i]) != -1) {
-                    isAPI = true;
-                    break;
-                }
-            }
-            render_righttoc();
-            if ($('.leftsidebar').length) render_lefttoc();
-        }
-        if ($('div.sphinxsidebar').css('visibility') == 'hidden') $('.content').css('width', '100%');
-        if (url.indexOf('/api/') != -1) return;
-        if (url.indexOf('/install/') != -1) {
-            $('div.sphinxsidebar').hide();
-            $('.content').css('width', '100%');
-        }
-        if (url.indexOf('/gluon/index.html') != -1) {
-            $('div.sphinxsidebar').hide();
-            $('.content').css('width', '100%');
-        }
-        if (url.indexOf('/tutorials/index.html') != -1) {
-            $('div.sphinxsidebar').hide();
-            $('.content').css('width', '100%');
-        }
-        if (showRightToc) {
-            $(window).scroll(function () {
-                scroll_righttoc();
-            });
-        }
-        else {
-            $('.rightsidebar').hide();
-        }
-        // move right toc to left if current left toc is empty
-        if ($('.leftsidebar > .sphinxsidebarwrapper').children().length == 0) {
-            $('.leftsidebar > .sphinxsidebarwrapper').append($('.rightsidebar > .sphinxsidebarwrapper > ul'));
-        }
-    }
-    catch(err) {
-        if ($('div.sphinxsidebar').css('visibility') == 'hidden') $('.content').css('width', '100%');
-        return;
-    }
-});
diff --git a/docs/_static/mxnet-theme/footer.html b/docs/_static/mxnet-theme/footer.html
deleted file mode 100644
index 0eeb611eb373..000000000000
--- a/docs/_static/mxnet-theme/footer.html
+++ /dev/null
@@ -1,16 +0,0 @@
-<div class="footer">
-<div class="section-disclaimer">
-<div class="container">
-    <div>
-        <img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/apache_incubator_logo.png" height=60>
-        <p>
-            Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), <strong>sponsored by the <i>Apache Incubator</i></strong>. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF.
-        </p>
-        <p>
-            "Copyright © 2017-2018, The Apache Software Foundation
-            Apache MXNet, MXNet, Apache, the Apache feather, and the Apache MXNet project logo are either registered trademarks or trademarks of the Apache Software Foundation."
-        </p>
-    </div>
-</div>
-</div>
-</div>
\ No newline at end of file
diff --git a/docs/_static/mxnet-theme/index.html b/docs/_static/mxnet-theme/index.html
deleted file mode 100644
index d9311d7ae39f..000000000000
--- a/docs/_static/mxnet-theme/index.html
+++ /dev/null
@@ -1,73 +0,0 @@
-<div id="splash">
-  <div class="container">
-    <div class="row">
-      <div class="col-lg-6">
-          <div id="banner-title"><span>Apache MXNet (Incubating)</span>
-            <p id="landing-title">A flexible and efficient library for deep learning.</p>
-            <div id='landing-btn-blk'>
-                <div id="install_blk">
-                    <a href="install/index.html" id="install_btn">Install</a>
-                </div>
-                <div id="why_mxnet">
-                    <a href="faq/why_mxnet.html" id="why_mxnet_btn">Learn More</a>
-                </div>
-            </div>
-          </div>
-      </div>
-      <div class="col-lg-6" style="padding-top: 60px;"><!--reserved for future promos-->
-      </div>
-    </div>
-  </div>
-</div>
-<div class="section-tout">
-  <div class="container">
-    <div class="row">
-      <div class="col-lg-4 col-sm-12">
-        <h3>MXNet 1.5.0 Released</h3>
-        <p>This release features Automatic Mixed Precision, MKL-DNN updates, CUDA10.1 support and more. </p>
-        <a href="https://github.com/apache/incubator-mxnet/releases/tag/1.5.0">Learn More</a>
-      </div>
-      <div class="col-lg-4 col-sm-12">
-        <h3>A 60-minute Gluon Crash Course</h3>
-        <p>Check out our quick overview of how to use Gluon, the imperative interface of MXNet.</p>
-        <a href="https://beta.mxnet.io/guide/getting-started/crash-course/index.html">Learn More</a>
-      </div>
-      <div class="col-lg-4 col-sm-12">
-        <h3>Get the latest news from MXNet blogs on Medium</h3>
-        <p>Contributors and customers of MXNet post about how-tos, guides, examples, and stories about their MXNet experiences.
-        </p>
-        <a href="https://medium.com/apache-mxnet">Learn More</a>
-      </div>
-    </div>
-  </div>
-</div>
-<div class="section-util">
-    <div class="container">
-      <div class="row">
-        <div id="model-zoo-blk" class="col-lg-4 col-sm-12">
-          <span class="glyphicon glyphicon-folder-open"></span>
-          <h2>Gluon Model Zoo</h2>
-          <p>One-click pre-trained models, included in Gluon. Fast implementations of many state-of-the-art models, for plug-and-play effortless use.</p>
-          <div class='util-btn'>
-            <a id="model-zoo-link" href="api/python/gluon/model_zoo.html">Gluon model zoo</a>
-          </div>
-        </div>
-        <div id="example-blk" class="col-lg-4 col-sm-12">
-          <span class="glyphicon glyphicon-list-alt"></span>
-          <h2>Examples</h2>
-          <p>Explore projects from simple demos, examples, tutorials to state-of-the-art research.</p>
-          <div class='util-btn'>
-            <a id="example-link" href="https://github.com/dmlc/mxnet/tree/master/example">MXNet examples</a>
-          </div>
-        </div>
-        <div id="tutorial-blk" class="col-lg-4 col-sm-12">
-          <span class="glyphicon glyphicon-ok-circle"></span>
-          <h2>Tutorials</h2>
-          <p>These tutorials introduce a few fundamental concepts in deep learning and how to implement them in MXNet.</p>
-          <div class='util-btn'>
-            <a id="tutorial-link" href="tutorials/index.html">MXNet tutorials</a>
-          </div>
-        </div>
-      </div>
-    </div>
-</div>
diff --git a/docs/_static/mxnet-theme/layout.html b/docs/_static/mxnet-theme/layout.html
deleted file mode 100644
index eb7ab7aa9068..000000000000
--- a/docs/_static/mxnet-theme/layout.html
+++ /dev/null
@@ -1,201 +0,0 @@
-{%- block doctype -%}
-<!DOCTYPE html>
-{%- endblock %}
-{%- set reldelim1 = reldelim1 is not defined and ' &raquo;' or reldelim1 %}
-{%- set reldelim2 = reldelim2 is not defined and ' |' or reldelim2 %}
-{%- set render_sidebar = (not embedded) and (not theme_nosidebar|tobool) and
-                         (sidebars != []) %}
-{%- set url_root = pathto('', 1) %}
-{%- if url_root == '#' %}{% set url_root = '' %}{% endif %}
-{%- if not embedded and docstitle %}
-  {%- set titlesuffix = " &mdash; "|safe + docstitle|e %}
-{%- else %}
-  {%- set titlesuffix = "" %}
-{%- endif %}
-
-{%- macro searchform(classes, button) %}
-<form class="{{classes}}" role="search" action="{{ pathto('search') }}" method="get" autocomplete="off">
-  <div class="form-group inner-addon left-addon">
-    <i class="glyphicon glyphicon-search"></i>
-    <input type="text" name="q" class="form-control" placeholder="Search">
-  </div>
-  <input type="hidden" name="check_keywords" value="yes" />
-  <input type="hidden" name="area" value="default" />
-  {% if button %}
-    <input type="submit" class="btn btn-default" value="search">
-  {% endif %}
-</form>
-{%- endmacro %}
-
-{%- macro sidebarglobal() %}
-  {{ toctree(maxdepth=4|toint, collapse=True,includehidden=theme_globaltoc_includehidden|tobool) }}
-{%- endmacro %}
-
-{%- macro sidebar() %}
-      {%- if render_sidebar %}
-      <div class="sphinxsidebar rightsidebar" role="navigation" aria-label="main navigation">
-        <div class="sphinxsidebarwrapper">
-          {%- block sidebartoc %}
-          {%- include "localtoc.html" %}
-          {%- endblock %}
-        </div>
-      </div>
-      {%- endif %}
-{%- endmacro %}
-
-
-{%- macro script() %}
-    <script type="text/javascript">
-      var DOCUMENTATION_OPTIONS = {
-        URL_ROOT:    '{{ url_root }}',
-        VERSION:     '{{ release|e }}',
-        COLLAPSE_INDEX: false,
-        FILE_SUFFIX: '{{ '' if no_search_suffix else file_suffix }}',
-        HAS_SOURCE:  {{ has_source|lower }},
-        SOURCELINK_SUFFIX: '{{ sourcelink_suffix }}'
-      };
-    </script>
-
-    <script type="text/javascript" src="https://code.jquery.com/jquery-1.11.1.min.js"></script>
-    {% for name in ['underscore.js', 'searchtools_custom.js', 'doctools.js', 'selectlang.js'] %}
-    <script type="text/javascript" src="{{ pathto('_static/' + name, 1) }}"></script>
-    {% endfor %}
-
-    <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script>
-    <script type="text/javascript"> jQuery(function() { Search.loadIndex("{{ pathto('/searchindex.js', 1) }}"); Search.init();}); </script>
-
-    <script>
-      (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
-      (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
-      Date();a=s.createElement(o),
-      m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
-      })(window,document,'script','https://www.google-analytics.com/analytics.js','ga');
-
-      ga('create', 'UA-96378503-1', 'auto');
-      ga('send', 'pageview');
-
-    </script>
-
-    <!-- {%- for scriptfile in script_files %} -->
-    <!-- <script type="text/javascript" src="{{ pathto(scriptfile, 1) }}"></script> -->
-    <!-- {%- endfor %} -->
-{%- endmacro %}
-
-{%- macro css() %}
-    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" crossorigin="anonymous">
-    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css">
-
-    {% if pagename != 'index' %}
-    <link rel="stylesheet" href="{{ pathto('_static/' + style, 1) }}" type="text/css" />
-    <link rel="stylesheet" href="{{ pathto('_static/pygments.css', 1) }}" type="text/css" />
-    {%- endif %}
-
-    <link rel="stylesheet" href="{{ pathto('_static/mxnet.css', 1) }}" type="text/css">
-{%- endmacro %}
-
-<html lang="en">
-  <head>
-    <meta charset="{{ encoding }}">
-    <meta http-equiv="X-UA-Compatible" content="IE=edge">
-    <meta name="viewport" content="width=device-width, initial-scale=1">
-    {# The above 3 meta tags *must* come first in the head; any other head content
-       must come *after* these tags. #}
-    {{ metatags }}
-    {%- block htmltitle %}
-    <meta property="og:title"              content="{{ title }}" />
-    <meta property="og:image"              content="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/og-logo.png" />
-    <meta property="og:image:secure_url"              content="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/og-logo.png" />
-    {%- if pagename != 'index' and 'no title' not in title %}
-    <meta property="og:description"        content="{{ title }}" />
-    <title>{{ title|striptags|e }}{{ titlesuffix }}</title>
-    {%- elif pagename == 'index' %}
-    <meta property="og:description"        content="A flexible and efficient library for deep learning." />
-    <title>MXNet: A Scalable Deep Learning Framework</title>
-    {%- else %}
-    <meta property="og:description"        content="A flexible and efficient library for deep learning." />
-    <title>{{ pagename.split('/')[0]|capitalize }}{{ titlesuffix }}</title>
-    {%- endif %}
-    {%- endblock %}
-    {{ css() }}
-    {%- if not embedded %}
-    {{ script() }}
-    {%- if use_opensearch %}
-    <link rel="search" type="application/opensearchdescription+xml"
-          title="{% trans docstitle=docstitle|e %}Search within {{ docstitle }}{% endtrans %}"
-          href="{{ pathto('_static/opensearch.xml', 1) }}"/>
-    {%- endif %}
-    {%- if favicon %}
-    <link rel="shortcut icon" href="{{ pathto('_static/' + favicon, 1) }}"/>
-    {%- endif %}
-    {%- endif %}
-{%- block linktags %}
-    {%- if hasdoc('about') %}
-    <link rel="author" title="{{ _('About these documents') }}" href="{{ pathto('about') }}" />
-    {%- endif %}
-    {%- if hasdoc('genindex') %}
-    <link rel="index" title="{{ _('Index') }}" href="{{ pathto('genindex') }}" />
-    {%- endif %}
-    {%- if hasdoc('search') %}
-    <link rel="search" title="{{ _('Search') }}" href="{{ pathto('search') }}" />
-    {%- endif %}
-    {%- if hasdoc('copyright') %}
-    <link rel="copyright" title="{{ _('Copyright') }}" href="{{ pathto('copyright') }}" />
-    {%- endif %}
-    {%- if parents %}
-    <link rel="up" title="{{ parents[-1].title|striptags|e }}" href="{{ parents[-1].link|e }}" />
-    {%- endif %}
-    {%- if next %}
-    <link rel="next" title="{{ next.title|striptags|e }}" href="{{ next.link|e }}" />
-    {%- endif %}
-    {%- if prev %}
-    <link rel="prev" title="{{ prev.title|striptags|e }}" href="{{ prev.link|e }}" />
-    {%- endif %}
-{%- endblock %}
-{%- block extrahead %} {% endblock %}
-
-    <link rel="icon" type="image/png" href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png">
-  </head>
-  <body role="document" background="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-background-compressed.jpeg">
-    <div class='content-block'>
-    {%- include "navbar.html" %}
-    
-    {% if pagename != 'index' %}
-    <script type="text/javascript">
-        $('body').css('background', 'white');
-    </script>
-    <div class="container">
-      <div class="row">
-        <div class="sphinxsidebar leftsidebar" role="navigation" aria-label="main navigation">
-          <div class="sphinxsidebarwrapper">
-            {{sidebarglobal()}}
-          </div>
-        </div>
-        <div class="content">
-          <div class="page-tracker"></div>
-          {% block body %} {% endblock %}
-        </div>
-      </div>
-      {{ sidebar() }}
-    </div>
-    {%- include "footer.html" %}
-    {%- else %}
-    {%- include "index.html" %}
-    {%- include "footer.html" %}
-    {%- endif %} <!-- pagename != index -->
-    </div>
-
-    <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/js/bootstrap.min.js" integrity="sha384-0mSbJDEHialfmuBBQP6A4Qrprq5OVfW37PRR3j5ELqxss1yVqOtnepnHVP9aJ7xS" crossorigin="anonymous"></script>
-    <script type="text/javascript" src="{{ pathto('_static/js/sidebar.js', 1) }}"></script>
-    <script type="text/javascript" src="{{ pathto('_static/js/search.js', 1) }}"></script>
-    <script type="text/javascript" src="{{ pathto('_static/js/navbar.js', 1) }}"></script>
-    <script type="text/javascript" src="{{ pathto('_static/js/clipboard.min.js', 1) }}"></script>
-    <script type="text/javascript" src="{{ pathto('_static/js/copycode.js', 1) }}"></script>
-    <script type="text/javascript" src="{{ pathto('_static/js/page.js', 1) }}"></script>
-    <script type="text/javascript" src="{{ pathto('_static/js/docversion.js', 1) }}"></script>
-    <script type="text/javascript">
-        $('body').ready(function () {
-            $('body').css('visibility', 'visible');
-        });
-    </script>
-  </body>
-</html>
diff --git a/docs/_static/mxnet-theme/navbar.html b/docs/_static/mxnet-theme/navbar.html
deleted file mode 100644
index d2449fa76e1b..000000000000
--- a/docs/_static/mxnet-theme/navbar.html
+++ /dev/null
@@ -1,141 +0,0 @@
-<div class="navbar navbar-fixed-top">
-  <div class="container" id="navContainer">
-    <div id="header-inner" class="innder">
-      <h1 id="logo-wrap">
-        <a href="{{ url_root }}" id="logo"><img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet_logo.png"></a>
-      </h1>
-      <nav id="main-nav" class='nav-bar'>
-        <a class="main-nav-link" href="{{url_root}}install/index.html">Install</a>
-
-        <span id="dropdown-menu-position-anchor">
-          <a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Gluon <span class="caret"></span></a>
-          <ul id="package-dropdown-menu" class="dropdown-menu navbar-menu">
-            <li><a class="main-nav-link" href="{{url_root}}gluon/index.html">About</a></li>
-            <li><a class="main-nav-link" href="https://www.d2l.ai/">Dive into Deep Learning</a></li>
-            <li><a class="main-nav-link" href="https://gluon-cv.mxnet.io">GluonCV Toolkit</a></li>
-            <li><a class="main-nav-link" href="https://gluon-nlp.mxnet.io/">GluonNLP Toolkit</a></li>
-          </ul>
-        </span>
-
-        <span id="dropdown-menu-position-anchor">
-          <a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">API <span class="caret"></span></a>
-          <ul id="package-dropdown-menu" class="dropdown-menu navbar-menu">
-            <li><a class="main-nav-link" href="{{url_root}}api/python/index.html">Python</a></li>
-            <li><a class="main-nav-link" href="{{url_root}}api/c++/index.html">C++</a></li>
-            <li><a class="main-nav-link" href="{{url_root}}api/clojure/index.html">Clojure</a></li>
-            <li><a class="main-nav-link" href="{{url_root}}api/java/index.html">Java</a></li>
-            <li><a class="main-nav-link" href="{{url_root}}api/julia/index.html">Julia</a></li>
-            <li><a class="main-nav-link" href="{{url_root}}api/perl/index.html">Perl</a></li>
-            <li><a class="main-nav-link" href="{{url_root}}api/r/index.html">R</a></li>
-            <li><a class="main-nav-link" href="{{url_root}}api/scala/index.html">Scala</a></li>
-          </ul>
-        </span>
-
-        <span id="dropdown-menu-position-anchor-docs">
-          <a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Docs <span class="caret"></span></a>
-          <ul id="package-dropdown-menu-docs" class="dropdown-menu navbar-menu">
-            <li><a class="main-nav-link" href="{{url_root}}faq/index.html">FAQ</a></li>
-            <li><a class="main-nav-link" href="{{url_root}}tutorials/index.html">Tutorials</a>
-            <li><a class="main-nav-link" href="https://github.com/apache/incubator-mxnet/tree/master/example">Examples</a></li>
-            <li><a class="main-nav-link" href="{{url_root}}architecture/index.html">Architecture</a></li>
-            <li><a class="main-nav-link" href="https://cwiki.apache.org/confluence/display/MXNET/Apache+MXNet+Home">Developer Wiki</a></li>
-            <li><a class="main-nav-link" href="{{url_root}}api/python/gluon/model_zoo.html">Model Zoo</a></li>
-            <li><a class="main-nav-link" href="{{url_root}}api/python/contrib/onnx.html">ONNX</a></li>
-          </ul>
-        </span>
-
-        <span id="dropdown-menu-position-anchor-community">
-          <a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Community <span class="caret"></span></a>
-          <ul id="package-dropdown-menu-community" class="dropdown-menu navbar-menu">
-            <li><a class="main-nav-link" href="http://discuss.mxnet.io">Forum</a></li>
-            <li><a class="main-nav-link" href="https://github.com/apache/incubator-mxnet">Github</a></li>
-            <li><a class="main-nav-link" href="{{url_root}}community/contribute.html">Contribute</a></li>
-            <li><a class="main-nav-link" href="{{url_root}}community/ecosystem.html">Ecosystem</a></li>
-            <li><a class="main-nav-link" href="{{url_root}}community/powered_by.html">Powered By</a></li>
-          </ul>
-        </span>
-      </nav>
-
-      <script> function getRootPath(){ return "{{url_root}}" } </script>
-      <div class="burgerIcon dropdown">
-          <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button">☰</a>
-          <ul id="burgerMenu" class="dropdown-menu">
-              <li><a href="{{url_root}}install/index.html">Install</a></li>
-              <li><a class="main-nav-link" href="{{url_root}}tutorials/index.html">Tutorials</a></li>
-              <li class="dropdown-submenu dropdown">
-                <a href="#" tabindex="-1" aria-haspopup="true" aria-expanded="true" class="dropdown-toggle burger-link" data-toggle="dropdown">Gluon</a>
-                <ul id="package-dropdown-menu" class="dropdown-menu navbar-menu">
-                  <li><a class="main-nav-link" href="{{url_root}}gluon/index.html">About</a></li>
-                  <li><a class="main-nav-link" href="http://gluon.mxnet.io">The Straight Dope (Tutorials)</a></li>
-                  <li><a class="main-nav-link" href="https://gluon-cv.mxnet.io">GluonCV Toolkit</a></li>
-                  <li><a class="main-nav-link" href="https://gluon-nlp.mxnet.io/">GluonNLP Toolkit</a></li>
-                </ul>
-              </li>
-              <li class="dropdown-submenu">
-                <a href="#" tabindex="-1" aria-haspopup="true" aria-expanded="true" class="dropdown-toggle burger-link" data-toggle="dropdown">API</a>
-                <ul class="dropdown-menu">
-                  <li><a class="main-nav-link" href="{{url_root}}api/python/index.html">Python</a></li>
-                  <li><a class="main-nav-link" href="{{url_root}}api/c++/index.html">C++</a></li>
-                  <li><a class="main-nav-link" href="{{url_root}}api/clojure/index.html">Clojure</a></li>
-                  <li><a class="main-nav-link" href="{{url_root}}api/java/index.html">Java</a></li>
-                  <li><a class="main-nav-link" href="{{url_root}}api/julia/index.html">Julia</a></li>
-                  <li><a class="main-nav-link" href="{{url_root}}api/perl/index.html">Perl</a></li>
-                  <li><a class="main-nav-link" href="{{url_root}}api/r/index.html">R</a></li>
-                  <li><a class="main-nav-link" href="{{url_root}}api/scala/index.html">Scala</a></li>
-                </ul>
-              </li>
-              <li class="dropdown-submenu">
-                <a href="#" tabindex="-1" aria-haspopup="true" aria-expanded="true" class="dropdown-toggle burger-link" data-toggle="dropdown">Docs</a>
-                <ul class="dropdown-menu">
-                  <li><a tabindex="-1"  href="{{url_root}}faq/index.html">FAQ</a></li>
-                  <li><a tabindex="-1"  href="{{url_root}}tutorials/index.html">Tutorials</a></li>
-                  <li><a tabindex="-1"  href="https://github.com/apache/incubator-mxnet/tree/master/example">Examples</a></li>
-                  <li><a tabindex="-1"  href="{{url_root}}architecture/index.html">Architecture</a></li>
-                  <li><a tabindex="-1"  href="https://cwiki.apache.org/confluence/display/MXNET/Apache+MXNet+Home">Developer Wiki</a></li>
-                  <li><a tabindex="-1"  href="{{url_root}}api/python/gluon/model_zoo.html">Gluon Model Zoo</a></li>
-                  <li><a tabindex="-1" href="{{url_root}}api/python/contrib/onnx.html">ONNX</a></li>
-                </ul>
-              </li>
-              <li class="dropdown-submenu dropdown">
-                <a href="#" tabindex="-1" aria-haspopup="true" role="button" class="dropdown-toggle burger-link" data-toggle="dropdown">Community</a>
-                <ul class="dropdown-menu">
-                  <li><a tabindex="-1"  href="http://discuss.mxnet.io">Forum</a></li>
-                  <li><a tabindex="-1"  href="https://github.com/apache/incubator-mxnet">Github</a></li>
-                  <li><a tabindex="-1"  href="{{url_root}}community/contribute.html">Contribute</a></li>
-                  <li><a tabindex="-1"  href="{{url_root}}community/ecosystem.html">Ecosystem</a></li>
-                  <li><a tabindex="-1"  href="{{url_root}}community/powered_by.html">Powered By</a></li>
-                </ul>
-              </li>
-          </ul>
-      </div>
-
-      <div class="plusIcon dropdown">
-        <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button"><span class="glyphicon glyphicon-plus" aria-hidden="true"></span></a>
-        <ul id="plusMenu" class="dropdown-menu dropdown-menu-right"></ul>
-      </div>
-      <div id="search-input-wrap">
-          {{searchform('', False)}}
-          <div id="search-preview"></div>
-      </div>
-      <div id='searchIcon'>
-          <span class="glyphicon glyphicon-search" aria-hidden="true"></span>
-      </div>
-
-      <!-- <div id="lang-select-wrap"> -->
-      <!--   <label id="lang-select-label"> -->
-      <!--     <\!-- <i class="fa fa-globe"></i> -\-> -->
-      <!--     <span></span> -->
-      <!--   </label> -->
-      <!--   <select id="lang-select"> -->
-      <!--     <option value="en">Eng</option> -->
-      <!--     <option value="zh">中文</option> -->
-      <!--   </select> -->
-      <!-- </div> -->
-  <!--     <a id="mobile-nav-toggle">
-        <span class="mobile-nav-toggle-bar"></span>
-        <span class="mobile-nav-toggle-bar"></span>
-        <span class="mobile-nav-toggle-bar"></span>
-      </a> -->
-    </div>
-  </div>
-</div>
diff --git a/docs/_static/mxnet-theme/theme.conf b/docs/_static/mxnet-theme/theme.conf
deleted file mode 100644
index 89e03bbdac5e..000000000000
--- a/docs/_static/mxnet-theme/theme.conf
+++ /dev/null
@@ -1,2 +0,0 @@
-[theme]
-inherit = basic
diff --git a/docs/_static/mxnet.css b/docs/_static/mxnet.css
deleted file mode 100644
index d949f7fd5ba1..000000000000
--- a/docs/_static/mxnet.css
+++ /dev/null
@@ -1,1474 +0,0 @@
-/*-------------------- AmazonEmber font -----------------------------------*/
-@font-face {
-    font-family: AmazonEmber;
-    src: url(https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/font/AmazonEmber_Rg.ttf);
-    font-weight: normal;
-}
-
-@font-face {
-    font-family: AmazonEmber;
-    src: url(https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/font/AmazonEmber_Bd.ttf);
-    font-weight: bold;
-}
-
-@font-face {
-    font-family: AmazonEmber;
-    src: url(https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/font/AmazonEmber_Th.ttf);
-    font-weight: 300;
-}
-
-
-/*-------------------- basic html elements and classes --------------------*/
-html, body {
-    margin: 0;
-    padding: 0;
-    background-color: #ffffff;
-}
-
-body {
-    display: block;
-    visibility: hidden;
-    background-size: cover;
-    background-attachment: fixed;
-}
-
-body, div {
-    font-family: AmazonEmber;
-    font-size: 16px;
-    color: #000;
-}
-
-p {
-    font-family: AmazonEmber;
-    color: #000;
-    font-size: 16px;
-    line-height: 1.5em;
-}
-
-#language-filter {
-    float: right;
-    margin-top: 5px;
-}
-
-li, dt a, dt span {
-    font-size: 16px;
-    line-height: 1.5em;
-}
-
-.footer {
-    z-index: 10;
-    position: relative;
-    bottom: 0;
-    width: 100%;
-}
-
-/* /\*Content paragraph space*\/ */
-/* div.content p { */
-/*     margin-top: 20px; */
-/* } */
-
-/*------------------ Headings -------------------*/
-h1, h2, h3 {
-    font-family: AmazonEmber;
-    margin-top: 18px;
-    margin-bottom: 18px;
-    color: #444;
-}
-
-h1 {
-    margin-top: 0px;
-    margin-bottom: 25px;
-    font-size: 28px;
-    font-weight: bold;
-}
-
-h2 {
-    font-size: 24px;
-}
-
-h3 {
-    font-size: 20px;
-}
-
-h4 {
-    font-size: 16px;
-}
-
-img {
-    border-style: hidden;
-    border-width: 0px 0px 0px 0px;
-    border-color: #ffffff;
-    /* white  */
-    padding: 0px 0px 0px 0px;
-    margin: 0px 0px 0px 0px;
-}
-
-.topictitle {
-    font-size: 24px;
-    font-weight: bold;
-    color: #e47911;
-    padding: 0 0 14px 0;
-}
-
-.section:before {
-    content: " ";
-    display: block;
-    height: 60px; /* fixed header height*/
-    margin: -60px 0 0; /* negative fixed header height */
-}
-
-/*-----------------------nav bar-----------------------*/
-.navbar {
-    opacity: 0.9;
-    border: 0px;
-    height: 60px;
-    margin-bottom: 0px;
-    border-bottom: 0.01em solid #fff;
-    background-image: url("https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-navbar.png");
-    background-size: cover;
-    background-attachment: scroll;
-}
-
-.navbar .container {
-    position: relative;
-    width: 100%;
-}
-
-@media screen and (max-width: 510px) {
-    .navbar {
-        height: 60px;
-    }
-}
-
-#header-inner {
-    display: -webkit-box;
-    display: -webkit-flex;
-    display: -ms-flexbox;
-    display: box;
-    display: flex;
-    -webkit-box-orient: horizontal;
-    -moz-box-orient: horizontal;
-    -webkit-box-lines: single;
-    -moz-box-lines: single;
-    -webkit-flex-flow: row nowrap;
-    -ms-flex-flow: row nowrap;
-    flex-flow: row;
-    -webkit-box-align: center;
-    -ms-flex-align: center;
-    -webkit-align-items: center;
-    align-items: center;
-    padding-left: 10px;
-}
-
-@media screen and(max-width: 768 px) {
-    #header-inner {
-        -webkit-box-pack: center;
-       -ms-flex-pack: center;
-       -webkit-justify-content: center;
-        justify-content: center
-    }
-}
-
-#logo-wrap {
-    -webkit-box-flex: 1;
-   padding-top: 0;
-    margin-top: 0;
-    width: 125px;
-    padding-left: 25px;
-    padding-right: 200px;
-}
-
-#logo {
-    color: #fff;
-    width: 150px;
-    display: block;
-    float: left;
-    height: 60px;
-    padding: 15px 0 0 0;
-    text-decoration: none;
-}
-
-#logo > img {
-  display: block;
-  width: 110px;
-}
-
-.nav-bar {
-    display: block;
-    -webkit-box-flex: 1;
-    flex: 1 auto;
-    position: relative;
-    top: -12px;
-    white-space: nowrap;
-}
-
-.main-nav-link {
-    color: #fff;
-    text-decoration: none;
-    line-height: 50px;
-    font-family: AmazonEmber;
-    display: inline-block;
-    padding: 0 15px;
-    font-weight: 500; 
-}
-
-.main-nav-link:hover {
-    opacity: 1;
-    color: #fff;
-    text-decoration: none;
-}
-
-#dropdown-menu-position-anchor, 
-#dropdown-menu-position-anchor-docs,
-#dropdown-menu-position-anchor-community,
-#dropdown-menu-position-anchor-version, 
-#dropdown-menu-position-anchor-version-mobile {
-    position: relative;
-}
-
-.navbar-menu {
-    top: 36px;
-    border-radius: 4px;
-    padding: 0;
-}
-
-.navbar-menu > li > a {
-    color: #0079b2;
-    padding: 6px 16px;
-
-}
-
-#search-input-wrap {
-    display: block;
-    position: absolute;
-    top: 15px;
-    right: 20px;
-    width: 200px;
-    -webkit-transition: width 0.5s;
-    transition: width 0.5s;
-}
-
-#search-input-wrap ul {
-    list-style-type: none;
-}
-
-#searchIcon {
-    display: none;
-    position: fixed;
-    top: 20px;
-    right: 35px;
-    color: white;
-}
-
-.searchBox {
-    position: absolute;
-    top: 0;
-    left: 0;
-    width: 200px;
-    background: transparent;
-    border-radius: 5px;
-    -webkit-transition: width 0.5s;
-    transition: width 0.5s;
-}
-
-.searchBox input {
-    background: transparent;
-}
-
-.searchBox input::placeholder {
-    font-weight: 200;
-}
-
-.searchBoxNorm input::placeholder {
-    color: white;
-}
-
-.searchBoxExp input::placeholder {
-    color: dimgray;
-}
-
-.searchBtn {
-    display: none;
-    position: absolute;
-    top: 0;
-    right: 0;
-    width: 40px;
-}
-
-@media screen and (max-width: 510px) {
-    #search-input-wrap {
-        width: 120px;
-        margin-right: 10px;
-    }
-    
-    .searchBox {
-        width: 120px;
-    }
-}
-
-#lang-select-wrap {
-    display: block;
-    position: fixed;
-    right: 120px;
-    top: 5px
-}
-
-#lang-select-label {
-    color: #fff;
-    opacity: .7;
-    font-family: AmazonEmber;
-    line-height: 50px
-}
-
-#lang-select-label span {
-    padding-left: 8px
-}
-
-#lang-select-label i {
-    opacity: .7
-}
-
-#lang-select {
-    opacity: 0;
-    position: absolute;
-    top: 0;
-    left: 0;
-    width: 100%;
-    height: 100%;
-    -webkit-appearance: menulist-button;
-    font-size: inherit
-}
-
-.github-btn { border: 0; overflow: hidden }
-
-.boxed {
-    border: 1px solid rgb(57, 144, 211);
-    background: #0079b2;
-    color: white;
-    font-weight: bold
-}
-
-.boxed-bordered {
-    border: 3px solid rgba(0,0,0,.05);
-    background: #0079b2;
-    font-weight: bold;
-    color: white;
-}
-
-/*burger icon*/
-div .burgerIcon {
-    font-size: 25px;
-    position: fixed;
-    right: 155px;
-    top: 11px;
-    display: none;
-    width: 30px;
-}
-
-div .burgerIcon a {
-    color: white;
-}
-
-/*burger menu*/
-#burgerMenu, #plusMenu {
-    list-style-type: none;
-    margin-top: 10px;
-    padding: 0;
-    background-color: white;
-    list-style-position: inside;
-    min-width: 100px;
-}
-
-#burgerMenu a, #plusMenu a {
-    display: table-cell;
-    font-size: 15px;
-    color: #0079b2;
-    vertical-align: middle;
-    height: 30px;
-    padding-left: 5px;
-    width: 108px;
-    max-width: 250px;
-}
-
-#plusMenu .dropdown-submenu>.dropdown-menu {
-    left: 127px;
-    top: 0;
-    background: #fff;
-}
-
-#burgerMenu li, #plusMenu li {
-    height: 30px;
-    list-style-position: inside;
-}
-
-li.dropdown-submenu ul.dropdown-menu {
-    min-width: 75px;
-}
-
-li.dropdown-submenu ul.dropdown-menu li {
-    border-style: none !important;
-}
-
-li.dropdown-submenu ul.dropdown-menu a {
-    padding-left: 20px !important;
-}
-
-@media screen and (max-width: 420px) {
-    li.dropdown-submenu ul.dropdown-menu a {
-        font-size: 12px !important;
-    }
-}
-
-/*dropdown submenu*/
-.dropdown-submenu {
-    position: relative;
-}
-
-.dropdown-submenu>.dropdown-menu {
-    top: -8px;
-    left: 250px;
-    -webkit-border-radius: 0 6px 6px 6px;
-    -moz-border-radius: 0 6px 6px;
-    border-radius: 0 6px 6px 6px;
-    background: transparent;
-}
-
-.dropdown-submenu:hover>.dropdown-menu {
-    display: block;
-}
-
-.dropdown-submenu>a:after {
-    display: block;
-    content: " ";
-    float: right;
-    width: 0;
-    height: 0;
-    border-color: transparent;
-    border-style: solid;
-    border-width: 5px 0 5px 5px;
-    border-left-color: #ccc;
-    margin-top: 10px;
-    margin-right: -10px;
-}
-
-.dropdown-submenu:hover>a:after {
-    border-left-color: #fff;
-}
-
-.dropdown-submenu.pull-left {
-    float: none;
-}
-
-.dropdown-submenu.pull-left>.dropdown-menu {
-    left: -100%;
-    margin-left: 10px;
-    -webkit-border-radius: 6px 0 6px 6px;
-    -moz-border-radius: 6px 0 6px 6px;
-    border-radius: 6px 0 6px 6px
-}
-
-/*Plus icon*/
-.plusIcon {
-    display: none;
-    position: absolute;
-    top: 20px;
-    right: 210px;
-    width: 30px;
-}
-
-.glyphicon-plus {
-    color: white;
-}
-
-@media screen and (max-width:600px) {
-    div .plusIcon, .nav-bar {
-        display: none !important;
-    }
-
-    div.burgerIcon {
-        display: block;
-        position: fixed;
-        left: 45px;
-    }
-    
-    #burgerMenu {
-        position: fixed;
-        left: 0;
-        top: 49px;
-        width: 100%;
-        height: 100%;
-        background: url(https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-background-compressed.jpeg);
-        background-attachment: initial;
-        background-size: cover;
-    }
-    
-    #burgerMenu a {
-        display: block;
-        font-size: 19px;
-        width: auto;
-        padding-left: 75px;
-        padding-top: 10px;
-        color: #fff;
-        text-decoration: none;
-    }
-    
-    #burgerMenu a:hover {
-        color: initial;
-        background-color: inherit;
-    }
-    
-    #burgerMenu li {
-        border-bottom: 1px solid lightgray;
-        height: 50px;
-    }
-    
-    #logo-wrap {
-        margin-left: 30px;
-    }
-}
-/*--------------------------banner---------------------------*/
-#splash{
-    padding:60px 0 0 0;
-    background-size:cover;
-    background-attachment:fixed;
-    color:#fff;
-    text-align:center
-}
-
-#splash #banner {
-    text-align: center
-}
-
-#banner-title span {
-    font-family: AmazonEmber;
-    font-weight: bold;
-}
-
-#banner-title {
-    padding: 60px 0 10px 0;
-    font-size: 40px;
-    line-height: 55px;
-    font-weight: 500;
-    font-family: AmazonEmber;
-    color: #fff;
-    max-width: 600px;
-    text-align: left;
-    margin-left: 30px;
-}
-
-#landing-title {
-    margin-right: 40px;
-    font-size: 35px;
-    color: #fff;
-    font-family: AmazonEmber;
-    font-weight: 300;
-}
-
-#intro {
-    font-size: 16px;
-    color: #fff;
-    font-family: AmazonEmber;
-    font-weight: 300;
-}
-
-@media screen and (max-width:768px) {
-    #banner-title {
-        font-size: 30px;
-        padding: 60px 30px 10px 30px;
-        margin-left: 0;
-    }
-}
-
-#splash h1{
-    font-size: 40px;
-    margin-bottom: 20px;
-}
-
-#splash #social{
-    margin:2em 0 4em 0;
-}
-
-#landing-btn-blk {
-    margin-top: 2em;
-}
-
-#why_mxnet, #install_blk {
-    margin:2em 0;
-    display: inline;
-}
-
-#why_mxnet {
-    padding-left: 20px;
-}
-
-@media screen and (max-width:400px) {
-    #install_blk, #install_blk{
-        display: block;
-    }
-    
-    #install_blk {
-        padding-left: 0;
-        margin-top: 0;
-    }
-}
-
-#why_mxnet_btn {
-    border: 1.8px solid #FFFFFF;
-    color: #FFFFFF;
-    display: inline;
-    font-size: 18px;
-    font-family: AmazonEmber;
-    padding: 8px 20px;
-    -webkit-transition: .2s;
-    transition: .2s;
-}
-
-#install_btn {
-    border: 1.8px solid #FFFFFF;
-    color: #fff;
-    display: inline;
-    font-size: 18px;
-    font-family: AmazonEmber;
-    padding: 8px 40px;
-    -webkit-transition: .2s;
-    transition: .2s;
-}
-
-#release_btn {
-    width: auto;
-    border: none;
-}
-
-#why_mxnet_btn:hover, #install_btn:hover {
-    background-color: #FFFFFF;
-    color: #0079b2;
-    opacity: 0.9;
-    text-decoration: none;
-}
-
-/*------------------------index page section----------------------------*/
-.section-tout {
-    padding:3em 0 1em;
-}
-
-.section-tout .container {
-    height: 220px;
-}
-
-.section-tout .row {
-    height: 100%;
-}
-
-.section-tout .row div {
-    height: 110%;
-    padding-left: 50px;
-    background-color:#fff;
-    box-shadow: 0px 2px 4px 0px rgba(0, 28, 36, 0.5);
-}
-
-.section-tout .row a {
-    font-family: AmazonEmber;
-    position: absolute;
-    bottom: 20px;
-    border: solid 1px;
-    padding: 5px 10px;
-    color: #111;
-}
-
-.section-tout h3{
-    font-family: AmazonEmber;
-    font-size:20px;
-    color:  #007dbc;
-    padding-top: 10px
-}
-
-.section-tout p {
-    font-family: AmazonEmber;
-    margin-bottom:2em;
-}
-
-@media (max-width: 1199px) {
-    .section-tout .container {
-        height: auto;
-    }
-
-    .section-tout .row a {
-        position: inherit;
-    }
-
-    .section-tout .row div {
-        padding-left: 45px;
-    }
-    
-    .section-tout .row p {
-        padding-bottom: 10px;
-    }
-}
-
-@media (max-width: 767px) {
-    .section-tout .row div {
-        margin-bottom: -20px;
-    }
-}
-
-.section-util {
-    padding:3em 0 3em;
-    text-align: left;
-    margin-bottom: 50px;
-}
-
-.section-util .row > div {
-    height: 320px;
-    padding-left: 50px;
-    padding-top: 40px;
-}
-
-.section-util .row div h2 {
-    font-family: AmazonEmber;
-    color: #fff;   
-}
-
-#model-zoo-blk {
-    background-color:  #00446e;
-    box-shadow: 0px 2px 4px 0px rgba(0, 28, 36, 0.5);
-}
-
-#example-blk {
-    background-color:  #006ea7;
-    box-shadow: 0px 2px 4px 0px rgba(0, 28, 36, 0.5);
-}
-
-#tutorial-blk {
-    background-color:  #00a1c9;
-    box-shadow: 0px 2px 4px 0px rgba(0, 28, 36, 0.5);
-}
-
-@media (min-width: 1200px) {
-    #model-zoo-blk {
-        width: 370px;
-        margin-right: 20px;
-    }
-
-    #example-blk {
-        width: 370px;
-        margin-right: 20px;
-    }
-}
-
-.section-util p {
-    font-family: AmazonEmber;
-    color: #fff;
-    position: absolute;
-    width: 70%;
-    font-size: 14px;
-    font-weight: 300;
-}
-
-.section-util .util-btn {
-    position: absolute;
-    bottom: 30px;
-}
-
-@media (max-width: 600px) {
-    .section-util .util-btn {
-        margin-top: 100px;
-    }
-}
-
-.section-util .glyphicon {
-    font-size: 50px;
-    color: #b3ffff;
-}
-
-.util-btn a {
-    font-family: AmazonEmber;
-    display: inline-block;
-    border: 1.8px solid;
-    -webkit-transition: .2s;
-    transition: .2s;
-    padding: 5px 30px;
-    color: #fff;
-}
-
-.util-btn a:hover {
-    background-color: #0079b2;
-    color: #FFFFFF;
-    opacity: 0.9;
-    text-decoration: none;
-}
-
-.section-util .container {
-    height: 250px;
-}
-
-@media (max-width: 1199px) {
-    .section-util .container {
-        height: auto
-    }
-}
-
-.section-inst {
-    padding:3em 0 3em;
-    border-bottom:1px solid rgba(0,0,0,.05);
-    text-align: left;
-    background-color: #fff;
-}
-
-.section-inst .row div {
-    text-align: center;
-    margin-bottom: 30px;
-    height: 80px;
-}
-
-.section-inst p {
-    font-family: AmazonEmber;
-    font-weight: 300;
-    margin-bottom:2em;
-    padding-left: 50px;
-    color: #111;
-}
-
-.section-inst h2 {
-    font-family: AmazonEmber;
-    margin-top: -20px;
-    margin-bottom: 50px;
-    color: dimgray;
-    padding-left: 40px;
-}
-
-@media (min-width: 1200px) {
-    .section-inst .row div.clear {
-        clear: both;
-    }
-}
-
-@media (max-width: 1199px) {
-    .section-inst .row div.smallClear {
-        clear: both;
-    }
-}
-
-.section-inst img {
-    -webkit-filter: grayscale(90%); /* Chrome, Safari, Opera */
-    filter: grayscale(90%);
-    margin-bottom:2em
-}
-.section-inst img:hover {
-    -webkit-filter: grayscale(0%); /* Chrome, Safari, Opera */
-    filter: grayscale(0%);
-}
-
-.section-disclaimer {
-    padding: 3em 0 3em;
-    background-color:  #f2f2f2;
-}
-
-.section-disclaimer .container div {
-    padding-left: 50px;
-}
-
-.section-disclaimer p {
-    padding-top: 2em;
-}
-
-.footer li{
-    float:right;
-    margin-right:1.5em;
-    margin-bottom:1.5em
-}
-.footer p{
-    font-family: AmazonEmber;
-    font-weight: 300;
-    font-size: 12px;
-    clear:right;
-    margin-bottom:0;
-}
-
-/*------------------content----------------------*/
-div.navbar + div.container {
-    padding-top: 65px;
-}
-
-div.content {
-    padding: 80px 40px 40px 45px;
-    background: #fff;
-    width: calc(100% - 300px);
-    float: right;
-    overflow-x: hidden;
-    z-index: -1;
-}
-
-@media (max-width: 999px) {
-    div.content {
-        overflow-x: auto;
-        width: 100%;
-    }
-}
-
-div.page-tracker {
-    margin-bottom: 30px;
-    font-family: AmazonEmber;
-}
-
-div.page-tracker ul {
-    padding-left: 0;
-}
-
-div.page-tracker ul li {
-    text-transform: capitalize;
-    display: inline;
-}
-
-div.page-tracker a {
-    text-transform: capitalize;
-    color: #337ab7;
-}
-
-div.page-tracker li:last-child {
-    color: #aab7b8;
-}
-
-div.page-tracker i {
-    margin: 0 10px;
-}
-
-/*------------------sidebar-----------------------*/
-div.sphinxsidebar {
-    position: fixed;
-    padding-top: 40px;
-    height: 100%;
-    display: none;
-    visibility: hidden;
-    overflow: auto;
-}
-
-div.leftsidebar {
-    width: 300px;
-    margin-left: 25px;
-    background: #f2f2f2;
-}
-
-div.rightsidebar {
-    right: 20px;
-    width: 200px;
-    padding: 40px 0 15px 0;
-}
-
-div.sphinxsidebar ul {
-    padding: 0;
-    list-style-type: none !important;
-}
-
-div.sphinxsidebar a {
-    font-family: AmazonEmber;
-    font-size: 13px;
-}
-
-div.rightsidebar div.sphinxsidebarwrapper > ul {
-    padding-left: 15px;
-}
-
-div.sphinxsidebar li {
-    padding-top: 5px;
-    margin-bottom: 5px;
-    margin-left: -10px;
-}
-
-div.leftsidebar > div.sphinxsidebarwrapper {
-    padding: 40px 20px 10px 20px;
-    background: #f2f2f2;
-}
-
-div.leftsidebar > div.sphinxsidebarwrapper > ul > li {
-    margin-left: 20px;
-}
-
-div.rightsidebar > div.sphinxsidebarwrapper > ul {
-    margin-left: 2px;
-    border-left: 4px solid #337ab7;
-}
-
-div.rightsidebar > div.sphinxsidebarwrapper > ul > li {
-    margin-left: 5px;
-}
-
-div.sphinxsidebar li.opened .tocToggle:before {
-    font-family: 'FontAwesome';
-    content: "\f0d7";
-    margin: 0 5px 0 -15px;
-    color: #999999;
-}
-
-div.sphinxsidebar li.closed .tocToggle:before {
-    font-family: 'FontAwesome';
-    content: "\f0da";
-    margin: 0 5px 0 -15px;
-    color: #999999;
-}
-
-div.sphinxsidebar li.leaf .tocToggle:before {
-    /*font-family: 'FontAwesome';*/
-    content: " ";
-    margin: 0 5px 0 -15px;
-    color: #f2f2f2;
-}
-
-@media (min-width:1015px) {
-    div.sphinxsidebar {display: block}
-}
-
-div.sphinxsidebarwrapper h3 {
-    padding-top: 0;
-}
-
-/*-------------------- table styles --------------------*/
-table {
-  margin-bottom: 10px;
-  border-collapse: collapse; }
-
-table p {
-  font-size: 16px; }
-
-td p:first-child {
-  margin-top: 0px; }
-
-td p:last-child {
-  margin-bottom: 0px; }
-
-td ul:first-child {
-  margin-top: 0px;
-  /*  new 8/22/07 */ }
-
-td ol:first-child {
-  margin-top: 0px;
-  /*  new 8/22/07 */ }
-
-div.table {
-  position: relative; }
-
-div.informaltable {
-  position: relative; }
-
-.table-expand-icon {
-  position: absolute;
-  right: -20px;
-  font-size: 24px;
-  background: #cccccc;
-  float: right;
-  bottom: 0;
-  cursor: pointer; }
-
-.table-contents table {
-  border-top: 1px solid #cccccc;
-  /* medium gray  */
-  border-left: 1px solid #cccccc;
-  /* medium gray  */
-  border-bottom: 0px;
-  border-right: 0px;
-  border-spacing: 0px; }
-
-.table-contents td {
-  font-size: 16px;
-  padding: 5px 5px 5px 5px;
-  border-bottom: 1px solid #cccccc;
-  /* medium gray  */
-  border-right: 1px solid #cccccc;
-  /* medium gray  */
-  border-left: 0px;
-  border-top: 0px;
-  vertical-align: top; }
-
-.table-contents th {
-  font-size: 16px;
-  padding: 5px 5px 5px 5px;
-  border-bottom: 1px solid #cccccc;
-  /* medium gray  */
-  border-right: 1px solid #cccccc;
-  /* medium gray  */
-  border-left: 0px;
-  border-top: 0px;
-  vertical-align: top;
-  background-color: #eeeeee;
-  /* light gray  */
-  color: #333333;
-  /* Dark gray  */
-  font-size: 16px;
-  font-weight: bold;
-  text-align: left; }
-
-/*----------------API class and function formatting---------------------*/
-p.rubric {
-    margin-top: 10px;
-}
-
-dl {
-    padding-top: 20px;
-}
-
-dt:target, .highlighted {
-    background-color: #e7f2fa;
-    border-bottom: 3px solid #c7254e;
-    margin-bottom: -3px;
-}
-
-dt:target:before {
-    background-color: white;
-    content: '';
-    display: block;
-    height: 60px;
-}
-
-dt {
-    background: #e7f2fa;
-    border-bottom: solid #0079b2;
-}
-
-dl.method dt {
-    background: #f0f0f0;
-    border-bottom: solid #ccc;
-}
-
-dl.method dt code.descname {
-    color:#555;
-}
-
-dl.attribute dt {
-    background: #f0f0f0;
-    border-bottom: solid #ccc;
-}
-
-dl.attribute dt code.descname {
-    color:#555;
-}
-
-dt em {
-    font-weight: normal;
-    font-style: normal;
-    font-size: 90%;
-}
-
-code {
-    color: #337ab7;
-    background-color: #f5f5f5;
-}
-
-code.docutils.literal {
-    color: #111;
-    font-weight: bold;
-}
-
-a code.docutils.literal {
-    color: #337ab7;
-}
-
-dt code {
-    color: #555;
-}
-
-dl.last.docutils dt{
-    background-color: transparent;
-    border-bottom: none;
-}
-
-dl.docutils dt {
-    color: #555;
-    background-color: #f0f0f0;
-    border-bottom: solid #ccc;
-}
-
-/*----------------Model zoo page style------------------*/
-#mxnet-model-zoo table, #mxnet-model-zoo td, #mxnet-model-zoo th {
-    border: 1px solid lightgray;
-}
-
-#mxnet-model-zoo table {
-    margin-top: 30px;
-    border-collapse: collapse;
-}
-
-#mxnet-model-zoo th {
-    vertical-align: middle;
-    text-align: center;
-    height: 50px;
-}
-
-#mxnet-model-zoo td {
-    vertical-align: middle;
-    height: 50px;
-    padding: 10px;
-}
-
-#mxnet-model-zoo tr:nth-child(even) {
-    background-color: #f2f2f2;
-}
-
-/*--------- note block -----------*/
-div.admonition {
-    padding: 15px;
-    background-color: #f6f8fa;
-    border: 1px solid rgb(204, 204, 204);
-    border-radius: 4px;
-}
-
-p.admonition-title {
-    color: #187dbb;
-}
-
-/*------------ code block ------------*/
-pre {
-    background-color: #f6f8fa;
-}
-
-/*-------------API table---------------------*/
-table.docutils {
-    margin-top: 15px;
-}
-
-table.docutils td, table.docutils th {
-    padding-top: 5px;
-    padding-bottom: 5px;
-    padding-left: 10px;
-    padding-right: 10px;
-    /* border-top: 0; */
-    /* border-left: 0; */
-    /* border-right: 0; */
-    border: 1px solid rgb(223, 226, 229);
-}
-
-table.docutils tr:nth-child(even) {
-    background-color: #f6f8fa;
-}
-
-/*---------------getting started homepage---------*/
-
-#lang-demo ul {
-    margin-top: 20px;
-    margin-bottom: 15px;
-}
-
-.option-title {
-    width: 100px;
-    float: left;
-    clear: none;
-    text-align: right;
-    font-size: 14px;
-    padding-top: 7px;
-    padding-bottom: 8px;
-    padding-right: 10px;
-    font-weight: bold;
-}
-.option-row {
-    padding-bottom: 8px;
-}
-
-.install-inst {
-}
-
-.btn-default:hover, .btn-default:focus, .btn-default:active,
-.btn-default.active.focus, .btn-default.active:focus, .btn-default.active:hover,
-.btn-default:active.focus, .btn-default:active:focus, .btn-default:active:hover,
-.btn-default.active, .open>.dropdown-toggle.btn-default,
-.btn-default:active:focus {
-    color: #fff;
-    background-color: #0079b2;
-    border-color: #0079b2;
-}
-
-.btn-default:hover a {
-    color: #fff;
-}
-
-#setup-options {
-    margin-top: 15px;
-    margin-bottom: 15px;
-    margin-left: 30px;
-}
-
-.opt-group {
-    margin-top: 10px;
-    margin-bottom: 10px;
-}
-
-/*----------------Copy code button------------------------*/
-.copy-btn {
-    display: none;
-    position: absolute;
-    right: 0;
-    width: 40px;
-    height: 38px;
-}
-
-/*----------------Search function style------------------*/
-#searchPage {
-    width: 60%;
-    margin-left: 20%;
-}
-
-#search-preview {
-    display: none;
-    background-color: white;
-    margin-top: -10px;
-    border: solid;
-    border-width: 1px;
-    border-color: #87CEFA;
-    width: 100%;
-}
-
-
-#search-preview p {
-    color: dimgray;
-    padding: 5px 20px 0 20px;
-}
-
-#search-preview li {
-    padding-top: 10px;
-}
-
-#search-preview ul {
-    padding-left: 20px;
-    padding-right: 20px;
-}
-
-#search-preview hr {
-    margin: 10px 0 10px 0;
-    border-top: 1px solid dimgray;
-}
-
-#search-results ul {
-    list-style-type: none;
-}
-
-@media screen and (max-width: 510px) {
-    #search-preview {
-        position: fixed;
-        left: 0;
-        width: 100vw;
-    }
-
-    #search-preview li {
-        font-size: 12px;
-    }
-}
-
-/* enable absolute positioning */
-.inner-addon {
-    position: relative;
-}
-
-/* style icon */
-.inner-addon .glyphicon {
-    position: absolute;
-    padding: 10px;
-    pointer-events: none;
-}
-
-/* align icon */
-.left-addon .glyphicon  {
-    right: 0px;
-}
-.right-addon .glyphicon {
-    right: 0px;
-}
-
-/* add padding  */
-.left-addon input  {
-    font-family: AmazonEmber;
-    font-style: italic;
-}
-.right-addon input {
-    padding-right: 40px;
-}
-
-.form-group .glyphicon-search {
-    color: white;
-}
-
-/*----------------------Get started page----------------------------*/
-#why-mxnet img {
-    position: relative;
-    width: 60%;
-    left: 50%;
-    margin-right: -50%;
-    transform: translate(-50%);
-}
-
-button.download {
-    color: #0079b2;
-}
-
-/*----------------------Download button------------------------*/
-div.download-btn {
-
-    border: solid 1px #000;
-    border-radius: 3px;
-    font-size: 90%;
-    height: 40px;
-    display: table;
-    float: left;
-}
-
-div.download-btn a {
-    padding: 0 10px;
-    display: table-cell;
-    vertical-align: middle;
-    color: #000;
-}
-
-div.download-btn a:hover {
-    background-color: #0079b2;
-    color: white;
-    text-decoration: none;
-}
-
-div.download-btn.download-btn-top {
-    border-color: #ec7211;
-    background: #ec7211;
-    float: right;
-}
-
-div.download-btn.download-btn-top a {
-    color: #fff;
-}
-
-/*-------------output blocks----------------*/
-
-.highlight-results .highlight pre {
-    background-color: #eeffcc;
-}
-
-.cell-results-header {
-    color: #888;
-    padding-bottom: 3px;
-    font-style: italic;
-}
-
-/*------------Download source-----------------*/
-#download-source-package {
-    display: none;
-    padding-top: 40px;
-}
-
-/*------------Mobile dropdown menu smooth transition---------------*/
-@keyframes slide {
-    0% {
-        left: -100%;
-    }
-    25% {
-        left: -75%;
-    }
-    50% {
-        left: -50%;
-    }
-    75% {
-        left: -25%;
-    }
-    100% {
-        left: 0;
-    }
-}
-
-#burgerMenu {
-    animation-name: slide;
-    animation-duration: 0.3s;
-    animation-timing-function: ease-in-out;
-}
diff --git a/docs/_static/searchtools_custom.js b/docs/_static/searchtools_custom.js
deleted file mode 100644
index adeb0c2ab68e..000000000000
--- a/docs/_static/searchtools_custom.js
+++ /dev/null
@@ -1,853 +0,0 @@
-/*
- * searchtools.js_t
- * ~~~~~~~~~~~~~~~~
- *
- * Sphinx JavaScript utilties for the full-text search.
- *
- * Copyright (c) 2007-2017 by the Sphinx team
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are
- * met:
- *
- * * Redistributions of source code must retain the above copyright
- *   notice, this list of conditions and the following disclaimer.
- *
- * * Redistributions in binary form must reproduce the above copyright
- *   notice, this list of conditions and the following disclaimer in the
- *   documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
- * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
- * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
- * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
- * HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
- * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
- * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- */
-
-
-/* Non-minified version JS is _stemmer.js if file is provided */
-/**
- * Porter Stemmer
- */
-var Stemmer = function() {
-
-  var step2list = {
-    ational: 'ate',
-    tional: 'tion',
-    enci: 'ence',
-    anci: 'ance',
-    izer: 'ize',
-    bli: 'ble',
-    alli: 'al',
-    entli: 'ent',
-    eli: 'e',
-    ousli: 'ous',
-    ization: 'ize',
-    ation: 'ate',
-    ator: 'ate',
-    alism: 'al',
-    iveness: 'ive',
-    fulness: 'ful',
-    ousness: 'ous',
-    aliti: 'al',
-    iviti: 'ive',
-    biliti: 'ble',
-    logi: 'log'
-  };
-
-  var step3list = {
-    icate: 'ic',
-    ative: '',
-    alize: 'al',
-    iciti: 'ic',
-    ical: 'ic',
-    ful: '',
-    ness: ''
-  };
-
-  var c = "[^aeiou]";          // consonant
-  var v = "[aeiouy]";          // vowel
-  var C = c + "[^aeiouy]*";    // consonant sequence
-  var V = v + "[aeiou]*";      // vowel sequence
-
-  var mgr0 = "^(" + C + ")?" + V + C;                      // [C]VC... is m>0
-  var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$";    // [C]VC[V] is m=1
-  var mgr1 = "^(" + C + ")?" + V + C + V + C;              // [C]VCVC... is m>1
-  var s_v   = "^(" + C + ")?" + v;                         // vowel in stem
-
-  this.stemWord = function (w) {
-    var stem;
-    var suffix;
-    var firstch;
-    var origword = w;
-
-    if (w.length < 3)
-      return w;
-
-    var re;
-    var re2;
-    var re3;
-    var re4;
-
-    firstch = w.substr(0,1);
-    if (firstch == "y")
-      w = firstch.toUpperCase() + w.substr(1);
-
-    // Step 1a
-    re = /^(.+?)(ss|i)es$/;
-    re2 = /^(.+?)([^s])s$/;
-
-    if (re.test(w))
-      w = w.replace(re,"$1$2");
-    else if (re2.test(w))
-      w = w.replace(re2,"$1$2");
-
-    // Step 1b
-    re = /^(.+?)eed$/;
-    re2 = /^(.+?)(ed|ing)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      re = new RegExp(mgr0);
-      if (re.test(fp[1])) {
-        re = /.$/;
-        w = w.replace(re,"");
-      }
-    }
-    else if (re2.test(w)) {
-      var fp = re2.exec(w);
-      stem = fp[1];
-      re2 = new RegExp(s_v);
-      if (re2.test(stem)) {
-        w = stem;
-        re2 = /(at|bl|iz)$/;
-        re3 = new RegExp("([^aeiouylsz])\\1$");
-        re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
-        if (re2.test(w))
-          w = w + "e";
-        else if (re3.test(w)) {
-          re = /.$/;
-          w = w.replace(re,"");
-        }
-        else if (re4.test(w))
-          w = w + "e";
-      }
-    }
-
-    // Step 1c
-    re = /^(.+?)y$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      re = new RegExp(s_v);
-      if (re.test(stem))
-        w = stem + "i";
-    }
-
-    // Step 2
-    re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      suffix = fp[2];
-      re = new RegExp(mgr0);
-      if (re.test(stem))
-        w = stem + step2list[suffix];
-    }
-
-    // Step 3
-    re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      suffix = fp[2];
-      re = new RegExp(mgr0);
-      if (re.test(stem))
-        w = stem + step3list[suffix];
-    }
-
-    // Step 4
-    re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
-    re2 = /^(.+?)(s|t)(ion)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      re = new RegExp(mgr1);
-      if (re.test(stem))
-        w = stem;
-    }
-    else if (re2.test(w)) {
-      var fp = re2.exec(w);
-      stem = fp[1] + fp[2];
-      re2 = new RegExp(mgr1);
-      if (re2.test(stem))
-        w = stem;
-    }
-
-    // Step 5
-    re = /^(.+?)e$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      re = new RegExp(mgr1);
-      re2 = new RegExp(meq1);
-      re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
-      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
-        w = stem;
-    }
-    re = /ll$/;
-    re2 = new RegExp(mgr1);
-    if (re.test(w) && re2.test(w)) {
-      re = /.$/;
-      w = w.replace(re,"");
-    }
-
-    // and turn initial Y back to y
-    if (firstch == "y")
-      w = firstch.toLowerCase() + w.substr(1);
-    return w;
-  }
-}
-
-
-
-/**
- * Simple result scoring code.
- */
-var Scorer = {
-  // Implement the following function to further tweak the score for each result
-  // The function takes a result array [filename, title, anchor, descr, score]
-  // and returns the new score.
-  /*
-  score: function(result) {
-    return result[4];
-  },
-  */
-
-  // query matches the full name of an object
-  objNameMatch: 11,
-  // or matches in the last dotted part of the object name
-  objPartialMatch: 6,
-  // Additive scores depending on the priority of the object
-  objPrio: {0:  15,   // used to be importantResults
-            1:  5,   // used to be objectResults
-            2: -5},  // used to be unimportantResults
-  //  Used when the priority is not in the mapping.
-  objPrioDefault: 0,
-
-  // query for obj
-  objFullName: 20,
-  // Highest rank obj
-  objHigh: 100,
-  // query found in title
-  title: 30,
-  // query found in terms
-  term: 5
-};
-
-
-/**
- * Search Module
- */
-var isPreview = false;
-var Search = {
-
-  _index : null,
-  _queued_query : null,
-  _pulse_status : -1,
-
-  init : function() {
-      var params = $.getQueryParameters();
-      if (params.q) {
-          var query = params.q[0];
-          $('input[name="q"]')[0].value = query;
-          this.performSearch(query);
-      }
-  },
-
-  loadIndex : function(url) {
-    $.ajax({type: "GET", url: url, data: null,
-            dataType: "script", cache: true,
-            complete: function(jqxhr, textstatus) {
-              if (textstatus != "success") {
-                document.getElementById("searchindexloader").src = url;
-              }
-            }});
-  },
-
-  setIndex : function(index) {
-    var q;
-    this._index = index;
-    if ((q = this._queued_query) !== null) {
-      this._queued_query = null;
-      Search.query(q);
-    }
-  },
-
-  hasIndex : function() {
-      return this._index !== null;
-  },
-
-  deferQuery : function(query) {
-      this._queued_query = query;
-  },
-
-  stopPulse : function() {
-      this._pulse_status = 0;
-  },
-
-  startPulse : function() {
-    if (this._pulse_status >= 0)
-        return;
-    function pulse() {
-      var i;
-      Search._pulse_status = (Search._pulse_status + 1) % 4;
-      var dotString = '';
-      for (i = 0; i < Search._pulse_status; i++)
-        dotString += '.';
-      Search.dots.text(dotString);
-      if (Search._pulse_status > -1)
-        window.setTimeout(pulse, 500);
-    }
-    pulse();
-  },
-
-  /**
-   * perform a search for something (or wait until index is loaded)
-   */
-  performSearch : function(query) {
-    if(!isPreview) {
-      // create the required interface elements
-      this.out = $('#search-results');
-      this.title = $('<h2>' + _('Searching') + '</h2>').appendTo(this.out);
-      this.dots = $('<span></span>').appendTo(this.title);
-      this.status = $('<p style="display: none"></p>').appendTo(this.out);
-      this.output = $('<ul class="search"/>').appendTo(this.out);
-
-      $('#search-progress').text(_('Preparing search...'));
-      this.startPulse();
-    }
-
-    // index already loaded, the browser was quick!
-    if (this.hasIndex())
-      this.query(query);
-    else
-      this.deferQuery(query);
-  },
-
-  /**
-   * execute search (requires search index to be loaded)
-   */
-  query : function(query) {
-    var i;
-    var stopwords = ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"];
-
-    // stem the searchterms and add them to the correct list
-    var stemmer = new Stemmer();
-    var searchterms = [];
-    var excluded = [];
-    var hlterms = [];
-    var tmp = query.split(/\s+/);
-    var objectterms = [];
-    for (i = 0; i < tmp.length; i++) {
-      if (tmp[i] !== "") {
-          objectterms.push(tmp[i].toLowerCase());
-      }
-
-      if ($u.indexOf(stopwords, tmp[i].toLowerCase()) != -1 || tmp[i].match(/^\d+$/) ||
-          tmp[i] === "") {
-        // skip this "word"
-        continue;
-      }
-      // stem the word
-      var word = stemmer.stemWord(tmp[i].toLowerCase());
-      var toAppend;
-      // select the correct list
-      if (word[0] == '-') {
-        toAppend = excluded;
-        word = word.substr(1);
-      }
-      else {
-        toAppend = searchterms;
-        hlterms.push(tmp[i].toLowerCase());
-      }
-      // only add if not already in the list
-      if (!$u.contains(toAppend, word))
-        toAppend.push(word);
-    }
-    var highlightstring = '?highlight=' + $.urlencode(hlterms.join(" "));
-
-    // console.debug('SEARCH: searching for:');
-    // console.info('required: ', searchterms);
-    // console.info('excluded: ', excluded);
-
-    // prepare search
-    var terms = this._index.terms;
-    var titleterms = this._index.titleterms;
-
-    // array of [filename, title, anchor, descr, score]
-    var results = [];
-    $('#search-progress').empty();
-
-    // lookup as object
-    for (i = 0; i < objectterms.length; i++) {
-      var others = [].concat(objectterms.slice(0, i),
-                             objectterms.slice(i+1, objectterms.length));
-      results = results.concat(this.performObjectSearch(objectterms[i], others));
-    }
-
-    // lookup as search terms in fulltext
-    results = results.concat(this.performTermsSearch(searchterms, excluded, terms, titleterms));
-
-    // let the scorer override scores with a custom scoring function
-    if (Scorer.score) {
-      for (i = 0; i < results.length; i++)
-        results[i][4] = Scorer.score(results[i]);
-    }
-
-    // now sort the results by score (in opposite order of appearance, since the
-    // display function below uses pop() to retrieve items) and then
-    // alphabetically
-    results.sort(function(a, b) {
-      var left = a[4];
-      var right = b[4];
-      if (left > right) {
-        return 1;
-      } else if (left < right) {
-        return -1;
-      } else {
-        // same score: sort alphabetically
-        left = a[1].toLowerCase();
-        right = b[1].toLowerCase();
-        return (left > right) ? -1 : ((left < right) ? 1 : 0);
-      }
-    });
-
-    //Render result for preview
-    if(isPreview) {
-        var prevNum = 0;
-        $('#search-preview').children().remove();
-        if(results.length == 0) {
-          $('#search-preview').append('<p>' + 'No suggestion for your query.' + '</p>');
-        }
-        else {
-          $('#search-preview').append('<p>' + 'Suggestions for your query. Press enter to see full results.' + '</p>');
-          $('#search-preview').append('<ul id="preview-list"></ul>');
-          while(prevNum < 10 && results.length > 0) {
-            var item = results.pop();
-            if(item[0].endsWith('.md')) {
-              item[0] = item[0].substring(0, item[0].length - 3);
-            }
-            var listItem = $('<li></li>');
-            if (DOCUMENTATION_OPTIONS.FILE_SUFFIX === '') {
-              // dirhtml builder
-              var dirname = item[0] + '/';
-              if (dirname.match(/\/index\/$/)) {
-                dirname = dirname.substring(0, dirname.length-6);
-              } else if (dirname == 'index/') {
-                dirname = '';
-              }
-              listItem.append($('<a/>').attr('href',
-                DOCUMENTATION_OPTIONS.URL_ROOT + dirname +
-                highlightstring + item[2]).html(item[1]));
-            } else {
-                // normal html builders
-                var baseURL = window.location.protocol + '//' + window.location.hostname + '/';
-                var urlHref = window.location.href;
-                let urlSplits = urlHref.split("/");
-                let versionString = '';
-                for (var idx = 0; idx < urlSplits.length; ++idx) {
-                    if(urlSplits[idx] == 'versions') {
-                        versionString = 'versions/' + urlSplits[idx + 1] + '/';
-                        break;
-                    }
-                }
-                baseURL = baseURL.concat(versionString);
-                listItem.append($('<a/>').attr('href',
-                baseURL + item[0] + DOCUMENTATION_OPTIONS.FILE_SUFFIX +
-                highlightstring + item[2]).html(item[1]));
-            }
-            var html = listItem.children().html();
-            listItem.children().html(html.replace(RegExp(query, 'gi'), '<strong>$&</strong>'));
-            $('#preview-list').append(listItem);
-            if(prevNum < 9 && results.length > 0) $('#preview-list').append('<hr>');
-            prevNum++;
-          }
-        }
-        $('#search-preview').show();
-        return;
-    }
-    else $('#search-preview').hide();
-
-    // for debugging
-    //Search.lastresults = results.slice();  // a copy
-    //console.info('search results:', Search.lastresults);
-
-    // print the results, at most 100 results.
-    if(results.length > 100) results = results.slice(results.length - 100, results.length);
-    var resultCount = results.length;
-    function displayNextItem() {
-      // results left, load the summary and display it
-      if (results.length) {
-        var item = results.pop();
-        if(item[0].endsWith('.md')) {
-          item[0] = item[0].substring(0, item[0].length - 3);
-        }
-        var listItem = $('<li style="display:none"></li>');
-        if (DOCUMENTATION_OPTIONS.FILE_SUFFIX === '') {
-          // dirhtml builder
-          var dirname = item[0] + '/';
-          if (dirname.match(/\/index\/$/)) {
-            dirname = dirname.substring(0, dirname.length-6);
-          } else if (dirname == 'index/') {
-            dirname = '';
-          }
-          listItem.append($('<a/>').attr('href',
-            DOCUMENTATION_OPTIONS.URL_ROOT + dirname +
-            highlightstring + item[2]).html(item[1]));
-        } else {
-          // normal html builders
-          listItem.append($('<a/>').attr('href',
-            item[0] + DOCUMENTATION_OPTIONS.FILE_SUFFIX +
-            highlightstring + item[2]).html(item[1]));
-        }
-        if (item[3]) {
-          listItem.append($('<span> (' + item[3] + ')</span>'));
-          Search.output.append(listItem);
-          listItem.slideDown(5, function() {
-            displayNextItem();
-          });
-        } else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) {
-          $.ajax({url: DOCUMENTATION_OPTIONS.URL_ROOT + '_sources/' + item[0] + '.md.txt',
-                  dataType: "text",
-                  complete: function(jqxhr, textstatus) {
-                    var data = jqxhr.responseText;
-                    if (data !== '' && data !== undefined) {
-                      listItem.append(Search.makeSearchSummary(data, searchterms, hlterms));
-                    }
-                    Search.output.append(listItem);
-                    listItem.slideDown(5, function() {
-                      displayNextItem();
-                    });
-                  }});
-        } else {
-          // no source available, just display title
-          Search.output.append(listItem);
-          listItem.slideDown(5, function() {
-            displayNextItem();
-          });
-        }
-      }
-      // search finished, update title and status message
-      else {
-        Search.stopPulse();
-        //Add pagination
-        if(resultCount > 0) {
-          var numPerPage = 10;
-          var pageEnd =  Math.trunc(resultCount/numPerPage);
-          if(resultCount % numPerPage != 0) pageEnd++;
-          $('#search-results').children().slice(0, 3).remove();
-          $('#search-results').append('<ul id="searchPage" class="pagination"></ul>');
-          for(var k = 0; k < pageEnd; ++k) {
-            var page = $('<li class="page-item"><a class="page-link" href="#">' + (k + 1) + '</a></li>');
-            page.click(function () {
-                $('#search-results > ul.search').children().hide();
-                var pageNum = parseInt($(this).children().first().html())
-                $('#search-results > ul.search').children().slice((pageNum - 1) * numPerPage, pageNum * numPerPage).show();
-                $('#searchPage').children().removeClass('active');
-                $(this).addClass('active');
-            });
-            $('ul.pagination').append(page);
-          }
-          $('#search-results > ul.search').children().hide();
-          $('#search-results > ul.search').children().slice(0, numPerPage).show();
-          $('#searchPage').children().first().addClass('active');
-        }
-        Search.title.text(_('Search Results'));
-        if (!resultCount)
-          Search.status.text(_('Your search did not match any documents in this version of the documentation. You can use the dropdown selector in the navigation bar to try another version. Please make sure that all words are spelled correctly and that you\'ve selected enough categories.'));
-        else
-            Search.status.text(_('Search finished, found %s page(s) matching the search query.').replace('%s', resultCount));
-        Search.status.fadeIn(500);
-      }
-    }
-    displayNextItem();
-  },
-
-  /**
-   * search for object names
-   */
-  performObjectSearch : function(object, otherterms) {
-    var filenames = this._index.filenames;
-    var objects = this._index.objects;
-    var objnames = this._index.objnames;
-    var titles = this._index.titles;
-    var objLowLimit = 10;
-
-    var i;
-    var results = [];
-
-    for (var prefix in objects) {
-      for (var name in objects[prefix]) {
-        var fullname = (prefix ? prefix + '.' : '') + name;
-        if (fullname.toLowerCase().indexOf(object.toLowerCase()) > -1) {
-          var score = Scorer.objFullName;
-          var parts = fullname.split('.');
-          // check for different match types: exact matches of full name or
-          // "last name" (i.e. last dotted part)
-          if (fullname == object || parts[parts.length - 1] == object) {
-            score += Scorer.objNameMatch;
-          // matches in last name
-          } else if (parts[parts.length - 1].indexOf(object) > -1) {
-            score += Scorer.objPartialMatch;
-          }
-          var match = objects[prefix][name];
-          var objname = objnames[match[1]][2];
-          var title = titles[match[0]];
-          // If more than one term searched for, we require other words to be
-          // found in the name/title/description
-          if (otherterms.length > 0) {
-            var haystack = (prefix + ' ' + name + ' ' +
-                            objname + ' ' + title).toLowerCase();
-            var allfound = true;
-            for (i = 0; i < otherterms.length; i++) {
-              if (haystack.indexOf(otherterms[i]) == -1) {
-                allfound = false;
-                break;
-              }
-            }
-            if (!allfound) {
-              continue;
-            }
-          }
-          var descr = objname + _(', in ') + title;
-
-          var anchor = match[3];
-          if (anchor === '')
-            anchor = fullname;
-          else if (anchor == '-')
-            anchor = objnames[match[1]][1] + '-' + fullname;
-          // add custom score for some objects according to scorer
-          if(title.toLowerCase().indexOf(object.toLowerCase()) != -1) {
-              score += Scorer.title;
-          }
-          if (Scorer.objPrio.hasOwnProperty(match[2])) {
-            score += Scorer.objPrio[match[2]];
-          } else {
-            score += Scorer.objPrioDefault;
-          }
-          results.push([filenames[match[0]], fullname, '#'+anchor, descr, score]);
-        }
-      }
-    }
-    //If number of obj is no greater than objLowLimit, add higher priority score
-    if(results.length <= objLowLimit) {
-      for(var i = 0; i < results.length; ++i) {
-        results[i][4] += Scorer.objHigh;
-      }
-    }
-    return results;
-  },
-
-  /**
-   * search for full-text terms in the index
-   */
-  performTermsSearch : function(searchterms, excluded, terms, titleterms) {
-    var filenames = this._index.filenames;
-    var titles = this._index.titles;
-
-    var i, j, file;
-    var fileMap = {};
-    var scoreMap = {};
-    var results = [];
-    var scoreDict = {};
-
-    // perform the search on the required terms
-    for (i = 0; i < searchterms.length; i++) {
-      var word = searchterms[i];
-      var files = [];
-      var _o = [
-        {files: terms[word], score: Scorer.term},
-        {files: titleterms[word], score: Scorer.title}
-      ];
-
-      // no match but word was a required one
-      if ($u.every(_o, function(o){return o.files === undefined;})) {
-        break;
-      }
-      // found search word in contents
-      $u.each(_o, function(o) {
-        var _files = o.files;
-        if (_files === undefined)
-          return
-
-        if (_files.length === undefined)
-          _files = [_files];
-        files = files.concat(_files);
-
-        // set score for the word in each file to Scorer.term
-        for (j = 0; j < _files.length; j++) {
-          file = _files[j];
-          if (!(file in scoreMap))
-            scoreMap[file] = {}
-          scoreMap[file][word] = o.score;
-        }
-      });
-
-      // create the mapping
-      for (j = 0; j < files.length; j++) {
-        file = files[j];
-        if (file in fileMap)
-          fileMap[file].push(word);
-        else
-          fileMap[file] = [word];
-      }
-    }
-
-    // now check if the files don't contain excluded terms
-    for (file in fileMap) {
-      var valid = true;
-
-      // check if all requirements are matched
-      if (fileMap[file].length != searchterms.length)
-          continue;
-
-      // ensure that none of the excluded terms is in the search result
-      for (i = 0; i < excluded.length; i++) {
-        if (terms[excluded[i]] == file ||
-          titleterms[excluded[i]] == file ||
-          $u.contains(terms[excluded[i]] || [], file) ||
-          $u.contains(titleterms[excluded[i]] || [], file)) {
-            valid = false;
-            break;
-        }
-      }
-
-      // if we have still a valid result we can add it to the result list
-      if (valid) {
-        // select one (max) score for the file.
-        // for better ranking, we should calculate ranking by using words statistics like basic tf-idf...
-        var score = $u.max($u.map(fileMap[file], function(w){return scoreMap[file][w]}));
-        for(i = 0; i < searchterms.length; i++) {
-          var word = searchterms[i];
-          if(word == 'ndarrai') word = 'ndarray';
-          if(titles[file].toLowerCase().indexOf(word.toLowerCase()) != -1) {
-            score += Scorer.title;
-          }
-        }
-
-        if(!(filenames[file] in scoreDict)) {
-          scoreDict[filenames[file]] = {};
-        }
-        if(!(titles[file] in scoreDict[filenames[file]])) {
-          scoreDict[filenames[file]][titles[file]] = 0;
-        }
-        scoreDict[filenames[file]][titles[file]] += score;
-      }
-    }
-
-    for(var file in scoreDict) {
-      for(var title in scoreDict[file]) {
-        results.push([file, title, '', null, scoreDict[file][title]]);
-      }
-    }
-    return results;
-  },
-
-  /**
-   * helper function to return a node containing the
-   * search summary for a given text. keywords is a list
-   * of stemmed words, hlwords is the list of normal, unstemmed
-   * words. the first one is used to find the occurance, the
-   * latter for highlighting it.
-   */
-  makeSearchSummary : function(text, keywords, hlwords) {
-    var textLower = text.toLowerCase();
-    var start = 0;
-    $.each(keywords, function() {
-      var i = textLower.indexOf(this.toLowerCase());
-      if (i > -1)
-        start = i;
-    });
-    start = Math.max(start - 120, 0);
-    var excerpt = ((start > 0) ? '...' : '') +
-      $.trim(text.substr(start, 240)) +
-      ((start + 240 - text.length) ? '...' : '');
-    var rv = $('<div class="context"></div>').text(excerpt);
-    $.each(hlwords, function() {
-      rv = rv.highlightText(this, 'highlighted');
-    });
-    return rv;
-  }
-};
-
-$(document).ready(function() {
-  var searchBoxWidth = $('#search-input-wrap').width();
-  var searchBoxWidthModifier = 150;
-  var focusInputColor = "white";
-  var focusIconColor = "dimgray";
-  var focusPlaceColor = "searchBoxExp";
-  var normalInputColor = "transparent";
-  var normalIconColor = "white";
-  var normalPlaceColor = "searchBoxNorm";
-
-  function focusOut() {
-    $("#search-input-wrap").css('width', '');
-    $(".searchBox").width(searchBoxWidth);
-    $(".searchBox").css("background-color", normalInputColor);
-    $('#search-input-wrap input').css("background-color", normalInputColor);
-    $(".searchBox .glyphicon-search").css("color", normalIconColor);
-    $(".searchBox").addClass(normalPlaceColor);
-    $(".searchBox").removeClass(focusPlaceColor);
-    $(".searchBox").css('width', '');
-    $('#search-preview').hide();
-  }
-
-  Search.init();
-  $('#search-input-wrap input').focus(function () {
-    var modifiedWidth = Math.max($('#navContainer').width() - searchBoxWidthModifier, searchBoxWidth);
-    $("#search-input-wrap").width(modifiedWidth);
-    $(".searchBox").width(modifiedWidth);
-    $(".searchBox").css("background-color", focusInputColor);
-    $(this).css("background-color", focusInputColor);
-    $(".searchBox .glyphicon-search").css("color", focusIconColor);
-    $(".searchBox").addClass(focusPlaceColor);
-    $(".searchBox").removeClass(normalPlaceColor);
-    if($(this).val().length > 0) {
-      isPreview = true;
-      Search.performSearch($(this).val());
-      isPreview = false;
-    }
-  });
-
-  //Click to focus out
-  $('body').click(function (e) {
-    if(e.target.id == 'search-preview' || e.target.name == 'q' || $(e.target).parents("#search-preview").size()) return;
-
-    focusOut();
-  });
-
-  //Press esc to focus out
-  $(document).keyup(function(e) {
-     if (e.keyCode == 27) { // escape key maps to keycode `27`
-       $('#search-input-wrap input').blur();
-       focusOut();
-     }
-  });
-
-  //Add search result preview
-  $('#search-input-wrap input').on('input', function () {
-      if($(this).val().length == 0) {
-        $('#search-preview').hide();
-        return;
-      }
-      isPreview = true;
-      Search.performSearch($(this).val());
-      isPreview = false;
-  });
-});
diff --git a/docs/_static/selectlang.js b/docs/_static/selectlang.js
deleted file mode 100644
index c0075f956bd0..000000000000
--- a/docs/_static/selectlang.js
+++ /dev/null
@@ -1,44 +0,0 @@
-/*!
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- */
-
-function changeLanguage(langSelect, langSelectLabel, rootpath){
-	langSelect.change(function() {
-		var lang = langSelect.val();
-		if(lang == 'zh'){
-			location.href = rootpath + 'zh/index.html';
-		} else {
-			location.href = rootpath + 'index.html';	
-		}
-	});
-}
-
-$(document).ready(function () {
-	var langSelect = $("#lang-select");
-	var langSelectLabel = $("#lang-select-label > span");
-	currHref = location.href;
-	
-	if(/\/zh\//.test(currHref)){
-		langSelect.val("zh");
-	} else {
-		langSelect.val("en");
-	}
-	langSelectLabel.text($("option:selected").text());
-
-	changeLanguage(langSelect, langSelectLabel, getRootPath());
-})
diff --git a/docs/_static/tutorials/tensorrt/wavenet_optimized.png b/docs/_static/tutorials/tensorrt/wavenet_optimized.png
deleted file mode 100644
index 59139e5f45c824a1a85ee5ae55164f4643ddfff7..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 275440
zcmb@ubzGF)_b56h3KA+ME!`m92uMqJD+mnTLkt5LfJ#YAGlWRj0MaM|(nG^gDkU)>
z0z(XOo*Ccw+<VXG-gD0Vo*#d}6Kk)%_S&o0et4s+tx8NtLkI$ah}G2|>4QK-U=WD@
z=Z!1Cld8`*96_K<(z=fgl`k$XfG5Jj!UF>X+uPgr_V$#NlpY=){QUgv?Cd%^I=_DX
zA|)kNQBfHl9<HpcJUu-nARxGP>sCWUgRs#f3<k5mzdtcCF*7r>ySp159o^a4Sz20}
zl$2y^Yy0@|<Np4BZf@?Alatr4U;Fs@xVpODzI~gHj?Toy#M#+dMn;C2nYp&M_UF%^
zM@L6RMMZ6GZJV2$3JMA^7%U?r<H3UmFJ8P56%`E%3PPjN^78VZKYtDn4^K!)`1bAF
z%F0SyT%4q&q_MH_=;&x@Xz2U*?>RX+3kwUgv$Gc$7w6{Y?%liR=jX@7#AIM#(Ad~$
zV`Jm*@2{?|UR+$<+}!Nw=xAwaX>M+wo12@Lmsed~-PP6g>eVY@O?hEyaRbk%uHgYy
zZ4JVzav5d$Z?lsWpX%GeJcAP=Kh#w~ha|W`SA{&)A1N3Hpte3g2N($o%RMWmD!z=n
z{Lg-#V7@=ZkNkE~ciYhDKmG}ql_108`4j4P^*_d9Wb~Aj<SGtN*r^Sz!v6uD>6ce7
z;qWkBOsVMp=XfaotJnjRHg=*W%oPllY~3N-c*h%ibC8q%T5mQRQ{crKQbE(gS6zxT
zc+udrQxZq^|NZB;KYhI<B?$DLYAm5VMcw5Js6(flKL^D(q&{Q^EONV*&1Z4kEj>g#
z8&E(`7mZ1{yLg!fE$}9$w|IMuW0D1UoZqiG@1nJK`z8ovvLH<ASNU|v5MXoETINOw
zECrAtOIEx2N_7CkZ}j_dRW*`pAkdNbHy79|V=yqIAGkpg!1ziG1UkKfyVAIgV%(bl
zp+T-k-ShO^k51Sv6|ET+elNwo`dyxVaWVXpvvYB>(_tw6U<EZso$Aqz?6uC!&H>_6
z>V?irps<VQ$m^gmlv_AtueTU?nL&cCHe6v;?eCYy;dhrn>~O?|1M}T*1A#t?nnYrL
zjqG_8QNdwV5)Tl11dx2JT;P;jYE7%qso$sB+b9r7Pz}6OR)oo8L}g8_XK?YXyQH}D
z$O;VOsJ=ypB28$0b237N&gmUHPUd!^8*WjT8-6UdaY$=XnP7sOw1N?(*{fER6cYlj
zjN>g!5lS4e82}H*MMtNOsxROP2?6V{864E0G>n2Ab|8ldgZ+{B8{#4pWZ}dh&=e_p
zN4<6&69jMFJ2i7oRl@eZTxZ2$zD|f1=rt}zf8fqPOqhz|=uB0_vZiHIhtA#kOSlKS
z(!r=&iFfAK6t*4SYS?EfWY$+Ak9(;L_U|4=om9zA5ux*!Rk@_sgi+Yu-_IQH2O9Pz
z;du94vH4V6C`IP65vqK7>1pz_mZYxnbb*BVvvAgA9Ka>T<}xh~(onvA{h(3}?@yCC
z?CW&(39B1ptYbX>3H3Ob%}5A(yJ*JSeSZrLX%s|lqh|?X7EDa4fbFM%%L?Kah-VTr
z;u7dPDHc+eE=b<xbrkTa@aU=$0Sm(JYEQzQtHr(*nz=g{Oj+E<zTgD-^Bs#FqSGUv
zVRGIYE<1^vxXpZbY#1@?zz;ZwBRzYoh*^6I5U68@o3Y=s@|Xd4DX01#u{i((v>-JR
zQp?xrO8XMnLd*3(Ra{E0Br}rXS3zNmDOL^^CoaM~jH-Zcp#HGcnl&8EE>57E)S&?I
z9HO+QeS-%O@eE4Fo=B+n-tm0Hgx75l=ppHFtr>tiKN17@CH#`k<#CI<ja%J^hk&?0
ze1&5O7a72PvirblpAZ5f)FIyN_Yj~tOz_v4K}ILBK8Ou?|N9~EKKBtYlHoNlZ4ypd
zu8aVq?*J<eU;-d?^8nnWI0y|K<DTR2zX3pH$N&gV94G+};{OAgT>}Z=N<n1TTSJ|F
zJ}D6XPl5ldW{%F1Hg#el>=Zmb^ObUoufL;~NA7HO6-C0$JQBlnUHmO{_uS*_eQqyD
z<|gWZ?7?6`@!$MIlAt#@w%y0E?Hx{8s4wH#`oI6Mmmm^SdsIUY>?ceKY7X6eh0#W@
zdx1cv(um3(Z9x3PaT5#<gT<SdWYT1YlWB1i(7wVL`zL2wYF`*npr6XG$y1%32FHi^
zYbERgA_23`KYr{C><YUh;=*X+W4Y9@_hd_n>@WZ3Tj(=PUg<(84oPJsW+P>(Or<>`
zf1~@X42$0KJ&;b5jlfLRrv;aIG$#U^KpT$HOXv-meTF$YRITd~P@J__12$O`tdsM$
zDJQgM?#!P8m3?^S`|~WQi4)V?FM=l?WH+zgtrCp=e(nB^7s?OG6j~m+@;>@~w%GNO
zOTXZO7a_qUcf?@ei@2w?m!|I{rj%tXdkT(m97O7XJ<dnAakgv!E{fD?Ve|Kg!5(!7
zdsRS^&goF1T9p2!rJ=*UV;@m_W!Brt0H7-^YO|EZDBm92!agRJ-B2wXSIQd)XcQ&9
z)fEn_OI`kRvKEV3cQm3O7|ldDUAT;A`&gvl6etzm>SUehyTs3<x}*#UIfEjW8PJts
zIE)=9;1|e{Qgz^Q7EVWS<o&+Dsj?d0wBk+qBb?-u;kNz%<zbiyWHanKz{00UY>`+b
zZNY!04F5mnyW5=}3CziKE9hc;)$=9!|A89*@6Sx#45=MeA@BA&8ptrXs%h@5WYDZ8
zK5uv<BJ6DiIYso1>$`UOW&syeKX|{cJo?cw@NKU}e0OP>2&8L#eol$1^#EKnr(Gb1
z2GygAyL_hSwkn0#t4U`AfpU}V>l*at(MHz+K66SrnaFPl<Lt=~^=1d0YWz%u*yH|=
zrr-fQo&q=Dg{RMDf2B;LXngx6H~^dbdbadw)xznb-(^skzxkXh`pHBIbqE*u4ng8i
zv3!hY8Xyj`+H1E<i}aeamv||BsXHwf_Sfvb4l!K<bp%CaVT7AG;8wIcKc8iSKu@x4
zLYD%^+uB}1hI$^%X#)X&caCX{RIWT6c0`5+R*(*h0bh6&n834iSUv#vG-!w&a@zpB
z7V`;EALOr44mkvt0=4!)9e4MEHixB^5N$>WEEg;ir^m6+g($lHh6=M?&-I4$*3uDd
zO<haP%u;Zy?|y$@K%YkOReJBMykp(HeGZh@DUZ%`a$WqNBLtJarG3~uv~4}ys%bkT
z^#K8qKl4es$t`rh9r!Mg72H;urH&B8T@ZgkI^Z9Tw1>e5k?c6g+Yw?)89);8`uv)F
zWW52p8rxw%C1+EIlJO}GrRGK$)YfX6kfY#TUD1+u4qg#(tw+EQ3o$x<f1hW0ia8e6
z_UZjXOPED#k7y(z`rQUk<GY`ellzR}7`_RP)#%8RDshvA7Ik678(@-O7er|KiNSVi
zRNw^kmK%-$8U=6;;xguj;w)tT{vmjHxTlg6;j`lX{O?SmvWU&rMCVdx@lC(2NeB%(
zV)iC_`<9XA=>xH5_`EB^LIEsp!8Zl*^t3=n*eU(y^jJTHBF5IE2a3kJ#((~qHM<>}
zeIZjVN;$xBWYD2612Z{bvZ5nHMHT9gzPBjUXO~bgqk@ofKzJmGlQnF1Ihvb;CG6Ug
z99|%Y=(M+9MXQGz!1U6e{^i^aC26kSriZN*yMHEkm724^WV!{(W|0dn{Gz;uQUEtx
z`%nb6P^DpxAxmNAvvg$>r0~@A`#l*iIYA&!vHez@@i8}@N=u;4BL`1?<3&^$QqCCW
zJ^%|%RQopCn%P^%9nI+15Nnvt>H5lVo)pa&-dkX%H~jA&NgzDxV80v%3R2%*4!_3o
z2Sw^ujNoBb|LXVnXI<iJR<iGbbNZd1iO>;GS}7C9;C%7b??XovY6Ql;y+0!(+$#Nw
z&Lo3QxyqUXr4T-MhdtN6ZiOMWOBrZ|3!TGnSwrRrltqJ2NAz9F?xL_IJ3Bwk6u?W%
z*GN1Dey~hT^c>zp;kI9-gj7k440T?;>R9(O38MtP<*yZm5QM&&Coc}9>gT^R>)~{_
zEaxWrg-i2?+A!6oJt`7(3+s0A!tUDLQ-4j9J4K4v7f^(f-n_7brBQqO&53)yb#>1M
z3?^POA$pKw>UJs3@H0NgNyDN~s(A5Kp+xPzs9cE@`_sra+D*a2^$J4v&B%j`Kbd=(
zH6lqxPDwGEYQEBqGn)i0MfqpJB{6`|GGOzY)aq=B%lm53$L+@c2(icaXZD=SH>HJ(
zf0W(aTxO1T5xId**ob)WUK&m>#Gc>uS1a<`Yg8>fU9V}vwZYBr>El~?WZfGwh;E>r
z*KXDcLlMnh^oj3?W1=E3hT<O|*icCr!lWL7<xN`K3o9pmoAXY-4LIq1CPk-~_}?;B
z07pJZF{*s<R{=^CXKyMtujWN;UTRoaU!_xQ<{FigM-Zh3t2R@Ozn8zsTN=_4OuDqq
zrYP;SJ_wta(04I7h@u+d>3?3phzjUS_s@XH-Ri|_ZypZ`nsPOwAt^a2YQ{-wex5Cs
z31JmqLkw?&XB{$rUyZnlHhS(hQIpzBy$Df)y6A#CwPIp7UB${uohhPc40zs5tPdzw
z`LX5)UFml@Q^d0TH3lkVs1@^$bw-p~55(JAeoAku!YW365VDl;5w)Fg@${S1#d*^{
zF}g%8&sgA_AiSKm_$#}Hss4L^@0^}9WJpC&?R$D^Q~+E>X*K)$kCw8N0RCp`i6th)
zVAIBOGuIg#BGvFP_U%6=U`D4(92*I-TxC0IVc}nT9Br3h_&|mEfdC@oX1t-sPnZ(z
zC6W(fkHL`>Z%fQonXwyzJCT?#*V<HR-;JFNyvNeGcJr6nc8qlwNyD>3D?P<eeZY~t
zA#r%KXQd35HchT4cIONC8mNXpKZ!N+c(Z=`POD&Hn-=xSyizn%YEpn6g_YJD_q)#1
z%TXd#_sTDmE`lIZ%(b-bU@^Cz`+5RF%FobcEe?d%3Z`f?r<QV681AOQ^Ung<5dwp5
zP4gp?qEbcM9dkCdBfl$R-bla{vB3(yp^iJTMl=y$1aIZM3R8lsB;03CQS{=}ugZH#
zkFxUhPO+`qKi_$1&}$nR@QCgCn%TKUKx&QvW5t)EV<DoLQ}^tK2n_uAsHBhPW$*Ip
z68S;Zph>MW6A-SvCP9bR5y8zpZ1x0sL&;E!#*vWBe%7=(`9gL5X9O}zmqEw_B!B8t
zkNRy>Fk=oYlr#<Sl(~^`tL>E9jTJWwl?aTph*TUAT5V|^y>y@)kBfGK?xAA7AuGBf
zAu>S77>1+-;vnB7{GV4$2wbiT`}^nq4L1PbA>-daHxw-ZAB`l4^4*)emf;(OiF)g+
zcS?_gfP(L8;odQz##oqfQ=pWvB4p~oBPN6k5Nd@{DmGui#aN2XxG)TSA0ds)PyTu7
z57}<uC>D%fI@;kjP&6drdtGol^18hu-K&>8k4MOEkJ!IwfnHSRUx^-^VT>y$*xo;r
zX|6<voX@wq9P-^jL&JbYgFtXu1pp`LJLM~kEDk%cvTPg|L7qg4SO)ynL$D4m=mUM~
zY9&HDn_tl5!a$He5&DXYb7S*|-`0g~Vj)9Au2ad*JN>j73Ef%V!A5$Dc(mK_vk@u6
z!P+Zl%!oL?a?+P|1M#&Lkg}8+by<T~c(gYdQO(EtrH+G^pY_XaT`WrKQfSKagY$$F
z_L);mC6QW=k;dT1irAx;{iEiBiNoTr_9qsC+mNV)2-&Xggg!gv8@L!g3<=NTo&NFG
zZm_l~8KYYNew=pmWR-kl_1SoR!{?StT5c=pzN_%os(_#gi#cO&&p^NWqr7&zrRZf$
zLj=*E68%MMZbe)O3wlL@)`~J(88%Uss;_`AU2`<yfW&JN=WH})aaMUsP>en5lDP)A
z3f|OcH4Hi)P5h}jHgS6$Zl|mjKsrRhhTAfbVkBg?zF<HGak#~57kIp?SvqOfpde%2
z5PNyMGi?)ob`oE3&BCb0Sf(xztSLaCtX}sg5Yfht%Q!(~QV4l73#sf#Oa(n<ImhDL
zQDGj}>u~yjeA-nwy-mXY_+)MMs<C@0B}%}btxmka)>(A~&u}JR@FA}NEG+_aMcogY
zq0!$kH7ecDIQvjzO^*9rxlXwyDcXcKp~AIM4^lX02CkGzFm&ZIk<$J8cQ5Es>j`|K
zeUpaWnT2gl4%h^b`g!_ks;t?NrPde<PjSbJNJux$A1SX^>Sv<f{4cydG?@e?1RF2t
zKNX<&q`3dj+Uw<#250I&Bm~y_1v<DSc%;U?EJo0Y(}s-h7^^1HvSxnbs9aAn4qj9D
z?hHPRN%Z6gl*9c05(xd3;twTeMk*;EHQJ6#3`OHBe#NCYruvU16y0mMQ|#~hR|HAW
z8GR1KgNn5HY0r(oJ9RFE(Q<KD@vlQEP<Z(bsq(Ls*1GzXuKPkDg%VY`?fxZ&*>pEr
zG2FbV8wFx`XMX>6rY-bh>W6!V);l|=$FB~KVecQW5@tVM7zjh2AL50A4D|qyb&3h$
zaS4C*3KES|p$^d-Xp4W8iJk?oI>d@t%70X+2S51M+D;^7q@twfgvz9-j+dk7vQ_ae
zcTJv}yRrVk{MXG+qe2i~h~Y}j$kI*kY@^|DjB8~HPx+vK^SlwC2c4PD=y(sbYnRW&
zpYb0^DEpj&Y_QMr<Dp{eHdy*R>K$cgo*|JY-0Tw{vCOpZAL4%Hwa<@_&U&M=#OJlz
z^^s;qm0R5U?=xsO)$duPv?=$`tKzj(C7*@Zr-pv7Uu&-h+csfT`(b%{K(%&Wk{BTi
zZC@o;QGL7WKRMu|*wgK2{GfTqZ<V5SpS6K${p*?v&K6v}6o$77%4XHMkG1&m%^2T7
zeO@1l-+^#WBp@+_g!yOBwK!82H+Z;f&^bvCXp=jrYSkGIjlXOZzlBzE>PtyCPF$Ye
zSBA^l^Na@HLERnI#Fo^fIgOj9s5tLDV@0p%ghTqFMIwKVQzz10?N|bF$mZu-=fLCL
zDzAiX^(zdzgR&c~J{zw=|57Z!#s;?<Y?sjG#!EK!1B7(N<e*7c+poj}CFyt9yWNb<
zTUZf{lP4N)m&I6R%eii%y9D!n@%RO8!L!@FANaT(rE{nTF59C6l&@IriOU6~zJmA^
z3*@WuSm=+;Xe%~<mnCHWXB{jN7}KsYePie-uej55n(Tvu=P4e;Im8d7e#-4-V~N?|
zu*H*6&r+Pt2R$hen9eXBb5^NdsX3XeDgC-sUkW#zO4h(jvI+^>N6zSEbl3f_X^W3x
zK%p7^aH%$^eWtE<f%S_7;nLctFixZEOHBNRx+R~G8c&$Ui7Bpm6&o42oSo+=T^CY_
zs%K?KMXdSnT4ye|hVq|xEQX8yBg+?fAp$8%tKbEI>=owrKh{4MS4;&-T!q7~;u*(;
zu*1_tq5x*Rgul9hrp8O4(rG8MZ80=Aa4<w$9=e67A@R$loo(2$HsE!IjaL5^EdfR7
zETf2kCyXCa8C<!^lu>-pzo=3vYOI0Yn8s^MRRGvTD$Uhjr=)IJGX_(bTqZo>0If<#
z!;EVvQ4lDTmKEB5SVcST(6d!`+T4XAJ+01J8(XNE0Qu>7W}J%G!8`B6F|j*t1R;45
z;)P|Mqu>1VzbQ2S$bDCqnlrmo>EulPX_oLq5zBC<z#@cuk?7PMOQBSp(8xtRn~G-%
z?@LyM(eGwmo7Oy51V<4|yah$;LF$Z@G=18ZGb<d%?9cuCnxCc7)>i48z4X3hl_JAN
z>?1rsQWWl>P<~oX%j4(B*n++7Gcklo%Y7>y#b)lpQ$w59Len41k3PP6Dq!$VicuNA
z`&_K>^36836}pc<h*vIiz+nbWTi!M2cTiS4>VvEZ4|SHDv5E$fH6M(oUeGW7axrJi
zDK9)jpZq{Bc$!G93!D4d2*}(-W8Vg=njdbs#@JaH6v32B&h8^LW8S>zjySf8p%}Ak
zFgcOh{<}arRHJYONuN76+Fzg_@^NjOn7Mh3@p#NtE6`EN06HWOB|p875Z}?s95p>~
zl`u~Ix_$$YgcPMasL_mytWZ{BVTja{F4a`aoj{JI+`TcHYv=r0vP01FrjB*B{%R4t
zP!!&dgycyKaA9)a-#Yjf&oCZp=F^pVCwVuH4Ob)rJ&8xkJ)}eVSeI%0BFb=-g;ZJe
zu!Ful1Su!lvo@PvtGVFye&Xnr8%`DA5oQzmeF<FINCTLeF!B5tpkUeoz8D4&Jwdz#
zZkV1Thy($7rA9vi46hnB`f@bjd{v~W0larqnnC$Mq&7l=8~jx)AGYs!wDlA*OXsGe
zg0-F6PEUBf#2K5D-zdX}xbS-@c>8`A$K<oF3Dz8a=0}gYm^aVb<DNW+M>&6rUJb5*
zKrnNmj%ysa{QSZYY@y(iTY{tnDhTlx3nGXNWjj9mDyY6$RWrkH+j*fZkOWr?H>OnD
zzE=0ryEs%Y?c*e7^x1H)?6~$MWInk!4>1ud8cK^=yHPk~*_?APqJGM+KhEj<*bA#~
zw$_{oWgw+|wu)@~0r_4_kh99$r+w3k{iRs@%lfWJf{@k*Eux)IJGFCPOkWwpU2k3P
z{sy2F4{+J@hr?!R!p62Qj-IVV!sq#TLuTZ^gj>v-ZVU|8eNwc*rK%1jXpjA|#xb6w
zxfecz;zLVQx5Vy+@dm7ur0)LNXJ3Piny$#IkXVj=5hA{vp4vDXu=|CY^uP;Dz^F)k
z%Z$$Z&KWmZ+$|;PldI$M`JO~fC%1hR;e0sG2uDID3ugz5e$<j!?%o{oM*0U52@&ze
zGuG<|bU|dc_h?vnqA%aL03SZ@{YfwkGR;R~D3xyBGTXUxCCumO<oQ#ucz`$J5+V_3
z%~rDw+zhYB6=DYHP@kFw5*&XwUK06afwJ@BVu+}}A~E<$lfig8mUPROwV(O>E5Kwu
zru1XK<a%+eB26VLXAWhI5Ho%utMWvCj#T6U2RRByqL2@G%A_{H{{7wzD9Ez)`(;l6
zO4t1C^-BpBf7dH%bwtqxRv&&pei?R2&@6zLk?FG@I0p+~t%O_6@?RqDL_(Ye!hC!@
z69{zGpKAa2opKjS3+fdMPT70@AeDB`+Oj_2>YaxJGK=KX4^wws9D}>3*|o;SmXO)J
z@17mbE&x>vZg{helJw%xkEnHGMOthT$q?VTl$qr;!|JPl_sNaJ_S?|?V0RR6<t5nY
zh7Ub!SQoPsgQnWo-XT8r%+l?laC8J*)+%6XS$k`^h-JdgK>NBwAQ|c))?%TeBhVXd
zdS&mwZ;jZyp&(H3q$b}jHgl2>{I&U{!VDj@p$z&m-QRrmU{UQ&1E7iBCHqfaciuA+
zy4}8g+`BMeAw(VKMc3x<@+P4{F|SvNS!4C()N<jY^g(C2t<zy8G$>nEr=Z+wO7B!|
z&ObCPz5iW4GPb(HoMqS~Ccmq0V&y$9a0)}gVKdOEjgyz!Q0EY8T)uR%%3kCU=jDwv
z0^Lq44!Xl;)<FL^X=8Q3%Jc%|2mu<C8uRwI$9;0pKzy&?+46~GW7@QD4|l_(Bgr^E
zGBg<i)TtX=2M%(ZW(Q`07haIPUD1!uYpe}`vFXNsbCtVlWXb5r0C-MAy=SZ6w|!&1
zi<HHMZ2;sY_d+-(WEzKo<S*hd>u0W{SsNu~dZK;XFAtyr`nVx)1TcW#hn1SCaOnk5
zS?%QjtPB$_(C!eyIY(T7=;-PH(4_gVX#D&yE6&mLQn^5)@Q`}{ym;`(b*}%imec=a
z=I7Ne;^kVe^r$0psBAgkiaq+^$I9yF{O-N|xO;)E)$5AWF#eN^(ARb}O5n(MT2y1!
zR09sy5{^&-5)kqlE@Zxga}id!2#X09W&CFkJ#FByc_N^NW_?J3TJkrlH%z^|LJaC)
zsWAbY6rIth$p~<`$#cvW!p4Q*uy_Un(D(b(0tiyb!A=<;A~e}sa<JPs(8w`I87Gzm
zKit7neiNhZe#Y1RT6qwvj5d|YYaV?8>CzhudNpYiO@ItE?Lfdb0vcR8Z5(F&LFy%Y
z${vBbeUSnOap!+vd(#(*%T>Wzc9Owf60Ya1vO|5JTxIA7`h(xqZ3OGDto;627I=In
z>6GJFs|NI<kgx_ttbvn$L(&f~p`F7?lQDVC{6(k{4Z)y)N_1cEi44}_c#WB-wzu|T
z<5oqVYZWY5mRo#Gr^qR%&U`uGRr}Vwl?&<#b>!}aR@A3nksC3^JbIl4V-=8@ZWYzE
z@>`e}1mPpq?%4AjyEt`jcGh<<>Bl|KND^N6W{pLWF#>W!!^DWPs{Nt3I4c$>;zsY_
zp@q$QbFGJPz&*wM*5GwgFPXaCFYUEcA^T*gYy`y_!tZ1zMqLSORdA&?*VfpZg#qWw
zb1(gY%hu@*iPvsxgNtadP0FYGxO8So42jvp%cr*t_z|7^Cth8=A-90Sf7l04xK)k5
z4Vb_lp5@>>jMh|a9=eOhZspDK;?t?YL{fCoqybGUl|5YcaM{<*h#ut?oc7C7!(Z$m
zi3sfu8QJ~|Nt{HAwxIRMan-Tt=|WludWUf6z?<#i^r%Ty4+nUWCD<dXNg@gt7(VPI
zUmUUSH$$-__}+<Bi6G(%JIpmDU7)3Ro8K+#j3T$W_m*I*J2R1W<;+H}AYHtZKv)5K
zXt1n=jhksDA4aPeq*-b9W7rXA>Y-#@g0x+uZ)QwwTvuFnZGAqb9WYar&-cccC^p*~
zLs)=t>B&wbl7HVT^qB*J{&1PB##9|<b=D7I;^<(Yv;C=E$dT8It@%Y-Ol$#PXhJvy
zW3AD}>oFvSi@CwC6v3ORlEc@%BYJJb>xW|t*7&6T;IPDCC|W(perT$SQEkm5L2#JJ
zBN+X9wTK@vHdZmGyiz+#i;vl-L%WbE{?oh3f26A+S26kv_8j_O==luMKFa(i!+^a5
z(Xie`OV#)xTt3PI3Iqa|oxsaKhej`5!iR1urHrVz9PlC<=OJ8|AB238fny;EpBRAR
z{$>pL{vN1`6dzMQ#KVQb{zBY=$AoT8UIJ7c^d<LD$Bjf>4Z!jrH^2WUmgxW4Nk>;(
z5QqSi-gvnnUne=o1laEXv$p&HdghMlYX9VjJkGay*sZUQKUkFCk-xY>m}h&Js9LxB
zH|2)%jq{6~0HSJ<!3UY&sW&;;dGJm{>R+b;dP0w5;76C1i@PXsD8Q!i5&6(aOs5sF
zj$~hZf0<X9PXgk&=cB2|#%2RiN`U#~-q4VwTg5q|k`p0MKm#8hXf{g#Ta{bCfZM?X
zKN0}xd2Q(kV{h-XebpvF>msZMv%O_ov)%%4%tiY@2Rnw>@44l_h@zV#0D(lnP4SJj
zfu_dmq5}C5+urekG5{X%;AgL);l7=8V)SRMYKS!s&>@AxJ}pH_`a@12KnXG>e?Z;r
z!#Xl{`++JzrIy)ppjinT8JoIw1r#O#*)HcQ77Y9H+dL`q(LfAK9=w2hOHo0v&QPWx
z?W?cgSmwsXMXcEC7`?=RG1rX4#_6)Ba@&hDd&I93A2a{R=#oM3(7pls#Go*0GmTqV
zhX<r?ts2X?!(m_5SoAQ1KN(PwZ(iWvr>P@?Yd=L|xGtmt5f2OCV!aN--Rs*L0q-A1
z<C9q6Ob4!n3i2fQA8vt+Co+;IoWp)m|3R`wqv|^iw|y#<!QH!uxPmO`R6c|V{WAg8
ztuxk^yptezZ1Qr{I1~<ZsNA0!heAd1hRbV;7WJ9M>3&8MV%fqh#rK3%Q5x}ALB3jf
zfIT#3QnLEgT-w5un3MBDMWxcW1%M2*sOO&<f|Rh2P(I7HPk)q=M_}tWpHfek3qGFy
z{Ox43wKf5LCb-`n^#!Zv-xANDuH#l|b?(f1D@uWS(v6D1!Mf7pTokcmR4Uq9Zk*V=
zrsL3EFyt;45u1^UEib6Xm`uQ$b+vG(j6km&7LanO2~WSrCM4BaC5oYqoI3XUoil>%
z*9yA4)CU7qRXFNe*wNGcjv|4}lIu!`8)~xo^~w8MCaMnTZt2<2)hK8kSUPKS%S>Gf
z^R-&q7;gi<T6lmPoD+l?weQ5rq-bQP`hq>2f{|KI4$u57`?HXf)2_a@;#Dwq3K=$f
ztL-fRv68wbcx!2@&r{#v);J{OoyP3T<RO0IWj98kIGF_}z#QwQ;B9xyK}7vw#s)Ri
zq5a+Jso`kK7*WvjpYic<G*rJ|(y7-yG;?MlJ_5!`L1&hbFbcJafT%Y)5_mAUAKpf-
zSZR@>4A533>crA545&GrS?`$tZ2^`(I`TA+9}s3#H_F+l<yNpZ_q7N*Qdj1d<d|@)
ztcwe<mh!|NZ4Wp6?AeZhyo;|1Km7>Q$#T^C{0~bC!t)65`!*+=h1{l)nJrb5lrBwA
zEO9jSP_xJW`c$kYj?k>XvR-ZVI(v3@_TG9TOVnt<CO0BWhr8w-&%tdJ)I@<EE&g_E
z_8wk({Ixy6p;l&GQVFTfSz+gSj|JfeWh7gFU`8cT2gCD7{#k9!MLJf$LTBmumUER^
zl0PwA>UOYKn5{aXr}Qn|>J28k;BO_(Uj>jhbw>KM)EbP+{y2faM!7FI$JcpEdLHS{
z_*k84Nb`^sRnR-4ec!Pl-W{AeJgkWssTgmv0uQUFlUR<1pXrUR8iC_*?&LcG)PEKw
zLf-xKG_<E2ZCNZ$+dYZM(5kYxD@P-QUOA0OiaTHP6TIDiu_ma~R!mA=^*(y@keijY
zQh>yN=u)4%C_;zr!p<%bnGkdw7*{l@>zcynnK20YgJd{xJ5o7GvSc)8R!ca3=o|?D
zP{)g)9kmcXe3<ou2eqAjN?Uu_BxbSY7FsKUNR)QV!3#uVc|<)u&y;)mjF}jFE1#A9
z9&5-UD%JO?RdIfC!5iXk!rDri6l)S|uFM&Obr(6QJ8I?rZp9{oh>S^Jhq~hKsI4p!
zL{4oSu0qfKy$A_8h_?+9shWPXEM3cS92mcLSor%#+W!4{Kh}5MHklKJvYLF5f+dj%
zD^1jZBJ?m#z=@^i1zdK~*Flv<{WEeP>ktSb=KSz=APoVav)E6?q3PH@zJGF@STWv!
z8kn(}Zk0fJ*VCXv6O<b+KIRWhWL1aUD54#}M^MY7NPd1&wBp-H%+7CNR)DO&SiIA<
zExd{D?C<|UqXeyKm&bPnbj{Skm1euhA-uQ$_%{}MeH(?JocnJD+V+(g-j@^N%a~!*
zaD25f_cC5ppAa*9OrMh0w*>ws9mGp<lIHR}$zS5x{#Z|y?nM0M^P>evBSvAI+2H&u
zrD|mOX%=Ui+f&$=fUeVFmXM1ECg5EZ10@CP^k@JlM3+~QCh<|KuJ3njDE*(u*U#_S
zy(W<Vu@^|O_>zb}Hy9`F=J;-yt{jd=J~$s>k~wiZ8<v9aqb2F!e3~Yh2r#{UxKt~b
z4c}N>X{JCGHJ-`jeB~p+hT6e@aK6PYk8@xixzKWSN1SeA-t}k22$tPO^)_xX;f<82
z_U<hC`MGW4A}=8sm->Yk)UPcq<{@FK2#mWG{<vpKB<88{8gyd9w;)4WzM*Ch>tfvF
z6)e)rPKm-XxJm{Q8A!d!xC(E*{jyBVPJZ+CkqjxicK5LjKxM~UxYbJY>#;!aRCUHM
z^M~V$AaG^n;685pGN3C{!p(d>s01ZzrBmInRGjq%T-P=gYWKku^8&El9)kn;gK-o}
zSZFNAnI!{C{<<&=jww75n9m}6PE)u63`bR~D@6eX0M1k(t^#7{TUX)d<vk6bo_`=?
zau_++0hsXmHoz?E5lZ0q3GSGo3PKskYUBWG7k1kX_??Rbdh*Bu`2FiH9#js328+KD
ze4F=M?nRgq-zl}l;~SICkN8F<s-Nd3Bi<yU#I8k)`ivZgk`%p^SbzNUpwV=(D=;EC
zO9>~k9qszyMN`KqhV|#$R-@7uCALB?^z$JJ1b8vZERKL+w`&<B8h&B=y1Ai2*0Zoo
z-xYjw1LXJ?IeA!69a}b*Q{gLX)h#VxBSM=`#8<|ah0<X2!*W_V^!&ACL*#In>yI@~
zS8G+~%`CL((#F>>aa~f$lVS$y4SZbaY8)jX;NVO#ed#%b7Nr%#hdB6rz3-OdCqsv{
z<%pBb++A}9ys!8Ca0C4$a&%A(L4a!D812(vuX1X2Y%-}_IYCJf3pe6p*N(JM_&l+~
z=#CpWZld84jd@Ayv^mU3D{n;L_#Xh<5xJW5{Sq2fkmIh8Tksp7siwma^VGY4m(8V!
zEoGxpDviLj&GZJ#h2{^~W@0})mZsaXU&cq*xW#9e4VdeRA`JSwxYtdGx=H}u>|n%f
zyVc#~e0}*>=E%EO)?jiAmWbEN@d4_$!1+%!a&5a_n7S&TU0jeURFT5?Bst0sq~xmz
zB4$8_tIkgcLn!2{uK~L%*0Gp7FQ)+PeBTW~c0I3<_|Ty50(<^(fRphNR?dN*I2ky$
zH!LDO(&nBGctXQx;v6GwdzQfE__OTxJil5?G!QqBFN-XCD9=Xh(cy0FGj;OZ92<QE
zfK-p4pmeJ(3PMa3=nI_n*Ui{T+cQIK9BQvd0YTRZFGt#%qkL+E`QEQF+ssC1e@!40
zs&&hvv#+J>0phM?njw{_s3AXo=$W-+>%u-%z9NTLE|?ye9<jC!>0L6doJ|x8pa#@V
zav{b0n0wN#(xOj183^lL-=2Bh8`rxgOrO-q0-WF#>s$*7Y>JlJ3gH^w0U}d`N4-^$
zKt5%q$x3YK3~;|wmE^c7KYYHktIWl5>33u+*3C&O`U@yn`>UGOs>etf>+Bt|PRk_I
zW%E(Sgf}tYp6s8st9d~09{iCEu*&%v_<in>7*sfDoEDeQ*WMH?bqfg8H;=S2v$U3{
z$o`mqV8JD;iF5+_vTF_I!54rrxG?i=3?k!8r*~ig^wtoOb1Y|G_f5ui?=DVCe~oM|
z#z6)-0)v3_&AR>DBJF6E{uG<q2H>o#Zf}%A_IzwTap*ID+-;A->bbVa4)-453UXi1
zNcr5Ru=H(oY6jGq#=3RzT*jGN%bWMkP0+(@IYohnj{ds=nXP(2oVyu9@@GqL{J|Xg
zo2#H)7**OL(O0GmUuGQWX4|v2H_jE!mhwQ>2c8&`YEIP~2J3ImT>}nC+XsjfK=npe
z^xSy?oia!GrH|E{OkHfxtF8d~EMonKNRNfy#Fhc%5D2f$Yv+fj3RNj9O9sp?gFF+r
zh`o(k8kf{p_dp@F{;E8BU0!5ip;zj=X(DBL3iaac-QChuG#04mDJWK6km>DQFvvY`
za6CLcYb&CTczy{a*6WpH6f?gqGZqO59PN^bWXbL(d*d`W2sBN$)@xt)9a|ZE27FRf
zbz6=tw3Jx-aPI`jH#)9MXj~x7caPjCt$;fe-<XY&w03sf!NMJ&&cHhs`Y~m|cs&kS
z?WA*BNCS<}<j^V|C@kLjfujjbn*{gVA?>vEdKM-SND`{pXL}o*M~xQ0ULgfehWk#t
z4pp4Urvlnz8n?M@Cc60~8wybO5;}Smd;DF&7`VPNo8xG%waE&o-t~q(VJeP^diQwo
zTHs9O51F8Ne~XExR^<-@z&sH`*!Nvn=fwrqc0WKdZ>{Q_RBXREdy@tDiI<I!X=JS1
z-DGQktWWUO=5ovSTlehR7qpn|O3_{k;IyNkXyCL^R$Oj|!87(T<Hs}=H7~&q{p^j*
zH&=r%c38ag3kGbDZk%94Vm-E(Hou<(8~CopdrOC~hZTa$Bp;40$f5SHpxTe=u!^|*
z8t+O=9!^e*W<aGuOnUTd1cZ+lD{yTdHKY%;;Jc+nHvZ;u(~pwN<6d?)xIy~a*Tr*T
zYki#tfF^@_1MYnf!;~9Ok(vVn0ZP!VucY`m9$N_rK8Tfx;yCMt-@R)GMSy04O7|v)
z-x5we-HO9Orl31^vlWehd}GAjc*tYYzF-#g%z>K@eZMTdUqaifI2^nq)4OjJQ)z0*
za7Cj+4N*&~plT0<&`dV)vg$`$;97O=F1HP4KVNf@MpS(PcuMQgzxut~aDvgiDNd13
zW4$sK_EFxDE(_e9I+NGciW^7iEou_ZXg(`92_vtkx`xgV{BhLn{^6kiI8pX2lH~=k
z9UV;){w!>diEUF41iJLSw2}Ny+$P+7ggrAupUcH#H{7#_*3X+*&$g6tF_Njp`t;P7
ztft6ofagI%9GIP^WYwM5YSC_3<pz=0D98Hz_yPryNrXd7#>LP=E1Law_*3fhw%7UB
z#SI=m{itM7V(Q0BN#vea{o=t#J&|IXS!_F*!S#)+W~{6X4})_O-7jBUQuR3Jot$2%
zbaMSgr04gmtf093SE8Is5rAEMG|%kfFh3kv-DKA{*i-K9<F}N@&drX}$@)9m+FGHt
z=#NR$7^7ML!mq^Y8uQ;~Phz@9KN@HMQk!>jTC+=%$g$>f+2sYsm$l3;sJ$YpiwUQ<
z7?uerKCxHLasoHBSn!EwjVDi^+0`{{nct6{onVwJ+tyNQu6PlF9c#A;1UGDK$oMyT
zQks0lVR=3v@al!*5Q?yArHtQcudz8CnH_vj$ZLhE{o%7xvYule_R)_8FC_L--omro
zG-#S`zwf#SXi%7+*H5qk=f<&`f|B^3oINEd$B;`f2T3?)O9gAL-i2JqZY%y<&NG$Q
z%DySNnIIlZG5XtnWn?y;3KeIo-n6bDfsjwOUhNwtaAYf{M$PwTysgE>ykQ54HOTW>
z9S?yyuui`C!Qc#%;cJ4g*n*<J<IkfV3svn*Kf_ySMZMtxd@XB|hzKniD}Jhd$tmQ8
zbjWgrzI?o)+bY~D^^R&!!v2h^#-EA9PMKJb^F~#y+4lCYQwDU+7dU-3U=+fP{JRyQ
z*~4w{FzsbyBQ%Q%iO7C#2}TxbGT~5IU-WfhU9GsaG(A6Vw0Qm<!^z}A=J3MEbKK~z
zLcrF7cq)5<@h31w?(bhQ9S{?Zt-j;R1*6knKN-*p4h^<$)^!B&Jzl3lr|I|BKV^5~
z2L1qCVwlk((Ebkgn2)CT&0R|m8<dxyWvu6U<IC|Ugv;w9)vY~-Z6dusZb2%UBpa0~
z!crcruh*aY!6xE#+c3%z(CR0J)ku<Q>`zj{lGM$<E`ffxv-qRW$L*LTp5#(O%`1&`
z_yvmYCWk}5y*U)GD?8oZtXjbj<vTC~6Nt_uN1ZPOV_k6JP)C!L?^mx^?+)Faow8OT
zW$#uT@hQuu(^p05vY`1$yg4wzsHomJ-iikn9=0O8cjf-C4t~I5f=p3zjx3Zk$2iFj
zvzLXi^Lq9i22SEWjZ3Lt;(HpOtphL{bn39PU_|rwg<$m1>xQpPZ{8^?U+(U{q4M$t
zvmRN?I}NS7unU6fs^K()nX&_NXlIW++I982py`@4j%suZ^#s^kQmqz{e}qWCQ`Lgc
z0$_a*bR!JcH?{V70Ph>q=v^@5`A!M~xV^MxA+S&%;42=^*LfHGlx+r3`0)s+T>#ua
zpC0>IJ$M<^5xRc(hYJizz!lhyOd4r{kDdVkuj`lMuh3P$^bQE9T35YvM+S3%b)~m%
zU1M81#f27uK9n^Z_c?$;q85^*8WEBb#6a4$z8s=k8y^Qer8o>kk~%~FZ~`>hrsd~?
zOB@M6xxZw36h@c@08Vnd+xPgLJ<bAJAOcYThJ+{Nk1qK}<hJ)Wk-fO}_HzE0*MwKq
zZ;n+(kqvx242@gp*dz9BoHWQ39@x^NT6-bFO;Q8&w~&~OVFgFWru#si@GSG?!!5uM
zTx{`BDL582k^oTyw0|nMHp3F&ye}68RlZ(TB91n#&dlrct;<3HX->9lpV)<gi{hN6
z+$Z8YxM1Rg<SjxBJz)KjpW`pN4SpL2KHaLHZ6V|)F~Xg~gk<LD+9u-Kc@c>%x40)I
zf$azXf{IbXmX!fkch-%y<rM`3gn&&~p}G%x0ZSf+-rlD(9yD|UhL88Z%zYMrhI<&|
zO_3D5<_L%$B$4ha+uqz&PzNJWzZiW+7k=q9vwiP@R(%z<nebO3YVy2CF9lJr3)y9l
z*neR|V<{DOAj7mDT*ccLi#~ml$RsMEU+KwB5f7vT_71JOeZ6jL%@3faZjcVRTs6jB
zLLe{APi6pWKaTfl>*_4#Q-VM(BiWU^7rAFhZ&?s%Y<^kY6PoW)9;yIs0c1pQLO>qw
z;^dYCJElcU`2eT|C!Shg6OH!93i@9^76ml0$qy>($AcblEdmndFpZvL;g5DncL@O-
zRzm#8Nw;AX_bH}eXH@W=aUp%&<(R$P4QD4mav-c2NVpf{CuiAs*CMMfx!J24*iR8f
zaT^sDKb@RZ`*Ex+AZICy%1Ttz{si_v_f}yuoUId*5?pr|0YUDlk^bgC@{mzu#&mP}
ztdV!=edM01Nui~51_8)4NWAgx;9gmhiJAS=mzb}gjn`!XX-Jt333=NcXfcE`bBccn
z`KlL44><0IG0T__GqYSSW^UClF<6Cl0?_ZsEa^=r=3BtWWHML2jZ(V4Nnx%d9OYu>
zi+WL$0=umjo!5PFA-|Pp$^?IP_g4>$KA2$f+3L~I@*t5fLXVM0q*<?h_R}J~{0HaP
z=Ro)zMqvNBlyJ~E$(Qy}29f;sIv|i?+LN0tmxZa6of^Crm72xPaZ$I_jL(JslWb*}
z`&wHh#(f;nXpnCWUmTZU?FeGFaR=V|WdUb8Ne(AE2?y4IGqya09`^t~b#&HjITGH&
zS5ca&BtIQc;xoMMh3q(T>c15`0ZhbYd@lTbn<C5LNySZcT=BT?-ly`Z;_(<o=?5~w
z_@gc|Pwu5symwJ-W<`s{&Gu=C6QbX?=F<iml>e>Ze6FTQCm}*7wBl<tuN>y?g@3RD
z7bW&0Ja`fxFK{Ayd>Vytbo};Qu`~o%L}2zy>*~hNL*ewP8UnNpHYKI<qZOK@TaRy%
z&*ynHezG`YXg>cEhMp?aZ*Nc0sE?A(Z;tAp3dcMyOfag@pW%;dMcqsBH7sT&nF=B$
zDLgszP^YcyiA>gl<|W_@Mqd&<%aE2g(FUTAE$Qeb`AZ$_W-z0#o$qM6A9$<y>C|T~
zq_hQ8mc``NCdghG`qie1<+GSUg;vtn^cSo-QA5$iA)(Ap-{fRHJ9%-Wlcn6#;Sos%
zKIq$!T3=6CwNQd;XtZR(%C@JywCCj`yAPJXKqE(gb$qcKoQB!2$dzkyi7P>s7_(Em
z3%W3HH;?Xmg5(DJLvmLS;KX;3M>}j%8h_IDmHk%%iu8Wq6E!1r8ywEa2hYkJvvk!_
zORV@f=OLikTppM`c#fE7g8@CiSBO;yaF2d!)&iq>s{**vrRB+5a>G(%x41?*d=PhL
z=;8Z%dM+HH2BUr0mHqH?mc5u0NR93<pN8uOqBmE9^*(si@OIUku-cD>DoLLi(xh>+
zqA~DRR;O`m-L??5C1rVh1o7qYO9tWK3KH|>?!>an7$01g?cfvFWMlNjth9uolB;xr
zp_I*ziP}epU|x4d)Rl`DQXV^{4r3u2xSGb*owQVnbRIZb)v^6fLJ1#yo<*yXi)*k<
zj1A`AWU<m$dh9WXWN<mlcPk=tF;jnuR>BsrAr?zkv#f?t*VM6quzn|n9-_uXs3W{i
zn<$^XP@1~UeMM}@*pZ3p1!vJyi8s+ouN0yeFMQq$-5`4P{-)RVj|=F;T<_S_g^0fe
z=ySSHj-rpFUx9VpmbFtA7eZV>dMa6h;ZMYM!g`mqwKlKOfaO+xHcTVA=0qtn#HNx1
zGZeAkT8QM=<W|w%AJk@bvx>xM;#7r6<cz@x>>dfh2stFSoMb3U2bI53IxJIzowFI;
zJ?uj5#i*~(Sr2HwN#yTZ!j49&faO!`vligLQUK7xqd7Tb85%uIHNS4T{!9?x!TIuM
ztd(#Ojnua03_Y>44w@xLRSal!b)Tz<eC*$(5?!QD{uw`JIvR^DbPA1k@Q#^?f4aW1
zoondKhwvFvh8(u|{xamta(T0ZXE*q(4MqAa9KCzqQgCJ`9$DWLxu@SkKEv-yTCfJ0
zb9H%`PHmn@GF(70%jVQ@8x<Z`T~MLkK#7`gjJHaQ7cr=wmrfZn+KRyko8PyZDv)zG
z-LQi{9R~f$g=FI^_k%Yue2oJE%TRI%_xg-99M+y-r7vS_*m)Y1TA?-IUk#j51ChO#
z1-4xTaiQ;aU_KK%83bO8Oa)vqNU;_z9KL!9INEMgdKa+tpn$N1MJ^F1u>71|B*wB)
z27f3}=AO$Cx5$MfxCl=MNB#y^_}@zItk%gRIKFy}gp^tvgYk7>#C$>nISt;Hxix%e
z`xlfCYV<ECN4DX=pwA=8)AJweZ0D5PQ${e3T*GNKa^{1j5ja-pKHg|ZnTvfxZudi)
z>~JV^YwcS<AnEL-E;wYia6I<?<dlk^?RQ32vzrF<+fKodMde3kZ$z3A5j14+y5^VC
z5?7Q`l0|0{9S+XqRcoszH13ODv3oGJbFF!R?!2?#1v10{lJJTODN+AxcfZ0|5f|$C
zSTl4xdNy4IZ=<#6T5?=Q@l`(wuf6kve}U1*x`}vpbiAQ80q-myP~C9Dk^1CIG#8?C
z)uiy61<^fX6)>K`cO4d`X*S+c{h?vybQIp8T9zqIstO(XZuYmbE_mr&5YZ5O6Fukg
zQ=;8=c)du&MSj#%A@*;Bk^PGRJS#5m?Mch&ZIq_oQHDh9xIv&{7TU}g-7<S(#7<aX
z2L!`-%CAaD(4aKqT$^b$KKBL7xo>Ty{kH81*XEPA3TShKuA~<j3+D%D36J&7WDRo8
zF#TjiJ)Mc%cw@qU)!B{G0~J!oN}MKIfo%J(t5mkLVS_JJp01I!WNvRoSbakc>M8`@
zU!2XR7j<in7np(cVCbD5WT@90?#w_=%ds#%;p7{>4VeSl#?B+H>n@}3%$5kzGnJZB
z#!lF|=<{p;uon!LuUk|jMT=f*EN4aR{cy^OQcT`5c`k(YikPZNd#33pVT4Kk1=Vgy
z7Mj@i4chY?3@;t6$@u)vET0HHTAcQ!D{rVwX6i?u*}TQgJ<<)~N=MO-EPdSnCIEGO
zRFeL7oX84;$ZCW`{s=wmUE@y3t6}``^V8w3EMhGQG7~-!kF?e%VY(;e@(g~~CBCrW
zs~QLU(HoPN$1joTV~5R575(<N6U%ZFeF%&E%{FBSK9n>3lW8kDO#FK3oC9T_>dT6V
z>U9g6lu&}6JW-A3o3Q*{{}Z{ecf~45kWW3$(Ml4p57oP^w*nj1sakm<yZ-Q2xGDBM
z(z<`n3Z4E|ABsp!E@murh!IBkHN)!dH1+0(Q-D^c-3~*rahZ3MYW1;7z89{rk6+f=
zBk7~#M_79)#O&Kvbhp<yRnJ*b?7%h222myi)-i?$;W0=)R5>#`UshW>kDTm0wt`xm
z$r+VS%;KfF`!-6UW@_o>64L#80%R15piOqajjeHRV=n&Xy82BLHGzF&4B2g26Olj=
z4Aa<y+xLLpK4PcrFh~dCs5IEbqCV1E5#>q`n30ZeXsoWh+o#{AK?#1EPiH&|#$uJ4
ze+^EwXh2-llT(^G4&2l>8;`_zsoqi?6fbWP6W>DLuLV9ZByyKEL+qtm*(@HcNFauf
zf~r?uZs)X2;Pvp~5;0oPaZL;zq*5|-C^k=wF5WJD)?8%|HA}DeA%f8L9IG698|q@x
z^}l%ELG_{mksw5unn6pqZRZ<A&_TYRe{4w4#`tN|UkdZsnw!@)S0Io0;i1e)K2r4{
zOdrYpQ90U}_3e>Conh(pWC?*N=Dae$Dg%}E@MiZsQ{thdIYFY|se{lxAp7!jlP4FP
zs)}7{-98!`a2MlaFLsBdb5DC0&vtHZeDg1w4G>+L+HwYZq&#4!a-U~`qCev%qOYR~
zA0X;R)-1NaZKYVt;O)Y|F`OPz-H<%u;gfj#8hKg8)^c`mmXmIUg9X<CHN=D(E{i`@
z9rj=?65~l|Y^WCG|5vHQ3@XsI5xxwzbUAnNS{^pG8cB*KDCHq<ZT@R@Iv&g;A;kFj
zGCrfB$r8`WC(C&l^$NV97WAgz=@Z3^fJ!VfBn@xUA5hgQHU70>-@3L;|An;g?-vbK
zg7!ehr>c`hJ0k0|@t>qT>_|>tPQR~+{k4{LUW^PvulM2&|5cH#hZQ#?Fo&DTwfEG!
z0=zf!5#%l=9z~OgM(D@B?Q`=J)*^--A~w7(4A;6zdus_ITDv|3rb<;g4;u56*P0OV
zWUtB77n{{=m>IxY*nlc2=(h}?ORwl>WUK&}^Gf4>YV;pST|9<g#4c1(y5#2Gc(TNv
z7T&hr=y@)>oc;vdP#|p)V26H`=fEKur5V&RvS8R(TPEdww+$aOhIuZR^G;n=YG#!+
z^_fS#iR~-n(<tsiE~F-;tl$r&fAM#H(8x}WvME%GvNpHqql^`D%9w0Dh`<C7rsBUU
zAdAg2!cAq7{~qDdR6Q0nwrwOPkiF%T-`c(%1^9@NYM*vl^=>`7?sio)i96Xf4mBzA
z2Y%O+F2ds9Q1+24brx#{T~x9#(Un#IQXoD;pU6nBQCN}=0r}5GcYGt2?@RcZu6U`&
z{lzU8BXb#-?d%A<%viuhf0xI)`+EbQMPj|Z{J#1)^T;lfSKDd*6|KIBE%l1^2#hD&
z?5^F&wSS+Mn<-TsD}gu9tG%d4mhmh2Fjsqw!%~F%SP&$*lHzLp&$dGt%Xp{oZjQRy
z;9ptk=C{4{kAzH@o9g}xYEsYgFX+2K@4sSKt!e(R1VI9$`b1e2u~GD!{x_lJ^#fm9
zp5UeMgVp#S?}1-Ph8m&<R(l5#;HyR;TXLeRmy~p1LiiBx)Ns>NEXqf`bkVnmHJj4X
zpmJXHw<}_s8tG5(qY0Ry)qji}Npj>}MDVioaw8?t?gTEorkvq7tN;0n^}KCw7s6^t
zH8KM7@wl88_#hzPXiuZW)a!>>RcV2Ltrz7i^6y!*>(|_rW7i`gz5GdsdDv^30s_#<
zK69o@MFGTFe}$R=qSCH53pvy>o8(*_&_!90pSzI6*4jD-#;eeEy4f<iF;esu>ECBf
z^ORc(=?z<N=r!J0gE<_Uf3_lx*{jmhMz(p>eJG1fwSyK$45X4Zl3&uq;LUiL50<lM
zU#S^v-|klT<XUfo>x6c}=8qy4)MFE2;XgK-=JgYA!<*n}Di0JP&0o`>8)%ULEcFab
z_1}p4>bR($@9&EU3P?!_NSAbXN{4{b9a2kogP^2z2uMqJFCZYHNY~P^lt@V|-3UB)
z{d|9~=fB;#({pC#%!&7z=ruTIfZ8{4EL}*8dcsnCFdQ56)p_CY<f(d?3S=c9m2hB(
z6A#{{4*GHyZF5C+UtT<4`e};QY?isf<vQ;&JbhOPB9b*vkkM&9_Pgh#kJ^+^V1502
z(InSqi^AynXdq~9mHyF~nY#f-saMcpYQU~0({?<ea~%sB$xdC{UNJ&bcy*_*ce1QA
z!o3=&CXOO9i2qR+m(Aq0@-Q3pjN7@$NqZ7D^QK!lB1niLG4+IFT7pDbe~S@%#$wuk
zVE3Qf+lkIz`cqyK_=JhR=A>_;!p6#G{@p)_mtbF+U1n}cX;Bg6N>jHI7`}P?@3i?F
zv~@1`vE{rj|6hyw75c?LpZu-JH#{K$#a2?5voqRnXKufg{@S?%r7A$A!@%+j?gdp-
zU6;%bMleCP$p*W~dhaHx%I_Ca<QLoz*U_*WT@)mo_u3o?L>lHE{t~N;(s2J1FT)wC
z{%sz7&;xJn=fX5J2Lhg6ylOM{4q^wB9&R-its8kP-9&A(xl4k=5g@+}nmXa0)gtIE
zPp(quV0^aR=C2B-KjyS~qubfc5}!diHHs9;QFr8Erxc!lkz5>EiXPap-G3Le(&&Q@
zswNV`6E{MGChJweTtM@+_1HfPXTaNGz~~=W?9wNq10tdX*c>?N(BO1);jG{)5f8E{
z4)-e1<jcN!k<S;)jYYokH@<s$GrRe2M0(bfuyf>O5ddV!rvs6@`^nz9B*zSBp$00V
zTJ?>QoOf4O@fjYHu9d_l_K~xy*E-gU1I~N!d(|B}<gBB636&4Q)_!DwGw4X(Nahz~
z4+^IspQru*xN4Vx*jwSVA~@n5_Mj{U@@&@n&(S^|Ets>-^QqsrX%HFdi@Cj)XkNEP
z6{x^5aI%;55Grp27i;L-vUG$kQblY)xpaP}Uy+GS)T9W-;p7JoL^iG;9TddP`let5
z#Wt&RRy`0<3Kp$blUw`sBqP-HeE&H^6A+dEM?6<O{gd(Ah|(7fAH*XW-^RZvvPS>?
zcWV`IIq=G0Z7vJcrhYluBM#I6X~|%jmmSW*%Z%(lghqaDs1sDMg)?=-=p42xz^<;c
zD9Fv~5QDH~WNLD7y5Zy3;M2r4t0>==dkE|)#ftEYjADII)%9m?^zFEN?lf3D^<H0`
z)j;i756tSGoSeTQfYjRMr0o&1tNVgNqCdW)tIFftW9}$`)T9kZRCv)Vu>MwwPkrLE
zv|yj)zYcwa_;~1o{d{(^NE9NF+z6I$(kG63DOnu|nqq7SPTs!le?tkDlJ_B^!Zl2{
zE&!hzp&@(3L3>Lhb_6JdXutelh6v~-M-CFcjHOAEk2MaJmoA4I#A>i>$gbSW84(s<
zrT4$uracT)cO|*VI-!~rLkb&zQ=d@J?KP=;figkA4FB))a!lUf1B<04x8%EggBVhn
zT)fb4TRXsY56ZH&_y)4gLZbI7q(Fsmy|rj3`i7U=Q&6Zm?~C(6V`SJGTtTb&2F-_7
zj91`deF^u!qJ1%r*jfO08X2Xf47_HTEdnI&#;nZ}v#R`Da1_A?@>dAeS~bo-Fr9y?
zuDfITTebr7cl+R7f!VYxXlxHmd3~k|AySynLH-sP8ErG07eZRN&K~jxp{dBv3vPgO
z=cySa`2lUIA*&~LLHwiD2<;uS*cd9{(A*xdU1&HpWX|fexYa909N1F1&oQb_YjEQo
zi-7_-G|mV;`c6cm-wr=mJ?X7_!QXFF_ghK8q)+?6z0=><IVdvH_6{V-rg@O&s+m7>
zA+@MPPPE|*0(;TyjCgPG2vJf~Xoci>Qq<tl=eL~-td;di`Bs!&CY=PIvm@qL_asHf
zmg5T&$8Dytt5=<^HkraeCL1O}tHLZ>c_i^mTd<!kIH094@Hf(#XtL^i;b_Ej-vJ)R
z2JxFL*g2P(-*O>Lt<!`O{^gBXS)3+D!Q1ZDI<^Pm*(jVy@1qSnrH=4WOruxkNefu?
z2X_x@e>y%S(`n^aZxS(Cf$%Aj-2m6UH0bs?X>3Uhk5I*Pc=jk+F|vd#QA(G#N5k@r
zt@PXexrY@mFm6ksPh4C+N#09xNIXT!tysm`6Gx8;9qJMrc<>9yecr}vZYJ~DAvP0H
zIqHa_ZH)`j<Dd@~{yOByzVl2+Iix29U!;38a+cP%;W)?1--53sqee~PR!XD#NqNU~
z586EAhR&m~Y)K49Tzq}6Z<8ZTWes_^pDt*SHE51-)BzZ5wc6eEDwRF}Pt9CRsMjS=
zp-M$m7iAh9XeS%rL{%Bh#cCASp+<_0FgavK7MpZTyYQmcD{c(IkUv`xm4s4dJZ3}7
zB?&vfBtTd`Ky|bbg9iR{{OUiw8BJx3|0TR_QO~yZ1Ct|%1a3v}p0AB0!b`GF;?o0(
zxO0f879<+!BSu~GgDSF(<bp$e!KCm*U~!*Jn?0=ypHKeMb1Z~pN!(t8hC=CoK6~j(
zaYQ$c@C9PYRUQdee!x0i^)djbYZO~%0o91i@u5T=N}Bs)F^X_6KRyM2$I`=|7qaS=
z1MAAm^Dv43HTS`09p!hWU9EqSJ6*JZ9kzk?=^S>41g)<578?_8T7cf8ziX^Gu@t`&
zh99W6eCqp3mm?gqWb{Q;z*CmVD+<};P=1bceAADZrkk!Ufl0<ms$C3K9H<psLnZFe
zHllh6Ew8#4HjebolG(_!px3|Y4z2{g$vlH&Erx@oBZq@I|C-TNgR@@mO61H+A8Y(D
zhx^dA$L;+ibQO%W?A#{~=|I-=fnMUkFV{)mV!1n}6{ZAqe~{OH3?3>}3nv7f7t=A$
z#i(eDhillTUFA#$yAxV+%FTanB(JIXK4H)h;Mj~WYQE}Kq<I@6f%F`-WYi+LBCGpk
z`fE+1nmMcBb_{3kXJj_3FCNkna^9uOM?$VDuqdVXOn4U4@kEvRy{7nG=)cZJu^Vs)
zpm1syH-5bTl4HnybdWVzS~YTDjQHn8nNB0AXP_|bL&359Z*cwC<&8e9)&&YrPxziA
zD~Mby^-IduOYvgqo4`T#Mb=60hIhphkNo?JYSTY*?3{0Fn`X@C!u(>RS?LHjv*zH@
z!6URSC}h9X3`3V=?+J%xK6`R`hx=Rev!&XMUr5W%X4i7&{k~n&>ar31L@90=WDe$n
zuV}k>>q%O>|H5>@D>qzBa@dWtb;G*ks#Y-m0<=gn^-la=s&zo4f6#*q-ZO2#_xYUd
zJE00&1DPOWlj#B5R4;?iJHm&M|IVsLk~QkC_@c#)K!u9guC?l@N~1MNi!gYt)%sQx
zFPHw3;dw0x>JPn)1X`-<E&V)l0@%QB3>5->CmPue<{2wsBTdDI>xbH9uP*df!2=h@
zsarEC#;WZ*zaIUJ9smrz1YUKd`o%mNm^7dR4JA(dA=%$5tm*4Bi*F6V+vib$TI!lc
z*bSosQL!kf;VUs!rC8%wds*MX0k%YE?Z{}n-R6|B30imrFO92CR4aEb3Hp8m@55RZ
zHK&OHPH};|^e5$6E#Or$+dE`JRsN`NAo>At7~d{xWGjNXCiL!|S+rSdP`BKRXDP|H
z2H|9~LiAB?v81g^Q@5rH7r!#Ic_njMg*b6dM|~+v$9#DlPxH=uJ2{d&!NJ*ih`wsp
zBs)FfnHLAA<#oxCK98NV*mC@cLx#WuK&mHBVPO6%pOvHFl?SMV{bDU@96qKj<HWQ*
zs%sr025QcCd-!~z+GO0HnIhD|t5VC^C0opPegE2AG94GZiJ`xWbF0JBrwj`$C$1$_
zK-<cMf{=BKvmn~$k^#>)R`5pk2^JV_d$N*};fP=m7X~@1UZYpZZ+iaacv}VDl@{pH
z>xcPMwp_}7AYL25_s5#o*U)Mz2f^g+26AMvBG*P%QznM=$@<_VT4$f{M#B&M2Nr8V
zrZhvqq=wB-*P9vyQSqL-(^j3jn^C{G`3ly0!eZC2n>H{%3(^YfDl}J&@{^VSZIQ|a
zl56OSdRD`9(@<|z)4}ve7n~@a{#+9`Z;jNg4HF(Qc2MYXzVC^&<@|AILPbdPY6Aw-
zD1bmw%BwbDk)#tZP(qbozY?2Xz7G_V;{0MOkuG#1(RqQkN9A9GMy+3uUFK`yDs1RL
z4Du}iwQD$9nYW|s<)NGb4mOyenBRQBQZB)~Fsh2Gy_^hc^FS}^^3FIKlNyO6bWf1+
z0G>KfDz4Zy$pn^(fGSLYdcKvl%+sumCxEWe_XEg+NMSy~nmBP^2zzU2KnWW60SV~4
z;k`Q*X-(QZD<VzW9hh3%Cl5hpI(SNGB@MH=3Ez68;hUr7v4$c6NY#Kw(Nq$dadarb
ziTzW9^-Hb65s=3L6w|ts2b-P-7WHjTdg5iwJJ4iPf}9zUAfkY3>taa|wy9bAF%YLH
zpchOG)@pSqcjHOf+*DWExnRYt9z+LI3$>bQHVUcjJ1o;Cr0q!l4YzLL11;;xMk4J{
z9LTKzwln%@1~*RcETP1e%E&T*x1!dDyN&oTr$t9fNU84D%e{$Xqldd=T13G7QhRot
z7xEmg=bI78%s;p6$K)i4{<=GjmB4OW5B{i=Y-f{#AMnyz+^^)c?>~^It4l6owKX4&
zbhwWphZWg3+>+Y#v=NdyErGLGgaRC<WsC0Fk|B{e#UAYe5w+|*k0RqxIP&AZgp|Ii
zkd>nQfc0YV{+`Nfnk(`*89xQv#o5uRs^p68G9~aVG!bv`&bD9LusocL39OzvD4M{g
z!v@zFVlU`H3C=mtzZ6h<Gj$l&BoSeO9C3>%W}-T_IYjq|4f3yVTMOx+1b=AWEl21Z
zWP)%-c$0rP7xYXd27Gz}{qwQ4h#d1L2>Zo<o;QDELtQVPSU@<#_>sB6C(OSGc=bP!
zRp5UH<iP*^H9CUjRA}8fLsks*d`dNA!<hc%<_M7tJ=e>Fd8wDYj=U3wHal7!^TknF
zVjwV9G-u-ijWay!{)4P|dMN{;QbN0c7S{chZnIOje2i#2w_uR(n}EHu>ie&<_gOOj
z#X2`NAOL3PETpJzhY!lX-ERAzM8ew~l9s7nxMbZv=MZ<L7621Vl;ESwZEX4byStEJ
zDew17Bgh37Lb-w=8)`5)J^urtn^bHNk#P;J>nuI}#%L?)``2t{*`ewT3$fjQQ50BG
zts>I2_0O*U$#Pg;d&!i%vE81crgBxAjg^8i)0&sD$g)O$qLt@*fw*Zs#Zm%o1gF}o
zxZ*8sCC8g_aqp!A61d@c{i#?~{zN>i8m{th6zB&(U?v}D3md;`rY$S^k*_3m^x8yh
z6;&+^z;2RuldQcuW(qeP@({mU5g5$%OTzF5$s8<1EUJIwVZJHk0FGUhbNd*P5g74L
zr19%)X}ns+uSK8ZzP_NHgCcbT7MO+8-n0<yr#DClceXyAh%0;%*gdJ;<|hE<-G^|_
zSGng^<^Sid^u#0;)<Jr^Z_|%mX5lX<vWFw?{W}wy%?xF=CGa;$RN+msMl=WE!ylb5
zUcsu{po*Z4GYC~x#rly8bM11OlP9a^JJFWR2Obn<6gNdYSxvl$vC@2?t16v*wE1Jl
z7t#4!-rsw^H7xJmza0UM`)WPprP~Hiq(xtSTCID|Kb3aw%l49wix9r+5oDN3KMI>;
zJn2ynZOWc6!9|F<qatNx0H9(+5AAlBxe13v_dQtGHErFfqfQ)BgxJ4iuamaO{mi=K
zPJllj6X6xrQjY7$>i(a7I-t`08babbh?ba6H9DP(6TgIiMvUz!!IY2bo>@Ib<+sXB
z30D|q4}>u3_X;AlaX@VV*mn9KH<gS~ujm)%Cx@~JXb63MO#Jz2SaAm10p7IlD$J|u
z&GbM{mcha{!Aj)x*1JuozypQ6cHuV9pF<A+06~DRV{v@AEg7q-bQ@Qj!h*D{Fzt1h
z9purUu(TIBrb1Aw^km$I6sF1~qBCD3fqI3uZin%(rg-5f<eGwJl0z+2xQ=QMi)9wJ
z<91~-Ehx^=hh`5H!cFo|#5kYBHH^)@&~5j$tkw3<r?*ZGxbfiR#!frZZ9M@Y|J7g>
zDL5n9goD+6_dWIJ9=^akBaPq8^`ElDIlcAIUa#qd*?eRaSAF}jbgkf#Yi>N_gSfqT
z>CC6Y6d62b!vFc61WPi&%7W~enrhyT<V2hyDsd4eh|Cvb$+=lu`UR@5?zAA}Jd)5^
zF(Yc#5MSAUG9utIR^WMu;HskSmi96{#WzE9$BTifqT@!vuq?oR<fM>kYrB&cY8&BS
zh*7`z52_64av$1H7jTYevG#|juG`j6HDu587~|TDHJu)*AsT9rYBb@9sj;vo2D_^w
zm_)ohIbUL=JS0_43wi1RW^bHtX)fN4xQXNOnugKy&bWyzv02lnGg-?091c^wtUmd$
ze-)MzL^oczRT$Rf+V?{iSqwn$BQ>QW*0@-UnY8T5pqxXTe}CW6nrw45?)Cfpm|mg5
zP5Ka;f`xF<-$TlwfUdQ*m^Q>uIlh@;A>Rz%$r$6z2<8)QN#_(jx{JZ{)izD<TQL>&
zXOEWxSxMkbzs*9Br2vn$nCU~?0UkFZqVy6a!$*B((q-o@F5G*9{`LF)o`Lud?MGFK
z-292=BRgSUa)ijE7`XF~kK^_8d+Xv@hFEEGQVUs_t*%->H7}l5PdWEijTK@q*Cf`|
ztulgi;{Q-bmN+yuI9X}AAXsqkC3UoIYjN{v^YzyC%<UkD4@C`=l?i?I3%bnP!G+a4
zj>`AiEe|_4F!}UfTjWQ*1DUfC2O51>>`J;VZnHanjha!R6%r9^>&J}dq#I*dyee00
zpyP9^MM#c<7k#C{uNoZ)=;?jHfNWaN_WjBAzN*W<kcF_9XCmZaMik}POL|q2fs9~q
zSfc|%^AyMP+v(ru+%$xDc16;nw4qq63doW=F!}wDuDy4)E_2skmm@1I;&(F<85=^5
zC$+u4sZ{#VqJYk^g+x5L%-^SCq9rdfb0!s`uh7G2%b<9HkC3-gmtFsUm1$EP%jUal
z2d{xfm$J2({RByc^5D+v)jzk>UtVl4Poy5-avv>x)02CIVEb1{(qV)uLvr*Flpis!
ze7JeM(UP{`L(b`ap;GpacP(9iu(^tT!|;J-+b5oT6&wePAN+oMV%m0{^q?>7-V#E`
z-27SZk>k&}{slX}L6y7&bOsDM9z_guTF<*a?i+LKx{E306P=xV(T4$FXV+S5$bnjy
z-rbnxBayWtuk0hKO5XFNl<kh+AKOeX_?&UHiUQ&)>V-4P;vW2r3{vPOm}IX0aqEbr
z?&vCf7i@9;T;V30anQfcTQtwi&!bu5guZ>|1-v{0-H~~vzR}%SIZ{L7Z(g!{gXUjn
zu0L<sLFTg-!dNmUlf8eGa`N4?w@J??i{_jx#EssjzLaC|r@`tEU!x$F)tYOu<ByST
z-8&lvAUo=FruM1d^X%OOQ9gSMWq*3PhFkVTjDKvK>OtOEkVlzj+A(QwQNXw6J56uZ
zWVIt2%|x(|hww;R31`OV!g>NC(2_BxGQ6U70}Cbk8kvk}9#O+bAqiVerb<ee&Sc+}
z%na4wNTT`1b)(EMPjC~{!d|fX;nn;>+xZjYd{)_^cjY9FG@HjMGB-ZPjYJpQ$^JC7
zwd@^<6c0c-7Qp$v<G0#*$8&kZ7I>V~OdPYdfTc=#f1?0-(wV3BJYESiNn%L1K>TdY
zPrllbY%=gDy{FkA=A3=|RiHd#*WlG2FXMHJX4`2({O*;k?ZjY7754Uql1ZohIrhH>
z?x!YNh2Bx>*j4j)H`?+TjX{m<*;|r5<Nsj0N_@i3gI-M=;-+FafZXz5)m)B!`Rxy4
zwH&4*(7J@9p+>u}Gm6^q?k|f`2w^QID}(VH_gNODP_$&_o4)smOw~JYa158?3pakV
zu*p3S$<m6;EdPz%bbEwLm1BE{uCqYY#4fL}&w_WZqMng`lag7RA%isN#K2WE<x_B!
zDQsQ;$6Dy;``%)kKNFJphl<)ieX1npA>142H0t65J-yA{9r!%kgnciYL2f-*A;5vK
zuLZB@&-E-FPuOYc<&<0g<A^MhA63WIGShy6lWYug4gn4C9yY0th$5+tS9SK*ZyUZf
zyC~TlBS_XR?wWVRoV$3t@MTdWOool0+vGxk1JeEuS7W;R@;EkJXhjetnpuhHl3jn{
z{CzC^b}CvDx7`V1V@aK1-HTA#7V#|y#MyUhdXt)2`ft2c{;1KH7+DO8=-`ved-G?u
zuEs37yZ)<Q&EBd-eId=pk2hN<dgJlm=HnWz-&6_6TPC(MK0oeoyeMc}+vw+`01Z=r
zji|IFD{~c2io-qsUV9ijNAq;FR2r}EzSIw`(e4RY+r*>f;wI{!@rcs1MWVv^xXbRr
zC-?N?IVds&Bp5u~(@jk?g49f7bvv&s4O8crdaDoYryG^?$SIBE#AcY|cnCqN4i==@
z&?(;Ci}jo@U9yc~`JV7rCL{Nj&+NucydP1PQ!{3%E<WZ&x9Pgm7)2G&Ob*%u1H=<s
z(XrrO`OdjhUgBlf5^1E^hdL%|-BC}bZj--xTy57<mweWHChK%$q$1OB`DTC6w;BrS
zeB|mhlo)^NSiEYS{=?T}uqxeJwGy;B-<Mxi9<xKm;i=hc)k)*N%orFe$wvE8sIMrf
z+7YA6`+VPnIhGo;+a(&)l2DFK=0`CtW~l@12kPkH`pAA_yngVh4jCj2Q7X?@d(wZi
z2n2<f^NO1WJp#B2II5T<PJZO7OK`47vsS#>+N^y_$eBC>bwp>VHoYFPCPjhJ`@n^g
z@Qe2u^XNblROvO3b5>q#EA--9W=O^ft783Kq=Q*9D$7}B!0t|B-y&b&SNwxIH0a0T
z(a;zJac;;pc*q~-RyjHhRi0|?`vq!|)@Ssmh=f(@fBW3V<FRhnzRomcX%NpG@rcGh
z_%GY?`=wtg4PN3uD?z4j*;v*|x`y9CGbKRbrdXbLsj2fAD$J8H7hO^H*(8Pk%F>N!
zO!TyRwcSeW;uu9sc^G8ieo@F`u6D3}6If<1t6UkZ^jI(<s^R6>+Ep98lHd1;y3han
zS`WV~s3Gg}Un6&UW9lgZA_p^CtDZbTm$v~47+-sKABNLp=G|3h)nqD)^6dTTu6!Zu
z+}Gfhi^N9!Vt&{f;Hj6E5-?W0TQTloj9+6lYW-YT-9+4!iq!DaiMh3YL30)u*d@5x
z?a?{9V9&s6V|kJ~hy5ac4axko!pshf>42(u=DU!0JJEo3R^wOBdyK_caKCaA^*e|Z
za3sHxr1-W!?NC&EcI;T-&;Qq_&aSnwbaU7EURzSzisA9*Kw9arz2PyA9Rq0XYnoyb
zuU2EB$x3X&uKQ#ASx0-}^{(5^V9IIv@hs!4obd1!|E3O&D{byNWt#(dV>(yqQBl_X
z9c=Wr3-KKyYA4;Z;!j_Rh#z0J3@NyP>#E=iaG-JBr`p$_IJX~3<%)t^=xOELf^hsI
zz-#O$zJKY0#r|-wD8#wyHdnH?vD4ngi2(1rLNe(={Wuje)Qq0l=H9Z|^9~6O?LXP{
z{*;C<w6QT&zgA?s)wv2Ve>aKW$lNj~yFPR+`g<Hb5(s4hd-VZBN3#f3Mtncp3FpXE
zTtZJbRNf1uuHpl~r-w7UJK>9rr1)$PPP{AvPhq=m_~1QG++}6{t!vTeROQmoR~549
zzRHIv+hvseExym-)Kn$<K#%_WdG+6z$$S$~{DZ5)5v(-pw}ZB6croi6!v3lJVVIX!
zWyz?)JGTMX-SuH@wulNN%<dnpulhDj^KPiYPCa^y^JHO+_;bxl1{ucI<)y(SivLaV
zb|z(QXWE`Vij-DCefZCX!e-D*t!1lvPKM`Q)lNsp<iM$C-?jd(l7x1g7Zd4T)eY~g
zoFvh&hvc7=3>stB;6@<FX2#L7an>s%SCUcFe$@J%xZYm!QvMwNsG?ZuX-}pD;ZPYS
zs=(nR(Y(|E674xM44~^T*CUWFUyg5UBWPxQ)vLv)-gTtX>_K008>Hs^S?l=e8$s*v
zi?eb*rap5y7i3w@ise!TC;4+(g4#WU_D<HHr&sDjoJW>9z2Xm2bM#`OXvAN<E&{&w
zhrjH=Ux7pZcH^G!JAenulWrH1P&wKGyr^LFH>sf%@A2s|-w5by!MO1wTkd!JV;FcT
z3pMn!@K*z!f(ycnZ+1ba$;vO;)t#KXOPm?*{8agfnlMu33xzrP6pQ6u#pS&^72xho
zKG#thtL<+l2pT8}OMo69S3X@47#v?}n_vIs^9irGG=Ox<Lc#WS(!ox?@YQ4T%b*$q
zgHU?*cyM$0$R9L1_2_drLJM+zE!tQ42=N%1DXEOS?XJV`>dPt37rY+^mspJ1sSLM`
z4Jm>uOsnT+2d&LiXtE+@sp%UV6Fy|BzQ2bQ2fZBK#&eH{@wpVtTnmkfT0VexgdQz7
z2ku5ifr|Kh#KLj!&PhEpxd~l(;Psn%gLj1ufeVI<i9gVX1)Lyb(F-TR;8DNHjCOn5
zyEpzAzeLr46|!H^EnDi6ab`l+P-~$oO&c6m_@S?a(!S(R%tPSUHrmwVbGa>;t=90m
zb!>;E)>G@G-n$tEhMK<G_7#b52)A-{v5;9$#JwlKv@#YDT*CV9V=-fsdhZj5DuX%)
zKYBmD*jBHzPl~M>;_RW9?;qtPpBj&wnD@(p``}K7nGYez<G5AuxM{7A+KAA)ViCKB
z$u&3UsVn^oI;js(89v^4p3U_y@KlXb!SU;k>Ect;9?p^POCPz6%gAj_8q*ZEMvW>-
zePxs%>TwsnR5SjvPZJb`0$f`39OhG2kZH<7Bm4h;eSvX8R5Biat=Z?@7$Y`Uz(b*@
z|NZl|_^Lw|_?S2}e7|oPXq~euCYU^J-4j|X^~Ha3Xeza8+qZ{^J^AjT&dQdhLHqvB
zBFJqVvetA*FFW2CQB8V(ZrN9tVKDzVc=S@ah3_q`9bRThT-OlI5(Fa&o`sYs#($Cy
zOC2HzD(Q67swW;DPVNoE<re>#y`oAHYKj4Hs$UG2n7n65hu{KTfr|9or8{HKCFgl!
zL)_jxC02S!cNVj7k?^x=WG9g$!n{jGr!2Q3T3AjG<jlW&?Fo+Pso{P!(xFgNi6&B6
z3iNf`^zRSWZKz^aaxi13-*i1Q*o*q12y|_h1(2RPo$1eYG#Kj+Iol#sx;h{}O{wGo
z4E%q8@blK1U>fKdn%4^eW)^#U*nSP&h>f3O7wh@=p7a=446PyIHuKpX8!aYt>2r@N
zOT^<_2NysYeU~5Bw-#HIjV$>mhLqvzp+$wS3zvOMy-s_WjwRkWn2p3z>_kbjfipa#
z!CczmA*Q@r#hKmvx0;qsfQ8=K9#?<wo5He<B~>krN!wJ4^9`s4&|_uV=ujEh6?RZB
zH<iHG@u!M=HzY2rJ)vypR3b(($97@p=q>w;$^T+GMV3$w<?mi$BDGmC|F;MDky5W?
zfS#X^UL;0c1=Cg>+zV%r_LAjZDN9{ASLx`^W)}o)o6Ne8fuQ7?uaD1D(K#PHgI`$A
zerPKf6X0L0XgfF8kg!~k^j%8)<ICcWDpg)Gc=;!0Qcy-#HrYHcx8Lua`_%8M7Jo%~
zXwCgp(8fe>WUjG$?!95;A7MjbOkng&O!Vucv^ime$ydv=vK~bIzwHBV+#_#Uu9{kR
zQr|?L?)3R{Z6uxba0U6|CssS~B@li)mi(ERz$%xZwIMe0@$+0LBD1=xhsst~b}VWW
zq193Nm#f`=)G~ms)wXm*XDb8c*@KZZ`-(k68rsY-8Q_Y7Tg(pTROXh`8pAub_axPc
z7;5+gj07<z>v8KYsQ>fzDDRR9`w-;QP2Ee(;`TMac+L>Ie3ek+U`fN&o7V;7r_(Rn
z2(br*+k(1X{n!b}Gh}AM%8k||?cQSOtN62{JJROcP($yos+cY=ia1wnWO!)zuNZW3
ziq%7gU+PCjR@a*VCeFnh)ynoTD`{Hzag3d;>4p`33D78ageKvPs+U8Qs60;7o0Gqw
zDf1;&y9ZBsHNO1r`^PLdlIF9Bj8(B<CneLF0QSHux3wgL7_jr|sRC#1nTrtv2gA-6
z@|P27u~Z8K;uYHK_Ws%KH<5pGKk)z3Hr+4Q0v=$#e)2I@xxBl2o9hYpM;YD8`*xCa
z&0&`;4S^^akrp?kEWF!syfEv>?{rFp@RJI`)2%9l!5=*a$VpOvJ#fC`7R1uI9$m`A
zgVixumr{o{$@J|=QC9Fg5Z49KZw!5pny5{OWj~rq1f7Iqyv?wFh^S_abC!QHhSOcA
z=^9#m$U^p%6{6eIYaBZEZiqX&i#$<|kU#Jvfi|t&aDR`1tn6ta+pyqq$t;CwVA<&p
zTheTZ3v*f5;q{sa6Pv7T^HY%ruV>e1_?SAP_aC{9cfK@i^n<SXq!1@UFe{o|96Gy7
zi=MtEk?nG2gm7luGDOLkQhpr8Dsrgk8A<>Lp#o_a_r5mid0W4Hlxi8OEGhU%?wLuD
z&%$bo0&Q!iF-h&ciBsQT*UWty8Y1*rz2aPVUCY`r0fO+ijXJb@M&{aWM)|y<$>8~c
zg6yfK?C+FGwM1{KhH3m}sS3mC8h%NSJL~gM(#**HBGZQ*;p<+5a(jALf&u!lWkQ*=
zj4w+rpLAdCmsTk(uvqxNi1y?TFb88`_}Mw2mfeoNWc*4~pndn|9xs@Ne=<iI3x7P&
z)|h&>bKzst`Gu_iEG4w!tq@O$*J+ZAL#{{?EbIENBBTj4TORzPDtk&_d1|qu!L<<=
zFJfp*%I?W>RQ0&~9c#m&ij?fBS+Pb}o{ZDW20!s@t`A;sCeyObVbvKg+Y%_l*S=Wd
zbK88(LR6^Pq_PoNjfRC95kSfF7v#)V)!EO%kTI&Y6s8*<)Sh;DyEXl#DS--9sIV(6
z<6OhYNJ=fgh4^;QmN3<ar?-ZfEWO*O@B0a3LKk^$O#9v|D@_p5;g6YXT6WvAbwjF4
zNjjhe*KxZa1OjL`tZpZ_?73~h>OZ5IE}e{$DT>eLP~1w{-BSCTQCQ;6SWimJo>GNo
zcO|h15>H%+W=f6H$P?eeLP?>zDD*L3Vx_RU@*~w((jLaR><L^LiIF?^Z@gT{5XmRu
zvUAL?>Dk1m&BxHlS!oL2V9we9E%c;tMzr2HK~*-Mb_osBsih7=(YhB^H@sW1Cig1(
zQ}O%qpNS*D>rFWlULEm<_Cic32Rb5ZE?t~PpRQA-)ogTJ?fHYlP|m5hCy(*REh8!U
zjD+p=mfr-tlvh8?@v_`H&J^os%)jTn#2NH&X_-*gPtGe{pb?NS@#=4<Wen6#tq5z<
zZc!sPX%Cpv0c{`{<^2Lj>_!d+OZqIS&oWJCAc-5>%i(zV;!&}Tu!NiXO^76+#m-}V
zeAO>Yqo>C%)Sd{dX;I>!P%RtYK9oO%y4H>Kva<0CRjBN$`&B2wFa5(`W=2AO%Z8}*
zMe(z7o?E|qS|ynJM5<Y^G_G%41Yex@1Z8T`%~bMDO2kCCZe%3HO;)1w@563;_1nI$
zr#FsTCzu|}9V6f*f!g`WZ0L^{nB6OOgThfl0eC$agjrE~6+$`=6fRGSX5K5;fe|Cc
zmEUIY3E08ECSJ6BIKdLQVVil!oKjx?stE|*()&gl`>3i`@s=JD^UUCJJRwGh^{^O0
z@aR+nP0$9&^(DoRocPu#{(c*{y@6GKATux7{Egl;<8AcAUmNWELl@xVgwp=1K4G#7
ze$gGpYhmLVq?2g$nTPiZ^^ZrM#~xTk=mzc?om_xcs?DFBp{_Y!wZvbo=6}k7E(%4`
zM?lxg+4H_uqN9^cn^dn}nY5J$$Fx8UJf#@)>BNQGT1H1*R;4h|-3GFkw6&XP@XtW`
zZcw}02^bxLE*bf2EIGNPB2Nn&(`%hpLVd$kbX1^?X95?jmuht@L|iVZeZj)lW^m5q
zO;WDVdaVesfK&Jh75F9Co$e!@7@JaGM<W`fQ=_bG6pt$m)K*j9&x6(g^<Q6?GHXP~
z#tRvuSJyWreb5`6yTk=>=VsYBS3v7THPE9(>7!5YGTMn7O@bEI5DoyB>nP;c0ntd0
zj&&y$qSiau=5KU9XXPaGksRX?OZk&Bm+u2Wwu23S&ZkdVLIDTEWe&s)XlP#Z09+mJ
zPWu!P(et~BrS0Les`z;@dhC=|C@aAK3;2S~zs?Hcy?$gAjHka^Huf)b*g78hnwB(s
zd;WtBI8@a4Bwu_BGNCo^-GrE!kM<`l^-UO_(5H}Py?Y27MYo500Kst+<wuX-VlnEn
zhg6U!MeU;Vt^tSiR#%|t!y_iM7S1x%KpLn<@>GLoXoU*p8@hhg{NGA60Q&c){@b>a
z&BX74oWe$#WM5*yr#?}t00%g{K3huJ(6;H#Qtjr&U?%0g1|<|Z<}U}D!Edc+F`M*g
zXACx44fJKK-8|%%J<Ajv3?N>^^B}4cu7vz0dCbX#Qf<_Xm(oL0Z>*(cu^AZMTkm&Y
zv;|A>1^8U--FxHPjOCnUZOOgPIi`FH{5W@gx7(J~`Yk0bpL7j`&EF(dfCG5-YjSU*
z$}>4c<tej_7$=~8&5xymF<ZF-se~CnUnO;P#}hk$!K7Bn^jE0ViZVU#NX$TN8G9Xu
z4IPS$ukR$2w7!hsO4U{~Q=$YPLj83$JOU5+Ki?{xXhp4<J_8=^X3C=jj|XR$6z!%u
zKbvM6m(9^+U{gD29!-z~4>Y!C>9tKin~wGIGtX8$1463`!cc$*-1@Oai(9F}I%G<d
zv7V^GFjc~aITWd9O=I+)2S&|4-%Jv{uAOesQOwfg_yho{>l6t^map0Om$QdDgV;v*
z;7jxP)YBRW{$#jfr+AceKpa1S%e<d3f}TJs!+;JbHoA#3A*KSI&4<zw^LsK-q`c^l
z1Azy?WwRS+BH7j`D(PAo^nMVaR>2ez1f&d7f*3R$_k09KLee5MI&6Ui_P};<ibS+=
zo|d)Qc4(g${tiE(^9J-f^2osI3z|b@MZO7cZ3=I@E&$a2wBv=?fGwZ1BLBt0USl~Q
zTmb0L{}vAB%v>X~-AX=jXqq?9{}Vj*I5?)f+!6@(XnCR7(|SWbFw^0oJo`9!*kC#w
z1(>kBWz;y`PxKXx*IwiS`YSump8yH8MXa_nJO_ikWqxnFoX_{YN<O|o8ehCl!g-Eq
zqljMq0xh2?!}hR1@c~&mQK2W__j1W^_cyQU6B+-~*}8jOOOSBS>o;78bp3_H8c~47
z7rI5QA-fs!3RmQ)!M;sJ!vLV>F_!<MZQYrAgi;PspN(xc%1`tJ_|<LA7^qjbP9!>^
zF$<V=AehO3u8^~}(w+VZ?-b_OwrBvc+;?Wchs!7-hrx9FV%8SclY#<#m~f7K0L=Mn
z`8(ibsZ@X~?m$1722lHM<egU7Ztr#^bO?Q&c^FLE|5INGD0HHG;gE^illQv_1#p_0
z<^myP;)-&jSK=GdHsY~?Lv_;wY#^cbt%8Wlo7Cp|iY*+<Nt9sH?cHW@%J_!~jVojI
z^vlHgfZ%z9HP9O#1Y}aOL(|UO{65qq!a-~Vm|fdV!x3MM4Xt75!F;g=*OYMi;iw!D
z9FwYt!Sl}`F1!#yKd|2#r^c;bbvcf^HGC}CfJ+52^smm%%ZG3vp^#}plc{J*lBhHq
zEm#zlIZgHpHlQSus5TriJ7|awe2#%K;6qTQlFn}fzVt;YfGJ#f?*#z*or$q5WAs2C
zpsUb;8rgba^5}3xdus<7@XMr7oClC{Gk7C8{E@+!5FIe#kBHWEB|8r3DZ5n6WC6q$
zbq)!hI<s!Q?3dhIT2Lc+Ll|*%wh~^z&c(p>oXwz+>-CrCh1#@kPh;?;Uf1s2bVgwR
z#G=_|j%=7X@a85#pq}n#q_m^>oY|eQ2tijg$$0^(M>=Ya;`S0hg|EKSQUa8EoZ+o^
z@0kds$LPLdc#@N#1CRwjrCXC!?p_rZGT`g!6-#Ktm7+S+Lw`b<##p@NXTTTZ1(vEz
z0y#(D^mf#)m6?XT1Wmd!Y{!P9GP_H2AozTpsHMe<TjwW;a8u$WtrS}%1RV*N{#DKe
zfS8Yk6en+$+|dD!K+Ib^+UY#UtHUJSC;%3h$Zj<fO|p~C2OL5im@TJdTV)oK4yOU2
zssge+^5J~vgB@6^D(pjsk}m`|f)W*g?r}}1pXBG4qXes`xD;D!P{IlFb~Yke0LAyy
zC?lUq+7dylqJRvHc00z6JaG-|IuyWQssv4P?IWr}5GL>|=YXaJS?ZiY44fJJx7#7U
z+*Mhbj&8tLPK?A1C4UGfx@0FQaM;O^WBrrd&mQ<i1S<QD8B%qIqY1WKP=a+qa|w?W
zRAG@aTeU58zzM`0fvFl{Lv?u#cmWp>4m#M$t=itbpx6Qx=p+k0*AVc(_%mfIU}y&h
znN&~!4L5uq)X^%ajNORv0EZPXA$``~@oEg#REC%civfdUCue6cP*`vrw%zz*WXg&A
zFG0Wu7okT+4@LPRiVmOM0b2njW?MX@o-Biw`Ki+W0d7fPfuI@%xf8kq0#u-nGSrLj
z^6ro4(_0Kg1^pXi56|>m);~I{ekEUv{FUAPuLi>l9g?0Xrqh#7NhcA~Bb+x&i2cFP
zvyc{t^9m!%k0sZ~ZPo%tch=o$Dbf0Ew*eEw96P3*>PpkcWZ-O2kfzUuwj5=)``atC
z#1wGdu~k&zQ5z~8eoB4z>CvbBLbj29ZuVHVx`B@SEy!_#Xco_S^s-SW7RP7v@S&~W
zowFw6Ppz$KcAp>yxqy>0Q#Az*Z<I?~W{wS%ow_6wR|t2(qt`KKXk-c{bgq)X^r>$N
zo#=!Q8}s1H!kj>*!DX?(9goEl%ZYBe)?fm>yMy`r_;DrgSPRq9s(GP;zg$}%S`5JA
zwpQH(iR6pk2z_lp^JIWhd+(lMzEZ^E62Y(#!9*C=CgfCe^SQi(u%0MtkkKl&`7cc1
z48Y#OldSzg-Py=+>+x&n$9*xW+`L%&HV|iSBBhn<zBO5h;1~K7tSJx9F+-*3SK1ys
z+I4eusy|w_Oc+cpEN_IMy9ue3rxzF5RPY+CN=1BG4UC)-<sM7Ojr_v!9?ziN)0hwF
z$Dh-~DtV!jQjUS0$GbcIJXiIFMFRt0Ns1fh_g=ip)gQTg;;Ez`oC<nSqD-sf-q3^9
zg-FQ{5-@WB1ry*-8QzOxW0e!?P3awGgJkE4FFZ7}Vkd?3>v&d<eoT6L-WwiV0GoOK
zWS1Vg<_c^w-O)nbrE;|xCfK^ZoG#}%T6@XuKk_^cQ1RyC#e&cKb~Xw=(tKS?FYTXV
z@Vl%pmLy_A<m_ow(adfE>>UppRNWh@+3t=;9jFFj3K7vj`&BVzrqGH~_#AF+okqZ{
zAjCp>!P@}y3_X`>TcS&m><*EI;7Th`nB~3hKvQSv5f@KXBbtjTGA>ndCBTf)ijxst
z%K7yBX{G54Xd?z+VKeky02kjca6Q{Tace(T;4Tfaw>~GytFl>tYfF*(G;b-q$8GTg
zs+?f@*v=CA6+$+rySC7^RE6Pd!=vcFyrnT%6BPG=lNdgq$!5d%x`1YcFZw2_{E5ne
z&L#!eNn;2k!==(5m$#wTGyELuv+1)CYd23>_XN%^7=i7BXWL@q9at^u-CZB7j~#Fz
z_{H(k{(|axLDhqbJJNX+eJm<|n5wsUx`iy7b>2ww_@phl>I33k@?x3TKz7$yUf&Wj
zlfqps9t!Ct7w&Prz9}u#!g;a3X^Z0kA~&~5oh_R~ShG=|8BT;riyD+EroRb3(_<M!
zC~E7Kf(PbsYw|ebs?R<VSmAOyEY;fPnn%RKzpT<Cy=ZEls;YhRKr7WRf7G{AGMeza
zP9mLV53OYEVl|RfIh?dfA3QA!PuHKrk!cep;8b(iH8Ku8cfK@N<ZW;Yn5O%2^vY^@
z_<{FFn3JHI7IM+<TLoW=W3^OneNIQ?AcZfr?ZEc4|9C<&(~ZlZ0*@mU6o&UC51qpW
z{;i1y^Ah!QD(xS`Y%VyCybZfFGmbyTCrH35w~$Ll?8L%ItsmI+zt8r0W%>^$N*JxJ
z-@79##0oJV@sWK=(fT=E80>C!O)LDNELfKZvaW-@C4F@Q^Jn3!TV<ZIbv)9=P&9*R
zJ~pC?PZ}W(I+3pN59Jc>??q!$`yblwe&`)ro}H)XJy>0rxJ=E1ozjI_EO4R$d7!6~
z*^5u&1ulzjDga(<*ej2dnjy<#j!7Eu%nH#Gt8#h%(9={5IK@QVBhfNZD<659EPr->
z3`jR8wD_@mu!2YXlL<G)&OsXqLLk(MM)UO_JrFnnby~d4W$&sTp!PuK2<u<KR2Xo7
z5DWv)el9|%wo;cpKV2}K9%%6w{_^<@HkUgG1|SV$CUkN2Ul%_(UiSZA-Y2QzHNgRg
ziT|r8<U<WBb@rF)Srnx8V)$%f1!g~a{I<FeF&1!53k9!=OW|g*{NTmIy1U?jTUp!r
zQT{=4DKaG7Zr51rTFe8DBXMj#1nHTU3&V`>K%|Ot`m4?<Z(?yAq^*<@WXKWCa)qFi
z0-mk6)#E67J95(9NQH&K$;1Bd`=AP@;^-p{10%N4a~wnf_qZ_U=x-md6Q?r!@kVl!
z>5KXv*+6S>l%N%r6fW+!>fp-D_Zs`o@bjf_pP)l^g(TzCKLRY9$?TeXMVe}yRP1IN
zu8-9M`DTxODu}%&TXvjvPNt^@-WQx1mw~F$@8=aaJEGB3=q|o_?Q6o=ZitnyUy)bl
zOPp`{!E9Hqtqo~=NBz}XDO#+q-qWXroiA+1n+(06Z0z!;YhTG6XC3{SQ#&M_Efv%m
z{Pa9!w)x@F`)Vt%S3ul^;2|tTRWj;X<F}LVOxGl!ZyQ&Cs5@pqLC{3H_)XR6-12Ll
z31Mu+{Ca;8{hJYqDDBcVw&_+^v8Prix&iH<XKf)hELPC{x>Jt6jJ}e>@Lg1El&+cx
zucqfq@7jy2yf|{#;p*fF)g^tZ&K6Txk9<i9FGvret2wNgiVKFyxkSfrlH$Zo#jp^U
z9<g2{2lU&i1rcRJYR+b_%6XMD4}EmD!GLmux&;pL`VQd_4;S*<4;JAnRQ4-184^=*
zTfhID7u45Olwjsz|Kb((ZvJFR7;b*)I}wSm`4i<wJs%xrH|>-9*EHJ-tPQ5TqW5c%
zOUtK3uO0~+xS5J_4gX&1uo)4>GXH$~`GLksq=*Ri;?*7z+&!BtcQ<bBkL&?C!gpmN
z5jN^|H)TUSc7yT^33@Hb)4ElzTvk@5*FphjSTUU)K8c~%UXe8ceW~&Ce@XkR2Bna+
z&u_0d#KZQVFG(;!cMCRB6lrJM3n(adt|wPPgy^oY@TR26>7`GG8r{p99tF3(nj<r<
zaAms0P<jMObPqC?DGCM~G<sRmw;)>r^u*rrF38@+?^~*g8!lcm(aD8LNPu+U@-i>5
z`Oj5_g_DwY8)aCN8?T&bDva9@3kxeA+zpQv&GK%4aGvS5HtZgO=qx6$RQ&w$;6ra9
zvqVMS<%Qpz6$h{Xn4B$z*(orby9_t%kif-lK<E>*Lzu}4(RPU8pjwfJ=DZigc{D)t
zcR`TS#Li^Dp^gGTE&D#~eB2vKu2!W)&aL|?{esE%Cqnv;tUWNhnW@MP@vEpK97^fj
z=Q=ZzQW)1l-b}tj4lfzWhoU^egS&IYyy`Pj5@;7$v#wwD-{la~H}vo@TYF=Pq-{Jj
zV)*Q>QUs5y=rR<m;Ek@NBglQEHQ6jk3f&E!hx6I?9n2ww)ShPvvxpzG`bBs_b0>m`
zhv)!w3`YdK&UnSdlRZ6PQ@;D+l-D|5(8u#3@PR}RvT_7%UC&wYdRKIN_8!3*AE;{3
zYR$=dFi){C+mOU(A$rblz^UJ4n*9V3)hZ`SbM*rPm1_AcVfC+SOh*QRWD&-emd3(+
zN^p77*r0uXRTS8&K23@wsKk`3EE5bexQQeveVR5cPSN7LwL$3$&Z9t{aU7t&)?dTs
z(?u5gG#KIIAKn0mGeq)`J$oF16|;r}mXHR6dIYObz}wzc^7<h#u*?04fq$mjM$53}
zBT*EoKj4w_3>}<x8NrAD9o^3!62o(iiGWZLnmpJXPY=R*8S;MzWtX)74CtTo?}$g{
zb8cq^9wR;0yQyno@76{C?|glLg~)tI13X?bhb-(kE|Q)#&a%a*{#gVYyG%33L10S~
z0HOO&^2AU4m2Rij)axtC$VHgIG2PjImT6m;1N+q~<L~z_fQc@p$->zn>D+$J;=s<|
z&tT})6wBE~Sd?1iffCE!cGhD@Bmh!trWRh9`K4jX-h?BD!&91R$`^PUcD4pFiiOG*
zO^q~mg=M|nx26%#>0f@_|HVlRI!0J;K45g_NoJ$g?$0xB%m=#K=3&dvWt%EqeR^`O
zqgw;1au~ORLp5dvlCc{IZnsbMTI-^SLHk{)j071&?4id%Rwg#WaEZQYx=w{cVZe65
zOZ)5R@Fsquh4gS9T?_qNw^jkz4{*ea1RfB{2GHkc%UW8LSuwt9qF(3u1!B#9NJX+Z
zFOc70mS3w~Fz;h>$V;VY5LJp=8<y8@qwp_(+Y4r!rLA*XO25+{r=oK{sNqD*G=GUs
zKv@rX9VD>)fk{9?G!6F#Q-#5D`I#GsGz}~N3dcI-`77A6UF-&5{gU2?W^r(4b=aX}
zm2`lMlK`~g;B6a>RkXA*Iaay`qMqLy0MU(S<f{)dAuG3g2~tfVrU<l?Cxjw6h&PXd
zA_IZzJ)6i-Rj;)?(q1*+9TZ40FsEmN^}ntLzO@It`i}g&N$a@Y$@{_sZ&3}073e8A
za=k`ahqKa4;MW2lbnA?k+!FEQyBzX9E)Q$E)*%Ki&o5v)4FoI3I*osk*;`=C4fY^5
z2pA>~NqnGrZHW#Ea0$8qNIhN*^ICR$0IY!d9=~scIT^?RzsNz#qj4dD`#Bq<1S7#H
zL%U2NLG_;h|Br4ZC*T5l_Xp!$VmY2w(E^7cWJWNa&=Z8`?qfg<WL2mif{dCy`2%1~
zL;5CUV{0r*6`Mi9;3-&Jr5U8+JE0fQajOg2dhGbu5(KYKj?CwJ5@dfuhw~*n<AWBO
zTv0w9;kAsGvSXuSUTmst-O=5FOrcu2(uhy%YM>h9jRVe#enUOVWltY$bgl#FvIq)2
zFEGlxe7Q5dFK+u>w=miKoc1d-l(%L403<--C@b6hUvKLh%dW1@3k{NzkjrejA#;<1
zzyQR&DBywq(~Z4lY)^7OBT5}tJy^dnLo+4GBBEi-&H+@zm=sowV~?n+Dobkey*W{t
zNx>OX(vg7fZlyj2E?IFLwP(fzp|&<WZCR4hJNky(z1ol<N)D}{h^7v7jc1LRbheIP
z3tp*B=(L&4r5>-A{a^crOaeszDiE(#!t+2VT3=m#XNI=^ra*Yut-gB>-*zxG*J_ec
zYJOl<o^<YYu+VJR*m|dgL?Tg?hBPzAfU(oqa2(|XFg7!IlJ`FwV#z42I2lnS!speV
z6|u2z=a;^6$A^3G&Cf4NeMFX$h3tycIRX;VGKLnJibWA2LeR58jfrFsg>2SXNb@<I
zrQ>lyl%om@7-o+{Uw^_$;U2VZ52k0Of|Fy*!vQhkh$g<qwWVgSG9l=Vz;kT`3lz5?
z?7Jr<UM8AvVQ6<^|7Tv+JX9m-lc6RLxI>cg;2JA-4**l+2rmgP(fb!Yc}v%CQ?c&q
zL}<_0HLiu&EEhDM!?UT`o{PUm7I=ye*RaY?6aa#ZBbv1Qs`LWXCI)vo$!%{*{;SSi
z6gli&UC0fRoUO1CV7%ssz&mi`m=Rr)!QoH`Ip9|ja*bHK7GOGK)(-$qtHq`OU3V)K
zAe9#iKBmA)|1&I}w52?{fd=S}!e-v{FN1{3=hnR5(pSw2;yH#lnfU$P2Xx?NqGUtP
zW5%YSg8(*S(({HMID9=hGPB;$e-?1B1%iL$!RrWEpJM`ldA*$I<2{#KOCA7hu8<0Q
zCvY$2MpS#hPOFiaT69$bkyvpMqp8z>?`|;7w8cI5!)k^|9*wHDxIF|?#i0C7dYZtm
zyu{3s^Ska(Ht*`agIM37n*$Lmvs`nvRu`ys#8^#-ro>g>b)FTOKf>71PuKttU&^RQ
zZSNla*c(1E`u-eSGCbY8<C~C=d&6M?@Z}g5XP#DqQu|@lq`ImUH*G%_0AucP4=v&Z
zYoG3~tj}I|>yhu(NJfUlOH!c$s-a#+o0ma=%fbbe_rWO>^x*5@k_`&TO}|h^%cBT7
zh6y=>ULLPg$)rU&+?-j#I}boBqlGHmw1_?dLItFR%O4{mv-hn36_IDiqo{HJD}l^a
zRLTHdbKZ<c#=#=Y&w$0v%S!}Of|qMB?GyprRboU2wGj-AD@DqJiJ1G=zwZb}Xh*?(
ztqD<h#p5l2`o%Z**bsoF!At_)_`47TRhcE=eR)j)kIs6&<?|GX%`C=sM?wD2jyt)e
z2Ym$<<GWG(2;f5wBkmTWfWf)w7;11w=s8b~gpGu2DK^xB6Y&861xOp=Cwq(qAfWr2
z7$yLQKEB|-hHp$cu>Q(u7d5fv!9*4M+wn4kBP#H%?U!Fs^mS;Pl2%a`L*W*0<@J~D
z!=Ng2^B)GhbGB%4ByqL^)f+RQyM$k|U^zoKBg26tB2q;>sKFZ2{w949SLfQ2z|Q1=
z67X6XGUn(%wSFoCXxT8;JP3X(?f)+Oc32_j|1tH}0aZOu-0-21P6g={k&=||?(Pr}
zu5<`UgER=zNOPs7;R4bqt;7Y9xO7WgI;Gyj_xHZf^Zt8wc6WAncF)f2%x4_X{<I8A
z16pd33<}yeE*4l)`c21N02?dp*UiG<e&;8ukgoy1u7QHjaIQ%z&0;~Vj9+GiK<p!=
zb;obna{NhKaqj>6Bfb?c$Jevv%f|wW!_Te%ZKdoj5~3BLY91RubkF@YxFy}Uo*byU
z;FrXBAX)uaYM6=D<&Oe)n4851em+9Jq0YC}usFb!iZfpaS<--T+h0~>kJx-_41Ii^
zC4?d+4DF~Kg(M4K{w56Z_RFwD4e^li_wAs|=bP>{y}*L~rpFILtF!=ZHz&vTDZW0`
z0P?r(Gl1<G>^^1TB!0N((=-a4OH}v~U{JjZ&l7HQ=Ka*qjanezub;mM5GpJ{Y3lq%
z2Ay2SPzwZHJh;Ih8A8_IZfnG3^PMd($z7d~keG6xHub>RCo*QKmRAfyw8^-h>;?|#
zt2%CJgjSlZ@>Li2BCV#Tj^ajTbK=v1k~OPs_u^Z$^Q3*r33vdJsEp`@nFIQSUHk?E
zZ~=x?IRM=lGS_Xn2&IYr6JfNF@Lv#XAmox5A(<0_s{_9rc3=*%{T16m3LJtt&5etL
z888S-+a1xAs`)nJFo9?~=xtn&w+RkTP3AOaqLwTT70`_$b1l>tHr!e?veJYibxg)D
z0ED20pVNu&+p`qQj4GfiWtkPwpaHL>AouDUj)oK2Qw=33QXcI(-@$iQBE<BouYq_)
zH%F<{41n^uT7?bxMv9HxP0QGMt6ca5?DdgbIT%R#4Xi%5viPCs=sE>NwAsSM!3UO6
zq2fJ6F|D^~tW7BI4YX?=0N@EW^1iV?$2djLp*_GBSe}gB!2}LN;d%1=r@AH+eF14t
zm;l-|)vpwwE2fOULEMT9{d0kBqB)?w{H-kjJi|s(8MW+OkZr4{{{jufVwAhR;Cl-Z
zLI{R<>qWjDNDp>J>`(#ukJNOgrfHfPqg*X5MG#mO%9wtF2E3PsEa~iT-=s6n{sT?U
zQaOVBJU|Fl_<3%xcU#5aCGrr((ix?9!|Vn;7sVK02TE59iv`T({19^&s>ApWf20Z{
zULjAr&qS}ZXAy{TNRLANoh;^KSvkbPSE*t0c=G<9<~ts3<gwQCZ3j!s)W*oM#oZ&{
zCF1QanguY$u16^w%vB32@Kk8VZar(R^+r?fpJTc*%@bnc7F9BY5NF__z)u37;5z-V
zXr=u17F6)NEE%Ht`>Z1Ye*KzspFbXP(%jZ~t^2Nl`e`lct2YisT#R4f`%$Sv=b<!A
zx@nE61j@04RVsKM_|2o<T}&LStzZ`JI98sDT?~0SGQ{nw*xP<d%trITpwE%fgPi3a
zs9i65IJi>nK+o<MKefXv-5xt@EJg#HbM>R8(a~(MYc}`EnU^y!FVDxn*W1l8dYokO
z?+DQ#R9XEpb@77jL;s*U@D2DrB?kOY=@}Vukas3y0httgF3ACBnD10{<Xm*(=6j0O
z2A-QyBLoH%KE*e{xrU!8qLHVsLeob*$c-2&!2m!XP7owOrtMzPyEN)!K~tg4iKa7%
z8h~CptZr5!IM(z9-$>VnH3Wa{b*ociiYrf!nJI(t8J<*6e8I`bY>F&fk9u3wAc>RI
z={vV*QjJ9=V67$NyPW6@Gl*>vaV}YZ_?2?$3-@`sFN}aVsf%pO7A2!hf2c^hRj@K(
z6i={LcaK6o(m92BAT)L*%6rksZPq{l<^IHsdHfADZ;R?(vBu*T+;MtX#%##LwA>dD
zTDVIpELk<KH=cGG5MOy7WSVuYqPU<ntW#J1)e(Z>PBAEQ;!=A2mRYAVrz)n0&HZsA
z<k_708`%`)Nx~IHOkqbXAxanSomGbl;vqn9YmBQd(st+r>xvH){k+<uZ(>fdqueQ9
z{6vG%)a~U&G|euIkD@#8tqc2e!N%w!VaLWf7vV$L0X?U{yDpEX^NgS<bW%-4W#`YQ
ztej6@x!=Z<BrWOq2uG@7oKfA}=tHn5&8-Eg5y)pj;^U)yVNxLI1H@GO0jxvj+T|!}
zPWj(QsG@}`7K{A4U*&TRgkj00pVoJ^TXxu!@fC9tglE#fkK8Na-mrm(Y|J>w>({sX
z?vf%hO9n1}Ud9y(j2;-CQ5z!FLgP)NtO%+t**V#PCz7z-JotWx7qg@M_DXN;Lw00n
z;HS^!3kqTiOG+FP9si6nT;c{JI*<A=tF63z#Ls6QIZvb3J`Hc$it=t;e#V<$(90h}
z)+36Dux_Y$t+zV7s#oHpI5(c~ALG>xm;`x*X_RiBmIPQZHAY5BN1iJRLlfJ23=S%%
z?om%F3VKgg?-pY;UfaS*nJ@)(axPTd@zI*+o!KwP3yD~sxNm+gc7v2={@!(c*d!OB
zo_o0)=ToC6jp=B_NxHPc2^F*2y#Eq76hlaaj}n3)W68sevb-&F$M`&I3ZCB`X-U8H
zy*9}6eD%y)4;P9|wGSu<aWS+ZOLX3j&V0>%-2*W{Z<@GJ$&wP7j)|sM)EcVz(Yk$=
zJ+|=aP#P7BqVQDZllx!5;*<HkZ_h|Rnf()nQDI*VlZoJfcQj{miO>g`JD_x2b`zns
zp%-ph8s>2HHC?}-dit7pS}(iY6#G>SHql~36Dc3|OpFMZUCn1~VY`=NE8b^!oDUm7
zT;Ft*|ALMgoTagjQm^R2;BtY%mp2`F4VN|%<@I5FZU5W7{>U@XFWmXLGaF9#p+(b&
z57?ncA<?K|kFeQ4pmIXKM^lSv)#=s#yBizFwVKo@<zuf>zU;zUPL~vQtic<d@RjX9
z{dd;mlcLLPHLAydgg!YZ!1C3C`<TC~$2}~L8N7#y%DnP6-0u^h<U8IrDeDnnw$cda
zKVqK5o9{xB{bL{vI@Tq|0Iy-WOz=m4CM0D0VJ|d~i&pi4oRONLL@Y<3mS1M>CBX=C
z$ICpUZu!Sy{m{O1cU=}E_=^5e^!CSAp@*w-%1guB*7syKb|h|D=}I81LuEYNyDUt4
zGB73SQ3ro}yQsbRqh&IIq%2gxh#HE7#Ek6CE{@c*+PuIm|H1a-M$pO!BPks$q=AYP
zqUO44U+kx`+I(lx<b8@zV*{GjAHQV>lOGTxPKv}K%;N6qtgTnMJKB3Gs8-LSo_x6?
z2fF`ZBZW^{c-iNt%z5iP5W7N{&gbDMP0O4yK{a49Bq_*Pjo0t4v>@u`Dc-BTopE8(
z`H<pFOP1dT03b>a+p<`upJp#rX;njCYuiU?rjXBvkiQ8#&Wy+ftiaq=tUoI5syY@1
z9=RAk|07BH?i=qTBPD%i@Vyq8!GOjuy+6qA%MCCr`FJ}g^J03Xi{`yg)RW3HG(eOA
zc4Qwjx?GgQ-jatQ^lZLcRD^3tMIQ7o2!r=8Rk_+04lSa7$8Q(<o#+B0mh6FF<35;X
zJTH{k6Oy5F#Oc0I#8<g31bq{*q462ULJjs~A510BzB$g(C6I>%Kht_ldCd3-*o%Po
z?^Zg8KfPV)WYGOykxlo<Zau^_rF+kJ_lrC@K^EA~?dk>Ql;l1Ezp~UI3%9Q&vjltx
z@Q2k#S^B?%&ErAiDUbS_n$N{bTh%buI{Q}H1{#&EFm0G5I(5*1b6Lo}^}&8N4(=LH
zLjKZY6$n8gLhLk0Sx=?Dmr<veUZZ&Nes|_yAvK?9LxX7ceDlGUzM1FfS)0qpn`RB=
zrW8^BjogzjNcITdd78^DM6@j)5aNf8xa3gnJ8b>sOohpB+7jhrJGj7nn9)f5Y<CtY
zVE!N|F*j~<zgg9X%<$P<DNn0nsSct5?#!s3pNL`ulXCF>-lBjQr74ldG?kmU#ZhCC
zT9s?E%Z}o2gIu~2KmpqaL00Y!Duq^q(Y$XpmZUzMD~0-~GZ8tfbYK*tJTdGjB7HL*
zcOmNiFIc0B2cb{I#z;P9NaY~vr)sp2R&2y)%$k_%Dt+q%_ko|})Bghhrh7>Ze{J+k
z`kRCTXg!vu(tUHso+uIL*DQ7ny&CCg)*TbVrvA)HtSbgy*$Q|*2$tx&Ro!3m&b;#Z
ziM4grzr0b`^_U+(a-PX~=#>Fd&gjl+EroLdfdhX!AwRLk492wIRHBPe?Z>safXHA9
z@JQa|v5Vq16=BvU-)`L(rr1wXFbh)B$_CHAf*epwQ|<VQ=I)ua$a}UMBr9W8&tQdP
z%5V}tQG5gxNPG|^OFzb#&>t{LGPQnl@qI(Rd%)CBOV3S{xj%asC_wul2utLM#5LbN
zMQ}6K;a*6y(j2;LVWx$fFw*Q30#dl>&VN~E2I(^PEtr}W6|%NEAWcLB0tR?iUop%X
zM?*ljI#g+@RvbZ@i{k2y1w;n%{p<Esgs;eJCL-PP8ItW`FuF^e2nOv!u5Z>F(0t>z
zE(v6U&kZR=dcJFUDR7yC4`eYwY)Xm@t~EugRoVrnHe3$O7nubqAN5ZEq>D8Q7{vtQ
ziC`}abvor*cm8tj{ff4KxhOF>nV$dL(`L0(^r4AXu!&89<FhmKUv0}|R1NyxguL6m
z)Y&YbqvcZShygf(G*vCRsz!q9?B9N)Ow+6-LFzvUH@+5UAfHK}_-!r-yV7xEeV~ys
z5&eM&FP{~4pTnR<o{Z%S1;b#8v~jAs)+0ttv;BJ!-oiQ+c#)c11mA(~JW6n<@R^s8
zG}Zgj!JE6^xhx6e<xMB_uk}>l`^rgrePGlOTmeG7u@T)%@;{6-xI5?7ybV<alXWHI
z;eqOZ`yC@}7~g<!eUPXE{C1<!u0ZO`2?#<^?0BYsC00Do7?J<Xb8YEO`|9%vRaAh6
z7#4c}()7&L2emYQb3e0rmwt5ReVLKRn+jr#_U;_@UP17%6Jdm))sUAja9pjPA1&}{
zUQ~A%p3U_N%BEg64CX1eQ(^#cQfaEcg%~!Gyf%BB+guswzqZxwm*3loY#B&h#AK0k
zv7m%BflSX`N<67r*Q64^ZlS5DQ}>Kac6l^8SU)(F46+;tvMg}Xd#d(q*=gXX^!B!=
zWq)8l9*bc!XZb48qnBV7H4!EV+5v>~W@vDJljdhi-kZ>o{_R!r4U%1K&6tn%zX^d;
z1vsWev~SeJo@X{+>O#bRq1M-4ed?-~CmpMg4)rmBEU*bW*Cy{Zm7*yjn+kD5cPB!^
z1t+d?mG7*)`XI{D2skOBt9fiN_N$Y{C>Kvts|CnA%e^rq8(y<<X>{Qi06>6kpgA_B
zcWBIu_|vuay?p8@N=O1zjnv(c>m3C+03#G>Dhiy7c$e$T6~hBQWb)|nv;9@Prm|MU
z_?SvN@c6_5XQMVI{B~|&b0>LS!KbZv<_To!m!ZiX7o}MB7hZWC+M~@Yf1sNYB;+yu
ze80yoPE(k=Y+fvS!YLvo{gnz}N*Rw*O+3p1|8wZlRPgXQNQT*EEYz=+I(2}$V!Kdl
zyt$WpW58WSuROPD&{z)z;0P<ghYZe`TkEy1Wt>&j6^O0A)a<S)RSDm}5a9fh>Z&8=
zZ?=d9ykLN!WqEE5M}%%tZ_L<^srr1(*uQwmuA>xN<cI@Uv7kG5%S?IvmfV-Qm`Z`?
zcJ97w#+mQm?rM+jxio(cgftStP~v!He0F{Q5~v)UCvSMJ(HD2Aj6)?;60Nc8PN_i{
zU>|$cw{qGP5f{P)(ew%&Ti6L~nDL|pRwQU;^wwlpYNxmPO!zqIRSNtefrzsO-N3f8
zox6)D5VpCj^EZt;i_<R4_YYio!)o^T0b8#|v(L)vFS&%KQI-oq<jxt_HD><{7crJ=
z4WA{m_EL`|52u+UAABr=IqE{PN|PDorVjjIPoNvbx((TiH!@tVT6^_O-u(UhB;F<c
zJZ|^Rj8s{n1%y|uBPlAt1EwV@kWg)jxvLoet@SMcbMcPl4J?4>88UqL6;mmK<U-IQ
zgTRMhOZl0|&YwcghB>g#!<*$<$z#V87UFB=pFe3XEkk{>xQ}QYZ&@Zvv*>H4i$hUG
zn7vMNUEk7da=HRMxL_yJ)9xhKx#w5OwE~tNe|}yie|Hh5)FZFPaaI*6_RqP<BLG0t
z8k}T`=5l@OD1P?pPj$dj&sFls&j97hp%az{M8*C$PMCbtX)y?7Dg!}-B%iO;1&?Kz
z2?caAm#j4^ws6C+;{Hj2k#V58j6W@~)3*8`<|?@Zb0mYzOHLmw-Z0_D!v<kC!I^nX
zHLTuSt?)@^Wn|!lr5s{+v)6dSGW&5}W%t`x_t%hLbs9MTqr9-n=f2uL>k8~-);3D7
zcSq_AxZoiLIzWG48_<|Xz54sp>6PaR%feK^(7|0(AB(TbXicjpBX8l!*Zz2Dpw%rV
zV1*|`6@nIcjL(@LZ~vwI&(Ns-rm9DnPKy4|eBu~~uCMMk5Uh8~0MHMz{%XP7tF!3E
zJ*0E}*t@f+EH#$9O#(*frSiYx&qaZktv-PRp2o9yu($^lY%EYLK7Zx4<PCgBvdDq1
zpP2!A1iZqA;&%sxs#qO>c)<Uop{SXk4bmLFrIQ8ocUzHxENxegYS{KoAmHa`7j%Mx
zuu|EjBD+^)CSZfz4`|0djZegclb*=5^>{+}<;C(2!Hn{T#k@KhGyD50G*B9?o%2T)
zUJShC+EgGl1^5VJ^)1B60>?QYR^flm*uja1^evw73imd`P~128&Mn9MRU`v6ya&&=
zU6e3?dG&E^Oco~;n@gRo@5_7BMBqEwZ#_0{EtR*dYUg*Bid?Z~D#jJheYUQKyLs$`
z8-j!Fw{qm?)j+dF-jS=UzeEz-%?Lb?o;zeP<8h7bb#mnk5XbU=<?i+nL*7Tde3Vnw
z5_FnAc27bV)H2aVHGJxKnMGyOMk4Iq%R#o{`rb0f_FY7*uio*AhQ;;8^lKC>0()nw
zfWKh|Ro~MVtsDw+1gkH><X|>jAwiQIR54vjKs{>g8y{@$uNBG6(;hwACg*eIvF`?i
zziCZ!P~l3VGa|g|T<XUvI6Om*Z<6bn_s&>D*nCaw2X@NfF3GMTd|i?Oq=<vTa8U2;
z?E}@;RZ5>o?~132P?I+#5J1<_d0EygM!ZhU%|!!E5f(-Caw|l<M;nqN5NC_ds_Yn$
zzJ7fNU&Kcrit8ROgEvvg4(zByxRW&>G+?g(*rRAs6n!*|Re3n(4-*OroS4Bh*n=8Q
zH(2IIoO5m3E0eOmO$(vn{Y{4;xPUwxT(*K34lz9_-Eq%h^1XaH;pcIdw2=WZS3E;R
zwQmX2l&jI?6`|(RTh)#{NN%g3%)2FQB&IPf9+J7G2AmJ~mtf`hktuMldtTTQ{n(4Q
z%Y(G_N>96U3gHA^VTg8LRCA^^LP8{VCM6!y*~1K6!&Pe>XW}EgXVBZz4CsPXjNw!$
znm?N&FQyszXl`c-@nM}B`oS-ZyFBO%$q4fMLc@XvRv><&3!-z{&r=~{cHtUJ;pal<
z{ut=WTqvsNaQoyGsq631T+~D!2vphxIB$c}P*Ng{Xcr4RWP=RBdfz^Sb1t@)2VX?X
z`aTCIK=mh&6Q{KD%G@Q5Ha*vpP~R1II|rShzC<?<+K?&Hw!ghbdd<AxJJN=lQwHr0
zzNFOu|F<GBkTwSw%p>elyrv2v3E@CO<+d)^>8KH}Bk?CJQL|vVGrKcxr?g014<~vU
zpRL9J8TLni8Bv;TLT9^US?I{xOMov$=ozaeYDCk~A-p<)YF&j^Dk#DhG79D^wta<-
zL_7X11;NVvEh!rt{VntV9Rn+V@W;R(L1AH)DxaG);;*OikGJ5R8OmAFV=Qh}Fww~B
zs|gB)2=9PQ&eK^Iq5mVigqLoa{1*d`VbEN^Ij<jFSkrBvI)b@j2mHLSx*;QKabpjK
z2S)9{>2@!_x|y$-UdqUN@tvXu155VWy5ZMq;>zwPM%V@3lzT<(Fxi9H{kJO&XXC^w
zxY2nxdfuGZ<xMsHG?lRY9L!}}W7<WIs3@P@$qyI&`kb`6M7Lc0LD(ulVOu|Xp`K6I
z@#;?OYEn|Gb2kO5_JWk%C&N=h^1dE~x*GL`h&Jy~MMXz{iv-6_Ti!c-o{>8;Svkf#
zv!<D0I@`A<)#Q~<aG~#3OlhQar7^Si9WEGH3Id2uYeC`$ZFDZ(C<QLMBH5o|`v3S|
z<rCU0YUlNuQ^1$PCd0ix*|?I`=R~(wJ}_$U$O?iL0Gjxs9oay>x^}4@=Zzey*RA(r
zHXNXP{nDqi%|*U`b!mJvZ`PZ*G)DfWJi>c=a^)v<Xax~+=){zh>lFa!;y74MT`sTj
z32N1MoA^lmOd3nR6mEMv@034%!zxQD$eES(t;R6JB$EC5P1Te1EgAo>8^2`af@*y%
zP2b9azHt{HCm1NqREVWfw8`}JVj1OYL-5h560f@wc)le=(QU}$!wx*Z_I!6cuA9EU
zrGgWD5DfYBW$NicltcU(LjASloG?uphm$Yfbh-X^b8TI<YI=pn1+GM(xdC0yw*vEY
zVeki`4qk=cTRK$LNtij}VPyYFmZuz9birh@GhRB`3YYY_{D#cBZf>M@n8>RwpcyP#
zF9T7tv8AcHXUZG<7bK^j{u(cDpRmmFG5DoyzCb~1MCXw%Oc-tGo4WBPYEr;hdajZ6
z7cHptkbvABO+7XB9~yM4Ix;C2*knh6e&!n4NB(pAHI+BMS7==Z2VJ9ksb)7cCOaoY
z`wvt!G~%^oximUbg=S-BJwOz3{)htn<tjTrS4Zk<fxNM}XX)ir)~!u89;elWzTbus
z8OPQEqlW7->z+6ucs6)RYu9K$4XDzNUlDdaKZd#nM!zjhH<*t4XeyS~G5&Tip<K3l
zX;-236lRSI6bPU@1NT;l%vtuLz_Z?nNv8QK0SRx%_#GnUww0G;pZxxPntGFc@ZPnF
zpA$YnE|x5!@mm-L_)HJh<1Q2Md*b8O)7UT2I8}D%y2<g)I-UF9RMh<K`*yF9M;~p4
z>z;G_2zXEt0U?j1;h2Z^X=b~&WmA{8JA(n!RRRQdN65|Bz4`D9u5F$gwq03442#}C
z13inzvqe#E07wZbz%K|sY^B7*UzlRM4%~KPTJwk-zcMkWvp(d1%uYSr`NK}H)X?Hf
zS35|ar3lC5x!`}gO7~5(AJRtXJL;0_{*tX=I7)69?Mg%sM4RG6b>(d!LL`b1;7rn?
zNbQ0baYJ%$eY&v7Mzj=t9bvWIK60<aR?C8Vcy1S~Wmkg^FM}6zG7Gqvpq74EJgh(2
zvkQ%%MiQS)Ugb1febYEQr5c((;52xZC?Y6>3(lxMy7Mw_v}jw(G*+pBGP%TpFSc)1
z^XRR%rur<_x#f$K=?Dcbh0Z(d>o;A;J7=wIfjR)3^@AW!IclBR?)v3u&W>bzdRmX8
ze>E>}dy8trv$jT>lz*uff>dIr&c|m_*%ynZX`y+mnWms{fI6XCy02l0_P6rc^q#O6
zHEFnSR;`QFr_XJ43@w|!DlRGnuh(V@+XoNcla(nLq**F8k;Z}Yj4Ms`8NDkmKH+W-
z9=KiUvl5^o#+2{)9<KvKF=}X2l*=%8p8MqOx{F3}0+XgZ*i|^FXiR<FFnz~ONtuJM
zoXIrQ@i+C2g4lq0Y+ex^!6eGwf#EKpP+XO(d#L6hxtcOfzs=($P_qSPGX7C-x=7))
zNLK(uIW^U;yqDm*@YT5i&#I7#KbMkF&HE;VZ>Z*IJ>(k1o|$+IX2YZh)fJ-XjlH<G
zPCuC?A-}3UeBn9omcAvYR+kGA`~mzk;QFmpZ=Vb<XQoS5n(HB>&KoycfFBf@0PC*R
zMs4o$AHSLq>d++Ne1B!$Yr8QEi_+7b-;Wx;ubF=_D%C$Z-)BsAtLk%QzOg-!$e6(H
z;lbn9G2n^*A%~5Lg2Iq07aOfYPOL5WHG1>r6I0vsJyyP_U-$k7(sbXgTpt~dngS3S
zP)DQoYB!*hb5%`=*Nh)p{i9H~MNlp(_hbBZ^`_O2BhJLfNrQp1=Sr?1AP<BBq>s)&
z61i|~Pk$UWXj(&h{X<Sd<e<9V-=IO`4}<lMgour4pXmu%ds?u?uV{=pFe(Mz*{U%)
z`^e;9%X?0>K`k#^uJg*TG&6Kl9SkCJi-Lip9QH_;eh@Q-Kc)b`^2c=lzK{>kmmHyt
zd1~MIZA4)Wj780%&}fXRYG-4%d0eSsd8Vj%Ind3b@?ONQM@IpGGlHTW>%BfoVB>Qu
zC1ls7*62x_&CQ^;qWj0-aYs<w1buSrm@n-aS?yB(Zg-Z3$Ii+C7BB)HqrZG~;_W9?
z?4vjWoE~8)L{;ZL*VXTu61Jq;dUxF_&oR*Q@??}{o;;V?#Ew$rKdCcH*R1YoZ4=f5
zjU3kFuS*YGjI#cj{<G+Y#5=!WN(hr0Q|#_{Etb)HR-V|1BBcs$QESSaTWzV2IbW$t
zk(+v=hS|l(Ig@igN2U<p$Xve8m(qEYq8`@ZYsP}$MF<%<=Hrz@y$*utE$#Bn%5PP$
z&h;8<{V<-!30?8S;tGc!=l*@iOs2I*Us9eLQvongu7{y|duiDh<*NE;exi@XpP&&2
zZpH9~_0F=jWyIb#Vv0&4ScG273`rVi&nf{%;^1!WsOnz{%ZX}Elj6{?>FX{#xM3b<
zu6q%DXds*##6hQ0`iP28$xp*8xh!f64bTOx3l-L7i+@CVAVz6X9br!WFRu}wifW!v
z7>%s;x|ld(;2yDaGJgKmUo$(Xhm->VwNTJ@A}Fgbu?W!@h@RJDq|pq>OMhEhNN+MV
zU@2zg=!HFFR1ohW|Ex~Jt&j#_1y4VV80b#f#=fqdewY%+pU!VPmSd#Bv1d-#@61S>
zuREaiRVt5vkfhEJ99J}+UZP<EDWDxl_3RIefE86+(DlvS)#TG#liwcvcaY0>+?L$D
zwUHYR>(d)g#6BK?&?t|HV4-i6T>ANDLoa5EbQc6%OC3Q)&3CVj8e`mX(ZEF*Q=H&Q
z9ac?WX2Q^ja(@peh`cKa%5P}Vu^0X1*Kn^`H~tBZnN#K08q*0fgMJkv0-i6wgvaY2
zVr*aqG3|ihG{3<04oHoQBmZqVO?4o;v8XHa*|hM0xMTBZxoDDk;slA^jgyPNJ`j}Y
zj17c50a=<A@K#)kH~({&SQ}tb610gh9`r+YKKt7-Z$RR`%oitDKzw}!z1c6MRB%OE
z_67NIpvz4#(dYo?_74b-&l-Bu>5bpv&yhLXcWL)TZti`oJU;v`T$ltLQiCh~vZthq
z!z6*=UGV1w)9)Xg`C4j8lYD5k%U@@+>-?C~<q7w8{&st;T5Nue<?pj-u0YoBels!2
zFXJFwu4PTB&BRa`p30*x&@>Ut@^t$;m_|U{X4|_J`u*Yr^TuoJYUIp+9}SR{gJTA9
z(i~qWglRgR5L(&q-1ejzkyU+mHddZ7l4W>aGTr!w)e~jeAN;?R!mjyzc?`dM?<43e
zr}hQvUM*^Il1lm{)z)<~7GC@lvTn{G0YYlP$vG&J75BAT@*35zCBJg2`?Oum9(2X_
zI`ig-ub3__SfRul)h=boR`(}p-;jl4+O@9{oV)YvWi3JWPogHQq5NkiJ5A3r*7Y4<
zm`<YCZi;E&HoV*`ltBgf(4D2I-)o?aUo<c%+Ba5Q-h8rnv>?M>C%LfsO#PtA{CP`u
z<#bQKTh0zSi9LMm1v6kp53$K+iKF9dHazIuvDzh1lAn8Fp}LSVB;Mltc~`%z!5XcD
zJ$JVObG!shAcBpUkr`vx3{>&?_cN;D_L$A*LhSkDQTJELqJ|BsPR|Q;NWUvoDEUP&
zyAPBy1K85=enEHs$-O=v*O!#mn?gdfL41sl53IV<ed)Y4>}aA|7A1zzOc_?2cT<uE
zz$+Ta4leb3g_^Ye{ZpLlUWvrpYl}L(?bZ3(!eSn_KqVZj3TEz88H3JI8Qllsu;O2h
zX3vgXntr;!*EXags3Y@tkb16G{Y&%1_}*;-(3EULHhKSxvX+ko0GSt47VtmWoVVkJ
zb(y{9Mg}=+a^mB>Tx-2izClv<B+U4k?Dt)YeU|ii41^TK<F7zY(Z-kGjgFHysi64q
zF{aD!W?;t56vYO<`1HPxkmuF>)RFSX#;2%&6ue)=lV1Y8$oNOXMpWiM9ii`gWN0d{
zN$O5$u2T6?0vr#jEaq`qF&^3#I931AWVWz5n%*{YW8A_u8^74h8NBXES{IrhbBgnC
z_=h8C5+;JxNu0;drVF(H2<OzfZp^lt`)i%=v#v`^h7vTy=a#9)6(IlyEyMet9!5H3
zew%H~3U^k@v*?f4I>751x+2`RM(g@z`gI3Cx$~j5Bdz~Q+Dr9F!nMq{dP9y`>8pR=
ztoUe=4HlE^#3;*zfqxSS;VB}nAby`T6=6aNCbPePy<3Sxyj@(DQESP4)mbl_&9l^9
z-fN+dF7GlSC5}wke@*igv3Jb7nUKDnOH1}A=l)y#bNXl6QN^E2mN!nzOUOZPHCFpl
zMlf5E^NnSeNb|4xH?*666~<_VEgSFulm&OC?tHqep)yJxG!6==-TV4qo0hM#m|9Yr
z)w%16L%zo7yS;bdY#Rrb;w@VRP?q`peGsXF4}l7SDo>4Jm?9`L(t^(!$s0o;3#(J?
zE@k?Y=lAys9`x+&MvXW#zSV^62{{2C?ISrk7ZJB6SXz|ijEvS0UGDNg#%xlT;7_jB
zFE9+B8y?9%s?2q%`Ya<?W5d3M)tP@%Zq3LTDR`#fwsAAKD;;--oRr8(rhRl58=rQo
z<^5?(^9M-#%ZDG&Hsk7^gP2%0+afKi;Z;zMysEb?#L->auP-ZRva9fUaP4W*00Yrk
zbYl^kUwFB?Tra$2`gQB*MfDbkU;D|En1WL^x0gC&h1>q(yggTNxsEqn_L(4{44$)}
zZv^2J#Un8jvBlMQno-?k2G)(tWL<4dH!)E*q-XM9VT6ALb+e<Rq3Ya4A|gJ^(s7C-
z#vDq7lyoWwxwkVzCQcF0E{M-`9bVw>A#UWE!J+?_oUq%gQ(3l4y^8v9Iyrg@&!USh
zb>Tji5F7{Cdi5N*3AMni_u<hH`j@csh7XSZ0k#)*m});#7wZYORz*^8!}u?3NbwV8
zoSqC?42EED2CIrX2S#tG%Y}tP)d}N`PrIlZg1MAF6_eEQ5vGnY^>JqrjfcGa-6M6J
z(*A*GRpDZn)eCtiv=FV7ooKS6{lxkcsWuikT9)$oxTD>daah^owWsrYe;14B@b6Ui
zKWu^Bu{+Nb_VygF{xAxrhb_>^%#{HzTk3T4`DlF@A_a*TdcaAw0uhvs|9tK<v+U=1
zN@Z&A%XZZ|5=9$>Ij=|4y<K^O)>E(Jp?KCBjB8LU3o)HYHqlTt@fdusoqWyzi)_2q
zAPuT6m&*g&ep3~cd15!78vmqFe;aMr&C`|w|8A&Il>o}%M=IEf($mB+re~xeE5c@N
z&_Btpe;epl^ifLK<|Ag45Q-iNqt3A7mmt;}@&zg15fYZqy^T6+eGz6y8$$B2j?hPd
zKSol5<CV!1YX{PCb8y@*MGO1Z%-QaaKQELj5(GZW<W1dc%~g6t+(8-jhQ8%;cr*_k
zRV0xxHuED@mHBT2TKp6RGW<6HAEU6jXOsOjyud=hlKZVh)bj>qwIxJhuB_u33)n?f
z6IU;s^-CKC;*{jaZ-1tIC^{?O9Nj;%Z!0>SONb=#clGRYr&Fo#tgQ9(UgN$ZGuGpD
zYMsu73x42wUs({lCaw$POIx9Q=maZJIh@lS%=_&e;QO&ja^th~a>^k8v0ZcHWW;+r
zHe#=Ccu8<Q>B6-L-p0xF5go%eVb2@7zYy$?!y|5rg5y-hxu|zAvCQrM7Jf5E$Hs`J
zlaJP9d8yGx${bC7?z=tukb$BFMRb@cRD5CH@{6PS)M`(n+$5!JcHWqevDg&!@4(48
z%{D1hk_174^abM4T@{nxZ<&-3QD)6m{@Rl(Z^%G4l!lZgY^DRgAD}22Q#Ox-Ogt&!
zay)u3$+!~<RUfmWxjVPZ{awBV=7P8UD7XHe0{_~+IFV?4&IgL-dCcG7yZ6ecNj6tz
z?s4&{6omV21FpKnE|~DAL2WMEWy%^tI?e=&M>*c~AN0@N%}3i`Pvci&drgzfMe|7*
z|7mhehWf17FN_Pi?n4acz+8_%wfPdN2%rl=J4(av3YpDiuJ=vwoEiNnz)@MTiv)?I
zY>L?`-Rm_IWF_TRIrG)W-0@|I+`N^g9ea1~cN30-Y=6!uiMW7hmSo9){FEH3atXSn
zPYENog%!2Oe2G(BpbPGt`{S%%_Q6GFi|8Aap2`Fo+LMzdCNvfg3K{&H%*h%5-E*h`
zjupr61yNrxQi;TpsI6-CV(1StRB}>~V8mi5lj{?2S;bYh{>k`mOP`u7s^ij~UJYGv
z=JaAn2lv(hyre5To<RQwf*p|CWtJGkek0}=75UorBj2BGLVus>RJn)5+?^#dk5s)W
zUs@)kOXFkdXeW}%;R*b-;8(r9TVk@xc2J<mJPN_OxEFm*xJ;SgT{GKp;dl6cK`ylo
z24#6xJQNB0H_7qBSzr<uQMWkoh524dXWYpoOAP}9an!u<sqLcwqRJIcu&Ik#NuEMM
zi{kb~n(E^7t*%DZMzPLs1;>WddGL1R<7hS>GT_|6?mXaZ<Vo&jd(j%@nB4h9;I$pg
zHd|+zlY)+IkH}`5;**Y}<cMg415BjzwI&Z~GBS|!w&|o$+A*#75IZCY8$nT2o2a}o
zsoIp*n-f3tYoT6*)sVyq7bRS(ol)OL5cXqn^CQNlwI)(Bj@_8I$v)K9={Ke-?Lbxj
zcSn?ND|F{mp_CbyvKJXY=MjS}Q-&<+xt5;#XfkWOrAohPcPu|O2{(apO0XWtrfTsD
z#>ONaHCySTsQ3j-+NaDXC_joV(z{+0dLIi>w>ADpXUD4jw(Lb~5OKq}qutZwl4^~-
zry;e<HOiF!WnfScVd)eq@WK0Y?~{bE^u4K1UvQb8ZvTpDFFOC10YkwqHs5~n49uAU
z`P4GolX{>v*z{xMPioOra~2AA&6m<A7-%3b1NwW@`l2&-M~Db_;)cw={LUhe$Z9A0
zJqKa^a`3+M;oL}NQH=vjw43_X;i`?{@#<hk`;(dl>{~!#S6^-;=^_rbdyp75FuE7;
z2iH5K#j9w%kf&o)`ELBtv_25}sRFU$1p%OnaM~9Ua)Jjl1UyGRs=7MsvzVlrz@6}Q
z<(oV=6l~EC=a*%{N>|A@TcsN9S2HJeUpTKMNfYKxv6esjS#aigpkNDsIR6aU-!Eg5
zbl=u+G!Q3EgL&=z9Pt9U%A57QwgNB!Sw={d`pvD1MRK#(UoEI2^wM1CU6%_0AFFs?
zMG1scO3!&k#1}{V)jVx|w{|(=r}4AucLu&yZC0r!CcvNo_cnRmI<3ACl<u~T;^Y)$
ze4MA*cA5{o`m4|?2<BD%(9|~ZT_SE=vw1VwXDd%HNZtKOh=1C;u+BN*f1OMFtvPbP
zaTOY2@x<aBx?h1;z5+7dr1`wv!3Xwb;XyYOF~vDWkd0#D;;Z3|FH!v7C=@!c6`!Mp
z)G|WaO4H+&ezv98JGZhwPvZe75ScZyXd#J=5Ydv(rR3E6^WOPnu|d_AG`=1Xw#B(T
zh90Nz@a{P_;|g3?{*VX`@IIF2Ig(ORd)-S>fM>#{FnJM7(6aV3ZrQe8w&sArZb}Wg
zh;K!qii4Mp5={<8DsLA(e|tSKy*=Q_-4r{~u^)@lort+Z0rbYe&(|*e7$dGX#635+
zPYMp9ZvdWl6K!*}5H@0jhFVvRQkDZ<me5kR&dQ&5l<umWG9x}9niz_daU?228b@8*
zf$>s^O^2sI^Yl8qH;}{#8#r+D)X+KkxiA=Qd=;Nsoiq#F4ZKZkK3M<`3B%79>Mxm;
zBC7a2?OO~P8c=MP8@TWQMmfl*Km9?9I)O_z!|7XtPC#ZE%gqEP5sQrfM}qqzo#Ww^
zF{g$pvP^vX7x)$ez!$K>$c|s)SBV2Y$xeE(9ea`SIuxmQ*vOZ@E%fD`wzMO`Ea2y_
zP*t>$zeEU|qY9=7%KT}PgAUbH?;$6EQbhNME{0S9Hqvat>4Z%%$6{G)|3$mkx3@&y
za&zOIC{j+?$abVxh0g2W8<3{iodlSh1CUrX?R1Go#2_PTNwuK2(`-0gJW)A$AuL2-
zRiE}@6CYrdg=~E_Op~}h6)FFg&CZ__ga&+eLvwx#$VI|O$93eto)4E7dY12dlnRB2
zE@yj#g9oc@{t6XyJu!O0zEMdMqFw&{Ti_i^H@=KNiFldn+^2b$lUEMxPHA|6!W#_`
z+GbS-vbDP6q;)**I%q-u$5;TM$fb;gp~ywSN53DNNSpYy3SA>kTTr>BfWkk&Nzp>S
z5g`hvzbawHZBBv72HgbW*|2*K1A1e#{@CFYJE1k{%v+h56f&g3;y-X|Q{g&G|DL}O
zOeGp5-xJ33U);iZd_MN;_2;WLZFehxXfhs*wtx{Kpz7W_E8c&iXU8?)E}sSvgs_+I
zhM`F5U?aQt6@QinMKw6rYe@(J*e*~<w2&eqgme{GQQcPDCLz^=aGnE-7K7-*Q-C=F
zPVg79s4vJjV6ovK9OQzf#iFZ@4<yS#q8eJ0F{3u857n-GK#4p@Q2s}4m;ct&A>nL5
z#NJmBd=qwj4J4Auh$h|{Je?e_^U}3xiU*=M6+nC_u*=tCWIL^SqfM?%jSCjxSX%PQ
z|1n74sTAMn>z(B&$`3?8n~p(|Qo%+7ex}>G2eAvP+<1WX)3??@B8f~}z-~#sji)pB
zloLvH)iOVGNG}nhSafvqR9w)SZ@U6T%1piPFSe8kHnN_xxaow7hi|(OManiqga=T1
z1U0W-$-IYt^xpuXh^(o05db|)xOce*;^+Nm*ZiVjOklk6?AL!EGyfUx_yyN)zgv)O
zK`UdGp^|dIMv~A7^6z|h-6amDmU@>v>IoUc2Xv$%8TJ!;D*+49XQOB7am2tE$Mygm
zq=_SlKZPUH#-ATt=GMh@-n_HlMJ$D};BZBO$o*Am_g^7LN94{6PnWMZ@hDOOJ`U!P
z%or?Z;1L7tzQHF-R9#M*B65pLCrk;yt;ku6gTnC<gFG4oIvx>AWJ&b%GWr-0^zIzT
zJ2)pIccgPnzdbWYSU2g;Fuo_>+9-n-A8_DF^H9cvp}@eAC+TjiaK9Y(h{ppQ+zT-<
z5r9bqyt-d;W9#ukU8^G|(D*qvR4}B80rsNqd7os{`;sGY8Pr`UKnDyenKqm-Gf9rt
zI5R^`z$;Tl7|j`|l7SeW2%o*Z5u!Awp#V~MM$+^8U){HyGILo{0&;Q?Lo3zkqb1t_
z9q-qG*527lx{$U#LI{YCglk-A8ebABI@wh6p@qm$F<BY~T{+E1<-WV<bR!DZ#gy75
zLJS3|E^)TEB;W%$=9S+L;`zk$nGTG}x#-&V4?uZffa#uD{{8-giY=QGAe;Sma?uhS
z4uTgg&ejQ8{Y5D-$42TgnO|_UlOvx3WILC6u^0r-4WZYjXdz12NVx{JE{=AT>_p(F
z(?9JJYpCp8Nl>#ZK!gp={aZkszZs}I02F9V8|pK9L+0l1p91JIkkVPF-np&JdT?5j
zJS`0}m!Tc|7nndO5kmRg=bht`IK&0e`Wt^xu678|`{wCKBE=l;o!q0~Br{rs`Edbd
z(s8;*pY=UtuH2Oh=#_?)e&)9(o!C^r3?Y&liR}E!J1%?uDflH29R}BU>D0@*&TIp|
z2Tj5~(_&~5O!rOz5f(IenfZ51pHD?K%0u3v4QwQv#NWZER*Jf402n3slNcdgiTwL>
zb<SW)=pmMDS<>I<B2|to%KgT-ES>)GE1nO_rm-kY?AD!DS1z`1pLV{MzrSB1uKt{@
z{=776);Vts<B7jr%gUyxeNfH`fP=JpRmc~yoW02<2l}QnG7>`J1d){0^yt(j^o*$2
z@H}^Oj|s0|uy=o38?hqJl>{r`kCX1BWadng?eM=(zY)f}-e<H%E=76M(wla_F!_q=
zN(*L+QWjHlY%2yy3v?l-j!|o^eQfzsa_hmk&QWi`=VEkFHJHkJO~SCObs-czXo>Bd
zq6AO4sP@%@Y~f&l1UHgoh$fR9X>4X=&^cWml6~n{p(h{&r71U{N!~oQb@EHj8;Xm6
z@~gd2utU75UTLDrz{d8ANqmD&m4=@x71gu4r3mjP3#`^NKM~813C<`=f5wtTjIUl{
z9QdMi4=f(riGNq?s0KN-(uK^$dg3wg^y5ZH<Iy70UYljc-SUW#_$R!`SIW|PDy|wC
zG<V3sY=m(fnIB>o{B9jRZz8aeX<)sKVVfBxDoOSH+oz5#U*OkzKz1GseAz}-hdRIL
z5f1NWk(8CQJ%le}S@f%RM@zy+sNFhY7-U0?a2in{{qJwgWKD0OQ1^NQvS;nwhca!L
zVj@Au*hcM=uuLOnN-XN+(kF`RM}*K0x)HcsA!7VLZcTT1BYDaW0Y=8KsB@GO>SITT
z$xE5GN2!Az1o#>yM+xhwCG72uxnqihj|$||n-g6M+$IT;1hwiC>cw0KgvbSILFEBl
zBuT9S)5#rt>SI%M_y?J}393#hY^ORrV&XXX9X7v7MjD+ajc@$B%=Mk#@EFwJ)w_>F
zxAQedB(TzzTK~RnphJiP7DASk$9M2iRHekXulR)l`TB=7higMj5g@|EGI*bfo{8{w
zCNvcExq&WpKfX?PE0oKROWJ*BVLisd6X4&zr0QC%#eWgyP13FFK}+EHcPN<-ltU5`
z9A{DCEZ=acRQc#=BbtNelM+#kb@op9qD)xeT=!k;vr$tjsm{s84g%!Hb0GHss$P0>
zMHzzhmzkrRBzW=nN=C3IPZE^~bTLsfVuK-z{xae*?#VnX$LKo|;`#sry+CO0nMlU0
zS@jLN5yM+X;;3ei_pXDw+^`YaL|Lc@pUA{E1up>}AsHfout2#}_Id|RHZutF?18^`
zAb(F*W}ef28Nc0Sv?fKE$xgiY?brcvT57cfoeNaAUT&Jv?lT3h|2o<INP8yJhW^nU
zz`cbhpz7BrD|pw!z3JAxK4fBMtq)t=C0jt~k_&r4Cyhsf5ly+x!%XN3Kin%4qS3n-
z;1*b$J51eqM-hVdq3;MNOjrj{$o3N;l32<1J-|+m@>*adZuhAd#4wK`FlY++iWbrI
zk|V_o<@|l0UOU`kQW#`60LS^o2|soiOM<LiFDuNq9>eNx7%B-<ZvYC~dT4zpq}IZN
z>X<`r-ahruM5~GX<ncNGR3<xRmOms+5DUlIzCngH_U~KDdNKdK2=5?c(5ED{6yB7)
z&ikWasD_BR4#&A(@Pz++K6O?4Y=%*MX1)bNc)cby!zW=-6nbk)K(kE$$x-)8tdq(w
z{rBJKX!LBExt|i;#F%ZB;1&siH@)l(T8qA@$3|Mh^BLrs|B@jLvdk1I<SwZh8Sh`g
zL`SH|AN)*Yt_k}xWhj`xVLr%d(`mD;s0c0S$fE>#4y&4c^^wTP29=SKfdB~tkqio*
z?-#J_(Bmy1UPo3&wk1cgJ@4cEDTl|zfgNkjFiZZMr|jTf`DZp(*z2Lq`=2M&Tl}S&
zOWi!O#xnlTGg<OV^EUV&;(z&|3v+%6Up^Gx5P_#~K_-0PIo(nF`<ozK@Q{Y9Kzw}i
zQp&7EFVMg+V180W$L-I!vSss5{D)FC$i=x>mm&uDvCTtVuz?JihxUe={AM`iA%J)!
z+}pJ#_QpwKhz<o#fhar*_6a!nJ0uBXf`v*GBYt7*`RW}m67EU)LzciK@)RV{j*r||
z&)Gk3asW?d{p}lDW8j#W@(^yDDy44Pbw==GTmO&E1VODRxJAG5XyJ$7U?A=P*bw|F
zr2YL>Gb+d=j0CaZyt!Lu#g~r(`f5U0VE+Dzl;nDu&v?*fsFn%A@v3xw#A))Nei&~q
ze1DJU;>(9KvHx#GO@r6k(rIWY|53++#o9sfIGetYn_m~+x!{4rq<VxLwG)oVdF)Hn
zykg7$X_xC_$8`+s>Kh|WbZ&61Y}x%6c}CRlah01tzvnyOy1k>AHd}Y$pBY;KFjXNZ
zAWB`J3S(>!=t~ruE&p`++c%g+_aJKDp@N1oJGqK?rVGK;{k;#&-q84EQ;~bg!g~pB
zTl@Ljjunh0Vb52fB^Q1-pwk|~%vSp@9-c=n3>zCv!;`uGg!?TLj1glVefZZ!%3RYq
z*2OSGJW0Zw253nT1gHtRcj^#L$rqUC5KhnHP|KM%0-{kJNF4J+U|DY={BD}|5KwmW
zic{U;8Y(}yn_G;u=acLZR(7F#wb3Wrtl;7M_^*lh<;7|1i^nY;1eQ^O=?kwp<8kFH
z;%q8>?CyS}Mvk@SKWotmiSE_2g&dqP+^u^PyBC1R=6h|(eZ*EJ<D+UP_bVxn>>}K6
zW+F=hw@I`woHLX*IqOaOskg?SG~n{b9<~@Tcvho0MHD;tTP1uy4Z!TN_kzIa8DEl9
zBC6;t4^17fmoCx%Z?mQIQY0L8F^RfsD_l8`Oyc9<)ow$hsjs%IypylemsWFC-E(r0
zb+1JIi4g=`nOmV@QY_fW*f50@m)^?O+cZriuN-s954Z-mQHzf#M4)tUw%pFYqa~_;
zO|iwaf`V(UwfSL6agbC;p}I;R9us0N?qs1^H4Rm%LTLghCwh<A#S|;kdtMRT<#>(+
zLY?CKLgCe!^apMd*6HVvxLz{~ME<MBlXUyV-62-Z+bS@jHFDizpF;cbgR>jh?#q-!
z_}wVGOi5_`=pmedw^j884LQQl5Oz=m2K27nKtqjjoRfP0MtPsF2~iQYgf#|m*$dpj
zg9a$)gEyCIPx>!Rz+>wRoZ#bI2pwMP5jtK*8Wg$^GEf&pidZ;xp+}>xOYfJ0xSETR
z{kP^ylCY%QS>cfr@><RF<*0M?s}f#(8_tbi-6tI;RR;eX?-4HYaki`hO#QSqhqm)e
zpc?6bX&6*|eKKjRQov0Z*7K!PL>0=JjCTv?GBnQm<|yMI6+j&B-Q+2xd8bpiu(7e>
z_20qfe<LH>=NxV}wFA`a2k#Dw0BvO9O*S&3m*^b_2NiiCTgpTT(-mQaXz0epCN&a|
zQAX6mAmTSs(#%ejcU*G_X{QWB#@H4P7**fLG<k3=DCSSqnb4H*5Zs4mLy7pEBUkpi
z&bqL3=;o=2*eMrXvTCjBmCS5!-5*TE<1Ot1FT7L8>Ls7%&bOV9uqh&PL{dZEVJ86%
zaZF7Rgxj4(z{Z`=zq^Bxu-;DoTBCZ}V%_mQqN1}J+!}IF8j4?Vmxgdvi4-yB7L4H}
zU(X>H+lr#J^VQGuM$C?HKkv%M1yM^9T41)6<Q2+_I!xi}WGl+8R?s0{u~g}D?bT(D
zxOgswdw-SNlt}m5c?j6OHU!zm==}a>%#pc@@UU{i6PB2dMr&ktBr~^@rexqf;<@SI
zna`!w<^1CYM`)?<4S5MyC*D;A*B84g=fvmHN@@|%_+j_Qh8;PcME>CKH3m#1A54w#
zY!8#x=$EE%%Pub*>#d7_*RRlRChw^mO(dWyw>lQ?JpsBiGFy-#?%g3iR|M0fIb^)8
ze~lTF`5)X?WufA&b)e&TwV7ywst^T&C89|Q6|5kK7jgc#pBHACK!g}Ec`DR1{BUx7
zdN?_>AfH?~WZS+g5`l4>anWY^GXC$tv3YIMg97=OxJBZBHIoX17!>}yhfkNn?{L>B
z5f`6BJYD6P!8^6&Y+FWN$rm=fo~5Yw9M7sINPfzfYdxh(v}Qw)(CcB#XR%r!3Ougv
zyLy_!W9YXN2zG=I_)H4LgRoxsqjVENX}EHs=3c5`j3iq33^vkj8xKjzjq`vKM?v*p
zp?KerC-}Pq!s%f>#eA@fKzaZh&)JmlS8JIfndB)sm{2s)7SwoT+qO7Sy1Q_Y8`(%j
z%V@6uTX5YQh$7_%8LN8J^|%<Z^><Hi!i$e8Ux-`Z^>MzC4`JIMbMa;KH~FB`qKqgf
zG;pN}pnnBx=on;CSG=;ACU3;4w#TtBoa#ffKjAw^6Q6rK3cAHhK}uO+REy*QdX%?i
zZhOZ0{FHa<(%WxEvukZ|Z}mDt0iHm?<Z&(E6EIQ&{HvBfLS6*11K9Y;n@ZUBpVnRX
z`Lhhtu0)r8nQNz4hWuJV9;&kbOL7o2015^VcOE;UNI@V^m^T98r$J(e$5GchYh1A1
z2}Q%xY?=%~!PYZ!3=oBdnG%}24!1Z<1?c%<9R(iiQ$Eebe+*Ue2iWcQqt0~#nT4|Q
z@%22NF+iXo<|hxt)h!_66#;VCch82VrFC&|Y`6(0h^nK74zW>X+S<2+4RZS&$9Wb+
z!h$CV?lsD9>#hd`N}I54XiRt9l>Q+Gbv|q)S)})R0ziL>?q64!glI3?jU#`4p2i9S
zfLYZOAz}!SC0pnK12oX`g$MWAnz6bVi0x*}03!vF;ka>uJ1&_1TpTyzjk?7D`Ti@Q
z2%nxFcJBkmPYlUGo{7M=+Mhq@lU8Qo-rrIqy+<(q(?8c3K}q0;>AU?;YTjf2KUBSS
zK-57KH-3bqfCvakw=~k-od$xm0>S~(4N^*X2nZ6Mlz{Zn-AW!IaimI!aCGOp=kq-8
z@BRIAe|+z|yR)<Not>T8`HW+jmIHMC@+o9DrqzE_hdH`S;F1@+Cut&l##kTzr7Yqx
z{E3EfzFqHBnje&`wD0Cdj#VGg!{yH>T)7_L#O_eep7HU-@|81~m^3N!dw~d?=GX`=
zlEEB`QjPHNV~0UBI2mMD8Oo79g)mqI*Eqntm%v|8APSd5n*F0I5D&HTga(OAcvuFx
zmbyU%I;Soz{m6m#^k#y@)!;KlC@wwH$X)Li<c1xNE|m;~g2>U)*bI`$3U{W6E*L|!
znpv35nItW4t?PY`QIs<DxnO~BE*RG{x4!WdG<8<DH>DO$UkO3r+!0WeIz-4;0XRr(
z*g{P|O`X>Rcc&j)VaPSz=>-+o)$SZ}L>ykfu&g`!Bb8=xhvc=9H$(qi0`o9{qQaww
z(fL97=lq_5f^taiTJPz}N~??jIvK6M#wN412Z-xRxv5K1<T-hqQnLp;XMKUG!nj}a
zvp4K!o)P<?s4d<-bO#R8P6Ut^P_&pR58CbZ%F9#OdgZGK$k;c(-z<xddfmoL$sNR4
z{6xQ;)2_ySVI*_d1|?qu(4a6?p|}i4i`QD{;j4)9k|G3C2GWWpIX1SsevlSt2W?wY
za3}xOZHxf8NOP^fliF;6^kT;%h-Klq_Z*D?Y!*^Y^nIGsF$b)y-%G<-h6II*tNLvR
zEj{e$MS|;(^>X+qHj}hTUp^ARdKXe;XGGn$kuJKSWNqt6T0nNV*XZ$*C`uac{j2JF
z6d~(&uk`6wX;zR3zGHx_ncV`H{wkW!e2P+Fx3+WwC`U9%LzWtEEFGnb4YeA@!w5jB
zZB@xT<0`N5%<$ubZxr6nP*LEr`-X?wRWe~Ke~Fv;rz^97BlE*hjlOru`H$MKZ-8~J
zs0uA&gmY)@;&yPuDFO1_i#G7R;i3K_B>zd34HG=ov_#3onP)UjG}s7cHuV`yeuz?0
ziU?NrOzIxwa(Gqyt4e)(BP-+sz*68+^q-qgf-LjnWQ5-{E&V7#4jCd*l%jx@$y^KV
zW@r1)MRA2=0*C%PRlGkhLKQk0+1VcmCVjdi9sHY0z2`C6j?c}>ZWoqimxn*bwodyF
z+j+eiZwisydi%V^kiU5#mLoHK6AyJ|?nePx_s{V@A9HN>6@wTnL#IiR4>d{X4k0PD
z>pzV>W;I64mbf5>v50b3xR_j_7=}w*hPN-9<ZC<b1k6tW@d%(4y%XL^x+CyCJ8Q>L
z)$pk3z=sKf1U=nFKFl`v@V<46uR9{Eh%)b^fm==h03Tw8m*S%e^z39H>{KgR5qYaW
z92VSyUR`O7{LX+tDPd+h(0)_?1%hDw^QVn@$MN&C7-is_!AH%A781SRa;Bx#GmFjs
z_`8^}>e|g;1cFq98j>R)3KtSx%GGSRgT!U%G&zz`FU}6@24sB!Z!7Go+mfFSNB${T
zN@zMBDwp^v5-&I$h!KL16xlAV*_r}@qCX<^t8;O}a+DAqpc`OjtZ=;mjf4vfL@1CO
zB%9*JB+9kvS{8C2z39!#(3_WzAJ&#|<N+r4zrSN~i)|aR{`YV0k;qXR)+eC<|8<=H
z?!7r~$19}f66Pm%#pV%2eBS$TZ>+W&G*FewV@SlbAT15z1NFC;T$+TmR636`3BvEk
zRDNy2!`Jb<S$`1yXGeu{J&SPqM5gCw|Ezz-t%ZzLW(XH4QbkGXc*?!)cJN4x`c7qt
zaj4f1M0|bo>>NRG`OriY&!>AZGf}uzby9Z?<j=PwT3mTH#bzgH|FY7W8f9%rJZ(`e
zT%K34*#)Y$g|c3H%!i0ukj4+z4E5ST#IN#1)MI|aYL&)AKR_YkJAV%;O{fJETjR)c
zDmME-)ueORMpPj1*FtI-kD?KL49JH@!_sf4AZ5mD!VpbTq@t9iYi6D;2BOf_2^X|6
zfd+ZH^SbxW^M2B)cfSWIz;lFM4I9Ws?1*7~;!XGE|JUfE`3g7h%?T&Oc>jSR)+cfp
zFFfdZ+mLA^5Fww;-2Xl?er>y0`v?=->nkaY6%EF^pnR8iMBAE)E&to55ij^jXdU8u
zxDzShuLhQ}((e8Y2{{Wq?3h7!M5xFa2Lq9RA#pp{82#2@T-?xaDGbCX*Bl(c4HM{1
zw`Gom+<_qtbYzqJW)=Gm=)K)k_WPEFIttWOHqn|?pzZ&8rU*djZp?Wapf+zKhH5l8
zxjrhq*DOpj*trStph?=y*<SNq?3^EZ!)FtS6+5W1NC8sc9bo}WT+QECTWpg$e6-gJ
zA(JXlLX^KF1N%DNO*PhUfh7K0pZEX`CoX~+JV8Q9gjBP|Fc$d$K<KvJ-#U$&^J+-!
z3VZXdPlDhrx#dGk=8gLbn6WEdTK|3YCE!iQ*EiC<kZ!-^8q7!3fPWVPGSfQa=f;q5
zx8wuNM>Ob1LPtLAMmr!4;N9GyF~*lT#69Fyri{6ECPO=6$EU?Lo&P+;5^r)$?k!lq
z_2Q0{-J76WlWKt8kS!iF_Qp0>YfP+}QBRC7@u2ZLWLu_2+Vs#A&iT~9oBuv@SfcBr
zxckYuM&~x(EiP8T|3{+4Qz={9a$FFSjTO)|0W|HF^!3XTp7uszA|^EJ2>wl$e(~^x
zl1yU)IgXSc)j9ZHius5EkgZmI=HSV0XY9S52sttOW{&YC5p<AeA7l(owsBjWQ(q1e
z9iO2O=E8S`$>7w;ndl@tt-E8K_N&N}2j53Oq(?Q*MX`<&j}UZNh^D*iL+sIHF&uB#
zirERocAs7u_At{EXh}JQKfgnc(4mbia+J(=cjBVY8v(`Tc#0ZH*@Q{4Hjn(P!t!^M
z&3zj#q;ItCHcXA5<W=`HCPCugJcU9MfuRVy;&1#&LZ@*?C`GG_@oCf%+7Dq&0=TFb
z7IVn64=F3s(jG+ezZ{{t42=+0asb~v8tlD3$gA-KHD!jC2vvC2j05?u3?)Mfxt%Gf
zC26nX_Rm7_y*>drEJade=<${`61A;0@FQsPMKrd0VQZEs5#(+(f*o$_aAp)_*?eB^
z@&m#;BI*ytQWpTwt2vE7Mseer>zkUhh;dm53*EA;ZZS5Z32KLyPb#W(9mKxI!XKZ~
z!3ypHIMb+`!n5ME$N2|8)m}jEzR>y6VdFGi`!H}SPzwYu3ss^0YM{d87BW4SS=TY{
z;oW7p!?r`W&4}|QJmpDTxNVZ&tLfKTxHyo9caVDkW+boCt*z|aTt(R{EEm58!~WV2
z<t3*lCq6GFU;UO=o8Agd_p?lT;o@loqIHF8|9YpqxjO%ym2bbdK{g7>QXTc!EAIv7
z0X__M$pl}B*RQmy)90W4o7es5s<htYBateGb^;<k0g<Cr(Ct>m#pzvGJ=libQS2^J
zBqf?egUv>Bu=W6Mh;>5(qnnYp%X{Pasc_&{?N<F;ui-ihYHMG^Ej|K9Vnr!gMSoe|
z`ttTC);9~a-OsRG>`$Ga)o2)qk#k<yn6Ed@IcyzD+&nQk^XhF)4!U~sWIa5iOzg>2
zb77LPcmDCgi`_j~Jp41|k|QhLgi(!u@7zxTc)GqQ<O8s_7+^A^ZwrpJd^H+%SEUH`
zIylFk{n6RlYI?7yMYPmX`G>f#=3O|mr?*N{UB16ft%tpM&H`Q8lM@R$omUak4&lZ%
zu+_2m-ge^9UlE?7^cVsn|0rH;<o9Z#Rw+rJ+7Tw|cx&=>yF{m&rB%$ISw>`ZmOi|0
zGxr($uA_?NF3(YI`p;B!zbIQJGosg2NM0%SIYesSh4Jk9<(FaH^tGM)5bYQ=FU_$#
z$KP2RC)LkQ`>5W1Ztd?|`}*$am{`muqHOs&l!PJN^l{;2F6LN@6-?$-hyc|!L+LIn
z(o|8~N1y$5BOIdxj)ywMLxokHY}m+DsLw3S<ky9Skz2Dbn5qsA=iP89HP4Ws6jTlM
zPqm;470?Lds*q{|E0`HbE$)#=%K<<6iOObH9{svs2omR>uGz~MWFj<3n#szv8*=36
z*CG)ncy3{4!g+OP6g6rS?C!n_;Hz;Tl$!q|8NK3HJDkSa(dEJq4H_&o89NTFfVNA}
zBY8K^5G1G<0N~i0j?*XJSacuM%MjoP{^6|!v!{14K#7$Denmy1G#t^pkC>h)k16BV
z;K92db84v_tX>4gn?kj#lSOp8*1fyU1?C@0Z{^5nK~L&^c(d^(P{_p!epVv+n?dMG
zlo|7Nz?7AqIkcW3V^LZzPbS5&sG8oyd@-Vt0H^5~y3Vzf`Yx<;+>71wrK*1f0%sWu
zh60Fqa#E#^wOy$!Q@D^TUXCK0^b&3ZArq34*HIb)g3bK=a5E8u&uPg6Y=dd7O?+RR
zjzw4K*x9X|rUH|RG(0bUK0n(=#OvW0y}|~7baQfw7eR)Z&$)yxvx$P|bkfzhumbE2
zC94P76rX%y(z!LXKW?dnLeM+4=d<=su?~M!$w&CuhIo1O`8WDFUDjrk;f+`~7e2-8
z<tz`!_(P2*QHJrele@q0m!~6xLm!_7N{6)7EJMHhC4K#?AJ{9Em3H{bT*;=6wM^yd
zz&h(KTh!#!9caCbx!}FY$zBAXNjhEQ+V^<x#g9(_Dverb9e|>Jk@2xC)MQ^f&Tw6#
zk?1f<?cuXBwSrVi*G+X|+@YrRuVmfaQ(k_DU4i_2eD-GXsWk^(zmjSoernJQH7H5W
z%JXvt+xI<2Ba@VvILpUn3DBi$b4|PE>aD@@$E1c6mo&HtDKs<uA33Tn2Jj5jh)BX8
zYf%N8=+lg)Z+<}*j(d4pnRRpDYQ%xGvcRuc;J8LDb?<YjlZvRyMW<_UmPNlx8uQGG
zh)*S7g{4^T&!@NoAVAqW$ZJyMJLhdzC)?REPFuI8yR*WOn<*=`m)Gl<5TM&hzWhm1
ze}z)ws82t!U~m-th=0jVs)qaKH^4GrhvS;qwVwI5uqBX5ibqNR>}P0j)hT`fK?7#D
z5#A-Us2_FnZ%Vc#VRU&X+S6nYPSW_OX12haUH66u2|{O8em=RwtQ|yOwB|kVfQJPZ
zK$%J}Qu;H({^NZ*zjn#jka9gEqyPj>$dVD8m}hkmKl|u`>e>d+i6-VWlRu;FG~{2q
z^<h*iCy!IMPRbh@-TT3%gu=qr0R0fqp{nNgtMdN!@;9=q?<>rFgux-DE++O>4QMn{
zMH<R`&ZDvcyY^a+y?eIW&RDXj-bp;Fe{<W@ii)!F<t0zCU0L)vtLziUN2D})bo^jM
z&%)7EvNfCjzGE@U*Q%)cBJx#mN3ONdlaYVa#O1u7SKoe}Wxe_1ooNp*b#M$OQLhcC
z1QpjRWunOvi77DO|0<SHIs6IS`Z3enPdHSx<etoTt)H^8@hL3bNW6V?$I>@N`6bTv
z4t2xnhOBKSVD+A%!9x9L+-1Cn@<h$cpqDBbr)7S;wQn)O89r3>`g_ySM5NBf_~RH{
zJ!cTC!7&$3Ur9v0{739%QE!pjZ>DAzI^*j_9;1GO57vd>Zq-Wz{{k7(hhy_2TMg-S
z{!Kt~KauXCK++Sty-FN>NN621d~d{H;d=Kq9~a$uQJ8uMh@r5uM-^zGG%)_LkKuPm
zy|*Z3)1kVWFr~1TfhNZ9QS$G5Cb;0V5r^pOKlgefN?&~+cmBhjV)(bQYBaIHz@~b;
zE&n~6drWFMUaU<nXX_~ol~fwLmd-6et;HQARpF|pp!=lU^6^mfc31t!`D&}z(lNB#
z+&~Z(6rfh*283k?jfk|x+y@y_&J;cqbi`sQ_hw%S0;e5{UXdmb4o%H@x8blcUideu
zBPU@wT*ydd?1}(P_v8+;G6bbr{d&GXZ@l0Ye>tW(!%jfQW9_6+JuoE{a0Q80n>pIV
zMuaTKUp=@x?-HtMkU!!G&_j{$@Em6l3P**lLKzhr>0S-HY?nt-74JLR2Aci=934hD
zE-A9I3RU~@m%NLXFhfjMK+f7L?-^|_*5tAWfb=U|MH*cMv6B^J16xwFShV@t%a<fN
zI@0Wve1NicRcN<@q|p3A!FN7hlZ33=c_9L-j8wBE-+DCHDoBwopY5yZ<{KS0#_AQl
ze~#mOpHRtiGgS}v@qz!H8R0))3|`O_Oc+fUsNobgRABO+RAGi&vN3=5r2qmQhy{yt
zt!&tkOPDMY-q;%|qEU6N=y-%LJL#dw+IJ%f=m(xZs}}u~S2Y1WWm*Y=8wB?=Xc&6q
zPP|>%KG%{%03y;zeRA|?NNjGl8(BIgWjaA0YbIml!$`)5oS#|Wt21G@-MEuCF+acm
z?kD!r4(aKpRj&I92JA{ek>;sgTcx44L7r-BQA6Gj>#A4L4_*ka#WHim)&TV2YXJ59
zl+V&$B4xVx6Q7v{*0tuMw3_Sh_AQI3EdhY}r05U4T|&RURJz@9=XK^g`pQ-OV`4qm
z*X3@)&xg@G`_4pQ@f7eJhcbU0F$}|DQ|<|Klu%Zoc%DIwht;(w%t(%ZE5rnUSQD?k
zHgv4<na2#NI85Q5S;JI;iGz2ceEE+O`nL$2*FVRLJL#rZ_<6U>mxQ}TZ_Nu?P;gps
zfi;xDh7n!9{O)Vq`vErfabC*x#Od7j)Y1aOyUl^wvl+co-FJ{3Sc*n!ZNKD>kM4O7
zrw)nBnttURs?IWD{QxkE6#d0Pz>@W(xuO0FBbU$2z-oELstIm6P*q`lOv@7$*n5Tp
zx?y30hmgk1ZN*a0<L4ee;JCLb#<rN9MO_#CO;G_Ys!3p+4{Oui>olpGt2$-g1KIZa
z`|+X|nuQrBk0=9T+;4*=RRd7_32Q{PbZ43@ui(c!+!8!x<Wz2Y*;^Uv(T<xy?h*i~
zHhV`~pi$GT*&D+&KACTc{+To0D)2(|Wb#0~Rik>PI)3PfCp_p|zy)!9ME~NFfV)xt
zz{6YM1uH;iIR~wqcgoxy?mFo$ZeJ@W@AF!;#L)v#9O;KzBUXHUrnL>nkKEj=R)}2q
zqUf!P7(h$lLk+S0P2}l?-DFRgIp6#|d6n<P;g!@q4KwuDBEzRh;}f{BvYqS3&tsTE
zt#&b3(p6ezJoc(6A86U*Q&6)&#aAgihAsSh#auE*v`%cKTeP`r(Ct*&6d)Ns`p+w=
zl`Ct<#KfC1QEexS@4R<fDWR?F)&|~+gUqV3Z?S`k6)!*yfDTq=g7o3ClO;?AvnCIV
zE=O_RI%(6jKsS&X`*Wc>T4Z>X2#CLVdNM(tcx=TH0V(|5mxu3(V-?8lzbSOclO+Uy
z$sm31bu&%cQ%PEN_f{?H;9`aD^!CB_4Q3M_Y77ivCMuOu5^7a8RPMR`@FaczNmM#p
z%1_BJnsdd5M|-#!5D(IS?7mT|_B!AH5q)*FvOIYAhNW}nwSy*WK>j%&BP5s}#79C!
zciT5)*K}!}Xt8T6a$*{4Dlm&gj&mx$R~V<PHsDx|0#Gj{=n|-I(eStiHZquOW5ZV-
zkXGNYVN}Ma>l(bT8C?Q<YtO_A2^RWCTd>fi<ly|aI;`BbXI+q2?=Zh}B6+a1#8@E?
zILcpu#Q=S|WO{sgjK3qsKtlUz(r^3pXaG4qPQSx+J@BenFdG!Fz(bjUss!ExP3^9C
zkKlsMd!76bNXX7sdy73g6O~Zn52mpp0zfQ)zj2=MJ4tTf)(V;I1)-+@1DE!e_XNWO
zyqZ&m+p&2?GL1jRK^?_rU=nyyf|te0AK4eT1PPzQS$!&hYOP3}xFoyf*?gA$E#A+a
zm4XdXGy(pu?FzAMSVyii$vS0D&c$27d*#B4b{P%pMiXfV@k?0}UI~vUhMM^|XW#+9
zFF=wXx*}2buWZf$v(5SL;?3pRlWp-6xVCcBt06_{FJzCz90XN`)>=)G{rZa^`u#Eg
z(;`5R{wz0JS!O53+`%R5qLssM-*+=9R?lrh8h$9I7UpI0<(1%NAraatAzHSa$gbHj
z^6qINBqqAm_Qv%J*70G_KA{^#+MPuu#=#zoe#>IqByQ*Db8aE;zz4f1d(Sbs0ipr@
z;p@Sbtzd6+sGizwHRpdO2}Z0iIn~VU^&0nWqFaHS{HfP6j_ZC6yk@co^8Q(r$X{YK
zTbCdZ04gdq{Cf1Fpk=8A`oN`8hWu2O@BO%G$y~Wat%Lx)z%B>>KmD2dKM=^Jq9=IW
z*OjXOCoju0|F3J5ew?AB?rqew8jx5A3@97{_L?9ng2TodsSv%|{K&y4wV25wJCkcY
z2;vWEkbibSuw#}|$d50!HGcw10;Zf?&F2W5$fjj+!;fQswxfeN;BfOjbCHIgz*Ha&
znMH-%WdCpHE=uDjx$F3j%as1!bLb0uFE86Ty54*XWskngr4>RDS&hq0G@)87jQU=9
zew~|Vc6%<Bl6J7}e#O%5q07;Xh3RPlTjCCUHDJj=u0!e6*hQTI_LGYx@HB`9%f0Ad
zG~UqwfsQQGf2=-5`K*_-g20KcAbM&pTTKgDhm?x``<G{i{WM6ilEJ7e`&xig27v_t
zcqvAcJf(}nnSR)wm{PN21@|}OdMUa)$cf|6mcOqb4D+6%i*bb>Gl8K5X2Xv2-IT0s
zU{~Yj#CWJuzBGzi4_c(X_!5Yb1tVn9F4?spVpOABqh}UO#x5wiaB0U^4&ILZ_%9G+
zhwj8E>^RTQsK9TbbZq@-d;f*DZ+-K;&+<2v-__C7oBAdZX81?cu}bP3s&;B?_~;<n
zyml_7b+u|ZhrA*vZcrS_S6h0l>+h%!HQXW@FR@~vXhfmQIsx}S=w2RlZ+qlx;Fmlx
z^g_EKP^W5`vzbX|NQ27}mzJ&(I-2X5kK|vyAgqh2iJq=!%dYYE-)@%Ug4d(iHq2z@
zj#%IpGq3)|-T)8fKYdm7uP}L1I(PJSGQ|exw_b1IA<XSD@=ruvW{fv$Ha>x)f31zb
zF~wZ%grQ>vo07lXR?e06*5wxv0Q}O{49<3i0U&q^VxD1uIb3r>$eQu_=Z^M4+I|np
z=E91`dByT=7IV^(FJY~p8%w=ZD4M5|(olYaaI8^iI%>}Y>Ww1$zvO(`Qo7jh{NSPD
zzL!Tf{`-22bS^BO{hi`P(z$bM1-MYtp3Z+m2trjm1xo%?<{ao%(FJQ0Iv@+*p^Mef
zvX^u-L%Jv--K|x)!b6XH7{72IhclOYZTx}BI8IN%t|n!{mjz;@Xb*X605n&ED#h=w
z_&#I&uW)_OmG?FTOt98$Jd}zG{Y)8D#_3nwE@J!k)#rc_dJsPO&tTXCg7Co$Sisc_
z?IxDExt#R1g+x(DJ+ns>YDB1#e78=Kqpi&RBL%3pT6o<(;@mOwx^@t*0-;{u!tb)C
zM2;4q_P|}czTCg#3%BGx=ohbrPiDHfDmA&TAo#ML+KmS0rJ>Tbau4>J?xAWNvLC~)
z{_K{qIB&WCUok+7Dz3^oESrj~P5rOB{R~m@*ZC~r3^_Nbo^Q#Eb*b?>E0evZa_oh?
ztM4NP!1XQ$bR{D^1;iLastHk>6X2q5tcJ6j>t&9o9K8SDdOo)Zg9N;}i14_qFn^7A
zj%@mur=2$!u;!7@Phm3KbC*J_|C7PlusXAElo5UJk$G1|G*7yFVTMFu$BEHqk2j2s
zE+n}nh=zDPZ2+c|U8le1^D2h_1iHcO1qCGGrK31?(swGCNKi~L7@a*RW-`<J#PanF
z8gh*hJ^_AbX^2sJ3ApyU)QdM9iWk0}A0-jSF3&=by}h>UIHMi_SE)piI=#{ieqz+K
zL;;Q4iFkVwDvvruzdzao?h)WN`Tm~bD|Pzj$^E6}j}F(}0ccYO1RPJ>MM-MMlQ!oA
zzFxZd^{VEHKZdS*%E;+JV*+HDcpjTA{aa*uU~!!?thu+=Uz9l=DX9~~e{i3TK8MN3
zaKjlK3_U@nHaN{;T#_aIIT=JftFYJ9I&FaJ-|Z|&v)76tQ@VHp$_@O}ySwk-@RyTO
zu(;5sr5O%1`?;UVG%atMrN(VV4}Hmpi$8>Om$3gka1y=n+bl9UI-Q^G)Kn)9lhtqD
zxYFfMzsuZICsh9dj%+T|e{Kx+2Y*~HV8o&o;fueUeX(%s`StR=apX&d58-6T!8a2|
zrN>9YY;~z7#9){3e1!+hFd*<BuA3f*8xW7IrEQuNZGLucm3!&$tTUq$aPj+z!fjst
zXW(@{xEvS7f9V#u^8B46AS3+p#?H{F00l?nR0J4!f*gGGj{DS9j36Z&|Drtp$#!Q^
zHU+YgK{NHgJkj=8c+jUaV`ZmVY<=<Ubffm!YA+c9SH;z6TC-+xD9GMM>-2szvH0vt
z)L=m>3w6bRTh25)!5_ZVd+~JcY|T#<lRkyO!|X(}{~bUuLXY}lDh|9L@^Ume#m691
zx-19X6$!9hXID%J*tt|)51U-~KF?Q{(>UtP)D%B5-Wt6gCUi7tM>j_?!b6Ob2aY5!
zHuz_cWv1h_XS`G-&D!6O-?WjDf~C5sz;k?YuV>$Iu5@u!$8L{f^e8U#xQS6)dnQHQ
zj`E6M*u#7uAMi2&7&GFAtmn0l=>uf)i(5(u4bNt1Tp8TEbosAyDh*4tXGmroee*eK
z<o@*w;>OG3{6Of!>DAAj+v)e&=yncupZqcpTR4w1f7KNT{}Y$YeuBe)4)%N*ph&a2
zwl<OBq`RSNfq_?!91SaEBvE!zX(tokLd|Tu0^iT;K4dL;J-WJ%W*a@){&uRgSn`u)
z-P&j$@xJoY=HYMo!_y271{L#*9*Qx5rE9AN^*0RPsHUB<L7Qu!r*n$b^||R5P@72P
z&q69I=hDZ%6@qS`GDHRpWwy^i85@8Niv1W8Uep`<t&N<NRJn^Qa_bW#9s#kUVq(L`
zlc4z9T)*?VQW>Gj>o?m+n9hM8CyLhBsz~}7N*r8T+0mW_4M3Y%uX|AYW2vBkrRh4E
z4U4wJd5k>Hm8{EUSH#S``6}d?TB5q|^fa#)hnV$0g6iC%t2aC9O@B!~F=An{g;VF)
zX!rN>+qP7Fi4t>FL61|tDt_A?B_>w#)JjA2=_XXP@)PaGk+!(K@%ib@^wRr_IE-c{
z4jq>1#KCTB3nA8hp_)dHpJ7LDcwu-|k=Xxio#z4)?^@|4UiXICx8zoAED38(Jgi9Q
zujKu^QSUiuYpcEzB<QVr5~FU<!wgDc0xQEjTQJy+=k@`&?!&z*>*(;+qr=Inw2^yy
zxVfg8I?Mxtub=EZ`}!Sg`qcTfbC`?rpUf_M(h3KjelC2Xt=$k`#xSrTv=cAP+yC8&
zW8Y_N?3twXZGVid`FUREDpTZ{B${c8G^asc+r*WnoJBSe|2Lg`P@~M8y!CRLcYm20
zUU5Eduryr4bhXagYc2@dx5Wp03K_B^W`O!qC$IP}8CjKHCscew+{d^h`wa<|n*Gau
zu5{EU4v8dRe&6)_J6+UUd|LleOgT#mEM$xK!%W-hUU<*zwdH>p@nSCgt?@GGHPPTc
zyIwwzxCxU-{fp-A8^!;O;{JWMPAcrBY(Yi`eRjm^+~I^)tAR9K=%morl!}Da3;LhK
zyVpGUDk2@$B>&b$z`|k)+lwF0t!&AbQnewrVsmO%Z`d5cmfm3dxKMco$9Bf~k)$bU
zv~`Mq3B)5FsMHXnud|nJWn<Bg8V&Zl75f8Ll5$3@E*`gic|K{z9JNqtVnebB_o!hb
z=e01#gj}IZk}h+4wQP4xcm9r-)%w;n#@CcEm2V{OH#dCn#&jV<MjSQuLEML9=&0n8
zKGYF%u7c*+`wDSUmUo&K(k}D~b#DUakV6y(-1g;?g{pnJX%g?e`)R)ss<3H0U?Dir
zi+^31j;a&;nn}9l<N3U;En6m9jz$orbu@bzc=T?kZ!qQU&a^oPKe-lM6e9DFCKBn=
z?Gd}rKPbt{5r5v?G<p3>wDH<+ZA;xLeK(WtnDL6aX%~T{U|2GOAZXA-!2P!UlrOaX
z<TAvG=4aB@OT0eL>uT3Ui5tF6A|5T=98z+q&(vPVLQwo8Xcse2D3zhEd3G^Ni4NB`
z<cmnTQ_mvak`LWo54(z|6w%h%py2!AJHS(zd(ID(A8W(D&)e22{)!?{x~)P+`fEc^
ztG>V!)1nHGQByqvm9}p*D2T^f^!tVz#`P{qTMmSC&Idyb+oFx1+BTuZw@<E0=RJEu
z8;wX6#or13K7$;hH$j-#VvUn;cjjHPr4pKpjA7e9IjeJ)E8ry1l&Vj4>&59D#i!d{
zA*r_o$?qsJ-i^iz86X#wV6?Xb*<TbshkB(yTpo>V1UPlbUHeEl1o`Xr`PCclKfHJR
zljFRYf^^JyF&PsQ!vfa%l4EMgRF!r_rumPce_717$EVva!j3BrN*N|PvAPXe@fP@=
z1}Z%Pfqgx)5C?RN=ZmFr*>Dc-rq@E=V-L#9xLFQkk{SY~%uK$c%<Imyqn4^f@5e-5
zEx<4#o9JbG{yYfTP%_KVCcOT%HLguCoPVWkN&kxELYcX@4fDDu+v)*x*oZtvunPKQ
z!t;QdK$sT)s-w}Mbyj3o#j?2dhuua69jf-t*C8kCOS+}Qd46X9hf|4=N_Mbn;RV<+
zNiDWKCtJe@`mQfb?}dHNp3oCaKN4?Z*kHNLn>NAMQFiy6)xqj`i`E2bm2LNQX6=m{
z)604{(^cukmu}~mmu3#da@d(mb&2ElKO_CVLOqT1LIQ0Kxce`}7ycrdtE_#?!LWy;
zH6UIw5LbHQlu>0!D;2>bAN~_**BnXiB}M^xjt@+N?tD!J=N^Oaf5Wage@5QI(RlUA
zx>;<0o};DN&fz@?c_m|;ye=83-cBthWDvapf6x)smk!M8o-$~sJcd`qu1dz3RzOem
zI4hp2$O>ETbuAP|o;rFHKzPtn^paj}wVdTK!n$0*cN*{Zn}O%wYe(%8%xtQouF4oW
zRCYaZE{sTxbX=d32aBM!xb}-H9cL-Sqrv%^6gj@|<N@f<jc(i5H#uc{Nhk3>Zs&O4
zSCR+SAUtTPs~Sq~oQp*LZ164gHBaDMIkbGxl>2Q!yR(swh;w<iB^ni_-bbnrdEo|F
z6SqpfI77{5aFinCtaqZfE&Jux13Iq*otw*wmdaky8P2zCcW8-J@_oD7Ss?Cc!Qr8{
z_#;XYl<P}NW`})l&(yX0ZP+BE%3oEcu&PS=&0<t!Lj{rmG8BU*MYt=1gHrd(si{44
z^L1J3q-6u9g&v_`4YCB+$1j}CWAb~ndhMXCBcu?mdY)sdO{lh6rZ>K1TN2+aRoPIh
z=jguZ4cNoUV}5R<QpEl%!CmNXum)Pda-OwQMTspWq|8_>@>IUL7$ekE4crwwkm5Jm
z3|?I?Nl>wV6(J=G8HxmS>`5ng{@K=fmP_XAYIYL-#@<!5^@6PVa=E;qT_B!_ETc}C
zwBG6yq~kSOw)BQ6W4(->MKZp()YIXL{$Or&sz2&0&#(VN@gZT;!9ZDBC6EzQfEtKx
z-^5<n=Vl3+s)$tYs%kA>eIH|Q;-j4znL4X(_?yM($k?a21Q6{|G!3BAlZhrxY?nYw
zn<(bnN$d!D2&sCArLcX7eeHAaFBBA;@sg-33pFwY2XFndp2#Kld;MqqSS03xF-QIi
zI`Ki9q2+g_?tcvsv98?u(RU_lTD8hZ^D(E*+VwU<)hCXKREFucW4I{MOn=nm<AvrP
zFSa)Qd$rpX!l`Cn_(X)Q8Mt?{?=XMncncdw6o!mV3=g<%NLGEcQ^L{BC|2!XzAxka
z)vW@LDC0~Z7{%@lU_B-}5H`>W2)U@YtX`_C-RrBbRiD2tQy9?D5tbXAn#|q#{1R>$
zi&4O4UahRGhoSTl;MZ^qzBDJYa)8B49B8yu3be_Ub;8PU^=!hRk<`Ul@|!E6k>7Tx
zkN~#jL6{+BMIMH!<Ok!-Ii5($NyC)&d!`0ON!)m2h^ImmkhD2P<rQc2oMeJeP?Nsu
zn+uITq8qcVYkkT`j<<;&{es>oMCQbqs$u=*nE$Gy+}4Mt_hwegNYd%&xI#mwMzo=g
z_sG6Ws@Rml8S}@S?AaH$smk_(awFfwqPcL95WR&{)W00f8!v2ddv9pXx@g{P$uR4J
zRe5yc!X6)5;K9Va&lxVN^l@83PpsOLs9fG7hyxaFy9Jkc_Lk+|g5+wlkl*6%np^K>
z1!|+d6`^O=L}MlUgSwY(b9Z4#Z7)%#z4mJ-skrH&t9Yt9fXI{oZf#GN#QzXZjr4Hn
zD?aOct3^mxRz~%U?E;Q8TehY9;RBN#6FG{xh5Z4kyrPC?z(V-AsF+`z8H3&0>plIJ
z*2%anRE@;;J*IP&mnXph6sfHTJ$1Pr!za%46;-|F8?E0*40Wkym4o!ZG!|#?kEP%9
z5}yNC)j?-R1#R{=N(s|5pUX4HsAyr9s-(<+Q;@GdJv-1^{5vJqs4<IozN*LaIp`&H
z2>{4!^E8r~PA?BI=zziJ*NlB`$B)$v`7?bEs+`u84^+fqjN*KD&7MQ@pdA1oy`N7!
z-3Se|-D43Md-M`21m6%-y;yL?FMp@xZce$wYnoNR{D2|g?18cecV99NkMqDgEz3MT
zXvFoM{=duuoN{mewlqJ3Mo=M@yTj0nl*?*AD{UI`yDa0DFZS_9;FspEiti|9W$*om
zgp4#vH=pjSDgelSI%FN}hAEzCgG+cw-|$wifc?HyZ)D7&Tufv*9$GX(p=f)Luii_)
zMslR1dyrK)^8OBO<jhSqJ>++U92+^BF(Qg%dMIm3=4L`jWsKFnn(OylHJd>0xideh
zC3g+#e^SEqsm&=;YxKAAux~>mH>rbGep8MQ){CWk8{YSxeuPE}fHhsw+_tIz$;D7O
zUMEcu@FxFuzn4p&+8|kE&WA(7j?8rbs-lqRkV=oJ^`gmbpSqWw56f(pzhJo#S75(i
zR6io6-JV!Iz-1dJOAnJ?3(OoV|Kxv;n<Q)N>Jtk6fF5Af{HFsKZLpF_P<BSDTC#fU
z4+!?+mTG9^b-xNB-spgga7f*^=((;vgepJ$*%g~Ak(ppepy_*evF;d*0<kA$YWwQ<
z#|crH^LMHQ0uMIdtMrUByI20Y8u>-&`1xfQ5Tqd6oyDePygY&U9S&Dz1uK__q0Az*
zN_Zl7lnHuxo^(Hg+D)C@&=N)5PS;F~@TJLF3UguFVY{5JoE7?EnqG9p_5<0aDZ(ZC
zkZ(fgxhC#2rqBLDZIA~8J(y0Wk*jx$5s#oGfw!sq-smeUvU4f9&fgqJ!KbZf1TV`S
zV69^x`NvGXvw8u{y#z5>I+@x!q#c>r0jboE-S*kc#{u28wq+DFl70ZBzcjLvQ48-b
z@A*O7L5iYW9@+C|(*AmjF;UVq2N)e~3-pvHTr?>K{kc8;KJg(tLWRFKcW$<xA)4*^
zyi{pR#;vh;1PoZd8AAp*5%0L?I_DJq^_mD+6fd$JO6Py$6AeR85~u^mei#AdAzLdT
z&_yFZRamiN+CJ`J_sEWon34szHS|53RVLvl#~3OIYWhrf>aKDD6^9d!dm>|gBY0h3
ze`u(_=aR?h9e#XN0|Z;Q7cg0mLXTxUw732F*OeaC#zBL-^7}20c%(AXBM%SXeTn54
z_;B>+6AdyWI8r#=7CxQV%G_7Yx~r)C7GXMl4DFA`^5ty}7NI6Z)@S$1AeH00(S61t
z4npx`PIwHCqOxOm8yV!?ol>w^loK9IV%9BvJ3U^mh<<}er0ni7OrrK6l!)}3<#@@M
zn*wb!>9O02nw(!K3f~}E{Brh%W+sIcCi9nv*^L;eKYuQ&L5rNu&-_TbSxaHvONi`h
zdT1p5Y&FhKb?mSU*0yA#TNG<>z2~9g+=M?K*6t40%pG6i28-i#^NXow60wT7_7uQa
zfn2wBqxn3Y6&|37>VB(m<8DeqT630$qFOOgx9B>&lEe%-{O+!*Fw&>GhIzfvc;BpN
zfh9iIkrhcE9AH5eMFrvIPNRSrH;b89moRw>!4CxU<=!O!xiaxs@EiMrW-c*Wf~7Kd
zp1mF;iIK{w4WN?DMCru7w~SC!)?4LxRPuesZM7`;Fie_{`mU<NZ9Sv&+oNgo!{WQ&
ziC9K{x#=-SSb|l~E<FzHB-Hvl7k-s%*)}}O3OQJyVftL8KlbU_>SDQ<MU15_X;K$j
z;oI()<z!YuFBlSf0wd1IgY~oAXO;CvpGLGXolG@ETh)#*Gl!uCkeJ*a-cq^MXQPbK
z*k=M+#Pg25GutgjNc%0|N?rlyy<|)zIeHJ1(h;a5j+wGAtxE7>N8PHtkYQA|PN!IA
z<xp0(dI8mohsm%7_6SwJUN8NOJ$>N_DW;?7GmI=WGUzWy$wjh%E=CK3RJgNHHs-R$
z10Q9gY<D_@UNtQ>Q}se@c=G)vH_|b3v~td7%8Y|TlFPK7wb7STe1r^9Tu|oRNm_jn
z=2-rP{d2n&dN35UZ8eRu!?h`IsP^}dTaarb753+m>MND_Pg5(JBcYmTwRF>}X^e%4
zt%&A&gSs1hG@6CS_C$mCowiP7q$()z9uF%^{A(JdaK!yMylNWz(BM&|I&Y4zwYZF;
za^6EYM?b==P^{M%V%)_1_e*c9yjw)dtTMrV{-gCm9p?K{uqzTO<cJly7lsduMQX-i
z9Z&02$eBoBGFkD)gD9cYMZw5#KdF%IGtKqc?4KVH|K3c1%X3D^;5-O_m!?z&m&a!6
zj9$G1!z5Md?7nD^h)Q?F{jgYhymBGaX{ytfVdPH)qbt}*r%@mntT*?Hy%h6&u(6wf
zv1>2*xD&z%inReRzTuhyKU=GFbLQv=BifX%t9%TX*BsC>2qOh7n+}%EBIK`>%x4%t
zCpvFWS+Gw*Lj>7zqA;g|<TJx-Sl|brs}0iN(yI;wZs)&@o1AJ!NFqDLems}KWF&%J
zZQ-HTpU6~bm1O(+JGbl_m6)B{2SGSXaOb-*q=OM7@raR-y@k)@nncbD7f143b!OQE
z*t@|tMi)2`klFy6|H)pwWmmJhjXRLy$vOK9iXEx0WT{OfPoO9TD#N->)b}T|-(S-y
zU=1TRoYR9ymwXail#Rpq#s&=8V`rZNb~ZWgHJt<1@woaZS4aYN+axy@!0|$N9T{+6
zBOe*v&WmAUtH5<O7z9Z$egwS_IaGq$f-9jVPw~e8I7e9c-Z*J`2I^t;*uA|F55C4j
zVcbEwN-iYopNM5Ting%+xOZDVD>DidH9P#rkvw6;WPLwJ;wvsqhXWi&g9}Mf$e(xU
zxyI|QDierV48cZV!hiQMART1GX-A!qPXH(b6fMaf$tF)T%`qt1#%1FsF6zUuMvO~g
z1)GO@d4ZD8nOX`>n;+EFtj0q}wXk+QYBnVWA%9fSl&ZZ76jA4pJz0BJk^id>Bhp+8
z;C#sZo1%}tT)9?(gnO|BD}^{V{z_*<*H;XA5IO@vTH(UJe5MLqBAQne-nZrX-c0BG
z?;lp(&GDngGkvp^zrS%98S0*i{cog~*JbbFbwfoXU*Ln)q>N%VcN<L^2^i(BtGxSV
zG{XMX`Hcz**<b#UI7N`sTx^HvxlbWM^Ylacn5oGQD8+o;oPI;EDIpsu=lm;>Kg*vO
z9851<B~u0h-0e$?kHA&r_-a`y2>Bf(w;FUQW;4XY%TBj;U>r7=4uK4cPi0_qU@A({
z!%$}4RQeZ|=C!5ciB+VGymV1)ijaGx$UQvAb+1MB`oMM}+J~Z;`CsxKv4Sxb{qaNV
z9O7O+T08Zvrx1Se8e4tT1VA3~FvHX3q5h)08s70FYAx>va2RFUMFb!Z7~#K3kvRc`
znG)9$bF!LCMDlMX2>`Cuf5x6FYVv5daS{oP+H{pKae`Yx5|yA8w6H6NfZ@plKm7fQ
zl*X<c4E3jG{WuWcP%ml4xl&4nb*qC?ci5UgR^k__vjr{!Mo>!|)~cA@=&|gpxbOR{
zRR=4vBtS;!zoXOZI8S=Wq*Zz4zqW@Y*Y*qD`R~lfz7%TA(Mm~7_Z*lK1_#v^M`Os-
z!{`zaUVk(*rOb02r)D1YW!z`9<GqCyEd7sJy1(TYZCNae`+o5=x+;_}onT0#&&jQK
z7l<|nOy>-#sDt5UF*jvszH2eU1MG+<J<O}*3Z7ymHfT@(Zw{#=VSedrNGF6XR$>!=
z5fcW_f7o=$o!MwEW;x~bHv&W5T%&>r;)aLv39aM!T~h76hV&`)>xQuIJQ^vQfk@kf
zVXqM9Y9)S4x@J$pbg(;~3QX5woOUlRYzvbryHd&BVElb@r)e&qNR<%-hW`u`+WD42
z8JmDWVV8|{@y0VU3A%y%jd>wQaCC<&Co4nZol~e+U}zmpt#Tax0V?Ckq8i5I2M$Qc
zCqh^NNoXA<;T)Im&5xEG`zc7zr?2Zc5Hmc~C7vTcSJ8D)NHMGNLrB5`a&igb)%Js-
z1A)GZuGsqQ?{(CT-YZjD4CX|S>(Jvrvuaqu4xz^~caX9z?{BP-ekB1|yQiLqE8b9s
zT6Q%eNI5C;j1(zLYkU5BgqIcEetalCcHAP$(JN2WA@`3!!dfiH<|W-L<BXHlcMy72
zwE@<Lps-XNVk7}^yxe@>(cMY&ml64$7&5b$07!=hmdyY|DLmWA@jZDT!y+#&X$hW!
zJWOyC)(4fm_|aP``Gj`~LO+5J(2ZKrb3%1qz<AxAwXAR$@6&MtG@fM~MJaImbh$+H
zYBai36W|<q8ziWm=e-|JIP4{_C;^Hr)a$X0rm^tJU0!O4?cbe&msG>884n?@z$t>V
z9O86spB*hs)un<M+FNv`PWikve(AJzlO4v*O^L&ZUQ4q`kpfTm9eMuS!Q5mFicr2H
zF5SDx<2%R1qe@h7Q>4*)2u)duaIM|;v(z#gh@SmwqV``=C#AX9o<U-#7IoNY{Rd|C
zJmMT~zn$%-M6+wd8N2BfuQay>6?-EB*+VZ1rhaCo$jMXzQ^O*ra>0G$VyAUUIEwl8
z<ZXF4@NYCj(v=<}%xS|_mgG%Gx~sRBrQj$Ns<#iZf@%IyfB`6Qz80B$2+7$BOWXbh
z^7p!mF(U5){d2NZaR1uyVm4Dy*9XJGrn>?C?;O)E(LHbgtJl#0rBC`RVhEC-FJQ6Y
z*|Sr<{>&6<lku4|BO`&+`1ndrt<uOzn`P>@k7&gb;fGl|pR3$#{~eZ-bVU=`mF>w+
zNL{~BWaKj&X0~HXU+8bvY68;63UVX?s;wEX7Yp@utTd6i&X-=Jl5$Q->YhYs<h&t)
zW0+Ta<EyEc?UF2aNSj+uNfiAb3gZ5^UEoJ6W|9lKM;r}NGw6aiKMN5C(33ge2R6A2
zto4k{ULY%5y@3->fea9}7bm_bb0cB4qy~BUp&tQB3$_&7EZDMpq{=;lXp9!IusX%&
z(O>hbc+zWl;F(zH@z>vp6Y)=1<dE)J`(=v$(y1Wz8ziu+5}>4#Md!n=?&s^4l2(ln
z!Ll*FvG0c8e8Z}9ra<m!%<H=6p{x0V9{pYZ&MV_hp5ZWCeoPGWdanY8e*h?Y_mr@=
zI5E<pF*h>IFRV_3_0;)tDbpm(OY$SSf|23{I~YVz^WY9sBEn6Ml+ZImROiyMW;r7l
zMz`1UK*QZlT0%%?>2MZ#z~;p{`dXWBT3s?nOqpGpe<XQb=c0aUDn7!A0Ht4mX==db
z#7_C~HH4}-%W}o%On~IZ?_S5ti(@~wPU`}6raGEVd0itVJn5HrnJu%o5qxnltH3|?
zV@WXEKMjm_1boQ9;R-anysUpPsJjVYtlE35P_-#mj3r^YbtT3UV?9<KnO&1Utgwaf
zB=INwaR=xQpTSy?^odfEVsefsDu11zLrQg6wm@QWmF9-9ijHs+S=>8TSJXa@1hc$2
z9c%Vn<}|`^dwR`q3Nyo@r<L}DO!`HLl8Gd{GHULxyv~(A<%N4&YGgjw)&n{DurdUF
z;{B(1A(**J|N3S~N=RN(W!Gke8wO(<Y3B{tQ8K9WF<ShpjnigV)Wy)Lo(VQl@w(xH
zn{?ou`B<;CcthK@tg>UzVP+8X^zYgNaFIM-W*~3J@~cEOcp>gnOw3ao#3-dtNNN77
zHcx8`r=*qAVoowF8`}Qwwe2&lA{XReQalh|J*9|6?8>J7zQk4~sauQ+O_AgI7%Sfj
z0YU&dk`An5)d*W?r*N*qKSEb|J-#~QC^bjpR7~zY4Z*Peo%iQUjUzEtv-wW_pQZ0>
z+B*n(oZSy^q8<!FwUTlH9t9}Y{s_7hWh$;JxNlAWZ++Os>iG}ko5(?puab~@dhf;a
zUoCYw)&y!hfuYAw=;b#Jp+d;>ee4d^1OzdT=X*AI0GpxYGy{a{p9BLKVW_LY9n9`G
zB&1b(hENWZGCU*cTP@(C#^uN>u53P8%ruQmb=Cm~yRG~CJtU4bxAK#CuW0G)YHw5H
z_FZ$q8wp>8T?Xfk;Hst=!m>dfr%3Qc$vjXIy@N@4$jE%WLEzZdSGjj^{)`cDTycyR
zBRy8Or=`1<uHhm?`X*_NED%Z={9Wk9PcFDsuv(gFKFDa#j(DykVAM+>(VQw##DMf)
z=|NWmLu-hT({BNxogCSOoR$UKh(N+2!@iKR<tfq;45#~4X3V*m-@Fo_a#-PV2^e2o
zrh1){2|I=o5PUKr{^yMZK0y@7zDjjB>KV%j&NG<V1U+hJYavLB>=Ak2-n!8Wxov~u
zdV+9Vmd-HzIYB`+4{yIV#}9@hP~omxaH_*{GW!G$N&vr+$nqyaheYW=5Y<z3S}_pH
z2l^?^t>Y$(@~h6%Ay*fSG%W772|r}9yO>|-+g2}ub9nlk<Uj2$vvcJ0R-79=VQ0(E
z0_E7~XDw>A+?x*TUK@((O2N#E!7UM>yZs*>o9bgb1ad;o8VOK*n+|zw&4vy|e;G8T
ztX`cwcNPxFwr?G)_E)^9SCwYY)<0b6)>M(xiPNi*1W*GYpTV-%Kl;<^31(t$IbwVI
zS1C8|&Ep$b03E#&CWvF8Fzosp2#W>VFEEVG0U8+G|5Ym3cu5qSokNd@sSaW>sl1;;
z1Sj!PJ?A8-UzSM-9C@fW6WlperWlfDdMEhyDm4;)RVCC*Oz{GT7CVF7`7{J#9SLqf
z(SYrv$o<b_6_>41%tR(|z3MgC6thP_VcWO2D)8jPL9;60+4qvvduJ+o^LPFKd+90$
zmPIbNpY{KAbkN67-s#v*7JM6A9<oGl!5;SJkFbl{_#xEb%2SUj2q$c`9EUM~*iD02
ze=&fj;W)uvoUa+q)uC8Qv49m58N0YDh}~rLzo!h~uJJB<@AR0GuE6^XauRR<Ah7NI
z0|E^KJ%I00*-g%zZ&^kGnf74;D*f@I0z>~Evlx;q4C-MsDuIg|Gyk6aV8oa^Z1{gj
zeR@>K{J_@$A_#6(MaH2==JeMjec#H7rnJ4vyB%zsuH^lr8uQnW@a8HThBBj>*g}#b
z9~NZj<JpNANj%@V@r3gvZuLD8RjNXrNe7p9U-ogtFEzTiax;&iH?&dWDD}vJQO>(k
zXs8o_B&O(Z!;=~LmP_bPl5akM7=k{9a>H%eJ9U&pdguOs&k9ws9=~uWt4SQUazTGb
zj!Dn|^X~Wh$hT{;ip`E50_D#Z!@-WE!YYgY&@y~G?|xoB@mgeXBZASEYo8ZB-V5{I
zRr*;2&|r9(Z!rE1&n2xw&KkrGk1-@h5mvwNDbQ2X0>pu>t>>Q}5Hd?6y~TfeFEGtf
z2j6jyPdn=O<iur%Y#dRV{b|$>c*r0?^+CUWS@dsEqcs4U8O9cC(x9?GVz@J%cWXL6
z!&p!w%`NN)OOdu43!z6Geaq-!6WhD_h47+bhTEmXZkQ~ir}T7r-U{R5kiy~dmrh9=
zuz+tDr>UF1LjWyJJqx|@3#I&~QC8?@0Gsl5vIiu>q5Jo&<1yXyO;(+E^IR`7xVwq|
z&|d8CimUQT=wto0DhTMK;BgmN(6&T=J^O=|6vQVCCus}l(ogN?zYO;Uh)z*L|5#vb
zsz&bN6RqWyp{6qvBW$0C;Bzap4_LD*O>9*-=y2FOeok9dBePaB`xrV$RnwK8SCOp%
zrF5J;{__I)6)N3iZXNy>mfc`$txF?5(`@x>XREgE<oL0FWd?rSO#W|E0CD#%_Yu@0
z8{k=~@8Nvz2ugbs^@>K`E8(aLLEqNkdWlzV<wU{v+vik}0EOL`KR9%u*qlXYDt}i{
z%NDM}9)7I0XxFkDb`e<J?Y{Hpn^d4gWA+B)Pzlhf*TBH|OO6b(h;EL2%jc}y5LzK4
z0mr3YC`iI1L@mc34~K$sgGA|VX!NBGSoZa9Z&!fmqlZG&d23<EoI92qc7)}9G@}k+
z4TmTJL!=nDrI6gFv^K7;meA?t>Nrl=f{rk5a>v6#d;nu8YZ^ZccAiTvcv9P(xY!wi
zIII7zqJPqxr`oWJ>Sc|t)CD(ywPVePHjQLmZ=;Cyu%wWAe>^{Aw&X4BdBHZX#dBOX
zzflj0oGSV2Pj;f|;)I#a{5t=|+lG06<p2P4&ed!0Kg1-U;X@Mz@Fo@KIDmJneR1?<
zBLQP;uOzJ#R^=41vHwN@N9lR8ce&pfVTc0lH2=<+)9u$SLZZ(#nWf>~wow4nk;I2c
z{3rK{U}47hBc<j<In|?k72UNrJrh%7q@)@AXgi_dd3fDTh=|BL6~Z5MnG|r}6~3Jm
zhYlq^igVZ3@ZhvgiWR6wF#Q7Ch|d!h#4S7hJNJ+ipmTos^mF3|2N#a3<O|<6?Pgfq
z#|(3a)QX?>GW#CDa1yjYf2q0R;a<z6TehqT>Qf=F#y!%_p>jLzjjr0VMr?!lhtp~}
zt>S@et+&L@Wg;THPI>6)N%2EPM>KwCog6o&>+bAVG9U>zt=E(k>f<B7T|NOSBH$Ml
zQsh3>$ye=hAChU7rudzVLHSku#>Cc_hf^B&K0@5CevB~Dee%ox%F^+$Ib^py;hK7%
zj7~eFpqquc6QNq2qKb%lMdPjq<GpM9Ulw?!eB7-`5ycrFwx&p*7q3fHH{SIrbs51_
z3?06kl&d5K#EjV$t;hZkSMME9_4_~mKkZ#c_8!?=R*38o%FM2WV;qu@RVpKUlsyk3
z*&I8gl96yw#yLiri9?a2%-?m?>;3)Qe*Zq7*Yj~buJydu<8gl+^vEFLbG<&>-juCv
zKWzB6^EwXJ-1(j^N*^D^Ow^0Kow#}&do{V}YERK~sHrgWvApI>4cfUAK$WF!F1#)D
zvlbI{sjT(mjNlH3ATSFKKgM4xU(1gQxD>!Ka@2>)2PTYQ=5fML7{XFo?O$lopO;yD
zIYJ~f*#QOTmmHY86SrlNaZHpN^k8Ng!~d^~UsJ;)t<HrLgNdaCKaJ`AVMQw&y<xZ+
zo&+YXHT+zo_L>@-{+Wd5(f@tmav%s%fWN-J;;C3UWCP9;q6d><vNxFeX5S5><{Ew#
zENeLZCwT9NEK;lqeDq5QIrJ;=k=j_@kcazAK{sI9s)?ZhCl>{XBeW9PBW*xdz^?lq
z4Z5t~_fq7@5CcZS2y7H8@%;$OvAzisxOxHUG`ws=Y4%5NrI2^+RW{X`ii~~7ykKC)
z>7F*R+r5--(M+HWGNwf*l4GwQQe))~gdvJ^G}syUUe{k281kFEF7Lj!tPjeuz9EK5
zry?7gxaZV*;Y99bKP!SJ92KAi>sxez`JfX|bHe~|=dE#EV>X4l*HpYNZWCZ0DccTy
zuU*!&;`I4gDe|`r;E**b1JU6*0;-A+!I`MP<<)cxn95E6WM)sV-qJky67af^{rw}!
zS&!$WvEyHB?LjM$!xh2FkYiN9`0(jxc^MaOV=RFQ{uPJocIXx=Y%J1~s%>lB&477i
z3783Lz}k6{i$7(NNmvVrUYH)OP<pHP`)ZTakcp|+^+HYDO_-_0#i)CpwY+Of+ST~=
zN)tOc{gKiN>CPwDAO!f15SFQY53B5Xax%T|L3YF9m#DD|hSq_><)yaA9UP}R>HXDd
z+K@0=BLy}5T0Vz8Bb${<%z13;<5lN5LGF%=X4mWOJ9({7TMu9I0z`)g0#w%s^F}1U
z|8u?b4tX7^ga<GB64LdS;{T_(9I_GSLeMqc-kdM^So+N3i8Z#gx=Qvu*N>}>9(7IE
z5jPU5bj~R=Uek|Y={nh~ZMwOpOKs<q>z_YA<QnL+;+-ECDEq@y6lp%euOf=>RUd)j
z`-hq)%#9uf3#<}?j{o?<JlBj5vF66dzym57zwR>3M1<W^A^cL{gvbMYC|bM&feD2i
zQe}b;IsxX%+xW3iM#6?x3E+c{ffM-B>5?u2u*$r&phMtO(2wuu`AjgFROaBjEKwdM
zFaksIL(TnBR8wsF$t=h3v!Lou1(_4lV52&ip{@T(j$}h)0(hXg|4AlhL$@<gga?B5
z`GX&0B0vfsh7p>#r$v(mEbjTFolj8m`JRp<1-u{d8WDwi1g8r30eg=@Q02hU5)|qs
z;%JVI@JmiP>cLg<G<Cz-=$)i>Zl)4byV0AWjxgp}mFx&yX!BSHZ^wf`CHvQ6Ynz{M
z?xxFvzX2_}ffw08A&(?ewiTmIY(Q<=`Mv+%F1yN=<Nrw^5czGG3fr5szpsQ0@!q*M
zKU5(dsxXDb1dn;`I`(`3%u5Z}2w>3*enRC}U{ay`f)v=u534zCo(HlAmx9wjH#Sv&
z0+EwlJS$%rz}D?l&tSLfO5WNr#-=i18pp)evN4vV_^uPe6cM0{h-zZPX+x^dz|k6e
z@6T9x{|5Toa%%49R9385M&D*`6fW(h#H1NgVolc|@>NOWXO#f^Dl0L}KSd&dB2%c%
z>FiTI(rFe`qCS?a2e=O_8}zLvChB8h(~TDUyMA-!D3)lPe@L6UEb2XeWZ=BWv0jg9
z;5to0&;tX_kddgds)HxvD64Tvqa-23tQ{-HRMT#GwfaS4V#)D@J8ZrllWLWbMu7iG
z<WnH>j9Y+X!9~I^83IWE+uT<3=sf}CK~tl7qXDd@xtG)p(Xm?j;`5lsWN}~_qUu?3
zB>Krht|XxU@F3m5^#c7Jn0kFF^m|zxS@YhtKrt->H(2BP{`c8pezSRCI8ymsG`dj$
znc-}WZYcu@(!UyCAv^<@mpBPjJGs5J8Ib+7NSgYg=d~XCqTRyXv*xH9nnXRe59x_%
zc&-U4)M?Z{I_Nt4#S2F#M<x&w*fBCa^G3zf6-y(YxgRP2#NoVm!PfY}sJTN|u`WJ?
z0I`SwxfHFCTVjOCX>h5NBM_~f58uz*`VkJ(2N{IP3}BL(#%~Acl#q=h*U%cSO^3EG
zz)0&qeW;9-+8E7;Zr@&66`fT-5rnSc2cYO3*m^?;U3mQK9Vt=!tqOJK@Rz5_FCqiI
z_N}08-{lNZ`(?dQQKAL>UPb#GYNB+Agom4RPe>H=$S<FcmIcgCLbsxEZT=)Y{0!Xb
zYG>!}IMIjKo{CXHes0`WtO?hV<@zJW=yhl1AqkHpevrREgLP7n2ni+?&z{^Q3rl--
z5!dht>cP*Q{-auU-d763D?iRg3yF^V>?Nf^#Q4!x{ssIktoFY%*51f(JNUU2>Rc{9
zS!Tz-_q7_*#wYOqs%0mK*`UzSeZ?sC>D%)Y-S97`YFt7d09RPDyK-~Nxf1H}#hc%^
zpG+P9SyK_?*0_&9C)I<kKT$j2hb~v5+n~ZIly?%!`zb2@7}E(613v|TH2U|X*E2vE
zPcea>^C2<(NOnG?&(M%0F%Ll50?trCNJM~E1TN3WE`%4N`@gBSHq@Hj^&qh0B@otu
zHS56?wPEwMf$9?1^saG4kHo}EH@IrUA2TJ&MCv>&6}T#n)ERoK&0H0HQp5l9)4U5u
zOJGP)zV%NVj?UGZ_raT{MBf0}ut)%jp}-ny9u(|+bRzg=3M0BkkH+GGTu1XeAKeJ<
z{hudc%Q`ErPzM8A<9S2?*xnn!oK%K>nm~E~_esw1&T%ue-;|z2bVCEy%zy@6KLJ3z
zA5s*0W+e863sYbNG}{wVT)*!m7w<s9xPJcYh5wWYfo&`6Z4rK(y2(Y-os9Ap!sEr>
z;tW=Np`(ii%(pZQ@B;lK0^HZ7cbuWwwrA`F(ma0)pknA_Dz`;~svvXkV-P0CXOfC?
zyqogJk4>Y%ag`5>`lqwhpA_YJ%0i=t*X)p%W_uo<NtOXm^xJcX)ILy$^CD(VVAk~L
z=4s`@(K2XXZeSPsD=rb$`FTm!4^FS)!xY2>k>C>o(3#kVODD34YIeH+3B+<*cM^z>
zx1-`ad(WV@?Mtf!S84xNXvc?lK`Y0TQ1G^z6N$#<e|6*0SDf2HQOF+>ME6(Z$<5VG
zt0GDe54Wp^{aD~wTt~=#@DLH-+x_9HEbTeoA|BcAhi3)QepLjk2d)A3aqAajLl(b^
z2q3v-+J@}e$fQ}iwgn;EhyXQ&gDy<nBQ*2RiUXuomyRL$k&LPqLQ+v+MXIf2Ya|+P
z3VZvVg8;KDA0^3;EMqN@Z}tBn8_o;6D+B?;@NPr^F9^&9<Mn(0fH>+4oQq5&2QOd{
zkjM&}H}r1M9<LZcU{D8Zw#TC+x0eIaN!?KB$6^Q!rPGfcRy?L35btl<%#mmSitlOO
zx3-wEdWBn^%fbKe)8^(+xBXh!G@P)m4XhA2?gt79AjMR>hP~2D6n=dU$S2c?{`bA;
zy61^GCY*SG%|D;yuR=zk3ujQ7vG0HPbR_wk5a<}peqhEP?EXBC0L6<ZziDcQkmi&P
zW(}o$ipp2+2Mp`~9xJe~f|RR#4@v)*i%ppNfY&Bg4WW*c-6JeCf=x1@BO`DhB`0Nf
z4o8&29hHXSKp^gaa?h;q8Lup4Im5q29stCdB*MWImO+p9vpKq#JN-JCuG_r1J+1iv
zr8iMg6wCVab@(xHeoM~G*>D4}8UE{P*?aTwSW(+Gm5N$S$(d9eAuvs@<epgiD_rL+
z+Ar*6WHi;-Kw>zbHDG#ReecZ5>v_+W89(npaNQe$JNG&(<K7-8i0D{uW!VRPaf)aH
zcj$bYA#Av2N5kL_cWBJRn#x|4fLsgK&X45hLzWc>wDGS6``m!c%F9(i_#<-oUs1oF
zvs0x`sHlFTDnS*$R-lG7Y0;bHSQt6heOrA?|3lZYtsJyAHvWo0gGCbqZntR=YA#QX
zubMTz`Jm5Z`MNUpKVjMtxL@!G;)fiQE7@%NNji<x(A!g4Psy=Lzn$*hvS-(SR(%HK
zK=&_)Ox2kXK9Pt0Eegq`1!=o9|L@Ij>fjN9*`s?O_Bb=^a7O8$q0FbK0B}RD4eMKj
zt!I^ldPi2Jbc^p!b)LB)d#V>5g0tbp1ItHZ9XX^o6u6j>o{%&F0t+tdLEsOKR>iXJ
z+MoH4fUA)AS`T!C{$E-0;urfINSH3E-MGQ7k50Yk1xW)=6!_>V%H+e1Wq}$EZ%}9S
z$YOb-@R-v&(~byujDFvUAe^jWG$Q3;-D^y)n^N1`kUFM0;D7gp<&H8~+2O!t@mS(_
z6}ACRan|tPmYe0?bvt(RaQo8FqBdWUn_1ZPG=dhe9?%ToK;$Vs{Q2(cqp#6Af=!QF
zhAQmj^jxZf6%5L=hYk-`bJO8hoZ6XQ=H^we_e1uCU=C7?LggpCcw+Bp_MoF{FhQ2K
zxJ;D#qoqwWa|F&_9~Occi_L4O+0Aa`4y*XSx)vs{M)czZzXI4H$4-kdvQIJ!=Zf$2
zD$qW=>v(oU)A5B_Ll?)%8EnA&vlQ6aymIX4jFHN6nVQ6-x}sIrXv$odKXz#}-6u(4
ziQPwsZa|K@yE67GsIZgXKpR?iG{zzK&b>T%Gm5pFds)|DePnMP>HW@5{Pi~@bSK=^
zt|~rZ7%6M#XSQeXUia|TgLPu{&Z+*Im=t!`oQk3EH7Lnf-q3^Ez-9-7cQ-luYUX2-
zi5XX>lOcCn-gn}JR6Uf8S}Iw$1LuB$18pzr<(hIWHKC~o1!S^GAs?nE*X=QfNx%}*
zN*a06o)=)EHGg?4v(ETe?FS`k?+q_1)LV`Bw6h=IldVseQ|?#ZCk<PN9OIt9;|)}6
zSIjk2>GFvxl5}{r^8S~q^33*ddz@gDyRUJ)FbK&-4JXkGp<f#36nirz7!0e>|2SX&
zCP6EOsa7QTHiib<@{`=uEb4Z+R9nP@X;w$-=$TzFevexs@*J6yF51Y)kvpF7%N)90
zLu6$hhj$r3%(5Ir)h(DQ`C@I|dmvGNlm&g4LG7{gnu?ByJUx1ID4A7tK*f#+mJosa
z(kG2<$dF{pJ}4vkBsF`{rdN)aZys;q$4HGexOE7l7R0=mx4A?W|1Ce>pJRY##<$_1
zJSt$A0$Vv1+waJZ{;rMD)+MFvccjGfY2^r$|0~c~ZeM`7wSEl+-Rx%$QRCKYjgP|u
z>+=q+Va@NYx_FU_TAxe#v&{mK8LVjSH+ak4B%rfE$8LaRRA<C4T>iZFJ+iJJ3oq{H
zz}A@=up`9?2WE_-5YpTO<`nc@-zuq4Qj0T6;hHqwsEjfSR}wqY<s|aPRdVuATR@So
zeBscb#J#}DMY0W4fMeDi=j>?Hwuz|owb<+QDYs8lmzB|x&DFLos&SRMgZ9&-n=;6l
z$KiilSO|SQ;LOl8WxtwS4CmYWuKzmSt`QMNJ55n67!{+HqF1Y>5>w@BFRKAJ-j;8#
z9)!h(#!p2@m7m|aYjBAWx%`$j)&sWg+9ImG6oNPMi$$odX~s3SvUsg5swFnXs4A0m
z4r1fUX{QMdc~kg)xKf$G^zxFZAvk-tSPBVGD$xci(QY`}P_a&gU-PC|jaONO==tNC
zE9W4*xW@=J#s!u34xvq0yYGiqmQjf@igP`27DQq1OT$_SzkFkK%?(m&T4X&(1--2p
z+xv8Bf3q^9<ejhs4K`aiSU*;xfIT<ovhmaY6VO`NYt&egTS-mwQ-)4d5?W8cu^F*_
zSQxc!cy+cyg^m8&US$HzoHQP0S?zXjp-8FSV!z=!J>RxEFp0E#gUXj3=;&LgnBp63
z-|^)w5=;F`);i{roTf6^Wp1UDdj0EU+W-0l<=R>qhOG8{Q*57sV`t1c6`AXvcON?3
zUfvvgIT$H>{|U)89=sVHXiem+e1(}10eR1rh{-!eh_f=+qf`OHVwvmM!c5kp3K{wI
zzs4#<o9+FHBF$;~`FfD?Dqo5fp<Za2C|$xMgxaO3CPivE_x10?*V(-460#OKy;H9i
zN(k2GG4t^O1N=p2SV#{F7kz*G_H|55pn71zn&vruDhhEbx=|JubYM3M7lptI9ibm|
z(mi>%(P$T{MoMDBoJq%5<SUKr>qFt5BCzTOatX!K9Api7kzu@)@oI(q+S-dZVM$2{
zES(mn;8JyGp2QovhS*)V*NX9v3u%6Q<<h>O5QY2Af}WE`9*OU^&DxoE|8##*`}^9{
zP;p0MT*No2kiDYk3bZ6Q?wgihPI>u8M3Z>ygYaA|^SQW-x9GIp5u(CdCtP{FoW`Yc
z64y{9$6F%DUc>n$p?r8On2I*+?CbKki?QzdIVlvIex~$9qTYtL%?ZN66#g(GAetP@
z3%oyOgk$*~I<C~-Wq(qdn{IfSApGI!Yd3Q2&3aB)bta>G-0jujz$8X#9$U}5($&xx
zMsoNGId&KTa8`FV|JW;R%NmW{qT<<i8c3xoErLQ-!Bz^#gN(Ki_qE|<(0lb@oSIhT
zo0IwFiG2bT5!jfu)1kGnMukD<rZN1SXiV1EHIHA2mm(7-gb)pw(VEy1G>kr2GPx+E
z&HL|jCXcEx--MQnSQsdwdw5qyaAtU{G&eNw6H#E=?3wiFCE>n?r!mts-B}I5zD8gH
zUukhe@?n8okFTARNZwsb`e3`Jl3_hw$N?{%43W?kruL^ZyZmW!s?EqeTtq|9*L>+J
zLAWHCBr#7=y<M)&L(i4?3b;DYw}?>;5u4r06^2lxe>MM|1pD&yr7f~#h>hxVrr;w5
zFQfX`W<(l7f3q&;H>w+0E|{)Wil2Gf5v2AaJn5XG&f`NDf<nAg9;lbE>(#gK9tGXU
z*iwJa=I~i8CN`rFGJ{TE2k;}mI^Fx~v32VL-x+7;o(HkMuM+}Y?PP7Oph9NwJxK7=
z!!B^Za>t5eVk%U6;4b~^jNh;IpJb##uxK!8s*%HA4+VUk{Q7R^SChPLRhnySeiT=Z
zeoSKxq@jY>@iMGftk=z`5mj_P@kd$j@@K+D%r`o_rF6YXf^T>yAt2(O`5!&m>!wr~
z1C*AVatdvMFNDN{KDi<k+I-ws2Dk<~yoAcR<V}MF*d0>I<I=35!c*%>9^|Op=-ZMo
zpHip9=8>`*#vg(|qxvsj`KpNT%ogW>ub(*0Qt-x?`+A#&S?5Is%k7`r*}{r=9&ep+
zEZ-dS%Puc>9qagdlD|+O?^#2mVT@N<8@AYGl;@-%OW?G$c5&dm_4JRV#0j0)P*CqS
z{}N0STOWk5oAcJm6FIRd{!%zL5#=8PR!D{7_-f-t{`?pab+oW{JRaJ9p}8lV{$bCR
z*q$jI8Nk&3eJ?Aj7uh$L9x(N+Gk+zK_<>NPn?a)bB%U%Wh;#te`Z90Zp(AjC(azhW
z_<DbV?^)U|7PmkUPw^@nzH88;ufC~g4O6;$Yk+7SKJ{!)a@^v#d+w|(q~ZHlM=cRI
z#e|Bu^4uxDf%{DtrKbnp!*Yivk0HRUWk5?tD4avu=Tx~a4m_Q@d+;c0tWfuksdz|k
zm>U$nkMEi>sHhK*mnZ6kHZoF@rIO2a%WOolF1SAPRruSp0fvdjA}ZjHow}9b#o2qF
z*JY$BR^<2)odl3AxYULN_7>Y8u9-D&A=Z=>ocBh{`{IG00iVD%-7sk()!%ABAgous
zMErceFxvGn_gDVJ4qdm!J-rVM^iU*!ONp8ag;!)JC`!~-apPh=iJnpH{hr#64PEBv
zhuC;QHbR>0i-q;*nmq;eLH!=?L@eV9ld^eH>z8tU6(1-ZoE+hc(#sO`Fn?=4P^B|B
zJ_1K1_j#I);hNg6AD`R~DfA8RD3(VKk1zi7K1WlH38(TT<OcoBMP&@5yC5}$q5`U@
z0KA;%o9%!$cAQ;ObW4NSD6!GZ_mI4%o>x1ajNt+QveCZ7z0h=cbL43Dy9!aPY#1Mz
z5$WHd0^0OS>J7fORGQzKw;IsV9k-mlQ`BxWihq2%#0Jvf#{xc05s6>*^H!ywmmX|#
zJ$yFDCA(ntcDb!z;y?}hg&!0q5ekB~-&If6v^Lz!N;-l<8k<}mTmTHfb|*toaw6%@
z_6T0l`}b&bWzC3n&JpOaCKIc?xIueS*nRfIpZLj?*>mpD`@?I0hVBsLcI8h8&Ch0}
z&Mld!LkjrgBRt$GM?|Efn%l(P+BfIGILc6PQ(Q114#2T;@TXswA`}X3D$aQ)R=>@+
znPwb}T1**NRM{b9xH@EH<4Qpvo&e6&ngINZl#-ZvZ;vg@T`K_o+;k%D0dw&?=jStW
zOB=Be#10niZ~$yNv_mgTJ(w2v>6heBabLOw-<etZCLvfdlS>EP09O}?SpWd#a(S?N
z;4ScdXfDaOX1Mk2a5nEsO!OkNgPf4~Wx_jn!;c97!eThDRo+0fxBUHxrCEqWz0JT<
zWOG$NtH`+%$wTwZl)M`U+6a%58fiSlu<Th(uqWg)iK=oO6E~*9{DMk8T980fT%f7f
z0e1oBK94oryjbwta^_}pYZume?)T-=D-?C1=a>IfLl7Chg9H8Jhp)K#);C(NGd9=x
zUQgl*^CY#&<EWGsGhc8u{O~Omf`mXye5FYT_i@jqPpn6;*7xZz*)i*!gq&$TSb9;}
z^l&<f5XyZ7n*RWpL;S9^q%2Fhuf)DCwaM5Ox8S{FB+`13J-irytj?{;o*AxDTkd2z
zc5OY^_hj<@(JMiWttugLH-OZg7VcRwv!dI~TL~2pR~;za96tqt#&Fc-`R#whcRsPP
z<r!m!OAm8!ee2`(AkE4w!dj+(^+(%lH{&j2o2KueJUl}(VnA3+`}a+_r8te~=6VWa
z;1`WXmohycONTbSmxvFWeUueVfqjI3y=(m=6mBR#l<=OXecv&2)#IXTzf7hXos6c+
zhl7Z&w0(qypfJ?U_D{<Rc`x6C&?2E}v;54ByJoQFNH&kSFhht5PpSodu=bIOZ_dMW
zftzm@aG48<<(CGzq9dDV>6VvW44?`8w2V*m%-eTFt*IqUcAbG!@&)zuVhhT#qM`_b
z#QrtU;q6Jsz-RbV=o+7;LbDhRuP2nd7I9>;NTjVX<G4p$RIFb7+(}pq*|h-v=a&;$
z-R4&NK33j5ic-9NlEKFm==i~HCX8k2m-&Jw#_I8nNr;COKhQxZI#INq$$PS2xiI|e
z#W9g5*(1SJW^_7DH5=4}r<)v*!+Cg1nfI=m9DnPfz!FCVDE5OOmH)kR?|pw%Us<q!
z*@=OI8mmaq<ptfs_kgenh2-m>(+pQPl#qVe`Zr*TM4crBJ%s-x4v^m6_gQ}fHx`ga
z%DpKi(4fY*ZxV`>-it?qCf3ys+*^Fqgh`!l^GQ=Z3wQK3!HIr1a8v*^IyySE#x4JL
zRylAhWdbn5e^Z%(8<X&?|0SS2#GoWCL~D+{Ve^$+)27S~yJFxzDHDu+I5&c(q+0}$
zH}o)9_V^mCxr8Y0b{9rMmmFL4sSc$ac5l)W4j@^#@X&31NeIg#0^Z1)_dYy_mMZBW
zM|8Zp`#G0CLGz%)LI4;GG5jkP$uS%@|I$kF*cHZHV(R*SPb=&!*2ridZ@7bJ9pHf8
zlpm?F(Fw046xrG)VXq}!JXiY`DYoV|XQ$H{BzGs<c8_yW-c7b*R`5ogV)fj`mzmQQ
zRuw4BAouRaw|h>r*sO#5t(F<2RYySxhi8{E)vUi2EBmKWf}&mjU(r6Ij=lKXoN?~+
zm9kuFlQlNqU<O~uOnTL%xb>7S*7H?4B%5=mZBM`rmMfS#)wzz&t-($C?RGQ|uA}8c
zG6p3;OcB8_Jg1rdzoNND;cDBx*k`?`P-=1*)+C1I(&=b)>@?!-dC!2&_VsCD1Xk54
z!vYE5OdGFC!qkz0J2_*Ep)QWFnhG!ev%QETowh5WB2L#krxl@A%&Bm1)ur_UTbPO~
znb}&*^z5DTtY;G+ZAUc^%4e^qd00K0z_^B(y*rD2>V~WsYkh`rcwYa1ORDi4bxDp@
z&3dCZwPeRN>Fo<ngBMMVe33Y<y-*u;z3qLOc3->Jd2(z%GDsGQ8FL2rD&qM6I_PR<
zJch8XD>PUK&kwEM0W6X<%c}BXdf*5~{c)QhXZDW0t)nV34rC73YXjrt|0%9bfo&`Y
zn5?E;IP?3jW)8!w?bl=J(S44vz>m}E3Nr_md-qw?keIH+;=Gh5!1SrZPj`TvIzQFg
zTrE%}9KIOB&8YK+!}i)e#M{T(=cTXgcpEH`42i9L2ZSY8+HL(<+e;i#CtHsiaoG(k
zX(#VCo^YXLkmk$?HD6`hQthHw`;JRtcV51FRVO4{wMT<RJ}prl8c5$)=VPxzIkw9q
z&tr}gaEhOm5H#HeVD^E7t0OLO#%isDD@s5$^%`+&$J*p^rrefPvS69$DeeE=p@j(%
z7-;)mea@nBds%!8gh}=|v|DRJ<$HGd-!}A52K;IP(1)8q)13uzy89M9Bfvs=_^4Ij
z&El7<hWm1S{3#*P0au*!AJz-hmp!rtEVP+5F0A?IV=;uoH8o&4p%eqg6FfozMRYL&
zhZ(hk*%iYTpU$}X!T}*#nSYzFZ0`)Vk-$`5o%7y3N6YB>3z~LkZUusPa^H{BP?xUF
z0&|1^C*v`yGlkjU9!pqjYbGh1e7^KCN*ReEK^z&T`j`#XA;+X7oSh7vn<E2cEBx!u
z)n|isTsS~<X=1>iM6FN)Ic_8&-*_+78-C^34i-43jC65iFz{40lK{4oGx#P0kz&3P
zfXEIP=TprG{YUc1v6iM46TfYXS%IXS|0MB{V^0*#tA-EFVa(%e3nvjxl`pEUj!^&h
z0Yz^4Uy*x<5-+K$Ai2Xi(R;f?KSoHqficZ#!z5!aN{;jml^oOcqAtl25khD1e|@l<
zi?CQ58vU&iI%e+lk$UX~Z4}OJ#~$j&JG0;$v^MBs<DvI`1%z?R?ta;JTbPIwu!BP0
z*e8ZK@H2}wEm}jrvh#QAPyT}O(jLs$H_PQ^rUJFDQL1nXCs;vd8x&6UuN1SA%KP8`
z9K74gSNO~{)}>&r+f%PbuP6_vaDWxC%ZWl?2>&&H^9N$jMD7(n^mBQqK(PZ1YaP5G
zsbE;}*Gr$xdZZQ;+SzTrR2=$3{4f2aNL8pkTV{Ds@SC_^q5Lim<Cw#|DQ35#5FKSj
zSK(5cuawx&NEtFh$opTh?<HtD5BUlsj8)dl3SPyH^C=49mVO+rz%CQ&ESBMJ3;g_X
zcg10zDA?=FRwypC+?!f45xPSL{!|!pcz0MuoEv-DA5OR-wfrQ9%K3oJJA0K|A5zwc
z7!3U-IS9U-`KwrXyV-H}V$aQAcZYlhJCLnr^lO<@DnH+B8*Zcbuu>yu@1@-Tq{v<~
zAG$F6dr*P^3dB!Q2`JK;XVaIeIYRRWZ9Nmo(iXAt_61W$RXvH~S1gzg&a0MY01yGY
zJqH8y4cxxt8;T$r)Yw{9wnEUTmgi92Y-YjKuIsJ7ZK1*UbQB1w&o@mi`Pa%HL*YAr
z8{@=vYdC$3ya=KW^wi*^XECe)Q1r5p?tN`6+Z}=wp2xPrGGN!on%sTe;7(M_m4TDl
z5Q4eGrZ5vC!P=LJ%ERt`9EvJy?W38C1u<9-!6p^%Gxq5{AuBfDEB7wB?|hVkx@SO6
zc5x`uIkUSbl4`CWeZ6jpO`8CYs<&{vx57e4(gu0QpmPYcg|AgluqL(pF(Ub%D@ob*
zOhB-6;yyhl{r6g>DW-85h7h&XlPQ3p2K?*;%%JWYV>(9=x^nG2WD`3vz$bG>DQd9H
zV8dhQ7g_}#qqY>sX<*q%%Lv^t1l4l{Ad;`6%xZHy85i$rlsi_D<aWsDsl}*;hO9(9
z(g=A!dZ4+$u4sM$you!SYi*E&^vB<#8?X4t7T#M`vx~=+{gRBGD&go<V{kaf@}q#W
z{l)Y3yz@yA<cHr?;WeVG-mxeJ_ib;z6+{cd#1;%z?dq(oQi_}1UJj_keIl#O3|%^b
zc|y1LziP?PSRFUJ@$RS2Xd~r8-O{Rt(pGxd#DYDn03P7ng0q$?d3D`{5|YMm{w4s5
zYxS!kE~&QaZg+N<@g&Y)p-{~qHf^6Aw!VeKX07h6eG;^s&b5ZZMetpS3G5bxOi!3_
zeWP?u&RU#)zrG!<9el9$y}G(teCJPIm)g!@>T;1qnAbxF=*Q_=AGDO9OV?I@Kw@RF
z-BgUGp#LWQ#j4!gMt<feJ^AkMn_38D5FNG=Is(uYJRpV}Ox^GN5*5>Q{eC;PZNI-6
zBQdle`M}?8^L|h*ONerc-vr5VlAF+|llIu%`aF{GYY|Pp5lvu6R)}fZ)3fn2=;=`H
zVr#3cAty4-SpNEYQAgV$zJu>aWt{%Z5a$FEl8!<tB%&t3rY3(SX_F&DuV$zc1ebhr
z{$~HJ3vzp(MGq>SrqO%!_T{Z}QU)=fE|Ej1xYJKS+}Yl#j;&H3y}{bW_dnk3Rywg*
z1m-KKALj=mIZCe|9X>9?&RJI}X^MLj052>!2cn7rR9U3jbuj@~Cf&i>$q#mO@SD%o
z{b7H?%u9lE4}2wmY@2YUAH%uo-;jh${;w>S=n-Vj^w+}TV4C*3ew*}BRk`=rt$nB6
z{3|D1n@Jo^)zULw6+;#FkZQtz#o-b#PwPFrllBxd;pJxb%o084Qs~USaW2a}&8J0V
zTQGImxco!kZ$-jzAzE}Vh`&aQ&J6y}8sPlECgm)XD}AIbN>XrPJ71n--evxM=stST
zaaGafEaMB&knUq`$ocf6i*)Er$M(Iz$i+&#2E&pAxb>C(0@3J(*bU!R2?URd&c~b0
z3s*~?H*r+ad?_G@FOtK-uKo)5!(SV}6uP7@zO$mjNC~dAOKLCFOK}-E9S~nx*22b4
zkV=!{>+2}4quobLj+#jNfs>suT~eapD;X=p3ygQ4Qi_=wy1Rb{=!JLA*s4a%SW3pu
z9s$=$eoOWGD4?6?ejK`RclYsbA4ik&`R#dy#{-Rz%TyAME_JP&v*>r`czf<hkazQw
zW9R75!Qh;MBv3x!hl?1W;NnYvBLO<=5t)=9PJv|Yrjk-0t~j5)=(HDz<!DcV9%#Y#
zKz9K-^uQ%8Ot5-U_gL05nqKTIy6O5JD!^+D`aXHw4=}%}^jkD92y2m<^ALJ)`M+<p
z;m?`;cHhQkR$hxc3Aj{}Uyg3Mlbv+IY-`6$^-NyOQaW;j<A)?WboNxSY@k^F+N>7F
z1$&dWeahB9^Z{LahZEc;8mV00l#`YG_73`j&#_L%Z!tm*X2h(DrX2$!VU%vW?d|Sk
ztt;}Y+hdlmxs&qBL!E+<7p2%{s#$`^|2#%2iFwr!!g!G(dN5Buqzrv%V4C^Cb6(DT
z3%OZg_{&R~7!~B}wQ5z;CWnu>#Q2tzgr_XH-dqqduH7x7iA$4RtD0@cuHH&Ur)gq)
zHSE<_6h_NvPv?7MLx>fZo~iuNWo^|V7H18>E_XbwUTU$u*Xx~GvL)|+-%0<&mb*S#
zY?Ri>xiesHe8RXx!e8;+hB+lois}^WPm2cSrY=Yhl<BzWeyAiwU(sgS^)Ul}EeZKK
z_A9ZQj_%xCy`{DXYYkAyjvY}Ijp~)ANMkFA<BpH!uJgf#zIs^dz*?q-HrZI4rilFc
zCBG^}Tfo2jK8RfnDyRyaJ>4M>VlNm;jE^?hn0Eeb+F6mS#s?S6z%-9jF1l3TRMd{w
zaMl8>#i~cB2_2YocaX!{LJ5-9CwtBGjq+z}_P{^uBJMld>JJtI=3BC_gN<CYi#14p
zWO`aZPbwNVVZ*oB%Uw5{PUtv%rrL~>o~19v+uf`ja=W;Q#`NJ!Dny5F10^jwvU#ZH
zB8a=aGbcZPLV5<vd5(Xqsq?$jmrPmojP)cJ-N0Ne2^9eQ)1T^oBPcMiCD%cwnKN!p
zr^aq~zI6S9hgB$UA)Q9)aKqueC|~|XegVAvzcm?qwLF*NQ;7t9i*SbKcaj#j^z8K&
zx5||#ugi&(UV7_k-&Pxn(*Q<(54B+H0Gm%<I&w6*BHhH_1|lDrbUcl@?mEaXq5Gz(
zr6#<~(4L*I-qN*{9F$e*w7X57%WnPi4H*z!_pYubn0IdJfZtt3d1v1D9nHy-5tDXL
zos}721D?d7CbVFSjT<9T?TR`klBR>_n}c~b3)OF&2`ft<<ai$5<RQq%r(&SM4^-*m
zNAA+1?~{1@{Q2ZAk7<68oOxAB5Si>3T=Jf+|2zwkM$N4NtsZ9zQGiect|KQ#z~hpm
zcVzM!g~M%T-KS=B*Jv5zuDm@=xybfXE8A8-=wx!#_a&>?N6Tr(vQ<Bt<Db8`H$$pH
z02N*&UG=Al<EyLp{C^^GqS)*&@m-EV^Q@W8xN*{!jM0SBlFtM2fbV#~42<M8*%7S9
zE33tAsh%B5PI8wG&#Q0Uug?~u;R@+_m%9~gcFSWBFe;Cz5ZJSb1ls3|V$=LKF?nOh
zzgj3h-B6V+6dOe-H0z+K(n2l98p4{A00dE;4t-3ECZG5^y*hj`;Omf=8aDgJi*(n5
zHN)zAy+#+lA$HS+lvBXMfgBzOE~-$vS7+|2Y|pLPGnN~3WEM(vIj&X4Rq8H0A^ld@
z7;0`NzR7k<mUJSA6E6g*)h8!^<|vm8lYZn^wj*O|<t;gE?MeP&th{7_R6YH_l)unl
zE2f7z+JCz*-bY37bSN+MdzgIIqoFN%H~n}i??*hanE*TgCn(*m=RzKJk1H4S<y&3f
zUiC&M-liGpT5cnMD}5YbId<TK*HiOAAwnUT?^&g5v<s(8g%Kk|?1yPok+@L%P&gfL
z@E?P*1wkp@Q&%fH(9kcJtA?yB(itk=>lrJ}Y>5%ozgEn!3(bwH<<#aF$dX{kYl+RF
zL*GySrQ_cHY^fv1Q(f_9x&24x+Z`2oep4(-{0+0ukq5J<fYAA;s9rUf)DRgxtIgYY
zgL{d~4ulWBEyeHf-M3(%N)1TINBM{d0LHzW{6_(zLY_)?YNlB~EYMYtD<czRh*aC^
z(uJ14i%SvUv)v?z&ls<?1b^4xCN;!rG4KU?E^`)p{mQy4z$-6ga}&gqXF!9irIsg8
zg4b+TH3J@mO%uO55XLw^Pk7MKWStzfbGS!__c}WPS5wS^UVfdzo{bh#Id?dh0&jB~
z%Y4vPH-s~vcT4Ir!83c0Krd^yb?k~iFL3@=NS_Ep{hDp!HHH0|EBvC$mS(_!*%)?`
zi0V~$No^!CHS>Mo_fekLZ)7z#l^!gkNWZyxtBEq|1M^G}W``4aOjru?Rn%WP)2N<_
zN-zzT&=<|^`GcZ1)evfw3X-J%Jpzsqa6aJ9$5Q!V``UztK&R_l#VMAb0*tGUe#$ha
zkr>D4l<Z;8(HvaFAgb9`A_^CS(#`0X^v4x>4(Pc@UEvoLv(8cIHkTd0{ya*^?aD)a
z3CHa9-oOU&n*{1p?c=3Lgf#WLt?{u7v7cy#-XO0NB#;YhJ~=SEX16!tU43%$@Iu6=
zs8}{6oHpOH_vbf``I<Qf(?qxoQl$@dCI(@A`7?4h-mA<kT{yFS(E!?wA>s|bd)Jv#
z`i{s+w{Wj~YeI_UEyb?#g+}5`iJZ;s_gq4^00{!Y37AO_WOM~r2?W-S=4oQIl{?ai
zYg<^Ei^>LOMjtJR{aM$o^Vfq_z!$FD5%(@N=lXdJ98sqsjC)X>+*&H<q7;IV6_JcJ
zzFqN|^fd65d*tD53S!!(*Bg_Ghh3NHE)0Gc;678W)$WxAUzkcQo!ReMFD2Z#{AsB)
z)8*-md|Y!GtqgMO4eCrZ!npd={ukQtB-{%Tx=~T7QixhfRYucv{*4e%t9d$^Xw5tM
zo3Si$K8?Mq%aK``(U*KY-|f$x34b5IcMxi4pLqP?+M_>h%iOeY8nX}HNB*91UyM@P
z{d6I`xL*HGx_NAniecS~c&P%N)&YR}-(f+AjqKwT&VKo^q$-)xT7Q;+gZpQ}*wv)|
zbB`7W$pQlCP>hAQV^cUMv-=vV=<RhRXeL#|WA)iseHoLL9~s5`(W{)i<y*k}S$xIz
zj1w~|w^NWl*YfJE<1;=?XOr;7dXG92iJ0v}CgfcVN4(j6uDyDsNzwgvP>lCG=bCD=
zQZm_ZD3r;DWTNM4^C4>D<0?z-6$2u@s2-DRvJWGh65?4$ZhhKM8<(eenjy`h8Vc8X
z$S)h6Ix0J8b(#w|i~;2G4&Mc4%N9b$&7pjWDi8X+ndTa()X^g1Wi!hesPEh1szq+x
zI(?8Bh;cQT>`uL;-SgoVz(u}cW=Yt{Bu>ysYK*|;J|r->W5t8~BP&3MZ`c4OYHTO+
z>qnxi!;-J)UtlxLq2B2B<++<hUG5KQh2i>ohom_5<|teXGkPe#Nd{cJV$?^6`6MI|
zdD|Ip@CdA^fIb#9LK=CHn04tI^od!PUc_^QHT)5U<FcNbr@+}OhHkIUZzwjflm{R>
zL}(gSG(#k;9B@5VUs&Nj=Cv<t+<>(S3p&pk7X22rDR;0QBOvYH#QqExrfx*IKk$ln
zSGobBR(*(KfG_SbV4vK8O_qF(V@B72U)PU`6d5fYV{hPqCT>>;SWmzWOd9#_CT#E7
zstJ<@4rcibHnhn>^m6hF?|0uc)gLd!zp@_NByGVW0tRSPX1}mvZw-sxiq}W0dna5O
zR6{dRUD6YwzIDZ$21sNt=1YA{zX(`b)Yz+k%NS`@x1%9j*C}<af|~HEpkCz5vzh9U
zzr|ZjL?>^3RkyqLFSnFcAf0m8#ds{-JUw?@c)`Id%PgN*c2id^?V0+L#SL@sv)C_{
zn$D{spB;i5exr#&U-hQOCWF5EhT~EeVN~gA(ZdM)IrB#X^AAfdnGI-}8o^2_U;6WK
zGt@l_^uLeb+`IIG;F}$^qOE&$Ubcu4GZO~CT{j`Cv>x5mMLo<$`pNgc7LspkR1o|C
zk`SDfTJ3u_%uft7f@I9o`z-^{Y1k|rBz>~3yj*zoOjj6%WJqgWF6j^dGg3Mwq+ags
zQw-2^R^OrS#yu-;#-L)JhjK_xiVuvl5={rA*VC!pvv%9+_Dj>_R{%;4D9?3TKWf$c
zyeY(qPPz}o+ixBSP`nIrpgq(+nRKP!IiR-<YW2{6#+YQF!6q-WI5<`!(KmQk%unx`
z0(#`GwIwg;M%Pb=Yll~WA4&KvAAQA^P_i!!HLQqEWJ({3J?Qm?-kx!nhNn+~U6OY+
zIIsQK!3Ygh4OT1Bz$-Jt%ZqJzznczH2#hR`2GlIJw7b&L0pqV(;5ct>X>tz|Yus!p
z9lT!!c?{7O9s3h=3^+R=1Usv<PZ@{-?9|{)d5eQcSmNHmj<Zw)10(NupVjXBznD*G
zM`@_B!!oDcipvkUCO^bTL?aT0w0K52`+8#dEJy36I3M2TE8>X?SY<(j2$CVj%NAtG
zE#K13*<4%|MjI`-c3xwi091IhiZSsuus~D4g>Z>GQ}URP@iAigMcOu#j;AF(btYTY
zb@KZslQ&VB8*DLD1;bbJ=NUILjPvvIl%JG<`6+-7k(WGgr~ArhCca#-;nCwarLVl_
zOreLgXkfLTp#>{*u8<UIf9T?P@8Gt=)e8=vSGpkqJb4w^AzG+@w?6+_{bC#S3MTm@
z>G6c!=#NiO3I0xmiX1DDd&SiZH#4p|eaM?dbNH^O({_W4AQf-(4=gJwn><q!GhXLD
ze2u#=`|`WXCdHQz+Lq>5{t%h{OYy5M#5`5kZ)Lzyr=dd9yM*|V)xu(R7oz|7eyP-{
z+Mr>bYGt59WG(mMVz+Uk>t_WBfX^wgd>{a^HI|2_;CCB=bFet46k9mE1Zfpp_&I!;
z2@sq$?`aI!O}Vqhn6v6T8{7zAw?6;PMv{s@(*?>Q)7Q;@-@V=WOV{jK&Q|z|^3_PJ
zrA91ReW`#bqn|#?iByKLt?msjXWLee6P(|GJyBQJz*B?UpP~{`p~jR$iOO=mlJcx}
zq|ZkyzEu$D<IzgYAR4L-%!sWZQ_+plGSrxrm_y}RHMNEZ{=;xo;?Tf#Gt8uFmuJG(
z*tawSVRtjjQg@;Oa7Kj%<RIBR*JO{Km0=8`o{H$IZSe~tqGT4ZaFpahI*-lLjKO*j
zZPL!nat;xP=9vFzCZPtwtSJ!=^uQ^s6@hH<I=*m8>=#WpHg_Ebl|=q+!29<+#!$;Q
z-wnswo(XJ#=5}S(q#=WU88XdsU%uJwd8Wg0JY@4F!RD7d9F2zAzdGo_l_LE-xOSiP
zel&qLi+&o|k@B1e>mmmbw)4i1%N`xlNo~%N=ZU&gai+LU@3I(S%6C8Zi9Zw=2GYPT
zo0nfNLYmP@5ENes)^JC2u@v_L_ZU-^-t4I1H<NjLt<eLo<)%HQP8V$ekVRp!=V`c}
z&(1tQXTEL@wG<=7$)vza^9019jYi=D4;;Ve46{{J`@@<kxUTa}zJB)THGUlmY=<{r
zmwqt5|6Nc%fT%mKO0bGF9E5L=z+KS9*mv;C+t>Wc6sRHz*NOM%gtqZFvLq2rjq`oI
zIX46iq=>pT(eYY@HuwtR)5KWsYN9Ww<#CRdHavQ7DOMlWk9XjrMepz;6KI0R0x|q3
zY)LUx`;tFjfCq22<e&{}-?uRz49s|MGByd-){5m3hkN~14DR70-f_%P9Z+%v)wZ~%
zuD_PU`(bH@m_#&{y(t~*^Jux4=H+(b(Rsh}G6EjvzjbRDe0>yl-pl{R4s<m((Dp3U
zieE<)YGOu<v-fvvcb7UZ5r^Ns_nGSN3-^eoVs`g+<Ed9k7tTQEr;Y179KZ%sjR>J8
zq=^}h{3RM{IfA@-$O?()(Wsq;vhdQtM|mq}*K}CyChw>~&s~i`_IUFzwFFdvd6&gL
zQ||+jFbZf{dHc8tIG~*XVceP+qt&a8719C8OYJ&j*9}LIOpxrq>P*8r({M-EnZ!jV
zYYYfpr23l+(AnVVYEIsr33Gnv`FE>gD47EE=6qe4$~1PpCxm&YQ#XX5&|>I$HUSR^
z<ABdz!7QvWcJE~4^4Mhv52>nF_zS5jF)pW(w{fp@daiI@Aw~DRuKOFs3-=go+AVB7
zw@x(UdFtZ|MO<f|t?jfwUCw&9k#*klsqrNZ@xKE$V5x$B^+1)hO&S_ZclUr6@Vk!l
zz*vsmLxr2UYeGme5gKe3nmo|}{$dZoWiqg0FA{Ue@f>f;XAOT5@<Nf6=M27iG+`>f
zq8-xnq~Zr8kCnbsQ)%M?@J8ft?%|~e>bnsD9L2J#N<?z{I1W5^jp54nIh##A8!mlm
zJ&xch-h_{t4lRl7Wo!P}OjmG*C)p@W4)Vpf3g~~4NrtW~7~diXBHFhvz<-0|ul~5g
z%@aL+6%>NtdQ&^5OH)N2s=!-$8<Atb`{#d4QEvXcqC>+IA%4V)4)TVqxIr!%z$@9#
z+Ic8<J&qjG)K3FuhmA6BHay^x7+((SP1}odtBEE2M6?>EKM{@!%7cWbTF`5@dyIs9
z&k1amq}CuW|CHXN!G5YX5NN3~xss@?Xc;mH5vRB(K2o5?H>e~pQY^p}dv?XaiJg=u
z-hvwW=stb~&><WeI~ZY8H{a60d2-`H>a+ONd`K~z#Xwz9;0p#KTEh=OI{tnENKf-{
zVap03nu{h~i5d@PNmvR~dx~u){jx=l&EZEfYr(8NiyZr2=aeg7_>zvn&Xrdcg)TfW
zR;R@q2LdH3kmD#7zpWc2xU+;e{dF5#f37@~t6p9|k*pb_F`?-$phGKywv9qnn^`CA
z4YeQTZ?gXY&f!>hw%B5&2g7;ZvMLU|uF_c-g|0djQeH{eHof<TT0ofX+T6(jEt_$;
z3#LQ61HSo3wQZnY4TFOptiw^>p^B~^GAk$J3WLAd+1x*8X3(bio<nb*LkFBgAK#`p
zplek$B}yr*+Q_z}>l=WBNQ3H~a_qDzGXEvIgsrpJ+vKdLNGasZ6iLijM3JT!WEK3M
z6kHu&$(9_$vKweArtQY#lpU+J*wu$}l%MwNQvx158~{6#NN*Exc^#)0@X#Y)iTFJZ
zjaCrVoqVCKYYuBShuN9ILXh2BOY@frxEw^brD(1sfe7VD?kp+Vl+pk6n5)!2j6L++
zQP$QBxFv(g+d(<X=rY{kD;7b{iXgGV$Ris!=0D9tEs&yD?4g#YYYbk-HF<ysxppce
z{TzA@NmZAlSI|yNfqrDOBoK5KJGmmMefwM_PF@&UC5-IIa<PvHgI=jV(nsZIK7F>N
zhE!Q$;gikQ#AIn=5;ZXyt}>mmbQ!$#7XVyA7#S^$&p6Cx`Qo-uy35lDW55QX#PXgN
z*>t6!xxb5h|4s52fOODCsA(hMdI;=*Oo(teNdON$Aej7>02e`aC@rv2en|@$P5+WW
zfpu$So38%M6l8qRXgev2WS5gu1n>ZoLI4kNDn;RnKT0gkFJ|uX&^O5-W}*H|KcXg|
zL)?{`2kh{(!3Qc~K(JUO2-VTV7}>;K_P%AHwnssm(7F~S>30nwv7nE@nw+ZA=gEbD
zY(t@xZevQUQtW-T=@91%RHve%wKXj6TsR;Zg~e!MlHA05z@&%crEfu?5-Zq`Oz2N2
z;LikH@`D;twiPnt$4#xMMaEenm8y&i0Z#*29bE&nWwTV^b)cZAZkj-aHOubocF&$*
zA2Rf0@1L+^C)-hksK@R>Ure&+<_!5w%E~qEdO%KjnwV5gOq~0iF2Se!+<cGP+0aG}
zO3kYN?)L=tANjoqlimE}>#DY*qtdUMlnEtd3U#l~7xNdStX*N_R2EouPF+?}a#9Rh
zF(-nhNI;BtSDt3I0nv|6TQ41mCfK`1?BJ~>aK~CJ<m~l|fbfG1%>x$LuX&>L^%S{H
zq}>;#ln6IxW@ZVwl0{IVnPD1CXotdk_Zx^SP0qk|D+DU~CpyZa>}^|F&>?B=y=k$-
zus9Gx>Te%DkRFi|4!`K!FNNEz7KxFjUi|HUjetV9g8z$H`BJLjW%ul5s-i5GKT*H-
z0Vb~g$-56Gts|dmH#c@w!rh|2dYwHc+nY<$o&}ajbu2;;6NYT7QH}(z;&++#C2#+X
zxy=}-aDdzS!(v06*uSiH+cVQ+SF@dH<Jmx6UCmxJ?(&)T+Z?kyE=<!oHT;1vPhGp7
zTut^BFwTXMPku((kw1Ezp7`{><^lCf&alQUZ_Xg9_+)ztCM@yQ54UB<H3k~w+i5DF
zef-j^9D;Bl`|-dNQ<OBCLIJtUV$`e&RQU!0ZG;RD9qZIWa6aX8qa$Px*A)0psuj}s
zsCu?d1z8hrIaW6}V%KIra;3;KPtlPu@;JClaahWHeBub9M#*Df)wZezJzD})uqA?P
z0LC$U4*jNfTAO_1hq89jaQj@%gWrC1{nOQ`OQcNuK5ASK@5I;oko8ODad%T-6Lg@w
zV=fT=FNCuD_z00lRy#_C6$}@tPgK6k*3AH5H$U(k9hGzF%lA1_r&o#hc<KGY;$UvA
zr+-=DYCt3|a=dls!*Z-?c(Xv&b@Aa&4ioY1F$#DE!IHGgd}89gi%iOd=RSgw6fU>T
zG2h1B4Z_!q14fS=CEO3dNPu9NAN$R#OJ|>I&uYMSICUk2Y7`w19T#IdlI#r-vRn`2
z8>vl`QsAxI945LW7ZDxAA03E3JHmv*W!>FmZZe?*xI7KE&!d;tw7)awp)Q?wqXOdj
zPsIbvt;<txgg0xuNwtf-rIG58XN_xkRLDBHI?;XVvZt#AS7;Ie-b=TJ5_0rCANS1H
z>Jfb&s>!^+sm4QNS{ykZ|16Zo!esjMUCo0=94wB-9F}7aJ19n-(R@5k{={X1hu%M*
z6&t4prBx`+B8E~JAWzP@{&UbL$#1xO=QNIG<9hFpmw&Z7lSU$hY?V$SL8+G6z?!Y~
z*F)*M<Be^j{Y+r1_@7bfIW*{3mqd_6KhAR)>1k*RY^#D(uxf$vUr#6DxDv$z+kExU
zrhkM6*>{=ND6n03irR5636)IR%JK&+Op5vWbWbJVdnWCoq$baMC2hVMZglU8`i#dY
z*&^zgsDoPMmM#Krg1|o0R3ZO)XU1y|6(-Mjat@tWW!D6PZ?fUry8RsbT{BB6_JL(*
zl>Smybd&N9DnF-S9$<r1Rq<>VK%RpxOlz{dULers+qxMQ05FV8U-4a!4E(=0g%s&X
z^<INdPyf+a1PW(_P`f9kwXGphN1W0R#5~o+Xd$pknkrDAI6w8=4_dyIvQt)Iv?fL>
z5{JV6AbHCpzW4npH6AXbLoJLP5<!NU!N|TyG}Drr-v3WZNHVx%hzx)X?KN4R2T}k3
zWbfGPv7A#T{Xb9RK&kn2by&O_C{y~y-f#Ruop*MYJGrKbkl0rIuRFfW_{#eBi(*LN
z;WKTPKHtL~I(~L5vdwTgMr9eA)Z1xG_UJ;F>=!SlE00gQiWgfhe(jC<&>wy9T4D9*
zZcf*aqt3kzR)s%Qd@<Hp*~DOYvruB!&0%K^5NZ!BH@k^zvL5R%Se6cN<x~U2Rty5w
zc?pF1JBL2G{A^JAau)olfTPQYLluj*VhMUvL|7Ol_Ny?m&!;yzJgl2f8$80#hvcW9
zyfe7f{a}UQzlXx751pHPX)?bs?D3@D2gBeROb@h435u)h0=x7ZbFE{O6k)(GvReX8
zJz0k0O4SV6xcl)Pal^g;((eIXL4!T8c}s4CrZrGKol_5hkQb=qv#=|`a{u(%3?Fix
zmHp&O`%@29y|;KER`G2aD*j|9ZzBesc+i|Fb`dz`xF5OI*SoorXf40UNP12>^4Q>e
z*tNSu6!4jB!B`f2X%x?5<*K_EpO242R#Up#DH~a`2lhR*Of63-5MX{zG7BrxJYdam
zE(;fo%{0M(a0cF-jnN1@u+Dk_+wz^&?4n6ErM>8>W-?`K0XmGH$Oy-k8!0X5@0Q>S
zkS_|)yGqas$uiQ2yRC@yeaqLzwzb+mH~b?kq5WxX6DKo4pRdq;2~m6*mVg8KsF<XG
zA1f_d#$1CzhZ<iL_Gp}(H(}0;-Mh4CKl{tpj}V%Vu6(WG<a$@lrS>Z2H8f-9iP&km
zMHJvSKzJv!)7swXS&h)r_ZN6{q2U>~n~#shB(p42*UV2C9*pJ*GAH>7Oj6e2<$qL=
zMsoX7S}S+3JN<frVq%Sk!Bz)nJEK&_!}>ESBtvzE;ckSI=~<rqcRuJ$$qA^a|9@P4
zby!r}_xI4<-7O#`h#(CE3W|WzA&4R{!~oJEp;C$<A)r!@luFAmsHCEV)BsXLDkU*e
zGDyGsp!a_7?|mNhk2&Y;z1G@m*NV@|nv@dtbL$Tqkj<1nnlQb?xA}w;nwJvDP034+
z+pz?lwWk%<629_hohDt|yjEDTdQ8>8Hr2Ii74IxFnb*Jrrk{)geIHiy<{)0ut1f#i
zs-m4$Hss?~B72&N8f!J}hL3|iPF@^mPi!|VSXQ#z@7>)bMDN$pKmcWrxj`cTl>LU>
zRjKCib3d<A<3kVClsEtO6NA!dc-#{)`O$0h`eek2Cr6ezqExHfSVMpnM?)hX<P-)F
z;aWwwaWXV~fj%$zB4V`rA}MoxT9<4R$Y5j{(yM&}3Ia9(Ej0+E{=|#N9V%REA#Ff~
z8(_Zed<l@E1t?;Hf0&9v9V9Z;YOWV=@BI*q{B5P*xVDc_3X9U2Uu`7-=*Wk<is&Hg
zB^vx-E;dOe^0mr`!@a#Fp@9LG_v~ztBI0Jmp9uEaklrQ#dFTc3djhEuFXBfJV=w`}
zx%x47Bkj)Bi?IT@Je9aMZ`>Mh<eCgv)KW{8(c3fdupT$hJq0Dxn>-pGRn9RURYZ@{
zaz|=j0-Y6E7su6=zMJT{+^G-$+vrCYq=(nCe$wilpjongY@>dJb$E2r;^PX+NA_Dl
zCd@CD(Y+&JT9U=$$P{99JmiD71l1!|vn!9A1fT?7S*esQ5=nXaXbFUjk~-pq%pgKo
z)KOmSX>>Ykkz3lm84%zhll+lbUU@DdV{d<QdM(%QK%d_eNGwx`r>RYNM$6?b2QB`-
z)bn#GK6L=INvR=Bo}Z|q!&VMG(Obpl%c1LQ5)PIP8ZuqAmNFDpvyKXHI=AJ0WoYAe
zv{v5d$yW6r5ib<->UZ>aRJTuwKzL$2x@v8Dj?$Km&D)P{lv~2q8h*qO!X3X;Bd&;!
zd+F{)`DrKU8;Dz4GB1LyZYb%>LwV|{h<7E0kCNg?q&ab7;(4?nDckQNXTRvXgbS{H
zZZNvIM=2Y@)LjFS+#H^4@;raK858SU$gq)vEzjx}EGphn+TjfTK%sK!Dv`{C=rJTh
z>)ZUkZX8=}Pmo7xT?E2OzR&CC4pZZ{nmq;JD2q&&M_!!&cSw^#gddx(!mMO#_UA)y
z(&Rbmr;lL09lkje)o)FDzSuQ@sf0;H(J_<si)Qlqw&4BGXq|1a3Qn9&`$ulbtEoL=
z%9B+~?<;~wsPG|=E@jXCDiIM@P(nW%J+CiY$F-PKPaW(0_TIG3c%4cDS@GP_B3dK0
zcM4`bAslj;dgVFiTxtDa2~ZVXRN_3vu4dr95fK}eS=GxDX`3uo+6ltnh8U2avSec$
zdP~1Ifi+0pcN=Bzi}B-RbRuu*M_&4#_nB3Aoi)^QhN+7i%o3i!SWH5c`Mwdo#-ZV)
z4;fm5**6DE53VfnDRm4;-gV90{&C;y!olpW2aM2CS1FHX>6-WejnXVQer!O^lMn2Y
z4Lq^z&)TVm)Yy(2_c`(!B;Fi=>{kTTh$S&1V1Yrt=H?l#W!Ed<*osj^rybQ3+p<z=
zfSxXK-~gno8G@%u0iDTtS1#oK!+g+A!X+J0=QRc+6ocX7(H*<aD?D_33J7})9|K!>
zd?Ko$gIKTh_zXY*+Y@+?iAOgqg)83r&vti(8mUW-6ag8a#5{~uxyR70`K-8DR@`}@
z+$R|XZ$Q0EfR!IjCy7oR;lz!N$Hi{}wq9WQ3$aQCSfv|I2)RvGVUWP0bbtNE_y^Ly
zGr!AkB=inExeE?H(a^RecubyJr&PRkuOM2k{Pgd%kKarjNX|NpxP9G_1V_Jw=oGM`
z^FhryBO>#?stp9u0-=Vkl7d_sJG-ZeKUEw0Sq)0q4(e~0q~nIWsn35J9$qLX%FZVC
zk*5QP>E93vq3$2D>G*X&h2BFo`2X}Fv33c+<JT+|+9{>3|4?o3>P6|)I!=)=x=*8T
zf63CE0{a4$I2dF#0~GSp7i>i9nRsjOQ)oHtC7&&d+9*%)@&@6?sc0pdLnpe%_TuuG
z4_Gg3Sm-*TfD)XsQy8IRRAg095O6DKbH+AZkuJO(Fh*K>ImY%%gyLPy%c#5YrX}R^
z2#`w>(Eg~^nIlV5-*Y~kex7^sp4`)FN@Qj-YNBR=_@iM=ps$%NdbB)UD*cUyH}0k4
zfiimPLQ`p(NLfj7KPr4eso*@mWS<iGJPI-@-@^t1pIsJ4P_W({F)V6<Jjn01NLu-c
zgP%lO70P%Vd8=-;7m1Z25tPwNm(_ux3P4%b0YBO{!CpIfak2fetyoxtD?{OAS6o0C
z7MpV$p=@blK7cAad{|)8t4Isyk>CZkpdLZAy>Kky4Id??9Kg$Mcz`UqauL^ESWujl
z-a5aDny}RjIDytzn{huevjJkLi6MUiEqqMu2I2^fB>D!5Iinqwf()O@WCgesq!5;_
zPy7rm5^)c&FuMKPgY*B=CTYjaIQf)gB~2s8+D1h)4xfSXG}kO+1Mu|P@=$nyO*F=^
z9m`*(rycwX+hdECvXI*HUj*T_j?siQN|5mf^;_a1N2;eRh!(HajI2{G?*yp{<ihB<
zb5Opoth{bmS0Jfg5HIc=$U(TrgliFj68yk0*w?&|`f-dVMGs|q?nTq$!^c<{K`s|4
z_VLe42VdV4zW@=)jKJ@KVL{=U70{6*JVbpE03C2UfPBJ0DlVLw(g{J3h6D<C>qF^*
zvO>ChE;k;Lk%Art(We1=9-z|&QQ}lV`sXJQrwozqf><3Zhd`MCq7^6;0nFi_Xz?<3
zjEv*c09Y#VWQm)y|Lk<WEESxG^C7koAP~j@E#ShLcwW)=;h@BK+eYg3>)2(IqUaE(
z;CsZBAkz|RwW;!1)U+vTLfXVUsGz0}l*dq}a|;c7(XSzgp5ps?M1|kAMdD+>PVDzb
z`q=g3s}6H6?0{JJb;$Ba4wyWR9;{^`zOl!F<H4ZaH#KZhyPj%T$W>+L%#LIE7l%#Y
zVHzQ7btzv1$|8>j?XvnC3g@;yKe!E)HJB1VwK8FEW_pD+Zv6;%PA;BI(YD|U(_FHo
z`>*qN`=7{NP*HfQa{FjC<%a2HY|&F$<K>-+Y`<Gxy<MlnhH}p#K-uK{vvl|%f<=_~
z0akWatLH~~jdP&yZnouEQbEloR&(U1WCR1!f`i%>rEzf1^mX~yGZ$zEO<m4C`+}FG
zN70Ew?xerX*sHNm+i9T}JhgHhsx!KZ6kT-&Fv1gR4EWB>(4SYMF-4Dh;Co+^3bMEg
zAEY{b{W+3(qw2}j6<#=x0W=@@;RyX(+&HrgCa9Ub(TH91b1waKG_UQfULOSd>+L3A
zTYQ}UPIA%G_|-)@w72!U@Yk;-K*|#|1vP-bYeo^hm7IQNty};n;Cby?jyFM9%{i#>
zO9-OLe%WnX*KRps>y3lDR@hi0CElh^<`w3(7MNFh(7e)#4rHxG*<)mg%C~gw_`<A8
z#x7`~>oTUU9z}5Z&e)i20w*NDtxe4(Gv#tcS-4$W4t#O4rt~`)Oz<z7AP84`FoxBv
z%zzC8&~bCz%1p*Cs_gt^t~$TGs&KcB&1l)}6Oq;cZPi^h)2&;Q`9H!7ro+!=vylc9
zHa|Wa6{C~DJ8w~pCVnt+k(B}w*rE)7WgJY03@AO>TwRSd22+6Z5jK|>#djZ@^j!(9
zj8B90LfTEJpu`&-^U`qrodr=+&Cxxq1H&fqDQ1+03S131;XZqV=@CSBmY^USspT%l
zOWBNwCd~f%stFZ7fFV0OL|KvUzvXiQc3v1Qeh#580@Q>azrwamy|(OUu&)vGYR%me
zc3u#DM2zB#!zRt1MF$|x5k+*P;r-kBUa=xXocE521JAyzptq%auXC!0v8r|)l6l3g
zS;AVu3IgDFgrR1UgGj5WYcVxzrc{{QTIQsVyznsJ=s;c|&SmUm6}}YK^sM5erEf1f
zGpXg5GZAOpzig|ZGdVo<qntK{{P`uG8pB^su}9sa#CI^^!2c4Bx^{J#_7tV7b!>2h
zrgF`<kEJJYgQ1i;8bq84|F)rqu1>IInI1J&5j8JK8GpqA!J2|XI6zJzh!S_2PU5R!
zekHe_>?-Fvm=qu0|FXF~!A11Db@HG_kBf|mH}_R?q1HS*yZ?aj<4?BA6JlhxNJ<9_
z)?Wu&26a-nJ@_=GKDN4dQz%v-uBFR4nlMO(=SJDTSw!(A>~l76{Z6kvYF86P#}FbZ
z@u`Unc&!~=9Vaa%QX4^o@1(+~8ZqGCrqza~oEpCg=h+nkFFwJ_j{e4KcA<r6d#H}z
zBSzT^qh~CP3B=YcxF{?;jtpK0u#JAIPf=8Y`t&FnG);6MP?RTg{%TQdC^||c%3cs{
z7)|JRla(HbJ@emR>dxhoSAtBze?D>;&7GHy0;vzsM?%m?Uox}R>_`mZJd8Maoi-v9
zcx)Q(?-1b+dc28#=>W2UM@1Ojo9($%MAyx?@CddGu?MJT!nwK(>~m|IZExO!g@dod
z^q{Z(D4tRfp?_)Qkz*-9uK;WGKDKd9xHyOe@#D%N?Q;gW9RffJ38TWl8F}T9VteA$
z|NQs}0E(dwdV4yvr)Zu(&EIF$7Kp}R`I&HH@G!@qRDG{1UA44*-v9ji<3po70OMR3
z`$JPa78GdF?G=~@aCAuZ4p*W`fJ2%t0q!d;9x({6pC>#|7p9qpI>L2?nUq}lMnrY`
zbhaaf_?phnl#cQ?sP~?`y|4MZ+5ut^V8jAmhJM=olpwKA>w>9*p$arS{tX}E5KiSt
zrx~zz0ids9b1y=_A`+VxVx%G~>1#un0I~!I>jiNW-rE}B)kx{+?7gWPWeh|<!3?<B
z%WcWw4R$86+?LZP@`nQv$3oIH%Rc;auK{N1pSDT@(&^c8bdcbSpWWmqt9%xuLi?Nr
zNUjzVXYSI8&EKb@EXL<n{}6UeELU#MTv{ek>GYKdn&Wo?^elMS`F0LP9*Om&izeNF
zeqHoWeTPpsu*RL|Ck4s*GH4!1$F$T1cHZ$c`)cm`!u9`QLc?3rm!;V>HiTDO4*HD>
zFa~~82xV(#J}f#i{}J~OtqZ;S5%pA#w;pW<z#1TRf)p(9x@KyACyd?y*&?R~sJcai
z&w#kyTw$P4;rvNanOqUVtgZY{XZAiSU+R4$NZC(!l)>IZLO<p$wPCYMM!DuXllYE!
zloupOMj*(#`5nvoojBO*R+Y-Pca#48*?s$@Ul^};_mxSqBS;05@&##IHZNRwO`fKu
zksST}%-yo{cXiyAhlV6f6ANVE(HY>-iT?Hc?6gbjzQ)t~UJdDBo33YMvyi75R9yl)
z*%@PZ_c!dEl<)friP|CeD<51f8k_rZp`zb%0;XPe4dDpe0RB`YC`loNF1}SS+Aw#A
zV+|cH!xg0mmY^{av@Run@oQ~CLnit3tvXdj*nP0iV0002jJN^!tBt8jy>!hY@6~p1
z!kD4#p}6CAhPBEJD)h;xb*_Cq4SV|U2h|8Zr9!&E10n|QrLVA?a6l$KL}stcWf^mG
zo7b>$Z~N{HqIL+BLF1UvzQLeZPflqQkGtx!kR(%sEeK#tQld1f`wEhk7-*wH%HYf7
zg2#X-lz2vX82!Ec2fya^hhgJ`Llh**5PZX*4>AuwzwvMARy^rrWy1I02Nii?rhb{%
zk<)n`uN6V5F|do{8F50<fmB01qGh~1QBAR+m{|wJgFeKx^V`=-g$v@Bl3|hl=oTAT
zKg9L1^g8=+jTsN@%4_cYeA4S46%C0`2ohhvznXGt#@H&IH3FG9`iW##_n+~}L~J>k
zoa|}js(hO}Xs!%f{3EJ1M%<k0TbY9O3JVf;a|>OPWN7nMgE+T;KlC}ZAU%92>$*^4
z!I}R&Inc_s@-Us_i@fYabl|e!P?sA2Q6g#8UQy@?{aY7D+L$|(F>tD`OPhByoVT;=
zn~oMJIZa*C{ZDKX<EMDYheFgm4u-#}h=0(HZwD1gq|nf1ixuaZ-{^nuX8kW(;iGh;
zB8<;G0V!CT>+d64)bd1FOA?(A?yOg!zr3e)JhxKS#<+(wj^fomoao86+oO->Q%FeK
z7ULQDP7`mC2Uk-C_fzPP!1YDZLsUT1)T8t0MlK7z@D~N|8PO!~&k+amJvPy|9jW*n
zD&K#yk2lZ;#pl397y8@tWb?8xu&k{WR5&bp`bF@y^q4MO?GU8cT6L(<N>~mAUAeY5
zUtJrTK%@Fly1y8k1TKA-P)I#g_)w#LMe^002AY(0f^>Hq=NCe~Fk_$(D=wh-0H_Eh
zt3bL7NrJTa$j2(;g*!@Wccmj+r1eCfW0Qb)68K6%6ZF(_?xI!%fBe&j)!(UEIExPB
z`NTMUEEkY4ilH;xt<(cj&S9zt{*PweiJJXS?e<+VIWsf+B`HpJcp=4dp_7BxW>%px
z``X$bN~48YufXs>jMZP#cp_*c8Ho+2R4AW(^Zj~!X!<3)o82)p3wTHMv68BM_l1!v
zxGz_G72~Ymx9)Y>GrW-^87qF+wRh2)!>e+Vt@kK~E0)f&EM7+F7h{#c&HV*dQ|oCz
zeMg6hp{!nOFNH5li~5OcB+Ikp*}-)JI80bNOKt-1&PSb<<TKl@h)f%wG|g)MUDU+K
zsGErcl)RO;v~0CA#b;iN_2>Ub42|4ndJis5WDSzOdYBdad6l#E{^n`b#y8*Tq63dk
z-ArOR2g~i<x`X9cr(OK*$En!jFReZoULTr57H^RG9|Z{UYwWQTy_Vs+#K^_7Wnn3A
zUOB;gJ62b1iUu#3-<hoj!U5HAntqg}rv>Pdj{ychu;>3cR!N|h9M`KuVs5Hx6*tv7
zUlp~!aO36q#NNByF7rXChQ#urWb9=8nW4~B2gLcXYbY6C;g~>wU<HBx@HG9wyY8v8
zY0IbS_0CVKi&vi->kCW@LNkCk#+dTj25c^M96_!&sY~S+hP<d{3|IkJB>t!7*RsEF
z(B$1*a=0*j*bzVG=*|*3{kx0IW*mT>kvK;BoUgN`#nibb&zLd}#b7mXg?r&f9`YYl
zcsqQBcyYn`SoTG;;~{zh4N;@DT^RwHI`l1qL^ywmM&+Is&Lxy(q-JF0{4bB`lXa+^
zZ#~Zvc}Gk0M+(?lG{<C@YTMzi*Hz6(KR&qStL9H%Js%ApTbF6LEKKxY9$mdBD~b$S
zE2jDyA{A0II@k}p64TcC-Y}DVnQh=Bz|}_g+EXsoUXUdkJrtp-EGb~y3-G>8a*x?k
zK{1E9#6mru66pR#6cYRa-B$@75V)3l39mRDz;XZKvsa2)r@m=8>W9!p)oO1{FjoK<
zH&U(5_kKYD1z*s}y1!-)nU`(pJ~z%NT78bgpKbF4jyH>u>CapSrYkU)`_Cd|tVIWu
zFIpkvA3nS{rXG#jwQbR9HjLd>X=5Ls<t8TBzDZ(v7p4T~wycRKqFO2nO^G^IAW$s~
ztRRJN>T>c#L)5A?3P&>f)>c%eM$gvpF05@6&c--#$^JNCqWbT!sS@T}8(2Foe8xPy
z`|z^`MR9H9Ypj>`J-3E-+T+QX4j=nNndxh}RgIO;ff1HYqQ0;2#v2dfBBy!miVdSC
zW|qp4x_Y~;xISPjlYsu@N_KY-99{P&T8;RU9}BEZ9GIr+utu$GB;LCwDEsinNeRTu
z+n1b|lF5yqxt(Aal)p576!1%-dt~KWSxoa@?m`e-Z%?j<S|Rr>$tr1z%McU@AE*on
z4|~m~M1KEq{D`ScH%8%7-f1ah(w!(MunI4T2B#FW3as@tiHnXuEnNFxW}?Ws7ml!(
zP^gwHe2fOfBR?&f%q4xM^`<`g6WYMGg9~;M+FR~n32;Q5{?_}fOzo5Fi#&d!PwG8V
zY)G_^QJENV4wQJGr?0-!n=wCGCV11G(y>WlA<?D-_u@b%=poj|;S1pzb3Brc-}-kH
zmD~NBbfAhJ*ywZ+VPLOwmk+Ue!`!}*B>NQC`nC-7`2-#8+@HsLLg<!~O8eDxv1C=G
z*c7*;=j)LBps?Sw|B*FuY0h3)_D;iIjxuekqckQVRZAIW4(+cTD0~by>28-%ka4VL
zWQFo?Ez;gnr^0R&JvR0qfvG82Dh+LSGFQscdnu3y%brP5Bhlu8C_iQk5%N@Xbob;P
zb2p3$vPJ&AYw|F8lqw`nVMv^PW)q%#tpXld>7ty4oAs=b+%S-+`d483gSh1hGk2bR
z3}4y?2Z^@OzpWbVnT-iBBxMAP-9NIZO`Sf9)P`_N{&e{xtP~lRUE*CSO?>1Tpi}S}
zPx+h`Y~*#I!ecaszBb2J?S$^;o*Kg{-P+BkMV|)90f_cL1U<;MBHuRR!i~KS7@}Px
zwUKJGAkmikXCRPB%ig7TPJ3ZKbzy_n@=*&T2AT4a+VcNCPtTCstBX6w^{nal<!{Q!
z{cz7uUfeKH#NsbY9+qX<<C)Gce<w7AUAcJj0r9E&Ob*uBXbSyPbZ{m{KDiEOW}&z*
zQdI7A<b=cCzUHT%647V$sI!m49z+hUGz@*as?_Skqmi2!tP(}~7Ai~-B#gG8MAp)1
zWEa&Q^nWj#C$S>`vEf<prIEXN4k$q#NQs0a^5@<>tq3g?v(8QAtv<=s6chAGmJ22e
z0h0kU9UGkIQHIl~o{*#rn-r6Z^{FVy`{p;uqsE{(WeT`m47N4TIODsrik)rh+~}Yf
zy;mLXV@~|%?*UM3?8F_cr7*hB;%xFd-XJTdPHrP`tnk@|Dl59<G%RuG`|}K)0~}eF
zb5>r%3EfgZz3VPYob+wQF#V0Kh2FS!nj%H-g9v@mm@M-c)99?haYowDyI4yhG|JDP
z_X3CdrP>K65s_3-J@;=;*2!ok@*ez7+5WHzt-82*utUL@T>WF3_#m#GD#tZ{#kVZi
z9UR^f%sQ2z<++~sC)cd}m}wF<+s>W|`vj5C-|-jTD2Uz2;XKYn1F4mu<wGOH?tXZ{
zM21r2&Ix`>Z_bA`pI^uvmV#u#ENlezaR(Agmc*ZT%`fv#)@F^?1d-3&2WV~J@y`T^
zY<a!9DPbxpv80<S+}`DtSvR|+Tai=>Js@Belu}`WI*Ik(89ZDlyLuV7a(W8?nIuoF
z?c7;$(~&dBEP^Ql5t21wD;fTTIj)#DvFSR;^vki%5oYzjZydLRCO?vXx^vb$VFq_G
zGeb7SSmDJD2d0Go(z32D>rogTAVVKEu|0Pq(K$-w#AC(_0Hzttw?F_2NsUT$MRU-9
z${*vu^}G1zX*#cohY#*OZfCE%0lk7DhxnZ#t4}(k@Tf^l%%*%{yI7Z8&aFt+^cB}}
zmLVN9(1#t9IRRp9=ZQEEF(j1o!9G-a(Nq~EH(pjMkbNf0_;UFy!@<$i75|K=BFupK
zbbT+`)t13J!st}cnlDDl_sl9<OlcVYf>F+dyFR)?HpPiw7Bo(WD$_-jfK0+$PxEu0
zzLLuraT(Tk`an6Hre5;!nkay&j>0NMV?YKiP<7>pt0t@y6pMl06}$suy@jmIB^{B#
z+N=}{fgpg%@+;$}f0g2Dg`sO>&W*fTxAZu@4JHy`9j5u)&5sL)DG5I(%ge=0*I^4{
zdCjH+%-8xr?jb<Q2sqZ9hSD=r5mTe$uVh83<9-p29vdYDzw6_Lp(f5FK$RXaXuH}Q
z+lQDo_okkM0^f)We43N(K`BeoP^KwR9X*Jf9|ALbQ<Lx2w$q|pxodJnCKnFhsvV50
z3xcr^V#L`V4@%?d#?0<P?w#b3QgmdaKhwA7S>2z%cuqoktVRiNBcQo6W!tD;k?mPm
zP~ChmGgH?~ezN1P7>fB32^czMM%=~YmNdcWn&nFcFpb5T6qmS4`y3e!iS)NaD(@}$
zmoE}wE_>-fIiCfd?(x<Po!aMBGI{Y`zLd-$=@m@mKU!_AP)7E%ia@-L4vHm7vm*A|
z3u_a%Oe+ioCP<mTB(-XXZn*>@z1qc<I#18Z8n1{aRwr86=FQc&2Yv;T45xwHft+D5
zN7^%#d!>qVNYdPnZk^;0MrOYf)p7dh#WegBl2ssBvY}CJq59Yo7xO8!#?R5$OC~b_
z2k*!=c{5*mQbF|w82m<(zeDGf+~IplDRP?0mHusm@Elvpq7fKbf;}Zt43bnViL+9m
zl}W9A54Vr&xlgH=J)0v>M0rr+K}hwVam$wSWO!RvZgr`ZJg`~AH1itw)nH1{0mt7#
zixMFpKjZeQZX238+yBVzty42T<X4p1>G9K+x>g$=2`vmPbBtosH^KTC4yvA~HPnF^
z-iO~$MfBWqA`WvD>bd0q_R=lKkMZPekI(Yz<z@(;I<;}o6gR<5K0%{GRcnZ-m@0Ac
z*u_{Ky4eCmpx@ig8Z*3a8T>j*>VIE<Wv2R$5yl1Opnx*g02q>#`|ZL>-xBAP?$Nbp
zmR~5oR`!NeI44})SfNV30%*tqwSs`P`uUx@Qa_dZgBvc@QWggcmYycWxHqY92b(Zm
zm`-9r5DpGky92>#-nmQ?q?5qLWs4Y@e~?sg5-k!JVyDIo<AQQi{&X45{nqc?;6Qdf
zy7U#dp&cS&WyI#<R1}aEzYqkh2#BUs@`*GZJdFL-^*U4}0M+2*EJKv6@h>^eBosg8
z)|)jf-N&jX4Xz<NQ@HXPh!h}TMwWlR-wt@`UgRW6i<BHOAxc*H_gifVo^y$;Swml|
z6k`dEbfntz&^ktp!ov|HcX(-~>Mm!LZv{vLPwma|vr)8=nV%E{uY(#o0fFmi;gpQ-
zAJ*KuVVy(&=Sv;)7hjUX4Z{!>W96`R$T88Fa4Zf-U|*Mn@EZT{P6;+%;B#_1c2<EN
zA)5;DRB;}Aml$zTfEKU~sDU&B;HHXJ5`3hB9_HY<7DtEIO0?jzYgf*I?DfEN0UTQZ
z1LdhLB9Dza=%|x}oPi!6GKjpj7*OY|j~Jd@ETrvT($>q^B7yv6C(r;?0BX;S2A|Y!
z)^JAdoh0CXDG6X$y>u{`)z(Lam)ioXFX*4}huvBEILz1On&+sAuZ;#f`xj@h5td(p
z{T9kHHUUmZU~Dd8#T^i8+AoC7hu4K)?QVMJF<dz0<?(T}B4^Mjk=A6X39q!ft&u;m
z=xNX}uvpe~ySi3FLACry)<5r4+pzG4#_p>jx0ktrq1!P8zrjbsz+RPXpj)yD$sh}R
z*x8eVS7eL1&Ux4<^FsTIZT{ISHMS@7(F(6GT_FWqr%(OSvQLom<MNScvY}S&w#shM
z(A2NW)elSbOt>q6KznNZ3xyi0#kE3zBd*$DpKAeXDr)w&D{1~tTHlx2U)u9~-98t7
z=rj82UdnF0+@kw#<#N|R8a!;UQ7U|8wf7wp&WXVAhi8C`4BSS~Wn&2atwxJ`t<BMB
zDYsiQu`bF=w@Xing!A*HW2!+i>pr@m^9l;-ZRvTfWB%iCOLhZ}8z+_NzBpg#As$1Z
zsJpV~=M_z0*i-s5FuK6+0(@BkTS<iIf}P=*eSsh}N1;{V;X6f}YtI&IJNwfBSfbft
zu0}?;Rv}G@)kPT;UzomHRUE(vyKxN|-68!2jx3-}OA&}`h&|VKwUcw63>P~`2bsop
zUdO(Ghh6AL?zx3W6n)hQ`$Y@t3Dw`lmgA0HKT0S*;86jZ_g(;(fYc2;ciLDZ8MAD7
zEyp&ES{~E~XZ^Z(`^qbluw7bIj$b4v?oII@4gB9{a~M=Z$!jy3e#`JzKfaPB&N{)v
zn#9ly>7h59OcugYF(ynnhHL>UP#M|*RFwG-xh@E~n<$NM=MhuBC2nrbfi@?HGXp&r
z$~{_(>F|<;O85!;I^o5^D+upwUfexGC?ie|fBpXqM|lhZ^ULQsmKGo27TMg!Z|FDU
zz}cVpV4!eRhhr)4YycuG1h`1$+=d84UuqAf(F^!*nS*KYy=Dg*co?n`AQwXEDU2vS
zFvGvrU?X0emvQ0p(tQ&IQeS^b8Yr||lDeRKP)v`{-Xv2yvcUySII!b_UQFgUI9;QI
zhjktv*J1ik3qAHSHL-+yQmNxh_IU+aEx>!(fBV9Fv-MZHVUZ<{tKAo#Jw<B-YtbP2
zW<j}(uMYI^y@UqC|BnvL=E2N2rw^zvrab{Ri5#ifB%p{(=DF?m^nf$w!@K@BZx*To
zG)|9^^yjbJG$j9zOd`2)H5~fxUd1EoQ+^53XfszDyaZl%uR){ODQ_j@&%0832z{`C
z91LJ{uWBkR2GVCOq#p^R14ae>S8^#X{+WT6f7R+X;-~@FV~oFqYH*NFI<P=olgb!0
ze{Y=)aEqYDh3!~{wByQ7umxS^#I1kVRh!I;7NCDl0o?;YZXnQ2K1>en+eP(Qg8zmE
zEEMtpQykAI>~)S*GUc2$Y-7$ob5t&5YbAoKQKRVman(1+^?R-(re2E$kv?C5^yW`f
zIhb_mS$wP?C+<<|2PUE)(BjS~2x+nQ!uYPr(T|zQ;zu2iCwva!d*P|2RNAXh3LNmx
zfiYISj_@kI8n)b3(bs+R<ZA3DJI+KY&4TEM4gfnK{6tD3C1Ck$ldmY-QKaXu=#@E^
z2wy%q%~fWR@IvB6)C+)1W&}qX+1+$%5VDyHcp`MQtRp&-1qZ+qKpy1u(QJ8Rs6t-B
zxoi7@xBVU~=k4y^?Vqj09`^SuR=}pu145Cy2q~~l4!?FDNbG)z)~g{*?k&ducGGey
zAmD<3;blAorW#OpG5NQVN)tnhNE4V3geZv-LZ<`JS*+>$?D^tSL&G&6O(r##NJ>`a
zGvc<_eMo(vyXN1Q7&pH4e(pSHZ`&GVn?6R;*LE0Cy`l^P>GGd1DnHicGr2bT%g6}!
zpHHGvUoiv?(|EjbRhiWoo%k7v$DcnfVKg_mm*O^X?_ILj<X9W=aySfZ^I-rihYCeU
zdrHY}BR0d?B>dZV|H$*dtm-^=3Kq>at4@iX@9a8o{<^=ExSd7}eX$3U5}5%?+4XAg
zfbgrf{zsoI&+OlbH2zs69%>~QYCLm8(I{}CJ1pLKJdp~z5a2Z_k!PZn)@rY=jJo7`
zJBx6c(EV<9ttc|oRWyIt9iPHJx;50j=TI?A3f(XWP2ivkg#XT<%wufDFsNAMyYtS$
zr@42EWoL|&csC;a-u_4<$KNu~>moUI9ALu6bo{}#%hZG3#!=<Vg75rC!jc>OCJuyo
z=cH3o$d5kXgF#ymM&wxPH{aUU?;m<pd)Js!#(xlAJ(=@fanS0dfa%Bk#DzsW)!DT-
zdg`^nX%RqKor}f@!+B&~JPGzAN_#GC(GTz|L&oOZzgh}!YM&kb+Wp|2Ip@mwLtHd9
z45SyR!|lKwB^n#6HmbBY)YYvyn53@i_PcO#Utv9&LpJ$AO;6$D(CXRBOeetWkv2j~
z7$V7s%cFXAym7P`F~4+{6|HG?XsyBGwc+zEGxpL)``Y7KxhIGo1aBse4{mlhdbcU-
zOQK2xkNg?CKRSF!Z+rJi;EdZEUmO{X1DtV?03!;FD#zJV%zLLKWcqx=RuBC3UK(=k
zg*VZ^c*-uDQBt~W`lY7OhNu|{#&j$RYYEQQeZ;kFCMP6hK6LNs5iZd>*M+6r`l6%g
zePhMzt`Ku`I`n2N)>0SIf+CK&xV(5S<z>o9RW_$-z2a(SQK+lzO7cC#<VW*213bbP
z6!eJSLg%9~Mrbab?IHXeU+Vd7c0<|oewoBkvO}e?{nRhj+fAz$rt*(=NS0YFayLl4
zAm<r4>jMR&SE#wRQLZJ188UF~{j}!t#*V9DySD9DxzrKirAy!|uf)R~FB+IEbccrS
zd~lwgWCp7Wj-~z=nPZDyM)l{*-!G*Vh1Riha0*`Cm!(_wQ~yPgy-E?bJ(i)!O76Iy
zefK%*^;=XIPe?w$uiYM*%;ioU&za(km^$+NL>~AqNc`!WzJonj7*YLO<y;L2cE1c<
zAEK46%Azq>RG#ubFVD#{R~mwUzw*LWxvqcOBwU!+qU=Dnv&w(D8diyg#0AV2K(L?R
zeEamg^n#hzVOc|Jm}hB|rxE*aw?yu`bB7j6fzoZSt4cxM?f>3S7Y=d_+qT<tV7pyd
z7av?Ye%%k@;1=>Exb4gPHk*DP-_r4VMi@I}&eGC@bm*Hb>c=)Nt5LB*gRr!-3zB(j
zhOk}MP5n00%2|cgcX>Cho**gMA>?21L5UFh8~@hw$U#9oW~(Y>jIBEUrfkXl=iPh#
zk>TU{;Y)>4>-Tdx+>nk$kjaYeSfgn?^O92USoMFU5!IJck$1W&UeaH=mERrqC~(6+
z_SyQoh;Nf*){4-Yr~|Eu-+Z<zs`4U`p}!wm^@e?VICQ#$V_;60ho5My?C3#OnLVoJ
zP7HKlwmcp@<E=IMz$2quPTjHO^$m|auO-h3Q@eN|BMYTq($vu8{WDXZ?`}=Cjo1#B
z2;i7Lr>u#klr>Kd$%>EcP)Jk$@2!l4L|mn~QQbPO7-29Xm?>v$Y$3-o+UpvwP;nsK
zYNOOF4EbH9j;BfSh{NLVqPnV@4HqeIGp4wtXJn_}^qT3oyvci7Y@Y1Yrdp9rTn^dD
z*#D5q7*gzQ%(0DUP0b%juDoC#=~NUJnT8kk^IdBcYNHnW=?e>jz#qZgmI{@qvUycp
zv#~L9X5Fw-%ClOL``4`XYEet}+EtmF*QOU`CX*;(<<J$5feH7364}+Q^LyJOGW4*k
z*LqT9h2`BAvZHG5ZO=DJV=Qtvkt|XCLpI2l`&Z8L9HWl%lUYelVZBM7Wfj;fVr5)b
z##TSiT283E_#8en<a$~T_Ld2Zyxs9S`LfEdtleJ?V>uz!FwTy#J(;s|De$-R6EmM|
zh7NnDtgg8SuxZFN3k{<%dfoHQ2U|yd?#Uvv9Kh_bd$5As#?t)oov71u4V|swmmdb1
z5!FIw;y>IjTw$;(suu2~y9VmX1loC!b%msy-z<zuo4ae(s?3G}2}`p78+8-pnY_14
zhGsbB2h@9IE(F5d$l;{Srm$C~!$qD*4_eM)@~e<P8kiHn#LG2D&up1Yh3sFkrJ|w<
z6L4nODv$EG%G!MkkBfm#L+1HEy0>n=^wf5`Owp6>m&m%}$5)NK%f$-BUVd-g_BH#(
z=s!~&Rt8nr9fOH&H_@I(bK2gpbwo(=3&wonW-uzeAi@8DapQ1RB-v8R+BvN2>6wq4
zbul{|D>kbU+hxZ_J4z%#=O(^9veo0U6dx{`M>=?AfI^^EOoe5V<({3}8;i%Yic93$
zv(SkNB%}kY8jpnr=Y;?J&OHar>k|?oy%u(!_hlk#?HcQaUs>LWCXH7G>ibg{VJC#X
zWo9ZqH~K`?U1jx!Bk|^bcX-W&oQCq-1B`K6>i7x$6N3F<t*E0wA%R7<`}g>6hK2r{
zU;<)-w77B?e>v4`JjC^spsA6S=>|WAxZp?VWrpLIBQK6m>;TkN&-tykXezA1=2=NM
z^`K_5_e5qggQCcSol0$Uh-A5Al8;heuLf8ph)(p+MDe(VH;qi3GR-^9sV&}?^tkIP
zC+rGj1<*JaMY+sjz^^3L8*QiSbZS>a-@nAD&Bc+Ec!Wn>G7XHI!fBI2%`(8p=uwHw
zPjKE}zpgJj%-yH5RmZ=^H?&z3n(fqrM2oAOVS2PcRe?bT!~v|msyw4r_Z%cB^=;Rl
z@LA7su8}bt)xGhl^WqnUGP?i1dEK{cR@L3^NzT;CC)1xByqs4*(Z#lXekCH785~a;
z&kV^{2LU1iuD`0R8BSbDZ#{fW)$*&cL@DzNzvjT?6VYv{ZHGJU$LAHW<iUBAQtV{|
zMb!M(dZJ&ft=tjmFaljKt(O+GXf$duG67u!H4-qu7V7S25_vvQX5dSfkLZxXOsbO0
z%hKx^S_v0=RC-5XDgRTKa)qF(3HvmAHRQaJHGiY^W3=fFy`Ud#&$3c8^2BJPmIa}T
zK1e2<B_(qI+>M|{p3#Kj^m;0z2sJ55%%^Ixx4+aE3~IOnlVQVf#NIiCod~*&5%)Xo
z_U?z@&Nr1$TCn7%95`R-7(W>_y=Hib(7BjoNe4TE3i%+_j+r_zeUxD-E3&sA@D^#-
ztCD)+nIy@+Y&w71uP7_e|MO<UrtqAzzUfASvNN1bZdeo<dNuM>UMaiS#C2<)HGp#g
zaJkPx0$n$(Pqn)|)_SGE5uP5bC?%=%WlMj5(7ZLJ_2eZsB27kIBNGk-5uhY%q{-j?
zT4o4QmX&PLOD|?rFO2dj{5|>>{lkbG25cdJ=oEwVwBF8s>LaT1c_;3KXAFr|{|!m+
zsK}?2Nf&75m`L^=S3yci{Kr#C*Q|t}OUNus*OjQ3r+z&70C7G;)Kc@Ni%rH5NGTzz
zTYB&%>Sd+xkqa*udC%_$n|Un_B|nm*13^h%HBr<A6BS;_>gxWP-uG457qQ~S3l1q&
z>*u%{*mC+(uU*(OB7?^#86bj8;B~spIKC7tze{1mt@PPXeYR{SCMuH2=&6paM=xl;
z*jSp{#T#Eh1R4GN=8fGIrRCOnCAMEWj)A#1L#g5Zk||>1Ulg>3^(aKqb)b?k7Zo0~
z&LHj~cC)(tR7t+v!#++GCR`ljwvX^D89jYaHu$+cf<uH`mP~rw-1gI23N`~+ZMr66
zs8W~u@?g6&x6@}A3dS|z<6|4HzTanHg@hg0febv@0I}WIjnyo?^FF7K`tqERSN{&3
zgBn30SAz;kg<lY<RN@j*$;*3#6_}^Un*D7;*+=5MeR^l9rFooH!2&yt8tgZn{2r2m
zjfet<d>chxKbmIgAfJ0W<;_l+7yJc3i0&K0?cjS)U!F~GSXX@jAUN^_#piFj=>57h
znk9~BQ769VMi$if<^#;D{dC#4i~G0)T$z}L>}@!_ZX1iCUSeh)Fi7EoS;$HtYV>?t
z$L4E5%}Y3ZjNxRSrH$3^%jc$~=bUa5(z+f3$c1j>WI4>*H0=;rL4IS-9vTlxUw!kX
z_yo>(I2Fqt0qU-XAzYsj6GoD6(PQY&UKA+iTW#4#_|Yg!unpXm@^WX!wHUy|qS8~1
zotqv#k9;`8X_LJ7Fg}ij@^gEB_^*%k@0|Ir#s>C1oMLV}arOT2fUI$kyz@8m(~K0j
z3bAN8AWwc=NI7h8hK9xqVRDO|KF7*GhxbE_QN%sVvk!5iy|Bl_*M<y|EQydJg|M@f
zN!*({z7tI?<IFf_+#&mgM(2sKMoXm^Goq26I`MJnqpXMFk7gQskg555urGnI#GKBg
zOXX(vy^?Z+4Ua^X6}nqTbnI;sGg@YMG53pD<r1Yr=Goq&6Vb7f0UjB%k4|E}WG*1T
z(J<pQq63!=Y?8PZ?%$0$anCG0z7rn1M1NKM5}YQ@bH5Z8GE|oKK@2g9{@(R|(ep{{
zwF?MC#GdtRgV|^Y<*u7^>g-4Q=wL)&?d(3)&xM?7=U1x94CL9b7GHP2znki_eYzY*
z<!aY;+OO+<=Y#^^J4d*Rv++9pwQJJJHCPuJE`Fv%LDYmFLSXGh$!6DC0oJAdbBIUH
zPO-1)^*>S}!vsF7%t!Ht6g5{qy~|$y4uy1c%^uoU%aBCLpzv0>(OE%rr5@$>k(XDA
zB>CIk8I^OtjgwHZ|D^MTjOLwVbI!A*M?PH-MRfSxDYN^%)O+5xU0yfHh`kS75I&z(
z=TP6d8jX`Z*v}i}PuTiCm)X~W?-9Z4ih@nh6VqO&p4=IhZ~0P#9U}U1NveIlZu1+P
zw0#Ul_%u#e2Ofqk<t-7l(n`l(Gg~g;ehrUr+j!QUjJHja{EAPzVuGmS&^>Y1h3HiE
z_^(e@n-A=CdBl8|Z4t%Ue5d`|Y<=Ym*1!sqs~eWKn1SsHf!E>hW03F9;TsG4Cc)&s
zE`rjyOz6CJA+}iLM>XQkZHsd4czv|FNK?;qn&G50Q{H#{I5)CP9;>7du$d6__PWB;
zf@G+W-%P-;#u2>gK*2r3^rHA!CSQtNEP*{B$qsQg70a*5jJwyeZ!XA1d#=x$Vy3`0
zL$2*}zFk#EcI?lGn}u~_GE?k00c_=vEZUGG%!%yx=j^*4D87Ix|C)?w%$gKL^Padz
zPz<I=N=;Se?hv+dPj62f9%c=eBT8p?q4TXtJ$pwco3AAmGURmP+lspbB438Da6RrK
z>mI^7=(cRT@ah96Bdc74#l|JcH2@Oy(hi!7ha!+F@LWYu;hnaXFU}2@Gfs_W>i{?D
zLRVOcq3y`f_`QvJ*N2hEN*J%X9W#607e=2#;wQgRdzDx+_e+LRtfIeAo9<T&P*0p2
z<{#>UrpfU0QH&wYB>Gfs%$wztQy+LxtxRuI?W2#Fs>&GGxBDF*9oP%-rEoYf&~H94
z1B>ugojUBi;dAtrNZ#HJo>L!N2gKE-Kh}z(#n1;IaT6z0j^<$p#Rpk};(J>4!m;t}
z!fpQkKjlKmA5-&ICofPu=k|)m^xz-dCAE?Lcv3lc)LAC!JMU8+^^zraTfS5`nxL4#
zx|-di?leYb+^~iz!bzR;U0{eobFOmVN5Q6V1nkfR*5&PaUy8)Gk}=O%!imM1Z%knI
zMMlOyZc1KSG@-rwNqy%A3taux(~5`q7k+UC&rYcHw^POr+!Tos>CTbTF)d5EN=~~i
zbow%N_b?i)qc!_Z)V*}ow-#)D1(4r3m~jnp1pZ#EVkeXMi%x{I50e8`xqURjbd9lH
zKID?GDTSkK@_tnp<7L{uwiXd+DT+8%%_vg6N->l0<G^C0lPfydeO*MM5akpZT;+WI
z+0C^Am0xeq4?QOJt4=Itzf#-&zTvSu?6gh~1y;C*FD}mFWs~T)mBLkX68sPt?nW^4
z!w{Kw$!Pdz6tNezbnR5mUq658Y*%~rZy2;&@dtly2v7(_-@S8rpVLX2HI6ZSO|rF@
zT(yW)_}1C)DW=Ohrs}45ba#3u8-6M>ox9HIkay>#V<ib{i#GxHU{1of@(!Gg^aFFu
zbE2O|ntEdF28tdZAKCCsE)dUOOA9IrD?7N3z#Ss1d&_rMtC=;8?%O@UHbx-Eiek%(
zUn@IrF7vH6-U8=ewYRUl1KhkD*V0E9T<T0q+_mME-g9%+cr2H7QWK;FDd2UN0TM{L
zzkHdvdChn(3$B&}WuH8%bHO{$meFIce-c9!6QU?lhO0pyCC*s)68j3GX#&om@pqI$
z2Pdx9?qmE(GNH0SfsSc>Za0N{wr5<Q{)#aZkr;aM^%}-Qwc(U4>!OV|eRQBsC<=<{
zF&?>Y<W9;JweS2x+g(HPx#Q`llk1HXj5y$@0JvK$sGQ1;TSnX?l6IWp0g0Ymm+Qp?
zWPHRWwGj&-cL{O;1-@^cQZ5XOyQ5IIVPX5g$BNz&Gxk;c@s8s(IdqUUguG{JHclmU
zZi}CYRq!Y6Xulr3ott-eFXsYoDR3_V8BRjz0rL=>R9pG-D0St-i`;MTRAUXSrtZGq
zZy{#HfkY)u$g`MJuu+}rkHr`a{WM^4A}8HITo60&2TZ#Z@G$_zC4^>C+_`|8s=SO7
z!^iA4(`Uc(H{bOH_Cl@WtSupQni=wfcRpX=*`BZ0TS&gY`ucU{G>C-f`RgjX|8zO?
z*>%}F>dFzK;R4Enxu{rG5Rroo6h?=GfE~D+*npJC`y&NgrLKU-w;B}h;>tXd?a#tc
zczwvD31ngF^4>J^Eem%`*y}d#E>c;}9yHWHVDi{?H?aOa>FaA-#nVsS&B&wAoJ{0C
zPVgc?K1%#e^Q3}1m$wJ@t7P%jgA6E*p>Wx9BB<X#5@J~|KzXW6*MF;5ysqs@VN3mc
zhNvI9#R4oS&^33fgW>jFWX~bKT+v(GC!B7Qg6NoSVW@Fdbln$r5n1D4*)kqR2epAx
z05=RSO@J^ERpq@>j%M_x+~{I(Yl|Z7D(RFUI(E?>3!TrQ?v<z5_l1=WeYdfwyp@Rw
zW0otdvEQ~(ieK}vZXZq7n?xT{F%@DiO7}+<k=Mj3B2EE(MWJGir#5j@#*+)eC%j!M
zYJ#Ye-tTe&zocb!AUi4tj;K>h9|~;2uHAXv4g2CFrQ)Rmn}hU)4Im++kY1JMH5?fZ
zCGY8<vo71xfOrTgxLO?6SP0#Kmqu4+_avQV8grKDI1sp`N(A{@a{w?SBW|eC!j}GH
zf8sCYsJjmb1I7bj7a?O_2@n8$Y`D+cO-tS0@EVCkN9E8M2yCboM5i4;8uEN`&n<Yj
ze>e~JMPaDqzmEogX{Xr)KMPR&&dC4S=7$@wCfhOMBq1vCOZU(bthwZVMi>%_ZtMI%
zpZOiP*uMr(U!|im9Bd<EbuFn-CW^v(=^=p2R1zT2eKS3%_Sv#YUrfKJT`hG0v3DA-
z76Y-zhfMUJ-ix0wE_5Y&jCkDw+Ef-nn;o}_oxZxY?Nc)`XP0ne@I4*)isi3R4tTwP
zV7K0<sP$T8HKIt+p%q)uheGH-#PpPw(9Kx17+J`XNPN}MGzFm{53$C=pjo7HW{oEI
zk$mHdhg_8D*6s#hCviQvB@<3U^bp>R_05!3p2_xMl7B#eA2R_*0JjNXBHc#6DfZj4
zIp0hb`}09fT^wli?%zg5?!C+CG0E!P#nC=@bbUqv(EO?Xj?nqgJoI&SW#(HSrYWh1
zOFp4!0iFL0Lr3~j-1U|FC)qnLzx8W?#6t3y!SI#kl&yjJhIhuqP-4X3i5C*|Gs`CT
zYNp*NgwH*`oM7<^VQOb*_6o!A^HS_xWm<6X(E>vDq7asAXu_d-Ct~%~>8X{`YL541
zLybW9d5)Ji5GC2BTj-Rqa=%B{+mnd5as53dBMum&L8@j9mVQQbG3T7p&*fJ{eB1hd
zB*!BG76P!GJ#g1wl|kDUllxC-@qtNPM+Q0j!5W~W-c(}6jw|XfBC>BoQUhIKI$pa>
zDkYN8>qGFY(?3Yg8GN@Q0qUTG*gKxNv>q3)$)GR3rv1@qf6G7&sHG5;uL`<}`cSTE
z<C)o4i-Bp{Q6HMpkU}Q9lH+H04f*tP>l+n~sl!{9rvqMsAWcY9{?HfYw3Q#(by$cZ
z5*u++6#Y+=O_T2$?K~SAhiD}}T=oW#m_QT2`UaMWmDu%)7KUe>m!qqv(@;?_!G;xf
z%+`YPV9&_iy8Kr}=bkJA=tFeYAD&P-WBD?8J0CN9&dagGp)O!@lOHdpKt(Xv^J^^n
zUG4|U&uOETK>x?twSc9H4~7Rd)8rTO=zi2w<Y!@B_9%}Ppy8OQ3S^G0KMkJia#Tm=
zGS(pv>=01Y4Fe?Ig{^f5ik|MeiWnj?PPZYu*g)?WUw{MSkF><$Y9#?VHvUe9ZRC#~
zU);0R=eVK6b3D+0_7015)yg-RGF+7K!-9eW`m2H3XMy>o;Q_R7R6kpeiP_d|qLXOR
zGn7V<fat5gJI3kPUm3JY<{vcFO}Gii6Q8=>qa%$lw2ha%A;x}*j?wU7TKB6Sb06y`
zi4$b?M>Hu-EsyZ`zjUqq3NLqzL{4VsXo%Q{3uz0Te+`iP1koVS>JK&jCKIVE;r$^*
z7jFT;K=9zNKIZhaYS*PUWDrOtKF8kyg0ni_dj#NWs0r>BY{qr{QI)%-t$sl>w|5+W
z8cW|QWBCGwf`FwSEFX&o6=Gph?`5>F&UCL@>ZcX2l1i`vOBThka8YJRda*FN2@b)c
zZ;cZpb`M>8Pi!7}O?dG(u|2;GgaQ2TKo9)6cdu@2pUb0S?3)pANw}BPI#6q|Bk;a~
z!X=LQF;tt&`*^=Hk2TUrUupPS1Y@F;LtcMw@rDkWLfBF&Of-eeY+&02yZYkdo!QlA
z%2MBPwT+gYXVK^Q8!`4o7a^OS&@ss_FS1gb&dcO7&pkQ4jHoHqwL2e}eRRegV71=C
z{@oa#1z5k3X;Wnu%)Otp$Qlxd;pIMJ$Txr}76b~9H=kU7H(N2$r%$Z6`Q=h^hdR10
zP*fYrp9Gbu!K8e3yX13EiWr`Tu~)8*3g4w0Hq)FzDgkBG#2rf}{am>4j#^&;(f3ib
zU(Sh5Y=+@7)n$HgJ_1o6nqY5r;xnKtUA_-nzm3^bHTsD<_|Qmw{Jaq?l7eU!o07Jd
z!8$vXNlzw6w2M15&|0{3*c{{{W`tB%+!qzV8Cqj8Mr;l_#=4+7U!Y~MYYTU(X6%<O
zCBY!S7Ed)4`>lI?)M<6sNJ1P9(GZ;k!c=mBfH42inphHAUpa&pYKFWViCmCn?~f#%
zO8AwtdaM0O>2JZq>IO~mwYHJ=-(AawlBCdeQc~NLNXV})1$9dhApFEg-j{$7;hcqp
zxf4i4qg|erzF?GaD@vA3=hQ4fY1}^QWs-g-ZRG2DVhC9=MRV3Wn51h%U|!R;;mZ1F
zN!&<3-N@%=AGjJ>a)4t(WCu9`)OMsXYWlrZ8xVsbjR9B>_#!C5fuMIcL++<%L_4>h
z7uydk{Vf3crvrf#)g4FBYploetlbfniRa|BMcDO893^eqc1oaG1_tB-ECmGm2fzbC
z#*ld6epQFhBer<X-$5wkIn44VCV&se;HmZrlgKezL#0YD*PrE_;FWNCb89E)mhVSQ
zTl-6-{*!9FO6pEbPR>Bp7(A@NTKZd96{lgLe)XAV8>?C^jd056SEQ9-m)H|#25&rG
zzX6E*03jRxL)_ivwL6mhIyax}>Z%PJO%KWs|1kLFp&ZX}0aMgxNeA-dwc9@MA_bk-
znMdjY>|cyi1olTe)6Q36u4j6#IWBgm$!>N?M_ut<icWe0w<Fd8xF3PX*-~LncfF@c
z1m2ORTwh|qGc?=Y<Zpiq!0-$Px1*SR*%4n$npp_#ZI@LOZ<EP(2q))~DXhc>@^h+$
zDiS}Yh*QdUPGvf~{H32n!RiMco+1C1ZGQf)zTMs99sttX6+{?n@sUw%#cyiimahUh
zI(3E=Eq<+<P<R+uyY)>rO1lz+&mG~-^K@$Iw!$;U5pqL}QrF*yu;FB;Ww;{tf8X#y
zG#S3Q7>l)uOcEN3c)cBqaY2h+hR{AVk5g$FKHYxP;nN|&BD*25$bffxZr;@X&V(<7
zOwVB6)z$C%wC{}@FUW4^k9}{Y#|o<4!LT^Q5M-m2v+UUE&g&P!^g66v515-N-K5ZB
zl@Lsg^6^M>m}cJ-(0faKESG(L)z@_ZzleNxG~kX$?}*cl!ssOOG0P}%<+i;tF|1M!
zhCGfS8g<I%N2v#lc{Ucv*btKOaZ*|q)%=|MJrPDOVR7_C;C7Q1Me4g;>>;eHN{aX<
zhL5KH7U|<RhTj&95mvb*hy!Y5tnIZnqIy1FoFu@c!+OD<oCr!08-VdkqQwyV8=V0a
z5C#b~Qm)I7C?a{YP2%0Y{w`c?(p^K4qnD^o9Bq=*Gv}h9l0~85Nu!0>Xk|Th-d6h_
znh+&g<TsozQkF6IYbUKPh9DN@v_YDgL*cl55}i+tq@o4@C1XZVrVT!Z%jFw|s9x+V
zE%Xj|T1MJ1j|M8o<T)X?b4KFRQzGN<>txSA^YljXsw_nl)RPa0>+1m+Q}YQw`AZWr
zXg|r)*bcVrv^cXFt@kRC2Abh7uo;e*K{6kJHUw!?4icN3n$H=`W6QPBwRZ+@kdG{D
z1Vrb-)K}PVouv|`ZVpON?$<7}COg$L$4USFHkGu$u9|Yv$c_zF<tjVwN~}S~osU9l
z&x{e70ZACrIB1$fc}%jKpLn;YNhFhWZob^r%_=?vi>YP|)6GgDftAQ$=6ap9uWR)1
z4>`dK^({U<qbrWhy1+W0jh!Dir`4W`4x+(tI`^wyfc-zVz62cV@B4ow`!3nH?Ae!)
z5ZNM8QIs`B#xlw__N8pumn_NFgk&!U*%d;>ge+sLY*~gxBia7<J=Ev(`Tl>;)8pxx
z_kG`c&bjw}?>Xn5^E#a~x=w0Kp~xaYbqhqbC4uVf>oceQpT4JgNS$&F>#{X_FS(fl
zH|O166gs`^{yJkmN8ALNVQUSadLOycuS;Y})>|YdlRfzD;n9%4`;Y>2!|42NOI}-Y
zpryR}fv=MNkRE~Ofy?C)HmiyAkn~mk<#QUY*sDC~yt&DbPE#zG02q2@C()}Q2@s(F
zf$6!gmor{|Crgf=ZKetIQy+mu-g*8Ulls&cPz#{mKt@o+>SMcK=di*Z0o_3}f#FCX
zqz8t7*QDRmZ;ujV0?N+W!oZ~e{a!)-q~Faf;qM{FE}FK|(32zT2k-3LuDbX>BYyy+
zHZ`C(vG6=WWbx+aMcZ7FI_Di;(l(MS6z_>1MDFeHe|Zx!RHU3jK43ht>Ykx=_4hAi
z-6L=c9~&8tV0@FgLUkEJLB2wWLLr=c_tSox@x6Y&O7a5ID@BOFA!ANBeOmMq#MoFa
zNVPz)=!t6JxB{0aKzqA?zD|t6|Dzm{BXBY(yoU}SX2Jn?55*(+z;jnfT@|#|0amNV
z7(tmhI_GJ?H!@^Q#4cY%I`Pi4sPdCONF3+a2ZtB9WCADP5yhX8THmivkz-z5Ng>wY
zP=ZcKkfa2WJ>(wwIVF(g>6P5YB?}J_!&w0-4hJKCH#8M|z~XBitP@+r+MzoBQOD<>
zQAH;nk)QF^5B7!5NZ>&T1=mDC5ik@aT~6dC{|wRH386j{qE2yhTnkZJ_f$L2Br<4l
zzzI=L2i*4giZf{45S9h8W(Y_^2O~>kY8c<Bt5IBrqA{)zYIYtpzrl6kXyrMDqn!Yy
z01fF~hr$f+)jeA2t4YW;isk!D4TVRQ6oaz`z%7|hW1@!SM)3~^%&npL%VjN5*-utO
zKG*{m)Pc9<kPQm)#R{#@lsNfD;eC(u!+t?qg?+P%JUyWcl?lrK<$%O8iM+0EeF~+z
z(v??GG)MD$eGOp^kEg)x0Kgq!oP<^r%n%CkZ>d_CPi!*Pzjsff8po}ZC_0+IdV70s
z7EHu(yDVn!plSw!c#ZD>Py=4{eGmshf#X>oM;J3+VSTr@M5&#WM`CX&BAEZ}T>q(a
zavTQVC@Rnqh49~dIk|Re!Gbs93=BGJ1EnmoTvu5dZthpQ_eNfZ&ZvE|8>#o$s1ewU
zX?j9xL~y~eFEuXpo>F>4+%^TsMng9|`Mu33!|!U9`c(3Bj&@{R+}kSg7eA*FXTP;n
z>$<92eIJB+;~W%a(Q*wBT+bTlkcyz~&On%o43r2tO58qgclXEWj|LeANhg!MIA?16
z1JZG?rb)Rmru>gM?hU&reMwr>2y5~rBj2+czvPwj^1Vf_>3NI$8H<Kf8p|c!Yj3*L
z$Z;F-qPLX9+=qpxBf-bMkOx!*0GI_n_I+Z;G!2h|IlsyE%SB0K#3<Zr#N<88<gV%v
z|1j;>g}#b$S@}oHnd{_!T)f$0FsBll+KMSV^~xv0uI)n|XNK-<c_{v(OnN{~^xJ_(
z>N$MV>}K!FfPEL_)rYM&tj=b{2tUy<fBp#UV_bxsUIp6#cugm%Kacm+LIiS??EMbR
zI=l;a|1|e_%j0=b(kz)-EsZha&s&PIkMlnyAiqW6&r2>h&3cW`t=XH5Q<r~MZO*jl
zR*-og+vWZ2<e{(tN*G9v)r5f1F4x>|b=qyf(8^&AT;<t2YWPrG>pJyD-zHDpN{!pB
z)RaPJRZ8i<ZnaEy>~W>a!UT3`TLkq7uWnp>`U0SG7GRpd{srNXfxpU{^&kS%<bsW~
zX=*POPSc|StiATP_&{D9-#Gw?j+->ty+Mhf`Uyg(zOD>&TEi$R9`d5?yG&=P&)i|W
zuj0Q_2XM^XK#;WdNwfh$<TRtK*hJ&=>?O+0ah1K5XYjE|Cy_W+yu6C+eFjv(q<C1h
zyf4bqY#zI;Vhtmk(O|{)E^TqvhMmYCI~!Kr=>xKdL56fL0o&}hJ-qL!aGwQL(^$jo
zj1tvUh%~zR99%7=ynQ@8qv$ovimtSHa&i>+WV({_E}sxOC?j7UI|N|*PL#uh{fRb3
zANdbZ;QC_~M>6L*{mJ5qh0V_JG3ujs$p}YRI1}~`z*(yg0kyv8)<|Dolze-;f&$%j
z{PVF(3ioAwxJD+IIO!_h8S|doH}zJT^peigbED$dRCY<d;J5fw!!Ko%F<NisGx0o~
z53Z9AVez4wqvjW5Q*Ok4O~4BPNR9rJf6q}NhpzcJpJ<_*q)p}$RU*t5ia&Sn<fDt9
z^b{U^3-s^djT`tPD0(UTE0|m_i+4T!UlSLGsSy5xkF0O>*p_qI#I0vx3`-J6E0YYl
z7;0f*`1U_pu8_>Tbm6%-3Rdmt+nfZvvfbH6GWm-|+-GBE9*{k~mow9GK9l&e;8Jr0
zUQ<i6{<j!fF-iDgdVV|q06@~!Rpg>pe$OJk#)Os4f>cNVRR`9`Nij|3*oXNWffT6p
z4Mx)B^m8XH)n6oJzkyqEG)LwL;=hIB?F#<$!GSd}om@*v{I0Ui1Y~CG<^TN@4Q{Yv
z@A@3oY;R`m&<?9^2M~6TE`T$Xn~-P?fb8Pdlh|0LvjwY-0IZ%UIAi??r~+dNVW@z3
z4th37ab)IzRJ_B_K_MI&Ch;%^8X}tJAF59PfW265t#=ScLOUtIJVX-mhQ|_OQI-R@
z9AutB?MeRIK0bn;?Gn^K)qncOpP}QGO$8sfgu-PwIO(l?pHRL%9kV}m0qgtp^Zn~D
zNG?!0w2R%OKc+58njfVT{rG9%e%}cRCKbW{n&{h4*LRpJGbSvu+XXZs?;<GVYWxt@
zcxQ@a13IwCp(Fk8&l45(Q!1}-V^I_K8Cr^^-L)6_pQL)!4L^J*57Pin2#}RZa0nwQ
zrP2Hu^q;?5Yi|GAjM=kuDp$!aPD7_yr%z^QCW0#4^jOP7uZ$lFD|Q<x-f|)flU)=S
zAaQ}tJx6VqHBm?Gc6ttn5*H%5>PmXi(5C*Pr>vknuJ%Vk5_<takpuUmpuiv@Ii~=o
zxn2B`>Yko3y2I>7MjB;xT1^bv<<r}|sxz0)i^mn;Dfh5+tz*<HmdblB(Fw5L$Z;UW
znQ(28b%YvcH^%%q!4S?<%x1n&ubL`~cOEE`+9>6~B8$$?vy60Z$2Iig%q&dC9G?p+
zLVT7(ZZ~kBfMdD^531BAqAd(X?x_3TqAHi*(f09^vsWtgXT#=-n(Np*ZuTx{;52vi
z`!D2P8w9TWV9W}^BLiU9PPSksD-@4#8d2c%^hm~3DjYrLKDVUq(;^(kWf!lQ`;267
zTrkTVA0oU4@&X9%KEnJUl%PSY4T@8%vNZj~<?Yp6%yRR#mcXX9B=SOHkGIe>Z}hh^
zP3V7k)u6^{X0SlK&eQRa8K2~k`k)${VOF!e`}-%ROjWs{8a|yR^!4~PuV{P6|4+t#
zLwJ(Yd5qXmyUN8Fbb_LJPKr+Ikm2)^Bv3n5nS`Ub&!O#%(2O>4f8mcqz8Ro7W@%6(
zXvu%&uaWq@DvbN|34gER>Ok47N9ETzQh;<9iYHmQ0+8-NTS^)96t)f)k*uM-5NSu5
zidY=tN6Taag$9I(0T+leSSJQf<lh09S#-U1pm^LO{{MTf9Zix~rifkzpkl3%pXm{#
zR*ju(!<aY=5(PjS=L1M{(>s_uTe#wKZg3zNf#U>paUgI&2haoXChFFZ;s-@?BN?I5
z{ldVB(a3~h0iOdul<bs6a12?U2*)SxGGicB@b)jrYXySC4w#|)6~YCwn^3V|HX|C9
zzd7(ly_Md{N{kx1rL0*}O%5+*KT14!L=PqM0De-`1j7o{Z+a1~0n%A#PF&BnxX`2V
z`uYsuhL8eX#}jg!3dF<lud0q(KstD0An0240YStStXQrk%GcZwX}f?MzoP_s*~x%y
zNR^0|A`CeuB=0yBb!I?$Fz5kB2NXgM#Y+?Dd{2NAghhZH8r%!z6NRS3e;be=G_ZyD
znO_NRwI;`nnuiryI@sJ^*G9WrKN&qaGh9xI+kKSVjme&Ts1VifyRE<JbIs@{(yL6I
z4Ejof2p=aj0U0P&higQUtTX_r(0QaXj@{%sb!mR@sJ%pkELym3fCbgawxWuqEJ#^8
zJ26?g?OX90y~QU5vYdF)!1YMjQ}FM>eBpG}AAH4>Y4NNUIz~8NH&hfKL}ls52Es2b
zTfg>X!<q$JDbe8y?<ToP%=WTmOAS+s^=G~0Cy?`}BP7H$wkh~%J4*HTgnyHbvg1g_
z1yo9bv-T5Q1QMq+#fz5rbmPV<PU(v>CuclD5BcAmnwy(@wh3U4h(HL=pKa%2IPxSq
zDtM+1<mFB5q?5=y8;f4AwJR`MfB%tTf!A!|(>0L`PiRxjUz6kNP1IB>B?yFU6RzaI
zDglHP+vSi52-D(-moCgkjJd++OGG6$H0%3cOCOJ@u4Xp(ENwiqKgJV^99_|OEL#NT
z2&e}2XP12shYuqx)!n{gO!_7j%<=rRIOFI^=diR~e*J)Eje7jZq9b#)=jI(hdhGAX
z;=wz2Q)bj5=QQxxA8ed4;ds*wmW0rcn5IXH+ph~wqd&zhj}A=5#OcbJm^w&_YFRtF
z!prAc-}c5>KzkQA<S!+BRG@0xmy2sGzqm_IHV-D4734W3ThZOERXhPIg8nmW5RXgG
zxyW@jII+`l|6Y4|Kz*ONaSIPvjS3ZzXEEa7^fA$_cg#5|a7T&+wJY@N1TPrx?~*=+
z=DB{AF(KLUyWw;hHxo%70a1`2c%2aiCE?Y~xa;ahXOBK&_ga7RJa_|gpE3}>_2f^O
zY;;|4UM8t(Y^@u838gL%FqgOi+9?2bV57m^u9x6FS;g`u>fKoVXI*tl=nFag036|I
ze3HL3A7IbrK6Az2<o_l9S=i635_^>?%;uN5xkd{67p9o%s>0LVyf0iaylBCce)dJW
zhb9|)tGSO1**m{vY_v=+1(bcA$Y?jCfy^G7(CRRZf*lPaAl{frGd-p|))9H-`4Kb7
zH02;iPyxgg4s6hE!JE?7hVKm;gRt{z-47@bXlO@MWWX{5kP&(GX1%w>P3ip$Z2P8J
zlO^vL!-9n%qDeKzG~gNi;nmNt?7x*CoY6cv>+SOsuV>j?=Ea;J<CFUA8rV2?=~0Li
zXoE=2ZSm~pW73&h6;~ZSFRu<#sl%uE(FO$iW@!+rv71N2(fiXLO1_*qIb8AF?aKqR
zU$oRK!HjGJpE0<wVvN9F5kGqB7V~*fS@!OE0D}b4|DF^3sPUKRQyOTw0yl{20mrn6
z*k>yr-oc>bUr1w23vN!1FrC8$9`~z1@`SK3)j(|m=vU)K%#Z}f9)=QHOWO=kYrvLK
z1pZ8&JCOwR71r~h5PWEf(CU+(a3^-Tzdzzm5PtN5_vL0#!3jfwJ@FWU-&#%c%Ey%d
z^JAB$U(M3ckwxe<f`Xj2;2GqIOCa>caGdf54@7F?;ro`On%lQoB90VoDoc_CV<tgy
zS$e{3oJD)mPgzmEm^fAT@87lHOQFT`@Rpp{jGzvg@*!&g!9u>3AKs$9!?-e1pOI4X
z45~M)Y;dvpS*TE={DO6-&>hm<ha1P)PQC2iSJyC-QNz!<zMiS;wP|g<LFaTIlMP(_
z8p?d&&zN^;#%GheOuXm<a$Eu?jvV(Gqrm+|$a}<#3Sg^+B6Z;agisgG5r6Mk$!7VH
zP&^YV3)~<>e+sxc;-%~C11<+c9fI3qsKf2S=WFD-euL&D<TwfqWg#@bhOz*<uY+HT
zlC*lc3UqJ^dY1vk#faVCS!pkL;3`47xAPgSyq^>Y6eM8Iu9v1sQR)0FVgbFdK^+|Q
zGQQZlO%h_ZWC!-pBup<vF)anoeRC53`_7xk)xTeYGHRM|`%t77JR%eyMyGEYFI|!~
zDW(qZ188a}E(Y)?;0yj)JY~4YU<c^?q=vFE)Hgp`YHMJ(?50-EwgKon9P0KV^hY1R
z-D~(JIF73WFe&_(NwrJ9Yq>X7{BS%Bp?LAX42k9AB6UeO8k?Dc`ACk7f#jJ0@>!c%
zI7mW9S0+J{&c6t04_O%|xvlhV9uNZXNcj(#G;LTtdy@5i+TL|ABLANe{XV&Dy1dcN
z#s>oO;{K9%O?X0$iu6ZXW4&c0Urg)iot+bM4po-?+PRASEV4%_tlcO?JB^rpi9>q#
zRnFY^oSVE`dC#&#T3YR~{mQ(%stZAvJX{NYab6ieoeJLckBHNWlj98k%6%T&R7mXP
zARZh01bmHN(DVaQvY}L`8rRu(9bR%8I_49UX@b-YDR5iw`ijrWU#Bdv+vN&HvY@z*
z9Za~FuR`HiD`~6O)kmd3g9alUGQFxWRz46xL%Le-#SK)!zrQP6SPcf`I#5rsbYy~k
zfbq{HPS`j#I<_nNkh-W8bszL}xJHy?y@Wm2%ikn?a4aRz6n8k2++!Wr)Hd0vhdoQ7
zErlM?PgoBV_NxJ(LU5#-^4X~$yy&q3f7kq`U#-cb2~y&pFIM8gCIX=Jlc5QX!O*JQ
zC_S@T!$w?XB@DjqiBO~tya4)h{>}i_AD5?~>2d859e4uz0zX0>+9Lz6a-AmchLsK>
zz%D@tEr0M09K)hUwLe7{leSjKixMaEqKW?s-FP}a_eo}LQ@G8RSqJD#H57UApf9P<
z@O;PL^>2b3idMmc-M<7&-I_)(mR>=GPdmiY0*d{}ahV4cTVZM(K4oN)w%)TE2Cslx
zn}p#Ru~gVI8H0O~gL@$IOi_@b-G(zLTT4L3(v^8cR?__HONVELquNx?l)3$<43>7A
z<L=lT|6Y+TASKpk|Bj9HV{_)z7NvdP`&jArtH%LYyTVg~Lj(LL53^_Qq~vz7P(tzC
z6~$su$b`d)@Q?5x>lU3@PV@M4Grj>cEI17F703?<H8^;xyBlF-US@fw*P0#}=#UTR
z@GqXEG?2H0IT`B~Yqda)08eE8^)O8JQbM6=9g{&>H&(#(1RvylgZ@EYB}!bN^OT^l
z`x#U7vW~Lf>Htm;_|xZPHT@JErgeMT^*+tB5~|~*pA8aJ3W6nyDH^(b?&oR?{vM_w
zfQ7DxjCUjOl9jN_JXS<QBW-$Yf#++kBih~~3V^Ad^WllNLBC)5z}`&M2WF)Q-D$-m
z<Tpe-nmHpu1s095%loLt*7M&HZpT~rhg>yMGnZ&q8}#7z>GAjC#ekNLdZ#GdHK<F>
zXZoV8oR*q3{#HF}{FhMKb-WT)m~I~AOT6=&4pEcz<o>L!yDd=*d(Ms1F5xr89;fiB
zJs{8*)3i$}kG{Xy&yuvo_0S?^K9e(oX#I+_upt#rqGB#wl8J$P1xO^U`qiFheGBLJ
z^rj;)XW8iL#oxQtUKC}17VQ*6QPX@0NN3JU6o&g~2nV)hd5^E_tNw(lR+)}lOgw!r
z1xI_O;qPRCEcMu`j^}e@M^Eh+Kwk18c{thm;CJ6D?ceNVo_R9OhP0KPp{EsW^c+c<
zdwt%Gr{(Qet=O6G>^G(6#bF$4Up5-GpEK(x8UUI!#@a&e3gje5@Vae@W}bgR96xi9
zxa9#$h+1N!t#p*IQO>ZQB#T+>m5BW(gTFK$SnPa$D%ioFYm#m!=7FM*l!dy`-A84e
z#pamaLhWj`YwM>Pi**G@A2I$3>;mM2ynT7HWIeaUn7$Utxl(+#dIx`3l*V>7$9D`V
z`v;k%NG~z^S_}OcQ-(Q&A5(#>x)Qh3DoCf-S@V!CP$IiniRqnxE2PI=dSq+n1lf?J
zzDcJBD&)LCKeRIV!O9?|>#$2FLC?84&Pk&qFthj78bVglnqy8k@iO}Ak>fQ9KOcu=
zy}0p4tY5QXDUabAG#4lRg0g33Y6?5|%ugo1ys(lfH{lb+%s~-UlJ@o0O){i(R6*DC
z*bzz9eb*B&<~%AoOF~*P)tE0U6%Jt%OneN_!P4VcR73Ad)9Sd`$NjDle>x^~%;XZ>
zo|caHq-*hR#`LE$%ustm$T<|wNnAy6YvU8CmSp65vN`#+TeL9t8E;cKS+4gJFH4wV
z;@vup`rRmFO^$1B^3Fi2&=n9Q)!p!|+v}BsV3n@I{S3c<|9*b?D3v9ubb6OGL__&y
zrJ`QN52d=Cubr`Tz3!vh4-I$cP7iWmw@lz_)H*wwk1({8AJ4j@F2nWF&`QzyO^3T=
zgHoNnYHF6G5Z-ylSEJr|P_XRnrU6Pdm<fA6Gr~*vIiA*oGIA%#FGi8xCHdGJrXW`{
zcyq+6-F3Jro&6r=_sYui+rT63!f{AsV=MN?x?F3V@4ZFcyYQUdID9yOJ{CdO+v$82
z$f54j{&mUPB~QrMGxK((llA$NE1Yz=wC{*24Zj^UrgyE=skkTeQ`Kb=^eng=7emKx
z@L+G8UFzw}z7o6m*63#FUDdPdy6|T0;xc17<l9Oj|9)=VeH3P8_Ux2F;*M+uW#aza
z3E}yB*^C*mmpW?bbdm&{VQyU1*oC`>HczBrdC@rV-HcdK?yRL|l+@Z#f$)T92EZGq
z&>sXhoz9++Q^S4jTF+${n5SpPX2OcPd(0R3nu#?xa(chFUhvR22W^`~{QNwUW`-pu
zo}2AGB6j1L|81~B{E2e4PGnv6W$H~8=9sSzZZ^y-5B7V6hx#7}Tx3p=d@d^)=al5L
z*#I`Ow};DBL1(%vyBmIM(wMBnwrlmYDq8LwT<!}0jfj2kJz{fImH>Z)4^-D~uX}Pr
zL69NLm9=?aJA))y|MuCNCL@pNWkmXkQz%kdN0SYVL6Q-7m@b3Ec=s6!w_Piu)+P5c
zCBNQL*0~G^y*kTTcE-$L=NBYXdyIB27tT!ek{eX1kw&(nyssENmn^hZ@cMP7#`#Rp
z_nfn0bxLygp26O+0#C$t8y)UB9@pY#e+>U@sAMXW8y`^arCHC~pnST0@0^iuH5V(L
z04~MjV~;5N^ap-mm2h3!(y>bFm2hUI*XPeY?!HyDa@m<G>A@xAuyklRuQWjtvfM3A
zJx58qxt@yqE5apx{d1lir>?-JV}NZG@HGOd)Hz?$VfW!i^XTq54&w3=f49jqbB`#L
ziGj(YT>bS&od7~|l~o!YtM2Lk#899<(qHvPHFtE;&d!3vX3(Wa@yp)k6qV<Wf!k~F
z#+Wb&!cHyQQ1G1Zi5?qO4Kf5Kny`%vE=izqt8-MBy6|<efA81D$z0s#YUC|v9+M*w
zAKE(@NwP-%xu`o56#&8e0RuMVRMAq>;BS8kd-bcz5);`qzp8V?@xhTtSe?KNI3_hT
zu84i;nq))8H>;(nEC@Uzz%5G3L{qPjOmLDT5`Kr+AO&~S-?aVKw;Yk+H7H_>wlzWl
zGQ`yMmz(=^DF@0Qk$TG;VP^vAo`zuJ;tqoHK=AMOsUClGN`Zwn_w4%CdAke6a-;_h
z4xFdL8r$fL_Nz<+g)e_37znVwU|y6}-o7uXp)7hB6rDwm8~Mr;M?Fw}edL{*CDqTc
zG1cHHavUgxK?rjM(SMhQb!G!DBq7y;ZrgBl)UrEv!FB+2^g(3{C<;8_w_8=baS~IR
z<ja?_xz745H0pE!i1UvVmK^u-<;P`l=c(qGo$ZX@j8n2Xf~`W49*2-b$_&_CK2yK`
z(N~SNCewo)f$4=};|j1KfUj{F76^)Ja0_<bQ5DonZw!<BwJ1!@Ut8ukMWYUauE8jg
zkjn-HdL_zb;l!QAUYJg@3t8sozl|gMbqvLI9EH;%-0p_rMMTpinFg!<P*)W$zAR?T
zAFZO>0Rhf`q$#Z7clCINwV7i^oqKHbzR<O6W?z0ge<ml8N(eH9NOi`)oApa#nyabE
zjL%ewUo;wkao&eI`6F?^HiI1N$!=XEA<I%#*^3D?xAPrIKTCz;=?ShK0<|6IB<p{Q
zFPAv0*>H5V!O`e$*Xy5I{~2ii=4x;16Ut{I)SE{rwAkEkg~2EvGMsqGWPJYtZ%Et2
z2Z^Z1ll!VA<W`pjU;fr{Gu&G}xwo>|2n^f;Er-4CThE@yDH31osJcI^HFoJpa1cLw
zngPpAFtkCiuus4>rNwIlT0b8n1kz2giFH0{bmIVd|2$+K0~4PQJ&=#7^;G38+!;~Y
zPN{ShDgSB*y9ciLnIT{~V3<c@40C^c+whV%$ik%%%NAH&jWuSv2Lcg6*%E@04jL5i
z`cA7G)E4lYmhg8a^~ZUbP{KflYsVia1Ta?c%S#Rh23<YnH%RAYZ;6!KD#2cpfeP#x
z1%g`uL~Tzz8qII2aV+xuG)kHl*bzyKBo6jtz^?r<+d;fIwdGaiqzo^CjO8NTS4Ns=
zZUyjv2X!vE4}A!cnBJ(`;?jkXjJpW$^GQbAK7uK*&!Cb9sMbR8M}Ra22`XY9=IErW
z%9eAx=N83TQv4WTD!@kk<MZG{%e}Br!zJc@#m1+t68GP-u`rwF=WEe`xH)iu0ITbo
zSvjYno{`JWK-W$6CFQP@H%=af(f(T|y%k~I`At@OMe;hz>50w``9;aAbTAbHEd-AV
zpv6;@<xz2Do^6%c)a+Zd_Rslz<FkcC7KAn+p_Uv$hnirXJG1%DcW)4npYch@y2yv6
zXw}XW1xNpT3dhbnxaZ28&}&a$iFQ-(9$8I@d;H+&EqX|HAk`YUh(eJWY!*C9-TS48
zm1^^IHUd{%rC<+%5AvTG!oh##xq0u_d_Y!B_9f5cVy#o|G?BorkmI~Vt3h%x<m;(N
z1nk@x^%2(7J3l;GTnmftYwn#ShkYfCbl{u`Mf!1M^C%VT8TAj0JmI1BrXL<v2QP=>
z5d`lM1C~6*lw(*~&*R=}*=?oIYA2mtvuISHeDkJQ%-)B|EPUQ^Eh5(JTsKQ~+eqfZ
zPa;}j&*Yiq85E<aqNw0i3cvS*&ZCBfXc4$`%)+w)P2;PZ0~uYJN}>P{0Yx*lc=N1r
zj9!*v;9jIm7q>3;y-b=0C>X^>1A;dM=tJ3h<AY_Yh0(|5d?a1GjAs(3om)u2P7Rj(
zH3*T9qnYBJSN1!B`R4qu7J>^=-^n{hg8}v%^hqtzW6uEW%Ug@PE6X}p7aM+VsNRJk
zhZbvfMef1~!&F1qmw=F17V+e!f)3UFYf1Gp*Dz1{DS5Odw=xbo(m5n$@1%E19Y^GE
z_oRhwX~mkcQ(W|GZlr$(10kAVFc0Rpn+u+o*yGZ?+{8%BlZzkr{IFH}OhBRdGnUD6
zM-Z1wL`8aT#K{n5lIDjNOw=$_Na*gN)yxnIVXj?bzaGw4k6rTJ*(m{UcXKHoM!iey
zFi`V}0>J@+8G_Ng^B3N_v`pPQHt|BphvYJU;_*>(4j3)ue-(nz8UaFNE|>kWjI`YT
zm#?#wd_OO)Q86pzGAe)G;kgF^?1Myu0}&!JIK>M(#QI;JGF3jg_6?u2%oW`uqeb}|
z!srDH;h-<$a7rGR$`e7kGe4%=(%;Ap)bORS5w`$*p2NwI(BR;zV!U*1H#X^2siSN!
zMcLiwd`Vw!(R}9p_l%yAN_wo+z<FG)#y&lHoab2jlpKr`a>FV>@QNk?2Ufl~WcEt|
zmm=!c=g&^fb4UTUNARc<x-ABOhXV6%$=q<^Ebet;`!a6D46~6Q;gh&zOnlG=KqQeP
zxHLE#>3ufcI}CVc@lV!hNVHN}9~>QrE?EDLguusTlwP-<LB3uL>dx+z&R8S@4sj?$
z1CTOw0{zjcp!TZg=b%0uPSZF|B+5C38um{EKr2ee#VIA<xyM#QH2OSg@BpM6kokGl
z3|yrTv+*v~zOV7?)v2e26jyF*1wOfYggLTJr`Ul6P3C>GVSoE2$=LM@j%Eb!@~BVF
zx>Ar_dXE>Vjd2349jdUBl|5!U+lhN0Ra4S5eb)OnEGibv2FR9S)G*h9Prpf#tRY*O
zN}EUvWD5p`uPAU1q1B*@2Lo0l>WWKE)%eN{RMmJutJA7AYw%m>IoP2nP!fFTD<T86
zszIqr?gQp!*Y95ofT#TQ;iUkgRSm<W(qm;cI7l|i$D10QZ`fG=>YV-hgh=Epn7?ct
zI7kX(K*fGt<T-U`2HD-{6y(%<T*u39I)3_5NbUamYm6dZy%1W*r$CA~JenNAG*LRf
zVlj^|+;*69@rO6W^(*<`P=r;2<S+&--(jOXly%N~9F>>#jhaM5YA&gX#(CG%K@rut
zs^!0#HF<JagPlW>fuYq8A?g64(#eHk;kz~G4b?{HT-wsYo=k<9y4rZb<?t7)f1(*)
zZT7%=K%U!qxDz>o1mZU_`%a_lx8dGl<h1vjyTI^{c%t@T3wcvWLs?D39Dr~UxMKS0
zpI5zK&G6~Tl!!SzgR6Z}nK%I)QSq1|dMqU<zX#_(H?g$3Zg$6-AK?6Xpb%pp<zeaM
zxE*o?z-{7%=B!`tyQBRF9p>8|9T!-!A09Rlb%F$le^K(}y>Gd!%P?}#za$ie5O%eN
z$r5t**db9AlOqI{MT^c}d576!BPLD=`_JGuIk%^T-?L)fTjdU464fw22MJkVS-dyk
zSq!T2xDBj=AX(7e!IP3>R8V)-NEKNa+kZ%9B%syVg;oPlCW2Hc2({B>{}D(ip>o>P
z^0=ijqYV@-W<->7?M9}w>~J2rG)eog6*cDo`YpQWebfFX+)JNC^R+2gK~`-bSx>9V
zX7)bailrPVDhak>6e16k3RMyWQ5&GBLoTNJGMocM=Du4|L>E7LbZJEa-Pb@VY0Ef?
z24&WD3Dp%q&?az*8Nu~qev;#)3a@Tk^8LgOp|XUm-`Ez6a)L@TWo^bs8OMpGyX(M9
z7YHu_|I|M(u}k;pJ5W#DhOZ`>hhqY@vO_JiX*9RRl*&M1Nh1P~Kk%S|z)vPD<Lylj
zZ0EeY7dzJ7nTU+4kJC+x^`3Xp|Iu(b99g_W_3Nr(lSrCcU1a)4H||=ubgm}OyVr)k
zQH7heKObJK$+aw&IAL9kyUL^Hr$`YxzY~AeoxUj&do)<16q!`)Gjjz|U=6>NDApoG
z?DyTrV*u3ZJVq!201R_b!Cn_7A%jGKTb)m0Eeo{Lk@RCds^@M_oe*pJO&>gtvhlQy
z5|8^hf8sO`&45=XQPRaDosY)Is7p71+v<nRccJu)do(z<9~V&)x1JFSK!A`b;3R}Z
zUPgn9ozwhm@1A#r6L$UX++~D<&&hi7%$|!xtF4%KUl)%A+Ze+ijR_`Ae^J`r9XwHk
z)!eo#TDkOGXgdtguyqs#YJBOO1%Wt^2^jrjB4`5;FGY!neAT3kJq|1OyRn(Wh4cK(
zwa`R57lLXRxN+368WR}(R$S>=b`VjBq<&a6rg{ZVm#`3r;TAsjf3SXs0{5m&O4Bg+
za{?!cLhC1nq!roz=a@;@_lFh~z2%=X1Pff~u@ZIP$Z>W*Qo!tM{m+uQq2br)1aEHR
z3vh4QzWGf#;O_krLG*c|iwPZXl!&HeYVUy>jgOb95eZTt)<}p@okD>U0V=<PQkS=S
z7_seTsDNO*-$TI@H#>9h(z6XSVj~c!0Lm%3A96_^uv2`Yc+QE7=izjTPX7n}L(1sk
zx0tdW8l1|Q1dLxGT3v<}E4-l6!UwERs$bPz#OMb+4QJ_aclLq##KDDX)Qmfc*1kw6
zPJm#-3IXR>SasX=cuXLj3Rf4f$cRsKe#w`8xsmDQFms^<_UX!<5MjOu1;yep2{+=$
z!pCC!IBekMNw<}|cQv*j`;ReTscbL&AG7Zpil_Ad8I3u6KOWHw6X$OzgZWS&5B89C
zgDZPf=J_3Qmihj1RzVu^I}U$V36xH(pP>WU9Fc_L4d7D-9~ks0FEL<yLCiIJ{vFZF
ziGmX_x(?QGq+=EAVj8Own$D}z)`>!0{+!#YX3`iej!P4)^b9UgwO9pIC;tCd4Tztp
z>zdCBs$9N`y~j%Kq@w&e!BV3>O}b8Qnr0`F!=kOWr+fBdGv*zuKHNB<*9Vjh{$ELr
zq+rULl}zmwC))#}5pOX~Z@IBT6Pnv{wW0dfUDp5yvH!zCJtKBKV}5S*!$Nz&LNhsn
zdYwG&j*<}|aO8iNH9)p5Z)&z~z5-WNxjScqQe8Oiic0vNdhb&UOl!vaptQ-~#p4|G
zw5{HT96@{=J|@3ouQdaTs@G>|(TCf3PoBHVbk*@0NLN6knW9Q*j4pFHJCrI^MPcdu
z_U6Boop{IkvXAwc*Y~DC&9{5JwUs_dB%_8hC#v)W`Z#p@>9o20R^7H?*7W;e3~SK}
zMe;(5fIf8m#6F(*$pt;yMooFZgh0gx2JD|hpv`$>K-Z5{bGx^bM+x?b9{c5=1M3!Y
zPT7tl;|+%UcL##(=6{D9EZcez`@ss{=hr+3yQl#t<44OwDuxLI_93?ZxV6ZbmmCY0
z&cy!%(PMDI^UpEIP{T6mwV3+k6=p@kcj_MOIq$#e(UKhA+;d!ab6qJ{{=PMC?XhA#
zfAxG7-Q+e6*-bu(VQ^*(hE_*mbe4xAEY+eXEBi>F^Fdww(HOK4_WRL_V%(g}7q+I9
zB|Rx4r+1n+UnhJ^PF**)EDR_*4Krqd_+&U#rfz+5LL^6}I%YRui}v;|seF5K!^gw7
z&+g67i7TT$sqE*kk@2#E&`*M7_yC&<e1m6s#Cgn$&azK7iDdiL2k38j`AI6Ts7_?d
zRetRXQKnUw{)bjDIToV!G}hHxwe{X6wWr~i&My?yhb&1tVM}*rH_mXcmMg<-K#30M
zygkrj)?O#aXfIB`>bzMjk@;eCA;4_Z)Vj%&O}-g})(U#L2m{+2XiiXiL^U{M;CE>Z
zpQIM0DVU{YKE<9%*U#{uF6%~T&2#B{ge%hqM;*=pLB&h-NvUji8D+GaI$e6YtF+30
zq9>KvP4Y-_NR!|bs`VO97zv6i4$^FZG6^)B?v)AdxW&{n7TlRNS>OEET6!cEtzN@7
z@Z~83Q<-<GxPqw&@}xPGr)*K(Pe&EzlR~!>vmZ_T@_rn+@bQYXSH!By>9UN`aeRg|
zrv~{B52OsU9BQSV*<N?cVmkP;-Z@~}>SV?9O6&aPdbi(x>Wj_asL-z;RTdz54B?FL
z0x&p&@@V`1((Odt_PR}NgV|DEiZ9M#@Vv3w=y+w@68mcS(dYb!tLmWfyVCL2=?D2y
zeG1KG_0o+IVyPmyzLu3|MoVV5?#e}-S6Yj0^qaWA2LROfR`^sdf5h-TQ#aCOj5*py
zoP6t<2rIjz9w^TDA-MGYdwh#Gd-fCw+9c3-s6=hM9U$@_!#4jhIBPUSToa<H-|-dr
z$!xCASq(ELTJ$SkY7R|`&)mO)e1;r;KGy=aJ@5)2x{6?jD3*Tzp)D*H=PYa)O}j=H
zs)o<W*V|tHnC4x1j3Mzeb%buP8a;Lefb1O_ThpXfzU6&W#(j2-LCA`|o5-f+Zf*Bd
z{->t)UFX-bva^V5*nkCRt$t{ly6B)&vuUGlFY5o;OW1xma?V5xdyL~<O?IX@TD$E+
z*49mb+Hnm?x&YpjU}U)x<ENB(D#(N@a%d;wQ5&}VH}R3Hrac^FTW>z9O<ly?AA`!y
zN%0=qh{HuM*Pab9cdYcd^-$wRtqOzQOUVdr&*I+YjTG@H(7<k!BS5ku*PreKG$MOa
z1*>%(5Hiy5GjFXqX=%AB6UphAh2#dAOZnq-FSV#@jHyBp{=kg#Bu7}p<Fcf3+7kDl
z_^V)t_){sOItSl3u_@nOy7p>9zlIuME`ZKKYl3lSQ3b~ZY@$X=K3#kU*QvBBf7rQG
z7$3CfUHjEg&-sy<3`o^R-iJ)M0~6P@=y7L>*{zIAjpfIC9?rcTNPU>Xo@RqrE0Nmd
zsT`HK%K;E22)6fu+o7yyy)n1p^z*pX+jl(1j-6vD92}-O;%C{t)m^5l0764a4r%!W
zD8Dbfxs>&Eq@?uXYq$#irO9K7$nZC9bZe6vPph`dz~%A2HoW-I`ll(^PBY=wZo|JU
zz8?LJv+0#qN$;~&sXO)SB}#CFf&i$J07_{P3Kjqx|GN>d|Jg2=pq=j{#iEl^_S&{d
zU7doZjbq<uk8QL7xZA&LMjPJB-##67!}X%-X(zVw9<DU4nJ;g}Lb}cUkesVrM?$N)
z56ce#r-}-_WA+Ly?JtAS;aD%T-AAMGPo@Lxv_4`RlLxRVr|ymd1luXXY6SH#G~u_e
zW;CWZjFgOHZ3ewxV2Y!3>o2M}wvl=1nk+SZq@~A#(g%Rp15e7AuqX40mJ&HkF$LL|
zdZKkjdscZq9qAT)b<OK~C5S!MhWss{Qzr&2tM8+LGlB}mgV#k_?|u->wCz#&b!N#(
zB>fmeu^+P{$V|6}oGJ(Na-01A_odM{Y0EmLHyf&NxJ2u2a`03}tSMMBFciCU%7fN9
zhb0<@0II3s#`mYPBerR2&))gz5AZSW^iafpQQhyIY?Kyc2nR^=*(Z4^5+~kCIEuW{
zQL*#Z<%)knLBWl2)?MG<kE?FIsQ2XVv3l9c&7Wc`cHB*yy9ol#?r#ey)#3XZ5HKxp
zu>e~*=<-ON*}8y_Sb^~U=oWaPxI?VH&YtPsOVPqCpU)tY6$EU>U=&WE0Xo5B)rp=t
zCQ*AyVwOk~A>pl@*Ny!wQfCcx9b@D?00j0bIf6rDEEJ=_hqjTKT1Fo)LP~PiKa8;x
z{4lu1VLf$KQdm`wDu<X13flwy9RuaKFz+nqdV^vrge&sxC1op}l6tdGZ(RFsV~{ju
z2Q55^NQ4HQg8}<1qjA19Ti~v*kJ#I*VyAfY_O5oL_Gwd#lduuuZcuVr6USlG*AuPL
zE@r63LEX>K&_M!Kil-|X)1?YCLG{8rN)YP*)P0DhQgXz&c(Y9DWl3|#THWL2{r&aP
zQJTq<v|^{a6v&|1Ua(~vKumEcl1mm~DI3i#ycjane&(CXM|2|M>GX=qWHCh0ApyVw
zC_tJXJa_obT)J>qpQI}Cin6Xx|4MeqxhWw5W&Mk~1PV$1Fk_jIuR~!j&ozAI+w?H{
zVU!shGXQr#mo&BY6$&55$Nmw|rF9;pds4#}UvZo{sOCBx&vgrb$4AGgb`Jv}5h!qK
zz@JT+$-8EKUQ9*Lb&jw^unZXarVrKgRFw()On4H+4PrS7&1mL-MB9ZS%YI05n^PHi
zx(|M7QYldspo#5M<^}qO#x}V22!-%wAC<k#R!W*_G>a;&eHWg;cy@JbqLPK{(vP@H
zoC$ES5l@6bGK?k1X=P*tq^HhY3Z%BM_+f8Ue`@~TYE=IDq&wM_pU)*h&L{vS1NsCw
zIK?C5KLzuRD`iYptxPTqcP*cjN&O+XYBx!w2YIzr51BT6@pS9`Bah@8Qm=ohsHXN$
zJ6DqFxmKQVDCp{-V~hf;r<^b`Ac!kbM~&Mr>3y^hO93wK)GLP5)Q_>R@-`N$i5a09
z8h_Z%I_=JHX>Bwe(A{42da+A+R_Kd5*6N}@b~c8F(C}}EHlml#DCx-+`$<}*7?~}8
z&sb<FI#nQ8ddi2IR1Z26hYtmwB@nhg7@C*cN+#*TZ#`N+P=>w_#pDfyKO~Wl6jm4K
zjZQuykt!-#K$%h|`ST`=x%2-1s>p@Q>P5K{G@<jqzj}vm@!7wocx21`*pL5I@7yH=
z-sV=P1dN9bTn@c0gHDk~AD2NFbQC|J<yA9&v=0j7=J{~EU1P!e9(jEKF}(p@xPoRq
zou)a1ra6tK--YG7vqaxWq#SGdkN!{#6}rE>q8ePQec}l-H6k3(8(z&AUd<O?y|1*2
zhpABb%g^k~AzR7UqXLiFy3(rG*^z&|dJ_yxCVe>YZtrz}$0KvOVKLq)=A%i+QF@G2
zk6Q|ks&V~qL)>Qpx~IQ=%~h^A9C`BulUL{*Mx@#uEz{3L=<p9GyWM)uM=<u!zLIzi
zcS_dqUK9}aBM+_lWp{LoTN4BZ#{;ZoROv$$&iq|vIK|@*`*80WdwRI+ppc1A<I(G4
zCr<p32ORssG|ed>rpIzLIDBAqNcpu3d9Xa6f;W+O{0p-S6x75M%Vp4P*Z@{+6f4$|
z72CA&%;rdhn9N#lv`UY-SM0@xO3|BKGF4q2tXOJXAT>ge8lg>%*mp%v5qq=sJ@m?i
zeGjftT?-52y_qFl)l~yEBLdC%X>bYrX_*hH*KFA7W@XSFZ!&^OLhT!3$IByLT2XCj
z30KwFIcKg(t_gtDtK0Bm8@L#o13akBu>fAJ9rM*$rz|o#P*18<Mvi3m0|swVTSfM?
z&LuhxEp&OD<wv|XJNatN2hXZU+UC9^v39A7GU&S<7?W0viR^n!S?=S+Y9U9yACzh{
z?8*-X7Z}<rF2Flps&uLLV(^cvQN(vDg(l4g8LU`-fD^j2W@hTLHGUag)+J9OKISIa
zzxgy{?pFLUAnE|+@aiOna6Bn<`@LIwyn=k`w<Z(My%S1E5Zj(o9Mq_PgL&?EM&!Es
z$$E)NQL^CLx`e6Zv~1Wy#}BC&QhvyL*Ew@wu`w3!OR~<0qPT3~P^YC2P^GrEsM3Y}
zhyHD~$>@NVA`e?+r9KzQ47ZJC*#M`x1%ubfbTG<bGx-eZWefW9_I~LpimgO<+aukt
znlq-K+m$bCg}1%pFZ=Z_iOO9}_fB|EnYNb8lW4Cx0Wk5~NH7JtR5<1ezU}zzv!0sU
zN>S-6ERSEKC-?F_<+T?|JF!+9Cxdg{{H|NVVdR7Ps*(<Tx9+BDxL>>eX?*6ng65dq
znkua0<l6XoDhDg)wwgtQmdwb`fUFeQQGA4iAJtGt4NSq9Wnx_iJ5;h&#d~?eef@N#
zetgUk%JA3AAMDJTOU;;D=Ic5Q+-0gFQ6Df($2HBnIyICjYV6%so;rN0H-~wl6S`9`
zuonmTp{ZcvTqB9mCyIji8dQg>dhu_3DG=J>3KOweB1Ct-sH?Czz^2}7>>b~w<_t{O
z9$gUkGx6xGU^zb@?|p)CRlJlW?+JX#<yWUfL>;G$VWOO`qP3T>uKi4S?Q5S<XjP7Z
zk1IwLjeE+?=$huTIe*Km)_T>?ih`=KV0-4KD)SBPE*H9wvH~U=Mgj#+EBH5^nmuP2
z(yvn^q^=HS!+wvsev8;*d^>s93iZ)fo@!Zpbu%s`zk>7Vl^zRg{MVL53GB%Wi7uS;
z2~nZ_50(095SnGShOV|7&P{Q|OKd*mZD4xH(0@fCqG`UQ_H36Z(giL|YV|O6hy`0b
zacsu@^ui9=#)Up*+DqYi^+hF*TbAozosMd7ytP`Juxcmq^mAv{Y(jE%z|Fni6)YX?
zVMbY}tbVq+&NFCgnv1HJDx#Gu6%&8ATBxukv0@Q~FIEtgRINM9iggtT=lN*JFBt&A
zJRTb_sP54H*)aY6N1%t|nDEf`Od_A&>qON;TeJG_H40k3m|hv)=eo&AN0!!DY<DW6
zt?IOi%CiZ#oIzz4%?ws-8ezwX(gBzQ<Mdc%GrMh#PS;I10HG+e3_}|9a>=0=fKOyB
zS&J#$b*G8+&h#7S8xbTi>ca^~+sTvL-WMdlk9b9v(bELV9D7_rYO8!hbAA5Z=*Ftr
zG^(`j<<*A^v2$d8xo&}`4-6^f(Kz<ihpBT^Hj_2GR(amB88ew0_xmZP47oW>lPJe@
zaV->&-quufk^wTtwPQ@)VrWy<(5Am5s)H6gqiv6Ru0<gCm9cE$3N#<Aa*nXeJ&V|o
zdC#8tF7N5p$s_H<vdIH0g{gsKRznj~%~tQ$Jep7UelZ)c(CB5l3-^k~HZwz953^!{
z!~e~X=3G_l9&gR<1`cfR+q&XA;_cWRT5_CMKb~YLiYb9Pk?B{QLSZEf^ZhEjk8Lqw
zLh<Pash62fSK5)2aO#aQOMCf^bw8W2nDBp`SmKvS;+{P^JT+_?^gfn<`HY(>eA7DG
zov>ISJ|3%~Kd|;sVyxbht-12GR8vzTRE`xYls(@=L_2F^_D+Xo#WF}D!jYFnD@p0Q
zq9ljz3-kMb=?}Tubfjb8*~*dAEQP7`o-TL&Zag(%C>Ywx3$rvAmAbF=YQ8j?Kk&Tz
zWFiqt^`-<Lw0E4P#;I><n&V$BsA7+`X8#hHUo!+kRQ5djaQR49jz6i^4^v{R=@yY@
z6+l|2Bg5(I$1NY1C;SpP<)iWO%O1@)hMTiQp}(E|Zoq~lgRdo`w*}|g#D8W4G=};c
zS*$$A%1N8ASRAs~#G<KuZx1u6X(%bd>v8<!m3bt5IIrVV>qiY4aX!NZw>W&XG3=6Y
zzXDauFK&k*anG0^-TZ*Wyfd{g%lqt%x>|iDPDP3;eQsm6YrZ#Mou+L=6#PmY(GA~D
zuI%UQqhV_O`e<xz$Ee`yGKdAnPTp`}LldTw8V6Jd;#oRGr9)eOho5bG-yZxuFUduH
zq3ne~uLe=D0dob%%JHrl<(eAE30^dyGI(Kf<fH19JK@vFeS}oB?{;qN$|j;s%&jKg
zW{u-@Dzp2Xo;lDVssU5*JxnUki*A}RRnEfRfcSem(&&Aq^<6&t0f&b3Di2zdF>M#L
z6R)S9U!%R-?=0~I2E9k7sqDRnQOIbF=P~~!sM#%pULaDS3*IvL*g1cjbG`YT;i>JT
z*Avb=S$4d#a{71_(i*_weQrIXI`6$A0<kylX$#+G?TElp<ARERQ%d8tg-;SEzQ)*Q
zkQj3L<WUXg>lakPk4S?<ADZV{lmRtvAMr{3YbSG73Fosnt#;^4^~hnOSaGlv#{TFT
zs*FW4Pf-Kiqlh(GK6b9|ynP{C-uJ>Br;5PNil7UxW)H6x4ae)kCHQ2D`*fT2zxZf%
z+_s`s9lF3Y+oy2~>_lK2CFoVM;njOz*(ZCM>+gRBOEPr$@*s{<@D_aY7F_HWqy`k<
zf-kN5GejajxY+CGQ-s)QTc(r7py^|I|4mV8?F_}`eo2Q9D$h@=Ww?weOTTk{%owW%
zwTV8|j!Nq{h)NJ24T*+M_dY0y&{uf%VuB}BiyFs4P+|^Lna15}`cK~@{iz-^JhSSO
z=Un#?kLCW)NHX&j^aD;ve179mM~#_l61NRni&KE^MW9v;=q<P4PU4g5w@fx3o&1_X
zzg5cWT{Ko`2^2^`NI0aXL4RKv5J#{)<5@rXzQI*5VEKkOpH^-dI2Q4MZ$cmYcB?BU
z84p3WN_k%@y71&3(cRqRnH#1W?An6ul=-@7_LAe2*zIP9wR@yVCD{Q0-Bffky>Zj5
z>8!qDs;QM!m7O<&la^Mrk^CtrS_ajY1UJYa6|>+?t%tJ@^?qtZ!egf*37z<-;?QFd
zrbKvLh%;C)KaI_CRfer+6;IMW<^A{5f=`h}5idQ;Pn-y=Mb_2P9X!>5-n|i8jan*S
zOkWA{IazvCde|ku74{wibCiIf7eW3e$M3-B=?m_ypJ<PvUyUT*q0T>d4j4}LAT9|2
zj{&TA>obdSZwJDM$cV%-<v)djL0}BH0fDe53mC9S+UgBwHI>+FIMSCG%HinC5IVmL
zFNCysN@MK9(uIt&a+*zLxAChKa!y4`o9Z%9-}j-u*FgfHpDF^Y^ZwN$EOqqkz^Pyj
zsA~XF3ZT9~kz^e?1B-q8ju-vcVEU?y1-M~8C~cJr!hS@8bky02%Re1oV=<Pn?w=9A
z>DAf(_6(>NR$gL1OJ{0YWD$XC>3M986(I$7at#4-k`aZl{jSF~om}e4rhcKcnTQF(
z3p0nXA(cVU*~ez%K5y^It6ca5UWXDrm^NgVG81qmLCSK2sa>7dZgX^;3vnk~=Hhu6
z!118KDI7kwRMCz2vXoLsWSjNP>)_F!H!UqT(acSxa;l^WRnID67a(3hrFSp~Aa;t?
zf6ze9e?nI0$R(pCQy9s=hcw9iHt6-zTC2IcOiiULaqsA5$n<tO0m&F7>$ajA(j?>L
z>&bcVUQ(UsXNQ~-^MnZqTAr}hXR=}HSX&1*L2-Q|PA4CWC#{6BA=pkliDDXgr(w%t
zwz}`>p_<FmZ10jSx4gMdIp_Gx?at{Au<pL!w)9IJ)nXw^e!S!Z1MvZ&NZ^nlL_<+#
zOjU8DPdJ>SecE<anP>*8r^0m@pTt(O$h#@=INgv~q@wi_mK6eaS>c63;i55RtBD)(
zvh^mF$JG-}#ecQ(Li0@qb!r)k3^=A5H`oMcS4RajQt?7DM+dJ(g4Z;*Yv<dg=1a76
zYVGR|9s&UvK#^5tcM+DW{8gGCh(&%zT$5)#av+?;XQ)S=xWcn!mmSTEiSi&a5fGUm
z$|wc#qBd^u?5Q(!PY0uC^u>t4Yk{B|EP+47bjVDBBVV;o?z4C?gCn|7uJPDmz@O80
zfgWD-Q^#*E^;fT+!1|#`4v<C<67$G$MGEJ!5662yagtni(RL!Tg$4wS69)9mT0EVk
zJir&so&8NTco|x(;CFfu`cgmcMuWcnd<^FMdT<I>0d=Tw*kK+12OYX5Z+zY(Vd$db
zL5Bnm6d17AVog0*yCxi&)p_bO!`mP?%YiIGxLR#KJG?`~o@hhnL*P0{jHAG9ACey}
zF|5A*&`JK=QtVlh1TOGVfP5ilDB;g^0|6t(We}<GC;4^{(*sP)e?9Y13J&1r`6(7`
z75Kc_ml7I_<xp7K<?`=?DCPwArzAR~Fz@K0XB?>AoJPMf2R_PH>7D)J<maNE&xP^|
zgDWCA6rR&l8EGCJgfUPvFw%bBD{tjyJvI+(ihFA&*L0~Im9A)QGL!^`Kqx;m4st3q
ze;+UutmjGEthqfBoD8i(!X=V?DDu=OT2b7SN9kFtk<K<q^!0R6e-DW@_~1rI`N>bw
z9io~>yOtT!z_aqSxWdF7)&yBF&ypZN6}riZo4azn%y#6567dDrt(Oa>a`qUVub_wR
zONZBLubk(6eqYLvnr(7Yry$<+-?@#^S^W19sR7LuGnRg1!A3e~-#v{bl!zfZxRXJO
zE1!Ki^Rv-GeN=CZ4V?&u?ED?rb|A+#wZT0{@o6SQh(Zzc%~Bmk1Mw^2HT9=3IUJXJ
zgU@kXgSH9ap(O+ox%!v4tS_amS$qhx$#qp5szbR-o#7y*#~uSUkpHx?wY;?7%doxd
z6z)6rjVD~R4(sO(LrH)t$A4N_{Jx*R`jGDO#Lw1|m%BH^zZ7k4GSj{tF(oF)=|O@g
zT=(Ea%mQyODF2iSXU{-{W3qd9zo0cc_QEE|Gw4mIdC|h^P(Tj_suWZhg!^^$D^6dc
z`I#aS`bDhSpaO+RwT#i|1_kM~4}z4y72@>BM%lHN^iArYpXj0k>q9lioV|oNIn?3Z
z&@G<?6l8^Da^Gw1PpdWkTy{746BTNyM*j>NXE(BC3xjT5|L}OF<X3fm`FuIe&rj4-
zJG2fF)f1s~Kn&o9RTCPH3&qu1WNr34dn*-H{aoXFy#P0eSO1ws1WDlFj_o}Be8TLt
z*V<$F28S1=o?k+dUr`9MFIYhFIVkW45X6VR4gdA*ntFE{O;r`k(2itb1X7L@AVq=^
z2usi|6nS+0)8i=XUty@~)G&T*VFW(V`z|1r4;@tDRc@dcYQx%5u7501vtkK`V!I(E
zLjL7M*2Ux~Bl`YR=vGkH#1&)FHFKljKC(VgDtmO)E}YG(B?#$~Ef3;mz`tA!cO<iF
zA{ozV+O^X8+eEZK$|LaM+;k@?5i9@?=MOLrKmCTz8?5*_e|?k$gqgArvcf=Uzy*{l
zC3E9TQ;i4AZOtIC0wn9;B?#|;H*+tqzuOzc>WoeO=Lvw>;ZN9nXF5nLM!yfDN+6Jx
z8W0{t$Z3mj5f}bKq`kZI3k!lI=wl^+rutPp`zVo%G3@OD(FEO%BIt>~C^|-<bi^^=
zXSDcQ>a}uZje5$46^TtI7%~RnTW&Gx6m5e*fYT@c=LJfy&qU{Uh}NXy!;r!HlnB?$
z@cILcAs~)C3^!4RjkkI<8T~#x6V^oFtzWd3b1o*XzzC$!jYnvV2@;a&2+4N%@A>dn
zs<ZB-U#*)XkahuqNTqwnvA0K^H%ZK;=7w{Zi*ov0%5O9{EOf0euGc8krhhQe4X+p#
z6w9#WPd6W*P))BBLw7{CGdi~4I^D2#tJ-}5U|xzfEBWrgzGapZd5<(%Nb0~@qA3xz
z&oRhL1epV|>jLyUy|_12qLbbwaXa}K0C0JQe1erL(jQ0Snq>=MINgE`M^B%j{eP^z
zc_7r^_dkvnvdg|_iwMbD)(9a=${NZ#mLXzfSN1)7LQJ+~XONu=*@v=?rG#V|ii{9`
z_c^q@U*FH4zklYBc|M=_dG9&rKJUHfJkH}VKG8b0_wZW4HNaHBpW-Y5bF+Fj0e_65
z3^;wEyISpJ#`-$J>CRM31YqD<iv+Pbd;;O&;CwAu%+9s3ylviA@g@=oVbBU2??Wpb
ziy!i6kx4ynKXm?E7-cP!CO@*Y<=ECS0qGk8L*B@YBkgIlGcA};=sfzWP9;L^__S_I
ztGrL-*w_TA;!cgpfn*_4_~viZYNx}fc(j~&a7pRfoy{t6{3B4dLU>&SfpGdeecatj
zXk*pE(;tr*nRTB|=+2q!UX`Oh{&YDQK{m=C`2PKTpo(lw=*@tu&o%r2@g;9zA9^^d
z`iJg63e7`v)EW3tiJr2g-UsBJ@T_}vYz?(b?+TrK#Zz=_AGN4Tu)qmFK6pPkEOMR4
zNR&|KlQql$ZB}D$BEiIdf$;GPo3t$1IUO5%j4N?)wfFB#NlQ{Ja_2S01EtqI`(ugC
zlG(dV?1k6T0b*!&^nQ%cwFT<l6tu48I5%~nd9c*T3{0!nI8eoVU+_iREJuJ$Z7w=l
z_J1vt%KWM4gBC$?YR)ApxU2p7Oaf}xAe{Q0hw@x^R<uXBOC=u;{&M|hB#pwV%(KJK
zqqkX(mnq7i=Gl>^M;>zvWvo{coTkIL0zet)B}nEx$mg6M|IzNmvHFVOd-)!kvT87A
zgYCsd<(-`I+6f)r!3%d{;H~AyOM40Qa*loQvYa|`%1!d(puN$gg8!@tN|eBdJJw~u
zC}1Q7$*v2F48)rV^Y1j%Z^vE{Q;klOzU=@e!t(vNCnX!m&}QtIA}$HiExSO>Hjd*)
zf3C_`3P}fqs4Nd;aOAm4qA31{XH&!PwwuCi;|(d|J=5bhQoqHT^^J6P#QrhEgLj+>
z#+}+<B2L<TCla)lqP%mTJbb~2u;7e3Y@MVf(2RzdR;hu&r{)2iVN@uYFsK$4YMXYO
zxM@RKmR4<f8FkcZG8hp=81JBj?2A#8Mvb6it0%JX6A4cd3IDpk)Db5nMkFe*CJYTC
z4e4~GA3ng0A!jEtgqI6@FdQLe#W}~;p3zREA)|X}kh4Mk<ZFl&qm2cDeTN$(##SiD
zosIZh3Rm&w`XpM%Nf0%6{2wa;`LuTn%LzqgK8d~UxQLwOuN=BCB6_{^TYMEWYAoNZ
z^ZT`ADItmqQPk=O-UNYYM)dtr`Ni;1S{8PIGs9*sk9TXb-K7TWXCJpsSSNSn(GTH+
zA)ks~M{%{n<Q}nJ`c?!044;*$>w`;mNeg{4g?>s;Hca~L|3#h1VqD{iox4ZKgWZ))
zz(r~I3=Yh-Pv?0#xKW`z&@KhP@K5*oyg3i^9lAa$uI#<GITeIfLxA(&F3I7t`KpcA
zdE-bXGig1jC+r0ijqVr^lA;U3ffofGZiXhj;<4*-H6gFfY+)a{P=M+Ow2y}m0fh22
zBHl66|7Q&H9K<wn0?YCHy;8gH&dY4_soaGqEbsv>Je=iCFSx&>$w?pz5G#QlS0uEm
zf(_e*J$Om5IWs9lOogw~hF>e$oqB7|2Ffc+&;gJubZ!ki=#<G1+-YVW^FM#%_X_k#
z_LcLUnV(nvEPppo{Ck*j{?+7Pb|aBx)mvL)6PouMzdf=E0_k61Ipzl22e751^oZxZ
zlkaCEPy`n|g}MaoEe<h92$ZK{HM!KU6!7$zcjjam`Z%;u<DUY(kFQ}WqusX!&*=!u
zN?`zZ3*f)`Er!!gcbJz?U*LzhU-a?&3<%?bz32a{+4}KZI>f{Qwha!4JGeKJdwu6&
zK4aIJAl?jUqYO4TV6%X_uy4iuOUG4*8j$=0fHJ;$0NxtV|5a#xZhj}^HPrrTh(8R!
zt$O48%{lo}G8GZ1E-Wm&iAlUn5bW|xE}Tq*3w^fiG>c5yG=th5t9XIrx#H#X^xB}-
zDT?tlBlSgDdY*5;%wNs)tho3fchWN6oWwjOfrH%>Kq#{!<-fC~cS*Qih4lj)vIOez
z1!pC21#lsJ7rDp%9=-nvH;yVxqek{ZDU9Zf$l?gJKFGP?M(y9aFrOb3>Hay<BN6C5
z?HK4hK;h6}UXsAAgK#<ZEwrg2H|u8$1-jmm8gfdZQd=T|x<aq9$VPH(0@4@w7gd-#
z0B{5J1vhGl29w})KPGFEx!t8MyvaeQNq*!~y$VzgB}i(W2bHZ3TLSx98VrU6ZW>g5
z1yj+Ljm)UL%KY^QX~&!atI#F-Q7zcJdt%NlUxAkcBA*ghRF?G$suoc;0d-EJ(a#>F
z!MY_R2(XJnui&$nb)uc_c`QD2Vrl8C{@{@;OTf@*UbOP|@>|D?o%OQsRz?>B`uKj8
zO#8iW+}?3Di~K;f9!rpJz$e%3Sd!be;~80dhoM0DJRvOqp4Q%n`Ls*4%4sy1ZG7+W
zk}Y<iL{9Fryk2B@EJ?u6e~iwXs&f6CaUAIcf?y{6O4~Q2>3Iz0L^TmxTCV=tvF=3U
z6XU)BrF-<f$;oHd$;0nky{hSZxKWLJvZw?_*7MbFVt(^B6V@l-fPn!3ZSZ1q2Vz?#
z*GLW7M(!T}d8&dKrHs}|Gc9_OH>xBpe2mzE8$o~#qUjd?RGpN-f!bM4b}M7sOU#a5
z_Kkzf_*V8@=AV?!mX5TGpu}eCw7)_XrSvAgP5VgJt5qQH^`v&=0UgS~K-9V2G#PCZ
zgaCRzFdSxNvHc1Xe)^l~gx{U_b&n9Mk)uDEerbu>bHPFtTVTl%NA`0o%N1bGJ3D-8
zXXS*B!u^<Tn7hH8vwqZtzw(y*Mbz;_Uwvw3X)B{AJj#CVzXMC6!O*UfOQwbZe>M4p
ztQj$uSUy^j<dQ7)+Qv1MmSK_tY~GtO*fuz;cVuTFk`g;yllZGe{LJd9$o$Wjc8E5u
zw%X!e`O4rxDaA2I2qAXlw_&~&&-)&Z=xx>LZ{AwUY7{kF?NF+xFT`&16@QDV_P_D<
zc_FZ%issPjvutOdP3L+a9ex{%<G$yZ|5m8_lL58#{qAVAjX1<byNS2TD$w#5P%}Z7
z$?rLQ`x=VXG81O9i{|iJx-uYN$`y#_*uA_1m^N;@fne#F^mrhS<DPT?VjFvd;ep_h
zB<TqfxD-G{2H}(rCuOny6cUa@$TzhF;-0^{MFf-ffz`Zabisv>?pb>Vd7$L8<X3M=
z3u*~;3BW>aQ%`iz#nns<#N2R$<yWRYk!#lybAHnzONE(B^0;BDe0u`-Y%Alwdr&X<
zJ|g9*p(#8Jrtni07$=XY0sE<XQCF+16|U{eIb^ZjnJ#7<S<%*U#^Mk3K0dr6JAFwu
z_RS<0dkSsK!>Ay{1O1X9DsHlGecx<!F;AbXm{j~MZw?mT3d{AKu&YNWkEgB-T=Z$H
z=G?*3VjQI)|HVVR)qt#?p$Qe)6oQkXEjB$H=g?xGRJ{8YM+r`{6R3mzqXql7tc;@}
zj0!y`ja}tYjk}3WMpECF&oRXxeI@r?=pxG8`f<T0?xfoH$uh}uFww+-9_v(R_PdqP
zT&DuHj{%(TKoNYie2E)GPPme=SuKJ^Vnh0Z_J<*(0%k<7(l$>VZ~3|G>?;w_cMi35
zQgHM{xwJeirG~B2IeCMmB_IB_Ev;>i#&iHFtdA57tRhRE_(=jyxUj<s2fcy>UbE=b
zo0w&Io*(I$^H$UNWTMuvm0Rt|o7)`n-J0z|cEBF(Vxb8W^n1d9v@m-Gq%Uumqa3yB
zqLHH&&_wSekT8sI4@@dZJaV8SMJNuV`1U&sQXX;<*f<3te*FHVutR#|7-7x19OuQr
z3RQgGw^&_4B#;9Cvp<;wk|ca|G)!|z?MCiNdO-{0ef!Hh*+VbkjvITWAZP7$-cTE}
z_ZMHv)AtDziZ<`?1p?k3@Oi{%=z_1T2u%ng<ue7IhcXCv0Z(WK>MMnxLwPUX3yQ`s
z?K^CE<}g6}e3=Pjf>R+mSkwOJ?h!}5-+$xD195<|+JYP#BhNr|u-?muLriOcKq`ev
zrQR_!C;()#fUyA9=nQ=FG#^N?u@L6EbFj$(&P!7~Hyqz<v<N1gx~v&FS>78trKH99
z)Pe4zFNLssz9vGMRU9JK?!dF9fyz@S@4{Bc;aqoO(0kkKAtJ8wM$JE7p+{QQewF7b
z!7THjg&33?0vv|;f#X=a_iPB$k-NUxsB$6Ge*X#CrwJuRiLU&O4GKS;3Ti5z6g3qH
z!o9VQ81Tv4J5pobi2&^YxAmXCDO6Z;-N{CpF7Q@gHyEoXO-`swPAnZuc|Sx`c=KeX
zdoKGI2L}^h4cW$}AgS|EYQEk-A{J$(phXR!ofhv}6gwl#jmwAg_^8~!$)d1^()}Te
z6ZLw>D6pcAKE7rPg-y5{|I-2xXSf(@9cN5pXs_-)>7-DlTy9uqE7`48vloO*YL(A_
zxScD`B3L4#;Y=$pOO1>^3r$KFyedJCH&7)p?l#FN4Az|ZY<!$5nT~kd*C8eZch7J(
zv3ep7WoS3<T3I_%&>4$vzY66qg0fGbLI=<*H#s)HTI8ss1j^yYz?Ad2uiZRuC2z9O
z-t#=FFhkFpV(6sCxcAVnmPS`|K+b4G3iJ%35P&C?5wD|<RUy=6189*U%AyY?eFiL5
z#7XS#aKF3m=TS?=;w)b7*zgiw3XP(eQ^1L@?9KLDV`3mv!-15Ll>%!^Zz3PJLf2DQ
zlUu7ce{Vql1~OyK4(W!4g`?GmA^+gNYNp*ed(?5647crMs)EIRlX3j8fmz#*MVLP2
zKt_*2Wxr<rC`Am#DdziH>LkX_Az5hrSBwn@sO>f$dzh~dKwrR4%}_DaW&T09!KK~o
zdZPz<VE2hV^P2}1%#ycE7|~+>qsvYrJO0aHvO0M^4SNoFXk(4d@7c?~54*`@Uwn&@
zUI>$O?CsGPB!VBZ!hQBv?`oA-iH7r+w0mcMwD|IDV^S_M;kGhsP&LS9JD`0$5ItW#
z*U^J5w>O?c9kOL08Esj*-!ly)(r`nw3d}T<WKzO|efTr*FTNI1ihxS}-CJC@4<=gW
zpOK$OagMS7Uw=ndiv&gDKLp;#q3Uk3;Dx$?f73nq-oYI1KOhebs@94^`dWDWUw=1R
zjs)G?<wMncxxMczR8Z=X12)WHXa2u}-LvTjcd5XftwRlXHx|22{CD`^i{d@Z@OncX
z=Aj?2@Bd(GvB-*Z<;tc4FWKssVBFrqcU72yu~8v!F}nw+WQ?<xxhNl1@+=_9H-IeZ
zfWP#h`}|XPs4y}p+2OC&`ZwOk4}HK~{OUiIcVA_v>+YN!N_Ju8O)u*C%8*5V8rDvB
z{DI-m+eyU8=-bdtIP?2^2sCZ|EQB1iW3U_}Vxpxj^kcsuX8EQ)C1_@^b_r~o4Xzai
zqDA~tZo&%Fnv(s}7h6kmmN@4?m#!UlNd^Bo5(}x;$10Hw3!u8~>dML<8yDM^ZU+i8
zz)kBE$+4I@<VR%j=LJBxi{MK->2AO`)Vx&os-eLr`xM{?t%Zy@;g2fCQQrDAk0eR4
zs#=W4UurL12^X%$Ud24AzsT*|HSF!q$ET5Wa}rcq<TsDmt^-t_aYE1z0Z&ug2<!$}
zh?A^xnHsV<znZsr_roe$WYlzF3y^vD-fz=|Ioz)@9T}Gne7`4vGTG!So{T3$M$18h
z0WnOlus{n1GsYWyU4~^)oF)#Xce=CD3YCtw;*EyvMyxD=Su}2_D$a3fc&Jzx=ImoO
zh*(qR2$FJzKIVTv6xJ}0qBBPw*f3QIA6OI_)RD!-JuKJ0l*as+tgP#*H;T{rpoTP6
z=!|FW`^xwK4q+!xp8k;}CA<J}efZrEIrA-FPuHe1oXV_Qkg#a+iw^V=6-OnFYH{5R
zMDGD`^(oZkA@2m3LZ;jl*yM~8&7-w%k^}+;2;SK02y>}^;bhDfzQSWzv25bt5LjIG
zT8T?sIB;eE;`VN^;uKM<^B3Qe>6Qj^@qGP|YAYBkCgmK8MkVb$Dh|N>W-)OQL)4f|
z3OF++9h|K&VMq#Q%$00pqq$#*y=3Q(m|LYBc_%uA9NvyrBZqgP)c_zW)>`;x;TyI#
zH2dtxW9SOF{1%-@4$lE#ts32G<)pf_NTydE>pq<ENNpy}6J#_K<}or_f(0WcY{oQP
z*ljE3$mmH8E;55Rf{TgBXcP-ZcFyC4{!T4ZpMaT-N751(Gx#6hCvY3+)9xhg3yXfv
zxdf()gdde9<k(Mtd&NS5opj~DS^QnlxiK2)wRMiM37w4WWyVDPZT>sztjf^E?q!_C
zli~bS%KZOn;3)-GcZ=@ji0f0&ZS2Ted)e9MWw+Ex8D5n%aTJ#ds~GfSCxzlny!FDO
z#&(@8ZwuE82H%6Wfy4qNkTZs!I@^|BFg`pr+q?Bu)6F42TgXKrtzjGpaF0Jk$`ZyC
zB4rKZ=?y^?bwxhj>vkEVT&NRAQD7CI_RoO!rBUW7382+m-DoM)&TpwsqRo=C|BruT
zvRxVCk;TT$m=Hwf;W#DOFdcL1S3;`ot%0un#SV3iQ1QMkHL-e1#boIGzb}+Q&D^Rb
z=|t=2lEYQ%`ww+&Oo^q+CHp`ARa~BE)zpV-L{>eIiXdUX9FZ7wm*)6zi$)CZ;BhJH
z^}SWz&}ZV?G1*xEXY<@Rug#D~Gj~o#aI{E3Ghp-}E=x;g*$TiEOto971glI9szyJ9
zIP)@2V&ZOv;@tg(sWFbHk-{iE+azHBv+70ex^xJ_IX;(dlB)5ef^m4ZK=4y^5w(Q&
zxWe&SFOxn>2S$d)(<g!-BM(@n<L0H-8`8UlcifasVAmk7LcpK+t`2-j$hkQ_`I^(=
zpTKh!9z0==$YebGqtu`Af1C0IM7|#x&i<$^CBwfQ&pipfTn_co-JId<{UPW7^hzGJ
zWUVM<_@%1xu^bV2`A2?;6qFCeQ%HpkoZ8bXdshIL#9#T-twojkCRu%>Mb-KYF$FMo
zeu!yv9*lhhlssrOFMF#GG9{o9o`AR&FMtvH!bY??eTDAvmfig$L9tun%Jqv2`uhU!
z8nGFVrcc0;@`pT-JmA9fnpiT64&R&S<o=N^2(f0csGoH~Un32HWczz~Aa1<A(tNC7
zw@Hxc8PJ?Kq&d9$R^?W+K{ZG-P6qO8zhy24b;|=$Bc~g`GiZD44J}65TN5Hx)P(KZ
zuIlJ#M?60lwNhMuG+O(FJzf2au+htO^sV8{8gB(cWXe4E=cymHPo!zCQ7yeT?!PxW
zKSSOJ2qESB>&zLgR&f1~a-pt)*eJ;XQ=~kVcauJ~(Q4!|EvO5C@9~g2(|e_P!QH_j
z?Dn%w&DKGv7kHXgs4F;g6N7EXOP}gGJ9$ZfV7MVxP@oiL?|q%c$wvvk%NdX6{=5dy
z*y{Vy8&~lOP?wsvu0d^GJ{S>bOVMD&d)>ZOL*z&C!9$mzwt$v_J8JytM&H~c+9F*3
ze=kwJl3o=*)?u<V^ymcj|Fn0{xi|2UrSI2{W3;EhNbzsSTlvHBNm0_6^_Tv1R|9|+
z@Nd9tA+!(o%eQr)#Qmed6A(#7g+dn70sT<<E(|RlQ?DCJJEjLU_E&7k=-y|=^lWoF
z`yx+>z%xKU08#<{#1=VjE?czH*kB)=qW~sLMFQpvp5Rkqb_-EDC1m&&Ro;0Mk{g~5
zln+I#?EP?k$#>46mD_o@st<qZ6eI!Yy7$8*J^6j(vX=KvH_x2_?ZravQ9_f@Y!;L7
zOi@V2s)iFzclFW#jENERVCX(o3LB!Nnf_`ZxO4;(=zZvivCh9_$ENiG47dKs4w9V`
zq*74oZbQgUCT8a6{Qaj^H!ch_UzB`JBY3v_Moy&a-MOlm$s)O3KrO?Y^5mqSZ%aXK
z{}OWwKU*K;)VSaHGL%G(ou1gS>JELE#V^RHK?xY|WI>qnIT{p^09pV0_WT`QwA}td
zS|5xm;+ddOB{M#WHwnse-Zf|=7q9EdBp<zdG%g3KXElRQ%LcKuaAofxn~Yl1upc+K
zh`2SNf_RxoD81eUlsN&x%%6Hzd_nIDn7us1c%fo?>>!%>`(idce#0p27w#c_7%wPz
z_CMDcpWwbW29tb?ua*BlE~dgCXO?^>PUwEQPR-p3y|e@WI#5Z2HW(K?(aV;ibIPvf
zO)kOzbG5n^>tS`twl()QHo$``{?y;0#^DpXb!B-!GIH5fx4jF#@(2pQy8M*`T)g?J
z*@zC7&U^Md;pKRU+b#x_s1raH<HeEv>!@eh5sku9pW|_tAp*byDHXbeAf%McA9qm8
z+R451$yH%};1}qV=U|`zLE%DRxva}QHQ}Y~{(;^EkVFnDaNubhs#yS2gDVBRbwCPm
zD+kH&bMC%9mG3TpY|gh$g>^c;W5TzXQzN^qm8iY+`UZ_hj}Z!fP43v!x`r%Jxl0dr
zKt!N#NrpXLcsIYsobW;4s!T`bEZgy(Z_hyG_O__N*}6p}g@noMn(dWEx#@?2T2Has
zjlRP|4fB?0#^i6~H0DGCF09<7ST6kN03E>4sIqs0N5GwpW1fskhNhJFlE-Cy%bi!p
z^Uk37=`s7Yp>e~pIcT{HvE8A74`sH<pVx$IkEC!)aN<t25(A+`ZTur1OQK-h99~->
zvA@tIxH=(x?FrWye1W|L$>XE@bm>KzTpLCD{85EE`%8ZBGM(dB!{S2FYQ?i+1{$rz
zNs1io_t>+KJ&YB{jk#)9HkDl%X*1b0A{j8SA{?D_Rz<PvvU)y8a~x1+fWUdQFv`E|
z=!cS;({;$kF8eUOEma&G#raF=nv~a(qUL8tT%LwU@){?ld0ABnmg0`IIA8AD<~8!q
znE0Rr%19LI<!tiSnkU@GSW4ZFay=QBKFZ2Azg&E=(QAx7eZ$Tv8$Majp0+`>lQ%&s
zB;qCLL$a)0T;!&*L#air3(LflO+Yr%&VV__;9`%Y#mKBZZM04?(W&n4$W|?B8-^KN
z@nUu*GrVJxF{_dl?^35F6+4>zsg38%3HT?^z}tdbkKG=$r4=$+7mD{y^|Z)H^HkrO
zY&9~!Q=ZV@Hd>UjYVA$*s^e_^S9sm#$JZ9~;V(txfa2B<6^Bz)YNy(=nL(OPXPCj9
z?<h%q{_)99*V=2ng@uQcY!*p1|3sH#F6zB$MQXo@t5mDErXDS(rIWk10l868a8I^_
zIQyeg-%y!ir@f3!-^c@=cO5p3tQczs%SyAD3<+kA^|GA@nT;hSWusf+-}Siw&4wQI
z1@JHb4Z~P1gb^DK9t#Z&IptubG)*0qm*{E#B2mZ2eq&cr{cS7GNOjf5*kNR8Q%!Ap
zPI%w++5DwoWHHG2>F7bP+;usLiEMh<`!)(~mNAhdXz@Vclg}~Jepzv8m5|W;!^9_5
zk?*=UbQ6Sb>Bc{-X^FB;S5M<=tXw$(w;b+8Co_~;7p-wL&dR+CXTqEYNL&>Y&^YZ)
zSaU@b@D(@wia{Jx6$AV?eq;*%8uXFm*vGEs#w{75$^&0~)N;?fXWB4%Z7PoHj$x`S
zA%V}tAQ-L9VFt{Yx9Fw-WHG?e19a*!dW`378jPq#aWt2=A4&}%)`W1fsG7MHN)_3+
zu8KF5cEhhHPO7{-WuRq&xZPR^ic&n;C_qj={q=E_`odnLT73r^bdD0=xz}iYQbAOv
z4jFv?LtL998+$4XW~SMfB?Kp@BAfGNgoE(=t&*Xb63LvJSr$(DrHjXB9D}OGmQ;Ig
zGhq1A$KC7&E}X|5t0#v8<n1}sEhda0IlL+W*qZ*AUbI45P<4HS)Nr-E#@Q}<q^U<}
ztQh|-x_0R<&lto{s~W0+_NBb*ycvY|T{8qJtv}J1xheD|NMhQH7(2!#%B*^}2QZ_t
z?c2yrRtM4t%VgMC=so5D!VCI+APb$7$My?&Og_)FKZMr`q6VBI#@j!+P>gezes1RY
zBADtEc5SsT8Yw*JGf6<1{KhBv^7|e_Ti4>%H?~!aS6)rI^jX@z5wmFwolKYDB+}Be
z*`*fn_8YHYG2%g0(iiewCWq6in1n-b+#@FV{vf)9oIP4aHeCLDr~Wr(5m1UzPd%Uq
zg4P?IGUH%>x%2@HjpRZj>0_@xqKNcV37qOQ^}9sE3CZ@c<JS=lm1azeZwC{NN|EXG
zu{qYPJHnRH^kxGfFAM;@LGc!7RE!vJfzIoU8M>GGCn5p6UF<;UyP>2fVXro(FJOkO
z35IEz4GHeBw-Gld6Eo;DVYno`iLa!rDeJDS&}cq)jd-}@`BBCrz_jDZ20zmSy)L@E
z9xpq7elzELr&YRN2^~>I1&1MlAPdDHg=szvuCBCWmft$5`n?+~>zc%UF}_mzbbocW
zi}(+Q(CT-g+5~Bp6O7F4I8p`K=QD4Y)5ZGqY$nIb(#}u%T_qPTT!~OWd5p^fwu3hS
zV{&X7ao7l>TZ6Xz1Y3beQ-iFRlRGT3$4C3f?%X#&?n}pIA8r&09KEo#^F*wV!M8`~
z0#GV`>`UiRRgcKvw(oc1oT>8HI=slS0u>{a0X_0lZc&f&eR(aOQ-3Y0IqPgZ(lVq0
zrUd|WgFG_}*he`f@~!rQt1{*urdyUD2jafgZXks}<Aw?4c23i|I#WF^Q|0<(l-c&|
zDvD)X89YgTX#Y)~0n{occ1yezKNug#Tz)Yp{5HG%{v#(b2zVX}>3tYT3M33%tcMe(
z#!>~nwigX%Hxtwyg|hXI10emOc~B~^FH;7*C0Y#+d9P2}U!hFr3Z)U?P3NGABNcAx
zM1!mI4>T|TbbEg#<R_~!_)w5zweWf{CWq69AnsJ;yAudu^Oce3Q`%3I#tINYczw>0
zYEl&wfMEwwGjzQQS<CKF-gOvTt}g9!5-9IL8+x{y0U8qfS&wS+PXl5eY9Vbz-)@IJ
z4}g&E0Lu?V3(Zb!)|i7Y!7fe~x&O=?l&^n&1!{l-Bq!mA0*ZUl)@|M(7&dnz*&Q*K
zz2Y7$zyftLb9r)7lNCCs?i$f2!QKU&h=BLrjIOe~nPX4$t48qYhX6_Y)qMrh!GzRo
zs;5?YX`8h{ZEOua%SN(zW<)%EHdfWvy5#hbf>Whr$y*j-_pNUMFvw|7c>oUvAX<);
z&O%|0&Nq~-!kUX>17lVM77&pA!XfaOLD@UqY15XkYK4_>uJSB}VP<sLe;Ozq+OX5;
zZHx@Kp+P*DPjER1IGunO6qK-nifG_QbFsZobH?5T2Mj)u(;K8zx(eKMvXKx<7j*Fw
zjPZHXq!uqAs38*wRx|MMu~M@7_XcGcV@{Ln-Y{mfkTAIL^u<rps*eA;Zd-ow3a|S}
z%#nx>squNQPIC@GuCucc+80Fq$&jWm`j+%mZ#ZOR__x)<Un1B8zCuF)WTFs68puGz
z7~?nkSYj91D?6loleV|QnMMq%rt(h{L<=)sS~slAZcs6m_K6mBjMDId$VDH%ObC7~
zeC1{P+Be`U)@NGIy|v`YCk#6Yy!7*jFkllB?3#?HqIrV-x<Qo7?U&)kzak<GA;0=R
zy-3=|WpUdr{cvb`mz$ZpwMrO92b!?Omt}#dH?b94-goN96g7{iEg;nD$dyq4D~ObV
z*K<KRIhzfv=LALR;c~}MnE@|k#v#in7_654=9dO;JB!sP6yw=t9}*~ah^WP&Vc<Da
z@x#bX;@Gg|g*#528v(_OG(yvRFM+V?UwtXVQpP)nZgUsZ;_i#xF}&z-W7C7+GZYub
zTMPo~Z2j_J#@=`Yc55y&;4bXfV~$rj#{r_@1eWcON_G`T8~y4M+%|vogzbY3U(yJ{
z<yiFIo4!V@1WoW=CNs_ogDgE|8j>_a$2jF9!kVvjBF-2JROmiD2^@o0Keb$y+zwaI
z*icA0Cr-TjYiooX1<LIJ#5G<{W@2VVeDN_Kr^KUnsfuqEw1-A8K^g~3GlVDyCNZg%
zCxVGJ;f>yABi?#ut5|BKNFWA*bR_<CaneDmnKE6gOO&IcVz<ZzhqEJi4#9N*0U_|~
z;>XFDA=RA7OHtxT6TNStP@40uaW?E2P?^VHc{ZMrR`AY9x!Lrqf#Z|m$GGjE;+Nzg
zfkq!r0A<)_*+i?EaNJEJndwgPJ_!1=6Z>cSAYu!hJ{Zty@Em!I`&eDMV@7<68&awo
zM8%2+i;HKS8(T@p=~4=n{$^4;7UUxJ9Lhld+f7*>o8jb43-9R_OC`(??}Ant)^ccq
zfbh}bKxAb}9k6hXi_K%t1^r(R)6UHwp=N~wb9k~{yj?KsJ!!z`@Mf_0-tWg{=hE0k
zr5RCk{pX&WKlMLq@Fp?XrdWK`5+T>f)gj=9C@(>D@9=AYkh(RGYhR0Bv!tpYqArfE
zKvggYFhTQx<<D&FE1QUXGZ3F+wP6oGefh<{+QoVjrSv~C!`JJvopM{RKCAI&RmY+}
zCP!SrH+GHc2&6wirSaPem{1pwbj06mg16sSuDF$;MoC013_84`{6};=1O6YK<T4(g
z?Ncp7WeA<tRA@M%)bPJ=QqQg1xwor%V-R-DYA5K^&ob0|Ql%bnPd#8N#%D_a4rlEZ
zz4bh94W1N}_grbDDubU)59Y)_O3)PLPtL?$DdCjLQy#vZm>axDa5?56d<jTuq3;Ny
z;Y&nzVEhMZxA<JX$X=d;3f6$HyLUin`YFlhb5Z^N)T9eUPSH27Qs`f5Qz{51VzAG#
zo2?8}Bhmotz-`A%HP_dMX))7VldU$JpDv81rhw+(AHtsaf^ahkH%~Lv+wTkAqIcQi
ziqsjT-#nlTH~^UeaC#wh=kC{DMU&Fmv*k60OdHZa>+JPCr|~%uqKDj#Cy>#7VOG0u
zytPmwi<Z|W!_L&lY*|-^`{D^Cobl+o2ndMws=TiHX4U1A>c+LP=$JQ7+RL12!j(sX
z-zW$NLTPv`7GIFmjW2$19relaeKl*<ieb0#?5AV1PmVzI%;S(B3u<fqB{rGw`NEe?
z&k-K+nUL-x$qb?54u&E1m3+o5Mx?yVAx51Z0)-8zS3e$$5Ei@OmwM6k0gTw`iClQm
za+h%B@k~yrVP8DO7NBM!f&bX_vGthF;5*HL*ryy%H)^Mdi!wsq9Tt>@>hMkCF=p-%
zUaft1jn!5!Ic)ca?dTVDk(6A0q__=(QA6L*7@jI56rv=>WBgUV+2O22S189(TDCSr
z$Sqy(!IM=yc<HPj;^;vik!Pyv6R<7=J<pA#>83DA!{`k3Q4Xlg-X=bc0xv=)rM){1
zFqf6C{<YCJJ1lc`dDRB(d)NFDSI7a83Yh<R5FWrhfc_~}Y>;uL8{4!IwnB5;AK#nH
z5UQShHtE{UX6<k{RdZ&>cMY5dl0Kg$Rs&j~4<ZXBFMI<bC8)2yF5FV7B5PVYr`D%B
zzhK-~cVmvchGILwjPZnMpk&q&!yiK@c6eT(b*#kwBDWAnA@bbcMn(^qfm!{|SI3~0
z$7?0cr`J^t4<-}PA;5B7f~i#3&D|fSH-pvR_x~L5_}?x(LWeAeI*(NmsL1XN+&^{l
z5;P2nL#sQ6q!>z*=Nt!D$Kl&`M~ME@tC!WKm}qL1`cJHsKYRNcHzepULu&}|Al))n
zc96f>dPMahsq43(#8dfqXy|mm?eA0BuHL!JeS&P9sdBxE@xGX;_(*@7o2@_=1!^Xs
zS||%k0rMup%wnzX7*1A@V2x!|5|IH?=$Ipzl7MRIEbIwbLQUJlGw$7hvMtqxr%OkP
z71lurH&7a9MdOa5I>*1XJgc)liE$=gR7=eG-zRC&$1#b$|Ns5>e*Xlvw!B<@HyCGU
ziK&bJ!m=44Wi9v5Q0j>=A7oL@bflgcDMCI=%osp}P>X@pIA6#z%@{1_n?unLhZ+-L
z62Jf3D^i%=D%vS^678wVx*dw1O4MgZHwm%lij;yHK7x}(m_+RV-u~YI1iWyh&^H(t
zbkU|>{F)|6mM|L>L+3dXqaGsI(1>HGMo30!Ucrd`t@q1%;``6XCWPiWq9m5|;ZpP)
zbNAf6G>}#=)=`sRZ*Qp84|y?Xp)XdXa4Kf>qz;)BM{ma}d!x^eHM0+Q>My;(*k2(o
zjxNkHO)yJs)E`U^W)Wn3C}n-|KO+}Hu_jlagCp2lWNqx~&~JIPFlRLG!aU#gyxe(~
z&*NE~BW3oI|AxXVBzhWFkttnShiWPWaJ@_3x9Xg63Yq<xmv$byk)qDLCxmCQ6QfKC
z;m%6q^yoY@WaD6yC1Q|+GGwai(^tgM7YsbZAXQS<>$><j@WWO{ir;OVepLN|`SFKp
zW7mPrnRt{J4aS+2>~6(51U-r>M?D#F9ILk=g3i189}#W;P?MGX@mW9jR|hNBx-$00
zR>s)66--L>*sU5LG$o7QS!_Of&rZs4_`~cmnD<8*)s;39Oid2k|Iri)wpDAN+_V3*
zR=iq|8NW(*eAl}Ek4xTijT^N?@zH3N0vscH<%J^+YW|I{QrdZTV4yD02yw@u!7lQk
zQbdDstZBDkuN`HXw@c8(l@Y`kzRKC^jiZ<gWbB&a{)E{0H8GU77vcXaB?aZ3g<SMh
zm1l7BYxd6TD#%N$!robv$K16qR-<Vb)|p#IW<zm7UD-6K&>X%j?NmzG5v)uWmK5d<
z%{HlxV;HS7sQmsc(~&lto)A8H3hHYi2;QW2xc*2mj*P6B|K2KZWaAD67B__Eh#TgH
zIbV_fKg<9)ZF)JV4C>I7&nG#DJ+&)S`cXGgi;1t%6Jdg#;SMS$rSqo^a_KAQZ#ZGs
zH9@d{;Y<6kTWEzuh0Ew?aisr;VAwIGXHn>2TwJglQcpas=EwZGn>9#{N{c-VTBP81
zT-WsWEk0}#E$ricUv+QwI`&K6dfSA+f#&QiuYF9HDP)8No|~g;Y|*M0_dGe^<_m$a
z;|;nrvsqzJe(vvw=01G=_T|SFF0nT|6U9A!&(bu$G&1u-1lg8{U*wa@tbw73?}%M{
zSikm@RP&9kywu*r23kzb@S@Eo>odSqI%P1iY0y)YdViCueyb8)Evm|e%Ab^ej#hBl
z2c(F0V4-9GyKH|IMOn`MdjFv4K~cHc5Ah6^(x5)HEXsQc6a?0B%#0mk?MB>P+gO}!
zRvy=!DieBo&36EhGykVgyTOPhY;f}Cjir)R?~%F4&OK$5L0J^lq`~rF`3?(+l|x&;
zC}^Jm)&*r)b~D;+;#Jw#MxWIZ={>OF6jQ9wsx*5+i@CxI)<jU1^!I+@d=T!Fv8-%y
zld^ZczPHSE*tMaO<?H=J`pcW>5|+o5V1pM!1MxlLVeerIA-<e-GAMU1*wRZbrpMI(
zToxjMzsP%??vcxcQ9QzUHXd41#Qs@t5F}Gc7O>SIljFS5iT1a|!5UZ*_;{1-L<r)(
z)P&S2u7BjGPmOsjPa@jwC(??3MwKi3|2$du{#$E*E+a--`GJj+CbTx<sZ9Z{`V<Oj
zGDRL-;lay@>6dyFLZ|_)e@ut#GpMY4+=Q#4XD9Wo^3KW(wf*rN2-@*apNy{7C^MO0
zg!Ds1IAfg1^+;krTx*^z45n6Kea!UFsKJK7%H_VFd=BgvI^YAe0OPfQ-@5Q*FQ$cx
z>bUwNez-TH^}DR^Ul%w#t9G{mEj7H|-S6D%pDuPv{-5yz^)Wq9t!W(}p7#(=n|t0|
zF<@w#CS1Q`=Je98#r<E2f_>xN)CXeo@U+!r;leLZ`Y~T`#$JnDF}wZ1O!5<eiJX8(
z;`Mg0XK5Rr;qwqpnJP8r)bX`@d*ZgD+}QoiwGm0mGbLnTIMu%;1GYX{rwyJ-Of<^v
z&6I4+Wf<hae^fWoHNd}5l@N|Vo3nqc=eS<x%el$MihH-QvH_4ATUI;h5?lMcvpiCF
z?5PMHSC|0c<(m7%5Nu5I@(L@Rq(#I2(!EN_cGGJ|P`UM<eg<f(o$0M};-wPsH)7vE
zr&`=E<U0}p?c>FNgN3+IE?(1a=YKWad-ZEnr`XeoSW2!p)$5eq_pbLYM<O7|BG@m1
zqbNXyf_sgJ5H)8PCI>sj>6aSa;glCVMLGDJeWt(Zw*|b0oDkqdibn~`lfct;EU_x5
z3hwX6WDM{NOF4cOtf6G&O)6zKm&keoSrZ<T)(ExE%0E1Tx#=h>IF?#PzdTyY<nP5d
zHeKW_Ng)80DF7!s)5B81?I#*DoX=D@iQV<RPWqvGU2k!6&ktwuI^g-i4(#vS-_5qy
zSrix0FGaRLPhR%HUX#D!xV&3U7<{-J17|#dRHDY*8~2pYH}uW(|F{)mjMyvLJl@Bc
zB6Il>H`E|FpqU+(-g%Qx7tMD~u|%@SpIbQUosZjtA7vh4vqGU)uG0Z4%TR>@W;PuN
z8b%R{4Sbq&2d6!>ZD-`=#542Vb#)=bsoq|R5HewaqC0?~f-)F0E4w;tEnc#yJ&dTs
zlv>x%^(n4+DhG-qYmk=yk^J{-xq&e`YlZyC4U<>-My&F=uu|#pzGmhNP=Yx#9+8GG
z#Qn2AO;@;JIXi+v$)F0YHaJ`3-p0{*TkKRbByzkKGw>q!udaORsEEi6lA<;djQ#d~
zF{vQgg7M2omdAPr!vV4ZV+Yu-k67pB+%FY7t2SA7tac-|ixH-iPb5x(V?RWBZ~!(0
zI&8MnF})&1)O-_u=Ow#odEAI$J<XM;CX-^PJpQBYGtraUZHx63tT_&KUj1uX+cr{<
zB^Z*(-!cACwjyLl?bnhJYnLsS=CYb+OS-n0{Yp5@$#~r<VenZ9RBHPhRH_V{3H?5v
z*&Sa(d3WEL8!g<OP&P@yJ2dqk^NtEM+4mbx9E?`8*lD%a$bbzV7cE+dpVNNu!2$a<
zkFXXxY8^-sZxrXo{Y?|xJQrdp+Jt&<oelLv;SAq8@vcF+IhB9sLvy8vv#ho%@O#dO
zb+uZ0M62HXP)!;}52t<!%RNp-GI)wZtZD!L6FO(i6Pap8?26g`=JBA@yYG7>Z5J6*
zF2=w7XcEm7yV-@a)ZDehdN_}6h2rZ%(ERmwz@syFuXe>;vlkPQW!#X;GsJ%X1h?{a
z@OpNi8!Em5YM)o*os)o!W4UKJDyP;=wjJiZ-Tcm;KQO~TP1^~T-2xQgP4~wGpfkKa
zm7bG27R*#wk~}W9%dmO8#&NjL=d13`|1?-I$PyLx!z78RbSc$O?ZU5G?0q|Du>c}y
zsskj<Z;y<|P9`O&CzcWI;?T1o&}ErF5|^0aZ%&p4gI;{}kF!LlZ@Uw7$I43j^c@u;
zK8Hc7=EcBVVFMvTI!K>Xe;qglMz5OBF8tKzIXzwRLr~LpbL<Yq*6ztYyC?W$20+M(
zuXzMWLUOA&jR&WPbM|zj>uj<Ub*LQroXbOwwn1*;H-9}j${f9Bi4}W8Po4;~>&$6f
zQJ-6^d6`p{FBg6J5}1@w75GE{Yep3RJYTQ$xY&iAR427?Q`uVHGbV7cl4x$oWAl$<
zPAqxfzcIXjl6GidM4(n(lz0ZSi8%685nmW1{Et%jKfP{`aw{vRO&AiIY>#WM+qq*j
zXtRl@(*TSb`0~$qFQ2Jby?QEYx0Z%>xMWc)XL^`jW3h(lT`W{`0u1-JqsX<sO4ypJ
zq+>QiPchzQTFrE@wIi4Spg2Lklsv?+fxox@gMDM@pk^ht_c?~d+^U@wx1gL#H>m(Q
ze8ma8Gw8sXTuxQVm^%_nRzYNGlF2Q$lo`us{n7mBCmyIhkQMgZdzfREo>DPXuS;zp
zk?>mUovv)Jr^dSU1LhN;3KQpn^Bfeq0+z_<p35Wbf3~kk%~JImkG6E|`7<WICj$b}
zePE~bcNiyM{(8XQD@wn>ILrD{y@H|semsZNIN>kQ9B>r=wS?UJ#ohw$C70vUiv_Wl
z<_u~I^XFF|`UCEmzs01Q$LdEi@*WA0zgf~dBapT7^pWJ1DpEswIYugK@)tCOZA=<N
zJIz#tJQjA}S!v_P=6pr&?5FjlWu!`ihynUTis0bP#Bu$a(eM0I&*D(-k?%1w9LS8A
zf5rZA?d$pzH?B0+$N319xwAvDSixu$lahZB0?>=%oz{5&CUK`v;q~%_H!~8ZW{ZeB
z23TxZPFtTcfiE>?;ZGSZAXE+}A(h|={TTlkb33U>>H8Jm7InCKol1z&tk8>bf8E1`
zVizk@#)RUsT};W?nBe^yb@%F=L!X0kZtRB>IuyD+aJtRrR@?PwsqD6r`ebZ_>0W0R
zjPjY4r1AG1dj1jMzN|f~t8H#Ep=vnE50q}H=^{DNQrT4l(P*8+ie~sg2x39|Y_F#5
z7DyqwDVd*tzvBA~)3^v^C&3>0%;2^2+c1_z6el0ASjpJUwy4Wf{ir}Q$4T@FNy$W^
z0disd?+mPM^y2sJ+0OrZi#|Q}>(X77!Cq&!;dajd2(a7H^|o=(uiXT83gYdZb9zbD
z$+aR8pvWF5f%i93lroadwJ>#a7H+DV_vU(qONnWmpjH%1hXOQO?4d&&jCSspgMlPM
zct@OAWt-(^C}u7F=A_l`9jd=FhAN3+D;9eSU>T7U#dNAo>(|c@E;l4Z-1XbzdGsHl
zFc-@wuyAtwXI8P7-8SZ}DvyM2@cuhUW^HmU6RPZ^Pb<5;cs0#Rmh+_wK}|evh+GA@
z!0;y0ZELU7mJlouC~8d3n}>rB(*P15s7hQl{xj|xkCZo#Jwi7BJal!7^L$_5uU9`_
zk>{)7<c8lXMeNw-*y|MZvIvWUb!`t%#slW@9IwhwBgfm4ZJhCCR27H1_Q&EjuBxHq
z_1oW{Y6VMiT-NAW-}b6o?a#57O{>Ke4Xi;Q@ZT==<ODJ-{Um&E5j#R7W}Mo@rjGjX
zqK%VQTBz@|S-sJPV(;D9^}&0d7<av5rHTUY!S<c*={3_^Gf(=f-2x>qUI8EZ9g@Qj
zXtzc%!o+}kalLzRV{}7}(YrSXZ7)tKk-7XKL)&LI(dly)vz?2uW?<pt{Q}QM?r0y<
zG)_!mM-VQ`sZ5Rd1Xu?=Ap62y!>yn$*a?u%{Pa9(DXgi&ru-t2FXD=>eK4i~GuY<)
zQg3r($Di`VWpcYAy;I`ho_?Z^YFcKVU-lZm<biN$(hFr-2RFwvgx^$lNVLD-;waj_
zw{iMqN&1aG%;%+KtT@B>n|pMP+tzgYe3zsi(7Ea(`iVG_tvKdfb(PJumB-Jf^pnE-
zzU~Ks5Gm!K5NTX6&R~Li!R%tg*U;*E_^ee?M7VWjboH60&xm4yk)lRcf+>p2`bCV_
z2pmULb4EI2nz9qH!h|No1t#s2W`vCKt|am5T=U60h6EU+)S(*6fJo86?1cKdV-AIO
zP3OIQ3EFpbBZ2FozpK=cz&JP6R^mZvVjpY*qJq-{5qW$tR?`h>s%okxXt>_J^DIiz
zq1L?1!cX}<xx%PXE1Cg)k3VIKe_U;NKZce(cSSKz%S1r#9G`~ScY)d4>DDb@yB#@V
z)jI@_soA1i(RB-VU?*BAoaL7GX2$xb$I?920yj11quMD!zCC+89(DkN(9cP+pX1}w
zD6ynopcL-$?41)GHlJGiNYNgAM`WhvTy!fk(dD^zR=cZZ_bFuT#?Ow6X2qfAGo051
zX46X)k5r3S7C$n?b`|m!)wd3GesUdSAeLde;z2L48!>Zw(R7DrWo%_xoaL2)psj=-
z6jeU=JFtBcP!Z&-)CWEF9Of2DYnl4MZI*<j{gC^pyCf`;^#R?BU^)&Irk1q9o$oHt
zq@RO9K%~)ynlBF`%!syHzWxm5VmI{Y5U*wlv)oS^vz544O4kQtgF^KAcvJ$AX;K&{
zvkM4mF7<xECi`v}EOn}<8EU!1R;$pRR|apZtH8|CZneBRZ9FZ#aVsvB68m(^o6aD_
z9Gr$TbVCq$?cBu&+Cj*l?<HDf+8NncTPq$vPO}x3cSbMr!QLi!ljt+QIu-A#fd2BW
zEj};l0oteLHe@oLU0Y`@+g3kDS1Nl?vWX^z=Bs!QGh4$tsQxGbU?1Fp0sxCGW#j9b
zi11Jw)?0dcjNi9cRU1T~)nubZ@&PocTcAjTsOmAJF~R1g>4`K9|BGvg>fM18LDh;8
zAJt?rTi0Rz3fc6t%ovx~Xd77Q0mOqGp7G+@fD@|gJ<?ZVY`|~7Wiz|dH%i4|uOY0o
zKKo<4q}}W9(p~R2Vy3c3V0`Bd*I_4w)$A4;(lFX=-DrXr*VOpGMOBMEMq2*Jc|<`|
z&N>4Y`yPethu*mkYL=1E`~k$lfP@)940MC5YsvY44afPOkUef+^2?e=y;R7lUn{73
z^LuN&hn;lMjn6vA`(VWZ9-U{CIYdqfhwbD93_ttoOWYLUSTa+=%9e4G5^>cf0Fx&j
z_oDkg8)mR^!Bmq~XoU^3otHKeO@D0%;44nvf_3Z3#m8;>!AhqigQ|3ex||U+o7__z
zqMyqs2_3_V2(+rccYF43pc=AA<Ouz)oR|D|1$K9hf)1y;%2Z}ga9%Gv;mCDyc5-$^
zK;D@cMT@Bq^0=mh@xjz)qW*jzpz#fWl1wkpuaWG2ujCyzJyFOpdh%0AO@pc&k4mht
zx*%+d;Lb!G^499&?u~o_*GVNIipW>KoWqhDr-^HV7+#DYPwsw>JEmfwg&bHo5}Fqn
zIFPxL#_dY1BKk6!$8}TTaVmax_D8j|0ORllzz={TdxIVh7ew`js_78n(207_tmd2r
zzeZ+^$BD_-oGEavA)L8#a#2i#RQP7w#gQ3s+_KG-zewp6S(|XbZN}8y+Tt1W#;U4I
zD`XRgFdcZ(J7p7vEKx%EY`;0fmDwdJ242f7vBx=e0XZve9VzSN!4#<}`+2>UE^7Py
z0Ly{^>1Kyr5dmE(UjC54o0mSaE+P8agY=cN){VJ4E)MsnBQhWIpRXCr1bH6YiV!O}
zn1KL#0?oj$$|jl_qUXzpS>Dz9s+$kmGs?uxVmfS*ZR5wL=&W_pq7`=wQ^dE;p~qD4
zU^3y5>aIsBX$)4<wWX}g1$R{hh(^xdzA|QX%mTLm8=7^1ubF`{c1{UM2=7^ky^DL;
zrv)HM*A7gQ91fZxRgq0bJQR($@^lM&E(Xim&<tb3G#|oZAovToA?D=Av5Lr-*DrUC
zpJM~h?*pJ29{zKHo%yNZXWPx%Kphd`OTZ%vuwk4K_-8kvvEBsE9r5~HgMy>bfPTUi
zznAn^6X_f^=(Llrv>2agzfN;Zb#-4k%J7@gIde)IkNz*dB3%5QwuY6-zYkcl$J`K6
zZ||uDUlhkGk_Mog4v_%|do>#cI`o))F%dlrC#<<zU8S55*$4`G{N0s`ud8{nxo^e`
zW9V5Ghs=EYLq0c?V6lg1sCsDsl99CMSG@Y+_Evjs%S(1jlni+QdN2CljcC#G?ZqtS
zs+t?3*NewHp1RC&1mReq%@){z;J2U`n4dWeL}N_s>0V$uXk_Uw!S*jfyPn_Cbp56j
zu?IVW`<rwtsHOd}vwNyA=if&g{5CATXGqW@>21jh@k<W_VKp};7$64xyLG*=P$dz5
z^6JK^$1No~KTn+u0vkMhnO-meVq`{~Q+7}=k3(-VDU(ELU|&Ey5t1D`4;^f)lwr0r
zl`Pgj?94kJ@@_}j1)|Lk4-g3GP$J8iU~$|j_6OhN_S@O>ke{ad;h!hxEyjlBy6)xi
zKfK&WmMZ<)JP3z7tP2aq4r&6vxo6zx@W~@eJm<ahf)Nyrg&)!#f|q;Bytn5k6+=fW
z35vRPKD+|89r&kh+q+|fjRKeTSdQOdmL4^RT*Ba)--o@kJwMK9x**j~Wn^NOjzUg~
zw0|!8+^SFi=yU_Eq9S~CLkgmVoC^Efa8|vb&CENDLn9Th+e>;q$ZLu1{i}`RPWIGu
zuX3}MG)sS+!#^7UJ*xoi<IL;H=&SN(F7GO7<{RT*+dNvIGW6p4DbhDp#~*!Bb|rtI
zODk#5V&{CheBdZX>T)?(5Q9X}&%OTX(Mx?sop%^be|_Xx9#DK?DhcW&9Qr5bWzmH%
zW{T^2mGpSzDIi};=-<&ehSt@yyR)S{4K$)8YXsJJ8nyHHxagB$oJnjxa>1om;tR&s
z9b~F2d*i7Z@e0tX$*uC%eHY_L%#!wv5CnFS@_*l&Utc`0+o`PnupJ>F2%Ys$K|>tJ
z8@+?dPSwhG#*%tgdBH^+bx6QR{)(t#b*OK<_lE!)^TdD{a!~vQP!&ui)h|j9rYSkp
zw;g}11T)81f)+lsw}NL2yqaTrjIb5uYebOT75*HgWk;-Q8yywhSzt~tt3b^f<$BIP
z?vXyX64ei`FGL^Wblepom8|?Tg?(4LC?w3kUdw8vPi};y12TVsnW#hOm#gX<ryUd*
zRI(Zww^<=CGGJQy-4{<lNEu<_v|yRlNQ*s=hZH~~JOFXzQA9s~>Yi~7Bm4dmWVXJN
z&`Bi?FB>~ks_dt-Ueqw>zG_AwHw5s>0csZfHQQU0h?|vp#*A|*ZD#ovk=!5$7@85_
zdn6Bi4f@t^eSFS_yTH%vz=7}2;dDSyW5R8`*oLl3Qd&J(I1i1n=^rdj<;#+(#J2L&
zgq+RSzjG?@0C*9ozKoxzq39;5)9PLgBc)0q^l)I4`<APICeJ};bxh{{-4W8-k~>*1
z<TYGr;d;XsG`BT`o?q37Tib1p#a%iYA-<E~*1s~nlj?DfT-?0x_6i>g)W$ifJPr<(
zLFgFQWkul(Ny-p%*WQ80kh+Zj1gv)M8eTa6wxFMx+wtBFP?TJ`p5c&a2s}5qDc(Kb
zLPbvVr2$a>X~PWgAP{iQ2YF*wqBC7IY3uGPL1c;&kh%)-0Rt$4f<09)?0<Lb_Kt~4
z(2<YQ&_xG))h{RtggupVVYAhHCFM9hGP<4w3n-cZKT!mP*Z`&A-Ua)Y1x1K5sW~FI
zL{VuIevn@1p*T<^{;j#QY`cD&>~CId=<J2yH2NU{mni?CE3~}o?J#oEShIG`XTsgb
z&x;!Ketk*|>VVK;*)gaC4$oY1ua<=0eBEdm$E7;;g^~v0u0s+@J`8{n+s#inZ@)8V
zRV$sDV!2HYQn_g{l7GTph^UZ{hRO{-fj@i#9s+76<@%GWUI6_8#qAJb9!*xv?mqq0
zSu-mK4{rQ@Fo@I7k$Lw^H%Cr~5-T)|kN$ytP`Z3gOCGRomb(1)9H4CilkpNXEIfur
zC4Gyr`|F2N@+t&F-m}Y=cHWD2W#Zd8&><I?<zX}q5d`0dF&A%3tK??y>8D;c+2wgN
z3px46|K1Wt?<{PrwsXNl*oSxbJQ^N#wZ^gcS97$qb(ZrJ8EAsA{_WAg_s!%%A^vwS
z&pIcs*;UVFzZa0kF*Ql`YmMEOx(daynj-(o%&@mEajWYZ#ip@i%1Y4)^EjFVwvvSC
z%RH9~!izZgACr@PTU%<PIltF^uXJOrXs)XL0t!B7t+Cy68W-AF57z{Qsxp42T3muJ
zA_Gb6^chNYPby3sX4xbv-}sUv52yrauIQYdd7Ggvl3wyLQe3vabhBr_t<jqfLyA@S
zn@GeSod1h}zv>l1t<IbE!C#F?O88Yzvm@tkK==8P!pVZG@rhUL!7{PaIoHyIZ`<~m
zOcT6e$Gr2ld>jXnOorksjrxOXvd_^??)QI8=b1JI-R9Bz?n8NQ7F1=t^+SFZ1|=cA
z!Pg@N6*NKFN+L0Zpfo`rg4%X6(bh{La;Ug9w;u<$sQ}hNQaIqpi&oyjC5=8xI~l|A
zc6~&uk(-zqhvrz9;>w2dohb47PQ6fK36{ihoz96RU*So;{*Z&~<GWPYI)mV-lTd0I
zsA~%dBk}k0-0S0cWf$Df6%&G!6{TY40~uEcPJCfkP3W$e7QMQQ%Ph9c&p{S*7Bn(^
zauDbYr~!L4B0V%MzULAh<~$uF8)~Q~DJTdFJ@=^p!mG}Cmdi+Cl}n&!E5EC{YSUq8
z1jJ9MFPQ4Wy3tU!+74b;QW3<gphe1z-Dn(-?X}Nap~&5HNrG-EtZRA&5ob`9m6aZJ
zn0-L-s9Yb5Ps7TEa@Epc{L-nGENh`iQ({DYH$~gSlsxp_)uw>X%H7=xW$)QDs4Dr*
z-?ek`Vp-#vNVFvU47O0SNc;`-n7jWd{~>&(McW#CMw%S1m^vHHmtdy{vvsXy4#xTa
z6>;%_h>KiloG>$_Zy7MetdUzoKX1XjFHzf?%yoWNi%5COxRv3ac{g~cdt+|I&Q*wn
z@`}#UORZFo+~eCih87oot}4sw3nmQ+B%S7$&YaCB@DWY2u`itxoglop$x4uiiH+4F
zo^`dmft3IJ9}D%}5qU)*&0a}T4$iO9F;(H{uUFQDE?Q{E&tE_deO{)wLc9EW+;+ig
zYE9wW^!(R%XGDalSlEqt3;B6>d9qZ$j3MGx=l8e+#9VP<DP{sF^>mQoHwjoef2SNe
zn|6%_a?M|$EDyq&W=VX16?L^v8r2`*xJw-tr=^r_cI3-gwoKsK!drt+WLwNHGG?zk
zAyn!~-Wq#Ywrm3m%}mXN?~<r1PQ_lcUB=F^k@oh^^>R;;1*!*B4$N5%UpE%AdU~B!
zZ6oae$JUhxLiK%dS+a#>&6-`=LiU~PNhKs(_GuYHMvN_Mh>DPXsVHkQMzU3kB#aQo
zmOaZ*WRUH5-{70-_s{$>@4a)+IrrTA?z{JMKBuMuGe|1<;gYXnI|J7E7E;xM<j|(4
zW5*c#k*^XOvQ-ATRjV5Z9+F<14aX}yHx|szlh_}DaHp%3Pu7lJKM|75Y%xy*Z-FUk
z{x=SGI*HNefSi##wLK+{^Ahfw4E^TJ2fUMi*-I2Ej;QwWxo4hV+3Tqij$w}Uijv5W
zv|&`$kpsO;mjQ()+4IgyS1;xxI{T^M<VQiap+6yIYiG2QO?=H|6*<^(2T!b+F*Hwu
zlg|23Ol{@!n(LX1(~T27c-DuPZNg;bq+reC8*j2}0+~9+Jm}QJ3xz?(o41RK(MfmK
zh9MTGQr5=@r_WJG88RId4L<Zpw1Flp>b7?0{FXwfnWmD)21C<h6`uf>_@>H~Xp4+f
zt)KJ{f=M16@$rCVem18jO!vAbS&^^S?NGG;<?F@#&p^WJ{Np~?qk7{kM&AB8;$6q+
zB63r+RnWbgI(=L)jsu1lx4dEISk1MN$20hb_7xia4Y1b?UPs^*l$HnO@GXZ`Q^~vh
zhPl^Kq|WV7M7E?8%e<Z&c)+vFv*z3}qZR`vZno(@e5CtSob`EWRMO+P^YR&rpJ9xt
z{sFja*&9Zmg^;>Kv9g~RM=@MHC?tf41VB(N3Y@T}QYeOYw3}lK-_sVR2eWN|hpv%;
zmxtkJL%$6AI$v0H5I0nEaPWQ-ZrmT2kG}-IgCF0oz1+3`Vzx?8;pp_!hBjtwSAp*d
z$F|FDoARpbWeJOG_%G#$vIhWL4Rm%6ObDUC9n@3`toWS%p?XrA=Mc8)(J|}f)b1ri
zcs5Q<pT$yJ`Fxz<#1n4q%$GbxVuc25_zf8&iM7a|HB8tpyO=ruXZm!l%-AHAOUSb+
zZk%87?JqMHxN(fos$xIE>GY8Q8B6?qbkc2MRmXF*#$8<L;4HF$5NQQ7JNWcO-HC}=
zDY^1Z(->OO6IWl#^DfD0^;I<L3i$W@=+#8JHHgoh(mQ638=3jo{<4@QDikr#hpLS9
z`t7FHp)~J=rwYcD*%)*|+pqnzg|}kz14YE?ViZP48w$6^HWwPu>7a65exJE%)$x+V
zmI<5Y8#rE16q3MQzE@JP?a{N!J@wg~6)Ph>Iin-l8$x~udKX`EQQ}UAA}F#swPE0s
zV--2iDKMQo^Hw1O|LJ4tVZbwkpCb4YEb0j2`fGk_2~1OGb5h_qNVTe@6e>ZHhG?)(
zk|x{+3cK>4JiQw&4LXClDQXz7y5M{Q0tpeEMow_rrp;R9p{3R};_R3?Xw{ugfdlad
z^1Q*56(gAjowwL`FCG4^T4mLYlEL}6Lq3Oqv^HpUasUdY9jyiyUmaQ+jQ@YntxZQA
zDjy5Dh0*bbR<L`YGV;}r^o3CN-`KzkW*`z$0fY_|{cEaca*l0+ucB|X;8MB<<ejsS
zHtAKbSROnzcy;y1x;M<twsoMd_Q`^RuK{csTGxOB_1z$EwSFU)@HCXAW_=!WdSCW{
zCA8jSfMO?t;D)(eKkZ20DdgL!Y-{dWFE7oiMku<3ep|JU^F1ATaXIU{FGOu-lNv`+
ze>~RK{LtBnSM5RSp&a&X*^h3sDyB@_IFoX``Q!R#<|C$i7xpjYCL<DYzv?czJnUV=
zZB8Vojw?;5;9zZ*!RRcN#4NZ01}z9nJ%l20i(IB}CxvTRh6!m{V(<bmb_P@oB{r87
z_tkPl=p-sFOAJ<k;fGOUYf0e_!Dva1z6V*HelzRAw-ZPCgu!!s&~rkNu%hkfu6nZ5
z7x988j-8;y2BX!WC*VdHSpgWiZ4BMP_G7c^l-Oe&e_tC?4dl(ieJ3@*m<{V;m{A*$
zH2BqE^id6^6?|p%Z9zz+^52iyihRTjzODPF9IjxDF_Y$pwPir&f!2T}MwHlF-S+hK
zI9rXXGlq7LJKs!KH(6rLCP;C;gsZzP^kroE!&#VNE-$C;uUr*9{maAaJa+0_KQZq7
zQ7*Q-)Yy+1St>|Km*2(=(ug0)Ih^hP)&5GCK1P*??jx(2Ynqc7<}?L1C~IR2(sz_#
z1n?VpFfnVx8`J7+!a4&^{^e3P7*LC;l^RyQ?=%myO0XM}Y+3|>6rng$xRWs^Lj}4Y
zvSYiOO7zL;;hqPulU{LhYDBP32cmODzedi*H#v8yM9InOH5engZjzMfe6~0p=T{wU
zLq=OQTI}A!N%g8c<?Y6pYuMPN%B6)uo#1P85usR<Y~1(##WN>3tPY_j#X$dY8UA({
z@J%u(@%}<z&^)O8W?`L7jd`AVOAAIs;|1}U1xKMv-KbO5H&bit>SNT)cTBx+tSYu`
z{ycX$-o<N*DRKP%GmK4j&ueZ^?!i~=u|joODsG_5@D~U!cnFz9TT420Bxf}ncdyb#
z-r%{Jjtm$3HKRP1n@XuV;r&DPWgL|rVWY>Q+dkW_@><shqoaos+Irk|6H6AybPiKH
zI)5zuX&;$<j{|mBb1f%8Vz9-zPmH|c;*fNacz;pjvg&EA!qhhJ`X6H~+tzJIXA7Y#
zOoG8VJI&B5uYES$dM>2c5N<8!hkjHg;+}l+Tz;p5Wo_cBRADT?mRa&XQBB?D5fSA?
z-)yd$3$#-i|GuB=1>$CEn`Dude8ze(zFGkE@Fpo7$W$Q21?VoXHw~e3OH41nMxe>R
zy%BmoYb~VS=aIAFa6R75>|1utV#^za?z65^O)M`%5lNQU(TT%LQ}B}DtRhm};X^1m
zK}k<S(0@DBKG2S@^BSW~YS*1#)W;N8e)qU}oD(ZyTRUC8LbdWSpPCz${>JE0*;_ie
zXmzd6C$zT!^*4<*PM>ugN78#{U#G7L`973b15B;zZafi2Di{}KThfk6?}wWZG8s!6
z$Q4=K;^0w~G$odd_G;Bt(U;#fkiyeHEQZ7JkrC6!`d-Ucc1FmesT;?58jhJ)lfngH
zwwXKQEOloj;p&1A37j`^KMr9(5nUMm5R34mv*DDzH|a?<b&eZLyFcBP_SMr0MP*?J
zDirYUBoZ|+R>PX{Jf;)guv#0KlWxe@C|Hf|MtY$Xa|rGLdV&#3a0eK78__jNaD!t9
zz$e}STJI(4iG9!XG4@%K&)_KrSo`Rf$7mRotbYCC?D6~NF%q%$TU!%phrb5A`82#*
zb93U&wD0WnQwjQr&-NnXCpzy4ofGN~K?um2Q|0S)GCxVqcw6C6P>7EiEJ|@N&E7=t
zUZJ50#%EcK`#$@8M)nsOJYi%}N8}<lW?koww=YJOMebPh5B&i}YJGO~)FAYF5Sj^t
zCdMMv`>0@+```>%C4W`%F5ZzlHzawxYj7G!QQj*<ZS8|8cV8xFRiT*ft0ToKe|Fu$
zI&qp7b8A%f)*N_f3Um6wfm^a?O#Oj)AwSp-8$)7jdZTZfMVROcQ^K~8JHnN+;AE(x
zApDlPC3!nMC+?Eu>P$oby3>@+J2Mt+YEywRRUC6pUvuv{(^q8=UMmmGI$P9r!CIEG
z(yQS`YuOrQxowSB6In$fUFcs*me%JdNU(Wg0BsL|@giApav&SBCMAm9z*JrdAOB&z
z^npNuyJc8HX?WZ#Z#d0dFOfK1MnXC818yI#gBjq=rwEUbV9g*M`!KSGtz42XT%44$
zj1cN;L%aVI<ddO{*{}%=@7&2?bO&H`?jYqNk8lGm<T;?g62FcU`a-76^=oa!SQ28a
z7RSF02xb}eQUHMY4MKZ6a*Mu<=2E~+WQnorN}PMUqhRuf1}W+&I{^Nt6I$7EA-b#P
zA=5tiN1cCk@H#kuQa$wrJ!si~uU8P*SYzJ*+cw_8aHoj@mc4B=<Ku7P&nbW5Zf*?T
z{2X92Kr1)75PuH2`hLAJHZ2JMy<w5!FVA1Ro$^gByJo9AUxTSQHr810V)H;zVN!mI
zb&c$*Yi-sj$^Y?5nNB(@iq5Rj+}1Ejcc^ytrWsp7%w4|#`TX>V4&Mt^=j+P7v^SWX
zod=EdK5`nQD4mB}V7bY$q$q)ace8uD&vGC}pxqrKF5We&sl`y598EtJ&?#h4nskjb
z(!P|dv{3DY4A13Kv6`r>uYNisb2m@vs3UKa{NwZ~SY&Jn!UX3lT79?goES5gZcWiZ
zCMH(PXyd}SMTt1UyIJWd`4>Xgy?0(No&Wzbu1fMs;b9Vuogp8_7b|bxkkNTEWsBkJ
z_<2{N)I;!DS~z~7vgQTqRp-%p=JZLL{~JyxrgKKKYIus?m!&_QtWpW&Y8lEFGivzo
zx`^+U&BY|tA!C6Scryiz&h3vd0N~C6Yqvuhtl`6xrcnm3U$H)W7aT>(bcgCPobTBe
zV}l=$>@g=dw(7JDWZd-=@*>F%U(Sd0&Q3YIOej8dEDS<-+1)r94bobuB;{`-KVM&b
z&h1^0pGl1Kb^Fqgi5a|Bqi9jJ7B|QZb5}=(0skSzp^=T-nM+Q$Iq+X3#o6Ob7^}Mc
zJ{Nk$Wuy55pLtQC*65ta0huXo<0C5EH7Oix7=DohV>1l0cuo@rFCq59NX%y!Yv+Z{
z3%36f9C*cAT61(frKYLt<rD{J)B8d%`0Pm^lsI2j&D(#5g{i>DoG|m}a<uG<#eFJV
zf-T|r(co6IrOLt}JQbATqPSPlPYGM(xEnZm^QiSR$#gIYOW?*fXof<w-26_AwNdXA
ziHyi8)2Mo}HQb!;^iWY9+568BE%7+79e4{5D7%yXnTjU3iGXvOz%$z(Y5^YlumB{d
zzZ3`QOOgK=epRV88stv#{ynGRujtx>LCWxt!QJv)w|5?xB!(uj|K=hoE+GE@NXl<a
z1XGRRzjjcUO6=&AK@Cl4a@(^ty4zKmKAjVbbk|tyvbXMNn166T2>*iYZ)e(g=N$c|
zh8YyO>k-$GyqfcWeO2@{dXokhQ1x=r;#FOTTaxWHJvkdjYU9%(_`arg2^e>*Wm141
zUF`k}OWp3rcPFFdN}8!*C9Z$OH<)U1<xQ=%6xa<|Omk_m4Wp92a@TdmYeIRNN^-9h
zLlELy!)r(C3N&gvxgQLDv+$Yuf?dl8Q`WyBU~_l$*|$uLYh!q8W2|qftiPZy3&S&S
zVqAZp1xuiY<=r{6yV?GE9ryN+e>^f}U@P=$_2TFEBnta*Oi-H8AG3IkP3ekw5I%wb
zC?l2r3Ev;EUxyOTDJNbL!nmfXP0685Q#iEN22&neS#jOqm#KrD&+nc|`r=52GMC%a
z-=N5lx02CGkepLju5Tm?zjn1SD~G;-^d0GgGorUWvTl!Xlb)Q}u#GqpoSfY$A$hXG
zKeH@dAw9%fN(5;Eo#o(s7^UAn$GGa2`8qDR7aEkqmUAS;XgE0U52Bb6iuqhB)y#5o
zww!>ra|54kWI=&zhYFj6LkUp3jBo@6(CpauL*Zd)XM4?*t^9aQ@jIH!czKi%_tqIT
z8hDY)tXF4peCORC@tC>O`YCclyqZYD*D^hH@&z`&fuMufT;~&^cG><CuOvj=*G{0;
z+I>sEtScx=1<snojE7=kiyPyZ<?<CNaZkJ%E|#do0Su)z_-3^x0P6@qIB?>1SulgT
zUK9DtM4HRbH8UKYQ9kl!!&681accX0*P0@kg6O>LftAjD>wu$l*($G~Z&kvHsVsCP
z2tHj+q9pBB-lj|yh0`Xq*2mb2aIlX`(PR0bJP2^wuzScCfsq}E^s&1Rn~cS@(tuqb
z7J?%PY3|^jSDc366FqR>KNl`Wy1m^xjJ5qt%_H=W?o$^1EUmuj3tL1MhU-NisKL`o
zgyYd2vAEut<+m2*pKCpqC9UZ_pfm=7KRV%8DNmZr{a2N?#|OXVQf{|EuxiIUvPwN$
zA5LRU2VDh-`K6)u_g+OmJKXWsBK0!gys2v2<mcRbOyEOboB*XHWdDP+#-HMY6eQ<O
zv=s{YbHwESGk}0<1kNvFp?*tb>3JD(w$fKIRTdpu4SnfT{?W-BAGY3pi!Mz$Hy^Ta
zqde(z{=LIcGXzBGZZl<vn%}BB)I6B(BcE~bu~6$N&g*1n@PKwO2vG+P>}jE+$Ud7!
zUAyP+$6Y^~cq<G&OQMH}9sY1h>aTXD)()G87kdk=ZvPth9oL*KIwgh*I{@v~{Ha(I
zj56?zhcZ31RWtdAI*6kSwGxWvP>~VMH905<HqvXV%!`gu)C(({TyZ)mc0M@x;_a{_
z^n5y^BivL}lDXeE=j*9^(&|&39eyZ#A0N0<>m!m4Hh@mTTqGvuHTY4z2eC7u2tE`*
zJ5z#x!ET6BI!lNitc(ZMO^MTi{$Rj@@|&Ruy!+<ilbWeC*)^wOx|)D|IT`ww7gZ3k
zxOn6c_8J8aY^l(Ldt0G@fxLmt4wvf*7-ub52?G|OdP5O{(BBlexuz2UkOY?v#e+gO
z1YIC;k(jxF7V;3Cou>8nG>wpS+Yf3U5Cq-dBWQNMS_T#>%dUw`$HYRdN^jd%6~10)
z-P#Nap}@EaZEl-}oVr!AABj{Nq618VYsaB`PeZNHV+WS}q<i{I<embiL;tirEzDi1
zWuL_*Pa#9m7*)c}JKpeCHJ+nEAXL;qRh1oCr?zE@v`)0WX+1sJO=zCyw`Y-WI#wN?
zm!+;cL6nXWkWMb!hTHt`$N<8SHUwcuAjQabu3X=8%%H<B*$;9;oQ`(TzdL?sc{JaC
z$<}yc^q+R#&-f_zbT6-h0xo~GvtXxMC*9K(IZUtzkaRkL3bHS#a^z`CyzEKO)@pDg
zFiD}TPC5tEwa)|#pEF}bY%RB6)^Ocg3=Y98_P+@yTkJbt7)gP92$6E$#&CI#G2xr$
z@cHV1k5d3*Y;=Hr02Sm1S2Yo5-(Dp$&<PR;0nsZb1N1|Wb*NoBE7{@D$OSIgLUfLZ
zQ_v3}u<Wh2BX6*>T|+^_fCGLD!%S|TJB1pkA_$}3E3EaGuVlNcnlQu-3HB3zD^^x9
zjeM`1=U?!TuulCtkqLzriCn7e4dZR%N+m&v6%>>?YFpuR`*I33T?6rCwh2N1?p}hs
z{*1Oy)Vd0CFg4eR_iESUp2--^&lD)dWPS^H{P!cA_-QZ6rqs;~P~Y)F#uErr>9bX4
zE5ke&m8Z*~W>_G8Oakd9P~T2%E$g9U-Vk8U&8{JknC^O~%Cjnv$>iEn%s<b__|0>_
zUu9$plw|%>f$r;d11=B8>;l2QswVRO9{1QZUbYo+?)h#A(QSfv#!mO7AH-hmo$oSp
zUv<TU&0R5+#1Y5^5M3(pPWO5zZe*bbHws-zg0yZWs18Pc^>j?$`zLFdHK>QSoaY0~
zbE0yHqULEH9no(U6HK}HVn4@cwiIFMcT{9~YR<g(9p|^WS{frA=SHk(g$swIugO!l
zPNa=P%*PGTkIn%c?}mE-ZI|pP<OZNX)<P;kT*;Eq54~2^r#vg_F4^WX=t@->M1f4;
zn!cTFy?(jH`RNP8c^ZiF8n;)TCUSM4C>;C6lPxYA4wG<#nGjf+!)5JZEWXdv-84-H
z+A09|bT~p}Z&Mh%Vp>g^^l^xYS*t2$Z(CQSWJzXHWMfkKAZ}#@B*_8#;R{EU4pq)A
z-;;@1kmA?s!??OY6wuDlk3B4DI&&=GX@)yjD5PT1UKKqgUbr8hUQMndsG+o1jSz1i
zuKaOfI<0@2BDYKq)bu))+Q4)ZC@*Wbrz!6r%H0`Z`K&(HM}QW9tNOQhX~PQ?s7y;!
zxix`6_-lv&`V;i?4m0PN*wTE4tYqObV3UT-1x(#LblvIIt<h)5SQG$7v@D8puX?E^
zMy{bpTt$q3L8=pcuxqb(S>})*N5}}mLmKjh^Ik1n1s&9=@?LUhfSL=FJFMOWH6CAq
zE$32eV56^EEsda+^pbRss~VW^yL1gN=)Ir1ex^P~E(!GFRx_k?EA;ai(5tm1JY>({
z)_rFa5V@r?c=%oLoA&rV^TE7IoZrEIov=s6aQsBTc>O~q8z1f;lEzwc3E3M-HAnzu
zfcCkpAJJ<M8`L-5sb@QSk5NO{kU|OO5W^`{0L}(4+I0-b44Yj7^sWICKlin>_$n=C
zrn_)6V$c3Q%!W(VVG!(*jLKEBBzgh{w5OkyaTKOo$=Fds2ceI++yuN9K3r4izDYf4
za;f)C@^s;;G?lOH-Pgy0`iQ=~B#Ar<!379F09gVPEqsCnJJho;Dz)T;()VgRwmd&U
za`D*8bZ%`!m=GXT6&5X{PEOZSeXqbIfBkwcn+Ssoohx4%W!?p0_mycbxf^r(R?tD}
zwvOAJuF`dw+E(QS{te8a9ZhYQkcjGFb#q*_NmR7L%EkBnf#8%}$iLd&nNCWiRoB%n
z&0aG4HeF*jjxpLn6ZvV@^a3$r>Tu1kATmAWe?(URnf2N;(_f>wv2z_KsrUrAs^&T^
zyuUW9in+Lpl-1}g7}8z%I9toJiXwJv;k>Dsd_zcIHGng(v$ko!quJmBeZxu@zmF50
zdS0bGM~d_Ggo86zcN$&f=_JRIt1z2HDgN$724^}iRZ@Jh+qIQ!e?zwtZc7M7@ldl5
zu$*qA6h29yh8AhV%{JciaN9pNJH25Nx)>rO^m5fVA*0c;OT}h^r`qfNkSkK%xKQ@)
z<GCN=j<Qk9FJsDTIATr;(seRKKgynxnYt`b<{QV~9lW@Zp^I+(WV5mv#3u4ch)wY(
ze=Y<OI1T|s`nLNk*%sFQl8F{)iip+fdqcTQzMTGgV#f5vjQuT94zcC33+r#3=Y-SN
zsXji*7kxJsEc;x$#$l!K!OL4^Pnr^#Rr!0bpEVTyr4?`awcMVTY{84Jh)Ih3VUpRL
z68A(uW!dsA8o3)Yj_8!$(06eVKQMt<ICYyl*7E?)KpwRysx;j5qr3@s2A1?y;nI6c
z8_#`=1^rZ^CjGAA$}iqJah{W8u&i!x=)Ll2s)w)R76$JYE_%JQf!vDnQ}mYIr&%&@
zpS2Qvh^4(gIMt6m^P9~#zA_BG#><8!OXTch!fB~_$JUiMC`r=6{YHGN#b4N-E4tfH
zCYaf2KB6g)YHn@1E#{L+lg*h@BbLaQZEXT;O!!;nMJk+?mldp{uPx$S+pYOR1&T1l
zFMiaT{O6cJ*koMwv(c})%gGYQTfM|rnbyAsW)EEds_%JS5he6#5D7?gA)aGh0+fZo
zbBu23FNVpTj=;ZG@LH&+&TEYexehZwty!g%Ut$N#8|A?ERKIb#cXYh6I>Mcncunz2
zq&+`sgMMvXMKPIWt3I@Mt>KpD+6xFF!$*J}NE1*wjyBmbiXFq4L4{h!m8Hme<Hade
za`9GLT)2{7N=5Y&$(E+iY->B$YA!o(-h}dm;T_XaRb`bIBSqBpvUIcUr+<F!brF)6
zcq&PA>R}DB9B;ON7A6*OMgKL<nX&%GI>ww+d{;6>t5_MZv#76#Zz>xZAsiRIo2Wdx
zPN{|qm8c9aFgvpOn_1skN>wJs(%5*Ob>aRda5tB)t-Fnr@q-8Z1p$#aJ{swUbB}wk
z3Z?X(fw>e!%JL`f1Z$*b-!>Kr1grVW@a&QR*wC=?$u5i5k(0%~hp<a$Vban^R*8gH
z49&itut&tA-9qFR?nTdtNykv)n@%N<5kEgE*v!$Y8Rgh}6eA6blL4mP)PEsmhrh0J
zpK8FZvZoVQ<o@KZzG+(5tXe5CLHDREUKsg2!$I8N@ars0;`O4G$@j!^nYhTc@BMi$
z6=NzfeU!zG$58_;R^3le4GWgo=Vbou|5RV5DfL69_Y<+T$5+M`-<7UfhK!^Z-Lbc!
zz4hXBlBkBTz{y%e!fZtU=VYvtNk{*@h3&MDv}%%hueSIS2Vd*rdFoO#zeYz|4-M;L
z%K&7n{jrGqR{1eO#B#B@?_^pGZ{V?!;n6`eqUs~iFxUg~g2yX_;|1l?KD69N2=!G-
zB-m(1tXp3QNUo41+ahhEMlz&el;wUV-68ZZ0gbi|`!WLa_&0Ly`R8L_s|Rh-n1<8g
z0w1&;Q)e$Ju0GSON~g&6O2@iw&tstkv{fzvZS_eK6&<2(Ib<<E+#gUDZ8*{Q0*$vh
z6-h(haoOCk_53Q@bKy~nzaC+d-?lxO@VL|{CXsxMYOKF@A$+stWHx6m22DU^5x{B?
zFZib#UR#aU%M$gLfR`EI=iqyS9#C&1MaFo>tor#&G{+>6-YD!z#;~(D-AcRkB&9(~
zrjwnA3cp-&C^&mk`)BTiCQN|IZ)^GBe4MkhRgJt>*pkY7`-f$LTF8X$G9J6j2Qm(q
zS}N=JMb-W^6{=pKxJNkt*nf)%O0BBf`V+_BrB8l40kQ}WX{ACdJ$7m62e_f<!!MWH
zuPoO>&<Ai{I}0}lm^4&fBdPSmFWuY8t&4EIa6Fq9yVTz2p5n^_=y@|feG)v3H97$Q
zuxFvuP;7&(y?v5-^(IGLHULD0?_HVL%Sr`?y&m|{Qh)CNZhyF}Bebj{{D20fo3`9E
zY6EpG)To*;uJ(``zn6X*jjks*JEjt6RHaaTtlNc1c2}pLT_;?h*5i)|fmKq=pW28|
zIZRZXDVX+BEdfh1_QO!&l;6wRFZvT@XHvCkUBew=6(9lcKevI!@N>I#Z<}zon&y@^
zAXe-zvABBSx_&wt`KESIvju0iW5n)S6fNC$D<X%zzxkIizgAT`R0Zbua_>DS*O;Hs
zjix-sHn8ke`qP$ykYzcKpGv5aHM6-eN0>KAV%w_}-Dp{1M7BUj-s*86IR4K-sCg>>
zdA_0<Jv5d~jso>{b}FsxuI`19$t)SGmTdXSF;KgIx?&)S+^aP=yDoo2`TEldI;3|Q
zoAyDnGb=Xa4XnZENOW}D^*V3lj2N7ZY~p`)_C@?bsrfD5Rb-Gt`BZT6qKak-ZBjAP
z3Fc-G)7sN(c$o7dhn1?w{4oiuo5EU1>76iwVhIFdV;T12dnaMliFGFsV>*92@ve)v
z#G>e{WfkwV*=CI3-ev4N3|H6PPZewJ74cS|K)3GuGpZZCtQV^1h3?OYHiDi5`JH<g
z+k8m>dn_t+H!_`msChvJZT{#Rhqa1?am%06hWf^iAE6pNdn22xue<(J!D+W{jwGuH
zN61h@4Vs-!m;0VO_Q>=G4E-S>VilB1+wt!MsASKb?16Vv%9EV66zgU2bwFduKQ&Ih
zknS?&`UoE#vJT4lwEWdI?>5g)3@?6bg1e~EHere{{o!H+-%Ombui{Bpmu5_{^PiVQ
zj3cbpV{CeQ3F?5VK!nmjkT(We+qRPHydO~`&~GutE`L6zRbmh@H<H@h8wJWK{-w+3
zse9x6g7fIIyTFR8e_ibKQ1Wy;+~Mpp6Ps<__UFiNXf>cMdnl!3xqJZU`)5yA;9WoX
zad$Ym%-s6tu?bLnGSf`*tlaE?zj4PI*6<&bN4?k|SKo+!ZO^)&SeRlT)1mi4Bb_`J
zexPjqCilBx4$bvR*B5ae@iWWxDJLpD@>C*P8*D=n?ojby0CAUs;Pe0oPgLz`n^`Vg
z)AH>a*e`s)!<g6%fyDt2q1N-B>vb%nF|)ciWOXQTBNT8N=x?VToV?wIXH~`x%PX{=
zt!)hy+14D{MLbX~X99l_0P-_tUdbqFwK#vVQce2OZH##AHo)JKz_0|;2cl{&FU#dI
z-|y|WWj}rr#-h6o@F!sC0TQ3~!?}(!Zsr)z<&1rN1l7a$s(w#&u1X@0IU-|?oTQkb
zI~!!wuI?c(KDTVlzO{cowpzJw^h*^jF4>wS+dm9q*W9)KwQ|45maJQX_gA0hpwgLu
z(-GMNk0K~=^m_$qc?P!lkKQu18d#JM_X!nj!5f!oR<&{yh2mu(lFMicI2n{KQD0Uy
zY*T{A$=t2+aa2G2DuZ*SJ84kvAXY0B4HPG2MrgvwVYQ=!B_2<0d}Zy_kAD6%kjimc
z3${hzz9f)N$%G;vieF*kDxW;<=l>=s?uLJm1_0Sd?lt|$lcYJzuJv<Bm)<sJ8Vpk=
z>M(%Ar4qV_ke(5aq3tS(ipAoL=H1xOWQd0lK>eV1cDpXy*L`ZRr2lD6_xJrJ)CLI)
zVc-sGsQ<tn1O`%`Dz@_Oe3>h5ZmZ6HL29usf(DXZg+>oXM`Pbdfs=xm-zJG}%nhoc
zrZ31ZPTe#l(?(@_#~n-YWtz&Y2HWT0=z|b(Y??~fic*kyQVc&%P24p*a9nBV)c!%^
z!2}&CFSi33f+V)QFH;hL;4R(%`jDi1?qzqz1%X*v|CXRzUHq3jQv$qY<1%Gw8;gA7
z(q=9Ez;-prY&Z->CE!za!_O57#R%Vd`~Bi0O@##77_vL~ET%KZLT<vMwZC^7+&8;=
zr5wMt6-$f{y3A`^gHdr*VTtEFMA2mRJ$!Z}W+vZSicxL7OjVBObH_q8t=q^o*s_)C
z;)ugaCK`NY6zxFmjTo_Wsj+ck)Gz1KtRnQ<T-@p>W2|W5mztvOXQ5gqXJCCtQDOvS
z`N5l9d{<-X@&+HPQQyxHl970uTFHqm@S2xyhyfyL!qD5xsvHb$$j6knQ#17a<sVKl
z@%nq(+~Ir4iCvlVfAbQ`FW5(b<b&_fM;O|$LS5I{H)j_G1ico=lU^o`QN%{bP7n6b
ztU{P<CM<%mRt1o3QMSXsbX`?zbK~q?W?!9jEtv9A)xEBQN~$Kg1FG8t1>oC-VuVrk
z4&#S=z1#zJ0wSCmz3(-1=KIpgN6lmpe2+cu3|9H@J(|Y%()vvFKdwt)l^fI?OKP-9
ze)N(HYw9+u^A>9U%uZDrS@0d(WPCqNOJ1PQ?#h8Y+UBB~g;yN!moS6)#D9b)7qM2R
zg(#t=`n`NOqFA9F04M>&maw=4Ye*iG-sP!z^RKr}G9^Ie3q<-~?n}3dxuf7A>=B+8
za$B>{2PiDdh~55z1D?1e&O2609l@LQEmQMkHck~7&rSv3{t^R=Se<xdYk5YCNa->%
zdQX`5xn~Pq<LE?_qb4vRF15|4Ko1dAi3yCI@KFrxx{mb?oR8pLRJ!vmws{L2wDi=y
z>NjIP9)?h#W3O{9D9^_*gC>w*>wr*ahW6Pi_p+a`6um<Q&HZ~3h{%2uSTry|g>V(5
zKSY>U=`^6P8+&NGzI<+El_q@*wc+@A67CeIZvtzZTx25jmOt1O*#6D}PxM@l@KcOj
zyPoW5efJr?B`nESmQAR1DBD^z!RCOCifhf<L|NP2d5L$_)vb5kxKOpsy@6V3V;;{K
zN0ZJ)I@(>CxKtKk0xKcaVCD`va{q#Dnh(W+FF2i_&uNZulO_?RXgu$xMSKW5l1;E>
zO@i>*3Y_I^oZ~n~>@9)w^ycDC#HhY`{dTE{PnYTEi5Y&|Nlg6B7CQ+uj9iUjAf5cg
z8dVCJ^S&2&Qva?`w~P2Z9UmEfRy!G3V|FAtne4@w5Ygp@v3n$@VQ6UJ1QI^GL2}1E
z<hT3f9CH^!?r6Sa#5!FX>F6Xq!%mA6I8tikAKJtl?Q<$4Z&K2F;-2hr23uq((}D)s
z>YVJo5mT7l5B%lJ%sm{O*q<hcrOq8Lj5cZ0o1=-qBTndKXnRt@q0zKO*aQOu*-kF$
z@s=~2_mn^-5%6K=KCuN2#SOSHD!$-?SRD0hUXt_nj-2wWt&7+2X=9`qp1VP$n6IWV
zmRppcx9$bVDWaN<#`ql3b>hXes*gaaJnSPe5PEnu!srJz1Fjl(Tw-nbF_S;~yolqX
zX5*P;_A6d56_l%Zt~t}5iD10}`w1vMDrg;VR=bBh_ua6OF~Z8h;{#Ljyn%Y768?JO
z-Phgc?k)3x(xCf;ux0t)SEfINJguFY@aQ;o36U!jBWk>u=az8mo+FKVgAx^7f+*3<
zQ+#z96F_%F*DU7=9}!iB0>~u*5wv!f@GS;1emFYg#y*LQ>!9<8I&aIM_~=;L6Rh$3
zHP^(Czo|Vqw5r^W?bYVzpE`1EBmgFzq+>pOaRm@Ml04*P@$KvTcIDnJO-tz8Uv1Zs
z?q@D6NnCk~P;8Sse7i?0H9o!VGSSj4Oa{^1i#9R?c7O3YOT#R`t%(r24Up%!qLcd5
zLqdwkd5a6%O|H2{|B5Sq{6I3v^P+7<&36?&bq#kPN0wG~>VqRCFCOE1Ly;?)IB`EX
z5cCp{)_DF&O!OCjU+d4QvjPnKa-aw&DREW+Blc?gX+1PLNX~@{$3$dtmj}^wP<>Gz
z<+44e(EX?MSqnZBfwYp8HXUZ(=MnhxjAg^h%=<O_nzNoaI?_BGHqYYn5PVH*Tu2--
zHtKW4@uB*?KpN$55%?3|qiUobeR47QiGv(*Zf6Bor&sfugi%OjBJp-C3+-BP?LnRI
zPq~lsy5w9YPPfwpQ1vG!)Hn)By!UISzL&a3_T0$V@HV<5q;WjxD>i~iJ(~KpnAxp3
z_AuEJ;<7%&*Gpw#Xv1zkq3&BMc+?|IK#l@oV%$=Pl6=6_(WrM+yV&%)YhqK+cm(SK
z7VIa9Pb4>M+5pwT=Ctxb<sW*#N|^UmUv)p@NiQr~M}$`;meFac9-xD7oh3R_`mrnw
z;s2H|T7c*YZ&PQCFskgRaW)}_kM6!k%$Tub&0=?YIC#CAoNj~aR2W=ijBMbDlKx%Y
zIAftzlI|wtI!&GFomdqxh8eZJuk%IMF13)xGJRpVxIG>ycP9_&@m$@&@}OEO9Xs^l
zbOr5sBHjs%Y8AerlnL9bwdVa(@-Z|MgzS2+f422r6C3X{>pQOl7}U`k%hV@ZrBU@R
zjfzw_FwcYe6C|V&GT^{)`61mcNZ0Z0ikW0}XdkJ8ez@Vm(lESsX#~Q_=}8VDvii0+
zPk`InN2Kq%`g1+?q}A%PV@Q_!G;{QxBrT3>yZe}TjnA-AJF=Mi6k~Mf>#N)=ss(JB
zVpKTET<}WJ%7$-mrU;`PrjPJ1Umar%KhI;yWy^d76vndMeghE_?4YE;;@}D{xA?+K
z@@uoc)bwF^`R#Z0f7crl^JjhUWb?B=o9n(xd0QCO_yD{h_N+O6lv}!$O{#>Ke&Xve
zVcNZ6&kCZpAMISS9*@;&R~wNnS+DvCue%B_v8j@xO7_z0KZ=qN`17sj@1qjf?rxu_
zC_^486NZ2Cc-tnzMmH#ICQ+nIlUI<yQJH9x&iW*;^<!u|XP(HDHU2Mi3i``$ixiBD
zV==LsYk|<F-16?s-8`K<Iws_wK{90VMwls`$n4F;QurvW+&gW+b3{FRBl8@XenFz<
zP*3GhE`N@E)ac|j%9K#C)2yM6=k#;!S+f(hLo}Ra9i2C)HtJ$~x=20m4V{Yd>{S_x
z@W!k5C%1)2cY{m>0}KM?Rg!q=!sDy~+_+pDl7gu>v3g_dX*WG;GzFwPknaM;Q_E{o
z+RAeF!@s`kvkH&8VmQdAqXI`cr0%^bzGnUFlbbd_vtoqW9qt@L4V(tK3uOOcmT}G{
zYh13kkD%o82RY84WI&~#`s;eCO?Y4ws5885aE9IpgOCDnd}dV3t`6}}YS<*Acu{#Z
z{!c*$gi<nSn;JVo0G)%Jf+i}PXcldHtdh-gY?N~_Iv!x{Wp}bB0Ad>F2zR>$T^~?l
zKhu6{TY7jF{4@mj2N@gIv+@g=!R4PO+A=^ENCRR24P{42YFNg&{wH!5gpIQgMzal&
z!i5-sl@z}<c~1(Lc}0mm4P`wX{H=d9CHBgV9jkd@yHIgBOO*wz<dkvShAk|UaqXdp
zTj9zXOzheJ(^|JWQZ4z^^X>}^jcQuRxc<;*&X4ZPsw-8!)*_85CHqQW5RACPb^ym%
z#`<~9`sKYW&OTs=lHPw7vcb%Q-0>;za3GOM*Q1#Tr|fg^l`@0ono=?|YP91LY%;Lu
zE0(H{LDfnR6AK3kGMxO7ZvggO98reR5l`AE!32C#x#TjXAH{>3&$}rMYs>>V0?KNh
z-uoSvjOB}k&K9}M7fGxjeGb6N&m02d1rh+K+~b}7n#dAAD!-#bHV>MsHhX5%J!pG9
zl9Te)27Y-Y4V)WaxS8Lf_az*T5=k(<l<yJLg_CF$Q7=Asc_KD0#!~&dD%qvla|$2K
zq8#%+bL__*_Mo}oa)x>!>ZcGHuBS5ue>Bryq%7R~luYKTw4S6=^Lren+3}|e8f)iv
za!-Eu`L!dcXo><=a@y3_C*JmSm*Ee)3w~&PefH@{oRebQB^8vr7>iz|bgS#o9N8o#
zk-|bfZT**U4P>+^ksCwgI|Y<W6N845>{6D>QdtZKrLt3aA4S%{u;S+#lW&MBCRuTq
zZ9Fv0(7+43Nj5C(6gc0}eb@UR-lUx+x;gN{{s};ekYy=UQTgFXV0?Y5t^16PFoMg<
zaWtPuxi^$g-M0j%fqG_5En`2A!@NHzgr;k)g?^&OmOdkgi`E%@(K}M{`ilVtPPTGw
zkyK-`qE+*ZySuxj7|d25>iWJzdt#7g07g!Av+LuExh&T7>uVvy*B2>pbdGSY=2}Xu
zPu9r*8bB7*zfgI6h#Wqn)5j+yp`m0HO9{1w6HxP<6r-#@b-a{blie+<YD{)>?&<ks
zV$@i3LX4nEaYM8HmgXJmtYO17A#i%jYafFzs$V$&_%pBOIREYPoU~&f_0NMs1+?)7
zm!nzLI-?~A=C#e@^e28<Sj0>2=3|IE-$D$<sA&}wHS)UzbsWU9A5bJm8)3LyTQBO=
z`j9;2UkoI|k^K4=jOVQshb^Ci!?+hY`1r3k@Y4nQwR3U45l*5%|5UaaHl=s+pFaOY
zj7wn8R-rx5in<MK7uc^t919YaNQSR@jWv-5$9||(3qqtcxxzvUDX=Og?AUm}S{Xy<
zhzt2L@{F2FZ>H8g19Z-~&*=su{;%*xIn6cd<L|SGxk53UAG6LLaR@@Ncls<G(C8Rz
zGz{v7Mc^4XuCbuz?==S_+B(DW7vlU(>}%gC^t(JesJ`}IvdP1!DwdTHF6iGGA2|DV
zjE$=%5`PFwhQM>_-ODa*tglxM5f2LK)2s^4Qml*!`5qL*jIsr*K@!IzYOJNB4rVSH
zi)mO7Aujfwe8Zb&jG60&W`t9_GlD2FR_XPls!9=9cMzR-@7yI;<F#ZClKp@w_sP84
zew!a?%mmiYJ>H7!qhk)<y_^y!AXQerq6Fi<U-BwTMHl48WgHE~Z)Ic+l!56MK}c!>
zAy;*Q6qOqO%tm94QDat^?8xKqlv*&B<I2R01r_0llz`Zu#<z^ZW&FO}lI&2$R*h|l
z!|M3zfV<<tJPoED8jZfRos=3#RL633IwpgS^mVJ?veuP6@$S2RZ2R!})iNlx>gN08
z)I<{u{1wabg(UI@>{;nT-LJAXBKsLoNWp(+@=^*|e1J#pnpESQvl;uC0IF&8&|w<f
zj7)aS>?ImCwkc$kSuaa;?~n%j;Y3XP`hn(y|6k5kOmXcsQzw!7n+(|Q7g|a$V6Z@b
zP;ej=B$6S8{!hbKG2$iV$I1p`MnPGP4ds(n`h#Lr*qZ-mF$J!!1XZQ9ri&>yI;FfW
z0;4lhZZLiS?j12HZczXKyt-=5hwGY%;95GczSQMv-t)@;zFZyIZPZGl_)z3EQs+<o
zpUcr1jRhkVIEHzK|99Eh#U`Z49_(L|yOAHu{rBA#Pvg_9V8g5hbMx&YK7Z<}7BWoa
zWodU>?9DokpKKaJBou5VNmBmLq3p?E#Q9(}c**W8Kq%d!lHSu{ecIR~qOR_T)%`+5
z*(MrTSy|2UtQ}8iCF;4HHktI<^7#LG6lpqN+~!;wg4gLv4UdASV^kH>dbx<dKUYid
zC60QgGtO_)P}XJiso3>_=aO>3l?~<Stl5<nJHhG$F3r^uJcRYdqn&I`D0i)Z=1a{u
zf4%*F+}o7N^cJo6=+m$@lbN5(KjIROx!~{Fdw4V~ryA<!pECC=9?kn~>@n3#Pa5M)
z#4$^(r+jN$Am5((woK*2O`E_c_ggm>;|5b}11$#F%T!+-R~+`Sxx#v^7GN^#Zj<Iw
zSBBW#S=jJv9jZTQi8-BhSM}|FbSe!mEc5E>565XLYz!ySvwf_Gz%oNaiFeP~u(U(X
zy)SK2cq$LzpcSu=3N`kyvOay0;l|eBrm=STg{tns2lH3IC`$~IJWqAiS`!Jf05-?!
zVK!R_Yw#y%!rfW7hN$B0lCQ?YM|@IwsYr5PGvANr$W4un+();vp%%d&C226xB6|pJ
zQzvI+`_rcbRr&cu8x9cs&|?8HC^gs)cAB57@sm|m?`v8Q`4Ro_lpuF39VVu`JncAk
z^8haAhJDFIU-kfg7Crvt=RC$Hp165hS;<V58n>SKxR)F-5>o<rHMM463`8AoO@Ui^
z7~uLQT->><P&lg*<7zM?Yf};apr+z}r0A0pyc<mIru(rxG<LF;&2Cr}mD(5=r>y`G
z#2{svEE`~A0Ux<|D8lc7P~rU!ZLw4pdW<E^aw*^vxgtugxn#D0%Tk^`ZBZKu-|T#d
zJvS_rAR?9?hPQudc7e{VIRxyU0>)F2lnN1x1ARtwPPljqSohapE?(YO6N*1uf6e@r
z!exB6|0y@lR#(!q%eK0Mt)#=VJ@c3^F~{!=e!N8x;ksAE;PZqRzNL)4^}#_II?t0{
zOn*r1G*dZt)JbGo-1;IWliA?1&$b0jzb#OH>^!TDzI(Oz9-T@kekdP*LyF36>LVY@
z0ZK3jTeg60A5`gzsJbshJ#D)2%(Q{@Cp-4njeb9c10|H2N@xhRzU$Kq@)U3@|DOH|
ze=gz`Eo2WpwiH6=1L8A)?AL@fzP5+?=eG4k`QH=@!f=@ZXBz$2!<;0|&dt*%_%H!h
z84^t-41l6#|5FqHt*`w0S$~leVqZ6dx1SXI-IZtMT!-HTF-Ikmf<635ptd#)Kx9GO
z095@5RT=ZO19KfK)kK;|s9M?{p)kvbFLYe*sUZUa(3%da$R>yy*s+-Faz|UbL-)pL
zPdC{?Y<&de?*F<=*TUzm9Ad!jC@LRFg(@QMkxJ2*wis)@+(%I;khP2%EM?jdbCK&b
zEBpq&IS5WQ-ic|I{AuG2(1b@!YyRsaTFAy=2L1O}U-44ldLXVKfGQ`%hOX%`JKa~L
zPaYdNk;-dUb)K4?H)u|HKYGN+80#l*@+_SlJJa;34O7hB%3o)=SadNUH(@G`yl-d>
z!l;AFeW7S}$on8-A5HVW$QReHwD0R6-eku*M1Fs6cenhCMvh(k4Mn##li|v2|Hbd4
zmtl7WQiRG+nwG!oyeNk4xG}QvsOZ~$Wzf}t1uO~!pyHqc0*fZ{gqZcn=8@h>*|W^w
z>oCRG4CX_2K3xmRr{12PfZJ;n)M+9;w$4wR!@3{Mq+`TenI2z!P!#4gwRnH7I_~FV
z0d62~^UYw5m<;_%JH}0KhS#KwIwB_x_z#^wqxIk^C2{Zv9*>1kUeoF=<kN>oE-KDZ
z$IIh>%}wmfb#BR+yR50SXDmL*k4%4h^~$_ho%mO}Z0ic&?B{N7Z1}UUu63CrMH8^u
z)@<_9?X>U~oBU7vTj&fv_9lEG(XY6>blKV0*H=LTQbrJEauDmf8?N8p#U1rJJN~4_
zn&loB*2q%a&;5}d&9h5gVwMT`fSMa86OxO1v)(>`oHGE!`BCFE7r#b_Z+q!9AnxDk
zX3p%%MCEhd17%gI#OJiudeBcSP2$rrt`-)GS0eB?HcmZbz$zN4k`2^KOm*rgX8V6X
z_c<XSIxqpXCIFM!u9(b5gqP6(+VRm<;?J7MmI-He{Qyv{hQS-Y(qyHCGS7GMn9MHH
zpgz{iAm+;0Nr^jmuIpFpgMS63zZpD`YI|WpL)`#YAmgZC^Y}FEs#9y+lOa*g`yN^v
zDOheJs%Ri9LhF&%;5Pwi=2QYx2BD&ZYAC|7VG<r_T=V^7oaDY|<X=P?yAZ>Q1A0I5
zX51?YLjh2R?}mtyc)|-#@uzaJhptktXv17Mvp0sI;4aynSPV*CmH4I;u0{?ps>56$
z-ZF;&IzU&T8|8eurp%*@jX2=CnCQ_N3fvYmxCQXDf(b<n*68TiIIX?(`Snv8>X+Yi
zZWa`#Qq40$IUI=;aBj^$Km-Zq*3#>+@hIPd6OJ9k;=*AQFQtm<otdDUevkIp#<|*-
zW!38lI(RMbS1WhE*<+)zTBi2_<pnsm+MV8DR`VU#eE8j*4A@x?M3_4DB^(qaw8p$s
zy#u@vL=C)g8Nck^8^pUwZyX4{hcnT-I(~3S&=#~uxD5$d4MP$9vTH9zB$qoAqlp!1
z->#ja3w5dAaEXGlcYtvW(0E+~<QJ^0<zh=Mjvca6oMtOkg3WdT#ulcQG{fhiGOcj>
z?Ft{j5xRD`{F*b|M%ONQo>T9$Q3*BwnA_q(ArwUi1cEXfI|EjUSj1l~yY%HXV^3l%
z^G6=hp<Y$a4~dq#cWomMHZVdnAkcC5<%ajyE1d7B$2z52Fk)m&{nZ>huTKP?)&o6M
z_d71ry&NLTJG~QLj8__ew}#`IzSO)azM0-ehiI&t?7A?-0z8=dTZ6?|(N&?^8|GB_
zOV;YkF+XO1eHt7n)HzIQ$BHvMBq`h^7p(T-+DS=GtCw7u!V?FHsa!p_<Z@_(Pd~h0
z;ce~FYN~wZd;(^W07u_}<wvi5P7;DsxlC?MY;nAj28WC&@4a~Xk>XzqH7z4o11*=I
zvU@?Gdc!>_2i@qo=G>Oir`&38&+lTmgiu-#&fP>)i3cUES%q${O_}LlN)1>qe4`2q
z`eJDJN`5Q8_Gk?*UXmb7PJwg(Gml{AVq14_k-rIcRtVKdiMz80d0%|>W2+gKgZJ<M
z#jg&SOC!*qCy26DPQzTFgosAy2c$-r&h+AsB*<<F{`1V05g=<;;P3y7FHg6+;%<GO
z5Qk(%{3lB&6k!zajYp$4ZaYT&XRZTeGlOQ;<0+*zbC4DQ-y}42??0tOF~v{ST}0q>
zhp?leh~G&GpvLfxE^~>#^#`)geqdBDQ{dG0Hl?DiXnDEJ5O$XV%ky9E17)^#D?9;c
zXI&RC^i}Rp{eZ1vdH%hqW3j(l^7dQ)=W>GITPbk%;)&xqUG9V20kG!;qkrpo8&m99
z_Orw6L$ChUdd?f;|DN4VtT0e5{sk8q?-~u5zZ}9nJMiWT!&%0lVNX;j^RW~6b4!`R
zf?${VPRQ<yz8LrNput%y;V;X9bOX6JezfH;AJ|$ZEjzq2=`oqMbpSW_C)lQVbV^v_
z(Zl)zE6#96JDB$lMumVbk`703>aO{4jNi!|D_iCX%O3a(Vr6=2|0)laZ)vI;9e}A+
zN&g9=it070jf_*B8&@*4BuM@PXhz>%u__Xkzdn2AK7hiz5s-2_aJj2b{f}8KpRh54
zvIUTPacxu$(b(lQp%5zoB=dIs6S+p#;bYU4(O-I0KKV=w^dx)_LJC1P(_d7(H17D&
z8(@{vS^vPN4b*lhSKXeT^SF5^E_)#CPaqR+?Lmk|r+4xrBfW40`Co{dIpf&a9J<7f
zl}ngWP~iJdWgAr5hKIgInESsZezczzzy)RZLd@K4!+p3^+@H5<FJ&rr5rf=<K?1^Q
z2j)}(e~~$qBbU3FW(-hdJb&Ss5dQJDk8~TNckodJWZy16>Y9s0;K>rJv~iFD0gzLK
zp4{!u`rHCPYo8y#w46iM0c;QemG11p=?y#|s?4vsxxtfa7eLnpVZrvovRuDNxq0UV
zwysIBSaBZ44*Ug5%1^3Pi&Rx?t*AY-1+m-;ha3rdYq~%DB$*4Z{%uk|HWvn=rxc0|
z)x%_tj4#fFtZdE?zfE|k=DBIz?qBQj?S1yqd`B3=pAKeOx|BHCp1W3A!va8#_x}Wu
z$%qMxiD5RoZzMqYv(!KP)VgDMQaRF@b8ZX*4*rFmEPlGAHYBl#*qV`fjp6zWK^2#h
zX}>uzNBSz96*~$c*MOIH5s>XQRu!9LDFe@PG{W&W{{*J7+{mcn&hg@2yrBjVpo)K9
z>@Isa84nwB?{oO}>u$D6#-EmSrNNg=;~ch5a04u&ZT+8!t}Ei6GBI2nvlAV_5WBzp
zv&h@s{H^|M>kt?abWofCwCOGoF|nxknuxD^oE#T)QF8kt;f}*vRf*$@YT9zS^NYFw
z5=itX?9?#1ctJ|h<7ylPp(AGn&^`d(Bf#1~ic)hyziM<wb(6)KNc~b0WJ=3k++TuU
zTVM3I=9%@00k*?fl0QSJc(j*){-m4hgf?i@bQ@}jqlX4K$e2>hm~8vj>H(@c9LAde
zg~yEbu2j9V+21lSIN?M<1n!M^bNAyjKhKP4*UpYI<{rS6{t00$ws#Za)=k=c?k#6^
z+0vb(F|h8i^sxfx*@L%fjNKKe&Wn+F6}OUOyQXKcEnQuR`7N&lmF=K1R)tF6k)K;C
zdF0oA1X87S-koac@_#SRfB6^2CSy|UIw_VIehckR41Yw5)uBYW94R3AD%Ph>WXas{
zbMP(4@@>DUtr8Q3z>K9$5m+YHMGsM_s=L4pqekt!g|;I+=1hso8<b+X)i^dn6=a=Z
zhq_WAUq?h#J5j>CS<;Um(leXwp7_=G(B~5c_I)^<r(AG`wJEPA@}wJz+$c?EjS@wM
z4N~trehYn;7+yw-?GT6c`dpA@>GcSzw)Q%HF1~!EpO{Fv_A8TWiPY8Byt4%lNAvf)
zEgd02M>e_$NW&%tYaE7OI*h7RX6W|+(4pKPmMq^28AgGMVSup<U}O!lRB)S)EEmQ^
zUd$8SRGl+jPoE`UqzclY9*b`aLkz8+xT1h`By-nT{un~pM~ap15Jp+IaW2OCuuj|@
zeC*(`+GyI<hU$Jf_(pH8mMzQK>8Csc@BtY%ml9=k2$m{<$p|nM9ljURS=sAyfPQ?w
zSm(#^a^Zf~rn3H767>xuTlp1OhWWdi*7>X&naGP20XCMVGKa-WBicn}RQ$gry<8o+
zhTz6h8}f4pux>U<ADCuNtWxlpe5X`Vr4ol12=ZLJ=#O7AxHaZWS%2Cm377E#K8!Y9
z<8Z+*A>;5o$9=vO3SpRGT3PTk^*&+9m$;+LcZ!H?e{}IO9S%`ZqBBkK7E>^i<)k4R
zVxsNV+jM_SawJj5jOcp2izgHJlz-~j$l){n1&UhJqOa(@FZ58RC%kQ*<X@g1eaa+u
zaM-%$4S9EI*76nY8^#9g{1WJ(u|n5AHq{*XGYd^z?>8=#ZG1n^6p89gd(JaCEqO~P
z+K>0GNIn)`y4M!<Vu>ew6>*fiYoG6Z)FVk@6C;T+$^WOa_eZLLVL8sUqp;Bw<1DJH
zt9{Dy<_FUGiPkBIV$@00MPmwyF7+re#~v>Y`Jg1g$S$KR)`rJ&bb7ejQmY=;DuP8W
z-AcH7ET`iu8olyL|HY+va&-^04s?$00hgwc`-k--(A~~~+=INhR$LYLi@6DQQKWFZ
zkZ#XK0Ygd-w@SCwcQmem$o_1YPz84c6*fDfm22bfrxZ{w_K%U0XZ<1Xi(KKmq@~r_
zL5)5QtLyKnh4v9;&XQd?(#%F=f7y|YD4$vG_%kODFX~AJbg(_3JnD02S$XX_9M7VC
z!Teg)qs}P#0j(UrRFS6l-8|HHgW%|BY$0Y_RZ1LC6;IX26*R(yjP$mxDHYaLyZMvG
zoI(CeqUq`{xihCz3)oP7+DkO<9}5db+Yf<w<iW@qWx*dn0f0wQTi80%N}>#>5v15}
z_afL(1?~I!cQre-O2I(Y)uF<d#DIwv5C6G(gy^fx%8dsX=}{ufooAn)fVt_i--DIe
z!<ygUD?3k&{rGws0bm1m<x(#>-?)D~j3ZCnkGt|yA1itbUo8kCZ-^fnXP|N9D>Aho
zDTR#~vkDy-RLP?%Hli0)aK9FoTW`$5OvS%piWQ=irY+3RWfx_(yJ8~t<IdfXQ!va@
zc{GJ7zD`e7Gb|6Q;CP=GponTK@;BwW*@Pye!Pb0>j{}o~XR5komWtl4kq<oCua!kH
z!n_sRySMzmifNIvp^_d|=@U7!U^7}|9#_0e`1Z{0ZU|n|b=oKhf6Hsl-uFrj;t;m<
z>8J%UE*bvw4kFaX>-;y2S(0iscShLUlss42%m;x!P8cRu+utn_(62xvotzRCa|oJC
zGCVY**Bd@(4|wY!I9Ho4znF#*-PU-v@AagIY<A4dgBhNs%=QjPm0&zzZB*(1xh8)l
zW>)ds(TN@{Dpa{eHV#a(fYMC0&ypyO8TtK@jQaHdxO?x1rk<v6IP?zEdlwL?qS68g
zM4CvDE`k(kp+uzx5a~_nh$6)R0TD%-5PFv`A^}7oROv1BUY`ScUDx-z@ArNFfaeF@
z!`a>0na|Gd$(ge=Gqx8fh2y8JwqmyPT&nr>w3cHnyYBOYBb+@qPC-8tsU$*wwt2ow
z;=`vec4>eU-I}Wh6t6J;Ok?<OYxDvt_QZ#1;_=~Bpf5K{@B?cetndSwUsya^*kPZ$
z`d>Mt&6@7D96t_ykY@##$#a%@dtZeO;D)ygbO$Xqtl^sk2=s-udT6Pn_@%)viC2Tr
z@>BLO{Xi+xWss7o#>-vSr#i-gf+oSPw^;keZ_A_m9qcAkZGy^y+4#?f<G4pZh*B{V
zdx)vwamMS*EWrD-!I-=>SAM|V;hfn&719V_zgevOi{rI-$y%dOjTvb*x<JoqOr#^B
zT{Em)`g5nv<Q0t(wjba6qT~h_F>L~!N}XKy1S$Ne^O_yUaM@!|5>X+KVpm2uruMD&
zlYI{$in$Q5pE2?2nr5aLCR}+$_ToVTJkR<Iw#1l&W*+l1);FJ|<}+Rcj7Yz3f?E1I
z$U#$i=BiH2wx~8#D`Yc*o*KwoH@+X8zO&2Xq*63A8B~8qDHs8$rE}Q#0a=bIv@JIf
zfq)CWf!68sk5P$}Td*s;N6a;BUhz%Ar(LC;I#55vb}w0}_@FuY$@1}6xmRK4m31te
zx`)XM&cDtT2w0lXe`&2p&1A(g*W+&^ehv$bU*|q5>yFZb_LrQGR8aYUAiLzHo)nK=
z7giF*TbI^6{nA~8w=G+Xf5V*L@fH`PKo91XAPOrmBPD5gyb5HsurGLpmT<>qr2_gH
z!+=io@u9iuaH}D#QcC8rzewP>Z#9}dmdbY=OQl`|6Gruq2Yzm<(WIG@N){xXWwFYR
z8YB?of@qzHPp9u{>oQ}D9|}pEi0Gu4RPvG(_Df1zQ%)^oK<ns>lu$~gzZw4XjHUYX
zj0G0(SoP@eOn6A8;M)dC5KK4J?zy^>h-8O%(v6W62zySz#?TeJ!;1&!Khe11PVqc?
zdS-by+Gw7FK)HnKG^}S~+p=Zjld|vJ^$2Nsst|&2&yB<5uKy2p`&8b=*|;oJ|J>wP
zT>TBuWswAj{cWXUl8|Tzt<o_nv_KGo??M;;y=u;yf7BHivZ`6LaAjY)7Hu9b_6kVU
z%0Q;BW@V*%uP+>vMKyNjY5SR4|4d+FSivV<n?>?BGO?2d=3Y?5cy{!&f+BR+QWea9
z?u`G}`l0a*vlmG}rw9h(A5i?a38H<tc2uKP%j?iN2xD;x%lhv(a3J~srpDj=o!b|O
zawC2;^WQi6J21dHpbm@M?(mNyJNgieIo0?tE5xoWiDHfEyWM;VhKu+7|Cj-Q$PHVl
z21bX`<ege&823Nl_JOg!9nPNWzuT!YArD2o`ELt~wg2wXqpy#P#(?+`M*q#dMpfUF
zNMY_H;~-2S-5=)V&Lsc{c0l(muFaZoGm+x1qLBX%T>w;OyX^Eo>PrWcs`{$@H}6+I
zpOhyfhKc|l{#s++om)46_!Tze>?5t{Y<~+LwDy1Z;W0~sNmMAh&s@T`pCbbpC;k<R
z!L2NSp(}T*?mV;v5Cnk~_xdk2Ti;*cR;i-8J$`iDf17={rmHPG=2P|_oJWZGugPe;
z(;%AUJLE+Hgct!xy}&^K235Fb>|1evRlngpwgq6J0cQO&V3h){+P(YOms@=V$}EAn
z5`RoY=bW9NthdU^yAnCQ{@~3Ex|zCOv6inYq(qFNbYTgE(BQ|3Pcwn23-wLIXT8IT
zYrdh)kB}2qrCzsTx~?NfkD4C$_mBfTT;TFAs|joDQOW(y?28KMIdeb$GDik2Klyau
zZEWql%SFw<yQJ^x{if$V4-j!-p1Jrh8k0U%62Kg#<n_NhkST3@UQ7KDaUN9yK>9Y|
zwtEgXz}ET~OQx>XL-;vnc>h%3<_`gkUC^V<^9cz2x1H(*CVf)pmIU$XUd1^rf^)1c
zj9WlBl3?F}+AnK$V&_=${$m9V?v>I+B~Uzp@BvW%bCQeJ{4$vMP1*n-oAc)Xpj0>C
zO3;3ibw2imUF*%?z$VMSee<my))NeXl~M6xf&Pt~;ZP}gFNj@e7ziBlXCD8q-od>*
znO00nvg(Bz0l5DSs$wybacV}XSP3L?{~u%f&HEWq)~#m$FY!Fj4xO#z_|X@*X^1X-
z`#ViLev5wBJRaTR{imV;&=#$g(?ouOUrYuU-JkFuaVukHQT_KA!he^2w|{-@1Iq>>
z*Cj@pkA`2ej3irMyrheX{J3y-#z~h|*H?Ed{=MNuL{;*#+Wp#6K{Sv#UKQ4}meL^e
zer&yl82A)`VEd;RQfbYc36+8_58B|bdy?lxmWfO1;HO;@Y<<$F-RB8Hpcj5Ie?60Y
z{ofwv@WOH0Kpq@b@PE119PEs<^l|NjFKPfEm;X2m4UcV`grjNv+y6hvJvqtd#bKB)
zm?J-DPDkTBaR(Vnv8IdgazK0_#`qHUzuc3IYU!?3^wd5g0C)rH03rW+MJYDW`+tl7
zb;NwZLD;#=)YU)ET7dIa!F0drI&l7t`sN5m{C*$pfHc}{d@TxAj^YIVGTLXvqNI-X
z=*v;vTMS=d73YN>gAHs;p;avM8ajoZ6h~?_24uuzHg~p3^<;tg4ihzVi^YWjPOybH
zZ_gP<qaNn(>%VwWF4Bgf{w^%&(RB^ooGKY9SIz<fZ<qb<sh)Fy?bYD{pLEF%@;Fs%
z<{EChJ~W|3J(N3=b3!shU^2kPwcN0(5k5<HzQbBp)fy|PYncgffaamY3s^0eYR$u0
zj=NLtOA#1qk$Ee8)uW<;bjux*h#&s3&I$oDCeIvhwTq33+>%{dnV)&L^gH5-T&(k0
z9G7?NJ37_X6UKno?vh#y$Lm3s-Ezs7=h24>3c4J3fnYxi6xceCUH3clt3t?=cO|>l
z&=m)3C;@dDS>2J=+-u__Ng--M;TYe=A5FKD`?b8N1$od!eZls(oTDO6@{vx%JxlLo
ze+rg;*J?<9D%mS8$Z;1f=PHSZ61x=o?nHEPsOQzqBh!+(OtHn3#P@Sne2%ionrs(T
zvQke(9f5iQpnm3_h+|6P<r$8<mpMu3wc48qL(7%vy&+~>;U}WQ#Tf$5)MZOrzBc#7
z1)os%nPEBZ_5nopxgkgUQ7&{5vntXtgSlexCvzTk-!tqJWxd7`lc&QjbVjD*o$6;T
z@6zwy$c?Z&kW6bC7AhzBZ7%&1Y-ehhDC;|cm^{Ai{w($FV5wHE>|^lLLr>|Lkj<yZ
zvlGevb<$~VDpj3=dI~ZtWdYLYo*QptSF*b%EQj8$`kkQVN+OgLPVGyppPuX{C~k-N
z)r{Q%vbo8gez&<S5o0elen(A8M~_T{HiRMJIT<6Bn&{e@QmT07kn>H4B1KXEx5c-f
zo*unxYVZ%t!wn1CTuB}a&x>%*xMpZ-7MeHTjA!%ldhJKm{!x{euCFV<s~9}F*)3ZQ
zPE)$tGW0%t_jw{}>vhs)?U$s4lzyVB-62KbTq%;5q?9b!(XiBMI>_wnB&m?wgm)+v
zSCw@tfLeEHdRke#j~NlE<NT1$*GcXOI@%$tk7)dxDX6)3sxFuLBp-DYspxEEZ@ybH
zhaNX*gr_!S3Rbk=Q_T7Bqo(Do#^&QDy@BgZ6w2?mpk@8Wwz(^}9&ExQ@r3fVLPDoV
zw_M%K)vWcLat-Qd_eykr8O@`AINwGAuhSvouhE+R&t?~Wn(c1$?mbsUq@!#L4;-Kr
z>8V5?Xv0=E^uwN02oPn|np&=J7YAQ4bPy-ly#5;9Ka;fdZs|KO`4~lpdGIS{eyJS!
zTq&<hVaI-{3XO($b?jjh2^qDncf%1dfo^@;tE+16B`jFfF>wf`;m?jVq9+_tD9$CZ
z(!P+$D&mT^p3X<G2&}WqcV)Gr=AT1yDU>+ODz9ls<lRR1&S%aNXh>@jmAI;!Yh#HO
zlnKJ#!FAJ{m~p^BP+$;53}N4YYMPRSaM7`a4Qe)Bv%iTBGJUMt%prW>Bh7|w<kG;b
z*M0STqL7WEdRP29B=~OQ+i`Do2)p;AVhW<xSGEmhKr1ujdWec=h)jBDCx$q-Q~U;H
z)hyW%B|KBPJo7h6k0MzjiVB_fuH8^n=Ml`ouh-{^47!2vm}t);*KWRU81o~^n~cb-
zWNX|7*1)yF$zXncmMobHPU%BR^jXw*dYB5%A%#bDboUt_F!GxBA=_$qY5gSGeJZ$u
zYkZim*y6kBaOv{lcc=VP>2r!SiT0L=@ewNcXkUcy*4J~YHepDPcg|!zzNM%b{FESf
zpWkx!GU)4!Oq1qjyOZ{^Xm_QI-{xQV{hpSxVKqOGChtF=)dDye{WO}qox}Xp7{Xrq
zJmQmVY71sOUU8e98ovK7$UW`r1ZNE@hW5$tQLZ8dGT`#2f4IBG*QfyC@`-t{J99~d
ze={nEpR2K|Vk*MF6y?`+Q?`Pyj<lE^Tfxy(#kHIsJ&Z|5#Q~HXG2@IDET+^rQ?916
z{OzwDPhs3oX?_rWrQmNy)wSrzdR;XirHt5Bl$jj8S6&G;Ol=DaxN=(3?E>rhVi4vm
z)Ysbm7`7HKF^Ns|cD>GyH7W`G!~)RuXA&<=f*eJcJ}ppw#upCpO^Dl%@|Ji^8-a)-
zkk$A-1;}6HGRZx4)W_`#tGM|UcG+U|dpM%3iObw9Q5?tu!>+^CT=rzD6&NbJ2Y$B|
zLl};T5!U_c25XqVROo5Cdz&%>k?vRdE2;iF8#RuSpwd`Ck+vB{Mfc;TT~yjEM!k{J
zTsw+c27upS?)L-L>Pl8>oKE>9mS#gJ2LU;f@%0)?yTK6Vddv}i7p11nKP1QW=B1~+
zcy*;3N8z1ol6oo?A5$jI+!s)ZIqo9u4<##h6eN<uT9c1)Z!BvBMmh^ZM8mpx&~eEV
zTGv^zcAL$wOy<VpStxOE;Ig8-j`tc1*6951tiMpFE$qp)<IFQJ9tU+uji&$o&BzsY
zI(OjPg-CNrMbkyw>`_`#-)u_dnuH_#lGkv_5JVN#aLW1XB@bW?I=7AD<4VcU2{J+D
zE<qIS2c+1L<#^P&83S}~NCO&*VU*I8*?s6?T}+j=JC8Im7Oi-;FrZR8@S^sdN{=8$
zeVh<mq9%TOndA}4E#;(hTN454g*61Aa^!*!pV7$_Gn?6D>MPk%29sge;73~9zw<^)
z;7n-kLf>Qym@d6sd0lS*OCtEvYYUjfdwL42*kIjT3zz|49dr*~(-|b~c}D^*tmmP#
z;4!6~CZ7YizH5G_N18LC*Qy^!qI6znIjd&VlVE=ew_+?bg_zLJg=x&NvnLcAgHpTa
zgDNSr_*E`Bfka^<Etl^5vJv*&4cWb+hd22K3bZ~zK2y0TO@*acYzan?eQ92^WkU-m
zSmY`x&(p!2J*23xdRc6N#zNFUOAF#AgXv72muUIkPCFp9JzqnjE9S%P-kYf2mjrNs
zp#>5@AhL5jR20U|Up|Z^#Il->e;X&jp;bmSR>;uvMR7xnuq}Bqv=30XWdac;zqpEK
zEEdMhdD6ig?5uaWU_D{wHm1}To{U|ORE~n?%k-(?=={iYUOyRpLlukwSgqGO0apW^
zC@3Y&AQufuD<nfgIW8{EN)wxd5d&L&)NqZ`3s`WNU$~awU3gE80OoWpsmOLUXqO1%
zUown?K0Xfb<)3J{<>$$<65ts>$Iz_)<>iK4>zM!3W70($23psyy(g0n6GFrf&6d{?
zuQGvGbSj0MX9YXm7@|N(&YkAfB`Z?pq6|=DkbWKNS;){m0sc(RwVC}>_iiZEwP9i%
zNeFWX8x0Uo-QS50+-*gsat9E`QOG`YxXu#L=<Zu;uohb=<Z&kuq5TPwPw%(tXGUeb
z@L6U+r`l0(X~9c~KT{DbNZ8S^HdDLfN3jvU_Qd+f9z`jX4D$i{bD9+Ud%@TFG0=*0
z?e<`(z0hGFc3PDK`x2Se^8|{xrTD@#IM?}wZyuZw?QnKXm~ARFzxBS}=j`>(_OS?w
z^|)!apI(c7rHd^nwf4AiLVM(^Ox#sZpIrK!7jH9OS9@YRNnshw=cIEN0%|YA41|_k
zKbMz3xK&x(?`&tSCd{Ad=PESndw=4Kr@VlQui7VvFHj){y8v{0-1spyUS_`GB05po
z_dsOFIH}=LPRS;Xv`bLi4U}TgBHIQxCu>>Yr9o7prvuhk!e6AC3O(|qcM&go;~@og
zYVXVE7DNwfq~<j=oL<o63LywGV=MbFv*r`jonAY%Q%S^w4oc%;=jD5A>Me3LZVgu*
z6{5Rkqo)TM@$mHM6bRR?4Ph~As7HQI1vEdeDlwp)dJFx|ifpv;kHV-B+96|ph}zTj
z8JAp#tu%q>;R&@81Gl-+0eSs$b#()H*bwm8T_0A9gj(~E<=0n2v~f)%@ACTBH8kEQ
z4$jH1o{S_bM_(Hpx6K~ZB7t?;O$oBq(d!11Jn%_o+*_~n&{veFoBmOl8x)+ENR3H?
zZZ2qkgc@VpJxXY>XV^|f>D9$0Ub+>+h}Em|%4X1j_1=UQib`J|$2U5lAvDV<?QyMe
zAxJKYrm@g#_Kr|%Ha`Sh*2xhlR`Lt?o&cSbs6a>iA#rrWFfJC|Nfc%4Vh_u8es=w4
z;O->!(-Wl^5|DEi_>i^I?w5zZ;YH2x1!}pK4=%D|b>)vlosJuRiCglts4Dw1Cc`v@
z0%Lg4mIMZA%YuB=NPIq;MY<BdfK`rU?fRQ~0@F7c9*{J8>_sZH)JzyN1;THlJHsJv
z#8H=#u!I@cOYtO$WF^;A20_?9Ef8y=0ljycD8bfM*k^EE+9=P2?hhvrt_e9MSW-bD
z9e>%f@ot7%pf53k_`QjL8vcW3urNvtIXeB_Pe^T*#QNtlAl<B#EB(ps-0wv9-VuB|
zjmq@Uhorf%UXC+FdIXkx(7_^>7>LF%TmDMuzLF`1Jf2P~5-S-a!Qq_UJl!mT*;u3j
z;;rL*rA0Jy$dNp^tOZH44&9WbKk)_~tJmYrMK(U)3rNUG1hN^(6X6a2!4o89C{#85
z3Dy0RxN`;r?e3&3=+xpI@8ueLS)sf-!;(zeuSEu5{E4usm)6W>3Pn`(Z-KG8)n!zJ
zA97!w`MYLwayF0GO!VZ6{$#&}1~<~O>gKKyV4wqfUL8{Fkx%Yv$FBt?^5nWzk-VaB
z6gXf_#@}7EOo;Vje9*lep`m;PcyCs{Q0x0da5Qu0QaqbtwiuEy9wonlpTly%UuPwp
zS+M%P_d1pUyg^;bn;S&rPgK}xh$lxZosrfN@7@?$(*G2uEwr}}mbC5_CbBwj>y|=_
z)>!!|sV8&I+S8ISCy@<X4<2cjcifJ?z;PM9xba*78T%Q4adcX0{qdXcMDmM3<-1c5
z+ABz^$|d`U0KJ0;r-9oQ@V8mYk_F`YEF{^J;lV=|Eg>Rwga<jkezpUkj{k!1q_+)^
zl^v@E+$2Hdfwq`@KrMhOs+h@FwaW;8m-e9x-QBv5ZLo?8r;W4w*!)2KnZg58UpNaA
z|1|&`J7^a%VP*@$vX9?vGW^2gKzO}B;4wMTt^|F65dp_5Uyz$Ivw9NMOjj$zr|{j_
zv*xji`yLIT9J{s~VSq@=;Y%$dnf`8Tl&5x@o<ce(#o|PxuasKj3^K{;i*r3tlSZZ^
z`vQLj68Ue?W<c@W(q9MirS#MPfeN=xpuGEfj9F#zeK_LRv~x~TVmh#R@o`U)h#)L(
zx$@8eSpU8*!(&2vz<B2<?O9bn#|kqQXid&$Am;+#u1w5sa~5m4v4IMGO$t5qm3=h<
zHvUXyC#NnI&J-lvM~UcT045Snp!Mo=YZiT0B009NQ_+G%Ql(G<ax}R5U6R8|DhRPq
zFu@{X`cc{+wpFmFEB4(K57t1OwHd4bg!LP==g04EUdKj8*R@DdXj9o_aCSJNgrrri
zgbYO0_ly3|V(ia5i{ZRMtZFu<r3<53K@;AXMZOw;9PDxG>hkrWgSQYPFn7KjM1c}B
zseq(iw=)%;ghcS)6;o)QLqM(^>ay+GcTOymvoNs?<TWs12mrFWaTWCaRmROf5G>2u
zdZm4(CEoQ^qG*hu9Sl^ck$eRuhtVv*7ygH8C=&2Me+46NgzYv$D;k~lp;s@|^^$VZ
zs89H=#F$qzW_oM|I9@UAJS}#v46Mt~)9{}%1R@x`bpnJl51TBnK4nzD=ey-L{exsi
zbpjlZjy#fko96_pAP5}2y=R}u9|cNH1$2#!r`hw-bpIZm6_^|*BJW6yQ$f--iq)hA
zneKvH3Hnk%zb49WfjB*Le`6k#WRBqlzMU~*#*YB{iqp&V>UusS;z?V;)0Y>{6uZYU
zq;(zOGzifFec5?)Bqxs!i@(Ie3ED@F;yn8(v2KS17^T~a>t;?m4F<1D{CG_2PZyeD
zp9wSjLlVvv;C`;O93Bsf`9D}>ie+*QX-NPx@{=&>tJk09!CZG%$<!$a>UDsf1rL0P
z(RM0zRb?LtcLfc!oh@kQ+9YW4a0eoqx7bC>726@U*T*Z^a#tuW3HVInZE_U{Ws64}
zgE=D<6bZ#Og3GmogVjjp-xEw4&|t4WP(H^5;`z-(p1A5cN&Xd7!K#NUmm?l72nowx
zs($iK$%=kxJ)a}=Yb<vOr3V9&iwZZe3O<{MRaq7f63iqpVW`aoA7^DOzAhevN>ul7
z(JZ_V-EHvd!^Z{z>q8QmyuzUMnetsUA-sYL(OGICRF}!AaDtCB?RoO93^#kF80Ys{
zJz9!=p2=srsY9OFh2foe^!t8IK$`|vnQgB>Pyu6r$44IUOyi=#p^%qs?ogcL<EuDQ
zhI{k^nyqq#*U0D-L$XJ)UQdDhc^#ecl%`F1XZSuU=EDYUWDGYpv5|Z+Ihjg%<QwXz
z{Ue&>Ihx6i-{_d@-@Y{}R@vcGBS}|sD6E<rs@j9-`@W$X6EfjB))R>tdi*|{tGsh~
zeJ3J5W(2@g<Jo?MK968MD+|ydgL~%cxE)p&;sN{Fl;FMcq`e@Tcf_kx3f;WADFF@^
z^&t63Xk56omvfF^oH?V^Gw(8p^fTm*z;bgz|Eyl{255i34ZhMqdM8%{Vm6>f03!i@
z14BIT@Q?7V1@&aB_{s;@F<jrNhx%-8X6E6cLvk$vh5Yrlz<CmELu;7pGaNch{`8we
zPw?B%l%oDbzs?sIKcM8eS8t|A6+0hPBobq&FSfqRUbXEIUzm{jD+(W~OvTE6C#Hzp
z{JW?mvR=m<FtD8A^bFf-N7wKt?#m7}$aK_+?J`T(PlxF+grkN}0M{)vQaIVG!qZ9s
z1UGh4ZYsOwIQyGZx9o}t{_a;K)#U5aMv;1wJmIs*cO>?yK31+bV7bX99rrn5M{q^u
z$IF{?e>WYH%tL!F+49#IW3SY~S$8yv*MmZQ1X)LJ#}j{{>sqGYi3|G-K=oJU71^-D
z4_N!E33eA(``maXi@N78eoGy`gw{0T+jzc490Pmtwl|Ds?LAO+z6d`;Wdv8YA5ew0
zXkyAm#S=$Y*9=QM4mjX)C;0HULXvQvnt>C&p$YC!et6$U=jzOKT3dX=mm9UuzP-7C
zjq?c3$$VEp`)yAzu(NmEL(atR0R!MdsU9k6_&&|;0rAzpfOxQxVTIc?AA`!YtDSu?
z<KCwnxLorVNztX!4OMo|Y_iriYPfiJDoDJLHB|cToKE97!o~qw0xE>Ej^>KqOC2G>
zS_Ub;ic;=x;JcnC9jKWCXz&%Mgg1H4737^W6g2T+?{i2lwght_e>uW?H8FaCvI#xc
zTExTgB9j^hKsPvFJvSnW7m=!bAC)zc-B_R1pN$$*>WnY&1eIOs?2lq$w-5r}G2gyS
zgKa}M*p{ws^2hghO9b~hGMpnu22fW9o9DjItvTR~CBzK{K|ai#jxWY!lc8s~Nl<WH
zHPhiZPp2cZ@{R<h`0w(Lv1HjF`?qd84Sy8#0Ulle27P2gOjWhN1cBEKV5Ey5B%PdV
z8yK!};ffqGU}hg}awwP4=k>!d^`GN^c``NDLLOu=b(&W6pRZ8*Hl~}v@xGI$p*92E
zOfi>XC-i=tU36&cDjoRRDO}j5b1L2rDzfQ7gl(QbQ~h$11q#>+A$=w?z%Q?meZqjO
zeDE^2LkV`~A++^y>?a<!S>9B8gR;h*?Z>UN@`PuGLP5Lh2Mf{;)V|-4A9p?b@PG%o
zg35)S$SWTS%gcc`t$^>f`_;*{9aIaUUk!CAfZvB}md^7MDmjQ#R<|<OPpg+PT%Qbt
z-!3n|CqkcDtv!C02xM%J&Ir*f_PBwCLFnyy^#!w#^`CC@2I|l|x@5gv{4jCmoBRwn
zddV#%B!xF|aVy`xb*h1<p_Su`wa{72JmAg%u5)TA{Q8-{37P(#By42{#rXquG}D_<
z$>gQ$FJ!?f{CT9!px_=6<jBY7a?rPs^19^>zc1KQ2-r{{mREbClNxvLjcoYnd)K7@
zbyL(&2#kgpO|iHoPPya)Hrxor$2%O5r20foEb6K&rRgaY0VA1WDXFcO4u60m58qu2
z<i3iJYg(vh&xDVV!HS$;6QSW?JopGy+4rH$FqGip_6PzWyF&_lO9uB<gB;1m#yJzf
zA;d5euRI#GpBluMt$plJc_f_^>z@mhCbH->*HR@+xkE(x#Hat1)P{uU8dn7(Y+Il-
zM(OQnV0j3_LU}|5ERl)JL^5L#m>(1{Fl?R(&57Q^$5Laf@ZiqM?64>b7y&%9PCT>V
z1JIEeix1@EVFJo2Ah8cStcU{kSvZpl9aIj2XW~Yr(GzNVyh_noztu61*KiMkG(uEp
z69TLg`TuCPr-lw3UCSTI#GP5S{5%?7Av_<23jOw?EpRCY5O=`Cy`dYRI43WSv7r73
z3FMAd=E;YCxj#G*qX!r;#s)wS0yrL4d8C2}Na>{Iyf>eJa4lpPT9Tb7@oL7<h=!Zn
z-2X7Ei)sQY*l_Bz<lf-RRMmc9>b03Yi}GXH6Zf64SQ^tvcXA%3Q9XjBu67xvmXFMz
zggEd8WH2Yr?S;MY=-W2F{lW3s7tQ&+Iy4pftk|naM@vnQr9Fd$o`JdvwZRjdnZnU8
zwV^JfSE|3I@2H;ynAv^?8Gb;h&bs`v|8+KyV!x7NTIAoqqJ9y3YAi)HP4PU`hY0#S
zLb9bD0k*&KE%MhF3DiSK%zKg>9kU=kZ8^j?ZRIiE8iyW)<ozCgfgs>0AQS%a#IYp2
z4Pv!8m&>0x(?+WmK-0T-=Z$Tl$Bx;M)J(f#`GX}2w1(m`XRC2!#SpUyCVe-bxs9&X
zOw@)5%b&jU^?sFilwSz}yyw}*kn4u@NI5<(&!NU!^kHpxpK&JVUOw}P__%$$bmmwb
zxS&;u@yVAGMp#7pj{2jNDX}CVAX}hx@Yoi<q;ue|VEUW+JB{*1W}xuch=@SDO@@w!
zVZ87TMV{*>P&^GFfWNKYQL+L_ef&)q@m+lQ;UWjCrHfxgGvg~T;9+%Hz0>|ttptWE
z)J56%QVAYTrGpqv!pHtUZt#YcQ^B4jI`3@^ol#+}qcEqB=LCwJ@y%v51-DYRlzWBP
z8%d)MP#ki2-^);nZ+mWCc5jq5CD8A5DilF8OY6NzJXiqd<FU_}3!#q=)xpT8b%00`
zLgs7V$<g!lg)+`W*qzX=fdXY;>EF+`j5Ga`p4QokV8n&YCE!HZ4EjP7JwWVTidV<v
zwI@_qe^7Lfp#q|uYO=L|Ua8ZQN6QVRPo~vp>0L&1qX<YNSym4O2~w$1EfGQA!v7er
z-MdE~cx2Sps(f^*(7`(!H`A8kwhWvy`}0S*-26C<<f&paJ6uS{>c<xutk>^n;WAk0
zW0rmyEI#01_W80L!MkH(xUv3=S~K$D-ft`*2Epg)_$C2Qb{+{)OtN7YHEx1#p^!Vi
zLAy+Wggz37<3xdQse7=RP%GfwfXXJ8_dZGt)|_lZY7FZdmYib=X2d>-!zc-LlJWSF
z;qW^{n9z$R(wnpyL#DMp>G;Kr*50YOV_qxA+5K5YbX*X2ra9pV4!dO|fk|Hp+`S6Q
zPl)m<!M8F&;qU=kZqm_m;lNOte}{7TGnBKU>Jg`wKS3-OGagsJ&jEXi8m8>5NNdC^
z!akPvlOl`O*yI#ExIub#hbc_Xoe1aKcx5r03Wv3X)KF$lUT;xi#rFpIt09Ls{e$!1
zzoY#XtYC)}Ub}r_n%N|cj%r*|c}5O<O6Uza^kMLRnAJKa8Rcuv(mZGOBeysijAz`r
zR3ie@#p6xccYqzS5@vKlL|;AMTG6g0!Y$~w5rmcBClo=?y0ExYHjxAIU)nez#j0jM
z!mqVo%E$bA7K*^L!Fw8TAcS_HeZvOH4qL6MnQpvXL}Ae3FJDkr$@zPWI`DcM!K9xc
zqhAF|5zj@*5u|y-j#hl8#9D;cj>~XV@kL&K%Yc1En~zB<wf=q$7>)@3XE?dP!!6Td
zb4o!&mUFtWqbHylW+zyD)c{Y{HYGT%{w>k^DuI^Z9{-IS*+mS2Ma_-s>{x$dWq4(f
z%W(#9dUz#qD-KT~T`1+9lS)neHY4`c|7|2j?2Kkdtef-ioxPC*y^IFKha*Z;JyRpR
zW(WP(;*gv!?CMk)>7S}zl19ok52mTbYb<NBpP*;?0EPNH%M{o@3iU4~*wuh>*VX2a
zhL_82>8>{{abimV>;PH6-MoM{*^L^2R*wCH`5$P31>WHwgxKFMv1f*{`>UdGPC)ga
z`JUyt(5|~x=i2p-3PKJ~P{T+wui_Nn&sIZxKbc#+3cIK=C_GnjhvwJre><{WbfgqU
zt1+pV-Mf{E`*Y)8lnj3SZSVZ2{d)`(5I-`E%Kh=5`xi^)e@X^K=>GEin(|v|8XjJ4
z4B`}e5{7schbeeQfL}rgpI@`3!2X!{RD>CyBJdEohDT-^T)ySCB!XU(3V)3Jwm*0t
zInGQ8EhNt5YLhdVt#sLz?7Tw*MyZaaJ?5r;l4)q5sLIFzc=&HecIJnMyWXEWDKtCi
z(8vY@OBAyDert@K5WNK(-Ff^cBGnp(E%!x*@MT}wBB5e)ni5^e84O>t!Z0-fPcsT5
zJwaB}@Bdsj-e1TUvs&A4>&JBP?Ac1OMePCfK);h2kG~6uo4;{f79HYMLI&SLen_9M
zO?2JxDxtuEXDb8HvFeJP(K*Xt-6eLJ31OqFt+t>(K6JNNUboT75I@k45a`sl%>B6D
zpxp2<6T_9yfUT(5h#81;H;06bLkD`AZ6rGn2D6_B-?}M45YL2y*Y{I5@7SelH6$v+
zyv9F0ZPe(gioDG2Swru}X9XP0Re9qOp_EbOC3McdF#BFclH66`hZM^1>-HgD!&GR5
z$T5fF%Y|pTk(A2Vkn#hlto;^dYcFS_6NtIsRo!)e5Ug_rO(GJu>$SUDC;Um4bb7xO
zmPzW}s3)E@mve=IpZ1z&LajE_uv+i<+42kiH@0c%Z1XZT=$?u0m2D*bR*FK%o|nz>
z3dodik}L<tti&^d4r*x|m~iwybK<ANTfBsQp6mVq`Tenr_8M^b>;%cBAL+Di{d174
z*WzQSGD}q$VlJQ3@MB?7A_TUDjNC>CjkXPN-(T~@CJuE9qw99x3XAs1M}D%EI_@L6
zJgCLTGp3TZ7D}JmxZf7L4UNt0Mr@+SCKmmQ`lD5R;~g;d?d+{(nqj0JbB+PC<nbi8
z3rZ>W)3}$YPVLFVR8^^YI91cuUTf(sZTHSRK}y#ksb@XhNO7mo7y44}MA`LF%{_UY
zla<RP>W_gSCs8r7dRmr35tuo!&(Uj_Rq4s1q2(LIptoaCT|!Sca=17BvV$;|sQjh;
zI-ZZSkV>Ej3A~=3>k{sk){C_nQml&*Hz0MmFH#C5X}a(uQ!(&!#I8-SG}||wiD@pm
zuw6GPMn4_GT1tcc0Y)CZ3`bhr)Z7ndCZ<R?&>|gx-gJfg?VF?KpGFq-p#UGNYoSJ2
ziy|yzSS{-4QPmoj57;TvH`|_WWbk?eX%imuxZiEDHAoB6r5Oh77E!A2rJRRW$OTWD
z_zF)tLd^?7NO~Fcw4MoR074xA|E&E{;E-KVAs5?9Rp_qpL#s&AeN}6xy;BXaOc;j|
zh^OXRS!Ig}9|r)OVUL|8@_qg~lo|ltG3M(H`NigsMfCR1P<{9TKi8PB6lhYzPw)TC
zW~^I=Btn%LR6M-w!Sf~20QN+W)LXkoQ^??xE%tmer$lI1GrPS~dpI8n+@W1Wa;T)?
zW*{{U26cmr1$FXL9iJnn5}L+RfOw?(KJ#)3GAak^QG(Bvl`>mBQhtD}{HtVM26~VJ
z3k9#0e){LD@lH<k5%}h#8#lC{__6GGs@4rU-sb^e0&C<hDrV^(%FBgHPkd52==()y
z2eQf?T)h8CC>P^@_W<=$@=cu}eD33-Ctj!LD^g9bb6{=^52xfPUhuW{GYk=st6tQf
z1ZbHFjhbYo`%&z!NAiMBdY{jOHrd(IhsyQx0$Hk%!wN@PV3f|N%P*mK`oMA_K))&Q
zgWK2C;%X!h$F&YCZdZowcF|_tiTSbP=>~95|D*Z#7x)J9ohrM-?cdi^hu`PT8^K0M
zY>N{8mOFN+>iaPqdL@byb&34I8hH*2ShJmw-)#7v=@%zsEM+=6`gBg9rAHWj08L~i
zY5)z>q6fU(DBzQ@yAFVKfaYqni(I-@foU5`$SqCO254Y9Y<c}-GYjeyRYo#_bA(Rq
z%4V}vh-at*1SQ&N+$xvl?=9e)?cZDW7$&)p@^JQJ58k0nv3X*!9jP>oqPhvn8cc<C
zdt7V~ZWU7AomsF<eCr`l(^d7SCirj7JNA{=Ku7p>*m8A~F7rS7Wd5o77s(MyZ1v;y
z{xE>r-#%gse+K&(N$HUC!v9CuIAVvj!^kVvtgmx(2e(v(?aCK~j7f`4#hHnFR8XzF
zn@b}BQRf}(GX?9=2cNKedEB{o#aqZKZ<99Y19{VFOe1o?zzD?AAopnnkHvz+bPSUD
zNp*cEM!w+GEEoBd0EX68mliC__zVI7JtH1@c@{knet65oY~%SBM^G+F)1b_Y)13b7
zdrGOq#@~*#Eem~#Qm+yo$*e9g<k%g%BJGt@L2O~G*uaX1pYv(>CmDDvefgDzhV!Lh
z6v4YH%Y1cABSDR1LA$>O%}z}<xXdjqxOnM(Ut9d(&gy@bF%GrA^pt=I_oc`=2#l9Z
z=5@)1fEZ^6##_u0;GrMaeyeJC_s5%vJN$S#idLBV?EaanjK5=)v~(lv3jwCtWnuI6
zg`>TThgEkt?UQVpaun}j>I?dp=REi}Z$zbFr*Hdq9*v6(5mRBg8|+U<9pAqmziTJS
zwd9{J6u4V+IPKs-S{)ROs7Q1%W<{@YW5?B%d|nY?$a3a>1^!NXx@lWSf<yM-FOiD9
zT~^CH-W)UV4K&3!k@1uYM}x~fq6cxsv`m2;pT95LzGl%FwIrBsvK(f3J!ZnVW*wAi
zPOx4bX-);hTndH?0L!n#a0C!jg2_3A8pn-X3cJ6<A6VDB9zip(U4SgsUlzrq@Xw0S
zbsZuE(KLNv#8Mpm!k=&~IGoaU+2&35tIy<{vk{m)SlMxBw0ef}%kH<k$)uQ%D1Lyf
z&hGD-UvhOL8tUO)@KE)AB>pQMKXUEsQB4Elj$v1k*@2XSBfe}FpM6(jd$QMn^CYU<
z>+8tUHa9rN12h93dKgMFDJ!>QlA$Y)I;rcooq4}w$HI~p6DFRQU@8C7_KnIByRTQx
z>vAS#Yj3ez_@O*2vV<^N?a9E8AFWi#8PQqFjs=erjDf(5?X-N<kI;*(5hcRQ5r;h~
zUee9e#Z7||yKFMcT&r1H1=(99^4p9fwTqAu%`Zo4>yR)mH!7PWEMakNknrexl-QJA
zWY2y_fr5cp<;VQoP!@B}PJ<CT(dkY!)x$29%1W+d#wZv0jfdUM8-UUNI?}juvXcG*
zNmEM1u3s*>0VmRbU-(zQ{AUTo*IV6nVv~~8nVnXHW#v8!%O)D3eYD4pBcC*N91Y?o
zJF}Rrn1&>tn;95$|9F$#Nsjvhy@~Xx_(6Dstzqegf=Q5Dky{GlhlL!fz>}e^`27oL
zTBio`>d(-K(Nb^%%9HUvTqbLydcb)Ob#hne%}5X88xAB^A5vXNYDrNORu>Cq4ejqF
zgMX%fu7GgIyCS*TKkW0<@v7;~h}~t#QLsS%>tjuK+7VF_@j|N0rlIQmqa!5b`Gy^b
z5u6}xhmfjX%No4Kn{U=8Q*WTh1d~#t7c4`(n<c>2FzYuEn1oFN{>dlfdPdOxcl|4Q
zmBn8u8;kJH;$0d6WXj<}6xeKJq;al64Rp?)@j4B_hBWI}KAQM>-5C#K%camX&lNbH
z$Tq*e)$XW4{oxh+<E0lJSxrPEP_?!Ym9PrXdiN4TseBDTfpWOPOjBQTm7BCF>ZH;j
z;TmDAnDQxbg20^jP6>M;AR|r$O~@~h{z!wisu#*AMe&9^!vjC|$~b<4jg+U<!p$R0
z`I;@B+mPdHieGLNoIC~fzBR>{WbbC&Oj|T<kbmTP_=C!-ZbGm=L>s~7nR>^#8+CGs
zc^r=$r*hi1DsFW8l3Pk0L(Eqrz+Gix%@&dp^(&vgcOGg_4QDVVsdqmS8_7$ZcYW~~
z1cX+5NJvNpA~oHKR=Fb)_#xbaveAfqXFH3P(O)(r6jP3~Rq;g|{92kRTQ;n2S%Hi!
zF<eqLGEy=4O*d(?lLNh}+x;w9;!8Z%;kFK|vLbbEA|2MQ@%`hGPaWcu)6yVOQ{-3B
zr6F^oWVPGcY!;Tkf1Asty~l7ZZ@=`Pw4{i?&>i1LgXUT_liomf2f{#|`mXCmd2oMr
zoZ)ZNv-z26=&$eLm1=U9*Tuv33I_oe>Jy1@*14|YAm7DUJZ}Q!C=rcXqTPkCW-T(@
z5mc?(XrQ}~?O~%CLLICl^mz{B?+44Kb57j3M6u8_YWv)@EE8#TA6k8c8E@%tjRU#d
zopq?tVZ)M5Ce6aj2WsrTMiT^)z)aEuY!=DfLn3}IGLc7so^?G{U`4?PJA=#np>l0f
zDF}b6AA-W@0w{49*7AKfN4x_7$64idR--O-V4`~E)k|Fj_Zgqc%Av;acy6~88YHNV
zc<pJtxhvSb{KQ`X6l%(@bjqP)42$zz+91F3An@2I_ji>WmAucShrX&A$)wF};H-ac
zMK<#4_Ywc-*Hz4flOt!1^q|GIVrC8<eb@+sO?9N6J|07J&3tJo0(5Cc>>X4cHsN*n
z)w`j8QTRF&T0{N<fVR(&nvJ$bb|%C}c2eQ`tIUBCOlbPM_#~sg-XqU0-%6%AL0S^5
zUzQ9@yMgWrf`|v$vrA?TWd2}0o&nOd<v&NM>~aOdt-1WEKwX^POCWLa^T`@1?>B&P
zKR~icBl!wza{O}7g@t>nrdnGa5&LlQir)A8)W7=ht$X*If&m?#g!Ts<%B55hiS$@F
zz0hb0?gn{B<iH{YNVIyjZD_pkF-WXDJnoi5A_LY28EI%;*^Yt(?>~jrcA#7JUpH(+
zr6H1xzY2ZGYopZjp*DaH5%z*G2UB)-Pn!@!6tTc$z`-#pzV9e{f0BVhDO7mA-gzOH
z*(%8ay#t`)<VFBfyJeCb%d^XZ?eEN!LtAoVyY7bX4ny7|_bBex+B|bL`mJ<>?Fry$
z{Az+b6aLr?5>S)%h$~X^!_&lYX>Dh=22``|TG#Q*%VB2px~PX=mc*!zg$v`smCD+e
zznk^m7JR5?G!fPeY-1x(U6dcNQe6hg_9+TuN4@T>PkN+W+t;c{f3!0U{gwYVzh^x8
z_pa~H?Y;H3#HZ3)z>)tCX#ykA?7)0eWFAYbscq|QA9QWy)VzDY{V2vWZ*f##e;U{t
zfX!A<y*ssrA{Rh%^}{zC!xSh(n2?nxTga}r(9R^G|Cfl$qA*A=zT}aKhRYvFHu$F>
ze;7uj%#}Ehgi*NXnw)P$@XrRfX*<irdR2oWg?9df3b*`a8Rr)*ggr}m+nPD;EH~KX
zWK6GYMG)bH+{sWf*J1!QsezYJdmC*!bCPm+O6R4XZVn{a-=SyMR))}j2elF3tOK?J
zdh9Mz6b4@2_EJ8oPxKK^USe(R&fRH~XU+g7A2&eoP#1rHu6Nrx_Z=|l^*Z`DSLBB+
zIa!I`-x6z4g?Sfp=sbYMd6Bn1-yxh(@bYY-#$H*f-e(A!?j(f^K-!v>A*hZBF3)GX
zIj5;{51gn-Iqy9=+AvW*N`Xo|e0hJ$%HOcZ<AjIMHqP^8|2hX03ZntnEkmx7iwbOw
z85PDMQlWn~PvWGna@!S&Y60WMpysk#Z)@-{=!08gXn9$_wXjuu@z3r@VMMjH$_SE>
zu>hJ=V;wTo2|3c=OJCLZeWIMVsC3PQ^;JF+hprTw2_&FydvCW+&!FQu6N)Bosz+M6
zBdctgo5T%e%F?9mzzzwDW+Lt)MtVs(v54w7uK|34S8Z=SnW;A+{2>H5?8wi$ok)zm
zf5~4Bx+{0SgXzF}Rt1USM{kmiJ%6|Ve4>g;s_$>fgf|#D&BLLy4dI9U|GJa<H8!n1
z;CS&l;LhThOQvFW2&E4xKasAQ%<lX8^9eOh4lNKUU0B$&XCir$b_t!cwjF_raWc^v
zw>p&Jg=tSL(f}thUSSn)Ui(vh<L}K8S@{O2H*gbjRJMzthLicXUr%F&bxE-kY9D}I
zWFVN+I8I+vM^W-DN%P!l$EH<}ir@`41|pSfe8du0$mwpA&^3LkKXWH_WqLcQJX6$t
z7~xQoITR9SAQ&|HioM!C^Wnus2?Dr)94+r(e~GB+d3)(RcPvv=xU>VrnChZ1`cnYG
zD%kDC)k+m-;2l63UiL^UNLnfCYQ#m67k*ywN}&@@8aiBWj6Amp{|4lz{X{B_mSZn*
zx+wDg6nv1h;VV7a1L<4#U_&6gt&UnTs(bj}8^}drbWs3Id^eVg%j!O?Gp9%aBI_8?
zy1?9}Jz6iF7q)*2!*J=+euYz39qDh={tN&AETMLU<Qh0|1KhL26PR=97%#*!q5LD{
zpTZ6XVc(S^>3uL*_dRC+3J}O9oNov-=hBhKdtQ9}Q@o&ryEF);9QlUqa2KFGz3Nj^
z;%KYaLU${{FNc$Asuo3*C^SK^x0UpG*qSk?@4F%u{8QE0FCsxWaxXq*-@X&yGq|hM
zkFbzH!a&4JFka@vDJTeL>FIuLWD<waO}6w3-+hbl4oK+Uk&N!yIl*vw1*GQ|$?~Y6
zVru9au{BZPE6VyPof>*(tRho%F(!PBi~>G{%*6QHt^G(%4F4(_drKnxlIne^d>~r0
zi;saK=N|dD2k5XQ4?T22B=G6#inhE>CiGQra{uqDF5KCYOycz|gpm$Nva?BGMEU5+
zleH|p?bD;NeG&_j-Ev=%#vs<rhxN1(4$5_=RVK$;nGI=AVt?V6EXRq1<kDwOZUyEs
zJj*T@cbND#3H}~y02^6g*kS^lFGrC3p}QppAALr9$=j9Z%I3HWh-{^~GYI+wkR0KN
zpI!xH6iM>dz8?vv8&`2LQpfl$wNsK5r9e{MHmEFjWX6oA<mMe_cb2N0#Ab}YfN&Fy
zvb&cEG9aV4pKQcmj4@=`l^o8FEKha1PI;p`1O2sp2l`xfWXRDWvm?e6_;&914l*K!
zl*Y_?SWlhdEhS=@ZcF!Gi69ovQg`Be^}#wGV909Iky=xwHYK*DV!`10a}gXruLTa7
z=PM}#1u`bkJN4n-CI;c4=Bj3EKLx`MUdBGvm?~6V1|7(OUwj8GzJgLiz?uXv6_Vs{
zZd&@%++jRjUfouYvXV*>EEB&Az`+hSdZ9z^!WkXe|2%-0zrw?f3Ck(}QSX$l<$mhI
z5Hxp1<<p6Jc;|&I*cF$e=JLq_i@hIL9+%N)V5AY%wm;>qoq^9gX4hdgIsL@gcdMq5
zBQfOkJ=+1|oNT>>4+5{Kd++1xbNyg`fbW)HpNuEv8;_^%l&@=@x!R7PXk3o_BaA_u
z1->1^wuU|q^9x+|@>~2Teo13>U$Tz2to{SK_>}EM%6Rd`8BsJ~urAgXpg>Z;q2Q5H
z2%jLSecza#3VJmt2^L4eS3(7icxHra`xZaE0Q+1>#DFE+k&lE|<M+zA7_3)7E25yE
zZh3vXqM;o4@as%vXn72jbev$leDV#5;c60igB14$YT`liv!kN#6Zm#ceB<T$Bq%$~
zz1o2a$2MSVnms~1hNp7XbAjcB1RiU^HA@#VP1~Hize4;ZKtZa0R|zI4yft(6KP7R`
zwE@3FJmfQk5}-K56dm?it~6B;_v<u$ioi;ctxvCTG2!J8OO+be-k#%5^~%;XtAa_s
z^%G5_Q1cm;CT3sZ>~p`;j`ibn#UYE9I!wd1L*=6hzu+6YX$xtf>L972gD_AQ2+^@+
zCS={aIGX{kdw*n!`bm2?RvXZDLf53r`HjE<I{k{#mm07l5Ar_R^~mSpP<I%=cLb1m
z{X+kG*swZIoekyvnZ!H>i_!9p-Y1EqwtWt!G`8G<S8&_Hk`W1`wTETIcVl${q8?ZA
z8Yd&h{%G9?wx@Thq>1ntekr{KtvV@2R=rOw#nfkQ=l55IK_xUn>%ydG^K#M9vd_c#
zk}^ub3^7<!HlAQDPn6a2OJ14D%aP;g97~~Um+*KuhZY#JM)M751lt7w;DYIhi(4nf
ze&9J)0a_uAP>uSa?1$^8waVwxaeKT7e5TL!{e`K|b+?j8!QjbdJ)GR4i~j^f3+Iea
zE+L_DyDoZHIJ0?SNipyqnP9sRK=H6pF!1B%FhYn6Rp8ZUZTR7s7at9>_3CxpCe>8A
ztM^?1`7Ugi2uJX?^Gg_%OdkJ|=#nJ1k573L-yUEo2KiK3BJf5S6_QC(^=ZbN(Y{VS
zSezEYqt0~d><RfYW@O>%sIa;#Xy@wPI0yh$Xrc`WiK5nAM#_fn-Vu_36R2&n%8O;j
z{7@13k5X|lmQc;bEd)l-cA7|t;th*;3n}2!S#0t^Jq<~uJXl&y$xf{4&KUrdsrJ-6
zxvD>_9MDsET|a+x@Qs5*d$JCiDCo3?@}t`f?pi_)z{xq&)d9R3G=_{u2=94zJazW>
z9oNeU%g|w0GC;dI*(~0__!<t?W{F|_BZQ6W+OpW;hvtO}=!shHcn0{l^{bBBSA3Fk
zz?#Z6c(}-W8R4n?0M{abHj&rSQp8D~%Yi~_8T?0wSYEa`xh@3Wu2EjW(d+VoRJK7{
zI@WGGwAha?BJYQu?;&8Mk~d4DYB|`I?1K4k1X+G(K-)l`R`b42Q<#k|pS;$CrOUMr
zk@uVUqgCk)W<cjkSL7jota-Up_jrj!-VjiI2NY-g%fg<_<_>6X-uKr{)aQE{u(it>
zFzy(rle{0e*lNihzS%6w@RrA4x%V$F*|ExU<bL66Ld-AN>iH$X7AW9{uj>98u;=eO
zy<Gx}%ggx}e|S(fx`=61<0bD0w)@Y>M|Mi`a>Q^O<|AYl{@plWq$P$$jne>tjXJD=
zJL~VPi;b@elGa}}DRr6Iz1dEaYc9Mi-uye!Ti&W<3UP?rHD>ehnMdN4l%1{TTRuCi
z#tryK(ggS{j3nJ?mZY9Oqh^elVrXf{Qa%(FK$?1kwR6S3Xrb-XVn_GRIBeD)T^~wY
z{<^h#>K9~iX=eRdq^hr%y^Eb#a5iQf$vSdBYVr!&%b|@)52m3C#Et|0uy2$8#V1RS
zJ2SAly7hqpZ3HBvhpoW~BlyuZSRg&u6*Po56s2e{w#=pVt&9j_T460Pc$LtT)x=(S
ze$ucFN!L*&V8?1g^Rc6hk=<0rmUr-#!nbN0>e&Yw?52a4i(aVkQsUG|zkY0zsA8f~
zVHt~m?LI`4Eh-{cf#y~}5}h5z`m?+toiXq8XUReF4IUcWnJZfH4vw$u{GyUGY{|S7
zj;OP{6+P~j>2TY6yG3l??J@8t+YR(X26bKJ7PRNZ*M*m&Pj;fOM5k9~3eIzBc2v?e
zzcXLP-XcgAEDl&%Q6sm8Ehjk-8<T(Rs3O)G{*|tu<Svu%E`_s+i+R_jzubXVjiHyT
z&@JNYcXRgtRz=sZTY@7olJmM_<!84T9rRA*E^Al$%ab!h2}A!UpNbDCk4o~>AMxyA
z@k~?7`FQuH#D3UeX-(whOgb>}j&E(r-NfIP_&@Yj7ZKlEEwCI^;(>M5nH9QlCyh2b
zR1I1=`YKiuGDqJ3IaV6LzsgM@_2F%_I=XMuYSSUs+WksbNJeqIwU6v;5}`|p);1ko
zA=jS2v9?QIwzQwGeD(O+E0RTFepchor-C$B6-Ru>kgpFE7nq$(nS*7jPL1!nI(S_#
z=npJ>zL5WESS))~bTZ_5$IVU_3$0{bAASj9N?qGu)A7ze*B&`l^2o5p&a}R>vfump
zYgu5=^B_JVZ_L1K7Q7m{Y0dG}-b<&Zj*s4?<^7;<5rdNDv*a<HM9#6_t_PK1$zm7V
z;byisrC)XfW|6QqQ7FKkb?k1W5HGLpxXRZBd;JZz*r?5H8j<WcN3Xnl;5Xd!@mD)P
zux?{Fr-9668MtIf*OC{9<64~wsox!yWmq|P>FoON6>`~liH)1+)a<C_Ys074BoifC
z3FDJJY}yno3IoGy4~T0E{Nq@+>Rf3kXSxN7o!+%XFGA^*ORrdtgO>fJ?Z3A!v@;mH
z9&Cb+?bjZArIMZ>(krco=_NQ_-4Xk8<&lp;F`uzW(^bZn@+?}~wKMr>;uGR6nPJxJ
zhsWa9N-pWhl+vT6jRLU4rrVZ|FX~EoTGF?VS0X!OBB~1d_2$hhuI!f9H|}S+_xQ$6
z5yt#bP54yxf5`gA@VtVx>nFC2#%RzqZj1(vZQE#UHMX5RanjhfZQHh;d{56gf4=Me
zJ+o)cteJcEz4tY<mbRp+bCrsb#ag3d;rC0NR4$I10MU4nLMndspFfB(Q&&E@tdFH4
z!==iROul8Bv<@jX)*2ksUt*=MoEW<oh7lil4Sq;}=dN5e#_U~D!5h@EHJ?XMf2rY9
z6}6M|P#Cbg9N0IOEc1CHo8gMIEN71LaIrntFc|M%cQ7Wb*T5TWZbeB?TDllCJDn?u
z9Q}HrLpY&kFq&+;Mz`y#!2LBZMe~!{IJBJM{$Yfp!J?Nr?7{soiqiSP;!+6jS0lT+
z=(P9S!C(EMr9Y-T2lvTZ_mV-MPJnE89M2w--#=ifcxDl{LNgy}gqA1|2RFm2O+QB&
zgcV+_L2j!NQ!4+nl(qbyIcaQ{<|oLw#)q^{IiMlzpT5F>^c_lJt@{=qMIdtCK_;YR
z|H=aXS0-Xc-f`Sw`jRul{~wc7w14Fwli7fOMy_N(31|@C)BUFSM9n}=VExwz6jTO+
z71JBm1lif){@?Zj;fV2I&$Is3|Nq+(zZ)X``TFChrpUk6K=K^_v9k@E5{0k5<zqx$
z;s1{ogUElS9YBT30qk+OPp{^nZbSTEn8@1=mLs6YA}V`tM)_XqCddjlvH5ZmQGb=s
z5cc%NL1-za6^x@lPIsAY=;drqBFSq5b>XT7BugW;P7qY(_CZQ&?zV~Au%(N{F>Sip
zVIpUZ9&<LAh86=>79ZsFB6;zuF0{nG$r|wOg&abr&VC~pTDi9pw?}2EcwWnH!vSt+
zL~Hjs-_DdnT>cSDNG-EejAMR6q{7^Rl|Vn!1W6yG!-Av^uL8-CT%adh(|s`$2R*}P
z&wI2>UZa4)o|VpRQd|CKD;IZ%8LZ}hV1Rl$JexXofR)@z3${x&#kZ&#iq#EP)D^5$
zeYNz-Xce<QE^%a#Q|oLbjrFphEpCs_^DiN_?!#Kx^Anz%qM*yOb&wddK1c=RJ;eYm
zf#us2J^7n2rI~OTE~PmxNg<`NxzoqsG~CdcYU+!VZt#{pXY}hjafhEbz9-I${#9ZK
z)YW)wosjnF&=&VXb@z{jdcW+<xSSg3&5j4ejs|?_#aS_J2p$58a{+0QvQXG5y;SKK
zq>d-Z^Sd*%^{vsHb_DqZAikE6Mje`wwgx-sdSyJjaP(}9Ao5+0z?94|N;$443N9(!
zb@<~diD5Y&s@{(o&hS(4vuh3l;j%Aq)%}=Ag511pBLGs9XD9f$d1C+5MzW+jvaxo>
zL|H_jN&YYBMYUGw#oP%*$nyjxHs&U0HufeWG!h}qk{2t16ccIA5z>xHT0AY)5dy7J
ztqBsdA`?IVV8NRf<|c_2#wLdOLk*;!@o;6d4l0ZRKmR3Lq>37M=s)bIIiFeTB4ba_
zEn&}0&pLEFvK%&fXvchBXh4L-W0S|kyA6<XnP+5nW)g%oeoC!)e`$v^B2XvrhQh{N
zNwKchLQKIkThjk9l=+Q+r*+2>T9Er2@$v{!cFL@azp0E9`*P6U8U49NQ+cA=zDE#6
zcq|bC&o_2aXx!sD5H}(sW3{gfY8p6O&15PhpsGxX;%gh?zgmnIIHtpPlA$~E$0I=Z
z206&giVX9^x)q0x!n>y<)d==I;uL|=gcn9~$~LK=GjT?VdCBYZxr5)kI49pB&m9pt
z2l4f?{xnqLoaS}g(K{VQ_kF>7B%iM|=SeGDk~bcXDI482^cCtb169sAo!WAxKqhTU
zqAiIf`67EegK)Ck)EZx=^kJNm@0verEmY%H?}~RFfyv|`JSEd@C5#hiSwHE7w)jxI
z@+NOjKaYikm@-g{XMT{X|7RznB*F4aAd59Fp>-N5+=}^AenWS4F_(zgujq_(4Yep`
zV;}ay1}EJUMmA<~rpgqdv?vd$xhLc~Ce*qj=X?mE8q!4(txdzo;pJiXZ{}m1#Q8bb
z0%`iO>yQYyWFqd)lt2+e6v~zu*cC<YSQDhs4e_=IZsY<tp}3wFrF|SY8g<;Y<PnGg
zGTR676(a+RW!3$oD!YjLL4hgY3{WCSIGm|*fH08r<QMQzS;WKhIX1UEO-VaX!9H)X
z`b1mk&wLUy#kmoZjTw5~tQyMuc#IKKuVX`=rs_pzH=ge6OSi8QFP-=`;pmx7jduJ3
zoVa+JS6qYPeFQ-o<hl8`(vjJ`<l5H^14)IHWQ)6M%H#b49_x=Mm?U<?W2Ds&o0^~U
ztJ7t4EX)+R3Nut@pT?Iub#WoNj8T)FkcdHBQl(mPF}`D8zv_T}mv<&r5}t-1bqC%1
zaXMrc(n%_Kekan$MkIbcYoDa#bzH6%=6to^x1zQduQHXbi;%9EFe-sOFPV@GI>*-6
zWik&?3r&h^kXV6VQKb<PhqB&3lazVFst@rBfJv8|D`9GuD6-q)$Vq;VX%W)I7|M%p
zDDCK!cdlZC&dK(IGh`>GhifpVGmXBhj?BA{=_X>YL0!B|(W-CH!>)@`${?)M^bhOs
zBad&Gj@>(oa~?v6I!74&o6W1R{?#<PCQ+HwBjNCsLD<Qq^9^Zu_OBn#F_Pp9tK{!8
zxKM#nks-4U{v}K)&D?A+!P#u`bG>E;OeYgo`<}{t#bp6a7`<Og1__;1Nu4kU!?`VS
zuX`u@=WlqV>b^K7U!k~7)<9_C;!zm&6hk)%A%C}sjdBpS>rP0(l5W*q;}^73Z(Mlk
z0=kb0PsZm@<>$N4P8AkNh>?I6AAZ<~78UYS9obwmV{w2--mzTDj{y@z8D*v!UAGgs
zlnQ*IePS^pXF}LcpQ5sLLr#M#)4D*`u61mx0u-qj%qR^Cth(<;lY)j;f^lv_lH>JY
z*d|s|!lscOagwAs7x~J3@je9^9ClsfW2b$`jq40480~ZZD8DUF91vp4<Z1p5*`}FZ
zMDp!?ey$GN%pLovgniG_eE^~{+fePfaU@h7<}6wIef!necEV43{d|{Bl`X|u((_vr
zCcXR=Un8d#Thx<eA!1>PK}@AES_Wq#-<uS1Vppp4-v`n~0XK^Uuv&#h+$~u{H<SxD
zR{9fD11-sX@bTU@O8Aw-gsDRlB~A~|o;ZD!S<0&$+PLQ@)>&wb`2m<_zLDD;Y{m}v
z#W-90ac;fI;~|>Af7?rN)Z~m6ikd(V->s1vghu~5r8R|V;x-j3FE(YNXjZ=J;xx11
zQL0EcJyp+6bSwQF+~nm0(BkL9ezove0oy9dFxIMcfjW0c9gCZPZpbPuvTvvyc8B6{
zwHo%V?zh1`v-w#-(hS6IP(_q0JK2b|1*)!UAnehpyW|vx$`iNh@6a;Sm}Lo7h^e)!
z@m(x-Nn4Ii#}r^doR_5W!nC_<KuW|?w*T4Z@?gcf@%`OcOnO<lk%{|tpRI`1%n%v&
zScutbh0!wf&qHc^-(OSPC3{#YLFzDngDgosnI!t-+UYY_0j5d})MhxUhF$2tV?0xM
zO~%rNbOLuuPKjU`>3X)M_KZeED&Ii-=Q$z$d>OJ{ttuX4B}wKGwaf|LOIo-D0J(Jc
zsoY$fbit(ToFv=*7fSyiY{yhg6UKZM^pKYKki)M7$P2zkC`?u5>)I4S76eeX+oLd7
zEc`@~kU88%PH~l(6tMwDHs-MFspx!Pf2q>eiGZ*<@KZ3ZwBPV^7D6!=5Ej?`*Fu%8
zc$QWkl8nw}qE7R31VSH<?R%wNrRu6YK@(2u;#uCBnnxrA4rBj)@9GC<)cg;y#ym?_
z4{Jist%7^07bg;15EaA)Bu3(uS}PETX1#GhsJ1TzOJ$ZCe~aQW!-BvnZv_dI_Xk>a
z`C%ar`lqcgm!sHJ0IeP+X+5@vr^v2Oym5S_l-B=t6@;&gyj7}$^v}f^M=N?N_-VYK
z#cfKF+ybXY(v1cqK8CqWtKfgf*lZt(N+Ro<z^Gda$0a(YDppXv%XR&9NSz?Rnte_q
zw$yaWTCpKMSW4Hu-?X7QbrDt;_k0Rj(JHVA(A4I|eEZC7N6DcVI3>A1W?%eH?E=Kj
zuK5t}jpdh8(PsPtx*{p>o80(pc@x;~U@b`i*L(F^hRgXiz%>wcpCl&)T?CDX{OeJ!
z$~P5F+1F2>K^rQ;;Gc}jM^q#4VC9L;cMs!x!(&Fw$Sai!h~T<rV=UuY@_Kfbgtg!L
zp1<(>n!Pxd1&{{CyCeg)Cf6fyrX!xK{?c&=e)T<iP&&oXl^!5Fi@gtbDZfT65<epW
z=zVAIcyOgcAdH-h-p#fScW2en?IWdv2Y?Sxdb7v{uLrk^0G@}3sCBE&b0(60Pyl9G
z1~&)ZPf}2#Rah<sePcFS-rKwcnlu2uc_*%fi$=!KUHfZIC}+PhM7A8h*WtsQgJnRW
zN>AZpuzA4gnY86JiMyV{0^oOR!~J-CTyX%t0<T7GftxAOLQnPfe74eGMAVNrzfAZn
zFu`uF$adp9RK;TN7!<v1f0sxY{z_7RI7|cs3?nv4+A_!gnX(~eUeLHBq4NM4!<#j(
zsMci=3C-*-TaC&~o^<46*!c}?dYGg6PtsGTdl;pHE*LETq86VS(-Ourt2=*xpwWme
z(&>21TCjs`f6SO0{^jz$xjCF^pF{j*d|Q3s-Xs8#`P}F{zT#JT;wN*~SuI2QjH^ub
z0GV^EI0ZI1@ux4X@+|<|ffC<0vglxYPUa`&N>4Bzbd)aHN^WEB7J(DS0L8T1&FF;J
zdN>e(&;9_!b%<w5cIS%x$zRkRu@*18Xsm10Pol6kNdEpkp#}`_T%ySkV(`N6#e+r@
ztf&}D7C9I1+1d+T1@hL~bAEA$7g3QSGi`D$3fEw+Hy?_D*R@%5MeJM#=^tbm#NNzl
zMgHaMu>9~RF@+>vu8Dv;AqCS<<XBP5{`lLtTq+3&v5`^1SIb7}u@$3Re}lyZXHMax
z26DltrIMqBnh`(Vfjt*^_ld3iwyP&gb4nmo(rPPWMuY^N+&$?nyW3cYP!O3=B(~!1
z_jB5rhW{C2)1~&9O}+JaQk)f6cQh3*2>E9+5xB>#CtC0rBXH9WJi>i-L<p**yVzHL
z*$tuV{_0z|+sZ9^DgIzsv529ppZzL&Ag=Qr2NB1Q_y^@n{1xrlWY7c;O%<xAI_q&{
z-vP>sw@T8l=GVef<V}6o7Lg<sC)|A25ra0u10j-E7~=u0PK;IZ7!<otJ}m|4gV5#j
zJ5^jrZxd7gz6qC3nsV>W2?zoGa1WDrY?IKrc?gnWnNPQ9884=cMcKb45JSn6W3?Gx
zcGo}?zp^+0IR2O2U3w|ZQQyyq$2}gTGVqkki>y&c=)Hs@-lLjF9x&w7;Uo_HTo%~{
zA6P#7oT8`AuInj?d<WS#s0*5sSYa|dj66-HBa~W=35cmu(wf6)5A;Sqqv#^6t*bYA
z0VfJz*z2H&{OvZK)64AtMmvv!^0CVbp<Mw=!O)sJ6s#fFQmcda#p%RKF#|B=k$r!?
zOvb-@k26)GDjIryywEZUD}Qk`K)4)BB@`GU(><AesgN+5RpC|WfHbr-EUDQl2wWA4
zqk0A-X}O+^AE26n<Q0VnoN`#|N47<759<V$SQu4%<3LqaHw5i%?VPyIz^9NiTd@@%
zKeq>lYYnf-2ua+z*2qE>>_$=ZM>)+M1(IM|kW!*QI|j5L!X51ncK{76WQpygf{w;!
zeL_6w%~~fFfA;L06P$*APknTk$Q7xhFLz!UK4N|stgFWZOt(7WW9ODw*xu)VG5RJy
zIs_&E+#laC?tbZI)Ti$;bI{113kVn(=8VMLh;I)w_^i!gk)<k#$VaV5`z+5YdA>64
zT{F=?$Sr6h^(-mj0hdJ(lc)EGBjXZYxY~<L#~9!+CWF;G2n``S@Dx^uPk!$@h;?U3
zpaTRD<f#R~IGEg%l7S7;^_gwskuLnX$U}DM;mm|Xi3SAVPy04tC;PiGf~yXq$y$MN
zD4B<|%mVV;PX}ipYJ~fX2lBD%ROPSkXwSWY^jI1#u~T41`%%=J6jJPqI&=)Zic-lZ
znSl^14O_>&3PQ~laq8=k4-4+R!>cs0UXw;V-`iXjuKwf#Fy?a1=VT#X=_TE#F{XBh
zFQ#a1MezYtr)@<`V^sO$8Ao05ZX2E75%}!WsW)ZyYGq`q9vy9x##(ub@koVl8g%wl
z#olQ8R_Dk7pMDa(xvl`fvRal<_@l6K-X)XwHR>fzQa4MY8Z^o}a7V9k$xQY-$DNi}
zr*@xgw!4=q64OK0b67Zk))26Y&_<o*#O}Bgnu8TXlB;esjAu%>%-y*S_A<W^rdilW
z>;#B6t#sW{^d|=;?oM&<3ZdH2I;i7OF3)$kn*b|t(M0MhJ*eb{W&>JefHy!rmm_YW
z-x~p%&42=u(ny}H<hTCN5WihYxg@E3LHYXX+4`oQ*lIk?>xB*zla+aaMk11&9zOSY
zg<xXCUJ`?m8Hn04)mX^v>~_}{wLgN}$B8=<c;(gw%Isu%o*%wWHw>mK!=7m0u=?@z
zsu$xk#KRBL_@SU3`a%qye;DOVgXdGJ*F`KOq-}#7qvJr43c-6Fyiha03|pE(t!#Qv
z!F6S{f1a^Lp6u3w<x4DLAJ9%+YP7L0E~t?o7u9?Bu-9Q;Cgl@Uw5AG}<;M<6B}O>N
zDH!Cy^5U7U*<wkaU2v1NFFZj|A5t38i+nWV*tC9UX=Feik6+?3uO%koiZ2gkwK(Iw
zri`!R(a*fytDOjv;bD@QLRDYz^fDT5^k#Hp9cQ&FT*JaDQQ44(_V^FE)t?b@Dgx^H
zam0@UIBmS(kH>OOtj^+@0dgmA_YP_yfy^PLm@rW%N*NHC6}=1t-g>-+PRw}{gyMuw
zADJxv&N2lv?C3HpWNtak^`ySnP9`M^4#yYH<gqHABhWb0%i5h7b;(SUz=BR{$-uK<
zB}4VLemC@a!dw7DWiLCh?6_fSRQ0yxdV$$3nQjkjhPLY?93F1y?L_?HN7;sEJTW8n
zXl6FAAmOd(FWb0U=o!ytk}H9YTS&yaK4J~dEy4VzD9l6g2x*e+febg$LRlMvzqr9n
zhwP|mYX!#Tsxx2SU<y7McfI}@BCP+yyB#hGP#~Aj*S81vBwv9kdCQqU4YmI>sUb7!
z5yyhH1A82D1w?0@mh8RCpCGDDm4)qYE}G_Wxd3e}kiSU@;u+i{ZjFFQnXU!_zD>{N
zGqlS!FXvl>Ks^bhq(#g|<8ekt)qDqj_!R+JPmAAs#~I^IGy|l7UB%g1+)Gryike{}
zrKZk~`k(yBx0TBSgp2wv-+stAR_@_AjX6PCYc~vpLpSy-W@WxM<3W*szVCSVI7v4C
zqLM}ylLW)vyH+HX%rrf|9CnX=*E`<Ego=Dj=-Spm1$=Cz)m|H=nV6~TjT7SA^@6uI
z+95koUE@pc)P^;)`@$a<w>};$nhpAJ2CZC$ua_q8GUDH6Q|o0@e@u;Rukb9xQEn&_
zB2DKM8Lrhz?bFJ>CJShK`S4OMk8<;hM37ksYFuYZgS{@ut%jHr)izITNk;^Qk3qwP
z62+==VzmRC0K*^(#dlHz_jY+G0ZIpS0w?XjgjsQZMDX*lpA)$4)RH1SZ9#G@ONTtw
zIKP0nnnOryf?}a>4v)!@{*{}Z87+6t6@&^40Sc~F)O?^+x3BWX=1<NgK9Fz1#yabd
zOpN9Ic*Mdo)gQ;p&~cOB>|3YsD>SFlGAlc1iGA1eoAQizTQYQKxzZrQ${Aq!XekRD
z#+@h#xMRbTL02(k&-N+lvxKhh^Zk~D(r8lBaIAUpSXsJgMxiU<L^~0ZPcT`Z;?$Et
zhe{szD9mojV^(wyeJ0WdyZk{B{my$25}z~rM!5s(y(#cPzm1OiFGER@0zuOnu>@~*
z@}pZWecV9PPAXof;(|QS&%XEXV1Vg6p@^V}MDOzGj4&NpE#Eqx`!9ZDB;6hofLp07
zIiJ=dIV00vEvjnX#ILaqqL;&-Ox8@g!@%tGzIZ`#f!a^xpBCqLT(}`US~N3!S}wB^
zXsuqNiYd5fp|VIaJm&=8=KRjzZxyS$fCMnsvax;nsqZnT@p$F^X8NpWEW6FNixk3+
z@aw;{1zQc;&Gj@J%*N|8vym})z3em``t{kF%|~u6$_<SQyRTFiwOjriNCTg;wGDWS
zrwHnh=3emtDK>Q{&cHf)5~-L&e`ll?lMi(F2-Rzdt~;>9VRp0063SAbQ5qm^(p2o?
zXv9GLE`z#jK(RAho7}&218_VPfA&iR?niRKeboWBEcI+x0#Am!!S~BsVVaAU&d@rJ
zmFzF!Z(5MPsUC@<g5razi3M0PBGJ^Z+1mF`JjDlT8XJl|Z!+W2E9pGNjq_WoDW!0Y
z3&t;-TfFxOF~LT1$4DztJgNKYQ{G4`rklvzr^3E(W63a$=S|C!sA#9jY6N?sC@T{@
z^%2-a;|1{us1m!`=WH685~e=ySKm{o(<nWR{A&~WQmZ`-LkNy2cl_BGN4d>LqYR$5
zxxC=wD+xx2SGnGiH)0zsActgTzfYF^+OnCLSG}3wxhJ0H^WWO#xdyv&tfD@rj+p~C
z(mDw-x)VsB21n;zOAgnugPBj?+!5AI`oOKg>zLiN3Y$TxB)OIP)g5ul`a;M$ldl|V
z`FhS{xJ-7|og&MQOY60Ami?EZK~V%#1bn<peCsI&<)!?EyLF8SQ2@UJooSQMsEM%V
zIY!;HHd)+&lSX4vK>e~^Ot>Pl{9^3_e$Uag5}k|>{2}lOpib&<Nx><5)*nmlPp70~
z;IGlTY5qjXn44Of84MSV$W~uHg||%f-}SVftDj(;T87YQH}!xIS5=<Y2cEvJ>w&k<
z6qUjOiEaK}u8@OD9WIr+;c8T6P?Ktzjx^je<jlXpOEW4&y3V<RS)MV!^P<6~hncg7
zduC5c=X_)2o=CD75!apE<230Pa_3-t!OsW7bu9zMDOXuIB@Wp^>(h^$%XXkl(>ZF|
z1+Jebft}dlL5hRMTY=G1%7OCO4aKX|GCpX+6Gt?$75|>393C?F2HjRIs~3q(x`7kA
zGwYO^e8j78%F<hN-6)Ms;0bV546JR@utU)U5o>|yf7?dmm;W@GZs>a$v`Ky$$G0wM
zK>{QzHsVJjFQ=t<25#*t-MQxy?kou7Uy6@9^CSlDlf<n9#h7g6ui@mpduJ~it%P8D
zv$){G?>-H8hZXcAMMu5I?14+mQ>oOsaa|j#R#YWih59wF2_t+1>sPPbRHl6R?a1_?
zO{@r-8n~l|UToY@aWXKyP6P^5dGwfY`D&{>s&A|xuXhMbM^X7^m@qSxc|WW42zg9}
zQ3T@5@X*RPdtU>TYY<laSM8&4qm5QqXKn*#Zn_&;+@UVTF#JPYV9t%_#)?Kwx|$Ge
zc-;@2cRxUhObzkR_`ftH%lpUBI3g!xZTC2UJs)9%HflD1CtCa%&hLlneMp4mHF2ru
z-%?-5NR)&RK$+W*TsV?%_CSt_srRunA&L<Dlwu5N*uU<R7+Z4~`@XR9<^<-kkNI0>
zs*XFuI~pO)Ilw*)Ftg+!b=fkbjl*L&&RomA$&d%X!C3m`FcEJp@Gf0VeSD_i7>hCl
z+S_22N@@-Ek(`U*t<y{KTZrIJ_qg|=Va!)Q--EH)lU+MT6wyL2#!Dc<$BRde5{0jQ
zY+Vg$@NM5jP!#BMRBbJ7ZK#(b4NfO(B6-Mz2Mk5H033Wp@83&yr<mf1dES?;0Zv|r
zjS8pj>k)<nM7F+-I4TUqt|EPmn(GF7OAZ95VHBzvtlk%AtfO|I1TJrj*Oc>2lEcX<
zwc_S|GQ@3ejV`rj8~HipYhP3KA7@l)=GZLYYy+c1^+BlQdk2WvAOfp=u=T}#^Ub#@
zX{c$?pA8+hw=;3Sz%4^reA3HYKnj{KrY>_Ewb&Slu}E@&d39$I$5kW;b`0B0+_KSz
zKM+s^MEr5}1HjtK6SxEe1H?U0>H6#}T1G^5#kb*2rMzX3<;%&QE}4Eoi&;Rl>-ECE
z6Rz(<zDqR<5tSW7d5ZS+>;$FUm|$<_3-fo66sB_L*TT0dkBy%t)bi$D%W9Cln|!YJ
zvI9h0!wRv!^YxY2z_QwHI^fUn)HBH*QE#gHuT58YCsECSd{*C3Ri5kO?oeuYc+y<s
zQ|($|KHkgj`uwakuP?q`Ewfkv4ry__TsJT`UBX|FfZt{b9dTfCCPMtB1YN?HLVqSh
zse?l&#$y`AVs0BU@@QhMPR!7#W=!%tKb3O%Ttzi7XbjHsetI<&Ct6bTHxx`bc*Wh?
z=(a9Q{@Vf&P=YeoY#NJpFy)SSX&#!&AbhWC>qmLm^kX00yfvq5p#d3p2@%J~A9TK>
zP-F_p08icCgvcTem5^Waw-!bBr`jkso$ws@!kiSV0Fwk8StT(1Tnt$%9QQMy$NV#}
z9zXUq_Zo=<ZQx&|K(qRBE)MIV*SBP%3rN6JGO^*Eshmq!3yF>hzbq?^K-2Z9y$>kG
zUOOE=`Dnm_c2{^rq2ktlnU0Q68)bd~$R>qB+N<|YNF-CATA3Z1yXc>1&FK(*>5wrY
zmtIzWUF;0?sEWfoD%Q1dEeFb63*;*U^lpc>U*Dqb!tfP_h1Za<iY<*}t|=p>JacfD
z<EuhPAFPoO>L28EUQXT27a!JDN*^!aY{f7h15oV<Nv2(%b`3<2=qYKawce-PHDzmb
zLsFeHJh)^FDcMWgYM0N8g9zQy)yx`oht+^84)R;SLZ9zqc}IjM^Do_toMhd>GeBEf
zPo{-OJoVz7qB(kqPU#)A=*2OddWTb1_NdUxfUPM8Am8z3(%H0sJ#MVH>K+pz5qg;q
z+k;2_LfJ$1TA0)InMZtCVT~uj@{-0cOdbW9H?v&T8Gn-65_V}qG)?3LRXBqu(u~OZ
z0r*s#bL&nE2Q_Y`{f-M9_>GG&y0)a^^YgZ7QPmrcs%Lurx>qqor(b)<M*g^EebxKV
zU%qth9U_g+i}F{B%{GY22Jh`)zfZ=o&9Q@z_N<LV^LykNXYbV|+9ctdDr#TCsaA+e
z7vARz%B>0+Tm3s=-FXGkF*KGt|90~wI(Po}1?{`CPI>p0c3vr`3)Yb}W7E?;ZFI4j
z)4rbA=KV{*uTr^wm(9Ju3ZBBLp(JfOR&blCJ)+P)D1dyV@H4pttN24FFT=@OoAN1Q
zAEn{(t1Pi+c8RQZ(OIGv+dyaqt-pH@h1~vRx-!&ScWPOxF`FH_g2&$GXaMZWxa*0q
zbd=T0x6zGEGs-AoeZYYM2DW<KZ`9?+p^oo<Ch;a{G0Fl0*Y8<|uq1t{$DG#O6KcsX
zL|qPL0V7$OjkJSy%-CReJ08gv_`z^#Q+DpZrkp#GM2(_ml{HaN-_H|1pLh3Seu<_l
z!9L-ZS`r3)GpYYDXl>;)&7j`^a*cLHb(B$=4}1g*Y57DYN-S-YLBPE9JdtKy)ZW-}
zh59P{%@6Y3Wa#&txkb$4jXbTLslI7;`ksI15?ZhB81F|!YaM=aq1g{dN@H^#SvaeC
zmc#-I^Hj}bbmXR5c;&QA$*PA{qhM~UEB^Me6B^a|xeXtyh5oW{$BY9=@fEoyYB)G{
zrudz)fmX-uQ}HwYhfI<Hs(`tQC#`t~-xj4wJewE?pAyD#Iq0<H<eBd8oc&FrBBN4|
zw-@{(b*LSq^Q5QZ@y$vL3PRajZtk5ey#!@t#f>rkC`BsKg;cRiq9PPV!Bd`rP_pR)
z!%R&q|KDAhs?9o3!-$U{1+s5$qE)@?Y$DWXhjE?(KIA5xOJT(dx@Gte>Qa2Zf{{;T
zORaw%mc`g{0g>W%*bJ>D+DCge-wDnzuq-nh2P?zN$Yp8n3tFrBkL+j5OAwimk^hVw
z2$|Z+{yY!P{fknq%>fZzq|0B##shtz6L5T10#qe{t~8FXI;z_NZqpyne4M7bL~GBK
z)OQw?Lcz{=>`g;bvzm2g6)*v&3NcN$9>kp*KTq@-B3}!pH`5x^`7WODu65o^voP*g
z(UEENEIB!*EFDjRhQ&~F^8PyXKHxkD7c#@bw22l~@HS3Kd$a#Kq#A8gX&h{>K)by?
zqh-JpU`{)@zU(*gAO}$SUI2M7v#m>+0eM6_5{KPR-kjj#f+2<-!QTdK#2@jwVbq<x
z{aNg5-W@B?CxEm|ZIcd$E;^p7a+ppVR-*Cm1G~GjUnsYOCm>gE78)fE)z*Vjeht}*
zai{JrF7Nz|^)DB!T~OFMxHJd*LpA~Ps1AK@+f3q%?KQ@}Fo0-f*RM}_x&AsHi(`_v
zORYBd1!lnfu>{M7w=9=5M!BjHj9{0RNkpF5UR}2Rm>NMuwP0coU-@~lVHlwn^^9qM
zZ~)Rc{3Xdamd>FH>u%}vuMooOJ=hhBsT><uaCRo%4ImBkgnL03#r3YLKhXi9EK=6X
zK}gv|&z2R#-N~~Oqw!veWM}$l8!L|xq284IU>+_k_XEl*cV_gPvF)K$HK${Lc?c1#
z>PFGb^^}lKv!~!nTc#sOCfn*7y86#a;IXWf1VG2DSOD2)szccUATp*&5^v7r@g%MC
z<*QG;cd4Ql){|kS&hat0`jwQ<_nYS8GNAy1#N>jg<**xf1<ZT5x5sc#8=6-}tEz0p
z)l4T=y8&A^<>Zk?m_G~Tj<jYUjTj4-i%^e}b0?e|U*ZnF0uDvJfwT|V)|vKz=}_>}
zJ}vFFO6KD;odCAiRmWev;oU^K87k6?De$;f2J!G~M+t~+J6^S7*{5P8ZAq9S@5xKv
z#@|}@D=#(tHO~sFS?#E)>WAz?6)UwuX~=VT-gQQ?h<qvLtmb018u=ZL0A0R63UW|Z
zOdtS9ZSV<i_o6J2ae_;MHk$$axvM%fZc7A4<n>UwE-#Xm#Z;_50b6EQd7Ndu1WXO-
zikSDEE$d=P=6vl|EpRB>lix6BBg;P$EG;;e5R8cl?2LZdKDNe!i!~ih1=uG~ypzLh
zoo;J)iV%Xl)QAC(u9X5uGprHzRsC3G>mA@2pH6EPv?x)BDr^q2I`m#-$vA~8y(NUZ
zTgq~~p-QcZd@~AABJ#R}KXx6Y&9Gr^pjZ=|WH!mgI6?zY2N=QCxyAA2%|-1JE8PE(
zgnrW&)%uPeCzTF{+m`Km&s~(AiG~L#jJ#~A#;KTxrHCj>8>myDst<LoTeGXpTdD7=
zuGA{gMNyqbgQ<Mt_HSKJ*IOwNmd7LoO`(ov2PI_6p9`v(71Yg|q&hB$i?0wKo;m6#
zoL6<Wx|PcKF<1LJ7=yjn6wA{))(fKeb(%KFAknxh;D=}rO@dpD8PTF^R9$D;orED0
z3g`^S7e>-0$RVnei6B((_{#ca^9qc?Y6D4m%x#9#i6D0^!Y=uvUyAD~i*T}Qt<G`a
zK31E^hrkmtcLuBY20@fKKZclrnx$6LFmQoo@rQO7cEQ!GI0jYBML}}a@DdvUdb?l-
z3FiVst$MP|EUE62OmJV;QgU0RBZO_1_8Plxa>MfrViZEb*D#(B?*h6nboid}O@TqR
z-jenw&E<Q+T*TC)tCocK)~8$wTLL={E{tq(xJ85lf5R^1L%*Au_MrlPu+dKGMP?SG
zAb(l5#H6EZOw^$%o9iVi!sFkgGx{;-Wt)1NJEEw!C3eZu<lx3KzKAIj+il-|6k3u>
zZ|4x)Cw=vnA?6aWyS>^)?z$!xmoDlGiOxqmHq9Dmq~i>RynKzk^ryjY7%V`TqEr}O
zgr8EFGbW-32{PD-dggh%STf-DI6x+Ci>7CDbBv(J#v`w7l}SOnsM7qCO+-F`i^3*L
z2iP=d;nDc>WJ0{5^9U9+DxW9%mjG3Nmg}{g*nsJFdNEz(joZmQz!iDmaR5L`aCP$?
z^pA!!3c)Bngy>+3J}w+X2iy1+Bbjx0TWo2wEFSpuROmV0KSGzov^jMv^fjyp<Tf^h
z(HKDEO4Ps6Ct*?{nSYB?V-W4^JnKs3Av;wZE8JS6%2;Q8$-ayv(MJV2Cdhr=1^8`)
zD2c3!^(ujYE+g(*8`A%>7BW_cn{-aUm{_3=n{3EK$dr~^lKG=?iatjU6xorbr~NJX
z(=>Q7mlryGa~b!Le)~|xUe)STy@G$wyv5yy%S|)!#kXvhf1_qKbi(`uFnUmV_Je6o
znMgwmObh!co0nbaQDfU!4S7vZ%(d>Ya`{4dkxCs1N)C4zZs0Q1y0~-fh)!4O$#TBz
z=@5nG{@vV`wK}-DO`z}>icN2VNc_0Tz(?UmP~gveUVqC)=#H}0SdCe4AL5(Lms+H%
zr4ME{+dO~<FtWtHKNv3074j{?9qWwsNvnH&yo#H0Bq_t^DppIbO-l@a45-sdBXy8u
zASSf@icPAd3CnF@9&jyc-{hjX%pr(d6rwI~+sIJS(N9o@I=~U;^JlS>|Ka;3a#fkS
z88_i85}*r%ntKU!XzHvMM(pxXe8Ct`lNBR%ufyX|S=kxV)_rs6g(93^-le7CS_2~>
zKHKDAq8ZpOoaG=Qk9n^i=k;rZAt2AzIq;H7216a=koYV!RN+JoHOTx+LlxdvV;oZ0
z8n_x=QWEYVvYMfnk4YwqN3w2iWfsNS{7ESv+20ZvH;+Ca7c$B(&C23Mj7`1X!Qs5s
z77`5*LNQw?7U|ly+<WnP@FLKcJ)fFCSLdR@eftMh-pjTI=wy(1#1J9QDLn9Br}wXd
zv9KE|sz>hW5EFS9D>s^-ykizySlp~rlSDS7W=J6gQGH^HW-}CxD}+}sD85UNiCv^8
zjg!}7(_9+=&{_D2cyDb}2$8xqh)}8>=O2p<;ApV6ZUD(Rv1tClH%r-z8T^3#6Je9I
z%KV7$v3PFIQ&LrBNTeB_*tmQ?a>uhhul|7V2t8`Od|Armd?}6!=@%wf@}T$<6`j<y
z)bTkmW}`K{^}#{e^jvWlS<B33UC?3eAkQ&DG0f=nAgk#y&{95LGgUEa@(>};vf7SH
z6fsyP<}n)c>A}%@-?l<Lz;ACUN`F?gu&OcjV;Alfr<d}uk!hJ*7KD_#c}6YU2RgO3
zW>rSe<V@AiXj;{aLWVQ`I6)xvEnYV2gaV8?w6IFg)<n2HO^>U5ybL5{XhL05w%ip=
zU%Qi<rGz(P7_4QRDSx{&o6ShjRK-8s1vx_(&zAaqph+R&)1U(Lc385IEC9q~1evVZ
zJb%Wwry7@iG(T?aih`_*gFg%QTwZsn1;U}+yPPtt1hNl^G|`tJ9ZFxbbZO0W`5f&@
z5wNq_u%K^J!#mg5MWa*SWY$}0WU}(8S$cA@4qTQtXXQ#vPWS5k!bvl#sCT5>tGx4u
z|IHiiwll**&wD=8!Q{i1*>cJW>(HG;=>Te!qp2jRw&73k5ng=~mW=%a1|*>-Zc0GP
zJ?mL_{?x@dAUnZSq-dn+pn2KuU_!~r5a`*Sgq_4fPKY;R9W|60?BDi~-08@k*19}D
zG&{=JG7>=oyVE%wKOg&@0iu9319y<q`njO7YKJ}CXACSPV<mwern|k@%Pe%Tb)u%v
zKcF4$bS`D+a)_gs%U7(dP#^Ij;K9ZmdMa?axUa%8Q>_;n-P3%zxtF;9@1?tP`95Y(
zRCbo#o_FHf>1wJV%;9dqNW_{rI;7xRC8sNeft&jV5cxoOm!zfNtv_%v!(R7=$9>{A
zmrGCXx^3IiYEs$hZ+38I(TcP_UE-mD20|3QGG;rUu9B&W%SZr^sbM|>X=0hZXiTV4
zuVpFpkKz#%A4_pxvVii(Cx+OSF4>8&%G$+B`oOPWr?5;`w5Z(%hbEx~Q6cS!_JOaf
z)x!>|FuL|WkMkY9VakTh<?Qm4G=vwz&MOzDazCVrSS4m*nYqOC^!QF=b^E{pMvJbJ
z-wOjDr1=p5IAjQUem@sF_(~!03>~uiwAO-jaNgJM+{%7eakhiJnmr@dD@L^FHz(kQ
zILI|Pnijn^k#09rNnrmxIe1GPkhCOg9k0p6E)cr9BV@Q;q%Wu-$}*6JP3`x#@`t*3
ztU8s~@@%xgsQ!WIl%)J6c2@dBg$j?6ysnavj!17vz$pI$Ri)eT!#vcLW>%W+n3(sg
z2A3mFd{v6oHuqJw$tkAcVJ3^7OageLu`ItjfgU{^>>rYl&Pdw{kx3Q}V}CR`M*6Wr
z1z>C7<Y9+17uRK}C}sloLii=Ru~YVRlP9N*Cu`&V()hVQS)F89Re?U3Xuv~(PK7RE
z(`j;y)4RE0baMuZxx8M>t=mOnXpNbt@&!lC{A#rS3cHWoqFo-I%+)S54xMvDJ?2gy
z2Z;1*Su}}t%G>$ZgWBo0{zIMPK<^v$Wzm2s%fii$ao_;>N+h<mT_m%j{qDV`<S&QU
zoUj*$4--Q$fWPl^1R~sdHy<njdoDCXu;%4=UA3eJ|3ER>%&JbC@*T&`z}fk>H8Dwv
zP6sEQLv{H+KHTeMp$ONm5h<H6DrmiN4=rZuBH=N@)DmBQza62YeHBaF#X^8Q50esS
zp|>eN!XElMX6?Xp@JMz(%hePF1PAnSFC~Jx=e&<^0(}3#Y_?AogGcm_hghHKmT5Ar
zDNzWI1qn!{?54h?wm6pYUMh~#xRke{k8#B6)S-dG*i1~j<38}}<Z!uSJJ}Ix)@AQX
zI!Pg2p2GlTqHbOqD*o6hhP`z{d<AmEoVsMzYll7>>`4G$S-d}Ks^enZ)z@yhxO*s(
zMH@L|q+<d(G14os<gR2iHP=0A<O|+6#QM#|;zaRq<KY%|fACh;Q5xzAIvGbcJ`mI0
zkW{6+MaEs(2z;^zoJ^VKslUwzrm}06Ex)bTMmwt2=}XuZ#bxoDw&_!5kHwuC*w=A{
zGKTr<n(^BdRJAf3%x?*~KOw$FlVDT?U#4r(4^D>dQ$`s)AJjABM2{LJ47Z#~=#JZ3
zvR<YAThOnrHC!*RxH$ZcjwA*VvIhOgag`H4N~IBhND9fFbu?Q)ZdTD(!q&6rsbd<A
zMHZ-a>n+m24VTBFS&Mq5_9jeqXsn6u;X|!%o+n5AwN4SF;v%@$5DWYJYw@Rqo=f6&
zWpHd$eaVMtewnSM;HD@0%B1%J^2X0q{$EZxzy3|{kLHp_Y;&f@H?FMJH81Ze1h3%w
zik21Fb`xam#v39~HhK|w$58Mxjod`{vCkyQTo>@_@^=P*(L4$U_e9<%vX7?D<xvO#
z>`iQG?l=wp)~o0*043rt(9ZsN)Qpfx=J&qdrBVxc>aVZe54>_z!C##e4N7QIW9n=#
zB`=ow7h9jiRHl^g3cv~Y-tQh?A!dKG*_Lw5pX=K<#%10F8#F@^NAgF=1xP3$Z|8OS
zOZ}nCyT(Updc_9dcJ68-g~*v{{xSqq-N_%RrOK769-x!-;_yKRzlg_DpDuiMgwbvE
z_T2`v=E=5aG_l&5`)rOuJN*t~NYOqH9RFjm2wHKF3{vHdk%wN!oZig|>G*~EIax~I
zbaEYM5>BZczWkEZ_Hx|6sizi=*=~(*K#?rU7q<fVR!3TWg<{V+9})j-3kor!7)ZRm
z;&5_{VUn@8;FtL!^Na7tIR0~d#5d`??;RT2dEjAgh3l|it3ICsS1z<ls8vBjhuv{$
zcDHvgXkA(r=*9KhG@F$zWrsYyW~*heFcrLA&W|(j$uWs|y|#bHTR%W5J$X>}_f3!k
z0EDnaUWoFhWX_QQa;S3j0IziwRp<|vi1rSpOSdH5ns<fgxKt!RW2d}{K99!H(_0+F
z_^d`K&o&Qsgwhvpe$bc&-=MtzUZ6<oKix+?Y<q83yD3-2de2(X{btHJRQ~qOy>`@x
z;iOhqifJqe7t<f`dt3gK;wZpbq6y%uC;!fD6oXla^DwEL?>0a1pk2^rzOCbCZItXD
zduvNeZ^v5C+@Acns!k3s5d=NzxVLPCdeKP<8ZzjH|5)qHLhe@}oe(N1s#>KUE)-;{
z1w3R76YNkKEbQO+kF4Et53dubGFfZ%Y~IfK&lM=BKNlITlN-M1mcNWEE9<Ajdr>G(
z6^&GME$Idey)cpB7t5;3`^9-T5+ca{<Za<JSXWtNSFbjbob@d^s|Gl0eATlRbsil^
zt5dwfAq+v4UQ_Nb$0a^HUQsgnP9r`VEfKDnbpWN{-Z;20k)@NQGHw|qPpbD-Wim$s
z8UO%1m5QC(B>kKw?kfFYWm*G#)}GIENtEi`!k;<m`>J3)IueAn^2O^aEYl<T^Q;z5
z?Auf?*bH$qLajmNXj9X2H}@+~kDu;}zMl5#RJ!fMmRC+^|HhkM@_jGYm2?`mQ%9T@
zu9V<#o_ko@%%oHXK?0Vr+p}xKZ|jkNvrK6GnD}x$FfHL2|B{aE1upYq%b@P2G0W9h
z<xO5GL*Gc{<V2xCUVUHCS&nc{-Yf{N?l_jjfnK`Gdrk84QihhtqYA^pHJhj2Kh~)8
zP2{sK``6+}?po@(L@f3{tTUp`7oy7Vmat`(Hr^!C14210vr-N|m&uoO*S>WnO>96r
z4F9etbsGXn&G^;sA!M`jbNKU?j4oOaT%(5W=`wd!W<JTY>IU@HkNirEy!%Y(dyJ~%
zzo*Xw<*rgS6U)DU)DuLi|AVUf_4YgT3n_CES4?rAZ-{d!*ycKi2&rCJE*o)UP<s5`
zh$d_G7Z&kv=;TCklf3uDWOcJYAx>MwBBycfYxKp0i`mr2g$sD!#oiVZS|UGiNOIjv
zLf?ld-MyY?Ya`6N$l-~?Ei(pGA#`E?;eEx#ReNZlJM*-!IyiFfqd48E+vm=*GoSZ)
z{M-dv)7sn?n>g@l*kIUJBq>U3So+Wg@1dZs$gW0{mn*ZYyI85QCNDhz4+$?po_K7X
z$bEE@^81)$hE~nkVcre~yf}`IjCj9#nqe5VAwY1xB{6Ix$QQno8HlJFSx%Xm2VNUB
zCG9p8G_LE)20SFx#}+a#X3g5a<@nZ#@<`csNNEE2E9xJ$%pQASR=7poY5ZOy>SLFT
zxc$x`Dqn==GIfhMuiI9_bWNln>VDoa>a%0+zY?rZ^htt);JS1N?7knhC0C#5@aqw6
z-J$PnD9KZLUMi-?&z;GNSpSseW=k+&?bb&>wDgg)ysV$PP^O4u%8eW;l>N4$>!S3C
zPed5wfS(9&^7Fos<9>fR(COCmWQ)@t78@T71fVy_aj{r+!k$7PK4?PZ_xn=GkX=+3
zV*HMkkrwLW%KX(td+;M20eAg;u>N5K{+v3|OKpxRZOR9BQa4i2Lh|hLVo?##;+CXr
z;-K5^b9&)J-2AlOh_rckuA7e>1OPZg$f=uX_N0TPUxt0M4LwpZ*1?fjwCdPda!1!*
zCLaB6N=tjuvSdO{F8o{Uwu*}KLhCw4DdsbaZ$&ZZJ-?>1hA@xo;X}xn;W~sdt;)K3
z;xFVf%TVPRp0I4$NA9GGB9G*t&Y{gk;P!xO%;3t39A*-Ynkgg*+V}l%>AZWl?m7QA
z?s|%2=_5j?E?;tR_{{bw?4&(2)1JdbAy7W};O?+ZPY_o}L#d5H)HrRKVqqoG#p#_7
z{@lAifM7vWTGUu;W27elHmRp=NBI=)UfPtwHYGz%=X6>!JjYW<<t4B5hw0(`!n*r&
z=`|!lMWD^ebh+uj!2_NYrwx6?*$4)UmayGpXN0k7igItfa0VH!`NnkEMc=|lhR4GB
zH02WIPV(%znZ?Nvee5&Of0wwcYCU7ni*J5;yGWfnsClAHy|ED$`Pk-KKE)u?kMk32
zEt{xEt$Pl;9UE;Y;+N2>+4h+BD%;y%FD*#wDc@WZ(@?qfo!D8n7{oj4Uc>*_mk)_3
zEh5d<9)>b>uQqFOcJSTR-;aXF&#TdeEQ>dqx-j9@18j7TBeW3#+6`~1$IFqZ!@MQb
z&TDROJj42tZsEKbEA{#c#))2UDd9Y?o>Hc6>EZGj-p%Ayi;+vG@?rCvBb@2{m~R^i
z;zRZGn40E%vRhP(QA2`q{|Qxq<@$smb1K5tFyR;7xaNq)l0%jXB87`)ty+fa5e0op
zm*<N}Np)T;msMJaVLwKHskNfE=j5oa@XRo0QA(rt+Np)n2#KYBMS231xth#H5nUb@
zSLYd3D;wj@a>GTWjzWsv5qklbgO|dw2P2Kcw&ZYVhaK9Mm;0yh3b!zjf&W+lT%rB!
zvrLQBO_qtED@}k>$k6Z2v*{;m?zOuu<+9f@9uV2|kE%icn$jk{Lys9nl_RMg0c{#0
z4>8@x3ui5Zj>iVeiS)$s^{>LftmO7@T&G;zXh)Qe9S(>6?4HU*_>{Ka9#+kfxnKFH
zVlnyB2fe!g0}FUV&QHFf-v6HCKU|VYpe0f4sttQRD;iK-grmR*ACrvjRw-#Nu9b|T
zHZo~VSMfVZJ|M0?D6nB%!2r`w+fHf~`zccVtIl^~d$(JJP*uK~?{l|OfkVayeY4x1
z@CgY)Cu?Ow>jpFH5ptf${~&$SVv>%orii~%e6i#*nozN4k3MyYta`ZHRf^JLH+UNf
zu`iB+%H}KGpv;Bs)lCh5Y|-T}*?4%qwx|!-j69t<&5Ih0Ko5bfA0m#G2fX+=tu+r2
z8yCMk3qhuTEFO@odrf=xlS{GMsb<b&)ewI3nK@qcEVILlEs8dOz0{sVS^sS$r9G#F
zc`$=_=4JR_8tVKJKW(_0l9=s`g)s+8i*C${E)c*2<8|5W#x=h%{LLxYAK{v4P+Wtl
z^RzAVQAIUut-7Xc<~($})s0@RRKxScqX$s0BLsDk8Yeizp-GG!S@kgmgs(w%wx>R*
zUPLeb&`sTl_Yi0MVPN>y3a-O{;k6|dC|O@?c$yf<b)kk>x}ldzIz)IYe)g*gSncF4
zs$8PbfB2L_)%V{Xo`E~>eDCH&RBI}GRlm(LOEjUpMj~JkH{YenbP765xeOs)CS&;w
z?pQM^hY)5=+96O>Z5P?;Fgmx^40XRp3*}T0H4hIVq0Ot6p5ygnd!#I7qE^2nD1y3<
zLqmgBY0xhG_&^|549T$0kayAGnFw5Cyb4Euown9JR0N%acWlpK0+s{ArN8pQ{Ogh4
zq8ndW7N#h29Xmsx>2>i6_jJz-+RHxcOYSr4vZvQvH`s=!N9oaefJA%r;dnn#Q*mHB
z_#=GHg0u{x^G}B)RX-Bx_z-L)7J+$k!t}D3iTlr8^P39OjsPpcZzZctJPc~6qz~7u
z{W@@o8((cPlM|bs^?Y@3jLJW(xaZqkJt(3?J3ZkZ#^1rY=<_3cf8zd!DBt%zHY}uQ
zH2#^G-ki_m#z6+t3X!yrqcS4{x*X!dd$>p2-<*gMsD3{(>kG$0cCwLUFO?fM^bl&R
z;vxZ4YQ?b)e<LPDMc_Iox^7C-z1&%TlD}QE7nQTjI(WJ3H6eQUe4DEw=__9n5*y%J
zr&t-`+>G`-(?CUEtZYh;(qfg1qB{8vb%`dx4SLgbb)K#3Dx!ltQLYa;HDi+IZ1T|O
z&`-?Lj8cu|W0NFQuw&uh-99VHk%#Q>41yu%r0(CfTpw5Um(nj`GoY>}IbRLVEN=SM
z9^p8z2m4*xgS#)6dB%t3Om0Dj2^ZVL_#2HViyhjfJQvynqh;@Kn+Xq6(mum0I6vIi
zCgzKT&Ml-4bXVqw9UHJ@77s<Ez0-S_zfWl1pJ_7E>DV{i6RTP@x+H1Lzvi_3D*3b`
zJ`8Tws<6-v9x8Z?KGl3+!Sq1RBIjDUAi}Ph&?f}|w)hQJhk){)7eeohVYzv9secV?
zzqlT2TX|4QXw~|p-FIyc>{!$iQkpFI$6_J0BWSCm4Af(ZXm&R(jK(T6U=B77NF{vz
zp^EbZ+~?lp5}Q(`A-(9~txeDMT$)>pA#45i>P??C>yii_#NOw`ht#=dPIa@jZ|wO7
z-JXtv8feq*f_nZ@n8(BBCM$J%1DPl_?7?E4wvjGtGT?DLVC8PBQFzkM$!58c9<+SF
z(Py%{`+j29jssd98H$=LCi`h0$S?=xQd3FGIJyLdBpnOmz$bV8q&`(WhVaU^X6g=|
z^th)q7G=DZa)a)adI8XbTSJ#=A56;upf9hWhUk&d<;;K0Q2-|qqok?o;H-bty`QDy
zP2hl%7u+Qln3Z2<7%O?f50Q$uN}ziGkGZ!Fi}H&WMu(72kxnI*?hfe?5Co)=9%4X1
z25C@2x<Q%|M5JSA2}Me30I8v*B?b^=0IB<qzu)=J`R;T7_?~m`^W4Kf%)4{#Rqxts
z?ftIjn~1lMF9;yYfxi)%vBK0qE2S>01GiTQE_goy<Q%+22($`d;3_6m&PA|r!{@m$
z2J-L9Y)vX)N_jo-j0_{DfUX$l9}sT(Fz+tRh+km*5fZ>}m@Y6S@LvEJfeQ}IaE%Q#
z)dd6Q;R^~R@RJmH$It?&HRMB&H{ZuZp<kz+HE?wH>)eM0d-<4t+VPlB2^0=-{LqTz
zXKeSn<l%%b>VBDPfuUNxR4!JXV3{`0CAMK#^41w&6NbEm#o>!1ug_QCH;`gR7SaX_
zIyZ1Kx1^3J(96R0jaa`u22u<}6QY=>PA3cu7AXjgY)T@V+FKhc%i9|r(pZqvQ~cRh
zM8=KADUpuBIk%DfeE}caCKrKk+_q=W6qNt{k)kbXc!kJx0Fr|G@CLM%1wYkk%f5{q
zRi{DkU6_R2otUG*thqP>9X{a8EJZnZ{TVU(^N0c_pF1vI$FPXV#L3qqg!na-SlJ59
z0ofw8iY+F-SAW!~FVDum4@0@j#KK5X`&^vx{E?Xuu%9HR?14skO8In}t}B?<C@CLt
zU7n*ujB;SFbA4+N$cTzU;2p;0UqH0i65mOMkUc0Wvh|`6eiKWCjfH!|^&p3#*iOk!
zTo`$JBlFF3X5n++QdpB{@N-2`;rKm5timA2Oz}RtV?{!g5m(xoq>8Lg0oHD=BOa_n
zzo`+CIa1mGWxL(2o{y0qZomHY)OBy6IUv_jF4@@}JXHF@poAC_S{&8n4yCV=-Mv;P
zh0KW!y02FCC%S(}Sli*B+T?q(^oV|x>T-qg=lXbi-+AJ--wok|CkXe^*+-G1BB_fS
zr`J^v^k0NJLa3xHEbO}UJUrSNxwl!x>OKi?8<3+?0y@H>9V~H>>QP;+mt`|lRab&@
z2HnBS)CWcf`zG?E4jtD+i2aEnHvK}`KY5l@$`%ss>-Up?{oHT_2qEYH+M@o}aH>_&
zmoge;{kGYU$bVtxGL52ghXZTmqxTX-%+3bR@xb!ABq^lcpVADB#{oHZbKMV1s^5}G
z&>#V5<AqhCq2dD?5{L&_<cE-kF%MGtDYsP8_`G*pA`0Cf*^k#BMwj?)mS_2dG<>+S
z%fqL*`=REi&gBC*arOP<mFH4G*1InY699P21Mz|A^?3Jm#D405Fss-7MXu&>pzt=b
z9&7WZfgg++1D9^}`?<8%j!(dEG6}}@)t?)=Dg)}IC>#-Do6T3b5381W0T3Uw5_3Gr
z`!6t}`<M|5=8ibg;?EAi%%+NMT)hvTw`5n{?OIo6e5)XhU;jE%qGOPQzPeR=-Q>P1
zjoTG0m_chIP8B2%9~*Q#Q;?5B)1(3Pb^5b6x`;MSNuWXYvPOT(-mRU)GZ~=dhdWLl
zwKVT5%g*uu0FclhX=akYP5^*6W53Z%<$Lc?9EqlGC9vT*<6kStn*<iz{AhDChp!T!
zkr)EtvF|NyVeul!jw*6pe+x`2^2X|HJYbFWx2zc$rx2eA_3f^Rtq4l_LMB`_OKzJS
zg*7%FqOqhviXuFEObnTm+A{EdBB2d-9`HI_br4~;Y+W}Zgv`Z5TS#e<_2U3oa>)zX
zJjrRF)q&!4tv;&)n?ZGA6b1cjQ8g#l8x{;iF@!R6{Rxpx7(N_!Qx05dC0CIMcSJK4
zZ<1+qYE^@paU#(NAeseK^#yi(vYQ|QO4R$p1)Mb?&kw(lTqbtVk}wAsrs<wle`85o
z4(B`90w#M@n@#X9TDF~z$=>)5sD&0s0OB!FWdQG4uf$Ls@%n(hpX#SeGHW_O<G{H1
zC4rRY&wa}C7#7>4hUFg?5TlIQ`U@1pyd)4#V;r?|kHM^01Unvw9^SDF{tv;dDct%h
zfw^+VDi#vOT`7Z9b7e4jHSI-~KtAli^|uVj&s@4to%e+GmNJ<m#{h{~HwSe8!a?(D
z>oBr%NSzQR)$<%ZV0AVS3y|%6^=9w(j01An6wHVr&!>Cv<>OXFC${!pE55+2QieQn
z_q7sCewh}D`f(YZw0}_@(~n1vQVa>ukX2p+zGs-b<6Iv^27s~1mOG$Y9Fo4D+z)v5
z%K%1ByuUQKd5*{=MQ;CIvpCSWRwsuvrx2&JCOg*~C<n$^W6M{pv*a0g>tJXoDvi?;
zZHkusSK<VTP=P>(<FP0-*>mlQxRnlB0_M~UC6AwoPMqi4U#>=L{DNMRpjudv-cG;L
z0BQ2f0B~Bayu<|pfJwmaU(C{G8Q>U<;zcS>0aApjr{d(@z{lW1PEYjBrssM=mjS>H
zlk3o0mvcsW_y+tpG1u|)#6c}ixMPE=AkL47yyO3XrY9OL^}OXtI)Vj9AnVJWCE+8h
z0Q~M0R%F(#kb!4haxcKNlV$3Iel2$eOYK7^$Wg>1vKJMIg7fWicG>knV8r7Xk}+m!
zVL;Jz9KUo-^4xB=5+Mq@y9~$?Be#^sKLQ2uzCVAhYGQbeVP6h`x0lxPY`o$?pDWop
zA_3zTc2>#+jNn<+=YIA|vHbm?;5B7=pYy=1t}lGyiqm0_NP&!0zc!x`KCNJ6l7X+0
zq`6}^63Z|v!gFz~G};G#P9n$}a4_rq%Bt=FsLH3_cj~Z%2k$GkRcHg+T@k*BfuKbB
zw?Fw`QF<n?wfr8?Mhfi9xc-Zg2N9p10Jy6o+K$w!#{Cb`UQx+)GbV&MOH)NOoqfsz
z8FG6~F#oP2im!fthUmPXwv<uvr&(>{LhK+K8sL_UurAI5L|jGW10W_xEC4hBATc(z
zQv!iB3r`rFZvlEp9S|unuweDC>}Hw$7<$d}2Cf_GNVtMO3@-jS&ZIp5ocEUt-)hv?
zpSRQ@Bry7#o(rxfw^yfsclg7nDleIb2~QQ$0>H%MoRi@A?-Wv57D^8!UfV=M;}%p4
z9gl#eIsz8bqwr<K6CM2#xogID%+v$wfW}#OwxFN;3DBs7h7nLO^VtrR!LGM5VbM+Q
zt_@WxbN|W>2_5p{74R{JMQMdp1dU2`6Ts?0K@5R9d|W_YSBU_yeb(Uho?uFYHkg-@
z6P^L6%x{1w>Udvmzc-aAB!>js!ythC(TRe=2Sf@Vc>F@ILL;D=a?z$7T`GhS2ml|f
zU-XT6xHOQB7|<mqfEL7zM7crDMk7@assKOvSEa=>AZz@dLl5f_1!EgzHdM$M!+pF}
zT>)%P_*4Q5QtI1?O3C7lIcCifO@JI)%z<I@`4*7_NgLC@g221~LCw(Lch`S)6-77c
zkwfxtBZ)ZS#sT164!Ey2*zGrBS_@2G!QlD@qq+KUxs-1a5krVkGZARVGGa^-9n~x;
z^?g1#KOBGnA4dAO-P>+Q9&K9~1XdN@q>xQg)$>npS7I1hXf2`%5uyh6jG#FZ>bvY+
z>JZ?hj<Tf-DXi(XEv|pg#=%MLSPU+^d*9sYj;jk#Vn)G@ziP4g-1qo6t7oStH|L>a
zIiAzRpzf-AAA`kva6;6mO8che2w;;PKAARt)%FlDN|~e4!}U5C`!&Sig~0l@EMTGT
z3&ES?M)~Q1!u{D7qa9;gs~wUD=1Bo%`#YVIW~bw)PGkJ%OH6I?2f97fL7rSzv!|PB
zjHey`JDIGDPw&sa7@Y#lPEgnVLYX37{7++mzx*uCh*y^8Wtqr|YgyIvn}-0Me&#Ee
zRWXd>vXs}k^Ev|U&${?5nO~-s2kt^W)iyp<MNglWr+#|PdmRGoX~Uvd+TNzY>;2so
z(o3FB+xO@4rh50f0Twh!eL;5t@2U^N@uBVHv#;<=#w(z(d%%3zFZGkM%jY1Zfw)s}
z-Qx$BFZ(bK()gnF8K1_4P^}zKm)@sUo~hA{uCksSBtRA2{ko51URoJ1zRDa<PsX1Z
zb6BO^>B@`6phD=uX5Xt+J?qj+_L$_HX+QnZqtsXSKA2cQd{}W+a*1V;Q$Ep84wu)D
ziB&lxn-c#H=sMrv$>IMY&273a^%Egua$|Naw~Ifz4%h>LR37}g%;t94>V^B>O`WVJ
zMf`4O*0OGCbOP$S5AQp_OWka!sgh+hZ^E)z%+I<rl8(VD-Gh$}pGq!QBYb(QewLFi
zeO<-ZI&Vn-JK()~o0L1A)?fD$S1uctm%j?+0E-D0m;<^!Bj8|Ib7edjImuGh%tDWG
zFl<=@-xmON3-L%EEWF=FrXFb>SCHFm`B#$$*&mETCt|+{k=zunhG~9#hZP}X^w>S$
z0I%v+^aE-V*9>ju{TrE_AL00rM<4f4eV@iSD|<2|m&rjOYkm9nDOAprX+g+GC+nXb
zlAfsAY5?xAr^x~WI<d>}s!*b0WE0!V%QD40EezxRD4td8*FwtZnk%Xqe=`^2@F-4Q
zICmWesz9M)&`gfVp^Ir7?pXZ$GqYn;oCyLY7|b48*5}4QN?fq$63>gJ;bT?Y;>;*#
z0%JI(8JXWwQZ*lOVpaK0nmF%dTUyIwRe(pA80fZC(72Xal5v#dfA_;>^(}+DqWa1h
zrl@hKDNbF@3fxR6os=amU+Mi+Ld44fRQzoCZfZSYE9_$Z@#g?G-^JVH7Xqet0mLbF
zal~bK$O=0y4dRM3`QILl<m6|r!Enl<cVkLRNyT=hf7$j6$Y;Oh;JBZCZ5RRxrc@{D
zxMr|#R>g9;vvGv*>wFUo5d|uKgrXLu2J^clYnX8rnfxa%y|`QeRsw324wUhIwN@-Y
z%gsDT)?(dm&wXwf)P!LXB~RA|J@0(o;~rY8xv-cZs}wP7$3uVwppN1AbdE?-m*ZZ)
zUUrF>$ilpJ4+V?^=1{PdhMUvxyF_8cQBGYrJolqc`BN1P3(E;gbZ~w=c`USi`8}Dm
zvLi8#sS&etEiis*ntG=rE;}^pefZKhV#hD3+8+opQg{8pce`Y;uJruAM2#@x^IQ$R
zZ-QbN20iO>HJUin`f`~uKF3H`l9)cmVO7O&nA_1l?{bfzN9tN3CcWlWKBoZx(cG#B
zmJUz)(MJgb)nt<tqx6?IGcfNWVFP{5wzTY<10spJG-s|cSwVnLS!Fyq7Zn-}+@$%<
zDLf=NPZ&wm0m}WzLR)JgU76;7l8V{G-m>bHIGBn9Q2dA<64rX+V-k_&%}=aRm8FwI
zxSq%YRFKy-+-uz^r?$6wN|Sj}eqoZ7kOCANR1_ZQyzgkDUeDKF;hfkJN=YSUz%amg
zIsf{^`dkw=C;vx#taAR}oWV$8pj@>q*fcZopi*hZyg|sA<v<&$Sc75b)g#x1L2>6Q
zP5uv5#M4)Q3WM%`a>a1$HOg*okkrDYG8~VletWk*y|Nio?Df`Dd+-cS?Ji#V;p&<T
zb;9W<>jsn)Ebqh`Dah4-fw(TddfF<T3?H%t;8FT%;#^X>tKxO``ZoE*H9_7ZEtwV!
z3q|an#HQz$ZoVe;<-a|Zq?aRAgCY4VF~#KiYF6QSnhgIV&SC`e`GiUI%r#Jh{bDZt
zn`aPD!lhBR(U_e`aE9jjL=(WKY&^7u?~j()k9%o1>Z}f?+vp8|g|P{tNI$G23g1Fb
z<c2qPZ?Yt`4MWtw1I$D4HO=GRX(fck9~P2c9PYx-?g}sn?z;(pL2<!h?vvC`^Sp^C
z?*MjEq9~}*>hG-L+ACwVP`POSZNYV7FAoNQ!L@@v$$2Zf{K$3ip2@D0^>rrju|TUQ
zw{7YAe(lDZ3iD+8X5PY58zzyU92Vmh?iCyO#R-Y+JGhZneuk$#Tk8^K)e^#8&~hro
zbhVY8>#U}#s_O7)3NR<|gSd6<`<`-7JSD78kA4IE;U=F8tGo3lsa?Eb7217go4R!$
zQ>=+_&>JWwJ$oEEP1144yX3ocw(0!qza0iY>l|_5*aUpz)wJkT?VK%KER2lBjD+Z%
z5|AofGFo)_^3*Rte?mViuPgj-7nLhYdNfM!3quy0d}J@(xp4*Z9`N_@aY*ium{38u
zX5Y_ca!ITyPX{}Jgu#l=SD0S$=q1P1>Hfg3`5+wn^-t^xPafs<5R;U*=TcBoQl=F2
z>o|NQWVKjv9ypNbO12=qe5$r7Y?vv%kamxk?)f*SzT|vVGkMILHv^R|!gLq7iWS0}
z{S_VDd{!J(6<pWFjrh@Myg`yb9*L_&5X^{V?V1G|D{fhNuNP=zlT+lbb*1ys1gIBn
zKPdaB)_SY22$HO(pC`4>upkQGBWQpgbI0xlb$pln`9(Ff7F_+8;FNwY7J)YPi55JB
zm^nDEfx20<S~hZ{xKhV^3U6<bv*B2A<$K%1&N<cS9)<q9Dp!zD>u!O3c^|DNP>q@S
zCT36<rzNhleUL))*oWj5vV!Dj#+PMyg9L+HT!zCZqrlf`KV(|ob)k3o2nuwZ!%beT
z?~#rBsp(}ah<u_{V8SdvGyFO6cj?m~@;Vr@yNZ)#n?@d>Ch%w6&Zfs`<9mN8ZCUy~
zp5<C5kAO;_CuBH$PDGSr(XQX$8!!+|B!<StH6<k8QK3tQZ4~j&lR}!qk9?{>2WA2!
z8GxJDO4&poh-W`e^DhhG>>0!ld0$zKiWM9Q0iQzQC;}T8N>nE@gemuR;MDz1;h(9L
z$IS>RND39LK0J}w1dQ=W{5W2YT>10<A?H^e-adc)wH@*2rw8M;2niNHw}qE`lt^oC
zq(|E+;s;+Oro-@?oxA>VY|rVVU&eX}kAFFHeL=kV44T%X+#cHmU!g=2Z|_)Zd{=){
zX)gr7AU|;ivc&=mH~HR)Fju080H2@bXDHaRq^*eb89RUQPua->dy*@Td;Hdi<%WK(
z>*;J$i4wX8j~+1u#{o+;x=?K+!?sP-N$zU%|E!5)uRZSi4w5nC6l)8oZiiiw)UsC=
z@h{C)Kp~(Hi&9JK`X?uPV@!%I!_SPsfg`s8PRdq)x^BYo`Nt97ksp40JdpFvr^1K(
zS*B%}N--9Efa$TrCy=aiG1+(0&1B1!TNJBSOg6`ra=#0@iPWQ-`uRF`p8%vQ9rvq$
z6L!|Sw1_StRkvkQ%(^Yc>+n57JOAgmQo1^K1tHeAdRC<d>Q1H$o2NJTeBvD)^qy$5
zEv(-J&LtnedipeHa`&q_4;^s5IP<0_)hs=IOq(~-WB0`0WU=VVdSb|i9NzF?O2o-+
zVl=yLrDg7FL{WQ!_QD3S-<T+;5Vt`I7<$29*aySWdj<6-q=S}!WNIC%M|}^TNK$B|
zLiHQnX$~|Sde8T)I9yPO;V-0G`I0%J6=Pa~<gd}0Cl=F7g+<KHit|(rrYwLeebOJd
z9xCoEH(7TV<|N>6Z~ueYWUo1%cGl)}Me1kZodEj4h1God!{2;>-Qq&soZ*%uN<{pC
zKJ`JPFU{=ZfGT!*Dlx!rc~;*GF}BjK)-@&_1pL{o$j7k;p;85TW`OFX8gZ*&$W<YG
z;YO$Qyi~m}%3z`9g#7JWOkLV=#m4|gf$SB&^{`ltSUEs1cT4$;H!ggegWaLSGaRcx
ze+HTfDSVDJpV>^;fiU114OTQkfHJx)Woxs3@216_eA~w-y5g~brVkTtO}QCo5-o0y
z3Xe>YS(wBH+NC|6$doQ!jgxF}d1)Msc@@R?__z#O`n)4nVVKz^%bx>`vgdP^HPk+O
z*{Ag``I=<Rpj$wZN$Gob{4A*F3s=o8)7|t-0AKNz8vL|^ztsY4HMiRts4?JbW^6dv
z@yB}Jovax1=>*>3&%SDRLPs9zszrTY%?p&rOsMx!1L^nt;k>(+WYlkg3{s34!tPUK
z=`MhLJSP-kOk3dul;jy4SuL`?0hci*X05RTpq~vcb=0Ubf3;=0?ldrS+N}QZCn<;8
z0(wv8US!Hk%>=ezVS8808nnN1Z{ZhgJ&0P#9~&O4v3y#n?r%i$OPai$i4+U;`-fj&
zKFd>m5rL@lcdBF{A#T49i!6---XG|kGC!UsgFrrYa|bV<70-_h@z)ndVuOl0v_vZO
z_<x(-bXw`D7pDOwt-Y63){u=Bx}79CX;3#4A9vc$RF52g6p}e11PC`IKsJm|Tsh5n
z@!GR!Z-JC7h6Y_ucEnEOR=DVilRzLbXhFr!w5v5OY2KDU76>s>vPF!N8Fm`)c5NaC
zg=IqPZLVYn9*%c^6Way4%et)v)r44W)`D$CZGd_x*>R~%(_3yeV$b6u5D4rn%Q#i%
zImc`Mvg=Nl3N{EXI@GdDb~5ydH>c%W5&&<;kfq6(y1$8-S6UJ<9<Ot@aX(a)A89&P
z26T52be?L^N{m&{#03nEob#;wow>+X6<Hv)G6)3sN++Lc$hD+R2>Pas1-f(w{MPTt
zUJHedg%!PZqA^W(aou~T<!+OX#D_b!6C2>$-d()$eL-RUIg||g<Fq~E^yiA>CcXBs
z&kJrD*>4{nrcBBK5t>-<9cy?~j>f~3))7Nc*aN|18T0mUiVstIhNts)K%k+u=k0;@
zNWbt7gei&uq?Dl1O4E_QuJ-serwbbth8;RT^N49(tn5hE84IKoKC+Oo)F(||{%HvK
zV2Dz9yFX0_WIe9v+sr<v7zm_eS1|Ujb$Chs$B?7s3=Sx4r3txJp1(l$ZDD8x;Lw(*
zC=G8<={iG~)GaJf_w!wpP8qE<*|(tKX3XQ7rK;!Yu?Uao$ZMc+?N;{qLRt`2y4mmL
z#9JU!yvgh3&$Rxx)6X4l<AaL6nCxr#D1LV^l4-CY0zF=_y};S?TI?)({*eF(uW%+F
ztB5t{7QVjSkda%|Ap+=%sM+C3@V<rhElw`C<C}*U=u>|{!oNByyxFwgvVOc4Wbd6>
z{1$Qv7@v6b8+u}=Qc15F(uPl^%}0I%;i8+__QC?LcRVC(2Z0Q#Ww`eV)z%8{xF(xN
zhAHF5Ao||1CA=|8Ke&;1k0EXrNfYwhJV!YTw{tshE)Y93tloORFLV_=x;op?@k|r}
z=!}7%#bfDh#)ZVvbHC|u0L>CM?7C#U1O3FmE;Zp)1c++#qTQd<De%0kvb6*~CMLGN
zQjW_F+^aSo(2Wkq>_XxqB#|L}mlaDji%SoS$43rd9(Mr6j}vclZAwC09TS(BYFK;o
zk-69W!1&)T%wM@2wiObEqQ-h>pKacdz7x%OZ5>Z~0qhsbT697UcS^Mn$^7Tuy!1mJ
zLd5J!<oK*uRljxuLzY9<ZL}9fyQn>0u#dVs6QlNaP_tD4e-%vxguMG6eC%xdj`dgf
zEdt2)y`~+^c=cQR4}95e2&p<ueT0=Qvc>^%RmvDSk2XA=?EhjaX)d4*o{{~m4A|~v
z@Wh&owyY(JzS1>oTV)H@Rp8tvh?+(()CKaB_@TLUth$$Sph6U8*xC!d8=RY3T;hCx
zshH<LvGgho17F2PsiEYq|3g>x3ME+88%{uJ8qLIKWwe?3Y@Di!;;9j#s$K$fh3y1v
zP`$4v(YOQT2?O|~VN+0>`Ja&EozagryuQmFJV1OdOmS}^5D0-Y_5Ip<_+GhbHjgc@
z9-FFf)r7(S?z0E{@d)+yM+Y~-xbw#4g!wrRS0UH4`R6EqtmQfBEs)d`^ujbea4lR3
zL$<v8T4QcVO5_a(yn>jxnGi$kV<sdP>`G8MaB1ngs)S7b1tuQa{SquGhQekgx)Fx^
zKw~jn!UkX45sYp+I_JYgET5b67EHm};h~I3zeIDt8A#T*jdawM_j&4*4SYRnR=kx$
zG<e3<cUv3v_{6zJ!RUQZ6<X}(TG?5hVWdX$!S|@b13~17dH%mcZ+}3L#nF>iMUoY2
zUo3qr?^um;f{Kf2>eu^nY;W3=S8Zi9oC*f~WE0Kd=G;J4YqOYfV#2hE(dgeZkAJis
z#v!cJ6_)n2+|a${M_1g2&m=kc<wEZ-zU70XLcTyXTF2k{c}_xiO~H!D<^wA*B?jmH
zDuF7Ki540m%t$|x0NeIz<-rin5gmRFSn#u&-zE8j%7F$CkTu1KYo3t^0qPI}2pWS%
z3gj;d79?%S1{(oQwBWo&F}3X-<V-cvIx~@r809t*-%)NUmh3SntqknS!Vn6QeJX*v
zHq;Pqjtu5V=$J~lQ63L`GSvxH?TyZhBHR0mau6)oBR(3cfm}>1abG^W)@WaaSxlfg
zfS7Ta4-{@U#}Y(%_%{x%1nysY2_Dl*a*vKtoe1G}e6M#0xii0%nW@KwJQe%iXj85j
z0FL_yv-X~H@U(_<+@*Qr(u0Z0dcS~)*O(OcOT)ama$evIuSP_D?nJr#42zhCk5%(&
zfe`_~6(D8Vf<Bdboz$b$SoEtGGr5MGs3@Wt5yn)OKVM<_(3ewW<nJ>z(8_-Al?UW8
zsEmLHUEM*FRqMDi&1y_!qH-sy8C_rSPVCj|vmk2<Z5l6pwWg%!fec3Nsji&=ASSVj
z2i{+jo2AS;^SGz8+5YA(yk8AgHN4TJFf?lPTQ{Ur`(JbeNdyU1VUR)*cxZGF@WKYB
zv;0dzX95^AD^(P9S8ewNY|+}h?c*Q>BThdo+2L!&sivpfYOpI>M=C<BZf@wVBPWc9
zr08&8P+W~Pl8Bj3bA`jzL9%TmM^)@i^3u)2)kkBM#4U3d7uL4AG)lekQ0c^bT<7s<
z`}o@+%;Q5nySHOt&v#iM&;utp@e$LNyZVJ41wJ9D$WFbTO@C&5#1XzFEb|D^8KOu|
z|2HQbN#iUUL7c#`EqYWc-?DvZP7k<nxmOn(RF*R?@D0Ug*LX5skgv`MOt2UaHFGMY
z+`V>{x@z_Kgt~s6bbjxiH5Eu{BOYp-nN>X77S0AZQ`_a%MyAZRPHC<}w#Wlc1^WTz
zSFAg<G^&RU>IO5S900*MvWE);Xdo?yz~|#7KuRI;66%bgB3pO@G<O^iG>|mj2cT9g
zzG>V;6lixYjHH8r!lK3{kPx69o1!{nG3oN&^j%Vr5D2HXqBS&T?C0TUrHXeTig)}-
zU0??E8;N(M{{x;-e(;0veh=nJKx?sqazm$x1&O50sc(T)E#Uh9gW%MK7cR<_0xC-h
z^u4R`SUeoR1Ypx7Fgt*LPUO1|RmT7J(-$D_Ek+t?%hXl#*v)rbT>OJoKZS)=vauwM
zB;m?KX1WN9@v}F-E<(uye`1YpB7YK7%~*BOTI9g}(vEiu_g|>qutq^!?QJs0yNFOL
zqIUr|i1|Gdl%Xy9iTmg}+A#eTC@0vmlO4uW1~!34Hab*D{>lqOb<S$`hV<wr(|c~p
zCkZs|{WpAY$zPh<zO@pbwzZs@_7^2PW<Up3VsMOdpdlr4DGz~e7M^;^y=;AL2G-BK
z3vUl)Mvh@seoGij<5I~5cGj+ynJ`qp*8@?#nu})EXdh+SOy#zl7OPp+Qvvm3Z84L3
z?;G@XX^~e_)j*D{*$po_IagY=Jd>kT^75H%FGw^FrVGp{ZqS3+KVUzVx??a;9-QB~
zUv;=d_qk<P^0l7^sL7Y3bAV}l%VCt5oUSWIjhM=kaRZ;$`+W-Y25m=70k+g`+;ILB
zj|Y6<qdX%rK>&0*xuI;WSsfGS1bW7<J;R;<NU*7%w!Lu$doA2D_BnfO7<FrXoR2|A
z!v%fO(^gj+Ngpao%5m&+8}Ml$kYLP==YiR2oYkaPl;zj~n<QQ>XZ^N}Qy?0D{rC9B
zuYyUTbP_6(QZ%4j-ga_}_)S-=s*4k$fNbhv+@0`RrvhPC6<GqR2Bwbh=6eL@7T$l(
zKI`>pZcx5b5mwlkOm{Bz)H>k<$o4MsNf#o7IJl+4!qlH?(k-Orm8@6U+S#L+V`Jx+
zGrq&<z1ox_>NrAFiYWtq4%#Bo3b*1OjzhUaX6rb1E+Oi4_6-L!R=WxKv`283=*h-i
zDGF))#HwDssar^Gt14uysR^a3;m<p;{Cv$D!Zt&Bz&-H>yz*TDo)LYJdJ&#5O9VMi
zw8Qr^q>Q23GxxF#x?@z&)r3!w_YuMJM)Pfp-e6^02Z=usllDG)!5>K&@Ji(+ALBSB
z>-cW%9vn43v1O*vef&XLRz#5|eR!1xf?-CZMq<cf_tRyQ8fw;=sp(#GE%2DaxE><q
zBO-BFjk93~eHzjh9(OR9hmhEX63yHvM}6Z)nz;+qI}5`BZ(A9jD=o5zgnM%kDkccG
ziIXTNK*>9|_<W?a2!xN&?Y`SJYa~P2>@C>nPd>&RHq5ehu}r@;0j1(<a(HNR4S_tq
zXD}<~@bxRt62;QZMQzgK8RmdD9PQgHr>$lU8PUHjgPZg6#x&E~Kj`<gAy~9vTk^M&
zj2+(cI4)REK1M({k~SOYkN{(@<8CRpU#iKzy?GaGOp3Dnt`BB9jYml6GjHQ7c-KWV
zNe_)93hXBCC*dF5tGQ7Nj5@bt^kTC0V8G(9i-0cjMM(hO9Pzh4Fy@7WlQU9OqZmcX
z&<+dA_8!BV*<`FXhF878#9QElyoI4p9a%r)_~CBOt<4(kaVNa@_wMK~_Y&XVTZ}}z
z1w)y1Y)N}c>Ss6nD8#?%?N_bx^_I@6%qw6>S{9y143Umzu@*i_)}q_x<_FiPwMb35
zsz7<PO4U9^`=khVT1Wi~NPlzp7tC)~d0s;2#-LayP#7+2Z^;CvwpY3LQZ$9JsHU+a
z6ATV~V&(pumgd5ooI!E~#1tnEXU)erfWOP_M?!z%T;Hl~zb69f>wS+lXZ%xt>sc`N
z4^0Qs!)C06wNZ*c^*nF9xWr*r`}%dmt~eu0a&}Ayv{)}UusmgvQ*U2}MWN}#{Qlte
z%`3IF%oY}%(zSJye<Vj~VwRGtfmuw<0x@JeSk?A<dDa2erOcP|H|yOc1gOtsAWs`2
z&)+-de)k7Tfv=~-^66nx_mwR?(;VL7+2v*w&*f^w11BAEBRxZ3OW`Hw+v}`9BY|Tt
zx5v2ATs)^9V2wg|8bR&~s$6tm2qX=!Zc+m5>Us}f#bW*N#hvu2MQh<T0r#vVrX~$L
z+K|Tedf!aOVFXR^x01_R=5LA&{jg4BGxzqN>U{1E0HAMjh6fQ%=9`o>qb$+35lzOe
zPlKgj&`jn4RhCD?$`{@-UO|HhvQ^F~=rDo{nCpWakdRSLV90y-=k&^l2z2xdGq1{j
zIW5(h2KhS_qYgu7tLzy7PY~|2<C=gKH7pI_MGm~jxq)|2peiF^5D<*hpt$~9Pa)9V
zx4`xQg!TAldUOVtB^i6%8ZZnFHQ<zhMR6PQKj4YtFNhRjSafHSx@a;SS##<|c4W|a
zC*bx1){S`s|G&k;e;0fIwI7SDUo+ByBGUGSlO#;BR1GHz`8t*$Qi8HGjSbhwka8k+
zq96u{zuCU~O(esgyKVmT*YHs793@iuV%uGv943#vgE5R@F)KedL&1fYE%$-Q4@#M_
zVz8DNHL_lf*~3Lbc~z=xTo-GmfPFpYQKjNf(nCg&5||j38P(<pY#Jz#%I$R-|CXWg
zUP}O57#gJhk8gi_*r<ZLIu--<cDG;Do#rV}gTlxNQR|)8{;Z4b3RLqTCBF!$>%qRg
zoB!L%hn7jK2bT|;Mu|X5HW5&#nh^D2!KLpdo5JZd9-)OA@y@qEMcm4=b#n$mZ>K*&
zVT%K+Qv23Gj)6o1)K`JES|^Dl*hcXxa4Gud8^ov^<q*6}JtrC}eZ}+v=*g-qTeH?u
z_EDS^b;RHIPeE0h>GT3J3e9H+oE&bu3!gOa|Fk(R0Ut8p1AveOZWUhq>e!r?f*;sh
z{nMZ=UTdVv3w-14ha1+lx8U|~MC+bff=VTshQV#Pnij>;V=n9m45=kiKaGGi#$S8x
zyL0tay7caz+O=*u`Tf|q=@sP_0XAWYz8l_0vhbUS;C`!RHb6Q>B!I%dVHryQ;s2AL
zzJ-YDiy-=ItIV-xpD_g4*oZC<CUT$(?5+F?5qdZpm+cGP7lBd0k;U+oPXbxTf5Sxd
zo<xv-hAo6%TJ(QDx9Ni|tZPngRAlKA@b3H#ZV^GEFMPr66E3_Bh8`nF#uaDKsFHj=
zLE$vLnZ>x?4gQH?`cw-PwTt|6Md<{Mr@uITg~vfXyN~r!dsEw*x4&&Wf*f+Z2W<>;
zOsg!JthBjvK!9>?wPg9wn%CdZdixmXENAEz`c$Eh8`Pk9>xXBk)sXRk(P;7C(3Dpx
z0$OPk4W%|E!3KL|fH)XCirFi=wMY19TsDnU9lJGTvF(GSREX}<F>XpKIE|YiQE~{Y
zz(80BoQ(aYQeXHYA#RA@3x{yNf)uy{h*YGnsX=Hs1graw^l3OV<3DS625`|P3SS9j
z#>PIUrR+XthvN)7m37p5M4|}~H~op;ZYAGd;xAq6hH`(C&t=4VG%JUHnTCJSZ6^qK
zQ`$Sv1x?Gsta>zIx<isWZ4Kb$1#{mNC{zl5`oU>n+DH|~50G~45e#Jt<jf{MV$gH~
z=VPPU;QG721H#^0z=C5-axc;MJiNN`3R0#A<KzR52j(4!Ol_r^peck!NK(`zsUVdj
z`p3>pntF3fl*+PCl)vjnjk5{j5Wp47dtg<f7-}8e;ej*njv^G;M7woTrJp%dAn&Kz
zgng+(T;r}+%t%C=-DOMFeyK$hCZgqePgu8j-wpWgEJDKR!_)e(5h)6GX=ShtNb_y0
ztw4T>J}-ui);}76WvBCD9Xd#*wDpnaVEvP|04ca=v=Ew?DfO_3pT2pdDs8Vlhy2UW
zX!NgWhIkJ!EpQmsTd|DMflw)V!QgK=>Pay`M@r?3$uU@m%pK&=rk-e8MsbZ6n}g4r
z44x>gGieu=-TT-p-_Jj@!9}SCHZzGwtik$8G)Q6<STGTrw#l#p-ZR6$c+`(jp08uM
zgWT#S9ZW#W&AYY^Jk@0z34c`KnAXo(WR0b#Sske?9QC+iVCW&(=Eb+S7aa)ewoveL
z(Xr&1m%Tu@ha&vyKe;~OG58{E!tsiK_>wH7;ut}OUmE_vC6ZD+%i*=?fo>WOW$Leo
z;16ML-n@rfFi3Qrf^MgJTIHC3=lGtNZb*E@Ur7-e1*G+)2+<@b<m$A9GwpNS4eMpy
z#JbVKEsdM0SGv<pcwXOewXk$=h7^TM5i6+zO1u$*cf3gB1I@`Kh&J>$Efr(f#9kj@
z6LKO*brsHB@ziLL0e14$-e&UL2t`=To%zg793}AlJ?Bf%mn<wMs7kKJLpFL|>;y#;
z)Fy*VK0em)JZc;Kd5<#K<DUTH@O?P*Py|{fuayt&I9=LtIk@)=89!BMbtU}dqtDx!
zMu%UtkHE_xA4a8wW~x0Mc_cr088HeFEy(%6BM?Z^E)~BV+U(2oEr%cSkqVE;mQhdb
zt&C)$5|Ei|^~oay;59C}gNMp%(WT;_Ik=@ep!Em+Z7^65%K>RagqnuvSh&v#H>-gQ
z#o<Hv_6IMl@%vxU&=R92pVQq2fgI+6Mn6<9Xy6Vh=-6!3E+Z_s4{`kmTnT*T@<lwx
zBh}Ip7mX&(*fUatv~b@%(*H5tlpnM(KVO}F(L#9}*#$T!o3de653&j|{+(xfS22kF
zw#+7Wm&MW`gLxph!()A=#;h!R0b{-k?C!hsWGIn-Uk+(usOW<COuEYLCr1H$T6%)H
z0j^X}@4P+Gj6LnTa-C!cZkb@5P7Y5Wz>j^kMz_ie^E*i^%R;|DFIjA#Ub5QOHV<&x
zl;>;j5!bnHw5r;lwu-~eA;5O|+!uhX&dzAlVQ@UxK~epC7o?t-9vPkLo$AqQV&nqP
zX6Er;Y!a*Gm6qK!mRxu#C+qI|Y`#&`z%B4RCC3<!Y)F}Tkg!Y<4b_MId%;8r%BgbE
z!4N(rGjVMMZnw6XkssSJTyC^`;&|-6XaH`%ekBmyNlM-L?8$T^gK1^;)%y70JAoPC
z(n>A{kJ%XnDE5!eIINA^dcegPd*5Jq_^Z5WBd|Ve6koaBqyo=YqDp%z38ZU>@Akhy
zi||q+KbO72@tuUp$2bap&=!G98p49THQLcq%t-4*;N}a)C7f&<g9s^i;;&vQy=g#%
zxTc(T5uh4t8zs|E-5>6l4cGj~c$?by;DI}BYZY~IJHrd(3fhs-qL`)MRqTxr<qIa*
z)#gqY>gfH%AJ!LX7?90U2~h7$lH7lkN-Vs~bbrQ6h8h)uldnG9pJEo2{DgPm>#E*9
z6(Uq$`MPu(HJeEU7`oZx1<XjFXiRtdR07Cpr}dtyRh2mLI#bRGbtKxkqk}54$z<4^
zym95vR2hW!j<HV>ZD93#rU&Pw=&d$kW+ZUXx_}bNnz{9j3*)%1=b%K=Xtr%?I?jB`
z`|c}7g1TBM7Pvo?@tslq^oo`t3aSvoL7fr59P7a~W?=#cHkw4J*b!<cfZ3EvIaKD!
z+8JJMsB2kfd}r}cTQ<v-F?qVJa%VL>J2E#_VR5QH+qYzY5YY*l{Nlt8*IbI3@o-5X
z2NI!T-0B0>JaDSZOY0H4IRE9+@5Z%4tmb^fg2?Sxot*MB^11at#pM4Nf9NU)mbM8{
zgvbsWq>Sy*{d+@+t$maN>1f4qSy1t;|BEX-$A2*PEWPqY1`#yGws{XZ>sQ72AMJbI
zA+DDxk#j%*;Oz5g2R|phx)<Gzpsx1k^)=AL0STHn?tKqQ`)9KW?xtw#ip^fxXdJl+
z2`4}ipNIO7WvwI+hjYY<LD-Q9C=CXILSqCF=fA$!<M{v6qUmDTBD^`k=zX=U^Xokq
z4FPrxHj5~bqkqU@R?GJ-v^!PTd!6oHXp#M|nw%(fMVhHYa`KlRYo}%@0Yky2PJddY
z;<5G52OllpcNR=lj=VtX4EjC4_t8A12jjJ-;E8Bz;DRxe`uEQ_l^ZTS7^}2us~bM4
zW<G~?*w<K42bR}YnQMkp0=-VG3_oy3W7fg;7+4UXIKbIaO^IvKO<czAq4u)|6T{@F
zox@gj6O*~4A+N=NWpqApuzx?cB;gH{I^a7EQ@RV!7heQ7kH|vA;dadDvC;ZT`zjVg
zBN+wxW$w|Z4URT1CE(HXshv*iLKxkHO9VqzYJquM8=Yy0LhDlch^*bK0$7nV(~O<R
zDuFfuKVNV(b8X8B{%>HlpXjc1CL~sIT15)rOBh}Xz>9?sVu(V(p~*ESXkRCl_UR;T
zUL5Ja+BIAmEbDd~NjkJYbDa4zpl-8~R=X4ax*^Z(#i#Maxw1~@z)6Gx@0q|g0m?+z
z%5!TZp+8U&2vXovAc<*^e<(%b#NH2e`n&nQS9W4;Fky;@j))36UwMHQpUX_JXy(6h
zmTvvenHZ3<P(f`5x39hx>6$yu4)01rjray#Fsx2+M|s|C4PnjmzaIX^mE_pr`56}F
zZm^Q9av+FCKmxQX%$hLg8F`-ry0kl3GG-uo1&XgOrZGThixsISi``WdcqMFoXvkx?
z5_zZdCQcLCl_|7Wa7;8GB-={@nxa6uEH+B~u<CWg0+mG}^mc_5be?2-0dc!>!2CzX
zNC}YA_g8{I-&Md{jc+>Eb8BuUM?|WD-GF=q)OylDF4Kv`9)W1a27$v6dhWNq{Ec!D
ztbLPOz;M8(V~^kN(2(J|Y+Tt1C03XTA?mvx`>Ox7Pn}g=^m5I?8w2|pe^A&TLe#=i
z^!TgY^AMn09AeZ%rt@ZYvE<m=(=W{=u0g8{06-l)S=Z1Ad?y66!vgp<EGR+(;EQLv
zz}`y$5Uu1S1ctFH2_djWOmORn5{YRg4&#Tn1ISZSP-X;L)p7y5wk$v)4rqYc;pg_v
zWG*0JXB%cUa-|cb9!-vi@qH`9)!JhuK;10k>cBw%|36(NqUGiOaTMTT&0bbmzclC`
zFXsV;f#cBsgW$ZF`|BnF;VNBO$l6f7R3gd*2y#gY0i5$iKs+JqzyCx5MU^$W@R0SK
z-8hQk;z=0kO+xb*SG60nt?*;xfpc;X7b!kG68v-3^#(`)`(mHOPxw)Ah-9m@zo+*x
z1zA`qIV^#j2-QFZ#2?R+D3CATAreC`9u58e4)|W0XkiH&k!VI{WCxy1t!&%Gu&c?5
z%HL2s-aYuQG6c(&FSsiM?wN5A_g9HG(ZLe%RRWQ_{>tq$I&xk?&^f^%65niB4?oqd
zIi*H5jDq8T2wn?EEHP>kd5rAIm|B#+_pRV}VdC!TWy|>eExVOmkbP)OZZ{EU`)YgB
z3CfxHo_W3nk<dE0wRNu4v^&2lA_*KdVN+(jMs%j$(UwW9tayFIdaT{*zols?o6Hr{
zT5QYGZ$)aq@h3MIxK(|s9m8DS0iS?0ZGQ0%cKQo`-(C0$IZBYBKl4+XfGbQMb&04<
zYk7=nolAg=&<cE$(pxYO-jV>+yZD7`zX=`Sm$sh!ZOVS^2}D}N$Y7+NU>hBsp`gj#
zG9uJbViU2iUABG>hcS~zZ;Y~}?9)?Y+uu6itwm;M!NrDNX5b})Fp7|ngIS3^|7Cf3
zAO3prUEgafeelIK_(R9h`#=9+4+OhP%fE1QVI*3Uy1p#a5&TyA=#ly6HC$zOV8@-G
zQG|X{sN^0&z|`rDy4;qL*2iC1<GJYvTjhCNW4hkIHl7nKAg(LC;Ut?Po<yjaTd#)+
zQH!6~Ikr(|8s+Zd@ZH=UGmN^NC5Vt%Kvbva%=~fD2%P>@EMMaX6P#IXd0bWAB{M;R
zByGUF_YiiNX8XHW?{C?t{6?Sjy*d~|gwU#3XwbNS@R`~u$hP8Jd+Bt_)!()6>ubLA
zTAmuYggMVfa}T}}g&s9>UNh}QHY(38xZe$qZ|sndh2<MN8Pk{?*#?m?_eyn6O~I;i
zb!0@i;rW;|YbX_P4=IF~qhq8*U$(Box8Qyx`eTWp*Tcy%!&XNqMM%N@-d}|;Mshk|
zKt^Cz5xIG0OqjEF5LIvwIV87`_pI>MOP=ksLi9-AlaExR#)7L84})UxmN^zLjb`?0
zc}G~H(c~75HFGN9qy}CeC$E>lSui*yj8qVw_6dm8pEYDz@PGB{tQY$Uk@ks;6hCwV
zhKJK*8U%0a+#OpPyH^El4!G{Zf4xGmblQQ*F@|le0RiL_kl8+KEY3dkFC`@iVF|#M
z<S^O)2i^4liGTFv|J^(K>VJes^ndrxM*Iy-;ywf6UF)WG^mHehsLlP)^N)u0FEOJ^
zDAjE5ECs$M(*BTH8ruih9=b86C7cp)Rjr57swDinx@#9*8rg4eAIyoeM4iZgv8*~S
zOe0BVgBQlW^9S1y($LV}L_>vX05-1l0PAdT*n?>$@n1zYY8Uq-68qQzKI$flhE~4j
zfEVk%=P|Wxp3qj7t$qYBAgF=~DKb6MME2lY5$fIhKUTgf7&j^CS+9z$NmcoS*G*~F
zuCM-<OsQD($@CPV-oF<m0ZdF#MAKKG&&MImVJaoCj@>Zu(EHEBn2n1l_`*d9(72!o
zT4Z3OGFrjtfG9gI=ZGoC;4Y-Y^#q>W^wSU+<!D3{9(ogGTd}TxU|Ijd-`7*xTWWy{
z5ai1vV$|kWC+&?NzI!1MujpXW-O!Z1-|nSPLOhsrEBQU_L=!#&BdljyfN#LL`9{_*
zXVs9Wa^K1JhdZ^^M<s@<XK>CsIp=Saw-@d>X#l2K@>6S=ysN2n7}2l*oN8W@v^~R}
z$->!6B{`xgslj#`4IB_<`;ey`_`!p_jUyF-uAgdR9Q=aN4$rgSgrR)o0{MXn&)4rD
zFVK2fk<Q?u*C%6~!6U>Ku5@xL7G#wj>_C0%Iu+8wDSWTWEV?PEDZJz$Sc(>}8ph9B
z#r<#X6W-m4LsC?ZZ!;&v^Un&CLy*eFX9>*YSwYGdQhA@ncM(gSwXaH0xxQj|I)OJq
z-@y{FyHGffKnqs<1o}d<XN@Lk@L$;fU_=@=XJXKhq7QEF@t;ff2eZ!EXwA=ud649(
zTukfT0v#sh2kvU`Vs636v4ALFc$zAy#;B3t8&K?g)VlLX<xWhji9Pc7MQEbPqJ>OF
zDa|9Z-or})(*`;x0c+1|o#r<Kr<$-U6lGAX!O$aLDS_)X#8U1bQot2-Z<#%^N!jAB
z*UOFvPL1sO>rHz@q6(`*up}y50B&9fHX=wQ`uRWJS{wtg;<p6#(`XWuk(|PDeiW3O
zf!~L24lJhV02b_D`|I>dsUt%@7HS#tqy{aK&?B4ICGaL(n+l$SX~R#M)QMlpJAgyk
zgb&aDacfEU@xVR9f4q!_hWSUL5im%S{$F^~RA32TCsK<MA>Lr_tjvGrztI4*+Psh3
zSFfA3II)&_8qLwr99;u^1>)1v@ZQv*sTaN!zf;xXHI`0@#3C{f#fp+xUTQ`2Um>79
zfmNH3&uwdkDuHe0RVzA;J0%mJAK`?Vv36Xwh~WIFG?&MzeOxulYRn#QT(L%tjI^su
zK9JD4)i&cIN|zbeD>ArfS9dG~PYN9_(ZOQaKQVTa*D;^miH2S~R%Zm#veOGp(+6Da
zq#zP=Q)bsJDwm%9ay{@KVSGP$IJDHHsPOlg{-fBGXegC@Zf@8R^^X%0rZ#DOdD-e_
zh`Yw<)qXwn0{_}jKY%qU<<j(_{#s*j94m5;pDxC4nmI{P#`|G)3kMFd(5_hE=o6)=
zVGu#;F98=RdupWGk0R1%ycc-#y7jo}pHftBSdjbo;<ewdFOUl<OcrFaB71S;p_!E{
zWw`D)^Tq7wGwi%Jd;J%9o57-cR`uTh0o)sdXf&UYkGbv_a+JgmA=041JS<rUTGO||
zez>28C7Z)hn~jb0iicTvApvHV!t$fHg}NG0&q|*yu0}%{HKRjc)NU*bKL(2$a?|4t
z2Q-VR(L8{H3vumVy#D;_PXcsY6|If){J}3r&|JTAAWd1+jGN}ypB`9}vZQPf{UKsh
z{cEK0+0(Z~H82BuHaP0nElum?f;8G%m3h{}kiYKBG`sJxj$7QS>IW);{HAXlnDZvS
zqRhibj9g<aZ<(Ktf#=(=vC!0EE)()*K|`9Pjo8^Z)@IEwVODzY71HwXWKR7^D3AxK
zQulC#cHQttZ_U-K1o9P*3t*!Nh{+I~`eFJ>k!UgsuDH0kyDT7SkwVpxf=pUuuTi;b
zUl^?e>09zGdz=VpXb_eN{-qJu6DW9>9&<4!TZo+u1$y37kj{#9NqzWCo*|($+?OH$
zCuuFPhws>Bu5H!&q2?j3Tu&yZrEc<6Wt!g_%vgu0AbQ-=Wgmt}#CemPXy%E-wH-Hh
z7cR=t97Z>StEfPO#8=wLGJGTN`D0yczoxov8vY3(adZ*gO>c%{f`2wm85o3>!<v>I
zpz~L5sNBE8<ZlHz=`WC@WCocRhEke`2tg(QT9&ZpcjN-5O^Eog8h+0L7`a-3llBm!
z{yc+qfY?+`hTE{%9_}kfBP8wzM~SBj-T+^Wp{BDC9AselJv(`%BHLz$^CP-xjf?^b
z@DE=xw_b?RU*|h6qZgPqOb7i4aDg7(K3qo?ev!j9Y~7dTg`=!)!bYs*A7PpP;CuY8
zzq2>+g#%v3PQX<kISK)SL8xJ7g;=Kdl2YzYf}9GC!8K{_Co1z2v<r{02^zu~<k7%l
zJ3+45hTsumRG}2$nqDKW4J%k4z&XAa3zPmwqzWjbC9-NIoL^0mqxf8I`zMviic>$k
z4axe{q&$y2A)EbHs6ahHPFmzcl9#-o$u)_=yv^ry;iOB%C>blV5X&;vDo8@G+f5P9
zzQ&5QI!Gdzw;@CwelVUG!Q$_;-O2jpAmXV%6w%bR^@SLPJcNx%gy9_Bu{wUG3OHrn
z{8f}({bec_enlw?kB~6tx1UYY^Qok^mJ}DV6PR<pb`IlJE=H#?VVjraeo6H=YyYvp
z$mY7Z8VMz9VfV}b5DmQ>Sw+zr!&pyMc$4qI^fQ@PuumRdthCnV6IK&}zIS_TJ^%bW
z_cPf8Xt*E-E7zYZ=`N!Xi9iC+h`OAQsD#2NejL2l|7z9y>deR#o!?9l3m7DTLI>n6
zrrUfN7gO_~HM~GR@YI$=5QmssFpQ+t;Fp4*-STQA`a!Z~t5yzCkR4cqj?E1J0W-sv
z15;Vj#wqxQAHL{MhXSBze7(NvbW6u)o$Cij_}6NAenI%8QgHz8v+Ecbs%<!J0Vkw%
zg=YAoY&Y=l<)YMh33!6tG5k(@guSoNcU3P`?D_(B*!M=9Cl0Ng&&HO5=?3faIl{7;
z0_%s!k{5hX7M~EC7r3>V3b+CVbVy0Yl6hT_sX+4n1qnUAVfz>xytVF@(NfkQ7=%!;
z$l1TJB(N6u$B;0}#UF+ez%CnM`AjIs5698CC6cPHKg4;Q+kuLuFZ_o-0|0%hjl8ua
zV<aFcSJF4C=d-Q@LEXr!w%O=}YbW<_oxL>K-^?ZDNKpMRr%ZFJyC)@hZR`}Jt&<Xi
zrp^_wrA%iFiGC|K72ZU<_$Cg)<p@yyZxMK-hKH5!4Qu;9XlSUelcP~ZoZ)OF-G_Rr
z>b^TPFofFpPBJwC5ex57k$}O?O*HPSdtXbRRP665jGfzW{t+3*e@x3rh#FUJ8LIE!
zzH-i7jN*~*?>(~tZ}IesIE0GLkozA+uk}VlS%#89O2O_NaC~Bz=%7=lUQZz@O$7Rp
zr_c;>1s^#v;F_^1p+F8H1$zc-&ndszJAx%Gs=Ph|-#S@UX&-mR_-?ROWa%3a)@tDX
zD6|ORya6KaO@Qm>EfBMpO$2_J#sQ&qFJP<JZ648tNW?flr|$v&9VU#UhZt2W_)q%*
z5ka5><{0*D<m=~8G{MdKsa|!b&#jA^t0K_9OZdo6t4E2yQzJo2YSh$7-=+jawGc3b
z(d0uZ0S<V3LYoXy8{8%FV8;qDGpSE3AA<`WeWxzzky=ZXpIHGweU$_G;5w|I0e=*m
zw6bg@0S#cb4sJ)d!L0uL^P3WRU$qv1Kyy?~ju17W@y~C^w*>$E?wo6HFXi|Gie1~P
zjQKTM{rXesLz<X(Vf8s&Ib|hw_tWgouevm9IqcCg?85Ey=T-_?&Yst_vSaM`?iKh9
zrz_8&P*!4s#FW4Cuq}unGa9vj`RJ<;L;QWX73=4}#t!9}@R=nH^@qR&qYB@=9F<*q
z@LwwL`)K@^O1=Cktq8ON?XUlfxVMa}s{7hTH=>}VNH+q4NJw`{hk%NJw2~VUq`Q=o
zZlrNbi8O3VQjo4q!zPuI*rb4nz?qBt{y)$2JMV{cKD_6=UzEM(Tw~5PbIdWWaZT^)
zla+$aqFn~SDjbP(4zF5oD<OwGtUvS~4}M`rMg$k>R{3G=?5V&-Ovh54zfW8iwpVm>
z$(#`5_Lbn_5SI3#N@2p){SX^PZB4jLDsjr1aCv<!=dWGaaieQ#8G&mLg^{*prm3BH
zk7n@<R_H&BN2Ns7k)quVU%y&?UrffJvT|TG7;JdyhW%D%;%t|Jm^14sJ;37_jm+A+
zQtRV-l19jmY=}f#|GlDg<!=D4y&CK&B?J;Iv-^dVPs?H8z|NqsUaxT@#fLP94~@2p
zefV8j_rHbmcnA!lnqNqzPNS7$^vAdasCTO4ZsQ^{>*})FneAfnlX6F&bw#uMqQ8bE
z`GO!zU>YU8#`VLnL{OdD#Uo|A1e<W1K<?DuHr+}x0k*q0#pf@BG_I|_?1;Y`;mS({
z7^Gs+DOZ@~_SeVLDK-T1v|;)U)_XTCXrg-)St6UGEMY=5i((0c7@PgymIQX1?Q!}u
z;ql{CQNt6$-VTzfPPsdwHJdg6bf4*fgL?)dYE<*htWrRGAo(a>6McwVMSq)1_YKTd
zzt|X<O2w6-)K_qJ^zDdZ=_O{Idsz1OrNLN5Ru8!TO-%alo>Fh7vWD+ijCQB?C+aI!
zI3%y{<FAGw=5n!IGEki;`?+By(})6DiTHrEhl0q6>EmaFj;o}#Z=zbi<MUieZ$~xH
zpz{g4H0Orv8PTOP2A7+=pXDfnHN2KOs>3rx2uFlys^iL}p}NIKM)Z(=S{k7PJ!^;o
zXS6l}9izp)YwwN3*5*-4sK2uYi;pW>Q_lp1tG4o^yS4aea!Qo5U0!z#1Ka(~{kiIY
z+w)#upJRxdStsjIk?e%obMZ){0Li-*BgA&Ux@BVynD$7uA0!;|Hd<FAq;!}DBA7M%
zEb61s+LaXp>6gukS@<hnY4OF@_HtYPJubi#b^JBW;>+7N-&C*o7OfL(2W94%;&~rX
zAFIkdt2(wDen^PT{z6pAFvi3DKQCFE#N0s|liK^YkAfiCe}}!O{QsPP()^!Meu>7x
zN9J_*pnc|Bf&B?l3?=`Q-=?mSlIPX5?IBtNv^wv6EO$)YN8WwpCGfW;k%Q+{*vXZD
zf7F4Nd(0XCDE4{i%X>#bY~0ne7rwtj-}8p{Z+T`Hc_cJdNrPyEUmZkb3uwc8Mb^}b
zKTslgPnS=geF8^r6?E@BM|0MY&Xa=eHMN;cEh6$<(im_|1Hf}%@50{{(&?WwI-4K=
z`Qn$)blAuSj>5dD7>;I3eQz6~J;aFnD!3pFxoHA|MrBi?F-Chjv!VBL^2(Be^W4d@
zIs+L2be`!EV{zeCq?FgJ_Ozs_x03d1(VJsu{<N%&@e#&eZ%W|TzB*)wV4ZDnFMY~W
z@<y55(tK0;`quE`gzp$YT*-b?I#Lh&S$~>+(xRryWpB%JoS9!)DdD_ua_;{4M%&RU
zroV}Hxyu<;j(rDH6kjpt(W6jyoRqol^Wy<Ek{0rV=nY77f*Td)9}9UO{uz3?`iD~F
zJ)vdMUWM;oLt2gI*@v_*=Z79&v49opW=;KE(PF>Jjj&X71vt#!oSDM&7lE9&z;>~3
z4L2sI`zMNMd5G5ezyD71)&5Fy>4lUfYATl}ha7~!<gkn)HY}V1US(j&U<$=?D>Q>#
z<)Er`=&79WsDGq7{Y)?}`mao#HIFMGN#|AW?wpSU9?*qazva?5?)^}1jEDCpC{O%n
zD^vG%i@cwK3QZUId(3|sw^|R3I(CBHtzZH4NSYQEL%1@xR~Y~UG%ZPgnhtUst62bq
zkXYVLpN7GY${pwj`x!!s6#cm9To<(e>}G)w8iCUE8Vd!%jIBB?g;2&X{^TH|mRAdu
z(c1Sh$S5hg(Iu3MiuRg*4WcH4+|TmZK-$nBXDrxrnkVqk%wJ!?sH&s$3o?c)XC5D4
zfib<9k+J1RWFpt+L1&VzHI;k~avK@B<^~H$SvbqVAw{ch`k4UafYG51AsaH`{F(R_
z*v+b+dO|5E%`c-xK!Qcfd!hFzBJu4UDDt!WVmKXa;i#5#Ll`XA=(r$F9gaIkf3|uB
zHYJoEdREbS3;m!>`GOhz7Ax@8HNSz$DD!<ZTkih4@Uh|pm|_uY(6am%C;Z)~#|I=n
zsC&rpA1usMFJEQ9S!Ow4RK{`Kl*OB%>5zU@fCu|9crchyFV`@b?L!A#O!Yz<r^87o
zo+_CYP~}HIO-pk)5yq>c*<dau>7H<YxrMgO67~beaU%`GC~M^)x1`Zoz$^SRb0rk6
z2G={u@CSx}gZoY*fKpO%!qb@(kg?bF#0)2(N-HFLmOzvp`m~Wz>IQ~}((~6{5CwO{
zQ7|(ckK)-g@1+G+m!`l*QtlfVya>G$9)LGpbf4_OLKuH{)m#8_Nf=gZf2zPKfAg>0
z1BpNUM+yDqyqj0-89?>SxrC+s+ize_2KT?=0gdE^?g<;sEp!$A>Mtgc*|L6)UqATB
zXo!Cj7<NUEovcYP*D<L_`G2WlFaqS%vL}c}Tl4fxgn~U0+itUY+(>KBh6K~}`K#Ap
z@MQxAB@wyP_{;`7HD<haQ9t8qJKLwFf(?CmX2m2o!s9=ZTjdGnA3aZydgOaCMAB;(
z@rg7ANeeJAgP$@o#mdgBoU@&K2L0Y&(m)thaJ@cNp$a~_BjF7pewm$C?Dqt2DIt6d
z2YCvsAi$o|3-@rDSS~yaf?Km3i4PPT66#<ZP1Y^RTsiM&Gi=A>)6k17W~Sz(VINTp
zBTa(rf+El(e*T$^UM5vCbo+@?GO7E%4v@y$GrvzBuYiz>+>kuE`$Aq0%6LQ1_kD36
zFeJ%)JhmQD3Ih2Q;$J(5>$8T+c{_v>r=AmC7F!{fCd3*d=lxt`WW*KdTg=QCt>)57
zW=1;jovGh4C+^)o<Se>|tu{)8?4NNUDV?%**|aL*oOpJ%`=pOMCk8HZ8i|j^ssrN=
zeq%Ll@8gu${aIW}r);G;3^#`IxA7+k#Ox^}GQSr`9f)2V{dtksVWZ#?N6zE-sMvc}
zhN*;r$2RNOv6Rkw5KYCya_??B2iNWt>k}*KiBky2ykA?)*A!*(yB#(Eg$Y?AAU9)T
zt#yCf&$wz&nK;~;m!WCU<c5Py#~XR?I137rP@}KRo({Dy_?q6a?LaXp7Icl)9Jg_q
zPvvHg&R_*wnZI6-y91~KBmLKd`t@i}ir0?hX9NpagT}V^2N9UHp59g|!^1OtIf*aR
zX9#0kO?S!KsbUAxmbs6iVuLsVwtKWS`awX)>=ylGvLuA7>TnNPG&C)|c4g3O9;e)T
z-eG4fwUiRm^DCXL=kOG!dk-i2yuhsZnZ;RB42%66cHOxvhCkJG(pq~tcSw3_br$=p
zpJd{82v!@OE^FWhs*<awWk-ut2G^+8u#K{+oA5Ry6~$&ae@xHhNVV|2(It7l>bdOd
zVpm1C7!V4d`A+cGRp1)v&N#<M-d*~v4_?Fd`Yh0_oIDwA#dE1?N1U%XXT)m8Re7)v
z@=2nt8OsL*+Z-{3EaXm)I2{VDmAtbu$3+4JCcTC|A92ag7_zJD6Tsi+D(=6DBCu{d
z9+!};T&HNm+{KL>IP$Qd>EdrZ79_`WkfBrA=oDg`ltPk=VI%7Zpc+)f)(;G+1k9uS
zH2h#R%Q$qloUdeNng(wyC`S8iw*GDlkN9X4v@~u1xivC|7pX@Q_t}BptGA-th2oi6
zhnRKA?t{Ihg^V9#r0yvs%*0>sO}`6r8$au}6mj6n7kNXxtZ7k&UC?3EwZ4sZRj^1s
zLOr&?MriEw#4h1oj!yZ&zwdr;-mvTXA%gU^?uiL<^HAdJ3IhkS=Pb9nGGpxFLrfbC
zqe@!v`&bV6`Hq7Ij#32E{5{7FR8tJ~Q8h6>78gVq2c?;cx_}xHF7V0GgK%pP#>cqG
ztx@fHe2aOU#*~FgXr}B_a>v=eYf^!S64ISVB2Cwpa$yk}>-d<e1vq7jycXR1`};wC
z&aBB(p^e<6j+<W&Hyw*)kPW9b%A<9n$kPQl?oeCH*uI6dN^8eENTR=Xgil!AX~#Uh
z@Ewxi$8<8*xxsP~9s03X$myOucpCJWTJ`ZO0wHcY{>@YWm?_J?Iaq;8x_ZU}ExmGC
z`#7clwxp39!kD{iDf?9cZm=7E=^rV?T;x;QEDtu(98Ag0--w3lNMg(pxy?9Vyl*mo
zld`EMAs*)57ybz;K<#@{kxi7}E$sE=#02ht$5V<zBo;bLNq!PPAqt`O_=uU!glFT(
zHxNxK-;Wmfj@oW;WnKIBtP6!ADzkhA*R$_l&kr0%o~fpY?I<)z|5j*dZ?y`Qml%-1
zES9hY$5W!kDyOI-XuF|RkYS4UKwUyI2Q%d5vyU#@f{ET3gull#A#tWq9Q3>mSCl)3
z-?uw`Ik6$I%FBhsEw!AFbYp!v8m+|pS&U6<xcXc0H@fT(w$+sAp{B0puu@7c^0(=k
zmh{ap?Pe$~tNQhR+l(837puoIok14dh1xe}GjDH{1%XoPYq*|cR}u0w>wPQPT!8vP
z`F9E?+s(UUv&<%z_@eLXv-9_RT(Da1e(R<P2|0?bZZx%ccwg8pVR;Qt>HXA?7uUMu
zt5ileV6_%R&sHMd(xP=|X10vrRb`&d(z{u$11QFO_&$s3#SJhEjSFQE?nUDHoO{%i
zgqjHRG%}JtzK`UCiaq^^ZA;zpZFPQ61t)Be2b~luBGIIGcH~nMtVsjqX0o-YD|Isj
zS1fA0vRJLrzd3E<1t`R_9U9V<DVk9B@kTB!ttHng>E^eZ=8uC0;@)3>w*2JzBWsfG
zJ$z^?u=Pst;{^uG)214aF#a7Vj%`FgCzH*TRI|`GVzwzcu|7nWHid=iQM^BuZwO&}
z&6fMq*AVg4jrUcF_y83{nDSizeAmJTUB#x|6<Kku%2j$ry_#+gcB{En+*e0liN(jE
zM>KEwS5_XfX)Tat_*7nj7JwcnJ7<PfCwU2rrpK<RgfQisv6l3O9@tWa9^~zL(WF0}
zcMRCX?xL5+nHd63@8bUq`=(ttN{C53&1<zHe&4M4EF%5-l=bD#nP4>`ycFx^0l&!S
za;7rAFvw3!ukEq6lf37z>uW(?%6ik!F&A2gzfG$<b$&Z0zW@j(&sI@sK4GK?rHV=J
zUfo{_GQKamyd^UkBFFU^P~IgQ!~S&f2dB7*vnbcdQh#pjJEiU_r#&3!Sk?t}Uw`r|
zzo{AIW|RdjXmP9FeNyAA3UCI-9IQ&6e6gBntOGLM&9V#0OW>1oApJjOw6OJw;X;Fp
zU+I=sHyZ)Cfbis3<g%u2!XWh2yg;#(%ERC@YTIdkRrPe07U~)cf&ondf4tGV$l)-}
z1nVwGNNBgm#<ExwPWwY2xG7Jpq$CGa1%=LJ=Lg#~!q+6H5yr;%5{^QJF9kGKcXL<S
z$D}W#vYN_!3huL9a!gg!9;aEK@^*g<aq~HqvRQZqZGQ^q__%kRW<Ko2^$=jbO<wyF
zI!$<4-UYMc!{mO?_AONc%;<r@h{^B^h`*@Y{b)NxsE{|Z{rA~xGqpj<&e>?OymjPv
z^&*N5YO#SHGRIeO9K**Kbc%uD-}^{9-FA&yv>f+#XOEjKO=5J2`j6sJ!8P7fiBGxr
zQwV`?-Iuvx#IgO#Z6idc*WpCmh{zhiYzCB!xsqua+zjGCD%X;$&PxX1ii1<iu53K1
z*!u^f@wd`Yg9KjjZ0}vcQKVJ4FGHmQ+uP~;bxaPU>A4T}i*s;rW4fx@<rJ>f{PR8m
z!dIGGeLsX6WR|6>^DGpr)F46APQTV*Z8{O-2WO$6&e~5S(}{K=sH2^aR0&Txu9@!w
ztsWM`#-AX4Xa`7MP2=9MbHuHg19XKf)p)0q0pSb`#GB6Re2vT4G?G0^0<sq?>a`2e
zd=~)q8vU{gx$KiEa1IFLZ(3!37IYoBFE=w8A2%m337x&1YDa!&o+!Y}Dz!hW?Bm#h
zw_(MM360nX!6=h^Cwx4GlfEy&v(`*|1F!8g;-DiliITcptD`pXFu;F)L1nnUL>Yb`
z#i7A^SP8WPY<oIPbtt|D2jOeF@vDxx=d|AtR2D`d5ur9(Q=$aFD0|$ee8-n+_dYnX
z!TRdipPcYo7TrYqTRK$*Et7?8QGph2r8(8$sHyb5fsw$x_!>7dsb`c29tSYie_lSc
z<B&V8^_oaX{C(~;1Z4PCxR<G`2EU*RStyXban-o!afljjPqHjp#UkJizP5Qc!5bWO
z#)YR~t03kTTf*6wvYa~q#)Si{3pVY8-0kMGYlzIdO01aGG^l4__Dt1F-8Ok!249IV
z51o1WDvdRvnmU+A6)%tTyG6l5-9Bd-$0hosc){bvi(K`Q(daXdPY4X7j617H?>Era
zF#C&p`<kPEl-;P(WR`fIHt!+8h61DNDC+hZ_am=EnV8NQ&}3NZfuRSZ3VS-7gf2L$
zdLCTkKkvrM?$`JlJZ}9-mQ24sAaQzI7U6rm^(cN4v&#<UR~jUi7)gG;aE%oy(j0W3
zwaNFwVE{Z2aD@M2T~qB3d3zImzPkH7w=<d%JnrP6PMUS<{zD;!oNOkdP)aUkaO4Gt
z_J9iYwY(&=?tTh!r2JtPJ4ag^RCBhK4}vO~YeblDn~kgmP3zFN^NJm7;S@H-BAO~_
z=964fXAA)}3iiA$tsOgF5g(((y{$4hyp#)i2&V1mAc!8DK*~!TIG)TeV$9hs!PC}X
zPnXWdO1Tu^T_y^~s(Dq=VIMY{Y_D!#&%Xn-0S2}G%4|F^<zW3m-*9{EbXG+`r8u=l
z+%}V-tOCo(Y_vp0JptxdAE~PSuMBDc`-JspS!g{O%&tQ5_-uQ{^Srlyz6FkAfL+wy
zI}ImCG&0*6ZkcGGBNXs1h7@=Ixd%-;OIu<b*H4Pdx*H*Y+HdPiF#Jcv_}smO&Exa!
zO-^==bC}=y5B`zvg}8;McV|P~<j+Hb69PYfRyw)FR=~E)>19TK?#n6BWLd;<63Nft
zr|oxPjO`M2T$_&VR6(5r8n%DA=6<Q*Dl{{$gxfgZL<g(=3^P<b>1lg8AZVN`1^^GR
z=i*x>9iJcEHhwmiZ!tU}d&o4FFS>F~CgjLx(>zs?24s1NaIbz;?H!+dU|*Bq*1Pv$
zrU+@7>ta*{|LFCN85#%qsNTo=uKs#&BZp@v-sCS0PJ|F-Fu1k3#I2_NBcdozkNBy~
zucw>w5Bkm?G@uxK%8{pH1z|>Zt`DH9a@0&cn|w1}%#O()9VF6`SbI?X+0mi<Qu;Y|
z-n0s7$18Tsl=IBAytA#T8W=RNzXB1I0@1UtItzE%+H*v=`|C!-v`?!se2f_bfEx%f
zSA1WrpL6$%xCRXz(dtU8u-Oc4-9uwEvdmA{b!N9~toGY^4M>9xX5d&EuBc>&bKU1T
zw9~KLn8v4`nRcDuf}kF-=wcU)W%1`@Nu(mhf*uACJHcQgJ~L15AaRoibb$#C+zuF~
z!>l7GF<!ZC2#pcCZo^NXgN{&n#=tBF<E<y{vw4eR@Phk7iEPInzeB-o8c6fi%hU>y
zBURF%BNZu6XwE;}N)1k9LcU(01K+SPT|WbS0ldH$kb=0*+CWfjYZ=DIjF-%jpGPIV
zSe2Tu4~5bBR`D+iZf&S&51ceTw=YXQIKKeeg=Y}TZ8{f4^pKeAa$|$Q5s+ITt|jhY
z>;$t0z(_!w*t@^jWaxkV0(|EVaLOlzZGevZ;NhyHy!R_JGb}r8(j9sEh9*_-GUt-0
zODnd*6@O7=gzO&Ra(}PD%J{MxFpx{NU5PB1y~aD3%gBIsOK5UYG2x#2H@m_4De9;@
z?mFTu%}7prt{Ty#Iw2=NV?Ux};JEPWADD#MSrsDl;7pkiBeINjv{sOSL`0Fri~`bV
z5ke=X(G=GATS}MqWM(A*hwyZ(QP@A&^4yFuCq?&EX^r*?X2f9%<EZ0%rl(&tf6hY}
zpiqNP@3q2Lu0+fzFZ98ilv|23R<Ztmdg=o!NK*6i%P7{RXqS_qU!p5lYY#aRI{D}h
z$4%#U!V<l+F#T`EBc(%+M4pGz;q(&KU;h@-dL#flxe-6kCwS-cGAnO2E?2eIaI^R=
zbJy{s#@F-Mdau+7udwIhK2PPMg!cyryJh-d`$Tq_J%D>JyX*ii`cNaB#RP1(%Q!v~
zlAIxgR6>`u{?&4&T7n_T(US<eSchl3d>&G1$~6{p-i)nJx;PBr30>H%wk5|WD}0_K
z<Y-Q73!9(X&)_269M2xWPnV140pWfrZ2|14yWoEa9?<^qtqi7{!q2F<xER}WmA414
z*U^b^iVqPL^?B1RgE{0#l-F`e+^XtWKJ1scyu=T8WI>Ob9-O7eLj{gYYtE<IRYm*H
z0HHb~!Z17ZT;(YrbkuHEoat!9ns>Z8^hD#vZ#HBVU*!^+5VES3)M>CWlP<u~X$=eE
zt0cWcgi#egnMcgcC$_{69zXx@Z}z<&*%@ajKV4}4HRSka4^8a74+EK3Csf_CJ_ftp
zYGtaooWCPz*`0$MbaZ{)HS|crsS5ZHMCSSRz5E*f0sFigUPG<o;6&6p_!wbjzk*W+
zqBO7IGaAMxwBpBS7Qhko*o(LbJAtzhBO%1uRar?dIAzb_Hj07m3~DY4<9(abrvHfA
zK}lcfEbpbIgV`D*1~e>;PZuKF3FI0MTrN<n(g8Y^Zv+nB6oZQbY?n>k;X|kgAfi;*
zHM9}OUgXau@8vrxUt-Lyv>MT?i`)(Jr|ge7d6VRG=+X2AxFC%8=BDbG*BW+1<4v6L
zmXUFxwD>O&nf0edEMiV|(1;UqDza)s{->#ZiR~r;_DhW>N&<n2#M*uH%~1j=p?OSf
z*Y%yChy24lnyj0DUo&xGSehyHi`)zgyPVvN{rrBit;oM~_zxqRQ#NFojn|1CxxQ_I
zm9bq3Rpm6)?NyE}Th}mhDOTWs-GcfP8FP@^pIAd<M3b^fXi&{u@xlKPLW}<XWBl1~
zj_a8DS1ybm@E&gl^j`%rE+n5K!-sY;`U?>R{THYO|C-gBxTRf^Xm56*p9&7g!=;cF
zG<LlA_8y8C(nOkl(8ig+@Ku?9%tlB%qA0rT*|{L%j|Cqi#uyATpxFUzpZ0A3cJzRn
z*t<K3xj#@a3D<Ruze(bm=+URy(<WA=p$xFA2e3>6zh2I^8lp*pX5dH5TFwiu8<>e0
z;7naKayhWH0Rp|!Q1+t?XiW2(0od$&-JG6)u<FjL`}JTGD6cHpN|Kjr*kdYi2WzeF
z5s%EFItK{S&YS@YGx^p^T_Q6w=5G%j*t(yVpL4V4UB?86?l=Pk-G}g~{xFmY)okwx
zpj;0S_p^vTm6Hfbb)N@$$10Ojm(J49((Kn<O7cCW#SYe({IlHtc+{QTyX0|vH1r{1
z`PbwOc=NMW8Nd$nJSh{|G5Rnt#Fz~<4I_!XS3nFCP(>E^q1%&gLJ@h3_n=FK{_D?H
zQ@70zI54W~Z&6gA{2O!}V>D!eksN^jVO%%ufOQ#=4+LBTgaLpA2Yjk5knjPZ?O_z}
zKn^+&DB=HI-bCQdXuSeSy9LBPV?ysvmtfO?ttsfiPBxm(J{E8YLMa|hpqxjLTw<sk
zHK@vOz`K`KlKihK;r^#eyZ==s2B=Dx#bE!ZnEyo>b%X0DT7fL&#cwg*iRUDta`~EH
zV`|98WQA?|KSw0LrJ0RbyttrO!m+g*iPxZHZz`QTS@f+1+Mp2BY_SlW`_mgRG6EE!
zIuSxBa8}4S^Ac)iCTNH~!UmpwSQwOt2Fk7i<r#zc-DH`e*29S*N1K9tt)qu3h&c_v
zm73L!`h76gZcN`pn!b0p+ZK?Xx1G4JymGgej)=78S#B2=Z0ed}$k8}9;1IZp#(Yc^
zIa!;D8M3P}U<DGVuGu2yj?PrKpUwZ;LGB0#xn+Ot=~y3LG#B%JJXlq_-qq7ol?)B^
zIR|!R0j1>%QoS>x)-HZ!zG{1=k*<FT4Q`$<(~F5XR<%lC^FM!>HvK8_WF;0o=UDI9
zEdICO^fY19!p-ydi^F~2v{xC`GF<WC1IBhPP+5NWTBhq8Ci1`XMlMZu$o2Psl~W$+
z$cuCdn%32|&3UG4WJvxv=p^Lb!bM;TyJw0g@1D>1KlFkeg6^6ZK@uz7?36ct{AGSw
zlRtWm^Wd)e%AKCI*8bxyuOmv`{@OpM6Zj=mMH%YW%HEw;e<*%txX#R~{IgjTXVap0
zdL$M@W|@hIgpoPR@49T>M*gL1Kegd++?8s`$_x@4dNPIL8NS8iG2+)(vG|kjnG?xz
zf29b-w@c!=n*mT}=W;6|Wg`F(=1wNSpoNty_S(R4)<^J%&)XX9Xi0YDdszJJv6t1l
zr3&;8bvVe0Q=`m|9Khw*d`_0}E5t1<nb#VJ9ocpCF-!lCx6EZWJa(iWz8_MnrVFfr
zfsfDGk+t(LwehvBm1iD;7-^Y*z-i2ZV<!p{=cfv%+7d{0D$`9-O$j-P9?#deaJhzQ
z(cAw}8NVZlY{;>5m-0ga;1kX+qA5ozdqS~an@kd$H-AU$BFFcn*Wk3OE91j`q=dDN
zG6Yjq2x-4zw<YR@-FR596n{V@`jKOq?>PNBb$xTEi(R-!C%pr0$|x&i6o|8?iIo6f
zvB2fbl60xG5d_epRb_F!_j|GN_B*m%;h~4OS;WL+jjpg;I6O!bjb1(cO1?Jx5y08}
z)P{;<$Mzvr?;XJ#TC|F3)st8K>)P=P$v#&<uo6G+{1KmfL6F40y5-$u;<SCRu>fvp
z$(z2xv5~E@Hm>MfrS{Sjs;)vvYf#OSS5^41iaaqlwL0r}oG4Z!gy}wy-1~EVVC4;+
z6aR#D6Xyz(#~~Ka?jw@l#}B%xKlDibv{Y(srVP+@LduABQVUfAwb_?D@`x*A$p*{}
zxZMlnj-z7KE8zJu-2pQY2FTN3=R?q341(4Odcdj%d&dg~b2)YnA*{9Co*o63Bqjo{
zi?LOQK(hbO)}jBgsI^X&1k%8sGhLU{zQ`~7!H^z+_x!&Wy#MDl_<vbf|6M)t|Fe4j
z--L<oLJ44{U957y&k?8am`rh}!QhEy9*2_rwFfu1&?r`tYZr`H_%LCE{;VMjB$Bro
ziiN@l*6BW6gBChwXgNb(E@wf|8W)$Y=`VK*j5`5p=cWE3NBbL~NnuI9R*s~Q1qVG&
z;72uA?@Nv<RX(WM6@5>hL$7_Nu)O^Lix(g;f&G0O4B_%OHr3Z5JU*{z3eWhctCsrV
zt1vgff9v9L6rbaU+!>*V1+31lJomdO`_d$Nru|i$sI(UJs=<CreeFn+DQd-eti0Yi
z)zg&$MBPN6Wt1fn=MZAPesyM806QV_htL}B%4mwb#K)oP1W;q)zcPcWC;Z!}=(!9q
za*!{QBlhi<5x#LJH(|S-&2xu>gq<&_S}qiYZ(hZDk%RL{_W6?KimN;!%98m5EnX^i
z+wld1wDI%)ZuO`e`e0c@_9RG1H4|lBk-Zhk<w@Nzyn-m9-;o7vAbV@eb7o=!gYf++
z1fA_zA1odk8rFs$#p(eY8Z7oI<^0ELS8q6_l-6Z#x;q?5og=HutT1Rp0sBY|_&4Ax
zw9!EO3kCQ$7N$ju?x?6ZhB^gi5`Zgr%9W`(T{8cx3^-7*s<guMXhP&K6K`$4K@sIj
zHwb;u)1pG-T0tR;U>t1P$Qw3iUvhQsUYVsuAO1~x+E<W#aI%v+UpVP$I_)!m{wQmq
zq=}E6W_+!E{g1gjGVr+u4H1pO>5~R7HNTrr&mvr=SQOXV<E;We#1ZBX_~Gb40|j=&
z^)+0p$TEjXsAwd`pt!va!K`;?W&hOc4u|)xwGEZtUWwILr@i%A+X8DYtm0a#`vdp7
zCMs2-dCG*?Cd4?2-Zl<7ySD!92}l2Ds*s5nPd;mws4noYDjCcf#EnsMA_q3Iyn}ib
za}HKNOxs(-{U5%rv-G-*f0dP!@=|VKvf12(ua-%mX`ir|3$xoP<%}mzH<EqB$JCBw
zOsjGt%W|_Kxd+{Xgx#T5jRiEZ4Qqqhx79~dy}4DQ;7|Fh`oG`+JgCQJk$SHp?w~{!
z1pR{rtg&dq-)|FORBfg+TrtWy&dx`<!=ebOyNaeo3Wzz*wXs*hV3@;N4@lUNYdZuO
ze49=>Q_^8LI)fp~;zy91#3yGf4Kc=kKO@}V(w8Eay+x6=h6v+2BD)3g@m!PRv^ch$
z;kZ=7Xxs5~A>{j&r~T17F${lvNYS^!3t-!=%@q827uIgeEhhS|7xmOuQvNex4cX(b
z=BlHzI?P!!xxN?2cjW^nR9R(L&D;+B-8m%^>dypH0rT%54GDTdG2FzD3QZUnUEn|G
zQskwpUBsNqgzTdpz}0$4hxRh!G5R#2sP?(1)2@jU9s2oT;Rr&Wz(ijvz!_EaT&d7n
z-=)wtBsZS?ejJE0iBouB!6W1dw;mrV`lH26NdrH90PL>H&`{u^1rhEYZ7qnVrki+n
zbB#n#Bubi4nv4_*y6&z&P}_nBpGC+?IM{WnT{H3I47aN;2j?t#&{5IfvB5sRviq2^
z8JeHt9^l^h9C?XiN_4y-{8v|Asuq8&#PrZ*xVyvCyU3z7Y#n;4C7<8Z-T)Lu_#V1u
zX0iAY{Cza88qF<fcBqpBD&)Mq#{2p<o+9R^rk=guONHzui*JQyM&ZHo5^{t^mu8-~
z;dYykX5|~qsG;d*gyJ1V)P=f@Mnf~wp$*q|Q?_6J6cgA4&ED1<EnD4_%_$%nTeuH0
zBGr8ltw!FTN1V+RGlQQ2EUTIW2i5Eoh^A+i0O|smek&~95=Ues<xiKJrmMgM7?FNy
z+Q4>o)k$~W*4o3X_7Uz@ZcN~LfcCA9FCE6A7gf-=WaU+@esy;Gp#9P_>o}Hfrcfju
zMEM_Mln>n7#f<;{)r3S9*yU+|{^H%jWq$hUk0SF^zy9B!4PKT)#?M`uy3d}U(=@Vr
zuCBTv*+|p{)jw}oPfTS%E6n7bi55-r+5&?i)nBDAOZ<Lew=y1WyRt_y5iI>a9s4B2
zRB7wRsap|F5*2>s9);rDF!r0o9<MNfl7RH({SS2w1OpfDSP7}G6M6)8fVSU_$B-<k
zdf1e4mh<+nT}0QE#hTQ=7f5Z{OLN<tcm&WocaFa8V3RoAQvV~F4Y>%OBQ10z;>C-o
zX=SIzGdu3p{q&5#_Z|T?TFyuPXUWS8QdV|Mp04r{hV_pVmNU<d_&`#tZ++7j*4H-d
z<yhR^ea~*8$2C-KX<tM3R?!kc=Oxvb+<vuh>`w1JdonM~#%iZK10~gMz<o4E#=a)`
zDrmT>qk1R4uoq6;g$C?m*3k>Sk#EZ)rzz%n>B+^rmCUM55v?Ev_CAp|oAu;=mHz1m
zKAX<?_h}0kr~Z)5(?Py`^ng{PC4{7STxUMSe7pH>p@&8S)Urn9=~~KD=lg=K7A>EK
zDzz>jhHP!@YTX~8jOa{GtayRvmi(ve;9E8LP+r0Ms$|xS6Jc8tj<WUE5RBC8EE%Y6
zm>j1|w6&hBC_f)g)g1^MdWZ2dYA|fNOj30&qF2@K(ujB>e_m66`}FsgIzSCUMtKz_
z`#tTJ`oG^k3azb=&yqCjvU=XH(v++O>+GV;F$49wR3evIp5<JMVOmQ*EBOuwbP1Gf
zBYKivbuBniapw^|t~1T+B^y;CW>z5Yokrbce4?Ltg{VcYvGZdGKJFgFmOIcTPytjv
z1E*(Lqjw@Ydh0s}bPC(*bIp>jL-XsS&h~;}1FlAdx=tpF&TS+ncI<xUJS(`=$a_we
zCGsP0<NF_<x}W0kXN3z#;$7p!`8w5U&PfwDv3R>MfBwQ1S)p<AVhIsKZRL@z{`(wr
z-*yMgM6)~TGRLetr`1xie-~9^{2MqpL~1U53!C)F-uN1dwUU^(q}<bswzbWw;`*PU
zuHB`XURPI5?TtIsvuzDzk(%6*>XEo;wSqqF-0<`xOQF|>VfKI*HY44-Dn>))o(N9f
zw_6}_?-rSnYTlmMX2X^O*IO2;oTNzB+MAcRLNVh;MduDgr3F9tWGZ^eu(I&<j+o2k
z<w|$<ZBz%<X5=ciZO}i~UUi%ZL;z8+p;!Zxgr`j<`YJ~C6zX4-yk%l{OTb78U~gLY
zdXmp?jo|2x*fDu=+IIW$alx2<%^eN8Q;MwAXbP^*w^16ZnG=c7q_}wcS}gw9vP({=
z)NJ<klkAd2KN$cugYC9%#j}e=ao|tJ3BIh*YoLyMY80uu3BY79xdit+IwD*yEHqUL
zQ#)$CC6!d5ivn8Sj($wk&#c-N(oxfw8{6rP=Q)68$=~=d&bdALuSrte$B<V!p5D=5
zh-U&tx9Z<vulJ1T)B~pNS>-u_CCIzh%Ilq;+_+t=XfZe1d}p9wt}@qs5=aTSoz#R*
zR-@FWNJS2Ezj=FlRdp}X$qgjDAQPO-t!|=@7}U&=xXtK3c9=V8Z$=>nO4xmP{zCtc
zQMW;2_#b$f&TG*prET>P9mWg(mQ9uIXvNKEnjeRpe|IIskH`?nOgBu<v3|T8zT`cw
zR{t=G8k<ak`D%S)1OlGdq5C$?fZR@<%gp0qt-SEY@HZ-%Bmseo1?}Rhs=;~im3!q<
zzjA+J&y*X<EpY!}*jD$_Av%o$#Q(6rp%0~E4X(}9O{y9ElFC%uX#4~)Z7^nl@aQY6
zD_^PqS!nC%&`hc_pgIVi-_#HEVru!(nI98Z!^Kv!Ae8&<GaNjBmz&ck`^Di!nlH=J
zW<`fm^DotXC2Y`Svdd(lSuU9VntJOmm7?n{*{Xh0=Fkg?e-Z558DY~C2Zs|s?lcsu
zVO?G$rS_8jMq#z}b{%T$M?nTFbXAl>Pa_LPTu634fo?r~p@LuA4m47?=v=%>^Z*CG
zvEjqBaI{YZP=T*E#Ml%%TiJ`fH`=I}MYxVJRBjQ2g{2?I&NM8{+4{zenqkI3(G#-~
z$N?6lU9!wURWBk=|ND#eUQLE-738F3itOaD+3TDY7^&zm_8*CjkAYU17o(@lI$8q{
z>O-!lSam#RqNluk3i@TtBVLFy>8a3c_w4&IX=t$u>a1g_p5wG$=oJ<cnY~A+lI5MP
z1NFCiM>ull75n$lG<!|7$j9{fC&o|%U6<7QwD&c`H81H!iSS-7HhY$$wPXMgbdIRy
ztfrHeoJ!E*JRuHz9q*r7rP1fEd(?ngC+Y#S{W*s9(Ls^eR+T|W_ST%C9=HyxW3MTK
zjf|M36st?&8co<UhU+TE@z*SaQ`6ikn=g*9<Md-;-8K{!z$`@lpvl57J0~E%KWC!X
z9b@>rC^@A~7T<_(xG&s^gh6#jQQo?+3&3bt|I!GJsJ3?T+s+35Qqzbtjc}(zyOK8b
z+qp><>|sve2xC1cAVTG5Q}&M;o!@AQOw6QSv1X}nS2mxWgn%fLv?iD#RL?QPGtT%`
z-)O1@^`zn}e0i%&QZd$F%$&<{3o+qEeAm{kIh9Mv_?n|AU-h!VY`v$iI>05LbyHhd
zlIHck%3Kb74^s=iZZJt^HcH;f86B3akk*ovPR!%Lq3eO2__BwcHAa}%CHV<tBxu_@
zz+LrgTO*=AKD6p<vB2~XPtmkgWyd=Pa<M+o)9XK)aEa%R=&mZ1quG?5cUTg&Yvd|l
zFT7C~Qxx|sG}tUH-B<$cSyQ0L=aPKsy=m*~1zv}<aGID~TDJpPN0$o-I-O;+VBQww
zy2$w(m`K;JihXb8O;VoSb)a@YJJJa;hn;4Wll}~Hqh_eMV%?PO<U-|t^;IRPqKPEn
z1y=D8ns?f`a<2EGEJopEl0Q#u-u5}2ZsLN(0=9)|aLx4eu-oq&8UljhS)xaN0mEtq
zi$X&lxtvImxwBg={Lho62{-`$<NFcIU>MHd*V(q=l?W>~D$_QqS)A>^Ta@UcOd8X}
z&I)hE3kkFh4}ue!kjYM%lDBjQ+K08jziZirX}-xlC6wSe#r!NG{F<=%Svq*_6fVX9
zbPN^8N%ATw2{eW;i{1F&tDr7EK4Wped3BYB@qs<BOh@mZDR*Q^kpwz4a2q}&<v8mN
z4nE<Pkywa1iGQp>h}xG9_BY<}4$&<uZIWvam@DX7Pc8|7lR!DF(Kgg=nh^7Ht^B>K
zs1YpI;@HHM?X)q+VwpX)?E`9CLR|uileuMf;M{+Qgus*GfqhLJdll6%XXo*oNgQ>R
zT+b`B)<v;Hb5&*ZP84_TYqd7KuQf)FykqJ?mxeK<-aWy?miM0Gx-|U+p|I+W6=av|
zu2FY9$tYk`ihmbBOp_W>XM9$yB!WCv$zP0(oKS7XvFiDWr^@m+#e3k;dr9XPp`E*o
zWj7ah3@`(!T*t&(Q(XBG>v(PYUA&ld72hrIP>n(R%}I9FA>osoQywbG-+5itoW#TO
z72`YW8%&<CZBEKJT)pqk{>|o4f?o@NB39pk0hrO^P(3~@2VZS6$n=<4!t=x`qK98?
zQE(%tE3Qk@4GLaZ<ZzR4nw5W}C2a~B!0V@ldYak1FC$L>Y8R`itdgbHsLGFIy3!x<
z*;ZgIz58uo74N)uok(EtT6cH9W$D18TXK`*Rt~T)F*Ey3Y;1X6?am@PrX4lcoU61W
z{;A=5(GOV)_xJ?hnNjq9Zz5L+m&6Mo*xa&xYS%LHRkh%%*gY<%?W>Rkr%=tF03Z70
zJLwN)JMUwBA){D#W)Dtjm4Ai1GUwyq$7BJ=`6>K@*0<z22!rY9Y|F{W+n+{0EMV$P
z|2Tgk_GJT0(t2<)WXC)BV%xf;$xKZ7YZLv-M?4N#>nxXble#Cn=dIZVE6qUwp|1B3
z>>l`JrK-?iv+4TX5h#tgB27l0t8h;i%$kX5sABe!^Ul~XGGz06)6H?b$x2GWO6f#!
zhh|Es%@N)?q3N0+eD!7Q4zRjM+gUV+du1&@K%rcl>wpp>UM>77m4!-YX43PK8)tqn
zu)^!l^)7`E)SWZQT-C5MU9|`16~vqo7ZobHu2&T`d%`3Gw6V;S1VvlNg?)WgoBlZ!
z&>`ukO+KYDp@Ls+inc&M#d^QLO#H>>e~sI)9MD0@vfJZWTK7}#JEMBvs4v4{#$p38
zkF{k+J-;NI965oF{%5N((dWVB*;P%{$P0U&#;xnH-NmTTlRM!CJ$)bY&UG1pY9UY?
zC!v@gOJaQ@0JoJ11x}aN$S8JC5}l@jmIt;uf9U~^VoFhSgD3gn0S_GGDh)o-%GQ-#
z<YRG@4+C<7aa9d$JuyuZ24VNY+hC2ld^qc(0wE9N^TuO!4c^?=eKrv+uSsY|Npf^P
zG^}16QMGuQwV2V+B(iy-SLn&`%NLbpb#(Uoc0Js1_d1OC>1fno6sgQU)7@P-dkd&w
zFvD73XE13k`YgPdmfZ{shGUaqJv_-e`5@T+L>xF2oRd81e5HEy9t6*&PLlvjTEuKb
zr{=!kUB?#Z69QQ4V&mL~@uu)b#xh;9r5_%k&U%<u>$bJ>_>q}FmE%1M*mHR40jqdJ
z!s&x3(~~<uBH!H0=4X!M;=N*Ap`O4PHs-P5<HQ;%F;?Hb3EWPdO@FB~?N*f@EemIZ
zjuX-y=yIyfB;1tDUIAVE=!JydFglP9SBK{S`uSb)xGB8vFX>b2HJ~Jaa!6>&sXo{i
z34TCbNDt_=_~t+W0@^0)n$GzOIPKmo&wf8V!@Tw&sl^j0$7CsMcfGvR7~kH@J}?Br
zo@e&`#CV^|P{l88ZvtI7<cEfd;%gC4vZtOY;2`{aXnw9*fn4zg%w$tAzvE}@8%F(n
zN*j1Mp8SWzx7s$sBrnb`vg4mBOl}j_YYjR3i&|$F4fOr_F#Ci@X~}J$^%I)_s6*|9
zpXzo7Ij4|5;SJcMj;g;DQ8NQcAJSeBfdOwOaKc05p5*<YW5JUKWOj(2>TUm!W^*uC
z0fV{A5&w9u*rC%jv;rDx%#YaW;jhK(?mf?5bHHS;tqt6N9^)IBU9{oE0^StrT^p_&
zr^C^S<hd|H4nZy~Ehk|b_mtzndpM(krj(ekdy3$r&4d>emX4exKAFc`53h8#xqxEH
zJX3Svvt6@<8&m`beWL8`WWex6Cf-{OpLMdwNqWdnDWDA~NFKecIy+p!7bDtGM2lPn
z#?g)%r<+mU>1BZiY#hME^a+70@M+SC{S&1PJ}__G6UBV}79A*w_fiS1hXo5zYV*iA
z_`0C0w>>xC<FKdrb!n{t%siQmbkpjV*~<J)t%JFiYQGAA!n<hLWXS!R<3sh=?WC!J
z?|6}-bMDW>`s^JUdp}VLm~7!=zvu}m%vb7{Vlo?FFyC_;qa14u)@Y@DFke=Wqpq|P
zyKUPQ6fzOC?;b~?aZ1qF;xzJ@C|ut3%DsKZ`byrClc}2rgqJ<rxIq<mm9Kc+D?35e
z%6@TRd5HLLjsPR4Tc`;+T9{o<&+ODh-Y~7WEa(f+@$l?AW5^m@3)+I`zwb^oU!aoz
z=(Qz+gz#5iEjzaR+1r8*s2ru`3^%{8;{zp<>{+`+_*~&}a5DRCR?i!Dgu##nhis~N
zlB|J{_y)KBPbYgh?|Zx`pjqPC4H9;glCJjeeDjzZ1aS1=T?HVyLuez-Y?2k}FLpiY
zZ&1x@dp%TK(UxUL={gI-Qk+!`55sp94RmZdTIau~(VGmE2CIS2WN7CXkoC61bvfUx
zc99SA6T-Tal%xG`7_x6&xwJls_cx1>HCV>rK^89A(yh~{75@fw&5E%hCAz?M;(b{R
zko|P=2;rMQ1^8CkkuB&isjE(xB%O5|-!{XI2PE2jL)>~^L;gjWT!Z<XBm9^M5IKNf
zwZxc*Kx7#q29toEbunWvvN5P=7;$OiwAmPPj+*OTkZWM?%RS&KIvVdiyrVi)#preJ
z>$X#kKE{qeG8l@`@0invc<x|E$$uU}lJGH0ZesV7xAI*!a!to?5Ub_rsHU@sP}5IW
z<C4zgSwHFd{*7CUv1aF#)wh!Ucux1yntUBI_{$qktZq1<Sh%n~A0J}-q@5x0SMj-;
zfto!9y7b|UA6L>VMmparcMwbORX?Lz-yZ|oq}9?!IWoH3by*9M>GR06wuvq8NKJ>a
z$SW2J2w7*kh6m)ltMww>3hB{=>wOXpJ5F!`;JW$UGdTB4kmTAUD(SVCoo~l@&Ho@u
z!mkTHxpw<04HMM~-rZY*IM)v^E{F_2bFtm#fXg|r`BEhG>kn=i&(;GT_PQ0h23F(5
zz7>J>k)!*nNr5k3l%cm(`f`Ggv{ki_khcoPi*_)+P|pOQc%qHq*_&n5Gwbp;3+*U-
z!uit!IO0s<Nsq~sA2oln&|Q}XOoTCAJt`}a#!zqd)sXt<hXnEsb`l9*K&0y<cKgDe
zk!_+Q+<3NsdQrlw9%Zjic%0NG;{EaSXF&nU9N(7>T*kBFNMF`MTgDBS-A8v$NzvN>
zh^8+wf;ZgGw3^Ovv3jj8(KnJcmpI?zW~=wd@f5`)Gq##oMed5}m_yg&HZ-FL#338A
zEL`~>BRcQ%Q~h|a!iFcKJ>gc9bBS3tiX*rS;>gi4{2Cdx1_l+wK;xoW(56s+E=IKd
zMMiS+1poVFWc-xG6DeD@qNsD9xOr)00w!7N(8%|+uxWSOqP%DVf){|6^su|SmY4Y?
zIX!K}mOXQ<`8M)(qG(+6n4q9x(YnEkV^R)SIsp-WuJGH}@x++shTtQO-_*du0$4vT
zS-*ItuVdatQlMWG6C1kYW0dbR+PEx0rsqfSAU6e*;C^>u0?frv$ydNo`=Jix%?PpZ
zk@I$w8UuUTBO{QfM$Xnpu7N>%2<(-YIey7;(5Iv3@w<SP>MpOG1oJ36LW&15hyOwk
z)a9BIyvu?uD0mNWrLUks(4uS{3Hq@oiFl7_klPvSLkNxc$O7DH6)m5lvIAhIa?hkd
zePt0O=r~VOIZ-&bg*)m-I`O%&xxXJ@P>&W=im;r7aMOXq4#niXE3m&-J48`8aXv8N
zv0aH{xY-{7)4Fl@&;7dxhdn9j7L(cj+W`@z=(U3u8Pi(q8jSDrvlCw_U{bqoH}XZA
z0<=~Q$^`VgEIlF$EGkrThHzZap^qSnNi0|hASrkNX6)EMgp{{IbR)DBxc)<5dEE{{
z|G)Fqt*r?@tXA2ZSIV7rE(ZS;-+iv~^lE_EM~ze9Sd>L7o<jFZNPUa~6*78P1E&Oj
z)+rV=z-F`t!z0}O|4sZ0>I%j&!4=+1PRsHfoyRbhuo^fqsMPO&Zoo(c6qf*165xM%
z&&L0`#MP<#hWXq}7)6qfLRijMB&z=FCt=b@Y;9v4_HSui^?lM`r~6$Dy<l8d>bvX3
z8wCi!pUY3ohYbzOEa!s*$o>UDO<kVDuZGB-ng$<*1?T;$1+2q_&|zu?gy-tLmU!pb
z8MShEr-ck{zlQsxc+WDa7MxR-r$&NmM!BA;jQ?0|6rm-`pZyl(Qd0cd<nLp6nKO3b
z%U4q=D`fQ{DI5KY>s4b*ptN|!K2mgtVk%v~Z6ERSR2Oy2MMpd1buzS2v|WECBfsNN
zdKvLK7qaUTJl6BkC!}Ug%vo**n_%XEqGRt*VD<H~IkUMgv8<IV)>X+&NV}nfQTZ7u
z|0&#2(WC5nqjWf5T`$&PRe!)Q{eNWSONJb&hA!uc!2ZzV2ON}}A4V(>c1zs-h0BVs
z4D0nFa@Me{Ho9G}b1hPQ$fz1np$EL^6r?jM+nQrNab$cZ$=Plt*KnGMNG}z8^M&w$
zvE}K^y;zBvpF4?NtL4W_p~hco+Iuyhw>zXu3S7T%jMUB>#Qz}kd7jO4s-sO`4dONG
z$)W?XNXta}g;z@6l9yPwSbusxPIWKM{^~?Mb;;9~j}^F74{}_31J8or&@b$ptXX)C
zcKy(&PA0(Erx%lTN8=Yvm$Ms=>d@H26}ukS(MoKc5y{(0h*u+Y!bN`y)n`r(+dK06
zz6-9pM6bq6o#111oJt}BnP;TmLM1ljvDs#YEe%V5v9%cg=7?M$c9l8E+RP5AiQ>4x
zCkDja$KqnU6><}P0-Wvfh_iSfTuDCeEwZl1@Y6xQNjh9C&sRAUFX;E$O}>*4DUzYF
zza@%ub+xQel{;ZiS*?jwfdj4rKf6DIzC0R5>m)Q`o51*~!if@bY!uPq_{-b`o};{d
zN3KD--e15{cis(|$=q_=rH%VF4cZNvL%)kHSH$(pER_D@O3vu4?5wM9u>$<HGO>$l
zYTkW$aOt~>?#g|n;bXRmdj9)Jc4}iH|ALUCA6m$mob+2&`?To%H(LHf;6{{Bog$=;
zuS!!c!rT%zRf61vzHnG3GwiT~!Ka_pf@<#Nj|`Xr_m3GI8?T5;A-$XCOw}4%t#O2J
zA)09B$g-1`P}{D**)|q78d39sdu|Wl#3iE#zivsOS3gc^jQ%^Qg8u?D(gv@AVAJYA
zkQ*}8p)JHN%~O+Z(sQ)}j87#=WPzfT1ZM%l_fJ@jz?s4+6Nc38HCwcGws<9piHNKk
zd}#3U5^$AH@LAXl%t)<K+hVLwPPfomVGV84a-m!1QU=6X-}de?D-$!zh!Ycw9mO%A
z*Q@xs>vQNaUOpdhV4|@X#{rSeDxK)nhhBQyn#2+ndYA>Do9mPYdi8IRsH}etJ@Lk1
zH0&(%p==MrV<@t*P8COO?W>*~K4zhndOr8`H)pi5%j?wl@QN+-iQCh={}gkEinD+(
zQFD5?9$YzfNcX`lMv?845I3ySJ;U)L9+0SLFLgrNnunO?)-y{XN39DX775nCeM)qG
z3@2ecr)(;hu*#^d+v%1VdWnd7iY<}TJVf}-?SV3CZecps_jk>R`#az2Pn$5PhSV6c
zMzA>by|wv%$}*C>OBzqRN=VVgn$1qsAXv}VW8?Jh1YsBL_Vr(55yc8GaxV&DJKmGs
z-4-|P(HbQ;6)qIrZ+g;?;}D9NJX9mB^Iix#iTu;87`ZUdkFeuu=WVpvD<)765pGl_
zxYn?`Vi(U?>q-67gbnw4-Jak43*zp*6Vx_KsTI$Shb`@|-7~ILAtk|>Vb)LVLWS5R
z89l7aGvVL1Z=dHr;q5Qt8EGnyWwz6m26O>Gw~y!^dfdV1vtpd%^sH#tTx%}pv)$u@
z)=#cJOL)PTZsljo$BtA2h|F!~?DF`)f@;Xj!{rRuyU!b4gl9Jf{@eU#wMx2!k1?sT
zUouP4S7$`aOxK$;!?2ie-tORC`(1l=A^+ot>+PlC>11N2jr@`=ei6X50iefk+K~hh
z4bc(A{_yLc4PF1=?H={dc5ej;s4sVP#!F=&<<7tRz3)H!ee%EB!2jl#P5-l%mY?V~
z?S2dZTPfJ@7a{IyXm9_A$(jd{Fh_ujZ(UL{{~t-5t}rJHdczd(Q6QH8r_NQ0A~M6f
zw|379hNGTW99}nUbPper0V<Icq#+%Upd+7v>Vn^MK)(!FzitO<O6;IPBf%f7E|)F(
zffSHCDFMa*x%ef+HwtQh7NCCd{-cfkgs7q7fLfLkeMu1R0=nyB=wuHc!wtMa=m%kz
zEk5$z^>g4zg`gjU*B_#!G2Xz90!m4;9MD^J)sP2@6F@7=aTAC9bDL9#`e{252oaI%
z3IGiclu%k*{BPo+!74Cu%x}%ksU<!p>-ygGUht8?PNG22VBEASt3|ds={=jgAk+gT
z1DA{AIA>hK#;G(6Aoz&6wi$y0m$8^eir5c-x7hbt61D>hvaENIn^Rd#*+<X!5x|P~
zflp4NwyKU#rX_pC-sRZKfm+0m2p=Y^4FKm0EN-|7X=6;$YK+p2;_rU)fVb7b#ZeI{
z#4k<yP}@Bdu8oePIIuKkB<U&r!9dy+KhoRzX7&+yUjneXw~tbd_j?gyx+L~W0o)2O
zz8?xVkB@;T{L$JH{xDg2Z~;`z5i^F?Vh7RWLOyy0h9W>@P9&sE&Uz7Ha-NaF0<5lM
zkbd7h2TImZepHj7zSMFOC8m4Ds}s)f*Ij}CzA@QAfo3!>036fu@B-*OI+2RfR0myM
z45n@}XxeAiauVF256cHZkwrmAR+CL-QZt)h$mP97Gfkiq86tD`9su;Llpnn+N+g#*
zwJb61l{xWo;s?}QvNPZ<?$9Isk}fbV;OQt$+ay$J&hYX@=+Wr(D^oZ&z*TP_a)jL_
zTKauJcI#~~4^hP353){#*YkdUy!(+l*~-A}?BXK&h@fv^<Y56dPWBM~s>AP!`yPjx
znN{!ZBH3rh5t;YN_UlLI%(9NA_Yd?~F>)^gorC3CrS<1!SO&h#r4z)ydd>De!Y{fa
z&=7a}tp<zxzVqa%<ojL*i(}??a0r~&kY3=8ko%q~l_1&te=+x-VNoq@yI^BNL;=Ye
zlq^v)lC%keBmo5_BdN&<+K4T|O$N!5lMRZ2B+`InP)U-S3{B3lQ9v5WQ>)$k{oeC^
z=bAHfX3m`X!^K*wR&~`=wVtYa!hK)E2$q?g-!LgO)KQqQJmqmGKe%c%W&%l4di=E?
zvtMGU75BF656Wp?DjfB|oDFw+N{!(<&mnV>`Hv+H=HVllaiD!BC)24<n6F|VZbY|=
zHoohKS0HbsGnYw@{<4&}kFF(W)LXoFO61!0Wzm~AAI$A3m<w=fx-Dp^Q;Y5M!M@D3
zh&Z|sVTx?i1TcJqW$36|4@f_Lh!lkN24c&xA7E!+1QX8RCRZxrVxGoc`pDFALeRDW
z{!3vRb|Xo+U-?WuiDfNT=`_7P!Q^^`+N(&GVCj*r1heb$wtn`BwhodQOC=nqrJ~6n
zFh)s3gkFKOO0|4T&FPF@+N~xrOgbhT)>MLZgWdQ<ML{b2DCkA@0h8|V!KsY6tp%3g
zdj%Tm0X(7@Z<`X*xhozGmjjy9!+Zm=ZkI}L@y2m)J+?|0dJ$3%arT6@`o6+u-5Ux)
z#@n6_i_Lz%1B%5hv^)VYm7)PFK35yAas=!(CM#&l2cE9#GLk{erHwYkG2oi3clE9Z
zs!d&rst-ZZG>BJ*zxE*!m)j$Po_S7!(FcO5ovaqn5tm}dS&J66bR+nz?ijG@uQwpd
zI+rf(FJ$*y11PYqTeJZn^WL2v*XVbfuTAAM?)4ugFO)i3vBZrA7aoT_(%p=_pHLc;
zB@6qDbztH+jwIti$V0cA?6w~L$<!MN_md@S%UKM#RT*-8LLAnuVf#fvvqoRfAEvTO
zK3W{jsxI31{2h(;^v6FRBw|W=-AOMqFi^IR@x8iEN>@tbX<~NDUXvvdi^P_-Nr+-1
z0pO|t>|wgkqc(+rLqEiiKLcI`4R|Jev^=1@vM=_2=tV`|`jkp^^1Y93CttOli7oRQ
zZJ-|tP}qIPft#?2(;id8;KedT=TLVv*=r6~WcCrUTbWhF0}gS5jtgK?1A3%ZCCGJu
zYtn7pr*I`n#GS*Iq~^nP&)iF!75`}HOmcP`KKgR!%Pw^QPFMvM?Ipcje3DNxx>L#M
zmonr!6W@mV=Z4O-B>7oOR!Sk#P}V#5E=C^qVbL$X()}(h)Tfu?<hyQ+)h-IH=v>Dv
zUy7}?L;UC(wtau%mBD2uGl~6%mcOAl<$?*{#L>PCM+>a`o1LV77Dghc_h^vUs4hi$
z1E!t#1bK@_fEyWaOWkF{s<@p*={wk7MWmmLb0pNm_px7i25!@yta}09=TJe}<hut}
zB;r=&7lg+|FvU5cm%zTvw(9UL7WrNIVcY5CscYRP4HT1t7{CIrCAxR3rZAGh*N2!{
z)tWi3h)MqJG<~H9Gr6;GAJERY6@LHoOzpCe{5d>J5W#le8KJE6CW|;Ub6Hi^h#%%O
zxaZ12<pbOajBx^LSKief#O0+nQ;;XYQ7bB=w}}&8aoZGOy7A;pGV29+HM92-74G3L
z+w#(OMYST*kp?WYVFSB)t&sA3Jjewk<^{$rKNi5`k3O<v<HTv-&nr2U=sJ*4&nh&X
zZXNZF@05b2+i`Sgk5n*mW|z&1Wk`}Tucx~+@^1?Kd{h=ofTQB>{|l43&shPJ&m%s=
z*G`eMd^g`)LEa)S-<D_Tsd}2N$e!=S##&#axzm<kJHw``z8+;U{&j7fc7srdx<D@0
z2tfq)vvrCJC6MfrJkfEx#5}wOAN6GSlwRk&Q5W;Y+gkh?2y=9kl1ED>&-{c}Kfj0;
z|GFgiKtRG^%wEz!AJjnnKE=zL;quH=#-HmXI5@~$e^|Gka*%+{IM%U~;U~0|F$s)i
ztzqs5FLykuTBZJJeEo2Bbr$bmV&}j9Fl;jrfWLRtvJ-UFoX@>yZjyNB8(cFiD~zAl
z@8Edy(pTzH0jofdkK+?Vja{rAS(B+XBc}}nDO}wfxGZY1NJ{d%Qg6{Kg=qXFVu;qn
zMK7NVJ<j<z&Cu%<2AsYj$1JnaZXR|$wQu((;zzgYxK?F^Os+2+0nuZ@U@2E*CJ+py
zGPvvzm1>(VhDvPFZ?rEypKIX3U}pWM&dne~rb@M3w##a2^Y@c`P(W^g<c0&r`P{4!
zAb}K08)^Ibf?%Xrx9;b3K{);X?o7rG8@{Z<7}vOv@qsY(Nm?Dg{}Rj2f-@vCLkY|T
zmv7&Z6<Z~BsvceLwvU;ER~LMBn}EL`|MYZpU_0-rJ;x_E(+_mtAG7Fp-%?}=`^&zE
z9REN#>@5B&T2V&+ZWm<zi{<r0B!4m>+&0O+>Z~Cj8c&YT7^A**g;jGsN==~pN+q_-
zy-m38fzH0Pi%6?apMXT?_Bpg7FJ^M|jx`kc|2l#LXEZVptUlx_!<|{%TL8GAa-Y3x
zPJ3-7A8jh|`>CA8Cu+puQ$)qh5Bka-?~9uqRLXWGxQR$?0lz%ac*n=NIZapOinCQ7
z7rb0GDR>#VhI}(y<CD{HOf#2}1USry<X_8DXYnQQyZB=^=f*SNajpNblJ%}q06MQ?
zZ1i8#n29?h|8RwYR7C92o9OYcIeURCZiE*A)Q#BR2O=0gHgqNhG-euffQJ*>mIP?3
zBl!N9N!tJmCzMhI<7Gl;-h)ataC2d3BEj|smIBO`4~TKG2`{j^1tsX(S1+QZ_B$T*
zY}uEOe>z3var!`o3*QE08J~8{>t|hSkrT?pa%KPV2=oFm+hkxrT2QzD`D2;GpmMt9
z(^AGzWXk<33Ie8!=AZBLkA|5s+84q1w+-6YuJdS#fbGx`KuS~8IjzqaQZF_C%;jpY
z+Z@&H>#?3L<n(>#-q3#Jr+$6+`1n{1rGj`DzTug2s8r!qwmzXPUJ5%Ej3pR}0)K}u
z>azEHRCN(f+Yj04hw&m@rNB>?6Ao(=@0RG7)*O}pV#JL<)_g43Y6<oGETC>G1SrO%
zf;t>h=gQgNyE+7SJ-<^vD~ow6FRW(&;hx*I@3;1lS@+f`@eA)>lcdMjL&Bt@g^!xn
zV%a6-Ei(<;&m@K+qj@`#vsj}NNi$t|b?&$8y!uO&_+C;?)L`ttztL6RY~O)oU|$rX
zxW7H#s)BHzw{KSBLHoXP*i%9I!3`!NHW?t3`(JPbYE=MZydZ*6wEyaDlz2<U!F|?;
zk^{a!=;tU+hL50b*$+qhBV^WYIqBHWi$V?tq8NUtqa=4qOKdw6?O2Wunw-3l>F#Oc
zP3T%%(x?liYleuS3hFWk^tef4mtO9S_~bsA2nQ$8ndR6nq4aq-qeo?q<s-clVfQPY
z#$_>+=TkF_Oo0^rYOxconyEb*_?UK^f0LcoHvIKH%-dl*(UgkWgkuTzHs^mmmBknm
zquFm<!qh_4Gq4)lpz?|;;*29#N1&W1$`Y?g4S`VT>tbhJ-SJgd-`pB11h#ihMo^BB
zfhv@g^V9+DI~9l}7`G(8yu3}5%<!kQziZ`J!8a{ATTk72fGHl$7M(6xOELDah!f0p
z4=Nlx%cOTCpt~yfSuz1;^|NQu_|lH>(G25rv_CTQK}<6zcToAzxr8n}rDebPrSzgl
z8D8X2K`<pM5bNLGG<Lp(O;J`qMttL>RVDe!MfL7;*t@W>ly@4EJXn80-~p0wPZ_^L
zKV_jRKkhYtwEygl-ENohUJZLa3Cu$#r*lNI76dN<x)Bxdt<8i<gUrL*;G7H5GKUcj
z+t)9;_PBw=FlRIZ;wn}HM`Fm#Elae4(*g!#h~(-2ofjzpUkQ{eL^53Y!Qa8xh*pgI
z%83Tno;+XBDo$L^;s1}$;(s$wpCMs9x+_}4KMWB{y#|5{1vBX2kKk@$)KE~1m5HMI
z-}lf95DUBaS}`_ntVWdOs18}3v#AnDQFq`X|I**&1CQ!q><$n8eW)Djdy0*-u-S0!
z;CKSo*|)!~+zDuk8|uL_2Yu|X0)XmH?&rpE@PHZk+n);eTMIqjrR-!ODW?u_7y?vY
zV4L-V3HRMU^jCHBm?TNppye{(sLYoxfe4=4Hvwj+DngH1SgQy%oz>!Ae0UF`2dOFO
zNgc?_s~8of#3N+};`vA)j@3W5ws9IAA&c2A<Uh}!O{9c+dKt5kiDe)2oQ9j;Dt70&
z<U5<Y%3y=?zL`5-7LL#L=8oFbnAV<9s9YU%OX5fD91MK^y<uS~xkcK$WU&~Je|z$7
z*JTice*KUN_l^SJXje@)>SOBo!%TcUZC!R4TTWh$e5Qtr=6WwnGudYTnKe3Oa$nzz
zci`h<W-uFGj8l8kcwPw7Vf}y*YX$3vP)>FWq1R<exR2QB+nLZXv?J`Ru2xpOc42Ob
zl6B(P*l;r~#j-o=O6=ecOrujx9bU67PNqT*H~ApxD=PA?zcbHqJS#y_;o4ko%5Q1W
z>B_NpHEkS_zi@On+|sS46AmIofAP~~P3)Ml(*Um-6JL}3ncRIB?dxYWyT~vjn{n?@
zwR*AC1URMF^5X9w)B@a?cY<)1M6_bZ;giAoOi<Lcu`~iZ5fg&SmZ~O~$4z6$bV!Nx
zO&<JU9MOEZ<whDjpPe42TI*rWK`~c)Q)S65ILe2(7CQmc&vrDyWoR}EST)|izS)Om
z7YriQ(e_q9R!be?d0g|^c)Z@j)t=Tp`)A&FIBH%936#Qh-;20+=UvAiua-YjCh#j{
z`iZbT<ewYzy9U&pl;aacvk`*%vM$%Rhb=n<YT;!WR^6Au$N`~SkbtQm0lUepH7o8v
zc#ZvH99euR*gfQKtVd&1IDWs^WMeynZRWF_Vktk~$1-0<e(mj@jt4#<2)O?u^rq2H
z2>FP`@272qd^V1RGc9W+;_h`HeLv-XQRkl>vaVINL2bGsVkF%aS=-?C`TlZxg7zf}
zU|z3;Vb$#rq=B{8AKtr3^k#ItjnFdJDN9#LbXL~D6g;H|iaitR7vAZ<>|T1%eW2Du
zNzqZ3XYqxTj4h_nvLSottm^?B;s2l|+K~$Oit(J^c`H)LE^00>AvvdeeKEO?qkBIQ
zByJv1qIFP}Nkkji5j9Y+D56nr3Jwfr(B_9gwAnQT1FI+W39S7wnUVuiK4>2_%!PqA
zK@7ko2Q9}R|5#8(fISV`saXAC^fLqYKXcHk26m!B_47ace~LtNp9kbmg(~?kKa2;f
ziZ)mc17R*i4L60jOA8i-&s45GB0wE~{NrFG2MfSu=yjrl@qfp|C&)EMDL8+`m!Gv(
zDfTI`nP)FWB?dQXP>mfs%}7|EEw7t-KItvJ;e`ZZ;s6RK00=euZ^NiLbiMG$FbYY6
z6*83mPhTIeW))-CIU4W(*b>CFfFPGDG}|g(6w`7^w$OBM61yJa00X!;*etK5a_sux
zTHsb5s}@2z32Y5Mdm>uW+Jz_UdeP=;-rZ7LTUivKDS)mxW48p*n6<r>4+Ud5pdvpw
zCW{fYz_EbK0Bn{-AKJWoj%ZD-9zdJ#^diADIg5XQL=4qo>QBO^LTmR&9WRHS5c69o
z2tLx3dPxgEq2EY<uIvn%Fk1b&bTGZ}t(R9e4zs@wYKdygWQ=#|)=!eVh#OC4$&CbC
zqQ{xlm;1i!zpT^0OaH1rGJde|4zvDDh%gikk!FX8Av9NMN7T#k=nQ06YF%h~;?55~
zi0Mz{cqhxZy5tk>&Q~3#YIS`_WBbPreP>>MwDMtFJkPpvC%pvP*(TpdD5Gu*_YY+p
z6Smcbo|zmMJiQzLa_rrLyP(^2-BRv7MroE0mKET;=Dcl$9#2Emt&3aJ<J2wa%06Fu
z|Ji8VTPY>DJnqNNHS0XEM#1gqaD3N1n68q$N=OxIGGD$+nK#mm79JqQgK&!LDZuuS
z>qK9!zNWJO+bli4W;};)%ViXsH2nt3XQATG#n$!GCDY}hg>`C^=Ui8v7s>jtNtV~Z
z<}ZMAB#2<D_&C*?1?O3$s5wUIZ(-}Af~v^nfn*V<gRpdPP#IOgI9G9L<|ChtV9P07
zQxOaPI$*09YlWz&9pThf#(4jV;&$!JWjQ&Ebz3F<`_1g#(vjGw2=B5>nyB@jsOJw6
z?yKQIjzO(gFcO=9;~OsDo53yI``1?RL&W5AD)cDq?}JnT6F8Pd6L+vFK${?1;Lskn
zBNRNgA;zpMs349ftt(k2Fj}jtxz|-tg=XuYW!>86b5qcY@_^S9tnrK3qT-^uM|$M~
z)DoCUxuN7rNl}q#2>_dO*_@%}xrQ=`AyFn46}k`uM{#a`0q8FtMK=+vvoz5VmIzEM
z5Suh!SV)g%0~Q-asAz!l+L)^$wLY}(=v6%>nWTREl%@tf&+UH9dHwHvlEUqP9PfBB
zth59vbk6QU&h%cbM~b4?dh`B(f0|kx*k3fgsV!+xMN?Uo+?IXZ-0Lv6e0Os<O@&K2
zTfVh6A}*Ehdf`X))n5x=D0SaGjRpCxS^ndu=Y|daf{?z7wOhQ1&fmU>WFsT$6!ZH&
z=DLa_{->sF?Rdt9nUvjN3%d-k>cZLPTbUh&L8MnOnQ-fGWb-2Gt$Ti(5w@>yze{*T
z9z0cy&g9TycX8`8d{$vx>xpo0r*!4R_<Y>yk>U$Jp(l#G7F=GdRQl;>^8?Qn!d}NB
z4a{&sALUE*k}**OfSY{BK2uGrc&+POQXe}OgiM@B;)5L-qfRGT)x3VZV)^m1&l&Z$
zM~kT9kvs;x03I7%6U4V`qn>PhlX`B3+jQZ%WMvi~lUAdGGU?CQ8reLD$FJb>uxC$C
zaqjzdkEVS+&8a4SYR-=gUncuvfU!-+4zuQFI=ZQXLZ7nPqf0$e&cTVp55!4Uda3uy
zl@BfY>Yile<Jes_B$~Wpm%I@i>KAof*K$C}8LXXYYVOsUj@TytD-3A82C1Rq=LxlH
zCU1{UPspAaRFSiaKCgcD@Dd#<eDEa_!Eut3e9L;OBdtdm(^jSBdCoC@{a(nNvpmLm
zmd4iWnLQTHvT^4`SPlVmUg+>D#(U#IUF-?z+VKz>queJ`CZkTF$dei&gSq@Z=<$Y3
zczT!Zi}Nq>+aqXv@gHU|m7GbmuPZL*Z6Xq3Gb=_fc<Cb7PN%Yk26s{`qu6YHF8<zI
zJUNAX1}6(=y^^+E&8dlmgzMkadMWUg-w-dfaT4oPI4N5RTV}`JLYTZQ00n&h?4~?S
zHM>U>HZ;fRaRBU%&-Hudp5C*2{a1w6LVy~|)B?dk6Yd<J+aO?g9XS>?b_o+%opK?T
z4CM>>pV*){2_|s>?Q|-SEq)n^h>1UcVkjXYL1#)L>R^z<V<xm58rGWAS4fRTH2ES0
z%4@m{MhpRwQ^rjG87#*B5#&14^kj3~I@!9w^qT`Ne`Av-c0>C2FFiE=+w)X2UN``p
z;$mvE{fkfzUVG5kBt9iRj}IDG8C@e~@*J?pCYp&u?$xWvN1rjBz^I@S=IEZqw6(pZ
zWAsCeg3;32ka}S<{%h`{?-8vsX3Ns@rnO`t(>Ppyj`VSw0$eNe#WBEnGI)CO9&a}|
z=}K5*Yz>R<w=*gZmLFi16x<qSg9*5CII5e{>7Ti50lG}{jV=Bt`4U4t-;~+F);Cob
zo?6<DJ+$v?f4zs6@39trST#)pY59mDH7rx#>hWO>jso8VBH$|Na4ZNPfG2KyrlDsl
z6nS_P;m_+cAb?r6MN-4^WX}lo*tq8(uC$(fX`EGHUalhlBotd7h_kRv(>}rYXQuvK
z_C_e5o`~D^kjLnZ(LI+dKXtQ9+fOH!0*5}_CA{};q<5CS*?Pcp+lrNEi<`9=lKI5u
z_moN`LUddivq;i$?NJY>Ic(n#p?BdKBD>MF!!HyoBAqVc^O-zju)Y|iz3AaaH{ttV
z1Z@U!=vt3%Z26G4PTk3m_)x;jKQdRZS3yzpyWi2X3YK0B@GcpqdDYeupcRb@S0Gt)
zt2}aC)bM*Q5UKzx*EU_qIH0&)_7;$Du@7@@@zsWwZ&7UsZeIA#O=)39+ToSLC$T{S
z)XBML9rfhiXYrgK*RPRvq;ZUR>DHX$#k=|dU^l-$haPs7QHx0)pFRGA#08&!hJ*T$
z!gT>ltpsGnNSfE)z!0iw<^ARir}nizn1#t(w#$|0UK%X~3{h<HaAuvhzf7P7@f#1P
z@L$COViZG=K^H^M-wE(yc36;;Cf)pjIHtvEHzFRLb?qWCyeV-#%}nHEDJ*);6)3Rt
zRC?ykB&-B#9AWSK+oL^plJq@dVve}>9y@MqLZM0)Md5Ocn3%Dn<#+#9bG&!zij9=&
zNn7-(8jS8Cd3mHcY@U)Nepk<qCSn~gKo)R5nB*DzN#xV>_}Z(XW+Ei~!U^XUrMs`P
zcpd>a%VLLUJ(wCcEDvFno-X%OB2R4Sv`h@{U>A-gV|CVHi;6(y*2yJ5BJ?d(-v9es
zxCzDu<@jD>liI`aVb*DzUm1guH5bWb2OS-n=B>VEf5JYD@D`^=7kVMQ7w*~|YzWom
z_p>)77b-9ocw^+xxv2eSDfv22)^e;MKK*FI$kw(IjYlFToqeNvKGiDK68Y%9=7^#d
zEw?kxNpf#^D@SS(zO%Qm|Dwm$OPsX~QOMt07%|^IgTmS%j-%^{saYzyqUiBw>t312
zL>`I}K~x5O)FXR8ZEbz`!XHNl2RHz@dKG1Ly|9RBkrk^7A*>u6tXvNz)Z2nsuDw4H
zfBvGFV-{XawE<#>6&FIBhY&8|9~v7*h_bPkABt*GT~`OU>lpr2Jr0pP?&J{R3o8Lk
z8hqaZs%|sNT;9o1{QRu!fdW^BZT=WVPF}ROW8oXsQh~UOOB(!+K2!08L*<*dvCqS<
zJ@N6)@*dhg?e?4Fkiu=s4T{tZ1Iztq7H|Y%fKDjuO+tRc+?f4Bm-EhitEaCC^I$4W
z-rp;HJVgEo;5P+CT)BctzR!d<w+35+cX^Rd?rR{rncJOA040?BoqlbwS)r4Psy|1D
z=diwSzLlFfA9eaSIA>7dhT25`3I+iS$h?g^2);k;Q1XiC%p0d5_N%pDGj&*9r9UN%
zW>o)GBJi^SCLzWOoQGmF)fK35ZA7xD)-K((bND%~d*JtM7g1yM|GIB|!2PfC`U)5S
zbze3*2)TJt2z2pNDVu-NMO=)H@Yve??`aR*`CUyejOm8sXPw;isX?e;AKb>kE#rv;
zCAWh0;5A!nT(zahj6!1}=)mo}=?`xs5cR)@9W#~5(Q^BQm2J1PEsKSOP`+b0O37vD
zZTHuysjXbU&ku?ZMS{w@Mq@KQRZ#eklS->0R3grqg@;t^zgF<28<W_h`t3%9L3WT}
z8VgIA&tInGu2YmK@9LMTjMpC?WRQ-ls*pHP@i)EvbX)WK>86GV)n@`+7joB+uceuX
ze9GASQgyOBYmz79aC6dupr-oG=&jj?1ZGw@r~C_lM0!Ej&(|qW8`C3CQI26P7E6s1
zV{Y5Oq&VuGW(rs$)y}X|L8S&8y)1tCy=9!+y~obwIKx*46KXod#FPBca=nGo?GIX}
zY1zn@1g2s)C)`E2to{D0@0pK^Ppp-!hY<AL*?p7r7woy`r3S-W1P>+CVfN8iAO3X#
zFE}V>Y1(jx3qYSAtT><ZS+7Pbj#cTFQ$)b>bQL7LRZwex6E_vo|Lz&0!PD?gl&7WG
zvt)Iwn!Ff$sKOFAyX?iD(_#K3?{d~J&ZBY?1yo=-jSHfpZOei~M}0b6bbTk@r4p*P
z>%x=4%P0SvI#WT5<J#yo9M-w^JzV7#I=X=eTA~}(4J^uyiacE>J?gAyPO;$L6=x(A
z@~k(O8BLWgkASGVilWkh{TeL$*FUUY5AxZP4$tFt3Cv!}YeT~)iZb+8J5Jse#mu&S
zqXo-((N{xJl*p8)?sHz_s}D~o^>pRM&RfK_(I#cw3q13gF^J%?y7x2Q{(IK&IF`No
zpGwb=`-LDASmdL0^5nzkHWq^t+CEkPdR~+D_D1}n`t#sP^!V7O<WyBSQruE>Hbu5E
zgdhpbNd%9t>qXOhS7a_nQV6<%#vak(oJ~*q2O;P3B#i#%12g(jx#)nH9lXTBaWY#Z
zHO2Lnq{=$m<{*F+CF8Z&=q1PRQ#{aikrwj-;}j^;2B%3)Jf&yy@R!Eh7CPei-#_Zk
z<+2=R%>Spk5^j6UxG?Xo2oEVkkH-sQFEtr9I8v|f8c*K~l@?1BX}miY;#@@2(%`X*
zZX5nXcvYU3J8Ost9e8rU<oSS@8~n{kfR_$u^EdrkT${=sH7@dO{@q{#KRwY~`5>QL
zf^j5nMD3n%iUPX{?veF-^HL)1h@hOy16z)c(_dEj0}O<i5}s#nUaGqZ!Q{8ARR+Zk
zbP9c0`TlETs>)uwu6eOK!v}(vzbJTjmyOF5#ey?4IXCxlw^iY<#JcnfX9)Sc#NdS~
zqxs-BgR{9a1@EV(Gw<qf(0~8d!2*TI4KMSIGMayIxpFqQK;R`+K!Kc4qf*(&iC0jT
zFXNa8lTz3n9iC@xcg$q$NpJ8IwAU4d65gk-C_ureLU(7C4nA6G4-GO9j^=&4QcO?T
zh&XgyE$9tnlIIA6+O<(2L1}5mV&hpxaN=e)s=qVAWRrE7pH$K2TyfIsMZtn{XE~b=
zDX6xB$KM5i+w6Y{>fDkz<$yOG8J^#gR-NNR?Zi_X)2dlNao@gmg;|3q_SI`^PEO75
z3e5Ba?@wL4ar$EZ^vR54Sr^$Llu%dF50<7$6Y_u|=cIgu-Rjdy#zA+f)bp#}r5T4&
zQlkpl{u~x#&2w%~9!Ab@J0DJwz@8vuTlB~L?6F<yL_!_5d23n_Y6kSTzu&xM#AvrW
zlrXf^;pV313JftPVUL!a{i1PTK>>HMSCrNNzKdureW!zfG(1J8*TpgrjdlTKWA4{P
z{ZlksA^e|z_1$j%dFvM<4W$o2ELU~1z)^ITZLywT2T}1jF&Sh_KJnhAdP9aUFNYv0
z8Gl@L&yhrr4@yEQowJ>r<}Z0M=})r(<bmYJgleb`CsAc!Z4hQ%P`d*j?OFQ$xCnd2
z;J&2=UE(^jfnAe)%S_b{Uj2S@muo7#cHcDo+W8q-1w$7;wO=#^MiE~?B$U9O3N40B
z6@DX}I{eA;(6r7oO5~JJ(ahEtc?np6qXwD{39&adNCL27`5z##i*x36_MjD9-l@<1
z94sL0u5vM8g6daf7$$)kbnWRu<WIUN0T@hteWxcEkUH_zmOVNJ<6Lx@EUW~SQHy?-
zz$U=DlMg)_UoM8Ipi2KxK~*Jxg=nH`h4!VAuoP9SL@p>f2H*RD`{~udlBFsPCcmgs
zUsMu=?3(Ubs=Nma$lNMQ?n}FcSj^bgB!N-=dQ<Ft1)W*C47GC!l|6I^F2A5Dx(cok
zqm<3;p|rTGErFA!pdu%UogZOXU7wnvNzjdV`F{E;WcZgHhamT`6*sx++R2SkDc3p{
zhQYoZZNE*^M1&svhy?ZE?T(95gOJT%*Dafb<-IEajAgL(6JbLY9$N9Z5PBh8R#<;f
z`CsR*T#hhV`EldylVgS%#f}d4swp2g>+Bj*XMcd8Bj1s%ACF~kYO<~auCwNyOEEdz
zXvK(%%4^W2ch&c9{Wzegg|<*2!}rVN*iAJ!>Qqbe;~kLbxn8|0;-1TiR{YC+LXH)n
z>)9D^$z)*J*EJWaf$t-3bd6p=2Ft#7KN*1k1lSXJ67C`>%TgDM!NbgiHEI25EIZ;*
zio*pw4MEa{f-nQI_S~9k3IO0{a%ZfU9v3LIGctgB1PgGonAf?(g$YFx?t*p<Kh3Jo
zWkf4}Eaf1D!8$YNb0goWAy!>r0sHPgS(Z8U=<!j4Bk~xeUkY@5k`FfsLRyl#i$4a|
zz&n0(Z~4^_JDAFj#A=XP+s1m_QTk35u_KYbI99{~YX~axEDM7NAw?&v6plf<s`ipz
z;Z+b0`jue4(0L8`(R-J-p0PbXi$7i)cQyv58UP=Wz#H)wO(7GbN^D8ERs3Z!!A^4A
zr$}?W1%#Sy#~l(YUK%o7&j26ED7#j>E2HEkTQ|?Wiz8A=92G^i*F?NWW0lG)7<}|&
zZh(MzfRn9-$#c-&$H4c!P8{>~aT_OXRN$Fjco;B>5osbi{*o`y>9lHe3`UmN+k}ma
z9CFMbK1zIM+%mav%JO33*%K^oXrJ|IijR3TwNR9hv}Be}w?eaMaJhLoudQcd<g8z*
z?W{cCr!RA5GcK#*#g>hlbe{vb=I@I$+VRQ7gqaszaxcku><Em2sIcOgKB_A<Kl)vU
z)z$rO>u<(d&pXO~re*=fP~Q;q7@_<=&Yf8nk9A{tA)f!jqO>h-vGCKUBto0ulXiMY
z#o9NrSBAX*X@jUP0`V8-zZi!yX<LG;w=P*!HRptKd?q7hERX`wn2GaEEwa3`RTDjE
z{K4lDz53Ad9S_I4;#<e?*KZg;hgKYhl6(+ie#<%xEikF4i+!%fIEpdjvQNf4WWx|=
z$Y2DgSOjldqU+B40hc%o1<dwyV6?q-W?Ov09M*8I;k2RM0H<wnl8afJn^4@dQZj7|
z!K7Y?Rvd5i+oM=EEnRQYh%@8$zek=mT{d<@SH?Sf%b>iTlo}=*rI>H%$a;KzZnVgd
z?a?Z1DzH`(9?hzCrn+xaSss#6!j+BPI~k|0QsY*+jaywP-kF4ZDD2b87pP{jX##83
z^T=t9ZT4zu>};|@s9DO+ug;$@y`LWrw6pUT)2XcA-`u2+es955O3x(wv7M(cyP>PZ
znSH=RzF;2RIdOhiUt+qbkk8-}OO&`fn(xJ`86L(KyuQySeF+L%qgj*C*8RHW*n`zg
z*m|s>^J$^6f_nN%dBPQmAD8vq+;f!l7AR(i7axrKw2VdrIFd7yK0hkmL-K0KZQH-H
zrXIJMY-}xE+ns6K5aqHw;XHS3%~t-rYxmJNpV1IKKk-&l0F_g^_+;WSRS1$?#ehU&
zsojGNt=?edJ-+pDOa>BwA@-dI>YUOWd1jna<kAy(UMQ~k__U9Ga?eJ@mdZm`KPCno
zB9<i%D?%u?RCrMMxeFm$6f0=_&;=RK(X)|Cw}V2Y1D2;n=SQy{r9z}pzpXW@YXYo|
zQ*>CtkNQ>yJRg-GoIjRT)(3NHZgG>IqyM6}H6kXX$arq-Gj`qKQQp?IOBlZKo+DZd
z+WUg*=tGQ-NzD0Z!Gg)3kIfLGAyRdW7x3|ozvXdu^w^4TrymV0ws8(r-yuTCmIAKn
zH^crOI+7Ssu|SdRism4s?6nO0{UxlM<oeF*^^-rASX2R!yM<(pQHM(Pbi$ao{|{V%
z-j}4VD02LTN3|(bxYh8lhmW`<a7`f2@zk6f8_}$pDkaabduja*+XcL_^`a39zo-sJ
ziS_-g^k%rGC=wmX`os!MYgIVvc7LvOlQbrCTxi#chK=uYuB(52?%}U@^0@UB5a!2v
z?_+|?G}g^M_=2YtDe(wvOX#p0iQeUM%9X(FFAdvP9Hx-Q+5jF{PQ85NjD44!2Gd%q
zwU&p>=)h`papQ{8D0}o-e7JK{XoXeW8&<z$wyvi&b~2cgEDaXQ7*p7@qTJ+(k@-gZ
zn+}-TH`VC8yR{9c&;K&gI2G8#`Z=u!VEf07d_tzW&zR$Sut#ve6U<UsJGbZQKc=pg
zXB&1cD9p7PoaB#<9JU#~acHt5{GsCsW0_u(0xKOZVKAc8&!;dz!HbUXW7FE1k2WP_
zZ#$A3SgX;#4TsmJ5}00<e2-_ESYxgD!Sdo~{G8Do$9Gbt5~H0Jwll4IjVbX!IkskF
zlIAn-`P*7bVoLPvwYV1p&Zi2+jT0ow8s%{KfL|9jZOe5CGf!%dsqfmgu`}f`UK3-X
z8H{NA8Md<|=TjFz^Dy4lGrfQtmR(cYX@Mw?GYilYAK|T}8Cj#<wUHdPE3(n}^1+8+
z90L>D{9`?^Zy!^0YuQ!6$6=dqBF(;U*Q=S30-~mTdckmo3e@DdILz(S?8B?+q)9(w
zy%_ahWVtR4Tp^>~^LMHB>X|D$s@?l~&gUq%k*soOczW_jUG0s{PT{PRD~ql4&qQjN
zD@KGr9oisdE^b7~)!4i{eaGiC_^L0L^6b25W7O-84w2TrJpZa&Q%a1xn?-ELkiDwv
z$BHb@?L;nb5nX;D6~55H)~9=l^4+BS@RFBdoX$pSNV+Fix-YH65a$eea*p@sz<bA2
zkS?B+6jFAO3;rAtMBw)KJTE)J)f8S$pQ_NXd~KRcaKfpn$(z*W1}JE|@cMhf9F<|q
zS^OBsi&--lm3>Vqts+~Gy$C)sS2H$U=L)m%t!KnBaeP=`;$5Mr6R6MH+@1YGxX-Of
z>V+%kXELwB=YLUZ^<TR{b+R36MVs;rBrN{CboJ^*W*XUFb$%SL-1in%mfL*a-pWeT
zlY^u3t39n*7oQ}xV~aR$eelKPe|%eY7+TtbSEkoY0tix^s<qE+j&RD9t_R{Mo+iuj
zBVXX!X_&OQMVHKF26%FiMu|CPCBREG#d|;cu5+4l5J9i-X85m<vm12K-Rud%fT9?%
z9eS0fz5W%Rt|b8O#(woP?!nev!W53D-rD#c4HdLEVbtiZ5q2Wwz(sC_OZraz*1ZIN
z%8Wm<3}3MHg#zE1aHp5R=AI33NnowZkMH+Y`A~ZTjds(Ze#XNKfNa2gq84c<C2oQ<
z;KEki>tB!hz`fh2Y9lZ!2w!e2c4GI77;q)MLWKD09GIoBJwPN+0FNwi%H6&`+uyUy
z)fkMlK?)}2+-iqjl#DW8J`Aa(x`(J3{2uSb!DtV>R<wP+sy*R8R0fEOQaQ~5nF{bW
zOzrLm&3yL1L{qSC=P7Lj(vI!HO@2(R>Y-gV@_7*9@cUfL_tw8aFJCQ58!Z3EQSOmn
z!t8kG$A56J0hL~-`lOmroy367Y&VjQzhmKG2YC}YNx7B{+cqy@M2mf%atp|@Kb6w~
zUOt#{Zcq5#BNrbkT=GEr1!@7M2UkI_rsPpOIq6_es{iD8V9?+-QwZ%l>$DTXs!uom
z;_Po#i!)hD$>GJ!>??=zrreHezoY3QzKV)SOeo!<$bDoQAtfvem#13XeR{)SG#}Il
z`_kq$|CF8;T|0JkL*IbMU$5u{G)(of;hK2NX*Gms&#Ef7t6x1tQWP|%b7SS+bpVjv
z!{;1dl?hBzFc`sue|J*oEMPEw=gr$z><U#Xmh|&Yx1wn@HY<1Jisq&$2L#_I7S~=K
zK^b38i&p%qc~r)I{K^{KZFsqG`%2cRcn|?|r|j}FqJc>BWbbVI#^5_W7skKTGE!qD
zX*U@RK)^0*F(=zP?nDqFXlp@V-{4Ie1aoGE-mALvB)xPOM1%It)?UzjLvQQ|?o)l+
zmsnnk>{LS>V^CXdi8CdX`d}1bCvMrkDGTXTN0|0SHu3C?`}BgF0rDOe+iwc9I;jcR
ziJ~ISm12%X2wClOzU|1}gJtr4Lx5+zvyDxrtz-pfG=QO&gNipDFL6X)z|WOV+i1Q~
z5LAcmcN@m}iaHp=nQ#n+$q(Dc_wGRuZ&q}MaYtn~6~UsI%pOkpqa<G^aJS)#>`+~Q
z=dkc4a=h(=lS;3BB~c_Rb2W8$G>28g;Ha7wQTDAgaF&D>4C~f$ma!1D?!iZy2Bdjh
z&FqGgfK>cGdRNFh20mnL&XyOLB0oo|emc+V^7ogOmuQ-FxmI!>rQec_4wpZky?4%5
zi%_WL<LG@D{feXbppS4yJ@@BTK1H+Cy5q|j@|Zvd^)%7@kceJ%XpY?S_L+_JMWC9h
z64y?OzJNOMt1G*pf7?)g5N%$i*Lmv$_tSQ$WsIc7g34t!thmKcFFDPYn~}tu*$13u
zHcYq?<4z{c<#rBI;!TNSo5TKSQ-r+CIGbkrq*5XzV==P*;{Eq*xmXK@+Sr$)_a-5U
zHO#G1Dn-6fLloOm(`gRic?Ji-Y!iF9SZ(yQnt2;b=-R7}*?uqqioy)7m3WptrM>{J
zu&l(F+8_79DJ$T;=j|>{FXOoLuI|2Ee9JR@^&6mT4Zq|N3$LSO^vUsW`paHtu6Kq*
zlQ_>{8gcW(P=~T7u3n|O9ML@8J`$MNkEpYSK76rSqw@IPiFl?Drz6*OofvV#LodcQ
ztMMOR|1w7rThQlLxqbKAda4p1J_V*wsjF}IHaCsTc%gW)wSvCjb*E<(kx5DQ+y1tx
z<3i7Gdovo<n-z9E%$_=*O$4@DZn^Im#oNU}GP-E!Wc+4zvB0t`J1`StXGcf4QO(tT
zzL1_lC{hwU5BspznRiD~sO*jQM{Yu;wRgFEhVQcv8lT98>50?+zVn6my)Sz$J|>jI
zl@Rh?hr{)_52l)lsv&>cVEW@;ojNw5oW;puzqUT<oks73z@M4}7hiMZ&nP$!eZj9i
zpWC544Fm>Q(cFdggC4TBWFOmzu-_h|+lnO>x=O7V8_D4K?o}dP;>Se7(d13}(mTo3
zpY)P`?~a{6e_$n2&mhqaJMQZGrNM-9D9p+?ec_oeWzf~Go-a<Ad5R5A&;X%(<h(KU
z^P%f5n&7Z{d+^q}ljV!#{w~X#<R<Mj6%*QX`8_0sZu3QNfSznxbDOJ~K&anm@VP|y
zC}T2jbT2EWuR+A!$ZK6T?G%6;lJ=~=c#%>8Q(gXcb4eSVQZG|%-jjeMyoGh2pg;jQ
zlEO<6P^Q=&iS47ffjWQI#*wRq7&7wY1J;?JSy>DAkI3-2*$iJwGT3zzWl#Al@M;HP
zcgR|Ud(0ja0oV?)<6sJ5DK@v>oyA+GJCFfjC1lp*O>p!TTFeElBpQ@{DpTX2u!EOG
zo6?jL2y_Bh-`PTu&Q2f9xiO0l4jgnB@Z()8Z-0$;uBZcJda2Kj?WYPT{HcFk0W)N1
zB0`PJ3_B#@Se|<pi&YB$Tu>x`5hW0%TC>7|tL^-5ubNR;Bm-58J9!qb?m|I@^NJe2
zmDv3l@isDLi3OBV?@#L+fDmSGS684FD?ZB_ier+ws=Nu5<v9$vn-`N0;nfpDk~UkN
zguCC2YQb+G*e<%I$DjSmADvwa%yR29r{I4*XS#hwI6Ns8ks6klx{RO%XK&1ojuMr`
z#0xj~v@O)MS!wCwp+Bp=nkwteS-eSYozADb#iVmLYS%}wS%T{s*bf=CmIw)P3-_yK
zCD%O<M+P_0@)!=u^h<uy>3(%7LXTl6ceiI*zGtoKM<ko%M_lq`(wXejbsP{JSNUJv
zYOfjRVlV?FQtquHx6d-(>HM2SilQV)T1-X!)L`3C!@bifRo88UZN+|!N{G3;NCy}z
z+*5shO&X_nGrDyo!1e+4f{3_h3h6Cdvg`2~N_q3u@O?SM>r!zVw)^O)Q=>)L@^r6H
zum&zK`@dO^LS^CUde4~!VaQhRpENzoHeO`cBZN*UYwb%{sXR*3K_~|+m+er=a!6rP
z9phL-Yez4{6bMfO^a1g9&eR|-3~G+7AX6DVZa;Vtci8!BBDnHo-9H>bvfrq2?g8Q~
zu!*atO*Cm(3!9H;CGTyN-0W)~U{>K&JW@cNVNFOqv6&k}2xXOSJJsPk^Huq>^7wiD
zMgQDdjjRiR`)Q;SFqla%y=hH6`bQ!Ug9swnIhl+Haw#XXN>21OJv&n%&ZL-<lER<1
zcuDN0=edD|r5=V?iVqNaCtG9dDlJK#Tp#>Fg+tA)SOGpN?}{Ko!@|DTrwxaH6NALr
zRCU47LDUqFU;}O2L#r&oB?<4d%AA!kT6|m~1nvOZN|$wtfR;HN8yppRAjyc!x7{hn
z)=elJT(|GE`<vD1VHgi)@wwOBUycP`Q?mn$hb{<rlJN`98J8x+#mW5rzTS#r#QDHn
zS#XEiqOqg=acVG|+)zRV`WV62#sM0(ZSR=;tmo_eKb<WJA~1^uiarbRwQ>-{v;^FZ
zf4<1Wc-i(fIYvWir)CPIfRId)`@A6EBE5G^gJbeW=*8OvqJ34B05R=!=WOy^Dy5m*
zAHU`dl@`K_tbrwu%8@bzIddtA_X#%G{|OV0rol0y+Na|u1=_sbMnFwu=A}Z4?s3iq
z{7Fw_GaarHT^wBy=k9*NwYua7UxcYT?q3z^A<|{XdHb%%1fn~|15R`}s*L`g@SlsB
z<iSX2$GVWTlS)#!A}ZAPqLESW8?m}SslK8WYu^9tJz3hb2*F~ie!G;1bt9>4XkHzh
zM0b+WbWa;wgi15OVY_k@JQzL8*v1VTn4VKlnc*jNrh5!E?(v!6boBV8_GmDhojllZ
z|C-B;@KNDX0{_%295U)rFhPuEd^*tX*Y?`Lj%;Qi0P$?28xzyZq6jTV{TcSSy5B1D
zNFaS-R&<nWUBd;s!Gmcm(*2LHsPz$+sXVC9n=cQd|LyK>47~b|i1#`T&M)^_grMO(
z_ZaBVABwJ4<(_SdWgz70N|n(jHk_NO1sQY>ySyWfzl-3<>^Il&LS0r}FP!y}q{aD;
zPTYjN8DV3SamiMyXjkE<!o=%uR)fx@+;lkg&+B;TETzg#u{a^38C+g6)d{ZsN=;=1
z$<#nf%+P(uNBm*Tzqn+>oD5Dq2nvljd83qBlj6jW<7>aDX{DEv7Mc7f4^F=dOWtu2
z4I<#CuGYJOz`O5D5|h!vNbS5-f3PihW2Psem*w9jqE*$tmHStfncLxRhg+ZH@v(gO
zvlXy7erFo%jvv_p%kFi3V-CQ~;}N>xDnEu+lgSzbYdLG0ejl{O2m55J<EXSyuzEvL
zxogT$6#GS>z5nJjf&%6KkQ}K(ap-@TkIq7ch;&Fq7O7Wfpz?qgq#<Jb+AGkw>x{pS
z-gi!)5r75!PqH-q8#@Z@e?<8thFlGRb|==@{kPJgEU&@A2l&SS7yoi|SLQbVg%E2-
zzpSuRQt&|QWsQ3uqDUS+`o_b|MZx|#=wvct?CAK%PaCFJt^INvu$tn)a`Z37y1p6#
zuud!e!{GrnoInD}^AAlF>?0*4XV`UII`&AP3jFu;c+VI;&fbqfOE2Gsc67gE%=j_R
zwGVW8cgDxJZQkaUJ++4)34te8$L2y9EuYkk^@!OR0(Z|X@jd3%69&-ez(}XNvC*@*
zXl%3;L(@YG)`~1v;oUvg3Yx=9BK>a$h!OiRQYU$g|9MwSKR!oEA&n1;Z1n0p`x}l2
zb+S)wtAF1&Xey?A@nQbf03kDE0cTOkygeR+p=y5QRHg6h(@uC>@=_T@kiv$&P0@#6
z2sYKY*uADNo{IZ{fe+BjuWa{|`aQiX4Gx<nM5U6EMVaArzK@w*-Fzx)QYN(id36LU
zGJ3P+spTf8zKM{~Q0eN>Q@3U(+*=zqj*+NRS>18PBL9uRr=Ik<xWV}1EOi#s3E_SH
z94+LQC;*Ry1^6ak!1ueJ{;{^Hju54As(Px|NXp^*ZDaGJAm;Id`i>wZYU9~X#{D%g
zv0+oUgRm1Dk88_C@#pYr>c(H>_%K@&2!BhZm#M@wr>UQ|D*GP<(=>hkjS-ZWPuuBr
zQQ{L49K43IG%YjKzXu_GcCib83y+@#$1!GJJE9x8fkfzCKCQgqj&84>`HW9o)Stv_
zq@!y^R`B<s&kP3GnWG7J8iz}<NJE3Kk=W=rd671!4P*K@eKofyE=RYU>>b!H5B*&3
zPf%S9LazTAT|1Tiw}<A}n10{Nsk79a?G?hT);e+N_pN7W2v_IJWc5FOhC;rt6AUlc
z!RzL1!R6iQIe^;+)<Du4Ql5t_v)PZoQ=&BuWY=T0&4zEo>T(6Iu6(;;{Pe1_>vFYW
zpGj>6$Pbg9|C1#EBefF5Y<D(pH_s%QADA%@zD`%0Bt!dNtKYv8|7fUj6j>%Pj*=zc
z62|vcNplCvU&1i>qb{<4&kmkk8!J5b#fmXx`sO{;`#9~tcoRN?T$7l#U7C<3tB+GV
z56zc#^y>P^Vb_<HROHS32tLcBS0(nx<;Ket=N+p=cJYIAk#B^7#%Wc+B-i>|ikg-I
zZ{&c<McmhrhYHX)@}c}Prezl0uAT?MI9zXHhrS}{!Eb+P##2L=JP^nSk+Y5eKRDaS
zpdlb~w%vk0!#|vDC!pd)&NgbO4kBmU%c-vaL<a>@mHtNrbN_c#*MGON7i{v<H#oy`
z`1h1vq>vG;NYYbWY?sb_`DGc!5=N)JIh`@|Z9Wca<kEj|%KZoB95Gpx*t={0>iqwe
zf*%K;9IK$JfspUnSoot4ap3z9^6XdY!m^i^gDIQev8S-IDFj05VM34YjCacc(bYi^
zB(%2~gQwj^uaX-+IDv0%P0%<)317Y*=G2gV`RrdSN5!v69xt3J|0tu-eYd6{@N_00
zUJ7D-dX$YxhbW((?%M(PcEHg(#QWso?qAlYM<1E#XS5GmLUXNt*CPpypI`@&d>!*E
zoX$g{ds;~O-w9<g#X!~AZ^~C5lpyq`#orEmovsg@%K=J|y?xH1TE+!o%;vlMaugi4
z?z8fVjs`wgsdh?yCITFK-zsX{V?rqKRjs32kjGGHtH$*O8*W=h2K=PqaC>R_)h7s1
zF9UG84(uxx!ZlB!4;42+=zIh1dVIOm6GZ)98gLf}kkzzRLzhG0lfWvr;E}OfF7*`g
zWBvnZ#0h8$eh5N><J&1{y4@kep8(LdUA59Y7elOB>qS2BT<B}k|I%@W|CM%_cE5st
z^rC|8eM8^lA3boI9Dy68Zwrsz?RT0EJJwc@BT*cjXNqJoKcb_d1Q^(FN^l%ZZ=1`|
zW8+I`6b&Wh6=n%ZGZRzAVBd_Au}X3AN_P|8EX)xOYd47czvOWfS`pZg{=9C+j9cFH
za;(nKN^F<$t$%puNT^nXbRDBCrLU|ZCw|Xwq4CSw?cae|_CljK-2*Ec_LqI!b$UIv
zGeai(DYu+DOXDeWXZdId&$b1i4<{g{N)od(V9>1@EbMqsxO>bRozvP;f=?@Q74S@1
zv3@+)kagabwBk)eIA!#CbN5b(_=UAwZ8|YfnltQM{~NTqi$Y?SbhKA*^y{c*wq+Ze
zhPUjuoV)o1zb~$(z3z!(bNp0+MSe(K3pB9W|7|;|Buj(a=0aVoB!=>H=J|cj%}l^t
z%hvU;$Tja+8O-t(s&x(~q$4nbt^`_KadVjyseu*4>n2C}J|-Md`*Th_CBv6Y<z!#V
z);xW4@NS7Sv(~5|XXY<jTyDGpA?!rxY&`GU1)}(lqb=a5Hilq=isSdVirxTc++$mf
zE1+b>z@CZP^S5+NWtI@bChWKOFKS!@LkJ=6S8JSJZ<ce~!?`-lm?5Gf@S2BaoE`!0
z<)_n2B=iaxUKPYFj#*paKCw;o=9niCTt&4E4@1hmxrK*EuQ}C8Ch<acOe&N)d_Uw-
z3OR_k<)J&%>qb75sO<6ivXlwh_uj9_g66<F71UByl7a3J#!A_u`<aKAU(1APCNZ;t
z$`HI<a(Nqx=+3IYxo@ZTUA@~4S0tb;{fXak@Gu0)ScRw-DXbO{l3Lu2MG=3`#vDEx
z?BalU8?ptmHPW@XdWY4&BX!D>C3?3>*X1#R1M!(@w{5vFk$MzqUU#*KOWI7nDPpv#
zf$htyrP42zBr;MeqgfLe(X;rX6QsRv>X!2oOl5ZNcOkt62D}nGVR>Z#-F3Ws6hxZ1
zBP>I*A*v=S(?xhS=4U6W!u**ov_iwiRrZGpU5XFP5PWareS-<+pcL$?SVGDcNB?;M
zfFM=^K4}=|nP!@BtgF8d!k9W-3ZNc~u8rNeb>cQ{@btbLljBN*rkw_7$ndM{SpFP1
zv!oVt<#FOEH8)PuAB{VOGaRfG*uPfSl54-Kpi+S4J%G2yu43&jJFfH@B@Bl6vZ|8x
zQW>xXJ1nSyof!*nC9W8gPl2DcQv%Tlk`Jmn`}BA3dLgoRJiS0pGd7~dVxi{Xoh*hg
z^<**^fH|7;Os`z&+Ry!JI^W)NY_3CJk(v%RD*;K`KS=Lazs(#6MnOE&`k+ka*WDGQ
zd}!%Ce^#K1#6);N*Pzd9n<W{_p@U}P3Irm|N!*r`Lvkl>%MrxIjkqlrhLk?EEuTvN
z&ztf8!iD<(c&YlY?1=wmGyeYpr7KCZ8(TjNcJc;Q+ReLz^|5^=Wbgjwr+o?L(0{`@
z3%n{2Z|r~KM(kAGuLrxq;j-L83|jX5>~V4xr*Pj?7<1ga<uA8W(}&$2o!1&~LW~5D
zzWeBRO!-MWmsh&8W;vL!YpzPwg*x<*6&>;lMvDCuYD$01?+(VQh7%QBKIC;yyt3GB
zBAczdd?ht_Mi@Mp`iW=2&F5%GZ_fOFD@pFkl_B(OR0SfFg-!7*0P43LPe1``JG2uy
z^ZD?7B6hXn931scbf8n3m^TTF?sr2dpXaHqk}7TcXlp;QXzgf_B;tM`@8Ef<ZP(TQ
zu5an^Yoqsr+_lcb2Ct$FD~ju!1vj)?N@dEHD_>g<**BSjRy3eT$?(PTB5zE-7W{Y_
zzc}L6lwn|4^69l#X?ay!!pe_52PZB4MVC$)Z*;3p{id69*#vpv6KH!c`)CAPq)`|*
zo+x!z*z;D~*4o~EJ)Oz4-p;Zp69XHzmbj|iy;`Ngua6Pc<#ZyyzUtI&x@iH|l1Aq-
zNaqBppdu`|xD%1%w-WClij@ZM;A@)}oZigH?#>05N4;_61^Q)ooyN2VoZ?^&C9NSy
z<>j&Kd_L&f?@LYxrc^jJ8JvH-N~$nXF}Lus;NE`Vl^NyOgrlZR3UWsJ(2DGq!kOl&
z<AR>TG`PgM+fRvr7!4P{hY-xhJ3Enczd_)vmoVXDO>og%)5BL$qHdPYotLo_^xNwo
znfJhM(a(hX>=)VSWA<}bUz-FWWzKcJ;V(N+9&!CA<qftPAAyy_`e=JIO%{(EYcrdN
zW=Jdh?5!O!;4$D&{RuYqUD#&`(<=fc3K+dN{Z~}v3{m}+<604>KCB#$)&$JYao;Q)
zcV8M-1R|Y;t<}||5A6s7+w#cR_pV%y_qNe&6)T!~{RJb*UB1EfBiu$mdPT<@XSLDq
z<ue@35e%I(J&+&nWdJ$;Rm`f6#Arfv=LffC75SBex?IKOG;=l2)xB>S<c?y;r*MM`
zJqBYYA;?kRbM_CR>|d}e4k}mbkkj52F8yABQ;h4ng2cm73X^PV@ot?y?)`?#>y4p4
zudYUyU0yY-#&*SA7}IMap03OpLdtUvO$yA+R-M(HWFjn_QWfe~9BYSs1@0(7lY>8w
z6GJ}dZt2cN?Apu0Gw93%8&*Ez5^>8l5W8N%99K@&K}0RN{Xhv?Z$KP7_@lfwNCLjh
z^Iu=>8T+zWp12Zl4Dpmg^w0l^!1Vuwk-FtS?brYQ-2Yb)N&icE5{3%_u;X*s*$1Y#
zb5Aq<HdYP&%iWDvAB+$nx&H4foc~G2?0>JUt7<|S*A1vGewNsf=IaCj)6%h5Z*v$6
zO$h=Ub&k30ipB?8hKs6<uOCxgT{^T3D_yE8O-(Y~Q<&7LdDge|!z_rf4vC*;dFGeZ
zS$v?967b<zUGdGR4g2aU3yB?W{>?K5Koi1v2U;4Sbtu6SWW|Hc`ulB=cneFhSOgbS
z((+m6ab|_~#;;LBz`tBHyPaQ?+Li4M$~&1XTA_BBg*Oj8P8552%nY7^y~@eeS=iYX
zFY3F0<ZQS!;U>P{0!n_UtZ&0Bh!{&_NbCLQC*Fbsom}<*(c6=UQ~9mCC6O^h$V?Ow
z$vn?83z5iN?964{wwWk0WR5a~j7e<cjtt4HjUTc_N@gD-WIpRt=bZbT;kozu-Fxo(
z>)XrcUF%!x9o9Rn_r3WptqYhw4m#anSg54BDB*k+te2I?Fc>FExABodl?ryhHvoha
zYQ<ho6)yHgD(oh0F##v}8V7@LnJ=;?pb;?Rs~v;>v{S{Ao0@^Z9(J`uFz8+FLe4(7
z0+64)x1OytUYElJ&CCLJa|mi`zTp0fAfNhRj^d@PlYAM^NO~ggSg6fyl$OZD(o%tf
zorA2iriH@N&AMT|yVx(!?oaPLd8Y7)I<oo+XlEvI-pCB_Fv>HD6BhxEkOfca0zY7n
z2U6n)jmGX{OPCCVA8Q~L<27U%1u=G&l|n3dd(buyfAV3XojiNrXy4(s3a;7d=ayv?
z#5fIbs@xbGE0MwU_Vn~Pf1-%+zC^X{Np3o*aiIwd?ttE&Z_06j0Zh4G!qjk#r_gYU
ziiI9mtSV%0n=1OEER)rF4H#XIMqng|j6NE*qvV53E~IYph;>aXI;M7C&zpQdQ`u0N
zkhg#D1{E8<W!q^K<+{7RVWrPV%)L6sq;3k$wufUM#A^;1@yTW`u?$sa0^Wzq5VtC}
zO3v%k0>q$Lfi9{RI_)U@YawE4nsEn+*Y~nDP7Wk2l=HYWg{NHb9QV6$+Xi(4d9mnh
zV6+ykH}}hkPtpUdzT*ng10?fi`uosaM<^zMi%;?eRw63Fa1<$oFdA5T&9(Sz4Vm4-
za>mODzbp>&FPMfWqy($X3yUN=HbczJBG>W+;%}OUk1GzGp%W#(($L_MkTbCyxhWX8
zoK#sZrHbU&$jj?=#Awq>X%aK2pdA+Kf7o1rj&`Z?yEr2r??$azra|>3c9|e9xl82L
zP+gse=JlMH0;QVcsUn&(mtLyy7TQtRu4*;lKy*HzZ{T|8shoy-ZF|G?#bra*>io4f
z+~%2Io6QnP>O$n^YL93+&8nok;;S##(Ogk)IOtPy)sUVkA^mD%%A}%n==h3$Tig&8
z#1Zbv$&eaB0vU0ff9bPzB3{UG;Mbm}>eJ!FLX#UV+{zdAYD0#LR@)d$OS=;MP{DHZ
z1*;VyqV+RnPkwnRonrH1{~{(in&4YyKBqGJ75g9K`z+R0q`!9Fd!9UEvtVQ6x%9oO
zsidoqFQwfU=?%g7#W=dueQ#vPmn0aj-cVYy73LqR|EX$DdT-1_@!kWRCCG)aMUZY-
zRp~}Z`x}o%PpIq)zcFa1$;&5>pQ%bg5kwB$xrW6*RK{*KAAibEJVIGNX(D^!+bP!{
zD(H>Y$OKOe8Pu=5OBsLi+Si+Ygw6qEP@c*dK<1wJ<JL%p@Kw(p?$mH1&C`=PF9^~b
zh(8&6ykN&C^CVMFy7+uH8j*d<N8HJeT1afbWkxs{Ub>>5d*9nVaW(fx!j>Ugd{fN;
z>GLX2<?om`%oH$Ro9AL}6{VzYNZGQqdrH!f{_-CoUZ+dGp+Dba@Q%WM%K&kF4Y*-h
z?yLeEn|HWaem0FnuVQFI&H(AQ6LdstOEiEW!-_~1O?iuR32Zoqto2zVYdC_?%$9s)
zF{r{!;`GoPYIu=y`Zo9Y@Q0Sjg5}7js!(-sY44$;SrKBLhfnh617#VHv+lo`!pCT3
zbW^9v6gN(&?UlgB=gyXda~UBK@zQ2>Y{*NPL*A%mcVQL%MaN{fwGAiv+#{;C6C{QD
zH&oEv!0)m0vppUrWUJ9CJLd2Z4n*i2ZMZ5?+&8+%bp$jtPfra7o!{G7rS}=uIn69E
z3ai(Q1L2!?W6LrToAm-EfMI8sl0YY4L>^TLNP;t?TL1C_AA@By`H`FWYB0>@mEbD|
z2vS%&mxoQrk0{h)0yF0Lz<+e)*Vw_`c>`mMH$PtoXV-Xbw-uuRHt>D|>?#Ha3;==S
z$eZvB%o?m6|CjDnyOjg8^hd3dl%IXNO8G{olmz`uuf<zyPvv}6dQEaihsHv=KV;?5
zt3%|>PlrdGY<uC0ey7=88{Jj3kLJ|{D?-^I^)Z<BGz=W~%eB2WP6`cv3e431ni97E
z_BGC(P7R(a-Ha>J&C>rNbL@hv_}!7YfW%k1B;W)Y+;Wo)vYP74a&_H`7?eDdDQ50w
z>EgM0j(YY98OU5v#t#d=Zk63jCF>0fj?x3Y&$7%%xA(<LrX=OlZ_$!LutsajhHsU9
zv_Z9wn|PpPlJPprOVvL!a&y%8g%G^JZMd>Ei+A&~n8lC14%CBtL7;-fh`k?tfHfh5
z7>rL-AI{{+J+O>D3vB%^(!krZ^#&Q#Q}v(*3~Vmq0iTutkb}%AO@-9R&A8?s4Op>>
z$_cpC>yH-$Hr`<3K-2X#BU#wkAKZe;+5ZV|Jt>^z?Vs03a1@)7Qkcsd+&Y_A6!YRB
z`47b&fr^2o6K&->bSye;sU2zefj3=L#6DMK;6<-)ro-JWpRFnPI%3`|7%{}b#YO_e
zzkRmSM`q2u^OcX1Iroam2<_1C01b9@O}8JJV6!jn=g9e0QTr@8Kx>lMb4LdEL%z4i
zbn?O|mwgFTE65i1H0XX<_i|lwRJtUt!B<`u<IjkdqIsP-+i!PCL6xN{_87e8q=OXD
zMrota^%4Cmj#5<;DJtz5Hv+~Qkm@&YS#|2gs;hc*!6(QAdc1iZTbLWD`6Fpk$rQ6C
z#eQ+!*z1pzc=dMWy=uuH7mm!pygCw?lABSI4JV=PwBGa7v!jnpnfi+6k<2V}Y0*wH
z#+(8C@K^U_;WA5$AN)ko;!A{6V;<ahey<~UW#;*3GnefvNpKa8t7*%sXdYrN%L!V%
zTjha3SwNZ3Q`Ir-%Vys2qmLZN9VEoG-UpfJMtgc82pZ|Hz~aA)nW>kwJxTe!O5-?7
znGY;Q8<E~3G7J57TryE<jz{F^fwt+@p$@Wzf3O96BM%Q@y1frm9Y|-fB7C$p-Oag|
zy=4wEVu<gO!ErJ|d=#n%bZTy{8fP``V80fq+O{533QMEWRCFfcdZ5(&VQ<!Ekox#U
zv{Q_2)-b_G&MPRIjf`1cVD3<2tBl+6f(v<FO;BS~g#y>U0B=M#ch=~N+Bp6kC^3b&
zJ-^1aAb!i}J*_OrK!;jh-xFLIqOAJ_j3>Ok0W)9a9IIR}gG4KcE|Fys2_dV+yDoZJ
z5rQI%79dJxsd{njw#gy{;^nfhLPOtIg-2t1J*7tzY&P)VM7F10+0P5q1DFb@Gh$}I
zaPa2;LcV|Jhy<}Jz%;&BVNMK(C6R<pC{#&(;vo8^`~OFk0r7t_fFS-K%r31T>oSI3
zJikQRy4cDXs!V%qTT02%{N%ui+*8rC2E~MTt`Ob*3RHme++mb7Rk`;jQPwK~WGmJn
zYWB!YV(~XW2#yG5O8@iDgo2Y$C+cl#uHYiTil!+Q^lA;xk`(OFPY0w#?>9xHMCWkf
z9T-Jrr7=OTiWjzB5_*eMZ>`-K8Y3?*b4$k_%-9)k1f4rGyoC{;O8in=JkbXjAPWWb
z!Pv_}ER!d7e<Cn5!0Kh7o8?O-^aS^-1!sj12L|R=@LM2badDfpPgs3@N5$u{50wQ%
zADkv`S$eIDEvgMUGvjxMI{bD@<aLC8y?p!G3omo8H4co=^%=3i)Zu`OY~j^0U~(+v
z5TU}xv*AjWoPEEf;qm}jtn39L-xk_<6YL*cMOwvp?<Tk@XMlt)y^cl(bP-?Na>6yr
z<&NtZ3b@b;;7$1ZA9Li{KMx~6e*OT|IP=w`qN6tC^0b~rsgrF|ym6){10`lipjTer
zTa^Lwk~=b(5nVF{w;&7^yf{m5avL<Aj4hBO0-cy0oV=F3m}!1h(Uw$Mjpn&^c?=bp
zzcUqNWvq}eeOvCvx96@2L*-;pr@ySlEfcSLomedC;mt+OGAmCz!37*_9jHBzsVrj3
zG8xMKR_^#Q)&eP!>f?hMxo$kGi$}dF8@{pjx@!#>9zQYQw=G{53?39YuDbYc2%zK(
zQ(cfj^#Kic62NPziQffK>t$pfKsQ7q3sAD@c@k$Xu1DTz&|0^_XcT0W0lIbuA!9#$
z0Cx<Iq=1TG5fMoRP4B@*t)@Pha$%DFc?gT7Bgfj(-`!OKe?z_UqlGx~Ty6e#)*af{
zP$B^ZpE}VH<mgeW1hNE8h5(?7u>Snt#RdFJ=h{D8EdLSM>wjPx|5<kN`!n88G~}t$
zsM;{byon&W_zXu%q&qmRNX*rEN{z1nf?6rlgNKxKpAO}FLh3^~!h1ZH&jmJi=9j+t
z$%b!t0jSY6H!L5yxpWzD$NK^q-JhtSPh#zWx$@>avH7GARjR{#@>~<UJ(vY53L@?&
zdh5eIUp2AR@CgB<2me}22?fiV`#P}3P}kYH^xEJAH`q1m#5PCr(H}qDfX55JssFr2
zhV{kB9Mk~*R=6ntb!-2v?9G+v*#{7?EQvL=Z4c?PRd0;S+Jn&->S7u1^&SlLO@WYm
z#fy>V!rgYO^yd@7o5`~8T-YM-?CArs(kU9dCh7eC73lWT;lj&Oc0ULM&&C+xw;gA{
zvm7)6k=p6yi4qvL6(K<Qmzzt->4K$-*lh}n(QBil=lO%<<ZW^j+o~v@{N%hewny3T
zs+HS8B+Sz|qUZ{1mCo>8ypG<C+{)-glOZ?1k;3VQWE5<8t0g;1?2E`Ihw&4UZOD0)
zb{CQKD<I?Ag>Z2gd4n(e*5X_<pZ>5wI<<F;4|>2X60v3c95<1&`IH&C4epg6#dEj^
zO_M=^wSQEop!ZvqbYZ$iuyxxauW&wcInViMt(AT3t)GmOgD#q`C6DpR*u>78&(E$@
zOQ`&m+>;o0m0Wl6H)cK5cx!WRpW@+LEuZksg?>-SdBiom{LZd1i9z7pr^f5fnVuyh
zvwcsgir<!e!7VfRxGHx9lIs_sSsk8dm`I-*b=5pFuB{^JRmK<$TLaHpjIQKsvo{(o
zH|0LJu+qM`i`Fd?GT$VduNUN79$UsF`-y+Azo)Q&Io63RXWY0f9daIPe=S;r9+-#F
z+cRhL1!=}|%6i7$l`t1m$;HdF%HXftr`-0#GJdSW-esZ1|NPK6zL7`q-iGmm*oxQ3
z3p34xOwA0JkKcc6aGIKhtG@5y*Re;^8@oZVj}tC(^%?0MyloYS##{My+3X$(zfYTg
zLJoPEnskRnc~Vl<M-DxUCB7qOqiYuORL{D6jiC6kzjYZ!sn#gD$Cd=8#NcO8Npd|k
zg3T&&lRS@2n$PqqVeEz~&88fQ)$+EYV?KdH$iQTP!e}tX{3)h(kEDu$bE4NzxSwZj
zo><Yx8MP>nF~Y;@e7J=aYI#hFV3;E;E17W4%{UIl%D0x8IrLoMUC-sWXpQw>#ny)>
zc!roKHZ~1Kx5g*tu(rC3)zmXNF-w~r^DCC}^Q)U}_0Q{wRDpW>1U1D9wbF^H*z(TT
zre14ibqXfS|60s$iex_e$|5u?{&8dhPkL;-QSBLvjBTl4Q%+IiNGGUOT|O)Bi@N-`
ze3KJgy^p)7P`4&1Ahqm@eV%wvM58~xcJg>;`=-Bd(luf$Ch3NK5<*RzhXyu5VdGyW
zq-9Ge@=Jc2Rw&@#!{24UV;Nap#^KX!nXyhhr?GDv0&dT92(*OQ9yoSH251tIRO}4~
z2w|#b5tkQ3uI1CA1aA56k<7oAW3Na!hSb>Jra=&bdK~p$?N*CgahXm6X1_mRK7$03
zUGftMEbt);AmS@rlJgHWl@8Yn3H3bIVEgrkysV~{k~``2R}E+!XTs>_+vL7WyEGY!
z>MNlWt)6Xtl*c5xfyhtJf2s|tnw(hXacMCS{%-DQVJX3StAhUb^cT)4%cSLMohzK`
zx|U~N8!t)<HJ&9x50L*5O)4p{0)-dc#Wd&r^vput4o^bel9UN%ue7`h2ZlumYuP@p
z+n|yTnDL=vMcOoYpZE~kB0@DyrG|->_jfH8YHZs7y7%L%$VHNmhS#`GPFuO3-ylAv
zjSuDaHJ39j^YY<j1BoS_?Q{Jj(DSFNGg~Bp4rbVTBQ4d;p{e>FVs{EHpG4((GuX;e
z(PW35aGsxTfl}z2<P{VrsSpQiGVk$$)1UsjW<?p~5V5uST8JH=C$8~(Fpp-pqLzvj
zai5Y16B*=sx+6F>g`S`fgy)tLySzQFz!Pv>aA+;N^t&AU<)q*!45b4DMU&CzR%nsQ
zHLnjD=T$^99nD6^MdkwI6r+e&V$QgiJ3s4Xtp!@DTB-231U(CyFDfD~P2)qN^!Q>6
zsqBF49Q`MU_p2$Z5yjn1X2Ck#7ELoHa_sc!lgorfSkfeEc8(v3BPaOgG{}sbcFS@g
zk*4h&)(FufbQ(+z-^7L%?96A3Y<Y<3Mh!3Is6P)>7zp~YB{cYQYfJqk`!FF-OSfm9
z#M6l~A}v*sXuGUiIWAy!dp5JKCNxnkyyR|;)uJTns&DBi9pepc>6OgVi>^`|=#keS
z?iM~=5B=CF^}&PKX)~67RVXFL;{8nLfW<+`ZR8<br^Who5;*5f55QS)u6kwlca$tx
zzy8=8G$+?b1JruqhxTRBHH_Rq*4fg%e!Td_o&^g;PuE-<$4D#p`)5Fq+}5#^8)@Ad
z0FU>V)L3Su=hRDqXO(wad`G`-M}6H_69}Q6%{!uzbqnHFc8ys;6g=&h{VI8V%s_QT
z3WzRE1}bXkeX~hTdiAn7^k4=BWMPFg`+i~drZb4pS426*jl5JBt9o%=g&62z|Dstf
zHHRE^#~(VKEiC@ijTG(9aRlevA)e$<Ln$t9VPgSjKhU<{iiiF$;FJ>+gX0HK6Axa+
yWdC_M7ygfyM+@z)0Z)fx?$Ak}5uAqpWJ@EW&HOl_H*DGl1JG2{Q!P`r4*d(dn>V}w

diff --git a/docs/_static/tutorials/tensorrt/wavenet_unoptimized.png b/docs/_static/tutorials/tensorrt/wavenet_unoptimized.png
deleted file mode 100644
index 591bde1377105669eca043c9857edc8a73b82bae..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 614819
zcmeEtWmuF?`{){oilC?niU`sT(o2JMceixP(v5+%z|ydUba$spxx^w%H{!z5NK2o6
zmf!oHb6x*)oe%%>o^QtwGCOn6-S^yc&&(65q9l!f_t{+#2!t;yBcTQY5kNp70jb+J
zFg^1pYkDBi^%)ff4av*P%WK!JZEbCFaBy&H2FsH^IXgQiCMG^QI_m4|<JJ!4;^HbU
zE*2FPjf;y@R8(YTWxcq#ARr*v+uNI(nrdrnd;IwEpY3f22M10~iLtS<+qZ9rhliul
zXdE1z)z#I&z`*0<<EpBvjg1XTO3H(S0|*2nC@7eppRcK@xwErVQBm>n<41XUd2MZN
zGBPqeJUk>4>E`D4_U+qm-@eh%(Ae7APESu05)w*DN%{Ht&Ck!Do}Sj!)KE}RFfuZ}
zfB$}Pa8OuS_}Q~(zkdBvR#qMv8A(Y=iHwZY)6-*MU{F<6rK6)87#QH;;ra6A%kSU6
zWo2cHii*U=#SIJ$tgNh}qoZ?kbKksqv$V97ot^#t`}fYy&QG5{g@lA?XlOV(IwmG2
zdV70YSXg9aWF#deH8eCtMMbf*v%}%=mX;PiKE9@=CNMw0k&#h+e0*A3T6cH1hlhuR
zgv9##It&IgH8m|SFSoI=nVXwyY;2sEm?$kRot&I3D=ULSp(Z9Ko}Qj&W@e+KqhVoT
zp=r(O>FM_N_H}i2v9YmU(Um`b{OIZFVPRpZudiofV&c;cS65e`ot^da^4i`#Tv*x^
zHHp%2hRMjt<kj}seJpJ5nHU)ReSC73T-3?T%#55`5;FXZUf)%9OfG2}46(M@-Ty0N
z87J`unpyEfi-B5&l5Asp-^3@2L(<GvTsYp-&0k+z-#z0~N<&mun~rNbB&-yUT#%r6
zZW)jl7+<T$%2?TsRIp7HqNA}C0=L1ZzE@Q^ek$e=)BQa?H8H<B+|%u;p%Rz(t-f=V
zgI5Gr+U+bay}iEX9$B93?^6^Tv%b8<$IN8?F26P_lS9UaL)m8nG0<IC%OUr+JSDNY
zFyDmd6$ih}_x_o@&*2~67-s~1nEtgkKQ<N(apDkGMK3JO4kHIUzE!2A_O&*Lrp5Zi
zd{VI07dKT+Dazv0lePWeq3dR~z4K>A-@hG%dwD4<A*SIuwejs8#`i&indgPXh1Y@R
zfA62k;esf3=8wx-LJf=)|2O{y3GqF^;@J&wxcT420ZcQ!PjC~!6L@A!D)xVYXX3++
zYXBaK%W)}{|2`g~|F53^lk)y!>hTlkbRfC?oxN#FEM})D$Am}&N~IIVY)nDVXk@Z<
z{iTp-x#1c$Yb8rii$}Qe{5`<5AkbHV_$jk^EqRRp2VOo`uKc!|gy}U=jciajXDNTX
zo-a?#jq!r7@1ML{I_P$hkjISv$?8_weL`;W6O0-0bPJ*P$`)RX#tbZ|hP%vdjN7na
z+JpXPBzu;>AI#su{8%n?9u#Qk!D2FqWY{(d8w6a`h2^B>1KS`B(-gE(-6fE-f?)Xg
zQ{Z;0pcMZI(+m6;Xu(Ja`qP7$tzCZJ<NSPk&#l5-4oR?)!5}_qAoWI1vHzM42XA7t
zb;OK=9kMs>2Eno@L4Rnl-FSRz+G@u0g7Ni*qvyh{rx&0FkuLy&pWXA?h@EfWoU!Nw
zRKVMG#CFAzLqk*;N+3`v6FP$qoo}wNgyC_4TmvzO(f}m=Tigpi-M2iNje<9hr5UFi
zx2w3Bb4}JkYKrEBn_4R_(|a+pimvw_zvYv)Y7_x$e;OY4(oEZD9v%6$pQZ-ju+{||
zQzz@E%OXeYIchGqDp5ME^S<}nyi^jQm7`mt5G8<Urr{DyzDYA*Kvuo@(IdstAoIm>
z7Ia^zm3FZbO)-|qO-8i&Edq_v(44~b+O8mT@AV@ws_OM&_#>=PTJbI{jcTbc4(ZSf
z$#80YI&}A!>Q$dCtoTKc&(s-q>by2<&K&z+8tb=@P|qjhpI@B(&AF05AF;YIt*@B~
z%`2lDEZ#aRfw^dg!xM{Aw6QYy^&NA23Artdy+w}HnYXO@2S6N^EuZw1)9@DY%wS2h
zL6qE}8whjcNRRpQZXNXvO{xyrggG*0cLY97V*wVX3^F#iCu>xGB(;=QNgX>&22U@F
zPc|>yBA`A#<kD5a($WO`l(p%c){3-OdmJ(1ONfD_oVEfVlsLS9M-zXuij4xVDyMeB
z@`H&LVNJs0yxoDW-Xs$90Ez*y(<|SO{+{2b%AnanmvoW#S+o;Yb7YKG#Ms8AHlj>J
z31e{Vf2?SlBEk0pSnW|s+V9=2b56yKE&QZ)_qy3UL5*qi)kJxx;p&g?!~p||94~fF
zGRkl<R()&stYbb$gK!vdYUwa*qdf{<x5t3vE5(fuEnl0e(89ySP)35J#IRQNOh<`r
z>Q6q*JU*++uxnc3z$`0fV#%kiaY63@k8dlZjRiUxo`SChvK_7`-NJO5MhA=w<YnDy
z0D&0e04Q~U_y2IyWn=smJ_c<E#bud?g5;yO7%oBiK!9q{1F`WRY_k8aaut~Ya%BO8
zT2uhJ{=>~T?F0^>o9`*Y-b-YCHo5cU{g($RnfP+S=8tZ!-L#B7+E=(nj!`KyH6WxI
z;GWSzD2xdxWB_KWq5;Nk!h!I90tIwW0XXLmaRBfEc8)F<@I7`;xH9C$`WR-$*Gu@M
zv4i&BRK`=xuMrw6zQoF9ZY(5Y?4DhEZ!^6l1_iHx#~-9QW^p?^Wtn(HJzOXz!=XT9
zukrJn$sI}wZ~3s2Tj~5khE&&ARdCtTPQp(SxSjG9b#6YbCL0}20pC)E2JeF`l@Qs>
z9)eC%N1WBik9SI*`ac~UZ{2wz`4nYcVPK_fgz+JeLJDkCKBZXK(UP^i-lb4DU$vRk
z;$?I8^HU<sD7zPA&LMVh(RAMl{oOO2tf#xRKVux~@@C0lZgLLe!Jxn6sF|s<W7cNw
zMB^Ib$rpz5O51=A*c~8k+qDk5E|rg#?EK^bomna)IvV5<xM}pbjZ}cPl3qjH%c0e3
zoV^RX#)#K|GQXZdtB)Tn*$~(Tl#EVeNK;@Yn&hGSrBeyfx;=K2zTqTGf5&Ifw!UNP
z6Og4mqN+OHYbaD-o;$Tg0Wqu)H3kshJP^y+YgwKwW~NKy*O@No7A*YD=YK_%0(|Mn
zO@73mdfsY1t78nrWRfz3)g3brw44F6!&>t)7IuD4j%}{SV$2Joq(+nfukLvjcA&t!
z*pz^czW-p8<kU?8Q-*zI|IxlqrapAj00bfe7UHiAfH(e!o8bSQT>meYl1CsCANLX@
z`{Pfhe_<D~<_XBux6TX%*XY~|ekw=`+$wq9IWUU%cIR{LEh{S61GRLk6USe_aRSr(
z0MQK^fRKw=XlaL?W8yFnGIccHcZ{UPbkY|HGe;%5cohJaiW^xz&_#@|GXrj@qLUU7
z0?dmWTc(Nu;&j&*`^UM45>O>op(Nm%-8+l`H0<N~rX%T@X|vCX45s(2XHQb&8-+(I
zhTK;ZMU5f1VjIm$85~2&YWl66We&=@hZv|ELJ~9Ow7kK6#}0c>ho~_<w<fhU>r>_w
zSkc5jM^#f2k8$7rHNO>y4AfxYUzD1?>1HY>7K%sw-jH5g4yTmJaKgLLT|@5M6K%=0
zsrp?_jz-~k!%QsiloTBl7fzof_a~z^0?v|?=~E(q$6(NZeF;kq!~Mb+Ob5=!+nMdd
zL{gxch_rO97`q@`H!X}Di0d%LF1RR3Ix1p$krMzAC&v9XB=$N1HZ<Pw3^Ps-k%U~d
zD({G6+L=H0-gz2qM$@3#wq-HEd>?bac^YlLr9s=D0rIF&$HRb7Sny=V?`Bf_#0-|E
z!6;9(GPj-_`i@-#ZUgw{f)Rox4^Z{x>t~lrhqfN>+)$%Pp+20%y|kuO@^cz=|ICam
z4cfc$`6j0-t8x;c@w0UwFsbP{1X=t-Txe43Z6oP-Pvkw*tag;I24hf%7w6@PHQcLN
zBkWL@DSj-AY>$^#5m-7tyRun1LCOweVQ+k(F80e3&^^~xgfp#GTPqMEq5<lNC?R;V
z!|Ds?Uhor3XwIblQ6dbD$Pg(+`Kpw@fWfe=p6i79#8$WZKrJFIwaR#*?)hVD_PiFH
z#kJ3-MG6ASJv`bH)(5=#YGYsn_O){>pDLnjs!L)o!IvvFwv*l6$GXL%lREr-;BmY7
z2Syr<`K(H9dcFy4%-VBNhL@0lUq%xK?Fr;<ONsNEhy+J1MjSXeJ&#J&^*~w=q3oEu
zD&hZE5PS|1>>WjN^$N9dox564D{Aw^RyXnaU1~jM$vHl>vBhXNV4}G^p6)$+#P4m+
z??pO2!lAF8k-EQes`Pl=`2z2W#$$~NGii;-syF-I(WQGXba^sOn#X$HXyXjcph;-Z
z`E^WYKjv&0b=lG^fH`%r$%8qyr@@@f9~t``TdGDKO35I^m6T6Ag<sU`S5ndh_z5EP
zQrUtStoE|XSmh-Sn^PjSz`UAUv|8g&#<--lL*?>%Pgq}+2;JNtyiQW7X@1k&;e8W3
zM@czxOsr@|_3f-}IyB)z$t&DI`^ltHQR7%r94D)bjt{k@RiFemkmFyJpa{v=q+m<h
z_d77iL(I_)Az`j(;;<XFm1#=7p@5pbtMjF2%*rT(qTuvCoS_`gua@3zH#ubH;F=z?
zcf<}6zw2Gna3T=XUlX*uV~FrDqw!D|k4k^SKzyRN$^)*n#tn262ER*ePPm~gB99Pq
zv7!?2C_DFu7?FKg>dvRWYpMT4KSCF@-<<6+J*k9{2Hi?C{NN5T>kph1-Jbcx<bBGd
z-R;sVi_w>aMBL)HDJMVSl_1*u1hWB6su;c+vyE66-+@L6REnLR5TABUdC``^54fID
zz!`UbTJgG?GUQXcBBkNa)5LT9CM5*Jqor5um@Fc8K99bUtQ=e2=K;nA=@0OOToeKV
z_;Br!OK=i(dm6NYNzJQd!c7z``jz~_+4K1&YRsV+CFM!(HYDLg&`U60Q^NhhDXl4R
z@`@Y_blvpJkTtdcc~FE0BcZ-CZYR${%TNkLF9Y`LmSRRm<;2?$WwCpqA!OGjQecyz
zO>KmHP)e}$4bs{dxC;>(P6WKT@8Zu-j{-mn_u+-X_D1E?(@KaA?~8{?-E3o5hB=3_
zjk&k=*}OJMpxF)hQ>$C~P#`{$w~U&6|57Eoa&Rz+@|V(KECX64BUy{1O}zuZ+btDV
zyzg6~+(($iwsw=9P7*SQV+s4dalLAZD9NH<$(Ow-9#-w98R<ZW789Ds*+<94VouXS
zLg;JdKM2e%)sV-k+cSX^8`Bx`OZg6zSer;GPh&Gxqs&bQ+hCG0dJXC|#`jAZ(9L7!
zdyeP%`7j|H@bzj5qQGo_zvA@4Z62_u$i1hL*N$KE5o~ZQ-(|N@t0<3$^}U)_28;L#
zucY4@|2NH*BYWsWX=E%+NF8BUT6}V{ms6H9P-L{*YqiYHL?m(^(Q&ZJV5JPvYg|vE
zls~wTjiUrtY7A!FI9jR``O!JKiF%HdgnY)KH3{`9lztpBiQhjoI`iijo<6*hgv7eP
zdd^rD@$z4OeqMwUoK<tK^i@E3&-f?qX>Qm`iWHZ$#`<=Q9)@ceQbLNBpOsS=HGG<Y
zjB4)vGDHmK!=9qKUS`nbm*&VK@T8XUhir15<Iob1JBiC6xJ55bMHCQiD&zfPpm5e_
zE-r+YvrS1}cS~$(&@3kXh{nF57?}6z8$a;Be2M53a=5Z?W+#vj+ujghg6@$&4wInO
zGc0S?Q=)#x_}VX_=?3Ml_+WbyWb8GBr!Py6O^q6z#QfwMN%7G~UP~<*1exJRUm?%y
zH>0o{74U*t&?gYdfvGW~L#5Tjof>Vs)%Q=}cpD?Osq=p%ZbS4)Me5ALHS?d@Np3V#
zpEb8>p;R~WVP!@?A29Q_^Dm8L$gOH^oJ=e#)1gCpssklSCCT7xx~nQOhy%K2IRyAC
z!TBow#;du~V~|t?cPUCE2prs9rguF~SQ+8+;o4hcC2XW*xt>BUO1!JrrfdgnTdiux
zoF((Ub04lNtE2R+ApVK8`9Ho##G4pcw#MJ5UH;U2Kel=pIh?<{`~lhOqpyGJ@nnFw
zcYJ);@E>$F+mJkM4-uQv+IYR+O){L45O)@8wDKefHe)O(a;sPo*S|91%6T{2U;!H0
zQJy@<%vzWJkYyvviQONr3&eJz*m1S3zT;p`Cv%8j?59%&Vz}4e-Cy&GFvfth?`N@`
z`A(ewb`jG1V|inWkUU~2Cyf&LYIYK=oeunO69!>~d~{Ka@*Qj=%T~GFsq*-oeF?YH
zE$A)}&t!X$5;<P-ZS=<C?n5R_J`9ZCvjjG(Bor6&_!)9aK%c0%{i)jbeCRMXc~=~o
zVF7+c!?Z9vqCf;2`Y);mNYT`tJiO^u*p|d=;7<$X)c)@;nm|(qkqXo|GSM&`Yz+gS
zlm#Z=gzE+Z{n4<r|2n*2KJMtF|M@%L=gTs=Ce|%)s?r+0ywWc^m1_184_EQ0UG-}^
zx_K04;#fmM`V;;HK6v5PIOUj=ETI>b(7uhPMpJ7eNdGQB2Z`d!AQZ8LKp^NIF|b7e
zf%rusj@PjT7>0a1woC*4d^eB?V?Fo4@(B7Hj5@Pqz@H~m@6p1m{PWe{fy-OzE{xei
zsWE3o;=50?G6A-X1AP^twi;4<R?oXOvGKD0LIE*W&;~ivYAtKMA+42vibhdf(Q!?N
z;RU4=N1_CO*CPv04qVhOPSy^xM<pDQREDg#<WGI43X<?0Z4Q>w#Say9!qC+*7luSm
z8)q;pAB~j36-{yE=*Q<D0abwlx1f8OlHIsw2~Cr4&Z(3g<W&0_SrT3h>Kc`$>--kX
zjGVK-tSn82QB4jhdtDllP48M%2GDv@otmkrS@Z9SrZ?m)IfHKl#S{oMOM})Dpx1tT
z{$|sU$!?C|$3|(EfALIp^X^S5XYPo{dQV1bc2y%$8++r_9=}SI@a5F+=g#<Z8&pb2
zD%SpB`4=8TntV31*T{Zv2m#KUFHhIwyJnC|&Bk?*%B1QZ+lHCxhYs?H3Qg*Pt+>x3
zseRt5RNOe0AK=~o!<TYOSIQLChOd5>q*g4U%cm_}8<a^%h^u$d-$Wul+0@#|BkZ0)
z_uI;rX5jbhTT8VOxoGGoWE7WH)4t*7f6p(BIFKXOgM*pz^}ZwIEKLhdS|8O>us8~o
zd@9^z8LdOzo1~JpGRkl?ny)<UJ)Z2Q_VAwAOmpqKD#bx7tl%h}!aB1A<Vzp5Sb^~(
z#p>Wa<;~*lYW$g}q2@ETe8Mg5jY{=2`MdgAMN?fBENiZL)M>^tw;*gxueYmrJcPc8
zQbfmn_{XaZA>IiCEt2x9@;}#*j(c((%~o1^@kL_caUHhf>9DfbgaLzM$FyeO`U}!V
zLuGV_lXaT73S!4l@&6Np+`Ct`e+iC)5M;Fi(0+eqHo61e^n#Q{9~$k9F=iHc%$3is
zaDb;r`#Ij#qHe;EV+~L3Y#2em`7V4O^K|pw>~rtz_~cz#>WXh48-H%9Xe#-FQHJF;
z^T%hkCa&@LwhxCQU4JRE)&Kr8LLcuCV!wNCASC#wC6G-J@R7F(a9yv~J%e}d`q(oJ
zkjVmu2-bbBL0xWN$tnu@^baG~;NY8AO$zYqEC1gQL_Pm^$i2J@LvK+C!L_TV4C28R
zI&_%;SleEUuBfF*eipi1rYn{24a4_cQBcKMYri|0zzVvWa3YV$-+RPAlt3mk+q*o5
zo?q8+`8{A+O<9{KZzJ1fzO`rIYM32$6)zW1mv8fN8JEuGzN4dAA_7MKRVA7Ms=dd?
z1b4L@R(|xG*VY*E@S@9cq??r}w;k#5<_|JfBPj10*f`|UIBd&$SL6>#1B)3*^fIb;
z*-K==5&mj$vc8#9(j_N_W#n*k$c@a)a>#eG-|tQG0{srF>ClS4Jlm>q;;!trr4^aH
ze3QFwli^9;KWM&!;5KX;)lm?8wU|#!-%%U>C1iUo7DSiSX#CkqS2<lfhKkdQH?M>5
zGF1+E3O=N@qNq}J<3734DhV}M-P^_)tQBWFCd>*>bvIVyP(UQ)l~DWDlHqilkv-y%
z3zNyPKX8Jua?7bVfq-d$QvC!h9XP_Sv-`3VXjNpEx>nt>aR_=(8(FA9`Z7z!S~H95
z{Rfi@@uJsabhu=$Z-15sOi-hk<cfT`C}mFsJsLb30v3W?Kzxc2DZwqwo~$#~wRx<s
zuX`J=EAK&>1*1W$`N|0p8YBH-*1sm5A#G`PrCnTD=g6kE63B`#Gm^Pa>3#~=jeKXR
zmlWFN3W1DSo}OfsyJ}I6sj<i-N_%TQrT{i0p#k&WCk+yJX^cM93(|<}&=3xLcYk&D
z;y{EN9iHpcx+l~mhjtoWFXxrhh4@dWZKs67>42(FN)|ENSmX67D@N_uv8cNvO(W#p
za&w%cBZ{Qte5XLoYnQtUwULvq{puL-Owl>ibDmV&gVMes?)m1EsCYgtgU`NU!<Eep
z0JaMoNOyELso%QuGB#@s)1ePfquyaz1cPIMQq~aaat~`3crx}kSO)P58zj_LQh>{#
zD8N1cWV)w-k4Jw%J--1su_(k6%K^dVD{%Pc;HwS)Qwsmubp9Q|(r#mxEyL#hs>edp
zXtPCGM8?L{v{ShA^PZhSbY2r*%}M9W?(7<b<=zO1rUY?tGKX7{`l3OodWcRmMf2Nt
zw=YbUr%Nqf^BCxy_@6`PM~}!o=`bgin4`EpR0|q6Wi_Q8A%pOl-d^_@J9NruXj#oO
zE8g`Q@VN@s{#Rh1qGaE=5qPhojmy$Omhy{&jgo!C-o!zggOXi^Fh=dR!{!;*Aw65`
zUpV8x*Y}FFwsR7t?bMS`d=!ra*;cK8%fAo?PAM^BCazGUW7uZ|R)jZq%~q4Li8a~g
zcaguo>P+^;yJ^Wax^84kdlb;@z_XQo495+fG-#iDKk)wc<Jx`OCUYfZ>ML0$Cl>6v
z94J%X73vb6NbmKNJP%sUT~cdoqu5<rh&uDv(T8}aRjI>Q9l{Kh?Kj<&DqmA6)^o2q
zZxh-0$z6%%8m<IcZq@Q$lpcn1D#$R*AL~Ml4un@_1i&s$TdBxv#6+Z9HhS543ueoW
z8l)vybAo<y^TQjz>gSe}Z|<6|stAB_smYGAN{(bl$zwU!i;?N>kXucg&J5dGc>+S-
zf5US;R(EzZEL#e^K3}aqjWlRlwpca)x!+Z;S(t4AjZZw+V*02sD?WEDCo)HVD;9A^
z2_9r~Y|7E0^ke-pu`K@O4_KXKOz>*S`8osLi@+s!aurU-^E)(Hk$JSm@_6&4H29*%
zH5E7ORdbH?)@4zG$&aAyC1Ywd0(sIsojp;E!}OkFB9o*8I0FT(JWE53%toA>i1V8t
zZM)=1^tPGJS4h*<1oRuVbkN{O@r~pdS_Jc2zA9m9c&T`0IPGIraBWS4IKG&3VG2~=
zf{Cy6lYt`3w_CONPAbHdKzx8q>fpx$b(U*(s`XbL3m6ur>#tv}<^d_Nt9(Hr1-Zce
zS4zmg$`|Z-)aU?gWXw-AR;SZuu@n5h)Q)A4y4I`{kxj*=RTEaHV#lJE*rb{B-cGed
zwUe8*p_9H7=TRcb>j++dgT8wWT?MVkwY}7EF#PzYGQx)d6o@hq#d81E33G`1?~H?Y
zMf)#4HQM-JyFr(0S4_5lv4QSI9<2Hh_t(q4dy=ZA{*NS4wCG`S8mqPdCE_c{%MWaD
zuK@WuL&3~1a;AP~KNuKVywZr<<B=bFs|?IXlXZE8U&L3<#`M1O<IxS>Xjcap$qi1}
z>kfy{{%9pePLdv<HwVPQoR7Z;K|QE{(OKya`SDUn_Ph?Zvwd>qq+AIw^rnPU=lWm@
z=`-1Bu~GZFYBRsJ8V5^d`}M8g?rOSy<Gj2!bJkOOiTS0peLb6JIbGY1mBniw4%=qi
z!TGSLwI{3M=C5gqGvpEVbD#S6Ts5f3>GI|CN2sr~V9g8;ch^;BwX9kuu>1OX#lTDE
zj-6(Hqq16J33qanr{rv%oV<PE@@8ETELAI3cd#uXcxhNJxo}*G(N|Cg(G%=9TD}E&
zGQ#uHq9i*VHoRI3L6qt~l$9Ossz|wUrK?C9bi?E??bp3}#W^9h)+;|f;fF=Ty=u%3
zt1;6opV#pT2)xH~ze@SgI~>Mx(%Q^_Yb&;#GG)rCqW}}L>~t+FgU3fCbAM&8*B`?t
zE1A^V;*b))mX?;`kwxHtHdi_jeyhX$fif!x2{rhZNWF?zZUziTcG|}yc~dcETsZ&r
zyDG9+Ou|`}xx+TwHmOj`!yxn*Zix60lU(;P119&v=Sfsg*+*}5iKfeyqh@|V%`~3m
z>fI`3v!jvDPH;!gQR4rFPcm3&E8Fx}k7%VrZ#^`=Wp~7Mj8ImRrAkm4QP9+9S$dD^
zuRHN>mqnBk=iKmzur{(vh6jzml-BbJ{)Uafzlpv|N)CGn^C<^PyTc2lVqXwdLc^HD
ztQaJ{&c}yGSC+e2L8G+DtAlX*NiQ3<RTL2inA55sna|@R2Kh{TYq_m{p4zG)ZzQv{
zkPe-!*3LgRyT$kfwh{_U`DZVN(fQAk<f9TMJ9(deZ~pymZZ@kf3UHe6cu9k}p@(5#
zVz%DfOz-k)%VJCt3Qu)lo)k`#)AwIezGXK(<9UV{+wz9+Yx1HT6>J9~yz_I+F17!7
zWfBazq9J#&CuS$0kr-CH5^ip9tm7tdO6JGozFRlqnEH4nEEhF>{9|a4I=Dkl>VrkV
zALBTU;eTuO9%fT2pZsDz_*Zl!bA9UP3pQ#r-QkucQ5N65g+BtVxrM4vs#0<A@G_Wk
zBZ4HLKO^`!)So~%I>oodcz9UHX<(NxZ7kE#to{f7n04oj9z35Ol{NDXJ+pMKrhv_?
zDk(6)!2?B9mdt+sR6+n}+-}0VeAX*G#F$JJ$Up7H<Ioe}Upfn~JVs-B{$!jqKH=i{
z5sLxLoKrXI$Hgz%vH~u(M(o<nd96)^+TAf%EP>av`;Rgw*!Q?FBlM8;Ne5iXgd=3|
zW6TibLw|?u1fK1Fog)v2F#v29#gv?u)0tO@F?*n;)cx`E9I4B#-YF)`sr=0(n_3_z
zQew6G8>m`*tBrdAy!SqwHP5LM$$!NGNdPOW=NDje@gHtJ{jYaMAQdbj7_eymCz}!H
z{COBLOh{1k2cHYAud{+O#R0kauum2K!_B9shkLzmNpRnu9p~6nzC4kOBY#8{DgpPZ
zk2f&ywb1T~*ZX2{K3vyU%JOF5+pp$Uqx?a2<qY(hFi_rrffY+s6}vb>fSp-j7IuL_
zVXY+j5@7oWu>AvtVe8E3Sim%oudaHvlTh}Xzy(v@bN~%ns%AK|y4Mt=OOv9AL|Edh
zME}|c;Wj^PJN2Hu74fv$XT?C$c6h=YFVLu#$dI0qBLhDyx?u}Gut$}>1Z2x<i7<I$
zYOcSPt;?N8IKb11t$B4odNLB&O(Yr2$GX@FQObGrF&M@_!`xUt>tP~v){`ISWE(|{
z_{tOtUcO3g@<=Jq&kafLn7fZ?(?hE$3lR?~Eq&PYrlI8b@lYzlo82iCZ0XBa24UU_
zZ02@_Qbc_J114<pQQ_i!bu*Y<NlNjLxW@Al(dQ4UA3Axyq^ECE^>CCRBs6SLxq05D
zr7TICX04GFIdIC%FIjMM$To@(T4GOxl{;uVkSmVtU#zzN?rdb^-!_R#Gg9T@kCrhV
zd>4fv8>`D}FDsWy`;y)${Bxv~ouybQUqt(jd2{+y&zS!!;I0^?a;pnf;<vY2_vy{)
zi~^*Ld+YB)w+f5TL$#Xq)7p}$RLey@_u1g;4J(Mza0G5yJ<(=3e6lrXqd+8RuPh*8
zZsMqh@86haYH;rwooH0W!gll#XUA&a5{3~<w6xCBBlk`%TRpfLsL?$Y_Q9`By(GS=
zlanI)7xe)<gXm)>Q4!oE0Y2eyu;21hLlreRaTel!I{aFfyR@@gk?Cq#j8lIF&S(^f
zSe<Y<V+6N%);lFqqsL#b5839~9f}5_B7)5z?vMA?uWHubq9oXjBWTj+V;1z^sCrnc
zOY+3wXR&G1%k9|etOX>31x<UEJeSlI5P9qKM1uE@$M<6^iqbHThjP!n#J}aOYggHN
zP%&?JDLl6;a@pA((G}S^dFP)2^DkS7kHSY=VSE(?>iv=ggFCc0ylky)FWTI%<#JAk
zq0b$Y=<?@f(^|jalTx+EXgyD8TH>@z@qizSVM;Go3Vh_BqwmbXbWw)KvFZ-f#A>In
zA6_5Y%AHd}1lMVgR2gngzMwar_y4u2AV_@ln0u4=#0^sS2J-0|-R9PpW8D@#tm0{2
zCBnRCUH*6q;7ixB%^m2kc5f!ZPUq3G-kNHcahs_<=LLBiB|=6D2r5Qe^j|B;oXp@z
zl`TKKr+$psa>o{U2?mOjFe|pst`^qN&~>nu>eFBIAYfQ4*V^~l3{AKLZ~P*c|BCa<
z&K#ko?W4P4?ynV;D6!{za$CmGy$e=abjZ~Xyy`R&<-5VM3AJ}hyy}6<+hY|)aka+}
zyu^WrlK*dBvHU-|OuqyAudi4Fu@7a~u#W`(!_7DCpKUPuB)N6$(fWolB@j^BfA|3q
zux4Uj0|ZuD^)%g+awZ8L+{ySTcAG3j=A%+Sl-8L3){sP(HN`G|+OrRM70+fXHQtmi
z`9huza<<n0S;qZZ^%huTSH2#eK@gat927U)c4>gH0iIH2W?CXs4H0uWvX~z#YN>--
zE^T~$*o*lf?&H)Sz|w8wkWL67pfAV%S&?D_vf0lVDZtp7q~!qg*8m>t45!md*OQDM
z4{gk>K=0O8iIT1=ejNa_hV5JOS4}hh0oJ@q>epI(qHH=te`26i3cs<_3V40dV?+0Q
zi@fLtCf;>0?6DSM)7o27_?_y>FNc{vf)?c2bt-Cut-dAk31xHN+{!KF1O5KHw||3F
zV<gWprU90xf44`js1)zf8&fAs+Au<iJVeApIgWz9E$??1?=ME(nQt4o){%l^x6x}Y
z&Rgw*A{e|NnY6YC5GCjqYUJnv-&J_64&NSDirYnv=uBW={!%#jf0S2>J4Bs5OaP9D
zK{Cnqw<YQ1d?00?D1hIhy%8T-=;VDMmgv{zKnG~G%6l<2HdhF&iPsG5M?g7!wpTkf
z(5Vg>o9aL-4@-bFhuKd3&z?&iFz#0WM#*USmP)Bs-)UfKtzK;@irK-jjT$fjXu;aX
z5S)l$s#Echo|8S<xREZOKP86{^U-p6YQy#EAI!H{n9!>)Y%18OEG%b3<NsO(N5jw?
z{~SKJen^O~HcqK3rpvwKLBU&;Cym?3k?5iiPiA@SeT9HePfrV>T4;M}TUetKk=3-B
zp~_{$>$I5J!d}<HxzBe&5%yU*!_Z%3(~m7DADOke*hSyF+0G^)p*nZCPAcF+zI!Q;
z=r_wXo+`F44p`~!h~P3RG+-@JW)X>AMEgMH;+NNdfqmMl{;7mdYD-|_xpYUDM)Or7
zvaj8B^(skqyXsupXa}Cha-H;7mLq2kEsE-RbKLIxYA7EceVbbRXqN1axe;zbkI&R_
zmrWn0ayoiXKN1;~XPhYKTGdGge6xL`xqkjh2k7(fc5+$mNppR9xLj$TE60c_XFmyN
z4{Pf0?uclqx(S02Fpl$WLW2-ej|<K1;oA572jW$<^MhSJJ4S*>^Jlv)Y#@`>VqLJ$
zV_nTPf}IAXlzLtnpN$SA_n>@N>g;w-P&DkBfPaPI=C43bWQ08O#&l4t+B*Mg5pk@G
zcyGPqmRif7BUBA;s)O-}4Bras_~ZFuFlqx<@8i)<gEcJ{zn@?pg$L7b@M_h4*|7X`
zVd~$ZpFdmrq-xxSgJD(Ua$eKJNVoEPvrm)53z_oF@}RJzoHvJu`O4E5FTu(+!5Xbv
zm}~B!*Y5Xx6JayJRuB}}ty&G4BF~e4JX1ez*Gg{9m>+-s%E5JVsJR;1in$)D<1~Cp
zEL5nY?zyJn#xRiPOape3nXLA6Ah<|@ITu<x4as;C+SCgCQ{2j!E|rXOsmASh(G;?j
zpVyCi>3N%;ipOQB(SVh;Se>5;vO27+u6i*j3P5#f;|&xMuHo;01qH|;E^PHaPdo|}
ztiKP%dj8L7e<QF60<q=JPIcgwQRAhi@4hN0TN!t*u$Gj*T}F-mNF~{YbW|NpqH^-r
z0dLN&<HkZip9_K2=V{Phl04J(@epw&^;J&CUqZ+4!A4@-Q6)9A$=rIm=9Z0B9aC=v
z3U561X@O!kPjrxH?g3hpX<}+qR!7Tmah63M;Hb2DORKzGH9vS`p&=4>uJYm67oky<
z{gq8J3C~&}v`smqzf-3OdLGZ48Yk@k*n-wfv(~IvZYIb<x|N&X%6c#Hs)c`e!Qj-M
z*tDl+MFZYu;<e6*b01BFwZ)KmT1Ls~Z=}Z7r*`<1Rz<au(*DcbjeTC!#bJ1LB68kF
zpB3<@j9%iW8PYJ2LtTo;?#Ym<vC8F>`9?2>D!&v}C*@*qN`n1<MU&weVW;Ti2Ty+6
zWMXBRYekTPd<m|$xd7g0fk2U84#R8v^v%NhH(YX}W2H9n)P`a>UqhvYdulkT(ObW7
z*k(-q;;~VjvLI8l(lS(Zuiwv6QBtZWVBp|hJAv-$6`I^HOL*LFb2>7oA)~b9@y}7f
z16wVGOqPSMS9?IIM;YA0Pb~v);Lc@ucJ_dxyCUR5Oh&2eQ+Ex$?(;ET{qdfwbwuan
zd#`T0Eo3bB0(C|vOpaYWBTcHXu?Ye(>oGE+8^_BB&wEhM^>rGsH=7{Q*LY0mOYH#2
zt$#}68u-#xD>EWK_G)`^>sxPV`NotO{#Vi45<|2;1}sD$1E_=^3xd1`yHNKrOR^eR
z5hzt(VqXzMdu@P(br1W_JX%8ucui;$!Y7{uxZ!N4kib6;uMC{$c9>=MB-9Ro|D%3|
z<S7<O*cFogOl&3h50YEhm&sX>6bvTv=0oH|3zBZ<zip)KX18zT4p=8qNVvY$Sjkmk
zS6t-^pPb;jJ!DJC@V&IymdS6)bsy*c4bG5o4^&h7z#ftRnN#473ATDOUy1`b`~3~L
z78Zr~VvowZJA&e?lUqGw_ng1EI|$vfX^*nS8fSF4&`=I>7`w{1zBAW<zR^wdNF*`*
zyx<lP7b-$lS_p)>>O9<txwdJzG`D?l{Kff6+h13nHjCx2C_%oz*!qhE^EUjf@BV3I
zY{%4rLCb8P)DYvYIx|=5!A3U)C$?z&#e}w<^C5AufbdvmryHBG-T(b#odZlaf8d8@
zH=+%)wcHm{$^pJlmA<6Z(p&2SKSnFf#{o7o*^hT&^`J6}gX@NSrfyCMDk=rnVq`S%
zTRMYDw;x8M&iv|~<N@8XpY(z?QM-rT(I|stV<G=~to5_YVU<-e_uKeOa4>0*UmJm3
z7LQ=K60wH~jgL*QMfvK9us<2C0_!%EcYm{Av?bM$aEXgi+@pvy`C^YF)~lr}F&Rox
zQxN%4Qt!dTP8M-9oyCWeSu=0Z+HO;ptk$nGO&JFEPOr!I&&FQD!59WXX8%gh4bOG3
zkqdgFrF2zrum;EJf_vnJ8!#+kE*_|RiRHV=3acPbifxxaw(A0&AY$eLFCSUOu0klR
zLwRny2rjJCpvMS|1ctv_%9t1q$@@A}uOW*Vr(C=k@}A8u`c(YOFc|;<A+*1py~&U=
zNN&?g2ph+p{Ej#1lO@#3VVpq&3<vI+;2j+-7r|H3y<*akyki&7(*Bmx_K2a)8i8>T
zLhWP#!5x+IJnH?R+@=2;ht^Ly+G&s<?&0-YpY<PpyuW5Z3Vr<9lbWXdP2RMM`bu@X
zB6gzH%fZPCgA!Vwr5;xO@6E6~aIjXIO9Qiz&@oFB_0BUFrm^A;f7x4FkZzm%NBC2t
zBkGM09EpLsUl|yS8k&m<6`e%5$@f^%Blu3Aj9(+!ce$)`O!E=il})HZ256vT$UvFH
zLo082iIaNjr#f)2xaFZqpUS<?Qv?^V(E-8U9|SWAN8VnFnuYR(GUYoc2aiQee4AoD
zn0RgLDc$lcdT4+F&dFuC*ibDwmFiCYw3E(ikzv{|@k`#9QeGU4BVn#%0_uLjC*G-b
z)LwS6IDa+*3vg9y$d#bD!l+;8FXe7Yg*zBn(JXe0j#M0^i7Ka4f9OOhXq>dNAF8)-
zf9rO|a&lI^-@W!EIORFZXmIaZQaootDdqhV)C^IfW_wMcvP9WQO`ZW|UAdHnyv8&{
z9jfq23-W@=ij_SxZ!u!ElHp36q~<rnM9mB<XjFwFGo2n-o{Judd1*!z<4+cQ8iOj~
z;7XzieM0ugQI<dpk}i{id6E?8V7RHh$A+1|aNoz{a9HATm-E?kCpEjDw@dXY2*krD
znno$ZGUFw6uWRwB(m%M~fVU{8ar;yB1M;s0Y!{7)+RVzAsueV8ho`IZ##YO#i7tcZ
z*S^kc9F`{(5vBW8)2Cke^@6ju+Amlr(KtPgw4@35&qi3w@+KVM#5@p3`Oy`*rW`Jw
zpAG5c!5;uQC`8CoPD!+i_mPFat89GK5hfqT+-e*M!BkUkzq*MT5EqwE&cARjb;mV4
zu6%4mg+63KupAW}c-fu+pCW-k9Dnr^D1Ank`a6rWtMHRsVZ_rT$upD?+Wq`EbI+Wy
zkEk1@U=LbXeqr@5WmLR9(tA`Qz{jwn!Ub}X2RI`{KBK-9Y^m;COEr!oA<~mS8eJ}i
zc0NdtEC~uVsxSZ~R5sq<Z>udD{W*KA<KC|X#tV#uT^61VOncXU|8O+jhs{D^8rEMB
z1@R;s^of@o2I8nQ7B2D(XPzPfbyyV;%_^=bN{WWscAJQ8Gub@Ya;<r&2)BRTOTS(o
zVRGQfF^K-*(or*_xQ_u0ZkMiFERcO7%DFU}bI0;X(WC#<Xi<Q`kr)7%S#f+Qv>}{T
z7B<#>#0}-P=x%v5$i%yBUT&_tByNb5owsP-%>s%XP$tE(x>ecD5uz1R(gF9wRgU#V
zuf05J?p>WKM8Hn}QD76JE!}uF*0phRm(R@|f|?HfpEfPUZk(ELg`)9O5KVz={r(x|
z#*;y4!#lNd+NXsofZbFmvPO4us-0hL;=*I*9_}d!mQD3*$klLuNg=r_r}SLJ%|@~G
zk3DW*8o-Aj>qxs^k=EeIj|vnrsc~^fvTGsm>BGcwk-}#oM{=Jf%3Dhm**uyw3vk}9
z0?G)?;u7Q-&DL72JbwwVT~HimP9L+T^p_<)&su{u(<E25&pajN+sTmvuz!_*B5>=`
z8U(ttR<IG~QJPmQE4U8nymzmUP4UsI(Wzq&IGJ&uT8_gW6&Y|=4Epo_5FOJ>DnvhM
zYkp*~#c8=J)`)B6{_XG}xEprP$u6ypDE=g}^w*hHdA=B9vWFgb0hxa|S+Klbof?>k
z=PYELv?wQy4CdaBxu?CIP{7mjJmERsW`<{u*7@$f_BH<sk9rYVDbwdYgYl#bPq=L+
zXua?rNh;n;xVQW6UZ+?7O(8y_*aYQxb<;OseFux$AG$6FMI!aprrUZh_*<uzr~Vf~
zdutwLF8;tGxFA}FI4))w@*Ns<uSF_2&KiZd)};N2`X;V=KA@-6$-Y;#B-Qp}n>5Q}
zpcjTUC$4i^0{Mx=I->Q5`uiybk@Z(G{R6C{-Y+f{SjPCej+K-MF^B^neN6v67aF?!
zqV1u!b<_G%iA)wpX32uL+izd3bG!5DI4rW$k7JfEa3z{89FOR0erOcFrmh_-E*T;!
zkFbyoAC(%P@|^@CC6I^c*?sRDS-JPysEPFKco`{`w;1}Ui*ap+uFqx8XJbG>U-@4I
zus?dha#mSUWD<l@Rp=C$%`M^7<+~UDBt&6@Pyo=h&bMlTp^((jc(wBPZmf|_lHcR6
zS>(<kEycOsTzcl?Yo`NjSyS;lO5GcJnA+!eR|}8GD9qJKCvE@qQ^Hobr6Av2V;(>+
z^9xOs4RPY9kkq;zX~~EW4OM!VOD9L&H7;+WY=EPSK+D~;!n<-0cpVZA?uLxG#~qx^
zj}A(3Qa)<##2zgwI^>N;*&H{o-ahAajHxb-t`eKi&Xydyp1i;pFAmU1sq)}#QOimm
z6B8e8rrbJ^?|pLOWVPt~?ye73oHOF_Rc{try^{(RjkhT_<uWD0h4xiVEl8bAH71WH
z%(Vn&-vVP2AwLLBxTxSRUQloze&Cg$UB~c*U^pumLMH@#&<|oY+cJMiqc`SzF7*u_
z_CXl)OuA~we$+L5nWxwQ%j(~H)M1i-&DJlp6A{cQ2j;+aXL5D;6sBDBht{tfgQ9)1
zdD@b`1M}p3T$QtEN29spS>FKdQ=){BNu4VCrxGv5KHjsXAEL0hm)A|A*rnhHGOo2N
z;pHBd-*nr)2*~*5?_!MhdVw1lko3&RL8eqziR;;4N%I{Exi}m}y(Q*B`;Qie?S2XY
z0K<Xp;TP(o#P4D_zMJ^wQyJea$@lx}SxbM2h()=1`2D5=j2Qh;&{j`Q)5ukEFMmP#
zL#-O$q%D%w(`Bo$MAYe98Q=}kOOykZ&y=;c&_rS9=NrHBE4bbc@MZdXGQTV~wd2@~
z)y4pUY}EN*J&bo#gYcR!cxdi9Snbp*t)v!&_v-1fg^>UdleAnZ_R&Pj9HG4=qskAF
zQL@r?+0({3N&?w#ryCOhH@Z2kUcyH$o_qOXw4p6dF^;VeOKJWF-WQ2P<nwsIHx{5|
zr}|8G#=^f#dWB-kaCkE_4Z-u1Z_~1yPCnd*eV_Y{0hSFyROZCbFnB(R<>|o_rx?@t
z2mOX!8JSdk8f*Y8=2^=V`nt26N;xTul!ohWSp|FdiNX)Msvt~>UfTc!M;~JRdteTt
z@w4p*`-)+<Aaq`Yew+4kFQZXfi#Zm@%~VCwM2(pM9mi;)y4duVpclWB27aCJ=yG#L
zVL$v0sGh8O4gEf7g{y1X&p#;leYdVI(hib`PBf>1c48&I&}<4A>kja~OmdiF)*N~q
z->nzvlP7%dCbieG#Z5XORbD(g^BfEI%ncYSz4!FYO0)gC+WBW^m7Ib?b*-~&#DHG~
zW^f^vebhXE2ik#?R7w<!Je-j>2PJ8WiydxJUe5rPoKbL{(7Y_nMTNi#)wVjbkO?kg
z;^?xrbh{XC*YZKYH51eGvgq%1oOa}}s8Y~~;|6u)e$1|`3l^^kObmH2@}iOcW4PRV
zl2;)H{8bM-M8(A^ULukX%BT2-_*7!Xwmxj{;T@XXcONra*T)d7(5Vq#lG}BBF-83J
zgWeiR9GBH}Z``(fqG9~Oe*H(Rf=FqWtvaA*7g2_{{=yfFvORfBOXB%Rj;NO`GoWU`
z!K)6f>kOCyZl_mS>+_IXdnT^s7iqUq%+_cHwUu?>+@%f%W}*F2YQP9%VVoh_=TUoA
zY9gdhYw)jBlu=s2h8o=v;GTgvCq_-sl)Xp8xBjNl<*WVuxr^-lq4$A`fwRH%SPY|}
zz;G4_`n42vei||j3P4T7Z#hoCKt9rMnqw~X;(7WCg9K!2;%!IE+7)~#b?@yh(nJ~&
z8~gDCOLi)_ym4jIBf!ZU0>uaMXk_ne#kO=iyThSeUgjIx#w#%>zecGL;F$hTIduXO
zEW53aDqdid%;!+Mh`ked+R>KdIjqqS&?u-gOJ!n6Ot13%MtME6n1ybjHkIP<j4l)f
zb97k+7-bCGdi+AZDUqx~=nUtYN@ApbTizaSy_%Eep?z)#7RP&;BXJ$%O2?}k8l4k)
zkJE3<otxPQs99wDgx|sdh)U8BbPK`z+RHdam-g{x>2A={=Zzc0H!`_mosvj>#)Bq^
zxM?yxDop<<H?NqMeTtNn8q6tgjvYr6`^6m490#QpTq~j&nPAN4m2s{h^Vz2MTBYU&
z)UY5ZPU>PRyX8Lw(tNb;G15d77lUw;Qlo=H@L1mQb7S(5_Tg50ZXx~W-+zxOW)$f=
z1=7B`b>!w%+l=S&4Ul&-C$U_&U}2(`lP^tBjY*0RF*l{C!~LmVDiXF9WoGJdWWPQB
zh7T8U<Qs798Z>DtQf_A_9)0U?`_!%6<mqn4jwKt}N!Mz?^Zz7%$9&d=GBi6EDtaTt
zwzM&UJnk4ikIoqi9mi^;i}GC77!thvH2Qs!b6GQld_pUK%|6~hwm6Mh3BbUJbS~|R
z<q(L-myGX%AR~nE)Z6f!$i1LX!dd|gU&$pM3A%Gte#JPzX-9$xNsH~;pXt0vL_Hw@
z+&pmp&Y<y5&gwqP*q2NxS^>XCWqZNE%2u%Ku@K%3OAJKj-G^+!z6`0f${$kn6y)wt
zM??~AH9~LIGsxm%hW(cm;td#@z-Di}@3>j6!xjzQKRL8^;m8gN5aR;`oL~o}PZ_G4
zB`;~Ozl0`8i98b+a95Pq^P6_S41-8qxXGYr&-ZKyDF$4S%Yl{Zc0TaLMQQc3UzpL%
zp{@jB`MIIcifiwwV|L>VdF!5e?&zr1h3f+8)!HOx2}hQWm-5zan6@Iatt;T=o28P+
z%5|i`Tte`+OIw^W%QK2-n1(^NUAGZp(h6!RJfTMci2l6%9jABi@{!=${L<^yvQfqE
z41_s#JG(t*EU=qUpFgezwJLk#rk<I&9vLSOUb`K1M7xF`0%(;nVTC+#q`vAKm!eK4
zm@Qy#M9psKRZn%@Z`m)qC4gPlX8-t0g{8NfPzo{ZRev^5A$ff5F{@0jkviuks>s#j
zP{y`#-u6#WtB-@jv(Mi_{^^fX9@}k7+9x?0+S}C^+3}{0mFYQ6goSC36cX)x;+Ncc
zf7G0rb$b!_PjO03p}Z^p(2pm-j$5tOtr5kSH_*{%kFehjk$1&JzpKeB3ZXgOof_0X
ztDznX>3E>^cQsqv42Mo#&)|MB{RT8}hG{~8(Q!_7N1w^f4JUAwsihtJQNxAw9F>)t
zW=VpFFSp8YhMczRQv-DW#{8-i>N)y{Y`8t``%yNU+edc~Klch6tL_-vNtIt;LzFxK
zKIsb5<w|w9_W~SgZ~1Pn9yYIXt)gVqzdjF5I7coj@1JkZT}f2>y1w)|*k*@2FYrd!
zf@-C9g_TB5{tnVgqs6Y#Lck}m$X^2c^~85MpF6|HSza|8iX*mPM&$>Y7#}>1Jy;dU
z1|=90$9Rhou8ZL0rr*@MR$!D8r|?Lmwx2vW*QtS>Q7wZs1;n1492^t;8^k)mZ#OU(
zPC+&Vc14=$>vtU`FQ_t1F?qh#pmedQ{2PTS3yet;+ixWCQDeXT`jQhLSabI=M;5oX
zgpbeg-1jP}MRrNh%!A1+#zX%q?FtQH%cgdLNz+u?M%r!z(viAMZNCp*ZHDr*>jiE(
zAoWlxI+mt<T-9bmK1K^493P3UlLKkhow9>www`OumlRkkFI{mwq0s8Dw}zm<-N_xS
zgDa(msR<N_B*CU`liuY=ie=AALG)ef?&Px%gMVE&;>#$?qViKRzZaCX@-?}jgn7Yr
z&5A`6-pQUeaC<1L&;I6=SoXtO?LXqGs_|xJq45^S)dim8dOuO2gbSw@!m?PMic8Bc
zRwRvOtGVwF9U39{^6&{6c%8?#{OIgJMa5bS=X<GloSVXG7T2<Sn(7~?pw}9h@(fj6
zJ`1Nr-OtM9yelY&6B$;UI7~#;vs)*8!N-_hdn-9j?nb=U>nj3Xw$7*Xc#*I>l`Ar?
zKJ$ap*EX!tFu$5pq~fE&nMB9ALsCn!SIy2*;1laM>Y#+5hoecipAoHh5XE16leJyo
zIrfZsxC|83r|<~tQN8EaEjMKUY>HD^=`)u)Zj;XAXQm*UPgj(9<~d<$^O=^3&OH76
z!r=@D@l<p{-Ds}Qw%@YM{P?mKUqMbH!mL=>r0j6n{XTgC(UJ9F)wpSBnQhkI?K6y}
zaz>KU&GqJ2iT=^kCi0HtpHNSV4=2_~ng|YQrMn+wiE<S(3v`$!h~h7HFQyCr7A%2X
zUkD~V#A1s6xiNj}_<b!KPm&NkN($DWjj0}4i40vQxK5Q%-uFPG>!9k}o`8ef!O%78
z&NrwTio8;pZ6dNk$$q_=K3VxqXPg-OXaN1wc(&FhG784I+Lou<j9-tep7R4=0MRom
zQJxq2l;*Q0)24@Xns9&ZVYdkBeAo0ca-2QVgo~zHwjk`dXJ6&DzbW;*I?qU$=S!&m
zn9oazW@EeC^}8^z&1%0=b~4_B__@1v*=oT>I(-5%8ondDW;11=@v*yrepTtsp_r%p
z1@x5sZ+!`7g}pfgKXzWr=`k%b6ud{xdtHSnG@;4ZQST9%ZbMXEh7KNesa`DQeY73^
zjh5Pk1U2DMGM`I+#>2Z8Pvc+31YgTuH0%qW_6fW_KaBk#?3c>Lt=RN>0y@xhvW}oQ
z>(s!6&YiX!>ZR%lBhNr<m0M&%Xblbj?d)}i<niWtmF<fG`eeFQnU?Dn4jCcET6h2z
z4KD&8tqTW@4I+DK$ywHid>B=ou?ufq`+WZnRtOT3;}%+>B~!`giw$q_Wjw;n7?yV#
zp3-wZBVyxsmjx#ht&T6cbKVVIbS??QiInTp8=({fSZ$BxHjy{FT5dK31J`D&&hYrn
z#2?Fb^C6>XLiipoK6tM;iAPnCW`7$)Y3TmpRvAqZeZ=BlJsdrArC1)^XM(hhpQK#w
zNhz^~#B)cius?1yP_Xz&s49^}sN*`TNI1Li{Gn`(Q6_nE<NVSu(69Z-d#w7M3YnI@
zlhT^!#@i){icg2LuyI0rAB}*!ze;w35BcMPlI<qrsmXMdn?d?XU7%=W>G4MNh4CQn
ziTndAKf`<^=Da<SUGu)};h^L-o0YMa?3EczDY0z{lq%n=hAOR$?#gtpHvJFg-a0Jm
zCyEya6_5~6Ku}7lr9%W<TBKuPNhuK#mIjxSMg?h*SaRv^7HI@UsYSX&*rmHu-tVHn
zd*A#1_ul9JaeaJvV7@bFX3o@^Gv|Cx3%lf?iYvGaVhN=CvrJZpa%o+=^XQd{hClC2
zMc*`5g+Lx;U1Tw9NcAa>R`OCwpx(%-xos0`=;&|if=YGgeE^<q@{$oqYE;I)qWej&
zQ<awSYlTv#h%VGA8b%Vg-@s~eZnl1Fp049~ptN<rIs1wE7NIC`4Dvfp=Ikj%b%?jN
zG`aZXD>hk^I|D6znO+%k5l(l5!*g*oWO(e(Q|xUU-*Tb!;=^4~blk0m9Ls|w)a*?l
zmBzoR-bMn~%5x5G(4S;CscwN)ne9(%Pj0H-RGfCHrK-@KwpqY-`?Tn+ZN|LnX1e|F
z{FWSJBUb??AT>SBBPSFO_mgD9?SthNuYe=0K6`)k$CR4IhTm)<$Xg9M*)_dF1iarS
zBiOIAle8|el26|-qs*|()@1RQe;1?uX+-84hpJwkrM2mU+w%RnI^@Bj={oVW@=s5y
zZd$M%H!szN?(L;s$7%i4s9K7m-X08JS^4-Q$)qILvk2%d`(eyl7T)=;r01}0m3p)Y
z(1T~?@r?`|bG5-?BBH(SmSQV__KCz`N$$Q>|2yC6s1ob?>CWO604|B&QY7Z$+8<{T
zr{1!Tj(YdWV^dzh=YD(%ZHw!>(vyQ%#ub0J(y0Kb#?du~3y*F`d~MiE%f!h6o20>Z
z*QrFS^>)^@c+q0AiIQ6}kjS5VxAIM%pP7fdHjm?pDO(GmWgz+HfkJZ4eJNP+D!RMu
zrDy@%((%*d1IDjGb{}^S6HO*cngC`PZUmiAaY0F)keYVgyOW;`@o--LT%;c0ntPoU
zW7T~<3KKTV2N-{p^{zjmzma#E>Nb2Qxo*1xI|`Dl6nIFB7J3mIGMACc({vy$QWsv~
zrzL@!SFHK=bEBumJe&sS`0LC?wd_3wC{8mhizg)H=er(_7;-3`;M4MnSTVPLrVKe~
zXf(&^Gx9-rtFbpG-Ft7KF(!F-&ntvD(38|Cs2fa2x$V~Au2=HqaTnjkFb+;n??Fr>
z=gW|V#u5u#<8FPy3@ity`^4Y8Xwxoof3fk!^V7vO9KVy}XeHc1@h*KKv}FP#Fi3m+
zs9jy$!N#BE29>E9cWnT`ka-^zae}?Tf_3MT?G!uE)vY(rbddZ-DaCp!cjd9Fu4Ox9
z&Nb2XB{Ly&bAd^KinrQsi63c9W9etF`WONrL4qDu<wu%#q7j~m-nPQUk(MGD=Spus
z6E7Mu>dgoE5?}K=!;iEUu32)A8>0qfVD!AjFtELaAAZZ}&zq$7GAt3>#V>x4P6MGO
zWlu(6RC`7@kkr4d5}pbzox$zz0txmtbt?o3otnCZ5|iVW{8PYK`D|j$aZ_BKv^U(}
zMi0-6c)FF{*P#qBeOr=Ell=5)W@b9erw&FMS$@&*htGqgUdr5u(!BXanMh$STGr}O
zXjJ;-M)5w<8~;w5PpaqJ(C-AE*KvML#)}iC4<|R$Q=LY+-31oCcT*P`ZnbDvEi|j0
z4v?1jU4$5K{IZT?%#t4^r~@YYufcZ&92ueR3<?OzB@f`vr~V8Ca(<K4lBt~Rc_IjK
zV5|eMjTG_o9e*MQ=2F8LqJZ@s5?(efE(Xq1!?{#5cZWkj(6kNO6BZ!%^Qx)_ad0xH
z6rF#xb{uFa8>KCY5yz5w;w0KrlJyuaW5}6TCsq6yaVCcFAqN&}NK((!h)NU&KSXo9
z`)N&qI3TFt;C$~mQON1OrI?dd106ZG2h`16Hjk<%F!<R6{R`s2rGZ_z)V&V3z`6Qo
zKX5mauE<@y`qnT7yaO*+O^Xw!OCSn;iNKvlxHzrf-A^w@y2nMAU^C+4&3BAMLs!r>
z+Ni;&d-H3q#nv140HTiH?D}+fks6e)b!XO~g$fK)iB<@tH8%A_d$=ggn9o6v7H`X>
zuH<Skfph3sneV+tzQYZ{W!A)JQ+38?%OoMWV63KJq3e>k>se;*Ep?<*>*SE~)nR+1
zc65eaY1T-N5Pyky{?T4o=8X^3-$!u;p8>m{Mg)LICDrt2xh;JS<4~V{wl3wQakjxI
zTCw!YQD>CsRpl!Up>*iIs1~b0$mh!*X`w1#%c)XCdtueLLF+1cEQyc_@#(|nd6_6h
zxKE9B((1jZ;<`74IR#krEEs&7-UP22?Fd>F7Z3t@m0vpONqIZ|Rs?}d=<oM4zkjZ3
zOf#0x5`!FhAzq;1J)1Vp4P;~g>h~E*40DRnhEG%(G(8OZ_Hg>{NIpv}(zU@_t+7Y_
zG@=Q}hSASE?Z<H9dS%BU52Jahzx}SXX3u*<p$hSQP8J%kWO2#@jHLAMkyV=|BUU6v
z&7f01IZu~7;NldVz@fws*6SIR+<7#!k5%Cii4-#$8M0pL&gg|0LK<ZI2s8QfAJU^$
zdBGcgq`~nY!N7Kg!#<&{GionEE$y5ekGy4f^A*3sZ~$usdaA(Yd-tm<MIehtwax^*
z?tEZ^+&&`uIec^A*?XFOt8Q#Ax`DcR7*Qk^X@&iTvEej;VI(=&SpL~0{^qOl5Hz<4
zm~R=rQ<eN98=ym3_QKA)Azvt#6$bCrX9X`v!{yVEo;^3Pefqk~1LOUK{n8mHc!veH
z->EN1i+y6kD_YAtr-P9bqgY~Mcwj7wsO1=~++<0hkRL${4=gnmQwG*jfqX_rJpeDL
zLsENyanWe>sKNNqE%xY?O4Y%adiT~!-D;D1lQ&@qh2BM#(UG+{r>zPzIo73N?kf5?
zi}4PI=&FT-8t%!ixIL-eBJ@5Z!k#R-_sl-l`J0(`WAxq<R3B*`>Zyd<gmSQf33|A(
zjjmXoD0H5#Je*z#-W_gQ+Q@;Cadi<e@rnAL>~{^8NtFgq*<EI(_QCB1<mJr|cNQ*u
zI_r99CQV}m@;2OlP}}}dgS$5Vi4}m2!T%`K#|Sb~bt+&=*7PMg>0cPTA1+Vke}N9%
zj)d+gqtX;|OjWbYw0&Zb(^;CTaMHwp_k8Oqlj=q~lpQTK#u<lWg#<kJ$qnSVtgAIj
zGA)Pa1FWH0x+NvG)=k=WR>RNNF5buQ2y@2E;MU<;UgM)wW?{8bOp;c7`@4zk4{K`E
zB8lMAFHu4Mlpy^Ul0olWn6US9e6h*MxF=UgCVNOGno_@;;~Szc5&rPYldqC0`B?=0
zKh}G4^%TTZG*L2{3VnI-()2D>gjNIclKC#@l9%?Bt*rU|O-uV&txWhI;K%N)HY(B*
zk5Uc_{Ewx(BN;y!A;0IB<tub61$TmAo-N6~rE2uM$yY;y{??xU+|qobKWDZw&%t~R
z^C3nYOV@RD#<fs2Iy&DcFz1XF(qM(@=h_`cMiQ-U1F;lY!klW*yN`4r!OGYgqlGZB
z`Dl4?l)Ab!wmwR^&U?R|z!)KXB<0DvznDD%#R3m~m2uCM5Uo`Nieb&t93)DcSbbED
zLV0W%R|)>;04=U9r;Z0T(S-LLk|~`2q7HWSWf`#DVk~vIXXO&T2snW*y)8dH19kh6
zbsxU!tq+ehxp}WaguZbrr2%)HldIu&>+j0SMjPuO2Cm6u8NgR)xa4u}{-mfO&aAE;
zY&31!X*T2Hh&B?mwi>3ep);9w^48pV`iCa9USs)1VOh9)9&cI~W=o5Gsf)UHbPV2@
zS-R=Rlb1Go{5>NnFic@*Z>{A^P3iLnWOvLbI9p~Lab_Ec$?070N5u}i0`naz`|zHL
zEmcSj9V0}R_0CFEzU(Q8DWz5M^#gLM&(L65c~(G!tCh(V^^>dE-Tg9xB#Wu5ZaF^)
zuDdboGiw)|?~f4N%YSVGj{LQz@ps_h!Oq~A6@n?kioT4*M2huoZ84*C&*&73baRqV
zq7Aoc9KRIN+mWi1wq<$yU+}^tH2O$uaY{fBpH$@GCFh!2t-X2cQ~CmQ5_EUTh-~EU
zFSCZug<jf?<M$=5=QC8TDzap_Z)29*dSBN`2EdzTs{0a*W#z=e#$~DpD4P`+So?aU
zc#a)!-?nQ`S*9?gzSK~X#j8y;RAg7nL7K!-Ko4e)2j3GEc53dI2vCi7m3!p4g5z0r
zV4)#lES$Dk*Mf<hu8YEOzT`E0xot0{qMeW|_7Hb<7g04MCs8G9PS<AifHOs+CwMGp
z?`4VXE1lc7sfH}>QbAF23g0=GzN&1@{`nN7{2VM8sXT&klGq5uE)VvQ=x5p8_uhUx
z;G26)yjyYb<LivphNZ)z#RIaGX+N>m9-JTM!4^$|Nr8u1AcA*0Z|TENL`4VYy{mEp
z%xu8wb;k|0w=Ba9yj_gNsO7J#p`5rt{fnVN%ZX#hKM#9lTkuwwUCx4pr%#^|Vb1gY
zg}b%<oWtESgFYt?5C_@M7${Z$D9iF?$y7Pii77+Iwgfba^|S|e6)gYCi0x96k~#wQ
z=hpbpH@#bK_~Axj$G?c5Io&s_y#E=?9a9W4Cja;t3SN<8EgR>|3*Cf_u_$W49U*O~
ze^)Uh|I))$`=i1A-;mmU*;mka)Cd7uVE(*gL_9{saHcV35(tKdurFn1F&!a_c|itH
zWRSArUk2^HG9bHJ@RY3aKO<@~>Q7V$NI$E2)2zetsA!yu;iMH#`1woFI=cyB$BEa_
z3EAtX^<PGBeYU3?m;5<PVUB{dd66U#^&qNU`dc_);i1$)FQRvdrB{IIjusW|tw9zA
zTj$J7Tk4M2-p{<~(8dgY)-1@fi(KS1BO>;~cx@9pCFDQYs6nci2-(z*6&kHBPpBn-
zzHc_e!udG401Y=AnLU450kDBN6Zt!zB}gs+^iH4%Nn@INErLzKT;6%W>iS1yIsDWH
zOaTq<$ZUI;pPh#kF$zozB6@`F_bXu4xZ7nE>~;`n7GrwYImdNTp36_KrbQ+@SkARr
z&DtsAIt=NLhr<0aw%|~0sJ8xk@pk$L`mGP8mW&fmR=e`1u918<6tT~YV32)g1cuE)
zCI;we7Fvzou`PiSdot;9%N}C9^`z3b`ni-`_J^%Fmvm%>EbvNl-Fi1<ZF+jOchQA9
zMX5~nYO9u8C6*-ui(nJ5-aV}(^?jfEu9INnW%+&MSF|XQYtwTXZBwq}_uihI&Oav1
zfvNoCcSMCcJEtx<tEXL>cF#-u%Pn`~9<R0}^fiogbgUEpr~EHp^RtVPEjad)&=>O1
z+ZZETZCS9%VTVGHy>bTfo*e6Bm%gH}%)hPn;qMwGDH(2fL|C$zXh$6bO>MlwyHtz0
zSP{vGIW7c_uSlPuvRWT|($!M`<^}y|K=O(nQ}Tbc?cTBff%?8vcWuC(7gK!05wCyJ
zwULmajKa=HHh#}0GkG?1*CyZcN7LcGW*ZJvOoeFu)Y*_(N*PkDX(vtg`wC}XNz4{V
zbbhp7%5GX(CZOO#2cNpJ)CQR24}k;0k)Ly2A%ON53~_roHor$%d;QX@Zd!hEwjSxD
zy-tk}CP(bY?)l)vPbj{=11$OP@^Z0zcn*yzmO4b39u`i0J=9wM&8q{EU72^stvI|T
zTe;(tk?Q1qHzQ^9ML}s_K}YB2!;K2!zE2{p6poP-Wf3@0C%j8H#ayR21GAs_(0kvp
z{7i9_5oOK`Du{5^bO93s9KJ4I`f$Q-ZP@@Pc|Z^1bQ@zLS2Vgf$dI|wajxpT=3xpe
zj+jx!e0HIjm0b3V@*L2GG%R%DX40PL;q4Wm`_Ck&m=0W4et(EWkR$Xg<DI~FbWv>7
zG$CpE<9C9HMAA)PfIa<U?oe&Usk_rGr|3b(UATq-j+85ibKc{C$?yn<05f?$Yo0M0
z&K>}_xR0>q0u>g}{Oc=e6fev#rgeqQBK7BWwiMHs6V{oq2%n8LB#s5$;-KYn2aRBD
z!?ceR?C}VO&duDZw<4Q2Z>XPnKmbvyRvzIx_CrYvTtL*yC_6h(eD-mz>q7K2`f;oi
zeSeK}Jb1ooPzZ3059~+~8!6_9818qwV{F^A#f8d5o>;LjEp;T^&y>oJ_}sF7%d7`a
zmP&nnylOPrO{kw>0%H`RePN?LCLeH-e<N1#HbPLEtZHA_tP32Gov(VmJvg5ZCdH|u
zfeOpKI@^E~O)3DMON%JxS~{pE{vZ9TLB8X$Avm3LWeiolb<K`c;Zpvrn8FU%K&EDk
zl3eTOi!ISr*<Htp;NgC!*16Z0lwO!`wvo(YBarGW&sc&9KZ-~3%uc<>l^jwloJ*eg
zwHQyHyX$QA&GQ*0uh*8lgA0AV9^}AFSt8pP@t6v|W)swdbhjQ$*=5;WNI)f-Io(f3
zdaI?uMZqr&6E(p_WC_N@ht!Jn-Z{2gbsvK`mAO{>=sVV#Jrsb`KB^r4F?0Sryiq?c
z{{})UbDja#-&w)N!0;{L;$8liHyH7eK`KHybB?Y=D6mec?;*<J%G#RjFb`;FJzP>P
z^4Z^os((#o3%)y*zSj2W$=9A|8`5b<gJV>M-XI+J=Nb?n%dI^h`3om-)`xQE21^7!
zDY?z`_Dw(hG5u>!;9%0cm{fQiC6x7c$#u@3nw4Ki`t_)D#N)Nz`mgW#5d>KlH8lnV
z?FKK8ADe#Qi}?|fN6o$zSB=6H$NEk<{4xDtE{lmD%VJ(us7lR0?XL~qG%hkmVHAh;
zGv8Oqvt}djAlMF{;C&Is{XxGe_k>eRX$c<jMfwBRL;d$~3rg&XEYDhuG}J_PC;Dht
zmLlEyMgQA#znOG*LI!MJ*5gWhI>Z<W)z(#nemhmsvz~>_g?Ux2ycYW;GSUvT0rd&-
zE5gHS`f(bB9i%R7uu=?jeedEd^JGv9^*&9QTxX<p0m0<p+^15RVK@G<I@XD~UkkoS
zC}G2=Vp1KjB`h{0%X0V2PL9jbtemsD2AIq!DFVK^%a&2*`mL{4>bb1;?sSoCB(0O4
zkUKS*PodZdugYC^*aJ=^CE^nt4)by_gk_USo7sqSBIyvNTJEhz?3{o|{cpP7z{;{R
z;K49J*w{1mApLLeiQ>&`e~JvQTl@y=|II+UaJ=;=O`;tNv1og7Rx3wP|28!@!^icG
zlRrK#MjVux=0;ghUdrpGDBl+TojO++6(H{Xh3U<kK9Sj>4pP=){)et!=YIP6NK;8~
z&`D`wQ-do_+L4X#xW0MV=JN(_V8d^fwNdBi<XC33Rz^k1%6-azjF%OC4T`dr1NO*9
z;KyG{QEKo5nCj}phldnn_G<)ISbr`Tdn_M+;D>~)wKhU5h23*XkXFoHiMPopn(C_N
zUlQ606p&+jC?(>Z5?>+qTI3A65_vO`OFW#J$ix_L%#_mTzxmqvZ~!CTKqvZJ1vHoR
z^qKLKXGX94!YSTT{2}hr_wx6*lS*a84Dl{Kl0)oGLG>kpGifsYYEJBuc^(Z;$dcnb
zzU@Fx&1J|?X8)S+{IIC8J(NX2=CMr8?jJIzbEE8HbpTg?ahj#R+}2g3FGi{t0s`O!
z869GP$x3UJ_IRVFr28;E$#tatS7pw`q-4`nfQn(^aB1aEOSIFGr-&t7UT@ad`@F*2
z_oyc}#vKGZ*1E?FUP$Zmir(XFQ3jA-^HjlPVNC7t;$D7C*dGauZ6Wn7EfnrI^m^Y_
z0siN3c|m1VD3TA*(=c;tp@bPP|8Un{zuvjcZg00bt@effwAIqxF#^CV1ye*j5q2iN
zSR5t9@$>n3*)dUmhp4h(LcmsvAw@uFl35GF27&W3fu%4FH*i{Swz-4bVc;T!MkKUG
zs#L)gc1D_uR7IkVWZ^!&4nTbh;R2Yjjpx&WHp7`I2iMQU0wxmRkL;qIZg<w9`T(1M
z=9^2HF!M2RVWpsT8u{qaq;D>j79>{{TZsu;?HW#c+3&vY@pZA~&sN9A&!x(nR@kid
zA7fs3u`Dq~?>+MY9Oh`Aiu1v0nHzSV+e6R4z>Ffh24-sc|7CGXo@E7e-uM4BBGvTQ
z8NOE)`1^+yRlIJW*_NvX8k=y{*zPo-EoK~@bp}$#BWpS?JlzLScWvGc<%SoV?XQ~3
zypy++hGS3>980QGrB6zpMmxnb0wuCII`0uz(m9{L2~tT;41BRi5;GX<#KyAJc`FJj
za`Fh!OUFecC%+Nev0w`}vp|-Y9(103+5*ZdD52!WkAF-genK{>34qJ?T(05Fv3J(7
z=OUdRf<2R8!Dp5N0VYGEouI1VB^mgk@ON}$j|ea_Su9JL32;;ZdfhIh^CZD7SZaM@
zT1D#$ET^`WU@<AV;8&}afn=1rT5nqv>V1+^4}r(88+ydt;pz3uZPx;R5(jUdJJAx5
z*1apDs7y(jDGR+INZaw;lK`|N!%@hmZHOx9CuHgg0MqmNA&{mm2gfJUL&-mddo2C{
zaN+_1MWZx;O8UtWV@t`B2lG)yA)A0s;S@4~uLp3!vL1jX_RQdtwJv~eBb_r^P~!_+
zm?(NsP>OAd!V^$N{(mUaJ^Hf)B>wS3lTel-!{4L#djS7m>vn84I)7A9Q6(k*=!7Y2
zsxNSSC<iz(L_ty&C42_xk#UA(&EXM$hP7<J@)iwPoI975{gP~o`lZdz>WCp>0)hEn
za&~P*D%?#_o{o+&1MF6$d$rkMizc)E99H&E8n)qeN-2$5b#VNc7$h}6f*O=E8eyiF
zsO%lR7b=1EHnh$K+$Zn}$f{#k3^v0#8{oBLMo4`n(vHK(Y}#6_m4nT_nf~d|EWL3)
z(f<)L*LtJy+lte=Pf2-c2>f7360E?3ePtO^Hixs}+v>z8dG1;@6lAL<8xtDZirR+`
z5O_}7XDoxamUCsbJ*XDCIuU~9NHlkHh!~N&7SuO%zYZ2(Fa!dZ8NLAM<VSU(#cz#+
z3SNp?uY??ddOgn0+72Hl)i7*~CpdR6PP;jyE11`svs4TkK7U{N$OY2?cj^mMxlyL(
z?Fz)gI9-%c?qYzB8OJ*N5xAEvRmtXaTxYf&pVdk}<>!Y&-%A#nYhaH+x{kB`3MCN{
zvs=q$JMX{!zX`p_8%RXC@gqc8mu!-;LvNk)2&^T{9bhY<S(#Y<RLN9DSzs-pk})be
zWv}_}dUse$)RSpoOt?Yd?%er^wn^=3i8plSoLFCyR^NGB_(ux2^@foW_KnZsEIeL|
z^cOQ7*`9V)n_{Q@>u;BvvtTmuETu>z@P}?CRAs@JtZiY$=TfEID}aLYS6eR(GD+H>
zkj^?h<y3l^^z;M8;=9TLd>e%F@+SgEb|&h3uk4BgO092aQ_3bo7&FQy)<T&E$9~_N
zda~tG=jPVFx6s!UE59c)VsGwbW>QrUPJr-SN}BDbFh=YUXAQdrZY?*AS{z-X=o+&E
z3nebVgW-rOGpf#UXU;%{C}++crvfRF1#O@^(j1RLOFhbm0;#tw5FowG6MA}y*~<0d
zGpUQ+_TFO04repWI<TaKQ@XxFvs1b-#pO$X%TacoG@L%CGefJ<Ie7CDNp@Rd)?doO
zKQjbqoe%?_HPV3Tp`poh`e1ev?j-wSWD18mBrjSK@X>L+1L$wlE*YR>$TSk*1N(m;
z$~{NgMG_HJVTUm>ELbO*&T&#M*gYlG>;L0__{FHBJnXp3;jXFT;1p#hpuw$Bc&Yiq
z4xgUMx?E`BxCL4mrLbS(qQiw)+5FN(cRQ_WCMe-?mH}AoX(|%JiU1sw*qgG@Q>T$`
z`)jsh21AvhePlEJjGspX16#r*BXSCYo~L@;zfBza4zai29Wz*vjrCm28AT$!YvqeR
zh*6Bv@-g!Y<(gT#XRLK(1<5nNs;wA2V{RDCuW8O2)vXV8+qbQGao(qXQKT<%ia#46
zPxAWcI^dlf56aV0Vga9gFP*M=e6QmT-Q|TuLa5<wn0~c^D1mT=5oi7GyI<QG6Zt)e
zl{j{0=Jw_E^5qA&+CEUOf2*3YKC(Bel>I&NdvgoMe~8a}?0W(jcBKo0Ra}1|YBTKC
z!Ysda^~Yj7?dsJnG*OLw#3PA+JWeB;-|P!~$=Y&Qe$H|xQ>Xp7xnw-HwQC&b9E~pc
za^(oNZ34f$<fLp+G&5ewMj>H%XmI-a`zpQnuEW>Qf$yev=)Gwg(xda@)pZUmp*eEX
zJZmSJ|512y1?;4ejO^+yQJ;(@5`JP<TW!{t?X8m5oaM%MLo{N`lFHX5Ui{V~6^+B_
z%)7e=`^+Wp?!H4*;U0H(YQ2%g`hNkvXffP7r%E1(mf#)d%HiR-H5t{zWFT3WWpl&L
z`6#V4I<Lf5yZKo|ma%8Fm1e2aP|^)u6#g6tr2hRj)+5X>2_5$Oj#^SVN5QaWBI$G$
zuN6ucQQ^RO!>u~66=L%;*F(XERdUOUW9Mmeovo}jL#N6{ij)M<j@wu5xB`~*H{R&F
zSB0K@v4-ffu(IM#t<w!+Ewyf+DHBT0GY%yS?-W5I^SzS(O!pgzs2haOhJ6N^;VIQp
z*hCKm&tBt#F>lAj<jr4UBuLxAH^1`;yo%9`9ET|-b5y;{u4b%y`&C?SqDNOd%R|A$
z{xJLSk1Z1b&&7h+h|bo(q%B5<w*t=sA<S3KW&n)Qasv%w>uaw02gds5?2w&yd%R3<
z(b+>w*_RkwaUb}uHr*B4!p-QtSllZ8->SvPhiEKMZe8(2FnUi-%6C_cfD1-*@;{j!
zjdc9pbx#I?_NTb2hXp3_9NZp+cD~|)AG#QuKMIMu^8WpVJa~yAVU8ZW!UMvIu3{)g
zn##gOuh8S9;Ugaqf?<jf*wrR$;<}NYSHMrwzP(qUPQ&`rruE5#C%<v-atI9*K|`6}
z4#gHiwdpc-KHLviu9kf-FG5OaJ9VPBal&Gy!j)RNw&ERCzFMiiJMB&!?Zg9|=*-bo
z_E^dQ;%&`b*LavBs{X3K55S}ExnP1!;I*qJr_Z2OTh!D@4RuFt(;(BHnU`;Oxn}e}
z3AV@qrCL{2Q8Rj3ZxZoRx*|Fy=xaR_iLmS;7j|J=!t0i}FXpZlX_vM+7Og8XRd?B*
z@zaG$A`E~I0MkBC>2ya`nheb{I&z9FT%_3_NW8o{Po&3U0f4hw-$z2wU#wgC)w5^$
z54XGxozrdV1^>u(H$FY{DLKZyq;I0E?Frk@WOi8(Lf=xoXiTTdr9M|axU@sfEYbHO
zsxs=(#R(ze>PdU4qe)%1-{jM}J?}blZXF&Y>H`a|d_7IIlu-<!uJX3qbLBNkOLmGd
z?o)2-D8-rj!Dy!k=0AAJ3`cww(k|^O0*4(sg;jFQ3m^?eRHPl*6rFvV4zqy^SfN4q
zaXgYrD=N{sQ+OyzS$$x)>@`qNNds%I$=?$a=n-gXYh(nH%&CI%moN4VJS|Q)cNb%d
zM|QW|UB6E^U9=(0QuVGvm#W6ZnO6zY9j+Q*?a=U6!3kq)%M0ep)l^2^*HRi&=;S`u
z-iJ?8YF%wEZMouLrvV#U4i(h<&jy-FwSkw)D8smWjC~VfJ;L25)ic4JN_{M~%8{oA
zSF{}e(JCy6jTT(iekGE*mGF1GL@}M?%r$47?gpYOfPlQXo>{%Ug{G@e^z4Tw*5pqN
zT5hIi&+5ToBC4vy!05%Rph611I9%{dYDge7;rJ<Ch~U9c)hm#pn`x_PBj8~~Tmw#O
z)&D4&a`ITsBD(6=B2owHv0dqP5iHww24(@oPa~mdr(cWCb_ZyZCy2NRBjUgPlF1hZ
zySYuiE)|o8H<uqrmrlGBv2(53k6@S)n!ha3>L1;C4lN2fu6dw`!q;O4?+H6eReHej
zk;)GTbdX&y{)!d;YA*2AZ2j}M0G4Zm<@7|qz3>EcVu8Vo*C2M(K1zq9&u`b*;g%wC
zbEEl92>$W3gLR3hVxJ9fzVwmjj~k`P-%HU>0TiK4s^(Nz%jFzR@^X&v>+h~{f9$VT
ztzag_e;;I1u<J?+4E+n-%(7$v^iiG#HmwLbH{IIQNhZz0Ax(oz6J0*hFQ9yOfg|=j
z{z2@0VmVFjCziIdAGDQSpwXP)o)U#DXF6+A^VOn>lDOU7f4gt~CXpgVUdJJC@}i_1
zvX1z0zuJk8(wR#P*kjTq^&UwPU~CtCG(IFJ_PvLfE@c2GalVF>2?2%#4$5y!J136?
zE}Vc9xgQa|u-p4rx{vfeaIP=KrzhCQtYJyW7$(A=a(r07;j%33F2#;fV89M!e*c?I
zW1_CHP750CdHF*|eDko%6=i@5=k%W~KQ)e~4X^t{uMS=`6Nb<n?!5uFG(XXcsh?Yr
zH*wpK1_NW0ifz&9)Z8<BZ(Ol*-jMaigCC3vHr-$6N%{;N1l>L)d8Ir)IUCC7);{xI
zlUuxk5I(SWi@x@G3*SZmGM<v(>Z%<RBY8HS3$Nd>fs&cKD~T?=-S)rba}Rv&++2ZU
zH`1;zjm|=T#0T*{c-T$1lTa(#Ng1e-AJj=$^$Cf^Gx7sqLJ|VdNUXroqXci`W?8~I
zQa^$HMVleVIdIspO_!?ni-EVvfK>p`<R=^;?${~-;pYFUB*MRtXnr{PzBaJy7a4$=
z3<%>N0FT3pk(!X_>@aMwkpCN!q{qt4?gPim4e1L*G1_G^p1EvfxduuT7%xZ{Au)y<
z@Xi0fDyhwfeh_Hy1Dw^_iE(LPOTfHE32d5y%mJnhGM3_Qmp*_W@V}cRJyw@I%J!Z=
zVxh0<-E`F5y~=mDSijzNwYn23^PQ-MoJ@wC?BIc=cLOEaMCjbp+tr!xFF#jVa2gJ<
zXa$)LdP$Kds6FblVtDHlsE_j{J0}Qv0L#G9>(oaZZRkF{lJh8|1R-wO>?_myg<tO{
zP=fUubgtqA9g{^l8JBg-T9D%{n2c2O;L#e~GXI>b+bwQyh8X|3;`W~U7iVc4*%e3;
zZ-H}EmFw4a+R~Lei6cgd?NFoy6R4|G5PsMeP#scmg;-Qh(!m5vU?GTMUedi|!hd!+
zy*szB=ZUWR<5T?X9yN<RvWJIw^EMc&D-1_jQn|ff8s{9ealdNjOI$DJJh0B1nuVT?
zRGXK7L`2I>gf^FP-S}~NL7~l->}#o8ESKu{*GJbQLEfF;uV26{8A&m!py}#lh_HRZ
zYH3AxS6-?y10$6Uy?3!?)&P@pW|%D+In0?T_I1w|u~H*Wjs+Gd$4QMEv@H^58h(6N
zF#ls#gO!xfXq-z8=i%Oq*L*}%-j!R{7<Jz?6A=B6pW*LZ`;0Xv2cZY_4YR``40o+1
zl!hnR;QuIl|JQ{FSWdsh6=z;D9OiRzPmHY39dUBnj{^^V7C2dNNLJc@oK|TFo!g8t
zcQvv<{NCIDjAJ?bwB%S95hwg2*jcD|UDL#WK=r`ANrS67*DdC`-<`@RC*30d%^Q6c
zI=Q2}(!j2SbKE}D)F^yg?hTz3h&U$QpT+nwr3#6a6+L54b4bM<;#Wkx--vW8sEmt}
zUqNN%mfHfDf-cLCdW=?8Ctly7X(#cL3Qbh8LRpg{8TY;<AohRi7pyVA;axExPtLk%
zKrZcD{78A<+#J-~-2YH>W%OgmpaqA3LfOYi$~YB!_XnuBxXB6wFUgxH7$%rU(O3gA
z@pD%YHUU^5c(+55KHm<OVxk5ka&u~;oSb(@)J&v64%2iejCJP2WShg!d4!do`qceQ
zoZ)o+S*<f)b2Sm!Q)SU!E!l2fcf>_`;P#>S!V<HNmKvhIpL`>>)@A#X759fSZzuU@
z@O#H7!6u#cPj*FgqfA$GI|$O$t$*USX2Hw=CU`~KHZG!wY6eNR>lY0jXvoW>z8RzZ
zeg;oDMG5BUc%gf$bw1i$%~TEUPB-wyg15u);&P68?Nm6^(t|K0S29%foR#NdK8|?v
z@=o%t=EkLiE=ERcwGLTS_q?s;-k&!-GZ@*_za7AXO>i<>SMXtFWui6($X0={Z%}c3
z4XoBwGFW>Ue~!LsqylW<Nk6_m-s-7ROS{`>-(Bb9WUBSid9`rz5ifjIFX~YLXz6VU
zp7$uL6H~kqTGfuq7Y{bZ$x$<K)lPoFl_;muQUl+tdfhuJ0qRo&_;a+I+EwFv$<))P
zI_MA97l3#9Tq0sSR(oZw87Zl7#y3)bP1i;OY6C?m*hH!G!gD`|w7jpfMMTw%#bb3k
za-YJGcg~@C!!;P4T93xUviq8KrJ|plPQ7P4rW=?Bo;VbxYzMbBRhFh8vpjDF6Q+sS
z23(Hl6CPoTy|soX$v#@&iv3?nzxg|mKP5tugems5Oi?c@SOgB`m*5Rt4-JK0XV(!g
zd@xg~WwGr0VS`(@$h=f+_<giXrRZuw@_VC^{<HCdTs$R28Z5kURHKep3EkDEFxMi7
znkIqG!ek+oVUfrxdxY`TZWLl0^0O{4Fd8vh&ng}D<tKPgcc%vKk3~`QTVV$I1v7!J
zW)`q4KmN43`ZeKB6h*>~sKLKgvND1A37PW#?mWt}ke7F4A<xj?O#a4)!@<?K96{lq
zJ_GFJ{_i}KaQw>u;;r8FJFRsU{>5nUzk2}vZ=YQMKMVR_h_YQHzxnb(5aEsAvy?F*
z5lV#)t`bIXfcJuK!du<<;sA*%3giC~Ljqtdwf=<5w8K9FqQJ}7;0#l6JFwdME0Mp-
zk*2^NPNNU8e2yvy&fhR@&|_}cb3k^GkE6IBC&&#f#{VPaAayMmFWxym1b3b`K(w{H
zbh=oU3Q!>Vrv4$p2Nfg)&@z*=Oe)HSS_@m1@fwBk#u7!-@QX0-U$dKZ!!=gs;(Jxf
zPBrR*zqsrak=7@1K6kR~oA2NV`whgDTeyJ~L{y>aHgeXm&}<sB$Kw-L@QA$!xJwWW
z3;3w?0;@X2Ik*L9=v~Sw8;_#kP3q!z=uHj*PJx$13&^G2EUyhd5yu_v7Z|V>_I|Ev
z_DTKAlwSc>?zM10I=R;2UZp>NIS0R<i`5ue{v5;Lt76gYe}z3geDCj}b&`s`?HW5d
zU^?3mEQ@|`o~aY^wqGC|cv8D&r@u!Q-~p|WuHC23+9(|Ua6`>-CX#ca<n$awr4CSW
z?us1FXH&TJh<CW-Fi0Xkb3%XHkTA)`jJfbjQh|1|U}9HSe9-YHWo$r$`q#f*zd_4V
zAI0pAz0A5rW;IcAxk!{(nP{<a@MIpwi3B>f96fEVd1k9+3(s|?>3>i5p?bWGBUf_~
zu{YEZlviMjy^%bVzkQI_j|YNz+@2Jncx(YnDxmblh#x*$JS(uoW)Qo}psmm}p$tL&
z*?Xe;EB23vssQ4B8Pdv~=D!^F-?Q~hIy)0dUnQh-sA#F>pt)cMp&JbaucKow*Qcku
zNlfWFcGf;m+tg@2D{lO(EGBEZ2hV2V%h6HPBqErBkb@T#Id&X+I@iB$A~^_>?|`^I
z4_Vt)MAf#mtt*DHKI=h2X8XRb+xBgN8&{iCXV<H+m@carPvJUMJ%}Cm6!~_V(Mi@4
zZe=4TmXuPMt2-YU2%M9Oice`>?$zb;t-kQN$js;!o;(Pm?7y#2t}&@k8(nqpwv@hJ
zI-!B@8$3%PZRY0pu<%$9q(~R@;V(G1D7Mv7|A-F$l5#F7m)4iZLS6lgG}1v`Lr~0i
zLowW1SSg4<2Cf4hhcmG$AYyp1Czyp+DJRGf@(rC2uf!H<we8ZuyzUN8pwj_tXMqd}
zYEnhW=?BYWCYE^b9GHVa(R!$4SRcx?JJ_~RO^6fIA*+4Wiv(P$0;G@9>LlSt-p2N+
zk%#Ty%vl}Wx=b)sibXeYIs(=e9kU7nSmZ4~N|rLD1C{kSqXg7^K2}^HN*9B)aV^Kl
z7FuhCE7$EDK{J5qhVuZglM6N*wZBfChtA!aOt?W2%(XP3oRe@&*BeJsP^c!t86KRk
zan-x)Y+Nt})LCJwm-mtDc8`t0<iTC5lBIyMnTw1immdTceV%kLOj<l7Ey)%rgoo^W
z>F|Wno-c|{JhnNH?J!8UG2;@7QbVK9mqR%Vn$Etk_P7%&rAcGAhHM~m*zWs=U$$`L
z&qzh6T_Kc1ge|L5qNi=uMn!M^)b1}66rZ+rov3=8$q=oN<)al>B|h^}1*hWCiF(ld
z;r{euT-QN-F1B&ZzvfST?L|Zl&&mRULc|L;kMhH8fxUQIv$)5Pn@vn*#M<ldI=DY+
zi!83oQE*gZWIe10!@JNQY8DiyW~{>WUDj&<2Xi=K>N>MA3}YIG9=qpI=vv@RdiLte
zZA|P`yr`?!WaSo`<V9o%q_2Z*op_~pPXRt7WT(O@dXbu~+ty{}N=L}&NjaT1t1E2b
zwsPjQUvbJ-)%e0~49+A`Qy`ynzSm`?YdrnBXKHmTQwBRjq>1k6prk!alUa4VZ7xDM
z+bt9K9v?EQPYnE=NM)EnwFfQ)^mwlL*v?n%XWYA3WEWL%E4g5)q0mLS*tm=)u->0#
z<Se9VMxOkk>xF@%nhgq}N?$Nj9)QPt$NGhi_Az!h$C0dZ<115>h0?UPWKF4N?+92u
zmg5mhA`g4zm^DYSQ%4w*DdYWs^H_H~cwWXagV^Qw<s6$ni?Q478wJUfhUBpZiXoZ9
zEp3U(@*q28gf82|6Q57~JySiIj&oi}6us};NMzZ&kUzZa4YUkZ5Oc2UC-uDU{C*F0
zu61?AbyL4;eLEfUi2vo~z_;u!JrkB4Qg+WUBp;3-_nq>{_-j9dY#k5#rz+yk<M(T{
z#UWsaYtOmi<NBOoNUXE#kW8)u%9ezN_TEW`=j^rZoZp`GAIWLBugm3xq&3SFZDmQV
zQXCdQ5d@M3fmAQC&XRa9gB5>CO5S+DKJc9bB&1hamoxAL&T!qagOd<ez<(7&5=xxN
z046R^D5n~xPXCo3Idba|=A~PhAhUk(ZBi-Wz<VF`pp2Wa5$Zm7Fe?W+TmN40v+ka>
zGCFBmff2_sLJMLYl3kWFZ`!pI7I#Y}AnGb4VUmLf`W&b6#P%M(ymr9^Zj0KDAM3nz
zXL^h&(;pp9AJ!031gw^Itm&#QXGEq>oUdl}`i3A0aK*{%BK8ic%N+|NNr5nu&C0^H
z+EdJVHvbwMMb;>X66Y_rb$ACwcz$>;M-}M2B~G-b^QJITSL!9ls+#%>4jrOax-vrn
zhHsWHZjs_5z!30YWW`e5Pgf(V@r!D=re#TwT1ECY4$ukvif_T&G&oB0AB)k~`J$Xi
zj!UcA`O}pJ(kCCgFp3kZltlh{b#1mV*%e<3!ls)3yk(x=&vcp8!(Izj&P@>&Al*Yg
zNeVFIw<ow0*0~rL;)!<VplXi4VuaV65S;WnYfe+3)lus++y_t;$J%ysRwSy!kI<Zh
z;N94mTlyor@2z?`{cg&=Z@S#U9F_AP<gLNgF4fjin^{Nt@vH#m@(Ji~Q|l2#%pk0{
zrcihUa<ek$6+{Pv?-X9TbTFRCRhjb?qpSeY6ZJVXfAy=Ug!p~F(!T4SK4cdPjC^#W
zO2IbcRZzWruru10bSr9)?28%C3Fr8b=1SMOXMy8cdQy)()(5@6m<C(~eCsXrW?J%+
zM_s1Rej&?ix3bW{y8q&fvcqS-cpj1GA&2f5cy?LED+kJ<<{)Mae=K9TTS*=YAbnZ)
zqMV}6ny^lx`8>>d;lMN@ERn!eyBMI2=I??%?|=U`%(U|2+|{%l{`Ob<PpqHox1XIH
zpxz<*sH&oNW*F1N^aFTqI$kEUHX#ArP`R2wlgH{!E66e;o3Y|4a5Z@a;P5V`7u8Vo
z`Z@>9qAX}il<;K=0Fk5eb~KKaX|F6>nAUJJDCo`D4Pn}gUpA+s0dg!&WD{>rzAMtj
zABzvs@xORg)Za4E3@3DKp~mv{ilWN|JKPnp%`6P+sg)#q_lY~fqkcE)d6@g{3hoNY
zJij3BYwJRxr{51r41PYJ88xiI|H8bJzxS{=NPCN6GvIHxgQ8-zAcccKZ({P0{a=cW
zKP-a<POYP=6z;N|x+=4n|M<ogNN&inJ9eAGY+AwG>Xji6Ja^9VS*a{^SSuW<uDQGx
z{fqku>nF`54}pLSAhdEQNGg0L<}HzQxyqp!)6&r4)7ykw1@@Ajt+y%GQd>y$*XXXj
z(buO2FAxGY;Qfr7-(lbtkf$49jf4##?QWQ$B=F$GPx-+OrN@v^qW3)hiX=CUgL7m`
z(rDxFu4nT4&$X$I{xMTW`3VftiALR6U-5o=(|^&nzOz@b2iQZSV-OU?a<NDecZ{=8
zLJJ=RH5t66dBmWMn(sPs_Q8l3U`&$4La{1_2Vi~hn}DF`J+?BW$wt-gc|$&rBFcve
zW~=`Md?Zp|2T_BKOpX}A8Q3oeW?@%PLo6UJQ0W~gyb^wT?o{?h09+-DDm7svBT9UG
z5>cZtA~tFSM@>{Z-o-uvl>@Jagq^`PD-IoKQC8TnhZ`%ax3B5Ob)|4<DpE{yaG(~R
zIwq|q6*zLZt4i0G+I=E^v@*J1hqMcb5%dDw`+zuL(G++rFv?E%)QJ?&LXzS?G7j7}
z`S4lWZWmaSkoMrZsa}$*l$HLeu?K8Wb*hT-8P!HoP9PCrYQ=5;9JtkOZ*|`5bKu}D
z{(k>Jgf`^+L4CQdf<;{2s7Qg8ZeP#1=D_8i5ehK$i~2G>$gR=~n^(9L!PA+I`i&+@
z`<_z+G=c?GOD1!>^PU2H)w{1}KnvX00~RX~$ssF{aP2(7AizI@vqygR;QtVgA~oSt
zMx8b{cGr@L)&#fxPo|9&`G%s6vK-+aVjFgtX#2d(#9WEOy2y`RcGa{*Ma@Ikepk((
zL;`R_+yUs<7+G*JvH~#B@r_#yIR->ZoCnx}#gt?Hq)MnPUR@2~wjx9q65HN&&l~P4
zFGW$}FLjmd_&<{YS!zy-S_c@eR}5T$DjX2Oa-xvMNcO<Lz^|<X|MDtf#jclD*x~Im
z1@0w)+_;^b(r}@~8P@*9=i=3ML*1M1mbU|k{4xpBmGrZz0)fbB+L{I2YRQS_CAx;X
zgSHafCDa$~(@4PrewEK!YvpST#P&@lvwY1BLrDVQI{3h$Bj)wrBds?9umiF3`~rV;
z6$SJs!yyQ^G^8<H$9Ai-x@7HQ!3c<OG1O&sq?PC!W^G2jZ36)11OMRJB{5YG_zF=%
zZ(70})=n>IOM%;jHKzKk0G*XDJQB*o|CeBJGYD2VM~rI1NBOFO%tqggfG)O7ANA#>
zky!Yh&#le{513|+;h;~e|2$J0d#$eL*hFz_hUA)iUwlI;-J#EysoTW#kE)aXE+K8t
zNfSml5xgclsQ+p^DWDXs20u;_-2bEN-*jETL^vyYFRQ;|%1<rfN)$jVBG6%$n&LSs
zwDLJNwJc;|o-D4n@9v#zls;tnXt@9S>nCy7x6TLm<Z9GUqlT0RW*?{onNLRMU`Ph!
zk0j#X)YU1xFn#T9I&*-7S<_1WO{?$$V99NOC3{*cVH<YeVz(4mt3MLn^J*^lxntHD
zbr|h%H~C-Zqc}V3Vx<VaWP2-xJ%My)sI~CC)syFEh6Xjt{sd7BJ_^9+8N<iCDzm!V
z)%}`gGx_0gtc?eaB{yj4ae4k0j;LgDr{se?k)k!l-J5fp^Ib|2>AH~QG@@PT%Sh2&
z7XF8Q?>2qHR!kq5d}I4>V3grZKCe%U6`hne&rivQP&OWOaqIre_Akq(=%b>VC7<0*
zN4qA;Xk4TAI!Sz@t;Jh<{bqcO6HNk#ubDF>C*!T>&_SU;ZkQ8>ML~lruU8!}3ySkh
zFKY!3gj_5k45P;<r~hv8ueMBO_@a1pc&W->rfltT8oM0F$$%dVE;{E;{OcpmQ7F%&
z#$u~8Z7l}mb@B;CNLeCM;syIrWYHRK@b$NSh#0!3CCEcbDjUrDVbl1a0R&Z!KsKpD
z*sv!zRHA@oDO>iP@j&AP{q#wvn|LeV@b{*812J?D;5f^Ol&nX@Q#N7G>ON0uzpr#+
z*mg_NQgi3NhZ}K$M3!_uAzz`<9mTaGAYb^)H_x<?8aP|7AJ>2Mn+kz=*&IfRREbiT
za`s0wl^?Tq=WcV4jN1j#;m9spFm%P|3Mr|+AjzH9&`c@8lnH7m%j3Um4=v;{414vm
zvmpxUWC8!|%u`!U86H&M(f`$PFwF6Pu&qyiVOy1H2K0~(6oZv;@+Ebm_X_l0^K$9E
zVPm^PdtK6I_eO5`p3lxT^2EC<CV0w8Hxx+IlRU+j2^oZv9|`4y?z0{(bOpz9(d?6^
zdriB_98F7g{$-l56xHL?8=miz4%u&;{ad}OTCCvySi-M&ZW>Aj)waG)xt>Vzh*0{*
zpN9$X+vmzG3$>C2zuv3V(@GKFDE~T(@5coKzcT>a`EbVl)SzJUImf+fXArVe6~&WM
zp1vNXI6)?TW0G>G$<EMwo7}e*5rdR2LDoniE-C+JwKn&K>oz==5X3b%$D4F1`}92W
zbVm$WArFVT^v|?T)CI1#f#uoq=&Bcvm~2khGUUPg&MT~rOXU$NnWitxobQ(T+~>P8
zUZ~d3{PyiYXFIG<tE<F9DO-PM5xkQ9c=wJxWfcFXM45qHDcUSt)|+%nIR$dsim{=Z
zVTU{<GL}m}!WW76BeuJ|`TF+gd8ZhsoXBuAB26X33~Vv_(kdgnDns)udYM>5t#pR9
zgB~eK??kC<2D?vu<ycGU7Q@-GsNMNLlu2#wEuN>A7E~{B*KWzuL!3Y%SVZ$xfn{pR
z;o-41h|f(+l`ZyJIVz9HT{B9o8c7@N!oZ+#%YG5cmoAibZuG;%uwk50wjZm+QG@Jy
z{66ZF_ksBkS@xfK>9nWD*7dxJ#g-d-{YI$FUiQa<Pwp*_>h&3Zy`{`G(#rGNeQYB+
zf7m2C#4~Wq>q*?(x+SQ9yOA{A)!0%kKx=&9)p*1t#=pmnZCGV_;@<e1?ia%Y&0p<Z
z&x(6se@^ay#|8zesRM8gPxM`f5C42k_Um@^+%IWYz<Bvq?F&GF)29`0A6AODZL%bo
zhmA-n4R4c*cEUak^V4?{swtufv3Z<XK#w1SL0GNWU=;F~KM*q>pscC@mjTS1v6;8y
zFGOXZvfsRhBl;m-|AT&cA~)Mt;-6%~A6~S+;`ekjRWB#@EpK!S6_E#L1j2=}Z+0{o
ze}=AcY@8CaSPBnzSQbDs3<_D!K!#(h|6ejn9izE40Nq+F1rax#2pix8<9{t)oB*KX
z2bX!L#&8X|6H9|leb#!U0d{CW*vtFu(gA|(Aj-FBj(<hP_wHk>>mtOnKOy}~<b)^k
z<kr7m)$0DE+|US6mO5P10B|sfvbHa%J6@Wqetk@L^Mj5l$smo{OA3<vw_Xjrq^jx<
zJY+51IGg$He;wyJWLX4>F~I6Wxk3A9y(U;~X*u$j!;|i2thyBVew6a;U*Z;Wr}}TA
z<q>~Z?tkB-zuWxFV4wWe_usFZJZ9m4sZM<rPoxBNwK&~`8@!#R|4JRcRUQcd7|;rr
zsm(Kob7ODraTN7UQlPZ|k~`Tyl(hkDeF@GO|G{~Zco(birS){{A;P8WcyoTKl95fW
z8A1xO{{@Bf#?Wr6zBY}1oj%3&Mec4p;wu`n8eMr7nby~Ed?>CSiT3YGb&H;~cnT1F
z0M00_3VeM4*eBxxfv`2kceQqF^gjXJRfOahxm2gRw*32|XZA~px0DE&)qpL4i}4nV
z*24R%87E`BP?zmi_xxWWYs_Q)t<x~9(iBT?gunA0Gj^u>q5bP~kAP@yj8{<(qbaPd
z_akG%Yh(UVQR{umXEkeBfB@=N1uBPCk0^;wZ(zT^l!42zRz<e62)dPE|DFp(`*X@~
zrLgRY0G83f-|=XFgX^f_>AtJK$T|rxA7AX&$D*hFwBT(3TE!{tGrAvd&d(CnT;*5C
zroRdgRrCxPYg5Sybr>s5X|9GmnGX}=m?ye(XXhtH-+58ky+=94uD0Rm3y)uR$>Z*i
zXVFKfbq7FvJ_k(Y9=W^CTzGtl;rkmFY+EpzBB3$w`n&9taHzfGRon;~pbe@6Ki7()
zt5>3dD3n;GAuo`PWNFt`N%YBit2DrSoH;#3$jdfYQbt2|tJx6+d@OHqT7Y4p(DZTp
z9m7(xDfaTJ>^2Y=WqYZSmD*V8)cLpZ_*Ln2#3d8B8x(?nZbP=YzKW=J>_DW%X)T0>
zw6IhCK&4}_YahqYh$WA)xVqZfjOSJehfeZd#<OTn2~c62$xePY_^C$Zi^x>2r11Q{
z-K)Uj(zMY|^%fI+fUW5Hd0%urw|S}sRY}cwl3e0EUxG+up<eXo#yb5f4hoa6`tq!m
z&(|{JExh)@B^7iJuX>5;)V~j-H(vjhaAZ5vaL^ZB^%TyOl{3w!r=q#_L!!)9`MaLu
zXK2N39>(g4frgA`2Jhz*?n#7KeZLn|^}K}ik0{ti#~111JoxHEpNZRdhSgr5TjPDm
zLRhfJxy=N?CVeS2Lk+a`>_JkEo`?2;o?{2Ju7pC@UNI!5>Rbkh;BsXN_*xmVRmYN2
zl<ZE=7utVkqUqSVhKs7+P7Z^5r9?m4WBb67Ta?WaUs?0F{2-H0{?^BHjllI}ysxjS
z-qG%PupGEsTo35~m>tTz;uJo`m#?|ZA`18=e2m;zj`mywkoy94vozI9=eV;5NT%^D
zoOBOZ+`t<pC<7ntQo#8cjhulO%qzchc=4o&W=qX8M=@S4KKp(4n#DzF%(|NA&V{S$
z{?YjAdw*D{j7#Kr_juXyI#=&$j8mPM){-cl;s6kdj-vL=wU1OO$+yKOyhf!6YQ?7K
zxCjj%Qr@ZX`68}BR&o^u<P$5HPbIS3RkTp1Y)EmWUuf%7y*O}hYOV+WpgJB|5$&9*
zKF!pqN{%be3gk9_nhsmZD|9Y|cN7~wofgf}l1rvvehqfv^-UVl^S;iO>GksMrIFkW
zpXa(hZOwheD=$3QeFQW`C{%0EW&Rl#`1IpnYpi?GgIwPkw+%Cfdxu0t$GgGCYc>`m
zBrc*g5fQ%M8XXtOx(7o9*}x4gCD~#wea&NRZqQXDamkw$+Kq=E!}3Z7V-<&;nl@D<
zS$8%tJ;Iz>_wS#TXTbj-s=hiP$|mY}5d}m{q@_z*B$p6GkzSN;P-5v=N*YB`k=&(O
zx}~H$lw4xzZdgi6y74}{yx({4{SS7YsWWHh#Qf%*#w=ZX91)MZXX+aS$=U(%u`ju3
zCchio((6g;L9MRH!iAFW8rC{5Sq+60^Yb^JQ}EX?l2K7U-zVHJ8riP0mW$D9-{77p
zBHUt_^eE#ypE*Z^Z$%(|Z)+LZutEor!SJq;{_;xu(ADZ2@V$e|yXHe?m&Nfv(5+z^
zbM6M4e{2vL6?;o-5PibDu%I(idyTdz61Hq}9BT~~NyDCP_5D)3*-#jTY+fcQ_!&y)
zz`;E+@Sy5>Ph!^T3#$6cqPj2^_RE%Eo1-EYtX6y94nGs<v*X&b)~Hj$N@LI^yO2AW
z#?N+oUyFSu9DMh})rF%f)aZ~BZDy({P$3Am*@i^}K)0GahgS=*J7F`A$_~mpb2Q)r
zQ#n`pU;awnoaxf7u5-|{r=9M%vY-Hx`E;nF-~D^X=KC|)C{{Obp|~mRwvo8rn@}mL
zHv?MoXx=^TwY5bcb5K1}=kutThsg^q_(YJGg>%J>7CqU~?K>iz-^`C-Xq<Z@$M^%L
z?3N;5>}F`*M#bBtcp+3ldasr03^!xc-iWc=_Z58V!wpy`tuVbp<>BOmlSn~ZF4k<L
z_$A>QKkdXk&7)g_?AEAF<on3z!9kySwA8=x@QHM_6M`>5<hLHLVbq$i8oj<5ZncRJ
z$Q}+3SJgk?;_`m)9bt;nj}#R3WYj#rWNccLs#(WiM*45d9+h~LT25KBLzljlt92&Y
zxX&5rv+x$QsugD&vU_4Odi~;ibRQ1UMRkgZcIUJ#v|cuq*A|B+!DVsjM*N?ktTI*M
zksMXc_6pF-^%`i1S0<y9rLTe7d3c=1=GH3GPj+6%K(cGJ-@Nz7@hFYOdF9gg%WW9j
z3d_N5UhrsmRT;UaV#3UZC^viUJjyH&8u7+igL8K7N%}&MRUh)w@cXt(j%`0yz=9Y6
z?D2_AYCm~YMN9WbkvP|7;$>&KqzjF`k*H?=bUdylJzpf349^G4uPVmtGkmiv)fkzX
zv}L}K&e%5mo1QX!++sMM`y`K!VvQ^lx>x_jk;xjiUGwTrm-`>gW?rHeR3=2x*K|&1
zLd#XoYBm`ViJ&H9CG>ZjSY(VmWN6`ljm?b~vE8w{>C>YX_8Vx~Vnck}pMXc^s$~dz
zSo`<<be})o@NV|3MUWMEJXae<=vrA>t(Csu>KeI`V9@?k_DZ14szT;wIh8BJvPxhB
zttWAV{k0CHIBo{IN(`LVKHP6z5_fZ3FyQNr2olB}>@4hmWV)v3t8<0H<3pBbF5UNh
z{;ToQz;qB((O|mUN^d7F(C3izKTW;VYA$UViMZG`mrAa4TQ5m1#IXni`2KUFZS)S1
zoN)I5>GR&)A9{TlfS(`#M%M$c6q@iJ`5Is6;eJ>8P$Hl8v-!kZ>(FI5c|-|}D(b`2
z40Dz(|DmtEAE^ypNFVsjH)*G?FC*=H_jr8I?*bYM#tdcleK<*czQJW{W~9xm$qIYA
zh9i;piuuOrhc5%hg0&Tkx(+Q>#c>)<#^$@d#nu?fFU8dZI-dU|Qb@7%WMQ`(^4dvG
z=sAX%?nBv9od5gLX?gwt6JktQ99LM!Kfdu2r%IvtX(n>Avx)*E_CF=`+`*bD;mAiZ
zGbbBr9HSJk?ER+l4XJOfUcF6GSifycF>^UXbMuHbY5Ctn^quQ2&#p#dp7v`wZ!he(
zyyuGk&fZ@c=}IiQ<RN<RERUv-&fhTwOXwty$Ct}ax?(90N2HfO@h%;Ur6gfqi~2KE
zU8{Yq1`aTeGz|9gT^!8L*_GGneX$4Qf!PI2S_Er}$V1|Vh4odJj$ZwiH!c(uyDS$0
zG(jxY{`pMsOf?=b(!E-^=t2n<Sz=EP;Pa=|_0a}fs$U`^THA9^Yqq7Y2+>n@xY4Rn
z{*?071Lr{jz?3KV$0~7rUPm)s=9C1E=N{JS1$uH5NEt;Q#+4TW%-|&ouu?)ntYUYW
zwcV?{w}TUy8~a+!y?r;sF28y|+g_+WYbaELl{ip{6;gDsuz!iD`_tNxsfeF&)uG?~
zK<VXaQcUjKjGWp^R*{}z9FwiHu%Ee@dpxccDjtt_6>!kE_w9?v)~JX;wjBH!e63=W
zX6<ZLYGY<^bqE0g%jKV}06FA#zF1aR@TRQx%W|<^P{RE#6@lE~Tv-?!D$goyoe$hE
zQejQ+-JW=X^&Xmtz!8aB_>$fdeTEdTg4!pQ;=9D~v8%HYg28Ajlymgti9obIN3;c=
zoL(+<;bDjHE&^%moWD5KKJ#Zc-|>a#WI;s3R2eCrHx>`@RRLDQ26eOjVYc*MlTtyY
zY(#=*U9b=Zh#L@yUjdzN=lK4yS3~OKDbEoDTMy?m8Bink`2$doPQ^ybQh>8&VxU?6
zO|#iy-`^CdiW}BDi7n*O-d;jSRfHwqW!7BeVpUIuI$s;ZCi9k7hVNN+<RU2zd6Zck
zE>bpsbchZ+ar;yL*W*8ZHM>rk9y8xJl&H>iy2q=@Xp*^E9M_<mIdb><12#sp*GqoO
zoBFEu$SB&Wfd~U}%tpt%LXdrSE2%ruA<JU-`|qRlZ4w*UJq5^d+suEjjvBW=<yvBh
zwMCx@l*)8@E4r}81;s~yjPBK~e$0}sah-PPv8n`7s@Ph7C3Qf^X4&rByI4!-x9f!;
zz?|H-`<KdF4|;yZy^kf!n_L|qcc^(F-H+AM<<Se|iTe6$2A-qzy~!zwTPl@$BN-2I
zJOgzK3N1j-=*K(QptG0XZzDqQ=PKO9@iltkfx^By`Wji*vJ0D#WJ>0BY$h=ls8f}|
z&vNm|8Y!e|&ktp5HzgItSlk?w^5e4tR*bVM<eEPY*{W75s&ro2>Pf-Q)3j@c+pfh=
zfY@XQNr|;;2-ub3)V}UxcY*mVa2wi;EX=#b$F^au7LD7udpa-Mw6w)U{3NT$EQBh1
z{45oOeS@#RuDB|dH$2_9cxRb9$NAdZ!qM5s$&Qy=di-i7n4PRMn5P@wXwxpY{Af#7
z85uPqNx^}=SytW#@%q&s?jMHCel`}$_HB?D%e$Gfi=)NL+pp)A$`#&;+hG(9$(zVc
zrQ;t8tXEC`dsAAOtUJ8XS!CUC=!>)L{0jJ!k<8CCYtR4g<;|@9(1{hO@!2cTRKc?R
zdHT!d6I&RY`twZ1{K9-G-dM4lUsSX8+$ssKVXd0KU+TSVzC9(H6P0L|CO0R%%Z=%A
zo8odEW_hbJM#S`6nw#rmxSpC!MZjyLuPHCx8T)~-))hALX+*S<?qfbls{B%y&E5Yj
zA;a#7QYxF&uE@5ny0hbyr_@Ipl`A7gr+=<v>C~P{ZkjG)WEeUv0NV<Ucb@cdQtci8
zvZzE;I(iLja*1m7P<p)+a=YyCupD|6Z!pa%GBeZg0DIsyeamUBK+}R%r@eN{gsitV
z=!F_pxozM3;bzZXL}j|s6j3T^ZBH%4h)v2a{lS`62-}Mdp<EAZIvbXz#Qoh~nEaSa
zpP_T>p-<QjAFJ^!dw=BS11hltCk=yN=7BbI{kP`8WMuN~#^IPV#aAjF-vEB<n7L3~
zu<YgC{%j6zYny-Ld_0#^Z9nvh8T%B0ckrKBW@3MCYHUGdRx^LReC2O?F`q0aflNWl
z2?FT5I-8+Sb-)1pyCor3f))i;7+ad!@Bu}l%x_~QebhpYR6>mO*zO&q23oDV^@~wd
zK(sG&lRE07IOIPK-^Qo+{2Q?{ss@!13FAYjAp{I26!UgsJkr@PSag(`9q*BTITZ`^
zy^%r!^QdW;h9n4#-8SH}aJP4d#HwgVUc1k)jgkGxa2kL(;iY`J>=s4G7^H9H>5Hxv
zi{#}``SOrs6uSPh?cNO&N<ZTS+nX}l8=ZyGYUM#3Z;RF3Gq;>V&J4eoksY;U%23B#
zzuj=`9HN_{|E*9-l(~1iS$Vk|9M`J~TLzv?l_<rz-kE7sg&de2<}UBv>$!OyXXg^>
zKD?ZN{+wkczS|X#=pHZ<;M*i+)UR8>Q!S`4k+eT{nf${ekSy%s)>xL2?vMv=&g6x#
zQk%hy1-SajpvF~u3I{U&so_#{6OG@6m*(o(9raojXl0*@@DysXE27nXeEfvL^s}{<
zhO@y_wLk(nE1YKdX@d|&rLcG59G@|KsUmBYhMBU-W4v{CMj;4CN^V(eZDdR{9f*wr
zH%T1-Rrm)%r_}?VKIFS1?ffSK3TRR{DPSO2HUq{lm4HMqX`KTvGBh8b_hY=@7HOtB
zWK1i-{#1xqC_or!ujb^FjLf9k>pje;K_C-C>AWJGxJhL*JCQTYTu7;1%)8dDptR?c
zUlyIb^dGA5$o9KTZql|JO;4=1jD%jMh7>wK5nKIr&=oxywWqr5eK}<EaLHCXJtr08
z?UBQs$Qx7II<qGNk*_gsn5o+zS^i+px|j&3Ybm1GpE&Cp1qfn`pishxl*x;kNHKvw
zxZqqIGKYpRh)<4A*JTlETnw&h3(fO+F4iDF&@@a6uj#E;^0Yk*6`VHWGyGwf@AIQY
zcTz<m$JKZj07QszRd^^QWPIK{6to&TB+niC+cx36Qv%;Sf5x%h&D(BI0pFno-a8>|
za%<nGVGOK#BeYibPDb=#<ePVZB_+IC(nW-`el~GTOobI}NeOnSDB@5T<Hm-{^|@uC
zF#fsuZl=fK!`!ZPMYbfh3JwLdqQv^tj?ci`v-pxDK2^BeFgw)QdDdECygY1K;fpUA
zT)*{m6G*suw%8wXSX+ZZN||YE)yF>uCr>gf+7_@vv+IGIwgIdp01=HJW4LX0jlA+)
zGNX~{x{{TW`gVp547Vq1Gq!@1wljH+BSXDHGKSIRYmo?y1QTuPEXm#N-8WVpXflm_
zRY+o`yJ9O@q5QTV7#zbrU?ZN)vO;C`wv{(j1x@g8Y2#;?D=HTWWGoNAbH&ST>?<sG
zxovtwx=QFhHMw&Mk%p6xWi9qGS)$*{a%Xs=eB|-t4dNMHhv3eycMh6Iv?h;+eV!Oy
zNZ05kBUKIstO_^Dc}|xw-dsW}8~MAuJn$hKV@d?l?l#*>xX%yZegE_X;2+P$xvD<d
z8o=xaMY!NV=*yyK#qQ7om6v#wfsMT%9>3&w{q}C7Y9uqqNp)(0cT1~4t2L9{U7w8W
zIR|gb*9C&pFFh7Q#VpjnlA6w%NV?~{Tc>)ux{)4>>jNa>T(>8mv9fOROFC7e(Idez
z1H0BR&ojZOecs^2!MoXh`iKf+>Jp!nO-z2bc4tynS*3=9WPNc+Hj;4j#sSJ>WQ!U6
zA@pjD`HWzw0^x{84{)1gx2AS*JoggiAuE=abewn?Y!zUGT66iAYp<|Uul<c{xKzCW
zE>LoD&Id2$<rzf(Fw2$Hpp#;>>Plu`(eiTkfn{wN!AVz%uBc57AN2X7>ri_y(?9`z
zYk+RjDN<QA5~*Mn8dxt=y^ERnUVd>=EU8Yt$GVok=@Qv3sDh?Z>qqaGB_n4T*AgzY
ziaKitG=a6SSjR{>+#O<v&hXE|t|eY^kFF`KFv5QOsAAkxLj|3y?qV{eP8j)EITE>*
z7ipx(>TZn!KQ1UOy;;APBZ%DXe-Xc!g{pp4bH3K)59H*P%eLI(E;7zw^jZphA!k6B
zY`Xo=F+~&RTTwK^x?(F1oa_I$Ba60-LL;j;sqx5$KGuWi3X!BI;8~>_2R^u4RWx$k
zo;9#nB4R|-!YU~;c3c9i&N(?g>I-Y5TCwAc7Ovt(N4LkAU1Aymc*L(7jDZP-0whHM
zXV@_UH`2W|<IGmaIRL5q`4zs?c`aZhxOzf2H3#PDQESu5q;S|>gQtFR8KVNz5@4S~
zx%d({&oflqWo-H07fqO?>597{SD20F;a#{u@hr0|3T%=L95Cqr))$EksbCqr)V#z*
zkNL6sK{Mv!FPL>J$>}c^#6nQx8WUC=l_^SOO5~6J4BV}s9@JyaE*t}2ER+e<v#|G3
zT^*iLh`mM~0dx<rGAd<?0JN#zP|C0@3~&pJVHIWI6#f^De6jdj3DV=9s<cQh2DnkA
zzg!)0K;GlIa%J?dg*hqKmC|>`s*8o>^Z+VZ?7#EdwcnGl2#zc$2M`I8A<M-7BZTZi
z2JRSvP}O`B3?MwLeh89r6Xz6tk4Q#Rq+<mCSANyL>HL*&j`5DIV*oK;eHSfCZcbaH
zpo-o)>*v8E!aY5|SLXHR*k%Oc39}E<x+NWso7hjg;L!FvlV<EI-z@>AeGbrVdt=&8
zRSifux3k_@`VFG+xlWf-UERr=NHq9qoJeHQcZHiUM4>;S75Uwt<+)Ky(bWb#bO*3>
zQGMr`l(}UpZ>dQS{FQO{(ZhduAQRhzTN7N#loJPR&@Xa>!Ws-IAWEFvHE|sInsqX1
zNZ|XYt_@Q+8jZ5jOHku4zVH_O3v*6`;61sAJ)oQZ&VU7xg~fj}9XK-EeA4y;#nYvp
zJq88hR+rMa-M-Um#BpF+{_q>;A06X3&$Bj`nufE*z2-AX;-*X-k5y5hKw`f3mS+42
za;>ov5*Fa9dE@M9H#%JeGQf?+pX;ypo0oMWKZM%;OmEjRfg4wqL+c}c1v1`9d59ea
zDC&n7^@E5hv%DWLNpVDfz^qa2!}3O>RmdYd4iFSa^w(7Db;d2^liEV*yp4&pHg_g6
zI?}-Eab^a1qF4<MB+VL4z|B<3?5?qCb=$VvgIwQ04P&jl-(7y=$7L=>)m2YRHjWdr
zWiRcFVIk~|S;%<VAlqNd#T#x(&sFEJIvNOMZhf(OFTz;ib7Mv5gXvqTZX6|E&4m4^
z#EDR+6s%1R+^L=3J$uQGPfFZQB196p0ai}iTVXP6-txSGD{qo*l{$M__eJaT;DM@0
zL1s^ST0-Kt0%4WCTC80Q0<l)dY%=wFR!6E&EY2(jBz38Ct&@0O!yaoO^ix~%2tFaf
zAhqf*B8@(t_b;VZsW?eIeT!PT8nK!(ko|SP`-ZHtIz~-*H$hj``L#>*tCaxd-3pwp
zk4+hRbcE2rn*2U6`~*FdJ;l#2<N6<jNp&zYf9xo*#5YqDaAcTgI>pN|G!rPeRkR^f
zz1ADz$_rl?1aF@J2@RbOrah95)@qzadEjWy^U3GE#aqvNlLK{Lt*1h4Jk;@UI}f>0
z_k_3P^!Yr@mbX$Y^)yF^q<4G^+DUp<np?%TAa3z_Z$Ks5y8my}=iPIN2X2b~=~cT_
z_e+~+=7+}}py~6e-<MEjJUE6Eeg4<nz-;+(j$?WL?#9WA?pE3Rw?w%1_Jyi#g6inq
zIL@Um)l@x3UJToM8RJ3Zu-?E`i={8B7+HmRv16}8iJmRVUTu+WuBKw_9t72Mf07OU
zvO+xCX)Lye^dKp&f`(NFB7(uq-*DxLNc6YZVXMy#b3GxO+%u4~7C1GB#H~kd59b)L
zE8_p`&a0PG`oT_zG@~&A`cVf|#m7wRN<D4rMc0tH_3rz0G#TD;-k&0q9OQFb`Qa<I
zcT`7_;^hzSl00|EH9>AL-p&+3{vzRmFSeUq_-U3-clRr%-gx$Xta=Y*PI46IU8ru?
zd`J^#Kl;NwY{$K4Z!?|j8N)s<HTKwyh5+j>e~O9JjDe|_74HH<%6qN6Rja=U<b&IS
z!Xo7Yd?g}TCZ@&BO)UxR_QkPrs&@k^#yUTf<0h&9J+Vdh5JKkSxH7$NnPdNzwL#BH
z7)JaPr+GKE8H|7JuzmBKea0YN_e6-9+;q-^DH#*Pz5yJhzNOBMl2<u0$!I#7mQ<Wd
zKa6#=4qBExzz;4@WlnI_Cb<?Z#%YTt_8d@~%n;J5SVkB*xvq?D^gi6fYu~%53*5uh
zmd@S)`B&usk&GBkAchXS=a1zUXKav26~S%^X?3l&im!Vz^S0l63uoUCo$ZoDB7QAY
zT`R-s=&M%lFI$?U^wb|+?kXoYURo?6uL<HfX4R6F;b}>pNvvnpbO*YFui^JzX1$#f
zUO+H*r}nAt$9Ht&>osdoY-u~IpYc1jN)&9(q+KI;h}}sk3GY={%G#28U1kza`t^R3
zYp<Gb?&MWnBe;Vrj#-0pP}%%kZ2s`=Z;T=nbD{FvJjt%fCTZNQoC_?^^t*Bo*If81
zdO(hym3Q{9fw;E<9zITg=#%H*SvV&9tG#zpzWYkqz%6xPJfADY7{?E}X`%A*0_CFC
ziyp4!8w%gJ5XbkEY$f{dVrkK&91--jzR-X1XT{$1-4Bsf8_6`iH*Y*5#E!{?X&s?z
z#+&Bv%e`D%AnmYta<WF6bT1SOH~>-Rtlr;wNalELq_QioK4eA4tz?Z}t*H^mBW>So
zza7U%UxT2%b_{p7E@P_(x~k{AimqMag0IA8{UNnd=9>HGk)AmP(=#Xz8Cks}XSYLx
z8xr^3QYMH7UN*F27TL}FwTsSv>cmqPj}XUP2%W@8vl&ir=(fFHTJUcWNpiL{g@T&y
z2X*!6K@nd~jd(@9PsUNHra7_Z)^9n7va~83NrU>(hJHfOzw8+e+wTn<=WXrL&PmPx
z+;@I$e;zJ<GkltMW>>19e+cy8Z<=wU(UQz{^Nw<FxtHBVYtCb&cjZnK5teo4VlCHr
z+e{oj28`@(xQ=d_Xq1b%i3sXy;ojrBPTplEp8`8s@+KKymj8=WZ2uG1%6F-T<*K3l
zXqx*40ypny8?LyFB2IIt?Gp5BlCW$wgj}9~?Cadsa=*Gxa8#ID^I(ylhK{NXyIY6C
z1!9Q%#iHjPM^%usYm22VY&4x`qqA=UD=U5by}&*GwY--lDTN+C*3UizBh}NHb>qbu
z>mnDTf7~CKweQtx(@duS@GFqW6UP>KxI$UpjiSj*R!&EIQu}kLlXQ~6G1zfndUU*8
zyye5+WXA2j{O9XlXlJYMgq>IiVz8CcUNPJ%b3CNH@y(m_x}TyJMOcx}K%uzJ99V~7
zrcf+hx&Tl9?mQn#zm6@Kj1Nrz`}DalQoPZlmXDK8YHjO)02|LG-0(JS|7aY817yR1
zw(%aB&x@skXj~O%w2Q3Zj6%4ol*4qR?pMiH?Y&CR2iPUy<_M81Ml$sNX&YgudnJ3(
zv7B#LArG0O_c|IGe0eX>>D)(g!0_6LU7meFq~7ENc|WmEpI$CCJZ<6RYY;!(1F{g}
zBpHd9{MHK`zSkOm2yAjrlJ_T6jQRLd{0twzDWY-=b+h>P)v_S={DX@2r`3N@Kkkib
zb;GSkn~C>#g>e|T{&+_yX%_qEoEO()Ivui8m)w%JxAJ3huJav#zCt+uiyFk&A-eTO
zZfCQx!JZ=05L#iofy}mTMlm{-mZsajzG_F0+#BC}PO4z&+j;x7FYGBKQ}pAF<f`p|
zWTh`H@kh=wldMvW4Nj*5v=X}z^sp-c-XvMrwe8P2rm_O2#wpWu(+wjfdwi(40}(Bk
zyVhId?#E4ytkCEyG?$&pR_n4X;~(3@k%(%%$CX8rrJ0iF6^7@|u(r*gLsZ7-mW6fq
z3(el0`Ny3D9ngdY`EiS8gDV`N^_O3QGVY%`@YRkAv-8HQH%)c)dc1EhSt}YI)E%r7
z3EJ0nQ%P&Ts`x@@kJpzj74$hnbw3u<Ekhw=c)!td{(&!79zCRkuOCD1I%qHp0WoLQ
zY<NcBa5S~}TrEMFi0dWoP|^DXKle+ch#LJIvlZ!@pW?hcW?3J?@mQMQMUR~2)pEt8
z94PTc@`BuD+kzF@qJt3%kF6v%{z=%#(Pp(Mb_{JSJ$m7K$mrTA5W_z%N^ymO)3ad<
zn|j8$8#%P#eK)e_){C&90j5g-))a#gO-nspcJSiz&HaQd2s$G282yJKL_?tesrS4+
zX7i^gN|!vw@<Gwm$~D=0HB9j5PcwO)P<%A-D$4xiuJ7$?0yy;C1c%rMs|K#*I!niO
zp<A7W_1fF=w_?d5UrT7Ke=TA5#l65@5n=06KR$I8D!b61m_}c7WhL7kW`Sny?ib9f
z$Lys&`gGs9r*$y^_sZZP&Erw!=Z^FTao<T_2WRvB9?*c?7MrC(sQV2w5e=E8(G~A<
zLT~a}YlCBu2}9<UgH_Xo)yMFMSmdvDwGN##2jgx@Ol1k<<6G*B1Il5Dv6aZV0Ywy;
zRnH>|gDfZ2d{b+|mraLh;P}{^cD+#m9EU^W+C{|%F16MhzAQhFS!sNZ3Y!U05~$J!
z_bh3mo!nv3vI#{jD<*bq!blyOCp;1nrx*i|DCck#3d4f!`R>`RP?k?$BT<Ce!uUi!
zjZ}!NCyDHDx1X~^pRzJh{6OgzvOsHedRmGe{8GBV_G0t0Yhf5a^yAL?<7*{~ush$L
zhW25$tE;Zwo9OXOefC!V+7U6~$4kiS^Ndr)kuFu0shNP-vlaFxgQkW|)s&?WLHfDs
z;jNn6?N7)esg#Tl=Y7*sNf*hVu;#I{8+~i>94cC0*?s4H1)rX1_9G&d$cxSy`D<IF
zJ4{spvboc7@=|*~4y*xp7V-CoKt7d#o-*}<YzR{X`1Pm;tBl0BxY)Cu-|?8NRR;o1
zVbaV0W7hXUlHT;}7iQa+M)5(-qM=gBH+n3;iQf_)2rV4UCy$y>1M6cxT8O|I<KFCd
z-GAGxSd*kZtRmyFX!y_Cewrlf>B^4yU9ZN%q`d8mggr+(7h-Fg2~EF>ck`VNHri~K
zp(3n2M#Bjiw^fuhl~&ui6n0ADSli<V2S;}8G;2oUkeM0fR(9PM@vJuETm`kr<^;F}
zook&?cwRYH#7i3sV*L0)UN?WxG-RB93C{y^gS<F2zI@j`!J<(<_1=W1hr{YjQRUI2
zP^unXUy13<5e8f^t<zKn9vc6PmLCEQ8J$Ow)!}_VKeMI_;9yKC$!M=rYtWqsXLXB8
z>PpFvY}0wa#3FkY03Rx#%{0C{ay9+wzUg1sLY>VrJ<(v(zLD9Wt21MLWvuV_gF`SR
z(iFVPZLB=`*|#Hbf?rgGZTlPEDtc{q{f(6i!r{x+&sB&0qtSS)MxGb>8RZnZFNSbR
zL}(_x^#JG*dh<wKt*-3dS8{B}O~te!Qv#qbQAv&5CyloPALG=cDeH~|A&{heGzIHL
zK_*EAPLUH8T3@fhTkTI?OVjWU7kP=Z(dRT|!){A&!^(}-oIa*=+{b-cRmREp1SF?s
z6khkBp9(8nLH;j3WNYF(YDgvYpM<Uk<7}y>R8~zAX`+e|7Kfx4iR^S(9cd2o{Fz_g
zjo;RBH7+agc{6~n=cYhVqx9gkC&62l0}nYKG2#)@K2#@4%X%F^@}}%25j9afzZr4A
z(Km*%ubCV&yo{Cc3CHO9hc?r#&c25PDG6t19tKSsm(P?#w;nQMZ)v9UovXP`o{Eh2
z!YN+CLX-h%y<Z~PWre4%cdm5!OuUOK29VRMOWcjbv14stFu&I)-eNBkAY15C{U%Ik
zvW2f>9wqj2com~gN7mPb=JfE=3C0Fl+}~j@X1GD^@iS^1cx403sIPt7n=2)v_06tt
zEoCUS$~z}MV`9r?@1%>ER;*`1ngZ&fCjFvTq@?l<2{szq{!amcLUL+x@5GaT@3?_g
zO8F9COWrrK*@J=dW0l{&U%Q3|4bonD7)|~Mxc?QBDEel^Jk&|BY~kV<wSB|oFaOD#
zV5|vJ<{n>Qjwk7@68iluR{S4oHN-ge%uJVR;I+MdbUCY(luB^h;N#%8fHIAt*Du}#
z+y@`X+!&@TTV1}zRP}W(D<FAHb*GMG^nk~=%V)5MQ^}ZP56e+KJVwj;{t(}##hO^`
z9fIYSLlSvk*K(M|822*P@QWAkT)9>Qo<aYJ4ZxOfs?tevrzBrsN@B6NM9WQGqlbNi
z+Z*KS*yaihSF-$*=p2^nVzFAd<%VI!7Fpl0(KGI#o7i!;9qyL;Hzd`e75MS6lOD6*
zqUakR_2yv1_BEjgtyY(Ye&4L)Y{Raxo#B!f-Zbs93V#x|v32!JioR((;h-HX#nr{T
z@^7s=FwX|f<Ng&9RyGM<0rLc6YpNJx?C|jOcZ(<?0QCf2fs|$h?~~KtiK1>!6&n-g
zs@!-@`FT6cgo%?#jq4*7s|#n&YmbK{ThAY|Gf`bH>#ct6h{yC5!CU#7n3nL=ljgWV
z68Q_yL~*b6oso0^g)aFfHUvPV?^2ono#efIODHh<tbLw;w^n~D2Dt$^RujeS6MgCL
zW||w_zyLPFPMB10S@L&vy@O&4{mwg@uX)z>=oSkAp$&dY&8&FfNj9uJFBZrCD{^VB
zE*+Nc&EaMNG<(?obp4wB^sfV$_0Qf@Pw-u003=)4x!{XeY5yMfz8slQyt?b^T#o!Q
znF{2!c5;9f`4cSeoWj8E?-QTBFf!&{IUPI32Pw~+J|pWhkr$Q&kGHM6Q12bTnX!9!
zjt64f5tq8P`UH5}yDL^7Xcm2!{}O;yk1ea>(FU^Yh)uV#V1FQ0SiVt4S&ycDLJ8bJ
zuOZEKrNe%$Pu)o0Aq2&C0}f!`vD(I{$G@?0iv)W8D*`^{vJkvwBDP9-!(~luVIjj&
z9GF8?OmL{pp|YA20QuN~?~w9+r~B4DiN8j$Y)N}*H;Y;K*=?vB`pD7mY$@?MVxz?M
z(UYFPgaGQWC-jimpDj-7a^I=xSu;IwS;c35!Rx<W(|E}1{q!N!&wbyt_Mx@?_OR=o
zih23J93{Z>a7{Q@?%J`PPsi)37Nbykr<zJ0eksinz%FGlC>3M$A5GHYt`-G{8NL=P
z4H6vq;S&l|hghU>c~-vJd@>17v%QmWyJEWUcQ^a@0T3yEI>N#jTUR6TN7(hh<6&?0
zpM@zEGuOo<g-H#kD@$}WgLaxWj$+BJX2>)7YqGjK0h91vj;kGM6O<5vYSrBce$5HB
z?y-Fl<S1cVdg>Rdh1TdboxBg6tWONpfv;Y;QD=vgVp_;vFV&S<qDWE3D{#qCZnPF!
zNvfv}2;jva&s@Xc-)^L{(8hs|_eaA<Hra|;{qPSDqb(e(VL>G0aA4lotyICL*NYA&
z6$*H=J`}IHKOzLObTr&0SJ<jR2=`!MOOXk*Z2=R2l#KmR!|LD8=|(<U^+Z%@UK5=&
zO2Fc<`OS*0k}ErSf(G!)67{F<iW5B(|H%?M{ocUtlvO4V*&i*5s0*2-?^$+Pij2zO
z1UhA`$riYFygmk557^$d{=?o~itG<kQJ4|lOtpa3!4Cf>0f`mw<(OZv`Zu0#{Ep~2
zao8u&2Fq<UU^eS-TMJimtsrTMfgyb*IM4z44YNUg{#M@uH|HS>#KEL&Sdoq&yk>}Z
zu33c4T&7>;&J>9f7$!y_0JuG<59GdI0iw&(M%Msg>i0u)kwOJez`01!Wr=&X)SD?F
zX0}wa({KL5c5X6ia`JnTJUkm8(-?R(@otfEYnEB>kXzN)Skn~f+1xE}r7P3SC#*=@
zk)KnjPm9Rl=kJgG+p^-**O<{byTOES?Oskey?jQDCRyAv`P(l(d(lJ0`01Et=qVB7
z10AzYstf81p~PrqFzvrDm`IN7doo0fEwd(f*k!(67lw7_HkX!eH|^ApiY0qOtm4`s
z$W_m&Swm5$G^tQ72{)dVNgCmEebmY4c2g38>BZa-ht8ALbpgqwtbe{RHf=5Pm%_b2
zewI8f<bWOq-WFg5f5vu@G{S=ZeD7wr<kFCFXMmBVB>u9xIN{V!<;pHX-n_0mpllw2
zK=%A4TbBDV=ZA+VKOS(Gf@!Q6Y;}5*Z@4`kR%1-hR{8G;QF^^2#5PHj?cQjlfb=pG
zZ&6ugBOi+66VS>1Vub5GrVo-e(R+orzt|ancdnqCeu+@uG+6O;dbug?KWLF(DK`px
z<_xod@BKqzlr37*)gcOKcD9u<s0#W)i3VI9Ak{sl1%^s2F>E_?4OOl|W>){4-Ck`t
zF1y9wJ6@IQ##=91RFMd+5mQ+oAAx0=n*rp|)z>8B0s`X0mNhiZ+2vuU^+xo`njPyQ
zHl1-X5fMyRBit$bQ%x<y@`N2c-Re$Uwe;wc<Sc9EA8hV8inK<)*M-Dj^HG({X@!-m
zo6FQl(X)R3lnO)qBfjYk5yEUFlu+!HXRt=B&#Aa*Q3CBwqC+7j7JA#Ti`tE6@Zg||
zV};@teiORsm2xMK7xN8t@x{X7c!DuOA2rcKyfIoL7L`JFitEB{@@R#5#~*Jo^dw&U
z16__)8k<IO_3QQEz>+7AMryP2Ta^wo0-L>%pSTT+Mb`zI#~vL%eR!um36@o8U{KUm
zF8F9f3Jmoazcf79BVqMA8M+w!J^t@AhC8+f)3q5AfTH5&I2i%Z$G)-wVet+!Tb(t;
zcUFoLrnMTY0`oaIa+!5?)CM|4loaK)xO?;v;zzw%$nsnGd#@i7MJ{_}E&Jk`STKZN
z%(v&=)xtaV=JK~V7^Nqu2u~)2v0SlJnQk}0O}3cGy^ptRXHT-WFE4C<+}?gJ?mF@`
z46nWD1sED9_u+fAM;1*s_nsSH<Q7*JRUC{CbSGzVvFV$>qfW%#5x3h$U60&&MAmQG
zfCsBhc%s~c_IF|lIK`j(Yholo+u<C;L&T;mE7Y*Ow`K4K^FaO>Wud3;uXp%7j(<&z
zdb8v~!@XQ(d~<8Ld}`jv$ob}TtgtgC>eCQP>C_ek#&4%s_g%9V%1sBrBu&MzV?vv*
z-4yP@c~Q5!ksCK$&MK%TA}Ln6ZXE6%Jmgk_vPttQem_rn9bAsw>saB+<-XB|DCLw7
zl;~U0#(N)ej$xps`ENIs4cmUXnm&OeyXS`BYEjen3ItD1+?9=OT<Qo^Wj#&EEAD<n
zEJf<uq|dGvpQE}e_zOF<eaGH~UdsN?Dy9tbtS)+~Mk}8KDhNjXDQ1az8R}<XpyDgS
z4R)@CFAX*XM<D4>b?qpV5lCCXvoT9d7~^*u&W;L87#$n<t<CLpltK~Y=exynL&j6L
z&CW2PCCsRjA5)ie8j6bLZhdhy33b~o%*4`Qhe~=9a!GpdN2W>>lRFFzDltxo{1nV!
z6BJ&2*(#9{IhaY@5iiAcvmDbRVWvS(zzIYJx4`)6E#O(S>vVy`I<pv1U@<V2fwpqV
zP$F_0!o{wiCCPrZrBcfsB|piV^mAkndQITXiTFHvI}Iw#bKu3xc7;v&WE~_7#-Y2m
zel?gR4a8X;(*HUU0KUC=0xnq9gFrh^=5|JdoI6pTG+Z5;=pAB)Mr5}OI}~XHZjVrb
zP)AeI)#tL$3IucV(d;p!bY(24URihwg4g3irp-L3Y`_#tlwfC6g*;3+Dn=w{Z#z8h
zmC!{mRol=)%|<^2O>T1Y<e<Ulb|GeenOSP}+&UQw;t0VQq=dX6ekw4EP`&|2J!K#~
zhDTsXuBl{f7x-|!Qw4ia@=+ibU10>y{$Z#G?yych2yiON5LJq8{db10d5|9WJf%lJ
z+QzROF1%G$D6j){^_FE%KM}9#bxCUHC><PuCjQ=_LBb{iP*#7$NA9q#_>6_sYSpQu
zAF|sJ9~3(f`+LGdxWIc0%4qHqqv1tSdwz>>MDV*)3Qvh7S&R!ic(^Sl-KbJ99!b~S
zaRlFP_iCT0x2_DAc&Gp=sCLav<{mCiXeck(mjVJh@PM-ZJsDN40di{eK?d{<Bq9iw
zH8{r~FF2+a-Ce?a4j;rbJ558asp22zBj;F6RlyW5FSjk;*3E!C)|8_$4*?<Z={=lw
z>Newpv5s=2Pt%*Oq0q!<K7$fB_tzh6%7(d;9BU>v5*R!!7;GvhY%-VJu7h&M42y&*
zgL{5)WETwVj<y?fp*f&8{7GPlXS`oZK5%;la;!kjc4|pfvK+?q0FHbe%oEH4l~2@!
zrK3(N#4f*ehq`y>B4Bq;Oa;M>)<kOkD+vvm!WPXr?Cj7?X^zg_TDUX8&&c~i?;p1?
z7SZQ_WR{0<yU@HsAX>*?8yya{B*8}T#uR+h5YFI86|;DjD|t$4`<4q}OXg9S8{Vq(
zwS78ARLzY?gDbs@IZ4V%f(4drNuPbTMd7^h7e7`1h#29C(LumPIixEtu$ttqlVh9d
zutR%bbJlQy175QsY+PZ&c#p#bz~#mJ7ET7CF?N`D&BuUZH@pMDNb{&V`-P}f&~|})
z@f+{6k-65DA>NFqzx4Pn_MqFy^$g3)t<78KW;rfH2*etX!>YW&f+E_riR9J$4-+v=
zb>2A)_#Pi}zYD*D58}_2{^DR?<1&KhHa}^L988Q}MBPmZW>g<Kxe1zqU#%16Tc#E1
z|Fng_keG=hcD%|0t|s$?5IRM%I8nj4x&UU$s=}Gbk96wbn+74uV225`sTO3M6U;NJ
zhmIHxa_>B`gq09C-NiiL7^P9ZDT{EHd4!5nB6p15NuPj~IIAZo{@LhOK$HNxP9anz
zcpE{FK+cF}A?foBB~M<k0K3l3^&N@s#MDT|8$<U;C%-J&SE8fI8~3^<78TJdkey=3
z&PZ-y@kTYRu(%G!$q%k9v##(`JbC<ybd+x2zylVV7(5i9rEFk^$l~Tr?`dk#Ft|Sf
zC;TU3n21G5Wmta7C3Si1NNgl2(Ykx3()$n0h9sGew5m4=v6J!)#wq`ZnYqDq)8kIn
zkd-UTd?;BQ>>0?@!MMssE*nWHy3VpfTIh<Zj}$<2u)9;nZkazOv_btI@VGsMI(ek)
zTqSG)%TgP+%V*F{)w0}TZLhcU9`5+Kx*2d7aT#qj?9BqU={U&c&qfMO29Hp~+*!cM
z?pvS4znNM2-tg12pwvh8q$eVQeAc(v=5J}kP6Z-Fl9vWQ(0bprEP88B{rk}_xKBD%
z;o8T1{_w-Wqn{MAaA&5zSEnnft7}=v1|K&)eBHo@aGTvx_Vv9!Xp$1*YJvOUaoBzM
zmm~WyWwavo;BcMhe5~pI<O$`*UzC}Ye+KVCp@5|Hk;lP`H4x?^u^wc*KPCn)df^0M
zZg@r`_b&f3W|Q?s4sM&?aQ&4M884NC5a^{it42<&4*{WhUX}cMttjuQ&>(bdfF9O>
z>(eQqtDPicN`IeZ`#@j91*8Mnf^S7g*ebKyEITk6_fTzONGzDG_I@9Ic%Tg{d2+YI
zUtoQI6K{iwAv^v>(1)`6glv!{hG6bq`*==)BOFm6J*e;He{`&&4&mcEH-p`IlB`$$
zENpz&0C$#t{UW0r0Eo~QTAPE>Rxde_OY-{D@<}<oM_`j`f&MsUKr^dB<Qi7ie8^ab
z_N+RC3!c%YX7jyZUnv$3K&{ni2^uGRbjDq3hUe#eDQU|NHPX#QkbW>RK<U2XRjV~K
zj8nW+oc<bes>7cBwAxm43#p6YYDPFuzJ&89ca7^ob_dP-F@hCm^8Zp@F*Jevd4U;?
zTj~9cK7Dz!EO@ciQCk>HWx*fY30ryvhXhDhPthQc$c^{lbyJhecP3(}ou*xRn5UFH
zzDYi2Pz5|WdV>0`2kQCE!d{oxo1TOIAEj7U=Bw0LZ9vT9G*SP>?ahtU88wse-+@7q
z*IBa&Il@UwB4wWcmM#sD{mkQ$)AHi}^_%yf%YWG=?@#45{1|#k_S{dkemZWsy}Qo!
z%ZL3QU#ZfXQws}8zZ0;)v+vo+kK-2ytVYLwFKo*6>DaWo+mk0Xt&ZB=hChi{Hu@C0
z?d$I9K`Ng}^^SUJ48Hcsl9NU6)5F-aC%a2`j$^*->+cn;xC>KcF0E)k8igtH(V^1W
zXewj4QbSJMxe$bNj2wQ3I{L{29H!;p@Mz<64*q;HEmvhPYaTlobndT)b*{M$39tX1
ze>PmQ^HWtekbuhoa8ZCvZm;v-IOa}*QS;k$oBcLis6FTK$+FUpX7hCiQuHPwy%4Cl
zuKW=ju0>UX{iPrT61MF61YWAH&K$v-*y~b&z_`pGJ_qbZ6?WI6Yu@A#@R7-auV4wQ
zxx2;FLRR<hTkfz!AWcqnB`pi|^&v*WU8E#d1dNY!wEnyySpZ5N$2{brYJlsj)mdUX
zo_a*1ba<XE8B81W#34^eX8gp8g~du<zwI1<wZj25bp~E+0_J$xkx{ame=jC-uq+YQ
z$r;tY?PMRypB)P$f6WbM%|hPj;K;@zctL$LTs{hT&%S3N$JM)jIK^b5kg(2DWosTM
zy|%Z*vqr4!>X7U;99H)ASlyk6a{>TN)keVl85==Tv;swrgp#6MwuBrvWfoG43XZ^r
z+Lm?-JE*zlZGli9Nq?kAywxIzipXR=5`Cu7VtdS`j@J3#@7fRU1H@HEoAGBkY%%Al
zc~{eDJRQgDEJvC{AX$KW3)CvvCr<n~P6fjJryq=YXcPLR2gqGqyBKcXNyYgB_7oe9
z?eSKA!w(#p4p!-4dZAU_;!A>Cgl;e%sM7y-C9<D`9g=|0tqJZ|FEb5X+;hGiWt+b$
z00^fRh%v)j*RQ8|>T_@nwL%9t;u@sF4V>?SLzsC<&u(NP|3xx_<FK@S=&stR;otk`
zIq)LWoAef{rvU@m2_nZ7mpk?pvl_b<pPh$cxN$4!lCE2y0ZJYY$RTS@@KVyq*ibs*
z4MmmSFdPS<$yoZ&!Q^JKjr|h(>@y_Ko*m=IO!^NSr{{Ga&4nJ`GiI>!9v>m#&^>N0
zoURon3w%r2NPnw9Ef1rS{$9qjC0<^yOiNucaNx)FO}TSw?pT7Jw17KWK}wv_AC(-#
zxlf!kqjJbXm&ktYg|Caubk#OcNPy;sxoTOP1mFUZ&5v-gZR$8<|0O>Pq(M=28{fgb
zfi|)mPXhJIqhH@~4hMXapug7R*4HGM!2u^-&vCx^yp{)eNfF3j=Sx`6F=$S+C;G<w
zS1^LTm;js)U0W!DY>w*xX|P-YA}R+9gvI_>E;;$&|0~~&KsttENS1N41%Zkx3Rs&;
z{UVT27S4*ZLJ2FM^8f2;SORr(#g}a**k^c0%l2iX7v0gUJoUEXh1G8cFL3N-Tbjxr
z%CNRbYzR*fC1g+Q-rRT*s`{`JRn6k6PdXAAd5<6oUi!pK*KbJShpai)K?@2bMcv#E
zWm~bYuFLbXIXPg-PHbEIygDGW=mz$zE&3H7jXZ#`vw=&Kk%<wao{dF4ZBR+uy1v1-
z;r^MB1-0iKXm>qqhfV*~OU|xIh{KWUgNHBK+k(Qj^%TBo_WU6w*EFpa&>4j9H31sR
z3C0_Gvblb!XZYQomwY|Qb~iW*;aZy|m<_m(LH+<DC>)NwaGH#mI8<Zb+lgvf2#e~!
zIoWNev(qjnk1oyoA}kNlMxD(66430<_%00nb_c$PIDm0&QYqB@^jyWJR|18TvEH$n
zpAvD~S}C^z9G1sNi>aoTRBE|D@5EGe*W{QrbE=J`qFztsxVg-Qp?F^)8I1PXi0mav
zgbzjd6lipWJs)p`f2lXF_HjO6-etSn+lq<QdX{ioaBL!Ie^7llD_>6QM=o5Y6eAI@
zqA3p~ZyFSyc_tp-eJ-7FAPAOH?5}N))=5w~;C33bE;qt@tU-eSX!j<ny3o9Il6d7^
zZIYN3tYYwqhg1^mr|_~pOqedeTQZ{iQbTA^S;S^jH3BnkP9NC(7WXbP7C4BM<<Ywf
zO0<)q4RIG$i(b3DEKuPd_})*X=L?uv6mrs9NA~Gtf3R=JC5%^Ni+8o@oiF$xApje=
zO7=PIx9bUwO{0y^n#Ql0yoo)vXJFN~L2mG5i2d#q6OiQzo&6`jAzTqkd>Rpo=-FDI
ze<Qc1{hFSq902|UTmJ^?=I9c_*-ri6rjlm)uR7pwiKVuQPqnC%a!ww(_`5^pe#GbR
zOMC1T3Z99KV5<PA%~5kyTxqeHu!k5U*pvm@j~M)O<XyXQIV;O0f!$0~$}H>#?n2<~
zN}7Xs;eknZ5@G-?hJ_<G^}GazJQ{(_d;8vk`E3>SvEJ4GEMx-D*RX*3$&O&*m=^Gv
z>uY?5d!Xke0z^&tmqMv;N$Ers*3&@JQ82Y^WT_D$5GOT75uB@z)-4JE&brr`$RUoE
z{L$yY0h*%*`4j0pvYrCWP=kZrwG+JVij5`uBN8FDggg+#hBnpgR7ixOJ!v(7yA95z
zjIASBL$8mM7SQaR>8O!sJW=!G(B$ScAd^y=rBG-vk5=lZE(VG`B2GJuMPAO(t&d+B
ztuYI%*01?FUz%!st#Vq?c8%@3r}krLI2bw0BCuDOJS?@%%(S4X2-&GAz;y44UM_b`
zS3ZV)Q2(hy@WFA$f%=ski01|9SqZVU4Oa^fiprEk){Xz7%l90gH5+@Q4p%<}YSuM=
z$9Q*uA6h{%hZeAg1e&n+MwVCnV+1BSb|liCpoLS6_n5?Y_)JTU^7*a*ty#4RJeGmP
z_Mc?6O$q*#8vRg{S|;T|)v@HCIpsY}U0~ql1Y7=s4}l%VQ;kJyS(kP*Jn~5b9TpQV
zBw@Uz=xL2^{CtfN8@|($eQv0oB$UD*Bj;XN%J{T0nCWa*nG-Av1PpXjLUE`)1+!O^
zcNLpPyrHiFlgd9CZE_)%XXu*C%L`^RreI%*NLEe6NF0F@ymQw(*sirP5j?m@!J6$M
z{L-1WXM>o3IN;8yzFOA2vQ&$F%rD=K6g3nFa4%n?uLy~Nqrm);vXU>}w>a&`$x*x~
zeE;j449uQqMC-12Ndl^xE_X^EO_TnZlByX)3F(=eplp#v>5{ff(n*;d&^EtKBGOrA
z($WRKnderq6&2ZteFF;uNE>-I@Ce$v7z?Rg38xP{$4y*KoQPIbwZU4>v@%55`}-uy
zgNd*`?#Z*Fd22E)xMkkQs>fi{!w8;5<*;Cbr76Y{&|4t&NXTC*m%#X3mmv-uNjftz
z4>{tH+iUb<9lleTR)rZE_<=OL-^cy8-dy1C<Tm&Jk(DBx8Oi+Z5;t|0&GW8}uPSIs
z8$=?xnn(jZ*NQ4!`F6PHfX5NyABb$qb+`ahEKp;a$PTqu+D*J)LQlm1c6dS1w22|x
z#ObC7J2=46n+VJcz;r7^QW@;y!`{*;pMd<_?-0pJ-C(p=M2kx&iHyYENw4^0)AWu!
zMhA&J315tcs{;#|VE_<W4fTy^onH7SUMwR*I_xbiTCV%W`=)9usQ{g<{(gLYsLytr
z`Ue@<0$o5;WIrX*sN60{ug40cut(UUf<mo*CW7*JV)pJi%!`J^cigE&Zq}nesa|o&
zf8<EM9Y4K+9}4Hg>t^ey@H7#UVeVz{9>&<&s>`GCFB$IHL*k-^=JB62Uw{ZUDyula
z?E_{cIeBbn8%5ifmg_NJGh6kr;UV4CU_ql`RCT76F{q-OTOED(kNvymxC7BgAK1>v
z7Zm7Q3Q$(Jpw`9nIs_H;rc`Dppu%j-!~6Fm>J?BNMk_4P2w@mVEhrn|Tx(SkqO+`2
zm4u_jAscJ<`)M@Fqfz(lr{7<CR`x=JGv8B*DODUddLiKIb@t5QOo@M*Fwf8bw@M<s
z)OM!p`PIJD<^Z>@M<UJljP6`bzfKY8^eCae_Dq2@bW!y3#Enr9#v*8|Ca<b@=$}e1
z8~si4V`OSvmigf)E}LObopI}-AKwl1r(ISWsgbRM{U7s07cayn$9*TO&kX7-XWit{
z@zwun6qUe{>Ba)>nfgyd(FYU`ad@f4)_)pJfjp#&7Nsk2^q-!g#lk)niph^V15Q{#
z19IeoliJI{M!J2#*voTJ6&dPgWgb5m!S^7&K6+4dy>Ct5`SeS5axwY_c*cS3j{x|t
zzENIcIThLT-&_dx);NV9=(3g?vG*}#nua=d$Y;Ip_E~D<`t41h(piDRH1VX5b4eHp
z+YcOAsW1mT`<-{|LY+v|x(i0=_BkGYEfYC|>};$rH~kukd%c11?Q)x37+xUE1kyH@
z4%O|ycU~BK%i-ntM_;xM{gB1*aeW2GLILe5k4|-YOltSW!&c>(v|&dGvG+%|2#Jvp
zgLgc#d~>!uk8UziJG~U(Z&V+~fuQ9ff;CH+U_Ay*@m8UcO*C5H0LuNB!GunAhDiZc
z%=fmP_du^O61hUb<19~@Uo`$eL6^x)q+C(^wOIern~DCDWdK!0h^qcYN~NItrPEZz
zr?k8DZ#-)JaxWKwEJ#gIZF^A~|8t}3%l9usOWz#oW+k&ZamWuAcIxR)jKQww3*mAm
zu-A}7#O9x}8yC@)&77>zlXrGDM<~e&3#T~RN4q~d{QCL1!SRKedYPeiKzcUd4bWzE
zUFzS$-7#a^y*sizMN-+$@I5aRQx0&pdvbVNZ2;+^npjMK#d8IH`dn(Pz6L^lBdB}|
zZqI;@G%|rHhAeyrylP90U`CZ1Vu__5adr`l&9CHMhhn^q6I_Z)zhUyfW4d*D6CvZx
zyJLfH24XfRL2E&%+_hHPR*Zy;{r~|@($B5|bSy778S&>|&or@U0w7XT|C|;ZRAV;=
zc#%cz2zSn&nUKx>=?gndXg79wX`y0M4l&7B8W`xv({AVv{@aKcga%xutuGa)3-iz#
zp1mt@<Cgu7TI1VXF3-Jb;|||b7B&)0`CpP5n#1hNA|neq!HcK%x#TxQe4^qoj|Iii
zKOT?!D6kTGdU}@3PW^L`kE#|1%WW14R_5kO9A?l)7V0#-t5Jhlb9(>E4uvSF+IoS(
zi)So}TiSp+2T%u28dC5S>@I8T!mTC?dsZ=aH)O0~rAN3LiL4htQ|cuk^MMe4uj5>J
zXJ^Q=qWyiElITb%s{IaI{)$28F4-U$l{>kto{(r`<rl6624JEW@uKAlnmFVRz-1Mk
zm?3}NjrU|IuuDJF#FmISRaCbIhXbnskRyyGo9zWTx|}C$I-#UvLyW>EQ!7kWPyWVl
z=!7N&vUzY_LQdm0cBlAbUZy+4ouf=jWHoPYMU+A19aUh8FT5~0qD2n1?!(o%4(BMm
z-1{DwKfGab-0|VbZ@rwyAO8<mZygY2^Tm%YASfwF2`DJtEx0t2N_Pv=9ZLyFh;&Kk
z(k&$=T_Ul>lG2EDhaey&ch>iN@BQ7oe?2?T%$zxA&dhVpnVHX+aPW?kQ84cmp?@Hw
z*k)G7BGMe4tQ!r?6R21S>wtz>XGdPNA9!YO3N8%ep2IPoKuZ^yp|@Ra;(>8_3AL(#
zcuB%qfm{CCn)<+?B(S+vSrIR{={F0LQa*r}Ihyea;|`maqj_x$5iUO8-y2e`d4qd%
z<zVW~O+zh`KFP=2y$(-9)tYyk!1cAKad47DnXJ3}Ug_jKj)LMF9wZ0s##j&ufVa`(
zlgcD%XUSV^wl{UcJ@BP5V+jM?bQYhm;$SX@J2yUP%;WcAf#ceH=L11hJ~}{f_~aH}
zRdr`y7h!i}Ey2|xgAg<41P~D*va^0wV|-mTXzGwHz@OX5Gt|aBy%Hmc1JQg18%I+~
zJ}IH$KlBp#65=j!a%M<kyY7;O2`ou^CL?Em)|4gROCAdTV*hr}#3o85D00DHxP_=t
zul3SZ%#{NI*`bGS2mRF_4;(fagQtC9|2~-sF8EDcE1<J%M;_uOY{30qKUS2@r*4Bb
z#ud&Uau_Mzg-BG;Gu7F9tvt7$zX~4)VHauGi(+}?EoyJg9|hP~r?cJB60|1~=s%5U
z?vY@i<HcYdF~5_xACR9#gfDex2(}p6lddbGK<A{Oxe+i%06G_t3s;qa5(Ch=PF3ZS
zJUBK$nloW$(o{p<0R}L4BJn3?D-?3w!vSoqM85Z6O2=CdVjW160H$vU?8X15y2is1
zOi1T{6}BE${Xfsw2ZP|6>;I~MeKrrCUqW*uVT^xft8l?KvVTqa^JDt|&3}l>^Iv|k
zvnBMOU4D=AG)XYgAek&=6U6+fB-bV+lGhK}Y3~KTO!)a?&5Wnnrm3|dv<Yu>Zv}!k
zfL+17iTC4}3plok#QJJTiiI`K|N9wxVN(liJ@^27!8QPWFC}&tz)3JO)#bs78~a0n
zOjVV)WS~q(>C<QsiGMX&wXL)JV?Y8Vp}FMi<>zTv;A-(O@P~B0+%tm)I3KV6J)fs8
z{ol{*o>@$C{}pdzwrZ++cOL=)c!CwoR`u(g{^*c^B-jhC0locLl@w3}pa_DhJm6ga
zJ(I7qd!%0(0UTsC<v&)_1X`3hIbaXJ+2=oy5Ny`KT4;tW4?7NPn53fVWYOZ9!TguS
zmWqSU<*Oenp&jH1y*)-1KmmAQ74k@Z9ViqJ0s)8i>u8~NN+%)!;V&LO8nMQHnl)_&
z=*6!EY13O3rs(+rF#zijWMdQEFaX3~0r`Nd7gH+8ASR0co{@*_*+2{+4ghNA^+~w^
zJ2>#<fwW-Sv)LP6HURz&Ankz`tTP?PfQ-PS0~u-MpiSUPTv>Xi-=GQC7GhwUxBEv(
zVTey0WOI)v){lde9`K2SX5;^RgKNkP6w~3~v!_$m1tsLV)=7{{P38?Gbi{z0Pd(Mr
zttSm#qcqMoqO*hu$51-RgyWL-(wSItzu==52m1UeS3|12#fA{2$1vYWZQEvhMz_)R
z^K%FuJ@hGH%Qm5EaTQ!T%m94~4!4>{vyENH0sR1czNWXT7l`Sbtp-y7*o?0S0xRAP
z4^9@rUzUG=wQMs#-ggC%FzWHd$F$Hg4Us6_bR_`$m;jFjyr+FvHoQuNKxV*2uI2K`
zPf>zqU!MVHp3*Y`>;Uc6*@`eF@$X;|0n(r$51f?y2Ce`sBA6p<fb2)x9{zIS0XN9Q
zLGkPSHTXMqh2N9<!`(tFhHY6ujOlAZH^Mjw=rez)gHD}Ol<kN1KNP>*cpgV=o^si=
z$UVl#fkXiq?JFH$SXR_8%^gMfP8Fi}8}g%1k(1hN`Ott{#pAo~-)0*%#FJlf^vvW$
zmsJ&VBrIK$B_Yuv{ef#izE`*LwTs!|^~BYynFlv5J_<+=ZNzNjV${avBpQCk3<tFM
zuEk1l7_zBuegAt}-Q5W=hxZ4s1=-@OAobPQQ<ygG$EH;T8+ctVcF(m(HfP=uo~w75
z$<W<=QRRPSd7AauH${l3s(IUqokRm8-u2fA^?q`A*=|KvpPD~KW;EKwW<+AhNcylT
zIk@tN5xPw7bADRJ|1i|$+KW4v;ci?ZAswx{w6l{8KIPrswuW8@lJhVhW9vK~HK%J=
z2d}cg^5=oYm+u@UaylY=;cFQUPQtIc6DIs*rx6cKKDOcc5LsvX)^yS`4S~1O6!d+Z
zXeH36Z#!IK7P2~l<gt4+Gne=m{P(`<K&`M;?UXZBVAc4Oj0<J4B0dG1<)H+K`4mEX
za*z05tA9v^1>#!w9}9;@{`|x+J-xCPWa%EP)uJ#ymjTh%l^eSp9)rG&#}&w5C+w(x
z6}lC4^qp*%PTN4vD!z^r?Soa@+|(rj@W*J;iq=Rda+eV{DTo3YA(olbmv3rYj!8u)
z2n+4d5rdsDHgga11CK4%(Bb7%;xzb(Y&&xHu@5w5LNObc{oG2A%4bO5u6-5f&P+9<
z&9eu8NOi7>j~<5ct{hgrQ%)g$H9^Ycg)Yyx!S#ZsaPzT#-qhqfBs03_w)vc(O_5Cz
zlPg$5dw;vA2wd5)J1OxR=0PD$n_kCn(ED0h>sCp{p}1$9%C!#Ch#rQZGat$H`WJr>
zzG?;y^0NFv%t~ayyNmbFF$Z|P9;GT32eEtcN7Z$Ahac#LQsA!k>&D)Sw9d<MmTUb|
z;3Z&_F-MLM_$wjV=?dRvZnKV?y9(k=^y@gJ>B>L;xi+3&h0v49*tNGbF;{}MSJI02
zZvE5?{Uh>0J$l{W*0TaM@Y*R6PMgms0^NV^+4U?G_tR1W-`?SR6ig3Iq8@4HkT&Bt
zNYg?s)q<-SA{6f`5TLmz$~<V`Tv42Kn4a`}sE5Mjq`CB7$zz}X?ZJ~jGUr>GXftN^
zbqvK&DS5u8v4wXY3I>en#((m(?>T%WD0ZhW)<Aa-tL~~LaQSYV<GmsZJYDHP;PUhG
z0^OR-xt(K!n)x++o8uk3&O2wd%KSJM2d}#Rpg8&VkYYQ_J;r?9kP_|L1s~#~?3bM)
ze){wegpKmK=y^RG@l`pDbJuWP1kQaIKdU}$t^EU0g$oEN$man6U)Jg^?1xL^99Nf4
zTf8f-NHiLVA!zW!(!R98EfudM^=c2ff92}HOuxvakbcubew)ao*Qw}pYDCs@Uz`h#
z(Lo2_)Jhm`X9EZ}8M^FFd?5-qdaG`6>;Ccv3-75HfDQUalrRO~B~AQoA85)Gg;E_Y
z*fLnv^AWck=u*|LwnkR2?Y&;#gYNbx^%Q8l=p;FCWP-AwuQq5ymHx^!{q0-hpXLFG
zAc_J)eARuA?as%WKAf%D+X2ci4!^|W?ZIkMS{1LcR<*k@xNVH>6Z{&M&f(7prK+H0
zsp3idL#-!<#+{Vu1D%E&ZkFktH}Hg@A)w?HIZBRu=}EeRT<ecld<mILZK(RUr>U0(
zWwuEmL4)>Bb`JfYA@m$Flj)o}Eb(o!Fk^Kcd0lSKH(`~0%6>;#gK1LPYmY?i4rkaP
zr984vz(9MT3*r-F6j|`+{c8p$aXsle(EIQ2wP`O5_Fz7FHHD+AaiZ=FZJ>9iKWNJw
zDC^<&Qw>Wn;AVziko13SqVXJNjrwJZmip-P+Vxx3mz*N*cRKcCdhD@1hIfBSl^2+d
z(9+3&PtcftqJ(5a|A+`n*}fl;Z;q#nEVet!hZfXR-Nd^dm6Mm72i$f4=i=W<`){yW
zu`2gwtOXLrGMqn~eT6KfHw%kb1Zaj>2XVi=3Uq>@CmG5fnM|X?&>P{Yco(OsJnGuB
z_yx1LNTP?~f<E%fX>ncIpHs|(*NmR*XS>Q0a{BvlTJ=0N4t>*yi0IK~%zDzY%Dv-m
ze!KSQxg9G)O9`g;lhcU|1z-+~h@#3wgzqpl*T@|1zpJ{oJkdlydr<S9+1Q7}v;s3W
zb)#*5zWL7=N$uu@+T>2bzXadPb!wOs4Mx_ZM9<?bT}Sjjax@cEN=j(n=cJ?Puv@(k
z-H5HL?5t0&qzX;2`PtZmnaiKivL*Boi@RRA#v}IQ`^7771}hk5ROS3#qh&L^tor=D
zTVvE!CD@z{#r-9CD$4n)>~q|onh58lXC+vSgO=MBa;heyxG(uyFv~6!m-I^CF><SH
z%5xb5!1hPo*_y)^CP9&2Ep$INnHD}yb>3T7-{NY<xWUiXVzsnKso+0Z^Mi@aS*`b|
z*!(u>ap2ilIDG{lG%Bb`+Dioa0Fb9h0pp>=_BX<7>ptV=vOgWv*g_}%(Y#h?zoI*z
z%l|(U-1Ap4e8c;W(WmMJ^J9rS`H58>cpv-pP_z1KJWA<#5iA`)H<p9xGX&klbpVAp
z7exfiacv@eRpQ{Ac08MkXkb`aKl#}~YJDkl)asF!a2@9ED2ebx)d%+`33xOo@i;IG
zKZKFw=4iVSJ(4!d{a`Sr=B(E7dD`!TXXE)s(B=i1IKk!(#vrg_lGpa;*M9R-svzYs
zApz@*(5C?G#rA5#l#Gb#u0PmTtDy2P)7WaqIi2ZSr+2!*`2gU>?1<Tv`fR~GmlM+t
zu(k${xa!5%k_#7e9ZG-u)glP$vXxf`n6HcT3&Iq#-+DiggCqba2a+CB87Wy2@%Z0(
zwSki4|4J%^%2|B~dkB9FeRv0=`t)BKU;ZysYNFchBurox3BCMRTdKX%T_PTl*?D#d
z%DF-{zw__K$s6e*zm7Q;;0P+9((LEwdQPbae$FPJD?tJV{uQlgzB)bjcS4ez-3kJT
zaV8X_UpCLKxW;B~j>dfTfvNrEUz*v?epk1(h_Bn=Uq(phr&u^ZlQrPJIP(>x>=XBM
zAcsJ-Ls0x*d~9pc3+Khd;J$9hf~pD$sL5i`#U+uXsy3CRF-PKER+lKrNIkq0@<3Wr
z758ek?)N<rEo0{jWd735Rpl<b<6FN^XAVt^!#(4x!oW%drY#`@l+FF`jVvcwR(TZg
z9d=}4FA~?v6>m?X7f**jVr<%=L2O_COY5DPzfk7<qBC9Y1WvXB6OtLB_r9Z>t!b+r
z{w@{t5f=hmE&e2ov)n&*xM#6Pk_`Zf+r<;-(Qm2^u{*iLs(&A<p+j=c=$WKnBD;68
zFNBgJe+?(-0PkN32;votdLR;U_JT8O$pCDL=La?$GlIgy#`(a8liD7s1Oh#jz^6=H
z!-JmMKOY-}o->A=gR=N^sIoDRmg}c)IW5VnGC|CPA7E@VBcSP1#e*76TXh|O+Q0!0
z&3|o?pQx*9ZhHR3yEYpFv1b3*W{6Y!NcM2gskkdSs4yC+<SW>L+2!;{gO-W9TYpAC
z8R#T{vzD^EIS)7dE#TN8oCOJZ>~Ua8ip~t_K7z6&&OFHr(fgwp_LAgSM_!#C=Yl1?
zhA15SWzFZ$&V$O)KF-MBBA&lE+waH1j}Fu6paxDz%GKc{zoj}9RAavkG5zM|Rj;99
zk)vlQ2u*?{*=5AVb>nxkgr@z<_y6KJHR{eVg2&VxeFXykerByG?8l&Ml61K5X;o6H
zW^ahqC$I?~81p5a;Dbo%5YDrm+D;7?5rK4df*CgZ2m6#67{zZ++yB)D(^iFjIp>Vh
zGUfGw9;5?sZ3OlO8$ER1Dn$6MS<Q8w&m*uC0QLeKM;^I@u%#26CODV^x4FnGGs|os
z(qOI~aTx{~qW?%H(yy+SXDRPZQ#N^>f|@`v8(t-3&qVm$rhm<>u}FnSo?D&Nr`9RG
zZo>i#n{d@nL-Dnz29Ezg(Dl|5;pdw^{!Lyyg#U_f^S(qHpB)qxeqzS!KJuRZdDn7>
zMdP#LL=8VTZnuag<sD45*q1UgMCoMpRQF_P2*+Dr68k$+k&eg18&5vBRU$6&1@D)8
z9wEGTK?edIuIGONYfp3h$o#1GKbo}7{=gfUW0Lc`em6}M{Oj%kNRI_>=cf|SGkeC(
z7g=YLBQt4V!oIHM`<w#bkmaRvNz6N24^imJpTizOw~_|FJZIm(dkFB3ik?YRPSo@b
zeI=({)~mWB9tY2r<?GC_ug8&9QlJmM0EQoXNxK?`smmb??Q9}daA`vFNBz%NO2<HP
z|9H46#i?#e@$p)9qJXbnt0;Bw-?)@)6`=Mr;cG!V{rRdZ{j(F)^;s+X>CfL)LUDXu
zF%~&M%^r-<+*dp6G4Rv%u=hn%27<+Tss=u1Zrs3!20+{;@vTE~t{$v;PK9K_r7^1&
zN~khVQDuniN*{5QRp(w@G=x`zMzkcrRbvh#Q-o_U%s8{X*6%(A2+6e2T)fjC>~@0$
z$w^lzMnI;(DJngIS;I(Kt-+0nEFmEQSBAkNFm&wcpl=S_kTuBO{b63PKHr>fk@Dcb
z<SJ3ek&yh{q*!=x1KlieE3Zw#flNDsRY;7#SQak>f<K{qn_XO}q&oz5El{B*ZClGm
zBtR#AP?Q)8>#Go-e#2*AV@1AMcDQre?470Jn{{>m9iyB9`et-v;Jm!SHl8o}6%^>s
zXE0ZyQLz-poocJWdZ&+}s4YMUYzz<k&FJ!`Pw^*5=1pKq1wn+b0S^9J>3XhO3|4gm
zA3zLJE1no@&(tf+T}Kb5oT3eO0vLmoUGJt$R0{Hn34Br9>6!4ROra^L6wWhhEJ#2H
zJ(D~NV81i@2^6(+r;`<d9j^f%J~PPwTQfkBx>Z1yEgoF#Ro%YwA8A>fXj;*1@UiV)
zdz6uFoCsoGlUZv)(eWoP-zxuQp`mr$jF2PZqhj359YV|`{t6D>1&(ZhQUd%5R4^O<
zk<W?&XhsI;`YFWhLu%weg6qiE)@#rViG2XxDF=I<nRcT;#kjdv9XzeWTRXO$xN*Rh
z{c2-ys>>j4cHb?8FVy>FT_N91=!*B`d$6G3(!N2==wd~v#-?lA#$>&fpq1kq7Z(!&
zwNoYiW*k$ktCVkoYv0Y8{5SP0nW#F(gM+Z=V~!s`iiz_1Fs*=dakRa2`{X|Ju4y#$
zt3>lfOqQJRMbtUfnbfr)fD3<N;qzNj@RojabbYJocY$X_;}r*OuOIvMYln^bFoU!!
zI;5pQOmEwI(gtFk7d|kQd6>UBs&aPVDi_WTrhprHUX#!4IVNN>YSrbKd%9$Urc#E}
z?H*>H`)zhnz46vj-jJ+MPiV_{bZNtTH!pe8S`ir+zDy^>zwH_YF(%h76}<0o^WV|3
z(YeE%eceQEm#`yVl+Gt=AIs@cx?!uD&GbIe?I1=>uS-?ZU6@!|nGgKz1`#$>%WDgF
zbU52SL=UftC^+wayEjwL{p~@%fJA3mDUt3cuOmfD%(yy_uc^HQPlhOk%_!pnFTLe6
zQxh1DziViJ{l>wDJ}{0a?=U})p-B>Kf<*8-Ulqq|u*xv(zxhz`b;HPId;Mq){>8eO
z-+K)AQnOWLx1#PV!p0K(9H7u)p$xbade%!y6@jI#zC~xXh~h2nhk>!Yi_R~E2vU*`
z-bke5MNK`OnbmDV+bz7fPvc=j7Wb)+K>3q}x92(bOS-4<EXB3xNtzE48=X0c?6+<J
zdCEmz=$Hk1|LauLaajn1)d~A%00~aN!;fR&`TZY+<=*rjuMaYPjgl4rVfy`S?k6W4
zTdJYJfm;PY-Pd7!B8A;e%d3d$=hu#r^ynMs{2pbpf1LjE_m*Ao^hj;&VW8S4Jv`EA
zlX%;rYd4szNg00vel`}j_Re2rm?f_I>CR-LIE^`qp!m-<{#|>RW5KpfK26G4ohV);
z!(u?1I<|M=g|nNWRG%(9s#+;J={NJvfZ+E&a?E<otctaSYwy&u^g&gYuae*QE@#)+
z1twr0`0pL*?%XKD#;LTGHueJ&CWgRo4hBni7l5jC|82@Hcly1mA1D<qslx{4;!ohq
zve<bYb4uKVZ7``Dm;7j739`GYnl(>2O+KMvZZ$juy>l_<tpCg>Hy&HJEPWLKJtE#9
zns7}*+_DN*I@^~KFAL7CWz<vtKa}pii7rrNPPhNP>Nmr}LJ?8akpSOe;0eclRUKt0
z8J%?6vhgyl+gTkWyNEToc@jn%qacKPA;PXY{J6xLV)k<ZF(@!$)`_`IP<R>KGYquw
z@a&LF$}^iE@n0Y9KmVs3%S4G)EUhUylUsdu&g}|G_)!z*@vemO=D!J3>7IS`-*`1{
zsaw+I+^jN4Y^Xh`F~c5=-fwrW$edRrAVRw{GOt(Gj|u9|-m()nd7rOv^$IDs>niYS
zkX-RK+kVI%RSyPJaT@s5um0;HTW%~-p0}4xH1+uMlH6x42nvFqiFG86Gm6N4wD3?3
zx`Je8-}|Pb+)V|^Ua^h-Vs|y&pQ1NCqRYuMr3fUU1GH9OkrxaRil`esH_C98c*bVR
zSIC!9Htx`&Yxn=?9vt56aX<B)*6`Y7kWFVo^`#?h2V$7?>GP*Q78ZN@0UvJSSD0Ko
zFCs!R9=z8grs*QvUck_G-(t}$JW_>lS#}>7D<kQcaMf)@2%(&%HSBsB-K&jtfmvOF
z1uI`W{|_zCgsWHkzm)AhHR|7;{(UOfeQE!aztrhlKFzo-PN@EQu(cXTcYJMyH|#<A
z<D93}^@<wY``(bX=KDVeFPT_SEk46gR<4bqx7tkF5WxRbv805gafPJ=na$n@ee`#1
zZHUHhZheg(OewXufe91kS#bXr8ph4e;fxeG{9g?XE+FV|uVQM6^G7M~J0krcl*siW
z6DIaXDDMUTZrIg@IpPfWpj5fM2Vrw&)iV{0b2;eF4*eJ^`vz1&Guu*|S(FD1QlFLt
zGIi332<%{e!jcDt7S_SM{do8qHiMd2X_#MgOf>IE_ArO%NIEv2XnltT5K?u`Fm8_C
z`n-3&v8r+>3(%lvBD7)~U~uPDLr{D!$wdc79t3=O-ajwX8DKo+P(#d?U8|>)cej@w
z2Y?j|2m~CkUKuVEDrje5hB~p3mL~OygjUrG3Pl350$79Szlg0>ElcgfUn09gZ4JYA
z>My3wdy*AP5p^Uqb;zo;|7CMIl41F1w4?hX-nH9n<Hakr8_O@}qh<+mdl$KgIFlVC
zR6}Cno&GP~SuH9@4`r<u68NkRKIxQV|6;Psc{f1*nm_xU_m?*3i`{v{|78|ORD3M|
z-AEQxLaBl1$<t(J{+nkaT;;^RA8KH0h<~_4+YJRKH!zdmpXfR{3LSX%mk9hCJo~-L
zpTz${!JQ9tg%*}2xAR({v7q;6e_7Y~HEs%uYrq!|KGK})akfmUlJFMS*u<!{bh>+!
zSelQkWs{q~fRyb}q~B8UF(YRg$PN&|_+_M4e}v#vq`fe!Xb}uL2r{o8t!^pp8bf@4
z*CJwit_jlvXJ>&8A8+)^%5ytcLAI#{>vH4pUM4sr7&9uW@%`Pdw2MyBq^9mcl_J->
zDqw4)*1ye7^*eTE&ik3Kvi#|tl(y#Ul;!Nqn$?a!4bM-!Nl2d??d)%BBL9y|msv~?
zu@I|z$^oaW#%a?ix#z|0Aa^xZNb}=2R{4|W1%l+Sm-MD5;(^Z#;RHb8NY9pQ@2Zox
zHm>!1v+%gTpY<*HzFIpdvgkcCuo(7nEVXgtgf@rXy+ELhcd%ICGFwofOsXv~P!iRH
zs4&n8S{8XW4hndBT+zI1)Mk73rLTjl%$}<B3iJ^Wqg856V7tf1sps2&$I=<!s7`UN
zMo;x7UTqD(-6qy@x!j`gaJ&C&3z(m=BJ#?HI7-j*|GH&F9oJn2>M_2t7Ds75V?OW{
zd~C*FNf5=-NcGfV%}c1or3LnvC}NsFpw~vnL>n}@r@_o>0{MXbH0y0|1*I_Bz}EZg
z?kmHuVpqo%ZQ2*U-7!;H%!AnvwjzzRUnXX8F{9j@p^yF8A6k+xyxfG0Xen72y9%fV
z@Nn?N^?l|nl4w3AJPWT@(eMVcO??zc+?!Olp4HGN?r?URV$MRG7E5NKf8oCDGniBu
zT4-hat0D8*6j(uoq!PvvV<mbp!5izgQ@3uKCkhp<G}hkpLJiC|7UUH|J>E{Z^2e@>
z=zuQZzE8^(#lSRq&7ilEBH)`{lcX0Nwy0UZPr<vRj-D_2>_NVJ-Jd|I4<LF1VU$=4
zQWJq2K0Fws|N6u1vqHvgrxq*5fcmGwJ}1)R1JYNT%aRE`9<8nVfVa-hF!*RkT|J4h
zfg^h;J(oZzERl3CB9WPm@)6EuOw2rzOVCH~0wsA9MQP=&x%$#`D#Wi<rYq#pV>S-K
z#bn9^!6vJLa~i4!UzjUDp`Y#gT~IGKJuQ>l!212>&HZrb97n<<@usLKEEetLO?}^~
zjvKz}#V=j8x}(DpPeGHrpTJ)DYq!>WRXkX36f;YL&JhPmU*iycS)vi8sqafDKFgiH
zu$&GmRzUSeNI$ly*B{^Vp#)B%XQk~2_8B#G6tiYNNS-gBN~`mVFhZrZ-0GeitBiXA
zId7525erq<tl6_guisOdo$15FOezf5Saz~8V!+)!WE^LBm664=b#^iW{1=JPFnA+O
zu59?MHSlgUj>{s<Uu#(tD&@L+)qu+|a4>r>C|mz<0<7HzWD!AKl0LQ8U;VPJ1uG5o
zHp?ni=K9MENIuu{lI^Bd=Z1YzCjlMQG~xEXKfKI^&Su%s7xVju7v<vznvJn_pGNI}
z<$rUWPajw|$dzR?`VM#-rK4r~+tQWU@X>&Qc2VOj-u6ThZLEfbvrD^uqQfYSO)X~L
zi!QT;{UPA2@i{FM-k{d>Ht1;DhT*2VJZEZy7CdIdEVg;7hetCEuTJdmBnwb{!9@95
zuYG3mx*;)3)5)KE%u1393wKl%bv`k?+J2;?ZV_W)U2ZA0@tHkg*i<PNQS&QMQ0(Bi
z?src-2Z5;N?NagEzazd&)0jUg<(1d+V5=<^rjc84Gq(9iyn?n$nK+r?d0Tz|@2C7c
zCMAw}KbEu$0ozAh^h~hGX~)OMFBK!{crTKs-39rW@^3G^$6MGa7G_?zmnhhuEa+eM
zz5_}S0PZ=9<7+nPTMVWi(^D=A6|^OFtcUZJ$KrkGs<WEF#0t0bY`n&OgzD7m@HJ`9
z&%QHREl%xlBDd@9r*f|v1UW6SE*UinUA~JhCp;h7aGY`60X`h0Q5r@&_`O}6k|HS;
zO-_3EC+)tG!z+oWv!4v=e|(E8w%{kk#&kdporpE*Gs-%HP<{HMHfU>~%-q^@5*rQq
zXs^{XSh32=JA&zRV|z`|PoSy3$hs3G@0i|{M?S7%fl`r$7xGSSVhyHLTxW@lDVCU+
z*RUa2s|LGo^zzI*#-6PcWxAfcW$LdTF0ec9FMlzhMPu7G++!KevU_{oFaH*B6$|gr
zY3+9L?2vTJ`g$1~Zk55ZLf$PJ{JVd*1luW^qVlWjMrgG<pbvJ-cqi?JF5oMVCAnwW
zS>do~)-dwn*gSiyU+qJhl@*e<g|!TZtOSf!v^AlU<--<MMEO|?4$HYlfy&dHR#8#|
zOk~-SVL0U=DQKg-a)Dw}+b-;t>a2_oKG)*7rF1U|Z_m7I;=Pgg$V<VlyC~^^k5ZY9
z8OtcGkW{Y132VVbs|J5?)TKIc7H4wfS%B6&YN`T~A&IP|+ke3KMZ{5EUcBs&Zel#<
zv0{Rel>38K_SDEP)xBV#1+0C0ymoq_GsT`K4AsT=U^ADsX_qFg33Bl56HVgc;fRsX
z4+nb|P*ffFT>2MH$c&6mjslgQCS0$uX!SED`H)WZkd39`5?E4j0OLUdw$hxgTtQAf
zwoZQ9zOEsJdu@4$+SQ!T9n%pi!y;)~LWbS*UVY7!r7rPxiRydxTzSdL_pmtWu!n`x
z;j)C-I8P~O9}4;zS^?wm+v$+-?UGLFu!w6Gv@ao+2ImWLRMO@Bzjz`}mdXSunT5gs
z+*fhw5aA$O`dUn^oHHYvapx_i2dTHa=+W<J2ZxVX|H~ye=yI#8$=;$cuXKvzQNvV>
zBS7|OF=o23$8;}?vsUjOkVb!aN9*qJqOU1J^v9#0eM(fU5#LDm4kR0&WhX7owFt(%
z03Fad90vbf@Cu<XrTD%r=E4OtgH>L-i|Et!$Y4^QgR!4^qYMyOBT3xEY2fbW1y(gZ
zj<K;bM$eRb2np#UI$geMO)5eK&Uow-Ca~eenU1!&Ecok<j-2YdyJ^d;nDbZc;-qj3
z>)*6Z<fIDj1@+NhAj05GL0fF++p3#Jrf{{Q93?7Az~k6o7<lrQdf3tZl2vp*i?00;
zh~xYTta;2mWhym%$z*GVS)nH9r|PLvVFj{8w`OtkuK}w4m|<{7Vw{eqo$(?%DvZ~d
zp?VfW+s;`tGBCy`$)(4BWBH(>-?D3N3CWe`_j}@*Rp4WV?nMQYEYBBJT~=ltE#%x<
zcU1wCTao?SWM#E%*gO<m!pd9YLNlM9+TjS@d(BBc*E3E?0d1}VRn<waxqX<NhiNOm
zZ`Kw!(l1tkWoR&Ib*FXJ=@zMYnoioIK?3~3;LXDGhr{74-lMf-n$GP~arbfbn~ZGX
zJgn0Z?VpE%g|!vjUs^k<>{Orn&NXA{VYYStlSrJoU+r1%(#;mYF#9dFHu-&Vyrn#K
z`~pGw+PMA!@oIQra_{D9lFR1Lu-hy!xJMyz8gia72^yvm$Y<nwie=eQ8_m&V&$bI6
zWkp$s>r^dc$Wdh5)wLRJL>|QNJ|o}5YE_cJmdMVWz{-1Sqojj#qs(7)ojIyyr(c_K
zspT_%DKWlw_3e%tj7ltVAq_dX`e_PpvRp=5BHO(ViPV<N)_$iC^{wF9mF6h*vAuZh
zhKCDX8d;rETDXNIxKvP*g9V^GvEKe|hOW8rSvu9TcWqyjY((wJl5xFWAMB?M<L0u@
zxW9#eWCM#{Y8|F)zoWkW$P`MvU#z~cJhF;D3KjR{*8G9LKb~7o@i=`gR2eMR-h{<X
z5XYxPoaAf?pTiEOA){_0WRwp-A`ge8^qM>_Tn^k&OVc+YakaNS_YaM+P0Y`*D@pm8
z1>SramAE**yuf0{b;^D_GKRVydxvR)6q+IvTr`rIs0({v>&0BAz;aN+Eiq2VJjh>0
z8st4V7G7JH8#Jjt8I=j@o#ZNFgH5HFf9uMAY=LzXx$?H;?>p`~idWY2+o8Scxso-+
z3~83cml!UrpAp_&M{&(>92&2pI*7s2YhL@2LL6UUQ@Y#AQpr_;40gVs4NeQej<y}4
zpg;4QA~%vwxJUNAEK*OYv}YcQ#LyLy5+9D^pbXu#v<*gFi6thD8FNwd-6lIPyMwc)
zH&0OvxO6;()*8`nwtDdVB8#o)PaK%(g1cEvQ}*{$TrKj)mVT{+P4Cw68zRIkC?B$C
zt7iUSOdyGEK*!H0zne5tHJWQLQlB+4NesF`#V#7&RATI>o)rN{ST`yu4u$Xk%6m#R
z#n~GfQP-b_+5G+Z@-H>Qx=w+zUxv--;O8O#j~~@WEpjGvaf#VBHd;A)kv5$z%4W50
z@!=u2jR;$-5cbgAZ0Wzs89Uo0Kq4>R^fmMjyvay$y+wf(?ih*7ArsMl?g<f5@V&FV
z2e)|CCW?_u!Q-H})k_PhWt1_ez{!gW?(N$^<Kvu{td3!suY2$+T~{+rt)msc%^%a`
zRxQr4zV(q%s2SuphGblg*wX=CMRr`YekYX}OAM@l1izDg@7!>&p;;!c$Enqtlq=Ty
zly_~9yIDm@ZsSdd*>iS0%6cx|{`g7NrEOF81;2BDwNjxB9CCcD>mkrfq&UVWXY$8T
z>5Fo<JlZIejL@WM!VLhWF}9M>s|_5P0V}3zA1_&8oOih~?)AvLY92{9Y8nZsk;d)3
zlrwyQZBisEz6T{aQPr^L=dq8B@E~;J-=rU=!4%LgP~hMa*S~^via;Wtg`32P((|KD
zTph_mo<7!%*L0dT98Q<Idcb(aCls<6{WO|*wL3gQkyev%9!+ale5BIvW%J}p(eu}F
zJ=ivz5xzE}9<TK)ls2|H8*6#cy|4RZ;ABm>bUXGn_x7?0yT^0MGWM1fXi7G74Mkkb
zX;Gu3t@kn1F1q+x$53~C#5cEW(H342;<EfV*pQy6KLlU7jYsNerUvN+Xyb_$hU$k@
z*PleBo5fo~8Vz`EKgu`gQ&X?u`N@iLCb51Va7oC*qxpKRD8tY@DjXNuNp$LAv7egy
z`>-c=iHlaw(-4ch=GIWxzK<v(lEFkq*(~!9`Kr=ql!BlBp?8qRH=lSQiYvO@Sf`&c
zH1<9n@Ni%?U$qLG95W~Ssigd%mN1r}e#ZQ^FCW*A9Q~eedy5KcN!vR@7}l$LKcZjA
zWPu2}zy1MTH<Ki8LH3dB!l1rfT{Ju%@}q^eSWooNBR=8D<I7`7i<~C|9Oa|wg4<IK
zjz7AJrJ=>gete9!8$=#IG?CIh%g_y*%Ki&=>6M)Vg8JvfNCp1KtK&P=QHHB(9-5gc
z+I9(0q0!lgXy*(i6c1rC%5-KNujg~gHer9o(@q&M270OyIjD8~Q#_qDyg4eCJG&)K
zbeo11UThTIaL%~eUFOZ#SM*jDXc2Hr%DQ&Td1>V<A1<l$CbN~8>5N?ab}^eajmL7(
zXt-~DEg4<04yjtAHY>iq@nT$UJwo5qG*k`w{Xh-rJ?g#vsi%LIjW)D{<Id;8tQFob
zs5y#lA<mU5dG@8VO-p~1l;U^JI9L8Sc|rQ|2O>d&>5a6TjlSx&QxY=Bmk&wRXt}@c
z(^|a%S4l*K&rw7{8oi&t`Fmr=??7>;fX^I8hyPsJb_&eRl`ap*cZ8609KRU1BH#y%
zyAPm)?R5-9iB#NqD-m$5tHOLTIx7`K!Hxlh{gLR%X%Og~5~fDFP1hm#oPb!-re6$P
zi)YdH+qYjHJYXQ#)<1Y{y%GkGvA=MwKnsUsn%C1@!DUq)#0Q!xp}cy%dn;s+-n<G9
zwkfg|RV()>aBspxsOYA!kLO&5G>I(QA9y-9p$pnhylsUpNvSsL6uz1!zk8W>xoHyR
zd85-<V@9L&O<wq4wA)X7QqdSGJTIady1m5s6JB017Fxwqr#3Zw6T4M`=AWh{Q}8;W
zFLEhqmviu)HgcM0InHt`B-MWS=D6WB>T&&o3pH9ck}ue;2)?igC4n83$*W$;d`8am
zbRDz<r;rCSvO8zv>{UMuYI&ZC_bH-@B~4gqkDjmZSzHU-Pk7wyu9Kc?Z|JWtY_^j%
znE337`KRgQ;wg^{e3eV(iCq#l!!6b*|5^o0uT?G8Lh{I)h^%5h%u{4Q3mSiP@><`r
zA3&2}nOz_MNXK1*`$DglHJ~?^Eas0*SK56d9eBhEP`fIiX4(L`kv8N#e*@X)=up#C
z{XI>j*pJ6<mpCV2*%+CI!-pL-9Y;hT@v9x3a=m{U{hF=#wj5G*Oe54YPu$6NTsE<u
zjj`Du+$qq`@}xC&%F?8^Cs_IA+iLpSh&2Q(xQU)}pSI~vds3|n8DXw3rG-`UogS;A
zH*f7-F7$&=qG*l`L0em~?Wv>i3!b5M3~UQ3Jl%a1eq8h%A!;Big&GxU#Fpz(>-~SY
z5%-P~4fh`Di*6Nb=;qO{QpE4R|B)soOZZA~Ucr?rwEGW-TAjB@%bl;D+Mj_a>6#{J
zmx33J`1!~X!JYhwFxSB1kZmy$&-7=)QV-obiY2)&(1+QITTClFr(jn{0tlG{_QlhO
zj}SzhA959WrH;eT$9bhTluS-cq%*0vjiJ6M5i&Gg?WxBDXb%ok)X6Z{u5x&ok$a?a
zwrgX2ftH8U;3GP|TxM}4;bM>OFBxC%TCKK8d^epc3Ab`$`WdHal{o+<n3uf@{makR
znM6FoS7!u3oZ`&?;u)h67cU5yWV$MI(0sd^psOpKJ~72x=2%PBgHCs{4OAN^xqcn!
z5vdKxRian<n_k#Ljj{GbrPEDJX1rO$(<bfd3FBuk?z~_7vi+9SbFJMvM`?wz?&+gj
zJ?x0_o=}u9FAj$Wu!Fp@9+c<_b3ZJ*{Wt*)P9Oa#@vz;r%*W)<M0w?`@Q=S0xFy@$
zDme!~R_i|-Uz;;1!FGS|QvcZ%zMBBWeI|7yS^3Lo51VOhNcEQYWSsZIGUKSR+oKTi
zk$7cySEFQe0rK}?F!$eDXe;~Q!Wdpv*xID)LHK{=N*`}M9+Dl^`I|(c!9L9R5J!(Z
zTq`9-zUapR3c*+&_pj4>`E_@?uG+di38%g&hSRD!i}<rEJzET;H}@MF9^(a<&wrE6
z=3wx$U*!ZP!mh{QT1eLH9N!K0QsS)awuV5F<LV<W%fd;l<}Oh&Yd%0j2gg^mHPHMr
z{jVG7u;Zo1Bn_diKZ5a|g4RYaFMRGl$Bxsc?<E`k!i)n%L?r$RF6rirhFCr!wCVCl
zelng{iYpU8`&|PiKpW*GKIcy-2(1LJr{n1lvZV?v;v!w*<%}i3h%dm_Lgsk2g{>`P
z5ZZhf)P6D^a@4m<ufawmg?byuYW4YdHbc{vsI-1V-0qX{EMFbje$vpge--BpP&|=J
zk?XG$uueHM+(Zwnv)lSh9xr}yUlJ7lrjW$<oRti!GNSRaCE4Y!v}LYPQ{bbfn)`qy
z+Ky5fR!^nI8qdDgliJvI&#<B1p92;2=ereLL~+@Bi?gqi-jMVypEAvmsg=gb3X|H<
zf8q$#jJ2fG9CuTd#vsakHYO?E{hrK?<n17)B>AYH;2*@vLXT6IJ}y|hbtER&xzc;S
z!1l*~`XtBXIkA)D2%~WO1r8qMngQAnT5N~F{P^o3(pCT`#N*IJT+he78m!dE!sk1G
zd&5o*x}3$<6(uoTsS*$-hhXP4>c#u&wVuC$PahAcOA=2M7sppyA*azH@wF%3TnSxO
zfw`v-c021}d&^JK-a&yQA7492-Jx8mLeBp=Q3|tS$lvf4+3N*15Nn>*Ge%l1n@JL1
z9hc^0{y_XP_ZU;f12l}>g($>zMte=ONcrop1=8kry*WTUprz!T6Dnpr+CNfG&i~~d
zo1OviJ`uCeuFeY%-No@mhvx&Qybp`x*-~(U7w<7JRH*33Y;DD8hPj`qzsIyh1NK?3
z?ZwdO@cq#r9sOp%x4`pY8p()T)`x@2|1J7t7S3XA%aDyLYdU>k?(CTdf(BOPhE#)%
zeHe8fv?@5VYiqAyec=wM>|G0E3qtCnGs7aR50fr76Sip%NJ0GzE6NU*HkCQ`JtABW
zGt;gR1PWWI<Q--6o_#sLT2Idk(NF-**pW><h&DBu&3+plH*5XO=9c!HKZGN0p`6U6
zW+CX&b8Ur)(4VE=4=S^Ff1Y|1bbL5JY;G3AzwUo6sh}d4Cc2r#ifV-e6I%n$-?sGy
zPHWo(RbJm>RFfP0EbKKi8T~99a;oL>qc&AYP>fGZ(ci_@*B$H$oZ3J#cHBJ209lI(
z8n5Kp<ak-EoxBo%vE9u?<s36*#Ddx|RGV}Wk{vKk7+?b7`_%F$eZ-7bTvzBKOfKg2
zRzvkHANg@*PEuKQTE7Gtl71JJujSaD9ou?K_B9!v*^+XNOEaoLa@r^DIcd@C<aib?
z0{)yRIqmh1M@cE`U#1TSb{QHY0CFw3#q;&Mk<CDlv*TQo=dLXX4^)z}A)+DBL+m1i
zVrm`0Yjyu?i!*zEU`VA!yNPPX>Vm;PU3$OY%4uyaT87y@=g*gL7nSX{BNVDPI+`KE
z@^EIVbjVs6C9o&eZ{##aP8v<D`ws1tQCHPg&D|lvbpG=wSe*s)WjI9y<+H+ZmLBWC
z7dhV>e~;)FaJ~u&i1=U{%6TAjJfP0}8{1Ji^w1}y_PTMw6+*xH_;Yq28i&!ETIayc
zYs*bm6u%WIuz=on;clsLpU6kHD7s`m8O9j;l<RLK-ir=n1HNE#USy-=n+y0QpBv)3
zPf#(tU}aa5(Q&OTti+kiT(I2%p=X!oAv8YqVc}j$b!}SrAS#nZ5tP7so1Pp^X3Bs4
zrwj<WAz42jyGSTpU2x|NT;41g;etV-XD;lCu78<27{Hs&cmZ}T(OX5=4+Ru)o;=S%
zGP_cvI`5md2YNc?=5D|X@ssfSdeA53V+rs&0i>ivp*ZvctiQy&_B1yjya-2MoC`o(
z&}6cF2mSpjFhKhEjVj0Z+H)QJQP~dv1bAE8(|qK_ITS^@$#i1)hdZp@;1L%8*zS<`
zG{VcIpV_@8(-<Adp>j&3cyo4JdHjmF;<xKBk-Ov}QKnW<w+{~iZ)F0*%8pjv{wv}>
zo}V9Rk@UG-`y+23%s508VbLLEIW0M*>A96`3lpVH&EqIa`Jd@VOF=aWYi+bfKap?Y
zZ!pZ&3tBJ3e&iRC6K){eT#=$nBYfAA9Vcc;bIUsW0n7Jv-J=!hK-GYDoM`ch?y2;v
zCeeK$*2mpI#hGK_7tI!USH!%}^f&r{29aAP>)3(30Iz}ainvC5z5i!O$Sgpvg~an2
z*MhP$)@h)ySab%_zZzY?oBO|*@_5eMxSV<6@&e>*RYX<@e>`M^@t+*oS^LPW&tA#M
z#2|+bk=Ququ&Kxn5EuKYyUeip<BRD%G2=vMO>Gh>?pq_(N5(@lVB|pFroeTy>V5t&
z!M^5dTLe51{X9GNy!xieaJDVylM_9O()W7DP0zKUm??{Z%3Y(3GzN}YF$eJR*37(Q
zWR9_^1?mWCXRuN_-7?03N5OyUz-&#g&OKzGB`(dDoyQEt5mUW`^*S<Ijg-lJFAn^w
zODU)TVoV^Pqs=uREYui5!6Xo@AXqn+Ot_%AFMV*&eEr(#COtVBGr0Oq7U=K*9fVh9
zfsxe|(_y>@amGNqW9SjYqAZuwx14Bvz{bH&{~R-TOkgzwRjmeW>Qra#K}S5FavFS}
zMU<gO$9=E=g+8>RjeOiQ1mm-5B`S(3hwwUkFLdk}n}R3}SK54e4|npL{$78J<kL#{
zlz_XO>O(rqArJ@*5C?nbTk6HVlHo3MYl@~Ee>*-Jc#}XG%9AEIcXzQR-#kkF1SIe$
z0Zvm#1?{O?oBUQ(m*=DI_G~(a{d3Yp^6%ChZdS`T>Zs0oiA*;*Z2kl4+F66&|LNoL
z5$;_n(3&*ic;~1|R^HUr09<V?ym?04LlN@p+$$|0n1fV5$HgeJXU;*AKpn?|t@Le4
zQJy9dCaEOmu2(&Ej#612n!5v)9a^Lot$cyf5tbU=*tb~zuGHVXK3vxdLQ8m!?5iHK
z_AD)}9k}O<BX2nSU4PxYZKseV3<?8opBnPEVf~tcF(EHA-C75^{R8Ir@B|HetXn8C
z{>i<-C-+|5VcF!bec!K+3`)^K-ceKbDQbc#pC@Z1@-@Hhi-tY8BT0BiGVIZMVzP!l
z@-{U+h*?M31{%y_@vKH3ydk{v6qe|kJ}Y6)Qd|cghrJqPQGBNGIpAM0LU5|B5R^Re
zk{@K@DCSlQ=lIG-k0KjL$TMJ5|2{^6F7tz69XZqA-a-ZBR3_Wzbx4S)Mgg2U#f~d9
zc6-2W2Bu#nz6X2ua?nPVZ}EkI8dMY?KUiZDhIDBfD1Yh-x-&K$hLrdrEryM%_la~A
zOkW?F%vR9&PQmBRTsUKx%;3+@O=5cMzafQdVXccTDeZ|cKm69grv<c0eT}h6>-bA3
zA_T1*9Gd{yvkgV0P2!UNg+N;J#QjIuG&LN0dNec4#SG?oaK2d4ut)}!ObCPI)sWZ%
zgttMmHZ5nz0?yG}U79Rbk5w;dST~_2aY}<W)o3<QeA^;}e#R;|tN9m>&9x*~($7kX
z8lQ_H`2Ujw%K$WY-TvvT9m=hTj!ey$RkISFa((7Wd@LOMr!4A7yzHt4WoTvtTuW?8
z%?8@1kK87$2_Jxpb2v=>YvKt<_6Q%vIvx$L8Zu2@i1Ousy`rATH;E-Q7Zx%(>Y2IK
z&RP&6C*(x%Q20tB=kPoi*=3;r&iF|Vb+<6hy@l_<(scK@`F=Bm<#-2uXRuI0f{L2H
zneuHp{;q4P&n&=pIsHN6y|g==aj2i_7t)_P;jylhoOYs3%Q~I%`rHvq+3UnUabo@a
zDz)R+mBtX4){s_4%%rxliRH(RXBscNih|{okA!ZqT|GM*n!X^r_lD6;uhY&rHq%z_
z8+^BoSh~gH6V#fbE_X0O3Rz^^FQm200VLF?6IkeIr~ckkocdB8!p`$}Kh5qwt<^%V
z@3yf?z0lq|^-fow`f|A^1@@+i`uRWOb2_~``OSZEvzz9HEG~}ZOS0RzxC!kJE?+Q4
zD?f9pXD&ACAE*)VYj}QQ%?-M9OqpUT_P>Epz-(nbNb$t>qhPVz$KP$2J!J)r(-k@z
zFDJ+IN2;afZs=`wv|s*~pOCGLRUWDB+xDYVk2zu*yP#G#oSI9J$93hByKAR|?&NnT
zbGTsRLoEPG)E=G>XC}6G43w;nhG|2xd8Z%BS&KI5CU^PZ<`6gd@0mEfVR7>yc~eo{
zQWb>!I0(HPf#JbnrPl7Ew>*<3*FhBl?;h&mE=fwBQ0t%6YP)3cClvh>QXV~**zzfP
zQmudQ{!~a1I}tM{dh9Oke5{@U@_`A#iCr(z97CtaMzi_Ww|5TI<@n?^h1|OK`EX*X
z<gt4n)QwTRt0~)aiu_LtpAJv}KTA4u!~D6V=l!wFA0Bz38#etj!nI+SBX&CBl{WfZ
zV;uDh{)b(p5pC95$VwmS{Y(y9(Y>}utW2L(^p@1kkVZyvc<{$g7!(&&0XiomV8oE?
z4!5E^>$os}PSate1GtPQKm9B;p$!-e8G?`^d<cUvWTtm`wllDovL%`L&+$**9Megx
zfq=>1JN8d9jL|F)Kf&?gslPaiYh&GwbE&(zuU7klhd#lTr7Wx9U9k-4{j*K?B(fa(
z%ST{YSOhMzpNNWkIyrybNqnRN!HJ<9px`tor&Ku2wlQ=<aqn?2)o3;y0)I>nMhmkQ
z=BNLSmKTY91?!sa&r2}9q$dj_yDajkesM_e`|8Ve@D7lz)==9{3sq|PXBXiUFIKZ!
z^=BA;T^KBdW#xY)M+@(5O)9achKW&IVGv}m|E4!fYqA?zd-wB>Kh`<*M#FoJB3LpS
z?z<t}KKmzZA0pqADrc`t^PN1pp&O}tZ-$BKnwElrX?Wk>{7193+D()b^l``@+E{=G
zN4U4t3rjWS;-|GbNR$2t@8Jz$%ILG^<h*u+vyj%l&^OmcbZ7@K6@sZmJ{q(b3KX|7
zS?hHn<<&VRT(vqf&maC{Q!pmsKOGNZwkp9Fp{E=RG%T8NyjlIC@9^|eDm%O6LyYfB
zH$5uV`Kvx#txXoA@CIh9ccWNee+=frD!*@PNmQpRyxe?df{bJ<W_><#13A}c-<W0g
zj*L9YS70JQ6u~avKOVa7O~u#bDTdsIw^vWK!PA!Rw05P03+yjpCo<pfYUxoBIc6`O
zh`lL9I^2J-h>x8n1I-K>dJX;XR6+BWa9BkKUIEiLKt?_P_RFx`oo?dp^?*Tuy{MVH
ztu~4^f}{z`n!2dN4h~G#tYF2g^(gP;yrBPmX{4c221ug3A8*Yl4|bZ%>eW7`b3G8%
z6#hfzdqCe@VB^WWeJ+>pyE$s@>b_57fOI~GmB874U)&ij-xw~Kz++Dff>C39sd!<-
zLqLK)+xlRHSupX0_(_~QOo>W<N{%M>XWcsco6K5?sS^A~G1kBs{iS7Xsbiawe;rot
zLniwDag8zV|H;&yPcN-#jxHk`;O}r|jww7^BYJiN5)41`nBuMDk@Lz8*x=8OX*Dmf
zvi1fmCh`r056ck$?<*Dp3z_S5$lG1C4Lf{FIprOd*|x*yBjtG{7#@<fBb$Ryv^Y7$
zS$dcTBVfiz1z*X-bwlWf7QbgYsU5m|)0@-3T_$jui=C*&E-8f1x_!xX`^27D<%W-_
zB875hxC6!nTka%Hs0}tnxo@B+72)rpt9N9ySY*AycAB*4Lz{WNY5?@kN8XJtTqv8o
zMxeRU|4?JVhMaeEfC5jC);Y?}G(ayocJ>Q=4_z$0TV?yXGY<2bt;y1h59}JyivK>$
z`*)@$sckX;<8Dsxz1ja}8=$Cjuq-F0W^$)?E&eZ3KBldV{WGnh2S0^Pw^j0p%v^6(
z5mMsy(Fd1@6iJg87sUF?gKgj=iW?uc1sN>}QlpvZvo}d<gB_>zM@w~&nX?D9z`fGE
z6kTiv$OIDkFm*M046jAjuNT_ar78IQvDkSWOfWu&HsLTYSMBVI4rK%cQY^v#4*zIt
z=O^uRd35XNM_o7USV>hk7oqV|)ot&Ej78dR2x)wGtfQu^O|XnY^C)QxA*s|W#G^!+
zvnM{INqJ8qa_(Icdyg8AUt|t%oK5(Je#muQA}3XH{wQ|;-AU5p209-pn7Qa*KiMfQ
z+U&|>WyWGl^VbQrX<<cossj?^wyWT8nmE}+uybD};w)nj;Lamok}AUpL~5ctMU-Hy
zbdh?xCR}z@g!_EF%E-7%vWzgE-iK!Fg7GI!ak(E#*18&~&L2pN!x`VsTeiNH&2Q%O
z4?Mr6ou|7J?<hSgCTi&L=DNY=%&eO4&|rmTpFR&|Ic;lYVmcc(^g^3?l@Hl_wi^kj
zoPn@&;o2qCk_C{Gox~~jLpw`PtH;Rl5Njj`?+tLvawY@rmx!nrwZKgGZBA_OCD0+(
z*dcpPvzUp4{>nINSn&QUw&MA?o@_$3l(D6Lp>S$XPmx$eDMi}!8eSGx+GlLt5}Gvc
zbrM&!$$PF8@F4#C7)o(F&pKZ-LGM!Qaj4e1<^eE7Suz(f?CD2$rLUr*5?l+)I@wL5
z0K<dPYuBdc-bpzH%3h(D(weZ)u-BKheu4ff(#H4(DuG+!H|Gc4n)t1ZrJofTtwf$K
z$@>}kavdTlBu3F+L`!SM4rEf!<oa_KyCF-_#?W&RetHdeBp|7Vaj%f8>z|DX%%>%u
zZ*4Te(fY-oGsh#m@Jq^BA*%EP{}*-d85L#IEQ%rs2Ec?MK{5=Ia~QIKWQHg?C?Z2f
zhMYkKMDh$#MnG~#iIPzy3}MJ1AbDU&f=C8|`waN*@7w$CyVgBxoxRpMe|UZ8>8`G>
zs;;iCuC96;#J>h2==gowwc=w%|7O{|nYmtWu;R5kq_<NN1jXx?lhrnq5!mfwU!D82
zx-O3Q{C9)xr7sLx_dVdQvvFo)GD*6$<BYSTbw+rFN4QCSBeuMY<6-2Y0!&)8ykSHA
z)&PPtpmiybl~==4f`oaE?Vws=vm?6NGw~KHT@6(MHJ^Ex^nsrRD9<H}^D)7yueT;z
z>Izqe-<{i!lkh3WW{2_2uG=7V#jp3=%Di;7>beQW@>WZ7DpLY7)BTxSpns2?tu^VJ
zX9+FZQwbGbzIY#V!}Umqd#`*fuBJ?W5Ixo+QWP%pX>_+!{7rt|Ok5lNWZ9;49Wz2#
zP#^~7{UY${gQ(9`0P~7(;42|#5VXA6-1e6)XWgD-!OAFBKgdvnxLc9d;5pEd7E=T&
zwWk|O{3dTv!ZGd{I1Xt+64Knb@a*WZr)KJw^VYo>0gIZNJPS!bYR?mMJ6Iu7;6ZVI
zZnk0T-POl7?boYN1p+kGY;Vooxw+XwZq)>9FipO#7#iV(inw;qe^gHOu~t*huH=!{
zeR}<i0s0H&c<a5XX~kSno*froqV;j^vSMNrY;~A>`nHRpK!v-lx(dK6^_i3cre{`v
zV&f%mJ(Btt{X@w;Q3W4BdA@2vi8+&RgV*HbYhiU?`xfu}Y`}Gaf8UtsbVC(%pi-`Q
zJiGBvzX1Nw1V&BtHpPA7kTjpg;RQ}?Kg~LZtMs>|00go$z`@0a<M1b>GWw}L!33t*
z$;s&`9mVdKcfHHLbc*6-8PeN#(ZRvV>HcfZyZ|dCvZshVpXHxEru8t6O4Q123-I=S
zS3$oyxN#0+HEonEyM9x`)8QrFt;^aw)z=?>!zcOnji^{oNMPjDmH1an&t%+w9i=qj
z{RFawmjoB13Sg(u#XrAtVDFHBri(TR@ED!~<mSLeb``(|C#vYqe-JStFhb}q>VY3a
zM!(k<6XMB%@__H?d?W{3$<(SC9^M@gv_d#m7-*27yJ!^!H3Jd|eZ>GB&{V(=*;S73
zfrL9>F%H#4<>y@sk;R`1h5)ZpGLj>&3!DXvxl>)%isx0~*-@xXq)i#x_q4m}&4X-y
z%7t?6JGVKtxgs;7p5#1yW2x{6-;&vqQQ!DG;a2ElEqR9SzS#Nn6#q1PG2I5gHawwN
z<j}Qe#%~i4E-GlA-rugk>&jSUq&>Sn`r30AwpO~u3g8_j1Na0aUAUWL2rj$KJduPn
zt4qM;Haf*1Rngidylq|VU^Nv8k(tI#Vmup0a0#-FB`=|Z8(yKQxeX*J8BRy)qb=b>
zUE>s}@jUMUAoI=<VyMeV9cB!kWC9<%i8=!lK0(<oa)E%(j&Vr;iQW_7&8f`4gDTth
z|F56~51D{Rt(p){C_m8IAp-EHDw_XS=sP?zKnj)r486Peze2wPCdFa3UCD`k<+?Sq
zm>P_cv|V(w!V$p<^Ynb=4g)lOZ50CXz1RCs#rAIoisNz^^yikt;$hB#<L(RLSR?@_
zc80k=ohI6W5rxXw27GTgm<L^ld^MxhGVPN{`dzux$Po1OvD?<s-dJO=!B$5ITQKHN
z8@lnd8t|>%Kl9^WVXZQJ%%xo@Z?;s<K_)+?YC~#oD6r3E7)z2;uYC39?Js13rD+R0
zSb=6CHeXm>gN6U#&K4FWG2=S1&_xwhj6g>AL|_y&^NJY(cMOIN>NM)_m~&5D_~X*o
zRz9#ngJx1RU0)!5MSH`J&GPIwb8a2_u?V2Tw}Uo=t{HPI@{|+18Apghbv7tNb}9!(
z0k00jA!?-*Ia%_Zjx(fBG88pl9S&>tNIP*r_*QneU->yZ*T(<JB&8QVT}A$`fio7F
z#)-9HCWa&8yyK9|w!?*hr%5>gR>uiNSKGH4Cm{>Mk~k;q7JsF5%&iW*9W~LQZ7u1C
z5~ell&AM#W$&pF!0L67mkxo`T;6`TOsq4Lb0&p^Oa!w#13&3T(5w;ZJp-{I7*Da`B
z=(W=$7%O(!_`#ZL&=Kdb+<?C=%0h0f-`vkDXh8@c@?4BmT!Iftl#`G5Q!6UMzH;J9
z*#$Kzqc;)+A9JYIt;4rFMUG*|Zh8dnYGtcp8G)qh@LZ5)fUZ>?dn~ev6DlN-^p~p4
z)_<qs3GU$Lq;*Ak&k+4C3=!5E61B%XHat|J&3g5L?YjC8eLEZRoKg=X2C%;Y+U9&7
zlQNWr{J43X=MMyGe%E<|J;yN%`%7>^`)A{|m9&G*G{>dYKfOe33`Y3N3+Vh-uXFwB
zPu+R(K`UQ*v1TyoCq8PW9;V3bidvx^Pq1J<KG^J3%)?Srp~Bx7sRAyua7~EkVo!r0
zjW`xAI3uJc`m+{NRh&&))DxXp)%l8h0vgO%FfyvV`$`r^;DJt4SUcUgVm+!;8KTgm
z2t-@P9$<QU3yb)2WE{niZv)Dd3z36?m=5}U`@-A^$6(BkIXFqWyvQA(B$G~gBrxWk
zbR<EA@mq=dk*Z&+`;e<Fd7E9eJ8|SUtN^^OB_YWHUo49>*i}sp?YCWkbQcChekK7C
z*QNl=tjQ$581sHI`8<$**8+Ulnw|{2Y^^Db!U^6MDuo%mz=Zptg8mE$Wc5TS@QMOJ
z64OORgF+=#0A;PahwX4r_=CQ&u^FJhD?>_=Cm|Tq+|dcei;-(#?TRqi;yN!2Rsg8o
ze1Mv!Z4@F81=gF?H8mq<m${Ztiw^Rc4k>Ditm9q9qxv4#_21*K+`aoW?w%YW;ndxS
z!3NX2WxykB$O`Lve;emajOXUA>s->z!%Hm@7^}q}qHsjQ5CHztc_DXR;Qw4_lp*>M
z0J$r;GK+Y?TGB`RGlPx5Cvq@4EnI5DUrb;`8M2Ndv~?8~!q|)-hdg_>h$L0|78eIR
z#{t3@JyGKpVC;J8sS_%<a%_I2L~bs?dq>6AqbNX$0ZhvYeaTE5P!i6}eEu{QZxiln
z!v>Z)v4@qec<t^abqw`o6A8=0#P!h%N)!tB0bT&lO!2&ukt<&?l7mkAdZ~2wGl->0
z-mxOPKEFm&nUtZuB2KVq7d)|h7!S2#2u9Eu-Ey(Qy@3Th#ZO^3N|8?k{N#j-%8*sM
zUEjTFk`}jRdU?K;m16EoGp=l3zZfn1KaU2eML+*yR*lKmid-d;2`1zzQiG%>F%}Ed
zUV%k<WNgr}Nk3)MSY&j9;7T0fZ6!q*uA(Cz9q@pv%Qg|hp1>=T+MAgmP|bxZ&gdUJ
z8T&~sCN${kzy_{(|A|zafF<wQqPmaO!~dXL7z+FmMp3k<NX|+3v+MbK0*he5^Oe)0
zg;wLK#;NajFR=Xf|H2YuPs5pSN1qM&VvDPF32+1DKleemp;D;*WA6aH>h#h7rC0r_
z<xlwg0bP5UO+%<B>9;Z$Qc0xo@~K}8VtZlER3x=T>%YUVcI^!2wDePH#cX<2w8Gbp
zA;8_wPy%X}_1}^`*Pv=FCxRVya+HTzpHEeS57ojz(&Y^Ez?#J+<!k|-8XG^>i4ZJ-
z`;?b3+67_Uijm=$5o2xtlB5(!8d5t;TdOZYaUnz&c4|Gg0aVOg5Q`3MqJ4ql81K3U
z8AG&b=PzmN+V9O)4%%4?uoL)}=V$ERkJG!Sc=!3!DK8+2D-Dmn8b(jFxnh6K`Cz)?
z&`;C^+`2~dlj0w)p;_}h<*$Ze{zN2@eTY4mV_7}mZUGkh2o`D%tl`)ToEbP!s4gmM
z;Li#LTrl%oz!YHRuuw(dPhAKm@&Qg(#o~~~$Pc(bPK;pYw>YtYu&{ALfm)}Wc_zx}
zH@GC+L1h0wL7jOoI@r<oTw}pHbFwK>0Od|`V<@nI151$$)taeb|An;G^^c-(AzEb#
z=jRoP%_Y_}@ottHgPB#tq-jW(GmDsYW0*R8kolj*B$j|QPsSi?HVe1;Q)ifLh5OM}
z_aeg6)K)w-<TrE1KJ}2Vc$nMlV0QilN|h&IJ_6JJhbg+K!|tweiTCO={#h9xHC7*}
z7kGol$sI=8zG|a*8%_eg{l2-1a4k2~VFkY`(}263)f$^C)2n6a4l1YsAR~|6l@h&-
z#dtLCCJJGnZ>Eial&WFg-XGw|e?zZ|6B`^){wngP#mDVI6<>c4f{N~={A$h5K`=3z
zdorXoP#InQJxLXY-m#rp4;mJ5FJxK7zE;$P^p(k!!+Q&derrG-m$kaYMZo0=Nikui
z@d(5}Dkl$K;jw6n6a#sW&LpCpq@2>~9K)*9D*HyL4-U5v?8@hV`@p(n26Z9mQty(r
zDzs_&wu||zQFb98So_HlbNw?x<BzYxlXva-5I^eY;!4=^-YzU+F#ia40jAgtJhCuz
zU&Maov;X&}w5#P)gJiyj1_UPWI6WRCbX(hCuy%q3R+wsbwV9bNPwkG4hRp#$?IY&y
z90A9%V(%UhE4hPhD{~PDYSLri^ZGxbem`P-`O9<r1m`we8r>eBY$=f$=d8g3JJR7o
z5Vpo)%`Ri(ae|a8>#+ZcVfwIu$toGOG<e~ZKDyY(6?Lg9mCd%^f8tkXA&OmQmrLsF
z1!w;}8QU<<yxPhaZQ)6*@9=5WipGi5@v!`IwYW|0TG#5kr?>5l7^=VS$?3nLyZVrj
z_WQ%z?+*x%3O~KduH^lFU%KLKrsaOmmj7=oHQp}{ux1%@QiLhTQMPqq<VU`c9f3}}
zv{_~0j#z21GASbI*+4T!&(Vf|kTo?iyNAG*cXumn$I+pQ%grB_d!0MlVKCG||L}#R
z)@kAUH7_d{nB9_7Y0p1G2wWBVEH*f;bvyS;D|1@=4(g^((0}?sTq8f}MQ5BX#o10g
zyi@{PICO_24`nC{pwLr)%_a)zxF$oMSfW<GTXoXlp%4nCQC(r0_>J)%yLOS`!_Ik(
zWS3FOKcp^$Z|`(@_uE%j*c1&K5Z`2JToav?is!IyGm5C(>HL}~GpfqV4c{onw6`99
z{!kdv;T(%BkSk)Yw{C>L9Q!b?xEIMBfzTD^bp2;9dJM~56CW_BV^lfRX&a4^u^PD*
ze{`2PbY$SiT&^rkqy(v<l*k=5UQPXb=$=^l*<Veh0~dU3fMBlp@w)#8YNZJ#GA^1r
zANIZauTEk_rIZ$_c<i;61E1OV6Dm+&i$om~fFlWRKbFPyzV{;*39|st0Y!>&+d3SM
zw1p3mqgEPVaAfpfFzh(3773UIJH)7zf3|uf@Ozvd3t$A3o$E1koC*`XFdLw9aX2-O
zvn1Sb`tK+NqkgXcyl`F6e+`Li|6yAUQQ=0o2N(Qst~Vb6UBv&I7NCEujhX~k7}x%<
z+{bP+_z7$F;;pMr{1q9Y;oJ6dH6UnROB|T#+-wMX&5}3N1vaSMBr{3i-ZjZ^KQ2bw
zHD4hL+_Q)i`qPv6+DQZ5ygs$&tOmr$Z1s)tu5dx+P3ja<p0!3lwdP#XVW)jqrSm>n
zO@UR-RPl|P@{c=rZg&v`1_dSzDWjIkb8o(LJqi$B$r^&DOM77_vw5Ca0okB&*))QZ
zS)glO-WhNjwJF~swgk38y~T;6uHpK|j+bGcZ2Jz0@4C>%FkwH2=PUB;68q+pc`@YJ
zP`%Ro=F2Aich=Bq9HGwll=6e=AQ0YV8>@<(9<WRSTS72FK~!Yq1&1=@ktLkijGR5_
z&)0t2IaP@=P6+=RE(zU~Pu>M9KUHX^$2UzFi-QlPX*ieTrp%z`0?^~$QJe6Ak-_b`
z$-xLw6}yZu#1P^8NQcKWFRE-LFk#FRZGSZsgDPs}L=}B#H69vR6ufTj?I9mA+OH`u
z!hEQ2bF$<z0Lqq1>`OV!GHGQ)i&RN(eJ@h2h+?$M0I~@Xi&Kqj6xq9jBvwuKM!a7B
zU6Qi#@a-B-XokY*V6$BYqsDhu+E32{OM+jPa3)ZD2twPk<&=(7yL5&^Erm<IeTQlj
zG$N5a%T`vxMG2~pOm==o;;ioTosAP5SPxsuj?W?XFa*vhv)O_k4=c{{gV~SE_riS8
z?Wf<V5%lmu*`@Ics7TF&9UGX>LpJ8^bp^5Vq9$7Fzl-hGCybjK!WHp8&RSoOz+39$
zwUy46FG$itTfdJhadHGH{_tTLDJlO};3>1zyK_`rs~M)~DEE7z{{ms^vH0UTLie%B
zL(=t1-<$2n<r1R~Gy&79=o03`UkL`wlPfznl~}h@JJN8>w6aQf*xZ%#2>dFV{tsck
z!Cl7_wrxfnURV+e{{laY8J^**-<5j`;}NvdD9aBv*M!`26DsGV>K+y8`uA=_MfL>)
z-Ntd4gVA3r9r&ojbWUg(bLte&U$t--F*LFpc)Ta%PtFpD{AmHEG6Hhi;Y=$A9@NT6
z$X+bc?yM^uNb_^92Tb8ZKr1HjWD~IH|0T*;Z|9o+N1JAU&QyO_nJ~KzAlL3$zQNfn
z+REtLo)*iQWadULo^4aN>e#eh`a&pi#*6G-_;yYa%j?qdc~fH-&q>-lb=aKxjwH!M
z+|s@kg3;@Zx@uN2o5rzs=h;S9zwp>p<t-%^wxG@RDH*kwfcIz1WP{a<pYaZ(T{sgl
zwViUNDHAF*|BZ702iZ3y*N#MmLi^|8>7O+Rd?m4_&CV_^+rNNOQQk|bJJ9Ods=f8<
zK~Wd1;Z;ojFIi<EcP<`YrZT#>kUm6qud#$gs9N$a{vrCQN*_>x2lolGODuEkVWpJ0
z{*6(oX|k7}cTfqjEI4ud7KfysC?b{v44$97iy!RiT8Op+oHav)aLSDVj?o)>ge?u^
zh_}(FZyIM_oNtqMzL7ksIzsKQCzUIPz-hN2L%8mcMV3Rzl!><VsxWg<dP*$ExhNx$
zI?a>yBC3_X^<EzuhS}1?6vOSV+(h>00_z+P?>3O;hdO~32seK`ev2;<N4sAg0kpBe
z>68KQ=&X)_^g;hU4rmq<xTF;Us$gXxqhK&a@OU;7F9;^mg`!%tLIC4;jXz+6#v?R<
z4Ac+)R~x@R5oZ97o3eO2XYN<1wK8}mvycutK%^O%W)Tw!D^~%yz332+5bQ#M7BAz)
zS7<}f57<(HwAWf_MKJjPq{a;EW0wGC<KbtVl}Ypm;rlcG?;_Iw9Qpry$SU8<2!JG2
zc$a0*EIczV5gvX0zkvMzA0qP=-B>AdJH&-D@Qhr5naI)7iiOfLwP50Hq8F!)teK6$
z!`qcQyu|Jcgm+8c%#)gZ*1CN1Ep1Qzy^8N0J!O@JZIenP^=XaiM|3yXfVjWVSmbwW
zWk@6lx4vSLK%(esDmYnIIBq$yK(C_09YZnSY;ZaX4@D&hvrJ_K^8+atanEr>9bz!1
zDI!lkqsAR+fD8|~xWsHyPVkz~vqwL7-pt7e7h_DL%`2>M>JkqH9z5j&_u1s_J|1<N
z%m)3ec$w2LKW9WK$^#B7w|f4_z(@e6YlATNp9dx)kI(9eN>%Hg*RoY?76hLqsG&b_
zjaLfIJVE^(>9r#SUBE)6Mc}FeZ4o_4cy*=WCPF-Uwa8J&uJ+O)m)&gOM=%%)Z#-+T
zn)1EUK=XFPE}%^$#g?@lj>}jNM2J54o{Wq-foIy2k6jxm2P;+iRdU##{vQ4`4&zvH
za-i5e(kmZ<ZZxOHWYa1+XqeS@yBFB6K~|#L!Zh7<Rh%|5jDB>`h|(Y`d{qV`ChaFj
zM6eFKmQPT>NlhQ{w8H};tP|rQJ6z+nHr~cMJN?CdT!|z&pAQ4LMpg`Lq4qA`E#FG`
zCp!9dV%6&8^ftUO&dsZ9e%n`H@Kdf-`eJdQqOiZSaD#ORUExG^kFY3rT2HP_`K5}3
zh7NQn#*fK1EW5_~`OltG7otS2nHC=x{x0pt@n6Q)ya}K-)*_T<naIBl8pex6CeXht
zGl6Q0KR$p<SMAsB^}3nz>o>n^j7v0?LR+VZky{J|i<xVpOUZxeS2>xFvq62(^YTYJ
zRFcFCIig^MUIn+>2B%#q8#dr4371h*aL&ACd={9~tFS}mDavjz32eWiGk(-#`468C
zzM^Uws(QUk7jpin`FQm5qzzYsZ@PU(KrK-+b0a@oAJGdNt*W5=to1#eR|O;NJ)RYa
zf-Tt19NWE;D2P#1hUH(0DDGw5*D!p@J`$XUQHPe(my!0(^PKymWMMbV#K8WfM`slu
zFp(fXSp~8agzGFjR6MD%Kr8}t-!lZWBfNYTh7h!flm!uKf;IQkVAI71Nm3OTn>!E#
zt}}=6%dcD*;-8{XMdyND2ZAtv(kdGi9n1<G+b}&d@qJ9-4hv_P^vH<=Ym=TFDuuf}
z2xFRT<K6pKO81?iCXC0Lm-t`sEaxx|-6QF`CK8LLB<ZlUZcWfV5i3ixS`V`(HaQg4
zyGO9-qJH)~i|}$DYupU|2r6WPEs7g+-EyF)-_<_=#Lo{F%IFd@2>M;2OmFSN@3LwS
z%E#aZ#P+-$>2e&nY;?ooh{jc#>>l@!q%WhZj;GF4bpQ%%LlN-F^GcaYJ7B2G(BG7f
zVR_~nLHf|DK=>Y^iFPH0$5lyE2e}Y!y|JJDA8ybLevn~Umf08xi>piTd#Zlhzd8Em
zQR6{v!$Y_6NH*xeF7Tk^ic`_Dlu9c1J$z;wquFIXNK+%k|5W0@0{u1@1U;Z&Ccs$*
z%II-oHfYmYMf7mZS^oO6-0Hafpx~Lfe#6GHtoWM-kvFf^3T7XI(vDsjAj~J4iXt&R
zpF>y$(~vSun?iT#Yj+ekr+(=9&G1}S6e(}{#YZ=xtppS&Y-sEJyXN=4q6*4?-itet
zDD#?fQmh`!)~qIeb!9`kd~I?tdP-`Wk1Rd6jH>3j1qTwjh|!>`IT&3Sq|$`EpKzKu
z1tV1oRumZd%Hs$_!?f?aSt-Ioo%#PN9ptMQu+_|!Z(~oXs~zYYT`gAK!h~<_62*Af
z!T0DV1M0E7G;h<t|6D_PJ7?aQFhGrm-w+jW+A8VD5sX6~8P?Fg=iKTF-=tSH366{6
z;**TDTsf?YbIjqD@U+l%`j274UcgkN?0y;+m_FA%Yg^NnuU#F<I#GoTCHP+NtP9Bz
zPD8F-X)4ic(vKo&O*VXDxTSE(s0PR}fz!_0w;*T-Ae-~F!U08GtpFTjWo0OPp-?J%
zWWXjhh6LPpPuy96TbqwT7fRYXB@i}UfOR9AGZ}8_QU>l~o?gOPVnG;Wl@K_XN)<t7
zC<$CS@2v#^IuY+u|JfL=jcjn!t^YZD<m$1zEU;Mw;Vqx7-4M!<wYd5}V2AzphQR+0
z3-8|`-}$dp`}Kd9h4*hJM({^yM=q(t!@G}!3B`<V$-F41^qf_AVi^1IvDlO>t(g0Y
zbzBQd^NRH>(&oAs>N&7|Mq;w5P%0XLXvQJ|7lt(pn=nuURTc(#D&EB*hjBGaQDA1U
zts+bZ0c^PcLmg7q{-h(v$gIR<fZUpLxI492WbIxI6c%kEEF10a55j(?qG|&cgDea*
zY2F@(Oy4gf%ckshypQr07U%-jLJ&s1x?_*F`|!@Grj^mcDaEFYqN3C}pz&(MdEu{T
z;X=>CS$Px{aYa>Qk>h(}2Qph7O((?)R3R?+Wp;ll@;}Uu2xcf}VpY!4n9mJa&Wy69
zx~pn?#-R1e;WiKp8S+4`WJ$+*@sZafVb9r4ZwaL%ztXv<v&W}Pl{P(b+CXs+Kz)u%
ziMh6)DiqZ*M@Is=sR4|uH=<6$tE0dIK*1h>n&VCNdu+y|v@TY9DuBHOukfc|*Q|8x
zjnig52hhAx64-P%Z)EAuM9hl9Po8z@_mM+(^J52DwU^IPXZv*=fb^j+1KD{jnjn3i
zLCMYubHhH6X@2?R%pczYHLA$cQkjVF(<;OE%-3m+WO8+grV^$~-l)j4or7pKj(QWI
ztB{u@08WkyFAFzrj1c2+wK_rTbz$ZeB#qQ@)*cERSv!u_)>Yoo;TbdU&=J+nd*6$G
zs@wi#vFG{L(}sowoJNPcUNzA!7P|sOp=1eeacJkbZZAeVe1uuMx{91m(q9KO8v|Gq
zqo&Zz&g#RSe)48BoLVZ{MwO}<d3aypz@GaYA^8KUFb)3q=3UE>2IXO!+F5ASm#&ln
zuLdCHG=Pb>>DawYcD8a57Q)5KF&qWaRR%76f40H!&q=lbH5SUjn1lP2B0;Ma1{<u<
zy@YLVg})ztEIty!%8<5`67?L8@e<|hoY+Oe8Q_v15E{v*1PBVVj?q9*^1sjTi-K*2
zQ~*su(yT!CI{Lkh_if=1NEQxUN!TuP?RXJCn9lr?5#o6(l8GbJi$GgjPvQ2au??fK
zE%V!i?+1jrDeUk9-PbK}Au716?D%E%7e;0>Az1ZHtA=k`8<_*xEA1|OHoHvQ*ykSM
zZ<3LT=s#*E5D<2TUHDiN1dT+g$V8x2fM<<F2n6nT8|4%EO_%^U{QO6Ch!c3F>?=3h
zNnbJ&Xta~g##V#e(fIMN7U-05*in;3?2Pu7KNtyqy0`{Jk#{GhAkWgK6NdsiC1=Wv
zNyf_@!3O3{eo-iuR(^0e@FMjI4E;T^0zZSN88d3vbn2-7aL+LP;|z@M4!*v=SuNZW
z7X4PQ$D<QgWen-U*Q%y0x0r<SkXzQA!~y^=kVD(fz!LE;|5kf6Tjf1^#j5WkS)yh0
zJ7h!%P$A(yxlgw(*vdOYBo?_RZ{55ogtC>fQioW=aGCNEu5~p?C%H?%nzxHLYfINF
zq@Fe}BGc6cz-Lkg$&0bO2;I6;D8E&6^`agM;|F3R?ZB`A&pThCz`7=ANi8DP^1OG>
z9vW0`b}gKFO#U7G&FXhB_wyKXB($AJX+?d${;JnKv@^VRWct*yF!gvYaU1oC<OQ!Q
zEwO=v_&ZszlF_MgcR}J=2T%n?%PVzI+63>nxE^<a0sSWEIr$~e)|FpKihdX@Z9#86
zw8woqQnRuZJR%CK+VY!IGC_a))bq77wxrie1jOqo?M=zIzj?_pWx!(NCKUp4&Z}=2
z<TN*yGsH9~p>+5MMWiR;xK@7`j3kzuu;aeMR|EvV-3kJk-@b;pUY1diNVD{)=@Ns)
z(_J1clM5!ZzqC|%>=S`ped=X2@u!<Dj~VYsgnSA`fvI&qeUR5*k9o_*LHJYS(gPDx
zqr6KG5|&rH$7H=YQYyTX1O=>)Uw@V|u<@K<I%V39x#~i&<}2ThVF9rLl;;QMxpx~~
zHb;SClZ>x<P5Ke<LYizJYCslSq5kxREYBa#RRO6c+$5~Q<Y2}|qF+s53c4PGt@UG}
zTV5iYFrGVd>&Oxr$X=;MT7Sr)Bpk`<MEuEpTla!=jPexGs%n9e^$VN9zdu_9aZHjJ
z@kA>WkmHffWw(zt#SoNnPcKH2j7y)?&b4fL%SlGDKm$9Ik>Aew3&K`9f!Aa;^wsYu
z8xeMyJF}7>HgR8qry=p~uS7<zIQ$CYrvh&gd1Y-Tec%9r*@y>8gOF2+y6XmFY~K!l
z9o_$ES9B}l3IeeSi;mZG_#W#XTo>;|{bjc1W;s>4fL2^N?UNFFcm*aUSi{%>%q(vW
z-{yMZm13Aqs|;!CR#C?#x3Pqwza<XYGnmJgU%K1+Vtv{7XY@?8<eF&k+do0C*+^@|
z3DpCP2#gFYJcCMm!ZbegWm(oPs^f))7(Oda%D%LIZ~iOrG|vhaeYbJ(zJS&92Z9`r
zn-@q~^4em(RoRMio3(^dD{E_qS`;Se&e)%VqLpAdE^C<1o4=~S)w&ufeQS7a_Y)tP
zTJANufsEn@>b2M6dk$|_;Q1vWRfMt=2~<y_V;6#)YZiZu2Cj;ZnS3y#uq3NNGINsC
zh*({so;hG-r;tx1Fg|IHPs*9Tal$=o<N&@B+QWinw~2ZE{R<}h76Ji0947%`dmO-O
z{qgwye1TfE&a8P383WfuGot1Cd$8peAS?@h!Q)`-<jW_RN<artE2DS&b>vKiP~Ma+
z`XFrnfC-vWepL2X6sD50Tto|QO!`8a6)VwFanX38@r>Y`hJPSk(9l1tKN%@lzUWrb
z*(5v&x>e5++2+JF5DvnM{kmbR*>!V{ZdksO$Y=lf8b1pddL{;L^T6YM?G+ieM6g17
zf<XfJ<>Uq5KQgYxbcHEQ0BlQTRLf$l8k2QWhM`YgV5RzaPS;%MC3>$^`>CSA{|t(7
zHLgK=XRt(x>#0zX+1!23+M=pS5xh9!Ap<VLiw7TEFXVpSI}GG#Bxvw_E-YJj;%fqN
z@uaBkA)`rL$h>7QvLV5s4&%9H36us?N5`yBp(2K!+;88)DydLwSK1aOM4Jfn(6+h!
z;4l`wB}z%oyYZQX?(Rb^?VZf!FRz%E&t`L^dLAdgy87}SA>mU6sRtw+r{qn}WVXhW
z$rE3NmJWl&OHz>2-AQ?BLiq`Tv#}bhSvv1uOFbP9praW>Q7y$bUYXX9%6sl7T4w@Y
zH2vUbg2DS}YPIt{XXVIy^WXF`T`6)Vm^V~}$dFvh)v~{o_1Va8f5yx$(v1-MU{79<
z!>q;-IXxc6p%Ly;1LL8L)nF>WN=f1PB^HAIir1VZC{DqKe{)=uW=V%8QMKJ*cXN@%
zrITYbNRH^EdC~T0Z&ylUurL!b4}Q%fwHx`^5ypjm*qw7>N=7!CfjffKNAkU|nS{(I
zIpZ~HP<IV^HevjTNmhxA=<y^(!IxYcR^FC@@pvCw0pp{+#aPO1dE+YxR`W{o++mh&
zw-AB?!(HQhu`5|y!UW&R%IvWm3#`z5dHq8@ydL_#C>{g!5caW)Qz~f-HF?}g4tUku
z|6e);y3Yyy=&~!IsQJ8MV)ZPYMzNbyy1A4xc&Cf%8|N#XX+;gj=E-*FSKHhmsL*nP
zVC-4GUV9Ye#`yN9!rRJa$Y3|v*B{<jOM5zb4*pG_ufrCR;^BO#@f$MI4>)$Zz|YfI
zzp_9j{v0r@j$s<ADS;jOF8h`SqzVRkzoL#7>*vJAdyBkBsnABq!j={({=nMYLBNX3
zk7*MHANwv!x}K0A1b-g^|8hGydJbZ9&pE~*83hOQ^{rF5)8fZ}p^ka4H{9!vfAi)6
z7YKS^9}<G1@;+&V*{MV9`aMxbLIdluZ;-N6iT)B_S)p+_^>LR1A$Wt64Y3{AKLh*+
zfxjyaCayJ=h4J(n+2|BP=c^uVo7U)-cg9^gcvZ_{pxDxQzF)n60t8uE!4xkUK;Bm5
zVvsTh3h*$;jCjc#&vWkclCnO!!}$^HlJ$Tos{_ye$c*gh-9U~38646h{9!T2sm6y-
zr@5@+>gew(=IA&1$3)W+12|;p5C{N?iZhqRY%zOvbSe{EConOkvrFB1wkJa5Ce0(2
zH!PlCCZxOFW_-ZQZ^}(&24m>CQ#Oqa+X+=}9?x{!<}}n>D9m2q_>>=93z$AxL7Z%D
zM)I(UnCA~-e$&P6HerNHUJ3M-^BG`1hZi#|IdVSU5g6!EN08Q%G+^!r_o=90=ep`p
zD-OX`>Kl_^_5mfI`7s%33Qj@ZZb?AQ7c+v@m|C?;JMAjckkb~)oCX=|@fbM^(2>k<
zHqN10PbD2|dD!$eQJ={Ee8&}FA9a5Ngs&k`RF_P_>_8EYm642N=TDDebq@==T#TQA
z(7#!svt27BJ~Gv{_nKKk0H({U1EFo|=&Q3`DlVh3$a~+|>w}lAj9Ibr`y(}%P=^m{
zz6sBKeKW1@k6Jm-Il7p!KT{KK9OL;e6efF`4f}DwZq&hX@yGU9Ld9@=xOV{!I-;&g
z16Eyk54ZmEUDeU%#mF;q5LCt(c~Hg&UI7Z6ajvjwK?;6%cRj%K%}Pc3+M#N%tEvE5
z@!~7)w#4P`Tk!m7Xsv)iz_2?%;T0o|2j-AWi}fE<YP#o_#s*JyFK-pFBltzwC$DP@
z`aQTU2$x-Im9@J2c}-h%T8QR0;nbqL=Sk5|<n1bN5fQ1b@qo>--T>IFr@Pa%-wk}(
zK4r9)K6>t<986K$<D)qcMJcL@HXPtk(&=4SRf#ajfqSF8EsLml!9OG7>9$X+)}zf-
zv-D*+Y!gC~jT`tXuGpm_-*03B2ZLn_6cxJ5yEOPqSk*v0?et*FY^(7P5I8}oqWAN7
zO|l}W2j;SkaP2)y5v&MDrj&F_+~Zt=S{7~%Ikd0qd_koY(@Xql%c}yC0|sG8o-ML~
zT{_C20p+MikjTQN;?niJx#$$f_r@C3-qzJ+V4Aa4))g9+LsmhF00%5$u*qn{2WD+@
ziayamXC16sh1DNaQs4#A#R7NzEKvF<i?6$l;krJ_=3WJ%ABHPW1)=p_dC-wqP11r!
zE0s&)Q$YNqQxFg<zydzT<76`(g(Ke!y67=(Z34a(?Rh648i0wClMNN_3qe=h65PUF
zkTF>P&;Y^_FI(b}ok1B@n}vL92A+y5gx3S11mUv4$ZT?IfO8)&41q`>3pNC+oZo+5
z43ia;K*obB_Yk!3AFVH(Oy$50=s-u;wWRX^hAjXo1qOWxob19iWdy`~Ek6V1gKIqN
z1*^6I?{|O7b&V;VpOQK?Vgg@5*1$slz!$rzs(b=6MS$>5AR#p#o-}+r+5qj{E%e?u
zeqv)|Yf~QX+TzU#?c^P=1FB>!-^77`AqhS!K~~Kj7DR079Q`!6Tyd{@BC;nhef9@P
z_V<qat`f0H%$+NF@KK7q>uhzPZT3&5$btIIO4z5ZRa9=Z3?1D!O&MOEK*OZb7||PP
zkfWG;u<%9~`1VbS{ZoYA{dSi>+ioKLao&s{yzzsDhM4$U9-!2@<l;*~31*44BwRy0
z9?j$WrR9$UWzX!e#E6ud8>uoMTd|9|(15Arg~Q-|x*Lgk)ySn#2QJquHt5&s)*GXu
zi>|3R#=qP?6bu>oqNQ6%+mv%N{F^z0&)z3#WUG%lgoEsS8@vm`ia3L@--H7VbLV1I
za~~>3d%XSQ7xi*GWG!Ij%OjOX3N;p2Rqoslr;@p8S(54)H$nW$usTavWY3*_G-@k%
z_8T18@VkVOux4%a$LF<Z@lfXU<G~C;a0$c_+)FxM{MlhSfjxbhjW>M1$69`LAA+v>
zey1Ru>X=6zBptY`LY!HeQ=5K`X7njcC*@qhXY+w;yt`{-sg;th!gX$nWqVKg(#dq<
z(Wx>p9eE>TZ@VcI+qGTF3HbcvZm1Nk%Z|T}hIiH_*|Ip~2c8!9%b($2qN>FrEySW0
z)?jpE9O{rFT@wgGJ~3%+d4FR&e5vC$m5G$q#@x%z9MMr99nBy&N(8Cwvw6JCn&7GV
zFXI1B9gmrAa6E}-1Ow*+&5X6DszB6UvJ8_u-n(xFzC$aOH3v!&RfG^qyJsiq1R@E@
z_r7?hLU30~*)w~Fm6kabJPE5|u{5b`^6@BJce`#|SZ8TxoZy|;ntm<$7{{VeE^`w;
zV^zrach1xB$>bjV{xl{Z(q%ZQVh~E@`S}qv?kncU5;wGkrcj3ErOBP|pNaUGuCPYD
zF~y6GW!xI&bSfSZ_nk;B<9{bWN%Z0gEBM}Zlu;R7nIb51p@LdQI-et-I}gQJ&%rFZ
zNbUFgnisRogeZ+b0&*jCWkEtB3gZ`T2{X5X=@6a|r=7qCwe9wPi2vg4Z1h3U{vOrx
z{I|DXt28HA!rrhreCA+u>*kzQ!0YO$dnw3wnIf1DMWsPSclp+-D0tr!FPSXH<^EN0
zFwb<itv4`VkIvO>Zp>@V8w6KaKKpdmtzM@~f^XY;-hTqq$$5r5VqXkM6~7?W$W(pL
zXL-6|Y??4B`<=F4LjZH32s4w+rgW_NkN6%v{c<g)V6s*FD<tC`ke$x?@~MN<eE;!X
zXIQmz`6uD@P?z;hy6v&ZEptdW!0-xr5!S(L2MPh2teE*##_fC1^~>>$Mmv3=AISq9
z$w<BNb7|9CL>W!MAfFY)$pl^}on+>15)jngm;2JS>sd>x@a^{9Yjtz!W2}iVOK#0D
zCueFcjqvx5gM1@<ny`8IOcj|aV=?Jn`J2zfQjq$<DzpxBNbh{9iq_L>Xr>XR6)*`S
zkxUtV)<M+Jtqv))IX|`){>E@Qg6C$YLNbefYB@O3kZ_KrwFT6c!7FZ*I6c^srV%MG
z>${n=OaAAxPMENyFyN$@x`P;+A5I*?#FF+Z7b~*n-INzLxsd`JxmG#f3`_=eerRS_
zM>|f!oJ!dYf6;V3kGuO8Wjm-abk7qVaL?o^%s71l_U?YUQyj}%l+lO(ILbm(fYot>
zE^zY3Y@6R5>nJQ)%DAFKFv^G!7%$s5?A_DOUaZ!CZr}X`rVpxGtRw-ko%?Ctpo~I|
zxe2=|htaP%`XaOL>EG_Nf!zB0&32HN``N2UUP9bA3MI297>Y9=bq8IXA=M!j4zv?a
zh6)vlM2{Kra*dXR_h0UjDEvL&zM~kG{=(#BKXSyPU$<|3hr(rC{Or18@cH@kI>Ob&
zbnJfflWY6JO7!`62rC<|B;M+$<D<vH4jc!HzCUPXz-uMT3%`7gn&o53{GF%7juetX
zArpoVxZZ|=LYOX#To-}!T)BhK7>fyYC9v4O7wmj0*=N>3A_&xZ4-@Mw<0mQ`t(dfk
zu9-Tyc_`Wb<XM~r{<-7uiWj(dlOJCRJV|mSWYj&zkIxIU2g0{;e#bZTDU&gopKI#D
zw7s<1x5fK@1X8#4*1)#7FTN*NJfA+U7mO*?XY&nAtObs&6R1D?+fS#e=A!no&4SSC
zkaNfJ{f*x&-SyNc|91o8KYFXUHhSL!CDs5}F6G8}+1Z-W$nU8=m#MAspDz15+gwHO
z%q0&G*w2TkP)g;ziSIP*x9B1pc#f|LP(EH6a&+P7WdgG+!UoySVNsw|_~8iY3x}{W
z<ZT>s=feB@2&M!0;5E;^pxkhTB@3HB9Os#I04mfogICU-?$8KKHYG|W{l6Qc)_QZ}
zF*qno>_&P;r=@(CWt@&e<^#o?*htLtZ5<L3;#3k<k)$=EV(i%A*tY4n6~Elg*TCrK
zC>l9~Me$X8mt16J&JiH6B8nGB9R~$m5Z|z8F-SG3_D+Ayx$8er(o8+M?+@v6$1afF
z3X6B;kXma+(i#8gf{e2gq68fN7}tMbA(sqfbM}8+TUI(}S5S%{NxoYG@1>IqclC?-
zdzTU@e)D9|pA+a(B8)d<&}rk!4$eA3fm<GxUeOcp9RCLwug>EjxuF!@f4YBs7jY@%
zhO0ydP!kg1)$OHit_-wEDWTBCmBm+GG~sY9x+Ey{d@4$xd)I=*Wjwr=0jdWA@m8E|
zPboX*jv<GLhm<5rI-m~^+^({7S@BUL=ZYPa#uXIJD@C__H`3<n%-gnCCZ_;F<y>Dh
zk`*j^w&`=hU!$i|O7||6a4q`F#+(nL#wD?o{Xr#-2zc>?bL@Zfgb)~~EJFYJ+^}db
z%N&vQ<LYu8j-Jr)wi3S^HVe#H{JINnEb#0TOc<h)f_Tp5Wb|`SQ4nZa(DR?~&rU%x
z1jgTc>Vqx>iDDdG7DNI0Es${`KabK}E9v+eW=xZBE?)>v!Ykxu5@*pG0&!LxRgD&T
zJFFq0p7CMQi`gdUEPtN%Tl|R)r#D?c=Q$w-V!+jk{FOPT6=_32sYrmd;y1yhl%qXS
zd{PLUU>0v9vL=H9_cqK~(F{PruVc_HGC2m~Iby4ER(2MvEQfjemkc(~7_VQSr@H!9
z#E#7n$fQp93Hxz|Pl9;N{VD;0t7u=mTihf)!Mh8j28_}!->sDF%?t84pSS)_WA{)H
z++ZJ+@$?)=;rG}<mW=o_aKKfY<a5$Jwt#VF#D4Lm&+ZpHN3zg7%$ruLU3FvPMyBI=
zCwD2RC%x9!MKNB2ccAmhkK0WX-sMYKQk9)N=NG4M4dx^JJYV2V{E3SelZw$X_($TM
zPIv!;T@6?oripBlCf(-90IJLb7e^!hV(2~(#TDrAqL1wMIh-{bjcrZYo81<!X9j<N
z!p?Tc=w|Kk8}|09$R3}!!p@0rJGT@&RZLzp#9al)B3-xQTN{JMktR~s+UIKm-C^u8
zgKxbhfbIwi0m|Hn?rju{hVw<vAApm4)R~#IIC>3@*>!J6>in?daB?YlH(RRkqa^$j
zwJ?u%YMlb_yWDn#6TE=O?1CY;@!5qxWnewEefT9s{onP-AnqwB#3SvQVJRKca@nqe
zt;m<k&RQ^_K=d29YZRqKio}gLh}ldJ;hUYS=LLlyfXCjtybK#g4i>bzoYbh^1j_rK
zH}1X5^Q&L@PjFIJ?3qT67~9r8@@<>D${*UV%POGQfy1;adL!QNAy!PkZ;9*AZ?s5f
z^UroM?3QRkZ%x$gu9d4n>m8Co+{58ke<4<w7~S*g35C)h_v^$m3WdiJ{9-@kwUbfv
z8CteJZQ&>mK)n>|Yb}cvA<O^Vr;B%IiH(3s6#|^QwP7B4a-qi&FGxL139u^WN>d9x
zzN}aoN1EE8$40yGD0uB<KWnjTRxuVizPl8w#MY1gV(5g3?9e9nZruuQr|xVgcC?#a
zOk^Aj<Es%fRUl(tyz$GL9ehSkjPWo2ZrDKY{+`=tYdUs~qt~RTQ9%Q9S*8`V!U|2>
zzWwF__z~dgL5)AU^NOW6;`WoTT$(U1CyDOKEEZQbgoMDuFbp^G$I(E6k8Zs?^+=V{
z$xx0|cQ(iJH_v^=h&daJmSedHep;{eLvJ2^nbR)T+q#UpCyM6)r6WfSWNE1BPs!?)
zv1=aO%lby`TCBhp^xQj__~V9EZX-s$RFv%2KQ#1hV5I`pj>twbN;0JL=`F!|Pr_6b
zH3Z(jcK6D<#Lym<UTbyrF@Jytt0^;~q?ORkzDJ=?LK;^DZ3NW<H>VMjy}bm61syD7
zaY%xf>)X*TBD_ZK1XEX#)q(skbEV)fa)J!p<~6Q_VjC>FkikG1ZeZH+NHgZ&QDB-7
zc36yWtBk06fbyQjT#MnC>w<Mxd*G~wbCF5mDr@({xa`5;h+nl|@<|Q)H6U2L;u2&`
zWjbpkYgL-{ZBBB}WEDnf4Oc5L_{#WLgB9Sx`4e$lsN$D*my*k8qT0M_$LTc|U^JYk
z2D460W<^KdS3C5=8K8>f-4`+AMo*(N=cR{2d_dHv&pi0|zHBnLwmYK^se;VBbt8<#
zyT>Ee?+2Q<zHQ7!5I!lv#`A&)8mx9PI{{p5P(10-)(@dW85<)Tg)4&g|47KjC%*_M
z!39>f4S1MC8BtHrWR7))JFMEjYsIQj^bpDp_MQCVVourGl|Tuzpn+G|qTazP+H}#4
zE_&o5LLewmocSLiMTEIs&wn$pQa1G~eeU2|8Iht^obV(IcFe0Q;iDGI_Al1FO^sb6
zRWzNbf>TrmfhPE8$v3}jcJNxn0C2B?&pxJnxo;H;KEBBb{s+I(L}=CNts?0~q}-!e
zVozo1VwoWE7Gn@Z$SSk9u0dn(!*iHOpAKv<<8<;qPZl$4s2%5R%s?)tm^-vS_!Q%C
z`fVb@&HO7uU6c)~_%B>zD6?j?T@W-;lzEpF_V{1BV#e<l^Fg(?>U>TIsR*=Sf-cw7
z;!w<KQw=M>h>TgG^+tmd>y1Bl2;W~DRKe#PM9RfZ+QqP4@xI&4o#ISz5~1DT`OSST
z)jOj0vfbX+@+K-}Oc(Nx+@?|{9V|=oDFuXRY;$XY<Yhx8_xi67^$>U?KIAAT6CQO&
z5%Itb?nK>)y)WC1agb00BBMNcHQE4gR5SgnpOM8o4?v-0x~<&5*@Xq|RbN^rRy>xD
zx6DY;e_$0%{%Be`)?jJ}D<V3)01KXP^Y&`y>6ojD@aNgHo;1?xF&(G{OMK}4ygJ!K
z6)#~Ys5+ADs-c>^yEYl?9Xe+=yVd%cr>(-n#xx<vvzS&Cs0{`L9p(iJDc8{^RX<zO
zQz`hI9brw}Wl{`}umA8h2D_T`!-QPmQ_TyFaO0H<+Eiql#Au>SMA;}&NCmf)Z8e!M
z7dz|9v!@^@Rqycm&O2#Oz3zGa;p5nUH~j1|@o}G4gNK<e(M1sDPFzX+Z*z)4K4#lr
zDNXO5yLP#*EeXO`O?tSd7;Wp!)JB$m#J8Vnpo>`D`Kr^A<6fFoy5zi`6c#0<Z%WKq
z11D@HofGZ`LcKi`YWUYiDU=d_k+jygEQD#VObe8`*+VDxb|}i+1vc_80ecj1QSYBq
zQt-U<EeEyzdW)WpamCx*ld!=ka((mGh7-*ut_fAN+()kZ!sF+8@lFj6k3J=W@_z8W
zCFUyo{ef1vRCwEPB>FETg84@}M~->=?~n@Cv5zHV>3YW*El8M)qLXR!H^$!l)9PzV
z15;yxRBD9)3p94OJ?9If#Mhgxl>3U-HP*Ej+!wAXqEOc+_nyaNT~zNM!HD#?;cm_&
zs)tV~SK{*o#w4&L7qO{SEaUrw^wT+yOj=*>6to9r;0#IN?W@eEw(Qjj(l~M&h}~@A
zr7t*h6c0WWQ+J%L78Pn^_dNmD@aHNauq6X~aua4pnP5BgsvVtrml;39-3tVc^elDW
zEsb&w3`sGX&4wr${^d{6*Dy1hP-WZGxZ#%aOHXkRVF1X@iiO!XvqG1ZcT_~Z!lG|6
zUf}UezE#D&aj9%CE7Mx3FqNM{;V<xsBv&2lkz0g6_j87GFFlpJfHGq_W42;Dam&LK
zri0}q_!@$_{41%k-Xt9V>VlYLAikM4mki$v+wQw^%m_#OHoG`8cJ_i!vYW5OuV=n_
zlX3MWT6{UNt^S2|I+kOpqo;v42-C3cv<|A+%Mo%~&=+l#d5<4;Zn-!7k&cFMee5s&
zqW^mZ8oYFFi|A*#x)AQU=k(`_PNLrRJ`lP%u3T>&va(FHMTm#_qK?-HjV}z>@Q`Cv
z5j14<$CPSm54p{S?a9q!KDN|h{eiV854E0@gCBK%t|{(VNJ5=rQ{*`Hj~aLA41?uc
z&&?xy0k7mgX9hN{YbpZfy#0I^R-Gi+98w)~eK$2#Q<0ePuN{}-JcDqWG3lA0b!3}*
z1qrUcuo|Hokn-wfHE6HL(UjGgqwvoQbG5&$1cR~tpaVpuq8i#BsN=#MmC@9W_cv)I
zbxS6B;RBQSLl?(6Nb05!kdM^Vf3ySjdnO8B7spU|g?^C%qEUVux(eIoT9KOI;$yuq
z`-DFys;8wg>&3i()rgt?*fM+`Qd0bVFiZvZyZ!}wn)V{NNcybT@bf@iyD}~LZ57fy
z2Zfip0{0$f5Iwp*&-9uhlZnsfc8Q4_>6G2)-#3uX=zYnW7y2^h)BEbLM`9W=Gq%AL
z=WIgStW8c?8MmnijW1{mh^KS+<Kn$zkz`Jeo{Zk^&HWBBQ1~>l!7Ce@^C^aV9<ZX;
zTIF1)ekc>six5{votw^t8$Z;J>d_e@BV|b{XT3VXd*{%LzpiKxn(;rf&qUCxY>4Zs
zNnB9Xa!sW?u-e=vaj`J~_N!R-;^)y!S83JUQ!%X}7W<n-r~xd#<Ap6BGg8)vcpm}z
zK((Y_hw9M}-n-yBDyq#~z;A7{17s+(O?e*FW0X{zH-#ztGV#6SG)+};CK722K1Y0_
zCy<_HX!J@)q3fq-pD50WT~z5;pYGtt5vhXpOWr{OW1b&P-Z#`oqD?~hm+#+1Y;>98
zv<h%g+uarrzo~$Aue**uQQ9W_%NEr1%jE_|gD>Ng(_A<`&H`Ob^tgih()v|+rGXx#
z8{r`a@cV4MJ}EJO;X!n3Gw+uNZeK{;ZyPR=b1IR!lvC@(adCZmly7#GnmsdLBf>^I
zn3%wqv~fiC>vMD1#GiDmnV(n~khU1gN)oV1@&2qe;KgXO2gQYOF&!;Yqn3x{Qb$A#
z;7f@$4=#wnzA)y4Amr7ZC>%9bu{N&4zgT@ezhFu*pS*q^`c%}bPk(>Z#>V)-R_#8J
ziua8mN(HgR7a+6=OnNccXQ}CIj$YpU;F6^av7K-Vq*m3}e}q+`4xCPHsHq@17nfmu
zDExzObYa>ucs9iIf;idn6NUylf%EPdmCtTj616KU?B{5dZ+2y6FSLbxZ5{_88~GUG
z93y{v{bmAS(;RwVCM#4Hem|KzupT=`99vumun8jI>N!(Er{%~AubXRvltiv~2nxsT
zG#{Dbdfv|;vH8i0UE8@^XvzC-6iTdAQ=g~~A@%?`)G{X|C<U*dDc8!6Z5-(jFZepJ
z9tny9+oNz`TkTkPx!PkZ>_l?e5LM6+`J$*s74d3X3tWuX{fB}oBw$XP7o~BL)9m-V
zT1M3e|ID_L08^M%HU>Tw+4|KpA66N0;);zKXOgsvMb4b<Q?2vm79>a5f><28<P&Kr
zQ3`jzK4ey6(zIQ?q#?u<@zO{^@OIU8*ifl+f07NhspZiOYq7>{iZ1)@e%BM}uk}8>
z8)3(%siET!SGd!0v&U;G-Q~xSjD2aNC@2dZm`Z>DBR^?$f)qD*JP*fUMrdbYdt%Nr
zT8+^6rrwG0>qA8628FM78HId{e$0z}SLk9xJSyqDhwSqlP0tyW_#X)ZD7KJUp5z9p
zTFRX;@xZYIb5%1sVW8$39$v!8iyt5G%OgU??y6B0B&M=A=hLXE)`HP5a1Jy)^8JHI
zHK%}yBlxecxinRjy>%frGm@Ta19WEgo11P<X@cQf%ASE1ak|ihx!FcFoiVMVOSqZj
zSLP4Z#$0p03`b5{BfBe{gh7rKuLe7dtX~=Fo?TjLjWQ>|$+ea@nbQ#B64VAoWoGIb
zL&9$xAizZ0<WSMCVJ^l-YI=dCwAV6RnjESHxUnVnCvGLx?Ew`bB^((!v4VrY+|L2c
zhCEmsDw^nr3$DJ93Z<MXO%S;GmZETkj$QZ+4QM$a+ePMtzlIlUF-F}}ymZGo!rR~R
z!vMaXa1+<!(t!pMz->H9*qA>A*CZ0CGau@eV}Z%W7$$_|xEH9c*0;fpanx?%48e@f
zz1GB6L+6!Ry)kL+_waALR?q}-z_5tY8AAzVaCXBlol(|5cMM_Eo~@w=yAWE^l_=X!
z@o*n~5*#ywoZ&3cM~I5$>!JRi>UeTMa^G1hbz%iXyU!WgBE!OlI-C(tzxf2AwK-g_
z7ic$#n?lnk&vY4$M=q2dfqh%K3=~!QHs2`N=80}U8W4Y>=rZ5ZSonD@Qli}vc<`ea
zn|WJfCra>+Mr3$+`?vpxtG5n|@_YV=5d;;GkP-n=q`Prxm5!yPQ>0Ut20=n4C3a~R
zkd~J2l3ZfxE|p!DMmojk2Hv0NdtJZ3xVZP6bI+MM_c?QBUNa-oa{TE-*zr82JV(v%
zh8PU2O@g*h(d%@u{@942;u$mGR=>xARIr^@dtS39i1l!|;0sAC?-jbwAq;^)IgI)k
zstT9e3MXriGwp{EklJ3`e#OiI2YTxB4aUs4K-C?j)<(;(%#BZ>Kdz1T9tYW|CL===
zYE}V%<H3m{k>%8ADG+gqLGGmn#K}X{v%uL>ENy0&$@&1Z0LB<F=c^Xe<9i7Um%u(w
zI#2u~8zN*l1CWdWz%VKcwl5oRQ_Jzil#(Ruq)1o(@xr}95kZhO&K4tg%S69F56BbC
zc%qgFCqz3f+3%bv42<-VYQ3|HKvjKtMI+G``C?S&`APzox8eLt8PjW%w*wnHsBAIe
z`<_$`+_~%U#FWVzd+*W;0>-1{1<~*vw~?Ezn<NVQOMtx&_X{LGhPHHE!OAMKdr_Pv
zl-_@HR;=nno6g7(Y|(?V{e4O_DR7C|TF;AY6en_M>8J-<Y3P6H#++ZGm*2Q@_8`|q
zD^2S#`Gx8+xe0@>*_B)6Z!W5s{PcC+9Z%WxjSYhRcI5F337-ciX5vLnhW3-oMbgr0
z5_fNJKa?9S5<wHwzgI+>J$bi8J9Qg7+Vpmz%wyc`q~-p@VhRCJ>d=5n;-}=~s3@{T
zCw9v6ZkBT*w;HP0r^(5$WGvjQ@_bdT@GJNQ-ZEO(EVcGE9qgVjDADbmPp!*8XF;0G
zgOg`Z|2hsTzsa9*JrX=VKi0dK+-iID?S;~pqgpJ{^W!cR<UW_Dhm%v(!ezE!obB<R
zH;1TU$$EN*%afiRE1>97M}8XKgR`2%ZjKM{D8I*B<B|Todm}vt*gjODBy*z4_qeyI
zsenYQdht&atM-me=b@;LxTj(pyZ{>h)umpk3e)}$VnSJ;m#v9%D$$c%Jw2NY-{_uY
z3J|z2ZI`*~!jjaqENg(xCChk6ubPxn9&l(w;@1=G7GC<^O3I$6`$~~JwV)^VRFy~a
z;dBuhMA;rIooxL!r=K|!<^@kcm8!eQ<jV0!%kP>q3z-yx){slJBCA{aps`$+zHMSJ
z=G}oOS@hQ~uNe&)NP?(LUu0QS=)CV`#Y@^mcjks1gJU%5a$&DwrIPX9=v|!%@n&~L
zH5q11<2gF9^RVV3FTKpU#0y5|U5OP*W^cq_hpQfQ;s`+wB3>GRqd2<1R5MA}%o3VK
zXwn4u+3^m)^fcFa4pPImsV>L*h5E<>B2ai&wv<e8Fgqiz1IhZ?#`&a#7Y>I7_|IV&
zv9h&!%uX+8&FEnJ|0*g<KDgSt85x%tIr%vIKCNK7@-B^-DuNdx!n$=WYtbfO-ZVY%
zE)>vsOx;am9H)4U@+jCHX4?7UsRL#<rmD{%ukY7C=FGn#xl)%Hgqsuoc<5bdN7G4*
zD)M;-nig+BBMr+z{jG#v{)Q_Qv_O`=sSY{1Bg!_xCj^Hth!wtfZ2~P?gUF6pg-BTO
zSuhRr&^0CNEf4gk{1Q#-zuol!c3A%sDQFg~^(g*vi_w!zE$63MXV*K+CMyHTIsElQ
zQ@`N_n=?iVMNu;4`^$m7?j3bZd+jHz#zEcgF1KHOI_(ZsLgthq^p^~FnK9I)y7)hb
zmpvFX?;bcyL(;LCi(C}s40esamKofRgovi~984=AjdF51a#udVTnj)g*au-EAb|np
zmOD(DsPR#PXrEgdN_d(gXBFP>W9X_(jj+6QYh$~!PF_BK=VM6)kaxX5T#*N3m$4({
z($m+srM)LL<E@IyOAf0&?9Nw+e<SXy=>7d=En2pK$T_-2$iJdrf9Hxm8&cc*jO*o>
zG7Q66w}4E6v9nya{3?d6h1I`>tLMLB1y%A&CyR!_Tw_7$_n$ATf-_o(q?Mk7hO8q$
zcID#_y|k|ZJc4&R^5e1CsJ|>{B7ZOt^SafNjHvxb)!xb1DW6(RU)Itoxsu*j9S0gA
ztFrYcLS#h%&Zn15Vz|<$V{bAMbzuX(&ENsbo;>S9(ef}b{X)?)Ff<3=7X`6|_lBD)
zAZg98D@JS0&A5^5)nu#jZ<6Viz-6b(xvv*?UxdV8s$3vI@}^oEBudDGN$DX14%cGx
zts39{*TgaLgQ%*IY5}Z+Sx#Zhx&YoE+MmfUWXf;O3pRd`y)k4raB=&B5uzJ{GW^>i
zVH~<-X<pxJugF=Azh|`y&wQ3y_5SH(i?!tfHzrseB3Cx?2L)$lXbk*Pq4|MKk%+}w
zi55{$xf*3LL1nUWHxtw*?K-(ox)7rdA;L9acbEJnj{vcHK#oLsSOWPnQAWgY0hR>p
zmNm&G1J?x$Gcnj0_tAhd&g5X#J_oliycc&bq486w`L+LiM#BQ~NSmS7dE&7*7o=P+
zB!9VC!+=}!actPJ?8|%9B;E#A;&m)xLYY;Z?qTAbMa{a2a8GBv!}kXU_tlWMLM%3I
z%{~_2!H;S@|DlA$ZkrLdf_QLX@v3lP6@Q#h47qh)>n?sLl86c8-=-C}Ec+V5eJeqj
zo`wS?!7*`~-u7J`g1j9*fIPr%aq_OyC?1zHlYos$j)vr7<2d-Ts_nLkKdH4Oewy|C
zzYm2SWcpe$bUB{8A3t_(Wh&Rqf?20gCVjPCnRH5))H}(7DL;L<&{!0-^xm7t`kG4e
z9Bc*3H7M4gJrkyQdIvqOgEd{!ur{im2O8eEMYq1#Ur(O*G3y)3=<$><Xg}son<1?d
z(obJ{FY+}OnKqs}uQwepe;UG;C9H)_x^MJ-dBzY>@+evvaD44zJi!79_eRPE`9`QW
zrVE%7G{X&kFor(SGGtX^eev<O?gOd^hUQ^U$~z&y<QRV=GprWv-rCAUjI|t`*W0G#
zVc_sa=k%r>F$XQp^~MJxScg|i;~z~`pxlLox0Tf{@z7h~?&VV1!?WGxKhP<%f>fjy
zy!fju(0I;ES;UAblDFDb+ZkZOjH%JZql)V%=@~J*k_C-|;{$v9Iope|Xq1s@F$c)H
zpvl0s((UQHQqBayQju@W*1Wdumy4SaTl6s^c`>2*8Hk{t@h~^2@8|_iX&60iJP~gD
zUNsb2-|s+=N<PUEf(l~>Ip`A47za=83=!jAUL%e)nDSkBmK*a;$qp9wr+J!ue0h#a
zrh=D3bpKt>{?)-$+rcVzKvKSEEIN><5)R+ARnvH9{gdI<qbyN}x3xLSOt$DR9zM+t
zqCbeN*K16u>~2_I4lZNf1f>(PBM+B)^FfSV@_(Jur|a!MothsYBgd<UduBL>nXlB;
zOB2XhZVRe7RT=r&AtwBEJUM6bb}I^6slk|^Q3Kw9u52mmX*1Xl3LW)?)UR*gYklRq
z(tu;&BaE1jci873X($JtY!R!R)2VGeS?)K3aa{Uc->ISDIG2$|fi*IKVm@4h@Ls<-
zEdgda=6bUE`}h2eAr7>1!A)f+F3!-xG8ZnSk)_9*|Mpx_(Y5<gz-Pb;fOWFEqzq>1
zLlpxVF*GDu*l3e%tZ?bRujpr2B@ep6CbwTH6Sg^Di<%Un2>EU3$vu!bxqYAWDMhh9
z5$oFolWDZDTDk+4!Ij520L2I*4Kq6=vAz7nl$}#7B>#eatDeiY|8pi@+nuZH37{s2
zg{p}AX=%zCq<pvP^{-ErD~_bW0yMg-*)p<Zxh@yOHSR^2G$;o{Q#JA0X#Usj?w19I
z{O2=S33MzN#>R1M$tx?{69DTLrz%m=;3h0na;H&b<>n&wRsq1VE!9<Vb`EVxSpjvl
zv9KP!0N`#Xg(8IVmfi0ChDAS^j;QGF-zhdJFuv2l3wOG1WRNEyqZ|wWU}f+S96P1s
zUc(Mba~J#Z-R%0;b$Q4ounSAMJm=(&UosFu7q?`^;9g+|ehT<{9*@4J1xoCkuiY`^
zo9ZZ5O-Yd*eY#o4X<zhRh0_<>$EKrcXEP&kAY<tkJ1k4}_uk*4werst>xj2oUOug*
z-O>ARzc-4|Pv24(p-$u<R|A5myTmYi;q^YH@Ic{2N5Sjs-<w#tB7mdhUvHrBJTw87
z?55E0UH<@NTDu4QP~NlPtUW*BhpwU)vACxC$1@Nt7!~=W|A6&qy#<nT*ywtFXL-U2
z6?mb5WJT6VcA5Ou1=~Z)ZkVUXXdM&-*Kn(^`)BMZu7d<E!mzKzb8!zU00KI68-vTK
zg5&p_eC=*tzyKoqh*I%~040>fGXSXoR)>H;3dq|}L9h$}7uhCvwLF19K5Rtt1%NWV
zeN`6~Fq;aHDj-oTEF3J~Nw1b-3_J|TZI_F3D0$6~U*^G-H5RYr9Us5Hd~lHGu8M5#
zC|fibY|%9?hF@LCq~90IL!!QHRmq0*)I3x`_GYnI&u)JEQDhuu^MkXouMfnzRiU1@
zt@=?BNy>KeIaM=ahGLD9gXQWDOF~nP;$(F$+K8`QT6L&Q{z9DOZs|kFh{3<?K)Kng
zBCGnILS0==?l_pd@r{9dX#D-}r4F2m$h{QUevUC+L1D*$M=YEX6K)QN_o0>knV}tc
z$Tc^T;rB-cI8=tVrpzH83O)Ka?5I0o1I_W=7uGwEO>ePlFC|P*Qc`QCJt#!?S04R!
zn?X4wr@6m?Y>ULeNo7=!ziVpd{-T)>$t}a;$y`e72Y-<lFy9_g?WW21%rvy(34*9|
z*@B^C0okP?CH81xwW>7jp9g<>S@JXLPBD$Cc!f;pB6{XVCW)iJ@SO0S5Cc%H(ecrA
z5+>J!I8v(l4dwgG`H=~A-pb|l+COe(nCby9P`tSF1ga_f_X}UfHD5@2T#DX*wCkn7
z3;g}~eM>{;;%8i5FN{tKqxWp%i7uZTbAvE+SGd&duylB`XM41$o-)#|9jGC|wB)e&
zCE56|4+pD=drEWS0YhjfnUyD18KKZ@&>f}%cW#Q?h8sL$D>jYE5)G{}5&7E+_pMAB
z5B=Znn0P_)ad@Moq8&mz2+T$(KY6`~AY^r}hN=wqRTOh_^*b5p<QY3!>K9Gg_0O;4
z9B2pYM&Y<JJT9s{x|k<7J@OCum+pe~a^sTIdc`2r;vH7<lvq;2Iu1KR35yGrX8ULx
z2<Jy(4?iNr>rh67X*N_r!mr)Ba0U_39B8YDe7ViUqXrs#HhcGjkN69dYH$gvcwGb!
z0@LEihmKL6BWB1tYIY^6|5RDRD)CqAPAQTE5Nq_Dt%P;<QqkCY7Su6683OKt@IBW?
z<6n%oMknGJqwf9O9q{~l5{~MWHWvC>nvsEHhtHC4e~3Z_5->)$H>EoqLnYGZQM68q
z{Ntm)d1Bx^2v^>QO2nGEG)Mlm1GbY50u?ABTFu%SFTh>2vy7$>{loAlE^V?P9!}pE
zX;%VQ0@DbKfjrw?Q#SFK6QBa~5iN|=w|+<BgkY=G1$v@*c{>f;?#|t+;#ZFt$>q3b
z7C?A^$aFc2CPTqV){#L9gh9aCehGhW!3q0P3UAClQNmM&q`P*cT?)je!Z$8Bt?9h^
zfS8%yeglx~wS$%jc+QrWH9gc-Se07hz<;iG`<r>byB$-$>=m;<4}y^oXeE+r$jWp5
z4{lkH{Ug5R9*HBs=o^?5R`qxDj>oQe-p_m5x4z6MmEf&(7h*FyV|`hh6AS3RK+UTP
zV_D|8{90J;JH7wsj)F+3HId3%H}IJ(Tj=^jYG5V*M+FRy&?7ruj!h2k6Eo0{g~6|w
zvcnN6dBYSC$oD&^?b$9P=kdck42)v$l2DxAGvFNp+nt~6UN_-BvMGI6hN$U4ONbNo
z_bjC=reftOq~kWx7=xm*|HP&bY`nmFJlYtDYLZzTP(yO>h3kMxuYnV<P!i=H0l)mp
zN7dh2zB;k=hZyARKx!u4t^Ad$AnYK=X%vbOt9eivJC8p4R`#IWvqV^>2MAPli-q+5
z2e~QQBQ7Zy)~e%C4=j0as=84JT8=n4H`F=Y2mpzzSivfBq$A&lD(&44nA-qSty?fk
zft+J}|F>po8F)Z^KSC}ma3Om_doZ#YEs1hUb%<zpI12S7OA&gXdr4EO=2o`Ke!XT9
zQ$E#n5SwsTN0W7_AmM$c-m%2MQBr}2{G0B$Qz}Rovjjrf2m{bQY@ahg7bVy&&>9f#
z0Ko(9i5hzz{*ZKrGCMT+wI}yEu(49CXSB1vZ-B!;>wuZ^>5gEP)OB+m!4qC_X`J%E
zGQi9<fteFfSTL6V@Ex)b{q+CfPdfZU%d2iTtLcjpdfE-59PmoHIf**D`;t!r7I7=c
zv@C`&X@i1@nO6zUY;o|BrF6Xwrj6?VK2O>ojDX3hGR3vu<yxi=%+@wUe|iE^UJHY$
zvI2T=#EHsM;HFsOPwWF;Sse9go0)kLB|VkDkRBj@k4izTz3Tq;f`-F7;02UI+L28_
zlNrQQyx|KXJ3zBovuT}~{3lQ?2RVp`r)lMbuVO?RkXbVh5Oq>0cG7z7S?vAeg#e6q
zKoK(r_p7MNHa2*;;enUQ{jV00wrUuxaq(ZX2t<U&R}1I*FY){SWYj4~SPxZWG8J+a
zku8WrMr(Mco)g4`+4`^<euHSq?`6y<e}!11Wd`Zm?BgYK2(Bqtup%z=#@=v)-sN!(
z1pW9MVH95tDMg^H2c@|P|7G$MbB?4<u*Shn5zD=Na$wv4iR2TsQcnqt;YFN^L<$;e
za!!&?`9NLo1D;OenUA#cN$>3cpOz=~2#{MplvNVc`~gU91LG+#jua;-`5$TO5K3i$
zvSgH7QxFGcU~}_p8p^G9XtAe)s*=m!=KmSY>o@-B?m(X{@Lf`Fq}bB}$PIE*QhgRC
z%s&}~ZBG)%iypDxXBY%%t1$QmMKh_2IQ^#%ndRFNRqZ)H=Ey&f+_(>5YuFfCryHl1
z5`JSvE)P}0;G~~R<BH=3mB{Ce<cx(q+QTO;DW}5b<{vORJ>5=Jv5@)GE%vXlO650r
z_O11MDZjCUCi_vUB}okG&QB2_HS+^#WrH)1g4i~*KY9}%5BJI{{Ij-86i9lsejB>a
z10r=3-DZA|Rw2R@yM6WJSM~`O>u8+6qfxNTmRNjY<wvfX4Rv#07AMQ%aTT-ExeH2w
zIQ3^0l6Uu_>hGC?hH$4Uu&=G-&8skhEi1og6VET$K=O0Pm<OI#M}u@WiLY(wq2oPm
zfQp8(;N@8MN?nb2wIiV=yF!KqtZQe$!zfCU$I<hfqhhvBw!99K6EoAkDh9gjO2N5#
zPzlG^rpos*sq6@EiDpwMbU!tGTbIE-TRUY-6&pdF%=BWOSSfir-nH){fK2mxO!jd>
zQ1tmk7If;{Tv62$F2(#A_ZETOAA^FC<Aj;Aopt+yza5D`AdtuIVERv|!R$3)#ihw8
zoj_FNaQBl{6`^jgb$DC6>)1%}Tpq6c4PWquRFJ=3YH&FXKXab2tab48)bwq7BMvBf
z&c$i<m05tAle(0_!Gejc>fU-Zy8G1e;Yi?9V93z}`&<5I1|&_7Z)VIs&JWV2eZGJ(
zIjoEOHYBpM%f+@aUW+)ju#?@>f~@QHr1G&e!nO4uuAg$;2TzSz+GPMK|6=>n7krX8
zFVg)&@S^aq6+sFhI4Ii4Gznv3Tbp<uQ|c8)E?R;&q>=Pk4auVoRIa$s19#X<>YJrs
z4LkN2FGw<QMyfmov9y-&ADzD~%<N2;^qup$6?&C~BH%7mV6%6;puc&Pq4q3`J7I}Z
zLYYG%OS_cOTGKL`hP~zk7+HTPlBUum{zOssSmAI(Nt5BwAw?PjhNODT_*p+FEZLkS
zvIhT2BqY(gy1I}uT4#-LQ6=~kxD9O+P_!IWj_9^z3T>MlEP~n5KWG&V=0>&sC;Y%*
zI!@D;4Xubni+*lJ$7C&Ng5@GUBPK_umU%f!R)1pg$14y2uliWmX$hYMp~m;C1UBZm
z)^gPp43^A130bNN#!SZ6a%%FLe>aLuD$&5>3>wmoJG!89;_{<JLx8reSnU18dCBOj
zjo{&IiTE~Q=T*u;=@M8agNUzEZg9HW`HvoACtYrqGuOhqn9pYgpNcAgjEiM;bGAIj
zxvOqlRmu|BBqCSS(qP2Y;ds0_xdq*C9nq@rtYy{w6(48(oD*yJ3eG@c3E^Jy$|%|E
zQ9%Cd_!X3vLdtGeUu2uVv~v3i`074pmjpi{W5oP>>dn4&unxccd@0=M|Iw6zgys6e
zhmisnP5!%_MW>CarM<vrUuRqF@Q&w~;<#Gn8l@G(DTAZ#`i>wj1hmg7ujDmvrLPWm
z_BpO1R{`%!g>O_p4e#s5F=j>#cU<@*X`X*b=fU)VDB_O>sBYc8@+1Su*ar&8Ckq+X
z92`|}pSdlV5H`u(>uX6RV+{OOA;`9NXOdspA!6ng+$B)@=6cE$h#~|p#L%554euH5
zvu&#s8ev_3_R^4q+fV^sbV1T`h=-jet%-B~oub=jT{ridt8PjYX0wY@nencUlSnm!
zHmR#C^<7egJ}0!&O%<~>V{f3Ltwradn~Ft#<+Xnz!)RTufYh9rwDeRdxp-~g5f!;S
z(~Paod9_NhC=%gem@V6xb<gZ|r`8Du1`K#5x@-iFC;>Ku@69uH0%DKIgSN`H0KXR&
zdC1j((p^j#{bN<O_|fk)hi%bSp@>vn4k9<D(S?d*pi`-9$x$1{r{6N;i$dI3vR8NO
zyTfIurWi3ubM~@AJOpIRaIB|VFETQH$yHl8rgdgpHLw*O@B2IvzF`GN(7kr$PuPej
zLO*U&*dR8w7f}jVm>u^F0W1O?+Dncs#O#cpxY9@h7_K;ZdsP=AET%JO?wr77{4N(P
zcEr0<QPH&Qc=kp<Vt>eThzXM_sk=jM?_sajeNOHkQFIuxa2lI_Ulpy%0P^?<4EoAF
zZ^&sTzfC6n^;}s;y%(+ykN^)W;9mw@GWQTXsEjO*0<{<`MsMU7FDV}Fw9n?)zy75p
z+r05In2}D(^qm@1($h^%Le*30%bW~g83^<NS=18IYkSoQcBd)E6SR$95&F@_1mdC0
zCIXD-GEpa+a<I_KWSt|tI59!t$*^9@{#;w*yD{|hUzOR-hI7PSz(Vr5;6iR-w?vF0
zuPlG-03H89hDz_#wF&40L@w7ANRGa0_Y|}t^lN|gyp_c|czFZk5auI8U=*pVi1rtr
z6A(3+@l$>RdNnrZw6m1+PH1S_J|EZY$~%vRUBwF$NLT*{g;r`R`KpNOlzmIp9;WAd
z7?<;?HEy<vEq>6-Rp9D8NFY=6wUT)e(ZpUU#&fUjIz{_Sp!dGNTfW0R#A$o=Obrck
zf#y%!4ocjs*x!k65`JO9FjB7reOL@+EY}jT)m@5Wb)#vWXb_H}e-$_p41kf+ntgr4
z7zmf2UtajM<2-t-GpRlo(^hiWRL4}RXpr8<S&f5E_{IJrSCh+aOx9gXSMi9r=aGfo
z#x1v%v|s1->8XK%-mL>oJ2*e*q5JZcvub<sm4z?Z-CCTd{y3P6fQTC#`FQ`VFavz_
z`IcubUEt82_2h%``Ui8Ys4IxW&rIO7;%ykSWzmGOnWmLh^zo1Ajb`8@sP7!U9YsaS
z%4TY$lnG(UUoGE18X!>gagk@DY31Hq;0v5xWJW%Xh{>zATYIV{8kl`0-;;01)7C^T
zqymz}=#-slD*Nasi_V<TyHvI`X`p@#5Y5N{Qj0)E)*<&8k)^DcyJ@De-IVvGukb)X
z<eZpE;hPkQ)cqIhNX6!nna+CCH-<{lGy(svGB5ra$uSiEn7>GIzundl3Q1S{Ev_`-
zrpcZ_*!JeCyGdVz?JF-IqmZYpC5f7k{Kf4w64sP}H1e~@STeVOJ*tiRbd_1Z`7?{a
zuSM!0jq3Qg$Hvv)?Db<-T4(}rt`<a79H7#D{@(PinRbah<_A13z2do78Z5zpu<a_K
z1IwggM|hf=GNGx+*Z%I0U<%vfMRW6m3BZ^3@zx0y@QQ#8$;QNiGXnXIy#7bn>H-gH
zlM`~YUN=u#U>#laXYEIj{X@aq&ZohtU$k&f?aNKqeeHJnC3)%%fC_TJzS5?aD~U;8
zdgLH<JKwF$x3iw1RSwULoy(Dm(?5M7R#=}L=fkz3k)3auBuH@I7JJy|m6B7}eoS6x
zVqA*YbI79;U~OTwy-h5$)Ey;iub-|xTMPQtBvitZ>%99nsCA43{h3f2NQ;bxg)~VN
zCH(faMnkDqAO6>B$H^cIKN~A5qq2U4;hR3WppT;sL$7977&E~a&IkDTJ|E`lR=e6{
zlemJu=KkzcD>fl{JrDAvlx$rRsq;Jz*@y2}XPj<YVvzz{$MiE0HSDl@AlK8S@33XS
z@;F@LH3{(CMgiQNWw34Wcf$bgPjAIfb$J`uy911&Fqem%RxPtp{DSnpB3GKoM^ug<
zOdy$HOe1pLk!6Eotoo{947_TqU52G`-x7Cv++bz1^~zfJ1Y|q$(|e`lTq9)&qdFA?
zFyJL*w9qOUX-EETme#6S^T!Xy=Lx{$M^G50c0W@FVe)J*oNM(S2Q9F+0zRX1B%~+>
zb|j)8LQ-FwGR*-hvCN`TIb6CTwBL$b+$(%5sjd`RMwVx8zt(s+{^cUaA4`&q(O(gd
z1!S5tx=cP@btWmF1&W6}{;%aoYT5ok&({&(>A?t|E0Z4(L43Pr+oGkFd~>cP-5dGn
z>GeZYr&AMR^8-H7nZ!VS82i=S6~JI~AGUpucbzEMz>YNzkW7il*K<r$o4m`#>#sbu
zOQtUaU8u;8^rx@IZFQvh8YCL(FcVIK{vf=-z%N-Yu$ZrE^Xl&#(_P_`L-Ny=^!7W|
z0Jf{yQBw7Yp1kL8LkuSm#Wm@QJRJ+n`#THt5;ODt7R%OG*2Wn4!3DMDpR%MH-L<fP
zn%D3vn2A*OfIoNML!Kyp7rC3R@;fSjX2C(aDnZwPDHzepqF4Sj^3Pk?De+?Cl@u6R
z982c9XB~@nEDil`8tM4YtF+jCJDQ}oI<PgmVU@MF#73Tt{FEou`UCzAqYCXspvp(T
z+}z!t>8IaBi^0bJrlTf{-T>LiJt}InZ@T7t@hRwRQ~5cRFnN}d-$+BzBUR8eJl$%W
zv1sXLI8>5+Fly0iWT~qa5%uCKXQng+GQP|V!*tb0QpxKdW?hA8KLLR(-tf#AvLZK*
z{3{&Yuh6qVyPsQ(gU44@-^*q#u;g7%K5#x1Rk&c4dqmh>MIp<_rT98BxQdkvjL*IP
z<ar#YyZW&ck;h1GBFDiasORdrxaV##j`h9I`3Se>8`*M4HXwq7%|Vhtc9@PUn9&nZ
z!I__!pDA3q%8$kSxZ%1Pa&m)?*?uV8(qPm2owLdHbgdgzMXAMG3^=E<O%c0GH+>Bq
z>Jn8vBhZci#FsO0uW7EC?AGVsUoC&+DaE*8awF3%H-50SMsZ({zbNd#jy7)T@aXV~
z8F8hf`P`cqoO6m&f8f%wtAoKEZ`e7*zMqEDBPdzSf$bju*AZpLb&3(GxYz8o_pb4V
z+8Qr+fiyK)fyJZB!@QeEIuDUhd#UieP4Jl1e*u5ECMIev^Q<YZ8@%4X|6@8&>+$mD
zKem)ZM|l3m4gDr|`}%vol}4%tvk4>ZfrSI03NsSEW$^}dU>x}b0&@!rzTLAmf18Xi
zngag(k|0xsU14)b7<=J0f&B=>4@XlFB$lkeEqfPyEID-cPIzbKa{IRg07V@xb?i#Y
zTrr2R{EiyD+E4(^)rjX#)|qf@bkDP0N46VYbsY$q_j{W7Pflfdir+lK9iO7(8&^SD
z|FP{1w`{-ZD*iWuZ#2GF+mh*b>D0}KQfae2*tPkN!Fjp4+=?hJ{pu^QZjY36@S@2j
z<K*g|Hwl7V18X_sR;*;Qp{uvYXh@wO#HCy}j8)l`r)3tE$t5kfjV8;ziK=?iQCZ#;
zvq~F6dQNt)PZ>D5p_R`meU^XOWx+T(ds33s&9A@OPrDYJi^oi&{p;q0#Vo8h@n~5O
zznqx6sAH(-YwHpo0B@EygzYEP{u%0z4-TDUOC??oJ1UY0omTbs7R-2Z)#u`m6e9*X
zZegcUG-NY;RCg(&FF>22S#(MxLPl+ILidLqZBctR5`&NfD3M1>fzGoVp<t~>)8-ie
zvCw1#(aGcf%*q{x2!t}<C>_;ULBsDoMY1nN1zr1-udJ73naiFLjkXT=ila)uInnUz
z11t8Y0yjb*z>WEW`#L>I53SA$>h~PZ)S#8}5G$?J@W8{&0ig;yQl8QB<}+)dy|iYr
zzb;Rs0L|$%W<Vu-RaV4O+kJAjLho3kz?VsI`<(EB%ctiIovJT?D|n%jKWJm7GFzRj
zyC**1$(X{K8~!Uf?aVS$yQXqyc^JDzhW_FbN)^lTx!U5C?mFUx4-XEn&I)0h{HK6a
z;-ilZ6Z-g=2TuRHyX(Ead~^zR%yVVv>u^~&uMkkJ#e_nMo*HYY@6yyhXU8YWrOXg5
zN8-EI1#)LUmxGohhd`sgHWz!)RH_ap`fT4Qj%`_$GRjOQWyWh`=R+J-q^u-DIrWyq
zUjD5=RwpbwU~8wdm`XuU-=_#_-CLaK+d&vqjbmTTSlN^r{3mfE+}#NC#H>5g*DKZc
zUka)nCOyh2Yu)tgDReuN^ZTNNWjG9zu%b{pZA?wCe9k!J-5!0O8~hWLm|px2v8HSm
zH(lN+BfPutNHR#Ip^%#JIWFVi=wU#x6LN(c>N(^__o!J}xD0C_uC=k%MT;Tc?sYjE
zYe;CCk*E&M@udh&&Si?Yj$Xn`h6Cv=SX`OR(tspjzaIaY;a|_0xs*4-8CCf-l_+7Y
zyiw3S?B)IZ^+(r_&a~$YgF85n+!+T(4eggD7?YpYPrnGPR0j>j!`;IJk5BOsaOEhS
zpF&n*TlViv`AD|w+M*|Z)j;TUlT)QvNvzxZKaQ0>eUl*h8gtBL+%|DyDe~jha=D+4
z3}=0t{x3xYv^lBD@XVh?C-D3r;s{DW82#r>=fEiRCgKN21<72WA2dWAHOYqp6O@$>
z<k!_C$)^|z1=-54-ig-wmHJn%?>?oS4Nc0{*KGffMYJ0XgM!|zQOx=ZDY-Csc|}HD
z*2go(dgjoNeN;}<V`osQKF~9Fo{o93pQPTjCRUsSo#C|^`x3-*fvS2)Ufq|54QcR8
zE3b%C=2UTI|0&H&A6H7hC~b`<nOcV{HrMcvlvBYx?7z?dKFq2{VV~xG-XqK-6;M{D
zyj~q<?+~C)GJUL6grg4Rb1kuwt0S&-N&DF3&W|l?8D_xK>?;NuJlJ&2Gq+khdV!dP
zk=fLDpuvGd23;Uhi;t%xxVYtaxe`GJI~G}QO;dEu@M|yw&y5kM_|jpOX-WDG13PND
z2&xky;?=1E!x4}z-6K-vZ3~9YuAD~Vr#+a9Qq+O`kJKr-q0wsiwE>cpkQ-FFk;HGR
z_T82#AMpiQmh}owkpV|ogN{R%EP#NiOLnc`t+Kb^K>DQAfOz<?At#9iTcCKISHf7?
zr|T9+1t5`Mm>-V>O|zk6CMs&o-Eg$goL|DejPQFKU<4(F>nOwSpHNq-#LrB1jO?J}
zC%nmkmjVr$VAYM4=o2AofVvu<sHOoiUrX=e{hQOG{99yJQdYr8egiwFmk@tWow?5v
zf{1&1Lgs#2)(JofPH?RCE{D^uW*t;y<{OHT3Kj$3$k(3h)1S{XmC=QOKNFYeD}hg6
z{O{fSAT23L5C7g0RaK<o?d>F$dCK@OmXi(@YdTZW@6a*3{({Olc&kNF-lh6aJ<Dep
zp}^2ec<SjhvMDu0=L0cjB@28$1htSd0(7!cH3Z3PB>ED{<hjmw@$7e0b;#<{hC#(d
zf))@cnR~NB6K)yz*W+`sP&2Y*Bq^1bqG?M~eNgf_^|z&4Mv$G4UWQOQIU24E>2;Nh
znnMMw5+oA^^aJaMY(V|H`zY>xomCHrlYmsey^Xh7X)9sbv_SInFm>;-$zi+nLvK76
zM6#grWfQ3ECua130}DvuYY-yW2_<McSf()J%Jb%%->p|`$3TdlF@u(^Y!TPuj=cpS
z&LAHs(Hf;CZ|ROLT=>a=z-y7BsCwFxptHQ6&QY$M#Lj%lFI!?cCio`UAx%+rD3x?3
zK6poyKbDuuHd?8HShafqdve!Sg7vN^cG5c#M`)b9bodMA$G;2l^JY~bMCLF7mxPPV
zIh4!AxJ2TsXMcqGYR5zc84`>tOm4+S=qokcAxVV0R94+Yy!K+j1RBE!zQ9y&5%&%}
zr<VD}8~jrNHt;3Z2%G#zh)O%P&``2DU5|ttiY5(Z)I)A=z2*vn#VI(sA(ByZgrJpC
znIHO4Hl9~|f1a;V1wP&T<6bFL%r}&rB`x{V0+QlO&NMLE$o+5rB{H>5jgA2%x7TvD
z&CFC@k)xOz6{(6$(l@BMc5+$-Ai_2+YaBRni7_xY;^bOwF!U`r#Tf~8tNm(8VCa<5
zOA#Qzt$SecJ8Z*Sk3E|s2<4Dvsb3n911w%IV$}d6qqa&`Aq6MV?w+>5jy$<}t^3ed
z+E~?SMq=(?pE#Q)P!`S*?jWu~#16u{%{X2)Dc95Z@{>#Hx8M2TbH)W4<10Cq=t>`$
zkJcYbC=>C_HnsQs-#*Rz4J8P-MT^hM^-VY&I~TW`W)2o4NObmbN8atp(LdWfH8oGr
zTx>VCNfvgn^iwB!w#r#146h0tWjg?NzJGYD5f_q9o_}1ce`UGbbaHn$VgJreU5yo2
zzKK`v4uA^iOYK+`G@4)Dw~jNFAI^^UJfBDlzGLYZMRl3jWrG-1zWJ(JfvVVk1D(rC
zNU5XGG#*)rQ>$;k2)=$6>=G<~nZYTioM3Wu0$*xF(tr6Kge5b5K=FG&B5k7f&%pmN
zp!tQc`#sYBl0IStni~R~LI5Our%(e+`k@?#<)=Z4j6-KU$%EVN1fgDvPxaM<tw`H{
z{OBW~)F>z8<Q%&jq{ewdXln&QY7O+X5w?A>&VUO-ng+_U%Cd_+2#{B10tMvf(z3m3
zo6X;HL_FjJELSSx%n(x4{==ECZ)2}*`}=EM>#ukp#VaGv*2L4-yf-?YSYjfGt-%*M
z=Ziab9r{yKiPRWbZ13NsQn?vru+jnKA=-Y7a%z8fJ;>mu6Z#cjd$dEHLBHG0lli_k
zd_RsDh6t>#mBdXnV}DN<p@{3DcDv1Wi;~Tfz0#!LvX3bvueHOOKL1NvaxJ4q*c2}8
z`n?`|f*|gdlY}mhcC+g)UmX0AwtT%SiA`(|Rj#$iSQ-!Wu?wRW=XRr;7DjzN<_X93
zro4&QYAF5jo>~4$&*DPyC^nEK-rp1l`7T4?dDtvd@+QNpusuFfMNNl3X;#EX{uqLH
zbEV6zKIx4os*`;cqjMX$<=0o5p7NPS*_1&^*3j$7CQ#%~IybqkiSL@)3-)V1zY#xb
z>fImBz%96LFJI2PwZkwZn{ml?_oheqLJKGM7T2Nw3)brnFN(Jg>|<A3%J5%aPn76)
zCW~y&j0ZiX#dVMrZKv`QMRLM<`IGAaI?O{SU-yz;x9NZP?~*EA&D)i5%1<Xpbo)3+
z;d<I{>(=ubCe%KV%iQ$;QS7Ue<FhgL-_Nh=*0LEUf45$;E8HZ-_~;ElhHK91pIqmk
zz>qMf(_~8=H~shTU16UFJ(TE<;tK}Ey^9J@k%A^H^@kxt)Jurw1!^Wd1x2=;_bMt1
zk`d!e0sMmTbb44cHBtLV;ziF}5xzh|klPV0+(9KJ2R<*JT+jC&^8Bm9Q9c!J;5CC-
z>Q>7bW`g}#`LAaW{=U>GTM@m;cK3$3HkV_U*v%P#lH}{wcTTn|8D<^nDS-$S!16$A
zHe}}5@PXML@n36IXNnoHZ^4tB)FfDQ%^DVSo5w-o*NnjtKX@BENKbq7CZcy!8L~AQ
zCS1M$r517L33tH!)r|QsF-fbRwuSFj{{Q-J8VY<lo}|L{zM<p!MmEEopD85@uv-Ci
z{2U&k+{xL&-@Cr35`CZl7vTWZR9jLacX`;od%Q43rvKyNLl!~D%+2VS4;_;q3(|NA
zWz~lx85z3`ls6ey-7>Rtkqyz!sX^Rr=ie4;M8l;gw?uaQrE0&6{gE8P5h%C9K6wQ!
zV=NgYwmQZ0V+|49KOwz<y(<)s`*j%`vvQb)bx8tz*I2Ov@3myW|GM2?VSNY%?3Z5?
zE~4N~6hhLw8NJj5?+gD%!Y_LN<W7EpUzFd%dg!KKLIYaXe(`Rv%QSYMk?Q32%$6L|
zBEFy2zmr8#1fs(Zgs<Z1nGA}>?#Dy6KC?E*R6VoSRKo=`X*{!)M;fHnPX2%uw~R)?
z5q5ERv9KOm6aX=vckZ{0kmMPSsfn5tu|LZ<r6{4_=sst!CN>JIeiUtIq$>y5Ch?3)
zMT>~z?M2iJblf%LW5>!FM-}AbXYU{_3SA$uJp;^>I6T_gr3+^0+3wyo1KAH>j{-%?
zYxsb59{{%(u_*wCc2$(w!inbBw!GpF1KX5SFGLRfk9}P753AMHBRbHO)36maXhE65
zj@~Qox^I+1*}Wk5-rlvP2i3&l;o4L{J962Jrg8__%Iex0p1^6-xjFuWk*&0q?-w{~
zitS%G>HJB^=i*ZdYWX>mXg2Pgh68mD9*aIM4`LspRm6(2Dt1_HgvsxDiZc<#4RX7j
zQq7E8a^zLaG<yR4^Rb*NPLBezVLQZOa__8Uyce!?Jl&}o+^jluWW8&;`RQP|$})F<
zKTljkG&M{t<c`&m=9%S`!+zDFJL_F8dnMv8Gb(HI^VR54Y6-uEdA|P7xU%kt<uiY0
z)^m>f>q?Y~Riu39c;~XVv93(;r!H()CU|iSNCT9A_od}GdsdCw&XUie{dJu}q@F)W
z(&}E5JT-swROZLaPNq(wNvo#{oQ&qe<{Xrin-}zXKAK#j&WBYJLvxpsqOS_aK8Yl+
zq%I|62l!C4RG5j2jL9X-ACA<USP;lMMEBNSlJ6Q(Y$|~jMXMY6lr5-*dg?Bo`YIvo
zwt*bRD##|XIhof+eON*YZeg8X(xNZ{XDcP0tgFSkzTBh`dEc!R-s%>ynXRxuvgW!k
zPSFaCCAIc32F_aXIptT=NwZy0rK1kgR3|$2so}liD&zD>bl{pQSSdZOG!_sY)~_@P
z?xJa#t-cxOuvSM#S(`t?r@tEP<iPz<Ehw0^F~^=wXmMtFG#?!>=YV#D-!q0JwaFRE
zM0OQC2eI+Vj9w2y2Sq#Nc%gG*si~DnAFVrkD0^%8>m|bd`7MTWEJTGCpZ<dt*&v*X
z;>TQ~!UtHst1&R~36H?xf=U*oz+|NLf<^lEgh!hoqZ%J*WLck?8cz{8nH-GR>Oz!8
z?^!n#=YOVYnwi?~XmG5Bh$4xS<;*hnD`LOAW?`Y*;~@nm@=-{M@_X`1Jj^D^P2GaK
zFW)@;TV2Z}aAE0S#>By&kK1IU41c|Qw)Fj6YStO;=A>vx66l_Xun`_jgL7EmT~lFi
zNt9Y(0<z6i$?H`~aPEo~2t|H#5;oD}P-Mx$sfd2$SaR{F%INu4^{>7))a@NwyG-un
zez^wNesS+RV6_IsbSp*<x|PAtEeT|;@(*kufgBsZPZFoW@BFnKw^_~+`R7dYceiF)
zY;t8Vf#UWgHN*0Z0uYf7&Lg>+LtMg$L!K{sGvOI|a?$9quf=O#h(QAUmoAd&LB5=_
zav8>M`g^e9TnT&*Eg0wy`C-)ub4~d9VHKeMyCCTA8FN0pCFHD72RsKIBhG;P3qbb?
zbb!LZRc|3@zz&Orr3|Z_&_SBL5BJC0cTfh~>wtB~2EyXW9EKktfImR3%}D?w8ed8B
z%)pAb5jvI^uhnyEj=pVJB?AHe0Iv}O89JglI~5KD9_k<;lr%zqTZ7v1xnkh}pb-E9
zr@;FKlDj^s2|da{oM@N86D#$5X5+Pd7NpDXZTnw({p&M4{^!l}Sp1t;tKz3x;*_|-
z$Bvp1=H=bD;N;}p<h=OG-^*$}%Tx!Y@Ii5l{l4S@D=Hyd^k!&}2sP163_Lfwl`{Gn
z$WRB2=kKUs$q$rpMI(~n?Z;98j1+L4^RZZqjKuuNf7BJaqFW7dvVJV94H_LQaYl2N
zss(aHv+n%b`1hf4Mob3nx!An7{%Y61V{3`S8tToYZE2!56t0f&js>CPs0XtnJ^=L$
z)I^HmmeEo@;`&>C)H6?g|EVC?2FQw}Q0g2QwiSVRVsB?riKQ5zjcHpX%0VAQeNvqn
z9BNPA#GeeSJlpx8<>SNr_KJ0^6;hzT1~7r{i0U%<q9^{mKjxCEOr6eho3xpye(5>n
zvm(-Ih3>uza$}&9kOO0iD6m5l2J!4z35L*$2kSls*-N5`3Unc*Zx?}c8mO@q?2V%X
zNHrkKiWxIa`mE0CncP5$9U7jZKv*aW(k_KF2|+&DoGd4LF3msnS;zgLz2y~O7PZIY
z4L)f3cWFJeosv=bXD$N)5|4d2lY@ef&eTuD!6(Y#jbdU$>;@oZdE{`2(Ge~>#KETq
zC<YDlukbul(}=r8`_F$Wt<m*G;H%8tpX$UYrRPME((3r$ipnH`>hC+I9*{TVFI5O~
z2ysR9B?^`c$~($^&?rJ6lJoMYQ>`v?m(DX=bNr5K+^Ob|itoCmtNGL(zpEAU8}i#;
z_RMIWpO;>L`mZRV@@tLozg8_|_pCtyJet#_kNO`~WkgbPQ~a3s#l8-B2BO6HAWX)b
zuc2?ow9@a_Hf>5D=qV-HoP&gT{fhRZ6Do8`26+~29c?dM;w`S=6xDU6DBNW}k*VkK
z0P#cLSl&ec!jlWrd1P~|Fl*!@h%OpEful$9<!q6oKw${C(~Ba&=WkE*$dl!f)jB6o
z8SNdXurK|I0dwY?18HL-rA4BqixNKQzLGJKR-Nj8`5hB7%bA7jrM00tzl|bS&mlwH
z>FP%Lp$#c<*MridW!0Y%jjd~Q^4f`h-p1v^!;jO#S+Yj<sMjj<S_vM`)ze_ys->-U
z-4b~dY^r#~8sP5*47M}jYhfR`?>RF*(1H;+xQiGa_03YyM^wYZUR8dI<4is5<F-^Z
z*B#|PBPuq|S5~U}2#-nHT7a||?vH1NwnzxYCS@&ctr;{rZe_}KO^Y{Gnp;^o7=VG)
z4tt2#FLlb6Bzz2oDkO#YQO;5Ru=h9@d5bKlU<O^)56`~kJEga{%*;R*<v}!a*@ICr
zT^V^2=TnahRq<NzrBg58a#$QEjP>%Bzic&(SBTrNWqz<_eh*U_Vs91ky^ky`ap9$t
zvKutwL6La;7=FD(EXgY_CY~m3KHs+K_bZ?7kNM_8B=IBFk?UyhjXa+3ajv$UR*^?>
zobUV8f^jQNW_5aET}Lm&RroKwI4K!9-j<uYT2<t6=%UGcq+H+|9ph^h%GjknQUgW`
zHFD<`{cN!cz?wkVuv>cx-wtH%39D{rQWQFXZ4P__mt}aE-@0ojWnn3d9Z^r_HF%-t
zf`w<_GzkGSM~}x6(qsGdim#6gy^2)B;|+Q5LUaH9atXSMo3YT9{CQ?U_oQy?`tNt^
z1b17c%EIh6f8t`b7_M(|!bx=Q!N<nMUJciqabo&|3jOflqqQIK@OY%(O@1d&^aCmJ
z){@z$B6?OdtwEhfH7e^(zi}W*I8PGiD?;#u9{0^CN*1x&_nI54{&omu^@|_hb^g22
zIa`Q3u5UW1)4|>F&9zvAE&J<I5xO}2t$4|L8JRuVQ~W-+!DPC8vlnxEHSod&yWUxi
zpIMi2WdTdC9rb$GU1+JgnE!lQzpD>qDJjgj<rz{DuxvV)`g#K0X&~PVL!{V2cio=-
zd5l$wz=?`kFhq(%QnD-_<_{xTe90}1V?@c0QdlM%J{qY7;}fH+UJ9!ff7ujv&(?{C
z;n@RX!zR>4MmgL)`yEAQpSbcWf0Yk<c{-e28|V?F_Tx$P{A~vd8=m)Fh-5@cX%o13
z@C^V7bnySNbrQmlA8z@7lEOfqr0{VBNza$Ux1{$QAZ857w_ca{(oZYXe3B$@8m+pm
z_p$}+U=qzR8grqiaj)ut)A^RDks(+aMC~sGKC`}yMy=$J_I?g91|<%L2?$z)C+qA$
zW1V5ul(V3pRpqg?5+81z939;INu@m<I&{3n^s_K84Y%d>L`2(^`N{gR6yclIIQ;<X
zz5?n#?`_|ooom77^Nyy$Nc;h95Q58+apTL@Nfz>ncjsj+2Y-GrTo7YSjE;mxkmrUG
zx^q+_&`Pc`?Xt=pgsOt^l_AB3i!YcRWj=gQM%kgIBRS`ryNl3_rVEqO9w~h@FIQAd
zNuRm1*qI$((otJ<=~cl~{P-|s1s}z|{O?j7U@kH-65<wGplaAlknzjOPMUVlHADJ8
z9e|lp8&nd*LZG^&u2cbV3lQ2A<S}zCM=b3#Bt9g&BC>ccSx$%OVc#0%UQZ)cOjjdN
zPkao2<~4H5!fGm0a4(c!V`+XmUU3`Rclt-b2sB6H&5=MP{#F;MLLWXmRv8=r;^;@M
zeKs~{)|W4BEz^7Nzeeq{KY2#1r~1~aLRB}tPjNCgb`v6Y`Q}Wx0)iy{-xkcD(pQgT
z!|?X}$Z|HW@}V9z^U|HDPc$R6EcrNmz6g-xtd@m1Th@Lc_RnYgr~>fU2=N-{C5D}Z
z$_ko#{|bFpj3)j2vjyfxNYq$0{3Z6l(r2DUazpIF7?^9Juoq=r-U1fbNJs54m7Mpm
z*DNkvUCrdj1V`c7vp*&J0W${R<v#vGzU>AcYs6Y9eCLe`8)#<Q1h?v=zVOgdEJs&0
z{Pcw|W67Hue)**KeZoWbK9#+}97s*fCt&Z3$omo}BjY*|w{8G_tR823X6&sA@_enc
z!lv|0zaS2lOqzS1Jqmg|3Ngscd%Q>7=Dwx;^LKBIkYWGY-BY-uX~wOL`*u?S%Q{Wi
zgT_}Pf?49W`SV-pBFU#9EeevQO)ASf6r&IA`gxYkn+^Rx&A(yI_w8M@2c<ow>8o$7
zs8G|$`ps1p_Wp&2PJDvXwRBWK{Q;ExRSrvbjSwf?1gLA^)G7A&FN7=Rj~t8P!hGPB
zTYMEuA)&-vZ?r&;WAXVA?tuq+v&$YAFnbUaa4s^q@E7*gKTaCCe4V!+UOn`XOpHKT
znT9`*$rXd#x@|S8=)OVvH9Y47R+}8KE{Xph!EoP<Hlp9-O5@FhJm$lyis1v~F~>1E
zo3l^#Gpd9{j37fV@$}0>iM#J}i7$yR@=TCF^bZ?`BbK1q>+}ytWkzym48i+-{M{#^
zerx~2=>v9XnerPUHQ0IzlV9}-S1n@39vgvV+fNtI39Bn!A}rhkH3#3WiTdNg`jVea
zZK$Qb9FuFHuw<XfSf{XmWj?X;)<UoV9wcEX665|`&_t)ySVs)VaBXX~PF-<V?&Eu^
z6Yb^#`5e&88UwJZkOA1Rx6o>7%DRpEuXbX9Z_U~z6I)aaO9YLX9MRv>zO_Y&+{FQh
zx5IrG53bXK3TWgSbs5>*OVGlrBOS1H?+S&z>oeyaLun8E8B3SJi}&>j`x<6Rkk6HY
z&G!63IjW$JeRZ|8++0{4!G6MeWxsu!mFQh6$(B2-jQZGLZOTCsGkF}&Xu^kNO67CA
zY9{463|Hq7OLieV)9vmf&)N7&x^EUx*#Jcz$AL8Jz^~z=6ai)JXL=fByaP-;n+p(+
zTY4Ph6(J5dBu~a32PJ4#yWXiw8V{l$N;?NneIh8dI?!u<*ne@bJJv?6{g+Z-*I(!y
zX=o*JmfMVWW6gg^U(Ul-&!g&YG~2BTO2yh_AP(I^4}+wU|L*kF2OBlV5ZFzd%67+D
z8B|gCof3ldN5WVf8NWGCLTw9b;O@V-@9LYgTl?%0EOmK}tEIh@+Xwci4}Xa%k4>K1
zTlHdaPF@|46s`Nctf_{lDEI!JF->*8ZP;H>&zk=Xg+Sr2Iy|k^at3iNGU`eW9m4d=
zKhE<s$iwb^hBSn1mcWrQ_tCU^-}+0wYH3W;7`AtuYJ%wfKeMqY?`I0|G`Hu$)o?g$
zG;xzS(GrEqeSRc&(S#gr%qe}gsioram9yeVP}GMCbllY;-x;r=4c1^B9Ksv6&_Y_a
zjj4>@F6QJ*>he9Xcw=_1l`KGrCY;1p(Cb6MkxZvp4Nbt2!R6b_o+)P0`q1>QS%;S+
zXv`n-KC?@t%|%1Yb{6-i{Nc+4UV;&86SwJ5ADp_w5GTfUuAiS%IHh<D?LlQlDbtU~
z9dMKUOo{xe-?hGv85HgGU~#jGHO$<=4%oh<j-2Ji_;(%}Qj5OiCe70j=1?rjgm3gd
z>X07b=zktJ)`)f>@$|OP)dYz^LC)wBLYjn8I=?r3h@qY)^!Te5L7PvTj|X_#syX2;
zroBBbiXUbzi-5EDb2ZQY&SSw$_|Zq>xG~Z_3&<Te=9pW4;y7*mI|*Y9Y45v-i1RA{
zsa7cIzi?8tGBUs3V1POqGwR6usNdCd#Ib2vAY-b1*!vgkn2w=$Xp3)E_KyO5QXx9n
zTY?24&hB9^n7Mv%wT-7l>#)S{e{V)tsp^{a*%qMvqW^!Ef6~xI<{>rtoA9Oqp5uJp
zb@9{CkYoA|742aj-+fH~P{@XL1|msdfuouSIr8-Qj36{CYepaFAepdCp4Gm|uDivY
z+(%9Ofh#|6_M!N#_s%90<DQz&yAvB&A2L9A`>md__?=MKW#6kmk*(|0rWHv%^>co0
z9{j4q5E*+u8tFGv>Hy1vP+I+ewEbmVRnHecjvhcl>5!0;?v^-6DP2bi=~C(D&>$@#
zeGV;~E<w7aL6JDbLAoS_0|*k*0*ZIz=llEp?|r@QgZtz@-~oG|S+i!%?3p!dX1(87
z-?`57Vcp~+SwpQ~OrdFpbmzx?VfP8?9Q9J8Fb>F5Jwx6oIE;hF_Xnq+{D#@i61-ly
zJ1`f>(?^rk8~SRG!<(HK^s5g_!T<?sC#Z|K-4II%4*8Mq&S0{)cAty(AGH0=`uiI0
z*lP-_Lm=V%x02~!&^eZ`KqmWI$m@NFjqgt+&}iATJWA5fQgbEmeP235KeWws^tk@B
zDYy)PLp`Q}-%@8G65iG9kkZu}UzvKd?rK2^A3hxZOppI_aRyFZkYFv-{av;iBCWQm
zrA9bjc-Ke;o=^a!DG%ZWj5Yzy+q^a#Uh>!4Y-0b%z3sE--4djlM#P2Q0lN?H%Nax5
zF*yO8qrSOsy&)8~TtvgoHhXW#_|DXm%rNZk0^o(PO8Ujm{fH*O#;xd(7Rqdjit;_y
z<nA9=D1Aa_DNtrK?9d%NMhCGwV33u)^}FIW*Tq?Jz#m@0B?D5{A42rw`g^`TwQ(Qi
zSKHqc?-Z4d7XC0?-JLB94l4%?Yv}qiJuT7cdU^u%>gv$e;Qo$3)_hO3T)!{BRdL12
z<=9H6@#D3lDPN4y9V&i|X7!aMEgqP6u&8TJlQB4yQJou9s(<d(YB-F-4P=e}G~%Ge
zyLC%91m#!&@S!BV=}bmed*&JegJJvEz-i>&dMDwC42aoNMix^-PmjZ)ETPx%WqC}G
z%>dmD-a<qpP!ME1A2)dCmRfb3^GTeCm~~CMo<Cq6O;KK2kc<$(ob@-`4SL^9Nne$H
zD&MY^0|tYSq4WuS-&8~gVTcnFWgafewEwzFj98pVa{cx~swaM}G|<4z{p~%7i6u81
z&(krWyRL0&ZleV~z)4aK+b)GKl)l$boBZ=jGSb<vMyDCLS%eXp@uhIVQ<lJ$5eS^m
zWe9Z#(yA^gV{q#hcF*whWj0d#xHDU<i^~1}j?qdiTTXd{)Q#Q`O+k<tS)*aP;$NiM
zw?=;IJ>}{j7ZL2v(DMWq0z3)S(Xk%S(+9A=KFz_+3?S?ot3WrgtWJ=*uXPxE1rdiG
zU<=!O<w#{S7>3Zc*6;&$`rN1KVuMcsqI0Y(o}bYzeU&v%GHJ)ey(+1`YOb@)K8Cux
z$<WhMc$b-K&WG?`m*V@WL+5Zjp5Q|cj&dLx6}82ce8Pt?5Iru^II5~M8Fp~6jAe1!
zIfuYjBry?WW{eYQlN{Cq-o?&XPCUGJ$<xm#z-+N6k8)@w70(=@X$fn_{Y`~Nf;*#9
zbaxOAxHs1SUA=V<msX@6DtG2g62FUlWx;cvlJXWl_*x$w%^VGXOA~9}Of%<dh~AON
zx5ulMR0F}hA9N8d_gcQLtZE>P_g|yDp996soOK-0`aj>vC;{r=e+xvvJujEoPC-LO
zn$L#yBoibdXNqU{7%PnN(E`C94w>;a(<`XXDxIhqDgTq;O211S=Fke4SGDO4HJL;%
zgb*WRm8jAu)a&-kS`(7hrZ985u{)71UQflMTrIHOAJZtB4)ZYOFktV~3SAsIQww_?
zcGyx|?GS(Ck`J~`HB}X&mB9Y~#CtJg`*HaPKsI~LdPyG=M$De~e&`!D1V21?i03<3
z8J~s_-L;X7^D8kW_{3FgIWROp_)yXroI2(l*fa{mcF)1ROgb*gFBe>xeR)tj)eqpY
z6LWMlq%$P(7bdXt<UN71`6!F_Cxf7;tkbb}=1~Bg6B~P)sSd%-1vb);ONgJ>w~#BC
zilE{xL|mhG=i$wfgH`?h^;xm`Hdv^qc116E%bW&1!<UB~BAt2d4n&Y~lc+N9jJ<r=
zHy{7L1=Y}RLv&ve#0tiW?eO{lb;zKJj!Mn|D`Xj+pHun@A|?sB60^I%#>I@k921v?
zk}xl+2KEQK?dgp9O<^}5ftr>IxRZ*Sc_6Qv+)xtFSumRALDIWosU}$=c|MC^7>Bn_
zDoPaP+u#w^J96PcQVM_X9yX^>+Olqz`s|GC1x94l<mg8iKtJHuglvCBmFrdXUGP1Y
zGCMCZmP|~aWbD;?b?TyqT$&)r{KbkRXSm_THL)n!7x3i|`r*TqcG5xRA$P~~&Gu%1
z8j;RL=uBGHIU{QI=F1iHNi0qLk_qhigX-X0?=7Z!pKI_4A$e>iFiYZ9(%Yu=$sCO3
zDwXz#GBRx0EF-xpeKKyJ%TS_}0*+a)z@pjkB8G|{HaW~hB;~!Tl4I>=-_TLuE=&34
z*%#N#0Q+?uD5jwpGC#;%hU@T6#QgTg@3H}T83c2(K-rC5*f~MpuzXVIh=x&%?mf6r
z!f5)}?>aGxmqmK^K-ctdXRr{y|IE-Tx%HzFoLd-4H%>~CugWT96J8k}Dm4P6Xrm@T
zV$YLw{&D+CsjSHR7B50$&%l?(?*Jw#6SNM&yg9TJF?7LwKlPYd_r)VO(EnaaHmpp$
z@t|&Tt}8?1X8$USbo&2HpN#Yf?pwIJjJCaAe-WGRYxJSI&~a~glP!_nWhBkh62Q9~
zOUNWpCi$`<eUdIBaIo3V1Uiqe%DzQ3ACPlwGt;Z1gKnIYCo$$Nv!+V*?Ckqn0UX6P
z{hI}QtUI{N68^SreC_hITkM^H-*bYB|Cm)fr?q$k_3FJC{Kf(M(1>U51QY0^bXxS;
z|0?%(s9fu-Y(e$<PyE;;?q;C5xG!PvBf$eZ$HZcDPw%&7k?V-fDj?{g?^%NFutOw&
zn%-IoP?V$x?ficnmK0S5C|7E4N_5!<mFVi5X?^HHWuQ<)BeC?-)bpHDcmTL_bvRUO
z0+<-f#4^bXq@!ITW-^b<(BlZw*WlY&fwD+_7)r4ez`oh*BIW1XBY>7LZh~Gmf66A$
z)Jji2*JMJBXsePSHEy!<VViZB%FCV8zw^e$10&&*YN<o65WtZPTC(w=&;+&zZEC3e
zfS&pF-E*L0mZ5r+;W<LAYJ*{e|L^$t6bnh~jLKxd!_#lq2v0Wnnmj9)0C<)sbWaIw
zD^S*t^(Hjff9gT`BGQr7`<vcMcDmlzD`Rwp;80{!flT~8;idMp7R})|w*>wxSB_}V
z8*csU%~58a*B|50D^A|GR0o}4wQETCv`;bS8@e=pdb*bg?wVdZkV7570KQrR&}jl&
zw7ooQU|0R%TNTG$FBsy%#s}|j1Z+%(G5}kPwtyM<PKnI$8-o9jTK@1XX1=|rM{Pc8
zd{A{tKlJ@ECZWk#U`t9?+aOgZeK-iOWP=@!=k$N;Spw&U+x7P0MV0e1<t&XB^nNd0
zw1$mKZ3b85vs^gD(UhejsVR(3<i*OEVlaMwUmWh^2;~|B!4m)ppAOTihv}E^vf6^4
z8VvJ%tOH6jywvN_9UY5{&rD#&|4(zk4}0-R7A?<wA8uk1eM(K~#_xqOczW1(GrQ3<
zRpt%r0TXNc|CDMO5Sk=5TZHc~{wOG`WQ-g8i;=2EJnOX2Q0{EwuiPXT*y`FoHyNlX
zBhkk%KYnQ8E5sZfANY~ZO$n{^#1y9yJb)mB=9w?TU9T`-2rg`OETukeqYmZ#v?d1I
zvW~G3lSHd2V#i`K@-qw~2r=EbLG@uU6-72U>pED|_b{=k+pXnGQnbwQ#g8b$KkRVM
zfQ7E5i9>#H2q>Xze)4l6jw6obJ~9St6p};Pnqq*P8m+yQ#2SIYi(9_Gc;2XB;-^^Q
z6W}qgLWtsUtF#Fkm+b%k^2^E>cy(gBCzcXg?SFlDBH0MMFWN8z55NDX4df58z}AjE
z^lzP~kgT>67S+Jm`0EL)B7^+?PXuaJZg;4vNMGqI6g2-zv{zoIS5{M^(YUL;&Zea9
z)r+oiv|ETr7;P7F`buvNxJs~heoYQR7t7^iPKw*qU|{YuE9-(=Bm6SQ0LJ_j9-wQ^
zUIr%mlaudBnn3@+_{vDRp|a(INDv4DBu6--uL6S0(kAg!fn0qWRC(GBNd8rV0jzs+
zXd6cZ_M{>XGc^QqBozKp;VWBl*f_!BDC3$|hJ$JJAog3Z9u42tQ@IraRrb6U5|KTj
z7V7RIN#@ri#;9#BZv+5>8z?DVuLpa^{-{fjqs#0`PHV`U{=D^U|Fa|4lbS}HEgXdd
z4M&C-3)tJ&iM*u#ndU}&6JvDWlhc4;T`lwwjkSQbrf}Fz0}y{(R~ioj!$KT0_nwS?
zycJam{4wiUV7eB7+J!y_`6mh+9Y15hvn`gI9N^q*Py-fHo(a&Li?8RUb1IJX-ukDE
zW7~@J@~le}diTw1c-facdN=gw>P48o*B`4A3_q1EkvdGz0FCQBX!Am?zqd86=i}EY
zGgA1Ke4g4~V*W`Z9NtXD`HaoN2s$f=(Lj|e8o)1y4=}r(WQlSC5%XWU;xQxxmvBik
zEv`>|HHW@zk&ma6|1+WP@E=>`FeCJA9Vbt#;h}A}QD9ug)u$O9{Rvw&_`L@BW>M8*
zvh~j6@Idl4i0MaruPm80xvFG9`O$O?mjSwowDmE32ei!f8mC=ln&Bn?ehjc__112e
zaDuEhnE!}@SA!P$pUoXxSH3cg7RIqK1b)6{YQ?qXA=i`ru#-DLe6z(Cyl__!XVQvn
zPyiOmtSKMwqWcvr28_|IPli!+be^xC!2?`2D%X;SQQH>xI6w%DjIS7gSr)s}jslmH
zcEQ;)nWdD|{R~Y_<-*`;ouKvKpc7w;skb}GwY^7xjB`gG3qsF$2p8M(i|CZ={1ECW
zhoK>L3GsXuM;lSPxEz*TEQMyaG0t-bFv$&<(U_xb)&T8D(wYkFm*{|y6G13#*zUad
zmhe0_k=O!}-G+?n4~KzU&Ib`BT=K_<ey`rE|2J5R)2~J!;;DRCKxMI$W`LKXFWtjm
z#Xpmlr^-qNN-F^aQakdZrg7@q&^(6Q*7F#->9{5d(i&izq28iWKnH%Aoed~rmHGxZ
zr5scE_o~B`7S^nP_*BB*{Tj2$_&=3IU1=+RGKqdvIxf`%kzYXhSMIUh6WdkQ5ygBL
zJL)q5hfJbF)Z%!rzf~<E3unk|%vB=*1U^oL{(os0T7Cc4+qrNWBKQ39uKPlZrQ^Xs
zpiUz#Jf8OAMb*Edes5ngyLb0(tXQJm2)~H<^BMqW=5#s1E5NnT>C3gTs3=t17)m2N
zf|F^ze=Tlt)u8gVYy-2)ACwybBI;EE&luoP@KQ+;Mjw8O%m!RKgyDIwWDqZW<ga!K
z;kmK`dP{(+PQ91T3|-d$9WbxnOh^x|8}rG8EiyEN7e2L2+#a4LG(F?mI_K{8mWvY=
zZ?k!pkeatc?=&pa4ma=^yzE~P<tXcJMP0n2yx<-^B)Sq#wffR^e;rHI``L;SAL~(^
zdEk}0XlO5(atT*7MI4#bM<(l2zr`!W+syhvp=rt1=x^_nJOrbHW@}cBnhXm#%_R6<
zzh_~xx<!~pbMWoL%<tQ1`$gdVtKS;dxX^@m7hZRdv;`y;&%Jb^4FPQ~3U5|MVX#mq
zrSr6g-)sYvGNN2%O$=HvskWs-yB{R}1%Z_})bn@_O1N1y@N~C)@2R->h4DMp<bOIp
zsvgGs^$PqP1OS72F=#?!+70yU{e*X9dWPs;4JB4rYa$iaF@=D@h@>^TJp|{!U%rS1
zc_=}0*y5yMJrV<$D4O0m>dBh|5BNPK4=0EQ1<q7*m)cMxC`!t$mQIc%svqT`R+<R{
zNr8Iof=As3149m2Am)hm{{(#w?j3UQt^}xi{{Q^L()U1fLjhBA*YfI#m>;Hh4bv#2
zrv*}Tg427^uB6Ad!XwEInb><RfDfKSQXwP&&ry(bi92$e*Y0df|Lf{H&94ETi`R`t
zWbbkaZz%=cG4&%*&_!cCT?s$Yg$~OdDQ@6Rn03yxF=vp4cDX=?;nbf|4x;!e4T@{r
z{QOPa{a3}Mo@)$4z5ndTHXr;_NCJA$1gPj7yXlor;ePFu7ZiYO3d-9DKG2P%$gGB^
z*Q?lWkY3j@Jx#)zyHn_M)mvY5k^6(MA_bz~O=^@jdB0O1U7yrZ?zIO)?;>2jx%D`f
z*xm7~2$zD6a@xTcbc}-HX!^Xw6G+erb5E&n+RDm^I-_rA-;I^M^`QB@d>z(f&M4<$
z;iURBJ8b5%Q~oPPNv>wtLGf*%O-Ui{&j2)}SwTp{YG}ycxoToka+Sc5D=ff;XkdM)
z&+4v9lT+xEtKS1cJM<XSZdt_F9N?p+L~*B?QX{GTv85$W)TKgO1>hpmC;k^zm+y_!
zyFjC5cxy8{RM~}`z>)M0v%e>t4Uq*vRjaQdm0WBud)z5Nt4Xw`c+S+Ir9%^kV|_>!
zH1u4%Ci}@|@6m{_`1x7mUI1_$f|70qrhntD3u6FF>a<h|sJ&m~2r_^lPjr(d8*P4Y
z_Gk@$W2V|Q-X@R~aOa_kX{hT3aehN1u6_RyT}m|`2n&HB`6BfGgG_EU%wg#dTZn%6
zI5AzTi4|O$FT|G9u7fcUZ$q%qNi5jPkD|D2Xx@m6@==nR+E7r7aucz7OA~Bo{64Wl
z(+RcQ(WWl1Oj;H&&$e=Kt6~5T87fV@hIx<;ptj57TdnUgQY7MH?v!G`sJT7JPgU+h
zR#L5tJv#XE;TDAA&c9Y1Zh!$yzYiO~om%fSY8=F$1Re&5z=s|b)`vdW+KVUN^($jX
z&kU#iLACPhK^$|}|7saST~LQAW=Om;zNgayckPjS?{+enuTqXzLj>iQ(V1Vffu9%I
zzMNZq#=9O<7quJ5cn06j!%p;}8PPR3cP9?D*3<?$R7)CJFghJqDGfgnk{QAtVq(w+
zRaZ*iq%~MM!5jJ$ki_DzI&0`n_S5%%R3(|3eQvJV#Zx17!+Y3v#dE9yg`XRzhUy!+
z%<4@@P5C|;I91zppJe}aT+Z@Bh-^8Up9HlY;{RG|?flb|jHwh)#ORB)d<yVtz_pNI
z9rf^Ia%JCgR8)*fxjPm=O8>TBVsMD(HQD19%{~z4)>*rOC&P483n3Yb0-cZN$ONO5
zqXqQG31j?Kgc}>wR}5$MGi`GrwIOUQ$4jE0K;@S!jp_JDpH-1Kc@Ddr+2%ktsL0b=
zWW#u9UmmvHspRJcgiv)Y<0oxCuEHxjyunR<HA2fW$zCxC+7p!pq^W|Ff8}npqjcgf
z>0V27tiF}Q^Sh6c4V%Az8N+f!k}5Xf(1c4YIb0G~b~`MiV&lURBa3peIcj5QB-vvE
zLyv0UxQB`jPyTY|D_|q<UIhR>P>C0M2Tz>AOa_BDyfn4AO)c=!wu{3V0=Bua$GLRe
z@z~@r=5?}UANH~o@UmFlrVt*<t1ufDjA~uEKZ%s=kq^nIXMA=l-=5BjX6vj@D=?cF
zxyj2ivuZ&y7*X3+TwG_-8Wr0zM`84DibCCX$92H)-P?6Fv<&1~obIvlCu0lji61EA
zYfEho+1WtSMOMBtP}gsQ=rm87<n?J>COqWBM9Y_MRBpjA^mo`IE5GUhg!Z8%lCl8T
zD*SjZ+{6sJWqc6*kwE%oucPFbQQMw@G7AzM=tC&6QQ}@B-3I2f{y=gVqfDZB7kPdp
zE{+rht@wR2kW9US(B4`su`c5FcN2aB+0ShTwqa7E+DXn_TzDk!OOFTpF0T$k4Wse2
z|5F*p5y`fzC}auQv?zv#qc>tPm9K0s>3{s3Mg1EVzc<swEMAQ&@Kn4rZ<lVgduR9E
z`S%uP>iq3Y((U0uo7-ILZo??!@0u#A__F`>?%X|H*2#p_Ta&kZ?ur!jd9XVvW%9*7
zf6yWjE8FNVNARy);F6cvZxe4z_A=Ght#1LRr^bGa1ysISG{iBz1Ah$k!%jR`!;^}>
zKOY%P+1v!jh^KN1Xm7%bNBPQ=;bcfad;kIb_6p1r%iO$}_eflS5gMe?!3yQe&5{_U
zH;dRyPSYb`dF_68YY18-x{TwOfojcAm}@cf8&Cv&g&*@pk=PC)Q0O`F)4gAz0`E|1
zlX)Cz{CN<e@-)a-;$+f{&<FF``C%h;j_$l%T$~P&@ydIXc!9N??<MAHa>%N|t5n*U
zuVg}yz`0xZ{WRHBd(}2jInfTT!PXC<BFe|nY^0<?fp?KW6R0T85=M>e@@3kgGntfl
z7j2fUQ<LP(*%yOom;b4m#w@`a#DP29MG>l7z!y0w6T5Q~7c8Z`Sj*A$0lu(+WP${U
zj>_;~ThpdnO^}+_Qa&q5=e%#$4zFHmBzF3QQUs!u<(DDDUAG4jjO@_Ao(AyrrVqWy
z`oE81)UlySvhK3~zLyyJc5?5AV8oDEORlB?Ec&jlEVfRcd>dSvKRM`8j3pBDdj*PY
z_@o?p!6|prb85Xo^_YO=dRK+2<I2Hn$MRPciDVV<<I|fuJ+f!NhxxWZaxB+j1LFh`
zn`VC&^=8D*^0`6u6*zXf>g@(3AfduUx6W7Yjk5(T`iikfo<d$fC@k9<LOof=m3*cC
zAVmzkthucZgAh0<2`#0i<aH3b{zBSw+7-uw(9+nU!A0<DJt%%AfZ*jU8@dg-Oc-i~
zZGgnRDixUkFBjyyB=44VnrA4AuKWeO0)XVNyaRsdqp|*f70~m13mx({Y8`}6WX|xS
zP%<jZE)acqsFWxo!A~%3<p&XE%<v1vFDxu%WqRjFpT>RflqTfZ(i@*%s&wjI!TYh{
z&k#qgOB?S4nFOV;ZY}WZ1Mm;jR~bcpLq#7G^F8lC@_6F)EBcMwNQy^BN+MN2f%IOj
zpbqVt%GJfXk@9+j`=8ShRan<CPkNf?1q-uWjn&<O(tAS>{RXvQi^d#K*){VEaaH<I
z-?b=Z_YDxD|ML85bP^i{Wsa%B`8h3(GM3@9GmYqQlwp)5e)d&uuy;CE?5M47#r{^U
zr$U>8avl8IeemtCL#HCbY`(2&Z5eCMvZYESpi+R*R~1MH%x-uPu6Ked7pnTN!Z!Vm
zBFZy*ww?Mv+81DU>DuZt!Q5EK?I4A`5T-a6+@G~m?*fTZcAMe0LSL7Dnct_Cj|dCX
zyqmk)3_l%uXng`zS}W7*estG2j#qWX%1)16;=8hChxaFFn2e+#<f$1Xs<}a>vKGF{
zd{pZ9^O>Uc*h!Ou`Li%Z*Z8>9E)~O?t#|tx*9F9*jvPn&3^-&D`xR~B3)+IvjHOML
zmvsg-zfE6!qr^?|oO)-bg|3oS(d2k~T|4`|^-jw`j_ga}H~-f668=3&77+@p`Djs{
z-nU+B3P)VgHCFI|9OelW-O)BjQ<o-fQm22JCdcyoXs!V^ap686KD)C|+l4qA6C~4i
zS{I+^DG|b=7EDwt%M|8?<roGKLm#|IZxWf>_LzctK1N3MD!aPzHDpBAv+lq4z@u*g
zhDu^<QSnq#7}Y>!xzQ@mOA3*BTv_kI=;X2h*`Ga<sQ=R#SI-J+>iKvUj~O~$Zs?V2
zS*jOYCE?{k+q4UT>SmdI9~FBSY^*Qp`5nU>H`2*?0lbFIp;1g_Bf7Rj$s8_vYgU01
z+9~Tm7>9ZzODtZFai+T^y!__yPS4%N2x&(5XJ)VI&P<^VG_E{Q8|HAha`|}iJvs{!
zh<RZvzEh2+>{G5i7V^%8ypVF)Z-=eqD=c0De#UI+M$lu@R>el%)FG%@1}>Gfwf66p
z`&)I(MbCZ|%9iUKkw7<K4%q(&k#&mag-bQzhHg7-vW?6{3vK^o;qwdlWw$wqaA?_y
z%Pm_)0CJM6KYL?>GooxJ9R06r(lBTA%EzZ?XP2KXIRlY-G409{IP_wuc?0B3hBmB`
zs02CbkKIFgcbyHw;7gwEf4HXX$51-wzA<9fVuLbHI2I*Q!t{(G#dz;_b`ru_*hOdz
z%p99=73>=7uW*dmK;Ixl218;D+1a>7qJ}9$LGR;qc8auCYf2h0iPl2X-6Nt$Fpb&H
zaKZLbMqEb@DS;f#zd=t8)lH^fz}K`^Jw>5>8|<N;8w7dSj@Ex)b6q591|7wf{`>su
zQ@;i`=)KzDSr)yGRnanReXqsz=SP!OPb;w4^c6(#!fd@E3!^`Vmjde{dRPr)5z?d?
zSl!P7T(t)IYuC3>bg4-j>RXES^nA*N5Z5v1pCf4W(5m;Bg&IJf2_7vK>bk8uD}-#&
zmmW@m{g{Zw?#DhB4E=vpPQy}Vr$tgjh;f-KaifNo<JyJ(#EsdJ?LXn#sujzuC+76H
znJ4O#Oa7ETV#3;6^mMCGwdVT*fL=;{W0o2Gg_o)wS)u`?NOq9Z?l))X8A;~L?ldva
zZ_OH#R+$=TpRdS()t;z7+vhL%1W%W3A(CyHZ{NN}qXwVdC^<|zuMck%;il}tMBOt#
z!H(|uI2psvB$_hmT93+VAGJw2Zt<Hb(hx;K&C-2%GXRS%S&zUC-q7@UXLy%4Z%?zS
zU}o>X{jWy)S+>ZVC-H-7``V|Cx1yYavM?uZZcqN$TWY}mO5R?p6?r2!<}L^&Rb_gv
zyBE&WYz{qFzn@7)iE1@0b)A-KGN<?b#bk{X`>T7Przu?Sg5P5&Sk(l#C7D-(2NVY$
z2RhKkIX+jW>Fi+%dAJ61TxSu3?r~-S^NVH@P!YB_aqToI4Rx}A;B$GJFo4XXXi+Cp
zZvvqW3n_l)K`PP9UwyNcrmk;X`Q_&H!mAy$r3eGrOU-ZiPAO<LvREcz?6E~Ss$7Qj
zovQX`UXUTW`L&vH=NruLRRmose~ej{q6G9f9!l&@R7pNu1%Uq|03B^dk0G`N1&#?+
z))=3|lbgA|9mQdHCXu0uUY}Lk>5XKr)OIA7oY|Y!-(s>(p=7-KR;P=2^M507gI;G5
z3wO9l_G#^%jh3VJt!KihV7@KnPKt2fvvvHZLHOP76S(#0p^K~NO24}h^M{YL(0{cA
zp@d&Io1eWD|MC83g!jYe_x?>SeCm6WeP~v`t(pfjB|%tNedmn8Z*ckPPC#9BYjW7t
zt>5gR_1Wol9{E^6w=-f79?p@}APf63{2I+_uz&XHt0TXAlvf2bY8?jQFMfNbJN5s*
z2#zs&XywmMOT(Uz?s|OB7oWoT#ohhITbB`_ivR9A5!K(v@3f#ZVPJ^(`it^_AXWQE
zYp=>I%|zEP_cWw9>nJQad_Yu)D<=ou8&n8$=48}6!P48DzqryVmm)2+qAU`$`rwsS
zX3%uy5F@l2!{>cgwp3Pbw}2MePzDQUKlHipa*e9H)K6L9`~X;i{-FE2hg9kocSWDF
z%dO%;dV1dSW_q%<n=}ocq#8>qaq`*hg=nd(F;$ENbt}>Y*r0RjiU}Y+WFt&Fbr;R=
zkjn2W#-ava87>L-rrxqv=fexx)Xx!L>Do!hf`o<%Y;VN)r&n#KD1T3IG*(n<Q#EPS
z6yf+tOKA!<ADV7-sP=BW`U>V|^z1WkNBT>Yq%<mX2Nk+S#hBTbJs}k3`1tO<{ZH<i
z-fC#Npoa%O#7c@<T;3tLsf3xjJWqF$>`meWhah2&u(>Ji+y~I<ry_R2qxJ*G-*6yg
zTmlzCS3I&M-<jo~6{r9GI|fY+638)@*HR~IQ+AkIom6^+`(ZIZaFJJD7y=Py7%L9A
zo?Edbm^nL_DDF_+e<MmCc;yu3bYj<`i(EDi(R$;%T}PU2GpYv!FU_T=Z>DpNWPRPP
z@!USB0NeU}n@SYU&E`<xBJ#x|*(~?*mYkp|XgV@c{K^f#^u?n<2f(9~{4ON)7oA%W
z4=UU7<&U`+g%@2qcdK>2ZC|`I599IJ_c`1v|M+41_t0Zw^vD5&zR?fRI7wIKJc`a6
zy1v9=*;=xq_ho!_C-K1CB6vSNDhtFO`g8-74g#ZJgW&1+q2xDge;~Hep(>mcf_fu-
z1EUp+iI+!;VX47hzq!eOW4HSa!oDlOS+&5_MG%!|Jqe=)f`2{-F``zP1AX36JclSf
z_riM~_puhF=q4j-&WltYB@8d@EB(k${jsceTk*hY1PvS$eRyT20H5K4;)761|MD%3
z^Dqnw@QMG39fOdb5S03Vl;fxBXb_l5_FqQlO@WqC7X^7=e`-+g1__YG-E<%O5@m)9
zYFeocC#gkq<TLi-fw`hsAa9{HUgLTf{{Q~rZzcJ@DU)}D3{TAk<(k`2R*?LA!FVG_
z3I+Wr@dT&sV(99|0DbD{+lfaU`Ps7_|BxF246w>xgWYy9stp0tpVz=d)a>5`-#0~8
zaQ-Lk*GOKdYwj`2jfFgrp5fgxB=(!qLWVbl0dXk+Wu@osv)!dp-bfF!o^M%Hgc32h
z8dE$xmg?$qM@`hyT*~9>br0u_bgRF*bR2**e}ciI4-`c#p?7bz`@wEUS$)izpRRs=
z)689I)cos#R|M}i#6xcC_W`LIK1t<aYs?#Y%MyU<%if1(|3n62l{jze+~|u=0kktM
z2h&0|x{ldzG~7~0OYClP=O=Hk%YaFisCj>61F^3^^V30OrwJ(W49vgp4XDn=n>tKi
zzcI)X7nEKk+Y`%%DDhbIM#8x|S|$F7H$T4VH_?p%koSQ+0{00~`OBBku^S7Yg<_oh
zo|E9jW@40YbdXC1s#;Kg_ChIOW5?joVYg#eL)(7cR8_cr;4i{_(@au!JF#3(Te7D;
zYu>K^8j6dWPt1|I&25$05CuT~f(F!}a;NS4c21S~C{~MVyhnj~pWS8hR>@N}vYp32
zBe2MEecL>_mc!{gpVzE!{FRw?m*}3x0?mi?WH{EM!QIQTkr2AkHv+>jsGNAG&2$;%
z51#O%IoY=qEPjtXdgRGm->iS5&21DEnNs;j(B9=?LlQ37jqS+*n{hx^SvY#r)L7s~
zNEC))=+)a<+@Pw1t&kfja_n112FKp5`Bj}VM#5m3hX^3T80GQEa4Tz<8tgh}B{+$#
z2rB5&6E|cwHfY!!HT1Z%-H*UWs=f`*R5!m+hLhN_Wn134gl2EPyRnlH(o-hz;)i-(
z@U`~QDEubbJ^{lpcBvRl_2BpyZ*=Sp9NC^48fy;**Znf|LhM7{otsg?237S+0jM7w
zIdRRBfXCRV`D6^k#IvK;`~>-fhNI>i+44wFt&FTrzTElt^W4&p+(fHebn1(d<?Rj`
zJc)JaR&5|ZT*Lb<x8vFx2e+`0NMV0_zh=iRAstF_3d%p&!O!}9b{QiKRYkvkI=<@7
ztOVqx_8geoK9mU7+0o!#RGENY_Y2IfMm*p&WwR^Is$^c!a+mZM=Kg=rVc_%Wm(BqK
zO6IHN!u+6*pcD@*y+L@6N|So3#YpFP-2KJ+-&6Bg@M&y^f)^}CIy<L#)0pKy5eA?L
zBYBAx@<NzIFhPmO{~nuwkLMMgWx14coUFw5ZC``y;`eSPbfLghE8>{P@@kH_qKD16
z?#^tuuBmc07G?GCyJa-s0qMQj>OpAQaO|fcQqc&Xrx>lZw0BX!-s7CImb<goB^8^R
z5?KpnZj8-xsnhv+7Cym}mgA3=l%~e7dEotZACotXY{c$ss|eexn^!((3leY?hbP|W
z9=`MZk1k`MijGzV{kGG%V_OU9Ks}!`oM4j<6t^+WnHoNUy>wkFfBL=5P<KhqG!B&-
z6Y~!DJu~$9?I4{6FQ**dQ8m9isTP&ea82J}YxrQ6Jv0Hpa@X0}-&he&@G52wJoF5(
z(Pn~Z1UZW~DPQuskEA!NqB{;}x5a&qYU>fz)ND8GUpwQf=x`SlYZ2krP9vi!D<}15
z$;Ixz=s$};m(w_LYeK83w^{8Ln#B<fR_$;3+RB>FI*5x_J{mf_u<pPP7AQshWqZ$X
z?@T%~jry%wYjd9`*5=*RTtOY#m&5W8=D9z=ws$D{P<v6(_$klg?O@h45MYvRacNSQ
z$p`l2l*Q#BbQO|GM~T3JDJWH1*j=iV7Ruf(q79lN%|CCBp%P&~UQ|kAAi$CJ{gW>h
zi8~Na9#6a05P&am!H{!Bu)5nAQ%$Y7b%baT!s?NsA*U06e`HEN{enutK}I%{6bh*}
zI_};-j~a0kJ8rYamc72$#lF*@qM+<}wwh+t`YjeGpSSRR-)ZQc0Tgc)RkBd<d<4Rr
z{kC!+tH|*sXI;+-?9=TB5Bx_eNg%>*ao?SADSpbaIr_Y!fhA~aPHj(jbTfYU&&3xO
ztAYWQAv+htCp(^()7vFA8`aV0bl|yt6G9HY5BY+ll)}np+hc0(-;WgvkcXeM?8m$&
zJDJVs#0g!PeNGusE|(|Je@l*Thjr8>te)?!$#2KKBg8RBsBoxD)aSb8Zsn(a9j!)m
z3RAb>4s}EHL~e%@_#?-G3<Dpu^0+pJT$G|e`;z?VyK^;t5K~HRSuyE){#)^-&$lmw
zSax88cCpl(L|DX(9}<YMj;+o)rmX)=`YAU)Qy&n<9r~QbgkPMX<3q9d&WYo)uj+5c
zNtp^QAUtGeTw4gdhb-E8(!_)#jKy`+YsHiOgT`2};tf?=s3C=IkEJ;s;for^6f$8T
zt>+eCv}Ym}W+m}VrQ?3CQtvU{*V{z&L#(4@KS$E;kj+l7X`^k}>@W{C)IE}L0#oCf
zy>?|JY|Z)NzZ)V9dy0*|b%Ye8HN#%U*-W$ADN|*TX~%M5lwo#Sl`g-i#;4bUYR>8z
zF=8*qdbFlV6N0{H8BJN+kP1)rza9zaW?HMRdH^MP#j939!!kajQTTvUbXXpu)9~Y}
zww$|4x6JUkD;rV~^_fvid53b~Crox>d&8|<OUR|lh>l*Fmvi%GYB*V-hEit6#thQB
zE4=}(J!u}#Wm6*_)6t}5es?KQf+^q0)MMMq+d=pe2<pWpkxr<hb*u&hB_?4-!Q-1k
z#|@uolUJ(Qws>hwc0{|S!V3eUYsbl;^1nV1rI&+gHt$!VKzNK%r<8^K1J~t&%|2~|
zGf0z2LBHaldu<5|T-x~HThpb9dS-?at7P?Y7~hnA0HeDon}FUw{d-<s>=BHlih6XM
zX8d3Eo$00<2R{nX-OPBMKFa8rzpPP%RJ;iY&j_9FN^Pr7PPpR%JohX_t*URZJPFY3
zAiDqNl_#e1&%VlYyaxz9-;7C~U-HE>FED1ZJMXnPrw8dCM1}Db6`;Tr*Ufeykp2J;
zQ||b?<8+syXSci8jd_AqvQSuFlhA8YGXZ#XpcIx5sua>VIlQ8qWwBs_@P$w`@UXBJ
zT7oQKZWKDSxIaDpGI_{N!+39185OYFPZv@z78-qU{Ob6lTHIB{<NVP}vvq25k2txD
zt$Wf%W_P~Eh-w#Yom&wg4hmLs+ybOW%n*=ywk?b5<CN6}TZE^41t&kkQ#c1bqG3lS
z`_Y$VhjpvvZ-I$`GFpOl%iDGs@@3e$!x*E5qwujw2|e~EzO05Y8pT5E@cGx%s&v+^
zSM9@m!J5m;35qzfm};*ok@P|1J2o=g-1{Qg3g#H#*TsZ|(-H1_Tdy=dq7>+n0=7#n
zOh^nKe9{%PNK3glIs7-TKYF(b&!?U@FBBX)bGXz{OX1&6ix<VFne<Su-vW~DqbK(L
zpC^CY)QOE4lEe=>SP!>0Q9(oa;3OgkzrR|c$4C+kp#zSFoSL35Uc`zk@`%Q#BO2<`
zS?zv~yay1Mia6>_h4do#URFX5@;IMV=DawQ+hMcwty(!V!C80Xv%baJb$yz>(DB(J
z$Kvcgz`+I*fEd2SG|s{`NJ)_gg=FBF_A1Ee#*f`YXTZjlhov^&k|Abz2E*i$e=~s`
z-s$xYbbwVYY#Zm9>sxolQzIIb1@?v=l7P<mAe>OTGpWuF?EBKY%IL%SybWTApH$%A
z65oMb8cMEJ&7}Pkmrbqot#MxXf}?sZ>&O|7I~))uC1$5v)^dF>-fx6u`;x?RdAs#z
z6PS9&Y@!O&tSb0Q6>6^&@-o@d)q1h(M(@o9ZMZBXm3Hx^7Q?~R%N&aY#H0xyB7_dN
zHc_@MSP1kSXv{D&PnZ%|bJk}>qgv%-#WcT1C9mzpM-V_4l8)?8b3l-y@unyuHj6hU
zLz(Z@NYel5<dCtSj+o5a993WCxuD}|OA2kUmJ}Y@+Heeaj}B$YSei~gq9sU?B`G!9
z9WZ;zp7DtSD$vCK^xT{i|BLw09Mk%jpKpKr(Y21c#kXR=m9%BthJB)jE~Pgud%nEQ
z&0vV3S}`JR^(I^ZwFaAk91A;PJ(KGHd<c`B*`B6_RyA=N2Niv-8U*iQQLqPM;;B({
z_ff(>lg&iG@1@ME)(^Z%j^9wP#trzBLDY*9U)1!>j4*%q#_(%$9Tu0#%tRNEKKO<T
zMV|3$Lmvm{SP<oE6=mIWiC2b?a>iCePD<LfM+sQJbo~wjCi-Kv>IUkjbn$L4P@!Z{
z_?rdaB>kN!QGFIK<L`dy5Fu|%vPu9jUU4RecUm#DC^s^!HkAyq=E8Uh<FbrapA_~C
zj5<jc<$(O@Txmw0Si-hVjks2SBEpOqT+HeS`QfYqY3!=H(;0kfZF&usT5s<k!(JcQ
zs<ka*Sv^!=e#rO(7v27q_7g*yTmSR#V=hE2Yc7?|ax;Xc;8&TBpiDYq_eWtRS0ppY
zqRM(OC(anJF#|PDF~}WyH73*HI2|t+POf`wTaKBmz%M!|*V2nAT}{g>;;*gz>gO|n
zVz1i~=Y-S0p5Ub56Mrz@{brv!?~^n9WmOjqJ5=-*`Tcr?OtQ*i<>auLo4QZ;eQ14+
z>)Nj}rp1$^0}}Dlj821DT%_0RBXB9hwoU><DK!SZ(W%)Lo_5z6tb`Asirc^FdOaf`
z-fjBK1a;Ep>80F>E81@##(5`R^TqCm!VdqvImhiW$Z^G&3}KRl%l=m*NE&?5y1E#5
zdTrOrY38Bt?@}uc87?+(uA_ni{LhPMCf>;BTDVNX?_^1V)v`;S4LZ!C2GVxM%R<PY
zd(sz;ma<U4f(i-F;OLR%rztkMyHEw!_BbmJI*a5cu!MCk+g3SA|7X2efH4V3BT+%e
zP7H=)#BH}pj$mrq>Hbf-1-FL8ac}KD=q0RA2WO%YPmIhMzI{?>x|al8a46~vNuWRp
zB684eL;+#uqoJLif0}2e;yM@6W4`t)ypjW4H*#WudaGw_Woi+JPcVb*O5s%V8cgFH
zieSg<V18%tz%@M4*&AOU6q)OCNPS35Hf08VT_q*Nv1REXPgT4CXT$7$ikKuQ+}M{6
z6vrA1{C%Jdb8y*ng&)u=N$>yk6>ZkY29HK!VpzB2pCI)3*ZR`Tx-{DDG@?7@)Lh$u
zn<?~v?<#Y(QXbAcD)!XCAKY--SNyLg`TRki+Q2ph1J9%2T3;c)XDS}F(X*P#%3e=z
zvv_1gs)Tb5K4|)WYc2m>V?;Q)(gU?(9qw@J1XhZUaMvcS!jY-Y52IaMt#Qmwhu0VK
zOma0PJ}%WaTT+c=fB`f2>#QnRsw1sx^|xQm&C^)$sq<QZ=-JdqDiSAe3Dk)O7P!Gm
zV}X_r^nb9nuIgbSa^q~`$ZNz3WoEbYR)g)py(&2T-P;vX63@As47;YVA*SqhAO`==
zl$>0u79ZYQxDO{?fWrd<$Q&n>PYJI3q553i?w|xa!NEK*yRj+{+>$Q-D{;?#A2#9h
zzEj~(TbT;7j*t--vPy$8W<jPz@iVEL$kEr7SOgIYpjNYUu^~V4QO1nOl>SCJCUsZz
z2WqJ~GdEMXZE!sVvU?x;UJ*X=4mInWA|`o<!OFWl9r(%(frO+0(=1R2v$423%uxpR
zRm(yiX6O$#q&5yyNrqAvyPSELqUsplsU`Oi;U)M4+>wG{>Nh1|vneL<fR-LtM#2cx
zTuX#Bq=)IB%5R(MQ>~Bn!g5qHqLBza6-TVlPIdH7GVs97OB&VV7`8%vl`>-1=s*P7
zRfQWwV=%%7_{fn&>R;toOS8qt5g*(jT@C*__BliJY+erzyAp!f$AwV(>2u@pFcS86
z?_MiKg^Ax5BcN))K@t$d9dQ`_TbIIk_O&eoWyi!TQEqiJSD!wDbm{C|0}Y+o)-M|r
z=vwFUPAnOCAQ1c%;4T7Sn9V6K{`0*4Y3p8s!BOBJT`b5iK9n&j3UxS|5Q|<o&0)|G
zZ8L#DNFKorz>JgI5m)Mgqj_kPAm;9z_&@GoAgmV&oF)Ta{j2s!)BgS`d%NZwvs|bl
zSVR*(0~(vh*C$15^lpV{?JHK)d%N^P2t@Thl#dm<4OGpuUp<U;)Cy*VF)c0ffiGC0
zKUkpK#iI#6qF&8UBd5#R%SvlNYqR}V$qeQ(pK<GUmd)5hNl$qP%Mh@dM6G|CIm8{9
zqK-435Xw5+Ri&NCcRdB!KB1_2dt?fl-Z%g}SXuA1sgXWD)I%2d&w`NvHZ`<L3gFe7
za7>B%+0sPUX@B+6fh__O%839HIHB8#VcS=zx#_;gPw4yt1yDmr3Rn>PJO2c_ZfDCQ
z8bf6HPvSe<dr@<re85*^|6U221m#%!R(~P93=Y<W?;K}=e4yOml@)d_bMarSs4oql
z{_dkDlqO8BbRm$pDsYLnnBw7Pi3Qa0sb-}7?$UG3l;>2@hsToc$`>!_J%yM=2Q(&&
ztGp!4N$RYKO^Yblcw;6O;#_ndJ$<D7n1iaQOTqHlE0$pFysNM7f4chmz5$dq6GhQT
zN8c8FTnI#l6>81~^;l!F98=GV6j21ua%W45&B6FABts20s|N1X*7JC#ZI&ICxL4Y^
zwgquOkQngW+P;T+qo3OLm*bD@%b20XgJ(SR0WZKp!3dxl0jLev+*XeeGFD_Cs!>I%
zED}k9@me?bGADMfiOH-K{XkFDs%3MCw}wX46Jjvl3PnzeBPksme^+Xp(rgiTE#<mu
zTsDk_f?-vqP>fdsMyaiThO4B!D)D30#Kt+Yf;pHT4961v)3A8Y%Zc4#{f=gioOk*U
zBzhlx^N!))yIVgUlqdRv(xsPpT+i9H1^4N|R}cPsb$DUJx?y_{PriS4lS2J`EDemv
z-Dmx$BsNct9lOiX;dmVVfvb9jfcqjC&-@(qPZQx1*C!L@f?`cSH+}Ju6)WjC#iaig
zdu+0hnwr&RMZmr2#)Xs!t~DcsKnj)MZO^1&=y0q@4{YIJva2ltX5CR)*XA@21UdTr
z2IR4{JLHB&Kg7F*{A-f(;A*v$#rtBWvbcC@%LX(~3}|da&qBXUpwD75)V<z`56L?=
z6!jpX+mQ>2Ctv}BMY;DZcb+_;Q}UqA>rR2}7n><6;y!+Ke-2L0mweBeMwys~1^by&
ztr|yo)Y@Y<K?0AG%>B8GXT;B+)ahAW9{uj>6Wg*Tskn#Qt^ttURJj^4EL_#b>qcgG
znBgS#L@@cO{{HTJ;J3V^0uSIXf6Ypv2s7)xu5wj>(ltwK)|LK547nD!$Pdoop&<8a
z0DqYt5%gQ+Wi|Fyli_oCzTcUmO9a?h0tj-_PW=rF*Kdzz%l3yh?hdXWBs6#S)xZu5
zwYm=tN`E<cyZfa3)qMxfMF;oW3oA0|R3^bT;JCt$z?_)GvvA4ig~$k%`@HPSuP<!C
zRYzO|dV_)a`wD8X8W&N_>kv*A3S?{%-d(-!PWp`0vE6@?S)fZLc7d^-%|j>E#6nH<
z`TXOf-N@_8<B!@l47+{oB~goDCn~Ha1J$nHa}`Y*BKc-T%IXC(DmbcFSslf{P6A23
zfGR4mLK#c&tAcXxY!AKOHB#1Sb@<WM6y$tZ;LNw)1ae`fA|ve2l#tYLnZMxA^I4%w
zBLZ`;8qn(w@Z7geKFMrx+?HDtiN$lgLNpuY7cKM|Cs#3yzF?|{6?h-)``e&<;}^fR
zVaGqSWFLe^6l~N|S^T#8*}V{@ht!n?py2oVQ3Wmyt-sLi+xCRNZDqXbg9w_4E+Kcf
z-hJ$AVqQW}S^n-f0Yl9M(#T2Wg@xA|Kc%6g$h}S*$beo>1d@X76^GX&VtE?&ZJl&2
zd<X<2#MpkZ<nx}YJEh_JOP6ry`!}7o>XFPNyBcqIcL+7>v)V!Xu5#Xonrkg4Yy6af
zj@AV^L43XE8P&^4f<HlLf7IEkqY`j%AP{%OWI(Vo;T_djisxW1Z9Z$%a$kv>Wy(kS
zv;E4lH^T92zo1&W!jTX?2ja*(cXY|h1mYT>^5w4z7RC@1K8oeXz9;@sfIG8AiSlK~
z;rd(ZFX8<ZmAba*gBOCEg$@%ZYk9u@!8Jt<iPm25N|17$l{K*d^PBKA>SN`R3^b_U
zCI+K68GXzyL?Zd9Sz2nBbNg?-86oJDk#p(5uOqRG;*B@_;h$W-|H{EUHGLUMlw^N2
zz?mCHtA3WN6CVe9R4FIFg4yk8K=fH8<@6GWd;UQ=xp6@;J<oc6FB{w)l6#~t=bmCJ
zzej)wz3|W8C)uT?m3-Z&Q<|bD?<oFhloHQ5iGpe`oflxJP$kvWLMY^W4=Iz4rCX@)
z*4ctY6rdD4joMR!cFP7D5D6JozA%o5jLbnVX$LeoMiqA-zWeV-0~$z{+(K%oXC4q{
zG}3uCSDsR5<n<6`9C)x?iPSrq$S&hVb@@vrZ|#XAUjZiypyT)XBL#c8GCCv~h*Vfy
zcv;37irizjJ8cf>89ebyLh4=fjp@^&e9E-7-_)4XS_85KH`cM!L{71m>4E1j3<mI_
zzh@5OT2WkV?(s<n9qePXUN?f3C`>TFqfzRUM4n~8OHrWE*0C)exQ3ilfUqL``4qY8
zy>`b1bxs3@)VUiogn$z5U@2nKy5c&M?Fb-vm{+~KbMn=hAIoGwl9mu>OW)(I2txrI
z>-_P0t@@?<d-g`@8AK=IPZCV8<=O4Vr>$^nsQ^V~?O_G-FH}S-uTV4JG!=IKP=qd6
zxuYo=SS<0`G+^_%SDd}Y=RX|gOg~28BFjwV|2f(-S(OnOUTBSfvM9}RW1Ua!J;)Ae
zGQSKK<WSvKbTQdL8HS~PY1uPf+gvBn$jno=*x*umdURxt1RNgAw`N{jB$<_;TR`NF
zuEk2dd7(Cje&`nUbh!@)jmd>;U6Acgi-Hg^(j2pU`{lFuD@0dP5>Oe!#S0D)0{dsh
zGa@fLPP;6^Q^Pn}ZXpi4ZzdYlkgZIX+x(_%(+c6=Nb(!+&~BN)6R~rG)_aW3Fzl4B
z$lmW<baIl`NM_NLWA`88^~I;1NIf4-Oq=>lf_xZp>!x8*aqF+;ZxL!>rjhj+ZM)T6
zV&FS`i3V|~Vh_he$V-12G3Zv<BT>8#jJFlyLC`zvsJfw`=k<4L2XSdg+TD3-a{<P8
zr^wOazUy6-e>1~6CdUiD{CG#~@nJk>${02ubc&OqZc)25m1=r$M?Kg$3T)sY)8Q9k
zTZ(1j;~u*rLg*K<g%`B_EOWy%7$Uz=nh%=VqHItckCSA~Y8{0jiyUoSUoMW=SN5l~
zzldmA=ZW&KS!Yu`nfye(1(iet;nXD&AF%#x(<ty5mt#_29dmY0`7f$y<{;8;q2@yI
z++)ECmqnh_(%_7cmSaW|h8{-M-7=kj3&<D{Lk`#U=urL?v>R<C&a{{(<C`&rJc{f%
zxII*o$Oggi)cMF=6y1{Aa4`H9#9)w81BQ}6)f2PM_drvZN#}XzvHSl`OQ0|T$1><Q
z`^u~LVbKocl{Gv{uppx}!Kf=DH4qwH={jYV@o8s|rNyfr1f&_coi&TjDm!`WEqQHq
zFNA7MwOWr$ZT);D$S_&39PEz{?v@sIqP191K1EW_y`<d_X!g;Di++XH(}zAw2V{Gm
zUE6*Xo7d4lQ#q#i;<dYI%>$LM0y8Geh7*GG>weUr==e3!poh<8M{W)2$wcZoZwyBZ
z&x_p+YY->BR4aYbdD96oDGbi%-s2J#S-_l)SKE$l*EDKA9p#0f8GszX;i9p_?3AP9
zLJauT2%>vk!hdP8Rn%d3QyREpRaV15C~zgtb+(iSg^oguvlAql&64feukig641+#A
zR`#{ZL@`|m!N5Z5l1lxLn07f;N-zl4ChWjJAy@Gh$Zn?Vbw^C2w(;wW{YmhR(*dDS
zbulSYub*_nKkP0UeBkqUS7owx1vs}1R~3ltc{sS(YCl<1(^hbUi8%}MmhtmGa9igY
zRXN`4w65O3G~fm0l1Iw!{Q6*dgGa|Cdy3dnwV$*R*eg5nTaC}-JR`1E;cE$p#Dev_
zP)+>4#4g;U4OCVKk&<cH2P`<P0>RJSRZ%x8;)dum>=rb0j{)9!VaU5wfU!rx8C%NP
zAQixhqAb23%d`uV98v9gy}v;f_(rP<VcT&VJ#l%Ykg4_gymhDGCP-7xY#RT-rMM0C
zW>Q7$>q~7op2Y*|F4?{#j5XX(?2GWSGP0c&BDZhV+As<^8-*NS8z^;PuvX0rGXXPN
zoG$^>!?DO?TZu=dZz0aPELr=+N93WAp-8>ml5hLQ(b_q=^Q{mE3%XgXCU8nz(-$d9
zkkH-0jQj#5?DbOtYu2UUag5_;q5Q`cG^TCldX8o_U}51;SW`xEWlbjV(<nn{!p{Ms
zDTCNz;wYQb^wgxG>-s{BZ_jPsGoR}VW4&3jDiPx7FDexPFj?KkWdnus%^u?atv<q-
zB_)zlTpco8svoUc5ez5x23MBxAmdVeey`lj=Mt`;RV0h`<)P=aMea!<>ga8GkR%$1
za5+uSZjx~duz7BLQ2qcl_38P|0F(lTQzpwWW>sKHnE(HarmqZ$s`(yXq@+WTPU&t~
zT0x|{k?w}2q(P;7X;`|u8w8|lk?sbO66q3rFVFA)e!g?(<eZr~bHa(EW|yGkpu631
zV3wrx4iPVfN?{PipYBTygS%uEFuJuTd~yy*Tf2~8w2pvkkpFs^vM@b_N%rm(hGf)3
zTT}@~vKi7_UGk}(w+g4+<QXskTdT=i=mU>GC(^2^W9E$fG>cJ>_W<1le2VEA-;eKD
zOWc4>xBO(M0z?8={5t$VGaH?3fu6ZR^)k_6(&_Uvp1uwadwdV~ewCjS{ryJ(;q0t+
zWInfce*ctdb1goSDAwel%qnLkjwE|ug7W;Fy3Imn0Pd0$tk@K&t;*l+R2DFms=slv
z-(ypSL~8!({g{SKrAnp^eP4F4_TK>@6R(ec<iJv$)jAleQD}xJ#YT*thr-ZFXA2j7
z4kHT3Sn}J84t<x6#aN;Ze?ea|TQNLObv7f@W%I8&P#0$lFmmCDmEDl*{+;#N>V&pg
zbqLD#8TknBS!<5P%2UFm3MZs!weIg`1vSQa4#r9xTZ&P(STaRt0MO7O>U5&z;~NsD
zztwPG|09O|l#05pR=g;0Pn`A9ID~-(W8qi_EQDV%13n14b-EW{bb}&v6<HbO05nSc
z;H4t$!nVJNe7zo4-hp~t|K7NblvU~)99S6IOD)wljoXNg_X%TGP5X>;shE>ie9HJV
zEFB)kW%Ow$D^TnAvYk21H1*@vjd$J0lr<0M?mNR%Kr;Oo0bRhu-8ClyZcZt~K4Xsx
zgrHUSThIwUS-(KvHW@v*5fk)+u`+tR5C61+et!bQR+Q^8qF^0t2p61Jj(>VN;_`al
zl}HYo>V<YZD+m)RINF%54+eIKlt$Y@E^&HwZXd0VGf-5S`Cr2&u6us>XBIAGag#$|
z-Szn*z8s%WfzwcnxN=|+=z3xQGHf%Y@RO6_kv!P)&W&2da;I3aq}5P@^Tc<}-%zI2
zakP<P$SD`2Uw>E`(hyl5#xpjDWK$^e@CXs#PU^t_eTHg$#69JW#`8%}hDA+CI1-#c
zOa6>+D4F*agAQWXMPkEC7|p(w^^oojE-^i-mGz1@;ivbZQlEORva%NBI1p2`p%=2&
z)?`aKiu$Sj4<C8%R(6l0b`VKIJ^ZG>g5N3&Fjz<VrCxOB?h+7TlDf(HWRQ(S{7vJ=
z3wu_fRB$zHjn4cCH!BA=18NQwCpIsrken=|IH3b$^D6QzY*5jR?5<`~QmX~}{TP14
z|MR20kW(?!4HAI#;_Y<**DDNa1#CV5hG<=LP;m*F{Qy=RX5Y@hfilEw?QH?Z^SU$o
z*$P>H^%IpE%n5EALj`5Fn^i{LnvRUe+s9i^Of7iI%qL!4h6n}OL1Nu#M!Z3iA_onz
z7a%_i1B_~*eSJdC8KG!^PL=&gL|kZ|fjw|8>7OCjfxFSAhme%f@9RU&Js~_FMe&;8
z)&+TEzEU6=Z%TXJrf-O1U1aD*rz3adzdAL410A{>km>hnKpJzqtF}!XU`P6WJkUZT
zL;z^o-kNLSm%jjH%PyKIKA-IR`v3q7bYEO8Y#8^s;%Sx~H*^iy28WY-H0EPo1O6C#
zlWDsi8YtCH-mI7iy7!SUB(zpUCcBcM<X>Fk1yT^B1<67cRnNH{1f>Zg0OWYL=C0_<
z*SN8QH^|;Uv;I@s&gg;2;I7`L7Crb4gNv|Mtp12M2{H96hV}a2c-wu~=<PA@bCV*I
zf-(G%qZ2~)6yO#;zcv3EGg@!c^SJFAtulOAmudN-49A=9mS-*5X9f=-o6E!-?Q27_
z1u_3Wj5ah1-yr@idB`~lTU^zyqO|F{_!WrQWKHf>-cpmeJEq>-m%K>*C5t#;v)5t~
zg^cvTsR*dbdLD}pULzzHX}gfzlJgpzl@UFgkeg=%7hqL{m$%TC3=0Mx&7Yxo0ZQ~G
z?1tKfrq>R_Fc{qPQ-BrRc3OaDVx1nNO$iFZ07L60DL}uK6OXSEeA}}Thk&lttM?UB
z#)=M)9lLCfbZ;!Fg3gdg4vb70_w$&c#oEd>8jwhgX_@F}YG@rr|8qk>0}@Gdz^H#K
zlxn7AXXle9K{_&5BM8HodF}8(YFdzYa7U%DqWr-#pnBxG=k;ROcv!|KzQR+8Pc~G8
zB?-_aV0qbM0acen*!ht~z!c*WU;bHbjoe}Qm^r>ug~*3MHU8`JQMDD8?6xzP5raK)
z7sV@@2Bh?M$&4@)$^$Xq0L6`=#(}8~f9^ek1BV}XJ9&8%7e<%*lVIvVwgCjN8M5|5
zGe662mb4OQ(zW3X#k)x|zZv(Z-F?I;*x<~~7Q$=rjCVe5ZjjiGR^avqoP)Go1`TRd
zja7j%01jsI%C@@!5Nyzzd#zB(;&QOjJloq@Agw!5Hx1~~3+!ODwC6Eh9VfPj*g&Z0
zqnx@O@t4<37$Jx+LL#f#Wl@Esca;}WUJ__Z?hQ_uwMOK-VSjPMZyD$q7h7uRnp@J&
zH7y+<AJ?c+No43+{)ch<^AJ4L<uG<YS)&3Em^&9hj=RIvg=Fw4z`rnBe_Lh;$8Zl4
z?-(`jxyLOq?%$rNum3vRdpB`TQ@hJ&O9g=l2L%3kWVDv^Zlf;&Mw*MGJP)nhb)dXJ
zP0Xx}`TM;`_Cu+qMd-nPZ;u(;16ivVr_G2hWL_eXH?S0v$CZ{0D`6}N7)dPbl%*s7
z=fH)hLG2nylTF!APZ9H%nVBTQ{r#H+AoiE6IH2P$X4;3ey2{fwE;-Tu#vn2oKO9rQ
z?}`PSRrfpNdkf=!&6aB#R+t#!7dLyNV3Dn(Z{~s>sUmWdnL$*>0bK;wtgo^A5=1Z(
zalRWVD4R!f54J`-g4fIi!BPlH%|hj@r*mbLSw+T86Oo=OHqeDgJxB)Z3J9c3LS)->
zW-MvgLl8QWy>OvHVq@fdk)@Lq<Xlv;i6y^4lMOx(Y8-=}t01^@uw-^8H_FR-?}MBL
zzZOZ_`{)PcO3WK2h@;7)V@gSSOF<irzFqK@7Va8qW$b_a$ULR~f5=4r4;jHw<#*Zo
zZ6#^w{y<`emT9T&7Af<4<032rNJd=$`Yzk&<~zpPx-8fi7xNQjSn|){t!4Z_vyM)Z
zP0MjEI`=P_G{?szf69`F)_f!mMYBbl%&G`8k=3jIZngW4(4AQuD^7v}Ww1#96L;V!
z>Kv+qa+8YeVOUV11Y<|x#)vZfg3$~;TWE_6+I}h;Px^mK!0{~EwcSGI&L1?%Z@WJi
z6+#>MiWe6%Qj@~MX5>Ku_6`ny7JzJ5vUlC@iNodiaYrt9+;HOPl3nS_y%&>0b9t*b
zF~aRg4*9WXOA*_7Kj3rGi><AaPt#S(KS5ZMEQ_~Y6wAMcEBBNZ>J#hWQ{ES)ATsVx
z;i?pZkO~!4iBehW6Wt#4;XdHG+CNu>HUHDOj|0uH-*uTV|GPumPvtym@mA9#at33z
zWno>T&CShL>|iCQl|<M8*Yea<LeR3J1pIB^*jsu&8_HPuD;HgLV)RQy#d4rmCI3tB
z^f#=MM^s|5g(B{;U80X~=!zVaAE!Fve=vxqYQEgb_`*qt+=Z$Hx8A%^pnQQI8uZpS
zpnLaY>Wiouf6oW%|5R#1(@1%2ihbXbUov>KwaRax3&>unS~Zzhd(|@;VvBe^q<@jH
zm<W?{Fc`hu(Q9_Jr9>}cVpAXud2w~W_%~FQX5EC>f0TSmj#E;js6e1O##ZOy%nFAY
zEeqj;I>8`6@)7iMJ9oqzg(;;Ii2(Ih*=~swY)rsvX)zC-AqH^x&7&2cIs@Sav+@i4
zsd9fb>j;(5QD6a2+CPqSUqPq!eOa!A0a1zL`$8M2tYS;}E$+`q6|GSoALvT5RiBW@
zlwV14HD$rj>0~NovEZ9+NnpcD3OH9%*55~bx>@u9IagvGtV@OI0YrLhPkj^u<kIBV
z>KCTe*~ze5r`bv&^<&uo3CEJ<l%<VTr)IBHi;dow@4iX@SJZ(^x1AW{XLX(QwYqt~
z=xaJ_ax0$Xh2(=Fj34YH%)=PRsW5ic(a(0aU*1{9R?^|eY9f(kgrdm>f9}lnjPCK4
zOQp$M`O{RWMXdAUx5D9hmlWhsVH4*7Poil3<$w`=36*?t369iSJ3;d<A7GZED-;QL
z-621JrM%im)jB3*Ik`CNZP?`_IpqcOe{tr2Z673EOm%@X)l&WU;#2#z0$ivk@@J5+
z?f&^>IKkDQih0vEU1KcOazk?$Z9C^LUrD!q3q}T_?VP9XutWcmoI+toVK!H++7q8=
z7e%=WXJJMMiRvmHkzKV+?0OK=XT8vRHnU~B?qUkciDyQM7q%}ujas)X?$wC=e(Zi>
zKS_nzh2nph46axiA*uS_AR2%{dtj`FolSc(inC=Pu8pNAKNKX4-P<|~B0v9>u}%Zd
z%3Y?PlWb6j+1SCq(s{owv?)~cS+*Uq1)ve@)UlWN>Ox4}YIijsJ#aobKX`GBf7}3-
zJKO!+Jr1>oa=c4uj)MjKL|E3QKWGr%G)<_#(m`m7axsR?zx$E$wYG4;;KyvY^jx<x
zyeF}AR{SLwRnoGGq1*(Cto*r4nGV#gaoLH=;u)CN{|DByoAh~PJFUJcuAkzO^<*TO
zqiebYe~uDvjQq5QGg5iFQ<7XK2;N}I1vP5j%8ICSZ<-sFmLvUXoDdLPl2U)3Y)1gF
zG&q1sTu+0YZk$p06nH7YTL5W23vy*KWk!q*9CMeG=w=%BKOF;XxZ~IzLOmeQ-->X<
zfBq1-zl8P3ouIGR{pvgNAlJX%$Kx^|3g`%V4F#0Qr-bTlU&(9e>@Yc&^4X|amsL20
z3J+m&l2de^!MjHXf8^1laCcYlClY1qcsC?w-N73gSb?1vp)lF)8o08JJB021a1Q{U
z7V|9L{}<KDQ3Y!N8hTk+#Z?h2>D25x)X6^|=eg6g;Y(5@%sdU|+M&f2I*|1`r{}lx
zeiMDLH4c5r@Hk}tMMkyJ2-xQ2zw=aYBLq$f9DCCVQO0w?M@cZbBglVFXq1XJh*LpG
z4y%J<t)CyWBEf7j@6fq3tm?5$WiU81?4m}1K8T2nmuNDVfxaY-)0X5PDDR%my3v8+
zIfyQN1Y1841}o9{(7<^A^Sk6*L$DjBxs1ZX>UW%~=4!)^W~`7A`D;sW@`?3^2n!1;
zJ9S9powBY!p8*i>pEzf=+oS{FZ4sJ$@v~id=nw_{^xWw1WjsZ=kU5S7<G%k`#OkL=
zgQCH!gx7woTO2POYRFK~e~*$-j$B5wAwkMy4?sav-+m+>6=h8nxvsKQkscp+#M$cW
zd`GDr&cf)lXzZ2vy%dv?1T*{g%hjIkUE<suuwbL!9?diM#Ey1*qVV49@Rg9aPS`W{
zdU)nVYkNEr;2LgEe~O_WqoQrEHV(H9KzC}yOs%XXWTY-a(c~hiqSb?T!uGtBlMmJv
z$qBmQMx-0|`ebebo?D72J-Cd=CtYnv>5rvKBg{Xe)|)FQxF?Iso;)EkUf;y6_3S(T
z3x#ejOLpU(+}<iXM)IAuu|ASMW#}=3$EAsMg~c;W$?uykqqsP$%$b7HHgSWH_f2v)
zt6|D`VKy`aulZakN6BUW+Cu`;L<9#5K(!t!b4-5e6j&dAPBeVDcye|nBbC#3m@V8*
z(%TxfJnDAy#|F48jVag!$!53_JssjwDPZY3oRxtjP;oQ;J|wPSu;wiP$*0MWD9IZI
zY9_vW83lkdrG7i<x!UD>)2{fnmALhc9?bf5;{;+7&}0vBF2h>2{8zmP+8#G@(ILF&
zrMN|wS5C)D;s0jPvUYEr#E=?B?wO*4ZGDyV{A`e@R0X^KgB{n&mBYHl8rtf=S1~z~
z?%q|*4_uwT+B(%!N!n0h609b>fr5654+QRB-*90kb<<h1{^)laXY$PDevun85ZeWk
zxp?MlFJV_Q?E%($gBmJM3hxDhN5Ze}dr`u?$#k#V(f@f}qAxx6*x0;7+kBT?BUXY9
z^XBm+#P<D_fjnnC`U-B1WDK3AKB5kE%rntvxV)N@Dc%-B2=0t(Kf!z=Gl>@_t#-tq
zj3I51?az8Y4D!guWUe6IOtnm?#*0z$m7{pJ)2MvqpM}4&-mkH({`h2<8>V&FY+f_r
zXOIl&*58d{ZFr{$%yHYs6w2s%^Jfnq`<R@b^A@imCA0oW?!g{{P2cf2iE+zvSADr@
z^+t-em6dS=<~^W6Y-)SSg=5a&)^h~QhAka>%R7OBq)Q7_g|bv~YaCtA`h-ZA(ujSu
zdO3j$2CkURDGeB`zM!lSortPteCGS6%fAx6E3p2s_uaFWr|le#Mm)()`@?!d3DSkX
zW?~H2oviME7@s0un(xb};+<h%k+r_VNCQ-Se~_T<O=Q1ESXP%B^-{BEYXp`=SKYN`
z&VF+Ci${O#K39{9{WeG2XWas2Tny8a9RB*y6%$|LIJ*`}#K=k?CVqN;Z@7Qe{(LXu
z?w=h+!&EBVG9X6{3NchTd!G-E$&t<U5!6xDQ8pIEHqIMEaVdtNMWl6LN)V|8x-~|z
z{$Wjm;d4cViF};aTX5W=lJ&vl9F#C^A-C(sGoQ0Kg<_07FQD^;E|DW!T6Cank$5(e
zd^xjY5&vtJsF&I)>IRj$;aM!QYDjf5EF9FMEcvcO^fn(qXMhA(&$OD}M36?*dnym@
zdo37E42;wLgSoBOrL~RP8haY<{q6hkNgahYUxI;Aulm;)9Lenj9Y|prZe8Ind?4`z
z1J%^dy=h$h6@UOOMp0I?vNBl><jLu&{z$cG7XkRG;`p8!wB{i<k{31bXF|a#gr|lo
z4kckvZne|D{XcOC1XaL3?u`RQed^=QH-$jP7iqgd7SHD<s4roOGnG<*0wKA7Vr=NM
zePvn@)+}3GK)jMZ{xTxhi2VB(4D}@&d0G6b+lL^UI@zO&*E(+$Q8iAIi9()-hj~_*
z*46N`yt(e(rCV;CPZ0oVya}k2Rl#Kr(=D;<Yz(9iXWQr-?lBM#2Mj{e8znn=hq1gQ
z<Nu}y=f#qOP{#}g-lYp^rt6NW%}$9D%4qk1Ma4AOpACq}M3!oznGWJ%?c&SCM4MOW
z;sQuPw|n+xbpyoF40YKLmK)O$8pSoNg;R1|fJ6CNm-<)B9?-n*!6in4?*K}*k9deL
z+dYr|WL%uS)y?dkM*W5@&~L)ylX`#M{!D_6^`}>dDT^9y<t<w4T;K?SViqy<p1U&n
zOH~(~Uj#9qtc%045G+FMGejcEpWP~rkAum=?4FwWN!Xc-X)u7R(gRL9xHe?M45ZA+
zp&q_S=x}WE_FXA$m!fVrztrdb)3L_3XxdC<9a0n<UIhS|cdP_sfBGr!zQU<bG6+=m
z%%Epo(QxGAZy(KRL<fGDx;}6z?`v9sno(6v1sfGLg87mQb}xJX?FSxy4Il4g%`kze
zV}GPLM)_e-u_HdkfLQl_N|PxgK!aRb+i!9zyZF_AxU%zDCp})SICcE#hVp*XhWG&8
zqAvr>0s_%`Y^>9Nl_}Cx-=Kb(-00TZ36>KJ$U;MV20m{ozn_u}WJ{C`^?L>YkyZtc
zGTp{-V;d5_=+`Zp$Gm1b^NR4^+b61#0}3a;_1pI!CpFHhUGJVcYLD70F%lEcLpyej
zh`DGV-W$gfd=|^^7+6ZVmq{}dJXAI!d=^xv%gds71}gBdG^WrQiV#LzMEPAF^rIbJ
zR`3H#3HtbM=cOg&qf$iwviJlWG4gbWD6f5?rC1s)i7^|C|HYQuq(}EiN)XKms#fAD
z+@p6_gt9H)Z5Viu!Q%$EaDy4AsMX^V)u@>{W>3(Op!kML@N15lVLOO+<>~~(zV$Jo
z9Et*+r-lXRXrw4j!jeGCUorsg&w)NKB)7i_bPWH?XADn@MO9kZB_{J0#*@+hw0$tF
zyZ%uKfbf`_23vu`PMDO7zL_vZIwc58TgBgk#hLUhSPYbu+g=;N!fvz`zp?&KS`3nX
zIV9uf=H`ag%5v1ROLvl0+KC4;JGtXQVc!X!zN$UQKsTBd7czP|Q|4Bb*zR|DN#=WV
zpQuTW;_}y9ICC+&kjb_HI}fU@|2aW+IY!I<;(L;|(b(w6F`fyhW_^1l>j^#Qd$(8U
z2J8wiSMDgfsn#2~;Y)C@zn>_9U=XO=8z^tF!W^p;rACdczWGq2p>*uiwMmt1_sy64
z8F4+=Zn^DrKZuoB9W1^QaRe=_L-<flDH<yhY&2&-+Bwj>Ad)Gw9iqfC(zXvIa&Gd`
zS?fVB5-9P&byYweLNR>}=7&olCa}v}P&<)#GhHjtaf~KGmR3U+?yV9c`wI`%-n28l
zi%RR{XSC_QSc|*um?3vUpvhJh;Vdj`eV%N;tq6#C0{TE(%E6_wH-k^Et}os}MjNl!
zoq!rNQ}E^03P*nMUPifu-?@^|YEC2pl-G|(Z|hWq<LDjad)fDu3`Z?vADQS^;`teb
z8XnRe23=?qXr5Qbi*~wyfLMzgTJRVYR)1A+*ag;8f@eRJf7rGsuOzh9>6WfC`QfF7
zKEiD6*DLKIJ0G(9l~;*erPN1XiK4LG*kk<oVXOaRy<icz*_meyMFpTANNFR1^42)X
z1mKq^cB3H$cz1_a;*6j%qGRQ#H*qeI`n~60KEw(?cz2;Rc9lUSA$0k<_Z`sIQkJg7
zd>1<NiH>nJzy2($T$|kiuYjF_&uKJ%O+utmWUZypyX<h+)XNVDqBNm}ChpRK&nAlp
z@yp8=S-ICsTIbm!4WwT?$7&(-msknU-$KFw`2r$4AfRKcHQP}A$p6V@okB;wr=mBy
zRqn&(wN0P$gsm;{No@O$FCe->YVg0ei{*#7eRowH2=Y2hi`C_ZI1~R+E#3|k9@e%_
z#2Tg#o$zm<E>i|V7}$Wx?SUYkf?ggEp8ay>3cVn8_G?B>vAWc4mcDZ5wL1-y$t?>v
zc{d09V>nV2kkQyfDyv+Q)|4H&wQhil#3W~P3oY+jJ@_UJRIYFIy@~oNtdb*A4zSYG
zl!X?U9*Cl**!j%QY;{yxz04qa_C4K5tI4Rt4RL7xE5}uM=3EysY1u-jhX2mcUjYBw
zw`TXss^$KM+Ev6=E5c(943S<QD({+#`B|Liv}>|?w>^W`d^rdCIRm(rfG(CnSgs10
zNc3ba#W4Z$7^1c9$V|$H*V*O%zst`08lDQP<qdvX^8UfGz`zi#`H=;P=<oGWz}pep
zF>Q6A{gB-Rx@vUf9iD*)v)CIB9dG5UJcri_y$gvF?QTK8fkfrKnZg#NqN6i?)RlhK
zDZ7dVu?qG-5{>@KWxtPe;+0H>c<0g_7!rCNHp<?4#m)c#{47t`0}U|yxMDX_1rNgA
z$Y_PN%<Yo(qtxDNkr%TGkge4tjzwcNm@2R(puM4WMGrXJA7wRwwL2wUuVtU^OMD9S
zt-cA!J{s&+J6%ja%n`{x?@YMaPhHz$O9y+eU1WF?*J>u{pvf6_!7JxWQ!`+9i{3H*
z0~JJ969W~yKs=&qTRsX_vTpKCN)-k<W1YoggDo?Wggy2_^x@NJ<*p&0SFGP;)`0jm
zkQf?-u4uEYn%;2YE5Xod$>6%PDJ}!kEib!|Qg3weYG~Ut)%A_dETHv&6C&V$v9;8n
zWe5a7{SnlkGk;kr^B$|T?#GA%_eiG8+16>Im80R^nEGfq`8Tdq5%cvX)bw~D%<XTU
zX$;wmsNsl=2R?V1QS9nfLfGC3v6jy`InmO2J<tt6*tOJmut`r41S(Z*K@aVrh2%GD
zx74J%JLL4GvXC(r8GShk$l1_kG;Te*6E0c51Oy++XH!5^8xAWi8c(Qoi+S6PLD+x4
z?M)FE#?Wv+SG3KTRjCaqRA9OB4OZ~`UFiurMp0%TlW2pa*Qr+C{kLmV{=Tnl?;uK7
zVPy?KB-x&rTGW5@C44(8y{!IaX0D3XH%&Kk$e1@?@y6c#-0{#>Oo<kV5yQxs)|N8C
z1FK<ZaIO@(Pm=Y)LQ7@5NCWDQ;Gt*DZTfj#S#YOBfbzW%x&ReaqJ&pkL_Yq^42DjY
z<u?USwU7^3^mM1#FcIFHPTs)GHX#t`R!N&r66twl^P6=xOH^axfRr)~&lpn#3Y8qM
zkUe{5f(_x1E<jkGyv<ZJO({Tkv1*@;+);-dCO!Ei^Wopm@OlAfD)H?BI_73i15RF3
zprpWQHYMrj$l-{?gFmP+4^#;f`Q^J<<gBQbt72W71ZR5|iPzk~8dWIk#np_FCsRev
zdt{bN%wojEkYgxB=-Y<2>=@UdXJt4}s1gCHa^!TFLR0Fg!4z$`nC_ec$5aFBuOBK*
z^ZClB+7$cy@G;08T;<$o070ceV<?xyxSAAp0-F*de$)(x-s4<$YMIXR``TpR>b<(z
zl|oF(hia&TotTmtNUMu2CwSxxC`AnD=NmCKAmQrz*F&SDdW9r(a%H8#B=^JC-{y3?
z5Y~Pp4xK!B6Yh)Ocm&pbiBzn_vPxQT^Du!5eg}b)$|^a?zLzU!BfJ&9ay2ghw`wgE
z-k4<1OFmd|C~bcns8#wPmj&!Y^{?i*rF0Evz9IDL9ablo7q_*F)ILn-G=xf(hf`8r
zCGDT`JG<GSf^s_>hXudRiD;4FCY^Oae>P)=#U~3dvT4PKPPmWiWDEL;%9o*og3h-p
zdU4ea?f9v2<7P<83jBLrgig)Njrj>mU46`D{_Wx~F#wy?o{5Z9XuMBB5&}XRMG{}P
zB2OvEAnUv!)U5oSwWn~Bm+9q|7-<hcNhgIC**m+BI#@1MTUCsA3(Rc<OotV2-&JA7
zhZf4-Ps1}Dz<fn)RaR26Rm`SA2431`>OlRe-0xNMtj)Ge`WEL)5k7K}IE*Rj^~j)H
z3q!STn!;@R9#9zVOrN^LSympg2gyJK3L|MZo6qQlcCj-t&_(mD{~_1c+1%U85UCC{
z<8u_IP{OL35BC->MSJo*M5%?NWpy-0#E2kOi0pjmLM;Q9OM#V}wE90mJ9Z8}?`i)p
zQV9{)UHWq6N>`rTH&`G^IXT-ETxF6qzMha~Zk2LH34zur2+&2?rNYd{p<Erh-L~r;
z5c&e7%41Z%hAYpHe>A=I{r7?XLEkO-3$No)Auq~}Q{}(b+|G@)rPlz-^tt+9LRIgp
zDG?*J+<a9c<JP%_Jj%5W`H}LOeSKuSae#Cfdte6-KhhY)l}{~2>zj~vEcWi*)VGk9
zC;dF_DFP->EzEB7PzMs%$sKV#CU9jWmeM0>=RCZ4)X35d9${caPgendK?b>W)EY7%
z;Fw%XPK^KSRQ5Mg)qZKm$cSZ0Ek!x9P?PrzKKml{W#{Kn^Z;UkOC1}j#oJ7=JNr^D
zN-R|!^)}$T=9J}Cz9y}P^v5Stl;q^JBujm!khf_}w(oK6)Z*^ld~1ge30B33)@!=A
z#P3#5_hGL-h>enpmYbWhdveT37I>y=@$7e+E+AKIxaHB!c_>o~0Ku_>Z-*_FQ}s2u
zOsumf2yJJbg2qWy-abZr)MZ{;w`hI^3Q9YeE$z*DB;_+<40S3=va1k0u9yEQYF(PJ
zd2-wRnQo8j@zw!|6Bdw}v;DK5%z(MrY{JbNQbI*6Ao(-8X?po$)M0<GMnxYmIQ`$%
zLV|m<I(;w~i~D8u<>Tlu$=<jP8Xhh7^cB)2$9ZrO1C<)c$Dxko`s%?F2#dw2B~r{C
z!>JkyZwngDN}J}hqn48BPBJq`03G{t0&dwOzb}(PY%chwB=3PuVpk#kGg^LI=Y~>F
z9t%7AIqdv)u1EyM8Dhtk>qDVew)i-%YMwmqJ&zkx5aoYER~AnOb^=Z!6`i{L+e*dM
zzd^ZnMGKb9fl&db1T5(nMKnAU1vwlF({p=_CBST-_-Z&&j2W~9a)r}hdE5G@S(!=$
z!<uO1_l?)t<)?dpr*{E+TIa!n{})cnHNITZ5{AfWlWv-KMHtfoQjMSIf_sw+cZbgM
zq<~?f%=`zTy@^_czhv`CQz;pXB#tp7m)G@*USlRGTy`@lsL!033Ip2pi6^fFMI~j<
zx+kT5xp23lJ9N0|pEhKC^X<i-0%5rfqk}w0Y)}x<gnTpEUmKOfOauen4r)S1eN4*6
zLPAdx*e_CvcqoBUVBiE`Pf@-2E&rIyH=sEDR!X67s{YtfF1#J{COy&LR_;)&<%b<0
zO}o=s`#*20Oc4cU>QoLNB^`ex7BLj1Q(f-)NmWyq{TMc91X{5l%|-V{y+L0(k5fXj
zo-aDLF>%?GFFp9WCE@EezpP0RV*05IU2Z_O$^=&`-$S~BA%31_E(iq`rnW=8kKo#m
zKuFFhoxr00?0-H~$~v-yTnFFWVT2+2)-)~LPxJVcc;lM<%P<fSp_dz-M#cWBUKhbn
z^Qf}5jI_#@{vsjUC}zhq`xT#epgFS5@wzV`ot(OiwqR-KbZz}$O1zbzPa9TC%$DK|
zx~?@lwVae7hZ2ablV^_zO2<q#X%jMC8@fX?VRsoo;dzHCu6Merz@%oN98d)s5ajgx
z$0nzaa;<tPrc26TA05UQb>h2Hdt%OHXuN62q*pOi?HRP(yzu)Kl+JeZwF_I)XWLMz
zd!4OemQQ?~NsYFZB=k<}=WU=Is_WGrdbw%I<V8aHo)56ySvDDEimy%k?;^<B$MTEv
z8ntS0bb`(kxf31f<w#-g@KhR@TStoGpLo>oQM|IY#=n4H%~FM_3pN(4Ah2}QkQB4X
zP3Lx61u(f;>+vxbO2n*vZF4G{h#09FZ}CdOxX|AKd7ik{YCsEvnHs(EMi*7X{AnId
z?ZnyqNJpbk3BSa;nL{@8gH+f{3IT{g_>Nx87LJG~Q}F>85aMO)82s@>d>GE&z^GRr
zVeb0zU8Wp#*4eF%YIL?9rRLpzkq_Z0W0lnIII_zh0Tu1;_7=ilom4G<Sme}=ub498
zvZ`iQu@6eFs!(F)?^V}DO%pOpE{a?#43Hw%4AzC|<qiuF*>Egv(6K}K%pZ<;nw<EP
zBvwu#KhTZV2?kT$gCJgYEy{Ax`#pNBxE2z9<9#DpUL(bx_Xd_=E%thmB#}2=RuoK5
zlwQ>UjZce;f=TJNql0(0gYdrEdHRgIE0FltbDMHKRAGfx20$ewDjoiaCM7gDkx$`R
zEBBPwsm68{HS;DZyy>E+$esC145jn8K3&hwXe|K6<~YNa)-ahZ1nWkSxck{czZUap
za=c?(CcB14mLi|=XdgGOmuoapP^<_qmz~&T**;9SJXM6$rp5FyAcb)yUU>hArrW34
zA3B2_Kb>pTtf+}6o{-6hFW2KxtS4u=+ys@bv3s@=?VHI6Xfa1DU-^9Eq*0m~?dVtx
z8&urv3RKTeQkO4V80A45Yi8d?IsE)HY8j2+BCt1*WC@L#GTeBCf}kmi*f{eK6Fb7|
ziHD3nLr#Iw?rG7g&W;#Y#{(tm`y)LydFG;7d-pgSy~F+kZVTS|MiQvUAd|lOs|sIr
zmrA%t@On-mviVm_4ZL>CETLn<9PfXLpUulcn{)cC1HVtW{CS}41uESC_9-%oRk&z8
z|1G|^A>obs$y_GPt0+nR@T#sOElJq*93e)<y})YEclZHi;e7YtGoRIak+w7Dll_!N
z+P1v}x7R|9{DS8aN3GM^Pvox$H@^(XoIW5B|DFM+jxxBXmSa7wCNL0fqw<l6()}Oh
zdGVj7$GFeP0+((r@awQLl0Sc<ygt2fxuuR9BYS`I0%?d;)_pzjWS;50EmNW_QZ#Fb
zG}xrSwIe0lIRCOkG|?lvl89O;MeWB9N{cBq9>(Za1kR?qLn660hcEaq{snEHmtc6d
zQaERrFr?WaoAdZ5OAUSYwWbl_?r$n%&@1cg2z7ve%|}5PtGhGNitRLbb?vJSUe}w)
za->r5lYrc#@^DNwsAVF&ZiMN$%_zPc8K@#cY(9<~H>cIO>$QJZ(=*aQ+gnH3+LKi=
ztv6`nmzT$hmgd$x+}g%87OG1Q3EGO|9q{W%=Fn$#zA=~5&ThDzsYUZeDPnm)F~nRt
zoFOHO%POeu8vH(CQ#X&UX8``7x_MRVhqMYdZaMb{`{8iqIA-*D8}Xq2bd~Y|!+3~r
zKtHttu^*!>J4qD;{eKr8BMoI%b}DGlo>X)Sy9>B<2NK*~=2IG00s7@QY0yI%gh#?F
zRpIMrWe8LQ>3!JZPk3qSjJW(}bFVUqt@uF{ja&I*_K!26J_eUzN{{Y`&K!?<v|GVV
z)xH$e@E5395i=)-4f;r%-VUTLio=%u8Na!;y*`T}nB-^<cqd#<CvuEw!Jwah{(8-N
z_`aJVxUQ1JB?wQgYA;=^N9y0`%|7mD2r;*YeYd$@f^REYAofnBjMx2N@Fjan5|P45
zF4*Up0A=}QZSSl3Wd>1+9T4L_&womyt+n|{<|+1tPNLQD%FZhc-i&ZLBpDPscT&OT
z`ARx8oWha_A`7khq4<n*TN%s^?Yx((tP056y>+jD3G6!PSo05RydqCRIqoJ_mh(oz
zzAFw_JjV1z9n~h-Paj51BVx_C8BC7$`0SrTujR_J?MBJC*Re7kxvd=G5ntkIQc2LK
zL5tHQ<tX=^J}0>!-)gz#d;xL`cKO7@6o%J8E#A_a{b!F0TLlC9o0j$)`TgrCwU{EJ
zQZMZU&SM5v=W4c-@=l+G2Il#{rY76dub$VdrahExk(LL!)tr-z>QsS^Hrh(MkZS^L
z+)#fXjYUQ!*XI_vqY>jIK}$|NW@JJ~17y)D3uCbJE%)Cs?WjU6H!hmhGHE;ggS3+n
z>yOdfAdVlo|N7o+e;qi*Q!8-V^`oU__KEJ%Wgfqvy<X74R5Q4K<CN#q{f75j^6BFl
zsv;)JK9{=00U5``p^UOu&%?QXAhvzf`x{8Jl+S0*%&N{Iy0(jCoo;a(1uM?z7Yoif
z;r}fZ__eZKEzvL9LuWsqX#JXuLXq4vw7->)UvKg^b#;RNCbzoBk1LJi(8mj6RX=5I
znCxDjbH$RN6NA50L)wu~-v`&BbEG{kG_KjbJz+|`yLw~2LGddK9lmv60V}xD0TVf#
z=1DxS)}_wWMFtk<_fQc{Z;##8P`8b%&kY2x0gEK>nEZ$*Wh4O^Gc_~Zar>w}j>IQE
z;4W;C43G2=EeW0F7a!^*;t%Jyd67opo_z6Yomoyw$91ftpnz<}W>;~n{xb;|&;-~*
z=SojK7<xNMxDoZ)d_w%i=4YclhhFxSPOraZh7u6UoE{RVw7zgK<!H713WQ9M#^cR?
z^v#bJO7~rdZrU~m*3(?N3BYYD3~|!fns1S8G{8C1d^RV;0n(pNKHk4_xt<%e9u&E)
zTG{Bk%}ho%^64YqsoRpco><u8i}tu4eA!CjMsvd6$`F%0OlDtlHITk^%d)R|g1&Ui
zx+5Q%p^pB&{$QAd+<tS>c?K76?pzBl`#73Pzxj9;3a<P&xC~zH@Bm*<q&$%ircWg_
zR83>IWK?!S`7lJZpSc!0N`mS`B>&$+s*>eVr?{nNJ3aICkUu+N8cnVc!{ns<o*Uj@
zjJA;VOJE_;ea^sX;pJVu!}KlEV13+cb6WgU*l9^bPd*<_>_rP(hh!X{b(xRU`r)v5
z<-L*i&^%Se1E`DLf6moXM)Y8$Als{L6JjJl21YbbhPct1#r^s<9r@BpMmn0D_bIu%
zCG~M*QE9gpvZSyz&^;c7c{5`5rbHsexlYr(jKb@)S22LRT)^_dXvIFH*=AE7dl)O_
zJu@J4B@rA(2f2M>_Awk6CAbW?6m;8aKK`YmRiS9xIp@W$xf#XR+Wz7!O+6$27X$SU
z^8f*(?q^Ce7S2P_uq`8oB9cMo>d8pauSC%Aot*VA*J#cEetgXusfW*;?ylgFfl6!o
zi`ybLA-dBJeUTO6jkRRu1_>n*Gi*MgfzV3{ifE>^?Wb#Sr_+t<<j5T+WdfJ)8P<#1
zGhkz_y9^2w5E`&n(q89|T{!{tL7=JdvXw~umrB(0qRFs`3(`RPPAl>KsV|5GDxNzO
z4pOEhCW!FGL5oT-|FY#0olnnSXUn@Qf};Gwd;)=SA{Kn>dZ?tV;HKVV`ihdw>^+V1
zi#1xJpg*G$F6A$s-qE3zRO>V_gqb|L5Qm}GpADzy550^g%nbb{x9O~AAc(pf?E5Vl
zK-CExhBy9z&jq<RyCWK-`GJG6y!jOHT}fx%#&w9bCVOnD-u+)@4<q4!X36cC6%?ky
zigSbT^W)X^H<Dp8ylf-T_?!CUmpv!Qj2w`43n981-B!w1If_nq%y=yI$Lx$8z~+|$
zGWHxS`5mIBOrmsC<sDR0Ua-Z-bw`i$-bzJlh|tZIRMtx8^EWJ@=mC^{zJseFU{{nn
z0MBY(2CnDUj~zRA^K!HHw~5^Z(5J?ERyzPkNXlT+w4cN{RmyqhCl+NSXG&mQW&gB+
z{mK@8o1w9{mOT&SH{^98T7bVvcCmQ!Ll$#uY8>6u)7XU#_LF19>`O^&R8SC>`M`7p
z_MN!qj{j4X+>G{mQ+*}+)j;8I>~@4Q`kEg^Nx`%ENH)Z*uSWIr^|gK}+-g5@KHOaf
zV1T1FGvIkGx|$-E8(XpkqXrN3|8lw>d~JDRw}k@4kyrX71vCl{%!^t7&dER{^<`Lj
z1ZSjWHOiwDV|u34^W{w6d0V!uUDbp#X$m?OSledb9O2xqlV|tch{D{yodqU)-^KS$
zr6sz%tpRznO(=mKbRlhr(5Ccm2c{J}Q=vt$_c8o?p88}_7_-?g$(r+oH<KwA>lq|4
zj6w&DZco7PInQp1^6%3+4Vy~q?J?QB>c~616|&{}9k18eGd@NmFSy-lUeAsF_Zz9*
zn!IcTSru>@E$6B98^va`Tl-$mBpiQz$<k)C6+Y*cU2p#BHFjN=mOGSKxUSH>Lo0av
zVZ+u&PN7$31i+AR`~4o!BD`|<b8ppP^!Wt}BdvE6v2ycN85Q(?7^wKESpcrG4qevO
zIB)8$LRC)~;rt+wY$E`37qO-nTaE^@h)ZXSqN|DROqCD_ON`Gdrr$IArq|UNIb$(y
zSfm!##R15%T*=58nN)^+{%rU~C*f$C=nE^xs5==sF%Xi)+PDCCed<&qmC3$q$NOL!
z#r!0|4fOhLx3u|gMH09L1CAHXpIr=TLKrJNgkjbN{^Mspd<Dufe>+<RS(ycG08c)C
zLVn~pEad3<`-%VP5BGglx^jvyL`4r!0UP`mpj=w4R~futOLHRo8g$hnguh66T`q2Z
zSmS5D+kSQfp8sqh75Ga3ln#6!_TsODj+Mkpy6d-}Z#5>7Sn&a+BhiyW8=YQ$C{&Qr
zB#)h7@#)>1sl3O^?w{N(Dn3gCrhsTCW^jL{(b9BZ*_=<or~jf+Azz4DEsbu={5T3Q
z8r*@t==CZzmT--dyVXM(!L2j4%4TF!@!?ZqQYPT;Rvpx^{A7N7s^oQOkl9;BImNlT
zoHU5}+dVLuhut9nH7MtJ%~*lJlwj!W!rpWZ_e<7*p^b~f@C2r#Km!*c;mGKdO{AJZ
z-C|CZMN@t<bv_4MIYz;Xk(2Gxn&Q##y}JOTJ}#VnZ}=U>W*{lcE5E6#8!>$$YnP*U
zrJ=ob>2g<Of7=ohe+mHXw&<wbEzctxLS!FAX<<Qo2<?0rW!%&_)1nqD1spfS>K_-_
zW|-D)U)#0V{HZ8%^f`j9s4s0!Z$dQ#Ch!1ai~o8Xnqk+uEfjfwF~>?q#r(F1$W3@U
z*B{@gXaWX-($AyX<&0IsB8dpI_=TDo2X9RjRz>d!<3b|`mlA;zq$%lId-9>!1cL;%
z<b6%Olw$m?EycyXRR^ro$Jqe4-CY%woNPpD6E)6zH<#E^z-I>h_!nr|{)&a(0e}nN
zf{D#0%?tkKA&IHQ{7<FvmLcMCi8f|z@_f<HM&N(Vx#E;Ea9nA3aoy+MhEKABm|VX3
zcwg-8`H=x<C8&_)O5KfeDngcwZbxL{2Zql<ZB2EMN^jdiG2gq_KyA?j`4#=2l@7*F
zRr#DK8z}D#c}<LK7yvR=<j|Pk0TWa>v3{6Xsy4^S@N;l+(y_)A;wl$JIlacWjob%3
zR_a<0Ng|FmLnC@?3{2m<d^8KlC<4W%YkRv!Uq1hD!F+ij!f!3oY^Pr|-xE5Bbz4v#
zzrdbWg@i84i2|b5%6mVt6gxRB)cdydE1n4C&m<0tuWK6ZW%Lp7EHT~BJ~4-tzD@rg
z2P<t_3=z9))(sK+``+vaD!>V5yZxU26qDo-eC0r@nnm!&;C5hrt(lK-$dtUB14Wq`
zZJ)w{+mKd0lv2UGp>fscNBq1h&5u#lJku0ag3DG^9{p1wi5m`HIm0@B@P75r?Gb)j
zTyVgPKxDKuc?3MPbYW>`J+af-DG3e4nZQIKT9O81{0IvdPX{yr>(^x{JBAg~OVe+Z
z-v+r44ZZmO@CE7PD2K2Y@ZL(@+xSH+FZFxug%ZNP)dh*bS8BEsW`tBLrzI~ytoSft
zK~DFI645Dq7t^S3`qKW_R0ulgHzzeoML?;Ydiw@w(+Z_I?3eDJl%V+X4Ro8#<}gO!
zN1x2e%%`bVm1x)eg7}Ndpm?or+#lRI7sfn*A53l#F^~*TSmq{6g3!fsf*!&2!xJ<0
zuE#R-^H;&nB#g<g_U5eBE-|kt;{K{iE^=GG?EhOX#Lof({aBgtNVOsF^b~RNmXS0V
zv48Ph0EUmtEwDwJ4tQVbG_V$A_5KlNFQPh7O6v}AB7dFhVxneI_=yDCL@DSq+N!w!
z*tInt5fm>+ge;9IjEkGd-YzqQ3t)wwS<O~<nnkci?eoTN$r3>=7!|pnjZo|Z@Gteq
zaQYY5tCnD`hxiqyaNGNWpwjmX$o=e=q93?{CoyTNW5Gjf4a<F@(^!tMCmQPVEi44X
z&=<~$K;2f^#>|`G`-P6kg@zZ~@>ZQhu9l~0zvj_9l^Ou`h)CgGKVB(0@9O#|DVfUo
zQ;~K1n?|^#+0ROJ(4V-j4*LjN%pI<Pe^~Rva->TPm4nG^Mr<~W(Zqh~kQOB@9{LDj
zrq|2Uktez1FWEl}<6-703m;X;m=$$gjQ>6xzk0ysbz2j?SPdFGdCQKiOPQQX=s*^~
z87hD!<dAMz<&u{0DHCwpH#b&{dX41}d1#%I#urw9Kp~(1!H9^jBrgK&Cm%WH2=-2i
zig<%V{Sfg(%#IL*4X55C;%9xT=v4z;@EqnC=#XpXJsjpVL2A9XJh^?5`Y3c4tUg*m
z`Ib#SW+nYt@+hr6A7UXaCg7u#7;ld$-RxK3ovsDEckr$2PIAl=@uL&7KyIH0g<y}R
z!ZJ`eVpIFFvDK-q_DSR3E@%Z6`f4r1$0TURJLK3KSdkM$k>$p0sF7bH<487D|K`Bn
zBVLc+|AV@X21$oxumA<5L8~YnOny#!f{!w!`voAUqUiw0Jr?v;;GES>V#6XAwO(6w
z2{24Ox=Z`#WQ-Dp+ASO;xM6=A2zB$Uvg-M!J-e&`*WwNj(ooyR1gz9Swk+99is{X}
z6DQL6;=(49fzvn3FHrk4Y!1(?xx*zR3H{AA(1CGQ@0uEXlaKzi&XV&|C!daJ&;i4m
z3fm9OaD(}d5`YYHP$6kqxzAj^E#v%1D$1og6c`aU040{r4Fh{y|7Q_f2#uOCS&|AL
z3Q#V0eH)S68-cGljxoRqKvuAeX035t&+dqCKkG*hzGJWx_m`KK%_M)0fz7$)L^8%L
zh20?gcWnWg|GC)m3qyTO+8<$5<>dUp0_tm$?dECv?f2=e@IcqwUj~WIJ<dQ?j1Qq)
z=sUMy4;GpBW4?SP`v+ljhgV<1qs6Y%_$VGo&AX`06e`isXj@(P$;MM%7H$|^x2A7u
zrMF)kFlc6#-~2l|bXJv{(@<G=3lMm#gZAV5Fjya`n)73HxuxBwh+uHj=fuQG_4R06
z^$m$S-}NBc%}zHdY6?}Rv+U%r--%wuFQ?V#M_B=#s2X3Zoa<1>d^2DAjrsRaqM^}D
zERtUykL@C7;WUUBE$GwJRhfX1Y1Gy@;pq2H(uuf+;TE2L)jPMf5%M%0ZXS_vpYfFb
zH^Fce_oQCnSG3^p#OqpdQA9uCMFm@ZJyA&W?+4UE<#J=Tit@3DJ<8tDs)PZ3dP>zF
zhz-eY(i0PQs@YvnrxX*Ei~rWXG_<9DEKQwoQE{%-(6_BQYbii5RF_CrVEa!1k})}a
z_kLfkA^4r2^mvGdFf}j%R?GgcT`8zD?|SLFF{A88V$Ia|xFA~VMz4@7XZ#$L%Db*j
zHt=o8G$f$myu9mmp?Q@y8DPhkYfDusJVqMy5<R+BzLhNID_nDU(iIMq7#i554ir_T
zU=9Vy!-MqUobb#w{6h0Q@z(%<W6$g%{<&_y-_DJZ8;{CNWYjUfeGj)uUwZFqb4^cn
zk0+?G^iRqTTu^HDfOMc+oGc3*7`AZSvEfs54M^!}-Xk!~a5Nr>j1IJH`^SriTQU0-
zGt^+-+7#ZyRqI$(1wzG~YT%IZ<0%;avAPj<r|%J1LQNKSVty=dlHitTn8y5cx%{x0
zT&oW+b(%pwU^@Ex{+XJFQiZG1U3lZZKJ%#Al&G*{j+xGjDA-g$2m90IlFP?P3PaN-
z*i`9m5;c7U^b{yewSh860T#gn2N;CRF{QU3*?n{jN^tARj+E$NzoxHExu|6)D71*<
z0BWlLXl|(RC>a{vZMnX-ZyK~qVpC#-SdoFL4D?n>oL>(u>10Y&|Imkr&C|<{3`hF>
zJw^lt8Q2@!sKlb29jE73c%nTKCp1VV@Vb(K6Bko^^p$C?Gn$WF0sTA4T&cSE`MUf4
zxURhGZtYkdR~jCka-qX+&3s|TyW0ShhVy03#ll4S5I}#-6ty0#6|g_TD#<9mIfR7{
zlS~1fit)ix0{K#gPD99`p1NM_H6f;TCBNMEtf|YTHxQXdShpRx+at=UTv=cF?}v-a
zY!FDi!OK%Z?V~AiV9pHMCg{+KkZb|@n8oC+>HDwmit=x@2GrlPy<=s%)$`C=zE`Vi
zHalrvuG%ZeRsHrNtp`gzMjBtr%uMQg%uUxX<7u$9=aS*;_P*s&Xm*Z}+cj4w_cynT
z9k8_wZ8{!WCT?Kp_smH5-9o6vb-Fy5%)p2b2aAdK1BHz(w}A6UaMg!ci*AO!kEE|O
z`v1zt$nlrLf){dPBHDz)G?e*sBb53k9N~kwzazh;q~Muf3~Ip7-+VCA7$sSh^;L{s
zO9RRmILAbQX_9$YnS#LN8?mfFM|gZW-B+&ILaos6se9=?cSz8$2pUY*hODl?|5cBt
zq2$Y5>kIOgPllDlm?ddRz6V^|^s+LSAPNor4VNOnP|Z$h<_L$}8~#K7bG@npX8b0z
zJR&VE=D7Z6#^&7to!!O1gsJvC@r}Fm)4f&Xcy7B`?T{ph^2RV(57tjp6@7hA+`Y@L
zC!Tn=WZvUXRK^MawW)`aK*pkRIMiF!YcYO0(xG9zNYZ#rT%GmmYKM5@iTN6i>LDjR
zg^jjR+*%Zmf&uaea#AQKTQZVKXlK>WsFBWb@)_P!>|caxz*UoZ@xi?s@E^%;jH(>T
zZq*me0SpoUf#KcTM&$}S_&#@ozmxD$7W#);5_}Y{==de3089GhA5dSAPaw1$HkQg3
z;iEK~9-nIcc$3t?##!TuT>^VzE~rTSbyx7M+KQI+N|9_rBt$=w=HD1j2E>k}y`o^s
zvQ|@a^zJ8=o`%VqiMhmPx4RhD+}UYX1~VP9)ZKl^qkryIpAKgA0@?SS^`52@eXn1W
zhH{zKLj>kuVO_j+#+iD#@MVf<0tYojTsW)Czi&b+gAG$S>&;)%k_I`E7@N$$gCYz`
z7YP>$0`A}}&g3bYe9r(+;p8%VWG(o2F=m5<tR#Ym^R&W#1L>qJs9D#o$KHe}t+?@9
z>4D{z4aGa+))cdU)_aY@<fx4rA$_I`1T$NUeJw6d4;+HY9mQ)~LSqwdfzxfAOJT8`
z9wDNxUG<}*krebj*dKs~U!BPvPsK`PoSs6jZA<bS{WmI=sx19lT{pJ+ciedP9Nyel
zB#dn!=9hFPzUjk}p7id>VNzM96f)E&INa4ZJcXZ$pUl!`oXCMi2D1?LgN^oYPv7jE
zNK3)RT_0=|tN8_v(K8_H%Zcwa6QKBY?!J5JJkvuGg;g<n`EeUG`3lRkX9T=F>gt~h
zVfy=VHYJ*2edPz)eXgIKw^qNM&J2u~B#d1r8)fgie0+_b?q#(x=n+jQlpicTl$<;2
z?e!GW@4b!EnLfJDL2-ZUlJBB*6kKSZj$NS%*Q3WLq<dZ-5f(o+^;|~fO=bE^UUw!a
zL2Ci|2uF}=`tu1CqnT=mxMthQs9NW@E3fyP257a={@p3c@3f(hfk%jCUTIFQC6^Rt
z^|#gVrOBojr{sk+0cA?oLk5SWFM?OeK_uM$`#AV&EH00<NNsP`;eIB}<{16>%UC^c
z2h0B-bMGA$)${C&k`aj#1QZ033<3fI8ziXYFysshC^^ZHb5IcwNke9nbCjGj2+jaQ
z2FZvsBmpG}2;$rNJ?Hm3=dQc%duzRP|C_z1cXxGFb$3;F*QbWZe7@A$S~~yWv|~c`
zxa7tajy0Pb;q=T~pwbW|?{w$(j#C>+shm*H`02w?E%ET5iqUyBe#L%+1L5^N*;%u?
z=M44_zG-DszjHKf(sg{(G*?yyYRI27>rqQHaQ3L(d;fDvr=#KG5N-bhev810A7?G9
z--l83ped@xtKga3l>HU9O8{GsZyY_h<}>t3P>!drlMtp7))w!S@tXcAWxxlzgT+TU
z`zv+dZYz@)fh!pNYH**wVni+6Li*Vcx>ctvt|WIco*u=7Kdh9e##7jeAgx<jT;El^
z7<e)K<ad|40a`52CXl_qE+4V!=8Zqj@-tx}Vo+QDS5=I`HZ9x4`-82^;!zWxJRU7z
zA&dIH=<NQPy(6O@Z1uTh2woMaEZcq~WXTY4PV*1_G;I1X@la|t`o-Jl12cHjPO@>o
z6-49ozZ#%l&i4<R+%*S6FP`DZwD?=n#06Mt3SkM**~?^!%^Fy;>gppK(eKh3#pLB`
z^mtFv9f^~452tchXz?^hO*8Z=mpC1KVDg?QwPn+WQTdaAjJwxo%AR1EZraVPXN!67
z`>vQoY2JP84^ZNL{9HMo7aLm{OC+Xr#=CJjOlBl3qVW*oG5*dDe?R$)tIfSL_~VN-
z((f*CyYZHd&4Fty8MyK$vTbpM6|F*osc@^qhK`WJkCmWQ$aHp!A|9a=-HS~*&KEko
z8%{d!I{r~jcG_f+Sf)^D?JL<ueG>cU{^!_sCD8&OMjC^hWv&}qUn6e#B@G1Sabg+A
zb0)0wTncO6{Pd@@PTX`0f&Y||v70whOgbd?bG;7*4B7hOjn8)9c#0KmPC2|%t=jy$
zFj@h+8l^E<Wi^1t&HjoqCaC!Mj31c~+Kb)Nbh+1>LNm)~T9&!fT59$YCD~(i^~Jks
zoO6b9qmEV^ib8PjF^zU&RfbjgN&D#Jp<mu@ecy*3n)>IZgN0;=6?M*ebDHfu#bw1{
z6x$C7=~AXaFWcnuT&h?TFsv+K36;2a$sNX4ulw#j{Kn!?+7M#I8V6R>>jPrEKT<eL
zGVbpwR^cW`qPI~kRy;$K1Gew>u+i=OL4De5255xwU=-u*{HU?a8vVWGdcU{&yc_&h
zNg2T3IhTZPQgJn(gO-za0B;t%FxpENjHMO+{%9Yb%_iw1_QCe+tGnF6)E$_f=~UtV
zz_mh|@(YDZhY&a<Z?G}>A$Cziej(H>eUwsu=nEBN7<``X8Zz0kiLlhS`+WtI5e*HE
z?uS7$;OsNiGpBeZV2Ht#3=}Q{lOOFKU5{Jy9owQ_XfhPX<uLP#2itli=}(|lt-&wf
zb=+P5Tg#YyN*>w4@uX_gjOMYXgJ-GC2!)YRIrm9B3edQl&$GeIhG2|XeRjzL3528!
z4CUE~?S?fH)>J3%5>I^M>Q>xsJdKG}xKn4LVi6t4pQ!s3isp=S&;t<lx=$X&Y@v`U
z+^uPpQ`JVSdmiu1Aw5V&_DcT82)*$Y9_LbAZIQufe5g!q0}W4l`K4CF=*9-=ai$zZ
zY{n@bQZlgPnTYn=iE9eM_7rpPzt35EKbSv`<=OCPAmiP*&i0eK8J%>9V-FH6gDvn{
zN4YZ?Q`Cof%Fy@5<PX|&`8E_SWF|k3B~1>S<3@;;foB&-zh0G2xy)jcb$dVEz9$@P
zoB5chm}}mNW1H?vOk*Q`+0)UlrM7uXn(9-F<EW+DH{zz#8PYPjV40E8*AzHRyZq(s
z!2+@rmI1#`HU5Y-D)wz8_tglB+t}yE5p@b|_wh^5L)Z-PC|$SS4a-SRD$}A$S4hr7
z81hj(3A-MtODJFQt-vb|M&p`4m%Qb9H!9!^k^eEs<fFX%s42d6^Gi|cDaNxerWSx^
zBz{Nc{+aUms$8>5@lu}W4ta6mxHKz20QDoSar~htxydfBo8-y>z5^o^gVg|12EcB(
z;%Lub;IYli;$x$6#T3_IY%p{D+Bw|S=LxJDhG<?^L%X8${f8oY=#h(hGVW;&tWb6N
z!LUXy=%hWJu8ecOZh+#MX^=U2wL%%0^;O#NIO}VdrPgcCgInG)G|~sIjguWIhnfm@
z>-Y-C&`DdvtInS}5JUW?6JqNkH<?#yS?KpyQ9dZ7aH#V~Wr^4Z<I~6*((W?$9^8)j
zvyZDh#T)w%#TmK=n&HWQdlb1K32DO@!RfR|3eG9$-56rw9UZ_HLCcQ#$41lg6(spo
zlVFp!&4hU~mmkfp%Uk?bc1~e>i8U|&tz1gpmB&ZZ^0`H$XGQ7)bips~ZWjB+nxxyw
zleXjOKn~Mwz72Z$`u;Ev)Jrc`)g$hWAFHS|i-D^eEZ6S@iHv5fx3Dbg)HKT(qCa$3
z2@k-Fb<p*zs7N+W(dRFPlIF<@Oc@vYk`8q=-ia+zJ|8JeoE)i++$L@byi_>qZrZr~
zTH^aSTQ^bd*s)P-=o<BH?+jbZ-JB{=cbfX=c1rC*2iTKz_?WGg04#Fs*2qg@bis<=
zR0cG0_^5^^;skHq)+>_*KehU}HCBP&qQ0NIVDwNe0?qKMahNPlh6nb7?9@1;guo%n
z(E(=X`7Cs_5RT>)>mPa=srqNFJGi<i{N<&BXoit29<QyrKZjzXuKjJ{hOQT4P1Ns5
z_<R;i$bRVRz3<{|^;4E0NQsMZ`%pL{rnxg$u(zD-6$}4C8F3_1ZLo>5Z*lNLbooHY
z$9i*RcV5F?@Ru972y|hMSG%9s425bbFQ}}lTkb37s2ai=Z#H*$ap5J_(S9<m;Vyx@
zwA!^X#x_5)<bA!S(%eXB2FPABup~y0LhO1LxGOKc)aN-Gx$RF!|E;C$WKkB&#JYBM
zYsD!yS%7A|Z{Tx~@K!9P-`I4|2EJqIqo?{S(Yp7O;YGn}ubV>2J+3G>x-Q}7f*GNy
z3J-N*3pU?<+L)-W`f?C&89Jt1lF@FWeCZ+tk;6(MkbS|sK}V#EK6UUCXZ$#w<Q}Ws
z@eL9}Bh!V!z$g1vky5Y@brt0J$28B?^&w*{74>I|yJmYzodcq@__h`e8#G5F)Q{zl
zv@#1KH6zu+212|7V}CT|_EmzO{adbBiZuu9>(c;>N@#A027OqCL+!XE&K?0nFFR9o
zweBv4IpbPv?As7o)ZmPhxL_Y-P`+~=MkF03=bpgRd%9jEZWY1FnmeosLo$bNk@UMn
z;xoAmV5iV`b_3EQ*cA0wvdV)@y%qX3Vre_(3dBvhxw2P1(tS{8z2{HR_71+zvpy($
zMWzrVHYthytw+EKQJ`S)s!)j9+A*;8=w`0?!Cmh=#vGz-YIuo<aZsb{cEKSR!sfvH
z=QuSi`@uV2$plWzzSKUl51}cjmhf{mz5*L$g(d*Q2gh<)e^DuReuyCqG$kPF+er)8
zkEzmLv2Y)7uKUB-QyS9<JGsxIOXAlWyu|&kkF~ZojFOul_ud3EU9e;obgl4P(oB@>
z^wipJ!Xbo%ui+D(b=R=`jS=$pQq^&8QDV5&iL}B^@Y=QoH44L78`im}fN{}W2wd@-
zI6mW0>VjU(3Q;v5k<U|i<J+=)=;1=H8NY_n=^5nfLpMb4*1?ZOL9E^_s^jKUO23R<
z1N8E_$&&3$Htdei^?SLX>SSWWBWQTB<0nsQ%j;R=!o`?qQ7y*|hG+{OF16R6DqCop
zyCn?kaQZp?ZXiD`I8t(6$Kc((ND}#$?}Lvr{w3gZfbzgTCuYOF@S~L&HrlhP8Z@o?
zI7mDq(DIS}AH8|spG5}p7tO7fOJGtE>}H=)j3M(=!fU^#q{d1vM5=lOKBUC-JjtdE
z6zFTfOA?7Y>kcphTuHX3ZqeU)_^MKjtprY_(^%C$cK&&Qyt90dqWYyqua|h6m>hs#
zO;@S*{Y>=RgwFcsy;e0sv4M4U6kzY>{AH+z;#b{`F?Y{3R-!!{Z|=^cGtrgl4d(Ny
zbf<I&ITXPBNR}ol8DViYQOS8wJ@J#1Py@h6p6@g_#ou&U<eq@lgk);4S&o0`?f`LV
zvy4#fMtm@g??8isH|h|hrel*KEus0xUyN6N@=l@a_69|Vms9!ke#Ye&STeq7o?`7+
zaC9{Ps^X6<yVdKYVOnTLkvGdjr9X{Vjtn;G&|~|_)K5csimzcsqm%*L{*|3YHAfn4
zrttmWQuqcm1yStR8tmoUsXq9XUwNeUG9piFKgwHvKTMo9j8xk#m$2vEP1&lW=hp^B
z@biJR;rdZQd&IMF7qiVTj&PWt-}(22M)*aHKQav;2{<oIK}UH)MaNGIvcFas4Qbr{
zE`Zj#1N{E659N9rf1Xh1$G0Iwh<Jk;%6Hvc&$@7b(fVfJ0L|2%B$D0o6fO|N1sE24
zIy*zL7(!Jy7mnfRt5cGK=3g}T;pI=@GAv|S#Okn>@DsOhs3sQe`WZvCwpmQr1amvD
zdN$zb>l(RwF`9IEY*9_?f;}3M4Qe_)S`96Xz?c<^MS6!o7sk6A74F9xTPE9134UHF
z(%#2-^l}xuzIzWwMjT}ocVG-1f&`@A#QH*O_i56k61>X5@c0vS!}_#wF-0<?l4{9t
zVx%e;qm1Tv48(fuZ!~28US#f=U@}J>4wZ7;>PQj+@jVl0R8H!rp>4e@u}xo>+UZsm
zp8lc_pST0uF+d+BO}ciO{1{Gq!0~42;^}4al^S>+>(D%OjLRC)Cq1%Qqw;k*QAWhg
z02j$v1NRe~{qmgP=V-^5&BR3+_c9IvTP!>9)=P&J+9~hdM3)7Qa+Z(J4%g;hRYpf7
zi3DORd_`<)@jKiV71mp9&CvdUc53E?4Rk0FYUcVCKE!D|bbrfXNQ;EswjY(&2>(g9
zIg-9SH>5lpeKu_bsGzs(9hM=|xkrDRG(Sg77-=CQ>xBQ-?TJ0g8&}hKPbz(8i=J}A
z(l~18Xh=Fkgr}OPOkX~SeR-ZUR-d~xSopGyO#dNVu>xewHp*zUwuuF+Zcc`y+b@P3
zD?o0v;Jzz#Tt(n%n7-Sp7hwGmyte_<9QhT#&H9H!^+_UixOJDo&Rz=Zov{KebOL-_
zSB1xJLS4AMl&1adyDGfQ8utMl*>V{wI@E8>BW0y-sA7oziCU6wcHNVa?C<N#79@*B
z`d^x^*&pl#O{71DU*F4xLBTjz94@09vqc<Bo_%pVhLHHq^U}}Bk{-cf<~#_Im^VFX
z<8OGnOk(kubb=rld!&CJ@gFvu%*La?xopZ04a|%)nszCrV`+Yu*(e3{2`MNIwZI3$
zc}&=Vw<r49&_Tf~uZmLASXztq2fmLo$=^!44QbtH-$-P>yM?k(%`1cwqc>AZLU9?a
z%`GVoeBHxms`Gs2S&-=$BZ)lwW(0h;#8xzKK<W^(va__Ssbk4Bu>W<V12=|=HT>>X
zWQjV@ZW;;<(+io5z9m{G+E7wY>`!ItY;9WEunxKg%y!*iB24CLECP35ppSh=`OpIb
zohN&b_55QJKkkWagwXceJoJo6a#u0}?4=CQlC#_a+fX_Y2N&07715E(Pf5+R9JZzp
zJs~(XE=V2TB-NoClnJ0na6Qw{1%{1GgDwx^6EK-WIX`<*E%+5+K_ANa+dMlC6y}`q
z)vPrU#oK124C`pcDGN)Qlr#b&lO}1I*dR!O+NSvC+DwIIB|SpEV$W(gHD576fTe<h
z(w&Cy7fyZKGtwI7-Cd3~IatZg(&lYrITEY@XL?X772rA*0tN+^&wWbOS+cUR*FX1^
zSSg5cb2sQi*<Y&w`%(53B=-0aC|lq$pKD!ex&h$0yBmE2d$;*_FUp?zpAy#?37`_r
z8r;?gAMBN}1Tjt}3URmoMM?pd<_{@+r9b;IWTl{7zD}{6A7>e8_={U=*fiW2xS#n{
z`(C%2u@Ug~A?(y00)+W-Kd0TwYd{ZXF~Ll@#b^M`7C6e$CQQU6gl-j=>>--yyYIiw
zo6*1Zs?iGjfCJc#kBDU9m@Q&1alzig3Etz3`pNYW>fMQ1Ru}|tToqLEDmC+A5)J(n
zxAI^!DlNY1<+yjS2Aa4wrYvBe1LyqCH9Lw$BIEZ^w$+UhJJR$Izk>x6eTo8b2EaSq
z2Pb!58UY&bRnUJcE7Fr2Mwg(rbfT7q8GfRG6fXgrf^zHA*J8j;12DHUXvMFI{d!2s
z@~02FV=Zus5Iz~|(1Gbmyjndh(@FoLH^-aFKAL&VMM>C*Yo`F^+vYAyAsU9O=Q;Vm
zV#nP?W$-jkuSX<J9*JS@L5$nkuG_v=CsGEt>I-BncqWhCiCeYvE`e8j=?R3>On5?R
znKI?|(Dzl;f15v$z_nP}S-v7hJ`!4?2QZflA8|JORyRrjPt`2;3otHB^v36(R>|IR
zVoEN*^e9^Re$r0|nPF6X#akSOq>wIpCFwl8Cza33r4#jjUG!BaytNRYJoEtL(cbJy
zpu|zZT;aUCB^8Dbzd^_{k$#W1V-$b)RwCDiLRLkC0k)lO`SBG)&JCSy0Z9ch${Cpi
z`)0hJKJHCT$8{|sZs$$uA2fx?GZTt1BVYu&hl~s9jrsHkjK|IUz!C^5jzNoqmS5<b
zB-EDAV)ix!LQRwK@p)}+NLaX{25HoF&U-;F?ZUv|jGRFkh=7wco1}pZ?OvRQ>7X&v
zMwldR*2X4ZaUuEeA<Ilx&I7>pJEs;j65|pw{tT1y)>N<p^ei(2m~&Y}OtHv0e{}=f
z1XILel!P?lMonhaTAO(GiRgx4{-)Q}=Y=T7WR=xN*6_wReh<bcFdk})x<aAO^N%~l
zjFB5Ob1B@CA5WMsEjRXp(jR;giU}4}zNJc)t1x7h8-iaE-Jo0=qDvI^7>sTzi#SES
zt;TKLTIXa$+ZQQjGG^^K{odZxwPU1m{wSt)e*OZk3qL90GVFZNfNCM`r|X4)osfq9
zR&W#SV!loiA~ovs;S>fm%uO-yhBwxhJrSfke}Y?|)M}!#39`|?Q~dp1;ar)a^aDl*
z3yu{FrDuV6B-It*a09AYvf*!zc+E!J={P=xLrLTJJ2FMshlw?<D&RJ492yo7z!auj
z`W{dT-_tHdsnnrYe(9GYjVqM*o#J6Hl;WGBH#biT({Y*uPW9F{I7n80sr2^v*|iFh
zy7JRt46qfOTJ>Gx(tzxA<g-Bz;w~bJP92PiH|shMlN2^cU>-L;Oi)Eanjj^sr}L1Z
z95nt$Om6wNQEzp$g{^I*aQ!)7RWce4cV_s?ALxw@5SB8vNOBicHiV4|>YSyNLf{Z_
zQA9`hfg7|1t?Jul)*0`=>LThCXG<J?!;pn271Q@&O%n+B2|?yveU>HM`y!tWEq16D
zm>5+x&@A{bj=2YTZD(%I{(;L<@AMml-~z?0?M@8Muv8P_cx5Z*fS@ul^5gJsh5;a(
z<AHq@WhqvEIozW^jw5<hgYUZID5eft*%2UglV~;_EfB5v8qJ_8*YRK_XH3D00}hv1
zYz->yc)*h$LN()jx*Lf!u7xl{3n<D#7Ufp>-ttj#MY+VDWfd{PG|Am?+v-Eb*QcvJ
zjM4h&9r3~-h!?2=$KPPEvw*w51F=pe6&}J_AVf->2X;~VTX&!iMS~T*3cfec$2(!N
z+f@lkijZFPB?JtHc;BdrmNVXM5Mj>X+}NNOkl~w3?|mUbb-wA;u5fvpZ+ef1nnF<Z
zhN-{#Hu)Phyw5L63w{o~73`D}oZT8&P&1#m+V9U)s>G^(e={nUWtt84#P7{doEW0l
z#<j>1xpD5BJMW}YdZfFYT$T(;>fb!tmDA+blV&+6<)95~C2kUUK;a815~n|JaSh5o
zp#r|=Ht0$Gmk-VvAjc5S^iyYb^r|j#jx6`mt9>F1%RS*22d!-1>k7`bcr!yvF-e2X
zRVuuagfHN#8E5k^^i_D7h#o9aC0sb~l_xPu5BZwU)5l?0*xjlo(yIh5y6RaXRO{5>
zK;JzMc?u)!u<{Q**t4X`W~gb;(R!ADn$83}H1uG>I&ZidWq{G%CT{~MM<Ymfs|si1
zOOka-eAMkcak)wl!s3q|)GvF!et9XxIr8N}sz3;?voJZ|=Y5Aw9X0ayMcFISs&6ch
z*Leh|o`2`x@Kj-bmVjQ(+K5R}?#LQJ9O~37gxXC>v3kn-@H0a?aIG8!`u=btjoD@M
z+Fx~>mjoXBpw)^kIRRir;(~LF)$`iYD>{1|*Z{53>j;;g<`{hEgK0MoUk(cT!dX5(
z{8k*bg!c)L=P{_<wd<u<d!u^e7Hfbqz-J9%lFM0#@~sawOvvu{?eA$37;(x1Z;rxe
z_>AzII@|Z4hH=Zmmj56jPx9?QIs!xE`AN>|O(rhJis!iPp77dDX-M!+KdvdDO59|6
zC5pn!l4BI*e9w>P?5au3A-#(EIKTTws5tn_49hQ)HIq>B1XW8t@zv&n5UxqonUqgp
z0z!g?fT-|03~`u+$xh#_bFCPO(?^4rNt2LBD8&Atq+;ssBI;5SuY1e>sQX;%@~D(H
zlu+fd7TaC-r_<5;T0gIBW1ozZP|E4*YwbEQXqzY<-h1<^*^ia`L|V!7+MfN0Ls3#4
zF?Y&?$U~vkcvY`-upFy;>M)Uo9e$;cUi=@Mnj{V!QIYYMT@H`5nN7>&+|XypxqS-m
zS;rK1VrE!a%7_HYh&LSS%FTFs9!mJE#g<bU-aKXhV4>IWLeWvc)&%InS)bvul^SEe
zTl-X!RNXlp^y#WJCsV!KX8?#;CW+V@VMPsqx|bS%fKtz#hA>k?<aZNVuMQ#3-I!WA
z2uam0q%_df65U}p?BAGhyyqYI^01UF`9AKmgp1c4xP<|PDeUGin>*hKa@_4HS^KW6
zlOp~S)#41Nz@;qYC|dH)_Xvdl%(1e{W02|OdJPS$ZeH8HYLq?ni#P*>)ahVu202I8
z<UeiPvxEua=gs++6(#W5V^<WoPu8vvbb~sv!Qq7weeO#yU&Axc!|+vah6a`9Gp|YF
z$z~evh%h9*r`Ajh=&H*=G5S7QaonOM)%kX~ZPS9NGo1JC-qLCzE-LUX2h%=Qp?sZc
zQwvZ?<PB(=dWA&dXUMQZ(`aMeA23DEZ0Afc)cU*FLk&Z?tivLy#uJ}1X08p=)tjY6
zGKd62wrL5{{FB>YbZpXUB2Q%r?zdj9`^eo>lJEBjg?R7d5yni`XP~waPjz^v=y-C~
z(ls~@fTjZF(}<&=1^VdUhOKbL<FFH{{644qKW3??_kSLgdk$XiF!W-ypctg=ldsyh
z7dAcU9>0yRy=9`-Dg?<T^RHZj>%48HHC{otSx4OxbaeQ}co}#b@EW4jw9BUm$Jhj>
z46bQt^)y>XMcsJ#u5Fz3zDr~fPFI<hPcE;1nh}TwS-dqLHGf{m$Y%P@jUo`aH4-7F
z_+SPI2H;uzk`|Bw-h{kJTqI!1KTBHHl)*7?0-T+1hRk~MPp{zR9P@q;i(ZbzHz%~9
zL6+;vDgBl(Nqz#jvVZ*OEE__qB1~<n4}wZp*#jAN?=UX4KXI)vw;@e(r+@k>6P&G<
zX*oWA-LKD@kK8JQP?YO;ku5CwEVH6rq?H3VG+_F$jmbX{$$}z?x~V<6J8Ff8ZTV&i
z9+r7;+<$T=T&NtGvdZ5vQBJ<tdUE5f9vU8X=pN{TA9dr$>`%*n(_Wka^E*<KI2|>O
zYbZlBy;0ieK)mX!gW^!>vwbA{!NT&7B60B^vmtf#n_ZlkMn`z-dw9{c(RD+%&hp}K
z>NN1Pz-RZLog8MM3p5|RZb{AnkGXJ<GW*lLB6y1@?f<|%r=%f`-Ap&!N{R4-MS$PR
zD?=?zP+RfJ&qQ?8DZSvNVT&V*LeyvKdm^)N#N-OmDmI6+n3S*heYR_e%5sHm&g~CN
zm@<aP@9yvH2th^aMkpUd#gxO#;0E{_5H{O>t#t-yxW3=yWQdm4!J2s$Cb?@$CgM{&
zDc;4uQY%eQ?0aia!oJD|E^~|fh8V(qVCiGTpB6T5ExUBzbs=u7ky03#7wbPA`R0m=
zXh}yJi)$r{Al&EmEU3!BGH_ZBp(`)n?qma(Tw>wd$RwtOmBJ;p?6K_lGN)|gPGUE)
z;jFQEtfoS}-4~uN@T!u#CLKaRUtjK<F^tDkEI4zO>3N{odAy;{jw6oM15)+c1+x)M
zfVnB{lT^_0CsCgkRu5Z2ln!B6uUWsZpvrW~(YXhokR+?=fnlRLeTm%^zd|>|eerQf
zFbt_($-dRsi($_@<q(svlMvE7k<YkfXEoYDD?GXAdpR*n>n3|WCT2SUvpD)NeRNsQ
zAJFb6jpJpxljM5biZEjX2CJ8uD8{xATBHd9cpgn`AE$aTqK1bS&b&tTlj^Xizl>Q#
zfh8kAN{V_hvTc!K=jmLMRrPatdU+c7K1@Mr7-Kfqj32^V%xkMZ_E-IGlGaad@#+JD
zM*g?OJ_S5;mTmgNcKEE1>C)}*R~sg6wn57o$g}z)9wv%G$hJ2GCO_V5&Clnob<_7h
z8jnRxLM%}qxiqbS;$jzSzSrabSJuDLkC^xj)h|w@2-Sb_1TA<iX#Ry@@6rBuP3X&^
z2S#mgMYjK`wp)nnhJ$Rv>*7;4xYeG?de1!Pb!o$YbJ#wk@|L98Yu=4!y3H`Xgi^4O
zV&?=m*!ynav&vf)o|ahtaWTRR?6e-5EihR(sZP@Fj~W*TR#(6zO|5x1LM0tp`iZ2H
zCOdOKT^qi-!LI=`)Q5c<G^hg%S@fZHJY@c=Wn=2}*Y8S>e7eA_@j^PQ*r!tP$1cig
zpyF}buxEr6--eF0aDJ*MPaSg}YRluV@ZAI}AEm<#B2m8Eg-Jd{#?f`W#U=2oIVfYS
zD4-zU|K%cuzr3SnvZeI!sUa-*@XHX*jF!mrYp`-K55)t`gV2%)>5%U%b@E1lb11)J
z(xieOppZ1l_~&zZ$y<9;GS7WK$p0>E^D}fdE(7;+Q9NZJ`mc%{bU9fv_jl+4yIR=9
zrzP+p+i$R-^ay<Jm@Z-@Ydq0HycGlE-LR`y6PaHb*lAbH=gBSg&=E$LxJ5?!lRLyw
z9k0C$)|aF;+ZOBiw0$mWpKc?77{+Fts1=Rz{Aqp>0)~ZhapWfH*2C9{|6;}8RE3SB
zj9Kgd{BZ$gVSrM5L13^Z0O>0O=#Mm(zi{p@+yK_K5>(VTWRd6FXo@0=Vl@E{A@E8F
zCYBD$9q<I8tS0IbdEs`$7=2Du(|Z+n9cvLcu98?wD(A!4&2;QFjrRl~Hi}-k(Q3!~
zwyT-48&AVxojdS>P{aU3h;K5kokN%%c3AK)0={@6xMqd*hzDsdBKW@rN~d8&Q@)M*
z-c+nGR6Kd`y`W!)TwU?@>o)kMHr#}a_3esymJ0f8J)Y>BnQ#4pmPm+^JGGq0mAoX8
zE+|pT-(IV_@YxQzW#2!Z^5+_dE}rm!JQOf;+<kejYN`F5XCnFHwTQ#1wWMi`leuuU
z)%q>eH+)yyJAHCa*L6Pe6??l@?q)lG0#6C_I2GS@7g9XMFpZ@};O9q{5wy6;NCAUw
z<shcr@twu%mIhw*L!VzK4xuAEum5Y85++f`8@9F6>oLYza`*EGsM>D+T8#4Y3wi7$
zUH>s(V-9(?>UCnz1t=}Vx#i(=v+QLP=Gn5o?tsHy*!zb$U#TO!6McJNUh{hKXG^Ko
zF9^SbUte}GOpINY1&Blbo7JdInMj{qqOZ5Z9d1L?iT*1ca*kpQgxCAw2kr-_E|j*}
zXP7IA>6rC7o+Gj)sQSma<zT5goXxje1wBvlyuP}1)G$ygO2FXpRevl~><8_~luxQz
z>MDSb)sdDzDmaRvZyGPE??C+SK4|cOXVp}%D^t^`9I0FJlR(;H`=8pvQJh3W-X?e)
zM;92>cJM%r*q1>rth55d&+F;*L=BBfSlYrYeu@%xWof^xtsB}rEOfd1_-X)F{$dQv
zeTTuK*K~rJVI4mw=5hz4o+LS}4qr3--iJ3r^)=3h!KdxjxNv)!k~^b3pKX@*pSb}_
zQ;-N6pnIqly##9URI>IxbD}=rJTSAfCD<Hg-|z-8{Vu=F`Ck3hRBpqb<X!f5isfAN
zJKZekhSpsENM}m|36%7M*BOf>4E3v)gk*dO(D>qx+{9sw4IO>l%mo?GEz;|^_rB#V
zr$K7<?O$pI<J%zc#=B`8>NNvr!SfoE?;g$5Q4GBUgEz8OE`6~x*MuF38sAQ=A>~6+
z;)uU}--VlXKm2iA?-Sr%j`=I9JKfNSL?T<+ZZ41RaE(9W0yGLbjwC3mxNNPFf$7U1
z?o~v%<8W`O<2?EO^&M3ucm}0;faaI*1YNiZ&~$LxqXH*VOsv3@oD(ra-`lAMlI!Q9
zGMv0@(3AIbk8D4y!Go$9!tT*!iQ#?mVoJ@o#?x<fegsMP=%<lL$J;h25y(_X&Bxh8
zwe~k+;Z1@F&WmV3e7y6g5p5+G7c7DE)*%#*2gO~CJ%5U)<r@47Qfcx39Ho#-`+Cy=
zbf1N)UuWQ?rZ~eZs5iMXPHO00zOn!P-Qungnre)yF3ih^(m?)Bnk&!uBpTPd^O<D$
zMi@dI^IO6adkU_^b2ATVeqBGggvyUSrYRw*%NX^>NqtTja(n%=^afoe7_9!M&j*xR
z@XG}-$vv~F=>{LK|4ksYUT&7CAg$wx#IWRr&eLsC%!)C<OrDWhoFhrMezeU(Nxo6r
zLnINcir-aVSxE6`LipD-n2{thbhq{~u~jg(%NXbqQygHGEljHG{!;)W;_s#2CqzZ~
zkXDPKfHa9)B5diM)DV~J2?SR2c04W)WKTI<JN)IF#Ft@?p8Wdjgx9qNJZ9&@VQ1$;
zV%Zxuu+NcLCoTY&^)}!e1aR28j1D2ghUPTkcyf>6Yw+Y3fE!>N2jW0gHG>>xFf%mc
zKVJ*jlBCe2xDz2sa7d7mL&m+>ek}O#fBG45+mD$^QHDRj;i7Zw?rC2=7`7euyC0~F
zyNS>wsf#Nt8KfP`3Ex}2Mi4zHaV;rg+$WSX2)C=u@wT#%g@aXX+J^Udc_+Pw-{I(?
z*Of{zFR4!YuV!ETT_!00E)$LaEE6oMurnn-+aD3Y^SY1+&m=@-qGg+f5iw90p3w`2
zgX}7>W0Yu;)$L{AC+SOy;JD^S*9AL4vqLFpDq48;cPfIg#4j?E4n5LYV((uiXNb-n
zFm`hCa8nwke<l_3%QKnv_ZAB1|H)OL<MUBQ9Zqy}Arp32G{JiuE_jLy(Wdb)+I}Y;
zzCvi+5Fz#1YA5)aX}XYk8N1bkuURatL~W$Aq%KK*Lin-<LByA>%{$2FRqtu{l_a0o
z(WwnDXH%x?n>O4FgEICcQhfU%l$(jz=j#K-E|HsJF&?oWCbO$67%{8QBy?TiF24q%
z-N`DSB3rgEbrGYJMqj37a31eH^Zyl-pvz~w05K^d;uOk{@|({#RS}H=KGla_83nC5
zB_4`=!vJ9%q#O3kJGI6y5bo?BpIhpVmC?-+X8;FyGoRWBeVjG$+z+)mY?Z1&;~O?H
zi@4M!!mkcRi^zyx($vE&BQwABi^RFW(KC#)r<_9pcl-LThzv^1e5$4I4j|vu=w$qx
ziv?P}A<R-JW7%3Tmdu^qq_BY(7$JDX)F(<`Q<34@ZDWbG+GUP;rnb1~I2qv0z1rzE
zR|j8HC?QI`-j)Prw!m$W)NJaNs^~8gkNHB`Hw-#Mzu_Ltewb(XANxSnyJ)x+o7`(X
z8p!W&wT$=E>miiQtS<0B1%X<r0EwxD7|)^fP~F<g{X7&^8KCFP5SFdj`AOJ@M@n%N
zhg4Jp10M3d7TjwaWsIw{^E3%`-?XGi-RL+@5V0`9!so19Qy+g%Y<)oDV|8bMZxxE)
z!Azm%6#p(IL4OPn12q3)BL54Vk~U-uV{?Bww{skLJL_$|sME>VHy7JZ`OwOJn>wW9
zWE_PV(ATXX2;kfKAX|uCeHl-ygW!9`bZ(w+&aQObtOCy|R+TE}$n>GXKG)Wj!kI4-
z^JQg{z9=u@xr7~&#x-{bNhS3cFBwSQ*;yB*MUiQ%TRPZZJNl9*@aW3R_N}VmhFzZd
z*A^Q-pWVFzy1d%T-CUN>nX=v_O;SSR7e_!D)?5XSV0Of<<L%2a{3ww+p4z1(d-eQy
zb#IK^jn<#FI<xUZ*fJY?rLi!+jTH<i%3?jv3rHz&4*SQi$$1p9y<Lj8E>%0?XD@K6
zfVLA>iU}&fAXX@l>%~L(+6CW6Ng9|gavcd+Ih?Ny_s@4sE%kR{)G29iX6K1s_>Gww
zQe6|6Y5a*go0yte;7>SwtAe$eQMYzS^4~{@m`0-&o@~q1b9^nfQr>Ph8bWPdOMv2J
z=-Ii@UGVGc(OaF*;=#edb-Cnob%)Dv(S*aqg2(_i0N)6ReV>*%f=X+47<TySiBeDT
z&lmAjMT;KAK^yp4xBnrq7uqYm5n##4nN}aM$U;rf1{b3XD)^x4U$noQ_9`T)t`M5^
zaGF_x&#myRyHdA+YZq?z256DYXr<aPA_MewB{&|=Y8d9#5Q@Yw#SPm(X9Squf+oWd
z(gd0xd|b$_m?WCwYIvjMe^BkjycN%?*w%XYveZtID!U@)iqoGKUWH)D7TkaFMrh{c
z`R<Q4cT)Fnly=fwus~JGF({*}J?&JevfF0@0_q)M;spCGg{F9<e<b*j73fQ5n4x><
z^s|BY*7k%ki`r@J$Hew_7v{fB`m=L^>9&om5lHpo><=rZ6V?yS1|(T@E)0}mI5=T)
za3TSq;yf%hV5VuUkes=bEn?8~cz*==eCH2?<lwzMH8Vab!Roghw<1sN4a;}KB-n>q
z1*#uG{iY&F5b-fB^ACF~ZloW~Lgs&}YkS7WM!|PzQuM<){y)04lI3vrqwKxAY0iO^
zYiq;0vs=_6leRgZjXxp%#`p2fRxg3?`KEuG>zZpY)qCA56rGn12HZ32x0-z}bmjV+
z+o>06FT*~te`2m#dOoosU3LF0d4^0JC~G0Qa~gJ@Pk7$t8PIby<%7TwgakqFEk%L%
zk}`J95Bh`47v`+QcTd5tvwcaIb`5A<5>$pIm=k>^68!N}140<axeq<7z}UGU`KTg5
zb_A#lJ>VpA0xFyUln@<;!>s{NfJ{#40Y@tx!=8qbhaPZqM3E}O$Vlh`PtHC8@jQVB
zdLRgyfY#GLbwL#us{G%oIH2|^u_%OjdFAELrpEA<971j`oB|%M(L@@oJhV=00KP>a
zHy4To#~y_gfnI6DaFlgnSv*{*NQk6#_<=g~Dv<_fA`xK=y~=~u?jrDkF7ygZ5F(1j
zq(QI#->LFh7X@@(0*8v|*wvZ5>mlj?CS?6zV;=D1Ybuc(G%^?jsCr&eT`W8#M6%eM
z)Ba`J^M5mK=wEU${<j>2|0Rdyf61Zt-*T}2mmK*2C5Qj#syr_PM+3)>^?~<?t4)bC
zAt@dH{?0eZpp0f0yzGqSb#GME=1Lk$&C5L{Jo^|+!%SSmB`GrwLrTQ6v>vcVMU@CW
zymllg=6cx5E=_=sGC4dqv*X@%pETT@#b|I-aB^{c=u$<KA2tv$*KWLm63eTikNq_8
zDiq(6|NLzMhw@x}=)n{eoYrB?`d^VH4NT?GkC0MVUk}hd;-3!+P6Q`ES$`BVSWqW7
zQNF7da5k2aXFLA!+20SlF$Mk5hx`LDbVv6^#p$j4%Ob$wAPcAM9X3GZrSLxE*o)_H
z>jGMM;*a<_HzfXP?$1}7nrM^TEb-t-7)^X6pSR5eL(Apf3(_s!^6T;!#_H(tsch&!
z4(a5^ggTv%lpp(x>|9TV?Iap0S(vvb_3-~Y4VxP1BoUmfmCdTf$7?3&%=v75q2R^|
zhXQAuCjM)V2l=^qeeLHi%LHD-Dzi5lQycDo;yK&Bvi#A_{h1)nVP}M_8u{G!%R(o9
zPg}RijUQh%&>dnSZG+Q(n?a>B1%bO~6c{hscfVSS{c+TnDJ#>U@P;qB6dydDfM@(x
z<5VN*PR0iuC%YmcZ)fH6Z0<=a-9jLs)CNQl_Z2naGI-Va-KO!PoJV44a_*G_XB>d0
z#IWDtn{fM5Kz7csvf(^`lC89Ud`dNGUZs_4(Pxd(5!DwnqD4m}=!7hV1y){wa#OI1
zz1FU%eJP3Fsd-!RhTq78D^+QfFG`zuKN$EL@I|;WW+=WutbfCGR${Z+pO_yX#IUom
zMTH_&BCo;FVeb_6uX<Z12mi(hTID5lg)QA5M6sUeW5}yH4T^FRbyk^!(bm6U=pup#
zJ$+$56HnkSVucSP>j`YnkoKF#^-I0T7Cf;0<0#p74DCqbvUyr|_xPhH)@zS6bW$v#
z>#RivF3(zdvaB}69;2=K87?_B?ynZ!;p_I8;?Ke`1**d!$bJS0e4pVnCV;SQu4DOH
zX18Y0uQy3OPu!LsFYO~Huk3ZHzZq3v6tj7Rw4Xg1aeP^Kfx$dMYeV$2&D62MnI!0x
zVS(p4qA>>Y1#<((-Rb+w_k@5#vH((!;`FRvA(6N1e9m+3L5U>(v**e%^OvakZp@VI
zMT$ky&jk06#_0Udb>LUyKnCywhDkaXGrVn!c|Kc14MUrU|4yR2M;8yq+XACsDG6=w
z0fUtPmZSuby;?=lKeCg9;eT6hugJckQ?|`xi{;q~dzDa^Pl;cb3g&Zzpl$Wjl-0!$
z%v2@>_JB5MsW4Q~uQjZ;SLOyZw<yn`QePsFD)REQ#0W$}-f#Y5w5&ef%Tf5PlTo<9
z8FDUoa9;nDAbTnpWEyVM!hymt-3!G`&9?JyJK2sq{ou4!O0@MGA^X_x=`?50zm|Wy
z2XnMg>wO*J-VP;(h={O)5<6JELWq#Jv9T#KGoL_lHV4jvfM%&oH@;iP_8#`A(b^9=
zx1T-aJK_HIji10q1(_x+ct&*3&(Ia-`|HuddvC#wu;!|V99+wh$|Rv3pl7jZ?CC8d
zkz8B*sy$!}u+S99`TQ{s!zBE66w5^OSGW?8aA_goTILziI5M_O)`lxSsR-11SRHW^
za~1b6?&ED5-7<pZ2z5D*4c_a#_%qVOq}1`?Wd-%{EZfZ&lpV(PyS+$Xe2MHNII|M7
zaeqsN*-#0F|Fhe4oM^S#HCy8jY9o;|uPTi4d%_&7vOHXII8Xb(YB|i9a17_W&hr}|
zOCQq_h)+K?AnGo>-YO;fcJK3J55(aWy}Lh@%VVNdVZq6gE~!hm9j*&jGUI;63=812
zBsbAM!A-lKllP+W{8w=<J<7~(gyDG4-;Y1fI#O_D23h)ko>}rwccI&o|EbH;NS;||
zob@|ynIT`d{9H^hy@f9D^qbB&AIc&`De;;m-Ygi`P~c~FVutS?v)d^h6^$tQ@Z}nF
zc-99@2O5v86*k;q1;K<4;@O%6Nf6hf%1f|f4~P8DyOE6TLK<nu_3+5EPIa^3)RYj_
zz$Y9VQOy+1EK%u@=KB7d>AXN+dL7|D!*nf2ZaJ%C0`ErCGCdvc7G30+6yN;}`Wv?;
zfxz(J*3+nIr&C#)DYc&aB6?n$sz2pN^Y;JCU{Ocg1s`_iwi829W$d=~UA_p*<?2)V
zb5Ex}R$9V`S}{xE=Q(85O%H!ZFx{4pT!jS}g^V<T&?1XFgcRl+YUyYOGqCDnM(AH3
zG#2mLiVTt9;3p%j)sqj;Go07lhahj8f8)11*_Y9~4#qGXhH8-iiwUgR7*-A9jP<}1
zhT&E~4P>y5{Qv*+#s6r?{cmTm{jV$|#&b_O=xS$(P)V?(<0-jG&i{9I<o}AS|8r|s
z$ZIF1Fc;_Qm+#U>^-tnVU1=^}K75rck=n{i^pb(FkC49Ks@mk&%(j?yZOLaQpRA*x
z*DI!wfiLoJtA6<3ro8aK9RuWl@dp0qg=^=Zuy0uPE*6}tpeKnScLKu&Aj`g`Am=nV
zxK_|yLzu-ZF~~(`l|`x;m`m+;Cb$brxmwsWkRM_K$dm{mUxYUfESMhL;U9Q^$GIoU
zjY+!ym#G9B<SJ@~Vc09H-iayZ-Zi=}&orR$&)b(6_V+R}_M|~Km0sP!gWL~&W!S;C
ziE~eS&$Up<F?qz!6?r@#=K1Vz?c30f;7r4O*!`D(%X%uqg#E{^S2%uY#{I`7x_tii
z*PcawS?1ULjG^%0rZ#W5XPfr)(Bb8zO&_tpcf}2Ts&LEG!Psk->bI{yVr3vPiMZPO
znqf}o#Wi)&cMNf#D)@H>(1ej1l4|e!99r>15fcO3X7TBguNWB<Wc$viilwHdR=VrM
zF%Vn~<mHMRP<pl~*dIpaR#noOyJ))(gb)02jA<d)yWebL%=`1Ar=Om6^un)BLFwn4
z72ES!YeF9865buj20hz7KVy!1#;!wr3kiT6-c+^6Hb$rF=wOQLCb@v4AJ_5cM@Me<
zo=!U|8R13$S#58V0@EYTE~Nbe&97$15^Sgyz3`bCpvhl&5}y2@g?gsxQG&2Rr2S1P
zv%CshL+GK2Z{w+HRK<$MpFIR_$VGnnRp;Y60PL0@Eou#xe|qB-V+gAggi>8!ftV?W
zn?Q4Gr#$Db8CUVrq|#%$*uuEcLzMjj>5^#uOQ`obI7kv=7oPBdPK0|=WNy@`0`avf
zL)gv86djX4Uba>U<`upO`R(j)7&8Q!esomA<T1)#@EAEp@u$Cd>|9K#&<T|Ildqk*
z;g3C`a!H4kGvvE}wqQOgLDad8gIFnGH_hdyLn<vt@i!>co-5qzKRehhkpR9(2Fo(Q
zc#IRte-KPj4evFF*TTaf`np*XXevN<+dD`kiUevACs-L)4Nm~yv2*cp{n<pgxD9B#
zVbyt<Ur+v0-}07Nc%OJF0(?_17D<?VIhk;H8vQglal%a^eqUh=)gqQDV~r(%+Bxl8
z?r3P%9&ls&Esc_8)}AUZe`n-;)6>~#+@PGh^*UvPPYy2|<eTVK&h))jYOlG%7V-X>
zi;Vzh3tHg{f15Z-TKA(z)SvioLU3{cSzHTVbr~*PE>V`}=W@l9b6g#1mY>^xXENS^
zOz45`9G(p*{HHOhY+AVhXBb6CFkSX;gz??)RPcoN7>~N;_hQR=Do7G71lWeEvzlVd
zr^6yBJ6NSv4B4~cHc65R-yLMU_qbrdi3BGXl$MGr6%-{R(g4(Ocx&}3O7=idA;%Iy
zGb;mpGuXb*X^WSyhGvUi%z4XE`!JGlOQn_VEK$T4E|WKv4?_3b{w}$<g#ddpG6?=5
zizto-1gQx=>nDhig+Vk)W@cHyV_rfr27M(@Z*%u`4zZ0#VRX^zlDiz5u>XMwb-=xw
zUn?Q&Vr@R}E>Ra@{RQ{YF93PoIr8S?2`Sq{@vPWias|4C+a2$tuAP#OxR^%Q0#y1L
zExpqTlm2}ET;CmkJlK11@ag6)p`;O+^vjEWDO()>#bjkp$IgJ2A=Qp7=~0Zjg&xwl
z4?|5AN#kMVEwd2BY;;8Gyb?Q{T^rHNCtuJm;VJ-L(plBFjS$LG<v*Jlx!b?=-9Z<v
z>uRm}1?lu-xleY*bM!D|;rDJYSFkj`cVxhoRSoyTi{xLhQrWsU0CsUQ6@;!2Kem8R
zFfa&VD;(vM-Yxuy@?>b`P^h%zZ+=?gY=7!?yIm`Qat6*mRBa<3RMV)7U;pE}LIG~G
z{ucMR;LI(p_rXQc?~7iTlzwwH9H8X!zX<uInfQ}vFwrv+nbsYVAVOJRCbxL5L>O+E
zBp@0Muo&4n4j)|?P!=LBo@;s}f4vxY)8vUfZ^__|F}{Zh{(!6g_BVuT>Eqy@J=tH0
zbFx9CI~)f!=M*KEr)t2)e<%Bs43;0yGws__BmGRsbn=|&%XMC%g<<*}TMkNdY92e$
zg3L$OHP5(mYRAH!JQ;$vlvR898hGUl3kV8|40Pr0Si*Td4{YjuiHn^L+Yf#OA#MgD
zeKG!pSrYr=2$9fh9JVAMn{-PeKf_$?sY_uk0DgVC0)3)q4!1C+`~=>OpeDTfa|30%
zl7<_M>Pl?(1=VK6S{iV{jY=``;0IMxozV-jUOz1B0zKT}L4j)|h08K-#KoZhjX&Or
z-4%7%4g7^-u*NwA$Z@gNajBqF&ztytnsap`*8}(IMOqrDd}a7ZR>tmnDviCN5Zxye
zp4U1=6Do*9>vys0s?C@SDhYRG=8WY;usua2bez+6HFpi|@I>BV>8Wqd_{obLrfP1T
zzG!2KI_fCV`xl*bJq1;?i(G~ZymHg#R)w#0(Ok<)e%U(3yO%Y6-Gqg==0K>X!&WF;
z85`RTygx&^k;m5PahXLQu0jTbBvm}TljaA<Mmssm(<j&Hp)a}7o*G&!Utr*dR<e$u
z9%l}MvlvPVu+0ryz#ni{U4pKRxEw*@P&12-?9{Ml3ef>URf624Jj1GUdw@bPqiKRW
zMZ^cAM~dv^udHTAIYId4SQ|BZ;a4&4bBdC{3<N^6qC?=XdlU|BK-{Z#Mx_z+x-pG5
zOO)h@edpkDzu_4ACc(Tb2*2VI6CE-$@CIH5nAx(bCxe>K1%<njCl(jEQ+4WSjAAzE
znL|5x7(%aHFn;6oYNCF@RZ=-^_J|an>efKMvb_)~y~L|U{S{LsTp+v?f0d@Nv;Lf=
z`&6-%1O~hp|Mw)~lmSn+up|F8Uy&;exv}9U#G4VPH<HfGL_0Wu1v!;y-qok-=%jw6
zuZ1qWll;rW!p{4HC-^?u(1<8Oi1cy*-dV()wiKHJmMZLK<3{G2{iemB`MKLOW^Uzq
zb2D5I>CcIW2D3LiFc*vi?^Jz@lg^h<1d72~(tpo}jWFP)y}F<=LT<U};rR71amFJq
zTN<OD=iE2>dMkBdY4RG;L{z&10LvIor|z~mSby_D&^^HQ8Jtrn598!8l^kuIBe2$j
zxM|SdN{WIDaxZ<T!{d)i^p|yu2z6PBs`VNy3_BW7#A65HKe#poaVChT?>)#LCX#^P
z14dt<S_bd^@ml{8izjMpZ>G*_Gr=Qtnsw*V?1x>Y*Y7a=_+ws{%&m8II#R(mL%UDl
zXnZ&BPJ603b9mXP@FabrAG1W+)Z9r|#~jB7vuelbC=cg`MZPBbDplk)A31H49~dIX
zu^Oosw|hGr#T9+rQJ7HQIv>XG=Z5@S(So^WnDiYNl<#+M>HR33*dATgK)>G`=VtKv
zJKpLLfyMugz!p4@{wN&5IL~)e1>y9O!{$MP#Wm5(y_{Nn)AyZZUM*u!;6(RXMR0wr
z;|E+4+`A-w%t{|`gp#7>y~j9h*I8GFAn`#P1~u$3{D>vwaZzZR+;g`>Q}MPNM%UuQ
zh&$#>a6TTt=eEt^67GgK5AkdS9Z%hMzsE%MM0Z9d1svV)0R371J@QS9z-vO+qC_UY
zz9x~I9kS}D$w&|(D%$C-#@+O2+Pmfcbyk3*n4pp@&g3o%l0HkY+ake-Oq}*Q)l$Rk
z2NnpLxo7HV0_Z4=+(Mg)r6iJe2dPl*JnH`JCmc?c_U?7&)tp)P?)hZ84|BAuBgC~3
zMJ;tj60uo$-`BMZr<|X*VL(45lk_OhM%KW;Mtn#ne6PZs;`w8(xZSJRypLGATr-{M
zM{|*C=*(t}{PvmnxQN?2tw37q>@)b2I|6|Bb%oHQSFbI`W_PVc>=cXyP%(>%?n(l(
zT&}A58^!OJvJgN3J_LNU@b3kuS{MCW;Q-T4EhDp;?uI=3{X!Sbj`ZaSu-d)lU72v0
zD+<NYK)WsZ>qStW%^P)?c3*KMl56I2Xt|?a?AmKLhTz{k(Iko^g%(#-D>U9k65X@t
zm!NKoYrQ6@r><?-s!bndwZt89rIm_4$ZF%cl}phV)>h!~!r*phyLtE<aoYj+-+X{l
z<=qT~tGB%tgj)wY?ZUR#b^T7jsDCKNTude(o?TwLH-g4Vya04pwE89H%^UcSjLufy
zj8Xx!#0R7}A<>Yz{3UtvHZb{}7x;45R`^e8VPJs-X>5`d`M%K0PWQ7#(=+6nG31r7
zN<mFDLjjAE2T+AN>|Cbkil5Se)Zk$w=0Fq>k1;fSzE;n<k}Y{Jm(TWyr+&o)+Ke)W
z@IN8@tr27|=0YNqX|@oD9=a2VavL$bbu)rnE_)ruy4ID~K`1Q%$JY0{eQ%7yTQc5-
zmkIShtf-^zXe?d%xlOztJ?L@xqv2q;uAt4|Mx`zSh9jpK2U-lN!2J?HCm>!D7*#Vt
z@Osu==9&ex9>8?t4Yc$wl%&P^$}HkLud>j&rARioe8Kd)k$N3-q{<eYId%)xzcRk4
zzm5|}$IkT$vQV?29>EGU(54e3<mcKteD(9Wix>~<h8(rQase8D)8qyHx}SVBj=WCy
z!%e;!RW!cH$bDhhVd14bN$zvA^><B{=vxkmO?;5S3vo$W1=a{EXMp$Lp=`YL=d;w%
z$laGwt@V~RnYp*n)>Z)@P?-AgBj?+|($Rqu9YyMg-IoxU>?3&CUFaN81zwAlM`8F+
zzWn5i^>Dlg#J)ouZWgG;a<bd5B_IC4JU*<0tZ6xe`2WHMF~D_mv4CK-N)>%Nk@*U<
zBX%R>**gus6>nRBKEd7pAa{-*GRfo2|9gjHc~d71Z>vJ#Fz~(9Lq9Nbp<LLlp!Cke
zr`dR>%4mC2fl=rP3&;5XVC^mAqU?hIVUSQlx<pF4L3*XTmj-E61Zh|hBqS6GC3k6-
z5RmTfPz07(Ql)cYK}t#K|Fz!t{rf+^7tgEbdBf+!b)9o&&YU^do|*a192Ll_2W*m$
z9o?k`j;~%n8ayyZW~-i{phnEY<&`aU@Cr|>_;aQXITwd-9|?&@C))jf2J8eKdg|?t
zk!T^m)x8n~#ABoZ2Fyt=L<^xMxC-#jvu*j#&<j`-R#as^pW|ymm2ZD_uv2xaezMZt
zaj39cx<0D$u9%wdcr4|(!2JaQB3&3zTvry*?<NFD*yGoi*^pHD%R@Xz&{b_3d_CKE
zORMtklIEvMRNk+Gf@g{bhMxplKA{3GC#>n}`+o3mm1Q=i{{&2!*VH@R<}#^(I2hRh
z!vwgq62)J1N&}l5)T3@O^b^u+UTimWc2D2c*yRRw$@qF$*A0lP+Qjm1JH(T0DIrs0
zY>eHIYTIwLk(M+d=GWX#G=P~ha0f~WOb)YrN5GQfDOMUZOq)Kda}`zHZEoVHag{jm
z%{?fgUP;63zw!qlML_c~inrf_(m}{-i@uwx11^d}WHZiT-RTx4BQ~0-#=l^b@KhvE
zZ^NGNy7UI3g*+gRqSQK@Y`AxDT67U$bOAnyBsg?($XrMd*%B9c>o*P7B^(GgYJm}3
z>*c4MTv%jKI6n)>Ne?XKNDZ(FgZ+Q~m)UpkimL>Rc<s;O06vJ7jL4FT_0zPf%p2O;
zc6QG6(5EFc)<Y>yv<(})a~TaOe2@{_b8l%lr+|TD9W!$FaAx`<?l)=BY4+R8eMAW#
zIz;s^3W^rEeIBQc-7#R$ymNoyDRJA`#+!9^0D;k1=MAyR$Pk!2MNKQZiX$vs^~}J3
z!Ew{EP@`*q2)?;l=f3sO9te`Bjc~G+=0aP$C+!A8eJ@oZKL>gKB1zf|AcKJ2{1u2?
zy~c9BZ4~zSW}9K_8uvp({CZ&+TT5@*ymD2@9zp2$VtL8;B3J%wXXQ+5Ub)YCX8cZM
zTC?vuTCI8H_f3-IvgHiJ%RcYAC^U|8mve*MHvNF>d0UkhI!wBCBgYR3?~mxnzFYsn
zG|Gq@hmM%@CRZ0(BAw!DHTvOFZQ|%XmRj*s=bZ@o)OoEz$P%6o#AiX+@t!s4uMnO}
zYk_AxAcDfxbZgJ18Xlsb6|RY$y~ayPT)l>4bIpP%9jejv$~Z5ci+KFvZ#*AQdOh(y
z`YW9??3wL%=t$Zx$pxEdqkqhnSe$R2(s16I{Il_y;MwtZH6Rz|E|}=(;R@C0DjrOp
zSgzgBjx)lx{OUnuC{yR5@Fq`n8;LgjaNPQxfI~kxwiA9J*;s#D7%_hk^uPhh<dQO(
zZ1fko;+v03y5dA1EaM^*G$x6f{R?(_W<iTeew+4bagTaI5!~?~q3&tUl@^5Dy!^MC
z)IK2m`)}ti=z!OV$)a%9K=YuN<Quf#93UqSSV4x6S$!T*)@bFA{IHc$s=Cdmwli-V
z#1+@N@g6O&7)a&RdSyS%jkXr<XmM^a2&g_8BOd#lp(%*7MJ(Y6`cG^Ca1Rh$o_y9T
zomG6U#*kER_-|~92gAY%>u^yzdU|?7bNSZCPKdl05qn52D%jzX0am^P#0GDy<5s~!
zlq{BhPkB1LXbw9DSkU4mxToAtMlm|3CvznG5StCOvJ45SWf<T5<|mIA#obP6yu1RD
z+KA}Hm59ctkT|SF2Un!q=D&)T2`E}HOq4>T9j!6{%>Duex8D%-U>%O{mahCEiV`U8
zG3^h?*P(*d@nGkAiGorqP)CcmJwuyDP)8D*`)^%Kli;-ZRw170E7*^GHIQm?>ocUJ
zj$!v2`&;(Jb)S^g&t3j8BUOEmFn9U1vB6)&SM(RPv;*KLFLaSIY{i@pM}&VZipR5*
z)gWeNWu6<e4bzPcaEcuYj8TVe#q`(W!bGjH%isMRM(G@3Yh0$o?;2nc$vok7e0#gQ
z9Xc7Ig#c-*Q?C*;=NiP4RvtDFNlBOob|ez2VUrexK4c2%q4Qj^yRB2fq}}eb?wWlI
z{~CySf!4VK{U$Pv>ApU>3<;v_&Q*K=l$Gbxtmy~Sa<Ag=8O~*QO-t&?`srL6NW@=e
z)BIFht4@ezHWM_{g==fAu8$l(^H!S1@;gM$18zvHT-^ltV^;{tbaqdcLk-tVh3H|+
zE5&<!6{g(qqLK4Y?dEO)!{E)l<RU&(1MAb)#AWe3h0)}+MTa*6=-h?ftF*I+pdYg;
zrP~_?KM`lMg@)FeP|-J?Txdf+!48@xS)gtz7u46l+q9hL=%rx#2p$3?CYdqB$k$u<
znlAvNXKZ$t^n)Lv&M|-VSD{j2rz}_CgMABYELa1~F98~L96s-NwbDB^_dSRZ%V-!O
ze0La7r*62YCCzW9T@ongqWq~o*w@`wy|5X;P6=$+Bu~o9vlnH@zqBXy#Y2?TrN>p!
zY9Q~NXTwXN=I!>RCa;p~*v``WNG-u1s}8+~7D%CWRbL`fG_~c^tV1eocX(%8sAqIG
z+`VutfuB*JOfTSW`=AScS;fn3ylcYJ$XOWS$<*~+nSjT`KbDJXTAE?fqoB0^^J!al
ziKS!8HdhwGYoohz8W1%w8%Q~8=^EABOh3`#d<VU^Vjx|~0&3c41oKp1cU~8qKGy1R
zx}yAtj>=s`v?@EA`7Vjr6cu-dF1HR6@6pn4hY{Dy((4FROdfDSbGgKraB-`i!%UAb
zh+5YbbcOLpjsfmzax!-Gtr1t7iq@GkbAhRu%xCK@N&ohubr{JOjWm{gc%w4jn=&bc
zK`H67fXJ~GApvhuuFoOoZv={LF^<Cw1qkachlOWntfszBAE|lw;6XLx>v$m;G4L}W
zqPeH{Nd{<ztju3TyX}PqCHi^%^G+-brGi_KwVlKz(ITy+t@Fy~YK`X9_}6WW_Yydb
zT!vK5*UNbEZjG3oE!niqovueJ$4ypr{Kvh4@Tz-2?FNI_^F5|1-m6MZp@PO{zBlUs
z@kD3{tJIUzfcuCleQ5XID-QZKkX-ifYbAi?@0<&d{8{z|Il^hK;c{>b>7a=$>Y)Ed
zAOAJpl8V>RVtwTfnkzRgZN~XAwO0t&8_mMQ@CO|7RKB=QEVQ+r!)8s+$NKN3GSJaj
zaC^<PbgFC5ogM&*5R}wt!8^ei_woyTM0C_+OE1kI8)~U{D#ROl8ZNwQ0#$wy17mg5
zp6r@(d=|cw`-|9??CqU5<Mw2};smUOGy<%7%X-4;cj<L(-uStN2((o{aX9tgTMe(n
zMQAe4bqR49nW_5~c}VsQj%?62=^2?V<@7V}Egl0fEIThKY~(l04EybA&|P3D#@?a|
z{zE#sKw*Rb#zZPK!My**MCv&~7*IY2AvKoq|4-w#B>1IYiY?$f#|lV*8=s{8z<!Gk
zy7MnPwQuJM>2@|`6ab_XDZmoRp^RNM?Bc)H5<?xKJxM@<&d$8Fpei<5=t_EDvi_L<
z9oCQ3@po$Q@v>eSE6uBXso*hA@-2sXSKb|FUuyag;vn)bxyIDn*xtT}V*icr3*oav
zTDac^6l7BoVrb#e4Ue@V{+AgsuO9jBY%O55a!C=IPOy1mI{!iQP$1^Q$<FmD3l-Qs
z@R|t^==wqlVmNCE#!)*t<iw0j3588KoB{XbBt~L5&q#nLoxp~$l>=KE$N=YkE&(uF
z&KaHXQ8FLqeJkdD3L0CP)Sm(Knh1Ek<j*Kv9gtG>KkpOGuyL9|1Q^yLPzCi)a<_>Y
z+R9V`YFq#{!HRmiOq4Z-Hwl2Z3Wq)1OMj7YfSGQgz|%@YKP((iDW{M6n|Tes+|x9Y
zLd(?cxp#P1Sy-j+tfH1zv3tfxdcK*RgsSH}Sh;zvm&hDeK#kCJy8u!Ca!S(1aB-dh
zpYl!ofXyrc*>=Pbl4Iv;s6gv>NA_RRV%R1J+H?8e6ZT5s>%sxP4PvaIlHEsL_&WS(
zFs40#zH1mk=Y;xo@&8q*SX4o?t_NNEW2xabAX0)=p#gs129b}7o@CJdD^*p9;k>{5
z?HGZLK?BSHHtbwqcQ=ljLjSZSll~QKBHY+7Z3*W(ey0tiq6w&iR&5yTv!a$84$L}8
zLSa9_2U2wHZ{gX0B^L)YD1zUO*i+w7!;Bcr&b2<W;VkTW>U~CmnZpBwrvIp%H_5`H
zRV9XpO$~_Q;_FU)WeGv#TuG>;?_OHNmQ>W*FY8D#MuDiO4IhmSN}+0ZxW_-H_>{6h
zHC~QPD3WdIGd$X~qorM{dBgJ4l=~>;Om?*G4@18hz>_KzhTN{$uo<hg{p!zzp~;ER
zJY6_4nGyMl!5IR(aN(3Ea*;RlSiqVASZ@6^R6*zy8`6v^>CvG3m=631O>El(?ib_8
zq4}%73MB|U$+ub}oE5!?{$+}p*=Kex>X<{Z7s#yuCJ&5y7wbR_d-7!n=qGLM!GAXt
z0E#pgo*r(<<+&Z%@<a*~(puL9Yo(#ZULg8oWn?gWQa=k-5U#mlJ{FVQpYE;K01Wi_
z78FMB03x#Z?I52+9|L?GyA1)=@Js;_Wc)fe--uVl(0|SX;(3~Jn2X&p*Vb~wicv&J
z0-UNQCG%}Ff0de|<SwHl(r;Burk(s|hW=<RJ{hfO4QQF55@aF|X4U)Lzv~=yEzx?x
zvKcCEE84%b_MTy(i}t2j;-bSs+`?78eTeIChu~VjB6-1HFNS{k94&{!hjC8YnJ<l<
zOomz;%C#1foA+O;k*yMXEs^6!^IhUxn|RM4%V)V&OLKnUNK0ETab4}cJw;v?TI)jw
z6)Y6pmDI&Ke=AEA`95;mQ|=w#{J@#b5dwBvgdiDt0ACy2VOhPrFnzN=AwodKdYz{k
z?sz2fN9?6nd;H&gj@DdMQjbqg;Fv+>HVUpMm`SPoVbF*J-L=s^tF%|1uP#!qlK`KT
z=0NM#PAFLB!)FQEE?y*E6VCO2!Y{D>XAB^NC@d)pvBv#;l9Js~d@hZJ(k~~AQA+7h
zEOnvJPm8ElAut7cC}eT<4t`0~RhZEr41UcIBAW4uM3u81y;|HM`yqfj#P73OEq;4=
z#D4W5>)dl%t`L(_xAg{Ugg?cHWZ?AQF6e@ob&Er1>!nm7Gbf)tzY0QS+_1l+(D<RP
z{(A@@FkXMBGoRsEN?dK)3p5Gy(+>Zj&QqASf{*Ef4AW&HL)ArH{nlapE58JOrtYeA
zTXuhU*--Ku-3CtD_=Sw#sRDS7)XQf~2t?{r7YF-Cpe-%xPJ_4^hpES+WDw_e;_{^y
zAEyNPt1m_g!JqzzyKnSRn1&p_E9VzCc>k7vsykFC-M$5@3^a1b_pl?^Q6i`jl$1mH
z7;FB?!w+NFVATi!BFv!T_*_c7y&FUYz~S23)2-U_Lv8EWgVrKKGt%$)I`8pKm`J$d
z%cIseGyK(3(?f+M;W?CppQU<#;NRNXnAT1s1}7bl&367@K8$_urK_AUjI^hcJ|Uu-
zhy>+NRTqjL)4bAPdMDmN!RgpDJ2aDWEC4EtYDV&z<L<D4Y|RpLbnYjbC!)kYn@>D5
z8$un_nOx>A6j+w!6xGcuAa%l}#V0i@5rt1+eA*fePaiUxi+&1%rSslunSK^QAK5H&
z*|`Ufk<A@+E^JgfPr{_D6cA~F@9CeWtqOE!44bkuzX@sAfs_95Pr1K~63e>~auW@^
zlRq$1gW^wl6Zxahp#f$T2)c<P{V`x(=DwD?F7*@Zv7Y;TcE>+|STbD*u}XEbqsr5l
zsTpCE{s-2sk}v+)TE(SiPZa&N7BL$k^wHo9XHKiFG^ZmS`OA!G5&#Lti_Z9Cf;8Zg
ziWxD4Tx-Uz!3J6)<5zyDNRQoH-+KiCi_UtO#~DepqkGLjX5JPuphP3ci4Z2UQv>i6
zh~!ot&SMuge%^2?GJ<TZlx6^`EnCBq;OF`FEB{l(3?V67T@pOR@OWJW8rirC1e=?s
zc6wA9p`tr6-*FKj_-JI=Q*8K`-43{VGW>?}{|?+9Xrtaz^yK2Ky5!IVdCZ`V43Gh>
z>6OcNuezzg>K0OJ%-y4T0g1dWGejNCI$koN@4=qk7xZ>`83buuZn<YI`WwZtp2njV
zJ9;;~vNjKO8S>XcYXvr9kA2<aYB|XBbb)CrT$Tl5s5dP2UOVv**$^SeY5`FIyLaIY
zWFmt@4&+k3NdSy`jrqN7qP;~p{n3XbwOS>}5EM7qGLCQMk(v<b47Zz%Ef6$xS6=Dt
zL9N4609|Ld*w+nRMJ1Pu{nCxl?%!PxDn#wrVmu&;g503z3JVhHe9s*cODd!WwdlVq
zi4aWvsb5ij*K+Ie6G*8UHE9@o2TG<p`5no2_J$nrY1uVb7GuWh-(teAL!FwVPZNS}
zN<*2XH1}R62zl$`i0^rNy0)gmmD?M;Lv`b*#^}68RetESrqesywI`!IQLc;pDNhkj
z;v;hja(}nNnLnU?k8f+3YV2W&*q17qQAn-7_7Y8$k<tl?bU@0sE6Ib*Cab<m&20}5
zK<i|O<SWyz$vjfrE3RIro49CJwuLTwnnFuf?0DwciUK1YZh2pxE;%&o(|r9NV{l?C
z3Xc6^>Y7@d9{*ZHE3dhnaLed_W^b3p4TXQL#dgiz?t8(Dvm?F9Rpj5UDbj?c@RA2{
z_2E@tSi^hQCk)ai@gbu$pLH|M&gg2)l^4ec$WQAfwVS?Q7e>5Ju;)F!BfADKa#=|5
zlgcdc@9wH;RW&8ftkUx@lEA7F_ntu2;`ZI^fT~UT@4eeMPJm0Ovs`Imd|VSi(kgM~
zPq?eoU3Dxvq63=Qc^&=-iF)xJf?>>ce);*xItuTJgls8^+IOe|Qv%uN^-JZ3!C{m)
z&))bEhW*L7H=3SG7sBP&LLMTqN2$?eh{@FQ0n(b^x?S+w>?K7L9H~fY2UOVYbAh(^
zI4%XcE)rJ!=ffKx2#Wv510KLbE6tDZ597Y=Jb_3g!)2SchIOEAf)2kW8Lui(!<mT#
zKF+|^*_O65ZK)~T`NE-VXvJ!BN7w>QijP+-G%oy6n#ELJgy$1gfmSjLeY=Kxq82I@
z)+(=8Beqyu18gbK?UVbb)X1M9ub40?E?+glkK@;oi#iNh^Ia&0tR(ow&-wfx<l~pW
z*cq#fYSOQJOow-l&;|A-B0zHv4e)i~m$HXb8pdCKO`JwYH3N6}8UXm8&>j|0NkN}}
z`D|HP_%3ZE>(hW0^*0dUY)A}PE8;Ix)~s3m4vFf>AIcpgZZM}0EV?^T*!v85CP_+=
z^h-bPR<)%Z4gKtYhbm3}-JCY?ZGGhSxOm%g5{EhFgaSu}XMakY)<6}Tc1bdkH;u-#
z4*=+!&{cM>%LCqwx#;<+#@T1&;}-J1Qi_5AZ7MF%N|R%TsAR*ru_$mCLY*UpJto=1
z3rcw{RRQvzXbvg)k!f^n@unR(?r?)4pKWc$9R=fL_I5qu8sxWiMc9Eum=uDbEyJ5*
z+#^fcEU@G3*L#191I%+EfFfP_HEm<3YMV<)#cX>By2x$`9Bi}_;JG00hqWgHt%Ya_
z8cLKlKoUURoQR=y@<?eUHwyE&`(`t7{b7HRVBywsYTDjR&1xMB?<;{haSmXF0Nll=
zt)1jW)xB~vlV<pa*3h%vnD=pUnz*n&uK&lB2s}j#3PN6AYh#T}r3scq&$a&DSYMk$
zzbW#?75~n!@!nEg@(2R;%*nJ}rIAY+ZdH|=9xdB9f6-?^%1mTX{6uW;*B1f5y@W&<
z*VvjUsKNoNmjDpydnaQJytr#GF=V@5S^GY6(vTgP)Bz0cIc3^Ar1JXMRrgq3<;K`U
z@6A)-D4vjm3V+6b+V2~fXH)6He^<3|yX2+CdktXD!BEP$I`j8lZP~{Un?$6^(-*GZ
z72>L5WQUJ=Wg2~k>;<)j_(3E^K5^}DP*5cUJkV=^rA$YC%1Jnp221f~jEOu#yJB42
z!hrgnX@aGCbU=fm(TC&pzXHTXDC_z%sdD~d9BywXUg*JvbYH%D#UbK$VMZy2u{AJ#
z3>;l_T-lNKuh2*vd)~Dn90Gcb-T<>T{w<Lw>E$f&q2l+COc!PfF_M3}6gxazh3PRB
zi5zY^nAs27%3XA5z6EBh56JFYkigfZr==#R$&Sj2C6_i{VbS=&oB$vo!M6sh_Xiy+
z?jzevg6f4+k6IlRbe=)~?KaY&`dl_qeX?r&%+TJHOEQvDjQ1aEe{xnHs)s{fg#39`
z&L%>d9Z$^p!aigg*y5#m!f{`=vmTS%XVCey!xCr~Cj=ev>7z4JrwsL@o%MU7!#Uq6
zij%mG(YpJ+jr!c-7+IH%zF)pVYIBWIJ3K(nlM;+IK_b6HBCDAF)K+>2J|E+bfd73b
zDNN(|gYxER&(1}TaJM49j}nD3r+{u+QtfnEZP8s7Rj=W(N^agabrmeN7y}sqCO#+j
zP#cZxWo~WC(N+EESm`k_^Uxe4oiJ3mp+%=!%n?`cX2;R5V5$Hc*^10U4rxGx24Qgh
z?-v&B&6$6)GQ9#;H?7_f1D^qIJ&2MMtwVlCcJ24J-@aRB?u#jz_88v%p_GI7-D5Xh
zw9|hsH2bQ%14y<|XJF07sDEz+4eEL6@JV8_l!0QfyffP|<H<?MmM+ppzZXj9G1xQ{
zO>l8Rptv&;4=p2+Wxjl>lRTq&A;y364S3_Q;oSQa|3<$tHs@YOypo%HYj#f{X0$aJ
z+<xWYaS%vnq<sCk*>)`Vl*9$+Iw$afctHk*$@uVpomO-<ylOLZZ&d$y)pNb51HkvN
z#E21^ocMmM5gDFZj@5FqW*?N$2ZzZixR`$2P?)RhkEbWb&YWLxD9dCG(rkkuLKwTY
zv&K(Sz(DN@L*(cPKZO$iGqE7V!`k}T))!)B*y@(k6E-vrJomJ@V+v^BNEl419kKl}
z$*dv3y_<R{<6XIJdD(oHV<ZEG1;xm|ccCoN({cMQq}m@l+LiLx$W3GCx|co^)qL?V
zdGUwo>D);E`HnG$U8+<RJ6}%U+UH>2y02#UUB-W(Y#fhcnB|s0XxjAc^=gO%l_#%z
z$295zM{|3oCdSlT1!S`^=-z9?n>AO}lresFdtDVXT2eTQT{@G<z+A7tvp^(m{<4FT
z@$mjWtq>PRnfw71Yn>0>myl<5-?i{08?Oq;8P)Z2mb5WLLKETkFLId-m^T4*BK41>
z>(M=*!|&OtKvv@|1&}7V_Q+0(B-OA~^#Kx|-(WO3^0s2*RlPt{Q6>(?&et~tmZZY$
z>BnSegK$ywPq28FFvz8qzzvY?Ie@Y?I_**)W>mpO!oRIYY;nRl0R;xK2Ww3jqZ>A_
z((AsBEN+n&W_m&aRytWgh#x4h%IGM(jr{JUt`z1Qfq6Lfvo6xhRql9f(@sT3Vj+AI
znDWtWgl3AVt;AYR@|5V(2AhuLit9f#3tkjDoNi<!?^u<?U5Y!e*f8rnr~)zM7xkF^
z{WYZSsPq~4<&*tu^HSn3QgOC^wCoS=a){BXn)`Gs=9h@7rd&Oce9LXgs!wl`M<VE^
zs4UR&X0a43m&v66s`BF3*-jVjuPor(iWR^L;#r^A9_Gr7_h_nxND5BO1wHhOz41Sj
z_TJXQ0$Q{_0F~H3mp(M-bBjSS1i>AG2{3X6{JF^t8#kF1jb1R(@67zRubDAx0oDK$
z`lp(ANcDq}%@;!Qw=pgVz{B;(v=LHbBIyh1bKbDQ40{gz*gq5Sv+&R7ats<&#|G49
z6Ywy`dHCliO9n%NM-l5-fQ)<qKU;rh(DdMadC;26BQ$1LW&sX_n#^}MQbZi+zJtmC
zX~u5(8V`CXe1ngnNd(YjFG7W5K+o?0TFOAolnx;B7&tQgzrO%$B0p^%1!v9)gNhJ7
z0j7|>-F;hE$TfT<yFc2t4)Doi)=eAWi3ia_?o~A6mIj}6?lniWOj-=DFZT4z${<=6
zB&xpFkX9_bfAy`hN*m+N_v~HlxM8QpU|fSB${8;2sinq@Y(#mxq17U_?Gq#XwShLR
z<{vA>+NO6lTKPwwcO9X9m%|A_51n`6C;n~>*^JpjmzPX3Uuu9bD)WsW<Qze{t$~b1
zDW(Ncr=!|c7J-QAUo(V38^Lq(+YdHbTuyxX7B#<fc=Zhxq}R9HCi|mX2l5o^l?s@N
ze~WP#Yk_OxZDDm{2C}U@)vTa+ICD_Wj0puHUY^No#GMXAYX11}-NV7Gj~?v(N*`ZI
zQCYChjEVjEJ+A4%;L<8e?id#~hSg8Hx2UWZX=~b$xI^+1CZ_ZeP#h$wV`doF$Z7S<
zi^X6`5tq?Qb;c53EVInaw^-Sdl5IcF2PQWfZy~mL*GX7ZVaf0-6p(Qh^|#(a31*62
z?2Z$+Cl@AMA@tsYX#sa3xXsgdSj2q^6fVeM-2xD7&)Wlm3RCp{u`%21+N58mg5UC;
zt?Y-EJaZHXJ;#R-8nN?*ogLp)e;=C67mLN4+=k-n!sP6tcCdxjS}Ud&47fp-1H8H4
zwvXve0cmpnFY0FqbCO=Qjj(iA$O$$nGe34mI;yv%btJSb*cX4guhBw=?G$IaH|oCZ
zca5oeai8B*o^kC&KVX^nLsi@%0WwXN*v(Ic8nGRL2#$!28f45OM4Fka3&oB;ArNWZ
zfA$d-q>g;BmX-{Z$cX=6%h8S!YaOF}(1i1#jG5N@l{$pa`3nQK0|Ix9>A4-j9Z@{7
zU;GpINwFBeYf#X!(>gd(%8=?LB+LErhEaa87g~-NCgi8ig1=_6Vv7{nc2RhpbX28-
ze3`xO%9;rv43cAbL8^Z(cQ9bDT?}X=33G3{!*Dcnkw5#N<RaP)+~mA)r!^(8?x#oB
zi^$<2>+pH%Ye~6l)cUaNgJm5Og5I29d!K`Zfr4$HQ1^2)#ER~J0D~nx-_Ix7+@{Aq
zN@U}M*7;^3K#Dd9U8E}gE?}QwtnVmg9?)001E^?Nd7G*H39CySTQ{c&ae>AO&hwok
zyAyVL$sWcRinJBvOg>+z$|dNOzbm){8P~V++1uXhAtkWn4uS(1-)%{>G(~xU+B8Gl
zA=zxBfXe@!4avW=abF(Xi{UI|-p5L<xW7=w&V^o?i%%j~xkWe3$<=|lrZ4bdc(<J)
zZf(E<ddn^il$Gn?1F+UcjG*@q4yZym&tcy3GZ~q1YI}VIPu}0EQrsLx)9N-5wJ<(0
zfu!Dw;PyK{31V7<?PVNj8#^L3^T@~N;x6_w>0$#AyNJ{<Z}tw3=V9WoLozb1c<LN@
zdQ-RA0i(?)p%*ci8pwms2#|)&fG$#k{?X9C1bF`;a6l+SnW#aTP=8+IgVV}s9)Ml>
z#~E?&ofG^E)=$LxTFwKJozJ(yw@L_^_Iqe5)8S@ei%3LU2ow&xKW2d#kcfdwTLe(B
zFKor{UQU^CgXCd9yrJHr=0K&7zi<kvWT?>&n>>h?9=qxOs~3;220k#cypY>ywt&>)
zO}l!McaOM2B3zcWva)U|rCa~Pt~_Xf)e$<0@2vu<l-02)-`sajYH2?C64lu^>sG9h
z-q1-sFyH^uCq1ylyf^k<fAY|6Q7%U;*TCLyusWPSItLW`$en@2Q9-O4f{fCi*zE%7
zS;W2Ob?nQ#Mqjy#{N!{Rk9))@gv}x&H6+IvY}6sGk3iWlIPF02|Js-ikPV_36P0Ge
zxvk=!r3ncMCNyc*+7lWY9>hX&4&0^oKy>4jCI9=<nxbiSpEWngD>mlIJ8b+n|7oES
zMhk~gmy*6h&#+ZF(e#8I=!iR{t3}%OxBgd~Yja4PV4W=_@4XjkipgXY;B5V?X>k}$
z6NXJ*6LO+|9sOeFK-X<;i9YsyeU1&hUz%xqn+(|0abM883iK4|5vqkxpFH_6rSXAE
zOLbUxO1OdhPRT>7=8T#8V%_=*UE1XeiJ@xylqHbps5h<rqI^5>(^$#@;!zhGRr`(4
zj%Ila^R8ZRPr;ZWCP3t8O);3_tBV|{iSwWgbJQw+gDPOqJ9MY2Izw3vNt;n(e)|Za
zFv#VgeEzEuF7)BNF6MXnkp_71;bPhH@XRZS&5*w|=0E`y^5@auV;)ssq1QwDb|zwI
zBm*>1MmF)E#k1fSX9rf8qnR-fu!3F$`a^i%e|Lolx}t)3Fx{OWfOrPqy@i`Om=b0G
zkVu8EErCz`4rIS#vO{=*bH&ia+^|3ALGOwKW|S#qh+)P%Q$K&M!>W$h{LH^cFG>WW
zxOw_fTom8F(U%l=X1NcY>2Hxrw~&|sq?86&?aS(TK=0*gf5{#1tdqw_S&LUP*`YUp
zogmD>&XsR>{>8o5xT$!b)kVmrO0Cy*zu)&35-^s8fr3U~_L2J~QOTB*C1M}q<vL?J
zYfhx^TS_Sd!3EC*IC0yEP#t+uR)ullw(U~6`V#63<$cRP9{^d&?<^Q-PkK>B{yu!3
z3!8wH5nOw_gPoq&fJRzGIEp|*u5nWa_I-CKO7I9CC{>r=k8a(<J2E}YYZE+zbk(5`
z1(}d549ozi^2AMfV)@y&bJ@0^Av90Rtq<!GAdj^S*gvBi^w1N@ii^9XJF@ZYCUYZj
zY++3Ci(i2KV>`)Qu=ywP9c!@Rqp_ChADQMjz>y1U8A!@1qS+oY{Q2Ys+ZU_rdX|#u
zX-09(`3I|q4YV>b<*sP)D6^LA^*sx14PN+$Ix%KR6~+K-SPW{Oa6KhpS0&Q)Qt<mD
zvA>;GSV1eu4>Nhj1oGdYlpZM@)b|^T@BwwfV$lK8r+EC>-2(k3L`;|VQj?s!7$pDc
z^Z>LnxRF6M;*riJ+Mvb06|lwb4uDjF8yJ)zpA&8QCY1Q<*&0$4WG|^;RE!G;BAs}G
zfX4)D8L)eW&NFTs9tTdq2iN6k*-fN%BX#dy#sdVqft#x3dgZ}dq~Gf{W;ff^*m=HZ
zSpLH%HUMVrS?CDf8vOR;Q0Thz$jESYR(}QyI9Or%0A}IJ&=puieI-P-rT%x*eI6hQ
zfztg)6!yQ;RN3rv_%IP%mG$i&Q*#qEW#VmB4jtwM8d|9eVRf~XPMp|XoPVebW){-?
z>464u<Nh6&2TJ*X&u`+ruW5zff>fJr(h>h*{wUVv^L=-VJi;$hB=UYlT#}zCdBtA5
zzh^4EHTAUHhJ`?ch`fh^OO$o&ma<4eZqyyZ9^K}xwFpYLyccn;X)7(29>3S*&}Z7f
zVOI(Y+dKY5|M0jxQAF~j^gAv2@z2liFnfBF9}06n-n3CUiyd@!a;-|W+~(2k$DAX_
zG=X@Z(~Y{yYaj04b0Zr)4k}4Mn(DePFlH_C0cZ<=9Y;-Myi1V38`5DFAJzGmD=rK3
zMPSnaw~jvXM7qPlm#TV)XB$=W(o27%&p?#FeU}*@z4&-FC!(n<)pB(D<m9i-^$-e!
z@9wJ%2SZjab85n7-$rro2LC#H2yn=)0)bYUdI}ODPln;<<ti~d{G;pxSpXBjPEV(R
zztf!$@DpHV`tJTpFRC4|&|l=B*!3nqkaNW2Q%iO<Qj;IIAovJsIRMtM5SkH-W^7ug
zTomdLJ1r{IWBA?v^rl$-+e<z+IkC8k0Qqe5UUTq6*1aH!8O;<x<LmVx(9GZ3$;DG(
zj<~JM$%-gCxTW}vrZ4agNESp4wv0o1wb;BU`fP{w8aSnGGj`bdfK{IbQZNS>?Rqc!
zdl4<Imhn+NKVY2Fh{%6<pV<rvl3sQ9ShYVU4$6#;8k~9e5f|e)`~~Z{2z25l0m^6u
zaW?lQxL&mmWa-Y+DawO6wnVgs$5>MC@yAF+#jrkALw*np7i7hwEaBU~G?NYa45FPw
z`kw?}5iONT2IZguGq|@oEhhy`a`&z0QGVgT9{4NP4Q2zavVk6Qo_J{cHQfU-zyrTM
z1CCF3ao`w5)&M$56$d40Wf4V$S!^~^@%YjZ|BAtERp48E{jKcdo424Z%We&3RG?1P
zm!BVE6dX{d=F`3Oqxf6&8P^t~2}hsm>_hc1pNrp#@L!c<(874)FFseJPApD&ZO);}
z|GlX=&>q~c5r6Rq5;&q@_*-%=e3=yUApsVbA#*o{YQD>rE_!~plC>gT0LKc0PXX8q
zx3zVdIJ}7%Pro=)OYDn;V*~LGEUaE2^|0bVcgucJ_~}&^wPw#(UH7SO;6nMo?EMh~
zQI;P0PbA!0KPJ%pv?#fV161_+r&rOb-Y4nj$`(8FrJ95C+;j=A|KqQC0_J>KqMP1z
z`v-f6)wPpCAMk^K#SWxk=Ip92JkAy`7+L#5`>~+UFXax~97eK$Cu3!#FKNn3yuk2k
zcUyrl(5xl-JuybIQSqp7(bU<G2bmE^KdW*y0}QQ2Sn~htG?<(>wT5p$OU)#xl@VYf
z$?^d?)7G>^w<;X7wm-=!%16_%kWr{U=WCKW`$%B+lZyVwpH>;rWhDlRRjQ)!;_zM`
zuk}%rIcqfw7q!;mn0>VG?5RUe`<ZNdx-~Db5)br2i7d!<F!dbqc<OAky5YueQ=HXE
zprDU}r!km)V49$$S8}rVD`G^NBdd9+0^CcI49IS|7_Jc~&BlwQe_G(C9iPNAa*vG9
z7?|msd<ZL#U?r5v>w~EKd0X?h=szEUk1rWRG9|&o2FvY5`lm1Y$HDF|S#62|p@l1~
zt5DRFXcnE{{f*LPNEJ)-C62<wFxF!LO2oZ=;)IHN(C1#6XdGlM%z}^Uf~&r+a;trF
zs>m_o)&03@OL~8}ILpD`!N@)gf)F03D?i$wY<clo8Okajp{ioE&kx2~qd0IeqZ*7A
z4}#X@9(GNt#)TAv+V#>qXprxEWTM{}3?4S*6b{me^8+FQmgS8?vZ(l;#EejK{bw4@
zAz3H+^4D88`R8>BKBLm#S_ED5wCKT!#V<-_Y%PNloAB>6hEG_GP~5^u=LMlD$ns~_
z$!zMQkH?x{-7i+oQ%Hz%vnFj;L30I$e^eG=5rgc!c@|tGTH7aq--Sz3mW;tEWSB{)
zOs)>5pIW3!?NXP2{@otHRgWmMCPEH%l&|7gvM=qYUj>^YweL~n{hEy2$}$)=URIH{
zT{8J3d;h4ked@^6HgA*kMr&^m>}3B%^@8n5N&<B|1Sp#Pf`B~zh77|U8NCIFi&V2!
z-m(QKs~YP-64R(L(nQ!cKOPC4pL7}VZ4o89k*(ulD|t1|TX>w6ne$x%_D!cb$89)k
z3x--0E|YiJd%T|U0Q(%D-TJ<0-MEK8-5gf~p57GRYrg@W64Yf<aZ~zf6-QR<re2bK
zD{zq{5!|P4s;*<{T49VM={}PdsYWaOK;6M?JUK&BaEO9cBwBSH_?CpLnIh+)862{b
z+AgZcUkg(+&=SwZt5hdA>dH;c_63GD4i_y|XLmPuZN@xK^l`Fpu8PZP3LRT3_R6`w
zxx9{;p0m0dmLAq}hmGugzjT53P=1q}pR-<1_crxl2+5oHrV^X_I{HApNrEV(f7irt
z!*EfFs5RW8pX0felQ8;&PNZ0si+KuJq9*^}-62*D3BaNMDY>69zc}kuWzBccu{KD<
z&$sZ%sYTEU>8CW*kZokyX(hc&J&cZX_wS7SRy&dT=D5<h^}5`t<A`6rRI#7T8#bYW
zlxuoX?YiqesS{7~+B5SFSLy3mdEE87*^NIG$tg@JZia*Bj%Rso!h8WspE9iKxC-8?
zGZ67?QfM`BA0l^3#CAW#z%mTYcpLb&qXyV%Fe_QLvoIG=<i&vZ%bRzr-k^3;!f8^q
zqOr7CyGGDb%ZjE(5pVl^`6P6?N8F2mNkb!?%j4Q)a#!dwqelFb_-yv*mwNK6PN}o?
zKp#I&;_Jb)@UO>MPqjfVZh;#}Mkh|vhW(3IQ?p~UAzw74$RFpaR&Gw8yyw6|x3piB
zAI{6-ly@E2%5CcfK-b%e>>^}B%j;sx8$MI<8(a@Y%bbl@!WVtKxeQ%WsI9M_E_O9-
z-1iT{384H%c2H@BGkP~=+t<EbJBQ?fs)KzACe|3<TM2g{w|sT=a^1D+Mz7s$DWY+J
zmRR~xmtC6QNMtK=R_kE6rMYvBA-K|iIb3=aWY2}jBzi&!Ew)>NWQ%!7JCr~ow)^C2
z8PtVtF)3{j?i5h|HbHhtg&TwVfwJE~c%SYv`B1JZpQFdGvI~-t<0IQWxSH(kSTL62
z_dE0}@uS8L^TUrz!{_=ALpB2m-lX1d&rWS4_25AsTFT2R&&$r*35>D#%sW5i<wKZ+
zJ$8Xw)K4-4))P(zS(<Yr6Z}TL)Gclqf&%3+B*XrajK@C{D4J;>e&k_>)S3!ncczha
zQ9fMauSKjXPtRpHe7|xxMXeb?od!HFeji;q_2odD?06lQRYndYpN~6rM*Ff4I<uef
z3$>QQSG24%Q7KmsajAciiI~|%fP3R&{Lbc-HIluj{*hsiEr^mIj2)E%`hfcbTSJ^q
zGQL-PvIR>oHZWo1CDxu#pt3a9v%T5t#<CpC_+HCs)y9s!x(w~5TN(o2HY6`=%X_@T
zWYiAuLK(6&eyNC?WvaOG3s%$^_Om8WW<OVLlGRocu8W>;C&9*(32*1~_;MJ&-pZJc
z>v-XFhew1>I~jn1uw%4o>`7zzG!rN3iVPo5?{}zI0X@Qs(W%w~D;ws8+LXZ&gbsx{
zb(Y^e70fc)tlfQ?ew>j^{Hy%qAm~yM#$}Bfn*VBI!t~PX$r~_AkHUnl>?OY`XxH4-
zht^?hFMs7vbvS>BB8wvF$7$TGif+@$A!*i0d$&^g$OKl=TA+HkipuBZD)<X%c|s6w
z0mA-Wwp89avz|dY6~fP;mvi9#mv?t}3AcnIqrVgWcs{o+!KV&6WrU~`h@j@_)5~k0
zf1!lr4Vorhvl~p7_x;))1dTLjar3nj)g9T=KJe0GLZSM2aLJCyoWF)UmfAm@Bv0S1
zNbO`$dOtj1xDLRV2;Ox;Rt*yiylJyLj%3pG3d4p-B-JO{y`kCO23Rfl2wC(W2C0`D
zX33&0lr$GeM!z$L1;>%(Ed&jV@@R+RD1ivJAD(6r$bkP!GjC(R0GN&UFISzyU6q;f
zV}}y?aIR#I_z)E=Y(NCz-Zf81ZcB^QxS?Ea|DRjR7(92Le6L=W=!`B{%Nw?RWx(i`
zn9Rd3`0#cPlS49LI-Nbc3foyeO7)f2*_(^VXo#75fJV|O`0PxmHy)<<gDq4cGQkX_
z9fdN&c@%D+^pBc-HbyE88&O?!4^KRk?C--l2%``$EIiRWSk^vz+p<){%2o<gbZ1rm
z6gil_y%v!&*(fO|2s=$K{+-;ZY^t^S;qjMfZV{mPJ1eV*aX>NrtS^)M92l%ol9P}m
zhu2_j*t+KZSGLW_>4~Q*iR302kjcjhV9d8`f@{50_T`M+kOMiDfP%jxP#A-rS4%*K
zi0y}ynPA_DRRVJVK4%4<=L*7HSSEBJwj`__`j~aO2i%G+qH!;BpXJstd^x3!Z0qkn
z9nb*E$19$f+M=k~US`amtxE^`RZTe1MR;9yT5e8!QXGJ0fCWDqTN1uAWc~D!)rrTL
z3fIe%;_@-Hj*LnMTkqS*e*6CRFDaSJV8`Fgf|Y3CD5(zwSrqs0f?CF<2j(xy#}a{q
zKE@f6P7FL-yRmSu!1cjIlQBM%!ZV)b@91wb^msm)(vBoh*wfApJIUp^b@?--IpG3u
zC@cnI6iJH#!KBTO!HL_<w*WLQmOdUrljr`~kb8Wj4dccuiZM)N6v#b$7ju3lwES^g
zGY*Yi0i0c@fLO*rstoZf*DYRKA-sA_R{-355B4M*Pv(igxTD7{i+@SPIJx7?Y+1_d
z;ml&5yg{KN-cs2NEGh2c{_O@(B(Iw%d5n^Yl*h>cOeKsTD12df37cpa{fcS~6FLX7
zA31`Co3K9|JAm%Kz*vD4P#8sD3;CGcq9nTD9Uur^AX+m$)Q}v?*$-+a#<W?^$dWcK
z;fwk#+$xFvj0IacD&p}S3-0%?n%}WZbKjw1je3{+II3cL?s3$g`Oa?8B6*)f-FH!)
zhfBhj0J5XAyW=+tJs43HN(%7Jtq7S|TD6mjYB5bYd}N0SkkPYqU0JU;h59c-{5k$U
zp#YvR(vjwUby&Qug9!iw5^jrSxW*kmmY;k>2Ez!B1+=y2XoD;EYWOM?Fw8OV2%n~$
zD(t&)kZF=M%+vsch@aybTkh8yG(jOIBn*(Avmoihv5-M8Ds=<+3k>iipbZHN<s=Gh
zp%&HxBy<-FTfAO(Q^=g0ze@*Hy~d2<k=nvEc6s+g*bXqRFzj}`9xV!0(>{~@??ILu
zifqg|r7jdxf%ZMXi7yOTUnU5TT@ug(m`>Khy0${u*KbxtbeLc_K0?z`lvY}9jm7WQ
z;!N8!Q^H)pKe#WgNk=?guEmP4G}vGf#Xa*_!|&l^s+6`f;?B}t`n|-!pO3+#hXTR-
zzpgPAUXOqYO+W9%)!FSXCg}oHXAIDRXTa?O!VQ@W_(_1YHvtu>shTHBn^i{Q{m+AS
zE7G*tRue`5Dg1Yg?@uf7jnfcyp_%~qKoHJpo4@H6FC1m0fQgg=$t<(8$k9;3On+u!
zO+ZA<>|Aam0r<*UZ9zob|5CMVYN648!X<f&6A&~eXjjqSey7+I&WsJj{IOawV{(t;
z@LH|A|Ie5KBa@+o`3(3}|MFR}Jx^RLysxE**^Y7m(bmz^P(r;HW_`?J0<pVBH6To<
z-9q*s6T8HHh!&AMf996CK&iK``|X~?;gQ%_L3Mue0tOg7Z^+Ua3A>vqT^UR92V)yo
zlk&^D`t`y0Vee=Pqf;Wo>Rs_tp&CzNWN!r54>vV;M%t7j<1S<@zCPP5-{bGfISd{#
zO8e4|77bBi0X-?X-0*_Lt<MsBwkmnT;ck5OSgf%7(qN=LaOrC3WFoxU#L!S3(E@^t
zDBQWn!^nZ27PhTW^?9?h%ne$)^Xw58*2e^(5%|FDQxcR$4IFNiknHY6qCy8E|5#2;
zqU8aUzsDF~kk1yHiJd<m#K7<HuuTZ1hTD3x8ir})#1OGzY(s#MDYz8qWTS}rTT^cQ
z5M#?xAP53ERKpV~8%E%t2cnDdSU(Q{_Tme6uASs(C>1xt82F1&e#{6fzzC0_KtqHu
zBoF(R8ekwg14N%1O&|uMEn+YaEl`EnK*I#M*H|Mc!`)O2JUTi@G}-q=*Cn1%Rdua>
zRA`_0-joTXL@ci|Jw3+?s74zKTws`BBxa=)xWM?LX}(Rri%jAX@lS`{xJSH+HsXI9
z^jBPz2CiS>ED<KFOiYba>-r~deSf|mBPaVO#dM@g%w4Nw&%_l36hj3WsegF1t?FPh
zxp+^vN;lY8AXuUpMqB!$uHtG!*yz!dpLTY8Wu<bWf5+`>be?HaQw7RBNBSCnaE~*+
zUz+@OsFP!m{sT&;9ylTSzHNQ=DWJ*-Z07<oea=)SNC#Y$3wgwv{OY{FNj(NSdH+?#
z7Ux<vX~y(v1rJh+qe!y8LUd0q&MjG}v;xBV0XCWT@*6X-J_&GebD6V|S068^<AmwT
z9e${N+~kpn>0v!({q}JCRnZzmqzpD0^V6Gl-wbfxhOnc*`s(s^>OM_|FT$Nuy+&MD
zt2|(jIf_nAUw&Sm_dWgy|GbB4f0(!ZcYKmpd4ZiKt3cy+{Jw|Q8P#Z6<R2ghc!J~Y
zq4RnF;#Iy1u?jXr_8*$w*w{O)0}>K%{uGfB5h<!v*H)9c_6{l$$%aKOQ=i^hRQ`P{
ztbF-C`U1+ZDk<te*>5Vz;s;6TZx#fWtGwH?a9r^&C)#N~Ww5-@v2@i{0A!ZbC-mz5
zn!CmUivQxb1r`<}eaT^%t3)gXZsujf>z(SBoL7T_tM1Uy9FavXB<vvkDd+G2shr88
z>6xaiAV3jKE$hW+a_O7_7TU;9u<N|)-1x_P5%zjW+0z0H+0TmXEu&rID684%W5d*Z
z*D|UK4s^$1(g*1evF;itsGuz&A}kirAq)=n)`5VzmI^^~2+d5yJ>Wya?syjn;4{GB
z(RoG?5mwLzkPzkz!d8HoDnhi7<cKp^fceIEE$|MoC7_}LK=O3|%@FA7;zy4K@F=Lp
zyHHja*Ng5(ejDGXME!TS{>MercNV>b6H&Cez4yrt^xL~F&8SpkPdl7d)}y$czvXhC
z<E!L_h5yld_W6ESSWfTEmTBr!>vE;=;eEWitVT-MYmpVS=m!NBvcWU?L%wy_kkfUO
zL(#L*lF}*<LP_Qlhz&m<>|6sxI_Jv8E^00@$3V1Bd~@a^F{fY(<_zq(cW+(y_3Huw
zM<sJ}3uYe2_L0U~OWg8g(IES9h{f1#0rVUH!wDRDR(7w&b~=}JF$u|W2qEsRFokaN
zK~Tb0CHz`qqNb@M=_V;vg|rP>ZxuZEbXdI@zKt#SWaQGiE$%uQ!i2-=`%<69k`S7j
zl;rRcZY{TuhhGcH&<3-K81RdPOdA}$@X0C*3~YH9E3P7Ip<vp|Q$$3^tZ2__0H)?9
zWe&?LC;-t6-lM<w5l(ZYIMfpR;>EIhxC-S6K@ptX-ThNrV|0oU3)vmqTOZ+9$?k(Q
zHp=*VrPnr~%xJ;WWJqgRl8rHPT_I5)nKKIEy<rv6Y98=hukWs297u+#7t$ts->Wv~
zM&BSlD_&`V@)HFuz+r$;?}S56%vyQSIC*`fqc&oa6dmVggFHXJpR<`DdAz&~UKhac
zzwXSx0fXv-I>XA~g-~FyM|A1(+fuOuY3Nt>)61tnEMCjc_skM_dA+^R0A<P9e(o{^
zGyR>2$<S6*tZHv>UqE@1sE>CYa8J-bvSmrC>!K)y??==BwC_`|mH*a1ixjGos~?Jy
z%;5ad(Xn*-rlZ8ERawGmR@>Y(LvSg?^5zTkWyVQ_b=O!uP1kp|slsn2PO}%nF6O-G
z1esLWgjFT3<JW1g4?b+3EqU(jBaL6lw+@|LANMZGtvtfAf`CG*;5?-keG%!UgE}jD
zrF+kXNQ5lAp4a!%Rg~sWdp6ZWp48}>d}txnne}Xt(Sy>IJnVjTT>lBeWS#XWu(Nl-
z$mF$DONVFwAJ%Bw>@HzHt62pDFvn(=j7aOPVD^B)qMz~$9`1zm8^~oh_n%X<{``-&
zV+MjuUJW@p4xWJn`xnc8PAy(Y<0K|-i|g7Bj<QkK(tP3X_&AuJj2?Wb9$}j!)pq<o
zKFXol>Z|MHHOQwlHcA^1rSVJ3`|nM}-|-ZdZq(5J)~<3G*13b)s&>!5Uzoz2YFgGm
zl4zd4fz#sbRSdCEb9g>+PE_Z}w4O@zW1p@|ox4b}su6x$ghZf>fzlYM=q`EFU0U`<
zBrcRPOWOTH|8^_Xrumc4(9vVNrn%Qx3XOAFd!B8Px6))9mIKb-0~Z*&x(6wLux&gZ
z>-&{C$7IGYBbfREga>R9U{}t_6PO^;DsCd^@l>H;PMoUf@m~`94lgC9qf0YU64F~r
zzryJ+A-j$f{s+VQ$VYj%MJ`y!%Y0X5Ru7+i|2f_>`=t9=2P@hvN-NaM&VD07PF^RK
zC3lBVDo`Ma@|V^`!NbctyRx|oT69ENL5{!dcH!n&3`Aiit)%Se1HVt(%ft1C$dB9I
z+{D??BV8OvL8H`$F)vs8JA);-u~mWgNv&K3lN)BiiyJhmYyrs5U+*_vng#~G8IN@C
zIU4m0aEseZsuvDfY-EVe_gHw_Prp=qgcTpleVcnlnjCqX?b$fQ;nr`c7@YZI7HN{k
zMB`&&{47GIa$QT}z7wBv5p@T0sXFE4NmdaUZ;$yf{V1_#BjGyg$YCg53g(0F=m&ay
zzwJ?O#5mT$T2u`ey~BK%+=Acz#YC}JcyZ)eVkF<QxsWtvZyPv5N3iXN2dgvZ6g~4)
zhIlZWJxm`NtXT|^V-Ii)n4x&?@oMZOV`s#QayPcZH;<$xdsEb<d}qgSkLCQNJ-N8B
zG!ycD(ebe2RQ}*h*yY$5l=!L_M@27}0;>((hoz058NxRQ`-yj8q;AeozHqldBeF+#
z1<TVSgQ(YP0QcbTx)1V}DYab0N4UZ;1(xasUq|=R-~-b^g3%)iQhnz?%7O`224r{n
zp@gC9_n{Lj1N2yfCHZvX`Q+)jSm!wu4RTd01pW$C-=^tRr#}9<{|e7Sxr(~w3zGFv
zKc3hFba&m@IURz}M)~JZv1efH_Mruh0GAoq+y2jSIU@qgaCq=uOw2o=iU7&)n@?$+
znss4W6ZkBLkBOIMow*$eGP$R|dL@HI{P<{bCV3B)FhY+ECC4*+8oE6}=G#(dhwEv@
z%~eEeiBw|!@v8*(qPo8v?ymxxGfeZo^(A_uae@629wjV)z&v}T-ABAS`|O1SgRTXI
zK3Hgc5UNy|xLux1xpf{{%QL^x_~09kx^t;@J^Zht-&x;D4d-<fH!w28c|AH`n?Zkd
zfU^M&KD+x+R^P_xS@OJV5%`SHz$Ll7$t)*He_ib6EY}mW1|9c#1nCDpQrNjssn&d)
zy0kPW%~>BF%N+5-BGE99p5|i2hrIl){8UQ;1VGZ+IMDUEkiAs+VW_t3>lDgEjxDJK
zr$1lh_V>i0l?~K}a@~Z;_b60eST209iuFqoGr25b)aHHFt`QThWeLCPc%EIAQGa_X
zHj$44MdftB?XuH6>>`B?G4Chy)``{By0X{V9}zW=49<LU1Pz65U4DNgq4m(STr=$*
z^-f_54mI|=6@iXJGRoKv5cNv-dVQ46ryp%*P^)fG36<SV(kcpN_z@1baRu>3)ncEf
zi?Znqf?|ug$?fv|`5u0N$OKdmxCw&R*3h@IL!*#f5Q+sw1<+wMVW_1e5@o@MKH~AA
zq*Gpzko?VGg!v7^U7C@;$SJH8j+0+<QPzH^Nwiw4MU;{X>haaVA5fv2Y5|QoMN-bI
z*hnZ$bu8OF?(;cxa~IyAx=k!OmdklS1S5-O@9VYi>y__eK1tnyqfg=*&0HTmxQ7a2
zJ#Hylzl3zQ9Kq0w-pLR_r@UnEm={k^iscnGLhbb9NGR_{2f!J8hfMrmqbLXI8)R5p
zh16~{h`ITvK#(9a_~2Pdu^Bd_j{*tVH>~@#xo~-G>3S^aJNX^FgND<v_XPz7qInMM
zNkXqzy^E9wTMYjXZEqPDRn+~D4xpg4f*^==NOug~l7rHXbb}5hjUpvT%n$-YNOyND
z5<?6~ry$ILv`R_eGd|D%cYpWIeRDte?LPagz4qGsoU`^iYkj{{XY$q^Lnh^S4MEep
zWcPJOx1hNOLse2WP1f`740fA?FWsAKT-oEccW%V_>tz~75@P7*J=LRvlu6@>U)eFY
zBkoDfoJdIs`N|0Oo7*j^(3TH7E33|As$`{*?%*a*x<(k7#hOx!CF68Elk}+GOAr4y
zzV@1zv%LFjyET2_ftq>Uec=E2j>f#o_YGc}ZA%n+>>S4J{@L7>iv~Kr@xkt=LB1y?
z8cYi&rlz#4rcr7dE}@KE_%HUD^Qhh>9}2|GT3Pww)uz-Z4F0%ve?zjh^s(y$!U)Ar
z&xTqHVhmBvd0qiN+kY_WoO$iOM6Nwnf23}bOBl$H%{-)>UV6WaZ1oq-KW+1nH3M&E
za6>Q^6h17!m>z?CV;1l$5XDd-6*X$o+Rq6jHXF3OcfvP)Fg&-p`UWIF1cFs8$U5#<
z1Mq7A!99GRY}2dxt@HFr<yzi$l>H~fNh>o8;-yvxukEjK`mT|DvGb=5yq>g4wXuV7
z>FtQr8oo_Rt1Q=eErbQ&eAQKPd}d<w2^m7t5zzYPa?(Udz42@`d{{A_Qm<I>jN<R}
zNA&3}$&+;HReOpaIP{)tL;mbaoE-Jh77(NR>Sc0dMyO?Fm2rPK@xndTElGa$_e@V%
zHA;k%DKgzzUprSyH9-yEOWv-D$XU`))BE)72_<16VmQ(XL&uu2q4*@+_J!(p{%UH&
zd+R*S8|Sr$A7nNH|L$++e-YukE=K5l{oU$>5g8Sg!=OE_m@qt4$C^FX;4X`)(ZgpD
z!GTA=0%et~?}f}X@V_~3)#|K5D9G<Sw~H4b-uQEE256Im?$0wwbIO0O=B)-*+DQDq
z(v=H4$K)b#ZK-n<pjuwRF?QizamU|;)V)O;Mtp<Q8SZOKI6X^<yEKT`Dj5dzvA0ky
zV-QSH(Q#QU6tYH$Qz&gx(y`$E3Xt1k&t`7t;WHF#?w9vnT6D6cl*1BqL-v1sFw%G2
zs2I43e86~smR@!Z4y$|p`NcjDw53|U5w2PkBJg9IZQY-*r|L5kzT+$&oHqP<E1LFC
zy7>sSN5SH$l{OOKY)rlPZ9pKIV(W7jjj}$q85raA=tbZ*QridofA-lFu+O+zl>O{p
zrF7pcz8<6A7l?3WF5kkdJFZu_E_}2tqr`jW$M<N+U(*P3Xij8=%F5fvkAD^$#H1mX
zT+iMF^##qV&HQ<elIot?GJEe(VQEpp?Ra}-X>0jV1hYhG1`f9TR$TVBJ@)Do#b1>;
zaScp`XYlh$={)H!x@6%Bqyz<3Isw%%MIQIsmEHthlnI}Idg}r2`$f8RsgqxW#{G14
z2mYBX14(z6hrw&n(Q$%luW0xwa-bo6+oF7bONiGZo=iSNWy}q2y<&b8GUn1!j;2zK
z>0ttQg!&X#A<Ex0pZ<3L;(!@R_=G4L^gjDRp*^~+3vM?vQ)#7sqC14-u}Kpq=K(D-
zkT*thZr&A>1^8nsxR_nSa{W}m4qNMDC9%ffh?~Oux8sOh!xhBQEA7?mMyOUD4?_uq
z|Jjh5DNitHZ40NrQ(4x`v^KFV-#o!-@?^V784?O_quO4^X`56XHWi-BWRfy{!t?Wp
zOr8`B-jYnn+%Ro{o_7^*18J8&xCV~Gf0zcgv~vY`MN^>x<&2rSBp`0IINPsd@6KOD
z&j92pqmbOANz;$XhoJF~#*a0@-+m$`@OHn=F&NV|90u__;+UsooZu(6(j~(Sg$qKM
zg-B(D;+Z@=b;yj_JtzKZp@mKpD1NIn-%I+zSNx>f^3gdwMK6Ka4}3g0r=4UXpEfzY
zu9kws^e8O+I~DiKFLX5ZP@x?rqbW07L$w2@Rj1=BLO~s;fd$jt=9dIoLnZ{f^Ep&X
z;CyDiQK=sRMo4rA8QnU;MNm;P+~h0YCUvqNDs7A^lG8EigKuX<2XP(vn|wS=NGRWP
z^7;mfvoPT2EN<S=0~2&@9dzjPhcNy(sD9v&M9xxKSb@kiMjv?IBNRC|C%zUS^8Gl!
zlDrZTP!$p43jwgXR?Y_KCLZ2uR(_>$N&IMeB#`yq!OETVEqi+L(Z-h;Z|2AHLeEnl
zz@T8r1HqhOqzK;6jVQ~%XzR!5pq|5iFR<AqXvtA)Xr6M`3#_^7hqJHa9s0MQjI|q1
z`l5YGCsd)72=x{73E>u~6{>9a`ih@iyxTv0Pt{R3#O+sU5?BS6$#PVDkEU}N?4s<z
z6~K2bp1@a3!RFDk&9q>xyz%Z{`p2Pp+%9i1(5n6X_ai($>Y51(U6jrc(_a)#CBo5G
zHf@rQrjq0*i5=eDr%t!qFubjv0S$1v$`168YHg{^j$1>~6M$vsN#^>wNr`yWpaaYK
z*AFH$_xA#7mlcxVz=DrVABj;^PNqzzDcsF!Z#R}+H^6Srz$vf)dCa}7F>&bMa(9)z
zf6FouFFj_`_}FC=GxPP4@_HNJ_wFZ;*OU8xMY(oUfcLexaiO--1&EDSsw%`||FE6t
z57D$ic|$>v_(@G1Kk1=XoL_-|CFl9iT8i(;2NXFxDW1u0m2GZ+Byn$x%>JNd{TZrX
zQG9hVFXHmkcf!@?gfIu~#S4O4jakEX7rDQJZO0;1`aQZu`N$%7zYR+(qd&fOu{*CG
zTKU+@$N<ZGsv63->1Ma#6gRMpxh$C{K^wrIy^{u&FTA0kOMjj0l}7Xvy<?~Mw*~m?
zSoNZjN*A(sdGf+bGmXJNDe0T#tA1VJeYp@=n8}J`*4OSvJO6Y#zH!9H7hGEgqwc2q
zF%;8dq^$oKZU2*;srj)NGqDlbJ^z)9r27+AlwP<Vg2RCoh`9w0S)IP8<lRtIFm!Q-
zJw@;Ter2*I4LRq9uAPMS^@P}$t^9GrXRlJBv&wn+g2fuYYH>Y#xB}tu_fbYMd+fuf
zCV3x->!fM>^dZ9bS$p)Oxf^ICFYT#Fg3I&Q6J0_p!_HsoyO*T`lWV*4wS?zp=6@dx
z1*mBZO8NUg1q%f+wus%6TxHOsp`oFt$s&H7$s#;2x6>47NsGsh5$Qcohqpv}w=J}0
zun1KkYUi3uKLwnuqLH?1-Lk9Nyo3j5YkY8Me{`I9?ZxfU)ytxNjp_ik_oy`=aC+H7
zis45!IkeIpBeVAm&yC3(ZTU17NYenp=5t714i<yu;Zp3kHbNIw7^9E6dGH&E?9@O}
zvF>4qv1@Hy$;r-wFE9xHVI}a5{bsxYZR8gLVz|VaA9=^EG-e4W>dy)tAcES=d|T?~
zRW$3*f@YqpAFIxCAqjM9$h!ym$`mZi@6xK-AK1m5DZCYKnf~IXX?4Zoc%zMa`vg23
zloiw+V(z`{$nYLsABW~CF$Z&3^22<L^pg-c*~UabTyLHW=Cz`|3CRh730s2ov^K~R
z8apY}N4(kE`$BXk+3e>%kwf4&=%Du9r7{k%*$-~VER0v3v>o)%S0FA3)U7wBA^3ZL
z2^d1#>JJYt;L`>=&{PJ70^blj-8d>8M4i3qIbU%x2E9bw>rjFA9;klWypvF!jdqKy
zK;%!L<8E4NfiRss6r~-A<OIOYqR?@0VNZ=kSLK_T>OFdUn(1R*NLE$pd~pRrR0O;O
z)TXzXX$gXxje%SsM(}1Nniafb2pQJUH3=F?M<D|GuDNu9bh*qFuUH@x0|fK(0H7Z0
z*})b^2PvONN*@&(7IdZ0ybZkU8M<o-guUgVkVgP|qakaty$|QGEgd2tKg9@=)=3GF
z^C5=-NPDcAfIoid3M;TS?(=L}SO1qoAGQ>or{XM!jKQC=9n?_jEMRruz!UeKCKI*3
zthb+B)1c_$@H&Xx7#up+8ou<-nvpP9?fVa(>d!QJ^c9ZP?~Gp%E91q+?VT!eN5~t-
ziC8fJxlIUU41#%cfOm6I{&m;Ij^$Lus3C*3)$&(r*KdHCm>pf1iw9{E5ZWAPd9fW9
zAr{Yk4V*7F+Tk1e!<<kTa3cc52qjfUQ-b9@z<zCm<z1NF(7GnCkiePmKX{;dKq`+i
zs)H}WmgFH=v$U`&Vewm5nrWM3^-UYKx40{6pT1C*Zs#7jyD5bx8Y?Sa9xc6;n{+t;
z`$&<OjN0#elivZC*L#H3V$t8Z|MXb336_~T2unKLRE|#9cH|B%{&r@=3(Uo~rmMe_
z9$Y?1W9KkAzkMr74`69w-XyQ6epUVy!t0ovuKg5fIlK?9ul?7v5%JtX>@hC~Xhah|
z(tl3d9DtsJs;19-Hvoy-{7J%(c3hXX0Vy>04wHa6dODO*ZvVduJ9pfKm}cJTk%;TA
zelvGJYM~+DX2L1Fw!|P)Y}(2FTLGUa``X*Nhs|xPrk^o+%v^cVZ&#6-R?{d6B<u9^
z@HZoN*;f4aq%v1{hrc3Wh>+eL*NZRN2y^gapW}>Q3$Uc)?P`$jt*<+-m14V>VpiqH
zcLCT5sh60Mne%jF<UD&7&>6eY!v}+dJM&G>+B>dI)lAji56;8Xjx$k2KyO<IGdQ@+
z*<^3&)Sg`l>YH$yau}_O9^3tKj$;Jua5dBmCvot4w<d}vMmA$eF6qIM(gBBK0UpvH
zj!Z;Ypo1}VG@<OFYs9l;Sd{?IM<rJ%pFyfJedQR#;Cp@F*)E=BZUTPlSOS8q;_t|_
zKNz7aau_-;ogO}K)Oul8EtS4x+yR~824i*B-cRv*8D!?t`v>(w6%(yx03kC<#cX8<
zF$-~b+mp=#&(*&tQ<&ToCR}<NWoKSYGCCH$A^vk3(N8g1=5SSN1g%n=AY)QTMQ;hx
zC>8rC*$RP2bJE8x02CNIHIxUm8ew!aJo6rcl<K^ZZ?>=CpRt@ig#Vt3d#!%X3U2++
zEEK(<d|T(Db+2srSk$v9r<V_d!PEXU<G=g$IKX5%4a>}6rH=@~Nn4>vfKGiIo+P-;
zb46PY)JDLYVgXFoExi4d!#|296E1_Fq$EET86&~P&6sK3CjX``El3rR6PSp2Gmb!%
zEvoQ2*Xm0ald1R^vlbUC_xsfSo>E7hg)H7d(tJV3eWZsA9<@lNJ24w@rRYj-vu<XD
zED8W`o%66jO!A+YM=HG0N9A1)q`;mf*_Y2!r|U=KDu3xgp}_MB5P!bgK=ZKcnxs!R
z;&GqExJ+#mD52j$W^(@YnQ9Kgg&hL4`o{zJ?SwLDYuwPWja0Bnt0nHuM;7fh>O?cp
z<DM~eoM8f_M0CS2|G@As=l?QKhGszMuW~8^d#7c`=MS10XmzsC9WUm}SRL=cg>A5M
z5mGes<^0MHlWVMws2Xhmy0(~1qzdsZW{Ng_`t6bSj%lzb+vX>9;1>6m{-Im&S)b|u
zZJ7B_d6sFyZR_curf1M@(mpU@sm!L36F7oN3-xhJFntEyk(XjdeQYBGrk*@;kWKop
z^zc{OD~~4y{VNJ}GOQY^5R;`YzVe=;V>4<0YwPHhWiZgtJj+_-Du+$_4^g}FGU;IV
z3m}y)7%_hNh5V*=MiqLlxjw|nK#{Rf?1j+}dS12loEtoBrLR~0^emwfNLr;pZi#mX
z29LIotX?&Ug58glBw)f0*Xt6bqso2L83?UN^e*Y?(AFGyiKCpiYmT#P;6J<fq2%xL
zvaJBG_~fmq@4(2MH#<u+w%G23r$aQ}|9xyfQB+q)e6inbK8^T7rI5hkIC6Q@{e12-
zu#5R5>2}_r(f__MG<Q0>n)r8-G=jI?e+chLVekHF!CJiX>wKZBhQl!q?M9Jfvhri-
zEIe4sU4tt=Um^rDB2SuhH|E2WnDi8fDmK{17+zLuhfK5bRCm*Pn|Y6X(G7`{634FT
zcXtE}@;+__$mD!iIBXZbIiLe_Z3F>Ooo_$SGCDtGBTT{dg`P7n8?dQ<AJbryPDeqb
z-&J)Wm5BKXaaRdbY{u3IfF&yu(B<Hn&rxiMa@)!jrK`l|5p5KrMQCNL>YKm80TSt^
zqfy#;LoA2$p)Qp6Lw5E2;!l8LS=j8{f#fMSw%pI36Z}DSY<AcE2w+7{=MAop%*W=X
zeeFm_sKR$PcQ*2a$-Ov=3!iboAKQl3KZ5;_w9%o%X&veij@Ns{6D_=zppBon*{1<n
zbnc4kykVP&+X6P>AGyJM5jO!sMu;rqjk{F{E&5EH240+xHiF8?bcHG-l%<%p_JDD`
zm47>4s1jBmHGB=oq^W<G<V`L^z+YVFCDbQn=uEp@avuNcW9iY&9yRw{@)E>?Mo?42
zn`1kwEZ!KjR(9O999~DA|3z35IaV;r2=cTDE!`d}6=FSiLjNDhgBi&P_9jWNm8mo;
zW^3<Twfysj+3og_@p-{J(lN3)w0YsY5LJgsxYwZ7r3`ov9-Z!Qee8!9YbKK=@AS~7
z3f)l5?iidfDSY1cf;r&fou|d$JWC(R{BDPvbco2&>7Y{J3fFq8lg3ZQ$?3zNfQ1Lo
zhPGB~dbgbLeZ`w1P!Dhl&q4zI8}5;O!%=pXrT=}vt=P8LIeQZbb-`v^J%?5imBS91
zmJ>){Qo2_Rm_*)l=rbbo%#X8|mkm#ITbj(yMmR?8o7p0rj}2LV%#j#hWpxTWZO}xw
z!i0S=OM4q<`22n!e&cG;)Me*(_m9F>5F=}7rBEv#upVUNivnYJdob~xW2Iz7IywY%
zBpEL-@&4}jWz|3&Re!s@c$wb}GZX73jCgA6i^3270;tf~ouG_t<*U#@IsS+(Vg4Pz
z_t5t2Ra5KuXsVqYYbe`%2$u(I5~8cntQIZZyNC>le!=(llR&mIBfBPQD#eS74#eZu
zhO{KEuj}a#5DCocYJmH{;@a0`2EV#C#;R46U08{lh+-0{r_4i-u%2>nvIc4LbPwRk
zlZ4|*ul43LAdw*m^sUqfS?@ESn%m#K4*lQeqR8T)eX34p-_Wyh>f?2PPTMa4>budG
z!VaSd_p#de3H{AtQ(-ve<#@s4u}Fv-RRm}~O1U}8PAol{h|HG1=W&nhpgBR$A;noE
zMI9}Ba<KZrChF`LLrBV&W@4UC)8MNYcLBu|Nv~6ddtkmEJKGtX$FD4J<HM0A?-dqm
zD@6JCMQl9wYiSw5jo1hF33xWb0BN#cIlQ8Sd(ZIje!t0G<1GamJbBML1}whH=Hr~L
zw|hc1D4$O~CvEw(hBx09T%wWKCERVO&4*@3NQ{MSDo{@1_Iz#~;ee|_c75bdZs|er
zFRJiq-&_*eejsOaRBg2ad*UhaxUMQ**SWGt-y&v(Q%Gn_g)~hGgPjx1RZjlBJ}T6a
z&JN|U`Y6oHBzYR{*93lZIEW3gWC7y8>sRE;Be$QLhYEpX-E_9TxJ8hKaeEWZ`St4E
z_8tWyHUF0IP>35xis;){Lj2XknbuV=FTDk%2zZSahKxzY%YLzqj5FI0x%GXj;WhwZ
z9|!G*dfaZCy9px*>ov<DFgA-g-9nmRqBG@3uKWA@e=6eD6=OG*ox28v!Yl<LN<78D
zT=`lhvZ4m%<?HZ9=h<jmB)w2{aK{a3csm?Z8#;<ae^r@?fH;m>?{1g*;IMp`0rhvD
zGC5%PSN%Jgfv1B~HIaihtK?>FhyHw5cIRu<6bLhQ6hA$_o{qzNdg^Dp$5qWYiE{a9
zk>shANxAEI)PRAQ4rYe%z{~h&{NG9V<muJha5aG*GS`1A7-YKw3CMU4l#X<VW>9MP
zjoZA5W-KXD8_zS`d8b$&-D_~`K1UpL%)_rvJx>TpKR+}n`IaKpT}G)jG|n|p({g%p
z#!D-1@e!eff&<{f^2SLBKiAQPx>R`AT(J5;`G{`stE_$Ta!Du0qsAHbMlB|=&li`1
z`B%epT|_X4N|OmODb9Di;IwLl-n4_QjIVZCvx~YYS>_1-js^ojP~b9$;*k>?I=MW%
zh1}Y|F+wHb4>xFLc7OaZs^$|#5GiZ+wA8BN_&LA`_RoF%GPLPB+=OR3E-`DLkmC13
zvaDmdx#3Aju1cuEqzThIDX^{jgReTGE(*(~r0vBWsq2{%LavDjSv_Mgb;G4ZWyi7}
ztSIs%dq#z*$BMH4zfS45&7EA6oM*b}9FDA|qS@(%;U^4tffH=FA?p5#iQNQOF$Rb{
zV7;=>&yLO(LjJgyKhtRCaO5c!ZF{QfdAAVY0EJnBHB0=Df?vKazD)S<hx~O{8}}(X
zj`cRjlF16P8lf=opKmg;wAFG-Ahcf%@)WoLrY2JOKNG|qGxEb^13d9nE%jZ!KyUfi
zNq~2MIBn6&!NFb3y<c%q11L>yiT#2H`55g3N6D@CQae&u_1;kfF2{dF*FoFXQ0JuF
z*o-U{=sC&%XR+~PvzFCc+9}IhP0>>qjAfxtayeBiT}OS<Sbv=j6JnQKo8d#3o|?3M
zG!DV|7+O-RK?oi-Ls5(ODx0RMx+g-7W&fRNku<~}W3ziR$5m@5txGmgG2__leZAv!
z)DM};GyeT~mUpaR7WGyJ8nz0D$4@tu7pM25jPhWn1Uwvx2vRW*Opp;PcvEklP+jf{
z4p{1+v;6FJSHR<(F^7e1?~!&clGl|No!^SnYg@m2y9euQ227^@Hns9!UZzSv8L`Rn
z_O|-c&h+?@UWa~y>vt6gEDt7jYz}}}!x6Q1F)a0?QkTD<&0cS=m&W%`KUKZEV=gJw
zI^1ZKu^#Zl9h^E>m9ovl@pvrM$%z_pwI#i*z(jJrRM&!n9LqFg2stAjFh7Y5pqo{v
z@^0aj^bJ$yGSik1&!k}au{Gd$=RSl9j}VR-xjOxI@YOZ&)jw0;PAWvv*DXvb!<T1Z
zk0?^z7aNXZ8)p|sy2j`lwL*s$4D6lDr@21N%%7I?bCiaNs6!*!!7Rx8Pm30HQ*37v
ze+&6~9Vb2i@|}l1{rynUb;X`Rec>pdQ{hiki%3SahVa2U_S`WNjN!Q?)+~5b@wAvq
zmRVZ`ekdO3H#4bB38XyxgON_cj7<_##=Lq)6_1*1^fh`N$hn=A3rli!1=p&+8jHSa
z|NVvS8~>_Nqp*tUp1$Km>{i@Z!lQuE#okMQ9i^aNBPnnK+vwnD^yB1yxey`yN3sL#
zMjB@#ORK2>&DY>PxbVm}Os{G!u40C*cw$Z6Kb&pe^y2IRND4g5H?t3SkB*M#o0sV%
zcu<X6*j<z0lB42_$B5fFiho4>P=_o#2?^!H=5Q=L*4_l20sr~<XZVo3ZoAsMLTta;
zE2k4iGSm*31%9)k(00&(&ER1ZsowYBoYk3G((YwdvO8W@><ybj^KR4@G*Iv~P;&pX
z1!Coai&ulU@-jVpNj$JDPIQ9c-U?Z;-)h@WA7tx;ImW{l-h&Lb;#4&%!ia)`HoN;x
z@zL%?T&ngsY6E;IGL$eF@I!{Mni)FNr-F&qyPTn@A-7Kgd}LbbE)Qvj7r)-m-Mb)U
z=YUTNADE%qgn=R&#Q?~A9JexdM?Ra(>lB29|2#l(?gje(^%)oLvoXQ0^B=uYpYQ)~
zv%x7oHeGxUORa%<@eA+;ORPwPPu1!c?;pSdodlR>K_hgREv@GJ@hMUApVa<Y>cm(J
zsiJks2KJ%ZscLZ;;uO(%5N`~hnm6N1!%X0j!zYOVbrxuvPNLv^{)Ezg9mYgK3<6w8
znn0XYh>$6l5nR}pMNg1G9N_9<W`=%66W8~nrCEn1xT;|F1W(bT0v(gzz2@V6=7mb4
zJ$~Lw%-J1ts9&)$*{@K9A%<Sk(HJ4;!!_%OE4+)e1s^@dA2=q}SLR~z(tCd@uLa@n
z@C5IS2}prs>mEy3YNNc<+<j!gwKah}cs@ht`#d3Rn<KWM-u#ylW&m9=^HVJpxvLhc
z0zJy$0joDEic?89;nS#1MI1^+RaWcN*d3f$>*bMw_-7)YfPH$M?aWz=WrT7tgKKIw
z&xHHj=+Yk})pTxiy{=6~Ze^YFa=17ei-L28dQ!rXF3_T~dayb0DJDa{`16&pD6qPM
zW7F<h)X;`+Amx7#|4AWfH#Zhdr#|?3s{S3~<8~?Arl+o6t?@M}uthZ>_7d@NvFHX>
ze4{`%!FH0Oj}U@)mElMv+PnCe*YS$RL1!xk2VC2T>@!_$v&&7#<G}yEIRjv#%8^So
z6C3@HrG=vgW`8QvRyn;Q86<Ib;+rNQG=pxdy0V1F$S|@OSG$;@`h|H;b$jKUs2f(S
z4#K3Cu#EO#TWDl@`@n3gQWH24Bc}tge`zOe3vHry9j5XA*Fz8-{wAUN`xW37KVsDR
zo9!;e5&%FH{hK4ZBjc~%)9p7h;0N$YmM+Id(q=3%;*UPGoA|+$USg;i*8`Oro>E9+
zLjTn5XR%FDFO($ms@?*}uaAg5&+0akKVu~sDm`vSM2t5_oGEyXKVya%e-=#{kpm|;
zc>UF~?t#MfmXZCf5uD>Y=p5T^krIPh{ngM17>6m%|Mq!oKqe(05<^e=eYu6Suq!+W
zZV;PmSh!#Ad~1=&=RTkhuo2&0ZBq{NXSrC>DbyQ1PzW)3eokIu&$1mE$+(k_iDLez
z5zZ@%(tO6yf`KU^Tt~G{$H5@@jv;%APdk0ti<`;0Jr^Gqpx;l9z|+b5K9qg0{*pQh
zf2!xY`*qMDpD4VDu|iu}_gj%%vI<GV9mR*m5rxlqdre!CzSo!;K_h;PZw?)H#FDLe
zwZiJ}me*c)3Q*@qY!)i$*dD4|^BLOYcUnjn_C4fF^L?zcO9@23#B6B8)jtJx$J-t_
zIDdO$Q#sz5Hu=z!d*rLc+EZu!8=ZzzLOKwN8O+~$y%PQHbBfq{v4PRSrCH4D-=7+s
zMc4M$P#+Zi&WGi?_xdwa(^~Dmt7y%}FI=FHehlnYM$ckXWl7G2;*9UKiFuqV|2ef3
zAh@x=Ce{|qGuWwe0Ww*|HqJkXFNWtz^{$&O?u~L6A8{lU)zxdZo8^!lP&>FdcXZQK
z`X68K6ypL23zE>eMgEthnw<)7Vtt!WD?lJ0pxRy?KuW)htpEWE+p*~T>e$*38Kk-c
zz$c`x2;|Xjt3&>=7T?E$2m)2}=>UHaED^}N4}ih3%*OC{*b)l>mW>?%T4%-9!esx4
z{g{;rsG2zbe;oqu)ixVwzM7X+<9Aa*8TOoRU`+i?I5Am{6aV@4{ozqY6`?kKnhK$9
zQ#U`MOgGD_=3mmidX9gCzV|gly6e*(;eZ+dSax>s))Blu?Ayi}urNTF-W~>r__fjq
zEGR*6K~Ah-PA%w!bJAqkw;3-fP?*?1_JORbL*^F^g+auiTmTzh2&rBiZhrUSo<ucp
z2Md;nV8U6v8CP`?Sq%aKMKl0Ofw$>sZ`e1XG`#=QVMj;@w?AO?0t7NuhY-RMM;RL-
zJ;vf^)F63oq`EZ>ZaH>#PhiF*ga9<I0o4Vd)n}zw@cPe3CqW?Ef4m1r0mF-7weeY=
zc=A&J)V9n4ko>3A>1iu&hdKPyvorpW^otU86rdK{Keb$eiG13a2?ymyp$i0%F#`zw
z`i>M=9MEOG=c(lLNQH-yj<laOc$Y+3Ju2cN2o?Iil5>BE>Bl7uxes}vp3C^|ZR*?i
zUK^CYUAvEpTB_<>IL0b8pcConf?eird}8nHMI2BC1Udoe>&Bmei{Cc33<4PfHU>c5
zPdNDT@D~Q{K_G6xAnahyz-%&{b)G#JoLuF9BHThbHgS3pXuv3htQ;TIA=|Q;sIcb{
z>N6RDWEdzpfx+8Y#t_6u>~aJkpjrbfv^k|dOreU}AF$gSbb&AudF#=c+>5y7%f}#j
z0O=OnAbuwJBKj;o93Mof4v~Z-v|fG;#fzL92AI(RI7VO?_BjM*CkzJ#t$hHJkNI~K
zrO_Gxiz~5ET6a8oU~+&M%5I?*{EFqYpfes&81p}6)Gg5oVLoOV)3`kV_mvuiwq<la
zq$gms`OpFcG63N7SvlYx=cxlY3A@XJnweM@h<4JMKHZvvRr0$wMDB&0`owoQMGT*C
z@p0jQ1wUC^-DW1`lRS&MB01|KgZ3U?#->T_OE!9^&Ca;sya@TXrlSaqwtO-DpQ`{6
z2*_QhWCuHMEDD_BNqXm8ZGu1>9RC)2+&P=T+tK}FGY*IcOWLLTCFH~puTAJG{RR(Y
zEDdmb6-0(#QjaHn*b6ct1-<x<eHdwdFV3WLDKZ(nq)D)x&_Ji?*~|6(+gT4Q(02Tw
zFkn?+aAvW<Gp5|m!phxXLJ&~41oV2pPS;7~<XV%G4}?2i5&=^TeZ=U=YnF3^F9DMR
z#Y#Y_*;sl4PGwNG{lODZ7#CKMB;=2${G**H@cdm+E-_Y4{FXpBfk?AB%Doxb6!)=B
zi40)+TOy6Z$az%|C_)W*;Y9Ex9BRrd8RcKL(SqdJu(H{xof}h&_=I(|kT|O!I;E~&
zF~#?SuBB5(wCWoJz7@qANEz@pQudWqXq(3bTC`VD$G0fa-E+L9utpeKn~}Z@DP<t}
z(3CwEyO(znJjt;#0?$d`6D&gjC5kfk1n@=139uuBKQ7>UOxOa&r73y=8aA@)k|i+I
z<9M%OXW-MJR{b24d|iSl7f!tSS~4pB*Nb3A=^9AEe03vsiC@VMR4`iUVv^}+Du=)p
zJdAEGS`jucuCf-vI`d=?(r=;~ZE2c|#^3B$uovq;CwKsa&M=f8eY&BN6u!6Q3~U>g
ztqkcz*s`Nm?6TQ{2imjh!w0`gEba5}^}Zjuu_+p~ccl2M=ge0V7r2vF&N=;u{g4M3
z^5z{^@n!4~jktw}Jxw3keqy0y24D5)4{^I`hVyUDi{Qszb|DsEqeNRxK#ATHUb@(|
zu|ku&e^N?iLn?^#U!-E|m@nn@!G(({^@vvs<{l|OYK3SfMQ`93P28n682|R2cg4kF
zttQ)Kc%KsI9S*<fFo#2nJW3D*DE{ABxxw+arwdvPqn8l7in@?a+wPa&6WfLU@@`Bw
z_*X$UgiD(O%MYjRvJD0$fbTxd%Q6J6cRjS~rMl%D{)?p#pqG(LgRc<?zs`<%`fi<x
zYz<K6o?ZyOP9+3Jw@5N3vbkI5QepD3arXLcGxD@rjXoXcOTD3&3~fIgUSD63LpP@a
zjYbd#!xZfPr09;Y`J?VWbvXP8kTTvLQ%BVjacrrYGS`pFHoh$gejqTw53}zDH@*q^
z^_PNr<%_3aby0fz;YUPTqalYUKL^<R!C%35MbqdHr4l~?rH=>M{7<<6PXs@MtJu2{
zTEzrT(Ao&gczoBxIsk<)1#?hBpiLM@f2=laQ+E54=`;z|i6p-9iSw-Z_1I7Fe3E1P
z&e(fF+74Ua)T5+F*HIdLT??12;e!S2YV7WHAIVhM2Ru`IDC)iH(`jz+PmaE0fjf{7
z77Fx_(J;eV%M&dfc}+&H+J6U*lyB7xN9KvPo4h9Z%LJ%M>DKbgMF_P(f4S%Y>`-DM
z1IU&GMp}M<bX^nCCt2sq+7eMJAoSUr#`({=H99SkUupL8Z0Z#s&(sejU(L9_T>tiN
zPJIZb!)gFWXjzE6-|1KUyvc-|5T9oLp!uqe2Uze_9Y5*SnpPJceG-RKQY}Ksd`+T0
zr9LjvtRwrb3cGd}r>$MIpw}goZV_Ml(|+@g{}wkIpWv}TMUj4TrV!`XK^L4PTkl6J
zp$v+XE`<H>4B`GWB2l~}#INpRjhfC?oEw`WJiK))%pGm_vki6gRs=3}#SyblVsUse
zoxA7rSatC9$IzDcX|^(L#)G!1$xg)8CeE@D$T2#n#eN@7%)I`!6_<qCq(Yo!J@oEZ
zQ?on_R9zF}O$35K=jgiW_JrlvCRB(fhATv4TeludzA)GRydG||luYm}d)4`?sgjeW
z_UDh|fF(7w>N9YB4+a&0U|(V>l(5YROd+b;IqOXCWCXq{ZzA|$q=XU4g3=Rqxu+Kn
z&5zN#3%u_1NnH9Hj5$(#H`>4=XNWo}Mr4}mfQu=v$<o)2eANTb8gUDXD{thhyvK14
zUH<5P8_`ScjT~~8z_?YhA6CS&YEn;Pp9{A##9KZ?l!f_YbB{_FBXBGgExXCNGo~Ug
zE-pY-HAw;`PG!oqY4^+hbt;#mReK-Nul!%^LjA9=FNVNxBkSBt5Sc9bT}ih*CP#A%
zq5J7qOkS6ya+~6gK0pfepY>r$sWlORwH&h)>v?i%Ofg!{`H;d=d&8vMO>9mQh~_7K
zE*j$eX@dzu{ak{Z)%l)X44KruB{%)VMP3y%i4a~X`yCPSEcA|5PlRKiMCs6cbB7*^
zkuY%Zwqd&-%DsVQbnuqdf^@C#`~A+;L6LS0|GLq~AQ^>z#!i>@B1nS7hjLDH@Rjsx
zyb%RQujsdqjJJZHrjLeAgvHAW$`8E(8#t9A*0vTp5e6b7iO=oMDCOfrFg$q93&Kt%
zP&#KzP-4m1>?Qn;7VpXrvvpDfld^Tk6k%!Zeglv$Mcg^?F8KO$1IJipUm;E%5}0F0
zPQ!t6b$Z=n15`(KGv6Xbto)EVotGV`E&QvF5gG6denlvsH)O5mDcO;b#qcV_Nbjd5
z%I2}<lI;0T0gYm>i;EffL;ZazE>4iMOpyk#UlITI>mjlKlQ&7s;?vL9zu$6LYhY2+
zX7%zZKn^|m^)8P2=>^lv_jLF!L52gTWp;(6N*FhkBvO|Ur6IIEI)UeUF(i{YkBfN4
z?zhI;T(u58fFH4}e|4@Vm|3Lrh#8?QKNB1NaAN(CtC%Uo6Wm1egh3Cp`yBPB&%C)`
zVRNLMb09OH$_D+K{`l2G(u=H-i1C_?1w+Vs#U(la=9>s?X}-`Z@H)>-5q{1Fe-N;D
zfJ0NA*uYZr-m5#U9u4#4k$6>S2dBeFoga7_*!C#^>ugK;!^Bz0#71c<v+%}r(W*!-
zRqZ>$1X?nLf`s{ymtESFX<19tPVAh&(#A4DL?5DbsY_vZc?Zd#NPm>_55Fo7z^}SO
zym0&!eeou;i8phyRU}luBwtj>?tXNUHOy38B?;f>-YpGfam&|CN7UlQ^Is+Nz)SUq
zAr>V3*_pv-b<NUJ?UVc@ybCI~s*95K+cQHOl>Yd(K(?cP#Z4zaxv;!)A(VDipAgPj
z3B3(!kc3gp^q?E&9vNt#(sgujBCWP}$I>e=XpdnF=Fg@zzK#S8!QOK(2%4E+YqD2)
zu<v?9QD)OGrJXrYT&3HSF4F!lsgPS4jUnpSsaGK!qK|dxKWH^fhMJ^$vjjrz@=gne
zO3`sTrdj6$1n$lQZwL5~Ees~tBR3AcOafl6@I84gYM>v8JHH>v+HH5dgLb+zW~?0?
zo{!a2)^(tIl+j2K1giXierJ*o<PWXc-}ilzhV0)>DA@03o*z+?LKogY(Wy1dz76&_
zJT{nGtxzmmj9)>%@0XpjFddoyEq^;9u5rvPOn9JrY*<Knv_v4$Flb+nh4-~+V>$E}
z2az$y=khH&M>e5lX(}4f__i9vcpWBey!Rt<^Ii~D&Z6nmd;PMDY&sSS4{p7Q!(>tY
zcFBxCJRlIA3UqA<R=;^)?(lS+tg<}sNW45IH8>&ZZv7>T^RHUbIU*ks48X%I@gDs(
zh0U?(lEnrq@@l+}s^HQZ+H@Qkr}cWTQC-K=thaJUQ!o2AC=5WhG7o{`rg7&O_K`#3
z^{|`N8z6sbl@1GSoMFjXX%s17X0Gj8@loJ^T2~CKyZVW(Q|7fb%Q^8tAW~+4Eb$9Y
z&U(h4@H<`KJfnb4cMzxt0H*^0$6f^L3*T1yOZonlv)vL<u?q~Ch<K^!Vs)`pL7cVJ
zpWF}x97n<c{yU&q$yzlZ9w`vhbyzqmij~ZQm3%K)DI-r-kyZRuxy?`rP(uW(9OAit
zz-MyOh#!2;p#y+~^?wqc%2wVD;b9}L>uWjclCc5Q01m|f;Gtc$CJ;i7)g*~iXK2+W
z0`>5p>=&~UGRCd{z2s9(L^DkFn9r9?F`J8zvQZHm{cQ3m_2DE+Wvn61BHX^OK<Qm3
zzO?i4_n|+L*SXM`@Z>)qoBb(4dM2f*n14eCg69D!7eKm1svA%_diCYdkX!^TsMAZ>
zfSe++zJ6M|v!OVV`$BN4oog`&G>Ub|T@I_@&Ap0V8{Hq^(_1UJAQ5HYz+Q^VdCoUO
z<h~`ltckz!m<J@HiftV`S5a-c%77jmKcQJR=K;yvV*PbOlCcReUxQ6$cOu>8E)^&j
zj8!2eUJ6`4J|CGi38{P44Fd7PfYG+SeUg7a|Kxj^Lk2e&=O6)9Vq3WyLq0}WMU?S&
zwPYQ2?NWns4X_d_9q2i390;c~mMqko#*7F-<9t}p-;x3Uti`ZovRqufKT`~flk1M{
z+{b=U-(glV_6s9{oB=`;A`q;?Ca0=@?7E(%r5x_rjptB;aslvtr22Bzv5;#^6PH|3
zr`^G67B0vf7u$?E*{ti(FZufG3@G$Bt17}-h5fV8Z1Be;8AZK_o89G6jbv+3<d0MJ
zkGH7jAKrDbjd>6=WBd?y|Lq%feFqg~iZ@Id0?N;resD3F-coRvTw*l!z9l?)YB6iF
zBz-V;baq^Db<_d`Ntl4R#^sGHucK3;z|ZgQO|ZhwR$S0j6n2?##`udnEgstST1CQm
zf_(C*K-&N+B)If#zv-X(K#}kr)NwuX4;z3)ITerXPqH#29M~-oO)E^Kt|}f$1bPMl
zP$Jb;5XNdd_W1%%-^W%Urbd>)?BlV^H8kg5+-~sj2|=|fvaxXs7!{6{^4RfYQksi6
z?ssV+Yl1`E80eW5R!T(0wU*e`XF*0u9d<ZeD$)^DB8puFUCAAj1Ilxc-90DRz+(CB
z|2CU$?6XNp7LQR`HN!EF<Oo5}{IF7{rCl9D^Tx$(+ga4x$oMIN#6~~{w5ct<W2Y}e
zb}Pt@W3+c27f3ea1UT~)K9nN8jR<ALyG~`0gZ+Kj_A>ygRfXch*^@G0`{?sDvcSK^
z7{J%#2vvua0Vx99-KYlv4gn!z$Z#@zfT|q&haa#&r^(Qt=bt!eg^Nwq4~rfFkcpX{
zfzC0w?|UVJzJGY2XfJkGU2Nh5aR$?uT@4haHKmG5YvP?+s=|A>HB{7i+vK_BUOoAi
zr~h?PYyB`NmVtj0sP_gao+nCu<uv=x3C;HZ*a;&?jPL-eaqt-sI`J}aakk`jEB(xI
z0E-I}ehE~8!!baVY;K%dX>+DcN*ZS8;Nph^1mSp|LMOfzWzHXW*07y#oN~<j?Av17
zE99XQ^O61Vr;(`9+2b|uP@x*neT{!tfMCdPswk9jgm$BVi6(S&>l@ZECP1WVotHGr
zG2wxj=0T>O`t~}-ZNLC^l1L;(z+(6lL4@{me+!+7kAK(*vHkIY_Hil1u9WUv$)ni>
zj#3NLCqS|)d{6Y81AL)M)Q}$IO4DjI7fo?W&%VWwxzG9Us;}3+A2eQUiJ<TbA(3w0
zVP#yLeOQ0YTAfYy09a0SB7I#84ODBA%NwzVP2cT9{5GFf^Ul<Qvol4k_6=$*;-?Nx
z8F;FYg~`?9H{J|<6+4RVH*--{*jYHIYv^k73PLBENPQi1C5?^StF~$bV0z5~0CPFI
zppaJG23?SKd|9%2GWLZwIKa%T?b$^akYETgt%Q9YRL^V=6{PXSb>xkg*@x(c9!=mG
z12&kTqE&@l<LQtV`ZxZH?UZtJ<|1Af2)PNA|G??IeJjnYo|)52X?a0kH8QUx#W~W@
zp)fI>mbpRvocWFw&6^wv?fV+bKVQcQj~!o7QJ)6O={O@7TwkgC4D(Fk0#>0=_%@PA
zk+M1z*)ZoJvAE#|nXsGf0H7-p5qIL%`)HtfxFXcakL)duh<nL|VR`cj0K$SibZup?
z)41(`Yjj3+*7O68Z0j;hu!|7lrQcCf!C&R1s9-<>fJ+;h-9AM5YOJL@JT%hqFF)qp
z=fGG&bsbqNz&QZMw)B?R^e`wsUZFJq7Bt?L_3_pI2b_>nT21c5p_gv_k5cr1hQ0uL
zvT}5AN}jS5>EdP}2t2xcs#ZWN#OOkBRYC$Ws}KyTsVo5kR^Z37?WQglXAkm>`=b@@
zYR-a-fgi2u_vIu?hIxlc0IwnuArWQw-Co}`7e1NYHdupm?VO(S=Yq-wxscRS;6o~b
z1L|P{Qa);Sw%55>#sOM%=1%;-&YtGA5x+=JY0o#HFrgS6;Daim3|*sRU-C)0_X~|M
z_4|PTm$Q|8^0gZOL%*&fR+aipz(~Iy0dT}cOYev7zJ{7suNPDPb(iBGjqogQxaxmj
zVC*-L3)qMb02p3&vD3xek^v7a5#U{lSyVc;WEZ)Zf1h}hZZQMM>B&)pyf*T_o$>$}
z%l9r`&@3!vJA;Q6U#e{qw%y%G19Y(hy3B6ra&i1bwr&Ivw}6bgxl>DO$zNxE(9k$;
zpar^cszHFv0TxycKGkVs(+GohwC{InR!r(SEivE<=(BQs$wFv>Us-Q(6zmUoxGwk}
zy%@I<?8x~7%mfa^ozx{sS0%2`(D)6c_cTm@lHDb%yHzLuvu32GLWEmW`%rem!PhBm
zI9}9f$5du}?1K`8dtp-?!!LiCbI9ffUQWm9*}2M0Avzfsn;BOZ(I4*J(agv{&1dQO
z<@llz`&;BphOL<LHIOfqTxl2RKK?zjV?9`CmEFIXC+Dd-a9xmz&Cwcoi)P37<u&x%
zq+DvM@-*2hAx&guzvrgtjktznbo@}1<=lE419PJb!2xbbItB>bG+|zM=K27-@@~7^
z@$OH+S9Z6>&{JQ(Rv1FgZd5f*B-7-ocSH*4iMc);0S91ZJC<aG?h_;pV}cVVnP=`K
z(oODdkU*dpf%-tF9|sc<DytI&xYO&<B6OtUH>th%7M?)^Ech1tfgHYKJCy?!A=qKF
zYq>`-2aROQGjaxTF5s4;0yGa#H~Z$%?{p#^%`~|1!#hFNikNbuX0v6jx~5c;1E^P{
zli!W;jRJu`2l+>K8nRn?`|>Z0w$1F79ys14BQ~qf%*Izw(*ie6Qbch|59`yyV}V}`
z=U2fX$;)e&vCB!cSvQa-XcvB3lUbPG8q_}Xr$bnow$B0r#7Lsu0M^NC3n*<=qFrot
zm$BJ_isG0FEQ^m-%QT(yh0;U}Ke3c``^Z4gVIlBWK1(fwu+pp6SNyCish~C*efB!p
z&WU4Ok+k4i;4C1CQ_mF4Ce_skJ@a)p^AScqJ@pDH1wwmCbj83lUE%eQeo-4;ydbh?
zcf9pPu6q{OD6vnzcl_y#Y#J`!U(#__I8}+_VMtZ#pixcaJkCmH4`g-Z9I3wkJh~x3
zZ*x`cF_I-63HBG!@}*w}QXK30JlRe$YpQcEH<*zb=_dasl<{vuD4bV9H8n5B)F4r0
zU5x&Z<{S}9n$Z;?T=RQ#-ExYN6HEbwzd%;$5ESFMYFj&%M|BS32YpM0o#r39BM?7Q
z-T}HD3TE3e*HL$+hH5@FxraQdBgGD2mjkP>snAAQ_XP6VSY6rd`Mh{rkY9!JAgKCk
zQwv!I8O1hr6VR%eI^x7ZIl&|x0xMR#jI%nZDA0RkDf&zT<jGl?jx42i?Yp(`ez2Zo
zkfy7)p0)P@_=r0eh^vO^L8($3kz3zm3cM18;ljAy!j%t(+0&VPd!h>P*{ASQH!x^B
z3-Eo&zMG{S*c105>i~SyOsz#z63Bc)Cda8b6L0p%R-?>}Na|Vxq=2ofJfes+F`i-P
zprMz>+#(-SL)~Ni(jYM&C{dWfjjtgfMNu25huo6DS-l<u0KxV*LO|L(9Gf1Xr)e-R
z(z_}Jl@UDn<Ao`DTD`N{_yg9yO%dT9LJKnXZ?FdOe*z{NUO>nB%_OPH38VsvQhI#@
z8D(X40^`H)bW}c=|E9SE-#;4ir+<n2yATb)77)YI0zo@PutPymTBZVmF&xB|3a{4&
zHt9-1R@`aNSDrB|u-a)41wU4KEtP-DZ&K)s!Ybv1RiU&OKRUnu5?s9(_#DblS%xp!
zYIFY%EW8w5ftT^V<CpOy(wU%NE&q07rWtsdhz(^}M=u6b>U!?V1%_MAuTKqenmDG%
z2u+3F-_W0+u7$;Y*jQU{1D;0MK{6u3-W_V$*wgy52j5771#Gf@+Z@z3sACW|iZ3nD
zl1KdWmrg1O69*JDJ*FohEr7#=<;`ZL1?pST-U2R(hGZ`}uY^m{()nW;%KBd&%>1XH
z-(_rvvo2Z%FajgkC~Ba-)QN#jmfpSV@^xV6=sq$5ZPJJ%hd7B|WO@mN2jYZPU*Pc+
zUh7lSYRmgc0s(6XXz{$Y6^(c(IGEuDl5|m{(kX(~i+LmhPg@`5`7T<9VFW`U<+nIt
zM;B>*DR~i2rj`UZ&(-d8Q~pt83=wgKTRI5~vQ4IBJm>4XZBvy?{bQ#~$x3vCk~yiR
z_<K7;{7SQgL~GUW>%4+i#}yLbtNBq4^%@LEOpFZ28)hZ#y-wD(&^dE3ym4A?FF&-~
zoajqNv2LDipMj^c8`{Fh)TKnn{XUpMZYdA%yn`}GReDThcr?zc55;X@rPTn^O4%I4
zQ|Yr!CvN<8P=~*2sXl*e=4rhrmAcYYL>pw4n0z5H;|zS&aa5uG!?$6=ojzQ9uFyZV
zR0@AiO1;~MUGBp-iqR`Ei;XoOVRgM^ub%<MR&V3canuLclm_$?SbE7Cqb$*I#6Hyq
zMIdHJpCX~tkcO+dpGms0uF@s90gNwsXuqZaygo98!upk?F;`sX1AWmI*5OGG<V7hg
zvB7V>2hiY47&<OZjg^C&Bg3ttWXf>S3j^Codn-0tQGe88HgVvG^Vi}Q*4<&w15(gp
z95PMExaxV_GjzCgzi&Jhh6!D;03T>_dRE5{WoEDm_*+o<qq0`}t`arjD;4$S6^(zo
z{7?jRVTHj{C~%%>YwFHlKKoX?y9>u2`R~vGm1E3FK!TT3)8!aqJAQYFWWUV8&wdcA
z9Z=2nAMI%Mwq-J2GR^$xFb=A}8V0Uq0Ot7bwdVmpY#e<71T~N}3;5n4+76?P*ClC`
zy^rE5aaek5QFgh|<yGYZT(?X@w5UN<LEIc27i;E%g`je_P5VdP2TR<5D!<;Nl{?^o
zk|b{HRR(W1n7}e~zEwa>0VEA%|Gq(+H`lDEQe<0bw#U8QbAe6nxBl~l04}`x?;qDB
z;?~D(VW|QIjd4F87TbV<wB5%3i8+ls(`DqEdvT4-cNBN;-Cz4c^vf`vX1K-atUM-v
z^P)8)9qlKD6d$uA{gQ|n^aT<GnWxnD;d)iTi{K$@5Ae$6$zc?seA%*hvG#kj;IT1r
z(XCLa4av$t`AGu?RDr%tp}6x>w0XsOwhTR$wL}GYRkxV1m1VaiQg^%-8hOB0XpcL<
z-UP_C4Izv@txJ5x7Q-;%H85~0U?l(7s4TjtqvmW6HS~%C)|(ul&E)2ghXQ~0L924|
zPzNGzj=E?76drU(5jz|sz<vf8jDZncACW>~f@NX&?~s>SXmiU_iD&DqOE1|DJgfA%
zWY*a?zYM1ZV($<D$T{Y}48`!!@`I_>NT($3imMP^jZ$p&v><5~lR}1@T#osG<N%Ov
zEP5oh)(EAYU1{~u&4{)J`WEe$I_C<&{DBfpSsk0=I-yiXey-*T`6O(GINTi9ub?O1
zzD=^=cA|(<AnyS5@s=9O?W`8+TVbi2A>LM($T0WG2QNxQx~5^zwsp#A>fwg0Y!(dI
zEMLFEy*BH%ovierO$XfBV}A2|9P0kxoqTNPEtLB9{M(aRubSk7&pNIh29?xp+NZkD
zC)6e|MLRjXCca08BD)zA<0nsUc)``xyj4%Ggus3b@m6Kx@zi{6;@vGuW!)ivk9K!>
zVTS#`In~2ob!=D%O@=(+hj~0DOI>aQ*aSgg$xPsS%)pTU0z)27^(41d_YXGB>8*L;
zPHD+iW83u9e`*3O!oiuW?w!A6zzLMBTQ2QcYMTriw3ps>AugvH?YjfIcKm7aqL3jk
zTKkqyXKf~pN}{upd;U9YrN$*3o0I3ovmrZCjlz4)!A+Xg27#Ua@V#uB3Hpn;0)vyo
z*{b%L<%5hW1FZ4XsA0lpAr%Og3sRTb!-ar8ne1{?sO;fD{Nqg{bzSSw<qLgw;HANy
zfiRl(y;-AlUYSKB*p9OUIb_N>90yk8)^*KJGvP4VmM0$;VhfH>e7CgL(eD~sLzN}x
zgO<_4fv3}x`k*N5OMmx?nHCKN*NUbvo2-WYnD=I{VgqAKa$8>YyT|oE==`(pLPlA}
z#Fs}ebC8g!_a*0ogTEo|Mc}}Q(Mvs-cVxw;E{y!zQ6nh4`mjnlAVbfBUp*#rwRKL=
zXrf~qy=z*d=3@1%S=}YO)BkN<BLpHWEKKx(!?A(53gNc0R10ynE;xF}2qsh8X&NbV
z!wmNL4rK#1w=?8y#YT`v4$yrfW}z%=T`TCuOnoiuv{HSBwTfw<gfxs)zCB>I#t;u#
zVF~BC&0(t8d&?OW{%wAHM<J6#o0e@f0+JO~?CdOyP8o#<aqN|x0eE%Z(OQZN#vyzg
zKR!*FXCYE<Fw#$!-_p3dSN=aVOGud$dhTldsiA@&Z1VbLmj2kRuWaCnn^81ulDn8h
z80=XKJ*~4J-v4rz%b-_tc(~*1q%Ulq+CZ<Nkcmnw5>ROdFMR!2_=@%c;wSUc&n4aD
zu9oYGww>kia(Iogu9&(d=R;m~qEHcVHO_j${+~nWlPC|H5;TGZ<6xP+*-R!REL>mX
z><v{N>KUTp$Ec3yHL_B3e%cJ?-b62CASKBJT(wpMo&ZE>|MW?FRx9;$fLGtUlx91e
z!Gv?mArX<4nDO~f>{Rn-_%#|KR(+7W8N0)E5ij<9ru^!q1xz+^Hj<IFkXtST@qaP(
z)?raUU)b;>3Me2T4N^*jfb@cN*V0`|cP!l~DJ3i=u!M9=cPY8Vf^>(Jba%ZUzQ6Z+
zp6BA9z1Z2AGiT1!nRDOg=5ze$%<;*mKuongT*S<&k$Ly#|MWrpI8p#~*P6$jP-8iD
z*2sG62NddVbv?E|`($KfTJ7HExjEE(z2+0WCCibi%afukD!z_5@aL@{#0*C)eBk3X
z&5IN8sGP5O090P+Oh#MWzVh08MB=;a?Wp2EO(DD3T!}RvG^E_<XDTIRWyhEMwcTgo
za){_r{$){3sQAKtBb{759ByG@LBt%L8Y$J=(T^A?g+|ue_^|nf$Iq%jkVVaOgzi@q
zwtE35Zcxh5Rv3L$gn5v^!BZJpCPIB44d@2vxN2JDpNfnSH77M2M=H8^tr$ZSQ$aSm
zaq;dFKWA5d<ZAM)X$E`2TsO60C-kw1;=UI}7~|S77r}Xf`6t*@xqknTeU!pEO4txn
zoEOKz8GwyCK#js8nd}#24Ij+Bf>RapMqRC_n89u&4J=C7+dm)7n9FpF@ZB|xaTq6@
zlt-=pX$nX{;iHZ;>~0c%Y}&&=X)Vvil=g_Mh2`W5@-yU~B6RN|hI?q{2OTI|4e?`x
zgM;FE-3U^q^~4k7&BU?LH2CauGiu@TcB&<AG0x}OE)w*cKFiJ{-%5*VH(%?BtYQof
zXd2^?JumawzMgd!;CMrsDP^=RC$5hF)}IbMrk5K3%F9Z9rkEN$C98IUSv1beSK^xf
z|F<O7T>0-i-VM%wmd*#}ab`YrHOQmlDHD8&SgZ+EM5^KM#-9Ij$A*6pe#*Tor(vu;
zGx@^qmc%k6H-?<UR)Sca*02GG<(p8Dy+}HIQvj;=NvNWP+S(xbwBa_=MUAgo9j4*A
z{y!Uca)wGrYC|Hk<)K>oQ|cPEyH^@=B9?kIiT6Ec*%^`(krQOG;pT*w0^oM0zTx~h
z((Czu{VIQAsZc6PY}W_^WFt~iQbL9>0Yt3ldJW?X*oiVfe}PG2+m?3xN%Q{<hZlmh
zI_kGpJ?({6-?>Wt0WdCrIeC_R({X+L4Kst~v>zY1*>Q2#9h#bwGSl9RAbn>9eitEN
zDivZF3vcf{p0)ypVemeqS}#;8>``8!N~sskOG$7BlhyaYns!49ft>VPpL6M%GKDN0
z$RpPZbtr-VSGRAL$8)K*K=lYFN~7Ot_T81Q&Ewm40+k6@dN7D6b)gXSE}6pWBw6Y-
z-xJ9cKPlJw@lju1m)W6K14RraIs%gfkIZBB)t?5*xI$vb`;yrvJi<zZtLdWUOP92p
z0S|vJ7_kxr2x&nKRJ<SN)XqXQmQ0a6UYBH_8*VCv``s49gRoY9hR%f3x$*hcQtAk`
zva3b2F0Fx*^7@(b)^~Vbu{u6Wh5JM)zkj@Btx3hJmlneJ^XNCnGj?=F*A(gFXF}t;
zCY8cEZ%~$NxT4pKpz_v298uH*dY|YwzkkYoyl=o5lpvK^&?Ya$o*Na2qnh{i49-)e
z;2fF*?<biDj_ZrI?H327-3L$Z_0U15(kPO8RX9LQxv5t%T+PHbit_*-@*R%75PBaA
zRQ4v%Jvs>YM#liG<U#zcw<65mAyjZgesi&63jpJM@qc(i+I^r#E}Zg}N7R?7Lz2s>
zeQBbkpDA&<;u9->tA7&90E4BBmFv$>^C6F#XyXQVr}lf{d>)?HBah<Cig{O_<wbRH
z=?8@p$h4s!rkVnWvF~R_vl_h%PB9?<r7)c_@<OP*If(*U-5B4qM^8?RH8jH95}Z>?
zZu%zW_1Y<Y`Lhoq%nW>{@RHcGQh{D{5Vj`BVVpV+Z6u?sw*W|spb=5o)ENGW{KWn_
z`w&7<xySv{yOZlWIfh;Ntvk`>1^>V`M8LMqCZLP0<H!a2gsJ?uaqIr?-*})Q7kz$@
z10xeBv;p<?gcq-TociTPja`af5cMDOM{M4&S`7(r*aa*t{1-X(h7Qk!y-S!#8_6RI
z%c1&y*inC7#?nVHdg}s3HoCk-5#8YkB;s4dLCL7wtSvL9dy{-b1w2!WbAR|iM!zN0
zG>abDog(IJ->k(~(*eik)0gH2KMtyznW^2E>E6Hnq#0iF2k+o($T^3Y?6~{d#dZzk
z$Ru)CGZ7PxwEXwHz2hYpfsaW!DkyzDAu*SEVUuAC-ta2$3<-Tgw_GedFHB*U)c!CY
zQjh)UX=BQ8R)Xm38i3iaNf)v0%4t^50#~;Y8+?n)BF3d|cHDfP%|fCjTNlVDfk<e7
zzq7?qmK^6I|Gt<Q{L@IttD_HOp4SKje06A(((XT7S2g1Ylxnwr%XCN+S{fPzIy@Q|
z%jN$XfC)eJZ5NCs)`T!Qq9f1Z*L?kOUNHRTmbW=+t`?m89?$RPyc~%BXmrocL+@~J
z`m~UA25W6*Bc*}umjEgMy4s&-FnIZns91DD_r<L5Di?mq?cgzQYl|U8;G=P^C|W~j
z<?Enq<{$t0)u68{C#tiqW7PJ@rN&{Vsalm&3#w4Q4wEKF?;PaZ42voK`54J9E2iN>
z*d<(*SUn1~;r1lGfG-w{UdC1x3qL9w{(4?7Gm490QM<j_;JgATUP{YEhI=#psWcE#
z)Xy||h|TRI7wS0g_CuCaF=6QlF*`CrZmVPwvQV~l_unkmDF}<ly#aXnuPo+vjYJTV
zH*V+=j7WAMwTUv!8Ps#MSv1rc8=L$a4YP}Tkn=OOT<1%Ti0qkPBd^M$`gb|1*#yc<
zCuXbanzqUVYCF0n1xCa?kNkw^J1B?`hN=&4+S+$G@;VL&%EW@a7Nb{x4mUp<%yA~Y
z<=ZX+Adn9fl_4Y!tL!6Z=w}%q7#x^c7#T}36SMR7FnRK*><8vSx^TBbXoRP`BQ3%<
zEGFlc{vQuO^Xk7UWfS|X_U7$g1{u)v@Y~i^U-ZhCnvlwd8B1#z*2~vY=DU_JC$j8$
z`FM#<jI113`R33bXK4H173#;fyMNo%wQRrH5}XMjCX6r24=S?xuorh%%Sxiy+E7XH
z*m^*JC-;9vi?`i{+~TqJ>^y$Jw#9wJw>jgcg(ytLs;Ucq%=sS713pDOE}ub?{|dtd
zILByU``t+b8naj>$4}hT1BBWD4xhITXX{R&$9n0uCf8~bmNbwYKUaKuy-+~;b{x+&
zAy-`Y^D<P3a!h^2BTBeelhTpMeb48^udV0&C%)13<<e>YDNpcV=0#<U`y7=rk|SEq
zMF|2Jt>J-aEbSPT#V4Zoby>w{``ga0m081oT1to?+iQ2q^uyh`%!f(`_4F})T<S|^
z=gUOJk7;;!(uweLuW;~!x@SfecnHQdB!Wo-^w2+4bPH1Da0833|H_6mtj3(LQ?lx{
z_}N^A`SKv_o7d5pATp$+ZXST9_UY|R0G*lWCpEt{3vd2dfOm3?VYB$Yb@^^(?KGf8
z{X1M~f9oph<`w6D$UpGgFh1#rT}5Mbc_6gYoR`#^PVQz#_4@#$A9)32d})phtFnW)
z&3g1yCI+Zn_E}UFrU^w!B9|=AwslbYD)Q5H>_=13&_H~(b;U>EF5JeqeG<<#=MCx$
zHXzNUl8ydRP3pvJBrOLlTv1Q7du-&qDUyfy_lYjacEYQhS4I)Em?nbNj(7jL@_U}f
z<L)qOA<2F^T3Pk=2Bz;B`;R1=<2d<xK`9H_NsLn9eQeM2?WVa<Kca?lm4V!Wq`jY<
zo!|2Y!GMLov4Knt4dGe*13TX}Cf|$m`r%0A`)s_0*j_JA0q+j&qgh<_59@`y5wKX2
z`^B`-3+4ohX>(PJUVL*bm0DImNx+W51a0%DkQ!vbdtBEdvHnv_<y}>$0w?%<88leT
z*0rgVJiBNBCq3e1cXcri@LT_u`)J4gb4dO!frvzWLthM9y$Vk7a3wx}EkG=?`wX)i
z@m0>TKa@fYbTVJZ36^m{m3{rF67xO#IoDKL(>N~|zPj%152@9XJi}aZ&-hM!73z`F
zf{*r<G%1N{Lebs-l__Se2W$j~m-AnoGbA%uX3@nx&}uO-SXc#x-9aAaakc0z1C}HP
z$z)VtX+m^OTiG{cp{qYQ2HE;&-5$P7{YjFL@asnZ@be#vG9d5{^!?My7{E>%R?R=G
zGeaPf@u!B@i0E2T^)hVF1n!pJ+iryJ39DJI3u7E}L21fgXU1Qja|+SQTAr}{o91-d
zpumKs3n{yP0m5N@Ee;mFO{20oVt)2QESNFW<xK|7wnvI2qSb5B@a3b$(#b+?n51yS
zf;FVxyd>7(epNnyRg9G$N|FE@T)cU>L33P;yzo1pwS6a9BxNn}!A29=B-D(5o*uNb
z>%sZ%yiBsk;wv-5eJ0;r6P>w-;Yof%l6rUcpW}_==`W|JXv6jsiNA<Ffv32O*+ibM
z9If?J3QdUb+dt>qfw`^^UkNw9UC7JS{pPGZ1H2Zteib8^qRl9vK%HD#i4OvXptyKu
zhWy$Cze)o&+p%(qtD=^EqD+n9t<7dwF=Em4(v`nz8XF{9le<B=hmq4S2Y9?nZ<_pF
zt{UZVSS(ii^-G0uh_>0cB25!D?XhZ}fv7!oy)K(W+iTwkzW7uyKx$I|>7SLJ+9%hr
z%=FI+W8RMM(>!c7*LD1|_PXazfY-EOmUl^RGHYLU_?Fh^Z6(GhqcZ#Dx7z~7S~OMv
zWY;@fZdKFiP9(%QG_~~r9<JEBaQD=3Gx*7U_@`<gyKwD|#VlGsR^Er}A>+pKFF*3v
zH`d=LYbyN^slNbTW&=lR`U}9{zL%Vp+xQEvy)K~rBj9-%L)#cfd1COU=#zxujp)^%
zqZ%8SVRtkTp8|TCt8HKrAy+VPv%r%$yIW3@J<j!)mMP#e>)FR<B#FOHE^H|~b)6~#
zU#!If@N4O+E!rvfid1YSY<7Ck&pSITc3aJ~(ZRSq*-bx@sIceEk;#_6-qslxh!-sK
z0C2j2sDIIgnNg9oDedk!-T_=B0S>lD;kmnEP+1SoNt@pzI>iMp`a&KVZ-COv&0j84
zd(D5ifF09ll=*w%!_$KSi|?3PFC{<PZ@%@mybKwyN|{fgeJooIb0^QP^8UO-HoLN9
zD<a}{4R?7P`d!g2f!5wj`|SMtS^<Qad{JWJc4vO(eY{`_3qbjj*|dK5Pv-W=U0V82
z_dOoTw>&Ma)J3&bBR50B*k?s{-wpRmVS+$ffoZ%l?k<b%U7h5Up`hk8hY(Vs5z+16
z|4`&wJ+#pU%?e6|J4^F2Gz72nT`aX7ICJZaKp^pYNq=??nRu@vOSC~Hn_eWl*U8En
zCFay9Yx=26UwWPb@mxa+7ATOI$f^xR62cuRIhT{0yGZOPflSMsZoWuj{L2`%y7^wE
z(`x2jot6EuntMDgOHDLunM$)xQhphsLRz`5jm4jka)Ly&FQ-p@ONI*L%&)?b$Wr9R
z$xF12%&;Z!@X=tz#M(soLBE-d`mw>j7vIn>;Kc2A&=^C*1*;Os%Xkma(%po~+jshu
zbA@U$+uILg3qrx2jpvgXw`?F#sj)p7M_}LA!$vPICZ-f0!f24{LMclq590$bkGpEd
zxE1dSy#olq@haJLf!;8P6B&OmP}-oBdh6)zt!b%KV&BVxQ`Z;&y~V@x8(@?sV`Vg`
z;c$$&DPn@gmMt<BQXH3iMp6NCK?dbcI-3v>h>`RC1c~UHq}5+k!Qr6;M`^Q+^LkIz
zNNoS6^K-=CD<s!JFQ4P-IpCF@{HEK=3kVlGZ`9CE`n4^~ob2rPa>-@yO>5fgMm3!3
zls&-j{(Gtj73eHpD7qW=$pxQf_IEx*?eW)t*x5^uzxr+(d8I1n+u8sVAlOEc^iej0
zov>C|>QX@T;nTI}LnJS9)@(IB)H0IZUTxU`%KNl6W%*ca^8D0X?rX*v9x|<*IhyoE
zAyakr@3?`j{_|EKNe6DI5jNR!nq@y}MU}ecCT-r!Hn#e3TeUBn_2MNNeN@(Q1*}>k
z;%V<dpw9y~B`9rMBkxhQobHhov<#E=!yh|mO)y0FR;VKF>y});i9mr+fk5gBCdWX>
z;soC1fO5a$*fn@dK;Lp>iLQjG{I|M`VMKg`^EWLT5a`K?SSHq?QVuirWr040A#{O+
z^*yTqd)O&Oq5a#l$|~bBar`JC;14&jW*hq>nB-ZVpa`?}+@*Tws~fio+!}TC7$HRj
zK;(nq2BHQS3%xg}R|+6!q=(5ALH5wS_Qu}B0wy=|-zs;@3xAK0W*!r`jzb_20VIzM
z`%o#(21}>-_WGHOns%>4CugnVWN1+?y--T*rsS|^2{51HTYdbjIbU}2`x`$`4Ni&2
z@`YO2P^z71C?<C%s3@d+{@zlP%fTJ1c*Cjfo&`IUYTX{TxBbI{Gj=Dwd2)A)-A>$m
zVQ}HL8ZiAQ8wD9WZqXrfRWgSncaioxREW@e#tA0hpInf^OgA#oEFK4hFe0(7`a#+z
zFE+>zwLi(U=icwU4ZSNZnQ{7SBe)qkkN~UKq2dCZ_$nH6Xbn|Gm}s|0`A{7-D~pf-
znwM%~Rp;ZfzA2<y@@7_dY{Nt#koNY3Ngzv*9%sky`e`BUK`)Uu<I6Fbs&nq5wmQ{K
zCZ@v@>;1<yc(-*D71bbRP5Q;4e2$@fswZ-@8OwlH*JoCvTV#nv9j1NpBMe9sEn3(1
zX9UNiSnq6X+3_k(q6~IiuBM_;#hctpp%cltK04(b17N~pqY0Hrmiw@^z;|qa7TJsP
z1wZv^WM+q|7iHg{m#f)K8Xe`LfcVQBwy~UY#e&ctiv3{>Of>|2v86&RWIntZ&k{8%
zD#o;cK+Wa87gwy$EKS7lL{G|!k2bOh>?jL3!p-({UD~SaS+a6fjy}?8)&Zd>s%h@n
zPB}rG%u5mMA++mC+T^m68FE)4LES%G6ZbkVDS$aas_-uf1!8b?iddFd=_ixB+p}w?
z81^9~hl<I~K_ouITE6xV7^naoYZ9NNu$=zlu~_?$^Q$lvxk2MwR-C&DN_o~Ix;jRb
z=)C+EqS8SivuyEaXbl`2)?Lf&bR`lQxnI}SI*$xTl4z2HbEs@bO)bqCOaUK!7=J-5
z3Y%+D_cF`Met7Om{&yM{rBv><)ahMC?_E}t-KU^s(b{7yr{(k~^Y<6=vsQ7r9itu+
zb^V0YN$~id?76s82g`%knt=W8%5YNq<URAYJB+?m6jl4fDi{uttg-1+U0JU^b=Zvb
zdRYhpRTO9)rQy9~wHjZ>Cp_tdy;=AaMBYBr{E(dgn=xi}Grod|xv8vtc{@KZkFCBD
zI#gJwwZ^DW$S6O=E!O>WD~l5q|94^dZDg~Vh-7<XW5}>y`+dk}n9r~a@&v5kPyVuU
z0a{cjx`Ir02Ne5oSmPjCyU(m}hxamF=?(_V3;Dj1HCSZDzs76adP=~rr`Ko?j3ztj
zk+bB)Y`lOe#^(XBvf3&%ZH1bU$CCflgfHx7^@Xd_1)LYyw0r&RNmSCivgomqJHGx3
zFb{`0r;g)UPJWsy?;LQEsO`Scuoeh(X`f^mQK+?<N>)zxy8gXxEcbYq>QqO|J~<pO
zJ=42_*i7*KZ;${8R9E;OwRkieYjq}O$G?@ah`(EGN}9HO6)pKR?>Oj%XY0BuDu{Z1
z5@S=w?Txk90Tvs%CwcOCrpYtOp;Mdb<<vjWS@n+`ncKkh`&|+;O{_t8uJdg2NdxQq
zF-E2_=YADIoU^{=tm(ckJvCUsea~yII#6JRd`WL$zY>e!N2kg8>e98PxgWoD_3hPu
zFX#UgchExlc^dm#A0*1?H)@H>OsK1Np7u-i&v9vPx$}Lzt)r?Enn${y(J_IQpG)=_
z@LHGBl7wBT;!zDt#&Vc#)rq}5{jeH%#!*feG-lGv2EU*)KN!Sid<w!yhC|3yQq;?s
zeR&!fVPr5@Ki>rvV$|cpy@ljU{&dx4pZ!;Jp#aUupC2z#nX3#nrpkYb?GSUqLc<Ho
za-v97al+2yzxm1?WwJ;@Q9$A*I$%`hqc<h!GFbbhELUnPZ>FBRB~B4a$kM9$3?XNV
z-j}-pkJKDF3}_lzz1pd-u`XPPYvgShWw709bI>Hhu@vWz=?LY8?19~3E~D72qfP20
zn`t%ckAaxHutit(eYTvccd+78n5C4Q|H&bcYihZI%jW&ZcD4tV+tgq0OIl&&%5a`r
zbX#v5jP$q|>1Sxe>D0;?Apifoo>GsHV&(Xk&Y+^Yp2;xHN~VZs=!|;KqCt#|jQry?
z5wEZylDdVAs@(7C9TK7iA2j7?#VSMoo?bh&XRhndcY8G=8au(n$l2UTw9*mD60J;b
z{ye8EcrO0fc+wSZ-c+dG__Ry0rkcNF++R;nU|P1GX-7m?)u^!h+CtT~G~_t+8r$Dm
zeOe`FR3>Acd$Wtg#|s2Pw^z+XVZqx|yASzAr`}nnc8lxCd&ykNE3qfwzYE_Or!rYZ
z2Q8<ftYZ}#cu%Tt6#t@`+NSl(lPOyYb)8iiqxV6Jhd~c|8>OFsoaDr@(KL2f#cK0;
zN#K>AE+pjM;b8f*6pyLwm=JrPz>ZsOO#%JB6^Ee^+E6BOhG8RRmEmzMDKCQ_$P(9b
zNi9Oq_rP+9rjsF{7(UE~GwY2^(}dI9a+FT7zRk4EbIZg+;;^AhHi2VJ7$3hFzCI`r
z^REqe){}VMqIhPA2X`AnVSx`UTSRulW82XZLtaAXr7Qy+$^!%EnD)niJP8@|>W)Oy
z_HBz#xb_|mEq-4Xg@Osm-?v3}t0DzZK)>9TRZ){%a+Uk5X7;0EjN~o~te<LXy7%R%
zGWolosY%18z;OVi;E658goMsyPo&yX!!NvHDmfeQ$3jj^*P%cuG7&RcuON@cr#K*t
z;_9Z49cr)YKXgxpVL9-FSLHGn$%>H`=Ol+EWu}|7UT#uG5ERhU(I{^$M)p%0HphV9
zzO`uz<^cm86w3XO8IwDEw06R?ZvUPSzgs?gl+e*tHy5>|fu0VtwQ^;Z9tjtj=UnZ|
z=Og@GX?mY*)nj4#j4m?WXuiLN<KFBQlZn^$iNIyz>@aOdsKLHDMr!_-WKz?BI%XvF
z{S%1&u5lvx-^z5Bl$N?nDfroao&JLTld$$h&6JHxDJRXMv%=_z2BM-=(=!nFrcB}P
z!I`pqLwcD3eV`<cP0p}dA2@e|yh<(662?Us-4?KCjERbjng1*pFt_@>F~_X?Xy+(J
zI<=vvl&BEeV67a)J+4a6I+r)ECB9oNqxV3ysk$<v!X*NBDzERQTTrm)0Rm6{R(WGH
z{wRZNx24yKjKRYbWD_^>7gXL<YIxs3^=&c)JgoCoY)-$=9Gm9h<GE<7ztIv4m&jQd
zEz*t@pBI5$@56rcC8n{yzmDm`p|RjO22i|y4op#h`R{Lf6deS=SkUBKX}pTldPh+V
zTmkfKis+>F2D8rnme9GdI)P;OC7eUu*p1w8rC|Vymsi3^-~zrm_`!1j+)-%79s44#
z2(OVxAqmCrswV1v&-bRo&|%IEVs_y#T+_Mj)zr`6M(>J52xG^&c?kbGer7VID1qMt
zxSH2VTHr9v5yT1-qrx?^C;D(<<0Focm><_a-_f+b=H!k=2^gOi!+_=pmF~m4$w-R8
zu|j1F%2}*=lEjO?fm~04ik&@nCge$aJTm@8Lat}6izmu5JlWrvO`R^MMq^rLed<2=
zx)3+NWNX;ZOsZV!&w`)m+9?mIY1DhSWeQWy4F;Sf(SyG=_oF5sUr6fZQ8Fwq`kxbY
zJnsVO1?H$lZ82lKzvBQMa67nbn`E$*FZ&yzxAG;2FiKPPA;?)It8effN`3Yp-l@E8
z(ouYZ(<*bX5Fz97`J_QHLISc=|C%9BKlkq<L9Y`gzj8Z!auJNXMtky6tf$v~*e9F9
zRd1#Wz1YzM6C?Q$B&JbCZe_a(@9B~(X-1cRwDUX-+PfYXr!Sa+bhO^Frjt-l{ve6k
zQ@t_pTuJ#fn}hy61=*;-PAYa6JE!b1;*w{K;jqsAv=XL6le&=UWV-G}rrngO__j@#
zJL657#w|DGx64|4E{Vlat^puk*oSfsqKjO9w4Z_r-{t?|taDCO#h(zK#SPD9ic&Rx
z0{oR)YqM_m4(x|lKQwzuOckRQI23w34H{5fn&Pj0s!5+K=wa^&>97AK?hdzO7AAEY
z;p%L-h)$eiW<fb<d2312bYrHe%sQ+!a|bJMtQ~hgZ6zC_P@`DYmaUrbSz9FF8wvyO
zBqb|5_dV}>``yF=(U&5x(9g9{VBnr+Tx-8-C*`+Rea<i<<M3VNB95>zsTL;y4Zf3U
z2AJ7+1;;c?+n~ovnEH<kjVk8Kc@LIg84~7tO*V(%1A?~Xluz`~B$xV|O4><w{q^;Z
zwxFSC&lne9GS_w=8Hi(qpt`7W3PG~SKx5S@>!$4V9i3x@{o1X^2Egehd~AG!&p)s=
zCBqAyjP}}@d@^>t$Js+-YNN&eoRq8lZW}Vq&sSWG;g~;`ZbXzg!`!UhRL-y?2z94>
zg>e3w`y1VpLIcs)xOn$3TGOKBmKU{yj3<j_Kls&Ow}!#Kl&^b1Ngs3{;#@STbrUdl
zn2Q?ZrIBU(>|W}2Z#PA_<C*^NIQU-oNh7^qwSBh_TExfsuj_@0(>WMJ4X4Y}(_XcP
znkz;n7khHt)#)*r_5CwhF9dp()9l~zbw(X7)O^Gl4mw6kWC&IAC0U~1327}aP~)nv
zBDf44wp*%$(oEdz-tX8o*hLHg*wOFfLofm#i-F93b3h7M{yQBns;i%V55myS+x%~K
zYo%`yzxG5eruVnGdq~vNI7?&S&(>5B_v{C23RQ1u{i3m&w4;=>$!pNuk2T>B(-=`|
zH-RS+>@y)bcGk%<+5wgw8ymhE_^mX=u@pCwRtnw9-4t8dkA_L0mZpA)Mr{xFg|yB)
zW?t!t+KKOejsPbes_wg+;}Vs3vL*t(P9D%ujW`2T^DujH@HR7Uj3J4z5fWu&ghswO
zXRh!#K7TC4IpY_*Njnw^i;GpKDYV{n_QSnNuy#~1A)<di%i2QT|C5);`Jr3Jf05hC
zVYt2G3=!E0Er<I8Hc|RvrZAOAyqbK6bf&@8F^fh9nf9`J%x0UR3gNQ0%CzW;I?b^{
z;PIte=KgI#K;v$UJdb&!rriIObW9HRGtrIPk@nqJvzoIw_)keb()=u3jd{xcf#yQ*
zHFX=v-x!KkyQ1qgff%wrZrt2Ft!4r&MIF<O;$xF5fj`AdpMN%LS8>&07G9>^)UaoO
zf@+`mnO1O?p>boGebzsw^x{CRD1{M(^=!&M#Epq)Ng$6OhE%<ow{wnd15Eq~D4d=I
z?7wA!3L(Cs7?<}yE-UY*bQ!o(?6>zr643=U{8qR?er2%VE-01_bcspQ6iH|%QG00o
zBnjgJpi`=~A2pW7DSLZ;IvVXUa0zCe*I$chC&>a!-i8DKqh$`VV3>%F$pvj@%YSRB
z#?Ft{g8fx7OZ?FC4X3G3;wpI{Kd!iGSVziF!+%e?^xG_#0_uPA_*ol`YWZTo-vg#2
zu>lpgIBu#SoxTw+4R}i;E2)_Pp=}MI>w!6Or1TXra<DTSuUw%duHcs(7Rk|#B0S^C
zw0c__`~{Ay@w`2>6HG!vH9`X#?PP8oyR0#_7G)M|jk|?ymdmZp!pdc+p?(>UTT!ZW
zs6A)A#`Oeoc*}k619eZ-$9Fw8ptO9kDJ2DGKfQkF&f61@>>!07dsobl+X9aXwjXEw
z=rJnZC6u%5Av=-umtK_S!#->0bKQuY2@7|9s+9Ib!TBeHB#Dn5!C!<zERMfIGM}(E
zW<AioKm4E*S-EXEhVrl3nyFlW=U<&Lr>0W?mD3hP2QbgUhx*iL&iJ9ZIP;3*b)^)v
z>TOK>AkH(EE@x78?15yEV13;HP)z%LKd+#OG2-u~QS1mA_3WFnO1&p5?{d4tX~IJe
z!+Bet#sz*k9>rjkB-Jzx|3-i={CAMlc;Yh`zGZ9cR@=jpb^`(d7Q87Bsi;*@6w9IW
zyEg16(8u0q-JFoK4^T?eZbeBvT9!A8u+ImMg0XZH7U3}rETmh$iGtx0M{l?K%<rs%
zb9qce^Tw6Ba@U^r0{9}cPBKo(uIrA9goo3f<&o3I<AG2zzBdJgRTwh2R}~3*=B-2^
z++w*_E=jQgqw3sidon`5S>+;87V$mX@ir8~@d}+-sCm^7;NG!4mi?=>>zArggqtzX
z<tcv&LO^0@NBt)X;e<a~{6;EpimI^ND*Dwp3W4;WE|?GQX=dvSte$uus-~f7=uC;K
z+4!0Rel=6|1+?MOxiHwvdO57)5_7qks$W4qdR$q5NBOwahMxg9L@aYcYzH3K4+FA|
z>9AivPwDb_vDjW0&IE!=Mfw58KA`{GH^pISnXky})z$9*1#W9j&Gzu{*&qJMg`<3C
zB%~{I0#I_rjz+6!-$ECOXn79eRCeaQ#iH-f7<#J6xTr}o(erk#=0Tvs@*VDPKCYI_
z1-Z$_lh{b5&C}G%FK^SYKZ>ffQOJi7qk%*<>1YMA)2#)C61YF~5Ai%<Q!Z*8SnmDm
zPl5|yXWAkjlLd*otxE>9Fx>et!19~q=%a$SzC)&AF6P?3Xip2x75bM7G@gT0lrWpj
zo!%^Rr^QLT>p#be2z4z$n^8LqS!90dP-sN9#0SW1q1B3pGwVnYuhusGi=3AEFXlFl
zZBL5%5D9K+m$RB)PAMr!g~zhVqR=Z~DQzaQ^>&Jw0=u0@MyrgE+=iRl%3g{|(}D^O
zNPt`FypnmbA+OfYws&+No<P`*f#ST0<8v|hN(!L#y1gaf7Au-mvvP5DH7_dcfeyfG
zyC=pK>j@C|R+WVL3BSZ8Rj8kT3Ksr{eW|r0hBq^|zdM+IuxWS;q?Gj#w*C#ekj5PS
zTaE(44NGMsRf}p#(M0W!Ch_G02H`yhi`TQAaSRmD-$Okq3B8b@wV_gG7EilFq}<n+
zCS9h!y37#3pNLFX{S_<Ue!L-dY(aDueS|ruEM#Ix=IlkxbOD4XJC$jFrt#}bo0-%a
zr=6fOYI=K~hnKd)kULz1L;=Agxy@vg_w&HV7Im5lJ6xBAh3`=RzT(Xl2l7!bYkf`e
zZmMbF&tP!0n9m=#J{)aH!U`YWD*nQw7YqV9xNUXB65+J7p{t#qqIhu)%)YF(KOA#u
z<)M~-H&z2kZZJ2gAi)gR(@)^8bNMVi5LHqXmJ;-HyO;#GBt;fXIfVjZThLgD^~F5X
zhZRrN=>+z-R1{+e6<C}Nf-Yj}F!o%@L7?)%itJ=h`y6cs+C;J?tO{LCiJ$_TR4!_Z
z6~dw}=sQqH3aT6FD^}K@bqY3R^SsLiC#J=uzO@D9XkF#6i^4YaK!GgH%p|bli$9I<
zTm*K{@MT%KL|w_<#1AdhzkF(%_c=gsyDFYQ`WeQ?rM0Q0#*7=_5hB+ovs|9G2Fch~
zU+%R>n}9Nskog4VtfA|+-17;sZR?|C!q)efCHiqFLr!*jvm2TGppLJ8_GGW<Sbu8J
zE!clpK_QdDLf7N)WW_b5=SBo->;iY2`}Uh{Zdw@ydtsQy2Y_}lz}4o@a&_@O>BwsZ
zuf#TT&@#HuMctSguJor%eOL{y3NQAA%FHvCIZ@0w0?FVfcv!f21XlJYPqW2?K{E7|
zRv&QjyQvv6oJ$r_Fcbydj)kWV8BPMYsx5vs5~uPcd=2xce$)2VrL^!P#!O2p)}a|q
zF_ygn2y}7HZN`Xtc4JJ5ztna02K(9uLpPls`_TMO@vA*5sG|}N!D{Qa5tgA75}IZJ
zYOu4N$E~p$Jv(G-%$0mJE|#A*LE2Qtly$Q21{}XS{(a)XZH1v%aJdww9Os{RiG~7-
zQDSj8v?x8gZ^^yfKlzSAoMJ-xd_l>%49`f)BH_*ObC6R>o0MD-F-7BjRBmR$&o^iW
zc4E#BM(7Tby=a%*J%d1reaeG4&p0;U*aV%~oL49hHVQ1E6%)l%pr1WS3E2<Cpylx@
zN>cffV=GV5(Q!R?hQWqlF(s1U?&9j4Ew&;Ws9X%7jy#?{Qh6)C5?;EZ?6Uqqg%s6f
z8ao#~o{s8%*2nLQo-cN=rpw;fK-6qD<f>7U-RRGm#Oq6xP<<6FCSn11p8=w1PUS^e
z+5)NEYNyN|nt_<OP-um{)d1)KCfLv6@&p7&*#1$mi=)b!fQ{>Z#Ou{OV>HE>uV^D*
zHZB$aqXp<R+A@MHqWCQzadJY+zyil|e=3h7#(b;=HX|wp>g<npOpxgN&4;YEVtW~w
zKlX-hi?dMs84|Yvl@N;&eJj`qm=6vgRN{XyI(Y0!)6w0}r}8d0jT(HxSt^{xV#F*N
zWdIuaDnUU$pT_DcKRS-Kp60aLS{2%HwiS)CzLKW4D~k$Z++fwNOsy@XeRr;Ek7h~6
zV}dbne7?WSm(KxE3Y?tq<`w_5&QJWvO2?xXT4c^YS*Z~{tsbmqx|=|K9vq&l#QKmR
zcro9&`#_V_K$8TvjG5ZGX%x}k+_*&^_+@T_^-GqcCrk83O+ldCtW0INhFjg)#X6l-
z$TbD(DAy0&&<<?d4v;eI_u{#9V1IxPXz=9bUUV!}(b~T=d{1o{S^FEefq{&#gP8qf
z7I+<{_0X-xX0j5}b%7CRqHmJN7Vj;+u~fFhP``Z#J-6fQK)1}Z1u8OWO|_NX?)u0B
zqt!MgeUbXH=AH-IL%6RMVxzrpyEc!6GhUj=E(`$sU)P4sXPVCd3Cg*=+QrCcBh*xy
z$&Ssw6qag<w>n_#fEf50bO9v7`fj@ofFV!QwQ_LVTx4Lo&~07+9#GU3J&7t>{jO!8
zA&DR@kI2ZFyezCES&n>O1bDt>i;GP|rB7e`YbE6VJtAELaci?GX{u(gVd+3-e^9)l
zWB8yp!g3Fg4ZLD`A6Jf2R=~TToU0iYI+GZU6(#qI&T^U=CW(0dbMPBC;82zQSGrGj
z@1!%5Qbjw&W~gr_N@OCJ0RKE7m%SEj>A+}$;093_g_(T#OQqA~<+_yoNzAQ>jYh&W
z0lreA0{tzRzDEYU+Q4*wX0Oz{)}48*(XF%G*&8^B)tSg657!yNE146c05FC+PHB|B
zlRdqmu}U8{8N<HMN$nz&P_TxzpeKdqnV;)7PlE!Yt&#eM)h?g}<Sp&DM8hGww>JDq
zOeyp?HhpK}kGEY)>TyF|RVR(0pQU{72KkfPnnJwCnqS)U-mY=)^(Bwh_+c8?k=;9o
zedC|nkFA}h7(iYLhqZqUKB4-NI>Ky042TLY_|*B0;x((Q!YJ(K;xhGx*id7A{J~=V
z<;UyXW=p1~NnwsW>&b>nX{rUUPm8LXFT0cC%3YAHeya!thpS~a{fL(hW9q3J70na>
za&UOR5x`kErnxo3GyumqjK9pe^P*|LOrJ$g^=ua+fxE%aH}f|daqjxI;Dih6U$xs*
z@}WXeksP|O6v=lV-*<)Z+SEEF`{Jth2Z{>vH9LR=n1-+N#{{&Wq>WeM+l9O+E;Ox(
zF)RkM>WCNRAvGlv_<KpU|F-qu#Wf7WvT!`JxM|LUEon2t(J4LuJsei+|Ck&_PFoBf
zoOoBwA##=dm6{LyE&Wj`UoV$DRa&R>v3F=m<^5V>jk;Qc5iokU8cn8-Ot`D=s)3A?
zy^O5)*E0)&sk3C#$KXCy|6T$BZ90NTl5ua%9=2Xyu?~U3B-&$Wa*E;Kn!fqs^(%sT
zhGuE#RayAiy9(T!0tyr0v!~G}#A4KED{Rv>L?BQL#(UG)3&YArGZF>E(0x65Tvp7^
zOg9l#>;59^)ey$K`Lu(A5*tkg$Pf`K_ZRAjghM?5e{=YeIr$ZC2ygc@g#}`bL>Y`P
z7C*n2+(f9ULzE&Tr4n4^j<hZaYhl%%k+Qf?M?Yi)J}99AkM!ydsT#!A`n6et3KXk1
z;!nl6*>`iwg2vOJHUr)A{f*2=l8--A=jvkqB1*=Ws06g-bbwLMke67dB=W+1dcS+x
z>3u`bb(u6l8q|p3jd){SmvQ|Lb21#k!JC6wJmwn)V5sKxf4!B}eI{*goOE1JyQGY3
zR&uNLHbMzpJ7mJJJ}bHszv;}J3Qzj3fUB6^bi(`C%Fr@RZth5E=cgoZNQx_wN6}NG
zy!JXyO^5y>Y&^mQTZ&ltB5bUrqT}aiR%(ch=VN!`y<Rdg0J#AbJ(7^oBun2!Q4)VE
z58G-u^?wBzGJXf_e)!h&7l7RO&{(JqbdzqS@fA4eYDs2cEYUIK!=oR`LJr$oc&Dv#
zfswH4Bx1^$HsJc4cl#2(8IQ5c1M!u~k_=$Oc>WNC*8;rhgAB+!(qy0CLC%bqJWniM
zQAKI2Hm`Lha6JRaF@hh&!JDgW08@t30j^_S$AAJPbu>Qj>~45<I6uNa8UAy7X<~9=
zN`L?N*^Y8Gg@@oeYiT(fz#VDH*U32LuVq2<HZ297vYE+=CBk3{A)2v=QkIoZ>M?=q
zNqZKF-c<oS$(zJvJ!rHA+w{;ck7|xUyJupml%hD+O|9tZis#czOo8)YpDN|9#4W0Q
z3wPL7`XF+@>vi2Q$5eork4!bcZE_P7QUcp=>?xfDd!?Q)99y}XSwJmB+Jk4>5h*!R
z?rHGE!So{C$#VU@cVG{$3q+gMSF0LsE^u{5d!`o%rtYTsA?>kk`9t3q<q`PRvlZj!
z%Tb(9+{|e=OBBZc?LRx<QIm0PG3Pn%af7}zk1<b&dMhJS23fwq;Ej4~^9+{cl<5O&
z@S&F1i0ALrehH!D0Yp3s@r}V>iuhG^e~%uP2+_-6BmjV0zHrKxmWPUc6xF@<V!XTK
zb+USO<l?huGyp5UMZd0@ZpZ^0cz|`F9+6V=DEi%t7sr6UlJzE$0ZH)gB+buj8b&1A
zlmD5(2FPFWpNDY9B8yeWbX@E6gbQ0}8+DVLRI-u!S_Ye;|3mrtI*XzAF8_6+<<vIi
z_O*KNmHWRRt9>qWLPGv~GiOvhY6s&37sRhOC8Ebf$CnM_4vfvNi~`c3Npyo-8aAmR
zQq4{0-xU+Q+b@=j@rm%TIfrQRG{0Ne{cKFR`mTGr$z=J%o;{vjZY~DVRkIg-n>`m5
zZ;BpZJM`V&F`k`xYCk2R#!fJ$IPz~dL;XNC^KQss_3oN6fLUw7J31=TsI*sN{-#Cw
z6$he;Z0nSfK4Ux+gYr7Y84E(9GxT<3NFxna`M-^cfQGBtp{*xk0nW|m>mlv*mUF?n
z<7RR3`6(XsWPC41G4B(B=!J;lm`5tF+vycZ6XSGE6=qyE&EAV_M!ETT+%ZYq3Q;_K
zNjizQie)W%V<A{fJc_YmDW4gPOzd2HDy`{zNi$Dd(1%U~PKDad<K2XyI&KYq#$11g
zJNGM(=1`aJk>KVp;m4PA(UB%Ax;0i&Yir>B<xDkwJ<nnpeZADEKczFLVsI1Zckf(=
zLg7=dp3&C5ejy8~reUao%TB~E)HMoMN(+%y(QYcjVw7luamAkwL<ez~wKUSb?|#nf
zv$74Q?ZSsue>?e=Z|HlN6;}NzR_6lyU~;<CK~Qy*XU;lXSeR4vG`Va?n3T=%oX(QL
zjcol_*nX_LDit3%InM`d`~<QNa>}4KZDqy`WJoO}DWuERSdPRWN$r_lA4D-ywIS-D
zs3a@Yw6SH?=@VNDsU4UHy>9Ff&tV&t@95X0tU0X&GC;kZwgA3D*pD1nYyL(fc|C!j
z37-u0or4ry_0etbKPNf<CP^Ii^%eGaoW^)Iq2=1UZ<H*=lh@ddMBj0e)JxItU*Va9
z1kXh=Dc*uVE8gdPn@S$J2Wb=rCx2q}@M16JAwOoaD#}M}iI@Qm%`lB5mB>gWt-*mD
zFG+x2!asH|Yo_w<dfD=Nb{NH5KKoigyE!b`K1<n1YID%$<P*C)<rY1C6SgPmVK1~`
zwi}b4_e*xpFgxHn^efW+DXrxcSXkJ1KSS8Ty}b9<0S0&S-$nTiJDoMmMEkh)Z_RG7
z?`c2RMvOE2rrbyNsc=Y$>&NEer$G8Zl$5f?HP^<^ZfOCZ>!o!xPy$oA?5{-S=X^|o
z<5r}tkZ7kYbpM%!Haz?Zr07Lf4OEWkbqIdOqkb~}2|Jq2dI6n!SmUOGGRzcRyuzcX
z2Xfy@ls~I*@gh*c6&3)qaD8wxGn*cYSD$;%I?=hTGmrgRZQyeuyGtO{5t@qK*!T`#
zXPyWz7H%PNPH*`<wyymRRv$T!nH)0Z?floLuf#EEzSKNFvG(RaIVV5*_{niM`B6jk
zmJEM*9{3x(Xa30D2}wsA{Hw%{Q%&%QPByGl&)j3*kAB&@a%H);irBg^BaL^_U05G1
z&Gz~vT3PAd93BI5jDkQSFQEG%q8%G2jUq<=Nrr|>_g1x=z`Mn@56=kbShOR4N~b2;
z(m4%ox59PG#u-GBvd}tn3tpyLj7x(=)Cu`LWrdQI!?haTwLY=!g9^j?vCcr6ZveQ5
z#^cqI#b)NM8VivBV@>a)1Y^jtGImb<*xy!oN#F|}styOue_d>*Y;?Z-&yaZn;Gs}E
zclLs<*j7X%i_50Bx!K;m>VFn;tSSd6Kr(hRD<fZcDtie0vC^7~jgF>A7HO1CGxs1M
z-bg+J7PcAh^D?@K7aBc}{r7n<G>#Z2e4jMLy5Q5?7;Nm+fXD-ULAv#nn{-FB1rj0i
z-5Q#D0HqJ3iLy0IM=hG^P%aJ~;1Lk;X8GBQ@B&>X8GD%IN@shvK9#Lch3^nxqP%c}
zR(T<0a5hobrP0BW8&is*z395Hp_$GLr%MUrt9)yLYupr^3=AIIJi-oi;JZ)*+t<|0
zt6NXw;KrzV#Tf6MG&G+mjNgc|;at)FbOQ!{h83(7RvJIH%q$p(%6%Ei`VA*l_f=)e
z<Ak7YjUWL4gvw<%&jwR6o2Bmi-<HyDa{RGQbHDxDEy(xPL1xxf!+_6bjsE8Yhj=yv
z{C<U^OssXutGC&O_wsa&TQIrX5c$Gr3Pmu=ji=JE1Aep*ts4HEs%94{cHP{8Hv!7T
zb%d5bJ-##_YMZ<DG{iR3Q9?qyFWb#h_`v#8PV=0?Mm(6IU(|ucfGEs&0J7=bwDn~v
z78N$;+t)I&bwl+_97yBRNW^ik^ZRolHB8<7jW8Iih%)y$Wk3V$Ycz?l<~6GZv{J<f
z9%l8$)a%TlQGg<8c|4>4>mr3DijpDb?1j`XT-ah;U~oked^0@s>`Tt?MYmo?O!px2
z?!q{#hvBXpNeKg5-Z}eK#+`!dwzcs*=k=q<4u%*)+P9a_{7q;?J>PRgCMRlnfOWZJ
z$wM9SE7Mk8LR86`iEI>F5V|jSmy<PP#%2HT$GcRTNQnmXfp<RFy7?%3ve5m#OQZx%
z025j(Y+j)i=c_+9NfiXyel=A91`mbJ%cTPpd+jt+$9A4~0u^)oZJ|?VtFlO}WqVHp
zs<%S;OH30zF1m%9J8RseRe)8!)MY5J<qf#|<byWCVbceLbM>XYY)nxHOn5~6vr{Sz
zX7lPK{o?`agB|7bPeNdJ5Bxg|Q@<vH-$lt&-UkEi(*AURyH#@Wh^JbQpdPefpS_->
z>37D^#ly+)tH#?qTN*IVp?3zVLqA#ojr=K{<;-+5yj-WHhvyZ#C5l=DZlGa={nvpL
zddu>*19q>~c$f=f8nFe@Dy{_~wbJ2blnwX|dQoeo4mw@!J`{&n!3+fxG?`qXuaS{~
zG)pvh8If>!zJ!0zns4&dUI*o@gD*`4sT@)V6EX9Y5*nrV<#k|C<?oxqmEeX3LSW*C
z8N6V{RD_Jt?5x?LwQa7tiFOwLH_j}T>Hm$hd3IDjd?egd#H$0!tKB4vv4beW#{Cnr
z+V0FX;<Z4FN>MlV<5VU$AlmgbzFuJ8RsBF|PIHx}@LLA;n8*5UUffy@&V#2W6%N1l
zVKY3Y^BhbkDiDG?o!TO#$WY*Hwhym#2Tz!Q$TguvuHy6uPtJ+Thu2R$<4@FaDxgaO
zPse4Swc8KOSHke!%7f#39JAu9RYacic>zZ>Yo|4)Gu~MK6@;~0pG4Qbh;<)@d(24r
z653Vze_C**%KPmYI5Cbz;G0<SQ~-<__^;8F-V%xus9B6Q^4yCNbHB6fr!UUh*5e-V
zPK8*N<Z&gRL=-)UiKQb@$fU7#VJ`G?UC{q6<S3C=Hel~!Zp_lGTq#N=mM`+ij5%(T
zj(;IW+57(Y&O_%q^knRUmZp5WlIgdrUmSCG_({>4qen@+PvfFdHedKBa}nu6s0XDq
zUP#11kOr7Qrt-QSGF2&164_iDWycCA+du$(b>j;{#-s2YC*}>W4}R%I;TJikFPMGq
ztA0ursFtJn!J364Jo@|PT8+S(k)X^5ewc4E*6kh27>$T2wC4?_D3+?)cgH?Bocx_H
z8$${DNVy1BZ<)@sKpj7pg2x5kk?YC;5)Taehcy%v-Y!%fuK%oEj{8^+gC-b54egcn
zHsieUE3_od>rob3Nhbt?@*ZDa((+&i%do!rt9Tgu9OcirlQo~+t=8(6-46~Y6S6Xy
z=}Z2N>d8H#?LVcTAPH~THp9*!)OoStlLesf6NT|7XK#dOxi{E0z0pTXf5xYmVVPfn
zUayDP(1JBh|Nafx+w+xwAX6o+B!X45Ej(|~E{R*SyG84mwOW3c#~gqHP{JI2!>Iyf
z&c2UfcnN?~B+r|t8gz$3Z%Yv~2~YUXgYi|P)3m>`Z>_N|S~G#wQNAe{GtA{<g>j*D
zF)#xgHV)V)XzT+R6<E5}HN#LYw<NOe|HQ5ziE;JQbj9O}W8mesGX5y~rP&BZAFTf5
zpK`k({XrdC74t*ikT<h$fKSj)d5XuiI{<%gw$RwaFuKN-?ZFV@(xgZvIr#SHq;%^j
z%%K0)2;!nSVDlnTd8X1-n!^G0yttP(Q}>Te23)xOT{I$R5&zG9#4(eDg8)CNFztFH
z*44I3^1IQ$u}ZKpgLf*5<yEw!yEAwzYt_`BUZ&6&H12LlCnwT0Z}=B0!p2<tp6jPp
z-G#DJ!8kjE1!(Fk9|ukAwYb%B+67|3@iW*2THhik9wi87NL+eHSD@*g>|zk{d0JUZ
zgMgYvKjpb1WQSe}3L)4*Sd%hrVv+-k<`HZSZY@3{`M6&@6)$HY?0?0aGIDH|+bgM`
zZZ(<A=X*f!Z{YFq9^hU}!kTFtI>c4GbrL)$loi305hTPf{@+e-qAC@yHCok0z%acV
zrM(%8JN6b}TU)miaLU^1-h)<Y69?40YhA0+A*?sL88#PD(I>yLfP*NiXbU_kH%#%L
z(*)y+y%2k?B>w}Cs`NC_+`*eYCHT8AqR0bWR?#*<`dSY8@o6Uf*BAo$2stvSc^mTI
zLY4TDMM=0@CZeii-e)nrE3LxB=DRE6>c$0vWR!zE{vDZU{*nNMyf;0#H%fi+=*khn
zg2&MeZTwzICD8l$GJwPUugM<{bAm<=GFc-lo0<jI3TjE#aM_lB?E9Z`va4!Pd+mq4
z+S!nMjUR^74#1m{fmNxs`^RpLt<TLbk>@<9w+fJaxIK^>>+53W$dK_02-Gy6nxS(+
zQbs|zPP@OaXw1_wux8djBMwlW{G-871zb~Kn6{GK!p3g^^s=klF=iMsKvS;>)(5hI
z;|rO|4+AfRAFuP+_?fWM2fNnHwnJ#sRF=J<oa=T#DsRYXjIIz6D*vQ^E2ufY)U;>g
z#hwyl^QwZKxLD*80+CQ10^}Y4BOz?l0|aRn7NJ&a%m`#VI_9J#sUvVIo<Cdi%J@Hx
z<>nKjf|ef=i5Ax5c?ZeT+>i37(D9+ruv%4A|Kktf6lfigUxmTDm&Fhu6@0>IseY~-
zHmQPOo4h^OFo5{l_$&tEHm>b*CY!3d;32*OnP>d?vLk?(t`3vzhBLjh&<LXJ(fm7;
z)1yxih>4TmY@qsOI{c3WK$L^nYUCpW-0feclegs+(uguf#Yg=cz=w5j7#8NISA)Nd
z)W*9EkB9{Y&|4}mqCRv))-LYE1G*9Iw_(z?s&ieM4NdCtSU018;bMw*ONiJagmFEQ
zg*@bSi>w2|eYp|P2%=O9>w&@R_z-Vth}}kT0TB{70|l=|fnZ|+&O*R5;uE=kYaJaj
zwaNp}s??<FR1sNHCwhP#fY$P9Ed0L2steE|J&X0y?>J_KrsP=l55<5p-&Yor84Je~
zg<c=T#XN=^Li=_2+1AHj8SDC6b~MJpjcCAc1J_QC)!DH@%OP>=zF;EKi2_~W-7f3@
z`_m8ya!>~<PI>Q}f+mZOga3VT7~Se~W&I`s$f4`M_|y)Rmo=VS7o43<Xjeq1RuPd$
z){UO6rNC48Vg1LKtgjyptg_#SJ1kuN^romB`=UtZ1`}NO)lnhBC$c;nqG(V=ZYL*8
z;o`+z81^wva81+ZN0dCj)wYDV=O>V%{~=OD+WcOdfA$?daS-<b9S`sSqwc+<n(Ur`
zQL2biM3gQy5RfiX0wSpN5<rw*rFRI@q>F%bNa%##yYyb92QXAYx?&&#0s_*D_W|Gc
zcfMzxwa&fk-gVYF_b<qkC$r1Uo;@@BvuC_A)QaU?y(gkDlc}fi;siOc|MmtyOA>Iw
z+lack{7sKFAE?m%!!utz4O$K>VN=)^9?;S>I32i$S2<L={Yu0GPDzp9{N%-3l<v!9
zWD5?W=EO~j3AlCneT(}pbxl(|(lak3uqa*^rF(L;aW^&)a#Y#Dc9Ov+1VN2iC;~T+
zAMt63a)!8aL*|@H{ca^V3qkZj&f3x6a%6t#pHyj9l3=Sc!au3u&7R*GIK#{{G-;=D
zGaMTRUQu}bP0EOwb+<XS@IKEa4Z*A%%+*b)$hO<bn+BZ9XbRolXg#xBqGhHjR@Lr+
zsov7HN3lSbdr?JK3$_t1DS1m6JU0n`IV1X7T!QI#C`j=UH7kZu0y0NNdN4mB3_I&H
z<c74!r@mENWc>`9e!To+%{9ma?+b?@uCXV-z-!u3xL_;_r7I37+CjOBmj1XDB&Lkn
zLs~{=ajHC|=XC`;7H?aTg?a{+f|#u^it8M%q_&HxSKy;aJn$)hO=6<EN}rF8DQ~|g
zr@E@NEBjM>;+;9Exic&=x(rd16L<cE>9U(OZ@jJpH<Lw-)4z%4HKrij#oH^MRa7Bd
z)dZ4d$D!ytZr=cS-adNeEfTL8MC#x+04J(-Z3uWnf%<GeMQq&s5CR#Jn?f~xAHaW>
z$Yrf-vofFpg;B*t0L*mSOY!3>EJYP3BQbOmkR%X(R(>+_JtLD^7(=bCxinp+(L$^X
zx4usz{^-Hr?bWXS5u=#&kd^D(al-#aRyUKKz=7ol_LA&@>_O1`_pMQdNSUn<2;goM
zPSq=zr5(Ggy;+Ui?OnHy0~-B@L-@${8zv=0iFTUIu810Y^K|6hC<fwF$OE8SPPq~_
zAVzbZgNhh3peDJ}kkPxmWTe>%{dyUlqL~I1_k16?@k!4^EPZqFSp{w<2BNd97~6!>
z6#*D=PriN5giHFACzl}wrHH1&(EkUmDjqh(J$D#mVw|!jtE(7`8g*{nN3+xDcgD}?
zqw@Uy%JFBT1Sge!=YGhMjm<yQ0uekA{dejXZ0sz!rE<URFAuaET#C=QyKC1?yRi4I
z=(YGgpZlM=(MYCZ1D=HN1V48bbp6_zZUzj#^Apnw;fIwJCL_6{Sk)dph~($3CRqx(
zSR<`p^Cl$e`TmVkqjr!c(e?=kxKOe>0htgrFKEDUoHz_K=Kwo+s3!tTa&h(b;4zK$
zEh}`)i!F*+?(?6?NR~K~`{R;Au7QO}%wsHOQg2S9Yl&(0?3M%mX!~2K9{@5$cqVuB
zhD|FyehL!lFAU4Z?~McFT~5m!kn~DM=66t=#D)zk3809`R11WQMz!>xW$V@HQDon@
zo>?Eyd8?x#Q#uvZOGxO^%U;pL?x^9^x>qW0L#t20D*~_WK3@2>%R#A-QR>eaqE*jM
zCIQA00!K#3>VR;t)mp}9vk;HaQWnOLcn?@2^JG1N&>~YnNEs%6DSg!lxIk@jI--vb
z=<g;}gElN}rvr74hq(S^A>lQqx4q&tZ=JG(8K>+9l%U6@B%)7cWbV%~=%Wpv^&3mv
zK)7y)C?j0pfvcACA1qIrf?Ask+LO$^U28&Lrye#r;YF`nGNHnvH;4*+ny#1Yy4HjH
zj0wHnWA4!?#TM+d^-rMfE%mUkP;26Jy2SG_k!CBY4xxrXD)Z{kQqAW_EI6w`PS9)x
z^Lctj#0aZ?V9EJhh`8RuEKEsoB_3IQ9j*x^IPFn|0xSLgoRdEKYc1}?MOFe5?U>vw
zH%#e##1=5xpe$fWynAg_xdKlQB~63hB>y(e)#A&u>JthTQZA~Khd9YyX$p+4xC+s@
z&yM$6FFs85PYS3e(O1PF%ktL}agToBe+5@QHqj#L%gwVHE#)n|lZ~mRV8^m%VQM=(
zT5nKL6x(+=K*(`KEB$c~^if`fedlBMvyz~ln}*Hg0>AvqZ+`j6+sO^%`g+6pmQIE@
zh*ZW_q%4SgP=@U+H|?2oU6nLg-Ty<kG`#Fu<R>#l9L->?7dx1?lEaJPR6))=#%rJ<
z7}ex>%G_jsN)MuGp*U7?w;tk><%Bo@b+WH>LN>Q=XaalBposU-F{+ar&P`#9+W&q_
zK=I94Q7)wb1IZL_F6O#(P@o)1Y$-0!cUlvH!vVvrP<y}KyhMt|lbS%WzSey=w_H#X
z4%G45cpg8o%F4)|DjMGr$ueSr5W;7;QJr7P3@P`35GjNPI&Xih1h_z<1f|!2@!=^!
zPxYS?0VzqUKP|#Ch(u}|8m7~XVdtJqhVsJ^p`8|Bb@^R^55Yf)no+sZ{!Cr8W#3p{
z5W2>Y;AiJb*xHnIWw>kN(;b)o2%Toh3VvywWkV{EAT=TiG3;c4u^m|O2hdeJMq@~1
zQ5X7l=O;0B&*R|}a`DKB0Qkg3JWwqo!Ra8;Rf=b#$#0%`hypySw9EG)F4787Ln|ir
zZpXCtWL|7|o-HsxfXgd}`0S2_(NZbYysjsJM72Ml_{=K<MO!ltV@e5GgUm?E!*b>n
zHGk~|)FYq2`yZRxyX{=URSKSt@isxC3@29+u3bt{wd;0jCSrusl3%~7z~BzpmNuL>
zkEE-)`nxlyPAS6eX&8v~-d;NxeTa8CuPRXECosk!8+<R)$8T_uJSxz}=8?m=vd@W{
z;p(v2_i$GV;7=x8Qzma0n9+j31yE45t#JjD8g3BCH+sF30fln(q!)N1<<82Gu87Vs
z(;xV%YZ8e+qu+P0Zn%XoGPV`3)*1X13eW9)abfTjR^h>R3Pfy?J~*{{mtj2E`it8|
z%CV%gu)!s+|B@m#6c_9BV@oGU`b&kyf<1yr&k80d{At^n26ApOZCC-WXOIt`iI7d(
z1#L8<ty4q<H|e(h;qtY;f<U8jQyku(KuJ`c?|22f*xPVKcXmag`He3M(a2V<bXD}Z
z`RUYW?*SI(-(if;^r6QsFlBUk3j2Vf<pVy2rj@UMj%ZVcZ;<MuxRjt26sM_eE9SGL
z51RwmQv5KcIDVZ)DMFC+YtC`=xF+LDXtryuYE#W$oy@*ce03zy>Q8AP{Pnn<>e}dt
z&TWCSqQ703=$4KriN;rRU1Jc~c1bND14<CUoTMQ69+3R^!HVdi`S9gezhBjV+(tC+
zmRo!`L!R`3@_Z0Azgb+J+5i=KhaE^H;H#^TZk>z4DQlPIhBVpU3rG@13PHT+8f6dg
z4XZL^CVvKUAz0UFZ{jyj^*^;Ukk&re{K~w>AQ~W=7LeVwCk4hfRdLc96L$(ft#&FL
zX(h_=9w2*IkP9-KBWWArV%I;y{2op~=34K|GCH_t;JjcHQ9w6^3`75&4EiA_$lj~s
zdghz#9C}Aiw~K9obZRe8#Uct%yaN)g-F_bK{luK5*~7ZN2n8@~XM=e9nkB9UI2*2N
zTptBQN~aOS042qrwlmw*51dRKE?j(894!<k6FS#?t_jQV$IUtnw#~zX*VMAqXg<?|
zg#&`u(^tleeH$UFJI<3+i{XK00_uiW2R5}=Lal1A-=_nn+1oMTmJAeu<-460V(fq8
z89Gdq#O<n~Z-@Rz&ui+DH!Cc@z{-5!n)*<#CtN7V@a+Ag+#P}SCQ?jdUTK0#PKfGM
z`AfEz)9Su5AXK>a&RF>Q0ZMm0cBYtx8zKvc<SVn)M8rykHH>V&#lsLFBN~iSbi{!Q
zx%~Rd43@6I(VHQE=#u7U_gMj^sP*hIL;^g!o<+xXis5k^_&FKt|G`7FTvbMcp>O0{
zfR;Uv;GLca!gs>8DD;fSyGEaakKR5npQNI@^-B9?66<>jiq~v6{9aKG<*O(sM7FK0
z4(|<Z@xgoj5upYTE{liYIRA_|pPVE`2!*RnQD=dk!`!VblgwX^sFA*w*`NO)N5>ko
z4;7PKdsW`r*6OQS6!#lZvp$e2^Z*4^1i@h#8h+?D26yeB#bXFhLQZ&}R2(a}sM@Id
zQNM(g#W~a2niuZK^MkC~j||??(FS44#H&{$Re;o)1UUVv!MwK1IqX>Z78YsHN(Z^E
z+kRc^XfQeHW`Z3E8A7M4mrmcF_zdRfJxiCIB?nhSm$2EP2d=vP%YJ0)p-UCqbuVRS
zpUh$muKA`JZlPIX=Ky)m_U-q(h`Lkp$wIktHFWrY{AL0ECq+mL=19Is{T>9en(^Tb
z_s<N$FnmR?(15J=<AkluFUzmsG`ja@SkJPuV<l@WaFv!pR&M&6p)lsTEV(`qL_XgY
z!iGRHLvTB57DC*FZCv0&Ee=TpoXWhAZK|8FuXY*+l%6W+9M9greRbY^J9Dn9suPcC
zbxQ~5eND!|DTOa9x?GZIi|RUP;TB3Q(NshK{AAw$vj)ckR0uedq<|wChXjC!mP@v)
zIc?ZuPO%Wo>tIY7E_^}?nELs~lcK#&TeA&|)BaW*`8Z@nEK(ToE#-miofn$V3JHIO
zVHjqPR+v$`xL3UTr40h|r$!UPYpwy_?q%9i6p;JWh81E5_?$5SLt>FQs1%O>{2HkN
zgO+XIxIOgoqm{t!`^!E9wJy@V&mFkPI>jy?fq3IgCC9^_m$rPe=q;naTc(imAk9wX
zP_SoV{;doLM~brNO1l3`C$Iw7e#z=7q!PW|>H|mkAP_-I%#grq2%+!N9f&sP7sBXX
z*nK7QeDE_=a({!(n&n-mN9;B{_aCalF7^j@W+d2EB~1SECMz66K&OQM!ZbOMm08l)
zuj3>Pnb^@Ddfm(qfqhZStNMfw-}TD2f$vqyNB7HdqfmdjAbl4_{1Ay8u>`+51nX~8
zyDJ=b<)MeS=%koc?{MVt2FEQX6BXDZ_9k0~5-`X^9rnshxd-y$*gZ-}@t<noikmL2
zm&Ui6NiCZ2JU;jSgJ^}ho1yvHUF(@K3{0PRxWM4QQh@{HD;>3lExMg8a>Q&Ts#&L;
zvJoL?Xbzv(wHWGs{aIWzy;Y%#?)MNXR7TM%x|i54vCBSa%AQ^q^slg~z>vr<uGB_D
zDqFREp+~8Dt>tHDShoI{sKv;5I3iB*k_Q3z;gr@7AxtZ3CEZhZ$=9l<Yvt0l)E*?>
zKxlpwO==-ejCzUC>_1F^?-f-RulEEL5Iszt?g;<}K^uKG-r_dIec-K~#kK`_*}`nv
z2%-HITP_G~3Wfm4bXx>)CIq3`gs<Q4uo(%SLwh-*iP-Nx(nTPfnk<I8F!m7@nymCk
zQA(^7`E5Rj@fEWAp@r`Xl+ex?NxoV+p<*^ao^tnA5lD9&-~+~0s7;X3{3mHw*=oCw
z*IGQ+g{mBRckC5;GaNlKAKu}>dURnXn~#EIw8m=g(>~6d`VrBV9&UbMzjY_|K0r+P
zD>QT5p)!sY8o%Tk_bw<kt{XGCli2WI)L9{catPN^?mJ(`MJi#L&ruc9#f?_HZbpOg
z2%%CqC9j3m9ge~`qU_bFdI|75zl{^7jFnz1gciW>J=J8l604qV8=Q7q@XqfxDvDy%
zoF%iBCqvZmo{ZxDcbs*6ZTzO?o<`o15vzm8b{rw2R$)s$$*gYq;U|gvN><wO6&X&b
zW3F#yu&R&u^t`NHnWNaJm73FAxgKi6LfAH%7L1W6>4irAzuwiq(LSY?D7ptxfu`5u
z%W(iDq403-0ORO`yMi1mw>_YGKZD_Kl@+}kRV9cv6IIdczVnCsK>`rr^|Ie*Awoti
z#^bIH%6gc`*-h}hxq)pTqvvW73&OJ{_ZAMO9EpV4{FR~m;{UmJ6wu+5Ib?V1HoyF+
zNeIe-+}HKevnapE_4#g|6|rg>aw)3S9+t#C=kX{kzTuOb!0zGIx({epfM$II(0)}d
z_}{cIHZf&%^?Oh6U1?)!LlF4aG^z=^yfV>MIUI@k`O~{fuPErRJVjs#E*?V0l2hW4
zMSz~U6fxkD0@@!BbqwTdwt5U`Giewe$jX%!@&_M;9llgy3eW(IJrso1cHjKd5G)q`
z{b)lHHS_$_59=hu*Sz5;@aX#Z<90a>31g*KY`29>U->b^1!=<wh{KL<o|V4Qj=GV!
z$m(L38Kb;aczX8xWF-StNFIx9`&ZG?s)DS5#=V(<?f`liE|jW(ek=_;_^-}9j6?bc
zV1D`n+d;r(Z>?D&r#PsY(|;A`m<;Ry7hZD+=xCs7%8@9L%xQecrNwtjM4`?*S}Q4i
z&Y&H#C_eD#OS2+6I<9}a{QW<RR|?>-e+JUbef?AC&lI43ZG}Z$v)S=l%^|<U#Ye-I
zcl&Y3bT<AkIsdGhBQ`kY^#tVN$CyIy<M?RLiRaxzoBOMoT8wR_35eWiJzI-X&nEV5
z`x-f;H<ne0B~yqbA^qQ7bFV>y1;+Vh_wVf+2VpDJ*)$Z;v20E!uP7H;5)MGE8@7yk
zo<_I+k!@U{ktF15v+-~=w<9~3DI2-N^6QUq$_SgZmqYuqX@lhTTLwm-(tLd-KM49d
z47zihS=8*EBnY+3rmBgJ|2Pw|1>Hc=!Z6yT%BxArT1_IRr*|8w+5g!D1AvL|;wb#I
z{#J6kD2^0*-+?=|M&ta$(mLFjZ8M_I)m<$}(Zi~5KEXFSPdf>(;di(CZbhw`&ZNRa
zxDaOGWACh_NC8U(mt#wT>#M(8K_?(6*$CH`EuFGlIl25Okz6nD(`k&oPOedAHJG-g
zsIybuq*QOQ(REzKD}*jg2^K;JQGoWVPH*hjT71_s`%6X|2*clc0Lfp=lB-9#oYT$l
zs?Ic4l);gL5~B03CCvKwTkP9@>5PYdQdaH`%UAsN2W(Xo7lawsee)$Le9r8jWjiSc
z!?ROD2OUU<mX%@{#z?)HdF^MDsv`07J->eNk$;76JFCw_)xi<|lI1>XW^>>s72-xc
zPx^dzW5U0j4#N;k!I82O3CB~aFpkaT`Vp;0?>=6MP0ZwQG~!}UQFU(O8Jnu@GTNdR
zUNaZBl%kJpwOb6*cOO!N{`dVrRqIyG%fd2THUlgXt0H|EFxKfn-z0!8%mC&tQ7vTe
z4n7^O8|<Z!`d9G-wZsE~RL3^TZkf%?c?^o5ksplULT3X(7am{^MUE@W$j1sxrdM(!
z2!|ww^j#J#tVR88=Zy&-Lf-HXojnR+@y{v4RO~4N`Q8>~G2ed=a$XpTn!StE=i55|
zIgdTQ+0A2gEkp{aS!WB=nnRvg2ptvUqHG5I!9d+IoYx@8H)8}M*Tx#QOAlZb39{G#
z!)31nSfzk2c#I+rz`VPmy=$jF>X#AYn1-Hv0@*Ymrh~%pm?7C+K99)<Q-&VhU8s*E
z4}ux3z8G}|^a_gjE(eT_6DyFEyO9Wc%!ySty8iDkURCg1>-8B7`qAtJc}N=%s}UBU
zd5FXiNW-4G?X{9B%?t&W|7b>(``nJNPQAIA^$k!3aLN)NbSquF%YmGuJn6xMB|;05
zZLIC+>#MdB7*{5>)O%K1g{a(;Kka|ZdUuiTbMIls70tRh27k&)Cr1c1RW8-}MALy-
z(UJ-`Q_(pipnqbKqaXW+?SlBx1pBlSzMiqi=WizS&Usu9$IrE&_;Oe|I#?Oi>b3!T
z5*REZEIW`vVO<0$a<X_&?S{^-c+sUT4+6iQe9T~Ith`?1?_}l1XY00nzV%!EdFD=G
z4J8yQ`Sj|?+35Ycw;`vaqPLyPfDHH`@WXh_Ph2>{OzrR2AS?2tkAC-J0r(^{&dHx<
zdX1B(D|s0(yey=E5`>%+ydO`Av+_=3&`TOpT!_Ts1V=tc;}0rIt`JB=OygmU>d<vA
zFwP)xZv0*V?712QR~<@|k0gpmycr}8fOT=MJjy~&YC>`1Ddo7Zxc{q3zdILa#sxCE
z;r#yNCYtj*`2X<k{?8|w_Fy9bD0zT`JNjpO;r%%E|MuDapLh3vA`{n7-J}6PbpXzf
zKO2rRYc~`AmkLAh`H6=O+f_-}yjW}xSt<#*_i2@41oimW+cqTeWQlx%T1uap$;96`
z9-G}_kYQjzo<&4y@Qp=aiy4qjhjxBPe3@2W2qXlkJF@rJ1c>B<3lS+m%^9yOh(JME
z*eXzdaF`7O+=2iL6aax8^jj8g;7_U(poVY;2;h_hC4ko6h<5m*6m-ouS~L!6XIv`M
zlU6yA1*ia=usEbNA4mzh9m@SBH#POc`$L$&y~)YzBaJiOch6PP(=qce7HR@v+jwk%
zWrYI*YFr%0@<0lg^<K$Q=5Cejj9qhY_x(U%Srm8YH(V$o?lAwjK6tj=ANRk#e@hiz
zV$7()7Px=cSE_<pT)(E_V#uMy#hT*tNV~3Q1YKC1CG1-J;c}eX<4=oYhy3Dp`Z8aW
zXlVi6_mvm~jwL5Y)YR~-nRE}^>V3%2TC=iKdecD7(>xUiYIxvK|D$=zf1*4AB<!cV
zRPwZ<xkQPj^>of~2EKt-gtb`iG?)}~U=3u)55_+=pNF-glvp0@{vvM_9Zr2Z)LW3+
zE1(D7$S{oO8(}{;6s7l#h^w4wNt@!x;l%02nhQ?6nuOlAxt>@Bs^icL4!A3P`)0Uf
z`16istenT)*QQ)g?~cieI{v2i27AmdVxT!W`1yN})hU%%uV>7(f)IcojPn7oVsG`q
z_sUa$2}nt>TBNJ;x1Q$6>68=;TKO4;PyT*KV<EYtLb+s7#2~UvSjttMU+#5y(v~{A
zmOz;D_$J&6W~4AZDqu&fyk-Gl{TGBx%mw!oJXwVKKR#O^oiBYdv<5$wL}m2CFKu}q
zI3?%1F$~hlriE*-QaJLEHM^ol*~Qo{J&##rduZ9_vlEnqPm5`-IDgRy=H9ad6~*?J
z%wgs?6?{~NI#LKxf{^K#+kAr~5W_7+NuK;9mlI`obU<A1q}nxD)}eaK!M0_jD!SZg
z*J+Yt_!a6>kf>0^ce<=$j&`L&XYcYBU06TXzniRu4f!4aGz3#_azD4&6PF(ypI}4H
z=+XI|9Q3@?HCV+C@Ip4k-^*Z{*HaP53t(WK7vI2z6s3M1hzS)YMkxtXD&=ec&L9#~
zD{ReFgP5rAtM~dXBzS6w9?IQ@k>&hm_Mk$gut`C3e>1X=Nl!0eekc93Sk7!4^5U1Y
zHV3)}?34o07=jz74<Soyc2&m>^Z+Iw#@v@$4+diso==3cc{x?CyoT>V-^5A)MOP9s
zkq!Gk&2U#J>_j;OIP-ErZZ&d^v3c_rB9L}#Pqu(L2Bh=S7?l0mF_f~J=@2@Gu_t<k
zWNH`kvf`mUcPd*Log#8UOxO%n>4FNu9hkG6n$x8pFUTcMQER+28MsCqWUrTYOkQ?e
zTjnU7NHn`D*^3iE0RpRE&qe7@P362N80sdU%Akz1T>>Dv#c{uiX$;#eGddXk<R&ba
zXFvKvob)RFK!+;^vBW5d8x=#7!O+az@5LOb;!K&mY*UnRwfD8gh#4{0iRMx9sn70e
z9PCyHJc%U0bi@J<0V|Cvnml_d$$T|c%MnHCplz{gf1Bg8U%p@~wpU5hy#I_NEIx*H
z<RPl(<@r3q1^cr5^Z^&7@Y8aOO8Go|FL=F8VcDW>9`Uk`9yI|JkGkX_v$vq(-g1VD
z3i_um&*c*C1X_CPjF|YM{<;2m1`eXR?;vc+uCv%-Vb+r0OrP8=p!V#Z<?_%syS{it
zG!lu_wA?9H=5D}5HkTyaJ!NVu7f*Ubs{9Y_(&h(jL?!1z7z1JQnHrr3fL;uch2aGY
zBqDcP69@+M1s#ia{M3uXTCeZJi{n0d+dd+b$s5!DY!?HhV!a3|!;~3N44s~2HLPZ#
zYKkbhJ23G-qUf}N0ZgEpMOXFyZ7Aqdi5OK9KRc?4uaD4G{XeJe18QxZXE@^S!|${Z
z^@)hM<Ckh><4<35a92iHo$z3KISRQsdtX*1E?dS|(flF#C2OU$3@qczW#SE}0-AXY
z61`}-!{k{LODzS_eLyA(Fa)r&<R^_mR^O$lWP<2Wh)g68Z75JW67UoQ+1B~tLR<c%
zn5D*=qPVjo6bkOr_}2*(%YkEhD3U;6=c0cfD4+p*&E*I1F~IoeG%ip_2nR3gPh|^5
zKL3mVomSER>wWuQTkeA~5eOK}^?-|xExGvi5C6Zl53`V_9b^i?P;TLz`!7Us`m+2V
zZO;DRFs|)`ooKNtkqbj8w*MB-JJuIZ4ez*of2!}o@@kDKSnl=rr?&a+@k%^|Pi$ZO
z##u*Ocrdb;GIUYWDy>xr2byq9U{lzJ*M*h<fmMJO0TFA!mgz-YeHdM}f$@oki3=B^
znp#t`UR-%Rch~PBK~@un(8)CyEC?dnLLAP1>CLETb8In!t*p-9<UCLR0)e7~_Jas|
zf#O{NRShXB<Gz){DsxPkwrG1tRB4-Ma+bjrIbmB?zTWifY*xME6ftt(NMyXql~ba0
zP9q4LDyL_YgAQ_B(a+~#jYKAF$h?a~bX|Edlf_P$GUMq1#hoiRZGDmkvN0_Pd;aLk
zZ&Y&V7iP2C-0FS)Hs|W~;lZuJktHu9H>V5F3*H^_o1u2+m0m^;9ba_X5`gtDK#L-l
z{o<R7UFc5_?(x!UfJuxr^jZNX5DsM=QvK}+1pCc-^kn;*mz3(q4RUUI$f56o<j7(f
zpI5SfK3C%(6DMwLMkwJw?+185{?OK1nvO22Y3_1ibGgH6>?8S_gcF}4Trqum5*<o_
zMoI=8+!=}CO;bAq*$QNT#yq;MhjtNjkR_Y}LkQ*bM|klEVhBv|;CuY12fN~$H2p4T
zuHuq(Q5cY%5^ZqMZQVvkg;&`!iY)QUd>TCFE7h^VQR)6)?a@4aGsRp8Qu<U{E;>y!
z)}%jOLjCEGM?%m~ird=fe(5P*?n^VRr)YeYxxtkSd?9vnm<n{0^R(t&Tz%3)r~w8u
z*;?(de`&`$17QwLkH)j%M((Xd&E~J$-wT$k<$VuXsKp_5C}S2NScU^sZVa?ZtNPU8
z)Tch#f}Bc}xPX|x3*V-<>V=}4lFsS?yd98r0Zy4*pYoj-64AQy;)m01Ih_Y+c+D47
zar%WI?&W3$MC?`c#Vn)l9P$;u9yR8T9Ihqm)BT8Wt?W?wudcYiYNg})o#<NQuT5jR
z;~$S~IH$iNF9eXUJ~x|E2;7F*=69&ld|MVfbi0{>ym@Z;-`X$zGnR<J3_Di=f(En+
z^q%2dN*_%GvZAU-(o!W}#oKW_0E-^C9)L9J17S%5oJL?DAU>1Qhi<_vUk#xWDY*dY
zRR;m)@kpW&6y9TA#Ezmwz#e~VUn$FY<dupUj@?VFISv&RhUcvbU{G+^F%)8B2G#`#
z84l-dz%j5Injy<uN~>A4{p=8>5Tg<jNx9_#q4vr!dsrdqX3LE_=GRBdKNlA1P|_<8
z5sZ7=j*87t2z_J<3(08s;yYk}6*<1~4nwdz19JySKI~`)*U8n&^$~*)!BDhWMnfj%
zER@$ub4qfrH1xS|GICG}{ac0%fUB6-Umir^5CYMGCVL1%`nfRUy?BmjJC0|4#D^`&
zC;1POfc|eM*6?n~%BfFP%O__H&{XTgw73)Cr?E3vXL)ykI)0ey6(3H?lXhI?YfMDl
zH^Ph*D|6oPhBMA}fX+F?fBvy7J#GDSgr)2Y&-FWW7|~Jt+4QE=yK<+2*UH98OpUwS
z*}-k9y0;<kcN5HK;bNhhFEok$W%sY)`CkE8!<*A^B&8EXLhnSb1jyXhoDMCdZi~I9
zkxMPRpBZ{){%hWkv_11k9J4s*C>0*HKij_3xa6mQst<EqqTf2Q8QAD{9!n^dcXGUV
z^y?Y?;7d#gSqkrE)I{1e7AeiBk8aF^o^M<$JASJ5Yw%%^w3gJl?21IODY@2W?%FlF
zlUs+syNT17?{QsM!aqHF9<Btx@0Qeplz8OyQEo&$>)YNjB*V=m<l#~pfAO-HRi$UK
zDhQjWkCu7!89tk7{3zk2ZA!xpbIItui<oE8OkAJO-Is@AiS$8)>85FqKS#JT6K<P@
zGEH4P#LT$E>3@Dt)m`h1QP5lQn!f5OP#Vcfl3&J!rZe*y`gKnt%Zs0~<y8^sGOwsv
z-%IvTRmr}Jq93~Ff|dAfxtcStm}(8`Ql+1e8JA;M2;Sv7!%iks<<I7L`cdy?AP;Lr
z{~WHO_Ru73U<!(T5;<s&V=dAH*H{X#y87UTbRAC;c5CncucKp0gs_k1+kyb*{I3gX
zFDN__#ZuW5R6j#kT7Ra^PNo{w1w7|IgjKv;p0sZ&d$0kacPG(U*Yd?6#r#{I<71c1
zG4>5B@p1FsrrBA+w4b{{W!g<xpiJ0aSs2po86STBWZI)&tdBJ4cQv!r`Pe@^DtBX`
zTBKs>*N;Gs#S)?14&qW^>H18NZkC!AkQ|hib)7a6DYIY=iICRoC=)iR{6sDq=F{CB
zINnJV#v7e5Ho_!1m@@uTx10E!#xrL>;!#1%lXkBkYYexX{P?ZRFs)H5x1U@~+4nQq
z(-HReGQ?ookoW456;Z7z8l!4jE1|Te#TPqI_MiEk{YbYu`T3&zd(eCyVkB8js}7y^
zts_yRfeMn#LJseNkTn)emYocsydfwP42U(ysB1uRGB{C)^hGTi&+Fd1+x-blP2QEQ
zA=1a!iAN%a1_$O#ySPMUpd(K-A`Yd|FKnwBZxr?>y50=fSK>{mvw#0lDasu4(`bkb
z@rGX;LEg6FS=ig=t+vjFO?rkdSX_=6I41a2SHzs{#t^HMia4KQwR(to@{v%1M7s*;
z2zUzqaX?W<rVZE@i*E83lHc)NQcF!vmj9MkDeblP^)uyH#}DUXfzoCel{C^Cx9Pk)
z2$ipO?)+FfoFsNPf{le+qVakk2WHw5Ypy@~eQp)7xn3KzD{2hCO**sBO@-$~$BNag
zF|DuGJF(YoCJQf@MOzQth8#XcKDXu6ho0QNlBe5%Jn_{cy{;1pkm}$bSF#rFeTmOn
zIsEXTpw9=>q3C-YQ^)@{CZbUKek5`mr!qfLNa<enBCeor@x*OND9)Geff<*fls+qu
zAl=0FyTELFZ!^3wZr|;&hb$2=Q8NWZMyE;LrG`et3DH%{pd)ViCRAJa3j`S2L%4eW
zYXbHX_ZV+g5-@ymyv0%dq~HU>(J<@bj>{X|m%5;xDWObTdx%9xmMpP?KNxoK#|4AL
z;E3f;-Yp~H)43&w-j76X)+0ym-MPQa%ZxiFlDoU>K1MKTzzdrqg<!v$mjCgi%n*1c
za>^Rh*pxgF=FW;`$Jx=a6-nf3!uj4k(3HUo7KGiV&>%O(fYw`Acv4oC9+6Qw1(a;G
z5tne+K&`qO%y2jYJjobS)F3N;lp{Dx6I`yDCi1+6;z%98PxHs3vc+uP&_mHU*YxMg
ztI=i{`^uGJB7BMpQgb&5O~NT-w3FP5l_VvwI6}!oVM}<$zhoQ+?t<gSGkkX2w#rr*
z+&<GbKPmPC3fY8mFh-&L!~ku7H9?X=fP{QcoKQ9Kk|WAUZC?+<=U(efe7Bo-QJ%&h
zF#C~J-Ro!ymr*3c`KI(MP7^FBRwM#RuJ<1KY_bTV6eUS)pc(k3h#9u^-5UT~LyRFs
z*0}^nmM;!qU*ovBgx>H2f*DC#s5gk;NS{$e3P}nOdWhS)FK!PBE$TJ1sQn(GdjG??
z4BFR+AB+pEXqic68c3M9BP0`#sWz@EpbQk59iTaE^NpMoKcCZ2JlU5h3pVm}C4$ls
zf~)hnZ0p0yBTxHinOIYtpKMCSQ4t|Tag?GV0<iiySYp8RtO70}t((C8cw+)hZeDUW
ztA$wO6<ln$;a6YJ<%!su7~W35)pas`^mloX3sjfXWaAF)?pMlcrpsO7iftpnlD6$w
zvEPzKlO{RJ`SYelxYMhdPh>dqH7C_T5GrLa^)-ZE@7ca93!~<dLr>|Wx0MUKS-fcv
z^*drNuG6V`HkCQ@g(E)^#<|{tpD!0R0m;b({yPS=CGN#EEj1(z%#biKO<E4k_@>LF
zEsuKiPUu;&t4++TIe+lkz#87XSf;&zxV;9k=|iS)q&=kgkQKZ5<?iI1NE&VY`yWib
z1B$;Z>yY~VhT=m5$1x2^Fz_EL5ivV;KG3AT|4b8qGRw)Kuby?;pz=@1wm&rm#knCc
z-51q~6Z1pIY>~)6o<p=ILy8U?ei(+Gi8$A{&ujXbVLnp|fXdDBr(^?<bxgq?z%Gb*
ztpa$#b@_V(DUsyoPz;jM_#!>I@%Yz|)b#uA(x2J`Dna1Bz#lCD_4gNk0Au}ihmHf{
zYG~_$exr`^<6QUX@+XhaaV(3PZYhVBr3%U%SXQbIBSlQ$Wn^DEbG{fQ3wcw2ist(4
zaX-><y+#L?lsZY2y<a7vLp3MCa`cJ#>*<oLjEp~}96ILbf~qpT22IGfL&c9NaAx5u
znVwtreX}~vKMGBFi`558Uc{H^%tlq{J)sVNk*80+S@5SZMM&xPex9xTP~>5qfFq1*
zd37}ffm4PwSs~y{M(V@26vQ8LuWjuJquqM_Uh2UI10PD-<kCH6WbeP5CQx`Ma@sB|
zWF{lWcH3c8gS{}xPUMIOM>-L(BKoudi||bqP%^ozsSi^o2Owjcv$Oo*NV8@M-Q%KD
z&Ao4u3I3_~6iOvi#P0Zw%Ftwhu#x*;rW#6sJdc=1sa0<@*y(+!aF~^W^X~-8GGu2j
z+|^iPKGIZUGXtaI*OPM8DJ~)`x%8>$I};eC`+@OU9LEW8cnwoqBJ)Ns^MP^9DNw}b
z_A3~^LTYZH9xif28LAz$wSR_Dc?;8e@=b-~jTSoToEtl<uv^d$#JO+=MBtRb(7D0R
zRhaoHIe4@<9fTjUF(pVzqJW-x%BI0obx-!?hBEp9mDX$pIEJxqfT3HJfM`J)l>t?<
ze<^scyk5~B?>gqj+BK-up9<|D)yfQ?*V?tAW*mklu6|+)1Vs`sXEEVinewm`Ei^h)
zTvP#?7ms9@MOcbF++-~CHMdOAf+KIbz}8FMe)b>oFEEJj-beMgA9_QUINHymJCbTF
zFEGC|32_Ff;FOK62@G}DQEP^fy>}QHB~%l=wf$fhBw=Z$B=zo1ru$H|_K6^q)vI5V
zKW(y1>9v0?*c-ZV@U6|{r61_rp!$@A3>v}1F~5N#Hn3QkuWkMMQO^m+sAJ@uL|M#U
z$g60pG%G}(FsO=R$6}B25n<!Ilw2ixXWl}wLp{`6EAj9R1%KGd$nuyT&HxS2oxwpx
z*s4(l9GDdaUdW~<i0eyrwCMU5UGA7vUcMF#$&^04k8Y>K@N8~Aoa|E<_W7v_EYa!{
z<aFnWzIZLJ)s8hUjyVa6_yde}`?9b(018eie#t91Srj_;8P&tZJ;|NOIeC9o`;8N0
zdks(p0*JJgAqTuK>vOkZ7?5Fv%3%^Wabj<Rz@39(tzdxnjvNquE)FXIY!T23nD|;y
zj3iJH-9-qc8#%6weg&K#NME9fxWT&_03VMU0M7>SZZ4t|5SZ3FpbwX!T4%uHb!IO^
zg0Mh=Z!PGVDHi=E^*tVEbS4`r{@VR|TjAm<Q{y{&B_<Zd3S)XzFYhEZ-#XMeymu%m
zm#Yv@y>wQBR{31CS;U@_*SGgQFwJ0bYeK5<1ptL)eW?x$=Ly$>vSa<zka}0}$p7!p
z5)Zkt&4`#G^ykL0L+$Buq&h5dhYzyA<TZzSb$4a@z46^fGRIn&k}A;p`}c6oyc<T9
zNLeF33GLjDqfk5cLWyno#Ce=mV?ak@aVZC+4yNSr&#OJITijO8&-5Sq<P-hcChxty
z8MPOQJ^kj47CP4K96alrI~G#Sj-bXdFie~uk9k0;xr0e?XHda0Bt@;|7n(d)%=2)1
z^N4A^lxPwAjI_qN!u9)3Q>YS@+6aV&W81B5d34s|s9&iVLoxJ$!pD+9$BE6DNrKUK
z!GsK>U}Mr=)*;xBS-KquoYXBf4i7`=z|+<5-@A*XxeF(+T>^YLA+nFK?J%_S;;<{5
zkxY*MxX&AZsqO{xug+*{8WDkk8V)x7L!oATda+w3)sF-p@2~QakbIM4Ld%l1x6x<z
zt(kZEgAJy|=mt$RCx1J=&#0-K?#t|$`>qLV{>2I@Z0uEW1ycJjLi!hct;C4!S8{RK
z0wD#dE(q$RDwg-gcgSluTk3-PU1_&ErPn|0liTEWme(Y$o~T2CK3@NiJ~EE1j&|Wx
z&xh&nDEQ70+41jkJ|ELUxP4Oi)mh?Ma?dU~z~VDsKZCuiXh81i&w_avUZ)i|L?V!y
zfv_I@!Gf-H&E@osy=Z_oFn^4oLKlnXd-oo4Ki(yw{&4?xC6nbnJOk424_f%D$bYPG
z@*H|V$o&79Y$~04AvOd}&W%3S5u))@7XF91V{O=bo%)@|BzyM{jM4lRk+<nM0j$+0
zehq~>&zGXxk$`<jx9E;#a@P!D!fUi+#~PX>mrIM_F0tUmJUehZWDaP%WM~<(uc^u&
zX_+tY4yT+ue#14*+^O0L<%@Kg!C)BFK34Z^>+DEE#)Kp$QMx1yRnhYXUyZWZLtuK4
zd^v!PGBPNxWyoQ|9<A@pX9oFX$pl1A=Yr59JOqEBJd!ED67@hQ`$nI5)0pGn(Hk71
zc6RK2FyvVc4q7&7%1tx0{qaDsJPI<7I-Z!bnT;`kKw3Oa!YQwRxQ<*gdAp~Yh&Xy#
zfcq=d+lsHJ3^RWy44H0XFVun-lU`!hHGmqFpL|yyE|0O(tcZy>-DcZc%V4GdrMkvo
zqVEGyIlwjic|P#^))4&?q>9>k{$1|?5ALE&T6w`ECp@t~u7Hh<E7pE<%1s`plbyZr
zCON!jpqcsh6?-V9y&v6MBfxt^%<whU?k9c-y=Zqe*H;A}V@P4S#5;c#XgTuv+~rM(
z9L$}a)YCy)lB)bKDClb!=7~>SttRH*1(cMqHRhrw@S9c>^PybsOJu*U^L*mXJs9D%
zcPJ!jC7x4ldU)n|tb)N}hCvYn!Q`{k{EgtoiYKVGt{}{Bp;amDclBQR_Q5ZBvFw$2
zxEz(yvbo;gK5^0c4rAUQ?kL5aQPoR2C`WD?b&H|-%8|Px|Ed7HEpG6l49z{@Isk3X
z_HR8JRg7SKJh~jiWAZ?v^T*R$c4-6gVGrarvz^O&9hJ;D8D<uD&I&OPhdm!$*!Q&e
zWjk~F8C>;)#8S~X(c}K(-5x%<z|hD=JiMRR1!t}wVF&ieH7s2t<ztSYW>YTgFaqsY
z{%8B6H93rAW?urz+lkoo_j=elHgFLf9P6fbl?D+XyNq%t*$E$-wS;BTq8$`JKE4l;
zTm@O7W=*@#<B}Rg$zxn@NC%|Jg#>-5TYQ@q7iLM*@XHF2I+1^+j^!|6Vj<h8pXJLd
z^K1E-GiCb6hYgmj+`lB0pd%2}nq&6#82OO=gK=Xe=p;uu5?~WWTnQ(n1Hh(pYlN6G
zgl;#AhB!Wzxz3{bUWL0lfi?b_Md^z>HSs^pC`fI-_IJG<7P<T6E4up=esYx;zLyna
z<pyU4lYb*q(UXXT8p`d$kmVPyg|zol>3y9&EI853kU22`*yZHuzNv`R`vQ5tWxfJz
zU+O?YGgo!D6`o+sBQ}V#>D9Af%)4e@j6(b}b5anM+@4qbE1_w8AhdT6fmE*v^r6l_
zN-2GEGZaFX|7Bzi2qQ}OgTRwNjT~jUGFH2@f~CB`N`mR9030qLS`k__lOX38`QY~_
zb0KR}fj#ATQz!0906&c*4u)h~hBo;~hC3&NU7v8vH{G)kgGmB$Flk{prLD-90a1;&
z`Bo6B+S<uXgvQ(B;p5A;2;r3LQ#{}AN;PIw28mF)Ht0*umc2R?GbRLRmH=_{7PS<+
zv5m*Wm>rW*^xSX~@W1Gd=1P@9U<8IhwzxmKKSsjR(qeM7qefz)EdLxhk1oDgJ?Y%0
z9xJUdym-U5=VuIT;#q)UvtaVw<)FFT_!Y(;AvCi=7jZczB>*H878SP*o++iHl*jk}
zDD_*ouzl0AQcpMiUdvs;Oem6rb@d9kueqx?b}LKaHK$&om$E@vLT1R?o2xK=FdRu8
zfXUmqilpCBf*Ik!5gk9Ls}%7~Ze1H~xodCm^uv2rD%Nmo7QL`w6Uv*KdCGC@4_;4+
zwf|}3UG%)f0X^khF$jP?xH>nwr39hQLPlPJiQYX&6LNyTUm=LH<6UtFQUA|hXN(tG
z@^HE#cLgqbx_PSp?sU`PqxDsDMQMI~T@I%uCJ!%hN-UGbQW4H+7yLA;m-wXX`(rJv
z^Tr)_fgC0Ppy}P4&3@K-?LT+^`PjTM$2#xLbn_p=ZNJ?pw%QK~bN_ad?=<In!$#rT
zu}fUkqZ*GtB@6)?451^6Ve#f4&dv<kL0mx(bV!~meBl{1xuwk#j#_Dsnr^^0MU2dO
zqXS=*wrQBB{Z{!n3a}VX`F!)YEX8Jd(v7~WgN|6+l96z0CdbMLe-AQVbezAGY1!O-
zTXi-0d2iPCSZ7_Ns3KJ_4@y`*8!=IjwV$?`*k3WKc=fdTef?w&q1n|Lj>5iugGys&
zh6?q+uN(4Hp|(yxnEb`cRk?eYUw1dtOWL_2-jli!%KF7>o0{YbW5+U8=UXtQ^~SB>
z-`6uvvIgqrlL&LKsJ|DUyD3}I)Xq<nekC$xYl==(#n3m)#=PIJUKnuuEH7J9{+O5M
zFCwmD-;eh>uc!J~sUx%PEjL=6TrsjtvtNP=qnwJlW2!PaK_xwURHoJ>G;VPU)ZE0c
zewe`e9dox)d-J{ge=tB|KH$Kl*?o_lb6unbcjzyEt>SY;E17NcAU0qHe05Uv#m|q3
z{T$=WZ*u%sUJud==bp&=%Ry%WX$O|v4Clz(w~@%&zwo%~ZX_Y010BQ$h@}HaK#TA5
zv;->*77di6cvtO>Z|$-b71SiE;i>|*vjUWJQ^@J9Efs{^8UW=0Kd|ARks`C58MyG4
zroUpSZOaHED}Fz2?n&*ltHZ2%7yo|ID7|*rkSb>?%T*WVpS2SPtGIg7jsoiz7}PSy
z`Qj7xKF{Qd082nIfWR*r2KKVT6Ex6xvwwfFV8#!ALK-xUtR(tN_Rma912y|HkfL|Q
zOyK)?b$PwHeQ{l>gH5@t#irw(bTt{GoP~Ec#K!}(I61{{Ey$=7ocE@r+T@#u2D`Lp
zO1OBod16;1&MX768&K52-vl<!iE$}_IRVCK@<VcFOVO0Z{?{TguEsyljtmbqOt0I!
zZ^Z>D0DZayJ)J$6lVq1!+(1$j%G~^WTu#%t(&ojoY&5q`C<AymmnZ0A$$Q9K4r!a;
z@p3`{mjm#d88FkHD(|an?tobV*yrUFp811j>Zh*_v&AS>fmsAJ+VZy)sVz#@`<_1x
zRHcD*TbHQo_o3z=V}h;kqakg|(s%xO2EeGKelUsEJkv2!>obHDth_+~Afn$>tV__k
zI`)?YU{;SD+#0hwv~OmXP6Xc|J@H?V{HdnPTbiGe)11e5lCbVRpPcwMWr3dX^0GyS
z0+c!Nt;1rvdIKg<AUmUkaZF9_UdD!foM<}rerB+gVB%gj{Myc;a>X0{=jn_`N_KNh
zpv_&~%R{Uza-f^J%Suc9CCac?<EXOpmr*J+31#gqLK4MG9}+QVY`t~|XkWTvWB%i+
zw){Q$!%ulJoCB>o)$(*q-0W90zyOW>Wo-Bek<^reBIy&3iAp)Nf$`F7)VG(~8E3Cf
zUd!2z0O0v3Y$1}*4kusb`FJMp@k+=(Z^j&^;oc{&X=Sd3*M@uRvQAXM*(S~#fD1a}
zu9NO)(Y$JpBFD$R&VKVoE*-UCq`$31Yb^ZWo$%)bL+--O12a3S1u80PLxmq{l;5m)
z-_>!=eKRcn!$Y!aGs~2@aj1INen}_x;gXI9%z|ZS0xq!~P|?fNv}vvGCakVpvhF?J
zwKg-O1<TkYshqzAVEqUNcnKtLyIawcqY+a}w$tWfn4Ijq;Q3ub)l<bSm$CG?l-!nv
z6udW6V6#?yGX-4}K<Nlov-<2$bxp8s@NvrI_S~#qcisz9%5pQL6o1oqvLAZGvku4h
z%5ooNJ8n+aHa{-dkQ$pz`)z@1{H3LViMmS+SQVNm718i?_b-+aPxK|#^RqY9r*5#^
z4&|xe;AxCyCi^JIz-rLQeEZ_n1o~(F#DT2x)8EGiKeKI?A}&@}(^5R-E?w4ZO3=6+
zp#(Foh++v)grD=f{n$gxwZw1m!x6I$x!q^l9FD4n%O^f&Kpul1yYCMRN5gI<o#})$
zq=qq7<WrhGo3Wh8=)Z-IEv#Rj1KlLe+tg`{a_x+jke4<WSaYKir=!-r`6qKnJ95#!
z2+e;Bu*2WK4}@!)pYaH4I~rRnIzDc^&G0SV!hj=&sDVxLb3$00gCDU(2%lCoJsT=z
z2;Ul3Zpyp4!l1WK*u))Sn%DFLM@$Co#%}c|wgo_Q3j+C^<1&*FHRf&wQO=XBm&U#z
z&`M<bij4IxjnFxYwzd!cr-h%A)CzJaI|Abq{}{?@#|4o;T)Do<PSQ)P_C!455v3x!
zorNAg;hE^lKfMpKdiwXOtKf5hQnq|6WkFZ}jpHIw$W1CASDqAvD@P(J5a$*W4OW0<
zkVs9oCBj|JlbqnxW;D^RZza0o?f_Uc33D@;)+2tT3l%yaZG<kkyb;gJ`hbMJaNvj-
z4ZC)FU)yExn>8$CT6N1hGvyKOryg97vUIC0T2=tHK9o@xk7WBm1%Fgc-3?oGd1cXU
zG0P_p7kZFPfV~FXaMv!YTK92c`5gfGBbSygW||8k+o$%nsT`*aPc28mk&jiF9%M!9
zY-1c!*%##OMHHY({=lVT&j6hpNN>DJU*`B0NET;@;)e*KhChmhBw3QS581PWmvE8`
z?$HZi2qL6UufHd;^cx!MM9;6&DU<TJHkE=Ql|XBkvLgq`j^OWMOYxYaCmb=kC^k%&
zJFKfR8uJ>TPZxz(i3-!oYbB_NwF?wq5*f84vdIj+Y;P^3uA|HQ31iwv&WboJgD2sZ
zT^3rCj8APmnv@2H$iK5(&ekcAOsBi{=5vkfu{CUJzX!O<4#-UkAU7nR5Ik`{WQ`ch
zdX>)mWsj<>;@PD1-mAgc(>7IF7yZ;?WLnrhp=^wllcp{Ve6WtQ+5&Wiava<rsoP|r
zT3=GP0$5tit7VqnnF_dREyo$=)KhyYguA}=X1+B0DewXAh&89O*+>O!3+R_9Qa4)G
z(7Ag3Veq|46|klYC!#*06Z1D!hjCcl`qAP$ir*gM#mkNzxm!9sIY^V>n$(UzfrE?!
zZ62cx+WiN<QT*~0T%)^I<0%#F=;1}?FdcN*Jl}BlubcutP-Nk50T)48+4S%))B2j1
zBRj^n%(v#^-|8-tkf;E>NflFGd=$N8+S_^u&x?P9hUojByP<J4>{H%`?%9ESH&vto
zznYab2;hC*iXvRz=p*&x>YYQkDLBROJZRhZX{EoJfWBC0^rU?q3B_f&A{k&`&*A!Y
z52t5CznO-6Y}m01=nm#mZHC{+qSwC9wD6Q!?=r|-%Id2EU`mYZr5yuD(tMr1kzb#F
z4kO!~DJK!Nk2DMLHy2Qz53-o*g>OCPxmtIcuyBRWS*TX+^ll~h(&8r!73tFp<$wI|
z$eLFng;vb|niqY3TYln%aev^zUFOSn%p58P7nEZ=INZ7|IX0hQr1CF0!H)Gf*s91U
z7P#^C$klN&>z0-72ayDu(lmo0`vbY^G|)g~FsEHC1&=~&Q3N0BP5$QUPBjCK->(eU
zL`+Av4|y+;-H_OZLA{>2s-7@0sfqW?!BGUyl&|bck93$%m4Wx|f{oy!&#GdywpZrm
zjGbuifta94%;UScA6_a7OkNp0MXk{~J9GY72YM^k!(wzjqgTcsv+C=?F?;Z}mCw(6
zCN-Tj27o;s*o&50kr72B9M??D+vr_<`#*71Um0bc9(!u62O}IO4i^^-?>9XtiT`;{
zo++Q(nD(P|Ehg$cQQ>XOmC+WMdH*|wl_UF!rB^;fU2hP<@s(-zHR-e5q<gu~LI2P7
z?)SmbY;_nQXbS9oOG<VWpXtLgxEC0riZU1#W2rB#E`p2NnfJ@Grjuryr7N6%jfu<>
zSED@3f5<py2>m^biExzJGh%-day3jbbKm>R<K{Ww1FD-8b4gC%0r=I3LfdR)dFB-v
z=Dt`8=N!z-@g85np1vAmS6M6ULD4H&kMCA>n3<@cugDz5RF%W-WE>~KUbAf{v4)qs
zzb*5%*f!G33FrdMVgEqdvEDd?q7*9HR19-Sj@TW_cgeRo%~>dfZ*x9Lj&S6r9)ntc
z?vK)YZ8o0b0vXxk^+WEpS6+2Rm2`{4t~5B>XTB8d5pwU_KOWnt!IC@sOs!R&FM`|%
z%tk43Y33+Ezxp4L!;f!9zpl@BvdT@JEI7Edmtv8f8e8d5K3aMEg=K4AR4=>chrf?^
z=<rb?3w69}4oI~JAAS`91{vVu*5>#-fj*Bdb_v#ta4RnQ?NVF<Pl5ZY3-d}3&TF0D
zfj#ULuUS(4r7f**Nsg$i30^&+-%t#3^xx?Og3bW|O0|C9K2vaf#$Y6L#TLG>iMmAW
zC;^rm5r<XeYY{;)O||FVJM947fK1@JZCoX{;(1g@ZigXldwd>DU;_lCXvXo2Lv|9&
ziIXSYkW%q8QiD6iw4DJr&%dg{DSy0BGZSyJiBYF*5f+Tc%$Js4&cPWV9_bstT8bRa
zr*{;VR+9D<ZEw0>eFH25u#2q^h;5`Y$+1q{NbSKd$I{E@Ie_^1gTo}m;AGTz@TwdW
zM=_r<J@=&x&V@QoZ}YYZ6Ti_Z=Ck-Vm9yD;=BZG@O<k&)UcTo~RMl?>$L6f%u)Y%z
zjWe_!dq50Z>gUzW)k|I`*vkZRYB|?Sp})0uYAv)Q9%gvxxl69iW#s*tH<t&l7(#JG
z6kvGzhc5oGSIt3vPpI0FN^psqxf=j$bV4x%{}*Hb9Szs_1q|bakOUEtMDL^bZlZUi
zjOd-{T^PMfL>Far28j~UJJF>Oqm1aCghBLZ(RuGApYQW~et*1cy=z(4ntRSYXP<rc
z*?0Go{i7QlW3yqnaVKi80|H+-Nqhtxwi+ObN~q_}j8a|T`-ZmN4bLk`I%<&{0QLt4
zi=f8NJoNdO(OxGU;(!#wL~1Ue<vA13BDBCHj~w$^D5=;1OQxbPd(1FMaY+Iua`>tJ
z;=rw<A@}^puV?Vy`l-JyKpIuHlMBa&g@o}eB-p*36N&1{x__QZrVr>~#(<FDKQRW`
z>scw*s5oAWk`KT{aObdqU%qdv;oYDF>y%YXiOr@J*;~0OV<g}lR`AQ4A3vMXvok5h
zqem>4z%m_};iQJg#~!0&2Aq1uqLzSM`8Exp;*2Z<i1mvO-Dg+Rr^Aau??iz({O^F-
z<9E)PGP1H+RJ9S9@@xCzYi(fQO2CQ~`teMFB9G@W%yU(8XWSGUi23>p;`Kp@Blu~#
zhlSyVP-|<H_M1bw)xXNvcNd&5$jag332$PFUmBwyVJ%ooGxc4-rPM&4>Jl;W2YzRp
zO&%X(%DazyQ$4yXqeX~#Bcc_)PxmlYP7+7I%;0`iz<IO!9Vy`3RR4TiqVM9^qTT-n
zAU-TPfS-{I)zd|VJSC8_NY$;PUKGK~05qGhBmm*?!BhMjg_7nZuY_`D$o>fx-VY!X
z9P0rPK_*jY-)76c*cOdaTBy_Eoo2>g0<KIpo)f}?05HqQ0RO&Jgv{(J+t=&WRGel^
zHj_3oBP)3+!)yRF&;zk+VL#Uv2h9~q45kE@S1av3I+!8(1vEH(j{u?l&Ryi)=c$bj
zXA)1hMq_M0JAXpCOhTDA*7n-)o`WzTJb;sSJ2)$w*pINgN;VSuZPd?PRM!@1djl~J
z+5qT(;UASSH%f}t9(X^jc5cM3#<6yC509SazwznK-T^nFya#S#8}2QP@+Isx!~m`W
z{M!gpThA60(I`(df6*zGC5W}c2<V#(4B(er)*G(Pn^h@@oOq{`U*XsQSzxUI|3Jq5
z;)H2|2P2rG24P;n;y}#^XGx32XEj#74x|lxj8s#JXK9h1dxQYX!Xn23mM4QYHP!B8
z$wjpEU4QAYKy~l|DlHa05QO-=J9yphaZ@~x{SxQ;8WH9V6+o5$ST^P}ch6Kmqkd0p
zO6p(l+sL!14skPAM#8szK?yi9R(hr?J-uH!X6XfbncsMnUfkoEn7p0#>~W!D`LdGU
zyoapfZ@hNBl2R3-Y>2|p_s-$`B;KG<99~u%ETLJ-ZLd-9fYsFd_`JBlQN;QREKMjg
znCq9vsCUrn_-SoR`EAT|At3j`p$5zb-hbK!a}xWp4^(ovixmyP=-|#r?!N3DvO3=S
zTmj(mSTddfGVr1v-uN~m4Z(@{WdFYb-1+;&_8->3st|2@f4k}c>xUl>>!Z>D#SMc<
za?FF2G}2RUV9S%(0F9q$un%psiW~9<fC5PWy{nERC;<2vUs_<&pke@G9+Rm@Z<p&*
zGyPG$@X_Zmv4S5?OqADOZ}!iKU;Y?LZ4atRP9w$a8x61sOm9;bu+nzgpEoLpR&dYh
z%8;w=4->nMHL$#<f!OVOf>Est2~MDp#VxE^;Dt;hB*iS%vH@y_;)K2Kx4$IV`|E`f
zkk3tu^&Lp%?(Wh+Ef*->Fn|qweM^irQKxD31D%x-g(nvhS%|G|f&WGVVp^j$QIgJ1
z);KzF!C<jKNAtv;W6O_Hg8Ew)dwHL>tNeX<2v6p&DRZZm2m|;CU0;B6XpCyVd5cGB
zb`f2s&=Ur#su2@C&ZP+ODYa>P6iLcE^_g!$6@~&E^_82laSbn7WG`@U$h}KCuW_?B
zXcqD?Pj3)E_iJ|_3o+~aR*BF%snDz5x(9EXQTq>m9^h2Fz~1|^)m<GVqNz9a(E+pf
zi`7=(&>+cbl_4Irz#Z1ndu^Ie1^rjYtu)BFXIrM&74NT6`V7{WGcSDffsXzuTi`vO
ztAeDZhZ>46g-AaaKT#^1HB!dA@30)yS)}o2LbQtNjR{!r8ss``BcRy!R%I6ZKb!#U
zC|U!xr7GDjp!W1q5=^GDnw&UlEuQ)$@7Gh-;((>xjH4MN$3c`@&W@S3_d);N$^Z%<
zNZYpjXs(GxQ8*E@w2+fbtyE8wPVGz@3SHlQvg1`<)>M5w+<1b@%SKFzrft8|P_xoU
zc?3T5=Ims(|Dgy(Jc$0;ttg4%3;X;VEaK?AdCU7vnyQ(Mmg7pN{+npd<yQo6mm>*o
zDZTmF`?1H5f&V-%f_<Wifks&fwl77d@pQv?Nt9I(AbqR?fJ?k)UXP$>92;mTNJY%t
z_kOZ%SGuq>y4Y~&u~8We+1!sFU&n#NDMGdC9#A9I%zV~HbIPwNr&#CIQU?rJ*QqW(
zHoRyPTq?fIXIcMa&aF0`OIb7A`<Si<pEKo9bZ^zv)#3vb72(7C4{415rWD)}Zwoxx
z%^2-?OGj0-8?z@l>eQ@8?CPr$K|k6wt>JXwX=YS%*0yXcsaG0TWH9yQy{korF$=Uh
z)wnFhGrU7Tdy9TwJ2V@SsOBwJ^b$!RyV0bXfneZVcfEM-4co10uKgRXOwS0=h#_oO
zcV^}-U1Irm=UnmH57rBp#M?+Pn{dTco~3Yp>Y}*V@{mS$&5K1#I=va?DXA0@UZT0(
zJXx4|s@FniA@)UlQBm%Bc-mn>aN$!h@oaF#KmHMo#(j{Eh<eo3%KrVq(F#dv*HOo+
z0c<UVW!<f`*r3v6T>8W5Xervu?y%=E{Qg<hxWUt45Et0Qg;+@iRn|bw)(Fdy6!5>w
zEHdfpg5}lcl&2iFcKmnVNr11~XL!Bl(-(n`TxoaDJ=(_i?yl4>dm*#T-uD=%zG0tw
z=j!8b<Ep8-C^f;!%!42PuD*7Bv=s^F@SZal2uE+HAC1yZ>>!C9LjdoloB>MyPvs_B
z!qLKV6&J(m6aZFNgAQUO5<OKYd7`P2e&s~X{m8f|Zv#FSTs&%`6WevLlOOKWq~i)5
z)cQus%ENA6k%Q1ITC9)Bc_XNTy2SU6pSuutp}U@MKmt|Hnwz=IAJxICJa<T)b^cK5
z{|yFL07Qs8YN+SjrYt>VKrF-QQ}(P>TH6v0)C>6H%pvHpq&GTt+<PPK=zHfpTcfZl
zotY@OUL*U+3J;m}M|<wD*z|KsEHL_I<q2$U;aM)?E}1{?Uk>+|f>}y3s*{f_EnDb1
zeAsxH^WRIl!k$b*N_GgeGX|7LHOU~^p4^5S77*j(>Eo2KO8X+4!vqS@`m^#}3JulG
z;km4}+p@4G8sw}z182!>u4B16$~-Xs<R1Y6@`H>YY&J^ypenUx>X|zzg0!-vj%UU@
zAaL?$Y;8Q}1dF{Hj!%ic_v#~ud2!Lwy7@BjJri)(w6fmMY{|j?zaNG^0No78vtiRk
zU41WH5_o+=Qko8vn_dJC>f1kV_5k>f{^(-1A0Dc^4An%<rs-7gkfq(EEO7saMS$I8
z@__9-zN3T0M11U6IPHVJO(%r}h%_~7{YQ^L<)3F;Zre5X9rWNY3MSyfR+=^r%t!hy
z!0N-Cbet;fJGycawZD*OV&1xcKVd-mjxfqZtChTCyHEZ8peBvKPtAa*#x3}@4fbs}
zT`=0%V)!l02W68943yWZmIg<8P=Bl53o+p*1iOiX=kKsbzR$FF;N+Aa`CxlrO=o7X
zZyM%j1tq@xvMWN1PIs!iI2qVFCNtGsx-f0y`C%YGGu79YU5)UToN2*O;x&+5px3`*
z6pS_<GC*C;_+dU!Vg5k=nX0Eq0O+7{l5>6QeZ{}05a`?fCayiY;&H##Aov*rCWV9m
z(1@H;j3zr;-toiABRSV6G^beVT<HG}1A+c%?ll%zn>-M}{06)n&+q3%mWT_Kd8-q@
zVaCY)fOK~Ht%*1^A^K0?x2Y0|GLsfp*FasN2Exz^wQj&y6lMzON?cUC7KzcLFW1c2
zzfI=T{hFbEtei*Nn!A-E_vx?eq{e0w+MLc<Q20WgBh1&=M7*%T>nD0T78fIr_W@T>
zT^>0UNihcC`xGsU?Yk_i>k1`wooPoyBVPfB9q=Qtn@adHGtT-zi$7Crw@o||)ZcNI
z$^?WJnFPSdUd&CL%~)_f-LI=F{-;ZfN^sKE%nWnJ=vhImmnS}wd+drr>e4*tTh*%u
zOojY~Z8vtwJoxf?e}Vfgx_i{DEM#unp}x2T{&?}LEOX>f)1EFSts@RlEs6!aVVVjR
z8?*5Aln&x9VPU17pHya47}>)Rka4+U4x$#L$%Cac)89R|7N1HjD5&>7fZgh>_Z8*V
ziU6G2`EAct14v3T88tXQJgd|F+N(0a;=wZNP?%<E`GWiv0Vf;(y;lw-+T~Ji_=BpW
z)ZT@8TjzJ|PgbPXFPT}fyi*X}lRI>K(a~x;tfM@Wp21au0;|2XpD+}%32ZPoNBiZo
zc(!9H^)_?Ma~)-Vfz@|p;To6&Rsnp^Oax(74@XS5G+RcV(Qqig_iJwTw158A9N0Wp
zMVMUYe7ie0<JvAY1Uz=Us>LqQlaL;n+lhyB1GvGap&(YY%Ej-q=xLLoEr->ZnYfGe
zs@yt^+T{%Fyq&4!*d#SSUt<!?^`}wg@+?LRZv+Tw<$YoXW4|WLLv|XP>)c$Xzi)5?
z4dJ^MA8AHr{eF8{&5W!%+gt1TU{q^fIO%jWlP0m^*Iz#+V?xdA%+8zYC4?qzWiVu{
z2ISK=e{M6j|Jt3_95XR=(~r`g+YZ9)5CnG1+xS<N!lS)o(BxUlPP4WRN8{8IW_JJ!
zL~8<#)B}>>l?&1>XUYnZCrpCB*FGUJhy$_KG|7dUcz#{BF((rfE#{M)nWD%t>DWue
z5I$h*nK0e0Qb?5+1&M4PeJ!P`OrDv$x^%%z38;_nf_;Pv8ox01zt7D*6Qj(UJS<_6
zP(1kS?tCeoZRa#Z631Qr&}1}ZZGWoI>)L^$Az$t<xvgvd*=-Uo<pT;69m_xabVv97
zrx`@KA*eRhb%^VJ)orI4hsAL>`gpw6ev%nO;Z51Eo3f%u32o6kH0r(*QzwT{P=&lV
zo>w<m(1p@d?Up%FF@pNa-MWfFxt&gIYK$%jtnQ|#c@947@<CE3aY@9guBy|)j6W<K
zXaJ5(H^%~pT1cMb{GHTIDn+AY-@qdY1V-)wnj_Xpkj~rtzua|qcXjc=kVV%#pU?cj
znt{V)+FWmuYHo&xqoa#?LtpgA3i~t@{<sH7%mBim1Cu%0WdnCn`_oU&`}zWEPIJn0
z-2wFoQzR*hTUf%;UHvLv3wAvBJq1lvqK0=z^52V9{ZP?j*k0fB`Q`kzo4(i`Y1>3@
zIUvs)(7?iSLVP3~YX<I3913_IB)T##B~sEi{9A<>TgZdt{tR;WylRK80B%<HG0W+g
z?->0K#QFn~X0Cd!XWdPK8!<T*UooYpFUFAi@4b)AXV&ulW)=c>Vr``JlNy`Om|eq?
z{`nz)z~vS)1kxzNpJ!EGX&{}tE?vU-{%cAoBvy0`4n)z1t-W8%sY@qY`W^9r_8(@b
zNhDp=%<U)WKT1hatbrcW(egzrsQ||hAQgAHKUusB?fZ-LKSrZ+oa-J=z_D5WD;|Yk
z;U&4TczahG8m&CxZ4~k%Rx=WElGqVwRLT+mMXY@hS6E0vmY^b6pJP>KN2cu=D`RR_
z_;hj1vrL6lW*>5|x3ai+$<H)e=f9RuZ_N#R5;z~NtlaSYa|N5xsZhRX&zk7JAjT<#
zWc$(?))r6xCEtFLuEn%?7jdGB{Fsdwk`T(~KePtV41f(m-eqn(2xe)i71h#G3paUT
z;92B6xJ@ejFE_Kw(;y12Lx#ucw7oJ@N^OLgKVc|k_&b~S)r>>Y2<)>_Se*EYT?!XX
zCTW4szsZD7Kv^AYuj?Omj8)pfliz&Vzvl#qK0rAdg|<C-a_!jT+Z59C<d9JpjT8N6
zWPhRPq1fJ2v!k6M<j_3%{Ye`>GZ2z1X9nybi1pF?z+hpcnXGEI(I3}nEBMIL)#B4q
zfYJc>cY)W7Di&u8*PJIi*MFSg?<QZhL1+KP49F&gA0C&)E)QLqsgKV4Ietn1^DiMZ
ze&t8daeIN_Wl(Es(fVMzsOxMd#t16Mer^D7{1S^Ak-V(Pvf2MLbZ<8GZv%zT1-y~&
zF^yf&JK<Ct*FA;XJAh3MSPw313EX1raqb7-D+_q<0hzU2!$0htU$i4kXT3(IT2M>=
z$wg=GU9Us{MK=inIN$3RK?Qt--Ag9$ECzbpQaLAro>yG1l?B?x4kne$4^Jp%U4`8|
z)P#2V-D;IOK2ToaT<b+ysuJI;jQPN|=gBWKfLIK&Kkvu_3o7im`P~05De+}u^<3}*
zd)w3xq)-WD^h8GkLG`mHPf2^L&OSby8p|7Yz5QAF<aZ1ExEv<jJ{m~r$aS0&r4Isg
zE7-pL?16RRnIbpN_`kL{JEymf4zMYT4CgzsyT5>{8{SWLW3pu`DZs(&ewO4T*_||4
zoffIWZ*U`8RgRA2vD$ceRR^pfJ|vjTE2p78is(179GSByLRH;?KG)agzmc=CF@8e{
zrTik7G;SiI_vwGSP2jMq)y44vsV^g4(FixcA_i;Ky|d#ebHeBXx{mCMO5%>n+2<s}
z;l^vt*3*8>-`%B3>&OLd6lJbPDLqqW(|lI6$;tES(xnf-DKBy%xR6!77$|R~VF6o5
z53&h_FVt!Ct~>g&tk6YJ1NOtdCqD+a_l8*gv5F3~(}stNZCxWXu7U&O2sgTWmkTaq
zq@@lbf7n2Lg#WI>2R;7kQ_xBu1rgrn(F4cQzOE3_XZwV72?g|Bn|CR#;^tmFCRK5q
zSR(Nb^STl^@xPo74LCjn@kZjgOm|ScI6PJ&?kn?5@%`-TIdaH@bz$xp+5sxR!P-=W
zrrQ`;V=}?kl%OlZ>I5DnUyk)jlkZ+_4tUVGgKWQshTaL=N{>ccNorq1D}dtW)V`yC
z8udPKWYR(V>OM^;sq8{;o?uaY3EJ|@zUAInl%N;zD{P5m6zxyg{l!FXd`+YW%QW}0
zc(Ci2%&EDz)SKrynXqdLZz%|g9)1H?UG_BLR3-!6mD(N_f+I#)l<G~-{jLRWn)^6`
zdlaF~AQ}c&xnW7XxA}icZH1Qc`9hMIXL>LM@}V?&Oa&{1Ci?c*%*_c7=b$HABJ^&V
z)L3qDHpjGqW+<x6{``vJ!%wm~49V;u=Si5I7Dccw$T_iin8P|Z{!{ArKhvR6nh9@3
zM2*>PLvMI(6PO74gJ54AtFv_0Q(pHnhBv)+@H~CX&beGh3gh72?X}*2+Iycba}LXe
zoalN2uI>=b=Z`&DZtF#BtHy=o`Mc{HZxB;2*QHvp;$wem%p?msA-cmXqpAA77TZ@*
ztH=i=cJQ#$X31Fuv~00OTk%C2Y%Z|N{kC`nVSJMDz}-BhlRQ?+wjzvFJRF<-b0Gv7
zfz9A#|1C^j>ugPtu{ynl>dyMXW>8(=!2`r*5d7JYq}hRIpF@AFnCNdY-fv-2Mz&me
zTAOzw?Ww0*{cARuOKJTt!o*pbpJH)w*ghgc^ql+U{SSp%$z#8lUuP=u<(bDIvOChp
z-sEbLKs0~88m-V38Au*uDx>;+GOmC^ALJ6r`_aFr_(kH)wZ-zJlTko6IehF&ZX6}Y
zcCKLaFw=<r@Mi>{k863BrmCiS)*V%}u0j}dV^_1%Nyp3jPloLVYOKhvrMs{5)rFct
z6HL`D$HTFD+EJ+jbt3;eFGFz%AIb)cEk$VJLW3Wr5^A;{q2s3&nC|J}U-AIq)9YPk
zZDf(+jHNww{`y1ZS)IFawIX$S`WYTsn0ck0lyE%48KM2ua<3?%=epC{5vhsQq%PEm
znXOS+QrIFi_WvBsp?E~1Rc1rV537zof0h1w<qD{?AMQQRpP3Cf5*6+~`V7wu!T<u<
z<lB${vW$j&v6y`3a!q*-JGzI4e@%>^jf=q&6dAvy_tl)Dmcv<uudd=d#`+Dn_r&M=
zJg{D9iU9NWWqtoD6h1zjoWeXafuY%7FA%Fj`Y0ImLvlU6EpbI(Ruw6DLVPlSke60%
zrLQz^ReefC>iy^f;e$8=t&2JyKCS_t4F79^XYt0iRVda*J%hjl#<EFAqxK1fjlDFb
z>-E~{CEu#y`}CI#qYdziHwgBjdrfPs#qs@A|2x|6t5zGTUw6d8&<zilT2#?ohRK4-
z)nC63Km4plvLV`K00ay~IGR91eIC&oF~bsK9dBI&L<Hn;9T~oDCwSqa;>z<nK2tSp
zjN=fFGi{sUKNJL;8$kTjT~9&KW>=BFX%SFaEtU;kdd?+<Tg%jOddp=9H%AA)nLIVW
z$;@w?q%)u*muU&{X{6g;P;(~LBJbTlL0YkV`j7=;`D8-4pYBvKX;QhbtyURZQ2C6v
z=q=+N{gF3!79Wste0*?(bE=n1QJnW-L(X2v^}0(VQXsg^AH}B$&Uyz3kMIhFNcA#)
zJx(V}!9}>e9az!K18PlF-!3##gL-`#Pczce?e^&Nm)~EPdlRmTw31LWFhG|d+;sj-
z*H$mGX*uRxG13t^gU7+D|FOyoTyxGtv#Y?rr{Cbnz9zY~k!{R8u$u})1;E}wb`p-;
zx-3GT=u_;i`8WnGs?ozG4a_;!cHYkru&?*BN$fvPrgqW=EI9>f^}0#-;zk{MD_G3T
zbWOp;ko{e@(rI|Z+;*w1gW7@M+D@!=fUTzErw{{hMxG((;{qf606W*1n?*#Ckp-pT
zb7q3xA}bSZo`e>3@4T7j>Rwbocnj&Ruv2mD_R@FJ$068$|KV4?Qb9ouGl`><<n4}w
z^v6!F8H@gI@h%MK^l<t3e!%YE0GsWzk8m3L<(^+Ov(Wn3@XuQzYyS?DBy>Js{c}$*
zA+i^pJLi;FjW8(gTJ(wQ#PS$r+cJ<L9mu^gJ(F)Y#P)PQqWZhU4dJ(YWb=Bzrj-N_
zC$ArQ`by5+ji9yQP0u|>w{{u|C|eu&shVeSHHcmouiANpc2U}D?q$4BywL!K0+2QO
z8NKtb7F9B+QUx=DzkUFW^B|6r@(~xiot$KD3Qx4KwfLq+oTBo}W4+n68JJUcLvldl
zUWInYCPmG%e^vlH>a-%3HCuM+7%jYIw8c4{UMqDz^qIrAnAx?per1^HY%8s~B3PC@
z?|w3F8er{rTixcZ_D)aDhT##up|bEga5&gP=0;eKU9r89s=*ao3Z;j?1qev)rV?hZ
z_M3M3X&R|KdMtI)M%z?#>SGt3(qf^<hQ<WEPHRh&@h%78S~=+Aus6`;Vs939>`wCA
zbr#9#4S?BO-kCUoTwi>BpBu130(k~#0<}e)tYMFGD;=`!Eg2pM25kh5g_#3n(M=Cf
z>_#@VL0pt21M|d`wk;}*_J7Yy1He_HFR>l{`IvK;EW^Tq%_9ftLg_U`-+L{FJ<=;F
zCa}c`!9a|6H;q(9>$qliyG*g4)^+r{9a{~9P+7TE+|d-X!YCpfZ6BXDuQ+>MOQ0(j
zpUMY>Ou&0Ra(;SmapTdfGVCP`l;N-Y?-tp~>UiT^M>Jn}?|;PR2NwA}=^6NC!~E2w
z8r~&U%40nkp2B})U-mE3lq*r(wj|FZwzOvc_==rzlMy?nChgZloF^With5-P;F%DR
z6gXHj#70EC5;3ne|9W=g8N1#R#lC1|_na{_!(WFlL%EH5p7RuledT+IygMc~zbgAF
z=npTP__iLP!Y8XhGkeP=4rwEd%obTh?*Wp$d@_PB#@8s1Y|v?6;7kut>&Ss9dUonu
zW3)|3ocBRHpat?hK;G3k&8vSk!##;y0ODS|#Xzj;KkU<`V548Ga@u(Sorq8#CKp()
zlOex>w#gYv!E^!~LJcQEiDu=+DHFw)U@lBAIhdTMa?U*OFLV&^TsaW_9>%W%b=Xar
zlRY{T2V(|`mxakWU*P*2ejiRlTVo2G+<}uCi=!@H<)P}t*)cr;Dc*WY<jnZ{Sfz#=
z8K6EW0aAJ`od|ei*V8Qo(}fV|Vp6^p8GAk1!uG%4O9xGFo5chDwSFgdv?)9C5f<=0
z)p53tHT;T8RP?NY1ot+{h{LOhE_!3b=yXS}ov$<>Z#8dHn%@ZuN7TpR^Oyp5sS^+@
z*OU(m=c(v`#X4{kJJ!7<M2&8W?0uZ4zCn2_7}M1{&}G8O9RJbpOiqje4h4bUX*0fl
z2azVj`~=c}HnRy~l+Qk?<zb@yXMwB&=VSwH=Ix^cK>*p`)nRgGZD%ykzL>aSEE?HB
z`xc`;ws1T*V4A`5(u92$I6f438(FDU1=JhFGKN6ewnPH4^|n_v0CoOt8v1pe{vq4j
z*r`j5si*;tcsA+ihNruah{8;-4=3#p=)VXPfWpXu&2|8R-&I6i-eFte`3s#A(EPN|
zQHJ6I43tO45(3_ym}Q%~)$cc4jaeKM^6cB<LWL5Mk_WXEzN`lW;fk^dqdb}SJ4Uv?
zw-40g-creu$dWB9%PcfB^DX#JQSCMm?wk*?*zZdiq3=CB7@&LjVbg>bZ+zc^3o}2E
zH&HfrN&f8HLXg+r<txGD^a9V=-zGI`P6B$r0}Ie$__|$_J9v=*vmT(@&P1lgt$Rl3
z9SlG~87{r6erfCo37<H?0f@n#-rk|T48!mN1cQz@=+|X=YY-U*x&xqW?7!b3>l|;B
z4E(7v^D_a%lb!LsO{%{n`QPtgB!s(~2)IiPgM2oJUsdo!jqc0HT@G+t7$kBFeN+P&
zPG0-d5VoP)Z+PB@_0*`LEJ^r~n_E2KHu1$`!0&8nz7o)`j_RsncOv&}S3wO3s*iGu
zE-1q8Uupe)`S=Vbz2tU4|Ms<%=p0(2U+Q^*-u2u7)ymU17}IFxT;`bs@ffq>W9zlb
z9ZR%Lt6SDUP?94?Q~i8-J}&^%R?at3f}&<xa?&|I17Y4L5>xN$YRSB`Uh6AZje@n4
zM+sDEg@r{+AG~saN8ZUQc%|@qx-0^BclMa*{NXODvpZsm+10ln75yz^F6(AbP$KSZ
zSa?`h)N|MBpP2?{OzROp>~r!06S6PH$sQG*{MRNV<s|_7oB>sW>Ta<x{WT%AXWF4(
z^Nz(MK1`+s#{adC{4U^Wbq!<k9$~^M6X@x~*o%@y-uTkCyFE$JtUcCn<-44E^Rp+t
zz&$9I+xg2t<W<|`)MEMAn%q`O8Ydo>3SdWC92dRA`u42S@`Z-r=ewDK-l!UBNds@=
z$7csrSfh#P*MbAwv1&OcT`->1qtW{#uit-3Hkwcud%NzN|G20$;0x{NutFyP&LT~p
zX#JHlcboI&AhEa3zK0#7flq3#HaJvW7raMm!R2`3xO7ih?r8_6U}KE}1jGA|9>|kV
zPDalQ^APcI?7RzkHY<^TSn!C>4YtXaqI32vBE&I#ivB{2$*gKR$2`UJTMJ%HJ}I`$
zY%=1vtu8a2v-TQXP{MSy;3wTqRf3JaK1zBrO2|=VT&$q!1LHT~j%4uyR$!GtZcr5$
zo6nJ7ekGdFdX(&o#jcn*e6D}6pW<^sg|R$bD>#wEGZ!I36^BoPr6LL^&3kR9X)$5P
z4VE`gBwF2!+fA_X(MLTv7ydl82<a^f)UxeN89QjT!3q;%0{?_mHhEUraDu4_C<RkD
zp0Tg1M()CP4R<HR<!y<VciCPZqFp#X09QP+16e*PkgLHKH~<LQiidnw<MdQtRw+=?
z=E=u3pyCgrO8G8=wPeWof1Tg?4GdSy3IYWf&t2H+8SIq>bbP_`r)B!`ijvx?Ujt!_
z+HKfa+VSYutV8{0@LDut;_bUZcy&I$?GBIq$Lbf6vAbJ}n2tV<-TwiQiIx~aUw&Ya
z&njqR!c6{7Sy5h5*uw<VO={Ja46BF=d6tt~9~fH}zjAsKDDh=WRqig)z`?^i&fPrG
zuVj+BeyBxeF>*K=$7a89O)t)RpOx;+YZLiSHZ0*6;o|9J-!Ju*ctJ4AZzY)QiaFQb
zm&DK$&>#k%UP_S#($e}Qidy6@ecSWbk7iZ?D}t@#4RugRpLCtnvDB~Z1&CMlcjV%k
z`q{reEI<?>_?0Ty*KNcH*!$VJOX%;s`hEF-H4U<tG$399fAn`gdq%RAJWz{<#lm{Y
zYfBKYbe*Jhod>wGew`0iA6doRXrspD%_Lt!uQn*eJl7D7!CU?QHiveuuTl@0qO6xP
z-@u{@=gVM9olC3kE~Kl}#GNtGHs7)Y4SEC9breU<2ON3fEZp|vDzPxBm2|<**4D6@
zi3ub%uj0f+dg@rCzh@d720;3Lb_rsMDnxVOyN}-srG>HSmvwv-%-hIk>8fE>ZzsC=
zcT5)ROAz_+>iC({>`P-RJ0l1rEe4hlauZUglIkgO%EAaId%4u>kv;|m4VgeMl6C*j
zykj_M0ou%=efV8NZK!A#fm-T|8ExKv_B*fmX8x}8Tk7H9?bjRUbZZxZDjiZ<{S?D3
z!`fi76lVo6E7lUO!a-^3m;=~VaWr7SQ5sGFR|Mxv#^~g6t_CH#JYIc-E1T&O4ZByX
zPz3Wi!ts4Uh}gR2zJ8Or=dtk7(cb=>Uz(?qlF~db`}*eWtmd`o>R@%H8Am^uHi(o2
z(otXkg4!Oq5Nfn&wSfk52p~>7)sxW}`o1{Lqdm2_*hm}I9v_dZ1(gMMCRN9eHd+@&
z`y(IP_z`zrdupziy%j&$$(!l-v#uFW$G)0x;xEHyTOZyr9X~D-5A`<dIB@9PBf#aM
z+IlJT<9tVk*PYva#4tbPbBB7GphuQCzVpKF$H53HDN`ccDOOcGumF~B%Yq)(=7QT4
z{;p7Z%K}Tk(eB9$5uToqF14|Q)2q30|CR{O#oQ_i^^*$=0iGcvPU92S^Q(*S!+@PA
z&b6V*hJa9ktS#y1We8TCKWi+M&KWm`wX+wOS9(;Q;Z{GKI3;v{`o_Sre+b6etcPD@
zM7qtz*(lSyk6D2xjG*GlLXO}{g#(+5MSo}KtLsKPU^7crRmxU>R&&&sDS%y@5w{~M
zzxj3vS0JE?_D=36-#>fDA#}wo`huGsdA`9HgsOvo#^Tj|T5)7FLhrZt_)6scs8*ew
z@<(W;)_Y_HN|}4N4IENOv@Asytk0gTMp`$9%~}vVFWuiFTPW|7--Th&{Tdm8IUfof
z>%84bRpcK-Z?Zkk%Bk?K%FLcdEM8z~@BP_m)+XWe2^d)@$={7w*0=EMlqN#xkxIBb
z-jSs(yJB4!7!7aSf<&d*1PG2ljl=UkeECS=w#@Aof3oneS!}G}Zyy3j!-qMq)tj8E
zGD;ng6d|!5FLf5$;g@nlTWuxDgHlyA_DNQPtCwX7MTcL0mXJHgkr$1g77~1LP<gZ(
zpD+x9i{e>+4<6D7O@uc-VJbzzW@Bwq)5nZK_JMjI){-OsqVHIxb@AQj8J2#>`c(<)
z>nXeK@W%Rs=QXp+0{+s3rfOo@bQyB|)-1WJ-3*;sVG|pJxGH;U&xBC?{xv>QXE0$R
z{o92Gg+)z+*o|GZGW`C2bm8Lw(Z8Zs=TQJh7&6L%#h1gN!LJLue^g4hk@y-9p-X@5
z-FDZN#+es<ME^K+Gx(~TZaq|sD*5p0YQU_E{@8p9+;~$IXru>4nL+FBemb7)x2a>W
zJPPWnD~LzM#_FcvJ2%5uXO%&|&a=>iFd*W!{9QY<#8N)}+2<;}$h4Fhoq;~Tse}&C
zzN|pfq0|(jsPq)n+V7yQXs(4ZO3g`ULFCCCN8e5XVq{-25q<n|e7C+CFF9ZW3QB_+
zC!(=UlH(U)*~$Xn)Od`J#gTILDrM<o0U#4(52xDN=nPxGWBDoqN2uCjf{=@@5|I|>
z{OL%{#&6~~hk(fAbq349b*GmVJ;ELippsRF!d={FLrJhEY+;S9FyXa+h}#Lg!eFG(
z*P0!9Fw&o8QHwwr?^j}2-e<I2s!lXBW9p$$(%Z!E{@CF-ts(fQ@<Zs4c1LnfL7J3b
zBko^0anSxc34fY(MCA@<uhXWK5-a(W@5Ni*5B9|CWEy)nx%ZU;^|2u_Z;v}~wL(i+
z`w24F*jl8yTq`uW*6i!{!S|p&hI%1xLCFfl?JVQU+UveeBh=|twn&`I-QknDbr-WT
z_NeK?3H}Ra4NfjsSPV?5hfEmWn%s~RX_EIjLW(L`x8D&AN8-CE3INQH7P(@h)0wWL
z@FZJNyAd(TG$TiOdp&P-RJZ`qWQ=<9Htq8I$0x<YR6%aKGjkZtZ-E6Bzqljp9qM=V
zf}W&Eg)~ysEm+tJA*cQ0gHa?r{C-IbEU(+S*d{hMPxM<K*#<==<}>sF*Am8v`J9pT
z```aHsqFV5(kndldv1~4!F{u?hZRvM9OC1y<SBb{8B+@f#En|0OQ$BK7793TWw2|k
zqW8%jPLeb-f*TZ3un=XQ$!xJS)C4E;p(_n|_@{f2b%p%l74=ik_>;<eoa=bjSDZ#J
zdSHS#YOW&92|<z$(8JoNb!IuL@AsT|GpdONzL0Ul%Z|47rg(nTy;0#<Z6Jqur;q7_
zG>oAD!DP_IpU^N73^A4(L${JEw3PAcC;O)GXI#O)w=%Eld2^_X5d$x|L1U_FC0g>q
zjV}qRVO+LLp(*%&5ENe02DLtlrkiyqZoIP;#)DmxR3RH1@DeuR_dkx0Arrw;D)R4-
zgC!*;&qsdqx7K%q7k;Jb?%x$YSW;Isun<L+!9GaOD=v%`HXyWiZ8gAK*y>EA<Hj9K
zFV$(IV3ev^d33|e0#B;xa~hxAKZH4S@)jBh_7QQeKeg_7<z=Xc^6@?v;0BHsS5P>f
zQ`ea?$9tD20DIwe0#)NQ!{h0yZW*{LXtS5}sFc+sL>L_>fn!sbT_L*I6wW0SSL6h)
zRE8ZB=n7Gjtv2|l*Rx>bRomGnYNJ%2Kb9BzL1Ih&bI7{!&T?_={U1MTU{6xz_dh{B
zS6y!XrtmjZtR}*nLy{?iZ_7Slkt{&Sagn@3oSW$pu~#u3S4kDE1%!7^jECMUQL;M-
zIg-Y5`w%g!xAXl>`S5C~SBbu0H6F&R)(@uZp8OqTar;B-b5`xX2rLn)2$+5nfnoX~
zzx~k9#O$UFgb{fWx6sm5t$HKkiv`_L<NHNxGqA~oa4yliGb#J*{qw{3u|MYXZ_aj^
zXKajG*z9$Kw3z%&HVQup(?4f|s{)HW2z#TF-H2FAc43&f(Ww9tXfXZV_wk2QdYt%9
zq^k{QZyGnaU$kL0Bxs}F$S}(nVZUK!7(I=CMTl-8$aq1(UV_t|{4;&*x&7|!R2W%@
zT|c-WogGa}uFgOje?^8>@#wEe(eR&0ald$Z{P6_$6^^O4&V}Uh7kcQ1wfzCl51xll
zU#*<Cd_N(iAsRYqCgI8oh~zBhWLTy4<jv_n6A2qenoSp(hn4<3lNx#jb7LY9)8n)>
z`+Z?-j5O-4ay~yQQ%=*4)w<ntlL14C7(f#}z_RCZhgbIxSgoybW;z=>f^$5UO3}1h
zs1K(FO4^z-p&zr_-7sk2Ru(w>A07~767yMIh<`-Dt*q_r?CWftP%8n#@Xq;}Q8<lP
z`&D78XW;i!Lm_LWCmB|e+83?tV`87(o--kdU-h@#@gtdjsE?{kPq%^X-tRut?LT7=
zBV_IPP`rN&eV=n(?%&7wudu4XQL;#2Dkkl9#N+NHqjBaze3ZcRmeY!*i!eO!-R7@E
z=v6=cLp-^|U}X;XMo$NKzs@eND$Sk5&J45FBU;&&6ZF=!tVqxnhoyVx3wj#fEDrk+
z93-VH5d~t3c(C~72Wyse^6|T~Yx=AOZLM$LQavSG7-~n<!S$k!tZ{wW--Ej1UHBz*
zXb=PW14{<Zvple<e{@oskI&=%>!sMdMjZ*H81e5@98(0ru+3mwo8w((&_5<IkUXxs
zQ6VZNw-yVVbpnfC4lH(b3O)YjL46YhwSND_$waqdzG1G_uvI0yvSPf-&Z~>Ui`8sP
zF2;--_Z|I*Xk(I@o`Qk!yy-V@=>x{4M{Y(#>}l+%c6@tZifx?SE+D0rI+Z%@ID`EW
z9IHXaZG8c>zQ@9@E{22%TW<G?h&QqbJE(L`9`%YIa>k1O6n+14t`3Q$Fnr9Icq>O$
z74`jbsJmp#9cu_J!lP(V9o1-K&BcghfBm2^4picv@(O)<{>W%?Z<Aaat9>wNAuv6Z
zh3*K4Zi09T>&6Ugk7Mw#<2*v(m%;i4P+1auxHtfT;fclAFhA2#Dn?2yy%<{>D5Ylc
zA8E(0g?7wC1Y}^b-glR=?NVKNRZ#LxyQ}enRoCw%XAXm3NE)>$Ue#~K&)BHVdB7AE
z;PoHYmYK3>aq-OUIwKMBMXJypm9@N8FClm~cS#5+aXf|4f)f}eI}q&6)RI<V@txcX
zDBL_Yl3rhRZ*hFm?fK6dv`qtECZXpPMJw*<I(9P48W+~944rx`7u_$gH)$aaz4b^V
zf(FzEmeo%<T(H|OlPeC(b&~uKHVuro<bhng931gkqXIqHa^?3R>}Jd}5q6VINA8#W
z2c3^xk}*?F*4n>j4htlf%v)N&Fi6C@X=d;&KHAQiQbo0&z~BfZ8^u%b6B%V1iZWXJ
zyuB%}Syj}TQ2wL~(=b(k8Xi2QjA$$9g}OyUSHOfe>@0u%;%j=%8??lziU4K(?!r>}
zE%Yb;gBnXNR|X361x`9TUDJ9y(3k}tR&~J7=O8`UEFIEZnW=ORwy9iK9<aL|M~0Nz
zQ7Pjtxk{cteoc|1eXuWkKY!KKV~pTA5^`S?x;VRj`xU1=2uE4Z_`Qhx$IaUAI!A+-
zO*Avo<oMe|EUXuQus+Nf_rl?a(?9trTyNKaRbfFHS%_Z*DzJMDUq3J*+2<9Q?Oz_P
zzq4;Sy#DT)*jwK0IP6|6Sj|}>9h&>cVX>SZ949b*p8SV0^D^e3wq~&Go?H$h;$dl3
zw58GF(o)`;KBPBZA~}XER${=>Q3EOrEBnJtIyt<sBP#w728o5%`+zv}LO)*dDvwTW
zIe>&sfW2=zIpGx@%w1z2QZY7xUa1&Q7Qr*n@JCT5Q07Jdf|%Qz!9<qVlRt94N?<Rg
zJh-v5^OB>#fu|M>607CuNl_Rrn59_mqkjES#jN8OK<4wH6vVC-FF5Wys03wPSI^|b
zGH55S#%x~-0()nhiNH6Fpa60Ke?=t&&U*T`i=~9+uMgaqW||k^9p|#f?f2#z$TLv@
z$#Qr2X8{C>CZh*+j>F!+5e0!=sYZ|+XB%8#Zxd)ovC!>5l*Z2ARA94fK?6put@7zR
zaD?0)hHDU#U8&-R7p!@F<l>WrFitMCr+P{@=_Z01VFp>+gqThCs#zV+kvf^|-H)@1
zWH5mme0KrUxC8W+msbP=2=xvG`cv-40>*(#(b0sDb&}*EI?aE9mtdR7^f8+LU^jKB
zB3$PV!)xGFGeDpaXoiOgY%_mV7x4moY%{R&aCaVtK}$&OizuZBw`3mR_6!V5Tl~HQ
zs{fw*C8o3z2MI|<ry}?p(GsEHN`xU$9-I-(o{WGCvSc9ip+HD%d@=$EhC)(|sX@Ci
z?bH9YzfVVmfPwh%8YP%EBbeD<of~`wvs%S{{svv708>R}ngTVZfOfwBwe$Yh9)~VM
z->hiC53>>c`E4Xf|29}BV2}3`M*7z{f2+_?1cD%!)IL32rdQe-jeT`H`Kc~Rft;+_
zBVn&Q%SoS#>r+fR!!3+H7><XVCp2fC<Vg%?o-rfnZq!&zP7qLKn4Ko7k_jBBo_z|G
zcf!(0LD&c^v^;b{y+m`Rcs_AK;UH(aU?K6fhnCzjD<IY^6_gXao*qoa2oBVE57@HN
z|9{Ua0Tkq;L8M3{4cHTqy*#KSjWlc@7?crQ4ne&_yF{ZKfK`UTQ`ABB!bqbe1W_`8
zIjmky7(51D!2%|L*E7J!`tNq1LZAZ4hzXFrEKEcJKn^y!G!g>bgqn@!Qb9$kKsi~l
zR#?GE1(<#c;x=;T0n!n`=H>r4U|<x;7UPBJy##|48JL>{IJXFWnVY5qyX8U(%t3~1
znOcYq;cd-j^Wr^WS{~bqRvrU7-q%27r^Ne<dcxtJVX~a|{_&DfUnU?r>bn^5mNfJF
zWq!(`KO)4?N14Q9?7|#&Ula4f*1{k}6=i+{lr)rXr3!8AJVg8009I~F0D5!=S|X_J
zBV|;graAY?^40Rp%<fD^&(-SX!qbq;^Afa#VYM%6>Ez@V(w_oplxM?5Th-awB8EH-
za=kt}{R=tu^S=I;>t0mupO24#I!|4^53S|iXfvn3*D2Gon@uo&H95*?XB8*>hgr_b
zfKjepCTaSiMYq93o`*y3BTZEF*vie}E~GunG;2`$MBTpHa6G`Vgt(*WOBByct-QbZ
zp`+rtQ<wER+#ciQk&y<mT9t`|*S9CA>eJnL#@lW5a*A#najO!SnjTqPFx$_Ovogay
zfqP`dAY+paJR$jLJXckRtv^th00b`h`hfxJLRFf4iYTx|F|9HyIG0~vch*~-_58*j
z2v7S?jEu?LgrbI8QyNStMX7WuIcyCQ?T&4C=NnLG)giD1^?>_6H$gxlA2i|<nh-i#
zRJ&#x3RfFAZm%4FosVc^k(|l|AvfsJX@~nSb@WpQ(@QHVd|}cR3Y~tdt<66m!}!2F
zzwK3^Uv&*&O{;@Gjz7o8@y$xhl7$8~UmK|j-%MztGQQ;XU`{eV50U~=1SzFUktpvS
zALn2AlTr`fHtGREp!k+I>Gdt4D@4c&!%_5y3`A*Nzw#ccv5DqOUijpG!85jPAPNqv
zOAf~PzBQ%3DqxZ2$gyY#rgvL#fWuj&Ap2&MwIng+A#$Ue862J><cLayObU(rFU+5@
z=R@EKhSwiqYXYmuMDUp}4UzgeqP^Ut;>ACve*Z2vs7?U9Fku!9U=kfOq!G^(A<GjX
zx2E9h^s<L$$c?UZ<Qe17nQs7&ynetDpCH-e!pLDKkn8^CNeC=Y-4DeFfse`dajt8v
zy42eEn98@VFxz{t>WF7(jSR-#)r_;KxQ%s50Vn03IE-n;Pb+;CrC(uI!*W<83cfOs
z2A8)OuP<>yRY4^<8NeRYiGp`NOiHT$iqA)+Wj7<a)!}Hb2M7J&_pWI}qliQf8pmI*
z_VFd-vmNF{&Q!VAB1V{0w9rVjg8DJ2BzJNL{TqLyS8z)Mdhw^9b+RcLu{#gY4>BL_
zIT5WpOqp*urv!_9u-5O-!(Hj@;13ZFtkSNP>;+R9s2no~S#e`^(*SgHkWxRl8D%$l
z(x?e*@&Nd(Js&t!x-%#R0%Zcr-|<{+7}4ebIG`)&s0DbbWCoKF(GPjS^4e}~8frW1
z03VLjgtBZzqrDzo_k(`{#2yWXlWGZ&B3tZ-{d0UAhd;hVU#=sOAPB0<!4GrCShN_>
zVH5%tV*ukYW+3Rnp}jz%PTa7YyT}+e25@dN0vlu>pM)^Q_@Wch3aLp5lnM$9E~ume
ztpaxNlQN7^RRwAfvd2PF07<<DfCK#p6PSb<R3Zo$3`570z*7K5_xv{oRQMPLl0pX7
zdk2Zcc)U+lP}SQDMw+CS<URYId|6fva^i;Zg^wR7_Kz~YdR%m`(kepV*5ljl9`1WU
zN@EWZ4*y4mj(t>h8BN5U$2l({F8i15{O*A~i_*k#Xj0}9YgH7NDoRfQS;`5NSa>AG
zT=M_+v^k~zL;als^k5-+9F&HjXoYbA#XGK9RiQ}ML*xunxUcl0?j_et;S21+QCzp<
z(P8<!LLh8)Psy-erD*t=C9G8%$!&>6iooVue#r(lYk}$4+%keCoD4ctczq;K04hOX
zYAxPI8o`V!6w<>&e@$J4Je?;ykmQcZpu3Bs5{ZxfwT(d)8im}r2I(NMls<Q0!6DIb
zPelV10@>0SLaoMcETWNXazY9HZ?FhQS=jkzdHBnbXGZ6o&+>LfIigH1&DLK|dfoe_
z`2w1<^}g2dIa}*Y$dYu;`S0)+ThG|Lr?*xnWGLnsR8h99>-ECvH`n9+td0ta*!d!c
zcj-eO7ofd1`p=w%+>da=YDa%{vb;TjmlW@X5cQ81I%w>b?k{D1wz{~YdijmnT@_R0
zLKVucP5?i=C9PjH6HlJ={Tm2gaqYCv(ms!K3N}&;35e{u6)f*dI5;Zt?Lmk@K}k{>
zPZP^`Ga@Y*k2rWwz`Z(IR9^~0-Fk~D`&WLiIc*exzE&6>u^+`{d_a>Qg{!ilB+<xQ
z+r%lg(6}BR8%L8vn?X;a{~HVUnRPxCkX8FVD6Duom>FX|FM84`y51F6i~hiUycKM5
zw)51^F|U!Y#Qp&pUhNB9obU(C>vbuIS_hBhY<3~E5>8Y`FgR-<|6JTVP;Vfx4b4@E
z;<S&Sb61n6@4NimT~=|wMJJYlC<$iAt3NptQhguy1M#m=hIaH55UaLw)z_H_==<P2
zhgE(lJRiQKtIm95XTi{pJrAh^|8zTiU-~b6zyC0uPpxQhf8P3we%6NzL!y22K#lK4
zS2Gw~W8TLdPgJt=B?UlGJ^8EcJ&}Ng>*(kZWtV=d6R3O4L;hr_=Ys2GPpEmZ3%dkU
z3#0&@`niD~Y%!8D|CQ%$hPT8U{#{%`<3A2Wacd8|%8QA~k2(umvg-Tpbag*URE|x$
zw_SWK69M-G2=Q@z>Uau|`slj<q8>ieEJal~8+f4=)``74Iwz}%B(6-6z#{iVf=|4Y
z6*q6;@gW7>RsOf(U92S}cb~iqH<P(t?oZGxaHCQCh=^L2bmycaCVt9%?&sM_aLYP_
z#?l4$Ws()^4@V80&hEjd06Cu_{a@+vA?H&hHX3uy0^p4X6%W1(k`l_MBHYZtyti2W
z&jnYcGPjl<D`$S@w_{#^6}%gpDI-4jGxa}wCxvK2^YisYoego+;T|}>fhhG*v{*Jp
z`KeO&9X`ilUrjybHH(qU3)&JD(qXZ78Xf!qEFruo>~Xv<nN-ApCJL`fxDl>|f{1ue
z=ghYqfs|hfWA(oBrB{VcRUxQp4-ZA4H9qqL_0y2@@qF)9(~rZiy{p^BW(d~*!@1m1
zAZ)p^=Q*b}*5cvAunuzp0knJ|!OXlIDL(h4HUC9$VzAK5GV;Qp7MkwpjzhOfGh|Bv
zT7r(=c}4vr9}=RE#b!;VfZUi(^W=l-W0|P+8Qy+wDm@~$m1x7fe#`sk+s0i!YM^op
zrgrc>crM~l&bG$~U!%wGa2=mzTl3-}!*o0hC$zEB5g!;v2{y|yAeU{!qYA}0?SRdy
z2_@Guy`<|TWJy{kLf0JU-^8~8*s}p7LRBdw$UYrg;I2_Dz<$?}rI4pZXs_-bPWvXy
z$Uw$V%6)-AX_#a^D3bjQorpA=`ePdPu9zL1>yd}ZE)x7Vf!!9>>G)&}P;m4SiP35*
z0<I~5WKUoN=fd(3%K-IaXH+2~cEtmITyXfXzVNy5oZoaw(#t0g@%QoKLZD$VcnS#a
zsR6qWua8Dw9=8KhxBLJ7$P0}95s;r1U{w&5EbK`#0w_6t0)fI+P{K&C3W^avwu<{h
z3JH0LY+(W8BaIYbtq81nRxpGSJOx?@)KnE{25Hy@h!p}_&qh;&>`9Otuv_kc93O)=
zO+kdCxfsDf&R3#Y;1z@;VMXlvYz<G$>H*7Bb{;z`C0VvrFCXjy0>6Gff)}MmKfH^D
zjhy)BE1D1k%94i`L<xrKF7CavMZxC8D2w0z?u!@KAI|x~8(bslh~36Esp#kG#y=5P
zuOE8hyhC4k_CZ8nWg;lyIx8SL6%BAoyTq(j7L2K+3-2rX65jx20{;q0w81kPQ$moa
z19k9ks8do<4_%QnytQV29V#lEPi$^87g<2Z7pWB{+of=<U`{Gf*A(59`Xf-T)E?^f
z`eWd#d1D!M)V(T%G5j#ykTnaq=|J~ZO3JlPSBj_MM&7-sHeMO6Fjw2h*sF!efI`dO
zwK+v|RbF3%cU-IR!^_UoG?QOPhG`|;uC|s7QKa{`y|KKoczV||5$aG^q>%(t#5San
zvt^8~L3k6YE|7t+RfiScmI>Xpz9VS|6998|!u}|_XieP?dN!-nR$wACKW`fp;si?|
zqDX+(@22r0+``5Lwre8;!ou^-IEUD9Qh-E*OoVQ32&;XG;f`!_Ks44DtFQ94JVuJu
znz4oW%BHfADp^nlg3j8it!akcj47n$ehO|+B<U<XjwwA1+dWXEE$e^ay*}ML`?_Y~
z(RUgHS%cTdLny1TR1&cq5Y_e==sI9|W8zAmNAG1Ax(4hwZ~b9zb(0nb=Soi{zy+I{
z+YReqePp8=2oxQuA|7YCBw%}k1X2Oip=Hq9NcLd9xMq5=_;=2Pf!}>k4xXX~=?~T|
zE0wdKaVljN*`9LL?Gl%oGi{lOwO&k$-f1ug5RWY72gk8yB~hA%%S%~8DG6A!G*P1|
zx*d(0w}$Naz*&gL7T|T8CwMtmv1<2;@N(2c9>-Bt1;MhTbibUTC2A(u;D>FdxaZWz
z9y3_yg&!Z?`u;IifDe;@frRLl(t``b3~t><wnVVpz2%s718Du*2`ZrF2hEW4cbnyU
zW=i^Ui`d7@K`SHtz+}cL^?vZXj<`n;zqJ=>ssWJTZkij4hWYWK&^9prkkxny*TcI5
zb!vIoTchI43*Ci5u-);p%>!gispLrmNxiI%5#I6vuEQV8-+U>^*RTH1{-xc6kKq!2
zxH);M{sB1??@Dn?07$$oU*=mE%CsR-q!s7E!t?hPgR4>$3evNbM54K}i=K+V{(pFT
z%ebh%sBL&ir9?tH6o!`00R*ITq#Hp}QXHfN36)l2hGqaI1!<(a8B#*J8M>5ipEKV7
z`}e+|o{x{8blkDl+H0S4_FmUoS64(&K8M4W+KQUU8_-e|TY`|8-jj#y4s1ry?2iK`
z6nw9-?6*8ol<phHVO8$T^?9FZ${sqS=&AElk)#Q76Yt`K|63vTF4R_%>!*YSq;e}q
zn%pY@5k>GTSxq#$h~1a;xL4_wib_}oGe{63%I>fX*1$}<{djo1sm6}uqGM*{jy22>
zw#JY!+1h?_x{78<qHi7ZhuLcus`Q{D)ULje4c>Y!IZckOqKVCMet)4}vjQ?s$PkEt
za&K>bt&rECo<Ij#_r8$@WnS|{vtHOQbzY6ZzLaUc3BMO6?7|91Y`SfP3JT%x?-r%q
z&b_e}P0H{P@EY<s4`|f+ZB0#Tvv}lpD$QP~IYi)`2UH+MemBg@cId2!$k>wHT=*|e
zq|KbyJ)~zhgrge!B*g&U_62w)Oihv_{|Ig#*=<v9993jv!;7lB;@Y6z8R-4hd67DZ
z{$%k(68|cYZKF=#aZ4$HBma|0HJHB?j3B)<6GK!Aj_|~kj7I@Q{4t=hIBvwz4i?X{
zeV<9?WCg$#K|aytUvzxYJX)1f>5vbgC&Bq=0Es4i2({G+mMS8-QiZ~gWas-Y&SMj5
zCpptlTpO3g*<G$;6Oo<Sr<bLvs=GYB_OQN)&ES{$wE*}KgFGvUN3QX`(}?7$O8NYE
zzaVbUP6!)5<_y~pP<xL)?z@VwM4Lv%U!T+<e9Zeq>dedj6HXnqwRyD#7i*J*kI@61
z1@Pl=bWz@D{1{Mg1@w^t74fh<AtF`<oNu&%pvZ_t{xWa?DuD$B0cQ>{I3GBN;K79z
zRiQw|UC>b+@-F-b6UF&PeF;I|8?H&a%Prn#Pmx8T4=1-p_(4JUF`aNEAOC*V6iv`)
zdy?KNn|a)9rJ_|>WnJ4N$^Bo8JxR{r!(2~TILC_nvH#Bf{QjPCZ`d)Dro`;*F6d#W
zDijx$b<dpqoDDHH`Cf?s2M^b84cDY*%H5)GgVxnPrbO7FyMl0vr{w)x9>JE%$09xA
zt~yesrMLkm6U@>dVWas@=O@yGGxe4r&?99Si?-X=!24F_Cr;0Zs=rMZum{V!Esn>x
zqF!{6F4mQZS+3j(at9=T%p>V^4X~6%4m&k`X{Tpg&7chI43#kG{;WbG$+sS9`+1R;
z7(mRui0@^bcRptqR3I64&Y1ZdHW0LsLaD!Kn^dB6<u+a*BXAl}@Zs!@Aqcb#%x*<h
zQTWVncIExH2`6&bFw}Q89PR1~JyF8_<<ZFhtLXIc0nR=pER_iv@M=ytJEYxOr)sQr
zk_0nT?G#A}>FC_E%Jjsr!s@IUdl>|a+dPw~S45sHA?V>QV5(<h5vLB0N+%+=Q5wX5
zyxe#9&7Gk-PwFN}PmthsrB$nqpwzravrWC=D-J1dmsAjq(u?CKx_CnJk@_YNV_7I{
zap}vc@bF%P(&uQITDtItr&m_n@q7SOYQ{!`EYbVxDhDd6rZY1vz}4@<5dv@^gS-`q
z{;2=|`0ti*gu#^zC9oI5A3_K!_srVhHkO1yj-p~L^5exp=6g2gCLSP<_5M>B`TBSZ
zp2pBS{{Q{wOd7Tx1f&3`VA`FR<>`Jw*|Y3HoYD61nId5dcuVJ&*yTAkJ8kEeoL=i1
znnuUcwu9%*9iC004Gl7$k{t19@W8vp2UAb+TK}p-RoNf}9%Es63ec-uAQ@L47p0b*
z{hXKPaGn_tNI_*$g|;J4JlkSt19<<N-o9a&D0IND@_$?BW>9~s-B!XFlo*Z>X=t((
zSo$7A1gK&g3*<o@8?*)W2XF(OW1qs{_PK#X_(i8$|J%CfoxZ^QVdLcgHoth?o%!h1
zlYg4a!{F;pnM8RFn`77j3ilD1!%^(=i&edeEXHhiA3@YACpqvCH>WPe7+LP3)R2e;
zuH@~?8jKxtVv(JY-zZ_L5eJMSx~k9>>9_HkH7`mMWxe(-a!WvwS)0#sUB*7<)V{Q~
zCVT8A0uN((Pq>1kbKi1mI^y%U+^QwZE21-`6Sd8CmkT?fACS?$cRirfruuA}6Hw@9
zC^{#7%%6{DLxShu+WmM0+1fCYdGP5_Fa|T}Wi0Yoc+k0x#xF&h8jyKY0X8~pRzldF
zZ2wPWRyZQSu+l)F-GZtFV*wEsh~wrnH$r?f^S&C4&grU9x|v?zHmeqoS`%OkeFg4s
z75*LumRNn+vu(g=%>ze7%b@+*1k%FlFe~&ExKPi&(Ig7JNXzZQ4AG<tl}sJBt+NX6
zFDjw}gm|e88yz))65>l_p#LqoI2@74RPkJ(fw{p1WB8xZ=$zcOkO2U#@5XV)*MsS!
z3L72M;Ul#7jGZsVXtscX1b<uM4C>ebe<Cy?X5+&jcsNE#M#TI6d1naEBbToOEQJ<C
zW_UV^Kb$F!$u0}ksK(GD9z1mSE~WeX*IFda_hUMmPE?^zse_&xJgIL}kEF1GfeGPA
zN??*8WhVRPdw?+7ap;_HH4{jY<Ht8kG(o@*@#vgF0%s`QbpPDH1_<Eaen5kAWQH|}
z0l<<24T>V>h-h9&9k~I;yr7)wFx?41XQ;AiulZdNP%Ym;4cg}vSiNrhF{XJHcPSh|
zZ-Y5J_e?#6^mpurT8Kar0AQUPrDn0`=?o36ZG<!O<AVGF5IHAG?Zv#g1u@O;=+gyX
zGSF`f@Egn#QpIuT)GC)|=DX$vY6ZBEfC;$G(uTK!uo9&%0HTampdfQ392tKq-E#in
zeovK~-GT@m=osLo-3H#upX-7G#XV&+Y19HlYvR+_Xd2S1Kud1`Rb?S(NmN(u%QKPG
z0k_>QUJEp8m+wpmiw9|1_HF+P?3Xxc2T`cQ+LIAS))}w54m6)6OH=f?p!@WX%{Lny
z&rN<;-XJeJ;y!&wiorRIogaCu8;DH$L*EXq*FB}?U{-o|rLah#FucO!=Y6><&k%-n
z?aQ5#qC65M=BZz0!0T-Cra*Van8kSKn`e`I_k)$@RYG!>Wqz@}ct>aA+$!$j4JE0G
z(+2)a+ao{8t28n)TE0RgOJEa>>K(Vkl(SYq)MLXxT2-(d+`ys7#Rs_>?dY}zmI&~N
zNc3Ol`c}4l)^iV32G52N?2Vv#l3~6+=9=vpFvN!<WbVfhzSYHvvlPVv8?EQ^{fOKs
z(^^M}rQx4+xKyiOBHvRki@e&`hdps3<OHSMBWJ4i<Wy+OGCNk451R%uWJf49E}fHt
zh^t{f7(=#iXtuX>o&!beumf!_jiREh#5Y(ZaS4pCKTf)aR*sDtWHmUXwV2VhS(H;Q
z^wxt5HJyZ2gf^Yx9@$F}fb2LCX!TqS@?{THV1+%g?`&__44VX#YT*P_>PxOsvqIbq
z_u(S>EPH~cv}}<2hSE6ys?rOOhr)wz<@VLTs9br+HNGQcf}lQ8DnQ^4kFDmUgV)cH
zG|WJZi07yNhPZw6kUcS@-@x#cONO!XXe06YFd^7cDm#WxQ8DyAcYbdx_XamWX!4Sf
zGY!juXVx~l`aN0JQD!6dM;e#Ckhjn@S~)!!xT%&6YxV9T-JaA+^Sy5d%fcFgJ)HA#
zXi^s1d|u7NH-4X>Wy=b(tQTb`9*Ixw4c&357vxGysupHBm>?0qRF$<VVxJ;C%sANe
zT#P^F#im@QW<7RKs#Cmj`{~028R`0+Za2uD4eoi!!lEFTpl7R{;2~Sb@nQG{RiSEX
z@PuKtaV$N#;K$x_X>66`sCO1MY_tRU&YzN&RELC@3YnIZN2*!*6c>uEWoggSauLW;
zM%4W+T9kyZCwYnz`g=8vowW)?)^g7QyyKrC*WCWz4HuGqxYLUhGxA+Ay6Z9d#>>2x
z;fM$J$4`ET7uEby?2znLN<688CH`E;FJ)g+l}tEtCD@uKZu2&b%uWmDxYXAAZ?Owu
z?7q_2R>E~nuJ0D>4K>2)g+S_}87^iuki}&aNF2NDo1%lgtsy0*CWkFk-j&U0wAou_
zp`lHv2y0b7ukw(BC9BCs-k{vpu}UxTWmy8zFwN%lyLEL?(KPa0hQlzq-%4s&{#S9t
zkb=gyqe*z&YF2a~P#*i6N6FtJ%0WFjbi406w?{yEX~56%^&zZw_-VsEbuAw6WQX08
ztki~13XskD@%O5qkIQHz=t;j(77~tn9`}YR`0CWdC}HyzcoHt{|M**p0K!@Yg|oVu
zFovJ*aL_mX=|Q(6zBG6KhRF~~x&tIe1xbA|%)O@Sv6UbVxH=8S)eB+!E9xTPy7up|
z{ihutpRF;d<yUHLJ!Vn4n2_ta4Dj1g+J9^S{R1orDr9a40MOyP%$n?$Am2r!udwN>
z<?Q>eb@=>Um%QheAR-^n#a0X;V`T}NnE>*+S8|kW&XzAYx)1W_MCrDYJ+pH8hfLjl
zGFp7hUM@iHe;({7W^qic=h?uea}%9J&o()&ybciV;XKL>K0Rq_nA*w6?c>UrQ{89T
z@KzdalG;$evUK}|3=2>Z`u&0wC1I<qS?cxd_3f$2Q0&pd+sw5&P2r{Lo{0}V51jLp
z8$O)&ZF%))4)whX)MHh+PZk%L$(?@}zdFf-xY|*Sn<EojwcM58;p>cf;SsC6Io3l%
zo3hpCsd^eU5W_Y5m~!K;n2EE6$~TtK*FH*cqJ2*lA*cskwV>gQ%|~#}nK)dp(h@6t
z(Eh#<j*!EI`!Wko3s<K(evr;m=NY8Dy%Br%X8pF`1H@vN&kn6UTbo;#I4XE;In;Ij
zbnQ5^s#*XKD8>jF<eyx<U${584y|yvu*LOt)-&$rb%@}o{&_ng7hYtmT`bb9>mZ2l
z453vlqS7)Las9+l(C+Z=Kv<)b70zn@lMy8Wf#YSwww=;^9dNk$j3%O@XzfVl-uP4t
zXWek^e$`+jloDp};>`v|Fp)`YXRRTn$aRG!+&u-&*N;EA@u+!_%Os3BOh`pX<K(ge
z3&f)E^xsYv7E)GLQN<6wvaNmn62C0j<#vsQmKSc0)oB(++|KDUuTeQbMpR-vXt9_?
z+ezxM)r`dUQ5LEWo{#%9W;hV{>hmeh@{gnO7S%jew|aHApH-H(%_s}oeZt#LN)!vK
z;ol}fFOvx+$*tDDtT3UNlzNsf|Cwn^4{~GQ<z#z)A2s~{*@%N>!*Mp=iGh#B5gOv*
zYxRC`r?T;h*61AeEY*w~Q%rZT9=i(8ChXdY*i{Jc%1nNabe8){33wtyx`g2cY`c-R
zNF&lwuYt`^Bj^`GQ<JLu{67;1<Zp$Xx8zG8>1~;i6jsOQA5@@4WxiRbCxcp|(cC@f
zxMdS@6r1Fc(H*~DUwm=)$Np;)l_{RxNCnh|$Hi=1LkJdYY;;@PGY3}p5Q&j<at38F
z*NfwixmZ5G)7VxHKiQ@e@p~x>4gDgMlK#~~hVmEt8hh&*cWDSa5xF&YeBFN4ROM{V
zwEwE9btTzS{d83dsA+~<K^Pa_8>_uWNvpkfOub@$tf6|9lH<KoYsFBq?&<sh^>?bk
z#Mbo*e&_u775@-9SzJZ|Eb==0?Kk9!ObKeobW!8r)4RF*K|Ec)p)41RZzW^q1YcN{
z6{XUiQ#(Y2s}2LgH<x+e;n7hBKo!G_R=Sa@c;fAwQ>srxT*2QsRdc8e(el|M?<qf+
zNZ>xbtE3pU-)>ec=6YoIapP*;mv6};eK4j2@+y}OyzyKnA<$8XD3$J$kST8<?~}^~
z53tns%tpZe<I=>xu9u_kh216~5g@TnHxN!SW9{^W-!Eh+tywgw(UKeF4rB;Au|XUY
zu81xSH*4GeD8sHau!1T9x+ETyT7$INK!beJNjE8i)DI7o3VaCxzxE@KE6q2Hrsq#=
zce-7$f|LOYC3V=S7!L~iHM=>3y#wdK6YC|AnpX_wD6ULnpse5A2(jJ!{S+%`9UwCS
zn#$Zkt;ugR(y~!|<Kuyb0LB;;N^w@r(!*;IKteC?Ibj76szPItn$_>lkc=Kjz7=J%
zmY^>{nIbR;#lEVCm+Gr4r;0ys>OpdBU=CF%d;0qBr-i6u+r7vo+%G^`M4;ILr?17!
zrZxanCG$sv7i16z1WliMc(q=~e75apf`Xh_0oHVR2(&wd%Ip#dA?*_a98CXy<61?3
z`)TNH$;&=zL<kyJ0Z7h2E>>UF8NJL+83d;Z;{lgU;=BgRz?uF`4=3dPXgfZ?&$!%{
zoF}2?9jDg=!5zeG?-@kN@0i?&S5i7^pC@ILK9*hICz(9q-ybyf!x~q`3~AY{Wgl4d
z6PZ$%<5#H-`U1s>eO*0fH_vTllemxxUS$Ss4lK@7SUdA#Po<c}%bqIl@2KsObZpQG
z98hPul!Joyl<2eZ6a#oTrimP+Esg?ecR^Y0**NS)X3MU)QTc1wpacY_t^NU+M<%Oa
z@i>fG?d+rsBzJ<b+<Ct2K$bIo!j9HwFst4f5lBQ1Bimn1S=Ab|Z<EU#e08oNKprV(
zHQc)c4y(2E%hx8=nD!|_FM*oNC;|sYHoXwSlbUrzoA7Kk=!YVvyN6i4v`~#nddJ|?
zL!YbXSV4*Z`Y$Z_tjB8^B)Z*05Q`@gf_^Ar8WY_!6#84C9kmek%eKrHFa!__2XmN@
zH^dS2=yI3W$fOLsIspXxXEcpZCAn!_=5-^hJ8Ew4xIjPIF&gs;i>0|xmrTr0ciFBK
zJaPcdutAnN{LU)LJt@0ipZgObSsqc!zqBR}`zaQk@@Pilk<HH*1HFBhN3P)PkQMg3
zGs&|qV<l#L_;l%A1HIR8Gn^&TNcj(Z**LCelh9vdoJ+O+>XXoyTc5E(2n~$XGWU#N
z-%V=iq|&N#0$|!8t1LCZL_de;p{6l}tKyZOQmF41`A(o@AB;A~o_ijK)r2-;Et6_=
z{mytG_DqcNZmOQGSj0@HuPKjCr}2ZdfedLtb6auWX}(XWx&1V7&9FToNFU%9LJ?3)
zA_qArYW^7e89l2{bISs0Kl<0&r3ta`^<q3)R$6T|O-v8}3sBiw6<XVA&VQ2I&Z(8&
z<OZ(#b%YK2#e#7pyF5cgk6wt*blsvl?06!GB4``4F_vK(C&zQ)44+SrmEkjI`FJ4i
zf9A$=sTsX6rKvypWeO*uM}qVdFrs{T$!Bl(RY0FqhL3$~|0;_rXdB~>F9c-aH|@U}
z8Z)&HuhMu;K!utZOJSw6t58XX<y@>hn_I$Nk_EOQto@5y_2}!b9(VJq1H|CnM|e0;
z5ce}o>tIofO@{YJmh<4@eMagfe!sJ`QGJ@8*Vk81lRxnFRT?gREpj(A{2^E~Y62Xz
zM|-C?St!)nSsjy=JoWI4H(54I0>1=`-X*v!P#Fr@zY4e^Ae9EnGAxGO!2lkB>!+!f
z0gbI0-;Ut5=kZ`XXG>xb2*4Ww*}`xiqmk|>9c|;)JaE$}JRA_F96eA(P7yjGY9I`r
zbd1*E7H_5lfdT+b42l4ZFiR+*fus0Z;FLqJKGT3ez%{2FxBaufnnScW`14nl+kec|
z$$6ZEbM__k@j>oDW%kjL)OoF;YhE1uh4r#NqR<=PsUi(0BX2;4Kvn2N#y|SktdLCi
z{H1ndjb)pT1;VH^wh#*Oi}w_uCE$K62Rv%4GuUj3?-L8*C|)Q2D+)g_0()jwY7>Fn
z0j5`7at*txu7vmWReFUnbhoZ~WNJ=<wQeBLIZ#s0F#;U%h=T!gq`8dKv1nn+uSU4B
zSON3|4$ruw<Qmq!1V%yz1(pKWk33$m#Ov1&ZVQ51fy`W0t<G4AegAuJ!q#RMc{T58
zMAE$i1O}9KvJOT5&L6X@J2kS&Y*r&obixM#mlO{Tg1brF*OhfHdtrMeyN5r(Cw%rK
zm>$2m{(h5v@@D)vaovzu0?Bu$zQLw!U~ErrG@@?uUtftTPyfwuNiWZ_Acca;uj(!f
zpAGxrGfElN*uX7Id-=ic{GtZp1yJ?m>JZC;Sc8P)hp35(6@FS?l?j(*AUM@}J%)_1
zQp9F7B7{ki1JCoJ=MwYJmQ)eW?Ju7N(bHG{vUd(H?sfNfoh+O+Ezh+nvg8w0QZiSw
z(=14P3_THU)kyIhwff0Cd76I@yhe3e7mf`A#Zf%cfQTnfD4D`KCKyPmk!FIe&ti_~
zk}Kur3-7rz6}yw+fj}%A?Owa)nxf~^uv=ZP=wRr1p}r1m7<)Y6T$-bR=hrI`D5y80
ztm%7gmeZf=HED4s%WS3K(wX6$X&It{u~{XxE;4)xEYL$3cJ&UsLDVpBz6Eb>KmK{4
z_}5)FB2~w$hMc)sh5wq^GxqZpr24x-_O$#5L9Z+h^xx*sw*M5D<>hLsr#+qlgFxZP
zR^}!*O;O)@SgOTq|CT#3p0DzM(YEXK4fJ&7<g9(ZAMh9iQiOhyIpX^+c|Hf5dFR~$
zib=PB5OwLC>vwGTe6;l(2M5z+sU|+EfPL6($aO!b)#F{|s9U@PeQCOusyebv2G|oU
z9KUNC_`c3uyFvQ-dW9)pa;@cX3sY(34qL$C9~5E$hq({IVrFq@tcw$dqCcLkCysl#
zjCgFk%O7HFS}`QUOwyXxtJWX!`%RIC8JQV6^`a~}9-VPa55idFRuO6n{kU1Hf}=-8
zJKtpXm>3(dCV>wv#1TT4lM)H1ry|i~HBpP;2lg7Extv0rM}>B;5i^N8HQ}Fn)1hZq
zSFS&hcjbVp&Q!rM+8R=_!16XL`x5bkK!1S=2BVR7l|$RvPX#MSfaadRt}<u+Eb`VA
zdHi(rSJJ9}Y`1?o-nQ?=*TVvx%d&9%RF4y0c!~bR^{hW4al$YbDp_P;7mAO7=k<$|
zv;q|(|F@8}V9G=pb#Avj*7Wv-VHu^i&f_kUZ}LiaRmpU)>~zSKD6#}~BC>Q|UDs8o
zKg5YlV4F%jG9T#r5x6v%a-ocYCso{_-#{4Vgu*)hKI3utJmz@q0drc5Cq8Q(Y~`}e
z>ETyb^|E9N3jap|T%C;6;}uS_j4uLj$drm-gzcYQ@IFhiZP1lW-B>Zzy6HFzjgjnq
zUFscaJGJ8q^KJ44e|Nr0%y@I_x;D4C%BAvim5V3P>)Q{K*4Q2!e`%e}9bEFW51^S9
z*=Ifs$9~eL5Vp#ys!<EmkD8S~b+syfpJFQle*iguq2Dm8ynoWT-e#)Osi{DZ`bP1Z
zLd?{6PgUY5B-naN3PReLb78ed4<^Zfn<SiR?!>kgaHq3Zx4X*g;cHoDIUL`>RJBk;
zD9WWv1r=r23pUW02jlRDJz~Kin&Mo+4zf#J--_SQztS3F`m#F2WhH9@!aeZEem;^l
zBX@yt#|@7a4~{V8D3rH{;Rlr%2U7n`Td^4OhwtYHj2cGJp#F3-2-()KZxrLpT9A+^
z0lJKRzL-8{NXe3bb!c5~HvktZn5p*IHiRE)zNMxn;zgO<A+AR~YprNtWDKal{tZhw
z`@O6@`YLo2@`d4CT^N!r$6VJ~>%(@^M@thlrtI}lc|pLcv)69|J>GzXi{AT$5Eovh
z{MuCtc0CCDZ+usd4NmRpG2`0P&KV-qJ-ZVW;;a0_(YZMWylD60ynVKZs-t3etUie#
zJP7~noJF|_o%CJc%3(nwj92h~D!na66;L>{`RPv}(iv4fx^EosZ28=%GA{vSKmh;K
zt}3h^7WY#IW2T34V-JdwzbGBWaf5MbjYw&}CZKJj7+?3O&leaC$$(hV?>c*TK)bfl
zfZYxAUEiEM`uu=IEHSpkxh|!=8^F5QlC$OSGTOsT@sx)i;M_HXA5pny<OvA)uoNVI
zw^m9{b9`K+$)3MT95ihSgaTjY5X)IcCXA&f3JTp_qJbpT2GX`#6eHoTNIcskhEaW^
zrWo6UA_~CJ<N#D<It5Lv@(jNJVkv_SvIvff$rcsG!?KEf-G60Tw=I1Cp#^})B;dqv
z=OLX?&UUK+`Pf-N^f(-FTQy*GHW5UzD#8#(U>*GiE*<?ML@0|k!6_b)%IIGY$jDF(
z>d<Ma362eq@gH!bptl+@{N37s1W&s%DT(kb$CehVaM&;0B~}$U%9s4*tWg9kEd|LM
zlyppcURIYdc1E?=kA_IIC@}Xy1yYcYgwBEh8__Lxqh{1|l7dIyfpH!@1-4x;H^77y
z4c;KZ>55X>b;o~<LcIk+D@l@^K@E&PyX%FzOZ5&}cR6jwKts`$coNp^`K83&*%^@5
z24I#pa=5Qt?q`Bx6ngiUH-xPL$;d`DR<KuSzpWZdcd7vFts(&7B!xgeeyK30EnbX5
z{l*SD<y<Y~AZfiFcuF+M5>$8_BcLwbiZ?osf6ReEY{=4s-jB`A^+8Nj>htzM`0sMQ
zv&g0(8CSt1z(Od6${!E2JfTzxs72-7sxfNwu&`8Q0E}T_a0IdE#?*NlJWOU3O~-%i
z!8ofgDn2SA^Gh9Jw|1RIGaY<bq7HtLKK-gEGPT0Gsh`VaZAw&VS8?>|we0C!Dc|S=
zGtHVi?*Lq7MJ!zJ^qw%JCg-d(^0^z-VW<VXf&a$3*fu@<L-RA$ZxZ2uS9doY@1XK+
zF!z`&fjNLT?4gV3Ep&R~tIJ?+avT%trx1jKAq|N8uuLPP^a8XIMGl{Tw|1Y5EbcQv
z&gBJx6nnj0L#AZ5lqBe#-p>y4pSq{-PTXMa@^=1?gcSc{Ilz3k07cy)qUp*pf;)0m
z!apEg>r3e8*sow$Zq>1I@ZWfvi7X|W%`Bz&KZB}bP48B6g}+R~-J3fx><^`Su|({{
ze<m?pDfw!-Bgq5$6qT|umQsWyj`!Xf+0KC$<xBp^9(&?nUq?yiLBvlEa;HKObO9yU
zNr6lO+p<r4ivAZ&RRhR9w`rN1OEH$jMd{j9P>?VX58(oGJKSe_3|*8Ng&m}<`5A97
zf;?beGdNYCpTOrH9vhI<aJy<3y?CnEHA7ZjF7!aqvr#k=6tn<z=EQVXcX<5-`lDMf
zcV@)XeN6xh^aMD$YXeyf%a*HG#pli(2Ad<+$Xc;Vpp(phGsYh*hwTjCgVlw?WR){Y
zp^I7=xB&p(>Qmctn;XJ$u;sx7fB?8K4@XGa)R3=Z<pV9z6abn$_O8*7f1|0N0Qi*q
zX$dg@8bY1KUUS6oseJH69Qn_uqAm=uf$$kICds5=GIzC6<bl?N*3glc2;7?j6E-VF
zGeC1ak`Sr9=#iJzPH^F-p$ksTZ1|4>b;h^VhR(95xz&e$qy~a9QMCj6P(C@#V84_z
zqC&rOLwXDk{V`J*L%s^l`U=sN6rwe+F5dn36Za}aWy~NE+2JdQ8MFwgoltcxoLW+W
zzD|_oSfZ9XyF}<S1iilYwZ$<tk;fy6|EP!}=B)aXbC_83@W7;BsMOBx%*Db*)WNy9
zZku_Z;a;MsMz!ATw7*QV)$LNx)d3UWPbjXe1U+=1@9K8`*i8-Ee^p%`a57+vnnIMs
zbpBzK6&V29KdCT7C_p2J0Z8T$1Mt0KB#W8e|MkDCr=ATRO}m87mc8qO_u1<`Rs$$|
z0=0%Vudf*~e01R+oDjQ>pM6}1q=+LbZItU&vvmq~x3MlB_%;<OYI~;-X8Yn1hKY`B
zIufpj&MZOhy?G(kf#Z%{jI1DGbGSg!vfRPIC0WdX*TnT$LS^0-?QP%97swGV^h<(X
z_3|c~`0D2R0JTMgf5wA>M<W#gO?rkC#wPWt2_B2$^APR5<TIDiHf|rYW#8?4;k-Xt
zngqoAOd(xVS18{Am+KGCfESPRKTgkn!3pWSa!8Fv6zjbrwd-{e+TwVE%8BX<{3M}?
zuez#4`qmQnCE>yMrjhjJQkHq@^|w8ej)iHonZM)V!m+!>2a95mrikSatuSk{JI>FZ
zpJIQG=!{r$Ir3o=7*4`cr05<ie_mlFot3qLo=Tz0#F*Ol16;2Ycx8OpslpSpN$@@>
z=UaeMyTOigWMNc+x_hzc@jfmwz|g&g^r0EO%CNUEfggjh#E;%!g%YV`gQCa(Jo>4^
zgl*dE&hTL(p;s`uaUSg5^8RaH>)^zl7m>_tkZru5qDGp~B;Wle?S(teIFB{Q+#ATK
zQMuf7ObvraprD7o)Lf}_WyyJtwk`jbAMhD#k2U0OKWn%8k9qslJ1^KbAVW5Gm$=0I
zS5fQ?02mC?g@1*bCX#;p`@!b4>X4K>|F9wDtPpPIt7g2QtK*%HR)H8CXEakQ{rcUq
z$$B9y@aZq+VDZ5rWn70CrDfMOc8B2YU}3j6aSd2!<#l`@{$v?q|CzKq0_KJ=_=XpP
zK^%eDYSxjx_>+V#vS2Ywlg&b;ghlY9X)&&)8u8C;5&j3798y6RXg^AXN9gX~*sua?
zqY*5c6%u?7wtPcqd7Brkq@i5-@L?9$cjlfTwozuqaAGVyThjZ4l@HGQ+*$Ea9RijT
z1fPtJGO*@tVRpHZ1U~!^a9&5t{tA(=1bbsKG2yWH$^l7^gROS(@(M)dWSH8ay&$9%
z=i;uOYZ?Hcv>19ix~AOtYANmiXFPwEXN!-#K)kjS>d>>QeUZBGMMWbLs5-P5Y5idj
zjl|xroOY@DV|B7BG!y#iLsF}2D2Vie=^MMr2MGwV`L@cnq*IEDQfIvoiDK9@gAwiE
z)1t5VpRWxrkHR=(t?`^*eiKD_;2Cq6n0Ue721`!|MXd+FV5Xg6fn+-#j;<PO%QAym
zSPn7nwa~W(RpE}cxzkvD>^`VDRJ|?gb7zWJLBnoDO(merkSBl2N($mv_Ttxe&)Aee
z=Y2UCWitD8h(ro*UU!+q=foh=Bu5K^gW!T4h}0j2RN0f#bR`{b9P|8>4F0KmimG{i
zT0;ZMrq96~Rz520)v8dmw1{FeF(&;%Q61R7r-lI7x?GCHrPb!1zx8@y9jscpBz>v}
zLQ~QWTVLc*VgGNf_&}4Xk@dv^E@6J7lHUMbZge_Jtm;7r9#%mU4=OylYnbI~m^Ym&
z&My=!?vE(m^s>Z8){a0sKBXXegBBaj-C+1)Y#W@!C+H`*n|ItjJz(u=Y1<qo|9B_4
zfQN{`!@)v;86ij_`fmVq6q;&RZ=Gcr$*o{$V^|}!*PC45GiSFy;JHtMOSbYNg5%Y#
zK}vB+ZagdF;&6wr1PbMQyY9``QUlvI7%iyc+f}b(n&3M7%&}1-D0KoJq;?Kw25)@%
z(dETWHIT8{!#Wx;TWRyOLLK%NLB3bNB6h-wNYfl@#^Ko;C)g@F^OxA^Y9kJW=}Ed?
zWsPq!<z`n8tDgB^DO6oLJweq;@NGrDvkdw3VL~5Xsr1$Zt*Vx4_G1<xkpQs+TmuKi
zNZp<R_IG=~Uxv{!@o+fZ6^CSx>C?SFj%5jn2w;c{b@1sC8+=oRW{8hhy@<<M^IAI@
z)@<8Ps&AB+v^=i=SaOBf&~0H<%z>6BJzt+61<!jJ*2^yKlK=@wVWafSvvyq0nKIH0
z^s=QXUVl6bCS^Etd*<jc73z}#OrB>cQjhU}=6ynwB)KwrgbFxzQ*+hL$#sG$vyMXi
zigUgvpy%jyRz}*{t30J3++9*R7kGgcy6VylL#lM{&0wV&5v_U${-htOW55g5ki)}k
z8-6^snx$r$II1=~?K9E*Q`Ty6wapaJ8DLdR=h={rMHV5yJ(@!w_kPQ{5Vuf+4x50t
zOrpNC=f|jv?_oLG+^(|mHo@N%xip-(D3AV55;yq?c14~U&p4{^E9E5h=X~(mHdWrP
zd&qmUhL-jfbD*|YbCPuRQyF%#O#Ja<Y^cyiz(H#9!9P8L%JD@=InNgy`RV-L`PX$T
z3DCc())1<boc$@6z+BN&V3EupA4HCaaC@syaCiJc>*sjKu!Fr4j&)T05IF;LHrb8=
zaBNU3zzx#cNAR%}j$Gy``bP7H<b=(^6+j?@n1SkvJRI8dlv+bWUn6laKmDbGQ~V`0
zgnrRMRE9394?XeTH^-!hoj>?D$Tdl>RVclMP?1n0*&J{k0c1`ZLLWYkc&J*}7LthM
zf6s@7;R3K)!<DZTf9d&wh!@iEeh;`D`X3S2eafat_=?bto@`U>AmG~tqvFfA??W>o
z9qL10zW|^?;Er+I1ajv0xp4bP{-zfJ(Y-b;Dk}mz`dXeL>n>@FkjWLH?*+xHW8szJ
zci#dxN&rbrMndI<AFA$Em*!1t%BJ4d(uB=0l#+C(>yy^^X#Sy1jxv~apXssK<{Jef
z4hVHRaWv7tyEzLUhst4*8nH9glwTn$!D0nXY|LiH&x=bAbrOu4=a7~Yd9McNWRLax
zn&L%HC7#nHQtG$@67*%*eFzS{b#moIU3`H^@cvWfqy$;8KFBonjm$D*+1Q-A(HFeY
z1SkPXVW^zkq2Jowyv!fneFrDB*n9r#Z%Iqej4?zocW|B=x66O@^L7l!#`TtW5^h@o
zJC@Jz!u$=Gwi`$w4JZ$e7W&#w#VrH3%E^jV!;Q}^2$r<eAQRRY8yBg{o&H{B&*MCy
z=5YJ{WG>A$fr#hn^Age?0jR|Je!Z_73ky5PNJ|Nl=Vx^;%y2u%l%Qt~9@>?Tj(_o^
zpBxl#ZY1`#R{bl7!<#(9`HF>yvk?nCaoFIl1Vn{d%5bdZ0RD$GSrOn%2pA^tHc6N>
zHz`+wZa)5xFD;I806+$<+(``jDxXVHuU4bOwow^g;3Ku|l!&`je#$~yf{Z^&rGi!u
z(H@3CsLCT0O?if@F*h*DNX5TDU{D-_g5Rk8?8W(v*dw`XAPX?vy1TU6&(Lt7T(Fhm
z9<{mZ4^;*^BNF&ZpBe-;Dm_IPd!XsCK3Nus^tuT8pbuMF_^<*S!Mik{Cs76tI{>zM
zliz5F<so+mk?tLH={ZX3(mOjqzRyN@1@4Jg%i`h*Dr}h|$o`3g;8rG;qk^tEglof?
z?zy6Y+e~$2$%{a?9HKkVcFZNVsw0QoYE497#K1ReCmK9+)km8g$TndYgWBhov}h*$
zFl8OQh|v2^yi+RRg*zF%-JSR|Yx3^jcpN3KI~aNE*2b@S(^5fZcjzBay;4T#vnm*;
z-iw)h{vh{{=3w`IzzP5w{zra-#~fd9rQC8}@3`KV&PnmIM^%B+#tjH$cT)A`l(XDU
znJFtmHJXQGExIZ~645u9y*H?wBVXzxybJzrbE?(jsiR3v^?g!kcLy+log_QQ8fg`5
z|EEmY7Y{xhV<z{IpL_wkpGOFkJglD8!gQf8op!XNfFS>mpW*r`2p!umg=Z8v7AINX
zGfh&G*B@{L3Tr~MER2Tc>$=Loc?PyXalAJIhSZl$+%?V7Q2IFeLEVF=c|sZW%MDf(
zYq~01MHK&Iq)Xqmx%qNGvm1gtLd-@`^_Ij2QWkFUBywhX)$#z7r=j$OlXFg#U1s$D
zm$d)c)dW(>2sADIy6l;dcG}#}N~c>|WM09#Ed9rO2ftyn$f$ck%o%g;1r%$bQ@Dpo
z%T!ORp!cRTaAPWv$ba<Y{pg2XGN;W`W`gjN#`CqMx`8L;nu?0YNr0D`Ln3rhI#2HF
zv8~<9;FO!8#I>cGUSC;_G%V$e+g$6T`X8@3q%eYJHMuj%1gfpA&z;|J=lrsWcqGMG
zzqrt1!6#{!>3mN2N*B0avxe#3F88U$e>%EAX(glymuBcc+RH<eRTw{gG3)Eyw7I)3
zbwnBy{LjbZRcc}6H)<%8!1B$3=FcA@a%*ub{x)m$|5@OFVx;6)q~Sjhs`7$@jO^w;
zO06>i@zT<l+X8=t*8BzOy}m@;I93Kkwx^(ure({qOeL^yI4uPsT!H>Jz^6ni{;EK<
ztgKpLKhj32&Ba*UitI(bX*a{&NP2s0c{!+_G^xJaZ@K$j;aS}Z(%_ZP-nZXNJ`;=5
z<mpTn4~Knpsc8U$<g-aU93%FH&*|L+4XQ;Zzl7r}@sz3w_@8-AFy2SVP(N!>4{T?{
zs7?NQ4SY$my!HzyC9rT{QJ@k>EA;*_TUz$jWBn+#D<NGez2nT3@bH(;fb#^F!<yr$
zu3eIf9y;^}X}PcEv~Wm!xDNFVQv6Bx+Wb<+&7o6*zf*rn*GYa-T`G+W+*8;HKv0ls
zV@Re89Qo>aTnv=ChX68lw@Rl3#{%Rui%ExO)&zFqeY6)+t!<7HtaT8){Qcr(Ku$?A
z60Nydj4a35ZZ&x*6MH3a#*mz>h1{Nb4DnOhXkpt3!}`^Pi@R8qr&|R|oR3h5(V;0p
z+Hy!)x7oJ#4ep-HOsSL>wPMM0aFl`#1Rcc>WK&*Z12=@eHZZt{OA%5~jd*L6IObb1
zmfDK(zGX<YBW%LuuN6=?f~cNl#?w}>bOFCB#@p$wq+cTe9^f9;f+M1j^Hx*-c$6iW
zksB=0IP>f+d^4uIWbym}xH7kyCKML^IpHYie3@Ka8vf@b3SU8anZ6oMA=;F9wg{*A
zJi;yvnN<eaB6<z6oAp`m;bB>>feG6a5W8Pi<!ztVz>c<T#a()M_?HAugsYx#bm}%)
zeY#?dpHoysgE6CD_Z8s&Xl|C#l4o)pco$bFHjT4e0jFT()A;)qmgPFGIkdM5GSs&f
z260oxnfjdWnmAKoMDNOLY#O&;vwwq*eqw0>l+tow5}X%&A}aYkic3I1@<fanNZdSZ
zM}s$dOOY=|8uRF4Iza}7(lBb{xnnEdvb=)}7dZ=TcZ(xC=2ms+NkyD+bUWYxEV|}Z
z3WeyLx1%aAjCkQ89$dHGCu#aQH5*;L4UMC~G6GM-V-6mUti!vTMICQp_BmAHW~d1E
z7`sQA$l9q`5$zGTfbFiguzPd(KYhtVfJdy-vJ|W%Ef%?Vr&Q+s=MaNG9a}@5UhiD)
zYxK=F+jfi%|IedL-VkaI4=(s*dx-KiIxY_f0{`-!>FbsoKo)y*c<>u*x|?4%7idl9
zz>khq^dT4{EVbN>&<z#J?!Ud4Hp|xW<&}7WbW=eDm*Bu1X@CfX7~`vAhC@G_g1~PJ
zfkY!g3v$1}*JV_-5Aox_Wc)OeIz$x|ZEvTvAI9)-JZKwvGwk)<6#AL*Pur<K$tZCj
z(FuR|#v8#ck3U_vm^Xns6#{{@v01~FjOIFj-lKqLrj4J#+;X7sv=%n{cj5_zxyst^
z+k^oN{j_I9q8!a%hseM=8YueJ??!DrU_o?&SJZ}p(U5LW0;0O?z&{UQbS8@PW`n2Y
zJ%W(T=%48}h_fY5bR@a``Vb!etrH_Z%L6-1D*V>u_HcAYIZT-LQ^JJWfaplNuY&s5
zQ+`Y7DelB?;!^|EM8H<ydI4+&U>SuB$Nblt&@VkE?Y+K?o5xAK<T0QCwqt5?;jGNw
zkBytce>R7ejoFpmSLPo2u*tE!=I16@_nDOKqS4YgNmTo(e97^L>DEgkS9<RwGULm>
zh52elG9@eq*)SsKk!)uZbbY<qzA<}r?tcAgkkYhuy?vsIlWC$F;jd8~%0~|<6%`Mz
zYX@_5wAI$^JvMFwGp($i-_&hn5P{-UVC{?^>>Snf!UwqL(7f-{8qP}{nzJ(H;|6;V
zu7f)-4nJ);bNa5Idot-_ffVUb1m2pdUghT2Q)oP6gmH4=Io)Qnkkg4_ibCpx>w=k$
z)jCEsl8c4gI%qvW;j9DZn2W1>724_M8rt!&sBjOtm@wX2uy*)y$~BH!_x5?fu}<!n
zuc6BsJvUy(01N^sQSebAIyKEJ6KC8)j1)KaI=$nB?i&|L=kh)D7bt;P)<IWs!dnti
zmj)pLMQ>~nO9;^69-m3GKZhOn=^l$xf@orrl!!LX+=Z$2SDi7#7(A6ORkQM&-I{&r
zM?g3bD0q_egZ3b3%DFB13_FQ&?shi||2kn@+h18HN|OB>JkRCGdw%*EW^CXBQ!no`
z?R^bbHQIhs$MQ#>93E#Pv8So8I-T4P6`wK_7COG)#|0@8pa^QrMeU1Dmcys=4!X}F
zHl}!PeY?DG9Z8aHyv@M&rYAqF9?Sz37g*sG(Izg7_Kzt8j+gO*KaiW{^N`ThP93$+
zgo#hC7F1>58KL%a7@XYq*91fephgk6bS`YIJ-12InB)`i+st}WnMp{uY0M|CF5NT%
z)+nFQncrs!Fq65TX$I6l)&K(NCuk8ixdai5z>50R)Ae1Zh*Aeh*7Gy=ims}C&3y^6
z8Q|Db1mFf(BGHNo<8}@*POS&;D6)V3kR4lbeqN@goUu?yDWcz^J0L~?0<{2#4l*TJ
z7NL+uSMHC(yrf8ra#O7<J*g^|DgBEH<c9M%8+WHa;ULg&K)G;EI0e1_Y^LcS(cjBH
z)lhSHRZBuKi<N8hVfPQA^%_Tra<dP_pm0hI2!f1+l~i4nPQuf5E+wvquUPHwZOp*L
zv{A!J)O^hy$qO=2&?_MDpQQ@DE`G=FS1+o4{-&qk`)4*+s_SRGh@)ILl8fijRq~)9
zQGkp+rK#b@j)!<vBEZrjt8@2jUg8_6A5I@f@6GSxb?Clz>U?ZaS(axzmHJ84OkS))
zExB_yMErIxot4!#gFSm=`RamxpL)Z4aiczfN(tSmg!>)VVSTp|`PgGxLl40Y<qa%P
z$nys;KR~qPdd+74iyu!YqB}#v#5ZOR`HJ#2)#ck*luK$1Iy))Fx}_|9=&(RxKzuri
z1-6nompRS6z)-ALLYc%&NCj&MeDWke;%S-&zrX@e*&K#VrGW0N`{iwyS^wJSDEwZ8
zWP|5;hB~#wNm;V>1F1p2CK4=6tXl&Z%Upe(^_|~f+PgSkqZZb!(QGTNjJ+AEmgM@A
za^DT=PHa##3ou|pf)*aq&2~vEpGJ2&c6Ro@36rg-M#^p(duZ#`sdDu+HYT7wvIW;^
zMHRN>tXlux*|Fu#g$sSN@@y+EpKufOrO;z^YhlMs9K14<V26h^2oVzP1g&P&HX4+L
z)UT%#W+uDkPEJa+U}E*!;5FU6Ae-;n8P^5!CVBqWmJj+rxa6XjThI9gF9_@In}z(t
z1>9|K|3Ptk==Ag~^3i2e-0*S7n+VP+&9#PJt<vOrGl!ZfZd{B>jQTke2qnPDubxG;
zCDr~hSPpm6Vtqf0s59Vvh+ph))851=i<#Gb%}G(OH8^)LF**Dt$&;v(5_w1)H!$^{
z)pzX;H`k0}`G3tG2<estJvp(Ue=P`b()(P>EIcFNnliKCH2NK}H8Zc1D1d_jH`vi9
zA+h64HK?#z$T`82lu!Y1z(N4y*S~s~b;((mVb6ghbR0#k^b6nOC9edYF&)8=;`Qg#
zzeQru+3)py<QM6%t$**G>c9RCJTak5uyNH-M)XNL>APc!;8W|9==K8jce~zR$x9PW
zPfKZjEZ-S)3B|^k`0pz@a>D&J+M8=l;(}AVLe`<gIDEVfUuoeCay_TF@1^jr?GN0{
zvclUXJ}v&m)l!Gmn74&EiW+GV)l^ZR=7gk}R%@sIQV&T9QOk1#r&yWhXj83F@6$AK
zvS>RzPxrWK?g*~4U!N1lz-%f^f4#UnUe~h+18N$V(w}A(`F_Z}=lMWm<e)!&Ll+-(
z7bp<}wpGo(Hlok*pERyPURvX1ik}r`VI%zCdHJx{7{`wJ?H-8_;el`gRz~>9Tyb{j
zlDTP9Y=S-wLm|y6U%of~ut5t0(?BmO(Ssz61a$u~0Q~7rttNiqnBhpDFecfyH~3DS
zL|_4OUrXWa60Nw0=>JrK7bFK<Ah8Vb)Vtp8MP2Ou$oVFH-BxB<I__{++3tG!8}ggJ
z%a#a8&K-C-5TuJr+T2yz-s$3oi0<&1t4ha`4QafDGR}XSEZK^9AodL;XN74u%PlCy
zc|YCTa<Z3e&57iWhcr(~Gyb>o?|b21H&YL#upWZJ9I-*))hp>)?<R%ru&8Z~>AfPk
zBbF<vkbKDRHNy%yq0smY*aaXa={(aXyMqe+6+?9ipm~Qa#X5B+s1<4tg%Oo;$gK>j
z3JMT5@Z2naa{o%FG0E~GIgT>n;OL1|b!5d{Fo{KgZ!smRz^S2K^%3Yd12Eh#7A;zT
z!;}U@!no9ETjgu=jF+7tsb&-DEQ{Q9+EXv=pdLVnyg#at4=qj}V=z-cy5#!_u{uox
zOG!hC)!dNj`tPqKUVxUkfJZQ!0p$+U^)G)r7rCKb#Iw+xIm6KER%W^+vCFlb;d_=E
zw4hcgFvoqB+h`HD-@2>kUQczGCn&EN$bV&LVFgNEST#L^t{)#!;Dde>0m8Yz(+IKq
z5XS8h6_*oSK4`CyRVeHvv}Ab@rtE}%BfGmKN(wsvh{nC=##X#w`oTMuDG5D?^mkO9
z^ZrHun_1lO&KRtaDwz`(v;Y9#GI!@^p#Z~F=<<o<s8g#`)T3@Lh3^wtI#?s>N;-~N
z`aGQS1a1P@PPl~M6eH;Fb}wOJkFX9m8f@TytoEv5Kky(zsI8}0UG#f;>DLcX2`o;V
z52J*|QI*BfI`4#ATksa<7~I()TbsN%px*#Ak8^D7)0<C=zi(l#0c@-&E&gg+eHCdI
z<uq&lh@B@=hq#r(jZq*g0F%b4ah*<y6#X5Sxb9M#Q?e10(rlviotx&%-Mx<Nr@mg$
z;Uf=HP?0hWdrhLAjdA@w5wpp2$pclVo44;{X167?8?hqCpPZLzz>fs}PJ=)$06;ER
zLld%HZ?i<*9~Fk)I;iVx>~+$Ru+9dnlVt+wJkS0fJ4IoErT}7eb+L@N)BxIZ1;3Ya
z8LUQi1vylbZ+by&PK&iac~FeWyJ8^F4zO_}+(^}$9vOCAe|2@0%eWc7Bz9WKcux1_
zP4J-c(~P05n8$#-*a4S(V2&Peu0+=sk?Tc{v=(CSi>a`K#h$R7@2TfwT(-KHtR50j
zD?n7L{l3+-G``W*swH9)&o?yd*q)b!$2I6MgtR*py~QjQCjbIT0TYj4$M9>Uv*J&r
z(LC-IrhaFotru4HBIvINOnR0Zb-}oThY#Yxv};R2AZb`difS0t`}Am#0^iC%@`Pq&
zVPmHmrJ<)&-}bk?1-<kECXWAoZh^Xh=1i5?N$jbsucwUbkPg1J4eEPoSwG2E;Q2M>
z>Lch&Iq)<fYiAS(b11o+?zbjwMCaV-WZ0(LJ`j@Y>a@(gWUD(knuCylNIs!)Pbrx$
zSRq_cl4)LnzmHhBs2i-cIfBEDEtEq%GkJO|g*|_QKy*MHeD~2@BU>F0ZvK1#1Cjc6
zqtD`(0GR?5=s}J2kuVDvA~l8{B)9qq5^TXn<pGycb~i2N_fps0=|hw~eNw!cf>rpa
z`AK3NB`Vszi~>Y=4;V44ltbQqreqDOt84orEsA%Cmw}SvT1Rww5J+zpzj?nm8#vuC
zo0-?fNw{<-H<~CwR<A~o_GxQ<?*2$YBXlG@k}xv-kUg9*GE!Y%ghfzUO9ab$od32?
z%q*D2EM2`Eadahz@9f5T7JBURE`yBBmxz@^=ee*HmVV9Ac5XOV<~@8Ye`a*ptWPU>
z>PO&p+RfZ{%sI{m9CB$tAr<So@?>y0)6t_}SPUBuM1t0|jeCi7eYh3*h@M#xU~@9A
zD!))veBRcp7O!Dc_~4ty+<k7-;f8#yZz@m%Uque(kluH<ZBb7_{U;6~;o;Ijafp{6
z!Vjc^wr3l&t9=}AK6}auyhaj9G-Z>9P2HPe5nOCj0P&6A`4}F4i_6Lp7xkRE0AJuq
ze^+4F_cc|XpmTlQUWIjco$%^Ur-zOAtS)TB?vsriSfjzP&wuCGK$q!kU#%ZZx8Sr!
zcs;j&tA(v6|098gBxM}(WN023G=LfyR7!b~3HyHLCxQpxqxD7aOaGPYbuN7Kdo}cB
zzavqKluN%t2M|aP0Z24q3O<c@f>IBc_`*m+wI9WYh=KYiW0<H8k!;7k?AvyT0y)#a
zNe_e0i^(6cWXTJ{$X&($+L|$H*HpG4X48nfY^<}{=u)0~Y&y%5FwmJ&(&|`7%r9>S
zd<`sSqNs2b6RT^9pp8E~B+Vee_LMsFh?61H`3leqiXqmo$Z_3Wq#v6-3`&ngXL}a1
zRXi1@6E#`2^p4dl{<|uAV;`^fTt;8Ddw>>{G}KigtAEvLfB%8?s(zID(cPZ(u&Un#
zy(8$t8cv=!sV~idl8ks?WHZjtL{R{ZDZoNHvmf6O;|bdC%yRh*BEJ_`DKwdIruQ%|
z(O~N|I7gzw>4qb&a(yyRFYS`}2edJ%=!x8zn~5;Or=$0xv@0liwTwy<%Qw~kz{>UM
zSZ8nD=e>Jc4!5(5)GREQY|79qEm*b*-j|kSoz4ep3W&@fMao0r%fqkN(kc7p-}Sz)
ze&>@kWH8UYWX@wxIoxC`*l}>59Ih;BqzQ!HuoFC@Y7|T+zeZ<o`9^R1k;GM*enOf#
zQui6nEQy>)zL4nZT#!$5`311Ao;bCngMJEk7Z(A`8v;0(F!JqNY`Nd-^jdS}9E3~I
zu<#!$8j6ZMG52J7LY|vui)>b&fcg%iRB`S_6<EmrdQK28qp99~-!NS#b)+a<iRaZg
z8qs`=)tc~vY{T?O2Q(bHYH+h449b`{_GTiH49csF>;A}3Xk|_T6xIWK45(@#2!CML
z`smx<|2{tTFpI5Xg>yYufpHo~u8Ds|`+Izp%sdQ4;hW|{;v6+2b4PBkv_iZ)6;pMj
z&g{nNRfPxdOn0GjX%+U%)e3;)Rt4C3fd{`(P>`o<(#=XD4oDrV$uP%+7sY416V*;>
z<lie3afd}F-Xv;Zq1~1#RcDN5!BtR^wpFF8dEYfx&cT$ynXxA0Xi2$|e!#e8=lzYe
zGH49mLJ?Gg{U;n(`$ZF%2~qO<f|7wVr@cN)m(pH_tWe`iokv*4dsDp7DtY<++V{7O
zTGjuDwzrImGg!g~2M+`Z?!n!iK|%zV!Gc3@_W?qHAPMg710f0S?ivU%$PnBmFoOh1
z@F0PGx%cjOc7N~LGw1MQ=(nr7y1KgO?W*UA5CiG5g$UJ&0ooGpu;<zyXuK`|p18Vt
z#QZqkCnP97^|^c+0}IEbxs<6Kfo|t!TWbmBZo=@q>cF(l*9^nQbGNpVc7MO)6k6#B
zhm>AX+0M}i*LiTDtH8^RTE?~fL0sRPkH&Gvii#*7NzT)}3}@~~eScCE$4L_Ky%j^u
zMLIF+p62cMDPy)Xg=9Yq555;+zbeq0z8R{psMPs=jEp!;SOonM;`#~w%W7YRr_d3X
zG$`?G<%_kXduo*>0f^{^l>~ilnZ{ZpL^ric_<~(}{)?*s-8FteNkGjLVr|HSsb5g9
zQihj7E7eT(LO*j8?|~qT%q2~MNkl`;%qa&0rH3QZ0rH4pE=vpeQ}Fn&Iq^J5Q+Y&^
zuskBal6)c{+CkWBT@bN|=)eA=XO)=6PONEyYen&*VCB8barTb$S9xM%O7K@9=hG#+
zrZJY*jA^|lhmmy~=RX)fI#@+!SDUl4WWUwH^{Gu#PiHobA;!9MD|j<(cI808f?6P1
zulv^W{)rNYYlH3y*+tkWY?inSmDOC6n+?OcOSKB|jKwz5xxWYY1;#tawbI5<I?Pb6
z-x_o|)<{(y7Cc($I}OOLtK*>VQ8{}TDcyji9?R9LhCLdVX*gS^s+3Vw1M01}>^mG^
ze0_!THbt$J^dMnkSd2pCPbjq%UNG?JLH=<x5Zjr3et{1oK)Z^+-sIsAInStuZu12?
zO<18!8Nro4RQ1)cYpj=-vvrm1Qba~W3z06szY0f~k3B&q^{zlX?Xf_y=EQGDr>=rt
z=4!UUZyTDws@>~KzA1wqk!eE-(?!S$l^%c7t|R=}#Ke~FRdF|kZ{$fDv-eRCK`UAD
zc+<H;2ZbM-BKlbDSOMMPJ56%RNLwAhF<CQ`-D+G%2kdn{lkt6|&En{%N;=ark9`T#
zh(7Cx^Ow^kpE%dy3toLlx!cC6V90Aq^Enmy*9qyVyNMcWt@P+W^0JgiZX0@Xx;5!d
z#%%mhV|xZ};=eBbs-ecVBC)>`O%T-SK}=d;Le3{nG*LdvRX5$B?b&&4&vdVXTH!0S
zg_&ozHPNWt>;$H_N(brF`qHWm&`Q{2OQK)&5tTe8K_9dXENzADYk*4NIiY5zO#3>h
zj+mkWUayBZFOVtJKcaFL`D;1zxzK4Sv5_F3a;z}!OvR}ZGfCK?Jy=>K{37b|mkdAi
z9n(x^XQo%BtVtt#vuM1$V)<*x5HC;eNvtyH7f%x+)=-{44uU+GXX0BhZfzg^vZ7Sd
zf=l&gujTrO;la_;Znn#0bmBH{rE@BG`gDFeE}8ynklp01@|W4tXEuRbI2MqY6vdte
zoUccqNG5|ySb~v!(Neuh$Pa!u3pIJ0AwbU?siU39&jb1lVm3_l7#Bt)NAm89*R|6r
zaQ4zo%7La*rZ)w0%-KPun(Rq`V(rDW1n!v31zAv2&$(G<xl63|ZMc9E_Kc3t&^4jS
zDW?3Ozlwmqf$w12lmrb*g+l{^pmmmHmgirGsiugz=CQJFzL!CNRVw0sd#{zEaB4+P
zLl;r$ehW&9iBZQmZ|>Wb?)4i+rB8Fk(#}l@RO)SkGSodsotT;9f%$ET@4TAAW1RVO
z63#2iL-1FJv$6Q*xo-Ji#5k~%;|qYy(Vv^L7HDb<C~|P8^*<|=IY|vxHh}z_skWY@
z3c+Ac%2<-XW<=X?gZm77mWGx`_rAj$$$&{|W^sx}5rn^@cK>W8`5KZ(?o6TSNWoB*
z$1&kCXgPdsNAO%_AY?wmRq&e7g9=(SvX}H-$+;{ge+MKkyQL2-2Zc(Qd{4jR^#Kpr
z>7Jp^9hNiR$aX>LbUJ9PuGlbAnNXSPP3JShUOF<{CnMvX0*uhHfp#gb)SgYsT;-B$
zzBh3(^@@RTu=7$Fbb*{lTFDv&uvX941$SzJ$5~!!jnNY0mW!M*3^qpD%O<4?zgZug
zGH1CAw9l@{qR0!+C{;H4kG{)-yMi`>4cH#G3~Bulfe-r90BiYz$>3rAa(^=-eY6_D
z31kk|LP^G1spVSHN2V^+GHyi_%@za{=zo^#>3^J2(y@{6e_A%;R6~j*Vt3U)UcDGE
zv^ZyaCG}e4ee2_e#`?dOX1V6&rwK8n>9b7!qEfNj2F2v?>`1y^W3|9E;_QD(qYaWa
z*QB}~XuQjez>V;T3esh01e>!v@#aW5{^Uy?dxKMkeW@6xc}Fm$KF9P=YE|qg+d&5P
z-YV=q9|0W?Zq(uf%Mn`qZUj!Rz~iqKM1$Pv(}?y6nr?abFln5HEA$oWpF2xQ8Jwr5
zdwrl+d}Bue>(8OGK@}SPGJ0HaN#{!fT*RSR%aKrj+_bsZTOl*e)~zO~Mq*C2l)Ho@
zbjQ5$bx{7w{da64kBMX@YzTLOppsMcA;YbAOdNQLUk4>wJU_UdBCv(;px#e^-#l@o
zbMWUuz6lWGmOs+Y6#H<bdjp(<4Z+bdif%{3pDRa)zP^lA8)|qmMi*Q^GTYu)LIyXJ
z+42AIqxvau8LAKY;&JPOALG7Ad*s^lNCTgx@fjD=&ef1Zp5I#IH&$(4FtCf&0XG<Y
zQmZHAs{73cb;fJFQFhLSXjDXx+Loa!6wH_4cKqHx;{SAaEhpw%)$@o`&E4$qiE$d_
z#v%c?ZPoD%H<|XF4)ik6^1rRt|Kd!mfl!;*{BGQ>4)LNOoVJz&W)D2gu;IKlGqm=J
zKR@r>A4;HfJ*kF{cV5-9bB||BWkF7r%Ca>y@_I6)`1l%<)I}cYVy4gWVz7N<#yJ00
z*uGaC+~oDsXI^^3ujeitgZ8&q_xjF5hOG9~>XLc3_J;efD5Dqo>Gj<2g(U@<pR#^)
zW@bXY@yY^N3nB8a`uk9pOG>ZQkj3sIvs3Km6z_*CRwz;E_u37@oaCzbKyeR%MAM{~
z2UGj-7%GD9#*rRu>%mmrh-N9?t(UZ9U^s^fq{-aUm@Jznq3@J&18oL%r$9Rkomo2H
zLOf!F0hn!l4B%at$L>4lyK1t9H*yxQwM55wxqT-#6iV9hA9WnXSBlEIJP67k;NG^|
zCm$x0Q}?J65Qd~0zqj-Fs_+#%Y<=jpV)kS!8ld!Ft(#_Bxr*m+dx{cz{yvI6SL53D
zem7Mih9N_hpK;QG4dnL_`)lNt@#qu;HG6W{MWrMJ+E(D_v%a<rA>uJ8`XoRAB=InJ
zI$f)t`f>fZJNjA2<doAQ_HK1U)wl^;a#EJncIO@C1H#d_GHc%xO`Q+wsONeOOdlgo
zxuMSU*+S&_f3{R!w)a>+D9t3&46WzGN?}PgU5%@Zf!X<8ug#P>gs{{#6gG~0<9|@+
zX<Z(r85SVxR)b+Eo%ukt%KgCDw?wxAyLD=YXZH6oP&f)mE3mZsDpm2keIa<jXenwr
zm7U<|{bW5F;$XYTY48ym2p)Yxk-K!=&WKh0%<X+}{}Uf)tIP;B+DcT|tK*DDDDbj~
z5v*OjWE^Z@U-W#_<1iD=PL2~&qOT|1`w53~N;q=@K!uXOqOOt5b<adU_w|3fm=~fZ
z?RZWl(_TD*ElFmcNl}asSm#UJ81HIldItJ(q^42#-^8wfJxAu4e!;t7vmYefd{ljp
z2*Y%UnaMB@4U3pe@r#YMKNb`uf5hDU_=xHc1u=mWe!I_(f!Hh4CtKk!iJ{lG&%Vou
z>~x0G>vXf@Lq;4&Kpb6<;1%`?kANAaBlwvgxrm1KauSDw6#KxNy^PcPd%_5(ZAh;E
zk`Xb$UQVj?e0FcK&JU||<?Hbd_U$jHa44^G+lX#1T6MY2e><suryI0r!07ew$o>m@
zPfMXqRiB3I3BCs<&r4uHn?G3D_f16bUMFtnZeriUDg7Q}D;1>)^;%Z`$FQ%o=FVN%
zi$f&jn*HKkv2JIhYAi!5iqk}T9s6Q_fFC^gucjh1<+U{Vus6P@=fb1=x2Eg(2>&7`
zjFmY-fpcmg32fj)Mc#faA?JoU<Pw@j(b2#i{C%U+ahz(S^-t1FpD7y9i==Wi{AYq4
z)-Pzu(dnqh!5hgkh0qR~bn#w4KQ7;~hom#Ymy6>=q(ZJY?cJ@t&Tq)rYxvx5+Fdl#
zv0Hy9lxzd#l)x;T>ixEplPS|>oHkxh6)eyil1Znf%g`GLA*t2+`b*_OVa}YHzl)zl
zUVOe#<ae{GnHr%nony}brcHzGKHOpZ;JXTfRDK-?JR6Dp3e{A#_PC!CVF}ARnP_Z3
z2kO%#K!C#qIy$JXS8EqE6a~Pt84u=Z&uL+AwFL+`32axVursFte;Rlrb|$WfLsnYR
z+PJ7^*L(MU#@WMI5f=7*Zy`<mKi*_<oEZ2@1*>ymz#IIvku&4P>>q_*Po`Co0-H*I
zW040z=mR?l`_!e2z<<-zwP*@wZ$z+LuB^=Pi){2ncZe>Is*B))30r^fGdI$Bq0&^M
zr=PX#m8D!8g$Wp(D&=Xnp=Dw&VHxbK%7tI@xFo|9r1q=k+&Cs*EWAK?nypWmEVTX%
zSf>P#r+|yB3KONS2}Lr=yVj0ga?Zc^&pfK<{QkvIZ6vpfIy~6L-huX+tV)HF0Ca{O
zp%QB20XzEv`lLP$d+?;O^n&hb_uMu=C(@l#ZMM+mUw&1f--V74U|NnTuX0Or8@UfX
zQz&+7O5^%m^J1tsvK!B4A13xru(2zRKu_FJqULEmNm%*r)R#Wj%rxO%37iL#Rb>dw
zVJ(_ytnl?*yG%>>OV=;{;)X=fHG**bKakjWpRs{1g32`2Dck$GV>QN$+uTWK#mjI*
zmm_BELxPP9GanoyaBeayKQE??cg2j8{TD%Lx99PM*J?rv_B#jty08Z!athmC0rSRs
z89CHZ?qakaFTPb_53!x~gNS@)$^@K%@lQ**D#gmGV7*){I%LA&Py2>FBvy_9F`!;8
zZRGI-p7rc%t}%_?fN^oU<CDb3w^ci%!NAAHK!t7O*c^a}wihq&>A_psd$N7>ejsG&
zk0}fGP}e*;#;rGF6%DXXyiII=T2xmHlZfhbCJ3v<=F}IJ04;RqK$^B>cImW$lD??O
zfOS@Ks)=U~?e@>d+p1MUw=T|CL7HM|uQ%(Rfq~M`bdukD%N6z_^XAQ$@JnUC_N1nk
zBvmMCrlc}ZujMFnS%WsDQ>KCUsCyI6|F~n#f;xg09G}NKP3WbFTxS7I@xH4<6*Cuy
zB0e3dE@Ol8S-^S3X0C8Lo}`>T|MM`~i{N@+_bwrTAZ2jAW1YT4L$HyBP)wQl<7;~3
ztr@98LavyESM(o+vyRw*QL&Qqi+;v+Te-siN7Ch0<J)y`X~DiE`trxoqFF%A&iomv
zX!cq5{fKd`iTLy<J`Q`ex>?4elPN<LL`LxskejhU!_@_4$i6ay%&@c3dh=iEKP6~x
zTLmaODb-2^^2~Ab73cStMn9N7xkn2^zv-PX+W<b(k_>}iY%v~If5LIxrwI3T5~pV5
zs;7o4S3P2}k3lKejWQ7{LR$B$hP^OA0EYqTC{Gvo?Q`xN3el50+!PKD+xOT-#+-6M
zkB%ILL!gjpWd!E+IV0cw7*lpVHL)ThZ8Y`~3&%<`H%kjXs5K6$o^f5hcDjY*7@H{V
z>ybpEl3|Th?@3A~MeL_7RRXuI$*}0;_4j}J5z(QIB{UZ_j6dtC6%jL|BG7Msu^4TR
zpk7N>d-SpF8wVrj)Yk`{Z$0=VrkMHO3PFp_ffpaJc}C%RP)LU|q{HlefbG6#+3=an
zVbIXlo4<KE75~O#sQ6Z9|1Axa-(B?msbqd8%Tc}VZx^C0Iuh`76njH3Wm-SL1X3ku
zA|0gkBcHN^;7>qNcuXr+`REDzGy1?7Y7grsG-j<}<#qy)WGu4rNBh)>SM0As7X=T?
zjS~`#TcZ-En}r`y?%_=Ig2cwvCSB!=RpV=9W}OlqZ<>6nqeq|4)>yCeUa=72oN9zQ
zowXZ?nfW7fzx0Q?e#RbuzZ|JdX48WPvIkgc7MDF715RDaB)zw12T^nbee0F4>FKj2
zCR$$2z^NCqVYWpL<-Qs+G<rkto$)IG&YQWq7bcDndCR#VE-H`*IovT8K5bXmbNH09
zeCw0Uq{VQ~hPNH6QnFK>5+?4sJA2{YEelja{<bZB>=%oty_+t84Kh`LB07yyOU5UN
z@RYK}k0+k4&07Y1oGweBKGkQK%w-2|1YNlyB`MRRmyQU~bW5@R1vSWn3=aO<sbgYD
z_euO!vBqr+ZJ$ApRX({KP=%52Yv2=E);-i$>P`wgM*BL@F=VfUnHC=;8H0o|IU7SX
za>QFGX|lwHu)LN+iPu{kw!NKDXPpo;_wu90<{N!a9&nfDl3mE&a#}J!2FQ&Wyyffp
z=U3gOkB+`UzPK!gx^{d5#0Az((BoDGTT@yU9DQxlS6mTFr;gy4wG4SAlf;L;zSz<6
zsrX1RWNYGdUnM(Ic)sm3@e}%PZwxH?w5Cw5P@x7m74vj7sn1_=bMEQSB8%Rn1MQS{
z1^AeXeIcx^kKeF-2zyt=^fThXN}@w>@xE^Tx01=|nFn6uj{})cNm>c0cgMVo-2n(N
zTNo87+m4mLV|IfK``qR8B~@eBlyGjre_y%3O$`-)q45}nBXa1TxH)SCa}s~IfbT_#
zS7E%Q;P)gb#$XDgB4OKM_Ux=X9Z&pmX_Q-3Gi{d3O15bmUz+UYTGiJKbBT4N3|k%?
z_J5c3GbI(J$N^$vs-<oD@1yn$uB}>ifzuV7DBc!I^P*XH+-$OGhVF&eESsYaCWye|
z$4}grHG;W_zdOLQ-J!s<TCqlF44|+l)?bkLEc?zB0eAjsZ)7`uzhA^-WF3HCakzrH
zz&=_~9cW6FMZz~@#;_+WK-0wZn#>~s^;cY{^G$jNAxTN800~&i9IO!qQ-rI)RfbQ6
zYcU@&4McH*Uugtg!vi>NQPtd%=Oj-^*rB7;+Rl(AC{VPL1`CsMCmM7G0qk9js)#2e
zqt6;|D1VoHx^28UU1DZLhTqkgK-x3CXxKQ|IG6-Z_?JSSyu^=65n<I&J66K9{hW5K
zg0kN`QxE*Y;$i}sl|64QyZ$Wf`zv(Yu4?Oa70OO0*KM8L46ESFkadZ$QLBeQoqBra
ztfG-fI@+Awz2C35tE=1EG%s6&pb>7v!r&b_q)e=BF3_yYoG?2jmACv=D;+SB5Kb`V
zaA*79-4nXBscxCK&a{EoxRaEFd?h-ocrQmc=p@oiF?z>BkA8hny4&(M7f^@LZW=#Q
zy^bdBplC#SGwat=TM1=c(W$E}7H7gf{GO1H1F9?HK3dRs)r`#shEUi>^!@tq;`i}J
zs#pFi#52#$FN#ov*D&Ri)22w?RNei5(BU+gjW+quoEE?cagixrcO;1S9tg6?DWynX
z{na{jCd;Dxfu;7IzKS6AY*HU7K6?jDDk9110Bj#LxF2TSYu{1uJ@OWG{NtVtJVeu5
z(dG6NzDT6iWe9J>5|Vun=ZaK^t1uhmQ-A<hBx2in?;`0__THUWwJt!!DPxl5<8Vrn
z34&S;;RJFv`4mu!R$#_jAW4Ia&?!cZneSp!QL2e0n%q`;HB-8Q=c`PwrespQo|z!z
zBUzZdfht)Kc^Bv!)-KK7!O?H@U0gjVw_A&SJ|MPJ68J+`j!b)1jAV_iNUN>{780-)
zR<df04;bvekolgN-ezMm+LR6Rl?b3?&6*JjG6(G*p;M76<uFK`Uvio56z<>sOJ?^Z
zwp}lbz0Y1c%yB^iq^15#?6P-!J4w#UE@~WRx+7oFLN4H3MT;n%&Ykk7X%fDW_O!td
z#dmION!K5dV?51WMDB|&d;7e!^K<CEgt>e_M|@FYgdopOd{E3^hML+#rA|AwPl2P^
z8{R?e!r%7Pcg}x$Q^a=M;JGcODnt}Y=TXY1y>G9pj|n`e%z+F~&yOau)-v1UZo1iL
z2SMkSTIN}g=f6WZwk`?}Zi1Xo6t18<-PX>U3eTa>FdbZ9w<=j2K3QK)C6#4?mi_BR
zF{pnUWh3<IFtr*EoYlY>??#>xEuwNxS{2b0&Ejv*U&VXvv6ax()46OG=^b}8M!x_4
zmf|>qPn!oE%5ahwGb%kw2eHpqA1>5%*QXG<PXa1)H;1TA8WwpUtZkW^;AjUd>;BJ3
z*r-qA{QLRI>WLQ0n&M#BBsPv_*oW^s=nh)seX}Bc`?YoSVcLn@7oTS7K^9N7%|Mrb
zP5eQ{Wapkd9`q;q8a$k;vNN&Y*`Iezp}idSjw!$Mf{QKNRFD@033{ZKJD3^XJlia9
zNnR%UBm717G4V7nV*&$qh|R#hfmZ##3)t(4fk?CI8?)^3;M_Wv8fN?UUme>}C4Q(<
zL#-CO4Hr}nb@<Y;;tkZ!meKcZ<Ymri;V36cimKu3>+UnSACblD<Rx}?nNEg)56<*j
z=-AUGn1qY}FvBh&?oj{8gYC${=T50ZVhHlF@FuI;7U&aCjri>(XDz9!WLO`Op9wef
z;Gz5qCkgb_4XK*tKI{*tZZNoO7`CGpX5+xk<u%0)dtxP-279sK(NQ2X=u7W9R{oJ;
z&#Or}Y{LX1_Qwu^8^W$|%u!H+VbNNKwh`TloO5}4guYvK+V~PDctUqKooz{4YK{BS
zj$lI#qBj#YC64#w$7{X>oP0HMF{>jQag4Zodw5woY$cPK^ldav-;8xGv5;)7Vv6tu
z^PtVlua!|h(qkF57=@LA1^oemg_0<+xe|-ro3puL%OHy$2fS?9ZDeZHyIlmz^iDc6
z_B@63xWel<Q^q@Iq|7Eyk=SHZ%YyLTN$ixtlZp%(un8z4%^rJvZm#2Nm&IXbw!<M!
zb4=eb(`j}LQzC{;mjwO~txHpAdv3&cQ-<g*;~hM4u;;jeW0dxOW5j!I7ScM_EwMc0
z3_a9}+v?}tx~CMl&Ec7d%r0&H8>rP%Zshb69y{6x^JVhA!vF%Q2PgPEU&(pGJW&U>
zXL>7v6O#0Zxdm1(aAm`e|2H@TYpJ-@hYv(F6BH8RreNx~%t=zHc}V6g=u3IFMw#jG
z?ax_;F2MqHWC-V~`I9T!&SvoVs?<5!uvEHvij*33Ey7ovrJL7@Rg8E;dVueP7zwfq
zHblIhHv7akM8Vor`z3eK>j6Wy&7wV;#n9HZz6C{dXfbQ_xL#20C3MqgQ2*3@Z(0lr
z7C9BQ79Vh~ME7JM${Nq*0gSbec5O$o`$}Q2I+8z;8GB0`X@N{ihww0*ko9`xnZA3b
zf1eBckVrHoWl#&x_5<lFGIiI8+2jR)L!X}Tc?$8VtsvD9BD}}01GIf>qtT0TdQ983
zMiD^N{Z<5$Lj$%!;84bglQ8s2T4jDRWqA9aDJ1w@7VRBf?YRqDLu4>5iBbis!EVMg
zgv*x1;w3fcT`A=F>t>baXT88?)ax#>DXvuArv?}ZHmAbudwt4~9&wa;X&x*b)EnB%
zU(<#e;!{Uelnq-lK@@VvSsfcvMZyUO))!t3@KMB~qTca24@AF96S~L|022vbNNp}!
zq>At-2AhOCq>6p3QosJWY&t+P`Jxq`pZ5gq68aV-qjmdh?7fa@sw6k|*Qh?jS^HNM
z_d7q6In{aac^cSCw%b0cBUC|#3Ga-pho#`uw@E#Ux7kAaTN((5J1Hzr=UTYYuq3Hf
z{&v#d8wklWX~9E!AR2xBjC{WqQy-JtcDL^^`q*vSrx9s>foR}};l%6TsU!_^UTtFg
z65D<;__52<{e{Z{&&5;PxrNZKD#bc0vPVwL=Db2QDsj=#g9D#7aDt;R^H-_uItd#b
z#D@`nFk4^#2Bnm*lN`VErSEx!QKIK5`8}Z5k6#_{0W9)5l-vX_EKvqIC^8#TpzbvL
zkM8yE<5yMPnl2xWi^icb+$I3!a#3{<%e)-MfD2cN!*g&6MlX{i{fMG|hz*Wa<X{nO
zfNLN<ytSKPd*YQ0mo--^g(q!x*n&7?^kBLOt9b$omkI>@cxGB*pwE+*E03*9iK)p}
zwhJ%s_Zo8Kiee+>1uXEa^oXDr$1(}r4ct*X38V6Sj^CXr^pEW6^MFx>cj=67b2$fP
zuz2SassZ>(ecm>2$@?yJPs92VmB%L>)N9XEjlkJy+93uZ06La?Ep%xwyTxif$@J8#
zvBd5;sBtVfK?GHZU(a3)Q7BSv<_>L{`K1=(qyu3AYS|*@uxA%`0=OuDjmv@ZbGYo|
zmv+xC)22%?D>CYdn+VER6~lh!L+4CzxOUKz8lR7)?H__H%6vy0Cuux&)H>%dd{qRf
zi`1=^%&k0D6m8P$3bUgRLutxjy0f|xSE&Q8HW-_*l+~Thj49969~n`tt#ANr;7^s`
z@{ENaeJ_U3iE#|4*x;0W1)O1q8!Mwlh0j&vEq952h3Cj31t=mJ+Ag~I3Y_YC!aOj3
zQeT9Nf~_f|=$_L=XU+b1<epD(F!}}K)-6zRq;z5qQ$M0DS+`eqC$A-g6EYmJON}5V
zV~zuE9MEz#5H(7bwE`(IEIxtFhhh~==RQurKdJEl{kSz<S%fE4izCk07?^anwD_oX
z9f*H&8uTe<2>)W%<1MWqv|UT?9p2w*A{=2|LOIj8{kpS9!bVey%Dhq<3CqeH1K?4|
z9c?o(P5=)F1-#_t0Im&7k@i@+-~3Ae5nICb7bHXE3p~A{oS9g|AXV6IO8f<x>XO)`
z3$#iL`)Wj4eLKZiCzZfSm}|#5bdJ91?ge-?M}5QySp-|t$Pbg0eiB7?ukz5ny_>vZ
zuC9>j(tiz5hkp4#1B4w(x4^P#6__vG?mEAyTV&ax7r`#lj-NgMbDXJm$qnkpWy^OT
zjn5P)C`M#M2c1B}{8n;ITOCpG*?=@Vyrxj~^M;$m@w8I~zM@af#@U)aw8Zf$;08@h
z=`qEzNOgHr%Eg5g7aR7?tWLU`G!a1%aV$pir1D$h#Sg6kOSdZZ5UZi3wkU(s$J)9G
zwiDM1DSz&rG?rv9%L6)w(_F>eLd7L6TB9oQ8eAmPkvkOi`Ucb&+VGnJbY4%h;WyTr
zhW07EK}bJ!pS$2B{y6S!J6lsByPSsT6*=bmnxEqSh4Fm>>_|hOhhF*}XNi4kqCD}?
zL2hggbTIdz2xgcv{jc+yfW|?Xha;8QA&ZnM!tyqvCcU9QJ)7-&UNY6HJMt(U<AY@m
z`5bRaQD6-ZBjS~%mMCsZ$V>i!5w19*0#;oeo|>BE#AQtq$8966&QdLLk_xTx&HX{8
z2K{e}VcF1}DAsh5u&@15d|n_tZSCjc^;4(aumMFM@<4u)(==^);)aHz*W0XVUhZhB
zh_E07#AB7!Op&l(y?oK*>Mg3lMSK;HG5n^om(+j9wU+FqpTcpJ|Cnmn=%c@$86HyW
zFN;3c2%=A`N=?ME%W-svR{5Ail&XTYAeZc#C#?eZDH>~>l!EWy=>Df;FHuF?x<6|X
zIb$R6!URDCPJ|`Zr=Pkgx=$_MfgK;Ob3eIk2ZOTgqP;Rw-<@jtS&bVBYaJW2nYGwb
zY^#I02&#%&^knhTU%!?kj?Qzmp8ZAd)8g=ZN-ez*20GDc$naQ*LbWsRrFo4;2wL?x
zf&_aQn46as#nDS%6~dl!?cBNy<ed?ZTzJ0`RrtsDMwBn9Ho14)C|2BsH{MFF1d1II
zo}z`&S3~Jdx_t<Xcj<_U@-k+Dq*d$0rL1-21`Qd*FF#S2Q-0blybV2gH@M8*KS||O
z-_~--vG}spXHGC(M4vv*y(UKD$}heQ)@*$L1@M5UlClS%VQ?`&lSH(@#HZ<O^dl-7
z7y5{lTS2R%Fu)qpbHZGO<ZjsiF^3$AZr=I)pkn*S_R`a#cMhqJgN6;T?N>}kD>Yv;
z2d0;x{R5`t3F!-?+&3xjPEN}Bv;!2sS<d<E+>0LvD14rP{z}R|ga1PJRBf5$VP?_E
z`I!y<#mmqQHU6h!w1{@)9bfzpChwNOJ)5S>q9ZCZ^ftq%vBC%H^>-4Ue80iYC0}`A
z7HF<8o0~N$h-#X7{a7h=^iwp_IT?EAlNzv%DPsrj$~AqhUmWHsyXjWLo(gqBRxVLE
zmcrkslCh`5`iy!7@wY^73t=B191;h3OvxP~k>c5+AvzG!&+tM)(MD(9uvNx$tCuw!
zKOoF1DDNQ_)R`To)naLQ)e`|s8Ag<g=zT0yoE*+m!t-=wZybN%$7gM|;>}%8G$Lg%
zEdj7(RGT+mSWrbAQ{QPJ#wxu6FIz+-#feUTu&445*n{^xlni@zOU+XZW7}2ma@(a;
z!RvN;#m~W9C>J`8FUm+9LonZzIvB$``BBxQTBz`NVxA-=ums;Lw)5C4&WBq+R$@%<
z^3h%td%9#Zg=;dxRT<j701*wNFp~oEN+o<wSsO)uR>78up04DVr=<={t$Zo?d)ox!
zv5#Ryow-4Q?{>vDu7Of%KcGmzn0hL&rP7vaFu1S|is;d5c5oIaVZj?}J{ozItG;A8
z4)terfs{LLGH^d~*0`y5)stgFow0^<m0<DMj{qIAhL1S*jlf0K6j|=0t$^LLun0<M
z+IHj7GpVe;y6$UDLF6jz7_-$4L%fon+*CE5lbN<%(5<+MEEL5WS^Suu`tVT^i19=h
z#P9zbE2@ha`{H-y>y`ZIaLJ=im(n6jbP$G`nWUtr{DoT-{sU|JYfdd81dOW>(T7BM
z7Gcf<bbN6=%Cso_w=$E~hjgsp4D4%;zTYcZynjB$RpP;(eIcSjw@R-sPy#$AW(YO{
zQxC>#wBkPmHU%lpos+x<NYjYomx9&nJCQ+*{=XQBMv&i0(_({UOFwlGLBtc#K}(bk
z%7)6o|0nvx+)|F4fTa&sy}LE+S*0j5$2wPVs0KB&xJv%Wn3_&L;m_YAhC)Z+4<Er`
z1qwGTU;zbBnx;3{uT4$A!b^%mm^Ir55DLcKd*q?KwH56J_ik^i{)nqzYD3oQ=FoV6
zipT!eIyw{d)7UQQeMe?I48hyLd`#AK!ym<pX&c>IO`H~L{W=9gF{<AvRvuYKTYPg6
zRsBLG(7B1t65QM4B+1LXMddt*h}#L2a%Bgb)hxA`7h`S2jU^&^)u5uT%{$e>j<bAK
z|K@R)Byur5(taBv%n2khwo^W^?In&C1T=K0g98CpEYe3ag(Eu@pd-$r25ETPh6*z*
zKuGMLV9i{-a}9$pjJVfPk8X7Drl??}M|;t%{6Y&7RQDDKt^!5xi#SULz`8nLF0z+U
z-UqXF*WO;tz3Va#?>qWlVt^Hb)EO)sy9i%x7C2aJIb3d%IL^DExSR{k)ZOkqy4cP)
zX65*z0A)5+q{Rfmv~|oJW*XFJLYeH1&}Ra>WemVI`mx85uAPn-7Mch;-ky;BGXd<H
zo0t>L8dc~KGx%PBmE)z}3{X+z`>y;vXMQ&lwy3Ij8xinnRimZc=iYgFX-Ep~j~BEW
zmwDz;6CwW$JIFc=Dc@SXYYRM+=B;-23ISc;_<(t6`pMK$-?{Xi*IAz>+D4{`66UOm
zDPan17T~C|KW*m(Z9LJ``^o@qJ`BK%q^01eh@5fi7QLz$;Q@U_>ag0+reuV&?wAAj
z)YcL{ilAPmA}V}vL;SMl=cE4Q=4AdR<0(>+u730oF8j)47$MNE9xaa)<PlAIl_a98
z<@!bv#n)8o(>*2fHYHRXtr({A94ZCeGY>y=YypL+sg^55|Cse{FS#49G8m=A1nD9Q
zf$JOK3f|_c6cgx4bgFMV&mi>{sVnV}d|{dakC<FKm=%--Wq%Cc11{s3oaeuygNUh~
zS;9n~v8U`AI7dr(1G=F9PnS4x0L%N0-;b>7&vx+@o!OaMt8NqI@!1VEf#Ox=;q@Sk
zk!(?bXIdE&=3-=k3396L%kq97AaaXrOgN|VdRaMj8bcxcFuHF^{~aBn840MgX?m+z
z&3c^Oepfhito>lP+P~tVP5uAcZ24<~3g(K(CE_J=VLzpv)gvF|82(R=;SC_?AX>;T
zz9l#fChRPO_9w4PIzW{><f~*5WUMyMh#70VwlG`K_zNdimd)q%#gA<^xwei9Mf#2I
z)8c&7;<IYOZZCZF;=W-2)ZdvN9j7jiTz_?XXxi|R$DSs3wHsSVn~)}0kt?zD>r;S~
zcEoU$RbGCC*EGEl&Zh?z$KP{@CX-adelOOCQk;3Nni{JYYA^H&fw*Wv?{b_X0D3kL
zUTh(EX!{<u?es?$U(LcGALJ{H$B8Rm=nwt<5yT@-KB<S8po_K@sY1>S6JE-zHbDt%
zzxV82f25(_Q6g&;_r|>Z#f%2J@IC%lv98YK?DfQI2^lw)y5{ZJmyF}LQ=-(2cq=dR
zIPpMrC4v)N#C&k}QogX$pN}GEHHB@bkd2U->-z|}Y5uKG2ww%^7}CjR(b|M@a_g4L
z3QChz6+O6Ar~DVc_Im9ds23>;Dj_$@V~V0qZ3xm3hDxex{^@l%8a@ndT013rQi1B;
zaX3(Bjg*y>sXt%q%2r=X-j4v7h0E}P93<4N+oD>7Nj<XD#t4I$t8aw5&{vQwE1>Re
zQe8W%$wlYSZhaT4!kkHd<P8!7qy*|!9y*BF!$tn}qqO>lr2FjZm39WZxwdlsn#=MN
zL_6rR@*@;qV6Jw=Xk)k=5EUv85XW-&s|AWQMWtnb9YM-5*>a^$Yl~q&NHhA*n9&xG
zY((Hoe2MmHqjZ*ajaK|ig&iUt;5C?(E@{KMbEACzS!0dM2HfS}Fc$}J8{0or*fE8!
z9VTK>yxo3N*)^bVA=2@fZZQ)seL{ZyFt8}&c1C6$eZq!&Ed-keBsN92kc_8fdepU7
zg#~OWyF9&q!3Rzh0t*Tb?3p%8LiN!?@(kjT2eIuT?T5og_as)`$j)zhpcO^v7w|hN
z#cBkbJ&`ju_Szrs4=gvreo7E+xvv1{H2J2Th`f9Z03ydzqKu;EDG}Si5_DnRX(o8@
zTm0Ju`gXecB$jV@Ytxej?2eE2j`lgCn=9%nr6E-a#KZy)a9vqE+ctqlT=54WqtTTv
zY)=vnb^;&l^-Y85|JgAIvo~lt9;rUPDGvbmTfVoL!ks^vZ*wU_;IfPcEGQ!ueL?K0
z!;br5Spr~P@8zE*$*?UK^l~VCF@Dqqt3AV=&%?%?{rB)*?|-J_+aNYTL;i{fK+H;x
z5z&XZY<7<(I<l68G7TBgZe`i-vS7Cz`->%__a^(SE}GXQ`B<zdBVHf6{cVxx6PgNl
zz806$W#<s4gRKZ=!k!Tx<uD6au#^Z$EO%<~%;C6EO4bXc`XW&`0F!Z99P2#b+>*A2
z;FcjPZ3?ZNM5>o{hMd3KJfL%*WOL_6xODuycuM{;8Hrf1mNa~7JU_+uzZ3@Ds|ssJ
zDr`!`AqVo5U6*SgM`jI@|FbXNR!7Vz)QefM?ljSy+(MoXm`I3tbTUn>^LYPfj2z-G
z(_lK(D*qbtqsS`g0Nxj^adg#F;(jg*)(4^2@H|e{n>H`$MraI@=idl!t4`L$x~)P&
z1@UuhfuI7OUwyEqbCAbi=+M>^5q6H+X0%huj@$L8Is0g$raX7UbILRik>JtsBOtPj
zSYBnOdVQpu9pS2HjY~hBkUe9X*q8moLBj+nnnHhN#{eMVk&{^AF*Enrt))+!n2fS%
zLQ00VxSxTWpfS9-)~H*rJ8E7E8N92=l+#EhLb(q>NO(zd5FVxMNA?EXk{I`_WlR&1
zr?F#4!rE0;mC-v=lE{nvE@dtUH*|L9bG$h@ujx59Ha1yhE!k=aaBIs(nHfo}QKDXc
z=*};bWl^!Kk`s}#8?`GwzO6tuGT>EXyI~7h_?Z1Ipm!j|UY1Rz|9bP`VBQ_Qln7g(
z^`kYi-$J_9#rGZS+8%%$h@RzuO1ozS1Ne^Y4f!oh%sP-*14OSb59omKy(Yv&0^}n7
zYAGbt9c?C93J0_Y62SO}q$x-}6K6g@XZb*3vv{MdzlsIAM?+llhgS~9vqm0*{7XWJ
zx7u1MomXW%a#$xMtSP~|uTl)ELUP?ePfs>ow+B2w#~Z!k2M{C$Cr7w~Ul4MGg8^{7
zF}`nAPl&lnzVqxn-55E*vADcMs`vYQL8*Ue(r$S3tz#J;`{&Uwd>ii-fr2eAp^prY
z&snUWd)Vq?9Js2B_lqp*5Cw#tdZTrJXeIBmA-8bN{)Oe?eCMSNS79$#POzecH{bm<
zK0)0gU3RIME2Q5r!go;{0m1;<syqN3CJ(=2(1NO#DYW99WOqs5t4KZYHv7Xb!WAJ2
zAF)-mQ*3D*3>{Rd4gHe!DX7yOOBU-O(sNx2K2_I6>(>U4cVh=_(z(8LJ#{;3xU^{x
z3_5O;h>S|pJI*-$`XQ6mfQ1Tixh3e4?(O&UE4d&q{@;)Wq@+jQ9X5W-e$v0b)ZU-B
zuiP+Jk<ChpbSwBK!Te7qJdN`vd^3=Ey(3l6bIw>llzqEOOg#-D<$zDOA;&-%Xc5wO
ztR+{*0LY*f<IMD>f(eA!6`V=TwiEmu&UIS#=~>i&6!0Qjn3<bvrdUY(J=C#FbuH9N
zWZH=SECuHSdp*N0D-qhiu1B`D`52{mNCshSOrk@NBpdSfrJzy;j9_xVd+tYJM&ZUO
zrbO!Gh+r5@+#;lMsCxxe$`shZYA^WmVtrv_R(dYR#&NXGg%f;_p$b7OK$0aFRlOT+
zV<@J)3R=OPP9l#==_Hs*JvN29VJJol2}P6+MTd^b4fK#0!KKv;7{57o3HSoA{usin
zewbO@uzOLkwVm-1i`U`bv;!C$c+!y#-dpm*6|7Lew{!9tc}v6z#tH#Y6*HPQ`F5}@
z3WZ9f*AvYIA=@|!d&o`zn3yMuU`R0I<00V>gLkRRbCVE>5I4#smocxp0z~Y_wnijJ
zh)3^pN|gi*-SE>->?QV!(5Gl28c0?dZ#2ngB4EpXx<j_T9~rusOHh6WRm43Y4Fn+d
z+Bov8L5n}gvSLtlaI7b?^jfAoja`*fgVvKC8Jm8llqQb3RYx4LXxMC!c_f6kacxoH
zl&pdVRo<X~TZt}6M;cj_d*rJ@=fKG~4M$A*{~}7WVK^ZZEG#O$nT*a&D6+{6uPZI+
z9RP2N4$LF7n#pm-v~f66!#J@oYUk#dU2xCh_p{1P7kD=I2H_v#kgCF~lL;-@0h;l`
zBU9zt@%Vk%1^O-%ANCVF=|56~G_-<xk`ruTFvY<;_2LkJ=<VM^S?Qsm`YJy&N-#dA
z+?1`uD3^|<31WK;59ue8?USTB`r_1gY=VtLgc_ic=L%N&pBkx-96vTN-`pN3J;mI>
zipw!dopu84!0AENIdHH|?>~o7f;IhX1`q<<!VET@1G?;oKM4sMObPj^K>lAP%mAvh
zUd%sPM*4CB7#zy=t45k{4)!=o&Ws_%yu%q@((4<S?QLk^34=RgmRNieQv0@R>OzwM
z6v<eZyGXU}GcnWqCoJaKVVq!84HqyQTs$t|$8OI+wMW?FNMuetD1Y8#4vECwz+NQs
z9>60i+d~ggk15<Yebj3sU~lY>AvO05`&xNF46Snw9e+aR-UpihjFM@q0&QVX<>Kh<
zk8nr{e?2|H-XpkS?B{uC4CrwkY6UUmRh8UeNa^-I=t~gYLIu*a816@*J+X~rQZ)p6
zfX3Z^`CucXx1&b98BPQq#RKVr_Yh{=mYf~^jz|U53-_QL=8FZFolPV!UnWej9XomF
zsq!})mrh&Iqbp&ek0H%4=H72EU<Pa3a)B_1E*_sp3(4)?B5|*$_gCxq-egUah(TKj
zd+(&!c1V;Fbu&~R<HXL+*g6{7iLw}F7pc+m9n-LkcI2cz$UmP8oDQqs1@JX>AmM!W
zMgz1Oj(3AADW{ibZ7}9ytOcmu?Nf#?aRdsj<g+eCIA=F<DTmvQuj3cQ(ccLdA}eo^
zrlX^7qV-KVb!HZ0{nOg*w^c|dFXLY+v@6e*_8&itm?95Yej{bIDBjpVS$oPT))HU{
zr%k98VlM*F`I#lwVX58f^GC=>gnx>`V$R4QcgRv@a^?PATuQ@cs#jbB_dAy3Cv){!
zHo8+3Y^kM|)|STCDe>OlDv+<CBVTl(F~bbTA59R<r#Y}t0ON2vL$|OprUh2G8uF9q
z?ED;ys&YFBWWy)iNLeiBT<D~LDBq*;EHU%_jI8{NWR*bU$lBeP9^+v&K-0d!h<D*q
zS9^0(%~SP^^Zyv<?F1i#(cTym=6lKa^+~edA{6^uXT%c>F{3pnAi?ni+S21`l8Lqf
zi~F?+Er7@z%6|L7&U?!Ew4X2u`-!cXM+jfIW0_jM2Z6+Mco{P7I{*WpTwl>-Xt+ng
z3r;kidnLbZJK7eMQf(Y|1=?K+aZ)TvordLzkyIfZg`5XG@M*Q6{2!2}vV)_D33bVz
zZ>|Vgi-8uvJennk0ucu|WIV(G+np8X=-O|5hERx+IMC@Hy>mw!K}nWWrrq^CQnbV_
zo^Jg|_CJ5;x#3q8^M`LE(_rMEVB<%a6Bbb>ZU$b+hek#GS9+graDeR=&J_y*dPPat
zMK^rodkrNl=Dl85vRQkUdD`%a;iJTD+<9wk3ZTY21nR9FI1QHm2}WgE+1wlvSEHm2
z&6y_2e4lLeRTaWk!Yc)U?xvY%7H!VM9;d_lGd!b)eJ_rVipnH`1lFCQ57}AKsjQ8B
z0fJl&@7Lbrt%~Ztcj1Bzq`VeYexj|;6*aK??d2`TtwDc@O{l3o<U%_Ca{pxIU^AdW
z;uFRWl*hgukr0yh)6qDd?&o*oo77WNpPIrr&FRFk*d|tvN&xd<I8+)@P`6CZX5X+S
zkI6{MV}C)SSL6M~Wq}FYl?+>C0E?<aQ(zv6NX!i%xD|(6<y$d0b-9-t?sJF%<TA%f
z)B6htg$Y@;H8h{WPXY+*KmtENL$l!%o-j=!PSKHqtgQO^m@5Q}hvR+H149sT3hDDV
z_(QOwca2jE+&ge;<L<>@lIxg^zj-)#Ctjq80Nm#;K+*niV%=rJ1R2=*Jp+~n>P#|a
z8V6uVW(K2~J|_QH6`0`8x~BiHoC3ffcV__y0^mQaDWo9&&kHLQphPQ__k<T1;6DHm
zlbitD&mI7Bl*s!3+n1vNvqNc=H{M&6MRo9&qNWZ&nS=+&1u|_nba!~{Klv4?`)_9y
zdH?>{mGXL#Ej_TbM&cNYJSVC!Gcu+5%HBpBs;H4Iyq81U@n`rG>CXZqr|n^Y>Ir}!
z1wcV>cZ`yAfzm%5o%Vkg%Z~AB^lmPh&jNMZNTZHa&GEjeI5P1Yro#H}l@Xo|K9k9`
zBfc+Hn<MQdg#nN!<%fh~G&xl>FzsMyx|+G#yrNX$hveTmZ+~T87o8{jlzG~0Z-hq&
z1D%(3U8T}E-qI%(Chl`DCIwXwC>DJn?<+@+c_5n$sINWyk^#b58{b%#RG0@q?*+V8
z`BRjZ-q60HE*df3PcBlH6peGO(OA8*qL*HlqN+Z>FDr6px_2RA@D;vAqawODV-x#Y
zc&Ur|dkI!<ipX?w37)#fWltjAqj{)J3h5%CDKOsmT(+o{rw*BP)13|^ITDF#kJUq&
ze{TG1pt-uSI-J|6E1Ra7lwbFRKFgUc!zTTOAECb$la*wl3$>FuS&VI34IQG;NtCh6
z01LyTU4WGP{_cNWqU1xCbiq%<vkP}++QO$_sfh#^Aj-!7NrpDFm8QjI2=<ff+o*tt
zJEHjCCNug>>H(;Mp4~Rkx1QTa3Hdu>3vz!v_}AVehylQMWlp~y6f74!obok1i$3`K
z9bOAa#=1flJUQ*Y(pH-MZavO#N0`8%F~q@T0|mxosoDm!YQgtJ>LtKD{|3W+ZDLD2
z6qsp@;9O<^Kr)aPr|=T)g{nNO6aAO^^<7_AvRGW*yv(NkWfn%hIcrH~8S&TvgoM!U
zzIQfGLFh)R1s(q|=*<UAZm@KmOUkr6vK!m9$<@Jzt;8DuV`u(?B*SH!NN?zK+z?l)
z8{yhcYSg7j5xR);A~lqNXGb{u{7}BHA-16;CB)7`3;E2^>-l?<>rde4-RJ1(FnXox
zsSnP+KNg&6uZCWegy;p;B&P%XuMZ&LDY~q2G6o1weTlVX24b#M>s}T+-w4Bg8QneZ
zy^Soj?J=XC->p3jqcm4}1);;1$FEr1+~u&kg)Pynn_XZ0r}unR2|I{a+76|@Je;2F
z^fYx@EkjWss_OdyI}b3c2%eaNH6!5tB)%aXbl4VMb#P<4aBgtx?)D&sYomzR$3Usx
z6+32I${n-<)9XutK2<~l%YDpo?E3I}LMtMY_Q9^J|HrOf&^IyL93aMga|?`Wn{5iy
zRG;43r@aQB^&&CF!8M{#Z+=|!IVcfu9y8PVzbC^KyetM6>*CqlGUg~z?51HW!OQ}7
z<!_2`E@*r<kA24wj=N9AnHOn_E{ksgG7SXms>%z%DZSo)NE9FQIX-6W58UAQ+K)Ps
zN4NQwb;g_*J+QDh$uOA|7>phKK@8xT4wOp~(aQ`?5#a!XHK6FutQ=%sU@spqC)g_`
zb()o<RuTGAC{x7X|Bog&b<Q<id{`cIL&5iATxw#|2r193cKqD&>Y|0>CSj3^@u=}9
z^8>C#m?~x;!&<|LkI_5zWJ^Bl9JW78YC>F27bf~Y{r5ERM$Q!MJnsR7)42qsycrO$
z{u01(MQdqlVEtVP`4t^i;|lg_9KG+0Mco;VrWpHnVS~zuXqTbWTE1ew0=>A-t--;8
z@WXH%40L#O%>RN_`QZq7%HAD^LN_+XxPor^bGn(4+wzG}yNb>Z%r-1S8NnxhQNE|c
zy~SpKo=oJcQR1GD+fH<}#+L*u1>kG`6PGsAPc7cQbNAkh&$s!c9J)A~TOaCvGk77}
zxxU_^fi;cRaPZ|Ow3NVR{N?Yo?3lc0r4u4oNvhQ^B(xK%G`>Cj2dv<ICU8R+LjqgJ
z(y6tkn%5#im_?J7H~VnRcG~f=Ow}`Jhx7r{rSrez238Ju^vHkJH|Kr>?x)a@d~50%
ztHvju(|qUSJEmfnDqizZf}`?l1=x*C;m|L0v4v7&U&?OCHE}aTP`ZqyV@^#|`bbsa
zjgm6LUm4;BtFcoeYqo4F`6bNq$)x_c`aGP{*}fe375g;VO(Aq@+XMiz{(lP<XUJ|&
zkV~0fEKV|;#xUW}Z%ol)0zVd*TdUnSX7GCZecW%6-aFWH>GURg=(7=5!wLjCrvzPM
z%d1!Vj^q_N3Cc~ugO{DK$Vne&XX52p8x-a7F@segl|WSfk5T1nU^isml}%Z&(J8c!
z(y!wBLf{=2Hz%i+rvt7!`N?(@jd)9g_9o;jHwvuwd8|9)X3bhcU^PX44a5nHgjj%h
z*+#n8D`REeBp54){!1uO=YjiI=Xm6*;-$`8`J4vr8cFs;m@p5k{cu7Hl5g-ZYNU1P
zUWnN>3IzpA-#<6JL8cXVAze=I%sCTGnZOl^oz~^Q^9wI&z1lJy5*nGo+5m*#!^W#7
z`*{kv;*_cb)q^jVpT0F|@A$kd8}HxVpRWXUjUwQz@pEw4%^6=O{lx|jIBdqQ^oM3J
z_(>j6r|nyms`%ZUumr~vdXBP#wE?F1haQ!J&B2!@_ZyW}%wTKr#f!!3>SCDuAK&<c
z1@NyRMbx!txtO~=iNM^P0hDmj@OB8`0lyu79G)!4<!++XAYFg*t}X*P01&4<Y{}-=
zHzQzSXNqUFF-Udr+*dUrukGgHie#O`Z9f(0oEh|~Miv2EIS1h3m$@a;NLH{m*8fI!
zl-@8?ja8*uE6d3>%_XV|y`zA8i<|)dHzSln8nO-`_h5L{z-9@~{JS6L4{9QW`7iwW
z@t&rT`T)2(8vy*ykKPpkME)$8o#Rd(`lT8GL%)qcV$v9DMj<Qp`caH<BWa*9K>YXL
z#t<yG$IiRY8LNU_H7!bNBY=uNEP&(?I3sp@^VBLCO0#_Kjk{~g|DkVrIEIN%j0Th#
z)%#wSYh!DJA~Yo#BLtj&7UN>#lIVN3)cuur6)la8u}~*trlAZLV*lvyS^s1FS`&8X
z2fnuvKKVNo#o3u&8-Ty%obe95koAQ+--*?y)O_?OJhgfb7!QuCvGg~I=%bUOA9&kM
zR@y+D#~=0^os|PP=qbjZe2*?jd9gj7)zf`LGgF_l`UY@bfRCp1ROw2z)JEclb3=9e
zh1SsETM0a141g3LuE;z7boG;&H)$SHLqwt6`8$UVXuz?Ml|$57&Wj}NvS`9ILAV&~
z9nutG;_LO7Z$ki%3$WD~X$t!aycfpe*+Xtv)Tj5OI8nBCJtsl3oc3Oahrh=rLPM;9
z6D{;*2jP&Jj;Tb~Q<Th-N{wk>lNZ|1f7C6mI=XMoA@eQ{J{5^sx((Xeg6JS7|Lr-e
zH33YIfC9Bia~Vfi_ig5C%V+=)l;!`$+<QhvxipQUBvAxJ1(Bp6ISYcoh=2i*A?GMj
zf@ByF1VKOqB`M618FG@Gb5!CGh9n>vVMvm5meYgo{l4d{bN}4CzP0WjWu8t|)m7Ei
z)zw{a@@O9nHi;@gsqslDk`!w<`?Nw8jR@i3DJ1M@c*KukLfAB|1T<_AA8HvzY{E}K
zG;RXa2>1_9{t}5H_+4n&CW(W?NAmupCf`4}?&3|7aPm2VIRa(H=?ilt%8JU042lpR
zA6N=935nA|RGWmv?I7wPYUW_$Q&tQo4<~;F@wpi;@Cfo4;v+IgK-dugdk4c~XWMuO
zBV=b|XS<q-Y<vV!0sY|25f~_=!M}3^GvVY^>}=S6l8}wW9n?vn^G8S+9{!vp=m$5P
z{Pkl9KDP6ZYKlflNNhjeFuc3$7T(y-9Xp8r0Ezig@PZKshl=pYZ+)$&ZR5Vgpm)~)
z)Oz^u7zF=8<>EhMME?hsjeo}={|_p+{~?6Yf1%?04<Y3LgUaiF2|@H9RLK4z1mAy9
znST5aAwHcxU<G*zjTtagukJf{iB<j&jPXB5h&J?Tt>g_qU`Hojd&op8^r`g*^|e?2
zuoS}N>++#V^mfeGI;c9NE?m3JBg+_YRk!quhL_`m{p@GYloDE!qK;3w)lFXu_e^+D
zn|r?l>@A7S^$O$`+y%se`Czz#FapI$0iMxg059{M|G>&FF|!5kc}mr!X~4+Wu(^T(
z9|Mi-{hR?p3~GR{7r+zxA45N5p2rJ6zwr!(Y6mrvqCo*aEDU*TzuRLwM9)8{Qwo-F
z+pSK)mk}!xQcpn}M<UeWqo*$Uf&M*5nR)^}{%0v0JOn#;A)TH16x#$lpB@%3G_ZRR
ze*`bUfj$|ccq5UseJBW18QbcQy>T+Qz&93f_#Kgw;5`{8F&<w?OvMIqT>7W`-gS%(
zYpRdTOBOjF`RAT`N_F95>c>I5FVU>#Z51+T?|PO@iQ+^g&oYfXT$xomc)U&uHgg$l
zWhsCm#qh)n;j|l&)>F{EQa*1xWwP408k@E^uq5gf;h)xBJo0W?d;fmCUpa!%PG{Kv
zQ{}ur4U4F=Qq5lPD|6<m&*sT`9*Y7`Bw_x~402`h_3|!MR5GFRJ|8rhDm9)-8#sEM
zhBZ}qi~OB}C3Tpc98KuXL~cs*im&UfTL%Kw+*$K!aRWjbKUWk`M7O@^X|*WGD`c2=
zyL^}7F3C5hRdcTsvizx6D9&v08Y8yql)fgd0k!X<WqpJ9gvr9<<bUhm6vj&0-L1Q&
zX!om7y7j)G*wGW=Sh=}utA=iubkYI_!p<IzyHRA;X=VvZqq5u@P{g)YXGVUO$xpqH
zSHqCWVR34mG}gClO3Ta#UM*z`%kL4qmqw!xm_FF!h5?cT?!ok-Fellqj}^lUmSVJ+
zMn1zd1j^Uz?09e8l-!#wl$M>Rw)ygw5klL&(`nsP)nWl*l4Jv*uPpnvs=q<LJzNQl
z3YgG({|t?O&6MaA)B~I0#4;tAjp^b8uz7lNbi9I**L(3e|2raJp%&NW3{!(FkS>-u
z)nm3OJpi|%R@fGHzuHBuovopod)r5-y-d$2Q2{w%!>lX_nAU4$Lcy9`g~I!%Q*}E5
z$rJG;7Ug%$Wf|$RIi<>6{0ycnk(Aj(pA}qn3xfhLwfM#5pSGdnsaIChAP!|uuP=Be
znO^E1L5|dRF_R~?V@^`obQlbV?7=Rmd5Dn-;!0VarKpcCtzaL9JB8nJFOfliJ3v|9
z9Z^CLGLwH8?XgG!_ET_$sTTl>8i)WpEu6fOf=Ua5xc6mgV?GC|G-x)%9u+VvVeXz(
z=d_6!==?O!4HuAq4I7o)LzE<%&bY(OV3@!CU{1hOpaQ_^Ru%%t;YhJsD7@L1Oe>BZ
zcRG9|8S5(u`M?X=MKss5Of@{b7Ix$k8<3jwOh{bG^JMP!NF_Hp@Q3+n;{pgFZ+I=_
zQY)^H_3LlmEJ=I%1~rSaMB{w})(KeEqMr^UwjcJV-^g0Cn)Rz4daIVbKghCB{`-eh
z43%x5jkLSgD2?Y$h^E%ni`B1`wuUrPJ^A4S(SQ1`g1ju<PfM@vy8+TdZBil<aOdG|
zh=JG^vLQyQKgy)aSquVRXgJ=S(sKOT`;iQRIhi(?kyjTRS6<}_6kU53OVNzLfEf1c
zFJLg5lFG6n4o1i-i<aiu!>E!ewYuEbK`1ni6Bg*MnIYZxf1EdbpJ6DDb@yzVc-fVh
zR`jHxF5~eBC``HyUqk;_bx&351XJDBEOE_SA%ik6*tFP~EYr_ivTvGTyT}a1lt!J1
z_dj!ly?vWrlp5F&y+TqLiDUsUL7_^(6?9VI)&gwr#IB$?HM*6O#yO#=k>4Cs$w(ZZ
zkwzIAcTZ<EJ-Eo?tC|s*I=iJN_miQ?2Hugx7C8SK(9R6Z{v~A_*l0{8gYk+EA4LSR
zd_^a=JV~cf9%!I&tOjb_0k5A2p&$$p9yK}i9*?<69HzPJze=|i?{br`>M&;-TcJj%
zPe?i?-(VRFs7owkJ^H$WY4$?1umRovC;-eP9@bz1Z+V`>4Rk*E6Z{Ux>il!#1pELR
zo$sa$`ucgjN2vd&Mw@Z}=VFQj?2G>wi}L^6f{8VP{%@9p$lh}FB^(@opHQ#i!O)`p
z>39GCG_?M&)*#*SE1P$Zt`ZvW*nBlrd&74vShMiL3w+<8+^_voVtkW}2lkWJ#zxa1
zsgm+L49WQ#|C5=G4}@xAmwRBv5}>Hh?bC0-Yy}Zsf2`R@L~sEx^C+VC2xKF8$^rW)
zFm10hTb%R7C1A?}UioX{vdEwTLxGoX6@l@{%!d6Fw3Q7-0+Sg$uL}%Z!hh}Fa0Up7
z+5&qb5fo8w${AvS*jD3=B{K}bf&vD1V3REr?Xvqs@0AxfYGi&JSQ88g41s3fUcnv|
zEpEsv^s83%El_(2fKh#v;pDGNtR-?3Bvt6zdVcb;lju|RIjUGX7=P*Yj$=&Y4Gv66
za_MR{pOeUDe4M%{OHgw?Ej0c!T;n#QQrhZ~q9}gy{$$lu4;LNP0ebzYZ_t%~w{Kt1
z<Grp;igo&0y^7hnJ%a)gcA(;1ni{1+4bihna#vi>zK6Ayyq$qOJY4#9t^RSIP~RC9
zR+b)NJr4;Kw=|^Dk+fepnb?53lmRdNH%9R?SsW|;sWSuW)KlD&Mi0^Qop;m>I;tHp
z$JjL74aOQ&48Wh@ZME)IKUu83Yd*Z6K%^*OaJD;aa(^5dX?WoyaA-4t*V(-}h1n?U
z?Cyx57Gk9A&uVe5ft^ws*gY!$?BzD(ogayb8<WvYqB^@BGhwm#5T#b)@pGYFO~)sP
z{x(QiccT4B9zc=>8dXjR=?%$;@gcTlEcZ_Qk~;9co}n{~8Si%AHl$#n0YiNUmLBp-
z7p0EEPu9%wWxYg;C=K5GBCu$#g(6>`G0J}{J$G@jjTyY*0PAS&-MgSD?Cy3tL*hC|
zak$wmi)(`AI-tuM&3d+S%R>=r`7xWNL$(v#IXu%KPiJ1<W+`PKP&Qa3ru!@0H1&E>
z99k<Nbl_VdD(tBN#4OYP1D+&y&qV{a*G<x?VFD`{x-ns&rg)E-!J9iB9O-q}8Ls~Y
zn65R%iW!t+#C)DwDtw{?#a=dQ-ZH=u-8NZQDs&BNKpQl1;NE`_C{579ZgsVp_FdrR
z%ad2{e)&i&MX`CA*ZGc}q-$W}3n76ZRBj2oPpHjx_`9pn%VkuhM;s5?hB{xSu-$n#
z;P$rHIU1QiIhZIMbDf10f%teN60R$Pa^s8W@OkTM@}+I?_mm!bJFAT$kcgG-+Mg<~
zh15e%rceRtS=@`RG0FZXUiJ{JjLMFTA4ah2D*b$rfG?jdf?|-w2Ijc7kQgKdle{9M
zEDdPy*cl^FY}JXnyQ1m=s<B&wG(*ii7WW)Gp{7A7C^lOZBe>Cy308x_I-vnA80M5T
zW7zeuxyS+HCS}s;55@|oI`>JQ$)m?t$M&O<vr*8HH|_JyAN3RkOzj{X)S$m`K`>hJ
zI85l)^gbOCO~|7xMrD=lqeNx1i%hHw55iIli<i*xA@YiPF-2q}$igYuClQRk>l)q9
zNW@Gq00P<spy=dTpzwwtY2dz;)P2!Nq|w@-gf3&uj6v?9Ad`2<qL5iCh;9FNGcYLn
z-$BP<{#U$_!VK(UJ!f*TqXc0S*n^Q3S=NOEvsQ6aCzEELOzL7!ENYIe|DZMzSwf)h
z{jEYM=2VcDeXizK(kx;9vZlsjHLD2TFc0V4t2q1E{QcASZd#TJq62G+TzMJx1R*ZT
z2Nf83!}bEa1kC!2LH~(`{>E~Tw_1Bc37r-=PG>~k&kY*tHq+WiPCQvNZDCg=Oi#%<
zSC<9ZXjg>=1{aBz??@bZwwo7LvFHiaHaFfW=rX+KmN3=A!`5;W@B!Q(P(o8r@oq>K
z&P$FL#TUAlzChc2gwzN)vu7KfWr!^et1PML-&uNy@t=Jibyf48O|#gY(@S#Yq}BE;
zbSXpDag0vbituhZRK5Jn+I2dJQ_M(K_%t|AUQskg#s37C{g*GqGi&rpFvOllLXm`~
zxWyIHZ8W@cbM-PI>0#Cm-87gFEK{7%M-Gn#?#oyk<oDs<6nw*dcWn#AE~<|jUsIIw
zbC8w2ajRBifc#5Oy+BUE%t=52idvpQ|JQdPJNs0jB3nDW*VZve#t%1_qdGIBO&7A{
zxFijv;7(0X!|L7`@I!17PCxFXro`5<d?6N8zj^TEmENO@g8@LIeFeqUuUAfCroExU
zt5xj9&OLHq!t4K?@KIQ~_oHqc1y8ZD<2hStv7_<m2gUV7*R-nUim#B<vp=J!SI5nk
zRTwVn7Pzmvr9s+0%DMo(I4qgJZ-wD>Ft1w?IZzAPs6jTNg70B)4szxCW7xA@s-CNH
z5==@el28@TMXDLhPP>G4CEeMsK~i}ABS|AR5d7&nRm3Cxm<)%9r#Y1M+e~?zt);CN
zX`W`U%aj^F$iR-S=*kY*f0qw=f8@?WH-?eNw~#EKsjN!oA=l}|(8<9@$*O{p>lWP+
zDkKZj&WtEaQOs`;J<$ja!}s;?VjEID;r}b7rX_PWczNa}RaIJ7U&-&Du2X4TOso}@
zJ#sm4yB7QsM)=6$_vgzQ(s-Vyw>MVdPE#JQqG_t|R{h%Vur9RUV<L5H{aVAR%vZq$
zp$fy$iI~>#Eb!wfNg7D{ck!_Cu>XUWr}4U?LENP#km@tRKPFf`8}Wt1YS1JlFYx%C
zZg7h7&F6NyT7`>WFC+)jT7l~%?wg+%Ss>~?5Q@h;{U*duB;P3O>aH68tc{Trh?l3k
zE|s}`Ojp|CI@#%Tv=tqY$g^;jn-%9qI1^R2I@2$D>3SSP!MvX4-Vafyw+7%&>SA_o
zWIGX7rv0ukUUoV3w}2arT7{2HaH^w`_t`Sf?!?&>%k}n5B`;jI?h<aA^&I&9B5(bU
zmLl2-M{^9dbo*WfQjiI&*=zuhU|w_{UX}^wU=0oyGHWn0?~pM=q7I3oB{_3$D4Ay3
zL(0PI9volV8ID3i$s0AoM1Q3JZk$(zp^afjgX&|UY(-WCQ-b%q0&?9us^)e?F|EH)
zOAylt<Z;zp8p8{qYkz>D+S?+xUo3UfOl8xz`q;;;h;-Fz9#lza<%T`R$<TG2GyX)m
z_nbVP+y(X)$G=3^@FHv35nK&j;j$A*hpQRr?5c0c{k{ERSY6K^Kjai_zMlTR+*z4n
zrclfA(Cn=XazhjYh84pohON>@0@d{utCb6ZR_<E|cK|h=F@keqMl9{&L}F&&3aw`J
z4{zSHBFCx!v5XEIb|a_aj4wn82;h~r;=WKWlf8waMo2A7MLx%KmkQujz29H`ajJ94
zP|fz~?F%cuZbmD+?}=(%kPcU>hIlgfLzOn)KscrWDJg^iha8<oZj14y-@rH<HI6#B
z;0j25(ea-(LqSM#Rc*sgFGUAzKHNcirbHuk=bgBcyl8uWjl&XZ3Jpw$J#eHeKGKbs
zKN`F=I>ZkFiPp}ZqHmI2k;kKYlm4R1@!NW64x2^99f{z86`*(CVfBtNb{+W4WAQ50
zmpYFfF)$AE%@*N|r&{c>;$6e+m`4TFFHqTRHXUyaWK;U0mXy>cp5CVF8hnUpO^E%F
zd$%Fk>A$EI2Bs--BN1tEaKvziI#l{FuGSWM*s6QExFH-l?|2J5-OU14NR4sWJ%~lE
zR~#~Amzix!Tki2*6kH(Txi2hWPr*i+@kj#8T@HHa7sB=iFJOcZAxvTo^yh4MD2pEH
zCs5ip{ZzvmQdLZ4Mp=EuZKCBK3!^Z$<Le1LncpxZuVK{{NV{=ZfA9_)yksIiz&%wY
zVZ_a-q5)%L0a%I!5E@3@6Nx}U@Zkb8Oc20p)TjugJ7eF^cx2XlAItL7j^<C3Y(cga
zb4z!Y-s|~RHy-nX5!}G)A4Xh(lt<&RuvH)_bO+!Ai#Iw)D2xQf`!xupYt{>b0wqTa
znP55klGQahD>6cl;-N13pJllFbDsue&SfetN2Rh`LV59Bo*PtZN(#|@ale_0_{fRv
zm=@ddH1m1KXpEp{U~>v1N+fuPZsND|+zS=T7dhIDjD4PsLoe@;5A?l&7Uh2T`L=Fx
zokxl=G9$fvQwN=AGF4Bgu@3U_sa(J!3D$cQbrJ>>iFnc(gS;uZ7AumT-5Wh-Adl9Z
z{lYCoA{d1H&VQGKO<r&;Mnrh@Y9fo?bw>(B3Avdrd@Jds?os!3QPNWAJ&jXdM#h_f
z0#%N{b|F#5F7U~7GKw$q>HN&fa`kq(AiFabkht`Vkz&{Nbt41j3QqX@71&)>Oh`Xr
zZ1ArtJ)?cNHnZ&ev7Htyz0WJ>Oksp<Si<+hm@~7#_D@B?oivN&EFkw(;=k0+$#;q^
zL~c87-uYZ}z``Mk2>{FrX7nD!7l9myslJ53x208GDCIUB(=i@bf(}z;VF^&~iZW)p
z#5wg39k-8O-9&6(e6znjDtYyT@yW5!hYW&m8Df<bawL|$gkH)wnx~U5@Ly2U>~&Y(
zp(~KOMlvaOM7C^7>=H9!t47n&^)OM5!+4EsT^?^522AnWnu>6NB#h~tWHF}}dhaY^
zTW6vJ!fTPTA5u2qlA)FZj_!1TZA%RH3!0XbftOJ_@krs!0ZaY6nu_mJPa?NhSXZ>F
zFypskMvfUFnL(IZ`=Cn`9+p*aV@Jw6*6$j6JP;AkIs2`vZ!M#E#yttc`vSbR8{h=c
zLIgEp6^4}2<bbTk3wgz@7Ff?vcG8>E!t!3{w&6!1W8-#3Th`<0c2}Ox-*eO}NgfXH
zm48q?im?F?)e=mDO+@Z!?JJ|cX%kz0Sb{G1>&^i0aD)>VqBv&frL9V|y@f9Gf*MqR
zpUmtHe6PD6I)aZfn^D}P&Mb|3Tlx#*T;WQKp0VQbmIW?QGH{_QBMw<RCn?f_vYeGT
zVxzB?pqF)bQc8R?^p;V9M{7SSZK0qm*LCdv%;wD2s`}_Q!l|E&CG%KjO?HUT)%6*v
zg<~1zBNpILlkFB|w%AFL1sC|<H?dftj6`hojB88G7cdV>9nT_fNS_+_SlLc&Hf_Eu
z!ASyVmW|-CY3%U1$V(rhkh?J3AA^S$xQ%mw>K?g(RMjX{M}Px&7?N!`IsoJ~_MQWq
zqlf=niJ|jLt%bx3IC5Y!bD#}@qD>Kq2=bo}z`w--NL2{S;K;_#v&i2K*#F3f6SIZ2
zxA4GOkxyRrMLylgcWyRemohk*10RIT<`F^DjI1o@pJr6Cnv-dkTzpfik>9T&Jh5Bg
z@-~!<Knc|viR|g*qNcr>n6qC`x92h!JXtssHA;K8P>YL)uh{ZwhhqRAgp@)EGFa#q
zvQqQ&tK!#dEAO-v@38?1j=}DMKqKeCiw)cz_x7@FuSr|EM<ZJ=CnA@Ow`r>6IQK)5
zwJ<mYSVryGMc(xFT&eJIO{u7Ok`U#PE~C<Q#`N`2*h@LQfS6`F<%q@C;gvdT+Ol|+
zde<Hd`g4c<e6*;D-#Y43elfWok=g5H@v>KFFPFb?{DL#-2g113XqpQ%o<2R+^5`dO
zs46FnZ|bL^J^$~8wr;^5Fy*)0NlUo{LnM{L1|OQ*yr=yxqJyBOvk?gSK%fQ8u}fAH
z*FvyO6?s3dlJfN%_kPUXI4t%L&rD5mL!{{RaS+=%CfvHAB@xS_&6+ImW2r_Wfh3_-
zme}g4mbl$}UH-KVCENCqBBdMr>ej>SpRJvKlqR!o%nyWypmZEKAp*|~T&orR<}|E+
zSO9?L(@AcvMlHQoKjNo<d)CME?AmvsH7_E)u#o>IC`kcwph)Cb8i@+WMcH)D<)AXT
zdjST+juqMuUq*2a`b-kiAzT)-?`C-TZ@FEFrc*&d^l_8al+fN}y`w%w1R2XU$qmX`
zYztM7an_@e7c$`jPMGNDjZNMqlB99N?fR+#**ZAMi0A^Gm7lBxyJuu_q{W*Z(0S~B
zvIM(<+$=$E5^ozXHEB@yoN_Njm+lAA+d1Mp>Li43G&a6`pi@nj(VWaJv$W(3!mPyb
zj3kxNK{)Qi-Y+?H=W*#mD-)2DiZ~!E?w_%f<WFu3C5CChzE!wat7!6j-SGZ>?W?u7
zQg!J6iFiQ^r6{9kCGV>$gwqp33k=Qp7UgSRJuwrm;a7~EeNG|$$?$<G+$2j;h?W{k
zN1QlGGGhnBE%7yxj5Blv(?zF4KDBPVO|8SS@VHw7VKa!>-Yme-jj%x6=9m#+F$){P
z8i9))mZX(elAay$vDr;WtO_Aj4{M}lal@gWN!RTxabug$G`_j0-##LUFW{cTbsk#8
zHENpcLzJYFJ$hyiOHgicFtpmiJX9f^7DALvtHaDcjk9YkPF_C#Ug#E#R77|3;FL-r
z5L${Lm+-Y1Y9J-S`AC-!-+6>&nG!_sD>R=A(F+D>+_BY1-m)lUn7J^{I7RvG-aF$>
zaZZGFuiG#8?}lXIM-VO{!B=xOh&QBhy5_Lk5nt+DEOFdn`JJJ4<>7xB%nok!ovR5*
z8#RP+`F6pSReU6?0VDv(8c43pcywHXq*Zi$C3|<HR11k8)CkEGWa`%Cz2`T}n3FAS
z^4QU{Bvsn0XgGu$QFkS;Op%4{%Aj1!6HjTF3D1f{LV*S<z_I&Uv^k72Ru&!4Wa#kG
zww_hYMx05Hc5Xbp`)Bknp+_z3I*@N{s%=w`IdzW@a-l+fI4!!iE#=qg87o-SQ>>ap
zWRBuVuVG9QD^2}Wk4bI^M;L#Iy>1hyY;OvqjFd&^hUHo0jy28SFYdp!*?4r-tBs2-
zh8)nX3%j!>)IyK9o^s48>q@1_47W?f!aTkxj@o^@iYVz-ht2bEodswqjy}TzfVF(@
zRl|Ny<jr#~!nvR)Ih*MYGx@jtqmh0YaDmTZXG*oOesPdi_MFxGc(u?eUoqoike~|%
z$kNYP=D_Ny@TLqi%9fOx@BO45E<N{rO|BIo@%qHS{%xjmg`TI0ozgZ^T=_~)WDYx}
z5)c2?RdVduv|z^P92!kFlV#|NyL@4b72K)X!8*T(>4iXY<9;QVwM>>@3?$PBsbuee
z-Z2ee*fLRemeKxbBsvHW-HBp<mm3t*cZc#`D8aRdOOE!(u-ywMZ#&*G2y7pAalJO5
z-|{m~a)XmE7RQLHe2Cb!lCCo0njH^$z#X#1kaQJ98-OU1>Xn5=FBQLS=-NenR)}sO
zr#!3x4t?o^A<$)l`0xtb&AEIx;zL7wD}*yud<R{t=#_!Du@pr5#{28EskN*=CvzW1
zd(h6&dZ06LTYxVYM9cNs@@$VVTy_=dt{Y71>FF|5sg5i0&_7W~2kS3(RQDD0Pm>ad
z^yxGtN(N?jwIoNYriu+V5jD?(?hYkbywxRP2e~t`5QsN*&v8%ei++`=w>9m{to5i@
z^t>era-Q}AFY3;*=gzES<gcGQUx&%Qnw78J>M2&AedZ;hxm9*=^$#o#wrjk#M4Yb0
ze8j-_N(wyKbpLv#0~sK+=T7jD{|}$hKPp~m1kUmW&#sH|v%jK*F}6^%$VyriwVQ^e
zX=4bvxp^C3mQ-mBZzR5r-#_)>oyl6?$ORD}=Pq?Y;5;Y#7a+}d7@Kqddn^Em&k$Zx
zZ+80vHi`r|>AN&;Uu?0(C;{*KOL-Vx)5^TA^0?aM#ea%5(eGyLw)la&SP+CU8V@<$
zWna5ffe)wvB3-E11lZSDl>Yf;kr0$rep*WcC=voZ{m7t)m#p6OEP#Xi-e?R#E2L{X
zseIT2OR)1|Yhv|Dd)|K<F9@x_8~we-{C6bFlapwZ`2u3VXdqT{)?>j!d6aPh8xkXc
zA`0FPi+gM->PG2}!6ep+YWwj8<MDYY%A!o}wb<;C{ra4e``kONt5q6mu4&FhTZ{;=
z!mC)hSAT@w9&Z_g#N$Um6_es`iS^fPdt3tGod4u^B5`*ybp>u{WpJE8Q{AD_TQYX?
zPjRwT{Y9~wu4RCXattVb(3P3=qJ*v-3%(hk-Af5q-%oXOo`8<*0ASJ(e<RNtC$USQ
zdOYAc_g4~MtPu~fxTk}$)1$@-CLY{|0R#wS5y(e!IVZ%`ui%+j{DgoYlnQqjBxx<z
zVn;6n^|PeJ3F{tgM1W%t<pc~ttUEd{Nv*S%2o$$qiydP-lavrASjuC6=P4+@<2}Xj
zk^S@gFm&qi=b=MdFkCj^0SQsp;C|(+e#SeKtlFF@A=ks0L%q`%pE5HPT@8A4_4^ZU
z`BV-bNj{K+fpa{&^EKF9>P4FzOfQtL@GLy`yHb5f4SE{FBDQDzRwpwPiDjoUz;xx^
zTijpJNt+Fz98l)irI?zatg5>KkPUp%t%zxOZ1w;_M+gj7z!lXsbgu8JNt|X>{+tux
z#6t}(XpB%TXaO<;*e__$Pg*J?5G(Y58YsV0QLK39{?N3Z;JgFn_XddL$M?(I4r%_3
zcPDb>eqLT%jSdg6cu_>*rr~NA6|IZ-azG><fG}kz)`KLwPz<&H2cFBtU296pzHSC_
zgXaNOk94FDZFKc~rRJsn03fw=-;F-Ewo|`E7Z~4QYI_`oKfk*%yu8ruh66`<?X9I}
z27o6%Hjcli4M?kxX1}<a`{)|=ciEI7_JGY?6%$|CG8L2WeijPhuRrqNrsREia^Wmn
zjKo={NFk`TV9Apw$!E(p!EQ}n6dTJ>Iq$13v{TxJf7(F>M#CPNuIYr{rgqmp$=+fj
z1UQ1P-a&hd)5v63{eixBxS$dEI-T|%*OpDkeOQ0h{Ux>)_$*?z6TUU;KUvDGK>N2Z
z^kv@V$E`yacC8!q@^W%RWxJn|yS7q|V!_kkNdFP`Vnk*7YhBtlTm6nny`Wgs>irX^
z?V24__3&wX^3CjSIu9g&kkakj_Zt<!e7*&z$qrr0x>d774wBI0R9stw<qrmZjIw=f
zW#Yv8df^$laslK9U9pk|xU0{~Q&=tXX6kvPNABqJ|EGO-m!++QC6|jc68S>+H1R=#
zye9+Xtmr%Kl1}Y1hR!AeDH?7gQM@{&R5IQ5&Po8SIFFByl!W0ef~ZEE5Ap}p0<_fM
zzp_~qJe%$*Z*Q)uX?jJxMH%5$*19xXuQSUH-(H6zBPd0wsvwZ2N5RK$gD^Jyu&z!s
z8<`us)N(Tu-@y~cL`fiq5(mles@dPEXHHI*RvdFnE)l6H*O5p&A#pzxQ5wz1_1Y{a
zTC*y2KaJE_H@x&)96QI&60;9G=udCv_@R?eOEfLQIP^CYtroODFpZU(=ZbF?J+^F`
zO`5)?2gh`0rZl}T@OEK}dNs!FXz?qd)NRhQ+&c(N)+zQFDv%8>B;#%`bl1{#^{I!M
zhG!2lG<+`&r81V?{Zk5cM}t(a+gmvNRu-+Wo+`Scw^nFOb5Kd?v-WaHZ`F*Mt&+>b
zEpPZww)+y<-+~5(Xu_Sx82=17rQB~dIed0+m)~;$=O)-)9$}BhuDw-Mo1LFRM3v#L
zhViz#vO{94@@>gW0v@L~#MB{V!)OB7@?VOCY3GMEGA1k++5fcvg*v=nxFSu89kR$N
z;Y#9?ImYy$d?a9n$d)vlR*ZLav2cgwp8Ub(mjW=8URujseWj5^!b*@Jvlk?T#`*L*
z@42$=hmZ$`d0|Dq?uXJSUATJClNXwfhKQ078lnd7VVN$*83<{CEP7n>_4a64&rAt&
z;z9TjYeP*~6Nbc|zEopLpllgYeHDrx6&MYnOSr0lzV7r5EZl#%QPL7};~P!PcBz;=
zT3Uqy(-JG-xJqpFLCbKiHtaIJbPpqB*!J<!_(Xd*s*1&6xxZsS17jnwrEnLH94EaD
zUOLC!0YMDjObN>d(U%R?%4j%#hf>m_V#)#=t;3LhKVIt#ncWTFw$*?okkLz{L3ccr
z%&W^V%sd8}%d!AfxX)ry6ZQeb*lGcW!cux#_>F7-s5(Wf@yRLI_?sZdcB6<P*1#-&
z$4i4ENqyRMjPG<W(k%Gle~j#xk_s1@esb+X7I+U{qkB~fp&qAAK01auw1w$?8>tQ2
zaDY2KN$C&0d@Up{W?$;4Fx4_@yYfVyTi9a8@QOfP_D=47Rtqs>%6uu9i_u9Mp|I;c
z09n@q%@T{ZQ6=NiNZSt&AHz%rfquZ^`A9}GeA3ulQQ9o%WkpPj9L><IWymoqi%oOe
z<M)0PSr@mvNP9Evw4+tc=Z(|vh!Cc78CdyqV!18WtS*_%r&r(t3v8Kh2CS|uM&G|;
zp3^#ZWrS<asT<`Mgpri_%W4eF=K8j`?ypCiSmdVIjMwU`?!JS&>+cBk-4dX6D*Nh>
zsox0trWBa-ps<N@l&Ok*h!LWf>*p19#5>{ofPTxnMXo40g(b~Xv^%^1(SXsF<yS=$
zq46c|WgE?-J%sN;u=ct1ew)co%T2Q|C*qiwurk`2I0>qN_Lq68x(6Nh=xA-<SG{Bq
zD|PZ*2RwS3+tT{uy86ZYl~r6%h1j-_4bV2aN7I6&^Z929E>GX4JvpTuie1zJ`QxSk
za@M7gF-z!L_#jfbyDLQdTP`vj{U&Z)j|Hmd8aont=4_*?A+VmLUVeeGh^gBv=VbXH
zi9;i=Mc1QjFL5qkPD^!yX6u&Lp)O3kE;kmM>BdJsoaW>5^!7l=m&40nuaZA#PPqRK
zgg~4N${jfEhzYKRMMudat5oOH529Aqs*c3D)B$5Nj5qUc*bmTK8)se4yb-rF8j4S}
z`g!QL#!0~)(dCJ2)_hqXag+aQNAHa{RBBs`G|Qi+U#mNtbmDV3wN`UgMr}7JEX54A
zoX)=YRt8ZASVm~%ba==Vub|`lngw1Wa80WTp|V__{0TmwlW0$6Efqz234~mnFA%we
zsch+~b8G6KDtHxOF4ObkmZw_F_3BIU1JI(3<%p%{J(T}d`%2bn|G;}=KPib;!Gm6G
z+$UJp7kTb((}=flq;Xllj2G-?<p<xuYMr#-><aymK&2_aW~6E<e*f>PRkt+GVmYhT
z6!;NcNi(@d{r9t2HcYo^N}9CO-Y8E{*!b_Nx08S1lcuOe(rL4o=lsR!pReW3_bn&b
zn(l4wwNi|=)-i$6uoi%L;X<0AYjdG|%KCP{a|ZzL05v&LDoO3<NGE<qDL$KW^Tu;i
zqFFvt-3QQX1KLkydcUoZJ|~cUY+SiZEJ<{Yz;<j`L(W$@T53qZHevbbl7-WO<CT3<
zV8#)Re(>#T_(k#i!`KvJ(ieomU@hC0D!uKP$@^ljPI#vjgbHo~%t2k~5GCSwXVy0n
zea}So0=COQJb`t@htNZ%`+fW2oZme+?~noT2DM3@&-9DS45@<Be>%@g#Ly6&e^t1@
z>mjh@$?-kEMnFdVA<rLc$Vs7AM=3#!_j`7oJ_{FRU!&4_<&dFzWtN*-#ATMAYiymL
zC_X{YL3{4%8p{1<DHm$}hG|KGavGTIz=E6QnV@2xQdcF_{hW=d&B1+(z}i7cT`OIz
z7YT_@Q`d|mY8`?d;MmGI&&mXIB7ZHqd~R`w{qvz(D?6=1`G6}vc?!iBc>b-SbWaFg
zptN=aZMBL<Qp^ASoVovIXTRF7L|Y%a)vHTEb#4)QlaJaDBfb`@9jF~&gi=aS8iBPM
z7;^M06DZjTN4mD3XBCa_1KS%Zhl)OawCQEA)htY5_<B9_(YHIPek{xc##hZG<F2pZ
zRS(L%G<N^grZIo~&~?*is*%v}2(goQBbI+t0JT_#_Z`?i%xsN>JmmP$+YrB!yb}5&
z#Av4Lg)hL!kx}&SmNsC8xPIw<v|yvx-?v|hvwKdoLcg3S_zrJ1vwBa*7|3n>y&g{x
zh!p54GLlUczATYIDO6Jup5~fSh6DCIfCH4DEp+dt3H_SD7^2Z+ED(>MFLBGyBmW=i
z=aCV@N$Tx6P8_#p-9Nr*BI*V-1;kFiDGYb^5;r)o{pG=9nJA{?J%7$ky}eu7O8wx;
zY|izajjzl(+nOTDmgiG!vv5$d#w>R;Tg-6GZTk^ij`+e$ux|j}jafsSd7)<kztF>q
z`uQxj0~MDGna<k?&UP$+O0%#_S65!%K83os)%;ts+|{zi?8{UkS2q4;AOVBYQc^Km
z0P=$&`0>+YbwcgNgCFiY$6?I21J)M~K!Gy2?)D`iqADTH<+oMV`}_}e(E4sVu0~!k
zb=e>#^o>^`WGp)R<Hyu4X2r5@q-VG&B6YBV=d}9y7G7R2<|+H-)xqG=RYwb47h)9Z
z?%vvZI!YvP_Tca;cQxM_-pz+&RyK^b0oU}je?E_1><{AA)Fi$@1mg>4pdit0u&ch5
zob!9!<erR*{ps7p?YE~FdRF7Mk=qw)u_o@W)p2oJOh|G1!1^0CIO}-4EZ#TZdDy_3
zVcKplr2D@QQ`D=+jCe6{xXvPQF9FX)Bi1OepITpFKFzY=RtT(^XV%w^5yJ5S{k;Yp
zvaxp5@hL6+Lk~AgF);!Tzw3#A72Lk@#@eXbUhDa3C_lmA2lkVeQ1>H7dmIp;eLkGG
zmsQhKsMBh@eT_NUkgRI|PX!7FZO{g5E{XInGpkzKhL&k-2<5<IB{&KHEu!kY8JlW)
z{F2`)8180mg!AMt_}*tb^>b-y110U>{e$57dH1FXo?uU20A}7VG7ObubUo+?4&&bn
zhs`G%^5c93e20a;5vnw*z_BEK?^l=MM3`)6VkaR3HcI~-3aNE7Mh)SnGo`X3wk+LC
zXHT)A`5u7Dr`5|68`AoR9=Bh#;qu#M7goaU0boypVuwhwnt<<CH+f`6zOKSc>uCo1
zIRGPjgVgmnNFIrY;evWSKYpx9QBMBWqva~sQ|-5;@~#98vGX(i+`c-XwDh$7Q>|V(
z@%C$Rd#0G9+%#5N<fHe0`~O|+D$JuIZA|x}XTnFBllvz68~pjAcKBWf>17|;q`uA+
zn}L*%cP{b^6Y`E%z9d)a&DS@)-}>_YtN^}Wj{y3W=f|eQO$mp^<h>)P<VueMy@erN
zIy_hCH)gHlc=uzLTc$7xGm40W7Dd!M7T1mX7%<Wl-k(~TAgf~C;vzfXbbPJoV5E@<
zUlzDb0P%NH%QpoXe|5*WUh7tWr^{tB=itI<@~c%5k1zs5;15`Palm))JLI=Ohb6k~
zqn~@J1o3IO_g5C+QUQ~2q*+r<Wg9Z+Ji3=+`)k!M;*99Rxp*Diqz#$fvCdF?wQW{9
zNY8522>dEPWE7`ua&B?=7<M>U*X^1K;)<QC76<se?!4)<{QBOwb5Ymt=AJvTPUfeM
z7_8<){7!x2#<pvT83`IeZrkCx18M`+l2eDgnbNOYm5Z^d5slp5C!qMF-{a;y+#Y7<
zrA2HPgy|j58si22(Ky|TXpOsQm=?M`@8rM(@ACP;y_UJZ3amN}A!^l{JvYgf0e1tH
zshE1Gr*5#sW$~cB;JUKR`L`=$ZepATI}rl<ZV5dsN$1NHPJASciL>A&Lg2LBkX-qE
zMf6sTJ5*FRI0TOtBuX%YdKlqjm!Sbk@Ve!J8#4!$plnD>sjhHy;OLC%4c{7eDo6Z%
zPyGAdjw~Rz$w#@A>$}Z~%U$XrA+0VBQftde-5#ftI|t}ET+n-C1$x-@r04izIWow~
z|HAY|fEieDjfz<prl!F229E&gv4#qt4g$f$#s{2_I2j-eu<^_a#>}RtXcP!|R2~}_
zc{YfVQ7lI`QUDQ(wgSDJ<<K)*l;`D5Zb~vqSi0%yrSmK-)z=%wAwRE8P37HfU7G^F
z$I3tBF=)buWS4I#32q_!RYAqKdV^J?UPZB(6^G=<K5Bb-Spz6aIQd8Legc>%99B!%
zkdh$8x-JW(&at!COmcJyN3yNH{ZEazPOt|tHmCS{Xh=v%gCHbvjCSyc@DPR_@~TY_
z8#)VOX@Sv$qaKEg(FsDovN_^}RNNBVt8Mj(^$=G>4v&oeQeU5yld1{rAsbyILa*u{
zdU&!XCR)~H9scMXJH9nCGA>a%Xm(|R?2W^A_Mpq=w^_HJhNSb`&@tOX4-Gx^&1bOw
znCHA<YqbvBX=y*lL;NEuD#%K?9@vNNFdQ7Z^DYfN&sT-9TGc(LabTB4`|dpj@B^PL
z7Q<{A^wG5olynD3<Vewbyf-C#QcIFQ-r^5`>#~+uf49@Vr*D~2r&Il$#x12CGFGNX
zVNkbHkRCCa-?(=ph=s?t*g|Hu+*Kwfdur|>eXyBLW_5bFbv0e(09E{Ym~(eNEsR;^
z4rKVpcG_;1Iw#1(UOE9ho||BP0arYl!;BB6v~qFX%*j9CfQY8+2~R=aq+-<f;Sg1m
zrNxh>y$QTG9^R&m@zf0%eVNAoDp8`3XZOc&1XcY#g<qUXXn+6uUHElcdfxC)>Wb*2
zb+%05-5+bAs^t=N6IKm8a_HG*-FTgf{NyJv@sc)*5gl1H<(vB`qGrH8SbH=AA&^;p
zx~I5g#Q|v}Hx6^lr@RTC5G<=n+e;BxPkVP4%2xaMXA8z8o;PGiwryoNHC11nlXL_n
zo!*I1ySY-QT0JPWH1WOy$rZU25g!QO&Mhr{nc1L-=A4?dDR(kMWu*lHHX!@U28w9Q
z(LPG;q)1f>gV0#tg~P8}(MYZpR?F&CnVfxbjs+$fWqtF=@6Tzd)i(~`S`6(Kj*?v*
zAWO5JL+MycM9hoI*P4+}!s^3l;4V+VGnlmTdo4uF3!$FX4<T*ww!j;x{fE_)OkivM
zH}g~^)&?zJ7{kz|#Qol@zN&R%DK!R336R)BZ7Z1IO!uqHrCmMlsp6u~6OiR^(&frs
ztw#tdkbR!r<;&qP6(8|8s|4_pCdDW=i_f0GsbB&G5oi$aKvYb)O93ZT`t--$cup}_
zkGqf_SrE73nY_xR`{EgsOZL8zwq0B8(}0rnrI84DN$XxJl^(jdRK6;y@%pjq>q(w0
zZBLk8;cyh&f=vy$w)3~=jj%dV&-go6rbT`q9t@@282Zf8S*fe_gJq?fGOT2LU*Qwa
za=R6=5=8)v;;e9`xLxptjKHr`cfQZ_#}{jlyDA=A!H%MTDU8MQIeq`UkfvXrs+`}-
zVxbe8mk^c1!n3#7UZ<AH_{M*y>!!bNa!&GdIeBzke`Ee2xh6Usvm*peT+wp>J(ujh
zZ4E=qDxn4TMiLPvs~>BSkHFm<tda^%Kw77XzNs~PLiX%b#0|c;Cw7=C8()dpDKcK_
z{?7Niby#9}Bm{0yb9dMLs8_t(#RQfg##?vnpy^c)tCoP$T2xiZ2(R2&-oLfO2igBI
zV4lgc@aDKf2?a6kVTSnF{MA-s5bK__4l7F)i|?PswZb&D(xN^opoQUUqd{r5dy6tJ
zl4a}2Kc~NHsu0xT?3PhPt4!NU?18sX)SbrmOf2gfKGHN{9v+IG&7Hh7Jm!EbjN!w{
z+ks(%75#q1K#AQT%kGtsyXJd|P~DyOO_ZBz?8lXeeq1ln;%!f;>T<npj~?q%3ke%r
zrqXaa)H?I@utUdltZ?ii^OAeF-FoE%d9<f~CYy!j=k?QYq{V6~Gg~O|zhG4!GwZ^S
z4A`KA75xYW1mUu8&}O(=HSAEm*n9DFOCB;HJ6|^M@CGSQOhZA6{V!s+a5%Esreeo;
zHa`y>dIbL^!5!e2y#E57tEZ@>$RZ<$hAP+(x-P8xGX953ZJ1jA`L61pQ>-BKg<9Va
zyk|TBOTepGEN2iU8>(7pYhP=x2&59B?+comd#@S(Q~vf9XBX@zOFnX`A0aSNwxNy#
z|ErtiWffe0zUr)EvyhO#;m!PK>NkRxl4rkldd+#zeS43?{8tzZSFTL7OGTY^R7s5A
z{I_aO|Iag{`7Y17=dXpi|Lx)GtjQ&Xo6py{7W@mdnQ@?h@kEC>d|6B!ny-V^U-=n>
z!wd`*te8=$UGcXY^NU>Xs5(-)RB?fU`VVoNV~Zj070Yy{vjo~Awr7!Xr&rzaLj5t#
z7TENgOYGjjY_~1E9@Oep84@|qP$9jNT;`Y8Eh7*@t!q5pDqH%YXh=E~oQS{kA$Zjn
z;#xQO@!h2x+l(S-@#_m3$h!?q5bQQyWR&k#Gk>e^*UxmGJ_z7j!Y<{GCN&Y`hYVLb
z^VdI9<FEm%6btED@@Pm8RP|`Y^F_L;@T+I=dAlKV5*#nE%2#4d;nZdtYNdXg{A+wd
zH5Kan%q&+Du8`o+|5N;wuyY$;3XlClbF3jZH40as^msLdQwVM>HG*Xy=Y-`ucfL%l
z`_`yZ$xqBL-!ExT%Du*L3IRF(lwX4k2gwHR-F_B4^<G@5;jpaR1wrVY2XM&8nzt1p
zzaKwQeZ8#TexN>WwH}=I%`eKD&^zLv^2{PxJ=GbNsr@{ciu)2QOV*C>ipUGE{_UOF
zdYKqbSzZv*Zpaiiv0BSqP+w|{^8+ZYY3yBv*-5gS#PHTk`q(g-<Dn)Pr(kK!`dcIR
ztt2e&)5S`874XYJqEX$KVzXL0;pPH&SxIr^0HZwz>yU%yg0z9E`0tkB!mn@38M5>(
zX>f%8(q%eLn`r?mTah0Z7PUgrAmD;m{!d8)SyVr~w#%xq(!@4%4Qgf2?}C>JHXhhz
z?TlSrHiAWeWJNthFFU=ika_(u)WXJR0JH^jwc%SQ`U}b#4Bm)5f#K7!<36^-#8BjQ
z>n25fjS(ObZl&>w6;dOwDE~C~y`#z`kJ!GSs|}>Dt{CmGiDYYR&e=Y#4P2;MaQp4$
z%qpaYhE{Km=djiL<wZ1gqFGvh;NAMmy?a)tZk;cBEGG^q8${r+Ig4QuZ|uEd?9xwL
zSI%T<Vl-jhW2sMScRv+=`=&b+;Un5Va@H$rWuMQ^p&czKa_=_1SSV(Sh?EwG=Pw5~
zJkJ*@QH8ljT)4v~pXXl!A9VG@=j#P~My%qg%<kUTQ8i(5o@ms_R%u}45bKqp###F#
zXwzwdo+_$GXnp^Z^1YvPxh!T)ERmW2mXg)K7GuiF<TL5@`O$W(fw|4#MZ(n$VW3E`
z(!lHB#B@hz{iv!;)UQ2uf(g}d%wdj0_t%za(ElRdAv+S0Nn#>a_qD&r?aARO2mbV>
zKTy$SulDGwn(SLOPM;C%O<FMDs@0z%=H4LwQ%vRL1!1>*=lIw{2)107){MBSWaC5s
z2j<t4X{QSP_%$ZIq+@quRZt!FTlAECe+X5(+Wk^j)!`8Sct4Hi1vM0Xm744ig*UR>
zP3qW}etL8#EptBol-9MNHA{$P4$$!PW=mTje7CIB)<Ka{Xge~TYr6=0Ll7{>G)w(1
zq#>L{OXmX&?QdcCEFB|g2D&^zPfp|kC!H~sfs&6c!JwdV?~k<booA-kZfHJ@R^?Pr
z`AJq8Mjd^DRY!YT`f5h6!NuMz!Xf^}{gB~Tl>55N`gegNqA7WyDQ+_De);s8ts#lW
zm<JpKoBHdgb1plpgFteG_f=*m)&rO`kL+&%Mgg8Wx!v6Q(dBL~U%yiq^G!FnT*WZ$
z1%M<trWV5gJij+NP(AgKZ<{S+3{_OM45vTe#Z^PMI|c`!%V~$*_%5byxe=uq20edW
zf3$)XnQ+sitTW3TBM0Mqgra{=Lb*tuH;l@{PDcEmXb4)g=|WkLdMN&YP`+j}snkzh
zuk|Hzek+9eaQnwEigO6Qq&rQCLfyT7bE@~1&1Jd3BU<_Z%z)0{J5D~ULH%1@-7iPH
z+S|@_fzNv0B?d$Wt{QURyBUbmOiG*CFSSATnF!3*Xv?2g>ISpur~&BYx*dg;@e5mK
z=y7W<Ik?UVko{8L_gfJjCuc}}A69QaHzm6G;SUwQWX$i$z>{MJrIYQ-{Q)%V`F|0G
z(c}m`g-VMH5$~41tq_J<SmuC^07@FL-;QihiyyIVx^F54#KSR*YheI@=|XT&ZMe6V
zQ5L{=xoN}gYK9-@eYN<Hw$ijv-H@hIgsj>L11quKch1M8fl2DkDMo!Mj2c(*^0QMK
ze)uVmS30xCI~ktM%-1f6%`(1Zfk)OOF^^Y&4pXrfQB2ts4G{rMkU%>Gf9TZngvJRU
zte0}5#ePImloenPOub)#x#Q<+sN~9WvN8qn<lRKjw&5ygaNv(~0<<ZGT^Tj>msqS|
zxPtN-wcO@TrW-!M#sN6-m2<g#6Vd`!qLPu>qH6Gz`E2Yttq9l_v-ii?8TOB=h{tf0
z_2?L?+P8nf^7;V~TXytpi1sg2q$Ec}Qi9B{(tVPRPe25K1(U>pSTAR}b=X0eN$%Q{
z0jJa+@pA=|gLTjNeX*75U#au-rT;o;$v@=(=I{3l+HY^kElJr~RH`j!z~SpR@M{6g
zSZ7bWUiw@4{+!1~4(wkFpw&Aw?{LL|F2j0WFgZm|a{UNl!V1NQr0MAvO!yD~X{1(0
z3pI{26JMa|X@KfdInZ6a@=v48NI%sFZ3x-uJmlweXS-G$3ji0^3$(Sh`8#)K_6isB
zVAhyY_UBkEZa(;YKGKWQJC@tNxEIZb3ZR?EV)%4m_}ANs@REC`pKzz|9tWgNMD3B~
zWq(aKa+FmqHkqB@E5-#o^%L{;--)BFvm)LNQ**q~&$_YYqXy~W`AbO`{D)LZ)rmU@
zdY)3P$r<z2@lPlyyB@=wXweh%x7^ougwBlPSl_=FigVsxsqR_6cq#XEqm!*?yEaNZ
z;A+Y-qeU0h2fdVy1XJZZ28MFNGU7V#3XWuM+vOmNr@d^EbPR{5iHp3;ziwGVe}=Ym
zyuW((3PZwAVyLS?TKfCmx5t%fB1zMSGjyS*oXuS88($pODIhyWG?DjwU#iT%_s2iN
zC)~7A<k|ccC**p#NAKI&X^&zmSNZrWur#d@ilKP&@eBf!9__-&N{Fq;9NO$nNul3K
zD&8@0n^gIUriM#&y)cA7F0E9g%ocH&le~QPM0IFij8P3_Hmo6SJMXNIA9jz2hlBe%
z#P8}%y0juo*q5VcXdA{MbJn}xR8QxIA9pH^A0vrh-jI1&I2Zr4O^`Tn+y2Q%t;hVy
zho3LY&P<Nn+1h@yI*h4l@)dgPfF#RV&2D?{i^yGnqrJAyPEEA*+SqL>;MbvTsk3D-
z?9(qP#Yr1Av8d2Uit{lqFlO!%)AG*a=A9+u8R<?Oxv8=oOY>9K3CS0qae4<{^FE|u
zQVt099FvMRx&MHYMuPi=;V2a$=U5gwvmA}`QjI=CRoQ%gnM>;DzWTIxHH0Wytvl}*
zUD`<CdIGD`ewJmhpbT0$s@a;CUnCb=Q<&K#q}1X+smaEeT{xy3%sk*ZI?#e=4&&5B
zR%fRw@buAMzaijkH{HS=&*=7|DU&5$C{}a0kFWKW5^a3u)9%E~Ya6AQ;Oe)*Y3>@j
zsxsJVP-My!dLpgS|M77SDf`e`$1_1v#%6zU_!O?$eWUeHZjFT7ne#ny0i_C0Gg?)8
z4AA%Te$74_$xlbuSmjaKP^pNQo}mTbU!AZWf1sC|C6xLES+2m)oK)9i8=ukkQdZHl
zznmakU|j9F=nRrSQ&Xx$v$-I0@kYSUeH5G0CZ5X*k?C*&%c@YIfQ{G3wgKro_sgab
zkZH=0_cbiDrL^K;ikV@T??`?K+os*h|3r)tDV6#938`Zn;~VRLRi}f=!C#sLiiH(r
zVHJGS_!;^_Wg6+E>(otl)FL6DEYoM^s(c2!!u%SmmUl?mW@QWQueuT!0iCVnFea2r
zOmtc0pDsHJ$>&{xf8HC$KnY8VN<0J%rPTVJnhJkWM^b3RH8HgsE!U}qt!)b!meu5T
zKFw_2xe^h$sQ%69&WC2oK@eY3J=T5$B4$gW8su;oHb$f+*NUoRymv|hmAf2(d~&Hz
z2lE}|L20653%VGlR<sZE7m2u_oV)qXnzf={yHuCTZt5P*h9p`Arzn=@5gbg~g8mKd
z3bOuXZWeDP3o}W|luj0%Vcf1zC!W-Id-o52uWKxlRW9lx#tcWL6iwUP654f!S4I6=
zspBtR8HR^A)xDhO+RC%1aGn<mVZ_NxB<A5+vSZT1?3Aj=kTy#3i!25prEfR7`KPn^
z0l|B}$P62`?EO8-3-e!f6JDW()M-Ji$Nkt~;>A;@3LW$OzWKUCoenXkKVfuFKCWeq
zk-hI-e*WMgr-wzT8W9%(3H#fyZHN{mYW1nLO`$dFsD9XXC(J8e@);V;tbGVFLC7iq
zDXs;{tkr@nSMRb~LhppBUApri2q{d8{sG-kscEmeH1JJ;alxF?0?PLM4J7a0nlMYA
zHT4m~C5$|klUAKN%x}@|h~v{p&Zf%xmyIDJ*gK}nH1paUAxQEV`Fsw9@7u^Xmmh{X
zp>=-Ie~0g$S{v;!ZIuW<*kGzKy6mWAPY#I}+WL$f@IkVq&eDVsfT~~LB1+TFRoq>d
z2sP2l4#?^!Br%`9y)@b+uAMrk@7fS!?S+Q+rMeEC9ZAK3QN;kkt*cL*`4^TB%+_Oa
z72vIYrwc2e?%&1lF4IE{FTDntqLcCLG0v1*AFQxsuq?3`sQ%*oHbUULdCFF47~*z(
z`-pqns0NL&7>`@1+CLNEa5wW5`r}q?*uZ4*y`&$;0?lFBH^|Q<;S%^VDx|LyI^7(H
zG*!Hgkj4+0V4qTrWBc4PBTJ&3To?%%?0y3t>xOeD8;6ioqcNHEAoXi=R(^7F^W`Ud
ziXa2G_{Fhb8@XoVi+78Z{k$+c1uX9UT{t?T0jZb!+VPa7D|_#$xQ()-d6r9l8Y|KA
z=*N~l_b?HU5+F@%=lCmv7GdNIRHyi_wahLQ(X8V=j@5CDNaw!F$fcvsrH;?;q|&C_
z{4jVhC5!V4O1fR@As1{*JalQndnP|(W*@>b*2ASUtpKFKxrP*`^UVCc+Mut1+s3W>
zQ^o8+5l9%m3yJ|<a@E&m{GsIk;_W@dn(Vr^!O)dzp$Q@)fzUyE=prqY&_O!VktU&c
z5CxT<Py|9zdPk~&6s3j|Py_@K5CfqJN>ve^jraXL?>F!KoH^zP*Fmmp_q}#o`&{SB
zKB|(fRzsS6;)I`g;6C06n`-4uJGhp-S~~1>#qz<MADNGaCpGvhgQ5e(w<a|li`z-N
z|69)ZGzn%d?hgbfh!)z;yYUiSA5F`xCT@8ZPCd`deQ}x$o^ie<?q<z^UQ+@V&*6c4
zXt8OdU*o^GQ-fBhLGzs9YR|E~R&)^>OXa$IuBbajmf?mFD<S{h3&l{eGoF@<FNd)c
z6H2JeOMMb|a75Oy%pdD>`hj;PtDrPcpAcAOX6CzVPB(Zi9l95#W+NiF%l?%x4h<yZ
zAD5fru2=G}hJ>cuZB8t3vz%LA;6)tVg#0WFX`1`>^-J6^ZhFY#&8PaH323h9NU~(X
z$fZb?-B^8wDa*niuWd5uh=i!e57q*II!lMAqVNPA0*l~u`8ndDI6IynqU6-u94bj)
z9X{)#XS<mER0<{X^tJ+9e3Y=o;!#7k=uo`+WgJCpar@^y>$xVUN{t3oa)*)^<6~s+
zr_P88`k?=aqa=!S)*2aG@`S#Jr;mal0l&qjDHq-!$Mn51u!B+J?4sp!UDX({nc$Dn
z9ff0vt+JA}Jr{^)ZE((&Zg(!be=T94TI#LK4Cdf7mwPc`q1yptS(H}2$`RW=fo>|6
zy?Q$OZCwcaEDo%q@$-}V5A*jK!h*(^)fg%4zh6l(d3NqRSEDYYxz>s|_4Nk{s(t;}
z&iAR`;5y^bxZLo-jQN`Mz2B}Kg=4Q*UE)UPi9bk;^On2zd|jS%9w+=+EE{j>%Iq+3
z4t<gMJ<{W7VCYmUz@~+tvC;*ayo;ekbq>3olIAze;{<Nvu>;*<<rC3B@5DqXxG0Jt
zgtOSaZF$+MaV2#14dNYUKjqtJ&e(LX2w@OZk5et+rH7*v6l@B!5XfljAbrQ}6$&<A
zN2YJ{t>}HAdC&$|AHu<b=gK~lkI7bj9-kyX0f(@+fP7<dtXKA%6?aaf?8DiRd$s^{
z;0{!GU#vJ>&$9VDUz!>r2av$OA!p*xTvzGco=^FnT}TDaE&#0U6nbH*j9jmS<F<~_
ze}c)7$C*vNjYDt<jv<Z!999zO*mecuCds{m<r6ZbO#Q)C_-S!25BjcMf_YsUTW()1
z-uyScCu#@sH>^r?Fxvo^3kp)-R#fgqzwD56ioFHcTu)EKDb2HUk{kn7<N(`#S}uFz
z&mq=26SaE+hZp<-Y~nO+I=akn@Ead$HNtf#Y|xnQt&gv?-x)Fve)FRqR`=pfRk@q<
zjFv`&kG1GbrTW;jKw-us>T6vwG&U~|lOp1d?ShFYrGoeuUv1_WV*qQl*c-2WVbUw}
zRSGZ8vStDlZyL5oa>wtOuQ0a58c(JHE)q{e;bu7MsPa5*2z$|npyHr?bQ`k%ko|nZ
zX<6%F4bE-guG07yekCCqsYJjF*z5lD>f$KFF0?Q=Kq>Nq7`XN{0p&kC{e!=-;KVM3
z?LTh<qx`}I3yZ3-7B$ZQ#`}Wb(^oN9-jlOe`zN8X{FO;{6?2(vWl~C@JO)LYxbsa>
zluRxx`_F&hY{+H}sIG6nx!pz23aG?S0RdHt%J5lo&OM!;V9?6ox|FR36{K9)A=A-D
z%?ge|r(ueM+UVay@V(;ymQZ@p<p<oJ3`^MZ>T&xg*7j^i*pxsIE2I}^#31`9_MUQV
zY?ZQs)w%NzuAQMj(8|m6myKnV^XKFEP*D;z?=h75RdL*uz=3}E3x5*L?7e5z17zhE
z$p6mvZiotvYa87Y&|*~}_p#ggR83!H3%p3<K+}VY8SIL1l1qLQ3nJ+S5&!@Xd~n~o
zOeN%AM32}%X=o>3_I#RR^P7hC-!%G*T0_oH^sPE8oqYo6l1`KDzj1}K>6?@fsms5q
z`sNLic)i49qJafwJ?K9Z>s3<~_Nd*PJpiJ6S~7wB)x~J(Kv1&(B;ngd?e^KR>wnVh
zvUp@BE<;lEZ@J!&%+suW4*PwE_1_q;Xq1iCoO26EHU7=?uDxHfZSt;b58pp|P<IiC
z(HfB}f1&}h@@VU0r#8n8*1=C9h&+uao)=%Y-8&OQ&IMR@bl4DVbX?GLUQOqk<jpop
zjvuu&PG#TDC$jzSKh70q)&PP;@p~YJoUZM0F1ZcJ4Rq9!=)?U@<6k-xOaRCaT874Y
z5L?K#xD35v4kQ><S?DW_{-Pv7@;7V^Acp>?(KTpzFV1>k=RaxG_dFmJ3oAzimw|bU
z^|VzYA`p1@Jp1EmeVjYmpSzp4Y>f0Xm`Y0E$}nQP?%5Wf1qI$R#M>kV{c&7KyEdfg
z^yB63D;U2<qRF$fa5DxFalZ&#!-@85|M*RpG!Yz@o3-b=Qi@DvGu+f3{g9>CLq`(c
zf4JQD!y=fMgc9ieE#VF_hrJbQf~da8tsE!^|M?w<L5Z_s0HtIi=sRAaCa;TLAiN-E
zle2<*+)C%N3tra|?ZyfiNH6I8&SjHl!n_Ed2G<MrvS0vZ<!LwdL)Vd~nV&XHcrpW$
z#}Z|a_UF|MM?kpKu=Ny^2e`pjpBIih_y}iez`LMKJjuKNq)1h3>;Cg(DOQ~=!s>ur
zAG5sqR^I}j8{?sNlR(lvwX(N{7{2u13_AU{=!0r!_T(1zLnQiz7j##{B8j7Y#*6Y{
zALx7a^|IURt9bg1yJdWd8nITN6UdU7v`2AMy&|;Ikh|cXcG=2`g>iBFMjYpd9;VbN
z2U@Zk^W({gto)%VDabZxQO{U<?K<18klZk_rB1vh=bS|@^Y0wh#+#xx77eZq{yxQ@
zsDAGF`9t>@<Xj@wDw*TDT^_AIu(kAeRJ&<8HnIM;(Bp01B^nLqH|0~XKS#5@RwdJH
zWDq;BPP7P1b&h@vw%hzbNFOKs@~qjnZ~7GWVP#zlxIt|Wc}f;v%$$&Z^Mw8F@Rm82
zmk88mm3A_aQ{wt-@`dI*D4EIzROIuQR#vo&*=ANdYg(ll*c=wQ=PnIwCv*$C?`pDV
zMd3&!dz8i4H%dr8sE#$PIEW0TEYO6Ct6zOV&px+>R$y|q#91_;GIQ)^&c{l8E^b)f
z2hMzMx4Qrrd4S*8*K60G#<AZFe~x|~YX0X#z~;?ugb}VH<f%k1k9SQ#X;kB{TFV)n
z`)2V9B_tVSVj^=O5L99$=z6A168AO9Vj<bd2xrlP%KT6%L1`YhhBO-pLd~5KmVn#^
ztb5Pcp2FH-Ojht-o^KboypYFpA1~@41F+cEfHzCS;s+zcvif-SJC`7`pfHABQ@QgQ
zk87}+0r;;;@X+P;Ps@Gi#=Q)`%neF&r27rCfe@5PX7GJTG_Wu}XIiwnk!vZ?imFA^
zF1}dZI&Gl5IxMl|Ii<dtNAPLjIscC`kgu_z92?VCb{Hd2(fa)HBd_*-=^&m8!dCbS
zEt%B2B^+<k@Nlp`Uf9wRvP6YDt2XszYcFC7Se|1D{w)Ay0fYte6<9$Rk657^P-a(v
zu`?h5h0K9r(f*PLCg=+6psP=r;|e+c8^|_m;Ya98*$QCN{EGqXH2%il)WrQ*{;@eY
zeq|+Ud%HJYU+4GlK|$g{e*QW46$L9s^Vc(GO#Sm{!mssBP3HdV=th}#EC=oo*leO#
zaI-(sgzVtmQ71{yuELG?_XwA#Jf|C~7d%}$o112>zgpqis><RCeVT{(eORR+?dNg7
zJjml|dOpd-QTqzgVex0i&rJj<7%iXP;TM^B)?$*t9^hV68t7NyU&Q1q$UIRF{n=ll
z5&P)6``dv&(US#+6P-{%?l^)a4I1Eb@?5ZcJugATWA(?OJ1Ix`VHBVK#PXG0?Nk3X
zJgZEe6t?zQ(5x1stcjnOL+xc==XA{46Grhqb6C$54*R@;id0kLwD^jVGJ7KR;6cKm
z0djAT3a4O*hdmW}bFCs>;<uKZAfV)w?7*%P$%^@ili->yn^x#rBSeWmH`?8hpQwGo
zFs;LRWbYaF-c_g^c%EbUCP?hWoOv_D6crIgChIUN9|$infhXW7=xy-98^<#^7eoBn
zLXlV3-1%G;(j+tdesQ<?qxpz`Qp$ATjYBi)aEcpR{%UXvt}Dytc)y;K4i=Hc7v!|b
z<`Fv^>+4Z(MTpqZ(c<8Ai8vAB&)wb5&!yP5m6a+rc!9sDtE#$@U>UeeGeQ!z2Hvhz
zi-@wUgae=Q$S6y;0>+e|-X_D@`Pux#{AXVOp)y3?cw(Tt@{(*n|7|SQU4}Ks@uK+H
zpq=$M&3NQYMXsy2wHZuaKHybkXXD=EJG!>YsMXck!Gnk>SE7_u*ZgcqRkz<6)(wTJ
zso4-1s>V+KkN-!wa&YU&^AUK6UKc~c-d<zGael7p*+L#=<@MaxZ!5B+^X+I0jq-j&
zqo_h;7i9vo{BCFt9&d<s7jb`R=-OCwn=uXfVzo2c#3&&z=ThFQ?{%EjoF3%g^!^}p
zd(N?A=9ZbWra<=Ow~8Xvc7)^Ybt5VKwyd1pILG^!+aIwmBUd87tt2!uC!pjEfZK(4
zzQ?9XrnG+mu=K4Z{qQ9pXfsMqUEHJpdDg1yARD4_#9ia9<>Z^|4p4RY#mmNUix%(c
zQmi?rAaP^aLEvRscW~)G%iPOdv&rWO$>7`GleJhGgwG9MU$#qpBKXh6%geNAdD}VD
z4rNCG7z@=?`sy1g@3NZSl|~x2mERpUdzclg91{YeFSmQW*X(pNvI9uynMX<BsmoW2
zvL%nZSG32#d06FeBEZ-w6FXL_+38()-0m@ygp`_$Ux+jx=bYHLIWu)s7VT##5c4Xm
z9_}-)Sv{516%U#3KG1B=+PkzHOo7-jEX=^FtM{YUez-z}po_}dxrfg?urx!_8!9gE
zX9V-F=2mYJ%iZ@M)Hzt2sJ?AX#8zz{Jd-=F4!}z9<)!=0<(gE}*xj<q*JNKInXRh$
z=6P<#-vIwDxj5OXby`QtSq;_Kd^ueLuXShTvOIdaR8oMKBk?-gC2KOVnCYr!axtH)
zgQEa3cYUJim|LWpPB-rLNgQ(J6O_+PEz&DN{(G}z3(3wKzjKI?S2P}-druJ+zU}qW
zv2?i=@8$GUV6%qvn6pjjIlfjjKhrzHBNX+$)b|fj)UWS52oT46>UtLzv{6U-?CsP?
zbRE1wcI{s%w>$8pQRNz?R&+K;G2NmIl*qsnO?Vi-{Kp5^D{DW7n%h4OsBlJe8-7^{
zk1GyJDbUSNGDBP0>GD(=#myXX3elz}(YVTPt;}nSOa#aVSxwXL8gHtDg@C%@y2;|P
z_E20vx0uOdRii01w{?dhmEssC&ksAHsEKS4F2)8pz3D1-YIu9+nw_21Ee-u<KH`&i
zNv=@66(DcRr}E|s<Sm&4*X-1<d8fH&;r%uDK={~c1M;AsVb21`>s6`8&fiSzXYa1;
zn~;4uo+fJ?vRvx5Q6>?TP0Uj{>7ZDIaK7ngeWFAYoO@%%nu?%bkek5RU4%k2RX}s^
zeV+6&O;y(5bHyXY>%YGEzsXi0*$nr{iF{@1&Ok||4W?i-$cC)l#<+IbC~Gs0J;geT
zw6(*!_Qui?e<@01zKX}8##<#IGt!(hsb%ESt&~wG6x<Rwh0rpJZ~1f(ksb!Od+Swf
z0S&q}W$Qi36OxyMFABO*-=&P4CRld8yc^N|aaWj&L9-(Zqa(cO*l?{F`+?NT+``i1
z$ReqDgZGP|z#Zll|M?NPH<<S(9}*sV!1-KXp8lWc6nNIWa$<}3sGs@okTen`@})7Z
z=(^Fa2$5_c#^-MOFqq@VdTxtzbA<i4c_G=T%s=q#pAu<7!3-hNxgie3ke+I%OfmL2
znyumc#8cP2jK=92z2>Pa-E;)Xq3Tb={9#~X4Hh`J!g&8|zX_gXDQEM#4PtF_P$Km3
z@?Ene(N~K70q4I@9<aC^bzC83@Q3dVc+K1zzSL$WQ&_yzo|Dot(#GE7-@s85`ntC5
zagiaiXyk|Rk9N~u_x)-+9cD`(%t$FRFH|=rAfnE$%e+9It#ZeB>P4zhk{O(R-}i!r
zmtfg|c~sLq&q<e0HQ7@_tqiDV-U{6COjL4vW-Myp62chBhBT&M0G&HD`5atLp>opR
z4a1F~D~C58B|pV3_%AKVPflLjx%DBjm=;Z^uAZONGtZ-wIch|uVbom4@m{mxTn{#b
zmY?Wif0)D4>;o3C$9ek-iie{l236CnrP#*P_2qw>;e(3nU4O!_A7QQ0TsF~*5bw2|
zUzu!wO1-`%v%CHbYqnaT+NKCIG#@m>KWsL!<0nAg1j2J6p6>#}1>2}qsI%ETvWzd<
zYj}$jkKuMT);?0=qkgY`MxM2(P-;<p>5EK~Nu?<=#cM<e@v=~4?3K0EO_YvVD(B@?
zetw3fABHEVUABU2T8-R*e@-l}z`8&`u)3gcLOI&OzhLBCMi}Dz1`LqQ&Nsj9zN`E+
zyxuDicPmCL9-i20*>sIEW731fi&n7ARX}oz6E5g_DOwkmdxfenxIidF1kAJg9AHrv
zd-`NJgz|+K1CnGrKg^PoNhSsfhC`?|hQWR*t#i;@Yg|C&69M>2)#=;;EAn^C^Ylye
zD~aWwt#JL2)inA2aN+2nq8*gwog}9H<HLH%9!?<-Wec$xmz(unKF|X?wVAk$;~dtE
zMiowvAzTY!Lo}^}Em&!_RIKBCM89F?uMWd`13q8DO+?9x<PO7sT9%>)&LcEeB)QvS
z=A~y!C%Dk`Hb@DyC0z3nm_}6{1z?KmdKM6YctK+5cUA|Q`ll16aesC%r5@n%_X<&0
z64p@Ym?WJ^l6SHKt^;B#LNUuBC<#kkD1-zo>*w;B!4BfK>h+90RnfcV3?g#}A=H%b
z-fu(HrwOjW<;62fnHcsP94|O)KY!W5G6)jK>?1gzo83p{*dSe3!pV9_P_Ttb(VnKe
z5q3NCE1odBCCRE(ro$7<9Icw9SqWDW`st&Qt+Bi|pJbCoQIr`TZ9Ot)rM_cp1#iVD
zSN=$9?^>M5^s!Kq`emIy=_^Y7uJKlCX*yzDUS+X74J<3N*Vc<cAHy&o_WegjhyVA)
z_!KKDDV>=rI75c(P2Y?7@csJ<cE2H@M;yI?sJQei%RfB~H`RioT)oQi{p!oC#?t2x
z_q)iN`irqbRCn(wJRU!wS^a)<7;ckpeD1!QCV2AF8^fhyA$X3qF!~;gj$zI%Q~>X%
zR{k`pA5wh34SaSE9F{USA9vNbxy54HU%c@<x}ydE*s5>yX79X5|Lls;59+DKG@a*p
z)j#DrQbVr<IISo8mx;J~GvTtTUEbEtC<{B{o_1O&qo4l#eNnm1DTvH{`lyC5fSW=N
z<C40z90|FZHCT_W5w@rX89dV)V|NR|q05H&>lN%rTJ7(?F0V93{M$Ie*VYR`<M_3v
zbyS)QbIbfcXT|Vovzt4pQTBxuPx84D1^F0eUaRtjUP7~0<&xts@5@m5lo{S9=3e+!
zSTUB<5lLY|l|E344HFX5E?SHqb6U8!5&))_*y^99mZweBrvaYJT#+kVoT?9R2tOcG
z+2J_|`sn<QK(;;6DCk{zqxGjei#Nx|c#S+xTKAq1MIbI%8@!}TDwc8-Sz4+(?wGyj
z2bbDGE7&VjJ?}G;G_2E9Vu?Jn92SXQaFpCU9y3(TYD|n|SU(Xf#}@p=xmQVOO>bB(
z+BNm{HsPLO9gSpurzwz*P1)`zu_35d5@BfMu)9V>zdo>8uM}<30<!skj1WGURrJqh
zsu-Kz42(`pvA;du43)?JgecvDLQ)FMj6zb!FZP(YeE*8NX#h8eah*);@ek4V6=Q7~
zMzmHTH09qi-Bz$)(uk+qjPHN98P75%m)<NQAV{UV?xAs^F~uKPtX=e!&YT!fkHumj
z?I|x_ADpZdkQ|+C-nhZ~lCg4!d-`$X$ZWo5XkaPCO~kMW>$<P7Iu$&{UnWu}Rj_}&
zm2lz6D(5B%`V!pi7QBW5MZJ1ORweSh#bQ?7p~h1zyH2MQ&S=C8d0;AhwX8LD$8vhk
zuH5at2v{4y)+@znMqTFauByyWjfCQVR|t-47Jmc~`i=pKrzewtXUVn*1F-dSo$K<4
zOv@)4Aa@?=LO8bz9ma1!x;|dU4{Jk4^kKJOH8!T@>M}zLetNAC{3Gav=@Joei7sKT
zkvmV{6AX(=YAoStcGtk^K=m~@cwUTtisiML+1~#p7{lTn&^Ay?oLhN#WL|slqEtaC
z#95oxBijavnYcL^almEhm!%I56thYm#hqAWy3d~;*9R5K;kV3*xj3857@q+c_tuK!
zx90Fv4@KUNlFoVR#_+J?8zEZSl40;cMfAzc)bSd(yTCs?f=34lIaNhT#j`7kaP)3!
zy4s)#^6ekX1#Kx;qc^Fq&N<VOh|-bDqv33(bQR}eV8eBGowzI%#J6rS^6`v>sJ!}r
zrJ9!!fK=~qpfrlG^sF}cTepy<5ieg<Ja%5myl4q`R8OCL)J~q@ls>$k*oYe7M0{LH
zWHQ974H|%;=B%anlKX10MO>{67s$EZoL^xir~17({Da{*@ZscN5{OqACaBg0T6%RA
z)mt~tOWrV*@`TKI)jP9Y7_E8IlCu9X8}3Ty*!9bg!5U8UdN?GVm|!=vse=pQ!@&V4
z_5jf(0(A}Jg%Sse1>md?d?DI<!CXUDe$9C=!m6>(K}U-|=K3jF=so*9;Ccal5#6{C
zt-;z*1M*S;yY~b-yz)%K{d+AKiE1nbz&>BYg&@CWN?=mKFLdM3@N5|t*9LTncMwQE
zH+gvPFnl(Y3odB9IGoK7uffuxu@HIm#f=PM@Z%!}YX&^vs6gRQZD-Mw!?TJw_Z0xW
z0UGLm1Ne>nuNRFdAT(G=8sUDZvkp?w^$1#)0gNTiA&}+fErcdip#J|3F#kU<gf&-r
z;74e`>*6q6_UIi?z;Pp<hBW{jFx+t9H$W0MD~>xWMvJ~E&~n+1(uLsoB@(qjJ32Ht
z=V|u&i-A0+M~pQTmJTS%wUD^%M8YW0{B3C=%d-dyF@!{@>rJSuH2R{wCQ7vvN<0g7
zg~Hjd!5h=Czv}Jc)gX|o=X!p&u1Z~?n4fk~;e*YWV9ADO0d{}5EeS<>ANAU17+#3Y
zLn%GOe&B=s{}RCUxkpqTqTiQwu%IlaJ>mIEDUHAPx!+eA-&t1_ol#`TajD^ay`jd{
z$J`wFe55+ztH`qi??d8h;^!?f7!^ZsV6bi0;^ue^G3l|joj0`1-Fu0%SMugL9wHW3
zf$`VIt-ER?&!=(mlnosOq+vCM>|4ZVZVy^s?%8Ah`ZJMI4F`8cZ9wykP(Nqi5w^r1
zSS-jaQGh7lm_Zaz8eRNX=i^9*7)-~CF|GTeAe^X=%quq5Mk?p#U^yELd`nzEW{tk4
z0p1Chg+Vl`c_iMpj0#(wAxKe(k%*Eh@UQgQJlN3N4A|(}IqG%;N!V!rn?wh2z7Z)K
zxX+<1UxqU%F&^@7zg4@^>$-TittRrX`)A;*(E=KV8u;PPVX<9X|B@=RDBRpZwnayy
zddB@YlCL(Rj^NJ^4?rXN;U}J0ERN5P>Eg?z2<9r!<<trlv2R4G4^ISuf86uH<DpQh
zB0S8xBWCPZFLYVx-sq*L7u@YLEH<a+j#93qgimytqb#j;LD?L&k(}%>;*1N^Er+uj
zO6L>23t(lhc1hSe|8$Z3<LI%^m=}Z5CGolC(`@^dl|X6Wz&_OA)@gaXaO=wK+y9jk
zY}+Z6Nu2aDTL1dbjK;CPZ+Mce#prBgnGf(Ktp=LTRxBHPaO+t#6>Tm3N3u&0ZEF%X
zA<Md-ET7>#WqBk2>B-Lo$$Ql$k=IGLQfCcO4ifpiMIY+hlv!!TLCXWrn-5uZd5Ly`
z!2YvB3{Y0>T^;SwXn%gbrBQYN8trP&U8jpVOyt@0x0mkTdo>gpY|c`ZAVS@7%lxFj
zb8sFj`bd6|Yv*|Pgckok=F0{=o=6kVw6HPQrrZvP{rJ$WZi+uTel`0la&b8SPU9j5
zi*Z3?(sUCEw1BCXLDyk8k(r<w;=HmDj>UGqg*}q?57}p1dS!WPFIJYw>nhb<5&!mG
zyj{aw*Na64JpJMEod~YTn&<y3C7rqOS<{nL__P@!MFN-+HyH(YmoIA60VaQd<?Z2T
zK^xCmCOng`WM}A`+Zo62GCC#_n)GDhmvF^e_*6Q~-d^>F;my|W&0Jj<?Zs?D`WN>p
z%Y>+E3V>6*YmUXXln@;n$LEeu_z%O-e&RTs7Oy%g4$z2+w_bT9+}O4HN)c#|mw-9h
z3$8#^X&FuI;=`8rI|%$H491<!h`ZDv3y%PJ?YjU0Y(4gz;3+A$JB}#wwNpXaO8i?2
zFhSa<Vxzm432Lg)vax31uHh13`+|U(?JEl|n-u5H4JT7YwV#@F0QK-MbyTSeY7RI&
zO2wYS-g`ikMWtc+Q?b%$iL1~u4*(Jr1C_Ufx>7^Sk_fbCaO(iT{{K2T3=(fOen<{2
zy9rtWjm-xD&#PcYU69kq=d7*>9Q1OWxANVnfFFLYI|>|uD_Uk4sgF{fI46H}C&6V4
z{kO^eOoRHx7n;Q<SsZ`#f9}{52a~W$4}hh{y~ky<J9hhQn4h=0J_G--Q`KLPwx!6w
z<KwPhY!$*m)hGCGLVcj}Nl;g_-*x+IyT@98u2vndo;bDGei0JEz0ksUZ++O*H}ZuH
zd?QwTF%T3id^62YJ|Z$fA&?*<cA+Wl{xwUpwRQ`O*|Ax#>FK$Knrc&@P+~V5Rr;If
zlqQbBhvE2<y~9siN6y5D4`acwvmDCD`g!P$k9O3warU%WzdEz9_HCaz=5rg33fD};
z+ukjOt?<v!zhvy4#Ie-BjB@UyjH$~Vl}v4?FhofM3Q`z6H<u!r@Hofm0eTX52jjPV
z_s(m&Vyf`jX!c<A-o7FpumBKe=eyEw1=0)dkB?=m_v8gbdQ^H(=Sq9v%60lVjCk>9
z{*`4LC~Lo%#L(R%28{9A((~Pu>numSw~6dm3Z$#|IzKTuA{O;npZkAOMBY8~jNmB(
z-_#fHfz)UF>NTk1)}5qq>p?4ewy)T-lna)xSSQyz>fFEhb%MQckmvxtzxa?bCNI&2
zS;AdX7kIy=FC{R#bA+Lk#;sK!_F3_MOSL)C4Zc2vnIcqo^*>RtlWz0xVh~w)jz7AI
zeT`1A2p@G&h@BNu!_7LOrt_Sn;8Hkho-)i7v@Cmor?#KYc2KUMO%QjTb#esP_QE?a
zMLqqcoSn2;F4j;anc;|c`l%t9oqWZBW8lsAn2YOvnmcIO5$p#h8v0#|3BAQ(f^MDY
z2au`e_1!ProwkwD{bJ{wT~SwzMUI9P{?Ba1dCH1TehRy$o@czp%BuD4RiqLXy%3DZ
zC)O*gJzXvO!7>-?3a7uX(Cd~C+|gbJ)qN{>NZ8udl;*q|Bh-7jmwTIpHfLqA<hB`R
zazKVQF);iYOYPZP8k9?co$GYH5Azf+$ML~)b@4wjv|kww?5#1?1}HfTJ=7}6lSoIh
zFqtgtvAe8VX6VMBGKpg-RA~s4$~J_p1gtMqz{hP<vLbwM>s}T)>HJvOi$U8Z($=M`
zu=CDv_~&}-ExryT#YG=K#Uhryj#$Xc3i+Pjxh`i|pB%+s)yDH;8h=BB#K`(sXP|=V
zSfm*STPyLffr=YTUMd}j-nx18dYGUvZ5UWA4gXl11i3(*-~$uBEbzNq9I%B8NiBOe
zh#;%OrIR?u?Co^lW!B6-BZRiyBJMk;t9`ba4tN&YHOR9WIEPVAvLTD_>?w{Cjn@3_
z{t(?bOQ=@HiE(T0I$2|^O9}q9IRwIt70t^SeFbn~5this5N7>60~gXJ=xxL4fw`!;
z6X<}MeG~@<QC}O7<r+tQ{Iit#0tdEROHvS++|bYq_Oh^{BE|jX#uSO}h=lX92+^-U
zl^o|kFf2)tL0WbhL9cqErV9w!3bVcs|0~5IDA8FWMeTPt<`%LOM~RrT+irw@OLB=T
z`peR6Ky3eM<A;iH;a4?W>09AhON>HKvnaLa*Ut^lKb4CiB-O?MC&Sml@VL++;(<2R
zMrt;q8!G{)^}08t|Ba@ZOWd0$_OP77|D+Wy4F94rL`z6X#|Hj^1Ic#bhv!xE!oA|G
zM}0mu#k7E(L)szOgt*7Z;Vvm1l(O$?Y9l-TV6iz)m~?FNA(5c=^LX_QZtCk9u))v@
z1COS3;0_wfQ}*!>66p>|Vct{|tiOO}dTR?F2O#~MxC+3cq>T?O0>$75F{*8~@!f@l
zCS#Nw=#G!T-?f@H2n)zE_C)#MATB)^^fF)&rCk7fvW*hLFB)y6JWp9HAN)fS5cFc;
zoVg+N#A(MiGUwqkUvdE_)L5(L!8768#m&@~<&SSw9lW_yLa9?C(8c$dfqapTk)^zk
zidV@Y@l}TWA5&M$>@_|w(S%Zod<J&aCm-8zCvj(;QMF{4$z<I6HC?>RDe<m2;M&9{
z;M(YciwoYe1TlE-DVy+64*`l20iH+1;9s6#MP3s;PZ>S0kzp`HZBMY9J%lDlRQnlB
z<0*~nLt5~OXrzmO<M6#izt*XQDJ{3oK<AfJLr2-Jz2HM!1sc17>08sIn^Jd`L$h#W
zp_oa;9Vqc}-|Zm9)Yu#PMF@~gc^h~zO3J~*pnL7k8^L(su^6LS{wYE;G`PKPF}%Kx
zp^spKKpr4ev-m3ICB^bP11vv7v$6C>+;Ex;@gKF9&ViCY#39~6s|jkpM}v+PG3V$R
z2V?g7f8X*w^xfF*c;i%Wb+g~i$f*B!zk{@34SUz{!m`-m4lN_hUcukz_8IBC?0ASI
z#0!T18aYld(FVCGi9QZqG|zL=LiOCh`J#1^RL{{DPacg9Jvdig6w`Knd@_ocNXVMC
z!?igJ1(Z{L?dB^U*e0`SRdAd!U^pdMXT@O3Z*1ZC+kcx9#KIdm&uGnX%!YYt-3#M<
z;7rYQF~!NLux6L}bHd(*%fW4SM#Mwy%Ss6CbJCsS*gt9GBy0H}M~@XT#{BRpSV*9X
zlXJxev{?%##y%m7;4O>2g?mI1GQqWg+;r<xQ<h=;ZH^LALSzi%LKFg4{glq#5Z_zJ
z4L^+YTPzStvR35)ys88TUF0*3=dd!}Pw7Ix0=s>tRBwkoxU@bX>I;B^Z4RBcI*dan
zL~Oqo`@H0gV{vrGh|!LutUtP#E$z7%dlbf7=76fyztFB$rT_Nbm6w!AG;ST3@++k)
z_IM+#G<yZFH<9yrv$tnsUj)C^#k<*GnHZqw4k3FF?fepR!=8Wg5fH3LL!F=~bYPaT
zwPxD!IXd?4;pQ0~&TU2;Pxd}g<q|S1NPJdZ(&>4T)*_pdlI&uM)1%X@RYBHowegi0
z$<Aq4$iG=%$x2rJlrUO-B?)PhOgh8k%?UE+(x(k=*VKl1e^0}bQm!mn-wg=~Nl7>O
zOIQ!Q=DZU-1dN>hR!aACycc$YAlw&TWjA9Q?OsP355bPUWnryJ4CW7>I{LohyhSK`
zE(T{p8+)P6P-X?tXAijc22Ta~Mm1p`W_J>oTcvrEX47TtWUt@yDsy;g*&q-3(ic+n
z#TCL!!mR7#Lh1jA0!bwg(L1}h{v8HuYp-QXy6%<HeKY&;c=+(u#QH4cssx)3^nzfF
z8a)tqE#wN~_0U@|d`Ns~X#dR*o2A&3Mn_dsl$IX2^rX-0W*rnp|K-gjSDQ~{>kDvv
zQ)D}rO-yK3+b;nAHl$RV3A$P?n<;YJM>}fzm)m&z+Ys6J1G+`MbaL$qZfnmc*%w_Q
z>1k!n##Z7gln~@R-EAAtu--2=lz0CTcfX0Eo%ioqBE311Y*3+*7Eo`_P#$>qI$C2P
zI9R)(3U_Jj@^rh%WfjF!*|G()MOmJS)kI%=<n-mZbCsoyEuvCKV6i#0yY<m3S<I-{
z!cI2!F|G}dSbibWQHc~6aTfl!ZfeIYX&pNC$r>Nn(~@7aiB6MFqBTaMuYB#R&%q7_
zuJK2a2b_9VNB~sFZ2j8ET>Sjijkhkp7v6BQO*1D&KLK@U1ICwJHR1>dRC{~|Htx&#
z*ZA7T@{$^sD7SS@oBt7ibYsg%$B0O*t}c>i1BXLYf(Y5!SmYq^6|xGbiSLxF3jXmZ
z6Xz{sJ0L)%bdlzcfI0YZy9FG>l0Sc6j1=QFzD9prZ9-)?b-@1O;|DjAHIkhDYEV3K
zRzc)?kJtpSac$i=N50CAFVmr+Zu4&0Au3{f4?4X`E(6$9a2obc4B;0C4z8+&p8!aZ
zyTC+x*>tk_Re{<O?aet$neUD1lV~pJ!TF))QYALXeQ=_{w&#47#Ol{gNz(FH_<SuZ
zf6T?_^~I8K!F>U86|nCvz2jb?`z_hz%!;z&iB&cZbvZL!DlLTaF_ng#0hW)lQWfbq
z*C_FTk218g#yg#_vLq-JWyqZsXS9X9d2Fu#6DXr%v-DM#l&J%!PX5agIkE5J20qj0
z(nsBG>dS?7W@sU#09NCjm!Wj&*As+=q1;AClfzf;3b(;?PdJB9njSiDY=Gi|)7E8q
zgCtWr<%Wbg>C0*6@ioo){-$E&u7)43cU$jij~P=!xWKV!utBQ%`&Rh-s@36lzkTN_
zUrN3>3bET+jv_^o+X$3j{_^2Ql~>8jW}2(O$a2YY*Wjo4t=l%ZlHy=T;|;Nw%4UH&
zIqbT&k}oif-5=8`TWLwO=Zqq6!`t~Qw|>az8|$&qL0*FrZyy-HimkKe*oP&V44<v_
z>U$aC*VOs&NYp0O6LO9foZ;+UTCJWi&m-hZqy}zBUGKizIelRsaYW3oX1gaPKP7C!
zm~DGtUC?odgdB9%SB5}dCDwDP1ASy<&rm`4dNUyd;P4ntP<yF`R0BvcF>i3dgTj{8
zQP<>vA)PM((_Mw17e!xu18SXkN;xMQxV=zJ0--Ki3p?|h@gCpftLOpM`i3(V>O$66
zC5p8wRW!6R1TMNJrv%}dTiehWYcvL+<W2#Z{~!JjAg<?}XRAn`02Luf*6zgo+)616
zg%~j7gTmTcIz=DuPPhK&C$dve3-!?k0+5v|DD-9rCpB8=X9)zrD5D9`nKor=bakl3
zf4+7`(V*uKOi2;}$Qp;JEsGS=+MPe5IHfy-!d}fQpY_|i`R~Viy+4fPAzuPsJ?NCz
zaOb0bEhabV#!tfaz)ev4?0GT<x)ggy*Yju%`J^+i1_pSFnXI30vaVwFtd(AjG62JJ
z6_n9?J#SL#Hhc%f4S+n+nAZy^4uc_eBv~OqS!yAv<yWN(llLx@6|(&O88$|YmXi))
zhIIZDxg>*8dlrxv@Bb~UbJ-6TgMO#Gif7z_QUHw^Zu!Az&>uvPfvo*ad^j0SQ@$)6
zL^1)q%R^xqiiC3p{R#~K{wzsqJ5zo;trr221cz@N!d>~tC4*kMMY2M`rlf_`4#N#b
zUodlwq)-8R-&AZmSnDyW=Z~#_n$zt6jo>Bm;pe-%4Wki|rhk6NW3A+h@9E*qNhSam
z7z%6F)6l=f^J`TT0wTl!65g_sVZJj<Uv+O#*3{m(@g_mWd!$~u;~am7`k6<9-|o>E
zT0bnLQCG8WpG=B$=pJ}UR2X-az5D$-88`e=TR{k$!NTA#MY`zUr;Q<w`jDLvy<D2x
zb`ipX1|93P6{x7YAwQJO1)JbGt-*k|6762}+eqgtR4OS@*j4zY&RzxDa!)UbZVHvG
zzb_OWk(E}@&1n2}PTTKW$8daf`*zI~>B-*&e~Dy9xH7if8qR>MrJq)aPIhE=Bs!;b
zd@uY0B=0m#F$u=}2X|rV2)2_h+7<!I_4ZyJT!GTWKizJDLZ$UI7x&kZ-koR4iJ?Qe
zkhQGSbo7Edy_+*1A1)A&AZq}K4uyGrS0>n~T#w4yOZg!`0oZ1=kZiuWRpX@EA=blt
z&5$*~WetTTJ@Ev9Uv1{9wol;<6Y$eemft?<{iZ2*?8=7~kmkSdm??nv$|ip9rjgDc
zL_Dznq*XyHZdBB(@j%EU&o9ujsu(3WXBp2@;~7{#Rar9^ZXD%%Cgma7+2(t%j`r}A
zCmT6oU6bt5+hFuwg}=F;ud_(!#`0W(TAdcs4wx0d#6cX|m<S`A>)rRYdBg_s2MT@Q
z6ck`t9(7VS#fHT<J@`|3s_z0Xk;{j7?gtgC$OpF?8;8-J+VF!k5vy+nSSL_4zDz&=
z^UA^@UeZM+;Y(%%<*CXp6}u+;A!s9>q=xa5`L9Y6pnL;Ul-xwei{Lp;K|Sl~<SS&S
zq$*lS|Kh@?#b~pTQ0u3c{=S;yg=>av%Kb7;DGvFNxZX?YIZ-XSGWTaXC^zd4<wPk+
z$tx8bq=7d3qLD)t={Zc+@AICQkk_hvfqj`hYx~0=7DS>&v)cwA9d)51$m4e)svVO&
zKAmo;->IM6(ei8u6Iy-tlCKrDhqtMy^mSYz)&s=7dz6U%(&B!@t6A#6!^%sqor}&g
z<JN(UltO|yg#oy1DRi&V!W$G3k7Gi|38Ez?cryhh)<$01ui+_DD<y;M1%&$aD-P&e
zbt-0Z*IoZ8z>S-;S=Xj23$kDMSGEy!fm-hl__HjLhBK&rl*5JeXp_EiD{|d^@G<sE
z-utp~_?}6@mFrd5{yoK<RO>5aZq^JlaNK%)taB@2R6x<3xZnB4g|`j1!PY(jHhbfi
zdjiJ8HF;Rv+;t8sT;Kd)U97XuzLz#jb!Ht(rMK}~8kR%_&~X>?00omQSB2PZm)ZN{
zqw@>*McL86#_2EnHPrFIWqyuo1iXLT@M*H>He!9x45i6LB)#NJ+OqX*Z=@@prCYmQ
z$#MF-kbVC?98tHO!3*y;_CX)OLPl{}l_oz-CE9DKVIfv$7b_7+*&nwNc#N{%?uf~{
zN1V3==QQq$73`?!u?xn<dxsb;TvPRx>x<U!lgxwhGANTvWfTtoa#wJR^Q)?_5fjT}
zPU~jpn~y%a%(_00MJK<Q{$4$A#>5nyZ37tr-7ajHi*-c*GLm)4^bKFgYtt7%g`Wse
zBienKO;Uo!h3j|{%*4gIJcced_vl5?m@Gb|sSN+=gl8+DFi{Xq(V!_oh{OeKqdp=I
zJ*t4eAyWxzx#uf-+frdQYE7Z&lU+*Xcfn|%&N@2w_QyM=>5t#bwQ(E0PrGp557tUC
z+$z}ZhAVV_IR<yFip1IKl)FKfY)ra1Id)&Pco-d<e_R<Y5C8c@N@=qMs;Ld7gJ^>?
z*BIntC%b1mT34B0%hE3Wfw6St8m8%~ia#}jo<Cdi_?fEz<vVeSjRQ{yvJG9t5*u%b
zT8q&tMc3VFJk}2M3gmcA9udhh)-riwr#LxVw$s(o^ZQ=7?lYq->=om%)nPNT>>Q`)
z*<Pbtl%o-yyyIN9S_M7JC>vAcqOst{;Pu0UW2_Z?XQt$=(>2#`W?~z|nFhkzz<6fl
z5n<k2$mT6KI)8XQ;Z3UT9x+0C^N|x}u+2pelDKEw@FW%se*MDN!l=FBWS;kO!pdK$
zzrIhG=&`(xHZ_IQ3~&ia(BeqSq;TmI;)+pDlfIt#QlRGN|HdlvsqLsu1zwDQT&+=Y
z87}y-$04m0|L4<rLn=Yoc{-JP)arz|x|6)vdfPqUZY|0R#chnzQCKZ*iY02rL};m|
z4%MPd{1rSTw7pX7qo`7`IxqT=Uk2S<5F`IeD^#FNRO4o-u0qp=%n-q{4ob-+HGgQ?
zbFU69uhqPXU%qwJLJfqnONh%CKCo5S1qha{x)`Hg2(}>BGKneGM`_9$++4QiX^(#h
z5bqGTtrVHzRr5XT_lA<4)w!-2-GV=IyZS3*(nR#Wn3daMCD!|CxP4Td8Gg+ae{*9(
zCLsg!T95mwlIcpi19g#kY#OZzKGhUI_S+PnS%G|<NT7raA_v+A;04&<%{<5{$|={n
z;Y;S7lG{~}1fmoHyeoLaZUt)$Q^$+jL538Y?>{V$&A>dQylMbHa!6`tv-?<ts{B9=
zGo-7*a%Y<Q-%`J>ztqgzH4Lwy?`*sU=NB)`n%{UhvpS=dOnOX~Mq7ima!}859`<U^
zYRv8B4|p4N?MLao77o?D`^~}*k0j<gFSJzg%Fa}wx|ni*a6Loeof2w4c?OA=v3-W~
zhnj`3%vD<aTXTrX3T+oh)O_2$iXQ^5M0}kTUVOr-F6X`KpsLs9I>~S5_!n}&0+|J2
z>eZ-9v-e2^Df`XVwEZScrlO^b9x2cK$60XR`X}B{N1r3Ip)+GtbIs|N%Ug~alWVmo
z@3U4b)jvEOmL4^q<$iR0!4h8N>TY%AGQ>7lRqfrtFhVW%Jb?W1wiv6-D@F2N3we>z
z$cv7N)b@0IlFHSbH`K{nz__yZ*rv9*gu=0Wnx_~An87#LR4cAtv!pQ2*ILlJ{ev<J
zD)EA_z@GoLzdXcHeRn?oFbp1l8dP(4?Oxj<1M^qqYkR%3v2S#+Z)gKxTY61`Wplz?
zCU|~WkVilN(+b5rwc{hy^!OJe-u-y0)YSUnOK?G}2>13ss-I4l)b^SFN^v17pMpNy
z!L+_j<nw!<;R@*flVbLYa2xR+EkgqTxh!{+Nr&n?oWrvDGb>h(Jz6%e1zg_=Db;5x
zY_AVx^gmI#lOluuvBGMzo?v+8CwJPU$~CBl2jrywzA1i9tIOY8Km48j3YGy`Ll&r%
zY8P~&LgpH~zphq$%-}WOri4YqM5ej6Ucd7mF(()8p7~a>uEjlo*4wEHx)Q2;dmPh3
z?*XY|G(%ovduYERIT-!!O!SK(va7R7?;M|*%8A~n&WxS`bB77jwkZAuXXIzr>%~DU
zms^#uxqPe4f0;~LPWoaeg&+)JF9>#Ug2q(RUp;@Jh^q_}cUrtv63<);={=74Kb@~f
z`7&$8iW%0S07Ni7h>Z*JWJa#c6N)9Mu3Avkzsg#>O$@rXSt9Wk$aP0hk!uoJ1$TJ1
z(D{&h;3^YBE?D*?>-L9CA5IanLe79~8u=>62%*^7oAr6`)SRe1TdgV*hdwzf*6vs}
zx}P<}m%g8#gFP5`Sb=s+@IZc!*+XL}7Fbu<A-u?Ccdf)YBdfmi;o%c{3MWWtF4KEd
z_+nqwj>g&dVYhx7AHRE~<HZrpUFEyL=*W%z(0fr7;`!l7?^#*&oq81V$=j#WqQp5p
zGwKW@cxa|Cy>vv<x%2$hST)Hrm8hR?*9C=Z8%!lj1Y-kA&6+lr>r-gYI@dl#sO$?p
zJJ<Zlvren_G6i#}1N8Z)ru*!74w-PM>m#YZkMffsxiW}9)cw!iJo3?1Ks`y%S<&$?
z->23ijSSB;6D)o3a82;EqoVCs5;K1!<=zvYw^xf8siVS6p!YjyyGb?1aRnJSf7M~t
z)%w6Fu_3F%{#Xpw%O@Tv9@L#(I#JR<N5=0yHe9;T6LpK`TQ`dN{COax0kpL!%Ovxh
z{a)pSnWJ}6iOF95Z^^k45tF5XWC{5=&L9#@)QZrxYany#nWz=$PuILk2=~$ba#8sO
zvYBz}^XH!fJxEdprd%i2BAF*#0Cw=%i{d@p>z^-%?1Hc~^Oxk+pEq^Z%%xgMtg@Ky
zKYR$L(4qce1i=^|LzB0kuzE;ZRM4Qh|DZF>y+VNcv+T>?k_K+YG2|UBx3wAlC3evc
z29L2rsVZ9SwBl&WsZnvRXN7FC6<v3c<f5(xjU2}ci_5JG%?lL3%&vfNpW9Xl$}!dR
zNN-WAQvM1V8MrFEiaYy}GLNjA2HUr6W)x*B3U-!DfR2YQ7SBML*VL+H3bRU}052}{
z!k;EQvcvUElGaosO%bl`V7nT_4TVWE$hi`!_nS{Tk#h2XL&D%sJ09WWrUbifCI(ZG
zMm}CFU=KTyM7Yn}I?kICNF!-wp}o;OMGE+n+R@kv&$Z8Y8*V3^(+hOPY`he120Lgs
zkX!sg6>ez!Dd}L64Z>S5nfWvgU6iW69WxpxNI{<)<)?fpGNSFY7`9N@zI1S%_Vj%1
zO5r0K8BDxr9n)#}T*CdxRToT}bF0U!%ugTQke8l$*Fm;FG1!U4_ra)Dn;69?nM%S5
z%=O%9wl`6#G_hHggzTWO^bBQ0t@3GmoSsi>ghY5Ga`zrgdaiB87XNNB2Agmd9rQ)Z
zQ2G3M4neFXQF;*b3HP_De!1@0T2@l`iqS^t-?+pf#i#YW>GM@>8ZUE))Iiqz4|oH`
z&NUSB<!E89GUZ8bak4NnI=4(uKk~4f>3%`Avu6e>Um4St@3gA+l>6h$IZWcwr3#Ag
z$iGg2%5S?%<H|93`u$%X=%dLnYDftvAtml?z<g(3)3*HjmXM?0G3K|<F*QUPe38SQ
zJ)ljN1+P17b)hwa`ZN$_V0H^4NYTyvz}L4f&$z^+({8%bK)OM200cV7yP~)I_OAP0
z0E4Z*xWs!K$ammH2l(Wfh`T6(o1pt#G8XvKM`7LOk_hA%pn{r0Al*hbxpB`$1MlZ}
zd`I}8ek>aSf*gRDLZVc|+Bt2J8l^OPl7XMX(Xmtt>Ll+|fiWaVuBG<CtRbpvLK3F0
z2fbNTKn-CB)V6f!i*v1;);_5B!CsA{b{qOvggV|)e1`#wsH^bXg1nqCix3mHhee#p
z(6+r=NIfvnGJ(Nwd&K#`PG~e-*lv3T^1)l=EFkZ}$^T9&w%*jIEL_+9UTuE`lvohJ
z1d#@;pTL8X=SQLy!o#L=%V~z$5;4AJNwU-pOlD&V_r2;}eHF5USM-SVQh}fB(I1b1
z@Ckta21RS>@7Y}-j9LfX9?Z7>n7bbtZoI@EcGO^G${O$cJ?yL;?X&kc=&MaXJ$mQq
zK{cjz&)>kz>Go|0b><tc6EU*Xciduv=7qOv*iY^g+a5JXzN|i=k^^SQfZ%>D<LNgu
z)S>MsEx1xkI^o+$7pY1akYR8TQJj>SDhNNemK9%h<%Evi58{Xh1Z{<mz#!h;xed`(
z&76Esa9jpGT4N&s@dpZ#JkY$xdz*Tw&@zLL^-p_<#_!|ARtNx|sQ~Wt{r8P4ylsA!
z2G5@|a3fX+=&+D@eV_%n*xrchP__s)Dt|{X6Lb%31s|jUpn`DU+a`Rb0GloJ&UJtA
zIW*QImm*r=R1=|kB(mTJ>*%HlusB&j=2Y2%7*l^^biR<BLM~X!2{wd$GDKVXP*Ot<
z{`oL_*&CMdtdV|jkU6I~`>qUAq4=qirQV!u#|n3k^DIZ#dc{S=??~~xq$a1jor{Ow
z_AMFM^Lg<b^W$ky2`iBq$UDH*3_bv~xL{jpApO{w-502+ZJyA7KCtIW;0BPNmc|x2
zbx{$A-Xw)afAg>_`b@-3=9g1#5jK;cCk%ONB;|kA3tNkA$}n`=m)9|~7Ct;7KcCk@
z41gCeFqF5ga!!p{?Ua2c9;vrI(U{~xkHxR+=J3@%&E>j7Vd<8m!Ovp}U&Hm0a(^Yk
zl*=qx=?VPg_P^pajQ?~Ta#jYb++|Jd5>5H}o7eSAG|6{!w`(xd=1X|hlPwNJTP?Vo
zO$R!|Xw6pcD74?z5d#ugExv;Ez}ykKGC|2%LH@(ev?9vC)Q`c8Z~E#8*6Z$K5Grk-
z;^KE2b9X~sR(sKvEhDtRh7k~J)iqewO~z9`xP&5&7buv=phK7gvFHjlZKK*v3<)G0
z_>fvMRnr#WzD^*gwyZ!4Y=~S^fd8gx=CJx@Km$<%df2Z*RS~pcYFA9rQKSnOP&efW
z;mq9Mu&5$|{5pl`Sg2jNiR?+p^(n@08pmC;$Q#kp#@Dbxdj67XIfI;SO_Q~shfr6r
z-q{m{KA6d>q=PJ-@~ZD#qlJ^~OlLY84bt^SAzG6Lpa)2qOOT!vaNrwEHA>`yEzQ~o
znXnz-k$v7jB8Bc!td)U8>jQ(!ucJZ=m;E9qI}|#cRF<qyE@v*c(!#DkVNEmnMyLfP
z1GdZeN>T5B7-x7yxvMO_<GoY-5iuQl*?hC3y4X#JTaya%8z^>2_TIp=D%Rd*<6a00
z-V-FU;pJQHRlZ7kxfbgxIeiuS`2Io$Bc}s5HRSsh+*xv+Mn7&tCIfJ5vE$Y1cS1>S
z@MAyUByEYeNmD*o3T<=}O9}a^jJ|j#U7d&$D8G-H^qg<=B_pl4F7luwn4IVqal(&~
z%9W>toFNCH&=V0sdJi)EQFEysgWKWcQ$>ONa({XaR4kN!ACtPyK_DbxLcOv3&gR{D
zZF%^Ym@qFDgNRGXh$4mV&?gh$A3P}*Vv&t0*!pOtf6<UJ?$g1W;+j|2=;zp~D2yjA
z1X!xY+*^J+ZESL`pO5ldIUgT7__5nP4Y~ofZ?Ny?dNcwKs)FWU@{igq-Ro9&$F^o^
z?gAFb7f{Y!6NDFx_<BlXy0iCoZ-JkY=iJwnuUmrpZ*jg#OXQ$)u+cX|t3q%VA8_u9
zNuJs<lWc(3TteP>8Xi%6wW7Yjya%!b+zH$rLajX!sQgB_%rM5j)RY_)`*w+Cf%qpd
zY&jmfvwl(aGQ=9FvFUO58!C&ZF@8e+Ly*A-gE=L^QFHd0vQZjF8+YS6OCU?Y%>V9?
z0Ta8?9YwhQ{hxGq7g4&}hqSkP)LWU2(W^N_K9IXWQ7j82a$_(9iO9S0JJ9#wb>T=E
zsoer(2|%Ilf(w;*n3()xMynS4_J;1O6%(EP5QsNW*v5m&JOa_$U*dCfP}q%<-UdzR
zB)+~1IdckDJB^l`TpGw9_OzcAs;CU(H@*Dn^dsoOyF-+?5b=T?V`zcT3~$NqkAX_`
z-sgRPGO!2<E4<}9&}+67Q<I1{M&lLS#2_kSr|l-Kg-0|#32&74GV0rMGsVFLzEn4e
za4Lm^zIKtXL*Vi${1y%~Xkxqcuu>3}QwZD54Iy|$1(0cNC0kttOUI~tM~V|qvp)Ry
z6Ub>9%}WTnA+|=jD<N%_=tl0eGk}Qj58FZprj|p>{L9HZp^6DbM@V!uxX6hAm5cQ|
z*<-OCjN);fKI+zaJ2kF%GH%M;_QF-5rR(PZMbulyMfpAd!wU!~(j_U<QcA;8BGR#R
zOLxOkl7b?gOT!Y<-Q9u$OLs{L3rI^h+!vqk?{)tl@MM{5t~qn&oa>yK_srZlDv6~0
zJ<XHpc*k7)_r_0dB_{D<foI;sG8ehX4YR3VR<v~59W?NPq$h5D^Yl!{(AqTQ5jMDH
z;bHb0sDr_~!h;){PA^wIT$Km!c42F|G6ueaAmGUOr0*K3NL>fBKC1j$zdR;SA<eKP
zo6wsYI&++>#r@BqPmkSOw{mE?>}6T|vMFP2GZv;@dfwZwFf|3Ow3{1-jfSKxCZ64-
zf@BSTz)9Wdwm2l8d~nE97^HkDhwy0`k{|{>WqO>HYh;^6(tN`^hw;S)Bo&viz`R9+
z)Pm)V@YVEQ`HVH5{%-AB?0hV*Ek936m7574IE3Tw$BL~-2^7kDCc-Rrp18Hw-UDly
zVcJr>;M$hHz=aWAu3ygK6?0~CYTI|72))@5dZ8PN3JL_G3%I&j@VCR^u+FBrHYPA$
z%F-2LIpctr09}RZoea@JFRMWUg$zb`IbZNX3-KVGp2CvOB5UYtd?_gnXA_sOCjYS}
zh8?uak^*&GI}$c70wK{(WdWsY(1ZDbDAN_nrS<u$4%{B&yvlmC!NI{?geE5>fN7QI
z1VN5=EoWwZB)@@F#HL?;6|*r?&9n+`F60HfC#xeH^`c?D^gbd+y9?y><kTg_^Qr`o
z0NgokFQ_5<m#qbncbz&uKX+7ubH?j11(%kWGtu=fYmr^TtFPqx*aM!uVJzIn%#5D7
z=^sE`fy70@iNc<sr~G5zk;GTe?|fwaJ-!c!+CgGPyg#DB>FD?%2o_WN4TMco+$4`t
z8hk6@$q6K9l~kdwaSs%$3^_pCnfQh@uOk4pEN%}Vz)y|gZE?azr~$w8j1Vlx0z!?w
zh}Hwvj@!8tOBfP)ZW0<QTMpp4=48E~u&|>K{-9JCk_r`<=Pg=fot{UH^-#ooD!=lW
zJj94Oz~`3%gkGo#N4!K0N;=8ynTZx~mgh?3h1$gjpmnm)^{CDN8jwv}doW3GPaR>?
z(a`}&9$^;c5k-FawA`*U7}D2<gr+jM=j@qr5W~BQL(6gISMMpRU?+mBnA{2o@&QXT
z^A*lj9aZQE+EcPflP_?Ls_Izy9e^OTcoK6Fhp3Dy;%;SLP@3SnfB8T5=>U85pjJeD
zR|gEKDCn|DOdsWZEr@#PCelIFU-Op~pT4gg8Hrka_MR7fjMzr0h~MPe+YE<I>YQFH
z!)}~I6gwU4^Aq9CM*KI4aJs>N4U)31*L)=*lS_6uIU>btPG6)Xp8x~78bpfdV?LNh
z5CuxgL7_AO;vG2ef>8>Inw)B^g`F*DIo^)-BRN}`=27a9@$t!l_umV<h6Z4heR*<1
zY4~o)2Q$Vkf_-SX#wrE<)jy09iP_&}F}&VO6p{r0L7GJqnVEOhLC#(Q`3S_28AwzY
zhOIP>G6JSiAd3KbeDsZJ2{zDrk;bxWDhBp*^#|=rH`gHByoMR*MM2W0dE;#e`H>}Z
zbp(srdn<kTEkD6eiY-y+?}5lG@Qr<VcxWhqpkifYPJKDIy84#Fv9IZ8%IQztUKfF%
z8$Xq$t`(CQw`g>)$UV@Fk33r7ub=XlPZ~htux9uKVxwpH+WBBKtBcM#JC2#*q5X#d
zb}LT}TCsRF^W~(>egn}=B}&7fmU`m-C9=`)2Odiq?WhnT1JN)hEgc;IE32;mEIZOa
z{RNQ0eE8_iBICRdxdJyzS>_Ze@mVAUrg-T2UQ!Z(cbR%>LGP^+<6Uk2{pA6Zt^C-|
z;MBSs%hN$n$}}DIWnPH@)}gKLovkJLh$pW%c3`rKhN&Z0{F;qpm%f^Sp8E!B!sH<j
zWUCA|ksLdLdjBoRsiy#juzmlp^{aS13W8V6Rjg`qZ$QqOyL_@@@Hw3SW<a)F1y~6N
zpO_@mCVfQBu}WXX=HVHH)QG{*zAEAWj}1P+=5D$fQ?|F?9Q0sd45O;W5WI;N2@ZW!
zZ(akS7Ta!FQCAU^TWu{Dy$RtPTDq0_`t(HB+}EHUyG9H~!aJ&=z*C+u{O-wqbsVh7
zUM5N2&`@sM35M?&6R)ikmcKNXEPu@^tGxi4(mn?&5}N=g)!wRiPFo#DVG8xo`i$nr
zdJi@ry44-Pz|8*bY-O~uV(Ybk+x@8%0SH?GDj@^>{Piw_YGkw(r7N3(*-a8E;fL4A
zqr9mspr`rPmEeWV`JdgJp|3rl$4XB^HR(X)>2!bAc0}J|3*j{$?Rh+&1u~We{zV7=
z-iGOQ;=$$+j*Pr9f;x$U$bfv9=)KKG9gkkU>xLxVHo-!<krdIvQIxLG|GmpyK+C9Z
ztw3(%GV4kJL>3Fs%R*i|GREk+6ERwejKrXH0g^vT<#KqeS?XAV6d7s<8H@k#eNFpq
zlO^e_;7W@T)PN&d$lp+8(Ax55yw;Kif*kXT2?P{}`X56c0X_G4#x2V?|Bnth@B$Td
z#(>^`K)+j1Fg^G?J&Y(E=v{*iB+AG@=36H-nQ}P8*SCzh6Q-~@p7u_#yc*M!)BlFm
zl&zww5@FAJfb~Wb<pr;_>SCzEVvawkv!q=;DO_`0_RHDrQ|H3P!VSg;P99AF%LoNF
z1%j)`g@E;F)rmK9(1rbd$IXiuGE_(B=iDQdwimY|;4Gt#k9h$8kpQq9fL5zLi@}O(
zto!zUEi@PVsjDFY>O19;+>ZxYSjiP10R#NcjsosD!NScLo!p<EmIlJqvGh!st+;*z
z_Wrn*Sd8B(j)+eh+$Ol^HalSbN3gJ>1iAJ-N(B0il&~rQ=;z117oh}kQg1f^lNTSK
zy5}?>%1W&;l#Oz==2Beg;?Y1@!BWS%e~_oiGw^-KLAeo8nV9yEJ&}ClMenM=TM3Q~
z{OQYm#!VUpiKQA4fiUH&Hol`ci!E~!Iln$P5o)2RRP=BT3!@fz>BN?+O1^I+BA5(6
z?^>o9t&FcV=cM|%?L4<I=X&4!tV_4kW}%@0OWaP--orDvC$Q9nX8jMRijULj-^r@~
zlQvGGHU(~V^}QfwIA!C^Wkw){$ZnQP!!^a^Ru%xjF=QnwK`wZ?QT<L3Hlup|g2wSL
z0^%@0>o${Kts`6*B6MDu|6+v$s`vWU1xM2CmvCy`Bb$9WoHf!T_c2!&b{VWJB2KKQ
zYanH!G8Jd(=1M=xN>j(HX>?>N(8d%EGxK_Ol*d9ffhl_2hSZEn<4+Wwu-s3LwbNWG
zLgYr6Z};}8mcB${Vp7usA0#9FaipTOio1&f$H%Z{<Q|IS(&ZB>XBbg&3$$y2mj~ww
zg78%@9>}&|y(&B-?n$hW01P#_zqIPWfF0F4b)Alri250bIubKbU{?)<fe>1q{I+EH
zs;LcW{u>|RD7bs-A&}~}!L)U>DR?(Lyle<PY%esI{ol;Sp#r*+J&R<VM^A_yU1ivE
z;ht|rmsSnSMeVIsc|BDOBomoj#q(g#n$$dRN>CcXBQ+djFmxDV*utKFz9&ES@GR7Y
znCy>7N>qm)d>|Sdt*_scho@ZxOA*$=YHuE`PN}eF&WCTo;~WIB$K}QmxrqCnlmSnt
z{FhV~q>HCh#l7+dknOe}epdIj>+g40;2abBXx><Zrcf~pmqQE>6b^GAe(*RqjKQvh
zCM_dlUmI96RksM;ppo-P5Z%1L2p+FxbZMY;JUkuYo&D2By~zvzsVn}v^OZti?_Ph9
z+}NlAKf-5@FE-U5ly`;G>VM*AoWj$E343Wg);8q<^Ph#rpNOuW#U<%hV#d<tUcp?$
z%cz8#m4S@L$K@vbLsu5U=J>)ypcP(wc1d?*G_nJA;+yQZJ`!x${;3DdAp)sr`hIb{
z4=qK&4mSCrH^2oZ0}=p731b@<QDydti)IiUIMdRrIk;B;#b_!jw<$$8OUVdO+5%&G
z4TXI;p5%_7#LkU$ORaa6d%Zo949^;{KF!ejzg67D&QLhp*HTIw?3^C5T(uu0H^Rj)
zw(`T!9A5O>L2Mb&m%jsJ{3Pa{-M}&U?i0FTs^|g0#45m$cdoL^lR_Vy3@@V+BLq1|
zW>k%s7Y;UcK~Q!*eMQV*wdnui6jP98C@rZjrp<_E8gcJ8;Nj?}U=vs^W|s~G+U-+n
z+0C>L9G?FBauFweL;kdTx4*w9JKsShIO+(f9YZ$w+PzONF0tVjY5tC4jbV2-!Q#f{
z$(+K3M`iI)GTbp6b=_W)O{HhA|L4Cdr6klzk!RScPu#aU<Y}DYNZok2XW$0_UjoHN
zi;?sA^uPd)0ev#g{F`%y`e_q3iJ6ckqKj&uM%VNI3-Jpn2{g9IFDdw{bCv*C8%tuc
zB64O#oI@9f&dpp1&XuY!1<Rs+5eR=!mM%u&J?Wc=NEp}iN<s9<<fB!84d-!?bAY`a
zAA%h9fX33aP!3{5@-hFky${$`_rT}v^q%P}99OJklFZ<E<N{V{l*Jq4$hMOI?Z2`n
zT!^bmb*TJ%{WSQ!O&2A~7wUO`Wax`fb1|z_0k9ET69gkL!`y>`G=NX+6K1GTs|vbK
z{zr{P(BWm0aRYTDXfB8@9&Z0c#auTIaOu*p$$1AXm{L$fptxK;6pUu$_*ag}z%lFb
zX!1=80ILsh0Q>SAJ`mZvCTEk)Dc*ophfU=X+U!N99&x-ane(<rP#uKZtcM9Ss*qYL
z49a%I{-n4dKoKy*i_Zl1zzlUWl3KlpKhX<4Rc-;n!{hGL+#%2d<@h>hCb@}as;T|2
z{~epuj306IaJi9u!UOqD+g*jZTMmcqFx(M2$j_#ryI=eIBC=bzBGn<%%g9?U=67F<
z^#t356+WiS;=jf;uFqVi7*Ed&^|e@8_R+`c5{!PxgqxRZlv8R|m)k7uOsMWus9$84
z{?0>K!qTca8X$^fba4bnn51nWC(TyQsNcHriR<vgt-~!G-xCo$;JW;e8u)9<N7~m9
z5!y)*A+`a08{oeWI0XLs?&^_L27ucOlm<8(fmp(WswlX9gb!@>QncmqXP~NE0FVT>
zUIHofGWf1(@oAu$4kZ2$cr@nm@eO#amq974cGl$>74!sXTy+Wf4?ZnMx-Pa=Xd;@I
zS+GF(;s7oZh?K0lFw+w~J89Blv|^l~2Z@6KOx`1p+5La_X|+-a@U~?1v;U)BiFX}4
z-%TE9Q2Kmq`Ze!Qe<r<C{%zP?l5OQ8P@@pK6e_5D(lEnd)v+W8yg~Bu4J+|H*R4qS
zkm5F8uf)m3zwP4W^`%jFVbPjKZu}%Rpl)J=>Q+vlvYmcByb|8g1OmMWTrpx3DR8s#
zk-xMyb_bW+yDlu6&#0UD(`U{VEDUyh)CJq<E3(QdsWOtiA#xwI<QZoa9OnI-0YzDA
zWQ6r>S^(G{9!=(U2q-`tK<3fdD2_<h8KZd(FSMsf2_@93to)H|>~%;lYxViD_rfAs
z{++PU+l+Zv*AmeMmmx8EA!nHiAUbu`jY+-rNqeTXbR-cn@~^j$XEN?b6rfQc$@x;&
z3_8McHqWm#?^rx~-827_sJ*cgVbdezR9j-#*;JI*;vibWY}JQR8Y5CmD_8B(3;=j7
zBl9!5uBL#)sraLwZ~qN?O?g9f%5;3Kaw$2O+!;C;ttzG2b6S#1Ny{~dm)w4vLYXeg
z1}Y(YdRA5pBm+<Y4>}z7POL?E=gF_r`GMUSKpfKli35=r;AkEtU9fW$ZTvFT0PG$J
zy6f?+Qdifwz;LDk*qcRvYeu>6gHXU-pyXZSXC!3jj`=1sCa+G-(QUq)<c?DKhDMRO
zjOq0IM_VEg2$(+MC`7X~>%u?3-yGEXey`#6us6R)5nvhvHwYilu+G!QBntxuGt`)N
zH=?MKdrURA_`R6bwqW7@{hUa7T~BT!h@^3c2J3K?YP&9Ngg*7o1mzKS)^YqTLwH|_
z4v0tHcBGB*JBqKvBEO)shw9-cqUzZ@EY<7fuXRk}B<Hw<*-iK2O=ojWzrFwHLzw$B
zpHbRaaB-G_=j6o!Jhe~=%yUsN{qTDE&*__(Cz*1ULWU1C5O8A%#!$U%2Y^{BAZpBm
zMItVvGma#d>7Ubr0~hQC8$HYnaUN=>CJ3%Zg>E$7%1?SlXQu6sfdl`Zihl%w_5r*k
zyIKaEDB6-g%*{`Oqn-F@k!rW}GXC>jRf7<?^=2yT<6FX!5~AY%FF?>E7`N5`I+r34
z=sM)&cl?lXWV_kW^mA$*Dv9GHL`n=27X}=B5h=*q?^`EtxVmG8?glSIsit951+G8o
zrF}D|fbieuv{9SLc9Bcozm5s|vm=wlm-8%+gpv8}wvq!eg-<|ZfJt`IF@YaUC5S@e
z1DV&n8JNomu{UbS`GPMDo2Zkon+eA_mezz^(<M~-zk#yb0TYwjl!Jg(Q-*QEZh$J7
zQWpaewa@st@t8B8<X3o*Xkh-zpCd*MI0u~2q+E#k9a)G@cHyp}S0ly9-?ZAdzG_ZC
zgwxt+9eWG#L3KdztP0pic+(GmRi~=TL@7HRxbMTH|5D8KKX-Ze_(q_)Pk<Dlj@0PR
zYv@F!^xDL)#HESXeW+<biTw|c6#Kx|@<<`K<|%MUtwkR{{H=RJJd=F-SymnFvWque
z0yHM}I8GqO^hq>v_8YaW;JCvtf14_2jf~j3mybLEz~Fy8zWd%(h#F9-^!Qv~$I4JR
zz5agXH<9D-h@~WEto{^K2ADZ1Fx|oAx@mez8uTlktBV*m(J8si+6oG00D(QQw>}a`
z>-d>DD}+##_Re(bGbpWZYbwqEduz_g-vf+V2k>{Y16!{9jMx3<k5CGPXfcs+ez8`T
z##a8^7bRLy6%dHkF%Lv8mM~sTL;WmXnz6V56&W2j=35?YS+ZSG^N()y(`lq`j?>5p
zOEny@aB5hdfF4^wMcjUXb|~Y%C5!{IF*sxHO`WDx&H8?zZT%uK6l!5+C*2WpZu$^~
z*v1F6VhkibSIKHZ9b>L#ltc4mwhWn8!r{7cx@x!T(3cc%KP!*iR}aMvi;=&6;c2z(
z7A|50o9AZaqXnB(I=@af$d~R5|FKme_sR%z20c;M`wR^3UsSu#og8a4&_wF0S6V3d
zRHey_FkfZQG*&f^#imG6wRlP;-i<Y1OUQLMcy~=N*`j$PKJ%)wH!f(8iLcbv-`7xN
zjhs^0YODR^#_zM_Pb7nyju`kWVGBhpk#nerR`Os~rQ9Lxz6V*hcw@o?W;0lics9#G
zSdqV4l+;~9nvsD;BSV`4&tus7p3K!r#@iP`M9oRCh3?Y#y&=tyXv0)cm`3SqXefIG
zLM|`X7kZtCm9K^of~|cTMp>?Q@ZNzR%;v-2{s4np|DzFr@oYPw5TaOh8=$%2eXk}h
zdYvVW0&s^XO?=z3Gj&eRNUtafXoepWXtRWatN_#lOTZ)FkI)5oPj*t*h(21J)VFJj
z@;qaVJo8rDN`Cr?u;(A7p_yr@RWjFvhM=>rq3q>lW$+9M6M%1IxZACJ=bPZ=AC0vy
zQ%2!T4Ftpyw~F7DnVp;q@CoA19@Eoc3L`=;G@Kej3{S1s$`Toz??ay31;zw^NJWZp
zqGU_<5{k|vr+U-5312^nk0*B7*uD;63YN8kj_^_nbh_vu6UY@V-_|5AkBksc`0Y9k
zU3UVmmiTyCuV!SB(S-Oefj#3^5ija&4Z>@&+J+?=F6;eP;QKPBv1&;ptoAdCxScT8
z4<y5`>j<MTz3CXn3{Bc6b0q`|!ERR94fDkKk18@trc73FX>zEnDv+1=4H<t@a9S8_
z0o+zcV&P%ZHHdBSgsrdM31WLbmPtddE@u;ht&bqbDdZ}3&wix{si_(29lm<W_4lrF
zz2J9K^O9_HkYl#BCi6>&&_5IO(KU>8HBXtC<0Jt8VsoWJ(eG3R$P@at%v(gap0Q$D
zz(#0+=L`}yc~6&pR-l5<#H>5>V?0c_h0J^FA|1NE=~4Pdw>5N4%PXft5qmjtyYCep
zT`iEO#KMpT7ax=oZrCpBfs`ox<MZLe0+Mj-xFL(!e)COU6LpjVjS;Q+)wn?|`tkFs
z4fh8cB%z`GLqI|gR@m>d&K|(59wZF@{j075Z0~M^t?KCwI8IN+F~Q-txH$KD^gCgP
zgN-psNvSBnP5{Biqxbg5|3JB#fDEdZG7VN4KSDv&YrVnN^r$rRAY7`<jUO43k_C?o
z;c0yQ-((@6y#}i3KaUl|x&?w51OI1*?^miDjbfS^=>M@a59|TSl+=%ksbGn<bI~W1
zY<>F~BfPZw`~zc|m2NT$+x@}kJl5&`<jAfG1lNgjARQem>*EwH)>D6-F8{6eYC-e-
zd}<(#?9|7KqD*M5PB$;p>7W1pdZ|~Z`Cl}L_m<2AaW;=;IAOwO7x?R!eH#nGeXrWW
z^3^xBT|*kij2wXg4-c1XFCA_-nSI()jtnL`HDbWJ7_}X4j2-qHxWyPJ4&LTF_?SlV
zsQ4FHN}rzxV(}7wjva;=olrTwmXv&;ek6BE?^(c`IZug_5ywxS@ZWWl<d+uj%=LDg
zV<UGo;#u(^B)Ag;^4a_>50^dCuyR1yqfq8As&vFCAL>a}zp7Q4#(mLCq*I9@MhELC
zOFhX8wRwdxRYVpVmy#{8d{lBk{fZVv{<(zk<bzMJ-^pRBuAS>Qx#5sQH~lbmthp$4
z8cIE-CizxKxlxW4iAQY4k#u#lemhDH5r`YuzUR7?2PrhJg=R|bhK<eDO;&Fvq+AkY
z+S82k4f>cTvL~;{7AKstJSw7zO<^3MJfOaMRU&f|$>h1yeVfG<aTO7x>xn4++CM5o
zGHbQXBB8gLDBqU;>iPZNR_-N@61>0wwF93e6EXdn@xULY@&X1COGHW2#Q~9;S^kM`
zVe}dgo9!mdy3-+yml`Is3&C2XMVAj^los>YAba2wQ&s<Ai^D+5ia0jTF1I}{R`6qt
z;j|Xpx`ttb^xgfdjgpo4m>yHtY4>GjVU$-SKuraCWuKdzB*(PQpS{Gffei<P&l-VF
z@9O)OVjff%3KJ;;#m(P~=?O{o{~g50ql4yvWPw+9%W^TOH5Huy_8fJn&}GalHgR1=
zX`$aHn;x9b-TeaRy5kW10PPci){lW1`(*o12tmx82%aHX6_n?j9YLJexAyU6pWloP
zJVzA#JSbflK#tOB9Gp{TJ5qq+V>(ft^&C7K2bbX|iOQTXri__f<6M%_6x5F#Gy|M~
z%)cOmp2;s1UOR^N7@bc41iz=`{l;KEQIkxisW}7t7rOr=i_76c0Yy9-ppJ$2JCO7&
zTP6!0D0X%vGCNUX8o&gzt7zmavnUDDZ&deGGJOp}Rlr3`{2K6smgTjt|E6|%lHT4!
z!s;~jDR22Sy$76sEvuY6_eJ3?SRVrfWYWBH09>n@N2QG0gndxonu#IC*ZR#Y6FF6*
z*IV@Hrq9o&kv-E6AXy-hDHaIWQm{5pakYClk+*|0J>v~<A)$rRw3Kh$rSD8BZBn>A
zuW&*3fU(4i!HCkDIcv|MwM|ymS{vUd=#KDM%@s7WH_GQ0r8-nIJs!I{8Uu&}UT$Gx
zBT^Z!45u=Ujly^V29d&8%@?*W4#GGt+j15D%dP;*eT?<Ya~m04?flv7F=PEa{4M`7
zPvZOxuUurm&GVMm)BY%?U3k}qHN2a%O-#=E8O1j}&KWIn2`Tb$)nZv{=P!9!s9%N%
zROGwyi|8cj@S{E!aScRy-fAkZe~aTJ!CmOSxeY%#a6dN$E|Z`K+2j$j)rM~eyI}dS
zdv>t5fWX6M%wLSac&?ohMl_fNGsbcB44T&C+5J-4vfeTvqRsCso``(dsHk_pwqGf4
z_uY@kH9yTGrRba_)q>GSLP2Zw67DWWf!0xE2r?8-7ER5Q!YBJRIFg8}oCUVNyEiTU
zUycYaL`?ys3B1@g1HMbCUUtgo!{VUeR(o%#sV=MbXN);G<3DbkyWUO#c0I%O+*$_@
zs#IF1wY08R16q}cII@GSlqZkFz|4`AfhyB2!qS0K!pbZg*a8Ary_)wuv40j`;z%({
z+36Wkf`Fs;YRwH{Vu0we60e&iGL*OcqnOiNc8At8%Z_=I0tR*&|DMSIror*Ls0w9!
zx49`zC49^0M6i!Ey_r&zXi&8#<Z$1&nPK$UCF_5LUO6ae6KW~~OHc)tMj7c{ob>e2
zqME5#&7HzhG?-A@i~mw<oIj|P@EFg^-(X~t4iG#YO{zw>pxmEmqrLfLc2W;pBk>3N
zSU#G`LvNj9*TKi|@*p%QaR$|%m49Qq%?!|wq$N;J?ptE{$6t>rJzZjqQ;uCDUtUyZ
zsST8mB-3CEH`AR_)XaTbPAkeou->Dg{6`hZV$y{R=}cwS9=P`Pj4`ol=D<=UtNEDQ
zN%yAsTzAOsF?`=;I29scg)^u3V<}%ghUz<l&Ei+E!4Z>Yij^QXX$y>&MddDM`0-X4
z;I^^(?;}UUj3AE=bs{~iZQW|9*5}t%iJz4qyKjW+OS!_}&MNGuk2A8ZtA(4TGM6xG
zG(iwY?|n?AFEB|I$@Y?$iV1xk2MiM#H4{lke`IR3w{)YRZbH>}wDpyfWY})l+J*bq
zE_<?i`gmK_mu>||GKCC>;_o`m#zAXUMql1!%bnph=9}-NHSQ;i9?m{EueObzh^}ny
z5n>&gy@c5Q*4uMVAZST9cOPiZ_tE_I6#6VpselSGOR~@Z-*|P|``aA?=v(x!3#crx
zD?fKQIs0FrV)1<Yn3I;{l7+->-z26CI0FV@RaF+ug+^st4k=@`Gqs7<-g#bPukzw+
zKVuo)WzNTm)?{s*|B!voEib_Up>-7Y0gIg{XZQZa3xUJQN$;;PEt*}*Sl*g`EmIIF
zu(iPpaugiHi9Wl!O6pqN<M>OoCGf@Yvp^_)cZy+CHx^O!4xl8!a{)K?&?Jh%c1}sx
z^!p+Gb?`{&m{P~Ig=s@J@GOcS4Rj7*DUbrX6~tbFGq%v$Y`Te}K&iCNH8I4C7?liA
z*<;_rzUlR%3>(ftJ})A7q@~HQoE+NVUM11-ZMG1K4p4O{R;ESqpb~@B4!a?lrL;`6
zpX4ige8MuNPXF|~%R(XHVh9MhC869V+x^JZ<A*JjuI*Q{Sd?G4mtnEyk+7H=7k}NM
zIy5&21#kem@`@HjQ$-UNjf^rpdTn-bEh8pOJckB0M2k?(0PO)^;{Nj1QPpta`*LJj
z8~HhlnQ?LaBD+YZNMxyg%+3>I0$?+TR4zh4b5@N;r5y!uOYjU%Vn~g#kG7#i?k@Pd
zO9S#S0@w4{4}c5KBk7mU)=ceLEcfD(`9iiJ>1)WXdPe&Ej<2FS>T942O#l&VNZvvg
zhL(-JT;%KAKO^zg-d|01<E(~3eWkU7KEFZYVSt{gc*v8M_sDd9lGq(MGk~dwxg1S6
zO7f_}iNK|kcOsUc?8gp0(R0rfG*@u;;_kJ6XGaQsjx9xv;9v<Ud=74Eu=Jhyx7L8-
zU+Y=Pp(>?H);Ak({EIBV`>9e!9s399W+a%;D@;~`F(YMwE2JPIC1DAB-xNbtlCj`R
z$M(91M9{MNa@x_}$%9|8tMNOZ{d&^Xmvqbl?|hx&lHQT!tWC`ev!JVG$w0{NTUi=%
zS1TbgNYUbwEF*#d*HAcUMsvlN{g>_E^@z{{gt8{q6m@1{joM48u%BT#L%u_DX)mo(
z@Ij6EV3abOeW#=X8=i>JW{G9(SF`iu4zFW!G|ak!Eh%}_=)7Pi5+EuGAXEAGfPhg;
z7QdwU-S0nxSa}DF_M8byQ5w9fS)?7m@-KyhpP@Ko#1RF|OBxPjZgUG(dK9@$$0N6%
z%iwI(F({E&kuQlNg4~+pnGZpW;y^D6r%gj?J^%hHwTpe>8`DnIi2cz|_1ccggx=O&
zmeVUKNeR2T<wOB!p>XlK={E)!31ojc1uM>16SuH_#v74`>Y}*n)>BrI^eK=dKKG#M
zgTBiE?G)@i*coxoC&#v0&W<h1T)!`<pyakwa2l5i8D37TCX=HDaMD9j<EF3wHotb3
z7H^r#5%<mRZ?0FPz;F+%irKf`_hI##^}h561(C%Og%<3DnM(|`=XGs2Oo|?=*=-Ed
zz;)QR#39va81|zdZ~Tir?SFw@<pDwxbBO#+RLgop@zLW}RbN7F>CR;!oka6ao&1ew
z721*$o`R4Dat&2nkN_Om4;GqXziP}<J%cW&m?;JhUC9McYAraNMN?GKGap#w?R}#~
z3;0J1MIE2e)Jte<Aiy|mEi1MXWJI)j+s~-Dln&Gw611)!P#T3tsqZ|&7m|d$l6aG3
z3M()=<Y_AX)9Y@eW>C|wKO$-(8ODsU7kphz%Cfv{0m5emC|M_>WEkp)Y7@pSa=+<k
z=51oeF9nPKNi4j;*h^|QF|tiB;sZU!qhrQ@hN+FoNPR6UlR7vNrHS=(Bp=5|N?QvZ
z%|q3Wmfm8n5c`#6C_LaKZP`dx&amS!;m-@7i|Z+Al3<iuuZh&9VSL(Wapp}7O!gFn
zKk-=`@zLu@N}{A*qnp0xIGgyQ#a6FwZ^r>_66$@|c&#S2JrfMO`KalqcYaTWW7`0g
zUsWl4j>?@ZP)^86_c>jNH_6;^b_U`<)b8^`optY><erYd2D&(oFLvj<#bO)o#ov8N
z(^(x>uEiV_u+V><tlF^^Qe##nh_MwS)L)qllozL^cx(rnrsK#b#ogM<vU{DDQqx=n
z+xv8!^wF<n1l++EQTXd<%wP0FiI*-1ZICqx=*elVhWKfkl=p50-)Md2?I7$vqx^~D
zN|aly#)}O*ttIO)1F2gpDKAGg!QY)^7N<EwM6^M>KsVAHa;00|WhvYSKmL&3D!6>N
zqZpgEYP3)@jxld#{E+{(%d5hR_f5l|svWZW@;iQWa`94$<;Cn*cT7R}-P4aFvxkK(
znZUe}5rU8q{$g*H*{ALG{^0CCDy!9ZN+b1Yf$LtEbK$JC$IH{Cu=($zWJ!BY&<aM#
zz#`dbyKKCl#+C}L5~=%Z1^)>n*ff#EQ-Sz?7D=9GpR|p_VB_EBX<WQ{JJpeqV-c*L
z{cb2B9lp6l58`=pbXG{C!nhFjR=4)2la|+Uc?6BRtKHA}!0#=6jANOe%lv+Bey%Vt
ztoZbg;iHBqswrOx%Nm9=zy!gmRa*2=OK0(CAsK7aUuRp`1pSkgvFb}2(LNioy_^|)
zy%uc-!?weHW;#e!Rlh-+v?NZFFYr;2s_jc&++-a4=<iZ0{o;A^tzP0v&-4m412d0O
z>9b|dJVf`K3k>jip}i(!kTV<5@sIbL;zMC)Jk^c4B|0<9_xO)lpx9YaP#x(~{6)MK
zKX<O}{Tn%VG0-Uw<_9ssB^riQKRnDNxj}S`AbKqJ+8v%-(#3~wNEx;O*YXTJ-1}hZ
zF{0`bGYyU(W1}$lmsdO&6AZK31M8QP@2^C6@oHJU3P$yQcv)M9uy-FbHGa<z4i`cY
z;?v09420O}xpU~^eOe5EA?v^L!Xn4EW`V*i9VPd?mpzV@uUT@*H4i~#%ko~W{n=D8
zqmn;(SPEmlUIdFa$eI3eC?fkaXLxebKr%KKWM-FPH1-F?Sn^CG$wjQ-{aKLpzxa>n
zzNcDRwTU_nF=%mtz`dAkfaHMn+}%kmZJPPHbD4YRapRN4(d))cO?*?kjidxVCi+Ao
zt<ewZ_UfRZxrUw^N2;esz0?_z(VxW#*jAaof;EOHr+Gi;Uvty}tqe=dSq*cO1hvvF
zDG_Yb@zXP8zp?+;3G^0v^m;3{cNy%M_{4wUEyeUvDNN$Mve-!7dG)*E{ZraS8M;Dz
z9Hr*wmNBV`&u_+~iL@E4)<11pl>~(D6tk}@_A-$VypnVrqFq)x(9vEd@Lu1K@a5C(
z3Y1O%^5GjBm6~yQO5BMfiWE2Uqg_}`kL+eD^kFvW$-Qv{(KqsAC4JedUIvLI;vly-
zn7p4`2#MAgEIjNe24Hr)d5%8DEn2uYOj+dMvuD;;&33+-KSYhwKLSlQD7MIvy~H!A
z2U3@8$GT_c^?bejXv98dzP1?rjm-oNd|=4~OMJWUc<#1x@kWLV4jGG|hy(B?nj(Q6
zf+DG=@mw_)`ESklEkr745CueptmA;Dy?x5{X?fAtW5#2{z^j-9OhA9JA2(U#=xDeL
z$i7Y_Zh6M?(J5i*hjWP2>k!%>6!A|FyEv8jJjjShPN*>sO=Eq1Y*9mAPF7Nd!G%FW
zQF!Db&&V7(HLIp!7XHkuFVC>6l%WOQH4Bu0@tpTwYT(%uXDk6K&g2!SELT^Ufp$z0
z8$G}3P?jjf@3wf{BVw^;wfZpSaNj0tIqRV&R{A|<uhn6LrUf2n9!=6hg_btj?5Txj
z2I4+8?YKXPLU?e}-c4%8O?q=MKXcMl#BTJL!u2<eGRSC!dlwwj)}nX4){K!Hi8n8J
zUuSitTHbHtq^PM_=9=t!`PX1%LwsY*P=T(n%GV|MwuB&w9O$;l^tc5GE|kxbV#w`m
zG<l_=zL+z57V1||XC)tKlD6Fv=lY6tR}_C&$~dkSD_U~IW_PT4#xUH%>BpI)+S@Mt
zoMl5c=s~*jik8%fcBNY!2T!k6X0VTvC`GtAZUe9BP3-s=GHh~9x-GfZVfWQ%Z_iu=
zROo>WIG0d(Zl<9+dw^4MhzkRBL<85M9?(fEW;`+^9be+<1<-<3@&ke)m_LVM<^Inx
z6(sd)sI-1wS#nr}20V#{eTV7ERA?tPVli<X&fU$7Wgj=`5%Sl`&g<MtE_l_W8K~4e
z+Bbwd;t7@+<T`pI++S^Qk~;sP88Q)Z9XE;A^F%X7Q-N;lsP+KLL^hn**8;5j$En*?
zh@!UJDt4l_so~HU(|%Og>tuKCdOTpgKYv{Bk#n~dS;2^3`6NveK_58Fs;IzM_~OjN
z{i!Gt8rsHWrZ2_LQLM9gE87ZHxgHi0w%yw;Lbp+OBq3XRCmg~XMs;J)8=rGsVh3wB
zu+Ubd<1shtnGyck<&5P1WO$;jwTE+R^y&%-iR!Yy({zb06{`I1k3@2i9dKz@*`~(1
zVyu0-DjJP)(Uf!ff96J9shm1<uQHpm|30H%w=#)W7j!hb%-9zJsM7OMmGn=MVx2kh
zkqz+ui!Q#3xMZ}@e8G=hPp!~&j*{uEZp^D{1MtCP&qZFf19ubZh52Jz>C2*kEu1(s
zbU{f-h~7IMYK#MAesQD*X%m;H^GoHy?OQPwI{k94u-KRYo#(%a>GSN-3~M5=p`y`S
zO0m8qkT~^W6mVjBm2D#ZmPp7NexI$v2BV|37sjZzvX;Kt9#6K1*|nTIE^b_{C5KJT
z>q$%D{^!E@Yj4jFDhtD2g`-}I#_r98%D(vUEU`hQq#-;zR9=;p_?NAUO0kYqIH6Qg
zn5o7sr6fHQVbt|npS!1mlzln+*P)~n9}}8YjB`kT=Exp)92OZkOyfnG*x3?jb%|g&
zNRs8CCtw`wCh(d?#mr!bBxnE5>y1?ty3W+kHvaB@|8<@7L0g&e9oTJus(!tGofcX^
z1$nl5b*p;lUF%NQ7fW=EAa~GovyU2HHWT)0tj=t5q47dy8iq>7JEQO|HBRWJM;YJq
zI#iBK`*QNRxRa+{n#q*=gaIa?7{<?4_Y)~bGU9)FHFUu>vPkxi5lRJR3jrvRorH8b
z!+rg7c{TTb5s93?`bL-4UDmoqpQ&qqb#JB&016!Z^!gx^P7bzu)15FSiDXd3la4Z=
z`;h!xa%4PJ4-o_ZYAR%WuX3dK)yJd+FC0zec@M9NOb=OrHQ`&=&oZJVTPSWB1abZE
z=eiwT+(BqpakW>CZrrgE@DJA}jiu7czxL8$rF?C8Hm#)E298axoZI9qoI2VRwYE=)
z9TgK@*eel(ZyhZEsokppQd5oE3;R;Pq@~k)Aq~kn))p4^5W=0D8ZTYSVK48_dLBXh
zd90n4nA*+m{ll~BRP747aXuB=EBQ(iEN?2;^LDRvh^&^B+5QfEiH)FBMo}qVU3{rr
z`YJv+f!HKJF1D<WX`*Nhx>uu(p4&Q3@AZ|hlWXSbYb|S)W8zAwy0nMa&b|_%p)-MT
z@kMPmUx}1WImrzIBh6^&%eApI%knF-e6BFiggDQ-7<Vo534(`|CZ>X0?X0oW6)ATV
zY5rXW;}euq(=)5iU$;1o`n^>=`a0ddW<JCAjdezC)|ww`sNfTLB7-z)GF7Bc-=G#s
zmv?x4_Rt*<U+YsbHh}|rBwTXfpD$M3%#UL)hK*ayC!g)#t5+wrFpaTs^R9h6^ys{$
zoINKkh#OWl$!`AHTZK3^YQnASp>*#$x0TfB04y`1DnYTXYocR)*#F?X%0kp<?W;g}
ze|2fyOV__dboC!wQR@sy=yK|fkqoT#dWY+A@-&Ja1I>s=$L}1wQmCAkcM>D+*sH3c
zd6_YZUFc^qZ<unRzxw6sycThaPyODqXM`bP;f2{_4<{Z}f-hHz8*4?o9ca~JeAF-}
zJ{l-U=9>XEA4PZ^JWk20PJPJ-C4(B8?w@3@xb5BlOgO{Guqu{Qs;?D0bw1RiV<n+y
zj?1;NTv+^#tKhp;jQ5^4oa($=u#5a@A7!5at=d$Cym8!vWoVHkpc>qK>a;Q=NV<E^
z_}wC7u)PvO=yKxr-;b-{)pZ8uVg;WqDLk5+&?1IM+NnARWAoM*q<(_eEk>b9n(nC2
zwz9%M&`~$hNn+J932ik6WQEHA=$Avj{+_?{jfIBY+s^GF7QcMX1Lf~72`d`GTij=H
z17%?&{9;!G<11WHLa?;6w<jHjzOPkwDx1x=0Lh8?F!lGEDoWuXS(h2ft*6XFVSk-~
zIe=&1*tl6y+GqT!R`%_Qz>=N*zC|}`%0Z+@H7t=Q<defg4}^~Zj>Y9R|NL4G1=%~&
zu8{8j@Y}ELJ`OhAkwEbEfkg2uM9*OrYLS#CHImOiIizR7q{YVems38fjw<mF3N3mZ
zgFLF=TJ16r4d=}8nw#2PyYV8p7(Tqts%j{Zmcn?BlUbzpd=%69hs5_<I%7~3E**1~
z)i<Ci*05yVt29Tl9d@2q5#p>RV$u&HPz@TKM?xM0TTm=X9MN0^s%Iw72y;T5?1<>K
zd^6f^3s>$xVt0&~HVt@uh_jSK#20rAkS=hKZD({_Tl#lj#qD3OjI+nH`VO^RjWI;E
zZQ{El+DFxl1VeI=xfJrrd8oMD^v4=$@?VKK4wxx}J;4cDtoMl3c-t`7R%bbR`Cx2N
z%_}HsYvt|T(#FK*eDvhHHuhXmb+(u@S07LN4o?@O`}=852<|INU?AxOLOyIA#IJJ&
zMXxs*PxR*!HdA=}i3^<^OQ%7F3Cr3|!ysc0Nr-VHZ$|T{ZA0_sIxx3gmnGl6HXN_g
zb41E*5!A?Ql(gW*hh9YrFg%fP56Q}lNF?Y4FP+C{@itg8$g7vf^tvyBwx}XYDi=Y<
zBmh}FmN3mv3Zj9~p}N8fQC{Ogy?ipI`j<NsnrMrzZH895nK;IL038o1Bc`(Fl$|Oa
zxsRc~B~U|JD{o*mOT2on2bEcqM(tlPO4mEUW3bF;px{o8!$$AjX_XVLble}9BloQj
z`z3`+6bgiOSH-OhWXwp%94oVNn03yM;~?#C)bh!sp+5j_Emrxd^M?!$1#g#<l<0&j
zS^x>a&oIcaEme$oDkq74Y4mTd6oiB+@io_{VsT7t=V>+;3s4nm98rJy&Zt9WCqrG)
z>e23Zn!Q}95%f*X&x5P^de^pp>I`iXqF;j8@WFu}!7~r#In~k;Yl+GoS;qPz$L2a`
zh1mYyr5<Qh9!zDwzhQtr12(WPJi9`v&|q!zVGOy%<;X~v)uJgLHL2f|#@S_Y=aN4k
zAM$Q*$pLWYl64*JvX05s&T#SZX9!`r@{js@*a~qCRFAP|O;G*Tkae|{Z<pPS_@Y9W
zqdcZvzG#A_Y94p6c=I6EW4QNu<)Zn`Dlw4t^rEp;tCfyk5C0Qad`lmM`_{8^+B7V<
zSvs*<#TndL-cbb#04g2c3-K3v(9`Qt$xyy)`l03zk6^@k^GuOs=2cJcs>ekiU*92_
zH~@WzYhJF0RK(BSF3#-vy<_k4Dz2R33#{fp%Lh3oS57|ds{#Ra5uFRBnekf9JdP|+
zeC)5ol;Djv8Kfzt?_1HG+qo2ohRV@b0l?Y2@g|r0$`-ZfU$TVO4Bu@vHsSxa<w@=_
zXKSGkxDmzsTL$Xl0)|k&`yqtrd2`@eK=#k2uYRUPFA7-(oXKl7?P5R}pD3r^qksU{
z=VgAl@6GYhMiHrGrG37{T^nJyl>WF_Aj00Fk8o&JGlK|(4<J^uym}9?8~kvr0~7VF
z1<lW%$$#jSe)|l2b36cKsiT?k;y)_&9ix3WDaYd63U?33_~)1ju+TTp6=#aYP_W}v
zah`zyEDx1WgNqwCjqHBATS1{*0Ac*BOH7x!p%RvHcoT*YI^e-iXblH$FLo1ZGKcj`
z(`Qv6MwW7xz;PyCjsPx8w;+Pwpdh5=RY|elkUu$&?fb6T@!Pz3N`{0QQiWz;fsb12
zsAN6slR$zXB8+=VCta@3-W?mc%olV$@B3v!pwb7mw)}4zXp=1b7)1z(LTqgGu`LKR
zqqxsiyA?F3f|6zbMt9lEonaZr#vUKyKXM^3%o<>dsI>l?i`X;Oi~?0ml==tG>($GE
z;?3)$a}9uC0`|qHlcK}zCZRG!`Np}Q;dpmcx9v0Ri%>IkvVt5v*B(@WD01q!i&FVx
zbSAr4b94KTBqd+7Lr0^u@LW)I)`#q{aG}Y-$#nHWw{8ApA*JASb>fp^%z71tjGCW?
zd(XyY=>|CeN;!GYcBb__=fUz$bhKI-?R5Tgu|C**u_)3|f%l|$dWQ`~(dOku=wnRB
zjN+hdY0p|Uxw5AGv*Q<ox_+Op*9OM$T)1Xc!OwA~$54&*r3|i<ld|-*CR~SFbh(nS
zD+uTl&k4~4*?I6TumfQ*x)lqIbRLc!=mE~5pZEBi0LC8g*Hy!n(V)#!o5$EuSlb3(
zat1EH=uXL!cP(*|nK}x)ymNn=b~!D|Q0n=1%OlP0Ns}cQ>R+WhUf<fahC`-R>=NiO
zOhM9>>5a3D4-~J6dw2Wi=Bru${140Bko+!`uSzK|rt-QPFgQrNGIO#P7#~H!(^y)%
zJ#Kz`eq%hzCT{YSWrbNkc`6%(kysS`XgbK6^DT8YheR=VoWyq&4&K@f;<P5I#eVGz
z905|Jr&u@Zk0D+qG|a%5lk#6X;Se#ZHj=Ffd}6w!RMG%DV1`O?ccLHVUF0^XCEfn(
zaT<B}`*Qi9+Z_c<O^F{2wk{9PVr0f6=P$P_dJhElBpr*yye3w3cht4ZD!1-7J3!yq
zNeBeMjq0fB6jn>fQ(V{d?rax7RzUnU7|s;>&KA+Hc_aIm94~|KP{mC*mKCAilU#wS
zhNE!;@$e%z36Kvg`pr`Sm)pcXNjbP>n1iF)P_F1_A;27)IH40JbX3HRtSeQW<XgDo
zAF#km-#iO>6!`h9Klk)X5^iy+4^vHToOv<zzmOE^GvW`Bp0<}~)*6Yl%=Yp^LbQ)^
zbbrK2=6LBaU1ImTT92lP5+C$Ac>+5aUchII;mJ~_H;wgZ8V^RQhe!6KgV`-2`DRhB
zjJIb`E|bYVKu+3TY6ior*tY+Mf;#6~Yd1-A1*6xuL>{czmCNeg*;VsR6`tl2v>Jzg
zum45r<EE%lTJ*hl1Us0kCs8|2U|jfO=(O8nImD8Xf``HFn4u87qMR%+7I7}zndu6=
zSB$0Iy9T=X=U7|$AitT5`ch_=44iI6Nza_77p!0h%-}!7@$V(=L0tQT$i@yo!JmOj
zs}FnTNPF++dSu}7p>y=JkB<R9+v4^9qa|ja#sum)-}-aL%1qEYGzPdv)N?~~iu2vn
zPyoH9>F{57TQXH^Cl4L&ulU_Q#pE{$I+-1%BAQYaJmD?fv0MIS<?^0-cyBL^H9M_#
z=&|VF6cU;}dz8_VCq<{<y27Cnz*4}3j^kXDDa!5CnT;aU$dVln$xs7yZR%_3)aqrY
z{>fG4g+t2O(G@5e?T28DBtsUZi_)rJqHsy@rfmO_vh0Y^dV-j<F6y!L^dLuEh3`SS
zT@W0u#z!2l5bYF<4B~-UZ4JwY1r@Mn*C>1U5I+v^{(){G>c*2}Lh8o{9_{l*!GR5N
z&I#7Ix4A59&C-_?MGjC5Y^4#^`6}7gl9;b_E1l%W^bJhl=Z?!HUX=j@*Qc!iv6+UQ
zcAdo3^8d|Q-Tb#fju*nj(xsyt_s2Bi6O->;4V0AKhA6DW+xi89oCvPPSXf)BNhm|2
zq8by;2CQ7w>DB#%asr!isUIX+`dR*=<7u4B2}CeY12buer(~HfSSBC!&;G#YukGA>
z6hDC?{}|c|ov5s7wX$zDAI-6H#$36Pv9%#mv})eVwr_nOD}}5=Hd%j-q?7M_upn{L
z#q=4t@U~I2qlu}S6`Vm&cS{?<N_U-u3Ruf$Ut?E;&nve^<z52rRerR1_NHIL24%U<
zMR;;1D-tJDnFuyoEeudT9N^S&gEJay<WNZ3$6JYk1@?Nf+G^q3i|>dQbD&iETK!ua
zB}Liln!CZ!hst`p4L7)$Zq98|(qV-vyTXVA;o6HQtm(YEuJv5NYt712=Wa^*3at#6
zVxYBPrW(9&6i5F4q@QqZ>kilp*CiRsZ~0$&&Av_o+Ev7+pt3F$89gd%e}*8#<7nux
z{3>Whl1GQ6-^Yx|YF!3W>R84x$FM)(G|RX}@v8x;h=oR4(!Ybi0<2BJU6N)z&DwH_
z`_|Q%*A!XjxM3f^KH&61cBGeS3r&tKJ7=Cls07%J>abgx_Ecm?2uB0r;hFAM;9VFT
z1y);)AP1&N2`2owct}~`vm!lSN^@(NK2|rxnk0QkD;J+It>3%M2J|Lk8ccG;GSd^l
zRK<Jn8KsDSc4M-uLN$F$xM;BIIU<HQ71_rE(|!nuMMsYaj_~HLV>|gyryE`-yN$FD
zo*lI8S_lwMtysZ)M0~(}C-nt*D?THM1K<jIh$C2rW*QoOg-2uvG=xivug)hR3Ns-K
zPCUuq8kS<4J<KpL5e@NJA@oq%WbCsCQ?F-lxV3%p0XJ{>k|f0ILwb{V%@t{bO~|xH
zl9N~}y<Bleqdy<}58r9tju35XntXa|B@5JkV`OfGYc*~7hW+<UrsUA8|2+~@S8i9c
zv!G@Yt9tr}^6WWojU9I-u^N~fWtO+R5`cyrM5kBRB_xkzMhQB_&@UQ}Z0k4J`$0IY
zyw%i7pVA4l{Dy$(mpge_w!1#5j;to};2+-|lqP2?^q&u;c32FyZRNq$<6*WrSh_4*
zJRdfb;>oGy|GnYD?x})yNY;IxO{sVDV#QvhVHjOHx+)elP0_|%|DhKmB}7dvnkVSV
zaSc1R%2`EJM3-I+d8K!DH^R5Y!BL@Kl*M#Uv-C#Z2E?YT$Js_JZy-PICro-j7#jBi
zf)wgQc+5F$HB#Q|NJOiG+r_msX!|PQV#eCY*jb>iyICRpJFV*=A`*9nHij(NL<eky
z!JV#^fMg==dG%oKZlYk9A5XTKD8*990>3_^!E(NaB%EoJ?FxQy6R=+H*of2x3xLiV
zIIQ*tt~fZiq%<K4gX9#)-ySO9FU<Q#6~LPP54lh+l-*oSV3Jc+eU#)m{x|4;-nR9d
z?Q&%UPG`UC8RZU7ick|nuLP((Rl%`4K6upVpydtvnrFx++4cxzQo3x#bV1h1DS2Ow
zKo4T2w~$a|&v2eJsjjCd>^(5AqPHMsn7~uFfgR+l{7uAmVE*?sNhuVftf24-GLWjP
zmnP;tP%QDNc1G9{<NqWG6=c)b$0}9c?8z2%u|b3vD4LQn4IRBdUT{zQn?!+qO0=Zv
z3LRZZ9Bk3WzIwP^*SeUtOEc?wm)C&efqUgNAbhZReR8W(IS)0YIKCLb8@&#px~PyZ
zB==_M^XItLYha#XDmyJ0`)%m-<1|lKmpSy*Wnr`VjE`Y{YpF2u*RDgxn!$6bbJDpw
zxSW_#F+QeW2l2W9jvD36o~@%u{yFqI+JcQmm&a~ath&2#v?^kTewZ>Z?~`l5vkrTo
z4H4Dj1FjgV!b{&uHnU=<`?4#Y{IQU}3(}E)k$!_VL1nrU9ab3Y7kE#;H1A!Mj$+*2
z^BhCBv>LJJHAZ&tcX#jtRvu(0KWJ%_Loe&?6KkRQo^mSvCvD58Xd!7ai4=hX2!-<7
zIqWO)fnw+>-+)5=sP?PL(<`kK4)`I>TZC4H?&`^YBr&wS5?5!(u0|860&fS^qTv{F
zd|}(<d{$6az6j;!s=|Lq095*7+j=gwSUl=Eq;%hf^YyY}l(b@g2UxR!E#rQ;zLk7O
z*`tp~Bi}4?BFy4=v2$l7lXacEo8-|a_l~WULFjPo;376pd1YjaVfctG4w}#zDs&_3
z@<t`{o57%(tMu|<^OH|zVhnb2QSZ{R5fy?t8hM1O6jl2mH+g%#5usH-S}|0lo3|LN
zNKXAz*yVl?-pmMFh`AY8(yoUp9s(gBhxz|e_uf%Wea+u!sESerDN>{a0@9msK#I~^
zKtOu$9Z95j3xY^VLX}XYNbg-l5T%3?q)S%_pnxJpkRsfJ-{*Pn^Zwqu*1hi^zq{^T
zthh|h*?VTs%$_rI_THaS%gRPD4o~=I$(#%p4O6M*jAJ*$`1?J3->igPsk4poqEO$@
z1*cT-lv?mWu4<B$PM>;4@wWzq=>+-(*T7Gn3hDd{o|Rns#M;k@2(WL_k-DnWov1I#
z-WGTXyE0nb5l_Qq$l(`qCMf+|F^-A^h8^y&3^V+$Zhbj_#L&W5^GW4~K#`5?Dg@B~
z6<Pedy+@bV7k}6AlaOCs(U_{P_>yid{(Q&=9`{-ix=u|wp{BK*;3L37BUKdV=O=R%
zN6wyzRArL-4!)z5RtP%wq4|ZS_!L`(rM3o!7t;`9^Y()WG1lcsXef}bT&HKg$Q_RD
zXuy+&Io^X0&)w_%&<gXifQ!}`$96}x-F71J?7qTerK2<$b=(+*h@ecVO!Z{BNt_aX
z$*x@%eR**143!POIZ-NytP|ffBq?-}UL|h5W1RQ<V~$ha{Xt9o?hPkN1W!i1ix|Fz
zmEuOm0>`>hjKJ<ql|=d7286{O5Q<kbW$fAxwCG#bDulo0#@hY9oBhFTpSW(7hG3s`
zy3~^M&t_d@Xx!*$5g-^H4@6HNoBkD<d=&0i@0DoZ51xpY)(DB3BuhA-$LMG7zdqd$
ze&^j=_wvAWF6HSmJ=;k0pM2t!j*$-DpVNQDo9cZTr`mhNpzofM&SqL@PWhF_IcG#}
zZ}!M(YI;KdQKZ|9g8Q`>cORHr!2kT_Eco-hB@Gn05ztX|unNKK`O)9iBi~u5=U=;c
zfOyqrU~i_<lu<sIckM^zok0jSYri?kpwLx^$(jU0kTJ3`s4x`pQ;jX%F|_;Cmy@6G
ze#e+tEgzDxNk^rG&y-&kF6O9<qkF#k@zFQ2hb+eL=6GLMSl=I2Qy>sY66{tQIIc4U
z3x}>yUJAaFoUZlYVqfEpbHYAE3s>Y{o>_6x{g~OiB~*UZfvHBr6tBrOySeh5VdpeI
z--PSUdXmBA8jILh0Mepm#VNm-^1|N-D-tVF<g#*9uyS>ELpl;T#XT=Q3DJvTDMeTe
z(}XBLunk>`TH~)WwW%tfrp{MdAt<fi9w35ckBJDj2RRLTB$f8Qtaxwl4G7;{3i1#>
zY)&>r?y`gemHn+&iRDpOq(xv_5@OQN5i<?gvd<^`@-68F2D<8gulRd#P_0{5F^Hq6
zgq^0Tr{1b^&X+#kG+cRL+CRV=HC02`9sNBkO5zu@=(877DT$BhJ%1(Zb&)zgzNNZn
z&{oE6$mN-q{3vB?8%pT&oECI7=);UN<$o)UO}Y&zY{xQ<)yySq8h&P~rS3~*@ilNP
z!lLdZmS6+8ii6)C)v=Wr)x}TqULFk=h0$NWUV!@h)azyazIfARqA|02>VzwpvBL&q
z?+{)y*)j)v?RIjyaJVbe7_n}&>l(}P^;mQzOlHJfukAKoiG%zIZyCM1*)=A-Mg~6k
zUZ=e`(`mLmZpX&21YN(cnlObXT&&?dN;BFdnlyd+hSYmE%Bq%>ukkfPgp`j$>#aSH
z2p~juMWYPsyuL$~Okaa4JnT*`3HH4OWx?t6yjh{7IBcL)zf6ztC00nI21>}DoD8N(
zE?MmNvoP#Hna{S`sWPs#Bc8qQeC^?djqSSH$u;uOp6@F-Rh(EsBS3_vrdX<F)3W|T
zZ3M|RV|Dk!9=kYzV_spCk*bhy5)XX1k_yw<FC5?5kY?fQNgHzrn~FP<pLkx7^fhO)
zIP1x}q^O1T-LWc7ieRAx)i$*UYLflKL`cy!=b@K9*|*8ea>we@Ce$|%5Ed0G2^*Rc
zmp{P`nZ_gO^4$O(NwG5xuQ}=ina)ej*MjRQwf$F8%2Sxh9k4?5MF^9U0ja!&8q|>T
z`iT36!~-4opH(*GpPR*f!LXs-!!CwRG2?=2I(D4>qIrEx*o&hH_okP^#r~CUsptN}
zjz)_QLV-{;r=C)MiPh@t3JS}8()7Ob*2qOKNeR4oilmg2{7P6rUsja!kbEMMe;F{J
z%t~V?eJ+NE{Dglrk8LrUl^L|~db<Plx3o4{hDWSWygxBrF=lplEM|B*OTBD7)*`aP
z%tW~G`ql$kVe*ly8j*DKf&8sOd2a)g;TPZ)?Ukku*$Q03t{IJN9>N3ahTV%i2KY=k
zw+9KeLFUn&ElNSx)bIg;03Ia{%Ngqoioi<Rt38hBc*98wYrrVVd6Y+|OJMEket+Dx
z;8Jo%=9_hwKHac3YQ#zl3zH#IFWId#H)Fr3Oj3!P(N|+G9U2<+tq#+S&))NiX~No1
zKD!4TlpW0^+`94P<W7oo1@;~obe4_|4eQ=kCv!TjCYpU*DBz5$!lG2Fh!LBqlCp&T
zFhbGgT0?=gHDO^y3pxK%8YV7B%IW7Li}Q#yk&D81s3hcoX_tBCO`PW;vqcOG?+T@2
z$C?mo_u%;S2S@%>>J_NW>xO|x<B=NI9wR?-7GkxVKYq{O74N13<W{^6F!WA%Yx&(M
z;(j1VOSI?VWZg_`E9wD37<LmLxt08O*Y{>kseNdE6?Thc_p-!y3;10cO;$Rj9X~<H
z?hL(O7&gw*FOWt8yIfA^!P9SfqlxL$4c9}W;abW<lWYCpe*DEc8-X>s&owD8o$og$
z`3<nJ)$Kzw9!Gz+lRJ(p(RVJmGg-lv%kx_H{(fK)HZA#m#npPm>y8I-^Gghd#8Ff^
zL)#vTv~}5tXU&j`D>)8dFMXRP-z=%H=ARl#l|=OY>MVOT^*sfYmi8a0mA-^QF?xYV
zm8d2Qe9VyV{5}D52Q#4ear4TEvgg3=U_tb1ho$tgpduK^sv7~1T4z+q*HS1JQXh>K
z=9E+V02B3Td+B_w(5<cXj))&r4VhD#bW_DC_4U1B&W@iNu^yErmT;!ILuGZr5ib6v
zd2L{7x+C4l&!CxZTRMt;-F=F?gbekXKMyNEzzM8hYDDQCXf6zV8)bSA>oUMEE35q?
z{guRjYakk@#E2{E4HI?ENX`xsOQh9%qo+Er|3tUYL$}*5$rub^^2I#6E3hIAu8{3a
z)d_=?(nICGrv6s*dEO5WU;9O0><~4xY7UN{ivi}fFu-z?pdXEicO@=iB-`%%&|MmJ
z{;VbM_j|~^YP<iVqBxG9lH}*%D^lmOk`g;rztU&d(#x33m>C4(N;ogk`RR8Ssouss
zBu{V2TT*9e{=nPAyPhB;lD-hMa{cF->{w}T@p7qx+U4)R6@^ofa=HmHuHan|Po@ue
z=<;K}sp)NP2HU$e{}w4)$bi9sdr$u2T5-VRAY^xg>!5zX*ssohO2mMB`>lqXM~ugn
zrus07?ag18B_o_?-XsMm9v*#2#0JaQAg*cEiHZQ(kq_Ym!JiG1OJ+`&<(jf%Z<;dB
zqKvGBmZU(RHSL!_9Ye=;gH=N4%jJMHw#b244Y;G5kxr<Fl;%pD`v=*(<#fQGN;TDY
zEb6xs8m65++=_GEp3(VG%shHDa#tzc$i^v@Q&`tQH_S_~W4K-c!Fc#(n8a=CO;Znz
z&3rR=@teM#x2F;a4tG(W8#I=ZdMm0@Tn<CK+{MqZdPS<e4!t<Ilou6iTuB43mn=&1
zkh}VZHXU#^o~>0+)v}kavboMn(tOo_`B|eFTY8BBSUnn%7zMbJUn&}tRyjRtWDlJo
zg*cHmV<mP+mPLa%zaKyC@J{}{n@v-V1y28H1aEe?$w$tl3q&rbNdmA;!j#mu9=hwq
zB!crrwHshWp(<DWGq19vqc?;FS|&4Bh95N6fSc#NbV!rJE=<v`a6a%9bNJ46*#e&u
zzqu+@x7q!)*&=>$9A#9QTw*XN%1@W*R;b$8x87GeX;KTTsPBt@*<T`u;Ikzt-(Gyc
zL3wX)f|Qa(7)JU2iUlVGLJLT`PYz8|wTE34p77bUuB*vYg+y7BTjD<u`gppZ3xw+{
zc4Nu!)yf=bL`*%yzHV6=Z+=nIfq0th)~OlI<7!d&`7O2pE{gE@+?R<`TkXrdIhRf!
z<IFWd0fDIEbu*AYHKFg);%*~SeC8Fs*X9Ns$skS)8gTzLe#z{o5&{Ur)M94er3I$p
zzA5TaU%=mQpmZxN*}>%6LL4?=AX86W0ihj_MfJs{(Z}%KX(od}xYv}fCYUv@231L`
zBn<rcW&D^7@|-piJBR3}OI|Z>hxzg*5vX*(y$i~_SUn@`YUJx1$Y%jJxj$n%ALjk>
zrvYKGHLbqBxmgm40!^j?o2*7`eB)dgXK`9zN)?!x4xjN?y>1OZON{9zhNv@MH_NJ9
zO?ja^b-Ck}X4)+QSEGZQbBI^ycWIbl0mQ1+08WAdV6lMr-Qap5@V6g)W_0$2bf2P@
z(KY|_OEk+I#*l)?JjG?J1g(je_3OX+&pxQA9T`WE(!4!e?)#=f7<`vD=82*ghJD>0
z(ISF?^Y0eMv*)|7Z`G@oC3UMOjGQ{M!o{Vs!A92pWn)Df{FWSjamLo+{cY+40xARF
zUY^Sb1T3E?hCon~diDE0S@VcD8B1wNi;Xl-KN369`vCO0RY14&qq!`8qtXw?J|Zx~
zHCO{y+zn8;3XC8g=A<^*o?Em>fA#vU26Xiu9q01z<sad>JP^(SS=5;DVK#j33rc{x
z^IrG8Rd|^jK%cj=XG~BXpa?ngbA&^b3FBe`M{_(=P1Pbi8Y3fZf;9dnE0-aC@Y+82
z{^?|r{Pofn<7A2OGEr_Pqh_pZ`WTsTaWOZ{0{#)1`kEBftgB)C<$%q$20x%X0a%a-
zJ;0e0L+V(Yu_CWROs=v`d^4)eE)9FSs4|cV5iWlEx2a@$+9gO-gYKWJfal}D#_!+E
zEEmm$7PkprLrz2x;9VkiUC~IPv_kaII&h>Eb1(Qg@-OEGe>wNLTL6J*>P&n<2N5=Z
zF8N*AxN-cNX<UH@UGQLc^xo73{FeuMQFC3w#aX2}NDU1mbL38%7Ip7EGIQ)e=EZ+A
zVWhQ73Q-PueMh>sZ(8kMm&$6jtn~8Ui(qHM#Y4vVjZ(K4PzdJ*@7=?&h>rDdNSogS
z5puRWk0NIk1eH#H=&j7l=DxrF`-~;7kD}l*Xa)i=xrrdjIXChXx1|nBOyT4-sunU<
z{MAJ*Gdq`AEqI8)p$RbpXTQPXrxLI0iX>BazRVcUB!otu*{`xi_Z?n8t#)Big@06m
zLLj5#YEm%|Q#N?l3ST|T35^fqLZl2?B13vgukMmT2yd@%&ikI9x#`*7n4+rUw{MlI
zZF*&Hg1_15IApYhHL(ytI;@ThrQRzvbQ!&l6Dr9g-q0EjDgWNUi*}YTcn>tUDf-&c
zL^%Fjkz1xL73x#N%wmiNhV$5Ukt>DV-H*3KVBBrGwO{2Clhtp}J}yYySKV<~Jcf%X
zDn-(%Hh78w-eaN{GO;3AtNmJPJ2+uly6Id%trsPk90W*XexM(|cz24Qri>(u3*h1_
z4xd@!#MF~o0XV7{Hex!I-YXxTwOWnkFR0*L2G!6>*wce;E}H!Lc$|Ay1kgulND_p#
zF6GQY=up;|ZCHRI(f~ty#SG8c7E|6}E}mZ^vj)vfstp6;fKHwzwFIE^E)`?-5qeTZ
zdsj=+m5rlj^pR3|wI_3>9t#&P{;;}nNI^_cS#>2E=bjq?_Af+a3P-4DD?W;qG6^gy
z&`(X+)BKHuWKkbzNk<@8dwvqkoQ*GetZH<r(D#Dvp8RLK_wE3Md(_0Y@`n63dxi?D
zWHPTyE-v!N7d#8|1QjIxIq4vDsl5Nwp!J<Esuq^3CK;;NX}`Jg%H^kRwr}Ex)Yq2>
zz679d(3kw)`H9E9)fdOfOMI00vp(vf5L<#32fmMqfHLO&+^^;HHQAXK^=e;W<-o^R
zE*YDqTx{3~DQi=OF3|Z>YT2a9{RwG(eZUzTn}IF*DE#kMZs}^YsGNuH&auS+h<ogh
zIZ9?p)_eQv$&c+MeP!_6|K24&R~E4UZV&oXZu)N?y5UT|NIqaBFQPGEWh8Zyo|D@F
z5kiTnr@X&X=rEruK#NL~(C+>)%cc$2El)^LuYG)5m7t2Sp|;C%4a0UB0U5`X9Md9I
zSVBzAgGaFq<4sBe9Bal14t+s-mq}4>PfqS5SA>ry@(zRg3N%7%t~IH{@|P`TgRP9*
zKI9!r{me-LA(0bwy_UmD6W@h8E`0f$C#$*b1l;$1yqK>i`i=*=0(Dg_q*pC0couy1
zN)qx0FjCn=WHy0Yh30dkcV9?d;2G%i(ZUNHKI@SCRR6H(*JNA~FI&9&g3}8ZJ9mI=
z#|yEV<i+bcqM?|>RvDV}{Ik_iDbvu~tXmSGS@hfursN*ITWz@HpUAsbv96kvxpLMR
z>()!avi?-SPxQTf_aqnB>N?M~zOMLN`UWo8ZX}Xx;hDDx%O^_~QWvTUDdLvVivz9v
zeHdKpN?$8M=&0}dFtHiy`#rg+Q-6+3K^Wj@Jab!B&f^}*Um_~^E_3HQpm$T)wA54%
zT}3Jhx>~oeDXZnVgd=m+4<#v=`66T{9%A3K@Wi+G5>*u0QS@I<zJNgvpehLsNW$m)
zL!@}psQ4bse>%S_r@Z&*{FEs-q(Cf;TJJUK)fo@Z65}!k!5hY;Maq6e45USu?tP#l
zGd7qbvsz+YzS3MGb86)(_n@NX9?48su<iqF;r3G7_RwvV13^Ie=T>rE2JV{x`@>jG
zMVxs*vo>)6Lav%x{qFm+!6_^9<?nYZ97!nOJ!0{-f06U#W&8xp?lp%T8Ahg1^tBK;
zpA)@cYx+rH!*V+YcvRN(O3XW8oNgS|i0EIE?UYh2WDdUcCkr9sfsg~yp3tU<5EU;H
z{0xp_?TLcIcI!0&m$<M%1b_E&6(>tNHtwEr!V}H^`TEXKr{SMVhCw(W&`BPM3?W?6
z?^&PYDc)x%`Y1PH{F29WB}Gl5=WeW~_-MZX_s%ilL5vE7{5!#G_QOGeHBuT~SUy-o
zzY8=i?NdR#x3vn7_QOA5-}-5O;a!_UeJ2ba_qE-CU@xhOf76*-@3R1$Yxj&Iedz_@
z79g5lL@w4X|H3B_>zb6^M#{;fkm&mKmnV_ktjr#!E^`kB#GlFN65)eJ&2s{{1s5&~
zydhxa>$0&*3Wx%KLd+&gUxp|nuS0r)hTUASSm;FrTt)R6$^@(teG82gJg!nI%Eod`
zhKjU&dgxhk_Lyfb%$4`4eXN(}3njGJ{0U`b?_OLJY>4&x>xU5?O3!BY$mU#l3n7>>
zxxYkqC^q`4>oxX&-fe)^(ujuqA(y%J&EzksH}*D9Vi^i-E3s|#aW1RFr+v01`p|V`
zZe%7Fb(x8;0r9Lae*i=!bgX+b1&906d|Uq_B}XIiykDZO{nBD)1v&iYljZP)Pq&SL
z$z4jH7S1Ob*vWHwo%JmbZU3x<TjjfxYJeD(paPx*Vjt0>9QMjnciC#thxJN3OzKe&
ze(Z{3>d|vg-*|Nd`Bj61HV2RQo(K@iy!`R)NyM=MOBdegVIV$>>WN>VIXTe`4v5n~
zsCEG*#B*|gr%G(yPlwIuSO%UXM*-Lxu#@pm4=3`Dvtmh+#cg#8mjnh0RvA1U-(nIg
zQjHiF?t-v+D2vajl7wBq5zH7X<<YJ_yb{a3vxpv>MY1l&QK_UEAkKBXa^3u$9B48}
z@A_Ni!#AJs$N&cuR&QqD62_)$7t_Jrnw+Ahl^a}{CFQ#)k>&PZS;+-eBFc!e6^jF}
zs8<H&6PZ;m5rT7#u5aY$ZyWvkTFaH5nmM`aix~@-Y9h~w({s?fr_#Y%lF;3Bp*Mg{
zo-||26{6};s}<=L8^$(Eox<j(*(?>(2x=i0R(z8Au>akiLrGQ8MxrjvQPK&hFIHl|
zxWbvKAJ^l)X?K0Rsr@?YG!Vj_E7~PSx9Xqw)atSn`X#^Nq(+ye9U>4V)J~(aV!s-a
z`%vI@CLxbic}+ICDR=j{YX(A-Tl~dPcpqar;NJ30YViVu6gLo<Ir0!Et49(6N++Kn
z#HWpSRu(KKc*b1k<nI33mRir+;`g2BK4%t|{yzSBDn#J{k<Hn&KT-6)H(R2!=g4ot
zOd&sLFGSs&)QE{PGapiKS>7yByYHn#`h#!PjcoE)lkA9A8ZlmYIUC}F%-z+7*R`h*
z#3cciEtWl0H3EZNP9#FCB&L70+x_+FLY<o6IlgFo92&{;v0vbDrppFY&WjGj+rSre
zIO*7N%Rd?9Rp_^zH@*^Msbd7A#1RZFcmrWWLI^}K_!@JKBI3eV?hOt_Ay>*DZ*@5n
zcgxqGJrhTBbao4O(z1Ldb@{&b22(#9KSr3=MdT1uKQUiS!(gW)2Qs<Y;i{n_O71HR
zbiA(OcM&2a767j;YYP>(;q%pMERV7tH_FWQ#NpR6<xC>8=ntZFE3ndEgdOYj1t_jS
zdGWTY{>x`i4X!jPA~<5%=FQ<M6#IOXsU<7TOTM<MQ6Xu0=$IRJlM#4%V7`+JLBF3R
zO6ly>O@o%a0z3Bcxwsx#aTTQO_YdWB)V=W`TY!dhBXc6DE!qDAnH2gBi5K^*B+mT8
z^Fr}pjszmnBlmD<1Mw@odq5}Sn(2kyX6}NwEV63vImJAc*0Qie(+zX_)bD;i);$Gx
z3q2U2JxxA$joNsJe1a7@IPpQi*V3#Ggbis-v&#eXKP^o7vB~(IY8(Kfl-iQACN7`Y
zw-iS&v=X3MV6`KNwPGH!yj#Q4pB7~{)Co1X9^fEKRnS@S2kVO<N1&0kG1qo35$_Sn
zqO!2}VH(tEp{f5Xa@0hjnlbD}MCI3?MiZflG~B;lz~P`LsTmHmZL|r)yslH9Z%Txb
zGrQi{P5YjRi14Rp<^_nxkxbkoXh)FRi44yCJDHf;f?C@l{k3}BG*uWI#}CpQa(&u=
zt@xyCA^VpUUCdoJrCzO9$rd9fm9<S~h57u9S8KTQ8LZ4K{BD;|@-A^$ajVBMk9>X7
zQxa#oaCooC(N<ZaP^Ho3<1P;rBjX;jZS`k&@PP>}?^>j?B^(4qKiOdP1xU)p#BiNt
zpW2m<{jOw~4ZK`7({CmSL1Ll5FLjEH-a5@(=e-aeK1Vh8ZSCDf4&;KoU2=mL=aA3m
z@E9up&ha^aX&jCfm%}1K(~!K(1bYZa<Z#mb@ZRL<jUq6`czXkC1Yg*ia;@0~jRrHX
z$G?RyoP7w~6rNO3nM^aX0^v44th8gF_$zO0p0y#Ab{L}g?POZ`Tsz5qFxC)`7ld2h
zJUeBj)FbeoSJD2J%8sIIjYOz)dI1Q}ASEP#p**Uzb;AQKIcO%I*+Mf|1Em$@U312-
z+f3bAU}xhh-V-X0HO|By9}=ZzuLTGNG@M!9il_?yo=RBo%`~Dt<123Fy6#BND1ZEi
zOUlU`Oa?)y$iXxu7;i%o^xe>ytR|wMS>-7L!TB=<K)mO)syO=bsd@J;+|Npes5mZ>
zhDPeI(-I_<nCRo1KO<mbO+Q1XalzkB<fq$+l;>q}FG*N+aB(vBb*z<70??V^O*tzJ
zs4@(aJayhr2mjPfcPk>a)tRlYO#LJqD>=cprblIN3HK`o;W1qYTFv+tGe6(?Lo;fX
za~dYrS%?3uE^>uBB5A~p#>J}28Z!n~xT!f--!_8EK;AFxkCP|QSYhGBGoS<Zg|4li
z+`q&w{N;otPcYI~A|Y({m?ty)ZU$JoWj9zdihigJbu}5A`gAh~d#$bsy95H+bGr<f
zhqkvR5DS)w0D@zop!NdM^~|#^EbN0=pEZ%kpKu9wLi<lPZx-wDy3XH(RO^?kpX6dW
zV*YC<-&lKqGiY%WmU{^R+9bZPee4=4ai!(Dxn{^I`UMIbD%HY&O<u-m;nBUd`5qq*
zaNq3v_yAi<;L-|VJh^d&ArM3;^=raLn2G}$xeB3$!o{)7jaqOCxA%#>F;Ab;>&65T
zLs&FW?^?$Yi2ZSS1XvkB;Bl^s^wKe5PlA9zRFwr*h{B3vDujz$KtTPA3l_Ft1L26t
zuFD&8s;X~zUAc!eG{r{@u25|&Lay|Q%LcywkkH7?j*EEW@!<^j&AIP~%%n%(45I%E
z7~5TG7CN#XLhrt5yvohJfJSbRzV|BxsMvh&#Io;7O9WcL@sr7pqtN2^fWa6KCo*Vp
zI#)WTo`>+s6b}C%I;|M6_`cWrf_E*Yk%XJuCbD*)`QY=7-R4U|Cl)-b<51VSZ}-1#
z)o9_<quroav`W>(&V}oR-d!5ziWK^1FhaLMMYY)f8eQ{9#TEp6LcoqNyPnT;G5QXJ
zl<JHVv4Hhb&&(eo^YUxvL^$){e~;stJO~L42~XRa!ZvE-#XZ@$xpUA+n_({2p8+1|
zdesA~BPF1mrB3UA@QX6fs^B`ZOhDJaucHRjE+d4=mYbyrg$C5MRNX`!Au=^x+53{q
zb3nic6p_S+L*IIF^#mWV>^*rcHg;SN@O7#8lRc`No=uJv2M$~V@BEKxf7jPMo8rh!
z4~;a9Uq}XrfHq3&4FtYE-J%7Z1_(2sJK$W|_9v71&w3Kf^<-gPWFUm(97DKT05~H7
zEgVFczYrRDfH8(V^*af6ATbvu=kqau@k`fT4XUFgsY_-M>`$32Z%=Q(z1284Q`cx=
zUhnf}*Op34MO|3GJa{fx(Q7Uzer>&NaXOI)+d@EOOjKzcDLw<A+27%~Z;;pLG(K=W
z{1aL)(#y<25$OZs!lB1jQ?S%|g2kZgB6M(baXVOQ!v{=ksBZw3W2r`Ix}a_b&Sz4<
zD?)?U%1R!3OwJl`#)0Cg+XVk{aQ)xB{Y(y+fA<6W!3Hpkh*57+H-($afoc>w*X1X3
zX`p`Yp%M^u$hcmyESN%-sXR6jgw5QzzqN6}fZzhjyz)T=cR0mkyLa$XD5WfM(^)2J
zPbkWdoa^uCRKd+%&tfIbbntg0RI+Kd2X8<m`Cho>CLYVdV;1sjFLKhy6VuN@%(ZZb
z1E}IZao2I??9=UA>|$TU@sKG=TI+LFai^X8aV`%Ga)LDp_2XCOp>$`@iC@suKb0;d
zi7N-hBPSca;x0cXU~6_@QVa}<`lQT9JdBq5$g&YkvRBO2q>aaOMY?UKZrzPgN*>T(
zst{D1Oj3G&D?DV~f)`*)nETD}+u+s%w!W+X;=AG;mfdKaZ;h7z3uU0yGTmngjG9&5
zfhI6N-ZPr$1&PtE*BeZ@>7_#}E&P}HjKE}=*w0lf%D(KFPXfR!u}}tiGh^_jtPH;c
z<RTs-b81icwnnp`T?%#eCm4lB^(?omaUvP>|LXa}B~aF~YI=y*@jTvct}VO_zMu4&
znvm?*sDF;#NjM2xt8=b#@K6Nk68r~wT{rkw4~g#%s`6}8e8{Jj=Uoe`x^TaCgZ!yq
z!goa)`4R8mJpMc#%oVZ<xmbQDK~SQtm@M?E9*xA;RX%VR2Z$$6CkUF#5<{aY{2*b$
z3Ty9l7{M-+pPCi@RA^#CbJWt$iwi*U5Av>g&?CdN>_U81!X#s~ZChC!iV3P3d)g1Z
z;N;KK;`jFbXq9s?{yfz?CSGQmct(AOm*e@U@M!5Z7u25qLhh5V{Q{tytp(xLx#4#)
zSe*9>LV7?fUx>1nh9dxCpBS)B`_ZxzRrZ8%a?LkKyf&O1`Qt(uf2A)633=UC4=1QO
zMH-q${aO~XoL^hFlSLq&huh6OqEJ|VbnnaYB~-KR`ZSeP&W#^2|AkcZ*PVAx%--9&
zZmcy4L&`k4!I?yf#vUI&U=tg!CPnbUL>f3GYGda4JqL+zGwSAzDLLHn0{xxW1(SLf
zYcFRSZuK`5D2VAq!dzoh*GVQ;bMsOWW0DnOKA7gsLMHt7gB1o)ZD!}vEH2T4i#jf_
za_mEZI#Mhl=lHvq_a~40g?1lR<7$2R<Gr+o{uoXWo)3;b4iyR?U~jc!Bl#@;gIF<X
zo7kb8FxU6;{S<EYzH1+6O?upN0^9BRKG%rnC$%rIwko>99KqO^e4K8~djFaZlmAQy
z^jJEN0-#7{Ga5ws`Bm;48Hzo0Eys(mtcB5PWw^Lqspv#2exJg?JRv5Jl9T4!Z|glp
z74#n8W$PIpdF)`|UaUz))o9lue36Mrkj&6RB(4iWL`5!ryL+7WN<y81d4{r7Ps>Mr
zuc!G5l}`?wB_;O`h54)-=CKo)GtwKtqQ<@WU~9V8(z|qV9h#*+(Dm!BXzY%0@fm>N
zirgU;gn{5W0pKTLNfr>4tJhm_${hOkBFJI-anRCqMv4*^m2C7hi7Z+VzoRE#1Vyor
zyDoiuJv)f<tVMJ&;3z)dI&a*igTK~cjNy40(v^*cEtDkg-q{o|1MER?>H8cY9FF?-
zB0*PI(SmjyDUL?&AL=yqkJbE7?ctwVnQ?JOV5iJDiq1f=4JQv!`9$=B7Cf{9D+VIP
z{$vMH)cQ2xo?ixTqsKS|Vb4KqoG=hf^`ZoN%;Gm9nb6SNPvHobnpgxcJbX@QV0_C4
z1-}0`cUiBCuYE^wus}*w&r7tohpcvNQ8nV9!E6dcy3p9jMC?e5kdI8}^<Ze-4Z9T(
z;;s|?<lX(myEdN8k>It`l!!s<DdnWfDfv7IL(UXBN!D>+R70F9)D`$?d8BWnLMQ#<
zo&6xTYZUlVE+~PnJ(izb+hp&5+a@Zpu88{e%XDOa#Ih2;-IgM3YvSLmZTl0wn^T};
zTeODPwddg)_(6a>X_Tq94+;e(oi5zMu|yFn6cJhSAyQIfX90mhHTA1~iHR*`!nRI7
zZXX-UnH-HwP!t!OqOJ#EuoYMkGd2SRE0T)UM%Wm+-YN~&^0X+tSKO`Tyl;4|RjFXv
zFhI7tG+GcQI_aa{APU=hAX6q>E6#4(E^^ODCO%BFT|{Bd1uc0IXC6*4XHR61&h(Rf
zZH{!(P@*b1z16~?Re80+baVm5w9aw(8Tp*xbS|b7mn|Ec)Y$i8iAthM8P7IjiatKb
ztSg@%`1;DPpWeyPUfuLr)yHlXHDOBb66=&kx3Q{7X$>AM+{lsBap+*V=Sfx0M0%iI
zGA(E}g6f#v{%7?~K2X5>>HaK9^`@nkikiF-6$bg+&bv22r>(+7{IWJ{mo1j%>$f(h
zcuPb7jZhSJ422y*>1TA9#D7ndn;g^UISZ=UPn@6ZdT4uTFw%N|Nj>NLRV{ecxs0G7
z@NeI<5yBkbaPx^KBJIKK5AELXW>yINryZAi*T5Slwl59*M>@@^D$AO8M%QGxfnq37
zz>`~((=aQtk-?s%hi_`k8hY(d?8zVC<h!AP!dd}fT{KN_7A0RTz?t7QW04Fn_OKK_
zsXCraz1a<0i#Tqm*b3-wT4(b45#D^q!q>NLKIAwqIRc@sp=b~H_(V2z$_Fsb;_eeR
zqS+I!1EyaC+5&2M?w?DT6U6paHW}2%OHU?K!?f`)a%OzKnf);6PIJq9<Mg@pPHWfO
zcOBGw6G?MSuXur{Jm5WVL}tRj*;lcd(JJlapY}9w_Va*>2VDR~G8R(j5P|qn(|&B3
z#tAgf&W-I5M-GhYZP=V@;;C3YzZAdvI5l?owhYQqkzGY#O9X{>dD86HyZt%rHcd!0
zSqFRO1<zIlSe7yQ95s?>73SLL+~gGakZ@vFvt}_a8TwP(9)UM%h@OJ3jq|SECy?TR
z<ypA3V8)_NW#_}+sDEkVId~{9`RaSZ^BdOpxCQ$`t#;6!rV`j9ovOcTy_grezoJSB
zBF`;?N^??!0*>EjrGJ`(8@|vO)LE1u><WmKb^_KnRW^Byi`zaa#>HK%!v0l)9N0>V
z2k713TH(Y2ir}wcUDf2jzqWl&yd*HQb<<;11XzfBQ5Itf1z!n@ovV(8z1doGMC3ph
z6R<FUWz<MvEs+8wt@>ADlfqS(N^$b0<Ave(Z6EJd1R!(3lLgA>>XEb%ffcU4@pN0d
zc`IP+$@tuTOH!~6Hz@6CzXf4|%L2jTYc}pXKCg}D>Wu0~TWN^FOLsxnIcWBKZl2Z<
zF)pp3XXB2ar#D25%wGku5o5u5?9><9t3%RJ8+i7HGU_(uscq|%2Vy09(ARcfKR)JT
z7h>0h3B^XzJ@`nAiv7@K+N`tIEP4PIPYz5U5bj5Q$Q%C{pZ>n<&aIx`h2RJyz=av^
zH9DDrC+m|x3dRKey;OBC|7xds&n1ke&j5koGQ-p=Y;1y;4JOtWw)vOx#&@oXPY2ig
zl)Jae%U|nlQh}eNP!mWc%%`c=H+0F1a?CsEji*{A2SC$Df)xj{HtOTY^72pL9~hN-
z{@HhAFm8~;QeOgr@~LqYKQ+9By%|qie~d;%GeBXWSg-K_U<K7e5IPP=@sSyq8AX6n
z;V3lWQ9YV?QYg&*yi_Z(lsbZs6VL+LL(}Jpqa}%OTG!5tqCyf-Sn%Fa5L1;v>h_$V
ztBgQ{SandCvLn=$7>$woe@NoDeFwE01YDaZ|K;mV)vR*a03IdT&yt6A;)ms7A+9Ns
z$^gy)t_%?2`CX$B=$F_i1Tpq59<l;e0f<&eWec!G{aP^y;(m(z>HtX!hH`~8h5qw%
z1UWy9rJsTy*2%I$X#kf<q5tpg7}(#4A_vD|Z*Vp_d0)PP^Zy@g|1W}OKK558fk2|f
zN+LZyTi7lBzbWnih3)^vK5CJo)q1La484)XU2_!VdZT?QFS&YjE4J?~6}-EdN7h(w
zSL!D1!8DmbW6;3OX#2J63HMjq&1{XKY$*K$Kdr*Gdbc{wt<}NR0jD||F~EJ%4siuU
z^^SGx7XuE6_+Z~zL7Zm?@HagcOI#~b*UjRSHr_`k_SI$ZCJ?_`)u_h=arEh`k)SNs
z9?j`f!e(hRTju|Hb#)7kqyTU!y)W;kKH_osB&8&EDs%-b7RacMSE)ogy*S5_{@Z_M
zhYr3qvI^x7IVjvR-zD*My3<8ykk}EotL;L$zW~9bV{uxlQqD1$`qf}ZkHRmlj+Y$P
zzJ_f&5^0cxVYuM)8=6anQ}zi?HCQZ_Z@^&R^3!jM*rfTGnXaWoO80SvFMWH>Aj?w0
zAW3e_JfUKc$beN1)wCWLaH-vh7ZN|^eAt=A7xlf3`^UTC#K*bQ5gjE_Brxbw!PaY<
z2hPJ}o6p+rSFu7qu1(^lcE8LD2mTgPq+zbh#Ljy{z%yTy-f>M<7JBMGXdG>bypb7t
zCoivMU34+zsV-R%hpg9X_L#F}f+a=%&B6pB>~bFDa|@3%o3@YEFWMs!osZOz^|F?9
z8u_~eY^M)Dp4iNul$bh9B;Hal<&C2Xe_^Q!kL0dTCrV+cF<|v$j2j|^F%HKDetF$W
z5XU<?IWjL_`Z;93(_d}k;E$TRcne98SQ^v0ij$4!F}en-j4ufkq7%q*cdpr9Q9Zep
z828JYp_etTv1gKm$f=Rc3A}#df{v3uAYZi(toDBG(2|4}tqsFs-#u&T-EJ&dOnV!*
zNt`7S#^^qY9=rUpBEFgG-n<STAfQnR_Rj@7#h>EJF%ChA#W~^#mfs<UWfxHC&+BC+
zs6fLS`5+EzQTHaMo~l7LG_D@5bR{1e*F`9DD>s;k;FJ3TJMzZRkHHwQ#)t?yI@>c$
zM5u0qhS^tU*KP3*MNixz`Rz~7f84HM!DXI^KNYWnAT!>)pnF7br<jVp&2zdv^Su7I
z5bQ50!oNsKR=oS&2w<_e#TqDL8VBr)xQ@%M@B_;}?kYoKfUqP+vg+M$e`Cap_ms{K
zJ=Xa6Z*ADXXE@7UAwHNhXqhAb?rB&2A$*Si-DU~lBq!|nCb&Wy^vkR}RpcYu7;!T@
z0%4*iUz&m4FT#Keuo$hkj8D;H%zW~|tmH4VMt)J+Ahb5Vkx=Q!h{5!n%i`bN);}pZ
zEv&FT{+=A3UaycGr+8$cn2gyZVClSzBGc=K2)ebQ>5`eyq=^S0kIbe~;<`sno-(wX
zh3Y?=S3aBT!moj-@T!<z0*K;1t&5k??obWQ+pT7srxX<F#<gApcYBP?@LT&|-lh-r
z<Ezrje;vl_$3a6pm%d5(KtHlz_fj&5(VY<^nUu%JjlZ+kJwwke5z5l)<b(vRNerAs
zx;Gb&N}FoTP|pd2a{twk@OrnbnSw8z&|Y7!)TFV83Vl(5d0h#kE#(;ZF5z)Dg+3Ld
zm$_fX_s9gohVsLeT5t<LQIUAS=)R9eYS>qC{gBW$b*3LO_e&xHNjfs`USd`l6i}|z
zF+sRwccTRUb8zr&A}hmAR~RL^)YW{=AP6TDAMBDIq>^9BS5Yk`j;U*y(v&*D`2pg{
z53x>yheXQ+F?RT~)Ma4nh&Zv^{!&Jz6vFAxy(q!?S@0D1lsz*W%i`+iqz$6@kN5`s
zSS+&VeE$A-D2r|<_x=U`I}{m=&_|D`AM$s1!|$ji8#1*8-oMIrGBz}8CYt~wMM$~l
z45z$of4L|`BQ*^|Z$rFnFh&bgM0HlhU!ZNTBW_;jt}ng?&?ri4ZK**OhO0P+2R%*m
zbsS%H_XB_K!N&9u_^x=L2h1XSzn2T|GoHORR>T<DsP|;t=gqXl1ZN$PFY;Kv{}kA{
z`2InN=@Zn2!(Nl=bT(IUi3l)q^SX|sfZWW*mG<}{cv|f~F~uuxruS%|b)H7IWD60l
zTktqiEWPz-Jm)I%=g{*|Ew<LDBl4Y<yPVWj^-*A~qf7<HICi_)Sf3;9F%P!{+RV4p
zICB?fb#)||GlS7%Nu~r2s1^fY<FSDOh%}ZM)QA<BQG-MEc%_RF8Ok<>&RoCCvvQ~p
zXaA3D>KJ-?IELPh47?HKQCbDj1RkrN1j8|qo`EFP^>Q-S4GgKli=X5F$mJ11Aj-_+
z9gWg+)WZLrQBC51p>^^9g_!C8{we;yALaE#uzv)@Fiy(##hGh9PZ|IJ6hr=pJdm5y
z;@YHlY1KwbKXw+{|IXawP|YZ@CvE<GNiL&re7wkWd647T89wet_17IRnQ{0hSmyyq
z{#*Z#h&Zp*SiBL52$5j<C&Hh$y^cF<%*_}(3f9MOa{-yTf3pDC?5->tIoCvwxi&B?
zi{1?hReHDgw}sraXJ}daM$vQZuEj<`Rr8h(3viz0fk{ZeN&1l^0sG0!16c4PK%@Wm
zBpAy69BX8`VMl<12``vnh-RUXA&#XIh$|pkHJFv8&-Vcx6Mb5gIEs$V`62ClcbTKU
z&?q)>!sw1}Q*hlLS7u|je|!b%E~`mOS4W=U^$LRk>(rt-ZwDgPZ62F4B*?p1GFBq-
z5jmD-1%crk7jP6}PBzSl#P-a#80>>5=TtkB0ZA|~uK)LUWy{6Hsb-{cw%2gZJdKY;
za74?C0*}MB(Kb^e6)+zI`N~e1W((HQc(=G2HiVK<q89_Z0mo^PIoJQ@^`^|3EnJQ0
zKqQeyk|yBcMmSD8er|Z4-){BKq5JAygmU=bEX!Nh#igT^yEs4jf>G-mMyoZA)qB}$
zr`gyia{!KH2uJ%*{`JM}M)ivu+I+4;6>Gc0@9b#wL(=uA)G(6!fxGV=lta<<%pgZ!
z%bMFQy3M5kM?dNGhtYgZp5B6BY0aR9R3y=NEx6r9u<lbqj>~?QTtA=d8(bI*<&JE9
zX~BI&&w;LnXDR%ARG$|Wa09Q<T{spbaGkQw(%bUo14NJ4TWU9b1o1B|cwQDCybeT*
zK==@Di*@SmztDaLTT{y+Z-4>OM_|n;Jyt*w*P8i_=;dvZ^?8C&4)(Z}>0=IobRC0L
zmZ`i~ff~iJj2#O79_tm?#t*gcxd=CdC_aCiVwO{|#os*fw@udPr-XB`+X76)7GN__
zR9|0hYvXeWY)(b6^A|y26C!9actjgEr~5CqdIW*U+Cb3`k;fo}eWYlx=atJg&3qsJ
zOxE0|iPIsa3yk9=r8{UNyKMg`f%c}u^7V|NK?k`Fk;1r|Os&~MEj8X5+UAa~<L7<?
z+WydIi>`O8fvwtLA-v2mP&|h~3~23HgW>bhE*Qn`MIJSy!87<iK-^off{kh<2N+<U
z>M!cy#VB=N$M@Qo7=<rthG$`q6R)qO)iX^uV{JZ#>qaoEvpk(s0E#)3BA(&2B4xmG
zEa$<R1n-(7WWtxV$Pm>Z<d$bN@4J_UwM@CA|0-8jJi!t#WQi{$dRy6(XGqZ?LX;#y
z-5QF38++(&skk6UInQ$lkUYN;B8}+7kta<QZ1h?b{8!lW0c<U(=q7R?(|N$0#9-1^
zkT_KesHX-(5&g-TSgG?Q2CS3A-H*oRI6@v<Tf9HGmAxKOut;Fb9zL+)nM1gILd1}F
z8gCurN)?8OWeGoU?mLGgy6~g6bFRU0r|<&todv_JH6>=gIuT7bl;4L}s7y_uHWcfV
za$0@z8V~9$s$bizQPDLmG8PmXS~VMo95%6^NCCb-_#x?+ip2x^yS`JS>oY^`e@$P7
z>@!_LLaYQB5Mwv_8!PX>u*AP@#;!Q^sR=t7;~s=HQ%KEs1}Z(j`&zQ*{f45`aQNxo
ziX@=6f7>9~Lg<eWkj?(2mR%*sh+nAhx^+XSAm^~C)@7#6r(;}_fcwvXB~l>MtmL<`
zKAms)K5U??Sj>|4RFvD4tv#*DI6SAKgRXux{ry12u!v|ts=d%YUK%23v%i<nxIEtU
zzF!W|z^lmgA3AWzmW+o$V5tZb0r4zLd_*%g=aB_Osv#&jQCmrjd~z54yw;ub-gMM*
zAUmR*oO!w7`!+@LT>k}yFh}p+<S9Y_h(fF=fp92$sF5P~DENBJAffQxdr%#qP27zk
zgmLm3UU>FRVpHu8*`Vj2Hx#?;-ghl{C>Sfhi|^;I&^BAvv2C=x{B!N+t5e!nSg5N{
zcUET2RHzQT$b_m{v`q=SwN>{dxS0ni#+rKI@-62O`)w;i#P?XnNwO2m+--A0^cNvX
zIWfE$8gjZWrV>H<ZB#(tI{_KT&(1qayfSNg)Ip;vhycpq_VDZuzcv0MAAhK;oD(06
zisRpA3w6C(^0oK+O*^`ueP=ZXo4?z|^`kU7#v6}+zVtS_9#kUlbL&FR>{^l?htA@g
z8}6dy4>>T%NAHM=s6n8P&OKi3gfj`?upZ0OjHTjCX1VvhHT&etkm}sw#v=zp_@68H
z+o%sw6|KMOnUS}&@O9NZoQs}_S1NEcQmMut{i1m(o<JBzjDG&t6eu|(?0NR3HdGPO
zpznLRvtF(IX|CbfB6e>+?K7>-9%Ili|AdO4o2_IG{@W@uZ!pL0yu~D5_dJ@gutCzA
z+odYD&^6=Jo*6GoJR}G;C^#u`gQwtGR~opA;-5JsFB{R!bxcm>-A=}>h5wkN@qRBE
z%dTQ0N>f3no#2WXwT%X+1n462Pcf3DOCF6!Vz>`=k6rU6`Tt}F*&7w{9V}8s|Gok2
zmK1p#!R6JJ$hsELNOKEzgbndVi{a(gm6LEw{Ed$_Dmj_DQ1$O^zUZA#pDWk$=KTAJ
zKI$r!mJD1E_#_JHNGF`!assPi*Hj5MqCr1G_AiAT>Ek7Y93kTbQNG_ci2h1`@QhL$
zxnDbT#cw&lmTS_4bQ}XcEF!QSs3GvjKgn|BgmA4_JFgV6`2h9afTJ0?i8<rU_5s>R
z(ZG|avPva_FW2WD<iHW}_6Gp;hNH0Z_Ctp272%4PI&DBhkoq@WaBC@<$s1Z6d4)IH
z%u60#E(zB9sgCZo=+N2EJC(772j}qVHW7>mh~|ve$*XXeUGVxCBzRowj}M^2d3J|c
zcA@ufJJKbD9Vb8%nvfZBz%{I@XQ>~>X^Fs#LGS|pEae2U_?2LZbNf4=BIge12#)7A
zn9RRqG)coY&hfQZ{MFIuHB5o4JL0Gyx=`kt*l<IYX2OHH`_VEvi4KEdY2tt=^}D6J
z9;Z%hCLa3_f3c=Q*O-eY7i@ZkT_Y{=FbUdiO=?kR<5QiC7&!j6fFAy0l)#x?pMT!E
zXCRr)h8M1sTHo65Ua5DdS5e}og1l8u@wp5BZ2yl7F4-c4Q8hGNg!E#hVXGO124QPs
ztP&P_5PhWwPL*?F%5%Xgb>zW3mZM%c!2O}ctCSbSLIw__plz4Z|9*=N_XXNZ&F+ro
z6Mw>oxh7}1e<rn1Iz4%dofL#rUZ<QcCo<9?fkSt}qHqP{j}HSHilfW5DYNWyXSsI6
z0e7)~dF63%$6n`GDHe{dPX=CE0dGP|g`4-)5HS#L^FT!+-BV|h)=^WLnP3QwO13!A
zJ71g7>P1qaLci(0a$5_|usJpjI*Z1Y3dq)j{1g(7Pj?f@b$Lcj{P&n}=47kCm9EuI
zVJ{IDZhZu&?S-W2B`ayhD5R=Dqx(16W2<vN77adDLWURGD{j)o_%TP?KFTD0^x_ee
zRHZ^|ywL6L$2$yCpWW#%lIh=8d%%^S_nG3h>isW02{thzZy-zyQR$z%e-G(;N$T-U
zJ|7*F8K$}O4&I?x`15D3F8Jv0WW%wnX0;|D2)oUqV{d(*Ij&z-iVRy|0k_=LKks*H
ze8Dtq_{+g2Vq;vQ0*kD`5*aO&4f6?}E$N+}`Y)ZSY9eBOqK!t|qklj9vnz4RhrycN
z`X{mDhOQ|wDgf_)ewgVn2bysvV14o=nlA1mTio=T46~gKm^MmGr-BQCT!Jth#RpE{
zDPEAJxv8sKs`Cc8*x+RGlkjGDiCVCDMR4tNcYH4t=HRS@%LFzylMRZc1nCj$lujzA
z-k<X`U5R2z&U+IMLR_oBrd4A_(Db!+jf6RQK|t_Dc0g6txYPSTj`yY?UIGc7uYqK4
zYQs(N%vD&hQ&$PVa%Cxnuo(-p78&mUa613zZTb4#WU8yg%ZZ=V%-P@qTrK#~<ZDA?
zQZcFE^1}zhrmew5v5cCNc~bm{@ZRgtdqy<!<?w^nGC9v+)IC@J1?l%I56h~o7>1>-
z*BYwak&dkGn_=iN{Lj4O><irW!^Abe!MqsfWQwQ*|A>*XY1kxVX=j^iHN*?{@Yg$)
z*YJtxYoS9MX_{Jr@u;zR75{KreSGPp=i8V*q<31cQlYuw+1?#Jms~QtZ7|D5<Ep?F
zPH>HLl~w>4(DRll4Aaj(+9FiAHQiGxFG|dcqq$JmVEb#{j8Eih_1CwPnTxn-lz7rF
zd-JfXO2A@Z;aeSKr0nI+C2*Yzim}Gw`f>dfgItCnT>Gop=b6?a?m~b@63q?wFK^C%
zP7gR{ezgUcIHnS`Wc(vJfh^&zAG7mZuG{8tPO#+Ze~v5#`-=Qe^%w@JKeJ)D>(5QE
zVuHuKOWh4wID6S?4u#QboU6M|5HJV-q5)NHd~b;U$w_E#E*krE%weyAJqKGZf9-9E
zO^#c=P&DC<)X~fBdT<FR?LX<zsagPfr<ta{NJP_^gHY%Ip~#L89_;Y_#m!tTPGD*l
zv3pJnELj%i)c?w>tDgmpHQ@qk=O5xS$5zp?g*2fulN@xYC37V}-UlHk^Fj8dwLmb^
z?KCAfPJ!jyqQY9+pOHUpSZNA<{LgyG2QUZwmmMXudK%sQr>n!k5p5j_Yhi4<=ZuK<
zYO=@V1J{=!>e^)*tlZV}k)>t3+D;`td^w{su1@vN_gp!@;8-FUX)4GZ!2B&)cY0i*
zmOOWb(h{JFm2RnJoD~J%IlU_L-VG7)xym@>;WV_~dKw7K>P;ND*N2uhvHz0TP7563
z1FCu6V<b<6d@BFdQ2_562p;~cxd?>M`@N<!if(-%o!#KV>Nr?w)+i!*_x3ludck8V
zq}C^w!Pwb5Ru8*{bAp>9C(L=F8=2S!w9DEXTm7SfL*5QphsIWzen<uaja8;v*MVDq
zm#F{x*eZNABnVjGrqb}P&cZG~TD_qG<vkwSyg%(y_!2y-(o<hna(bLcku|d93Kr8I
z1sBII7TfW{jNoYclH1uhfnN=c>&<;>`&W<3yZ5Eqk`mXOoxL|2z=JkB4ZqDHX&_Ll
zvMiY(kO|U_y8a7Rte@)ZKKXK82%-b-<lw|UOvUN~TJTPz0<`4ebxCn81hLNf^6r0L
zsBb^>X3sS92P;YoomWq~f5`+Pu>u8%l-Bj(AYd91=VKf$1w$ac1<s#q;9?;y{GB!&
z3;<!D!FqWggZ{ILKKP{voEeXeYS0(~&|w9g?_J>dPl{k2?YwQ!B)>3J5S;o!hH}6!
z3FkXWiX)+G-$pcl&2;$QjhwZu7F(&ft$_O$j{9CvO|TqxD`xDsN(LPw5x8;)vh_I<
z>e???K6Tpo>u@%Q)v4e7Q3!hM7`-(eewfzi4~5+UYb=5iRTjnp^|!9(M!*`GcH$IZ
zx$-W0*NyL*C&?-mnG`pS0&j#tc)9iOiSit&BZopLkw&)`==Usq_NQRYaLl7_w?pT^
zqX?7!oZ+j1(xy$#o0^2FK`M!rz0y;H*TO`jfcW&XKd$_(<xK19T*{>Ug&f)Dsh-|m
zs6y-t_4jhcS~p~%)O~JtQLlTwn^|EI1u1}Jww(Mc4=9WnT;4qAkO~Jl$7zIaR>?`O
zeyGTpx8HYEzp9e@OMJHYvdPy*<GrvMc=;6K#}-`pL1lU1$|3y`<Gs)yOKa0}HrCI!
zIC2_Kchb#nSH^7^rC*U*Yo3r&+!si5=>KN=2Nyi_Hc&R8a|3OgB2?=*c!iF=L7qKr
zU3B`D(TsEEM4Eg)S>l_fcTP}S=A787k;;?aVfHj*K5_Y9JlF4nMJN9y?L06+uxKgd
zkj!psG{jU^ht%#Tuum0w2Vjtz;U>n-{|8-f84z{z{ednmA)qKFh~U!QERA$4DJe*|
zEF}$MfRalIETNRr-K8k7#8T2NOGyeSjdwtw-~Zkh_suHbnK|donKN<Dd}fBZdMH)M
zZFAx^R=tQP!${o0o+j*iyyt)>+A}6$oHvm^%9B3v#3H>haO7o95&zPD;W>tmoy5Mf
z_;(Yg`%U$tw|=LH%Uan|`lK6`$#7?lWA<ip;V<W}V=}y>H8*douaK@9XgH$X43}Si
zd+bth56)zvqBTz?ad1ARL*40qy7v?6Vw1Y|`ZYy!eXrU?k?pwcsDN&KU5S-kSs9HL
z*VdS7Y#navp+~xh<8^P!W)+eAOvCtPsP=GPV36O+)JizOo#(v7C|H*5;YVCoZt92M
z{vM%HCXr}GElZqj(Dab2qlY)uWbJi>+r-R)2>la8rHfREt~dNP&cMM9YUj^dD|-e{
z#?3RNuT0(6gmLNz!yg~6jz|(nicV;vJ*ESn8$nZWU;3_N?|uF5V28D8v_^ZLh`dtN
z{4^rjy&AWz_i?}Po%ZI3Dk%2z3(OqA&Z%5Qr9{_?Zr^c_aM;Ftad)*Ngk|8y+J*kK
zRVE2m(_o06Bbm@l*~Be#k%lqUp(X-oWW%j~?>@juL&FV<&M^UB=Lh^q#nLv?k=c?#
zaVv3{Tk|wB2Lj{H>$^>X_nm)5ofS@QU;+*WVcP7lTz-I?yFeo9$ip+NX&+oSe2Z(2
zpKrnc+IPzfkp_#P+rx^ipl>|HUDa&=DZU|&ZQ@6|I-fI>kd6Zp2hUL#7elt-+097T
z7;KvKr6RpE6pejMsVpzhf$uX-$meNiH+6(Ws7r5<jr6nC0<toj&n5n?2oG;VvMfvj
ze$M~Zdg(lxM_{MRu~nLa%(g6A-syosOOR5&hzBOwsdoIu8%cii$Ar<Q4P(AVA;q4&
zuSwB?M3enKH37y4{&3fM3#fbL6@OUux-*BTf!$?eC>g)au(#oC67h{TSiy>ncacS$
zie#*?Bdw>L->z7yCmz&MMPsk%3?EVF6;-NfIfwyV<^?$STP)IQy{kr&`02EAp%~V_
z$u!p3=`Uq-e7JKrVuD&nkh1aB`@`W51J3cGZ6Rg@m*pQn`&yY<GnP$?+6jG*2J8x(
zJvDUEL?fBVr*8bSU!|YAz_U{5+F5q7Vle}^J6aXX%2PJfABW0vo@~#RH5~lvlmU$~
z_<xPTRLv4rG06P{_c2yo_t>qKV=*iYwGtD)a(^hK$yq%1+1;PG>_}o>`yuk>86O!{
zU3B-iOk`IPA$JSv9SM={1}T;*%Y!SQ%5y^$qm)8%&(%=u(%JvCJ%U;*zPo;m-$?_%
zvV<ubc-Yf8ZoNZ$eoq(u8c>;4swlZ8<{cNt#=0Vqi%H16I2mzan}Mh`my`1q6NqFg
zjY6)H>$0|n@(xuh+@SOvv+o_)X*|wi$5O7r4zpQf|7TYE@Z*<x;EL{*igY6m*i!I+
z4gOWUm+DYybmIk=kj?C8UcXcgGKB)bCyx2zr$fjw_1;dz2Y&wWF}9Q2r;o!NUZ{**
z;JHbFigNfB@lQR@e}+vE{B-?!vJ<hAhZ|P_I9k&Hh_XtUpUv?Ch2Z%S$DeT*!7G_!
zR<?{f#6bRUmxf*{-ys>wOD7=ST_0Wro;v5xKhyL)xMKwW*Z6wxdZu48;J#kIX<a^N
ziyuTT<zg<I;|EfW@PPzFAC%h9566~Zdx3agrtq!&7H|xEwdSTb)y}Q{qhR&<Z3*tV
zhs=0mn8B57Hc0!yx~siTH(TFhM-diB%7h$M#;_L^u>uBcxBAA7jE%aKe!e-w79#FD
zYuS^O`R+ZZl`hzV9@hHKdIFns%S)Y_=}KP2^<ce-Z}Y`}4U!fQbRP2#s2WI_{<WuL
zC9-|~M%8uGtSBhA!5DTV*5k{k!54fP`mLmyU7nm0IX|8m)h%fp4Q(vEaT!;(FeoK+
z%QiI}9-Q=$_?i6mpir))@O<UC&;1zyJb@YNMxLP;#nrJA@9WRO%!?wtPkn)7go5xm
z!1jDrL4&JsE&pazg5%$T5&p&Y@F4^tC{^l9)-U3aSSnhdFJsGWkr)zL`1QZo9!sbl
z1QE}78OJ69n+>tZ=a=xI|6djf9e1Y!ct?Dnk%JY8DDM4K*AaE0Da&f)9pJOfZA`WD
z;8@(~SnyyP3?;oDTq8h3UGOE#Qa((MHe%otkCGGX8^wk?NbL_1t(I-yt?$WAydbci
z8ZSvS`8%Eg%j{CZ54NHQVKti=zAWNP=|>{>#O<f_(Kro%tKaAAO5kF$EH6uSDR;q9
zkLo~;;w+x$r8=`xqXNh#MS4m@crKNq=Mh(!<>KZ`JAikoFh_GZW!0uc=KrY4X@%9y
zU<CeSc6e0n*7qpc{BPmP?*1&7uz;(pf43@x<D1A*y;HJ_|07<Roc$}yrWQuJ3XpeT
zF9Q~a#N{>_*Bbtdyn-(uQCDs71>+q56&)?q|13#r$yS!(QuJ;Z+H$9V_N?e0gaC^!
zQ#CHEean)9pgwj<R%e&@UCxI<EK@pqWCGRrn|RURw5_(%nYD)^!x}cjO(d&-jYsT_
zBpH!^mxC<DrLGo@1aUbx2Y%f-zEmZ3zg!Tm*XEMTf5}H(2U#U)0fhFKTP+rn6248e
zdew=QAv~7?;a4Ft<@DW-=m-98l7#TU1wS~>8EVnSUEso~2i#@}6uAXfu*@#iLt?*+
z?8(Y&J|(ulG&&1~M@N!`9I4tKQdxf$aBZ_>$?N{0$ri!>rD5^qj7?u5YDitOlx4US
zSU%NlMfHBaK!1_OFj&!ojhVMvdw1CE;7Ku9QI||29pa8C^gPllXF`<z3i&G`O-|Uy
z_r^d3=6SI9CVlQFwkz#BkuUC0zNe&&3mFf8W|2t#{((XnIWg~iZd~|_-v+Lq@ajx!
z-OmO4D_?w^T>#Pw{eUWS$~_vTO9X2{&^pFmnK5#PB4c~pSpeJ(dX|Bibql^!J$fxj
zmi<!HzSN}&?|~Q;8yU9ar8?|K11ycF;`@Fz5wQW7Hn3zfs+31^yuFSeRWARutZWi0
z-u!Bf^uZdrtc#Fy3upjh+v;y%i4Qi&1coPFH&RD*5SPX?oRn7`$XA%lcF8}lkLswF
zmShGUWyCG4dNB}Gxd`;VvY7wvy0^@ib>}iu$5}L-l4SCSlDRa2-d~yx!@b)UT^aCo
z)-32{p5;L6+j#9>8H-~fsh3rqtnK}2$EU|@j(>yYavXzn!i0NI?Y3Kj67d%S>^O$S
zB_f>bMb-!Vo1Y?x6>kWm6_^}N>pEWu(kf4%+@nx_A6#BtT=lH`J|nAiCh<IUNs6j<
zHej#Drr=m%E3E4enBkbf2pq?r_2i(RLB_-Fnl*N+NBy<Mp`xs*s;{eMd2-eekRe43
z%mrO<bEV-XH-n*_omUFSd+z+1K8D&c^YOUsJ@9$HtAj0jWP{gsPx_h4ql*%f7bWVJ
zYTb2lL!%<+lix?aQ%*%j#V#&{1+^wcZtMh55>pSY%0~}5GZvW(?L9GFmWKH5fn$>`
zV9F41zYwe3Nm%TFUc5qnOyv!R9oID>gA?|r+849TOg*|PZ>~^+zKfxClJy<q_fzJ@
zkcRJr**Xpd4$*~2>N3V-#ipvxo~uT2p&qt=jPoa6@iqTaKd|h-&qrzZV<%D2i+N=(
z{;7(&z(9;l$2VR?<sXYwsukbpLCKzxgMqm5KGCkh1$wiEDO`nP9dJ}p1<1vut0S#G
zB#8_uPb<QDi2l6`MUD91Pdm?2*d0=BOI<J*G!o{OGI0TiDn2DzS!hQ)e{cL1KvGy&
z;}GwR^CB_#gXV70NMn-gF<nHp|GeFZh);Zz|75x=UbHd%l?-ObqQLgnEY1C^WXoa_
zz092s+U-6!Dc8%;$vj6WqqmmJj6<)$5}ZXFG@O7)j7o<pk8mtY{hVw<xk&3#NR+1r
zYoA#eZ6yiB0qxiBw)`n^IsdC~|1<Ft0i)1uKC80t=Rd|`9b+|8t;=`ftF51=q?uM;
z!Kq(}iqj5dyL;|}JdbS;Qx#lxd$RmFEF!fY&$s{ay!56<|J6qAGcp(RFcpUXYh9?c
zhZpIuW{h8suP4toPlSMh3jNXsvzIlhCbX)Lf{@dESFqfI9wXWFjI)pBo|a%r80!z_
z{ARpSoffZ_evJP-zb{?06KcSiz}|o_HnMN%u{F=2>5devGV)NKl#E41>#pS64@OE|
z+~)ml@k_@Rle7L;na;q1#`^n3Yb@$8b3U0~;JDlO(3-S#+O}8odM(>lTY0-f;vI@8
ztw(ji%H+Qh?u)N50XTP70#HYFSE75kM&+H>%+c32`6uix`N-^(u^tN6i6$V=)t~ao
z!I=O<*j2`teZFQsVSdW~*`tC^HWz(><^@o^HPLa#6<AUWGi!~I7(kUDj1(ifWKk21
zJ!>^ulbTwkCIv$I;N;)<E>tv`KWj14cUwjqZNVpnI(lP<zDiT~L$EPkuo#mdcTtBM
z7e@YZm1DCZnaI2QYV@wgu4ycip{dX<I3AfP@J7m)ShM081ESIz7gZ05uK0*K^;qwE
zMLo`KzN@?|G8m8iWDyjP9C3IfMSQ)Ee!3^ckqu@e^nbz@3>3@c%$T8@A(7W%oO^|4
zXh%bqW$F)@t}8+5$l>|%>l<(ba9U~N=E`(C^|X73_}vJ&vJV_TOx4595dK@b{5RjG
z|IW?V%_t)sxLWZ4D08R@9^pj`YMtl$63RZlJND!kN3BMMDVA~~%FJNe9DBYlIl_Oz
z<o?u_NvEKQ_Tm@{<@7|-qI|YFiNwYUL6Bi_<-sH#L}{?O%-hJ$U*N>0YvGe8I)wUj
zn1lKB7VP9`Y8$>hSRN-4q=A0z4ZCcCzoSqcwer(uJV|C5M&-S011$X;P?};Kqg_~;
z#mnJqAMqJ*u8A|azjY#Bt$BfQKIMV+5#8sfk?%JasQX5^oBP6YS}?^7yRZYpLubd1
z=fqBfC9>;F|Lzm6oh*qALoXZ*M`baCTdbp7VV46LO~ggBb9Bc|J0nV?+<tySs0!GA
zTMfUXE?~}N<i5Ap<<*c<mtjp}HiWS{g~GyEM-j>f2=6?9ZZl(v=fhKC2YfF*-&#jt
z0(~@42vIZS(QnGz8c*=&SHdD1G->O)1<!+bVw%5(k*_I(UQKb~InZP(GmG=oIgH;i
zlIX~9+pEWLTGRNe!uM`Nmh_=y+>1xJMP8WMS&Zp7eOCWgR=Xb4w6gEEjdB8wb#@ng
z%RU>!{cg%j*u)FDwUsAEPq88#vb>&0KF6n!#tD*sYhUrYtfILg(rwhg(>Myl^LOYh
z-I#vlq~i{rn&0ju{&Sz|Bh}~LyN-|JlhDg$E;k4|o(!qg;2Th1CstB0+lMp|HqZ`c
z$ZA)Q(Bx9F^!cI}4uhmZ=K2nN_LdJ8Z;1gg)u|r0fp;$jr*&VvR8llPyx6=qCEq-J
z!jqGC^AS&Xo@4oA?+vcg(qDqG_Bu1g=2$`PCLIl}Tai9Ed5yjn@eO{WM?a26LMA}?
z%MdK2WWO|NcRlD{<~I^iSPoD#iEi1P$bCYRS9)Sah@4GuYU2X2wRsSNBgd~_8CpB?
zO6;>jcu1E{4jG`%uXI{8EXLKBV4XPf-vZG#5R`*FZ_i6;HEhVJsCR#bLCU+{NpBEg
zBnkUi8m+(F*+GnB3*vOR;8FFE*e>Z<KX&;6QVPe}WxQ)(Ap&q?<osJHLfy2_zCDV+
z4KBDr5rVZ>$cKnvhXVI8^0z}}A8{!zAZvh+g6^Njeb0XKNVp05gX@1EzbToBmr$Sj
z1VKj(Nd6UHom{j1cGP=J+iGJ%+L{VZz_fv5iVy{ppJCrPZw1^4PP>1uIRf5=xF8bV
z^o{%DGVQ6(ma5YfKH&ZY#C}7%)McDhP8#uL>V(%iUt$4SPr<UM?Yc?}nr-f}|BvsR
zTk28qRTl|Q0g;D9iE-Ilwbl=F{5VT{`5Z0?CtrN0oGKj3rbrs&(efIP6MRhrKz419
zfGk{5VcJ9S^3s9l*4wVe?;}5pk!A*eQehLS`JhOy$!?HF6ZY;)vf?#`I7KbRlsoSj
z6RX1CW#tYKCk%-4<VJm5e7pFgz9i;}w~W|9AVW;Y@VgwV$COa;tQZFOL+sauoUO;*
z{BMn1SPo>Uz;a4LRVzd-rI>ZkjR;5IpFc@MiVf8C9NbS)V6(3#kwTu1a~186n)(Xx
zSQb>k?}Amk3Z^_Yqz<2K_pVs36#$>L!J_XO?RFaKc>QzSh{2?4U=FCw_yxn9jvuB1
ze~O<#1!u}Jn9d6#G8fK{ZG#7D$9dt3iDPZXK(cWxQue|JDsX(%fvC2t{``l!bxLDd
zSOQ>oL;+8;sWi9cmKZA1_1!|i#TE--Zqfktip`VTcDxyEbRGNgdnj)8U|_`r@kq#s
z$Z=gpRf{yozSFKFf=q@8fNjD+MZ0eqsSl-a?Jo}9Lk#C%d<_Ck(Y@@4z>AL{a4b62
zfGcMJALbZT|Io^VL$%RlbzySoJ_4<D!IBr8-m8g1zrg%PXx=+XF?VGyw$UvGRN8^T
zoc=mJPRaSTres;0+*+ZJS-BuTw=r#|9`8=w`Ey;l4F8_9)%Yi=?bOE;+%-1S_2slS
za;mY=O4458&stpmvSjb$#S5~;Kg>P}Pau(-9y~Yyv%O*#P=8wqv=fBXhO7ZWS#wpM
z$f*>XSo7n;Ao;+l6$sxA!x(7d?9HoxtcCB<@m=&OkOU!^{euuXdfZ)Bdi;)Ec+5x?
zA5?V_M7cwV4|w!*_J5GQF+Vae(aG*7WVHYt`@-nwziiZ-4t;T)?tC*5Z#uM8{S51Z
zUgQPv)HR{C`tDq5@vOa?!*(jx_(i-N^eRY(UaLXQe)mho50b5%Yg=E5@#n$kI~V=(
z5E0nToVe9*Y-)RKrYhkfdbKSWaQX>Iwx#WM;;AOejeK`s(PU!IocMtE5(t0|Y8oP+
z{xvCQ=144K^+qiV)ayhL-vT@uc1f{L?-Z`2W98A`w9cia=BV8nH`b;C>UGiE+6uRu
z71RtfQmkIAbs~(%f^UUn|CdzS-1jmk(@k*C;fJ@p&YF#LDIz2uYz$m#s-HA3q|YlR
z*sCTpUuVce>MPzo;1~jm5M{4(WMWmNv5+=;46f=Ml`PlB{~yWb<LQc=Q43Vl_(Rp4
z{e|@kEHV6lQhu>;7(i5=R=MWSfm?I8g+Gc#w;vubs#r$1mHYBPoK?9W?z-P@6a}M*
zuy9eZ^13DvNf;+u%ur?@8XFqvdg76dD$ylK_mJ#gZ+-eSbMI(Y`UFp}#tmQfj|E)q
zbq0!EbuD(Y1;;=H5j5qs|B)ymkU}vv1jWvduc4)?7WFAva)&8-`eWsO4S~?QzuXWM
zd>#~pF|#YGZ92&=ud1rNcNYlmf;~}-7l1J<_G1!qJRLj@LUtE(yi96NZjW=YR3_#6
z0%jmtHU4lKn`5f<9vn%!cYky4Dk9JFpA_t#@PV*ugL|$!)|uOoH#0i1LO0`ebx7VK
z5eZuB>5|qze*V$&;_H>Ke4C?bkgCm+4~^GF2#q{_N<7{xMf9`!n>oZ4Y8{=poQ7gY
zVG^L4D^<E-y&YlX+S%<KdatD;n7%&yo6_ff#e2m|#8jN;`KnOEg3iJ`9h<}xHFqG&
zg^ZQwTt>G@KR>LcGV_ow68=2CJFIU%N-vgs$OmIiK_YTe5`s3c8#i;`c*T7skGto_
zdFr*he7h{TGgS9A1rQ48G<9S?=yt)YZ1Q_=wi&O#xSdX3@+Y7MzYlkMC{d>alVA;3
zL)aP~*Flh(H>2NCydQYA)chFl@T?Dzp#Mvv0r4x@_n`~c%+8-*?;6Ogqv^zQ#ejSl
z5^!=<Q-!FcR(8GUD<wthNP=(YWj@;l50oBIzZ|m_zQ;uUN$54fee!a`uq@5{ccJ6q
z_hj$iDpz47dY%4W<XM(tQPVV^AnXPs>Rh#JRz72e$6WfMIvo2+0Ixy!c;^c-P0`Vw
z@wqW9Pqj->BbWHW`NFGyRM4MC=Ds(<n1GK!LN<;-6lP$0ITLhHzHkh##n`W2;+{9j
z>a`imwPJE}duHE^8_q-oGx5Oi#RxjFgtW~pmzh%{Qdc|3x&(yQLcvf7nQFmwoDJ1_
zqhQskzU-jGe*?p=##xUkSGrR=(p|KFju3R^5s*`N-M4hV#1}s6m*E8BZ&|5;=XYaQ
zQ|@Lb0Cr?!;Kc<bjluEeh$B;;8K+ESd5Zdf`8G6561Kbm@B>K(s8s`M6f%_u9@T-+
zl~ft_f1*%iH$7|^lY^gk&;2VW3qq$KK<Y9Gr1aAEscNAJUx8gw4+MpUtr3iwl@3f3
z;A=>V&C>Ma?b%?&>q~#%RaRcNtjz01?&q!zSt{KgjXi`>Qf{7-DCcnZ#+E@*Pl3Kk
zipd$)Sz)?I9TfAt>Z^UflWt4*(P>-EcA!`m<_1b0tT7cY)|l)@PR_6}dU|t89>EH^
zP6_eJJ?Ki~RBg|>Bm89t)uTIBo!_yT^SgM#qM!ZZE)Nxyu~p9+K56-rl(?&@_#7a+
zSp7}Bl$3NHD2x~3Qnnv=&Ejn&CrmL2><D!E0S`yv%LM#<47FzKkA+$zd?L!#BB}*{
z9Db&z6-NRn-blaLPO?6m;buKMAD2-<KXEBucVn@`J0fps?DrN-L2_HCA#|ad|0i>+
z8!W1zqws8#6EPfEu%(nMC*YVm*!Fp2fh}s!YbFsbvlUM#tQoc;tXXQAnh2d@UC+Cv
zJ>VX2`n?lw%U)aJHX|ZNX=SnTE6M<IpgF8v(Jn-+hSsEFx>`pUQCCA|9?XX)w$jf{
zz~P8hrO4z0@necRmbfNk6{j+bX;R9jVHDpej6qBR_tWA3G;{3Iv!>am6D$&sw5l$f
zc8_OyvIq-_V{m_HOM_>Q2>7A!{o1@O+`0<CrjcLZa2BrzWr4o!AwXgg<VqyFB3yy}
zkYxgg8n3l{^tPZ1{!7cub^A41h)8_eCY92xc~*NOFAT>NTO21;mMaDMq|y|{j>rSS
zj`tVAj)bCVZaX(YN4L$ZpN3P_&>UGV$HcKu5W}K6{CIWuGQUHT>IuW)G_rSTUiGBY
z(R@PJ+Tsmpl48f)4#3d@Civ3l8>A$?^tx<&Lwx*ZHuPixxkBoXGVkxkAjPP&-FLEh
zwc*mN{q2Kq)zI+c#Q#s#yJz4^^Dy}gh%Ev|v(YXa>5Qw9+V(&un`izin3#X!^P$?y
zc=unCYey@DS`NK^>h+?BCm?I))jNA{cngy@R(i2e3)bK-mX!>?mRPLul|7!iqE{)2
zD5Lpiq(s-x4NMVJKG62X4IuyQ5U@;om*1`vvurJ6fU{E6+8IJG6iNU+33*H5Kp=*z
z@Dyurhgx4&w?M}!<jV+$3~F6AUf9e|%9?fMHk_&~LhHNlSQ_#XR%WK>#7glKwJ}p=
z_$*UnW&9qWFW6Z`fStwvREy-c;}v+UC6jS)l0H={zWi!Blxh=Wjje4IGf1n~g6};@
zv(V!AP@L1x_qxM&@QKDG8=v;>G(Mt|KvsCIy#FplGRKD=<r{}YYoT3t5x|@;?xOkE
z1z{#yG6Udk4xi(4hVWXk)F5U%ws@pri0%~vnRgPQXBy0`QGryoII9a>PuO8InY^$b
z)t1wC3>}0oSq&`$>B9s{zzDFWxg=q{u~Fbkuk`6c%neo0|5OGq_LI~MwwjT5uXL(-
zCmOPzl-~>=uF#vU2EJh^_gg!PN#qJL&dkc{!EW!3`3&3!Rs5djP~rGJ$}$z&g{#mN
z7=*VAZ@MBFEAb;{e?=R8d)L+*p25Nky0{K#5gkv^ByosDe_lIczNwyQT1ky!uEibh
zFON0b#vM_pYmhmktE}e&wZZ7vPgp|3W-nV<J+<3Ls%^pHo8;sq*0NAcWQ+XVbzhP_
z>+SqwN^#VLx`8t)8M!bgT|TP5r3ZhV14faY)r(jUGpA3F0~GQ|a9|)&4V}w3{}U4P
ztdJ$2{_#N<{ybX>Z9NHzHZ+6)lS5`-6Ey45hbejq&U50R*6c#8naSb9qf5{gd%3wD
z8XGTfc*8_4uj(~b(0%@=sHFJw6uyw7cb+ovF*B-p-{qA86pZK_Zu<RO)_!096yeKG
z=o@E1AwrVNlPJ_rzWX($k8x1GKB5j>Pa$8ja|Y`sRMDaJVBMZG*dzeU?@|O;Vy1!-
z?)Ak*OF9)B+;$}fZXujB9wYqcapMD9f^sa?nJ!laO6Lazf>H6vdc@HLAKkNdAJ2FK
zhlOKWj5?YZqgwPN5orj2Z4DHGVfFVJzHW1)!~JlY{=WQsXUkX?mtMtB3J($<P~vh6
z_z6~2DL&i3NBN+SyzYZdT9z3@t%EZQm14i{f(L%7Q(#M5%pVxC6@>U#O95!~NMSDb
z!+UU0P(%eC8ueCvic7&s0e%mhB%uWxXD<!hmZk~SodSz@5gvly18K)^xeKaltpemf
zcxt?D;J5`&xgCQ#Dwt{c#fyT$YtAIEdQRrEf@fyIB<c>PGW+5s6q`9OjPnCP>T0g|
zfGZee^}#&Kz&`T&JGj)Xr#>Fc%X+|-S%qa#Wk1D(t|&<(|NAr%zo(Z_a)dcWZnIp>
z)T&w#zvv~w2MrzZ9vcBcm-#?kd>n2mnfd0ka0W7Sw`BqsuoeeSP?FP;?3$rdnil#N
ztQ7jM__5ZKB(0*87%bCT=HhCAA0SddLf7Z(zB{jRqwo`^sR91SZ!3cU%6k7526sNc
zPc&tYy8ozW{emih{<Mi$@$$4Lq_^&R`A!OIf!er$+WfKpwRq1{Dm_>vPEDvnT;{e}
zHp3R3nyeXu;=)lK&$X4<91It$4?dIQn3#H{9)FEr@L*L98*rE11B!I@v_AW-r%<(J
zMyI}X(lI+zBSiDnold6pdGWiM-cQf^Mn_ZCzZq}7tRDE{U~O;vLW_vPbD`_jWTXm*
z9F$yb_2#}oJn{RY-lMK{@v(zFQgl1-ACGfZI$G2Z+3Menk0Di2pf=cGZE}d#tB@x!
zCwuDP{Pbg?L1Kbft^XXvo(xnC4|okbiQj_O@^ZG4d(t{QTsNgP#VRL)UZ%?eCl>-P
zV8spz&Pi@vf|{9_YSP5KHs!vmu2yyX3rUC-@QX|@Ji5$qZfj;{C4b2|M+Up}S0CDQ
z-$i2WfK4<A)rI>*fc;|jea`L|By(^Sa;95b?e$zrpc{k^Y+_xCH_o$b{hn)Wc5?;G
zD!%;qX>HE;@0`5OGk8q^Q3d!z&V_1b)Knryf16po(|hOpK*x2bDX|l08W6ZhCOy(Q
z;$b&lukih}zEm4%qZ)t2jWf>&+6bKPgIMJEHl}CgB&ywYcBXo&>x&_g5T*9UZUw$x
z9k4kFW$Ij7+>OkMr_QHLQnk0e%)Pg6L1ci5Xk9p$Nra*toQMMTv!IiEP&!&84>kW7
z;hGTOivJZIDqN+P`0W89O|ZB}pcLE3w~sVwzyda8_`;tR9?I<W&<QA$c01*g6pl?y
zXuN;WU;TJ~?mxxSKPGlUJd<c?)bjsQwr8X_G?r%*9EZ&cHo%^M5D=t^_x>ukn%{VU
z<sa|KniscFTB=JeYn=E%?s;IGkT%{FZFIrt;BD{aQyY(CB_ka>0fjd>fIqm({>-V*
z68QHgf?lyzDft5Fl3Q$((frrYWEso+{JG^xwoZf*jPgMiaa>C>qXsw1MO<`GTFn1^
zZ9Hixb$SOHPew9$a5hn>)eQI~4_uyBS-E~coLRnzlw2&oEA#ShsgBOdxWLv=U*b=u
zsRqJ7%kzQ6i&gm2r`08>VP+pSjam52BbjQ%zK@%fJ`lSXxCMHqn}g-E&=}>WN$!qU
zae}{cDp#V}tz(gG0bq|daWmL|{i#kXzw5R2?=G!@Yflj!T`vIFc@Pkjwb?uypH1}q
zO;<*|GRND!Z>BkPygy0x5ENzr3ui~#<EFueEM|UT{)gJBh8@2tfq}Gw#?%LAaV-&}
zagC#R+!1}EE9exFytx|}iNHkkJ)RmWBYLvOh}82%Bt;)j&H2<C0AWE~7mH(Q(mog=
z{Ho?g()2g962gU-tb`L7{W92tBr^Kr{0PzU)#HWDkoTF1phE_OhXfvQUoI;7Rl|o9
z)xnB>$5rW-zg?;cx4kz3%tq}(7{1||H0vj)zLt2-v|H=iL*?sFfYv}?F{tIOY&MeN
zpAYX;n{>CAM+63HyWj!$9dOfMm-FHLs7-okQn&k3nrt+_vh;R32M`4eCXTuiex;%)
zOx;1>pIn!X0K=gP4}brEG-GD`XWi5VK8Utf`uK1@P_%@+99x&;pb?)+{fz31v^~d#
z9pVi3hYXXXSg8n4CTLjbJ-wrJ<DP}V9WUq*J;AJk|K^!=ZCSMW;poAconYsTzbuIL
z-T{6w@H};Rdi%ygdnC5<`<^Q+6AnB)rh@_x5;*p+P1cNgu|x{zo>jaT8PN>+n!E5{
znX1vG+c-;8d78^ePtLaQjdpSzl7Agle#c+^p_rX{o%{G#qk6|eM^0{U=neiOh;EzZ
zij8ieCh9Iqsn^0A|6qPqC-h%hPrF3=jkhy<5WD55R+?gnReouhp59-_{&7W(<Q3$B
ziQI#S<9->~eXO(VdUF5Db?hHg-)~tkNc}((V2|zK{{?1$Gw#y0&k_3;t*omu#af*;
zUNxJ6h0?B-`uG%73=sWlai~o*K9@rJdiYBzK40C2=B=BNe@pc?o-b~^H(+_jWH33y
zgOhfKx^wf@-=s(rU*pg!!6gmbQ%7B&^|oZgpmjY<5R?E!yDLib3)?yxaH@Uz&l?G$
z%`<my{tYqY^l}8ArrkRm5Jx2wjNm^h{E>U>X6QePsZCen&$Nl<e07as1vrMYF$C-@
ze?_zzlDnl+Gvk(OC}_eP>eo+YfCg1QFZaoBR#gCR+s+XnXs&#ajS|wif8+gt@sr|r
zQR=2YXrpKc8@`0AN2==yx$^S}^Y9OTc~rz*Go+GB%emMa82IB}e{u`V!<EJIV(Q4)
zeRCJ#I>${Lu(2^<uw~VL0KS3&;AvlFr~b#uw?c=r`<>Se9|a4wmHtb4P4S4yVclyh
z08iogm{Bz%_q2UM7Zd^Gon+2w9+SgFbq=E#I=x$Eq(ccO0E7ZSHx03iG%rx&6#2Ok
z*`}H~%kD69+=LYb#>tBb<}R8($<to)@s;=7xyULzuTe-aSnGg}J&&g)#q#lJmi*yF
zD(BM6ewul(Ou1m>$GdC!D#0IR_YL2)CCEhQG4j=7oAO@)_l^C!5)Hl?(3JhuP?|*Z
zwaNVZK3ZVN{t=#f%fz1WYQ0V#|Kku|zyl<%<~C0W48?I;;rDj6o^d(MN#O*5emS3u
z(HN!23Ay9z&B6NhyXyJ|o3<lKfwrb<ErHZQfPvC=Fi;<z5uhdvsVltKhHPnXgv3OV
zsEhmdEDJA4mwU5v&>iAf?P_U>W2z4x)2IS&=T}h;Z=62%aaIgpJv(iGj$hQL#_dtm
zxMaLs&M5%lvRIS=w-au@G42sjlyJ$}az9}_(s{66P)&IrJYhJMTii)!LNde3T#_3J
z1(LV6TcJ``W06`K9_P0E^Cd5s?EuVUKF~w7g&<jA=%s4fV5xn6b}EO_$-?oDnn_3y
z`apWn$MS_<orLY1oK|(X00OkY{%dOw*xLU1=~zKPLuosz(3#tccb23|a<s9Xwl?#{
z(6y72@#Bh_a5ePCHl&T>b?fwDclJTyP=o!7;L%_Y4tM^Nx!#i8tC6w1so#$P4+FX<
zx%7s6Z}<_L@j6>iY5s)8ITwFb0XUx>pLoIDdk7xNKBn?9v>t9l)XGgmqSD$=-)2S-
zk41Zn46aOkb0T_Ye2-|3Ybv27JyrO8GtElmuE|3egT{LJ!=b!zBkt1=(2wplfyI@5
zWjYH%of(u+VK-{X{`6oVB6X$D`TniVG-JnugDBqCcQ<%dbPhj8)lrC?rAB;8$mtGB
zGZ^EleQ3yNEl67T$oX6P!@H<v{_2U*+iGxaXmPvoVfSp{FScu6A3$UXC~QTvV)Mws
z<{sC0<6}9iPvib$<5AqS{-qJp_Cg)JMS?!xh@aupSH503XsfvOyOo!<T}om)Wntd@
ziOQcrhH&eI1l~Bwg-L#6+u06+NKCEK>zoh3e~C;SPS>)fAL{?)5~ZFxlgLZ+7MIub
zVO$d~m)G?{*E~BYO#oF;aMS(hx(`)i+2C$!oz%6;(A+^TyD(v=WNpN7d|`HL_N|nN
zt`CjZ>0@tW-Bx~I>^vCCJNBNx`L(3(;6%<{k|xD254FUK2RN?7yyFtzcZG&4ZT5sT
zcg6gA5ta+mX4+bhsQjVXInU#I#m(Zl`1u^#RE#TuD+(@>KI8yT2*}|pqb8mV3ubp4
zdHKK*>!0sZjmC{r-57cwAMPzU!pXgSE1bjbR#HVj>u;~n*S)uKYzIoSRc@;?ZSURh
zNKL%8fVfBc>aE)MorhY_!n;;E4v|Lk3nb~?$-e^Q?hc0b2Wk&FwB98%1{*4_GM!_F
zj2PI}hb2GZSW1IM+mA%=)2Q=4_4B1Fi0?|C>l=!aeih*_sXhy%^8ixUIoQrY$i(la
z2)QldC=I3Ns=VgN5C38Sn-TI!jY0Q11r#Dt_7vfr%=(Zh${1teK}l*?PP{2!TMGI_
z5Bw0*9@VU_TadD7t*@yzJPZl2FE8*ch9|EpC@77Y9Y^hC|8X{1_xJeHEy>KP^%2Jo
z7C2HXT<@#+#p8MzG*Iiu!?_bL2tmrdjESqu1@F>|e>IDsuVzn5!OxvVXH`-OMEgF<
z`at{Iv*N1kKzIUN@y@TWHH@EK+RR}`6w0I<$O$wDe-ktC0QM;8D3?k3eYC{mJ^|i#
zBZOf8G9#0~B&mQ+OVA@2RU|WOQg32EQlYhbB2djib%i}wo@P&daDlAu<fw4;puMCj
z4!QD`Ms<%Izfu{n4EY~RcYU;pw4svbj!kKIJx%CuGLDs>_xjkVikgwOk!R|PmDiwq
z?+ll|ki3G@r-No6^p3J4-#HW?HiBs*{A*!1A@*W2ACNuQg|iW#ypHiai?*i@jbZt$
zeXnF}3*hGjlx~a%(vj>4@1>2Ks(XAE&|;$k<V9tFW-fWQrZ1V=8lpw8ML%~>tUiEk
za*pOr3%O+nFIDx!MZ`Jm=64cb<UD#6-t|EsKcUxnYO$d-(c|hG-6{OggeC*u(WeCE
zZ+hF4?98NW>|pfXd5=qk-3vR|l<^!{Bj)20^0eu;3A9gsTP5|C++Bx$9jIcWuEOJ~
z9-6@Gzlp-S9e#Vf?ViA&cR%LPrI>ry)d^AayiR|xBv~#p<hkWyLj<Z;d5v;%1K&Ss
zqNm<li&R$nFce6$yJ`py#aCYCos2=pnFyi+@#-ARu|pacY-<G}0_uIK0W-B}cS?k{
z0<O3|d#LoMdyNi1<c0Hl|0I&!Nk(`E6$pUp94if3`Om9N{_9b79IT|zlk-jH%OXtQ
zkcC{sH*+VWGQf&)HYhIu&-Ug{BR?&8?g))_CmFm`7uIy`h~Z$(gd~=KiYYc?HZNu_
zwDNsN6ln+=>48s=WjSdrY6G7R(@Qt)fLSI)O14%Jxu&9@?YvF77l96(zmkHTw2R{K
zOj8tZ+zIPO!G!0p!G@&N4GHKCs5)aCd)yV^U!b{<pnX$^7XLGspZaLiRE<Y0E6gEG
z^#=m&-Suy+iEg7dG!TLzjn;nA^^A6Kzr)=4sqgOlCCI^>PAE@4C%Mid_~F6t*y+t!
zVqmu2zhjZAyCShM(V-a3vi{3Ve8zeo(lsh_$;`D_4XjSp=&UzXT$8~MVW<@8Bk>6-
z^-L=|;}nmc`cr3;IJyYL5G!KYCE))(6tU7Ws8&2A&OkHQ`{Qfd&hs!$?HnG9soDvL
z^ybV@WbCxpcG96wTHqprWzRF;a=+v))FRvNN%2#Hf6Xf~a>3^fz{Fy(&Ti=|D9t^{
z%<;g37{pr(s<&E`2DnbbhK}LATzLJd1cz6~t7{FHdU?XWwixu>l)jgd?DJB&#rlA&
zc>9%+4V~}a&bH$2@jGW}HMLBY)Y_j|caL0>nAi)zCGLhxLIu^S#MuwD+P;@Z`Q^U6
zzrz*Q0@p>X_B-P_=n)0lEpfbnDkz-eM}gt3!uwFDzKW<_Q^E_%0i%w9lb-ob{k-do
z?t?ld>W^|A@%FxL+bEn1xBmL#njLiW-@GxMSSb_3{zaaui6Sm|_^cecgEo(6C6AZv
zTw`$gli%&?@m`RW1ra>QMMDXM+Rf)&`9#gR!l<ux?dSlR+24HApI9!yX&1q<@@KsZ
zZd-bX^a1owgOLE29tc%w8=|e6qUw-C!lR0qOu;l$;x`hb+ILtIM~U^(e@08_tp&s8
ztd)0p)0o@ufsPhLNO<kq5zL3Pa)oi1peR*ownz@cn-4la;LQ}=TOw4;9oCh}&)M^9
zigDLR7nrX0#!RD{*#q*Bg%ma2hZxx_)}2=o&*`pwF<tn;M)~Z;_uQY};+%MLMNJ`5
ziBT`yHq?u%`#D;+xr1J&0OzYZAhqWvmBh!DbEZZHEY0`}<m?9w9HG69^TTKobxJoN
zaOrM|-)@*D`LHgzhrY&cd<n$7|Fy2aGGy}iiR3Be0M}(E+Kve+f^VYqOBsollOUi4
zAOW9TeuFzno|=v#g}`MP06WlBC_RSrvi$Io#}LnDq3`EjFjDI7?=P2gj4q+8xy>hu
zCs^&U0i+P{96^z?VpUfHk~NFFRHth1R4?<`^tEC#LoACmccEEwf4`jdZg*&sY~}T5
zhy1?8uio%V=y`;V?}I#I%z|C!Vb-iXq36RXg!z%!gJ=+Npye1=cEv4N%sqVA!3C2S
zS4-lhWA3#%_XhX1Vu4#Gfi~FeQnQCcM1ODDBx17>we1)u^bFW|Gv(gZP+^0!EAxC5
zl<1xYCj`C{BR@X1;VpM6{d6Gp^?a(hTHHtkttxd%wU4B9S2!!Jm~N9G0qkc3G;ObM
z2Hw60e`Y>b?xnO<y8`1)S6A(mV8kC3E~}kLCZn-0f=UZfyL5(WQ<g05W);?tOkZXD
zn;|Z3ns9XkrraJP{ue5Hh-%*qK~t9OF=mlo>Q<yh6Rz{gHKPbO;S$k2@a0LY57vws
z*Rq=|&81+P8Xk1_+v=&L6aQj<e<nY|!gIE6AXBHmOy_P|dMEYnTUoL)mi}KkcT(a*
zu1D|P$pVLefgeuUadi8B`{NRV8-F>p8N2e}ecO3E9K!Th?7FJbB=N01_ACGnIIZ+K
zD57qAK>eyey+VyGZ7e~DI{XV)*gZ=<0XCZ;14=a-uKvznjOYDNUalXL=LhxFGqbjl
zY&;#m#}H4)MY#`F*Wf4vjt7i)^*d83p46pCk!jjVL=8`SedNe4WnN73&>QIRHz4-=
ziz`1!bdbB{2IKyRLZ1$dCY}`QQT#8rfrrt6@?+K{mP>g%G%Jk;I`v730oVn!K6Pz+
z|1)VyxXHjJsJ^W(%=za2Pvprn0M!A)bTHq-3*4|S&H9Viw^T<nH5aY??@uQfyFErg
z8;|=^PLee1)~vYXfE!hRtFgh?iWenmUHe~QnR*_3YeCWfa+{#cKFLV<luar?zE}pZ
zP<$|6e3zpjX-_^CBK)Dse(1$e729jJ8o8)v<etU3QN+T2_Z6D_e&GdByUh-#b+){6
zA@Sqew2XJoLml|Pl<5iTt%&AYeGTFH=cXCSr(ug#WKUt&0gn<$l*O7DjDm5u?dYFF
zS>wNee^~5z$S==zM*EGcqNiC(^>*$MNuW}C_*6*@r8b~;Wx#m^CGSMbu1ukf*cGwG
z$XAoS;RZ=>p)a`xj=bSo$-CGW4)7NWhviBUd3_$W9mb))m<IZb96Uq46x=PN?9qQ=
zT%7mQ2lPRXCrtO#hX`2+2`2kH3k&G}g3KP}oQT0cld%wrPFi8QE-_~2xCPycgwcGe
zb=N*{C|~;7`z+tz<34%|oemfKTO~s*D(1t_&;_NoxQkoOAW}~72hVt&G_ITL{gV7!
zhQU@ODLao5=H@!m?)FSR-QLs^p}MBAyKz!<$M>LLl4H_cH){8mAhv}97%%bXbtm|6
z-FUAwq?uHz%73@bBtm$V*mmlBnL#31z#-c|OMO9G)!xHAr)6Gi*?5g{v~TGf(_XJx
zE^1jDw6L!(*K0~Y5klQRUd<WN!O<=E|H*n(XKT>Rtuj=VVwPH$7h$y7kF50Qv8klO
zwtxbb$F8H!ZU=gk5&WuX9B|BYUcJMWvM(rihB_W2`Gnu<#riXhb4s90g=jO&N=wfh
zTQ4lW*T9=S``yiA;vo7JriDuBRx-e>-2UTlJF0fVuY^iD-WlrN##Kd8I;+ND#9)dw
zM8EYRky&#aGgIm}F5;vsDLz?YChrEZ)MrS?5jD9qx$2g=xt`LQMUm(Sd@1Qy#N+6f
z-!nPNa}Gx8ec<uDdHqFM+Pxi?hSm2y=P|aONe`WTJSXXAy@N7|eNqidGu|8>M_z=`
zRmwQ;HN~@v%_Jhbmp$G;w|HA2^iwM_9Bgq<R4QVwi$Tw_9`^rKO-ClM;?{hJd<4S=
zgs?_pvr*&gS_wu^i#Se|yx2Ie(QzgTsTN1hNr-gUM|LeAVzjghD4$;8mgNAbAlb)D
z;ZeJ%^!X<UUG{6WSKez-I8;RC0469Zb8I_Pa@~n%7!=IzS(~msmJT^E<Lf=FK`LIp
z+cg+Tc9v@jDcnIHbli>Ll;JM8(QmnMP2wbBD9+#4AvtuwoS_B|sd~X8D-9Sf3haK}
z7z0M>f{WavM4@El@DBW+A;n$brJkgJ;z#JSjo5`X@WK*Zu*v3PmJ_#Y@06%%*@U(&
zK2GOX<g(<fU|n&LWW~F46AYqv$_u`V>T|fNqrK`{81j??5;x7=e3<Q+rJsLeJorjf
z!{iT-rg%jN(IEwL$09!_A{|uF_QJc|vt~&b{{`V+plCzTt04%%Y6OLe7Bt@)6~S$%
zp#pAk4&ekn7J^DK247eax)qo&NrGVL1F%N083hT!XcU7l6exBh_~RZLa8L&X*TF5$
z5S1YKZ!fYcpPCQZ2W9Wx-5vF9M5TCbjO)BY2o5h!kbAxFU1YCNL62{@uK%>?aS(*X
z%ODo#M1;mQv^=2nYTzdu<Gag+5ZqFE+`}F(hJxkQJ>43hYFxfaWx`@-jRd9+23&~5
z2CJP1?~B}nnvdBdO8cf!L;6%dXvN(`VS&Bov~f$FF=OYtaK~QZK~<D#iNZ!tKQCbW
z;egk&BWc(c&f)<L^LiJwxI`G&jH!}AjG`Q*5Z!tiwEoaOOr!*29NrDpf*(gT$v@iY
zhE}L(Ni2ff!JmNbE(jC`JINT})g=3KcH9#G0S)$h1*xF?FwVCJF#@pirK%JpiVj8j
zo{W?^ABI4wgDNSfAOS&)j5>Np3~ZrHgV_n9s)e>wDD^ismUu)_&B_5Au_&8xg`-nN
zT#DpTDHK<%nR#G6*jUam7nc05DlAN}v@p3eltXlmAo#I<j~J#?b%xnIPDD=7&<Ku~
zDALd*p=fCAfPkRk+VH1PM;WVMbw>uY*Ld~ipFGKG&es-h8u9w&@S&wrUthvgDxg4r
zEb1V7`$Hj_@M%lS_Gn|&;;V-9T%&E~A0wHxo!eHYTs;`BsUIft>VYjq$h~2^5~x57
z@1w#N`aAEBq#xb9=JNZ9i01^&H@Ca=MH|hfIpx%(=U^QA9%Dagu*i_&w4H#lp&`Ia
z^&Wld>$$Jvpf5XI(7XN%*0}YJdV8|S#jfOA0atvb_we`6ORp^p^VnZrJ(6&xTpme%
zl`BE&ul=~kAszWs86NN_{}@dw$l%pnU7RodP1qSa(vor9@oBj=GG~&3$&sk_$8neP
zZdc}Ahe-2JT-qfETWrch8tB1jYjnwU>YHws&F}Z&84V8t+13wKjG_6zfN`ynuVavD
zjeF*O$0~51{S$__cBO_JrF5_RY2up*x|7|jHg<=yZ%P;lmi|8JN%75?Yt5)VJnL<8
zKIoO7)#pbHk6t$B#e{*<L>nqJ7kqTL$THGzoQqvhw6~uSI%G;;b$?Yr(JX+jq6mBt
zmdRf9kr^KN=k4uVf2f4Ce#Lzplw?@6i(2iNjOaZu;Pd$HLd1~FWwoq$`@L{4K?GaO
zQHP62dc`=r!~(6Cpt^kax+iMeAVHqSncR@cho6$$dYLA-w|-&$7ha<CcuA49?mK<B
z)_BLunYc#k71PJV1tISf%0AMXpffkp&$RWu=sM@NOYK4|*6<RS6V;z9(LOC@`}i;i
z`}ZAk;(Myx46=LdJv)ppw#<$qQ7N+07Q1|9SA%{s=C$|LS1(N-=gyscYR&OzNiHOa
zJ(03cPC3|{eDbKJZLsfPdS&SZxy{dE_f4h7{AGJcv-AGDava&u#f))P=k{TmoilN9
zRY*6V->HG&*H!nTsyR_qNvo|+R?w^5pqsp@5^y%|B`QV=SHbkJN94%CniX`6(e9s$
zkaMzog9Epm_9`uO*K_VHkvj`;tS)z|iS=g4{|T7CGN*ZO(-RHpDi9KLCaX`_BV6R_
zX20??gf0Khm2!Rbn>M$t#o+4!E>!q_3ypbp@JBwaWOY{6weRF*$i~u~XRx+>e&O_K
z-d#RP^7tL}r@qlIIKjzRUkktGN?e4IAN@2^8Q$W5XR;<Mq{y6aBn#Pzn&I#lPs!Gk
zHp(}MS3J?_diHWBAd?2lIePk8ZV9UUTD3&V!vZhGV?Hn*E+DkBNNyw}Hn+`^|BX(a
zvDJ`b)5fgt2E4Tj$+8qliTX{N)mlhXXUEApufD2{?Uyzk=UgR<zg5Nf&MOCQP76ux
z7oMEEA`gw~R^7USFm-(mloJ)F$1ruvsi3P2j!?{uHD$Qan3Up$#@dRIc(O;GPTip#
zweS{v>n34ttTj2xenr2TLL(K9tT(xBz4|W**%<S!8JkxLEC}1XjUqPORy47bQWA#!
zk%}-p#@pmeH*gtix_v3@#67259gL*x24}W<F;tVq(qfAz^ea>1fvXkg_{V9Ujr+>G
zJ4dsD0R#49y9G$RY87p$q4>H5%Z@OtUy9r}AjyLmrrf`9Zk%{8*hVSVCQ)sZ@!;jk
z$u?@pIWdLe0Xm(-gV6WedL=SI{^ObT;Cw)ER+dGhPHC*Np43sO&id}|kK838cDpK-
zb3)UKBFr1vNASA?(^l2{*u^C(HJ;hEo%Li?XLl2Z0|R0i5w=UGSfwj*{b@`N_qD8`
zJt!mfL_gSjWc@@zESIoM&zcls2p)}NyHtel*rof5taVWb{mt2$0QIYlH~>%821AsU
zL6#zHr9_X4kY!3Cv&NniEJDg<A@@tAn`U#6%VIs1$Y<o*;c%j7<R(iSkmDi(A?EoC
z8xQnEGXz;XHnYQQ5{wmWJQkW3jWzAn;mdGbY~Kz0Q7y@_avKtl+C(>y%Z_@oNWX;J
zvI;!tvwo3T%EKIN;DqvxXVIKoE&3JE8Y5YpATII&$^KhBx%Z#|d9&LV+vqy%wvg0G
zV>@S;V<*>J-9x39l06OvXtua2Vg7eB^~{uq?T#c2Z2M$8wVa6HS3boj_P35&@1S^=
zgcOEykT)T-EW}sigxK==5oq4D1RdSG-A0Aqgy6}lux4eQj3qj%-tKzhw$GUcxGKqT
z11uBOyyYd$cb|8eMQGmkynC)i-;ezaN`?J)fk6)~OZ#Ls8}-rif#gfEH*lhRO-!KI
zf#;km8N?hMXnD;V0&%ESYN}x79=5x;ejoc}p&Ye82d*>udCmDsOe_9UGDHuSEIoiZ
zA!WGgzZwN!Nu#%(98c7xXB;c}p11P$&+Vh?0=wsg3r9hO^``9Ag1cP#&F8VE^ZKyD
z#6C&5<<l0or&A(?I@@kTh2IuwhTy3Qf@;vsr(R;~2J;!&$S6x2$?exU&Hhe1Ia6qR
z?tDgs(h?V5S>|a6xoQ|Z^=^G+fceDp&)q4GyvYc5+ok>6st&MDtV(zVAEAhc9lmMz
z5p5aE<2O%b!^wOP#&ztUL3Oh{s0_50ZQ7Q9sea97%V$Tw2d0q!&lDsH_SY3T=bh@W
znwB}2;IO8rH2wOd5k$7Sw}7;{`rN13RY>MO+I3}hV4)k<UERd7vIn6g;aD*r*SHgJ
zfQZK`x*Z1BVoGM--G4Lf_1qM*iiu7Zu3tE6Nu7S5U@Y|knQNtohIQv9AgMF9MdNL5
zc{33ApXrLC)uBhBm&3_*)k698Y_FTEaL#;AL@iwyNphxC``tBH7-)D7J8Mc&g`q6u
zvWhE|xZ0NVHiPwjbZ10P6>>4u2nK6DZYF^U^DIr1G<EQOlvQ=5Hgtz^(i@dFnN~{`
zA>$zdR1PrJVDqTvHaJk=_lt4X{pG)L7G3k{^#^rMD?~|xCytOO%b%-dRDmdTT@ZH>
zjt_ofW&6s0hUndHUqI+?>951?dNSFDOgS#*Fo=01iNccYQm@7m*GqwX{`<Xjxhx2Y
zguVuc9tJ(N8f9BUDK(f!UpGy!LBYGXq4-jBqR**&@Bav_Fs3gm?H}MUxx<~W3d>g9
zWyl(?sB&pY9KMMvY?JaAt*5e6Rv4Or(=x3YT_}JJOf<t?6~?(l47td9{H1;Bix$TL
zlhMcs*5=b!rrq`Ow-`KZ`!i5#y`*tmseCWUCUBft>NPo6BxGsJyzay7^bHBp_aPCW
zFs<DKExB+q1sI-Ocro0#6}xWW={oj4^&H;mq;<MR+crwxZa2?Wwtu~LYQVj{;eE`_
zz$Y5_4FO0htb1+Ut@-`I%$VQ3rG19qxy|y+cOx(z8x0Cl<S+@{VU6yO%dtf|6K_7Q
z{Mw+$Y$3XBFdaJqe{8}{EApS+O{jcXWGp(8gPe{{ai2cAx*zc9eGGkHhK51mFN}Ha
za{MEe9tRz?eVioWKG<{fkNdW0pk)Y+-@d!RTZo#KItW+s#%>OjG6q`NFF@w0&?XF1
ztLn>TbU*rP*-i8RnM`g~jp9)}`J9UkrRg&M`s_s;RWDu*rH3l5^rC$9k1S0U$TKbj
zm6Iis0Da3PB=)!X(8RY+j<DwchqeEXiZW>WMbROHf@CB~92hbxamY$eg9J$`IcEn{
zau6iPA;W-)AW1TkgMc!Gk(@!qAxcs}K!T_7efPJ|UhA%V*FEd}12fOlUDZ|HUER}N
z^{dZjX@*qGctA%~Hs$DTg5iiHE9VW!Z1Z+f*<@9AAOx%8>AgULKE%7u<?g`E>OY4*
zY5#I)ftae#zXvyLl2+n%z%N_BS3vT{{?ifCWmAJ;Q<eh$bpv&`;HzGd-8Jmd6HZjv
zckZ<Ph~guph(n(qu>!js+X3w_qYWWc+4z~tucpxroZ~;sU=FR24c!$Ab;WqB_^8)Z
zQmi*9=FG8EV~QlD1BOC5QTgd(bpOo4NfSe=r(u-f8d-P1rCt2blG;TM-3si$%UUx)
ztlHsi3_mLVOSP1H)vh^2gi_<wWz7v@KabD4=B<z>G}(`NqI9yU-02aH^pC4;%>-&7
z{-=f#bWX<(4C2)83a=xt!y`M-o7~Gjlj9<ZS#&4%Y8Z*-b2QAPblQ=N8$c*e+|a*N
zg%SLN$F*cQ4@3Z@&si)YdDB#gC)#he0eiNYOH<4j^@|>QKEac6v{U9TiuF5Ly7Mdd
zb$<y%f&V=$#R=Z+V>Mjc=^P?OC|9N3Iix;Tvi9jfOugDlJTnIOV*v|Gv;))S24*r&
z;Z;T1N=k~{47EN&8QLcB&s>VD{~DZ|$o;{5ckr=bapL73L(KqDeuHwbA0wqFd&lyC
zf1w}4J~h+gJa7gz{Bfgx{zPP<HH)xSVc*qif~52AW{8j?xuE0bqBvxe8;mm7fBOr>
zfA<$9&YLYWHmVvsts{1-#f6oQPQp(n4*dqw?YisL_#BPug0cBoi8m?c4zyC!ORS8r
zDdQp!AH3m8W0&95S<CHPaABNrWROs~kI-FhcAt6wGTYZHGS<uCTi43(sRFQR08$w1
zS;o=l6_w?^Rj)bM8k^=aJ5u@2B3Q4Uv~yo8kN4{>6$8Pw@0a_{EL&s5(6ZiB`p1N?
z)1E^}_%*Vz)&)gp%WHfQi$nZZhgE;3_L_l&zY&c}V0cITwSd1AXytdLBky;%;mK|e
z(bv6oUGzRkUY*F~Ct6Ih5?H!dgx0^wSDb?XYyHiUVIOUOs4?~y_wEWS`(Xb@-kW(+
zS0{(9emFBSFqxx=1gs|<(a29m@cWz+CG4fse8qL>H)t0()@%Byl{RnDqe%bN5`FAa
z5$VKbQR&O2XOC|GuOW8(^tXFbA+j`c^~_Wf9@1_Aib5CD4b^W)_LuOoW!*D+a#jZ(
zDi}JguzJR}_57Qktd|3G`4OBVD{V{`)Zkf69}5Nh0dC>7+TO7e^tH<cZtao_NB@Z%
z|G(DgWh1o)-~w+z!=6)J|Nr*xY@q$d90xh!9l9s6d?}qqOZZOVku&qnaYA2fPA#rm
zzP&s;FHc7sgR>_gcm-M+Wr)1{4<V%<HP+7$Pvt32_tpUMA5NFZAhpIwkn`NJ23Lo?
zb{Pfo@3?eQ3n5sH)5$;^V>vPW@Yk0otXOl5^GX^T18BgZAVTEdL8_S`V1N=c12m$b
z#kdG?GVApOS(A~zpN1~CUugvia6!U@D**QnC!~hd!rDRCqH$^H%LCo_hr#WV46_yJ
zV%(XVkdG;Rml|`jaIx?m@Z-RcMQEEKFiqngfMpXa=v>Uk0ND)ylxPb7QFTeE98G&E
z7YT}%)EFyM4jREExE66q8$_T!Pp<`Fn>d|M{cujuR38xNVzpA<eJOl35}^Ua{Qr;C
zT2foUx%(~h@6y5N8j<&E({;;TZWuZk@t1T%eXbdn+1k7tOO_^l92=oZNF8JNk!AO+
zEMI=Vu|-RJS#y57ojs=K{Ez>LXRvMxI^TN5?Kut#4{HJ^E>u=4ZtUe~XtARyJQt+f
zLRg7_g9buV_#6p965U`j#sUZekR|LYI0eEp#{TXCSbYAG>9u4i5IMREE@+fGYtzLn
zAK8hil})@LHpc$VL+?NX$|fX;jS=kNR7Vb=eMa}RAs3AnFScG0moIMY9zE~SOCQ39
zjJ*p>7mLKjZ$S6I#(t(KM=Q9X(W*S#ln9}l?7+470k|yb`O&}3h}v&{GXK>g&<+oP
zhK@zy$^imB6ed{wk{RacB5kXyd$_BSO9?!bg#knKu?ZH_UodlS-}}g~wtsbVt@>yG
zPF&&W&inQHZ)>L|B>|wWV>xC6QIov=9({Yxtm%+%4RJfr-5+&H^@|J2+a=iY=<SrG
zzMt!7L6a>X`>OA*B5k7f6y;gX;QHkqkZEtaJYB3%R+D8iF(NiVr<d)K$h#KLs5&lx
z_k$CKWG(=|`CSL`3`In*=P|*n?G)wzCN<t7HxCN1KsUrP(Ujx+UU43?0Z-QAHk2nT
za0d3LNzTgOC&59|IFQ@7{OJmMyvA>`<;mAqg>^#(LvOEM9ewx2McO|>;j5x7a}EdK
zp!^e7EUP)-k}f=IqwTbCY~_O*aXr^W0U+3lk34H*)wPn)?22*Y!#N*-JUaFMJT+Hn
zI6(X$1t2nB=J2$w7c8z@+<LCH^2>Qa!nw)p&ga3%df`hJUmnnzBLJFe<gy-+;zrO0
zvI%X>`?`Av8qzlTPl_3VhsfFWZ`)ElwXe~M4j*xHg6W?=F4wii-TnSe(ZarV<{TV2
zeg)@>XZKr>8GlCoSVo>>I}xcegIBb&Zj3anr;AQTg6Y)z5)2uV^d0nM?y&naRENch
zjeB%@?O`L1Rl{?eHDi$N$f;}gCcNUNKI%`!;!i2h#mi)l<3LrHYI=C9MD~%T(Pj+}
zMai3e8pz(JhB4EKXh;ECczp~~uNSNw%)8lj(g-wy9{8b$0?@8*^dfLQmrfLFhDmt1
zEjwK-eM@Vjh$xcgwR&JR1IQ!?gCq_8>7BLHG}$hYRM1>M+rq_Ly!~>tMyyu!w-p9-
z&;1za9;014pEcZb`SO1gmU4X-Ts;0$vx%0c4Y=RCn*a6x@JT2+yJ?#m%$NVrz6gPT
zDQon9Qe0R0brZF}CTLksQH{Ldy}<dvvi+uumrZL#fVuw_SI=pTWx%0oXSv;lobMyJ
zf8T9-4G>uK)6(R-L?`i&0R}51sVRMo$!BKzY$B+g@>1+--s0M4dQoQKQw}gq(24$o
zJjM1IlxA&<Yp#P}_zz#Ay@q9(U_+iX|4kk;mA~xiPs!+?d3MM^h076Iyn%BA;F!Z1
z!Bvi*CCLeHQ4vlg+L|nhekyGApuKkiGX5Zoh<OK6(Vf}*>HG5mZSUtiD@e{(-kJhe
zm;E!SqHB<7IXVWQ#DYLa9=DaAex?kxDV!Y~Ouq)v1s{zOz+~1^3a&*0lX(-E%-D4B
ztr$mcyE31QM$z&D15l0pZ4Jz?bvc>^nA(qoAe_J$t8i&*<I}yB!2*&6sP*>GM5P-d
zBdd`fmv)sFm;qDx>7|LjX^f=+i-$EnxO<%WF!i!v834Gx^z>kT1ji+RPT+QwfuS;K
z(tEBtyzzEf7A6zb78G>XgxC6&7%!I_H_yDyKdFq2T~&#*w58bG|HBeY+3u-;CVY?n
zd+j}12P@Q_zJEOnt%G>;7Z0R4psDd)8SIe9Tkg+R#b98VbTMrJd(C(GM<Z;spNR?<
z3_G@B<aapmfM(sXavvPEZzm%fC4Xhh-Wl1b^bnqoO4a+N=e05@p_p2$)9>{@+SluK
zLw4dD1D2}2bF~Q;K9Hm8GNA&Uo!83vSmUm`-3|WV2N}4aU9pDwET{S+_b~UR3I|<C
zk}}78GzdR}l_Ux}?r3tuue-eb&PD$^+Ev~tj<>r-|LubE?0=Fhyy<X}+yxHdYChf<
zdA9I*;)568vj)pv?h+Y)6jW07ZC1^cWq&+r;p4co-rms~OECANs)JbJb8}>9J7ct9
zu{rXWX6g6->;MF8Eh=lI<LZ*C{;OV+v@b3fBP#;F-PH5sQujCF9-|KrWkQei2R-oD
z=T!d7n~R9a0xn3w<XKg~#Y^4DWA`InDUY*IU9oG9hjF5yl-6;(LWGDHBIZ=<l1%rL
z;P-1RBUKLJ;}s)qgo5Uihj8yN8@M_T`q)E?`eo5HB^GAYva6=N`LQG~-kj=ge1y-x
z{UlzRr_>U1)fr@y`j6^-SLWAUiGCGq#Kc=8<gV!kIyD^p`hI$WvyA_79`y!u%JyA8
zl!-Ni2zt`9@V+JUt`mdQf8`Vaa)u*dj{0!6nBs!=w={*L$(EiK)+ULmVw;R~S4p<c
zqlh0vIpj|?s#KMEG}u930WBAB)C$5%+bAEX#LiKa*{NM=E@W3SUFJVJFMO_SlC*nD
zGjvsKJxx#As<zAeVfL(g3tLDN<#IpBTo#DxZ$z%X68nl&NNwgPqacsRywQ$@d<GNr
z#Sk5gW|=4ot--v*j5pk{{){u|KY9C7o|9Li7jk_3o)$2Ezg=?r4J8M8@vBhBJggcQ
z^wP$Yevi3wkfZKY+UpslQF3mUbeg+bjmiYki^HX!bz$2s8t6VhX8juoW4bH$BSzbp
z`3Z&1j{;jUk_WDFLPqwq-q}nx9(SlHRiIp)oHS?M!6dJ(6VKmZuq)^0lL{N%yW#uT
z;u1w;?18f$R%+B%FruOPGyH56p|Jo4b_34S`Nl@7Phs7IXK`YuD<BR4Oya%anRIXL
zZ;c;rZ(SM&5=#x<6PLQxysvm_&fIWM_=vrPz8!wBe`3LXUU<***?lO{;oNBV{i8+A
z%4=+tM8=3OI2?2(2_Ra{rtatXbcqBv_*XtZWS=zJj?ek+h-?=L6ysdmOfHaRh&Rst
z1(tBIe)z7h_cu+L(|#D-DQ)qXgxzRJaY)kr5qh7pe(>&gElCn?aEJ75$5BWAu41N;
z(c5X}32aH2lD~DK(xzZ0L0I~j7#yy7V!NV~_ZwIDB1*NTGGjZyxJrB0wveKi#~3?%
zB)f~MowV5AVP=||E9o?QYY&zzupY|!_^t#tcey>D&lzQy<KS=9m&{!(w-v6v+wtO0
zBPL(yP*`oP^u_2vfTUXh3^)Ecr1g3Fm=xd^QjXpj%SVensofrJV5|MU({}o_>~Byt
z^J%a70`vQfm*RC!DSpBL+y(6ImR_d{Iu<<h$wd#nC2>cj^ruL~ZlTYAhMm**ULkHF
z1e-N&1Y(p$(D4e0M=t(A%~#XHxi+Aq?C7Td)Bj7wu^q^)lMR%$w^WO42(Xi^TLxEQ
zUHwfeNF-{}6mH0;h3zn{3M+9rX`;DI4}0k^-Q~FACe`5OZG0?fj1axRU^M-KhF)jk
z#!oi%)IX<zxRzv1ux1r^3&H}ck^VpR5XUy403Un|pv+v77&hB4(?EEZqd{DfSVX!Q
zF$Tat`+-5?WeTqiPfx|^Fao%FD1eQ|fg86@>y^Q4#t4*{wJ97#VFBuww|P<NVzQ=i
zO>BhqO8lKmv~4z^cCd@zvI3CRw2}WW5=Ey!fWy9ISki6PL<$e%4n5Ah!wi)l{Gnrd
z7=*A|*$PBXUd!P9Fo#*mx7+Vh`oajq^k)<P)xh3)Fuu#{W`UiT;Q@%0b~Koi^hW{b
zngWU6r7uqWB|Bd;MMbP?${QP-XZZ39aMCm<YZ?M?Z?lS1`yUPE4<%eHj3-NB^4~2f
zk_c6Lg2%I81z9K{_rI-m`JJ9qrxa?LU>V3d%5xKYfP-M9&#TrCEXrZyzY&l;S{j0%
z{muc%Yul+(VcC80%TPn@A>rLaT{N^3EwLnu665$I1Z)?`<V>T#-ERq}mETPyY&n~L
z$w<UYlb1^q<oWx>vxSa@xv^M!7%@-QI$DqQg-BRL^r^57cl%foNfIr^=^?{eKW#ff
zXIMO@;;elw-4G6s<2{l7$+)gPn308h$BW9soQ2IotACP*t}`J}CRpZVJ?xpwvpkA?
z3rJDU^Bp@3PVQNMJqk_o>3Q*yFI97<98KT~etR(G;=&dB{~9}*(@L}r<4e_Rul0!`
zk6P6Wr~H)Tt+@Ess%_5RtF$~!xJ~dV|5;3+**ous1XX(y9VR+>qGi1nM}FdGh*0aV
zz0-UX#0pL*r+}Y#82XwfH84nLws})G=AACh+{4bXhTMNz;dqHYnb=e)K6omtFt8D&
zhQYc0z39|JbnGsl7wq0fwMP+*{kiWWd%9V9v>1W^pF|)UG3O?T6lZi=6cNU`?xV1G
zBe!<ntT4CITb$)BiwteAlX&+cM$zUQko>$FQ&p;DAD2Mst-EfRFS`L2@QKt~CsY$o
z=$2*&A)X<ZR%)FbGAS$-mtIQ@_JdaS&Y1(S+Fd(al0i3+?6M}_A$7mZq63XC*;ZDn
z0e4`oB+IGOAijQI{@s5!4r$XND{01qu`L|%%+NMek2TrfTX2}2jU80rnNCc%fr;l#
zMEqM6e)Hku&se>sBl$}7CdN!0^AO3PQzH$(&+F`*nCRD~`K#ZI0EscHn$=u-9bteZ
zmCo12P8~9MO2SjjH~XM#i-M3%JMpF)or$&4#n-Q2k0N;;@t+Jd1~z}wX&LKLwM^G+
z;cy8Kk?+yznurhNQuc#cNYaiN40&izKJnMHvsQE)!E(Bx1pF$@xx)4C-504s2t>Um
z{&oMa0g(R;7Hu$CYX3gE#Y4o(cV5MC!&xj9tqZa=-OiN*W9BnW5EJ$P65MzU-69PS
zzzN{LNkdzp)5|)CB0UUsOo^F=gZlQQSs)R5<3~qFUoa;RI4t0QdEp;4SV=SV0K+%H
zp3%WDf{qk@JdWR7DsbowHY>SYbX-Elcg0lNTDAFv8?t!wnUgOuGgCxMPNZ7@-B(=!
zy57pH$~M+SyzI$U>~FtHZ9AND+WvBu+5F-p)c#y*wz|uSq|+q66?qYa+{svMzs3I&
z9h<82^1y-oF{%|g85)RWC<iAYp7;LfsnlZNRW(b^50T5UjU!wzx$O_?dmx!jJXKMm
zxYFhR6Yw>n29qI*MD-5|Jxa``k4@*aC(9{}pEr1ldg^db;&P~CL$t83Z>4-{TS+x^
zM+H~6E<ycP%JEKcLZ%hzXvS!*xfMmZa-uVYR29`kg6(z_u1BPJXP%PhztauBr4s!@
zuG)j|@$n6aP2O>B;!~<hG(F6*+TD;If%07`4+69-DrRFJJW}nDef7Wk-Rf_8ZL3`p
z4_VD#e@SN~PKbNh_JkOGX(A39oxBLnMyvdw0a6GxSino}wjvp7W#J_Tt;qLx0OL}9
z@CW2}$;Ft#E#PrC;ZxKW@Bu62ZFtND$-h?w+hp%zBkTF^aT<On*eTuo_AMx#ygdBj
zX~9rf^yCdN9cO+#e~M=$Z<r%VC~#mPc4y-Igt(%U1Yw<Z`ID)7(rBjzVC_=cES%zA
zZ8}f3hsI)g1MW=h>&udw)93Bf|NUfbz(PV@A$!Trq-^!#N@<R!`_*^H{l8fc!rNM$
zHTj}HN(Zlpk?vG5@I-Lb-6C_<zcVuM=9RHNlY{A<gGehs`ZR{7{wD*HLG!c5lUk1G
zfxkyg*9g80!W~<k_uW<o+sqjnMyroWuWE2|!9wv`^e2`E(%-muKa-cBXy1y_M>x9p
z_nQq1m`g!6lmW1m6^lXl(q}Bcs8&Zh7d=IAJbS65m%Y9#Lk;<LIwcuuI3D?I1wxxx
z^u2_<?eT_6-u3Y7YkwHU8gJ+FHF&LLgzu6QD}g2Z({maIrQ@oQBK+{W^Lfoo;v<n#
zts3HI%m%X4L|wi3iUkmURlv{pfdS8Z5(PuLtiT3Yo+s0(V_#85316RoU1ds(ar@rz
z3PT^Rovo1<rQ^F$(&XPN(#8^M#D&CXeBLP3NWU?_(8BiDV4hCSlxMXiqe3g?BQ3wc
z7#l$WeF|;oO<YUMP~s8->ASCFxUI;`mTAGSnTZN-S~Cj|P_O8(xohZwo%%)0Q5cPe
zbXG^}t7+QU-HSiAE54=yvTia)9D1HJt2dBy`I3YR8*poSL{slwgFGluL(NlhFWrDN
zXe2<JIMq63A#N$mnn>mN&}WcoE(sJak`ZqgQl;tw#ZcVBU&OPD8`nUw*buf7bTX8r
zK&5&}eLqi^5YPY~#Mb8zIzFWchm-z)DD?lB>s4X=RlpUefx2!^I0(36QlO%Bq*;I<
zid)mdiTWlb<N;J2)jG^&#RkMkg!;W!?UVyzu<0W2#)kR<G30Y_5p;O_Kn#Cz3>p7F
zf(P+=vNCv#k=A?~N0!ZkJ(d~03*^i7P^w&L1V8>V9!DJAZ7r_A4ASFxygsfM@hH+r
z(sv}GLZf|rF;>x`{*XSd^l}v8(|->QFanxZQU7@;H+L5zMtJrB)G8cA1sRj}fm#Or
zLNkspLIXjqiY?R=wSNys`$d>)?r=l~gQq9tsZ6wlHtV1)>Wk}Z9cON;yNVFI6at}C
z>L@lP@^eVCs>n@U;w{QPP7X*-ACX74uVb(h6&_x)Dxyj^-;A9~<xcR^?R7#c#>m%8
z7Z09=@>VPIH0E~6ahXHR3c|nJ4gAErLr18@u(9-2Z8CzokCPKpqd}INDr`-N)F1_t
zeO$FuzaGZHjL#7_?)6frQI;b%5;RSol~2raDva;)`N7_?;~j=DC>QulR}E)sgmc74
zgEG3Uf3x3lQK_neCJRWyJl~MorBDO5aYL9qZ-V7MK;GaJ>CzqAu$wZVYF#zF38li3
zkPNCmaYJ}OsWrHvpf#LtQ@K?Mm-)yp^}LGvoQ@Zh23lNVM-jL+uj@*yPxtBPzDi#K
zqf4%FQJPk{?z<Z!uU_>U^ApuSkAC9>eRtK8%2Mf$D&2LWJJ(~cDpse?JJCAIOm%XM
z9sd>fAkcL@-1EZf&_ea2A+b>ccn}xr09sktg$g@9A4fAG<elo4{uY{1r4vF&`v@O0
z&h=ZMn%0Vn1s2NnMh6mV-m}<i9I8-51n`t;A;;J2eGd!p5VwBgbH#idjE<mY1WBtP
zd!jDYw5=cRNSP0s_C?vK&^!uweU*%RkKJa4Ff_%rx`sN<&BRgMn(RK&l>6s{-%GhQ
zl|;T8{W@CrQ&nhA<P?h8@u~nAV}~icxJIqCsqWT#^tg<jY(6^cL&wd}8ZUrvLoJd<
z6Ic6qGznq~Tq1YP-6F;Kx$oN{=<){`!`~~IdU*&oR!*{LJBhrQG`Xg%CQ$HGeH6;W
zm2C=Fk1mi}Tygqz^AmJ`@8E6m*srb6y;U}l*!Xl>NR7+SO;ydx-JF?Qd3Dp~?%wWN
zvqy5~a_t808qJos9#F_k2I#Gs>Kgn&9o8K_7<Mbuudx&%QFN1EXHB7#+rBuTo!c2;
z8FQY(ui<8b6$;9u$|{s*pZWA?IDBpqFP;t}KCkA<jw)uC&&#k?3~nAh&BNmpF-Ac8
z-t}Hb{nq^BWIUnrcT9OMmZ&hBLbG1U+@j7*_xDTQC*y}h{-s@k=gGBpzl{^T_LMbh
zktUTlkfO!Vh1xV%yqT1(KFtIG(7~V<Z|Be|cv_fKU>NxmnjKYn$;?u*efRH9G#;N2
zU^d;x&>d-<Ar$!T)rD(8&X`JV`|FuL0mITDiBpbxl25OCvmeMz%N@BE#JY8A%To?z
zdvi}@_HCZt-);{KUSPOhpvQkH;RcF}Ul_I0FClCs@K*J<?Bc#lMQ)JMz1#8JnycS7
z)b)y2rjy*=qb47YA!i_T^nx(RmPOp|c=TX-jpYLZ?(|dt_+y`=33lg`c`PGtam3^X
z8)kq)m^{HKp(wjvf~lkYk(cGTub)-HaxL1cHN{6=)!;U2hw&C_=4ZZ%msH}9@9H`y
zwwRX3e@;inoZ-K1A2?18y$SeAp=4bR#*jz-deMy|F}OjKSVQTnG}e;Q-Q|~T?QtQF
zv*-F}zm#Td!1r>r#-8fvjh{J;+G@8I)DrBTw!V#|V-21ZkLUjTX=C?4V!(J7TIIVI
zC|~t~A!l`D9#|6PAwHV9QI}rpQOyS)nrLeyf@UpBsAq>Y>g_w!1&%DObIq++RGy2X
z+PYN@s=sM8+xBSqF9!uZcyyGt|2Y{9z@z^Rz{-J58BfTSI4}eQ_23J>h7%5d46%oc
zwy!F_-QrteVe)+U^HZdHDJCz)rO>U~wyov(K{=Wcmrx-45K7MyTOg|z4~F&7&=(ei
ziJKnK?4*v%PWM#IXn&L;?h1*GO{Z0yX?Z!t`&|0U((X#Krt`gAiUPvB{F(_R<ET<$
z;q?1dNP$VCQseF3-0iM#t}u=Z>Gk`6ZD85THsZ2dPbqV8LD#maKBC!@cBPUNqID{J
zxmbQce(65lI$;Rq@Zp}|-Lh;CKXg8}|L_4~{B(iw!0DjHn=)_Tauq!%p~GG>sBJ1M
z*NJ<?wW!ft(5Ufz(lSM;hE@tS^3y}ACH(r+t*f^kH9ih}P0B}TSF^Vae8nRi_~VO!
zcGm#_FTjp6vW$g~wd#QJ%gmK~jhZ5yq}mhRT{<4KN^^V6rQ-5S@xP-V1uOIv%oV`Z
zdNg_~WQJjxyt|_WuD$G<nQ8jaifYCuO|@)dfa3*bTi$9v+ZfxVOcQ(&eBfgqdw!f=
zab@^wJ-`vlG6tYc(dJtyx0Ex|G>k#qB+W0~QQVdeBHt|~;R^9u#y2l=f3d(X5nQ^I
z`rPRgF3F0&QlB{|(}X}D8K|WcNW_lFABh@6xxe3tz$FwzLzGvw;uFTkE=8+kFM}S5
z_MQ6Yq-#8NUZJCoDeX0(K&{eD5}5B=ruBEacDuJAb83@uE;p2(c*#iHvr8+SeAT!=
z8@~VL`z?`~51->;b%=eyZ-wp{Ou^f1n%9~b>32|hSt;>}&$h2toJ-oh@0pekI$z(Y
zqdWciPV>4EHAbic1c{B*H=31oVy339FjkA55F=gj&<}+>eR{c7`1i4~Z5Li!A}J}*
zl!A0|)P-;5+9pw7AZPYn#-O~6U`3B{!7FbyiY}Y1X&krH6wlAU-yH05SI$NqHJQGH
zdU2Pg&!)J9UDYA)Qhx=4j#-O3784pgY%yG#f*X&_L1gWK7K5zrIp6IcmwgI-1kYrF
z7uX4(6{Tp&(Ws(~Kep5%Md(mfHPg<U>M3T$Am~k{{y6GfrU4H^9LB->+fs&S<E{+h
zZ}RotTc#?~F8L41y!Id9L2F6h&~}lDJJ>)5xw>Nzbg;c{Pom^Ou4wRaI4cG3W}Fa*
zj6PZ^{p%HuY*nf}KE$hAVgyzUW_NwwNQYhNpjWyb$00+FR<g|??i8wtUYFaNtET9_
zNk!0U_qXs-O$`r$2;1_)6?Q5+Lfgmqg3a_w(W<@!>pjbaY#YW9PKGW+*o8lYm{t;E
zlcJ#JD6a@v%J(3mvZ0Pg2qCO)`Vnw2Y}olQc>6EK{CixE2>e7w&ifeB!_^y)y^FXe
zwEmCGB}HOd89?Q!u_N^d;z=LDP^vg9pXC@@Mx7l<wCYV6ZRy)p${+Aat4S?S9^@5!
zTUjOjBPsXfU!mXs3Vm6t>6Bc!!qLNZR*sN+jPD)84yh(pTibX@=X>YFvdvneE+pa&
z2kC6c1TiuDV7y#LHUpbbe@rK5+}@rbXQ0tl9U2aZ8KI$LjyC>R-v)eXJL>n{<1sO`
zrf=^+YDkN)BG(BO_T7lQ`nX!NWo|vjSGFTnV(1!YT0wUHjrjL58|TuA&x4<=dh-cG
z6XVT0aS%Idojm#XMDbMIfV5{G{2Ebt^5sYN=}(O`Rmw^K3J6~#B-e-j;~nz@VhAPs
zM|SyUCxbqqwKtjljjxmW{^iFo_ZQ(1Z=Zh!{Ayj*VVBoXL}MWn)U1+!yzp8+lQ-fq
zv00#GeVLRvtU-+`naU-3NKC<H4R$xtB_2q2-Q{P!yz_Z>(n16f%-B)(1BnfWHdN^O
z#LUo4m=!(|2hhK<w>L-jU9hD!&Fe+8^cCs<D=L~v{IP^CROn-sj9}AkD!n2gz_qg4
z1Ofu0h$~7A->iL%A3uB*W`q}Lr_a&_d{?TJNGkAs<9utJ9)J9@8NXJfbhRFTS`)3#
zY;Br1Gf(@D(*$zL;=yJg(Y+bJlBPu6g+Hx(aGyMPB)g9(OaRsUGEY|H4hQjbzMSOK
zuQOY$!(F6YB?kdgH?2lOUU#N0M_e!UmddB<v#Ev5cYA&L;3j54sbO0W5t;wEm$*6q
zB8uSGv@qm|HLytL+0sa;QsmX8etD83qqPr&FmNKwm7qg$FYh@m-zQ%@KehM7$GJ|u
zWN9{Sq0+XSAqOq@=<;645W(&xoa2v{6Rs6uW^z^uC-|kq%!L@K9QdU_=pu&5f+tJB
z10BX>`v{n0D*9iA7;1tVA^J5erUZWH$rZ#a8ZlxOGeK}0*YpP`r;qN0Z!uD-BJbh_
zAzzasmZ&nwqmfET7sS7&^km@aLt$wn#UGz(ZBSCO_U2rEHySN|Q`{fkCcEJ*5Zit8
zqCJOH%|KV*l~0xVj$psA^!4#~(cFAms^W={Ib7x$qZnz|q<a{z2V3jCvnkORXc_Pe
zj!Rwj83~a5I}dAee=9E?S{U`1m;=Nya@@YQ-0~pi=O83Qh?qIlh!Y-mEFHZW$y4(k
z-*l7F8DapywBF!2aso1<6R&Vw85D<<fT*HsBvrS&Hfu2<6>SlybrOJVFcOfjx332O
zbivus&O#fwFJ&!FT_U8CUHwl9UV~@uX64sPN^n9m3oiX(+1Z~#-02e<G{okU9YM0d
zh8DJm4R~UNZWc)6sl<(d_AzK+-@8ff_A>W5<Z5AUM5e&?>EF0bs5D+r%s24<LIayW
zWcZRj;T!-_q5^Xlf`XPeG3J4*CeJKc7S>Lh)hHL>kF9&yG*S9<4^Vc4Yp9;z?HkzK
z1$B@{PW?+}Vo9YDvrvQk&IQ!jv&6{vV)Ij!wWjso*n*h7-}|G=r$Nr#04nxg9vCr<
zw-zA<S|H2vNV)4id-;OCkr#i&Ta&hX_oTo3f8{NB^{i|^eAM-&%S7Q}J&7x9u5jt1
z6Q6RDtyap2RY#jCfKR1@WVWCx<k4t@movNE4+Q{w^;(7u0U5FG#M4NR#LWvQSiWS-
z<o%KAon$24?(qU?pLD?gEZ~8DMav^c(@i<~y1VMz5~?fLf>Q+E^Q=Wkw&~6mC|~7u
z+<V2E(cK?WA6TYX9)5`N^K?8O-oyg@*?UQs+U$8jM#6EQ8C5HwJ7a0tE@zgBp3$8(
zYN*s=d1&w>D@djJ`&WM%8IL&&{`zUzf`n1WDO9ac!rV!mXr*Kd+On?WE0)hTQ%b9H
zt3UyMA_CjHZUpNh<KP;UhLnsZqbYf|{{$DfE{aYfw8R=}&(B?3b)p3C+zHC}T>A0(
zjZs6Jwt@PEBl2mzw&?o`?Ce$88j~h$i_`uiS4rcd@c#^zt3|A#M$Q@t%6ftIA6uo`
zSozU9S2c0R&W^l4H_KmTpv#~<-XW+_%X?KF?sngBtmy|r1%<bN+Y?c4Jw?$vF-&NB
z;YZ_pUsEa-=PjNx`N9Czo#0s$c207w86DY{F#EEBDKVXu^Xte!yQ3CYLU@Yz&keu5
zxCQ`w4xO{BU-FSoO)~#B(QXErfOMnVWyYTMLcnE<=`1sQd4+L{1Iw=JBcwu>u!85t
z_Vz)9KZzUr!}d=d@+gm)cBx!6tnjJxsw48ZQJJ;p7Ha&LCFaD)KxDTj@&-f6sR=R>
zI0z{AKFlj=HU$w(VE~J5LEG>$zrq)FYi3lvFpc>T)Q)2knnrFzX1=6MC2tj>qUR7R
zue6$=FLmb;{Q>21<)=i7&jvH-!TtTC;740FCLOu^E2pbo*gJI0sC_@wla=`3Ngl_a
z&m&jmZ;1r`dRKRQJJ&Dh;Qj7s`9fYwfBud^@M%&F<ulZNd7az!U6&qVFkM|G6<sY&
zDz~qjSR6qyX6I`U^KpdN7@VI(BLAf`?J3}d4`im9g;a4qD$_<}Mi$^{#yz#x?oZSR
zr{8TBSm`LI)HdH^*@)6}ln~2?7H1X&#61{|nna1MVcsl=@eQ?<qM&PZUsnioXf2-*
zd#PMTaH+iGga@6PsEsBZz1KthwdmJ4DS5s<|EsFt5I)s1FSyWrteX=8hT%?T{tnb}
z$J@eSO6)@hTCg(qsk7SXIci(1OcR+n&kwG&QkP4yY3!7js7uu@I*5Q@+byww#U`cn
z4-{#G))qLXK0Mk}__V5ZRaFYQ7WQX5nBpb#aALv)-UK<t)1N}H;X2b&u{Ib%=w%XT
z3r!@OmVMo7Oztoexf3--f>$BkHR1f6RtcTu2YHS>#tC(l`+>W_5e@w(Il#ohir7Ly
z+NK#iVV<?w)m3H;SwT8&sjWoRG9f^fQgSoMM&AdQaW)9nAHQvB(GafQB?79;M6{<9
z&Shh6fN%k05ac;9tWF&=?q#(PdnbuFw8#}9cR&<mon;>8OBdgNIs%|x4YN(X3wNtU
z;ziT%Q!3p95`SCp_IX}-6|^q+ACYP0XLf{;Z<oGnz<N^!dC}zkdrk@h)vYR*ePVB%
zOXzvylac{h`4(W$A*^|6B#tPwdXna^%tx}u&Ky@h-t-q`Fn8sQjxDL9qPaREye;fj
zH_7{xv&ZoRS*+@$5|x9)myHiutG`03l$cSp8upbD#03{eoJz<)miQU~fUCra(Tv_B
z^yc#QwP#3wtx#vsO9J$g#xGbnV)sHOF2HrsB)%cA_eKIb<yWMX=*VLCVemGdPN*h=
zBmD|(*+&X0(E4b^C8;b^K#C767e0Vh;m4j*F9H>ynl}J^RDhoMA5DH(@n^z5;6zzs
zWcq)W{`h~CX1FX36hW1TBQz+fsuGxo=!59}b<jUrFJ9dLMjUMe@T;R<C(WIw)*o4$
z9@L-Q98^!;cWY|h&w|i$T{c+gUxT?Zexv5>A5LMeFDvAK*H2C6gJPveaSiFY6PsPO
z_ysUfR#K$=G<f`CukP#JDw;b;kClp}>q%tEV160QG*%%h9o@%2_ip=L?b3>PhiQ_Q
zJNt`6-(Y74c!_tRu)SX3fdHld=Ye2Dkh4mpWzP5fT{5$k{i|&IH$3T?HR3}x<LMa-
zyU)s=n0=_vKPV58z~1TY%c!4_EB-PtM26m9Ittxw-;FWX3hwq_-wW`N`>gR*``}1-
z$<#7$-&<~2Kj!81!CoE8u;(dAu5*64Ci+ZB8xi8X=ku)pn3KVqP|I>qwlA)$-{Lk^
z+>_KZ=D6M=l5p1eUJc^r+@jC6!ORca($^%9Puj-&Ha5l{$csOmw`h{6N=DbG!qgsi
z=wJ^<*1g45-VVK9{<Gu?!8p}zE~dt0w%~m~F@zA;hz7jn#*I0NC6}XppZp1njEOc)
z11Pyd_RihSpJIR9vgmx^pJN!t=DRLz4fV~^KO6K1SmE1ryv44RHM6ixRBfM5eW}7I
z(Lo4=>JO65Obm}VOyn0Q6_mTvYbWDajd4^M8Wp%&C?ay*ySwU?8w2ah$&*O+?sK1)
zY%8Ul{GFWpAX@@IE!mwP6=TEr<t5&*2KMXQ5{mEQkEU)?Q@es}U(tm^?xxTDqehhQ
zbF;0e)Q}1*YrnAG34Oopb#?&&wxmfGTFh%P+nnS;1nOu)5NMdd+4x``zrU?lK9qLX
zWxi9y7(Wx^Vg+lWlum82r*rzjM)Il8fWz<ijlIx;aT)!fwifnv*bh9*zg`5P<Z%$k
z?>@|(3-@_oezy=cv^>!7sdE~Vq98$dqAf1+MrCTj+vCu1^aN<1_P_Swq{<J%Zd})R
z#fjDF)4^KTlzpI%{C!x&eN!$@g)0w0Gd~c}z<zg7@eiD?Nk;28Ql_4UIX6R!!^4C-
z>EL7!S6FZMw?XEE+rwp3T6kja5zQPlPP1#gh20?zW8AlS(6Gfts}}vI?AwLL?{oqt
zNqlZQRsRXh3B&eWA5B{FPnRV!rU&`xxR3$376LL9buGaqD>|A7z)trVWJs3hD7~qR
zA@ndFrk6~ml~r%<<G6Cz;rr7@3BzxrO@#3y*$%I$s5lC{+7$dS3OiXYAU;gQtujOD
z#!X2O&?jK@Qv@+#t=u1z^FphQa2~yrkSav*r&fv*PvXp?beAGoT!hbI62Z~^<eiPN
zS3PEiWQk)YlIA;0{P2^CG<31X4*OT8QJ}53|J9q}<zbnGk?YGV^PX_Qww3hO(DF!%
zB9=c^712ZVP)C|L;-TQJkEn>N)0)2+s5oT@acJWHH%=`POPotHcaf8l-8aQPTL|TR
zyImn+&4}6}&dGX0-&cTq-1mBd*HJ1|7AL3Gr*HQN+9n8v{XWTFnPI!+|DWChqiJq>
zvNg^p*edW<Tx9|dZFj!kFrUa+I%6BS4e+|BAKpPe{Rm&Q6A(KcAt=a2%AMfQ4|K4`
zuP&fmbGIZZ9DDB~QQlpK+o4j66!8+-o7vBbSfwhr1g>&oC_4QdC<wAcY4Zki9@-;U
z$7&*4?ekyw93>Y*A~RvI($lDiP?)%mN)HM^Y1{B?vrLv;LAqZ5(9=Yc#N@d*%)$m`
zVj2!9r$0%egRb#;#oMW-bie%Z8E>3NV^XHS@h*}$aGKP!Gwj~1?@%kxEpa22A`!f5
z2CH~8`^0OjK@f8d#bi5HCx=OSj&A5(5tLyF3jG1^_$=O5iiw2kp$;~@vwmbG*>A;~
zf|j}(3MRBEuuoUq-IO@LhP#vp&C;k|m?IY<BI&or!V?C09BIAU<0>p3KH`VNuPiAr
z5m$v@{SYy-0V@tuNPK;U<kF`kzvW3Bol2{OLK8g?Q=(FebnpB@r@a6eUwmF!v_Q(v
zL%&28KXgE{aXiQ^(@WO33S}MtpAMO}?yl)(Qkjf>j<ZvQurCQcT37!|@TwK>-Hz!k
zxFTK_EDq1-re(>-I^!_w(y2`6Ht~C+EE6(v6-N+-io{ZkPgvhbwicj}4l+FgzT(yb
zO3kHV%Hlp*82G312E6Njwys1>|9iUr55JE9$m(Y3T4CI|#uuL(q1sIEv(Pazuae}h
zv3?sB-iP5uBsObIVrB4-gefzBDiTw%`CWA+^7~0WtZ;!Zt<xMIXX#0l_!j`Nza&pi
z5f2R`#f&nE!Efzy&h4FDtu-&Y?B<+*-TZXAYRii_rT*hLtJm+Fq&vn8jJm!WvheTU
z{2cOHs0;sn)2uxuYp{qGUE%Rt%eFP`KAQ20BhIzec*;2Rw_LE#z`7pwO8|PnaR3aP
z|4na6@o~BH$gTH_o2iD~Dk@vr0O35tCW>O<nj@8&;KhBU8df;=G6BS8J<I=Fy_}=6
zqqD8;xmspk@B$dAYC2gr)2Gtmedg9txN#wB>VV6X28Gksz5lxB3dleq^?SSi^9#Pj
z@5j3@c^Bw&8Vo=1>0&nDH`a|efBg{CDD>F%9qD6OTy%7-hA9mtY1h>~<9gc3Dz_-A
zwZTBepkc?ne3CU>pf?us{?cojhwMQ)G#<|jgjM|ic%b?zeDzG-TcO(=zmpO;R#gtV
zRhJr1=+##gHL(uz9k`VgV9);imWl-)ysYV~I6BDjrRQ0C7sk(ZA#JtF%}yoAB&w(o
z+cO@=vQo?(AH=mJaFn}0fOh1;OE$QqsDj!*S?G=Ws?}^V-4OJ)?$_w>F^1$Vv+U$#
zhHb8TxU8u7++NssYov(W6F+nFU?myHROa{ut|g(h-AU}A-}?!C4IY4SZNh~yo5`*1
zC#k6YiFThWDWa(OYRvy5aw4tPs@07|d~UQXP7PYY8dPIz?lK{&v*N8v0kVSa^h>kl
zg@j-oNf8h>u)*@>L7`uyH`~<nB*>T5&r4EB2_zj(w)3vdEEn7#|FG+|p7Y0eg`U^Z
zz3C6e?4gUpjb5+=^SCW$Z=eBL0%0zpChZ}a`wbO}p0w(T=o;C9at?Y&1pITF@B>Zx
z3cuI%n{cUM5gqK0w=*~4Wruz(3`Uaz0L{ZIK8UAKM1}}*ap{~0S|M6^nlb0De3CD$
z=U$nXr->ahu;Mviy8n7^h-4w}CBmm=KgnWrsG&gFL>zeYH}<rTd=*<dY7L};oP_{H
z{#F`IY@ERyHn=M0jn>sgaw#{1&+bo=4c@O4{XgXlt)9M`F`qhB`0bIJXv`zuQf8`w
z=)_~-#LSV4m(nV^RL$b;X+h!JzuG#Bd_GRf4VtM-2ws2w5{>kjZ!7&fQ!2`kz*lE$
zK5NiXIF)egQVStHhi`|byRswH@s)tN4IPh#u4R+emez53HWUlkd!ncJWSj;cS3iA<
z%#bu4ekZ_yqCJ*x?^)fQWLM9-{Mo5d<@BEZ0ncHDN6Fgv><r%RqTnxHf|pI>P+;E~
zx%j(uw)=%R)Dn#1g2_tn?V5=sae*Y90#M(w(^$&7*c)1<kJWv6d;P3UPk$Yp-Z)(W
z=;2;VPGf~j-phIrC6F?gqh6u*nYyTMuQltZ4n<L9jg5>okujTaUBO_4m(ks!+vEJ%
z8RZSPxwWb~p{*M(3qIUoCW|MP4L2HiZY@8|KU&ytnxF7{_VZ(Rz_c7LCyFUlOekM+
zY|_8Pj|vhEVo>}uqaN<xBp9}XOXiNI`x${zZt4Ni`I+p><^YLrSI?bYg390>_qq^R
z#r`X4^}8;6b_r)`2=VQ;_@9Rdb=jLsxN)*hSOYV}@E<2gYVptk39J|axmG<2$y(-|
zk{g6f$LkVwVM=I2Wrf5cMZSF!lwy#g#kUd+Xf)vRQSyHXZq+&e^mY2-jCVOlyZ?G`
zdmNXC2uHe0v<?Y?xT}NMakOLwFS-pz41U9fKG96w-*P+=z>q<gf7Ln5evMa<yv#sW
z{wLQ}&-luUlg8I64>yJZ#R9GOSqqyv=*kSS*i>(iHq&;Af|0HXOyEYCPoTls?e}(I
zWVyI8i%NATUAsht3Xo}<oC<jo#Hqt~<>YQiR<_KMY#OCnGw<3u=7Tg`lwon(lm4j@
znQK&l`dH59%NxhywPh-Rh?DzlWrKKd;pI^ADou5j{nmE-MT)Dw^pM?^*M9`b6Zu_u
z96jI-vs&2iXG&sl*C}?b2V&jnUVQR`KaiTEg_T+$pE&uaqVOdjF@n(i-V1yyE!dRg
z$4~wJcc<e%YHx51PJIk=u1-fQlGt=t;EcAN%2Uu|?9js(6I#9^313G>o!cx#7YuJh
zVOC49=VzbHV>@gvf7(lrV(Q)aR+tx6I8jJzg)0)y?@#0EnPn50G1+q&X=D>xj0vgx
z8?$cR+&th{ZxJFF`+V%{O^82P%hgbxyoYn-?1c;ahTFKn8y?!C<`}aMst0bc;!<vH
zGhH36SxS>x3_QSa^t&Gq_!XXEGiLv`iG?&|y2*Bew_7^4+ml@U?xXTo$*+j+czIXF
zYU@Xjc8zF~klbh^677mJ?oy%F5?e(d`Q5fZy%-2%w<I0xLgH!uZu)Keb8A0$C@%F8
zaxtLZH@N;h6@5KPG$h1hJJmbz5jyd3d2{FF!;x(!`p3}9@$nz5bUZGiV77d_7F!nT
z;c!*sm(-NPf~A{6`DYRM=iaA3*9RmN%Wr(|+VqLvovR&c*$+Y{7MImd3_NDUU=E(!
z)XEO|ixQYK33FgNntuPrOwR;$V7ll21}QR;pwNRt<&q&}{zf4@gyXfQN~p+(u8m>N
zxL$`4FaHU$f@+ihK4P+)TxIRzmkHyWyrMCh>`EauEG#JryMmY?BfNG0zlF?4>fvTy
zu?UVAuklghIP-1_I<2H3rf@?ae5OIV<N}T<a9FLY?2ceYm(prjy$yvDwHbTi%WK(q
zZ!Xd0b&k~DQ%XCt8t4eUrdkgV0F;*cW?&rsx7(&iU%n*1U(GLlALg|cPn*mmH?y3Y
z?e?frlBjr{kSmLqL&v91RZ(t++1Bu~0b$noqe8YV!bg}G;}aJ{uTlg@<bC{QA#aMt
zm{~gX8`^c7)X<k(^LMU%RH7mp2|enl^&q0KlbSeRq-H+3XLoab!lz4~()p75YPAN$
zI7L4)<X^?;YIX39|LJ(ql2M*g>@EDNSn?{Kf6Z{wp$@$dbss9$y;Kxbnb3;9#GE1b
zIS^mffwWVArmjkoC{2q56;B1q+0QAPcWrnn<q`oBMq3rS3pih;jB~uISVFf%-6344
zQuZPEf_4vZ-{esz9Cf1%y^k|0V;}uPfF9rsqZYdSuH<3}3zFu&8M<&;RMpL;Ky+~m
z-(8KQQvgX`&o3Q9ou`zIxqPSOeg(?{<i0HpQA;TvUuAz@`|->6&|+I52RVT?<IshP
zyXRd&=NQUm#cyaHapkNe4*W0WCJ@<YToWa_U2q*BM3XyYK89RBSMT5+T;aCYfIpF!
z7t8>qsPwZZyNewE87R(T!nN{Fw~MKl4?pO)U}%D1iWbn6jvZGR+bMmgo@uM}&z2$m
z=e5$|WIo_b<ONI074SaZQ6Wr^K`1m_9f2y*{bu;Wlky?RsgSS?&6qvtF^hL50%_<w
zwH~81fD}38!d@U`k0CZy9Q16)Dq|?fhdG?H42_*Nk`eQw>jdXH;=P(cd~e=m@z8V}
z;XSlnaxRUd41&-}7m3e@xInn~=bFLkFV9`yAIHuP;%Fmp&jTZE0Y=2N2o5S0{oN23
z6A_)^rG5e5UD5^RkCYC3Js7?5qUHIpuhOsJcn|%ummZ5{-f^hC46*WE#=V#wbjUc_
zA_WF}$1wgRO)~GX@m?)(jt_02d&VZklI28E1~Q_A2GCtk%Dq8eAL9-d%FdU!kne`L
zR?R<Y0y`bL)Kgq19N&;!2wo|9EkCXe+jxo;0?<fHs}b{qj_vhm3c!pMG43EK_TC`~
zG<Z0-Z|Gss!!N|ZHJ?K|CHFPCKVAkYP%Ozu{Q8r0<1Q||A+n_~+9tEky3B7lu*`H4
zz>s2bVWPb<pvo&LCjHzox+}*1HQtrdqy+kmY7YpsIquL9@{LwqBS$^4vN{}}Opb3!
z@_x8|cH<{-4+E<$ZBurrt!Ur7fBcVy-AS*t^GaO-0c0RwY7^7*TG4?z@ahkWv2Tb2
z9c(WGQn7?(h!Z2lt`N{US^){Rqseg&ti12O$b-1j;pqNO#W6LT7Yu{sPKZ}z*<thX
zz1JQ!A_0y5RHnWmC!LX)YS37^BC$PYLc4g2Eea5H#+Mg1PmW<*XnYRJZBjEIk8|>+
z`q=pZ^67oLt;COK3Scb+AcXC%<I<0z;la^#7675i(J|O`V#=%5s)rPN>nCyhc6wt5
zsDC?G>}Ykf{;ym9ODT#!Q;#n^@+qfI5uTABvK@SJD}mX4gA;_}usz#dgk>)2$D5%Y
zdJ$X1;k?dv%1+S|EUwF{ZL#$RM54(y6;AbRzF|)Ey#)HL=Sw82!yVHzc|Ca&a}%ZR
zj)x~fPHMUB+pBpuepRX8?r*N!K)~T<;|g?kO4QhRm444bSmD&4>vYu!hWovL0zZb>
zC=2DigQ@Owv@uj%kubwM(@b5f+A(pYHR~^WkrOS{eHa$)X8&lOys@yZ&rCZuCO0&X
zB4&P8KhyHwsUnx7<IRLYNF!eg!K#WzMQ>k|mdm}1cbI3gm5lIOY{NuO^j6nOShSEG
zrm4=|laCYmRP~2{D*vs=fBsZGy@sXV3Pj&)ThY{8+-FEYbLR`1-CFkJ=X@>S$n#yF
z*{}7nX~3h8YV7Raz3-$2YPT)(*E7OtD^ybP&h%63O;ZoqKJ{hum$lo`rc^Y_P^H)l
z#pDWzvk~pBqnO=$v-oJ!yJ>l>hVDMI3n*SEXk3nsm))mPa13@O>cra8<a9qn1Y8S7
z{6R=tu7rwpkcE279-qX(y1W|DMnBFmdjTk-gxRI1@w+~u5)bTbhQ_XivRr>Ybt~}J
zeEr1>b=P}lhGTvOiG9^FH*X9Pg}iNi+foz+DSfZ6P4AbPxpcJP^uwGyk07H1^WZhi
zRm;{Or+mB1yB0IT<}m5cgypq3a291@@ZhSUo+9R|At{f^?SBMug|t%fzZq_^+NPj`
zNr@A^T86ykHt20${FdoQsG#|7eLR<<g69`uyQ4*c*^BoU&g<4>o%T%Shvi3YLi3BY
ze?NF&S>|4geX82};a=sf(gr~wEa_#xD>F?v7sP(4h@3>H&An3^n?g1hS{%ySj2oy<
zt{qN1fAlCWMa;oICw{p2;1g8JPsiWkzQkM1CpL$1|F5~acA~Pzo?JB)nV_ZNy3S%t
zC+l@REqKK<NoWbW*&%+TmgS6lCw#wHv&0ttlF0LVBY)c|BA@npv0u%1lDGj^^eJp&
z=(TFR$<5u}dD>;}?fYFbfa|J$=^2sikK2B%)3$Evi}Ek5r_z~kRvN@6?c0}Z^qCoq
zw1u`UCLfIU9W40_e;r8~Q{>oj2;|GHpErPg4N$9O=q*=CrI-I{$ecQD*A$_(AZ<J8
zRMQltTCetTCM}P4=&$zg@^31@8G>A;ZnIpbaps6FSW51k_H#lX6ETmzC5Vj3Wz36T
zzQ1LTedC|cxoa!U06%YN*DYo?PF!c1$xjY5_nr<>DhN2KHT7g=q+Q{Bic8oYwk(K@
zdc$f=wiu0G&9g5ZaV4opyDo-o-(6^(EVqZUSPW#_@#K)NV{%3rYcWMqr5Jsjc#hkS
zwVE(^h4)k#r(#C}8g(FhF%zDfg9!g~3y$1Wf?tD4F~U=mkUI(ND{_{TiUnUa1@$Jr
zD^=WrIAZB*s0yFIdc`gqlTyoJjaA`fgHQd<QllCr=^X6acov{sn~|b+%*n5r(S}hH
z2TGxpfUocskJx!r+TsQouV#94nLl~R812M=5mh%c5H02J`9d)*mCMmE?R_c@{Ij=k
zg;@s4o_vfd_WNiOx~<lMQeA)O-Qi=puyC8UxjopE(!D4Y*BUIy#z6~rwpCB%(s-L9
zK86~eFikySgL{a#p&LmJ2Yg*?7(8nd(Ib?q0gT*^zo(nu(T*700^vtRW_|+hzx08w
zg_ga*e*#jjwV%E^I*qp5LT8_C)DYaMhhKyjVh>LW%$xLJ5To9ww*ImoxAo^z{L^Z4
zso+1N<0oF{XVP6NDNzi``(THcAe#5z>_W4`hvdsG!8n$6vzU!ppSomdY|r5y8M~)G
zt{f~bSN$}O`I_!V-wgdB{WBFmW{Uwo<%Isz1KHi~ku@aS|3%zehDFtWeZxa{cS(0Q
zLrX~x-5?FpIw*pqNJ>cO(2aB>DLHgX3kXAZNJ+e-*LD5levaqE{oy^Hk9+TPuXEM8
z)^EqT)>Nf0hIC3DedIIHb`V=DA3aio{agp0|G5cqucy9V$49U+Gm?kIN2|MXs|>b{
z5^C*@6sy~C^Wlhz+%7k>%U3F-kq~?Crh=*noCKVzP3rnK4OC0eUAAZuF!z?q9SnvE
zUGe1`h-I)Lm0PSu>=^RFIYhN%b`5<ZQPR+ti7qfjEiS+AhKkAY)?hE;DeQH>LO*pm
zR^GH=oOem?LC<HEn@NMNw75iQ#36SaSsDCQZ_|z!!+4PZKJeV3nvckBV0+$k#ClG-
zdeVz|%8aTv)K5%18LN;EJo~CDi7aCLBN9MFSrr!Lgro7JMH#_~eAap^%5;T=bIaCT
z$L4i{E`vjT7)8T{OCC0~UqDkq<||Z1AuYbNzH}6E2jIo<l%xE04slR^){jzuz?ch~
zuV&DzfytD5b5ztGD}Ja&X`C*}$$Pw;uO@E*zsLgHU2C3SH;H$(UV9#3&^iHBX#gD0
zB=vg^W$}SjfLTWT$W#t%re|YcDEpmCPt#C>9TmQ<mo^M@uGrr38K=yU!$Irsg>zt4
zW8wqIGReR(HaUoRK@bH|NTi@p&sbEX4fHTf{Gjy&Ql+QJ1Dr2qC>E4K$(bdLd&ie9
z7DmP(=n&7~U}wfqvxN4&4@=lg3}w?yx^(t}?c#Va(1C_+&VL^Ft>{euZ1s^wtU*kK
zvNw64AXOHRH+3%U6tsx;;rs5hF$xDmpuy)>q^@1=W)obe)0ss(5_f^X>)$$C*O=Y5
zpwfuBtoGs-&hT<_RkC#-WX(eufhgF@*owGbzNX50abqx@6mAM$jCaP3NOs=Qu3fJ$
zD7AiOoRn==ZqT}Ew^et%G-|N!xf6N+MT7U<bK^wVpmuhO-l@rVCD04z-VAJ6QSZTM
z6?d>No=t)vR~?oNf!_e!GzP=Ii|U2Pjkj;_SVc`La$_Y`{EH1xD_!ol5tN;xqJ7A@
zQ3P548hd#=Fl`Uby!CMp-+xquNq&->61r(OiGqB_m9@5US?+nEk_zmsapx7qMAF6w
zNI_!JgRs2m<mgG@#gE1j7@3Ai-B$Eas#A_KU)cb%)IoIGcsA#5tr9MwAqSsQa9Orn
zGL`hx&YO(%oew?uA|9>OZ9EY1SXl9kvKt0noX)ObVWSw$Vu8F-8b$nKr)z=mBk{m&
z=UEmaM>Q0}-Yw*1ny&Dg!CT@YWcobbz(I_*Qz;WfHVqsd1sgyDk=LnH0`WWDVDd?d
zE*P>~lt$hThyo11&5Sm%MuqfeseF71&KeVdbTUc<8`M_Woxrk22MGN-&y+%`&!8^p
zggA!KHO~~z@r^;l*94(i4j{9@6e>-^2yy{+(I9%&s9+YBDP?)hBU2;631aqfJj>a^
zTO$TUX$Y_xN)al40E7Wv+KtCl&;`FI$CSQxPfrXCjQW0@M#{8Q+%s#7jDr==&Vif?
z39K1JcQRnoh0~QloKMHjaaPgkJJ#ruN0c=0XQB1h<|FkgR0`w-oLD7T)$^#9Uq8b<
zNd!o{oJov+Cgl2v+>Eeh*~lWlW07R3&D@0ocvdS+p@>z-6ln_YpVwvOqR~Iu$1}Np
zj0L|o04I`r!LQ!<f@tkdKD%P@g^4kX*)mDs9CEjd2I<78z>hr5zzsZ=Mi%E6FVbJX
zG6$>>y{Hj3R??jAja-GmFOf8|a5_3?cxkhD?S>ulm?reS76EmX5U*YIm^88=q*Jk4
zzTUH+!v&KEThRfE9v@$4WW|Q#D^o*$j*xkp+pt+)TgkX-Sns%tA>``??uKPH3uEN>
z?;?*(aCDvY;6l=Er?y@bZVi0q=u&<E#dnq|9W+41Z&N~Id6~=5#DY`eDTyI?vkTxN
zDe0|Hp~ZZn*{l8$CDpH_t5ikeW7gkCUi}pqB4`3z(xZq`(#z|!$Qei!x`Fmj)v{8)
zmSSX45D0LVp^*C-ft|bK+TUMI66cys#809Xv&>ULQ%N=v)ur!RgntCJzmF=FYsq0u
zzyoPii^lRhKQ!RXbDji!G9_}s2_8xFL|~!yoeg$wM3pPTJq(Ibm!`QXzp;ahj~@yb
zUu69;02P;-uH-^)-^&4+cfQ;Ot7}FUO1<Wd@Da~{HrTufek1-k7NMgw1|;3`1)VMC
zNgG|<)^u9HL{RSg*wWr#L9!n;pMf;I_=WHVlDsVNl}{b}N-=1$ae~#ELj{?1e8o@d
zSngK6MzL}B)u1CTS%K>bBc&MI6dO0fq9#NF7W0w_eXOy%L_B=`8#XQ%EVlL{nzE-D
z>odVv?9T!8=ON#Sw0sepg>wNvvFQG~=m;keQep+57p(I}{HQ4p(kM};LPAeV`$jb3
zbNkXs&d!8F7CKT6pQ}!~n1$;p5PtD(7J1G!*J!Cq5moV$KRy_VN{#P;K6c$BSeIy-
zA#%wRy(J==B=z>K=TT}y1RwU=b^ERc*aI#1!yBy^WM6r6c6R1oObm*nuS43=4Jcm|
zJQHldtORA6-N~iXPb9WF#|Zss_wny64Q9lD^2F-_2YJ2bsy;4V%Y{y9$n4-?`OJaJ
zb<D(lC5tiC1zHb&he(=zprT;}EmcJP1`QaMvWCi2)NUQ183K@PPg~@iy6x@0G2}Yg
zKA<Xc%Zu2$t(_rrNiW2uPlQ!<27OcZh{66MhLd(6hDG;8DsU#)-pN*3Sm*Io*u8tm
zOL-n>);R#N_F7rvg}}i&wgF=U&>9*#GHR9;*ymuULnN-v=+qmFlh(b7VdUG$r2_96
z4?pg~rAT45JU5O_k0lW%%u&JycE!H(MkJ*z#W2=k-2bM`3+|0&X64cBT4xltQgpJ_
z5@rA*VH<-l-}@lKXS>#?D=Ah&)n|+s?22V}pcUhyW61c#E1a85Nud|5JO+?YhWM>@
z1%|u$ztka0FNu5lEgXi;XAA#EjEfTv-9lp)wxU{ehS`b$TkpJeJg*$ZrObc)<PioR
z@9b<QJK0v1J&#V7tSoalWS_jd`(E}gPAz5z`{wmKZ%=;C)>gp`&@esDO^&z`J#ar;
zrn2m9+6Z|H>qO=(x{^~jX~P_J^|)_&PR7n*_2ar>3(ObXarbVHazSm^u_pf4#_5BJ
z@R@WWT0Zl*uK4r2`yVGKR({blGLPbgqBkttFg5<Gn`a9P19{B`RSA)+OO$8%r`Lnr
zc;2FiF^gf#cXvyPmx5{VmL1hMa81D5_;qM%>swI=WKCx#8E_san=*DT6|o6EEtuf8
zCzc!GQ_X{NO_wv(S5(jVlf!@U5pn)H#SzPeyEbx$YMe@*>X`ZpHI!oYdWGzPIdgl(
ze6jVQyU^S&#ZMY@d%0(GZ6rm|LpsZ2+)gLKTSTBDD6}kQ4+n$Y0dRYq6kYyo*^eyg
zTyniXDGxHcgrC>0>8_#x`~`SuF{BR&oPSA{lpqH$gsJBR|HCK^{nH%@4zH7ddKtbe
z<5QBgv(=SceiBTypGCb%(;GicNUV27@}zHf;LQVpG$04_vqZtw-4PA_O&26BpU`$)
z8h{~jCUVmG^APo+w3pC9-7%%&I%Y)yd$09DKI~7vq526{Itgr4H8BY4Y`r8}1Ep5Y
zqoUvl%6H$7a<;b`ve^5?*F5p!Y@5H^rmkev&GoZxCwf2+kCJ|>xiytv$|rM$eLwm-
z@5k-LXzWg0fpq<)1Y?%O#h)8%EiErE{+SGyYY9ruWfekBwX*-sDP^xzt=YWNY_l1N
zzQdzsc9$$#hic_^y-T8Vc-U-i0Y?s8wsy18(%E8$t3j`J*_n2pkFPk+u(G{a-3(rN
zT*(jy8S}nsQI^pZp%(L8b4i*g+wNGrYqq&bnqI=?10}up4^)xo^K-lET5EP^BBwcn
zHCJuhyBh%+<+CW`*7rJu$<Ai#k8i7Uc|GWLt3#Z#xUMPIL+@TrE``ewCou`z>OhNT
zk!WG**l9<P-b?2W?9ewnbBz4*_M7VRUaH-E6c~8-Nyr&2Jp~-vA_9OM5J&IC!9huV
z0tH>=1-mM1pgVS8;%l7{TteJHV?_#_zycqpa!3k-xZOQ!paY^%{iT>++LDLkfE1ec
z*`T0Sw3iFFYDcwJD-Ga@;vKOF{pk#Yh|Z^G-SDgW?@29vM!WbLc5KGqs8>7|;I)w~
z{3B8|F>lm>42w<Z?Z5IPIUJn%84N^1L}$O$#?3PwddTw=O6(<@1HS`k4BAB`sDzNG
zi{vtB0bCpXvBj-V*RwI%9c-cgbYJA?-#$mM&YPP^iHK#NA85>jxrURSsm#nL&&1kY
z;jp!6+40y)B&Rqvwdc5(N7D#!I^2HsAPOB+_S&fecxl6ZCyR}$1?45<*)46Lgy8@{
zT(Tg7!|Zf;{HfwQ#ccc3r_3+3TX{8+W!t4KfZsdhb}nz~FX#Q9P85I{49b;yQELgf
zR`lHyL|$l(1=)n#6?Sr2Gb)C+3JH(65mOa>4|k$k^lZ-xTeu2yDL|Ms=xim2|KZX|
zquk1Enl10U-s=;|!TQ|m7jJK^uQ||};1J~)>h1>$SLKrNyQA+N9j=5(0i@gQqjA$3
zmreR>0f<@Qv86!1Q%zAL@oi3rab(%BDh<3ALwj*g{S|?H@>_voh`_Vjj>A(q&%`4|
z-<*r>g3$7NH@~3pU!x%|tj1CX5k^&<4hONuz-hd!d_HxhZh4t#t8ccc^<Q5w7GN|*
zql&Zm{h;g;a?G<+mhdi$cB9fJmf`;J#!(zNfqUP6o;dx|R}0cH!r>5&`HNt6&IM*3
zxG1@NVI3Tu>YFS$7n<}%*ag2Z+#>Qj{J-Py0fGSZ1t`lxJN{}|()p(NE6ho^TjR0z
z8wLy2Ju7qIr<9~kbnOr$7FGN?afOY(=t8*A0=aSXaPOu;`Y1{#7=^yDg!djnF)dzn
z%n;Ob`m_!pHD%ih2rVb@OVb}aKUr7mMcW8d9|yCD+I~4U4x4Am7x~4-+K5^6oRm2o
z+>Z;dc!u+15VOaYLGo-GUqmBRMIz6|mU@QY4p$(Y-PlWimooF#Sdfv41c~D{dyn3^
zk?rSRG`{*AaR2^rIoKXyW~zU|$thMcxY>VqNX5YG!Ej{dLq?wo*ErdxmsPYa$)XTd
zt|lJ(;Ai?xGsfVa(0OYEU}sVvRrvPnO(u{Ctz3ByO_q`88n*)`HLVa5AB}|V)QEGf
zKh>In0<@__AB^^;0<KxgQi!?bZqiTfTMW2tdSg-mtq;xI{?G!53hqM8JX^1LZ@zt!
zV5=(WCwfxHAqq}gX=yg@AI4ZcwoH;2)2Pp9Y*R0ohm=AA;uj_kn&V=wEs%mpPjt4-
zFUC<$4E(5jYg~@c@GZMJGOCcwEVO_dKkamY!#%Uw8hF1{Ob9G)X@|V2Uj^VC&9l(<
z<!8oz6ML3v&!|z)u1n~j?2%pm7?SF2093QBp*bCo1y?SZ#3+Q|D}L6BztZ|y%egJo
zrT<w@{pSVw2gm8TOg}&hgIsdOM(*ZhM~ESC=_~K&#N#M<;2OFSpdk&Or<AeP8Dk1_
zd6rQwPGFd!KZeQS|J2t&cWj=%4qQYp>0^}|6&&V2)KfOdx2t(!Rm4$YKq#1Vn}{KR
zr6Th>R>=(4nSo;Knx;9QEn%jN*XRp8xEqPQR$zA+=@{WGmXZVRKRsizu5)QZkli3m
zoK6cf<z4@3KaxClLYdZI#QFx39-SCW+BF-Rm;lObuSVW%vmni8l&;|14hz2&1pYb`
zQgh^bcdoB+=M1mdMEruRzIP8TRRGy$qPHb>VE2*e0%aJ%9N0tutO|UaYaWNKR(el6
zbR!LLk#&it6<~LWmv7AXix@IiQN*?WPz0&@D+-hL{E5PPAsPu*2Kd?{au2V1IEPiR
z7NFrrl=W<mGYVBGZaYhYbzQV30Ibk&@yWfCA=*QZJDmKRzH%sm`Qfa&LDFB7tHILs
z&C%h!HC`nCGuAz<e68@%;g<oseDmu<D&-8zTZc-fn|$cz3&y4-cO+%6%<IkNNG}B^
zbnd`!Po&KlGK&ab-NUPF7c<ri9imoy;d~_*;-?m%fMHWO1qR0_PUC|Mz$>#c)Kgzn
zWPux@>O;l}BeBs#jvTfU))icyj2#8+4v@b>$rzuyeYcHFpCvq$kSN2en_hm{>)gu1
z1-v#+8gx5BOZv(asGZL;H7N7N+$?GCNgwxNulUu7y<xQns@xEU=&yQDd5Z{k)+b!P
zr}dYxRo(C~$_ztY3CSQM?+zzzDo5Ggb>A9-H?3ZTrwVU|)2++rT!SeMG9~mRHG#(|
z-$hfn${<PEk#9K(G<S!*vm2*9Wfq{);%!Q>&X0}Bn+BkFrxEY$77zDPQ}waog1+Lh
z-KVKu)|0ILR?JyU50eh;nh1PSOJzCo85@nxnGRtyiuMfDTfuxYh_z4xDUxesJQCX#
z%Lu#V&;V`(j|`>xpVWt12@pN`#g6!yrKj8ys)W6a8HlN18GkuE^sZGkh`caj1OHv-
z$2q)-@4Pah@>u3i>A3tgfCI954OF+P>Oj|2^QhE0<d>8Y`U|#<Aq$ZmDGArO1Xq}+
z30%_o(Xn>QG<kkLP$h_ucI|EHi_MX7k)O}Uz&B{8c@s(J)Yh9vzjm_OsD1F~o+rv2
zj?}uBCuzU_0806WU;C7ZQSM3n)z$E)nCI})xb!|>O7kCAg7X`Zy52Y=4pGR!{MZ(f
z+y1g<>p{fB`heW;m3Q91C=^^Qik*;lGH=1*l8)WOWY5^UhRrU2rC%kd{stiHlR;P!
zv2t`$Q@ny<+z>VSvDl4fZcwv`$9ZVMo3R`c+Civ0c7;dAXi$z6SS}xaJ%r_1OTe~h
zs|4)R?nR`Jq1(C#rRdAV+Do2%Mqdbj0ra;v1ZBP^40{R;`8*W`?ojjeDB>@GMAwfQ
ztW;B;5@_pNsFR_Y=*R+-EXlmB12TTg2bbxsXX%es3>}aS)a@}~2wrFnEfDwi2su&E
zA6g*-4=tfCS|CCCrB^>qMS9n%bN^0z_*vV3e07zp{I_(YO5uZQeg@1@hHmL*<jxio
zMBazH2T{%B2m0z)g-cMm9eeAA=V;=1a|U0j$6@w;4RP0EF+x%dTk|)%p!}RBbG`5v
zkX2ZJJptpYu%V(^g^a#LG3}eq2TvwZl;`nzR)=juEs2(rJ$|aou|8;0L)1v}E3M?U
zeUsM4m$5Vrre|cBX%_ME$tS*HRszIN?jpO22Km$pU6>m9>==vasf;NH@b{bCy(v#~
zjEMJqOA9`Nc~EeHKLzc*X5lax?}7L?4i=znjAK*Hl8u)6Yy|9i2`$a$@0kXLT6rB5
zKlyFOg2Lz-zn|Y6-cBp^4hH3uJR3hC4oVWl4ujlM2udE_rQC}vyON^^v2i5)S_O(l
zB1dpZqApGAAJaS?4ZjT>=)^0<h^mZJ)I|FLyW4>NS|$HXbaE{Nazu2p?V*f=pgc>4
z$j>Wj4Q-^PcZE<!lo1RXzqB0$21g?oO8OUKn3cqp{4<PPZDMF2J}m7&hD{<@w84i#
zO8-5~Nb~<3MuO7dpVes*IECzA8dvZS!Bkx>HL$A2ntC)7{MnC7tQJPyT^?l8l<|Au
z^X*7YiArkV*jMCB61^`n_j|qZ>0L52%BdSeaU0a?mJUygeQZgRlc$hZ1}L8ko&jRo
zdavB=UOp|YguzELmj7Uj7tB7E3I2gnK{=)7h^Y3jGbpGHK~(Bah*Z^~b5W9kjJ&7!
zrpDG2oRxivEY(ELUZYSXu`|Ne&LkDoJ{^C64pGNKOH&q09=yGx^HM<wn`)$qO)dlJ
zQxn%*y;3c`69Z0tS?usWtRO;^DmK-bl8AmUdT6BkgT4exQCDKvWGp1hID336*my9j
zT>wp4aXIvmeHcJq%kBFB>iZU@OwSW$HO<73`HHHsm<!1g+IVs!LGo6J!LFGa{ZZCP
zIHH8><$qlgX3%dwQ!qE>`zHDtMBqOIzxTfeq=+yGmwXp(Dgq-qOBAEaI1$DfG~+Dk
zCL{3KnqJ>!ejKKXqttVUTyhlOr-X!`<g*a$hAdS}*--%G8$-rdOPl)$*q`3?JAMW)
z2AC>8`Ay*Ye-T)sj>AmsG-ypqnntNvcVWn@z3TKh-_tju7Dv!2jk!Cf*mkx&y+u*Z
zjfzN0P$t?F1=WGwPRGa66ciSXWJEhvscCdP%yDhLy_YzyZuz)Jgr2Q-u!PrxLWcc-
zBBPuuUi%QGh>=Y|HXG`@HAXCQ)%azY^6Uj+fmVO-m`VYL0q6%81H<?7?2KdPm__K<
zU(SorMEJW|R`(5$H)X<PPRP|V#Bxs$@&6(f6}-<-XGC?D%4Mayl4V#ZC%uTR!)jx_
z$ut!VuYF19O|7I*khZ=|?Kl7GsJ@p!)m3OQT$B*=DARGFhOWkib1}nr0m10{1LMlk
zGr%%{<6UL2mKMPhj!tn5@px)8%sV;m<mdo9Dz?e*0=ZSwCnax60!Om)NV&0_AxXA0
zb}66kjR1=Qh(SZF9gN7(?Wmyej(^+ULH3>5(}*5cVI?Yxgpk|v-c{_O6~^<!sY%)d
zK9O5e`ONyF$AWRh`M2(x=)*jkfitCyTNI;&JD*TfcxNf;uheQg47<q!pqn<~%6Y2d
z;x&`=lkxO(gObW=e>9J0HzC&445kd;2oJBfj%gC12zpy>1oYUI7E=Cp*0;#A@JV$c
zM)I*Rh?dW+CD`C5fof;BQnrUDHA!$Vqk-ca!Fkl-RFtdF=+JIf%WQa$jzzQ4{--yh
zc?OGI^qmk+idxaGj0Q|Q^>T>9JIhV8uigp@l>7sxI+p+mcr^(>vH_pMVlCkJ_m(UP
z7(-71=1C}ie9Ic^QLsSL)h=c00wZ7;+0(t5xNm}O2)l@GX-Py!gVVe7s+-hFE9&-I
zMK%1eU9;U-d5fn*wtr8;#4z;<xKajV=Y!XvpzTdqY0?2wF*gsd4WNc)Z<I$NLX|r7
z#zzm&M&ESvC8Cy|`x{I3&|m}LjSR;sS&YeL;Nx}I`30oi?pF!aplwnWX8pZGDg|h8
z`!L+6Znzpbz2C6kd_54f6w9GEb||a2^<EF@%%lu6t0z{iYkvVD=a-y^<P(dFzVYq}
z3p}0ed+V&YSk<*eV_<RbrlM&sxba8I#XIe~jaFtD%xgE+=8(?rkNKsl;bmTT2dLde
znLVXyx5m+K3arXh(oa+nPvHgJLD<85XSgB-Q334a{-}Z1ACTrL!~i)jw`=WQ5E`vj
z-IQMV^N)u^H(6=>${|UF6_5+4ie&N*;q4~WC26@j`0n8CV625Q%^&NZ^GaOZPS4N`
zyt=!OD|k)NLM%td*^Z5wWM!(yrr)pdk&3K^ZKZivPeE(>_46%=_;#(MC%?~D-Th~0
ztVGQ3Fm;>|e+xY5CwWZ}xMT1*<t-w4o#34zxREIV%<wkSV=iIP>;0P_FqdXeZmWS1
z{(cvG9|<bMxdG?Fn<>_S;&(6gRi<+#q~GAo)IP7^ReaI4vD?z`6pbvT#`Zw1E4i(9
zL~%r0xR+}Np2b$)MEauXq~%XpR**$U#U-Ns0@^gp-zR8eGw3)nc49HuLIzVfBHhmF
zdii_KDCMUjn{_ugt)6D&HmaH{m$Ua1idnPbqQ*xCIloFG5yxl|<Pgr({rh*sio(H6
z#}o}VFlQqgXL!CWX3HtEshNlkNbe&akBVk4=9lOh1C5o;xWR=ex@g<DG?*jX(rCzZ
ztGu%Q$o6dIl)@0N`V|yM%1ZroWZno&Str=G4U6)Tq?HEBKd)MfpC-4FUcMA*g`)c4
zS$|~zeTOj8+XXs-2=+V*Dm9ReXDH&JcboTvf#!4YZCOy=y?MO#<cso$SzibBj#IyZ
zOXY_b^%hgByZa07{lZV8RnM-PMn@I=hS+H5-+F)2bl($~kq~WN&5xo&yE?pb5D;wg
zHw71g6hRY|5%pml=^uCl6_}<@PIe0|V&t}Nh4_*mPJ5v77amN22)`8nOh4P-vgyrV
zZjCfj2jVOc_&hQX$4{qLG{JX~${_7@#H)>c{{)~^Dqu77M~}LfLgC|O&e6O@2-3lV
zn07E5JX0!@p9&_k%r(2oa}$W^h`%7Vd_@rIv9ED&eTcEUQ7|<oU--H}O{Xnr<Y#18
z@l>5fVgod)_Gtc<RTMixV>_^p8_iPQvv(z6uofm{zwfF%hGK?Sc@*?M33lMz=ks$K
z{|Hwx*S>rmv009{D9~^bt*HP&ogDMEZ_||Jhw>w~x{7}6_1;^r0!a-X#J9K4tn{2v
zk9S%OEcpW3OJRv_y-YuK*x5Zd0}C$iU}j=7^cvJ<YRYyb7K!kUvA^BqgRA?P%P_)x
zEKSXoMJ*RFdg0xE!nwK#gVgiON+t*7tLoR(Tf`yxPx{PG@?Yb!)FUU{Qon?MO+UIm
z<HnHtsk4-TnJv>7(M%j%GOKWPbeOn)qTB&EGMrMz!QPsMmr`Hdk*3qvVedmc(fhc?
zy1lmw2C1gx_`P^B6@z7??pS>|n&t`wP}MSCKBy(b9naL=4=kPIBbf&8*EE;%>lIo1
zU_Gh{Nk|oQAzha1^amXzoZFaPKA$gUzO_1~qCbw7Wz}_rql(<K0yQs+D&taOwS@K9
z4*7o1t}#ZirnZZol*Vf-*VLv32C0cldx-sC?Jf-@DyJO3w4nu_vLV;e?k2yM$$)rh
z(fojF1j(OFtAa%u5^Aao5Dd84G|v*01PJ%SL<;?oKD7L_PpG5yqLa$kNk?v`|3<0|
zin&SL0+QEmbd&2+y7+k5o8oqb9)!0)#@oTy?yPAxq`ldGv9_%4e&@a3l7J=;P`(AG
zE_aS%f-4{#_F4K$(=8=v`nwj@epqQ^xqU^8QP1}(iO=#(^=Mm3p17zU`=jt)zucUK
zJ^;J;U%`4PkYP;_Vm{GkJkP>!=fe3vovT7~Oht^lw8N#zy@hrFeSrCAqW)+2HrVzV
z?R{Rx#P#ar1VL&38q>Wm7?ZfEI*YV+QYPoDua%{h*-#Hveb7I6J8utq)1KTCZ<G)t
z1$`d$CUnyTh1O<MH~!zHT<sop!pX`34G%7$p8KiJo@wO5W|&UeXV9sqhPb0IZVQ0q
zlf=SJ>!<dq6X_`7Dk*gI+;S7gc!+<d+x5}Wm7t^P<=*|=|9LZQ-?nn*TUHVSA4~Mo
zA1RNmeAF~FTa87e%){8fP<vO&UxGBz`1(Zt3X#>)Kw(U;ulfyBpE{mtO=G^#3L3m2
zJ5etKysI}TErk--|N8lieA%t}a4y}CkJPy2J*wj%&JVs;OFs8QNubo;nc5}-xsH<N
zmiRzoqnH7YUu);{kAGG#PRrq;?tR}<roP}l&A!if{4ZwTU$J!U7yU}j8;)BR#NFML
zz;3b1D}Bc@^YF!)Sl2~RBpK3wfDNyTPb-Qz{@LJ{c2*1DOAjrS1i>ev1iRP}y`y9Y
zvcQQ_u!=?`&&$<wwok_x6J8IZW<RH*2PUM>1+llQXjr%JNIGZY5&KP;^to*-rx%Y?
zNWC9S9BB*>oB8?^!`oog>xWxd-=N&eM=}dOcJqo8@pb4POuWGChevueYuE8&lgh4F
zYGdmeH8t><<1n8J(OK0|ci)*e=XFRoDU&kUYJE;KC%&ns;k}x8gISKn>R^?m!~Ek@
z8&_-Vnw>sdHC9f|)nyN*1Wy!y;d(U8cX{e8bw=y{Wp$_zBS~gfdnRL;sifMtam%f{
z@VY)Gp(?y9uU$-A9E5~X-fBvh2G}<g`wX}3y3M)s%2nQf0@-)ccF;KSZw0l$?j}G@
z72*|59$ApzG3vo;+utHR97!xDKpGb`AUuP^;^nncZ^tf7pJc_6N3ttE$YsPLm#n}*
zta6#NGf+pzd7>TQ=GK~!;bI=r-?V>zwr~}JC;if5<v`3a4f0{zn~Wy&Xpe!nYlF#K
zu;-wlC*-8wJlg0(dsjla|EAEne+0{RXu|OT^6=*kYAmIq4PVCM4W%PW)DtWHK}T4B
z9*kHe6keNw(KMF;z9$fx_K{`#ZAMifx3rw$3dQA48&!iq`Q`2FPnsVncTFpQ5zZ=W
zh<9Qz7sp+xF#(H*jA_ZOWTxsT$<xdRxR36Kpu7+q_(iJ7cwHt69d=6I%hHaz&d&UO
zIfVj!aQp^dpm3!=N96L)W@Nga$ZG1X?&%yQ`>8kn1xhOtrdr^1?@opIgnDkH7}HWc
zGg3;%1f=<#==nI0ZwJn7!02To_<lBwQ#>IDSZO7kCQLNJBPIoqf%W~_lXs9x)@|fi
zJ2z%A*Q=Wxxx#uR=HlGnY<?%q&R1?~Evn)dz__lr8i?}UzU-z?gxI@rse<BywSHHN
z0x<yvKiK(qOSvJ9k43+`U>KyxLKPNBFI|$TjAL&_QU&QArznHpwT|HC^on*J;7h)X
zgGEs+?6tT*9dz9JqeyhP29?)^{Aw=Qw?CqBxsFLB7<hpFaX}v_*|A4wAMAT5DPB&u
zFsgzCk5h_xXIIupT)=j%LwgZ0$?F@ayTq>;5gv|zq|AlEOJPBZU=ZW2XXV?w<v;U*
zz@BN5W3!l=N_n{L?xzwVVYr>7&&9cTH$hAeBTz%*>3o<F*ap6vl}TafZcFlW@jKPI
zkZ>lw&jU+88Sn!rE8-~IjhtO#>aS@9=gMU$jCp6%ztq6@3C6DnfI#8lOjGQVt6RLk
zP=u~OB|%W1^A(GO$28f20oBgDtmu8R#Ii`GJ=W^<mAwyYd~|JovUbwjiF`<+9`vW4
zQ9~GZ!N(B8G9NC$1If$r2Dc5HiUGQ1tn+Wot8?#g@(#(9h=$M~8!lDfbUGoJ^-wUC
zLQ(^eI9QSSZJNet3vOq`-VZ4(rzK)l!x*<#lZP<0M?z6$jRlU>hfs{p2+rNrHiO0N
z1(9<2*x#~jB68h?-!v71<$SCGmL51{`QTZH2V?jj1XBEf>Xy(*ZQ#D(BiC~JL>4(<
z-mIsu1oAU2fYfbNwDs~~NryV@)!j{OJ;~fgI@`dI(rBT>z;`F5=x>F@rr~PK(eoUY
zuPB1hUq#;Z!JqYA*`I9@NAxzeS*Rr!bqeh!_?}7bB0Ts&5CA24IFB}|kVol`M}wzx
z$bX#!x8oe|?BP>qFa}%;5}u2~P?`i#&4~aG0KoD^0}kGz&izQ<G@j6y(C1ELTxNH^
z-QsCyorvgGrs#PFm5`5XL$HIozS}g{VqRe!@7V>q>TYj>T3w0{l^B770D#-=VkqxJ
zH>?f*o@ZN8E0u0no=u8UI(e|oh1dBuyg-hJGkVb_H;H9i#b=cZW=!k;{ahTcD4Xy<
zNw|SQUXTl)ckR_eiY8i%RVrcjjbZGidhtqXjJhi(T6>gDN}hP|c9x8$cQj>`e&U}y
zpY08~`g0xb5x}NkQ5fD9j_rxuQoh-B-PP;zxT`FBZ*Gdv7Yd``YUrW>07U&nfy0fZ
z6_%W13r`$BM;~3>YwWuu*|Hqg4Zg)&Sl+zquZr^?6#XeYB~ifJxPb6eMmjm+<1^$I
zA)t-ib7$Q`f2>~?ODo?#;@wP8%6anJOb8>qL<MwdOb(`z1DA6PXGM5v3ck4JB`*mT
zHZFedhuz&;9=3gdQ`K2cVCRw9APg=j=WS~-_Ji67X>ZmBQKv&*(TlP;Y<yj8j$P6u
ztd0DaMn8f;%sdyQNY1+1QrY%cGj5Ru&IS~s!1IK?OX1mY5uBOq7OqmB<K6GKdezFC
zL#f<^unAZc7Em0R5h4lHi%ub<Z%yRiNe74_0oICkU?aSBO%A`xHH@j)mT;LXbe-xF
z1k`qnY`{vwvrr?xZTXU+{=~BWa<wE1kI;gL4hQ*ei)_ZpK?<@nlO>*?A}kKR&|#U9
ztv$0(vF1bMuQdR0XM3sp8vMLAB1_I$_nDS5Z$k0{wd0rX2A27h<(gxIWxEiQxA*&$
zlM$DPxiPnf`rkVFb8kfORm}zQ!u~~_iBs8UFG0|S^o)2Zr)<}c!8YYzzDb-3N#GLQ
zm75Y#Q>^>=dScex-06@VXW=BRKx39jt!g(p(YUSfvT1XPa?rP4OFU_;sI5;BjhXqL
zGd|_w7&E{xMuFAA465cUA-QH+@^N#8QySK*HD0K&72NqLHuFE_c&AurJL#r!HQK4|
z<6g9ILbN-ot~Tetsje<6QCZSYIr$2U*?m(<1|7u2L^54^`-uCaYCLWS7KPI?$JMkT
zcR26bksiEktd8bCj`?4UqXUQc0Hi$h`pU#u`1zIOH`stcncADO76~fCAR7<ZoLtto
z!`<^pT;Zeuu-;3qf4P;6Q;&`PE)P9uc$7fy!Mq~nU*@E3_~D&__7=0o#4QH@?AWRO
zUp_t=8lb6$DhurlIezR^{CoXp&o{KDz_-OkZOm2Wu-ok!U{Va%PDM@4+El!*F*)8E
znTcP4c2}opTZS0mJK_^ey=Tq*S><{BYT`xF6LPn!^Lz9syMumTKkWyh;kwQXs)HU+
z9~y!xF%+J_BM9|kwL>c8VdLHz#hfTyatr_vtG62G1Ktn(n#26Ug75v?QC{?9S%Ggq
zT~(31)}TB4?UNIsQa!K>+G7jOopf2)EBHFf#qkLp+?84BG?);658c291j<ugVkb-o
zv;|b;I3valz!!0t$N>dQ@uSzH34B#Ee1Zp`v8~1*4{vvCx|H4w;_lqR1S@_ljAxB~
z;!sszC3La@qYf-q)|-l80|mWxoymDFgtAy|`GHch|8cvs$is#8rCYPolf`%gvijG4
zVwA0n4h$k(GrchCof=-&BfH}e`cLyd6-Qs!kEB-DThY@7cM<-P?ZK4FKjWBEZS}e&
z_l~`k^HC)~Uo*o*=NIM1^s0XRaYNtI5#C2<+tL9r3XQlokLc{)!|s8ei)w(k5bv(Z
zso~rYtnxUHR7mmJQ-`+dwQhgT!m}$wbnh3RjUTdpeXxc)tb1<k@6n5rd>F+c5=fXv
z`bUX(O(M%~r^%^5J<(?A%o`Hd2Vc`W*6eSWYcfBZ^0aThusZyv*!s#$@G@_(Q50>~
zp;0$r7o?2jXHn4NOXFNX$>Q*#@7Ll%5LT<XQ~d}gJ+E0UEbewHtbvGsf28sW_*te<
zimwt>9fbF{O=Mf5vmu>(dnN}D*@KSHl4^Hdzq`)n#yd*nz62*rm-{&m6xt+Xj4VoA
z4m-9zp`5C9C2BFidF@Z;Tp-S}t#BU8Jj~)?2dzj>R!z`lyzCHw&F<$aCbca{GsxM4
z-v$2dRYR_KFzOV0E?p6zgpi|VfyeA))nHmf(bunvKGa5)#<?N4T5`D$a;r}qck7H8
z^#y>f^%bLpb_sP1u#bbr1ZT2-FO-Kpx0MKkxmb$P{`Rq)k$%mwDNKxmphO&22RU64
zPMW(bnEJveB?p0OP{o?JZ=30&!+Bj@%cNoVmgbmjpJkn=0cC@_9W*Jyqav2}(%+c{
zrZN6^{3`*GB-E{dr`Eo(IH*<~HqUf!ytc$K5n09m!d2Y))(Et(!ur-;wIq44Wv)_X
z_l?FFoX~fiTnduI7r>`b{n)=(v{ohui^H?2{{DphR7h31L#f29r`PsZ*mCwD1vQj7
zpb>f_p89f+>2%Z%P9uLdgR<t0`WT~umpQ@N!)BBq$L$;8AK3VRJY#efUd)q6K^q@+
z4Y;71i;t11puqUFo{g9zQ>S=F_oHaeY4_1{!jL|Fmjie+S~9Ug^~-UY1v55HY=E!P
z9z5Vz&}4()yN;_CLFH0So>$}>!e@Qv8Le-kd&@E*+jA8av>IYSi^j$KPiiY}`K)T5
zU{Be(;Im#p*Na_OuX7hhmWAa{;q76TedBQ`?g!s{nW)fOlOa|EJL!Ss03(g*#giYc
zmOS5q0=v-(J(JG(aqj<IT0B`TFbiaUjJQcDbe8jm1F0OGxBY_)GUwxRe@!7vbu>Kk
zKTqV{Z2KagYij;$NMu4C;6HEquVaY#azaQpn8go8rsm+a#&5x+AdED|l6U3fmbWdt
z=~{4vM$X=Qf9-bZP1}7yG%cJf$qzXB`UuIq_CHI@?p~{#`g5w9NwDGi?Lx8JAW`bO
zS6^5X;XB2X?fN^zX^p_FL6U#b_62`}k#$zXaFNq?{*Ydo|NH@7L7yBs@ZR)T2Aj^y
z@TvAo+{j?{0>?lKz?ugxdYVBx>iGyfwpqo+MhM~XApv~H5a_2>r!BK609`;XLV>s<
z1baZ>IFQcP#F27D5D6d_rYOfUCDertI02#st8;36@U{m4wyTw)3hAh+2mtBrz*xsZ
zN4{}vz#26vMw;?8KoHQiO^q*5A%wG)h5&%{Fwhe@Qd*({t|3TrMM*_pB?SS29!5;I
z3>rvqUa~!8q4iTjsSc=sznL9)PLl`#bkPA0R^L5Z0lv0kenk9#&eZ=-gJPF^Imqx{
z1_+jqv%iKB(O2}fh~xiT&=IxYnvw<ow^h9VXiE8iScUhGrUw7pDqDrW%}Vz_tkU+!
ztW5vIDk*==3j1%Xoc}Rv?EkRJ^FL<&i`jac&KPZC^gOyi5NmgLZ3bIVF8K?#Aj4Az
zrL2|ttqeBhPd}d*g{%0L+0$iF0=mSQ2nCo^P)iU1@A?tw1vyesPiFvte)_mr_7)*o
z9KeJiTw3iGq3~i<03U8du=-#jf)gMx>y<1tYN+t3q%a`xl`@m9Di4kp65t{VNzPu6
z2WPz$0T3n6K(7mS45S3)IKYL-2Rq_x;s7kE2{F>*)sWzGNNohhQY5K;@NxnGHe!{b
ziDr~JsDPbOJL<}%2rKk7)~#2LUA}O}q=j)aJbkX)q#>k4Aybm_(nps>lakJI@VUF3
zH%^uwii(pD1GIe?4Dj4p`#$YGp4l(n=s~wH&+VZpd+K}6>k{6x!SHFC>)!HKX((6`
zcXcLok02|a$7AQx&#IRbStvMvaHS*d-ODpF_3cCZ=;rIH$Hr}<NXC;G^w2k^wpEn5
zLXf8|u-!C4u;}j5-G0R<7`juX2bzohZh)|yFu&fQ)@cYdHyG=>mT!!s<%gvliEuhJ
zo|da5dYhq!hqL*4q?Q^8Gw13z*rvEGE3G`q#PwraEBR1W8#Y;fsUR4cBq;C@>k506
zgC07;+qFq}%`y#1@*pWFBsKzPTmf4q+6B(Iy&i-gj*N*?#p$thsdxfe-zwUE9gGNn
z)7A3J9<z{r3Eu*zhEv#*IcuMq#QAdhBT`oe)%aPM?^!^rv$SJKosqcI2JVe5M%Myo
zy5*F7R!=hJ*@)v+PQcBCjzc@1w1)4lhrqv?w)2=r)&jg-j>W`PY$|X%(HNGL)D84D
zwq3#Ia9A~avz8{@YxosZ_r&P}Mxjdc(;V~}Un78LkLz>5Jo+lBOIV!|@bM`W>AJ%)
zNCnS(G@~fk><Ec1t?M94VBh1yF;AtpjzxRx6$Ksq^VgQvT}++uE;o{gDiweEerh9E
z&HMNCBxr9xT8AaoS#Aw^o0TLpYkmF5c9gG|pv7Dc8Ot*oI4vw0tHrD&xK|L0?590b
zzhQ}~f0u4~LI2D~0b`++%a3FM(M?U#ndMe!l4;#If$h8*8=oM~-(v@n=CrM!g3*m}
zlGb{o!7JHPByp(pVO)R&rudTp#ij)8ALHg#R+nhs^A>1hgST+mGmV@;2o_R;la*x2
zDo#CQue;{iM&9^`=;RaGZo{;hH5P-hHe@NHn;%2oMvjb&tq_pJ7d;1d*I<iyLW%&W
zc3Zloj$BMX8$DV!t|pm%Op+kgzl>e5N}cs}H>9GQomGF#{l|y2^r04A*U-J>asZ7s
zN3o8?x^X!~rJuUi1eowa1rJEM@u*`4q*`~(D2Et-{SReMym!94CGE#dZg>k^!bqwM
zH7Z7hF_(f_RyPl7!5saxU}6<8-!46?;TXh=bfXml_uaw%{~)0CbT`cF$y&AcS0V!T
zv9Kf$;0wm$-f6|%+IcV9`cFUJ0;#Z4;5!bxzWOaxfy?U3gQf}NmY&xAXobX`hPl|N
zC=f-7z+giW+v^KmE}Cg1zUeRi?OMv6K=OUB8jdsE`O|$^5ftE0W!%$3K|nQ6>PUU!
z+#^{@y6N^6-gB+ZiL!Q>A&@bz6mnV)rZr{o_DcE6<q5iBr60FxWnHw4m!H)K)iF--
zzfw*#{!tvkLO7nTkTzf!7#^-85=I#q$#OD?{~6HC5+ou(c4vpg)(jA5ppyL|v}QOa
z9>l<3j_9HS-YoY{+2eYAcdsZ`fO|5z2w%ni0J<s+C0e+mza3kd%ixH8%Qf}@&{kw>
zNjekSJxM`eU{GD67BTmFf>x1>XZeG4CBkU?y<N?#9mc>&Lxln{oGvr0Cn;vA%k8eP
z`O1LSPLAy|?@BSD&wi*~(L`Q)7Y?U?CVFm;b_QRJa=6)@26By<TV1s7qG1lk+h3nK
zvl9PHmMPINgZzaAt;*-5A<@i^qGhM!Bz6`J@0nDyzEy4q6F;g!f3N6E4hJ=Gw1E5=
zZkBL_z^7pV0YP*va2`5bGGknyJ&3;W8N!uD(esa;^Rz|6Bm$~gR;)H>Qlnxkq&XdC
zPyUHnFA_(8@F&~q?Qt!R+XlPPyfgv|hB}O9AAEa?0|1~S@jq7H5Fy8!Ia>{*{s9<T
zOtzpunMeel%5GGr>HYwWkHI4<e*nf7JPTMjQo?=%4EeZN;GeWP4^L%VaD?&)V6YJi
zu>MJ#8SuR8Tj&`32Vf}5ZT&$*mcP+Zl@QL^Z#0yv42Aqjn=bIYd!AJE^bZ;$#z;%}
zlQu`;dADOq2>J&NG0=PbNt@O1ygOo;{+n6y|4jCHNPm+3{~q=H?*PXC4-jp!#t@Ae
z0N_Ih{AyYWAeTL<49fd|&eZ<~H^P~XxfdULN{$=^Fq!gt<TTZ|V?*d<LOg%INHk*a
z`96I!Fg7ho<qt?0hJzAEfj@}C9&TTIyQx121`LNE>FU3+Ly0lmLyXh@V3lU}>`lw3
z2WrrZ7GVT9P*5&x^T)DwmfON=Vu24OFHPL9qrGIYy<JX+Llnz`GkX~qjeiDfJz+!$
zR!=b{f@7LxuV^~kBD=BQbLDLD6Uss_n*UJDoI{thH$eRZcDTY6<cbChej}sdmc|c<
z^3q)u7$pdS!kJWJ_yPpW>A$fL*LIua!w&~{^l<`uqMnKy+wmA}%J4k4zz(_(jRrx5
zZrxHlxaC|zK6R58rr~r2+Sc?th-Tq6G(>|;3R{nFL2<IFN8O{X*adJb=2Uk{N^wjX
z!S^g!ozP7~8BSl%{+?f8OPJ6qrk=KTG~y5U4HSGzO6^J`1oR0XOo^s+kpOStXZqWf
z+O~)V*qepvrGx=mN$sAq;Xr}@K6YD%Kk9%EQ>wrres2ZFZYe@wnGqufdRp?|gGrp3
z;Ljg~O)S5;D6@`cf<Jex&jw%20A%&c<B)^Z)t1v^;kqjMC>#%e&VaM=dsh7#B|>}w
z?pMxo@0<WR9J9)Zaz)I4vu(FP>CL`bV`^ofdezjb<0fjxm?A1Vt2|yQ$5SYYjn(oP
z9BcfSF}%5r8Jo}uXgdQk$A{PVZ}`6GA8lVnj$ef@ggV2l4`t%^cawx`HK29jUR0#u
z#|ZN<U-8T=bc#Pl-)ppu_U-F7pF1Z%y{Qvs_@e)OW2cj4`vEpWxxfM|4);1l4OtGb
zo};JLWZ9O3?~p7(Sj%Yl@z!U+>;gPwuFoqx&3r<sh=(=!apyj8<syfagt$FtrzZS4
zF)?wy6Nw^$LdL`Ug%K|5IUa@BMK*TyP)zoarX`C>V1C@#s6Yku)*tKm<<{EPhn^d;
zISNYG)fbF*C^-}P&do9B_d|~M*o#W`&lt)fi^q5iHN=6o0rY4N*znybtgot66n-Pg
z2Qs21WlATmk8Li4-i4u#EI&nM7yP*s8o50h&8Ovsmq5z9v#UuQ&o%ZY7s4BkiVBil
zqI`glSU+X=db}7<dqmE5jHtz^K80=3w!PfP4)i&7cKUYsU9_=>)&gU#qr*>FTCF^K
zU_l8LcLC`+LYlw4r!=uD%F=EYzXFuSXvDnsq2!wctPm|kjIgsmHo`}PVO>C0L1>7j
zF%I$MhwCrok<xB*wZXigOI3RWN2J2p-Z_CEt6It2pHCVdYecj|i^^gw+}jue4{2|2
zvxm>+)Gg?O(P>e;t<UIlzeikepaIMXy1%gd(l^FEmFMAPItv%S4{<T&OUGE~oulXp
zrY3#HgK;=C1g|kc+z&PYtv^o)|BdRRYa?6>!b@zLG`j@4eogt5@ZVHoexts+tNdZa
zrdrB-t|OAm@l#2eq|m$uze3L>X__06R?WC1YE?;YNv1!rZ3*7d()82*r+ZnWM{jSr
zluz5{uCSTV2J5e(I3HbYyJDwh7mzo964jbw(XW;mzIU37vXT?|dITww0bOEy4{Feh
zxO$BYsVqAa62JihJiF#bGrez`YPch+ral>RrHObWCA!5OD~QCLHfI0%-D_;+H&W6&
zG$$a{E0P)KU4+rZ*JrYM<3lYBp~Z5+_ab0iqehkRn@rc>&P{3o7~+Xihe&%}cI=_c
z?6ei4@dD5$JXWI`jbsetRfhxgj{DzUf<UyWj9T(5J8PGC%VZYb2ElNyIv2Cns;uw_
zOB<Wkei@{y`upVEn;iP~*`9Fe-BW4KZ9&;lLWbaKBYdQnTH%rKF2g*wP2pOqLkzJ;
zqPDD}xI(QQ+kEt4_r!QuHPljaF|}aAt8|54IMmvq#dl!@5tk5SW(U?<NKUW5lL)2i
z@wvfN6f|>{!!j5P?<i#sjJgOEz5Rm7^s5}=0~ir1Wa1Ke+RD}Y&S1=f3`__m!wlil
z+Tn!3id6tXNgk=OJ^ua#^p*L7wpsr%5s?ey6Co5vZb7XG_XYw#q!#@CpF64wg3)ud
zxnDRPh`dWT1LJR0s~$zBzKM5R<J1?*nF(x!A-Gs?Qj5c|g$e=0cC_?Mm^bk$^G`}3
zzrE>4%n*>@_TPSVPZi`^fNi_#?43hLHFQjXGhFtzDeg;ms$;T+ed1Cvq^?tMOjGuY
zzU3;mA+S#bz#Hrpj<$tV({MNhjC8e~h|N5le^?1e7?hTC3)w~01R~MnnE-u4TsXF|
zL)HKBj!DuNj2Lvh(Q~reMcl;GXK$Wp&%KWBYcXk!ZmzRBQZoj)uzS!yCf1?b6`Lka
zqe_blq&zDZvE6JF#GrDBXo!5J627^QOHF7`?GutJ9p-9V9V<o07>%e`^eJ}O|BJi&
z<^hBN-FsqaK@bL%JMgRhL{Id$!CoYyEGnarr*tU)LdNDyT4gc&yb%&ri<vWZt8aO5
zIFNca-YV_cmU7M=7_M^ydzs`hiCCkLUW07J8l~twA{KMr4*3u!i8@ml2TyAQ^oD`f
zMdg9MmP&N6uN)5JvH4in&;PwHzsOPzA^~?fZ>mhoMG=H#lt+0y>*JUfiB^qe>jjQd
zQhVp;hS;eH01E}wwcya~z1y0fboG%~7vEye?Wxf!tHHRGV8qlzRSVLgU$3A?rm~9C
zi3F-B_Q-NiO2C)6z>d(QgyuOWhTqT2jG(5tW3^4E+EJAjFd}QpwfLUrSji^!jh{?8
zp1<INfW=p`%mCRRk@MXPE{p*zwjMIy<F2!@yf@_{|3g2!B4?&Jt4i;FB-oJ)+@}P0
z9anQ8GEfr0e>YWN3~`0Ybj9vkFd+MfO%L1bymlEC$Ddq<|8gAuqTuekE3ErFmJ1_x
z8vXS6k#L2%E=z9lsP$eGs-fw%suVhC@7`SD!sa)n$EwcO=ofzt)ifMSTm1|<@6acb
zse3D00e?|J9=ggJqS9BE#xzBkSmIit5^dcT{)&n8Zb#{Ewth$TtD-1FI66mO5RchS
zq|~7LQd+uGBKn&#KkjrS9Cx5#hQ~DMR>w)9@$e=sJ=zN1M!X+hU@@?>gaGelLL6(n
zov=gkGbV@W(4d6{3%m6tn`eYyymV+8Jv>2GsR<+S^7ifUl3Rg~n8ND_ZUSctwe>_;
z@mbHopO56Mv@(8?VSbMnYM5-(I_QYkYokgxXRFMpEC0D!TTnOWT2jS57ZfA@-qnQL
zzo}}|J)8Nvv7p+x$t=+0%VyH3F~A?*7#(1t&G=-Eg;A494an`N$}u-0-Y0iPzKsU#
zlhX&OOcB+U;ie1bf7qwIwwL#)xSi}RN4fgRtSnOi%=dc7TYFJXaj3RZ1H|eghqpdB
zA^wL?SJA?68<*_`h0v{!Au#^2zIU46iu4Pkmpt#nFW8eH1DtrTz4%q*&$TR<@_sp?
zCHu01#tbLd9P|Y1%)l(i^AMD^tKSq!{-!u2sk(qSdJtb9uR3-esjuHb8E}2z!~xt$
zuh$-Qe6wrH0{mrl_oHFgxD*C623f*uyaKKxDNg~S6y6LA$<;(;7V~qX+Jh8mo6c9_
z48VaK?(GWOv@}I{X;`>f00DpNL155ZV^{@_HIpD}?}4072kDTCcYgjQ((XTy{4lr@
zAe@5&Qjsv^2YAy0Ku4aEWS53gmTmmGypAu;mAIkHk@}dD<=ELt)BnfZdxb^SJbR-<
z&RNM4hnzu#AtOj;KqM+TCkX=*B!eJ13|WRGNf1y0K{5ye132W2fJjtwl>DvX{qOzm
zZ=Z{Eb1u&F%*C43-Bs1Sx_i~C`gIjTLWRS@;u$ISy&z)#Tts<2aORN%$e0rnj~;dH
z)!49{_?EeSeup`PI0^j8?tD$DBqS$dg}*v`%y6nIpOo@571w$!#|~37rF84Z_9_+I
zX0U_(WIVLdprkBlpC9xK2<_Qn^rX=mcz(O!xfA22yHA7E*V+F#ir0M}WdY)-!vtz7
z#@|Q;@NF}sZ7|fnAc5Bwou+J$XFN^W&Ol_FB#pnDK2mRBL@i^vgn0t->Xxcxa+eP4
zXawW&0y?o#Z>*m2Hr%glpVC7L;34VwpI=#^dcu!5K5oLFQHyJ#xb`n<rJS|rHdeO2
zWFGN12uj=`^Q`?l_SBLQ7=AxK9j?DJ+mS@SI?aV+YVH4Op}x&p`YY@0yE$2nY<WI*
zFazTC!ViUoYoF@qofa;<`Ozl9Ms>k(_W}ZOf8*6;`gyQ&0A_wPFww~mr=lf8k9hwW
zft*gj&#!(TX|&cV>F)f#8z(Tit~6&jku99{L6w@1v1+>ds|^l=9U#w}2cQsO{eVJN
zH?i}}T0N{}rire%O~lUH5mYIIm_WP?AZe%t@?Vmde`@WYhi4nJ0}C$#z3?>hpnLwa
zL^M4StxFd9;(uC`!nC7vfJqYo1gKuZyLjOk0R+Xw@D@>^0&-o`hO1ywt8QRAVmvJ{
zfP1Od&_EHEC$gV?R4xY(jNsIPdeq<UC*wS$0-y>990Grf`D2|}i0V0)h(Rkr#ujyx
zJ7WseuG$@RpuWtyrPLbk(lGw%Hvp_7S;(oA%kHrjmdQtP3<nU4#b~%r|Jy9w8cP>U
z6iA`17$-mNkqC)x=Oi`)BQ)?Br@fgmU^m4)PSh6CoQXQzlF$<i61f;?IlZ2-de&wp
zQPPu1bt?wFfyG|gOw}oq(`P=VwWJrDEo8y$;$t+2R_hCS&+xm1L*<hO)ua{KN2)e!
zo@d;D!$!amqg2%78Lb>$M_DGNycshpO8PC0e$bKbS5Ll6Eq7awN9y~QcJrgw1>d!t
z_3X2@4Pu_dkl1Y9iM%EEVi+Ov%|m<$?kn=QkFGTKV)PX9{RkM(;PI-0zbr!|eLWfv
z)%N*@4fq>q$c~*Ni9j{}Rm0766~j~l<_qC`b_IOvSKf1}+xrbVWxJ2OHdk24E5B`h
z6RbNUvr%ogcZc`SX#IZezcYe$sHvcXnmn=3r&eEl_GSL;_ZyC9DGK{C`ZQN6rBU}9
z>zHQ3I|I^-HorX>ulO9=N<yNxdIv5ZNe&~%XH|axRC~`Vq3D8mrvqP%fo`|@B^}*Y
z-yMV%=6ZJRy8unkyl$xvk85V|CZ?h9Tkmg~S0)fY!%{C596i1E=~!rxvBp|W&*m92
zJGHKIfBC!BBD25Tc>&I>n=>~+@Cwd<ZlZRanxEhG0+l<#@k~ql2Co1=UuVc<`p=mp
zU1L=219vmR_#*0%)56Rj#+tlk$(1~>l5G_5S)Wcghg<aB!<4C`c2xb8)aoKQ*|to+
zGg9|1zl(+F3zETc?C73{H^L`3%*JH}pR~Q)2xV7<H?8{tA*RqyCfTzm+5j>s8Y@R+
z`je^wM$HO;&0xfP7L8@FG>BPxBJQ(G2BVe4>}Euh?KG!H2d1D_fu4nz+Z}QFk24FA
zxc~bJj+jZ1!E-Z>T`n^YY2$KQH<Q05fG}P8Y7s+oh%lS-A9r6B$>#byRDUo+&D;ga
z9{0~+@w?&Z5ih8H{VBhykD4j+0GGfj-h5fvLS9jUD=O~?4wMT*-Z7j7E?BCWiCS}p
zcDo8}tJ-mewo!~%fMk+}L-nDoCQtBLdqc?|*1em+&Oe5IagWTc-EuL7JYE95|7eV9
zd03d#buV#1_jr&=qI3??-}oG}<0L=*u<02@=j><J!nQu@3DenG0p}C>c&Bb@pVTiZ
zwN|V8nTV2+$5S}z&oSdpt{aT5-XX3ZL-K){5%Mp`$_Z@w9=X%%RpQrSsSD<5lF|9|
zjhWIAoVb&hrnZdijCh<9%0K;DM_yo(dJ(93^p_*B_JxJ_99HlDddz~5+tr7PF=4+w
z$f%7OCwuZXh9xFv3x~Q@Y?!W_Y^PEX(t~3;{Hn>eTdR1Q#1-BT#JIE7W_`to^$>rg
z!jS6nK?XEF^@*z7{y9(1z{dKIi@%+@79Y)(`40}Onq@-mdU~RcR$fZ-dwslaAWi%o
zNKPOKNf)zC@)7uZQ;8@XwC29={5rRvE;b=*R%KPdfvTL@o{WrxH2F{d?|o^nC<(%Z
zMrhzV>NmXCnDIjm;R|G6{Oe{&jD8JE`jbntf7fpn=ik@CFzYObl-T4H!oA18WK8AP
z@0#QBpPwWLG=*x?AHyW{=h&Mhgt5zjtPuCD8M3ZgVQfD~Ufj@0rc?C-pFO{w$`KW7
z0%x%*Cv$_#CQ&bgDQQO=8LD?{n^uMqC~v`N@srKf@9a?-G6PC_kZ<zplb1cHtJ=D|
zs#YL5CzgPlj$Bv#pFSVcMnCgJJ}mU!(Dm47m)u|x6clWdI*a^vNiNa@j$4vl_b+9U
zY*~MmdP<28?6+yh!%K^TCvP`Px5+MQjh#Q73Wg*mvh4($*w20&-Ibfn7Mhhmxv{2h
zmxC^Us*VeAXe~>j`7D(+^ZLq>bZ&#|2M}-fPnqWRr>t(TwXtZ4+Ub7y2zqlasQ6BD
ztx4res#4TGlBeP`-roGif+@cnH&3PB@2oS>MY_$z?10Y>iS|H?frPo6CH@XA3Y|ww
z%3o16OP${wpO0NkcZ~H(MLa$5$%wBe03bSccFud*HMCk`{D9*gcpq4l;^y<jb>8U%
zkUv22+5by%0+qpE<;TRO52?M;Zx?PBoyeioZbw2efZPFI81$oo-?KT@Ud<Gf{X@^r
z)@fhe`hErig4=|CJEg(j_LkxAcLa_`0bm`V1*|9H!$FtM$Np$a&VTN_?IFO<&>{dF
z@9V@M;!(fp@FPu90F?)zl9)*iOhjG$%P>$NkAeT;s17l?byPq6|Fu8&zrF1Lm-E?Y
zYvL0HmLf6Mr|V`Pw@ui0p<zr2_`gi+Cja>E(H?!He+=#|N-6RG+@)>7Vq4=jxHY)@
z;!}!98=yV<5xxrDj3v%!gZmz-BAb1gGhmsu+#`+7D$>6-xG%7(b#LjT;RnPE7!+P<
z6}JX=*(3ZcJTo+b)BuH!`C^w_i#?ev=9=OZ8dI>sL@q3`d28~o57;AH<g{S&M$_QG
z#9Cej+o6THoNg?liVmZV3cvEr#hpZb`1!q#g_YS=(t{b+J)k^;CKGWH>IYGux%DR~
zjN@mf)T0p(G<cXlVJY>uf`m*?_GSEYn~Mf>1eV-%dpk0kLO|>VT0VCuc3buu(~H}F
zQ;o10Q=g-u5V%ZT$$aY@4Cs-YkG}J%=CNc4F-UlyF-pHt<>xI!wKV+n^d2<B6g&~z
zei3>+w6nvAO}ThWpA_iJAc6q9UqI^iZoZX$UusCOOYqjLyyn-(cW{@y7|yXCV*_@<
z$FJ0EhAm0NA7F1MJpZmZKFuZMAuMwICYfgFP>W686!o+9)Br;S)M1Pwh)*pwmQn+-
zC`$!>Uy~5}<g5T##u8Hir?!~2o^+H7fOzo0g*8fRebDzsRdI0DrMK=Ca{&*}cks?&
ziqsAoU1KR~hV!KOE;Sjtfg>M57VGDEaWsef=Y*VU43$ji2E)b~`Dc%jI~dlWP(l<@
zpq&EM);|R%8M&JxSiaf{#30?m&dGz9^y7{mK8M8qtFC)5gE-WB%&5?WFOA$G{LK4f
z!KW~B+K~kySoFci*9R?B6w<8?(ULs7ih?_UIhYo-<o?~R*7cilvt7V(ro_O4t~A|C
z$598TQX*%Bk(~{SwO89=``x09^r8-PRS><s=s(RYqjvNxAk9VJmp`Q)+`*bUZFrGh
z`@V#KLjo01BeI_StK^6cl_aEbvzZE`DPFE7*o?tgifavUdd?PG1PV@!9K^QpSC8t{
zbt!XRF6ved7uO|{Yod6+%*V}L4IDsJykoKPHIi+E>x5H6D)p(mEBw)Z-r`$r&A;x@
z#`!U6F$%ip)lVZ8Z6f*`bxA%!rJu740V!t|Xub&jq`733u-NVp(M<Qk1mDQos;)9h
ztStKnKZLM24Jqebn!ft09H33H>qG~5oF6+=AeWr?eb;3M-2y~d97wI4*6eauBIZCD
z@aLef+sN&8G5+t$^-0}YW<ZdE&3RkquMxdM9N=aJ`wY6#_UoE^GR7Zk^`&|r+SIZ(
zL<qX}z19TN2uX(1-Gim#Nq1DAW6g$3HORhG41C*K3ItRlj*jlb|DZ;##?)8%uqdR%
zNxH|p&T%lg6H;*Zh;JSCF=JTLaZW9Aam{7Y6_3os*?xDGm?&a1DLLCNY6~^fjKkoQ
z!MiT$7*BCf-O1yVDQj`BGoJLUG#9s}CY{QR>hr(dqi9(673|qC1?!y5;v>x&kdc{<
z62ufKKNxul-0xD%^qb(<1ChWC1d2X<)$IGyrAPeUn$H1-`x7Hr(B2_3@}=8eVkiHM
z4BX}w0?18%@M6bg<F;qrsZO&#JCxmAvo5iIIs<@L_seG_XS{<<#-Gm=aOpvbqjr{M
zF<L%id$Dw}3)^hM!@l#5hi-uPo}SUq-ofro!p<rq4boF7>ig8fz$R(Wy_($QD#Rxy
zg)KTDd1_0Uor1FR{PLnfq1zlj(sgyP3!i9^%n0=(`@yozsuq1j=8Q?UeG!>D9Tjq4
z31tZN72Zjfmaa{IlJEbE{a*@9qMOqd$!X6S3R4VGA5Ok%sjglc<~BI93Z`h=@;N-U
z8a1gvJW+s!C)cKr-HVrAIjG?++pU|rv!9oNK|$f^QseeM{LK94qdAp65*-KwgFY&&
z++J(#Qyc91Dy_DvAHqqW8j1La|2nBXUzwc!gzCS8VqkoZ@vFL=_^upC*A+O6?aoP&
z)Ztj`O-iwd84U>(THhL-38QMs*(1F+hm4t6v6_H9#x9@Uvm*v`k<J*R?siZNZX`c5
zrK?70U*UC%{Y!bZdVimOFX}QXtCO!lbLXvRAZc`;Z^zb&d6J{&=jgde^VRq>Te;US
zP|5RHs;gQK>6vHrePb<KLwf2n#PnnMbdLLGc!`!OuUq=>{mD0OCftuXdBe6aCJLZF
z*t@|xcq_e(UolO{`i^)WQXn7Z+EV#BnKN+&q}Qrk`)3)jTG;&Rr_0Gys%1NvwUI7X
z`EBVc=BUN9(hNRCQ@u>~eVwm+l$OaVH)8&zJK!m<va)TO(F^+1iwTeHl~Iy5jED5m
zU;bbr$7n!zPT|DWCqk_)YLhpv>=#p4rZ)zVsqkOl3QZDhz#kZwTdmzQ1gm~v!=DiO
zZ_{V{Vv9+wsMJg==m>1IOPC7gM*KVHQ%0!m*QGz!PxR9kP<Y}b%s8L7v0d}-e0B0O
zEUG7|ak^hhStFelLgt^Il)Rsu&uad!e1Z25vNEFm`;HPN8&rf{WHP$kzfl{p{Er%#
z_k{%-^dSv;jAMlM;?G2LT1s4sWsClMWrKIdvL^ZOg+BlG72+hE@UqlS&a8!GKkmPW
z_F3IVba?Vj=rw;xjTasd@x#Z&pzSN6A9@-T^G#}lgA~^IGIIE6XrWs!-W~;hNfsIq
zNPa#>ABe%EAZ~?rs53m1-cQw(xV@ej(>?-^L2p5SXZNY3XO5`QSV2blZ?B+|IFaa=
z;;D{67?sYW+v+6x8q;@l^=_-<8BpH8JAMaU-R%TMWTu{N@RAVCx%cN)RYJ5=VL6)h
z-cPm)HuIMoyl4z#U}LOvbOz__Z8dqpB0}quxVEo=zR;2XhdWQrspu2BiO2H8SWf8q
z20zfGBPTaN-HJBa3Z$_cvgtGPfVn59J8?leT-aKfWv@%aSu;&*#hDfGOfDsrjKgWO
zkw0z`WKo~T6Y;bAS8xB2#+BdQ7s;H91khrknk|Ez5vj`6G-x<mB5f{-TEfqHq3$PG
zAV$&rSTOFwt>-wGz<@<yZxS0ow29k~v#E#Ox*EUa^Kfv4f3ZQs=8Tt^E6cF4Z>twj
zFz?$c;r0@vQFboG@6BKs?%HUWU5Y0MRo;-I5&&vb2#Y+{W?VvhA3;xRYrvgFjm~H~
z+@hc5ia`r++<kUBu&r65!dN!jH|snnQTpVvD$^?jfLr^^&w<5fXUw5yoD2s510n83
z)77mlvH4`6xu4SUUd3AJS3e*+AZY1s=gkx@`xBcER={(-mpq9xsiB!h_Zp3<dm`mv
z%*EqW%7`ZMfoI-2GTw3u-9MeD@_mzsVP<HzxXkmv%~D+>9o1+ltEXEAA3XV|03miN
z=OIWwed{B))0CLiqyFfE2@EFzyF0F`b?9yD%EpkYA37i(bk~Ld97%s2BO|;0qw8_(
z)_gvH%WtpNL6wIDN0A)VfV|qRe5|WXqLaLZ(we}4U#XYnggquSCnA!g^G2&E!Uk11
zMt?r<L8q|00d+RLks-sMJFvtB6VPjBcwHL_-Cr?p-fBfnT|&{X<;j>v(Yg?55fyDB
z{Vj~@mdLRB0^=vhuMXfVf(1Y#a{6A}j8Ni1fv*S=kb-=^(YGKQp%^T#TkLW{$^7$q
zggT=n@CgHOG+XVOW%=*2PXq5ij7TWq1TzW)s!zddmw^uhQxES3Gok-lrZTjf3`bQH
z{EEQ<35rNEi%9)LWBp5Of9G|;(be{0JoJ26+zLP%cO2eplW5REeRhR54U1b$e^$zf
z`pTWywd<lA5k>N&qn<@9hSQ9kBsemgKZ8Uo3epY!`|Td#WNqiP&#r%-Pghk4Dk*?f
z7FalI+ku{LvXHoyVh*&2o({y4_Cc~CAMqwN!vk>30}8Hg)1tF4J4Br8`4ZsJ2iE5&
zZ)S)u3nXZ*_}+h-ut7Mu0Qdudf>lz3ephD#T=?i+qt6%trvjh@bfAJ1aM=o&0)PiN
z;DUhkCP)G|Z9t_SCWk+P!J`2%0I;~RfhyPyoR+vCM|>m?L>-MY0QLsV94*vOM}4fH
zeD9l?6WoB%L3vsylW!0a`<Jzs(%C}}@Ki(CKazHErXE(N(c@c~{Ltj-RfHwF$O>pS
z=0F`Z!Rt?|B<y~EnvB!KZ9Qj!IC1=!D5!M!Va?<pNeEfM3u9_2i*}@m-0#l9mEwf|
z^zGv>r2oXW8w+IU;_B)J=lbI+_mabNxylgaS&Lr=qAulD`VO)H-`O!`S{19mjOw%W
z3K*YgiAoGIG~PHpB6@4Ai1Fx=VjTa3{$pDL%R7%KA{ablC;{cG-C0_<IF|U~n~lLn
z%B;bTi({l0mzi?qiaca!GU0po6h~QeGpu-8Rn(f6Vt3H^$*Z=u{(9?CciLQVds%*t
zM9`KPG5i{}w;|Bg8!vVxu|OlN8fNv^8cz`X_K4Fwv0JT&nB9v`g80-B?sfWESNx4q
zVa0DQJl;961X{vIxk!dM-q68Ad(if#{Lt%CWQp!)1G&--7lq_f@=@n6l`D6oN2__3
z{phjS*sNK1k(+V}b(765$n19tv#)aQho<<0S{GB*7}#^dyjF-<6eh8-_*B_e`!=Fe
zt~qE%Q5TJ<dzK%mJI4Q#hfdy^!M>lui1oDo^6RYZO0T5)jWo+6Ls5Z+$iYKdP-4pI
z;>dlc^rQtXtNNF%3meWbCRT<tgtgMF_#;Lg8$cuDUy+4yExml{PBYy41`!apCY>LK
zBL>k7h>HRl?I}@q&=_Kh*@}B0T(R`{RHMRDa~ndz{uq9Lml!;*Ggp*{3WGxb>@8#J
zwr;N!hdj_|icm{obms*R`A>4KcYmVImNaxrK^)pLYlTg$RI>Z>LB2)Rxx`B5^#B^C
zai1^jB!|MpFf4)(xw)%gltv7~C0Wv^V;5Yy3ju*8Nr(@LK}kvZKV#mgPRAEy5(`nE
zwSC!3%$6fofu(w-Y~en&ISRsJo6|;3lk?8o(&*Xd6ICDCc?SWph*}Y^`>y9#S`<jR
z|2I>)M=lncngd>z^IN@?OU~zVDFk9zehM{M(Qi^F)eH+`hdgSjzj2vLDj(vPh4vQx
zpnx}oH63mCA8|-Pl9I(c+}^|(efhBxh;1nZn9J5MhLz~&)KCGo&)i_tDHt4O)5$*C
z6uo1#<P;P{XHb;A?Q!<ibV4Noy+X%jCNOX8VTqU=tO&%zCCWW}afnM|HDlJAzpcR4
zNp>BDW$entJ@7gSs6osrn=JstukY(~;O_y{4aSL7{C^Pl`QjF-&0#O^gNgiDz60p`
zQ}9}xcMK1M*x^MU{!UElIi&NhI46&KLVtcNALE7mQA@mj1Y0Y%b1q3|=-V7TBm(UK
z+tr7Zu=$lh4JIJ-CyCS49Z#7AKwbWnyt~4PTb+7Dedl~II37d2RDh;iLIWCt)BkD6
zBGeKuF?B7)k?pZsUKVD&VnIT2d%<wC6AT41Qr}rrgqh>)ArPIdgF*$8ISAPd<P3D-
zyEZD*))>k2pxEq5_x^oAhwOgXmg+^F#ryM{VA_Wx_EhfN7?nSv-6}}r&-77g*PeWT
zu-DiQyJ{HUj@d>Q5F(=bZz&Q&M10(+s^OcANNLzG=G3u?l{qcs)e?g=BYO^lsF5ZY
zb6k@T;BgwzPpsQ-*b>V`0413E0D-W_e2eL^ZMNOJ`n2O02GhBO7U~1YJoOQ1d1i$e
z>s}$)V|=JnuYmQcwp%-O%px-W>>k*Gt`!^?R**yIDF&IVKWar#WUO$ODf;}cc@+RV
z=&%C0pW0ufh^Gp692Unja5~gce|dO<C2J~u@Dg8P?@i9i7G~&g)p;3+$s&;-rna;i
z3i)zW;ul78d~#a{lNf|S>gt&qG@>mEvC=X~|Gzkdz~Jkp?50B!=9H_8<>f*=zhbG3
ztb?D%(fs|(mC=~V)~x%3>Q(vCxEonb_(Zd1dQv{}(AXiI5jYKg?|(ehvX#O1P7gx_
zb$vD>#6CM|tg=^1dnFwHWJ|5NH2Rkl@I@OPdGi25#cq-kA_TEWJ~XD1>68Et$^Q>3
zdyAZ9k+~e}5si%tx!|(*PHvPgq^R#CJdkf0|1NP|m>YnEdW__$|M!8%Qso>ntD!J~
zsT}6>oq20kku*Y>9INB)<;~FZBt~7Bf`*UmVK@t`4W9z~)f|sJ>k(^S0jE|I>;o!*
z7-t#sAI*W=`pz`sf~)Mtc~f!fl^>vj;CN0UxU^7Y_X~hy3qVuTg|&G(hye7>usADP
zB~YCI@Of7U+CBkS|EIydo}cf%fJsix^?U;W4-$huep`y^ROf2q>;{Z3{Gw9%v<ToJ
zBtTlFx;?ess+XBO?^Z8id73WnYXIS%ztwhF3_sBsZGG7*x~;0qfrl7?*7%>Ic^14H
z?W#KZ`H$++DxT#kC{3_h@)|8|*YS*VP#*TJPGoViJq<+a#|^i<F_ZQN8#8AQ<!&lq
zd;Y~@HMTTU_u4A%saHaGp`vQr^^Y&0{Q|1fmv1{y*QV8@#9yO3E14iS`B&p$OL7HJ
zc<gz^MS7tpwTsDI016X4-BAbishz+5^LDP4QQBnC`j%XH;ATHrXqBrty3sXs4z^`0
zz(4{V0}EBE4zTFK0JLd<DKE~zr_}9AQFNt+QHlI462IG?D(drArQ&K5qw8Cwm1k-Z
zw%nS&C<J~-`~M@OpLz&==k%AX?~!O3soLF9g)d-yE*Dnoq*~<o;k2OHkcn`7wx<d;
z**sQ4*qB3IXx-T2#Gd|rs)i`hA)pVpZ++Lb=X$YncdZ-))JCiRlQB*3D<QgsdF?Td
zLrHY;S9F^ktMP769@bJ}w5DzCKIv&gAI&`od`8H)78(j_%bdOP2adp41U{&hLUJO|
zwJhE1y?kKmoajola(I$*h24~B9$I+}G8i$ox7YExVlvpC*tgf0?Q4`D6setV2@$Q+
zx<4d;e0x2&#w4Uf2)Vs}zXsk_!e&KZ`_(wCKLB+J6N63{rR<niKJoIc{UD^oAHfZ>
zHz@56d_cYo4Cwvr$n3iYZnqY|x81MAgf##S4;XaBdHAQtK*k8PqJ4*kIEnZ6nP)w;
zJCm+TWxcJxr~a&ycC+KFA~3e0WGL`cE{of?OX+Pflj<~vBxw3~P%=IJ?6-~m-exVm
zPc?U?L(gt*L}4A&4S?+e&xIt2%BGZ=2p7G;-l&OM<eL6-KZY_K54Wl28G30HgmfSF
zcZ5BoBF6%4-GOT5^;Ayu1m7!SAy!rdUCnWaI~FCB7*Ch(Q%{Dke;gTOP=1I_RfoS-
zvzyNPgS3nJ9q>KW@w?-2%=7j;g(eP*jwOH+X97L=41E~)Za?Vol_DfeSlmak(YIeE
zgiWaT-hQAFL*?jlZ8ee$UQ$hTJZA>ovGRV3$SKgvPK45^c*5c-giF)tr%P0i#9!Nh
zlJm!7t)|Y*a*g-~MmE24LxWi{uO)&=5Tk6k&qrULtlG?~3~^K~;|-ih<ZaClxKKT&
z#o61X&64gc@JGH%Cb!|7jB$MT4&&bA&R$;b@DJRA;|XNsfh@5?-ao&x{bdd)^Kx->
z_0lXQafAiO-H%(d7enkytCuM&XWPbjIim0d70etELZAFIpkC@tLhNp|H}Zziw<UOe
zC(b+lbp8hoe?U9P>B9{wJWPra)9*~{!}T#sN(${rNelZ!f!xpAyj&9TH_UuoBcoWh
zdeZoeScMFK4>CKyz}n=d)q(_<Eu)!hVx|bJMP4e(ykODi{X|YNLh2n6%Z_@1t&~%f
zO2+&^uc3gc;O1rAm~@!2C+aFVW&4YF6QH=--5-nU)I7)sLgLjOfj)`5fW{NxeF_Q6
z(y=2WqM_G!A*lfj7=GSH-HR6!sVlxrFDzDdQ4=!i<03vKq{Mbz>Mb-OtKGub1w+r7
z;|P&scx^$Gy=|B<Rq(>=qaffER?$y8(1)E5>wha89r|gs5jpsi^|4DY8unlRUQqes
zz=j&C7-0qdj=8sfGEZBX|4n*}x;Ig?ETGH79jCd*Y7dT}<Kv?E&V##v!Hp@orGEEf
zuq>P%Jh^hej<G;6CM38$bD?L3sIbZp{%+Z!EZh0p0rskah}*{4<y**AxjqVuo6oZ3
zdG0YuOjHYYLFh+5;CvVI@czerR^)z5qXVkDF>$Fo4d2{I_t;Qug2F#S_~Wk@=N`pI
zr`|?!iQjAdPZ@{7(x*{B7YKdqx|TQ~>_F(iH%7#1!G#u?e|bGd>G8MwwHY^h^srR&
z^p)m$&c5}%`eKCqrLQ|fi?8%)UbRuM{Gk;2*V<*~9^b2%X(7=qcy0rKE9*l@NEWBY
zmP7k=t+auWieph8Pg$bScL73|BujYi&=hLNEG6{MPz?~t5LHTkz+4O%osA1O_H_GB
zGK6>(hyBKObwL>MtaHbewgv3a)32_47!!u^k#|;;@w5l4bKa?t*&^15qQcGqdSQ;@
zJ+QtAe=8wE-WH=~)~+Tm3!b&9?4C5)Y~JjLIL9GeykA8}*Z9Q=Z^mDnn||gk^WOsm
zuq-M#0pgvGWW7UJO<k7hN~3zcA|y}(oR07W;2Ax~X|DV4XT*xDWmqO(^c|~HhO;rR
zLCI9!IDPDl&URW%=pb(MfmrBltrJUH8|Yn-BLM}4UC~^@Sz{yYVGdL0leSeEaR8o`
z3-@>Kh48I^ReD(C;Z9p!Wy!^W%eUShmY(Xki6MG|ZH2%MFrKM^RhNV?{CbCwkAPNe
z4v-tC$Appl{|XzXtSYOg$Nxd3rW{62ab>@2Zk|TiC7CEMPs79!<4yR8oM(I48-ow-
z{1*4EiLxcm-5(n@e;x|Na<f%vHy2zlFZ>db={>0O8r>{d9I{ri^)nJ`Uq4Iu70qwH
zu`e0euAiDNwvw>qxypD)D#d5Qyy97d^6LEgnFr;%J}N!=v)8!<@uFdJP*(wL4JVz-
z*dMNaG{JX$`R==8(O!E$B&!0Ut$mcEH)BusDjed#)gHmvjrY)VZ>?tjtt}{Y@B>Wc
z=d%&}8rV7e->Dnp0PP<}F_KB*QGZR`+;kzC7WJc>i^73MFeV1vbTJ@QPAJZ|4!4Q6
z9Iv6s!<~^2eecWOhJAGUImNz4tlw-rd$%=-s{$c7%)Tu~(*xZwH}ZXcqA*P5wXm_I
za#^V%8Tq?a$7)K3Q4RytSMYYnT#S!tJR=~mPsKe|C}}QSBAnfb$t-&QhB{c#VZh!9
zg*4f3q{xfpNN@87I(n0i05*?wt+;*yOGOdgmg+L4+S5@Z7hXRXpJzW6BumH`>`68w
zOafj5$vm1`#Q6A#_e=A9V;`(m7R#<N;43axGJh%i<zP0p-}*rRZ+8!rmkVTj#H(x5
zo{oCwGvZd!U|)izXObA_<&Lc*&5xsWs1JznsgWWVEdqM10TG7KA|%|3Fr4Nsl`Kco
zb!j9t?8q{os$U;fY$mH7M8s?nQt>#t;jO!RbJy(Oi?HE%+;HiocjHxDC@CH%hvPK;
zW?rX!43KCeXF1pAF?X_y>?0M=odnAqJ%YKgG8h!4F-YP|9n$-yTQ3uO6ZNwl?lmL)
z1@811g2B&^yKu51!-#Y0ukPTs(K@sDQXk?QoaTKF&#q!l^*&B!(*Isxyzn7iY=(w!
z_8dYU)w%ENWaJ?&(>9`>XCPI$hqpUGK*|7ODUvo*?-cVwA^mxr7}u}y^YqF|f@2Q%
zpY7*uwrZm^Q2)p<uIKZ=We~<aX^>{1=Bb<HFD=x5<~}7*v%ov;`h9Ng7cLObqHfAQ
z*T8hQ60ko>(^S2PyCeM^l82vH1>?|0&^&#0T9ItS<M$vQDgJaYDvbXf0(7lOd|$u*
z{v}oz1qd{!q9+<2<8>*f4rx&_MP3%dF!_WY5zoJ|S(O=50EIqhcj5)gSKfKSI5HW~
zv!2I#sqn#bsD^YCF*azL6JE-mCE3f^hny2%)>tD$*8o%teu<c3RZ~HWjc&?ca1dV-
zEhw}^g-e0>lqdwSYJvbmVF3jTv)|o&TW>(o7$9S_aSW|r@Si$>l@m}pK>Z+CgbS{Z
zHV7b939bMIzmCI||L1QgTPWkt3V?|G^ygHDXe%IU_L;+WgJo_zhRNNo0>vsCTTsEh
zFb8B#DhUs!EeMnZdYi=nC{eher~BBJy-UVkE|{XAP&|;87Ac@BVS%1Y-atg83;}%#
za1j6HN}TyQi4D-eDU+8VPJ-N4QIihiC#wYkcjfHX;oJKTchPkh6JCkR#bYL8gwD1|
zHaY}r0fsy}kirzwHe?zbAe?^I-_8CS2P+k52r}WyV3sB40L1vM_kwALg*^^^*CGqw
z{uM2{GPAU=2PTNPOW5P4N=zmGOHOArT(w9!H-wbAj}j=?UO49$R@%Y<xPi4D!p88L
zu=_v?@KUi(auGa$YB)HTBqJIE5(SZiH{*pLRibM$K5~c@uqFY7UE!AE3uJjEiXjS0
zF4`CrR*6TK(<(Z=7Plq_%DeHUq$??G0GU`+hUu&mr!fJZ0^ulfuWo7(9Y}4&D`T3y
z9t5zA%@g~^B7y_3)Hp)$;KuL(EOp<mYl*07h1v9yLRqjwS@3JlxPtnEh^-vocMwNS
z-US9uc_`aDISWmd<0a}&ME4zlDt?Mry)n2HO*I-(`;G0E>cbU;2xkljKy{dPJzhke
zA1x>;#35eYng9^gyANWcMX8B?7D^~O8@Mr=<UJUQHnp1)Ai0t3)sqFzK$Cop5bFVd
z!UhDD`j_PXP-CfB7Y*)hjo#e2R5}N^3={$4;!^1Rai}IGrUgG8!lU5|1hVdQB$-J+
z)uxRO2Q}qv--LL5UMtj9ESyCB2p~P@b&PruRon&<65^7AD8t3@56?5OH@?$>bQcj%
z-m}N3!PxPEB!M4ssuG3SVvqz`2++mwZ+v!m;J&edB@A;*@jZLInl%iQldD6nPU>qk
zSRx!Gi3_S<lH|I*e@zYwHg5T$0IrRXGStR;2V@oGUTo@Th_=lb8LV&tooPWscdP!e
z_dFH4hV?F<O}utVFc9T|&po9%OfWyaIB7Q2g(C?(G<Z*Q&#yeW6WDm*H=83%#CZst
z`#@PLP}W}olm#_}3&GD)WTYM){9KC9LlljbgjWE>`(7?qExMqa4OO&Ben=f#dj$n<
zcRSGL9A6lorYY-zQ!7b09Wq^T?6MJ{6yLNr#}{%q7T`ys15|Nhfv~3U1p8W0(3N?+
z4j9hn4}`RVJrv>O3Jw0S$VI81(lWGC2RXA{9}Ko3>=M3i%U6F*KWEI_Q%7r+<<nH|
zBX!dH>F%R#XQR1yxziJ((Hv<{>7VfvNKBGbrl>`CCj`)3Q||gg2ZkZ``l|7A?L_Oc
z7CJiMAlSjB=ptRRtpWrCw3J_Yx^XIy7b2J%y%Iu@Mg=0r;pPGk+uj7Us<yjtvcRK@
z@9IK<TSy$B5H!3yvU{UQuC6>v$jy~T6}p1iPXGdK05on`qQPuVK&5^H1sKLEHVAYS
zaC8wuu1uLy2UJ?cW@!wCVS+&CvgcRw_-rw1Pcc?s!7!6e(PDH|EKZreG`jd3wtcD|
z<guw2_-Fq#;57DbWJvkqsfQ%zd*(ezph$n?@iD`_#Csd3(JRc4Mz=lm+2GV*DC9LB
z<|9`!>~k2EIvMtCPYFUwQx^)lwkbe>K$auB??1rT`l;sJz4A7=4BiD_a?ZZIyjmET
zk6UK*UF}Z^8Q)bJH296z<k{eU*fKb%m@okFd6s=qu*eP%xq~ztysBdNc_20xr^cxO
z;ftRAl>d%Kf<k(Pg>Ti>IwPvuw2=<z^eDR1q3^Ebj>RUtc1jx$xUQ}_NB*==?DAzf
zaOgs~goPB^lQ7xn*x>|EP`?IEqHT#DL*=~$fnhAhLJ#BPiy-mGWlgWe-HYKCVn%NY
z7cD-Ne=;F|zjzAObh*kr4^k1{aFbg_P!YSSw5x(|xZ!!0ACIQ`W7Ie`AY-{u8i~7Q
zVqAi&<0j!S%spl-v_OtmZEK7pM6&aZ+D96R<?ag;axWddk3IgPe$aeQvh)4b<?%PY
zjgzsB<+YKJ^B+0$o5|h$sQi9EDaLEtfJ0+`MdmK6oNukx#}^`Ft-l-#rgG-yMm|hE
zHsL;c`9u0dP>vBi`sZ*}msfsgOTb}a{rlC6cHh?zy;g<g@ATjOMjTl>pq{j^8XGh#
z?xj?iFe~G5`hK>wG^##3s1<gzOxVCnk}i_@YOQzYR5cEd`J``N{LXK-*T`%!vX!$3
ze6opIjdfGMsb_bo7?JwOWh^nDAqLe0MafDX!N@LxnBu&#jMbqx^J!|ls@!+<oqf5-
zCuFkO-6Wfx_vdR>%*ie)*~(k8-wLxbv(1?HH&**~z1sdkpC&f9EC21`Vh|N|TGoM+
z&}L(CE)zQsuf><ZpCGP7-u94tVoAprai+l`(!W$?`CcZ}7(&G~d)NhRG*9!`tC-!T
zTVQ{b7KnkRp^l4Q8gGv;gh$Twz0UU(XHO$tiv4*KTAQd07Ly^Xk!`;-OkqhIsozg_
zcnKuhT3LJ(QBIGCnbm)=e7`$)2DPpIVr1G(+biNr_E8r)p4a$At%nhB^Z1eSip@fM
z1WByT(S_BZY#M+>D7?qpUZo#7_Ab1c{=p<Q{asIW3LTO2bAD=QA{`Rf|I>NU<F*kq
zqCdVOFY9YNxl>}Ty-eptFM{5(pH)!hP8q5n6?%e&sLYbj1V%QMmG#z+@0q;QaC!(H
z$m-?$JUspI59;R#lYvP`cu-a2r%7Ho@7xhsxyC<D?1$=`Nt=*)<D99SSCDfZ-3*nl
z&eE#O?%Hpbcmy}Js<`$I-{u}OxHx!oYTH5M6FzTeCy|WZM08~&HFUY^qW1tKdJj;j
z3${?zcY&%5v<q@v&Ipum?|#2Z;2A2m{2?E7#rs3}_vMv-Yv9f5pZ967!cVZt);nhJ
zswV$roW_1wBy#co##Am%=OFamdTqWBx`xc}oEN-tj1LZZ<sWh2E%qjm=}=TW$A5aV
zeh??;Pr6qMf7(FnV6YI^`z-&?neCz#-+G;-iQR08D(3H|hKsur!oG+X#%>{6pHn<%
z_(f8m3)C!fNx>hasQQl`QjerYt35D80RuGz;jn|Q*=R=>&sQt+P~4khT&0RM_=VWw
z&(1(qKZt@oeWbU2Dt7v#jXcGbX_cSrz-ME<a1xRNeEIarwz&|N*m>c-{TfT9+$<U1
z#*_9~+=q8We=U2tsB5%OtjQdriS+8&Wud2d27v^nMwhoXO#FKOA{fKVB$(!Z%(4KU
z^(1zvJo$wBMEk=dmd{U{&hI_@9%Qy?K_y%FV@tWgRlH&I`d-d<g7>LPKi!#`05EP1
zi_B%LCWG5AxCT%xvjba6TehBUk`3Yhp|gD|?d}8BdcF^IcnvCI{o~3JsU?qa(|=D5
z?~2J*Wcj<c?7|L$OJ4S|PHmme5fSo|luwssXmS`h2OCZvP|xgAF(I9Q)i8L=iNX`o
z#VAt74+GyKHxaTcr|g?P2aZojf_N?dEGqj{8CE2{>d}S%QTy!+p+K(bp#+qdudZbS
zq*S=e#+340*Km}7jP(wGVZ18ik`L)!Eb6sfV|=3gn$nIu*XjwRmV<JzdM1Fn+Yl8F
zNwargaya(uYDR#-JiulTik{qEq-u?mY<<WxwYsPO!I0`l>RIiA_Qm5}i$JZT`4jaI
zgccjl&g`pN2^*(|r?It*Iw>ZVAEu}MIlXq>T;0QAFSlHcWc_2~f)v-{oSAADe|qTm
zx$>4>)+$&386DnT=blLSH*4wNq-TG<Gq@1-0Z;poab7I8QENc(REX)fTcHq&Lk8|E
zHF9@O$+NE~C0-vrZPp%xy)Ae4FH-bv9+~TWbRk?XPc%&{jQv}z^s#mfJsi%f%HSML
zNGADlp4^vTNsGqP5soGNlC?EbKHxX>_}Kl?=X*(av>__`+z~-y6av3qu4?V_vo(E;
zNQKnH472yk;{#h1AsBzj{<FGn5;qJhL|V(=2MC_~DGkv?FZ_X;3wQ~PaLgS~9z7zw
z2+A=JdVFIt{@o(D-|58%$%`xF><0DI(Og$S0+%dliNeKQn>>k>%(`DK<%0neFt>vA
zas+$U93N}%)+v-OV(|Gd1C*79WDmmW<l;M(vYWV_hJXm?I)!iC)+s?!@iyIyrVGXS
z8c#4gdPbx*B3@k$D&>$P1>ltgG8I`<G4+2ZB(GJ<4gc-F8R=;J43J|kBBp<)_!YhJ
zcM9xtKeZ=)51AhKZ{V)3@JkRVF&5eIl_esr*Y3*8EC6qJu>5}>eLiK7-R|JaH_=~(
zFN1nZBb(}@IStFK!zw)KGMnNIJ4k5Kn!+y#V;%XwJTeW}H2p{!8J)~cyNBtfU~~Ts
zEfrNljC?0iNX%k(p`LSZMpQ-mE<y1+f_NQsL2KbP)L}NSKkPHkQbc*=QOd^K$;F1$
z5ZsMA<j{00#>NARMbK=7Y+J(G_Rn2d)XrVi_NnH~YDBt%u7y_NamxMq$)sR;V$Vh#
z7cOfEay{@9)xL3k&wVq9Q|6<WWmCHoqdKdDcf0+%t6sLthkkK$|MsG4F>d6--k98T
zpkV7XCdpPJd(HPR6A!!?VfK<3D<&G85Tszs&XmLskGpH?$9&4HiWl=!g{~ocMk=lZ
zMz5;J%cTknO-tR|GJB))=Zv-AA7sh&dj;!`&RAcM`N?#Lg^0U;R4L-*8_46J4EEOM
zeA2USIiVWC?kMv*O>B8HAwAU!PGbn9Xv$K%l2b6atTJfVdDm!Oe`5HiPimndH5D%q
z{HLzJ>DuJC0-g;1iC<1&+P6P@z7Ud=SXIb-N14emF~xc>dL81Ei;0no*0IFwcT!`I
zk&hb4%Zm}=-0G*l`?H}5fv?Hq*m&0KHm;ba#xyaO^{N&x3=j~-=<hX)`SRY;QToiX
z5x{;d2S2u_1M+y`rvz#~&|t6wVN+75(>u&M>LvOKCaT4by?eU%(SmdAF}HGcut23m
ztFhYZ{X{U&I%ndF0*^zRyVTnnh9%u-7}Hoh*$)$wU$VsrwfTGSh}LmTKk|D8JnOun
z_sc7rVD{&MVypS29h9{&`uqho$W$#%RBN=eAIN1zwcmJ=0IKK8cawF-{rmRwrO|O|
zHL04@?i>>9IcS(I?#fp<L>$NQcw~sIue0`~hf8sFYr{434{pG>5=5f&vswD?nPec@
zr74~O?4BX3AYwIscDIbhI>kdkL7UaAmz?i_Fmp_<3Gh7Y^E%Kh30(#AT^S=c-dzQM
zgG{}^>0NQUlGACXs&*;fI&CKBi_D7jd#QVJ(XBdqP7*z-mU4+Xd{pyO?4QB;bsf)<
z$t~vj2>v#*;)#~38EY-YQ1ubnlaVr>GEeXm3a_N0Wi`@xSYB8q<MzSK3~PURd_+9v
z(IAdw!Z%Cl!ipJ?+*Zk*&=%N-Qp6ZWi;`%E(@45kTAh);;aB~56G(!NJIv0To{y^w
zOjn5jKFul4W%u9GdB3T=A3$aqppfe3V$2mnR5L=A>mA4tBp+CD8XyH79pO(G8|IgQ
z|1&N=rUfid5g;6HWXd01#w*rnc*FtscAfzD+{>8Q4G-N2-xKcjaz=3VY}dur_pH1D
zC)Kk^)t7l{o@utM@~PB>-TXw$dx4^%slYtB2~%q;{FX$)entsiqYhPIV^g=0meN-)
z;jdORAHODIbKWQkFO`;gSK=6N^aT1#P3m5qDJq4Lc6Hu1s_wTskMJbbTf_zmMEl<2
z$4dz!77m=HwD8t=JZ^-kGgek8kxvJ_=wL`z{R#9+uxCyXq~BeQ=qYPhy)=bJsjC=6
z=%TFHjsuqMQu$Y(PgTOKgGN+RUypz7lJaL4@O=X&(S8uR=5XBk{w~&#<MqemFn#D)
zRfc7|#55n%stim)(R+PFqmY3@HKYGUDBp?kc~BwN{uiIzEpAqmc1((UZ89T8h_`N^
zcRLi@V|UuF%m+X979f-wMjrq68yVZx++|yCvWgy1V~kDitE5FV;9X&*RF*}^J&G?b
zf2OhVx)&4%ypj^h%2#kV_aEt{jU_%D9^!rx-q*};2K#%jkPny`I%{wk42-7&@hql^
zhLg}%ApAab6DQZtvxYpjyRTJVXM>;U4Dxv1PO>JFDlVk&n7Q2qo8=n=DQxvjr;q(T
zdLV9LwQ8HS5&`TXw4AVZu1b=G-IosAxyPl8R2$CQVj;5=Orriw(Lq9N8Zo06CWgO_
zhxar4Qxa!ZB^XWuY<va<P=9jA$Mr`w1(xY4qTai;Eq+<V^Q_AML!4cb$IKNz{#|zW
z1<5Fv-sP$qE<HUZwYr)7_l2)vamDZJT)mIfDC2~*me6yXvXLR`=^o8^Arl98ma1ww
zZB&U2Rf6tR(-aSFDPliwK@<@#KY$cQ;sUc+SWBn<rZ-w0V)L5U?`Dr>a&S@R&VFe$
z;qwJ|T&MP%e0HAnRQ7D|WLS^-NFB=~+T3ftGK#<pmMC?p+eZ(tUF3QSd2cYDCGcgj
z7gJU52{h4ml%gHhbc*K*Y$U{8=zZky=J=wfqM}CmBp~Ibj~a9&;c4fU9H%5)*Vw?I
zGv%@~_+AzWA6tZctKH>OXei;sgTZkR#u*4JB!nC;)+&J{SO?DW=3HEt3{lOuh4iRP
zA+^(y{;3ELQY3-p;@oK@qBvCxfV{mDfKzp}HS7{W)VZ?J$hc=<uFxR&MH(0!Dw+ZS
z=<0zJ=NAiYLMz~0TQtzlH3b1WX$`XI14t^vG@#RL5R9#Wi?-xcZ+Of~8sZ~x5P16Z
zB#4!36HEmF=^Cd$+sxrwv*3r`m;1!$u|ZGVup~O2+(eE2h%k*{_<BJLFJNGalx2N+
z1^eUT+%A?sJa0t|3gzNL#HCU#tu3`~#pl@~#J@ha!ayPrAX%5Gase46zsswlub6NM
z1S4>eumD6wAXX9@g&?6Tc#}%i(NC#>!AWVafk6Uc5LFQ8;tEaY2*uO_52j(#!yed%
zIzyo#K1~o#mRc4M&ixhFKF|a}*)Rh{D4ZhvgwU~XCK;mwAYI9iamSL)s`SoqaxE*C
zz84JB*aVZiL;0%~PXVig67IV-K;{~3-O!j<*?I~}s3>CH@4)cd5&VB6(1)ic!KYJA
z7Yi2-Rt|xu%GamYM_^gx`G2~(<Y{+y@x^qev6eY=SvdaJkft8JM%_DZ)n^qapVG!f
zy`?_s=h`$2V^chHEq^5qu8$#C{F)$rQ<3MRG43<ocZmp=-vJD9D6f4Pk@Dc(g1ZyD
zM`ansR9?l;ncw%~Nd3}Mt$A?m$&mtvNBjUXWd;kxXHZ40KEt>X;N$`wonG`tKvC~U
zi!Px$&tII&=6bHNo48P@vq3g;`5pkrIes`otc=_4?kxf)x{lMU;NW5Yay=+sS+QB?
z_X~Ey2kkPtX88uEn?qdda8;VkQ*RX`wp#*p$EZg+lYW}d7;4HbwWPy;N!?4MWL9ap
z;ybdjssqe`z)x7WT|WO=lj+^}dGpQ;={33a`WXYQVsm2w=VM47j{lol+=(FbG^zIN
zwsd`Iah)BtF(hPga?=40nAU(FZSmr6MQKpE>p*&)zs3xGJGj<M%K9`wC*bP3b0m>~
zlPmWzObDpVMt6lGBC&X+>9j~P=d5`LisCx48kt^Aa2{aV_Ul@~1`Ld$?YpP;hm;yC
zE%vpWCaiX^IkKz9{c}!NH}%`|)3=NVSBk|nb{@fz>foCt=uKa`=X*`{T8H0SC^7qA
z2?>PTnm&ICW%)7(Z2lu$-C_W{j}gZqbyN*cGY3-Z;Q2yk*`?KUhTMGyQWZ3vqQ$fE
zwfh++W))f6J5Z<rXV3S8-@bt`*a3Db6$tp@Zl9h>XPB2$1aK!%r`PmP1>c~>`A{rX
zF9z9+hX<tC!YGk_zMS<hYwtV1`2NGmYwOWz2*k*iy6Pi?^IY=pFGI|en1IQ(!~0TZ
zLvikH9JOiA$C$QY`Qy5&-63k#u8<Ge2yYc+tC}bz#&$)TrliWR*46Gu?V8K`JQA+;
zyU#8Hlf=gjH*j6jkOY8H7c@4qySTpf{HHmpB0<ZdJ^y9D&}7s`{Gx~Oe+JqJa_BP?
zyr`K!!68ixMNM#huM7;gsKh~!amW*slWlG8-(w3a#3Q?#@rlYclWcHh(SuwD>LG*n
ztu6!D;>SR~rS@+?<;@EIvcuQo>H7|2Kvl6D73Z8QOkhrbY8NAw0DbEDr;7CNKNI4H
zA1r7#03@qQ_2eJE7;sAbRj%h3jA(ISy6c)ScHk`~NZK3ADKPHDcabPmia;<z`<Fp-
zAop&Ot^<r^4+lm!kaYskIelupJq$ngM7j(z^4HS8Jg~@JfTvkdBN<B8!QeUgF)&ne
zp6)fnUUpExl{#-?7w9_B1<RBI#_hxedBo#vN1^dHJfm08@L}wix|*CcLk$|f0YScJ
z@3R{Zw0AF!q{&UxXlBb3NQZjAi})Q&x37OYbXCacL8>cv_v_*MVFa?-mFfw05U$sK
zrHkfINKK87PA?(e!~Cn<uT^3idKcvsDMoHW&&5SS!KX>LgtHfumaWkU;zNuPI<VB-
zhXSep3^PPKh?b~$#L{6HhLUA>Q3|$B2y}Fqw>8twKn6#jo&$fjcrd%ujMvkj#LBnK
z^^Hvu`vosiDFaWqVUIT>6caG=-gg#kIO(fPhwd1F4)utv9c;lj4C$1}&t9A*B=Yy!
zFfP+1baS<ut@gA?3vFBY-o}0fx^c9A6>Ms#yAm;)vU{30fp@qgtMr@i*^2nycy$7%
z)4gs~pW3xNF9+OZ-TT@V2HkFs%=hH?C3StK(d99jr?BZ~q@4Z===ZxXi_DDr<4A{{
zfPKLN`L@tc37yDa9SWD^!*@d3U<yZ@%fTT&PRp9@WCC-RUp$$s@jB+R7qExrv8)~L
z)2Y8&&IxPs!!%GYkz26@6A8mPxz=Aj@vO({(^r)u#k|%$*&A+EVYg(DZfMzzxgYRp
zq5D3lfkfd+K_<Xm5)u=wv2{u!4kvV~2~bg4qWa-u$|fML_xgb+CKuS-S{=G8v~%j^
z%U0AFobUQ*L5<dfgLDXVP8*#&;tJ+kxiieJljQVN)%Ynb5;MLSi7bZ%ta#tK0B@zK
z(LDXOKslmsnh8=i$b2OA$-F{*OfrbBqv%V-f3e;MawFt+s5muqs%xorJkN}9oVs%}
z72JLz(9_H1g<89i)l>DlY71uc$NNTO5a{b&7f05Oa(n<c7vI=`CpcyZcpUXfs~iNN
zSwrw{Dz*-An;CSuSXFfcUY)}85u#C4Ffx<sR0vjMd}OZD#rXZV32{p+U^ZY1z16#r
zJ-KhTNj^heHPS*;QUT;CiynCRxP4JKz4jQ$05zLa(DRY!6~FJDoj2dt4Jiz$-9Cg*
z(`l=Y`d!(_jTf5xfP6W+)Tk}N+BX@sPky6ff`9AlmAaJBd$?F|m65I6QPogaz+@Ud
zfRHmNrpgw;14ZEp<djUG-@VuxWiW<6S3_n|s#Mv^d-q%C9AUmbylRUc*&Wdrd*3sB
zNnq08GVj<nK129L#r~<{R{R?K47glzGr|l=*J+1HxnyE@g2A8|G`0>}TY_DMWusWr
zlbl?_EIA(+BEX@iv!wnIi83xRZo1vrcW@7Y@7?1IPXbVmEyHZFxzx|8aHhX5wo2(6
z9p$F&WUTMMXHz}>bGkA8q35H?td++-98m`MHjKOm?_Pyl-sIyGDg`5c1er3n=!v$*
zxt`&g)gu?X6XkFyt*VQgoV*OAlFarZjv5pVCHOJtJS;u>fAN;0lgXfN#K;lUBVvRn
zzS4~P^epvf8YipmN(UivQM2_-`#1Ni>mpu}Y0l;{*Z)J@TLwh=L=U6SvUGO{BE29f
zy&$k4DYBGwNOvwB!lFobNh}CRNFybsBB3m?C?E|IODWQbAjn<({onV!AMX8dKizLL
zb7oFWoSEl2bLKDE93UoIP4w!kBo%J7h4Xw7%>c#lgv)cBP^vdkbGy1B)Wt<r^9G6~
zzXc63<CPi<njc_2{?Im2h@tdT`ljiC9?>aU8l2Acw=W~w%pICcXnO*y2$wsm<H^IK
z`nZVXc%$ECmX_$f`?mI^AE|KZruSos?CDa$mF`YbBzK0cY5{?d6-}P?|C(3~MmQJl
z7uscE5SuQ=_DFpa#s?U#)V}*ZGkv%<;d=FnG&WnXQe^J75*AMpitGZL6^Y0o?o#|N
z-|FR$D?ISl82=VvN_gZQ%z_SJ)@t57j!fdJDWQtVet3(}f%g6DzEq`8U0l`t*SKEH
zwl)`4^zxJTxx4qK$(=mZwzF4|U5VE7Y%S$59dIczXG;K67L$DBOu2JIN<I0bs$}T;
zm{G!$qR+_w$-bba%>=U0*YDqoOu;Rhn-(O74}SFJZzX{Og3}wEs*=Ryxz4;d*;-TV
zoX_KXMQ?ZEF$&}Mov+Ny&ea=^2J}4rKO*J2HMOTZB1#Ck&Qdqo<c`W~bQ56@0||u8
zar;r2BMFGH2N!q$A>(o{N@N6(O~%>sS3;aUc)_^1`$Kq^xf9P=fJ$H7_wsCBd{aP_
zR$Si<N#@rn242Rk;W&E}4UjG4;_lxj^Rb09IRU7T*S0B#+6L5(i@Wa#5*gX<B}Y!k
zUfVHRJRu|#N!}}=o@Y?2T#ygWIN)ra3X)(S7k59Kpk1tzOag$f;UWj!)+TNQta%(t
zn*gQ_e_O&HoL%^TVU|(Ptm$W!{kxOf-XsB!<jbKB*6V?bMdvjKSup2|z4W^wmG+Le
zaJJZQH1D-9!O6VQr4O{CF>a_HsfOGA1ApH&vF$Xz$xD38v~H&27$!YEFnc5-ku|0{
z7e1Fl?EmrVCOSP{%3UeA24}4;d(e4z_God8<AgW$ah$anY6}Tk+Vb6g-O}an8sK_5
zpHftJ>K)M5Fp>44+^IY<^J>}5?!7a3FQ6=!ZGTNkvBjg|jWH-lgIB7bw{Q{mzEWaJ
zD&uK+J!`TIedG7CvR*ANEuP4NBaM;!_as6l&a%%;9j7vn7UC<aGW{Mwl^P}Lce}Fh
zotfy2H=Dk@e6|r~1xoM5X+6?7XMJPcAkg&P+4%h*yUm!7zS`^BA*@qbG8)2f>t^3r
zhvYDOwX`kqCT}A?v<<#N;STeJF~5@b#_osyy$%15lUcN8y<X2pzHQfYKAhGh{G5az
z?Y7*U2yy@I0j4r+E<*iyukfk}C5#q46-4VBWl%LUb+evt-Ry!+HCy@Fo50F^5O}c0
zZ&aU%gfA`L>(&{L)PHZ&*{Wn5)_!*aze@0(tgjNto7=MZzW$3)OBhmJp_o3BtZ)l{
z`eRU)?6L@B`c2bm^dh+~N)sMRFPNxFi_U}2d(`C%OBcTJviN%Y+ed~6x*w+^Px2wU
zm~<VY^83!E8P=jVm1+VXB{X!_huE0Fvi@Vj+vwg9t;A)7%A6y^&Bmhq%fBs!B8+5#
z^KaIw<z_6UUTG~wXvLmNw>+l(AHsFiRH{vGIwdG-ob(fZ(q-cVc6U7M-XBWaSsA=f
zCK@+XfJ%vf358Zz*=OX;*f?gE2|GUw{(XM(V<C6rsnv>nSK$2k!|TV)&S@SvQqD$`
zhy+MHcsNo}cVnYJ|Ftn^Fc{v<JJ1vGB1~ND2|MAJOlzcfYazPL+}Vdc*%x!#Y_dOp
z73yQ<L(PHP(QWOXvOwQHo|i5ISAP4JmeAHvxO8c48zwom&*jQ`{%xwQEOE}I8}};i
zN>~(T=8dM4hqcI1jZ!Hsj{l$Xn;xMfcfJf+j9cG7T~w+y`hMGt(tYQ_cw-^E!rLO$
zYm*-o8i&X?cu%5nBWU;XZv{hnZDquZi4vo-?^|}`8E)oDl3%f=?T~GIU!Fqv3$yJy
znDdSWj|8J{Jk}*#he9<&GjP}PwYGrFJZ3&#So=@lQen{scG9(!dFp3~)+3K<DQJ+A
z){sILQ#ZpuiG21>wc9%1IkTT%K6(86V#0W6KEA8u7xuyE2I>(!rF<r?I^Ikxd_dEA
z$L~KnUo#J4ba1mmJ}AfYjX{%vy!V=!Hq+rXr61@j8OEtHhEj>WqADpUnZ@(!kKOnQ
zZ}9M*{Szp{Z%hdB>E8P*hBx`})g0N}ED5MpR&3Mjrma)|kAF3qpwKQI(+Qb>*egV6
zbA(gg992A6s(5tzyU{qGnN<7H(d+)TykA)I$B&W~8V1RhNQr5$&!*KvZ&KE^{O|C(
zI>W#Jl)e3#g-9TSBV#ehOOd#$&my`a=H$)ith+09NSG}y_~7YKQ`^F^g-fx--=~`A
z5A?XUW92V>z{~&b+Q|WSWTZn6{yONwr}CB;5dLqAp9AUv@28UI7T-nQ+CA>0KsMZg
z9>iM7w}Yr-Y=Pf4SA{GuQy$AGdnRNN1KipG6^z%`_}u&7w*@mOXhaBf+>jwAc*%sl
zAQgwn2tr`P3jz6mjcA?9ub!X?Xo^wUbUmMGVOO_Fn#UuRF;9r~FheBE)3v89d-r2z
zU9Z9oyh;0;5qMc6R?qWTtk7cjOb)}xk3hvQa19p@QM+GhovQw<nWf&k#b*iy{Ze#~
z{?cvNn<Nz}x4YffM=A*E8H>0%FGdblrVdA5p6-lbY9F*KVIFeOB!bTZEUk;6CrWJa
zXMf<2_ipW7BH9xmxE3n`^+_4zlFy-G6lr6bbj8?>)~_6BF~5@L34k>if%i~sGdi8;
zJ}?5nsQlo}TBZ0s<iPR&e}#%W3(7dh3GzPJ#<ydSET75}tl9sAGt@o6|9_mJnDL7L
zmopSKaPxnhp<;lo|8a)8gAXJ6=M3eX=|YFA$%4S#DCnoe)f`Ooo0{PXysjC8YW~Rg
z8jPzcjzvjH$KM3xfHcm{+Sgh;z-JJW9O|4rY~DihUou7CjsQswJRF4jCD953(Lk{Z
zCeJ?06vTlKjMd-7-Q2)kikSSJ$(}s;_T}fn2IE0!SS>9#t|EsDX4XEN`MM$v0>fU@
zPYiGZacO>pdFvs306z#8jglA?8j}FQNB}M@O4%u-hy=I~P;+hyH4Xm|0`%~Z#8BsS
zE8H__Ncw9AGRcpmPl?L@ao~{qrbCH?0AN61_=dYjPC&xf5=xtGIM!3SfN&y83`tV?
zg~Q0MWz-*&9z%q}$geeH)E1I60k*q#JsT0n)L61pgRgzha;E2AE)<Rx-J}NLI)i*|
ztW(n9y(%Zvdlp6k;2hSvJU03Kh$aCJRSa|_ROB#-b)OpGPxqtr3l|dihfxCev8dq%
zRM-gHehOY1*CxtId6OAhOaurZFrL!>NiR@H2mnh&`4x~kB!z&V;QqF3vTbgl>=uwg
z^hjptrkutgZ66^$h(jF%9UJZFs|D@x15yag<J~kCJ_=1dSTgDe<Sy6yi2!y1LSRw1
zW+o^$$X)PYDX1fY#LgaFIP4r0ibW~SOe!)#iZ1|V1O~Fe9l}Tc3a`h0&-zgT;Y%^(
zi%J#R*KQMP1_|X;6;pwjFV(DG*#f~}f$!a;As+-w>F7n{R;~1vX^usId7gV|J?ERU
zC_KDStTp?Yaw}98<?PyYMa>bZ(#5GNDB!0!Bh=CaO5<b>coz4UCh7Y8O#u5F9dh(K
zTB^HF<(P?Ei(PjnLy(oDPt2rFb7n_2Qdzyl;12FEa>)doy(6j3F?Fr|aatDgKm`;1
zD9bXDpAz}PON(fxC&v;EocT^A<(+>(OGVw$#``tApP1oJ^X0TQx&wFJRgi+?+w(sw
zmk|<T)@eOI=lph0kB5N^4{dg^Pl|8{Vr7%d`QwAjWo#$Wq&aQ-YXwp8AdW6YTsrP+
z!#fF#;I3wGlTnZbiG0YPp^x<wVxo2zQK}Dz>NWKe|0q0ugbv{DaQK`P-l+8kn@BDl
z@|&MV-Hb`I-f>bRnk%NuY|5%e7h9V+=CfFq``dp)oucc_azM?afJ;Wm^zDuuYJFOD
z+-HjWY@0v9!0rnZjh8NQ;@OKb`2=DSjgrRNVStD^*Hr#=Ox%NflNZSaJ=Jxq@=tl4
z*2%lGgy!NQ;+`09LYpOe61RYakwo!?Gp+ZG4UI1wRVa~A_aE>maiw7G4I0^3?_5t?
z%8WeQ<Ov=wV$G}AvJyvbyp8EH_4)-^7VbXajchS>@1A@}XxggxLb8%QW?q%W%8l+|
z)D{i0$D@l8lJkhjrkd6D2C*p#(oTMtAXp!zx%w6zXLsF4`m3_<DQp;!m0Yg1Q%qu`
zFV`jp$&s}rxTDBut&D2_hjW>&8UA-Id7_M+LtIoV9i^jog|b`5V~jkO!yn`xOA9o(
z(+G!<bAW9UOXLZ9IzL#9f7nEn)XZcQ3`+M3yE^)xSY((En}Z!dlF#t&{Wbk5t)O`5
zb~{htG0Ublztd1u1h=TBm>%Q2YIw(MJ7vEC+4xiS-&~_D>~INT61S7QCy74?=Wo&R
z+TsrNrN4c5LtkYNyd`aJZ}(Vl?36^1vfdUJe7d8N4(VZOt)4!HTQ{XtCoh;Mp#n%c
zo)X9$AE*)qKYG4;qm0*5aE6-VJ>wXUe$U@02i+}{Ao-_o5Fu_bdXhjJXtiJQ8iBmc
zYlVZoAyY&4;pSL@hvOwZdUVdLvX&VQEDtR*Bn;2RG5385;Z|%mE4f+Hc-|^tHC>UL
z&}mj$mqJy)gFlus2t^MqR-fM1Qp0_6>rR6_m9NxX34McFs;VSw7c||SS5Z=Iq{0^G
ziP;~V^y^FJNCOev;VYJ%LA)||J$B+z)@t7bB(GTw5rzoMkVvX_#Qs$0N7#MN@t^$B
zYt3bUtcsEOF4hpi|Js;+BZTJ9xfEe?+Dd&kbY%9s?Ts0x1k|GFDo5A*u%p?&lEEs7
zi6r8-+OnZ6qWb&!Mr(=r7lB~Hu1dA<Op0VQ1OK#I=375r9I?X+?TSLsI^Ax{{DfLx
z_!^euhnb39!D*8IUgd{f`%z*!f*~8>DlX?$Gk8ONB#!<fctq#TZI!4Muk5zJth~S2
z<{ooZdSz;Szax#{d$E4Enkvjqt-=P}6F*UNRzp;Zf4zWYNn-YvtCv20o4@38*3at9
zqlQ8Z*Xv7#gH}4m>7Z2GeR{&`@cRflYi)X>CCyic#pIJ2l!{&J^-p*`i)p;jpQ(7K
z<$lrLWMY>3OH57;rLuWQ)L(}~_LbW4mfP!ezgpAD<|?6I0*{5AsiCLaeLi(A*sN3|
z{~a2xgiqH!fp&(Tv{eK(Dmp_epH9FsKfu}F+W!>lCc@EwY9wZ<&8x2tPxylTE;*o|
zx&lXon9F#Nh$Rp8lG#|HneIlv-WGtyRp#{QD*ulBSRIW(I6XGQPI`(boOJ4N;Vg#s
z)D5~|Pfb|_;cQc-u><MtD&1I+tct2HGwypFzb&eAC42Iz{)v)lm(k6X5&!3PkN-aN
zd!rhHeY8^{I(!^-c<;lv^@aI>1MST{1S5|kg0Pj%@<d6dvgnsP4FQ|O`vin?t9iR2
zLXXe|y*g>_NhA_1E9#pr@!I&M-2B;#f{Wxh>7D*6>}UFQA-QzAu}NF@#X&PBUz;L^
z@_2!5&&<cAqh905jiVM5s&%}t(<NRp6h3KoU-B<9eObg&YI@yI^h%gpxr0Xi`6_|$
zQOcgGwZc7a=_Gf(1MEs?sa<nTr)NJD#nq}iLtdyg^9(2BZQRj{Ln+tY9r?s{q!+tr
zuBx3xXR4QcO#gdfLZF(r<N~G*2hVVjq)U`c5hdv+OzMgw2xHzF6cMG&8PwXDX!H5Z
z<TQW3sa<t?yTCghoRobjZT7ZX$GpkX@NGNq!GBCP==rI!kBK2@2kfsGgnnN|vSPyJ
zk~pw~`?<6*lN^;XVNd*>-zF{_lPh=^h2aqD#r}!HR#8rz4lma7XN61Md}}Hj?;;oZ
z_UDKL61=pQiOk1AMVj9RtM*I_^t2uI98$o)Z<kSoy=*Cf1=h-^v4dCwX5vxm0o!|s
zsk_UMUr47r@`*WVT@6A@*i~>7{j>Kc0vjf8UN!pEYuiKn=?qGR46(>k(A&j0-b}ZL
zTnxHm#(=?WwX%0Ndk*T_9THo?vh?w&S@hDLugk$N<u?ZpL*3TpH4H1iP86D7xCoyF
zTRuh1Zf(;qW&qbKQ~|ee75C<yW>?6YpcUS%MT(MdxElfe?+6`(-uboDAfZ1Z|DJWn
z`!Yb8QdFRe4LS~a9nWau)iG`S^ONml*X!tbed#=}Y8LW87DL`H-+!(zVos|kAEIV3
zkX}Ar&STvvOmom`@Qj8tL0=%pLb>v|g(eGRA+98<|MX3efKgjCULPElP_p!*7~(-9
zJL6Ro!r+x@`l}*~huDq4uu-FYr-|;}>!3Y8VpU~&Sl$ffg9khIVa*68az^s#fJb64
z{78e*<j*@uN#9qYfD4ArPoh)S{@n!A)BIeu4p!P9gDym!+ui`#bV$Q&KMPZ+%&CI=
z6n~&`(<`G}%nM3VcNQ&>LnJ*_Lm^@HHK;e0e~Q%H>1m9cDvYMF_F+Gz?tHU=WuyVi
z<i3>OxN3d1J`!q@AVK|5iY*ia%MA`bfRB39e>o8onndpN5=i2}Y%6aIee4EgNPEav
zQ6m1i_aBhq0OXt)*FL7g0A_||bx^JfY9emjEE)>(qDS88hZb(3cynp50ryZ7iQ{=M
zXs!Xu{m@1JLUbP0H9!?Lk%Gy~-v)31eH?Xx!VngUYk(SRB4a!+tK%9VgS#x!$3Z=y
zr-zGJL{CjHLzkmqh$o2KnJ{y2W{Jzuh0ymT<29;dTu<)Etp(HIGGZE-ZAU++12!Zp
zflV4}Vq}YaRrs2DE@)-Jm%QG4*LbxMaQT-KPfUOXexLx03jNDX-$VD06I`r^mq(}p
zl5RK@xIGA^OTye^gzPJB1FtwXlNzDArYIQj^ExvkCU>h@HVS_gq^FEwvGZ57^TA`m
zf{UM^BnGy+oqh2*kN;&pZgt!G;tAm6Uqe1^b=&#k-J$=N8Qi)UUd2af62l0f&KY0M
zhBY>Z-`zw3#Q4h8mPfjCsd2$1<jqE$3omU=pNZYT!_hGtVwZ#Z;^{xUM$nw|<;>m}
z&&=UpCilykqc5JV|G!NBm$N3>C`pxnZ7^G?gI6#fUJ#D!TIXA%vc7mxE!WkaesT51
zi|_wG$;tnc+c%?B5!X#ue9g4sC>X8sbyFMM{bU%I+W$#v{g*sXrqe7F02r`rNpJ|9
zBfT1UnC2%YCyYzhUMKg{Uk#A(j58n$tmI{J@jC7JnNPm?6T7P9INPw!^l`6-U30QM
zhIfO6tOp-TwC7$Z8>K#uC#H<~u+@S~&i$7>e#Pj*BLdL<N2fTw8+efB=kp2I2kPhl
zYE(@y6Wt&o?}=90%%@bZq=S4`vjg~e7$MAsjF8V7_CU(>a3w9GmlH3|i&F;ylxPU8
z8d1$eJlz(z1hA>S3cis9Po#4xlK{X%a4|WBY63YDiXPm**=xCqGbaI|_KQB2+Uq*y
zQUICAUpK;+hEhl;JMceN{a3M$W0VGLxbRvDod=hDcQfDOQR1svT`rL<6sX+liyxur
zQ4i8)>Sr}6?86W!@<2i6oqswxmR@b4`~~;@2#IVm|2+D6+-g8yIHG5CZ%NH?7awDT
z0Dc8iqbm6}16<)wuc|?!FKIOJh+abhZ+}sY3BnDoR~X@ocPkSG{JeV`^EC4)1C^w2
zgdTgjwT9%*yNSHX&aip?ONyne+TfOE$8*qTlRTh(nP`Z3^8GlG?zh4)xRD1Jx~bX7
zXhBe5kE<9wab3U*nJ_Y%lo!}xE2fzLuRztv8N&;cm?E;#!ugaCCt%VfkSQZnJsCKU
zk~F$P5CrgK;Xv}@{7E<~<O(7cKNk?-nP%YJQ&dWukKTYm52*jEN>ga{o>kK}8u&ZY
zb^0x575Aq2*u8P1?@etjO>T8x3=H4ev`9F%*)mJCj#zFO_sP+4a?v$YKPRUB3tUxC
z1hzi)?JUnL`W15dIXtrhy;-Syh=D3NYt>|<JZC+kOFX6qvgfi#r?OBhT|s+-P&M;9
zxu3Glagt1)oHXgYb;bxjB)kno)NXJSrh{hTNqxz;T~(#XTd8TNTDiEvkj_`8z{Ay4
zW(>y^U~@AJ9kg1E0_I$eX<W_CRdG>Qj5(p(2W9OB0;!c1SpPql*0`2}R^MBOQC#B-
zEeWcswO^H>-146k?<!j8U|?LYqhdxJE+#)n=io~RSl=4iH!1mG|IG0DIr^`vT^hDt
zbyY}|hjzVT^_hEok5a}2^v3*?dQ+jB*6*8-6!aAT?CsoQjaf0<VL|!`{pw+5g*+M<
zzYnJ=(5MuSp-<nawBq5itNM$ErZ)X}zhoN5Nqo;=P-|L<{DYt5AMqy{TV90YJ%UAn
zt4*mlfxLyJj3}=}R(IVri)P8Bm;Mnm@IHH@<(E{*7pMqK6FYBZTwP7`3K4y}afu@i
zars~LECso43Kf`X6g|5O^Y?$tw5zkT_v8vwB!*g)gpzy6PptI-v+OZSr!<3287Ml~
z*s|1eLI5ITLM!9(x}O{W9;Z@nrr)Q(iP)ISy=)X$U<xLN9j<DgFqMw`=Y0-wW@qMB
zdK(UPfMd9PqDJsUnV%jBg|PAa=$E}*qeKV5SUad@D8rXk5bP>58Xq6kQ<*|GHI8?A
zWpc($qE@Ig-&SBJBcW<Lw+fTx^Ws|Ae{$pgnV;xlSOB7TH>uJqu;eGDsWD_>GCmaN
zO)(suziTEdgKRmp4gq8eObVLVli916QRav!0{bNF2o$FFHOBAYg2PA}21sM|xW=nn
z(s7+SsRW%}i!O1u7|cRxM3ERDuT|1B6-I<HJ8ET!03^nl5*g~~OO_v4HvIi0lxX26
z+gwO0KU6IrBFE~@MdRvfuEoWb^Zf<tczuirl$4jz%*yQejEFhTB>1;sf2xni<^WMP
z){<&D;N*6tZN-tB=(3JE-zE=Ifef7&YYQ=pPGtw4<J8v{Y2<9L!_8G%_^;z-rZ$c^
z5p-hi_u=YyRKyvQBCy1z0vR2xKP``$+jR~jpwL)ARID`(RTaLZ=9B=lGefiZf^#`v
zaGRX>M7be~3=j!3#|F-854gBWvGg=mT9=te>QIVI%5E9Rrs1;rpbSwz3sJ>+-pMKr
z(_2u?U@IjD`mfY}#1-l3z<mUQ=p4@hB>Scoi>>q7V+*02QCoUv>7Si>#FMAvZGuBC
zCdcYPPD6tmTbe&{3P5wP4Sa#XdQqr68jc%;P735Bc)KDtVJV5f@Rpbc_>s-sWZi3o
zGKAvU*d#FcwIEEcr51ZEmd16D3JJ|=A&{l8{1OIL^Dr;Gip2Yeckp(jCY=3p2k_bA
z97Ur?LLkdUni<FRr9eWZ>(n-W=$lcHvxredBTeor9>}IBm^ih~*9;9c(@gIW22gLb
ziWISHlE{$M48#7IFSEd?;6GKES#4k?hZhRa2UI0{KXJEX0?&T4rv3;+M$*lG-7U#e
z<2F8=_#*R!bZ;N1W|J?H%|dZWC&CFT<0N^0^3kLl+3;+VuCE%c)mr^TpUH%Vr^Q)S
zXWoXV-`ZLD;E8vK8m6DxR@{<t@3tnmLq8Jf0#@;B`7#sH@KhmK@{zP&{tA9tbq4Cz
zw@OhoiC?gve~d;e%(a3JR(&7A7s|W2xqiqEwT*5MlU&iHsh5o!AAukz3DF_A!nF2~
zPi+h<33U9UNIXp~oY&yZQorAw4F9Sn@guWvtM=zAxmZ41OS4Vn&<H;=>AK_|YAz9a
zRZR@TeY56)+FBe`QqTlVL5syDNIAH1R^h?^FDbaRT(J=Nzx-YKU*iA|q~`$-WMpSX
z4~+9@JW4}}y<WRb%IYJl2Eo1cQh^XE`H+)9w3&x0gdQTX__>3xqY342KSHHqCs8o9
z3_6ZY{qIk{0fLO1kqEgXm=><YHo)SD)h6SS-E&QNEISVCgsH8c#R{v<fHqE)Nj@lu
zR{G6#j3Im&e@rRAvB)ZQ1>&mCLpZwFH1`vfwTu{hc$OytTjk!Af?jUm|J6X?Eo8XI
zOCYf=Coo+EUQ(t{JMRdPF);;JK$D2UdslMAz?yIis$-lqvLvw<z2qEFPleAo!@9=9
z=0)=VrQyCKg5BuP;fQ&wuqbM0{->^iFZ0KPALBYMPPZ8)HSv;ogk;TfX6P8`6-(^0
z1chI;LIi7Ohj8I^-KH#0DwN*%!A4Q}QgdbXW2_Nun2*7}-lswGs`jt>pR>F+{W^sX
zaSyjN6y=hS%`jAT;2vs?Jd}x}7l+0oN&glRFR$HV#&5DTCWvl>V<>6o4P@@ne4b1k
zB*YC)t^ucs0bw){Q65esAC;qfCVDx6-sYQ^I^k-<#d(X<d_C*$JIuD2C@j2GZ-a}$
zCj8>?(QOYz=uOJ8Z@QQp&$ZD4|3jAS*;wnWLF(GJ#c?^)=WKRW`*TJwkq{KO)XYrw
ztQl@5l>wyLsJ1C^A48E8I%Iple=;OLluIFe264<hEBQM#Qi3H>m90#eR={yL8FC|D
zD1AdG)j$;05`6pdo6ON4!;&nwv>in!5H8rz7oJdPVfy4s+at*4A3efclEps7NA}Ne
zcK8MK`CojPT?ipWDmg^3X{Ng#G4PtOHVZ>)u7!Gl<JZ97nCT6~AFd%AuO^ow6V||i
z?(tP$dplxh81s+IEY!56*kWrPPvuXx2hJLrohvDQ^h&EldO>liQN{=*pNwPtFNKkf
zQ}aNgf>zT7!-H1W`qoJb?ftlufIuMrwIw3QeER3XPX*H-4xzaf=*9pM&+_f6VL5qF
z`{19raf8$_LgBYk`JpsuTs*os`ogaRo)ssAy*!OC2p>0QG$%5A*x$z&%Jzg7-iN2(
zw0n#&#;c4~#A`6lI1(9hQrAX!ZmS2J{MG^-E1jwESlGdggNw+;K5H9YMd%---ujB%
z423mipVZuP?Av&E6o|6rO%|te4%m^xo&*qeVZ&!U^~W3Z5QL(S)_EE>$G$afqQ{hY
zE4>pu<PJd3XD?)r^E|#xCTbSpMpW6>h3NvViSC08nCy^rqzLr%hGZ!FtZhh_U?pJ%
z)yc^T8IDUleoI6sUAtYHB1eWUMo3Ie&eA8-%O<Uxv|Ygi@7-rQb(s+AsbnzBTM_yK
ztR*fkV=_R!a}&#JlcdCk=+e;I#q;n0&NZET?_=m-Oz57rObMf8&>?*HE-)#TzAJ_9
zIDCZhYHA_>F!ieer#iC=oN*G6u1U1$aev6p8|4h2`;I`zPb4!9YMXk0ySNB{|F1Wn
zql{oCLX2I6_=jld(`JQ@XU}_h7Lm!}U^Wi3M2<X=+#K~zY-%C@!A7PZ^S(3O=*z<=
ziFXWzdoS`1whM2?HQ~2Pp4DPe;2ce34|(WM7{tuGP(c~@6T{^x_L$Y-lYdWn71J38
z^Y$+fORO#1dy!f8nOgLRMy+&Hhx8LYWj#XHr{;4ZvvMx$El=}iKR8Mjw7QMfO;jrt
zM@lV&{F5rS=7QN9QuiW69ZnO&y59E*1mJdbG+kA<yi8_O0g|Ng(GJ`SA1G{hcZB)A
zqDO;ljZ!UxD;)(Inw0;^7*V0;m;Dw%>)T{Ffb<WE#e8o{WNP9+Zhs&My*ICCY-+<E
z;9Jjpb~l<-f+A_+yijceDW>K37M)jL>6oUKb(beqN(=6=AVhN2U0TOPlZfl|{=o0C
z^R`8Dp-&{=O>}2$B5-e6`6vdx3Jv8uzrIO>+vFCwXksLKW^4u`1fhOnyQWl2eCX}e
z;wwTv(GSf4s(}xZhZ@8OPzT^=%84%m{)AFP8?WZbZDJ^}rs4$jdB6ov<_{gray3>`
ze_R*NrKyCpsL7-&8tEt2YG}i~19Po|d7KW`ln-r+-ttM9ma+))R{Gvmbqd;2no4MS
z8H0U*XD@Wn*<_4ZBCJm*5A0OY$;f2E4_p%nOH+}w*1<@;j79HTErS{!T6c`CuO~e3
zp+|p0qUpK3GESqo9vM|<`>|@lDM7z=BGhxj!8~s0S%Qns$6dhj26}NWl%@)|?~9wk
z46QJ3r<CZobS>YTpEwY(Ft~?6kiY8)4p$O$<K4U~_G5j}%)i+Tv4l@e>_^hWX`Fc^
zRceM{mi?%_tX~8H2V)psAZ^DT0X|*Ouxnrll8Xo_!<5T5r-#4<ajZCpQxOhUSu_`B
zpL&U3m8WqxfYWasv;J@TDRh)FkP(iiZR`aj!Cm%cwVC^HYr@Z9W-D9}M^AtXO$Tha
z;eyV>Y^uIJE#kSCh!iE<K_doiIramk&0RnFgTz8VgXq!sfM2Ed+-MfcJ338N!Jou~
zUR|)0q_gc)q)qfO4VM0Io%{B#Y(%rDN*AIQWbA<=lL+I?O!-s1-!lUhX!M<B-Vl&%
zVLdOT3&GFwUUmG6gVZK4y5=MVInlKQ+SCd<Tg#!sn?~<9^QYw#Qnuh?WH#{SDH8Qa
zs3CY5gx;?qwJ13PCE((ZM?Oy_#;M_!WIK7d>;g*c>EK3YCb>@7nx*j7q=Wn6bGgOZ
z`+<bBJ>SkC{rSPp`BE#}PA^)f5awrj-%m=od|o3vXS}G8E_=W2R4qig{+l)bi~C&E
z)uX`14Zi8AP=wnLwK9T#<LQ5(bJEaJbiO{$#>rj}_cE6Cd~RoI*Lkdm@((ohwOC0V
zQ#vDVyRPc-i6pjw&WU<9A6<njoBF?(>0je@@ch5y%|Z$Lxc*R6y%vh@KcO^-Eu~#B
z=LolLwX&N6IF$LH*EdF3RN2m?DsiOe@~`O=6uetFpNXwN_^-T(*>!x|MZP?&b%cKW
zu=`tlz~>WzCW3bxwZfHw)~a(E|LR|fLa&;<8$@*meAzxJeT$wQwDq!EKAdE9E8lQ4
z|6z?OwG`Ij)9%!-=kH9TQB~Z!kH1(PSsd9q>ZDV!ZaKR*a_T<zYdPq*f~*pzU=M0_
zsoR3Oxm|^E-i~O6EMW%vm>x<_T-+DzS%WlE<~OfyV71}EE+-UJ%y<IX945MH|AL3&
zB&L~(mjNU79yMCzHKzFaugAgIpN7`SScMF0EW@$%eS2q~R^25IXw-UL!-i!$Q~(aZ
zao10-aa0D~by#AeFmvOwef+(7tH-8y7uCY|Oz19KD~-{YfY)nHc-+phPDG!|P4^Z!
zGw4hOby74omD-^N{BYbJ6ZoyAhVV++t~hx$EyvKJli%X%`6nH9)^@Z}h3%i|e)3Lx
zwkp{mF>f8uV9Yoj)idX69*h64;ipw^=-wQR<GZ6c%b4A0n_6gh8cLoa&RkXP<%bZf
z+pM?pK3jQjrXM}kTKrCthU$5tSBMV*Ah>;}UgCM?QS$Mi%Hl?Aj)&H~v@3!W8e7(=
z*3usZ*LwbP5uH@l+xQNC_i^X;LO>mWCb^*a)%VT%<Vq9-7~v2sOEBQxX3THuCiH~M
z-Iy+0G!8qS#-d0^3yi;H+iC>~Wsqs*Lo5C|i055)>k8gO;O%~O3UPEc1|fNI_j>l9
zCtzDxsrmwSO@u85Evz3q70^{Z%Jkd&61*J*Q&SFGdb_e5XI{%2xNQ$t>oz45>SJm(
zvtB4c5FpjA+vg=<N6JUrv|rD@D#xgo{jQJNHYO9fk0~duiarf~D3`7o+!dIKf*HN5
z8a*UHC7J-g<8dB@kyRtDZC~Q<r?63K=A#!4Yy0L)zPYiczTy#^tIJPAA^lp--o+GL
zk^)EnsI2?_EPN*6IuAKbN4bQ*uhV%>m#z!}VtrvCn8Nq5*3*uuqV{kkR`uQ=d(Tcw
z6uBIzJ?V%9sOFl!;Y-zX+kJwFPK&S*upHRN2aM9)liP`G0$R6IeR)PndUV*}fF47~
zR9qNfY*_FvPi-6pp!#R=fv@TA6zxRCzvhk&=?@XY0F$pc2xf#e=iUXqYL&h;j+TV%
zCl`ioyK<4PiT#b_EMb=7_-gm+e&Po*ePtD$<SFe$6A|U~-0IxmMEwu_%mHgpmJMba
z5uZ$2CmCPDZNBsPY>XWJs4tM%6k5Wf^T-$aZ19ja*!<WqSm~vS=f_&vjxGFUj*qke
zKG$^k0yx8M+TAaaY;dL?d_D5o`(iDRY^gLkAZ~uc^Pglz2>?l4>4*PH8BN?IM!s_6
z1hgOF0pj>W>m6%Pzg^9VYM*<#LZ4t&liD8^l(34`<DL`5A=~NnMAR{7p%jjIAk=^6
zJa~aMwhjPyQtN?}VOkI3M;vyMiaIYs^-veL*QrcMslb_j?`BQ~p%bDpg4%Dt?-j|-
z8ouMHyW|(4<Y&vE(^|FhY%`5K*tG<3$25-8t&XW2xxfZHNLF*vgCX@8N$09&HM0Ad
zKSzl-Pp~K}tKj)}uc~z-%}f<4OD4C9rn$Z8Dbeu`VlsZs&sL9}ZU3Qp64&u-p{PnC
zQ=zE&1cOOZa2<B~BsHapsqYFfOva8hykFA9Fv#;K-;#gWDmq@LbyGlF?!tN{4Ygm?
z)jGTC_|n83jGIP)ysN~tlV_k^sT~=#AXCwN`)PJ$q~g|mQKvsQ^u)oTE(bM6TD|?w
zs+Gmn#x!W^`Sa()1kY3uz3yJq8@t~m<-$aOjAv%}K#AH|>s#a9>JU^jYSOX%P8Ywc
z?EZkctJhURxpz>_#$sOe$#mw?$77dKsb{K)Uc>t73Oe-WuLD4nl#d38W>lDFpAp<1
zeOKayz?fDTYOsn<iR?9Wb>yMmR*mJbGqYq6CY~0cp`qsECmFq*(3KL|$qK=@o4pc%
zrX1ma7>qrBZ?}4syv#$LyPR4VdGKt`z+Bg+aS@9{U2-sw%!W2MZ$BNy?hNNVeA}tZ
znU31O|G;gcWF~RI47AT`PYm=X7P+U2=M?^&z;V$nA#Xw)@w#oJv^?OQLcz}>0|{ur
z?XQ`g#c7)GU~cu+Z9QMP6CQvzK>#19iz{+pdwPEF%Ga~<w1G=ut_1h7<Tr;?>-OAY
zsrDEiM?A9%Kpkg!<U<u#^80JW({!dcYv5%Tb!q^gb2_5Qn+QS))~6<*7*cYEh)K)E
z?;0E7M~1ruy96Cj_;1&aj`R+95eL(d;rsAR6Mlmw&&?%`c8!0d&9#XH;MloURu?_1
zg4Y8q;Y^KV7XWIobwDPqUQy=dd%g{8!lY(JKm*~qPl3<+YV>z)%~e2@K^xJsYW+`z
zsE6FGv!DI9PeBC_sCx2jSi?Z^fo{#EvQiMA0$iLZzg8xTKbP3q>d}0Z#rcP*8!<%7
z5BxAF-TaO@54VmS`6$jnqGh5l(!O`iekpoQJ+u5$)kEA2-jAzkn-3L*47+(sKVB8;
zW@NVU2a0@8&XIGLZtZPhQ3z?G<y&6`WDimc{6Hqb;(8-kfLv^YXRcxNqb_Q|1YAx|
z*#p?aa}SitZ@Rph>WWhNMQDS(1Nr3c)_iv%L6>MKmlX;6h$8o{9^KGIfv3Tb0)H~c
zE90WLEnh`?;t}atvIwsJn!E(_8k(z8zXmbGMnV>y-2M9HZ7*=nXXaorWN!1v1<9Eh
zHjrkq6mZspdT&+`q1_6N(x?{zT%bQ{brSuypvb9cLXH<XNBs-Oa<YH<VJ33mG~MB|
z4|8{eZ?r(882;52ZIs>=h8NJiYOer_*Y6&-pE;KNY%PiMlDux~XX{y%=PZwLDP~9p
z#X;6lzq5Z0t>PL7><R#y&>(ghZ=vQzT|&uXF<{%05gNQ86ujy$TMT*((yYYdax9MT
ztUxyh$zXE4pVpiPxcpR#2?DNRxdjAaBP>zu{RAASG<#3Ri-+!hcsed*Fj>y4Mt2Pe
zHO|M3U?TD>XKr}yPduL7?PWa1Kg($akbx>_6muIvaXu?eYsb-Gzf@X(6Yx-CZE!T1
zbYdDt--q{kB!OEdD8bMVPk)CLc4wN?&F`gb9Vzw4THqML$|VEQ9Z4lT%w<3ti`>I*
zVB^nbVH`DhdP7!mbEu1|sWu=!i4+Dg;Y9cupr#1o91MXQP?Ge5OvuJGxa0vTjFSil
zI_zhQg9&BgeO8ATOG9*bxI3ft2nmXl{FpnSA$Exy_~Ns4$lLX{*bQZOh15NuUmOCc
zIS?R@%32iP;inDd1Cx4;gQH}Ht-l#zx83pdR4h1TL?2F#O#{}yb~!X~LH$a%F1<$+
z2;x2GY921^-7F~=I3tsc7Uu&Ribc{2Wd06$NkY;-<p9_TrtqrKM+ryw{qTGrkig{P
zqZZgQO_);fc*J>uSye)W-$>#X#tf;NgU|7N1m*?TssY7<@K|V(59PoeP)KbIig3_g
z1K<*%xNx3hFq2x5g|W-yrD?eOLu9U~jSMm1{yW@&ZHu}eqPZvaWvwfmBc1`I$t4|{
zS-=qLHkynSGsfqLncLFw$_XaQ{*A;Vwo?PR29u|-PI;?B2j=)sm}!uk+sT;lg232S
zp+if2#0)jkGnDR@H+#>@Tc#*ZRy-ft4_|T@7Aj;D*~sGTRDf!FqIWuB<kLYSHgE)9
z_lwovjqXG;Krf6O|Ic1NspetJdLFG8Z!|$)KNSb^DJb%;GVtAut*?KqI4TpQq3DM<
zd3bMK{J`+lvP%!nVObBo%FGu*&DkxEr@`I@B!@3q@<q@Kc)*uOj=;sq9Z;Q1F}rrQ
zecS~_7h5m5p;9Wl7t!6<P!MTu!8}&CEba%$8yEb|XJbS5Od@`@{J{R(u6037uNd;Z
zE}2eQdBG^h%a$EMM|XMQg7IGIS5M?rWBSO36Lx#8Mt`d0t`dD$CGfs@sgv&^Pj--Y
z=kvtznN{6<aLFdLd#8kqro88;vZ{mZ)Tq{{3!MSe-zMBN<-ONMLJq}VyR{@%1Eb1O
zS8#cDz>?d+&K1Iii_%*vXt(%4yhSm=ZPbEh+^75N%+2<`P&>Y5InJ7ejk1`#e_2!F
z#+MH{xWtn`cs3`p9-8oGWxjL$nP6A^HzUHB(dAq0;u{35v+m1^h<9cK5A&tPpH`6w
z3tv$blbF#Z(IOl&?nxuYXwuKx<>TVS&1AsMCiSR9SfvP4O2H#D=vbnL%DNDJR1<<k
z`qnq?Y4!X(cWx)JB&Yk+c$J*bWw9RL>$$m_eS@fJ97{c=Ej0|G7P~nW)<3(epB44Z
zX>5A-9`l;0%$YsQ;a)3U{*H0@5I}p|SzE=TQpvr9Do*nOrd71`HE+f90cpG9qcoZZ
z)Va+n=T+zvPa507i`>k(8JtVdUy@v78MQ!mJlPOVF3;L{@0W=8R-n%zEj~47hrrG}
zSKvXIvTEqmhvz8-SQ7^aq@S@pAPRBbUCs0`?^6S&O5)^doGAvTKQ=Or4vlhM%rZCX
zuFQM$6l!%HI<dnJ#AH8xv7J#9eRGEbn`%z$bKZ`vLREzwaj|NAod>2ouZA!F@FAZn
z1WYYyv1ea+{5p@b6;UCi?y9}!)?5BklC$PbRr;4S9rG|%NyT=#xVVp<o`fkNEGV2&
zU`73yd6PQo8~he*056BBe^-~GH+Cd~LoYKu!>F((Hb-^ogCFl9xnrKP>{|n-fGP5!
zFV5;;eR8buZv#^U;lB{S74Je;9^8*hIcn9M9#Tv7jd&^=EERe=enLgYEk6BesQlAK
z=NEyx1pk+2Q((ubdZKTLjk!46tTdYUI2N<g?gb8|WzSCzxednrROCAETPl1fOA&1g
zM81faQ6(>r9d9yq^ZY?0dY7rjfZ+9$xXz_d39APmz-shV5Al@=)by@wCD1K}(zB+*
zom%G?J&SsQk7hz*Ia4#aZ?^kW)09I!Z)Y>MNtPp6jo;pa)|h_ff7CPgi?a(k&Xhjg
z!~2@d;+Vv@Pcb>5Z7~&9&&<B2tcC}thAMR!JfhG@GFQi<$lONlH<i<6Zs+hWSuF?X
z*MsJ{KK6qG-|z-mJ7!mBPrUJM`(ah<t5%s_)KeMsb{`Y&@PMKG2Uo!tx3JiDIf%P?
zyQQRCwu$W{UWI9n%E?A_&u@oh&yhy-99D-NQ_(RZ4UH1ypy9psoRX{b8|=wi#UY;X
z^m}n?1#ZN5(LfMuzaLQnB@3i~<1Gr~av$YxyQqovXg%q3A2shAta?Av*C}Gq9R@jL
zl@A0}4m2~!FYf2c&P-F&yh(Idzd`a2f9EyYhpK!*^TzDXqg>bZ?!L-ZC~1w?<j_W@
zBdybn1Bav;#3gBQG*LPvGkG>^Gz=QzDok_jX?o_w@0rp)gf(!$PHMS=j78WQN`s)M
z$-4+^k?;4KjkkoBJV8_Ee>}Am8R(Bs_jAeBD-y!}v1~6WTSb<VypWGSzo&13-L%iv
z7tGun)67@Se97$`ZhtJYc}r$CS7+7}QOb0W>;CO}HdWOsodP-Tr76&X;ho!vsev`~
zJL!I$)8vzhhxl~~NvfD4MM?K(+?A*hDo25{&Dk+?*;n^^V_yx1sAO3?V57aO#>ppd
zfAoy#Oghju3TMzgkmugKa*$@}iT&(QSbQh&G_~L^cX}oyMYTWsX`D;W_{N|}<<sud
zJr^81A67&*pU^<>>DF4Q{>aNspOCeZac9x;*Q<GaY|rGwvwhBL6DZ^MfbCc{<hurZ
zItjI(e0N0Bn`z%t#gzx~e8LfXp)|tB?bZbUocrDl5h?AcfT*yu;we>K8B$iwN~pva
z9#*d5<nk-{OsDb9j!_l!@^Ae9<Ad(~z4ggAj^_u}GPt)5(|eNlzQ09DnTa6sTc>_o
z)C^n>{zz+cpC~=IkvYupZ+oYC$XRO{{D5t*fsD3arfRh=Zlo_2_0-jO>myB_{oA|o
z-Th_ho~vfC?U`c6z4Z%G|I89DXP5ujYPwU<w19mmoT{2dZ7CZUH~yy8HQ1?Sj<M?{
z{Dr8kw%JayUb>&s(oF*`^c-)~u!=SBmuaq=Vt5?W>n_f<HoPg8_;^&G`B9^FyydeX
z=E?J<LNc!1GkR5cN8C8m)!vqBQasrI2IAFAuE7Wt|4{amAZ7)L8wAVI{pB>>bX5(%
z6s6tJciE+yZ)ni=d5hmI+`pa-^g7Z`h7|><PN|GIW9xHFeOz3$>qH~54H<Hi%N>~^
z_b}a0>+zAyCdM;1GS=y<d^m6w9V|*s8Ji*}m5TH76AtJeI84NE9kMF+VeFyO>PCS0
z?*f4vxVaj|-Z#o6itG&}H|^Lrp36|Jq`4F`EG~BYIo|q&ONyh}#ELJ*O}sdtt*G`O
zIj<DmH=DK1R~b;wQ>ygwsuuT#)XZdkD_}}B;Ky79d9LlQia{N5R|d3_70=Pr1Q8*d
z3w)dTf3X)s>`~)Sa@mg0Q>jAZWW){Jt#1a1gx>mL-g>&vHp45h{I|c!esy_*7WmN^
zM?YPnTW>MLl7cEMWxhxhsQH^JC4JW5G4kHzDWdz)u=%m*+a1oXYu>yNlMcz=I33|u
z3lACS;tyULohM{OG&B<%%x{d-dn}v1hLH*Jp6IW`PhSk94^f7+0-eV7#yK>}mJ>Ny
zE|LX-{kdDH#d;au&nbnR>n8_!DvR(RQvvP|zm|T!0!w=OHGPzE8scMZb|rMM`^r;l
zdObFnr6DDI(%N=)SVY?MC;?|JhMH(&QY4zSkMC&QoQw3@lk?m@x2i?pk|~PkwK2j<
zCE~oh%BpBUGK0CNEZH_Q<h!OS=l`IxZ}3{rFcs{mUpqUFGVFTxQbiEI#{pNVV^m9Z
z_}Zb_Zl)|U`iS>rMu{%=d7Lpj6D8LWM~sf$GRn+jnV(XOrcSn=RW(b1yr91aIzQ8U
zhs~DDJj7Baj-QoTdYkD_U|O^o@jVdzZlvp{<P^hl8z!FZFb!f!>~rJgeoMcy{+>XL
z2}g&B$9R47W4FeHZ_GENjPfOrjaq(v@l{SST;O@ALsWRQ4n91(#8q9xCi@rpLQX9@
zvnM{!sYo0Lb&VpA*0H|mkLPdsxJ6t$UL3``-OG0iAO1kno&Ls*?i#?AAqAzCy)(A6
z`2p18!T~kQe8dAEkpS^a75f5O9WxH9%l(pcPtC#aQh}y)Dd_svJ#x}}uR+P+L6s?o
z96|Vqxa}+HWh+r?YbI&4<(YfLq-q^FD6#ewma1O%;07oCI-x%$2fJA&^Uk}B+V8oa
zh_C4`Ip~l!6S+6l+LKb^7yAKq==_Jy5JpEc-B+^vHe-}2%!Dc&#5sq$bDV6tcl~2~
z>eH(%dSsY=+%qaujGjEcgRlV=fPLI)E3?cpu5;iTu#a0tMFB2%<cG)DC<naZbG$Lf
zJ129GNcvySD)a{5JT7P4D37cht3yYQPl?UKndKX!#_lYeW%8E`)9psT1`E#IL^|ta
zJe1Mtr29x&yPQZKJXNOobiQBSZnuXNcNR9w<WPXq?InEx%bT+MJU7e~3h>JOm;L>m
zgs4L*-tfCDh$`=H<I{CekgQaPa>oz+yAFpvv~5J#$6h*c{R26YYjAYri_hzmST3Um
zy8YZ)u=a21dntyQ!;<gdz4lI29=4BR&kX4zaM!vLRFui_KmYkQmn=^+fqP>pVvkth
zL1*rmtlOXX;E|mu=3hivhEgANx?<y~eoImk+eX3!u9%1?)Bg+xWA?AX{`yiM)ViK0
zZIa&_w2guh7}2pwI^1>s4Wkq_iK}pXLUcz3ZgY+#z^i$czl`Es<M~K6!_oMpnxvjZ
z9PV&|)O9b(uO?aQ8<fpE0Lwuh8c2NzV`ecCi|iqd;;ZHh&%5;{LVBI%h^Taib6+r@
z?{0?!aYy1ASh4Pz&EHofqTMkx2!<mfe>icOTd#$6A8|**8MuuYUtem4E{L4-ISpWw
z*mG5tFSee<i515K!p+J2^7(|;oP$7|FKB>$o4pjo4V+UTaxVJMi#)=9CO3WJIvM%B
zs<XO>sXL?m>E_7=4ti26xc*<ER#8+3U{DOrb$%m4ClWI|XEG?RI9SJ}m-G~0Qm4HX
zdOFPJt~1DB`nad2bzkReG{4`H;zv{O7bHLK##W(V!ZA`V`95mDTmVicn=?=$-9w#0
zhX%s5gV3p4K3T{LlDwT+K*f6O9E_pWlj6*J%L@BAtOfYw;6NoiD>#rY9uh-R%@^JD
z96T5dr2%53aR|ph)+9d~Y>)a9Jxe01vm9IH_C&;BF&H+elf*cp*W-)F?$(PIKeQfb
zyFBs^3oG(_^tUmUxI=BwQ?AmMG`N-V?qu})nn|@MbAzLoid+ty&+T5}$w6LA>TDUS
zm(C6T_^w%(G@-&y!Q6nvXx-4E>y*aSN+T*$h|LDS<~Ae2&#3eHcFdKo&TXcx4S?t<
z4${yo!!8E#-)7Lf`pziH{=1U(>eZj5n$o)tdI?bcBO9k??ab`lwo(QWAc>~*`GZvg
zHi4uv$&ym_$+-07i;+uem*KjiD*>JMa&^A1n%Ugy?9xqG{LiT4V#$@8QsPzWJeBEh
zmJ7*U;L<o`zcrIz(Qvb#<zew}_^nq~NZN8q(du!lQ!<C}LZdKjf|mR`xjM=CIOJe;
z>oayx%ZBNSY!;t`9ydtJ4vS1T*^Pnx`So}FA@e8Si$9O$a;J*nwoj)Hn_d~3hg1>Q
z^8YN&Vn{a#Ol@jvs{AulTG}F~Ut2tDEHN>Ncgb5&!mmnA-y5;5v?aP7k^+~OZ!Hw@
zYuo!lq#(~u)k_rPeV0}x<HiY4GS0(eq?cR<Oa444Al5{3AFiTl@KT&35??@yghl3o
zTmQt@tv?HE`nB%A{=7|yr+ok3ilo_;yUWd;&WJn6Lz>YsezZ+GFgf>;(u7mZgPa>$
zJ%jq2btx!Ai!dQ&Zh6nP&MQ9$x-3+58dg0Xww0P6T^4i89qSI!^yD09dJeq3pSAnZ
z>p-8#MZZ^wZ<9|oF7d};`ht=+raVq)NUncEXh$=mBLqiajAo2SJDJA@`|8M8BakbH
z&jm}^AEI>Yr+&mcX=@GXduT0UEiZbq==+&zS^M{r!;L=_S1TltBub5wG|*<FBxq#*
z4r5Vq!=K!*vEcJovYp-H+f?X(wie(Zam^u*hPUu_>xW{U>Q8R>OjvtQhcO_m1uJT&
z<39a3;U~oX{9E+WeTb+R7B~w-=NTESUQ0YVm`RU5AFN(fR8Z#bx_`gzuN?H#k3qhz
zQxz4>@t3EJD&<<5q|8R#nM>efK?A{8j}yc`Z@QDbr6v;fSJ)zX>w3ojU1eJ7_LE0c
z@u^FnmkZuSrQdL!J_*c|>RkF#{{D%-m5H4HTeGDL&zF0JkK(&ccCg*eI^gzKUzME#
zzvTFfrF`v{sL`zjkc;_k03hOSZtewLb*d9lPj)~Qady6ppYKoPMKLAl3Yv83*4g$-
zWAgN%HPxM*Pmc&+ijbZ@9PLEUkSJ1+$);LyvxL<QId(82M22~4Dz-Yi!WHOHQhp6g
zew#Cp%~$E8v3We|KMa!U!q#q_)z!cMJDNW6#x+l$TlD{8?!AMe`kF=2A)_EkB<CUL
zs0>-MWE4q~a~1{x5hO?s0}MF~Q6wli=O73(gdr&il7t~i4kA(C#^3qQ`)<ASs!rWk
z=hpjYYVS2`_v-G|t5@&d-Mz*e6fX-`erO2ecN2mj*=Nu|@Ha_nBC|Ca@6Au>Xn1Z1
zPvWhN`ixvZx#jtLkh=6l$<PfGrt`-?e_vc{yJmU4yj0Y$ic3x*Q`{Vl&~v6x)N(Dx
zQDD?7uIlY&Iwy1hh2MwY83oBr@TykI;5RCXtAO%&G<3NLL;b$fIU5w}GhBkMuB~W-
zYNjvoil<MJ0iS+Z?Gn!il102F!W@R*rbN$Sow8ODguIR3`k<Fh{Z^HPWm>F>eu7o1
zm{KHcR3`4@&JQ?&>jOzLuP>tge073*f@=M=nqS$(TWr1{Vc5$2ABg0={o9B+)KZWs
z5biYlcz<XD(`;ZS);*>;%_~rt7mR=G86D)JcdiUR6PHy|<jJX>2OPEH+rL^PSp{zf
zdsw+u9}-s9gNDt>!4GI0$6ECz`Opq32{JD(Q$|*h^P!VA`uYBN+-V-~Fzn%E7k)Z`
z01=9OfLDX^Qwd?9+-qj+q-lO4#fa(qAVS_S&_XN)_5@?ExWqw2_ubEXp0FNu*k%_4
z;aOlG>KtqNo8|;dqAdJZtPsQ=Pe--?N8u{h(;{JJRSrjA*_Fwab`clMq?_5NuL8;=
z_RknwVp=&_1S`l;UN0pf`>ZRe7orS0ItatqmD^3d<{>1_(hks-YsRrpq{$KnU{M>7
z=MZjac<p&KforUaB+MVGn&>7MPiJ6?H3!rFhaEJt{E$BIFsA=a?CzBE9bn)<uxs?a
z0h)wdeo`;Beq<lYM8J2UOMl(k5QBU`#ftXYx1Uz6qHN7J2Y309+uFBW&7Jc^tK6*8
z{hbA)xVzSr1Bb^QB<YQuRQ)Y6of*ooDt7_bnJ<m)Ujz0#{XTUyHTu<tEE+$g1T<kU
z{eI^`%1bo6i)y{YK-(j<r5V>Moe&fJN?ml~Ga_5#$y5V79l1uJR*T82xV?p@3L|A*
z6|#H#&mhe1j&n<=hI!i^PMWKyJZ(O^1TaZK=KgViQyUY%m_^CGVGc;tnG>(@f?S@)
zXYt$O>g=V%&F4=EZkybB^Ty@Kp0H*<GcPFKi4M#)Rahu;=f@`$qz36`^SC8QXya?r
zt)GtH`Xwk}qCq3r1)(UlngkJnInt5qCCrNivuWWY_eB0}soW+r82^SWY=^zn){b?9
zvgjj=)Tsu0X*=Benp;>E_uF*M;S5&-1K3f`DpurMI3fYQ)``edv!I6^b!TEJd{z!I
z)z&^dJk3o*WS2O*=xDzycQzG;$$*!hl&bET&U3Y;_PuEtib@PmgszyP1lpX^3{vaw
z%kBL^AoimzVr*WI7!AH|1v}bP5qdvNz!{B+rWFwjMZnCH>si|)e;PAtY8w?)IVUb>
zx_o&L!)xnD(yFlZFFoMVLAwly|HN^fPxg-M;G+D>JQZyqZf{B!P3XHVkVDTykU<Jf
zFk2UHe%-N;BL{s@hxjF4mjVz#fVx%1`D{TNVjraxMNSi=Q$ls57z6%>!3cf1^8lL=
zqDlq*J&B~x7{NbUv+syTXMTMm#9Zv0U9$e!AUnH+V+Q%UXexanYd9l6hCk0_9<PX=
zKRP4utlx0czUbG^MAT8>#xaB3b<*=I1VSjr!g!vi%@MLUd=GNG@*`=L+v_+)r~80^
z!$b9xVt5XV-aG*#%sEmB76TU>acl+7-P2iL<x{A72EEO5iRHLT2a#z2OF}GP5<iac
z|C5UVs-`im^PKP1u#UCRo1g>eW*_McBDK(u`gd_({lSLJU_Xf}2qxv<#ed-$@0Q|C
zF8tZ^Lue8(zooz6AAH8bQlD#m&{%#WLBIo%(4P7aF}0)P`H8w~GWqN%w>~dMq$N?g
z*@TR`>-Gvh7{l<i!?rW2ejZ#DW+_3@JrtjU7{e2q7dTCr&v*cx)?{&1Tge91ha(~a
z8GLkgp4|H#S;ll^D=55}30Byk@=dYDjHnsst?kfpZD{l@t%f3?UIl3i+h;_s*Mubd
z?OolKsU<mvFk<ZQ$UmH=-Wbs+a%;Iu<$QiW@UZ_FSQ=#_6pC1lcE#fVejFsnf2x4a
zd-y^=!9<fx5)=;wh!mp;POqmTgU0h?PGpW43Kpv(Q^FK=p3J*k5rT|TZ)EdA9yn8N
z^0RoQ7A%_9BEz&IK32IJW1sf?Too-V^)r{nVyb-mB$bqR>gU;UL89%CmsIV_TT}a}
zbM65n|Gn)8sF*zE7ZhLBq%lMiq=P7vDLe>0*64U2Vy)WA#`3eHZ2qocvxi4KVj+}n
z08y42jtHWrn?-jMW9iJ%!MokM0-4EpvZejVm!A7r?f#{+E{dm8y*g&Gg~0?}N;3<5
zVy=yI=Ba@ntr7Xak3G~ZV4fT!&sI0!I4TKncj(b`+$`8}QaIwRY@!U6F>)F=rQWlu
z1A3u{HO_*ADkr59*Vfb;E$6t!tF1j{=7d2D9GQ0q9E{2q#X$_wT<^~oc_C^q_M{(n
zu(B=nWs2{m8xxptK`g6Ty!XHCKIm#u-Mog;KrGFQwi7F*sbli41R+NBKUUNvmFsE9
z;188pF%>wJ40K74!B2bKMW0|{Dn%TK^j_CNWUxn9GDzp}aR+_SgQOUK(7M5+a`>Ax
z2aV7xye8Ej-;6$160uB*lN!asOz`>7_c1Tx%}#|O%rWiXp>~jb8#^IPf5kv&6An1W
z6uUky!ypZyKM%yhzyF<5QA)K5<!lN{I~^nWqk!gB=uM6ug!6%})JkqcxNxN7Z^1%>
zwFlr&n8{qS)02KvzbzM+&^|6EcVym!Cl&_?olReBDmR~PY`HGoTEQcK%D)yixviYx
zaX3a`sKv<fOTBIHE=)%I%nYq2SM)@#HDih0Q8+{f1dC=%dczojfZ>$O*Xh$axY}{7
z>~Cy?dPuE`V&GFFAS35_Qq0c&4{ylv8G+|~zxd>B!kG%e1_=8I4^0=%=G_Z`&;)?c
zvFOdlwSN(M0w%+M6Bpg2^qz7VpmOoHmD^6AXS%dn0=$01*z@VLAYF9Q{Pp8icg(dY
zfBW4uttq%d<*L4w(Ryc%Av38<_Nn=W#_bzjn;ig?Y(GAw6cO}DH=WFile-)sebaKW
zR#*CNOsG~af3e1f&blO4S8$(W0+}_lgmmtd(%yl0PVXnzFIwMsSR`9<z&|Vd^@5eF
z@yBK?BG(O;sew&awI%+&j}Sryg=$M_KmS}!F#hckI<0X&$y<YxIANtlb>uyWY+n9b
zkeYQO_V>e{%0Jgf57F>oQ7s;!%Yj`aRPGbf0ttETL4&N;R8Dimp_#5^%J-8!dRzb(
zAqri-HHh?rW=ps}Aa*wvPbct6=qOS^Zn<MtPC-YU26#U*u~t7qZ3pN$N=uxxv&Eh~
zaRz8^r-NG{y5Y93%Q3xowl~)1Uz#PqoO*S*{B1W`dZwZ}ZL!<^9rxN>?`NC~bZ5va
zuds)Kl-1fEYwUE7W(%~*j~f>QPALcEx>Ize_!ebY%2g5W=E-N~veeo<_YbEjnR4gr
z83NTclHvZDN8)u!DZHnAMaGRp1mnz%{s(!H9U2}q=j)fRJkUR7pkdk(mN1hSXPY1A
zwXTvVZxEF)Ss!^%3G5Kpk*Y&RRmd%=iRfm093oCNL%fNlVh!<?R;Us>IL8kPn6?>M
zs~0}bcSeQ~@RWC$&*B2G$GxdjH~WiyzUuG$5bv>%9{q(Q6Y6uYt7a6fGk$wLj;JHX
z?nY;8mVOpp<4r3maW<(S&#v_tSo$1&Yt)?4e7w*cj$(inFCbO6rv?6e06%wY5D(s}
z>g5bm)k#kVtdRzdr%y;81Kf1TSfe}qDip?=B3=Y`>_ws;LxHtpu@x-7cf;&-)pFH3
z1+Wy9Djvc9n!U`~wC&xWsbU==*$>X97w_m$*sx8ci7+(zB($K!c`H4+H2wzncRugo
z{NvFH2s|*CCJHbG+TNXc6$4sVK<r`!%~;Gy2_zgho?k9`v$5m6W0kB~{UGItLvmYg
zpTtz+G4LWQNaGJANvt9Zo@?ACe5-ofr5}k0-dESLVtR-n4GocWDK1crHxoWez~JIf
zs%qR-|Mxyc`5H$P`q>J%{|ywla_#ME7H1M>rF$Wd7Bc28=SS>0Vk##Ez=s|dg-YD~
z{2igJQD(~^TOlFZ_GR5D2pk-Cf@OBU2l8r2Oq=CVzlcm0hM!C%gq}cB-GoP*Mg2cp
zYzZWbrE60;$+%Z&^0c5-(KJ0@a2TejpXE7YPp~C?N3lDGMq?r5dzfX`4YEP4g9moW
z^$Pu<J(hy=?(sBEh4X*G4kO&WgO(l>$9kXmucuUdl)S1)Pa~`Myk7{uyEX1Ztjh@@
zQYKzj#SVJ?(zA;dvd@(5&7&PsXZunVWx`?u8l(uUeZDXM=#v-CV1A;T8Y1g9ZzY(!
zBt})y2CqELVeR_ycVU>nkk5jv;oKKP*DAB?Pr2To-?F~U>DSNC=q{j_Z*jaldOZfN
ztbOH~jmm0Tr~KaSOhL}rJbYk1fRVvZ=m&L3kWa9l(>rD-Psxm^^i8s}NqJ;^?~6zI
z6EnnMy?o79)OVzYKH(^JuM{-N28z)fi_R}4rlQU8O$t>fAxGbgSoq=*xIp<);=wA(
zsU*a@vmojqgq>}!&;Beb-17;qX+Qqdt_U8_EYV2?gFC@8qfiTt08V6{nE*|*(yFOf
zD8C-~nHou)w;tNYeD*8BjyQBr6!fce=na8|6@(%)C)`64yY3?rB?5_2AH--4@1wop
z(N6Li-xDc5G6qk`qsn8cdCX~5{w&KL;zWNwY0!GXH}vU=W?!pi0UzwmU+{uI1DHKD
z(6s*X?rIhZ8z@3Osso`rM!E&uXYPd`CkPh&nUUDw(w=p8wmT`u3c>zNQOe4foy4SL
z5zj3$$bJMw{lxH}7`@WS``opA4|N?G>7usJ==S}HxVN*#=@>`Q8Anj_S;||Qmno#V
zvc|YWfiO45bC2g~>4<G`zA@ZtKVnz4tFfgl0d$-O@-Si>v?Y-GR_IC?!Y>Sk0(GV*
zg(0M8DAT-~VcUrxyq|PAc9Sut&qzD6xb<@;`pUvB=|Djb%{l=kYda27;NipNWV~JV
zby0RSFS0P;cIr9ZtW=#6!W3GSc(qrR`k8-680v{zlaF=a4AO8_>x)y6f8k*QvJb*g
zW+Ie}t)Wa9FF`gLvY6;oAKI!6?%Vqw>>FVTOa|(UKf)TnK<OaKMCL)Ig#_7MIzTWO
zgu}~<m47k^Pbpk?+O$xWLiK*i6oW}{jqfqCN~cdZ6HJeCep%uoq!X=f5L%_TmeOcQ
z1Ht3uB6rS25CkHKbd}kPuFWFBkBJfU>N+P4y7NhfV932MQ(wNUkqe1yLh!l7SK*_m
zbA3hvvxIt`U&Cs`{Xt=(mf&099F%iH1=q$pJO@fFYs@^WrQ<jvLOSv)P+K;Xj5tkR
zA&dDxhYCg<s{_J!xs}B=7CJ+ZIyb44)~V!hOnZ6cBgig!2?aws%|lH<8yb|$ox~5R
zcR}`D|Dr}0y$g_4-`)O`G(?X&q&zP0)tn-@^ds<rBLsz7MMQIljZBGgzU$!-gQOL3
z5siL8Z7C~Ig{<G+?84A`JUm`rQH5FzjlLP6et`{}BwSE{a33>U{zHHaxb>?H)IIMz
zhoRd2lqWpVyyMEpAT5gs)~}$UtAC`s4}<a2QBHldz6D|Heb_yyg;tC*7%|Y4&|_o#
z<h7L>`-r$LGS=amk0{&neHOw)H{nUx{htxTixaT9P4SwK!N>I<2aP@=9e^VyFdX_y
zupHIXDv$a2FYr-}*@&+*z0vn@!~w~5JRP{3;sN(Dm}?NA?FkT)Y|2iNUQF^sphOL>
zt~ZU#2JdM?ZRO_WeZRp6LSMRJrT4LP1k+FYR3-z%>uG+k`&R_-cM>iN$d{&{7#Xfe
zegx6W&-2f?QzScjIrP@$yZ%E$P}H=u)^@TGr6N916+u{Dwy7;<W0r*|9>D<U46Q9)
zv<QoCvQ$}frW|OY&Ve%8BM0FDH{2PDh$4NK@cU+X-*VltlIGj@%LVHC3R)-&%Jm;o
z)bf2D%EcSKb~w2c0xa-$npulDSy)BG(@NMC2T5JeUw#r=LH7Vhn=*dl4V(rd!!}~-
zg)&7bTGLm?1<RxW&|h_zF86?i&(#O;0roECiH{`vF3MyR3dsWMu>{zZ4|@X97P(af
zf0bR`z8v!QKYg|50G_=#Xm@`CeSf}om}7mdVxBvi^5<v0wp~?6gOW0vj}HmlZFl3t
zQdv`58wI(4;F|L#{dL6oUT}o%Vfjm#(hKjO?<DdXF%<}!c>DcK#Hn<bjn(3VxRs*|
zIcdG^V{=dKNc;fM^~N!%LvE>Jiu&$3rX6G^$au|UDfxa7LOx;XyVp<}BOL$n0FtET
zdRrro%C<2s+G#=W*=<3X`a=OdJw5&H$dxyjRU$TKHsT%=x>sgVJm9xF0(76G5PamT
zA9UIMlbgLu73^QpoTmO^g{Tk#2`lPZnD{Pnt2ye%6fvuJbMLXYaPZ|p>RRUEThP=8
zp*R>Qi;AkLumN3+M~`?gDdUt*&UI$fF&I@UtgU5?f{4D(qtE|FJsoL6^LFboMJMJb
zezphw)P^-k8<VEiu+#I%)Fa?TikiG!i?lDV2xA87s09I;6fGPZLCe#yv7W9h%#Kdc
z)>S0#mGec2m94pu)$)Ri{5V>>hp&$0KuwaEUl10Ly`5g+9@#w#k#&WIe%;M`Xq99}
zBz}iU8yhJGqM;w~1~H*|ASkyG4Eag2o3t<m+md8Tz5k`KN%yr+WJTpSBTb!`$NvQy
z>>Fx0)#`#uHk#$kGrddCITj^fhv*M)-d>jRCuu>cQz+RHE6D0%_q~G$D3G@LL+g40
z9FAk}?b^{FWWs5#m{E#kNN*frjYw~A9rX1>1iS7tlnE|=(R|%79C7*!%EUDWdUkn{
z5b$3QPiy7w=(wSA-27O6y5Pf9!|g{GA@|q`LbdJWYjR|8Pc|kVD<g2qV$xLyLi!6&
z@srV8&wVHtLA3WlKH~2}WL#@vOU@7}Q>4U6m79$uAsm?785sV_tIL$~aHDE(a`@1R
z@D7g#8n1``X1=2}p?hJw@4h51oV_0<C{|y@F-29RA#y^+qC`X@4J4x<nu6m$Tbu65
z<xU~Kw6@VsiSA(CpL}r_>6f~a1oqZOlSm5$p!75I;O*wgcLUhOBbqom{Bz@?4N9V{
zb@icwphe>lAnU{|`IyZ!@a?<7AA@8`-;61;wb8D=&`1j7*Oaz+XZtv>#ZZq*#&$e#
z$5#>SMx|jb0dG55Ly5OT<HQ;9j`VTN#dC99p^=Oz2=<ie5${K0EXj2(oql|^K=!f!
zmzm_QiLz_BPbT<tmCSTb-NBW9GZ`@t8+h6>z-d<z><BxFcVl~78sfEaQbDSa9S@@Q
zz-pR2Aw-IWg(qUpmmZ-d9vbIQgY8P?S?sxP#Pe|VmKDzI{^+)ua<Ql^aX@UcH`c)b
zHjoUvl8or0!|aXwQK~k5PNWZwTi9{y*Pg*J;EmI)u~(Gv!DJLP(`7(z-a2D##BQu(
zKp{!&XtlRqApbp(g`a24AHU=j+Zc0Q6!y4Ap0uxo6~~5Loww@_VJis@8|kaRvQqJb
zDKh1g4Kc^FxHy)lM&RJ(KgfG=L8+8+nygQo?H&NRL?YJ<7>pB{dUm(P+*0N_4IyW!
z@w5cwHNI!48-eG74mu2<j?3tBBGp%C%=8v<WjjN|++jAelk>wnNsekH!r6Mq(kNP+
zVpzNmNEb3nEi&fyri4*R?g)J$+=|YbX^lp9Ko|4q&u*pp)KV=;lV`PM)3~ZLCs;pY
zAu4a9JO9MN{M|J0x$dve@3S~`D_cp++c?^RL{-8Azmya|CY<Pnrb~F?Ar;8z_=UDk
z9>^o*x~D3k8m!r7#Y?rB=^L$u?Zot&2&`q{Z<CN0c<Usk>lV(-PA18#VU+)?4%mnw
z&a%9kX#TC7jh=e4Rve)cCyo4Hn?8bUqGJe&$zScLEIg?h(igeg4{EefCJ_=xahhG5
z>4+J}3<e{p;rHYPAd3q3Y#8;(mvT~H>K%^t@9aIro?j`>ON|l<@}E_mT!~&BsNM50
z=Yv?kxKmi{#X^uX5jX=HpZD3;AOCQ}0U57qI9Fw*Zqhc<ufZ@H?j4$6ux9GVm4&WC
zA?drbd+-t{YPO#tNkqOl74g&<Y@ZQWtvADDYArM?Cy;_TO-@5RZM8G{J6(qu1a%+1
zdtb$JGayWs8*FUHqbA^537K#@%M+^yGi;y4TU+u9UxWvQN_R~5DAZ^{>1tU#<%<0v
zACA{P>?E&hrF5Pq?>_m3X~?Jm8og0_fIQk<2&L5fj7+}=@?6imAue{jJpny5V;`9l
z#7mJcy^HeR_F}1erR9Wb&$|Q1v2@s5aY9N3Zs%jCSB4ra3>(dI4AN$4(E)&`vM_E#
z&lbO-oP|tKCTW_!r2xpO;4B15unZ(>Z>hUB<VLIbuUC-Q?a~O3eDCV=u+XlRCj9Kp
z7*9z5tERAeUv_W@6IhF!l*~csf^9<<*D8iGs_QfpjzJC?Zuw~TjVhJ&(5xE*FcoNy
zj6z@p1ynh($}pDDY4!B&P|z<ykzDoKFYn=$WHzh!gAj7E&-MlhI4+vBK(8|Dr7XLK
z1R-d~1P*EkorUfiUk8*1n%BHe8rWDeQxR=w73JToj<C;zQfG8v1_Hqujwcm<pOM-g
z_`cKj0BRP`w;EIGW%Y12&C$HC&w1N1lB)?E-%7<$SryImwJB4kb;<Rn89O>Co}-A)
zc4v3C+ixX<Ja8&i%F8}Q6!O$+lfR28zb#mi`bfkOSrlzfbL(Ejk0PH{RlNxq%hQ=4
zvlIu49ABGkQJ=lxKN$;d4qnSy8=hRZ=E@LS-XNsHQDW9!X4E_8l1bCyr5uI|#Dmf!
zO|iHL(#|3OUl%SLV&q2X){iG?_jMq<fkAif_}4)928~vSOm-YP=#0=7NCO>dB^}i@
z+;P=4yKEc<{N!)QFs}6vd6p9Ar8DBj5Q*!l%m>U-*N=t-ysh3m(c#OtR8nH9Ky-dU
z2~D3Wl|W5tp`yH8AqnGB8?@RB2<9mU(C_x*!&ZyOZz6taOvk%&F0C~*!r}@2KYwX~
z`X1O+HnMURL9x{>(ZA8N`EA7BpqhubkLyib`O93J&^)CPq&1X#wN3Nf?>`*I4mn#R
zAC)0MuRcnx@93J7K(Ko7c}B)uD6b#S_!>}ExPp1+SA8f)%lTqW-}ta{6#~i;&lf$L
z(<ZJ1s=>y2A)|w@fLvt-0VoC!nOI1TS^ZddLV(E5M0bRF9YI%wK%KGzA!-&Z&>M?L
zM%6Xq5N0er2o~<V6ds5$kk*$bO$-!-tFVTfBnzbBNFo#MzgiohL1*NkIHr&tkr+_V
z!yx`XlDAO&o<xxG3CJ$-5iW@2$o5}P_7oAY1Zc5=Jd>D*Yx>F%SO;Jbl`Lr1mI5mz
zY|<if<^xA>-F-!*ZJlh$U7HD<WyYIf%DRc4XK}zp{~XmP_}evonU)eVpUdXO9Q&c2
z{goykct3pH%K!B-M3?a>9@5g%dh`H;y7YMz;9&81Hy1!Q<ZY%hC`4)a0DWr?`K<nf
z?fdnkxE5&8tUFcQWn9iHGkAl(KWDiiwOcu&l;r$!cP_kmi7!gAtA(rFu$A?-Xgzk?
zT~LAz1s3Rcjq7vyM*}zcp3L8ty`1nkn#$CKH41PNb?xa~N(^ltnphK%N{l)TH~1jZ
zsGUE|Yh)~V&U9zXSbKT;-f9~g9wqW<IdfaOUZm%cSdk^ae_daz{tDYkJ%8Z4UB2`y
z9QD%Bh!eL3-pUuQ{!kK9IvuEQ*0pi-fwN`o^}&T@ROq#q!h0TwE7gL7U*e3;3|7?S
zjDEl{brbAQe1}YCmQQlM6(-c9Kq4rJyk+uE=uM_-svuC-C-pN@(kRS1FRyed;=T^8
zb;+}@)Ht__H~szMes5kL0qUvCGr8W8&cEtZT0H{RBU2U5T2_K#uu-c?9ArM^^$aD-
z(5_$Q*R|P(XAsaY@U>$2Y0lZ6TUmLrld$hA7TnYVc&EwE#>OEbnyyut_4DNr`HLL2
z^)t}RzFHXF`LCQ|*{}oyImk?4V=JaZI{c227|mFL?d@hUG6Q{Ro5OSFPa^pAIO*_Z
zLFHEZ0lXkS`c|*;L)m;pbiXZf4i6k~O>PHfb;UX)#09iI{Bavl{ONHrLj8``X)SeM
zgU~TMmH%C1>joJ}h70565h|v3ztD?xN1P$og1>sjkWo&BygIBPHu-MVpzNc#hh{4?
z3o_xtIU&>tV5ItK_?)`V3%jI7BDLQW4#wIv+0Sh6{@Xy0!OUAaOjbgBhjWtq;30=)
z;(8Wy8q|zB4;MtK`^_rTNF%**^ME1$VdT@W)ISWaEM-NQ9k=g4FdX>g)(+9vo?`g8
zkgheP4z$YTj!C;GrnU3VR8P;lv8klk#10<W6J@)o2@VSK*~r)+Z|5wDO&cM-v6@EG
z4O(3MGcqb_;hoRX1PPyB4X(j3{Z1@fy|TxU={pVWAirmH2N&HhS?*QCKf?1pGpO%}
zNxJ;RGYz4-4+=PIa>E#RuC6U%`i)y=1R2g*#O{y(Xm?lQl{;EL8NUn9Rmo6Swxj2*
zAV2$hRU9GELrIHAfweteGbR)5ESKXs6~=+D!&S4V<!!WAz5X0(lgzYi&0_O1=XD=Q
zC+jY#XkAzRlWVaj#&p1yx!s>hJ=wcQJMVQ+y!Bf*%y>Sm{Wpi>CiQL+d|{n<*(Yb8
z<sP&DlUJKDNRqdJwA9_X$FTtoaiDJx1!JCp#H>XZi7U=%Xi5mGWHH>aYbo^;yI37u
zbK1jn-dLG7I3av)AqE|)x7AKLANeIe4<Qw|T$4M7kEJ&;K=KhZ@65%aqE?cB5)}dR
zN^ZzwRQ-p%AmYJOdgB@SK3^P2)IMvew*YPwdEfN<SS!m@L-401FUjxNWEv-)gpft$
zx2l4T*Q&oqs~G3G$&^GuxdydS?pU|H-hnnls5YZwomu)M>W(dNAjWl9@3d%NDbQ-;
zk~v95R@=&uqWjIk^$+B1ua-!3FcC7gn^^AR<O&5_G(nbEQP-?{bXlE;e!;RN=zse&
zL4?7LLIFennZRljYulME1PB?I1S)jlFk;cX>Uot-$f7q$L$Z#US5N~*GS%d9ViSHA
zi42249*mQR6cSXF#(=PW;=fzzpzplE{VzZq*v~8mCC9HyCIokxM~`YnlvLuFtg{Y_
zhZm_kT350=M;LxE^sU*nYV^rWtEw}kw)gO#GXLXpKo-&e#FK-x%hF>JA|&^1W+~v!
zmq89}5b1l^U`-Sp;i}+H+@nO=^J5jz-~?X=Y`^xYqa_ON1I0}VtbTd{8$y`Ns>qHt
z`v#pJJ<h>6PR&J&GHCf>nrx)4P|;m$&8;^+TVG)T7l{E&m+c#vm+dA8s?c>cIqP;_
zdPtI-5Rk5YK%YSf(%Z5<YtUY#Wo)fzpMK}sR1r+neA=<N-Ch>|tqZT09tHTXQV?md
zK$M>$kUG2kK#&|%uq<*VGb=_eE5^l<G8uRTS40`Xes?vMPybH5;DA`gQ4gdL)T3JG
zpC8h0d3m24VDZfQ`D``BY6+-I<z6@O_@rPe-e5)&5es^<s0SMF_QabbeZ|kG^5fLd
zoQ@tCbno{w<(z>OwUXCUd*EL$7`Y&ibU@ra0D~xHBEO9;{iqMM$a=IoU2u0jc=<uT
zkb7??^i+BGV7BI69m?Nj0=+XM7bk8i&KcJ<=W}oCj=CC{xj1*BIxz_WB&wgo`=+^b
z>yoZ|H?_tCZTqP7Cy=w45!lsdCh=!VK)8$py}4RYR>eP~_5-6<l);&Q{;c$EWPuP!
z*!AxIXo6fPH{lFTtN#vbbjRt-opWH#`U#und+R(RQiW*KJiK4-nxl^Xp^lzM1}9IQ
z^4e{+EmltJ%Rqq#NZq?XJ1SY%pC;sbfSGrH^CzbOr@kqgv4MJG>9SJu=JbGC?#^*4
z#m&Co1R&MMGH~_T_>udl!=O$ui<%o}ol4u3xf;~)5p-QuP#sOOQ1fiE<`WR2(4T!b
zMx~v$>1Es{a(G`Env4Lf6JU~Sid5&`adJVnUwooNtYkKeIQPmzqDE#?y~fWAyBB`x
zo@!ipdH6PcA4#pbKxNTG+y>u~b3t&jn@pUW1E7&^<vgpZU)$ECj!2$m=E6#uh=3ez
z3?PM|cYsb>_bIHv5O3vtdPe#6K*eFm#OVdXK-F!zqVDt#2?=M0qGBWa#;Z8Zn7U-K
zVy-63Sa(UvCe>f?Z}?&>QKfjvcR@#h?1nrBJ_2OrbcrS(*ZvgRM(p}8I%U|GAP%Yu
zcV?vQ12u3(f28#ixPIV;NWZx+LVE=i&;tLuX!3QRXAI2J)cFNJw-Nwysrmjv>-fV`
zQ~I$bdP0QAB_L(ivwe6eq`_Vrj(FdM`5bNX2QHdCMeBcEWb#T!2igYHg2n<<U;|8n
z0&4JJ;<@zGbcNDlw0_}`YjOWRVZSLaP3vlw-WoWgUx`!!_Wfp6rv0MX8z!%lF#^Y%
ziR{D7Zj;J_8Y4AvK}-hpiPoea@-WIwH%n9R-QT4tKU-}}A8E~kAA;5J2krc%KfUe2
zRy!WC3|96Jn!a8ch-Cp7q(qweFP)Qz5c;cxMy8ZOp;7~$!n)`nj$aQ>Li}&k7K$$6
z=l<_3=ECdWkxat6cd>AJP5NqYfMEWkft@$2uW<U)d4!o=8e(Z2XB|*~$^rqBQ=bka
z=8M<P{&`IKhyjuw5J1c99iT+|QcxQ=36*oKceEA}h+MlPyW9(?*s?G6u_^O;vRAH@
z77PEtF^D72r&km_EVtgoItIGbd{XUwW>>3Ec0{T~TJ|;u^!4!gA+oR^bc6iP**ObF
z&jth_0P?*GB!hB`fPjZU(m`^BkW`u*dO{fGnZp@0h!5<mNi;wpAVvG<b9dZ)Hn)2u
zsj`~53af!+=c&X!c^udUUUJca8u(UY6j<Yu%*t{cI5mzjASVPVTYfUu*Lr1;IGO;i
zivsPKuz~0{!jPqQpi_Qw5bXFN=7}gMAe}H|;GE@%hc@JY5bW0vVH5xnxHi|%=zB{E
zpGDxf|NjcV|0Xv-S}>*pWU!EJFU=9kseI*S;Ql|uzyBtF|E<}sF(Tjqh#>60XukhO
zm%QmV!QB6#*xd8o+iP^@j$b`HE>2RGnl@7B5%5T=X`;V<*C;dW6)WYTjRj%b1yrnK
ze1Xt{`;Csl!P05J@~FkogrZhEP;yC<cC__8!W*5KOo0=!QVYBpxf$9YJInau4Zlgs
zz$TCdeX<4iqpuZ1>r=P=*!gG{i}J2;6uaI?ZenpW@sWdiN-{?swP)-FgI6Hbe@tfb
z>|Qjk$C^KLO{KEkQcMVAwIg{2GW^@qV{0zjvbz}C_Egq;EcepyPZf-Kcvcl{(w4Mp
zMVJ-F6p}WmfeWfZ1s{8bEn5ZA&k~zv7@*<oBs$$cDfWL};^Ypg=qzjUYDAi>)-}Ps
zZxj0m35Sn7B<&gcizKG={H)ZaBYLFl;uGvL)bHxBq|}ra>3cjH`QFen1{t)eew8&o
zTm8XMN0q`=`=q(NRWjA`;(KK4R&jvRQ@__@6&>{1-w*YDtKK%>b2pS)+W}LZhs&Hg
z*tl)GA;R5mIrJvg>pn49OoIH2e9n#0!KKIUuijg0UOgJsw&=KgUz=A^t}hZ?t#T^E
z^%{0uPj~obY<uIO{Mzu(7!q{HdUGjiM|%DD3&DL0hpMPTx@Y!oU@HHJ`RSi0L}(9+
zI*v>gCA5t8+oMQ;G=)QBVB5P7c`hgPR9+n8Y({f+AuMn!=Xc?0pnMgg9WI%;QZ;#q
zIFUdjQxi_rC+~}<#Rrw;*U8gPO{|N-LnGm|KSt7g%XI0oT1{t8e8(xSN|VIWrnD8C
zaO*vh!Fim`9jxBtI6*ajLe<*JUX+8ur9>%3#QublR^_a*be3lWW&Lk5-w^5d`l*o5
zm(lA77O=H=780(=@5mIj+PYOUO3_Z82T^V5CAUNRI)5c)-HIL5!UbhY@mv#5@=-|@
zfMrYB*_+QJs=1^crs+4Urc{$NlzqrDw`=LV%kS3-bqR|F25XK9G!zdNi5dFm^RL2<
zkgG$GSBJPM5W5Yx6$>5End1nsXU}rY8k<q726OQ`9?`k*7&k&ox<T}tx$owXr4vyf
z`_A>Rv-}2l_xBxNV(uL&ubun@fcH7l?F2-bm_-O$PT5~MwrNHuXnZW~eu}n_Bc3Tk
z@JYADL@=V=gHXbIQKlCzg%^IjrD|2Ix&}JB!nA-psBQx1Am54;8)TQ%M{V`xuo+Rh
zFa&8p9=AYGC)2!FtV<p?mSi-&qW9|7+SZ52LT~%GM{W&m-Qx5{cB46Yxj+>JVn8~}
z>AUC0^ycj62KsCBom?^<Sq5lpGp*A+O}hREu3N8PekEDTxG+awQmbC1&1BsBO6QI#
zh|u}qb@<D8ei*{KpVYjq|I$GIT~Bh7Y_A>3EF@=$UX=c3kpj4B<=Q3Cy9A0zo9ZuR
z<8(^o7#UDhZ5ea@VE^H_V<!KerL@7U`CH>=4aBKoY?fI{!g+Su1$<%E)FN@!EN%E~
ztZl6V%mj&;H(Z0wGV;{0+vy5tDfyyH06|m*1Thm3#3x)?Z%tBMbc?~>Ugs^pmAxKG
zvI3uPOX)5-cEtiXzK}f9r=P8R@H@qj(dpCgMO9T&u{4(nW3?x?0_ygiKPJKXNguv{
z|E+-l<eV$Bg0<c8iNBJ=TY`d@H`805GV<lJQ11ghBUAbdpM0jzm9Na`=I+0?fEz%b
zJN(R@m}oqMq5|$QW>p}r+aN1(JkJilNd>ItyAjK2NO^B1)<*JfKh3zIfl2+x(zvvy
z;wf&5gxxQ<|3w1+tW0hBen{UG7|F8DiEMJLb&WYF^Z4MhXWIUxp~0^66XTxo0g?;T
z8sQLWB^u6<tGzbxF9yh|2+Dn<{Hp5gyeKR%`RWZTM&$l0(0y75Hc*HzJZ^0CP6eXb
z3H*e;l8Z)>$@VQ!dfXbAn={H*;2;XkZ``Dfc9~Spvb|(SG{C&&5Cc-t6ZMH8T;^>w
z);}zk<CkN>(5euF{;r|}>q9KoF>V`>p3Kb<q;aH#{;K4Ft`@nA1q!PBKf2rekJ&!q
zol@LbAdmvfbax|pU~J7(NxlE$M%4djBfz?v!~+zGMHXFtDYQU-7E$n(4}lv4*n#?@
zpx1yM*bmr&+_WKbfO)wRNamkPtdP%v%>tO0G8*^=fO(k&*a=J=IOBkMsScQLiOE=-
zfO*Ldn3rlO&<S8(QUK=VTRYGNU|#+Yf#rIQ-XLeQe29NG+loUOZp}g%a%b{Su!-qI
z%G*!5Tir#&x~#0d<5+$I*0g!!uhmsNx@kT;S_02atbdxCUdt*d-Gvg9gKq9$H#P;!
zD1g7+^|ixI!qNj?8pe&j86#}(LT}Ul?T~8%zMJGl$olt&tAac{&`1&V12EfP)vI!{
ztP(%|dxJIx6P}k8gFXYc{mJ^iizCY_q0QfaNM!chA0j1BLWlvAzPBV=md%dgl>&)2
zZci_b37=UL>j*jP>Nw70CpJ*mb%zRc23eO0RvaiKup3tfg~!8xPU2Z75YhrwkQMG?
zk%_7{N!TPXkku_g7+>|k05s&TMO5o%q9LMcNFowh76d2uGAa)^M}infglwmKTbvB}
zB*Cu)ub0)!uZr9o)6Z3!B+v7z#~paBBQZftBc)l1OeS!Ck8oi3&^ePsql*Z3T9rY@
zU;#xE{gV(`;7!@JSOv)%%T%*Lfn^12AcQDS;G6<aSHP)Qqqtx7<Z0rDNKCR}$W%zb
zddGljR87Mfj%vL31+|o{DSO6B0~?D7^~G22lC#R;>jx6E#d6=4DvOSel{!<Vq$D@P
z(|_T0%#2JXe&i6vmg@VcJXBHhS=pze>F$p?l)c=Qw7f-#rKF~2%hD%;zZ6WFPNFyW
zS9O$?mAjq6UX)K?#InT7j8f)tFRJD(w$z5W;|9xOUQuo_6P>TX=qnHqnN9MEKatYp
z<m96sU<+EOZK012PE`U0RaqbJe)@)lz|*)f7o)Cj;A7HR*Zbh7m8RqJR7a1<g)s~_
zW6AgBMn^|GG{ec`6@sTja`)*zajjcIs8K;p@bf4$EGg$T+!tup4L&X<^!JB}(Z9v!
zW1^#-eCeH^ffjYQ=(;wXbwXo4y4%IMXM9x+>AnXwg#3Ad<a5hO6H6qJWxE*j<N^w>
zXSo++Zy$4tcXiYGEVXyJ>{Rto%}6Bfee2pDc>kl_W5G<G*mR*LKVJ__GT5hnXUqrZ
zkCGqRbrXzVZq=<{ZqTIraN>tg6qXqjF*(fGo<670)<xQ5eSGTf*t+wAp$QI&V2G;Z
ze^07i;I0Vc6v@{6V#hc_2%$^pGls>Qyp*OR4O@!5x{4RO<vV)#;g=BT<<5L<lf2}M
zU!c05?x4KPW?E6wSIQ^VD>uv+`-Tj#`i#Y<eP@^|q_|4fu1=cwlIs0?IxL(!$uD|%
z+gooveSTDDu74-wkeZ0<RfI={Ip@(zLA2XTd^2(ru7O@#^_dK;uL983JUb0Hus7j(
zfI<F}OFD;(Dc)y1)0Eze&q-nTi9c+VM=E53B!ziBfeKPPlZ|Y3KPYQ7sPf2G3v(-T
z%Dgm-N0R)<!9~IM{MgvovS(E(+>XqabLm*eoxW&%o%U<Dcl+VXW&ZY#f%t2>O)ldF
z(!S(0Cl5vGo<YMq(_EG~1$K>hK@WR}c<7n-C`}r8+*zQm`xJ2S8Z+O<`GU0l?%o2`
zH>8YWP=@MH@@YrL6D_M%;SUH4+L-r-6XVXTm$weyJ&pJ&cv+H45;%)3e)T5p$!Wes
z##`q#N`LRY0_3*YnjP{+3cF+fN}&swd|ZEtl1TSUz4foBOE{zRe+I&P^dufW<@@5I
z`&1t1x%XY4<+%LU(9EZ3<DYzFR27H-!5#~CD8D;PsU~+;)V92#&hL@u39N$(qhmqy
zw3y#=WjOs?imon59IJvKGgkJNF}lycxZaS}>O*7^Q!&K5VQ+QJ1eXAwj2sDLB7beW
zuVb1M`%ySS{`nfa5ySKMC6a3KHjM9A+zk4$%%kfiSrW=5-xLvfvGV><G~aaG<TPP7
z@gu)I>G9a@p`J^@tyn|(hz*3egi*4wHszFzys(9?M|B*v;KrCliN)cKjHI~~-$l;N
zgo}RvTAfieYd1ed$0+xryx|rJm`XQ1WMoVtSzLb0M(@+H<=`Pc4UIKWPgnR6MpP#Y
z&ngiq%DG`lcN8LPnB^wA`UvgY(#cEkB7+f1&8Ghvfd}P;1pGXF&?)y36NuG<Mb2D;
ziWd$sXLHO%%mb`;>bYU{(=xad;pYDCwS$-lRv_>BE{*wt4>5@6-D76CzN2YmK)Y4?
zg@MeHh)IE8ILaTl$2m8>MBx<6Q+YrH@@FzhI@F+C=uu3>p}x=7c^V^hyvuq0TRFw)
zqm~~8$zUyXC5SVp0m1p9(45Q!g~6yMMyuj10;|7F5yoE7D*X7z-6U?H1iFg+%G(6E
zVYrm{LYQZ##1U3BpFsS~{YR>Kh`ff+cy&|?ACMCDT|_+)PDl5s`CAVx*dXq4C9ls+
zgOK?XZyUr9rp-h}jxZ<1YvRsf_kkl9O_p%1BvMl7W!c`_cHAW=*N>p{C+PTH_A^sK
zYs0oydgml(+KEXA?SyL?f_My}j=9pcmvO}G9P3|(cuPVS<TcQppuJeLLH}cHEgB6p
zpyD}|1mlG_Bu$<}9n8frdq+yY@r#eJE=$@lsW6a5jLg96<2MJ;PBg3cTQ%jMu{Vp2
z*Anbiphml(kRRKrs_v(raVJr(Ww*Q#Bi?8o@Rr)ih<7(E=MVw+8mBg5G*@UEDn+=H
z)?T+FInD0Pp1zo-5ZV69DH?R!wU|mtXG{N`fgh=&ND9JxGOG|1FVWXw8HC*Ju`+lU
zK!!HRLNsUB@UjeVELR{u-jnu=v)D*#od6fT`gAkVY1Qzc<qvglIGH9{3ub-XMM)I-
z*f+SS&x(R|vC)^kHAKZ7jvZLzICpcN%B>Mc>riBY!mn)H?r%#XtKBMQle`7Tf6Up%
zL;9A6%U^@W^O>P7n4~2f!ZtyQ_iY%2tkQH0(#$G?rvBTBRMZHJiTn%S--!-{oq(L}
zJ0o17wZjDA1U4GXK|q|A6Cskvy$4J2+A?%U*O6Twhp<ckJwSa1b?ivX9!PScByn9w
zeEX|^jKt%5R>#pYx!}j90hanuPOM@+5(qW!fmHc>EwuC4iPOyP8()V=xw+ycUr9TU
zljyR4mewL&Ie)g!Cj*NLu;GvE77GC%Rq=&F-b;JAucBG?T)4vvlG6_#zBCm3GiqNM
zIS)q@>+b%FL_AXaz)<%{$7=TlwV~66PKMGtAhSQ{_JPHUsf2fbxWMp-{iz?}rUg2Y
zoChy$3Xr^sjz$6J0luwL%|zT0w&Qe^bvsEAB!2rfwCNrTo6XG$nVmC8n6%$%t*YvI
zlI{{@6m!)~FY6fBeapTQUi*$JL+Y%zb01Q^(imNKC=Ar{D)VqrwuN~j8K9LZ-q3d#
zH=eYZ2P#x52YsLHr$n#?v~-Fh`+@jQwrO?S?@9F0gdL_~Ix!fxh&3X7KKqr<R<S7L
z1>O-0_He*Y2dgtvAR7Ay4|3OHEHC68W!z8eqttM3dMvlhCEOm`2i?wnc5o3R)0vA0
z^Hk)2u&9l<<w@LY87vvd;7z$T2}3oET@rNzQE$wDXS}3)d8)2_%8V0R-Dqs6@Qv)N
zX$uv(1RW3;O<t=?M-(-$psZ&Xv)M*@ON(IoEGJTKz~53lc#3j@bK8<Dlt+|&?q$|X
zvv|iE({yx}1UumptN#k$@1*sYD9hKHxGFP~!?saqZnL;(x;sEPZL~2M7NO+9!ggkz
zh^!*B&`FOVZ^*<nFs~Ai&9Xf))#kLAjT+a2N2#D_UF7(m^pP72qy(O^Le&kae$NAS
z_rH-J`)M7T4Re;tzV#Gfa}fu4!Da&sbOf?GatLp<yDm`<?mf{*BowIv>=N4FxN*&D
zR9zHmGbqZ9@0N3Q{#>afeA=%m6y&sIr<fe`Xp18wcf-#q^07r`?(pwNb+9-R2pju4
z6Lb^Q!G1}E+6pch7^44kFq;yAIgyBYii%0SqG#V2KaZNhuU|)%??YqzHGpE+Z13SM
zn@M0rn*sZ-v-zab56M#mYiXTT+URdfYYbW$z5GC&&rNM@WeEyuMyT=|OyF>sGs|A3
z2HFsN$}zwl>O0vW0>8g3cf|QH330sz^#ZbJlyA<kD58sr2atLQ{hdc;Igre;@f!+l
zx|<n>YDerFa%?1tktW9nQ{bW?uESR8X=TYz*n#t^$jMD=a{0&@p_ufp;n@gUILEU;
zxO%0!=+^}U9|=o_!Ow#6mPPXsueEY_J5EV*ezm#%s^Nr$nfz)cD0nrJoX^2_s1Jl*
zM!T>Jerv^UJ%%mfK|Ix;1uiNL+>O$oO`d|*Jl-!<^`+~PFrmxXBwZF8L}eWskq)=n
zl}7oXXh$ykeeZqZYC?=m;<&1?6$1eba72)fh;G0{M2wXX{X-kH4@x5{<)WQ!xQT(w
zaWebb678GP@ko#VTV2m1XwYFW^Uzlw8y~2{X`SToblUxEvYdB`)3M6xH|&OVhK)^G
zys&2bI;@&xyxcX>QI?pIs7{3*8Z}=o>Tyg*WZ+~U<0@QdVkRKfp}&|7?N$!zV*jKZ
zQ?A!MRubO6H~Opadd>lew6pmKx5eHKXQ4J>@*R7yI&N}h!|mq}+$Bf8oPFkiH4r@C
z_PXcVnkz8RiLn2`hG7oybLtZ&!_{vfO-W5&eC*%Dc03i@y?@WjnF`^MWG#|%H^eDS
z$lfax&%~mNy7N3^o%UD&I9n~m_g}4h)SVm4T7g<RKyAi?rDaVM28V~~8J8;i$Je=8
zF~<TacZDHXWe?6CQ$?91*>k^ANXQ`kPneNK1eljGpy>Pm?Z46ax$T#acoX_SzXQE-
z%uNVyX=5wn+!m`P(V(+q(&Tw}U>-p2?owWvQCXMm@S&+;x@~K0*HcGONd`CpxpZR{
zPc7JJBe1!8OibAW*bD2N-ZW2k9`s%j{Ianc2eS=-=LA~gIA8KD5HXJgdkW#>%d*um
z+;p9L`bKxL)6nx;jo04aXi#lnR`vE3ky6)OjAHBkoshIC2EaZuA$|btYrDL?j%zBt
zKbdPg<M)1?UDG$gvHyHbdPb+m{fk)IYE68wKuwvdPaDwRDt7~2r?+xn-$7i`C8|`d
zRy?^_>PzbO2taJ%PG&V~9b-g%iYYc*SN5u_d+!rGS%k6kGUgr>@8<76Jy{-)ii?S^
zw(wcPHEX5v`Sh0go8c2QoPn=2hJw^z^7zhyUF3UFMr*%6+`e>2;eGi-g(A5JswCi6
zNng3z$1(FS(dA4k<Q0f@nv-axP0Ni9Wc-G-)gIGeNhiWz?h^9__Kjl_FD$5&`|UxD
z%iUo94;plrJ3+e=@rumXgU^g?t29hhi=R2iYD%xT?3+crs%_G>uy$JQ4g7?CG+4SD
z_CV*Q2}C>f6_UsKQ$g{)!?}8qtp#UfVQ)VeuasAARK>$~<k~pv1)7CA;7_f^j~Yh4
zQpgR@X>NGVo4%Ye#i$U$JzS*fW%{G}dUQ>e_G3dh>k)-qsg@SX+@P%!HvYnVdQ{<9
zlqzJmM0Y!N`!~pw#S&5)!tqCr<<Q!k1V{F_>8>QDG|AV;6$nlZS<KU|k456#E1i+W
zC$`&3q_j#*3VCy&2A-Zx{(&P~okW%mkjv-QuL=J?rT4TCCRU#~s<?1*7+u*0ZOjLa
zxtG5=bMIkoeZa0{R8@8a%Fg4>C8ZnlcYGyvyYh*kJBSd&x4|Nu`$tuK5vH`dIh8QO
z4zfa|O(~a01wIs&I91kzyMT1c=cO=>yt&&Oj*w>=&|3rp30Q=r;0GiHD?ClM|1K(@
zC;}7B+xqAqx3hm=amNLVZpGtJ;3TEvT)w+ZT9>*Vfp0Xw*JoNPBSqBHYy$Wh)^@xV
zy#EyY%CPAFzGU%!#|-TjM@dqmu=UnZ2}*|aA@_ccwC9k-oB!5_<kv#eu>ii$1<V6d
zX~5sRlq_6h>B6l3VBED@%`o|_x5W^X`2xh|(_>#wmriT7r$CuK;j+Lk{`5G6?@OW2
zP>C_2(?uinet(eyw@4XdfHD@D^_sZ;5$lYrQeG6>`#X0j`leaC%csrf0yFB(e}sfp
zAnYT7SMO7%n>BM!^FWOwz**Jq_q=24ROyhE4)UWCO}uN)`(|zU?3T*8S-qe@b6bZO
zy?7-m^2$hBJjI1eW-z@$ht@z%W;kJV2=c)QZRNs2!=SG;C!%AAEiv_R$hiok6vT1f
zR306eWs!8>jj^t1OtO{KzdTDXccf5uaSVuX1Z3Z7-|NO4uf_e-&no&&z-WSrJSHA1
zfBuN3ZFC#F0%8&6nC!;aYWpN@5T)4URlN|PxSv`{8$67)HG3h*Wy;rKF$g*ugpCGm
zTtB%HGI`9MRvhlSZj<8K`qd<_IcWp29lyb94FWz#6KmkLw{!467Qc2WDZXyc*+X%{
zT(8|NxAbQq<>fYfb6vtu;Zo)U+n_fs%gx`7m`Lxp(#@)Br3N9p8dFEGVhzB{BD8gd
zBA^|jm1aphZ{k<Vb|N=3*RYMd<fgEw1aH|+41$Lvp3~1zQCYun@7LOjlHpH|)Q1dH
zMvVaK$38I!aFG1w?X}Fz&I@tyxz{d{;XX^)ZuPe>v(}TrZL*M!#8>(yl6HVx@cxwx
zy9wOq?*hJuos;f;>H&bH|J7ks+6POs)nSxT)-2IQ_)CA#^}<Su6yux)C%*z7INf`Z
zp;7-blYRsW_@x+f{%Zfx6>AUSVU-CSs2dxo=uVe-TvTf>KeY-evKH7549~{7mwX~E
zIK_wLRY=Q#CMUzQ$w3X|`6DJu*@6L1?1FRsVPYtQVioC;)&!0V03=yJPIN#8sc3i-
zQVZO^S%UYIkuGvc_tXCa^rcrq_0cic%e9y{cLPf{u^{)!LC?|WBuBJ2ZZy@J7|`Zx
zb&~M-B7!S?0LOcS1gxYS_*U}&``62f{}0de`2XOC`u{y2Gw*)bK>j~r1!=z?uIm01
zRxor~aLDyfSi#!sk2hXPLOc=5{@*!G;X4AV4yG@Y?#WX;pZx1YR<0ubZ(rJ>NpI`^
z^Vt7-iLFb4edCb^TK|hnh8FqFc6GuVN4DqkZ<c*FjvG&RPv~V>M_lS(pY@$YoD;_%
zK@k);e(&XvSRY9v9%}scj1v(MdJ#Ory76)A$b^62wn?N^SQ^gDMSwv4EFi$q+F)hD
zHqyYd$ppB0qwzj^e`L}Jy#}u118$K}d)r~10KQsLPQt`n|Mzt-njC)|2yG#pWeJ2<
z{pWRmn=0M^MciM9Mg4T~!}!ujhop2P-7JlCcY}0E2n!M_NGKiBpi4-KAl;21vcw`G
zjnvXDNXPSDKA-!(zrWveUC;IW_x!Wh?99B+IdkUB%xlh+TrL|bgX7yn82G<oqHRj=
zdy1K4-`F?E{|Q5{9uSZlAnvI{oUjwI{|(d4>%QM03BAux=>L$0D?$~06Yx#Hreu<0
zz)QkLvJLRokWZIXL`6y#Ja6FjIg^nz4{(4V+Q%f&N;ZTNVTUmUkjq?xuy>9}&~XXT
z-U>+S8{zQm2`p{M<7<UT0SGLEo6Ogp5eMLfa+v-#EQZw(<qzN+UMWutriQcNBIDmX
zS56psv02^cM{$QkPzj3e6Zk`6`dS^OZ|}cQK4c)P75|}+*$?=TGncPo>+TxAlYWmq
zX8Ut6J>@7#E_0&ox4&8M$^g7Qe(>=LiP={xgMLb$J(f=%5;Sm+cM?%yx{3}m*e$~s
zf)9Pe@(GRP**VUizcLeSx0M*%XFNN+kt)m>n;)b|xYFlUbRe$)dO3%PvNLi%Q;KZa
zrXGS1KbjEagl5%Y44N&Q0beRCnmBubsXo;^E?`)pLJe{G@)@S-xoZk6HmGO_mo@1X
zLPWj8hwBRaN}ur3#L?AM#PaCT6*$LEPl+)x0+aQA7E1sL^*_{&F^0d?UaQOSTFz=E
zJRmEBq8Y<UsGZ3n%Qk-$(t}R?Sa<P~ZZB!so&e6PB()}BGOKstzPLENt)czM?6@5|
zK&AHV?8JoYFI6Y~8actBKPzdm!a`KqF%D>m7^1z#O*B-**!e@~hz7LvCjTkT+j~Ot
z_k?EV4LJA|MAUJI`oXsi%b>Ilyhrpcp!DBS{o+;0)_fR~O$3J8OlkoFw;w)#lm<@`
zv}}J$`DFnTwGt`U!~!Lp{cS##HTwCvyvmmsv#X6U5sIc4Ax>GuNP+F-*RQu*r}Qk`
z-T|XNmgm0E49G%%cJL&05tmzveuO42*?UGBoo@Ka5Y$FC<6XW>xk1PQTbQ{?T7)-^
zMUQrRrTbuQ?e1(}xbtp=icY0~)DzQuh;`QqnxoB6u{NGj|5@vP_yByw3cP17K)wRF
zEbzXi0b`E(Db9HdcVt&f(_fU3LP$mPE0^X9!_Z;f@)9)@GbAJD>FCio7B^<n(K@u(
zQ`WMzVSTtsGGV&--ZmOSUB`|dya&_`K{_tM05h{Ps2?>X)W#`N>U&c9>CDFI2O=?G
zG$cUUi6L)eg0x=5bd=#*9SX}HqvX1Y7OvvMRb4mcE!MR@k<oVLo)%s3)w9j-2}Kd~
zO3j2N1;gK`uy}xn#~JU+pfg2s{Iv=>cKJm?bRyn>2W|bl=XZOZIQ~0Znm6<m4f(x&
zIGEKgrGnILOBmlJkQZ=sk+u0Ldb4gTKjiz2&1=aKcK%MfBzoMp53gNbd--lo7|(+|
zD8e7!qsFEQ?9fa56Z5|Fa=<k?CQV3?PtVXC{6nnk<)v`P;KklzB*|X?&Pc+y<^a`!
zIF{jue~Jgd^89Ozm7LT?+1c*~U{46;bdl#4p0$I^H3FB<MFH&L46J8oPA%p`D)x39
z%&%(Zu5r?8&p#>34oAN+08_|~xidch0wqN^poYB8y(h7RB(eQatZVzu4?N7PYF-Hq
zI(|yX<t*_Od_3Z2W%zvaYnlms#mQWK4H^!mQutkV8?Xjt$o%u3P!DXjiGod&0_?v1
z`Z72Pp>Z()e*Jkx8f-Q*UzGN%JpQ^yF?ZO#^VQ<C518H>_QLJp`QsX%#~a>0ye=d4
ze|^09HZ)m=Y5qKw7OFjOOMFK82faHrvdwL^9m;tIsriXUL=K$q$g}gW--w*q`p`1D
zyHzkl;Mxck@>t}U@R)lxW%B;Gg?KJJLe~BCbl18be-?i$!ovvs_xtXW_Sx!6^XZOJ
zBpghpRvWpyjlimn)OYZki<Cm@k?=&C*H~NhE3ydq$RbobDLiX?ukG<DJyCN1XCo#!
zX)n2MBeSr;>7cMLdN&k0XL3Eb{<Tiz>do5Fo&DE|!wm^(zlwB4jjRYL_(?@;kA1yu
ziyypGlSnhLf9@GqnLXj=>xz`O$eL0`s`*RN8w3mOn*jZ>uLUnjEUXgt#?D04>Ia?3
z=?_&JBPkXMo=Ma<L;UO?!FY!>Bc8ieHW9MMbf~uk1obpuu8hrcs(#=@+(@e}v|b{Z
zB)R^wV;4&y&dVx3PJMAakgl}GB!3A){l<a{GOS$Kn&3%kP}UOcEI97(PH>#pb1(W3
z*}$6+U19#x%lgPNGacHj@O$!EI=WC$e|UUrPDOdxJGzh%eqcAoax&>mKFh4F={&hJ
zWp|x_j=Be;;Sx#x36eUxW&+PZ&|_y+Z|=vLp}%Ka>NapVOaVPU0Q5*y(~W8YH?R_U
z+wdLi&wq5(<GOsdmJ|xm&O_1;C4EyC#t>%u!~rw!C9)N4Zc3mqyj@d#2Kw>?ApSP7
zLx`0GgewjDWCLs)O9#a}Q@_5k`Gmzl1LW?~&-<$xQ%T2t0($!vI6GuYBAnZ~yC8`Z
zBKHtYuI;Yq`#0rDoi(<>A>te&67Ew{f0a?Rd{rip`LxUd4XqL(HZy?sZ11FeydFw3
zB(R|7MdSO9R0HT#E*U$7*YBr}XvWhJ9qb`?0C?Kt;5P}z(7jh9LDBtj*N75MszeyY
z5)I;T@O?@N1^gKtYUpyTWt)gPAS?)kIR`hF%7L&b5a!wGR|ClU0%YyTlYR0xQD#_(
zLBGmbN`ST4WHlzxfSEcRSh+<cD}p9!)1kmptSW0X$TY-X0$&@DErkWu=7DlacXU1$
zOR}QmD?d3@5MGbm&(0l~DQppJiu=hH6sIdercLv*OB!H8rCt)f{1dGrNfCsn_em2s
zlFlm@AQ40Ds1Gb;YXH363_0JC0?`pbbfS!-91zV6M92C*UhD$Gq(Jy%s<uz}CoCEU
zK#(HKEVUdDv^zl52v^g-QK!YOHvk`3^K`XTGw@+9Kn4265yvW>;2KcYbjE?j1&V{2
zBr+i{Z@zip`ABWxk}RMJh57)a#drlm@h1SVAa=LzW(U^JJpno2)=^K}j}YiTj0&R+
z5j^WWaku2;s3>PS$j-z4@cyu|0+=Lt0_xGc_&8%L6bY!X1Q2GD+r2v5F+LxQ2FZ53
z<HziQ!YEo8P{(Y4D+CFFdg)>X<kZU^EI)n>C^7>O)Ohig&4N5oGg4~kT{Xq=lfJky
zbh-*8C{<tAN^22#z|0~Z$oBNse6TYF3z&BRR>E*yDs;IT2ypcPG{bVxuv<Tbt3>F6
zO!9BT2o^lxd@1(`u)$Bbyxz>mKs^DEj)RE6t9CM}l>jSbQlJIz@*{AUHR^Q^3u;JR
z34As82kkBC(izLB?5necaDF^=l!>zWJNQpsOsog4vcU(S_uoDY^(%q$uD`_A=qP(6
z;Jnu%LFq~<gPR8C58rTo7w49f0VrFWINv@J`i8+CL-#WcpuMm?6y&J<78fv`JT4$T
zQQ3L+cPusq(o9W~u|a+-_ItyZJ|BGCLtX=aPyaaOwj)Jmiw5N#Hr|_Do*1AUT?&8l
z65SJIrjR7wI3zg`5Un_6@$mFu^la{cD*MO3$)PcX@HW_mtq<LkQM5lm&8(Noh##|;
z3<qKZ^DdxrC`)cvhH{~U2&pi@&QlaYarTf|q(EdyJEG>+2A0EO==gzFXhWTH{JkUS
zMwl$bmhz3YmP>q-Zg>B)Y_hdFg5DMiMG}gbS0gQQd_+p|%#6K?69F69cV9N@dO>XI
zM0Ov?H~i%XTs!CZ7E|h2*u_skhWNk?kHJ$_`vWP>OVAf!QU@XuV_}CqAdm(bg=?XM
zq8=fSDn!zSeCY-@QT5`()F5HNe)wNDz~-lw2m}ilXasrBfQcc<TLCzIumo&=7nufn
z@Fqs!00*!u<uf%f&y<_LW(JFG9P<_o^;@$1xxjZ!uKl~*vwxuluTF}+wExxL&kr+;
zMan~WFTtHEygqr5Lr9P!H1%-`luEM546Mt6H^Kx?oaQ6j`h&r_u-U`n%(DWp3%qC0
zD+xw>b;X@rJKjw2u8)?r<f&v53)qbakbJ-%DXUX(iqO>0L;7T)g^5(?2*SM)tI%NM
z(o!A1bBdc>i|Gx>Ds``nTWxs3M;ryL5*Fv<nu*~Y>CspQ)9D8HT?6qW;a_9UjTrKY
zd)D&rd2YuKZW+AJYn<o8AIaA;M|Y&RYJ8A361{%Cs%z^%IefztnK8YO6UIB27{(y*
z5g`@KJ6@Kc>GY~4*kHT1a9^|d#YxZJQIP)0bxUFG@?1fd{44J9r@ZtTvpR+HD!d$Z
z-PUSw>Rv#@f9`dfl?9DP!`bURV+T`o8h!p-m;E08IwkeiOn1}!V{4K9X{#NJXF6}1
za!r}K?X=^oywda0w<Xwxtp$2yw=KoG-M@ME8m!*`slB!HsBC9D__<~$v>%@@Zc@2<
z!J}rer?blYQlFRp&Gb&uZC_sm3^8x#{~lT&2~_wR;9mfF3kiYg%l3cWVCXM8K<IXK
z$8Wy6vbI9s8<&?Kq-_gV^HLev#R@w#YP9b5jC~#JH*$SGg1_~lU30rbh_O983CabZ
zv`*R{%PrWvmYG7-<vMxLM<<WZyvlB9)X{?fkZj!z%`O{Yb)E#1ea+E?Yn?JB*PbHH
z=%1=_V%d0Srzk4yy5|6KN|kb@PoooO{KeyIaem$rr1NrFe(MXNn!4z#CooIbLW?cs
zS94dF>kX^h!!w+mnK^*-82U9uW;UZR6wJOsv3F3Hp$yCN$dH-to<Km?o`WTKQ^r${
z-v&h^%B6wk@Hi3K06tQ|F4V8CjxVksZRwTLQ*5frOnf0=fB5e6h-&tCRY{x1FTW~Z
z1Qn$3mX1qgDCem45MI_aWN0{lTSjqRI6=JjylP72V*8!Y%(k(mPrLtXY%?)`Olyb{
zd^WRD!tUIj%>;HM{KrmjoDTM%euzPyE|a0vEf{MyQZ6X{%=6@#^c-42e^^fL+nQfW
z>8Ztfsj)aXJ1w!dtMls<U+X!;;Sw7+Ez{qhUo8Y*bWlOjKv5rB5K;wSUabYUm5Vfc
zZWZpaa=L8{!oXat1Y7fA5uU1xCMiC3`hh;N9mQXoWpmYDDO0#p%&Ee;l4WKGx9jQM
zzPm}*|3$Pp(3fh#!)~GwarD3e<7ME0F(|*77tB|>vlU>LKcA@P3Tb0%&Vk1MLP&AZ
zLXA(GaB8<_TR%WgSAM48>Thq7mFhOXuF``LJXeK}5jr&jmr%LRxbe9v*_@R_&(vNv
zT_9-c{C}}+s9d4LsARccg1#Z)jiQznL?)eX`uC$<Rg=r3iatogvHUmuWFeuv1$9SM
ze4X?TE^C;U)?O7mc_DL2VVKD^-XAUCJyYKwr#Yg@wH&Stdj%2)X@styKR_GJ*-F@6
ze-Q<D&aARHUsnMTj2;QWL|(uyo|>zkUmETCv4VdTWn=`27B$8vk0-JQHK?#;A8w}+
zTdB|0j(Jc_+_7aya%hQY!OzA{+V`_=MU#bY>SbRH%T&<KjVM3$&43$8%w#`HgEsEu
zn!cL9RfqHu0*AQ|1%Q^LXOIEyV0M+;-nQ8LaY9jgS0$YJJQLc<!ZRnyy4L=*G$j&e
ztUB&Zf||7SSgYnZVu(f;RswL?ijG^5=2-Aj>+r1r4p{STq_%%$1A4)f|DRI)g>3~f
zCkLMxU5&zK1k`Y+7WlzF_G5dH&kKKM)7D(odd^xs65;-8(i&=FTH4MB>e31&KwW2y
z2>JQxk1dq%bu^DP6IjFCnZUU-xo*kF#VShwr}(7k75;%opWUFd)l(KiAGBDkl7iFv
zo9kb{KV7UFYJb{lVn2svcx3A-XVeouH}RO>fV86+e|?uKSttTHz5W(?dOhj`!=dbv
zP1|<pM;B)BN0+ojA$U>Ue%;MGL>%LPO>5m6xaPU|@mwGboPC7*l`7}ZKM5+(3mkWU
zjl{qc=%JEN2j@TXk#EeYX}5EGs!rZ~UlE2;sr=7wSdwegzHR_dL;o-Ce%w+bl>321
z%J~t9`P2|Z9Lj&0Mj#M6+JI4PQX+XEuCa#IAo&77a#9b{5(!{x-eJJkCHG&K9Dfu#
zL0k`a&=xWfp$mW%*YDntcYh49*mP*w#&~6F_oTH0F)u%gwhoN+yuM>98^<Z*D*%>d
z0{(mYepA`lMM#lvkOKoc09d?=dSo#-nEw4!8s6QUlaDn7K!C&5zr;t6sE!#7|LlW~
z0bunOK3rM#<5RR4Rwe)$7y_P4yZ|8A0+6D3pjCpzA6WprIc(mV0N~0AfCk@pie*ax
z$XXu&5o_kB#Rf<SqzXVe(_izx+kikgfItn;=09p@15kqs059tGuf|IO?EaYvfVCyB
zJ<X8`#6SYC|E|`XDgb4wtLBSc`N^Sy@WMeuojWr&KVul)-+=HqXYZ+W;R3vfU-;Zp
z36KML<@x?2=N2I6-!EtPLJ(;I^MtQ&Jnstt0}|SuPK!qYsS5$AQ)g#C8UvCdClfzi
zygaFqGB5$?ID4ku6<K0$-~%J_-0}MaULb++)%YjCD~_D7aM=FCEfC0qKcrRg?>r=6
z<Tvo(z)6T9Al>5beKa<J=gtSu{s{y63>W~iBxXwv*ieRqaeTO`Y&j`0R8rrv1Uo-?
zuxiB!;C|#DVy1RZ2>>cQ0D?6C1j&FPd3z}`mJtXN{S(Ur1abZef`K4<`-LPOA}C<I
z>kja-o^B1V9+m<#jQ0*mJ&WZkvJ*(>^^>6RYd!CX0_G`liL9tL8NXjMBdLl2-|M|R
zCLN7dAato#1OXS{v0rzPN7DVY1z>%F*hn<;iwNM0I6^4J{pW9iKheM2)O`VfL;~zq
zmONP(L-JtgA~~`d(e?yk13Qb?bN^yiQM6&s8FAJ*2%s(z$%r>nWe04c`~aMk;-T$<
zsQ9zQ50M_r{#r2|MnxeAY9MeHc<!~AD4Q~u-Tj}Bz@HLTX8rp=k9LQN9b6Su8Abs1
zS>50WVXb~_1hC(>yjsk8v;>1V6zNnY!`u+3e!;+a?^PYl%u*}9e29*8s|HVh^l>%H
z4weJ<+;*{t`-rwbGay8=4{+{eFn66rL2`fo+RM&|oSpXm>!GFIO>P}=B)_HT?&#5(
z5cm6!v%8~*1FQX=fzvd|O}eG;v+{f0lZniq`PzMK(aRurE5xxQBNm(gLe`Bj|I|`9
zwl$0}ChxZAH!KdW#ZGl>lR28R7@oZ{6tuM_xj2=jc87ae8m)S17!5d8$?&=Ib64-4
z$O;jw(MQ{IJOv)*QbZ7j52WzCf1^U~r2T?2%jHB-aja6&@i~_JugRyZZ=?FD713^f
zHKWeOiDAGh@a8uSBa@<)brccLP4e-F3Mz5L`rO(rmMFPjTov}#r#@n0x+^zJ=wnaq
zC9!4{>xM)J;$><GHtqffJ{(O^)+WBFypB(*CF5IZ{=3_<z7*XPj`76J12ZhD|IMeq
z(dn)%%axJ$Z881flWy_Q8D+F^y~rdKYOoS;>1xZ`mtTH&axTVJ<$@GsN2ebX?L#-N
zeUoFO9tq&$RT($WM?^(Am=>68QG&IiOGepwbKL20+c>>ye~d)IJmSB(w9HQSXR)G<
zK(luFC#mD1Ovy4NqG$N=J6b+<w?S~eQTVV)D=lQ<;rf)cTT)vfMpv5{>K)C&!0HR`
zuVwGTS5V(r2rokW_*Q>A`HbFN@+QBsWU2fG7dn?ECWd`JqE_3m936>frJQT7YgdGv
zRaf;s@=F)qYx*n%8G7x5)498fGY^w4kxF)2=z~vU-W|3)8ewfe$)cvFG;HgMY8aZ=
zm5e(-wTM+H0d%EC{~%UUEuChJtH)|O6Zdz}x!fY9lWRG1c0pJ9_=C3?8*wW3f>=GT
zJ1uDmn=?(945vPl*Wwv3h9c_el6+Mw9{^GnQn28%G_L8|%CuS3D=tzD2`<sp*Eq~3
z#Nu>%(h_Rcd1n++y@hA}nt#%CDJ|sk7A7#WnWT|SolL2V6iGAaGp+LLD_3A^j2ha5
zK@Uh#R2hOG<@{@vw%GhQjgQqtKkK+fK%1o^5BA$IA$_{F+h<g^4BX!Y_mAptCWGUP
z+`1mNJFS=(cTb~3A?FxOOvy^$pU+#Vyh@sP9wv<cgW>tCL9d|FV5NW?Hsh_g%xPLt
zZKz_Szz=zewd}dd?jx;{8%p*^KhZMz1W(4NPcqJB5D_tF+uOKEU6^E`IHuzF24~(C
zQ2MLcFXPJ}$wg$syx+p#l3}0XUhF@iYmnrQ_r>^TxG_WN%;NoZ9W{fx>r?etBH|?O
z56sRsQY#gf!m`&dGg6dISH5yrTNjsQ9=ks`>wggsrT^AyvhIZ&X90D$aNDwipNw`f
ztk6c(J4Q?Z>~nvw0ZJi^RxL4(w0;#2EwFtS=Z6&rE;u`2pg1S73O!_O<M95vVv$^*
zK?<;Q|5MVv<i;fmNsd!*%Vu2%-e=Kl8{E>0q<B%&omn81pCn|D2?ekIkHM{KOqA+N
zRmGeDDay$$!gggiU-zovgU~9=1!~Gl<>%&a2TdthD}PC8T9@Gfm7U*uGAr+WHc+Ol
zovasqAPWh;p{3x}P7<n8fp0RU;FIzf(*n&(XnKh1fWMqaXox_88EI*-s2qRr!Y1#A
zWoIjNd?A$RN~*aHe872$`Q(pzbF(u$Oh+jyN)>bXEg`3|gg=<#YtlF{b#wJJ8nJeE
zDAx7~zDx2!?~_+<E=&><!u;@W8Ag#x0>~#BCPg(Z`#dI;7~K~cUM^yorH~A*LhGZ^
zPyu$eYmfZADOS3=T>ja1e9dyaNr4^hW@K>?18S)J<wZmcYs1l4wB`P&ZndoCV+eJm
z6K+}kHb5fD%eGD!c?y|X6*-{?2NL!PX*ZZ`Bo`)PlAP=#%T`Cg+;MZlTN0&eu4>Bb
zCqw1cw=6Y)Yl)(~HMQk4KYoZ7S{WMsx4R^Xo^M6HoA9j!y=T19k#eY1!dp32(;d8K
z)VgD!=TQ`V?4NTs0^);J-nt*6nlc>=KW%JX`8JUJ+uQp+f{i5}3fE-M_;Uo9=crsw
zKqRpT6<%#m)bmb}8Dkg3!=#g>s#IKuWtrwzg$8#c9~C;a516x5;vGIIjTPPr`81`R
zpxH`^v!BilIi6s@J)HS~sCR;mnI&5=FfnBrvIllCE^aK5_ow$HI3^cZvC1GB>oJ=A
zv`56rR8MkOrJQ^z^HEEjbV5^OiT0-)O^4Y>A)D;iVQWXXfB0+N<>}3B)9*6{`o)OF
zc%3E3eipb(M}*pgV(eM6eX`k&$zjG)MC-8&p*Gs37b)D-DNlt0dCny8oh1?Mul%cK
z6(7DsIlncVc({;?P5wzS-YT<5gZPPwCvI5k{v~A?;eibko~8xn2QPN9#WPSXIPxwh
z7ZZnHud}o8_^^`NGx@cK{=nV{qK)F{rV(+pvEm_<)**Ir)Cu|BartueW@bD1_`DTS
zQco&S;d9Ctw=i%$kPGR;wQmY8xfnN3BmMmRy&}CyS>TMh^|1PgqvycOiwbuuJ`;*H
zwOB%DN1N+L_+(ki^_yYPkAm3!14lQ%dR4)w^JNRcliLodk}~L&V}s>M$M=c2O=vi7
zfkhUYHKnog8af*@r@hsB+wS*$3y?5VcDUke6R=LZ08J<hX$y`e<zse!5jCnE&qT)H
z4SA4@ax$9I_3Hu%T`0?V^Z$|=#T@zk7E$vas1cAI9Z%&N!I|GX2%3cADy*RwD+8;r
zB+(h@8vJH#XcGWr`zca|6)#Ac2YC5HibJ(rQky}40YrO(NAoaVkm5eNFWS*1wfa6f
z6|Y>OKojFLfVuPRY#6B>Ipl%>{~iV!*C>Y&$~J(wKU0W5rgmhzkB%p`80BETkKQDo
zj#bXW)&<<Pa<yG%M;TU<{GklkPZ%=aOhzyY8*z$n(qHM3pxgont~$iMsr69rgAHhl
zP`y7OWNb0o6frB|60rKbM2ovY%t~+{OhN7B&hJIJc4Y$LVezu4Q+EN&;$5Cgiur~2
zk=I|z#YkB3ka=xHy;$_V-P1A#hF!gye}m2>tho2-!iPgttT_J!y*Tvp@6$!mGD!Z7
z6!LnkmwX?|hSo^J`T)o&aJrv|X~Pf^YI9ydfTvMLg_E%Rsvh)Dx(!lRp~8TtifX9S
zz{<KQo~1@ALx{8%ddT;#dc_C-|B>BY&HqMH;>~NM(%k3l?rK+rp&&m$SHkh9qf~={
zP62J4qGFM<Vj(%$@Okm;{Xa#9`3Pv){qxcPg6m;Eg8#-MgCoJTP=2Cbeo)=za|ss4
z1J>N**`Y2Y2FAsG7R)?gr$bkBo}m-F0O$q)!f=ctLCI*8P3LHk7cWQJj2fPtEO5fh
zN4|7O+YWRIU~?$0x}zTn+D~8`#ge7&<JuI;kl=J|&_<YkR(3Dwq*f6mPLVpW`+p~m
zb&-wBj+U0i%!;{$lLH0OupErQ<Q$zYI<DKrL+=71sE6f+Bosgn)?7R$%%$f*9x1M9
zdJ{t@8cvW&l>&(7!duA^^A7^O!X^@Q7%b;#oQ&#)rhwj+h0MgDqrHR~5k=AJ_r8FE
zNJ+2m!}d+38Ljxlw>*UGEf|-dQKrh1NKV#9a0<Bn3FnXnwQB^U$AMQ3f{GD9f&z7g
zv?O`dB+F)Hxq?)+L+sdIiBo+XimYK5^1%loUl(t?s<eaiS<jtHM#@NC+PDkivk4vC
zYM->n#&$G@G_tVE6|4XK`I)|f=giVw5Q{CPCd<f+TpKO`IpA56hCLuEHIzQtj^sH3
zQt{bp@!(@H@RR2}HcAjwmdELY?U#(TV7K;F;jEIwmDemK>*&S+$(&~wdGy;tS@tPv
z=Wcs#auBxP7G1#2EZBxT^dJ5z*!g_W42SJ<z*k^fBJ99*L@_@J@h`g_G?jsy{<K4~
zPr`%GqhOhugNO-e2m<}HysYphR!Z}j!&47sU}Ll#1l6#Ro}~n)sP*q(mQljwaff_1
zW-u#~T=FIs%vDe?B?+2L3S5w+b`p*d&iV|SpO%~}`Mk}Mkn7W0OH={lMZw37Z7Q_D
z;by|oe~21!LB#n9p^(WbFOwHWDD7fw>S<=$gpSH7ktcK*ft5v>>6+<{8A-Kx-a&E8
zn(cFt@1L>YC#L@%ocrguPo->d?dpxhsx$oH);QH5)d1L9tSwLUcH_YjmY#O>kPjJ^
zo;ET@5sV>El*5jC9$peu<MG)#9-4W3>L)_<b#E_cOXA5JRY~8^r&JdTF*BAW=T&2E
zf=q9KJ@KBRx!LTOhbPSA^NSOrfsJPIGzooAVuq+|n^}_kDSxnM4O?t<LORlNb0hUG
zF1F6;t2Z|Ia5TUM1hnSt1Dx1l^!d;*2J8`fp_@;oq?rk}B6bz1=QD2OKhd*2QLam0
zM*pPOClZr0b(orXI6Oe#A!qsN{C&uS#2sssuaJm8SgYp-QG@HzGE#s0rFPMZuR^J*
zPznU5h0n{GRzH`Toyw{dKRgOxNfJn?RFM+6OY@QKvKvA-jk!zW2obU6EFtygeEa>Q
zhzLGXjwlFD7{idk^$p|t^TRQS!LQ7C;3U=gj@d;|aPL6LXQ(WbQxpW#5Uz&nV#@7r
z6mQ1qOn)L(*3ZJ|85}bJOCnx-O+JtnoymhN8b=8O_BW~Ti}W8r+*W8g4{Cnwe1Y$#
z)y}L&>xnR(Vrkgf%?s&ktH{e=+?7C$Ffj0s73p&z4iP+{soRs2L{o;nhA#pdYp;!y
zY}cSK9ByUTG<i5w1GQ7fJ?m!=JMHRQzUjm3dV5`v6?jdw_7Gh@;P&tsO9!`$gQ8s;
zv^xKn#^u?gXHzGmd;9Iuv!cS~eJ3poH{}?fVmsf?T`Z=;A0?*MF7B<z`ZZc)Q!;tK
zkTt}b-=CA3>UEe~3K_9vykUIVDn$3xU!rz=XfWdzl=mw-7h&j$u~K&|Ogj#Rw}@}>
z_Eu)kFkW|CT3hFFSpS;(u|r{mh>+AVd~?oaRXkyBSxS`4XEm4k$0w*=!Jxkd_t%vg
zI<$<)`m;5aIrxof=V^ZU_W9*y#+GkG&85rV?<7iGowD^Wv+O@{IuBuYWg1&y4Sv>f
z@N8P>87AEK)jC({JGR+|Z=TmdAT*vet@@8I7o>@_Mwk21bGXX;y@a<Jg15HM^23!e
z)6*C|3w(N$^nSm-8mi6@NwR%6^!rNwPou4J#E;IVgYX*EFSR>6iCA$%uXW+fJBex8
zYMI9H@}VK?_o{a-?gwm-1ob{9%RU_{N@}yZBG`X59CPmKv`*Z@J3KU0>*&Ng8O#-U
zH->*#3}v0meq@_Ho|Qa%t_~p%G0v5?4yp##MYcq0ubsG$NVskvKuiB;Wg6Gl-S}Q2
z%9X{%s@T$Jq0QRLKOI7gJ-=ubY&T(4)EJ>^mTo(+6D^RA4+P@prER|g4KH;023VMV
zoeHIDwS{t(8zny?wuY(LQFsLjr|*!mfgn;KRmJOF?q}tM%Y<Z62OI-39?}jmxUsbA
zR=gy7ktHT-qEPu2gj%nmalbkXZw7~1Kh1+8XdrmD>cu#&+m1)SLy~&AFJtYe*TCD!
zd?AjOWtSzz`<IHvWO*LwQ38wo^LuFdJ}%~9Fo9tsN?l@Tgr97`<+5T#LG_+Ji%gKT
zsRILU78$V$+ZGGS=>P~&cw4H9$pg1<F?flcUELfN;}nya2}4k>lAt2b<Dw2+H<J<+
zz3(fgN&Su<52;~sn8iyJL-FC8JOMaMJ_p}sX}!B9nxAl&2Swk?;a-qhqE}2E(^m}Q
zUF*uChNMMN@a8LLBU{>iKXObW`(_i>Wt_`O>JBx)*X^xTotXv1jXD~0)6H0o?)kG8
z{-MUJzT0hdgB?xaEw6%Hcek;dWlZpTKMys;S_Dp33`u5k82zMLX7%xLc~nN+?Na|t
zXp{IxB+>1X;(Xza<f3s=Im<gEQS;H3tsu$Srze+a;Np6_THF_Firm+!1h3L?v!b?~
zPv7ZkizXI)k0N`zHm|;4eWGwVs;R`;*LEDQL1e<mzxEp2YsBJGDMDlvb*gns+&yln
z3N{w#QBKGmK0fduF>QPy9W&d*WA>)Yr`s!F05;-DO#3)o2{WBeX=u3lp{oOpgFV$p
z)gS`QC{4~W3@%gUfTZQ44F4N>dKM!Ccvn)E&)Pi3l4Z?2$|!FEqyj@Vko`A$v{@6n
z&BARis6!5Ii%nHp?~<<TRTTaEL~^#Ug6T5o+R{1OINqV3%bMbq8ik$V2V-a2{yh5z
zJ+PY(gqRERlpNX1Acqm#rz<;zyRix#5&}>31F&q8+n1hIZg1HaxQ|7=FjyTJM+<8I
zA|a3F{J3P~RNeq&<xoQlx)F$I{$>QHUyjG?&8T}x*kvr~)NXqi$-lU!S#4Kc5y6#4
z(>p%kRQK!gfM9QB`|W$Bajt=8*EP#E-AU&3h$~+9r@E;u5{^3qGnpShmUFPOJX9C8
zD_1CONlvQmO09jcbkLP#t0nx`O;TTkimAZQMPg<SRa(r6qs03v&&KCn8Pt({TEmV+
zO7NQ38Ii=PAXUtPVLZQ*NtLCKqtW~a9B4m%0NGbH(if}hs)5{_sxz}3r`ch%Pc`OR
zokUmCyC#YyvR4n>eXALt6j#HtQtQ&)ttm#uQ1nqM@+zQaAeO0y?2Xe1WBB}I)wn0~
z{V&Ley>pyIAD6TZ{%2Td>l>lpN7JO{RU>8JL{xWSzv`y_a)%l4`P4j5fz~GSb-#bK
z%1<gG1+abGzzj<9Bf~|EOkt#XdKR?cc}xHD4L1$TeE10>Sj18(`^n?civ&9XJ|kGq
zpc50$*AI|Cw$)GEj~+Xx<@YkvJum@#u=>8l7%k;u_c=X8>3#AvCA#^SBrf^<seFMA
z)dP|&;~>H>j5sljg2QYrWsjWhIv+b%A#2J-%gq=)5swQ{6-@5RFJ_3KS44=ZCww?&
zrHV=PZ5h;h%vEyb>;nWdW=LvUX45{TZqLm=$iI96MYl(5Auev(H012h@i7-D{S#l{
z94|_F0n-;0kc@mKWOh|m@r<XM3D6ViWo_B&OX**(4=7pxJ97Gj=gMlL&c}>?iLR)S
z4YXYqx2}`Rt)z$xSvyT_Tm)}>B>uiW<8{XMHGpk<EsC3D0(*D<y7uB)9-ywEo@p_e
zz?FO39h+>W+n>YSC|ny{2Sna{UUf`;duFB0m4*=&3zYAPjMWp%H6y?oLPzp?k?Q-q
zAXpzpG4uvx^7l)9ncD=r;MdF5PF$}aL|L{W0`O^uuBCreV+faeE*z#3ktVd#&#@&w
zd`bm$b@N((>7zK8A3KX?#}K`lN)_^QShoawIN7LcQ>FRvbn#BippQ@Fhh94p*i153
zQ+F9keT8Nwq4qvnU->y;ruIT)g|R{weMP8O{C}8?;`(}-_wbntQ}IVhP5W|eMMBHJ
zf5YSjhgA-vURv(rzwD_wtA-F98b7(Uf_u8?MWZCoVyBHyk1n;KMy*9fhC<&De8Enw
z?Mk>XIb7{=VHS9u_(IUFSvz!{aOQWqQMvzB9(G!7rVi{x&3Ek@U10emV!=;KsSHZ*
zF5fFlo(Xx$4Z8VF-dr?HNj2H)_Ebw)(FuC6jjwTLzecl?dCuv4*!)DLFE72Dxo3dN
z+aYx@3jET+ZzO8yBqPT%K@MzPIPTp$CA@}5lLa=>_H68+!U7dxu1SHa$X}?V_*IUX
zCg_~^qvHRV>??L1aODAoT>cn{ZipLT0de4bh4SfTuNuLX88&N`#ucodto;wKu1f~r
z{(SvwE)0T}DYgBcH+A0j&o9Nq2m2_R&oBdX@#_=cC8woG+orOqAMZeFQhyhID9I{<
z6kBYrV6T{QAkfOQjNvu!Z@bz#OdirbtFn`r9Uv<*wZ^r0*lA&Hs?ll7jwSvUCH45`
z=RmZ@Rvp!mmKE5aVKQ0VYw+1lcleC3hJfAJH+}8kr86mCIx!SYN@n{BMFocmm(5er
zzR15Waoblv6V5qHyerH&oDch1k{T50vZjc1#ur{39dULfJ;;L|M1F#9Y#BzJe=LT|
zGYL%%n3*Sqx-4RO{mg^ry7onKJq;28XQYxw5Ar=+Aq~dtSE>WNn*N{_Q?nOqnw6jX
zpUmrH5RoN8a#kXr^>A~@N&GAUFwEEgK@9-VexmJy1dT0BKHyg(AAPXUv%N<U(kk<z
zf3As0tTX_<80(qPg2(aWjfZkSymo>PA3IyWsoW>@3?WXG_=z>zUTdg*Exs3jJi7xN
z{UkJ`eOh?YBN=ruQ_6VriG<kd`bo|{+92_WyE}ht)e{r#4&uKzC{uWAuZgSAz5G<y
z=DjApM-v{fwm>@acs|Y#-d26|=g`-0%&WHz<=oh#tMXm+);UG-?fqAM;spL5jKnxy
zhE_eoF4<nP^E_R9izQZn)z|Fw3EItkENFL>$eN_QuF?CwH7&Qnrlp5n@OFU8*g@TO
z$lH<7b!`LakfMhyxi(w_>UAw-T4(d1r!1;-am{WZ?G?`|nitLKXogFq&rtfg%0$+N
zKpZa_@%0OQ*Ww&sDP1WCNZ0U|>_J&QN%_X&m3wazbn=DQ5R!a9qLK3hWCz91$N1*Z
z@iQsqBvwZasLu4<`YQN?IL_~bzRi*&>64vp{7MJ`n*FxZn)^N?I_LH3yP|g$SoTh(
zP~*C0qP-WOjil(BYS~?an|A1sNmP@9UPr;WKuh_6QVtPmOxu826<V|s*L9lEFLL-9
z#60Ql2epz3{6LLEw6lv|&)Z!gu01*Sd>GZjwCoGvRTrw}Vv~+T+y|GqxX7B(8HG;F
zwuPZ}8JiZKOSf-t{T9WixKo7!9LC(|3uCzj6^rSp=C>%QWY_T61*K#XZx!0|T?VMc
z1${cIPJ>8$vn|N+qnk0;ddXZt(U;qkgO#X?Drhu~tE9;&1KIWD!7PDBhkYg2Q$AV6
z&`a`flAR!N@+^qCz(@jR8cr2e))Ou8V*0^<fx_*R+oc~~Sg-7KQv4|x$5dTh^>5O~
z$HDE59&VBEc9+vt{yr{|=qHizj!!dq;V-KP&-_HkAX1c@6Ays|y+8THPY22;WLRn4
zA(^HJ-h3^D2&xceJXYt)zcK0HzWDIn=13=Iias@>Pv~RvAdHDdW}tL}0%%pSe&)nh
zuox3?S(8w@Ni+75V^QI^?-alAakas}=FTkSMnVJxJCIs)j#D4U_LutWa#H)&PsG*R
zrCsbRG@{w52(Y%*A-lCWVZnr;-6#t`%&0g$a8EQ|;$ljyLz+>`KLCN9mlM@)L{#`O
z`)$n+bfU9{_<<9N#|4kRx^(#wfM{BS(G9{3j-N;l*Jzk%yD|H-rUg-EK_+Rcm@Agx
zz85&Rru82n9Z#Q8{G+q;$o4B|HIA57{@@Y$a`y)~Vg0u@j7s71TJl6{@($SbbrMAm
zJWLLCiuP|)B0h?5t}qE-{;uI|v@iZmz`L<|JQE!2wJdp#_Yx&j{ckwQW0TqK$09qS
z%Q0VP4_YmaRqFK&RocyP&JQngHrJgGIkuYCNxgrN#v#IjF1vMDvdwg-wg&&yr!HrT
zr?wN&^@Lx_N3)z77u6W5UZX4g`O8B~*P2n-G@E9wYA^U{R;Bt1ug+qdSM&?@@grA3
zxL<&|vtUljxBdPUy&TWg9ya?f4Sk6OKBtmi`s$e*GCuz2>vs7+4E+@FNF-x(Dx-LV
zN7mLi)`#*}2Z|_YM=rl08liyS2*httLO=8;H~8mEnbHmIsIr1~9!CAKjH~6BeZpW7
zekxqif@FiKwS*1*^Kl)x{P`6F2)H_!GA4Mlx|Ok`{fC-1oBfIdo(|k)fRq)CLJ7RM
z7;?+jPM>^n-TqY8<)!H$ol~w(5eqmgE;>a(3M{h8=FFBm0yt*?=2h`8Zt^xy!ex?E
zV5+Vw4s2rJ&VN(zZ{#3?RAp?)U5NKtAN;?FID`e$7Rq6x=@F4#-uma<FsN2Od3VN}
z*Vn14rk$epAE#03y_cvIrm78l0C=59ZWzE?Y$8-JPB04KILk*mm<s>nQBuEmAx${2
zlpxRn{}#;4@X%a}8$(RUBmhuES71+q+fmiS`60_?6hQKg=Jc=PcL=V<wKXi3+@l|2
zphe)LB)6HEW1WsaR>8YdpMY!Guu-e-IveKV`u##PyAZoD5H}mU98>*n?;W$w?f3r=
zBYJ{q>)FVFM;z|%S~E$V^E5C<Y7Dl@n^nDH6b;?%4dLSr3{2WjS~)-ak?sr_=~8@;
zB5pleb27tX&n|6>Jr3x|6+46$$gW-30WL@o+lsj;xy&18`oNa=(B(iZ5E|0ph^K0*
z4=)~F^pC$o;EhOvC;_itNHl!OykWnkc5JnU0^J~($4PJ%=QjmWln~}20Z18{Bec!U
zOsR9wOXr0i=7PnJ<--|Z4Uqi~IMFa=caWrU>5~sr-F**3_Z8}Jo_O&zVmd~Zf%1F~
zl^+$jpE_<l1Dfm5WOBC?DV@|{HLRAQC98Q;-^2%gM5?)7MP`!m&xhRH*6HIXw>YTX
zU5n%)#6XSOKuKZX4Pl%beN72TrYpE5Pu2_1*R2+vImBkq3&r-{K9?<=<w+J|nOY%*
z1?qCsT|ZKJE+|scn&)pFq3u+#(4Y#>^X!vku7-{jouvzUI~zTQ+Xt>ZbV_)>zRw+%
zXxIukRQb^Xb=iNA0$Xuqr;&Yqo>9-zU~D8dQIlZ!9M-Jr`AUo8=JFGlIwS&NKA#KC
z_|vi)Um%v%#8#I6o8DGXAGrLXh3X(Koo*(<7*W;{SauP{^5V9Qd{3*LEjS-&unIs?
z_;X+>wZ=N4N--bOKKJz+@2rl+ZTke~9XOg;54g{^TsG1RCf=UQLn02%=QE($nftL~
z3BMgyF1dk6>K7eUwV6d%vyWD64JuqLJv%3BiV^;h;K|kAZO_s>3uN1mM)dOe?H`In
zizlp}pN*q6+LDaBj1JGaPN(VZhvHg1-f@3V+enV-`VL+689(-9kMwLZ2f6enpXWyQ
zzEs2HC&KGJnM&zn*z-SEp05`<R1`-wbD(m9j1ROLJuFn<at4Ya<!vhZ<}NdSXs?20
z4Jj~b*qrgERsj1bqou-+6cgl}PkS3N1ZM6*7}_~$k9g&rQCBIFhcHvIY&^;vZQ~>9
z_{BUfoGS>t_Q?obKa%glrlZ~dmQOz<c(~rdX5sGnE(52U>3-S4`S)2;0qfXsnAs2M
ze<-J7+19Le4)9IJd=?Wb5F!wmYI~qjh$nTLY%s8gX`?zl7~I)83|s~C4tZ}(ewGwC
zUb?VW|68ak4<BZn9HgT!p@08Z{vzS`i7u0W#V$5}d@evufTqj<9-*RQD<KgQl47p@
zVx2_Ion_ra)A1@7!s|?)aRd%1ySC#Y=8Oiew|V#rp;NxltFUF~Rub>W0e<soV&Uf`
zNw6&9kH}d`A0lqqT<WxUR_8NCM2|oY^oMgIUW!3<7FAAlrgHGqTh6PhM7^yhvALe?
z=Q}Q#&(i>s>c}f;ltcm{x1)!Wjg_CqhTbL{{Qey>+47xzvc;~0JdxG;F9ty*lHP6E
zYrDM%f1R9s`*iRxnI~wa7m^64eV?D6a)H)Gb4@iA2kep5_<z-xU$QVd2N=H>SPmv)
zbC#O4J1`+2#8O$OpZJ(HE)+Ah!Ro$cqvVtu_QpcdR{*%SCh28yRl0|i7i7iz<!+=#
zC%KP9{)cwl%xIw}=XAjJc1XoO6n<B~Tj1#U-QLXVlST)Sh3P~0f3w&DmC8is&2u#v
zOiL|**ToOQ+a_A0&EsI<zZ!Yyx!y`Nb4S{*tRH))qMY*`G4F$>n+XL)Pt=g#CB{j`
z!kRZQ^MJcMNJ<@fZt1B4$y0nr@a1fQ6%YME^J`hbKiq5^IgdiVA<$I|)9cUQI3Ub4
z;nHHDcd!TVRe9Ww?)v*{Goi0MK{zR-3`ypbD|bmr&H~`GdC<fAF24Nx9W(QY9b~^5
zg+o(2M3HZ*X5_4mKzVAVk$LfMvAd@u`$m}fyB>|KtX46r?oQp~zV2$Hi5QK@?jkvi
zx;zGbZfw1WtGpsl;;$ke^|{P5b)2tvJ}Y@f+x<y6sH!n1F<OUh{5M_l71`&SVUDK3
z53dOo>p;NmJ*rCI-OO^`l{q7M9jf~u2gbC;;lJfGOE0XD)1blNXZ{E(gyel_A*ZnZ
z)8wwe`H@%33L<#fG$I@)D$8zQ^^sG?-tg3}dMx+0pW#nL$!fO(h;4gy??CSZWJ6K$
z8M4X)FZ^MF+8s7YjmFO~LMlbSE$&NCS}4bvV8cPG-|f4~%Taz!)Hj_cSMo4}YX#dU
z91dS!Y6@RgfO}wuQ%*4>*a5&^(JB0ZQHV$M9EeS}eBn^E4wJO>k<V0|S!&@tm^f(`
zcwbg=?TZxHr0pcjO1dT66ZB!M=kp_ck-1>JqBFGWlUhD+H7&mVNPBxB*~{+RLdJtj
zs}p~euSW?PU8B6Z9#*jnz}gd4;dqz(%i`q}(=Foz7>)3&B(bZ<LomZ%PL{AhFcA(7
z=)Fa@%X5tUji4)K&*v)K(g`Q~Vhho<@usS;{y6lh!GHCD^m|++@Sy38sl~JZr)WZB
zbZRj8pdhyI;4bmF`yzX5wF#<R=kyId;ykC5#kt_X{u90O+~GJQIP^Bdd8Mn1^S@$6
zffV%bTns1o0(PadayYe%AB(N3YYYA4`H?csdWD+2srd2Sk6p9ia4XjKHOW?&wJ;dg
z{GCurxOOvM=QlQ{@Bg7DM?g*WSt%CPT1@c~YX_-m(~M<H2kB>>$+hR<_3=M&WUhCy
zdRKU~WrcHlbS_&cJNoh`xmN`KBTSYxEV^8iPbABL>K{4aSW&?#!|}#b#=_BijYE;=
z9I`0}Y(M!Lm$B)%C?TvRtJ*=BKc`}kbu#4ZeVCZ)fk&021`GosPg-Y%Y}C+${y3!x
ze=-wAX=G{S0H?gbQ0Tlw*|XP{otX35R9iX@?yP^1HQ(lr`fIX&Ive^l#q2#aC#8Cv
zLv<UfY2>j!vbeVUzE~)knuaEA3%`c?=dd-bnb^6AE}^+4>@XMBESmNJEh)gNrh7g*
zIaQLKN&gXG`JXk@*qtG*uUjVAiF+NmncEN5aEX$9Uaj%}1XSDjM0Uu+t#fwzh%Djb
zjxWNvG8^^s>ZwlXvt9FDZS6{!oMKOw9pt4pTgD;t^QY$EtS_^1T;STSLPdfYzRboq
z#w{p&DiHEJd)rTTkeu4z+o0k;6g<l(Y6ID%#HH~-?`l{)kRrsz^<tW&+nCGI9jjr`
zoPpzPhXn(VJb65#fK}J4b(%2KUB<mYo`8XG8q58?z#|v@>^gZ7KOR5!k|_rp7<V5o
z%&c?5LHnav(p2tG@qdIcK?xTW;NzrSjyMa}h~VdrO*wYVTZ)ndKboxn<}HP~bb*&H
zU;Tep#H3o={udpI5R@buex@r=&lIOSas%yzEIVfQBfkDvPnpeY@OdkJ{$g*0=&!V2
z`6=r}pxi*TpF!))y7B+E98K7XQtpJkc3gw@0c@#__;}}|!YWkrg-2f+G<qKQ!GgT9
z-#mUldP8)zgafZOyj@minQR|%JxLGOuzMpF>u-!#RO+X#SSSkg_<C87b+m)zdc_#d
zLfGwodw*bJ5tPO{iRzW?RXz|9ST^3pf|5OQq2lMRbz}d7iT7st;A<XrD|u_mxQ`&c
zf}Qx{+C&(Wb4F%d2bZn+WMLn&Cjpx9==)x68++YCE>xE(!B@ZEm9JgfcoaO8QVK|X
zTaK-+e*leA^*{oE8lzyIx#?1rD8ExxOoH}W;smdfgfOpYI#qZ!=C9NGfWQ<rxQgD=
z^X@S^x}S1J4OC}B`NwMF4W~>8gQk2D4mfK^XU{ma+W({G$GS81?oWGNFapmPE5AcE
zXC~(2&V+c?U;h+5D7fT2uUAqmLbi*E<3=|#`yc1T?=$Xh%!gp9TJ6K!M{!9hxsn9(
zC^*3$#CopC!R+s$xiNW8XEI-N!}S`PE^u|np)}DyNn}U=P2vaN<(|nY;vz>#!(KNt
zvw&Om4#ey?#PyrsXv92Sh{A*2*##Ya;C;5Ekw(#^B+f|u22*S^+tfr_9D}%gL8a2y
z*jys?HMWjAhCAKHW@WK84Q*Q$Z$t=Kz#L0)vFEh1m7Xm>1L4mXeNKa(M*>{xhaLiQ
zWw`Tb!n1t{6%F44wqD3o;aGfloz~{yW((PezGBE{`gcSYdbd%DV70(a@!6P1BPO#2
z28_}uw=7PnH>`X!=9y5*4Z5h$*!Lrq<)G&B1y>+!@*83v43tU<+37!CF?Yflv4&|h
zkh5*To3WT4BcIOJPin5$JUAq{V2wD2fJWa9e$LzQXga3Dt2hzwr*^Au*4TeTo;Bgf
zBqr!gZmioqkXW%qt+L)ZqIuCd%Ix~EmEpMmgHPU5j&pZnJu)Pp0fG8-d|y~(&ZFu_
z&B0Afd|YH3OFwdsR0(T}Ju>7bO=E|x{#0o$$WXNAJjw#kC<eB)-u%+Dh+#%sORiO^
zqXMkD^c|oI2jxAtndm~g3FAe3?m$!*osRC_-aT^v=Wey>w{dON3rw&_Iw?Bw!JFd_
zC!_?l^l>^bYdU?O%{0U@ol&3&i=))h%M1or4VYUtAxdDUu>MW+<|O$V3(*RvH(iuj
z!@SXz>GW9_FF-vzG*lB>kZpM$@a@J;h}$r+%626z@I|3OE|k4eQ8^Jz+@32z8#ssE
z=Bp_2v;b%OKV1`2Mza(b+e+v=Uac;rt#|9O<(Ivpf*iJpc3Hj-=F>&`N$oinN(JTp
zhNzxW4O0icn79ObowF^_Q<g_Fgz09nxEZ??!yYO7x>-rxNl_<Omp8V1-|?yTxMc+y
zDfo$QyOQMFSxR`5xE3IFhrurAYz0&Y)+x1rf2>+UUe1fr7tP12HVa+8@Sw7;`;=DH
zP4w8{KaG>g=a~-NbtCh<fwhE2sj~3Vlc%l<IVwyc)^_g*-44{)e=_P>^)8ZwmZ)Ge
zA>Fi3%|D)6b;%tHO(O+sWZf9)Q*IneeMD7Qgw>+?P^O%Rib^;UIGs{yE3v=apZ7Fb
zh~vtJLS^}eC@mdyovb`M4!l$EKMefrcB3%-+Ec$U4_)T;Jx6gK<S~?Q{!bGhDwPJ$
zjV_T*H3Yl#gmI&UkKI)w;?y*lsu^Qjdb9cQrhWQseqMs0(xJotHnOJ9JipVS26D>z
zj>^R>U=0<_gFP@W;-oU-ER;E1LJf{t$(N*h&QkR^I5W+l3qy@Uc25~%=f}*yA-6#i
zVkMX^fxUFdvXb@p@83g`FQ5JkHr6L{pb~jhut4%CwaTM6+=D(#OPwjTCLJQqJewqn
zrQMVvKeCL?-rP#b9Dy97>jNwxm!VG4=~P+Ch;n|xrUI>Zpai^a7BFFS<;>E|OWAWr
za`B5ZLO0^`tz4hHKsvdCp+e<9u+oLyVf9Zq{~OT4sguWfy&3e3Aj(I&5}~)!>@QrO
zXn*EpN9#!<1?>~QN^t&ul`fxd8mp09%P*LgQrqb3Q1v_22gU4WA>;k`B>8L>_7(&g
zk+g1rtYrp)jpZ1tPHPf0^N2vwOB{PCX?IZ_jz9yA3Vn`v_8)lpD=z}!ehfpTR>mNt
zn92whL=TqRv4Z$;ZivK*L~fOA#lOr{uSkPJtkuk2czD&|-}(*J;aFq!W`cr0kA-S+
z&2mX9%9Vivvw}BRnx%Ng-<^eXG5v^KN#+fnJ%&ZIdfVjUfh`W$s?wke;gwbS*$R&T
zZ7GI8nMnnbDh!NRHcT2FY!->sDjBB;i`LzVJqUKO+`fKig_%)islx(26<@EZu<=9$
z-g81rY$u`ey9t!t^giObUF|XNviAYGc@MwpsDBT|KWM2Rbu?7?uVp>c9XDU!_Rpt=
z>w@IwNK?IsAJj~nTlVyKz2J})Jc+fE?9-ye{=YbT?{K=Jrg3!Zy#&#Ta`Y0NqxW8;
zw?ss5;b=kB=n>`U9D*nzh~A?lqUR936Aq$A4?)~5@B4kfKkmKHeV+T@+H1|4nKiT4
z?7jAyHB$t%jcamd7HDgvNOF09v66@t!Bpz7#JpJl&0kZ$o+9()5GCyG*VlhleRF&{
zKfU*4zICaSXmFQ2SqFt_=GEW0x9@p8B{BcULfYi`huS*la|iUTIut_~Rth;&Qlne^
zU$qRY*8{D2QUH}QxJ{X^<NEW!9LC48<#zyBk%Idp^WoB;p>~6fv519M3{M4cKV-+3
zTXdVkmI<r6&5pVL_;{7LJ7NHjbihJP<(5^8Uj?-|a{nLxlu!S`A3xBV%>xjyaL7Ym
z&tWbl@wFv{yt`qnyPZO3;pq0R%hDMO9=>iENr?;XJE8)r$)IQ~=HH^XgMH@M7I%UV
z6gY-(qGJ(2;kA>f>N<f@{(;|CYLZJlP9IoV-ge`bSDncvJH2A9C#Sp7EeVdyWb5E_
z^BOXCFJ-%xHf#Xz&`#3T?W=>sqrU^E+C=gw%<ioSLDf?BB(xXO&sl(%{7(P-&^h-3
z1561;jcIePcSFrM$IXeq${#!A26`6W`5sa!+lzs2h@dxN3qg@kF6dYYx*jfK8DxWw
zjd$q<4kF%<unXSBW&->bj2u8GL@<_9SUVGUJ?OD`=hJ9Snmz~mAl`W?{{Ke{rwGH*
z{tqz-7eJEr9~Lwo0gM7F{}}6k%rFK37|f(nw-uSK7s=D;nCu#865SLaRdxISry^&b
zIRtYRFa((`@Nq~X4a4|HU_4tz5g`7DRnw6Y@4qPiUx5{b0REag7Bn5P2Z5GuN|4_B
zG>0VIFaozbel?xXWB)&^6EVZc6Yik(T&HmXi5}{s*?Mm}ARNrcrgp3IZtAEQqMjjY
z_}O-E6&S%%a+$vX<T33r!&+_Bb?6=du>}c$02Lva2=A{vw)<T*QSJ8H_5WkJeC>}s
zuJ0X8YA$;sNu<6SkVPQY>b!RGztt=rB*kNQ)%+0?U$(<3{@?=MJu04o5;Dn(572D*
zFNT?+PcH4lP4Mx+1|67%gfyuVGuY6gp^G;<EKeK-qF-f}WI!+s6sCZjNh_B{sOd7r
zs-kyW9m4ZcWu%(W09*y2<#zf>rw6d<fvHq*h^~ec@g4#W@9iG_>L4D(Km+6e`^WYc
zN}eq5j$hLJK=6+l#lJE<-;bDB*+3HnVc+K38pN~vSEfAL-dvQYthxig_tpgjr~X$?
zed~}&lq7TI=uC~fF7a2I!B)AP&$v2`xT?uzM=rh~7Hvt{s<$NKVfR-J$=KNY_vl|v
z5c5Gw&uBFposVx4m|K~DFUtI0PZn^~0)TlemX;T}Pp(}7jW11@K!G(=1bU7oL>Gwr
z()sT(9zVzQ+6jE@12Y?0xBsjrsC#OW`TT1lYt|*(Rw5Y20EQ00SNOUA@IKw|YhubP
zHBv0;tB$1kl}z~O7N>oosx)$mz5xpWS9_S82bEeIXP3p)eaBuU>(u1LO}1$JZv>kO
z6&4OceuFJoRhsH-*KprTZOB9%()VCak$<y}nDL;MjI%T9NnH{aQ_vk6=f>A{GO)lz
zW$O<+d6<kHrYw#Yb1aV6aIcKhe(|!*ts|DQ%&39#R28HmDfAituA1>^X7^2qiY+HO
zG+dhn<e1%=%J&)X)?ld@+nR*LiNEVq=VJv|q8m6fGWy8A!2TtRBdtV5Ms`DH!phbA
zbD8a}^v44=Rsvz}m5&B9PamawS@bd7gId0V*73UrlpEum+8M6g7`)3=?n^QjuLcH)
zSvWf9+Gl1;3k3*QN6DEr3|v!HE#luzmrk3Mxw*tNl41Eq5dM;kA^%G7lcJa4@nLmA
z0q%~R+j#M&kHbFueYdN>vXgC%zhY9wr2^D-St3bY-}18`L*CR=3OwWOZcZhTa)$>c
zzH2>jeuH{59M<~xQ{t+yICs+Y_s5wt_c#UoI^>as&mKJKuGAfEg;0f!+qo+po>!Dl
z2R8OI%?uWe{;=&V{N<wJo}KNfRl)bF^aJzk{DFO=eH~+H?%Az3w}*;9&zKeW9y`>%
z$p5NwoG?_pNd2_-8xi(uf#jHluu`>U#VGYR4xV4r?kCs#S<5y`YW;ZYII;^*<lxfY
zH2SXlD2kUlSL$WcF{%423b=y&5Ewplq}hqj5yCDS<;Org@qMlW3%j#OVMTIW0)Jwm
zIA-_f4u7yQ16s_=5P6|3fxnX;Gc3b7EBpPQB>|^dY;!)YMp=OxEZf*D<8k(Fhx81t
zNXtj=G$Mq2R@N^p+-W8(M|706DP#_QS`5#{{;{J9Y+g}<V{e-Xz}1VIt$(b}V%Zlo
zHlNbud~xcxRQ~L-l<}5VR~a@odXierKfXvx^l2u-!WF~tJ4ca4p4<aiy9;w)w6ZQU
zDE~bE%3q6y@~#<+W%RijbyO{_<BIuKV?SCD%_G`;lAAO2TnE3byth1w<9QkgaNTiP
z%YsNZ{X&kr^sWdFBRV2mOa`UD@|9X;i5q#OJmgJYENMt!^w4OuPZn=*@-3VfDitUA
z8aX_(6C8fr@DY&uX%g7<WP07f2DxgQQ1my>J5k!;a}wbPKKlY5l_XsT;#MEp5<XN+
z<Wd{ouO#0}-~0(l#Y+L0tn<@EpGgY^@QTg7skYu`s8LR3;+Gu@=E#2o2^F>;Nj5x`
zL3zl?itY1G4xKQ+RCA(8e-`5VH2({Q(ev!@?JQd{Kljpa{penbo4a#0+VO3T2?|SN
zJ!masY}P(4YD$1dPFnuWdQ4lf(9l>t(25~HE!>d5Z+g_Ck525)^^79rC+maAz*VLI
zWtyLdSj7A9etdJ(TTNP$<boBchZwUu>dRb&XjYXdBY$p?k<erAyk237q$VeWMv*F3
zqGumk#iP5%r3l}TN=So^Jm}y|soyf!ZDtHE_vZLULz|<Fd^)WQo=93Kq7p1sH53ho
zA^97p7j;*>Amdsx3!{NrLbr9XDau78Jb|ilqNA;m12jax4Lvkxt4w_}PK-{QK1@GI
z@s3BezUG4VbM;;%4{$}2+Ez_`WoYBOpl_Qi9=H_k_|v3@y3XK<Vb>l>G5i^i5S2+p
zs9fwsD<glL&u%DOXu~y9gk7>3Q@oG-hf!;d2m>X~Cp$|a>0byZVbn5KDk$l`JOlZ~
z6bk5vz!woMO|~cmFXgcNSD{LR>*9AjFvhs$ipIYQh>J(|tE7>n&lFk8NEKrnb*T(1
zxAPnsBoX^T8Qu=&C|&_EV<tm0D9q)MAz>7TR7cUP=G7xg)j)`VjQV_1=#<oS={Lf+
zHc<m}A8$^x%fMT2l&U(ny)@P*v`)PkRmw=G=H)>hy!NwWscDZ%x|bi7-XJuaj9kBJ
zh*XyZ%P5S8liEtFqbL+{Yp1tA7v5lWem$?<CIU=YrNv|-R(+{n3O6L>&@`j3!f&Cz
zPaoF?NYrJ%=KpB3F)-MEC}ZDPGCkjI9{bc*)%5f7JB2<z@_$o%iwD5+`?%p|Eko~2
zJE|-6CY{A`^n7?~+59OnZR=}EjiN5;vLEf*rD@@b`NP{|Ercr~xn`Z<L6V0I{rpDt
zTZgKsbe1v;SEYurjPm!>b7v0#yBT3D=H3_oqq3-wM6I&$Oq4i<lQa(HfXLYDE@d*9
zhvn;8Pq3m)C-CTWrKtPLo?)0C(n|A*rA!b@n?_ry$~R+zE%qra<vjB|!!5#V_&=89
z#pll(ghYI;_L?-un1RYj#(n>s9ukY~lmvuX6c37v;4Am<=E-nmpkE<Y41%}Wf7j@<
z$EOhy=$vBG0L@HWuLb3%pV0lOz<vwj7Y7<B3R}1T2z7J2Nd7oXp>yV`!$6LL5D~AC
zbdaKh3#5#+L$0~^T<});LQg#AWjHudS8Asp3TU*oR$E-Dw>J2-*a_P?#4WxHPq1WP
zgu4t&vk9j{?pV}LdaWLd_&(MCY2l;7yrb$-Tr$MuOAh<K2E4pFeELJz+hKF>K|PK3
zGa(|~9Q8y5QE9<6Oub$833tWR-;-M9g9qRH-{CX=v&+=oK1Us@D`$Qpr|M!4kJ&u`
zSW!bKo@+2l5EpdjqOut}9MJ0R_D2U=i7JQ?`^w0Pz6N`Tgs6g-or~7Pi&`L~1Cyb3
z^=yN>!^(F?*_A+J-L0<*(wwj@%Gdju$KfPM1SqMn++GUVH2#y2M=c1c<(}60LrjrD
zbb9|)kVuSH+0XW@7R!giU1?w6rg#%~S~tC_wut!|B=B4+&%)dI&+)s<_q6{tV;DL7
z++Se?%<e1%D*0;q4>w6G^qkDhpQ3%bNU2A!$})C|D>UZL)M-eG<0Eg*h`Mjzyz{g0
z$<_85jPhMH#6;`&?=n=lU(3Syu!?W<<CI2YB{<6BuN1j8_XYxwxp($I)2n7eC7+8>
zo&w5dm&kJbq>=D=y;(6C+y!_WuH06E@trO}9V_Ar&TiX=gr!WR4nT7TLMMf?9<&c=
zkiF8!eYO^As)Z|f{T~_c`FQ222{3I2nzC%Evp`of3|!w(kzp8|F7I~V{vWvHZN<~y
zq4_!FLdlA;xB1tFWQw@V6H!04gti{~?axMoH{s9<7DFfad)!V}-*P?0kSN|oCCJNZ
zV1>1<is#m+@StNr?gKj!VU3ghMB!LP_<*m%=f^JV5#5;PAPkeOps$yl&8r%8dF$lb
z?3@-x0YwY60)AX`9hpU9Gv;bQpQ?DTl#(B3B-s>rPhgv2h~tmphsz60VnG+m!#|)Y
zK`24=7`xj69@xD1%t09by)srRjAb1M5RXJ{f$k!~3uxgTKYh`GWBrX`f{zFQ-=6eu
zG%#+U^{##>c}RjHOdJ!$vvtYu;)Q;z2|KdiB6b0=ZK-jO9T{kO9I?S0(3KzXLC$JQ
z7jb7HZ6IS5vxfYb+Hh--CK)gHZ!}80hag)-V(GiUt70k;8}5gp+w?G*HISY{l(oGV
zx;N^sns8!>0%uxX00d?bEBlI<aPKLILUWIY3axbDu9k~3BM8unqy}Lr>4r1=x=FdP
zL0R>U!0>His4?WN^`AKf9AY`(3f+m}4n<|M9f%^Ll205Ei@mE=`Sm*^^Rdk<UJyF@
z^d*cT$WI>=z`YBM=jxWWSp6!5OsIDh#}8K+1Qst*Grjb)c`X3~dm8)u`#!|k9Jp(6
zRLL~`6G~;qItC7RF`fR&;Ja{hG9plXPkSz>+NrbHC%^`TiKR_rP`Iz+U4wf$5#{w1
zR-c9_*uOu@!R)6|fnp`7iWYw1UT%MMwun2Z;5U5JEo8ANZXnmPq-M{uyQ~US8bGLv
zo@=IDInr3TJJ`}GAUoLf5#w_)9ViJs!jE-FGDK-{;vnL#ECz@A7_=8k0NADfr^rGF
zem7l2e?CJA6CtJo#mL&tuS(7kEzoC1Zwk+kN#F!G%vT}jvn0>H696iD8Y^H2y~=~>
zKjqdTvMN{FyRu`|@3tDDS!Kf#(QoMB1+fd5aizU|O-jy#JkeKoenr)L@r`bTevjZT
z42E~l{7-(fQOU~Cb`C_-+<7`>a_Ndu+#2+e0ATNA#3}wQcl{F*u<W@o!GGDV9U=wZ
zQv+R<6!tP{t~^1nm+n`-J*0)(8J8{~{cfLTb)1ok<}qDt&;g@<DS|<OV|9&@5bXcr
z{??zew^2o#c1EA=KT(1%42P#siDn(-`WPaiyygC>$sMw|wM7Z2D2Jiq0W$xlLF`_y
z7k&N%MvLT#>dCP0<0fOqLq%sKZ*Zc*^hxha$5N$4h?(gp$^4ntv7peN@JrQlY#Q~>
z`hq%^;ky(l*~0{x7_me^Im$Lsum&p{9yD--h87f#%Y)evj-}9_yf!S|k$2-!^is&Y
zk_`(5+o_%)Qo-wZc5JY}mgMFiF)ot+0T`HP9*KlGgU&UBcq%eJn{5O(l!JI-sK|(U
z{yHlxPwsyYw#WQkDu83^oevjh@gYmS46yZ|2iD^{T8uI9Y9L6Ah9vKj7tKirhCU_<
zoxy(fp})WAF%dSn5Al8$dfSeIP-Uk*wyVRik%g0oH4V0=jZdsUGYI0UYS2JR-TN{*
zwDjG{5C(hn+xB4$5kLTrG7W)fW?5F(bpVfd?F~KwRLEx10t}=E@_mI`?fVd8m0VQE
z^6pYH0%i9XwQF*Wj_R2K0dyf-$g+i?-7j5(bI)_q-z_|}Y#+^B8#rA4xiy@1y#Dp$
z%EAoGG^2>l{i90fgZ}i=R%mCyr?Y+U2{~41rrhKQE{16wXW!tf8<-}^?jNsTyQxXf
z4RaPBB#yobygu3V=xzT4&K52I3Oz6I#^c`_|BqdKU%^uuxd(8T+)7t21D^URD@~pO
z)tx#Uey_Rr?QokoEwwe;->c!cdc6-;pZMywYnc}F1l2J9nt3IAk%vN*1#eyLbA=5!
zX(PQgq7Lpm@J%P4aVA?cWc-xHif^`hSgU31y6?5!H1Uyw?mgf1rYWb-3;+7B^J#i3
zKC4K|H?t&b#M1FmTU;6+_O6DcMC4&>BrJkI?aC?dRzz%aABx_1C1yWwyyp)pp+8M#
zA=}75GuX6ttC>op-+HU*)bFZ<(YcG@)uS1U@;wXK<CJqoE-lksj<Qc(U(T`K%<|Je
zWZN69hkM^(1pP*iLO4Pml5MMbqJvWhFc?op1Z=0}bzU@Cy+z!29$9AnbRMuBo@Z7f
zTJ7!otuEq|$-IYN?<Cbk`G_5ixI)~1YuT?fmt(8eBYmH43e{11$araM4Li2f6`43c
z>yIV>$8|<DY6MbZI@3<(XAN_Rr}eXOTY$-#dby3}%RDTR%4eJCx{NZa%S%m@*&aky
zs_XS7ZMlo<AxoxK8OGFKkp8TjNk%xS<l->(;N2MlWo;!`pk)&JSEQ2fV1Rtvn#QXf
zp-md4RtCW+%_ia3#a?=Bj<>{72f8A9$ingl`r3zFhs(=qup&|S#jpekG|*0j4S+{_
zWzE)Rtj;a`P=75)8E3OvST*;7bGG65(5IE5M%1;4>yWcvWtT}B{IYZ~yFv>IN2lA5
zf26D4rVX=AoEO93(!Dc9Oo6oU`YxlP1&)VWS`P3D^EdPSr*>Tz@jggD7caH>30*BJ
z_a`6Ow+<@d<4f9nP=b<2F)~IeP0eYDn<Y5=9@YGV5T_X<m|qIQse%tD50e7`*A1xx
z(I8D`-~LE!6UgCP%?<6HytI>N<z&C(?o}No2yVZZ5i!$2ejMM_8UJGeYt#GU9AqWA
z^a%FH;rWrZr~}TPWhM{9kARCYr6<Ix2&dAU(xm^6UEM_;x%1lgVJDn%nqzD1ecFEd
zW41lvr&OA?{G(=YV}JWyNsX-=>m47Ly!bNL!R%Dx=WX2k0}W{iyfOm6&HU5q^H?9x
z>SdAJ;kZ_(c6!KvWa{LijqIH$E>@Ybev4p~tT)=}g!|k0zAa9&uJ$ih8;`aeZ-4Re
zPsVI+iFlL??9Yw8#wW+jmJ(El;NzO#AJZC6Gxo_}m9JXOr*S|9IoypS|I6@u7j4~k
zPuo?yzBfX0Ygl@cA}P0LFtyL2<8q{LgKN!MqX)}p58u1M%ZOd)GxAFy9GgYKMaip}
z0B96hc+`<y>A9Pm7J5jzA7=_#@1mBYO8+|xu3}If&^M^w&~CJ-%FCx2F8`Sv1n`rU
zZ~m}E*`V`eRByzyAjU4Vx&N6tmdS%OEYRA++5AzIUGIOUB;z37NFGL=j9jzr|EamQ
zTS<IGhiwbQx*Xm7T3l76`>BV)@9VH46{bLnMb!7vtV~J<w_tb$A`e>YTlKGv8Cv+@
z>*rKM_r=#gKHr`#J^}|&pbPu_yo$qAW(bM}PPcIghkO5H>;h#NI;Ohr=R_hR`55q4
zcQV3<fn>Mu?`kHGWH<$QXWSD)c<pntDxG=~c#ejff&kFeK6hW;-|~mK=z^<V-?&AW
zK=`o6(kq0mEb4B;nMdt52w;>o-)dh{4mz`j5rRncB|sp7`Xw#UKNqC^2fyF>DYipX
z(BLHk(Ybl|64QxV0DB3;Cp5ve9)|erZjlw9f-ph98wQ2P#Am_;R0G-FD9Su@DIIJe
zW$7TTmo@&s<TSo|QTM-96EB#<swoJECv)1P(LkImxUzBrnscoa>aK1=o`0COpRzd9
z3ia5MFG@*ooZ4GBflYi=^O2H!*)$p?RS}!jAGtIc8AQqn6(wU~xl7n}l2wzN<4J<f
zR;H?`tPCc(vSlRqGbBGyC_qiB$NozmHuCJVhs$Rf+`o@u2Llem*P=+brj#z`lyB@8
zDnhxww-V4Y-MsPt0k-{LU`8C1!<pW2?7z{@Ht;7c;j>Ap-pRd1m2$z7MJc2yw_#wa
zv0h+hN#`VC)zRhgGp?}^zFTt`_J9m6-n~nbyS!zDhf7O7ZWAuoW4rHoUQla=pSxa8
z4ALA2z6zCvy54}gh>g9&!t33>oni3&aD>&0F))0&(5=DO34gdPby<(3wq_ZJBtlcy
zyrL5fI2daERK@c#%uhV8b5dp4>17(#g19^T#6;4J!+@Lg0bAbD3(?naG0BfEq{aik
zZ*)q@&PRNpP23btx6fIDPKeJbS+2(g;~<{EC#Zh8(q06>IuqWYH9y;`DJ}G!jar+V
zfVpo)?SDOS2YzIoZKywh+66059ZV0Nr>U_rvy?spvzt*DbmXcABix9*K6S<9EiJy0
zW+is~xlT^J)(-#2rv?&Fj@L1-esFs)(*6n$j)v*?xz1aOy8DmnW3)emjq>q?s)COK
zYs4neb9waYHf4Fwln}p`aNWKWK{%`cnDAFrWz)xYKmLOriw80I4Tw3Xw;!&YTMwE?
zWr2`mX|e{<bt`z&k;R+HSn9hxa<*Z|9)Vq54DTKANwQG*NqGpU198zueS+bN@S;ze
zx|iZc>|5Lg`xkVpf8AjDQS0Y}6}Fbxt49b;G|vVZGn@o3;X{NKaZ3a}j!6dm1R%AX
zYGAB}1c`kPtz#oJ%{lP_e1qm022w=B-r2L`tP-d0hRyKo&(T;K!Qj@Skl*RZx9sf$
zzrmP`U)Y2`o!t-2Y9$NOYjTAs_v+tGQ;d=QQo3hRysg*+!Y%V554;G4d}@C%x#?xz
zpZa`U(b{_13OCGXh({4_jO9Y;!d`GeIl{GyXOaLKpmcCao@qejFudmtHS<hQ$d7wA
z9cc*haA}&*(}8c)R<G>D1-?Mvgm-JehXD@DFpT1djvQMiHd%IdU=+)(o_oh;M=vqW
z-m+tyz`f7?p=tvzI7K%>vM&G2T$=v`B6UgU`96xCqbz<9FVtFi+cE~<HD4yk&uoxO
z%R&X#7XHIcObSgIJZGg_P^%*N9VSc&=pN(gtJN_YzzC`7<xd4@Td0f+AHgWY4rj&0
z`HW}N8z-N!c4fT0Vj+Z>MFm>W?x9{{cCfbEejnUMKZk!ihf-sWsWCobf_~9}9a{8$
zOd-AZ49qR3OsK!ClT?TI{s--1ajJ7>9>RZ@zo7FqUyZc6!Q=-V*NqkZ6vR!IZ&Mn=
zJxg>50Id&a@(8nLU--5Lf`@4X3_MqYBIJ+Z^M5VFsRcuJpAb{933Wfs2-##fKv#s2
zzt%x&L-~TWzrf<%teK$Sw~N_MLzKY@9{oQPeA=tQKU&&f;LE4nITwyCTyj^W*s&~c
zxq8`+NeL<~R7nN48D6fVqEmue#OhBO$NG&vxJ|@b_aKV7#RDy9=r&VWX33m@=@%9(
zSF?lH^^}#~l~xW1qDZ_~v=Nzy;Oce_J+V!l5Pt|IuTdU|@I6PDkdTA^*g-HdZBK*7
zsPHa}AvWi)Qt?Gpwg>UAr#vtX{$b&WrySXHWnE@Gw*~PiZ4ASPmJjWqPH?F?wm*~;
z^Cu(rl-*_c$CJdAQ=h<v<!ZAD8L%8A9(4@$xrlFBVdy4H6F_F8Yf@1fQqbp=AJQ{J
zIG}TAtE`vw)+lQx(OkrbFp?PJjY@A4^E-72|5JxRb8evIj=|vf@05M;vKxz#90Vd_
z>ygkLrgj?qMAEb8TlnM3O~t_gK;Atz31~Rff-S2yyxzmHXvLFQ`r^h{?THjFcw6ZQ
z;5A`96t>!^ORcAAks9#nf6lgb#$DW@U9BV>u(leNvvxLRH@dsX4<p0vdN)(}zZ}^7
z*D8C$I^Ur4Q+b{VU!Bh{*wXiTUHd!3nuQdlEW9SF-wVZvWu;5lCD<5yneoO7^u;Qv
zvg_8yaxjwqAbRm(eom&*$9aI;@9ZjIY;ikv^X>IV)SpuKb4S=bmIe)%*@O^uJ=|w>
zub`O20QQ)0^2$R?F&m9wMSU=Y%YP(fj`gfF*eJ`rf2fASuAwLe(WEtQQgxAw8i_U$
zte17}qg{Kte)H1F;j7(uw_~@dJvLUfZZ7bfg#NH5H&6m&@&~HHodknRFt4r^)j(v)
z7Hsy6FLk0?YjmVyJVi4N(d3}+EAHZuk;ADT=!48TV=HFObtt}9|6+Yz_8?95Tb&-k
z_-b*(dQz(JO}U*3l$Fb?e*{TXO83iEZLsqF-&!z|%sEy9CQt@$A3VFic<lTj)Zc;&
zlj&CTS=Of}X`MUjP6w`uITmoZ6BGw_#!zzoHsXcnxt(dadmLDW5PN95nEbkY`fYaw
zk!&>kVqGUc1y}`lmjLmLSsfV!+KxKRZ@G3UMbLHqBRn+)82k2r2#pEpAje;z@Ih!6
zC>lWQZ%E=8mf;K&Gyj9?I9sD+MJvb!ze`rbVwNVq&@kO~zx!UhKKdscWtb4z-8v4F
zWAwZ0sE%YKe+B_06u>Hse^uR&Uh}=$M0B-kW8Qtgtft3ofOy8n+@E-ZH<j+X2AvFV
zqqSFNMDv!aZ1m3%Cu|n;eQmpX<Zv}VOs>)RHR26r+rBIE=cW+pKX^->!Bz`C_hp$X
z6^8kMke|AuV}EPQ%gaqQUqWAo+-%`BevJmZR6%n+_-|4FzAj(7=pu7VQpHox%4{qA
zG{%)h|Nb@KKW&>Q*O~FNDmPkjDNG>JdUcjbZizm%6wJ#-^Iir{Ugcxztty_m_7BCP
zizd?T+SX}iha~;A{X&T~<pEdveoyk(Nyl=Z#i1JurUAB+_Hw4|c}raeUMHX1iM90w
zmn2lL9O>2QyLt?9aaAw$L-1z-fRh6krk^%`UG7BJI3DOTSIs5<7O_Ge*RZ6&wCi2p
zC*o-Tr?hkKBF1?8=8t5}pYem_2h(eE_jVQ00l7QHt^U4+ZC24o&ZVTJRBo(iq~xm&
zLg!z|R<zycajcrq6aLZS1L4^A&@AKr^k~JaQ4MA*XvFbvTNYB|FJ1+P1*MLDE3Q})
zt1L9mujq)tz0WsKQmL#>ipNr-Ur}pj-I5#&dLphV-iDDHUH3agL^WLg)Gj<7S-2YF
z&qX*2qc$<oyWgasUUHW+uGF>UnD3df_>#a+a*P_A5;dDmS9ZI6wK{#-ZW7Zm2IC&p
zB<Q}OSt$d+Egdkb3O}*c+HEQ~zG7~q+nJZLu1ezAS{hcO8(M5P`riGTPh-F3Y&)s=
zvD#%a0(WV06(Wgd13D7{0&LLz=f5&PeRo|5Xn%fXA=q9wO|OT{eB*b%<7&f|pM-tN
z8B+RD>OrGrP*F+D@K88P`H&skQ9}+37>ey@1ryD~5x<{j$t0$Qlv{f9z-h&wcU8<^
zS3a1op4C@*oBQogUi@irebc%0_>sD9q=z3q>^mL+21f$Iwp%kGLk)~08aw;J`fa37
zO!>Xr%cJ2&cPyGEF5j_82C|qeJQHH!-u{dZ|G{8fI2jHH*dN>rw}`UN{0W-ch}Hym
z5Sok#We#8rTncG1Rwp%RJ)9H>5e|SJyw(IPZsf!e;VK>7AzT#z5w7yjNW017qF=Pb
z>*dhER3?rrK-$}X{lHi#ccQtI>2qlm*iQm2sYk>6-%o<h7XzXsboyyqBLur5fIcp`
zxDPX38N~{y%c$<C-z~&9|FEdMAki)Q_Xoi9i&AyKY}hqY$rC$R2*`>85mO8`Vp-CM
z{!-L>w8DZFfr=dd&>On9xTKZ(l)C?D#lD8iexgdm$~9~5_4{;pC{c_U3CyAVB4YgW
z<Ki*&ud(=g$L|H=BzG!98^xN+MUUpPM{i!rNi7Zr=UE+YbOz|2Zv(5Gpo9t%t%fJT
z<l5oj4(oE2_6YTs6!@zKO2lQ$;&+~#N42$KQZzy9qUQ(6hp6$_qN(-C+ngL*Utc=B
zSBig`GlH5ao_+24x4eTHYS0{pbtm^ddEl_?#%iJNj8bYTr_M>*-S6=U<-xF76$-)E
zL6i>n7CCw0&yr&#dF6Ye#$TNMBzK_|D?kxeq>Dxf%0zyw77eZYwxadC?@x&=oksQ>
z9@|euM;9%RHc<56GAlDu>rZ}vu!Z@liWlpLm~G$RMzG}+$&C9o89z=&yjKh3K|U$N
zy0ew~QZuSm_AuCJe%W5(q$@_QU+C?L!nzJy91ONISoOA@a<_&}gXdmJW(gymmHWcn
z+;%ftfC8+DxToVz#h8q{B*68@99N8Und6!IG*K-QHzs=}xX1KMGoEkaq4l#y+oPkF
znxh$TZ)R!zv@{L~M+d&2Cs`hVp0I%f{5Ad7NVJ=IkVjJq*=lGM<u(X0Yag?!v(iDH
z9|pZ{T*xUdBuPhIlM!nS5;0D%6(2kQ>-&1@KYjB(uXpBnGG$HRhL&O{$6%zL=Sf!l
zf3ZIdmaEifvFoxmxIIfp?Trw}*n0bK5Dnrz#03*-$cNQl0^PUg-c`<{duL4*gV0YN
z({E+ho>qz235qVps(qO;*3f;^*eje;464nG7yhjZ@6JSotwOV0t$+Eb*J<@TYyV7+
z_6Sgdt(jTf@f49*1@g3|Sluds@mJxl=I#wbU^O60<<QNH$?@$!>#Zls%KIrcz5M?G
z@caXi#|ogFEdOVPI9iik`A2|s&mIjQbO7w7>%}}{m4&AAUlf09Tydb_VF4*7-{oy6
zJ5vj@9Wm}`%MO+oak~ebeD3w*VL3FC2t%K{Fl@4yJsF|s{QrGY75+54M$%3FXhZ@0
zT`d3M(2gMVCBLj^{J#x7ujG8ugM6tTF3G#bW|{@}Ry}0uDn;Ji?t7U~Y!$USiI5k^
zATH=j5LrD18{|dTiGbk<BKj4p{w|;({ReiKeb34^-!H()z6s67{E#hl5{#M+G~)ui
z1b*xiDjZ|qY%sd3ZSq#H`95O2e^!lVV_Ku>*}1O75$1{s_+ho*LlluqglA)iS@+=g
zT*h^|qFJ#5H9WIX%N@1e55-A8>k|Uaw-r>(kr%iri*AC35hSBWY-?zppA|6VO%Fd8
z1+uaLqqu+%*1bwDF0{962($w37Y>?4phulQCQuOpb(q9-Mth+K6mU~-rOrExar~$L
z9=0P;h<39m2pD1cV>{xuqU|FD0TC=yY)9fbv<>>ZvLm)5<t5q_LI5nQVLQ^_L%%DZ
zVoJRdhEs>z5>o?#5pQ~EwBYi5zX^a}RwmevLeA(_R^ZVSCc!J=Jo#|lL@K}{;%$$)
z{Cjz}G6H}t+<j7^MxHhEwOqTOCk7Mg7kT~D6%jtBqro9zHdF+Z0O(*Ddrdn+iA!b$
zyih8jFwT0I%-RHa)_z^~p%x@%Z3H|&sQe%lbSO{(57oSi@Jg~ol3D5jeTEuQu2P~j
zbzZ>Ap;lB7o7e|Dne)L332^&{lW$~`-h$pON*Pt(KO2tgj1~4dt_n{mkV(-DZS~oG
z3%!(W47@sgAc{Oncn}S^I~trqvz$PYh5-s#0;5&;EmJq>!%k2X-D07Ww(jHfSf2S=
z=+vdqf(;auXjOs>t44E0`Nzw-dJMWS1tx4;``eOXYUWzg7x%W6v8d4M<~YMN)I3?9
zyGY=Zep5x;`<{!7<UT-$2pa&;UV0Wu>`D-kj>x0&e$RcMqg##9Tc-yIpRz~PXJVk$
z?vZy#yc0J-EG^=y+vfp0+d91?=#J<@G#p_bf#f}{pG(5D`|t`f7M1(7kCwyLOYsU)
zmzBTlc|BxGwnzWz4dU@mO+^bJB$uaZja#$YbdhIl#~f4gV6onW(kJ)vqWK(tiu35%
z50f{+TMdS#j#6>tkmK5?1NrRYtq=lY++HEg>%FC2*9m=U?8D!41sb(Z4^z{|AGMdd
z@K|uWDDZH<z~G86nBWfajCg_em7wT_lH7&Dz0-&1J{+QsyR>35HIU$t2W^5G!UNv4
z%Q3uk_t@^6iF0*ZB6`O)lxx4blHD^PtMwSi#KEvlP^Ng^0l9aNjc|cI`^D?jS!f*R
z@(UI4XBF}CVlmOIVXQfs!QxT8vN9M)HCH66St5>{@g5uSP+eKsE3>re(#4!J9}xH=
zGWVUQuZBZexe~k@p%ErB^{p0vIj(%`0Yy9-=ZJv4-(i~|Y&m{AFq0w8sk+1=ftf`!
z8?)gX`M{92)@<q=>h&y7^!>dIzJc=R?~>05(MBYHjR>>?v-6MzLNT1JoJjn)DI4s}
zIgVcW_OY>>M(FdKIWV6MH$79ND{!Fx)A@>(<9lAQFs8sshwv9n3>)WEH{n_P0?X8~
zFRb@H$ch3eZ{wn4o$`CnO`YlTsRZMiq)o+s#-yK2byl2nB?ZfN4yS2P(5V-!6FKM>
zm5j>|z@qh8kXLdJ17i1Z7P^ufIv(}X$w<!rxT)|XN6Pp-ajy%UAiu**0e%w2I_Z9G
za_4Ro0=5Dbihm_2LnFTzvV<L|KwB?LiEPfAV%vAMRQp!y=L1_~*KEx*k@$*KJl*>2
zi9hmMa1x1w0hgt|AAU$2&r#8tKV!aOUtbB$65V(YI7Dln1~g|fKlsxy9I{3K7&tsW
zm_PU#2djI*|KeRtPma|4=h*ELU5IJ?Ypm>+z3U7^q!UfpiMm~%#Uey``+om-khxAK
zq`UVXUzl{*4UPHdgH^v?+-Gtq%rup5=);3-8JZhv$Nm_YyeX3H+HJbwk33`HY-+b}
zV@@uKNhoiLFc6<8S@X2=?EW+30JLtj>x|?j!gr(X-v|d;uJTUGLwMFdsT0qw3L>|4
zZ1)P{Y21+IldcwqDAAXrFKK=70?%x994f!+D5gEGW$*;mr?9uMY4(Gv?LLhadLj*m
z?K>ZgAWIA`3|8Z3FBiF6F8%Zcg-Uo6gGw~4Y2ttI$KU@BYpcE}>s?BEzVm{j>vac)
z3B>BdkH;!gzEfFkrglG9W0I=~3mdRgww4^C70+kio=88~FMaXBb0>AkTvhDFJGvfP
zDb8oux!#htFQPf}o$RflUB1Pg1kmhSN^v}c_^$y&;FPR|r0G<VoGJ%|vu1nBPkN&+
z&Z0QCRwv&KIm-^WMpkCYvc5GpHSLU7S79iYwWi!aBPL8}Jk>Ky_nst$TD#+$50S<X
zvF~(P4kagTn>%)ScZRRryXQ~EPm?bELhzMnHM&C$)KP(u=E0P2`o#0?E>G!ae6qtD
zX%2YQezCFRZ1<YQI3m~!<(kTP%0jGj)$1L;>OWN7Na#@7^pAM8*pY93<w;q(qxnNP
z?D(n;9CUi)X%X`3)^1>khQP8N`BUt@oQDbYn@yR^T0%Rbe`aN)!x)!XXvTH}l0%M{
z{Q@My<m~SKjr9V<TIzb-A^jxg@3(KULzfe-E~2K<@_Hj}Ma1~Kn$~HF_2$;jd0)*I
z$m>#>3xpFSf8FLY^Eg_$ow=M4eo!c0!8c47CKM}zom1sld}12?5mZ+VdyG#kG<`>l
zPJ=cmHkOPh#olnYFfaT1gN}^2DtIyT`Fq6u$iVwdJoURnU|i16Qg*@h)pmcLSIfWo
zqDU!(Nm@+hyX+1Q=Q8G#RFFGM{5y$|PFXuLLR+%0`nU?@nUh#n`jeV`vB6|TSog)P
zmaV#RR4DG9k&vD~^kbi~w{<9j-%B05lrCmVyYd{}Ka}#3yc`XayXA3=Z9w+D*wDtw
zZNrbuOyHP){O(q9dnkJ+qpNab$hnDv^7$V@VM_?F=K}ql%E@b6|Mi`k*J^OuGNp}0
z{o_F8T49lg#{SW_)A;G$`UYb%{zti~i~HVcm%dT`@t@XKEdr|~bI0?0Vxo(`4AO}O
zMIW+$alU$N7j06!#_>t=<`bM7XU-^332w1FDMYNX!<|fgKJ?{v&!^GBEtp@LA@pSW
zm|2jg5sM*wWMp%tDDzuE-PhE@Q%YIqsGp5Ku12Q1CmuR3U!=<0WdgMKC7&15YJ4^O
zVg9^`E+5Cw)xtV1QLCTTkMq;CsP5iWGU`nuLp4Ih4)=P*bElIkyYT+|3xi)O%0XQf
zTTYM1iLm*MN#xy+lZ!v1&-E}yJH781%5Kgei8F8IJB8Reh-D5Lh#C4D=f7Ef#zsR)
zqg+_Mh~?sfA%)ZJ#kTTX=BbgO<d5MN>qFH!UwgL6`?HexH!{y$^U(c7bd^rHYxKmt
zqmzd~${%V7gJ<k&;~$(=qhYLWaBgmfnf(vh=~Tw-Cs6Ny<8BrjQF3rNvHkJu-M7O)
z`MsX^CY2al)h8yj9#KQB>oT6_b;VQ9x~G&DpR4BzWK|-r(o7srf~;v&IcTTyzE{~@
z`aOSHwW9%l(4odF$(t*x)F+JS{1s*Yvz`5xRldu=okUshe!KkM<+si1K_z%6z8#L`
z^Lzt=ql<#F4r8Q+EW}~Mq^NvgWbapt+uy>QrRAIm_q>cp^KMR!?P@(Pj=9HGi{li7
zkhUiaZB-qO+#fz=xNQ=K>+(Ebc|AI|iB_<J8@_L#zwP@WpeOo+eCMD0I$2E_p;g~9
z6fYfEh3i+m=6Ka}bK*(a4XDyWNwOCG9nJi@J1}>ZY?}}np2|YPJeb2)Vy|+sOpv~M
zMYoK}wZD)xr#6ox&X@%)9}P=#W@op$A$QQ|xYd1wR#Ja<VcYGu3-iilrP>6mk95|#
z_KN|p<OzZFn@rtwF*rf-R&X<V#*gqv8(VBZm(w;=!>39aQaYa#tg)Ui?9Hi*4kiy`
zotVSqqIGH(p525Sq>+Z1r!Gad<N?4vK1|Q?XR~&68mgFKk!0u>FJ>{NWU3j$j?zf=
zk(GlkkKRxKG;Elw<a{qy6bZvzIhh~(?XTj>Q%9rC`rej2#aNBXQy0!E_6T?P(V0&6
zM4X4+(kOP*wy_TnKof)H(Uowi^=9liocs4NlDkRK`y|`ga|fkA19k$DVO~Buo@EK^
zjLDxh(Kp9Ldji!M!$cfN$MmK!7Z}2UM>LA^e4{z51_W`yI9PsQO>V`(N+wUBg!{hG
zCE{D6HsFq!4}00hZnpOmA<v_P8(ZQM@hQ;=IMZfk`u1J4hm}J&sGA~#JXvuHb1EX&
z3iy(x*zNzfAWB*G0R#gV^#Z43%bXm38J|qZl){B6tuI;_k+f|)L##`UT||w)_BR*F
z+$`U%_Gu}bws;hu?g4d}PBrOaOgSE#W|SL{+i?~4pp+exi;A72W{3v8Q(FgjLNF2|
zYl9fAlznoWb{uWwm<3Z?C)N<!i$T44SKCm|xQ52-9Sazej~kI*0vQ23=pSWD+4sIH
zV`9#3l7b+S&Jc9;1TZ<y?Wkg=3ky@X#Hf0=n$FKh2AG&;TNEIKkux_7`kr_Es!LKi
z-c#DwvA~tIzz@v~c7(j|y?adEhU6Z$E}u1wsPCcS)3ZvvEx}V8w^zng_k43v^PEGR
z5JQj<C<F(0L`d4-l{`-~%i!6_LRg<JC;C*}k@g-M_pIVmIvw)FRX0{vdAxO8ze59Z
z<XQswKyZSa<DBEk8N<MgW-+T@m{N)t<Gpi_tRUL@e&EG>JSTEoys<v{2%jAG)nirl
zu*d9VcH<&w{^E-0yf#TuoH^_TI!usmZZQk>0vGgoOhxoPJNBO79QMOdb}U77AHlR$
zDjjlMTRSF*Jk<jK%XiM`;UmNz(aMj)D9#^W6gZ<7Zk{Cy^%$#0Vh%h*ix$@*R~J7C
zv|hr+)9=$p*CBr|aPrb>(TbWG4c$CmVFHtzT2Okn^&LfII8$sy+wRRMZ&7TB6QMyi
z(uhHxG{^S=9xWc}U@fL5dF<-@r*SK+1rQwaN>8JlZhUxZa{}hio*uAGeRc0?+yl-6
zb~SQN9i}iPTvjw8Xs*07Iv%}h%M@BRLbpJqFm-T+VO*PX7PkPZ;Dl~bl&AzB<Ooc{
zJS-*V)LAvwMpR^-46nFfBTGp}hvFX536j}7Tx@bybXN2(u_!XoNw}9ru4)VEeq-wT
z!%(dR&uS_@PFGY=Ra=*OFeAlDSKfO(ZVc0<U-?5$>>wViAla&}cf>em#7dT%2|05T
z^i8jN7)}m(hcp4ET$hUpISuqFL=|27h)S`6Lj+B<kUaqeAgtc$Jya*xCwE!<te$|W
zzBaDwtNHKo`(JU8cxOwex5L&*$g{h5GICT$uutgR#_fLe^rLoS+b6awzk^pk2EVrE
zdxodT#cfdpIa9nx6ep5G8b|eu@tO_U+MMN=kZrk}@N4VoL2I*rNbt|!KSBb*aOkP>
zB4|Jj-fZu01$9#Jzj}N)N5CNo+5{%h&2B@EC;zXqBHS_Xa3<Ud9q2^Q4by=D%nIrN
zz=e~4pioy(f(NLJ+K|`aMPf3e0oTEY2$B#&iyR;<4ND9y_aB08Ie_u9$6sCx{9TFt
za+Lk}V;2`^kW)ba@54W5$}^wll|9dP&_V~;BSLNi4WoENGdOZ%J3~LLbcyS@p)+5q
z@^poce<T>>>UuDQ2Xy=Ck`mkz5LuHFOEFa+`o@vrSWRk7p4w3tpS7Fs#?rf(4`*@w
zmh(f-p8P7iJ&RjpYxBvj*4$j7+5OniTmH>NXIpY-3OKXt^xj2z^6bdM+U<8LJICK=
zWblv?eO&-RkiSIZ(*fiy$exjRCK(NGNSP2mc3&g@{sBZeoaK0{x*`2#kpcXIVczaR
zoY19v)1R<#asU7luLJmw2^32SilwMgw48BvDPpcRZ5*G78Kk{^-uj{`j+oD4@=|Fi
zn}%5L#&-z%kbXIZT`WbF`O<mAjfxm2G!K@D4~Fdn011vTXLMjZXGbh0H2b~PYj}|I
z!^T46qtewQ)_y{k6!_H!mc1VD6Aehw!UexF%jm{oUB~M5RnbSWBLTASsk<q$#s=)V
zWS2I^>I{xm%3^{pflh?J1W_=_3#n!~71Si3$$7TSU=`KS(+VpWvYg?U5^1U3?AJ{U
zVw+*^;@*0jadgPJCZE4-P@;K`IwS5)pXH_n?OOkKznZ@T<Mu2xg>IV`ekQiL66p)K
z%B7Rv+}g|O+3iOq=cQa@zQblK@9)o)6zEKHLZ;uTD~Gb1x6;zV%Tmfb!eos|H2z-s
zlMJ1`riJXfnD?xc{bHse#mD;1Xc5R_B?<{r%FRlOnZJ>MC@kbZjEQQ>V|A|C-)@WY
z-Wy!x_5DV_uuS_9Vz{3@vc4ZR7-QGwo-2Zf2LK;Q(KYTAaGhk-9Ebb;{#(mWK^)7T
zXJT9rl_`(W(@c4NRa?)5<V`2J<Ce{*a}~+ryFB{a6^av&a(*fba*0>*pw+EarcaKb
zs5diSnPq8NJohL*Ya*=5BsHKsN%^#woGTCgiq}V5KTA_cbiS67?GP+S>GJ$q$V|!x
zlVBL<*sh=0q2ALYZF_L26cl<*lJ`Q`P|y5`%$|UvwD=5PW@-MNNCIc9TQ|_Y$IK>M
zl@3>A&a+j~!plk^6dC5a$;(vOvHWt!^l)r=WcZ`=V?!Tx-I_0dFqvtF7KgH;k+}P$
zmra7hwDtO~T~cOr4<TRX3jL3nzxT}l69N)11oJTtCUB8d?m^xhi@!MJ70+@M?&z;V
zw%j(6Ml-rYn*O+^SI~}p#kz<iu=Vuo2gRWl+7F24$FT?s)b?46E1M(vXY9t#AG%Af
z*V<lT6W_&B<{YuTZ)D5JW+QpUm$SZ)RBdJR){+aC3W_tzRq-`fTb3iVvSN_wjaIqI
zsg0EraJ4`I|M_}}6di%*E7I@)hZ2~_QxB0VWtq*1(&{uG$Je~SbfLNL&DG=a%Vz~g
z9z8Kqw@t6tRL{4=#DCtwS<?NCLp?lb6kXD9R}?~os^5w~IL;<#fcpmb9yy#l=P*Gy
zvYWF;Ho;_k!XHJvA=vFZg1;Aja5{D*Tg?kAcGf!v9y1$kXs0Fvz$$`?vH>)8Ow;VA
zf?d2jrwrfu?!C@cCJ7F(#bz@JDW|p1_qMD(l|1=nP_8X&66@Q`?KqkHbgeqQQU_7k
zQ$$LH2xR_V+_dQJnWh72%@m%^Qft$@F)vM$ozq*L-c8sZcsXl~)UeHX`b;vkHf~RQ
zU;2%%-$RZEf~D#7FsqHliH|FRM^@Is_fL4AQTqMr<lb;Aie?T_IUQ&tFKGYH9A}K=
zW=>JDGigT&E%A&$@qRZDs1ExulPp5I?xyAw+p5+8V{gs$7WFo*PtUv6np~eYd?gdG
zX7sE{@Rv`TaEQ9k!?Mie_pnk8&^pf|@vN9~03f-WeVOc2oa84B8WqWXPE%vy2kJKl
zIBZ(zJ`;h(6-Npm@!!55qk4zv<jjq2Hi3%?n(ugxv|1!F6*7DC{2D4JLJB*!p4lC^
z(;jvEIWWCFA!ux^{eTcVfwfj`Q{`qkY71}b401c}8o-<e>0xPxJCxb|#ED~x(=Hhs
z-Z7Ylp1F^Sl#U;7RS!=xzf&T6hxpFin#@cUiT>S`Xxz8_mqGUysNU08@0Gl(tuZ!6
zD!(=A(B&`Y)PEkC89^5`9UKs<GKQ|lD5p<osT)BjAdvsDjq;Dgj-r27kCt<LECupA
z=vST@7d5+(2e@D);!3~kzVT1LK^_kwXYwBSLD#F&i**t*0-McVO?zude3mZ89%?l#
zwxLfNNU!o?A#)%MwXb-G_>x6OU6|=g{-%Ubct*<v8ng35#JlMOLaHyWnN#?U`GL_J
zJ#0fHy9+hENb*$UWy54Vp2i~o7I_g>+wrra{=na<LrejLaok%k$;1T2kE#)y0w)o8
z9UZd2^FO~Fs9bwE3A<C35%g1}a=Ne%c)%LwjULMm01$g}Jq<NltS5p7QO0{&;pf7?
zOlt9bBaZcKDNI_2!WU+^FDnp9lS~!9(04P(6$pdrf-`M0N#W;dZG~yx(wvShd?alH
z!{V^ri4ns|ia<X<JgFhB52!ZmADuLP?_i0ON_3lZ)v#)t{1%^Kync4=340kA;gia=
z`na5E6yNtR!2OpGAwp1|Ew-t4#KSlbGesH$2zVEPQK@LCH8s*=#J4lD_Z93<cFfe~
z$$miUz!o6<HsDomiksU1T|eO00;nIaADZ@WxfIh<J|=pq=^8;Q=9NRR<M2DYl-H|<
z)VQpxjfSag-__~IxL6}wTmwvzx@ge3-VW0!gQ4>$Y2H@0xdIEVrtXhFy%Y3@TL<<f
z`1I4qh+D1_Pm4Xvml(}j{KW<4S{Lg{MG1rl#J)pF1$vDZ2u@6~pI2G1mBi|GfrA^}
zTe^C!{S=I=vvnmw6lCSUI3$posHOakS25Cl5$3E2fi%ym3}jis+^xxS#2mh8KW(NN
zx~}8B$5x_#$$HJ!Ia)95)%Szm`w{Y@!*w0u_x7!9S4Ym0_YT7#L80m+F$UG+_vW9h
zH>UJLhoS1BwiR-#ry5AY+fcX2-%siAQoZx8)NNfDj%J<W`PVfD*xAcaO-UY8sD?Mi
z83^IOHjo?ykq}uV=|~)tP9*7Z5#G&O>L$*Ek1Kq*N9MU}_Q;>X+}P=RIRbMu7POnD
z_x{-0T^=m`O2QlZ?Hf#XA12F}IT^fQE<32v&cQFO*YKE-rO0}EIoyJrCvKC&wi>ml
z-J6pvT>duMFjlu(og{9eGz2zurYvqbt^bC-K`Mhmv&F@Bn`>7ytq&p!P5r}MHtksw
z$DqtntWC;5f?(fa&}ibsBp-K%1TY6`{$}Ka&8v>H_NG>P$v|YYWCv-WGtw|ogV({3
z9pw9zYSwI3Wx5j4BoXGSSITPea<-R=>{}nz>as)Kt~f_ozoXoeQ<x$pq?;(wa&VbZ
z6?g{$3a6RkmpcsWYhq8Ng<Z2Qa-N3>v<QWUrgR-hS2`8xy;n?1qo~93O>TQGHOvA1
zLJ>!<vJhpaRH0%)zF@U7TrTxNlvyg4rYF;KD_JtRufb#>!0nz_=I6$oSe1^BOyNqB
zcQ@&o#q>5gyl<72UF{;i3Hv~U)}2;G41TLG<EoaftMd~~O`xYvh{5ScOZ8D7AAbSq
zh&<F35pZ}gNH+5uwsT&=we6^55b?0NKfC5$zJhk@$qZ|&s@3bQ6epP2fI6w700+9r
zJ;i}k_Lj~L7Wt#Pa{iGmm7Yg7a+6Lhrtf8&8hBPllz$G=s77UsrafF<nR(Rm_k9uG
zo$gw_pCIrAbAA<<l6^lEtHSdV1f!cB2)yNFyVY(mgbfl5!Ihp5;6v@VDs+O*OV>rF
zs__DRvT<O<%Vg~FNtyRyO-qoXMx|IvVM8%8@on`(MpcX7>IX;rb?fSNGB1P6uEbI&
zWZ{c+vJ$6t3x`_Bn;<iUVX7!=cBk}C6oPBHm-!~Bj<wij>5;9Eho+_uawWmeBx`Ux
z;Q!3e5aKaqTxK;LPluUL%_|=@HxNfxb)S~bclk>d8)K5{tN$<N-a9IauUQmra?Xe#
zAZY*<B<CQAh%>;DL?la+oO2Kz6a*v>8HON1a?UwQ2FW=|Msg4k0bh^5bH4N4citcG
zu66EO_pj;My}Nep+SOgPYge!kw`muD7@}sQcp7g{&y^og=-Ts`%3jBAFVn25h>?-=
zB27=e;I$pmkh0^bzqHN5?ljp;cuse(bxMV5jV{KUKVM(b^>!>GPoM+)&4o)*eByz@
zy>L@T1p$7Jr;Kbf5<YsP{m{apy6r#C5PTMv!%Y-ElNGCfWCh1fnN-wRl`h=VfmFSF
zp5#MtF>LDMP=*JQgue)-7U!q*SBu2bz`UIvOfE-~8QcONa&a21f?BecvZ=3if~)68
zYDl1f;E=-?PUp}_9EfVkBbae@Zp{dL=>W52t!1<NW;Tn4Qi^48vkri-s<jzB_sXVS
z+7gqk4N895)yy~t5Mw?jAOE>jx!#K57tpgmqD&HB=qZ*d&btYtl7;66$4Ai;IO2$p
zfxFRM0^u?NN;ZuBbX(H86dcPej&XnRuA1W7E&&|jZY>9=4UPh`PltGGDgaZkH+t(>
zsqu*DGXY;zk{WtnDqF<+J}Yt<J3$eii{5>vzohKlj^=1J`;UjeBB3guf0eqN7pmi^
z(;_Tt!`45N7rwuEtO2=4Bf^TrpAFK!#|p)cN`KF+7o?5<zA6aJyq%)1?}V?kG2oT@
z$}#S|6cyj(gMpbWrbjW`DWB=DgjuLsG*Mt9ElET^1^%})$xv@+$C%93DxuI2!dP)0
zsD{zW`qKq+06i*RU<;<pf3-kW!;U%Oh+2jUI}KcN<F3yHfWTNTH5{@0Z!OMaMN%N}
zB-3hmAHX^wCWImDeP1aqgMbge+cr4{Lq5UD5)*L)uZxMMh64;0(jnykjJGu(6PZDP
zFck^H|Bp`1@QT?oE8YHQXClmCPZoK6=JwDhRDf&IN&AeNxZc&GctM&FhU385137@g
zoFNHFw^$Gz+UT1BZ=gX{>rr3^@vRPH1B^Q@P9j=EXC+^BZ=pxM{`8-tYX1qVC~~fT
zCpv+?GwC(3ILFP!Xvj@G%%LK^nrs47ctr{2@>wqwbil$)|DJGdcuu;bnL3{GDt7s6
z3t>mj*P^}dmo(2@9K@zm>|&kYS{!Y8W)6^s6Jk{cxA68ZcyG(ZuYDgo-bCvEd9(Us
z&IHz_L)r5{cGb2RmGkE?i32EznECDI;Pc1V%PqtCQHe~SBE82W{o3k3>ofcP8ip-;
zJn`}Ubq=CTw!YFl6@zn3L`QR-s%LxGoO$;USQqCu^viwJ_pH*fIiBjs(&FUFe`dGJ
z{?fE${hG`tzDdtcdeUZs_quL@$edNvcH+4Ge3fjm%;(g0v%%$?hhcwUXD%u>z|8G*
zt3A?IrDa|UIbRs5sS1_1j(xC6_8^Is^|+y9cg^rOJQH3xEt67$a{IP-<J$F$y>Iu$
zvhH(%Dmr#=%GxPXBUQ=wk0MAu60F*b+412huuM8otv@l*99oI=ml&H-CsXX~R+1C&
zB}EM8{pSFj6GBhrvqn2?6Pmy0wj2&+>4*m=5}M77{7L;lVQZG**UQ80YE8vp14z+#
zHE)#M>^d72p3K%4SVY~fep0x^(T7I=D@)ziez^XRPPtcligE&Cqh9<~Pg(LZB7?^B
z@06g@AE!JAliW!S@VusV8+3^s6Oq2tu8VtopzLk3O|DBD*j6EWAg%0SVN|~_>9<o%
z;6NmKcz=Yid_<IBjHFtQ`#*+x%2L;kUm7gUCpDeyf&Jwa3WFw5tww}jcyK``s3@a!
zU+lz=$>zI&$+MpZ>f}T$v~Jh+5zPHk<`J3W2p5#(bBugx3~Ie|cw-aM!2C1)bA%cr
zVq4*qeL>a;Px;9_aoL;XdrXv>&NKm#jxT<G=Tdl2415;}X&D;TDJfqPwiXu=!hZV8
zXKCd*-^m@i&&nFhU~c!82{BUFH`5f()cxChPM&Np>|;@OhbZQg4-Jn@CzBa#eaKt!
zWR#2E{5$xeh_H_p+O30B>?|+~V7@W?KUc)UPXdfs&^&B}mfoG_e<_|*+ZpWH4DUQj
z)Zv`(Uu-M=%yK}ZoaZ#1)Md-(dKZ)2p|vUk;3(&V|7u`~`;QgRh}5^h-qNim`S_5;
z%PHYappsnqTQ0;lN=`$Xg(B}oKIr(&dP{~^#QE^jW$49_x5Cc1xjM!v+Uxhm#Vup4
zrhM3Q-UB6f6YSr9Wozi(a}PZ1y1|r;=H*|T{V>UBqb@TU@cIM!j!fxa_I=7SV{~3*
zu(;ns)-fUnU2L!XqX~mb=>JSOuKIq+bq_K@0j|!^5HR{Hdse4zlqELgi-!8pr)&*)
zOEIeZTiVnH1S}(NxP8)o6l!}Xp#U|+VD;^Y$NPLVVp2hXQ8rj<Z!0U0p!&s)`O7W>
zDQK-prhTX*A?MVS!auD%e^)Fp>qgq^h(gh}Meh#s`B~NuHQ(w7ISoeibX#&!pcT0<
zmNajk&M34Z32|7$waFa(sj$xRGdwvuDClaiqtFNcq{@B9T!st-|7QlV#R}yHb|9)p
zkI~<F-c(8XG05u=NPCm!?qzaq-={)mZg^Ob62jcj%<#f({S=8c3wjZ(y<;-x6#34%
z_;b~eDT{EnGi~Tdhv==zw_5>OigK(P1vmEVrw#he?_P;7+>LkuF7#F3io}3~OU=>q
zquY@WEUDf}bWd7=1iOo7XI=5Vx;Ph-X0KS6sac{it2-arIvVSJS2+ZnHn#I2br4Ee
zq(0Lx#bXl)GF*X`3AXyh=2~!2=+_;roJ%D4#zDXGS;T8AYbF`j&PRIU$!ym)=QqvH
z&36f~&fDWl1;}NVWp8F@oCIC6>05&xJ+TmY-%E>%(&ioJKh{ehmF?-F(6f>c)Zr>`
z$J90se*q_AF87Sp98xDEetvckJGO}R<XjBK+ek+%u=4?!taPDHfGlB=GI<d|*8(pj
zjhs)T^e@RjPF|x9R>_q=|J*2X0k%>Gz%%=e&2Z({yptj>f5h+b*z=XduUXcvW2i(o
zVCX{_t&%3cYlMFJU~dOz3C-vz!~qi&6!LZFf59QwX{6B_QZSmE@o4Qhh;<>X_Mvi<
z4L@z)mTJL4*v(~5wBkoUp69H=v@{y<TL@rzhHzY+6?W`QSOC7sKO+yv-rUC2#jTzj
zR^q|2WsGhD%xU#V8V#DsTA@#4qm_Bty>cC|-MHazALOI_tOQ1W;p%Xd1j<CsRe|Y8
zgGeiA%mQ#q+ZhUyXk^Wfbk)SKo2D7ruw=7_nYs~Hqw9Y0r<fK!(g@Km?8C%H%t{yR
zjj;R7&uN|Uw%FKIQ-%16yP4~V64uD}3=gn-y<3M!m+@!ji3Uok#AM5hR{CZRI$_7I
zH<yr7j>q1cFNbFStTV92Pm}kNyct2iBoYI<k+*1C30(@Ub%Cg_HjmeBF-fyD^3>Cd
zhM5jN`%GUfV>m_`wyHeB(}h;E$`1jiT@$x#jb%!+_|_Q68D?hXpD?<AAugJ3H7PqU
zc`qB|W|_?b3>*?1MwvbN8asxu%Qlrb)l80fd%WA&M~5mxwb6=uRt#ni!sO*$6RIFw
z+=yEn+7p!kYsl35zQhiX=K(jhBugY?M3D?QLs7d=g9apy%*a3EioHo5>2D`r5=ihq
z5C{?HzbQR`efu~Awv0_A<ML4wL#Ed<J64dO^@*WK2^AlTbM^I2U+`T?k{i`0v#91W
zu=ptKBN)(D!X=VH@gW@+oz53bN~5jeI()2|2g55&)JRm<5tOX$BQ2=bEpVIt0YA8W
ziM6KwOk(V=l6|qN9`C+rnRLtb{$_(31;t3e?XCQecxohRBbMF(Yr)H>&hA(paQ3;J
z-(`$L{bP8|UdZS)aw)`756>xZyT2FqML`z}sK1tdIVL1yA%sh@P$unz2bD)zTC}+t
zeR_yUGy9<$U16`{%%L_c8@}>jhKyO5Q~bL%<2L*D7?=T;qG>?uv$ZEMaYz^T1&_Jh
z`vt^;kk-7-;@O<53@L^V(v8%^vUzPD&$ja(y(9TyOmu~h5gA<_=KQiwClWHp@My>z
zU!T*99(u@}VFTOnyZ;FD5~{+@Dei^c>T%3yc>9kWw~mSm@1dP#E>Z3Qq(c||vc%8)
zcuqo=e&<o<$SEMN1tyeS;}cPaM9=y8ZNEifclqv0`-P!+#h-&2)G2^D=uWqcsht^F
zX#~8rNaZf!@*|5T9B*hK74(qr@DaR(D8Yom(>p1TtzU7dSJ?7Muns5FtnrsT8SJ(Q
zGKHyOwwFlul<QjMAtwdJFIC<OgE|xudz-#JQD<dR>8hZ}=H%liCWmN6pl|Jp0ZX4}
z`LS6&lNYWmxuf?cTtx)S55EPoN3YjwD7dvB_xAhyA5*dD-7)lL#casPJ^Al)RhiZk
zIpUV#Ca`*7O@9>bhYdl4tdM$s<}^<!vxr8>d&O4Pw7*$7EB1r@d9(l8T}H;pX~9-D
zdo@L-{ktFNzF~P1aY`3tC2ZUsY4%P<_Fw3jtuk^hd#_oDi`QAvBSZ~{(oo%gybxXt
zLoV@cyJwGio{YX@!!>G*cd>KBcqZC{dD&C-_1*rp$}%Qh-EAz=a16;f6v{1)((IC&
zusEQ{b?3z>&Mq|4Y4?sYQu)Be8vJ1NEKPP6taEH)S3{B(iMva%81o5EtlPs`dN+h2
z3S`01q6ZL*?BN97-a3f`j#{A<x^vyQ?0L7sWsg6^BS_aAtKd-fi-xD}P;nWuNe5Y2
z<xMB9wtIj9VDhtWQyoq9vP_Oii%S#B;NOCqeprcf(FA5^jWThIbpCGjvS3y#Y{znS
zVPiA+G(Tjf@nh!}S|DmU7gttu^fTWsD_k1~c{PfVOOvWj%|Kc=;ZgjWy=Q_6BQ2pX
zJDFY%5qpjeJz{E8lS;vG+MO6CQo_RT4eF*7Es8^(xvyox&_LFS*Y<D*=<nZtVmoEw
z_MX$zOjzRn&ry{sMM<8MdIFa079-bcD#FHV0e0XLoZO+z3<Ra-Eu_nYo@-#J)ZLMP
zFnCUtHAi4^Z**$vw()pJG|<E6?D`EAXJay0j|{8mrK3HS@w0C4A)5O)>ItxlSAh~L
ztNqi2JfzJhh=1iFp|>X<KyW%H?NZ-*+s5h4wOcfoW1z8gj}~cb_{8KC<i!W*pwJ#l
zYm*(ZYh%a-gQXoJ@L`28zl&~hFpX;OBQEijW+Ihb4&tW&{uJ^pot5bM3ZbAVuDG^3
z)%x=S4j$o3nm=zUktbhgjhM-U@;E~O*U_Hc651Rf9kB=LoL%0+(HwBWe#zJAs+yj`
zQ8<Rvtlg0I^wETWlxQ{M6Xc)`G!&HmN!e@E6cjp_0ZykuR3RuW4vVN}L8~8O|3*Ob
z7j5rcbS32r(0t#S-^d?+upN;B89q>hCwRP`JEE)5Q~eKSFP>OJaaBBF(1nAWi33CP
z6Glekm`V@VdH)10s+Q>zQ!u2O78mi>GiiK=`8QcC$Lmj`hXMB%;IfF1kENrIuDu>c
zWF4{jwe>BbT4XxE60{n0*>Vx!K+J667;1AoZ(aABQmYM60Byy6N<wJQbP{HIt@JP^
zU@G!N4ugPB?>R7DW1#~W4Sa##V`ee6Ap~w3igADmx(W*gn#jm9VH`k31u*&l`?odT
zpy4;{K{%Mo@mE(@hX!4DCwxu*HK`p9^GDV-tOQlJ0esGlQarm^8mnx<6t~YV)h0z=
zV~l^SKct7Xby=-$Fgz-mb8+E$bi3vJj2+BsO+lws!BjDs3MDoxPO?0cnavSkjs|02
zUD3I`)pyFY)kN4tM|#Hl8ONv9kic><R!RvL2F$jNg#sWW1j4iN%yyG3_o0t+G@n*?
zFa+0uq0;r}ROq=?SFhk^>y*N8@0+eCZE-U&G~lLr4;5x^wg$ro0X>4qHk*t*HiG(Z
zjXm-cZsDDpV2ET2I$8I)b-oR2ZJ}p%3v*T#M$;@M!~JYtvu@M9N6S;qhKtdi4YNtw
zgAO-E!NaOtK;uH-32+gYY>2tx!u(-MzA38qk@ae8qx&aeB_ki2>8Ufn$m&H)$V)PH
z*SDQf3Dyw#EHW@7d62Hk3;4Xdl0;Tt5nKnGV{Pqoet1|9dHX2o0L+qyH)4Nsvh(|*
z<rht=JmZ+6P+%y#mmyLci>{I5M>vF&>rfHQQ<M3`emg+*4dVF<iGs<C14oEC0|CW$
z2bjY=BkuVNs_ql@R@U$Ks;!TBXo#VByHv-SNYo#DjtCk>t$ty@Q|0^Ozsv6teG>iR
zc}^J=bldDODOVCHifsRK+F(m5FaN?`6Isy)P3<9lX3_<Qg+D^4UAGp0A?)|~PXT`v
z&p^-9z|#!XVl1XLU%0cOQ;dGWxO~mvtNKLZ&po$3^e>z>A`+bI^#HmiG}0Sd4WgY<
zF*raHN(&8FVjc6T7S}VAd=RwCU(muYgrh2ZUEdkjFUOyjLyV&zw{p&uDq}^*t`#;V
zVqfj6ri$Ud&9t1_Yi|wN;#=15CHW9)NcHm>GEVq4wzS68G|!SRC5{HAgAk=2&C_o*
zZBYqU{}3DuR&aQz9$-w}J@wp3AviFFF5E6j>T!mP><OPe9piIlxg2hC^-?TdR&N$;
z#;x{r*7`DD1~v`i0kMkh9}5S6I6e7a@)o)sahn6Jk}qsbx<6*oPZqV=-WTR3(W@D^
zKPb_I=}M=eyk3OXr1bwWph~){^t9e88aaLgm8kbVG`JLyxJ^)85<JUJSA&tPE-`?s
zxKz#MTg?jB8PA0*;f^BTkKSd6X~F=ZV00`Jd?IP9;&_z4!ejK)Ow{Q(M|hxZdX-FN
z*yiVTH?zbgWTlr({?+wW4ablMP2THw`*up+l5bdjD>a#ael%vTZRW6!&&oEN<dUx3
zu5zCn4tjm^x*}H{M$^!T^RvVpQxTbq1T1Ny!2qhnd|{_i<LVY8n^8_8Rmh%OB4euJ
z(~5!6^{R{M_N11?nCgefyM<Zh-{*rMu{1n49lwOtb08jtPc#l62+eJc*v=H%4P<;|
z|MkY`xk#ft4Ecf*fSZ7!NpGI}4!G0oD{9oMRUUuElg`H{Op&-v{6nK6Ct0q2QhpG5
zO08h`?ACU#){9ez{5m9d9hV&F#+43JRsj$W)^{r`!0L-?fgUy)8}t!iT7$>7(l@c#
zve96L9bjo(%G}4?kelGAXAOsSv(wGGP4jSZe+xJ*QuMSK!)H7jiCY6iIqqNpMj$q*
zul)28@ffv-UGi)IfF7Yj%zX7o0Jy!FV2KSNK(ag1blXZ9;x4z-Q-uKlShfckT1g1&
z?g#WeO*1F}fP)ig)nTYO@}xr$IVcNZ0<B;Om{^FZ$X#A6$M0dm3IKpc&BAG*YxOn2
z)5t%94*+Onxc?h6w@wlmncxqXDl)!st5r4><4%$a?w#cK80g(*FrfOQj$B`$QCxZY
zFMen=br&&x<)ZiXp_@*6x$p3L^*guoo2`0wtHZ>HgyF@(GP|>O;IR?Vb+nU#>xE)e
z5Z@v$E6n*i?5#~wVOEw|W+n}ZIo9@PMY3w#`vo-TOLv41W(aOf`0oF4uz58&zCAw`
z^83|TH<WUU7Dmm65lID7BxtatfA>bq*KxUxUfNDN_N=nF*mlpWVW%hV+4)D0!Z9+c
zlfB8<T`a6(2HL~K^E;9T($R6kBNCoAZ4a-_zv{pScb5W4#^q~;kCxbG7Rqn8B-8bd
zO(L(cY-nNp&oG&J!T2xo#8>e%*>|VoFA_9zMx7(>{ycm?r=p<CmO4u25bkq5`81DS
z$64j!p^@q0`=5JXL<{zP&jhZ`SLL>UT}qDjPrbHgolm+rh-#d+e&;1Fh!mplB~Z{9
zhyr@Rq#}U4g*zOq+0yt{3l7<-oSt%9N;@61j)3OTFEC9mo{~H)<e=YiuP=-9UD14Y
zPOnN+lU=4-kQa=w$%u%86uUh`N_Yutnn1oX5Fi@C_)=Lj^QlgnSjv}gCptN;a(I74
zMqCMtb4q(%x{jUi*vG|+H|Cnk$rM^wb3Gt=OCwC-2tVaVc2?~}H6lWysNEodGdhyE
zEYX0<?RD(%;bSX-g5Y1Hwco6EnR@G{yW><FG|tm+mS+zG%PX@kcV`6CT6q0+sC+{q
zPDPT)aXdPhF-SQkH04t2gv%yZN1?@Q3=>{U$bD|Qe4jjtk8$o&TIMjfnlBG$8$1r&
zR2Ocw7Y094K-P{j!hXa=QM*HcV>Av?iF#CXQjIx#48mm-WOv`57TbiGy~;~QRgq-Q
zEaeI;sc3y+jO%pZcv1{LuJQ9CKQSsx<)86I(c{P02|Lx=@h&80ay)u@tsP&W!ZCGE
zX-HhFVfB_Ug(rIA3M+epY_JpoWP3Qa>?1Iiky+wh{eXRAmVVz8VVG&MzsZ!m4BP!8
z9)2XP0wyyj8f@~L_?ZPsiDtsFi~<6M(TWT3#^3oe@>Dbh9|LlpU2?0Sy8sw@YUGr-
z2amxcZx^JwgZDRf%|B$$RyE_z9~LRo&rSe3d6R5y?cf=t@0{uX)oJC;pT|;Wen&Ux
zT8jurr{p~t>OdwakehE&28=gbnko4}k1q#mX%VMOC$|u;e&0SVClZpEBWaC_orh9k
z+*EST90HJ5npi4??EVbZX9N2l!6hMfNGmDWB2PMNSNBe4<MY9Y7zX(NJE}4n`R?uT
zbEefz^#5OdI&(ySe(IF?essXoQ~^@e-q`CW-g7q##J0>u0c#H=>g7iwDVAkoserCB
z2MS8&m~C8u=yzOPB#>YaI1>D(VZo3~v<FH{9jpil5a7-D(*ON#Og?VPSODk_JlwKF
zxYerIo`QQUuYj3u52&>-f!=)g+^`VcOO$j3h<XfJf_EWNK;}3JaQB^(83g!&3$R~3
zRKN_?5CuH19!5n0x%5EHVGS(-pW*_rd{py43y9&rquP$8Cg%WpLB@DsA)Yw|hhn!j
zfLJztg(B1!K`fit!T9?F5X)0AP+!3d#PT$*2MRQTSPtujV$-gJSdQqivSA#8)BPU=
zB!2cI0T%#p{nFn}-5U0b^_L`BLt|Nw(YlJpQg#7I_isUC36e0P1&t+0X385hmU;Am
z)D0R-kj#n~Xe_hsfx`<lmTIEFz3+dg`#%TBX?J1HX3UW`@aVi?Sd-h@hMiLD$E5Ot
zypr^-%5=l6#`}G;oF|T--+Ka2IWs)vcpKHQuS4L;16S6wL2{o*HeJdVgbv__NNImp
z071N8bMY_^8>p?v0QlHym)^5u9Hy;+la$3h*Sn%o!UXgeKru-%@6d(>E>xY|umI-F
zd*^duI2|&kib|sgw7^xA3A#_~=|PR3VPXN*T_Ng;m^iZqAFJXC5fVb?SNz5HppTU5
zK@DfsAV<Ds;Op^h=?!fcnHU)&K?_ACjVO;9jfwNwE+o){4v|(9LS8Q}$`l9pBa8KZ
zjEw48XOw=agb-;o80Yys!;AyJO5isKkr8D(-gvM%-dVoXe;*O%>jJtCR`QNp^xvz>
zz}V)t&L9_1D&IP|*<s&aN)0iH^-$U)>DRXX1r9?CrZ5E3jUX<<XNt#K1?C103#@^a
zZ*w{Fe2s4oHP~ZZ3v9I(Z$6uS_%`n~c9vCyXLnEShaB@yyD_Jx@?*cuYc&laQ3o1c
z-yw!x{p%BC>H@9(vu@TL>rDZr6??TQ=`x$kUOoNiwO>UHPMhbf9DE-mJ(g_Sxj{;U
zVp`S@y5V`>Dn9F)y};(#G1STc>y>+2l;%km)``}miV8Dxx4o-eKXLl}9h(9Qo3q_!
zYeuC)g|WT{<Te@iI~wy)v+!rgYDI79>2SMV8`KQDlgK?sRokybNnzUkyLEC)zVwyF
zp58^qROV$6^cNK%lrPu(nLO+FQ!8tAi0Ri!Ly6@#6W#}1vlbOEM|CwLlwce~@w`^W
z`isg#&EodCG}?ufoEDz5eZPO^C4uXtP#(i%o%i~N(Q9yUZN9(`J!I}$>hz}?+wgAh
zEj?QY6e=yQsLtS4GG7Qg1{lQ*s4*k6>ovMrRo${;ZlaiKTGIg<Zs(VH-c}rO^DlRP
zjBM&3R22vzQA~r}L8M81AycR3(PnwK3GoKC7mIIkig$AdS3KSiF7nhHP$v`?MWVi&
zT#<BBnIG|RqQlS;tWn%i<3?9LR{G9eMZ>W*?405!sm>q!dmry}BV9INrZ-E2vzE^0
z@sQ|Xs#NDxTHijeq9lg3AyAY;{t`u@eB3vnNsO^$VHH_n74@2zQ@rJ}NITQkH_ueX
zSpqM+0y=~lsc`2=#aU72#n{>aH%!IZy{6J+Fkf1IYYysM48aqx070SCs~}9GxF^&#
z-$h*>-rwxI$U)_daYiq^65v<Mr@AL$ZQ^Wh5rq=u`W+)Eo_#B2&i3mrGK)UuiuNR|
zs(?^*06tXt@LdX+HGvk)c?R60?bUFbXx3XZk9qg=dq?4qUNP}+B-V0d+{Y#tH?Oa{
z3{b&bah9nP$zorUKif}dtqmjxFzqfpwxLp|4fyU--UFc}gmXO%P_w48V<jxg(B^Vt
zF?$h2k}FT`HpipCmBC(nh=+e;DoDT{zu1ePJ*@1RHsMIu3|1@>;~U^+(EI)%SrXYt
zHvfFk2RT_^Z0-+%s?|=fI+Wx*`UC&dKQlbvmna<)38epB>T2^othBT2sOx8XDnr?y
zn^>h!N-`@kAiqc7uEbWF=|f-S2p@IgN!I_!-fQT~Ly1?ETG|d?K^tc@)`x$JdLNu6
zJviU9W<c^io(b`_<n&$F92(H^#|A)Qioh(pHC=wqTJ|oU^WBIoXZ6c0)#`$6KfH+D
zjgOU9WGd2m!K$3+`epX7?lm}nS-RJk_*E}Dq+4sb(|zvjTW`|Nc6Naiv>p33Z*|;R
z59)uxTW-@X!Z$^;qj;~w<1XyFEej6mIJI<nsj*qMSL1V2p5I5+aQo$u58=doLm}#V
z%}CgS#sSWASMPHKe6BaK%g}50ci^S>KZqAiq;ZkIeY7>eHkkMn3aFtMGIGmnZgx?6
zR5P#aOQI8&WnQJP*SJ-T(9)z@)Rm65zB*JQj07I*QNyJZ5=KQ`z{rJ^Ap$^!0-z2p
zQhSSaZ@Yd)CuU%U83M#np{X(busV33q&k?#E<+Zd0I@E=jtwxohuE#CRAlXZvKZm`
z^dS<^Rsl5>fK|%j=#@WqtIYq0czgUmC(^|JJ9vM*bpJ)PHUbLsh()wE0t$ReZ?rZ7
z3UeuMv^D|?e8nxaHgXKg%Pq7v;t9&j&uDF=uK}Yq6RnLbqp|#-1LV9r!A}C9y=eXv
zbERW(5qR9`G5x<vXZn{Gw>n({?nU2|<0BH@jrzO^aFcl-`eMLr*p&?kZpsGzRA)(m
zkEJ#E1f?<VScuL=4N6L24;Oect>=%etq5vWCJy&OEfc)QvilGW#mQa(n9FKn{IO+0
z+LE7vx@g>}JrFq!dLv+_F91gPKH$r&WF-nfcOSB|U<}&>eEv=A7ElO|SQr5|!tm(I
zQTZ^If(0Xd9n5KANb#+v4jdb4j)gF%I2WvYgAd-xo(0l%8kKqEFm1EI%Vg8{A=eVJ
zk7;q=-@9{f@lPL(dXSbfP8;m3NszeVisCB)cfTDL0;{(d5rvjgP!<kabyRCUgD4<|
zL=n@1>4dQn)He^=K2d4M?BD_ocJ^Uxpc@jiAJ{&lC#Tkkc3Cn5Hv^@6Jbw-=a3iTo
z&;c*xV4C1#l^S__cqIpCD%#8GKDdvWNO1^WONykDMnm|xhMit(7X|)pa{Dv}Ma;ld
zfKPf~IFgb)`9Pm3XAm3f-y9q(HgYQSU}0<Q_vi+_T#mKQ6wrr?9L$5MmYmcI1-!uz
z|G0nBMJpIsgAdA;TsYha0!a4~ygb`SO$6UVtX$Of#Ug?XYY;2V(v^;QP8}rb@;IW@
z)J|;%L=QVv=+z55yLkBqmH7qTK9S?YqFD&ASz)cWscTffH%$da(+W%QM4^~+r<~pl
zqBg!su~AUiyca%VWFUAZC;tRpb5TAsO!zTqOjKt;+bV;E+mbFw!~NM<GFZ?R5;I1+
zl9<E&gYJc+yPgKxuo8EKUVSv7byxYY$nOc~u6NyjU}J*+lP;M4?Qeb`W9=0%?Hk&-
zDt~R0fuGLm^J~4l<XtaUkAq|XdZ|ly$wa3Vi@Pzi63;8cfg~vN5%d8eehWX6SS|8;
zM)GD9I%(M47!3~pF5)k8MXK;MK)(DX5Ci7|j_J!=iF~@#N`l4Z7>wYMNKeQMpzek(
znA2hj{p=8d)BFE=bZ;lg@j0RFMVQv1{&_>M6<C!!Np1U-m}8Ut(i`#_v$W^B3#vc$
z+t8WgE&(|C8&0%o@{ZlBDS3dpxv}j5PL+EuRhtziygUB$Y626-{WsmcTaBIuc`q0k
z$yZ;e?^EwFDonp+irI}HzJ2A))^nB1q4BKC9K)l#*m$ztE%14%Mb(e9aC*JHU&XyB
z{_F0La{?#J-8(j{er9aFGf~)QwoGnwe*`GMIel7S>y4wYivOBQXH|SsepDZ83*qYS
zn*73BEaOCOz?R7Gt`YRx)JB|=_0kKCXNHUpl^c%CkH$9VWWGgn-BQEZgCN@nVjBus
zQrlTZjYsSBv^Xw-urLXsiSbt(I7SDGzp;G3t=T$lH4YaS3T+1e4shqR8>+y_3aAaA
zXxDB7S7QV^Po4irEncDeB=@z}eMxXxw(h{5w0Uk;A8v;aLAweN8hO>q0>~!0`6Q0Y
zx#$(PI?lpDkbSPD&^G=Nro!OD`@wg}T@B5f@|)g!CtEdQ5rz#{%i+gH3uGS3RdcXd
zM~M1;c{Ahhee&o+lgjXx=ga;%dY9I$*aHp>Ufe<gmTJ{WnG}Syj7+mZP>w-w-C-O#
z{gjM3xZIWy3^jf-2`<TqdL5iQ_7)JjT)8)p62F>~6p6!n8->v{f6ZdL?HvLI6Xf&4
znRmAJ<PocEI^{RH<j*H%K+fFSM4uQ0-59|&5#`+N&20}baK1I>y<hrS$4fDqN<i>T
z;@6w>0<0d7cW;K3mb04;5f74*db~Dlvhj+7eF-i6#caM=AlT^x5~vCYZuExCkP)3c
zE9`0`o7LA}4L;txIeL5)C3O4?8N;F$Z6NpnczR!tLaQE)(20<vW3zUXun6Uio(k(7
zntzzBN%;E<{`;mfkwP{dPWrEG0*ng^Zdrf1^@w>L*72|jZ_$<00z=Qh_b%S-7Y;e!
zR19HDcoyP=Tp{0PT|cl(wZafB=(i+A$>jMu%>y!+skNaV-|oa4hiYQ$^*=~YGU(mD
zKc0Pd_Z~ZKuc6E1<OiJ$;6jnGpjRubdNB##FIOXPZLgq!A#(4$MUVQ{V#EElfomoX
z*&X<hP=1O?Z(_;H&Iwm5eIbX4TYei#RIUNiDZycZ+2RGcm0tX;e8Akq-@4@wg#8gR
zy%b-;!};>;eG@f=HdtjSdiXAW8M=t^lc0ZI<{A#?;TTLc;TH9`Eo(6^G`sic)t<5B
z<J?RxuD$sHwPsI0tK(QC)u*a3?X9~rYr-<L17<DFNkGBanPAxP$Nh;9<$aNYkC5lG
z&BwFb*Il!5zjk!#94SNPIXC$&`vYo^ydWT9@BFPBd&f>%_`Chr$(J8}KTK%hpMG_J
zAiS7@B3XVIw=H&IwDiGy3#Q`J6wTa=d~S<zs2mhH&C@Au)UmmS>ihdL$T45{sX^<~
zn0^M13y8*{j-t3b=&xO-PvXY(OBDBTmN}J%vI$$^O%c;jTj%{%kinMFwaiB>ck+UP
zo|z{&4>&9CYgUxg8b@6-CwRR55{+U9`sTF*tzkMhC=`)mk*bH-rTxn|R&E=1<snI`
zQsa8<3V_SM*6~M|nUC)m@`kisW03QWAiRjyyM&iUf#1DLKL02eq>M(DMR^i>{jSk`
z?mn1{tNExL{OZ8vF&Mhvk$G86P8nh07Cgr8)kx80!GE8Qujqjnva+(msuR-iM<Lz}
zKSj`AwvBz*Tycby<bLpJ$>F}5OWv7m+U&D~-*mjZ&)@dPw>DKT*jt)kBgYyGg{woo
zz63-`32ZDb?0{yM12#}Oy|57Gj}EuZV2jmt-_5F5B3jMt*Uz=g(eOTu7TS%_zhq5x
zzwg4k>6i7%<w*1z>5^?d8OAS~^)R|tAD%juKi|LI<^U;5x?_+$9j!2#D)zJnrI_Lg
zIq?w}WTS&c-;Beq*E-)Q6n)m@Qp$eZcsp%PUtmJAtFTdrq1R=99h)2*l%Hz0A;Ke3
zeRj#Su^}!;mJh7FZB2i#D?9Hi{aiEpE$bY+s(zU?xA$F2e6N<(%A?zxWAixhh3O=d
z(t{>}E-4*7mDHWH2ff>EHV~iDV7MDwp2c1*ciqVHZ|LaSKzNpDw9rhjkhpN><Yp4J
z@3zjjcjB)a!$VGPV#nCLT0l{bkKUButFGlZcEbxiOV&D%PdhJ6N$bJx_@4gyBuhAo
z_hCp?flA)-Sm5R7w?*=B&m$(-S<Vn6%z^J+y=TUh)$cF}T;(a5$mt0*Bl&<8)LY3w
ztpvErNjlt2Ls@(u<g>Ux2}Q1fS}&#oxMBMAa4QMSH%opB1@#I)^iq7KafMq})WP7W
zmh@-kBeu=2|E()jjcPLjOjHS?2?#%i-}?#$bt44Y2x^ka0cs)4A)rA6k(AEi%tr~D
zUcuNkAbmQQfR7UBL7R=!IdX#;Lj9uve01i#dm`4DU~<lKaAY8tz?v10%@`a?4vke<
z62OLm;6`s|Tee06SUYJ6*f7XJb?VhjiO*#2dp^R}3T$N-e1!!Ls5bToCaC=D%*ZZ$
z)<Sxr1UcKuAyyG9>aPl=V=c?CUQ`qK8`Q23ByQ&M%>QUqb|RHs63L*xIe|)1rn7=a
zGrmgJ%P^gMr0aARw=6w)z;hWl-Wpx5$-Z3*(?N)UCo`uH^ZYnp=P8aFN0$S3;Q7v$
zo(7g`pGIs}2ghrSS@4u-xF!oXH75H3ED+v@S=k2abPg~QsryPVkfg~mIF=uJ31pX$
zcRV%X@*#{O_lbsaqp1+h?81jx+%I4fwi-0)B>(3S*`dGa|C)Vk>BIF5!`bh1ADayK
z4|jWqR-V!A?we@vcpq06nmS{W2!hj^yTomlPAa^(ySvd=y7VrhFnE^48<M7w^SX3<
zE2)>jwH-aPO_HnMU1yUs-!$9}8N-<xFC>`4c>X+4>Ob%}iO8N$knxs&=k6mVc|Tif
zaUrQhn;@EZwP|%{y~F7~*|)Q_B_CbVM@)kvs?B@*DJn{d%`<5WE%jHvJ3|+TpTO$!
zU>-y}RSjbX+{2yxJ%k|U=XAGYN9N#kC?y|Z_OY5`meo*>fK6imxPa2sN&bvh@#qVt
z^paU|>|EKMqP{=fxaYNWwtozG_rVyZJ|jJq8mmQ4^>nN0g++?lJ4h`0P%`CMxpINS
z*6q|ZtkNyTG?@_VoZ^#n;!B^E{WtiRv(GxjaI-E`3#K;I0yR}wI_)`PpX0O0n--v2
z<F|a>KUUypi7l`EbfGxz)}toT-FfPL%hj0!HX%Z7!HZN*F3C}|%YQf{H2%Sj{%Sj_
z#Ae$)w9Yizrcvy2FgEa;kB8NO&(f^I$<_Sr_y@5r-37$%pXe_ED(x3lC%7`i_5{5<
z#@=i$*^yhCyrj<?K8-1hCk&-4Zb&;Yya>Hod%ws0CG6WPx4TdK8mdn_)YOz7<dB<)
zM)!fm-7?Bvs4Vjyn+74bvZ*XLz9-2{4xNaU&mKW09=o1Mv`c;B5b0QJ&^l#|WBbly
z_DCn?IGji}M@M{By>$*Flyq4|@AiPG)W(?|Ddy(Azqp;LG&4Pxh_qmeTg`8}jHp_V
z+?r>S#e4SV$QHkQTAd2%a+)sOLQGAW;EwUI3zoHy5jg!xR|70^*N_Mj&!0TZ?gTq^
z2R?wL-PFFe5q~!06w40*vr~R~HbEHhUh7JYQCH^+fMMtXlYgxF^0~SUH+*|{Gu`CH
zP!9zlI@sq$B7635crH|M3^*%Wnm263f3EmoGrUH8`li|HgEZ7BW4{UoVm0pf_E9rD
zQ;k+cH2;ao3zd6;Is3<VzjPO+X+Qo!L%uFP&H^rDSqN+$tYtMa{ebf@oyH@T!zsS)
zGS=n&RE{SRj1+?}A#<bhLT#|c^J#KT$sD;7Af{7{;{6mm=0%1`iQaHudRyl^yst*L
z`)v03*)$e!rW==qI7C7Q<Dhl~o;)_LbAI;t(bR4L7Da_~Gxv+3WHcpwqW+;okuxTf
z3He>h^E@mjTOzgX$2v}XMtbufAS5_cp_wTTh<#>d^=Bh4+V2Zmb%u)OBDXLirwzqb
z{V}-9Cgw?#mOkd{_t%}o;uUmO`EHq$wM&H-ld#nbLn5X_ouRI&7%Bp1(F#OTc+C_3
zo%txPJp%-qe^WXC#lNPFj@8kchIvPU--Pt24L8U#vuaaW*kqAcN6Y+@{9pYNox(e)
z@!j<z?6tend>rEnc0^8oSy?VLG&$dW?GW;=8E}yteneRs(Nl$zeC1X8&>oj5fUXPn
z#hZ<xeKN9818%0sGSd4>5ZA#ZDC!oT!-PMid!jYtd&heTRy6&)|BL=GUON7Sjvela
zcD7|?-`KqeMG?`w2uQN2*+5{cVC;4y-h^&P{-AH~j~KCGg(>B^>b~Hbk9tijy!Nja
z)mB%hvYhulW7~@J#w8MQF6S%ZHTFcoD-cd4mXSnN_1_ckwKx+e)SnQXj2;&zJ$eVz
ziwED4>A5U)iB3z~WX{9Z&-ZSU@Y^yjs1y2j<}t_InPGTWkF@Ir2!DvD611+o`v52~
z=8jrFn{kHDcJh>pPj9hEeu@dzprZ7l2H(%|sEQ8CzVB%qQV6C$oGcV_SH>-*POSJD
z%!o5wzmNWo1S0-|%#+65%rGeK<MMF&<vKnyzFcE1yFWk9_4_Za)L$fmwUwr|=r20n
z9#u*0do(=xor0p5e)Vn<G=kd&2&=dKFwVI)xjccz_0HT+!@fuZ<>sd4a*2oNUU&D=
zaCOq+-L%D$SwfqP_t7%FZ%}$UZzzqhKzohQ;UTxdKe7caY|8!$oBzEA9O1)|bZ%b)
z30pPlbV3kF+q&x^3bN2EZl^+-P8=QWaJI{gW5SG$33{gg*_7s$<LK5Zc)L729R+ZU
zZY(V)JjM+3(q+MDb_8>Yy0r5jsX+a)@xQNO8#PfZeP{cKFS6qMj2NSG6||z{T<3WH
zAz1nOZsmpwkQNumSeC+KxWNyO1n5vGVE9j}n5G;nL>7~i6QI6~<6^-m1<gqQ%}=3-
zY+KM+clz+99HtRyY{K_NTaRYIQ@Jg@V$k9Unw`im(Ke=X(115wqYql5K;sn4DcTbC
z8tnhsp$@cH&5;0dTWEV#AJ|`R>DaU++zJ4U$nh^d3@{kAVYOqjssHl_Yqr?5><;J?
zI!bzsNKqj7yCXk^QFJ3l>!u@tGPdj@kp8jbt2_dJj0>c7u=7y>URa1bq_?RsM0)9g
zH>B~c^1xb{=@h9w@X?qY*l%&W-RhO;EQ<9WN(xAG^I&2sk2(P4+WM*3*s?7cvJ@d9
z2q5~D9>AuF=fuDmb^wMu*I1caS+8+{;NJDeQUEmrpvhksgjWh}!;m#|se|Dm-eVzn
zMB*hGM!}?f;C0W2GbJfNf(O`z@|1&_FCBqMIs=}Y2~2GnAlK9_ir6Up6Bgpb1iQ-`
zHs4!7t!J0@cIzERJb-hGoAnz%0A2;`@-Q{HJ7X~ucLIFZF9(VI$i-6|j+KeDRSKDJ
z$<ngp0WPWHzrKs$rmq2DMjT!N46_kupmcd{{Rsr9WB_8;r*!APBJNp}0&%t~hHDt0
zI}KtK)f9w9F8>XqHK`*$@R<sba~Bvlf6YuJOZpTKkaWitz0D86@qmdL6h3C8+!#h{
zYF-<y3EIU+maGf6*jN_V0L0Q(#SHD8gW%Z>bEO2#)Z78bu9envJW%ONCrV~ZEFsHM
z^Uw!RK0_#t8M=-(`5hG~^$a>_wFca+$8x`_5V32j*@=#7jK?tsRVS0GxiafYsNXiA
zCi;2%yk1bE#I(=$@&5Qp-LQe<V)A+5#rkVad3mIUThN)|-*PRiNSYtve`4JY<!_k0
zJE$lqU@Ge@mjDNAhKCddMO0<F-_n^g6C@0};;>yw{<6TRbx>9wqJZ}7Jtq4VeR)_*
zs@E@rKXU_JDcP2A;M=7mYqxxBi!t3VN=^~14wfqkx_W|p?SvE$jkUmF>szze*4n!b
zH&m)No1@;G41}m!;8fq@sWg^aW(QtuKBr-2bwU^Hh<b-dLhDnlPsIgsU^&h@GVHBe
z7-@BwyNujryIoMayN)|7^)7`Q8w^DYf`)=MT;w6^KVk`4A%WHzI##M+JgCnmCX;#X
z)aJ|3o}px%jH(#a96k(E*&+~<(D`JvPUd#4$|le4S0>D>7Zrz=*d_KyN))WD9q6(T
zta(}sU>6PSIF$V~OQ0g;Ht!^v=<%c82A<{ptC*(;!ZrsqjLzMUZI5Z|wxSqM88;>7
z12P%Y%x))C#@Syg+e>_%sgphaY6k>Pzjw-awReSHQn>~#u=|+46kpzbm7VtZRlEmq
zZfN>YdFWClhma!5`0uQRe4aReT=V1Z<8JK{JheL`?nOS~8ULO-6N{s^QT%N;vEz$?
z=thB`E<Kr!HH&KiPOaeg9}#?B4`F7yUw`?pks=-Zq+Gt+p%$W8f;n=RXXukX8g+&F
z$Qm<VJ>F9IgF1akWc=aL`=A~^w3{>Z)2$YlktaThQua7lVGF<KzR)VY@^f>Z01Jl<
zbk_SMx)jf;4v})#=p2*x;Pq~&ee^-R`V>8dH2d-#bt^5*OLKB-{Ia($u<mlVYC2Mz
zDyB+Q<Uq*StFPPGrCv})I4N%V7qz_n%0E=ClCL(B+KjTLCQkP=<lp~rgdp0xM^$lV
z=ly!a=yy-zuA1{(+~vfOuEJ~|@tgn%r8m5Vpmm}5HJ5k?o{wn}K{*KB%&7MgMGvv)
zQXDeUJWZ)$6_7)7BU1M|;^Hp7yR!*d?_&z?kzR=6(8zO@7CJ+-lI%6qBP7&1HsH`p
zNIdYP8F*}}Dz2(oy8VSpb*LO<9tCun4f)X>mTQqvf$JG<=R1NbBno4Rktq7uU$PTV
z18v(BRd!fCO2Ne3mzK<BIr>C3r%Fx`@yxK4NR(3IFWI6Xi;*VO-B=VW5?z-_9@3;M
z^wT0nR8>5_Xl=kx0k>;=%gp(HT<~4ChY<tDZ=AymL9CGX#HiAo&uWCexs<I{iijko
zAAY~Rr0o-MqvWskHVQ=oE>b7DkdeGb;lL;vt%veWY#na8rvP-w1wsF%cW3Z7@AWmH
z^+8!*A=mwf?H299MPt}eZ|}3!NhJ1{m#Chx609B`$C)c%^449tjdqqY(a9ucA5+Qm
zZ@Bw!53YGEZu#yfe%)|%u6FIa&zkvnowj3}`ntJV^_;QprijYMa|4{enimQPupR~m
z7@8GgiJu{t2_q&cJd)Pzl@(QT0{CruRnPX9tE|LAJ1&0jeTJ1uRx%b+E4EdeP%~Pq
zGbmDUoUj(qxT;`Fdy?awER%jRY`kzt&az78I3jOX9FtBnC2$}+AnSYbq+H=xhi8eg
zDsba&u9v05deuM^%S6T#N<G^v@DTH?dQI8(^QC@Ev}&QRF%bFpu-$|a7Qwi!D$K3?
zO`VpQ2{Q4XOzwkpMYYg%J|ESy@4^HPqpJNXLtDXBqo*p9-1bEVoUphl8(3;qh~pq!
zwOQ8{Y7!EbmcILS@&iZMuR`@xl98%S2@wGL_c*$ODfLZSyAXqtglFa}^Jpt=s-bOd
z48aDsm!k#Am(zUbey%YpLp#CsVo98vopnnI->lD{SJYOy`>s`7Es{aREjD}}EY(DB
z*snxZ{^sJm=Tr7`gDs>XC;B6fLWRwO)5$P^KGp%!=RY>xP$lQN>Q%2zbq+shnv{}g
zxb?ebC@(wPB9S*Kpeov|?kUk+MWS`frujn=-}j?Gc<r9Lb#Lnq+`bn62i<9{5YeAp
zYMwMviAx+;U%e7Tq{jjt3g7*ah#`v!s&cK>&fOtNYU$J5NWqpt-l6v)Mf<xiwe~C4
zy{Z5Wv|*$=*2k`N7XycIkX8KT{u4(ULt6_+TcXY12gLt$D8_|dX~o|v!20Wo0DtBh
zN9}`k7$GhB1p@$NX*ZUHMYKnCmvzC#k@edmE~i7EWf6|(nDU{mI4l_zo!aOpP2?>C
zKBH?YP+KKpa$$f&2RXYn3zDOI^Nsu(5JPubk>BL%01wEziWyS?;ty)n@bg#o^jVkL
zjj4j~<(5qVXu8Dha&#q5;J*B(01dj*DRXd_PNx7Gawmm^(liztpnQf4h9uexFY<?a
ziotuTWfjoqqmsf!D~-PEsgm*nY)b|g0VUC$D28rhQcv-CPnC`~Sv{H-Ay7d)0LKt>
zi%aB}MVp*Z67<-df3GwxVwbTk!3rn{p$B{~5MGOLpl5B|D|{K7c5@vYp`CvM*59=U
zus<^s&c2&S6;h{IJ9Dy-Ky+~MLN2msj8Z{pnwvp*DhSE}PT&glgBk@0g2*#VNP;6U
za!>=5xhid703N!ZMe-u{y{>RfS<;F7Vi+I6Zs`(fV7JHB)WB%2N)y2_3uyJ4@M00e
z$QQ^3B4KWXDhLh`^W32i;FE*-n6d)sC++_}xq#J5$;}0%S4%l!qQI6?yZq;lnB1+6
zn83^gowqGQ6d9djAX)vG8UH<V?x2hnH#Y-0^WeVbQ^(EFW;ZfG72RLHq6{oM<fdgn
zvkaD60WM1J-!BW-!+fI%l8#q12b`Gv7aCxzV5s9IR*#7t2BQ2&qZb!K`HTk0{ZV0a
zj?rW8jDbk~A?11t!5GQ|3?GN=1xNzirXCoGPe1hEzCmPj^8#vWDK=!JrKlqup!nrx
zmmn5{@mmbU98S$nCD5&H1T1q#q`;P_D}*C3hRWK2R?&&XX_VkyF$}o`V_>=ehw|_I
z1UfPlz(ULq947xz-fqD2@rcA^A;wa3ZJ+-$R6+Y0pYVZP^l1}TOfhDfEIkDcSfelD
zE6~a{lb9{err4ZMG#ukZ47mPi6uSM_&1d25?tl{!n$b#6)(7y=Oo%@ewuKEvTNM8+
zhYlEqH~Vy3p{oS|Qt{Gaoa~$Fp#Ke6GNf9};y<qsp*Jh2G!$560elD^BKY*5dVv6R
zNU7IqkiZcNHY1-qV~&HIZRB?A6L?9@j=n@U5`*-PM1l1NaKRx8qQNVpNYvGI-FaHp
zidg9CH!Aw!R!we8MwX&`tdhfDBL&8W_v3CYw(}&#^L(jF(ktWPntJ`({wLmPiTjYB
zvKE!exV3Z%$<R4NbK1#opH&75Fp+u=D9+ZF($OJDzn1QcgA$Gv`~gLhd~Y7u_)GM!
ze7f<lfmlFN&dQv#*(N08R6Jr}Lodh1cC3?7MIVS%OHJn5|Fhbjy!=}mXK%!ZLj7&%
z&n~}ufnIT_z1-6;=3QK{Ar=9g{#ydXr_thQ{RAyZ<zrAbC#9M1*G_Tkh~m^fM5Dwy
z9{ua)P5GJ`bL16PFC9t1e3i0_j2F}A?XQg75DWT5bS<&J$Yq`NT8-D}@9b#DqEg2O
zsoZdT{jI>f_JV<wLOdcKQ{uPxqZ)0Ka1;6AX~s!z5J`okZoTj^CtJs@un>0r$(ysm
zoN^d$#W&7b*rMPd&|7Jx6@%L0{aS6pP2=Jq?q>Ry+Hvw=?(FvyK%-2XP*#<jrkRn5
zk1iw@KC~mWl90_tyo_I`1L`RmXgWqEapv%nDP3GCI-5T!oj;4@;5(viZH;H(7E}gQ
zVUL_ykN6xb_?+tqswH6p<y#-u&HM{OvDnmsiW&tBS3}P0R)OBTt+^KwRc8s01*XY-
z7L=8y2#m1mz+C3e-r=Mkb#I8B3B+iLaoGaa`~!LZMK0?URHB$%w2$t0J6;Y8>Bu{O
zfVGr0TZY33%>Bt4aiCwH3{p#+9x%|hcR!Ta=(o}snQL?o0`DXZ5zP%(Id!nXoS#cu
z_bYnA$tlSbz5Ah}*{$c9d1FDe%Y`hm%MUB;_3;tE7e)B$*ELsuG}HXu0ziwR1#u6=
zkHz?2K!>2bDv(~9)+_=B0>1CKhLIhzaa67NWNnMImQRcMkiaF(^kU`(Ps34?J5x1>
zV)O}}*PSifoFf9LW-_WF^FkKaxP-U<do}6vg6N8N3-Ow$581sss^Jenru?hFXsoX3
z2c}lChkAZyqDK%-dVw(uR0*6jM35Bd+&4kl(0D$g5_Pg&ZY^u7$QCUoX0MKVOSq_B
z;ON>sd>@#U3?q6~)%%OWk~q@dr-?viJLv^v6Eb6vgbM8WfL7Dd<O~`Y5f{{sa=knf
zYOaG*S+XR`arWX!iYIoneRQfLaQo)R=&DdFO01M4(-D1PeiZ02%7M^9-p*H1v5>g~
zEor(7EVKK9fXT%TlTZt5xBhF5l2I7xHAT$Jr>0DiT&RPqR8$dHuh%WHFw3=v^(WKa
zCeEYNl9f2JV3{tifbC}I-fh-jr?;a|un~G+*OW1WqR)5Nq;Qta*L%H!ZSTW}=-2N4
zJ8%YBAy|3;gxXfOJM*)CWWkO_?>3f*Nt|R<`8%%A-5ztQwL0}yK}^@^(Aqa`!qG02
z+RR~=6TRE>c&a_(%CoOz4fw^e)}t!|8+DRU7QOuc1ssL4YgPWZ%`wk=%I0E;QsVHy
zoJnKn!~@=#W1%keyslh6g`#9pscu`o&~JS61~wcP^nBHoQpB2RsNk1d2CNaYqJxbj
zH6vh}uk9sm9`Q_>#<Iz0_AmeSCU^B(t@(9{l~xrbpp?{GcW~~?gVpd3fO+;B%x66-
zJ+#9zD7=l{C<SJFUz$2|XOY&)U$IRMHgHS?Cf^&C_b#VxUrOnFg`xisd+!+)RTC_V
zZi3_}SyYgmL5YKeF`(ou0ulwu2n<n4g9svsNER4^l0i_i2ndRVAq+W3abQ4_NCtt|
zgWq@VS?8@A)?4eX^XKw|*`aoIb#?Em+7-HA6rBIYMYd9U{ebmd7MTw{@eQMEm$`rP
z6Ccedy<xTel%Q3Et5wDwhY{KPM`A^Argj!DMvXfxO&hS2DU0Ivl6Q|aF}W^};_x;H
z(uChWX=a%Fe&f`7Tm87;tpu8m{SoZs^Mb)E?^P7e@!0QYou$Y5Z1(2Ar!PCce3FjZ
ze;Xv{zWJ3AJ?3a@G?UCV=Pg<BcwS!|#<8LmhpH)m94H5~eBi^&53CW$?lp?&9&Q9-
z?CthIGRC%I%FnEGW=(0TXx_MLC+57P%lh_j&m;7$t#{M+#8fPg5WKDPKX$vGUAC3=
zv_ycdZ6GTW^dkj}=|$&xG6O!GgU#lKxy=3cBNyhs4KVY;G8>niF_ws+&Oc{~I#YUp
z62m`c9p~A;62Ez)rcO}{!MyHu_42P;fB!qDH{pomXKKSBe+XzFRj_5PL?cjcs7Fz~
zctvy>T$kJA$;T42;=^@Vg=4G;KnOrlm<7}`EEQgHy4!~ByI~k~v|I4|-699ZQIa~^
zcdH<7eAB}en-|mhQ&DIB-gIPSRl#o^4#B;4fIU`w2^L&_kCX=jNo`*bYJ#G<?~&0%
zdp~p`F5(OSZNIE_-f%*Y63+Q9czHywJdEx{juA<A4QzGv-}=k(U&$gsD$|_dyx#lY
z_giM!CP2}m%X%wb5H%7V`XoA+i?*ujtXX(z^ASsukdyqQk!6@UXU?BJhL|wIS5LEu
zpbcXINR3P$qWk~<LcQ?fvIzkcQKfqvlT8R8uqSRn8FeDEmurr*pr$6u2j!!TlxMOW
zBeHasXA0{3F9Zd5gqlzJ)PywF4F*v{dym~r1*Se?qQVG!C3JfaZ}qnIa0QfB%~-}9
zQu_PuzP-tMi{Q`sP>-9E-ZG4im+xtfl&iM$O6G}maK2qfEQMwX96S+t8}YdyOYCjb
z@tob3tG!E_n$surX6OTZ-|dyD3f$P($OW?%PP+8m5HaAhI(hfT*yIYWG$Vbi!%0w<
zZ--1!dG+Pq<p(&H8y|^&3}G&i(5@~VUXocNCh#!nE~f^@or&>tnS}2XF<0{}ehmn|
z{9w|d6Vn4K(RoM?N=eZ7o)J);6NG%K%Z|M$HSP%wXY~I_=c(9*R=yMXk>uKh64uU#
zur=E}lzX^6R%10fRvO)tE6$;oagTJa!a_z>seu&VhHfl%3+8hT^e)#;8uw7(WhXWF
zdZ^@UM}bh%p+Fx09nBl$MY>L@xw!^6c|t-yCu5n*4bL1b4Q{P`9V1Z`6>eK%dBo9v
znMfxy#dm9ucKgn!st4Ir%UsGP-?{0O2`4YzuXy?TeaSUoDl!|`-YA=L9o}NKw_gjt
zUpLa(86KV5Df5*&SEoFIZ<p|zQ2|9RpUJ_1DSa$4$${f<+Y2)kF4THz`CVgY_?tr?
zkiyDGZvNzA$HatKSm&8|)((C#e6T=w@{7ygXp!vZ%K(|J#y0(c;n$BSmrd4?zzz_}
zYY)HAV$|K?*gd7|;`{W~Jjo2_pstXJInlU_lzsUVAt`jfmNve<$6-IXkj>b#UX=8+
zO-}`5(q4~8(o((oF=X#iA?NlV*v)SZM6JJm-YxWUOqhuOrO_oYu2F*JZ=M=yzv~{;
zb&-wva8)GN&InWeXk$F(T^7aCeCW-aXU{FRC`rce+GJ#y!)mlh9#3l?1#ES&1t)L5
zcm!y5jVrE6=ES2C!lS~#*gF>LP-2$d)AN6<#F|Y^f#G!g1+I)yw8K;fS0OT<x-nKD
ze4MKipN~^SCU==&tRguibq=(Qu4?I+`r>VW<~z1G$V*V0(u|%-o)4UA&ntDp0(E;I
zJrOAZ-v=kQS&cR%3%!a$OPC&E9NB07u4mP4DVG!$!b^-Y*Gp1DF8*QMa~(ctzBuLp
zwhh0@tZ#ecvM}ykjftl$W7~Xqz4@%<%~I60B+IZ$p!#`&^xu;9$R8!fzb7zclkXfm
zr_Cqc$YoEXs7d;mv~@rIcK(h`xnd~|eHo(Fjw#0H0;KWyfgCWj!<fc&SFUH1&r;w~
zCw!02Zsk0hW891|fraAPcY?<*-TOY)KC+<7&pkp(hq=t+7tSRy#z+L)e(geY$VrN@
znYYzCpQQUF@S10Cx3AIZ3ER6EDR$O*Th$riUDfzT&L@k9P}O!Hgof*jzv8E_L6DeS
zW4dAjyyPQ94Wrx|=5cJZFq6f&W0ZNG@`xvoMlsJsQ(UbriBVLeG0ox1IGNnT4~U%x
z-!c=NT82ivBA8k2_ah$)F#Y}-swTZ33TAWt%ari@M%tciw%vx8igQmo1-atd;kP$r
z6rU9x&(p*$ZN@iRu*DV|z053)*W?JnktxQs<?_y&rXI>tf8QV?y|g_X6daiuZvTEa
zXYbuz$H!vJHP27y#iuu3JFyBQ3*B=ZQj8N97-gjsB6k-oWm3t?zus|3%}qRgH))Iu
zQ$q{wTyrDfFS$<)UY8p=$#60G=FwS~isTzr|D9r`Z@iKW_L{@zRw@^q1+Fmxae^gz
zTz`I3^(h)Gd_%8y1AIs}#=ag&B!2dbiQ4PmOY7j0w9*YO%_PwE0Q(vm2M8I$`E>Ot
zEhC!JQH9Q30CM%Eg{)rOAc&yzf~;cx8-Djzh6z9>5)}I3xWw{DJM(o$FKG53LjJpc
z|6eA@{(rL#bZUdO27xs@$yy{BdG4R2@4tm77`DH@`XD^t7?>OiN{q;}PiP?Q#v3RB
z3?*j_?U^jRf`1es0PzCrt^LOh0)b155Im_?iP4Rs36}yk7=CRc`<W3*ai0cyngZ)d
zCe(#@5g?^1j8PEN;CV<Dq!bP6dP1BpTgZE_V+fFPeEe`q7%B7{0Pt>|huQ%^La!&}
zP`z1AjW2dTw!)ETGt6Ruc}=sk!kQbxN!7H7;j^lUCjC%drgq7Xn9|Mafi<DU7nvtm
zNL`=!TEs;-`<GVCJvN>QkDAdBd{mO_-iPZeO+K5KOQI?`qAb=kPf8{;9;QW|+PQ2I
zf^wefnDRjs-j%7(a@WHy@vox?KF1}DsgJ8jIK30cY~o>;^v|7syu};={J}`Z79-R?
zZ~g)?-fby@9utL`d!CxV2r)HnWCSp;Rb#OLgC71q7g`WT@D|w6oGN(7&`q#yGd%JX
z)e}4_HDI07odLGDAlUr)ux2R$F)}kb;IcFNa9Ro0^Q_sN7w3AXgqGxvv_mp#dz<u*
zH}Ek5`y5-6fm)}X5og{!Zsk+iz!k@&CFAlQ9by%n14(Mnn|o<se$Wk*{-A4Brp?YC
zSirhi1Yh|2275|#jVS`d+|uDCy%dMaY&l*ojq?;oxk$p2Cp5H?GdwsJ|J|Xvn~dQe
ze*#}N7ddwq>B&4?X>hqeQngzxv@`7=-4Hbz{kE*dqMSZEOV;^@9^ExvVz>I;uR3#A
z<G)=g<}>=Hpe*5uKn41`ooHUU0rZG+Dk}C$EDP<~jZLoKljTSE5gmaS&PCr6nb6^}
zPe0f`F+$LY3GN4?$KK`Go>mRulY0{;Cee>nuz`d6H8(Sie-;LnZEAJt(|&a;xqbJc
z;!ERL_fd2o<#*YG)RRB+`*rgi@sby5+Pnjm&uc4CTQbzLd`=;I))Qo7)QVM*`droC
zHC(bu@vO&OQcG5*#$wH}ru3v^dTym=s9EM{{Xq#Oox_y;4a6{ISdkOWMY201vgX&{
zWL+Gl$&{+g(ED-YQnos~c4~ctlR)K^T6>&y{DJRfy9cUm&#tG$Z`yW?Mi>A5B;b{m
ze({{vdZmBouQ#`twA^azJPYud%HKfc16K#z8dib(_oxYvMdRoEXUWMbiDO}pOiLSv
zt8glsJ->NGgndlJOoqpt@B5$C;Kc>Stc+CjM49=CysX(duCaTt{bd{m_vV1y3L{t&
zZ^296udmQD#pc2jhupD&$}k*3adZm#ULKnyU4c{Nxh9!1y@SxI<^brxhsbA?ytY&Z
zI<X$0e`%Kz=c!}4_)B_;cDVEcaoRfa@|T4V)L^wQ9<KW!*_eKw;x6Kr)Y#i${zH|A
z%8z_Zeq+W{7GtlhYKW?Lx6~zVw_3Dz+`MQpq92&3t-b#m<%@An`z+5<v)d>wJb}F#
zLk@+HqRCNdgDi3zDhTbR^T_v}_C34tp2Zx2=~6chBr(tWTD^#h#?s|3C!%gcgQkjP
zh1jQ!x1a(uDVM01Gv&NCi2J>3<jD6@iKv#QFJ+q{_ie_Z&3s=HBMFdje_9Vx`!x||
zuTo^3dDE?Z_2~n}k)ldTy|txW_1LK4z*t{QL&(`-XMX-o@#m9hUw-?8I@Bo_(@?Ia
z?Z8LDgA<9hGY|RN6EE4hju?j2yXl~GP4B}8q7v6@?tS6V<g>q>_2zQev>`%9T#EOT
zMcS$_OxR5}k3r&c3M$vUZhRn_Ff32}k9V}=n6)ZBzS`w<-g29v*;r~_0)HD}lX9m!
z=L4=Xa3|dnL!ieZR345NYs9e(NM4MnuLR489p_{J48L=U-+UdB54AIh2Z(wF%So*B
z$5mXCFk3DoF-my|SCph&v}}019SZm7hi$ivsP?3#s$*(DDUnuo%E0dZeDf_P7Y$Zd
zPlHX+60eSKms&7lS401)nW=3~MC~W5oW%(F%tyw@#E5$9@sy_c-ALApb;g*7PdnV0
zuJWo8@qV<GxSd{%nn5MHB4~QOU9XBZEDAYY?p@*s;-S}?jZo`*+AwWPtL9D9P+J6v
z*Ij9CUYv-z4q=gf8Y=3A(5-7iu$Y$u;oJ`DG0jGfm;Zwq!e(g42D(Q}B5RSju<g-O
z^=)mQHz|GxHhirr8K~s?w4^(Ebwy^_gSN{0eD)FZyD(u)hZ3w&RYN>dMf%Lb#9{8I
zwNzI`k&}Q!#JOmS4`i=xyO^`IG}^Xo5j!ePpF2`M2lgsu<Nm-A+A<wc*9vKenVXc0
zP8LFZ5UF+(!_gZ=A)Nm~4)-K_f0R4FbNls)1vcqI-1`;Tnku@+u5T=ePns9`D6f=~
z;>PXdMKc#$o44*@e%m5G*&V4P-rgL^M-r7!1jRCk7$TT$V^Pr|3yTjhOTww=B-s{o
z>QuIWdh77n>ty9!<`)j;g;AEPVqQL$Bc#&g%ResX-FXCepM0y*>HUz}iu*qk<7m!M
z>v6Pk%)*t=;N!Q@P{xH*!PH&Z$c^w@Gu75sdSN<WldC@W!&k;lS*Lz0Cw|jN!EU|I
z?noa|D%+n6h1|Nr;lOlWo=_V>Ls&G@{Tg-WO?8S1^w>qh&YqTWeO%X%koA=&#vB=Z
zANRT76-qwHdguju$<jf)n(#4id_~Bn+Qeh%N%!N~pHenPM8YYc((1Gt=)B$~Sf)jM
zPCf2m4f8-$_sExTq2pWr4x65aBEtr1*w=S69?e!OT74xZ9VM=;lsq84d^c`j{RS-*
zt@7l<epEUZ^{T}aHl*tCF0~4}#VXAPCqR-c;SiXxB89T+2%4^+wJ1fDa>;t7Euxx)
zu*!)TI)7O-<XW~7baW11R&)B6!>-2<@FH?GcNJ-WB(1LS#<b5b>vg6-AK7B;ObT8P
zceriQ`#?yvbBn)pg{7}#w4&bd@0dM~VJ1=xk4nW2+{uIi_L!&MLHH2zExx%T(K71e
zeP-6xB@SJf>XSO)2N%!kNk-RJ`^cbcKky@suElF=S71o@XvLt`a|6zFTivhH>jUZN
zPv3EYyAB8=#z4c1hY4Bp9>SsD(hIiLK5thl{9bBvyH&!Gl6d)x6inq4%j0i-Iqn6w
z`%M{#Vk6NhdgzV^(VC{2frskxJ?m8Gk{u^V*?kd4QhtxV<0_JSP<Hw<2^Z7!zCO#L
zqd%=GLOkQcHS!i?yE2Tv$FPxzq|=+>+TiiAG$+>hA;MSO=&e~HJ~5_;+HBE-(>~wR
z=N6SUF|}z6$F*3)Xy%gjPn9-@9^pVVZJFaA<2q<MwE~H*(NRqX_Yk)WE~?38+L?|z
zP$qjW95R_|BkhwIpbw}dhFL0baLz=#lbr4gFkQGi9bp7q#@djBEliXjPTWZjMT_Lm
z6GvEqw5ZtmN)$vd!VgK6gC{wkfGF#&qyb>`d;ocS4jbZ~<anr&`|SwANdWb$W21`<
z`e33BHErZDKurL>c$*|Q)MN)jrET#b<PESlobP!7L22_~V(7>dKq9yn%u(G<f^dTK
z!5L2s|Cr~5+9o@;#=iAQWb|d!znLdxtRZVSn{bnpI>dllt8aX`G1a_2Ypl;)?tB|=
z^I^|{rsYLHf~;V{j5T8jjt|PV3@nS__c({2=qpT>LCDjxgZvQ_{7Nv$<6OT_HUv-@
zutgbl-dCK~I~*-DM&^&ZSiGDuO1l9JLSW`KZOo-$3ioPAAC<qdzy!=hMcj?y9u8;!
z=WZst_P7zqe!rdj{BAeDso-rwV8s2Z!J3UuUVI*w79E|wG&=sd?&^m_>%#-g!9b|M
zfDii7((J5@Fbqe8K6Sz}pdYPS2R$F{sbPRAm~`Gtu}M@=mVPBXbj39p^(@H@_Sa)P
z&Wl?OkY)3)JztivZE6RSuhfrO&!al*Qj)OegZs<9rC{d&g7V<r)J7b_%4+weX9f{A
zSKh|;aPL%D<ne-aE33@h=l|Nli##y)G2@7;KQcj!-l8nrZ{rdpHr^#Ws$sq+0-F|i
zezQWcW`$x4ru9AZ5&qo1PO!&3u+B;zTz+5D?NZ=$#>Y@E92wUoRum6~q&VAbR&d1d
z+XI5$bq@tMNd60=JXV{TocBZS<&X6mQc02fb|m9E=M%CSFY_$sR3!C%e%ePX@$Cuc
z6ZxkXc<R=S`bv~*%3uzU$FAGD#I$XA2Bv4!e;+VFv@ZWKlt1L&Y`z8x-OhhLD63XO
z5LN;*XM-c{gT&v~jA$dOYZ2;yo!%}RE_atV`((^L|LLw=?VO2)<q4Z>-E!350YuKR
z5tHDv6eQ{yb%Lp&(_ns!<RTRW40|g-!Hi>8DC-93W2AjA8-6Y|{nA%Bx(vae;pa_H
zmgMPN`ko7P9)zU9d)gmwyeNr2^OrFv%sr+%y-HOzKRgDrC!_Py(Gh!lQu)8X-<q6y
z5pgRcDx#$Y-~{xusvYs-%%)#tCwcztwnoS#RSV_1u@oh9h^u^C=#Pph7zU{%c#?R$
zdF_|9_s6?%9jkNZ-*WHsmOeD<5mAUT7X3-z6E()=S?-|#vs6lP;KfZ(8cCVgU%A&4
zEzlhxdRr40`fp>PPBOmnA!BWMrsR*Z)OG$2lgSPzUJR}wqVUh>SNn&veh7<`&D=!P
zgvs;jjl=hEEiztD#F)YWqvAHj{dYI}y;z$({%meYM8WQtI({_$BzUDyX7hH^$Bvuk
z)e-vFI+;2mR2Pksv-&xCL<%%yPlhI08t2H)T#a5mdMws+<oURnh!%oM3dLZu>JJ{A
zAGoJ{_i}n5F1g>|?I}7%F38w_$vP__0o5okJ{;$%nA_VQDJ1;KeCT&1e#QYt!<~yX
zv=dosF8Uc2e}=8oukICYHP;5md+y%n^hiLBkFFQrl>R|5bdziRu+aX(L8`&$8-RiM
z&`y^05HF6UCb`tl$T`Sx;}Zg-`r|nJb$n#bSL{0$9{ZVVZoEYogYRk`$NL=R^yVQ(
zL^tgzK%3yrhDv_gB5qME$#nY}Mb<XH>b<b59u<^Je^>e^Cyb$J?D?66X6#^;p<vP8
zaXC$5pruo0fJjT-XB99d@MY(a=^z^W!dHINt~XSF2%|Po_;qLq;}z3twxalf;U_|{
z@WzjnlyS$a+S;x#p};LWqdMPYo+(u~^^V+NN<aV@0;sYe9$nawFc2@MZgLo!vl?_Y
zGd7QZrHq{4OMKpXh&;ZcPEknEU@Jk{p7&0ALgj4Ezf6lN63=+k$>QbkaKhk=%E+)G
zLngaEXa9x1l+qX)vVYLeR?eX(D8D)aa>?+`%{E5jwk6iBdjJ+Vo`FDOhT1^*R|Imf
zVC>nKfS{-NzQH%7<iW~Dpr^<Ml?@av5d@F;3!eSI1K$Qqi26JJT|(5Sj7@%UpHWFi
z*T4=VIv}Mo&c&hA-b5)aRc7`)NuN_`P=fA>>Idbl;Hrwy4)>Iabie8MM9(}u_sj#_
zg4}|XBlD%GAV>^PJ94gemGyKrFlf>|mr2Lgx84$zKZ{j0>=&^U^JUuetqJlU!vC2W
zO_u7y8OcdXdQwHy%l-Zn{nX{c8oL}?=&Fkf%%F$(9{)=2oJ;Y%Wx*fMJ%l*F)IMf3
zIYt|Hc+P0~P{-?`xWkVLgp4QJ#KY;bJ)EOBJd;LMd*=62LaCf{Q7q#t!!!$S7;q$<
zVoK|rd*t#x?^=a?ikhcUDGT#7)jf&VWy5;t0VRhN^OX5GPi5C_MN&O>s<B@ME+40j
zyjq&Op6wU;r7$ijv0nNgi0ZMG2|}W&$tdz@6X&Q;tGc+eJ6c!j>WwGp*w+m!S{@81
z3&lI%AKx@8<v@?c>l_*A-vw*Q{4*y5_{CxNxdknoahtl)G6D}=k3M0|%CTLV<3|$3
ziTvI(16(8FtE%3^JEd3Q2#|P%pH8#NkI-y!d<|GC2ebx;=E7K|S3n@*rOAi>?Qe&L
zO~u1bg<U?!UF)lfJ*5=?%&a|iYeegcDy0gqeJw`Bq$D*m^Gdt|<)xmtX3;w)=c>jX
zxMJP4f8f*R@bB7Z{nseA2~$euSkz33;AMF?Ma%2e0+MJ+iQ!oP@Pv5uEiQv;LbG?k
zbAq3iO~DlLn|DW3BVqGYhN+@8AK!26EXz<7mKeKuMqqjV0m?kqQ|Q5P38|aB&%q=d
zXV={QckuHgg)?SA(exLvFb)zg;DLk}Z3A-%9L}!2^;2&K?*g7SNFZb4B}lzP?<bpI
zl_6jae<NV*aFPyo;k|?9i^!~_x21b*1-0k;Vf-K@KnAqO$KR*##S^s*hE`hlco3dl
zV8iU;Z3Ujqip>{u#Kzz-3&t<$j~8xBUOMq;A_Xu{vT}2DLd%F&z};Sh0Bv`?ve=86
z3!5b##m}M)$}^l#!)TzcAJ+-Utltnpn;&(d>&A;vlY$r{l8#v(l8E`QAT0kiGWmZ4
zrvH<`Y}tPac5nPQ`<?&#ySK{jwMjuph>AF6RPR|Zznm4fV0U^%4{+w5r+|}#$oFp$
z`9Agbdi?9$)rHhS6!%rzzuw<}oV^#H5$fV2(h4y&4_0B&#{=#~=)4a;uUO*6kG)Ne
zOKOf|PFEaQ*-h0<(!8I9-f5?>&ZpqA@Sz(VfcKgci)R?#{IzE3;!?h0m)6p0MLE+W
zy4vYUwI5e6j;z~#dNyCuGi)IA4FP1f`y-qNg4`!`1n!M*ty+HN`c--&*ZUGS*ZOuF
zSl{^9-Qn4ZCT1xWdw%lO^B|pZ_fnR9M?2>s{F+yIaS3-;^-+<bT8*?%M#Ijv%G%zy
zOKhphyqYL}uzg*l4pW@>KJCn3$g8TuzvL3Vd|a4!g7$))CFR(I*93m@90zSJ#pX<t
zN=}Rz2+#MaDZP@%nH=rE6BF%>x@#nyf8l9TrkirC7i(&5#qYs;C8`Zh%zb}Fkpn5(
zOj7NF;CS_i(XC=vlu1#4#?0OAe5KUwp{jnfsdb?<77a|BQ|>j)<IUIOTlu<ca|B-5
z*{!{MG&>}0RIyjde5(F?&&I(l)O&94_-8e!$h{s$FSVIpptf1ocxJPyo!H8PPARx5
z)`<|VBkRnC`4|Lx%R2H!PN!-mykxJ4Ybw6`&Tiu$p^e9Mwjs#MuC4!hJZjql^ZDg1
zyCkN^KX~%}u{y>sua_IR-p8Uqy@;uAQatKB2k|MB_nN0K)XPwZdz?Qx@=;|c%XK6b
zZqd6fTc?#L;eLOq;9|}hdOg|+D<<0Mn{?M0_2?A*Hr-^@i2Q#HUs_@Q%;B)9`_=PA
zKdwgG#auP!Jj;lIGHratp8lNCSZ+xo+T<wN^XGaI5AHpJ$~X3}ra-0^@!<?+L^a%Z
zi?AqR**ZQL3wv{Jcl0k4%v@fUsJPF+fa3sAr>)}xOe}uW`ZY>&UWp04pOcD8o;IFv
zsy;F)j+2}ma$W)A6R&}g=gOHD_jOYwap>9x?xh@6%x@D+Uyj3#6Fq&Uq@|AtF<e?(
zuE&pM$(qV5ux4|`lUIYjHqoAc6#?XZU)cR&qpCFP{+FDq+p>Z_8K}Tu)?4Z;8%caW
z>Zx_=NA7ICyij~~b&LO?&1fRS*&7jF_fkQ}dl=J-U^&$aP?eSNtBd35L=$C$6_$;l
z!)YzNWm97n4Z<bK*)}TK!kB&+?FQYftD^B0=bOzfS9U_8c>5y$C85e%IN`JBf}Umn
zvEqcd?~zQR-Ht`V-tJeQAI2#*_DUH(!?~N`>z3LWkD{hwfx6t}z~3JJx4)Ne87Vb5
z*(<Zu{rhexjuu4@lbDOHx|IsP^2<yAqN~y8-aDv1H4$5?H<hSU`1chnV7qrB?4cpc
zv_|+V`AEJR=q_u>&N3y}ZR@1;0ufK>rB|d5j6CjuMJ`iS&gS*cJLic7l7Z<uVqTXK
zv^)rw3j=5Ec5k4kx_B+aOaUju>mQr{^@ZdADoX!v-q8OfKZ)beTbBqTYm$)IlkKLZ
zeo)I$<^{oHt`o3Y13`T;j8Nh$2-_)AoUwWeB>iJXiYBs98fZ&^5lK&S0b0}(fVipT
zq26p=;Fa@&vTnbDUPyqj-v4I=^Yzzb1)&-IjP1<0kSjq)c#Y9;=h*ipdwYKDQ@V*a
z6g|CruO$NO*Fb#m$z@XL>(+5VkRWu16+{NLI2ifi<ij9V2$r4b_^<%M&$p24hJhI2
zoGQrg?}9<EZhH_iTK51I1ahj${Ar+oWqAW4D}1p4eW|B3WCs)j-R!vo<s%VBY5u0G
zK=_Tiv7u_wW2p<ouYn+F@>!nK)W!@;@i)Byc3Sm47W>B_p&LmK%&35$f4Fh!C=3c}
z0}`f*{5Iw)3?T6UIDta(i$&4yET@ye1xO&PjwkeIMS96)d&pW-M(;5z)p~kB@Bxn2
zLKKvl1FSU!p}QWi#?)Ym3|Z=c7ku-EpaZ~pm6~7xBPdE>fE@I-=ZOpS|1%INV`r`1
zt(BB}lT!9#xrmE4njPf7d^*V8%RL)KwZib5=|s)GcKAwD`vqgF?$kDlc~@1v-*o(L
zDJLnn_PAgv@`0M`V9~Qs&Ny9$pVT#yEUw?i`<{@SXY%jTVjdA$?oHK1{AtEg5!%wr
zwQwVl^g8HTO`7KTemgyo_&k?#n#@guQ`W&K<>B*mGfuO}b6D#6^B<%t6n$_s&4}|<
zpp(GgYfC?cK9+|ykEuF#mj&_R$lEq5)-XH0UWwFqlPM<}!+wt5d~>Dz=9N`B@+<Rl
zHLRYfqfi~o>FHww#E$s5eVNn5Er}YvN5oPK>Fv2+AY_g!KY>}zs-(DUyg_vdNKM{2
zEo0<T?1RB<^1pvdak%$Rnq~IqI(Ks>)nIqnn9~61NRk>UnW-6+A;aRV{Pw$AP0P49
zqL*u_r#bz`yR?)u;q8|okKd0{sKWuh-x%<jun}7~ao7MVP5TjKVl~!`*e;4CB<Koh
zePIC6!suHnY^K&4!~t}jO30myJobd`@0GiVG^XO?1$XmB&TU2s6WK1<IwO=KbowIO
z0`b|#I;Tm^{S4{xlF#*&*$SymMbVEgf>kc+nx;R8^zb~peC~&Mt(80S1tIShZ<bb=
z*=t8WTwoS_ZOCbJD&7-uzAlGjEuaUfr>tIrF02FWefBpEJ!27tdXMzY-8Vjrsv1Fx
zpvxSjDhM=l29|V>tm-tqDgAWHe-i21Ny03NKK?nn^?D@(Wl4~!c5Y39ic?1(5EuL7
zwD^uod9<!EGv_OCsb%W8x}%f{nnj5osJ*ti=xFmv^TqRF)55;Xg67UfQB)3{p9I8G
zqbS{eeyTejnuUIty!?{F;<xe0wWk7S>E|}1I1|N(Gozs9=&(a9gh-`b%qqFnJZ*iR
z+R~(k#PF(y{+emQy^r*(=_nJ3A!o>M9ju0C4H)14;0+j4`q(IOHNl>X(?r*+V^N1q
z@@dmV;_;<gK7XLn#-hoGeD;?~nwD1Psjgn7hkE?^am>j3!mwllkyj61#L^Pf<I~ms
zPWM-dy|$Qv(s(JUS(<=U;fCo<&Hd0*P_ls5;$K)?psC<~IwY_^sD)W#B=pc6Tm53%
z_B`$feCQrx4cgN-`c>^gkX?eM!snzP{10^X&sU`OX+~w@Bwej{=^5yaubnVqwHIfe
ziQ7DNYNGC-mxVbu<txsb3ImQu<*YWR)L<)vxpt$>Zaklqi{e@0!;!$Q;L9$SVoqNW
zlDGZ{zL3d31g|2`q0v{Nyi`|XGKg~9AlsU(eSx2#OY(d3&$BTh_qwr_+`kfkTOw85
z&}_D^u>y3P6_<YS;Urh*0kX{hK=ym<4AOX7M-o=MPYuUdPpL6Ab=jw0v?p*m+PG9z
zx#DBZN4)$#4P|z+O7y|(O$}B-`_%vwWmAZGaQDkvbnP8Oz`!jnd_^W#s10Afqu6D7
zEbM!X%sr#(0li~>^PP&c#QU%t20gdDHo;Q0RHaQnUxiis@#K5~DS1k#BEc5fh21KB
z<<j{<b{#zy_L=r9{ZjHNg$kc;qL<EBje(iB--eGZP!UQ%{$-h7KaPut{L49scUTq(
zJIEms0s9hh+||}>6qE)e<?IW@aRP&tB)B8M55)s<&Wo3r3I9h2K`Y~B)ZgF`;g{sk
zCmB=7(7JX)J9$nsa%eUVpMQ}L1h~5zaPuM!fIiq8ZTw1$WC2GXd4T)7Nf1c>Nl$gr
zNr(<_B}UZ_e<0w*f5S6-mRJ+P@!!`X3#QE^NW7!UxT`9h8p3~ve|YUo%?{zeudObR
zoRfi0z5n-1rClI0yn~>{%z5<x1fkz(ZDvxzTe+9bxP12^m8X|P_46-Fyvb=TYdT?_
zzt6}|@!X4nNAS$}S0_DvJv~a6&+}`VSFI!Sno??eJelo}%!6j#BVRi=d;rzxfSB3F
z`SFH5&vwOW)pXFEN<1x|L-Hf7`7?Pl9Tq*93%T0hnkjI0H9dl*IR{#js0$Z2^KWOP
zzw94$sUAr*)Jn^aH%@{WZVW9iKUfbMsHh_|f5y%ELl4QuVoE({rFVhM_l7ibTLwE`
zV~GXm0Ea5moyWcp+kUz@zm=tbt|6c*1tnaTtfZ8wR=c~br33I-<Gm9{W}Q1m&WQr~
zl^h!qpl(6-{!yEq(;~amIkj4JAqpTn_ZOLlfybx1VO0#`+tzz*m(Q>e{ri44h8YCV
z`VN5ky*{N4FOI((Mk5YPiwtkQmx}XrT=73Q88Mbn=lQ1ar?u$c_Eyc<BPVY76P3NX
z@GZVfuKhPw(^uoFB+Cz#4p+;24^d!a3SDC`tPWR4!ky`fs{D2Zf0_()97ba&6`}<K
zGk)2K;zxb2#*E#vtKVdT<6+KA{(rbP6YJbi`<Lli5w*jV?9iu>1!9Qe?^v+o*@pb*
zGMG&NaT$}jGGgu=2bB57jLNI_>Hr5`9&&Y^2v3bEeOY_k;r8xvQ;`o=idPC^v1)p{
zFKFar`-B%u@}BV1&^}znt{)wly`S~<2_%4!w4ScaG7u6!?9gnpSy~u1@HYW+p*@Ka
zJ8L57<EisisjJFl6u39RsQ)h>(M)yz!SVc4uGH|K4TPXT55Vb;A6V!(7_s(%SsHUq
z)O+4~qq-g}+SPOo3<30-3pR5idR#AH-s3jBW79vR>mfGOB|PG?D%C0OT!pxqy_U$E
zi1-}|dQS@MDaa}k`(lk`rFO84);cX}vArmrY2wb5{+X!OyWL`rgLe1&)7~in!>>v4
zAN0GG86QS(cdBa`Ihsfp_B3NF4=GG;DVE(PGT%N!!*gMHo()jAhL7DfVL=o?e5!KR
zoKp6jx&INGCYPldJDGGP35YRwtJ{H9ZE?fr{u3>~v1YM`<<~LVHx%DbkEITt<HJ3v
zd;p7<pNdORjJ*pekURB7$Mfea>Rz$Z8Ba<a#;sSnz)IpP&?y$Iiv{aFpBYV#c;EjA
zr}9)Wzb3*j2w#Yp`RFdXaR;~Z+h@ZC!Do<od}q*CSZ{PM);QrmW451-GTBaJTuHqU
zn0&uBzb9y-UlsAPoqw*c`jii+Y~E`NYN)))7EZt#H2sw4?Jqc)W4s<%E$<QX@+$HM
zCy9OzrwUkYfVfJR>>lGa-5XYw%Q&O>V%7(Cj48W^rOO<XoreN-GWCpFcYV3;+ixBq
z-sRQGO(yc^XM2L!?ClH&NUqvEO*3$!Fj)3+#yqjyD`2L|dpxO=`E!74?d<G36ya|Q
z0b$x+@;aj~5~K(S`ep^x89n_!I8^WhuwJLI3i!wVcZaibz9q2>bHTWQw!I%qE?Z=s
z<nF9JK)kFz^<6cHns@0M)K>Wh;>Ws5AR4`h7@4LvWLdxYZsuSo>#+^bTua)|>}MrO
zp01m#$B4BO+Vnt<%hP*i#nCF?Mz6a%bB$ZU2!7iFk+|mLDldxC7|K=6&Q{j{#IBL;
znESpT0lR&$o7xGpWK)(jf5fysKC=E}>3%@1k6X}hk4g~s#-f3SFkc;S?>c(Us!<W!
zziF&BaXC%K=GR!`{GSiQl~s+X%if0zGxKJX5fKOKgQQ#}&IX+~Sh&RQ_+(Q9e(Pjx
z%3k%w^uy^|t^b?|-Biiu$q17bB4h64I?GF85fPbHun~C&{z3H;>)`b-#^=%um;Mwj
zn2`&RPq_a6lb3Rz_4V(NWZ+B=Laeq{)6(jQidK;GmfS6t5!t>QSCO6XQjml)RTSs7
z9|_B;DbP>OO}p~mC#l;mol+XaFp*aHMfZ@IX7_4JL*6o_sNEhe{-CIuqD<<XTQ6zm
z&J-BuCCjF!m>lmpz&!aDbq>6boLQ#=hJWOD^@TnwON<6agFV?u2wi(#%(DSIxH_3s
zjH&8hgAZZPSJDc`rRdv$w}+Ui8wig-*zxD0c3*u~dF}gA-}JW+vx>*I`w#wTVN%>Y
zt61tr`c&U0p~l5uv>rBIcf$M13Gn`JNV!(#vQ_nEQE^zJyNJwAk5x&6)ZI6Z{0sGM
z4cLIm^cQ(<gZSkcpWuZHbc}Efy6rtDeo2~6zErt?>dgYsZghrHcpqW0nRA#d1b4>=
zcd|+lB}~&0^abQzHqjn>YZ6O9-y~KZ&-c_aemda3q<V|Upw{O*5j+CO@g&6RS5hQi
z1d7a$htWd3sSFV7&nhAoNFQ`hV$hjX461Yh3O66@EWS?!!2mhNg4IEiL|6qps9N=2
z0Fnnh9<Va`g&HYAMSP9tU_FI-<u0O4^GyA_dxCQ;-E2o2P%WTSV!PGn-dZxzQ~Vd|
zWZE);)lE-A$nz~I-hQ_qN(c3}@(~nZ>xm%Q4{DHn?K;$SM)NFEJ7bs-V)>v7sl$E&
zZO%*%7iPH#$zeDN9OfNGA$OD_p{@qN;|xdxqhb5u78D8l4K+nd6C#Cap&W@@Q0gO7
zN@xX)FCgO87e<1`Jecy%>MYo)CL+WtHn^V{Zv8&9{8Q-Yz<CHl0VaYTbB10bbiWD^
zb^(=s`WzL($4tPoJir*x^$4p_$gmf51PD+#Xr5k#4#^x7PYbO)Y9~iFvJoJOTS?&z
z0D;KpMd;%p1Sjhx%8I6yCjcv#FhFe_0D~7~h&U!dipLW{;v<9*+X^-Eeh?9aOisTl
zPe2z=AfUQqY04G(gly?qTz?&J+z5PKgdOtpBZd~N1|Z`MH3&M-$Pa~sokPxCC4oo?
zp;s#`5ULAAb89}BgVKEhB##_{-|uL^+gG#1O>cn9N`?ZmV5NuP;xs5I0t_rAsL`iO
zhng@U49K&<uS<oPCC<Wd^Fq3LvIML=0L9q`ayU^QDU>Bk@Dcxn@*7!#U$MQd8Tia<
zkW;g@J0oQqL7dXmpQ$+T&Cwfb(sCp0jg=;VuzV0LK>1GpF#`mCUtE$UXt%O}X5DjT
z3H<cZA><@6XIKdVa<g3v`uq{(yVR1y$(o5Ee%&;v%hQ_>s!)GO_$#fL0djvxNcZ(T
zL6Ze44cfY&2w73)$`CA~<q6<YRpf9_BMOMCPz%I(gdtTf83I+CYKXI`WxAn3fFAgC
zgM&z3#=XvFk7Rt{SUk*3?rIzEt;c@zCaHXHNS1&+@aNv-7g{U@<xSHlUuQn&Wv&9>
zgCMa<UvIyM9G|;h3Oj)vmV|~`An2sjtj#Xq4X6k>fm^V!K@dTww+r97Df1+wb0X5z
z&?}AoX$KJZy=gtX@I;1j)n0v|K;pzsEA0w-YOTX6NV1uIS~Cto?I~~@PiYt_s8wGj
zO))kTzq6W#-ka<l#)2IxAi5&BFPMlSl%>+oy5j=vPe!mSQtZm@-`?=@y`OPYR~UH4
zU|RO*jPemn#pAUr5ZvLqsV@W(i+>#bbN}Am{^(G<+-<igODD-seg5^@+ukK}n1kk$
zxZ#=073t&7n2$lWX7R&Q><7x3LwAMOk_{GW9W{8_XPnoQq=Zs(?W60$#+atQ{(Ofs
zSBnAr%Wx{Z5mkbquMD=%d_HQX{6iB)sSgGCVmxT-5X@0byc|DEO;jFuCfy0*<`>$#
zefhnNtUvF>l{?CtuNKO49`meT(daRKNpVb}y!`6KrI7%-&bUAUqK7m2NE^?E9Itwt
zOea%Rf)i0Ds^yl+VJda8YIX(_rf|Yo3zO8LcsGf%dFX>#SwgpYgSv9gnEp|uiRF}q
zosqE6p75V{QANzF{f}J%8wv<5!htmQf8wU~J~yf?<wC;CoVWqyPLsaRFH@-VKcUny
zIed5HE1Vo_48?8ZD%Kn8WfX-IQD}_-z3jZPvSwEz=&8t`1~RD0GOxbUj>|$@y<cSp
zZHV9#H_{vOb==NJr!;iJhA2CEa94vhdr~#D>jWL$J-<NE_s-5p(4}ODZp+9Rr+o1*
z$1!o69|OzEZy_vPAL_U~w9B0LV#yu9qUfvyE8##t{|K_Z_Fq-Ff}{=R!u^RkS)sHO
zQ_f7(;Q%^ivN=FX1H(OrMIE+|I~C^f@5E!fx|~#+T@dogYcV+wdmsoFI{;)$6UW<t
znuVgITNShMW(Z8=`d1OyQ3Wb0-h};U_SN0^ZzA@`;<&2jX}QNf*YDiphBbCOE#+RV
zW@3sn+4!E@x^i6PPXxW<el+tI<O8HOx~s@DO(dzTrhP;OJTyu&LsV~D`S%I+f3%4&
zGo!HR&9ctDpg-nq-=J8w?&Z|)0Wca=BzY@TYe@TQ+IbHz`?R@m19bq~YIHFKTZwgZ
zMBLU7RMQ`O$D7`eZ-RK3aa5pMM9Z_?&HSg&+}La|;CxcvCnv$^LLF_u(8oeUNmA@_
zUGm&ZUOdrMz!7>f;d2I>6+X%@+`kiGfC;I-O5>{`KQ*#F>Y{S)BleH`9euBM)D6T%
zi+AF$&7W7=E5X{za=n<RcuN$+#>yrtw%+;0PcCV8H>}z>L|{>wU^>?*RoJ%)kTqMe
zhHqYFB&GlMMNKFoie-aL%%b^FI70Qz8P^t`z>LBhY1_*hx2{$}FZve^Hs7RFc6wD<
z`l=(CpH({7?Cw^tN;OnYN-d?YnT+JO4D7BJsX>OA6++$GSE(GYTNFAgAc0hBk}P@k
zP2slJ_K%pMmWp<05bn6+8R?m1PoPV>Nd-><1_7MSMH*_MONDfK2B_hm%^V5g)KoAC
zrU$NYRM0e8hV>I8$Jri1(+sH)T=^CUl7ljr9Qv^UB+?sl$ZJs>LU2nFK;OxmUR`$}
zgg!CgFWY{l1r{y?<Tv+~0cjo9TmQknjSp%D5z>ugjeIiVIIhuj+ZnAlx1pxAW(Fvo
z9NPLI2UW{H<%2%l@*~WW1pZt;aySg!$f?kSJ}qiOSugtt>9p(#p`&-KNX{GJfW6!q
zGWsY<06pc0i}><E?g9g(vwYzEVh00fPj3odYQXIXp)UOlsL_@Wtjohqfj<ur(1B9O
zA>EdKLhVZcT+C;6lmv`N2xt9C{}KbS&-W*cYk9Lb<QK5yAFA*V()d#?=3gOz%Gn&@
zENz+)Qpty%P7{yXs)K;dkPm7yTe$E65Ao|EC2>4x`E>}g1H6*meGIBu;PR^s{3+*C
zCh`JJ2vRz)7$Tg6zaO(j44#I}T|f=6EzqECSHdiWK(Y?af{@`a2}%eq$<Ev#q3JhT
zWy6lN20LlA1NLdqH-Au9|D5)OmK=KBRv9Lr4{*N9c%=?7xXTK&;xYbF(;>SA8qR9J
zPZA5vL}7cKy$Ard*i^ru9|M|3DlU<8fe+c#%PlE4z=LoB%Ji2v!IN9}YZ$b^g(k8E
z1vV*gfy!>dlMA4N>~-|_lg@x-Cw}~cqnikg#CCuvIq<5wD=W!cfuFZ`8gX1?+RLW7
z<Tk9lxIZ1NTaQxcMFq$d(7=Y&-m<K12uyOdCIS3us?D?MnIIi!#$!hEOF8tMkCcoy
z?|n9vEopELpf50LL>#WZd*r@`HYqmow9^J8K@Hf&t$3A4PaVvz6Q=7q=#SNz{rR@B
zb1f-R<G+*}t3)Xw=$E?xv_^ighf<tp)j}5vkO)odzU0Zr9~4ySdT7U0l*1S%%0UU6
zHxG^*#t;hdAm!Prc{30e=_oZk6X3Cm6+E~Ge%DUWY69C759~I=jt<Zbh4J7xl3DOa
zaTPy*<0%Ep&1^?iMKbK&U;iAGN<k#?Lis&UZtEA_D92n1{g$C&J;8#H?z0zL6y384
zYCKv~Sfho1M*AKYXZ&zP@)`Dxev8`my*4W>KC{h0GuQ?Ggoc!0%XM{E4O^7cZX%|p
zV2Sf}b?m|ED1LFvV|z5YwzeexuR_S_HOj;|OXzrv=L#8-kn>ZFB|q-sb2SscdG*8f
zI7#e+)F~h(40gf^i|=2X;kR$^cC^F)k(~G<^3u`_s&p_WqM|1vux@<IT`q>J@fDwg
zr}x<kJ<zoe`VyBLD9(3X98~T3&7^PrYi#nZVRAP!k0?4QUMit`XQ6ydCr+OE!XtnG
z7^#Qmjybs2iaXI1-ez)$cb`$H=PMYMx}Z#y8;$qZLJY<jft!u~(R74y*6MudcFuzK
z`@a6H-Y>+VwxcCZ`gOU@5WFXqk3T;J|2Ou2Pzk0XN?bZzj6!X*lf7%?6jVbnglK7u
zBf~BTec@dq`ex8CEpRiQg-$*@a{KjyG+io-1q<nc7(Ji;6anXFM+EI~Mq*w*5AV+P
z>)uBcla7}ZW@xC?HY~!JJw@O5emD7-k>kY~DC%DCHAMv@80`Jox?m5AEe*BQRIz8Z
ztjmwDaL^Qvw%%|QmAJ7lH<7JIu#=%!%|dhcU7Oj{{4tx7-eO0(dp?D<ep!D4{A49Z
zJsc3|!Z~ZT3C>GP6Y%$2wOlY3vt)|mnesa=Y3Z4{7ys#@N%tKcj2FkmW<O#3ts^;T
zBzJ*1PX8=P?uA!f#gJu=^<wX(uWl>hT75ahw{vK9GIv0eC{68djm@uDlfGOQ>|^1>
zIgN@J2KJ+Cd72NcsK}S3L_}248)y@-w*`^D`3udHwfFyaG8J?(`a@$~T^;eNp7ON)
z(U;j-Jys93f@F(ZI^U|}VlOr!P1)?CeXg5Ju@MXhOvNP$4Kwv$(71SA&A{P~)wS0s
zo$v8EZ1LKdnBGB=7FB^n0v~l;E3f@M7;NHqf5ybB=<)8g)7fR$Xt<1Bc~)Mmr|ffW
z>>BSieR7P(Pi}5k*SV#OS0p3yjr#K)-e$;!<ZkveEnjY#7q)q-t|FJaXs^_3r>fDR
zB?D_Fh}K6vr63m5DCXM}<!ArNq#CR=i~T#Ji)(+PYpJ!xP8{_HW73|dzoa?Gud3aU
z|3I{PDR--xUWGwWZ+u4GUAOFF;xwh_W2MY+efd_qGh$ECwIr#$Onm96Y{wMcm+(3-
zC50JE##Ubb4eHQ39%=c1-UFx7#|Y-x972wtkF$dm2%hos{>;neQ^(wqxVl%KF82D3
z!xep@+*}erv$Bg-v@g}=iFm#HjKr7jzeep^)#NM($D>S10}?GZ75>2aK(+wM9%Ak$
z2TzT9$-l$#6D&g{uQsq95JaEwHmIJcIeNS*eJoV~MoD~2icBQoEt=Cd*X)HX)3P3^
z^)tg2iuJ`1SrzGT%~&%k+TDT=YwzPWF^t1f!pfPO{d0*EhfEngN!6d1Cq$^>6a3O`
zDAeDP7#dQ5Dcf>he0FcCfTN1TA;h@(DY`IGLbybyNkuRfYx<m;;}hjr<aWUUl~!}-
z0uhnW&b4=8g4PHp6V_7A{WPp*zF|ToWF(Y;3B$PxszsTIP;ZlFlLmblJ{1}7c8d7(
z<x5*QgWj9WV+SY0nu<xZP^h%o?3^FHoQeGG%B{%K<s##lv0;izOQgMipx;y)uXT`6
zAU8u-c-Cf2yyS)wKa=lSx#YM=c1$?VYvC)_?6HV=;YXo`|1{og>eSK;($mASsAEkJ
zhn!uO<*i-EZhey=Q;(IfH_sm)n6rhW$<5JxWL3O1&oWh#s#`-ee-HkUX;h0m6)3bj
zE2|{eI3wQ*#813bp^y3yBuy72MfY5?%ckVS0DOd&bLq3yXpMWzz+%96H-t1_1tCQI
z_6$%fU2KuBarmZ95^!=rLrYvROYkKQ!M*YSAz^%x0Z1h7gL^)FP#^~mQ}I3^sGaAh
z8^(A**8?aKsl4Nbu7Pm8Clykn8bN8W3*nm}QJL>cxkPgV50XGnlnSuzX@dppz^ykq
zH8UgwmbooJZ^13K2Q9+oHOqm%0cD^#^9&S5dlsE>f&DTa@nx#zMrYDbB<C9{iM+mC
zf}pngZ^CNY6##bj1&gm1+R+DKH~aOeR2<fg$LuP;s2)GC0`R_0Oj_1Hg|yNYddT_r
zwkY|>6s*)$K&TPVRCU}bAnPlqd!R}O-@NH#SDC+nMru_Xe*q78`o(zi$AF=s%Nyv!
z|M&J;L9h+XY1;wfdOlZRcL1-Nf`5iq2*DnJ!#|@nHtbIcz`A-zzR_f4uOtw&v%?un
z_q|WtO*!RoaR%TU95_VCFfG`GbikJrl_%h{^%P00raKLP{!h?>y-SR!3A7-d*w497
zJeB|6zbzuifGz~z^pKdZu8EE&Ms^5KTB@7@+7aJv)id@Yr;u=dIsd<aTwhW&wPWz@
zgw1yGo#Hm8#UK#!Wx(PY6~p)-itwzPnqB$ZFe^3Y9uEMsSefV~N5biDe)&@|T%oK>
zH<3|9ws5d$4)))Rs^IzCikUkz?<vZ53QQ^15Z{V_4VT(qlz*OA{N)Y&rh5_}PTdQ1
zTfxsj`0t~~-^S8WMS$<DqMXk@H(F`nxgw>BV))A+qA)|<Z}jU70A&-8AbEmE%OwLm
zn=OMqXf(9(y{4i!RJ!~O^}C4~Ootc-Ts>9_k7BAkl7A_QG7oghc(gV+srs!1vKhx3
zL7;JBqDoJB;jDv$<C_b_!i#)!vjXxT<>ciT`66aVc2wcnmoC$M*_~AANt*PZu3wJv
zCpX-Q@Vpj(I$qt3rCv`;B*PW~mkXBYaa@17b&k*K`?m=PA>fFCblWtl{t{13U*U3H
zY@6`(>J6~uF`nK*ai@M_^W_I`L7zE&+|{-5cL&AXJHJi2@KOFE)d9?_V$R1hf-b{{
zuHV(fPT5tGt&arByMT@(JHYCI!}RIJ4VI3EzQ1%rL07+o8X!Kad429&AGm4x``Je7
zN<i1L<G1lqt#~^@1K`2@u-GC9v%HYLse}3A$5(y#cf91uu8*At2Do!US}=trD$aAd
zZ*0w{hL7LAaYC)fMZs=vc&PiJ_rd3>o;t@#eKv^)NqAV2C_L=vzOmYl7PZE~ncp5l
zK?^^kl{;ZIK{?&SEm>v?ib9rdJ4tKn7E2e>YkuQ~N2~=(AL2;s=9|AXPr5a%&-adv
zuOsH&_@ldoZj^Q|{r1m4`YzH(?z=Ljf#2Q}?EK5|Mp8u}<z~C?x%+DB6SHUBn2PBr
z?-R33H~Fj3PZhB1)n{>i9E{!zQ}hhUh|8P3?||9+!@Mept|zwJ-s%G8F7#6nImZJN
zj>h=iaZ>5ea=l%t`_NrXsKS;VL8I2kR1qzpgVFQy<h1w3oZsE`$-fs_BHN!DES}G<
ztEp^v{LT++(c$*vJTpEO6v%|6>jJ>6?q6RDxD=?p5?g#-9EO`?H;uH>$bIJNW1DsO
zJXZxFlYPtI0w?b(9PjCpu?C;{gCop!Qk1zjVAYbzBs+rZ@J*-y$e=-8x)wEr5nH;4
zfox>4qYjO4T*V*j!s*nC+yWeL{z-eLU!~ROWG87KUYu#rw(H<8`$uSW170%2UBmQH
zJoeswCc{?y{_n9U@j9<*4a{4n-Rb%BOPy=x7>@aC$0Fje{WwNE-KB(rfbNdP0NLJ_
za^m2XL{wv!TF`ntY9c-8&BVs~eAf*2LZu{rwNu~(Cw;@nk2V?F-|~h12uSv<Ul+r<
z`y#Xz%6z^wtT$-!;0Dbxo2ME<_udyg==?2vGYj%VQxk-eZ?SE*D#jxN)z$@jKg?8f
z{4*(PJWw2$%b%=~*4MVY*6xU2>WNGXWvZ!9=I7tV`{*HQMs1HcRN^h1c~wwqdSqqF
zJeI%WS;yCnYHe|QbfKGFLvzb+x|znxHAxMOO2d+l*Brv&lbu2QK<3cJrHZlo$t#&E
zz&{KyG3eqkWsLY3IDL-OmJ#vk+*W;r$mkBvMTnPb$UoTmA{wJ%g78Tkn!^SyRj($Z
z=7;KzMQT287O~FRc>rfOcynbhJup+v+-^db1z-8Y&A(bVKi6I&v;H&6B;eM$dqrXE
zabjlD6}IS-w-fu)Wi}&u@#xx=*Zht5WxRQvtLuN#{I;jo`8ZS}t*BLQVg~%;V6Zh+
z)4+%g;e2XmVoG`!tGnZ!I>Q@YG8sKS`j+R!kxtdNR1rP}+{{3r<Yz_3W8e8?dw%-;
zyh>6I|9le_WwD$ZCe#^o_=pcPEdnk<6~Z6D<fdI?R31ozNy?srtx$-5zkXO>!C4tB
zjE5<Rnswr-?77OoIO#|N&X3ETakt38`U=FGJ?9vTdcUu!PSLwGxKTghU(8i~_$z$+
z1`z}WHqb4JPG<onj=XmuD)%T9_~aq6LOS$*`3S)BHIiC?QsFwD<2arOtd|0qVq9#T
z=srm^P+KL(r~I|pUXBAKUANCTNyi_8KGFB>L|~@BBSA-$Ibcljl;BbNp<e}S(|Si`
z;QnH}&u#DBS1S{sai+1K?KB*3@r_%)y@8PD0wdDr`f}|zZz&lB1*p9w75RUdd+WF;
zyKZ546tNK%5D^t5C8c3N1Ow>?=@Mk5W#~o`k&dAmLb^L7B?KIVp*w|vkydG`Z(rkm
zKj%5|e!ug5?>Xmv=dWw_wXe0;?$yhn@34c!eWc2V8up0xl=rs5R-CGvAx4h)?d}~`
zn0P~zkMXNb26KS)ZJ#tcS8`F%Tnr*AGlCZk!9{KtD>>|K36N^4v3u+;=5{MTI8k3|
zS}}f0dwCVyf|kMYKeW`qah(6ceBf<&stjO$wQfi(rtJc0Ak6Zqj{I--8UXuZaO$R(
zJt%TkFk{jK!Uk9$NitQ-96}ke)`q6!t=*~M3~KDW8$k0k0i4-I=%;-j<iTY=Qs#35
z2D^Ov`H`OSoTfIh<tO5?hR~q(ovN4MHsIoUw`Q|<v;hYCd4mloZO?{Ex!;{E|4%%8
z{thY^b2*+QJaBQnyIKyF{BJ!7Z7<KCdQN*fbta%o4B^#y?)vM5%z!wUb`{WA+>2*=
zVhe67aRWa2_p?c$pA9eLFB>Ph)v$nNF)YLMIkvhX0Zf2v0pd|e+y<Ob?8=0TO+XP!
zO&?uu1%vp#qVOC+Pn|Mc!^EHrjyZ4ry!bGXxeWT${1N#M7~oBb{<nI^p@(1^&0Px&
zA1T{F<Vs5J@Oh?{YxugdYXR|~v)>mh-keKlh1+mAra~)M8>;%qRv(iC7S)X5@TF(D
z?_0Po9gDp&zM!Kvb3(D4>$@Cr*#+$<<>xbySFBRA!}=oVzW8Pf&M4FwOWKH2o}XLZ
zusq$MRbRH>lrlWa6!bwz5v2-k09Ov{<_rh@U`99;QyNBx<S=@y4+Zvx(b5fH6$nZZ
zVg#$J2d9mL0{6mB$$Y7JO9Iqru(Oe27U!s;WAyUfJD?_X!_X={DrB^TG>qaYZ*^ry
z2$mM&=c=QNfua#;E3N|QE!R;S8w;6GW7e*q_H|=qL+y7zkMfENf=0FzeD|sVWmmma
z0M+5K`}4_>Ju9@M5|Tipg?HhUL$@s_;?zam`*wD6lXed4j<!7tY}`dXZS|42J-=)3
z_c@^E2iK1ukoZkn@2`~|7aiAGB6hM`jJ=%eOSLPetYjg4xdGz3@<>l~Wz)KEZgRn4
z_UOZePK%O?cGR~zC;M<3i%;qt*j)2P4+~ibi-OZ4{bl<JLt{m`<3&pYy2z8vE0dG5
zDBGLwe(pajCca>hi*Fp97>Elf*WwVAdF=l1PMK1uno7<WT6$Nwe+oT)yo7UK^0)vQ
z-(|sXi!=Bw_{fZ#BaQxjOK|RpzpuX5{7y5XOz*~czEtu`bL@bpyml2Tf@xYe@^;n@
zS%sKr%WkRg)oC`e?IL4L+-ON))hEcF1O|K5S#eq5b2xg@tn1oJkbS@BWM+c^NBS}b
zzV>@q#ux*SRBa6byg26;#p+rb24e%p5=<uA;*RBedbmh>E3&$deQWG?c%++iPkfAJ
zm5fg?Bc3#;?5njZPE7$M5OHCEml5tvZ*Mz}w<NX<__|A0;|f($-5z0R)k@VtJJ1SC
zPK&}_q_X<G6`lLQb^@GA3dlc|gUW?f1P%9>!?Vzbm9^;={iqF1?Wfqh+~KL*NlFF^
zh>?TAjPTlLsb{6IspU(#^+V@sWBXs2iRdX}Z;ih&D_O*Cic0Tq7d8w$h-3qbb4O?u
zf>=w?s0l^%=PgaJke^O}QA<ZcA`|C|R1kaRspi_}j%={#P2{9>Eq^h6Ju%T~)w4C}
z_XByty?vH@_|eE^S>)P$`jbbl3EK^k_5*4Aq3FnWTl#(Kf{EzGNZCr>zHE!Wy)n^*
zSpw+-d(;IDbp(3NYmkZYfqgZ7Uc-g$u+8eQ$mpS5JR)ZS#hZ&;?L+4Zs5+V*n5`Bz
z-G?>~jJ8Mjg3;sq7_%&WM{;*vmEVim{HtDxWADT1&GKti9Y)ti6>i;VNP_kZP!GmJ
zQ*#HdsG^9?En~z>@9xurVwlka7$E{N2Rm{84nY404QgX;ZEavQ!ftMkuti}r=y_1-
zxfC{&zwiB@IkSrf;Ql~9b@wem1US$GXfr_f|Ed}2{>6J<9qypXjfyo*g~L3l)xK0T
zzw=!H7K1QvU0Lhzj9If_Hm_Dc8~G7Zt0RpC%6XVL-SBzpYw;Tj5KoNc_-OP2z77=4
zFxufa)cST_K*Y%c?J$&uFUflKIcR;;gAY^&_dvTQ$bw=ce=*Qem+-OT&&i;fIw>p7
zS?CznPZwbS3BkFiLLI#erZ|i;heY-Op!+3o;2q-;Km;dY-g4CoH2aIL0)Tl9O#kJ;
z#p})0`N_gEEW-gd{ihDuUv<9y-PDi2n{xiUsr!F7mGpO0ihno7{C89T`(V651W`WF
z@3SojwQZ-hw9ot?0UiH8g$-f+{-aJcjuaV#iyijX2wdzw>;*)-rwP;W-#Rw`OKkou
zihn&*LG^BCR&+tLT%JcPa!lF${;yA&`Nup-7+_`1)Fk3hQM-~O)9O0;{!Pu5dw1zr
zS-&?2dp>98Em|E{P_e&bpV@8Go6cp|fu3wJQ3<20u6eim)&r~~&`YEP$@sZ_=*$w=
z$1UBcB-@5Ih<DwWL4Hf-73ib^Yc<beL}3K1O|bH2KN+-GPCBL^mv?s*G(try><oD4
zJjwBeb0T&kX%CP4AM8|w#1&1!<st4C2Cp)XD9fyW9Zbe~?Lu^4zwUQt)uoi5Wjw1z
z+toU#<Bu!F`79eNJEUXCYcI}&4@?Fx$zZY9Fj}{x{O_eJOb=~RKC6k2PLe=|=sFh5
zQe)vxdk52u2!g{ctdunwGg{zRi8;`&KDG7xz1&Lt#!+KrTAAc#wM}$VLpR#z=lEv6
zoa`#^n5LIIva(9Ma!brJQKoLNicPz;{0%ji={3TkV4`swwV@>2*J5W{*~5B~{*}h^
z_SbXj%r~0cZNF8d&jtsK<|}BMd=S>WD@)d`d$-WMt2HG{-_ZYMr|!$?Jh29u8cz}7
zPrnbm)AbbDMcMj$S>o;;WPkCg9CiO0(n{%uIC`QSA6w$<FPQkP)FxEBa$l%)h+X0n
z`#4R10a34X_cl$8Ju9EhGd`T1VaunT;>1omNJ!*u*<pmdjpDnbzLT&O;EP-Au;7pg
zIn6K85wVuA`V2!S8uftX>a80Vo%~seHN<ScukDhWtv%!MDEYMgOi{J0^ssj<c>iLk
z%wX!k7=mBEh8n%k&-X&K%4gR1%2!97T7zWAM;4ssrY&V?QO5qzI59Te32KH=TSt@@
z;TR2*-;>4W^=0^M=jEq)Sdd>>TkP;)Ow1uolkP4YZDLvcnk;9TJrE+3Jyh|8J~z?f
zkRlQJ$#0$kZm{F$l_ucsmTTo3b+=8RyJJ%xE5Gl~P@gw4nSCC(f;YdA_x-@1F7kR%
zTe~1VW;MbNbHAU%<Hsk;><>$#WmFYoNBPAQ=O(UL1NjilTdu<ZDe!L3*iqqzGX<`}
zH#2jIm^aV8kMSNEioOoF$(KRniG12Ww;|g*&U>aSzc#yeD!<OdLrHU}RKnCDwKfU8
z=EuL;Oh-hKu=t`Dhvu6$R=+?UVb|tCRV?;ZRsonlf_6&ToiJL9Ipm)TmvS{8N;fcw
z(c7dBt1%r->S13vMFbXp+sE8wM(FyiIjA9{bz`DczTWnTu~S~RdW`h6W!@{R!{L(C
z0U(%G^e0?hwwA^lUiyfB(Q86V2umcEKf%wqtL0fWmDtxsX1t*&{N8AfN+D`PT^6r<
z&XXz%EjF8RJV6IvA0}naTs|s8|9m(lz5GX(K`Zzx2A!-{`axg!;ba$vsw$|f%?W-c
zjmK8qV*8Y+$0bW+Wb_cq4)ad`GY0;}40%-jl?v2|X`7yxyC<?0T!HGfE#inm8?Ph5
zeb#8Ry@m(gyCHMC__D{;S}fAMT;!4(HKQ5%)Oo^OCiX<OMB(4w7NF$G>(!eAcYRo9
zUz}+QJT7)g+77GuYS<%N+P&PT6u<t>&8-FX6vX#qz@LvnpB;HT`!@5<E|GX*4g&s_
zS!(n82OiI$SCRg=f^KoH2IX^f?_6*DqJVX@{IoDVke^x~(@&NB_D~#!UVkbXlXv}$
zQzQR~K)z_4ekH}HDXPE@gXEHce4Jc2a$=qW)-C|wVu$>A-b;B>+=DA(h)9D4Xj(^R
z8~n(I)szxxbTSP_tWMxFB}1uxzgkO>{bZ2W9y>r^%Uvj~@KpYDc-#Yiwr;8Pn<d+4
zM%a0I;Cm#_63<>g-i{X+KRR_|Ba<X6TJ*zW#QqmK>?ZrC?)Ao7Ofinp@ldLZCk$Bu
zF=+kJd<W_NcLq{bvhnA&)6c~cGc~{X)x3b<NIx{nvGU?I3e-c)zBgMUMrXgeZ%7e?
zpjfU)H^|mJMjBrVk$v}K4Cnq*>|}(s6mlcr#T-voH?M9Y+Oc9$>%<=NrC+R;ur?cT
z&!CIYXq8oPUXOFu6^z-DiY8s<?h@sZi?DNU{H%PgQGhJ@bOrHDxn+cTv*CC8jL|7B
z%-&7^3S8#-d)UrhE4gk~C1mPGsDBD^$`muzt)Dkv-41g*t9ke$`fxMgg(5Qocc(Qe
zmSiP2eifIwnU595Q0BSM#r;d`#BvHLe8WGuOZ#5B#PA>{*(z>u=i@paGTAKtewF{K
zJ4n*}8MoYO*{C`EdYsN?_|;dGiEWdcrM!ExjB*`GgIb*qm4SqbvPJRs%N-w;H?y8!
zJiUL>`3&!Kgg@a;$wVnGzncpl8il^z$whVsa;O4MLe14LFr(j(IgOqw>{0pJ`*<|W
zbDDm}f{=va$1B6!S72P1Teyz$15@0v_qe45h{|uuDnK-hJ0|e^e@HL==g6+x|Bk4F
z2(kYT>gfLy1ptwS|4fer9SUcH4*f~y^rcVO+lD|E{vRj#{?REwRQZs3pSXVdxyf;c
z&iTovn^!Tg?e190oQtxXV4D7?AK2E`RkOeqQvD25JiB(-&jMsUg}ywUAKer)pyF#C
zTyqatiElfuUO)5b`q?)#ANG%vuY7+0rKu+8caxBX3$WSLMzq`n2CA?yixLt$h#L_v
zY!$ee3aQkphsL-F{sK5{ix!5dzv%aSM>IbGg&uJpVj&i1Af<qkI>cmD&E9X*eii^}
z5#k9iE$wYMWw8lB?BSjIsE|V7KD9ntgCLJcNd&Bf&NX~V`Bg|Hu}Kaw5$$DTkZUTC
zP+$Qx4l&@z1fIJqnokpyeYDwO;G+mg(jl&N2k;-2>>#`>492{_J=SM(-P`t{W-7b4
zG$Zk4b!Eu%rez*lLF7%~d11|Gj^!vV`!`=Y&KC$OlS?OqB-%-EtJC3mv>y+T^@D6f
zCs$ne{6x=+0Tzr9*#(S&_oFX>H4KB&Qvsn!%4LZ4Wnd=IRDUyFAnF!)_lI-)*Pk7{
zKvqV7O*jm-Vv9+T!xq*$x&2}Dj38$2hzCy3GUsKPIADTYSP8n8E+pXi77~xZnkgR@
z`W737Gvej<2_aTsZ|^}2UW)+}q9%=g>lnpbYbsAoz=Ad(VnJ0gaiOUPOYO!Pb*etg
zr1uiwagLJYjQ#Y>ygrq+Y1!l3^>@uH4zt%WB{6xTiZ!Jco7$1cd?%CP5|!o7@o&p%
z!&Oe5jEIBD98pXhn~rMB>KS14LWHoBte}r!GbPjErD52@A@#^q1*djof9|PEBXVh+
z7T&E^Rnrmhc*_Z42oRx?u40;X-*=QyH?BYhWo6`O<ld-~Rl#h>syVu5J-4C|{8FT5
zNqeckiBsK5i`rl^Q}ftf6fUxzW9qTm@gcQ*RnRM6)VzqJ)?CxPYTwn0@@hc0B*SX?
zwEV}+P{Q~;B8<<6QJk(VldZHl#`Areohv6?XVXPo1*wm`BwIbNBbZY#L&GWS5oOUw
zAu^iEfgnrNE}2NsDgS0RUv6KMgmy~Dy!<FDeBEHQh?p_!kk)2j3XR52Pg&KD#G(_n
zlgeUN4<VO1U~6{)V#<3gk=sr}({%L}_hcT6MQlX&pGohl!`+Nwb>xG`ZIX(16jnOP
zGpTsqB^i6M7@X#?EaUjl6$m=mmZQm6wp7Rsa(<HeZk~+TKy9yAj?dvbZNf5Fvbo_d
zz}W+60@(>GjV}SMXh|wXykA$2;Dn2fPO;ufia}SUuZ*hnZQPGc3clSvuCP9QMT2Bb
ze?fCSCsd_d4%yb1P^BpK_-7RJt$izGQiOfynyTADs*tq~K?GySO^_;p4wzc*y!^C^
z560{@J4)0D+=Hv0YdxJ*+q97Se1~Q)%zm`2gIbV9AOT%p8^95<gmG~;!e}Y4#?%F(
zHcE4Ek0cVdLGfANFd%x^EyHyl0YjsAxCwJNHP-R92HO->mDb?fFACRUgf}Mpw1$K8
zd&-zIOdJK^{mdEa9tK5&D7)2@5oO#Q<EuZ%$ZHE+8+7|52r?hdCxreVEaTkS6D#Xw
zSZjI8#}y1>o{DC1@s$s(dEAwlO6C12JshogMCUNh(#3?&(Wd|HDhikFV#eD^Tc}|3
zrm?aeolJP{2(m_}?X~<0|GSq!Ldd7FL2BmL_){~r6D|ikv`#+19Ci225Xo>r?EvwP
zhk<w#={F7jIg>FyO}2;1nceHpo8=TZvGXm-kkDo^=AEu=%k3k3<bz1;%`SbcBxX--
zaB{op<z)JuRV{M^)TR9rK7r!z1$-;EX7wJnbns9adAI$_DU{t}g8R>udg%|VvX3>&
zdhPgEZ&AlUQWlu_SjL^<zG6Qyu)|(RBuQdi_>_Kjbtq+UB=NDY>NqHj3C*mgsW>RP
zYW?6bvmbQUXKlyC6|j1_$q&3^c9X-tsUjU2o$tFN=_uLl5OPeQ>rH+6aN0Mve`KNh
zE0kGuOZa6Jx~J2kH~c0S52>Vm+Dj{|XwGFEjBfSHsr2<M7M;I#D5bD|PgdJS!gbsj
zIrWjpaClq#M!{&3*lp36DN4dgWq3{MabboLX+INU-LYG1>GM>Jhn7>`0Ft=<bi=&B
zuHQ;xfH6kUgIE!f`E;dfUp-MrWu@0y+TZZI@}JFPhFG@wl6-z3<Kz9Jb8+FvUZlsG
zPRQFd3OAjK6iAsJYtPg(;_ZbV05u9sN=(P=aS2a;4o{SfI!8O_sXi*3-^V*YLLHb9
ztH{&^{lt%GO+~_<+}*eAvV4s{6R$Ax#dLt9(<Lrb^IMmBPJ7-xXL=0W4tcb0YW_Jv
zF=+yXx&l|A)`}p?ZtGqI?+yxg@psLF4nmi@cc$m?omc+ttr~BfoCoshUp+44S65;U
z&i-2IkGsiE;T3^)Xni{6seR3Dq1$g_AiefM#)0{`EplOcj~^oq1posRS~I#t-r(@n
zH<x_d<810!r+&@@^jtH#wJE;2o}zNUjd3;KguEcf#BpH-UbPfJPMy?aIeAk#b?QrW
zhOfnNMHc$^EO!J7ipvF%wJ#gIlRxU4ugAh_*-1J(EVN8fHSos8+MnlpW;uU?*{8NW
z>*q+S$<U|{s}IqFbuw165mgV**!>)Po+j`Jp!A2cHLn!bEi=G5U~fN*)tOYlrU(t|
z$VhaR393iIuMjWJ3c+;_HLarN-_(p@M%Da~tv2Lt@fZ063uJ<0u5E_{D1?(eJzjJR
zJxXt`HMg}Dg0rNd;p^5p%{WN?U^pE8lTjM$=}@!wh)Ei|FwpYumyT!*=mg=|p&hhr
zjksd+YZO$I*tmIS{`rm~3O%WceP_TTFm$wvTWSR@{JA|@5EgxD0Zy%Y-$TV@u|xrU
zUg2otj1QXC1ACl^Bfh`KxiNpEp9LWVpPv|tL(5CtK9It?Y9JkxHOFZd4mABK{sC`#
zwDzwMn?=KKx?W)#*68R<O8kXYeunv#Xx`%apen7!l~LP^xqGrH;mp46cbbrOzI{*B
z%Zn#nyplq{?%0jYJGeLFTOw3Fzq+`PZ6|KvRybwH?lsKoQU#^;PM6P+NplRdW*pDo
zcel?m*x&55t5Ny19la5WJMhyv2UK=2ZzW5Lx?cy?<?40@T_40`s}h+JUnLWBmmEJ8
zJQQ2+v|2ZH?f(+m_08QPMXa29yU>$*kDZMX-&yP^_xBuLjxTs)+G2fkG=ajbX-edm
z5S+3-xn^~be>|^GvbMw;kI~Yj^6D^s_dw}kMkUYP$;kqHK{p2GVL38`ivWiwh`j8b
zmP$(DJ=>P;4fRi5=~>&_6>DWg;}w1prL}vDt-HTgvlqISF{9mTVTrDi)lI)!ZI;&p
zaSV-D|Avr1x!C#rkxdC*)U+|@JqNz%WS&Fy4SjV+dYWunw^_0X6in8G8?<&gu|RrI
zG%Ke9AxmkdGnE~IKBc)72(9Svmh$G)cJWhRXzUrsw?m{9PO@*cLL0|^o4i*jc*V0S
z%OJX@mJWWd7)J?+N?5PNmx{C0XNwvBoBw5=D{fDs?KSOo__n*{Q*>Bq%|p13-ZMQa
zlo<Kp6|r&fBSi8BwYyjK){FIe=xrlNHe8s20SPX5*O6Sod=?L0m%$78Bu51jqZGq9
z<@+CT$)V17UGI~AaY~;3>=NzcY!uct&vsg6=1uJ-n?et<V~rmo2hB8`Kv*B_r@K*n
zho^D-olQEKY9DS##q&qFnx=FH+nIJub+S~JzSls4TbFT;^<eDyw4x2`K)6}Nr2=_w
zC^Iauu6F*yPfATkwBlcIO*QUZ!I*vEeC<7lF`wscoTo5*eZmGLIucrJab1fxd-A?X
zdZ==1_15czZS6dFXZXJ5`-{b9ZiPMtj|NTcShXu_xHuUt`J}K_7c4sBSS%KObXEFl
zTYFl@a1$)j=EXVz;BE!Y{%Ct#=rVGd%5dB?Fgd2eX#D&<r_Cha^4SR0`Mq$NSoDSS
z{OI!)!%X-C3F-Q0N<jpTmbc(-WLL|(FECl_32ee@QppM8j$i@aA6r83ecrtxHX~%n
z^4l?-rGoTQW$AM$fDH^*_cP4mw!jnO5tD9a{>h4PN&)&VK~tJDi!Q%~;2QY`Rz^ru
ziW^qvC@fHAhSTX4YAXL`Wbz|N@0O2Yap9u%ycT|<l{{6(mDCJ)b?SRTag4_dc-LdK
z9}Q?J?24P2S$5t5iXQMzFj#81#cjbSr$>H;Yb`uZE+ma7DKuNp*Lu;mb_P>DY9_#E
zF`xf}{=pFEQCM_L-%wwk5W!XvW^*4G??-Zcws^~2;dNm#Rku8P*v4ZOewa0do(=w|
z9$hwcs0N`pVL!0YNQ!$8&OZnL8bwH}7mSEbJd1&L4Q!nN?Ube-CuAm3w*d`>lHryR
z^x>;+!iTS={avY{oxY2Qrjr)FY;zC%`w=KA0^(T#zlCQh$Hx;FYR>TNy)HXPr3Co1
z(*|PD0(j`F%VybQXl!u;X80Ilqtr&m6oF?66M+YN(DE)5I3k2W?An2gpo%5%#ej9P
z;Ixfeikv=!9eRoaV%6>o1Ct8c9Ec%{`3P7fh?D0eXx@R!N0*>^2TGuipG84;fOh1S
zzZ+P!K*RIxDx^7wbUuu002fFLBs=jJU-{2~W{f|Dn1TLWez)foOj25YX@;7hR{GZ(
zgnuhP_yh32P}lPh#-D)uztTK$Z7;_{vttzNH9SHqy9{f%^KYaX|C882D!2b|Y_9)1
z$<P0F>NF?8BKcEBu(oNz*OMGk0yMsFBb1s*<jspyV#Rf&d>@6~UMi>@(+|+!#GSdO
zeBs%T+1BF`X~&AP1)tmOogv-pDJ&}rF8J-@IF2tDicDXf+XpKPtd<|<r(=qNIugvo
z|4T83WEHW;bs4I5Oi6?6@}ts4KHk>~u6>^8aJwf36no>{zwpzTzQR;&Z$xuq<k)KN
zYoU~-sh?|A+>5XMqu;?rfeqYpk#mm{GaA?V*-)8x$5L#nULWa5ATDW({sQsY^K``+
zb_HiSNVg|@%u43O*}#7l|J%dBk0;0QT5Q=Ltqm^b4>V;89s3V$W?u&koY~v-MAvNI
z<+fH@Qi$2BZLLT>shn;8zSO$=-Ab9Q)%9w?;Oir9KCzo$Y`S(Us}~2CMAhqQ{Og~U
z@A8NBu8UYOT3i8w8kqQly)cUhoih<Do==x^qm=jyI|4W}NQE>U((j1!2jqTBlDc=3
z8BgneHLv70Llvold6yc41(6j3i`?eZs%P8Q?lpu<h1gm%SP;(ke}dPbbf_7P$kk(m
z`+OItR%UP*oD+Zc+(SFsH3FSctfbfDSCN~Jfs@Pf4&B>!d%dLX!r3?U@kJ3ul<-4W
zi5h|~MEBR7VfWZ3#@x}J&f5&rnUfPAWw4txIm^`Cx6MA90)D+C`DHOfWsEipaOf00
z+P2ax9xjbQl@X}%q(6PTY9ZEtL+&^^8hybmhEo$0S3bL$g(f<T^yqMjn=&bS!N}~z
z08ETwUwHpac*JP8Q;BsJqcfZX?)6^z2+j;wG<Uk%74H#k=N|6RKd4P0tY=96ROi`Z
zLGXEYz|s4WcJv8oGMjuop5gi!e;j%*vg{9=0>?it+~e|*Ek(flZJHZ~l#xksiu3vr
z=oVX~W9xT|7v1hh_dQ9iw%PI6MQd}m`_qC>SQ!Pkf^71t&UR1<8BMm^I(1mfcgfob
z<O1L<xOo%f@R3%-mGPz%$Hc88%@q~h0d->Khdo+oi7U2Fn>3Zu(NY}t-^1$M#bj%7
zzJlYF`hd3*cY_Ej5kCKdcwR5JsLGn8Yw$f<y_&P_WHzDcou1L}406{KZ)Vg;1>hz&
z^%{Eo(>_1w>jSdurS5~fR?bIN=s(ts929qk=~j9eD$RfUCr*qVi!3p2e_ZB)JAF;=
zbmXb5XPg|z7}QE(U)MWd8c*1hNoGPg)axWijb>F1hQ0107&G=fyMp9sz9j!V&-&Lw
zHG51syi8Pd12r&SLK?NV9JpFfB)v#FK8gCpwB2+29uuBVUEYN|p-cAJjl(1{wPgN1
z#Qtt*QUCL6SryPy*7>_@p-)|=R5N(L=P_+GFtx1b9^6>SiC#L^z1Tiq#^e(M>cYzP
z-s%PxKSqsct1plgNb*AlisI~lp<|(rx9I$AM<8*|GD8I52WiWKrM7+TP;<hv)TF~{
zzr7Xzb_bj+Q=|V*vHo9l5B+=bH*`P#&s=B!%M0X>&Bi~g3P6grelU8p7dO^#P@9<D
zY7`{g@&ET&Kp3tJH=S!yy7;6i`#6*A*qg1`sG`$8MIy$pF1zNAKY=Duc6fZ1K$D~{
zy<^xW&?JK-(kld-B$qm3gg}$13o?`th?`t%spkZH7PZN+&yZQ+e-4oG8g(?y)!9#3
z5O<Gpq4M8Mhr3-n07p-lp@(+oJn!<llx^F@F<4{bZx5}w#R4|CjiGjsGT4|{vfWa<
zGc0F6i(i&N#O_aPJ?x1ZBhpfHnyP&mVEN!R5Sw60{UO1Dw;a*zBf5eYknA4WuqPq~
zu?TTY1HMm?0DEsKi;dZ_ugXT#vA|r%`_miL##r}H)Qns_DTv|y9wdriTrt#J9LO~r
zX<QD;2k;E*USQ&S9}k+EYs#|_2_{1I&dFmVX><8iTV4i?IxW72B=en)Ni6}{19FF{
zEX<2K*fStf_zNb4+~vXeoM~PJwvhu5$i-X+gc`4hOBS^b`mg`^kS_9an|277+%F(f
zU8)LiMPOzM0VFM)5g!P>0hBtZ#`g-UHq(Sn+~T|93(i4(`LP?y<OA{6D(OlTnG$dN
zQa=`-WzD0Yd@>=ftn4E@aq>p6EP1f&T>DlhI+`l2IVIxa_MV;`sT~h9u72N-1$PZq
zMxKCqOr#$?M-S;D>8fJU86$GHZ~QR7Ndu|qu2i?;*jr71^>}N{?Lw-&_g^@e5G(>H
zzteR$+9e@n=@V;iIHg8R7iU>qDhfqaB-l@LNhrFjFlPP-qKyCR&FQZg48K0jU*NWm
znGhDLsy+u5my0`1WdhWiqKJkICk@mwi)Xem$m`;RJnc?0CV9hu0WUnP!nmA_-Eu_U
zpbM768Zc!2;|(|b*cRF5)8svzfN8<RojvXGk5($jBFYwezfgFU8SS>#z3sNNr2M(_
zl+)&HV9r*jum6Qw)+aa2U$e}mkJN4xRcdM+evVakwy$f*YF)qIs(@XbNUW|;xQRGu
zh!gfhO@3iO_1+?_l)OYV^!w*T>#!Bfkr{<QaghlX9E$3vD~b@gAB8q*(WGYc+IqA)
zogc6nAmwW$mlt}eWn|M1IW!PbmjJ&LeI!V2xASDhjDsO7{?`ZpB-L9BAx7`LOs&Il
zx`O#nXKzmLne^>6c-m+wh4K*16pwL+ghC0%o;*=|^z1$yL@H=_;_cRV5=Z@77<1->
zTwKM=Q-<dI42fL90reJQ#k3U?8qdqLNhLloREj9FFd$kVXwq#{$<}GsD&rF4GD?QI
zhO3`dD8!tYq??0nzpr!Vh^9`GQ4r($aYg)KoZrHnd+$_aPB5q8N*2!j#8=`nxij`$
zzi;)kGU2lx#$K&`(&0`2a5vhuWC-418~*<8>g3Q}!ILkq#<BksqQY>+U10gS$$lO)
zdOD@><`>=Hu=^A_KaJYuhGAT^aEC5kH~Q_hW}?!Wn_<g0$~x>(r2gMZWh^<Jl2TC{
ztXvjo|C6h~FKS>|O5Dt@cui<$74l_Ur18SD;?=${Bvd+czy&p;9f%&dXJ3MG1TrFc
zdjvf*NrhN=F4`NIpBjQ^sY_u6Xp#5vkF%oCRv@tc_HNtYZ`ZG`;UV6<@W44^DCgak
zD^X!l3fL1V60PlrkC3xXCH6IBlV-ddhVQSO&spXBQNF8(jjh*lrv*vE-rJDnBy(K&
z%3&zQaN)ikz|)SeTp{s_ZBmInQXpBP=U$&xp{j^C{7Oq4I_ZEji{?i`l;+uwtVrUI
zwVEJ)=9Tch(NX;zqNjs$4`3T1W{bG|bG`#crI&srV5n?l)Y%gOl4{6Hdo!vuWLO-|
z31q_pKbamGX2jWZW!r1y5oL-6f+vVZEd@x`0&^;kZ-w<@_T~yb8yBy=@=6uVcvf;K
z#NMIQavIMf43E<6Dr^^@dSHyfp-(7rB3pFvFkb7!ygTs6H7nHl=Wc7V!?Qkk9=S4P
zM*DelZYlTo^n8nrR!~rNJ(9mE6^9v}n-v=pZWpwkD$cZq67WIDOaNpU$DWMpNMRqF
zPX?y4gj;SY+;v0>J8cmu@CUnEXkR*se*%q8Sw%zd8CCt{Z>(@hn68?YNn!WH>KxBQ
z^)R%l+5_0qld%|(sh&7xCFHZo^_SPW;2eqQcKxb{EDU&Q4UAc+A|syG+xv?vl<^AW
zyz<vs#2us_cZ{k^VONFUJzeIg;Ax=R0g>7Q#xaW%gQN#9YnBc?Q+Nh1UJP6il3+&U
zla9pi-+u<G7e@43(^q)HUKB`)Ga}&L{ZooqGZA=RL2(tTzmh}jGBFoj)@z?scvgl%
zJ_%h?zNkxD7<ywZLNyfFPPARr2{+sDB=u$^WYVHb?JMuYXp!8fGz-2KQ6_OrmY}S>
zFkJanGDV9a=n-;l&qzaTCK8Ju&g-{6@s5G(<&;0ul!!tvX4Hm1Rrf9f;_ENe#vN}%
zO_i5q4kvO0Fr)ZAoLY>+m7L#$ffal1oig9Eel;(NSEHpOMKUu`c2C#kDIzxdf1&JP
zjb!2DAI<f>Nm_9$e=p%ku!P;j55A933Pq?sE!lGv*1Ldn82TbXYuvgpdK6i|pQk(F
z-(nPgE@e7DzhtBmo-$<{pn$zIlg--SnaqrEcC>p@^hN*F6I>rQ&yx+_C8EPBnuIo@
zd6m26xJij|>FsEUB(=$adp8-YP)ejUbAzcW^48~Png(AlF$&`)F3m(swU587&)#qS
z3DS`@lsO$2Nq@Gz*>14z*@NQlyFiXisv(ywo21ix^n!=)x{mz2Fh+9A;>_wTU&f$o
zX~}7xi=-o;;+@(H6al4ATiY@`H{88S<hGi{{5TXWyq6)J!ti;LHYCj;?m+7_AIYxC
zm#aPci&@_2_sXYxR24*#CFl7xTpK+en_H#mo@!X?;7~xeeU&27*my<?%v<!#-$~sk
zwG7O#W=7CQuFoIbgU{cL<_*R1*}W&(QFzUtm-#xVBCH04#NYAO!ME^uCwgOYWVN4M
zd;NSOasBekAN3$h9{6pTE?`&!gJY7HKfUhyw54(5Gi)```cUvnW8xa==RP_M`IIbl
zjabU>qx0gn-K=qAg{RZ?6OJdIGiQ|}VbP@Cf!>(CwZ_Xid?(7;=#XVL%=pRZE3a{U
zZsO(+P4w{j#iHxpe#|^W!bCkQ?;(6tdc50ZDH|OdaW~bTlrrD&PTIn=1cj2Wf-IbW
zP<k<hsje<jM(SHX51@04)TPup_{gBK>HG}7^(>dc!S;HLew7gOg&Mh;VIc*vC^9M`
zg?*}z0ejTnezRv~Z3f26DTxRd8i;OBp^Kv%Ud55zpVkRSd=b0X9PJv}Q|R%4Ko`Vo
zpNc5caTu)fe$vNI&K;IAu*~3Xh=EIAZ+MWc@8HlH^ZOO7DT3Ez1KP1yddpM8(1{)~
zaeD7ow?j^4=(pp_WA_U~c9m&N|D3+zp?4XwI!0W7ag^*E>OE?yvOS&FOKZ}_cUy?v
z-D{FGO<Ck$U`3<9@v6_g&5wJ;_lg`(pj1-LxZ(Jce-xyOLH0t8u*)sQza;fe*IRZO
zFr1yb7*>i7>{cV+eGURR(Y}CFCg<G!(drHe!ww|p16Ac20(BKRuj`(f+DN$p$WoTC
z)w^;MukS4YVofF5?q^)UNrO&6%cZm9XL%`z8)5<VCl$qsFUO-AKLLu)JLd4WnXhQb
ztdQq|g6;ZR{CGu^lz-pn5vgDI*zZ2UJh~>+-1gM5<&B@=+&5NP6VnfiJEHnU%&&f<
zClmCgu}l7;42Y;H(86YJDuzmIn0YR+u9q6(C@_0bClgqH`j9CCTo@2n=E%f^egac#
zapX1b2miChDT){u-GqQ5j-unlI;y~v>L8wk#c^sv*(&RiHXyIXpL@t)zZPO#9(V|Q
zRql&)sQTk9K~`d)xisy(fKQOidf12)Gd?-kac=&>b9aQDvm;xoDvjC11EBU)vM7bS
z^O9ZIAFi_diG8x+^;z7Fv7>(&chfn^qA=v?XX*NSN6Meew#6U`>Si>0!!I~I&u?Vh
zpyqnrd#5(xL{~x^KROEQH~Fp*E8P^6L{YuZQF;BmPbF(!`Ifty6-9PRA8SbENq=wt
zfOESkPlJoHznnHQ3cABk`faXbBJ~YJetc#>dU|qle)Q0;2pl?6Wb_s~PaNItq9hnf
z>xSE&e-(K;l{}X!jfGna?Kq#iV@lQJg*+gp-M`qRI*hyD5vht}shgj97O%+NdWX6D
zgcy9E4&87aq4-Yg*WGOqx6CV5&p=RS@Z;kxjUH8+F{G-(kGH#A6l%tHPqFLvG2o4h
z-ZDHsTT(UPXEnTjoyj9w>vYfdNeH~|H`Cp=19745t51bY>#L{zJcT`xoNg_RMy5RJ
z{U}(EL%Zg5B09JV_%%bvLVDbw0K9;1t|*b`_N!}8jSl)|6_Ba+Zg;x5-wIty>oK=C
z);V(lR@g=BycCZvGnt}pWGLgx#}C)r^TWR-#naG<@OJurs~t0sbYw=%SwV5y!OzUJ
zLSDhXRmB8{BV+m|+t%NXU17irj!m6Iq3@M+OGd*+$j&C!FfsFx{z|tC91P53v1Xsh
zXF@bBXdc%jqghrE5oP}}4rOCZc$F238c8^p;u*~n19@)$ZF8jd;_jWGSX^gg>^UmZ
zZ-I5h9r*yu{SGP%()N!d&GsTgD<962`q`GmqwQ7%ljC?VHobb><~NBMb=F}6fxf{n
zUdsw)7l9XHtJl>J$TX-69bePBWbjc=`-ly1%f!Z#dBlkK4NvzfY!poFs@{?0IHjnE
zvU`1K(Xz?aRq&G@Z85($gctBT#0A+d4e|b(pT@~CbT!huMc8e-`TVQVkQ>3xT>Rk>
z*;`9-KOih?gMAmXN8hpgA|xI+QG|HkJC%GkrG^R7O^z90m>3ia458ST8lPhpgNs#$
z2O6WlaM?GukKOA%J+4g%)=k&fX^uu0BQ>u6h}tja=>0ZJT9rn8=2h(#sV!NBG>bh3
zcMjs+8oO(bZ|&T!UHmcxSyF~iuMC%vG{P>naN&-##?RC^NS*pc<iaaH8Z=%k_Tc_2
zArhb|fh&IYJ98-u9CDm^|J@&r-@sYYIDh{N&21qx5Z!-7Gtbr%Iei7}=5Wwlu~>br
zO%>3nyn=_OJMFAS6FwuIr&&xIO@+8nqfa4LP{69QOCOHh9G^G7ru7Mbb-#;Gd<yZk
z^fNABJ~i@Nf!Q-*AX(atr;;vjTEp%;76MM(({$Us=<ZIQljl+h@VIQ`#ke>$6ww?h
zOdV8Qa09KML2WF#CmZ|jKShJXR`Y5CI#@YUT@Dcij=}GOc9z!1#9EM&<nnci3#9~Y
z3s=B36N0wIkeuTSLEF;&o$7KMB-!xo<)saRBpc_`5aQP%Z43F+Zf`C~+j2K3>4Xsk
zNa?yLQ8NJ$xA{?Wo9JsOY$y@zVk4K_oT4j(qe)b7$`Vis`E!?jYJ(hlKF5w4%qfDC
z_<83_5#(j_qi3(4+jE*`4w5+-udIaHg2EJT$L;oy0DjMPk$vL{(Bk``xIO+gfWwov
z&Vw6H04q9UQ_o;1bVN8e@255(k@oJ;)H0X@<05X_nP2o1Xq#wxO^1e^Auv>Ib9QwA
z%=e(EpYW+uFz>I0w-UK75^=-o*FM>sNz3wxi`Uc%z79L2w_FM0_%`<ve8t5VZ4#OW
zoLvpsgBeHX?t-FNZvJOax(Q`%1DaPvL!kgz)OD^^Pi=nPZyg3#kJL~tzbgvg%Luw=
zWXUd-BpCx{DyE`@*5$^rIjBw3Qt-g#hCJxO8Mm|t&Nu(@^yf8Me}S;mO$qSY>zbs?
z8sBY5EOTMJK*;eX<nN0}s+P-WPX>3gQktE;eP&47)0wWML~8DfAf9HYn8oOCsW_2G
zPN#GvEzZKc5w~kT_kEkb4SbBuOeO8Mu1kUu)IS%x@|~LiSFqzTvgQuBVU*=UXBW5?
zAWvkv2N9@t2AV3+Y|3+wQ_MED;$Y2Q=*o$m0Bb7yJF%3_&&U}?u<TRVG-#`vR3Z1S
zR}Xy#wykYNLCuJTpjEwDN6_iu{sdkDC;xshM@|Z);Ug1wDw4sPZZvoqC1tTnaF0D$
zzy-(#--2vJXPTz!D+Nf14S|rFq%X2-wA?qkq_@k#dRgG}2MP<5Ymp>RGG{YX@znQb
zNLnxG|0v{ocx98!t?%Zr<6@?IxT%Ndy)p8!$3pnQWK{Yu9oM?2&mBtj==Ckw31pav
zbiONt0)CADc||j!ptR+4j?{DeTs?Y)Wr8(1orq#kJZ?I9$?}rO__3beda92+4>742
zoEW-#-b)qvtJ}>^=gP~c`it;>D!Z{MR%Dea<oC>@b(TRvY;=F^H|r~JH(CPldZq_m
z($A^2)*Oo|oVa`a-t*w~_)O>w#$5A-c59<<;gtMPtC>P?t{LC=PRn;AK@2xsg^u4R
z>BFBh;a*W_-f;$?0bzQ;fN;0`!G@Q49}q$jievEq1d;<Xk5d^g+dPBMCo@eyBTbKe
z5K^oAhJfQH`nmvu1^*oNn3@8ILt62aoLZhAdEeUNwl^jDgeB%RzxAaw<5t6}!ZZnf
z-UXXPG-_O4;6RW@@=^Ck&(aLm^$ofqp>?7-a*Z%8c7;a-lw1|%Qc+JPljtxOlAX#K
zBa}~O#BH~f#rh9j@Jgw_-kQwjpXo%31<BNCONSpiS>7G``0cH#>FDeEOP#g5kw?G6
zadGM!`<p)85kys&Z{>}rm7ypcrh7gLo5}Flo@-;jcWW$$c0Wsb(My<2Zri*TQyD{V
z*AlT;+;?}U^>g-4bd`}VPRTj3;7=b@ocXduG4O~(6W8l^nSVFevMExU$-%X5utZoi
z(xSLJ<Qg)J8nk1Q?*(bYh43HO_@>k0UtVoWc+G`X(HlGLXH?vxz>~)*gtcBQy;(|)
zZn+qgVu`1<t?M!w*V6eKtj&2o5dDt7bMjKsC3o*|(x+41=l@I&WotHEG5qV?ZqjUI
zsC#PnlwO?lFGJ4vOjGdSpI3ghbjx+}!p&sv>O8t@^C-JXtetp&RNfb}_qm;ghx#U`
zT<SX7zog69VUGzRndjo;{_PtNd^i#vQrmoGSR$zoN2_Df6{t`tk7eYe;`qh?NU{3#
z@-;2u()&V}AR^+*qD09{1)eVrv|iryCKWkvR>-VM<!H{#)XyVTnchvmOhDfaGtdSC
zyg{$z&xV>(84~)|H}dvrXg-Gw^~u{3x3TX;-N2X`gyA1wS7$oki>buG-<k@>t?kF5
zWzXJXw73-J|Fz7MKndWIjW4F6PSqhbz4U{#wAsF-o9vq3=U)$(JE+&gQtZ=H>oxsU
zLW7{%4&}FAPLG2a){bU8Jfo{laLT=_ANWPQd~fVch4%gh&g8L;m3_nyg;zt-GxiC%
z@~Z34;AJn6SjzKc4!-A{@zrZ7jPSf*obFRY76ptnQKGXh?ut$n4M@{Wd3~zND#DIK
z@XC!Y{^_ragU!9$VfU%?KE)6QC7h&w5N+bzWs#y=l+u@pE7x$x4mrW|R(VR=NNUkM
z(;_eBu_pBJn9I(Zidb{@2-N8x%(ZB1;FDywtcU$>BAKKqV$!g4N-TI#@}pv*;^$==
zdBPB+f5?hQU1WWEqK(z({4ATDP-mtaoGg#)nr0JiRttkn<w4BCXo>NM>e<GVV4o(U
zRXt;M3=-OwmX7BSe2DE=ql%rV81VddxG4B3>IoNYlhEhQpI=P;4)WUbWoAdEFEy3Y
zqv^7YqU`VeaoDk0{!J|`t$Rn~K6$2dk7V4}+s{-V5|eC9z4c*-i=C)rKz;lYcX6V=
z$;gyjbj7%g7&+B0fWdSK9RyH`3O^J4T{c+TxQQ_IdJ{#<oJ<sR{q39<XV96A#$j<I
z@7l!gCN?}tbTPfJe3$hP;R<twC*0Bs-X9pQuj%y%oa74nROCa$cdF=oRFC1C(|HWx
zo|mPh!q{zDeJ|!H@sih&$8cuI`t>mC#2mGV-Y~NsjDO|-suf@5d>Vce8+SWo3LNjn
zSd}SsWhyw78yz41wx(=(rS@>#@os#8&$E_>Tfh~1J|)G{?jVsx^1&Cy!_R!%C$U^;
zxgRRW*eL7{QdsHae5&5fvBaQXMk5_AXtYf`jrw?N^s0uPfZKM%rZ#t+?ne2<){}?7
zXCMDD`?X{F^u3(I!|_!eBa5q%h?AD(inM4gMTFh%RFnF%zBpi$r6O?y_f(ocLyVox
zv}%-!5idf#pCNO7N`D-iXy#bB$%y!6XL0PPR3JOHH1?qS7b(ke+gi?27xj2MC1_Uw
zu1MGVM6lsyJ8I+0Cvb(r3@0;c;8xP3as~M!ZWXTSZWJ_6M_pIdjEy3Py(C1aI7+`w
zy>x9|kl{EZV_Bu&y{uqbn;)CoJy<B3JhrmeA)<OkIJRNyVhQBZX}FrPmulDBm$bY7
zhI(osKy&Q1x}MVW+HPU1$y$3rEP)GpDNsS@f~BYt(o-`EXP+|vy*34R`Y7|$*rbAa
z@+KH|*vuU^Rc={%to9D#8E?dy{_~nk${xR}BLcUnS4SK@p@=!K^n+C1f@Rlyit~Wl
z0@RFVLaa1C_qLhdB|sY;7<GqO!QMXvbm_j$ZxODLd?Hg;A^(@eFnOB9xV*x%;2ZCi
zbm<9u<8}w|wJSn?CVed6D|jqdO!)Y)f>;CG^s&l<wm!G_Puc)tg^NMBWkKx}pbx~;
zzISTdF+r{%+P(^X(P`hD0I=jl5up>;-@dsk_1i|6_z4lb+KX4UDK+a19bcP5PbB3e
zs035`T_f<aBWVT<`*o^B?#Go7yvCmIa-${m5WIz_h_(QdCeQ&cxG?=wc84TE@Ln5n
zidarF=e`AKhBE4oy=N1O5>oY2^tx2E$3no4mYpv<?EHCLbtB(<DD?`U9CSBr=x#rV
zYXZY>Wb<?qaDPNckr@Ylau$<bDol6@FHlo|`34Y#w5+k_H&(Br8@r}%p<!oFJ-p0!
zoPFp|dz2NL%O3w)4+a}b-3XE+eB?84|0wqyG>ToWx$W_Q426wYW!o1MisnO>1QMVk
z%}<|hbO}KHDK=1RMH0RY&MA9l;|bws-&UvgX%P^)-hHXVW<o^J*jfz*nxLUiKNe0@
zQr?9_g^g+aKZ4r{rOy89!}dLdZ;F&0umXv%0p2qjFMcF+3fF^X1os_-e7G(@R#rMb
zig~I@M{@Z+ZL_{<Zn~D$K8}Cox~dULzXCL!bvz8M@q>vcAn-b-o0@5j1mw<K%q|rO
z&k_W4;mP@;WPuMFs+V*c%l8Wgp@B?H&*xegfSwN1FWM+TP4=jDjAT`uhD<FIGvq)@
zN2tu8LnCSEfk?)DB7o$aV${SqDy?t<s&l+jC``IN=Z~Qxf<|u<xg~9v6#1qXg1M1l
z>19@;PX&P}eiu28@FDs71j3e`eqPGX)UNHtDdty3>mkb5&%TBQU}p~3(ZNi50jDB`
z*4%ufh$?BWoPY^QKiJw@er?qBa1(G1jYhv#i)`S$ThOyVa@Wese*AzEY}7{9)olRS
zJ8))24H|>(F-C-i)h{ZI?w1f+d#<l!uxFuQI!dWRY)n~DjFwnS@+{l{)Cc7>Aqyr1
z!G;G@-4EH@(eVF?Jg6U_3vw?30OKe$bfSTfwA5Je9;h%uEB8lgho`O)30<7qk$*(|
zL4w(Q5StTF_}Jpbwa6C=^qpu@+uWA-mmj0)uFzeQcb=Owr?KjHH42p=!~L#*nc(H-
z<;`ZMIzuUi{qzcOfe=`NZ8dC{804OxfJ$Jdk?6w=mE&Y7>}D`^)sZE#AvS(2D_T%B
zwAp58>W1|32o$^pu#JymBFgSk96v#BkGWBFEXictlS8gKd8zSdd1&@!8;^!17&BYq
zRV{o-2nM1sj$<!mVKJ$%6xL<?(%{9<Z*cdGHJYs)+inS6wj_)N?B$*Yig&EP3>V6T
z7jdg~+%5=&ODTwM{y-}w*t)ro`##)vEG7hAgr($s#lS^k*hZ$p_D%OqIR$kUu_N{V
z9D;f=nvpdhy?PwkV{}%!qEt5ctdLJb5Yf`1yj2zK74t8jbeQrGi4IpP+zemZX^-fO
z8yojX+tyrli6=+%4lif0RZ?SI20U+@l9iBR!yCqO3?CDWW5pj=q3m>Pti2-W&h1yK
z-Id94rMM(NnshA3UvKQFUgIn?)+_A0|J-WA_#xv|r}^iGsu6zDg@JWjY0>RlTea4Q
z_87R~y%>Skxt~wLNDTXq*6seRe&8nicb7hlW8kqvs<n5`K5SeG9x%XC#p}{-G3jA*
z@ndb}bi^7Ni4l(FiOq~zqW7n_-#)d(lLob~?A*e{)gUM<8<IKU(_=Q|FBuR+Q#Vcf
z_qSKQ)AP1R{|WtmRNoDnm`J4b*lKEd{mKYy&QcJapAWKbEjOLic+Y*D#RM7h)S;<?
zoB{8$DAjlP*a}0nU4>p=k&{HSE<E}k{B*T>>>mJ!Q5*jDef9AJ0--WxdXb!CYbXWh
z=<P^z*C+?|fX}Oysw21{Dan<s8|N<d(Ydu=jMzx>DCC9bDO~cMKx>)A?C=RNfMpHB
zAli$-zbe<?&#$;ZA${>>G5|IX!eGCsz~%M9$5*pnVc;WjU$5+!o9mC)nuekGS1A%P
zF3k_FS(kBJCkqWt{8^JdJ*&7)$t`mJxXvYSzTV%c$N;qf>3xC2%Xu>0R#nN(jEFi8
zcsup9W1`vww=}|&9v-+|iN)z!A|6{k8}r}k9ePKws`Y+X2y*F}<rIEY9MAB*g;_M#
zPN)*S?^cr6cM_m8t%&7UmUddrx=|?hQim5#DW8lc+s?t_Zrbw<EY#eZrso$tAoyXy
zZUAu|G4U;Dtv+)nCOlJ)ildqTnjordmCpya8MWEN<|e0YV<5^4|KvHWQhQ)DZI9U-
zZ5Rnh%dWH<3FMtw{V6lq_Ky+YLG=^$MWp9N6dp#R{ShjBYy~$!62Nh@(-<l{IvZvi
z*&<y*LFHPu#IqZp&;~1CZ(*vNlg^jH*D|}BK-YdGdekEP>4TcN+!gdM-;t1d#1KZ>
zck0PyE0d;2<W}ULE3RIsxGd6Y-RM=6FXBVfT9`&3qOajH6L+-m0x<jz$>`-YeB=F~
z-Cwlujq%ar_d?ScVfZ>)n*om*MJ|kmzK<2{>P&2!=n{SIX%>J(k9G#SVO+wh1-lju
zhOS8xybODyRDMZ6d(bT}6fFO;N*USK*~Vq<F+Ca3)|@;V8~H{u&gY}Cm={O+D0D1%
z7e+?hiWPuo?R>XOj<(}ZwT-QwH2)LKl>sq_DD((!TR#waSw^rNtk^-JTXJjMpCR?t
z2f1#v5553w1UP)N>yvTrsVY-zKvn_!>#S9!63l{f-U0sr6oTON`Qw|k0u-rBX<6EC
zn+Hya7Wlns8toxRDDDYdSTRDfLP$|+Sc4!$5Y17eqy&L5PF00&n&Fk<hv#72uu~;@
zUi})HM2D9cI8WMOfv68Q0}`0RV5}gYDa;$B@%t}&oI(7tX}*4X&#B&+HQpQ0e?pK8
zSIQ&EKIp&l@$VQl7kv(Dr<5sV8;LJN4bAvM=i`6%<MsWmkY-x)w?+fwZ^`eFUOoFZ
zQs7NupzX0p$*x6f+w&i2bHjIwV!#%Nu=feBt|{lCt45q~nf(7LeiDUq?~1tu=EqLt
zE`0iMM8e%q8Vb#V?kvRBRqX%%)qgt&^M1MPkhk@oWXYav958Bco=PkiCTTUg?lrCd
z5_zTY*4xu1y7%ONZVP`4QqO*)+3HIs#1`=V>7-D~dotFKJs_H=mD>PQB@3snFTTo*
z2;+4eW~bm?x=Vu@P3Zc(#Dq_RJ8pme#EAIgp6Xp%9Hn7Z5kkp~sG?ZDCz%=Nd~k@j
zYGU((<3E_HH+!f42|WUf^ef2Ut(RQ0&v5Q<&rPTAJ*;x0cQeyF%!m;k-zkz^%AyEf
zmo5_1K^n81`!I5IH}u>0**Z^)tnrMav;sHF_0Wn}P)>I6n>S>mpE44p6R~2wZCUtk
zGAj9grM#bLsflD(dP^?@*_mG70eX?#KHP7|yhGXi!PmofZg5f&x#<kwKe0DpchBfh
zk**9oOqM&b6mEHf_@3@l<FlIn9%;E8IYom}Z{_kOq83U?E8<n8upfBFa;9H&6U$6|
zWGoO_<xGOiXkmAWMZB;pF2O6CQ^97)XB68=CePe(RLC>lxLDC&%(wIJE`Oko(nS`^
znqNwlX<`o{1v3Tn=D{!_vLE_RyI`vGdosSp$z62n^!f__K|K$AWt$%pm+eYHj20}?
zzaR&n6Uq`fljVZhlc(QWzYwwEm`7Hw6IYKml3Vz&$WGuXVKNqLI#}o5(Hl~|I@n-$
zCM;UBmZxUj_dJSM>`y#V@2jQ!6n;^~t}C|4Og(9h$H+^Jj8hrXZv_-se(Ff;Bu;y|
z9ANzoe)s0t=_@NEv5Bqv&;bCuOQ}PpF|o_K8aSb0H#6K<+Fsy{*&j~-?8#hUeSTU5
zt~$ImCPY^CTN;u1|1kH~QBieo-}opZ3WBHzQX<_YIfS4h(jX1e-7!PA0n#xv3@IT}
z(k-ZjBQQvJ2t!G?Nd3+kyuY!&&+}XFyWV@P=lSdGbIv}y&)(O*u4{ijw}x%@$!{|)
zX6x`{*UY%r#o-p+CF11=nmMf>kH5xZ#H_rZgskcI>_1q})gQNy;wz(GM}^^iEUrV5
zYi^MCm|)#yA#s7lMG-W2Wku$iz3mM34kqhoarRY0v>uE+@X}TFO<h~+GuvAGzTVAb
z*<4ca!9<Wev1rIGXA-wU8w>OaF*jy+Ebb4vZCA<uO~yx+vXB#Za!^S>FlD@uO@$xa
ze?nC5SD$LknxD`ZgPyLC%n7@bLfbRmTczh<bShrer-R}d17|lZE&vChRst?V07{H#
z|6$v0HirvjfV#Yf9jqkPKPR#~e^2laW<?9n*X9*!wcbm5klO%{Dx`%K%<k_F{yEJN
zU<}kuG%x#VEigRQ{yFpioxwYz?$H<TUBi+5u6=f9(fiV}S4tO|)kNy0cku<t_zmG9
zz|M^TBPXyE2?UqKdxqGFGPE@wSr4vB>Nh$Bma)%UJ+Wfh=xipqz_DOa;aISX-8&F~
z5po`8G12D)TJ9Hx;d7wvPBWd~fLPHU>d0{xUHQ{--Nnhd>{}zF6{c~QUqY_E6{R(*
zP#lXJ(`EC3;64<8;oJA#{fc&;ssgLY?Lx^G%AE48M{ES2%Zc4|%Iz$sRu(eqS~>uW
zeWvoKWK|f^Wf1l<Sid=xuK+^@7W|%rI6Jx$3)}#4UeR%8N<?C#XibOmm0@<ly;rNk
z`DW@RCY%ck0YS`(8s&oef~b}<XhZA<i}G9;*bh9H1aSZTHjoPeKjrlvEn57tkWLns
zPvQL*)0n1EFNf|B<G#-9>1V(VE!^nv1SCnoMpCH}yW^dF;(0<LxSwF2F>fyLJpP~^
z<w^ZWv$NO5YlI7`{FvoB8STUwO<qUhb411VDc5T}uwQbD5y;eM&b$=GVObh5u8M;a
z1f1&gAXn_pJtVkAn|af8<YUi<KPP;897*<6V|OT|s+i)bMrBBawmWq(<-wG9d(%N=
zVTsjXy|d&|y9NmX-IdT8%ITxe&8>?QIgu+e$Sa4wnRSau1Er(=PDQ09cf?K>J8a9j
zvhj0#f;&~u2D7Czf69V(b2O4Lh5t|s1!a*q>tVEX^Ydehjm2u)Ra3d4{cKX{B10O^
z86$z-NAh>|oCd07FveS{4T%}HkEH2(s9kBdr48RY8ch4D9`ZsRrRaq08l>rJI_Yb2
zEEr}d!lc%Hyh~eAo>q2|U)zzneea$m_6oF7Hc$`m8csd$=!e5Zmdxdql=d_OTUeUb
zI#8o5R3+vLGm^EDO7($XXq$GIo2>^SZ`*^uNZVWYn))tmISJ1&npw5KUht04xvTV4
z#Xo}wYTJ!evFa2cAsRpKN*^;H&ihO&ZLVbBP-rV0d+rS?#rg%nLU=ME*TLq0@;hb$
z!(LWmBb9!OwSYKR)=P6@oFXz8hH?h5)!2Mh;*m%Cqos2<V+fSn=8F9-OEFX0<9FUJ
zJmOq8>KF{o91^^djC<`yOq*o)#}8wSGH7UvoM|2oxmq~+m4W-+LyycAi@Z$N>J_IJ
z>Qn4*Z9Cl!XrEND^tc#;fV&e*e(HNKG*rSB5brEWSt(HTQuo|Xj!y3OM~n~q_Uz6f
zU(A7!08tO=xPr_4PuClsOO2#;8^VrKbKAzQHM_I@_nkMi81sD;&@!^}temV{L(Mf>
z2-MZxVnQPmJ0HJChBFx86p;zz_okH}mr!+30^f*Hyv%s=*cUgLR0x;U<M23wnHQz*
z`5tfHGeH%^zW6TM^<D9yncY|%57gOi`*x<cn-~=S;?>*5i*@alcneBv_#A)y=bpXa
zWX#{6ZQv%T%pP%G4{fYW)^3`R^<H$%b023vD%wFL#@6(FEw(iT^!FWjzLuqCu@&Fr
z$y+!a#U3+!r?%SngfSiJpzB*?xCtAQMD}xuwapd$M5Hg7pppp`ge;zFz=-YwbbJx9
zryPs5cCq5b+Qjncn8^mR<=-Zm$;)9yLG6`m{QvCP&xuIo)?%jzo}Aly+|RVw?3fG&
zw{T)QBX<nsN^egi<dvLV^&@Uu99{?hSZ~Huq`(vDi)*XGZzD>`F2WzkqZ4GAEa90d
zD`i}oNp~gb`>*Plt_&3L5iKn+E^20$JfojQ>2xGXihST2#Z2TSmbHA+hUuE-^>kf)
zB<Mz2sz*G#^$=?&iWBic>2Se@wvY`zhatY)cDZlSGRfVomLfh3uwiGeutraI*z|)9
zpOn1L1J~^@nByq}An$adR7mG)ZNs}j$wWhsZ*^o#m6twgeogAT4m7@We3=1^3pX_U
z{cny%erm%D_hS{6QsZy}cD68Lx^V>N!%x!#H0){r`fUTbp>Iokb*-Cj*0g%E!O+iY
zFh$+1VKjVOK?xQi>dkZNV;Xys4BNcWrnj$;QOnaSi-?LXO8~m%{cRrL*sTn;yRslu
z!#+po>Ex;l2ZZSk{Mypk5MQ@ETIHD4^27sHBDkN9iLFR5(8O7rb1YJPI<$F8iRZ;i
zF;Z@!PD*?YaA*W)5SAyu5dA?v!rmFM7lw(|(0Up&?`P$3-!lA~yrP=*Dg`$AjO@wX
z_P%Y|RjM(3L)h>A-)R`Bk4j27cxH*dF@m2wU_<hB(C3ii-cIDOR^>L)-srnRaY})2
zy9juibhgFZBOiKbf2<5T&5QMtk~hlwY=q96hM1zl=##3c(je{--#-cxyMyL*E}f2O
z<hWHAx1gTzZSM^_O`a~wC;6AE)Wne>v#Y_X)cek`%BYS@@#4)VeG$z0jdeeYkM@5T
zWAtouo{PL3y-#LEMxlBs6-oHwCH<?LOqQJQI4v#*G`hCi;rvwMd-^of_4rq2wPeJr
zotK8pmKS)MH9uoGwC(fAmS}<*E!8#{CZr*cUWn->n{>$;LI3Kn*G!9vQwbsLnmW1Y
zyGy^qZE<=NL*nUF#oxbLa*gSG{u|DH%3o2dP_$B_h{k_5Rqt3jEsHL?hPhxxd-G*{
zJaBs1TzYn6zB1^c>0r@i6*F~9$TEZ}8E~PFdi8#Cy%0l{G;jc0@H`uny(N#Px~E<0
zK-_X;yd3vS;)Rp)<a3wEI68i)?|P#My>7J7+sM7+A}f}4_mAIN3pFgqr85$2d^IP&
z8*^}W+P^lK;ueJVG`*8knl@;TMMgv|T=WdgUNBpF*Ta#DM8u}5!`v>kFXsjoE8I@9
zrgVNC3DAiQ6;G=V_;d;1+cbxjlw>Htl!g^QSjve(yHl>+TbU6vs^o3L&y4*v?&(8}
zHSOi%E22v^3~FQcoo{kBuN`ZTMZ9UtP)_^iow3m7UILZf$i2ywl$ar&S~qJwY8T9|
zA!0V9fJ7QnmNS|?pr|jcyKHJf>>Dv1SNtkm0@_^dd${O6&z<(5t+kla(?|C~Geh9{
zGWUjQ@r<zmqmZuoqetgzIL-HBah_p@oYXV^b5H6UxkOC)%I^fk7V-WJ>|j++sHJ1l
z$pJ#m-k~geBUJoxgz>qD=8ark*eTMm<Pq>~FW&yj$Q2M5UHj9isG8XYRi{m;HLliW
z$Oj~-FPc$H=m&*0l1^(-&QjG@0{9&vDjfNs@GD~s<piWu$uexzdx7(npxz;oW2zju
zw&;a1fpML_Zta5LCo>+Z;H7Kc!wdKn;v~ijUKU_tpyLQcIGqH%q^5~lg|LOs3j0J7
zu)7d<IQ_tFiZdUjBV)V4m`jVvQK^1M_4;LSOG0&D=ES9H>&1?4CPy;?{#y<f9_bB7
zjy*r|5^MZf)7OQ8!~$lQZxaF6UGBDiYCIsIBG&X7s6@bNu1EbGa1RO1*8(GcZ$@W&
zSfq9r{K^__yn|Ko^tZw3o7<Zwdn5>~3x&r*g0sE^zg<26Svw%V#G#aDsYv-7Z^o<a
zq@`G~n(&rs1l-@kLgFreJ5BuCvIgEp?F{XSq>O@Q(O?_<L}a12_aA`{z{#``$!Kk<
zLv^rw3VhT1s#k38oAq_x#y%C<vA~cIW?>bJeEPVKa*NHd{cvj*mfZGc^poxbO8p*G
zhHX5O-j;I(p$2w~OEfYW-a{`4ujtiArQN5Imf|dJGm0@C)MZ@!6qvw!E<1|IqH`PX
z^2T-2cT|6CoRmW!275)?Ba8fsBCx6oeP&DhpPGJ=`G+F2Ynyh-^V}$%Vhxzz`>G;2
ztQP8fZ&;NhZ24PaW1VlQ_8qY_<Qrz796O2YerK1@OT5Ga-6)#x@Ze2vTaZF3p<@BJ
z_YMZ;7NPx>#oT3K0Hg8<-uvR=NR)mKr*(qdhxU7PGB(pO6suu3!i{);KUBvfb1Bq*
z?Lx3UmNA}0jv}ffb{U^@_R%-YXNI(#ktxF&XOmWp0SXapiJZ#Y`cb;Wvyv<E=VCIm
zmROba+*2wm{(5XDsfby=04xdt%efxy*uHM#W2FN6T7kQb?=CnPSkh&G02d=K*7PlK
z5J+w*;DgWm<+s8+fKdX;*A(j>lf!N`PPx+E>hGSZ`fV{viz)P&W;PI)jihsPat;!}
z-395h6TjVvX}S(>QFvlcNqSOE85SEVDd}yNKUYkn4qa<18jqW{h0zCebcH3-yZyOX
z=pepj85q}8QCw)#-r%u$>wM!~t?G)ys<dFSsHMdw_|dYa0i_~(|LtcU=tg`B@Aoq|
zXIs+`S{v(6>>NP)Y)F#gQ$$7Te(6HNDevvYSvN2JcR9z>--hUOgEe)3?K0dReh|fE
zdARt-7&hhaT`MjlT2?h&AZ;fBAiRMP3i?OhifrM40DtI~MHJJg8<r;0UD2jan$D5H
zKN)z^_toeb!hXm~N;>GF3dw5r%givsZ5#KKLh9?*)Sn&d#Wsd<u3z^#dnwc-1bE+!
z+E&f$NSE3@PhH>so-VghBqWPoHd*_e_Wct;aadR940|xv$35DQOMQF$^y4>NY%f8G
zm18jK4R!956hF1+6Z8ix8@Q=;w@LwNAaE=eqrP#EhQV?^{ZUoX$I|@coiU2Ym$cYD
zB4mq17Ok9MmsS>3?{*L(uGuTL97>Ji$Xc_8Ne7~m&&mLc!G{!8BeO()G2ZFK6rtvQ
zT&(s)QsE>Sd39PmuU;T0y!aQhYj;9U-rBbsU)RqV1>2&yt4(Q4`EE!}UHyZIi`_Pl
z!uCJh42NB3I|r(}mq*u(gzgxWjJ@BTC}Ob}J{?s;>#I*2NXYBz8g|t+(z0$0cI2Om
zLaWHat5!%<b-0IP;#}}l1(*Fu&P&dFTNdpYS+`Qn%9+}f$gj=I?%ijMMn>kw3p-T5
z4VHy*G4!jt761J0$4y%>pY!?X<vG$+aDmlUoKh}HE$;`LU*GFW+HKx8J}N+*u6-GN
zeIBEwXqGi~lGNfHG<`-Qbg^x$5!hyjWB@+&gusyg%yU-K+j7;Ka}|Khp7CG;C*)(8
z7&SQDse16ixzR8(pB}ginA4#JE}NZOF6Xl~J9zPfrbhx$5X0-9DWP9@!l$Ef;#%_n
zyk(h((OM5P8*@}xWx}>~<Eq>5^%AodJnsJ_`90~5vlPSF^%<=_6$&dBBk~`5&3wrD
z=E?qeF(5&b9>q_<1)x<ZUAe7nCWNG@%2_DV?-7*vDUnkDGdB4$^x~+%`B0hR8fH(&
zm3Qp09124nqVelf7Oi0Or^eEeLRE$5z0;zv6lTfX8;j5FKPYFS_fytK?S7s9^c|bM
z!wNh$*=wUeY!^Q{!$>c4Cd6*<{RlB;<^AAXnF+6kAt-DZ_FTIEKubYtpq%M~J3>Qa
zD%0;gb3F-AG}jhiYBt=d;*H-8vNx%6(H8Z$>Qo+ZG&LZ2YW73gyA<9>f4Dx8m(z3g
z`X9_Er?hg+@yZteSi25v%rubWdH=8-H5+Q|0}>-9USv-Z>h|+RpqU`?!Pg(pIdo&@
z4wBr9o1t&>C-Lb17(q+a5|Gx*v5N5kV7B1eE0U_O=BUFV6M1>rPSk38sNcYCTuc&y
zXmhpo?T-MW5x77@YL#_h)v_PwFvxg|P!zdJ`+m;TMCQe~!QWJK_Y0o|PUhqoe$pJD
z9uH&O`O|0`Y={ap;8L%FBJZOK6JT*V$nSD7nSQ>j7kM0$Q>@(|OfML7<9zUzsKy|v
zAIj&x(qOQhx=i3@-Xv=99Duay-7lEih@|2^;U2g@$r$oQ7yGXT5NI&uTXbD!E|fw4
z%Eld><qNBpwWx1N<ULbi`-3G3nOJ@~dsT`;Jzg?D2VGn8{kO{b_^nlUZR2x#<b++K
zfa!VS3TT&x%<N}-l)M3}3M}w-tardbi-g<yy+C6C`7@L3<gCXzlJ<?%<?$5PL4ww$
zfqMgqvvtExz!#E+p_ZX1K!8CR{|Hzd(yY_Trv`y#G&r5O9hm#Q`jNLlf6;)2a3fDZ
zN7zj3dujyuiU!bMPP_$*H1KPF#K0AjSUBmH@ZMfuqiPqlqkQ(&7=DFN1KHJlg7MLV
zVQ}n8=&6fvcG^WLAVEnJo4`sWsEqpBS(JnTDb*mx(+dpdz%!3Bcf2Q%7M^Qg9FAq9
zq$gsvfAs<o_9RHaH6M(ZH4}b2AN%PgOCIPGFg1GNur^i@T^#?Djv|)C?8CI`q=Pqz
zNNNNX?nGf<=Dp(6M`9ld3-6pi(tK56h&;Uld|4FKAEAvchj*__-T+w0mND_!EG6tS
z`A?n&z-c?+nI@~gJU^&{|DY;+@Vm<dP*iV@*1e*-lywjPrPrDK3h6JFk~^Xr%pT;L
z)K|i<piN+*fOaV0#;3#J>SWN#pjINH+}!QR9LUtKe<j<pw$nj^3rN|ujj7(ZMS8OZ
zsG}Bxw^hsQ;@KD=(#PQH@2?4;cZ&iY<fFowb%Wk5J0Gx-X?JRV55J8!2nU{1y(X~w
z7Wrx&1;lrI-@HoBXZ8?Op&h#(b?KC=JOHMamgf!LO!hjmG&I1FiHU4{W|}Z|fCqTV
zG@GYZSrnXtQ(tx!pB*~n*c1q|<e@9Y^L_LQHcf^jBOpP>#E|u7S0)+c&91XcNDW$Y
zNM@lEkx*%iF%Wq|W8)%ylN#6qDfmhzd0Le8K(KbU6*?sx57d5!s9=zZZ!kYZ1Bfi?
zUwSoS&)$p@2&)*_sKTkkIl#H}J*A>Uxg#IwBMynEFxZR+t|Hg1KJ_Z)fz77kAs$BL
zUM|1}G-i3fj!SJtnKckqeX{%WTv{<9@GE5Iwd5>{?l1s#egEq>B7K7_1Hd{8c!)d6
z_+UoDHWg@(8RHEH-PGW0$hC~vmM#EvMiKuiV-^<^zx6BJwo5j~B(Lvr%_>n!2tE;7
zcgYIYR{7Mt)=It>Vcl69yqxTr@kE{$?uF86x*FReS$jv=<iWgm8Bk1!WS!1CQk<t<
z1>qoh6i_Xul(x_0aJKnV@NHadgi)FEHy;le_AW3+54f9<tA7ICGMeUUgj4a=l&{|a
z?j)17S#Ps1c12i%{UP}yd6|TL@ffddbbhE`5_kfc`K^wRf~rM3F4;-pYzZ8>Td9DL
z-2k$#V}F9CEJ|2`{iI%ztX6-uhublpJ~Zq;XsW^QRB=*89=g}BVph2=Og@yrhbO#U
zBn)BuSof05cO18wQFS@Rm%A6X=24^eB8QzzVz;7tKGvww>~)w@cV^4op9wuTW2tE8
z0lDdYGnh<fGH`t&{X_jGo(MH{(EGu3Dh-KUjlEqdsmSbatGz3|sH68og~j|Nlx3&9
zN~cL*9n+R9V7=pM&)<8Ehe&i5`xBb&pD8sgbAZ1kgoVzw&3d`AF#fC#=KX$8MZo53
zwS$?X)B2La^PrS>?}M&C3Hnir3ID<z!?gc$cl(!lMXYl<rLE(-0Xi$MA8)n?wq+4L
z)n1o@#wx)BF5Vw<s#a>yJ#;_P-Rn;f)1vl3k=KS)r^P1x<d0sH$*`AY2VC&9t&5f?
zEgqL+sKWAZEt8$WU+C)k)AsehTj_yS`9+J<rGibE=@bsOQTSSYiV}l!=9{BMRPsr5
z!pGQF976Xd%&x?CM;qm=_uIX1da!+|oEvX$`zItKC%`Qqge1P|b%onhJZc)LLuk>=
zEr+fuk{@owC_LYqb%&asaKKTAoDE+y<{6m{5f^8ez1{oAq)6I~kB=KrBw1bjVMJ{z
zk8kon<kWZ;{)jV}wYL0`?~Bmc@_IF&hfkaeD7OewldtkSBPaq9-T*&FKiAlo`KiG#
zO~4-_`NWf5*fFu3KxjCq$$Dkz-KAzlc{Gh++_4xF^!ku)VEfoFOXv;_#Zm6t^Zs?h
zuO$$6Com9}g-^qz4>=cSMxLXm%wP$pCTnseE5-2$_yXr5mOl#ej?LdaXq~jt15O3w
z_quu{rvv-QUxD$8LB0}$a_de76}oc{TJk*~##llfB^vnpN}HNXZ>Pp1=PI&mCwVKL
zdcwAKfEfak>2(aWuIK%Z#?rwJi{}D+706?weRUn-^<}w!j`)vB_DI&#X_SMB@+=2*
zc&}dLXR8Ir+E^EcDnny>=xK#HVRN@-FSr&-Rta+`A$H6Ji|@*BWmVRhJ>zh-{T92U
zd&(G3;?8KF+!Q-2ubO})G_TVB$SwZC8l}@Xm6jAQcl~-8En&zx#7vuTm(Uw|q8DcV
zy+^rOmF5;J<A~o!zs*SX<1Z)J%opaQ9&~X)*$b)@n?tB&CSkDsTcWCsNs2-h7`t>w
zcP5KHE_pAfhdr@&q{_e~1PQR&N^-nAO!X4FUp#8_L{r;lz+T}!^@@FwPF6*)oL%1U
zI+!pzebAPJ9H>Ho-1e`c1dLv*alUS~tZ^4Gb3ur?5&@=0uGw0eL_XeN9pDM%^E}^_
zPl?DS#7QaHSee8nUeK^7`N5|;wMXO4izaX;;*pox;5fq_CApUWlnA(Qp(#E5;WBD$
zv;*8g6rT7B8@k^a0S<xy`#dL<EPvX9DX=C$W<tkzof<ZIKKO!f{3_s%LJ+`01R3?W
zdDZ^Q=taVW;&&21ig1EX;7klYE>J%Ipr^i@BoB&#KZP{tx$ia!1u$oSY;+-4AOaQ~
zmS<ujp>Ok5Xo1yoA+6H@b_^6WWC7KS6b~c<=IA$CT(&6iCzIp99tl9ERsyr%M58mg
zd<pPOCELqE{Y_7nG*B)R2fJGj-cSIa#8#C(`%ML^PMAzh-|j*|or=OKl_^_9)>B|O
zJvtm>iOP5!1627=Uc2;itkumKqeZ}DHVBZ4EY1qlJVMBvg<gD(l%#^pRPg7auR~8?
z2mr3TE<RRN63tTaKrdwRy84B%0lg<`x^haa0o2m1edvU-9>}vRyx>UQ1^BgpDHbH}
zE&=eL!J^Ig(Rf$DlSD@5cDVltuz*B-GpzaXyWoi=nTBTGb>=Hraz0ZIT$x_rrO{sj
zAXdySc$A5yK>~=y0>0d=FzjFgrJ+lxQ+Z}ZH{u>@4~ReWE&9DrB%pjt@OJmV3*gZ2
zeZC3C`MUx~*?V7z!36&;5WEAI1EioN+hiK)#<&dz+}hRQ-m9%V*Kj{2tu!z4vC`h*
zNFX0*>7Tv`ffZL8hgR($vH-@xVL8e?ZEhN%*D<<QU+$bk(N__gf^xNF$)I3Y7HHb6
zBOT|#lH*`>?Y`Wb0$O}2Y$zz!FA*HGX#k?(*WEmjoLCnCrFSY99v13&^bC}IvVd2@
z7(#R>s?Hin5KCco5C&gU0NpzS^avbk4Ux85SZ2NfUFX(h^m8@D4ruz8d^>cL-UiFk
z7a(2CD6Pkh)vp|n)-)&P%>p2xJ+VB5Os7`_N6bdr$BKkycwJNw>g6%IaRyR4tWdT4
zSzOu2c=$P+B@#8fS6AP|;YPkb%??C9J%jMW1!U>Fs%Xmdt-}Q@<mulP4&sFx?s|cW
z48Y|z-S&pyHB(s0m|SjkjTR$zBh=aj0IZ&_U^qsj&hzAjbAApoRzS12(P8~RXR;S{
zUlRiE_Ezo|mW_5=3kb6@2S(X1Z)9ip(zNo!&g9UJ%Bg*w7JOPi8=+oY%kUkepY0vq
z8yi+me)@gGrz<uc299^nw<mN9Kw6^=i_*`0kKXQ=><-PF&QqY}hhd11#|k^9Yqp|>
z)%x18_;;7<H`JV+KGH3xiC3E)2%Hz~23RzJ8RU`f$b*>3wL@HM6X~(?9YjJDm%Q?~
zWki2ikDWvuezm_n*J!>!<Kik_gX@tfwifqH02r_6Cos*sR_0Ub@i&in@^zJL;~&DN
z_GZeGxuA<bMal?XVI~kZoB$y`v<|yI-*Y<uNJ%bgd~H8Sy~<Zj)pxH*pu;{&rW^40
zdz_L|4QH#>OB^Vx>0w;dI9Hs^0LWsrVVKpQQNH8x@_i*eh@FpXurpzO7?*rXWrw<x
zys!8XsUh^jjF9)i#+Ve<8|PzBvG0@@1)n)u@z_@u*xy*cC{2G#p&LfTmi=60U^bHo
zcdLU*xa||^o6|3MAJXERlzQBJ;Mp8Rxt?d~1K&Kk2nm3qfPVZbgFfn8`q}z^ja$th
zs%2M`lLqM6Ty!na8uexy1wLmoeF31}6hbUIOlS9w9*WoZHTMgqM0{x(xm(XCi}oEC
z%4Lp^?XO{=9yRrKJ5(wC6@#?I3WE^rzz{+7(+eDgnP_-9(8#RV_H8pjYu2F~^2&wG
ziw$b^L$I<O@gq6^qpUUc#kKCz=jS62pg%Nz%!%?GiS1MO8sCZ;aH`H3kD81Y+%@RK
z)lxz$jXxgmRGsE>g^6m^bf1&)WrIVdiXmTX3YpM%os-+e^)S4N%a@I!uY#J@ceErO
zIr`AyI?cP?Z2RjQaT#jXoLCnL$fa(uA!L;rPHeI^ODXz<EM+^Ik8J5J&^eT0ii$2|
zHL1U?=S<{4y)Yy=3HT)j;*tG%u@x?W9^%%yFA^~y&^+hc>q{g6(4F0{D3qtK*p!$K
zY~63-{Kx6i0S1T4W0<BJQu<t{fPHeVV!FZt!p8k;n1`VI9OL@l@FR!dL?pO@h6E4;
z4fCb>TcepKg74fv`ZWl6Y>@?xeGT&RK{nG)M1+m+Kuqpg)t-7Qh4N@~@KMV&$J>4)
zB<S|cKc5RHHdBs{0Nf#vt}hvw;XHa2J%Dd1fXOfVJ&44Go#+04iS~dw_3z;8`)4u|
z{{e`4;XhGR|1V(X{(hX&|G<<@{qImyf%X4?j34JO-_3vF7yKVA4X_Xbh`#^Mta!)!
z;CHqZNxSp$VI5DTdTQiLx`w3Ug`Xu)G^GWN3F4o$x;fTRJ|ug`8O-I9MR}Wq<l)|R
zceKZtxJdm#SZ4BxuR+n%Bu~<w25!E$U18e3L1$MF&iz)B$j*-Wy5`LvU&I)G)q}mp
zTKNP?nHD|dtKMr*c)?BiS?--Zs=m)%?c;l$G7fytvLL7cTlg>-<2>PTpO)61Utcu&
z5ne>bZEOF^j8xvk#MHGsUdDuM-|zJm+Id)0|FfzWh>DL&u)L?{v-FwkoovnUkF>@=
zbQI7rscUwhTcCK#RHqe~qCYn1Y_@dN=+}n(W@k2a!iUGf3U<L;Hsf(Ga6~t!$gV2)
zggzVUyiq|%7=6L3<9G{zW_PS;^FMNazX5%aCZ+uOrdP`^oj-6&OQEJPhf+wbr%JI%
z{OiaT#rXM-TboSy=u@*r)&R_C_*W{ou)Zx)5o;rpw%4;aHK|?4tVFYMO;bvI_CNYv
za<b4#+Yq0)Y}BuRwI|fY<)R6E`ca*M#$IA0$2Tgjjp_JHOcuF(TSX;8-kt*%VF_y{
z8p=&NanpJ1;{&qnZD|nQlA`Xu$5W!&XVcv_{-kfQ4bPHdl7!rWFhNRL`jnV{D{G~f
zNESe$EY^@=DM5NhIm!ZemEA1eES(g=?O9|8bBCV{obPvk!|BhP2ahVUeQi1Kk=9)H
zkj+HIQ!#bpqUkDGty9Hi?vj^{S`3%W=M!ql>30!{b&g8=|85+qm{`}$Kf7c3RwC@&
z!G}a~OCGJ(vQ>U8^L9a=Hkn6R%R+4=ak%$&e7|b?#Rtde){Ckd8jtFNc5l0^6>ayt
z&7Dr=t>?@5{o5b2Tdon`>Pc<-xPs0%25u$ch{MeX!%eC|^gEOB&ey9#xI__!My$7{
z>|&APWeQrG?WYEr7aI%dD&DED={VG{C((DEA2tuo`Ne4Ya53uZL!(dc@9l8%Lp`2K
zG3*d^6f3OK+)z>~zT{ICtEN1>m_GnB)RCgl*DJGVTU&WG!B6@m`AbQ=E-XbtwT1o}
zX*36bzsj(fGqT<ye~n?W=<vRgcS1bUC*i!YznLT=lLb{jt=v>1)Qna_&6I8{G({?+
z_ex=0DX|JJ%X?<wv3&0y?uf8K)$-)kvbyj3##e-C-H%4nbjj{>utN{&S<eSDX){JF
z6sq>J;w^A|IJi9E_Xx%=L5}Z5U$`09k|h)m0XOD^MuiQ9xxyY4%w)2Dy8afiQ^=CM
zvBI!eb*wP$;y0tC<QwhUH^%V?d(5tt8@eZ6sw?^~v)u`HnUfy=Gp(fKMsn?Ijps<g
zDbf2$$bu@&YREo|3_380X>p?v>EIM!A#}=S=~3}`T3fUr(wi*{L**S0mpFHmV3`H#
zt12lUXTF60*|v(~vsVIN9fuqN^{Cxl`c+Gl$Ke&@L<ikkf>kO9=032YV?R3I3ZP-#
zq55#<Ro^8{<7l(1r>cSz84veEO0TD%Q>iygX#_=3i_Ck;rilLZ`8;-wszwq9=8Sm;
z@Em_=$uB@SGvcMsrPx30Yu^gz$alrPy=5eQK#;;mSLzaF@JO6}ov5kHttp%Pi>_pU
zqAQ?#F8@U>F3!*X4N|7}`rwI~`%UV!SZ*n<$8Y9zD!+NrY)a=;%GMZLRlJGoZ*eA1
zMecJ9u5&+8Rp~S$O+D||>{*~+NoNEuOa}D?RO<f3PYEJQqKfmv{Y8Sw*Lv!dsYVb<
z-Y4BpUEenF`+`yxKaPdDlV+n|B46s|KK7Q9*R?h}%5*a5rcd%0B<qQc4uZQA9y*W>
zo4LS#7FSs=@72W{1*f+?x5c{XWV899Sd*Wt5RvS`<e0yY&IoqjRdI)D>%4|N6Y8R6
zxt`dF{5qJU^qy9pU8gM0w!sCKe$#xavtT~o*6WxSm_DAt<mQ;;K8Nd)Hb6wkw&YqT
zadvY8Q0`t8Q4u4nPB6FoCaC4d_@6Z6ZR3Bo6tycsEG?Yr-&$@B>C*jfLr)g+Uz2vG
zdaz7P6-q3^@<^7k)@2j_(lUl|<WpPN=H*71#0O$lR?hCteT-+G5;yW}pOOMNT*0V-
z4)z*aH`&TsOYu+M85z(&B5D5Uuiutw;VMkEIM~^<)9cOtrb&FG!NGmpt}Zxz+~(HG
zexAgBX50Hn@mG$;iDYSOz{)g*q2m9!sJuAnn&YyX?tf-2u5|`Dy536+VO*RHMjf(2
zO+`(Z9KZv4Ie@3BU53QU9n|x^8?0#`7QL%N;CsMU6aoXBZ9s5Wqv%E%ug8BNVAX}<
z&T(F4T>t(@KL5cWtoR>m?Ejqn@sAb%zhV&n-vvkv044(`k%L~`3kFUi(?;KoG{4*s
z(C;`8QcC~DGw^o>LXS3<^FWnBp`Et#!6{q~Xs41uL4ay6BNiM*oR!AyzDVG_cC>98
zp(rTq-y1fe#KOO03H6>~^$oW6cL0?%z{fXnS@KTk%X`76-V4DuVsN~ZmuH~Q4{-p^
zNR=_uUrc*6ku)PfmN{551`({`NgbqL_)#c)U`@#W1hhXXf^-fZLavSmm)$@$#yQho
zV``)D2KET6q$oh~kidcVwPFO1t1e-w96~Ltl>f=YC+~-KzRt|R((3`+q;q;b2JkFw
zKqi<uhYt3P>(ZjG@hhxb2{Fhj{_nJW|MDHM;|$1*ZE84?y&w8T*iYXUM^`!^mlH#z
ziumS`dGlc>Og5^srHG@#l5e<QQ&&fEN3n1ec7l6SIQjHhSuyLvkX!8oZnQHVbnakV
z)MzByR<J7*qkax1ru{WYufjOUUqQ(~>w7!rkO9j;60(e0xjqhw#c(mvDjDVZBEghT
z&|25Fe?frBGBn)uarxYw?>PtOjiXU8JrzsTVL)TnIm}mC24S7WmuqK9NF|1wCugW+
zD+eN$b7Za75-_(E1m9Zya$e$G9sk?;1U}J3CDJ?2lhN=}MgykN<@&U#{Q=jLsr~Gu
z!N3DWy)Ue8;Su%EC=R`;$zZ3s5^pvlC&fv0WR-R%MoqJF*-v_Z7;Q|)R`~O{61`C?
zu*?2obj%)5qRu8L?tLbI-3Yw_Vez~!loy2RDJVL}H*0kh#H0}N4a6b2Hk~LWmxr&2
z&Bb3I;(}Dr%p}vnB_lDyDw*(0(wSe`mM3|BD<&nFZZaIC6yorBk+Px4p)a0rhG`L*
zzu0$upzR$w@jTP=&K1O@CnEWB(vCMIG8*2mH}D7ZtSk-N7*$M`wF6EBn~Wb*mh`k`
zm1sU5;9Mz|MH}C%S}|h3NM2G;=tB1N#f|%z$FL7sH-6~b?HHe}%xcWoFhXf?_|~1S
zEeUAt8_}bdJrnj59$Zo!e<1R+c=RX6PTVhI!t=QB-z%1a6u*(@+hV!R#8a7q#Bou&
zP$z(b;*!X^TQIe1=*#fOQ1cN!dw}=N?7}Z8cfbwTntaBT5&CrBz9*V9z_z4yAUC|?
z1qRt6Tgvx}3EF@3LZ`1-Ny#_y93IX9JBU<lVIQ}v{q8+Bz>6$qSQJCOYDpGA9TLl2
zTX>{52jzjf@hbK~Lmqg2d+Ofz^|}vEeFCE2X^cn_=kdZf_T8ckBo2ftdh$vNE|bH_
zA4*p*v&X=Idt8hnKd9zH?he%qq7Ea?Zrst2MfP64bK<Cz%Sm85i>r)NQ&;4ac{@EG
z)8%gF3t>ZfT9~4AT4&jl0YEUybW5|he~|2mu;cSX9Y*0e8A+%CoXN!8QQ53UALUbA
zAkkTSa*USE<ej{Ft$fztPHZVGs}%+l(Fzcslz7UHs&h9*)gcld!tn1pA_Ck$60|5X
zo<o#Sm(dQ}B^ejS?l3yVWA4Tx+p^x~z{&2+g?4@aPIUi_$|+e2g=__Qk1n_%o^dcQ
zwwTawJ1|1wRm_XeA>GgNdLKJpnC(P)K0JqQGm)T#bqA4Gi$JF#HQ!F(7y~TF(W{Ky
zDQC|ILqD=WFJE)QzvG%>@q$PRV!eGT2TORdWA@Q@2SuKCRmgxLidJ&_;fiT9R49?O
z{3!nX*R8JjQ^x*~Pf3C(o&7B51xZMEn%>}r-F)eQqAxyIWtak@Zd5~Z2c+%<b28>j
zglz86=Mk1V0-Ty%qB+wIv-cwm;E^{@xXx>>|KU%Hv1^R?h<lPTn34w`>(@SvO>ja&
z9!j8&2O-2C(*!qUFIrKjzbOSEHg&=;2yb3Vngq^QOTfPF`$2NuytvIjBXQt2{%Xnd
zG$%1d`Ww|OiD}d$KNd5_#vb3`a`p?nuZQvPZP!?cp}uk2?G<r{;gyVoV><#BV!^Rf
zrSuz|qnbw>5{H<wd5GpVBm!kyRro>>DjXCdw1S#pv$!VwT&-8Q8fS48Z#;R(?$LbD
zAQT_}C1vvm`2AQHCQWwSGKKVMIzHC?Fr5dDivY^mR}s`H?%8`wQ?>>lN|dYM8Q-zx
z9Lm$$cnKl+bL{i?tM)DJh#hwiUy$M?+81#`5O4IU5BBi;hwp8SJ*)LZ4yTsCdfH<v
zJiXr)MAZ9uTg3p=H=u{wkgfi>HnuRjK~jG>QR>fOou9ipL`zVWJt!|C2`kA!rcQcA
z{F<5X#KWrmcs>EVri*9M$Xo$L|G0?YbbX0M$4|I&Jw?%j(v!QW<U@=LR;g?BQZ`{h
zqmY^s?h=RGvoVjr*y-f#{MbQ%we^J-rWbqV6fX40ha&n7dqw}jjAs|yck=6DR<5*1
z&EDHZ8$SR1EjzjSf|Qz?V~?7g;)CuYYD`!0RoEJWziQ%se&3)B_c08O^pIF98jbWl
znq$rP)yVh<LEU(cy0c&|%mANFbID;R+Z0(WnU$xd{?+_DP6hp;o=-U3oWIUx`e<-e
z`;(h%Rfh@Rm;TH>)ynEVdtgBa3I#UMeZ}(E4Skf;4l)FqEZrTbWsT}3rz(>t%SK;d
ziTcq)*_dlbWj7M$_Y>MsqZO}5B{w*?ZG;DkUdK~#a%O<8OjzylSAN!$^ieqJSYewc
z9dvFO@3w30gAw{d$`DNWgCG<DS973_CXB5}^c#)#mwNs$+23atf9W!;4y(V;&W8Uj
zCC@(<xy)**<;d9=b>{Bs8u5?n&Z*Yzi+wNi7(28s2A~|2Hm;*aOVLj(3Q-lvQ-Oc9
zaY)5NT*U{Gd9f4)bUIWS4>d|5kKPg*QX-S{GJf0ul=r;iOiI5wEE{j_XX(K%6Y@2r
z0K58CDf;gln*E}DsaJ<{{Sx8JW<_cKzMB1ls>kyji}<Lz$bze8K$}7CKPi9B&gRc2
z8m%?1$T6K{A0v+@=j`#+j2m-ZsnoZ;+e<!Ok~m?X`#gPlB*{xw#I(@iXIO@Ul8}tA
z(qH6w(bfn?bvadQ9o??v!5sNc3Wmki8Pz!T^u)@J)p+$W<~q!7$}4%@tiq3snC)&Q
zn+*ppZMztx9beie^JSnE<x1r_L)Qas;OlP9IRxG=okA<JI|k1Te<QX#y1S;A*SUxU
zr>DAXw}vrxM+q~fOO?<x-`=t8F=^OMqg$t~KoXKeCuY+Ix~ZYrJlFgSU#KkOBF2br
zb_M3J;DyNYKe6Q`5fH$|Ez8w-^{5aLlUaR*$zrzUAv@9W7sM@+o4g4DPTGsU(%!!b
z4fm%kC^R_YEd)sPQevdfOiLEz@d=e!PX<+K3vR6SKJOXuJyHPjb;i>2d06J{q|`i7
zEEX2pjuteHXF$mYv|&m3X3~<;w$r2nP1R{jH3KOEPyXc_!6*Dm^sZG@ONl;*FF+E$
z{V+gr`wyNm4lLQ;lqbk3cjbfjzp#!G8<pvS-en%=EMqMu|3+Tc2IB?Z&7^@`b}t}d
z|3+m-+SvxY3vkC$zgb_j>%93pzfO&inuh;lG1+Z%?3{7Vg7*VpV_OcxwPml|@-21S
z#}sG(D+QdwwGVUOy6t8oE4SW0YdY3@FRo-MdSVRQzd5@5hFipwzc~SIrBE^4S$$g<
z-7RNXopa@1DcNq}tmdu__1BG$RdwSQT4-w7m8Mtp-Gfe#eme?vRsRsAqvrp<`nH#=
zVxQUy+h^<y)j|ofv3+TUj8_W%q#3Gr6H=MPpB$=>`#|{$b38JpWt8{Q{OF--Uq0Z&
z>jC@dAd^zY`Q%^O^^)!wJ+%-ur1_al%}SyNZ8WbIlTn%+{|LawR^)!~3jE%-g_+=z
z|Mpz*2EgYB5)0-r;<JR#Z9*?m#OaV;H5aK@iMzOVyGw9x_Yvz4xwv4#M>3smoHm1b
zkE{kuN-6kMGs95xPU|}p%A1C#wPainbv4>yHoH3%My5x~T71{J=S_C^+AA7<%>mSr
z7Fx`X*AZ}WCE+wASw7vY^uEb!(8TjD?@@`y2oFb#pvA}bLoLsZ$8PlLQSMXw{X3tX
zVe6PjdejV-%$L`duT6-jA#D%&Ox$faq?17L-i8gM8%X#VXZ6$S>6qDFJF58xveZLg
zCiiq|v%CATJ4z*`;$2iniAZk(<=>0rA(&O4$PwN2^^<*Bz(~x4)GMqtwhFMPF-Q9)
zM?IBKmT%bFf-M|5d|YQ<OkGLtOdVY@v;6vHEU}rcy2fFlRWJNLXx1A9_8s_?r?-r}
zGc>auy}pNQ-JK3?UMMYYCGRe71x^P5ZUVrLsPMzX00D5QzAucny)Ag<0~0X9C0&8N
z)91?<hA<{T)l0yt-)l($hxZ^rUye^nq8;|?0wl8$pe{FlbumnMc+3chSBi}X9Uljr
zDhL4v^4o&7d)RDLu-Uv4s0PkWj{$t-Y<4Qf#gogx?&%h`ZMrSBAFBcB(fW@7gh2rp
z?ghPw|CRy2;SzQ1ehtwLZrgtCOv#kd^8qNDNbDBRD+;jUobR}(qChi_43LuXmz(~;
z1`{C9GyaQ!?n7$^5Dze3XwQP6tbp=>0|aFSX^0Yj0e%CZ^5Fm3`14;;@cm6hM}Jc?
z|4G4z`rpWx^j}f%0j9VAJ$y+4W$w(}O0SHmIx-9cnX;|OvU0Vhtwa0ke?($oF<w#D
zvcFt<(o{S1l9J8id)Tk*M;6a+iBgNPFqBUB7&GCZ_!>9X<Piw~<sPB=cGP}_XPpBu
z`9n^*A7E0h+p@$0hL|<0H}kI2M_4z-7Wkj%5@S7kV@3q_eeH)S*C@yApiQ4;3%j!w
zfW;}`JxB$o{W$qJ0z=hpY2urPKTHMnUF|`@rLdM<jd_4#?L*N0n4))6%T)SE<QFBY
zX6P>at*DPDwa`7i478QSj*UYL+d0lfW<5y3H>ARZm=AQEpP;D!O;D0tp+8KtMwBDa
zDX6n!$FPjn;7gUN`nBrXJJ?}6pxdwbxJ-OdneU)O=O~ND-kEH2V+~O$R5`&Rgz=r6
zJ#)8ALeqiHt^qcG%1Uy;H^EeOk=16L$^NT@(>}?#9o~FV%*$IAc&`|s`Jd`!(A8dE
zhr1T}8CUJC-_L(;K;p$KO&cHT{BSW9_l<xf|9C9^Gr^sb-E!+;{o4zOio5B93p|x2
zF2xS1bo$o1LW*c<72DFm7fR#h7^IGpL|^Lh;zifNdgJOMtE7zEg3^1d;dUUZ1K>w?
z-j*_LJ;~y>Xt%YyfD^~+8`r(B$T|E37;r4)Ylc((r|tCu4R(e_*!HfyN@veEM&^z4
zmR>k-X#mVz_0{MG;P+=glxrJ?TGn-*Z$PRwl|O>{edYQFegJqa0{N*YgZzaQ-*P@=
zDAQ4+<Tk&^p$^-S3&R?S9Sh6opYHe@uy*EouHORZs2q(y&NI;2;<8Mdm`4_SCBpR-
z&~?g1lQU><8#t<NlWUO{D=xni208cy;$*k<(W|l}c%iX7k;wwr^H5NE1pE!7{fNkq
zNW)Ye<u-R{)Rag<@}-`hT6Qu<OFzw#;2;Y(K<b_XPv}A3cLG9-F`>EYM320Uui*GC
ztDB1$f(qNvFIzA79U@}Y9J>;Ra;M#4@2#~coniaCPCUaQ&nf#@pC=&?b=C?DQi|h6
z6)es@4WEF#gGE^ip+}<*G7(38>nVZjP9!X{=o3_FNlv>=-`xt!b*Fq1W=lg^PSQZN
z$;#UZK5wrYo=$4R#tb6o3Xe*#o8GaV*Db`%bIuV~kHfd3$f(G*9IsQWoT8&eIVCIN
z)7O=fIycoAEp|&ZR(b~owI+@2PwR7Kw5@i1&d<?Bv4L>$)2T__;<yWX;6dJhJFg|3
zPVdmCdzI?N)78k5$Z?MX(}Ev;nbrhVHAQzL+jQpqx*;>7qV9G$yw_SKRkX(3f9xJP
zeh$G`$qZn!z$b3$6ubf1bS11C`ap|X7C(rp(ps$nh85TOlP-}xy{^`$paL*0NY8d8
z+RP&DcaNif8K_>b2iWPq1Tmbmy}l=uJ!gB>phRHt$GP7NGMv-Ks@uz))5g9Vi$14~
zeO5(#P8*wT^Z1Vzckur_p_b17psD*`HR*!h7vK``A5ICB*mXSpuXX%infLyFmk5xO
z_b;j9zgduY+}39RXUX6fMkN579(%xKz%`Xt1-z$A6*Q=@zl|!-e4Ua=f}z!48Fbo1
z?A*U;ov-6f+6V|;_dab|^CtFC`9BR6SUBtfUI#n*J@1N|ov;Au`T$y`!<*+H(Z+zg
zis*uSTnv2%;8_8N#qQ~c;I#2|K!xlkDi!jp+_FOug!{bY>3a%&^_uWnA9;nl2KlN7
z@Vl@TcZVHuM7{R41-VfC*p$#mVo{}+Ec<-`87or|x{+x6(ls|$qu&CjR8$AZvSmMk
z(eml_+;094DY}>Mf|SU_tcvJ_d*4#5jra#w8+MIAnbo_Yq&t+wCeO2XAa<7?B*lA_
zhSIqurNh_l4d1^e1_NN>?*SmnDa&6n9L8s<pg=m&2qd$hTJOhOV06`Zl#aU8Growh
zyzGC_<fQ@!Yyu=?(6>?xtY$*6OfD!-6TaJoV$SF3Dcb?DnL#NshhZ_KTb+Ni75pR)
zBUY9idO2Mf$Ys-PV=Co&=x3ft32r{w@W<mMyowd$(N@t+tYZwEt~T<|it*)CI#hKn
z+pTqjobP;_X-_M)S!=qf!=m_D`s+@^i2nXh1JkL7GyXRwyiosQ#<X-xKHT<pMEsNj
zBhiRlK3S(vZU6Fjv5o^5JG+OuQ6RffaBwKdiD%QFr+H{k*dmt7O4k>jZP(<nwJ#Bm
zbTF#udV0+&eeoAjSkb%PE$o5vXT7K6n2GBO8g8(5sj(zv1%u^3(rU*Xe?gJ&9*O~Z
z-bNygGkP?Z&$<1wvrlYM<n`_tWP>;<Jj{11ierV;`9LUL|0mgV8_YnU+Dao`vF(ek
z5n9$&(z`4lyxx*2wZoz!r4L+cyVZd66{DW^UZ%y)?LDalmco5+%ouK3)mOZC#+xbH
zP|on1!Zq?`0Jd)`41I1A_gS|&`qZDHmZRE8bp?9T-<_~`N1V{jMzk>vM(LMnuSKEA
zb-giz-6ylcn<hGooX^4mQZ#k7DL><$ANu=B98Bw}{mnAyCtqwB7Jey7(UlDMM#%ek
z#mPcM2i*L8{`ilusqSM|4<Nt3UztD<-Rr+xDbtQ;K4C8uiC10X-iPyC6Tcx4?_((U
zaQsNg5FU=o=fW;E7;1U)+EddX;ZOtZ1_exfTJ8{hWCb0k%bWo5rF#nz3>Nd`itM}%
zHvT4Oz)XIaN;{_~9>+7vrWO<pV&^JV{_1_|flo_<dGX3<_s1UsheO`oSl#_3Fic?E
zy%iD4AKEv$7!D&3P^2?G1Szq=PJS;_7YlDDD0$itMVpA!hv?b_V3LJe9>+uzC<=Mw
zy?D5+K8$H2Gg)1ri7t)ANo!(GyI<@|E7q8E*HvHv;B@Q3Q7emqd@t@{VS~2S#}hl_
zrMVvK71mcLqo5yU105{-x8>x~^}iW?0|*>7y#pc*87)P5ctq|)sBV&9{*A7@5Y}Zq
zkEkH9niyVw6cvlqPZEU!#eKh*8`(zJAZ7tu=;**;9R}Xh%`2fFW<GA~Skr^eqNk*?
z@qDj}?hwanKYlf)1i8KgAT;1N9O7{tvW3jZ#VifnY*v8aJ?U$q-yumujs`vX87{pO
zUKd+&a>vIZ=7Bf0<HfuuCKfPFj5l;Lz&1;pu*EY5-m(Xj@Uk%7eLjIfZcfReo1Qpb
zcl!HK<Th3+Z%}kC_RN+}Y7?Sbu#`D9l;?Nc#NW=J6qG(DVx0@#;m3<YDT%9XH*~q6
zcmwsMcNFsb$&IQ;RPhWh$fhBu*MH(E+-g}PIB~8juQqxEz`7--<S${=i@w(%N#;4V
zBT=5WBqP3>drw{+ab7tkLsUFW^eyE3RNO3wmVM{%KVe-k@0O``a;2(z{XW3N)}N5%
zdE?To8*H6*_7>R9V?xM+jz1(^8YL4bohYtnf(r<EgYE&nqaH((7h&8ZSW3h13u^Ba
z9TmR`&eU3g-Kf?pNM=XwEBy@Rd8x&EP<sEja2)&3=CkVz`PA|50i-eOP=uO@xXO@_
z?)__(VHQgA%!^Zt`YJG#=euAGe@8w7ekI_k8Sl9-$-8erGe|5o!`4xZi)_@7N8gzi
zhY$VOuZkQqqJ?(rpS094r7GK9_&K-M^1-Sd$iMi4(UZla*gA}!h7>HtK4-{1lY?aI
z9j54lQzgJMS00TlbR7XKf93c2?m(QRA}@|h@d4C$+AEKF%J;v|>%q_@iAXOW5ooO2
zNT|Zt!2v}0uTi4}jGDm~(-`%8iAd_-kL}8{yEs-8Y9t$iY3eW&o816eVcb_U(!zeG
zfTY*FX46++8&U#YA~wUHjr_)F*>N1Z!v)DOoL#!<dLadn=2W&&1zh5EePZJTwtVEB
zvP*BaTbCuFltjbeF!5B|h*(7IOdiL=iCb`D3WXgF19A^BR7U6M6&mp{=0(nXwMQ7d
zJ1AD20ewY=p^M~c0x$|0kuuL!NwH>8Z?2M0M9p7uX9Dzfn2Y24B?kbT^5R3I8W}bn
zFWB$=pFfYD2!5ugvm8RYi=wQjbqoxDpqAY)yiwD_sbE;NW<HwbYK(Kc9<(nf54l?k
zGUN3G_z5s+Eu+Z&L(|D)ij#;(_wEzlc9oEL@W-9#Hmf`A8!O9;6$=>p^@S+BT4Pyy
z9uo3WoQre`^$|s7qVinQFVba5ttuD<F1>!$^0*YB>r-;S<9drSW%Vu_0Ca;Wmzww)
z>}#^(hJbrN@#9`}N3lj3HAu+A+Hsr|uT8sshSNhb#A)HWBGBhAem}is3^|flP%14V
ziOH4xo<=jBL_yR1=O-GA6Nj2F@4pplkhmyHut!$EdQr^1k?xcgVk>R%C6|J62*)e}
z97O3<IK))d@=O9t7k)-|G(+2Dm6i!1+P*|dT)a^3^a?3mFUYm3V43dPSmcU#vHv~j
zoKB|0msuTzT6L4_1sQ|exd#sd(!-O0j$`e<Ya~C%|Dm)M(^wR=(ERg4bVQ*>#n@eV
z`(Go83m8dh)YP;m^GwdXzqkjPT7RTpLunb*G=Dg{m6f1dYBjVts?;kQ*0g;KE)cn%
zzez_^IX?FQgPTgKKI9FxtlE~8^P_XME<ai+)eku{)17L|IAH{MB`s-EF28gdaOK=S
z-uuU5`T|20vfz4v6J|5l{ZKDKHMs*N7N3rkP`gS)GeF%<83Rpj7E{h;whWAj3SJ^O
zat-g3yz|=Fqh=Q$pVZ*q%PKtLOPsKman`HVJL@TuMzpk|IjAo5&OqmB*KiX=Qaune
zv92Vj%>AWzZqcLgaD!p-lZAUZbz+wUNR8I=5?EUgj<-&aHuv^L^2$bpkF3X-ZS&4v
zRA`GGSf+f8?FPgaDGhnF&!dRgqU4)|dRU=G4(4W;CxxR0(j)~VkkqNHy)Cyv2`QVw
zvcp1-->D6ha9B{IW&baquvx>Z1@+8l8hXG&$fA9M;0Qjf5UAdVq556#<bx720STmJ
z(6;!3{h@dUTh>6f7!K#-Kj9V{ET0l2fTye3*5yMbQTv-t&wtWRk!}5W@df<k?|Ilh
zQjwU>lnn`;TO4`)NZ|!3D^m-Pn0)4=m?6mayEL|I%jz=GZ?+yvUQ%RWqae$Crg!V+
z=%%yc8?Gx1AFP@9Vt1ZTHPl-k^xOtzt!J>yvs<)xg<F+xVMVVf1Uw$6cGTr#on!N@
z3z#qr(ulyJqHd<6_>a++cHtRpeJTYxUZ7=~n(K8biW|Y_{2azrT*9;V&q&hcd86M|
zzA~WwFX~svQ?Xur8nfT}$xsM7xBP&<PU7{``RjOZYqQ6#aDU$CyK+}Kwqo6GAZZP<
zc^{E{L^J`7q^LNoO+}h^hU_;PI(<_U?WNZ1^>iVTJ#x1FW0y^e5#Y>z=j_&XvmVYN
zRH1$rh$^L@fXD6-DtF)SxM3P8tGAVO2En)yi={JJ(yLj7y!zD@vU6?PI5{@2?S~v}
zpZRb$>(dkm+>E}&J9>W(OEm_Njqw!P!$_fJF9M4bJOwYfkN*B*@P&ApY0-B4;dRsC
zvYutUfaB~=#f--e5UZ&tn;t31W=~T8asjHEC8MCg`jdFSYu}3)7PG{0rINZ47LGyz
z)lY^|^O)V~f99~oZ5h(e498i~dsQeb@li`rNtj@qlpxsoqu21v&r1Q`92b+GW7D?H
zF~4paQulLaW5;cQ4$8}4D<E5KVcquZ|7-6p!=mcmzTq(d0|^DBL8JvldMH6b8brEL
zN*ace22m+NU<iRBM7moVlpMgJJB5Lv5oxJs?}6+8-pBLyeO%ACXFlw`X79CE$GOh>
zJ(`+@aj|5bRbHU(y#p}e?%2|>@r>=pie)c7;!#?*f}Oq`Y}jPR$mnl}5%?`RUHz1W
z3|^~|PUv~#$elE|yf+Yv=6I$<E|kM)abu(!xqRM(XJZwOv4GW6`238@59l3=a&y;`
z$JLhM%I|m;olI(3*`E0m^mG3RO@sZCR+m~-LQ6}jqy|!9UdMKPxNDL;)3a|~JkgBt
z++U;yjMoJw(UT80baM*y9`f7naZax1S{*i{@mCfUhUKH2`fewVwK#<;)eBC%=$Ygl
z|AuJwD$Gv$xZ*ByP0NF_Yb>c5JvodRlR_YL6r{#l+Rxy;-rtz9woNIw1fe7Rz7>Lb
zC-qNG)&pVWQ<^3(<!;P<Luje552*w4h?@mg$9k2cZBynu<uso?pCurQ_}qG*w+rkX
z^}VZX=YM?4tbSh^l~zM&q_7zdFMFXxFew~~=<!Kk8$K6WmHlGQDtO;tuT)lKOG>-H
z)*9`+?XNixAhkYsms@9qswC>DzTAt0zD`F%2V&IqzAbs`sALA{%iIw8OU;7G>gq>%
z7jUwR%r=idd>=iPfR!eD382yCsnz-=qr+hbjXV@%Mt!DjxIyxZ>3vKVbK|Ejp=O$#
znpU{3j3c6sk}`HmSk9a{yyL^pr~QK_S`ac50Lm18G(CUCQxx=R0zodfy!k}91|nzz
zN8zkH^J3z~?E~PS2!!&-{r#p>F7TrWMwVX@r^~0)BQs{;OIc-^%p*@D-#Ac0NDmDx
zuIq8`<d6oJS%`zVrJ}MuoRzcLTZvc20*oQV5GlpAwzu8j17h7l928;s^)E8ctm5c~
z2tlL=2to}uKH9n(=ntpy9cNnuzi~+ZX=!q|hYoPDg=<Xi_fk9L^vua<DVrRPx0ksc
z=zC`$h}CEccX||}+q$C+1$K-E3^cCRS2FZ5@W%g9d1@;t0cj%F?Zrw!jPW^NV<jNm
zAIQo<&Iu9u)0?`-z~TGa$1gaXrvNYDYFqDHGEk6m!^A@3Bbdzl5?*>Ny4{aa3L-4J
z-ODxnATXI!ZdcqEFq!A%B3E94$*_J*y7cJu>a58>rlZV950e-a>Vj&oUvqu;@{(Wh
zBY5L8uuT770%0Y$8yAOwjEu_)T8dP`fNA&t6qwyl4%<-!UJ<!@noTOFd<EonO`Y0u
zMoz~_z-7Wxbv=(=RuR^2)FS^~&;Kb<qzYP%R#9x1*y9OaYk7{3^X)8GGE_nW$EQyb
zC)R#gB|kk8#KH6X)6Z&=0)pesmhurG8=l1h8M(w$2AUduRRA6$hlLpaChVjb0;SY?
zWBu-Hkt!3=g-$c2a4ygmmUyb7PpRP<9QZ1<+EL=qqhAUDluI#JtzhZ*5mvw=_6@Jl
zpYQEL;DDtg3q<C~0K@95Ff_NiJ)0OXoPFig;$)Actp<t`w6qk|M*CG7$Te^%GQ3_o
z7)X?^_CCTR)S?2w)Tl7I|L!GyP#S~d#yxuHOC(ZfNkox;5Ej}Q`Av;tr;yW8$Zxs2
z)<Q06&@f7Dh11!Y4YVg+dSU<NtAe8>(6cgL@9el*F9Df}H)^vp?*pUy#nnBTS+qPz
zR2+0#DE~xz05d71?kZAGKWH8th(Qrm-(9Zy3;<hxX{(v+__Xj`GT<kuBX0PfrDxZ3
zkf1Rdh?5<i<msQL0Pt>}YKOf0S?~lbgNPE1qmFfG6)Vtt#^2%}`$>m!GOWG_vM-S$
zMXKs;eiO*$op$r)SpVswH_=rJSRNabMQtOelj_-t+g?)p(grxD3q#qdgMTQhWSaHm
ze6*QkRL3ReEPs<bpmeibwY>L<r=%pY`kBKf$HLNRL)f7gSNNT!&AIiJE|E?A-n^gm
zKMDoU;&7RQ8@Y@OK1t?Sx!?xc6}fxZ(T@HY!usorlza5-r`+7c4Sn-p3TxHQHy&{c
z*zPRgHSFx_1V6jdXmvfy$Dw4ML(8~{-R`^cU)t}wH6@p@)ImIviL>>(8?(j917ti+
zOt4@_O=!>LULWq<T-ohp1f&1s1jN_76T02sXS&N4X&F+SD^24k!XB)hW7ILX9`LaQ
z-wU&(dRpmSNN?@oA;l~}<WX!T9W*iWJN{-%7KWHTvFjk8adWt@>C={G_X!M!^0F=Y
zCGJzg;1Kt4pHO{)Ke_30B3DGuANoH|LlhwEA*dsrJaLink+Dk*dW;4#K12fXbaXjg
zWLP94EwEkQvyEE5Pf#45Xk6};<Ql8*Tz$#tQ_6aYu8q#`1Cs;+!-eTOdiOW9+HtiF
zA?Uf#pdNV}=cpFKQn~>l-YYf=Dep?cS-#mS!b6AC>X($K^wthlLusbNyL)OD>F~DX
zOt|X%-G3->D?6}P^=M-4G??3}fQn}CGTx1>UXXZ@bVFH-Hn%@p2ZvH8>SohzbK~;Q
z?oXPJa<fJ`3h<Shg*)RkcOn*V;|^dZd*bRy5@QDS0P%aP&V47#(gYcQwO_axn1^O6
zX`!UYeo!lcmhDRqvyNw$;nu{uC^NDR4j+QAOG?u%0@%z=VIR$4wDkKiTjE^_p|6BV
zlMA~os$9lHnGMC?{kieyFiVkxhu1_tyH_Ih^^@!#u}+-_Fj=ST?h~z-N@opFD0-K{
zmeJq&rgpIYCPERFX*Y)ueL6YiF7Xc1$}k4^{wH5LlkXU<aYJi^v~?ElU)^xH^d;03
zkJZ7N0;`hC18w^Xm6IZ=KfdG|ktB=rUZtFk`x}+t_PZm7cyccjQ&^f*1BEOqdHzg1
zfJyQm|Kb~Vo(I*R2pNLuEIive!dxatmsl(g@T8~x`d-$MfLUb`x%gKPW}(*@is5TI
z65?Auf3fr{vGH62Tq<SYRomIxR+BMKd^G+krCYZJ$DKy|xNYNuuiQ0tt2vKkl_?Ld
z8{+x=rH(<w7c20Vw9?jhLEG`JalBjAT8ZqLbbK=mq9O+c9WduZ@!(E`Bk!52T-9O2
z9ON{^jh_)eU*ya^(3qv3SGaivIkwD#5r0!-5`p0Rt!AfDeLuew1K8Qz!pBT$0Twtl
zx3!@(VqMESZ!PoWXemC8bYW8tvj33)Z(;c*R+JoTBpLP#ov+6|d{~P}QjH&-w@@e`
zOVJ@$L@7Ro*8ZOS4KIVwqSG(UYq>&gedEfG77S1aui60!JhKKjs+9acEc*A2G~3W&
znb;PeJKAS~oTVM;pmfN$ci4jnFL79)IV?PA+c+k)e^f}`Hp89Ha5o()Z2G%0r!#Ee
z1%+DMMd;bzFN@kY1e(Uu^$p7!sRz<spvuMl#!H90)wFE_i>~ctBD2Y3q*hs*8yJ!&
zzusLz>%OBD?qUP;<Z+LhK`=5X!|`aMttMB}TD=Jt%M5WiF+(BmFOTIz2s`kVi<1NE
z+iJUe7Gi731r!7fyqKV4T+OzL%Ue3Zl7v3r9TyF+B=*~QLfr|4<Q`Iga;U^7LCQz~
z{C}OTR@xV+s3Y9duZF^>PKeG*|KF+S$dZbmTLf8xuUW))^UIn0iI@)Sdytk#@ky(t
zsuo2(JUQ+jTK4VBJ)EKFe>Fhc&{}?WS=ot#Ks)ri&UKH6EMf;thaB<sigt?M1>$!3
zPSBHcz9e$ytBitS+G__s%a<8mEGJAft_xhzN8_HKG&<079!^m~FjU`Y!(^?#B#$?v
zUecu+(<%1fe<Uf}IUypJ`kM<knwHDs)Sr69Z3nTA2mnn9Y5S}HUB5y9ciX0vmnKgX
zoc1`k4Ue+*hTnqWy~<D9myGS1Ygu6{!QVzbWjFvA&JzT<DpMP_Qt-0YZ|4j3_2@NV
zS?iFG`E#v1nw{Y9jmcT<n5dg6)umg~s9cf;^Xs+KZ=}qbZipSwbjE~)NmwmNe7HL%
z`IV1GZ%y@29tXaVSlz1SD~<C-Ni|JSSk5bM#!J7~)7%=?7x1{C1K&s2eX9-kZ+P)d
z;OO&dZL(hJiX_SVzWCd+a<V}jjPk|Vml<VQ-{NLZ!d^kQP#u4Iyr%)1c-Y=@*j+yt
zc{`N&<MqS0$U_PQ!n?>|X5<Mh{A8!mwRo(-!h0D23?SDA5L*t-9!{JEqY;Feirz(2
zKkUz5V>~@Pr>IX5GXU+XT`;-$kwzfT3dBwm^>NDFsJRIug$+hi@_s4H6apVh8TB&A
z{KR@4TMk6EC<%_>0L0RB!&k#sl_{NO0g5lX7vUcI>?-h7BJ#0w<~qez8X#p&?fwEd
zw+g~Suq7>a>y~wZz-p<(^W*89UDTj?bqI}cKqiL+m;~!60;-^lMJ$u18_8S~`Wc`L
z(0FNqs0a!X*NstJ3Dyr^Qb&C)ac9+@w?1;alkQy_eHRzl?EPAwTC@<$cmQb|ANIyO
zH2Y(L;~qPsj2!5}Tqr-Fwyw~E$Ym19-Z29cFy_Ix9yvxR7Qrm!OIZaHi)(h!s}pY)
z2bSg<x$w~sMG(8@os@-6A{HACv1rrD3$_grX%heYOc2hkKdtP3KQ`gn&GEO&ShTnJ
zr<gr;Y^bEKx@H*744GV;l)LdnlH{sn-C4#^%rycdK~4D<&Zre1NHKNN)GKUcqc>A8
zqOfQ=j2}?O-wqpqjCzAmx;5Cm6`eCzl)&Odr<L7tfB_Ytca_N|c3e~f>Yu0Wj4O-<
zpqq>~94X8~Oae8ix*LL8K=6X!h(-mY#?r94s7R+d@vH!4f;3SDFHPPQ0qf%+MK9bw
zj!l4CfG+8&I}2DhaX#9l2f^p4b(_wB<7;>G&2UGet7)tuf96j2*V>8h+`J(Z%sb;m
z`I^35@RKPvRgM>b8MDCl|12*bi2jHTsdUaE*}x>tV@hpc31Db3w)7RO6D&MVdVUN7
zTMaX>XnS{N3{s>UJk0p6GtK>Rbz-*o6c2tNOQDFGa+SQ+27*-gN_9H&wiXpW0&BMu
zIW^BC31h~lp$zz@LKXQ2xq|~Op4ul7!G?YAiYvVNc8(8>&&Ma=WQTeJr1^Te4Hq&w
zR$)T&&YvFtD^<S-Uf-Rdq&GNv4+;WUWPdT$l|H&u8}#F<>+ky0K-<UWmr_Z-LvMo~
z@rJDNl*%h_`ONoIftU(J_V~(3C=vMPd)r(z;vpyrrI&PER8+>o-JN|KufzH`fml6;
zF)Jxs0K*o7)B(xd3*04fSv9QP&+Mig_7m<%>|gqu=k9Zv&wcHh-aB{n`!NIV<R&!v
zBx<|gr3CO>zrX*da((}?P58)eIp|RZ;uNcbl3~k>K#TG#9irg%M}IBZ0nG8!h6ECP
z%Hr47`I^uDLo$Vkr6=d4r01qR?<HvOEAl;?uiBym#H5uTeegCz@{}1Iufy4e)9ZMs
z-Nd+o=-KO@UoTs%wf2AZ7Fb_AjaaWu`Feq47kow!)f0^dypTI>sL{^=`uhZLR63Q4
z?!Tw`b%7q%@;DKZba-r}gc{qsvS43lv>^o58lzIPH6%?Ef%4exOH*OhP9V2`Dxv&g
zhso=~uxjH?_PB{Ow2jdq|3(LDI#ErJW>hWNq5TjCsWF8<ev{9b^dN@k=DtCVukP02
zc5X%4_Exn~|Mu*PE8FT&#*3-jbxThgnQ4OHPNRkqR!*(rYzsHJoCE*KN=aFHIc7bC
zuYH=s;8xW3E<X<Km>q?xao5n2NP}ono(NK-2G9E8c-hTLCzC$+$SZm>U!8udep?%6
zli}Hw=f)KE|JCQx`UqF!OpdoIo7o_2eC_t(pVWUUDfdL-QafkXtd(_^Uv2-`uogS`
zaz=9zSwW#%q2XMkSEJ?<!vhuLd+Dj-)xbY$zG+ne_$^-5tSz>iniOU_=%&>&#W=%I
z&H_V=ZGqfh0z;>XY|kCG$nrs<MY36<td{j<Q^Kp<P{LXvhFgED_{=P}bzf_xw|agk
z)zD&tJ4t_P7_D9sO;%+-aIOc0M5mmX7e{YwVU-hE19p7JL(^3D=P3s9i$Muxndk=6
zb#Jb}u>XumvE7%SIFZt86SN)Q5w&TSAG96<Qru4D6STJYvF#;i;#%#Gt~0kUQbI|3
zdXN8%yyxlQae)k&zl>bIoBO7OS(qOaRxYZDV%Z8P#?(L339C(0>?>@Lk4K2iSvc!8
zZ}-hkT7Bb>K(MTJRUoUU#Nl>uqnAYur%wN*bcl2}94|_qld<*v!jDvVk%pf9(tW5A
zNuX8U?YgVciB|0Y^NKqEWUv0WC>cU9T?J+0s$~l^EY->!rxr8azqN|F7=+r}@G_-@
zEV+I5rSFB~Xwmyhqo|)Y5315a)>rayqqb!oeC$+~3r-|gbO1HnvA@Xb<_6tF1m3!n
zrGQx1`aA{B+{K75$z-uOE?;$XV|B<AfYQmsX~em6-IJA3U^-5-Nv^&XGS00Vdb~9=
z0MoYAq9-p7A?ADBJ}%&?lb{qgRk~^ZTCV)uFA06$8v2-Vxv*Y}(p3r8))>>R5WmL=
z%`2poMddqHVq~qU*$JeO(8!f~#FdR~B@~&xQpMO}iurpg!zf@$gFu2aSupPgb;=d`
z8)o1GH)C<&ZtKcXWh=Qr_Y7Fv&D3Qw;2M2;|L#{bb$tvow2Ny;iv?qO<d~r3wxtF8
z&wy2uL+!XuQOP=zX2;n%`fyRKYNmVsTGBrmEUPNByY5r6pWBJ!SsHoK20w2zTHHxj
zMh%`GJMK?ru}_FbSel-9S`%Ls?^eH0W(&uW>{D<)_|dJ6*WUUeK_uN*@r)shjBzEE
z;C5+-H%GxeDK8uAc5xP89Q~h=Cex=#_q^RdDll5quCC$6uJf@OR>?eBAQ~|fyyOOO
z5t=3zqp_=_1kCjtu#QWd1KMavTPq{Sn88IP^^DhI7|j7R%+54|4Xy_A2Tb1-{QQiV
zwgNsdzx#EH#o`)BJ1|`ZxfaF@!cRd;=Xc~wz)u#0I|^JwfyEZwB(R)F9Z^uT7<l+W
z^3-7Sa|rY7Wl*<m7K>YV<6jQ`<CX*qDffs}sR`$Q5fl^9l>fC_*&$4`Z&?8%4)PFN
zuYWuE(ObmTmI3hgeSCVmD!tsej;t`~Rm^KSYuq@J9tlSWt>s<<W#tuyki>wX)BxY|
zyIcq+ErmFOIB`Piy5=djvnD{^rG;IqtiWFOE5tg!8pQUH0k*fYvF?;=o`4*%=b-L5
z#2E|glsX)b7UcjqWDvm#7beW|=~riz1J6(@_@EomvShI{TlW3X9$;t{-|GDNbj;9w
zxs(%lIowPNcY?{m!nrzIdWhlfR^46jfWhHu>;SR;nuNe><t`AiisF3`a+9Yd2Wju=
z7B@FGf1t{5;pr{HDVF`I;5->Cm^igeOD$$7hkBokxC$zM)c>ijF_Gq0XA$wEH%=lM
z&2JLe%}&#(rFR~4J>BA?d|0N_U8<gEp4q%3a<<LOU<f!}#H8%y&MzJfPGf11)t5}r
z6e+3442|%9o8CNotp-XNv&R#`o!K7KndPKZyJ0`WHdS@%YKlebeeH8($K;MXQAYif
zAs#1hrm5$jT6Yyy09EuItMw#f0c>lUdrTe)A5_389Ikhgms)?MeD8P3g^`Q3HjH&n
zMrwUcib&FiL@Ly_<%iK)JT)IY|AC&(yn~VSA7)1Tl|*hQ-5;9G-kw=mDb}>=x`gX@
ziwe8dj2^@Dif2UCH}g*b)>Wa%6h{^p7n|<mq}N{nTsSY9;TH7q^Pxu7!TC38GPIue
zjiy>tz+Qv&-mat^7VBc1D*Z-@q_C$Wp%rmsGBc_h=x8|z9zuVVKl0>-uCDLy4{#f9
z%GL1ghPH9p(E(Eu0<nE%eJ`=)Z<l#PBLZ=cY2M`bBhe!Bxl3mYUa1Ee+{rHxDp(o`
z()$9ivFwQ>-QJu7kP{+UNB=m?ps8cbd<)kec+O2pDIlvWWKlFjmFGUXUKcE&kLT1Y
zf(IfHahh<a&#YC{hOHix@L=9EN1pft>$C;5;$38Q!T6Co%<dwKCwU{Va8gmXB*ZC4
z&T3l)LMG(_`BA3wL0Y=kit{qZPZ`Xnl$JpFC$ao;Ul>8|;lyd*XlwO(8!%T=8jch4
z#9T2d;@eWr#8|t)Tu&-fvWsQBm*Gh(@qyLh2HEXi<I9@5{FO2>r;&(;F*O%no0(cb
znGa~g8<mu$B~uh*${C;bbjep)MJ<k;UkH)gjn-}-ui?|A%j5crC^*gaJ>o<UNW+w0
z!P;={R@8+$VFf&?{5Je%MKqI3!}DV`f^-G8Hu4jD+-%2-{sCFR=@SIgKZW1#{K+m_
zN4AV5>ffsWUIAd9KB($v`%!h2Yv`-z>2}cs1eLK&GM)N;=3_nO&kw9(ce>=AUJ7S=
zn_<pq!ddDJinL6CvnsG?o7vDjud`pjm(zpQ8-|o876h!&zJiILTN$P9+R&&&PZE|Q
z>UQvel^L)F`3dUsLLis0Zs;HfM(2+3{DJ^E&+OfE!SH_pwS%Z*SF|j!asSmI;2a?n
zD3CEZdS*8~=k;Z-?B^hXuMD>1zaG4>arm$GLSUD>FJQg?)!;`15uum#AHJ}@9$Ri)
zB*KRV=DS4}#9zKDEPjABP&CHdQc|7*4nR$R7C(aAc}V_UI?j)v`_-xpC@Hv=6ksP$
zCDya#XY5KnfsLq$-KUek4*<KW1Wtfa5V#vRhHS^Ql85@P3f4FqfWFLeOmCp%kz=)>
zim7s3o{$0)=qUjkv}Ur119EKYiz2ls0sSx3jTK!15+lNuIMJ&;Br$w_39!^s28WXN
z?)<T)G6o2Ruw{X5^$x}kl&y)hGY2g57X*lQ=<pu<P=XbV(Nt`2&#5Z?lDvJ!q9ndF
zvssEIAu}z=)igrKgqtXs=K2@7Yx?~$|4v(VZ8B$`*KYS?ct8H){u+71MNOBFwM8Q0
z+SJaizl;SH6bj6y*0Ds}$9BVAUCAzIf&5ZuH<?*3P>opad7^kDl`<!*_<am)ZIi^0
z99b{{HNyOI$T1@PY4!UtI00tj^+GHAk;cCnE<OsG*hE4j$mTUVDFwCCXSJ#qr#If7
z2*Jy6KeL#?_9k-xpDeeGt0|Z7{eFIHLQ7Zo7aVcF(f+dLg}PvN)k!SEwutee<>8hJ
zO7i-yk|s>~d!}zZqSpWGx$+}J+I2<?g}@HXzCn^4F;b`PDqQXFPR7=oP_tu6SfPF~
zJ+~avU5Ko9ZzRe|nxiuC0zIy^fE%jDFf(tyM7%7Bkr0UgoA7ABb3bwS(GYxS_p_5h
zna66)R4hN|KGWgGdJnq0YJ*D5t=ewJFOh87rFIuVzQ8pD_{+3-7-LdNqg}pjdh(Lk
z-seS)c$GvCX{EuJPx2Gt`o$RIB-<lKi}?A$Ws|UZsc4T`p~cPETO!usJ!<2k?eTe^
zR$BQhl$gQ?&`hTJ4dz|A8SF{RO+1)zdzg6ASXI7M4>6{XGGj{Ed#SthYbVv_)F-aO
z3+*n|K2}APW@Nsg^Q~LEEAtAquW6uS;&D3>$xl;wHGO6E>*W+Y+vL+*mwM1}-g|$3
zNM*>XXo}8s`;^zV?G*1_CBnb**@)BJ5Z`v&H8Q?Qk(do{rLgJFZ#iY-uP+`fdaArm
zxm3YkO4R0`(M)LV_U`9`>TIQ4M_$<_4)=W8=CZbj&`o<>qCz?!Z7Mw+6?MTSL-Jqu
zFFJ;u3<o)On<!YXkuS7v<7|-2{~fX=!ufN9P35>0u1m}ZG(2XR=<s*x5XTL;(~?rD
z#*+Y~av9xvVF@#fz&c*BQr)4Kv+#3%7gHE5hf$z|yL$)b<+^*SB0r=mchGD$Jmm6#
z5({ky3+4`_tWf=43PPL5goc|5mg9K8xTXYfS*7aY_L!&;#e(57Uf&&OlgP-$ai!y!
zdiPuN0tt#Vm~L%qSwUTuynPGfCiGcJM=4Zl@#DJZI0I)CXXT4Z`A{-xrg)t``J2+c
z%84;biMn6E;Ov=yOrsB?XMH=p({T5MDamP5QOzwkp6%@`A3Dbk-uA83hmHCh^~q?y
z`p*UPPi*`93U{s5Vg&9i1P|NWs{R#DFEDibyAa^BiB2z4MVIt=#`3yyW*MA~8Rjr!
zvR}f8J4i)}I-)q7vQGt|YIU(r!o|9`t`@8Pyk#6%&?9Z`e)aO5Ni;PfzPgo!9UnB1
z;5n1|G<>B3VGz#FPn;dZj0vAhSm-ogzoc<Vo!!NFvWoqp!e-Z0V3i?!%T=E$sls~t
z(Pi=4MJ`h5s;hVmbh|7RB*S4*q^>u&3zTJ*jYOfom0Gz-cb%3Qst%qq9U?ZowzFq2
z!>eR@?j4fTHT+H56_-8_)qC!jnY+Tv$lWEkt1J4@lctM$O`p}_PF2w8tIy||vq(7}
ztKCqa7`@d^rQJG#hm{ekF8*fBDw0b#(EGKxXznfSVTLh?)Px7yn&Ssgj{L1QvI|VK
z(G!qW*x6YUISDO8Jf3<mghtpd8cI|q#U(<imSXK-x~&~F{WvBe+-+<6h3rHFGfF51
z685k32?(lg8}U)GA#}dxvwI#g@a^&6@!V^FYm$pQQU5^5slAC(Yb>#NP@ufk*!NIM
zxMB<W;dKCC3vY?Eu6U4WMS#H&aU`A*d>cM&zK2-Y*@yv@6$PD?_@G&z-T8_Km@!Xy
z?d9%6SuWjtPBIx7+**}3c+YsyK_(*+k+B%KXi_qPm-J$h*OC!qWrj}cRzev+-1hJW
zWC+r=cAqt`rbb<?L=-;~5f6|1i5|(!SR{TVj&$2o3S?{9<lU<5ph6b9-6$QZ@9DJ^
z<TMI#nl6L7#`4ZFXIT_ImPeVHi(g)gD%g`|WJy5OtI48tjagxX+R?(_h;(l$^5q?#
z{8e&<J5lyN&9OG{iAKQTESNV=Rgi8+co{*J6`8;oIzOK_N4@tW%Y_DS^gV$2w;5V4
z_QvsVnC#QqTRr)NHPbr_wkpixWb;X$0^2&WGQy0tQ%YSPjj|R?(lQ)Sx{43$HQ4g}
zfb;C%=6<^3SL$?V@{7@eqPN~2P>y?nYh06i8Sze3fb@O)K4I03ZddrW5K;DL^^kYy
z$!j(%!h$+5+{#?*`+m!Iu)?eCijBemDf)EUCW8!ubFr9o5?x0g{%NQxHJm85xoyci
zBlu%l(Pk@Dw4(U|v2Q?-^IrtI{HA8;3^QipzzHhmc_Ihfd&cEQ9VuyG93zaZW<PX-
z0w<hsp-+n(>TOn|Cw`|T7?|DLz6^$YP{FJneN@ikO;!U8Gp2-r-s1g<$=&f|4oX2z
z!undC3(&!*pV2$n_IAX?XJwkUQ#L!Hd}7xu?U}`IA0bGZ_tTXWWv2Ut6A<fR45wPe
zB}fXN7*c?(X6PG@sJQP}XE4U_LgRJjTUuopsz{Cv+TK*H>-5M$R;NAm5}GVq8<zY1
zjv!fgGC}u&I8;INDgdlqFaY`ZIdu}P9#a`?Z+oSZwPKO>V}?te_c@}T*$p@%_({+G
zUvJ(%({;JM{^{voJ;I-!Mr$K<ADo3fnH0DM9|tT1wETLwoWS|0`OlH;D;TrJi*I7t
zugnp`8g>NGrmG9YYo6Ln%vmFo<CoaEe7$XhL*k9JJfjKa?t${VwC<Nt+cQxwV42%z
zIXfH>t6b)_TgKslonSK8KPw6VrYCdCDHN-Ju`3aq>B2U|-#8o^DJqlZPiyJ(`mDiP
z&voh<I?K_DJ)1KBU|R+ukryxi3O<5fAQwsjNna2UOTORHrhqDQ-`#)If<`0Gd^aNS
zR=%*>L_B<b8{(jY1=;KU0n-KPvaa(RSu7Ta&kOzgA?U+-P52!LNJc4yv)l*BD8G5j
zd%&%@Ys(a%v;cHHPVnXmP5ubnAmLf>fol_2I~>qNfi!KEgHBRL7vAsy<QM?GM+V+%
zX<aumm^!&X&j0qllRh`5csE0lmWNT=R$k6LqJ)(v1MuN;mF$`%-eqgz?uM8h;=j3L
zqc;->6n~}7)l5(M9_9N?{$@{}0aZx=T&Kn#pd!^-(Kc(chqq8T$8aqcZOEbY%`+#)
z@#3gBWm}zEO5$3=`>{27=dgPGP_49EPc4`*R-$8Ls6=8%opat&S(KB9Nghk4y~fBl
z855_~4a4^7tOBZu2cM`^^9!P!^82cK6;U}@r^E<(ROtS;LW@rWMaRZ>*feVG!9uUB
z3|BXqbsk!j57Ws8o(lkUyd<y3<n~VDuecs8boZkxUihr8Q6oJ{$dJ{0*_NAcU^VA?
zlL4^646iux1Oy2FXh28s?#mzYy?XqhRhJP{Uzqtw>l_|W8Kzk#OrvmEan{=fLrF=o
zDG;CNunjXnZx&*CWvO9k{m)6rjDv`6;ykggg*CBQ1ek=f{eaU&62o-Xf=*tJmGhmu
z4LMsqEbubzP_%x&;_*SO0aorHp@*dC`7>oaBDuKw455fRsQznW`wmp9JJ_C!KPK;6
z)Md1QKfuBV3Ss7u!D-Myx~lV9IB_KFu1iyo75u~lDSwHr<6EK-$jlhXRx<{0&NV*A
zJkELbTfMdB>+gXp5)c0wCpO@i>iy4{(gQFDAUiNo3b2c*d$7Z%o?7J@@Xyb_o!5;3
zK#cTjFPy62qkuf8ejGSUL2N#ucEhtf;q`2O=1yg5OI&<@rOp6Hw?TB6diIhSGUVE1
z>&lP9y4AeJHr%kO!QIowylv7`2gtF3XxVAto{krFc2`p*Rn!VW#`@bkh8>oy_buD`
z<%T|=yP#f@JBvd^s;%%uWS2)dJycU}f4tj6NLGJegBM5cu<oBoNyutXM)|r!pHgS~
zTd}bLgYXBz)h^3cIjo#<QA|gphHfy;QlvSoA*@rFgn%hvEvnpCv{P<d<pMHJ1@J$v
zMf=9PFEL>+x)b$avoD}SZ)Mwz?N`tI-l?4%EcQW9z%!UQOqcT})s52X1u=tdgLBlf
zUbt}7_R~}U{>ldTXAebTT?1cYz3P(?U&h<V<0q7cG+~)OkF6Ooy6KfeZ&z?M{&;8+
zol6AjbGEe;_3Fwk={8=7#wu|he&t*A-#~ZM*a}a{D;KDo=8DuUKjOdYpXp_unOUIi
zmBD;|1ou#rfraC%?4^9mc*}D9{Gqq<4eFepEE1`8^qTmv{Qi-TkYY{Yhi<J_6Qf0D
z5>pG_JLgDXse&$y7_m>R<r!tPyOot<ETrB~?&=RkCn70wUse=ZDy^fbKi0dEpH-(l
zmF|(Br&86eQKM46i9K2RP45JX)w-8J@c~zOQ#mHhKPH~@kXzQZ@wIhD&YMLuA2N~L
zd>175>}g21Y^FAH7}dB!o%ZZjQa&oa2ct9HWxczu6N?DU6LYM~jcLsV_$n!@1MLrY
z+2KQDy9IHG`}}2026rslJT_Z4(cM3%-M`STA^XR=<Y>dfS=`0{a)8Zd9HSeH_$HIP
zr!vxtcA8Rvz49LF6QP26<XXXU1r{dvgrm)Tm@JfCdMH*0ksggKNC4!ew0c|;JelSV
z{+S!AKa{9(<#4eQ5W|};@2~hZ=YQNgKHh1xju@o^E7~OA>h#r$IUhT=j%Bu}Z5p?=
zs<Ds)sc&%K0#n29SH_#aCcvbCwDPh7ZZ{zK001c+xE!)3OTGkd#&6h?PXVtTz(mBq
z0X}b5*xG3a?vN0R=iqa4!xPe;jk`(rb2<-o;Ys^53v_L5wX|8QE91^TeZ+Vgh`^E*
z3-4%&#;yEqrxxD154Bj;IeqzO`1trZ(e&lW;Oi)p`$yBX`kuVlX3;n>5Y<Z9OIlvu
z`@pAEI@)b!wC%8ghG!_K-Jj4sT;3gD>CSK;S3tQKt-R)qG?}MLv_@{3?=3_gFaOYf
zAf`vQ5zbt_e-GIm2)K_wKoy?U^N|KRQlE+9$OO_nvnUS^L?x7`F?rbN-4e)o&Z=ki
zxFc4&<vHaKhV;C2I^$l0yQi&5Nj9{IfT@)IRnJ53Q-8r8z*z}9erT3?vXNjd1FvPX
z`A*-DBZXMPBmecK6l<4N8U*#QkGJKaULe1<DB*ggex9D8h5jDd(@i^_NJaXbD7fyf
z(fQb=C%uidwC)^#!T^0J{JyCn&$>)lwSq2cZ29H7Qlc6&rR)0NXF8F1Dz7|4sP*=j
zUNi^;?pX(PIIo?LUUgYUjGE>54aO)Z&4g0H@rsoBsDZ5j*IF8FWQ9yff!Ql9QcF^%
zb&p`Q&EBfhMW!^jk<0R*=F!eO0?H_={#?=uesO3QnJM0l!u3_JE=_7?Ov8RI!}+{?
z@rn=n^eA&309b#fTl&<}%&>5XWWv=%B&5zzYlouzl*NK~^7i@U&v^tm3S4!|VwOeS
z-_HBFUs*j;g0h5h6?GF%i%HYsKHM?e9{63n=O%JDp6uci?gWId=r+q3OjxJPE#t}z
z<!ZGMRK^<+Gq&DrMz4)E;hir@5jQX-DyvoL!HQbA14=R`&>negb^ICum`GyCBm66p
zcmB?md~h&-OV3rX_9P-VKxT@x=WlppHkS1sVsRA@7^KxCDxbi?OVUF^0Nh|~$rre)
zSe(_Xa8a5?AX=}e+GoH7H>eWWY^Wgq07PHiq;C&>H3a`s{}wNkO=YHqvKKICWIFd0
zEdkZkS<fe|!0p&bC*{%@FqsV58q5Jb5<hPeL407$H<=F$1DVaf0=IDhe;ByF262l0
zNU&G)|Ch(Jrj6m2!1v_1hi>&I><NB1A%1GWvH@Yo3SIuy;Dz^FtRP91RG|q+70k#G
z%Le_ogZ~$N$$yo}`d<?JSnp|R%Wz|!`}9=rvuSqRB>fjwOB*XiD>7W+SJXk5TJ1va
zvNLBt&|-hfI`>Rcy;s`t7t0<aOXh%_UX5el^0uDobaZ#EuPkb({2Yk_LVM0a7UDb~
z$bN2CQBGx6Kx0At%9KjaUJZ^Hzw@e92mr~{CJLuCbpFySV;LkNOretgg`oXWXAU9F
zH!RmZ;ueb{YVQ$JM~LIjjKccp;@qiSdpPET8y-OsmEoN0u22nCL8a=7G2X%OAG)vi
zf3!0g`Zj9g=s9Li>7zf217_Uu!5L;~DevJvp>(p?&v;oYVV<yyopa=EV)dzvTy?v2
znbH(@mJ#LN&Hg`$I;`P*ZV!V<a2Biir|M4F*H0pLUaxIDtk;8=|LG)iBL;O-AQ88s
zm<-~|Ibt+_rcFjYHaIM1%OX`rlQ7nEi)8g8WPfQvH@>P>*q!`Q9C~YTf*%#g*MqdD
zYuQ#Uy6d_%po=ZmHRNsA^z{!XY}urGKPyrm8|V;JXmILDdu;bo#-(qb@swmbd=-D5
zVPd%6Wb$FoVE5SbTN?|eebhC*({#;tw7)Q6VTY>$BpV@qA0s%vWUBTV*eNti!Tz@6
zRgd%=@ILSva%H%4!qSgi9?GaUh1IMN96c4Z<L}Pr?JaBJqCJ=M=rD{myh?ZQps~al
z!)l^;icl<WN)~h18yCE|Yms4hyL;}QcV<U+0McD0vFvu8t2V6iveO4Ex_HEthqrfb
zgIz1jDTf6EH1G&nZF^_`T+q|Ofm}ScH)LUOD_9U-M%&;m+SIr;(B9>7Ot0RSOt<%^
zQA9aBhHg%@99e>3(AZW<-O&MD`I)deZ{HOxWOt0^6%@$IYUpaQwqqysVs^Dz+#09Z
z^l-|^a<!3Rv3QVts?&I<_Mj2uKSLszqL@VEXh{mD`!#JMD?<gi03aR>`;jYovQsW)
zJuhCX3~}k5=296vh}ASvSe2U^4VP2ir6Sv(+G{lc7AmvN4V|yozH;NIw7-ga)wd=?
z8m$pUbob+1X$kIhMW(m!GlP;0D5*-mA!l68vqr<d7q<$C=xe!z#AP)mQb3G}qI7wE
zQWQ~9g|~cJ@Yctqe}yvkB<Sdd&ZF((Q{d9Egoy(SiM|MoO`~RJ9B_!)gqP91G1&ih
z90v-|xubLiPa}r|n>W0YO*gx~Z^;d5l)g5vK0yqRWae`9|8*CO*O*#cm<mv=Ye*VS
z(8^ketBPx^{xls<cAKd^4E3nO!oGaCmvfZBvTeDmyeKoT8^5q#1x@hH&)6F;I-_j#
z5~Y@@URkuZ1tyuFzns<66F2K`rqNlzRB0+^s>o^PWX1@BlbHLBoUN75VO9=jHL?hp
zuEwKLz|}7n9B;<w(qj*-^!Qw2yoBvP*K;W`7UgD?6#F_E{9yLs|13p%{_l%f{v)%w
z@UrFiGQxL6Bs1-L6>u^J<rk?>z&+((4U*ToI$Y)kw%O+^jmty?`u-<EpO;^~#42y}
zV#Vh~*j6CJUa`bOIrfS@?xkXziylk1aAL3Lx)Q}KP#0i_gp4FQX@NQy4l}3bKS3e0
zsHJi%&^Jw3cY~=P=C1mE5)%==M1D1m{&n$La=>Vlx9l4W^BQe3$S5{FTUKyd34k5i
zq*|5-R9~WeYz%lrKfF?j(ZifWj$M6l)xzN%?NvW!?xQjaTZ|_fA-p0`exM){neNh9
z=i7b~8FTn5%89^mrH{gn1dcco>^b$oUnEKmbZT-HOIX;PE8014mAYAoDM>k!x20&o
zE1y08VihJ)@-~w}Q{pBk>s!)E?zFc@T&WRh&-$ubTbQ^ULk$ldljQndE0s@@TTRcj
zD*kzld1aS^Rt$#^bYp+=2!ygSFZe67`i15@4@LP#A`bJu;cj5cCC0pbG|i$8kN<Q=
zZMO~%-e$>)2Y+K{3*XTC!iua7k~f`bUo9k2zE6r_!c>)i2w7|J@kSvn&B34MtxkAZ
zBgHuFw@Ys2L^^&Oz7YtDoPq!P_HMW!t2^Fp-5^&#%b1*^&1?>+Ak%H)|KyV)3e7T>
z^>0wOGPPue$CNW|YxIXHW*W&8+)|_^e<(}YiRkTZovxK*<CFIy+KU!IvFrDsDS6^`
z<*En5v;@{pOki9h$#$76Np@BZ-wGXxgi@4crYqcr80iuFkbv)hns@Sdc&64aYOi+;
zU_{b#Oyos9`W!7TkuRCxpD!MEd%Rpa!DLLw@0GH@C}Qf;T_&^nmS@g%v_I+c#fVw>
z2DZKhR6I!kb`lj;G#fRTEpf*E%L}LYX@UpSdvgQ=+Zg7dM((i1Rlmjg`ImneUWi85
z`0Ki~kjMT_-Ey#h=iN{J+mVNSkTv3i$@^;-B;<k1r&l@O1NQwH3+|b?xa_jZOq;H<
zed?bPHGTE^>@7aZsAU=*OPhA9f7)tJD;Yug=}zv1ZbV3L?tByO!b^#Q)e#k0U0l_l
z-+9?ao=$z4CH)G`Npev1pX$X=(U3>2J4jgx?e09mLB@+cWnjM*h;-+8!uUq%6oEL6
zJ0B$Z9>=kW5Z5GyOZkR*c-)1|A!~z|Sl8DMehcGF@XO>s?O$|IsM;#vBe29tb833G
z2<N2;Y`?2DtU$_$p)t<jYtC?xAkU+?D5j3f4fwVo%~KWs5%$3?=XXjKEtJAzEX%!l
z;(jk+d#n+Gas7OQ<=>1BefTZzO5K=&t4$<8T!%>5Wl``ZGfQ?wlqx+lryDE*I}$ar
zO3~e@ZbepSh*kPNV+|YggwQpXjNz*}JBxRCGtkr;#!Tg_0}t1;|BBD<eL|}@SHf5Q
zzu$=3meDng^6(R|6hP2H3Zc-S*a?vfAeRcH2bHt;-h#ilo4=SV(0`?(pXSHLfHNC(
z^)Hiw-;ve3K9mGuU0gZc95-JIqQJytA|sNQ$UqxLi$g0X7=MlK`H>j(lh8jYO6tIJ
zuNk#HE6vg~4voh7JY<$C-LD|T${7|N1%IXNd8cKBIdK+#dG`@{p8&ajLK;+89;Ly}
z#uR8d@FU9U=;oexgsWQg4f}*~QdBeHk}l0Zb?5Y1KvJxmOtaw#-%B23F@Dl75cHVM
z?(#D}8Fn(uOCA-sg)@5ZR7Hq17gDXf56eIgx(mTp9!=^R!-lO3(sgB}NmmFl_y>l-
z5>RFjsQSwzBuV^!IhA*Oka(gP`KmdW=vDD^y}l>J(#QU`B+|Wxs*-~*L=^15>$yC}
zrBZdgX=dS`Y-Mho=^F}#JP!tta38ETre)O*Ad@qKj=Cp59Yl)VDF|wP{Eax#og$89
zDrn}6$DV<!b0I!x<Lq^5aT#?9U?8)W>+Xo=K2k)@=XCGsJYXO&_sk34{OPV(<O;64
z5~PNowI`+%9-l-#q&nK`MLmNf;(Z1Gh%E|aRtg_-KxbT|tI6gk;AZ<yzpNi5D~5kM
z09nD${i6`x50GOir|@n5Hc&fb5X+o+j;{<@tgri+!jg@Q+(2{?i6MEIPmLQ>8n_B)
zV=Z!1BhERLWZ1pt$vMAJ7fZX{I9GqR!mjJAjMx=ByLG7;<O1S$|8tKhAHQGg+8y<r
zT;k=y5pw`6vX}lw!dux+`l?xymtTn(IElj~i8wibP0pp*y$;%WvbykF^lbLyLqr5%
zL<muRqRVLtsA5Y*M`+%R2g**wls@#t9C#yj-Lm4}j3S_Gt1Tnw@?geQIWA*pqsBP^
zY=x!dFiq=~vA#o@Lz7a$ywFnHlfj}-8CX`2#Qt&RIP&}Y?o&0HqwQ}n{<Ag0wy&69
zty*mR6_w1GDJvINkT^JK9Ez<Fv|H&Zan-~t3K(ClADp+0SDs!?D+kaTAWed`uG#$%
z^7k(fj+t)>7&s`t{J@Vh_4cK-qC%Oa5pS>(bI?#C8Ot5`#8t`1$r%0bca4M~#ic`)
zHB)(%KFk>Ru!)yNxVDBGM_p(y1ji({cZTgNj$Cy#lFVSJ)zq-u<cT$FnB6+Z8kVF2
zUcRi&&K)p=?XBi0<r<1d4VZwsaF6XPELUx`io8=0%~S7ru{%6&!Cr5nMF1(%eS)rW
zCJ(zo2WTcvSGe;0(FO><ribO@eU2wu-~gH3{FgiBiSBxsfv0=0@C`sSu>mJR>N)&l
zWVW`ty5SE6_Jtq)WSZtd>WSA9rA@CB+qSUkT_xTX6byHNNiv@7BiiF%cE<G>p%w`a
zq?i|xnt2@nyUkDD(z?Q9uiH$o!3Cdlc)Bt4KJExgxsX7L7(2_{$8~Lm?RRc@hcu%M
zdfpGA5Mi#oBXm9WK7+(eSTbt?`nux#&3%>>B@31P{%0RKZIg3u?bqdg^7*uIOuBVn
zFii>2@K@w7!ZK_8{!o{XTe%XbU^d#v-{%*Ml$LEP?ak;+``j)`vhK??nbnv2x)=j8
zDSXSvgQuqtKaZ-Qwm$znZK#UX>nk6c5g}Dd4CU;ZFDm@o#rhWP0M;I#Jns%qV$~T1
zR6*5eII&R~v7?)){mJxte_`y#=x&A^XD9ab+*vkbbq9Kf_^T(<olP9`s^yzaOH7RD
z1dYrsacXa>_vaNFv#(glPYG!NFtBA{3!R2>$(9tMG~ZNGf?j}w30nm~m#xZG5hmUt
zI@aBt*S9m*zlJ)bQ1U)8z$B(!1A9MJ6tAGNE*Z+>&IhF$E4^)NXRbi;FzNhNpq}k4
z?6~N>iP`(DNWsbcoQ>47BBUGWp6uw5a$RX~UaQMX>lQHZLS_kTU7auA11xi%yB}h_
z@R)y<YY?w_32;Et`E(0P0ID#RS=BlilvqK7brnECRtMA{a3>4|SZ|<pje!^v175{H
z0d^6jL)TZqpb=m+rLN)2O#E2~9;G<M0_QVG-MEemP?FyU``*B<DFr*eu_W{?c45=T
zUf*Enl;-h&RmuI|n#TVvRpgIJwOYXst^vMTX1LZI<>)O&-WxojCl`=aOdwI!zQT}R
zd|fs-T{fnlXY}T@zt&oZB(bWN4Z3NYgtvL}d)F}p;*IT+C2W`c#h!<HvFeOF=Lbcs
zM&i;<aPg7=y>UW%?_xJER2L(L!~WUAJu1APsX)&+0WMz#?jy%QJRnGNG(u!M4Awra
z1Pfo>_Kb4M0%O#mkBb%m*f$nxc74})-k^JW__*7-ihP|vZ-DV9Akt3Ru29cg{*|lr
zfh7%`zyOE}6Ouc=fz#|8h4VxHPjNYwGG*$vQH>dOs4DeZij)jk1)vrQ#^Z&Kv){F?
zY>bnRL)h|;Qe!5(%DSWA^QF67&_-pHbQ|3ao#|r4d8*YNn{r8e@Mb;am^Ynz`|_t6
zUT9Ze0eS}uXA!qHC1$M#=)?Uwauu@t(woc|aFyw)?tpeK5qGMTJYJvl4T5nA8A0O7
z8rGmtqX`kj_q*wq5`jNlZeb2nKK-Ubx!Xk`*6SW1_ZW=Mk`c8^&OI;<?0#-shW3hV
z@5?~<0b02IQqZm(?EGj(ACJeFcCXe=cpg~5loMIocPrr**j5f}SU^bYnA?q<zHs{7
z=+!l*_bh&)Y!vxb_b97+-4JYRyC_6lJjV7T^Pws6w%1S_%svLud@N-5D#D#7{Ge{7
z&kZ{DsQ`~ZPvoc>aDo77sz8pRc#c@A*^yP9lX=zhs3Vs(fdGzyfbLDD!3~u%8yQ~5
zhXDNov4-=G@9QKJXl)x7OefX{GzZ1S56Ghk0r`;t@s?;;R_H1HTxr}Vkl2BYuvl1b
zeBAr$Bt-dbYk;1v;^&+4rD-c$kinM^pb*xa4FiLxsZj;>1+4Te;GchKClOnJtb(N<
z0AU5_Ql6i&X|Q{x?SDq*!S{2&tK9g1Ha*(a`7Qx!w4|UaV-p*g_rDsv@SXa9Fs=U=
zaI62r`fl@~i3Tv#C5gVCD(#mz(Bj^{dkOLkY>9Ji+J7m?`bJB66KwS|YZ(?ufCLfF
zrsA0x9@00$IAS24i?((kkr!(^!foUli2~Ue$NdP<T*U7hcH&cD!+~ILrQ&1R8(8+q
z@2QG!KwRUGvm_wXReFcfT4Vs^^M$U_B9cJ$RqbDXvw*@X1oG4dkpOOLK~F=<_`x)P
zD?3_&n;|=}hfM;akXt~cX8N;2H$~|?4woPf=L)MAJ&RRk{f8|M_WM7)DNwmp;yth|
zHH}t(jb&~Y!k^c7!(-qKQGNIeAs@=oZ?(wq@tN7pjB<?{7#Edr>)=X~xh0Y8+uK8O
zNjr|)FKI?YQtv2L`L_4ioo3yA*xgM@pbS&q+u6zc#b;kW($?|M8SWJMT@H2c4Lc6q
z*by3`D131cJMHsVK%Ev?thvCZMLn5<FL)p}zWgi}!r@&}?2d>S;rW9v>23om<~7V2
zHq;OKlMzwsbd)K=Uuc{h5r>n!(+xSsb6c>AV-5B;6K-lPz8e6)K9Xvl*Pw?~0K=H{
zX}-LiqW$Izi)wol+a*-bLWaN9dcxb?7rSOItCfQ@2TIWJ5x1Ey&WGJ?3&me~{r)M$
z48Qk6_tQjYlGu#Z--;tO_o~utzRun5&DxQBTcVr;sURWRRnYCYRL-%?eAQbwI1O!&
zghC{DG3Lx~)%pclbHrsM#&2{@LOMp6TG`%)x>Oi4?8?#oQZ?VF85_77P|z<yBK;4m
z$9p6iG4Y$7k$&n0-5?)+eO?AxRgUWG7;y<{t=Zp!q95gO=-qEF#cVLj5j<X^ANuRE
zPUc)BHW;q<6FGLH-73T6x8`<)290*CVUwr9yY-_1hmw-x1*9l`CjVtst;5Uu5g%rs
z3m}J-)*I%j72_T@r;+?~9NsffgQ31dhx`ZM5y0849tj%ZTejzBrYgisZoJ29j;dMi
z$c4O5o0eppV%imDWTvV;6<^v%Bi=ja^iPQ7U1H6ldGlq0hQo?6?q=6^FIlXX;R7|l
zdj*TI_5Sr|vV{^?`A=Xn$>H<;@zYPnFE~wmzOUs`iZ?l}EcjXvt$!OxVmdTw&4+Nm
z>iU_9y{8J6dPU`-?)NTQ?uWI(g2F4Ao5)6H40W%VgBa&B@1%zR{d8v9z=~Au_!U2*
zGDMs+gXs0)ICJ`kIh>b2WB@j!m;1WL^3g>Xe4fXn)wOZV%@FJWzi&k{_pH1UCsb#r
z<mc_5-@9$R=~or>x+$V-q{<X|Z+|Zp=9Uc|ma{m`v(5Q+oThArx4*fx@XOn9V_+3+
zdwU{EP2u^51*7Z{%^MaCxY%>8$nn$)8cn{AOq0&1I{lPVMcWfoCsAtmiTz?1vIixF
zh-YmQ$_P{G3nt|+*)U>U<d}r$X&NS_4V3w{{64*C4*#LGLLlmL!9U>-UPro<7hXMp
zMPU?ra}HErABnpXv-b<tt$vY72;@Z?eTWXEkjo?b`GmcitJWTtyNi4e`7Kd%O;EPo
zsBikaR!;Ae{Ikx)JSwD4g!3&sYy2N{%F!n16&5z*=V2nz+Mnxb0-W0}dSaNmlEtF#
zy;}PGy;t7la%#Vo`c1NBlOAcllwyBUoBo=0u$mt_B!f;!pMI`Wa~-n>fXvD>v5UPU
zu)M8IJP3txKS+AF<w0Y|h}rSDYSar+lx6CWqkPAQ&%aism!l{@L|^CnPt^j~kvY6g
zezy&ppv&U;@<Y_>jJF`v(PXu<^+_V9w>+q#c&~4dU<k5S-9oszCu^)>;%`S@;2O#W
zi>K<D`6={2%9gz+5XH;?RgrMg$iDY)E5ey__{pOmp?YqExJ5DPcexiX*oM{wB2iBG
zOR;|>p6Mz+{cT8I#b!w<l$*fNdzo78DJ)iaxTm?kpT%#_0M2{I&sZG)Aye4poAU4O
zF)j0@f4Vx*WU=t&J$ElN=ToDzOZ2H$expqFfpoV-qILz7B~>Q>(|WFqn5k=ua)N?$
zFjmMhd7!G?*fr<84O=`S@tijFU`56?B7N1Lt+Dk{YP?q<S(zG6Wc`jgbc-6iRQ{uX
zSSM9!rzbyj6hx^D6FQR;N?HSIL2X)Js$2qVj{LNJ`Wgv&usmSv+q>vVE$25pc?PCg
z%yZ*xj~<QH9BQv42558R$UN?hE4jj|7%h3t^`5Gpz`VY2e>)4W{t@?QIOm@=<}5cE
zarVp}rl#s^Dh54^y;$h-Esx94-Ol#8z<S^Vc;n^+oFqc7;@PAIUOoHZ*Mu6+bmI~S
z4lYb<=#RNQQ0%G6&>y~yJ+8T9&+uI5>=(dsE3glVCY;+*_J#0SU`^|M1+tLWn_l%0
z*g^ax=|5=HoVab=I3_Z-C&q!0&;7vcfX){@Og){SfwBN+qz{mpA7x(-XwU!u<0tOv
zK#z@%J{!N5```clprr=cMi9thqrlt$eloemcz8f{fF1U<C@l5ApAP%lcYiP$NW_`-
z{p|mKIye4z^ZY+G-oMp6{j}7e`6%St-?Q<%%wXvM?l*fIPGLW}^=^D0j3FnjELHf#
H`0f7yoj-rs

diff --git a/docs/_static/us.svg b/docs/_static/us.svg
deleted file mode 100644
index 1d621f96d8ad..000000000000
--- a/docs/_static/us.svg
+++ /dev/null
@@ -1,117 +0,0 @@
-<?xml version="1.0" encoding="UTF-8" standalone="no"?>
-<svg id="svg153" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns="http://www.w3.org/2000/svg" height="480" width="640" version="1.1" xmlns:cc="http://creativecommons.org/ns#" xmlns:dc="http://purl.org/dc/elements/1.1/">
- <metadata id="metadata3151">
-  <rdf:RDF>
-   <cc:Work rdf:about="">
-  <dc:format>image/svg+xml</dc:format>
-  <dc:type rdf:resource="http://purl.org/dc/dcmitype/StillImage"/>
-   </cc:Work>
-  </rdf:RDF>
- </metadata>
- <desc id="desc3066">The United States of America flag, produced by Daniel McRae</desc>
- <defs id="defs155">
-  <clipPath id="clipPath4293" clipPathUnits="userSpaceOnUse">
-   <rect id="rect4295" fill-opacity="0.67" height="512" width="682.67" y=".0000052307" x="0"/>
-  </clipPath>
- </defs>
- <g id="flag" fill-rule="evenodd" clip-path="url(#clipPath4293)" transform="matrix(.93750 0 0 .93750 0 -.0000049038)">
-  <g id="g390" stroke-width="1pt" transform="matrix(3.9385 0 0 3.9385 0 .000005)">
-   <g id="g169" fill="#bd3d44">
-  <rect id="rect156" height="10" width="247" y="0" x="0"/>
-  <rect id="rect158" height="10" width="247" y="20" x="0"/>
-  <rect id="rect160" height="10" width="247" y="40" x="0"/>
-  <rect id="rect162" height="10" width="247" y="60" x="0"/>
-  <rect id="rect164" height="10" width="247" y="80" x="0"/>
-  <rect id="rect166" height="10" width="247" y="100" x="0"/>
-  <rect id="rect168" height="10" width="247" y="120" x="0"/>
-   </g>
-   <g id="g177" fill="#fff">
-  <rect id="rect157" height="10" width="247" y="10" x="0"/>
-  <rect id="rect159" height="10" width="247" y="30" x="0"/>
-  <rect id="rect161" height="10" width="247" y="50" x="0"/>
-  <rect id="rect163" height="10" width="247" y="70" x="0"/>
-  <rect id="rect165" height="10" width="247" y="90" x="0"/>
-  <rect id="rect167" height="10" width="247" y="110" x="0"/>
-   </g>
-  </g>
-  <rect id="rect200" height="275.69" width="389.12" y="0.000005" x="0" stroke-width="1pt" fill="#192f5d"/>
-  <g id="g274" fill="#fff" transform="matrix(3.9385 0 0 3.9385 0 .000005)">
-   <g id="g218">
-  <g id="g194">
-   <polygon id="polygon207" transform="translate(8.2333 7)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon188" transform="translate(24.7,7)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon189" transform="translate(41.167 7)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon190" transform="translate(57.633 7)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon191" transform="translate(74.1,7)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon192" transform="translate(90.567 7)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-  </g>
-  <g id="g205">
-   <polygon id="polygon193" transform="translate(16.467 14)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon201" transform="translate(32.933 14)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon202" transform="translate(49.4,14)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon203" transform="translate(65.867 14)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon204" transform="translate(82.333 14)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-  </g>
-   </g>
-   <g id="g232" transform="translate(0,14)">
-  <g id="g233">
-   <polygon id="polygon234" transform="translate(8.2333 7)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon235" transform="translate(24.7,7)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon236" transform="translate(41.167 7)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon237" transform="translate(57.633 7)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon238" transform="translate(74.1,7)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon239" transform="translate(90.567 7)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-  </g>
-  <g id="g240">
-   <polygon id="polygon241" transform="translate(16.467 14)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon242" transform="translate(32.933 14)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon243" transform="translate(49.4,14)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon244" transform="translate(65.867 14)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon245" transform="translate(82.333 14)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-  </g>
-   </g>
-   <g id="g246" transform="translate(0,28)">
-  <g id="g247">
-   <polygon id="polygon248" transform="translate(8.2333 7)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon249" transform="translate(24.7,7)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon250" transform="translate(41.167 7)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon251" transform="translate(57.633 7)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon252" transform="translate(74.1,7)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon253" transform="translate(90.567 7)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-  </g>
-  <g id="g254">
-   <polygon id="polygon255" transform="translate(16.467 14)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon256" transform="translate(32.933 14)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon257" transform="translate(49.4,14)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon258" transform="translate(65.867 14)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon259" transform="translate(82.333 14)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-  </g>
-   </g>
-   <g id="g260" transform="translate(0,42)">
-  <g id="g261">
-   <polygon id="polygon262" transform="translate(8.2333 7)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon263" transform="translate(24.7,7)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon264" transform="translate(41.167 7)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon265" transform="translate(57.633 7)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon266" transform="translate(74.1,7)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon267" transform="translate(90.567 7)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-  </g>
-  <g id="g268">
-   <polygon id="polygon269" transform="translate(16.467 14)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon270" transform="translate(32.933 14)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon271" transform="translate(49.4,14)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon272" transform="translate(65.867 14)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   <polygon id="polygon273" transform="translate(82.333 14)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-  </g>
-   </g>
-   <g id="g211" transform="translate(0,56)">
-  <polygon id="polygon212" transform="translate(8.2333 7)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-  <polygon id="polygon213" transform="translate(24.7,7)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-  <polygon id="polygon214" transform="translate(41.167 7)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-  <polygon id="polygon215" transform="translate(57.633 7)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-  <polygon id="polygon216" transform="translate(74.1,7)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-  <polygon id="polygon217" transform="translate(90.567 7)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
-   </g>
-  </g>
- </g>
-</svg>
diff --git a/docs/api/c++/index.md b/docs/api/c++/index.md
deleted file mode 100644
index e09c16e60b28..000000000000
--- a/docs/api/c++/index.md
+++ /dev/null
@@ -1,72 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# MXNet - C++ API
-
-The MXNet C++ Package provides C++ API bindings to the users of MXNet.  Currently, these bindings are not available as standalone package.
-The users of these bindings are required to build this package as mentioned below.
-
-## Building C++ Package
-
-The cpp-package directory contains the implementation of C++ API. As mentioned above, users are required to build this directory or package before using it.
-**The cpp-package is built while building the MXNet shared library, *libmxnet.so*.**
-
-### Steps to build the C++ package:
-1.  Building the MXNet C++ package requires building MXNet from source.
-2.  Clone the MXNet GitHub repository **recursively** to ensure the code in submodules is available for building MXNet.
-	```
-	git clone --recursive https://github.com/apache/incubator-mxnet mxnet
-	```
-
-3.  Install the [prerequisites](<https://mxnet.incubator.apache.org/install/build_from_source#prerequisites>), desired [BLAS libraries](<https://mxnet.incubator.apache.org/install/build_from_source#blas-library>) and optional [OpenCV, CUDA, and cuDNN](<https://mxnet.incubator.apache.org/install/build_from_source#optional>) for building MXNet from source.
-4.  There is a configuration file for make, [make/config.mk](<https://github.com/apache/incubator-mxnet/blob/master/make/config.mk>) that contains all the compilation options. You can edit this file and set the appropriate options prior to running the **make** command.
-5.  Please refer to  [platform specific build instructions](<https://mxnet.incubator.apache.org/install/build_from_source#build-instructions-by-operating-system>) and available [build configurations](https://mxnet.incubator.apache.org/install/build_from_source#build-configurations) for more details.
-5.  For enabling the build of C++ Package, set the **USE\_CPP\_PACKAGE = 1** in [make/config.mk](<https://github.com/apache/incubator-mxnet/blob/master/make/config.mk>). Optionally, the compilation flag can also be specified on **make** command line as follows.
-	```
-	make -j USE_CPP_PACKAGE=1
-	```
-
-## Usage
-
-In order to consume the C++ API please follow the steps below.
-
-1. Ensure that the MXNet shared library is built from source with the **USE\_CPP\_PACKAGE = 1**.
-2. Include the [MxNetCpp.h](<https://github.com/apache/incubator-mxnet/blob/master/cpp-package/include/mxnet-cpp/MxNetCpp.h>) in the program that is going to consume MXNet C++ API.
-	```
-	#include <mxnet-cpp/MxNetCpp.h>
-	```
-3. While building the program, ensure that the correct paths to the directories containing header files and MXNet shared library.
-4. The program links the MXNet shared library dynamically. Hence the library needs to be accessible to the program during runtime. This can be achieved by including the path to the shared library in the environment variable  **LD\_LIBRARY\_PATH** for Linux, Mac. and Ubuntu OS and **PATH** for Windows OS.
-
-
-## Tutorial
-
-A basic tutorial can be found at <https://mxnet.incubator.apache.org/tutorials/c++/basics.html>.
-
-## Examples
-
-The example directory contains examples for you to get started.
-For namespaces, classes, and code files for the MXNet C++ package, see the  following:
-
-## Links to the documentation
-
-The classes and functions in MXNet C++ API are available under **mxnet::cpp** namespace. The links to the documenation are as follows:
-
-1. [Namespaces](../../doxygen/namespaces.html)
-2. [Classes in mxnet::cpp namespace](../../doxygen/namespacemxnet_1_1cpp.html)
-3. [Code Files](../../doxygen/files.html)
-4. [MXNet CPP Package](https://github.com/dmlc/mxnet/tree/master/cpp-package)
diff --git a/docs/api/clojure/index.md b/docs/api/clojure/index.md
deleted file mode 100644
index 38e1c5ff9d1e..000000000000
--- a/docs/api/clojure/index.md
+++ /dev/null
@@ -1,61 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# MXNet - Clojure API
-
-MXNet supports the Clojure programming language. The MXNet Clojure package brings flexible and efficient GPU
-computing and state-of-art deep learning to Clojure. It enables you to write seamless tensor/matrix computation with multiple GPUs in Clojure. It also lets you construct and customize the state-of-art deep learning models in Clojure, and apply them to tasks, such as image classification and data science challenges.
-
-See the [MXNet Clojure API Documentation](docs/index.html) for detailed API information.
-
-```eval_rst
-.. toctree::
-   :maxdepth: 1
-
-   kvstore.md
-   module.md
-   ndarray.md
-   symbol_in_pictures.md
-   symbol.md
-```
-
-## Tensor and Matrix Computations
-You can perform tensor or matrix computation in pure Clojure:
-
-```clojure
-(def arr (ndarray/ones [2 3]))
-
-arr ;=> #object[org.apache.mxnet.NDArray 0x597d72e "org.apache.mxnet.NDArray@e35c3ba9"]
-
-(ndarray/shape-vec arr) ;=>  [2 3]
-
-(-> (ndarray/* arr 2)
-    (ndarray/->vec)) ;=> [2.0 2.0 2.0 2.0 2.0 2.0]
-
-(ndarray/shape-vec (ndarray/* arr 2)) ;=> [2 3]
-
-```
-
-## Clojure API Tutorials
-* [Module API is a flexible high-level interface for training neural networks.](module.html)
-* [Symbolic API performs operations on NDArrays to assemble neural networks from layers.](symbol.html)
-* [NDArray API performs vector/matrix/tensor operations.](ndarray.html)
-* [KVStore API performs multi-GPU and multi-host distributed training.](kvstore.html)
-
-
-## Related Resources
-* [MXNet Clojure API Documentation](docs/index.html)
diff --git a/docs/api/clojure/kvstore.md b/docs/api/clojure/kvstore.md
deleted file mode 100644
index 36af3a8e6349..000000000000
--- a/docs/api/clojure/kvstore.md
+++ /dev/null
@@ -1,103 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# KVStore API
-
-Topics:
-
-* [Basic Push and Pull](#basic-push-and-pull)
-* [List Key-Value Pairs](#list-key-value-pairs)
-* [API Reference](http://mxnet.incubator.apache.org/api/clojure/docs/org.apache.clojure-mxnet.kvstore.html)
-
-To follow along with this documentation, you can use this namespace to with the needed requires:
-
-```clojure
-(ns docs.kvstore
-  (:require [org.apache.clojure-mxnet.kvstore :as kvstore]
-            [org.apache.clojure-mxnet.ndarray :as ndarray]
-            [org.apache.clojure-mxnet.context :as context]))
-```
-
-## Basic Push and Pull
-
-Provides basic operation over multiple devices (GPUs) on a single device.
-
-### Initialization
-
-Let's consider a simple example. It initializes
-a (`int`, `NDArray`) pair into the store, and then pulls the value out.
-
-```clojure
-(def kv (kvstore/create "local")) ;; create a local kvstore
-(def shape [2 3])
-;;; init the kvstore with a vector of keys (strings) and ndarrays
-(kvstore/init kv ["3"] [(ndarray/* (ndarray/ones shape) 2)])
-(def a (ndarray/zeros shape))
-(kvstore/pull kv ["3"] [a])
-(ndarray/->vec a) ;=> [2.0 2.0 2.0 2.0 2.0 2.0]
-```
-
-### Push, Aggregation, and Updater
-
-For any key that's been initialized, you can push a new value with the same shape to the key, as follows:
-
-```clojure
-(kvstore/push kv ["3"] [(ndarray/* (ndarray/ones shape) 8)])
-(kvstore/pull kv ["3"] [a])
-(ndarray/->vec a);=>[8.0 8.0 8.0 8.0 8.0 8.0]
-```
-
-The data that you want to push can be stored on any device. Furthermore, you can push multiple
-values into the same key, where KVStore first sums all of these
-values, and then pushes the aggregated value, as follows (Here we use multiple cpus):
-
-```clojure
-(def cpus [(context/cpu 0) (context/cpu 1) (context/cpu 2)])
-(def b [(ndarray/ones shape {:ctx (nth cpus 0)})
-        (ndarray/ones shape {:ctx (nth cpus 1)})
-        (ndarray/ones shape {:ctx (nth cpus 2)})])
-(kvstore/push kv ["3" "3" "3"] b)
-(kvstore/pull kv "3" a)
-(ndarray/->vec a) ;=> [3.0 3.0 3.0 3.0 3.0 3.0]
-```
-
-
-### Pull
-
-You've already seen how to pull a single key-value pair. Similar to the way that you use the push command, you can
-pull the value into several devices with a single call.
-
-```clojure
-(def b [(ndarray/ones shape {:ctx (context/cpu 0)})
-        (ndarray/ones shape {:ctx (context/cpu 1)})])
-(kvstore/pull kv ["3" "3"] b)
-(map ndarray/->vec b) ;=> ([3.0 3.0 3.0 3.0 3.0 3.0] [3.0 3.0 3.0 3.0 3.0 3.0])
-```
-
-## List Key-Value Pairs
-
-All of the operations that we've discussed so far are performed on a single key. KVStore also provides
-the interface for generating a list of key-value pairs. For a single device, use the following:
-
-```clojure
-(def ks ["5" "7" "9"])
-(kvstore/init kv ks [(ndarray/ones shape) (ndarray/ones shape) (ndarray/ones shape)])
-(kvstore/push kv ks [(ndarray/ones shape) (ndarray/ones shape) (ndarray/ones shape)])
-(def b [(ndarray/zeros shape) (ndarray/zeros shape) (ndarray/zeros shape)])
-(kvstore/pull kv ks b)
-(map ndarray/->vec b);=> ([1.0 1.0 1.0 1.0 1.0 1.0] [1.0 1.0 1.0 1.0 1.0 1.0] [1.0 1.0 1.0 1.0 1.0 1.0])
-```
diff --git a/docs/api/clojure/module.md b/docs/api/clojure/module.md
deleted file mode 100644
index e1414adeb674..000000000000
--- a/docs/api/clojure/module.md
+++ /dev/null
@@ -1,252 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Module API
-The module API provides an intermediate and high-level interface for performing computation with neural networks in MXNet. Module wraps a Symbol and one or more Executors. It has both a high level and intermediate level API.
-
-
-Topics:
-
-* [Prepare the Data](#prepare-the-data)
-* [List Key-Value Pairs](#list-key-value-pairs)
-* [Preparing a Module for Computation](#preparing-a-module-for-computation)
-* [Training and Predicting](#training-and-predicting)
-* [Saving and Loading](#saving-and-loading)
-* [API Reference](http://mxnet.incubator.apache.org/api/clojure/docs/org.apache.clojure-mxnet.module.html)
-
-
-To follow along with this documentation, you can use this namespace to with the needed requires:
-
-```clojure
-(ns docs.module
-  (:require [clojure.java.io :as io]
-            [clojure.java.shell :refer [sh]]
-            [org.apache.clojure-mxnet.eval-metric :as eval-metric]
-            [org.apache.clojure-mxnet.io :as mx-io]
-            [org.apache.clojure-mxnet.module :as m]
-            [org.apache.clojure-mxnet.symbol :as sym]
-            [org.apache.clojure-mxnet.ndarray :as ndarray]))
-```
-
-## Prepare the Data
-
-In this example, we are going to use the MNIST data set. If you have cloned the MXNet repo and `cd contrib/clojure-package`, we can run some helper scripts to download the data for us.
-
-```clojure
-(def data-dir "data/")
-
-(when-not (.exists (io/file (str data-dir "train-images-idx3-ubyte")))
-  (sh "../../scripts/get_mnist_data.sh"))
-```
-
-MXNet provides function in the `io` namespace to load the MNIST datasets into training and test data iterators that we can use with our module.
-
-```clojure
-(def train-data (mx-io/mnist-iter {:image (str data-dir "train-images-idx3-ubyte")
-                                   :label (str data-dir "train-labels-idx1-ubyte")
-                                   :label-name "softmax_label"
-                                   :input-shape [784]
-                                   :batch-size 10
-                                   :shuffle true
-                                   :flat true
-                                   :silent false
-                                   :seed 10}))
-
-(def test-data (mx-io/mnist-iter {:image (str data-dir "t10k-images-idx3-ubyte")
-                                  :label (str data-dir "t10k-labels-idx1-ubyte")
-                                  :input-shape [784]
-                                  :batch-size 10
-                                  :flat true
-                                  :silent false}))
-```
-
-
-## Preparing a Module for Computation
-
-To construct a module, we need to have a symbol as input. This symbol takes input data in the first layer and then has subsequent layers of fully connected and relu activation layers, ending up in a softmax layer for output.
-
-```clojure
-(let [data (sym/variable "data")
-      fc1 (sym/fully-connected "fc1" {:data data :num-hidden 128})
-      act1 (sym/activation "relu1" {:data fc1 :act-type "relu"})
-      fc2 (sym/fully-connected "fc2" {:data act1 :num-hidden 64})
-      act2 (sym/activation "relu2" {:data fc2 :act-type "relu"})
-      fc3 (sym/fully-connected "fc3" {:data act2 :num-hidden 10})
-      out (sym/softmax-output "softmax" {:data fc3})]
-  out)
-  ;=>#object[org.apache.mxnet.Symbol 0x1f43a406 "org.apache.mxnet.Symbol@1f43a406"]
-```
-
-You can also write this with the `as->` threading macro.
-
-```clojure
-(def out (as-> (sym/variable "data") data
-           (sym/fully-connected "fc1" {:data data :num-hidden 128})
-           (sym/activation "relu1" {:data data :act-type "relu"})
-           (sym/fully-connected "fc2" {:data data :num-hidden 64})
-           (sym/activation "relu2" {:data data :act-type "relu"})
-           (sym/fully-connected "fc3" {:data data :num-hidden 10})
-           (sym/softmax-output "softmax" {:data data})))
-;=> #'tutorial.module/out
-```
-
-
-By default, `context` is the CPU. If you need data parallelization, you can specify a GPU context or an array of GPU contexts like this `(m/module out {:contexts [(context/gpu)]})`
-
-Before you can compute with a module, you need to call `bind` to allocate the device memory and `init-params` or `set-params` to initialize the parameters. If you simply want to fit a module, you don’t need to call `bind` and `init-params` explicitly, because the `fit` function automatically calls them if they are needed.
-
-```clojure
-(let [mod (m/module out)]
-  (-> mod
-      (m/bind {:data-shapes (mx-io/provide-data train-data)
-               :label-shapes (mx-io/provide-label train-data)})
-      (m/init-params)))
-```
-
-Now you can compute with the module using functions like `forward`, `backward`, etc.
-
-## Training and Predicting
-
-Modules provide high-level APIs for training, predicting, and evaluating. To fit a module, call the `fit` function with some data iterators:
-
-```clojure
-(def mod (m/fit (m/module out) {:train-data train-data :eval-data test-data :num-epoch 1}))
-;; Epoch  0  Train- [accuracy 0.12521666]
-;; Epoch  0  Time cost- 8392
-;; Epoch  0  Validation-  [accuracy 0.2227]
-```
-
-You can pass in batch-end callbacks using batch-end-callback and epoch-end callbacks using epoch-end-callback in the `fit-params`. You can also set parameters using functions like in the fit-params like optimizer and eval-metric. To learn more about the fit-params, see the fit-param function options. To predict with a module, call `predict` with a DataIter:
-
-```clojure
-(def results (m/predict mod {:eval-data test-data}))
-(first results) ;=>#object[org.apache.mxnet.NDArray 0x3540b6d3 "org.apache.mxnet.NDArray@a48686ec"]
-
-(first (ndarray/->vec (first results))) ;=>0.08261358
-```
-
-The module collects and returns all of the prediction results. For more details about the format of the return values, see the documentation for the [`predict`](docs/org.apache.clojure-mxnet.module.html#var-fit-params) function.
-
-When prediction results might be too large to fit in memory, use the [`predict-every-batch`](docs/org.apache.clojure-mxnet.module.html#predict-every-batch) API.
-
-```clojure
-(let [preds (m/predict-every-batch mod {:eval-data test-data})]
-  (mx-io/reduce-batches test-data
-                        (fn [i batch]
-                          (println (str "pred is " (first (get preds i))))
-                          (println (str "label is " (mx-io/batch-label batch)))
-                          ;;; do something
-                          (inc i))))
-```
-
-If you need to evaluate on a test set and don’t need the prediction output, call the `score` function with a data iterator and an eval metric:
-
-```clojure
-(m/score mod {:eval-data test-data :eval-metric (eval-metric/accuracy)}) ;=>["accuracy" 0.2227]
-```
-
-This runs predictions on each batch in the provided data iterator and computes the evaluation score using the provided eval metric. The evaluation results are stored in `eval-metric` object itself so that you can query later.
-
-
-## Saving and Loading
-
-To save the module parameters in each training epoch, use the `save-checkpoint` function:
-
-```clojure
-(let [save-prefix "my-model"]
-  (doseq [epoch-num (range 3)]
-    (mx-io/do-batches train-data (fn [batch
-                                          ;; do something
-]))
-    (m/save-checkpoint mod {:prefix save-prefix :epoch epoch-num :save-opt-states true})))
-
-;; INFO  org.apache.mxnet.module.Module: Saved checkpoint to my-model-0000.params
-;; INFO  org.apache.mxnet.module.Module: Saved optimizer state to my-model-0000.states
-;; INFO  org.apache.mxnet.module.Module: Saved checkpoint to my-model-0001.params
-;; INFO  org.apache.mxnet.module.Module: Saved optimizer state to my-model-0001.states
-;; INFO  org.apache.mxnet.module.Module: Saved checkpoint to my-model-0002.params
-;; INFO  org.apache.mxnet.module.Module: Saved optimizer state to my-model-0002.states
-
-```
-
-To load the saved module parameters, call the `load-checkpoint` function:
-
-```clojure
-(def new-mod (m/load-checkpoint {:prefix "my-model" :epoch 1 :load-optimizer-states true}))
-
-new-mod ;=> #object[org.apache.mxnet.module.Module 0x5304d0f4 "org.apache.mxnet.module.Module@5304d0f4"]
-```
-
-To initialize parameters, Bind the symbols to construct executors first with `bind` function. Then, initialize the parameters and auxiliary states by calling `init-params` function.
-
-```clojure
-(-> new-mod
-    (m/bind {:data-shapes (mx-io/provide-data train-data) :label-shapes (mx-io/provide-label train-data)})
-    (m/init-params))
-```
-
-To get current parameters, use `params`
-
-```clojure
-
-(let [[arg-params aux-params] (m/params new-mod)]
-  {:arg-params arg-params
-   :aux-params aux-params})
-
-;; {:arg-params
-;;  {"fc3_bias"
-;;   #object[org.apache.mxnet.NDArray 0x39adc3b0 "org.apache.mxnet.NDArray@49caf426"],
-;;   "fc2_weight"
-;;   #object[org.apache.mxnet.NDArray 0x25baf623 "org.apache.mxnet.NDArray@a6c8f9ac"],
-;;   "fc1_bias"
-;;   #object[org.apache.mxnet.NDArray 0x6e089973 "org.apache.mxnet.NDArray@9f91d6eb"],
-;;   "fc3_weight"
-;;   #object[org.apache.mxnet.NDArray 0x756fd109 "org.apache.mxnet.NDArray@2dd0fe3c"],
-;;   "fc2_bias"
-;;   #object[org.apache.mxnet.NDArray 0x1dc69c8b "org.apache.mxnet.NDArray@d128f73d"],
-;;   "fc1_weight"
-;;   #object[org.apache.mxnet.NDArray 0x20abc769 "org.apache.mxnet.NDArray@b8e1c5e8"]},
-;;  :aux-params {}}
-
-```
-
-To assign parameter and aux state values, use the `set-params` function.
-
-```clojure
-(m/set-params new-mod {:arg-params (m/arg-params new-mod) :aux-params (m/aux-params new-mod)})
-;=> #object[org.apache.mxnet.module.Module 0x5304d0f4 "org.apache.mxnet.module.Module@5304d0f4"]
-```
-
-To resume training from a saved checkpoint, pass the loaded parameters to the `fit` function. This will prevent `fit` from initialzing randomly.
-
-Create fit-params and then use it to set `begin-epoch` so that `fit` knows to resume from a saved epoch.
-
-```clojure
-;; reset the training data before calling fit or you will get an error
-(mx-io/reset train-data)
-(mx-io/reset test-data)
-
-(m/fit new-mod {:train-data train-data :eval-data test-data :num-epoch 2
-                :fit-params (-> (m/fit-params {:begin-epoch 1}))})
-
-```
-
-
-## Next Steps
-* See [Symbolic API](symbol.md) for operations on NDArrays that assemble neural networks from layers.
-* See [NDArray API](ndarray.md) for vector/matrix/tensor operations.
-* See [KVStore API](kvstore.md) for multi-GPU and multi-host distributed training.
diff --git a/docs/api/clojure/ndarray.md b/docs/api/clojure/ndarray.md
deleted file mode 100644
index 991e458ccc04..000000000000
--- a/docs/api/clojure/ndarray.md
+++ /dev/null
@@ -1,136 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# NDArray API
-
-
-The NDArray API contains tensor operations similar to `numpy.ndarray`. The syntax is also similar, except for some additional calls for dealing with I/O and multiple devices.
-
-Topics:
-
-* [Create NDArray](#create-ndarray)
-* [NDArray Operations](#ndarray-operations)
-* [NDArray API Reference](http://mxnet.incubator.apache.org/api/clojure/docs/org.apache.clojure-mxnet.ndarray.html)
-
-To follow along with this documentation, you can use this namespace with the needed requires:
-
-```clojure
-(ns docs.ndarray
-  (:require [org.apache.clojure-mxnet.ndarray :as ndarray]
-            [org.apache.clojure-mxnet.context :as context]))
-```
-
-
-## Create NDArray
-
-Create `mxnet.ndarray` as follows:
-
-```clojure
-
-(def a (ndarray/zeros [100 50])) ;;all zero arrray of dimension 100 x 50
-(def b (ndarray/ones [256 32 128 1])) ;; all one array of dimension
-(def c (ndarray/array [1 2 3 4 5 6] [2 3])) ;; array with contents of a shape 2 x 3
-```
-
-There are also ways to convert a NDArray to a vec or get the shape or the NDArray as an object or vec as follows:
-
-```clojure
-(ndarray/->vec c) ;=> [1.0 2.0 3.0 4.0 5.0 6.0]
-(ndarray/shape c) ;=> #object[org.apache.mxnet.Shape 0x583c865 "(2,3)"]
-(ndarray/shape-vec c) ;=> [2 3]
-```
-
-
-## NDArray Operations
-
-There are some basic NDArray operations, like arithmetic and slice operations.
-
-### Arithmetic Operations
-
-```clojure
-(def a (ndarray/ones [1 5]))
-(def b (ndarray/ones [1 5]))
-(-> (ndarray/+ a b) (ndarray/->vec)) ;=>  [2.0 2.0 2.0 2.0 2.0]
-
-;; original ndarrays are unchanged
-(ndarray/->vec a) ;=> [1.0 1.0 1.0 1.0 1.0]
-(ndarray/->vec b) ;=> [1.0 1.0 1.0 1.0 1.0]
-
-;;inplace operators
-(ndarray/+= a b)
-(ndarray/->vec a) ;=>  [2.0 2.0 2.0 2.0 2.0]
-```
-
-Other arithmetic operations are similar.
-
-
-### Slice Operations
-
-```clojure
-(def a (ndarray/array [1 2 3 4 5 6] [3 2]))
-(def a1 (ndarray/slice a 1))
-(ndarray/shape-vec a1) ;=> [1 2]
-(ndarray/->vec a1) ;=> [3.0 4.0]
-
-(def a2 (ndarray/slice a 1 3))
-(ndarray/shape-vec a2) ;=>[2 2]
-(ndarray/->vec a2) ;=> [3.0 4.0 5.0 6.0]
-```
-
-### Dot Product
-
-```clojure
-(def arr1 (ndarray/array [1 2] [1 2]))
-(def arr2 (ndarray/array [3 4] [2 1]))
-(def res (ndarray/dot arr1 arr2))
-(ndarray/shape-vec res) ;=> [1 1]
-(ndarray/->vec res) ;=> [11.0]
-```
-
-### Save and Load NDArray
-
-You can use MXNet functions to save and load a list or dictionary of NDArrays from file systems, as follows:
-
-```clojure
-(ndarray/save "filename" {"arr1" arr1 "arr2" arr2})
-;; you can also do "s3://path" or "hdfs"
-```
-
-To load:
-
-```clojure
-(def from-file (ndarray/load "filename"))
-from-file
-;=>{"arr1" #object["org.apache.mxnet.NDArray@43d85753"], "arr2" #object["org.apache.mxnet.NDArray@5c93def4"]}
-```
-
-The good thing about using the `save` and `load` interface is that you can use the format across all `mxnet` language bindings. They also already support Amazon S3 and HDFS.
-
-### Multi-Device Support
-
-Device information is stored in the `mxnet.Context` structure. When creating NDArray in MXNet, you can use the context argument (the default is the CPU context) to create arrays on specific devices as follows:
-
-```clojure
-(def cpu-a (ndarray/zeros [100 200]))
-(ndarray/context cpu-a) ;=> #object[org.apache.mxnet.Context 0x3f376123 "cpu(0)"]
-
-(def gpu-b (ndarray/zeros [100 200] {:ctx (context/gpu 0)})) ;; to use with gpu
-
-```
-
-## Next Steps
-* See [KVStore API](kvstore.md) for multi-GPU and multi-host distributed training.
diff --git a/docs/api/clojure/symbol.md b/docs/api/clojure/symbol.md
deleted file mode 100644
index 2457dba6d530..000000000000
--- a/docs/api/clojure/symbol.md
+++ /dev/null
@@ -1,149 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# MXNet Clojure Symbolic API
-
-Topics:
-
-* [How to Compose Symbols](#how-to-compose-symbols)
-* [More Complicated Compositions](#more-complicated-compositions)
-* [Group Multiple Symbols](#group-multiple-symbols)
-* [Serialization](#serialization)
-* [Executing Symbols](#executing-symbols)
-* [Symbol API Reference](http://mxnet.incubator.apache.org/api/clojure/docs/org.apache.clojure-mxnet.symbol.html)
-
-
-We also highly encourage you to read [Symbolic Configuration and Execution in Pictures](symbol_in_pictures.md).
-
-To follow along with this documentation, you can use this namespace to with the following requirements:
-
-```clojure
-(ns docs.symbol
-  (:require [org.apache.clojure-mxnet.executor :as executor]
-            [org.apache.clojure-mxnet.ndarray :as ndarray]
-            [org.apache.clojure-mxnet.symbol :as sym]
-            [org.apache.clojure-mxnet.context :as context]))
-```
-
-
-## How to Compose Symbols
-
-The Symbolic API provides a way to configure computation graphs.
-You can configure the graphs either at the level of neural network layer operations or as fine-grained operations.
-
-The following example configures a two-layer neural network.
-
-```clojure
-(def data (sym/variable "data"))
-(def fc1 (sym/fully-connected "fc1" {:data data :num-hidden 128}))
-(def act1 (sym/activation "act1" {:data fc1 :act-type "relu"}))
-(def fc2 (sym/fully-connected "fc2" {:data act1 :num-hidden 64}))
-(def net (sym/softmax-output "out" {:data fc2}))
-```
-
-This can also be combined more dynamically with the `as->` Clojure threading form.
-
-```clojure
-(as-> (sym/variable "data") data
-  (sym/fully-connected "fc1" {:data data :num-hidden 128})
-  (sym/activation "act1" {:data data :act-type "relu"})
-  (sym/fully-connected "fc2" {:data data :num-hidden 64})
-  (sym/softmax-output "out" {:data data}))
-
-net ;=> #object[org.apache.mxnet.Symbol 0x5c78c8c2 "org.apache.mxnet.Symbol@5c78c8c2"] 
-```
-
-The basic arithmetic operators (plus, minus, div, multiplication) work as expected.
-
-The following example creates a computation graph that adds two inputs together.
-
-```clojure
-(def a (sym/variable "a"))
-(def b (sym/variable "b"))
-(def c (sym/+ a b))
-```
-
-## More Complicated Compositions
-
-MXNet provides well-optimized symbols for layers commonly used in deep learning (see src/operator). We can also define new operators in Python. The following example first performs an element-wise add between two symbols, then feeds them to the fully connected operator:
-
-```clojure
-(def lhs (sym/variable "data1"))
-(def rhs (sym/variable "data2"))
-(def net (sym/fully-connected "fc1" {:data (sym/+ lhs rhs) :num-hidden 128}))
-(sym/list-arguments net) ;=> ["data1" "data2" "fc1_weight" "fc1_bias"]
-```
-
-## Group Multiple Symbols
-
-To construct neural networks with multiple loss layers, we can use `group` to group multiple symbols together. The following example groups two outputs:
-
-```clojure
-(def net (sym/variable "data"))
-(def fc1 (sym/fully-connected {:data net :num-hidden 128}))
-(def net2 (sym/activation {:data fc1 :act-type "relu"}))
-(def out1 (sym/softmax-output {:data net2}))
-(def out2 (sym/linear-regression-output {:data net2}))
-(def group (sym/group [out1 out2]))
-(sym/list-outputs group)
-;=> ["softmaxoutput0_output" "linearregressionoutput0_output"]
-```
-
-## Serialization
-You can use the [`save`](docs/org.apache.clojure-mxnet.symbol.html#var-save) and [`load`](docs/org.apache.clojure-mxnet.symbol.html#var-load) functions to serialize the Symbol objects. The advantage of using save and load functions is that it is language agnostic and cloud friendly. The symbol is saved in JSON format. You can also get a JSON string directly using mxnet.Symbol.toJson. Refer to API documentation for more details.
-
- The following example shows how to save a symbol to a file, load it back, and compare two symbols using a JSON string. You can also save to S3 as well
-
-```clojure
-(def a (sym/variable "a"))
-(def b (sym/variable "b"))
-(def c (sym/+ a b))
-(sym/save c "symbol-c.json")
-(def c2 (sym/load "symbol-c.json"))
-(= (sym/to-json c) (sym/to-json c2)) ;=>true
-```
-
-
-## Executing Symbols
-
-To execute symbols, first we need to define the data that they should run on. We can do it by using the bind method, which accepts device context and a dict mapping free variable names to NDArrays as arguments and returns an executor. The executor provides forward method for evaluation and an attribute outputs to get all the results.
-
-```clojure
-(def a (sym/variable "a"))
-(def b (sym/variable "b"))
-(def c (sym/+ a b))
-
-(def ex (sym/bind c {"a" (ndarray/ones [2 2]) "b" (ndarray/ones [2 2])}))
-(-> (executor/forward ex)
-    (executor/outputs)
-    (first)
-    (ndarray/->vec));=>  [2.0 2.0 2.0 2.0]
-```
-
-We can evaluate the same symbol on GPU with different data.
-_To do this you must have the correct native library jar defined as a dependency_
-
-**Note In order to execute the following section on a cpu set gpu_device to (cpu)**
-
-
-```clojure
-(def ex (sym/bind c (context/gpu 0) {"a" (ndarray/ones [2 2]) "b" (ndarray/ones [2 2])}))
-```
-
-## Next Steps
-* See [NDArray API](ndarray.md) for vector/matrix/tensor operations.
-* See [KVStore API](kvstore.md) for multi-GPU and multi-host distributed training.
diff --git a/docs/api/clojure/symbol_in_pictures.md b/docs/api/clojure/symbol_in_pictures.md
deleted file mode 100644
index f5a56e8dcc3b..000000000000
--- a/docs/api/clojure/symbol_in_pictures.md
+++ /dev/null
@@ -1,94 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Symbolic Configuration and Execution in Pictures
-
-This topic explains symbolic construction and execution in pictures.
-
-We recommend that you read the [Symbolic API](symbol.md) as another useful reference.
-
-## Compose Symbols
-
-Symbols are a description of the computation that you want to perform. The symbolic construction API generates the computation
-graph that describes the computation. The following picture shows how you compose symbols to describe basic computations.
-
-![Symbol Compose](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/compose_basic.png)
-
-- The ```mxnet.Symbol.Variable``` function creates argument nodes that represent input to the computation.
-- The symbol is overloaded with basic element-wise mathematical operations.
-
-## Configure Neural Networks
-
-In addition to supporting fine-grained operations, MXNet provides a way to perform big operations that are analogous to layers in neural networks.
-You can use operators to describe the configuration of a neural network.
-
-![Net Compose](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/compose_net.png)
-
-
-## Example of a Multi-Input Network
-
-The following example shows how to configure multiple input neural networks.
-
-![Multi Input](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/compose_multi_in.png)
-
-
-## Bind and Execute Symbol
-
-When you need to execute a symbol graph, you call the bind function to bind ```NDArrays``` to the argument nodes
-in order to obtain an ```Executor```.
-
-![Bind](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/bind_basic.png)
-
-To get the output results, given the bound NDArrays as input, you can call ```Executor.Forward```.
-
-![Forward](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/executor_forward.png)
-
-
-## Bind Multiple Outputs
-
-To group symbols, then bind them to get outputs of both, use ```mx.symbol.Group```.
-
-![MultiOut](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/executor_multi_out.png)
-
-Remember: Bind only what you need, so that the system can perform more optimizations.
-
-
-## Calculate the Gradient
-
-In the bind function, you can specify NDArrays that will hold gradients. Calling ```Executor.backward``` after ```Executor.forward``` gives you the corresponding gradients.
-
-![Gradient](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/executor_backward.png)
-
-
-## Simple Bind Interface for Neural Networks
-
-It can be tedious to pass the argument NDArrays to the bind function, especially when you are binding a big
-graph. ```Symbol.simple_bind``` provides a way to simplify
-the procedure. You need to specify only input data shapes. The function allocates the arguments, and binds
-the Executor for you.
-
-![SimpleBind](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/executor_simple_bind.png)
-
-## Auxiliary States
-
-Auxiliary states are just like arguments, except that you can't take the gradient of them. Although auxiliary states might not be part of the computation, they can be helpful for tracking. You can pass auxiliary states in the same way that you pass arguments.
-
-![SimpleBind](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/executor_aux_state.png)
-
-## Next Steps
-
-See [Symbolic API](symbol.md) and [Python Documentation](index.md).
diff --git a/docs/api/index.md b/docs/api/index.md
deleted file mode 100644
index a1164fc72477..000000000000
--- a/docs/api/index.md
+++ /dev/null
@@ -1,33 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# MXNet APIs
-
-
-```eval_rst
-.. toctree::
-   :maxdepth: 1
-
-   c++/index.md
-   clojure/index.md
-   java/index.md
-   julia/index.md
-   perl/index.md
-   python/index.md
-   r/index.md
-   scala/index.md
-```
diff --git a/docs/api/java/index.md b/docs/api/java/index.md
deleted file mode 100644
index 099848cdc01b..000000000000
--- a/docs/api/java/index.md
+++ /dev/null
@@ -1,32 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# MXNet - Java Inference API
-
-MXNet supports Java for performing inference on a trained model. The MXNet Java Inference API is an extension of the [Scala Infer API](../../api/scala/infer.html) which provides model loading and inference functionality.
-
-The goal of the MXNet Java package is to provide an efficient and easy to use inference API.
-The MXNet Java package makes it easy to quickly deploy an existing model into a production level Java ecosystem.
-
-## Installation
-* [MXNet Java Inference API setup instructions](../../install/java_setup.md)
-
-## Tutorials
-See the [tutorial page](../../tutorials/index.html#java-tutorials) for detailed tutorials and examples using the Java Inference API.
-
-## Java Inference API Reference
-The [Java Infer API javadocs](docs/index.html#org.apache.mxnet.infer.package) provides detailed API information.
\ No newline at end of file
diff --git a/docs/api/julia/index.md b/docs/api/julia/index.md
deleted file mode 100644
index 3eb7df68b4e1..000000000000
--- a/docs/api/julia/index.md
+++ /dev/null
@@ -1,34 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# MXNet - Julia API
-
-See the [MXNet Julia Site](site/index.html) for examples and API reference docs.
-
-MXNet supports the Julia programming language. The MXNet Julia package brings flexible and efficient GPU
-computing and the state-of-art deep learning to Julia.
-
-- It enables you to write seamless tensor/matrix computation with multiple GPUs in Julia.
-- It also enables you to construct and customize the state-of-art deep learning models in Julia,
-  and apply them to tasks such as image classification and data science challenges.
-
-## Installation
-* [Ubuntu installation guide](../../install/ubuntu_setup.html)
-* Mac / Windows guides are not available (contributions welcome!)
-
-## Docs
-To build your own copy of the [MXNet Julia Site](site/index.html), run `make -C julia/docs` from the MXNet source root directory. You can also generate it with Docker by using `dev_menu.py` from the root directory and choosing to build the entire website. The Julia site will be located in `api/julia/site/`.
diff --git a/docs/api/perl/index.md b/docs/api/perl/index.md
deleted file mode 100644
index 8815e33a3e39..000000000000
--- a/docs/api/perl/index.md
+++ /dev/null
@@ -1,91 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# MXNet - Perl API
-
-MXNet supports the Perl programming language. The MXNet Perl package brings flexible and efficient GPU
-computing and state-of-art deep learning to Perl. It enables you to write seamless tensor/matrix computation with multiple GPUs in Perl.
-It also lets you construct and customize the state-of-art deep learning models in Perl,
-  and apply them to tasks, such as image classification and data science challenges.
-
-One important thing to internalize is that Perl interface is written to be as close as possible to the Python's API,
-so most if not all of Python's documentation and examples should just work in Perl after making few
-changes in order to make the code a bit more Perlish. In nutshell just add $ sigils and replace . = \n with -> => ; and in 99% of cases
-that's all that is needed there.
-In addition please refer to [excellent metacpan doc interface](https://metacpan.org/release/AI-MXNet) and to very detailed
-[MXNet Python API Documentation](http://mxnet.io/api/python/index.html).
-
-AI::MXNet supports new imperative PyTorch like Gluon MXNet interface. Please get acquainted with this new interface
-at [Dive into Deep Learning](https://www.d2l.ai/).
-
-For specific Perl Gluon usage please refer to Perl examples and tests directories on github, but be assured that the Python and Perl usage
-are extremely close in order to make the use of the Python Gluon docs and examples as easy as possible.
-
-AI::MXNet is seamlessly glued with [PDL](https://metacpan.org/release/PDL), the C++ level state can be easily initialized from PDL and the results can be
-transferred to PDL objects in order to allow you to use all the glory and power of the PDL!
-
-Here is how you can perform tensor or matrix computation in Perl with AI::MXNet and PDL:
-
-```perl
-pdl> use AI::MXNet qw(mx); # creates 'mx' module on the fly with the interface close to the Python's API
-
-pdl> print $arr = mx->nd->ones([2, 3])
-<AI::MXNet::NDArray 2x3 @cpu(0)>
-
-pdl> print Data::Dumper::Dumper($arr->shape)
-$VAR1 = [
-          2,
-          3
-        ];
-
-pdl> print (($arr*2)->aspdl) ## converts AI::MXNet::NDArray object to PDL object
-
-[
- [2 2 2]
- [2 2 2]
-]
-
-pdl> print $arr = mx->nd->array([[1,2],[3,4]]) ## init the NDArray from Perl array ref given in PDL::pdl constructor format
-<AI::MXNet::NDArray 2x2 @cpu(0)>
-pdl> print $arr->aspdl
-
-[
- [1 2]
- [3 4]
-]
-
-## init the NDArray from PDL but be aware that PDL methods expect the dimensions order in column major format
-## AI::MXNet::NDArray is row major
-pdl> print mx->nd->array(sequence(2,3))->aspdl ## 3 rows, 2 columns
-
-[
- [0 1]
- [2 3]
- [4 5]
-]
-```
-
-Export/import to/from sparse MXNet tensors are supported via [PDL::CCS](https://metacpan.org/release/PDL-CCS).
-Please check out the examples directory for the examples on how to use the sparse matrices.
-
- ## Perl API Reference
- * [Module API is a flexible high-level interface for training neural networks.](module.md)
- * [Symbolic API performs operations on NDArrays to assemble neural networks from layers.](symbol.md)
- * [IO Data Loading API performs parsing and data loading.](io.md)
- * [NDArray API performs vector/matrix/tensor operations.](ndarray.md)
- * [KVStore API performs multi-GPU and multi-host distributed training.](kvstore.md)
-
diff --git a/docs/api/perl/io.md b/docs/api/perl/io.md
deleted file mode 100644
index ca3b0f1e09f7..000000000000
--- a/docs/api/perl/io.md
+++ /dev/null
@@ -1,129 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Data Loading API
-
-## Overview
-
-A data iterator reads data batch by batch.
-
-```perl
-pdl> $data = mx->nd->ones([100,10])
-pdl> $nd_iter = mx->io->NDArrayIter($data, batch_size=>25)
-pdl> for my $batch (@{ $nd_iter }) { print $batch->data->[0],"\n" }
-<AI::MXNet::NDArray 25x10 @cpu(0)>
-<AI::MXNet::NDArray 25x10 @cpu(0)>
-<AI::MXNet::NDArray 25x10 @cpu(0)>
-<AI::MXNet::NDArray 25x10 @cpu(0)>
-```
-
-If `$nd_iter->reset()` is called, then reads the data again from beginning.
-
-In addition, an iterator provides information about the batch, including the
-shapes and name.
-
-```perl
-pdl> $nd_iter = mx->io->NDArrayIter(data=>{data => mx->nd->ones([100,10])}, label=>{softmax_label => mx->nd->ones([100])}, batch_size=>25)
-pdl> print($nd_iter->provide_data->[0],"\n")
-DataDesc[data,25x10,float32,NCHW]
-pdl> print($nd_iter->provide_label->[0],"\n")
-DataDesc[softmax_label,25,float32,NCHW]
-```
-
-So this iterator can be used to train a symbol whose input data variable has
-name `data` and input label variable has name `softmax_label`.
-
-
-```perl
-pdl> $data  = mx->sym->Variable('data')
-pdl> $label = mx->sym->Variable('softmax_label')
-pdl> $fullc = mx->sym->FullyConnected(data=>$data, num_hidden=>1)
-pdl> $loss  = mx->sym->SoftmaxOutput(data=>$data, label=>$label)
-pdl> $mod   = mx->mod->Module($loss)
-pdl> print($mod->data_names->[0])
-data
-pdl> print($mod->label_names->[0])
-softmax_label
-pdl> $mod->bind(data_shapes=>$nd_iter->provide_data, label_shapes=>$nd_iter->provide_label)
-```
-
-Then we can call `$mod->fit($nd_iter, num_epoch=>2)` to train `loss` by 2 epochs.
-
-## Predefined Data iterators
-
-```perl
-mx->io->NDArrayIter
-mx->io->CSVIter
-mx->io->ImageRecordIter
-mx->io->ImageRecordInt8Iter
-mx->io->ImageRecordUInt8Iter
-mx->io->MNISTIter
-mx->recordio->MXRecordIO
-mx->recordio->MXIndexedRecordIO
-mx->image->ImageIter
-```
-
-## Helper classes and functions
-
-Data structures and other iterators provided in the `AI::MXNet::IO` package.
-
-```perl
-AI::MXNet::DataDesc
-AI::MXNet::DataBatch
-AI::MXNet::DataIter
-AI::MXNet::ResizeIter
-AI::MXNet::MXDataIter
-```
-
-A list of image modification functions provided by `AI::MXNet::Image`.
-
-```perl
-mx->image->imdecode
-mx->image->scale_down
-mx->image->resize_short
-mx->image->fixed_crop
-mx->image->random_crop
-mx->image->center_crop
-mx->image->color_normalize
-mx->image->random_size_crop
-mx->image->ResizeAug
-mx->image->RandomCropAug
-mx->image->RandomSizedCropAug
-mx->image->CenterCropAug
-mx->image->RandomOrderAug
-mx->image->ColorJitterAug
-mx->image->LightingAug
-mx->image->ColorNormalizeAug
-mx->image->HorizontalFlipAug
-mx->image->CastAug
-mx->image->CreateAugmenter
-```
-
-Functions to read and write RecordIO files.
-
-```perl
-mx->recordio->pack
-mx->recordio->unpack
-mx->recordio->unpack_img
-```
-
-## Develop a new iterator
-
-Writing a new data iterator in Perl is straightforward. Most MXNet
-training/inference programs accept an object with ``provide_data``
-and ``provide_label`` properties.
-Please refer to AI-MXNet/examples for the examples of custom iterators.
diff --git a/docs/api/perl/kvstore.md b/docs/api/perl/kvstore.md
deleted file mode 100644
index 4556f68bd837..000000000000
--- a/docs/api/perl/kvstore.md
+++ /dev/null
@@ -1,126 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# KVStore API
-
-Topics:
-* [Basic Push and Pull](#basic-push-and-pull)
-* [List Key-Value Pairs](#list-key-value-pairs)
-
-## Basic Push and Pull
-
-Provides basic operation over multiple devices (GPUs) on a single device.
-
-### Initialization
-
-Let's consider a simple example. It initializes
-a (int, NDArray) pair into the store, and then pulls the value out.
-
-```perl
-pdl> $kv = mx->kv->create('local')
-pdl> $shape = [2,3]
-pdl> $kv->init(3, mx->nd->ones($shape)*2)
-pdl> $a = mx->nd->zeros($shape)
-pdl> $kv->pull(3, out => $a)
-pdl> print $a->aspdl
-[
- [2 2 2]
- [2 2 2]
-]
-```
-
-### Push, Aggregation, and Updater
-
-For any key that's been initialized, you can push a new value with the same shape to the key, as follows:
-
-```perl
-pdl> $kv->push(3, mx->nd->ones($shape)*8)
-pdl> $a = mx->nd->zeros($shape)
-pdl> $kv->pull(3, out => $a)
-pdl> print $a->aspdl
-[
- [8 8 8]
- [8 8 8]
-]
-```
-
-The data that you want to push can be stored on any device. Furthermore, you can push multiple
-values into the same key, where KVStore first sums all of these
-values, and then you pull the aggregated value, as follows:
-
-```perl
-pdl> $kv->push(3, [mx->nd->ones($shape, ctx=>mx->cpu(0)), mx->nd->ones($shape, ctx=>mx->cpu(1))])
-pdl> $kv->pull(3, out => $a)
-pdl> print $a->aspdl
-[
- [2 2 2]
- [2 2 2]
-]
-```
-
-For each push command, KVStore applies the pushed value to the value stored by an
-`updater`. The default updater is `ASSIGN`. You can replace the default to
-control how data is merged.
-
-```perl
-pdl> $updater = sub { my ($key, $input, $stored) = @_; print "update on key: $key\n"; $stored += $input * 3; }
-pdl> $kv->_set_updater($updater)
-pdl> $kv->push(3, [mx->nd->ones($shape, ctx=>mx->cpu(0)), mx->nd->ones($shape, ctx=>mx->cpu(1))])
-update on key: 3
-pdl> $kv->pull(3, out => $a)
-pdl> print $a->aspdl
-[
- [8 8 8]
- [8 8 8]
-]
-```
-
-### Pull
-
-You've already seen how to pull a single key-value pair. Similar to the way that you use the push command, you can
-pull the value into several devices with a single call.
-
-```perl
-pdl> $b = [mx->nd->zeros($shape, ctx=>mx->cpu(0)), mx->nd->zeros($shape, ctx=>mx->cpu(1))]
-pdl> $kv->pull(3, out => $b)
-pdl> print $b->[1]->aspdl
-[
- [8 8 8]
- [8 8 8]
-]
-```
-
-## List Key-Value Pairs
-
-All of the operations that we've discussed so far are performed on a single key. KVStore also provides
-the interface for generating a list of key-value pairs. For a single device, use the following:
-
-```perl
-pdl> $keys = [5,7,9]
-pdl> $kv->init($keys, [map { mx->nd->ones($shape) } 0..@$keys-1])
-pdl> $kv->push($keys, [map { mx->nd->ones($shape) } 0..@$keys-1])
-update on key: 5
-update on key: 7
-update on key: 9
-pdl> $b = [map { mx->nd->ones($shape) } 0..@$keys-1]
-pdl> $kv->pull($keys, out => $b)
-pdl> print $b->[1]->aspdl
-[
- [4 4 4]
- [4 4 4]
-]
-```
diff --git a/docs/api/perl/module.md b/docs/api/perl/module.md
deleted file mode 100644
index 7b7054646d62..000000000000
--- a/docs/api/perl/module.md
+++ /dev/null
@@ -1,62 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Module API
-
-## Overview
-
-The module API, defined in the `module` (or simply `mod`) package (`AI::MXNet::Module` under the hood), provides an
-intermediate and high-level interface for performing computation with a
-`AI::MXNet::Symbol` or just `mx->sym`. One can roughly think a module is a machine which can execute a
-program defined by a `Symbol`.
-
-The class `AI::MXNet::Module` is a commonly used module, which accepts a `AI::MXNet::Symbol` as
-the input:
-
-```perl
-pdl> $data = mx->symbol->Variable('data')
-pdl> $fc1  = mx->symbol->FullyConnected($data, name=>'fc1', num_hidden=>128)
-pdl> $act1 = mx->symbol->Activation($fc1, name=>'relu1', act_type=>"relu")
-pdl> $fc2  = mx->symbol->FullyConnected($act1, name=>'fc2', num_hidden=>10)
-pdl> $out  = mx->symbol->SoftmaxOutput($fc2, name => 'softmax')
-pdl> $mod  = mx->mod->Module($out)  # create a module by given a Symbol
-```
-
-Assume there is a valid MXNet data iterator `data`. We can initialize the
-module:
-
-```perl
-pdl> $mod->bind(data_shapes=>$data->provide_data,
-         label_shapes=>$data->provide_label)  # create memory by given input shapes
-pdl> $mod->init_params()  # initial parameters with the default random initializer
-```
-
-Now the module is able to compute. We can call high-level API to train and
-predict:
-
-```perl
-pdl> $mod->fit($data, num_epoch=>10, ...)  # train
-pdl> $mod->predict($new_data)  # predict on new data
-```
-
-or use intermediate APIs to perform step-by-step computations
-
-```perl
-pdl> $mod->forward($data_batch, is_train => 1)  # forward on the provided data batch
-pdl> $mod->backward()  # backward to calculate the gradients
-pdl> $mod->update()  # update parameters using the default optimizer
-```
diff --git a/docs/api/perl/ndarray.md b/docs/api/perl/ndarray.md
deleted file mode 100644
index 2ffef392261d..000000000000
--- a/docs/api/perl/ndarray.md
+++ /dev/null
@@ -1,59 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# NDArray API
-
-## Overview
-
-A `AI::MXNet::NDArray` is a multidimensional container of items of the same type and
-size. Various methods for data manipulation and computation are provided.
-
-```perl
-pdl> $x = mx->nd->array([[1, 2, 3], [4, 5, 6]])
-pdl> print $x->aspdl->shape
-[3, 2]
-pdl> $y = $x + mx->nd->ones($x->shape)*3
-pdl> print $y->aspdl
-[
- [4 5 6]
- [7 8 9]
-]
-pdl> $z = $y->as_in_context(mx->gpu(0))
-pdl> print $z,"\n"
-<AI::MXNet::NDArray 2x3 @gpu(0)>
-```
-
-A detailed tutorial is available at
-[http://mxnet.io/tutorials/basic/ndarray.html](http://mxnet.io/tutorials/basic/ndarray.html).
-
-Note: AI::MXNet::NDarray is similar to numpy.ndarray in some aspects. But the difference is not negligible. For example
-
-- AI::MXNet::NDArray->T does real data transpose to return new a copied array, instead
-     of returning a view of the input array.
-- AI::MXNet::NDArray->dot performs dot between the last axis of the first input array
-     and the first axis of the second input, while numpy.dot uses the second
-     last axis of the input array.
-
-In additional, NDArray supports GPU computation and various neural
-network layers.
-
-AI::MXNet::NDarray also provides almost same routines as AI::MXNet::symbol. Most
-routines between these two packages share the same C++ operator source
-codes. But AI::MXNet::NDarray differs from AI::MXNet::Symbol in several aspects:
-
-- AI::MXNet::NDArray adopts imperative programming, namely sentences are executed
-     step-by-step so that the results can be obtained immediately.
diff --git a/docs/api/perl/symbol.md b/docs/api/perl/symbol.md
deleted file mode 100644
index e3ad4816daa4..000000000000
--- a/docs/api/perl/symbol.md
+++ /dev/null
@@ -1,144 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# MXNet Perl Symbolic API
-
-Topics:
-
-* [How to Compose Symbols](#how-to-compose-symbols) introduces operator overloading of symbols.
-* [Symbol Attributes](#symbol-attributes) describes how to attach attributes to symbols.
-* [Serialization](#serialization) explains how to save and load symbols.
-* [Executing Symbols](#executing-symbols) explains how to evaluate the symbols with data.
-* [Multiple Outputs](#multiple-outputs) explains how to configure multiple outputs.
-
-## How to Compose Symbols
-
-The symbolic API provides a way to configure computation graphs.
-You can configure the graphs either at the level of neural network layer operations or as fine-grained operations.
-
-The following example configures a two-layer neural network.
-
-```perl
-pdl> use AI::MXNet qw(mx)
-pdl> $data = mx->symbol->Variable("data")
-pdl> $fc1  = mx->symbol->FullyConnected(data => $data, name => "fc1", num_hidden => 128)
-pdl> $act1 = mx->symbol->Activation(data => $fc1, name => "relu1", act_type => "relu")
-pdl> $fc2 =  mx->symbol->FullyConnected(data => $act1, name => "fc2", num_hidden => 64)
-pdl> $net =  mx->symbol->SoftmaxOutput(data => $fc2, name => "out")
-```
-
-The basic arithmetic operators (plus, minus, div, multiplication) are overloaded for
-*element-wise operations* of symbols.
-
-The following example creates a computation graph that adds two inputs together.
-
-```perl
-pdl> use AI::MXNet qw(mx)
-pdl> $a =  mx->symbol->Variable("a")
-pdl> $b =  mx->symbol->Variable("b")
-pdl> $c = $a + $b
-```
-
-## Symbol Attributes
-
-You can add an attribute to a symbol by providing an attribute hash when you create a symbol.
-
-```perl
-$data =  mx->symbol->Variable("data", attr => { mood => "angry" })
-$op   =  mx->symbol->Convolution(data => $data, kernel => [1, 1], num_filter => 1, attr => { mood => "so so" })
-```
-
-For proper communication with the C++ backend, both the key and values of the attribute dictionary should be strings. To retrieve the attributes, use `->attr($key)`:
-
-```
-    $data->attr("mood")
-```
-
-To attach attributes, you can use ```AI::MXNet::AttrScope```. ```AI::MXNet::AttrScopeAttrScope``` automatically adds 
-the specified attributes to all of the symbols created within that scope.
-The user can also inherit this object to change naming behavior. For example:
-
-```perl
-use AI::MXNet qw(mx);
-use Test::More tests => 3;
-my ($data, $gdata);
-{
-    local($mx::AttrScope) = mx->AttrScope(group=>4, data=>'great');
-    $data = mx->sym->Variable("data", attr => { dtype => "data", group => "1" });
-    $gdata = mx->sym->Variable("data2");
-}
-ok($gdata->attr("group") == 4);
-ok($data->attr("group") == 1);
-
-my $exceedScopeData = mx->sym->Variable("data3");
-ok((not defined $exceedScopeData->attr("group")), "No group attr in global attr scope");
-```
-
-## Serialization
-
-There are two ways to save and load the symbols. You can use the `mx->symbol->save` and `mxnet->symbol->load` functions to serialize the ```AI::MXNet::Symbol``` objects.
-The advantage of using `save` and `load` functions is that it is language agnostic and cloud friendly.
-The symbol is saved in JSON format. You can also get a JSON string directly using `$symbol->tojson`.
-
-The following example shows how to save a symbol to an S3 bucket, load it back, and compare two symbols using a JSON string.
-
-```perl
-pdl> use AI::MXNet qw(mx)
-pdl> $a = mx->sym->Variable("a")
-pdl> $b = mx->sym->Variable("b")
-pdl> $c = $a + $b
-pdl> $c->save("s3://my-bucket/symbol-c.json")
-pdl> $c2 = $c->load("s3://my-bucket/symbol-c.json")
-pdl> ok($c->tojson eq $c2->tojson)
-ok 1
-```
-
-## Executing Symbols
-
-After you have assembled a set of symbols into a computation graph, the MXNet engine can evaluate them.
-If you are training a neural network, this is typically
-handled by the high-level [AI::MXNet::Module package](module.md) and the [`fit()`] function.
-
-For neural networks used in "feed-forward", "prediction", or "inference" mode (all terms for the same
-thing: running a trained network), the input arguments are the
-input data, and the weights of the neural network that were learned during training.
-
-To manually execute a set of symbols, you need to create an [`AI::MXNet::Executor`] object,
-which is typically constructed by calling the [`simple_bind(<parameters>)`] method on a AI::MXNet::Symbol.
-
-## Multiple Outputs
-
-To group the symbols together, use the [AI::MXNet::Symbol->Group](#mxnet.symbol.Group) function.
-
-```perl
-pdl> use AI::MXNet qw(mx)
-pdl> use Data::Dumper
-pdl> $data  = mx->sym->Variable("data")
-pdl> $fc1   = mx->sym->FullyConnected($data, name => "fc1", num_hidden => 128)
-pdl> $act1  = mx->sym->Activation($fc1, name => "relu1", act_type => "relu")
-pdl> $fc2   = mx->sym->FullyConnected($act1, name => "fc2", num_hidden => 64)
-pdl> $net   = mx->sym->SoftmaxOutput($fc2, name => "softmax")
-pdl> $group = mx->sym->Group([$fc1, $net])
-pdl> print Dumper($group->list_outputs())
-$VAR1 = [
-    'fc1_output',
-    'softmax_output'
-];
-```
-
-After you get the ```Group```, you can bind on ```group``` instead.
-The resulting executor will have two outputs, one for fc1_output and one for softmax_output.
diff --git a/docs/api/python/autograd/autograd.md b/docs/api/python/autograd/autograd.md
deleted file mode 100644
index 82da4eac05b5..000000000000
--- a/docs/api/python/autograd/autograd.md
+++ /dev/null
@@ -1,176 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Autograd Package
-
-```eval_rst
-.. currentmodule:: mxnet.autograd
-```
-
-## Overview
-
-The `autograd` package enables automatic
-differentiation of NDArray operations.
-In machine learning applications,
-`autograd` is often used to calculate the gradients
-of loss functions with respect to parameters.
-
-
-## Record vs Pause
-
-`autograd` records computation history on the fly to calculate gradients later.
-This is only enabled inside a `with autograd.record():` block.
-A `with auto_grad.pause()` block can be used inside a `record()` block
-to temporarily disable recording.
-
-To compute gradient with respect to an `NDArray` `x`, first call `x.attach_grad()`
-to allocate space for the gradient. Then, start a `with autograd.record()` block,
-and do some computation. Finally, call `backward()` on the result:
-
-```python
-import mxnet as mx
-x = mx.nd.array([1,2,3,4])
-x.attach_grad()
-with mx.autograd.record():
-    y = x * x + 1
-y.backward()
-print(x.grad)
-```
-
-Which outputs:
-
-```
-[ 2.  4.  6.  8.]
-<NDArray 4 @cpu(0)>
-```
-
-Gradient recording is enabled during the scope of the `with mx.autograd.record():` statement, then
-disabled when we go out of that scope.
-
-It can be also set manually by executing `mx.autograd.set_recording(True)`, and turning it off after
-we no longer want to record operations with `mx.autograd.set_recording(False)`.
-
-
-## Train mode and Predict Mode
-
-Some operators (Dropout, BatchNorm, etc) behave differently in
-training and making predictions.
-This can be controlled with `train_mode` and `predict_mode` scope.
-
-By default, MXNet is in `predict_mode`.
-A `with autograd.record()` block by default turns on `train_mode`
-(equivalent to ``with autograd.record(train_mode=True)``).
-To compute a gradient in prediction mode (as when generating adversarial examples),
-call record with `train_mode=False` and then call `backward(train_mode=False)`
-
-Although training usually coincides with recording,
-this isn't always the case.
-To control *training* vs *predict_mode* without changing
-*recording* vs *not recording*,
-use a `with autograd.train_mode():`
-or `with autograd.predict_mode():` block.
-
-Detailed tutorials are available in Part 1 of
-[the MXNet gluon book](http://gluon.mxnet.io/).
-
-
-# Higher order gradient
-
-Some operators support higher order gradients. Some operators support differentiating multiple
-times, and others two, most just once.
-
-For calculating higher order gradients, we can use the `mx.autograd.grad` function while recording
-and then call backward, or call `mx.autograd.grad` two times. If we do the latter, is important that
-the first call uses `create_graph=True` and `retain_graph=True` and the second call uses
-`create_graph=False` and `retain_graph=True`. Otherwise we will not get the results that we want. If
-we would be to recreate the graph in the second call, we would end up with a graph of just the
-backward nodes, not the full initial graph that includes the forward nodes.
-
-The pattern to calculate higher order gradients is the following:
-
-```python
-from mxnet import ndarray as nd
-from mxnet import autograd as ag
-x = nd.array([1,2,3])
-x.attach_grad()
-def f(x):
-    # Any function which supports higher oder gradient
-    return nd.log(x)
-```
-
-If the operators used in `f` don't support higher order gradients you will get an error like
-`operator ... is non-differentiable because it didn't register FGradient attribute.`. This means
-that it doesn't support getting the gradient of the gradient. Which is, running backward on
-the backward graph.
-
-Using mxnet.autograd.grad multiple times:
-
-```python
-with ag.record():
-    y = f(x)
-    x_grad = ag.grad(heads=y, variables=x, create_graph=True, retain_graph=True)[0]
-    x_grad_grad = ag.grad(heads=x_grad, variables=x, create_graph=False, retain_graph=False)[0]
-```
-
-Running backward on the backward graph:
-
-```python
-with ag.record():
-    y = f(x)
-    x_grad = ag.grad(heads=y, variables=x, create_graph=True, retain_graph=True)[0]
-x_grad.backward()
-x_grad_grad = x.grad
-```
-
-Both methods are equivalent, except that in the second case, retain_graph on running backward is set
-to False by default. But both calls are running a backward pass as on the graph as usual to get the
-gradient of the first gradient `x_grad` with respect to `x` evaluated at the value of `x`.
-
-For more examples, check the [higher order gradient unit tests](https://github.com/apache/incubator-mxnet/blob/master/tests/python/unittest/test_higher_order_grad.py).
-
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-## Autograd
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    record
-    pause
-    train_mode
-    predict_mode
-    backward
-    set_training
-    is_training
-    set_recording
-    is_recording
-    mark_variables
-    Function
-```
-
-## API Reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-```eval_rst
-.. automodule:: mxnet.autograd
-    :members:
-```
-
-<script>auto_index("api-reference");</script>
diff --git a/docs/api/python/callback/callback.md b/docs/api/python/callback/callback.md
deleted file mode 100644
index 225ffcb9a2fd..000000000000
--- a/docs/api/python/callback/callback.md
+++ /dev/null
@@ -1,44 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Callback API
-
-```eval_rst
-.. currentmodule:: mxnet.ndarray
-```
-
-## Overview
-
-This document lists the routines of the callback package
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    mxnet.callback
-```
-
-## API Reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-```eval_rst
-.. automodule:: mxnet.callback
-    :members:
-```
-
-<script>auto_index("api-reference");</script>
\ No newline at end of file
diff --git a/docs/api/python/contrib/contrib.md b/docs/api/python/contrib/contrib.md
deleted file mode 100644
index 4fe80625766c..000000000000
--- a/docs/api/python/contrib/contrib.md
+++ /dev/null
@@ -1,29 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Contrib Package
-
-## Overview
-
-The `Contrib` APIs, defined in the `mxnet.contrib` package, provides
-many useful experimental APIs for new features.
-This is a place for the community to try out the new features,
-so that feature contributors can receive feedback.
-
-```eval_rst
-.. warning:: This package contains experimental APIs and may change in the near future.
-```
diff --git a/docs/api/python/contrib/onnx.md b/docs/api/python/contrib/onnx.md
deleted file mode 100644
index 7a1655c74afb..000000000000
--- a/docs/api/python/contrib/onnx.md
+++ /dev/null
@@ -1,78 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# ONNX-MXNet API
-
-## Overview
-
-[ONNX](https://onnx.ai/) is an open format to represent deep learning models. With ONNX as an intermediate representation, it is easier to move models between state-of-the-art tools and frameworks for training and inference.
-
-The `mxnet.contrib.onnx` package refers to the APIs and interfaces that implement ONNX model format support for Apache MXNet.
-
-With ONNX format support for MXNet, developers can build and train models with a [variety of deep learning frameworks](http://onnx.ai/supported-tools), and import these models into MXNet to run them for inference and training using MXNet’s highly optimized engine.
-
-```eval_rst
-.. warning:: This package contains experimental APIs and may change in the near future.
-```
-
-### Installation Instructions
-- To use this module developers need to **install ONNX**, which requires the protobuf compiler to be installed separately. Please follow the [instructions to install ONNX and its dependencies](https://github.com/onnx/onnx#installation). **MXNet currently supports ONNX v1.3**. Once installed, you can go through the tutorials on how to use this module.
-
-
-This document describes all the ONNX-MXNet APIs.
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    mxnet.contrib.onnx.onnx2mx.import_model
-    mxnet.contrib.onnx.onnx2mx.import_to_gluon
-    mxnet.contrib.onnx.mx2onnx.export_model
-```
-
-## ONNX Tutorials
-
-```eval_rst
-.. toctree::
-   :maxdepth: 1
-
-   /tutorials/onnx/super_resolution.md
-   /tutorials/onnx/export_mxnet_to_onnx.md
-   /tutorials/onnx/inference_on_onnx_model.md
-   /tutorials/onnx/fine_tuning_gluon.md
-```
-
-## ONNX Examples
-
-* Face Recognition with [ArcFace](https://github.com/onnx/models/tree/master/models/face_recognition/ArcFace)
-* Image Classification with [MobileNet](https://github.com/onnx/models/tree/master/models/image_classification/mobilenet), [ResNet](https://github.com/onnx/models/tree/master/models/image_classification/resnet), [SqueezeNet](https://github.com/onnx/models/tree/master/models/image_classification/squeezenet), [VGG](https://github.com/onnx/models/tree/master/models/image_classification/vgg)
-
-## API Reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-```eval_rst
-
-.. automodule:: mxnet.contrib.onnx.onnx2mx.import_model
-    :members: import_model, get_model_metadata
-.. automodule:: mxnet.contrib.onnx.onnx2mx.import_to_gluon
-    :members: import_to_gluon
-.. automodule:: mxnet.contrib.onnx.mx2onnx.export_model
-    :members: export_model
-```
-
-<script>auto_index("api-reference");</script>
diff --git a/docs/api/python/contrib/svrg_optimization.md b/docs/api/python/contrib/svrg_optimization.md
deleted file mode 100644
index 9c5c3a02c88f..000000000000
--- a/docs/api/python/contrib/svrg_optimization.md
+++ /dev/null
@@ -1,103 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# SVRG Optimization in Python Module API
-
-## Overview
-SVRG which stands for Stochastic Variance Reduced Gradients, is an optimization technique that was first introduced in 
-paper _Accelerating Stochastic Gradient Descent using Predictive Variance Reduction_ in 2013. It is complement to SGD 
-(Stochastic Gradient Descent), which is known for large scale optimization but suffers from slow convergence 
-asymptotically due to its inherent variance. SGD approximates the full gradients using a small batch of data or 
-a single data sample, which will introduce variance and thus requires to start with a small learning rate in order to 
-ensure convergence. SVRG remedies the problem by keeping track of a version of estimated weights that close to the 
-optimal parameter values and maintaining an average of full gradients over a full pass of data. The average of full 
-gradients is calculated with respect to the weights from the last m-th epochs in the training.  SVRG uses a different 
-update rule: gradients w.r.t current parameter values minus gradients w.r.t to parameters from the last m-th epochs 
-plus the average of full gradients over all data. 
-  
-Key Characteristics of SVRG:
-* Employs explicit variance reduction by using a different update rule compared to SGD.
-* Ability to use relatively large learning rate, which leads to faster convergence compared to SGD.
-* Guarantees for fast convergence for smooth and strongly convex functions.
-
-SVRG optimization is implemented as a SVRGModule in `mxnet.contrib.svrg_optimization`, which is an extension of the 
-existing `mxnet.module.Module` APIs and encapsulates SVRG optimization logic within several new functions. SVRGModule 
-API changes compared to Module API to end users are minimal. 
-
-In distributed training, each worker gets the same special weights from the last m-th epoch and calculates the full 
-gradients with respect to its own shard of data. The standard SVRG optimization requires building a global full 
-gradients, which is calculated by aggregating the full gradients from each worker and averaging over the number of 
-workers. The workaround is to keep an additional set of keys in the KVStore that maps to full gradients. 
-The `_SVRGOptimizer` is designed to wrap two optimizers, an `_AssignmentOptimizer` which is used for full gradients 
-accumulation in the KVStore and a regular optimizer that performs actual update rule to the parameters. 
-The `_SVRGOptimizer` and `_AssignmentOptimizer` are designed to be used in `SVRGModule` only.
-
-```eval_rst
-.. warning:: This package contains experimental APIs and may change in the near future.
-``` 
-
-This document lists the SVRGModule APIs in MXNet/Contrib package:
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    mxnet.contrib.svrg_optimization.svrg_module
-```
-
-### Intermediate Level API for SVRGModule
-
-The only extra step to use a SVRGModule compared to use a Module is to check if the current epoch should update the
-full gradients over all data. Code snippets below demonstrate the suggested usage of SVRGModule using intermediate 
-level APIs.
-
-```python
->>> mod = SVRGModule(symbol=model, update_freq=2, data_names=['data'], label_names=['lin_reg_label'])
->>> mod.bind(data_shapes=di.provide_data, label_shapes=di.provide_label)
->>> mod.init_params()
->>> mod.init_optimizer(optimizer='sgd', optimizer_params=(('learning_rate', 0.01), ), kvstore='local')
->>> for epoch in range(num_epochs):
-...     if epoch % mod.update_freq == 0:
-...         mod.update_full_grads(di)
-...     di.reset()
-...     for batch in di:
-...         mod.forward_backward(data_batch=batch)
-...         mod.update()
-```
-
-### High Level API for SVRGModule
-
-The high level API usage of SVRGModule remains exactly the same as Module API. Code snippets below gives an example of
-suggested usage of high level API.
-
-```python
->>> mod = SVRGModule(symbol=model, update_freq=2, data_names=['data'], label_names=['lin_reg_label'])
->>> mod.fit(di, num_epochs=100, optimizer='sgd', optimizer_params=(('learning_rate', 0.01), ))
-```
-
-## API reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-```eval_rst
-
-.. automodule:: mxnet.contrib.svrg_optimization.svrg_module
-.. autoclass:: mxnet.contrib.svrg_optimization.svrg_module.SVRGModule
-    :members: init_optimizer, bind, forward, backward, reshape, update, update_full_grads, fit, prepare
- 
-```
-<script>auto_index("api-reference");</script>
\ No newline at end of file
diff --git a/docs/api/python/contrib/text.md b/docs/api/python/contrib/text.md
deleted file mode 100644
index 577865f501cb..000000000000
--- a/docs/api/python/contrib/text.md
+++ /dev/null
@@ -1,418 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Text API
-
-## Overview
-
-The `mxnet.contrib.text` APIs refer to classes and functions related to text data processing, such
-as bulding indices and loading pre-trained embedding vectors for text tokens and storing them in the
-`mxnet.ndarray.NDArray` format.
-
-```eval_rst
-.. warning:: This package contains experimental APIs and may change in the near future.
-```
-
-This document lists the text APIs in mxnet:
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    mxnet.contrib.text.embedding
-    mxnet.contrib.text.vocab
-    mxnet.contrib.text.utils
-```
-
-All the code demonstrated in this document assumes that the following modules or packages are
-imported.
-
-```python
->>> from mxnet import gluon
->>> from mxnet import nd
->>> from mxnet.contrib import text
->>> import collections
-
-```
-
-### Looking up pre-trained word embeddings for indexed words
-
-As a common use case, let us look up pre-trained word embedding vectors for indexed words in just a
-few lines of code. 
-
-To begin with, Suppose that we have a simple text data set in the string format. We can count
-word frequency in the data set.
-
-```python
->>> text_data = " hello world \n hello nice world \n hi world \n"
->>> counter = text.utils.count_tokens_from_str(text_data)
-
-```
-
-The obtained `counter` has key-value pairs whose keys are words and values are word frequencies.
-Suppose that we want to build indices for all the keys in `counter` and load the defined fastText
-word embedding for all such indexed words. First, we need a Vocabulary object with `counter` as its
-argument
-
-```python
->>> my_vocab = text.vocab.Vocabulary(counter)
-
-```
-
-We can create a fastText word embedding object by specifying the embedding name `fasttext` and
-the pre-trained file `wiki.simple.vec`. We also specify that the indexed tokens for loading the
-fastText word embedding come from the defined Vocabulary object `my_vocab`.
-
-```python
->>> my_embedding = text.embedding.create('fasttext', pretrained_file_name='wiki.simple.vec',
-...     vocabulary=my_vocab)
-
-```
-
-Now we are ready to look up the fastText word embedding vectors for indexed words, such as 'hello'
-and 'world'.
-
-```python
->>> my_embedding.get_vecs_by_tokens(['hello', 'world'])
-
-[[  3.95669997e-01   2.14540005e-01  -3.53889987e-02  -2.42990002e-01
-    ...
-   -7.54180014e-01  -3.14429998e-01   2.40180008e-02  -7.61009976e-02]
- [  1.04440004e-01  -1.08580001e-01   2.72119999e-01   1.32990003e-01
-    ...
-   -3.73499990e-01   5.67310005e-02   5.60180008e-01   2.90190000e-02]]
-<NDArray 2x300 @cpu(0)>
-
-```
-
-### Using pre-trained word embeddings in `gluon`
-
-To demonstrate how to use pre-trained word embeddings in the `gluon` package, let us first obtain
-indices of the words 'hello' and 'world'.
-
-```python
->>> my_embedding.to_indices(['hello', 'world'])
-[2, 1]
-
-```
-
-We can obtain the vector representation for the words 'hello' and 'world' by specifying their
-indices (2 and 1) and the `my_embedding.idx_to_vec` in `mxnet.gluon.nn.Embedding`.
- 
-```python
->>> layer = gluon.nn.Embedding(len(my_embedding), my_embedding.vec_len)
->>> layer.initialize()
->>> layer.weight.set_data(my_embedding.idx_to_vec)
->>> layer(nd.array([2, 1]))
-
-[[  3.95669997e-01   2.14540005e-01  -3.53889987e-02  -2.42990002e-01
-    ...
-   -7.54180014e-01  -3.14429998e-01   2.40180008e-02  -7.61009976e-02]
- [  1.04440004e-01  -1.08580001e-01   2.72119999e-01   1.32990003e-01
-    ...
-   -3.73499990e-01   5.67310005e-02   5.60180008e-01   2.90190000e-02]]
-<NDArray 2x300 @cpu(0)>
-
-```
-
-## Vocabulary
-
-The vocabulary builds indices for text tokens. Such indexed tokens can be used by token embedding
-instances. The input counter whose keys are candidate indices may be obtained via
-[`count_tokens_from_str`](#mxnet.contrib.text.utils.count_tokens_from_str).
-
-
-```eval_rst
-.. currentmodule:: mxnet.contrib.text.vocab
-.. autosummary::
-    :nosignatures:
-
-    Vocabulary
-```
-
-Suppose that we have a simple text data set in the string format. We can count word frequency in the
-data set.
-
-```python
->>> text_data = " hello world \n hello nice world \n hi world \n"
->>> counter = text.utils.count_tokens_from_str(text_data)
-
-```
-
-The obtained `counter` has key-value pairs whose keys are words and values are word frequencies.
-Suppose that we want to build indices for the 2 most frequent keys in `counter` with the unknown
-token representation '&lt;unk&gt;' and a reserved token '&lt;pad&gt;'.
-
-```python
->>> my_vocab = text.vocab.Vocabulary(counter, most_freq_count=2, unknown_token='&lt;unk&gt;', 
-...     reserved_tokens=['&lt;pad&gt;'])
-
-```
-
-We can access properties such as `token_to_idx` (mapping tokens to indices), `idx_to_token` (mapping
-indices to tokens), `vec_len` (length of each embedding vector), and `unknown_token` (representation
-of any unknown token) and `reserved_tokens`.
-
-
-```python
->>> my_vocab.token_to_idx
-{'&lt;unk&gt;': 0, '&lt;pad&gt;': 1, 'world': 2, 'hello': 3}
->>> my_vocab.idx_to_token
-['&lt;unk&gt;', '&lt;pad&gt;', 'world', 'hello']
->>> my_vocab.unknown_token
-'&lt;unk&gt;'
->>> my_vocab.reserved_tokens
-['&lt;pad&gt;']
->>> len(my_vocab)
-4
-```
-
-Besides the specified unknown token '&lt;unk&gt;' and reserved_token '&lt;pad&gt;' are indexed, the 2 most
-frequent words 'world' and 'hello' are also indexed.
-
-
-
-
-## Text token embedding
-
-To load token embeddings from an externally hosted pre-trained token embedding file, such as those
-of GloVe and FastText, use
-[`embedding.create(embedding_name, pretrained_file_name)`](#mxnet.contrib.text.embedding.create).
-
-To get all the available `embedding_name` and `pretrained_file_name`, use
-[`embedding.get_pretrained_file_names()`](#mxnet.contrib.text.embedding.get_pretrained_file_names).
-
-```python
->>> text.embedding.get_pretrained_file_names()
-{'glove': ['glove.42B.300d.txt', 'glove.6B.50d.txt', 'glove.6B.100d.txt', ...],
-'fasttext': ['wiki.en.vec', 'wiki.simple.vec', 'wiki.zh.vec', ...]}
-
-```
-
-Alternatively, to load embedding vectors from a custom pre-trained text token
-embedding file, use [`CustomEmbedding`](#mxnet.contrib.text.embedding.CustomEmbedding).
-
-Moreover, to load composite embedding vectors, such as to concatenate embedding vectors,
-use [`CompositeEmbedding`](#mxnet.contrib.text.embedding.CompositeEmbedding).
-
-The indexed tokens in a text token embedding may come from a vocabulary or from the loaded embedding
-vectors. In the former case, only the indexed tokens in a vocabulary are associated with the loaded
-embedding vectors, such as loaded from a pre-trained token embedding file. In the later case, all
-the tokens from the loaded embedding vectors, such as loaded from a pre-trained token embedding
-file, are taken as the indexed tokens of the embedding.
-
-
-```eval_rst
-.. currentmodule:: mxnet.contrib.text.embedding
-.. autosummary::
-    :nosignatures:
-
-    register
-    create
-    get_pretrained_file_names
-    GloVe
-    FastText
-    CustomEmbedding
-    CompositeEmbedding
-```
-
-
-### Indexed tokens are from a vocabulary
-
-One can specify that only the indexed tokens in a vocabulary are associated with the loaded
-embedding vectors, such as loaded from a pre-trained token embedding file.
-
-To begin with, suppose that we have a simple text data set in the string format. We can count word
-frequency in the data set.
-
-```python
->>> text_data = " hello world \n hello nice world \n hi world \n"
->>> counter = text.utils.count_tokens_from_str(text_data)
-
-```
-
-The obtained `counter` has key-value pairs whose keys are words and values are word frequencies.
-Suppose that we want to build indices for the most frequent 2 keys in `counter` and load the defined
-fastText word embedding with pre-trained file `wiki.simple.vec` for all these 2 words. 
-
-```python
->>> my_vocab = text.vocab.Vocabulary(counter, most_freq_count=2)
->>> my_embedding = text.embedding.create('fasttext', pretrained_file_name='wiki.simple.vec',
-...     vocabulary=my_vocab)
-
-```
-
-Now we are ready to look up the fastText word embedding vectors for indexed words.
-
-```python
->>> my_embedding.get_vecs_by_tokens(['hello', 'world'])
-
-[[  3.95669997e-01   2.14540005e-01  -3.53889987e-02  -2.42990002e-01
-    ...
-   -7.54180014e-01  -3.14429998e-01   2.40180008e-02  -7.61009976e-02]
- [  1.04440004e-01  -1.08580001e-01   2.72119999e-01   1.32990003e-01
-    ...
-   -3.73499990e-01   5.67310005e-02   5.60180008e-01   2.90190000e-02]]
-<NDArray 2x300 @cpu(0)>
-
-```
-
-We can also access properties such as `token_to_idx` (mapping tokens to indices), `idx_to_token`
-(mapping indices to tokens), and `vec_len` (length of each embedding vector).
-
-```python
->>> my_embedding.token_to_idx
-{'&lt;unk&gt;': 0, 'world': 1, 'hello': 2}
->>> my_embedding.idx_to_token
-['&lt;unk&gt;', 'world', 'hello']
->>> len(my_embedding)
-3
->>> my_embedding.vec_len
-300
-
-```
-
-If a token is unknown to `glossary`, its embedding vector is initialized according to the default
-specification in `fasttext_simple` (all elements are 0).
-
-```python
-
->>> my_embedding.get_vecs_by_tokens('nice')
-
-[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.
-  ...
-  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]
-<NDArray 300 @cpu(0)>
-
-```
-
-
-### Indexed tokens are from the loaded embedding vectors
-
-One can also use all the tokens from the loaded embedding vectors, such as loaded from a pre-trained
-token embedding file, as the indexed tokens of the embedding.
-
-To begin with, we can create a fastText word embedding object by specifying the embedding name
-'fasttext' and the pre-trained file 'wiki.simple.vec'. The argument `init_unknown_vec` specifies
-default vector representation for any unknown token. To index all the tokens from this pre-trained
-word embedding file, we do not need to specify any vocabulary.
-
-```python
->>> my_embedding = text.embedding.create('fasttext', pretrained_file_name='wiki.simple.vec',
-...     init_unknown_vec=nd.zeros)
-
-```
-
-We can access properties such as `token_to_idx` (mapping tokens to indices), `idx_to_token` (mapping
-indices to tokens), `vec_len` (length of each embedding vector), and `unknown_token` (representation
-of any unknown token, default value is '&lt;unk&gt;').
-
-```python
->>> my_embedding.token_to_idx['nice']
-2586
->>> my_embedding.idx_to_token[2586]
-'nice'
->>> my_embedding.vec_len
-300
->>> my_embedding.unknown_token
-'&lt;unk&gt;'
-
-```
-
-For every unknown token, if its representation '&lt;unk&gt;' is encountered in the pre-trained token
-embedding file, index 0 of property `idx_to_vec` maps to the pre-trained token embedding vector
-loaded from the file; otherwise, index 0 of property `idx_to_vec` maps to the default token
-embedding vector specified via `init_unknown_vec` (set to nd.zeros here). Since the pre-trained file
-does not have a vector for the token '&lt;unk&gt;', index 0 has to map to an additional token '&lt;unk&gt;' and
-the number of tokens in the embedding is 111,052.
-
-
-```python
->>> len(my_embedding)
-111052
->>> my_embedding.idx_to_vec[0]
-
-[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.
-  ...
-  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]
-<NDArray 300 @cpu(0)>
->>> my_embedding.get_vecs_by_tokens('nice')
-
-[ 0.49397001  0.39996001  0.24000999 -0.15121    -0.087512    0.37114
-  ...
-  0.089521    0.29175001 -0.40917999 -0.089206   -0.1816     -0.36616999]
-<NDArray 300 @cpu(0)>
->>> my_embedding.get_vecs_by_tokens(['unknownT0kEN', 'unknownT0kEN'])
-
-[[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.
-   ...
-   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]
- [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.
-   ...
-   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]]
-<NDArray 2x50 @cpu(0)>
-
-```
-
-
-### Implement a new text token embedding
-
-For ``optimizer``, create a subclass of `mxnet.contrib.text.embedding._TokenEmbedding`.
-Also add ``@mxnet.contrib.text.embedding._TokenEmbedding.register`` before this class. See
-[`embedding.py`](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/contrib/text/embedding.py)
-for examples.
-
-
-## Text utilities
-
-The following functions provide utilities for text data processing.
-
-```eval_rst
-.. currentmodule:: mxnet.contrib.text.utils
-.. autosummary::
-    :nosignatures:
-
-    count_tokens_from_str
-```
-
-
-## API Reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-```eval_rst
-
-.. automodule:: mxnet.contrib.text.embedding
-    :members: register, create, get_pretrained_file_names
-.. autoclass:: mxnet.contrib.text.embedding.GloVe
-    :members: get_vecs_by_tokens, update_token_vectors, to_indices, to_tokens
-.. autoclass:: mxnet.contrib.text.embedding.FastText
-    :members: get_vecs_by_tokens, update_token_vectors, to_indices, to_tokens
-.. autoclass:: mxnet.contrib.text.embedding.CustomEmbedding
-    :members: get_vecs_by_tokens, update_token_vectors, to_indices, to_tokens
-.. autoclass:: mxnet.contrib.text.embedding.CompositeEmbedding
-    :members: get_vecs_by_tokens, update_token_vectors, to_indices, to_tokens
-
-.. automodule:: mxnet.contrib.text.vocab
-.. autoclass:: mxnet.contrib.text.vocab.Vocabulary
-    :members: to_indices, to_tokens
-
-.. automodule:: mxnet.contrib.text.utils
-    :members: count_tokens_from_str
-
-```
-<script>auto_index("api-reference");</script>
\ No newline at end of file
diff --git a/docs/api/python/executor/executor.md b/docs/api/python/executor/executor.md
deleted file mode 100644
index 2cf5062e1ed3..000000000000
--- a/docs/api/python/executor/executor.md
+++ /dev/null
@@ -1,59 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Executor and Executor Manager
-
-The executor and executor manager are internal classes for managing symbolic
-graph execution. This document is only intended for reference for advanced users.
-
-.. note:: Direct interactions with executor and executor manager are dangerous and not recommended.
-
-## Executor
-
-```eval_rst
-.. currentmodule:: mxnet.executor
-
-.. autosummary::
-    :nosignatures:
-
-    Executor
-```
-
-## Executor Manager
-
-```eval_rst
-.. currentmodule:: mxnet.executor_manager
-
-.. autosummary::
-    :nosignatures:
-
-    DataParallelExecutorGroup
-    DataParallelExecutorManager
-```
-
-## API Reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-```eval_rst
-.. automodule:: mxnet.executor
-    :members:
-.. automodule:: mxnet.executor_manager
-    :members:
-```
-
-<script>auto_index("api-reference");</script>
diff --git a/docs/api/python/gluon/contrib.md b/docs/api/python/gluon/contrib.md
deleted file mode 100644
index 22cdebb53b85..000000000000
--- a/docs/api/python/gluon/contrib.md
+++ /dev/null
@@ -1,179 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Gluon Contrib API
-
-## Overview
-
-This document lists the contrib APIs in Gluon:
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    mxnet.gluon.contrib
-```
-
-The `Gluon Contrib` API, defined in the `gluon.contrib` package, provides
-many useful experimental APIs for new features.
-This is a place for the community to try out the new features,
-so that feature contributors can receive feedback.
-
-```eval_rst
-.. warning:: This package contains experimental APIs and may change in the near future.
-```
-
-In the rest of this document, we list routines provided by the `gluon.contrib` package.
-
-## Contrib
-
-### Neural network
-
-```eval_rst
-.. currentmodule:: mxnet.gluon.contrib.nn
-
-.. autosummary::
-    :nosignatures:
-
-    Concurrent
-    HybridConcurrent
-    Identity
-    SparseEmbedding
-    SyncBatchNorm
-    PixelShuffle1D
-    PixelShuffle2D
-    PixelShuffle3D
-```
-
-### Convolutional neural network
-
-```eval_rst
-.. currentmodule:: mxnet.gluon.contrib.cnn
-
-.. autosummary::
-    :nosignatures:
-    
-    DeformableConvolution
-```
-
-### Recurrent neural network
-
-```eval_rst
-.. currentmodule:: mxnet.gluon.contrib.rnn
-
-.. autosummary::
-    :nosignatures:
-
-    VariationalDropoutCell
-    Conv1DRNNCell
-    Conv2DRNNCell
-    Conv3DRNNCell
-    Conv1DLSTMCell
-    Conv2DLSTMCell
-    Conv3DLSTMCell
-    Conv1DGRUCell
-    Conv2DGRUCell
-    Conv3DGRUCell
-    LSTMPCell
-```
-
-### Data
-
-```eval_rst
-.. currentmodule:: mxnet.gluon.contrib.data
-
-.. autosummary::
-    :nosignatures:
-
-    IntervalSampler
-```
-
-#### Text dataset
-
-```eval_rst
-.. currentmodule:: mxnet.gluon.contrib.data.text
-
-.. autosummary::
-    :nosignatures:
-
-    WikiText2
-    WikiText103
-```
-
-### Estimator
-
-```eval_rst
-.. currentmodule:: mxnet.gluon.contrib.estimator
-
-.. autosummary::
-    :nosignatures:
-    
-    Estimator
-```
-
-#### EventHandler
-
-```eval_rst
-.. currentmodule:: mxnet.gluon.contrib.estimator
-
-.. autosummary::
-    :nosignatures:
-
-    StoppingHandler
-    MetricHandler
-    ValidationHandler
-    LoggingHandler
-    CheckpointHandler
-    EarlyStoppingHandler
-```
-
-## API Reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-```eval_rst
-
-.. automodule:: mxnet.gluon.contrib
-    :members:
-    :imported-members:
-
-.. automodule:: mxnet.gluon.contrib.nn
-    :members:
-    :imported-members:
-    
-.. automodule:: mxnet.gluon.contrib.cnn
-    :members:
-    :imported-members:
-
-.. automodule:: mxnet.gluon.contrib.rnn
-    :members:
-    :imported-members:
-
-.. automodule:: mxnet.gluon.contrib.data
-    :members:
-    :imported-members:
-
-.. automodule:: mxnet.gluon.contrib.data.text
-    :members:
-    :imported-members:
-
-.. automodule:: mxnet.gluon.contrib.estimator
-    :members:
-    :imported-members:
-```
-
-<script>auto_index("api-reference");</script>
diff --git a/docs/api/python/gluon/data.md b/docs/api/python/gluon/data.md
deleted file mode 100644
index b695e1bc3209..000000000000
--- a/docs/api/python/gluon/data.md
+++ /dev/null
@@ -1,155 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Gluon Data API
-
-## Overview
-
-This document lists the data APIs in Gluon:
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    mxnet.gluon.data
-    mxnet.gluon.data.vision
-```
-
-The `Gluon Data` API, defined in the `gluon.data` package, provides useful dataset loading
-and processing tools, as well as common public datasets.
-
-In the rest of this document, we list routines provided by the `gluon.data` package.
-
-## Data
-
-```eval_rst
-.. currentmodule:: mxnet.gluon.data
-```
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Dataset
-    ArrayDataset
-    RecordFileDataset
-```
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Sampler
-    SequentialSampler
-    RandomSampler
-    BatchSampler
-```
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    DataLoader
-```
-
-### Vision
-
-#### Vision Datasets
-
-```eval_rst
-.. currentmodule:: mxnet.gluon.data.vision.datasets
-```
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    MNIST
-    FashionMNIST
-    CIFAR10
-    CIFAR100
-    ImageRecordDataset
-    ImageFolderDataset
-```
-
-#### Vision Transforms
-
-```eval_rst
-.. currentmodule:: mxnet.gluon.data.vision.transforms
-```
-
-Transforms can be used to augment input data during training. You
-can compose multiple transforms sequentially (taking note of which functions should be applied before and after `ToTensor`).
-
-```python
-from mxnet.gluon.data.vision import MNIST, transforms
-from mxnet import gluon
-transform = transforms.Compose([
-    transforms.Resize(300),
-    transforms.RandomResizedCrop(224),
-    transforms.RandomBrightness(0.1),
-    transforms.ToTensor(),
-    transforms.Normalize(0, 1)])
-data = MNIST(train=True).transform_first(transform)
-data_loader = gluon.data.DataLoader(data, batch_size=32, num_workers=1)
-for data, label in data_loader:
-    # do something with data and label
-```
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Compose
-    Cast
-    ToTensor
-    Normalize
-    RandomResizedCrop
-    CenterCrop
-    Resize
-    RandomFlipLeftRight
-    RandomFlipTopBottom
-    RandomBrightness
-    RandomContrast
-    RandomSaturation
-    RandomHue
-    RandomColorJitter
-    RandomLighting
-```
-
-## API Reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-```eval_rst
-
-.. automodule:: mxnet.gluon.data
-    :members:
-    :imported-members:
-
-.. automodule:: mxnet.gluon.data.vision
-    :members:
-
-.. automodule:: mxnet.gluon.data.vision.datasets
-    :members:
-    
-.. automodule:: mxnet.gluon.data.vision.transforms
-    :members:
-
-```
-
-<script>auto_index("api-reference");</script>
diff --git a/docs/api/python/gluon/gluon.md b/docs/api/python/gluon/gluon.md
deleted file mode 100644
index 19e462e8a773..000000000000
--- a/docs/api/python/gluon/gluon.md
+++ /dev/null
@@ -1,189 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Gluon Package
-
-
-```eval_rst
-.. currentmodule:: mxnet.gluon
-```
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-## Overview
-
-The Gluon package is a high-level interface for MXNet designed to be easy to use, while keeping most of the flexibility of a low level API. Gluon supports both imperative and symbolic programming, making it easy to train complex models imperatively in Python and then deploy with a symbolic graph in C++ and Scala.
-
-Based on the [Gluon API specification](https://github.com/gluon-api/gluon-api), the Gluon API in Apache MXNet provides a clear, concise, and simple API for deep learning. It makes it easy to prototype, build, and train deep learning models without sacrificing training speed.
-
-**Advantages**
-
-1. Simple, Easy-to-Understand Code: Gluon offers a full set of plug-and-play neural network building blocks, including predefined layers, optimizers, and initializers.
-2. Flexible, Imperative Structure: Gluon does not require the neural network model to be rigidly defined, but rather brings the training algorithm and model closer together to provide flexibility in the development process.
-3. Dynamic Graphs: Gluon enables developers to define neural network models that are dynamic, meaning they can be built on the fly, with any structure, and using any of Python’s native control flow.
-4. High Performance: Gluon provides all of the above benefits without impacting the training speed that the underlying engine provides. 
-
-**Examples**
-
-*Simple, Easy-to-Understand Code*
-
-Use plug-and-play neural network building blocks, including predefined layers, optimizers, and initializers:
-
-```
-net = gluon.nn.Sequential()
-# When instantiated, Sequential stores a chain of neural network layers. 
-# Once presented with data, Sequential executes each layer in turn, using 
-# the output of one layer as the input for the next
-with net.name_scope():
-    net.add(gluon.nn.Dense(256, activation="relu")) # 1st layer (256 nodes)
-    net.add(gluon.nn.Dense(256, activation="relu")) # 2nd hidden layer
-    net.add(gluon.nn.Dense(num_outputs))
-```
-
-*Flexible, Imperative Structure*
-
-Prototype, build, and train neural networks in fully imperative manner using the MXNet autograd package and the Gluon trainer method:
-
-```
-epochs = 10
-
-for e in range(epochs):
-    for i, (data, label) in enumerate(train_data):
-        with autograd.record():
-            output = net(data) # the forward iteration
-            loss = softmax_cross_entropy(output, label)
-            loss.backward()
-        trainer.step(data.shape[0])
-```
-
-*Dynamic Graphs*
-
-Build neural networks on the fly for use cases where neural networks must change in size and shape during model training:
-
-```
-def forward(self, F, inputs, tree):
-    children_outputs = [self.forward(F, inputs, child)
-                        for child in tree.children]
-    #Recursively builds the neural network based on each input sentence’s
-    #syntactic structure during the model definition and training process
-    ...
-```
-
-*High Performance*
-
-Easily cache the neural network to achieve high performance by defining your neural network with *HybridSequential* and calling the *hybridize* method:
-
-```
-net = nn.HybridSequential()
-with net.name_scope():
-    net.add(nn.Dense(256, activation="relu"))
-    net.add(nn.Dense(128, activation="relu"))
-    net.add(nn.Dense(2))
-    
-net.hybridize()
-```
-
-
-## Contents
-
-```eval_rst
-.. toctree::
-   :maxdepth: 1
-
-   nn.md
-   rnn.md
-   loss.md
-   data.md
-   model_zoo.md
-   contrib.md
-```
-
-
-## Parameter
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Parameter
-    Constant
-    ParameterDict
-```
-
-
-## Containers
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Block
-    HybridBlock
-    SymbolBlock
-    nn.Sequential
-    nn.HybridSequential
-```
-
-
-## Trainer
-
-```eval_rst
-.. currentmodule:: mxnet.gluon
-
-.. autosummary::
-    :nosignatures:
-
-    Trainer
-```
-
-## Utilities
-
-```eval_rst
-.. currentmodule:: mxnet.gluon.utils
-```
-
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    split_data
-    split_and_load
-    clip_global_norm
-```
-
-
-## API Reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-```eval_rst
-.. automodule:: mxnet.gluon
-    :members:
-    :imported-members:
-    :special-members:
-
-.. autoclass:: mxnet.gluon.nn.Sequential
-    :members:
-.. autoclass:: mxnet.gluon.nn.HybridSequential
-    :members:
-
-.. automodule:: mxnet.gluon.utils
-    :members:
-```
-
-<script>auto_index("api-reference");</script>
diff --git a/docs/api/python/gluon/loss.md b/docs/api/python/gluon/loss.md
deleted file mode 100644
index 8e8f92c5ee49..000000000000
--- a/docs/api/python/gluon/loss.md
+++ /dev/null
@@ -1,59 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Gluon Loss API
-
-## Overview
-
-This document lists the loss API in Gluon:
-
-```eval_rst
-.. currentmodule:: mxnet.gluon.loss
-```
-
-This package includes several commonly used loss functions in neural networks.
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    L2Loss
-    L1Loss
-    SigmoidBinaryCrossEntropyLoss
-    SoftmaxCrossEntropyLoss
-    KLDivLoss
-    HuberLoss
-    HingeLoss
-    SquaredHingeLoss
-    LogisticLoss
-    TripletLoss
-    CTCLoss
-    CosineEmbeddingLoss
-    PoissonNLLLoss
-```
-
-
-## API Reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-```eval_rst
-.. automodule:: mxnet.gluon.loss
-    :members:
-```
-
-<script>auto_index("api-reference");</script>
diff --git a/docs/api/python/gluon/model_zoo.md b/docs/api/python/gluon/model_zoo.md
deleted file mode 100644
index 4672a3b7ad81..000000000000
--- a/docs/api/python/gluon/model_zoo.md
+++ /dev/null
@@ -1,257 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Gluon Model Zoo
-
-```eval_rst
-    .. currentmodule:: mxnet.gluon.model_zoo
-```
-
-## Overview
-
-This document lists the model APIs in Gluon:
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    mxnet.gluon.model_zoo
-    mxnet.gluon.model_zoo.vision
-```
-
-The `Gluon Model Zoo` API, defined in the `gluon.model_zoo` package, provides pre-defined
-and pre-trained models to help bootstrap machine learning applications.
-
-In the rest of this document, we list routines provided by the `gluon.model_zoo` package.
-
-### Vision
-
-```eval_rst
-.. currentmodule:: mxnet.gluon.model_zoo.vision
-.. automodule:: mxnet.gluon.model_zoo.vision
-    :noindex:
-```
-
-The following table summarizes the available models.
-
-| Alias         | Network                                                                               | # Parameters | Top-1 Accuracy | Top-5 Accuracy | Origin                                                                                                                                               |
-|---------------|---------------------------------------------------------------------------------------|--------------|----------------|----------------|------------------------------------------------------------------------------------------------------------------------------------------------------|
-| alexnet       | [AlexNet](https://arxiv.org/abs/1404.5997)                                            | 61,100,840   | 0.5492         | 0.7803         | Converted from pytorch vision                                                                                                                        |
-| densenet121   | [DenseNet-121](https://arxiv.org/pdf/1608.06993.pdf)                                  | 8,062,504    | 0.7497         | 0.9225         | Converted from pytorch vision                                                                                                                        |
-| densenet161   | [DenseNet-161](https://arxiv.org/pdf/1608.06993.pdf)                                  | 28,900,936   | 0.7770         | 0.9380         | Converted from pytorch vision                                                                                                                        |
-| densenet169   | [DenseNet-169](https://arxiv.org/pdf/1608.06993.pdf)                                  | 14,307,880   | 0.7617         | 0.9317         | Converted from pytorch vision                                                                                                                        |
-| densenet201   | [DenseNet-201](https://arxiv.org/pdf/1608.06993.pdf)                                  | 20,242,984   | 0.7732         | 0.9362         | Converted from pytorch vision                                                                                                                        |
-| inceptionv3   | [Inception V3 299x299](http://arxiv.org/abs/1512.00567)                               | 23,869,000   | 0.7755         | 0.9364         | Converted from pytorch vision                                                                                                                        |
-| mobilenet0.25 | [MobileNet 0.25](https://arxiv.org/abs/1704.04861)                                    | 475,544      | 0.5185         | 0.7608         | Trained with [script](https://github.com/apache/incubator-mxnet/blob/master/example/gluon/image_classification.py)              |
-| mobilenet0.5  | [MobileNet 0.5](https://arxiv.org/abs/1704.04861)                                     | 1,342,536    | 0.6307         | 0.8475         | Trained with [script](https://github.com/apache/incubator-mxnet/blob/master/example/gluon/image_classification.py)              |
-| mobilenet0.75 | [MobileNet 0.75](https://arxiv.org/abs/1704.04861)                                    | 2,601,976    | 0.6738         | 0.8782         | Trained with [script](https://github.com/apache/incubator-mxnet/blob/master/example/gluon/image_classification.py)              |
-| mobilenet1.0  | [MobileNet 1.0](https://arxiv.org/abs/1704.04861)                                     | 4,253,864    | 0.7105         | 0.9006         | Trained with [script](https://github.com/apache/incubator-mxnet/blob/master/example/gluon/image_classification.py)              |
-| mobilenetv2_1.0  | [MobileNetV2 1.0](https://arxiv.org/abs/1801.04381)                                | 3,539,136    | 0.7192         | 0.9056         | Trained with [script](https://gluon-cv.mxnet.io/model_zoo/classification.html)                                      |
-| mobilenetv2_0.75 | [MobileNetV2 0.75](https://arxiv.org/abs/1801.04381)                               | 2,653,864    | 0.6961         | 0.8895         | Trained with [script](https://gluon-cv.mxnet.io/model_zoo/classification.html)                                      |
-| mobilenetv2_0.5  | [MobileNetV2 0.5](https://arxiv.org/abs/1801.04381)                                | 1,983,104    | 0.6449         | 0.8547         | Trained with [script](https://gluon-cv.mxnet.io/model_zoo/classification.html)                                      |
-| mobilenetv2_0.25 | [MobileNetV2 0.25](https://arxiv.org/abs/1801.04381)                               | 1,526,856    | 0.5074         | 0.7456         | Trained with [script](https://gluon-cv.mxnet.io/model_zoo/classification.html)                                      |
-| resnet18_v1   | [ResNet-18 V1](http://arxiv.org/abs/1512.03385)                                       | 11,699,112   | 0.7093         | 0.8992         | Trained with [script](https://gluon-cv.mxnet.io/model_zoo/classification.html)                                      |
-| resnet34_v1   | [ResNet-34 V1](http://arxiv.org/abs/1512.03385)                                       | 21,814,696   | 0.7437         | 0.9187         | Trained with [script](https://gluon-cv.mxnet.io/model_zoo/classification.html)                                      |
-| resnet50_v1   | [ResNet-50 V1](http://arxiv.org/abs/1512.03385)                                       | 25,629,032   | 0.7647         | 0.9313         | Trained with [script](https://gluon-cv.mxnet.io/model_zoo/classification.html)                                      |
-| resnet101_v1  | [ResNet-101 V1](http://arxiv.org/abs/1512.03385)                                      | 44,695,144   | 0.7834         | 0.9401         | Trained with [script](https://gluon-cv.mxnet.io/model_zoo/classification.html)                                      |
-| resnet152_v1  | [ResNet-152 V1](http://arxiv.org/abs/1512.03385)                                      | 60,404,072   | 0.7900         | 0.9438         | Trained with [script](https://gluon-cv.mxnet.io/model_zoo/classification.html)                                      |
-| resnet18_v2   | [ResNet-18 V2](https://arxiv.org/abs/1603.05027)                                      | 11,695,796   | 0.7100         | 0.8992         | Trained with [script](https://gluon-cv.mxnet.io/model_zoo/classification.html)                                      |
-| resnet34_v2   | [ResNet-34 V2](https://arxiv.org/abs/1603.05027)                                      | 21,811,380   | 0.7440         | 0.9208         | Trained with [script](https://gluon-cv.mxnet.io/model_zoo/classification.html)                                      |
-| resnet50_v2   | [ResNet-50 V2](https://arxiv.org/abs/1603.05027)                                      | 25,595,060   | 0.7711         | 0.9343         | Trained with [script](https://gluon-cv.mxnet.io/model_zoo/classification.html)                                      |
-| resnet101_v2  | [ResNet-101 V2](https://arxiv.org/abs/1603.05027)                                     | 44,639,412   | 0.7853         | 0.9417         | Trained with [script](https://gluon-cv.mxnet.io/model_zoo/classification.html)                                      |
-| resnet152_v2  | [ResNet-152 V2](https://arxiv.org/abs/1603.05027)                                     | 60,329,140   | 0.7921         | 0.9431         | Trained with [script](https://gluon-cv.mxnet.io/model_zoo/classification.html)                                      |
-| squeezenet1.0 | [SqueezeNet 1.0](https://arxiv.org/abs/1602.07360)                                    | 1,248,424    | 0.5611         | 0.7909         | Converted from pytorch vision                                                                                                                        |
-| squeezenet1.1 | [SqueezeNet 1.1](https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1) | 1,235,496    | 0.5496         | 0.7817         | Converted from pytorch vision                                                                                                                        |
-| vgg11         | [VGG-11](https://arxiv.org/abs/1409.1556)                                             | 132,863,336  | 0.6662         | 0.8734         | Converted from pytorch vision                                                                                                                        |
-| vgg13         | [VGG-13](https://arxiv.org/abs/1409.1556)                                             | 133,047,848  | 0.6774         | 0.8811         | Converted from pytorch vision                                                                                                                        |
-| vgg16         | [VGG-16](https://arxiv.org/abs/1409.1556)                                             | 138,357,544  | 0.7323         | 0.9132         | Trained with [script](https://github.com/apache/incubator-mxnet/blob/master/example/gluon/image_classification.py)                                                                                                                        |
-| vgg19         | [VGG-19](https://arxiv.org/abs/1409.1556)                                             | 143,667,240  | 0.7411         | 0.9135         | Trained with [script](https://github.com/apache/incubator-mxnet/blob/master/example/gluon/image_classification.py)                                                                                                                        |
-| vgg11_bn      | [VGG-11 with batch normalization](https://arxiv.org/abs/1409.1556)                    | 132,874,344  | 0.6859         | 0.8872         | Converted from pytorch vision                                                                                                                        |
-| vgg13_bn      | [VGG-13 with batch normalization](https://arxiv.org/abs/1409.1556)                    | 133,059,624  | 0.6884         | 0.8882         | Converted from pytorch vision                                                                                                                        |
-| vgg16_bn      | [VGG-16 with batch normalization](https://arxiv.org/abs/1409.1556)                    | 138,374,440  | 0.7310         | 0.9176         | Trained with [script](https://github.com/apache/incubator-mxnet/blob/master/example/gluon/image_classification.py)                                                                                                                        |
-| vgg19_bn      | [VGG-19 with batch normalization](https://arxiv.org/abs/1409.1556)                    | 143,689,256  | 0.7433         | 0.9185         | Trained with [script](https://github.com/apache/incubator-mxnet/blob/master/example/gluon/image_classification.py)                                                                                                                        |
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    get_model
-```
-
-#### ResNet
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    resnet18_v1
-    resnet34_v1
-    resnet50_v1
-    resnet101_v1
-    resnet152_v1
-    resnet18_v2
-    resnet34_v2
-    resnet50_v2
-    resnet101_v2
-    resnet152_v2
-```
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    ResNetV1
-    ResNetV2
-    BasicBlockV1
-    BasicBlockV2
-    BottleneckV1
-    BottleneckV2
-    get_resnet
-```
-
-#### VGG
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    vgg11
-    vgg13
-    vgg16
-    vgg19
-    vgg11_bn
-    vgg13_bn
-    vgg16_bn
-    vgg19_bn
-```
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    VGG
-    get_vgg
-```
-
-#### Alexnet
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    alexnet
-```
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    AlexNet
-```
-
-#### DenseNet
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    densenet121
-    densenet161
-    densenet169
-    densenet201
-```
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    DenseNet
-```
-
-#### SqueezeNet
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    squeezenet1_0
-    squeezenet1_1
-```
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    SqueezeNet
-```
-
-#### Inception
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    inception_v3
-```
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Inception3
-```
-
-#### MobileNet
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    mobilenet1_0
-    mobilenet0_75
-    mobilenet0_5
-    mobilenet0_25
-    mobilenet_v2_1_0
-    mobilenet_v2_0_75
-    mobilenet_v2_0_5
-    mobilenet_v2_0_25
-```
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    MobileNet
-    MobileNetV2
-```
-
-## API Reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-```eval_rst
-
-.. automodule:: mxnet.gluon.model_zoo
-
-.. automodule:: mxnet.gluon.model_zoo.vision
-    :members:
-    :imported-members:
-    :noindex:
-
-```
-
-<script>auto_index("api-reference");</script>
diff --git a/docs/api/python/gluon/nn.md b/docs/api/python/gluon/nn.md
deleted file mode 100644
index 2ec66f10416b..000000000000
--- a/docs/api/python/gluon/nn.md
+++ /dev/null
@@ -1,114 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Gluon Neural Network Layers
-
-## Overview
-
-This document lists the neural network blocks in Gluon:
-
-```eval_rst
-.. currentmodule:: mxnet.gluon.nn
-```
-
-
-## Basic Layers
-
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Dense
-    Dropout
-    BatchNorm
-    InstanceNorm
-    LayerNorm
-    Embedding
-    Flatten
-    Lambda
-    HybridLambda
-```
-
-
-## Convolutional Layers
-
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Conv1D
-    Conv2D
-    Conv3D
-    Conv1DTranspose
-    Conv2DTranspose
-    Conv3DTranspose
-```
-
-
-
-## Pooling Layers
-
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    MaxPool1D
-    MaxPool2D
-    MaxPool3D
-    AvgPool1D
-    AvgPool2D
-    AvgPool3D
-    GlobalMaxPool1D
-    GlobalMaxPool2D
-    GlobalMaxPool3D
-    GlobalAvgPool1D
-    GlobalAvgPool2D
-    GlobalAvgPool3D
-    ReflectionPad2D
-```
-
-## Activation Layers
-
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Activation
-    LeakyReLU
-    PReLU
-    ELU
-    SELU
-    Swish
-```
-
-
-## API Reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-```eval_rst
-.. automodule:: mxnet.gluon.nn
-    :members:
-    :imported-members:
-    :exclude-members: Block, HybridBlock, SymbolBlock, Sequential, HybridSequential
-```
-
-<script>auto_index("api-reference");</script>
diff --git a/docs/api/python/gluon/rnn.md b/docs/api/python/gluon/rnn.md
deleted file mode 100644
index f639479d5db3..000000000000
--- a/docs/api/python/gluon/rnn.md
+++ /dev/null
@@ -1,99 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Gluon Recurrent Neural Network API
-
-## Overview
-
-This document lists the recurrent neural network API in Gluon:
-
-```eval_rst
-.. currentmodule:: mxnet.gluon.rnn
-```
-
-### Recurrent Layers
-
-Recurrent layers can be used in `Sequential` with other regular neural network layers.
-For example, to construct a sequence labeling model where a prediction is made for each
-time-step:
-
-```python
-model = mx.gluon.nn.Sequential()
-with model.name_scope():
-    model.add(mx.gluon.nn.Embedding(30, 10))
-    model.add(mx.gluon.rnn.LSTM(20))
-    model.add(mx.gluon.nn.Dense(5, flatten=False))
-model.initialize()
-model(mx.nd.ones((2,3)))
-```
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    RNN
-    LSTM
-    GRU
-```
-
-### Recurrent Cells
-
-Recurrent cells allows fine-grained control when defining recurrent models. User
-can explicit step and unroll to construct complex networks. It provides more
-flexibility but is slower than recurrent layers. Recurrent cells can be stacked
-with `SequentialRNNCell`:
-
-```python
-model = mx.gluon.rnn.SequentialRNNCell()
-with model.name_scope():
-    model.add(mx.gluon.rnn.LSTMCell(20))
-    model.add(mx.gluon.rnn.LSTMCell(20))
-states = model.begin_state(batch_size=32)
-inputs = mx.nd.random.uniform(shape=(5, 32, 10))
-outputs = []
-for i in range(5):
-    output, states = model(inputs[i], states)
-    outputs.append(output)
-```
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    RNNCell
-    LSTMCell
-    GRUCell
-    RecurrentCell
-    SequentialRNNCell
-    BidirectionalCell
-    DropoutCell
-    ZoneoutCell
-    ResidualCell
-```
-
-
-## API Reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-```eval_rst
-.. automodule:: mxnet.gluon.rnn
-    :members:
-    :imported-members:
-```
-
-<script>auto_index("api-reference");</script>
diff --git a/docs/api/python/image/image.md b/docs/api/python/image/image.md
deleted file mode 100644
index 91e4a6b3ec1a..000000000000
--- a/docs/api/python/image/image.md
+++ /dev/null
@@ -1,232 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Image API
-
-## Overview
-This document summarizes supporting functions and iterators to read and process
-images provided in
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    mxnet.image
-```
-
-## Image processing functions
-```eval_rst
-.. currentmodule:: mxnet
-.. autosummary::
-    :nosignatures:
-
-    image.imread
-    image.imdecode
-    image.imresize
-    image.scale_down
-    image.copyMakeBorder
-    image.resize_short
-    image.fixed_crop
-    image.random_crop
-    image.center_crop
-    image.color_normalize
-    image.random_size_crop
-```    
-
-## Image iterators
-Iterators support loading image from binary `Record IO` and raw image files.
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    image.ImageIter
-```
-```python
->>> data_iter = mx.image.ImageIter(batch_size=4, data_shape=(3, 224, 224), label_width=1,
-                                   path_imglist='data/custom.lst')
->>> data_iter.reset()
->>> for data in data_iter:
-...     d = data.data[0]
-...     print(d.shape)
->>> # we can apply lots of augmentations as well
->>> data_iter = mx.image.ImageIter(4, (3, 224, 224), path_imglist='data/custom.lst',
-                                   rand_crop=True, rand_resize=True, rand_mirror=True, mean=True,
-                                   brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1,
-                                   pca_noise=0.1, rand_gray=0.05)
->>> data = data_iter.next()
->>> # specify augmenters manually is also supported
->>> data_iter = mx.image.ImageIter(32, (3, 224, 224), path_rec='data/caltech.rec',
-                                   path_imgidx='data/caltech.idx', shuffle=True,
-                                   aug_list=[mx.image.HorizontalFlipAug(0.5),
-                                   mx.image.ColorJitterAug(0.1, 0.1, 0.1)])
-```
-
-We use helper function to initialize augmenters
-```eval_rst
-.. currentmodule:: mxnet
-.. autosummary::
-    :nosignatures:
-
-    image.CreateAugmenter
-```
-
-A list of supporting augmenters
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    image.Augmenter
-    image.SequentialAug
-    image.RandomOrderAug
-    image.ResizeAug
-    image.ForceResizeAug
-    image.RandomCropAug
-    image.RandomSizedCropAug
-    image.CenterCropAug
-    image.BrightnessJitterAug
-    image.ContrastJitterAug
-    image.SaturationJitterAug
-    image.HueJitterAug
-    image.ColorJitterAug
-    image.LightingAug
-    image.ColorNormalizeAug
-    image.RandomGrayAug
-    image.HorizontalFlipAug
-    image.CastAug
-```
-
-#### Image Iterator for Object Detection
-Similar to `ImageIter`, `ImageDetIter` is designed for `Object Detection` tasks.
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    image.ImageDetIter
-```
-
-```python
->>> data_iter = mx.image.ImageDetIter(batch_size=4, data_shape=(3, 224, 224),
-                                      path_imglist='data/train.lst')
->>> data_iter.reset()
->>> for data in data_iter:
-...     d = data.data[0]
-...     l = data.label[0]
-...     print(d.shape)
-...     print(l.shape)
-```
-
-Unlike object classification with fixed label_width, object count may vary from
-image to image. Thus we have special format for object detection labels.
-Usually the `lst` file generated by `tools/im2rec.py` is a list of
-```
-index_0  label_0  image_path_0
-index_1  label_1  image_path_1
-```
-Where `label_N` is a number a of fixed-width vector.
-The format of label used in object detection is a variable length vector
-```
-A  B  [extra header]  [(object0), (object1), ... (objectN)]
-```
-Where A is the width of header (2 + length of extra header), B is the width of each object.
-Extra header is optional and used for inserting helper information such as (width, height).
-Each object is usually 5 or 6 numbers describing the object properties, for example:
-[id, xmin, ymin, xmax, ymax, difficulty]
-Putting all together, we have a `lst` file for object detection:
-```
-0  4  5  640  480  1  0.1  0.2  0.8  0.9  2  0.5  0.3  0.6  0.8  data/xxx.jpg
-1  4  5  480  640  3  0.05  0.16  0.75  0.9  data/yyy.jpg
-2  4  5  500  600  2  0.6  0.1  0.7  0.5  0  0.1  0.3  0.2  0.4  3  0.25  0.25  0.3  0.3 data/zzz.jpg
-...
-```
-
-A helper function to initialize Augmenters for `Object detection` task
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    image.CreateDetAugmenter
-  ```
-
-Since `Detection` task is sensitive to object localization, any modification
-to image that introduced localization shift will require correction to label,
-and a list of augmenters specific for `Object detection` is provided
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    image.DetBorrowAug
-    image.DetRandomSelectAug
-    image.DetHorizontalFlipAug
-    image.DetRandomCropAug
-    image.DetRandomPadAug
-```
-
-## API Reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-```eval_rst
-.. automodule:: mxnet.image
-.. autoclass:: mxnet.image.ImageIter
-    :members:
-
-.. automethod:: mxnet.image.imread
-.. automethod:: mxnet.image.imdecode
-.. automethod:: mxnet.image.imresize
-.. automethod:: mxnet.image.scale_down
-.. automethod:: mxnet.image.copyMakeBorder
-.. automethod:: mxnet.image.resize_short
-.. automethod:: mxnet.image.fixed_crop
-.. automethod:: mxnet.image.random_crop
-.. automethod:: mxnet.image.center_crop
-.. automethod:: mxnet.image.color_normalize
-.. automethod:: mxnet.image.random_size_crop
-
-.. autoclass:: mxnet.image.Augmenter
-    :members:
-.. autoclass:: mxnet.image.ResizeAug
-.. autoclass:: mxnet.image.ForceResizeAug
-.. autoclass:: mxnet.image.RandomCropAug
-.. autoclass:: mxnet.image.RandomSizedCropAug
-.. autoclass:: mxnet.image.CenterCropAug
-.. autoclass:: mxnet.image.SequentialAug
-.. autoclass:: mxnet.image.RandomOrderAug
-.. autoclass:: mxnet.image.BrightnessJitterAug
-.. autoclass:: mxnet.image.ContrastJitterAug
-.. autoclass:: mxnet.image.SaturationJitterAug
-.. autoclass:: mxnet.image.HueJitterAug
-.. autoclass:: mxnet.image.ColorJitterAug
-.. autoclass:: mxnet.image.LightingAug
-.. autoclass:: mxnet.image.ColorNormalizeAug
-.. autoclass:: mxnet.image.RandomGrayAug
-.. autoclass:: mxnet.image.HorizontalFlipAug
-.. autoclass:: mxnet.image.CastAug
-
-.. automethod:: mxnet.image.CreateAugmenter
-
-.. autoclass:: mxnet.image.ImageDetIter
-    :members:
-.. autoclass:: mxnet.image.DetAugmenter
-    :members:
-.. autoclass:: mxnet.image.DetBorrowAug
-.. autoclass:: mxnet.image.DetRandomSelectAug
-.. autoclass:: mxnet.image.DetHorizontalFlipAug
-.. autoclass:: mxnet.image.DetRandomCropAug
-.. autoclass:: mxnet.image.DetRandomPadAug
-
-.. automethod:: mxnet.image.CreateDetAugmenter
-```
-<script>auto_index("api-reference");</script>
diff --git a/docs/api/python/index.md b/docs/api/python/index.md
deleted file mode 100644
index 2e23b162ee77..000000000000
--- a/docs/api/python/index.md
+++ /dev/null
@@ -1,219 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# MXNet - Python API
-
-MXNet provides a comprehensive and flexible Python API to serve a broad community of developers with different levels of experience and wide ranging requirements. In this section, we provide an in-depth discussion of the functionality provided by various MXNet Python packages.
-
-MXNet's Python API has two primary high-level packages*: the Gluon API and Module API. We recommend that new users start with the Gluon API as it's more flexible and easier to debug. Underlying these high-level packages are the core packages of NDArray and Symbol.
-
-NDArray works with arrays in an imperative fashion, i.e. you define how arrays will be transformed to get to an end result. Symbol works with arrays in a declarative fashion, i.e. you define the end result that is required (via a symbolic graph) and the MXNet engine will use various optimizations to determine the steps required to obtain this. With NDArray you have a great deal of flexibility when composing operations (as you can use Python control flow), and you can easily step through your code and inspect the values of arrays, which helps with debugging. Unfortunately, this comes at a performance cost when compared to Symbol, which can perform optimizations on the symbolic graph.
-
-Module API is backed by Symbol, so, although it's very performant, it's also a little more restrictive. With the Gluon API, you can get the best of both worlds. You can develop and test your model imperatively using NDArray, a then switch to Symbol for faster model training and inference (if Symbol equivalents exist for your operations).
-
-Code examples are placed throughout the API documentation and these can be run after importing MXNet as follows:
-
-```python
->>> import mxnet as mx
-```
-
-```eval_rst
-
-.. note:: A convenient way to execute code examples is using the ``%doctest_mode`` mode of
-   Jupyter notebook, which allows for pasting multi-line examples containing
-   ``>>>`` while preserving indentation. Run ``%doctest_mode?`` in Jupyter notebook
-   for more details.
-
-```
-
-\* Some old references to Model API may exist, but this API has been deprecated.
-
-## Autograd API
-
-```eval_rst
-.. toctree::
-   :maxdepth: 1
-
-   autograd/autograd.md
-```
-
-## Callback API
-
-```eval_rst
-.. toctree::
-   :maxdepth: 1
-
-   callback/callback.md
-```
-
-## Contrib Package
-
-```eval_rst
-.. toctree::
-   :maxdepth: 1
-
-   contrib/contrib.md
-   contrib/text.md
-   contrib/onnx.md
-   contrib/svrg_optimization.md
-```
-
-## Gluon API
-
-```eval_rst
-.. toctree::
-   :maxdepth: 1
-
-   gluon/gluon.md
-   gluon/nn.md
-   gluon/rnn.md
-   gluon/loss.md
-   gluon/data.md
-   gluon/model_zoo.md
-   gluon/contrib.md
-```
-
-## Image API
-
-```eval_rst
-.. toctree::
-   :maxdepth: 1
-
-   image/image.md
-```
-
-## IO API
-
-```eval_rst
-.. toctree::
-   :maxdepth: 1
-
-   io/io.md
-```
-
-## KV Store API
-
-```eval_rst
-.. toctree::
-   :maxdepth: 1
-
-   kvstore/kvstore.md
-```
-
-## Metric API
-
-```eval_rst
-.. toctree::
-   :maxdepth: 1
-
-   metric/metric.md
-```
-
-## Module API
-
-```eval_rst
-.. toctree::
-   :maxdepth: 1
-
-   module/module.md
-   executor/executor.md
-```
-
-## NDArray API
-
-```eval_rst
-.. toctree::
-   :maxdepth: 1
-
-   ndarray/ndarray.md
-   ndarray/random.md
-   ndarray/linalg.md
-   ndarray/sparse.md
-   ndarray/contrib.md
-```
-
-## Optimization API
-
-```eval_rst
-.. toctree::
-   :maxdepth: 1
-
-   optimization/optimization.md
-   optimization/contrib.md
-```
-
-## Profiler API
-
-```eval_rst
-.. toctree::
-   :maxdepth: 1
-
-   profiler/profiler.md
-```
-
-## Run-Time Compilation API
-
-```eval_rst
-.. toctree::
-   :maxdepth: 1
-
-   rtc/rtc.md
-```
-
-## Run-Time Feature detection / Library Info
-
-```eval_rst
-.. toctree::
-   :maxdepth: 1
-
-   libinfo/libinfo.md
-```
-
-
-
-## Symbol API
-
-```eval_rst
-.. toctree::
-   :maxdepth: 1
-
-   symbol/symbol.md
-   symbol/random.md
-   symbol/linalg.md
-   symbol/sparse.md
-   symbol/contrib.md
-   symbol/rnn.md
-```
-
-## Symbol in Pictures API
-
-```eval_rst
-.. toctree::
-   :maxdepth: 1
-
-   symbol_in_pictures/symbol_in_pictures.md
-```
-
-## Tools
-
-```eval_rst
-.. toctree::
-    :maxdepth: 1
-
-    tools/test_utils.md
-    tools/visualization.md
-```
diff --git a/docs/api/python/io/io.md b/docs/api/python/io/io.md
deleted file mode 100644
index 13a612196eee..000000000000
--- a/docs/api/python/io/io.md
+++ /dev/null
@@ -1,203 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Data Loading API
-
-## Overview
-
-This document summarizes supported data formats and iterator APIs to read the
-data including:
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    mxnet.io
-    mxnet.recordio
-    mxnet.image
-```
-
-First, let's see how to write an iterator for a new data format.
-The following iterator can be used to train a symbol whose input data variable has
-name `data` and input label variable has name `softmax_label`.
-The iterator also provides information about the batch, including the
-shapes and name.
-
-```python
->>> nd_iter = mx.io.NDArrayIter(data={'data':mx.nd.ones((100,10))},
-...                             label={'softmax_label':mx.nd.ones((100,))},
-...                             batch_size=25)
->>> print(nd_iter.provide_data)
-[DataDesc[data,(25, 10L),<type 'numpy.float32'>,NCHW]]
->>> print(nd_iter.provide_label)
-[DataDesc[softmax_label,(25,),<type 'numpy.float32'>,NCHW]]
-```
-
-Let's see a complete example of how to use data iterator in model training.
-```python
->>> data = mx.sym.Variable('data')
->>> label = mx.sym.Variable('softmax_label')
->>> fullc = mx.sym.FullyConnected(data=data, num_hidden=1)
->>> loss = mx.sym.SoftmaxOutput(data=fullc, label=label)
->>> mod = mx.mod.Module(loss, data_names=['data'], label_names=['softmax_label'])
->>> mod.bind(data_shapes=nd_iter.provide_data, label_shapes=nd_iter.provide_label)
->>> mod.fit(nd_iter, num_epoch=2)
-```
-
-A detailed tutorial is available at
-[Iterators - Loading data](http://mxnet.io/tutorials/basic/data.html).
-
-## Data iterators
-
-```eval_rst
-    .. currentmodule:: mxnet
-```
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    io.NDArrayIter
-    io.CSVIter
-    io.LibSVMIter
-    io.ImageRecordIter
-    io.ImageRecordInt8Iter
-    io.ImageRecordUInt8Iter
-    io.MNISTIter
-    recordio.MXRecordIO
-    recordio.MXIndexedRecordIO
-    image.ImageIter
-    image.ImageDetIter
-```
-
-## Helper classes and functions
-
-### Data structures and other iterators
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    io.DataDesc
-    io.DataBatch
-    io.DataIter
-    io.ResizeIter
-    io.PrefetchingIter
-    io.MXDataIter
-```
-
-### Functions to read and write RecordIO files
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    recordio.pack
-    recordio.unpack
-    recordio.unpack_img
-    recordio.pack_img
-```
-
-## How to develop a new iterator
-
-Writing a new data iterator in Python is straightforward. Most MXNet
-training/inference programs accept an iterable object with ``provide_data``
-and ``provide_label`` properties.
-This [tutorial](http://mxnet.io/tutorials/basic/data.html) explains how to
-write an iterator from scratch.
-
-The following example demonstrates how to combine
-multiple data iterators into a single one. It can be used for multiple
-modality training such as image captioning, in which images are read by
-``ImageRecordIter`` while documents are read by ``CSVIter``
-
-```python
-class MultiIter:
-    def __init__(self, iter_list):
-        self.iters = iter_list
-    def next(self):
-        batches = [i.next() for i in self.iters]
-        return DataBatch(data=[*b.data for b in batches],
-                         label=[*b.label for b in batches])
-    def reset(self):
-        for i in self.iters:
-            i.reset()
-    @property
-    def provide_data(self):
-        return [*i.provide_data for i in self.iters]
-    @property
-    def provide_label(self):
-        return [*i.provide_label for i in self.iters]
-
-iter = MultiIter([mx.io.ImageRecordIter('image.rec'), mx.io.CSVIter('txt.csv')])
-```
-
-Parsing and performing another pre-processing such as augmentation may be expensive.
-If performance is critical, we can implement a data iterator in C++. Refer to
-[src/io](https://github.com/dmlc/mxnet/tree/master/src/io) for examples.
-
-### How to change the batch layout
-
-By default, the backend engine treats the first dimension of each data and label variable in data
-iterators as the batch size (i.e. `NCHW` or `NT` layout). In order to override the axis for batch size,
-the `provide_data` (and `provide_label` if there is label) properties should include the layouts. This
-is especially useful in RNN since `TNC` layouts are often more efficient. For example:
-
-```python
-@property
-def provide_data(self):
-    return [DataDesc(name='seq_var', shape=(seq_length, batch_size), layout='TN')]
-```
-The backend engine will recognize the index of `N` in the `layout` as the axis for batch size.
-
-## API Reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-### mxnet.io - Data Iterators
-
-```eval_rst
-.. automodule:: mxnet.io
-    :noindex:
-    :members: NDArrayIter, CSVIter, LibSVMIter, ImageRecordIter, ImageRecordUInt8Iter, MNISTIter
-```
-
-### mxnet.io - Helper Classes & Functions
-
-```eval_rst
-.. automodule:: mxnet.io
-    :noindex:
-    :members: DataBatch, DataDesc, DataIter, MXDataIter, PrefetchingIter, ResizeIter
-```
-
-### mxnet.recordio
-
-```eval_rst
-.. currentmodule:: mxnet.recordio
-
-.. automodule:: mxnet.recordio
-    :members:
-
-```
-
-```eval_rst
-.. _name: mxnet.symbol.Symbol.name
-.. _shape: mxnet.ndarray.NDArray.shape
-
-```
-
-<script>auto_index("api-reference");</script>
diff --git a/docs/api/python/kvstore/kvstore.md b/docs/api/python/kvstore/kvstore.md
deleted file mode 100644
index 7858ccd02699..000000000000
--- a/docs/api/python/kvstore/kvstore.md
+++ /dev/null
@@ -1,146 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# KVStore API
-
-.. note:: Direct interactions with ``KVStore`` are dangerous and not recommended.
-
-## Basic Push and Pull
-
-Provides basic operation over multiple devices (GPUs) on a single device.
-
-### Initialization
-
-Let's consider a simple example. It initializes
-a (`int`, `NDArray`) pair into the store, and then pulls the value out.
-
-```python
->>> kv = mx.kv.create('local') # create a local kv store.
->>> shape = (2,3)
->>> kv.init(3, mx.nd.ones(shape)*2)
->>> a = mx.nd.zeros(shape)
->>> kv.pull(3, out = a)
->>> print a.asnumpy()
-[[ 2.  2.  2.]
- [ 2.  2.  2.]]
-```
-
-### Push, Aggregation, and Updater
-
-For any key that's been initialized, you can push a new value with the same shape to the key, as follows:
-
-```python
->>> kv.push(3, mx.nd.ones(shape)*8)
->>> kv.pull(3, out = a) # pull out the value
->>> print a.asnumpy()
-[[ 8.  8.  8.]
- [ 8.  8.  8.]]
-```
-
-The data that you want to push can be stored on any device. Furthermore, you can push multiple
-values into the same key, where KVStore first sums all of these
-values, and then pushes the aggregated value, as follows:
-
-```python
->>> gpus = [mx.gpu(i) for i in range(4)]
->>> b = [mx.nd.ones(shape, gpu) for gpu in gpus]
->>> kv.push(3, b)
->>> kv.pull(3, out = a)
->>> print a.asnumpy()
-[[ 4.  4.  4.]
- [ 4.  4.  4.]]
-```
-
-For each push command, KVStore applies the pushed value to the value stored by an
-`updater`. The default updater is `ASSIGN`. You can replace the default to
-control how data is merged.
-
-```python
->>> def update(key, input, stored):
->>>     print "update on key: %d" % key
->>>     stored += input * 2
->>> kv._set_updater(update)
->>> kv.pull(3, out=a)
->>> print a.asnumpy()
-[[ 4.  4.  4.]
- [ 4.  4.  4.]]
->>> kv.push(3, mx.nd.ones(shape))
-update on key: 3
->>> kv.pull(3, out=a)
->>> print a.asnumpy()
-[[ 6.  6.  6.]
- [ 6.  6.  6.]]
-```
-
-### Pull
-
-You've already seen how to pull a single key-value pair. Similar to the way that you use the push command, you can
-pull the value into several devices with a single call.
-
-```python
->>> b = [mx.nd.ones(shape, gpu) for gpu in gpus]
->>> kv.pull(3, out = b)
->>> print b[1].asnumpy()
-[[ 6.  6.  6.]
- [ 6.  6.  6.]]
-```
-
-## List Key-Value Pairs
-
-All of the operations that we've discussed so far are performed on a single key. KVStore also provides
-the interface for generating a list of key-value pairs. For a single device, use the following:
-
-```python
->>> keys = [5, 7, 9]
->>> kv.init(keys, [mx.nd.ones(shape)]*len(keys))
->>> kv.push(keys, [mx.nd.ones(shape)]*len(keys))
-update on key: 5
-update on key: 7
-update on key: 9
->>> b = [mx.nd.zeros(shape)]*len(keys)
->>> kv.pull(keys, out = b)
->>> print b[1].asnumpy()
-[[ 3.  3.  3.]
- [ 3.  3.  3.]]
-```
-
-For multiple devices:
-
-```python
->>> b = [[mx.nd.ones(shape, gpu) for gpu in gpus]] * len(keys)
->>> kv.push(keys, b)
-update on key: 5
-update on key: 7
-update on key: 9
->>> kv.pull(keys, out = b)
->>> print b[1][1].asnumpy()
-[[ 11.  11.  11.]
- [ 11.  11.  11.]]
-```
-
-
-
-## API Reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-```eval_rst
-.. automodule:: mxnet.kvstore
-    :members:
-```
-
-<script>auto_index("api-reference");</script>
diff --git a/docs/api/python/libinfo/libinfo.md b/docs/api/python/libinfo/libinfo.md
deleted file mode 100644
index 3b8d4998aa5b..000000000000
--- a/docs/api/python/libinfo/libinfo.md
+++ /dev/null
@@ -1,110 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Run-Time Feature detection / Library info
-
-```eval_rst
-.. currentmodule:: mxnet.runtime
-```
-
-## Overview
-
-The libinfo functionality allows to check for compile-time features supported by the library.
-
-### Example usage
-
-```
-In []: import mxnet as mx
-   ...: import mxnet.runtime
-   ...: fs = mx.runtime.Features()
-
-In []: fs
-Out[]: [✖ CUDA, ✖ CUDNN, ✖ NCCL, ✖ CUDA_RTC, ✖ TENSORRT, ✔ CPU_SSE, ✔ CPU_SSE2, ✔ CPU_SSE3, ✔ CPU_SSE4_1, ✔ CPU_SSE4_2, ✖ CPU_SSE4A, ✔ CPU_AVX, ✖ CPU_AVX2, ✖ OPENMP, ✖ SSE, ✔ F16C, ✖ JEMALLOC, ✔ BLAS_OPEN, ✖ BLAS_ATLAS, ✖ BLAS_MKL, ✖ BLAS_APPLE, ✔ LAPACK, ✖ MKLDNN, ✔ OPENCV, ✖ CAFFE, ✖ PROFILER, ✖ DIST_KVSTORE, ✖ CXX14, ✔ SIGNAL_HANDLER, ✔ DEBUG]
-
-In []: fs.keys()
-Out[]: odict_keys(['CUDA', 'CUDNN', 'NCCL', 'CUDA_RTC', 'TENSORRT', 'CPU_SSE', 'CPU_SSE2', 'CPU_SSE3', 'CPU_SSE4_1', 'CPU_SSE4_2', 'CPU_SSE4A', 'CPU_AVX', 'CPU_AVX2', 'OPENMP', 'SSE', 'F16C', 'JEMALLOC', 'BLAS_OPEN', 'BLAS_ATLAS', 'BLAS_MKL', 'BLAS_APPLE', 'LAPACK', 'MKLDNN', 'OPENCV', 'CAFFE', 'PROFILER', 'DIST_KVSTORE', 'CXX14', 'SIGNAL_HANDLER', 'DEBUG'])
-
-In []: type(fs['CUDA'])
-Out[]: mxnet.runtime.Feature
-
-In []: fs['CUDA'].enabled
-Out[]: False
-
-In []: fs.is_enabled('CPU_SSE')
-Out[]: True
-
-In []: fs.is_enabled('CUDA')
-Out[]: False
-
-In []: features = mx.runtime.feature_list()
-
-In []: features
-Out[]:
-[✖ CUDA,
- ✖ CUDNN,
- ✖ NCCL,
- ✖ CUDA_RTC,
- ✖ TENSORRT,
- ✔ CPU_SSE,
- ✔ CPU_SSE2,
- ✔ CPU_SSE3,
- ✔ CPU_SSE4_1,
- ✔ CPU_SSE4_2,
- ✖ CPU_SSE4A,
- ✔ CPU_AVX,
- ✖ CPU_AVX2,
- ✖ OPENMP,
- ✖ SSE,
- ✔ F16C,
- ✖ JEMALLOC,
- ✔ BLAS_OPEN,
- ✖ BLAS_ATLAS,
- ✖ BLAS_MKL,
- ✖ BLAS_APPLE,
- ✔ LAPACK,
- ✖ MKLDNN,
- ✔ OPENCV,
- ✖ CAFFE,
- ✖ PROFILER,
- ✖ DIST_KVSTORE,
- ✖ CXX14,
- ✔ SIGNAL_HANDLER,
- ✔ DEBUG]
-
-In []: type(features)
-Out[]: list
-
-In []: type(features[0])
-Out[]: mxnet.runtime.Feature
-```
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-```
-
-## API Reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-```eval_rst
-.. automodule:: mxnet.runtime
-    :members:
-```
-
-<script>auto_index("api-reference");</script>
diff --git a/docs/api/python/metric/metric.md b/docs/api/python/metric/metric.md
deleted file mode 100644
index a3e3b545a56d..000000000000
--- a/docs/api/python/metric/metric.md
+++ /dev/null
@@ -1,45 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Evaluation Metric API
-
-```eval_rst
-.. currentmodule:: mxnet.metric
-```
-
-## Overview
-
-This document lists all the evaluation metrics available to evaluate
-the performance of a learned model.
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    mxnet.metric
-```
-
-## API Reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-```eval_rst
-.. automodule:: mxnet.metric
-    :members:
-```
-
-<script>auto_index("api-reference");</script>
diff --git a/docs/api/python/model.md b/docs/api/python/model.md
deleted file mode 100644
index 967765740aff..000000000000
--- a/docs/api/python/model.md
+++ /dev/null
@@ -1,175 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Model API
-
-The model API provides a simplified way to train neural networks using common best practices.
-It's a thin wrapper built on top of the [ndarray](../python/ndarray/ndarray.md) and [symbolic](../python/symbol/symbol.md)
-modules that make neural network training easy.
-
-Topics:
-
-* [Train a Model](#train-a-model)
-* [Save the Model](#save-the-model)
-* [Periodic Checkpoint](#periodic-checkpointing)
-* [Initializer API Reference](#initializer-api-reference)
-* [Evaluation Metric API Reference](#evaluation-metric-api-reference)
-* [Optimizer API Reference](#optimizer-api-reference)
-* [Model API Reference](#model-api-reference)
-
-## Train the Model
-
-To train a model, perform two steps: configure the model using the symbol parameter,
-then call ```model.Feedforward.create``` to create the model.
-The following example creates a two-layer neural network.
-
-```python
-    # configure a two layer neuralnetwork
-    data = mx.symbol.Variable('data')
-    fc1 = mx.symbol.FullyConnected(data, name='fc1', num_hidden=128)
-    act1 = mx.symbol.Activation(fc1, name='relu1', act_type='relu')
-    fc2 = mx.symbol.FullyConnected(act1, name='fc2', num_hidden=64)
-    softmax = mx.symbol.SoftmaxOutput(fc2, name='sm')
-    # create a model
-    model = mx.model.FeedForward.create(
-         softmax,
-         X=data_set,
-         num_epoch=num_epoch,
-         learning_rate=0.01)
-```
-You can also use the `scikit-learn-style` construct and `fit` function to create a model.
-
-```python
-    # create a model using sklearn-style two-step way
-    model = mx.model.FeedForward(
-         softmax,
-         num_epoch=num_epoch,
-         learning_rate=0.01)
-
-    model.fit(X=data_set)
-```
-For more information, see [Model API Reference](#model-api-reference).
-
-## Save the Model
-
-After the job is done, save your work.
-To save the model, you can directly pickle it with Python.
-We also provide `save` and `load` functions.
-
-```python
-    # save a model to mymodel-symbol.json and mymodel-0100.params
-    prefix = 'mymodel'
-    iteration = 100
-    model.save(prefix, iteration)
-
-    # load model back
-    model_loaded = mx.model.FeedForward.load(prefix, iteration)
-```
-The advantage of these two `save` and `load` functions are that they are language agnostic.
-You should be able to save and load directly into cloud storage, such as Amazon S3 and HDFS.
-
-##  Periodic Checkpointing
-
-We recommend checkpointing your model after each iteration.
-To do this, add a checkpoint callback ```do_checkpoint(path)``` to the function.
-The training process automatically checkpoints the specified location after
-each iteration.
-
-```python
-    prefix='models/chkpt'
-    model = mx.model.FeedForward.create(
-         softmax,
-         X=data_set,
-         iter_end_callback=mx.callback.do_checkpoint(prefix),
-         ...)
-```
-You can load the model checkpoint later using ```Feedforward.load```.
-
-## Use Multiple Devices
-
-Set ```ctx``` to the list of devices that you want to train on.
-
-```python
-    devices = [mx.gpu(i) for i in range(num_device)]
-    model = mx.model.FeedForward.create(
-         softmax,
-         X=dataset,
-         ctx=devices,
-         ...)
-```
-Training occurs in parallel on the GPUs that you specify.
-
-```eval_rst
-    .. raw:: html
-
-        <script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-```
-
-
-## Initializer API Reference
-
-
-```eval_rst
-    .. automodule:: mxnet.initializer
-        :members:
-
-    .. raw:: html
-
-        <script>auto_index("initializer-api-reference");</script>
-```
-
-## Evaluation Metric API Reference
-
-
-```eval_rst
-    .. automodule:: mxnet.metric
-        :members:
-
-    .. raw:: html
-
-        <script>auto_index("evaluation-metric-api-reference");</script>
-```
-
-## Optimizer API Reference
-
-
-```eval_rst
-    .. automodule:: mxnet.optimizer
-        :members:
-
-    .. raw:: html
-
-        <script>auto_index("optimizer-api-reference");</script>
-```
-
-## Model API Reference
-
-
-```eval_rst
-    .. automodule:: mxnet.model
-        :members:
-
-    .. raw:: html
-
-        <script>auto_index("model-api-reference");</script>
-```
-
-## Next Steps
-* See [Symbolic API](../python/symbol/symbol.md) for operations on NDArrays that assemble neural networks from layers.
-* See [IO Data Loading API](../python/io/io.md) for parsing and loading data.
-* See [NDArray API](../python/ndarray/ndarray.md) for vector/matrix/tensor operations.
-* See [KVStore API](../python/kvstore/kvstore.md) for multi-GPU and multi-host distributed training.
diff --git a/docs/api/python/module/module.md b/docs/api/python/module/module.md
deleted file mode 100644
index 86ffad559897..000000000000
--- a/docs/api/python/module/module.md
+++ /dev/null
@@ -1,227 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Module API
-
-```eval_rst
-.. currentmodule:: mxnet.module
-```
-
-## Overview
-
-The module API, defined in the `module` (or simply `mod`) package, provides an
-intermediate and high-level interface for performing computation with a
-`Symbol`. One can roughly think a module is a machine which can execute a
-program defined by a `Symbol`.
-
-The `module.Module` accepts a `Symbol` as the input.
-```python
->>> data = mx.sym.Variable('data')
->>> fc1  = mx.sym.FullyConnected(data, name='fc1', num_hidden=128)
->>> act1 = mx.sym.Activation(fc1, name='relu1', act_type="relu")
->>> fc2  = mx.sym.FullyConnected(act1, name='fc2', num_hidden=10)
->>> out  = mx.sym.SoftmaxOutput(fc2, name = 'softmax')
->>> mod = mx.mod.Module(out)  # create a module by given a Symbol
-```
-
-Assume there is a valid MXNet data iterator `nd_iter`. We can initialize the
-module:
-```python
->>> mod.bind(data_shapes=nd_iter.provide_data,
->>>          label_shapes=nd_iter.provide_label) # create memory by given input shapes
->>> mod.init_params()  # initial parameters with the default random initializer
-```
-
-Now the module is able to compute. We can call high-level API to train and
-predict:
-```python
->>> mod.fit(nd_iter, num_epoch=10, ...)  # train
->>> mod.predict(new_nd_iter)  # predict on new data
-```
-
-or use intermediate APIs to perform step-by-step computations
-```python
->>> mod.forward(data_batch)  # forward on the provided data batch
->>> mod.backward()  # backward to calculate the gradients
->>> mod.update()  # update parameters using the default optimizer
-```
-
-A detailed tutorial is available at
-[Module - Neural network training and inference](http://mxnet.io/tutorials/basic/module.html).
-
-The `module` package provides several modules:
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    BaseModule
-    Module
-    SequentialModule
-    BucketingModule
-    PythonModule
-    PythonLossModule
-```
-
-We summarize the interface for each class in the following sections.
-
-## The `BaseModule` class
-
-The `BaseModule` is the base class for all other module classes. It defines the
-interface each module class should provide.
-
-### Initialize memory
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    BaseModule.bind
-```
-
-### Get and set parameters
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    BaseModule.init_params
-    BaseModule.set_params
-    BaseModule.get_params
-    BaseModule.save_params
-    BaseModule.load_params
-```
-
-### Train and predict
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    BaseModule.fit
-    BaseModule.score
-    BaseModule.iter_predict
-    BaseModule.predict
-```
-
-### Forward and backward
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    BaseModule.forward
-    BaseModule.backward
-    BaseModule.forward_backward
-```
-
-### Update parameters
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    BaseModule.init_optimizer
-    BaseModule.update
-    BaseModule.update_metric
-```
-
-### Input and output
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    BaseModule.data_names
-    BaseModule.output_names
-    BaseModule.data_shapes
-    BaseModule.label_shapes
-    BaseModule.output_shapes
-    BaseModule.get_outputs
-    BaseModule.get_input_grads
-```
-
-### Others
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    BaseModule.get_states
-    BaseModule.set_states
-    BaseModule.install_monitor
-    BaseModule.symbol
-```
-
-
-## Other build-in modules
-
-Besides the basic interface defined in `BaseModule`, each module class supports
-additional functionality. We summarize them in this section.
-
-### Class `Module`
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Module.load
-    Module.save_checkpoint
-    Module.reshape
-    Module.borrow_optimizer
-    Module.save_optimizer_states
-    Module.load_optimizer_states
-```
-
-### Class `BucketModule`
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    BucketingModule.switch_bucket
-```
-
-### Class `SequentialModule`
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    SequentialModule.add
-```
-
-## API Reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-```eval_rst
-.. autoclass:: mxnet.module.BaseModule
-    :members:
-.. autoclass:: mxnet.module.Module
-    :members:
-.. autoclass:: mxnet.module.BucketingModule
-    :members:
-.. autoclass:: mxnet.module.SequentialModule
-    :members:
-.. autoclass:: mxnet.module.PythonModule
-    :members:
-.. autoclass:: mxnet.module.PythonLossModule
-    :members:
-```
-
-<script>auto_index("api-reference");</script>
\ No newline at end of file
diff --git a/docs/api/python/ndarray/contrib.md b/docs/api/python/ndarray/contrib.md
deleted file mode 100644
index d4358ddcea22..000000000000
--- a/docs/api/python/ndarray/contrib.md
+++ /dev/null
@@ -1,100 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Contrib NDArray API
-
-```eval_rst
-    .. currentmodule:: mxnet.ndarray.contrib
-```
-
-## Overview
-
-This document lists the contrib routines of the *n*-dimensional array package:
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    mxnet.ndarray.contrib
-```
-
-The `Contrib NDArray` API, defined in the `ndarray.contrib` package, provides
-many useful experimental APIs for new features.
-This is a place for the community to try out the new features,
-so that feature contributors can receive feedback.
-
-```eval_rst
-.. warning:: This package contains experimental APIs and may change in the near future.
-```
-
-In the rest of this document, we list routines provided by the `ndarray.contrib` package.
-
-## Contrib
-
-```eval_rst
-.. currentmodule:: mxnet.ndarray.contrib
-
-.. autosummary::
-    :nosignatures:
-
-    AdaptiveAvgPooling2D
-    BilinearResize2D
-    CTCLoss
-    DeformableConvolution
-    DeformablePSROIPooling
-    MultiBoxDetection
-    MultiBoxPrior
-    MultiBoxTarget
-    MultiProposal
-    PSROIPooling
-    Proposal
-    ROIAlign
-    count_sketch
-    ctc_loss
-    dequantize
-    fft
-    ifft
-    quantize
-    foreach
-    while_loop
-    cond
-    isinf
-    isfinite
-    isnan
-    index_array
-    index_copy
-    getnnz
-    edge_id
-    dgl_csr_neighbor_uniform_sample
-    dgl_csr_neighbor_non_uniform_sample
-    dgl_subgraph
-    dgl_adjacency
-    dgl_graph_compact
-```
-
-## API Reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-```eval_rst
-
-.. automodule:: mxnet.ndarray.contrib
-    :members:
-
-```
-
-<script>auto_index("api-reference");</script>
diff --git a/docs/api/python/ndarray/linalg.md b/docs/api/python/ndarray/linalg.md
deleted file mode 100644
index b73d9680a874..000000000000
--- a/docs/api/python/ndarray/linalg.md
+++ /dev/null
@@ -1,75 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Linear Algebra NDArray API
-
-```eval_rst
-    .. currentmodule:: mxnet.ndarray.linalg
-```
-
-## Overview
-
-This document lists the linear algebra routines of the *n*-dimensional array package:
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    mxnet.ndarray.linalg
-```
-
-The `Linear Algebra NDArray` API, defined in the `ndarray.linalg` package, provides
-imperative linear algebra tensor operations on CPU/GPU.
-
-In the rest of this document, we list routines provided by the `ndarray.linalg` package.
-
-## Linear Algebra
-
-```eval_rst
-.. currentmodule:: mxnet.ndarray.linalg
-
-.. autosummary::
-    :nosignatures:
-
-    gemm
-    gemm2
-    potrf
-    potri
-    trmm
-    trsm
-    syrk
-    gelqf
-    syevd
-    sumlogdiag
-    extractdiag
-    makediag
-    extracttrian
-    maketrian
-```
-
-## API Reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-```eval_rst
-
-.. automodule:: mxnet.ndarray.linalg
-    :members:
-
-```
-
-<script>auto_index("api-reference");</script>
diff --git a/docs/api/python/ndarray/ndarray.md b/docs/api/python/ndarray/ndarray.md
deleted file mode 100644
index 5addf92cd150..000000000000
--- a/docs/api/python/ndarray/ndarray.md
+++ /dev/null
@@ -1,745 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# NDArray API
-
-```eval_rst
-.. currentmodule:: mxnet.ndarray
-```
-
-## Overview
-
-This document lists the routines of the *n*-dimensional array package:
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    mxnet.ndarray
-```
-
-The `NDArray` API, defined in the `ndarray` (or simply `nd`) package, provides
-imperative tensor operations on CPU/GPU.
-An `NDArray` represents a multi-dimensional, fixed-size homogenous array.
-
-```python
->>> x = mx.nd.array([[1, 2, 3], [4, 5, 6]])
->>> type(x)
-<class 'mxnet.ndarray.NDArray'>
->>> x.shape
-(2, 3)
->>> y = x + mx.nd.ones(x.shape)*3
->>> print(y.asnumpy())
-[[ 4.  5.  6.]
- [ 7.  8.  9.]]
->>> z = y.as_in_context(mx.gpu(0))
->>> print(z)
-<NDArray 2x3 @gpu(0)>
-```
-
-A detailed tutorial is available at
-[NDArray - Imperative tensor operations on CPU/GPU](http://mxnet.io/tutorials/basic/ndarray.html).
-<br><br>
-
-```eval_rst
-
-.. note:: ``mxnet.ndarray`` is similar to ``numpy.ndarray`` in some aspects. But the differences are not negligible. For instance:
-
-   - ``mxnet.ndarray.NDArray.T`` does real data transpose to return new a copied
-     array, instead of returning a view of the input array.
-   - ``mxnet.ndarray.dot`` performs dot product between the last axis of the
-     first input array and the first axis of the second input, while `numpy.dot`
-     uses the second last axis of the input array.
-
-   In addition, ``mxnet.ndarray.NDArray`` supports GPU computation and various neural
-   network layers.
-
-.. note:: ``ndarray`` provides almost the same routines as ``symbol``. Most
-  routines between these two packages share the source code. But ``ndarray``
-  differs from ``symbol`` in few aspects:
-
-  - ``ndarray`` adopts imperative programming, namely sentences are executed
-    step-by-step so that the results can be obtained immediately whereas
-    ``symbol`` adopts declarative programming.
-
-  - Most binary operators in ``ndarray`` such as ``+`` and ``>`` have
-    broadcasting enabled by default.
-```
-
-In the rest of this document, we first overview the methods provided by the
-`ndarray.NDArray` class, and then list other routines provided by the `ndarray` package.
-
-The `ndarray` package provides several classes:
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    NDArray
-    sparse.CSRNDArray
-    sparse.RowSparseNDArray
-```
-
-## The `NDArray` class
-
-### Array attributes
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    NDArray.shape
-    NDArray.size
-    NDArray.context
-    NDArray.dtype
-    NDArray.stype
-```
-
-### Array conversion
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    NDArray.copy
-    NDArray.copyto
-    NDArray.as_in_context
-    NDArray.asnumpy
-    NDArray.asscalar
-    NDArray.astype
-    NDArray.tostype
-```
-
-### Array creation
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    NDArray.zeros_like
-    NDArray.ones_like
-```
-
-### Array change shape
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    NDArray.T
-    NDArray.shape_array
-    NDArray.size_array
-    NDArray.reshape
-    NDArray.reshape_like
-    NDArray.flatten
-    NDArray.expand_dims
-    NDArray.split
-    NDArray.diag
-    NDArray.squeeze
-```
-
-### Array expand elements
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    NDArray.broadcast_to
-    NDArray.broadcast_axes
-    NDArray.broadcast_like
-    NDArray.tile
-    NDArray.pad
-```
-
-### Array rearrange elements
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    NDArray.transpose
-    NDArray.swapaxes
-    NDArray.flip
-    NDArray.depth_to_space
-    NDArray.space_to_depth
-```
-
-### Array reduction
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    NDArray.sum
-    NDArray.nansum
-    NDArray.prod
-    NDArray.nanprod
-    NDArray.mean
-    NDArray.max
-    NDArray.min
-    NDArray.norm
-```
-
-### Array rounding
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    NDArray.round
-    NDArray.rint
-    NDArray.fix
-    NDArray.floor
-    NDArray.ceil
-    NDArray.trunc
-```
-
-### Array sorting and searching
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    NDArray.sort
-    NDArray.argsort
-    NDArray.topk
-    NDArray.argmax
-    NDArray.argmin
-    NDArray.argmax_channel
-```
-
-### Arithmetic operations
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    NDArray.__add__
-    NDArray.__sub__
-    NDArray.__rsub__
-    NDArray.__neg__
-    NDArray.__mul__
-    NDArray.__div__
-    NDArray.__rdiv__
-    NDArray.__mod__
-    NDArray.__rmod__
-    NDArray.__pow__
-```
-
-### Trigonometric functions
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    NDArray.sin
-    NDArray.cos
-    NDArray.tan
-    NDArray.arcsin
-    NDArray.arccos
-    NDArray.arctan
-    NDArray.degrees
-    NDArray.radians
-```
-
-### Hyperbolic functions
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    NDArray.sinh
-    NDArray.cosh
-    NDArray.tanh
-    NDArray.arcsinh
-    NDArray.arccosh
-    NDArray.arctanh
-```
-
-### Exponents and logarithms
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    NDArray.exp
-    NDArray.expm1
-    NDArray.log
-    NDArray.log10
-    NDArray.log2
-    NDArray.log1p
-```
-
-### Powers
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    NDArray.sqrt
-    NDArray.rsqrt
-    NDArray.cbrt
-    NDArray.rcbrt
-    NDArray.square
-    NDArray.reciprocal
-```
-
-## Basic neural network functions
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    NDArray.relu
-    NDArray.sigmoid
-    NDArray.softmax
-    NDArray.log_softmax
-```
-
-### In-place arithmetic operations
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    NDArray.__iadd__
-    NDArray.__isub__
-    NDArray.__imul__
-    NDArray.__idiv__
-    NDArray.__imod__
-```
-
-### Comparison operators
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    NDArray.__lt__
-    NDArray.__le__
-    NDArray.__gt__
-    NDArray.__ge__
-    NDArray.__eq__
-    NDArray.__ne__
-```
-
-### Indexing
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    NDArray.__getitem__
-    NDArray.__setitem__
-    NDArray.slice
-    NDArray.slice_axis
-    NDArray.slice_like
-    NDArray.take
-    NDArray.one_hot
-    NDArray.pick
-```
-
-### Lazy evaluation
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    NDArray.wait_to_read
-```
-
-### Miscellaneous
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    NDArray.clip
-    NDArray.sign
-```
-
-## Array creation routines
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    array
-    empty
-    zeros
-    zeros_like
-    ones
-    ones_like
-    full
-    arange
-    diag
-    load
-    save
-```
-
-## Array manipulation routines
-
-### Changing array shape and type
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    cast
-    shape_array
-    size_array
-    reshape
-    reshape_like
-    flatten
-    expand_dims
-```
-
-### Expanding array elements
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    broadcast_to
-    broadcast_axes
-    broadcast_like
-    repeat
-    tile
-    pad
-```
-
-### Rearranging elements
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    transpose
-    swapaxes
-    flip
-    depth_to_space
-    space_to_depth
-```
-
-### Joining and splitting arrays
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    concat
-    split
-    stack
-```
-
-### Indexing routines
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    slice
-    slice_axis
-    slice_like
-    take
-    batch_take
-    one_hot
-    pick
-    where
-    ravel_multi_index
-    unravel_index
-    gather_nd
-    scatter_nd
-```
-
-## Mathematical functions
-
-### Arithmetic operations
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    add
-    subtract
-    negative
-    multiply
-    divide
-    modulo
-    dot
-    batch_dot
-    add_n
-```
-
-### Trigonometric functions
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    sin
-    cos
-    tan
-    arcsin
-    arccos
-    arctan
-    broadcast_hypot
-    degrees
-    radians
-```
-
-### Hyperbolic functions
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    sinh
-    cosh
-    tanh
-    arcsinh
-    arccosh
-    arctanh
-```
-
-### Reduce functions
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    sum
-    nansum
-    prod
-    nanprod
-    mean
-    max
-    min
-    norm
-```
-
-### Rounding
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    round
-    rint
-    fix
-    floor
-    ceil
-    trunc
-```
-
-### Exponents and logarithms
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    exp
-    expm1
-    log
-    log10
-    log2
-    log1p
-```
-
-### Powers
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    power
-    sqrt
-    rsqrt
-    cbrt
-    rcbrt
-    square
-    reciprocal
-```
-
-### Comparison
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    equal
-    not_equal
-    greater
-    greater_equal
-    lesser
-    lesser_equal
-```
-
-### Logical operators
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    logical_and
-    logical_or
-    logical_xor
-    logical_not
-```
-
-### Random sampling
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    random.exponential
-    random.gamma
-    random.generalized_negative_binomial
-    random.multinomial
-    random.negative_binomial
-    random.normal
-    random.poisson
-    random.randint
-    random.randn
-    random.shuffle
-    random.uniform
-    mxnet.random.seed
-```
-
-### Sorting and searching
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    sort
-    topk
-    argsort
-    argmax
-    argmin
-```
-
-### Sequence operation
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    SequenceLast
-    SequenceMask
-    SequenceReverse
-```
-
-### Miscellaneous
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    maximum
-    minimum
-    clip
-    abs
-    sign
-    gamma
-    gammaln
-```
-
-## Neural network
-
-### Basic
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    FullyConnected
-    Convolution
-    Activation
-    BatchNorm
-    Pooling
-    SoftmaxOutput
-    softmax
-    log_softmax
-    relu
-    sigmoid
-    erf
-    erfinv
-```
-
-### More
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Correlation
-    Deconvolution
-    RNN
-    Embedding
-    LeakyReLU
-    InstanceNorm
-    LayerNorm
-    L2Normalization
-    LRN
-    ROIPooling
-    SoftmaxActivation
-    Dropout
-    BilinearSampler
-    GridGenerator
-    UpSampling
-    SpatialTransformer
-    LinearRegressionOutput
-    LogisticRegressionOutput
-    MAERegressionOutput
-    SVMOutput
-    softmax_cross_entropy
-    smooth_l1
-    IdentityAttachKLSparseReg
-    MakeLoss
-    BlockGrad
-    Custom
-```
-
-## API Reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-```eval_rst
-
-.. autoclass:: mxnet.ndarray.NDArray
-    :members:
-    :special-members:
-
-.. automodule:: mxnet.ndarray
-    :members:
-    :imported-members:
-    :special-members:
-    :exclude-members: CachedOp, NDArray, save, load
-
-.. automodule:: mxnet.ndarray
-    :noindex:
-    :members: save, load
-
-.. automodule:: mxnet.random
-    :noindex:
-    :members:
-
-```
-
-<script>auto_index("api-reference");</script>
diff --git a/docs/api/python/ndarray/random.md b/docs/api/python/ndarray/random.md
deleted file mode 100644
index 74e7d082df27..000000000000
--- a/docs/api/python/ndarray/random.md
+++ /dev/null
@@ -1,77 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Random Distribution Generator NDArray API
-
-```eval_rst
-    .. currentmodule:: mxnet.ndarray.random
-```
-
-## Overview
-
-This document lists the random distribution generator routines of the *n*-dimensional array package:
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    mxnet.ndarray.random
-```
-
-The `Random Distribution Generator NDArray` API, defined in the `ndarray.random` package, provides
-imperative random distribution generator operations on CPU/GPU.
-
-In the rest of this document, we list routines provided by the `ndarray.random` package.
-
-## Random Distribution Generator
-
-```eval_rst
-.. currentmodule:: mxnet.ndarray.random
-
-.. autosummary::
-    :nosignatures:
-
-    exponential
-    gamma
-    generalized_negative_binomial
-    multinomial
-    negative_binomial
-    normal
-    poisson
-    randint
-    randn
-    shuffle
-    uniform
-    mxnet.random.seed
-```
-
-## API Reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-```eval_rst
-
-.. automodule:: mxnet.ndarray.random
-    :members:
-
-.. automodule:: mxnet.random
-    :members:
-    :noindex:
-
-```
-
-<script>auto_index("api-reference");</script>
diff --git a/docs/api/python/ndarray/sparse.md b/docs/api/python/ndarray/sparse.md
deleted file mode 100644
index 71dfaafbf294..000000000000
--- a/docs/api/python/ndarray/sparse.md
+++ /dev/null
@@ -1,618 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Sparse NDArray API
-
-```eval_rst
-.. currentmodule:: mxnet.ndarray.sparse
-```
-
-## Overview
-
-This document lists the routines of the *n*-dimensional sparse array package:
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    mxnet.ndarray.sparse
-```
-
-The `CSRNDArray` and `RowSparseNDArray` API, defined in the `ndarray.sparse` package, provides
-imperative sparse tensor operations.
-
-An `CSRNDArray` inherits from `NDArray`, and represents a two-dimensional, fixed-size array in compressed sparse row format.
-
-```python
->>> x = mx.nd.array([[1, 0], [0, 0], [2, 3]])
->>> csr = x.tostype('csr')
->>> type(csr)
-<class 'mxnet.ndarray.sparse.CSRNDArray'>
->>> csr.shape
-(3, 2)
->>> csr.data.asnumpy()
-array([ 1.  2.  3.], dtype=float32)
->>> csr.indices.asnumpy()
-array([0, 0, 1])
->>> csr.indptr.asnumpy()
-array([0, 1, 1, 3])
->>> csr.stype
-'csr'
-```
-
-A detailed tutorial is available at
-[CSRNDArray - NDArray in Compressed Sparse Row Storage Format](https://mxnet.incubator.apache.org/versions/master/tutorials/sparse/csr.html).
-<br>
-
-An `RowSparseNDArray` inherits from `NDArray`, and represents a multi-dimensional, fixed-size array in row sparse format.
-
-```python
->>> x = mx.nd.array([[1, 0], [0, 0], [2, 3]])
->>> row_sparse = x.tostype('row_sparse')
->>> type(row_sparse)
-<class 'mxnet.ndarray.sparse.RowSparseNDArray'>
->>> row_sparse.data.asnumpy()
-array([[ 1.  0.],
-       [ 2.  3.]], dtype=float32)
->>> row_sparse.indices.asnumpy()
-array([0, 2])
->>> row_sparse.stype
-'row_sparse'
-```
-
-A detailed tutorial is available at
-[RowSparseNDArray - NDArray for Sparse Gradient Updates](https://mxnet.incubator.apache.org/versions/master/tutorials/sparse/row_sparse.html).
-<br><br>
-
-```eval_rst
-
-.. note:: ``mxnet.ndarray.sparse`` is similar to ``mxnet.ndarray`` in some aspects. But the differences are not negligible. For instance:
-
-   - Only a subset of operators in ``mxnet.ndarray`` have efficient sparse implementations in ``mxnet.ndarray.sparse``.
-   - If an operator do not occur in the ``mxnet.ndarray.sparse`` namespace, that means the operator does not have an efficient sparse implementation yet. If sparse inputs are passed to such an operator, it will convert inputs to the dense format and fallback to the already available dense implementation.
-   - The storage types (``stype``) of sparse operators' outputs depend on the storage types of inputs.
-     By default the operators not available in ``mxnet.ndarray.sparse`` infer "default" (dense) storage type for outputs.
-     Please refer to the [API Reference](#api-reference) section for further details on specific operators.
-
-.. note:: ``mxnet.ndarray.sparse.CSRNDArray`` is similar to ``scipy.sparse.csr_matrix`` in some aspects. But they differ in a few aspects:
-
-   - In MXNet the column indices (``CSRNDArray.indices``) for a given row are expected to be **sorted in ascending order**.
-     Duplicate column entries for the same row are not allowed.
-   - ``CSRNDArray.data``, ``CSRNDArray.indices`` and ``CSRNDArray.indptr`` always create deep copies, while it's not the case in ``scipy.sparse.csr_matrix``.
-
-```
-
-In the rest of this document, we first overview the methods provided by the
-`ndarray.sparse.CSRNDArray` class and the `ndarray.sparse.RowSparseNDArray` class,
-and then list other routines provided by the `ndarray.sparse` package.
-
-The `ndarray.sparse` package provides several classes:
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    CSRNDArray
-    RowSparseNDArray
-```
-
-We summarize the interface for each class in the following sections.
-
-## The `CSRNDArray` class
-
-### Array attributes
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    CSRNDArray.shape
-    CSRNDArray.context
-    CSRNDArray.dtype
-    CSRNDArray.stype
-    CSRNDArray.data
-    CSRNDArray.indices
-    CSRNDArray.indptr
-```
-
-### Array conversion
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    CSRNDArray.copy
-    CSRNDArray.copyto
-    CSRNDArray.as_in_context
-    CSRNDArray.asscipy
-    CSRNDArray.asnumpy
-    CSRNDArray.asscalar
-    CSRNDArray.astype
-    CSRNDArray.tostype
-```
-
-### Array inspection
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    CSRNDArray.check_format
-```
-
-### Array creation
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    CSRNDArray.zeros_like
-```
-
-### Array reduction
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    CSRNDArray.sum
-    CSRNDArray.mean
-    CSRNDArray.norm
-```
-
-### Array rounding
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    CSRNDArray.round
-    CSRNDArray.rint
-    CSRNDArray.fix
-    CSRNDArray.floor
-    CSRNDArray.ceil
-    CSRNDArray.trunc
-```
-
-### Trigonometric functions
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    CSRNDArray.sin
-    CSRNDArray.tan
-    CSRNDArray.arcsin
-    CSRNDArray.arctan
-    CSRNDArray.degrees
-    CSRNDArray.radians
-```
-
-### Hyperbolic functions
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    CSRNDArray.sinh
-    CSRNDArray.tanh
-    CSRNDArray.arcsinh
-    CSRNDArray.arctanh
-```
-
-### Exponents and logarithms
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    CSRNDArray.expm1
-    CSRNDArray.log1p
-```
-
-### Powers
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    CSRNDArray.sqrt
-    CSRNDArray.square
-```
-
-### Joining arrays
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    concat
-```
-
-### Indexing
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    CSRNDArray.__getitem__
-    CSRNDArray.__setitem__
-    CSRNDArray.slice
-```
-
-### Miscellaneous
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    CSRNDArray.abs
-    CSRNDArray.clip
-    CSRNDArray.sign
-```
-
-### Lazy evaluation
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    CSRNDArray.wait_to_read
-```
-
-## The `RowSparseNDArray` class
-
-### Array attributes
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    RowSparseNDArray.shape
-    RowSparseNDArray.context
-    RowSparseNDArray.dtype
-    RowSparseNDArray.stype
-    RowSparseNDArray.data
-    RowSparseNDArray.indices
-```
-
-### Array conversion
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    RowSparseNDArray.copy
-    RowSparseNDArray.copyto
-    RowSparseNDArray.as_in_context
-    RowSparseNDArray.asnumpy
-    RowSparseNDArray.asscalar
-    RowSparseNDArray.astype
-    RowSparseNDArray.tostype
-```
-
-### Array inspection
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    RowSparseNDArray.check_format
-```
-
-### Array creation
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    RowSparseNDArray.zeros_like
-```
-
-### Array reduction
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    RowSparseNDArray.norm
-```
-
-### Array rounding
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    RowSparseNDArray.round
-    RowSparseNDArray.rint
-    RowSparseNDArray.fix
-    RowSparseNDArray.floor
-    RowSparseNDArray.ceil
-    RowSparseNDArray.trunc
-```
-
-### Trigonometric functions
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    RowSparseNDArray.sin
-    RowSparseNDArray.tan
-    RowSparseNDArray.arcsin
-    RowSparseNDArray.arctan
-    RowSparseNDArray.degrees
-    RowSparseNDArray.radians
-```
-
-### Hyperbolic functions
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    RowSparseNDArray.sinh
-    RowSparseNDArray.tanh
-    RowSparseNDArray.arcsinh
-    RowSparseNDArray.arctanh
-```
-
-### Exponents and logarithms
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    RowSparseNDArray.expm1
-    RowSparseNDArray.log1p
-```
-
-### Powers
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    RowSparseNDArray.sqrt
-    RowSparseNDArray.square
-```
-
-### Indexing
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    RowSparseNDArray.__getitem__
-    RowSparseNDArray.__setitem__
-    RowSparseNDArray.retain
-```
-
-### Lazy evaluation
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    RowSparseNDArray.wait_to_read
-```
-
-### Miscellaneous
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    RowSparseNDArray.abs
-    RowSparseNDArray.clip
-    RowSparseNDArray.sign
-```
-
-## Array creation routines
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    array
-    empty
-    zeros
-    zeros_like
-    csr_matrix
-    row_sparse_array
-    mxnet.ndarray.load
-    mxnet.ndarray.save
-```
-
-## Array manipulation routines
-
-### Changing array storage type
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    cast_storage
-```
-
-### Indexing routines
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    slice
-    retain
-    where
-```
-
-## Mathematical functions
-
-### Arithmetic operations
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    elemwise_add
-    elemwise_sub
-    elemwise_mul
-    broadcast_add
-    broadcast_sub
-    broadcast_mul
-    broadcast_div
-    negative
-    dot
-    add_n
-```
-
-### Trigonometric functions
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    sin
-    tan
-    arcsin
-    arctan
-    degrees
-    radians
-```
-
-### Hyperbolic functions
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    sinh
-    tanh
-    arcsinh
-    arctanh
-```
-
-### Reduce functions
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    sum
-    mean
-    norm
-```
-
-### Rounding
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    round
-    rint
-    fix
-    floor
-    ceil
-    trunc
-```
-
-### Exponents and logarithms
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    expm1
-    log1p
-```
-
-### Powers
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    sqrt
-    square
-```
-
-### Miscellaneous
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    abs
-    sign
-```
-
-## Neural network
-
-### Updater
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    sgd_update
-    sgd_mom_update
-    adam_update
-    adagrad_update
-```
-
-### More
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    make_loss
-    stop_gradient
-    Embedding
-    LinearRegressionOutput
-    LogisticRegressionOutput
-```
-
-## API Reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-```eval_rst
-
-.. autoclass:: mxnet.ndarray.sparse.CSRNDArray
-    :members: shape, context, dtype, stype, data, indices, indptr, copy, copyto, as_in_context, asscipy, asnumpy, asscalar, astype, tostype, slice, wait_to_read, zeros_like, round, rint, fix, floor, ceil, trunc, sin, tan, arcsin, arctan, degrees, radians, sinh, tanh, arcsinh, arctanh, expm1, log1p, sqrt, square, __neg__, sum, mean, norm, square, __getitem__, __setitem__, check_format, abs, clip, sign
-
-.. autoclass:: mxnet.ndarray.sparse.RowSparseNDArray
-    :members: shape, context, dtype, stype, data, indices, copy, copyto, as_in_context, asnumpy, asscalar, astype, tostype, wait_to_read, zeros_like, round, rint, fix, floor, ceil, trunc, sin, tan, arcsin, arctan, degrees, radians, sinh, tanh, arcsinh, arctanh, expm1, log1p, sqrt, square, norm, __getitem__, __setitem__, check_format, retain, abs, clip, sign
-
-.. automodule:: mxnet.ndarray.sparse
-    :members:
-    :special-members:
-    :exclude-members: BaseSparseNDArray, RowSparseNDArray, CSRNDArray
-
-.. automodule:: mxnet.ndarray.sparse
-    :noindex:
-    :members: array, zeros, empty
-
-.. automodule:: mxnet.ndarray
-    :members: load, save
-
-```
-
-<script>auto_index("api-reference");</script>
diff --git a/docs/api/python/optimization/contrib.md b/docs/api/python/optimization/contrib.md
deleted file mode 100644
index a431508aa22d..000000000000
--- a/docs/api/python/optimization/contrib.md
+++ /dev/null
@@ -1,69 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Contrib Optimization API
-
-```eval_rst
-    .. currentmodule:: mxnet.optimizer.contrib
-```
-
-## Overview
-
-This document summaries the contrib APIs used to initialize and update the model
-weights during training
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    mxnet.optimizer.contrib
-```
-
-The `Contrib Optimization` API, defined in the `optimizer.contrib` package, provides
-many useful experimental APIs for new features.
-This is a place for the community to try out the new features,
-so that feature contributors can receive feedback.
-
-```eval_rst
-.. warning:: This package contains experimental APIs and may change in the near future.
-```
-
-In the rest of this document, we list routines provided by the `optimizer.contrib` package.
-
-## Contrib
-
-```eval_rst
-.. currentmodule:: mxnet.optimizer.contrib
-
-.. autosummary::
-    :nosignatures:
-
-     GroupAdaGrad
-```
-
-## API Reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-```eval_rst
-
-.. automodule:: mxnet.optimizer.contrib
-    :members:
-
-```
-
-<script>auto_index("api-reference");</script>
diff --git a/docs/api/python/optimization/optimization.md b/docs/api/python/optimization/optimization.md
deleted file mode 100644
index 47f99f3602f8..000000000000
--- a/docs/api/python/optimization/optimization.md
+++ /dev/null
@@ -1,183 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Optimization: initialize and update weights
-
-## Overview
-
-This document summaries the APIs used to initialize and update the model weights
-during training
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    mxnet.initializer
-    mxnet.optimizer
-    mxnet.lr_scheduler
-```
-
-and how to develop a new optimization algorithm in MXNet.
-
-Assume there there is a pre-defined ``Symbol`` and a ``Module`` is created for
-it
-
-```python
->>> data = mx.symbol.Variable('data')
->>> label = mx.symbol.Variable('softmax_label')
->>> fc = mx.symbol.FullyConnected(data, name='fc', num_hidden=10)
->>> loss = mx.symbol.SoftmaxOutput(fc, label, name='softmax')
->>> mod = mx.mod.Module(loss)
->>> mod.bind(data_shapes=[('data', (128,20))], label_shapes=[('softmax_label', (128,))])
-```
-
-Next we can initialize the weights with values sampled uniformly from
-``[-1,1]``:
-
-```python
->>> mod.init_params(mx.initializer.Uniform(scale=1.0))
-```
-
-Then we will train a model with standard SGD which decreases the learning rate
-by multiplying 0.9 for each 100 batches.
-
-```python
->>> lr_sch = mx.lr_scheduler.FactorScheduler(step=100, factor=0.9)
->>> mod.init_optimizer(
-...     optimizer='sgd', optimizer_params=(('learning_rate', 0.1), ('lr_scheduler', lr_sch)))
-```
-
-Finally run ``mod.fit(...)`` to start training.
-
-## The ``mxnet.initializer`` package
-
-```eval_rst
-.. currentmodule:: mxnet.initializer
-```
-
-The base class ``Initializer`` defines the default behaviors to initialize
-various parameters, such as set bias to 1, except for the weight. Other classes
-then defines how to initialize the weight.
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Initializer
-    Uniform
-    Normal
-    Load
-    Mixed
-    Zero
-    One
-    Constant
-    Orthogonal
-    Xavier
-    MSRAPrelu
-    Bilinear
-    FusedRNN
-```
-
-## The ``mxnet.optimizer`` package
-
-```eval_rst
-.. currentmodule:: mxnet.optimizer
-```
-
-The base class ``Optimizer`` accepts commonly shared arguments such as
-``learning_rate`` and defines the interface. Each other class in this package
-implements one weight updating function.
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Optimizer
-    SGD
-    NAG
-    RMSProp
-    Adam
-    AdaGrad
-    AdaDelta
-    Adamax
-    Nadam
-    DCASGD
-    SGLD
-    Signum
-    FTML
-    LBSGD
-    Ftrl
-```
-
-## The ``mxnet.lr_scheduler`` package
-
-```eval_rst
-.. currentmodule:: mxnet.lr_scheduler
-```
-
-The base class ``LRScheduler`` defines the interface, while other classes
-implement various schemes to change the learning rate during training.
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    LRScheduler
-    FactorScheduler
-    MultiFactorScheduler
-```
-
-## Implement a new algorithm
-
-Most classes listed in this document are implemented in Python by using ``NDArray``.
-So implementing new weight updating or initialization functions is
-straightforward.
-
-For `initializer`, create a subclass of ``Initializer`` and define the
-`_init_weight` method. We can also change the default behaviors to initialize
-other parameters such as `_init_bias`. See
-[`initializer.py`](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/initializer.py)
-for examples.
-
-For ``optimizer``, create a subclass of ``Optimizer``
-and implement two methods ``create_state`` and ``update``. Also add
-``@mx.optimizer.Optimizer.register`` before this class. See
-[`optimizer.py`](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/optimizer/optimizer.py)
-for examples.
-
-For `lr_scheduler`, create a subclass of `LRScheduler` and then implement the
-`__call__` method. See
-[`lr_scheduler.py`](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/lr_scheduler.py)
-for examples.
-
-## API Reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-```eval_rst
-.. automodule:: mxnet.optimizer
-    :members:
-    :exclude-members: NDabs
-
-.. automodule:: mxnet.lr_scheduler
-    :members:
-
-.. automodule:: mxnet.initializer
-    :members:
-```
-
-<script>auto_index("api-reference");</script>
diff --git a/docs/api/python/profiler/profiler.md b/docs/api/python/profiler/profiler.md
deleted file mode 100644
index c025811f5a5d..000000000000
--- a/docs/api/python/profiler/profiler.md
+++ /dev/null
@@ -1,82 +0,0 @@
-# Profiler API
-
-## Overview
-
-MXNet has a built-in profiler which is compatible with Intel® VTune™ Amplifier, NVIDIA NVTX and Chrome's chrome://tracing visualization engine.  When built with the USE_VTUNE=1 flag, MXNet makes VTune API calls to define Domains, Frames, Tasks, Events Counters, and Markers.  For a detailed explanation of these, see [Instrumentation and Tracing Technology API Reference ](https://software.intel.com/en-us/vtune-amplifier-help-instrumentation-and-tracing-technology-api-reference).  When built with CUDA NVTX ranges will be inserted into any profiles generated, which can subsequently be viewed view NVProf.
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    mxnet.profiler
-```
-
-## API Reference
-
-```eval_rst
-    .. currentmodule:: mxnet
-```
-
-### Profiling system control
-
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    profiler.set_config
-    profiler.set_state
-    profiler.pause
-    profiler.resume
-    profiler.dump
-    profiler.dumps
-```
-
-### Profiling Objects
-
-These profiling objects can be created and accessed from python in order to record performance information of the python code paths.
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    profiler.Domain
-    profiler.Task
-    profiler.Frame
-    profiler.Event
-    profiler.Counter
-    profiler.Marker
-```
-
-### Example usage
-```python
-    profiler.set_config(profile_all=True,
-                        filename='chrome_tracing_profile.json',  # File used for chrome://tracing visualization
-                        continuous_dump=True,
-                        aggregate_stats=True)  # Stats printed by dumps() call
-                        
-    profiler.set_state('run')  # Start profiling engine
-    #
-    # Profile this section of code
-    #
-    profiler.pause()  # Pause profiling
-    #
-    # Don't profile this section
-    #
-    profiler.resume()  # Resume profiling
-    #
-    # Profile this section of code 
-    #
-    profiler.set_state('stop')  # Stop profiling engine (optional)
-    print(profiler.dumps())  # Print aggregate statistics if aggregate_stats was set to True
-```
-
-## API Reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-```eval_rst
-.. automodule:: mxnet.profiler
-    :members:
-```
-<script>auto_index("api-reference");</script>
diff --git a/docs/api/python/rtc/rtc.md b/docs/api/python/rtc/rtc.md
deleted file mode 100644
index 8f5f4e773efc..000000000000
--- a/docs/api/python/rtc/rtc.md
+++ /dev/null
@@ -1,46 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Run-Time Compilation API
-
-```eval_rst
-.. currentmodule:: mxnet.rtc
-```
-
-## Overview
-
-The RTC package contains tools for compiling and running CUDA code from python
-frontend. The compiled kernels can be used stand-alone or combined with
-`autograd.Function` or `operator.CustomOpProp` to support differentiation.
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    mxnet.rtc
-```
-
-## API Reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-```eval_rst
-.. automodule:: mxnet.rtc
-    :members:
-```
-
-<script>auto_index("api-reference");</script>
diff --git a/docs/api/python/symbol/contrib.md b/docs/api/python/symbol/contrib.md
deleted file mode 100644
index 38537f7487c7..000000000000
--- a/docs/api/python/symbol/contrib.md
+++ /dev/null
@@ -1,97 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Contrib Symbol API
-
-```eval_rst
-    .. currentmodule:: mxnet.symbol.contrib
-```
-
-## Overview
-
-This document lists the contrib routines of the symbolic expression package:
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    mxnet.symbol.contrib
-```
-
-The `Contrib Symbol` API, defined in the `symbol.contrib` package, provides
-many useful experimental APIs for new features.
-This is a place for the community to try out the new features,
-so that feature contributors can receive feedback.
-
-```eval_rst
-.. warning:: This package contains experimental APIs and may change in the near future.
-```
-
-In the rest of this document, we list routines provided by the `symbol.contrib` package.
-
-## Contrib
-
-```eval_rst
-.. currentmodule:: mxnet.symbol.contrib
-
-.. autosummary::
-    :nosignatures:
-
-    AdaptiveAvgPooling2D
-    BilinearResize2D
-    CTCLoss
-    DeformableConvolution
-    DeformablePSROIPooling
-    MultiBoxDetection
-    MultiBoxPrior
-    MultiBoxTarget
-    MultiProposal
-    PSROIPooling
-    Proposal
-    ROIAlign
-    count_sketch
-    ctc_loss
-    dequantize
-    fft
-    ifft
-    quantize
-    foreach
-    while_loop
-    cond
-    index_array
-    index_copy
-    getnnz
-    edge_id
-    dgl_csr_neighbor_uniform_sample
-    dgl_csr_neighbor_non_uniform_sample
-    dgl_subgraph
-    dgl_adjacency
-    dgl_graph_compact
-```
-
-## API Reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-```eval_rst
-
-.. automodule:: mxnet.symbol.contrib
-    :members:
-
-```
-
-<script>auto_index("api-reference");</script>
diff --git a/docs/api/python/symbol/linalg.md b/docs/api/python/symbol/linalg.md
deleted file mode 100644
index 5b9afbcbb782..000000000000
--- a/docs/api/python/symbol/linalg.md
+++ /dev/null
@@ -1,78 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Linear Algebra Symbol API
-
-```eval_rst
-    .. currentmodule:: mxnet.symbol.linalg
-```
-
-## Overview
-
-This document lists the linear algebra routines of the symbolic expression package:
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    mxnet.symbol.linalg
-```
-
-The `Linear Algebra Symbol` API, defined in the `symbol.linalg` package, provides
-symbolic expressions for linear algebra routines.
-
-In the rest of this document, we list routines provided by the `symbol.linalg` package.
-
-## Linear Algebra
-
-```eval_rst
-.. currentmodule:: mxnet.symbol.linalg
-
-.. autosummary::
-    :nosignatures:
-
-    gemm
-    gemm2
-    potrf
-    potri
-    trmm
-    trsm
-    syrk
-    gelqf
-    syevd
-    sumlogdiag
-    extractdiag
-    makediag
-    extracttrian
-    maketrian
-    inverse
-    det
-    slogdet
-```
-
-## API Reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-```eval_rst
-
-.. automodule:: mxnet.symbol.linalg
-    :members:
-
-```
-
-<script>auto_index("api-reference");</script>
diff --git a/docs/api/python/symbol/random.md b/docs/api/python/symbol/random.md
deleted file mode 100644
index b62747c6ee7b..000000000000
--- a/docs/api/python/symbol/random.md
+++ /dev/null
@@ -1,76 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Random Distribution Generator Symbol API
-
-```eval_rst
-    .. currentmodule:: mxnet.symbol.random
-```
-
-## Overview
-
-This document lists the random distribution generator routines of the symbolic expression package:
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    mxnet.symbol.random
-```
-
-The `Random Distribution Generator Symbol` API, defined in the `symbol.random` package, provides
-symbolic expressions for random distribution generator routines.
-
-In the rest of this document, we list routines provided by the `symbol.random` package.
-
-## Random Distribution Generator
-
-```eval_rst
-.. currentmodule:: mxnet.symbol.random
-
-.. autosummary::
-    :nosignatures:
-
-    exponential
-    gamma
-    generalized_negative_binomial
-    multinomial
-    negative_binomial
-    normal
-    poisson
-    randint
-    shuffle
-    uniform
-    mxnet.random.seed
-```
-
-## API Reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-```eval_rst
-
-.. automodule:: mxnet.symbol.random
-    :members:
-    :noindex:
-
-.. automodule:: mxnet.random
-    :members:
-
-```
-
-<script>auto_index("api-reference");</script>
diff --git a/docs/api/python/symbol/rnn.md b/docs/api/python/symbol/rnn.md
deleted file mode 100644
index 287e5a178ad2..000000000000
--- a/docs/api/python/symbol/rnn.md
+++ /dev/null
@@ -1,378 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# RNN Cell Symbol API
-
-```eval_rst
-.. currentmodule:: mxnet.rnn
-```
-
-```eval_rst
-.. warning:: This package was experimental and may be deprecated in the near future.
-```
-
-## Overview
-
-The `rnn` module includes the recurrent neural network (RNN) cell APIs, a suite of tools for building an RNN's symbolic graph.
-```eval_rst
-.. note:: The `rnn` module offers higher-level interface while `symbol.RNN` is a lower-level interface. The cell APIs in `rnn` module are easier to use in most cases.
-```
-
-## The `rnn` module
-
-### Cell interfaces
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    BaseRNNCell.__call__
-    BaseRNNCell.unroll
-    BaseRNNCell.reset
-    BaseRNNCell.begin_state
-    BaseRNNCell.unpack_weights
-    BaseRNNCell.pack_weights
-```
-
-When working with the cell API, the precise input and output symbols
-depend on the type of RNN you are using. Take Long Short-Term Memory (LSTM) for example:
-
-```python
-import mxnet as mx
-# Shape of 'step_data' is (batch_size,).
-step_input = mx.symbol.Variable('step_data')
-
-# First we embed our raw input data to be used as LSTM's input.
-embedded_step = mx.symbol.Embedding(data=step_input, \
-                                    input_dim=input_dim, \
-                                    output_dim=embed_dim)
-
-# Then we create an LSTM cell.
-lstm_cell = mx.rnn.LSTMCell(num_hidden=50)
-# Initialize its hidden and memory states.
-# 'begin_state' method takes an initialization function, and uses 'zeros' by default.
-begin_state = lstm_cell.begin_state()
-```
-
-The LSTM cell and other non-fused RNN cells are callable. Calling the cell updates it's state once. This transformation depends on both the current input and the previous states. See this [blog post](http://colah.github.io/posts/2015-08-Understanding-LSTMs/) for a great introduction to LSTM and other RNN.
-```python
-# Call the cell to get the output of one time step for a batch.
-output, states = lstm_cell(embedded_step, begin_state)
-
-# 'output' is lstm_t0_out_output of shape (batch_size, hidden_dim).
-
-# 'states' has the recurrent states that will be carried over to the next step,
-# which includes both the "hidden state" and the "cell state":
-# Both 'lstm_t0_out_output' and 'lstm_t0_state_output' have shape (batch_size, hidden_dim).
-```
-
-Most of the time our goal is to process a sequence of many steps. For this, we need to unroll the LSTM according to the sequence length.
-```python
-# Embed a sequence. 'seq_data' has the shape of (batch_size, sequence_length).
-seq_input = mx.symbol.Variable('seq_data')
-embedded_seq = mx.symbol.Embedding(data=seq_input, \
-                                   input_dim=input_dim, \
-                                   output_dim=embed_dim)
-```
-```eval_rst
-.. note:: Remember to reset the cell when unrolling/stepping for a new sequence by calling `lstm_cell.reset()`.
-```
-```python
-# Note that when unrolling, if 'merge_outputs' is set to True, the 'outputs' is merged into a single symbol
-# In the layout, 'N' represents batch size, 'T' represents sequence length, and 'C' represents the
-# number of dimensions in hidden states.
-outputs, states = lstm_cell.unroll(length=sequence_length, \
-                                   inputs=embedded_seq, \
-                                   layout='NTC', \
-                                   merge_outputs=True)
-# 'outputs' is concat0_output of shape (batch_size, sequence_length, hidden_dim).
-# The hidden state and cell state from the final time step is returned:
-# Both 'lstm_t4_out_output' and 'lstm_t4_state_output' have shape (batch_size, hidden_dim).
-
-# If merge_outputs is set to False, a list of symbols for each of the time steps is returned.
-outputs, states = lstm_cell.unroll(length=sequence_length, \
-                                   inputs=embedded_seq, \
-                                   layout='NTC', \
-                                   merge_outputs=False)
-# In this case, 'outputs' is a list of symbols. Each symbol is of shape (batch_size, hidden_dim).
-```
-
-```eval_rst
-.. note:: Loading and saving models that are built with RNN cells API requires using
-    `mx.rnn.load_rnn_checkpoint`, `mx.rnn.save_rnn_checkpoint`, and `mx.rnn.do_rnn_checkpoint`.
-    The list of all the used cells should be provided as the first argument to those functions.
-```
-
-### Basic RNN cells
-
-`rnn` module supports the following RNN cell types.
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    LSTMCell
-    GRUCell
-    RNNCell
-```
-
-### Modifier cells
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    BidirectionalCell
-    DropoutCell
-    ZoneoutCell
-    ResidualCell
-```
-
-A modifier cell takes in one or more cells and transforms the output of those cells.
-`BidirectionalCell` is one example. It takes two cells for forward unroll and backward unroll
-respectively. After unrolling, the outputs of the forward and backward pass are concatenated.
-```python
-# Bidirectional cell takes two RNN cells, for forward and backward pass respectively.
-# Having different types of cells for forward and backward unrolling is allowed.
-bi_cell = mx.rnn.BidirectionalCell(
-                 mx.rnn.LSTMCell(num_hidden=50),
-                 mx.rnn.GRUCell(num_hidden=75))
-outputs, states = bi_cell.unroll(length=sequence_length, \
-                                 inputs=embedded_seq, \
-                                 merge_outputs=True)
-# The output feature is the concatenation of the forward and backward pass.
-# Thus, the number of output dimensions is the sum of the dimensions of the two cells.
-# 'outputs' is the symbol 'bi_out_output' of shape (batch_size, sequence_length, 125L)
-
-# The states of the BidirectionalCell is a list of two lists, corresponding to the
-# states of the forward and backward cells respectively.
-```
-```eval_rst
-.. note:: BidirectionalCell cannot be called or stepped, because the backward unroll requires the output of
-    future steps, and thus the whole sequence is required.
-```
-
-Dropout and zoneout are popular regularization techniques that can be applied to RNN. `rnn`
-module provides `DropoutCell` and `ZoneoutCell` for regularization on the output and recurrent
-states of RNN. `ZoneoutCell` takes one RNN cell in the constructor, and supports unrolling like
-other cells.
-```python
-zoneout_cell = mx.rnn.ZoneoutCell(lstm_cell, zoneout_states=0.5)
-outputs, states = zoneout_cell.unroll(length=sequence_length, \
-                                      inputs=embedded_seq, \
-                                      merge_outputs=True)
-```
-`DropoutCell` performs dropout on the input sequence. It can be used in a stacked
-multi-layer RNN setting, which we will cover next.
-
-Residual connection is a useful technique for training deep neural models because it helps the
-propagation of gradients by shortening the paths.  `ResidualCell` provides such functionality for
-RNN models.
-```python
-residual_cell = mx.rnn.ResidualCell(lstm_cell)
-outputs, states = residual_cell.unroll(length=sequence_length, \
-                                       inputs=embedded_seq, \
-                                       merge_outputs=True)
-```
-The `outputs` are the element-wise sum of both the input and the output of the LSTM cell.
-
-### Multi-layer cells
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    SequentialRNNCell
-    SequentialRNNCell.add
-```
-
-The `SequentialRNNCell` allows stacking multiple layers of RNN cells to improve the expressiveness
-and performance of the model. Cells can be added to a `SequentialRNNCell` in order, from bottom to
-top. When unrolling, the output of a lower-level cell is automatically passed to the cell above.
-
-```python
-stacked_rnn_cells = mx.rnn.SequentialRNNCell()
-stacked_rnn_cells.add(mx.rnn.BidirectionalCell(
-                          mx.rnn.LSTMCell(num_hidden=50),
-                          mx.rnn.LSTMCell(num_hidden=50)))
-
-# Dropout the output of the bottom layer BidirectionalCell with a retention probability of 0.5.
-stacked_rnn_cells.add(mx.rnn.DropoutCell(0.5))
-
-stacked_rnn_cells.add(mx.rnn.LSTMCell(num_hidden=50))
-outputs, states = stacked_rnn_cells.unroll(length=sequence_length, \
-                                           inputs=embedded_seq, \
-                                           merge_outputs=True)
-
-# The output of SequentialRNNCell is the same as that of the last layer.
-# In this case 'outputs' is the symbol 'concat6_output' of shape (batch_size, sequence_length, hidden_dim)
-# The states of the SequentialRNNCell is a list of lists, with each list
-# corresponding to the states of each of the added cells respectively.
-```
-
-### Fused RNN cell
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    FusedRNNCell
-    FusedRNNCell.unfuse
-```
-
-The computation of an RNN for an input sequence consists of many GEMM and point-wise operations with
-temporal dependencies dependencies. This could make the computation memory-bound especially on GPU,
-resulting in longer wall-time. By combining the computation of many small matrices into that of
-larger ones and streaming the computation whenever possible, the ratio of computation to memory I/O
-can be increased, which results in better performance on GPU. Such optimization technique is called
-"fusing".
-[This post](https://devblogs.nvidia.com/parallelforall/optimizing-recurrent-neural-networks-cudnn-5/)
-talks in greater detail.
-
-The `rnn` module includes a `FusedRNNCell`, which provides the optimized fused implementation.
-The FusedRNNCell supports bidirectional RNNs and dropout.
-
-```python
-fused_lstm_cell = mx.rnn.FusedRNNCell(num_hidden=50, \
-                                      num_layers=3, \
-                                      mode='lstm', \
-                                      bidirectional=True, \
-                                      dropout=0.5)
-outputs, _ = fused_lstm_cell.unroll(length=sequence_length, \
-                                    inputs=embedded_seq, \
-                                    merge_outputs=True)
-# The 'outputs' is the symbol 'lstm_rnn_output' that has the shape
-# (batch_size, sequence_length, forward_backward_concat_dim)
-```
-```eval_rst
-.. note:: `FusedRNNCell` supports GPU-only. It cannot be called or stepped.
-.. note:: When `dropout` is set to non-zero in `FusedRNNCell`, the dropout is applied to the
-    output of all layers except the last layer. If there is only one layer in the `FusedRNNCell`, the
-    dropout rate is ignored.
-.. note:: Similar to `BidirectionalCell`, when `bidirectional` flag is set to `True`, the output
-    of `FusedRNNCell` is twice the size specified by `num_hidden`.
-```
-
-When training a deep, complex model *on multiple GPUs* it's recommended to stack
-fused RNN cells (one layer per cell) together instead of one with all layers.
-The reason is that fused RNN cells don't set gradients to be ready until the
-computation for the entire layer is completed. Breaking a multi-layer fused RNN
-cell into several one-layer ones allows gradients to be processed ealier. This
-reduces communication overhead, especially with multiple GPUs.
-
-The `unfuse()` method can be used to convert the `FusedRNNCell` into an equivalent
-and CPU-compatible `SequentialRNNCell` that mirrors the settings of the `FusedRNNCell`.
-```python
-unfused_lstm_cell = fused_lstm_cell.unfuse()
-unfused_outputs, _ = unfused_lstm_cell.unroll(length=sequence_length, \
-                                              inputs=embedded_seq, \
-                                              merge_outputs=True)
-# The 'outputs' is the symbol 'lstm_bi_l2_out_output' that has the shape
-# (batch_size, sequence_length, forward_backward_concat_dim)
-```
-
-### RNN checkpoint methods and parameters
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    save_rnn_checkpoint
-    load_rnn_checkpoint
-    do_rnn_checkpoint
-```
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    RNNParams
-    RNNParams.get
-```
-
-The model parameters from the training with fused cell can be used for inference with unfused cell,
-and vice versa. As the parameters of fused and unfused cells are organized differently, they need to
-be converted first. `FusedRNNCell`'s parameters are merged and flattened. In the fused example above,
-the mode has `lstm_parameters` of shape `(total_num_params,)`, whereas the
-equivalent SequentialRNNCell's parameters are separate:
-```python
-'lstm_l0_i2h_weight': (out_dim, embed_dim)
-'lstm_l0_i2h_bias': (out_dim,)
-'lstm_l0_h2h_weight': (out_dim, hidden_dim)
-'lstm_l0_h2h_bias': (out_dim,)
-'lstm_r0_i2h_weight': (out_dim, embed_dim)
-...
-```
-
-All cells in the `rnn` module support the method `unpack_weights()` for converting `FusedRNNCell`
-parameters to the unfused format and `pack_weights()` for fusing the parameters. The RNN-specific
-checkpointing methods (`load_rnn_checkpoint, save_rnn_checkpoint, do_rnn_checkpoint`) handle the
-conversion transparently based on the provided cells.
-
-### I/O utilities
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    BucketSentenceIter
-    encode_sentences
-```
-
-## API Reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-```eval_rst
-.. autoclass:: mxnet.rnn.BaseRNNCell
-    :members:
-
-    .. automethod:: __call__
-.. autoclass:: mxnet.rnn.LSTMCell
-    :members:
-.. autoclass:: mxnet.rnn.GRUCell
-    :members:
-.. autoclass:: mxnet.rnn.RNNCell
-    :members:
-.. autoclass:: mxnet.rnn.FusedRNNCell
-    :members:
-.. autoclass:: mxnet.rnn.SequentialRNNCell
-    :members:
-.. autoclass:: mxnet.rnn.BidirectionalCell
-    :members:
-.. autoclass:: mxnet.rnn.DropoutCell
-    :members:
-.. autoclass:: mxnet.rnn.ZoneoutCell
-    :members:
-.. autoclass:: mxnet.rnn.ResidualCell
-    :members:
-.. autoclass:: mxnet.rnn.RNNParams
-    :members:
-
-
-.. autoclass:: mxnet.rnn.BucketSentenceIter
-    :members:
-.. automethod:: mxnet.rnn.encode_sentences
-
-.. automethod:: mxnet.rnn.save_rnn_checkpoint
-
-.. automethod:: mxnet.rnn.load_rnn_checkpoint
-
-.. automethod:: mxnet.rnn.do_rnn_checkpoint
-
-```
-
-<script>auto_index("api-reference");</script>
diff --git a/docs/api/python/symbol/sparse.md b/docs/api/python/symbol/sparse.md
deleted file mode 100644
index 473bb4bbe530..000000000000
--- a/docs/api/python/symbol/sparse.md
+++ /dev/null
@@ -1,241 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Sparse Symbol API
-
-```eval_rst
-    .. currentmodule:: mxnet.symbol.sparse
-```
-
-## Overview
-
-This document lists the routines of the sparse symbolic expression package:
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    mxnet.symbol.sparse
-```
-
-The `Sparse Symbol` API, defined in the `symbol.sparse` package, provides
-sparse neural network graphs and auto-differentiation.
-
-The storage type of a variable is speficied by the `stype` attribute of the variable.
-The storage type of a symbolic expression is inferred based on the storage types of the variables and the operators.
-
-```python
->>> a = mx.sym.Variable('a', stype='csr')
->>> b = mx.sym.Variable('b')
->>> c = mx.sym.dot(a, b, transpose_a=True)
->>> type(c)
-<class 'mxnet.symbol.Symbol'>
->>> e = c.bind(mx.cpu(), {'a': mx.nd.array([[1,0,0]]).tostype('csr'), 'b':mx.nd.ones((1,2))})
->>> y = e.forward()
-# the result storage type of dot(csr.T, dense) is inferred to be `row_sparse`
->>> y
-[<RowSparseNDArray 3x2 @cpu(0)>]
->>> y[0].asnumpy()
-array([ 1.,  1.],
-      [ 0.,  0.],
-      [ 0.,  0.]], dtype=float32)
-```
-
-```eval_rst
-
-.. note:: most operators provided in ``mxnet.symbol.sparse`` are similar to those in
-   ``mxnet.symbol`` although there are few differences:
-
-   - Only a subset of operators in ``mxnet.symbol`` have efficient sparse implementations in ``mxnet.symbol.sparse``.
-   - If an operator do not occur in the ``mxnet.symbol.sparse`` namespace, that means the operator does not have an efficient sparse implementation yet. If sparse inputs are passed to such an operator, it will convert inputs to the dense format and fallback to the already available dense implementation.
-   - The storage types (``stype``) of sparse operators' outputs depend on the storage types of inputs.
-     By default the operators not available in ``mxnet.symbol.sparse`` infer "default" (dense) storage type for outputs.
-     Please refer to the API reference section for further details on specific operators.
-
-```
-
-In the rest of this document, we list sparse related routines provided by the
-`symbol.sparse` package.
-
-## Symbol creation routines
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    zeros_like
-    mxnet.symbol.var
-```
-
-## Symbol manipulation routines
-
-### Changing symbol storage type
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    cast_storage
-```
-
-### Joining arrays
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    concat
-```
-
-### Indexing routines
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    slice
-    retain
-```
-
-## Mathematical functions
-
-### Arithmetic operations
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    elemwise_add
-    elemwise_sub
-    elemwise_mul
-    broadcast_add
-    broadcast_sub
-    broadcast_mul
-    broadcast_div
-    negative
-    dot
-    add_n
-```
-
-### Trigonometric functions
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    sin
-    tan
-    arcsin
-    arctan
-    degrees
-    radians
-```
-
-### Hyperbolic functions
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    sinh
-    tanh
-    arcsinh
-    arctanh
-```
-
-### Reduce functions
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    sum
-    mean
-```
-
-### Rounding
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    round
-    rint
-    fix
-    floor
-    ceil
-    trunc
-```
-
-### Exponents and logarithms
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    expm1
-    log1p
-```
-
-### Powers
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    sqrt
-    square
-```
-
-### Miscellaneous
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    clip
-    abs
-    sign
-```
-
-## Neural network
-
-### More
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    make_loss
-    stop_gradient
-    Embedding
-    LinearRegressionOutput
-    LogisticRegressionOutput
-```
-
-## API Reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-```eval_rst
-
-.. automodule:: mxnet.symbol.sparse
-    :members:
-
-```
-
-<script>auto_index("api-reference");</script>
diff --git a/docs/api/python/symbol/symbol.md b/docs/api/python/symbol/symbol.md
deleted file mode 100644
index 264f69093709..000000000000
--- a/docs/api/python/symbol/symbol.md
+++ /dev/null
@@ -1,739 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Symbol API
-
-```eval_rst
-    .. currentmodule:: mxnet.symbol
-```
-
-## Overview
-
-This document lists the routines of the symbolic expression package:
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    mxnet.symbol
-```
-
-The `Symbol` API, defined in the `symbol` (or simply `sym`) package, provides
-neural network graphs and auto-differentiation.
-A symbol represents a multi-output symbolic expression.
-They are composited by operators, such as simple matrix operations (e.g. “+”),
-or a neural network layer (e.g. convolution layer).
-An operator can take several input variables,
-produce more than one output variables, and have internal state variables.
-A variable can be either free, which we can bind with value later,
-or an output of another symbol.
-
-```python
->>> a = mx.sym.Variable('a')
->>> b = mx.sym.Variable('b')
->>> c = 2 * a + b
->>> type(c)
-<class 'mxnet.symbol.Symbol'>
->>> e = c.bind(mx.cpu(), {'a': mx.nd.array([1,2]), 'b':mx.nd.array([2,3])})
->>> y = e.forward()
->>> y
-[<NDArray 2 @cpu(0)>]
->>> y[0].asnumpy()
-array([ 4.,  7.], dtype=float32)
-```
-
-A detailed tutorial is available at [Symbol - Neural network graphs and auto-differentiation](http://mxnet.io/tutorials/basic/symbol.html).
-<br><br>
-
-```eval_rst
-
-.. note:: most operators provided in ``symbol`` are similar to those in ``ndarray``
-   although there are few differences:
-
-   - ``symbol`` adopts declarative programming. In other words, we need to first
-     compose the computations, and then feed it with data for execution whereas
-     ndarray adopts imperative programming.
-
-   - Most binary operators in ``symbol`` such as ``+`` and ``>`` don't broadcast.
-     We need to call the broadcast version of the operator such as ``broadcast_plus``
-     explicitly.
-```
-
-In the rest of this document, we first overview the methods provided by the
-`symbol.Symbol` class, and then list other routines provided by the
-`symbol` package.
-
-## The `Symbol` class
-
-### Composition
-
-Composite multiple symbols into a new one by an operator.
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Symbol.__call__
-```
-
-#### Arithmetic operations
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Symbol.__add__
-    Symbol.__sub__
-    Symbol.__rsub__
-    Symbol.__neg__
-    Symbol.__mul__
-    Symbol.__div__
-    Symbol.__rdiv__
-    Symbol.__mod__
-    Symbol.__rmod__
-    Symbol.__pow__
-```
-
-#### Trigonometric functions
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Symbol.sin
-    Symbol.cos
-    Symbol.tan
-    Symbol.arcsin
-    Symbol.arccos
-    Symbol.arctan
-    Symbol.degrees
-    Symbol.radians
-```
-
-#### Hyperbolic functions
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Symbol.sinh
-    Symbol.cosh
-    Symbol.tanh
-    Symbol.arcsinh
-    Symbol.arccosh
-    Symbol.arctanh
-```
-
-#### Exponents and logarithms
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Symbol.exp
-    Symbol.expm1
-    Symbol.log
-    Symbol.log10
-    Symbol.log2
-    Symbol.log1p
-```
-
-#### Powers
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Symbol.sqrt
-    Symbol.rsqrt
-    Symbol.cbrt
-    Symbol.rcbrt
-    Symbol.square
-```
-
-## Basic neural network functions
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Symbol.relu
-    Symbol.sigmoid
-    Symbol.softmax
-    Symbol.log_softmax
-```
-
-#### Comparison operators
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Symbol.__lt__
-    Symbol.__le__
-    Symbol.__gt__
-    Symbol.__ge__
-    Symbol.__eq__
-    Symbol.__ne__
-```
-
-### Symbol creation
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Symbol.zeros_like
-    Symbol.ones_like
-    Symbol.diag
-```
-
-### Changing shape and type
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Symbol.astype
-    Symbol.shape_array
-    Symbol.size_array
-    Symbol.reshape
-    Symbol.reshape_like
-    Symbol.flatten
-    Symbol.expand_dims
-```
-
-### Expanding elements
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Symbol.broadcast_to
-    Symbol.broadcast_axes
-    Symbol.broadcast_like
-    Symbol.tile
-    Symbol.pad
-```
-
-### Rearranging elements
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Symbol.transpose
-    Symbol.swapaxes
-    Symbol.flip
-    Symbol.depth_to_space
-    Symbol.space_to_depth
-```
-
-### Reduce functions
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Symbol.sum
-    Symbol.nansum
-    Symbol.prod
-    Symbol.nanprod
-    Symbol.mean
-    Symbol.max
-    Symbol.min
-    Symbol.norm
-```
-
-### Rounding
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Symbol.round
-    Symbol.rint
-    Symbol.fix
-    Symbol.floor
-    Symbol.ceil
-    Symbol.trunc
-```
-
-### Sorting and searching
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Symbol.sort
-    Symbol.argsort
-    Symbol.topk
-    Symbol.argmax
-    Symbol.argmin
-    Symbol.argmax_channel
-```
-
-### Query information
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Symbol.name
-    Symbol.list_arguments
-    Symbol.list_outputs
-    Symbol.list_auxiliary_states
-    Symbol.list_attr
-    Symbol.attr
-    Symbol.attr_dict
-```
-
-### Indexing
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Symbol.slice
-    Symbol.slice_axis
-    Symbol.slice_like
-    Symbol.take
-    Symbol.one_hot
-    Symbol.pick
-    ravel_multi_index
-    unravel_index
-```
-
-### Get internal and output symbol
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Symbol.__getitem__
-    Symbol.__iter__
-    Symbol.get_internals
-    Symbol.get_children
-```
-
-### Inference type and shape
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Symbol.infer_type
-    Symbol.infer_type_partial
-    Symbol.infer_shape
-    Symbol.infer_shape_partial
-```
-
-
-### Bind
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Symbol.bind
-    Symbol.simple_bind
-```
-
-### Save
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Symbol.save
-    Symbol.tojson
-    Symbol.debug_str
-```
-
-### Miscellaneous
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Symbol.clip
-    Symbol.sign
-```
-
-## Symbol creation routines
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    var
-    zeros
-    zeros_like
-    ones
-    ones_like
-    arange
-```
-
-## Symbol manipulation routines
-
-### Changing shape and type
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    cast
-    shape_array
-    size_array
-    reshape
-    reshape_like
-    flatten
-    expand_dims
-    diag
-```
-
-### Expanding elements
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    broadcast_to
-    broadcast_axes
-    broadcast_like
-    repeat
-    tile
-    pad
-```
-
-### Rearranging elements
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    transpose
-    swapaxes
-    flip
-    depth_to_space
-    space_to_depth
-```
-
-### Joining and splitting symbols
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    concat
-    split
-    stack
-```
-
-### Indexing routines
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    slice
-    slice_axis
-    slice_like
-    take
-    batch_take
-    one_hot
-    pick
-    where
-    gather_nd
-    scatter_nd
-```
-
-## Mathematical functions
-
-### Arithmetic operations
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    broadcast_add
-    broadcast_sub
-    broadcast_mul
-    broadcast_div
-    broadcast_mod
-    negative
-    dot
-    batch_dot
-    add_n
-```
-
-### Trigonometric functions
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    sin
-    cos
-    tan
-    arcsin
-    arccos
-    arctan
-    hypot
-    broadcast_hypot
-    degrees
-    radians
-```
-
-### Hyperbolic functions
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    sinh
-    cosh
-    tanh
-    arcsinh
-    arccosh
-    arctanh
-```
-
-### Reduce functions
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    sum
-    nansum
-    prod
-    nanprod
-    mean
-    max
-    min
-    norm
-```
-
-### Rounding
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    round
-    rint
-    fix
-    floor
-    ceil
-    trunc
-```
-
-### Exponents and logarithms
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    exp
-    expm1
-    log
-    log10
-    log2
-    log1p
-```
-
-### Powers
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    broadcast_power
-    sqrt
-    rsqrt
-    cbrt
-    rcbrt
-    square
-    reciprocal
-```
-
-### Comparison
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    broadcast_equal
-    broadcast_not_equal
-    broadcast_greater
-    broadcast_greater_equal
-    broadcast_lesser
-    broadcast_lesser_equal
-```
-
-### Logical
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    broadcast_logical_and
-    broadcast_logical_or
-    broadcast_logical_xor
-    logical_not
-```
-
-### Random sampling
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    random.exponential
-    random.gamma
-    random.generalized_negative_binomial
-    random.multinomial
-    random.negative_binomial
-    random.normal
-    random.poisson
-    random.randint
-    random.randn
-    random.shuffle
-    random.uniform
-    mxnet.random.seed
-```
-
-### Sorting and searching
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    sort
-    topk
-    argsort
-    argmax
-    argmin
-```
-
-### Sequence operation
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    SequenceLast
-    SequenceMask
-    SequenceReverse
-```
-
-### Miscellaneous
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    maximum
-    minimum
-    broadcast_maximum
-    broadcast_minimum
-    clip
-    abs
-    sign
-    gamma
-    gammaln
-```
-
-## Neural network
-
-### Basic
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    FullyConnected
-    Convolution
-    Activation
-    BatchNorm
-    Pooling
-    SoftmaxOutput
-    softmax
-    log_softmax
-    relu
-    sigmoid
-    erf
-    erfinv
-```
-
-### More
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    Correlation
-    Deconvolution
-    RNN
-    Embedding
-    LeakyReLU
-    InstanceNorm
-    LayerNorm
-    L2Normalization
-    LRN
-    ROIPooling
-    SoftmaxActivation
-    Dropout
-    BilinearSampler
-    GridGenerator
-    UpSampling
-    SpatialTransformer
-    LinearRegressionOutput
-    LogisticRegressionOutput
-    MAERegressionOutput
-    SVMOutput
-    softmax_cross_entropy
-    smooth_l1
-    IdentityAttachKLSparseReg
-    MakeLoss
-    BlockGrad
-    Custom
-```
-
-## API Reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-```eval_rst
-
-.. autoclass:: mxnet.symbol.Symbol
-    :members:
-    :special-members:
-
-.. automodule:: mxnet.symbol
-    :members:
-    :imported-members:
-    :special-members:
-    :exclude-members: Symbol
-
-.. automodule:: mxnet.symbol.random
-    :members:
-
-```
-
-<script>auto_index("api-reference");</script>
diff --git a/docs/api/python/symbol_in_pictures/symbol_in_pictures.md b/docs/api/python/symbol_in_pictures/symbol_in_pictures.md
deleted file mode 100644
index 23a9757b83d6..000000000000
--- a/docs/api/python/symbol_in_pictures/symbol_in_pictures.md
+++ /dev/null
@@ -1,94 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Symbolic Configuration and Execution in Pictures
-
-This topic explains symbolic construction and execution in pictures.
-We recommend that you also read [Symbolic API](../symbol/symbol.md).
-
-## Compose Symbols
-
-Symbols are a description of the computation that you want to perform. The symbolic construction API generates the computation
-graph that describes the computation. The following picture shows how you compose symbols to describe basic computations.
-
-![Symbol Compose](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/compose_basic.png)
-
-- The ```mxnet.symbol.Variable``` function creates argument nodes that represent input to the computation.
-- The symbol is overloaded with basic element-wise mathematical operations.
-
-## Configure Neural Networks
-
-In addition to supporting fine-grained operations, MXNet provides a way to perform big operations that are analogous to layers in neural networks.
-You can use operators to describe the configuration of a neural network.
-
-![Net Compose](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/compose_net.png)
-
-
-## Example of a Multi-Input Network
-
-The following example shows how to configure multiple input neural networks.
-
-![Multi Input](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/compose_multi_in.png)
-
-
-## Bind and Execute Symbol
-
-When you need to execute a symbol graph, you call the bind function to bind ```NDArrays``` to the argument nodes
-in order to obtain an ```Executor```.
-
-![Bind](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/bind_basic.png)
-
-To get the output results, given the bound NDArrays as input, you can call ```Executor.Forward```.
-
-![Forward](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/executor_forward.png)
-
-
-## Bind Multiple Outputs
-
-To group symbols, then bind them to
-get the outputs of both, use ```mx.symbol.Group```.
-
-![MultiOut](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/executor_multi_out.png)
-
-Remember: Bind only what you need, so that the system can perform more optimizations.
-
-
-## Calculate the Gradient
-
-In the bind function, you can specify NDArrays that will hold gradients. Calling ```Executor.backward``` after ```Executor.forward``` gives you the corresponding gradients.
-
-![Gradient](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/executor_backward.png)
-
-
-## Simple Bind Interface for Neural Networks
-
-It can be tedious to pass the argument NDArrays to the bind function, especially when you are binding a big
-graph. ```Symbol.simple_bind``` provides a way to simplify
-the procedure. You need to specify only input data shapes. The function allocates the arguments, and binds
-the Executor for you.
-
-![SimpleBind](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/executor_simple_bind.png)
-
-## Auxiliary States
-
-Auxiliary states are just like arguments, except that you can't take the gradient of them. Although auxiliary states might not be part of the computation, they can be helpful for tracking. You can pass auxiliary states in the same way that you pass arguments.
-
-![SimpleBind](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/executor_aux_state.png)
-
-## Next Steps
-
-See [Symbolic API](../symbol/symbol.md) and [Python Documentation](../index.md).
diff --git a/docs/api/python/tools/test_utils.md b/docs/api/python/tools/test_utils.md
deleted file mode 100644
index a442e9b46941..000000000000
--- a/docs/api/python/tools/test_utils.md
+++ /dev/null
@@ -1,44 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Test Utilities
-
-This module has a variety of tools that help using and testing MXNet.
-
-```eval_rst
-    .. currentmodule:: mxnet.test_utils
-```
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    mxnet.test_utils
-```
-
-## API Reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-```eval_rst
-
-.. automodule:: mxnet.test_utils
-    :members:
-
-```
-
-<script>auto_index("api-reference");</script>
diff --git a/docs/api/python/tools/visualization.md b/docs/api/python/tools/visualization.md
deleted file mode 100644
index c807ce6fbe05..000000000000
--- a/docs/api/python/tools/visualization.md
+++ /dev/null
@@ -1,44 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Visualization
-
-This module contains visualization features.
-
-```eval_rst
-    .. currentmodule:: mxnet.visualization
-```
-
-```eval_rst
-.. autosummary::
-    :nosignatures:
-
-    mxnet.visualization
-```
-
-## API Reference
-
-<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
-
-```eval_rst
-
-.. automodule:: mxnet.visualization
-    :members:
-
-```
-
-<script>auto_index("api-reference");</script>
diff --git a/docs/api/r/index.md b/docs/api/r/index.md
deleted file mode 100644
index e8032cf62bfb..000000000000
--- a/docs/api/r/index.md
+++ /dev/null
@@ -1,44 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# MXNet - R API
-
-See the [MXNet R Reference Manual](https://s3.amazonaws.com/mxnet-prod/docs/R/mxnet-r-reference-manual.pdf).
-
-MXNet supports the R programming language. The MXNet R package brings flexible and efficient GPU
-computing and state-of-art deep learning to R. It enables you to write seamless tensor/matrix computation with multiple GPUs in R. It also lets you construct and customize the state-of-art deep learning models in R,
-  and apply them to tasks, such as image classification and data science challenges.
-
-You can perform tensor or matrix computation in R:
-
-```r
-   > require(mxnet)
-   Loading required package: mxnet
-   > a <- mx.nd.ones(c(2,3))
-   > a
-        [,1] [,2] [,3]
-   [1,]    1    1    1
-   [2,]    1    1    1
-   > a + 1
-        [,1] [,2] [,3]
-   [1,]    2    2    2
-   [2,]    2    2    2
-```
-## Resources
-
-* [MXNet R Reference Manual](https://s3.amazonaws.com/mxnet-prod/docs/R/mxnet-r-reference-manual.pdf)
-* [MXNet for R Tutorials](../../tutorials/r/index.html)
diff --git a/docs/api/scala/index.md b/docs/api/scala/index.md
deleted file mode 100644
index bda1945b769d..000000000000
--- a/docs/api/scala/index.md
+++ /dev/null
@@ -1,82 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# MXNet - Scala API
-
-MXNet supports the Scala programming language. The MXNet Scala package brings flexible and efficient GPU
-computing and state-of-art deep learning to Scala. It enables you to write seamless tensor/matrix computation with multiple GPUs in Scala. It also lets you construct and customize the state-of-art deep learning models in Scala, and apply them to tasks, such as image classification and data science challenges.
-
-See the [MXNet Scala API Documentation](docs/index.html#org.apache.mxnet.package) for detailed API information.
-
-```eval_rst
-.. toctree::
-   :maxdepth: 1
-
-   infer.md
-   io.md
-   kvstore.md
-   model.md
-   module.md
-   ndarray.md
-   symbol_in_pictures.md
-   symbol.md
-```
-
-
-## Image Classification with the Scala Infer API
-The Infer API can be used for single and batch image classification. More information can be found at the following locations:
-
-* [Infer API Overview](infer.html)
-* [Infer API Scaladocs](docs/index.html#org.apache.mxnet.infer.package)
-* [Single Shot Detector Inference Example](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/objectdetector)
-* [Image Classification Example](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/imageclassifier)
-
-
-## Tensor and Matrix Computations
-You can perform tensor or matrix computation in pure Scala:
-
-```scala
-   import org.apache.mxnet._
-
-   val arr = NDArray.ones(2, 3)
-   // arr: org.apache.mxnet.NDArray = org.apache.mxnet.NDArray@f5e74790
-
-   arr.shape
-   // org.apache.mxnet.Shape = (2,3)
-
-   (arr * 2).toArray
-   // Array[Float] = Array(2.0, 2.0, 2.0, 2.0, 2.0, 2.0)
-
-   (arr * 2).shape
-   // org.apache.mxnet.Shape = (2,3)
-```
-
-
-## Scala API Tutorials
-* [Module API is a flexible high-level interface for training neural networks.](module.html)
-* [Symbolic API performs operations on NDArrays to assemble neural networks from layers.](symbol.html)
-* [IO Data Loading API performs parsing and data loading.](io.html)
-* [NDArray API performs vector/matrix/tensor operations.](ndarray.html)
-* [KVStore API performs multi-GPU and multi-host distributed training.](kvstore.html)
-* [Model API is an alternate simple high-level interface for training neural networks.](model.html) **DEPRECATED**
-
-## Related Resources
-* [MXNet Scala API Documentation](docs/index.html#org.apache.mxnet.package)
-* [Handwritten Digit Classification in Scala](../../tutorials/scala/mnist.html)
-* [Developing a Character-level Language Model in Scala](../../tutorials/scala/char_lstm.html)
-* [Neural Style in Scala on MXNet](https://github.com/apache/incubator-mxnet/blob/master/scala-package/examples/src/main/scala/org/apache/mxnetexamples/neuralstyle/NeuralStyle.scala)
-* [More Scala Examples](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/scala/org/apache/mxnetexamples)
diff --git a/docs/api/scala/infer.md b/docs/api/scala/infer.md
deleted file mode 100644
index d9a4a62bb7c7..000000000000
--- a/docs/api/scala/infer.md
+++ /dev/null
@@ -1,59 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Infer API
-The MXNet Scala Infer API provides you with model loading and inference functionality using the MXNet Scala package.
-
-
-## Prerequisites
-To use the Infer API you must first install the MXNet Scala package. Instructions for this are provided in the following variations:
-* [Tutorial for setting up a project in the IntelliJ IDE](../../tutorials/scala/mxnet_scala_on_intellij.html)
-* [Installing the MXNet Scala Package for macOS](../../install/ubuntu_setup.html#install-the-mxnet-package-for-scala)
-* [Installing the MXNet Scala for Linux](../../install/ubuntu_setup.html#install-the-mxnet-package-for-scala)
-
-
-## Inference
-The Scala Infer API includes both single image and batch modes. Here is an example of running inference on a single image by using the `ImageClassifier` class. A complete [image classification example](https://github.com/apache/incubator-mxnet/blob/master/scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/imageclassifier/ImageClassifierExample.scala) using ResNet-152 is provided in the [Scala package's example folder](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/scala/org/apache/mxnetexamples). This example also demonstrates inference with batches of images.
-
-```scala
-def runInferenceOnSingleImage(modelPathPrefix: String, inputImagePath: String,
-                              context: Array[Context]):
-IndexedSeq[IndexedSeq[(String, Float)]] = {
-  val dType = DType.Float32
-  val inputShape = Shape(1, 3, 224, 224)
-
-  val inputDescriptor = IndexedSeq(DataDesc("data", inputShape, dType, "NCHW"))
-
-  // Create object of ImageClassifier class
-  val imgClassifier: ImageClassifier = new
-      ImageClassifier(modelPathPrefix, inputDescriptor, context)
-
-  // Loading single image from file and getting BufferedImage
-  val img = ImageClassifier.loadImageFromFile(inputImagePath)
-
-  // Running inference on single image
-  val output = imgClassifier.classifyImage(img, Some(5))
-
-  output
-}
-```
-
-
-## Related Resources
-* [Infer API Scaladocs](docs/index.html#org.apache.mxnet.infer.package)
-* [Single Shot Detector Inference Example](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/objectdetector)
-* [Image Classification Example](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/imageclassifier)
diff --git a/docs/api/scala/io.md b/docs/api/scala/io.md
deleted file mode 100644
index 9a7a865f9ea5..000000000000
--- a/docs/api/scala/io.md
+++ /dev/null
@@ -1,182 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# MXNet Scala Data Loading API
-This topic introduces the data input method for MXNet. MXNet uses an iterator to provide data to the neural network.  Iterators do some preprocessing and generate batches for the neural network.
-
-MXNet provides basic iterators for MNIST and RecordIO images. To hide the cost of I/O, MXNet uses a prefetch strategy that enables parallelism for the learning process and data fetching. Data is automatically fetched by an independent thread.
-
-Topics:
-
-* [Data Iterator Parameters](#parameters-for-data-iterator) clarifies the different usages for dataiter parameters.
-* [Create a Data Iterator](#create-a-data-iterator) introduces how to create a data iterator in MXNet for Scala.
-* [How to Get Data](#how-to-get-data) introduces the data resource and data preparation tools.
-* [IO API Reference](http://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.IO$) explains the IO API.
-
-
-## Data Iterator Parameters
-
-To create a data iterator, you typically need to provide five parameters:
-
-* **Dataset Param** provides basic information about the dataset, e.g., file path, input shape.
-* **Batch Param** provides information required to form a batch, e.g., batch size.
-* **Augmentation Param** tells MXNet which augmentation operations (e.g., crop or mirror) to perform on an input image.
-* **Backend Param** controls the behavior of the back-end threads to hide the cost of data loading.
-* **Auxiliary Param** provides options for checking and debugging.
-
-You *must* provide the **Dataset Param** and **Batch Param**, otherwise MXNet can't create the data batch. Provide other parameters as required by your algorithm and performance needs. We provide a detailed explanation and examples of the options later.
-
-## Create a Data Iterator
-
-The IO API provides a simple way to create a data iterator in Scala.
-The following example code shows how to create a CIFAR data iterator.
-
-```scala
-     val dataiter = IO.ImageRecordIter(Map(
-            // Utility Parameter
-            // Optional
-            // Name of the data, should match the name of the data input of the network
-            // data_name='data',
-            // Utility Parameter
-            // Optional
-            // Name of the label, should match the name of the label parameter of the network
-            // Usually, if the loss layer is named 'foo', then the label input has the name
-            // 'foo_label', unless overwritten
-            // label_name='softmax_label',
-            // Dataset Parameter
-            // Impulsary
-            // indicating the data file, please check the data is already there
-            "path_imgrec" -> "data/cifar/train.rec",
-            // Dataset Parameter
-            // Impulsary
-            // indicating the image size after preprocessing
-            "data_shape" -> "(3,28,28)",
-            // Batch Parameter
-            // Impulsary
-            // tells how many images in a batch
-            "batch_size" -> "100",
-            // Augmentation Parameter
-            // Optional
-            // when offers mean_img, each image will subtract the mean value at each pixel
-            "mean_img" -> "data/cifar/cifar10_mean.bin",
-            // Augmentation Parameter
-            // Optional
-            // randomly crop a patch of the data_shape from the original image
-           "rand_crop" -> "True",
-            // Augmentation Parameter
-            // Optional
-            // randomly mirror the image horizontally
-            "rand_mirror" -> "True",
-            // Augmentation Parameter
-            // Optional
-            // randomly shuffle the data
-            "shuffle" -> "False",
-            // Backend Parameter
-            // Optional
-            // Preprocessing thread number
-            "preprocess_threads" -> "4",
-            // Backend Parameter
-            // Optional
-            // Prefetch buffer size
-            "prefetch_buffer" = "1"))
-```
-
-First, explicitly specify the kind of data (MNIST, ImageRecord, etc.) to fetch. Then, provide the options for the dataset, batching, image augmentation, multi-tread processing,  and prefetching operations. The code automatically validates the parameters. If a required parameter is missing, MXNet returns an error.
-
-## How to Get Data
-
-
-We provide [scripts](https://github.com/apache/incubator-mxnet/tree/master/scala-package/core/scripts) to download MNIST data and CIFAR10 ImageRecord data. If you want to create your own dataset, we recommend using the Image RecordIO data format.
-
-## Create a Dataset Using RecordIO
-
-RecordIO implements a file format for a sequence of records. We recommend storing images as records and packing them together. The benefits include:
-
-* Storing images in a compact format--e.g., JPEG, for records--greatly reduces the size of the dataset on the disk.
-* Packing data together allows continuous reading on the disk.
-* RecordIO has a simple way to partition, simplifying distributed setting. We provide an example later.
-
-We provide the [im2rec tool](https://github.com/apache/incubator-mxnet/blob/master/tools/im2rec.cc) so you can create an Image RecordIO dataset by yourself. The following walkthrough shows you how.
-
-### Prerequisites
-Download the data. You don't need to resize the images manually. You can use `im2rec` to resize them automatically. For details, see "Extension: Using Multiple Labels for a Single Image," later in this topic.
-
-### Step 1. Make an Image List File
-After you download the data, you need to make an image list file.  The format is:
-
-```
-    integer_image_index \t label_index \t path_to_image
-```
-Typically, the program takes the list of names of all of the images, shuffles them, then separates them into two lists: a training filename list and a testing filename list. Write the list in the right format.
-
-This is an example file:
-
-```bash
-    95099  464     n04467665_17283.JPEG
-    10025081        412     ILSVRC2010_val_00025082.JPEG
-    74181   789     n01915811_2739.JPEG
-    10035553        859     ILSVRC2010_val_00035554.JPEG
-    10048727        929     ILSVRC2010_val_00048728.JPEG
-    94028   924     n01980166_4956.JPEG
-    1080682 650     n11807979_571.JPEG
-    972457  633     n07723039_1627.JPEG
-    7534    11      n01630670_4486.JPEG
-    1191261 249     n12407079_5106.JPEG
-
-```
-
-### Step 2. Create the Binary File
-To generate a binary image, use `im2rec` in the tool folder. `im2rec` takes the path of the `_image list file_` you generated, the `_root path_` of the images, and the `_output file path_` as input. This process usually takes several hours, so be patient.
-
-A sample command:
-
-```bash
-    ./bin/im2rec image.lst image_root_dir output.bin resize=256
-```
-For more details, run ```./bin/im2rec```.
-
-### Extension: Multiple Labels for a Single Image
-
-The `im2rec` tool and `IO.ImageRecordIter` have multi-label support for a single image.
-For example, if you have four labels for a single image, you can use the following procedure to use the RecordIO tools.
-
-1. Write the image list files as follows:
-
-     ```
-         integer_image_index \t label_1 \t label_2 \t   label_3 \t label_4 \t path_to_image
-     ```
-
-2. Run `im2rec`, adding a 'label_width=4' to the command argument, for example:
-
-     ```bash
-         ./bin/im2rec image.lst image_root_dir output.bin resize=256 label_width=4
-     ```
-
-3. In the iterator generation code, set `label_width=4` and `path_imglist=<<The PATH TO YOUR image.lst>>`, for example:
-
-     ```scala
-         val dataiter = IO.ImageRecordIter(Map(
-           "path_imgrec" -> "data/cifar/train.rec",
-           "data_shape" -> "(3,28,28)",
-           "path_imglist" -> "data/cifar/image.lst",
-           "label_width" -> "4"
-         ))
-     ```
-
-## Next Steps
-* [NDArray API](ndarray.md) for vector/matrix/tensor operations
-* [KVStore API](kvstore.md) for multi-GPU and multi-host distributed training
diff --git a/docs/api/scala/kvstore.md b/docs/api/scala/kvstore.md
deleted file mode 100644
index 30ea97d207f6..000000000000
--- a/docs/api/scala/kvstore.md
+++ /dev/null
@@ -1,122 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# KVStore API
-
-Topics:
-* [Basic Push and Pull](#basic-push-and-pull)
-* [List Key-Value Pairs](#list-key-value-pairs)
-* [API Reference](http://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.KVStore)
-
-
-## Basic Push and Pull
-
-Provides basic operation over multiple devices (GPUs) on a single device.
-
-### Initialization
-
-Let's consider a simple example. It initializes
-a (`int`, `NDArray`) pair into the store, and then pulls the value out.
-
-```scala
-val kv = KVStore.create("local") // create a local kv store.
-val shape = Shape(2,3)
-kv.init(3, NDArray.ones(shape)*2)
-val a = NDArray.zeros(shape)
-kv.pull(3, out = a)
-a.toArray
-// Array[Float] = Array(2.0, 2.0, 2.0, 2.0, 2.0, 2.0)
-```
-
-### Push, Aggregation, and Updater
-
-For any key that's been initialized, you can push a new value with the same shape to the key, as follows:
-
-```scala
-kv.push(3, NDArray.ones(shape)*8)
-kv.pull(3, out = a) // pull out the value
-a.toArray
-// Array[Float] = Array(8.0, 8.0, 8.0, 8.0, 8.0, 8.0)
-```
-
-The data that you want to push can be stored on any device. Furthermore, you can push multiple
-values into the same key, where KVStore first sums all of these
-values, and then pushes the aggregated value, as follows:
-
-```scala
-val gpus = Array(Context.gpu(0), Context.gpu(1), Context.gpu(2), Context.gpu(3))
-val b = Array(NDArray.ones(shape, gpus(0)), NDArray.ones(shape, gpus(1)), \
-NDArray.ones(shape, gpus(2)), NDArray.ones(shape, gpus(3)))
-kv.push(3, b)
-kv.pull(3, out = a)
-a.toArray
-// Array[Float] = Array(4.0, 4.0, 4.0, 4.0, 4.0, 4.0)
-```
-
-For each push command, KVStore applies the pushed value to the value stored by an
-`updater`. The default updater is `ASSIGN`. You can replace the default to
-control how data is merged.
-
-```scala
-val updater = new MXKVStoreUpdater {
-          override def update(key: Int, input: NDArray, stored: NDArray): Unit = {
-            println(s"update on key $key")
-            stored += input * 2
-          }
-          override def dispose(): Unit = {}
-       }
-kv.setUpdater(updater)
-kv.pull(3, a)
-a.toArray
-// Array[Float] = Array(4.0, 4.0, 4.0, 4.0, 4.0, 4.0)
-kv.push(3, NDArray.ones(shape))
-// update on key 3
-kv.pull(3, a)
-a.toArray
-// Array[Float] = Array(6.0, 6.0, 6.0, 6.0, 6.0, 6.0)
-```
-
-### Pull
-
-You've already seen how to pull a single key-value pair. Similar to the way that you use the push command, you can
-pull the value into several devices with a single call.
-
-```scala
-val b = Array(NDArray.ones(shape, gpus(0)), NDArray.ones(shape, gpus(1)),\
-NDArray.ones(shape, gpus(2)), NDArray.ones(shape, gpus(3)))
-kv.pull(3, outs = b)
-b(1).toArray
-// Array[Float] = Array(6.0, 6.0, 6.0, 6.0, 6.0, 6.0)
-```
-
-## List Key-Value Pairs
-
-All of the operations that we've discussed so far are performed on a single key. KVStore also provides
-the interface for generating a list of key-value pairs. For a single device, use the following:
-
-```scala
-val keys = Array(5, 7, 9)
-kv.init(keys, Array.fill(keys.length)(NDArray.ones(shape)))
-kv.push(keys, Array.fill(keys.length)(NDArray.ones(shape)))
-// update on key: 5
-// update on key: 7
-// update on key: 9
-val b = Array.fill(keys.length)(NDArray.zeros(shape))
-kv.pull(keys, outs = b)
-b(1).toArray
-// Array[Float] = Array(3.0, 3.0, 3.0, 3.0, 3.0, 3.0)
-```
diff --git a/docs/api/scala/model.md b/docs/api/scala/model.md
deleted file mode 100644
index 12156132d35a..000000000000
--- a/docs/api/scala/model.md
+++ /dev/null
@@ -1,135 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# MXNet Scala Model API
-
-The model API provides a simplified way to train neural networks using common best practices.
-It's a thin wrapper built on top of the [ndarray](ndarray.md) and [symbolic](symbol.md)
-modules that make neural network training easy.
-
-Topics:
-
-* [Train a Model](#train-a-model)
-* [Save the Model](#save-the-model)
-* [Periodic Checkpoint](#periodic-checkpointing)
-* [Multiple Devices](#use-multiple-devices)
-* [Model API Reference](#http://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.Model)
-
-## Train the Model
-
-To train a model, perform two steps: configure the model using the symbol parameter,
-then call ```model.Feedforward.create``` to create the model.
-The following example creates a two-layer neural network.
-
-```scala
-    // configure a two layer neuralnetwork
-    val data = Symbol.Variable("data")
-    val fc1 = Symbol.api.FullyConnected(data, num_hidden = 128, name = "fc1")
-    val act1 = Symbol.api.Activation(Some(fc1), "relu", "relu1")
-    val fc2 = Symbol.api.FullyConnected(Some(act1), num_hidden = 64, name = "fc2")
-    val softmax = Symbol.api.SoftmaxOutput(Some(fc2), name = "sm")
-
-    // Construct the FeedForward model and fit on the input training data
-    val model = FeedForward.newBuilder(softmax)
-      .setContext(Context.cpu())
-      .setNumEpoch(num_epoch)
-      .setOptimizer(new SGD(learningRate = 0.01f, momentum = 0.9f, wd = 0.0001f))
-      .setTrainData(trainDataIter)
-      .setEvalData(valDataIter)
-      .build()
-```
-You can also use the `scikit-learn-style` construct and `fit` function to create a model.
-
-```scala
-    // create a model using sklearn-style two-step way
-    val model = new FeedForward(softmax,
-                                numEpoch = numEpochs,
-                                argParams = argParams,
-                                auxParams = auxParams,
-                                beginEpoch = beginEpoch,
-                                epochSize = epochSize)
-
-  model.fit(trainData = train)
-```
-For more information, see [API Reference](http://mxnet.incubator.apache.org/api/scala/docs/index.html).
-
-## Save the Model
-
-After the job is done, save your work.
-We also provide `save` and `load` functions. You can use the `load` function to load a model checkpoint from a file.
-
-```scala
-    // checkpoint the model data into file,
-    // save a model to modelPrefix-symbol.json and modelPrefix-0100.params
-    val modelPrefix: String = "checkpt"
-    val num_epoch = 100
-    Model.saveCheckpoint(modelPrefix, epoch + 1, symbol, argParams, auxStates)
-
-    // load model back
-    val model_loaded = FeedForward.load(modelPrefix, num_epoch)
-```
-The advantage of these two `save` and `load` functions is that they are language agnostic.
-You should be able to save and load directly into cloud storage, such as Amazon S3 and HDFS.
-
-##  Periodic Checkpointing
-
-We recommend checkpointing your model after each iteration.
-To do this, use ```EpochEndCallback``` to add a ```Model.saveCheckpoint(<parameters>)``` checkpoint callback to the function after each iteration .
-
-```scala
-    // modelPrefix-symbol.json will be saved for symbol.
-    // modelPrefix-epoch.params will be saved for parameters.
-    // Checkpoint the model into file. Can specify parameters.
-    // For more information, check API doc.
-    val modelPrefix: String = "checkpt"
-    val checkpoint: EpochEndCallback =
-    if (modelPrefix == null) null
-    else new EpochEndCallback {
-      override def invoke(epoch: Int, symbol: Symbol,
-                         argParams: Map[String, NDArray],
-                         auxStates: Map[String, NDArray]): Unit = {
-       Model.saveCheckpoint(modelPrefix, epoch + 1, symbol, argParams, auxParams)
-            }
-           }
-
-    // Load model checkpoint from file. Returns symbol, argParams, auxParams.
-    val (_, argParams, _) = Model.loadCheckpoint(modelPrefix, num_epoch)
-
-```
-You can load the model checkpoint later using ```Model.loadCheckpoint(modelPrefix, num_epoch)```.
-
-## Use Multiple Devices
-
-Set ```ctx``` to the list of devices that you want to train on. You can create a list of devices in any way you want.
-
-```scala
-    val devices = Array(Context.gpu(0), Context.gpu(1))
-
-    val model = new FeedForward(ctx = devices,
-             symbol = network,
-             numEpoch = numEpochs,
-             optimizer = optimizer,
-             epochSize = epochSize,
-             ...)
-```
-Training occurs in parallel on the GPUs that you specify.
-
-## Next Steps
-* See [Symbolic API](symbol.md) for operations on NDArrays that assemble neural networks from layers.
-* See [IO Data Loading API](io.md) for parsing and loading data.
-* See [NDArray API](ndarray.md) for vector/matrix/tensor operations.
-* See [KVStore API](kvstore.md) for multi-GPU and multi-host distributed training.
diff --git a/docs/api/scala/module.md b/docs/api/scala/module.md
deleted file mode 100644
index 09e7c657c12e..000000000000
--- a/docs/api/scala/module.md
+++ /dev/null
@@ -1,155 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Module API
-The module API provides an intermediate and high-level interface for performing computation with neural networks in MXNet. A *module* is an instance of subclasses of the `BaseModule`. The most widely used module class is called `Module`. Module wraps a `Symbol` and one or more `Executors`. For a full list of functions, see `BaseModule`.
-A subclass of modules might have extra interface functions. This topic provides some examples of common use cases. All of the module APIs are in the `Module` namespace.
-
-## Preparing a Module for Computation
-
-To construct a module, refer to the constructors for the module class. For example, the `Module` class accepts a `Symbol` as input:
-
-```scala
-    import org.apache.mxnet._
-    import org.apache.mxnet.module.{FitParams, Module}
-
-    // construct a simple MLP
-    val data = Symbol.Variable("data")
-    val fc1 = Symbol.api.FullyConnected(Some(data), num_hidden = 128, name = "fc1")
-    val act1 = Symbol.api.Activation(Some(fc1), "relu", "relu1")
-    val fc2 = Symbol.api.FullyConnected(Some(act1), num_hidden = 64, name = "fc2")
-    val act2 = Symbol.api.Activation(Some(fc2), "relu", "relu2")
-    val fc3 = Symbol.api.FullyConnected(Some(act2), num_hidden = 10, name = "fc3")
-    val out = Symbol.api.SoftmaxOutput(fc3, name = "softmax")
-
-    // construct the module
-    val mod = new Module(out)
-```
-
-By default, `context` is the CPU. If you need data parallelization, you can specify a GPU context or an array of GPU contexts.
-
-Before you can compute with a module, you need to call `bind()` to allocate the device memory and `initParams()` or `SetParams()` to initialize the parameters.
-If you simply want to fit a module, you don't need to call `bind()` and `initParams()` explicitly, because the fit() function automatically calls them if they are needed.
-
-```scala
-    mod.bind(dataShapes = train_dataiter.provideData, labelShapes = Some(train_dataiter.provideLabel))
-    mod.initParams()
-```
-
-Now you can compute with the module using functions like `forward()`, `backward()`, etc.
-
-## Training, Predicting, and Evaluating
-
-Modules provide high-level APIs for training, predicting, and evaluating. To fit a module, call the `fit()` function with some `DataIter`s:
-
-```scala
-    import org.apache.mxnet.optimizer.SGD
-    val mod = new Module(softmax)
-
-    mod.fit(train_dataiter, evalData = scala.Option(eval_dataiter), \
-    numEpoch = n_epoch, fitParams = new FitParams()\
-    .setOptimizer(new SGD(learningRate = 0.1f, momentum = 0.9f, wd = 0.0001f)))
-```
-
-The interface is very similar to the old `FeedForward` class. You can pass in batch-end callbacks using `setBatchEndCallback` and epoch-end callbacks using `setEpochEndCallback`. You can also set parameters using methods like `setOptimizer` and `setEvalMetric`. To learn more about the `FitParams()`, see the [API page](http://mxnet.io/api/scala/docs/index.html#org.apache.mxnet.module.FitParams). To predict with a module, call `predict()` with a `DataIter`:
-
-```scala
-    mod.predict(val_dataiter)
-```
-
-The module collects and returns all of the prediction results. For more details about the format of the return values, see the documentation for the [`predict()` function](http://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.module.BaseModule).
-
-When prediction results might be too large to fit in memory, use the `predictEveryBatch` API:
-
-```scala
-    val preds = mod.predictEveryBatch(val_dataiter)
-    val_dataiter.reset()
-    var i = 0
-    while (val_dataiter.hasNext) {
-       val batch = val_dataiter.next()
-       val predLabel: Array[Int] = NDArray.argmax_channel(preds(i)(0)).toArray.map(_.toInt)
-       val label = batch.label(0).toArray.map(_.toInt)
-       //do something...
-       i += 1
-    }
-```
-
-If you need to evaluate on a test set and don't need the prediction output, call the `score()` function with a `DataIter` and an `EvalMetric`:
-
-```scala
-    mod.score(val_dataiter, metric)
-```
-
-This runs predictions on each batch in the provided `DataIter` and computes the evaluation score using the provided `EvalMetric`. The evaluation results are stored in `metric` so that you can query later.
-
-## Saving and Loading Module Parameters
-
-To save the module parameters in each training epoch, use a `checkpoint` callback:
-
-```scala
-    val modelPrefix: String = "mymodel"
-
-    for (epoch <- 0 until 5) {
-      while(train_dataiter.hasNext){  
-          // forward backward pass
-         //do something...
-       }
-        val checkpoint = mod.saveCheckpoint(modelPrefix, epoch, saveOptStates = true)
-
-    }
-```
-
-To load the saved module parameters, call the `loadCheckpoint` function:
-
-```scala
-    val mod = Module.loadCheckpoint(modelPrefix, loadModelEpoch, loadOptimizerStates = true)
-```
-
-To initialize parameters, Bind the symbols to construct executors first with `bind` method. Then, initialize the parameters and auxiliary states by calling `initParams()` method.
-
-```scala
-    mod.bind(dataShapes = train_dataiter.provideData, labelShapes = Some(train_dataiter.provideLabel))
-    mod.initParams()
-```
-
-To get current parameters, use `getParams` method.
-
-```scala
-    val (argParams, auxParams) = mod.getParams
-```
-
-To assign parameter and aux state values, use `setParams` method.
-
-```scala
-    mod.setParams(argParams, auxParams)
-```
-
-To resume training from a saved checkpoint, instead of calling `setParams()`, directly call `fit()`, passing the loaded parameters, so that `fit()` knows to start from those parameters instead of initializing randomly:
-
-```scala
-    mod.fit(..., fitParams=new FitParams().setArgParams(argParams).\
-    setAuxParams(auxParams).setBeginEpoch(beginEpoch))
-```
-
-Create an object of the `FitParams()` class, and then use it to call the `setBeginEpoch()` method to pass `beginEpoch` so that `fit()` knows to resume from a saved epoch.
-
-## Next Steps
-* See [Model API](model.md) for an alternative simple high-level interface for training neural networks.
-* See [Symbolic API](symbol.md) for operations on NDArrays that assemble neural networks from layers.
-* See [IO Data Loading API](io.md) for parsing and loading data.
-* See [NDArray API](ndarray.md) for vector/matrix/tensor operations.
-* See [KVStore API](kvstore.md) for multi-GPU and multi-host distributed training.
diff --git a/docs/api/scala/ndarray.md b/docs/api/scala/ndarray.md
deleted file mode 100644
index 0c13669d64ab..000000000000
--- a/docs/api/scala/ndarray.md
+++ /dev/null
@@ -1,174 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# NDArray API
-
-
-The NDArray package (`mxnet.ndarray`) contains tensor operations similar to `numpy.ndarray`. The syntax is also similar, except for some additional calls for dealing with I/O and multiple devices.
-
-Topics:
-
-* [Create NDArray](#create-ndarray)
-* [NDArray Operations](#ndarray-operations)
-* [NDArray API Reference](http://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.NDArray)
-
-## Create NDArray
-
-Create `mxnet.ndarray` as follows:
-
-```scala
-import org.apache.mxnet._
-// all-zero array of dimension 100x50
-val a = NDArray.zeros(100, 50)
-// all-one array of dimension 256x32x128x1
-val b = NDArray.ones(256, 32, 128, 1)
-// initialize array with contents, you can specify dimensions of array using Shape parameter while creating array.
-val c = NDArray.array(Array(1, 2, 3, 4, 5, 6), shape = Shape(2, 3))
-```
-This is similar to the way you use `numpy`.
-## NDArray Operations
-
-We provide some basic ndarray operations, like arithmetic and slice operations.
-
-### Arithmetic Operations
-
-```scala
-import org.apache.mxnet._
-val a = NDArray.zeros(100, 50)
-a.shape
-// org.apache.mxnet.Shape = (100,50)
-val b = NDArray.ones(100, 50)
-// c and d will be calculated in parallel here!
-val c = a + b
-val d = a - b
-// inplace operation, b's contents will be modified, but c and d won't be affected.
-b += d
-```
-
-### Multiplication/Division Operations
-
-```scala
-import org.apache.mxnet._
-// Multiplication
-val ndones = NDArray.ones(2, 1)
-val ndtwos = ndones * 2
-ndtwos.toArray
-// Array[Float] = Array(2.0, 2.0)
-(ndones * ndones).toArray
-// Array[Float] = Array(1.0, 1.0)
-(ndtwos * ndtwos).toArray
-// Array[Float] = Array(4.0, 4.0)
-ndtwos *= ndtwos // inplace
-ndtwos.toArray
-// Array[Float] = Array(4.0, 4.0)
-
-//Division
-val ndones = NDArray.ones(2, 1)
-val ndzeros = ndones - 1f
-val ndhalves = ndones / 2
-ndhalves.toArray
-// Array[Float] = Array(0.5, 0.5)
-(ndhalves / ndhalves).toArray
-// Array[Float] = Array(1.0, 1.0)
-(ndones / ndones).toArray
-// Array[Float] = Array(1.0, 1.0)
-(ndzeros / ndones).toArray
-// Array[Float] = Array(0.0, 0.0)
-ndhalves /= ndhalves
-ndhalves.toArray
-// Array[Float] = Array(1.0, 1.0)
-```
-
-### Slice Operations
-
-```scala
-import org.apache.mxnet._
-val a = NDArray.array(Array(1f, 2f, 3f, 4f, 5f, 6f), shape = Shape(3, 2))
-val a1 = a.slice(1)
-assert(a1.shape === Shape(1, 2))
-assert(a1.toArray === Array(3f, 4f))
-
-val a2 = arr.slice(1, 3)
-assert(a2.shape === Shape(2, 2))
-assert(a2.toArray === Array(3f, 4f, 5f, 6f))
-```
-
-### Dot Product
-
-```scala
-import org.apache.mxnet._
-val arr1 = NDArray.array(Array(1f, 2f), shape = Shape(1, 2))
-val arr2 = NDArray.array(Array(3f, 4f), shape = Shape(2, 1))
-val res = NDArray.dot(arr1, arr2)
-res.shape
-// org.apache.mxnet.Shape = (1,1)
-res.toArray
-// Array[Float] = Array(11.0)
-```
-
-### Save and Load NDArray
-
-You can use MXNet functions to save and load a list or dictionary of NDArrays from file systems, as follows:
-
-```scala
-import org.apache.mxnet._
-val a = NDArray.zeros(100, 200)
-val b = NDArray.zeros(100, 200)
-// save list of NDArrays
-NDArray.save("/path/to/array/file", Array(a, b))
-// save dictionary of NDArrays to AWS S3
-NDArray.save("s3://path/to/s3/array", Map("A" -> a, "B" -> b))
-// save list of NDArrays to hdfs.
-NDArray.save("hdfs://path/to/hdfs/array", Array(a, b))
-val from_file = NDArray.load("/path/to/array/file")
-val from_s3 = NDArray.load("s3://path/to/s3/array")
-val from_hdfs = NDArray.load("hdfs://path/to/hdfs/array")
-```
-The good thing about using the `save` and `load` interface is that you can use the format across all `mxnet` language bindings. They also already support Amazon S3 and HDFS.
-
-### Multi-Device Support
-
-Device information is stored in the `mxnet.Context` structure. When creating NDArray in MXNet, you can use the context argument (the default is the CPU context) to create arrays on specific devices as follows:
-
-```scala
-import org.apache.mxnet._
-val cpu_a = NDArray.zeros(100, 200)
-cpu_a.context
-// org.apache.mxnet.Context = cpu(0)
-val ctx = Context.gpu(0)
-val gpu_b = NDArray.zeros(Shape(100, 200), ctx)
-gpu_b.context
-// org.apache.mxnet.Context = gpu(0)
-```
-
-Currently, we *do not* allow operations among arrays from different contexts. To manually enable this, use the `copyto` member function to copy the content to different devices, and continue computation:
-
-```scala
-import org.apache.mxnet._
-val x = NDArray.zeros(100, 200)
-val ctx = Context.gpu(0)
-val y = NDArray.zeros(Shape(100, 200), ctx)
-val z = x + y
-// mxnet.base.MXNetError: [13:29:12] src/ndarray/ndarray.cc:33:
-// Check failed: lhs.ctx() == rhs.ctx() operands context mismatch
-val cpu_y = NDArray.zeros(100, 200)
-y.copyto(cpu_y)
-val z = x + cpu_y
-```
-
-## Next Steps
-* See [KVStore API](kvstore.md) for multi-GPU and multi-host distributed training.
diff --git a/docs/api/scala/symbol.md b/docs/api/scala/symbol.md
deleted file mode 100644
index f92548e4820d..000000000000
--- a/docs/api/scala/symbol.md
+++ /dev/null
@@ -1,148 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# MXNet Scala Symbolic API
-
-Topics:
-
-* [How to Compose Symbols](#overloaded-operators) introduces operator overloading of symbols.
-* [Symbol Attributes](#symbol-attributes) describes how to attach attributes to symbols.
-* [Serialization](#serialization) explains how to save and load symbols.
-* [Executing Symbols](#executing-symbols) explains how to evaluate the symbols with data.
-* [Execution API Reference](http://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.Executor) documents the execution APIs.
-* [Multiple Outputs](#multiple-outputs) explains how to configure multiple outputs.
-* [Symbol Creation API Reference](http://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.Symbol) documents functions.
-
-We also highly encourage you to read [Symbolic Configuration and Execution in Pictures](symbol_in_pictures.md).
-
-## How to Compose Symbols
-
-The symbolic API provides a way to configure computation graphs.
-You can configure the graphs either at the level of neural network layer operations or as fine-grained operations.
-
-The following example configures a two-layer neural network.
-
-```scala
-    import org.apache.mxnet._
-    val data = Symbol.Variable("data")
-    val fc1 = Symbol.api.FullyConnected(Some(data), num_hidden = 128, name = "fc1")
-    val act1 = Symbol.api.Activation(Some(fc1), "relu", "relu1")
-    val fc2 = Symbol.api.FullyConnected(Some(act1), num_hidden = 64, name = "fc2")
-    val net = Symbol.api.SoftmaxOutput(Some(fc2), name = "out")
-    :type net
-    // org.apache.mxnet.Symbol
-```
-
-The basic arithmetic operators (plus, minus, div, multiplication) are overloaded for
-*element-wise operations* of symbols.
-
-The following example creates a computation graph that adds two inputs together.
-
-```scala
-    import org.apache.mxnet._
-    val a = Symbol.Variable("a")
-    val b = Symbol.Variable("b")
-    val c = a + b
-```
-
-## Symbol Attributes
-
-You can add an attribute to a symbol by providing an attribute dictionary when you create a symbol.
-
-```scala
-    val data = Symbol.Variable("data", Map("mood"-> "angry"))
-    val op = Symbol.api.Convolution(Some(data), kernel = Shape(1, 1), num_filter = 1, attr = Map("mood" -> "so so"))
-```
-For proper communication with the C++ backend, both the key and values of the attribute dictionary should be strings. To retrieve the attributes, use `attr(key)`:
-
-```
-    data.attr("mood")
-    // Option[String] = Some(angry)
-```
-
-To attach attributes, you can use ```AttrScope```. ```AttrScope``` automatically adds the specified attributes to all of the symbols created within that scope. The user can also inherit this object to change naming behavior. For example:
-
-```scala
-    val (data, gdata) =
-    AttrScope(Map("group" -> "4", "data" -> "great")).withScope {
-      val data = Symbol.Variable("data", attr = Map("dtype" -> "data", "group" -> "1"))
-      val gdata = Symbol.Variable("data2")
-      (data, gdata)
-    }
-    assert(gdata.attr("group").get === "4")
-    assert(data.attr("group").get === "1")
-
-    val exceedScopeData = Symbol.Variable("data3")
-    assert(exceedScopeData.attr("group") === None, "No group attr in global attr scope")
-```
-
-## Serialization
-
-There are two ways to save and load the symbols. You can use the `mxnet.Symbol.save` and `mxnet.Symbol.load` functions to serialize the ```Symbol``` objects.
-The advantage of using `save` and `load` functions is that it is language agnostic and cloud friendly.
-The symbol is saved in JSON format. You can also get a JSON string directly using `mxnet.Symbol.toJson`.
-Refer to [API documentation](http://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.Symbol) for more details.
-
-The following example shows how to save a symbol to an S3 bucket, load it back, and compare two symbols using a JSON string.
-
-```scala
-    import org.apache.mxnet._
-    val a = Symbol.Variable("a")
-    val b = Symbol.Variable("b")
-    val c = a + b
-    c.save("s3://my-bucket/symbol-c.json")
-    val c2 = Symbol.load("s3://my-bucket/symbol-c.json")
-    c.toJson == c2.toJson
-    // Boolean = true
-```
-
-## Executing Symbols
-
-After you have assembled a set of symbols into a computation graph, the MXNet engine can evaluate them.
-If you are training a neural network, this is typically
-handled by the high-level [Model class](model.md) and the [`fit()`] function.
-
-For neural networks used in "feed-forward", "prediction", or "inference" mode (all terms for the same
-thing: running a trained network), the input arguments are the
-input data, and the weights of the neural network that were learned during training.
-
-To manually execute a set of symbols, you need to create an [`Executor`] object,
-which is typically constructed by calling the [`simpleBind(<parameters>)`] method on a symbol.
-
-## Multiple Outputs
-
-To group the symbols together, use the [mxnet.symbol.Group](#mxnet.symbol.Group) function.
-
-```scala
-    import org.apache.mxnet._
-    val data = Symbol.Variable("data")
-    val fc1 = Symbol.api.FullyConnected(Some(data), num_hidden = 128, name = "fc1")
-    val act1 = Symbol.api.Activation(Some(fc1), "relu", "relu1")
-    val fc2 = Symbol.api.FullyConnected(Some(act1), num_hidden = 64, name = "fc2")
-    val net = Symbol.api.SoftmaxOutput(Some(fc2), name = "out")
-    val group = Symbol.Group(fc1, net)
-    group.listOutputs()
-    // IndexedSeq[String] = ArrayBuffer(fc1_output, out_output)
-```
-
-After you get the ```group```, you can bind on ```group``` instead.
-The resulting executor will have two outputs, one for fc1_output and one for softmax_output.
-
-## Next Steps
-* See [IO Data Loading API](io.md) for parsing and loading data.
-* See [NDArray API](ndarray.md) for vector/matrix/tensor operations.
-* See [KVStore API](kvstore.md) for multi-GPU and multi-host distributed training.
diff --git a/docs/api/scala/symbol_in_pictures.md b/docs/api/scala/symbol_in_pictures.md
deleted file mode 100644
index 23ece95e835a..000000000000
--- a/docs/api/scala/symbol_in_pictures.md
+++ /dev/null
@@ -1,93 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Symbolic Configuration and Execution in Pictures
-
-This topic explains symbolic construction and execution in pictures.
-We recommend that you also read [Symbolic API](symbol.md).
-
-## Compose Symbols
-
-Symbols are a description of the computation that you want to perform. The symbolic construction API generates the computation
-graph that describes the computation. The following picture shows how you compose symbols to describe basic computations.
-
-![Symbol Compose](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/compose_basic.png)
-
-- The ```mxnet.Symbol.Variable``` function creates argument nodes that represent input to the computation.
-- The symbol is overloaded with basic element-wise mathematical operations.
-
-## Configure Neural Networks
-
-In addition to supporting fine-grained operations, MXNet provides a way to perform big operations that are analogous to layers in neural networks.
-You can use operators to describe the configuration of a neural network.
-
-![Net Compose](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/compose_net.png)
-
-
-## Example of a Multi-Input Network
-
-The following example shows how to configure multiple input neural networks.
-
-![Multi Input](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/compose_multi_in.png)
-
-
-## Bind and Execute Symbol
-
-When you need to execute a symbol graph, you call the bind function to bind ```NDArrays``` to the argument nodes
-in order to obtain an ```Executor```.
-
-![Bind](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/bind_basic.png)
-
-To get the output results, given the bound NDArrays as input, you can call ```Executor.Forward```.
-
-![Forward](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/executor_forward.png)
-
-
-## Bind Multiple Outputs
-
-To group symbols, then bind them to get outputs of both, use ```mx.symbol.Group```.
-
-![MultiOut](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/executor_multi_out.png)
-
-Remember: Bind only what you need, so that the system can perform more optimizations.
-
-
-## Calculate the Gradient
-
-In the bind function, you can specify NDArrays that will hold gradients. Calling ```Executor.backward``` after ```Executor.forward``` gives you the corresponding gradients.
-
-![Gradient](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/executor_backward.png)
-
-
-## Simple Bind Interface for Neural Networks
-
-It can be tedious to pass the argument NDArrays to the bind function, especially when you are binding a big
-graph. ```Symbol.simple_bind``` provides a way to simplify
-the procedure. You need to specify only input data shapes. The function allocates the arguments, and binds
-the Executor for you.
-
-![SimpleBind](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/executor_simple_bind.png)
-
-## Auxiliary States
-
-Auxiliary states are just like arguments, except that you can't take the gradient of them. Although auxiliary states might not be part of the computation, they can be helpful for tracking. You can pass auxiliary states in the same way that you pass arguments.
-
-![SimpleBind](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/executor_aux_state.png)
-
-## Next Steps
-
-See [Symbolic API](symbol.md) and [Python Documentation](index.md).
diff --git a/docs/architecture/exception_handling.md b/docs/architecture/exception_handling.md
deleted file mode 100644
index 87481bcdb9bd..000000000000
--- a/docs/architecture/exception_handling.md
+++ /dev/null
@@ -1,125 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Exception Handling in MXNet
-
-This tutorial explains the exception handling support in MXNet, 
-and provides examples on how to throw and handle exceptions when in a multithreaded context.
-Although, the examples are in Python, they can be easily extended to MXNet
-language bindings.
-
-MXNet exceptions can be thrown from two areas:
-- MXNet main thread. For eg. Infershape and InferType.
-- Spawned threads:
-    * By dependency engine for operator execution in parallel
-    * By the iterators, during the data loading, text parsing phase etc.
-
-In the first case, the exception is thrown and can be handled in the main thread.
-In the second case, the exception is thrown in a spawned thread, caught and transported to the
-main thread, where it is rethrown. This tutorial will give more explanation and examples on how 
-to handle exceptions for the second case.
-
-## Prerequisites 
-
-To complete this tutorial, we need:
-- MXNet [7b24137](https://github.com/apache/incubator-mxnet/commit/7b24137ed45df605defa4ce72ec91554f6e445f0). See Instructions in [Setup and Installation](http://mxnet.io/install/index.html).
-
-## Exception Handling for Iterators
-
-The below example shows how to handle exceptions for iterators. In this example,
-we populate files for data and labels with fewer number of labels compared to the
-number of samples. This should throw an exception.
-
-CSVIter uses PrefetcherIter for loading and parsing data.
-The PrefetcherIter spawns a producer thread in the background which prefetches
-the data while the main thread consumes the data. The exception is thrown in the spawned
-producer thread during the prefetching, when the label is not found corresponding to a specific sample. 
-
-The exception is transported to the main thread, where it is rethrown when Next is 
-called as part of the following line: `for batch in iter(data_train)`.
-
-In general, Exception may be rethrown as part of `Next` and `BeforeFirst` calls which correspond to `reset()` and `next()` methods in `MXDataIter` for Python language bindings.
-
-```python
-import os
-import mxnet as mx
-
-cwd = os.getcwd()
-data_path = os.path.join(cwd, "data.csv")
-label_path = os.path.join(cwd, "label.csv")
-
-with open(data_path, "w") as fout:
-    for i in range(8):
-        fout.write("1,2,3,4,5,6,7,8,9,10\n")
-
-with open(label_path, "w") as fout:
-    for i in range(7):
-        fout.write("label"+str(i))
-
-try:
-    data_train = mx.io.CSVIter(data_csv=data_path, label_csv=label_path, data_shape=(1, 10),
-                               batch_size=4)
-
-    for batch in iter(data_train):
-        print(data_train.getdata().asnumpy())
-except mx.base.MXNetError as ex:
-    print("Exception handled")
-    print(ex)
-```
-
-### Limitation
-
-There is a race condition when your last `next()` call doesnt reach the batch in your dataset where exception occurs. Exception may or may not be thrown in this case depending on which thread wins the race. To avoid this situation, you should try and iterate through your full dataset if you think it can throw exceptions which need to be handled.
-
-
-## Exception Handling for Operators
-
-The below example shows how to handle exceptions for operators in the imperative mode.
-
-For the operator case, the dependency engine spawns a number of threads if it is running in the `ThreadedEnginePool` or `ThreadedEnginePerDevice` mode. The final operator is executed in one of the spawned threads. 
-
-If an operator throws an exception during execution, this exception is propagated
-down the dependency chain. Once there is a synchronizing call i.e. WaitToRead for a variable in the dependency chain, the propagated exception is rethrown. 
-
-In the below example, I illustrate how an exception that occured in the first line is propagated down the dependency chain, and finally is rethrown when we make a synchronizing call to WaitToRead.
-
-```python
-import mxnet as mx
-a = mx.nd.random.normal(0, 1, (2, 2))
-b = mx.nd.random.normal(0, 2, (2, 2))
-c = mx.nd.dot(a, b)
-d = mx.nd.random.normal(0, -1, (2, 2))
-e = mx.nd.dot(c, d)
-e.wait_to_read()
-```
-
-Although the above exception occurs when executing the operation which writes to the variable d in one of the child threads, it is thrown only when the synchronization happens as part of the line: `e.wait_to_read()`.
-
-Let us take another example. In the following case, we write to two variables and then `wait_to_read` for both. This example shows that any particular exception will not be thrown more than once.
-
-```python
-import mxnet as mx
-a = mx.nd.random.normal(0, 1, (2, 2))
-b = mx.nd.random.normal(0, -1, (2, 2))
-c, d  = mx.nd.dot(a, b)
-try:
-    c.asnumpy()
-except mx.base.MXNetError as ex:
-    print("Exception handled")    
-d.asnumpy()
-```
-
diff --git a/docs/architecture/index.md b/docs/architecture/index.md
deleted file mode 100644
index 3c71edfeeb05..000000000000
--- a/docs/architecture/index.md
+++ /dev/null
@@ -1,46 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# MXNet Architecture
-
-Building a high-performance deep learning library
-requires many systems-level design decisions.
-In this design note, we share the rationale
-for the specific choices made when designing _MXNet_.
-We imagine that these insights may be useful
-to both deep learning practitioners
-and builders of other deep learning systems.
-
-## Deep Learning System Design Concepts
-
-The following pages address general design concepts for deep learning systems.
-Mainly, they focus on the following 3 areas:
-abstraction, optimization, and trade-offs between efficiency and flexibility.
-Additionally, we provide an overview of the complete MXNet system.
-
-```eval_rst
-.. toctree::
-   :maxdepth: 1
-
-   overview.md
-   program_model.md
-   note_engine.md
-   note_memory.md
-   note_data_loading.md
-   exception_handling.md
-   rnn_interface.md
-```
diff --git a/docs/architecture/note_data_loading.md b/docs/architecture/note_data_loading.md
deleted file mode 100644
index a60bf905d0e4..000000000000
--- a/docs/architecture/note_data_loading.md
+++ /dev/null
@@ -1,261 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Designing Efficient Data Loaders for Deep Learning
-
-Data loading is an important component of any machine learning system.
-When we work with tiny datasets, we can get away with loading an entire dataset into GPU memory.
-With larger datasets, we must store examples in main memory.
-And when datasets grow too large to fit into main memory,
-data loading can become performance-critical.
-In designing a data loader,
-we aim to achieve more efficient data loading,
-to spend less effort on data preparation,
-and to present a clean and flexible interface.
-
-We organize this design note as follows:
-
-* **IO Design Insight:**  Guiding principles in data loading design.
-* **Data Format:** Our solution using dmlc-core's binary recordIO implementation.
-* **Data Loading:** Our method to reduce IO cost by utilizing the threaded iterator provided by dmlc-core.
-* **Interface Design:** Our approach to facilitate writing MXNet data iterators in just a few lines of Python.
-* **Future Extension:** Prospective ideas for making data loading more flexible.
-
-Our analysis will motivate several requirements that an effective IO system should fulfill.
-
-***List of Key Requirements***
-- Small file size.
-- Parallel (distributed) packing of data.
-- Fast data loading and online augmentation.
-- Quick reads from arbitrary parts of the dataset in the distributed setting.
-
-## Design Insight
-To design an IO system, we must address two kinds of tasks:
-data preparation and data loading.
-Data preparation is usually performed offline,
-whereas data loading influences the online performance.
-In this section, we will introduce our insight of IO design involving the two phases.
-
-### Data Preparation
-Data preparation describes the process of packing data
-into a desired format for later processing.
-When working with large datasets like ImageNet, this process can be time-consuming.
-In these cases, there are several heuristics we ought to follow:
-
-- Pack the dataset into small numbers of files. A dataset may contain millions of data instances. Packed data distributes easily from machine to machine.
-- Do the packing once. We don't want to repack data every time run-time settings, like the number of machines, are changed.
-- Process the packing in parallel to save time.
-- Be able to access arbitrary parts of the data easily. This is crucial for distributed machine learning when data parallelism is introduced. Things may get tricky when the data has been packed into several physical data files. The desired behavior could be: the packed data can be logically separated into arbitrary numbers of partitions, no matter how many physical data files there are. For example, if we pack 1000 images into 4 physical files, then each file contains 250 images. If we then use 10 machines to train a DNN, we should be able to load approximately 100 images per machine. Some machines may need images from different physical files.
-
-### Data Loading
-The next step to consider is how to load the packed data into RAM.
-Our goal is to load the data as quickly as possible.
-There are several heuristics we try to follow:
-- **Read continuously:** We can read faster when reading from contiguous locations on disk.
-- **Reduce the bytes to be loaded:** We can achieve this by storing data in a compact way, e.g. saving images in JPEG format.
-- **Load and train in different threads:** This avoids computational bottlenecks while loading data.
-- **Save RAM:** Judiciously decide whether to load entire files into RAM.
-
-## Data Format
-
-Since the training of deep neural network often involves large amounts of data,
-the format we choose should be both efficient and convenient.
-To achieve our goals, we need to pack binary data into a splittable format.
-In MXNet, we rely on the binary recordIO format implemented in dmlc-core.
-
-### Binary Record
-
-![baserecordio](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/io/baserecordio.jpg)
-In MXNet's binary RecordIO, we store each data instance as a record.
-**kMagic** is a *magic number* indicating the start of a record.
-**Lrecord** encodes length and a continue flag.
-In lrecord,
-- cflag == 0: this is a complete record
-- cflag == 1: start of a multiple-records
-- cflag == 2: middle of multiple-records
-- cflag == 3: end of multiple-records
-
-**Data** is the space to save data content.
-**Pad** is simply a padding space to make record align to 4 bytes.
-
-After we pack the data, each file contains multiple records.
-Then, loading can be continuous.
-This avoids the low performance that can result
-from reading random locations on disk.
-
-One advantage of storing data via records
-is that each record can vary in length.
-This allows us to save data compactly
-when good compression algorithms are available for our data.
-For example, we can use JPEG format to save image data.
-The packed data will be much smaller
-compared with storing uncompressed RGB values for each pixel.
-
-Take ImageNet_1K dataset as an example.
-If we store the data as 3 * 256 * 256 array of raw RGB values,
-the dataset would occupy more than **200G**.
-But after compressing the images using JPEG,
-they only occupy about **35G** of disk space.
-This significantly reduces the cost owing to reading from disk.
-
-Here's an example of binary recordIO:
-![baserecordio](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/io/ImageRecordIO.jpg)
-We first resize the image into 256 * 256,
-then compress into JPEG format.
-After that, we save a header that indicates the index and label
-for that image to be used when constructing the *Data* field for that record.
-We then pack several images together into a file.
-You may want to also review the [example using im2rec.py to create a RecordIO dataset](https://mxnet.incubator.apache.org/tutorials/basic/data.html#loading-data-using-image-iterators).
-
-### Access Arbitrary Parts Of Data
-
-One desirable property for a data loader might be:
-The packed data can be logically sliced into an arbitrary number of partitions,
-no matter how many physical packed data files there are.
-Since binary recordIO can easily locate
-the start and end of a record using the Magic Number,
-we can achieve the above goal using the InputSplit
-functionality provided by dmlc-core.
-
-InputSplit takes the following parameters:
-- FileSystem *filesys*: dmlc-core wrapper around the IO operations for different file systems, like hdfs, s3, local. User shouldn't need to worry about the difference between file systems anymore.
-- Char *uri*: The URI of files. Note that it could be a list of files because we may pack the data into several physical parts. File URIs are separated by ';'.
-- Unsigned *nsplit*: The number of logical splits. *nsplit* could be different from the number of physical files.
-- Unsigned *rank*: Which split to load in this process.
-
-The splitting process is demonstrated below:
-- Determine the size of each partition.
-
-![beforepartition](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/io/beforepartition.jpg)
-
-- Approximately partition the records according to file size. Note that the boundary of each part may be located in the middle of a record.
-
-![approxipartition](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/io/approximatepartition.jpg)
-
--  Set the beginning of partitions in such a way as to avoid splitting records across partitions.
-
-![afterpartition](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/io/afterpartition.jpg)
-
-By conducting the above operations,
-we now identify the records belong to each part,
-and the physical data files needed by each logical part.
-InputSplit greatly simplifies data parallelism,
-where each process only reads part of the data.
-
-Since our partitioning scheme does not depend on the number of physical data files,
-we can process a huge dataset like ImageNet_22K in parallel fashion as illustrated below.
-We don't need to consider distributed loading issue at the preparation time,
-just select the most efficient physical file number
-according to the dataset size and computing resources available.
-![parallelprepare](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/io/parallelprepare.jpg)
-
-## Data Loading and Preprocessing
-
-When the speed of loading and preprocessing can't keep up
-with the speed of training or evaluation,
-IO can bottleneck the speed of the whole system.
-In this section, we will introduce a few tricks
-to achieve greater efficiency when loading
-and preprocessing data packed in binary recordIO format.
-When applied to the ImageNet dataset, our approach achieves
-the IO speed of **3000** images/sec **with a normal HDD**.
-
-### Loading and preprocessing on the fly
-
-When training deep neural networks,
-we sometimes must load and preprocess the data
-while simultaneously training for the following reasons:
-- When the whole size of the dataset exceeds available RAM size, we can't load it in advance;
-- Sometimes, to make models robust to things like translations, rotations, and small amounts of color shift of noise, we introduce randomness into the training process. In these cases we must re-preprocess the data each time we revisit an example.
-
-In service of efficiency, we also address multi-threading techniques. Taking Imagenet training as an example, after loading a bunch of image records, we can start multiple threads to simultaneously perform image decoding and image augmentation. We depict this process in the following illustration:
-![process](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/io/process.jpg)
-
-### Hide IO Cost Using Threadediter
-
-One way to lower IO cost is to pre-fetch the data for next batch on one thread,
-while the main thread performs the forward and backward passes for training.
-To support more complicated training schemes,
-MXNet provides a more general IO processing pipeline
-using *threadediter* provided by dmlc-core.
-The key of *threadediter* is to start a stand-alone thread that acts as a data provider,
-while the main thread acts as a data consumer as illustrated below.
-
-The threadediter maintains a buffer of a certain size
-and automatically fills the buffer when it's not full.
-And after the consumer finishes consuming part of the data in the buffer,
-threadediter will reuse the space to save the next part of data.
-![threadediter](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/io/threadediter.png)
-
-## MXNet IO Python Interface
-We make the IO object as an iterator in numpy.
-By achieving that, the user can easily access the data
-using a for-loop or calling next() function.
-Defining a data iterator is very similar to defining a symbolic operator in MXNet.
-
-The following example code demonstrates a Cifar data iterator.
-
-```python
-dataiter = mx.io.ImageRecordIter(
-    # Dataset Parameter, indicating the data file, please check the data is already there
-    path_imgrec="data/cifar/train.rec",
-    # Dataset Parameter, indicating the image size after preprocessing
-    data_shape=(3,28,28),
-    # Batch Parameter, tells how many images in a batch
-    batch_size=100,
-    # Augmentation Parameter, when offers mean_img, each image will subtract the mean value at each pixel
-    mean_img="data/cifar/cifar10_mean.bin",
-    # Augmentation Parameter, randomly crop a patch of the data_shape from the original image
-    rand_crop=True,
-    # Augmentation Parameter, randomly mirror the image horizontally
-    rand_mirror=True,
-    # Augmentation Parameter, randomly shuffle the data
-    shuffle=False,
-    # Backend Parameter, preprocessing thread number
-    preprocess_threads=4,
-    # Backend Parameter, prefetch buffer size
-    prefetch_buffer=1,
-    # Optional, the device context which data loader optimized for, could be 'gpu' or 'cpu'
-    ctx="gpu",
-    # The out data type, could be 'float32' 'int8' or 'uint8'
-    dtype="float32")
-```
-
-Generally, to create a data iterator, you need to provide five kinds of parameters:
-
-* **Dataset Param:** Information needed to access the dataset, e.g. file path, input shape.
-* **Batch Param:** Specifies how to form a batch, e.g. batch size.
-* **Augmentation Param:** Which augmentation operations (e.g. crop, mirror) should be taken on an input image.
-* **Backend Param:** Controls the behavior of the backend threads to hide data loading cost.
-* **Auxiliary Param:** Provides options to help with debugging.
-
-Usually, **Dataset Param** and **Batch Param** MUST be given,
-otherwise the data batch can't be created.
-Other parameters can be given as needed.
-Ideally, we should separate the MX Data IO into modules,
-some of which might be useful to expose to users, for example:
-
-* **Efficient prefetcher:** allows the user to write a data loader that reads their customized binary format that automatically gets multi-threaded prefetcher support.
-* **Data transformer:** image random cropping, mirroring, etc. Allows the users to use those tools, or plug in their own customized transformers (maybe they want to add some specific kind of coherent random noise to data, etc.)
-
-## Future Extensions
-
-In the future, there are some extensions to our data IO
-that we might consider adding.
-Specifically, we might add specialized support
-for applications including image segmentation, object localization, and speech recognition.
-More detail will be provided when such applications have been running on MXNet.
diff --git a/docs/architecture/note_engine.md b/docs/architecture/note_engine.md
deleted file mode 100644
index 6d0e1e19fcef..000000000000
--- a/docs/architecture/note_engine.md
+++ /dev/null
@@ -1,385 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Dependency Engine for Deep Learning
-
-We always want deep learning libraries
-to run faster and scale to larger datasets.
-One natural approach is to see if we can benefit
-from throwing more hardware at the problem,
-as by using multiple GPUs simultaneously.
-
-Library designers then ask:
-How can we *parallelize* computation across devices?
-And, more importantly, how can we *synchronize* computation
-when we introduce multi-threading?
-A runtime dependency engine is a generic solution to these problems.
-
-In this document, we examine approaches for using
-runtime dependency scheduling to accelerate deep learning.
-We aim to explain how runtime dependency scheduling
-can both speed up and simplify multi-device deep learning.
-We also explore potential designs for a generic dependency engine
-that could be both library- and operation-independent.
-
-Most of the discussion of on this page draws inspiration
-from the MXNet dependency engine.
-The dependency tracking algorithm we discuss
-was primarily developed by [Yutian Li](https://github.com/hotpxl)
-and [Mingjie Wang](https://github.com/jermainewang).
-
-## Dependency Scheduling
-
-Although most users want to take advantage of parallel computation,
-most of us are more familiar with serial programs.
-So one natural question is: how can we write serial programs
-and build a library to automatically parallelize our programs
-in an asynchronous way?
-
-For example, in the following code, we can run `B = A + 1`
-and `C = A + 2` in any order, or in parallel:
-
-```python
-    A = 2
-    B = A + 1
-    C = A + 2
-    D = B * C
-```
-
-However, it's quite hard to code the sequence manually
-because the last operation, `D = B * C`, needs to wait
-for both of the preceding operations to complete before it starts.
-The following dependency graph/data flow graph illustrates this.
-
-![Dep Simple](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/engine/dep_simple.png)
-
-
-A dependency engine is a library that takes a sequence of operations
-and schedules them according to the dependency pattern,  potentially in parallel.
-So in this example, a dependency library
-could run ```B = A + 1``` and ```C = A + 2``` in parallel,
-and run ```D = B * C``` after those operations complete.
-
-## Problems in Dependency Scheduling
-
-A dependency engine relieves the burden of writing concurrent programs.
-However, as operations become parallelized,
-new dependency tracking problems arise.
-In this section, we discuss those problems.
-
-### Data Flow Dependency
-Data flow dependency describes how the outcome of one computation
-can be used in other computations.
-Every dependency engine has to solve the data flow dependency problem.
-
-![Dep Simple](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/engine/dep_simple.png)
-
-Because we discussed this issue in the preceding section,
-we include the same figure here. Libraries that have
-data flow tracking engines include Minerva and Purine2.
-
-### Memory Recycling
-When should we recycle the memory that we allocated to the arrays?
-In serial processing, this is easy to determine.
-We simply recycle the memory after the variable goes out of scope.
-However, as the following figure shows, this is a bit harder in parallel processing.
-
-![Dep Del](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/engine/dep_del.png)
-
-In this example, because both computations need to use values from A,
-we can't recycle the memory until both complete.
-The engine must schedule the memory recycling operations according to the dependencies,
-and ensure that they are executed after both ```B = A + 1``` and ```C = A + 2``` complete.
-
-
-### Random Number Generation
-Random number generators, which are commonly used in machine learning,
-pose interesting challenges for dependency engines.
-Consider the following example:
-
-![Dep Rand](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/engine/dep_rand.png)
-
-In this example, we are generating random numbers in a sequence.
-Although it seems that the two random number generations can be parallelized,
-this is usually not the case. A pseudo-random number generator (PRNG)
-is not thread-safe because it might cause some internal state
-to mutate when generating a new number.
-Even if the PRNG is thread-safe,
-it is preferable to serialize number generation,
-so we can get reproducible random numbers.
-
-## Case Study: A Dependency Engine for a Multi-GPU Neural Network
-
-In the last section, we discussed the problems
-we might face in designing a dependency engine.
-Before thinking about how to design a generic engine to solve those problems,
-let's consider how a dependency engine can help in multi-GPU training of a neural network.
-The following pseudocode Python program illustrates
-training one batch on a  two-layer neural network.
-
-```python
-    # Example of one iteration Two GPU neural Net
-    data = next_batch()
-    data[gpu0].copyfrom(data[0:50])
-    data[gpu1].copyfrom(data[50:100])
-    # forward, backprop on GPU 0
-    fc1[gpu0] = FullcForward(data[gpu0], fc1_weight[gpu0])
-    fc2[gpu0] = FullcForward(fc1[gpu0], fc2_weight[gpu0])
-    fc2_ograd[gpu0] = LossGrad(fc2[gpu0], label[0:50])
-    fc1_ograd[gpu0], fc2_wgrad[gpu0] =
-      FullcBackward(fc2_ograd[gpu0] , fc2_weight[gpu0])
-      _, fc1_wgrad[gpu0] = FullcBackward(fc1_ograd[gpu0] , fc1_weight[gpu0])
-    # forward, backprop on GPU 1
-    fc1[gpu1] = FullcForward(data[gpu1], fc1_weight[gpu1])
-    fc2[gpu1] = FullcForward(fc1[gpu1], fc2_weight[gpu1])
-    fc2_ograd[gpu1] = LossGrad(fc2[gpu1], label[50:100])
-    fc1_ograd[gpu1], fc2_wgrad[gpu1] =
-         FullcBackward(fc2_ograd[gpu1] , fc2_weight[gpu1])
-         _, fc1_wgrad[gpu1] = FullcBackward(fc1_ograd[gpu1] , fc1_weight[gpu1])
-    # aggregate gradient and update
-    fc1_wgrad[cpu]  = fc1_wgrad[gpu0] + fc1_wgrad[gpu1]
-    fc2_wgrad[cpu]  = fc2_wgrad[gpu0] + fc2_wgrad[gpu1]
-    fc1_weight[cpu] -= lr *  fc1_wgrad[cpu]
-    fc2_weight[cpu] -= lr *  fc2_wgrad[cpu]
-    fc1_weight[cpu].copyto(fc1_weight[gpu0] , fc1_weight[gpu1])
-    fc2_weight[cpu].copyto(fc2_weight[gpu0] , fc2_weight[gpu1])
-```
-In this program, the data 0 to 50  is copied to GPU 0,
-and the data 50 to 100 is copied to GPU 1.
-The calculated gradients are aggregated in the CPU,
-which then performs a simple SGD update,
-and copies the updated weight back to each GPU.
-This is a common way to write a parallel program in a serial manner.
-The following dependency graph shows how it can be parallelized:
-
-![Dep Net](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/engine/dep_net.png)
-
-***Notes:***
-
-- The gradient can be copied to the CPU as soon as we get the gradient of a layer.
-- The weight can be copied back soon as the weight is updated.
-- In the forward pass, we have a dependency on ```fc1_weight[cpu].copyto(fc1_weight[gpu0] , fc1_weight[gpu1])```
-  from the previous iteration.
-- There is a delay in computation between the last backward pass to layer k and the next forward call to layer k. We can synchronize the weight of layer k *in parallel* with other computation during this delay.
-
-This approach to optimization is used by multi-GPU deep learning libraries, such as CXXNet.
-The point is to overlap weight synchronization (communication) with computation.
-However, it's not easy to do that, because the copy operation needs to be triggered
-as soon as the backward pass of the layer completes,
-which then triggers the reduction, updates, etc.
-
-A dependency engine can schedule these operations and perform multi-threading
-and dependency tracking.
-
-## Designing a Generic Dependency Engine
-
-We hope that you're convinced that a dependency engine is useful
-for scaling deep learning programs to multiple devices.
-Now let's discuss how we can design and implement
-a generic interface for a dependency engine.
-This solution isn't the only possible design for a dependency engine.
-It's an example that we think is useful in most cases.
-
-Our goal is to create a dependency engine that is *generic* and *lightweight*.
-Ideally, we'd like the engine that easily plugs into existing deep learning code,
-and that can scale up to multiple machines with minor modifications.
-To do that, we need to focus only on dependency tracking,
-not on assumptions about what users can or can't do.
-
-Here's a summary of goals for the engine:
-
-- The engine should not be aware of what operations it performs, so that users can perform any operations they define.
-- It should not be restricted in what type of objects it can schedule.
-	- We should be able to schedule dependencies on GPU and CPU memory.
-	- We should be able to track dependencies on the random number generator, etc.
-- The engine should not allocate resources. It should only track dependencies. Users can allocate their own memory, PRNG, etc.
-
-The following Python snippet provides an engine interface that might help us reach our goal. Note that a real implementation will be closer to the metal, typically in C++.
-
-```python
-    class DepEngine(object):
-	    def new_variable():
-		    """Return a new variable tag
-		    Returns
-		    -------
-		    vtag : Variable Tag
-		        The token of the engine to represent dependencies.
-		    """
-		    pass
-
-	    def push(exec_func, read_vars, mutate_vars):
-		    """Push the operation to the engine.
-
-		    Parameters
-		    ----------
-		    exec_func : callable
-			    The real operation to be performed.
-
-		    read_vars : list of Variable Tags
-			    The list of variables this operation will read from.
-
-		    mutate_vars : list of Variable Tags
-			    The list of variables this operation will mutate.
-		    """
-		    pass
-```
-
-Because we can't make assumptions about what objects we are scheduling, we ask the user to allocate a
-_virtual tag_ that is associated with each object to represent what we need to schedule.
-So, at the beginning, the user can allocate the variable tag,
-and attach it to each of the objects that we want to schedule.
-
-![Dep Net](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/engine/tag_var.png)
-
-The user then calls `push` to tell the engine about the function to execute.
-The user also needs to specify the dependencies of the operation,
-using `read_vars` and `write_vars`:
-
-- `read_vars` are variable tags for objects that the operation will _read from_, without changing their internal state.
-- `mutate_vars` are variable tags for objects whose internal states the operation will mutate.
-
-![Push Op](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/engine/push_var.png)
-
-The preceding figure shows how to push operation `B = A + 1` to the dependency engine. `B.data` and
-`A.data` are the allocated space. Note that the engine is *only aware of variable tags*.
-Any execution function can be processed.
-This interface is generic for the operations and resources we want to schedule.
-
-For fun, let's look at how the engine internals work with the tags by considering the following code snippet:
-
-```
-    B = A + 1
-    C = A + 2
-    A = C * 2
-    D = A + 3
-```
-
-The first line reads variable `A` and mutates variable `B`. The second line reads variable `A` and mutates variable `C`. And so on.
-
-The engine maintains a queue for each variable, as the following animation shows for each of the four lines. Green blocks represents a read action, while red blocks represent mutations.
-
-![Dependency Queue](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/engine/dep_queue.gif)
-
-Upon building this queue, the engine sees that the first two green blocks at the beginning of `A`'s queue could actually be run in parallel because they are both read actions and won't conflict with each other. The following graph illustrates this point.
-
-![Dependency Parallelism](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/engine/dep_parallel.png)
-
-One cool thing about all this scheduling is that it's not confined to numerical calculations.
-Because everything that is scheduled is only a tag, the engine could schedule everything!
-
-The following figure gives a complete push sequence of the programs we mentioned in previous sections.
-
-![Push Seq](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/engine/push_seq.png)
-
-### Porting Existing Code to the Dependency Engine
-Because the generic interface doesn't control things like memory allocation and which operation to execute,
-most existing code can be scheduled by the dependency engine in two steps:
-
-
-1. Allocate the variable tags associated with resources like memory blob, PRNGS.
-2. Call `push` with the execution function as the original code to execute, and put the variable tags of
-  corresponding resources correctly in `read_vars` and `mutate_vars`.
-
-## Implementing the Generic Dependency Engine
-
-We have described the generic engine interface and
-how it can be used to schedule various operations.
-In this section, we provide a high-level discussion
-of how to implement such an engine.
-
-The general idea is as follows:
-
-- Use a queue to track all of the pending dependencies on each variable tag.
-- Use a counter on each operation to track how many dependencies are yet to be fulfilled.
-- When operations are completed, update the state of the queue and dependency counters to schedule new operations.
-
-The following figure illustrates the scheduling algorithm
-and might give you a better sense of what is going on in the engine.
-
-![Dep Tracking](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/engine/engine_queue_step.png)
-
-Below, we show another example involving random number generators.
-
-![Dep Rand](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/engine/engine_queue_rand.png)
-
-As you can see, the purpose of the algorithm is to update pending queues
-of operations and to make the right state transition when an operation has completed.
-More care should be taken to make sure the state transitions
-are done in a way that's safe for threads.
-
-### Separate Dependency Tracking with Running Policy
-If you're reading carefully, you might have noticed
-that the preceding section shows only the algorithm
-for deciding when an operation can be executed.
-We didn't show how to actually run an operation.
-In practice, there can be many different policies.
-For example, we can either use a global thread-pool to run all operations,
-or use a specific thread to run operations on each device.
-
-This running policy is usually independent of dependency tracking,
-and can be separated out as either an independent module
-or a virtual interface of base-dependency tracking modules.
-Developing an elegant runtime policy that is fair
-to all operations and schedules is an interesting systems problem itself.
-
-## Discussion
-
-The design that we discussed in this article
-isn't the only solution to the dependency tracking problem.
-It's just one example of how we might approach this.
-To be sure, some of these design choices are debatable.
-We'll discuss some of them in this section.
-
-### Dynamic vs. Static
-The dependency engine interface discussed in this topic is somewhat dynamic
-in the sense that the user can push operations one by one,
-instead of declaring the entire dependency graph (static).
-Dynamic scheduling can require more overhead
-than static declarations, in terms of data structure.
-However, it also enables more flexibility, such as supporting auto parallelism
-for imperative programs or a mixture of imperative and symbolic programs.
-You can also add some level of predeclared operations
-to the interface to enable data structure reuse.
-
-### Mutation vs. Immutable
-The generic engine interface presented in this page
-supports explicit scheduling for mutation.
-In a typical data flow engine, the data are usually immutable.
-Working with immutable data has a lot of benefits.
-For example, immutable data is generally more suitable for parallelization,
-and facilitates better fault tolerance in a distributed setting (by way of re-computation).
-
-However, immutability presents several challenges:
-
-- It's harder to schedule resource contention problems, as arise when dealing with random numbers and deletion.
-- The engine usually needs to manage resources (memory, random number) to avoid conflicts. It's harder to plug in user-allocated space, etc.
-- Preallocated static memory isn't available, again because the usual pattern is to write to a preallocated layer space, which is not supported if data is immutable.
-
-Allowing mutation mitigates these issues.
-
-
-## Source Code of the Generic Dependency Engine
-[MXNet](https://github.com/dmlc/mxnet) provides an implementation
-of the generic dependency engine described in this page.
-You can find more details in [this topic](http://mxnet.io/architecture/note_engine.html).
-We welcome your contributions.
-
-## Next Steps
-
-* [Squeeze the Memory Consumption of Deep Learning](http://mxnet.io/architecture/note_memory.html)
-* [Efficient Data Loading Module for Deep Learning](http://mxnet.io/architecture/note_data_loading.html)
-* [Survey of RNN Interface](http://mxnet.io/architecture/rnn_interface.html)
diff --git a/docs/architecture/note_memory.md b/docs/architecture/note_memory.md
deleted file mode 100644
index e8b795bbd5c4..000000000000
--- a/docs/architecture/note_memory.md
+++ /dev/null
@@ -1,345 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Optimizing Memory Consumption in Deep Learning
-
-Over the last ten years, a constant trend in deep learning
-is towards deeper and larger networks.
-Despite rapid advances in hardware performance,
-cutting-edge deep learning models continue to push the limits of GPU RAM.
-So even today, it's always desirable to find ways
-to train larger models while consuming less memory.
-Doing so enables us to train faster, using larger batch sizes,
-and consequently achieving a higher GPU utilization rate.
-
-In this document, we explore techniques for optimizing
-memory allocation for deep neural networks.
-We discuss a few candidate solutions.
-While our proposals are by no means exhaustive,
-these solutions are instructive and allow us to
-introduce the major design issues at play.
-
-## Computation Graph
-
-First, let's revisit the idea of the computation graph.
-A computation graph describes the (data flow) dependencies
-between the operations in the deep network.
-The operations performed in the graph
-can be either fine-grained or coarse-grained.
-The following figure shows two examples of computation graphs.
-
-![Comp Graph Example](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/memory/comp_graph_example.png)
-
-The concept of a computation graph is explicitly encoded in packages like Theano and CGT.
-In other libraries, computation graphs appear implicitly as network configuration files.
-The major difference in these libraries comes down to how they calculate gradients.
-There are mainly two ways: performing back-propagation on the _same_ graph
-or explicitly representing a _backwards path_ to calculate the required gradients.
-
-![Backward Graph](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/memory/back_graph.png)
-
-Libraries like Caffe, CXXNet, and Torch take the former approach,
-performing back-prop on the original graph.
-Libraries like Theano and CGT take the latter approach,
-explicitly representing the backward path.
-In this discussion, we adopt the *explicit backward path* approach
-because it has several advantages for optimization.
-
-However, we should emphasize that choosing the explicit backward path approach doesn't restrict us
-to symbolic libraries, such as Theano and CGT. We can also use the explicit backward path for gradient calculation of
-layer-based (which ties forward and backward together) libraries. The following graph shows how to do this.
-Basically, we introduce a backward node that links to the forward node of the graph and calls the ```layer.backward```
-in the backward operations.
-
-![Backward Layer](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/memory/explicit_back_layer.png)
-
-This discussion applies to almost all existing deep learning libraries.
-(There are differences between libraries,  e.g., higher-order differentiation, which is beyond the scope of this topic.)
-
-Why is the explicit backward path better? Let's explain it with two examples.
-The first reason is that the explicit backward path
-clearly describes the dependency between computations.
-Consider the following case, where we want to get
-the gradient of A and B. As we can see clearly from the graph,
-the computation of the ```d(C)``` gradient doesn't depend on F.
-This means that we can free the memory of ```F```
-right after the forward computation is done.
-Similarly, the memory of ```C``` can be recycled.
-
-![Backward Prune](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/memory/back_dep_prune.png)
-
-Another advantage of the explicit backward path
-is the ability to have a different backward path,
-instead of a mirror of forward one.
-A common example is the split connection case,
-as shown in the following figure.
-
-![Backward Agg](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/memory/back_agg_grad.png)
-
-In this example, the output of B is referenced by two operations.
-If we want to do the gradient calculation in the same
-network, we need to introduce an explicit split layer.
-This means we need to do the split for the forward pass, too.
-In this figure, the forward pass doesn't contain a split layer,
-but the graph will automatically insert a gradient
-aggregation node before passing the gradient back to B.
-This helps us to save the memory cost of allocating the output of the split layer,
-and the operation cost of replicating the data in the forward pass.
-
-If we adopt the explicit backward approach,
-there's no difference between the forward pass and the backward pass.
-We simply step through the computation graph in chronological order
-and carry out computations.
-This makes the explicit backward approach easy to analyze.
-We just need to answer the question:
-how do we allocate memory for each output node of a computation graph?
-
-
-## What Can Be Optimized?
-
-As you can see, the computation graph is a useful way
-to discuss memory allocation optimization techniques.
-Already, we've shown how you can save some memory
-by using the explicit backward graph.
-Now let's explore further optimizations,
-and see how we might determine reasonable baselines for benchmarking.
-
-Assume that we want to build a neural network with `n` layers.
-Typically, when implementing a neural network,
-we need to allocate node space for both the output of each layer
-and the gradient values used during back-propagation.
-This means we need roughly `2 n` memory cells.
-We face the same requirement when using the explicit backward graph approach
-because the number of nodes in a backward pass
-is roughly the same as in a forward pass.
-
-### In-place Operations
-One of the simplest techniques we can employ
-is _in-place memory sharing_ across operations.
-For neural networks, we can usually apply this technique
-for the operations corresponding to activation functions.
-Consider the following case, where we want
-to compute the value of three chained sigmoid functions.
-
-![Inplace op](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/memory/alloc_inline.png)
-
-Because we can compute sigmoid ```in-place```,
-using the same memory for input and output,
-we can compute an arbitrary-length chain
-of sigmoid functions using constant memory.
-
-Note: it's easy to make mistakes when implementing in-place optimization.
-Consider the following case, where the value of B is used not only by C, but also by F.
-
-![In-place trap](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/memory/alloc_inline_trap.png)
-
-We can't perform in-place optimization because the value of B
-is still needed after ```C=sigmoid(B)``` is computed.
-An algorithm that simply does in-place optimization
-for every sigmoid operation might fall into such trap,
-so we need to be careful about when we can use it.
-
-### Standard Memory Sharing
-In-place operations are not the only places where we can share memory.
-In the following example, because the value of B is no longer needed
-after we compute E, we can reuse B's memory to hold the result of E.
-
-![Normal Sharing](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/memory/alloc_normal.png)
-
-*Memory sharing doesn't necessarily require the same data shape*.
-Note that in the preceding example, the shapes of `B` and `E` can differ.
-To handle such a situation, we can allocate a memory region
-of size equal to the maximum of that required by `B` and `E` and share it between them.
-
-### Example of Real Neural Network Allocation
-Of course, these are only toy examples and they address only the computation of the forward pass.
-But the same ideas apply to real neural networks.
-The following figure shows an allocation plan for a two-layer perceptron.
-
-![Net Alloc](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/memory/alloc_mlp.png)
-
-In this example:
-
-- In-place optimization is applied when computing ```act1```, ```d(fc1)```, ```out``` and ```d(fc2)```.
-- Memory is shared between ```d(act1)``` and ```d(A)```.
-
-## Memory Allocation Algorithm
-
-So far, we've discussed general techniques for optimizing memory allocation.
-We've seen that there are traps to avoid,
-as demonstrated in the case of in-place memory optimization.
-So, how can we allocate memory correctly?
-This is not a new problem.
-For example, it is very similar
-to the problem with register allocation in compilers.
-There might be techniques that we can borrow.
-We're not attempting to give a comprehensive review of techniques here,
-but rather to introduce some simple
-but useful tricks to attack the problem.
-
-The key problem is that we need to place resources
-so that they don't conflict with each other.
-More specifically, each variable has a *life time*
-between the time it gets computed until the last time it is used.
-In the case of the multi-layer perceptron,
-the *life time* of ```fc1``` ends after ```act1``` get computed.
-
-![Net Alloc](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/memory/alloc_mlp.png)
-
-The principle is *to allow memory sharing only between variables whose lifetimes don't overlap*.
-There are multiple ways to do this.
-You can construct the conflicting graph
-with each variable as a node and link the edge
-between variables with overlapping lifespans,
-and then run a graph-coloring algorithm.
-This likely has ```$O(n^2)$``` complexity,
-where ```n``` is the number of nodes in the graph.
-This might be too costly.
-
-Let's consider another simple heuristic.
-The idea is to simulate the procedure of traversing the graph,
-and keep a count of future operations that depends on the node.
-
-![Alloc](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/memory/alloc_step.png)
-
-- An in-place optimization can be performed when only the current operation depends on the source (i.e., ```count==1```).
-- Memory can be recycled into the box on the upper right corner when the ```count``` goes to 0.
-- When we need new memory, we can either get it from the box or allocate a new one.
-
-***Note:*** During the simulation, no memory is allocated.
-Instead, we keep a record of how much memory each node needs,
-and allocate the maximum of the shared parts in the final memory plan.
-
-## Static vs. Dynamic Allocation
-
-The preceding strategy exactly simulates
-the dynamic memory allocation procedure
-in imperative languages, such as Python.
-The ```count``` is the reference counter for each memory object,
-and the object gets garbage collected
-when the reference counter goes to 0.
-In that sense,
-we are simulating dynamic memory allocation once
-to create a static allocation plan.
-Can we simply use an imperative language
-that dynamically allocates and deallocates memory?
-
-The major difference is that static allocation is only done once,
-so we can afford to use more complicated algorithms.
-For example, we can search for memory sizes
-that are similar to the required memory block.
-The Allocation can also be made graph aware.
-We'll talk about that in the next section.
-Dynamic allocation puts more pressure
-on fast memory allocation and garbage collection.
-
-There is also one takeaway for users
-who want to rely on dynamic memory allocations:
-*do not unnecessarily reference objects*.
-For example, if we organize all of the nodes in a list
-and store then in a Net object,
-these nodes will never get dereferenced, and we gain no space.
-Unfortunately, this is a common way to organize code.
-
-
-## Memory Allocation for Parallel Operations
-
-In the previous section, we discussed
-how we can *simulate* running the procedure
-for a computation graph to get a static allocation plan.
-However, optimizing for parallel computation presents other challenges
-because resource sharing and parallelization are on the two ends of a balance.
-Let's look at the following two allocation plans for the same graph:
-
-![Parallel Alloc](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/memory/parallel_alloc.png)
-
-Both allocation plans are valid
-if we run the computation serially,
-from ```A[1]``` to ```A[8]```.
-However, the allocation plan on the left
-introduces additional dependencies,
-which means we can't run computation of ```A[2]``` and ```A[5]``` in parallel.
-The plan on the right can.
-To parallelize computation, we need to take greater care.
-
-### Be Correct and Safe First
-Being correct is our first principle.
-This means to execute in a way that takes implicit dependency
-memory sharing into consideration.
-You can do this by adding the implicit dependency edge to the execution graph.
-Or, even simpler, if the execution engine is mutation aware,
-as described in [our discussion of dependency engine design](http://mxnet.io/architecture/note_engine.html),
-push the operation in sequence
-and write to the same variable tag
-that represents the same memory region.
-
-Always produce a safe memory allocation plan.
-This means never allocate the same memory
-to nodes that can be parallelized.
-This might not be ideal when memory reduction is more desirable,
-and we don't gain too much when we can get benefit
-from multiple computing streams simultaneously executing on the same GPU.
-
-### Try to Allow More Parallelization
-Now we can safely perform some optimizations.
-The general idea is to try and encourage memory sharing between nodes that can't be parallelized.
-You can do this by creating an ancestor relationship
-graph and querying it during allocation,
-which costs approximately ```$O(n^2)$``` in time to construct.
-We can also use a heuristic here,
-for example, color the path in the graph.
-As shown in the following figure,
-when you try to find the longest paths in the graph,
-color them the same color and continue.
-
-![Path Color](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/memory/graph_color.png)
-
-After you get the color of the node,
-you allow sharing (or encourage sharing)
-only between nodes of the same color.
-This is a stricter version of the ancestor relationship,
-but it costs only `$O(n)$` of time
-if you search for only the first `k` path.
-
-This is by no means the only solution.
-More sophisticated approaches might exist:
-
-## How Much Can you Save?
-
-We've discussed the techniques and algorithms you can use
-to squeeze memory usage for deep learning.
-How much can you really save by using these techniques?
-
-On coarse-grained operation graphs
-that are already optimized for big operations,
-you can reduce memory consumption roughly *by half*.
-You can reduce memory usage even more
-if you are optimizing a fine-grained computation network
-used by symbolic libraries, such as Theano. Most of the ideas in this article inspired the design of _MXNet_.
-
-Also, you will notice that memory cost, for forward pass only execution, is extremely low compared to running both forward and backward pass. This is simply because there's  more memory reuse if you run only the forward pass.
-
-So here are two takeaways:
-
-- Use a computation graph to allocate memory.
-- For deep learning models, prediction consumes much less memory than training.
-
-
-## Next Steps
-
-* [Efficient Data Loading Module for Deep Learning](http://mxnet.io/architecture/note_data_loading.html)
-* [Survey of RNN Interface](http://mxnet.io/architecture/rnn_interface.html)
diff --git a/docs/architecture/overview.md b/docs/architecture/overview.md
deleted file mode 100644
index e2e9ce2bd3eb..000000000000
--- a/docs/architecture/overview.md
+++ /dev/null
@@ -1,875 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# MXNet System Architecture
-
-![System Overview](https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/mxnet/system/overview.png)
-
-This figure shows the major modules and components of the MXNet system and their interaction. The modules are:
-
-- Runtime Dependency Engine: Schedules and executes the
-  operations according to their read/write dependency.
-- Storage Allocator: Efficiently allocates and recycles memory blocks
-  on host (CPU) and devices (GPUs).
-- Resource Manager: Manages global resources, such as the random number generator
-  and temporal space.
-- NDArray: Dynamic, asynchronous n-dimensional arrays,
-  which provide flexible imperative programs for MXNet.
-- Symbolic Execution: Static symbolic graph executor,
-  which provides efficient symbolic graph execution and optimization.
-- Operator: Operators that define static forward and gradient
-  calculation (backprop).
-- SimpleOp: Operators that extend NDArray operators and symbolic operators
-  in a unified fashion.
-- Symbol Construction: Symbolic construction, which provides a way to construct
-  a computation graph (net configuration).
-- KVStore: Key-value store interface for efficient parameter synchronization.
-- Data Loading(IO): Efficient distributed data loading and augmentation.
-
-# MXNet System Components
-
-## Execution Engine
-
-You can use MXNet's engine not only for deep learning,
-but for any domain-specific problem.
-It's designed to solve a general problem:
-execute a bunch of functions following their dependencies.
-Execution of any two functions with dependencies should be serialized.
-To boost performance, functions with no dependencies *can* be executed in parallel.
-For a general discussion of this topic,
-see our [notes on the dependency engine](note_engine.md).
-
-### Interface
-
-The following API is the core interface for the execution engine:
-
-```c++
-    virtual void PushSync(Fn exec_fun, Context exec_ctx,
-                          std::vector<VarHandle> const& const_vars,
-                          std::vector<VarHandle> const& mutate_vars) = 0;
-```
-This API allows you to push a function (`exec_fun`),
-along with its context information and dependencies, to the engine.
-`exec_ctx` is the context information in which the `exec_fun` should be executed,
-`const_vars` denotes the variables that the function reads from, 
-and `mutate_vars` are the variables to be modified.
-The engine provides the following guarantee:
-
->*The execution of any two functions
-that modify a common variable
-is serialized in their push order.*
-
-### Function
-
-The function type of the engine is:
-
-```c++
-    using Fn = std::function<void(RunContext)>;
-```
-`RunContext` contains runtime information, which is determined by the engine:
-
-```c++
-    struct RunContext {
-        // stream pointer which could be safely cast to
-        // cudaStream_t* type
-	    void *stream;
-    };
-```
-Alternatively, you could use `mxnet::engine::DAGEngine::Fn`, which has the same type definition.
-
-All of the functions are executed by the engine's internal threads.
-In such a model, it's usually not a good idea to push *blocking* functions
-to the engine (usually for dealing with I/O tasks like disk, web service, UI, etc.)
-because it will occupy the execution thread and reduce total throughput.
-In that case, we provide another *asynchronous* function type:
-
-```c++
-    using Callback = std::function<void()>;
-    using AsyncFn = std::function<void(RunContext, Callback)>;
-```
-In the `AsyncFn` function, you can pass the heavy part to your own threads
-and safely exit the body of the function.
-The engine doesn't consider the function finished
-until the `Callback` function is called.
-
-### Context
-
-You can specify the `Context` of the function to be executed within.
-This usually includes whether the function should be run on a CPU or a GPU,
-and if you specify a GPU, which GPU to use.
-`Context` is different from `RunContext`.
-`Context` contains device type (GPU/CPU) and device id,
- while `RunContext` contains information that can be decided only during runtime,
- for example, on which stream the function should be executed.
-
-### VarHandle
-
-`VarHandle` is used to specify the dependencies of functions.
-The MXNet engine is designed to be decoupled from other MXNet modules.
-So `VarHandle` is like an engine-provided token you use
-to represent the external resources the functions can use or modify.
-It's designed to be lightweight, so creating,
-deleting, or copying a variable incurs little overhead.
-Upon pushing the functions, you need to specify the variables
-that will be used (immutable) in the `const_vars` vector,
-and the variables that will be modified (mutable) in the `mutate_vars` vector.
-The engine uses one rule for resolving the dependencies among functions:
-
->*The execution of any two functions when one of them modifies at least one common variable is serialized in their push order.*
-
-For example, if `Fn1` and `Fn2` both mutate `V2` then `Fn2`
-is guaranteed to be executed after `Fn1`
-if `Fn2` is pushed after `Fn1`.
-On the other hand, if `Fn1` and `Fn2` both use `V2`,
-their actual execution order could be random.
-
-This design allows the engine to schedule *state-mutating* operations in a manner
-that minimizes calls to allocate new memory.
-For example, the weight update function in DNN
-can now use the `+=` operator
-to update the weights in place,
-rather than generating a new weight array each time.
-
-To create a variable, use the `NewVar()` API.
-To delete a variable, use the `PushDelete` API.
-
-### Push and Wait
-
-*All `Push` APIs are asynchronous.* The API call returns immediately
-regardless of whether the pushed `Fn` is finished or not.
-This allows the engine to start computing at the same time
-as the user thread is pushing functions.
-`Push` APIs are not thread-safe.
-To be specific, only one thread should make engine API calls at a time.
-
-If you want to wait for a specific `Fn` to finish,
-include a callback function in the closure,
-and call the function at the end of your `Fn`.
-
-If you want to wait for all `Fn`s
-that involve (use or mutate) a certain variable to finish,
-use the `WaitForVar(var)` API.
-
-If you want to wait for all pushed `Fn`s to finish,
-use the `WaitForAll()` API.
-
-### Save Object Creation Cost
-
-In some cases, you need to push several functions to the engine for a long period of time.
-If the computation of these functions is light,
-the overhead of copying lambdas and creating use/mutate variable lists becomes relatively high.
-We provide an API to create an `OprHandle` beforehand:
-
-```c++
-    virtual OprHandle NewOperator(AsyncFn fn,
-                                  std::vector<VarHandle> const& const_vars,
-                                  std::vector<VarHandle> const& mutate_vars) = 0;
-```
-You can keep pushing the `OprHandle` without repeatedly creating them:
-
-```c++
-    virtual void Push(OprHandle op, Context exec_ctx) = 0;
-```
-To delete it, call the `DeleteOperator(OprHandle op)` API.
-Ensure that the operator has finished computing before calling this API.
-
-
-### API Reference
-
-```eval_rst
-    .. doxygenclass:: mxnet::Engine
-       :members:
-```
-
-## Operators in MXNet
-
-In MXNet, an operator is a class that contains both actual computation logic
-and auxiliary information that can aid the system in performing optimizations,
-like in-place updates and auto-derivatives.
-To understand the remainder of the document,
-we recommend that you familiarize yourself with the `mshadow` library,
-because all operators compute on the tensor-like structure `mshadow::TBlob`
-provided by the system during runtime.
-
-MXNet's operator interface allows you to:
-
-* Reduce memory allocation cost by specifying in-place updates.
-* Hide some internal arguments from Python to make it cleaner.
-* Define the relationships among input tensors and output tensors,
-which allows the system to perform shape checking for you.
-* Acquire additional temporary spaces from the system
-to perform computation (e.g., calling `cudnn` routines).
-
-### Operator Interface
-
-`Forward` is the core operator interface:
-
-```c++
-    virtual void Forward(const OpContext &ctx,
-                         const std::vector<TBlob> &in_data,
-                         const std::vector<OpReqType> &req,
-                         const std::vector<TBlob> &out_data,
-                         const std::vector<TBlob> &aux_states) = 0;
-```
-The `OpContext` structure is:
-
-```c++
-           struct OpContext {
-             int is_train;
-             RunContext run_ctx;
-             std::vector<Resource> requested;
-           }
-```
-It describes whether the operator is in the train or test phase,
-which device the operator should be run on (in `run_ctx`),
-and requested resources (covered in the following sections).
-
-- `in_data` and `out_data` represent the input and output tensors, respectively.
-All of the tensor spaces have been allocated by the system.
-- `req` denotes how the computation results are written into the `out_data`.
-In other words, `req.size() == out_data.size()` and `req[i]`
-correspond to the write type of `out_data[i]`.
-
-- The `OpReqType` is defined as:
-
-```c++
-           enum OpReqType {
-             kNullOp,
-             kWriteTo,
-             kWriteInplace,
-             kAddTo
-           };
-```
-  Normally, the types of all `out_data` should be `kWriteTo`,
-  meaning that the provided `out_data` tensor is a *raw* memory block,
-  so the operator should write results directly into it.
-  In some cases, for example when calculating the `gradient` tensor,
-  it would be great if we could accumulate the result,
-  rather than directly overwrite the tensor contents
-  so that  no extra space needs to be created each time.
-  In such a case, the corresponding `req` type is set as `kAddTo`,
-  indicating that a `+=` should be called.
-
-- `aux_states` is intentionally designed for auxiliary tensors used to help computation. Currently, it is useless.
-
-Aside from the `Forward` operator, you could optionally implement the `Backward` interface:
-
-```c++
-    virtual void Backward(const OpContext &ctx,
-                          const std::vector<TBlob> &out_grad,
-                          const std::vector<TBlob> &in_data,
-                          const std::vector<TBlob> &out_data,
-                          const std::vector<OpReqType> &req,
-                          const std::vector<TBlob> &in_grad,
-                          const std::vector<TBlob> &aux_states);
-```
-This interface follows the same design principle as the `Forward` interface,
-except that `out_grad`, `in_data`, and `out_data` are given,
-and the operator computes `in_grad` as the results.
- The naming strategy is similar to Torch's convention,
- and can be summarized in following figure:
-
-[input/output semantics figure]
-
-Some operators might not require all of the following:
-`out_grad`, `in_data` and `out_data`.
-You can specify these dependencies with the `DeclareBackwardDependency` interface in `OperatorProperty`.
-
-### Operator Property
-
-One convolution might have several implementations,
-and you might want to switch among them to achieve the best performance.
-Therefore, we separate the operator *semantic* interfaces
-from the implementation interface (`Operator` class)
-into the `OperatorProperty` class.
-The `OperatorProperty` interface consists of:
-
-* **InferShape:**
-
-```c++
-           virtual bool InferShape(mxnet::ShapeVector *in_shape,
-                                   mxnet::ShapeVector *out_shape,
-                                   mxnet::ShapeVector *aux_shape) const = 0;
-```
-
-This interface has two purposes:
-* Tell the system the size of each input and output tensor,
-  so it can allocate space for them before the `Forward` and `Backward` call.
-* Perform a size check to make sure that there isn't an obvious error before running.
-  The shape in `in_shape` is set by the system
-  (from the `out_shape` of the previous operators).
-  It returns `false` when there is not enough information
-  to infer shapes or throws an error when the shape is inconsistent.
-
-* **Request Resources:** Operations like `cudnnConvolutionForward` need a work space for computation.
-If the system can manage that, it could then perform optimizations,
-like reuse the space, and so on.
-MXNet defines two interfaces to achieve this:
-
-```c++
-           virtual std::vector<ResourceRequest> ForwardResource(
-               const mxnet::ShapeVector &in_shape) const;
-           virtual std::vector<ResourceRequest> BackwardResource(
-               const mxnet::ShapeVector &in_shape) const;
-```
-  The `ResourceRequest` structure (in `resource.h`) currently contains only a type flag:
-
-```c++
-           struct ResourceRequest {
-             enum Type {
-               kRandom,  // get a mshadow::Random<xpu> object
-               kTempSpace,  // request temporary space
-             };
-             Type type;
-           };
-```
-  If `ForwardResource` and `BackwardResource` return non-empty arrays,
-  the system offers the corresponding resources through the `ctx` parameter
-  in the `Forward` and `Backward` interface of `Operator`.
-  Basically, to access those resources, simply write:
-
-```c++
-           auto tmp_space_res = ctx.requested[kTempSpace].get_space(some_shape, some_stream);
-           auto rand_res = ctx.requested[kRandom].get_random(some_stream);
-```
-  For an example, see `src/operator/cudnn_convolution-inl.h`.
-
-* **Backward dependency:** Let's look at two different operator signatures
-(we name all of the arguments for demonstration purposes):
-
-```c++
-           void FullyConnectedForward(TBlob weight, TBlob in_data, TBlob out_data);
-           void FullyConnectedBackward(TBlob weight, TBlob in_data, TBlob out_grad, TBlob in_grad);
-
-           void PoolingForward(TBlob in_data, TBlob out_data);
-           void PoolingBackward(TBlob in_data, TBlob out_data, TBlob out_grad, TBlob in_grad);
-```
-  Note that `out_data` in `FullyConnectedForward`
-  is not used by `FullyConnectedBackward`,
-  while `PoolingBackward` requires all of the arguments of `PoolingForward`.
-  Therefore, for `FullyConnectedForward`,
-  the `out_data` tensor once consumed could be safely freed
-  because the backward function will not need it.
-  This provides a chance for the system to collect some tensors
-  as garbage as soon as possible.
-  To specify this situation, we provide an interface:
-
-```c++
-          virtual std::vector<int> DeclareBackwardDependency(
-               const std::vector<int> &out_grad,
-               const std::vector<int> &in_data,
-               const std::vector<int> &out_data) const;
-```
-  The `int` element of the argument vector is an ID
-  to distinguish different arrays.
-  Let's see how this interface specifies different dependencies
-  for `FullyConnected` and `Pooling`:
-
- ```c++
-           std::vector<int> FullyConnectedProperty::DeclareBackwardDependency(
-               const std::vector<int> &out_grad,
-               const std::vector<int> &in_data,
-               const std::vector<int> &out_data) const {
-             return {out_grad[0], in_data[0]};  // NOTE: out_data[0] is NOT included
-           }
-           std::vector<int> PoolingProperty::DeclareBackwardDependency(
-               const std::vector<int> &out_grad,
-               const std::vector<int> &in_data,
-               const std::vector<int> &out_data) const {
-             return {out_grad[0], in_data[0], out_data[0]};
-           }
-```
-
-* **In place Option:** To further save the cost of memory allocation,
-you can use in-place updates.
-They are appropriate for element-wise operations
-when the input tensor and output tensor have the same shape.
-You specify and in-place update with the following interface:
-
-```c++
-           virtual std::vector<std::pair<int, void*>>    ElewiseOpProperty::ForwardInplaceOption(
-               const std::vector<int> &in_data,
-               const std::vector<void*> &out_data) const {
-             return { {in_data[0], out_data[0]} };
-           }
-           virtual std::vector<std::pair<int, void*>> ElewiseOpProperty::BackwardInplaceOption(
-               const std::vector<int> &out_grad,
-               const std::vector<int> &in_data,
-               const std::vector<int> &out_data,
-               const std::vector<void*> &in_grad) const {
-             return { {out_grad[0], in_grad[0]} }
-           }
-```
-  This tells the system that the `in_data[0]` and `out_data[0]` tensors could share the same memory spaces during `Forward`, and so do `out_grad[0]` and `in_grad[0]` during `Backward`.
-
-  >**Important:** Even if you use the preceding specification, it's *not* guaranteed that the input and output tensors will share the same space. In fact, this is only a suggestion for the system, which makes the final decision. However, in either case, the decision is completely transparent to you, so the actual `Forward` and `Backward` implementation does not need to consider that.
-
-* **Expose Operator to Python:** Because of the restrictions of C++, you need user to implement following interfaces:
-
-```c++
-           // initial the property class from a list of key-value string pairs
-           virtual void Init(const vector<pair<string, string>> &kwargs) = 0;
-           // return the parameters in a key-value string map
-           virtual map<string, string> GetParams() const = 0;
-           // return the name of arguments (for generating signature in python)
-           virtual vector<string> ListArguments() const;
-           // return the name of output values
-           virtual vector<string> ListOutputs() const;
-           // return the name of auxiliary states
-           virtual vector<string> ListAuxiliaryStates() const;
-           // return the number of output values
-           virtual int NumOutputs() const;
-           // return the number of visible outputs
-           virtual int NumVisibleOutputs() const;
-```
-
-### Create an Operator from the Operator Property
-
- `OperatorProperty` includes all *semantic* attributes of an operation. It's also responsible for creating the `Operator` pointer for actual computation.
-
-#### Create Operator
-Implement the following interface in `OperatorProperty`:
-
-```c++
-    virtual Operator* CreateOperator(Context ctx) const = 0;
-```
-For example:
-
-```c++
-    class ConvolutionOp {
-     public:
-      void Forward( ... ) { ... }
-      void Backward( ... ) { ... }
-    };
-    class ConvolutionOpProperty : public OperatorProperty {
-     public:
-      Operator* CreateOperator(Context ctx) const {
-        return new ConvolutionOp;
-      }
-    };
-```
-
-#### Parametrize Operator
-When implementing a convolution operator, you need to know the kernel size,
-the stride size, padding size, and so on.
-These parameters should be passed to the operator
-before any `Forward` or `Backward` interface is called.
-To do so, you could define a `ConvolutionParam` structure, as follows:
-
-```c++
-    #include <dmlc/parameter.h>
-    struct ConvolutionParam : public dmlc::Parameter<ConvolutionParam> {
-      mxnet::TShape kernel, stride, pad;
-      uint32_t num_filter, num_group, workspace;
-      bool no_bias;
-    };
-```
-Put it in `ConvolutionOpProperty`, and pass it to the operator class during construction:
-
-```c++
-    class ConvolutionOp {
-     public:
-      ConvolutionOp(ConvolutionParam p): param_(p) {}
-      void Forward( ... ) { ... }
-      void Backward( ... ) { ... }
-     private:
-      ConvolutionParam param_;
-    };
-    class ConvolutionOpProperty : public OperatorProperty {
-     public:
-      void Init(const vector<pair<string, string>& kwargs) {
-        // initialize param_ using kwargs
-      }
-      Operator* CreateOperator(Context ctx) const {
-        return new ConvolutionOp(param_);
-      }
-     private:
-      ConvolutionParam param_;
-    };
-```
-
-#### Register the Operator Property Class and the Parameter Class to MXNet
-Use the following macros to register the parameter structure and the operator property class to MXNet:
-
-```c++
-    DMLC_REGISTER_PARAMETER(ConvolutionParam);
-    MXNET_REGISTER_OP_PROPERTY(Convolution, ConvolutionOpProperty);
-```
-The first argument is the name string, the second is the property class name.
-
-### Interface Summary
-
-We've almost covered the entire interface required to define a new operator. Let's do a recap:
-
-* Use the `Operator` interface to write your computation logic (`Forward` and `Backward`).
-* Use the `OperatorProperty` interface to:
-  - Pass the parameter to the operator class (you can use the `Init` interface).
-  - Create an operator using the `CreateOperator` interface.
-  - Correctly implement the operator description interface, such as the names of arguments, etc.
-  - Correctly implement the `InferShape` interface to set the output tensor shape.
-  - [Optional] If additional resources are needed, check `ForwardResource` and `BackwardResource`.
-  - [Optional] If `Backward` doesn't need all of the input and output of `Forward`, check `DeclareBackwardDependency`.
-  - [Optional] If in-place update is supported, check `ForwardInplaceOption` and `BackwardInplaceOption`.
-* Register the `OperatorProperty` class and the parameter class.
-
-## Unifying the NDArray Operator and Symbolic Operator
-NDArray operations are similar to symbolic operations,
-except that sometimes you can't write in place to the operands
-without a complete dependency graph.
-However, the logic underlying NDArray and symbolic operations are almost identical.
-*SimpleOp*, a new unified operator API,
-unifies different invoking processes
-and returns to the fundamental elements of operators.
-Because most mathematical operators attend to one or two operands,
-and more operands make dependency-related optimization useful,
-the unified operator is specifically designed for unary and binary operations.
-
-Consider the elements of an operation.
-Ideally, you need only functions and derivatives
-to describe an operation.
-Let's restrict that to the space of unary and binary operations.
-How do we classify all operations to maximize the possibility
-of in-place write optimization?
-Note that you can separate functions by the number of operands.
-Derivatives are a bit more complex.
-To construct a dependency graph, you need to know whether output value,
-input data, or neither are needed alongside head gradient.
-Gradient functions in the unified API are differentiated
-by the types of operands it takes for calculation.
-
-Before you learn more about the SimpleOp interface,
- we recommend that you review the
- [mshadow library guide](https://github.com/dmlc/mshadow/tree/master/guide)
- because  calculations will be done in the `mshadow::TBlob` structure.
-
-In the following example, we'll create an operator
-functioning as a smooth l1 loss,
-which is a mixture of l1 loss and l2 loss. The loss itself can be written as:
-
-```
-    loss = outside_weight .* f(inside_weight .* (data - label))
-    grad = outside_weight .* inside_weight .* f'(inside_weight .* (data - label))
-```
- `.*` stands for element-wise multiplication, and `f`, `f'` is the smooth l1 loss function,
-which we are assuming is in `mshadow` for now.
-At first glance, it's impossible to implement
-this particular loss as a unary or binary operator.
-But we have automatic differentiation in symbolic execution.
-That simplifies the loss to `f` and `f'` directly.
-This loss is no more complex than a `sin` or an `abs` function,
-and can certainly be implemented as a unary operator.
-
-## SimpleOp: The Unified Operator API
-### Define Shapes
-The `mshadow` library requires explicit memory allocation.
-As a consequence, all data shapes
-must be provided before any calculation occurs.
- Before we proceed with defining functions and gradient,
-let's check input data shape consistency and provide output shape.
-
-```cpp
-    typedef mxnet::TShape (*UnaryShapeFunction)(const mxnet::TShape& src,
-                                         const EnvArguments& env);
-    typedef mxnet::TShape (*BinaryShapeFunction)(const mxnet::TShape& lhs,
-                                          const mxnet::TShape& rhs,
-                                          const EnvArguments& env);
-```
-You can use `mshadow::TShape` to check input data shape and designate output data shape.
-If you don't define this function, the default output shape is the same as the input shape.
-In the case of a binary operator, the shape of `lhs` and `rhs` is checked as the same by default.
-
-You can also use shape functions to check if any additional arguments and resources are present.
-Refer to the additional usages of `EnvArguments` to accomplish this.
-
-Before we start on our smooth l1 loss example, we define a `XPU` to `cpu` or `gpu` in the header
-`smooth_l1_unary-inl.h` implementation so that we reuse the same code in `smooth_l1_unary.cc` and
-`smooth_l1_unary.cu`.
-
-```cpp
-    #include <mxnet/operator_util.h>
-    #if defined(__CUDACC__)
-    #define XPU gpu
-    #else
-    #define XPU cpu
-    #endif
-```
-In our smooth l1 loss example, it's okay to use the default behavior whereby the output has the same shape as the source.
-Written explicitly, it is:
-
-```cpp
-    inline mxnet::TShape SmoothL1Shape_(const mxnet::TShape& src,
-                                 const EnvArguments& env) {
-      return mxnet::TShape(src);
-    }
-```
-
-### Define Functions
-Create a unary or binary function with one output: `mshadow::TBlob`.
-
-```cpp
-    typedef void (*UnaryFunction)(const TBlob& src,
-                                  const EnvArguments& env,
-                                  TBlob* ret,
-                                  OpReqType req,
-                                  RunContext ctx);
-    typedef void (*BinaryFunction)(const TBlob& lhs,
-                                   const TBlob& rhs,
-                                   const EnvArguments& env,
-                                   TBlob* ret,
-                                   OpReqType req,
-                                   RunContext ctx);
-```
-* Functions are differentiated by the types of input arguments.
-* `RunContext ctx` contains information needed during runtime for execution.
-
-```cpp
-        struct RunContext {
-          void *stream;  // the stream of the device, can be NULL or Stream<gpu>* in GPU mode
-          template<typename xpu> inline mshadow::Stream<xpu>* get_stream() // get mshadow stream from Context
-        }  // namespace mxnet
-```
-  `mshadow::stream<xpu> *s = ctx.get_stream<xpu>();` is an example of obtaining a stream from `ctx`.
-* `OpReqType req` denotes how computation results are written into `ret`.
-
-```cpp
-        enum OpReqType {
-          kNullOp,  // no operation, do not write anything
-          kWriteTo,  // write gradient to provided space
-          kWriteInplace,  // perform an in-place write
-          kAddTo  // add to the provided space
-        };
-```
-  A macro is defined in `operator_util.h` for a simplified use of `OpReqType`.
-  `ASSIGN_DISPATCH(out, req, exp)` checks `req` and performs an assignment.
-
-In our smooth l1 loss example, we use `UnaryFunction` to define the function of this operator.
-
-```cpp
-    template<typename xpu>
-    void SmoothL1Forward_(const TBlob& src,
-                          const EnvArguments& env,
-                          TBlob *ret,
-                          OpReqType req,
-                          RunContext ctx) {
-      using namespace mshadow;
-      using namespace mshadow::expr;
-      mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
-      real_t sigma2 = env.scalar * env.scalar;
-      MSHADOW_TYPE_SWITCH(ret->type_flag_, DType, {
-        mshadow::Tensor<xpu, 2, DType> out = ret->get<xpu, 2, DType>(s);
-        mshadow::Tensor<xpu, 2, DType> in = src.get<xpu, 2, DType>(s);
-        ASSIGN_DISPATCH(out, req,
-                        F<mshadow_op::smooth_l1_loss>(in, ScalarExp<DType>(sigma2)));
-      });
-    }
-```
-After obtaining `mshadow::Stream` from `RunContext`, we get `mshadow::Tensor` from `mshadow::TBlob`.
-`mshadow::F` is a shortcut to initiate a `mshadow` expression. The macro `MSHADOW_TYPE_SWITCH(type, DType, ...)`
-handles details on different types, and the macro `ASSIGN_DISPATCH(out, req, exp)` checks `OpReqType` and
-performs actions accordingly. `sigma2` is a special parameter in this loss, which we will cover later.
-
-### Define Gradients (Optional)
-Create a gradient function with various types of inputs.
-
-```cpp
-    // depending only on out_grad
-    typedef void (*UnaryGradFunctionT0)(const OutputGrad& out_grad,
-                                        const EnvArguments& env,
-                                        TBlob* in_grad,
-                                        OpReqType req,
-                                        RunContext ctx);
-    // depending only on out_value
-    typedef void (*UnaryGradFunctionT1)(const OutputGrad& out_grad,
-                                        const OutputValue& out_value,
-                                        const EnvArguments& env,
-                                        TBlob* in_grad,
-                                        OpReqType req,
-                                         RunContext ctx);
-    // depending only on in_data
-    typedef void (*UnaryGradFunctionT2)(const OutputGrad& out_grad,
-                                        const Input0& in_data0,
-                                        const EnvArguments& env,
-                                        TBlob* in_grad,
-                                        OpReqType req,
-                                        RunContext ctx);
-```
-Gradient functions of binary operators have similar structures, except that `Input`, `TBlob`, and `OpReqType`
-are doubled.
-
-`GradFunctionArgument`
-
-  `Input0`, `Input`, `OutputValue`, and `OutputGrad` all share the structure of `GradFunctionArgument`,
-  which is defined as:
-
-  ```cpp
-      struct GradFunctionArgument {
-          TBlob data;
-      }
-  ```
-
-In our smooth l1 loss example, note that it's an `f'(x)`,
-which utilizes input for the gradient calculation,
-so the `UnaryGradFunctionT2` is suitable.
-To enable the chain rule of the gradient,
-we also need to multiply `out_grad` from the top to the result of `in_grad`.
-
-```cpp
-    template<typename xpu>
-    void SmoothL1BackwardUseIn_(const OutputGrad& out_grad,
-                                const Input0& in_data0,
-                                const EnvArguments& env,
-                                TBlob *in_grad,
-                                OpReqType req,
-                                RunContext ctx) {
-      using namespace mshadow;
-      using namespace mshadow::expr;
-      mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
-      real_t sigma2 = env.scalar * env.scalar;
-      MSHADOW_TYPE_SWITCH(in_grad->type_flag_, DType, {
-        mshadow::Tensor<xpu, 2, DType> src = in_data0.data.get<xpu, 2, DType>(s);
-        mshadow::Tensor<xpu, 2, DType> ograd = out_grad.data.get<xpu, 2, DType>(s);
-        mshadow::Tensor<xpu, 2, DType> igrad = in_grad->get<xpu, 2, DType>(s);
-         ASSIGN_DISPATCH(igrad, req,
-                        ograd * F<mshadow_op::smooth_l1_gradient>(src, ScalarExp<DType>(sigma2)));
-      });
-    }
-```
-
-### Register SimpleOp to MXNet
-After creating the shape, function, and gradient, restore them into both an NDArray operator and
-a symbolic operator. To simplify this process, use the registration macro defined in `operator_util.h`.
-
-```cpp
-    MXNET_REGISTER_SIMPLE_OP(Name, DEV)
-    .set_shape_function(Shape)
-    .set_function(DEV::kDevMask, Function<XPU>, SimpleOpInplaceOption)
-    .set_gradient(DEV::kDevMask, Gradient<XPU>, SimpleOpInplaceOption)
-    .describe("description");
-```
-`SimpleOpInplaceOption` is defined as:
-
-```cpp
-    enum SimpleOpInplaceOption {
-      kNoInplace,  // do not allow inplace in arguments
-      kInplaceInOut,  // allow inplace in with out (unary)
-      kInplaceOutIn,  // allow inplace out_grad with in_grad (unary)
-      kInplaceLhsOut,  // allow inplace left operand with out (binary)
-      kInplaceOutLhs  // allow inplace out_grad with lhs_grad (binary)
-    };
-```
-
-In our example, we have a gradient function that relies on input data, so the function can't be written in
-place. The output gradient has no purpose after gradient computation, so the gradient can be written in place.
-
-```cpp
-    MXNET_REGISTER_SIMPLE_OP(smooth_l1, XPU)
-    .set_function(XPU::kDevMask, SmoothL1Forward_<XPU>, kNoInplace)
-    .set_gradient(XPU::kDevMask, SmoothL1BackwardUseIn_<XPU>, kInplaceOutIn)
-    .set_enable_scalar(true)
-    .describe("Calculate Smooth L1 Loss(lhs, scalar)");
-```
-Remember from the discussion of shape functions that a default behavior without `set_shape_function` forces the inputs
-(if they're binary) to be the same shape and yield the same shape for output. We'll discuss `set_enable_scalar` later.
-
-### NDArray Operator Summary
-* Create a shape function for determining the output shape.
-* Create a function as the forward routine by choosing a suitable function type.
-* Create a gradient as the backward routine by choosing a suitable gradient type.
-* Register the operator using the registration process.
-
-## Additional Information on SimpleOp
-### Using SimpleOp on EnvArguments
-Some operations might need a scalar as input, such as a  gradient scale, a set of keyword arguments
-controlling behavior, or a temporary space to speed up calculations.`EnvArguments` provides additional arguments and resources to make calculations more scalable
-and efficient.
-
-```cpp
-    struct EnvArguments {
-      real_t scalar;  // scalar argument, if enabled
-      std::vector<std::pair<std::string, std::string> > kwargs;  // keyword arguments
-      std::vector<Resource> resource;  // pointer to the resources requested
-    };
-```
-
-More registration parameters are required to enable these additional features. To prevent confusion on parameters, `scalar` and `kwargs`
-can't be present at the same time. To enable `scalar`, use
-`set_enable_scalar(bool enable_scalar)` in registration. Then, in forward functions and gradients, the `scalar` can be accessed from `env.scalar` as in the function parameter `EnvArguments env`.
-
-To enable `kwargs`, use `set_enable_kwargs(bool enable_kwargs)` in registration. Then, in forward
-functions and gradients, additional arguments are contained in `env.kwarg`, which is defined as
-`std::vector<std::pair<std::string, std::string> >`. Use the DMLC parameter structure to
-simplify parsing keyword arguments. For more details, see the [guide on parameter structure](https://github.com/dmlc/dmlc-core/blob/master/doc/parameter.md).
-
-Additional resources like `mshadow::Random<xpu>` and temporary memory space can also be requested and
-accessed from `EnvArguments.resource`. The registration routine is `set_resource_request(ResourceRequest req)`
-or `set_resource_request(const std::vector<ResourceRequest>)`, where `mxnet::ResourceRequest` is defined as:
-
-```cpp
-    struct ResourceRequest {
-      enum Type {  // Resource type, indicating what the pointer type is
-        kRandom,  // mshadow::Random<xpu> object
-        kTempSpace  // A dynamic temp space that can be arbitrary size
-      };
-      Type type;  // type of resources
-    };
-```
-Registration will request the declared resource requests from `mxnet::ResourceManager`, and place resources
-in `std::vector<Resource> resource` in `EnvArguments`. To access resources, use the following:
-
-```cpp
-    auto tmp_space_res = env.resources[0].get_space(some_shape, some_stream);
-    auto rand_res = env.resources[0].get_random(some_stream);
-```
-For an example, see `src/operator/loss_binary_op-inl.h`.
-
-In our smooth l1 loss example, a scalar input is needed to mark the turning point of a loss function. Therefore,
-in the registration process, we use `set_enable_scalar(true)`, and use `env.scalar` in function and gradient
-declarations.
-
-### Crafting a Tensor Operation
-Because computation utilizes the `mshadow` library and we sometimes don't have functions readily available, we
-can craft tensor operations in operator implementations. If you define such functions as element-wise, you
-can implement them as a `mxnet::op::mshadow_op`. `src/operator/mshadow_op.h` that contains a lot of `mshadow_op`,
-for example. `mshadow_op` are expression mappers. They deal with the scalar case of desired functions. For details, see
-[mshadow expression API guide](https://github.com/dmlc/mshadow/tree/master/doc).
-
-If an operation can't be done in an element-wise way, like the softmax loss and gradient, then you need to create a new tensor operation. You need to create as `mshadow` function and as `mshadow::cuda`
-function directly. For details, see the `mshadow` library. For an example, see `src/operator/roi_pooling.cc`.
-
-In our smooth l1 loss example, we create two mappers, namely the scalar cases of smooth l1 loss and gradient.
-
-```cpp
-    namespace mshadow_op {
-    struct smooth_l1_loss {
-      // a is x, b is sigma2
-      MSHADOW_XINLINE static real_t Map(real_t a, real_t b) {
-        if (a > 1.0f / b) {
-          return a - 0.5f / b;
-        } else if (a < -1.0f / b) {
-          return -a - 0.5f / b;
-        } else {
-          return 0.5f * a * a * b;
-        }
-      }
-    };
-    }
-```
-The gradient, which can be found in `src/operator/smooth_l1_unary-inl.h`, is similar.
-
-### Beyond Two Operands
-The new unified API is designed to fulfill the fundamentals of an operation. For operators with more than two inputs,
-more than one output, or that need more features, see the original [Operator API](http://mxnet.io/architecture/overview.html#operators-in-mxnet).
diff --git a/docs/architecture/program_model.md b/docs/architecture/program_model.md
deleted file mode 100644
index 2731a3df7394..000000000000
--- a/docs/architecture/program_model.md
+++ /dev/null
@@ -1,623 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Deep Learning Programming Style
-
-However much we might ultimately care about performance,
-we first need working code before we can start worrying about optimization.
-Writing clear, intuitive deep learning code can be challenging,
-and the first thing any practitioner must deal with is the language syntax itself.
-Complicating matters, of the many deep learning libraries out there,
-each has its own approach to programming style.
-
-In this document, we focus on two of the most important high-level design decisions:
-1. Whether to embrace the _symbolic_ or _imperative_ paradigm for mathematical computation.
-2. Whether to build networks with bigger (more abstract) or more atomic operations.
-
-Throughout, we'll focus on the programming models themselves.
-When programming style decisions may impact performance, we point this out,
-but we don't dwell on specific implementation details. 
-
-
-## Symbolic vs. Imperative Programs
-
-If you are a Python or C++ programmer, then you're already familiar with imperative programs.
-Imperative-style programs perform computation as you run them.
-Most code you write in Python is imperative, as is the following NumPy snippet.
-
-```python
-    import numpy as np
-    a = np.ones(10)
-    b = np.ones(10) * 2
-    c = b * a
-    d = c + 1
-```
-When the program executes ```c = b * a```, it runs the actual numerical computation.
-
-Symbolic programs are a bit different. With symbolic-style programs,
-we first define a (potentially complex) function abstractly.
-When defining the function, no actual numerical computation takes place.
-We define the abstract function in terms of placeholder values.
-Then we can compile the function, and evaluate it given real inputs.
-In the following example, we rewrite the imperative program from above
-as a symbolic-style program:
-
-```python
-    A = Variable('A')
-    B = Variable('B')
-    C = B * A
-    D = C + Constant(1)
-    # compiles the function
-    f = compile(D)
-    d = f(A=np.ones(10), B=np.ones(10)*2)
-```
-As you can see, in the symbolic version, when ```C = B * A``` is executed, no computation occurs.
-Instead, this operation generates a _computation graph_ (also called a _symbolic graph_)
-that represents the computation.
-The following figure shows a computation graph to compute ```D```.
-
-![Comp Graph](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/prog_model/comp_graph.png)
-
-Most symbolic-style programs contain, either explicitly or implicitly, a *compile* step.
-This converts the computation graph into a function that we can later call.
-In the above example, numerical computation only occurs in the last line of code.
-The defining characteristic of symbolic programs is their clear separation
-between building the computation graph and executing it.
-For neural networks, we typically define the entire model as a single compute graph.
-
-Among other popular deep learning libraries, Torch, Chainer, and Minerva embrace the imperative style.
-Examples of symbolic-style deep learning libraries include Theano, CGT, and TensorFlow.
-We might also view libraries like CXXNet and Caffe, which rely on configuration files, as symbolic-style libraries.
-In this interpretation, we'd consider the content of the configuration file as defining the computation graph.
-
-Now that you understand the difference between these two programming models, let's compare the advantages of each.
-
-
-### Imperative Programs Tend to be More Flexible
-
-When you're using an imperative-style library from Python, you are writing in Python.
-Nearly anything that would be intuitive to write in Python, you could accelerate by calling down in the appropriate places to the imperative deep learning library.
-On the other hand, when you write a symbolic program, you may not have access to all the familiar Python constructs, like iteration.
-Consider the following imperative program, and think about how you can translate this into a symbolic program.
-
-```python
-    a = 2
-    b = a + 1
-    d = np.zeros(10)
-    for i in range(d):
-        d += np.zeros(10)
-```
-This wouldn't be so easy if the Python for-loop weren't supported by the symbolic API.
-When you write a symbolic program in Python, you're *not* writing in Python.
-Instead, you're writing in a domain-specific language (DSL) defined by the symbolic API.
-The symbolic APIs found in deep learning libraries
-are powerful DSLs that generate callable computation graphs for neural networks.
-<!-- In that sense, config-file input libraries are all symbolic. -->
-
-Intuitively, you might say that imperative programs
-are more *native* than symbolic programs.
-It's easier to use native language features.
-For example, it's straightforward to print out the values
-in the middle of computation or to use native control flow and loops
-at any point in the flow of computation.
-
-### Symbolic Programs Tend to be More Efficient
-
-As we've seen, imperative programs tend to be flexible
-and fit nicely into the programming flow of a host language.
-So you might wonder, why do so many deep learning libraries
-embrace the symbolic paradigm?
-The main reason is efficiency, both in terms of memory and speed.
-Let's revisit our toy example from before.
-
-```python
-    import numpy as np
-    a = np.ones(10)
-    b = np.ones(10) * 2
-    c = b * a
-    d = c + 1
-    ...
-```
-
-![Comp Graph](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/prog_model/comp_graph.png)
-
-Assume that each cell in the array occupies 8 bytes of memory.
-How much memory do you need to execute this program in the Python console?
-
-As an imperative program we need to allocate memory at each line.
-That leaves us allocating 4 arrays of size 10.
-So we'll need `4 * 10 * 8 = 320` bytes.
-On the other hand, if we built a computation graph,
-and knew in advance that we only needed `d`,
-we could reuse the memory originally allocated for intermediate values.
-For example, by performing computations in-place,
-we might recycle the bits allocated for ```b``` to store `c`.
-And we might recycle the bits allocated for `c` to store `d`.
-In the end we could cut our memory requirement in half,
-requiring just `2 * 10 * 8 = 160` bytes.
-
-Symbolic programs are more *restricted*.
-When we call `compile` on D, we tell the system
-that only the value of `d` is needed.
-The intermediate values of the computation,
-in this case ```c```, is then invisible to us.
-
-We benefit because the symbolic programs
-can then safely reuse the memory for in-place computation.
-But on the other hand, if we later decide that we need to access `c`, we're out of luck.
-So imperative programs are better prepared to encounter all possible demands.
-If we ran the imperative version of the code in a Python console,
-we could inspect any of the intermediate variables in the future.
-
-<!-- Of course, this is somewhat misleading, because garbage collection can occur in imperative programs and memory could then be reused.
-However, imperative programs do need to be "prepared to encounter all possible demands," and this limits the optimization you can perform. This is true for non-trivial cases, such
-as gradient calculation, which we discuss in next section. -->
-
-Symbolic programs can also perform another kind of optimization, called operation folding.
-Returning to our toy example, the multiplication and addition operations
-can be folded into one operation, as shown in the following graph.
-If the computation runs on a GPU processor,
-one GPU kernel will be executed, instead of two.
-In fact, this is one way we hand-craft operations
-in optimized libraries, such as CXXNet and Caffe.
-Operation folding improves computation efficiency.
-
-![Comp Graph Folded](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/prog_model/comp_graph_fold.png)
-
-Note, you can't perform operation folding in imperative programs,
-because the intermediate values might be referenced in the future.
-Operation folding is possible in symbolic programs
-because you get the entire computation graph,
-and a clear specification of which values will be needed and which are not.
-
-
-### Case Study: Backprop and AutoDiff
-
-In this section, we compare the two programming models
-on the problem of auto differentiation, or backpropagation.
-Differentiation is of vital importance in deep learning
-because it's the mechanism by which we train our models.
-In any deep learning model, we define a _loss function_.
-A _loss function_ measures how far the model is from the desired output.
-We then typically pass over training examples (pairs of inputs and ground-truth outputs).
-At each step we update the model's _parameters_ to minimize the loss.
-To determine the direction in which to update the parameters,
-we need to take the derivative of the loss function with respect to the parameters.
-
-In the past, whenever someone defined a new model,
-they had to work out the derivative calculations by hand.
-While the math is reasonably straightforward,
-for complex models, it can be time-consuming and tedious work.
-All modern deep learning libraries make the practitioner/researcher's job
-much easier, by automatically solving the problem of gradient calculation.
-
-Both imperative and symbolic programs can perform gradient calculation.
-So let's take a look at how you might perform automatic differentiation with each.
-
-Let's start with imperative programs.
-The following example Python code performs automatic differentiation using our toy example:
-
-```python
-    class array(object) :
-        """Simple Array object that support autodiff."""
-        def __init__(self, value, name=None):
-            self.value = value
-            if name:
-                self.grad = lambda g : {name : g}
-
-        def __add__(self, other):
-            assert isinstance(other, int)
-            ret = array(self.value + other)
-            ret.grad = lambda g : self.grad(g)
-            return ret
-
-        def __mul__(self, other):
-            assert isinstance(other, array)
-            ret = array(self.value * other.value)
-            def grad(g):
-                x = self.grad(g * other.value)
-                x.update(other.grad(g * self.value))
-                return x
-            ret.grad = grad
-            return ret
-
-    # some examples
-    a = array(1, 'a')
-    b = array(2, 'b')
-    c = b * a
-    d = c + 1
-    print d.value
-    print d.grad(1)
-    # Results
-    # 3
-    # {'a': 2, 'b': 1}
-```
-
-In this code, each array object contains a grad function (it is actually a closure).
-When you run ```d.grad```, it recursively invokes the grad function of its inputs,
-backprops the gradient value back, and
-returns the gradient value of each input.
-
-This might look a bit complicated, so let's consider
-the gradient calculation for symbolic programs.
-The following program performs symbolic gradient calculation for the same task.
-
-```python
-    A = Variable('A')
-    B = Variable('B')
-    C = B * A
-    D = C + Constant(1)
-    # get gradient node.
-    gA, gB = D.grad(wrt=[A, B])
-    # compiles the gradient function.
-    f = compile([gA, gB])
-    grad_a, grad_b = f(A=np.ones(10), B=np.ones(10)*2)
-```
-
-The grad function of ```D``` generates a backward computation graph,
-and returns a gradient node, ```gA, gB```,
-which correspond to the red nodes in the following figure.
-
-![Comp Graph Folded](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/prog_model/comp_graph_backward.png)
-
-The imperative program actually does the same thing as the symbolic program.
-It implicitly saves a backward computation graph in the grad closure.
-When you invoked ```d.grad```, you start from ```d(D)```,
-backtrack through the graph to compute the gradient, and collect the results.
-
-The gradient calculations in both symbolic
-and imperative programming follow the same pattern.
-What's the difference then?
-Recall the *be prepared to encounter all possible demands* requirement of imperative programs.
-If you are creating an array library that supports automatic differentiation,
-you have to keep the grad closure along with the computation.
-This means that none of the history variables can be
-garbage-collected because they are referenced by variable `d` by way of function closure.
-
-What if you want to compute only the value of `d`,
-and don't want the gradient value?
-In symbolic programming, you declare this with `f=compiled([D])`.
-This also declares the boundary of computation,
-telling the system that you want to compute only the forward pass.
-As a result, the system can free the memory of previous results,
-and share the memory between inputs and outputs.
-
-Imagine running a deep neural network with ```n``` layers.
-If you are running only the forward pass,
-not the backward(gradient) pass,
-you need to allocate only two copies of
-temporal space to store the values of the intermediate layers,
-instead of ```n``` copies of them.
-However, because imperative programs need to be prepared
-to encounter all possible demands of getting the gradient,
-they have to store the intermediate values,
-which requires ```n``` copies of temporal space.
-
-As you can see, the level of optimization depends
-on the restrictions on what you can do.
-Symbolic programs ask you to clearly specify
-these restrictions when you compile the graph.
-One the other hand, imperative programs
-must be prepared for a wider range of demands.
-Symbolic programs have a natural advantage
-because they know more about what you do and don't want.
-
-There are ways in which we can modify imperative programs
-to incorporate similar restrictions.
-For example, one solution to the preceding
-problem is to introduce a context variable.
-You can introduce a no-gradient context variable
-to turn gradient calculation off.
-
-```python
-    with context.NoGradient():
-        a = array(1, 'a')
-        b = array(2, 'b')
-        c = b * a
-        d = c + 1
-```
-
-<!-- This provides an imperative program with the ability to impose some restrictions, but reduces efficiency. -->
-
-However, this example still must be prepared to encounter all possible demands,
-which means that you can't perform the in-place calculation
-to reuse memory in the forward pass (a trick commonly used to reduce GPU memory usage).
-The techniques we've discussed generate an explicit backward pass.
-Some of the libraries such as Caffe and CXXNet perform backprop implicitly on the same graph.
-The approach we've discussed in this section also applies to them.
-
-Most configuration-file-based libraries,
-such as CXXNet and Caffe are designed
-to meet one or two generic requirements:
-get the activation of each layer,
-or get the gradient of all of the weights.
-These libraries have the same problem:
-the more generic operations the library has to support,
-the less optimization (memory sharing) you can do,
-based on the same data structure.
-
-As you can see, the trade-off between restriction
-and flexibility is the same for most cases.
-
-### Model Checkpoint
-
-It's important to able to save a model and load it back later.
-There are different ways to *save* your work.
-Normally, to save a neural network,
-you need to save two things: a net configuration
-for the structure of the neural network and the weights of the neural network.
-
-The ability to check the configuration is a plus for symbolic programs.
-Because the symbolic construction phase does not perform computation,
-you can directly serialize the computation graph, and load it back later.
-This solves the problem of saving the configuration
-without introducing an additional layer.
-
-```python
-    A = Variable('A')
-    B = Variable('B')
-    C = B * A
-    D = C + Constant(1)
-    D.save('mygraph')
-    ...
-    D2 = load('mygraph')
-    f = compile([D2])
-    # more operations
-    ...
-```
-
-Because an imperative program executes as it describes the computation,
-you have to save the code itself as the ```configuration```,
-or build another configuration layer on top of the imperative language.
-
-### Parameter Updates
-
-Most symbolic programs are data flow (computation) graphs.
-Data flow graphs describe computation.
-But it's not obvious how to use graphs to describe parameter updates.
-That's because parameter updates introduce mutation,
-which is not a data flow concept.
-Most symbolic programs introduce a special update statement
-to update persistent state in the programs.
-
-It's usually easier to write parameter updates in an imperative style,
-especially when you need multiple updates that relate to each other.
-For symbolic programs, the update statement is also executed as you call it.
-So in that sense, most symbolic deep learning libraries
-fall back on the imperative approach to perform updates,
-while using the symbolic approach to perform gradient calculation.
-
-### There Is No Strict Boundary
-
-In comparing the two programming styles,
-some of our arguments might not be strictly true,
-i.e., it's possible to make an imperative program
-more like a traditional symbolic program or vice versa. 
-However, the two archetypes are useful abstractions,
-especially for understanding the differences between deep learning libraries.
-We might reasonably conclude that there is no clear boundary between programming styles.
-For example, you can create a just-in-time (JIT) compiler in Python
-to compile imperative Python programs,
-which provides some of the advantages of global
-information held in symbolic programs.
-
-
-## Big vs. Small Operations
-
-When designing a deep learning library, another important programming model decision 
-is precisely what operations to support.
-In general, there are two families of operations supported by most deep learning libraries:
-
-- Big operations - typically for computing neural network layers (e.g. FullyConnected and BatchNormalize).
-- Small operations - mathematical functions like matrix multiplication and element-wise addition.
-
-Libraries like CXXNet and Caffe support layer-level operations.
-Libraries like Theano and Minerva support fine-grained operations.
-
-### Smaller Operations Can Be More Flexible
-It's quite natural to use smaller operations to compose bigger operations.
-For example, the sigmoid unit can simply be composed of division, addition and an exponentiation:
-
-```python
-    sigmoid(x) = 1.0 / (1.0 + exp(-x))
-```
-Using smaller operations as building blocks, you can express nearly anything you want.
-If you're more familiar with CXXNet- or Caffe-style layers,
-note that these operations don't differ from a layer, except that they are smaller.
-
-```python
-    SigmoidLayer(x) = EWiseDivisionLayer(1.0, AddScalarLayer(ExpLayer(-x), 1.0))
-```
-This expression composes three layers,
-with each defining its forward and backward (gradient) function.
-Using smaller operations gives you the advantage of building new layers quickly,
-because you only need to compose the components.
-
-### Big Operations Are More Efficient
-Directly composing sigmoid layers requires three layers of operation, instead of one.
-
-```python
-    SigmoidLayer(x) = EWiseDivisionLayer(1.0, AddScalarLayer(ExpLayer(-x), 1.0))
-```
-This code creates overhead for computation and memory (which could be optimized, with cost).
-
-Libraries like CXXNet and Caffe take a different approach.
-To support coarse-grained operations,
-such as BatchNormalization and the SigmoidLayer directly,
-in each layer, the calculation kernel is hand crafted
-with one or only some CUDA kernel launches.
-This makes these implementations more efficient.
-
-### Compilation and Optimization
-
-Can small operations be optimized? Of course, they can.
-Let's look at the system optimization part of the compilation engine.
-Two types of optimization can be performed on the computation graph:
-
-- Memory allocation optimization, to reuse the memory of the intermediate computations.
-- Operator fusion, to detect sub-graph patterns, such as the sigmoid, and fuse them into a bigger operation kernel.
-
-Memory allocation optimization isn't restricted to small operations graphs.
-You can use it with bigger operations graph, too.
-However, optimization might not be essential
-for bigger operation libraries like CXXNet and Caffe,
-because you can't find the compilation step in them.
-However, there's a (dumb) ```compilation step``` in these libraries,
-that basically translates the layers into a fixed forward,
-backprop execution plan, by running each operation one by one.
-
-For computation graphs with smaller operations,
-these optimizations are crucial to performance.
-Because the operations are small,
-there are many sub-graph patterns that can be matched.
-Also, because the final, generated operations
-might not be enumerable,
-an explicit recompilation of the kernels is required,
-as opposed to the fixed amount of precompiled kernels
-in the big operation libraries.
-This creates compilation overhead for the symbolic libraries
-that support small operations.
-Requiring compilation optimization also creates engineering overhead
-for the libraries that solely support smaller operations.
-
-As in the case of symbolic vs. imperative,
-the bigger operation libraries "cheat"
-by asking you to provide restrictions (to the common layer),
-so that you actually perform the sub-graph matching.
-This moves the compilation overhead to the real brain, which is usually not too bad.
-
-### Expression Template and Statically Typed Language
-You always have a need to write small operations and compose them.
-Libraries like Caffe use hand-crafted kernels to build these bigger blocks.
-Otherwise, you would have to compose smaller operations using Python.
-
-There's a third choice that works pretty well.
-This is called the expression template.
-Basically, you use template programming to
-generate generic kernels from an expression tree at compile time.
-For details, see [Expression Template Tutorial](https://github.com/dmlc/mshadow/blob/master/guide/exp-template/README.md).
-CXXNet makes extensive use of an expression template,
-which enables creating much shorter and more readable code that matches
-the performance of hand-crafted kernels.
-
-The difference between using an expression template and Python kernel generation
-is that expression evaluation is done at compile time for C++ with an existing type,
-so there is no additional runtime overhead.
-In principle, this is also possible with other statically typed languages that support templates,
-but we've seen this trick used only in C++.
-
-Expression template libraries create a middle ground between Python operations
-and hand-crafted big kernels by allowing C++ users to craft efficient big
-operations by composing smaller operations. It's an option worth considering.
-
-## Mix the Approaches
-
-Now that we've compared the programming models, which one should you choose?
-Before delving into that, we should emphasize that depending on the problems you're trying to solve,
-our comparison might not necessarily have a big impact.
-
-Remember [Amdahl's law](https://en.wikipedia.org/wiki/Amdahl%27s_law):
-If you are optimizing a non-performance-critical part of your problem,
-you won't get much of a performance gain.
-
-As you've seen, there usually is a trade-off between efficiency,
-flexibility, and engineering complexity.
-The more suitable programming style depends on the problem you are trying to solve.
-For example, imperative programs are better for parameter updates,
-and symbolic programs for gradient calculation.
-
-We advocate *mixing* the approaches. 
-Sometimes the part that we want to be flexible
-isn't crucial to performance.
-In these cases, it's okay to leave some efficiency on the table
-to support more flexible interfaces.
-In machine learning, combining methods usually works better than using just one.
-
-If you can combine the programming models correctly,
-you can get better results than when using a single programming model.
-In this section, we discuss how to do so.
-
-### Symbolic and Imperative Programs
-There are two ways to mix symbolic and imperative programs:
-
-- Use imperative programs within symbolic programs as callbacks
-- Use symbolic programs as part of imperative programs
-
-We've observed that it's usually helpful to write parameter updates imperatively,
-and perform gradient calculations in symbolic programs.
-
-Symbolic libraries already mix programs because Python itself is imperative.
-For example, the following program mixes the symbolic approach with NumPy, which is imperative.
-
-```python
-    A = Variable('A')
-    B = Variable('B')
-    C = B * A
-    D = C + Constant(1)
-    # compiles the function
-    f = compile(D)
-    d = f(A=np.ones(10), B=np.ones(10)*2)
-    d = d + 1.0
-```
-The symbolic graphs are compiled into a function that can be executed imperatively.
-The internals are a black box to the user.
-This is exactly like writing C++ programs and exposing them to Python, which we commonly do.
-
-Because parameter memory resides on the GPU,
-you might not want to use NumPy as an imperative component.
-Supporting a GPU-compatible imperative library
-that interacts with symbolic compiled functions 
-or provides a limited amount of updating syntax
-in the update statement in symbolic program execution
-might be a better choice.
-
-### Small and Big Operations
-
-There might be a good reason to combine small and big operations.
-Consider applications that perform tasks such as changing
-a loss function or adding a few customized layers to an existing structure.
-Usually, you can use big operations to compose existing
-components, and use smaller operations to build the new parts.
-
-Recall Amdahl's law. Often, the new components
-are not the cause of the computation bottleneck.
-Because the performance-critical part is already optimized by
-the bigger operations, it's okay to forego optimizing the additional small operations,
-or to do a limited amount of memory optimization instead
-of operation fusion and directly running them.
-
-### Choose Your Own Approach
-
-In this document, we compared multiple approaches
-to developing programming environments for deep learning.
-We compared both the usability and efficiency implications of each,
-finding that many of these trade-offs (like imperative vs symbolic aren't necessarily black and white).
-You can choose your approach, or combine the approaches
-to create more interesting and intelligent deep learning libraries.
-
-## Contribute to MXNet
-
-This document is part of our effort to provide [open-source system design notes](index.md)
-for deep learning libraries. If you're interested in contributing to _MXNet_ or its
-documentation, [fork us on GitHub](http://github.com/dmlc/mxnet).
-
-## Next Steps
-
-* [Dependency Engine for Deep Learning](http://mxnet.io/architecture/note_engine.html)
-* [Squeeze the Memory Consumption of Deep Learning](http://mxnet.io/architecture/note_memory.html)
-* [Efficient Data Loading Module for Deep Learning](http://mxnet.io/architecture/note_data_loading.html)
-* [Survey of RNN Interface](http://mxnet.io/architecture/rnn_interface.html)
diff --git a/docs/architecture/rnn_interface.md b/docs/architecture/rnn_interface.md
deleted file mode 100644
index 5cd5a7b85835..000000000000
--- a/docs/architecture/rnn_interface.md
+++ /dev/null
@@ -1,117 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Survey of Existing Interfaces and Implementations
-
-Commonly used deep learning libraries with good RNN/LSTM support include [Theano](http://deeplearning.net/software/theano/library/scan.html) and its wrappers [Lasagne](http://lasagne.readthedocs.org/en/latest/modules/layers/recurrent.html) and [Keras](http://keras.io/layers/recurrent/); [CNTK](https://cntk.codeplex.com/); [TensorFlow](https://www.tensorflow.org/tutorials/sequences/recurrent); and various implementations in Torch, such as [this well-known character-level language model tutorial](https://github.com/karpathy/char-rnn), [this](https://github.com/Element-Research/rnn).
-
-In this document, we present a comparative analysis of the approaches taken by these libraries.
-
-## Theano
-
-In Theano, RNN support comes via its [scan operator](http://deeplearning.net/software/theano/library/scan.html),
-which allows construction of a loop where the number of iterations is specified
-as a runtime value of a symbolic variable.
-You can find an official example of an LSTM implementation with scan
-[here](http://deeplearning.net/tutorial/lstm.html).
-
-### Implementation
-
-I'm not very familiar with the Theano internals,
-but it seems from [theano/scan_module/scan_op.py#execute](https://github.com/Theano/Theano/blob/master/theano/scan_module/scan_op.py#L1225)
-that the scan operator is implemented with a loop in Python
-that performs one iteration at a time:
-
-```python
-    fn = self.fn.fn
-
-    while (i < n_steps) and cond:
-        # ...
-        fn()
-```
-
-The `grad` function in Theano constructs a symbolic graph for computing gradients. So the `grad` for the scan operator is actually implemented by [constructing another scan operator](https://github.com/Theano/Theano/blob/master/theano/scan_module/scan_op.py#L2527):
-
-```python
-    local_op = Scan(inner_gfn_ins, inner_gfn_outs, info)
-    outputs = local_op(*outer_inputs)
-```
-
-The [performance guide](http://deeplearning.net/software/theano/library/scan.html#optimizing-scan-s-performance) for Theano's scan operator suggests minimizing the usage of the scan. This might be due to the fact that the loop is executed in Python, which might be a bit slow (due to context switching and the performance of Python itself). Moreover, because no unrolling is performed, the graph optimizer can't see the big picture.
-
-If I understand correctly, when multiple RNN/LSTM layers are stacked, instead of a single loop with each iteration computing the whole feedforward network operation, the computation sequentially does a separate loop for each layer that uses the scan operator. If all of the intermediate values are stored to support computing the gradients, this is fine. Otherwise, using a single loop could be more memory efficient.
-
-### Lasagne
-
-The documentation for RNN in Lasagne can be found [here](http://lasagne.readthedocs.org/en/latest/modules/layers/recurrent.html). In Lasagne, a recurrent layer is just like a standard layer, except that the input shape is expected to be `(batch_size, sequence_length, feature_dimension)`. The output shape is then `(batch_size, sequence_length, output_dimension)`.
-
-Both `batch_size` and `sequence_length` are specified as `None`, and inferred from the data. Alternatively, when memory is sufficient and the (maximum) sequence length is known beforehand, you can set `unroll_scan` to `False`. Then Lasagne will unroll the graph explicitly, instead of using the Theano `scan` operator. Explicitly unrolling is implemented in [utils.py#unroll_scan](https://github.com/Lasagne/Lasagne/blob/master/lasagne/utils.py#L340).
-
-The recurrent layer also accepts a `mask_input`, to support variable length sequences (e.g., when sequences within a mini-batch have different lengths. The mask has the shape `(batch_size, sequence_length)`.
-
-### Keras
-
-The documentation for RNN in Keras can be found [here](http://keras.io/layers/recurrent/). The interface in Keras is similar to the interface in Lasagne. The input is expected to be of shape `(batch_size, sequence_length, feature_dimension)`, and the output shape (if `return_sequences` is `True`) is `(batch_size, sequence_length, feature_dimension)`.
-
-Keras currently supports both a Theano and a TensorFlow back end. RNN for the Theano back end is [implemented with the scan operator](https://github.com/fchollet/keras/blob/master/keras/backend/theano_backend.py#L432). For TensorFlow, it seems to be [implemented via explicitly unrolling](https://github.com/fchollet/keras/blob/master/keras/backend/tensorflow_backend.py#L396). The documentation says that for the TensorFlow back end, the sequence length must be specified beforehand, and masking is currently not working (because `tf.reduce_any` is not functioning yet).
-
-## Torch
-
-[karpathy/char-rnn](https://github.com/karpathy/char-rnn) is implemented by [explicitly unrolling](https://github.com/karpathy/char-rnn/blob/master/model/RNN.lua#L15). On the contrary, [Element-Research/rnn](https://github.com/Element-Research/rnn) runs sequence iteration in Lua. It actually has a very modular design:
-
-* The basic RNN/LSTM modules run only *one* time step per one call of `forward` (and accumulate/store necessary information to support backward computation, if needed). You could have detailed control when using this API directly.
-* A collection of `Sequencer`s are defined to model common scenarios, like forwarding sequence, bi-directional sequence, attention models, etc.
-* There are other utility modules, like masking to support variable length sequences, etc.
-
-## CNTK
-
-CNTK looks quite different from other common deep learning libraries. I don't understand it very well. I will talk with Yu to get more details.
-
-It seems that the basic data types are matrices (although there is also a `TensorView` utility class). The mini-batch data for sequence data is packed in a matrix with N-row being `feature_dimension` and N-column being `sequence_length * batch_size` (see Figure 2.9 on page 50 of the [CNTKBook](http://research.microsoft.com/pubs/226641/CNTKBook-20151201.pdf)).
-
-Recurrent networks are first-class citizens in CNTK. In section 5.2.1.8 of the CNTKBook, you can see an example of a customized computation node. The node needs to explicitly define the functions for standard forward and forward with a time index, which is used for RNN evaluation:
-
-```cpp
-    virtual void EvaluateThisNode()
-    {
-        EvaluateThisNodeS(FunctionValues(), Inputs(0)->
-            FunctionValues(), Inputs(1)->FunctionValues());
-    }
-    virtual void EvaluateThisNode(const size_t timeIdxInSeq)
-    {
-        Matrix<ElemType> sliceInputValue = Inputs(1)->
-            FunctionValues().ColumnSlice(timeIdxInSeq *
-            m_samplesInRecurrentStep, m_samplesInRecurrentStep);
-        Matrix<ElemType> sliceOutputValue =    m_functionValues.
-            ColumnSlice(timeIdxInSeq * m_samplesInRecurrentStep,
-            m_samplesInRecurrentStep);
-        EvaluateThisNodeS(sliceOutputValue, Inputs(0)->
-            FunctionValues(), sliceInput1Value);
-    }
-```
-
-The function `ColumnSlice(start_col, num_col)` takes out the packed data for that time index, as described above (here `m_samplesInRecurrentStep` must be the mini-batch size).
-
-The low-level API for recurrent connection seem to be a *delay node*. But I'm not sure how to use this low-level API. The [example of PTB language model](https://cntk.codeplex.com/SourceControl/latest#Examples/Text/PennTreebank/Config/rnn.config) uses a very high-level API (simply setting `recurrentLayer = 1` in the config).
-
-## TensorFlow
-
-The [current example of RNNLM](https://www.tensorflow.org/tutorials/sequences/recurrent#recurrent-neural-networks) in TensorFlow uses explicit unrolling for a predefined number of time steps. The white-paper mentions that an advanced control flow API (Theano's scan-like) is planned.
-
-## Next Steps
-
-* [MXNet System Overview](http://mxnet.io/architecture/overview.html)
diff --git a/docs/build_version_doc/AddPackageLink.py b/docs/build_version_doc/AddPackageLink.py
deleted file mode 100644
index 280f9d0927bd..000000000000
--- a/docs/build_version_doc/AddPackageLink.py
+++ /dev/null
@@ -1,58 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-import os
-import argparse
-from bs4 import BeautifulSoup as bs
-
-parser = argparse.ArgumentParser(description="Add download package link.",
-                                     formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-parser.add_argument('--file_path', type=str, default='docs/_build/html/install/index.html',
-                        help='file to be modified')
-parser.add_argument('--current_version', type=str, default='master',
-                        help='Current version')
-
-if __name__ == '__main__':
-    args = parser.parse_args()
-    tag = args.current_version
-
-    src_url = "https://www.apache.org/dyn/closer.cgi/incubator/" \
-              "mxnet/%s-incubating/apache-mxnet-src-%s-incubating.tar.gz" % (tag, tag)
-    pgp_url = "https://www.apache.org/dyn/closer.cgi/incubator/" \
-              "mxnet/%s-incubating/apache-mxnet-src-%s-incubating.tar.gz.asc" % (tag, tag)
-    sha_url = "https://www.apache.org/dyn/closer.cgi/incubator/" \
-              "mxnet/%s-incubating/apache-mxnet-src-%s-incubating.tar.gz.sha" % (tag, tag)
-    md5_url = "https://www.apache.org/dyn/closer.cgi/incubator/" \
-              "mxnet/%s-incubating/apache-mxnet-src-%s-incubating.tar.gz.md5" % (tag, tag)
-
-    download_str = "<div class='btn-group' role='group'>"
-    download_str += "<div class='download_btn'><a href=%s>" \
-                    "<span class='glyphicon glyphicon-download-alt'></span>" \
-                    " Source for %s</a></div>" % (src_url, tag)
-    download_str += "<div class='download_btn'><a href=%s>PGP</a></div>" % (pgp_url)
-    download_str += "<div class='download_btn'><a href=%s>SHA-256</a></div>" % (sha_url)
-    download_str += "<div class='download_btn'><a href=%s>MD5</a></div>" % (md5_url)
-    download_str += "</div>"
-
-    with open(args.file_path, 'r') as html_file:
-        content = bs(html_file, 'html.parser')
-    download_div = content.find(id="download-source-package")
-    download_div['style'] = "display:block"
-    download_div.append(download_str)
-    outstr = str(content).replace('&lt;', '<').replace('&gt;', '>')
-    with open(args.file_path, 'w') as outf:
-        outf.write(outstr)
\ No newline at end of file
diff --git a/docs/build_version_doc/AddVersion.py b/docs/build_version_doc/AddVersion.py
deleted file mode 100755
index ce40844d960e..000000000000
--- a/docs/build_version_doc/AddVersion.py
+++ /dev/null
@@ -1,91 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-import os
-import argparse
-from bs4 import BeautifulSoup as bs
-
-parser = argparse.ArgumentParser(description="Manipulate index page",
-                                     formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-parser.add_argument('--file_path', type=str, default='mxnet/docs/_build/html/',
-                        help='file to be modified')
-parser.add_argument('--current_version', type=str, default='master',
-                        help='Current version')
-parser.add_argument('--root_url', type=str, default='https://mxnet.incubator.apache.org/',
-                        help='Root URL')
-parser.add_argument('--tag_default', type=str, default='master', help='Default Tag')
-
-if __name__ == '__main__':
-    args = parser.parse_args()
-
-    root_url = args.root_url
-    tag_list = list()
-    with open('tag_list.txt', 'r') as tag_file:
-        for line in tag_file:
-            tag_list.append(line.lstrip().rstrip())
-
-    version_str = '<span id="dropdown-menu-position-anchor-version" ' \
-                  'style="position: relative">' \
-                  '<a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" ' \
-                  'role="button" aria-haspopup="true" aria-expanded="true">Versions(%s)<span class="caret">' \
-                  '</span></a><ul id="package-dropdown-menu" class="dropdown-menu">' % (args.current_version)
-    version_str_mobile = '<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" ' \
-                         'style="position: relative">' \
-                         '<a href="#" tabindex="-1">Versions(%s)</a><ul class="dropdown-menu">' % (args.current_version)
-    for i, tag in enumerate(tag_list):
-        url = root_url + 'versions/%s/index.html' % (tag)
-        version_str += '<li><a class="main-nav-link" href=%s>%s</a></li>' % (url, tag)
-        version_str_mobile += '<li><a tabindex="-1" href=%s>%s</a></li>' % (url, tag)
-    version_str += '</ul></span>'
-    version_str_mobile += '</ul></li>'
-
-    for path, subdirs, files in os.walk(args.file_path):
-        for name in files:
-            if not name.endswith('.html'):
-                continue
-            if 'install' in path:
-                print("Skipping this path: {}".format(path))
-                continue
-            with open(os.path.join(path, name), 'r') as html_file:
-                content = bs(html_file, 'html.parser')
-            navbar = content.find(id="main-nav")
-            navbar_mobile = content.find(id="burgerMenu")
-            outstr = str(content)
-            if navbar and navbar_mobile:
-                version_tag = content.find(id="dropdown-menu-position-anchor-version")
-                version_tag_mobile = content.find(id="dropdown-menu-position-anchor-version-mobile")
-                if version_tag:
-                    version_tag.extract()
-                if version_tag_mobile:
-                    version_tag_mobile.extract()
-                navbar.append(version_str)
-                navbar_mobile.append(version_str_mobile)
-                # The following causes rendering errors in code blocks; refer to #12168 and #12524
-                outstr = str(content).replace('&lt;', '<').replace('&gt;', '>')
-            # Fix link
-            if args.current_version == tag_list[0]:
-                print("Fixing " + os.path.join(path, name))
-                outstr = outstr.replace('https://mxnet.io', 'https://mxnet.incubator.apache.org')
-                outstr = outstr.replace('http://mxnet.io', 'https://mxnet.incubator.apache.org')
-            else:
-                outstr = outstr.replace('https://mxnet.io', 'https://mxnet.incubator.apache.org/'
-                                                                'versions/%s' % (args.current_version))
-                outstr = outstr.replace('http://mxnet.io', 'https://mxnet.incubator.apache.org/'
-                                                               'versions/%s' % (args.current_version))
-
-            with open(os.path.join(path, name), "w") as outf:
-                outf.write(outstr)
diff --git a/docs/build_version_doc/Dockerfile b/docs/build_version_doc/Dockerfile
deleted file mode 100755
index 8bae701199af..000000000000
--- a/docs/build_version_doc/Dockerfile
+++ /dev/null
@@ -1,65 +0,0 @@
-# -*- mode: dockerfile -*-
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-#
-#
-FROM ubuntu:16.04
-LABEL maintainer="markhama@amazon.com"
-
-# Install dependencies
-RUN apt-get update && apt-get install -y \
-    apt-transport-https \
-    build-essential \
-    ca-certificates \
-    curl \
-    doxygen \
-    git \
-    libatlas-base-dev \
-    libjemalloc-dev \
-    liblapack-dev \
-    libopenblas-dev \
-    libopencv-dev \
-    pandoc \
-    python-numpy \
-    python-pip \
-    software-properties-common \
-    unzip \
-    wget
-
-# Setup Scala
-RUN echo "deb https://dl.bintray.com/sbt/debian /" | tee -a /etc/apt/sources.list.d/sbt.list
-RUN apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv 2EE0EA64E40A89B84B2DF73499E82A75642AC823
-RUN apt-get update && apt-get install -y \
-    sbt \
-    scala
-
-RUN pip install --upgrade pip && pip install \
-    beautifulsoup4 \
-    breathe \
-    CommonMark==0.5.4 \
-    h5py \
-    mock==1.0.1 \
-    pypandoc \
-    recommonmark==0.4.0 \
-    sphinx==1.5.6
-
-
-COPY *.sh /
-COPY *.py /
-
-RUN /build_site_tag.sh "1.1.0 1.0.0 0.12.1 0.12.0 0.11.0 master" 1.1.0 http://mxnet.incubator.apache.org/
diff --git a/docs/build_version_doc/README.md b/docs/build_version_doc/README.md
deleted file mode 100644
index 4821f50f5d6e..000000000000
--- a/docs/build_version_doc/README.md
+++ /dev/null
@@ -1,334 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# MXNet Documentation Build Scripts
-
-This folder contains a variety of scripts to generate the MXNet.io website as well as the docs for different versions of MXNet.
-
-## Contents of the build_version_doc Folder
-
-* [AddPackageLink.py](AddPackageLink.py) - MXNet.io site data massaging; injects pip version numbers in the different versions' install pages
-* [AddVersion.py](AddVersion.py) - MXNet.io site data massaging; injects the versions dropdown menu in the navigation bar
-* [build_site_tag.sh](build_site_tag.sh) - takes version tags as input and generates static html; calls `build_all_version.sh` and `update_all_version.sh`
-* [build_all_version.sh](build_all_version.sh) - takes version tags as input and builds the basic static html for MXNet.io
-* [Dockerfile](Dockerfile) - has all dependencies needed to build and update MXNet.io's static html
-* [update_all_version.sh](update_all_version.sh) - takes the output of `build_all_version.sh` then uses `AddVersion.py` and `AddPackageLink.py` to update the static html
-
-## Dependencies
-
-The `make docs` process triggers several different documentation functions. Most of this logic is found in [mxdoc.py](https://github.com/apache/incubator-mxnet/blob/master/docs/mxdoc.py). If you review this file you will see several functions which will generate different API documentation sets. For example, they are called in this way:
-
-```python
-app.connect("builder-inited", generate_doxygen)
-app.connect("builder-inited", build_scala_docs)
-app.connect("builder-inited", build_clojure_docs)
-# app.connect("builder-inited", build_r_docs)
-```
-
-These four are mentioned here in dependencies since you will need to install several extra packages beyond just Sphinx and its collection of plugins and packages. You can, if you wish, simply comment out any of these lines to skip generating the related docs, thereby eliminating several dependencies, and reducing complexity in your builds.
-
-Note: If you review the file, you will see that the function to generate the R docs is commented out by default. Remove this comment mark to generate R docs manually.
-
-Covering each one, here are the related dependencies.
-
-* core MXNet dependencies - you need to build MXNet from source before generating the docs. Once you have accomplished this, you have further dependencies to consider for docs.
-
-* `generate_doxygen` - this generates the Python and C++ APIs.
-
-To enable this manually, run the following (on Ubuntu). Similar packages can be found for other operating systems, but pay attention to versions. Newer versions have been found to have incompatibilities between packages.
-
-```bash
-apt-get install \
-    doxygen \
-    pandoc
-
-pip install --upgrade pip && pip install \
-    beautifulsoup4 \
-    breathe \
-    CommonMark==0.5.4 \
-    h5py \
-    mock==1.0.1 \
-    pypandoc \
-    recommonmark==0.4.0 \
-    sphinx==1.5.6
-```
-
-* `build_scala_docs` - this generates the MXNet-Scala package & Scala API docs.
-
-To enable this manually, run the following:
-
-```bash
-apt-get install software-properties-common
-apt-get update
-apt-get install openjdk-8-jdk
-apt-get install openjdk-8-jre
-
-echo "deb https://dl.bintray.com/sbt/debian /" | tee -a /etc/apt/sources.list.d/sbt.list
-apt-key add sbt.gpg
-apt-get update && apt-get install \
-    maven \
-    sbt \
-    scala
-```
-
-* `build_clojure_docs` - this generates the Clojure API docs.
-
-To enable this manually, run the following:
-
-```bash
-wget https://raw.githubusercontent.com/technomancy/leiningen/stable/bin/lein
-chmod 775 lein
-sudo cp lein /usr/local/bin
-```
-
-* `build_r_docs` - this generates the R API docs in a PDF file found at `/docs/api/r/mxnet-r-reference-manual.pdf`.
-
-To enable this manually, run the following, and be sure to re-enable the `build_r_docs` function in `mxdoc.py` before you run `make docs`:
-
-```bash
-apt-get install \
-    libcairo2-dev \
-    libcurl4-openssl-dev \
-    libssl-dev \
-    libxml2-dev \
-    libxt-dev \
-    r-base \
-    r-base-dev
-
-Rscript -e "install.packages('devtools', repo = 'https://cran.rstudio.com')"
-cd R-package
-Rscript -e "library(devtools); library(methods); options(repos=c(CRAN='https://cran.rstudio.com')); install_deps(dependencies = TRUE)"
-```
-
-
-## Setting Up a Docs Dev Server
-
-Running docs builds locally on a Mac is not recommended. See [#10858](https://github.com/apache/incubator-mxnet/issues/10858) for workarounds.
-
-For these instructions, you will use an Ubuntu machine. This flow has been tested on a vanilla Ubuntu 16.04 cloud instance on AWS.
-
-**Step 1:** Spin up your Ubuntu server and SSH in.
-
-**Step 2:** Create a Python 2.7 virtual environment (see note).
-
-```bash
-sudo apt install virtualenv
-virtualenv -p python2.7 mxnet_docs
-source mxnet_docs/bin/activate
-```
-
-**Note:** Using a Python 2.7 environment is required to build older versions of the docs that have Python 3 incompatibilities. If you're only building the latest or version 1.0.0+, then you may use a Python 3 environment.
-
-**Step 3:** Clone the repo or your own fork of the repo.
-
-```bash
-git clone --recursive https://github.com/apache/incubator-mxnet.git mxnet
-cd mxnet/docs/build_version_doc
-```
-
-**Step 4:** Install dependencies and build MXNet.
-
-This script will install the dependencies for you and build MXNet from source.
-
-```bash
-./setup_docs_ubuntu.sh
-```
-
-**Step 5a:** Make the docs.
-
-Here you have two options (recommended for most situations):
-
-* Change branches first, or build master with the following:
-
-```bash
-cd mxnet
-make docs USE_OPENMP=1
-```
-
-**Step 5b:** Alternative option for all versions to be built.
-
-This will have the versions dropdown and any post-build processing that generates site artifacts and other requirements for the production site.
-
-**NOTE:** This option only builds what is checked into each branch.
-
-The following script will build all of the latest versions, set the default site to be `master` and use your dev server's IP or DNS for navigation items.
-
-```bash
-./build_site_tag.sh '1.2.0;1.1.0;1.0.0;0.12.0;0.11.0;master' master http://your-ip-or-dns
-```
-
-**Final Step:** Serve and test.
-
-Refer to [Serving Your Development Version](#serving-your-development-version) for detailed instructions.
-
-
-**Troubleshooting**: for users that have previously installed Python dependencies, or if you already have Conda, you might be stuck with the latest version of Sphinx and the docs build will have a conflict. To fix this, run `/home/ubuntu/anaconda3/bin/pip uninstall sphinx` and follow this with `pip install --user sphinx==1.5.6`.
-
-If you need to build <= v0.12.0, then use a Python 2 environment to avoid errors with `mxdoc.py`. This is a sphinx extension, that was not Python 3 compatible in the old versions. On the Deep Learning AMI, use `source activate mxnet_p27`, and then install the following dependencies.
-
-
-## Full Website Build
-The following three scripts will help you build multiple version tags and deploy a full site build that with each available API version. If you just want to run master or your current fork's branch you should skip ahead to the [Developer Instructions](#developer-instructions).
-
-The full site build scripts can be run stand-alone or in conjunction, but `build_all_version.sh` should be run first.
-
-### build_all_version.sh
-This will checkout each tag provided as an argument and build the docs in the `apache_mxnet` folder. The output is copied to the `VersionedWeb` folder and each version will have a subfolder in `VersionedWeb/versions/`.
-
-Takes one argument:
-* **tag list** - semicolon delimited list of Github tags; Example: "1.1.0;1.0.0;master"
-
-**Example Usage**:
-`./build_all_version.sh "1.1.0;1.0.0;0.12.1;0.12.0;0.11.0;master"`
-
-### update_all_version.sh
-This uses the output of `build_all_version.sh`. If you haven't built the specific tag yet, then you cannot update it.
-You can, however, elect to update one or more tags to target the updates you're making.
-Takes three arguments:
-* **tag list** - space delimited list of Github tags; Example: "1.1.0 1.0.0 master"
-* **default tag** - which version should the site default to; Example: 1.0.0
-* **root URL** - for the versions dropdown to change to production or dev server; Example: http://mxnet.incubator.apache.org/
-
-Each subfolder in `VersionedWeb/versions` will be processed with `AddVersion.py` and `AddPackageLink.py` to update the static html. Finally, the tag specified as the default tag, will be copied to the root of `VersionedWeb` to serve as MXNet.io's home (default) website. The other versions are accessible via the versions dropdown menu in the top level navigation of the website.
-
-**Example Usage**:
-`./update_all_version.sh "1.1.0;1.0.0;0.12.1;0.12.0;0.11.0;master" 1.1.0 http://mxnet.incubator.apache.org/`
-
-### build_site_tag.sh
-This one is useful for Docker, or to easily chain the two prior scripts. When you run the image you can call this script as a command a pass the tags, default tag, and root url.
-
-Takes the same three arguments that update_all_version.sh takes.
-It will execute `build_all_version.sh` first, then execute `update_all_version.sh` next.
-
-**Example Usage**:
-./build_site_tag.sh "1.1.0;master" 1.1.0 http://mxnet.incubator.apache.org/
-Then run a web server on the outputted `VersionedWeb` folder.
-
-
-## Developer Instructions
-
-### Build Docs for Your Current Branch
-From the MXNet source root run:
-
-```bash
-make docs USE_OPENMP=1
-```
-
-If you set `MXNET_DOCS_BUILD_MXNET=0`, the doc generation will skip the build step. This is useful when you have already built MXNet locally.
-
-The files from `make docs` are viewable in `docs/_build/html/`.
-
-**NOTE:** `make docs` doesn't add any version information, and the versions dropdown in the top level navigation is not present. UI bugs can be introduced when the versions dropdown is included, so just testing with `make docs` may be insufficient.
-
-**IMPORTANT:** There are several post-build modifications to the website. This may be responsible for magical or unexplained site behavior. Refer to [Full Site Build Instructions](#full-website-build) and the [Developer Notes](#developer-notes) for more information.
-
-
-### Developer Notes
-
-1. `AddVersion.py` depends on Beautiful library, which requires target html files to have close tags. Although open tag html can still be rendered by browser, it will be problematic for Beautifulsoup. **This is why the install/index.md page has many rendering issues and is very brittle.**
-
-2. `AddVersion.py` and `AddPackageLink.py` manipulates content for website. If there are layout changes, it may break these two scripts. You will need to change scripts respectively. The [Full Site Build Instructions](#full-website-build) and related scripts leverage these files. If you want to add further post-build site manipulations, this is your starting point, but make sure you include these processes in the related site build scripts.
-
-3. A table of contents feature is used on many pages. The docs build looks for toc-tree tags, so any additions to content may require updates to these tags.
-
-4. The install index page is used for installation validation by triggering from the comment tags, so do not alter these without reviewing the related CI processes.
-
-
-### Serving Your Development Version
-
-You can view the generated docs with whatever web server you prefer. The Ubuntu setup script described earlier provides instructions for Apache2. MacOS comes preinstalled with Apache.
-
-
-#### Serve the Website with Apache2
-
-```bash
-sudo apt-get install -y apache2
-sudo ufw allow 'Apache Full'
-```
-Copy your `docs/_build/html/` files to where your Apache server will pick them up. If you used the default Ubuntu setup, then this will be `/var/www/html`.
-
-For example, for a simple local development build, from the MXNet root folder:
-
-```bash
-cd docs/_build/html
-sudo cp -r . /var/www/html/
-```
-
-Or if you're using the output from the [Full Website Build](#full-website-build), from the `build_doc_version` folder:
-
-```bash
-cd VersionedWeb
-sudo cp -r . /var/www/html/
-```
-
-**Note**: When generating docs, many files and folders can be deleted or renamed, so it is a good practice to purge the web server directory first, or else you will have old files hanging around potentially introducing errors or hiding broken links.
-
-
-#### Serve the Website with Python3
-Python has a simple web server that you can use for a quick check on your site build. If your SSH tunnel breaks, the site will stop working, so if you plan to share your work as preview in a PR, use Apache2 instead.
-
-From the MXNet source root run:
-
-```bash
-cd docs/_build/html
-python3 -m http.server
-```
-
-
-### Enabling Redirects
-The website uses redirects with `mod_rewrite` for content and folders that have moved. To simulate this locally you need to configure Apache to allow the rewrite module.
-
-```bash
-sudo a2enmod rewrite
-```
-
-To enable redirects for the folder with the website you need to edit `/etc/apache2/apache2.conf`.
-Change directives for `Directory` for `/var/www/html`, or wherever the build files reside, using the following:
-
-```
-AllowOverride all
-```
-
-Then restart Apache on Ubuntu with:
-
-```bash
-sudo systemctl restart apache2
-```
-
-
-## Docker Usage
-
-The `Dockerfile` will build all of the docs when you create the docker image. You can also run the scripts listed above to regenerate any tag or collection of tags.
-
-Build like:
-sudo docker build -t mxnet:docs-base .
-
-Run like:
-sudo docker run -it mxnet:docs-base
-
-
-## Deploying the Website to Production
-
-The production website is hosted from the `asf-site` branch of [https://github.com/apache/incubator-mxnet-site](https://github.com/apache/incubator-mxnet-site).
-
-To deploy your website build, you must checkout this repo, delete all of the content, copy your build in, and submit a PR for the update.
-
-There are several manual and semi-automatic processes to be aware of, but the bottom line is that your build should have all of the necessary artifacts for the proper functioning of the site:
-
-1. The root should have the current `.htaccess` file from master in `/docs/`. Make sure you've updated this in master and included the most recent version in your PR.
-2. The css file from master `/docs/_static/` will be needed. Be sure that the different versions of the site work. They might need the old version, but the newer version might fix bugs that were in the tags from the legacy versions.
-3. Pay attention to `mxdocs.py` as some docs modifications are happening there.
diff --git a/docs/build_version_doc/artifacts/.htaccess b/docs/build_version_doc/artifacts/.htaccess
deleted file mode 100644
index f334982a356e..000000000000
--- a/docs/build_version_doc/artifacts/.htaccess
+++ /dev/null
@@ -1,24 +0,0 @@
-RewriteEngine on
-RewriteRule .* - [E=default_version:/versions/master]
-RewriteRule ^get_started/why_mxnet.html$ %{ENV:default_version}/faq/why_mxnet.html [R=301,L]
-RewriteRule ^get_started.*$ %{ENV:default_version}/install/ [R=301,L]
-RewriteRule ^how_to.*$ %{ENV:default_version}/faq/ [R=301,L]
-RewriteRule ^api/python/symbol.html$ %{ENV:default_version}/api/python/symbol/symbol.html [R=301,L]
-
-# Navigation bar redirects to latest info
-RewriteCond %{ENV:default_version}#\/$1 !^([^#]+)#\1$
-RewriteRule ^(versions\/[^\/]+)?(?:\/)?(faq|community|install|gluon|tutorials|architecture)(.*)?$ %{ENV:default_version}/$2$3 [R=301,L]
-
-# Redirect navbar APIs that did not exist
-RewriteRule ^versions/0.11.0/api/python/contrib/onnx.html %{ENV:default_version}/error/api.html [R=301,L]
-RewriteRule ^versions/0.12.1/api/python/contrib/onnx.html %{ENV:default_version}/error/api.html [R=301,L]
-RewriteRule ^versions/1.0.0/api/python/contrib/onnx.html %{ENV:default_version}/error/api.html [R=301,L]
-RewriteRule ^versions/1.1.0/api/python/contrib/onnx.html %{ENV:default_version}/error/api.html [R=301,L]
-
-RewriteRule ^versions/0.11.0/api/clojure/.*$ %{ENV:default_version}/error/api.html [R=301,L]
-RewriteRule ^versions/0.12.1/api/clojure/.*$ %{ENV:default_version}/error/api.html [R=301,L]
-RewriteRule ^versions/1.0.0/api/clojure/.*$ %{ENV:default_version}/error/api.html [R=301,L]
-RewriteRule ^versions/1.1.0/api/clojure/.*$ %{ENV:default_version}/error/api.html [R=301,L]
-RewriteRule ^versions/1.2.1/api/clojure/.*$ %{ENV:default_version}/error/api.html [R=301,L]
-
-ErrorDocument 404 /error/404.html
diff --git a/docs/build_version_doc/artifacts/404.html b/docs/build_version_doc/artifacts/404.html
deleted file mode 100644
index 86c91f738876..000000000000
--- a/docs/build_version_doc/artifacts/404.html
+++ /dev/null
@@ -1,50 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-  <head>
-    <meta charset="utf-8">
-    <meta http-equiv="X-UA-Compatible" content="IE=edge">
-    <meta name="viewport" content="width=device-width, initial-scale=1">
-<style>
-    { margin: 0; padding: 0; }
-
-    html {
-      background: url('https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-background-compressed.jpeg') no-repeat center center fixed;
-      -webkit-background-size: cover;
-      -moz-background-size: cover;
-      -o-background-size: cover;
-      background-size: cover;
-    }
-
-    h1, p {
-      color: white;
-      font-family: verdana;
-    }
-
-    a:link {
-      color: white;
-    }
-
-    a:visited {
-      color: linen;
-    }
-
-    a:hover {
-      color: powderblue;
-    }
-
-    a:active {
-      color: aqua;
-    }
-
-</style>
-  </head>
-  <body>
-    <div>
-      <a href="/" id="logo" ><img width="200"  src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet_logo.png"></a>
-    </div>
-    <div>
-    <span id="page-does-not-exist"></span><h1>Page Does Not Exist</h1>
-    <p>If you’re here that means you requested a page that doesn’t exist. Sorry about that! Maybe try the search box to find what you’re looking for, or navigate to the <a href="/">Home Page</a>. Also, make sure you’re looking in the correct version, as some features may only be available in <a href="https://github.com/apache/incubator-mxnet/releases">newer versions</a> or the <a href="/versions/master">master branch</a>.</p>
-    </div>
-  </body>
-</html>
diff --git a/docs/build_version_doc/artifacts/api.html b/docs/build_version_doc/artifacts/api.html
deleted file mode 100644
index 001b77192169..000000000000
--- a/docs/build_version_doc/artifacts/api.html
+++ /dev/null
@@ -1,50 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-  <head>
-    <meta charset="utf-8">
-    <meta http-equiv="X-UA-Compatible" content="IE=edge">
-    <meta name="viewport" content="width=device-width, initial-scale=1">
-<style>
-    { margin: 0; padding: 0; }
-
-    html {
-      background: url('https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-background-compressed.jpeg') no-repeat center center fixed;
-      -webkit-background-size: cover;
-      -moz-background-size: cover;
-      -o-background-size: cover;
-      background-size: cover;
-    }
-
-    h1, p {
-      color: white;
-      font-family: verdana;
-    }
-
-    a:link {
-      color: white;
-    }
-
-    a:visited {
-      color: linen;
-    }
-
-    a:hover {
-      color: powderblue;
-    }
-
-    a:active {
-      color: aqua;
-    }
-
-</style>
-  </head>
-  <body>
-    <div>
-      <a href="/" id="logo" ><img width="200"  src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet_logo.png"></a>
-    </div>
-    <div>
-    <span id="page-does-not-exist"></span><h1>API Not Available</h1>
-    <p>You selected an API that is not available for this version of MXNet. Try a more recent version of MXNet, or go the <a href="/versions/master">master branch</a>.</p>
-    </div>
-  </body>
-</html>
diff --git a/docs/build_version_doc/artifacts/mxnet.css b/docs/build_version_doc/artifacts/mxnet.css
deleted file mode 100644
index cb771967b622..000000000000
--- a/docs/build_version_doc/artifacts/mxnet.css
+++ /dev/null
@@ -1,1479 +0,0 @@
-/*-------------------- AmazonEmber font -----------------------------------*/
-@font-face {
-    font-family: AmazonEmber;
-    src: url(https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/font/AmazonEmber_Rg.ttf);
-    font-weight: normal;
-}
-
-@font-face {
-    font-family: AmazonEmber;
-    src: url(https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/font/AmazonEmber_Bd.ttf);
-    font-weight: bold;
-}
-
-@font-face {
-    font-family: AmazonEmber;
-    src: url(https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/font/AmazonEmber_Th.ttf);
-    font-weight: 300;
-}
-
-
-/*-------------------- basic html elements and classes --------------------*/
-html, body {
-    margin: 0;
-    padding: 0;
-    background-color: #ffffff;
-}
-
-body {
-    display: block;
-    visibility: hidden;
-    background-size: cover;
-    background-attachment: fixed;
-}
-
-body, div {
-    font-family: AmazonEmber;
-    font-size: 16px;
-    color: #000;
-}
-
-p {
-    font-family: AmazonEmber;
-    color: #000;
-    font-size: 16px;
-    line-height: 1.5em;
-}
-
-#language-filter {
-    float: right;
-    margin-top: 5px;
-}
-
-li, dt a, dt span {
-    font-size: 16px;
-    line-height: 1.5em;
-}
-
-.footer {
-    z-index: 10;
-    position: relative;
-    bottom: 0;
-    width: 100%;
-}
-
-/* /\*Content paragraph space*\/ */
-/* div.content p { */
-/*     margin-top: 20px; */
-/* } */
-
-/*------------------ Headings -------------------*/
-h1, h2, h3, h4 {
-    font-family: AmazonEmber;
-    margin-top: 18px;
-    color: #444;
-}
-
-h1 {
-    margin-top: 5px;
-    margin-bottom: 25px;
-    font-size: 28px;
-    font-weight: bold;
-}
-
-h2 {
-    font-size: 24px;
-    font-weight: bold;
-    margin-bottom: 15px;
-}
-
-h3 {
-    font-size: 20px;
-    font-weight: bold;
-    margin-bottom: 10px;
-}
-
-h4 {
-    font-size: 16px;
-    font-weight: bold;
-    margin-bottom: 10px;
-}
-
-img {
-    border-style: hidden;
-    border-width: 0px 0px 0px 0px;
-    border-color: #ffffff;
-    /* white  */
-    padding: 0px 0px 0px 0px;
-    margin: 0px 0px 0px 0px;
-}
-
-.topictitle {
-    font-size: 24px;
-    font-weight: bold;
-    color: #e47911;
-    padding: 0 0 14px 0;
-}
-
-.section:before {
-    content: " ";
-    display: block;
-    height: 60px; /* fixed header height*/
-    margin: -60px 0 0; /* negative fixed header height */
-}
-
-/*-----------------------nav bar-----------------------*/
-.navbar {
-    opacity: 0.9;
-    border: 0px;
-    height: 60px;
-    margin-bottom: 0px;
-    border-bottom: 0.01em solid #fff;
-    background-image: url("https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-navbar.png");
-    background-size: cover;
-    background-attachment: scroll;
-}
-
-.navbar .container {
-    position: relative;
-    width: 100%;
-}
-
-@media screen and (max-width: 510px) {
-    .navbar {
-        height: 60px;
-    }
-}
-
-#header-inner {
-    display: -webkit-box;
-    display: -webkit-flex;
-    display: -ms-flexbox;
-    display: box;
-    display: flex;
-    -webkit-box-orient: horizontal;
-    -moz-box-orient: horizontal;
-    -webkit-box-lines: single;
-    -moz-box-lines: single;
-    -webkit-flex-flow: row nowrap;
-    -ms-flex-flow: row nowrap;
-    flex-flow: row;
-    -webkit-box-align: center;
-    -ms-flex-align: center;
-    -webkit-align-items: center;
-    align-items: center;
-    padding-left: 10px;
-}
-
-@media screen and(max-width: 768 px) {
-    #header-inner {
-        -webkit-box-pack: center;
-       -ms-flex-pack: center;
-       -webkit-justify-content: center;
-        justify-content: center
-    }
-}
-
-#logo-wrap {
-    -webkit-box-flex: 1;
-   padding-top: 0;
-    margin-top: 0;
-    width: 125px;
-    padding-left: 25px;
-    padding-right: 200px;
-}
-
-#logo {
-    color: #fff;
-    width: 150px;
-    display: block;
-    float: left;
-    height: 60px;
-    padding: 15px 0 0 0;
-    text-decoration: none;
-}
-
-#logo > img {
-  display: block;
-  width: 110px;
-}
-
-.nav-bar {
-    display: block;
-    -webkit-box-flex: 1;
-    flex: 1 auto;
-    position: relative;
-    top: -12px;
-    white-space: nowrap;
-}
-
-.main-nav-link {
-    color: #fff;
-    text-decoration: none;
-    line-height: 50px;
-    font-family: AmazonEmber;
-    display: inline-block;
-    padding: 0 15px;
-    font-weight: 500;
-}
-
-.main-nav-link:hover {
-    opacity: 1;
-    color: #fff;
-    text-decoration: none;
-}
-
-#dropdown-menu-position-anchor,
-#dropdown-menu-position-anchor-docs,
-#dropdown-menu-position-anchor-community,
-#dropdown-menu-position-anchor-version,
-#dropdown-menu-position-anchor-version-mobile {
-    position: relative;
-}
-
-.navbar-menu {
-    top: 36px;
-    border-radius: 4px;
-    padding: 0;
-}
-
-.navbar-menu > li > a {
-    color: #0079b2;
-    padding: 6px 16px;
-
-}
-
-#search-input-wrap {
-    display: block;
-    position: absolute;
-    top: 15px;
-    right: 20px;
-    width: 200px;
-    -webkit-transition: width 0.5s;
-    transition: width 0.5s;
-}
-
-#search-input-wrap ul {
-    list-style-type: none;
-}
-
-#searchIcon {
-    display: none;
-    position: fixed;
-    top: 20px;
-    right: 35px;
-    color: white;
-}
-
-.searchBox {
-    position: absolute;
-    top: 0;
-    left: 0;
-    width: 200px;
-    background: transparent;
-    border-radius: 5px;
-    -webkit-transition: width 0.5s;
-    transition: width 0.5s;
-}
-
-.searchBox input {
-    background: transparent;
-}
-
-.searchBox input::placeholder {
-    font-weight: 200;
-}
-
-.searchBoxNorm input::placeholder {
-    color: white;
-}
-
-.searchBoxExp input::placeholder {
-    color: dimgray;
-}
-
-.searchBtn {
-    display: none;
-    position: absolute;
-    top: 0;
-    right: 0;
-    width: 40px;
-}
-
-@media screen and (max-width: 510px) {
-    #search-input-wrap {
-        width: 120px;
-        margin-right: 10px;
-    }
-
-    .searchBox {
-        width: 120px;
-    }
-}
-
-#lang-select-wrap {
-    display: block;
-    position: fixed;
-    right: 120px;
-    top: 5px
-}
-
-#lang-select-label {
-    color: #fff;
-    opacity: .7;
-    font-family: AmazonEmber;
-    line-height: 50px
-}
-
-#lang-select-label span {
-    padding-left: 8px
-}
-
-#lang-select-label i {
-    opacity: .7
-}
-
-#lang-select {
-    opacity: 0;
-    position: absolute;
-    top: 0;
-    left: 0;
-    width: 100%;
-    height: 100%;
-    -webkit-appearance: menulist-button;
-    font-size: inherit
-}
-
-.github-btn { border: 0; overflow: hidden }
-
-.boxed {
-    border: 1px solid rgb(57, 144, 211);
-    background: #0079b2;
-    color: white;
-    font-weight: bold
-}
-
-.boxed-bordered {
-    border: 3px solid rgba(0,0,0,.05);
-    background: #0079b2;
-    font-weight: bold;
-    color: white;
-}
-
-/*burger icon*/
-div .burgerIcon {
-    font-size: 25px;
-    position: fixed;
-    right: 155px;
-    top: 11px;
-    display: none;
-    width: 30px;
-}
-
-div .burgerIcon a {
-    color: white;
-}
-
-/*burger menu*/
-#burgerMenu, #plusMenu {
-    list-style-type: none;
-    margin-top: 10px;
-    padding: 0;
-    background-color: white;
-    list-style-position: inside;
-    min-width: 100px;
-}
-
-#burgerMenu a, #plusMenu a {
-    display: table-cell;
-    font-size: 15px;
-    color: #0079b2;
-    vertical-align: middle;
-    height: 30px;
-    padding-left: 5px;
-    width: 108px;
-    max-width: 250px;
-}
-
-#plusMenu .dropdown-submenu>.dropdown-menu {
-    left: 127px;
-    top: 0;
-    background: #fff;
-}
-
-#burgerMenu li, #plusMenu li {
-    height: 30px;
-    list-style-position: inside;
-}
-
-li.dropdown-submenu ul.dropdown-menu {
-    min-width: 75px;
-}
-
-li.dropdown-submenu ul.dropdown-menu li {
-    border-style: none !important;
-}
-
-li.dropdown-submenu ul.dropdown-menu a {
-    padding-left: 20px !important;
-}
-
-@media screen and (max-width: 420px) {
-    li.dropdown-submenu ul.dropdown-menu a {
-        font-size: 12px !important;
-    }
-}
-
-/*dropdown submenu*/
-.dropdown-submenu {
-    position: relative;
-}
-
-.dropdown-submenu>.dropdown-menu {
-    top: -8px;
-    left: 250px;
-    -webkit-border-radius: 0 6px 6px 6px;
-    -moz-border-radius: 0 6px 6px;
-    border-radius: 0 6px 6px 6px;
-    background: transparent;
-}
-
-.dropdown-submenu:hover>.dropdown-menu {
-    display: block;
-}
-
-.dropdown-submenu>a:after {
-    display: block;
-    content: " ";
-    float: right;
-    width: 0;
-    height: 0;
-    border-color: transparent;
-    border-style: solid;
-    border-width: 5px 0 5px 5px;
-    border-left-color: #ccc;
-    margin-top: 10px;
-    margin-right: -10px;
-}
-
-.dropdown-submenu:hover>a:after {
-    border-left-color: #fff;
-}
-
-.dropdown-submenu.pull-left {
-    float: none;
-}
-
-.dropdown-submenu.pull-left>.dropdown-menu {
-    left: -100%;
-    margin-left: 10px;
-    -webkit-border-radius: 6px 0 6px 6px;
-    -moz-border-radius: 6px 0 6px 6px;
-    border-radius: 6px 0 6px 6px
-}
-
-/*Plus icon*/
-.plusIcon {
-    display: none;
-    position: absolute;
-    top: 20px;
-    right: 210px;
-    width: 30px;
-}
-
-.glyphicon-plus {
-    color: white;
-}
-
-@media screen and (max-width:600px) {
-    div .plusIcon, .nav-bar {
-        display: none !important;
-    }
-
-    div.burgerIcon {
-        display: block;
-        position: fixed;
-        left: 45px;
-    }
-
-    #burgerMenu {
-        position: fixed;
-        left: 0;
-        top: 49px;
-        width: 100%;
-        height: 100%;
-        background: url(https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-background-compressed.jpeg);
-        background-attachment: initial;
-        background-size: cover;
-    }
-
-    #burgerMenu a {
-        display: block;
-        font-size: 19px;
-        width: auto;
-        padding-left: 75px;
-        padding-top: 10px;
-        color: #fff;
-        text-decoration: none;
-    }
-
-    #burgerMenu a:hover {
-        color: initial;
-        background-color: inherit;
-    }
-
-    #burgerMenu li {
-        border-bottom: 1px solid lightgray;
-        height: 50px;
-    }
-
-    #logo-wrap {
-        margin-left: 30px;
-    }
-}
-/*--------------------------banner---------------------------*/
-#splash{
-    padding:60px 0 0 0;
-    background-size:cover;
-    background-attachment:fixed;
-    color:#fff;
-    text-align:center
-}
-
-#splash #banner {
-    text-align: center
-}
-
-#banner-title span {
-    font-family: AmazonEmber;
-    font-weight: bold;
-}
-
-#banner-title {
-    padding: 60px 0 10px 0;
-    font-size: 40px;
-    line-height: 55px;
-    font-weight: 500;
-    font-family: AmazonEmber;
-    color: #fff;
-    max-width: 600px;
-    text-align: left;
-    margin-left: 30px;
-}
-
-#landing-title {
-    margin-right: 40px;
-    font-size: 35px;
-    color: #fff;
-    font-family: AmazonEmber;
-    font-weight: 300;
-}
-
-#intro {
-    font-size: 16px;
-    color: #fff;
-    font-family: AmazonEmber;
-    font-weight: 300;
-}
-
-@media screen and (max-width:768px) {
-    #banner-title {
-        font-size: 30px;
-        padding: 60px 30px 10px 30px;
-        margin-left: 0;
-    }
-}
-
-#splash h1{
-    font-size: 40px;
-    margin-bottom: 20px;
-}
-
-#splash #social{
-    margin:2em 0 4em 0;
-}
-
-#landing-btn-blk {
-    margin-top: 2em;
-}
-
-#why_mxnet, #install_blk {
-    margin:2em 0;
-    display: inline;
-}
-
-#why_mxnet {
-    padding-left: 20px;
-}
-
-@media screen and (max-width:400px) {
-    #install_blk, #install_blk{
-        display: block;
-    }
-
-    #install_blk {
-        padding-left: 0;
-        margin-top: 0;
-    }
-}
-
-#why_mxnet_btn {
-    border: 1.8px solid #FFFFFF;
-    color: #FFFFFF;
-    display: inline;
-    font-size: 18px;
-    font-family: AmazonEmber;
-    padding: 8px 20px;
-    -webkit-transition: .2s;
-    transition: .2s;
-}
-
-#install_btn {
-    border: 1.8px solid #FFFFFF;
-    color: #fff;
-    display: inline;
-    font-size: 18px;
-    font-family: AmazonEmber;
-    padding: 8px 40px;
-    -webkit-transition: .2s;
-    transition: .2s;
-}
-
-#release_btn {
-    width: auto;
-    border: none;
-}
-
-#why_mxnet_btn:hover, #install_btn:hover {
-    background-color: #FFFFFF;
-    color: #0079b2;
-    opacity: 0.9;
-    text-decoration: none;
-}
-
-/*------------------------index page section----------------------------*/
-.section-tout {
-    padding:3em 0 1em;
-}
-
-.section-tout .container {
-    height: 220px;
-}
-
-.section-tout .row {
-    height: 100%;
-}
-
-.section-tout .row div {
-    height: 110%;
-    padding-left: 50px;
-    background-color:#fff;
-    box-shadow: 0px 2px 4px 0px rgba(0, 28, 36, 0.5);
-}
-
-.section-tout .row a {
-    font-family: AmazonEmber;
-    position: absolute;
-    bottom: 20px;
-    border: solid 1px;
-    padding: 5px 10px;
-    color: #111;
-}
-
-.section-tout h3{
-    font-family: AmazonEmber;
-    font-size:20px;
-    color:  #007dbc;
-    padding-top: 10px
-}
-
-.section-tout p {
-    font-family: AmazonEmber;
-    margin-bottom:2em;
-}
-
-@media (max-width: 1199px) {
-    .section-tout .container {
-        height: auto;
-    }
-
-    .section-tout .row a {
-        position: inherit;
-    }
-
-    .section-tout .row div {
-        padding-left: 45px;
-    }
-
-    .section-tout .row p {
-        padding-bottom: 10px;
-    }
-}
-
-@media (max-width: 767px) {
-    .section-tout .row div {
-        margin-bottom: -20px;
-    }
-}
-
-.section-util {
-    padding:3em 0 3em;
-    text-align: left;
-    margin-bottom: 50px;
-}
-
-.section-util .row > div {
-    height: 320px;
-    padding-left: 50px;
-    padding-top: 40px;
-}
-
-.section-util .row div h2 {
-    font-family: AmazonEmber;
-    color: #fff;
-}
-
-#model-zoo-blk {
-    background-color:  #00446e;
-    box-shadow: 0px 2px 4px 0px rgba(0, 28, 36, 0.5);
-}
-
-#example-blk {
-    background-color:  #006ea7;
-    box-shadow: 0px 2px 4px 0px rgba(0, 28, 36, 0.5);
-}
-
-#tutorial-blk {
-    background-color:  #00a1c9;
-    box-shadow: 0px 2px 4px 0px rgba(0, 28, 36, 0.5);
-}
-
-@media (min-width: 1200px) {
-    #model-zoo-blk {
-        width: 370px;
-        margin-right: 20px;
-    }
-
-    #example-blk {
-        width: 370px;
-        margin-right: 20px;
-    }
-}
-
-.section-util p {
-    font-family: AmazonEmber;
-    color: #fff;
-    position: absolute;
-    width: 70%;
-    font-size: 14px;
-    font-weight: 300;
-}
-
-.section-util .util-btn {
-    position: absolute;
-    bottom: 30px;
-}
-
-@media (max-width: 600px) {
-    .section-util .util-btn {
-        margin-top: 100px;
-    }
-}
-
-.section-util .glyphicon {
-    font-size: 50px;
-    color: #b3ffff;
-}
-
-.util-btn a {
-    font-family: AmazonEmber;
-    display: inline-block;
-    border: 1.8px solid;
-    -webkit-transition: .2s;
-    transition: .2s;
-    padding: 5px 30px;
-    color: #fff;
-}
-
-.util-btn a:hover {
-    background-color: #0079b2;
-    color: #FFFFFF;
-    opacity: 0.9;
-    text-decoration: none;
-}
-
-.section-util .container {
-    height: 250px;
-}
-
-@media (max-width: 1199px) {
-    .section-util .container {
-        height: auto
-    }
-}
-
-.section-inst {
-    padding:3em 0 3em;
-    border-bottom:1px solid rgba(0,0,0,.05);
-    text-align: left;
-    background-color: #fff;
-}
-
-.section-inst .row div {
-    text-align: center;
-    margin-bottom: 30px;
-    height: 80px;
-}
-
-.section-inst p {
-    font-family: AmazonEmber;
-    font-weight: 300;
-    margin-bottom:2em;
-    padding-left: 50px;
-    color: #111;
-}
-
-.section-inst h2 {
-    font-family: AmazonEmber;
-    margin-top: -20px;
-    margin-bottom: 50px;
-    color: dimgray;
-    padding-left: 40px;
-}
-
-@media (min-width: 1200px) {
-    .section-inst .row div.clear {
-        clear: both;
-    }
-}
-
-@media (max-width: 1199px) {
-    .section-inst .row div.smallClear {
-        clear: both;
-    }
-}
-
-.section-inst img {
-    -webkit-filter: grayscale(90%); /* Chrome, Safari, Opera */
-    filter: grayscale(90%);
-    margin-bottom:2em
-}
-.section-inst img:hover {
-    -webkit-filter: grayscale(0%); /* Chrome, Safari, Opera */
-    filter: grayscale(0%);
-}
-
-.section-disclaimer {
-    padding: 3em 0 3em;
-    background-color:  #f2f2f2;
-}
-
-.section-disclaimer .container div {
-    padding-left: 50px;
-}
-
-.section-disclaimer p {
-    padding-top: 2em;
-}
-
-.footer li{
-    float:right;
-    margin-right:1.5em;
-    margin-bottom:1.5em
-}
-.footer p{
-    font-family: AmazonEmber;
-    font-weight: 300;
-    font-size: 12px;
-    clear:right;
-    margin-bottom:0;
-}
-
-/*------------------content----------------------*/
-div.navbar + div.container {
-    padding-top: 65px;
-}
-
-div.content {
-    padding: 80px 40px 40px 45px;
-    background: #fff;
-    width: calc(100% - 300px);
-    float: right;
-    overflow-x: hidden;
-    z-index: -1;
-}
-
-@media (max-width: 999px) {
-    div.content {
-        overflow-x: auto;
-        width: 100%;
-    }
-}
-
-div.page-tracker {
-    margin-bottom: 30px;
-    font-family: AmazonEmber;
-}
-
-div.page-tracker ul {
-    padding-left: 0;
-}
-
-div.page-tracker ul li {
-    text-transform: capitalize;
-    display: inline;
-}
-
-div.page-tracker a {
-    text-transform: capitalize;
-    color: #337ab7;
-}
-
-div.page-tracker li:last-child {
-    color: #aab7b8;
-}
-
-div.page-tracker i {
-    margin: 0 10px;
-}
-
-/*------------------sidebar-----------------------*/
-div.sphinxsidebar {
-    position: fixed;
-    padding-top: 40px;
-    height: 100%;
-    display: none;
-    visibility: hidden;
-    overflow: auto;
-}
-
-div.leftsidebar {
-    width: 300px;
-    margin-left: 25px;
-    background: #f2f2f2;
-}
-
-div.rightsidebar {
-    right: 20px;
-    width: 200px;
-    padding: 40px 0 15px 0;
-}
-
-div.sphinxsidebar ul {
-    padding: 0;
-    list-style-type: none !important;
-}
-
-div.sphinxsidebar a {
-    font-family: AmazonEmber;
-    font-size: 13px;
-}
-
-div.rightsidebar div.sphinxsidebarwrapper > ul {
-    padding-left: 15px;
-}
-
-div.sphinxsidebar li {
-    padding-top: 5px;
-    margin-bottom: 5px;
-    margin-left: -10px;
-}
-
-div.leftsidebar > div.sphinxsidebarwrapper {
-    padding: 40px 20px 10px 20px;
-    background: #f2f2f2;
-}
-
-div.leftsidebar > div.sphinxsidebarwrapper > ul > li {
-    margin-left: 20px;
-}
-
-div.rightsidebar > div.sphinxsidebarwrapper > ul {
-    margin-left: 2px;
-    border-left: 4px solid #337ab7;
-}
-
-div.rightsidebar > div.sphinxsidebarwrapper > ul > li {
-    margin-left: 5px;
-}
-
-div.sphinxsidebar li.opened .tocToggle:before {
-    font-family: 'FontAwesome';
-    content: "\f0d7";
-    margin: 0 5px 0 -15px;
-    color: #999999;
-}
-
-div.sphinxsidebar li.closed .tocToggle:before {
-    font-family: 'FontAwesome';
-    content: "\f0da";
-    margin: 0 5px 0 -15px;
-    color: #999999;
-}
-
-div.sphinxsidebar li.leaf .tocToggle:before {
-    /*font-family: 'FontAwesome';*/
-    content: " ";
-    margin: 0 5px 0 -15px;
-    color: #f2f2f2;
-}
-
-@media (min-width:1015px) {
-    div.sphinxsidebar {display: block}
-}
-
-div.sphinxsidebarwrapper h3 {
-    padding-top: 0;
-}
-
-/*-------------------- table styles --------------------*/
-table {
-  margin-bottom: 10px;
-  border-collapse: collapse; }
-
-table p {
-  font-size: 16px; }
-
-td p:first-child {
-  margin-top: 0px; }
-
-td p:last-child {
-  margin-bottom: 0px; }
-
-td ul:first-child {
-  margin-top: 0px;
-  /*  new 8/22/07 */ }
-
-td ol:first-child {
-  margin-top: 0px;
-  /*  new 8/22/07 */ }
-
-div.table {
-  position: relative; }
-
-div.informaltable {
-  position: relative; }
-
-.table-expand-icon {
-  position: absolute;
-  right: -20px;
-  font-size: 24px;
-  background: #cccccc;
-  float: right;
-  bottom: 0;
-  cursor: pointer; }
-
-.table-contents table {
-  border-top: 1px solid #cccccc;
-  /* medium gray  */
-  border-left: 1px solid #cccccc;
-  /* medium gray  */
-  border-bottom: 0px;
-  border-right: 0px;
-  border-spacing: 0px; }
-
-.table-contents td {
-  font-size: 16px;
-  padding: 5px 5px 5px 5px;
-  border-bottom: 1px solid #cccccc;
-  /* medium gray  */
-  border-right: 1px solid #cccccc;
-  /* medium gray  */
-  border-left: 0px;
-  border-top: 0px;
-  vertical-align: top; }
-
-.table-contents th {
-  font-size: 16px;
-  padding: 5px 5px 5px 5px;
-  border-bottom: 1px solid #cccccc;
-  /* medium gray  */
-  border-right: 1px solid #cccccc;
-  /* medium gray  */
-  border-left: 0px;
-  border-top: 0px;
-  vertical-align: top;
-  background-color: #eeeeee;
-  /* light gray  */
-  color: #333333;
-  /* Dark gray  */
-  font-size: 16px;
-  font-weight: bold;
-  text-align: left; }
-
-/*----------------API class and function formatting---------------------*/
-p.rubric {
-    margin-top: 10px;
-}
-
-dl {
-    padding-top: 20px;
-}
-
-dt:target, .highlighted {
-    background-color: #e7f2fa;
-    border-bottom: 3px solid #c7254e;
-    margin-bottom: -3px;
-}
-
-dt:target:before {
-    background-color: white;
-    content: '';
-    display: block;
-    height: 60px;
-}
-
-dt {
-    background: #e7f2fa;
-    border-bottom: solid #0079b2;
-}
-
-dl.method dt {
-    background: #f0f0f0;
-    border-bottom: solid #ccc;
-}
-
-dl.method dt code.descname {
-    color:#555;
-}
-
-dl.attribute dt {
-    background: #f0f0f0;
-    border-bottom: solid #ccc;
-}
-
-dl.attribute dt code.descname {
-    color:#555;
-}
-
-dt em {
-    font-weight: normal;
-    font-style: normal;
-    font-size: 90%;
-}
-
-code {
-    color: #337ab7;
-    background-color: #f5f5f5;
-}
-
-code.docutils.literal {
-    color: #111;
-    font-weight: bold;
-}
-
-a code.docutils.literal {
-    color: #337ab7;
-}
-
-dt code {
-    color: #555;
-}
-
-dl.last.docutils dt{
-    background-color: transparent;
-    border-bottom: none;
-}
-
-dl.docutils dt {
-    color: #555;
-    background-color: #f0f0f0;
-    border-bottom: solid #ccc;
-}
-
-/*----------------Model zoo page style------------------*/
-#mxnet-model-zoo table, #mxnet-model-zoo td, #mxnet-model-zoo th {
-    border: 1px solid lightgray;
-}
-
-#mxnet-model-zoo table {
-    margin-top: 30px;
-    border-collapse: collapse;
-}
-
-#mxnet-model-zoo th {
-    vertical-align: middle;
-    text-align: center;
-    height: 50px;
-}
-
-#mxnet-model-zoo td {
-    vertical-align: middle;
-    height: 50px;
-    padding: 10px;
-}
-
-#mxnet-model-zoo tr:nth-child(even) {
-    background-color: #f2f2f2;
-}
-
-/*--------- note block -----------*/
-div.admonition {
-    padding: 15px;
-    background-color: #f6f8fa;
-    border: 1px solid rgb(204, 204, 204);
-    border-radius: 4px;
-}
-
-p.admonition-title {
-    color: #187dbb;
-}
-
-/*------------ code block ------------*/
-pre {
-    background-color: #f6f8fa;
-}
-
-/*-------------API table---------------------*/
-table.docutils {
-    margin-top: 15px;
-}
-
-table.docutils td, table.docutils th {
-    padding-top: 5px;
-    padding-bottom: 5px;
-    padding-left: 10px;
-    padding-right: 10px;
-    /* border-top: 0; */
-    /* border-left: 0; */
-    /* border-right: 0; */
-    border: 1px solid rgb(223, 226, 229);
-}
-
-table.docutils tr:nth-child(even) {
-    background-color: #f6f8fa;
-}
-
-/*---------------getting started homepage---------*/
-
-#lang-demo ul {
-    margin-top: 20px;
-    margin-bottom: 15px;
-}
-
-.option-title {
-    width: 100px;
-    float: left;
-    clear: none;
-    text-align: right;
-    font-size: 14px;
-    padding-top: 7px;
-    padding-bottom: 8px;
-    padding-right: 10px;
-    font-weight: bold;
-}
-.option-row {
-    padding-bottom: 8px;
-}
-
-.install-inst {
-}
-
-.btn-default:hover, .btn-default:focus, .btn-default:active,
-.btn-default.active.focus, .btn-default.active:focus, .btn-default.active:hover,
-.btn-default:active.focus, .btn-default:active:focus, .btn-default:active:hover,
-.btn-default.active, .open>.dropdown-toggle.btn-default,
-.btn-default:active:focus {
-    color: #fff;
-    background-color: #0079b2;
-    border-color: #0079b2;
-}
-
-.btn-default:hover a {
-    color: #fff;
-}
-
-#setup-options {
-    margin-top: 15px;
-    margin-bottom: 15px;
-    margin-left: 30px;
-}
-
-.opt-group {
-    margin-top: 10px;
-    margin-bottom: 10px;
-}
-
-/*----------------Copy code button------------------------*/
-.copy-btn {
-    display: none;
-    position: absolute;
-    right: 0;
-    width: 40px;
-    height: 38px;
-}
-
-/*----------------Search function style------------------*/
-#searchPage {
-    width: 60%;
-    margin-left: 20%;
-}
-
-#search-preview {
-    display: none;
-    background-color: white;
-    margin-top: -10px;
-    border: solid;
-    border-width: 1px;
-    border-color: #87CEFA;
-    width: 100%;
-}
-
-
-#search-preview p {
-    color: dimgray;
-    padding: 5px 20px 0 20px;
-}
-
-#search-preview li {
-    padding-top: 10px;
-}
-
-#search-preview ul {
-    padding-left: 20px;
-    padding-right: 20px;
-}
-
-#search-preview hr {
-    margin: 10px 0 10px 0;
-    border-top: 1px solid dimgray;
-}
-
-#search-results ul {
-    list-style-type: none;
-}
-
-@media screen and (max-width: 510px) {
-    #search-preview {
-        position: fixed;
-        left: 0;
-        width: 100vw;
-    }
-
-    #search-preview li {
-        font-size: 12px;
-    }
-}
-
-/* enable absolute positioning */
-.inner-addon {
-    position: relative;
-}
-
-/* style icon */
-.inner-addon .glyphicon {
-    position: absolute;
-    padding: 10px;
-    pointer-events: none;
-}
-
-/* align icon */
-.left-addon .glyphicon  {
-    right: 0px;
-}
-.right-addon .glyphicon {
-    right: 0px;
-}
-
-/* add padding  */
-.left-addon input  {
-    font-family: AmazonEmber;
-    font-style: italic;
-}
-.right-addon input {
-    padding-right: 40px;
-}
-
-.form-group .glyphicon-search {
-    color: white;
-}
-
-/*----------------------Get started page----------------------------*/
-#why-mxnet img {
-    position: relative;
-    width: 60%;
-    left: 50%;
-    margin-right: -50%;
-    transform: translate(-50%);
-}
-
-button.download {
-    color: #0079b2;
-}
-
-/*----------------------Download button------------------------*/
-div.download-btn {
-
-    border: solid 1px #000;
-    border-radius: 3px;
-    font-size: 90%;
-    height: 40px;
-    display: table;
-    float: left;
-}
-
-div.download-btn a {
-    padding: 0 10px;
-    display: table-cell;
-    vertical-align: middle;
-    color: #000;
-}
-
-div.download-btn a:hover {
-    background-color: #0079b2;
-    color: white;
-    text-decoration: none;
-}
-
-div.download-btn.download-btn-top {
-    border-color: #ec7211;
-    background: #ec7211;
-    float: right;
-}
-
-div.download-btn.download-btn-top a {
-    color: #fff;
-}
-
-/*-------------output blocks----------------*/
-
-.highlight-results .highlight pre {
-    background-color: #eeffcc;
-}
-
-.cell-results-header {
-    color: #888;
-    padding-bottom: 3px;
-    font-style: italic;
-}
-
-/*------------Download source-----------------*/
-#download-source-package {
-    display: none;
-    padding-top: 40px;
-}
-
-/*------------Mobile dropdown menu smooth transition---------------*/
-@keyframes slide {
-    0% {
-        left: -100%;
-    }
-    25% {
-        left: -75%;
-    }
-    50% {
-        left: -50%;
-    }
-    75% {
-        left: -25%;
-    }
-    100% {
-        left: 0;
-    }
-}
-
-#burgerMenu {
-    animation-name: slide;
-    animation-duration: 0.3s;
-    animation-timing-function: ease-in-out;
-}
diff --git a/docs/build_version_doc/build_all_version.sh b/docs/build_version_doc/build_all_version.sh
deleted file mode 100755
index 3c432bbedfc9..000000000000
--- a/docs/build_version_doc/build_all_version.sh
+++ /dev/null
@@ -1,185 +0,0 @@
-#!/bin/bash
-
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-# This script is for locally building website for all versions
-# Built files are stored in $built
-# Default repo is mxnet_url="https://github.com/apache/incubator-mxnet.git"
-# Default build directory is mxnet_folder="apache-mxnet"
-# Takes two required arguments and one optional:
-# tag list (required)- semicolon delimited list of Github tags
-#   Example: "1.2.0;1.1.0;master"
-# display list (required) - semicolon delimited list of what to display on website
-#   Example: "1.2.1;1.1.0;master"
-# NOTE: The number of tags for the two arguments must be the same.
-# Repo URL (optional) - a GitHub URL that is a fork of the MXNet project
-#   When this is used the build directory will be {github_username}-mxnet
-
-# Example Usage:
-#  Build the content of the 1.2.0 branch in the main repo to the 1.2.1 folder.
-#   ./build_all_version.sh "1.2.0" "1.2.1"
-#  Using the main project repo, map the 1.2.0 branch to output to a 1.2.1 directory; others as is:
-#   ./build_all_version.sh "1.2.0;1.1.0;master" "1.2.1;1.1.0;master"
-#  Using a custom branch and fork of the repo, map the branch to master,
-#    map 1.2.0 branch to 1.2.1 and leave 1.1.0 in 1.1.0:
-#   ./build_all_version.sh "sphinx_error_reduction;1.2.0;1.1.0" \
-#   "master;1.2.1;1.1.0" https://github.com/aaronmarkham/incubator-mxnet.git
-
-set -e
-set -x
-
-# Set OPTS to any Sphinx build options, like -W for "warnings as errors"
-OPTS=
-
-# $1 is the list of branches/tags to build
-if [ -z "$1" ]
-  then
-    echo "Please provide a list of branches or tags you wish to build."
-    exit 1
-  else
-    IFS=$';'
-    tag_list=$1
-    echo "Using these tags: $tag_list"
-    build_arr=($tag_list)
-fi
-
-# $2 is the list of output folders which will be displayed on the site
-if [ -z "$2" ]
-  then
-    echo "Please provide a list of version tags you wish to display on the site."
-    exit 1
-  else
-    IFS=$';'
-    tags_to_display=$2
-    echo "Displaying these tags: $tags_to_display"
-    display_arr=($tags_to_display)
-    for key in ${!build_arr[@]}; do
-        echo "Branch/tag ${build_arr[${key}]} will be displayed as ${display_arr[${key}]}"
-    done
-fi
-
-# $3 is the GitHub project URL or fork
-if [ -z "$3" ]
-  then
-    echo "Using the main project URL."
-    mxnet_url="https://github.com/apache/incubator-mxnet.git"
-    mxnet_folder="apache-mxnet"
-  else
-    mxnet_url=$3
-    fork=${mxnet_url##"https://github.com/"}
-    fork_user=${fork%%"/incubator-mxnet.git"}
-    mxnet_folder=$fork_user"-mxnet"
-    echo "Building with a user supplied fork: $mxnet_url"
-fi
-
-# This is the output folder
-built="VersionedWeb"
-
-
-function create_repo () {
-  repo_folder=$1
-  mxnet_url=$2
-  git clone $mxnet_url $repo_folder --recursive
-  echo "Adding MXNet upstream repo..."
-  cd $repo_folder
-  git remote add upstream https://github.com/apache/incubator-mxnet
-  cd ..
-}
-
-
-function refresh_branches () {
-  repo_folder=$1
-  cd $repo_folder
-  git fetch
-  git fetch upstream
-  cd ..
-}
-
-
-function checkout () {
-  repo_folder=$1
-  cd $repo_folder
-  # Overriding configs later will cause a conflict here, so stashing...
-  git stash
-  # Fails to checkout if not available locally, so try upstream
-  git checkout "$repo_folder" || git branch $repo_folder "upstream/$repo_folder" && git checkout "$repo_folder" || exit 1
-  if [ $tag == 'master' ]; then
-    git pull
-    # master gets warnings as errors for Sphinx builds
-    OPTS="-W"
-  else
-    OPTS=
-  fi
-  git submodule update --init --recursive
-  cd ..
-}
-
-
-if [ ! -d "$mxnet_folder" ]; then
-  mkdir $mxnet_folder
-fi
-
-if [ ! -d "$built" ]; then
-  mkdir $built
-  mkdir "$built/versions"
-  else
-    if [ ! -d "$built/versions" ]; then
-      mkdir "$built/versions"
-    fi
-fi
-
-# Checkout each tag and build it
-# Then store it in a folder according to the desired display tag
-for key in ${!build_arr[@]}; do
-    tag=${build_arr[${key}]}
-    cd "$mxnet_folder"
-
-    # Each tag will get its own subfolder
-    if [ ! -d "$tag" ]; then
-      create_repo "$tag" "$mxnet_url"
-    fi
-
-    refresh_branches $tag
-
-    checkout $tag
-
-    # Bring over the current configurations, so we can anticipate results.
-    cp ../../mxdoc.py $tag/docs/
-    cp ../../settings.ini $tag/docs/
-    cp ../../conf.py $tag/docs/
-    cp ../../Doxyfile $tag/docs/
-    cp -a ../../_static $tag/docs/
-
-    echo "Building $tag..."
-    cd $tag/docs
-    make html USE_OPENMP=1 BUILD_VER=$tag SPHINXOPTS=$OPTS || exit 1
-    # Navigate back to build_version_doc folder
-    cd ../../../
-    # Use the display tag name for the folder name
-    file_loc="$built/versions/${display_arr[${key}]}"
-    if [ -d "$file_loc" ] ; then
-        rm -rf "$file_loc"
-    fi
-    mkdir "$file_loc"
-    echo "Storing artifacts for $tag in $file_loc folder..."
-    cp -a "$mxnet_folder/$tag/docs/_build/html/." "$file_loc"
-done
-
-echo "Now you may want to run update_all_version.sh to create the production layout with the versions dropdown and other per-version corrections."
-echo "The following pattern is recommended (tags, default tag, url base):"
-echo "./update_all_version.sh \"$2\" master http://mxnet.incubator.apache.org/"
diff --git a/docs/build_version_doc/build_doc.sh b/docs/build_version_doc/build_doc.sh
deleted file mode 100755
index b8a6974c7c99..000000000000
--- a/docs/build_version_doc/build_doc.sh
+++ /dev/null
@@ -1,148 +0,0 @@
-#!/bin/bash
-
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-set -e
-set -x
-
-# This script is run on a nightly basis. Refer to Job: http://jenkins.mxnet-ci.amazon-ml.com/job/incubator-mxnet-build-site/
-# Job should pass in paramters:
-# web_url=https://github.com/apache/incubator-mxnet-site
-# web_branch=asf-site
-# release_branch=v1.1.0 (example). This needs to come from the job config
-
-# First parameter sent by job configuration: https://github.com/apache/incubator-mxnet-site
-web_url="$1"
-
-# Second parameter sent by job configuration: asf-site
-web_branch="$2"
-
-web_folder="VersionedWeb"
-
-local_build="latest"
-
-git clone $web_url $web_folder
-cd $web_folder
-git checkout $web_branch
-cd ..
-mkdir "$local_build"
-
-# Fetch tag information
-tag_list_file="tag_list.txt"
-cp "$web_folder/tag.txt" "$tag_list_file"
-tag_list=()
-while read -r line
-do
-    tag_list+=("$line")
-done < "$tag_list_file"
-
-# This is the first tag found in tag.txt
-latest_tag=${tag_list[0]}
-echo "++++ LATEST TAG found in tag.txt file is : $latest_tag ++++"
-
-commit_id=$(git rev-parse HEAD)
-
-# Find the current TAG in GIT
-curr_tag=${TAG}
-curr_tag=${curr_tag:5}
-
-echo "++++ CURRENT TAG IN GIT is $curr_tag ++++"
-
-# If current tag in git is newer than latest tag found in tag.txt
-if [[ "$curr_tag" != 'master' ]] && [ $curr_tag != $latest_tag ]
-then
-    echo "++++ Found a git TAG $curr_tag newer than mxnet repo tag $latest_tag , we need to build a new release ++++"
-    echo "assigning curr_tag to latest_tag"
-    latest_tag=$curr_tag
-fi
-
-# Build new released tag
-if [ $latest_tag != ${tag_list[0]} ]
-then
-    echo " ******************************************  " 
-    echo " Building new release on: $latest_tag "
-    echo " ******************************************  " 
-    git submodule update
-
-    # checkout the latest release tag.
-    echo "++++ Checking out and building new tag $latest_tag ++++"
-    git checkout tags/$latest_tag
-    make docs || exit 1
-    
-    # Update the tag_list (tag.txt).
-    ###### content of tag.txt########
-    # <latest_tag_goes_here>
-    # 1.0.0
-    # 0.12.1
-    # 0.12.0
-    # 0.11.0
-    echo "++++ Adding $latest_tag to the top of the $tag_list_file ++++"
-    echo -e "$latest_tag\n$(cat $tag_list_file)" > "$tag_list_file"
-    cat $tag_list_file
-    
-    tests/ci_build/ci_build.sh doc python docs/build_version_doc/AddVersion.py --file_path "docs/_build/html/" --current_version "$latest_tag"
-    tests/ci_build/ci_build.sh doc python docs/build_version_doc/AddPackageLink.py \
-                                          --file_path "docs/_build/html/install/index.html" --current_version "$latest_tag"
-
-    # The following block does the following:
-    # a. copies the static html that was built from new tag to a local sandbox folder.
-    # b. copies the  $tag_list_file into local sandbox tag.txt        
-    # c. removes .git in VersionedWeb folder
-    # d. copies VersionedWeb/versions to local sandbox versions folder.
-    # e. makes a new directory with the previous TAG version. N-1 version name (example current: 1.1.0, Previous: 1.0.0)       
-    # f. Copies ReadMe.md to the local sandbox build.
-    # g. removes the content of VersionedWeb completely.
-    # f. Adds new content from local sandbox build to VersionedWeb.          
-    cp -a "docs/_build/html/." "$local_build"
-    cp $tag_list_file "$local_build/tag.txt"
-    rm -rf "$web_folder/.git"
-    cp -a "$web_folder/versions/." "$local_build/versions"
-    mkdir "$local_build/versions/${tag_list[0]}"
-    cp -a "$web_folder/." "$local_build/versions/${tag_list[0]}" || exit 1
-    cp "$web_folder/README.md" "$local_build"
-    rm -rf "$local_build/versions/${tag_list[0]}/versions"
-    rm -rf "$web_folder/*"
-    cp -a "$local_build/." "$web_folder"
-  
-    echo " ******************************************  " 
-    echo " Successfully built new release $latest_tag "
-    echo " ******************************************  " 
-else
-    # Build latest master
-    echo " ********** Building Master ************ "
-
-    make docs || exit 1
-
-    rm -rfv $web_folder/versions/master/*
-    cp -a "docs/_build/html/." "$web_folder/versions/master"
-    tests/ci_build/ci_build.sh doc python docs/build_version_doc/AddVersion.py --file_path "$web_folder/versions/master"
-fi
-
-# Update version list for all previous version website
-if [ $latest_tag != ${tag_list[0]} ]
-then
-    total=${#tag_list[*]}
-    for (( i=0; i<=$(( $total - 1 )); i++ ))
-    
-    do
-        tests/ci_build/ci_build.sh doc python docs/build_version_doc/AddVersion.py --file_path "$web_folder/versions/${tag_list[$i]}" \
-                                              --current_version "${tag_list[$i]}"
-    done
-
-    # Update master version dropdown
-    tests/ci_build/ci_build.sh doc python docs/build_version_doc/AddVersion.py --file_path "$web_folder/versions/master" 
-fi
diff --git a/docs/build_version_doc/build_site_tag.sh b/docs/build_version_doc/build_site_tag.sh
deleted file mode 100755
index d453e0cc9734..000000000000
--- a/docs/build_version_doc/build_site_tag.sh
+++ /dev/null
@@ -1,65 +0,0 @@
-#!/bin/bash
-
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-
-# How this script works:
-# 1. Receive tag list
-#    Looks like: tag_list="1.1.0 1.0.0 0.12.1 0.12.0 0.11.0 master"
-# 2. Receive default tag (for main website view)
-# 3. Receive root URL
-# 4. Call build and then update scripts
-
-# Take user input or check env var for tag list
-if [ -z "$1" ]
-  then
-    echo "No tag list supplied... trying environment variable $TAG_LIST"
-  else
-    tag_list="${TAG_LIST:-"$1"}"
-    echo "Using these tags: $1"
-fi
-
-if [ -z "$tag_list" ]
-  then
-    echo "No tags defined"
-    exit 1
-fi
-
-if [ -z "$2" ]
-  then
-    echo "Please pick a version to use as a default for the website. Ex: 1.1.0"
-    exit 1
-  else
-    tag_default=$2
-fi
-
-if [ -z "$3" ]
-  then
-    echo "Please provide the root url for the site. Ex: http://mxnet.incubator.apache.org/"
-    exit 1
-  else
-    root_url=$3
-fi
-
-# Pass params to build and update scripts
-for tag in $tag_list; do
-  ./build_all_version.sh $tag || exit 1
-done
-
-./update_all_version.sh "$tag_list" $tag_default $root_url || exit 1
-
diff --git a/docs/build_version_doc/setup_docs_ubuntu.sh b/docs/build_version_doc/setup_docs_ubuntu.sh
deleted file mode 100755
index baa23f684c09..000000000000
--- a/docs/build_version_doc/setup_docs_ubuntu.sh
+++ /dev/null
@@ -1,151 +0,0 @@
-#!/bin/bash
-
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-######################################################################
-# This script installs MXNet with required dependencies on a Ubuntu Machine.
-# Tested on Ubuntu 16.04+ distro.
-# Important Maintenance Instructions:
-#    Align changes with CI in /ci/docker/install/ubuntu_core.sh
-#    and ubuntu_python.sh
-######################################################################
-
-set -ex
-cd "$(dirname "$0")"
-
-sudo apt-get update
-echo "Installing MXNet core dependencies..."
-sudo apt-get install -y \
-    apt-transport-https \
-    build-essential \
-    ca-certificates \
-    cmake \
-    curl \
-    git \
-    libatlas-base-dev \
-    libcurl4-openssl-dev \
-    libjemalloc-dev \
-    liblapack-dev \
-    libopenblas-dev \
-    libopencv-dev \
-    libzmq3-dev \
-    ninja-build \
-    python-dev \
-    python3-dev \
-    software-properties-common \
-    sudo \
-    unzip \
-    virtualenv \
-    wget
-
-
-echo "Installing MXNet docs dependencies..."
-sudo apt-get install -y \
-    doxygen \
-    pandoc
-
-
-echo "Installing Clojure dependencies..."
-wget https://raw.githubusercontent.com/technomancy/leiningen/stable/bin/lein
-chmod 775 lein
-sudo cp lein /usr/local/bin
-echo "Y" | sudo lein downgrade 2.8.3
-
-
-echo "Installing R dependencies..."
-# install libraries for mxnet's r package on ubuntu
-sudo sh -c 'echo "deb http://cran.rstudio.com/bin/linux/ubuntu trusty/" >> /etc/apt/sources.list'
-
-key=E084DAB9
-
-sudo sh -c 'gpg --keyserver keyserver.ubuntu.com --recv-key $key || \
-    gpg --keyserver keyserver.pgp.com --recv-keys $key || \
-    gpg --keyserver ha.pool.sks-keyservers.net --recv-keys $key ;'
-
-# Installing the latest version (3.3+) that is compatible with MXNet
-sudo add-apt-repository 'deb [arch=amd64,i386] https://cran.rstudio.com/bin/linux/ubuntu xenial/'
-sudo apt-key add ../../ci/docker/install/sbt.gpg
-
-sudo apt-get update
-sudo apt-get install -y --allow-unauthenticated \
-    libcairo2-dev \
-    libssl-dev \
-    libxml2-dev \
-    libxt-dev \
-    r-base \
-    r-base-dev
-
-
-echo "Installing Scala dependencies..."
-sudo apt-get install -y software-properties-common
-sudo apt-get update
-sudo apt-get install -y openjdk-8-jdk
-sudo apt-get install -y openjdk-8-jre
-
-sudo sh -c 'echo "deb https://dl.bintray.com/sbt/debian /" | tee -a /etc/apt/sources.list.d/sbt.list'
-# ubuntu keyserver is very flaky
-#apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv 2EE0EA64E40A89B84B2DF73499E82A75642AC823
-#apt-key adv --keyserver keys.gnupg.net --recv 2EE0EA64E40A89B84B2DF73499E82A75642AC823
-sudo apt-key add ../../ci/docker/install/sbt.gpg
-sudo apt-get update && sudo apt-get install -y \
-    maven \
-    sbt \
-    scala
-
-
-wget -nv https://bootstrap.pypa.io/get-pip.py
-echo "Installing for Python 3..."
-sudo python3 get-pip.py
-pip3 install --user -r ../../ci/docker/install/docs_requirements
-echo "Installing for Python 2..."
-sudo python2 get-pip.py
-pip2 install --user -r ../../ci/docker/install/docs_requirements
-
-
-cd ../../
-
-echo "Checking for GPUs..."
-gpu_install=$(which nvidia-smi | wc -l)
-if [ "$gpu_install" = "0" ]; then
-    make_params="USE_OPENCV=1 USE_BLAS=openblas"
-    echo "nvidia-smi not found. Installing in CPU-only mode with these build flags: $make_params"
-else
-    make_params="USE_OPENCV=1 USE_BLAS=openblas USE_CUDA=1 USE_CUDA_PATH=/usr/local/cuda USE_CUDNN=1"
-    echo "nvidia-smi found! Installing with CUDA and cuDNN support with these build flags: $make_params"
-fi
-
-echo "Building MXNet core. This can take few minutes..."
-make -j $(nproc) $make_params
-
-
-# Setup Apache2
-echo "Setting up Apache web server..."
-sudo apt-get install -y apache2
-sudo ufw allow 'Apache Full'
-# Turn on mod_rewrite
-sudo a2enmod rewrite
-
-echo 'To enable redirects you need to edit /etc/apache2/apache2.conf '
-echo '--> Change directives for Directory for /var/www/html using the following: '
-echo '       AllowOverride all '
-echo '--> Then restart apache with: '
-echo '       sudo systemctl restart apache2'
-
-
-# Cleanup
-sudo apt autoremove -y
diff --git a/docs/build_version_doc/update_all_version.sh b/docs/build_version_doc/update_all_version.sh
deleted file mode 100755
index d3305d462dff..000000000000
--- a/docs/build_version_doc/update_all_version.sh
+++ /dev/null
@@ -1,106 +0,0 @@
-#!/bin/bash
-
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-# This script will update the html content from building
-# different tags.
-# It assumes you have already run build_all_version.sh for
-# the tags you want to update.
-
-# Takes three required arguments:
-# * tag list (required) - semicolon delimited list of tags to display on site
-#     Example: "1.1.0;1.0.0;master"
-# * default tag (required) - which version should the site default to
-#     Example: 1.0.0
-# * root URL (required) - for the versions dropdown to change to production or
-#     dev server.
-#     Example: http://mxnet.incubator.apache.org/
-
-# Example Usage:
-# ./update_all_version.sh "1.2.1;1.1.0;1.0.0;master" master  \
-#   http://mxnet.incubator.apache.org/
-
-set -e
-set -x
-
-if [ -z "$1" ]
-  then
-    echo "Please provide a list of version tags you wish to run."
-    exit 1
-  else
-    IFS=$';'
-    tag_list=$1
-    echo "Using these tags: $tag_list"
-    for tag in $tag_list; do echo $tag; done
-fi
-
-if [ -z "$2" ]
-  then
-    echo "Please pick a version to use as a default for the website. Ex: 1.1.0"
-    exit 1
-  else
-    tag_default=$2
-fi
-
-if [ -z "$3" ]
-  then
-    echo "Please provide the root url for the site. Ex: http://mxnet.incubator.apache.org/"
-    exit 1
-  else
-    root_url=$3
-fi
-
-built="VersionedWeb"
-tag_file="tag_list.txt"
-
-if [ -f "$tag_file" ]; then
-  rm $tag_file
-fi
-
-# Write all version numbers into $tag_file for AddVersion.py to use later
-# Master is added by that script by default
-for tag in $tag_list; do
-    echo "$tag" >> "$tag_file"
-done
-
-
-# Update the specified tags with the Versions dropdown
-# Add various artifacts depending on the version
-
-for tag in $tag_list; do
-    # This Python script is expecting the tag_list.txt and it will use that as the entries to populate
-
-    python AddVersion.py --root_url "$root_url" --file_path "$built/versions/$tag" --current_version "$tag" --tag_default "$tag_default" || exit 1
-
-    # Update all the files that are required to go into the root folder or live version
-    if [ $tag == $tag_default ]
-    then
-        cp -a "$built/versions/$tag/." "$built"
-        echo "Copying .htaccess from default branch to root folder...."
-        cp "artifacts/.htaccess"  "$built"
-    else
-        file_loc="$built/versions/$tag"
-    fi
-
-    # Copy the latest README.md; needs to come from local branch
-    if [ $tag == 'master' ]; then
-        cp ../../README.md $built
-    fi
-done
-
-echo "The output of this process can be found in the VersionedWeb folder."
diff --git a/docs/community/contribute.md b/docs/community/contribute.md
deleted file mode 100644
index 2dd92648715b..000000000000
--- a/docs/community/contribute.md
+++ /dev/null
@@ -1,188 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Contributing to MXNet
-
-Apache MXNet (incubating) is a community led, open source deep learning project. We welcome new members and look forward to your contributions. Here you will find how to get started and links to detailed information on MXNet best practices and processes.
-
-
-## Getting Started
-
-The following actions are recommended steps for you to get started with contributing to MXNet.
-
-| Action | Purpose |
-|---|---|
-| [Create a forum account](#forum) | Asking & answering MXNet usage questions |
-| [Join the dev comm channels](#mxnet-dev-communications) | Discussions about the direction of MXNet |
-| [Follow MXNet on Social Media](#social-media) | Get updates about new features and events |
-| [Create a JIRA account](#jira) | Tracking tasks & prioritizing issues |
-| [Check out the MXNet wiki](#confluence-wiki) | The wiki has detailed contributor information |
-| [Setup MXNet for development](#setup-mxnet-for-development) | Your very own fork for creating pull requests |
-| [Your first contribution](#your-first-contribution) | Complete a first contribution task |
-
-
-### FAQ
-
-* I found a bug. How do I report it?
-    * [Bug submission info](#file-a-bug-report)
-* I have a minor bug fix or docs update I'd like to submit. What do I do?
-    * [Minor fixes process](#minor-fixes)
-* I would like to submit a pull request for a significant update. What is the process?
-    * [Pull request process](#formal-pull-request-process)
-* I want to propose a new feature. What is the process for this?
-    * [New feature process](#new-feature-process)
-* What's coming next with MXNet, and how can I help?
-    * [Roadmap info](#roadmap)
-
-
-## MXNet Dev Communications
-
-### Forum
-
-If you need help with using MXNet, have questions about applying it to a particular kind of problem, or have a discussion topic, please use the discussion forum:
-* [discuss.mxnet.io](https://discuss.mxnet.io) <i class="fas fa-external-link-alt"></i>
-
-### Mailing Lists
-
-Please join either or both of the MXNet mailing lists:
-
-**For MXNet Users, join the USER mailing list**:
-
-- [MXNet Apache USER mailing list](https://lists.apache.org/list.html?user@mxnet.apache.org) (user@mxnet.apache.org): To subscribe, send an email to <a href="mailto:user-subscribe@mxnet.apache.org">user-subscribe@mxnet.apache.org</a> <i class="far fa-envelope"></i>
-
-**For Contributors to MXNet, join the DEV mailing list**:
-- [MXNet Apache DEV mailing list](https://lists.apache.org/list.html?dev@mxnet.apache.org) (dev@mxnet.apache.org): To subscribe, send an email to <a href="mailto:dev-subscribe@mxnet.apache.org">dev-subscribe@mxnet.apache.org</a> <i class="far fa-envelope"></i>
-
-
-* [archive](https://lists.apache.org/list.html?dev@mxnet.apache.org) <i class="fas fa-external-link-alt"></i>
-
-### Slack
-
-To join the MXNet slack channel send request to the contributor mailing list.
- * <a href="mailto:dev@mxnet.apache.org?subject=Requesting%20slack%20access">email</a> <i class="far fa-envelope"></i>
-
-
-### Social Media
-
-Keep connected with the latest MXNet news and updates.
-
-<p>
-<a id="medium">
-<a href="https://medium.com/apache-mxnet"><img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/social/medium_black.svg?sanitize=true" height="30px"/> Contributor and guest blogs about MXNet</a>
-</p>
-<p>
-<a id="reddit">
-<a href="https://reddit.com/r/mxnet"><img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/social/reddit_blue.svg?sanitize=true" height="30px" alt="reddit"/> Discuss MXNet on r/mxnet</a>
-</p>
-<p>
-<a id="twitter">
-<a href="https://twitter.com/apachemxnet"><img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/social/twitter.svg?sanitize=true" height="30px"/> Apache MXNet on Twitter</a>
-</p>
-<p>
-<a id="youtube">
-<a href="https://www.youtube.com/apachemxnet"><img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/social/youtube_red.svg?sanitize=true" height="30px"/> MXNet YouTube channel</a>
-</p>
-
-
-## JIRA
-
-MXNet uses Apache's JIRA to track issues and larger projects. Anyone can review open issues, but in order create issues or view JIRA boards, you must create an account.
-
-* [Open JIRA Issues](https://issues.apache.org/jira/projects/MXNET/issues)
-* [JIRA boards](https://issues.apache.org/jira/secure/RapidBoard.jspa) <i class="fas fa-lock"></i>
-
-
-## Confluence Wiki
-
-The [MXNet Confluence Wiki](https://cwiki.apache.org/confluence/display/MXNET/Apache+MXNet+Home) has detailed development environment setup info, design proposals, release process info, and more. This is generally where contributor information is maintained.
-
-* [MXNet Confluence Wiki](https://cwiki.apache.org/confluence/display/MXNET/Apache+MXNet+Home) <i class="fas fa-external-link-alt"></i>
-
-
-## Setup MXNet for Development
-
-The process for setting up MXNet for development depends on several factors, and is constantly being improved and expanded for more development languages. Setup information is on the MXNet Confluence Wiki.
-
-* [MXNet Confluence Wiki: Development](https://cwiki.apache.org/confluence/display/MXNET/Development) <i class="fas fa-external-link-alt"></i>
-
-
-## Your First Contribution
-
-**Step 1**: Visit the project on GitHub and review the [calls for contribution](https://github.com/apache/incubator-mxnet/labels/Call%20for%20Contribution). Click the GitHub button:
-<a class="github-button" href="https://github.com/apache/incubator-mxnet/labels/Call%20for%20Contribution" data-size="large" data-show-count="true" aria-label="Issue apache/incubator-mxnet on GitHub">Call for Contribution</a>
-
-**Step 2**: Tackle a smaller issue or improve documentation to get familiar with the process. As part of your pull request, add your name to [CONTRIBUTORS.md](https://github.com/apache/incubator-mxnet/blob/master/CONTRIBUTORS.md).
-
-**Step 3**: Follow the [formal pull request (PR) process](#formal-pull-request-process) to submit your PR for review.
-
-**Important**: keep an eye on your pull request, respond to comments and change requests, and rebase or resubmit your PR if fails the Jenkins continuous integration tests. Ask for help in the [forum or slack channel](#mxnet-dev-communications) if you get stuck.
-
-
-## File a bug report
-
-Please let us know if you experienced a problem with MXNet. Please provide detailed information about the problem you encountered and, if possible, add a description that helps to reproduce the problem. You have two alternatives for filing a bug report:
-<p><a href="http://issues.apache.org/jira/browse/MXNet"><i class="fas fa-bug"></i> JIRA</a></p>
-<p><a href="https://github.com/apache/incubator-mxnet/issues"><i class="fab fa-github"></i> GitHub</a></p>
-
-
-## Minor Fixes
-
-If you have found an issue and would like to contribute a bug fix or documentation update, follow these guidelines:
-
-* If it is trivial, just create a [pull request](https://github.com/apache/incubator-mxnet/pulls).
-* If it is non-trivial, you should follow the [formal pull request process](#formal-pull-request-process) described in the next section.
-
-
-## Formal Pull Request Process
-
-Any new features of improvements that are non-trivial should follow the complete flow of:
-
-1. [Review the contribution standards](https://cwiki.apache.org/confluence/display/MXNET/Development+Process) for your type of submission.
-1. [Create a JIRA issue](https://issues.apache.org/jira/secure/CreateIssue!default.jspa).
-1. [Create the PR on GitHub](https://github.com/apache/incubator-mxnet/pulls) and add the JIRA issue ID to the PR's title.
-
-Further details on this process can be found on the [Wiki](https://cwiki.apache.org/confluence/display/MXNET/Development).
-
-
-## New Feature Process
-
-Our community is constantly looking for feedback to improve Apache MXNet. If you have an idea how to improve MXNet or have a new feature in mind that would be beneficial for MXNet users, please open an issue in [MXNet’s JIRA](http://issues.apache.org/jira/browse/MXNet). The improvement or new feature should be described in appropriate detail and include the scope and its requirements if possible. Detailed information is important for a few reasons:<br/>
-- It ensures your requirements are met when the improvement or feature is implemented.<br/>
-- It helps to estimate the effort and to design a solution that addresses your needs. <br/>
-- It allows for constructive discussions that might arise around this issue.
-
-Detailed information is also required, if you plan to contribute the improvement or feature you proposed yourself. Please read the [contributions](http://mxnet.io/community/contribute.html) guide in this case as well.
-
-
-## Roadmap
-
-MXNet is evolving fast. To see what's next and what the community is currently working on, check out the Roadmap issues on GitHub and the JIRA Boards:
-
-<a class="github-button" href="https://github.com/apache/incubator-mxnet/labels/Roadmap" data-size="large" data-show-count="true" aria-label="Issue apache/incubator-mxnet on GitHub">Roadmap</a>
-<br/>
-[JIRA boards](https://issues.apache.org/jira/secure/RapidBoard.jspa) <i class="fas fa-lock"></i>
-
-
-<script defer src="https://use.fontawesome.com/releases/v5.0.12/js/all.js" integrity="sha384-Voup2lBiiyZYkRto2XWqbzxHXwzcm4A5RfdfG6466bu5LqjwwrjXCMBQBLMWh7qR" crossorigin="anonymous"></script>
-<script async defer src="https://buttons.github.io/buttons.js"></script>
-<script src="https://apis.google.com/js/platform.js"></script>
-
-
-## Contributors
-MXNet has been developed by and is used by a group of active community members. Contribute to improving it!
-
-<i class="fab fa-github"></i> [Contributors and Committers](https://github.com/apache/incubator-mxnet/blob/master/CONTRIBUTORS.md)
diff --git a/docs/community/ecosystem.md b/docs/community/ecosystem.md
deleted file mode 100644
index e7e101372115..000000000000
--- a/docs/community/ecosystem.md
+++ /dev/null
@@ -1,88 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# MXNet's Ecosystem
-
-Community contributions to MXNet have added many new valuable features and functionality to support use cases such as model serving & portability, easy and flexible APIs, and educational material like crash courses and online books. This ecosystem page lists the projects that use MXNet, teach MXNet, or augment MXNet in some way.
-
-
-## Highlighted Project
-
-[![Promo image](https://cdn-images-1.medium.com/max/800/1*PwIMdZM7tpt3rmcyhlk2FQ.png)](https://medium.com/apache-mxnet/announcing-keras-mxnet-v2-2-4b8404568e75)
-#### [Keras-MXNet v2.2 released!](https://medium.com/apache-mxnet/announcing-keras-mxnet-v2-2-4b8404568e75)
-
-
-## Contents
-
-* [Learning MXNet and other Educational Resources](#learning-mxnet-and-other-educational-resources)
-* [MXNet APIs](#mxnet-apis)
-* [Toolkits to Extend MXNet](#toolkits-to-extend-mxnet)
-* [Debugging and Visualization](#debugging-and-visualization)
-* [Model Serving](#model-serving)
-* [Model Zoos](#model-zoos)
-* [Contributions](#contributions)
-
-
-## Learning MXNet and other Educational Resources
-
-* [Gluon 60 Minute Crash Course](https://gluon-crash-course.mxnet.io/) - deep learning practitioners can learn Gluon quickly with these six 10-minute tutorials.
-    - [YouTube Series](https://www.youtube.com/playlist?list=PLkEvNnRk8uVmVKRDgznk3o3LxmjFRaW7s)
-* [Dive into Deep Learning](https://www.d2l.ai/) - a series of notebooks designed to teach deep learning using the Gluon Python API for MXNet.
-
-
-## MXNet APIs
-
-* [Clojure API](https://github.com/apache/incubator-mxnet/tree/master/contrib/clojure-package) - use MXNet with Clojure.
-* [C++ API](../api/c++/index.html) - not be confused with the C++ backend, this API allows C++ programmers to train networks in C++.
-* [Gluon Python Interface](../gluon/index.html) - train complex models imperatively and then deploy with a symbolic graph.
-* [Julia API](../api/julia/index.html) *(Community Supported)* - train models with multiple GPUs using Julia.
-* [Keras-MXNet](https://github.com/awslabs/keras-apache-mxnet) - design with Keras2 and train with MXNet as the backend for 2x or more speed improvement.
-* [MinPy](https://github.com/dmlc/minpy) - Pure numpy practice with third party operator integration and MXNet as backend for GPU computing
-* [Module Python API](../api/python/index.html) - backed by the Symbol API, you can define your network in a declarative fashion.
-* [ONNX-MXnet API](../api/python/contrib/onnx.html) - train and use Open Neural Network eXchange (ONNX) model files.
-* [Perl API](../api/perl/index.html) *(Community Supported)* - train models with multiple GPUs using Perl.
-* [R API](https://mxnet.incubator.apache.org/api/r/index.html) *(Community Supported)* - train models with multiple GPUs using R.
-* [Scala Infer API](../api/scala/infer.html) - model loading and inference functionality.
-* [TensorFuse](https://github.com/dementrock/tensorfuse) - Common interface for Theano, CGT, TensorFlow, and MXNet (experimental) by [dementrock](https://github.com/dementrock)
-
-
-## Toolkits to Extend MXNet
-
-* [Gluon CV](https://gluon-cv.mxnet.io/) - state-of-the-art deep learning algorithms in computer vision.
-* [Gluon NLP](https://gluon-nlp.mxnet.io/) - state-of-the-art deep learning models in natural language processing.
-* [Sockeye](https://github.com/awslabs/sockeye) - a sequence-to-sequence framework for Neural Machine Translation
-
-
-## Debugging and Visualization
-
-* [MXBoard](https://github.com/awslabs/mxboard) - lets you to visually inspect and interpret your MXNet runs and graphs using the TensorBoard software.
-
-
-## Model Serving
-
-* [MXNet Model Server (MMS)](https://github.com/awslabs/mxnet-model-server) - simple yet scalable solution for model inference.
-
-
-## Model Zoos
-
-* [Gluon Model Zoo](https://mxnet.incubator.apache.org/api/python/gluon/model_zoo.html) - models trained in Gluon and available through Gluon's model zoo API.
-* [ONNX Model Zoo](https://github.com/onnx/models) - ONNX models from a variety of ONNX-supported frameworks.
-
-
-## Contributions
-
-Do you know of a project or resource in the MXNet ecosystem that should be listed here? Or would you like to get involved by providing your own contribution? Check out the [guide for contributing to MXNet](contribute.html), and browse the [design proposals](https://cwiki.apache.org/confluence/display/MXNET/Proposals) to see what others are working on. You might find something you would like to help with or use those design docs as a template for your own proposal. Use one of the [developer communication channels](https://mxnet.incubator.apache.org/community/contribute.html#mxnet-dev-communications) if you would like to know more, or [create a GitHub issue](https://github.com/apache/incubator-mxnet/issues/new) if you would like to propose something for the MXNet ecosystem.
diff --git a/docs/community/index.md b/docs/community/index.md
deleted file mode 100644
index 5db4253a65df..000000000000
--- a/docs/community/index.md
+++ /dev/null
@@ -1,28 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# MXNet Community
-
-```eval_rst
-.. toctree::
-   :maxdepth: 1
-
-   contribute.md
-   ecosystem.md
-   powered_by.md
-   mxnet_channels.md
-```
diff --git a/docs/community/mxnet_channels.md b/docs/community/mxnet_channels.md
deleted file mode 100644
index df05855dfedd..000000000000
--- a/docs/community/mxnet_channels.md
+++ /dev/null
@@ -1,27 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Join MXNet Development Discussion
-
-Converse with the MXNet community via the following channels:
-
-- [Forum](https://discuss.mxnet.io/): [discuss.mxnet.io](https://discuss.mxnet.io/)
-- [MXNet Apache developer mailing list](https://lists.apache.org/list.html?dev@mxnet.apache.org) (dev@mxnet.apache.org): To subscribe, send an email to <a href="mailto:dev-subscribe@mxnet.apache.org">dev-subscribe@mxnet.apache.org</a>
-- [MXNet Apache user mailing list](https://lists.apache.org/list.html?user@mxnet.apache.org) (user@mxnet.apache.org): To subscribe, send an email to <a href="mailto:user-subscribe@mxnet.apache.org">user-subscribe@mxnet.apache.org</a>
-- [MXNet Slack channel](https://the-asf.slack.com/) (Channel: #mxnet): To request an invitation to the channel please subscribe to the dev mailing list above and then email: <a href="mailto:dev@mxnet.apache.org">dev@mxnet.apache.org</a>
-
-Note: if you have an email address with apache.org, you do not need an approval to join the MXNet Slack channel.
diff --git a/docs/community/powered_by.md b/docs/community/powered_by.md
deleted file mode 100644
index 4b854c4afdc6..000000000000
--- a/docs/community/powered_by.md
+++ /dev/null
@@ -1,83 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Powered By
-
-<div class="section-inst">
-<div class="container">
-<div class="row">
-<div class="col-lg-4 col-sm-6">
-    <img height="60px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/aws-logo.png">
-</div>
-
-<div class="col-lg-4 col-sm-6">
-    <img height="55px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/baidu-logo.jpg">
-</div>
-
-<div class="col-lg-4 col-sm-6">
-    <img height="70px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/cmu-logo.png">
-</div>
-
-<div class="col-lg-4 col-sm-6">
-    <img height="70px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/dato-logo.png">
-</div>
-
-<div class="col-lg-4 col-sm-6">
-    <img height="70px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/intel-logo.png">
-</div>
-
-<div class="col-lg-4 col-sm-6">
-    <img height="45px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/ms-logo.png">
-</div>
-        
-<div class="col-lg-4 col-sm-6">
-    <img height="45px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/mit-logo.png">
-</div>
-        
-<div class="col-lg-4 col-sm-6">
-    <img height="40px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/nvidia-logo.png">
-</div>
-
-<div class="col-lg-4 col-sm-6">
-    <img height="55px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/nyu-logo.jpg">
-</div>
-
-<div class="col-lg-4 col-sm-6">
-    <img height="55px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/hkust-logo.png">
-</div>
-
-<div class="col-lg-4 col-sm-6">
-    <img height="45px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/tusimple-logo.png">
-</div>
-
-<div class="col-lg-4 col-sm-6">
-    <img height="45px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/university-alberta-logo.png">
-</div>
-
-<div class="col-lg-4 col-sm-6">
-    <img height="45px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/university-washington-logo.jpg">
-</div>
-
-<div class="col-lg-4 col-sm-6">
-    <img height="45px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/wolfram-logo.png">
-</div>
-</div>
-    <p>
-        <b>These are some of the organizations that use MXNet. If you'd like to be added to this list, please send a message to <a href="mailto:dev@mxnet.incubator.apache.org">dev@mxnet.incubator.apache.org</a> </b>
-    </p>
-</div>
-</div>
diff --git a/docs/conf.py b/docs/conf.py
deleted file mode 100644
index d2a0ef0283f8..000000000000
--- a/docs/conf.py
+++ /dev/null
@@ -1,217 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-# -*- coding: utf-8 -*-
-import sys, os, re, subprocess
-import mock
-from recommonmark import parser
-
-curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
-libpath = os.path.join(curr_path, '../python/')
-sys.path.insert(0, libpath)
-sys.path.insert(0, curr_path)
-
-# -- mock out modules
-MOCK_MODULES = ['scipy', 'scipy.sparse', 'sklearn']
-for mod_name in MOCK_MODULES:
-    sys.modules[mod_name] = mock.Mock()
-
-# -- General configuration -----------------------------------------------------
-
-# If your documentation needs a minimal Sphinx version, state it here.
-needs_sphinx = '1.5.6'
-
-# General information about the project.
-project = u'mxnet'
-author = u'%s developers' % project
-copyright = u'2015-2018, %s' % author
-github_doc_root = 'https://github.com/apache/incubator-mxnet/tree/master/docs/'
-doc_root = 'http://mxnet.io/'
-
-# add markdown parser
-source_parsers = {
-    '.md': parser.CommonMarkParser,
-    '.Rmd': parser.CommonMarkParser
-}
-# Version information.
-# from mxnet import libinfo
-# version = libinfo.__version__
-# release = libinfo.__version__
-
-# Add any Sphinx extension module names here, as strings. They can be
-# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom ones
-extensions = [
-    'sphinx.ext.autodoc',
-    'sphinx.ext.autosummary',
-    'sphinx.ext.napoleon',
-    'sphinx.ext.mathjax',
-    'sphinx.ext.viewcode',
-    'breathe',
-    'mxdoc'
-]
-
-# Use breathe to include doxygen documents
-breathe_projects = {'mxnet' : 'doxygen/xml/'}
-breathe_default_project = 'mxnet'
-
-autodoc_member_order = 'bysource'
-
-# Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
-
-# The suffix of source filenames.
-# source_suffix = '.rst'
-source_suffix = ['.rst', '.md', '.Rmd']
-
-# The encoding of source files.
-#source_encoding = 'utf-8-sig'
-
-# The master toctree document.
-master_doc = 'index'
-
-# The version info for the project you're documenting, acts as replacement for
-# |version| and |release|, also used in various other places throughout the
-# built documents.
-#
-# The short X.Y version.
-
-# Version and release are passed from CMake.
-#version = None
-
-# The full version, including alpha/beta/rc tags.
-#release = version
-
-# The language for content autogenerated by Sphinx. Refer to documentation
-# for a list of supported languages.
-#language = None
-
-# There are two options for replacing |today|: either, you set today to some
-# non-false value, then it is used:
-#today = ''
-# Else, today_fmt is used as the format for a strftime call.
-#today_fmt = '%B %d, %Y'
-
-# List of patterns, relative to source directory, that match files and
-# directories to ignore when looking for source files.
-exclude_patterns = ['3rdparty', 'api/python/model.md', 'build_version_doc', 'cpp_docs', 'python_docs', 'README.md', 'static_site', 'tutorial_utils', 'virtualenv']
-
-
-# The reST default role (used for this markup: `text`) to use for all documents.
-#default_role = None
-
-# If true, '()' will be appended to :func: etc. cross-reference text.
-#add_function_parentheses = True
-
-# If true, the current module name will be prepended to all description
-# unit titles (such as .. function::).
-#add_module_names = True
-
-# If true, sectionauthor and moduleauthor directives will be shown in the
-# output. They are ignored by default.
-#show_authors = False
-
-# The name of the Pygments (syntax highlighting) style to use.
-pygments_style = 'sphinx'
-
-
-# A list of ignored prefixes for module index sorting.
-#modindex_common_prefix = []
-
-suppress_warnings = [
-   'image.nonlocal_uri',
-]
-
-# -- Options for HTML output ---------------------------------------------------
-
-# The theme to use for HTML and HTML Help pages.  See the documentation for
-# a list of builtin themes.
-html_theme = 'mxnet-theme'
-
-# Theme options are theme-specific and customize the look and feel of a theme
-# further.  For a list of options available for each theme, see the
-# documentation.
-#html_theme_options = {}
-
-# Add any paths that contain custom themes here, relative to this directory.
-html_theme_path = ['_static']
-
-# The name for this set of Sphinx documents.  If None, it defaults to
-# "<project> v<release> documentation".
-#html_title = None
-
-# A shorter title for the navigation bar.  Default is the same as html_title.
-#html_short_title = None
-
-# The name of an image file (relative to this directory) to place at the top
-# of the sidebar.
-#html_logo = None
-
-# The name of an image file (within the static path) to use as favicon of the
-# docs.  This file should be a Windows icon file (.ico) being 16x16 or 32x32
-# pixels large.
-#html_favicon = None
-
-# Add any paths that contain custom static files (such as style sheets) here,
-# relative to this directory. They are copied after the builtin static files,
-# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
-
-# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
-# using the given strftime format.
-#html_last_updated_fmt = '%b %d, %Y'
-
-# If true, SmartyPants will be used to convert quotes and dashes to
-# typographically correct entities.
-#html_use_smartypants = True
-
-# Custom sidebar templates, maps document names to template names.
-html_sidebars = {
-  '**': 'relations.html'
-}
-
-# Additional templates that should be rendered to pages, maps page names to
-# template names.
-#html_additional_pages = {}
-
-# If false, no module index is generated.
-#html_domain_indices = True
-
-# If false, no index is generated.
-#html_use_index = True
-
-# If true, the index is split into individual pages for each letter.
-#html_split_index = False
-
-# If true, links to the reST sources are added to the pages.
-#html_show_sourcelink = True
-
-# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
-#html_show_sphinx = True
-
-# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
-#html_show_copyright = True
-
-# If true, an OpenSearch description file will be output, and all pages will
-# contain a <link> tag referring to it.  The value of this option must be the
-# base URL from which the finished HTML is served.
-#html_use_opensearch = ''
-
-# This is the file name suffix for HTML files (e.g. ".xhtml").
-#html_file_suffix = None
-
-# Output file base name for HTML help builder.
-htmlhelp_basename = 'formatdoc'
diff --git a/docs/faq/add_op_in_backend.md b/docs/faq/add_op_in_backend.md
deleted file mode 100644
index 248a0b058370..000000000000
--- a/docs/faq/add_op_in_backend.md
+++ /dev/null
@@ -1,679 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# A Beginner's Guide to Implementing Operators in MXNet Backend
-
-## Introduction
-Operators are essential elements for constructing neural networks. They define mathematical formulas
-of transforming input data (tensors) to outputs. MXNet has a rich set of operators from simple ones,
-such as element-wise sum, to complicated ones, such as convolution, that is
-capable of constructing most of the popular neural networks. You may have noticed
-that many operators implemented in MXNet have their equivalent forms in Numpy, such as
-[repeat](https://docs.scipy.org/doc/numpy/reference/generated/numpy.repeat.html),
-[tile](https://docs.scipy.org/doc/numpy/reference/generated/numpy.tile.html),
-etc., and wonder why we could not simply use those Numpy operators in MXNet. One of the
-major reasons is that we need to support both CPU and GPU computing for the operators in MXNet,
-while Numpy operators do not possess GPU computing capability.
-In addition, we have performed plenty of
-optimizations for various components in MXNet, such as tensor data structure (`NDArray`),
-execution engine, computational graph and so on, for maximizing memory and runtime efficiency.
-An operator implemented under the MXNet operator framework would greatly
-leverage those optimizations for exhaustive performance enhancement.
-
-In this tutorial, we are going to practice implementing an operator using
-C++ in the MXNet backend. After finishing the implementation,
-we will add unit tests using Python for the operator we just implemented.
-
-## Implementation
-### An Operator Example
-Let's take the [quadratic function](https://en.wikipedia.org/wiki/Quadratic_function)
-as an example: `f(x) = ax^2+bx+c`. We want to implement an operator called `quadratic`
-taking `x`, which is a tensor, as an input and generating an output tensor `y`
-satisfying `y.shape=x.shape` and each element of `y` is calculated by feeding the
-corresponding element of `x` into the quadratic function `f`.
-Here variables `a`, `b`, and `c` are user input parameters.
-In frontend, the operator works like this:
-```python
-x = [[1, 2], [3, 4]]
-y = quadratic(data=x, a=1, b=2, c=3)
-y = [[6, 11], [18, 27]]
-```
-To implement this, we first create three files: `quadratic_op-inl.h`,
-`quadratic_op.cc`, and `quadratic_op.cu`. The header file's name
-is prefixed by the operator name and followed by `op` and `-inl`
-indicating that this is an operator implementation with inline
-functions shared by CPU and GPU computing. The CPU and GPU
-specific implementations reside in their own `.cc` and `.cu` files,
-respectively. We normally put pure tensor related operators
-(e.g. `tile`, `repeat`, etc.) under
-the directory `src/operator/tensor`, and neural network operators
-(e.g. `Convolution`, `Pooling`, etc.) under `src/operator/nn`.
-You may have noticed that many neural network operators including
-`Convolution` and `Pooling` are currently saved under `src/operator`.
-We plan to move them to `src/operator/nn` for better file organization
-and clearer hierarchy in the future.
-
-Next, we are going to
-1. Define the parameter struct
-for registering `a`, `b`, and `c` in `quadratic_op-inl.h`.
-2. Define type and shape inference functions in `quadratic_op-inl.h`.
-3. Define forward and backward functions in `quadratic_op-inl.h`.
-4. Register the operator using [nnvm](https://docs.tvm.ai/dev/nnvm_overview.html)
-in `quadratic_op.cc` and `quadratic_op.cu` for
-CPU and GPU computing, respectively.
-
-Now let's walk through the process step by step.
-
-### Parameter Registration
-We first define `struct QuadraticParam` as a placeholder for the
-parameters `a`, `b`, and `c` in `quadratic_op-inl.h`.
-The struct inherits from a base template
-struct named `dmlc::Parameter`, where the template argument is the derived struct
-`QuadraticParam`. This technique, which is called [curiously recurring template
-pattern](https://en.wikipedia.org/wiki/Curiously_recurring_template_pattern),
-achieves static polymorphism. It is similar to using a virtual function,
-but without the cost associated with dynamic polymorphism.
-
-```cpp
-struct QuadraticParam : public dmlc::Parameter<QuadraticParam> {
-  float a, b, c;
-  DMLC_DECLARE_PARAMETER(QuadraticParam) {
-    DMLC_DECLARE_FIELD(a)
-      .set_default(0.0)
-      .describe("Coefficient of the quadratic term in the quadratic function.");
-    DMLC_DECLARE_FIELD(b)
-      .set_default(0.0)
-      .describe("Coefficient of the linear term in the quadratic function.");
-    DMLC_DECLARE_FIELD(c)
-      .set_default(0.0)
-      .describe("Constant term in the quadratic function.");
-  }
-};
-```
-
-The function calls in the above parameter struct are self-explanatory by their names.
-Note that for each parameter, we set the default value to `0.0` such that users can
-skip passing 0-value parameters through the quadratic operator interface. You
-can choose not to define the default value for a parameter if it is required
-at runtime. Meanwhile, adding brief descriptions to the parameters enables
-the documentation engine to display them on
-[MXNet documentation web page](https://mxnet.incubator.apache.org/api/python/index.html).
-
-### Attribute Inference
-Attribute inference is the process of deducing the properties of `NDArray`s
-in neural networks from user provided information. Two most common attributes
-of an `NDArray` are data shape and data type.
-Let's take a look at the following example.
-Given an input `NDArray` called `data`, you invoke the `quadratic` operator
-like this: `output = mx.nd.quadratic(data, a=1, b=2, c=3)`. Before calculating
-the `output` values, its shape and data type are inferred from the input
-`data`'s shape and type following
-the rules you defined in order to allocate memory space for the output tensor.
-
-One important thing to note that inference functions should be capable of
-performing **mutual inference**, i.e.
-inferring one argument's attribute from another argument's attribute if
-possible according to the definition of the operator.
-This is very useful for a computational graph to deduce unknown attributes
-for a neural network in symbolic programming. Users can view the computational
-graph as a symbol with every element initialized for running data
-throughout the neural network, including memory allocation for each tensor,
-device placement for each operator, etc. Users normally just need
-to provide minimum necessary information, such as input data shapes, etc.,
-to the computational graph, and the graph will fill up the unknown attributes
-using the attribute inference functions defined in the operators building up
-the neural network.
-
-Let's consider the following example.
-```python
->>> import mxnet as mx
->>> a = mx.sym.Variable('a', shape=(2, 0))
->>> b = mx.sym.Variable('b')
->>> c = mx.sym.Variable('c', shape=(0, 3))
->>> d = a * b + b * c
->>> print d.infer_shape()
-([(2L, 3L), (2L, 3L), (2L, 3L)], [(2L, 3L)], [])
-```
-The last line of the above code snippet is a tuple of three lists returned
-by `d.infer_shape()`. The first list contains all the argument shapes
-of `a`, `b`, and `c`. The second contains the output shape of `d`. The
-third one represents the shapes of auxiliary states, which is not used
-in this case, and thus is empty.
-In this example, we only specified values for variable `a`'s first dimension
-and `c`'s second dimension. The `0` in shape `(2, 0)` indicates that the size
-of the second dimension is unknown, same meaning for shape `(0, 3)`.
-However, the symbol `d` still successfully inferred the shapes
-for all the variables and final output. This is a result of mutual
-inference. In MXNet, the whole process can be interpreted as this:
-1. `a` and `b` are combined via an element-wise multiplication operator,
-so the shapes of `a` and `b` are same and `b`'s first dimension size is `2`.
-2. `b` and `c` are combined via an element-wise multiplication operator too,
-so the shapes of `b` and `c` are same and `b`'s second dimension size is `3`.
-3. Now `b`'s shape is completely known, so `a` and `c` missing dimension sizes
-are known as well.
-4. `d` is a result from adding `a * b` and `b * c`, so d should also
-have the same shape as `b`.
-
-The above four steps illustrate how shape inference logic works in MXNet.
-It is actually implemented in the shape inference functions of the operators for
-element-wise multiplication and addition.
-
-For our `quadratic` operator, shape inference possesses quite similar logic.
-```cpp
-inline bool QuadraticOpShape(const nnvm::NodeAttrs& attrs,
-                             mxnet::ShapeVector* in_attrs,
-                             mxnet::ShapeVector* out_attrs) {
-  CHECK_EQ(in_attrs->size(), 1U);
-  CHECK_EQ(out_attrs->size(), 1U);
-
-  SHAPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
-  SHAPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
-  return out_attrs->at(0).ndim() != 0U && out_attrs->at(0).Size() != 0U;
-}
-```
-Here are a few things to note about the above function:
-
-1. `attrs` contains parameters `a`, `b`, and `c` from user input.
-It's not used here since we don't rely on that information for shape inference.
-2. `in_attrs` is a vector containing all input shapes. Since there is
-only one input argument for operator `quadratic`, we used macro `CHECK_EQ`
-to assert when the vector's size is wrong.
-3. `out_attrs` is a vector containing all output shapes. We also used
-`CHECK_EQ` to verify the size of the vector since there is only one output.
-4. We called macro `SHAPE_ASSIGN_CHECK` twice for mutual inference. One for
-inferring the output shape from the input shape, the other one is for inferring
-the input shape from the output shape.
-If there are any unequal non-zero values in the same
-dimension of two shapes, such as `(2, 3)` and `(3, 3)`, the macro would throw an
-exception with an error message for shape inference.
-5. At the end of the function body, we checked whether the output shape
-is completely known by testing whether the shape is not empty and
-the shape's size is greater than `0`. Note that in MXNet, an empty shape
-means that the shape is unknown, and
-a `0` in a shape means that the size of that dimension is unknown. In both
-situations, the missing shape information must
-be inferred from other shapes. If it cannot be inferred,
-the function should return `false` to notify the caller about shape inference failure.
-6. MXNet provides a convenience function implementing the logic of mutual inference
-for general element-wise operators with the following interface. Users can
-instantiate this function with `n_in=1` and `n_out=1` to replace the above
-function `QuadraticOpShape` in operator registration (explained later).
-The function `QuadraticOpShape` posted here is for the purpose of illustration only.
-```cpp
-template<int n_in, int n_out>
-inline bool ElemwiseShape(const nnvm::NodeAttrs& attrs,
-                          mxnet::ShapeVector *in_attrs,
-                          mxnet::ShapeVector *out_attrs);
-```
-
-The same logic goes for data type inference. We will leave the analysis of
-the following code sample to users. Note that `-1` means the data type
-is unknown and must be inferred from other input or output data types.
-```cpp
-inline bool QuadraticOpType(const nnvm::NodeAttrs& attrs,
-                            std::vector<int>* in_attrs,
-                            std::vector<int>* out_attrs) {
-  CHECK_EQ(in_attrs->size(), 1U);
-  CHECK_EQ(out_attrs->size(), 1U);
-
-  TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
-  TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
-  return out_attrs->at(0) != -1;
-}
-```
-
-Again, MXNet provides the following convenience function for mutual
-type inference of element-wise operators. Users can use that
-in operator registration (explained later).
-```cpp
-template<int n_in, int n_out>
-inline bool ElemwiseType(const nnvm::NodeAttrs& attrs,
-                         std::vector<int>* in_attrs,
-                         std::vector<int>* out_attrs);
-```
-
-### Forward Function
-Forward function defines the operator's behavior in the forward pass
-of neural networks. For our `quadratic` operator, it simply implements
-the logic of running a tensor through the quadratic function by performing
-a few element-wise operations. The forward function's signature is fixed
-in MXNet as follows:
-```cpp
-void (const nnvm::NodeAttrs& attrs,
-      const OpContext& ctx,
-      const std::vector<TBlob>& inputs,
-      const std::vector<OpReqType>& req,
-      const std::vector<TBlob>& outputs);
-```
-We first paste the whole forward function code here
-and then go through it line by line.
-```cpp
-template<typename xpu>                                                        // 1
-void QuadraticOpForward(const nnvm::NodeAttrs& attrs,                         // 2
-                        const OpContext& ctx,                                 // 3
-                        const std::vector<TBlob>& inputs,                     // 4
-                        const std::vector<OpReqType>& req,                    // 5
-                        const std::vector<TBlob>& outputs) {                  // 6
-  CHECK_EQ(inputs.size(), 1U);                                                // 7
-  CHECK_EQ(outputs.size(), 1U);                                               // 8
-  CHECK_EQ(req.size(), 1U);                                                   // 9
-  mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();                            // 10
-  const TBlob& in_data = inputs[0];                                           // 11
-  const TBlob& out_data = outputs[0];                                         // 12
-  const QuadraticParam& param = nnvm::get<QuadraticParam>(attrs.parsed);      // 13
-  using namespace mxnet_op;                                                   // 14
-  MSHADOW_TYPE_SWITCH(out_data.type_flag_, DType, {                           // 15
-    MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {                               // 16
-      Kernel<quadratic_forward<req_type>, xpu>::Launch(                       // 17
-          s, out_data.Size(), out_data.dptr<DType>(), in_data.dptr<DType>(),  // 18
-          param.a, param.b, param.c);                                         // 19
-    });                                                                       // 20
-  });                                                                         // 21
-}                                                                             // 22
-```
-- Line 1: `xpu` stands for a generic device type so that the function can be instantiated
-for both CPU and GPU computing using concrete types `cpu` and `gpu`. The instantiation happens
-at the time when the operator is registered in `.cc` and `.cu` files.
-- Line 2: `attrs` is a node attribute containing the user input parameters `a`, `b`, and `c`.
-Here the node represents a placeholder for the operator in the whole computational graph for
-the neural network.
-- Line 3: `ctx` holds something called `stream` for
-serializing asynchronous executions. Let's consider
-this example for understanding the functionality of `stream`.
-We want to launch several GPU kernels with the same `stream` from CPU.
-Even though the launching operation is non-blocking, the `stream` guarantees
-that the kernels execute in the same order on GPU as they are launched from CPU.
-- Line 4: `inputs` is a vector of input tensors (only one input tensor
-for the `quadratic` operator).
-- Line 5: `req` is a vector of `OpReqType` values. Each value defines
-the way of writing calculated values to the output tensors.
-Therefore, the number of `req`s must be the same as the number of output tensors.
-MXNet currently supports three types of `req` in frontend: `null`, `write`, and `add`.
-`null` means skipping calculating the corresponding output tensor,
-`write` means overwriting the values in the output tensor with the ones
-calculated by this operator, and `add` means adding the calculated values
-to the existing ones in the output tensor. Note that `null` and `add` are usually
-seen in backward passes. The former is for skipping calculating
-the gradients of un-learnable parameters (such as index arrays),
-and the latter is for accumulating gradients throughout networks.
-- Line 6: `outputs` is a vector of output tensors (only one
-output tensor for the `quadratic` operator).
-- Lines 7-9: Verify that the size of each vector is expected.
-Otherwise, stop moving forward and print error message.
-- Line 10: Get the `stream` from the `ctx` for launching kernels.
-- Lines 11-12: Define the references of the input and output tensors
-for later coding convenience. Note that `TBlob` can be understood
-as a uniform data structure for tensors of various dimensions, such
-that tensors of different dimensions can be put in a homogeneous container,
-such as `std::vector` and `std::list`. You can still
-get tensors of desired dimensions from a `TBlob` object through
-the interface `get_with_shape`.
-- Line 13: Get user input parameters from the node attribute.
-- Lines 15-21: This is the place where the mathematical formula of the operator
-is implemented. The macros `MSHADOW_TYPE_SWITCH` and `MXNET_ASSIGN_REQ_SWITCH` enable
-the code block to work for all the supported data types and `req` types in MXNet.
-Inside the inner-most macro, we launch the kernel for calculating
-the output tensor such that each thread takes an element from
-the input tensor, feeds it into the quadratic function, and assigns
-the output element to the output tensor based on `req` type. Note that
-`Kernel::Launch` serves as a universal interface for launching
-parallel computation on both CPU and GPU. This allows most of
-the simple operators to share the same piece of code for CPU and GPU as
-parallelization approaches are often identical on both types of devices.
-The kernel function is defined as the following, where the function
-`Map` is executed by each thread for each input element. The `out_data.Size()`,
-in the `Kernel::Launch` function corresponds to the factor by which the
-workload will get parallelized among the different threads, which here
-corresponds to the size of the output array. To explain a little
-bit more on the two macros used in the kernel struct: (1) `MSHADOW_XINLINE` is
-a consolidated macro for inlining functions compiled by both CPU and GPU
-compilers. It enables CPU and GPU computing to share the same piece of code.
-(2) `KERNEL_ASSIGN` is a macro for unifying the statements of different `req`s
-into the same line of code. It's named `KERNEL_ASSIGN` because we call
-the code blocks running parallel computation kernels.
-On CPUs, the kernels are normally wrapped by the OpenMP `parallel` directive;
-while on GPUs, they are the kernel functions launched by CUDA library.
-
-```cpp
-template<int req>
-struct quadratic_forward {
-  template<typename DType>
-  MSHADOW_XINLINE static void Map(int i, DType* out_data, const DType* in_data,
-                                  const float a, const float b, const float c) {
-    KERNEL_ASSIGN(out_data[i], req, in_data[i] * (a * in_data[i] + b) + c);
-  }
-};
-```
-
-### Backward Function
-Backward functions play the role of propagating derivatives of loss function
-with respect to the outputs of the last layer throughout the network to the first
-layer. The whole process is often known as backward propagation. We are not
-going to delineate the principle of backward propagation here since users can find
-great details covered in other resources, such as
-[CS231n](http://cs231n.github.io/optimization-2/) and
-[How the backgropagation algorithm works](http://neuralnetworksanddeeplearning.com/chap2.html).
-The problem we are going to solve here for the `quadratic` operator is that
-given a tensor representing the gradient of the loss function with respect
-to the output of the operator, calculate the gradient with respect to
-the input of the operator. There is no need to calculate the derivatives
-of loss function with respect to user input parameters `a`, `b`, and `c`
-since they are not learnable parameters in the network. To formulate the problem:
-given `dL/dy` and `y = a*x^2 + b*x + c`, where `L` represents the loss function and
-`y` stands for the output of the quadratic tensor, we need to solve for
-`dL/dx`. Using the chain-rule, it is obvious to find that
-```
-dL/dx = dL/dy * dy/dx = dL/dy * (2*a*x + b).
-```
-The above equation indicates that `dL/dx` depends on the gradient
-of the output tensor and value of the input tensor.
-The backward function's signature is the same as the forward function's.
-With the aforementioned information in mind,
-let's breakdown the following backward function line by line.
-```cpp
-template<typename xpu>                                                       // 1
-void QuadraticOpBackward(const nnvm::NodeAttrs& attrs,                       // 2
-                         const OpContext& ctx,                               // 3
-                         const std::vector<TBlob>& inputs,                   // 4
-                         const std::vector<OpReqType>& req,                  // 5
-                         const std::vector<TBlob>& outputs) {                // 6
-  CHECK_EQ(inputs.size(), 2U);                                               // 7
-  CHECK_EQ(outputs.size(), 1U);                                              // 8
-  CHECK_EQ(req.size(), 1U);                                                  // 9
-  mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();                           // 10
-  const TBlob& out_grad = inputs[0];                                         // 11
-  const TBlob& in_data = inputs[1];                                          // 12
-  const TBlob& in_grad = outputs[0];                                         // 13
-  const QuadraticParam& param = nnvm::get<QuadraticParam>(attrs.parsed);     // 14
-  using namespace mxnet_op;                                                  // 15
-  MSHADOW_TYPE_SWITCH(out_grad.type_flag_, DType, {                          // 16
-    MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {                              // 17
-      Kernel<quadratic_backward<req_type>, xpu>::Launch(                     // 18
-          s, in_grad.Size(), in_grad.dptr<DType>(), out_grad.dptr<DType>(),  // 19
-          in_data.dptr<DType>(), param.a, param.b);                          // 20
-    });                                                                      // 21
-  });                                                                        // 22
-}                                                                            // 23
-```
-- Lines 1-6: Backward function has the same signature as forward function.
-- Lines 7-9: Check the sizes of the function arguments. One thing to note
-that since the gradient of the input depends on both the gradient of the output and
-the input tensor itself, `inputs` must contain two `TBlob` objects.
-- Line 10: Get the `stream` of the context for serializing asynchronous executions.
-- Lines 11-13: Convenience reference variables for later use. We name `out_grad`
-as the gradient of the operator output, `in_data` as the input of the operator,
-and `in_grad` as the gradient of the operator input.
-- Line 14: Get the parameter object of `QuadraticParam`.
-- Lines 16-22: Same as in the forward function, this is where parallel
-computation for `in_grad` happens. The struct `quadratic_backward` implements
-the formula of calculating each element of `in_grad` by one thread as the following.
-
-```cpp
-template<int req>
-struct quadratic_backward {
-  template<typename DType>
-  MSHADOW_XINLINE static void Map(int i, DType* in_grad, const DType* out_grad,
-                                  const DType* in_data, const float a, const float b) {
-    KERNEL_ASSIGN(in_grad[i], req, out_grad[i] * (2 * a * in_data[i] + b));
-  }
-};
-```
-
-### Operator Registration
-So far, we have implemented necessary data structure and functions for the operator `quadratic`.
-Now let's register them using `nnvm` to expose the operator `quadratic`
-to frontend. Users can consider the registration process as creating the operator object
-instance, saving it in the operator manager (a singleton),
-and setting attributes for the operator instance.
-
-The following code is from `quadratic_op.cc`, which is responsible
-for registering the operator working on CPU.
-```cpp
-DMLC_REGISTER_PARAMETER(QuadraticParam);                                           // 1
-
-NNVM_REGISTER_OP(quadratic)                                                        // 2
-.describe(R"code(This operators implements the quadratic function:                 // 3
-.. math::
-
-    f(x) = ax^2+bx+c
-
-where :math:`x` is an input tensor and all operations
-in the function are element-wise.
-
-Example:
-
-  .. code-block:: python
-     :emphasize-lines: 1,3
-     x = [[1, 2], [3, 4]]
-     y = quadratic(data=x, a=1, b=2, c=3)
-     y = [[6, 11], [18, 27]]
-
-)code" ADD_FILELINE)                                                               // 4
-.set_attr_parser(ParamParser<QuadraticParam>)                                      // 5
-.set_num_inputs(1)                                                                 // 6
-.set_num_outputs(1)                                                                // 7
-.set_attr<nnvm::FListInputNames>("FListInputNames",                                // 8
-  [](const NodeAttrs& attrs) {                                                     // 9
-    return std::vector<std::string>{"data"};                                       // 10
-  })                                                                               // 11
-.set_attr<nnvm::FInferShape>("FInferShape", QuadraticOpShape)                      // 12
-.set_attr<nnvm::FInferType>("FInferType", QuadraticOpType)                         // 13
-.set_attr<FCompute>("FCompute<cpu>", QuadraticOpForward<cpu>)                      // 14
-.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_quadratic"})  // 15
-.set_attr<nnvm::FInplaceOption>("FInplaceOption",                                  // 16
-  [](const NodeAttrs& attrs) {                                                     // 17
-    return std::vector<std::pair<int, int> >{{0, 0}};                              // 18
-  })                                                                               // 19
-.add_argument("data", "NDArray-or-Symbol", "Input ndarray")                        // 20
-.add_arguments(QuadraticParam::__FIELDS__());                                      // 21
-
-NNVM_REGISTER_OP(_backward_quadratic)                                              // 22
-.set_attr_parser(ParamParser<QuadraticParam>)                                      // 23
-.set_num_inputs(2)                                                                 // 24
-.set_num_outputs(1)                                                                // 25
-.set_attr<nnvm::TIsBackward>("TIsBackward", true)                                  // 26
-.set_attr<FCompute>("FCompute<cpu>", QuadraticOpBackward<cpu>);                    // 27
-```
-
-- Line 1: Register the parameter struct.
-- Line 2: Register an operator named `quadratic` by creating an instance
-of `Op` type and save it in the operator manager and return a reference
-of the just created operator object.
-- Lines 3-4: Add description as an operator attribute
-including examples of the operator. The documentation engine will extract
-this description and display it on the documentation web page.
-`emphasize-lines` is optional.
-For more examples and troubleshooting with doc strings, refer to the [MXNet
-developer wiki's Documentation Guide](https://cwiki.apache.org/confluence/display/MXNET/Documentation+Guide).
-- Line 5: Set parameter struct parser for the operator. It is used for parsing
-the parameters `a`, `b`, and `c` input from frontend.
-- Line 6: Set the number of inputs for the operator.
-- Line 7: Set the number of outputs for the operator.
-- Lines 8-11: Defines a function generating a vector of names of
-the operator input arguments. This function is used to add missing
-arguments that users did not specify when creating a symbolic operator.
-For example, `quad_func=mx.sym.quadratic()` is still a valid symbol
-since we have added the attribute `FListInputNames` to the operator node
-in the computational graph. MXNet would
-add the missing argument with name `quadratic0_data`, where the prefix
-`quadratic0` is the operator name appended with an index and the postfix
-`data` comes from the return value of the user defined `FListInputName` function.
-Users still can generate an executor for the `quad_func` like the following:
-```python
-quad_exe = quad_func.simple_bind(ctx=mx.cpu(), quadratic0_data=(1,))
-```
-- Line 12: Register shape inference function.
-- Line 13: Register type inference function.
-- Line 14: Register forward function.
-- Line 15: Register the function for creating the node of the operator in
-a backward pass. Note that we used a convenience functor struct `ElemwiseGradUseIn`.
-As you can tell from the name, the registered functor creates the node for gradient computation
-with dependencies on the output gradient node and input node. Similarly, there are
-other three functors defined as `ElemwiseGradUseOut`, `ElemwiseGradUseInOut`,
-and `ElemwiseGradUseNone` for developers' convenience. In order to add
-this attribute, we also need to register a backward operator for `quadratic` with
-several basic attributes, as it can share attribute inference
-functions with the forward operator and is not exposed to frontend.
-- Lines 16-19: This registered function implies that which output tensor can reuse
-which input tensor's memory space instead of allocating a new memory space for the output.
-In the operator `quadratic`, there is only one input and output, and the output can reuse
-the input memory space, so we store a pair of zeros in the function return vector
-indicating that `inputs[0]`'s memory space can be reused by `outputs[0]`.
-Note that this function just provides a hint to the computational graph initializer.
-If there are other nodes depending on the input tensor, the memory space
-of the input tensor will not be overwritten by the output.
-- Line 20: Define the input argument name as `data` for the operator.
-- Line 21: Add user input parameters `a`, `b`, and `c` as the attributes of the operator.
-- Line 22: Register an operator named `_backward_quadratic` for backward pass
-of the operator `quadratic`. The underscore prefix in the operator name indicates
-that this is an operator not exposed to users. The convention
-of naming an internally used backward operator is prepending the prefix `_backward_`
-to the corresponding forward operator name.
-- Line 23: Set the parameter parser for the operator `_backward_quadratic`.
-- Line 24: Set the number of inputs.
-- Line 25: Set the number of outputs.
-- Line 26: Add `TIsBackward` attribute for the operator. The shape and type
-inference passes use this attribute to determine whether a node in the graph is a
-forward or backward node.
-- Line 27: Register backward function.
-
-So far, we have acquired an operator working on CPU in frontend.
-In order to register the operator working on GPUs, we just need to add the following
-code to `quadratic_op.cu`. Note that forward and backward functions
-are registered with attribute key `FCompute<gpu>`, rather than `FCompute<cpu>`.
-```cpp
-NNVM_REGISTER_OP(quadratic)
-.set_attr<FCompute>("FCompute<gpu>", QuadraticOpForward<gpu>);
-
-NNVM_REGISTER_OP(_backward_quadratic)
-.set_attr<FCompute>("FCompute<gpu>", QuadraticOpBackward<gpu>);
-```
-
-### Unit Test
-Now we have finished implementing the operator `quadratic` in MXNet backend.
-If you use python, when you type `import mxnet as mx`, two python
-functions for invoking your backend implementation are
-generated on the fly: one is for imperative programming
-registered as `mxnet.ndarray.quadratic` or `mx.nd.quadratic` for short;
-the other one is for symbolic programming registered under
-module `mxnet.symbol.quadratic` or `mx.sym.quadratic` for short.
-
-In order to unit test it in frontend, we need to add the following code
-to the python file `test_operator.py`. A typical operator implementation
-tests for both the `symbol` API and the `ndarray` API. The following test
-has both these tests. The imperative API test, tests for the `ndarray` API,
-`mx.nd.contrib.quadratic`. The `symbol` API test, tests for the complete
-functionality of the operator - the forward pass and the backward
-pass. To facilitate the testing of these functionalities we use three
-helper functions available in the `mxnet.test_utils` module:
- - `check_symbolic_forward`
- - `check_symbolic_backward`
- - `check_numeric_gradient`
-
-```python
-def test_quadratic_function():
-    def f(x, a, b, c):
-        return a * x**2 + b * x + c
-
-    a = np.random.random_sample()
-    b = np.random.random_sample()
-    c = np.random.random_sample()
-    data = mx.symbol.Variable('data')
-    quad_sym = mx.sym.contrib.quadratic(data=data, a=a, b=b, c=c)
-    for dtype in [np.float16, np.float32, np.float64]:
-        for ndim in range(1, 6):
-            shape = rand_shape_nd(ndim, 5)
-            data_np = np.random.randn(*shape).astype(dtype)
-            expected = f(data_np, a, b, c)
-            backward_expected = 2 * a * data_np + b
-
-            # check imperative forward
-            output = mx.nd.contrib.quadratic(mx.nd.array(data_np), a=a, b=b, c=c)
-            assert_almost_equal(output.asnumpy(),expected,
-                                rtol=1e-2 if dtype is np.float16 else 1e-5,
-                                atol=1e-2 if dtype is np.float16 else 1e-5)
-            # check forward
-            check_symbolic_forward(quad_sym, [data_np], [expected],
-                                    rtol=1e-2 if dtype is np.float16 else 1e-5,
-                                    atol=1e-2 if dtype is np.float16 else 1e-5)
-            # check backward
-            check_symbolic_backward(quad_sym, [data_np], [np.ones(expected.shape)],
-                                        [backward_expected],
-                                        rtol=1e-2 if dtype is np.float16 else 1e-5,
-                                        atol=1e-2 if dtype is np.float16 else 1e-5)
-            # check backward using finite difference
-            check_numeric_gradient(quad_sym, [data_np], atol=0.001)
-```
-
-In the above test we create a `quadratic` symbol and feed it into the three
-utility functions. The `check_symbolic_forward` and `check_symbolic_backward`
-tests the computed values against the expected values that we pass
-as an argument to the function. The `check_numeric_gradient` utility function
-performs [gradient checking](http://ufldl.stanford.edu/tutorial/supervised/DebuggingGradientChecking/)
-to verify the implementation for the backward function of the operator.
-It will perform a perturbation on the input and calculate the response
-rate of the output using the
-[finite difference method](https://en.wikipedia.org/wiki/Finite_difference_method).
-Then it will compare the gradient from the backward pass with the values
-from the finite difference method. All three of these tests will be successful
-once the comparison satisfies user specified `rtol` and `atol` values. Here `rtol`
-and `atol` expand to relative tolerance and absolute tolerance respectively. They
-are used to specify how far the computed values can deviate from the expected values.
-They are defined as follows
-```
-abs(Expected_Value - Computed_Value) < RTOL * abs(Expected_Value) + ATOL
-```
-
-For example, if `rtol` is `1e-5` and `atol` is `1e-5` and the expected value is
-`1.5623145`, then the computed value should lie within the range of
-`(1.562288876855, 1.562340123145)` else the test will fail. Make sure you
-tune the `rtol` and `atol` values accordingly. Giving very low values for `rtol`
-and `atol` will likely make the test very flaky. It is recommended that you
-use the flakiness checker tool to check if the test you have written is flaky
-or not. You can run the flakiness checker tool for the above test with the
-following command -
-
-```bash
-python tools/flakiness_checker.py test_operator.test_quadratic_function
-```
-
-Please note that for `check_symbolic_forward` and `check_symbolic_backward` we pass
-both the operator symbols and expected results for comparison, for
-`check_numeric_gradient` we only pass the operator symbol, as the
-`check_numeric_gradient` computes the expected value using finite difference
-method. Which is why it is highly recommended to add `check_numeric_gradient`
-test for every operator with backward function implemented as it eliminates
-the possibility of passing incorrect expected results into `check_symbolic_backward`.
-
-
-## Summary
-In this tutorial, we practiced implementing the operator `quadratic` in MXNet backend
-and unit testing the implementation in frontend. More specifically, we added parameter
-struct for user-input parameters, walked through shape and type inference workflow,
-implemented forward and backward functions, and registered the operator
-using nnvm. Congratulations! You now know how to add operators.
-We welcome your contributions to MXNet.
-
-**Note**: Source code in the tutorial can be found in
-[quadratic_op-inl.h](https://github.com/apache/incubator-mxnet/blob/master/src/operator/contrib/quadratic_op-inl.h),
-[quadratic_op.cc](https://github.com/apache/incubator-mxnet/blob/master/src/operator/contrib/quadratic_op.cc),
-[quadratic_op.cu](https://github.com/apache/incubator-mxnet/blob/master/src/operator/contrib/quadratic_op.cu),
-and
-[test_operator.py](https://github.com/apache/incubator-mxnet/blob/master/tests/python/unittest/test_operator.py#L6514).
-
-## Additional Resources
-- [Use TensorInspector to Help Debug Operators](tensor_inspector_tutorial.md)
diff --git a/docs/faq/bucketing.md b/docs/faq/bucketing.md
deleted file mode 100644
index b5fb987d23e4..000000000000
--- a/docs/faq/bucketing.md
+++ /dev/null
@@ -1,113 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Bucketing in MXNet
-When we train recurrent neural networks (RNNs), we _unroll_ the network in time.
-For a single example of length T, we would unroll the network T steps.
-In the unrolled view, the network is acyclic and thus we can think of it
-as a feedforward neural network in which weights are tied across adjacent time steps.
-The unrolled view allows us to train the network via the traditional backpropagation algorithm -
-in this case, we call the algorithm _back-propagation through time_.
-
-Things get complicated when we work with datasets where the example sequences have varying lengths.
-In the _recurrent_ view, each example can pass through the same architecture.
-But in the _unrolled_ view, each example requires a different number of unrollings, and thus corresponds to a _slightly_ different feedforward network.
-If we train on one example at a time, we can simply unroll the desired amount on each training iteration.
-But things get more complicated when we try to perform mini-batch training.
-A naive approach might be to pad all the sequences so that they appear to have the length of the longest example.
-However, this could be wasteful because on shorter sequences, most of the computations are done on padded data.
-
-Borrowed from [TensorFlow's sequence training code](https://www.tensorflow.org/versions/r0.7/tutorials/seq2seq/index.html),
-_bucketing_ offers an effective solution to make minibatches out of varying-length sequences.
-Instead of unrolling the network to the maximum possible sequence length,
-we unroll multiple instances of different lengths (e.g., length 5, 10, 20, 30).
-During training, we use the most appropriate unrolled model
-for each mini-batch of data.
-Although the models *with different numbers of unrollings*
-have different feedforward architectures,
-their parameters are shared in time.
-_MXNet_ reuses the internal memory buffers among all executors.
-
-For simple RNNs, you can use a for loop to explicitly
-go over the input sequences and perform a backpropagation-through-time
-by maintaining the connection of the states and gradients through time.
-However, this implementation approach results in slow processing.
-This approach works with variable length sequences. For more complicated models (e.g., translation that uses a sequence-to-sequence model), explicitly unrolling is the easiest way. In this example, we introduce the MXNet APIs that allows us to implement bucketing.
-
-## Variable-length Sequence Training for Sherlock Holmes
-
-We use the [Sherlock Holmes language model example](https://github.com/dmlc/mxnet/tree/master/example/rnn) for this example. If you are not familiar with this example, see [this tutorial (in Julia)](http://dmlc.ml/mxnet/2015/11/15/char-lstm-in-julia.html) first.
-
-In this example, we use a simple architecture
-consisting of a word-embedding layer
-followed by two LSTM layers.
-In the original example,
-the model is unrolled explicitly in time for a fixed length of 32.
- <!-- In this tutorial, we show how to use bucketing to implement variable-length sequence training. -->
-To enable bucketing, MXNet needs to know how to construct a new unrolled symbolic architecture for a different sequence length. To achieve this, instead of constructing a model with a fixed `Symbol`, we use a callback function that generates a new `Symbol` on a *bucket key*.
-
-
-```python
-model = mx.mod.BucketingModule(
-        sym_gen             = sym_gen,
-        default_bucket_key  = data_train.default_bucket_key,
-        context             = contexts)
-```
-
-`sym_gen` must be a function that takes one argument, `bucket_key`, and returns a `Symbol` for this bucket. We'll use the sequence length as the bucket key. A bucket key could be anything. For example, in neural translation, because different combinations of input-output sequence lengths correspond to different unrolling, the bucket key could be a pair of lengths.
-
-```python
-def sym_gen(seq_len):
-    return lstm_unroll(num_lstm_layer, seq_len, len(vocab),
-                       num_hidden=num_hidden, num_embed=num_embed,
-                       num_label=len(vocab))
-```
-The data iterator needs to report the `default_bucket_key`, which allows MXNet to do some parameter initialization before reading the data. Now the model is capable of training with different buckets by sharing the parameters and intermediate computation buffers between bucket executors.
-
-To train, we still need to add some extra bits to our `DataIter`. Apart from reporting the `default_bucket_key` as mentioned previously, we also need to report the current `bucket_key` for each mini-batch. More specifically, the `DataBatch` object returned in each mini-batch by the `DataIter` should contain the following additional properties:
-
-* `bucket_key`: The bucket key that corresponds to this batch of data.
-In our example, it is the sequence length for this batch of data.
-If the executors corresponding to this bucket key have not yet been created,
-they will be constructed according to the symbolic model returned by `gen_sym` on this bucket key.
-The executors will be cached for future use.
-Note that generated `Symbol`s could be arbitrary,
-but they should all have the same trainable parameters and auxiliary states.
-* `provide_data`: The same information reported by the `DataIter` object.
-Because each bucket now corresponds to a different architecture,
-they could have different input data.
-Also, make sure that the `provide_data` information returned by the `DataIter` object
-is compatible with the architecture for `default_bucket_key`.
-* `provide_label`: The same as `provide_data`.
-
-The `DataIter` is responsible for grouping the data into different buckets.
-Assuming that randomization is enabled, at each iteration,
-`DataIter` chooses a random bucket (according to a distribution balanced by the bucket sizes),
-and then randomly chooses sequences from that bucket to form a mini-batch.
-It also applies padding for sequences of different length within the mini-batch as necessary.
-
-For a full, working implementation of a `DataIter`
-that reads text sequences by as described above, see [example/rnn/lstm_ptb_bucketing.py](https://github.com/dmlc/mxnet/blob/master/example/rnn/bucketing/lstm_bucketing.py).
-In this example, you can use bucketing with a static configuration (e.g., `buckets = [10, 20, 30, 40, 50, 60]`), or let MXNet generate buckets automatically according to the characteristics of the dataset (`buckets = []`). The latter approach is implemented by adding a bucket as long as the number of sequences assigned to that bucket is exceeds some minimum count. For more information, see [default_gen_buckets()](https://github.com/dmlc/mxnet/blob/master/example/rnn/old/bucket_io.py#L43).
-
-## Beyond Sequence Training
-
-In this example, we briefly explained how the bucketing API works.
-However, the API is not limited to bucketing by sequence lengths.
-The bucket key can be an arbitrary object, as long
-as the architecture returned by `gen_sym`
-is compatible with (has the same set of parameters) as the object.
diff --git a/docs/faq/caffe.md b/docs/faq/caffe.md
deleted file mode 100644
index e0362ca2776b..000000000000
--- a/docs/faq/caffe.md
+++ /dev/null
@@ -1,201 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# How to | Convert from Caffe to MXNet
-
-Key topics covered include the following:
-
-- [Converting Caffe trained models to MXNet](#converting-caffe-trained-models-to-mxnet)
-- [Calling Caffe operators in MXNet](#calling-caffe-operators-in-mxnet)
-
-## Converting Caffe trained models to MXNet
-
-The converting tool is available at
-[tools/caffe_converter](https://github.com/dmlc/mxnet/tree/master/tools/caffe_converter). On
-the remaining of this section, we assume we are on the `tools/caffe_converter`
-directory.
-
-### How to build
-
-If Caffe's python package is installed, namely we can run `import caffe` in
-python, then we are ready to go.
-
-For example, we can used
-[AWS Deep Learning AMI](https://aws.amazon.com/marketplace/pp/B06VSPXKDX) with
-both Caffe and MXNet installed.
-
-Otherwise we can install the
-[Google protobuf](https://developers.google.com/protocol-buffers/?hl=en)
-compiler and its python binding. It is easier to install, but may be slower
-during running.
-
-1. Install the compiler:
-  - Linux: install `protobuf-compiler` e.g. `sudo apt-get install
-    protobuf-compiler` for Ubuntu and `sudo yum install protobuf-compiler` for
-     Redhat/Fedora.
-  - Windows: Download the win32 build of
-    [protobuf](https://github.com/google/protobuf/releases). Make sure to
-    download the version that corresponds to the version of the python binding
-    on the next step. Extract to any location then add that location to your
-    `PATH`
-  - Mac OS X: `brew install protobuf`
-
-2. Install the python binding by either `conda install -c conda-forge protobuf`
-   or `pip install protobuf`.
-
-3. Compile Caffe proto definition. Run `make` in Linux or Mac OS X, or
-   `make_win32.bat` in Windows
-
-### How to use
-
-There are three tools:
-
-- `convert_symbol.py` : convert Caffe model definition in protobuf into MXNet's
-  Symbol in JSON format.
-- `convert_model.py` : convert Caffe model parameters into MXNet's NDArray format
-- `convert_mean.py` : convert Caffe input mean file into MXNet's NDArray format
-
-In addition, there are two tools:
-- `convert_caffe_modelzoo.py` : download and convert models from Caffe model
-  zoo.
-- `test_converter.py` : test the converted models by checking the prediction
-  accuracy.
-
-## Calling Caffe operators in MXNet
-
-Besides converting Caffe models, MXNet supports calling most Caffe operators,
-including network layer, data layer, and loss function, directly. It is
-particularly useful if there are customized operators implemented in Caffe, then
-we do not need to re-implement them in MXNet.
-
-### How to install
-
-This feature requires Caffe. In particular, we need to re-compile Caffe before
-[PR #4527](https://github.com/BVLC/caffe/pull/4527) is merged into Caffe. There
-are the steps of how to rebuild Caffe:
-
-1. Download [Caffe](https://github.com/BVLC/caffe). E.g. `git clone
-   https://github.com/BVLC/caffe`
-2. Download the
-   [patch for the MXNet interface](https://github.com/BVLC/caffe/pull/4527.patch)
-   and apply to Caffe. E.g.
-   ```bash
-   cd caffe && wget https://github.com/BVLC/caffe/pull/4527.patch && git apply 4527.patch
-   ```
-3. Build and install Caffe by following the
-   [official guide](http://caffe.berkeleyvision.org/installation.html).
-
-Next we need to compile MXNet with Caffe supports
-
-1. Copy `make/config.mk` (for Linux) or `make/osx.mk`
-   (for Mac) into the MXNet root folder as `config.mk` if you have not done it yet
-2. Open the copied `config.mk` and uncomment these two lines
-   ```bash
-   CAFFE_PATH = $(HOME)/caffe
-   MXNET_PLUGINS += plugin/caffe/caffe.mk
-   ```
-   Modify `CAFFE_PATH` to your Caffe installation, if necessary.
-3. Then build with 8 threads `make clean && make -j8`.
-
-### How to use
-
-This Caffe plugin adds three components into MXNet:
-
-- `sym.CaffeOp` : Caffe neural network layer
-- `sym.CaffeLoss` : Caffe loss functions
-- `io.CaffeDataIter` : Caffe data layer
-
-#### Use `sym.CaffeOp`
-The following example shows the definition of a 10 classes multi-layer perceptron:
-
-```Python
-data = mx.sym.Variable('data')
-fc1  = mx.sym.CaffeOp(data_0=data, num_weight=2, name='fc1', prototxt="layer{type:\"InnerProduct\" inner_product_param{num_output: 128} }")
-act1 = mx.sym.CaffeOp(data_0=fc1, prototxt="layer{type:\"TanH\"}")
-fc2  = mx.sym.CaffeOp(data_0=act1, num_weight=2, name='fc2', prototxt="layer{type:\"InnerProduct\" inner_product_param{num_output: 64} }")
-act2 = mx.sym.CaffeOp(data_0=fc2, prototxt="layer{type:\"TanH\"}")
-fc3 = mx.sym.CaffeOp(data_0=act2, num_weight=2, name='fc3', prototxt="layer{type:\"InnerProduct\" inner_product_param{num_output: 10}}")
-mlp = mx.sym.SoftmaxOutput(data=fc3, name='softmax')
-```
-
-Let's break it down. First, `data = mx.sym.Variable('data')` defines a variable
-as a placeholder for input.  Then, it's fed through Caffe operators with `fc1 =
-mx.sym.CaffeOp(...)`. `CaffeOp` accepts several arguments:
-
-- The inputs to Caffe operators are named as `data_i` for *i=0, ..., num_data-1*
-- `num_data` is the number of inputs. In default it is 1, and therefore
-skipped in the above example.
-- `num_out` is the number of outputs. In default it is 1 and also skipped.
-- `num_weight` is the number of weights (`blobs_`).  Its default value is 0. We
-need to explicitly specify it for a non-zero value.
-- `prototxt` is the protobuf configuration string.
-
-#### Use `sym.CaffeLoss`
-
-Using Caffe loss is similar.
-We can replace the MXNet loss with Caffe loss.
-We can replace
-
-Replacing the last line of the above example with the following two lines we can
-call Caffe loss instead of MXNet loss.
-
-```Python
-label = mx.sym.Variable('softmax_label')
-mlp = mx.sym.CaffeLoss(data=fc3, label=label, grad_scale=1, name='softmax', prototxt="layer{type:\"SoftmaxWithLoss\"}")
-```
-
-Similar to `CaffeOp`, `CaffeLoss` has arguments `num_data` (2 in default) and
-`num_out` (1 in default). But there are two differences
-
-1. Inputs are `data` and `label`. And we need to explicitly create a variable
-   placeholder for label, which is implicitly done in MXNet loss.
-2. `grad_scale` is the weight of this loss.
-
-#### Use `io.CaffeDataIter`
-
-We can also wrap a Caffe data layer into MXNet's data iterator. Below is an
-example for creating a data iterator for MNIST
-
-```python
-train = mx.io.CaffeDataIter(
-    prototxt =
-    'layer { \
-        name: "mnist" \
-        type: "Data" \
-        top: "data" \
-        top: "label" \
-        include { \
-            phase: TEST \
-        } \
-        transform_param { \
-            scale: 0.00390625 \
-        } \
-        data_param { \
-            source: "caffe/examples/mnist/mnist_test_lmdb" \
-            batch_size: 100 \
-            backend: LMDB \
-        } \
-    }',
-    flat           = flat,
-    num_examples   = 60000,
-)
-```
-
-### Put it all together
-
-The complete example is available at
-[example/caffe](https://github.com/dmlc/mxnet/blob/master/example/caffe/)
diff --git a/docs/faq/cloud.md b/docs/faq/cloud.md
deleted file mode 100644
index ecc0f46e02c0..000000000000
--- a/docs/faq/cloud.md
+++ /dev/null
@@ -1,200 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# MXNet on the Cloud
-
-Deep learning can require extremely powerful hardware, often for unpredictable durations of time.
-Moreover, _MXNet_ can benefit from both multiple GPUs and multiple machines.
-Accordingly, cloud computing, as offered by AWS and others,
-is especially well suited to training deep learning models.
-Using AWS, we can rapidly fire up multiple machines with multiple GPUs each at will
-and maintain the resources for precisely the amount of time needed.
-
-## Set Up an AWS GPU Cluster from Scratch
-
-In this document, we provide a step-by-step guide that will teach you
-how to set up an AWS cluster with _MXNet_. We show how to:
-
-- [Use Amazon S3 to host data](#use-amazon-s3-to-host-data)
-- [Set up an EC2 GPU instance with all dependencies installed](#set-up-an-ec2-gpu-instance)
-- [Build and run MXNet on a single computer](#build-and-run-mxnet-on-a-gpu-instance)
-- [Set up an EC2 GPU cluster for distributed training](#set-up-an-ec2-gpu-cluster-for-distributed-training)
-
-### Use Amazon S3 to Host Data
-
-Amazon S3 provides distributed data storage which proves especially convenient for hosting large datasets.
-To use S3, you need [AWS credentials](http://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSGettingStartedGuide/AWSCredentials.html),
-including an `ACCESS_KEY_ID` and a `SECRET_ACCESS_KEY`.
-
-To use _MXNet_ with S3, set the environment variables `AWS_ACCESS_KEY_ID` and
-`AWS_SECRET_ACCESS_KEY` by adding the following two lines in
-`~/.bashrc` (replacing the strings with the correct ones):
-
-```bash
-export AWS_ACCESS_KEY_ID=AKIAIOSFODNN7EXAMPLE
-export AWS_SECRET_ACCESS_KEY=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
-```
-
-There are several ways to upload data to S3. One simple way is to use
-[s3cmd](http://s3tools.org/s3cmd). For example:
-
-```bash
-wget http://data.mxnet.io/mxnet/data/mnist.zip
-unzip mnist.zip && s3cmd put t*-ubyte s3://dmlc/mnist/
-```
-
-### Use Pre-installed EC2 GPU Instance
-The [Deep Learning AMI](https://aws.amazon.com/marketplace/pp/B01M0AXXQB?qid=1475211685369&sr=0-1&ref_=srh_res_product_title) is an Amazon Linux image
-supported and maintained by Amazon Web Services for use on Amazon Elastic Compute Cloud (Amazon EC2).
-It contains [MXNet-v0.9.3 tag](https://github.com/dmlc/mxnet) and the necessary components to get going with deep learning,
-including Nvidia drivers, CUDA, cuDNN, Anaconda, Python2 and Python3.   
-The AMI IDs are the following:
-
-* us-east-1: ami-e7c96af1
-* us-west-2: ami-dfb13ebf
-* eu-west-1: ami-6e5d6808
-
-Now you can launch _MXNet_ directly on an EC2 GPU instance.  
-You can also use [Jupyter](http://jupyter.org) notebook on EC2 machine.
-Here is a [good tutorial](https://github.com/dmlc/mxnet-notebooks)
-on how to connect to a Jupyter notebook running on an EC2 instance.
-
-### Set Up an EC2 GPU Instance from Scratch
-
-_MXNet_ requires the following libraries:
-
-- C++ compiler with C++11 support, such as `gcc >= 4.8`
-- `CUDA` (`CUDNN` in optional) for GPU linear algebra
-- `BLAS` (cblas, open-blas, atblas, mkl, or others) for CPU linear algebra
-- `opencv` for image augmentations
-- `curl` and `openssl` for the ability to read/write to Amazon S3
-
-Installing `CUDA` on EC2 instances requires some effort. Caffe has a good
-[tutorial](https://github.com/BVLC/caffe/wiki/Install-Caffe-on-EC2-from-scratch-(Ubuntu,-CUDA-7,-cuDNN-3))
-on how to install CUDA 7.0 on Ubuntu 14.04.
-
-***Note:*** We tried CUDA 7.5 on Nov 7, 2015, but found it problematic.
-
-You can install the rest using the package manager. For example, on Ubuntu:
-
-```
-sudo apt-get update
-sudo apt-get install -y build-essential git libcurl4-openssl-dev libatlas-base-dev libopencv-dev python-numpy
-```
-
-The Amazon Machine Image (AMI) [ami-12fd8178](https://console.aws.amazon.com/ec2/v2/home?region=us-east-1#LaunchInstanceWizard:ami=ami-12fd8178) has the  packages listed above installed.
-
-
-### Build and Run MXNet on a GPU Instance
-
-The following commands build _MXNet_ with CUDA/CUDNN, Amazon S3, and distributed
-training.
-
-```bash
-git clone --recursive https://github.com/dmlc/mxnet
-cd mxnet; cp make/config.mk .
-echo "USE_CUDA=1" >>config.mk
-echo "USE_CUDA_PATH=/usr/local/cuda" >>config.mk
-echo "USE_CUDNN=1" >>config.mk
-echo "USE_BLAS=atlas" >> config.mk
-echo "USE_DIST_KVSTORE = 1" >>config.mk
-echo "USE_S3=1" >>config.mk
-make -j$(nproc)
-```
-
-To test whether everything is installed properly, we can try training a convolutional neural network (CNN) on the MNIST dataset using a GPU:
-
-```bash
-python example/image-classification/train_mnist.py
-```
-
-If you've placed the MNIST data on `s3://dmlc/mnist`, you can read the data stored on Amazon S3 directly with the following command:
-
-```bash
-sed -i.bak "s!data_dir = 'data'!data_dir = 's3://dmlc/mnist'!" example/image-classification/train_mnist.py
-```
-
-***Note:*** You can use `sudo ln /dev/null /dev/raw1394` to fix the opencv error `libdc1394 error: Failed to initialize libdc1394`.
-
-### Set Up an EC2 GPU Cluster for Distributed Training
-
-A cluster consists of multiple computers.
-You can use one computer with _MXNet_ installed as the root computer for submitting jobs,and then launch several
-slave computers to run the jobs. For example, launch multiple instances using an
-AMI, e.g.,
-[ami-12fd8178](https://console.aws.amazon.com/ec2/v2/home?region=us-east-1#LaunchInstanceWizard:ami=ami-12fd8178),
-with dependencies installed. There are two options:
-
-- Make all slaves' ports accessible (same for the root) by setting type: All TCP,
-   Source: Anywhere in Configure Security Group.
-
-- Use the same `pem` as the root computer to access all slave computers, and
-   then copy the `pem` file into the root computer's `~/.ssh/id_rsa`. If you do this, all slave computers can be accessed with SSH from the root.
-
-Now, run the CNN on multiple computers. Assume that we are on a working
-directory of the root computer, such as `~/train`, and MXNet is built as `~/mxnet`.
-
-1. Pack the _MXNet_ Python library into this working directory for easy
-  synchronization:
-
-  ```bash
-  cp -r ~/mxnet/python/mxnet .
-  cp ~/mxnet/lib/libmxnet.so mxnet/
-  ```
-
-  And then copy the training program:
-
-  ```bash
-  cp ~/mxnet/example/image-classification/*.py .
-  cp -r ~/mxnet/example/image-classification/common .
-  ```
-
-2. Prepare a host file with all slaves private IPs. For example, `cat hosts`:
-
-  ```bash
-  172.30.0.172
-  172.30.0.171
-  ```
-
-3. Assuming that there are two computers, train the CNN using two workers:
-
-  ```bash
-  ../../tools/launch.py -n 2 -H hosts --sync-dir /tmp/mxnet python train_mnist.py --kv-store dist_sync
-  ```
-
-***Note:*** Sometimes the jobs linger at the slave computers even though you've pressed `Ctrl-c`
-at the root node. To terminate them, use the following command:
-
-```bash
-cat hosts | xargs -I{} ssh -o StrictHostKeyChecking=no {} 'uname -a; pgrep python | xargs kill -9'
-```
-
-***Note:*** The preceding example is very simple to train and therefore isn't a good
-benchmark for distributed training. Consider using other [examples](https://github.com/dmlc/mxnet/tree/master/example/image-classification).
-
-### More Options
-#### Use Multiple Data Shards
-It is common to pack a dataset into multiple files, especially when working in a distributed environment.
-_MXNet_ supports direct loading from multiple data shards.
-Put all of the record files into a folder, and point the data path to the folder.
-
-#### Use YARN and SGE
-Although using SSH can be simple when you don't have a cluster scheduling framework,
-_MXNet_ is designed to be portable to various platforms.  
-We provide scripts available in [tracker](https://github.com/dmlc/dmlc-core/tree/master/tracker)
-to allow running on other cluster frameworks, including Hadoop (YARN) and SGE.
-We welcome contributions from the community of examples of running _MXNet_ on your favorite distributed platform.
diff --git a/docs/faq/develop_and_hack.md b/docs/faq/develop_and_hack.md
deleted file mode 100644
index d2710e923be4..000000000000
--- a/docs/faq/develop_and_hack.md
+++ /dev/null
@@ -1,26 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Develop and Hack MXNet
-- [Create new operators](new_op.md)
-- [Use Torch from MXNet](torch.md)
-- [Set environment variables of MXNet](env_var.md)
-- [Use TensorInspector to Help Debug Operators](tensor_inspector_tutorial.md)
-
-# Other Resources
-- [MXNet System Architecture Overview](/architecture/overview.html)
-- [Contributor Guidelines](/community/contribute.html)
diff --git a/docs/faq/distributed_training.md b/docs/faq/distributed_training.md
deleted file mode 100644
index 4d4a220c5661..000000000000
--- a/docs/faq/distributed_training.md
+++ /dev/null
@@ -1,324 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Distributed Training in MXNet
-MXNet supports distributed training enabling us to leverage multiple machines for faster training.
-In this document, we describe how it works, how to launch a distributed training job and
-some environment variables which provide more control.
-
-## Types of Parallelism
-There are two ways in which we can distribute the workload of training a neural network across multiple devices (can be either GPU or CPU).
-The first way is *data parallelism*, which refers to the case where each device stores a complete copy of the model.
-Each device works with a different part of the dataset, and the devices collectively update a shared model.
-These devices can be located on a single machine or across multiple machines.
-In this document, we describe how to train a model with devices distributed across machines in a data parallel way.
-
-When models are so large that they don't fit into device memory, then a second way called *model parallelism* is useful.
-Here, different devices are assigned the task of learning different parts of the model.
-Currently, MXNet supports Model parallelism in a single machine only. Refer [Training with multiple GPUs using model parallelism](https://mxnet.incubator.apache.org/versions/master/faq/model_parallel_lstm.html) for more on this.
-
-## How Does Distributed Training Work?
-The following concepts are key to understanding distributed training in MXNet:
-### Types of Processes
-MXNet has three types of processes which communicate with each other to accomplish training of a model.
-- Worker: A worker node actually performs training on a batch of training samples.
-Before processing each batch, the workers pull weights from servers.
-The workers also send gradients to the servers after each batch.
-Depending on the workload for training a model, it might not be a good idea to run multiple worker processes on the same machine.
-- Server: There can be multiple servers which store the model's parameters, and communicate with workers.
-A server may or may not be co-located with the worker processes.
-- Scheduler: There is only one scheduler. The role of the scheduler is to set up the cluster. This includes waiting for messages that each node has come up and which port the node is listening on.
-The scheduler then lets all processes know about every other node in the cluster, so that they can communicate with each other.
-
-### KV Store
-MXNet provides a key-value store, which is a critical component used for multi-device training. The communication of parameters across devices on a single machine, as well as across multiple machines, is relayed through one or more servers with a key-value store for the parameters. Each value in this store is represented by a key and value, where each parameter array in the network is assigned a key, and value refers to the weights of that parameter array. Workers `push` gradients after processing a batch, and `pull` updated weights before processing a new batch. 
-We can also pass in optimizers for the KVStore to use while updating each weight. Optimizers like Stochastic Gradient Descent define an update rule,
-essentially a mathematical formula to compute the new weight based on the old weight, gradient, and some parameters.
-
-If you are using a Gluon Trainer object or the Module API,
-it uses a kvstore object internally to aggregate gradients from multiple devices on the same machine as well as across different machines.
-
-Although the API remains the same whether or not multiple machines are being used,
-the notion of kvstore server exists only during distributed training.
-In this case, each `push` and `pull` involves communication with the kvstore servers. When there are multiple devices on a single machine, gradients from these devices are first aggregated on the machine and then sent to the servers.  
-Note that we need to compile MXNet with the build flag `USE_DIST_KVSTORE=1` to use distributed training.
-
-The distributed mode of KVStore is enabled by calling `mxnet.kvstore.create` function
-with a string argument which contains the word `dist` as follows:
-> kv = mxnet.kvstore.create('dist_sync')
-
-Refer [KVStore API](https://mxnet.incubator.apache.org/versions/master/api/python/kvstore/kvstore.html) for more information about KVStore.
-
-### Distribution of Keys
-Each server doesn't necessarily store all the keys or parameter arrays.
-Parameters are distributed across different servers. The decision of which server stores a particular key is made at random.
-This distribution of keys across different servers is handled transparently by the KVStore.
-It ensures that when a key is pulled, that request is sent to the server which has the corresponding value.
-If the value of some key is very large, it may be sharded across different servers. This means that different servers hold different parts of the value.
-Again, this is handled transparently so that the worker does not have to do anything different.
-The threshold for this sharding can be controlled with the environment variable `MXNET_KVSTORE_BIGARRAY_BOUND`.
-See [environment variables](#environment-variables) for more details.
-
-### Split training data
-When running distributed training in data parallel mode, we want each machine to be working on different parts of the dataset.
-
-For data parallel training on a single worker,
-we can use `mxnet.gluon.utils.split_and_load` to split a batch of samples provided by the data iterator, and then load each part of the batch on the device which will process it.
-
-In the case of distributed training though, we would need to divide the dataset into `n` parts at the beginning, so that each worker gets a different part. Each worker can then use `split_and_load` to again divide that part of the dataset across different devices on a single machine.
-
-Typically, this split of data for each worker happens through the data iterator,
-on passing the number of parts and the index of parts to iterate over.
-Some iterators in MXNet that support this feature are [mxnet.io.MNISTIterator](https://mxnet.incubator.apache.org/versions/master/api/python/io/io.html#mxnet.io.MNISTIter) and [mxnet.io.ImageRecordIter](https://mxnet.incubator.apache.org/versions/master/api/python/io/io.html#mxnet.io.ImageRecordIter).
-If you are using a different iterator, you can look at how the above iterators implement this.
-We can use the kvstore object to get the number of workers (`kv.num_workers`) and rank of the current worker (`kv.rank`).
-These can be passed as arguments to the iterator.
-You can look at [example/gluon/image_classification.py](https://github.com/apache/incubator-mxnet/blob/master/example/gluon/image_classification.py)
-to see an example usage.
-
-### Updating weights
-KVStore server supports two modes, one which aggregates the gradients and updates the weights using those gradients, and second where the server only aggregates gradients. In the latter case, when a worker process pulls from kvstore, it gets the aggregated gradients. The worker then uses these gradients and applies the weights locally. 
-
-When using Gluon there is an option to choose between these modes by passing `update_on_kvstore` variable when you create the [Trainer](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/gluon.html#mxnet.gluon.Trainer) object like this:
-
-```
-trainer = gluon.Trainer(net.collect_params(), optimizer='sgd',
-                        optimizer_params={'learning_rate': opt.lr,
-                                          'wd': opt.wd,
-                                          'momentum': opt.momentum,
-                                          'multi_precision': True},
-                        kvstore=kv,
-                        update_on_kvstore=True)
-```
-
-When using the symbolic interface, it performs the weight updates on the server without the user having to do anything special.
-
-### Different Modes of Distributed Training
-Distributed training itself is enabled when kvstore creation string contains the word `dist`.
-
-Different modes of distributed training can be enabled by using different types of kvstore.
-
-- `dist_sync`: In synchronous distributed training, all workers use the same synchronized set of model parameters at the start of every batch.
-This means that after each batch, the server waits to receive gradients from each worker before it updates the model parameters.
-This synchronization comes at a cost because the worker pulling parameters would have to wait till the server finishes this process.
-In this mode, if a worker crashes, then it halts the progress of all workers.
-
-- `dist_async`: In asynchronous distributed training, the server receives gradients from one worker and immediately updates its store, which it uses to respond to any future pulls.
-This means that a worker who finishes processing a batch can pull the current parameters from server and start the next batch,
-even if other workers haven't finished processing the earlier batch.
-This is faster than `dist_sync` because there is no cost of synchronization, but can take more epochs to converge.
-The update of weights is atomic, meaning no two updates happen on the same weight at the same time. However, the order  of updates is not guaranteed.
-In `async` mode, it is required to pass an optimizer because in the absence of an optimizer kvstore would replace the stored weights with received weights and this doesn't make sense for training in asynchronous mode. Hence, when using Gluon with `async` mode we need to set `update_on_kvstore` to `True`. 
-
-- `dist_sync_device`: Same as `dist_sync` except that when there are multiple GPUs being used on each node,
-this mode aggregates gradients and updates weights on GPU while dist_sync does so on CPU memory.
-This is faster than `dist_sync` because it reduces expensive communication between GPU and CPU, but it increases memory usage on GPU.
-
-- `dist_async_device` : The analogue of `dist_sync_device` but in asynchronous mode.
-
-
-### Gradient Compression
-When communication is expensive, and the ratio of computation time to communication time is low, communication can become a bottleneck.
-In such cases, gradient compression can be used to reduce the cost of communication, thereby speeding up training.
-Refer [Gradient compression](https://mxnet.incubator.apache.org/versions/master/faq/gradient_compression.html) for more details.
-
-Note: For small models when the cost of computation is much lower than cost of communication,
-distributed training might actually be slower than training on a single machine because of the overhead of communication and synchronization.
-
-## How to Start Distributed Training?
-MXNet provides a script tools/launch.py to make it easy to launch a distributed training job. This supports various types of cluster resource managers like `ssh`, `mpirun`, `yarn` and `sge`.
-If you already have one of these clusters setup, you can skip the next section on setting up a cluster.
-If you want to use a type of cluster not mentioned above, skip ahead to Manually launching jobs section.
-
-### Setting up the Cluster
-An easy way to set up a cluster of EC2 instances for distributed deep learning is by using the [AWS CloudFormation template](https://github.com/awslabs/deeplearning-cfn).
-If you can not use the above, this section will help you manually set up a cluster of instances
-to enable you to use `ssh` for launching a distributed training job.
-Let us denote one machine as the `master` of the cluster through which we will launch and monitor the distributed training on all machines.
-
-If the machines in your cluster are a part of a cloud computing platform like AWS EC2, then your instances should be using key-based authentication already.
-Ensure that you create all instances using the same key, say `mxnet-key` and in the same security group.
-Next, we need to ensure that master has access to all other machines in the cluster through `ssh` by
-adding this key to [ssh-agent](https://en.wikipedia.org/wiki/Ssh-agent) and forwarding it to master when we log in. This will make `mxnet-key` the default key on master.
-
-```
-ssh-add .ssh/mxnet-key
-ssh -A user@MASTER_IP_ADDRESS
-```
-
-
-If your machines use passwords for authentication, see [here](https://help.ubuntu.com/community/SSH/OpenSSH/Keys) for instructions on setting up password-less authentication between machines.
-
-
-It is easier if all these machines have a shared file system so that they can access the training script. One way is to use [Amazon Elastic File System](https://aws.amazon.com/efs) to create your network file system.
-The options in the following command are the recommended options when mounting an AWS Elastic File System.
-
-```
-sudo mkdir efs && sudo mount -t nfs4 -o nfsvers=4.1,rsize=1048576,wsize=1048576,hard,timeo=600,retrans=2 NETWORK_FILE_SYSTEM_IP:/ efs
-```
-
-Tip: You might find it helpful to store large datasets on S3 for easy access from all machines in the cluster. Refer [Using data from S3 for training](https://mxnet.incubator.apache.org/versions/master/faq/s3_integration.html) for more information.
-
-### Using Launch.py
-MXNet provides a script [tools/launch.py](https://github.com/apache/incubator-mxnet/blob/master/tools/launch.py) to make it easy to launch distributed training on a cluster with `ssh`, `mpi`, `sge` or `yarn`.
-You can fetch this script by cloning the mxnet repository.
-
-```
-git clone --recursive https://github.com/apache/incubator-mxnet
-```
-
-#### Example
-Let us consider training a VGG11 model on the CIFAR10 dataset using [example/gluon/image_classification.py](https://github.com/apache/incubator-mxnet/blob/master/example/gluon/image_classification.py).
-```
-cd example/gluon/
-```
-On a single machine, we can run this script as follows:
-```
-python image_classification.py --dataset cifar10 --model vgg11 --epochs 1
-```
-
-For distributed training of this example, we would do the following:
-
-If the mxnet directory which contains the script `image_classification.py` is accessible to all machines in the cluster (for example if they are on a network file system), we can run:
-```
-../../tools/launch.py -n 3 -H hosts --launcher ssh python image_classification.py --dataset cifar10 --model vgg11 --epochs 1 --kvstore dist_sync
-```
-
-If the directory with the script is not accessible from the other machines in the cluster, then we can synchronize the current directory to all machines.
-```
-../../tools/launch.py -n 3 -H hosts --launcher ssh --sync-dst-dir /tmp/mxnet_job/ python image_classification.py --dataset cifar10 --model vgg11 --epochs 1 --kvstore dist_sync
-```
-
-> Tip: If you don't have a cluster ready and still want to try this out, pass the option `--launcher local` instead of `ssh`
-
-#### Options
-Here, launch.py is used to submit the distributed training job. It takes the following options:
-- `-n` denotes the number of worker nodes to be launched.
-- `-s` denotes the number of server nodes to be launched.
-If it is not specified, it is taken to be equal to the number of worker nodes.
-The script tries to cycle through the hosts file to launch the servers and workers.
-For example, if you have 5 hosts in the hosts file and you passed `n` as 3 (and nothing for `s`).
-The script will launch a total of 3 server processes,
-one each for the first three hosts and launch a total of 3 worker processes, one each for the fourth, fifth and first host.
-If the hosts file has exactly `n` number of worker nodes, it will launch a server process and a worker process on each of the `n` hosts.
-- `--launcher` denotes the mode of communication. The options are:
-    - `ssh` if machines can communicate through ssh without passwords. This is the default launcher mode.
-    - `mpi` if Open MPI is available
-    - `sge` for Sun Grid Engine
-    - `yarn` for Apache Yarn
-    - `local` for launching all processes on the same local machine. This can be used for debugging purposes.
-- `-H` requires the path of the hosts file
-  This file contains IPs of the machines in the cluster. These machines should be able to communicate with each other without using passwords.
-  This file is only applicable and required when the launcher mode is `ssh` or `mpi`.
-  An example of the contents of the hosts file would be:
-  ```
-  172.30.0.172
-  172.31.0.173
-  172.30.1.174
-  ```
-- `--sync-dst-dir` takes the path of a directory on all hosts to which the current working directory will be synchronized. This only supports `ssh` launcher mode.
-This is necessary when the working directory is not accessible to all machines in the cluster. Setting this option synchronizes the current directory using rsync before the job is launched.  
-If you have not installed MXNet system-wide
-then you have to copy the folder `python/mxnet` and the file `lib/libmxnet.so` into the current directory before running `launch.py`.
-For example if you are in `example/gluon`, you can do this with `cp -r ../../python/mxnet ../../lib/libmxnet.so .`. This would work if your `lib` folder contains `libmxnet.so`, as would be the case when you use make. If you use CMake, this file would be in your `build` directory.
-
-- `python image_classification.py --dataset cifar10 --model vgg11 --epochs 1 --kvstore dist_sync`
-is the command for the training job on each machine. Note the use of `dist_sync` for the kvstore used in the script.
-
-#### Terminating Jobs
-If the training job crashes due to an error or if we try to terminate the launch script while training is running,
-jobs on all machines might not have terminated. In such a case, we would need to terminate them manually.
-If we are using `ssh` launcher, this can be done by running the following command where `hosts` is the path of the hostfile.
-```
-while read -u 10 host; do ssh -o "StrictHostKeyChecking no" $host "pkill -f python" ; done 10<hosts
-```
-
-### Manually Launching Jobs
-If for some reason, you do not want to use the script above to start distributed training, then this section will be helpful.
-MXNet uses environment variables to assign roles to different processes and to let different processes find the scheduler.
-The environment variables are required to be set correctly as follows for the training to start:
-- `DMLC_ROLE`: Specifies the role of the process. This can be `server`, `worker` or `scheduler`. Note that there should only be one `scheduler`.
-When `DMLC_ROLE` is set to `server` or `scheduler`, these processes start when mxnet is imported.
-- `DMLC_PS_ROOT_URI`: Specifies the IP of the scheduler
-- `DMLC_PS_ROOT_PORT`: Specifies the port that the scheduler listens to
-- `DMLC_NUM_SERVER`: Specifies how many server nodes are in the cluster
-- `DMLC_NUM_WORKER`: Specifies how many worker nodes are in the cluster
-
-Below is an example to start all jobs locally on Linux or Mac. Note that starting all jobs on the same machine is not a good idea.
-This is only to make the usage clear.
-
-```bash
-export COMMAND='python example/gluon/image_classification.py --dataset cifar10 --model vgg11 --epochs 1 --kvstore dist_sync'
-DMLC_ROLE=server DMLC_PS_ROOT_URI=127.0.0.1 DMLC_PS_ROOT_PORT=9092 DMLC_NUM_SERVER=2 DMLC_NUM_WORKER=2 $COMMAND &
-DMLC_ROLE=server DMLC_PS_ROOT_URI=127.0.0.1 DMLC_PS_ROOT_PORT=9092 DMLC_NUM_SERVER=2 DMLC_NUM_WORKER=2 $COMMAND &
-DMLC_ROLE=scheduler DMLC_PS_ROOT_URI=127.0.0.1 DMLC_PS_ROOT_PORT=9092 DMLC_NUM_SERVER=2 DMLC_NUM_WORKER=2 $COMMAND &
-DMLC_ROLE=worker DMLC_PS_ROOT_URI=127.0.0.1 DMLC_PS_ROOT_PORT=9092 DMLC_NUM_SERVER=2 DMLC_NUM_WORKER=2 $COMMAND &
-DMLC_ROLE=worker DMLC_PS_ROOT_URI=127.0.0.1 DMLC_PS_ROOT_PORT=9092 DMLC_NUM_SERVER=2 DMLC_NUM_WORKER=2 $COMMAND
-```
-
-For an in-depth discussion of how the scheduler sets up the cluster, you can go [here](https://blog.kovalevskyi.com/mxnet-distributed-training-explained-in-depth-part-1-b90c84bda725).
-
-## Environment Variables
-### For tuning performance
- - `MXNET_KVSTORE_REDUCTION_NTHREADS`
-  Value type: Integer
-  Default value: 4
-  The number of CPU threads used for summing up big arrays on a single machine
-  This will also be used for `dist_sync` kvstore to sum up arrays from different contexts on a single machine.
-  This does not affect summing up of arrays from different machines on servers.
-  Summing up of arrays for `dist_sync_device` kvstore is also unaffected as that happens on GPUs.
-
-- `MXNET_KVSTORE_BIGARRAY_BOUND`
-  Value type: Integer
-  Default value: 1000000
-  The minimum size of a *big array*.
-  When the array size is bigger than this threshold, `MXNET_KVSTORE_REDUCTION_NTHREADS` threads are used for reduction.
-  This parameter is also used as a load balancer in kvstore.
-  It controls when to partition a single weight to all the servers.
-  If the size of a single weight matrix is less than this bound, then it is sent to a single randomly picked server; otherwise, it is partitioned to all the servers.
-
-- `MXNET_ENABLE_GPU_P2P` GPU Peer-to-Peer communication
-  Value type: 0(false) or 1(true)
-  Default value: 1
-  If true, MXNet tries to use GPU peer-to-peer communication, if available on your device. This is used only when kvstore has the type `device` in it.
-
-### Communication
-- `DMLC_INTERFACE` Using a particular network interface
-  Value type: Name of interface
-  Example: `eth0`
-  MXNet often chooses the first available network interface.
-  But for machines with multiple interfaces, we can specify which network interface to use for data communication using this environment variable.
-
-- `PS_VERBOSE` Logging communication
-  Value type: 1 or 2
-  Default value: (empty)
-    - `PS_VERBOSE=1` logs connection information like the IPs and ports of all nodes
-    - `PS_VERBOSE=2` logs all data communication information
-
-
-When the network is unreliable, messages being sent from one node to another might get lost.
-The training process can hang when a critical message is not successfully delivered.
-In such cases, an additional ACK can be sent for each message to track its delivery.
-This can be done by setting `PS_RESEND` and `PS_RESEND_TIMEOUT`
-- `PS_RESEND` Retransmission for unreliable network
-Value type: 0(false) or 1(true)
-Default value: 0
-Whether or not to enable retransmission of messages
-- `PS_RESEND_TIMEOUT` Timeout for ACK to be received
-Value type: Integer (in milliseconds)
-Default value: 1000
-If ACK is not received in `PS_RESEND_TIMEOUT` milliseconds, then the message will be resent.
diff --git a/docs/faq/env_var.md b/docs/faq/env_var.md
deleted file mode 100644
index ca5292c3a294..000000000000
--- a/docs/faq/env_var.md
+++ /dev/null
@@ -1,352 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-Environment Variables
-=====================
-MXNet has several settings that you can change with environment variables.
-Typically, you wouldn't need to change these settings, but they are listed here for reference.
-
-For example, you can set these environment variables in Linux or macOS as follows:
-```
-export MXNET_GPU_WORKER_NTHREADS=3
-```
-
-Or in powershell:
-```
-$env:MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
-```
-
-## Variables controlling the execution environment
-
-* MXNET_LIBRARY_PATH
-    Absolute path indicating where the mxnet dynamic library is to be located, this would be the absolute
-    path to `libmxnet.so` or `libmxnet.dll` depending on the platform. The logic for loading the
-    library is in `python/mxnet/libinfo.py`
-
-## Set the Number of Threads
-
-* MXNET_OMP_MAX_THREADS
-  - Values: Int ```(default=Number of processors / Number of processors * 2 in X86)```
-  - Maximum number of threads to use in individual operators through OpenMP. If not set, OMP_NUM_THREADS is considered after.
-* MXNET_GPU_WORKER_NTHREADS
-  - Values: Int ```(default=2)```
-  - The maximum number of threads to use on each GPU. This parameter is used to parallelize the computation within a single GPU card.
-* MXNET_GPU_COPY_NTHREADS
-  - Values: Int ```(default=2)```
-  - The maximum number of concurrent threads that do the memory copy job on each GPU.
-* MXNET_CPU_WORKER_NTHREADS
-  - Values: Int ```(default=1)```
-  - The maximum number of scheduling threads on CPU. It specifies how many operators can be run in parallel. Note that most CPU operators are parallelized by OpenMP. To change the number of threads used by individual operators, please set `MXNET_OMP_MAX_THREADS` instead.
-* MXNET_CPU_PRIORITY_NTHREADS
-  - Values: Int ```(default=4)```
-  - The number of threads given to prioritized CPU jobs.
-* MXNET_CPU_NNPACK_NTHREADS
-  - Values: Int ```(default=4)```
-  - The number of threads used for NNPACK. NNPACK package aims to provide high-performance implementations of some layers for multi-core CPUs. Checkout [NNPACK](http://mxnet.io/faq/nnpack.html) to know more about it.
-* MXNET_MP_WORKER_NTHREADS
-  - Values: Int ```(default=1)```
-  - The number of scheduling threads on CPU given to multiprocess workers (after fork). Enlarge this number allows more operators to run in parallel in individual workers but please consider reducing the overall `num_workers` to avoid thread contention (not available on Windows).
-* MXNET_MP_OPENCV_NUM_THREADS
-  - Values: Int ```(default=0)```
-  - The number of OpenCV execution threads given to multiprocess workers. OpenCV multithreading is disabled if `MXNET_MP_OPENCV_NUM_THREADS` < 1 (default). Enlarge this number may boost the performance of individual workers when executing underlying OpenCV functions but please consider reducing the overall `num_workers` to avoid thread contention (not available on Windows).
-* MXNET_GPU_COPY_NTHREADS
-  - Values:: Int ```(default=2)```
-  - Number of threads for copying data from CPU to GPU.
-
-## Memory Options
-
-* MXNET_EXEC_ENABLE_INPLACE
-  - Values: true or false ```(default=true)```
-  - Whether to enable in-place optimization in symbolic execution. Checkout [in-place optimization](http://mxnet.io/architecture/note_memory.html#in-place-operations) to know more about it.
-* NNVM_EXEC_MATCH_RANGE
-  - Values: Int ```(default=16)```
-  - The approximate matching scale in the symbolic execution memory allocator.
-  - Set this to 0 if you don't want to enable memory sharing between graph nodes(for debugging purposes).
-  - This variable has impact on the result of memory planning. So, MXNet sweep between [1, NNVM_EXEC_MATCH_RANGE], and selects the best value.
-* MXNET_EXEC_NUM_TEMP
-  - Values: Int ```(default=1)```
-  - The maximum number of temporary workspaces to allocate to each device. This controls space replicas and in turn reduces the memory usage.
-  - Setting this to a small number can save GPU memory. It will also likely decrease the level of parallelism, which is usually acceptable.
-  - MXNet internally uses graph coloring algorithm to [optimize memory consumption](http://mxnet.io/architecture/note_memory.html).
-  - This parameter is also used to get number of matching colors in graph and in turn how much parallelism one can get in each GPU. Color based match usually costs more memory but also enables more parallelism.
-* MXNET_GPU_MEM_POOL_RESERVE
-  - Values: Int ```(default=5)```
-  - The percentage of GPU memory to reserve for things other than the GPU array, such as kernel launch or cudnn handle space.
-  - If you see a strange out-of-memory error from the kernel launch, after multiple iterations, try setting this to a larger value.
-
-* MXNET_GPU_MEM_POOL_TYPE
-  - Values: String ```(default=Naive)```
-  - The type of memory pool.
-  - Choices:
-    - Naive: A simple memory pool that allocates memory for the exact requested size and cache memory buffers. If a buffered memory chunk matches the size of a new request, the chunk from the memory pool will be returned and reused.
-    - Round: A memory pool that always rounds the requested memory size and allocates memory of the rounded size. MXNET_GPU_MEM_POOL_ROUND_LINEAR_CUTOFF defines how to round up a memory size. Caching and allocating buffered memory works in the same way as the naive memory pool.
-    - Unpooled: No memory pool is used.
-
-* MXNET_GPU_MEM_POOL_ROUND_LINEAR_CUTOFF
-  - Values: Int ```(default=24)```
-  - The cutoff threshold that decides the rounding strategy. Let's denote the threshold as T. If the memory size is smaller than `2 ** T` (by default, it's 2 ** 24 = 16MB), it rounds to the smallest `2 ** n` that is larger than the requested memory size; if the memory size is larger than `2 ** T`, it rounds to the next k * 2 ** T.
-
-* MXNET_GPU_MEM_LARGE_ALLOC_ROUND_SIZE
-  - Values: Int ```(default=2097152)```
-  - When using the naive pool type, memory allocations larger than this threshhold are rounded up to a multiple of this value.
-  - The default was chosen to minimize global memory fragmentation within the GPU driver.  Set this to 1 to disable.
-
-## Engine Type
-
-* MXNET_ENGINE_TYPE
-  - Values: String ```(default=ThreadedEnginePerDevice)```
-  - The type of underlying execution engine of MXNet.
-  - Choices:
-    - NaiveEngine: A very simple engine that uses the master thread to do the computation synchronously. Setting this engine disables multi-threading. You can use this type for debugging in case of any error. Backtrace will give you the series of calls that lead to the error. Remember to set MXNET_ENGINE_TYPE back to empty after debugging.
-    - ThreadedEngine: A threaded engine that uses a global thread pool to schedule jobs.
-    - ThreadedEnginePerDevice: A threaded engine that allocates thread per GPU and executes jobs asynchronously.
-
-## Execution Options
-
-* MXNET_EXEC_BULK_EXEC_INFERENCE
-  - Values: 0(false) or 1(true) ```(default=1)```
-  - If set to `1`, during inference MXNet executes the entire computation graph in bulk mode, which reduces kernel launch gaps in between symbolic operators.
-* MXNET_EXEC_BULK_EXEC_TRAIN
-  - Values: 0(false) or 1(true) ```(default=1)```
-  - If set to `1`, during training MXNet executes the computation graph as several subgraphs in bulk mode.
-* MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN
-  - Values: Int ```(default=15)```
-  - The maximum number of nodes in the subgraph executed in bulk during training (not inference). Setting this to a larger number may reduce the degree of parallelism for multi-GPU training.
-* MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN_FWD
-  - Values: Int ```(default=<value of MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN>)```
-  - The maximum number of nodes in the subgraph executed in bulk during training (not inference) in the forward pass.
-* MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN_BWD
-  - Values: Int ```(default=<value of MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN>)```
-  - The maximum number of nodes in the subgraph executed in bulk during training (not inference) in the backward pass.
-
-## Control the Data Communication
-
-* MXNET_KVSTORE_REDUCTION_NTHREADS
-  - Values: Int ```(default=4)```
-  - The number of CPU threads used for summing up big arrays on a single machine
-  - This will also be used for `dist_sync` kvstore to sum up arrays from different contexts on a single machine.
-  - This does not affect summing up of arrays from different machines on servers.
-  - Summing up of arrays for `dist_sync_device` kvstore is also unaffected as that happens on GPUs.
-
-* MXNET_KVSTORE_BIGARRAY_BOUND
-  - Values: Int ```(default=1000000)```
-  - The minimum size of a "big array".
-  - When the array size is bigger than this threshold, MXNET_KVSTORE_REDUCTION_NTHREADS threads are used for reduction.
-  - This parameter is also used as a load balancer in kvstore. It controls when to partition a single weight to all the servers. If the size of a single weight is less than MXNET_KVSTORE_BIGARRAY_BOUND then, it is sent to a single randomly picked server otherwise it is partitioned to all the servers.
-
-* MXNET_KVSTORE_USETREE
-  - Values: 0(false) or 1(true) ```(default=0)```
-  - If true, MXNet tries to use tree reduction for Push and Pull communication.
-  - Otherwise, MXNet uses the default Push and Pull implementation.
-  - Tree reduction technology has been shown to be faster than the standard ```--kv-store device``` Push/Pull and ```--kv-store nccl``` Push/Pull for small batch sizes.
-
-* MXNET_KVSTORE_LOGTREE
-  - Values: 0(false) or 1(true) ```(default=0)```
-  - If true and MXNET_KVSTORE_USETREE is set to 1, MXNet will log the reduction trees that have been generated.
-
-* MXNET_KVSTORE_TREE_ARRAY_BOUND
-  - Values: Int ```(default=10000000)```
-  - The minimum size of a "big array".
-  - When the array size is bigger than this threshold and MXNET_KVSTORE_USETREE is set to 1, multiple trees are used to load balance the big gradient being communicated in order to better saturate link bandwidth.
-  - Note: This environmental variable only takes effect if Tree KVStore is being used (MXNET_KVSTORE_USETREE=1).
-
-* MXNET_KVSTORE_TREE_BACKTRACK
-  - Values: 0(false) or 1(true) ```(default=0)
-  - If true and MXNET_KVSTORE_USETREE is set to 1, MXNet tries to use backtracking to generate the trees required for tree reduction.
-  - If false and MXNET_KVSTORE_USETREE is set to 1, MXNet tries to use Kernighan-Lin heuristic to generate the trees required for tree reduction.
-
-* MXNET_KVSTORE_TREE_LINK_USAGE_PENALTY
-  - Values: Float ```(default=0.7)```
-  - The multiplicative penalty term to a link being used once.
-
-* MXNET_ENABLE_GPU_P2P
-  - Values: 0(false) or 1(true) ```(default=1)```
-  - If true, MXNet tries to use GPU peer-to-peer communication, if available on your device,
-    when kvstore's type is `device`.
-
-* MXNET_UPDATE_ON_KVSTORE
-  - Values: 0(false) or 1(true) ```(default=1)```
-  - If true, weight updates are performed during the communication step, if possible.
-
-## Memonger
-
-* MXNET_BACKWARD_DO_MIRROR
-  - Values: 0(false) or 1(true) ```(default=0)```
-  - MXNet uses mirroring concept to save memory. Normally backward pass needs some forward input and it is stored in memory but you can choose to release this saved input and recalculate it in backward pass when needed. This basically trades off the computation for memory consumption.
-  - This parameter decides whether to do `mirror` during training for saving device memory.
-  - When set to `1`, during forward propagation, graph executor will `mirror` some layer's feature map and drop others, but it will re-compute this dropped feature maps when needed.
-  - `MXNET_BACKWARD_DO_MIRROR=1` will save 30%~50% of device memory, but retains about 95% of running speed.
-  - One extension of `mirror` in MXNet is called [memonger technology](https://arxiv.org/abs/1604.06174), it will only use O(sqrt(N)) memory at 75% running speed. Checkout the code [here](https://github.com/dmlc/mxnet-memonger).
-
-## Control the profiler
-
-The following environments can be used to profile the application without changing code. Execution options may affect the granularity of profiling result. If you need profiling result of every operator, please set `MXNET_EXEC_BULK_EXEC_INFERENCE`, `MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN` and `MXNET_EXEC_BULK_EXEC_TRAIN` to 0.
-
-* MXNET_PROFILER_AUTOSTART
-  - Values: 0(false) or 1(true) ```(default=0)```
-	- Set to 1, MXNet starts the profiler automatically. The profiling result is stored into profile.json in the working directory.
-
-* MXNET_PROFILER_MODE
-  - Values: 0(false) or 1(true) ```(default=0)```
-	- If set to '0', profiler records the events of the symbolic operators.
-	- If set to '1', profiler records the events of all operators.
-
-## Interface between Python and the C API
-
-* MXNET_ENABLE_CYTHON
-  - Values: 0(false), 1(true) ```(default=1)```
-  - If set to 0, MXNet uses the ctypes to interface with the C API.
-  - If set to 1, MXNet tries to use the cython modules for the ndarray and symbol. If it fails, the ctypes is used or an error occurs depending on MXNET_ENFORCE_CYTHON.
-
-* MXNET_ENFORCE_CYTHON
-  - Values: 0(false) or 1(true) ```(default=0)```
-  - This has an effect only if MXNET_ENABLE_CYTHON is 1.
-  - If set to 0, MXNet fallbacks to the ctypes if importing the cython modules fails.
-  - If set to 1, MXNet raises an error if importing the cython modules fails.
-
-If cython modules are used, `mx.nd._internal.NDArrayBase` must be `mxnet._cy3.ndarray.NDArrayBase` for python 3 or `mxnet._cy2.ndarray.NDArrayBase` for python 2.
-If ctypes is used, it must be `mxnet._ctypes.ndarray.NDArrayBase`.
-
-## Other Environment Variables
-
-* MXNET_GPU_WORKER_NSTREAMS
-  - Values: 1, or 2 ```(default=1)```
-  - Determines the number of GPU streams available to operators for their functions.
-  - Setting this to 2 may yield a modest performance increase, since ops like the cuDNN convolution op can then calculate their data- and weight-gradients in parallel.
-  - Setting this to 2 may also increase a model's demand for GPU global memory.
-
-* MXNET_CUDNN_AUTOTUNE_DEFAULT
-  - Values: 0, 1, or 2 ```(default=1)```
-  - The default value of cudnn auto tuning for convolution layers.
-  - Value of 0 means there is no auto tuning to pick the convolution algo
-  - Performance tests are run to pick the convolution algo when value is 1 or 2
-  - Value of 1 chooses the best algo in a limited workspace
-  - Value of 2 chooses the fastest algo whose memory requirements may be larger than the default workspace threshold
-
-* MXNET_CUDA_ALLOW_TENSOR_CORE
-  - 0(false) or 1(true) ```(default=1)```
-	- If set to '0', disallows Tensor Core use in CUDA ops.
-	- If set to '1', allows Tensor Core use in CUDA ops.
-  - This variable can only be set once in a session.
-
-* MXNET_CUDA_TENSOR_OP_MATH_ALLOW_CONVERSION
-  - 0(false) or 1(true) ```(default=0)```
-	- If set to '0', disallows implicit type conversions to Float16 to use Tensor Cores
-	- If set to '1', allows CUDA ops like RNN and Convolution to use TensorCores even with Float32 input data by using implicit type casting to Float16. Only has an effect if `MXNET_CUDA_ALLOW_TENSOR_CORE` is `1`.
-
-* MXNET_CUDA_LIB_CHECKING
-  - 0(false) or 1(true) ```(default=1)```
-  - If set to '0', disallows various runtime checks of the cuda library version and associated warning messages.
-  - If set to '1', permits these checks (e.g. compile vs. link mismatch, old version no longer CI-tested)
-
-* MXNET_CUDNN_LIB_CHECKING
-  - 0(false) or 1(true) ```(default=1)```
-  - If set to '0', disallows various runtime checks of the cuDNN library version and associated warning messages.
-  - If set to '1', permits these checks (e.g. compile vs. link mismatch, old version no longer CI-tested)
-
-* MXNET_GLUON_REPO
-  - Values: String ```(default='https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/'```
-  - The repository url to be used for Gluon datasets and pre-trained models.
-
-* MXNET_HOME
-  - Data directory in the filesystem for storage, for example when downloading gluon models.
-  - Default in *nix is .mxnet APPDATA/mxnet in windows.
-
-* MXNET_MKLDNN_ENABLED
-  - Values: 0, 1 ```(default=1)```
-  - Flag to enable or disable MKLDNN accelerator. On by default.
-  - Only applies to mxnet that has been compiled with MKLDNN (```pip install mxnet-mkl``` or built from source with ```USE_MKLDNN=1```)
-
-* MXNET_MKLDNN_CACHE_NUM
-  - Values: Int ```(default=-1)```
-  - Flag to set num of elements that MKLDNN cache can hold. Default is -1 which means cache size is unbounded. Should only be set if your model has variable input shapes, as cache size may grow unbounded. The number represents the number of items in the cache and is proportional to the number of layers that use MKLDNN and different input shape.
-
-* MXNET_ENFORCE_DETERMINISM
-  - Values: 0(false) or 1(true) ```(default=0)```
-  - If set to true, MXNet will only use deterministic algorithms in forward and backward computation.
-  If no such algorithm exists given other constraints, MXNet will error out. This variable affects the choice
-  of CUDNN convolution algorithms. Please see [CUDNN developer guide](https://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html) for more details.
-
-* MXNET_CPU_PARALLEL_COPY_SIZE
-  - Values: Int ```(default=200000)```
-  - The minimum size to call parallel copy by OpenMP in CPU2CPU mode.
-  - When the array size is bigger than or equal to  this threshold, NDArray::Copy(from, to) is implemented by OpenMP with the Recommended OMP Thread Count.
-  - When the array size is less than this threshold, NDArray::Copy(from , to)) is implemented by memcpy in single thread.
-
-* MXNET_OPTIMIZER_AGGREGATION_SIZE
-  - Values: Int ```(default=4)```
-  - Maximum value is 60.
-  - This variable controls how many weights will be updated in a single call to optimizer (for optimizers that support aggregation, currently limited to SGD).
-
-* MXNET_CPU_TEMP_COPY
-  - Values: Int ```(default=4)```
-  - This variable controls how many temporary memory resources to create for all CPU context for use in operator.
-
-* MXNET_GPU_TEMP_COPY
-  - Values: Int ```(default=1)```
-  - This variable controls how many temporary memory resources to create for each GPU context for use in operator.
-
-* MXNET_CPU_PARALLEL_RAND_COPY
-  - Values: Int ```(default=1)```
-  - This variable controls how many parallel random number generator resources to create for all CPU context for use in operator.
-
-* MXNET_GPU_PARALLEL_RAND_COPY
-  - Values: Int ```(default=4)```
-  - This variable controls how many parallel random number generator resources to create for each GPU context for use in operator.
-
-* MXNET_GPU_CUDNN_DROPOUT_STATE_COPY
-  - Values: Int ```(default=4)```
-  - This variable controls how many CuDNN dropout state resources to create for each GPU context for use in operator.
-
-* MXNET_SUBGRAPH_BACKEND
-  - Values: String ```(default="MKLDNN")``` if MKLDNN is avaliable, otherwise ```(default="")```
-  - This variable controls the subgraph partitioning in MXNet.
-  - This variable is used to perform MKL-DNN FP32 operator fusion and quantization. Please refer to the [MKL-DNN operator list](../tutorials/mkldnn/operator_list.md) for how this variable is used and the list of fusion passes.
-  - Set ```MXNET_SUBGRAPH_BACKEND=NONE``` to disable subgraph backend.
-
-* MXNET_SAFE_ACCUMULATION
-  - Values: Values: 0(false) or 1(true) ```(default=0)```
-  - If this variable is set, the accumulation will enter the safe mode, meaning accumulation is done in a data type of higher precision than
-    the input data type, leading to more accurate accumulation results with a possible performance loss and backward compatibility loss.
-    For example, when the variable is set to 1(true), if the input data type is float16, then the accumulation will be done
-    with float32.
-  - Model accuracies do not necessarily improve with this environment variable turned on.
-
-Settings for Minimum Memory Usage
----------------------------------
-- Make sure ```min(MXNET_EXEC_NUM_TEMP, MXNET_GPU_WORKER_NTHREADS) = 1```
-  - The default setting satisfies this.
-
-Settings for More GPU Parallelism
----------------------------------
-- Set ```MXNET_GPU_WORKER_NTHREADS``` to a larger number (e.g., 2)
-  - To reduce memory usage, consider setting ```MXNET_EXEC_NUM_TEMP```.
-  - This might not speed things up, especially for image applications, because GPU is usually fully utilized even with serialized jobs.
-
-Settings for controlling OMP tuning
----------------------------------
-- Set ```MXNET_USE_OPERATOR_TUNING=0``` to disable Operator tuning code which decides whether to use OMP or not for operator
-   - Values: String representation of MXNET_ENABLE_OPERATOR_TUNING environment variable
-   -            0=disable all
-   -            1=enable all
-   -            float32, float16, float32=list of types to enable, and disable those not listed
-   - refer : https://github.com/apache/incubator-mxnet/blob/master/src/operator/operator_tune-inl.h#L444
-
-- Set ```MXNET_USE_NUM_CORES_OPERATOR_TUNING``` to define num_cores to be used by operator tuning code.
-  - This reduces operator tuning overhead when there are multiple instances of mxnet running in the system and we know that
-    each mxnet will take only partial num_cores available with system.
-  - refer: https://github.com/apache/incubator-mxnet/pull/13602
diff --git a/docs/faq/faq.md b/docs/faq/faq.md
deleted file mode 100644
index b7866be5c03a..000000000000
--- a/docs/faq/faq.md
+++ /dev/null
@@ -1,86 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Frequently Asked Questions
-
-This topic provides answers to the frequently asked questions on [mxnet/issues](https://github.com/dmlc/mxnet/issues). Before posting an issue, please check this page. If you would like to contribute to this page, please make the questions and answers simple. If your answer is  extremely detailed, please post it elsewhere and link to it.
-
-## Building and Installation
-
-You can find answers to most questions the [build page](http://mxnet.io/get_started/setup.html).
-
-## Speed
-
-#### It took a long time to start running on a GPU
-
-Try to disable opencv to use a GPU: [build opencv from source with GPU module disabled](http://mxnet.io/get_started/setup.html#build-opencv-from-source-code).
-
-#### It's slow on a single GPU
-
-Check the following:
-
-1. Ensure that your CUDA/driver version is not too old.
-2. Build with `USE_CUDNN=1`. This often increases speed 50+%. Try to use the newest version.
-3. Set `export MXNET_CUDNN_AUTOTUNE_DEFAULT=1` before running. This often increases speed 10%-15%.
-4. If you are using Tesla GPUs by `nvidia-smi -e 0`, disable ECC. You might need root permission and have to reboot.
-5. For Tesla cards by `nvidia-smi -ac ??`, set to the maximal clock. For details, see [this blog](https://devblogs.nvidia.com/parallelforall/increase-performance-gpu-boost-k80-autoboost/).
-6. No throttle reason `nvidia-smi -q -d PERFORMANCE` is often caused by temperature.
-
-#### No increase in speed when using more than one GPU or computer
-
-Check the following:
-
-1. Does your neural network already run fast, such as >1000 example/sec or >10 batches/sec? If yes, it's unlikely to speed up any further by adding more resources because of the communication overhead.
-2. Are you using a small batch size? Try to increase it.
-3. Are you using more than 4 GPUs? Try using `--kv-store=device`.
-
-## Memory Usage
-
-#### Abnormal CPU memory usage
-
-This might be due to the data pre-fetch. See [issue 2111](https://github.com/dmlc/mxnet/issues/2111).
-
-## Pending Review
-The following topics need to be reviewed.
-
-#### How to Copy Part of Parameters to Another Model
-Most MXNet model consists two parts, the argument arrays and symbol. You can simply copy the argument array to the argument array of another model. For example, in the Python model API, you can do this:
-
-```python
-copied_model =  mx.model.FeedForward(ctx=mx.gpu(), symbol=new_symbol,
-                                     arg_params=old_arg_params, aux_params=old_aux_params,
-                                     allow_extra_params=True);
-```
-For information about copying model parameters from an existing ```old_arg_params```, see this [notebook](https://github.com/dmlc/mxnet-notebooks/blob/master/python/faq/predict.ipynb). More notebooks please refer to [dmlc/mxnet-notebooks](https://github.com/dmlc/mxnet-notebooks).
-
-#### How to Extract the Feature Map of a Certain Layer
-See this [notebook](https://github.com/dmlc/mxnet-notebooks/blob/master/python/faq/predict.ipynb). More notebooks please refer to [dmlc/mxnet-notebooks](https://github.com/dmlc/mxnet-notebooks).
-
-
-#### What Is the Relationship Between MXNet and CXXNet, Minerva, and Purine2?
-MXNet is created in collaboration by authors from the three projects.
-MXNet reflects what we have learned from these projects.
-It combines the important aspects of the existing projects: general efficiency, flexibility, and memory efficiency.
-
-MXNet also contains new approaches that allow you to combine different
-ways of programming and write CPU/GPU applications that are more
-memory efficient than CXXNet and Purine, and more flexible than Minerva.
-
-
-#### What Is the Relationship Between MXNet and TensorFlow?
-Both MXNet and [TensorFlow](https://www.tensorflow.org/) use computation graph abstraction, which was initially used by Theano, then adopted by other packages, such as CGT, Caffe2, and Purine. Currently, TensorFlow adopts an optimized symbolic API. MXNet supports a [mixed approach](https://mxnet.io/architecture/program_model.html), with a dynamic dependency scheduler to combine symbolic and imperative programming.
-In short, MXNet is lightweight and *mixed* with flexibility from imperative programming, while using a computation graph to make it very fast and memory efficient.
diff --git a/docs/faq/finetune.md b/docs/faq/finetune.md
deleted file mode 100644
index 4a03eefa05c9..000000000000
--- a/docs/faq/finetune.md
+++ /dev/null
@@ -1,228 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-
-# Fine-tune with Pretrained Models
-
-Many of the exciting deep learning algorithms for computer vision require
-massive datasets for training. The most popular benchmark dataset,
-[ImageNet](http://www.image-net.org/), for example, contains one million images
-from one thousand categories. But for any practical problem, we typically have
-access to comparatively small datasets. In these cases, if we were to train a
-neural network's weights from scratch, starting from random initialized
-parameters, we would overfit the training set badly.
-
-One approach to get around this problem is to first pretrain a deep net on a
-large-scale dataset, like ImageNet. Then, given a new dataset, we can start
-with these pretrained weights when training on our new task. This process is
-commonly called _fine-tuning_. There are a number of variations of fine-tuning.
-Sometimes, the initial neural network is used only as a _feature extractor_.
-That means that we freeze every layer prior to the output layer and simply learn
-a new output layer. In [another document](https://github.com/dmlc/mxnet-notebooks/blob/master/python/how_to/predict.ipynb), we explained how to
-do this kind of feature extraction. Another approach is to update all of
-the network's weights for the new task, and that's the approach we demonstrate in
-this document.
-
-To fine-tune a network, we must first replace the last fully-connected layer
-with a new one that outputs the desired number of classes. We initialize its
-weights randomly. Then we continue training as normal. Sometimes it's common to
-use a smaller learning rate based on the intuition that we may already be close
-to a good result.
-
-In this demonstration, we'll fine-tune a model pretrained on ImageNet to the
-smaller caltech-256 dataset. Following this example, you can fine-tune to other
-datasets, even for strikingly different applications such as face
-identification.
-
-We will show that, even with simple hyper-parameters setting, we can match and
-even outperform state-of-the-art results on caltech-256.
-
-| Network | Accuracy |
-| --- | --- |
-| Resnet-50 | 77.4% |
-| Resnet-152 | 86.4% |
-
-## Prepare data
-
-We follow the standard protocol to sample 60 images from each class as the
-training set, and the rest for the validation set. We resize images into 256x256
-size and pack them into the rec file. The scripts to prepare the data is as
-following.
-
-> In order to successfully run the following bash script on Windows please use https://cygwin.com/install.html .
-
-```sh
-wget http://www.vision.caltech.edu/Image_Datasets/Caltech256/256_ObjectCategories.tar
-tar -xf 256_ObjectCategories.tar
-
-mkdir -p caltech_256_train_60
-for i in 256_ObjectCategories/*; do
-    c=`basename $i`
-    mkdir -p caltech_256_train_60/$c
-    for j in `ls $i/*.jpg | shuf | head -n 60`; do
-        mv $j caltech_256_train_60/$c/
-    done
-done
-
-python ~/mxnet/tools/im2rec.py --list --recursive caltech-256-60-train caltech_256_train_60/
-python ~/mxnet/tools/im2rec.py --list --recursive caltech-256-60-val 256_ObjectCategories/
-python ~/mxnet/tools/im2rec.py --resize 256 --quality 90 --num-thread 16 caltech-256-60-val 256_ObjectCategories/
-python ~/mxnet/tools/im2rec.py --resize 256 --quality 90 --num-thread 16 caltech-256-60-train caltech_256_train_60/
-```
-
-The following code downloads the pregenerated rec files. It may take a few minutes.
-
-
-```python
-import os, sys
-
-if sys.version_info[0] >= 3:
-    from urllib.request import urlretrieve
-else:
-    from urllib import urlretrieve
-
-def download(url):
-    filename = url.split("/")[-1]
-    if not os.path.exists(filename):
-        urlretrieve(url, filename)
-download('http://data.mxnet.io/data/caltech-256/caltech-256-60-train.rec')
-download('http://data.mxnet.io/data/caltech-256/caltech-256-60-val.rec')
-```
-
-Next, we define the function which returns the data iterators:
-
-```python
-import mxnet as mx
-
-def get_iterators(batch_size, data_shape=(3, 224, 224)):
-    train = mx.io.ImageRecordIter(
-        path_imgrec         = './caltech-256-60-train.rec',
-        data_name           = 'data',
-        label_name          = 'softmax_label',
-        batch_size          = batch_size,
-        data_shape          = data_shape,
-        shuffle             = True,
-        rand_crop           = True,
-        rand_mirror         = True)
-    val = mx.io.ImageRecordIter(
-        path_imgrec         = './caltech-256-60-val.rec',
-        data_name           = 'data',
-        label_name          = 'softmax_label',
-        batch_size          = batch_size,
-        data_shape          = data_shape,
-        rand_crop           = False,
-        rand_mirror         = False)
-    return (train, val)
-```
-
-We then download a pretrained 50-layer ResNet model and load it into memory. Note
-that if `load_checkpoint` reports an error, we can remove the downloaded files
-and try `get_model` again.
-
-```python
-def get_model(prefix, epoch):
-    download(prefix+'-symbol.json')
-    download(prefix+'-%04d.params' % (epoch,))
-
-get_model('http://data.mxnet.io/models/imagenet/resnet/50-layers/resnet-50', 0)
-sym, arg_params, aux_params = mx.model.load_checkpoint('resnet-50', 0)
-```
-
-## Train
-
-We first define a function which replaces the last fully-connected layer for a given network.
-
-```python
-def get_fine_tune_model(symbol, arg_params, num_classes, layer_name='flatten0'):
-    """
-    symbol: the pretrained network symbol
-    arg_params: the argument parameters of the pretrained model
-    num_classes: the number of classes for the fine-tune datasets
-    layer_name: the layer name before the last fully-connected layer
-    """
-    all_layers = symbol.get_internals()
-    net = all_layers[layer_name+'_output']
-    net = mx.symbol.FullyConnected(data=net, num_hidden=num_classes, name='fc1')
-    net = mx.symbol.SoftmaxOutput(data=net, name='softmax')
-    new_args = dict({k:arg_params[k] for k in arg_params if 'fc1' not in k})
-    return (net, new_args)
-```
-
-Now we create a module. Note we pass the existing parameters from the loaded model via the `arg_params` argument.
-The parameters of the last fully-connected layer will be randomly initialized by the `initializer`.
-
-```python
-import logging
-head = '%(asctime)-15s %(message)s'
-logging.basicConfig(level=logging.DEBUG, format=head)
-
-def fit(symbol, arg_params, aux_params, train, val, batch_size, num_gpus):
-    devs = [mx.gpu(i) for i in range(num_gpus)]
-    mod = mx.mod.Module(symbol=symbol, context=devs)
-    mod.fit(train, val,
-        num_epoch=8,
-        arg_params=arg_params,
-        aux_params=aux_params,
-        allow_missing=True,
-        batch_end_callback = mx.callback.Speedometer(batch_size, 10),
-        kvstore='device',
-        optimizer='sgd',
-        optimizer_params={'learning_rate':0.01},
-        initializer=mx.init.Xavier(rnd_type='gaussian', factor_type="in", magnitude=2),
-        eval_metric='acc')
-    metric = mx.metric.Accuracy()
-    return mod.score(val, metric)
-```
-
-Then we can start training. We use AWS EC2 g2.8xlarge, which has 8 GPUs.
-
-
-```python
-num_classes = 256
-batch_per_gpu = 16
-num_gpus = 8
-
-(new_sym, new_args) = get_fine_tune_model(sym, arg_params, num_classes)
-
-batch_size = batch_per_gpu * num_gpus
-(train, val) = get_iterators(batch_size)
-mod_score = fit(new_sym, new_args, aux_params, train, val, batch_size, num_gpus)
-assert mod_score > 0.77, "Low training accuracy."
-```
-
-You will see that, after only 8 epochs, we can get 78% validation accuracy. This
-matches the state-of-the-art results training on caltech-256 alone,
-e.g. [VGG](http://www.robots.ox.ac.uk/~vgg/research/deep_eval/).
-
-Next, we try to use another pretrained model. This model was trained on the
-complete Imagenet dataset, which is 10x larger than the Imagenet 1K classes
-version, and uses a 3x deeper Resnet architecture.
-
-
-```python
-get_model('http://data.mxnet.io/models/imagenet-11k/resnet-152/resnet-152', 0)
-sym, arg_params, aux_params = mx.model.load_checkpoint('resnet-152', 0)
-(new_sym, new_args) = get_fine_tune_model(sym, arg_params, num_classes)
-mod_score = fit(new_sym, new_args, aux_params, train, val, batch_size, num_gpus)
-assert mod_score > 0.86, "Low training accuracy."
-```
-
-
-As can be seen, even for a single data epoch, it reaches 83% validation
-accuracy. After 8 epoches, the validation accuracy increases to 86.4%.
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/faq/float16.md b/docs/faq/float16.md
deleted file mode 100644
index 22d4ef48c2f6..000000000000
--- a/docs/faq/float16.md
+++ /dev/null
@@ -1,248 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Mixed precision training using float16
-
-In this tutorial we will walk through how one can train deep learning neural networks with mixed precision on supported hardware. We will first see how to use float16 (both with Gluon and Symbolic APIs) and then some techniques on achieving good performance and accuracy.
-
-## Background
-
-The computational resources required for training deep neural networks have been lately increasing because of growing complexity and model size. Mixed precision training allows us to reduce the utilization of the resources by using lower precision arithmetic which is computationally less expensive and less costly in terms of space utilization. In this approach you can train using 16 bit floating point (half precision) while using 32 bit floating point (single precision) for output buffers of float16 computation. This allows one to achieve the same accuracy as training with single precision, while decreasing the required memory and training or inference time.
-
-The float16 data type is a 16 bit floating point representation according to the [IEEE 754 standard](https://ieeexplore.ieee.org/document/4610935). It has a dynamic range where the precision can go from 0.0000000596046 (highest, for values closest to 0) to 32 (lowest, for values in the range 32768-65536). Despite the inherent reduced precision when compared to single precision float (float32), using float16 has many advantages. The most obvious advantages are that you can reduce the size of the model by half allowing the training of larger models and using larger batch sizes. The reduced memory footprint also helps in reducing the pressure on memory bandwidth and lowering communication costs. On hardware with specialized support for float16 computation you can also greatly improve the speed of training and inference. The Volta range of Graphics Processing Units (GPUs) from Nvidia have [Tensor Cores](https://www.nvidia.com/en-us/data-center/tensorcore/) which perform efficient float16 computation. A tensor core allows accumulation of half precision products into single or half precision outputs. For the rest of this tutorial we assume that we are working with Nvidia's Tensor Cores on a Volta GPU.
-
-## Prerequisites
-
-- [Volta](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/) range of Nvidia GPUs (e.g. AWS P3 instance)
-- CUDA 9 or higher
-- cuDNN v7 or higher
-
-This tutorial also assumes understanding of how to train a network with float32 (the default). Please refer to [logistic regression tutorial](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/logistic_regression_explained.html) to get started with Apache MXNet and Gluon API. This tutorial focuses on the changes needed to switch from float32 to mixed precision and tips on achieving the best performance with mixed precision.
-
-## Using the Gluon API
-
-### Training or Inference
-
-With Gluon API, you need to take care of three things to convert a model to support computation with float16.
-
-1. Cast Gluon `Block`'s parameters and expected input type to float16 by calling the [cast](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.Block.cast) method of the `Block` representing the network.
-
-```python
-net.cast('float16')
-```
-
-2. Ensure the data input to the network is of float16 type. If your `DataLoader` or `Iterator` produces output in another datatype, then you would have to cast your data. There are different ways you can do this. The easiest would be to use the [astype](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.astype) method of NDArrays.
-
-```python
-data = data.astype('float16', copy=False)
-```
-
-If you are using images and DataLoader, you can also use a [Cast transform](https://mxnet.incubator.apache.org/api/python/gluon/data.html#mxnet.gluon.data.vision.transforms.Cast).
-
-3. It is preferable to use **multi_precision mode of optimizer** when training in float16. This mode of optimizer maintains a master copy of the weights in float32 even when the training (i.e. forward and backward pass) is in float16. This helps increase precision of the weight updates and can lead to faster convergence in some scenarios.
-
-```python
-optimizer = mx.optimizer.create('sgd', multi_precision=True, lr=0.01)
-```
-
-You can play around with mixed precision using the image classification [example](https://github.com/apache/incubator-mxnet/blob/master/example/gluon/image_classification.py). We suggest using the Caltech101 dataset option in that example and using a ResNet50V1 network so you can quickly see the performance improvement and how the accuracy is unaffected. Here's the starter command to run this example.
-
-```bash
-python image_classification.py --model resnet50_v1 --dataset caltech101 --gpus 0 --num-worker 30 --dtype float16
-```
-
-### Fine-tuning
-
-You can also fine-tune a model, which was originally trained in float32, to use float16. Below is an example of how to fine-tune a pretrained model from the Model Zoo. You would first need to fetch the pretrained network and then cast that network to float16.
-
-```python
-import numpy as np
-import mxnet as mx
-from mxnet.gluon.model_zoo.vision import get_model
-
-
-pretrained_net = get_model(name='resnet50_v2', ctx=mx.cpu(),
-                           pretrained=True, classes=1000)
-pretrained_net.cast('float16')
-```
-
-Then, if you have another Resnet50V2 model you want to fine-tune, you can just assign the features to that network and then cast it.
-
-```python
-net = get_model(name='resnet50_v2', ctx=mx.cpu(),
-                pretrained=False, classes=101)
-net.collect_params().initialize(mx.init.Xavier(magnitude=2.24), ctx=mx.cpu())
-net.features = pretrained_net.features
-net.cast('float16')
-```
-
-You can check the parameters of the model by calling [summary](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.Block.summary) with some fake data. Notice the provided `dtype=np.float16` in the line below. As it was mentioned earlier, we have to provide data as float16 as well.
-
-```python
-net.summary(mx.nd.uniform(shape=(1, 3, 224, 224), dtype=np.float16))
-```
-
-## Using the Symbolic API
-
-Training a network in float16 with the Symbolic API involves the following steps.
-
-1. Add a layer at the beginning of the network, to cast the data to float16. This will ensure that all the following layers compute in float16.
-2. It is advisable to cast the output of the layers before softmax to float32, so that the softmax computation is done in float32. This is because softmax involves large reductions and it helps to keep that in float32 for more precise answer.
-3. It is advisable to use the multi-precision mode of the optimizer for more precise weight updates. Here's how you would enable this mode when creating an optimizer.
-
-```python
-optimizer = mx.optimizer.create('sgd', multi_precision=True, lr=0.01)
-```
-
-For a full example, please refer to [resnet.py](https://github.com/apache/incubator-mxnet/blob/master/example/image-classification/symbols/resnet.py) file on GitHub. A small, relevant excerpt from that file is presented below.
-
-```python
-data = mx.sym.Variable(name="data")
-
-if dtype == 'float16':
-    data = mx.sym.Cast(data=data, dtype=np.float16)
-
-# ... the rest of the network
-net_out = net(data)
-
-if dtype == 'float16':
-    net_out = mx.sym.Cast(data=net_out, dtype=np.float32)
-
-output = mx.sym.SoftmaxOutput(data=net_out, name='softmax')
-```
-
-If you would like to train ResNet50 model on ImageNet using float16 precision, you can find the full script [here](https://github.com/apache/incubator-mxnet/tree/master/example/image-classificatiIfon/train_imagenet.py)
-
-If you don't have ImageNet dataset at your disposal, you can still run the script above using synthetic float16 data by providing the following command:
-
-```bash
-python train_imagenet.py --network resnet-v1 --num-layers 50 --benchmark 1 --gpus 0 --batch-size 256 --dtype float16
-```
-
-There's a similar example for float16 fine tuning [here](https://github.com/apache/incubator-mxnet/tree/master/example/image-classification/fine-tune.py) of selected models: Inception v3, Inception v4, ResNetV1, ResNet50, ResNext or VGG. The command below shows how to use that script to fine-tune a Resnet50 model trained on Imagenet for the Caltech 256 dataset using float16.
-
-```bash
-python fine-tune.py --network resnet --num-layers 50 --pretrained-model imagenet1k-resnet-50 --data-train ~/.mxnet/dataset/caltech-256/caltech256-train.rec --data-val ~/data/caltech-256/caltech256-val.rec --num-examples 15420 --num-classes 256 --gpus 0 --batch-size 64 --dtype float16
-```
-
-If you don't have the `Caltech256` dataset, you can download it using the script below, and convert it into .rec file format using [im2rec utility file](https://github.com/apache/incubator-mxnet/blob/master/tools/im2rec.py)
-
-```python
-import os
-from os.path import expanduser
-import tarfile
-import mxnet as mx
-
-
-data_folder = expanduser("~/.mxnet/datasets/")
-dataset_name = "256_ObjectCategories"
-archive_file = "{}.tar".format(dataset_name)
-archive_path = os.path.join(data_folder, archive_file)
-data_url = "http://www.vision.caltech.edu/Image_Datasets/Caltech256/"
-
-if not os.path.isfile(archive_path):
-    mx.test_utils.download("{}{}".format(data_url, archive_file),
-                           dirname=data_folder)
-    print('Extracting {} in {}...'.format(archive_file, data_folder))
-    tar = tarfile.open(archive_path)
-    tar.extractall(data_folder)
-    tar.close()
-    print('Data extracted.')
-```
-
-## Example training results
-
-Let us consider training a Resnet50V1 model on the ImageNet 2012 dataset. For this model, the GPU memory usage is close to the capacity of V100 GPU with a batch size of 128 when using float32. Using float16 allows the use of 256 batch size. Shared below are results using 8 V100 GPUs on a an [AWS p3.16xlarge](https://aws.amazon.com/ec2/instance-types/p3/#Amazon_EC2_P3_Instance_Product_Details) instance.
-
-Let us compare the three scenarios that arise here: float32 with 1024 batch size, float16 with 1024 batch size and float16 with 2048 batch size. These jobs trained for 90 epochs using a learning rate of 0.4 for 1024 batch size and 0.8 for 2048 batch size. This learning rate was decayed by a factor of 0.1 at the 30th, 60th and 80th epochs. The only changes made for the float16 jobs when compared to the float32 job were that the network and data were cast to float16, and the multi-precision mode was used for optimizer. The final accuracy at 90th epoch and the time to train are tabulated below for these three scenarios. The top-1 validation errors at the end of each epoch are also plotted below.
-
-Batch size | Data type | Top 1 Validation accuracy | Time to train | Speedup |
---- | --- | --- | --- | --- |
-1024 | float32 | 76.18% | 11.8 hrs | 1 |
-1024 | float16 | 76.34% | 7.3 hrs | 1.62x |
-2048 | float16 | 76.29% | 6.5 hrs | 1.82x |
-
-![Training curves of Resnet50V1 on Imagenet 2012](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tutorials/mixed-precision/resnet50v1b_imagenet_fp16_fp32_training.png)
-
-The difference in accuracies above are within normal random variation, and there is no reason to expect float16 to have better accuracy than float32 in general. As the plot indicates, training behaves similarly for these cases, even though we didn't have to change any other hyperparameters. We can also see from the table that using float16 helps train faster through faster computation with float16 as well as allowing the use of larger batch sizes.
-
-## Things to keep in mind
-
-### For performance
-
-Typical performance gains seen for float16 typically range 1.6x-2x for convolutional networks like Resnet and even about 3x for networks with LSTMs. The performance gain you see can depend on certain things which this section will introduce.
-
-1. Nvidia Tensor Cores essentially perform the computation `D = A * B + C`, where A and B are half precision matrices, while C and D could be either half precision or full precision. The tensor cores are most efficient when dimensions of these matrices are multiples of 8. This means that Tensor Cores can not be used in all cases for fast float16 computation. When training models like Resnet50 on the Cifar10 dataset, the tensors involved are sometimes smaller, and Tensor Cores can not always be used. The computation in that case falls back to slower algorithms and using float16 turns out to be slower than float32 on a single GPU. Note that when using multiple GPUs, using float16 can still be faster than float32 because of reduction in communication costs.
-
-2. When you scale up the batch size ensure that IO and data pre-processing is not your bottleneck. If you see a slowdown this would be the first thing to check.
-
-3. It is advisable to use batch sizes that are multiples of 8 because of the above reason when training with float16. As always, batch sizes which are powers of 2 would be best when compared to those around it.
-
-4. You can check whether your program is using Tensor cores for fast float16 computation by profiling with `nvprof`. The operations with `s884cudnn` in their names represent the use of Tensor cores.
-
-5. When not limited by GPU memory, it can help to set the environment variable `MXNET_CUDNN_AUTOTUNE_DEFAULT` to `2`. This configures MXNet to run tuning tests and choose the fastest convolution algorithm whose memory requirements may exceed the default memory of CUDA workspace.
-
-6. Please note that float16 on CPU might not be supported for all operators, as in most cases float16 on CPU is much slower than float32.
-
-### For accuracy
-
-#### Multi precision mode
-
-When training in float16, it is advisable to still store the master copy of the weights in float32 for better accuracy. The higher precision of float32 helps overcome cases where gradient update can become 0 if represented in float16. This mode can be activated by setting the parameter `multi_precision` of optimizer params to `True` as in the above example. It has been found that this is not required for all networks to achieve the same accuracy as with float32, but nevertheless recommended. Note that for distributed training, this is currently slightly slower than without `multi_precision`, but still much faster than using float32 for training.
-
-#### Large reductions
-
-Since float16 has low precision for large numbers, it is best to leave layers which perform large reductions in float32. This includes BatchNorm and Softmax. Ensuring that Batchnorm performs reduction in float32 is handled by default in both Gluon and Module APIs. While Softmax is set to use float32 even during float16 training in Gluon, in the Module API it needs to be a cast to float32 before softmax as the above symbolic example code shows.
-
-#### Loss scaling
-
-For some networks just switching the training to float16 mode was not found to be enough to reach the same accuracy as when training with float32. This is because the activation gradients computed are too small and could not be represented in float16 representable range. Such networks can be made to achieve the accuracy reached by float32 with a couple of changes.
-
-Most of the float16 representable range is not used by activation gradients generally. So you can shift the gradients into float16 range by scaling up the loss by a factor `S`. By the chain rule, this scales up the loss before backward pass, and then you can scale back the gradients before updating the weights. This ensures that training in float16 can use the same hyperparameters as used during float32 training.
-
-Here's how you can configure the loss to be scaled up by 128 and rescale the gradient down before updating the weights.
-
-*Gluon API*
-
-```python
-loss = gluon.loss.SoftmaxCrossEntropyLoss(weight=128)
-optimizer = mx.optimizer.create('sgd',
-                                multi_precision=True,
-                                rescale_grad=1.0/128)
-```
-
-*Module API*
-
-```python
-mxnet.sym.SoftmaxOutput(other_args, grad_scale=128.0)
-optimizer = mx.optimizer.create('sgd',
-                                multi_precision=True,
-                                rescale_grad=1.0/128)
-```
-
-Networks like Multibox SSD, R-CNN, bigLSTM and Seq2seq were found to exhibit such behavior.
-You can choose a constant scaling factor while ensuring that the absolute value of gradient when multiplied by this factor remains in the range of float16. Generally powers of 2 like 64, 128, 256, 512 are chosen. Refer to the linked articles below for more details on this.
-
-## References
-
-1. [Training with Mixed Precision User Guide](http://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html)
-2. [Mixed Precision Training at ICLR 2018](https://arxiv.org/pdf/1710.03740.pdf)
-3. [Mixed-Precision Training of Deep Neural Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/)
-
-## Recommended Next Steps
-
-* Check out our video tutorial on [Using Mixed Precision with MXNet](https://www.youtube.com/watch?v=pR4KMh1lGC0)
\ No newline at end of file
diff --git a/docs/faq/gradient_compression.md b/docs/faq/gradient_compression.md
deleted file mode 100644
index 9e926c52feca..000000000000
--- a/docs/faq/gradient_compression.md
+++ /dev/null
@@ -1,124 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Gradient Compression
-
-Gradient Compression reduces communication bandwidth, and in some scenarios, it can make training more scalable and efficient without significant loss in convergence rate or accuracy. Example implementations with GPUs, CPUs, and distributed training are provided in this document. 
-
-
-## Benefits
-
-**Increased Speed**
-
-For architectures with fully connected layers, the gradient compression capability is observed to speedup training by about 2x, depending on the size of the model and the network bandwidth of the instance. Bigger models see larger speedup with gradient compression.
-
-**Minimal Accuracy Loss**
-
-Gradient compression uses the approach of delaying the synchronization of weight updates which are small. Although small weight updates might not be sent for that batch, this information is not discarded. Once the weight updates for this location accumulate to become a larger value, they will be propagated. Since there is no information loss, but only delayed updates, it does not lead to a significant loss in accuracy or convergence rate. In distributed training experiments[1], the accuracy loss observed due to gradient compression was as low as 1%
-
-
-## When to Use Gradient Compression
-
-When training models whose architectures include large fully connected components, it can be helpful to use gradient compression. For larger models, as well as recurrent neural networks, the communication cost becomes a major factor. Such models stand to benefit greatly with gradient compression.
-
-
-### GPU versus CPU
-
-The greatest benefits from gradient compression are realized when using multi-node (single or multi-GPU) distributed training. Training on CPU would provide a lower compute density per compute node as compared to the massive compute density per compute node on a GPU. Due to this, the required communication bandwidth for CPU-based nodes during training is not as high as for GPU-based nodes. Hence, the benefits of gradient compression are lower for CPU-based nodes as compared to GPU-based nodes.
-
-
-### Network Latency
-
-Benefits of gradient compression can be found when using distributed training with network connected nodes. Depending on the network latency between nodes and the model's size, these can contribute to slow performance such that gradient compression may provide speed improvements.
-
-You may not want to use gradient compression if you have low latency network communication.
-
-
-### Model Size
-
-Distributed training involves synchronization of weights after each batch. Larger models have much higher communication costs during training, hence such models stand to benefit much more from gradient compression.
-When running distributed training with gradient compression, the quantize and dequantize operations happen on CPU parallelized with OpenMP. For smaller models, when training on GPUs, it helps to set `OMP_NUM_THREADS=1` on each node, so that the overhead of launching OMP threads doesn't cause the compression and decompression to be slow.
-
-### Model Architecture
-
-The communication bandwidth requirements during training vary across various neural network architectures and hence the benefits of gradient compression vary accordingly.
-
-In networks which have significant fully connected components, since such layers have low compute cost on GPUs, communication becomes a bottleneck limiting the speed of distributed training. Gradient compression can help reduce the communication cost, and thus speed up training in such cases. We have observed speedup of about 2x on large fully connected neural networks. Models like AlexNet and VGG have large fully connected components as part of the network, hence stand to benefit from gradient compression. As with these models, Long Short-Term Memory architectures require more communication bandwidth, so they also exhibit speed improvements with gradient compression.
-
-Architectures like Convolutional Neural Networks on the other hand have a higher compute cost, in which case some communication can be parallelized with computation. Since communication is not the bottleneck in such networks, gradient compression doesn't help much.
-
-
-### Single Node Gradient Compression
-
-When the training is configured to use device to device communication on a single node with multiple GPUs, gradient compression can be used to reduce the cost of communication. This can provide about 20% speedup for large models using older generation architectures. However, speed benefits may be negligible on a machine with a newer generation architecture where GPUs can communicate at low latency.
-
-
-## Approach
-
-The idea behind gradient compression comes from two observations:
-
-First, when training large neural networks, the gradients of weights computed for a small mini-batch of training data are typically sparse. Only a small fraction of the weights have significant updates after each mini-batch. The synchronization of updates that are near zero can be safely delayed longer than the typical mini-batch size. This essentially means that the rate of weight-update can vary depending on the value of an individual weight.
-
-Secondly, gradients can be compressed significantly by considering only those gradient elements whose absolute values exceed a threshold, and then quantizing them to use lower bits per gradient value. By compressing the gradients, we can reduce communication bandwidth. The delayed gradient values, in the form of quantization error and values that don't meet the threshold, are aggregated into a gradient residual which is communicated when it reaches the threshold.
-
-## Technical Implementation
-
-### Two Bit Quantization
-
-Currently the supported type of quantization uses two bits for each gradient value. Any positive value greater than or equal to the threshold sets two bits as `11`, any negative value whose absolute value is greater or equal to the threshold sets two bits as `10`, and others are set to `00`. This enables us to store 16 quantized gradients as one float. The error in quantization, which is `original_value - quantized_value` is stored in the form of a gradient residual.
-
-### Types of Kvstore
-
-Supported types of `kvstore` are `device` and all distributed kvstores such as `dist_sync`, `dist_async`, and `dist_sync_device`. When `kvstore` is `device`, the communication between GPUs is compressed. Please note that this increases the memory usage of GPUs because of the additional residual stored. When using a distributed kvstore, worker-to-server communication is compressed. In this case, compression and decompression happen on the CPU, and gradient residuals will be stored on the CPU. Server-to-worker communication and device-to-device communication are not compressed to avoid multiple levels of compression.
-
-## Enabling the Gradient Compression in MXNet
-
-Gradient compression is a run-time configuration parameter to be enabled during training. Here are the MXNet APIs to enable gradient compression:
-
-**Gluon API**:
-
-```
-trainer = gluon.Trainer(..., compression_params={'type’:'2bit', 'threshold':0.5})
-```
-A reference `gluon` implementation with a gradient compression option can be found in the [train.py script from a word-level language modeling RNN example](https://github.com/apache/incubator-mxnet/blob/master/example/gluon/word_language_model/train.py).
-
-**Module API**:
-
-```
-mod = mx.mod.Module(..., compression_params={'type’:'2bit', 'threshold':0.5})
-```
-
-A `module` example is provided with [this guide for setting up MXNet with distributed training](https://mxnet.incubator.apache.org/versions/master/faq/multi_devices.html#distributed-training-with-multiple-machines). It comes with the option of turning on gradient compression as an argument to the [train_mnist.py script](https://github.com/apache/incubator-mxnet/blob/master/example/image-classification/train_mnist.py).
-
-### Configuration Details
-
-**Threshold**
-
-A default `threshold` value of `0.5` is good for most use cases, but to get the most benefit from gradient compression for a particular scenario, it can be beneficial to experiment. If the threshold is set to a very large value, say `10.0`, then the updates become too infrequent and the training will converge slower. Setting the threshold automatically is expected in a future release.
-
-**Quantization**
-
-This release supports 2-bit quantization for encoding of gradients to reduce the communication bandwidth during training. Future releases will support 1-bit quantization and other approaches for encoding of gradients based on experimental evidence of benefits and user demand.
-
-**Sparse Format**
-
-We believe that the density of data will need to be really low (i.e. around > 90% zeros) to reap benefits of the sparse format. However, this is an area of experimentation that will be explored in a future release.
-
-
-## References
-
-1. [Nikko Storm, Amazon.com, Scalable Distributed Training using commodity GPU cloud computing.](https://s3-us-west-2.amazonaws.com/amazon.jobs-public-documents/strom_interspeech2015.pdf)
diff --git a/docs/faq/index.md b/docs/faq/index.md
deleted file mode 100644
index 6c398d46471a..000000000000
--- a/docs/faq/index.md
+++ /dev/null
@@ -1,103 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# MXNet FAQ
-
-```eval_rst
-.. toctree::
-   :hidden:
-   :glob:
-
-   *
-```
-
-This section addresses common questions about how to use _MXNet_. These include performance issues, e.g., how to train with multiple GPUs.
-They also include workflow questions, e.g., how to visualize a neural network computation graph.
-These answers are fairly focused. For more didactic, self-contained introductions to neural networks
-and full working examples, visit the [tutorials section](../tutorials/index.md).
-
-## API
-
-* [What's the difference between the Module and Gluon APIs for Python?](../api/python/index.html)
-
-## Modeling
-* [How do I fine-tune pre-trained models to a new dataset?](finetune.html)
-
-* [How do I work with variable-length input in MXNet (bucketing)?](bucketing.html)
-
-* [How do I visualize neural networks as computation graphs?](visualize_graph.html)
-
-
-## Scale
-* [How can I train with multiple CPU/GPUs on a single machine with data parallelism?](multi_devices.html)
-
-* [How can I train using multiple machines with data parallelism?](distributed_training.html)
-
-* [How can I train using multiple GPUs with model parallelism?](model_parallel_lstm.html)
-
-
-## Speed
-* [How do I use gradient compression with distributed training?](gradient_compression.html)
-
-* [Can I use nnpack to improve the CPU performance of MXNet?](nnpack.html)
-
-* [What are the best setup and data-handling tips and tricks for improving speed?](perf.html)
-
-* [How do I use mixed precision with MXNet or Gluon?](float16.html)
-
-## Deployment Environments
-* [Can I run MXNet on smart or mobile devices?](smart_device.html)
-
-* [How to use data from S3 for training?](s3_integration.md)
-
-* [How to run MXNet on AWS?](https://docs.aws.amazon.com/mxnet/latest/dg/whatis.html)
-
-* [How to do distributed training using MXNet on AWS?](http://docs.aws.amazon.com/mxnet/latest/dg/mxnet-on-ec2-cluster.html)
-
-* [How do I run MXNet on a Raspberry Pi for computer vision?](../tutorials/embedded/wine_detector.html)
-
-* [How do I run Keras 2 with MXNet backend?](https://github.com/awslabs/keras-apache-mxnet/blob/master/docs/mxnet_backend/installation.md)
-
-* [How to convert MXNet models to Apple CoreML format?](https://github.com/apache/incubator-mxnet/tree/master/tools/coreml)
-
-## Security
-* [How to run MXNet securely?](security.html)
-
-## Extend and Contribute to MXNet
-
-* [How do I join the MXNet development discussion?](../community/mxnet_channels.html)
-
-* [How do I contribute a patch to MXNet?](../community/contribute.html)
-
-* [How do I implement operators in MXNet backend?](add_op_in_backend.html)
-
-* [How do I create new operators in MXNet?](new_op.html)
-
-* [How do I implement sparse operators in MXNet backend?](https://cwiki.apache.org/confluence/display/MXNET/A+Guide+to+Implementing+Sparse+Operators+in+MXNet+Backend)
-
-* [How do I contribute an example or tutorial?](https://github.com/apache/incubator-mxnet/tree/master/example#contributing)
-
-* [How do I set MXNet's environmental variables?](env_var.html)
-
-## Questions about Using MXNet
-If you need help with using MXNet, have questions about applying it to a particular kind of problem, or have a discussion topic, please use our [forum](https://discuss.mxnet.io).
-
-## Issue Tracker
-We track bugs and new feature requests in the MXNet Github repo in the issues folder: [mxnet/issues](https://github.com/apache/incubator-mxnet/issues).
-
-## Roadmap
-MXNet is evolving fast. To see what's next and what we are working on internally, go to the [MXNet Roadmap](https://cwiki.apache.org/confluence/display/MXNET/Roadmap).
diff --git a/docs/faq/model_parallel_lstm.md b/docs/faq/model_parallel_lstm.md
deleted file mode 100644
index e9a8768b6fec..000000000000
--- a/docs/faq/model_parallel_lstm.md
+++ /dev/null
@@ -1,92 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Training with Multiple GPUs Using Model Parallelism
-Training deep learning models can be resource intensive.
-Even with a powerful GPU, some models can take days or weeks to train.
-Large long short-term memory (LSTM) recurrent neural networks
-can be especially slow to train,
-with each layer, at each time step, requiring eight matrix multiplications.
-Fortunately, given cloud services like AWS,
-machine learning practitioners often  have access
-to multiple machines and multiple GPUs.
-One key strength of _MXNet_ is its ability to leverage
-powerful heterogeneous hardware environments to achieve significant speedups.
-
-There are two primary ways that we can spread a workload across multiple devices.
-In a previous document, [we addressed data parallelism](./multi_devices.md),
-an approach in which samples within a batch are divided among the available devices.
-With data parallelism, each device stores a complete copy of the model.
-Here, we explore _model parallelism_, a different approach.
-Instead of splitting the batch among the devices, we partition the model itself.
-Most commonly, we achieve model parallelism by assigning the parameters (and computation)
-of different layers of the network to different devices.
-
-In particular, we will focus on LSTM recurrent networks.
-LSTMS are powerful sequence models, that have proven especially useful
-for [natural language translation](https://arxiv.org/pdf/1409.0473.pdf), [speech recognition](https://arxiv.org/abs/1512.02595),
-and working with [time series data](https://arxiv.org/abs/1511.03677).
-For a general high-level introduction to LSTMs,
-see the excellent [tutorial](http://colah.github.io/posts/2015-08-Understanding-LSTMs/) by Christopher Olah.
-
-
-## Model Parallelism: Using Multiple GPUs As a Pipeline
-Model parallelism in deep learning was first proposed
-for the _extraordinarily large_ convolutional layer in GoogleNet.
-From this implementation, we take the idea of placing each layer on a separate GPU.
-Using model parallelism in such a layer-wise fashion
-provides the benefit that no GPU has to maintain all of the model parameters in memory.
-
-<img width="517" alt="screen shot 2016-05-06 at 10 13 16 pm" src="https://cloud.githubusercontent.com/assets/5545640/15089697/d6f4fca0-13d7-11e6-9331-7f94fcc7b4c6.png">
-
-In the preceding figure, each LSTM layer is assigned to a different GPU.
-After GPU 1 finishes computing layer 1 for the first sentence, it passes its output to GPU 2.
-At the same time, GPU 1 fetches the next sentence and starts training.
-This differs significantly from data parallelism.
-Here, there is no contention to update the shared model at the end of each iteration,
-and most of the communication happens when passing intermediate results between GPUs.
-
-
-## Workload Partitioning
-
-Implementing model parallelism requires knowledge of the training task.
-Here are some general heuristics that we find useful:
-
-- To minimize communication time, place neighboring layers on the same GPUs.
-- Be careful to balance the workload between GPUs.
-- Remember that different kinds of layers have different computation-memory properties.
-
-<img width="449" alt="screen shot 2016-05-07 at 1 51 02 am" src="https://cloud.githubusercontent.com/assets/5545640/15090455/37a30ab0-13f6-11e6-863b-efe2b10ec2e6.png">
-
-Let's take a quick look at the two pipelines in the preceding diagram.
-They both have eight layers with a decoder and an encoder layer.
-Based on our first principle, it's unwise to place all neighboring layers on separate GPUs.
-We also want to balance the workload across GPUs.
-Although the LSTM layers consume less memory than the decoder/encoder layers, they consume more computation time because of the dependency of the unrolled LSTM.
-Thus, the partition on the left will be faster than the one on the right
-because the workload is more evenly distributed.
-
-
-## Apply Bucketing to Model Parallelism
-
-To achieve model parallelism while using bucketing,
-you need to unroll an LSTM model for each bucket
-to obtain an executor for each.
-
-On the other hand, because model parallelism partitions the model/layers,
-the input data has to be transformed/transposed to the agreed shape.
-For more details, see [bucket_io](https://github.com/apache/incubator-mxnet/blob/master/example/rnn/old/bucket_io.py).
diff --git a/docs/faq/multi_devices.md b/docs/faq/multi_devices.md
deleted file mode 100644
index 60a8003f75d0..000000000000
--- a/docs/faq/multi_devices.md
+++ /dev/null
@@ -1,104 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Run MXNet on Multiple CPU/GPUs with Data Parallelism
-
-_MXNet_ supports training with multiple CPUs and GPUs, which may be located on different physical machines.
-
-## Data Parallelism vs Model Parallelism
-
-By default, _MXNet_ uses data parallelism to partition the workload over multiple
-devices.
-Assume there are *n* devices.
-Then each one will receive a copy of the complete model
-and train it on *1/n* of the data.
-The results such as gradients and
-updated model are communicated across these devices.
-
-MXNet also supports model parallelism.
-In this approach, each device holds onto only part of the model.
-This proves useful when the model is too large to fit onto a single device.
-As an example, see the following [tutorial](./model_parallel_lstm.md)
-which shows how to use model parallelism for training a multi-layer LSTM model.
-In this tutorial, we'll focus on data parallelism.
-
-## Multiple GPUs within a Single Machine
-
-### Workload Partitioning
-
-By default, _MXNet_ partitions a data batch evenly among the available GPUs.
-Assume a batch size *b* and assume there are *k* GPUs, then in one iteration
-each GPU will perform forward and backward on *b/k* examples. The
-gradients are then summed over all GPUs before updating the model.
-
-### How to Use
-
-> To use GPUs, we need to compile MXNet with GPU support. For
-> example, set `USE_CUDA=1` in `config.mk` before `make`. (see
-> [MXNet installation guide](http://mxnet.io/install/index.html) for more options).
-
-If a machine has one or more GPU cards installed,
-then each card is labeled by a number starting from 0.
-To use a particular GPU, one can either
-specify the context `context` in code
-or pass `--gpus` at the command line.
-For example, to use GPU 0 and 2 in python,
-one can typically create a module with
-```python
-import mxnet as mx
-module = mx.module.Module(context=[mx.gpu(0), mx.gpu(2)], ...)
-```
-while if the program accepts a `--gpus` flag (as seen in
-[example/image-classification](https://github.com/dmlc/mxnet/tree/master/example/image-classification)),
-then we can try
-```bash
-python train_mnist.py --gpus 0,2 ...
-```
-
-### Advanced Usage
-If the available GPUs are not all equally powerful,
-we can partition the workload accordingly.
-For example, if GPU 0 is 3 times faster than GPU 2,
-then we might use the workload option `work_load_list=[3, 1]`,
-see [Module](http://mxnet.io/api/python/module/module.html#mxnet.module.Module)
-for more details.
-
-Training with multiple GPUs should yield the same results
-as training on a single GPU if all other hyper-parameters are the same.f
-In practice, the results may exhibit small differences,
-owing to the randomness of I/O (random order or other augmentations),
-weight initialization with different seeds, and CUDNN.
-
-We can control on which devices the gradient is aggregated
-and on which device the model is updated via [`KVStore`](http://mxnet.io/api/python/kvstore/kvstore.html),
-the _MXNet_ module that supports data communication.
-One can either use `mx.kvstore.create(type)` to get an instance
-or use the program flag `--kv-store type`.
-
-There are two commonly used types,
-
-- `local`: all gradients are copied to CPU memory and weights are updated there.
-- `device`: both gradient aggregation and weight updates are run on GPUs.
-With this setting, the `KVStore` also attempts to use GPU peer-to-peer communication,
-potentially accelerating the communication.
-Note that this option may result in higher GPU memory usage.
-
-When using a large number of GPUs, e.g. >=4, we suggest using `device` for better performance.
-
-## Distributed training with multiple devices across machines
-Refer [Distributed training](https://mxnet.incubator.apache.org/versions/master/faq/distributed_training.html)
-for information on how distributed training works and how to use it.
diff --git a/docs/faq/new_op.md b/docs/faq/new_op.md
deleted file mode 100644
index 2395379bafc1..000000000000
--- a/docs/faq/new_op.md
+++ /dev/null
@@ -1,388 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# How to Create New Operators (Layers)
-
-This tutorials walks you through the process of creating new MXNet operators (or layers).
-We've done our best to provide high-speed operators for most common use cases.
-However, if you're engaged in research,
-there's a good chance you'll want to define custom layers,
-like a novel loss function. In these cases, you have two options:
-
-* Use CustomOp to write new operators using a front-end language (e.g., Python) that run on CPUs or GPUs.
-Depending on your implementation, this can range from very fast (if you only use operators under mx.nd) to very slow (if you copy out the data, using `.asnumpy()`).
-
-* Use C++/mshadow (CUDA). This provides the best performance, but can be difficult
-if you're not familiar with MXNet, mshadow, or Cuda.
-
-## CustomOp
-Implementing an operator in Python is simple.
-As an example, let's create a softmax operator.
-Start by subclassing `mxnet.operator.CustomOp`,
-and then override a few methods:
-
-```python
-import os
-import mxnet as mx
-import numpy as np
-
-class Softmax(mx.operator.CustomOp):
-    def forward(self, is_train, req, in_data, out_data, aux):
-        x = in_data[0].asnumpy()
-        y = np.exp(x - x.max(axis=1).reshape((x.shape[0], 1)))
-        y /= y.sum(axis=1).reshape((x.shape[0], 1))
-        self.assign(out_data[0], req[0], mx.nd.array(y))
-```
-
-We defined the computation for the forward pass of our operator.
-The forward function takes a list of input and a list of output NDArrays.
-For convenience, we called `.asnumpy()` on the first NDArray in input
-and convert it to a CPU-based NumPy array.
-This can be very slow. If you want the best performance,
-keep data in the NDArray format and use operators under mx.nd to do the computation.
-
-At the end, we used CustomOp.assign to assign the resulting array y to out_data[0]. It handles assignment based on the value of req, which can be 'write', 'add', or 'null'.
-
-Then do the same for the backward pass:
-
-```python
-def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
-    l = in_data[1].asnumpy().ravel().astype(np.int)
-    y = out_data[0].asnumpy()
-    y[np.arange(l.shape[0]), l] -= 1.0
-    self.assign(in_grad[0], req[0], mx.nd.array(y))
-```
-
-Softmax defines the computation of our custom operator,
-but you still need to define its input/output format
-by subclassing mx.operator.CustomOpProp.
-First, register the new operator with the name 'softmax':
-
-```python
-@mx.operator.register("softmax")
-class SoftmaxProp(mx.operator.CustomOpProp):
-```
-
-Then, call the base constructor with `need_top_grad=False`
-because softmax is a loss layer and you don't need gradient input from preceding layers:
-
-```python
-def __init__(self):
-    super(SoftmaxProp, self).__init__(need_top_grad=False)
-```
-
-Then declare the input and output:
-
-```python
-def list_arguments(self):
-    return ['data', 'label']
-
-def list_outputs(self):
-    return ['output']
-```
-
-Note that list_arguments declares both input and parameter.
-We recommend ordering them as follows:  `['input1', 'input2', ... , 'weight1', 'weight2', ...]`
-
-Next, provide `infer_shape` to declare the shape of the output/weight
-and check the consistency of the input shapes:
-
-```python
-def infer_shape(self, in_shape):
-    data_shape = in_shape[0]
-    label_shape = (in_shape[0][0],)
-    output_shape = in_shape[0]
-    return [data_shape, label_shape], [output_shape], []
-```
-The first axis of an input/output tensor corresponds to different examples within the batch.
-The label is a set of integers, one for each data entry,
-and the output has the same shape as the input.
-The `infer_shape` function should always return three lists in this order:
-inputs, outputs, and auxiliary states (which we don't have here),
-even if one of them is empty.
-
-Optionally, you can also define `infer_type` to declare the input and output data type of your operator. Supported types are `np.float32`, `np.float64`, `np.float16`, `np.uint8`, and `np.int32`.
-
-```python
-def infer_type(self, in_type):
-    dtype = in_type[0]
-    return [dtype, dtype], [dtype], []
-```
-
-Finally, define a create_operator function that will be called by the back end to create an instance of softmax:
-
-```python
-def create_operator(self, ctx, shapes, dtypes):
-    return Softmax()
-```
-
-To use the custom operator, create a mx.sym.Custom symbol with op_type as the registered name:
-
-```python
-mlp = mx.symbol.Custom(data=fc3, name='softmax', op_type='softmax')
-```
-
-Please see the full code for this example [here](https://github.com/dmlc/mxnet/blob/master/example/numpy-ops/custom_softmax.py).
-
-## C++
-With MXNet v0.9 (the NNVM refactor) or later, creating new operators has become easier.
-Operators are now registered with NNVM.
-The following code is an example on how to register an operator (checkout [src/operator/tensor](https://github.com/dmlc/mxnet/tree/master/src/operator/tensor) for more examples):
-
-```c++
-NNVM_REGISTER_OP(abs)
-.MXNET_DESCRIBE("Take absolute value of the src")
-.set_num_inputs(1)
-.set_num_outputs(1)
-.set_attr<nnvm::FInferShape>("FInferShape", ElemwiseShape<1,1>);
-```
-
-The syntax is quite simple, we register the operator with a name,
-then set number of inputs and outputs.
-You can register attributes with any key (`FInferShape` for example) to any operator,
-without having to modify a central class interface definition.
-
-### Operator Attribute System
-
-One of the biggest improvements brought by NNVM is the operator attribute system.
-This is like traits for types in common languages like C++.
-We can register any attribute to any operator, with the syntax
-
-``` c++
-NNVM_REGISTER_OP(op-name)
-.set_attr<AttributeType>("AttributeKey", CorrespondingAttributeObject);
-```
-
-These attributes can be retrieved later for various purposes.
-For example, `FInferShape` is used for shape inference, `FCompute<cpu>` is used for carrying out actual computation on CPU.
-
-As long as all attributes registered with the same key have the same type,
-we can register any attributes to operators.
-The more attribute an operator provides,
-the more information the system can use for optimization.
-
-### List of basic attributes
-
-In this section, we will go through the basic attributes MXNet expect for all operators.
-You can find the definition for them in the following two files:
-
-- [nnvm/op_attr_types.h](https://github.com/dmlc/nnvm/blob/master/include/nnvm/op_attr_types.h)
-- [mxnet/op_attr_types.h](https://github.com/dmlc/mxnet/blob/master/include/mxnet/op_attr_types.h)
-
-#### Descriptions (Optional)
-
-`.describe(comment)` adds a comment to the operator. Use `.MXNET_DESCRIBE(comment)` to add the current file name and line number to comment.
-
-#### Attribute Parser (Optional)
-
-Set attribute parser with `.set_attr_parser(PARSER)` where PARSER is a function with prototype `void(nnvm::NodeAttr* attrs)`. This function should parse the key-word arguments in `attrs->dict` and store the result in `attrs->parsed`.
-
-Simple arguments can be parsed like
-```c++
-NNVM_REGISTER_OP(scalar_op)
-.set_attr_parser(
-  [](NodeAttrs* attrs) {
-    attrs->parsed = std::stod(attrs->dict["scalar"]);
-  })
-```
-
-The parsed arguments can then be accessed in other attribute functions with
-```
-double alpha = nnvm::get<double>(attrs.parsed);
-```
-
-More complex ops can use `dmlc::Parameters` and `ParamParser` (defined in operator_common.h) for parsing:
-
-``` c++
-#include <dmlc/parameter.h>
-#include <operator_common.h>
-struct ActivationParam : public dmlc::Parameter<ActivationParam> {
-  // use int for enumeration
-  int act_type;
-  DMLC_DECLARE_PARAMETER(ActivationParam) {
-    DMLC_DECLARE_FIELD(act_type)
-    .add_enum("relu", activation::kReLU)
-    .add_enum("sigmoid", activation::kSigmoid)
-    .add_enum("tanh", activation::kTanh)
-    .add_enum("softrelu", activation::kSoftReLU)
-    .describe("Activation function to be applied.");
-  }
-};
-NNVM_REGISTER_OP(Activation)
-.set_attr_parser(ParamParser<ActivationParam>);
-// access with:
-// const ActivationParam& param = nnvm::get<ActivationParam>(attrs.parsed);
-```
-
-#### Inputs & Outputs
-
-Number of inputs/outputs can be set with `.set_num_inputs(n_in)` and `.set_num_outputs(n_out)`
-where n_in and n_out are integers.
-
-Alternatively, if the number of inputs/outputs is variable and depends on arguments,
-you can set `n_in`/`n_out` to functions with prototype `uint32_t(const nnvm::NodeAttrs& attrs)`
-that return the number of inputs/outputs based on parsed arguments.
-
-Outputs can be made invisible to other operators by registering `FNumVisibleOutputs`
-and returning an integer smaller than `n_out`.
-
-Inputs/outputs can be named by registering `FListInputNames` and `FListOutputNames` with prototype `std::vector<std::string>(const NodeAttrs& attrs)`.
-
-
-#### Argument Descriptions
-
-Set argument descriptions with `.add_argument(name, type, comment)`.
-This is necessary for operators to be properly called imperatively.
-
-First, add NDArray arguments `num_inputs` times with type "NDArray"
-or one time with type "NDArray[]" for ops with variable length inputs.
-
-Then add key-word arguments with proper type (float, string, etc).
-Operators that parse key-word arguments with `dmlc::Parameter`
-can add argument descriptions in bulk with `.add_arguments(ActivationParam::__FIELDS__())`
-(NDArray arguments still need to be manually added with type "NDArray").
-
-#### FInferShape or TIsBackward (for Backward Only Ops)
-
-Normally operators need to have `FInferShape` with prototype `bool(const nnvm::NodeAttrs& attrs, mxnet::ShapeVector *in_attrs, mxnet::ShapeVector *out_attrs)`. `FInferShape` fills unknown shapes (`shape.ndim() == 0`) in in_attrs/out_attrs based on known shapes in in_attrs/out_attrs. Use `ElemwiseShape<n_in, n_out>` for simple operators with uniform shapes.
-
-Operators that are only used for a backward pass can instead register `.set_attr<nnvm::TIsBackward>("TIsBackward", true)`
-and their shapes with be copied from the corresponding forward operators.
-
-#### FInferType
-
-Similar to `FInferShape`, `FInferType` fills unknown types (-1) based on known types. Use `ElemwiseType<n_in, n_out>` for simple operators with uniform types. Operators that registered `TIsBackward` don't need to register this.
-
-
-#### FInplaceOption (Optional)
-
-`FInplaceOption` with prototype `std::vector<std::pair<int, int> >(const NodeAttrs& attrs)`
-specifies which input/output pairs can be computed in-place
-and share memory with each other.
-Each pair (i, j) in the returned list means
-that the i-th input can share memory with the j-th output.
-
-
-#### FGradient (Optional for imperative use, required for symbolic use)
-
-If an operator has gradient, it can be described with `FGradient` with prototype
-
-``` c++
-std::vector<nnvm::NodeEntry>(const nnvm::NodePtr& n,
-                             const std::vector<nnvm::NodeEntry>& ograds)
-```
-
-Use utility functions `ElemwiseGradUseIn{op_name}`, `ElemwiseGradUseOut{op_name}`, `ElemwiseGradUseNone{op_name}`  for ops that need corresponding forward op's input,
-output or nothing to calculating gradient.
-
-For more complicated patterns, use `MakeGradNode(op_name, n, heads, dict)` to create gradient entries,
-where heads are input entries to the backward op, composed from ograds and n->inputs.
-
-When assembling a return vector of `std::vector<nnvm::NodeEntry> ret;` a common pattern would be to
-either create nodes in place as in:
-
-```
-ret.emplace_back(MakeNode("zeros_like", n->attrs.name + "_xyz_backward",
-    {n->inputs[1]}, nullptr, &n))
-```
-
-Or create the node, modify and then move into NodeEntry's constructor if this node is not to be used
-again. This avoids uneccessary copies of the shared_ptr.
-
-```
-for (size_t i = 0; i < n->inputs.size(); ++i) {
-  nnvm::NodePtr node = nnvm::Node::Create();
-  node->attrs.op = copy_op;
-  node->inputs = {ograds[0]};
-  ret.emplace_back(std::move(node));
-}
-```
-
-The first case uses RVO and the second in place construction.
-
-#### FCompute\<xpu\>
-
-Simple operators can register FCompute<xpu> with `.set_attr<FCompute>("FCompute<cpu>", ...)` and `.set_attr<FCompute>("FCompute<gpu>", ...)` for both CPU and (optionally) GPU computation.
-
-FCompute has prototype
-
-```c++
-void(const nnvm::NodeAttrs& attrs,
-     const OpContext& ctx,
-     const std::vector<TBlob>& inputs,
-     const std::vector<OpReqType>& req,
-     const std::vector<TBlob>& outputs)
-```
-
-`req` has the same length as `outputs`.
-Each entry of `req` specifies
-how the corresponding `output` should be written to.
-`OpReqType` is defined as:
-
-```c++
-enum OpReqType {
-  kNullOp,
-  kWriteTo,
-  kWriteInplace,
-  kAddTo
-};
-```
-
-Normally, the `req` of all `outputs` should be `kWriteTo`,
-meaning that the provided `outputs` tensor is a *raw* memory block,
-so the operator should write results directly into it.
-In some cases, for example, when calculating the gradient tensor,
-it would be great if we could accumulate the result,
-rather than directly overwrite the tensor contents
-so that no extra space needs to be created each time.
-In such cases, the corresponding `req` is set to `kAddTo`,
-indicating that a `+=` should be used.
-
-### Example: abs operator
-
-```c++
-NNVM_REGISTER_OP(abs)
-.MXNET_DESCRIBE("Take absolute value of the src")
-.set_num_inputs(1)
-.set_num_outputs(1)
-.set_attr<nnvm::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
-.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
-.set_attr<nnvm::FInplaceOption>("FInplaceOption",
-[](const NodeAttrs& attrs){
-  return std::vector<std::pair<int, int> >{{0, 0}};
-})
-.set_attr<FCompute>("FCompute<cpu>", UnaryCompute<cpu, mshadow_op::abs>)
-.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_abs"});              
-.add_argument("data", "NDArray", "Source input")
-
-NNVM_REGISTER_OP(_backward_abs)
-.set_num_inputs(2)
-.set_num_outputs(1)
-.set_attr<nnvm::FInferShape>("FInferShape", ElemwiseShape<2, 1>)
-.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<2, 1>)
-.set_attr<nnvm::FInplaceOption>("FInplaceOption",
-[](const NodeAttrs& attrs){
-  return std::vector<std::pair<int, int> >{{0, 0}, {1, 0}};
-})
-.set_attr<FCompute>("FCompute<cpu>", BinaryCompute<cpu, backward_grad<mshadow_op::sign> >);
-```
-
-### Legacy Operators
-
-For the legacy (pre 0.9) way of defining operators with C++, please see:
-- [Developer Guide - Operators](http://mxnet.io/architecture/overview.html#operators-in-mxnet)
-- [Developer Guide - SimpleOp](http://mxnet.io/architecture/overview.html#simpleop-the-unified-operator-api)
diff --git a/docs/faq/nnpack.md b/docs/faq/nnpack.md
deleted file mode 100644
index c0b27b9bf4e6..000000000000
--- a/docs/faq/nnpack.md
+++ /dev/null
@@ -1,155 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-### NNPACK for Multi-Core CPU Support in MXNet
-[NNPACK](https://github.com/Maratyszcza/NNPACK) is an acceleration package
-for neural network computations, which can run on x86-64, ARMv7, or ARM64 architecture CPUs.
-Using NNPACK, higher-level libraries like _MXNet_ can speed up
-the execution on multi-core CPU computers, including laptops and mobile devices.
-
-_MXNet_ supports NNPACK for forward propagation (inference only) in convolution, max-pooling, and fully-connected layers.
-In this document, we give a high level overview of how to use NNPACK with _MXNet_.
-
-
-### Conditions
-The underlying implementation of NNPACK utilizes several acceleration methods,
-including [fft](https://arxiv.org/abs/1312.5851) and [winograd](https://arxiv.org/abs/1509.09308).
-These algorithms work better on some special `batch size`, `kernel size`, and `stride` settings than on other,
-so depending on the context, not all convolution, max-pooling, or fully-connected layers can be powered by NNPACK.
-When favorable conditions for running NNPACKS are not met,
-_MXNet_ will fall back to the default implementation automatically.  
-
-NNPACK only supports Linux and OS X systems. Windows is not supported at present.
-The following table explains under which conditions NNPACK will work.
-
-| operation      | conditions |
-|:---------      |:---------- |
-|convolution     |2d convolution `and` no-bias=False `and` dilate=(1,1) `and` num_group=1 `and` batch-size = 1 or batch-size > 1 && stride = (1,1);|
-|pooling         | max-pooling `and` kernel=(2,2) `and` stride=(2,2) `and` pooling_convention=full    |
-|fully-connected| without any restrictions |
-
-### Build/Install NNPACK with MXNet
-
-If the trained model meets some conditions of using NNPACK,
-you can build MXNet with NNPACK support.
-Follow these simple steps:  
-* Build NNPACK shared library with the following commands. _MXNet_ will link NNPACK dynamically.
-
-Note: The following NNPACK installation instructions have been tested on Ubuntu 14.04 and 16.04.
-
-```bash
-
-# Install Pip
-$ sudo apt-get update
-$ sudo apt-get install -y python-pip
-$ sudo pip install --upgrade pip
-
-# Install Peach
-$ git clone https://github.com/Maratyszcza/PeachPy.git
-$ cd PeachPy
-$ sudo pip install --upgrade -r requirements.txt
-$ python setup.py generate
-$ sudo pip install --upgrade .
-
-# Install Ninja Build System
-$ sudo apt-get install ninja-build
-$ pip install ninja-syntax
-
-# Build NNPack shared library
-$ cd ~
-$ git clone --recursive https://github.com/Maratyszcza/NNPACK.git
-$ cd NNPACK
-# Latest NNPACK do not support building NNPACK as shared library using --enable-shared flag
-# Reset to commit that supports it.
-$ git reset --hard 9c6747d7b80051b40e6f92d6828e2ed997529cd2
-$ git submodule init && git submodule update --recursive
-$ python ./configure.py --enable-shared
-$ ninja
-$ cd ~
-
-```
-
-* Set lib path of NNPACK as the environment variable, e.g. `export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$YOUR_NNPACK_INSTALL_PATH/lib`
-* Add the include file of NNPACK and its third-party to  `ADD_CFLAGS` in config.mk, e.g. `ADD_CFLAGS = -I$(YOUR_NNPACK_INSTALL_PATH)/include/ -I$(YOUR_NNPACK_INSTALL_PATH)/third-party/pthreadpool/include/`
-* Set `USE_NNPACK = 1` in config.mk.
-* Build MXNet from source following the [install guide](http://mxnet.io/install/index.html).
-
-### NNPACK Performance
-
-Though not all convolutional, pooling, and fully-connected layers can make full use of NNPACK,
-for some popular models it provides significant speedups. These include the most popular image recognition networks: Alexnet, VGG, and Inception-bn.
-
-To benchmark NNPACK, we use `example/image-classification/benchmark_score.py`(changed with  more range of batch-size). We use CPU e5-2670, MXNET_CPU_NNPACK_NTHREADS=4.
-
-build MXNet without NNPACK, the log is:
-```
-INFO:root:network: alexnet
-INFO:root:device: cpu(0)
-INFO:root:batch size  1, image/sec: 6.389429
-INFO:root:batch size  2, image/sec: 7.961457
-INFO:root:batch size  4, image/sec: 8.950112
-INFO:root:batch size  8, image/sec: 9.578176
-INFO:root:batch size 16, image/sec: 9.701248
-INFO:root:batch size 32, image/sec: 9.839940
-INFO:root:batch size 64, image/sec: 10.075369
-INFO:root:batch size 128, image/sec: 10.053556
-INFO:root:batch size 256, image/sec: 9.972228
-INFO:root:network: vgg
-INFO:root:device: cpu(0)
-INFO:root:batch size  1, image/sec: 1.223822
-INFO:root:batch size  2, image/sec: 1.322814
-INFO:root:batch size  4, image/sec: 1.383586
-INFO:root:batch size  8, image/sec: 1.402376
-INFO:root:batch size 16, image/sec: 1.415972
-INFO:root:batch size 32, image/sec: 1.428377
-INFO:root:batch size 64, image/sec: 1.443987
-INFO:root:batch size 128, image/sec: 1.427531
-INFO:root:batch size 256, image/sec: 1.435279
-```
-
-build MXNet with NNPACK, log is:
-
-```
-INFO:root:network: alexnet
-INFO:root:device: cpu(0)
-INFO:root:batch size  1, image/sec: 19.027215
-INFO:root:batch size  2, image/sec: 12.879975
-INFO:root:batch size  4, image/sec: 17.424076
-INFO:root:batch size  8, image/sec: 21.283966
-INFO:root:batch size 16, image/sec: 24.469325
-INFO:root:batch size 32, image/sec: 25.910348
-INFO:root:batch size 64, image/sec: 27.441672
-INFO:root:batch size 128, image/sec: 28.009156
-INFO:root:batch size 256, image/sec: 28.918950
-INFO:root:network: vgg
-INFO:root:device: cpu(0)
-INFO:root:batch size  1, image/sec: 3.980907
-INFO:root:batch size  2, image/sec: 2.392069
-INFO:root:batch size  4, image/sec: 3.610553
-INFO:root:batch size  8, image/sec: 4.994450
-INFO:root:batch size 16, image/sec: 6.396612
-INFO:root:batch size 32, image/sec: 7.614288
-INFO:root:batch size 64, image/sec: 8.826084
-INFO:root:batch size 128, image/sec: 9.193653
-INFO:root:batch size 256, image/sec: 9.991472
-```
-
-The results show that NNPACK can confer a speedup of about 2X~7X as compared to the original _MXNet_ CPU implementation.
-
-### Tips
-
-NNPACK aims to provide high-performance implementations of some layers for multi-core CPUs, so you can easily set the thread number by changing the environmental variable `MXNET_CPU_NNPACK_NTHREADS`. However, we found that the performance is not proportional to the number of threads, and suggest using 4~8 threads when using NNPACK.
diff --git a/docs/faq/perf.md b/docs/faq/perf.md
deleted file mode 100644
index 03a8dbbb20a1..000000000000
--- a/docs/faq/perf.md
+++ /dev/null
@@ -1,302 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Some Tips for Improving MXNet Performance
-Even after fixing the training or deployment environment and parallelization scheme,
-a number of configuration settings and data-handling choices can impact the _MXNet_ performance.
-In this document, we address some tips for improving _MXNet_ performance.
-
-Performance is mainly affected by the following 4 factors:
-
-1. Implementation of operators (Convolution, Pooling, ..)
-   - [Intel CPU](#intel-cpu)
-   - [Nvidia GPU](#nvidia-gpu)
-2. Input data loading and augmentation
-   - [Input Data](#input-data)
-3. Workloads (computation graph) optimization and scheduling
-   - [Profiler](#profiler)
-4. Communication for multi-devices training
-   - [Multiple Devices](#multiple-devices)
-
-## Intel CPU
-
-When using Intel Xeon CPUs for training and inference, the `mxnet-mkl` package is recommended. Adding `--pre` installs a nightly build from master. Without it you will install the latest patched release of MXNet:
-
-```
-$ pip install mxnet-mkl [--pre]
-```
-
-Or build MXNet from source code with `USE_MKLDNN=1`. For Linux users, `USE_MKLDNN=1` will be turned on by default.
-
-We also find that setting the following environment variables can help:
-
-| Variable  | Description |
-| :-------- | :---------- |
-| `OMP_NUM_THREADS`            | Suggested value: `vCPUs / 2` in which `vCPUs` is the number of virtual CPUs. For more information, please see the guide for [setting the number of threads using an OpenMP environment variable](https://software.intel.com/en-us/mkl-windows-developer-guide-setting-the-number-of-threads-using-an-openmp-environment-variable) |
-| `KMP_AFFINITY`               | Suggested value: `granularity=fine,compact,1,0`.  For more information, please see the guide for [Thread Affinity Interface (Linux* and Windows*)](https://software.intel.com/en-us/node/522691). |
-| `MXNET_SUBGRAPH_BACKEND` | Set to MKLDNN to enable the [subgraph feature](https://cwiki.apache.org/confluence/display/MXNET/MXNet+Graph+Optimization+and+Quantization+based+on+subgraph+and+MKL-DNN) for better performance. For more information please see [Build/Install MXNet with MKL-DNN](https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/mkldnn/MKLDNN_README.md)|
-
-Note that _MXNet_ treats all CPUs on a single machine as a single device.
-So whether you specify `cpu(0)` or `cpu()`, _MXNet_ will use all CPU cores on the machine.
-
-### Scoring results
-The following table shows performance of [MXNet-1.2.0.rc1](https://github.com/apache/incubator-mxnet/releases/download/1.2.0.rc1/apache-mxnet-src-1.2.0.rc1-incubating.tar.gz),
-namely number of images that can be predicted per second.
-We used [example/image-classification/benchmark_score.py](https://github.com/dmlc/mxnet/blob/master/example/image-classification/benchmark_score.py)
-to measure the performance on different AWS EC2 machines.
-
-AWS EC2 C5.18xlarge:
-
-| Batch | Alexnet | VGG 16    | Inception-BN | Inception-v3 | Resnet 50 | Resnet 152 |
-|-------|---------|--------|--------------|--------------|-----------|------------|
-| 1     | 390.53  | 81.57  | 124.13       | 62.26        | 76.22     | 32.92      |
-| 2     | 596.45  | 100.84 | 206.58       | 93.36        | 119.55    | 46.80      |
-| 4     | 710.77  | 119.04 | 275.55       | 127.86       | 148.62    | 59.36      |
-| 8     | 921.40  | 120.38 | 380.82       | 157.11       | 167.95    | 70.78      |
-| 16    | 1018.43 | 115.30 | 411.67       | 168.71       | 178.54    | 75.13      |
-| 32    | 1290.31 | 107.19 | 483.34       | 179.38       | 193.47    | 85.86      |
-
-
-AWS EC2 C5.9xlarge:
-
-| Batch | Alexnet | VGG 16   | Inception-BN | Inception-v3 | Resnet 50 | Resnet 152 |
-|-------|---------|-------|--------------|--------------|-----------|------------|
-| 1     | 257.77  | 50.61 | 130.99       | 66.95        | 75.38     | 32.33      |
-| 2     | 410.60  | 63.02 | 195.14       | 87.84        | 102.67    | 41.57      |
-| 4     | 462.59  | 62.64 | 263.15       | 109.87       | 127.15    | 50.69      |
-| 8     | 573.79  | 63.95 | 309.99       | 121.36       | 140.84    | 59.01      |
-| 16    | 709.47  | 67.79 | 350.19       | 128.26       | 147.41    | 64.15      |
-| 32    | 831.46  | 69.58 | 354.91       | 129.92       | 149.18    | 64.25      |
-
-
-AWS EC2 C5.4xlarge:
-
-| Batch | Alexnet | VGG 16   | Inception-BN | Inception-v3 | Resnet 50 | Resnet 152 |
-|-------|---------|-------|--------------|--------------|-----------|------------|
-| 1     | 214.15  | 29.32 | 114.97       | 47.96        | 61.01     | 23.92      |
-| 2     | 310.04  | 34.81 | 150.09       | 60.89        | 71.16     | 27.92      |
-| 4     | 330.69  | 34.56 | 186.63       | 74.15        | 86.86     | 34.37      |
-| 8     | 378.88  | 35.46 | 204.89       | 77.05        | 91.10     | 36.93      |
-| 16    | 424.00  | 36.49 | 211.55       | 78.39        | 91.23     | 37.34      |
-| 32    | 481.95  | 37.23 | 213.71       | 78.23        | 91.68     | 37.26      |
-
-
-AWS EC2 C5.2xlarge:
-
-| Batch | Alexnet | VGG 16   | Inception-BN | Inception-v3 | Resnet 50 | Resnet 152 |
-|-------|---------|-------|--------------|--------------|-----------|------------|
-| 1     | 131.01  | 15.67 | 78.75        | 31.12        | 37.30     | 14.75      |
-| 2     | 182.29  | 18.01 | 98.59        | 39.13        | 45.98     | 17.84      |
-| 4     | 189.31  | 18.25 | 110.26       | 41.35        | 49.21     | 19.32      |
-| 8     | 211.75  | 18.57 | 115.46       | 42.53        | 49.98     | 19.81      |
-| 16    | 236.06  | 19.11 | 117.18       | 42.59        | 50.20     | 19.92      |
-| 32    | 261.13  | 19.46 | 116.20       | 42.72        | 49.95     | 19.80      |
-
-
-AWS EC2 C5.xlarge:
-
-| Batch | Alexnet | VGG 16  | Inception-BN | Inception-v3 | Resnet 50 | Resnet 152 |
-|-------|---------|------|--------------|--------------|-----------|------------|
-| 1     | 36.64   | 3.93 | 27.06        | 10.09        | 12.98     | 5.06       |
-| 2     | 49.21   | 4.49 | 29.67        | 10.80        | 12.94     | 5.14       |
-| 4     | 50.12   | 4.50 | 30.31        | 10.83        | 13.17     | 5.19       |
-| 8     | 54.71   | 4.58 | 30.22        | 10.89        | 13.19     | 5.20       |
-| 16    | 60.23   | 4.70 | 30.20        | 10.91        | 13.23     | 5.19       |
-| 32    | 66.37   | 4.76 | 30.10        | 10.90        | 13.22     | 5.15       |
-
-
-## Other CPU
-
-If using CPUs (not just Intel CPUs -- ARMs also), NNPACK can improve the running performance with 2x~7x, please check [nnpack.md](./nnpack.md) for details.
-
-## Nvidia GPU
-
-`cuDNN` typically accelerates _MXNet_ performance on NVIDIA GPUs significantly,
-especially for convolution layers.
-We suggest always checking to make sure that a recent cuDNN version is used.
-
-Setting the environment `export MXNET_CUDNN_AUTOTUNE_DEFAULT=1` sometimes also helps.
-
-We show results when using various GPUs including K80 (EC2 p2.2xlarge), M60 (EC2 g3.4xlarge),
-and V100 (EC2 p3.2xlarge).
-
-### Scoring results
-
-Based on
-[example/image-classification/benchmark_score.py](https://github.com/dmlc/mxnet/blob/master/example/image-classification/benchmark_score.py)
-and  [MXNet-1.2.0.rc1](https://github.com/apache/incubator-mxnet/releases/download/1.2.0.rc1/apache-mxnet-src-1.2.0.rc1-incubating.tar.gz), with cuDNN 7.0.5
-
-- K80 (single GPU)
-
-| Batch | Alexnet | VGG 16    | Inception-BN | Inception-v3 | Resnet 50 | Resnet 152 |
-|-------|---------|--------|--------------|--------------|-----------|------------|
-| 1     | 243.93  | 43.59  | 68.62        | 35.52        | 67.41     | 23.65      |
-| 2     | 338.16  | 49.14  | 113.41       | 56.29        | 93.35     | 33.88      |
-| 4     | 478.92  | 53.44  | 159.61       | 74.43        | 119.18    | 45.23      |
-| 8     | 683.52  | 70.50  | 190.49       | 86.23        | 131.32    | 50.54      |
-| 16    | 1004.66 | 109.01 | 254.20       | 105.70       | 155.40    | 62.55      |
-| 32    | 1238.55 | 114.98 | 285.49       | 116.79       | 159.42    | 64.99      |
-| 64 | 1346.72 | 123.56 | 308.73 | 122.21 | 167.58 | 70.21 |
-| 128 | 1416.91 | OOM | 320.98 | 123.11 | 171.55 | 71.85 |
-| 256 | 1462.97 | OOM | 329.16 | 127.53 | 153.01 | 57.23 |
-
-- M60
-
-| Batch | Alexnet | VGG 16    | Inception-BN | Inception-v3 | Resnet 50 | Resnet 152 |
-|-------|---------|--------|--------------|--------------|-----------|------------|
-| 1     | 243.49  | 59.95  | 101.97       | 48.30        | 95.46     | 39.29      |
-| 2     | 491.04  | 69.14  | 170.35       | 80.27        | 142.61    | 60.17      |
-| 4     | 711.54  | 78.94  | 257.89       | 123.09       | 182.36    | 76.51      |
-| 8     | 1077.73 | 109.34 | 343.42       | 152.82       | 208.74    | 87.27      |
-| 16    | 1447.21 | 144.93 | 390.25       | 166.32       | 220.73    | 92.41      |
-| 32    | 1797.66 | 151.86 | 416.69       | 176.56       | 230.19    | 97.03      |
-| 64 | 1779.38 | 150.18 | 427.51 | 183.47 | 239.12 | 101.59 |
-| 128 | 1787.36 | OOM | 439.04 | 185.29 | 243.31 | 103.39 |
-| 256 | 1899.10 | OOM | 450.22 | 183.42 | 242.36 | 100.98 |
-
-
-- V100
-
-| Batch | Alexnet | VGG 16    | Inception-BN | Inception-v3 | Resnet 50 | Resnet 152 |
-|-------|---------|--------|--------------|--------------|-----------|------------|
-| 1     | 659.51  | 205.16 | 157.37 | 87.71 | 162.15    | 61.38      |
-| 2     | 1248.21 | 265.40 | 297.34 | 159.24 | 293.74    | 116.30     |
-| 4     | 2122.41 | 333.97 | 520.91 | 279.84 | 479.14    | 195.17     |
-| 8     | 3894.30 | 420.26 | 898.09 | 455.03 | 699.39    | 294.19     |
-| 16    | 5815.58 | 654.16 | 1430.97 | 672.54 | 947.45    | 398.79     |
-| 32    | 7906.09 | 708.43 | 1847.26 | 814.59 | 1076.81   | 451.82     |
-| 64 | 9486.26 | 701.59 | 2134.89 | 899.01 | 1168.37 | 480.44 |
-| 128 | 10177.84 | 703.30 | 2318.32 | 904.33 | 1233.15 | 511.79 |
-| 256 | 10990.46 | 473.62 | 2425.28 | 960.20 | 1155.07 | 449.35 |
-
-Below is the performance result on V100 using float 16.
-
-| Batch | VGG 16  | Inception-BN | Inception-v3 | Resnet 50 | Resnet 152 |
-| ----- | ------- | ------------ | ------------ | --------- | ---------- |
-| 1     | 276.29  | 155.53       | 150.99       | 270.89    | 96.79      |
-| 2     | 476.91  | 296.45       | 282.02       | 493.99    | 176.88     |
-| 4     | 711.92  | 525.05       | 492.45       | 851.15    | 321.52     |
-| 8     | 1047.11 | 900.26       | 807.94       | 1282.36   | 517.66     |
-| 16    | 1299.88 | 1441.41      | 1192.21      | 1722.97   | 724.57     |
-| 32    | 1486.63 | 1854.30      | 1512.08      | 2085.51   | 887.34     |
-| 64    | 1219.65 | 2138.61      | 1687.35      | 2341.67   | 1002.90    |
-| 128   | 1169.81 | 2317.39      | 1818.26      | 2355.04   | 1046.98    |
-| 256   | 764.16  | 2425.16      | 1653.74      | 1991.88   | 976.73     |
-
-### Training results
-
-Based on
-[example/image-classification/train_imagenet.py](https://github.com/dmlc/mxnet/blob/master/example/image-classification/train_imagenet.py)
-and  [MXNet-1.2.0.rc1](https://github.com/apache/incubator-mxnet/releases/download/1.2.0.rc1/apache-mxnet-src-1.2.0.rc1-incubating.tar.gz), with CUDNN 7.0.5. The benchmark script is available at
-[here](https://github.com/mli/mxnet-benchmark/blob/master/run_vary_batch.sh),
-where the batch size for Alexnet is increased by 16x.
-
-- K80 (single GPU)
-
-  | Batch | Alexnet(\*16) | Inception-v3 | Resnet 50 |
-  | --- | --- | --- | --- |
-  |   1 | 300.30 | 10.48 | 15.61 |
-  |   2 | 406.08 | 16.00 | 23.88 |
-  |   4 | 461.01 | 22.10 | 32.26 |
-  |   8 | 484.00 | 26.80 | 39.42 |
-  |  16 | 490.45 | 31.62 | 46.69 |
-  |  32 | 414.72 | 33.78 | 49.48 |
-
-- M60
-
-  | Batch | Alexnet(\*16) | Inception-v3 | Resnet 50 |
-  | --- | --- | --- | --- |
-  |   1 | 380.96 | 14.06 | 20.55 |
-  |   2 | 530.53 | 21.90 | 32.65 |
-  |   4 | 600.17 | 31.96 | 45.57 |
-  |   8 | 633.60 | 40.58 | 54.92 |
-  |  16 | 639.37 | 46.88 | 64.44 |
-  |  32 | 576.54 | 50.05 | 68.34 |
-
-- V100
-
-  | Batch | Alexnet(\*16) | Inception-v3 | Resnet 50 |
-  | --- | --- | --- | --- |
-  |   1 | 1629.52 | 21.83 | 34.54 |
-  |   2 | 2359.73 | 40.11 | 65.01 |
-  |   4 | 2687.89 | 72.79 | 113.49 |
-  |   8 | 2919.02 | 118.43 | 174.81 |
-  |  16 | 2994.32 | 173.15 | 251.22 |
-  |  32 | 2585.61 | 214.48 | 298.51 |
-  | 64 | 1984.21 | 247.43 | 343.19 |
-  | 128 | OOM | 253.68 | 363.69 |
-
-## Multiple Devices
-
-If more than one GPU or machine are used, MXNet uses `kvstore` to communicate data.
-It's critical to use the proper type of `kvstore` to get the best performance.
-Refer to [multi_device.md](http://mxnet.io/faq/multi_devices.html) for more
-details.
-
-Besides, we can use [tools/bandwidth](https://github.com/dmlc/mxnet/tree/master/tools/bandwidth)
-to find the communication cost per batch.
-Ideally, the communication cost should be less than the time to compute a batch.
-To reduce the communication cost, we can consider:
-
-- Exploring different `--kv-store` options.
-- Increasing the batch size to improve the computation to communication ratio.
-
-## Input Data
-
-To make sure you're handling input data in a reasonable way consider the following:
-
-* Data format: If you are using the `rec` format, then everything should be fine.
-* Decoding: By default, _MXNet_ uses 4 CPU threads for decoding images.
-This is often sufficient to decode more than 1K images per second.
-If you are using a low-end CPU or your GPUs are very powerful, you can increase the number of threads.
-* Storage location. Any local or distributed file system (HDFS, Amazon S3) should be fine.
-If multiple devices read the data from the shared network file system (NFS) at the same time, problems might occur.
-* Use a large batch size. We often choose the largest one that fits into GPU memory.
-A value that's too large can slow down convergence.
-For example, the safe batch size for CIFAR 10 is approximately 200, while for ImageNet 1K, the batch size can exceed 1K.
-
-## Profiler
-
-As of v0.9.1 (with the NNVM merge), _MXNet_ has a built-in profiler
-that gives detailed information about execution time at the symbol level.
-This feature complements general profiling tools like _nvprof_ and _gprof_
-by summarizing at the operator level, instead of a function, kernel, or instruction level.
-
-The profiler can be turned on with an [environment variable](http://mxnet.io/faq/env_var.html#control-the-profiler)
-for an entire program run, or programmatically for just part of a run.
-See [example/profiler](https://github.com/dmlc/mxnet/tree/master/example/profiler)
-for complete examples of how to use the profiler in code, but briefly, the Python code looks like:
-
-```
-    mx.profiler.set_config(profile_all=True, filename='profile_output.json')
-    mx.profiler.set_state('run')
-
-    # Code to be profiled goes here...
-
-    mx.profiler.set_state('stop')
-```
-
-The `mode` parameter can be set to
-
-* `symbolic` to only include symbolic operations
-* `all` to include all operations
-
-After the program finishes, navigate to your browser's tracing (Example - chrome://tracing in a Chrome browser) and load the `profile_output.json` file output by the profiler to inspect the results.
-
-![MLP Profile](https://cloud.githubusercontent.com/assets/17693755/18035938/0a43484a-6d93-11e6-80d4-241c6ca552ea.png)
-
-Note that the output file can grow extremely large, so this approach is not recommended for general use.
diff --git a/docs/faq/recordio.md b/docs/faq/recordio.md
deleted file mode 100644
index 7367b524224d..000000000000
--- a/docs/faq/recordio.md
+++ /dev/null
@@ -1,100 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-## Create a Dataset Using RecordIO
-
-RecordIO implements a file format for a sequence of records. We recommend storing images as records and packing them together. The benefits include:
-
-* Storing images in a compact format--e.g., JPEG, for records--greatly reduces the size of the dataset on the disk.
-* Packing data together allows continuous reading on the disk.
-* RecordIO has a simple way to partition, simplifying distributed setting. We provide an example later.
-
-We provide two tools for creating a RecordIO dataset.
-
-* [im2rec.cc](https://github.com/dmlc/mxnet/blob/master/tools/im2rec.cc) - implements the tool using the C++ API.
-* [im2rec.py](https://github.com/apache/incubator-mxnet/blob/master/tools/im2rec.py) - implements the tool using the Python API.
-
-Both provide the same output: a RecordIO dataset.
-You may want to also review the [example using real-world data with im2rec.py.](https://mxnet.incubator.apache.org/tutorials/basic/data.html#loading-data-using-image-iterators)
-
-### Prerequisites
-
-Download the data. You don't need to resize the images manually. You can use ```im2rec``` to resize them automatically. For details, see the "Extension: Using Multiple Labels for a Single Image," later in this topic.
-
-### Step 1. Make an Image List File
-
-* Note that the im2rec.py provides a param `--list` to generate the list for you, but im2rec.cc doesn't support it.
-
-After you download the data, you need to make an image list file.  The format is:
-
-```
-integer_image_index \t label_index \t path_to_image
-```
-Typically, the program takes the list of names of all of the images, shuffles them, then separates them into two lists: a training filename list and a testing filename list. Write the list in the right format.
-This is an example file:
-
-```bash
-95099  464.000000     n04467665_17283.JPEG
-10025081        412.000000     ILSVRC2010_val_00025082.JPEG
-74181   789.000000     n01915811_2739.JPEG
-10035553        859.000000     ILSVRC2010_val_00035554.JPEG
-10048727        929.000000     ILSVRC2010_val_00048728.JPEG
-94028   924.000000     n01980166_4956.JPEG
-1080682 650.000000     n11807979_571.JPEG
-972457  633.000000     n07723039_1627.JPEG
-7534    11.000000      n01630670_4486.JPEG
-1191261 249.000000     n12407079_5106.JPEG
-```
-
-### Step 2. Create the Binary File
-
-To generate a binary image, use `im2rec` in the tool folder. `im2rec` takes the path of the `image list file` you generated, the `root path` of the images, and the `output file path` as input. This process usually takes several hours, so be patient.
-
-Sample command:
-
-```bash
-./bin/im2rec image.lst image_root_dir output.bin resize=256
-```
-For more details, run ```./bin/im2rec```.
-
-### Extension: Multiple Labels for a Single Image
-
-The `im2rec` tool and `mx.io.ImageRecordIter` have multi-label support for a single image.
-For example, if you have four labels for a single image, you can use the following procedure to use the RecordIO tools.
-
-1. Write the image list files as follows:
-
-```
-integer_image_index \t label_1 \t label_2 \t   label_3 \t label_4 \t path_to_image
-```
-
-2. Run `im2rec`, adding a 'label_width=4' to the command argument, for example:
-
-```bash
-./bin/im2rec image.lst image_root_dir output.bin resize=256 label_width=4
-```
-
-3. In the iterator generation code, set `label_width=4` and `path_imglist=<<The PATH TO YOUR image.lst>>`, for example:
-
-```python
-dataiter = mx.io.ImageRecordIter(
-  path_imgrec="data/cifar/train.rec",
-  data_shape=(3,28,28),
-  path_imglist="data/cifar/image.lst",
-  label_width=4
-)
-```
diff --git a/docs/faq/s3_integration.md b/docs/faq/s3_integration.md
deleted file mode 100644
index 49520b5814e1..000000000000
--- a/docs/faq/s3_integration.md
+++ /dev/null
@@ -1,116 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Use data from S3 for training
-
-AWS S3 is a cloud-based object storage service that allows storage and retrieval of large amounts of data at a very low cost. This makes it an attractive option to store large training datasets. MXNet is deeply integrated with S3 for this purpose.
-
-An S3 protocol URL (like `s3://bucket-name/training-data`) can be provided as a parameter for any data iterator that takes a file path as input. For example,
-
-```
-data_iter = mx.io.ImageRecordIter(
-    path_imgrec="s3://bucket-name/training-data/caltech_train.rec",
-    data_shape=(3, 227, 227),
-    batch_size=4,
-    resize=256)
-```
-Following are detailed instructions on how to use data from S3 for training.
-
-## Step 1: Build MXNet with S3 integration enabled
-
-Follow instructions [here](http://mxnet.io/install/index.html) to install MXNet from source with the following additional steps to enable S3 integration.
-
-1. Install `libcurl4-openssl-dev` and `libssl-dev` before building MXNet. These packages are required to read/write from AWS S3.
-2. Append `USE_S3=1` to `config.mk` before building MXNet.
-    ```
-    echo "USE_S3=1" >> config.mk
-    ```
-
-## Step 2: Configure S3 authentication tokens
-
-MXNet requires the S3 environment variables `AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY` to be set. [Here](https://aws.amazon.com/blogs/security/wheres-my-secret-access-key/) are instructions to get the access keys from AWS console.
-
-```
-export AWS_ACCESS_KEY_ID=<your-access-key-id>
-AWS_SECRET_ACCESS_KEY=<your-secret-access-key>
-```
-
-## Step 3: Upload data to S3
-
-There are several ways to upload data to S3. One easy way is to use the AWS command line utility. For example, the following `sync` command will recursively copy contents from a local directory to a directory in S3.
-
-```
-aws s3 sync ./training-data s3://bucket-name/training-data
-```
-
-## Step 4: Train with data from S3
-
-Once the data is in S3, it is very straightforward to use it from MXNet. Any data iterator that can read/write data from a local drive can also read/write data from S3.
-
-Let's modify an existing example code in MXNet repository to read data from S3 instead of local disk. [`mxnet/tests/python/train/test_conv.py`](https://github.com/dmlc/mxnet/blob/master/tests/python/train/test_conv.py) trains a convolutional network using MNIST data from local disk. We'll do the following change to read the data from S3 instead.
-
-```
-~/mxnet$ sed -i -- 's/data\//s3:\/\/bucket-name\/training-data\//g' ./tests/python/train/test_conv.py
-
-~/mxnet$ git diff ./tests/python/train/test_conv.py
-diff --git a/tests/python/train/test_conv.py b/tests/python/train/test_conv.py
-index 039790e..66a60ce 100644
---- a/tests/python/train/test_conv.py
-+++ b/tests/python/train/test_conv.py
-@@ -39,14 +39,14 @@ def get_iters():
-
-     batch_size = 100
-     train_dataiter = mx.io.MNISTIter(
--            image="data/train-images-idx3-ubyte",
--            label="data/train-labels-idx1-ubyte",
-+            image="s3://bucket-name/training-data/train-images-idx3-ubyte",
-+            label="s3://bucket-name/training-data/train-labels-idx1-ubyte",
-             data_shape=(1, 28, 28),
-             label_name='sm_label',
-             batch_size=batch_size, shuffle=True, flat=False, silent=False, seed=10)
-     val_dataiter = mx.io.MNISTIter(
--            image="data/t10k-images-idx3-ubyte",
--            label="data/t10k-labels-idx1-ubyte",
-+            image="s3://bucket-name/training-data/t10k-images-idx3-ubyte",
-+            label="s3://bucket-name/training-data/t10k-labels-idx1-ubyte",
-             data_shape=(1, 28, 28),
-             label_name='sm_label',
-             batch_size=batch_size, shuffle=True, flat=False, silent=False)
-```
-
-After the above change `test_conv.py` will fetch data from S3 instead of the local disk.
-
-```
-python ./tests/python/train/test_conv.py
-[21:59:19] src/io/s3_filesys.cc:878: No AWS Region set, using default region us-east-1
-[21:59:21] src/io/iter_mnist.cc:94: MNISTIter: load 60000 images, shuffle=1, shape=(100,1,28,28)
-[21:59:21] src/io/iter_mnist.cc:94: MNISTIter: load 10000 images, shuffle=1, shape=(100,1,28,28)
-INFO:root:Start training with [cpu(0)]
-Start training with [cpu(0)]
-INFO:root:Epoch[0] Resetting Data Iterator
-Epoch[0] Resetting Data Iterator
-INFO:root:Epoch[0] Time cost=11.277
-Epoch[0] Time cost=11.277
-INFO:root:Epoch[0] Validation-accuracy=0.955100
-Epoch[0] Validation-accuracy=0.955100
-INFO:root:Finish fit...
-Finish fit...
-INFO:root:Finish predict...
-Finish predict...
-INFO:root:final accuracy = 0.955100
-final accuracy = 0.955100
-```
diff --git a/docs/faq/security.md b/docs/faq/security.md
deleted file mode 100644
index 88d48be35ecb..000000000000
--- a/docs/faq/security.md
+++ /dev/null
@@ -1,41 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# MXNet Security Best Practices
-
-MXNet framework has no built-in security protections. It assumes that the MXNet entities involved in model training and inferencing (hosting) are fully trusted. It also assumes that their communications cannot be eavesdropped or tampered with. MXNet consumers shall ensure that the above assumptions are met.
-
-In particular the following threat-vectors exist when training using MXNet:
-
-* When running distributed training using MXNet there is no built-in support for authenticating cluster nodes participating in the training job.
-* Data exchange between cluster nodes happens is in plain-text.
-* Using `kvstore.set_optimizer` one can use a custom optimizer to combine gradients. This optimizer code is sent to the server nodes as a pickle file. A server does not perform any further validation of the pickle file and simply executes the code trusting the sender (worker).
-* Since there is no authentication between nodes, a malicious actor running on the same network can launch a Denial of Service (DoS) attack by sending data that can overwhelm/crash a scheduler or other server nodes.
-
-It is highly recommended that the following best practices be followed when using MXNet:
-
-* Run MXNet with least privilege, i.e. not as root.
-* Run MXNet training jobs inside a secure and isolated environment. If you are using a cloud provider like Amazon AWS, running your training job inside a [private VPC](https://aws.amazon.com/vpc/) is a good way to accomplish this. Additionally, configure your network security settings so as to only allow connections that the cluster nodes require.
-* Make sure no unauthorized actors have physical or remote access to the nodes participating in MXNet training.
-* During training, one can configure MXNet to periodically save model checkpoints. To protect these model checkpoints from unauthorized access, make sure the checkpoints are written out to an encrypted storage volume, and have a provision to delete checkpoints that are no longer needed.
-* When sharing trained models, or when receiving trained models from other parties, ensure that model artifacts are authenticated and integrity protected using cryptographic signatures, thus ensuring that the data received comes from trusted sources and has not been maliciously (or accidentally) modified in transit.
-* By default, mx.random uses a static and fixed seed value. The random utilities in MXNet should therefore never be used to implement any type of security critical functionality where cryptographically secure pseudorandom number generation is required.
-
-# Deployment Considerations
-The following are not MXNet framework specific threats but are applicable to Machine Learning models in general.
-
-* When deploying high-value, proprietary models for inference, care should be taken to prevent an adversary from stealing the model. The research paper [Stealing Machine Learning Models via Prediction APIs](https://arxiv.org/pdf/1609.02943.pdf) outlines experiments performed to show how an attacker can use a prediction API to leak the ML model or construct a nearly identical replica. A simple way to thwart such an attack is to not expose the prediction probabilities to a high degree of precision in the API response.
diff --git a/docs/faq/smart_device.md b/docs/faq/smart_device.md
deleted file mode 100644
index c56457a626cb..000000000000
--- a/docs/faq/smart_device.md
+++ /dev/null
@@ -1,112 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Deep Learning in a Single File for Smart Devices
-
-Deep learning (DL) systems are complex and often depend on a number of libraries.
-Porting a DL library to different platforms can be painful, especially for smart devices.
-One simple solution to this problem is to provide a light interface to the library, complete with all required code in a single file with minimal dependencies.
-In this document, we explain how to amalgamate all necessary code into a single file,
-and demonstrate the approach with an example in which we run image recognition on a mobile device.
-
-## Amalgamation: Making the Whole System a Single File
-
-We come to the idea of of amalgamation following the example of SQLite,
-which pack all the code needed to run a simple database into a single source file.
-All that's necessary to create the library is to compile that single file.
-This simplifies the problem of porting to various platforms.
-
-Thanks to [Jack Deng](https://github.com/jdeng),
-MXNet provides an [amalgamation](https://github.com/dmlc/mxnet/tree/master/amalgamation) script
-that compiles all code needed for prediction based on trained DL models into a single `.cc` file,
-containing approximately 30K lines of code. This code only depends on the BLAS library.
-Moreover, we've also created an even more minimal version,
-with the BLAS dependency removed.
-You can compile the single file into JavaScript by using [emscripten](https://github.com/kripken/emscripten).
-
-The compiled library can be used by any other programming language.
-The `.h` file contains a light prediction API.
-Porting to another language with a C foreign function interface requires little effort.
-
-For examples, see the following examples on GitHub:
-
-- Go: [https://github.com/jdeng/gomxnet](https://github.com/jdeng/gomxnet)
-- Java: [https://github.com/dmlc/mxnet/tree/master/amalgamation/jni](https://github.com/dmlc/mxnet/tree/master/amalgamation/jni)
-- Python: [https://github.com/dmlc/mxnet/tree/master/amalgamation/python](https://github.com/dmlc/mxnet/tree/master/amalgamation/python)
-
-
-If you plan to amalgamate your system, there are a few guidelines you ought to observe when building the project:
-
-- Minimize dependence on other libraries.
-- Use namespace to encapsulate the types and operators.
-- Avoid running commands such as ```using namespace xyz``` on the global scope.
-- Avoid cyclic include dependencies.
-
-
-## Image Recognition Demo on Mobile Devices
-
-With amalgamation, deploying the system on smart devices (such as Android or iOS) is simple. But there are two additional considerations:
-
-- The model should be small enough to fit into the device's memory.
-- The model shouldn't be too expensive to run given the relatively low computational power of these devices.
-
-Let's use image recognition as an example.
-We start with the state-of-the-art inception model.
-We train it on an ImageNet dataset,
-using multiple servers with GTX 980 cards.
-The resulting model fits into memory,
-but it's too expensive to run.
-We remove some layers, but now the results are poor.
-
-Finally, we show an Android example, thanks to Leliana, [https://github.com/Leliana/WhatsThis](https://github.com/Leliana/WhatsThis) to demonstrate how to run on Android.
-
-<img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/apk/subinception.png" height="488" width="274">
-
-
-By using amalgamation, we can easily port the prediction library to mobile devices, with nearly no dependency.
-After compiling the library for smart platforms, the last thing we must do is to call C-API in the target language (Java/Swift).
-
-<img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/apk/poolnet.png" height="488" width="274">
-
-Besides this pre-trained Inception-BatchNorm network, we've provided two pre-trained models.
-
-We tested our model on a Nexus 5:
-
-
-|                  | Top-1 Validation on ILSVRC2012      | Time  | App Size  | Runtime Temp Memory Req |
-| ---------------- | ----------------------------------- | ----- | --- | ------------ |
-| FastPoorNet      | around 52%, similar to 2011 winner  | 1s    | <10MB    |  <5MB               |
-| Sub InceptionBN  | around 64%, similar to 2013 winner  | 2.7s  | <40MB    |  <10MB              |
-| InceptionBN      | around 70%                          | 4s-5s | <60MB    | 10MB               |
-
-These models are for demonstration only.
-They aren't fine-tuned for mobile devices,
-and there is definitely room for improvement.  
-We believe that making a lightweight, portable,
-and fast deep learning library is fun and interesting,
-and hope you enjoy using the library.
-
-## Source Code
-[https://github.com/Leliana/WhatsThis](https://github.com/Leliana/WhatsThis)
-
-
-## Demo APK Download
-
-- [FastPoorNet](https://github.com/dmlc/web-data/blob/master/mxnet/apk/fastpoornet.apk?raw=true)
-
-
-- [SubInception](https://github.com/dmlc/web-data/blob/master/mxnet/apk/subinception.apk?raw=true)
diff --git a/docs/faq/tensor_inspector_tutorial.md b/docs/faq/tensor_inspector_tutorial.md
deleted file mode 100644
index e77c7447fd17..000000000000
--- a/docs/faq/tensor_inspector_tutorial.md
+++ /dev/null
@@ -1,168 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Use TensorInspector to Help Debug Operators
-
-## Introduction
-
-When developing new operators, developers need to deal with tensor objects extensively. This new utility, Tensor Inspector, mainly aims to help developers debug by providing unified interfaces to print, check, and dump the tensor value. To developers' convenience, this utility works for all the three data types: Tensors, TBlobs, and NDArrays. Also, it supports both CPU and GPU tensors.
-
-
-## Usage 
-
-This utility is located in `src/common/tensor_inspector.h`. To use it in any operator code, just include it using `#include "{path}/tensor_inspector.h"`, construct an `TensorInspector` object, and call the APIs on that object. You can run any script that uses the operator you just modified then.
-
-The screenshot below shows a sample usage in `src/operator/nn/convolution-inl.h`.
-
-![tensor_inspector_example_usage](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/faq/tensor_inspector_tutorial/tensor_inspector_example_usage.png)
-
-
-## Functionalities/APIs
-
-### Create a TensorInspector Object from Tensor, TBlob, and NDArray Objects
-
-You can create a `TensorInspector` object by passing in two things: 1) an object of type `Tensor`, `Tbob`, or `NDArray`, and 2) an `RunContext` object.
-
-Essentially, `TensorInspector` can be understood as a wrapper class around `TBlob`. Internally, the `Tensor`, `Tbob`, or `NDArray` object that you passed in will be converted to a `TBlob` object. The `RunContext` object is used when the tensor is a GPU tensor; in such a case, we need to use the context information to copy the data from GPU memory to CPU/main memory.
-
-Following are the three constructors:
-
-```c++
-// Construct from Tensor object
-template<typename Device, int dimension, typename DType MSHADOW_DEFAULT_DTYPE>
-TensorInspector(const mshadow::Tensor<Device, dimension, DType>& ts, const RunContext& ctx);
-
-// Construct from TBlob object
-TensorInspector(const TBlob& tb, const RunContext& ctx);
-
-// Construct from NDArray object
-TensorInspector(const NDArray& arr, const RunContext& ctx):
-```
-
-### Print Tensor Value (Static) 
-
-To print out the tensor value in a nicely structured way, you can use this API:
-
-```c++
-void print_string();
-```
-
-This API will print the entire tensor to `std::cout` and preserve the shape (it supports all dimensions from 1 and up). You can copy the output and interpret it with any `JSON` loader. You can find some useful information about the tensor on the last line of the output. Refer to the case below, we are able to know that this is a float-typed tensor with shape 20x1x5x5.
-
-![tensor_inspector_to_string](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/faq/tensor_inspector_tutorial/tensor_inspector_to_string.png)
-
-If instead of printing the tensor to `std::cout`, you just need a `string`, you can use this API:
-```c++
-std::string void to_string();
-```
-
-### Interactively Print Tensor Value (Dynamic) 
-
-Sometimes at compilation time, you may not know which part of a tensor to inspect. Also, it may be nice to pause the operator control flow to “zoom into” a specific, erroneous part of a tensor multiple times until you are satisfied. In this regard, you can use this API to interactively inspect the tensor:
-
-```c++
-void  interactive_print(std::string tag =  "") {
-```
-
-This API will set a "break point" in your code. When that "break point" is reached, you will enter a loop that will keep asking you for further command input. In the API call, `tag` is an optional parameter to give the call a name, so that you can identify it when you have multiple `interactive_print()` calls in different parts of your code. A visit count will tell you how many times you stepped into this particular "break point", should this operator be called more than once. Note that all `interactive_print()` calls are properly locked, so you can use it in many different places without issues.
-
-![tensor_inspector_interactive_print](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/faq/tensor_inspector_tutorial/tensor_inspector_interactive_print.png)
-
-There are many useful commands available, as described in the previous screenshot: you can type "e" to print out the entire tensor, "d" to dump the tensor to file (see below), "b" to break from this command loop, and "s" to skip all future `interactive_print()`. Most importantly, in this screen, you can specify a part of the tensor that you are particularly interested in and want to print out. For example, for this 64x20x24x24 tensor, you can type in "0, 0" and presss enter to check the sub-tensor with shape 24x24 at coordinate (0, 0). 
-
-### Check Tensor Value
-
-Sometimes, developers might want to check if the tensor contains unexpected values which could be negative values, NaNs, infinities or others. To facilitate that, you can use these APIs:
-
-```c++
-template<typename ValueChecker>
-std::vector<std::vector<int>> check_value(const ValueChecker& checker,
-		bool interactive = false, std::string tag = "");
-// OR
-std::vector<std::vector<int>> check_value(CheckerType ct,
-		bool interactive = false, std::string tag =  "");
-```
-
-In the first API, `ValueChecker checker` is a bool lambda function that takes in a single parameter which is of the same data type as the tensor.  For example:
-
-```c++
-// use the same DType as in the tensor object
-[] (DType x) {return x == 0};
-```
-
-This checker is called on every value within the tensor. The return of the API is a `vector` of all the coordinates where the checker evaluates to `true`. The coordinates are themselves represented by `vector<int>`. If you set `interactive` to true, you will set a "break point" and enter a loop that asks for commands. This is similar to `interactive_print()`. You can type "p" to print the coordinates, "b" to break from the loop, and "s" to skip all future "break points" in `interactive_print()`. You can also specify a coordinate to print only a part of the tensor or type "e" to print out the entire tensor.  Just like `interactive_print()`, this this interactive screen is also properly locked.
-
-![tensor_inspector_value_check](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/faq/tensor_inspector_tutorial/tensor_inspector_value_check.png)
-
-Also, there are a bunch of built-int value checkers. Refer to the Enum below:
-
-```c++
-enum  CheckerType {
-	NegativeChecker, // check if negative
-	PositiveChecker, // check if positive
-	ZeroChecker, // check for zero
-	NaNChecker, // check if for NaN, will always return false if DType is not a float type
-	InfChecker, // check for infinity, will always return false if DType is not a float type
-	PositiveInfChecker, // check for positive infinity,
-						// will always return false if DType is not a float type
-	NegativeInfChecker, // check for nagative infinity,
-						// will always return false if DType is not a float type
-	FiniteChecker, // check if finite, will always return false if DType is not a float type
-	NormalChecker, // check if it is neither infinity nor NaN
-	AbnormalChecker, // chekck if it is infinity or nan
-};
-```
-
-Remember the second API?
-
-```c++
-std::vector<std::vector<int>> check_value(CheckerType ct,
-		bool interactive = false, std::string tag =  "");
-```
-
-You can simply pass in a value from `CheckerType` where you would have passed in your own lambda if you were using the first API. Note that it's the developer's responsibility to pass in a valid value checker.
-
-### Dump Tensor Value
-
-Sometimes, you might want to dump the tensor to a file in binary mode. Then, you might want to use a python script to further analyze the tensor value. Or, you might do that simply because a binary dump has better precision and is faster to load than the output copy-pasted from `print_string()` and loaded as a `JSON` string. Either way, you can use this API:
-
-```c++
-void dump_to_file(std::string tag);
-```
-
-This API will create a file with name  "{tag}_{visit_count}.npy", where tag is the name that we give to the call, and visit is the visit count, should the operated be called more than once.
-
-The output format is `.npy`, version 1.0. This is the Numpy format and we can easily load it with the following code:
-
-```
-import numpy as np
-a = np.load('abc_1.npy')
-print(a)
-```
-
-Let's see how it runs:
-
-![tensor_inspector_dump_to_file](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/faq/tensor_inspector_tutorial/tensor_inspector_dump_to_file.png)
-
-Notice: in `interactive_print()`, you could also do value dumping with command "d". You will be prompted to enter the `tag` value:
-
-![tensor_inspector_interactive_print](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/faq/tensor_inspector_tutorial/tensor_inspector_interactive_print.png)
-
-### Test Coverage and Limitations
-
-This utility has been tested on Mac and Ubuntu with and without CUDNN and MKLDNN. Supports for `Tensor`, `TBlob`, and `NDArray`, as well as for CPU and GPU have been manually tested. 
-
-Currently, this utility only supports non-empty tensors and tensors with known shapes i.e. `tb_.ndim() > 0`. Also, this utility only supports dense `NDArray` objects, i.e. when the type is `kDefaultStorage`. 
-
diff --git a/docs/faq/visualize_graph.md b/docs/faq/visualize_graph.md
deleted file mode 100644
index adeb0e49136f..000000000000
--- a/docs/faq/visualize_graph.md
+++ /dev/null
@@ -1,81 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# How to visualize Neural Networks as computation graph
-
-Here, we'll demonstrate how to use ```mx.viz.plot_network```
-for visualizing your neural networks. ```mx.viz.plot_network```
-represents the neural network as a computation graph consisting of nodes and edges.
-The visualizations make clear which nodes correspond to inputs,
-where the computation starts,
-and which correspond to output nodes,
-from which the result can be read.
-
-## Prerequisites
-You need the [Jupyter Notebook](http://jupyter.readthedocs.io/en/latest/)
-and [Graphviz](http://www.graphviz.org/) libraries to visualize the network.
-Please make sure you have followed [installation instructions](http://mxnet.io/install/index.html)
-in setting up above dependencies along with setting up MXNet.
-
-## Visualize the sample Neural Network
-
-```mx.viz.plot_network``` takes [Symbol](http://mxnet.io/api/python/symbol/symbol.html), with your Network definition, and optional node_attrs, parameters for the shape of the node in the graph,  as input and generates a computation graph.
-
-We will now try to visualize a sample Neural Network for linear matrix factorization:
-- Start Jupyter notebook server
-```bash
-  $ jupyter notebook
-```
-- Access Jupyter notebook in your browser - http://localhost:8888/.
-- Create a new notebook - "File -> New Notebook -> Python 2"
-- Copy and run below code to visualize a simple network.
-
-```python
-import mxnet as mx
-user = mx.symbol.Variable('user')
-item = mx.symbol.Variable('item')
-score = mx.symbol.Variable('score')
-
-# Set dummy dimensions
-k = 64
-max_user = 100
-max_item = 50
-
-# user feature lookup
-user = mx.symbol.Embedding(data = user, input_dim = max_user, output_dim = k)
-
-# item feature lookup
-item = mx.symbol.Embedding(data = item, input_dim = max_item, output_dim = k)
-
-# predict by the inner product, which is elementwise product and then sum
-net = user * item
-net = mx.symbol.sum_axis(data = net, axis = 1)
-net = mx.symbol.Flatten(data = net)
-
-# loss layer
-net = mx.symbol.LinearRegressionOutput(data = net, label = score)
-
-# Visualize your network
-mx.viz.plot_network(net)
-```
-You should see computation graph something like the following image:
-<img src=https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/SampleNetworkVisualization.png
-width=400/>
-
-# References
-* [Example MXNet Matrix Factorization](https://github.com/dmlc/mxnet/blob/master/example/recommenders/demo1-MF.ipynb)
-* [Visualizing CNN Architecture of MXNet Tutorials](http://josephpcohen.com/w/visualizing-cnn-architectures-side-by-side-with-mxnet/)
diff --git a/docs/faq/why_mxnet.md b/docs/faq/why_mxnet.md
deleted file mode 100644
index 140e853e2692..000000000000
--- a/docs/faq/why_mxnet.md
+++ /dev/null
@@ -1,216 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Why MXNet?
-
-Probably, if you've stumbled upon this page, you've heard of _deep learning_.
-Deep learning denotes the modern incarnation of neural networks,
-and it's the technology behind recent breakthroughs
-in self-driving cars, machine translation, speech recognition and more.
-While widespread interest in deep learning took off in 2012,
-deep learning has become an indispensable tool for countless industries.
-
-![alt text](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/get-started/image-classification.png)
-
-It might not come as a surprise that researchers
-have investigated neural networks for decades.
-Warren McCulloch and Walter Pitts
-suggested the forerunner of today's artificial neurons back in 1943.
-Each neuron is connected to other neurons along _edges_, analogous to the synapses that connect real neurons. 
-And associated with each edge is a _weight_ that indicates whether the connection is excitatory or inhibitatory and the strength of the connection. 
-
-![alt_text](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/get-started/artificial-neuron-2.png)
-
-In the 1980s, the modern version of neural networks took shape.
-Researchers arranged artificial neurons into _layers_.
-Neurons in any layer get input from the neurons in the layers below them.
-And, in turn, their output feeds into the neurons in the layer above.
-Typically, the lowest layer represents the _input_ to a neural network.
-After computing the values of each layer, the _output_ values are read out from the topmost layer.
-The behavior of the network is determined by the setting of the weights. 
-And the process of _learning_ in neural networks 
-is precisely the process of searching for good settings of these _weights_.
-
-All that we need is an algorithm that tells us how to perform this search.
-And since David Rumelhart and colleagues
-introduced the _backpropagation_ learning algorithm to train neural networks,
-nearly all the major ideas have been in place.
-Still, for many years neural networks took a backseat 
-to classical statistical methods like logistic regression and support vector machines (SVMs).
-So you might reasonably ask, what's changed to garner such interest?
-
-## Scale and Computation
-The two biggest factors driving innovation in deep learning now are data and computation.
-With distributed cloud computing and parallelism across GPU cores,
-we can train models millions of times faster than researchers could in the 1980s.
-The availability of large, high-quality datasets is another factor driving the field forward.
-In the 1990s, the best datasets in computer vision had thousands of low-resolution images and ground truth assignments to a small number of classes.
-Today, researchers cut their teeth on ImageNet, a massive dataset containing millions of high-resolution images from a thousand distinct classes.
-The falling price of storage and high network bandwidth
-make it affordable to work with big data at will.
-
-In this new world, with bigger datasets and abundant computation, 
-neural networks dominate on most pattern recognition problems.
-Over the last five years, neural networks have come to dominate on nearly every problem in computer vision,
-replacing classical models and hand-engineered features.
-Similarly, nearly every production speech recognition system now relies on neural networks, 
-where replacing the hidden Markov models that previously held sway.
-
-![alt text](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/get-started/nvidia-gpus.jpg)
-
-While GPUs and clusters present a huge opportunity for accelerating neural network training,
-adapting traditional machine learning code
-to take advantage of these resources can be challenging.
-The familiar scientific computing stacks (Matlab, R, or NumPy & SciPy)
-give no straight-forward way to exploit these distributed resources.
-
-Acceleration libraries like _MXNet_ offer powerful tools
-to help developers exploit the full capabilities of GPUs and cloud computing.
-While these tools are generally useful and applicable to any mathematical computation, _MXNet_ places a special emphasis on speeding up the development and deployment of large-scale deep neural networks. In particular, we offer the following capabilities:
-* __Device Placement:__ With _MXNet_, it's easy to specify where each data structures should live.
-* __Multi-GPU training__: _MXNet_ makes it easy to scale computation with number of available GPUs.
-* __Automatic differentiation__: _MXNet_ automates the derivative calculations that once bogged down neural network research.
-* __Optimized Predefined Layers__: While you can code up your own layers in _MXNet_, the predefined layers are optimized for speed, outperforming competing libraries.
-
-## Deep Nets on Fast Computers
-While MXNet can accelerate any numerical computation,
-we developed the library with neural networks in mind.
-However you plan to use MXNet, neural networks make for a powerful motivating example to display MXNet's capabilities.
-
-Neural networks are just functions for transforming input arrays `X` into output arrays `Y`.
-In the case of image classification, `X` might represent the pixel values of an image, and `Y` might represent the corresponding probabilities that the image belongs to each of `10` classes.
-For language translation, `X` and `Y` both might denote sequences of words. We'll revisit the way you might represent sequences in subsequent tutorials - so for now it's safe to think of `X` and `Y` as fixed length vectors.
-
-To perform this mapping, neural networks stack _layers_ of computation. Each layer consists of a linear function followed by a nonlinear transformation. In _MXNet_ we might express this as:
-~~~~
-hidden_linear = mx.sym.dot(X, W)
-hidden_activation = mx.sym.tanh(hidden_linear)
-~~~~
-The linear transformations consist of multiplication by parameter arrays (`W` above). 
-When we talk about learning we mean finding the right set of values for `W`.
-With just one layer, we can implement the familiar family of linear models, 
-including linear and logistic regression, linear support vector machines (SVMs), and the perceptron algorithm.
-With more layers and a few clever constraints, we can implement all of today's state-of-the-art deep learning techniques.
-
-Of course, tens or hundreds of matrix multiplications can be computationally taxing. 
-Generally, these linear operations are the computational bottleneck.
-Fortunately, linear operators can be parallelized trivially across the thousands of cores on a GPU.
-But low-level GPU programming requires specialized skills that are not common even among leading researchers in the ML community. Moreover, even for CUDA experts, implementing a new neural network architecture shouldn't require weeks of programming to implement low-level linear algebra operations. That's where _MXNet_ comes in.
-*  _MXNet_ provides optimized numerical computation for GPUs and distributed ecosystems, from the comfort of high-level environments like Python and R
-* _MXNet_ automates common workflows, so standard neural networks can be expressed concisely in just a few lines of code
-
-Now let's take a closer look at the computational demands of neural networks 
-and give a sense of how _MXNet_ helps us to write better, faster, code.
-Say we have a neural network trained to recognize spam from the content of emails. 
-The emails may be streaming from an online service (at inference time),
-or from a large offline dataset __D__ (at training time). 
-In either case, the dataset typically must be managed by the CPU.
-
-![alt text](https://raw.githubusercontent.com/kevinthesun/web-data/master/mxnet/get-started/architecture.png)
-
-To compute the transformation of a neural network quickly, we need both the parameters and data points to make it into GPU memory. For any example _X_, the parameters _W_ are the same. Moreover the size of the model tends to dwarf the size of an individual example. So we might arrive at the natural insight that parameters should always live on the GPU, even if the dataset itself must live on the CPU or stream in. This prevents IO from becoming the bottleneck during training or inference.
-
-Fortunately, _MXNet_ makes this kind of assignment easy.
-~~~~
-import mxnet.ndarray as nd
-
-X  = nd.zeros((10000, 40000), mx.cpu(0))           #Allocate an array to store 1000 datapoints (of 40k dimensions) that lives on the CPU
-W1 = nd.zeros(shape=(40000, 1024), mx.gpu(0))      #Allocate a 40k x 1024 weight matrix on GPU for the 1st layer of the net
-W2 = nd.zeros(shape=(1024, 10), mx.gpu(0))         #Allocate a 1024 x 1024 weight matrix on GPU for the 2nd layer of the net
-~~~~
-
-<!-- * __Talk about how mxnet also makes it easy to assign a context (on which device the computation happens__ -->
-Similarly, _MXNet_ makes it easy to specify the computing device
-
-~~~~
-with mx.Context(mx.gpu()):          # Absent this statement, by default, MXNet will execute on CPU
-    h = nd.tanh(nd.dot(X, W1))
-    y = nd.sigmoid(nd.dot(h1, W2))
-~~~~
-
-Thus, with only a high-level understanding of how our numerical computation maps onto an execution environment, _MXNet_ allows us to exert fine-grained control when needed.
-
-## Nuts and Bolts
-
-MXNet supports two styles of programming: _imperative programming_ (supported by the _NDArray_ API) and _symbolic programming_ (supported by the _Symbol_ API). In short, imperative programming is the style that you're likely to be most familiar with. Here if A and B are variables denoting matrices, then `C = A + B` is a piece of code that _when executed_ sums the values referenced by `A` and `B` and stores their sum `C` in a new variable. Symbolic programming, on the other hand, allows functions to be defined abstractly through computation graphs. In the symbolic style, we first express complex functions in terms of placeholder values. Then, we can execute these functions by _binding them_ to real values.
-
-
-### Imperative Programming with _NDArray_
-If you're familiar with NumPy, then the mechanics of _NDArray_ should be old hat. Like the corresponding `numpy.ndarray`, `mxnet.ndarray` (`mxnet.nd` for short) allows us to represent and manipulate multi-dimensional, homogenous arrays of fixed-size components. Converting between the two is effortless:
-
-~~~~
-# Create a numpy array from an mxnet NDArray
-A_np = np.array([[0,1,2,3,4],[5,6,7,8,9]])
-A_nd = nd.array(A)  
-
-# Convert back to a numpy array
-A2_np = A_nd.asnumpy()
-~~~~
-
-Other deep learning libraries tend to rely on NumPy exclusively for imperative programming and the syntax.
-So you might reasonably wonder, why do we need to bother with _NDArray_?
-Put simply, other libraries only reap the advantages of GPU computing when executing symbolic functions. By using _NDArray_, _MXNet_ users can specify device context and run on GPUs. In other words, _MXNet_ gives you access to the high-speed computation for imperative operations that Tensorflow and Theano only give for symbolic operations.
-
-
-~~~~
-X = mx.nd.array([[1,2],[3,4]])
-Y = mx.nd.array([[5,6],[7,8]])
-result = X + Y
-~~~~
-
-
-### Symbolic Programming in _MXNet_
-
-In addition to providing fast math operations through NDArray, _MXNet_ provides an interface for defining operations abstractly via a computation graph.
-With `mxnet.symbol`, we define operations abstractly in terms of place holders. For example, in the following code `a` and `b` stand in for real values that will be supplied at run time.
-When we call `c = a+b`, no numerical computation is performed. This operation simply builds a graph that defines the relationship between `a`, `b` and `c`. In order to perform a real calculation, we need to bind `c` to real values.
-
-~~~~
-a = mx.sym.Variable('a')
-b = mx.sym.Variable('b')
-c = a + b
-executor = c.bind(mx.cpu(), {'a': X, 'b': Y})
-result = executor.forward()
-~~~~
-
-
-Symbolic computation is useful for several reasons. First, because we define a full computation graph before executing it, _MXNet_ can perform sophisticated optimizations to eliminate unnecessary or repeated work. This tends to give better performance than imperative programming. Second, because we store the relationships between different variables in the computation graph, _MXNet_ can then perform efficient auto-differentiation.
-
-
-## Building Models with _MXNet_ Layers
-In addition to providing a general-purpose toolkit for optimizing mathematical operations,
-_MXNet_ provides predefined neural network layers.
-This higher-level interface has several benefits.
-
-* Predefined layers allow you to express large models concisely. Repetitive work, like allocating parameters and inferring their dimensions are eliminated.
-* Layers are written directly in C++, giving even faster performance than equivalent layers implemented manually in Python.
-
-
-~~~~
-data = mx.symbol.Variable('data')
-fc1  = mx.symbol.FullyConnected(data = data, name='fc1', num_hidden=128)
-act1 = mx.symbol.Activation(data = fc1, name='relu1', act_type="relu")
-fc2  = mx.symbol.FullyConnected(data = act1, name = 'fc2', num_hidden = 64)
-act2 = mx.symbol.Activation(data = fc2, name='relu2', act_type="relu")
-fc3  = mx.symbol.FullyConnected(data = act2, name='fc3', num_hidden=num_classes)
-mlp  = mx.symbol.SoftmaxOutput(data = fc3, name = 'softmax')
-~~~~
-
-
-## Conclusions
-Given its combination of high performance, clean code, access to a high-level API, and low-level control, _MXNet_ stands out as a unique choice among deep learning frameworks.
-
diff --git a/docs/gluon/index.md b/docs/gluon/index.md
deleted file mode 100644
index ea3eedce065e..000000000000
--- a/docs/gluon/index.md
+++ /dev/null
@@ -1,127 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# About Gluon
-
-![gluon logo](https://github.com/dmlc/web-data/blob/master/mxnet/image/image-gluon-logo.png?raw=true)
-
-Based on the [the Gluon API specification](https://github.com/gluon-api/gluon-api), the new Gluon library in Apache MXNet provides a clear, concise, and simple API for deep learning. It makes it easy to prototype, build, and train deep learning models without sacrificing training speed. [Install the latest version of MXNet](/install/) to get access to Gluon.
-
-To get started with Gluon, checkout the following resources and tutorials:
-* [60-minute Gluon Crash Course](https://gluon-crash-course.mxnet.io/) - six 10-minute lessons on using Gluon
-* [GluonCV Toolkit](https://gluon-cv.mxnet.io/) - implementations of state of the art deep learning algorithms in **Computer Vision (CV)**
-* [GluonNLP Toolkit](https://gluon-nlp.mxnet.io/) - implementations of state of the art deep learning algorithms in **Natural Language Processing (NLP)**
-* [Dive into Deep Learning](https://www.d2l.ai/) - notebooks designed to teach deep learning from the ground up, all using the Gluon API
-
-<br/>
-<div class="boxed">
-    Advantages
-</div>
-
-1. Simple, Easy-to-Understand Code: Gluon offers a full set of plug-and-play neural network building blocks, including predefined layers, optimizers, and initializers.
-
-2. Flexible, Imperative Structure: Gluon does not require the neural network model to be   rigidly defined, but rather brings the training algorithm and model closer together to provide flexibility in the development process.
-
-3. Dynamic Graphs: Gluon enables developers to define neural network models that are dynamic, meaning they can be built on the fly, with any structure, and using any of Python’s native control flow.
-
-4. High Performance: Gluon provides all of the above benefits without impacting the training speed that the underlying engine provides.
-
-<br/>
-<div class="boxed">
-    Dive into Deep Learning
-</div>
-
-The community is also working on parallel effort to create a foundational resource for learning about machine learning. Dive into Deep Learning is a book composed of introductory as well as advanced tutorials – all based on the Gluon interface. For example,
-
-* [Learn about machine learning basics](https://www.d2l.ai/chapter_introduction/intro.html).
-* [Develop and train a simple neural network model](https://www.d2l.ai/chapter_multilayer-perceptrons/mlp-scratch.html).
-* [Implement a Recurrent Neural Network (RNN) model for Language Modeling](https://www.d2l.ai/chapter_recurrent-neural-networks/rnn-scratch.html).
-
-<br/>
-<div class="boxed">
-    Code Examples
-</div>
-
-**__Simple, Easy-to-Understand Code__**
-
-Use plug-and-play neural network building blocks, including predefined layers, optimizers, and initializers:
-
-```
-net = gluon.nn.Sequential()
-# When instantiated, Sequential stores a chain of neural network layers.
-# Once presented with data, Sequential executes each layer in turn, using
-# the output of one layer as the input for the next
-with net.name_scope():
-    net.add(gluon.nn.Dense(256, activation="relu")) # 1st layer (256 nodes)
-    net.add(gluon.nn.Dense(256, activation="relu")) # 2nd hidden layer
-    net.add(gluon.nn.Dense(num_outputs))
-```
-<br/>
-
-**__Flexible, Imperative Structure__**
-
-Prototype, build, and train neural networks in fully imperative manner using the MXNet autograd package and the Gluon trainer method:
-
-```
-epochs = 10
-
-for e in range(epochs):
-    for i, (data, label) in enumerate(train_data):
-        with autograd.record():
-            output = net(data) # the forward iteration
-            loss = softmax_cross_entropy(output, label)
-            loss.backward()
-        trainer.step(data.shape[0])
-```
-
-<br/>
-**__Dynamic Graphs__**
-
-Build neural networks on the fly for use cases where neural networks must change in size and shape during model training:
-
-```
-def forward(self, F, inputs, tree):
-    children_outputs = [self.forward(F, inputs, child)
-                        for child in tree.children]
-    #Recursively builds the neural network based on each input sentence’s
-    #syntactic structure during the model definition and training process
-```
-<br/>
-**__High Performance__**
-
-Easily cache the neural network to achieve high performance by defining your neural network with ``HybridSequential`` and calling the ``hybridize`` method:
-
-```
-net = nn.HybridSequential()
-with net.name_scope():
-    net.add(nn.Dense(256, activation="relu"))
-    net.add(nn.Dense(128, activation="relu"))
-    net.add(nn.Dense(2))
-```
-
-```
-net.hybridize()
-```
-
-## Learn More
-
-* [Gluon API Documentation](/api/python/gluon/gluon.html)
-* [Gluon Tutorials](/tutorials/)
-* [60-minute Gluon Crash Course](https://gluon-crash-course.mxnet.io/)
-* [GluonCV Toolkit](https://gluon-cv.mxnet.io/)
-* [GluonNLP Toolkit](https://gluon-nlp.mxnet.io/)
-* [Dive into Deep Learning](https://www.d2l.ai)
diff --git a/docs/index.md b/docs/index.md
deleted file mode 100644
index e7f314eb169b..000000000000
--- a/docs/index.md
+++ /dev/null
@@ -1,32 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# MXNet
-
-```eval_rst
-.. toctree::
-   :maxdepth: 1
-
-   api/index.md
-   architecture/index.md
-   community/index.md
-   faq/index.md
-   gluon/index.md
-   install/index.md
-   model_zoo/index.md
-   tutorials/index.md
-```
diff --git a/docs/install/amazonlinux_setup.md b/docs/install/amazonlinux_setup.md
deleted file mode 100644
index a432d4815662..000000000000
--- a/docs/install/amazonlinux_setup.md
+++ /dev/null
@@ -1,25 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-<!-- This page should be deleted after sometime (Allowing search engines
-to update links) -->
-<meta http-equiv="refresh" content="3; url=http://mxnet.io/install/index.html" />
-<!-- Just in case redirection does not work -->
-<p>
-  <a href="http://mxnet.io/install/index.html">
-    This content is moved to a new MXNet install page. Redirecting... </a>
-</p>
diff --git a/docs/install/build_from_source.md b/docs/install/build_from_source.md
deleted file mode 100644
index 8101dd3a4ac1..000000000000
--- a/docs/install/build_from_source.md
+++ /dev/null
@@ -1,321 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Build MXNet from Source
-
-This document explains how to build MXNet from source code.
-
-**For Java/Scala/Clojure, please follow [this guide instead](./scala_setup.md)**
-
-## Overview
-
-Building from source follows this general two-step flow of building the shared library, then installing your preferred language binding. Use the following links to jump to the different sections of this guide.
-
-1. Build the MXNet shared library, `libmxnet.so`.
-    * [Clone the repository](#clone-the-mxnet-project)
-    * [Prerequisites](#prerequisites)
-        * [Math library selection](#math-library-selection)
-        * [Install GPU software](#install-gpu-software)
-        * [Install optional software](#install-optional-software)
-    * [Adjust your build configuration](#build-configurations)
-    * [Build MXNet](#build-mxnet)
-        * [with NCCL](#build-mxnet-with-nccl) (optional)
-        * [for C++](#build-mxnet-with-c++) (optional)
-        * [Usage Examples](#usage-examples)
-            * [systems with GPUs and Intel CPUs](#recommended-for-Systems-with-NVIDIA-GPUs-and-Intel-CPUs)
-            * [GPUs with non-Intel CPUs](#recommended-for-Systems-with-Intel-CPUs)
-            * [Intel CPUs](#recommended-for-Systems-with-Intel-CPUs)
-            * [non-Intel CPUs](#recommended-for-Systems-with-non-Intel-CPUs)
-2. [Install the language API binding(s)](#installing-mxnet-language-bindings) you would like to use for MXNet.
-MXNet's newest and most popular API is Gluon. Gluon is built into the Python binding. If Python isn't your preference, you still have more options. MXNet supports several other language APIs:
-    - [Python (includes Gluon)](../api/python/index.md)
-    - [C++](../api/c++/index.md)
-    - [Clojure](../api/clojure/index.md)
-    - [Java](../api/java/index.md)
-    - [Julia](../api/julia/index.md)
-    - [Perl](../api/perl/index.md)
-    - [R](../api/r/index.md)
-    - [Scala](../api/scala/index.md)
-
-<hr>
-
-## Build Instructions by Operating System
-
-Detailed instructions are provided per operating system. Each of these guides also covers how to install the specific [Language Bindings](#installing-mxnet-language-bindings) you require.
-You may jump to those, but it is recommended that you continue reading to understand more general "build from source" options.
-
-* [Amazon Linux / CentOS / RHEL](centos_setup.md)
-* [macOS](osx_setup.md)
-* [Devices](https://mxnet.incubator.apache.org/versions/master/install/index.html?platform=Devices&language=Python&processor=CPU)
-* [Ubuntu](ubuntu_setup.md)
-* [Windows](windows_setup.md)
-
-
-<hr>
-
-## Clone the MXNet Project
-
-1. Clone or fork the MXNet project.
-```bash
-git clone --recursive https://github.com/apache/incubator-mxnet mxnet
-cd mxnet
-```
-
-<hr>
-
-## Prerequisites
-
-The following sections will help you decide which specific prerequisites you need to install.
-
-#### Math Library Selection
-It is useful to consider your math library selection prior to your other prerequisites.
-MXNet relies on the
-[BLAS](https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) (Basic
-Linear Algebra Subprograms) library for numerical computations.
-Those can be extended with [LAPACK (Linear Algebra Package)](https://github.com/Reference-LAPACK/lapack), an additional set of mathematical functions.
-
-MXNet supports multiple mathematical backends for computations on the CPU:
-* [Apple Accelerate](https://developer.apple.com/documentation/accelerate)
-* [ATLAS](http://math-atlas.sourceforge.net/)
-* [MKL](https://software.intel.com/en-us/intel-mkl) (MKL, MKLML)
-* [MKL-DNN](https://github.com/intel/mkl-dnn)
-* [OpenBLAS](http://www.openblas.net/)
-
-The default order of choice for the libraries if found follows the path from the most
-(recommended) to less performant backends.
-The following lists show this order by library and `cmake` switch.
-
-For desktop platforms (x86_64):
-
-1. MKL-DNN (submodule) | `USE_MKLDNN`
-2. MKL | `USE_MKL_IF_AVAILABLE`
-3. MKLML (downloaded) | `USE_MKLML`
-4. Apple Accelerate | `USE_APPLE_ACCELERATE_IF_AVAILABLE` | Mac only
-5. OpenBLAS | `BLAS` | Options: Atlas, Open, MKL, Apple
-
-Note: If `USE_MKL_IF_AVAILABLE` is set to False then MKLML and MKL-DNN will be disabled as well for configuration
-backwards compatibility.
-
-For embedded platforms (all other and if cross compiled):
-
-1. OpenBLAS | `BLAS` | Options: Atlas, Open, MKL, Apple
-
-You can set the BLAS library explicitly by setting the BLAS variable to:
-
-* Atlas
-* Open
-* MKL
-* Apple
-
-See the [cmake/ChooseBLAS.cmake](https://github.com/apache/incubator-mxnet/blob/master/cmake/ChooseBlas.cmake) file for the options.
-
-[Intel's MKL (Math Kernel Library)](https://software.intel.com/en-us/mkl) is one of the most powerful math libraries
-
-It has following flavors:
-
-* MKL is a complete math library, containing all the functionality found in ATLAS, OpenBlas and LAPACK. It is free under
-community support licensing (https://software.intel.com/en-us/articles/free-mkl),
-but needs to be downloaded and installed manually.
-
-* MKLML is a subset of MKL. It contains a smaller number of functions to reduce the
-size of the download and reduce the number of dynamic libraries user needs.
-
-<!-- [Removed until #11148 is merged.] This is the most effective option since it can be downloaded and installed automatically
-by the cmake script (see cmake/DownloadMKLML.cmake).-->
-
-* MKL-DNN is a separate open-source library, it can be used separately from MKL or MKLML. It is
-shipped as a subrepo with MXNet source code (see 3rdparty/mkldnn or the [MKL-DNN project](https://github.com/intel/mkl-dnn))
-
-Since the full MKL library is almost always faster than any other BLAS library it's turned on by default,
-however it needs to be downloaded and installed manually before doing `cmake` configuration.
-Register and download on the [Intel performance libraries website](https://software.intel.com/en-us/performance-libraries).
-You can also install MKL through [YUM](https://software.intel.com/en-us/articles/installing-intel-free-libs-and-python-yum-repo)
-or [APT](https://software.intel.com/en-us/articles/installing-intel-free-libs-and-python-apt-repo) Repository.
-
-Note: MKL is supported only for desktop builds and the framework itself supports the following
-hardware:
-
-* Intel® Xeon Phi™ processor
-* Intel® Xeon® processor
-* Intel® Core™ processor family
-* Intel Atom® processor
-
-If you have a different processor you can still try to use MKL, but performance results are
-unpredictable.
-
-
-#### Install GPU Software
-
-If you want to run MXNet with GPUs, you must install [NVDIA CUDA and cuDNN](https://developer.nvidia.com/cuda-downloads).
-
-
-#### Install Optional Software
-
-These might be optional, but they're typically desirable as the extend or enhance MXNet's functionality.
-
-* [OpenCV](http://opencv.org/) - Image Loading and Augmentation. Each operating system has different packages and build from source options for OpenCV. Refer to your OS's link in the [Build Instructions by Operating System](#build-instructions-by-operating-system) section for further instructions.
-* [NCCL](https://developer.nvidia.com/nccl) - NVIDIA's Collective Communications Library. Instructions for installing NCCL are found in the following [Build MXNet with NCCL](#build-mxnet-with-nccl) section.
-
-More information on turning these features on or off are found in the following [build configurations](#build-configurations) section.
-
-
-<hr>
-
-## Build Configurations
-
-There is a configuration file for make,
-[`make/config.mk`](https://github.com/apache/incubator-mxnet/blob/master/make/config.mk), that contains all the compilation options. You can edit it and then run `make` or `cmake`. `cmake` is recommended for building MXNet (and is required to build with MKLDNN), however you may use `make` instead. For building with Java/Scala/Clojure, only `make` is supported.
-
-**NOTE:** When certain set of build flags are set, MXNet archive increases to more than 4 GB. Since MXNet uses archive internally archive runs into a bug ("File Truncated": [bugreport](https://sourceware.org/bugzilla/show_bug.cgi?id=14625)) for archives greater than 4 GB. Please use ar version 2.27 or greater to overcome this bug. Please see https://github.com/apache/incubator-mxnet/issues/15084 for more details.
-
-<hr>
-
-## Build MXNet
-
-### Build MXNet with NCCL
-- Download and install the latest NCCL library from NVIDIA.
-- Note the directory path in which NCCL libraries and header files are installed.
-- Ensure that the installation directory contains ```lib``` and ```include``` folders.
-- Ensure that the prerequisites for using NCCL such as Cuda libraries are met.
-- Append the ```config.mk``` file with following, in addition to the CUDA related options.
-- USE_NCCL=1
-- USE_NCCL_PATH=path-to-nccl-installation-folder
-
-``` bash
-echo "USE_NCCL=1" >> make/config.mk
-echo "USE_NCCL_PATH=path-to-nccl-installation-folder" >> make/config.mk
-cp make/config.mk .
-```
-- Run make command
-``` bash
-make -j"$(nproc)"
-```
-
-#### Validating NCCL
-- Follow the steps to install MXNet Python binding.
-- Comment the following line in ```test_nccl.py``` file at ```incubator-mxnet/tests/python/gpu/test_nccl.py```
-``` bash
-@unittest.skip("Test requires NCCL library installed and enabled during build")
-```
-- Run test_nccl.py script as follows. The test should complete. It does not produce any output.
-``` bash
-nosetests --verbose tests/python/gpu/test_nccl.py
-```
-
-**Recommendation to get the best performance out of NCCL:**
-It is recommended to set environment variable NCCL_LAUNCH_MODE to PARALLEL when using NCCL version 2.1 or newer.
-
-<hr>
-
-### Build MXNet with C++
-
-* To enable C++ package, just add `USE_CPP_PACKAGE=1` when you run `make` or `cmake` (see examples).
-
-<hr>
-
-### Usage Examples
-
-For example, you can specify using all cores on Linux as follows:
-
-```bash
-mkdir build && cd build
-cmake -GNinja ..
-ninja -v
-```
-
-
-#### Recommended for Systems with NVIDIA GPUs and Intel CPUs
-* Build MXNet with `cmake` and install with MKL DNN, GPU, and OpenCV support:
-
-```bash
-mkdir build && cd build
-cmake -DUSE_CUDA=1 -DUSE_CUDA_PATH=/usr/local/cuda -DUSE_CUDNN=1 -DUSE_MKLDNN=1 -GNinja ..
-ninja -v
-```
-
-#### Recommended for Systems with NVIDIA GPUs
-* Build with both OpenBLAS, GPU, and OpenCV support:
-
-```bash
-mkdir build && cd build
-cmake -DBLAS=open -DUSE_CUDA=1 -DUSE_CUDA_PATH=/usr/local/cuda -DUSE_CUDNN=1 -GNinja ..
-ninja -v
-```
-
-#### Recommended for Systems with Intel CPUs
-* Build MXNet with `cmake` and install with MKL DNN, and OpenCV support:
-
-```bash
-mkdir build && cd build
-cmake -DUSE_CUDA=0 -DUSE_MKLDNN=1 -GNinja ..
-ninja -v
-```
-
-#### Recommended for Systems with non-Intel CPUs
-* Build MXNet with `cmake` and install with OpenBLAS and OpenCV support:
-
-```bash
-mkdir build && cd build
-cmake -DUSE_CUDA=0 -DBLAS=open -GNinja ..
-ninja -v
-```
-
-#### Other Examples
-
-* Build without using OpenCV:
-
-```bash
-mkdir build && cd build
-cmake -DUSE_OPENCV=0 -GNinja ..
-ninja -v
-```
-
-* Build on **macOS** with the default BLAS library (Apple Accelerate) and Clang installed with `xcode` (OPENMP is disabled because it is not supported by the Apple version of Clang):
-
-```bash
-mkdir build && cd build
-cmake -DBLAS=apple -DUSE_OPENCV=0 -DUSE_OPENMP=0 -GNinja ..
-ninja -v
-```
-
-* To use OpenMP on **macOS** you need to install the Clang compiler, `llvm` (the one provided by Apple does not support OpenMP):
-
-```bash
-brew install llvm
-mkdir build && cd build
-cmake -DBLAS=apple -DUSE_OPENMP=1 -GNinja ..
-ninja -v
-```
-
-<hr>
-
-## Installing MXNet Language Bindings
-After building MXNet's shared library, you can install other language bindings.
-
-**NOTE:** The C++ API binding must be built when you build MXNet from source. See [Build MXNet with C++](#build-mxnet-with-c++).
-
-The following table provides links to each language binding by operating system:
-| | [Ubuntu](ubuntu_setup.html) | [macOS](osx_setup.html) | [Windows](windows_setup.html) |
-| --- | ----  | --- | ------- |
-| Python | [Ubuntu guide](ubuntu_setup.html#install-mxnet-for-python) | [OSX guide](osx_setup.html) | [Windows guide](windows_setup.html#install-mxnet-for-python) |
-| C++ | [C++ guide](c_plus_plus.html) | [C++ guide](c_plus_plus.html) | [C++ guide](c_plus_plus.html) |
-| Clojure | [Clojure guide](https://github.com/apache/incubator-mxnet/tree/master/contrib/clojure-package) | [Clojure guide](https://github.com/apache/incubator-mxnet/tree/master/contrib/clojure-package) | n/a |
-| Julia | [Ubuntu guide](ubuntu_setup.html#install-the-mxnet-package-for-julia) | [OSX guide](osx_setup.html#install-the-mxnet-package-for-julia) | [Windows guide](windows_setup.html#install-the-mxnet-package-for-julia) |
-| Perl | [Ubuntu guide](ubuntu_setup.html#install-the-mxnet-package-for-perl) | [OSX guide](osx_setup.html#install-the-mxnet-package-for-perl) | n/a |
-| R | [Ubuntu guide](ubuntu_setup.html#install-the-mxnet-package-for-r) | [OSX guide](osx_setup.html#install-the-mxnet-package-for-r) | [Windows guide](windows_setup.html#install-the-mxnet-package-for-r) |
-| Scala | [Scala guide](scala_setup.html) | [Scala guide](scala_setup.html) | n/a |
-| Java | [Java guide](java_setup.html) | [Java Guide](java_setup.html) | n/a |
diff --git a/docs/install/c_plus_plus.md b/docs/install/c_plus_plus.md
deleted file mode 100644
index 13c1a87cbd5f..000000000000
--- a/docs/install/c_plus_plus.md
+++ /dev/null
@@ -1,47 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-## Build the C++ package
-The C++ package has the same prerequisites as the MXNet library.
-
-To enable C++ package, just add `USE_CPP_PACKAGE=1` in the [build from source](build_from_source.md) options when building the MXNet shared library.
-
-For example to build MXNet with GPU support and the C++ package, OpenCV, and OpenBLAS, from the project root you would run:
-
-```bash
-cmake -DUSE_CUDA=1 -DUSE_CUDA_PATH=/usr/local/cuda -DUSE_CUDNN=1 -DUSE_MKLDNN=1 -DUSE_CPP_PACKAGE=1 -GNinja ..
-ninja -v
-```
-
-You may also want to add the MXNet shared library to your `LD_LIBRARY_PATH`:
-
-```bash
-export LD_LIBRARY_PATH=~/incubator-mxnet/lib
-```
-
-Setting the `LD_LIBRARY_PATH` is required to run the examples mentioned in the following section.
-
-## C++ Example Code
-You can find C++ code examples in the `cpp-package/example` folder of the MXNet project. Refer to the [cpp-package's README](https://github.com/apache/incubator-mxnet/tree/master/cpp-package) for instructions on building the examples.
-
-## Tutorials
-
-* [MXNet C++ API Basics](../tutorials/c++/basics.md)
-
-## Related Topics
-
-* [Image Classification using MXNet's C Predict API](https://github.com/apache/incubator-mxnet/tree/master/example/image-classification/predict-cpp)
diff --git a/docs/install/centos_setup.md b/docs/install/centos_setup.md
deleted file mode 100644
index e01b68221bd9..000000000000
--- a/docs/install/centos_setup.md
+++ /dev/null
@@ -1,107 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Installing MXNet on CentOS and other non-Ubuntu Linux systems
-
-Step 1. Install build tools and git on `CentOS >= 7` and `Fedora >= 19`:
-
-```bash
-sudo yum groupinstall -y "Development Tools" && sudo yum install -y git
-```
-
-Step 2. Install Atlas:
-
-```bash
-sudo yum install atlas-devel
-```
-
-Installing both `git` and `cmake` or `make` by following instructions on the websites is
-straightforward. Here we provide the instructions to build `gcc-4.8` from source codes.
-
-Step 3. Install the 32-bit `libc` with one of the following system-specific commands:
-
-```bash
-sudo apt-get install libc6-dev-i386 # In Ubuntu
-sudo yum install glibc-devel.i686   # In RHEL (Red Hat Linux)
-sudo yum install glibc-devel.i386   # In CentOS 5.8
-sudo yum install glibc-devel.i686   # In CentOS 6/7
-```
-
-Step 4. Download and extract the `gcc` source code with the prerequisites:
-
-```bash
-wget http://mirrors.concertpass.com/gcc/releases/gcc-4.8.5/gcc-4.8.5.tar.gz
-tar -zxf gcc-4.8.5.tar.gz
-cd gcc-4.8.5
-./contrib/download_prerequisites
-```
-
-Step 5. Build `gcc` by using 10 threads and then install to `/usr/local`
-
-```bash
-mkdir release && cd release
-../configure --prefix=/usr/local --enable-languages=c,c++
-make -j10
-sudo make install
-```
-
-Step 6. Add the lib path to your configure file such as `~/.bashrc`:
-
-```bash
-export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/lib64
-```
-
-Step 7. Build [OpenBLAS from source](https://github.com/xianyi/OpenBLAS#installation-from-source).
-
-Step 8. Build OpenCV
-
-To build OpenCV from source code, you need the [cmake](https://cmake.org) library.
-
-* If you don't have cmake or if your version of cmake is earlier than 3.6.1, run the following commands to install a newer version of cmake:
-
-```bash
-wget https://cmake.org/files/v3.6/cmake-3.6.1-Linux-x86_64.tar.gz
-tar -zxvf cmake-3.6.1-Linux-x86_64.tar.gz
-alias cmake="cmake-3.6.1-Linux-x86_64/bin/cmake"
-```
-
-* To download and extract the OpenCV source code, run the following commands:
-
-```bash
-wget https://codeload.github.com/opencv/opencv/zip/2.4.13
-unzip 2.4.13
-cd opencv-2.4.13
-mkdir release
-cd release/
-```
-
-* Build OpenCV. The following commands build OpenCV with 10 threads. We
-disabled GPU support, which might significantly slow down an MXNet program
-running on a GPU processor. It also disables 1394 which might generate a
-warning. Then install it on `/usr/local`.
-
-```bash
-cmake -D BUILD_opencv_gpu=OFF -D WITH_CUDA=OFF -D WITH_1394=OFF -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local ..
-make -j10
-sudo make install
-```
-
-* Add the lib path to your configuration such as `~/.bashrc`.
-
-```bash
-export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/usr/local/lib/pkgconfig/
-```
diff --git a/docs/install/download.md b/docs/install/download.md
deleted file mode 100644
index ead73e5a18c2..000000000000
--- a/docs/install/download.md
+++ /dev/null
@@ -1,75 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Source Download
-
-These source archives are generated from tagged releases. Updates and patches will not have been applied. For any updates refer to the corresponding branches in the [GitHub repository](https://github.com/apache/incubator-mxnet). Choose your flavor of download from the following links:
-
-| Version | Source                                                                                                      | PGP                                                                                                             | SHA                                                                                                                |
-|---------|-------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------|
-| 1.5.0   | [Download](https://apache.org/dist/incubator/mxnet/1.5.0/apache-mxnet-src-1.5.0-incubating.tar.gz)                 | [Download](https://apache.org/dist/incubator/mxnet/1.5.0/apache-mxnet-src-1.5.0-incubating.tar.gz.asc)    |  [Download](https://apache.org/dist/incubator/mxnet/1.5.0/apache-mxnet-src-1.5.0-incubating.tar.gz.sha512)     |
-| 1.4.1   | [Download](https://www.apache.org/dyn/closer.cgi/incubator/mxnet/1.4.1/apache-mxnet-src-1.4.1-incubating.tar.gz)   | [Download](https://apache.org/dist/incubator/mxnet/1.4.1/apache-mxnet-src-1.4.1-incubating.tar.gz.asc)    | [Download](https://apache.org/dist/incubator/mxnet/1.4.1/apache-mxnet-src-1.4.1-incubating.tar.gz.sha512)      |
-| 1.4.0   | [Download](https://www.apache.org/dyn/closer.cgi/incubator/mxnet/1.4.0/apache-mxnet-src-1.4.0-incubating.tar.gz)   | [Download](https://apache.org/dist/incubator/mxnet/1.4.0/apache-mxnet-src-1.4.0-incubating.tar.gz.asc)    | [Download](https://apache.org/dist/incubator/mxnet/1.4.0/apache-mxnet-src-1.4.0-incubating.tar.gz.sha512)      |
-| 1.3.1   | [Download](https://www.apache.org/dyn/closer.cgi/incubator/mxnet/1.3.1/apache-mxnet-src-1.3.1-incubating.tar.gz)   | [Download](https://apache.org/dist/incubator/mxnet/1.3.1/apache-mxnet-src-1.3.1-incubating.tar.gz.asc)    | [Download](https://apache.org/dist/incubator/mxnet/1.3.1/apache-mxnet-src-1.3.1-incubating.tar.gz.sha512)      |
-| 1.3.0   | [Download](https://archive.apache.org/dist/incubator/mxnet/1.3.0/apache-mxnet-src-1.3.0-incubating.tar.gz)   | [Download](https://archive.apache.org/dist/incubator/mxnet/1.3.0/apache-mxnet-src-1.3.0-incubating.tar.gz.asc)    | [Download](https://archive.apache.org/dist/incubator/mxnet/1.3.0/apache-mxnet-src-1.3.0-incubating.tar.gz.sha512)      |
-| 1.2.1   | [Download](https://archive.apache.org/dist/incubator/mxnet/1.2.1/apache-mxnet-src-1.2.1-incubating.tar.gz)   | [Download](https://archive.apache.org/dist/incubator/mxnet/1.2.1/apache-mxnet-src-1.2.1-incubating.tar.gz.asc)    | [Download](https://archive.apache.org/dist/incubator/mxnet/1.2.1/apache-mxnet-src-1.2.1-incubating.tar.gz.sha512)      |
-| 1.2.0   | [Download](https://archive.apache.org/dist/incubator/mxnet/1.2.0/apache-mxnet-src-1.2.0-incubating.tar.gz)   | [Download](https://archive.apache.org/dist/incubator/mxnet/1.2.0/apache-mxnet-src-1.2.0-incubating.tar.gz.asc)    | [Download](https://archive.apache.org/dist/incubator/mxnet/1.2.0/apache-mxnet-src-1.2.0-incubating.tar.gz.sha512)      |
-| 1.1.0   | [Download](https://archive.apache.org/dist/incubator/mxnet/1.1.0/apache-mxnet-src-1.1.0-incubating.tar.gz)      | [Download](https://archive.apache.org/dist/incubator/mxnet/1.1.0/apache-mxnet-src-1.1.0-incubating.tar.gz.asc)      | [Download](https://archive.apache.org/dist/incubator/mxnet/1.1.0/apache-mxnet-src-1.1.0-incubating.tar.gz.sha512)     |
-| 1.0.0   | [Download](https://archive.apache.org/dist/incubator/mxnet/1.0.0/apache-mxnet-src-1.0.0-incubating.tar.gz)   | [Download](https://archive.apache.org/dist/incubator/mxnet/1.0.0/apache-mxnet-src-1.0.0-incubating.tar.gz.asc)   | [Download](https://archive.apache.org/dist/incubator/mxnet/1.0.0/apache-mxnet-src-1.0.0-incubating.tar.gz.sha512)   |
-| 0.12.1  | [Download](https://archive.apache.org/dist/incubator/mxnet/0.12.1/apache-mxnet-src-0.12.1-incubating.tar.gz) | [Download](https://archive.apache.org/dist/incubator/mxnet/0.12.1/apache-mxnet-src-0.12.1-incubating.tar.gz.asc) | [Download](https://archive.apache.org/dist/incubator/mxnet/0.12.1/apache-mxnet-src-0.12.1-incubating.tar.gz.sha512) |
-| 0.12.0  | [Download](https://archive.apache.org/dist/incubator/mxnet/0.12.0/apache-mxnet-src-0.12.0-incubating.tar.gz) | [Download](https://archive.apache.org/dist/incubator/mxnet/0.12.0/apache-mxnet-src-0.12.0-incubating.tar.gz.asc) | [Download](https://archive.apache.org/dist/incubator/mxnet/0.12.0/apache-mxnet-src-0.12.0-incubating.tar.gz.sha512) |
-| 0.11.0  | [Download](https://archive.apache.org/dist/incubator/mxnet/0.11.0/apache-mxnet-src-0.11.0-incubating.tar.gz) | [Download](https://archive.apache.org/dist/incubator/mxnet/0.11.0/apache-mxnet-src-0.11.0-incubating.tar.gz.asc) | [Download](https://archive.apache.org/dist/incubator/mxnet/0.11.0/apache-mxnet-src-0.11.0-incubating.tar.gz.sha512) |
-
-## Verify the Integrity of the Files
-It is essential that you verify the integrity of the downloaded file using the PGP signature (.asc file) or a hash (.md5 or .sha* file). Please read [Verifying Apache Software Foundation Releases](https://www.apache.org/info/verification.html) for more information on why you should verify our releases.
-
-The PGP signature can be verified using PGP or GPG. First download the KEYS as well as the .asc signature file for the relevant distribution. Make sure you get these files from the main distribution site, rather than from a mirror. Then verify the signatures using one of the following alternatives:
-
-```bash
-% gpg --import KEYS
-% gpg --verify downloaded_file.asc downloaded_file
-```
-
-```bash
-% pgpk -a KEYS
-% pgpv downloaded_file.asc
-```
-
-```bash
-% pgp -ka KEYS
-% pgp downloaded_file.asc
-```
-
-Alternatively, you can verify the hash on the file.
-
-Hashes can be calculated using GPG:
-
-```bash
-% gpg --print-md SHA1 downloaded_file
-```
-
-The output should be compared with the contents of the SHA1 file. Similarly for other hashes (SHA256 MD5 etc) which may be provided.
-
-Windows 7 and later systems should all now have `certUtil`:
-
-```bash
-% certUtil -hashfile pathToFileToCheck
-```
-
-HashAlgorithm choices: MD2 MD4 MD5 SHA1 SHA256 SHA384 SHA512
-
-Unix-like systems (and macOS) will have a utility called `md5`, `md5sum` or `shasum`.
diff --git a/docs/install/index.md b/docs/install/index.md
deleted file mode 100644
index db8dae8e46d7..000000000000
--- a/docs/install/index.md
+++ /dev/null
@@ -1,1567 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Installing MXNet
-
-```eval_rst
-.. toctree::
-   :hidden:
-
-   amazonlinux_setup.md
-   build_from_source.md
-   c_plus_plus.md
-   centos_setup.md
-   download.md
-   install-jetson.md
-   java_setup.md
-   osx_setup.md
-   scala_setup.md
-   ubuntu_setup.md
-   validate_mxnet.md
-   windows_setup.md
-```
-
-Indicate your preferred configuration. Then, follow the customized commands to install MXNet.
-
-<div class="dropdown">
-  <button class="btn current-version btn-primary dropdown-toggle" type="button" data-toggle="dropdown">v1.5.0
-  <span class="caret"></span></button>
-  <ul class="dropdown-menu opt-group">
-    <li class="opt active versions"><a href="#">v1.5.0</a></li>
-    <li class="opt versions"><a href="#">v1.4.1</a></li>
-    <li class="opt versions"><a href="#">v1.3.1</a></li>
-    <li class="opt versions"><a href="#">v1.2.1</a></li>
-    <li class="opt versions"><a href="#">v1.1.0</a></li>
-    <li class="opt versions"><a href="#">v1.0.0</a></li>
-    <li class="opt versions"><a href="#">v0.12.1</a></li>
-    <li class="opt versions"><a href="#">v0.11.0</a></li>
-    <li class="opt versions"><a href="#">master</a></li>
-  </ul>
-</div>
-
-<script type="text/javascript" src='../_static/js/options.js'></script>
-
-<!-- START - OS Menu -->
-
-<div class="btn-group opt-group" role="group">
-  <button type="button" class="btn btn-default opt active platforms">Linux</button>
-  <button type="button" class="btn btn-default opt platforms">MacOS</button>
-  <button type="button" class="btn btn-default opt platforms">Windows</button>
-  <button type="button" class="btn btn-default opt platforms">Cloud</button>
-  <button type="button" class="btn btn-default opt platforms">Devices</button>
-</div>
-
-<!-- START - Language Menu -->
-
-<div class="linux macos windows">
-<div class="btn-group opt-group" role="group">
-  <button type="button" class="btn btn-default opt active languages">Python</button>
-  <button type="button" class="btn btn-default opt languages">Scala</button>
-  <button type="button" class="btn btn-default opt languages">Java</button>
-  <button type="button" class="btn btn-default opt languages">Clojure</button>
-  <button type="button" class="btn btn-default opt languages">R</button>
-  <button type="button" class="btn btn-default opt languages">Julia</button>
-  <button type="button" class="btn btn-default opt languages">Perl</button>
-  <button type="button" class="btn btn-default opt languages">Cpp</button>
-</div>
-</div>
-
-<!-- No CPU GPU for other Devices -->
-<div class="linux macos windows cloud">
-<div class="btn-group opt-group" role="group">
-  <button type="button" class="btn btn-default processors opt active">GPU</button>
-  <button type="button" class="btn btn-default processors opt">CPU</button>
-</div>
-</div>
-
-<!-- other devices -->
-<div class="devices">
-<div class="btn-group opt-group" role="group">
-  <button type="button" class="btn btn-default iots opt active">Raspberry Pi</button>
-  <button type="button" class="btn btn-default iots opt">NVIDIA Jetson</button>
-</div>
-</div>
-
-<!-- Linux Python GPU Options -->
-
-<div class="linux macos windows">
-<div class="python">
-<div class="cpu gpu">
-<div class="btn-group opt-group" role="group">
-  <button type="button" class="btn btn-default environs opt active">Pip</button>
-  <button type="button" class="btn btn-default environs opt">Docker</button>
-  <button type="button" class="btn btn-default environs opt">Build from Source</button>
-</div>
-</div>
-</div>
-</div>
-
-<!-- Linux Julia Options -->
-
-<div class="linux macos windows">
-<div class="julia">
-<div class="cpu gpu">
-<div class="btn-group opt-group" role="group">
-  <button type="button" class="btn btn-default environs opt active">Pkg</button>
-  <button type="button" class="btn btn-default environs opt">Build from Source</button>
-</div>
-</div>
-</div>
-</div>
-
-<hr>
-
-<!-- END - Main Menu -->
-
-<!-- START - Linux Python CPU Installation Instructions -->
-
-<div class="linux">
-<div class="python">
-<div class="cpu">
-<div class="pip">
-<div class="v1-5-0">
-
-```
-$ pip install mxnet
-```
-
-MKL-DNN enabled pip packages are optimized for Intel hardware. You can find performance numbers in the <a href="http://mxnet.io/faq/perf.html#intel-cpu">MXNet tuning guide</a>.
-
-```
-$ pip install mxnet-mkl
-```
-
-</div> <!-- End of v1-5-0 -->
-<div class="v1-4-1">
-
-```
-$ pip install mxnet==1.4.1
-```
-
-MKL-DNN enabled pip packages are optimized for Intel hardware. You can find performance numbers in the <a href="http://mxnet.io/faq/perf.html#intel-cpu">MXNet tuning guide</a>.
-
-```
-$ pip install mxnet-mkl==1.4.1
-```
-
-</div> <!-- End of v1-4-1 -->
-<div class="v1-3-1">
-
-```
-$ pip install mxnet==1.3.1
-```
-
-MKL-DNN enabled pip packages are optimized for Intel hardware. You can find performance numbers in the <a href="http://mxnet.io/faq/perf.html#intel-cpu">MXNet tuning guide</a>.
-
-```
-$ pip install mxnet-mkl==1.3.1
-```
-
-</div> <!-- End of v1-3-1 -->
-<div class="v1-2-1">
-
-```
-$ pip install mxnet==1.2.1
-```
-
-MKL-DNN enabled pip packages are optimized for Intel hardware. You can find performance numbers in the <a href="http://mxnet.io/faq/perf.html#intel-cpu">MXNet tuning guide</a>.
-
-```
-$ pip install mxnet-mkl==1.2.1
-```
-
-</div> <!-- End of v1-2-1 -->
-
-<div class="v1-1-0">
-
-```
-$ pip install mxnet==1.1.0
-```
-
-</div> <!-- End of v1-1-0-->
-
-<div class="v1-0-0">
-
-```
-$ pip install mxnet==1.0.0
-```
-
-</div> <!-- End of v1-0-0-->
-
-
-<div class="v0-12-1">
-
-```
-$ pip install mxnet==0.12.1
-```
-
-For MXNet 0.12.0:
-
-```
-$ pip install mxnet==0.12.0
-```
-
-</div> <!-- End of v0-12-1-->
-
-<div class="v0-11-0">
-
-```
-$ pip install mxnet==0.11.0
-```
-
-</div> <!-- End of v0-11-0-->
-
-<div class="master">
-
-```
-$ pip install mxnet --pre
-```
-
-MKL-DNN enabled pip packages are optimized for Intel hardware. You can find performance numbers in the <a href="http://mxnet.io/faq/perf.html#intel-cpu">MXNet tuning guide</a>.
-
-```
-$ pip install mxnet-mkl --pre
-```
-
-</div> <!-- End of master-->
-<hr> <!-- pip footer -->
-
-Check the chart below for other options, refer to <a href="https://pypi.org/project/mxnet/">PyPI for other MXNet pip packages</a>, or <a href="validate_mxnet.html">validate your MXNet installation</a>.
-
-<img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/install/pip-packages.png" alt="pip packages"/>
-
-**NOTES:**
-
-*mxnet-cu101mkl* means the package is built with CUDA/cuDNN and MKL-DNN enabled and the CUDA version is 10.1.
-
-All MKL pip packages are experimental prior to version 1.3.0.
-
-</div> <!-- End of pip -->
-
-
-<div class="docker">
-<br/>
-
-Docker images with *MXNet* are available at [Docker Hub](https://hub.docker.com/r/mxnet/).
-
-**Step 1**  Install Docker on your machine by following the [docker installation instructions](https://docs.docker.com/engine/installation/linux/ubuntu/#install-using-the-repository).
-
-*Note* - You can install Community Edition (CE) to get started with *MXNet*.
-
-**Step 2** [Optional] Post installation steps to manage Docker as a non-root user.
-
-Follow the four steps in this [docker documentation](https://docs.docker.com/engine/installation/linux/linux-postinstall/#manage-docker-as-a-non-root-user) to allow managing docker containers without *sudo*.
-
-If you skip this step, you need to use *sudo* each time you invoke Docker.
-
-**Step 3** Pull the MXNet docker image.
-
-```
-$ docker pull mxnet/python # Use sudo if you skip Step 2
-```
-
-You can list docker images to see if mxnet/python docker image pull was successful.
-
-```
-$ docker images # Use sudo if you skip Step 2
-
-REPOSITORY          TAG                 IMAGE ID            CREATED             SIZE
-mxnet/python        latest              00d026968b3c        3 weeks ago         1.41 GB
-```
-
-Using the latest MXNet with [Intel MKL-DNN](https://github.com/intel/mkl-dnn) is recommended for the fastest inference speeds with MXNet.
-
-```
-$ docker pull mxnet/python:1.3.0_cpu_mkl # Use sudo if you skip Step 2
-$ docker images # Use sudo if you skip Step 2
-
-REPOSITORY          TAG                 IMAGE ID            CREATED             SIZE
-mxnet/python        1.3.0_cpu_mkl       deaf9bf61d29        4 days ago          678 MB
-```
-
-**Step 4** <a href="validate_mxnet.html">Validate the installation</a>.
-
-</div> <!-- END of docker -->
-
-<div class="build-from-source">
-<br/>
-
-To build from source, refer to the <a href="ubuntu_setup.html">MXNet Ubuntu installation guide</a>.
-
-</div><!-- END of build from source -->
-
-</div><!-- END of CPU -->
-<!-- END - Linux Python CPU Installation Instructions -->
-
-<!-- START - Linux Python GPU Installation Instructions -->
-
-<div class="gpu">
-<div class="pip">
-<div class="v1-5-0">
-
-```
-$ pip install mxnet-cu101
-```
-
-</div> <!-- End of v1-5-0 -->
-<div class="v1-4-1">
-
-```
-$ pip install mxnet-cu101==1.4.1
-```
-
-</div> <!-- End of v1-4-1 -->
-<div class="v1-3-1">
-
-```
-$ pip install mxnet-cu92==1.3.1
-```
-
-</div> <!-- End of v1-3-1-->
-<div class="v1-2-1">
-
-```
-$ pip install mxnet-cu92==1.2.1
-```
-
-</div> <!-- End of v1-2-1-->
-
-<div class="v1-1-0">
-
-```
-$ pip install mxnet-cu91==1.1.0
-```
-
-</div> <!-- End of v1-1-0-->
-
-<div class="v1-0-0">
-
-```
-$ pip install mxnet-cu90==1.0.0
-```
-
-</div> <!-- End of v1-0-0-->
-
-<div class="v0-12-1">
-
-```
-$ pip install mxnet-cu90==0.12.1
-```
-
-</div> <!-- End of v0-12-1-->
-
-<div class="v0-11-0">
-
-```
-$ pip install mxnet-cu80==0.11.0
-```
-
-</div> <!-- End of v0-11-0-->
-
-<div class="master">
-
-```
-$ pip install mxnet-cu101 --pre
-```
-
-</div> <!-- End of master-->
-<hr> <!-- pip footer -->
-MXNet offers MKL pip packages that will be much faster when running on Intel hardware.
-Check the chart below for other options, refer to <a href="https://pypi.org/project/mxnet/">PyPI for other MXNet pip packages</a>, or <a href="validate_mxnet.html">validate your MXNet installation</a>.
-
-<img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/install/pip-packages.png" alt="pip packages"/>
-
-**NOTES:**
-
-*mxnet-cu101mkl* means the package is built with CUDA/cuDNN and MKL-DNN enabled and the CUDA version is 10.1.
-
-All MKL pip packages are experimental prior to version 1.3.0.
-
-CUDA should be installed first. Instructions can be found in the <a href="ubuntu_setup.html#cuda-dependencies">CUDA dependencies section of the MXNet Ubuntu installation guide</a>.
-
-**Important:** Make sure your installed CUDA version matches the CUDA version in the pip package. Check your CUDA version with the following command:
-
-```
-nvcc --version
-```
-
-You can either upgrade your CUDA install or install the MXNet package that supports your CUDA version.
-
-</div> <!-- END of pip -->
-
-<div class="docker">
-
-<br/>
-
-Docker images with *MXNet* are available at [Docker Hub](https://hub.docker.com/r/mxnet/).
-
-**Step 1**  Install Docker on your machine by following the [docker installation instructions](https://docs.docker.com/engine/installation/linux/ubuntu/#install-using-the-repository).
-
-*Note* - You can install Community Edition (CE) to get started with *MXNet*.
-
-**Step 2** [Optional] Post installation steps to manage Docker as a non-root user.
-
-Follow the four steps in this [docker documentation](https://docs.docker.com/engine/installation/linux/linux-postinstall/#manage-docker-as-a-non-root-user) to allow managing docker containers without *sudo*.
-
-If you skip this step, you need to use *sudo* each time you invoke Docker.
-
-**Step 3** Install *nvidia-docker-plugin* following the [installation instructions](https://github.com/NVIDIA/nvidia-docker/wiki). *nvidia-docker-plugin* is required to enable the usage of GPUs from the docker containers.
-
-**Step 4** Pull the MXNet docker image.
-
-```
-$ docker pull mxnet/python:gpu # Use sudo if you skip Step 2
-```
-
-You can list docker images to see if mxnet/python docker image pull was successful.
-
-```
-$ docker images # Use sudo if you skip Step 2
-
-REPOSITORY          TAG                 IMAGE ID            CREATED             SIZE
-mxnet/python        gpu                 493b2683c269        3 weeks ago         4.77 GB
-```
-
-Using the latest MXNet with [Intel MKL-DNN](https://github.com/intel/mkl-dnn) is recommended for the fastest inference speeds with MXNet.
-
-```
-$ docker pull mxnet/python:1.3.0_cpu_mkl # Use sudo if you skip Step 2
-$ docker images # Use sudo if you skip Step 2
-
-REPOSITORY          TAG                 IMAGE ID            CREATED             SIZE
-mxnet/python        1.3.0_gpu_cu92_mkl  adcb3ab19f50        4 days ago          4.23 GB
-```
-
-**Step 5** <a href="validate_mxnet.html">Validate the installation</a>.
-
-</div> <!-- END of docker -->
-
-<div class="build-from-source">
-<br/>
-Refer to the <a href="ubuntu_setup.html">MXNet Ubuntu installation guide</a>.
-
-
-</div> <!-- END of build from source -->
-</div> <!-- END of GPU -->
-</div> <!-- END of Python -->
-<!-- END - Linux Python GPU Installation Instructions -->
-
-
-<div class="r">
-<div class="cpu">
-
-The default version of R that is installed with `apt-get` is insufficient. You will need to first [install R v3.4.4+ and build MXNet from source](ubuntu_setup.html#install-the-mxnet-package-for-r).
-
-After you have setup R v3.4.4+ and MXNet, you can build and install the MXNet R bindings with the following, assuming that `incubator-mxnet` is the source directory you used to build MXNet as follows:
-
-```
-$ cd incubator-mxnet
-$ make rpkg
-```
-
-</div> <!-- END of CPU -->
-
-
-<div class="gpu">
-
-The default version of R that is installed with `apt-get` is insufficient. You will need to first [install R v3.4.4+ and build MXNet from source](ubuntu_setup.html#install-the-mxnet-package-for-r).
-
-After you have setup R v3.4.4+ and MXNet, you can build and install the MXNet R bindings with the following, assuming that `incubator-mxnet` is the source directory you used to build MXNet as follows:
-
-```
-$ cd incubator-mxnet
-$ make rpkg
-```
-
-</div> <!-- END of GPU -->
-</div> <!-- END of R -->
-
-
-<div class="scala">
-<div class="gpu">
-<br/>
-You can use the Maven packages defined in the following dependency to include MXNet in your Scala project. Please refer to the <a href="scala_setup.html">MXNet-Scala setup guide</a> for a detailed set of instructions to help you with the setup process.
-
-<a href="https://mvnrepository.com/artifact/org.apache.mxnet/mxnet-full_2.11-linux-x86_64-gpu"><img src="https://img.shields.io/badge/org.apache.mxnet-linux gpu-green.svg" alt="maven badge"/></a>
-
-```html
-<dependency>
-    <groupId>org.apache.mxnet</groupId>
-    <artifactId>mxnet-full_2.11-linux-x86_64-gpu</artifactId>
-</dependency>
-```
-<br>
-</div> <!-- End of gpu -->
-
-<div class="cpu">
-<br/>
-You can use the Maven packages defined in the following dependency to include MXNet in your Scala project. Please refer to the <a href="scala_setup.html">MXNet-Scala setup guide</a> for a detailed set of instructions to help you with the setup process.
-
-<a href="https://mvnrepository.com/artifact/org.apache.mxnet/mxnet-full_2.11-linux-x86_64-cpu"><img src="https://img.shields.io/badge/org.apache.mxnet-linux cpu-green.svg" alt="maven badge"/></a>
-
-```html
-<dependency>
-    <groupId>org.apache.mxnet</groupId>
-    <artifactId>mxnet-full_2.11-linux-x86_64-cpu</artifactId>
-</dependency>
-```
-<br>
-</div> <!-- End of cpu -->
-</div> <!-- End of scala -->
-
-
-<div class="clojure">
-<div class="gpu">
-<br/>
-
-You can use the Maven packages defined in the following dependency to include MXNet in your Clojure project. To maximize leverage, the Clojure package has been built on the existing Scala package. Please refer to the <a href="scala_setup.html">MXNet-Scala setup guide</a> for a detailed set of instructions to help you with the setup process that is required to use the Clojure dependency.
-
-<a href="https://mvnrepository.com/artifact/org.apache.mxnet.contrib.clojure/clojure-mxnet-linux-gpu"><img src="https://img.shields.io/badge/org.apache.mxnet-linux gpu-green.svg" alt="maven badge"/></a>
-
-```html
-<dependency>
-    <groupId>org.apache.mxnet.contrib.clojure</groupId>
-    <artifactId>clojure-mxnet-linux-gpu</artifactId>
-</dependency>
-```
-
-<br>
-</div> <!-- End of gpu -->
-<div class="cpu">
-<br/>
-You can use the Maven packages defined in the following dependency to include MXNet in your Clojure project. To maximize leverage, the Clojure package has been built on the existing Scala package. Please refer to the <a href="scala_setup.html">MXNet-Scala setup guide</a> for a detailed set of instructions to help you with the setup process that is required to use the Clojure dependency.
-
-<a href="https://mvnrepository.com/artifact/org.apache.mxnet.contrib.clojure/clojure-mxnet-linux-cpu"><img src="https://img.shields.io/badge/org.apache.mxnet-linux cpu-green.svg" alt="maven badge"/></a>
-
-```html
-<dependency>
-    <groupId>org.apache.mxnet.contrib.clojure</groupId>
-    <artifactId>clojure-mxnet-linux-cpu</artifactId>
-</dependency>
-```
-
-<br>
-</div> <!-- End of cpu -->
-</div> <!-- End of clojure -->
-
-
-<div class="java">
-<div class="gpu">
-<br/>
-You can use the Maven packages defined in the following dependency to include MXNet in your Java project. The Java API is provided as a subset of the Scala API and is intended for inference only. Please refer to the <a href="java_setup.html">MXNet-Java setup guide</a> for a detailed set of instructions to help you with the setup process.
-
-<a href="https://repository.apache.org/#nexus-search;gav~org.apache.mxnet~~1.5.0~~"><img src="https://img.shields.io/badge/org.apache.mxnet-linux gpu-green.svg" alt="maven badge"/></a>
-
-```html
-<dependency>
-    <groupId>org.apache.mxnet</groupId>
-    <artifactId>mxnet-full_2.11-linux-x86_64-gpu</artifactId>
-    <version>[1.5.0, )</version>
-</dependency>
-```
-
-<br>
-</div> <!-- End of gpu -->
-
-<div class="cpu">
-<br/>
-You can use the Maven packages defined in the following dependency to include MXNet in your Java project. The Java API is provided as a subset of the Scala API and is intended for inference only. Please refer to the <a href="java_setup.html">MXNet-Java setup guide</a> for a detailed set of instructions to help you with the setup process.
-
-<a href="https://repository.apache.org/#nexus-search;gav~org.apache.mxnet~~1.5.0~~"><img src="https://img.shields.io/badge/org.apache.mxnet-linux cpu-green.svg" alt="maven badge"/></a>
-
-```html
-<dependency>
-    <groupId>org.apache.mxnet</groupId>
-    <artifactId>mxnet-full_2.11-linux-x86_64-cpu</artifactId>
-    <version>[1.5.0, )</version>
-</dependency>
-```
-<br>
-</div> <!-- End of cpu -->
-</div> <!-- End of java -->
-
-
-<div class="julia">
-<div class="cpu gpu">
-</br>
-<div class="pkg">
-
-```
-]add MXNet#v1.5.0
-```
-
-Install the latest release:
-
-```
-]add MXNet
-```
-
-</div>
-<div class="build-from-source">
-Refer to the <a href="ubuntu_setup.html#install-the-mxnet-package-for-julia">Julia section of the MXNet Ubuntu installation guide</a>.
-
-</div>
-</div> <!-- End of cpu gpu -->
-</div> <!-- End of julia -->
-
-<div class="perl">
-<div class="cpu gpu">
-</br>
-Refer to the <a href="ubuntu_setup.html#install-the-mxnet-package-for-perl">Perl section of the MXNet Ubuntu installation guide</a>.
-
-</div> <!-- End of cpu gpu -->
-</div> <!-- End of perl -->
-
-
-
-<div class="cpp">
-<div class="cpu gpu">
-<br/>
-<p>To enable the C++ package, build from source using `make USE_CPP_PACKAGE=1`.
-<br/>Refer to the <a href="c_plus_plus.html">MXNet C++ setup guide</a> for more info.</p>
-<br/>
-</div> <!-- End of cpu gpu -->
-</div> <!-- END - C++-->
-<hr>
-
-</div> <!-- END - Linux -->
-
-
-<!-- START - MacOS Python CPU Installation Instructions -->
-
-<div class="macos">
-<div class="python">
-<div class="cpu">
-<div class="pip">
-<div class="v1-5-0">
-
-```
-$ pip install mxnet
-```
-</div> <!-- End of v1-5-0 -->
-<div class="v1-4-1">
-
-```
-$ pip install mxnet==1.4.1
-```
-</div> <!-- End of v1-4-1 -->
-<div class="v1-3-1">
-
-```
-$ pip install mxnet==1.3.1
-```
-
-</div> <!-- End of v1-3-1 -->
-<div class="v1-2-1">
-
-```
-$ pip install mxnet==1.2.1
-```
-
-</div> <!-- End of v1-2-1 -->
-
-
-<div class="v1-1-0">
-
-```
-$ pip install mxnet==1.1.0
-```
-
-</div> <!-- End of v1-1-0-->
-
-
-<div class="v1-0-0">
-
-```
-$ pip install mxnet==1.0.0
-```
-
-</div> <!-- End of v1-0-0-->
-
-<div class="v0-12-1">
-
-```
-$ pip install mxnet=0.12.1
-```
-
-</div> <!-- End of v0-12-1-->
-
-
-<div class="v0-11-0">
-
-```
-$ pip install mxnet==0.11.0
-```
-
-</div> <!-- End of v0-11-0-->
-
-<div class="master">
-
-```
-$ pip install mxnet --pre
-```
-
-</div> <!-- End of master-->
-<hr> <!-- pip footer -->
-MXNet offers MKL pip packages that will be much faster when running on Intel hardware.
-Check the chart below for other options, refer to <a href="https://pypi.org/project/mxnet/">PyPI for other MXNet pip packages</a>, or <a href="validate_mxnet.html">validate your MXNet installation</a>.
-
-<img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/install/pip-packages.png" alt="pip packages"/>
-
-**NOTES:**
-
-*mxnet-cu101mkl* means the package is built with CUDA/cuDNN and MKL-DNN enabled and the CUDA version is 10.1.
-
-All MKL pip packages are experimental prior to version 1.3.0.
-
-</div> <!-- END of pip -->
-
-
-<div class="docker">
-<br/>
-
-Docker images with *MXNet* are available at [Docker Hub](https://hub.docker.com/r/mxnet/).
-
-**Step 1**  Install Docker on your machine by following the [docker installation instructions](https://docs.docker.com/docker-for-mac/install/#install-and-run-docker-for-mac).
-
-*Note* - You can install Community Edition (CE) to get started with *MXNet*.
-
-**Step 2** Pull the MXNet docker image.
-
-```
-$ docker pull mxnet/python
-```
-
-You can list docker images to see if mxnet/python docker image pull was successful.
-
-```
-$ docker images
-
-REPOSITORY          TAG                 IMAGE ID            CREATED             SIZE
-mxnet/python        latest              00d026968b3c        3 weeks ago         1.41 GB
-```
-
-Using the latest MXNet with [Intel MKL-DNN](https://github.com/intel/mkl-dnn) is recommended for the fastest inference speeds with MXNet.
-
-```
-$ docker pull mxnet/python:1.3.0_cpu_mkl # Use sudo if you skip Step 2
-$ docker images # Use sudo if you skip Step 2
-
-REPOSITORY          TAG                 IMAGE ID            CREATED             SIZE
-mxnet/python        1.3.0_cpu_mkl       deaf9bf61d29        4 days ago          678 MB
-```
-
-**Step 4** <a href="validate_mxnet.html">Validate the installation</a>.
-
-</div> <!-- END of docker -->
-
-
-<div class="build-from-source">
-<br/>
-
-To build from source, refer to the <a href="osx_setup.html">MXNet macOS installation guide</a>.
-
-MXNet developers should refer to the MXNet wiki's <a href="https://cwiki.apache.org/confluence/display/MXNET/MXNet+Developer+Setup+on+Mac">Developer Setup on Mac</a>.
-<br/>
-</div> <!-- END of build from source -->
-</div> <!-- END of CPU -->
-
-
-<!-- START - Mac OS Python GPU Installation Instructions -->
-<div class="gpu">
-<div class="pip docker">
-<br/>
-This option is only available by building from source. Refer to the <a href="osx_setup.html">MXNet macOS installation guide</a>.
-<br/>
-</div>
-
-<div class="build-from-source">
-<br/>
-
-Refer to the <a href="osx_setup.html">MXNet macOS installation guide</a>.
-
-MXNet developers should refer to the MXNet wiki's <a href="https://cwiki.apache.org/confluence/display/MXNET/MXNet+Developer+Setup+on+Mac">Developer Setup on Mac</a>.
-<br/>
-</div> <!-- END of build from source -->
-</div> <!-- END of GPU -->
-</div> <!-- END of Python -->
-
-
-<!-- START - MacOS R CPU Installation Instructions -->
-
-<div class="r">
-<div class="cpu">
-</br>
-
-To run MXNet you also should have OpenCV and OpenBLAS installed. You may install them with `brew` as follows:
-
-```bash
-brew install opencv
-brew install openblas
-```
-
-To ensure MXNet R package runs with the version of OpenBLAS installed, create a symbolic link as follows:
-
-```bash
-ln -sf /usr/local/opt/openblas/lib/libopenblas.dylib /usr/local/opt/openblas/lib/libopenblasp-r0.3.1.dylib
-```
-
-Note: packages for 3.6.x are not yet available.
-
-Install 3.5.x of R from [CRAN](https://cran.r-project.org/bin/macosx/). The latest is [v3.5.3](https://cran.r-project.org/bin/macosx/R-3.5.3.pkg).
-
-You can [build MXNet-R from source](osx_setup.html#install-the-mxnet-package-for-r), or you can use a pre-built binary:
-
-```r
-cran <- getOption("repos")
-cran["dmlc"] <- "https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/R/CRAN/"
-options(repos = cran)
-install.packages("mxnet")
-```
-
-</div> <!-- END of CPU -->
-
-
-<div class="gpu">
-</br>
-Will be available soon.
-
-</div> <!-- END of GPU -->
-</div> <!-- END of R -->
-
-<div class="scala">
-<div class="cpu">
-</br>
-You can use the Maven packages defined in the following dependency to include MXNet in your Scala project. Please refer to the <a href="scala_setup.html">MXNet-Scala setup guide</a> for a detailed set of instructions to help you with the setup process.
-
-<a href="https://mvnrepository.com/artifact/org.apache.mxnet/mxnet-full_2.11-osx-x86_64-cpu"><img src="https://img.shields.io/badge/org.apache.mxnet-mac cpu-green.svg" alt="maven badge"/></a>
-
-```html
-<dependency>
-    <groupId>org.apache.mxnet</groupId>
-    <artifactId>mxnet-full_2.11-osx-x86_64-cpu</artifactId>
-</dependency>
-```
-<br>
-</div> <!-- End of cpu  -->
-<div class="gpu">
-
-Not available at this time. <br>
-
-</div>
-</div> <!-- End of scala -->
-
-
-<div class="clojure">
-<div class="cpu">
-</br>
-You can use the Maven packages defined in the following dependency to include MXNet in your Clojure project. To maximize leverage, the Clojure package has been built on the existing Scala package. Please refer to the <a href="scala_setup.html">MXNet-Scala setup guide</a> for a detailed set of instructions to help you with the setup process that is required to use the Clojure dependency.
-
-<a href="https://mvnrepository.com/artifact/org.apache.mxnet.contrib.clojure/clojure-mxnet-osx-cpu"><img src="https://img.shields.io/badge/org.apache.mxnet-mac cpu-green.svg" alt="maven badge"/></a>
-
-```html
-<dependency>
-    <groupId>org.apache.mxnet.contrib.clojure</groupId>
-    <artifactId>clojure-mxnet-osx-cpu</artifactId>
-</dependency>
-```
-
-<br>
-</div> <!-- End of cpu  -->
-<div class="gpu">
-Not available at this time. <br>
-</div> <!-- End of gpu -->
-</div> <!-- End of clojure -->
-
-
-<div class="java">
-<div class="cpu">
-</br>
-You can use the Maven packages defined in the following dependency to include MXNet in your Java project. The Java API is provided as a subset of the Scala API and is intended for inference only. Please refer to the <a href="java_setup.html">MXNet-Java setup guide</a> for a detailed set of instructions to help you with the setup process.
-
-<a href="https://repository.apache.org/#nexus-search;gav~org.apache.mxnet~~1.5.0~~"><img src="https://img.shields.io/badge/org.apache.mxnet-mac cpu-green.svg" alt="maven badge"/></a>
-
-```html
-<dependency>
-    <groupId>org.apache.mxnet</groupId>
-    <artifactId>mxnet-full_2.11-linux-x86_64-cpu</artifactId>
-    <version>[1.5.0, )</version>
-</dependency>
-```
-<br>
-</div> <!-- End of cpu  -->
-<div class="gpu">
-
-Not available at this time. <br>
-
-</div>
-</div> <!-- End of java -->
-
-
-<div class="julia">
-<div class="cpu gpu">
-</br>
-<div class="pkg">
-
-```
-]add MXNet#v1.5.0
-```
-
-Install the latest release:
-
-```
-]add MXNet
-```
-
-</div>
-<div class="build-from-source">
-Refer to the <a href="osx_setup.html#install-the-mxnet-package-for-julia">Julia section of the MXNet macOS installation guide</a>.
-
-</div>
-</div> <!-- End of cpu gpu -->
-</div> <!-- End of julia -->
-
-<div class="perl">
-<div class="cpu gpu">
-</br>
-Refer to the <a href="osx_setup.html#install-the-mxnet-package-for-perl">Perl section of the MXNet macOS installation guide</a>.
-
-</div> <!-- End of cpu gpu -->
-</div> <!-- End of julia -->
-
-
-
-<div class="cpp">
-<br/>
-<p>To enable the C++ package, build from source using `make USE_CPP_PACKAGE=1`.
-<br/>Refer to the <a href="c_plus_plus.html">MXNet C++ setup guide</a> for more info.</p>
-<br/>
-</div>
-<hr>
-For more installation options, refer to the <a href="osx_setup.html">MXNet macOS installation guide</a>.
-</div> <!-- END - Mac OS -->
-
-
-
-<div class="windows">
-<div class="python">
-<div class="cpu">
-<div class="pip">
-<div class="v1-5-0">
-
-```
-$ pip install mxnet
-```
-
-</div> <!-- End of v1-5-0 -->
-<div class="v1-4-1">
-
-```
-$ pip install mxnet==1.4.1
-```
-
-</div> <!-- End of v1-4-1 -->
-<div class="v1-3-1">
-
-```
-$ pip install mxnet==1.3.1
-```
-
-</div> <!-- End of v1-3-1 -->
-<div class="v1-2-1">
-
-```
-$ pip install mxnet==1.2.1
-```
-
-</div> <!-- End of v1-2-1 -->
-
-<div class="v1-1-0">
-
-```
-$ pip install mxnet==1.1.0
-```
-
-</div> <!-- End of v1-1-0-->
-
-<div class="v1-0-0">
-
-```
-$ pip install mxnet==1.0.0
-```
-
-</div> <!-- End of v1-0-0-->
-
-<div class="v0-12-1">
-
-```
-$ pip install mxnet==0.12.1
-```
-
-</div> <!-- End of v0-12-1-->
-
-<div class="v0-11-0">
-
-```
-$ pip install mxnet==0.11.0
-```
-
-</div> <!-- End of v0-11-0-->
-
-<div class="master">
-
-```
-$ pip install mxnet --pre
-```
-
-</div> <!-- End of master-->
-<hr> <!-- pip footer -->
-MXNet offers MKL pip packages that will be much faster when running on Intel hardware.
-Check the chart below for other options, refer to <a href="https://pypi.org/project/mxnet/">PyPI for other MXNet pip packages</a>, or <a href="validate_mxnet.html">validate your MXNet installation</a>.
-
-<img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/install/pip-packages.png" alt="pip packages"/>
-
-**NOTES:**
-
-*mxnet-cu101mkl* means the package is built with CUDA/cuDNN and MKL-DNN enabled and the CUDA version is 10.1.
-
-All MKL pip packages are experimental prior to version 1.3.0.
-
-</div> <!-- End of pip -->
-
-
-<div class="docker">
-<br/>
-
-Docker images with *MXNet* are available at [Docker Hub](https://hub.docker.com/r/mxnet/).
-
-**Step 1**  Install Docker on your machine by following the [docker installation instructions](https://docs.docker.com/engine/installation/linux/ubuntu/#install-using-the-repository).
-
-*Note* - You can install Community Edition (CE) to get started with *MXNet*.
-
-**Step 2** [Optional] Post installation steps to manage Docker as a non-root user.
-
-Follow the four steps in this [docker documentation](https://docs.docker.com/engine/installation/linux/linux-postinstall/#manage-docker-as-a-non-root-user) to allow managing docker containers without *sudo*.
-
-If you skip this step, you need to use *sudo* each time you invoke Docker.
-
-**Step 3** Pull the MXNet docker image.
-
-```
-$ docker pull mxnet/python # Use sudo if you skip Step 2
-```
-
-You can list docker images to see if mxnet/python docker image pull was successful.
-
-```
-$ docker images # Use sudo if you skip Step 2
-
-REPOSITORY          TAG                 IMAGE ID            CREATED             SIZE
-mxnet/python        latest              00d026968b3c        3 weeks ago         1.41 GB
-```
-
-Using the latest MXNet with [Intel MKL-DNN](https://github.com/intel/mkl-dnn) is recommended for the fastest inference speeds with MXNet.
-
-```
-$ docker pull mxnet/python:1.3.0_cpu_mkl # Use sudo if you skip Step 2
-$ docker images # Use sudo if you skip Step 2
-
-REPOSITORY          TAG                 IMAGE ID            CREATED             SIZE
-mxnet/python        1.3.0_cpu_mkl       deaf9bf61d29        4 days ago          678 MB
-```
-
-**Step 4** <a href="validate_mxnet.html">Validate the installation</a>.
-
-
-</div> <!-- End of docker -->
-
-<div class="build-from-source">
-<br/>
-Refer to the <a href="windows_setup.html">MXNet Windows installation guide</a>
-
-</div> <!-- End of Build from source -->
-</div> <!-- End of CPU -->
-
-
-<div class="gpu">
-<div class="pip">
-<div class="v1-5-0">
-
-```
-$ pip install mxnet-cu101
-```
-
-</div> <!-- End of v1-5-0 -->
-<div class="v1-4-1">
-
-```
-$ pip install mxnet-cu101==1.4.1
-```
-
-</div> <!-- End of v1-4-1 -->
-<div class="v1-3-1">
-
-```
-$ pip install mxnet-cu92==1.3.1
-```
-
-</div> <!-- End of v1-3-1 -->
-<div class="v1-2-1">
-
-```
-$ pip install mxnet-cu92==1.2.1
-```
-
-</div> <!-- End of v1-2-1 -->
-
-<div class="v1-1-0">
-
-```
-$ pip install mxnet-cu91==1.1.0
-```
-
-</div> <!-- End of v1-1-0-->
-
-<div class="v1-0-0">
-
-```
-$ pip install mxnet-cu90==1.0.0
-```
-
-</div> <!-- End of v1-0-0-->
-
-<div class="v0-12-1">
-
-```
-$ pip install mxnet-cu90==0.12.1
-```
-
-</div> <!-- End of v0-12-1-->
-
-<div class="v0-11-0">
-
-```
-$ pip install mxnet-cu80==0.11.0
-```
-
-</div> <!-- End of v0-11-0-->
-
-<div class="master">
-
-```
-$ pip install mxnet-cu101 --pre
-```
-
-</div> <!-- End of master-->
-<hr> <!-- pip footer -->
-MXNet offers MKL pip packages that will be much faster when running on Intel hardware.
-Check the chart below for other options, refer to <a href="https://pypi.org/project/mxnet/">PyPI for other MXNet pip packages</a>, or <a href="validate_mxnet.html">validate your MXNet installation</a>.
-
-<img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/install/pip-packages.png" alt="pip packages"/>
-
-**NOTES:**
-
-*mxnet-cu101mkl* means the package is built with CUDA/cuDNN and MKL-DNN enabled and the CUDA version is 10.1.
-
-All MKL pip packages are experimental prior to version 1.3.0.
-
-[Anaconda](https://www.anaconda.com/download/) is recommended.
-
-CUDA should be installed first. Instructions can be found in the <a href="ubuntu_setup.html#cuda-dependencies">CUDA dependencies section of the MXNet Ubuntu installation guide</a>.
-
-**Important:** Make sure your installed CUDA version matches the CUDA version in the pip package. Check your CUDA version with the following command:
-
-```
-nvcc --version
-```
-
-Refer to [#8671](https://github.com/apache/incubator-mxnet/issues/8671) for status on CUDA 9.1 support.
-
-You can either upgrade your CUDA install or install the MXNet package that supports your CUDA version.
-
-</div> <!-- End of pip -->
-
-<div class="build-from-source">
-<br/>
-
-To build from source, refer to the <a href="windows_setup.html">MXNet Windows installation guide</a>.
-
-
-</div> <!-- End of build from source -->
-</div> <!-- End of GPU -->
-</div> <!-- End of Python -->
-
-
-<!-- START - Windows R CPU Installation Instructions -->
-
-<div class="r">
-<div class="cpu">
-</br>
-
-Note: packages for 3.6.x are not yet available.
-Install 3.5.x of R from [CRAN](https://cran.r-project.org/bin/windows/base/old/).
-
-You can [build MXNet-R from source](windows_setup.html#install-mxnet-package-for-r), or you can use a pre-built binary:
-
-```r
-cran <- getOption("repos")
-cran["dmlc"] <- "https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/R/CRAN/"
-options(repos = cran)
-install.packages("mxnet")
-```
-
-To run MXNet you also should have OpenCV and OpenBLAS installed.
-
-</div> <!-- END - Windows R CPU -->
-
-<div class="gpu">
-</br>
-
-You can [build MXNet-R from source](windows_setup.html#install-mxnet-package-for-r), or you can use a pre-built binary:
-
-```r
-  cran <- getOption("repos")
-  cran["dmlc"] <- "https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/R/CRAN/GPU/cu92"
-  options(repos = cran)
-  install.packages("mxnet")
-```
-Change cu92 to cu90, cu91 or cuda100 based on your CUDA toolkit version. Currently, MXNet supports these versions of CUDA.
-Note : You also need to have cuDNN installed on Windows. Check out this [guide](https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installwindows) on the steps for installation.
-
-</div> <!-- END of GPU -->
-</div> <!-- END - Windows R -->
-
-<div class="scala">
-<div class="cpu gpu">
-<br/>
-MXNet-Scala for Windows is not yet available.
-<br/>
-</div> <!-- End of cpu gpu -->
-</div> <!-- End of scala -->
-
-<div class="clojure">
-<div class="cpu gpu">
-<br/>
-MXNet-Clojure for Windows is not yet available.
-<br/>
-</div> <!-- End of cpu gpu -->
-</div> <!-- End of clojure -->
-
-<div class="java">
-<div class="cpu gpu">
-<br/>
-MXNet-Java for Windows is not yet available.
-<br/>
-</div> <!-- End of cpu gpu -->
-</div> <!-- End of java -->
-
-<div class="julia">
-<div class="cpu gpu">
-</br>
-<div class="pkg">
-
-```
-]add MXNet#v1.5.0
-```
-
-Install the latest release:
-
-```
-]add MXNet
-```
-
-</div>
-<div class="build-from-source">
-Refer to the <a href="windows_setup.html#install-the-mxnet-package-for-julia">Julia section of the MXNet Windows installation guide</a>.
-
-</div>
-</div> <!-- End of cpu gpu -->
-</div> <!-- End of julia -->
-
-<div class="perl">
-<div class="cpu gpu">
-</br>
-Refer to the <a href="windows_setup.html#install-the-mxnet-package-for-perl">Perl section of the MXNet Windows installation guide</a>.
-
-</div> <!-- End of cpu gpu -->
-</div> <!-- End of perl -->
-
-<div class="cpp">
-<div class="cpu gpu">
-</br>
-<p>To enable the C++ package, build from source using `make USE_CPP_PACKAGE=1`.
-<br/>Refer to the <a href="c_plus_plus.html">MXNet C++ setup guide</a> for more info.</p>
-<br/>
-</div> <!-- End of cpu gpu -->
-</div> <!-- End of C++ -->
-<hr>
-For more installation options, refer to the <a href="windows_setup.html">MXNet Windows installation guide</a>.
-</div> <!-- End of Windows -->
-
-
-<!-- START - Cloud Python Installation Instructions -->
-
-<div class="cloud">
-<div class="gpu">
-
-MXNet is available on several cloud providers with GPU support. You can also find GPU/CPU-hybrid support for use cases like scalable inference, or even fractional GPU support with AWS Elastic Inference.
-
-* **Alibaba**
-    - [NVIDIA VM](https://docs.nvidia.com/ngc/ngc-alibaba-setup-guide/launching-nv-cloud-vm-console.html#launching-nv-cloud-vm-console)
-* **Amazon Web Services**
-    - [Amazon SageMaker](https://aws.amazon.com/sagemaker/) - Managed training and deployment of MXNet models
-    - [AWS Deep Learning AMI](https://aws.amazon.com/machine-learning/amis/) - Preinstalled Conda environments for Python 2 or 3 with MXNet, CUDA, cuDNN, MKL-DNN, and AWS Elastic Inference
-    - [Dynamic Training on AWS](https://github.com/awslabs/dynamic-training-with-apache-mxnet-on-aws) - experimental manual EC2 setup or semi-automated CloudFormation setup
-    - [NVIDIA VM](https://aws.amazon.com/marketplace/pp/B076K31M1S)
-* **Google Cloud Platform**
-    - [NVIDIA VM](https://console.cloud.google.com/marketplace/details/nvidia-ngc-public/nvidia_gpu_cloud_image)
-* **Microsoft Azure**
-    - [NVIDIA VM](https://azuremarketplace.microsoft.com/en-us/marketplace/apps/nvidia.ngc_azure_17_11?tab=Overview)
-* **Oracle Cloud**
-    - [NVIDIA VM](https://docs.cloud.oracle.com/iaas/Content/Compute/References/ngcimage.htm)
-
-All NVIDIA VMs use the [NVIDIA MXNet Docker container](https://ngc.nvidia.com/catalog/containers/nvidia:mxnet).
-Follow the [container usage instructions](https://ngc.nvidia.com/catalog/containers/nvidia:mxnet) found in [NVIDIA's container repository](https://ngc.nvidia.com/).
-
-</div> <!-- END gpu -->
-
-<div class="cpu">
-MXNet should work on any cloud provider's CPU-only instances. Follow the Python pip install instructions, Docker instructions, or try the following preinstalled option.
-
-* **Amazon Web Services**
-    - [AWS Deep Learning AMI](https://aws.amazon.com/machine-learning/amis/) - Preinstalled Conda environments for Python 2 or 3 with MXNet and MKL-DNN.
-
-</div> <!-- end cpu -->
-</div> <!-- END - Cloud Python Installation Instructions -->
-
-
-<!-- DEVICES -->
-<div class="devices">
-  <div class="raspberry-pi">
-
-MXNet supports the Debian based Raspbian ARM based operating system so you can run MXNet on Raspberry Pi 3B devices.
-
-These instructions will walk through how to build MXNet for the Raspberry Pi and install the Python bindings for the library.
-
-You can do a dockerized cross compilation build on your local machine or a native build on-device.
-
-The complete MXNet library and its requirements can take almost 200MB of RAM, and loading large models with the library can take over 1GB of RAM. Because of this, we recommend running MXNet on the Raspberry Pi 3 or an equivalent device that has more than 1 GB of RAM and a Secure Digital (SD) card that has at least 4 GB of free memory.
-
-## Quick installation
-You can use this [pre-built Python wheel](https://mxnet-public.s3.amazonaws.com/install/raspbian/mxnet-1.5.0-py2.py3-none-any.whl) on a Raspberry Pi 3B with Stretch. You will likely need to install several dependencies to get MXNet to work. Refer to the following **Build** section for details.
-
-## Docker installation
-**Step 1**  Install Docker on your machine by following the [docker installation instructions](https://docs.docker.com/engine/installation/linux/ubuntu/#install-using-the-repository).
-
-*Note* - You can install Community Edition (CE)
-
-**Step 2** [Optional] Post installation steps to manage Docker as a non-root user.
-
-Follow the four steps in this [docker documentation](https://docs.docker.com/engine/installation/linux/linux-postinstall/#manage-docker-as-a-non-root-user) to allow managing docker containers without *sudo*.
-
-## Build
-
-**This cross compilation build is experimental.**
-
-**Please use a Native build with gcc 4 as explained below, higher compiler versions currently cause test failures on ARM.**
-
-The following command will build a container with dependencies and tools,
-and then compile MXNet for ARMv7.
-You will want to run this on a fast cloud instance or locally on a fast PC to save time.
-The resulting artifact will be located in `build/mxnet-x.x.x-py2.py3-none-any.whl`.
-Copy this file to your Raspberry Pi.
-The previously mentioned pre-built wheel was created using this method.
-
-```
-ci/build.py -p armv7
-```
-
-## Install using a pip wheel
-
-Your Pi will need several dependencies.
-
-Install MXNet dependencies with the following:
-```
-sudo apt-get update
-sudo apt-get install -y \
-    apt-transport-https \
-    build-essential \
-    ca-certificates \
-    cmake \
-    curl \
-    git \
-    libatlas-base-dev \
-    libcurl4-openssl-dev \
-    libjemalloc-dev \
-    liblapack-dev \
-    libopenblas-dev \
-    libopencv-dev \
-    libzmq3-dev \
-    ninja-build \
-    python-dev \
-    python-pip \
-    software-properties-common \
-    sudo \
-    unzip \
-    virtualenv \
-    wget
-```
-Install virtualenv with:
-```
-sudo pip install virtualenv
-```
-Create a Python 2.7 environment for MXNet with:
-```
-virtualenv -p `which python` mxnet_py27
-```
-You may use Python 3, however the [wine bottle detection example](https://mxnet.incubator.apache.org/versions/master/tutorials/embedded/wine_detector.html) for the Pi with camera requires Python 2.7.
-
-Activate the environment, then install the wheel we created previously, or install this [prebuilt wheel](https://mxnet-public.s3.amazonaws.com/install/raspbian/mxnet-1.5.0-py2.py3-none-any.whl).
-
-```
-source mxnet_py27/bin/activate
-pip install mxnet-x.x.x-py2.py3-none-any.whl
-```
-
-Test MXNet with the Python interpreter:
-```
-$ python
-
->>> import mxnet
-```
-If there are no errors then you're ready to start using MXNet on your Pi!
-
-## Native Build
-
-Installing MXNet from source is a two-step process:
-
-1. Build the shared library from the MXNet C++ source code.
-2. Install the supported language-specific packages for MXNet.
-
-**Step 1** Build the Shared Library
-
-On Raspbian versions Wheezy and later, you need the following dependencies:
-
-- Git (to pull code from GitHub)
-
-- libblas (for linear algebraic operations)
-
-- libopencv (for computer vision operations. This is optional if you want to save RAM and Disk Space)
-
-- A C++ compiler that supports C++ 11. The C++ compiler compiles and builds MXNet source code. Supported compilers include the following:
-
-    - [G++ (4.8 or later)](https://gcc.gnu.org/gcc-4.8/). Make sure to use gcc 4 and not 5 or 6 as there are known bugs with these compilers.
-    - [Clang (3.9 - 6)](https://clang.llvm.org/)
-
-Install these dependencies using the following commands in any directory:
-
-```
-    sudo apt-get update
-    sudo apt-get -y install git cmake ninja-build build-essential g++-4.9 c++-4.9 liblapack* libblas* libopencv* libopenblas* python3-dev python-dev virtualenv
-```
-
-Clone the MXNet source code repository using the following `git` command in your home directory:
-```
-    git clone https://github.com/apache/incubator-mxnet.git --recursive
-    cd incubator-mxnet
-```
-
-Build:
-```
-    mkdir -p build && cd build
-    cmake \
-        -DUSE_SSE=OFF \
-        -DUSE_CUDA=OFF \
-        -DUSE_OPENCV=ON \
-        -DUSE_OPENMP=ON \
-        -DUSE_MKL_IF_AVAILABLE=OFF \
-        -DUSE_SIGNAL_HANDLER=ON \
-        -DCMAKE_BUILD_TYPE=Release \
-        -GNinja ..
-    ninja -j$(nproc)
-```
-Some compilation units require memory close to 1GB, so it's recommended that you enable swap as
-explained below and be cautious about increasing the number of jobs when building (-j)
-
-Executing these commands start the build process, which can take up to a couple hours, and creates a file called `libmxnet.so` in the build directory.
-
-If you are getting build errors in which the compiler is being killed, it is likely that the
-compiler is running out of memory (especially if you are on Raspberry Pi 1, 2 or Zero, which have
-less than 1GB of RAM), this can often be rectified by increasing the swapfile size on the Pi by
-editing the file /etc/dphys-swapfile and changing the line CONF_SWAPSIZE=100 to CONF_SWAPSIZE=1024,
-then running:
-```
-  sudo /etc/init.d/dphys-swapfile stop
-  sudo /etc/init.d/dphys-swapfile start
-  free -m # to verify the swapfile size has been increased
-```
-
-**Step 2** Build cython modules (optional)
-
-```bash
-$ pip install Cython
-$ make cython # You can set the python executable with `PYTHON` flag, e.g., make cython PYTHON=python3
-```
-*MXNet* tries to use the cython modules unless the environment variable `MXNET_ENABLE_CYTHON` is set to `0`. If loading the cython modules fails, the default behavior is falling back to ctypes without any warning. To raise an exception at the failure, set the environment variable `MXNET_ENFORCE_CYTHON` to `1`. See [here](/faq/env_var.html) for more details.
-
-
-**Step 3** Install MXNet Python Bindings
-
-To install Python bindings run the following commands in the MXNet directory:
-
-```
-    cd python
-    pip install --upgrade pip
-    pip install -e .
-```
-
-Note that the `-e` flag is optional. It is equivalent to `--editable` and means that if you edit the source files, these changes will be reflected in the package installed.
-
-Alternatively you can create a whl package installable with pip with the following command:
-```
-ci/docker/runtime_functions.sh build_wheel python/ $(realpath build)
-```
-
-
-You are now ready to run MXNet on your Raspberry Pi device. You can get started by following the tutorial on [Real-time Object Detection with MXNet On The Raspberry Pi](http://mxnet.io/tutorials/embedded/wine_detector.html).
-
-*Note - Because the complete MXNet library takes up a significant amount of the Raspberry Pi's limited RAM, when loading training data or large models into memory, you might have to turn off the GUI and terminate running processes to free RAM.*
-
-</div> <!-- End of raspberry pi -->
-
-
-<div class="nvidia-jetson">
-
-# NVIDIA Jetson Devices
-
-To install MXNet on a Jetson TX or Nano, please refer to the [Jetson installation guide](install-jetson.html).
-
-</div> <!-- End of jetson -->
-</div> <!-- End of devices -->
-
-
-<!-- This # tag restarts the page and allows reuse
- of the div classes for validation sections, etc -->
-
-
-<!-- Download -->
-<hr>
-
-
-# Source Download
-
-<a href="download.html">Download</a> your required version of MXNet and <a href="build_from_source.html">build from source</a>.
diff --git a/docs/install/install-jetson.md b/docs/install/install-jetson.md
deleted file mode 100644
index 0b50ceb3fcc5..000000000000
--- a/docs/install/install-jetson.md
+++ /dev/null
@@ -1,235 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Install MXNet on a Jetson
-
-MXNet supports the Ubuntu Arch64 based operating system so you can run MXNet on NVIDIA Jetson Devices, such as the [TX2](http://www.nvidia.com/object/embedded-systems-dev-kits-modules.html) or [Nano](https://developer.nvidia.com/embedded/learn/get-started-jetson-nano-devkit).
-
-These instructions will walk through how to build MXNet and install MXNet's Python language binding.
-
-For the purposes of this install guide we will assume that CUDA is already installed on your Jetson device. The disk image provided by NVIDIA's getting started guides will have the Jetson toolkit preinstalled, and this also includes CUDA. You should double check what versions are installed and which version you plan to use.
-
-After installing the prerequisites, you have several options for installing MXNet:
-1. Use a Jetson MXNet pip wheel for Python development and use a precompiled Jetson MXNet binary.
-3. Build MXNet from source
-   * On a faster Linux computer using cross-compilation
-   * On the Jetson itself (very slow and not recommended)
-
-
-## Prerequisites
-To build from source or to use the Python wheel, you must install the following dependencies on your Jetson.
-Cross-compiling will require dependencies installed on that machine as well.
-
-### Python Dependencies
-
-To use the Python API you need the following dependencies:
-
-```bash
-sudo apt update
-sudo apt -y install \
-                        build-essential \
-                        git \
-                        graphviz \
-                        libatlas-base-dev \
-                        libopencv-dev \
-                        python-pip
-
-sudo pip install --upgrade \
-                        pip \
-                        setuptools
-
-sudo pip install \
-                        graphviz==0.8.4 \
-                        jupyter \
-                        numpy==1.15.2
-```
-
-If you plan to cross-compile you will need to install these dependencies on that computer as well.
-If you get an error about something being busy, you can restart the Nano and this error will go away. You can then continue installation of the prerequisites.
-
-### Download the source & setup some environment variables:
-
-These steps are optional, but some of the following instructions expect MXNet source files and the `MXNET_HOME` environment variable. Also, CUDA commands will not work out of the box without updating your path.
-
-Clone the MXNet source code repository using the following `git` command in your home directory:
-
-```bash
-git clone --recursive https://github.com/apache/incubator-mxnet.git mxnet
-```
-
-Setup your environment variables for MXNet and CUDA in your `.profile` file in your home directory.
-Add the following to the file.
-
-```bash
-export PATH=/usr/local/cuda/bin:$PATH
-export MXNET_HOME=$HOME/mxnet/
-export PYTHONPATH=$MXNET_HOME/python:$PYTHONPATH
-```
-
-You can then apply this change immediately with the following:
-```bash
-source .profile
-```
-
-**Note:** Change the `~/.profile` steps according to how you prefer to use your shell. Otherwise, your environment variables will be gone after you logout.
-
-### Configure CUDA
-
-You can check to see what version of CUDA is running with `nvcc`.
-
-```bash
-nvcc --version
-```
-
-To switch CUDA versions on a device or computer that has more than one version installed, use the following and replace the symbolic link to the version you want. This one uses CUDA 10.0, which is preinstalled on the Nano.
-
-```bash
-sudo rm /usr/local/cuda
-sudo ln -s /usr/local/cuda-10.0 /usr/local/cuda
-```
-
-**Note:** When cross-compiling, change the CUDA version on the host computer you're using to match the version you're running on your Jetson device.
-**Note:** CUDA 10.1 is recommended but doesn't ship with the Nano's SD card image. You may want to go through CUDA upgrade steps first.
-
-
-## Option 1. Install MXNet for Python
-
-To use a prepared Python wheel, download it to your Jetson, and run it.
-* [MXNet 1.4.0 - Python 3](https://s3.us-east-2.amazonaws.com/mxnet-public/install/jetson/1.4.0/mxnet-1.4.0-cp36-cp36m-linux_aarch64.whl)
-* [MXNet 1.4.0 - Python 2](https://s3.us-east-2.amazonaws.com/mxnet-public/install/jetson/1.4.0/mxnet-1.4.0-cp27-cp27mu-linux_aarch64.whl)
-
-
-It should download the required dependencies, but if you have issues,
-install the dependencies in the prerequisites section, then run the pip wheel.
-
-```bash
-sudo pip install mxnet-1.4.0-cp27-cp27mu-linux_aarch64.whl
-```
-
-Now use a pre-compiled binary you can download it from S3 which is a patch v1.4.1:
-* https://s3.us-east-2.amazonaws.com/mxnet-public/install/jetson/1.4.1/libmxnet.so
-
-Place this file in `$MXNET_HOME/lib`.
-
-To use this with the MXNet Python binding, you must match the source directory's checked out version with the binary's source version, then install it with pip.
-
-```bash
-cd $MXNET_HOME
-git checkout v1.4.x
-git submodule update --init --recursive
-cd python
-sudo pip install -e .
-```
-
-Refer to the following Conclusion and Next Steps section to test your installation. 
-
-## Option 2. Build MXNet from Source
-
-Installing MXNet from source is a two-step process:
-
-1. Build the shared library from the MXNet C++ source code.
-2. Install the supported language-specific packages for MXNet.
-
-You can use a Docker method or you can build from source manually.
-
-### Docker
-
-You must have installed Docker and be able to run `docker` without `sudo`.
-Follow these [setup instructions to get to this point](https://docs.docker.com/install/linux/#manage-docker-as-a-non-root-user).
-Then run the following to execute cross-compilation via Docker.
-
-```bash
-$MXNET_HOME/ci/build.py -p jetson
-```
-
-### Manual
-
-**Step 1** Build the Shared Library
-
-(Skip this sub-step for compiling on the Jetson device directly.)
-Edit the Makefile to install the MXNet with CUDA bindings to leverage the GPU on the Jetson:
-
-```bash
-cp $MXNET_HOME/make/crosscompile.jetson.mk config.mk
-```
-
-Now edit `config.mk` to make some additional changes for the Nano. Update the following settings:
-
-1. Update the CUDA path. `USE_CUDA_PATH = /usr/local/cuda`
-2. Add `-gencode arch=compute-63, code=sm_62` to the `CUDA_ARCH` setting.
-3. Update the NVCC settings. `NVCCFLAGS := -m64`
-4. (optional, but recommended) Turn on OpenCV. `USE_OPENCV = 1`
-
-Now edit the Mshadow Makefile to ensure MXNet builds with Pascal's hardware level low precision acceleration by editing `3rdparty/mshadow/make/mshadow.mk`.
-The last line has `MSHADOW_USE_PASCAL` set to `0`. Change this to `1` to enable it.
-
-```bash
-MSHADOW_CFLAGS += -DMSHADOW_USE_PASCAL=1
-```
-
-Now you can build the complete MXNet library with the following command:
-
-```bash
-cd $MXNET_HOME
-make -j $(nproc)
-```
-
-Executing this command creates a file called `libmxnet.so` in the `mxnet/lib` directory.
-
-**Step 2** Install MXNet Python Bindings (optional)
-
-To install Python bindings run the following commands in the MXNet directory:
-
-```bash
-cd $MXNET_HOME/python
-sudo pip install -e .
-```
-
-Note that the `-e` flag is optional. It is equivalent to `--editable` and means that if you edit the source files, these changes will be reflected in the package installed.
-
-**Step 3** Install the MXNet Java & Scala Bindings (optional)
-
-Change directories to `scala-package` and run `mvn install`.
-
-```bash
-cd $MXNET_HOME/scala-package
-mvn install
-```
-
-This creates the required `.jar` file to use in your Java or Scala projects.
-
-## Conclusion and Next Steps
-
-You are now ready to run MXNet on your NVIDIA Jetson TX2 or Nano device.
-You can verify your MXNet Python installation with the following:
-
-```python
-import mxnet
-mxnet.__version__
-```
-
-You can also verify MXNet can use your GPU with the following test:
-
-```python
-import mxnet as mx
-a = mx.nd.ones((2, 3), mx.gpu())
-b = a * 2 + 1
-b.asnumpy()
-```
-
-If everything is working, it will report the version number.
-For assistance, head over to the [MXNet Forum](https://discuss.mxnet.io/).
diff --git a/docs/install/install_mxnet_ubuntu_python.sh b/docs/install/install_mxnet_ubuntu_python.sh
deleted file mode 100755
index dcf8bff713cc..000000000000
--- a/docs/install/install_mxnet_ubuntu_python.sh
+++ /dev/null
@@ -1,74 +0,0 @@
-#!/bin/bash
-
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-######################################################################
-# This script installs MXNet with required dependencies on a Ubuntu Machine.
-# Tested on Ubuntu 16.04+ distro.
-# Important Maintenance Instructions:
-#    Align changes with CI in /ci/docker/install/ubuntu_core.sh
-#    and ubuntu_python.sh
-######################################################################
-
-set -ex
-sudo apt-get update
-sudo apt-get install -y \
-    apt-transport-https \
-    build-essential \
-    ca-certificates \
-    cmake \
-    curl \
-    git \
-    libatlas-base-dev \
-    libcurl4-openssl-dev \
-    libjemalloc-dev \
-    liblapack-dev \
-    libopenblas-dev \
-    libopencv-dev \
-    libzmq3-dev \
-    ninja-build \
-    python-dev \
-    python3-dev \
-    software-properties-common \
-    sudo \
-    unzip \
-    virtualenv \
-    wget
-
-wget -nv https://bootstrap.pypa.io/get-pip.py
-echo "Installing for Python 3..."
-sudo python3 get-pip.py
-pip3 install --user -r requirements.txt
-echo "Installing for Python 2..."
-sudo python2 get-pip.py
-pip2 install --user -r requirements.txt
-
-cd ../../
-
-echo "Checking for GPUs..."
-gpu_install=$(which nvidia-smi | wc -l)
-if [ "$gpu_install" = "0" ]; then
-    make_params="USE_OPENCV=1 USE_BLAS=openblas"
-    echo "nvidia-smi not found. Installing in CPU-only mode with these build flags: $make_params"
-else
-    make_params="USE_OPENCV=1 USE_BLAS=openblas USE_CUDA=1 USE_CUDA_PATH=/usr/local/cuda USE_CUDNN=1"
-    echo "nvidia-smi found! Installing with CUDA and cuDNN support with these build flags: $make_params"
-fi
-
-echo "Building MXNet core. This can take few minutes..."
-make -j $(nproc) $make_params
diff --git a/docs/install/install_mxnet_ubuntu_r.sh b/docs/install/install_mxnet_ubuntu_r.sh
deleted file mode 100755
index 662cad679fab..000000000000
--- a/docs/install/install_mxnet_ubuntu_r.sh
+++ /dev/null
@@ -1,55 +0,0 @@
-#!/usr/bin/env bash
-
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-######################################################################
-# This script installs MXNet for R along with all required dependencies on a Ubuntu Machine.
-# Tested on Ubuntu 16.04+ distro.
-# Important Maintenance Instructions:
-#  Align changes with CI in /ci/docker/install/ubuntu_r.sh
-######################################################################
-set -e
-echo "This script assumes you have built MXNet already."
-
-read -p "Would you like to continue? [Y/n]"
-if [ x"$REPLY" = x"" -o x"$REPLY" = x"y" -o x"$REPLY" = x"Y" ]; then
-  echo "Installing R dependencies. This can take few minutes..."
-
-  sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9
-  sudo add-apt-repository 'deb [arch=amd64,i386] https://cran.rstudio.com/bin/linux/ubuntu xenial/'
-
-  sudo apt-get -qq update
-  sudo apt-get install -y --allow-unauthenticated \
-      libcairo2-dev \
-      libcurl4-openssl-dev \
-      libssl-dev \
-      libxml2-dev \
-      libxt-dev \
-      r-base \
-      r-base-dev
-
-  sudo Rscript -e "install.packages('devtools', repo = 'https://cran.rstudio.com')"
-  cd ../../R-package
-  sudo Rscript -e "library(devtools); library(methods); options(repos=c(CRAN='https://cran.rstudio.com')); install_deps(dependencies = TRUE)"
-  cd ..
-
-  echo "Compiling R package. This can take few minutes..."
-  sudo make rpkg
-
-  echo "Done! MXNet for R installation is complete. Go ahead and explore MXNet with R :-)"
-fi
diff --git a/docs/install/java_setup.md b/docs/install/java_setup.md
deleted file mode 100644
index a6aa8f8709d3..000000000000
--- a/docs/install/java_setup.md
+++ /dev/null
@@ -1,128 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Setup the MXNet Package for Java
-
-The following instructions are provided for macOS and Ubuntu. Windows is not yet available.
-
-**Note:** If you use IntelliJ or a similar IDE, you may want to follow the [MXNet-Java on IntelliJ tutorial](../tutorials/java/mxnet_java_on_intellij.md) instead of these instructions.
-
-<hr>
-
-## Maven
-
-### Setup Instructions
-
-**Step 1.** Install dependencies:
-
-**macOS Steps**
-
-```bash
-brew update
-brew tap caskroom/versions
-brew cask install java8
-brew install maven
-```
-
-**Ubuntu Steps**
-
-Please run the following lines:
-
-```bash
-sudo apt-get install openjdk-8-jdk maven
-```
-
-**Step 2.** Run the demo MXNet-Java project.
-
-Go to the [MXNet-Java demo project's README](https://github.com/apache/incubator-mxnet/tree/master/scala-package/mxnet-demo/java-demo) and follow the directions to test the MXNet-Java package installation.
-
-#### Maven Repository
-
-MXNet-Java can be easily included in your Maven managed project. The Java packages are currently available on Maven. Add the dependency which corresponds to your platform to the `dependencies` tag :
-
-**Linux CPU**
-```html
-<dependency>
-  <groupId>org.apache.mxnet</groupId>
-  <artifactId>mxnet-full_2.11-linux-x86_64-cpu</artifactId>
-  <version>1.4.0</version>
-</dependency>
-```
-
-**Linux GPU**
-```html
-<dependency>
-  <groupId>org.apache.mxnet</groupId>
-  <artifactId>mxnet-full_2.11-linux-x86_64-gpu</artifactId>
-  <version>1.4.0</version>
-</dependency>
-```
-
-**macOS CPU**
-```html
-<dependency>
-  <groupId>org.apache.mxnet</groupId>
-  <artifactId>mxnet-full_2.11-osx-x86_64-cpu</artifactId>
-  <version>1.4.0</version>
-</dependency>
-```
-
-The official Java Packages have been released as part of MXNet 1.4 and are available on the [MXNet Maven package repository](https://search.maven.org/#search%7Cga%7C1%7Cg%3A%22org.apache.mxnet%22).
-<hr>
-
-### Eclipse IDE Support
-You can convert your existing Maven project to a project that can run in Eclipse by:
-```
-mvn eclipse:eclipse
-```
-This can be done once you have your maven project properly configured.
-
-## Source
-
-The previously mentioned setup with Maven is recommended. Otherwise, the following instructions for macOS and Ubuntu are provided for reference only:
-
-**If you have already built mxnet from source using `cmake`, run `make clean` and then follow the appropriate guide below***
-
-| OS | Step 1 | Step 2 |
-|---|---|---|
-|macOS | [Shared Library for macOS](osx_setup.html#build-the-shared-library) | [Scala Package for macOS](osx_setup.html#install-the-mxnet-package-for-scala) |
-| Ubuntu | [Shared Library for Ubuntu](ubuntu_setup.html#installing-mxnet-on-ubuntu) | [Scala Package for Ubuntu](ubuntu_setup.html#install-the-mxnet-package-for-scala) |
-| Windows | <a class="github-button" href="https://github.com/apache/incubator-mxnet/issues/10549" data-size="large" data-show-count="true" aria-label="Issue apache/incubator-mxnet on GitHub"> | <a class="github-button" href="https://github.com/apache/incubator-mxnet/issues/10549" data-size="large" data-show-count="true" aria-label="Issue apache/incubator-mxnet on GitHub">Call for Contribution</a> |
-
-
-#### Build Java from an Existing MXNet Installation
-If you have already built MXNet **from source** and are looking to setup Java from that point, you may simply run the following from the MXNet `scala-package` folder:
-
-```
-mvn install
-```
-This will install both the Java Inference API and the required MXNet-Scala package.
-<hr>
-
-## Documentation
-
-Javadocs are generated as part of the docs build pipeline. You can find them published in the [Java API](../api/java/index.md) section of the website or by going to the [scaladocs output](https://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.package) directly.
-
-To build the docs yourself, follow the [developer build docs instructions](https://github.com/apache/incubator-mxnet/tree/master/docs/build_version_doc#developer-instructions).
-
-<hr>
-
-## Resources
-
-* [Java API](../api/java/)
-* [javadocs](https://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.package)
-* [MXNet-Java Tutorials](../tutorials/java/)
diff --git a/docs/install/osx_setup.md b/docs/install/osx_setup.md
deleted file mode 100644
index 525b61bc2488..000000000000
--- a/docs/install/osx_setup.md
+++ /dev/null
@@ -1,268 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Installing MXNet from source on OS X (Mac)
-
-**NOTE:** For pre-built MXNet with Python, please refer to the [new install guide](index.md).
-
-Installing MXNet is a two-step process:
-
-1. Build the shared library from the MXNet C++ source code.
-2. Install the supported language-specific packages for MXNet.
-
-**Note:** To change the compilation options for your build, edit the ```make/config.mk``` file and submit a build request with the ```make``` command.
-
-## Prepare Environment for GPU Installation
-
-This section is optional. Skip to next section if you don't plan to use GPUs. If you plan to build with GPU, you need to set up the environment for CUDA and cuDNN.
-
-First, download and install [CUDA 8 toolkit](https://developer.nvidia.com/cuda-toolkit).
-
-Once you have the CUDA Toolkit installed you will need to set up the required environment variables by adding the following to your ~/.bash_profile file:
-
-```bash
-    export CUDA_HOME=/usr/local/cuda
-    export DYLD_LIBRARY_PATH="$CUDA_HOME/lib:$DYLD_LIBRARY_PATH"
-    export PATH="$CUDA_HOME/bin:$PATH"
-```
-
-Reload ~/.bash_profile file and install dependencies:
-```bash
-    . ~/.bash_profile
-    brew install coreutils
-    brew tap caskroom/cask
-```
-
-Then download [cuDNN 5](https://developer.nvidia.com/cudnn).
-
-Unzip the file and change to the cudnn root directory. Move the header files and libraries to your local CUDA Toolkit folder:
-
-```bash
-    $ sudo mv include/cudnn.h /Developer/NVIDIA/CUDA-8.0/include/
-    $ sudo mv lib/libcudnn* /Developer/NVIDIA/CUDA-8.0/lib
-    $ sudo ln -s /Developer/NVIDIA/CUDA-8.0/lib/libcudnn* /usr/local/cuda/lib/
-```
-
-Now we can start to build MXNet.
-
-## Build the Shared Library
-
-### Install MXNet dependencies
-Install the dependencies, required for MXNet, with the following commands:
-- [Homebrew](http://brew.sh/)
-- OpenBLAS and homebrew/core (for linear algebraic operations)
-- OpenCV (for computer vision operations)
-
-```bash
-	# Paste this command in Mac terminal to install Homebrew
-	/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
-
-	# Insert the Homebrew directory at the top of your PATH environment variable
-	export PATH=/usr/local/bin:/usr/local/sbin:$PATH
-```
-
-```bash
-	brew update
-	brew install pkg-config
-	brew install graphviz
-	brew install openblas
-	brew tap homebrew/core
-	brew install opencv@3
-
-	# If building with MKLDNN
-	brew install llvm
-
-	# Get pip
-	easy_install pip
-	# For visualization of network graphs
-	pip install graphviz==0.8.4
-	# Jupyter notebook
-	pip install jupyter
-```
-
-### Build MXNet Shared Library
-After you have installed the dependencies, pull the MXNet source code from Git and build MXNet to produce an MXNet library called ```libmxnet.so```. You can clone the repository as described in the following code block, or you may try the [download links](download.md) for your desired MXNet version.
-
-The file called ```osx.mk``` has the configuration required for building MXNet on OS X. First copy ```make/osx.mk``` into ```config.mk```, which is used by the ```make``` command:
-
-```bash
-    git clone --recursive https://github.com/apache/incubator-mxnet ~/mxnet
-    cd ~/mxnet
-    cp make/osx.mk ./config.mk
-    echo "USE_BLAS = openblas" >> ./config.mk
-    echo "ADD_CFLAGS += -I/usr/local/opt/openblas/include" >> ./config.mk
-    echo "ADD_LDFLAGS += -L/usr/local/opt/openblas/lib" >> ./config.mk
-    echo "ADD_LDFLAGS += -L/usr/local/lib/graphviz/" >> ./config.mk
-    make -j$(sysctl -n hw.ncpu)
-```
-
-To build with MKLDNN
-
-```bash
-echo "CC=$(brew --prefix llvm)/bin/clang" >> ./config.mk
-echo "CXX=$(brew --prefix llvm)/bin/clang++" >> ./config.mk
-echo "USE_OPENCV=1" >> ./config.mk
-echo "USE_OPENMP=1" >> ./config.mk
-echo "USE_MKLDNN=1" >> ./config.mk
-echo "USE_BLAS=apple" >> ./config.mk
-LIBRARY_PATH=$(brew --prefix llvm)/lib/ make -j $(sysctl -n hw.ncpu)
-```
-
-If building with ```GPU``` support, add the following configuration to config.mk and build:
-```bash
-    echo "USE_CUDA = 1" >> ./config.mk
-    echo "USE_CUDA_PATH = /usr/local/cuda" >> ./config.mk
-    echo "USE_CUDNN = 1" >> ./config.mk
-    make -j$(sysctl -n hw.ncpu)
-```
-**Note:** To change build parameters, edit ```config.mk```.
-
-
-&nbsp;
-
-We have installed MXNet core library. Next, we will install MXNet interface package for the programming language of your choice:
-- [Python](#install-mxnet-for-python)
-- [R](#install-the-mxnet-package-for-r)
-- [Julia](#install-the-mxnet-package-for-julia)
-- [Scala](#install-the-mxnet-package-for-scala)
-- [Perl](#install-the-mxnet-package-for-perl)
-
-## Install MXNet for Python
-To install the MXNet Python binding navigate to the root of the MXNet folder then run the following:
-
-```bash
-$ cd python
-$ pip install -e .
-```
-
-Note that the `-e` flag is optional. It is equivalent to `--editable` and means that if you edit the source files, these changes will be reflected in the package installed.
-
-## Install the MXNet Package for R
-You have 2 options:
-1. Building MXNet with the Prebuilt Binary Package
-2. Building MXNet from Source Code
-
-### Building MXNet with the Prebuilt Binary Package
-Install OpenCV and OpenBLAS.
-
-```bash
-brew install opencv
-brew install openblas@0.3.1
-```
-
-Add a soft link to the OpenBLAS installation. This example links the 0.3.1 version:
-
-```bash
-ln -sf /usr/local/opt/openblas/lib/libopenblasp-r0.3.* /usr/local/opt/openblas/lib/libopenblasp-r0.3.1.dylib
-```
-
-Note: packages for 3.6.x are not yet available.
-
-Install 3.5.x of R from [CRAN](https://cran.r-project.org/bin/macosx/). The latest is [v3.5.3](https://cran.r-project.org/bin/macosx/R-3.5.3.pkg).
-
-For OS X (Mac) users, MXNet provides a prebuilt binary package for CPUs. The prebuilt package is updated weekly. You can install the package directly in the R console using the following commands:
-
-```r
-  cran <- getOption("repos")
-  cran["dmlc"] <- "https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/R/CRAN/"
-  options(repos = cran)
-  install.packages("mxnet")
-```
-
-### Building MXNet from Source Code
-
-Run the following commands to install the MXNet dependencies and build the MXNet R package.
-
-```r
-    Rscript -e "install.packages('devtools', repo = 'https://cran.rstudio.com')"
-```
-```bash
-    cd R-package
-    Rscript -e "library(devtools); library(methods); options(repos=c(CRAN='https://cran.rstudio.com')); install_deps(dependencies = TRUE)"
-    cd ..
-    make rpkg
-```
-
-## Install the MXNet Package for Julia
-The MXNet package for Julia is hosted in a separate repository, MXNet.jl, which is available on [GitHub](https://github.com/dmlc/MXNet.jl). To use Julia binding it with an existing libmxnet installation, set the ```MXNET_HOME``` environment variable by running the following command:
-
-```bash
-	export MXNET_HOME=/<path to>/libmxnet
-```
-
-The path to the existing libmxnet installation should be the root directory of libmxnet. In other words, you should be able to find the ```libmxnet.so``` file at ```$MXNET_HOME/lib```. For example, if the root directory of libmxnet is ```~```, you would run the following command:
-
-```bash
-	export MXNET_HOME=/~/libmxnet
-```
-
-You might want to add this command to your ```~/.bashrc``` file. If you do, you can install the Julia package in the Julia console using the following command:
-
-```julia
-	Pkg.add("MXNet")
-```
-
-For more details about installing and using MXNet with Julia, see the [MXNet Julia documentation](http://dmlc.ml/MXNet.jl/latest/user-guide/install/).
-
-
-## Install the MXNet Package for Scala
-
-To use the MXNet-Scala package, you can acquire the Maven package as a dependency.
-
-Further information is in the [MXNet-Scala Setup Instructions](./scala_setup.md).
-
-If you use IntelliJ or a similar IDE, you may want to follow the [MXNet-Scala on IntelliJ tutorial](../tutorials/scala/mxnet_scala_on_intellij.md) instead.
-
-
-## Install the MXNet Package for Perl
-Before you build MXNet for Perl from source code, you must complete [building the shared library](#build-the-shared-library).
-After you build the shared library, run the following command from the MXNet source root directory to build the MXNet Perl package:
-
-```bash
-    brew install swig
-    sudo sh -c 'curl -L https://cpanmin.us | perl - App::cpanminus'
-    sudo cpanm -q -n PDL Mouse Function::Parameters Hash::Ordered PDL::CCS
-
-    MXNET_HOME=${PWD}
-    export PERL5LIB=${HOME}/perl5/lib/perl5
-
-    cd ${MXNET_HOME}/perl-package/AI-MXNetCAPI/
-    perl Makefile.PL INSTALL_BASE=${HOME}/perl5
-    make
-    install_name_tool -change lib/libmxnet.so \
-        ${MXNET_HOME}/lib/libmxnet.so \
-        blib/arch/auto/AI/MXNetCAPI/MXNetCAPI.bundle
-    make install
-
-    cd ${MXNET_HOME}/perl-package/AI-NNVMCAPI/
-    perl Makefile.PL INSTALL_BASE=${HOME}/perl5
-    make
-    install_name_tool -change lib/libmxnet.so \
-            ${MXNET_HOME}/lib/libmxnet.so \
-            blib/arch/auto/AI/NNVMCAPI/NNVMCAPI.bundle
-    make install
-
-    cd ${MXNET_HOME}/perl-package/AI-MXNet/
-    perl Makefile.PL INSTALL_BASE=${HOME}/perl5
-    make install
-```
-
-## Next Steps
-
-* [Tutorials](../tutorials/index.md)
-* [How To](../faq/index.md)
-* [Architecture](../architecture/index.md)
diff --git a/docs/install/requirements.txt b/docs/install/requirements.txt
deleted file mode 100644
index 38de91b9da7b..000000000000
--- a/docs/install/requirements.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-cpplint==1.3.0
-h5py==2.8.0rc1
-nose
-nose-timer
-numpy>1.16.0,<2.0.0
-pylint==2.3.1; python_version >= '3.0'
-requests<2.19.0,>=2.18.4
-scipy==1.0.1
diff --git a/docs/install/scala_setup.md b/docs/install/scala_setup.md
deleted file mode 100644
index 2456c147ef93..000000000000
--- a/docs/install/scala_setup.md
+++ /dev/null
@@ -1,157 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Setup the MXNet Package for Scala
-
-The following instructions are provided for macOS and Ubuntu. Windows is not yet available.
-
-**Note:** If you use IntelliJ or a similar IDE, you may want to follow the [MXNet-Scala on IntelliJ tutorial](../tutorials/scala/mxnet_scala_on_intellij.md) instead of these instructions.
-**Note:** Currently, we only support scala 2.11
-
-<hr>
-
-## Maven
-
-### Setup Instructions
-
-**Step 1.** Install dependencies:
-
-**macOS Steps**
-
-```bash
-brew update
-brew tap caskroom/versions
-brew cask install java8
-brew install opencv
-brew install maven
-```
-
-**Ubuntu Steps**
-
-These scripts will install Maven and its dependencies.
-
-```bash
-wget https://raw.githubusercontent.com/apache/incubator-mxnet/master/ci/docker/install/ubuntu_core.sh
-wget https://raw.githubusercontent.com/apache/incubator-mxnet/master/ci/docker/install/ubuntu_scala.sh
-chmod +x ubuntu_core.sh
-chmod +x ubuntu_scala.sh
-sudo ./ubuntu_core.sh
-sudo ./ubuntu_scala.sh
-```
-
-**Step 2.** Run the demo MXNet-Scala project.
-
-Go to the [MXNet-Scala demo project's README](https://github.com/apache/incubator-mxnet/tree/master/scala-package/mxnet-demo) and follow the directions to test the MXNet-Scala package installation.
-
-#### Maven Repository
-
-Package information can be found in the Maven Repository:
-https://mvnrepository.com/artifact/org.apache.mxnet
-
-**Linux CPU**
-```html
-<!-- https://mvnrepository.com/artifact/org.apache.mxnet/mxnet-full_2.11-linux-x86_64-cpu -->
-<dependency>
-    <groupId>org.apache.mxnet</groupId>
-    <artifactId>mxnet-full_2.11-linux-x86_64-cpu</artifactId>
-</dependency>
-```
-
-**Linux GPU**
-```html
-<!-- https://mvnrepository.com/artifact/org.apache.mxnet/mxnet-full_2.11-linux-x86_64-gpu -->
-<dependency>
-    <groupId>org.apache.mxnet</groupId>
-    <artifactId>mxnet-full_2.11-linux-x86_64-gpu</artifactId>
-</dependency>
-```
-
-**macOS CPU**
-```html
-<!-- https://mvnrepository.com/artifact/org.apache.mxnet/mxnet-full_2.11-osx-x86_64-cpu -->
-<dependency>
-    <groupId>org.apache.mxnet</groupId>
-    <artifactId>mxnet-full_2.11-osx-x86_64-cpu</artifactId>
-</dependency>
-```
-
-**NOTE:** You may specify the version you wish to use by adding the version number to the `dependency` block. For example, to use v1.2.0 you would add `<version>1.2.0</version>`. Otherwise Maven will use the latest version available.
-
-<hr>
-
-## Source
-
-The previously mentioned setup with Maven is recommended. Otherwise, the following instructions for macOS, Ubuntu, and Windows are provided for reference only:
-
-**If you have already built mxnet from source using `cmake`, run `make clean` and then follow the appropriate guide below***
-
-| OS | Step 1 | Step 2 |
-|---|---|---|
-|macOS | [Shared Library for macOS](osx_setup.html#build-the-shared-library) | [Scala Package for macOS](osx_setup.html#install-the-mxnet-package-for-scala) |
-| Ubuntu | [Shared Library for Ubuntu](ubuntu_setup.html#installing-mxnet-on-ubuntu) | [Scala Package for Ubuntu](ubuntu_setup.html#install-the-mxnet-package-for-scala) |
-| Windows | <a class="github-button" href="https://github.com/apache/incubator-mxnet/issues/10549" data-size="large" data-show-count="true" aria-label="Issue apache/incubator-mxnet on GitHub"> | <a class="github-button" href="https://github.com/apache/incubator-mxnet/issues/10549" data-size="large" data-show-count="true" aria-label="Issue apache/incubator-mxnet on GitHub">Call for Contribution</a> |
-
-
-#### Build Scala from an Existing MXNet Installation
-If you have already built MXNet **from source** and are looking to setup Scala from that point, you may simply run the following from the MXNet `scala-package` folder:
-
-```
-mvn install
-```
-
-<hr>
-
-## Interpreter
-
-To run the scala interpreter, first download and install scala 2.11.x (run `scala -version` to make sure you have the right version installed.**
-
-### Installing the Interpreter
-
-**Ubuntu***
-
-```
-sudo apt-get install scala
-```
-
-**macOS***
-
-```
-brew install scala@2.11
-```
-
-Then, add scala to your path by following the instructions output by homebrew.
-
-### Running the Interpreter
-
-To run the interpreter, download the appropriate mxnet jar from [the maven repository](https://search.maven.org/search?q=g:org.apache.mxnet) or build from source following the instructions above.
-
-Then, run `scala -cp {path/to/mxnet-full_2.11-os-version.jar}` to start it.
-If you receive a "NumberFormatException" when running the interpreter, run `export TERM=xterm-color` before starting the interpreter.
-
-## Documentation
-
-Scaladocs are generated as part of the docs build pipeline. You can find them published in the [Scala API](../api/scala/index.md) section of the website or by going to the [scaladocs output](https://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.package) directly.
-
-To build the docs yourself, follow the [developer build docs instructions](https://github.com/apache/incubator-mxnet/tree/master/docs/build_version_doc#developer-instructions).
-
-<hr>
-
-## Resources
-
-* [Scala API](../api/scala/)
-* [scaladocs](https://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.package)
-* [MXNet-Scala Tutorials](../tutorials/scala/)
diff --git a/docs/install/ubuntu_setup.md b/docs/install/ubuntu_setup.md
deleted file mode 100644
index db8a0bb48067..000000000000
--- a/docs/install/ubuntu_setup.md
+++ /dev/null
@@ -1,554 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Installing MXNet on Ubuntu
-
-The following installation instructions are for installing MXNet on computers running **Ubuntu 16.04**. Support for later versions of Ubuntu is [not yet available](#contributions).
-<hr>
-
-## Contents
-
-* [CUDA Dependencies](#cuda-dependencies)
-* [Quick Installation](#quick-installation)
-    * [Python](#install-mxnet-for-python)
-    * [pip Packages](#pip-package-availability)
-* [Build from Source](#build-mxnet-from-source)
-* [Installing Language Packages](#installing-language-packages-for-mxnet)
-    * [R](#install-the-mxnet-package-for-r)
-    * [Julia](#install-the-mxnet-package-for-julia)
-    * [Scala](#install-the-mxnet-package-for-scala)
-    * [Java](#install-the-mxnet-package-for-java)
-    * [Perl](#install-the-mxnet-package-for-perl)
-  * [Contributions](#contributions)
-  * [Next Steps](#next-steps)
-
-<hr>
-
-## CUDA Dependencies
-
-If you plan to build with GPU, you need to set up the environment for CUDA and cuDNN.
-
-First, download and install [CUDA toolkit](https://developer.nvidia.com/cuda-toolkit). CUDA 9.2 is recommended.
-
-Then download [cuDNN 7.1.4](https://developer.nvidia.com/cudnn).
-
-Unzip the file and change to the cuDNN root directory. Move the header and libraries to your local CUDA Toolkit folder:
-
-```bash
-    tar xvzf cudnn-9.2-linux-x64-v7.1
-    sudo cp -P cuda/include/cudnn.h /usr/local/cuda/include
-    sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda/lib64
-    sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
-    sudo ldconfig
-```
-
-<hr>
-
-
-## Quick Installation
-
-### Install MXNet for Python
-
-#### Dependencies
-
-The following scripts will install Ubuntu 16.04 dependencies for MXNet Python development.
-
-```bash
-wget https://raw.githubusercontent.com/apache/incubator-mxnet/master/ci/docker/install/ubuntu_core.sh
-wget https://raw.githubusercontent.com/apache/incubator-mxnet/master/ci/docker/install/ubuntu_python.sh
-sudo ./ubuntu_core.sh
-sudo ./ubuntu_python.sh
-```
-
-Using the latest MXNet with CUDA 9.2 package is recommended for the fastest training speeds with MXNet.
-
-**Recommended for training:**
-```bash
-pip install mxnet-cu92
-```
-
-**Recommended for inference:**
-```bash
-pip install mxnet-cu92mkl
-```
-
-Alternatively, you can use the table below to select the package that suits your purpose.
-
-| MXNet Version | Basic | CUDA | MKL-DNN | CUDA/MKL-DNN |
-|-|-|-|-|-|
-| Latest | mxnet | mxnet-cu92 | mxnet-mkl | mxnet-cu92mkl |
-
-
-#### pip Package Availability
-
-The following table presents the pip packages that are recommended for each version of MXNet.
-
-![pip package table](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/install/pip-packages-1.4.0.png)
-
-To install an older version of MXNet with one of the packages in the previous table add `==` with the version you require. For example for version 1.1.0 of MXNet with CUDA 8, you would use `pip install mxnet-cu80==1.1.0`.
-
-<hr>
-
-
-## Build MXNet from Source
-
-You can build MXNet from source, and then you have the option of installing language-specific bindings, such as Scala, Java, Julia, R or Perl. This is a two-step process:
-
-1. Build the shared library from the MXNet C++ source code.
-2. (optional) Install the supported language-specific packages for MXNet. Be sure to check that section first, as some scripts may be available to handle all of the dependencies, MXNet build, and language bindings for you. Here they are again for quick access:
-
-* [R](#install-the-mxnet-package-for-r)
-* [Julia](#install-the-mxnet-package-for-julia)
-* [Scala](#install-the-mxnet-package-for-scala)
-* [Java](#install-the-mxnet-package-for-java)
-* [Perl](#install-the-mxnet-package-for-perl)
-
-**Note:** To change the compilation options for your build, edit the ```make/config.mk``` file prior to building MXNet. More information on this is mentioned in the different language package instructions.
-
-### Build the Shared Library
-
-#### Quick MXNet Build
-You can quickly build MXNet from source with the following script found in the `/docs/install` folder:
-
-```bash
-cd docs/install
-./install_mxnet_ubuntu_python.sh
-```
-
-Or you can go through a manual process described next.
-
-#### Manual MXNet Installation
-
-It is recommended that you review the general [build from source](build_from_source.md) instructions before continuing.
-
-On Ubuntu versions 16.04 or later, you need the following dependencies:
-
-**Step 1:** Install prerequisite packages.
-```bash
-    sudo apt-get update
-    sudo apt-get install -y build-essential git ninja-build ccache
-```
-
-**For Ubuntu 18.04 and CUDA builds you need to update CMake**
-
-```bash
-#!/usr/bin/env bash
-set -exuo pipefail
-sudo apt remove --purge --auto-remove cmake
-
-# Update CMAKE for correct cuda autotedetection: https://github.com/clab/dynet/issues/1457
-version=3.14
-build=0
-mkdir -p ~/tmp
-cd ~/tmp
-wget https://cmake.org/files/v$version/cmake-$version.$build.tar.gz
-tar -xzvf cmake-$version.$build.tar.gz
-cd cmake-$version.$build/
-./bootstrap
-make -j$(nproc)
-sudo make install
-```
-
-
-**Step 2:** Install a Math Library.
-
-Details on the different math libraries are found in the build from source guide's [Math Library Selection](build_from_source.md#math-library-selection) section.
-
-For OpenBLAS use:
-
-```bash
-    sudo apt-get install -y libopenblas-dev
-```
-
-For other libraries, visit the [Math Library Selection](build_from_source.md#math-library-selection) section.
-
-**Step 3:** Install OpenCV.
-
-*MXNet* uses [OpenCV](http://opencv.org/) for efficient image loading and augmentation operations.
-
-```bash
-    sudo apt-get install -y libopencv-dev
-```
-
-**Step 4:** Download MXNet sources and build MXNet core shared library.
-
-If building on CPU and using OpenBLAS:
-
-Clone the repository:
-
-```bash
-    git clone --recursive https://github.com/apache/incubator-mxnet.git
-    cd incubator-mxnet
-```
-
-Build with CMake and ninja, without GPU and without MKL.
-
-```bash
-    rm -rf build
-    mkdir -p build && cd build
-    cmake -GNinja \
-        -DUSE_CUDA=OFF \
-        -DUSE_MKL_IF_AVAILABLE=OFF \
-        -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache \
-        -DCMAKE_C_COMPILER_LAUNCHER=ccache \
-        -DCMAKE_CXX_COMPILER_LAUNCHER=ccache \
-    ..
-    ninja
-```
-
-If building on CPU and using MKL and MKL-DNN (make sure MKL is installed according to [Math Library Selection](build_from_source.md#math-library-selection) and [MKL-DNN README](https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/mkldnn/MKLDNN_README.md)):
-
-```bash
-    rm -rf build
-    mkdir -p build && cd build
-    cmake -GNinja \
-        -DUSE_CUDA=OFF \
-        -DUSE_MKL_IF_AVAILABLE=ON \
-        -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache \
-        -DCMAKE_C_COMPILER_LAUNCHER=ccache \
-        -DCMAKE_CXX_COMPILER_LAUNCHER=ccache \
-    ..
-    ninja
-```
-
-If building on GPU (make sure you have installed the [CUDA dependencies first](#cuda-dependencies)):
-Cuda 10.1 in Ubuntu 18.04 builds fine but is not currently tested in CI.
-
-```bash
-    rm -rf build
-    mkdir -p build && cd build
-    cmake -GNinja \
-        -DUSE_CUDA=ON \
-        -DUSE_MKL_IF_AVAILABLE=OFF \
-        -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache \
-        -DCMAKE_C_COMPILER_LAUNCHER=ccache \
-        -DCMAKE_CXX_COMPILER_LAUNCHER=ccache \
-    ..
-    ninja
-```
-
-*Note* - You can explore and use more compilation options as they are delcared in the top of `CMakeLists.txt` and also review common [usage examples](build_from_source.md#usage-examples).
-Optionally, you can also use a higher level, scripted version of the above with an editable CMake options file by doing the
-following:
-
-```bash
-cp cmake/cmake_options.yml .
-# Edit cmake_options.yml in the MXNet root to your taste
-$EDITOR cmake_options.yml
-# Launch a local CMake build
-./dev_menu.py build
-```
-
-Building from source creates a library called ```libmxnet.so``` in the `build` folder in your MXNet project root.
-
-After building the MXNet library, you may install language bindings.
-
-<hr>
-
-
-## Installing Language Packages for MXNet
-
-After you have installed the MXNet core library. You may install MXNet interface packages for the programming language of your choice:
-- [Python](#install-mxnet-for-python)
-- [C++](#install-the-mxnet-package-for-c&plus;&plus;)
-- [Clojure](#install-the-mxnet-package-for-clojure)
-- [Julia](#install-the-mxnet-package-for-julia)
-- [Perl](#install-the-mxnet-package-for-perl)
-- [R](#install-the-mxnet-package-for-r)
-- [Scala](#install-the-mxnet-package-for-scala)
-- [Java](#install-the-mxnet-package-for-java)
-
-<hr>
-
-### Install MXNet for Python
-
-To install the MXNet Python binding navigate to the root of the MXNet folder then run the following:
-
-```bash
-$ cd python
-$ pip install -e .
-```
-
-Note that the `-e` flag is optional. It is equivalent to `--editable` and means that if you edit the source files, these changes will be reflected in the package installed.
-
-#### Optional Python Packages
-
-You may optionally install ```graphviz``` library that is used for visualizing network graphs you build on MXNet. You may also install [Jupyter Notebook](http://jupyter.readthedocs.io/) which is used for running MXNet tutorials and examples.
-
-```bash
-sudo pip install graphviz==0.8.4 \
-                 jupyter
-```
-<hr>
-
-
-### Install the MXNet Package for C++
-
-Refer to the [C++ Package setup guide](c_plus_plus.md).
-<hr>
-
-
-### Install the MXNet Package for Clojure
-
-Refer to the [Clojure setup guide](https://github.com/apache/incubator-mxnet/tree/master/contrib/clojure-package).
-<hr>
-
-
-### Install the MXNet Package for Julia
-
-#### Install Julia
-The package available through `apt-get` is old and not compatible with the latest version of MXNet.
-Fetch the latest version (1.0.3 at the time of this writing).
-
-```bash
-wget -qO julia-10.tar.gz https://julialang-s3.julialang.org/bin/linux/x64/1.0/julia-1.0.3-linux-x86_64.tar.gz
-```
-
-Place the extracted files somewhere like a julia folder in your home dir.
-
-```bash
-mkdir ~/julia
-mv julia-10.tar.gz ~/julia
-cd ~/julia
-tar xvf julia-10.tar.gz
-```
-
-Test Julia.
-```bash
-cd julia-1.0.3/bin
-julia -e 'using InteractiveUtils; versioninfo()'
-```
-
-If you're still getting the old version, remove it.
-```bash
-sudo apt remove julia
-```
-
-Update your PATH to have Julia's new location. Add this to your `.zshrc`, `.bashrc`, `.profile` or `.bash_profile`.
-```bash
-export PATH=~/julia/julia-1.0.3/bin:$PATH
-```
-
-Validate your PATH.
-```bash
-echo $PATH
-```
-
-Validate Julia works and is the expected version.
-```bash
-julia -e 'using InteractiveUtils; versioninfo()'
-```
-
-#### Setup Your MXNet-Julia Environment
-
-**For each of the following environment variables, add the commands to your `.zshrc`, `.bashrc`, `.profile` or `.bash_profile` to make them persist.**
-
-Create a `julia-depot` folder and environment variable.
-```bash
-mkdir julia-depot
-export JULIA_DEPOT_PATH=$HOME/julia/julia-depot
-```
-
-To use the Julia binding with an existing `libmxnet` installation, set the `MXNET_HOME` environment variable to the MXNet source root. For example:
-```bash
-export MXNET_HOME=$HOME/incubator-mxnet
-```
-
-Now set the `LD_LIBRARY_PATH` environment variable to where `libmxnet.so` is found. If you can't find it, you might have skipped the building MXNet step. Go back and [build MXNet](#build-the-shared-library) first. For example:
-```bash
-export LD_LIBRARY_PATH=$HOME/incubator-mxnet/lib:$LD_LIBRARY_PATH
-```
-
-Verify the location of `libjemalloc.so` and set the `LD_PRELOAD` environment variable.
-```bash
-export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libjemalloc.so:$LD_PRELOAD
-```
-
-With all of these updates, here's an example of what you might want to have in your `.zshrc`, `.bashrc`, `.profile` or `.bash_profile`.
-
-```
-export PATH=$HOME/bin:$HOME/.local/bin:$HOME/julia/julia-1.0.3/bin:$PATH
-export JULIA_DEPOT_PATH=$HOME/julia/julia-depot
-export MXNET_HOME=$HOME/incubator-mxnet
-export LD_LIBRARY_PATH=$HOME/incubator-mxnet/lib:$LD_LIBRARY_PATH
-export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libjemalloc.so:$LD_PRELOAD
-```
-
-Install MXNet with Julia:
-
-```bash
-julia --color=yes --project=./ -e \
-	  'using Pkg; \
-	   Pkg.develop(PackageSpec(name="MXNet", path = joinpath(ENV["MXNET_HOME"], "julia")))'
-```
-
-For more details about installing and using MXNet with Julia, see the [MXNet Julia documentation](../api/julia/site/).
-<hr>
-
-
-### Install the MXNet Package for Perl
-
-Before you build MXNet for Perl from source code, you must complete [building the shared library](#build-the-shared-library). After you build the shared library, run the following command from the MXNet source root directory to build the MXNet Perl package:
-
-```bash
-    sudo apt-get install libmouse-perl pdl cpanminus swig libgraphviz-perl
-    cpanm -q -L "${HOME}/perl5" Function::Parameters Hash::Ordered PDL::CCS
-
-    MXNET_HOME=${PWD}
-    export LD_LIBRARY_PATH=${MXNET_HOME}/lib
-    export PERL5LIB=${HOME}/perl5/lib/perl5
-
-    cd ${MXNET_HOME}/perl-package/AI-MXNetCAPI/
-    perl Makefile.PL INSTALL_BASE=${HOME}/perl5
-    make install
-
-    cd ${MXNET_HOME}/perl-package/AI-NNVMCAPI/
-    perl Makefile.PL INSTALL_BASE=${HOME}/perl5
-    make install
-
-    cd ${MXNET_HOME}/perl-package/AI-MXNet/
-    perl Makefile.PL INSTALL_BASE=${HOME}/perl5
-    make install
-```
-<hr>
-
-
-### Install the MXNet Package for R
-
-Building *MXNet* from source is a 2 step process.
-1. Build the *MXNet* core shared library, `libmxnet.so`, from source.
-2. Build the R bindings.
-
-#### Quick MXNet-R Installation
-You can quickly build MXNet-R with the following two scripts found in the `/docs/install` folder:
-
-```bash
-git clone --recursive https://github.com/apache/incubator-mxnet.git mxnet
-cd mxnet/docs/install
-./install_mxnet_ubuntu_python.sh
-./install_mxnet_ubuntu_r.sh
-```
-
-Or you can go through a manual process described next.
-
-#### Manual MXNet-R Installation
-
-**Minimum Requirements**
-1. [GCC 4.8](https://gcc.gnu.org/gcc-4.8/) or later to compile C++ 11.
-2. [GNU Make](https://www.gnu.org/software/make/)
-
-<br/>
-
-**Build the MXNet core shared library**
-
-**Step 1** Install build tools and git.
-```bash
-$ sudo apt-get update
-$ sudo apt-get install -y build-essential git
-```
-
-**Step 2** Install OpenBLAS.
-
-*MXNet* uses [BLAS](https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) and [LAPACK](https://en.wikipedia.org/wiki/LAPACK) libraries for accelerated numerical computations on CPU machine. There are several flavors of BLAS/LAPACK libraries - [OpenBLAS](http://www.openblas.net/), [ATLAS](http://math-atlas.sourceforge.net/) and [MKL](https://software.intel.com/en-us/intel-mkl). In this step we install OpenBLAS. You can choose to install ATLAS or MKL.
-```bash
-$ sudo apt-get install -y libopenblas-dev liblapack-dev
-```
-
-**Step 3** Install OpenCV.
-
-*MXNet* uses [OpenCV](http://opencv.org/) for efficient image loading and augmentation operations.
-```bash
-$ sudo apt-get install -y libopencv-dev
-```
-
-**Step 4** Download MXNet sources and build MXNet core shared library. You can clone the repository as described in the following code block, or you may try the [download links](download.md) for your desired MXNet version.
-
-```bash
-$ git clone --recursive https://github.com/apache/incubator-mxnet
-$ cd incubator-mxnet
-$ echo "USE_OPENCV = 1" >> ./config.mk
-$ echo "USE_BLAS = openblas" >> ./config.mk
-$ make -j $(nproc)
-```
-
-*Note* - USE_OPENCV and USE_BLAS are make file flags to set compilation options to use OpenCV and BLAS library. You can explore and use more compilation options in `make/config.mk`.
-
-<br/>
-
-**Step 5** Make and install the MXNet-R bindings.
-
-```bash
-$ make rpkg
-```
-#### Verify MXNet-R Installation
-
-You can verify your MXNet-R installation as follows:
-
-```bash
-sudo -i R
-```
-
-At the R prompt enter the following:
-
-```r
-library(mxnet)
-a <- mx.nd.ones(c(2,3), ctx = mx.cpu())
-b <- a * 2 + 1
-b
-```
-
-You should see the following output:
-
-```
-     [,1] [,2] [,3]
-[1,]    3    3    3
-[2,]    3    3    3
-> quit()
-```
-<hr>
-
-
-### Install the MXNet Package for Scala
-
-To use the MXNet-Scala package, you can acquire the Maven package as a dependency.
-
-Further information is in the [MXNet-Scala Setup Instructions](scala_setup.md).
-
-If you use IntelliJ or a similar IDE, you may want to follow the [MXNet-Scala on IntelliJ tutorial](../tutorials/scala/mxnet_scala_on_intellij.md) instead.
-<hr>
-
-### Install the MXNet Package for Java
-
-To use the MXNet-Java package, you can acquire the Maven package as a dependency.
-
-Further information is in the [MXNet-Java Setup Instructions](java_setup.md).
-
-If you use IntelliJ or a similar IDE, you may want to follow the [MXNet-Java on IntelliJ tutorial](../tutorials/java/mxnet_java_on_intellij.md) instead.
-<hr>
-
-## Contributions
-
-You are more than welcome to contribute easy installation scripts for other operating systems and programming languages. See the [community contributions page](../community/contribute.md) for further information.
-
-## Next Steps
-
-* [Tutorials](../tutorials/index.md)
-* [How To](../faq/index.md)
-* [Architecture](../architecture/index.md)
-
-
-<link rel="stylesheet" href="https://use.fontawesome.com/releases/v5.1.0/css/all.css" integrity="sha384-lKuwvrZot6UHsBSfcMvOkWwlCMgc0TaWr+30HWe3a4ltaBwTZhyTEggF5tJv8tbt" crossorigin="anonymous">
diff --git a/docs/install/validate_mxnet.md b/docs/install/validate_mxnet.md
deleted file mode 100644
index c31463b63eee..000000000000
--- a/docs/install/validate_mxnet.md
+++ /dev/null
@@ -1,219 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Validate Your MXNet Installation
-
-- [Python](#python)
-- [Python with GPU](#python-with-gpu)
-- [Verify GPU training](#verify-gpu-training)
-- [Virtualenv](#virtualenv)
-- [Docker with CPU](#docker-with-cpu)
-- [Docker with GPU](#docker-with-gpu)
-- [Cloud](#cloud)
-- [C++](#alternative-language-bindings)
-- [Clojure](#clojure)
-- [Julia](#julia)
-- [Perl](#perl)
-- [R](#r)
-- [Scala](#scala)
-
-
-## Python
-
-Start the python terminal.
-
-```bash
-$ python
-```
-
-Run a short *MXNet* python program to create a 2X3 matrix of ones, multiply each element in the matrix by 2 followed by adding 1. We expect the output to be a 2X3 matrix with all elements being 3.
-
-```python
->>> import mxnet as mx
->>> a = mx.nd.ones((2, 3))
->>> b = a * 2 + 1
->>> b.asnumpy()
-array([[ 3.,  3.,  3.],
-       [ 3.,  3.,  3.]], dtype=float32)
-```
-
-
-## Python with GPU
-
-This is similar to the previous example, but this time we use *mx.gpu()*, to set *MXNet* context to be GPUs.
-
-```python
->>> import mxnet as mx
->>> a = mx.nd.ones((2, 3), mx.gpu())
->>> b = a * 2 + 1
->>> b.asnumpy()
-array([[ 3.,  3.,  3.],
-       [ 3.,  3.,  3.]], dtype=float32)
-```
-
-
-## Verify GPU Training
-
-From the MXNet root directory run: `python example/image-classification/train_mnist.py --network lenet --gpus 0` to test GPU training.
-
-
-## Virtualenv
-
-Activate the virtualenv environment created for *MXNet*.
-
-```bash
-$ source ~/mxnet/bin/activate
-```
-
-After activating the environment, you should see the prompt as below.
-
-```bash
-(mxnet)$
-```
-
-Start the python terminal.
-
-```bash
-$ python
-```
-
-Run the previous Python example.
-
-
-## Docker with CPU
-
-Launch a Docker container with `mxnet/python` image and run example *MXNet* python program on the terminal.
-
-```bash
-$ docker run -it mxnet/python bash # Use sudo if you skip Step 2 in the installation instruction
-
-# Start a python terminal
-root@4919c4f58cac:/# python
-```
-
-Run the previous Python example.
-
-
-## Docker with GPU
-
-Launch a NVIDIA Docker container with `mxnet/python:gpu` image and run example *MXNet* python program on the terminal.
-
-```bash
-$ nvidia-docker run -it mxnet/python:gpu bash # Use sudo if you skip Step 2 in the installation instruction
-
-# Start a python terminal
-root@4919c4f58cac:/# python
-```
-
-Run the previous Python example and run the previous GPU examples.
-
-
-## Cloud
-
-Login to the cloud instance you launched, with pre-installed *MXNet*, following the guide by corresponding cloud provider.
-
-Start the python terminal.
-
-```bash
-$ python
-```
-
-Run the previous Python example, and for GPU instances run the previous GPU example.
-
-
-## Alternative Language Bindings
-
-### C++
-
-Please contribute an example!
-
-
-### Clojure
-
-Please contribute an example!
-
-
-### Julia
-
-Please contribute an example!
-
-
-### Perl
-
-Start the pdl2 terminal.
-
-```bash
-$ pdl2
-```
-
-Run a short *MXNet* Perl program to create a 2X3 matrix of ones, multiply each element in the matrix by 2 followed by adding 1. We expect the output to be a 2X3 matrix with all elements being 3.
-
-```perl
-pdl> use AI::MXNet qw(mx)
-pdl> $a = mx->nd->ones([2, 3])
-pdl> $b = $a * 2 + 1
-pdl> print $b->aspdl
-
-[
- [3 3 3]
- [3 3 3]
-]
-```
-
-### R
-
-Run a short *MXNet* R program to create a 2X3 matrix of ones, multiply each element in the matrix by 2 followed by adding 1. We expect the output to be a 2X3 matrix with all elements being 3.
-
-```r
-library(mxnet)
-a <- mx.nd.ones(c(2,3), ctx = mx.cpu())
-b <- a * 2 + 1
-b
-```
-
-You should see the following output:
-
-```r
-[,1] [,2] [,3]
-[1,]    3    3    3
-[2,]    3    3    3
-```
-
-
-#### R with GPU
-
-This is similar to the previous example, but this time we use *mx.gpu()*, to set *MXNet* context to be GPUs.
-
-```r
-library(mxnet)
-a <- mx.nd.ones(c(2,3), ctx = mx.gpu())
-b <- a * 2 + 1
-b
-```
-
-You should see the following output:
-
-```r
-[,1] [,2] [,3]
-[1,]    3    3    3
-[2,]    3    3    3
-```
-
-
-### Scala
-
-Run the <a href="https://github.com/apache/incubator-mxnet/tree/master/scala-package/mxnet-demo">MXNet-Scala demo project</a> to validate your Maven package installation.
diff --git a/docs/install/windows_setup.md b/docs/install/windows_setup.md
deleted file mode 100644
index 8a3b1f3b099e..000000000000
--- a/docs/install/windows_setup.md
+++ /dev/null
@@ -1,508 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Installing MXNet on Windows
-
-The following describes how to install with pip for computers with CPUs, Intel CPUs, and NVIDIA GPUs. Further along in the document you can learn how to build MXNet from source on Windows, or how to install packages that support different language APIs to MXNet.
-
-- [Prerequisites](#prerequisites)
-- [Install MXNet with Python](#install-mxnet-with-python)
-    - [Install with CPUs](#install-with-cpus)
-    - [Install with Intel CPUs](#install-with-intel-cpus)
-    - [Install with GPUs](#install-with-gpus)
-    - [Notes on the Python Packages](#notes-on-the-python-packages)
-- [Build from Source](#build-from-source)
-- Install MXNet with a Programming Language API
-    - [Python](#install-the-mxnet-package-for-python)
-    - [R](#install-the-mxnet-package-for-r)
-    - [Julia](#install-the-mxnet-package-for-julia)
-
-
-## Prerequisites
-
-### Minimum System Requirements
-
-* Windows 7<sup><a href="#fn1" id="ref1">1</a></sup>, 10, Server 2012 R2, or Server 2016
-* Visual Studio 2015 or 2017 (any type)
-* Python 2.7 or 3.6
-* pip
-
-<sup id="fn1">1. There are [known issues](https://github.com/apache/incubator-mxnet/issues?utf8=%E2%9C%93&q=is%3Aissue+windows7+label%3AWindows+) with Windows 7. <a href="#ref1" title="Return to source text.">↩</a></sup>
-
-### Recommended System Requirements
-
-* Windows 10, Server 2012 R2, or Server 2016
-* Visual Studio 2017 (any type)
-* At least one [NVIDIA CUDA-enabled GPU](https://developer.nvidia.com/cuda-gpus)
-* MKL-enabled CPU: Intel® Xeon® processor, Intel® Core™ processor family, Intel Atom® processor, or Intel® Xeon Phi™ processor
-* Python 2.7 or 3.6
-* pip
-
-
-## Install MXNet with Python
-
-The easiest way to install MXNet on Windows is by using a [Python pip package](https://pip.pypa.io/en/stable/installing/).
-
-**Note**: Windows pip packages typically release a few days after a new version MXNet is released. Make sure you verify which version gets installed.
-
-### Install with CPUs
-
-Install MXNet with CPU support with Python:
-
-```bash
-pip install mxnet
-```
-
-Now [validate your MXNet installation with Python](validate_mxnet.md).
-
-### Install with Intel CPUs
-
-MXNet has experimental support for Intel [MKL](https://software.intel.com/en-us/mkl) and [MKL-DNN](https://github.com/intel/mkl-dnn). When using supported Intel hardware, inference and training can be vastly faster when using MXNet with [MKL](https://software.intel.com/en-us/mkl) or [MKL-DNN](https://github.com/intel/mkl-dnn).
-
-The following steps will setup MXNet with MKL. MKL-DNN can be enabled only when building from source.
-1. Download and install [Intel MKL](https://software.intel.com/en-us/mkl/choose-download/windows) (registration required).
-1. Install MXNet with MKL support with Python:
-
-```bash
-pip install mxnet-mkl
-```
-
-Now [validate your MXNet installation with Python](validate_mxnet.md).
-
-### Install with NVIDIA GPUs
-
-When using supported NVIDIA GPU hardware, inference and training can be vastly faster with [NVIDIA CUDA](https://developer.nvidia.com/cuda-toolkit) and [cuDNN](https://developer.nvidia.com/cudnn). You have two options for installing MXNet with CUDA support with a Python package.
-- [Install with CUDA support](#install-with-cuda-support)
-- [Install with CUDA and MKL support](#install-with-cuda-and-mkl-support)
-
-#### Install with CUDA Support
-
-The following steps will setup MXNet with CUDA. cuDNN can be enabled only when building from source.
-1. Install [Microsoft Visual Studio 2017](https://www.visualstudio.com/downloads/) or [Microsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/).
-1. Download and install [NVIDIA CUDA](https://developer.nvidia.com/cuda-downloads?target_os=Windows&target_arch=x86_64&target_version=10&target_type=exelocal). CUDA versions 9.2 or 9.0 are recommended. Some [issues with CUDA 9.1](https://github.com/apache/incubator-mxnet/labels/CUDA) have been identified in the past.
-1. Download and install [NVIDIA_CUDA_DNN](https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#install-windows)
-1. Install MXNet with CUDA support with pip:
-
-```bash
-pip install mxnet-cu92
-```
-
-Once you have installed a version of MXNet, [validate your MXNet installation with Python](validate_mxnet.md).
-
-#### Install with CUDA and MKL Support
-
-You can also use a combination of CPU/GPU enhancements provided by Intel and NVIDIA.
-
-The following steps will setup MXNet with CUDA and MKL.
-1. Install [Microsoft Visual Studio 2017](https://www.visualstudio.com/downloads/) or [Microsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/).
-1. Download and install [Intel MKL](https://software.intel.com/en-us/mkl/choose-download/windows) (registration required).
-1. Download and install [NVIDIA CUDA](https://developer.nvidia.com/cuda-downloads?target_os=Windows&target_arch=x86_64&target_version=10&target_type=exelocal).
-1. Download and install [NVIDIA_CUDA_DNN](https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#install-windows)
-1. Install MXNet with MKL support with pip:
-
-```bash
-pip install mxnet-cu92mkl
-```
-
-Once you have installed a version of MXNet, [validate your MXNet installation with Python](validate_mxnet.md).
-
-### Notes on the Python Packages
-To get further enhancements for deep neural networks, you may want to enable MKL-DNN and/or cuDNN. Each of these require you to [build from source](#build-from-source) and to enable the build flags for each.
-
-Check the chart below for other options or refer to [PyPI for other MXNet pip packages](https://pypi.org/project/mxnet/).
-
-![pip packages](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/install/pip-packages.png)
-
-
-## Build from Source
-
-**IMPORTANT: It is recommended that you review the [build from source guide](build_from_source.md) first.** It describes many of the build options that come with MXNet in more detail. You may decide to install additional dependencies and modify your build flags after reviewing this material.
-
-We provide two primary options to build and install MXNet yourself using [Microsoft Visual Studio 2017](https://www.visualstudio.com/downloads/) or [Microsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/).
-
-**NOTE:** Visual Studio 2017's compiler is `vc15`. This is not to be confused with Visual Studio 2015's compiler, `vc14`.
-
-You also have the option to install MXNet with MKL or MKL-DNN. In this case it is recommended that you refer to the [MKLDNN_README](https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/mkldnn/MKLDNN_README.md).
-
-**Option 1: Build with Microsoft Visual Studio 2017 (VS2017)**
-
-To build and install MXNet yourself using [VS2017](https://www.visualstudio.com/downloads/), you need the following dependencies. You may try a newer version of a particular dependency, but please open a pull request or [issue](https://github.com/apache/incubator-mxnet/issues/new) to update this guide if a newer version is validated.
-
-1. Install or update VS2017.
-    - If [VS2017](https://www.visualstudio.com/downloads/) is not already installed, download and install it. You can download and install the free community edition.
-    - When prompted about installing Git, go ahead and install it.
-    - If VS2017 is already installed you will want to update it. Proceed to the next step to modify your installation. You will be given the opportunity to update VS2017 as well
-1. Follow the [instructions for opening the Visual Studio Installer](https://docs.microsoft.com/en-us/visualstudio/install/modify-visual-studio) to modify `Individual components`.
-1. Once in the Visual Studio Installer application, update as needed, then look for and check `VC++ 2017 version 15.4 v14.11 toolset`, and click `Modify`.
-1. Change the version of the Visual studio 2017 to v14.11 using the following command (by default the VS2017 is installed in the following path):
-```
-"C:\Program Files (x86)\Microsoft Visual Studio\2017\Community\VC\Auxiliary\Build\vcvars64.bat" -vcvars_ver=14.11
-```
-1. Download and install [CMake](https://cmake.org/download) if it is not already installed. [CMake v3.12.2](https://cmake.org/files/v3.12/cmake-3.12.2-win64-x64.msi) has been tested with MXNet.
-1. Download and run the  [OpenCV](https://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.4.1/opencv-3.4.1-vc14_vc15.exe/download) package. There are more recent versions of OpenCV, so please create an issue/PR to update this info if you validate one of these later versions.
-1. This will unzip several files. You can place them in another directory if you wish. We will use `C:\utils`(```mkdir C:\utils```) as our default path.
-1. Set the environment variable `OpenCV_DIR` to point to the OpenCV build directory that you just unzipped. Start ```cmd``` and type `set OpenCV_DIR=C:\utils\opencv\build`.
-1. If you don’t have the Intel Math Kernel Library (MKL) installed, you can install it and follow the [MKLDNN_README](https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/mkldnn/MKLDNN_README.md) from here, or you can use OpenBLAS. These instructions will assume you're using OpenBLAS.
-1. Download the [OpenBlas](https://sourceforge.net/projects/openblas/files/v0.2.19/OpenBLAS-v0.2.19-Win64-int32.zip/download) package. Later versions of OpenBLAS are available, but you would need to build from source. v0.2.19 is the most recent version that ships with binaries. Contributions of more recent binaries would be appreciated.
-1. Unzip the file, rename it to ```OpenBLAS``` and put it under `C:\utils`. You can place the unzipped files and folders in another directory if you wish.
-1. Set the environment variable `OpenBLAS_HOME` to point to the OpenBLAS directory that contains the `include` and `lib` directories and type `set OpenBLAS_HOME=C:\utils\OpenBLAS` on the command prompt(```cmd```).
-1. Download and install [CUDA](https://developer.nvidia.com/cuda-downloads?target_os=Windows&target_arch=x86_64&target_version=10&target_type=exelocal). If you already had CUDA, then installed VS2017, you should reinstall CUDA now so that you get the CUDA toolkit components for VS2017 integration. Note that the latest CUDA version supported by MXNet is [9.2](https://developer.nvidia.com/cuda-92-download-archive). You might also want to find other CUDA verion on the [Legacy Releases](https://developer.nvidia.com/cuda-toolkit-archive).
-1. Download and install cuDNN. To get access to the download link, register as an NVIDIA community user. Then follow the [link](http://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#install-windows) to install the cuDNN and put those libraries into ```C:\cuda```.
-1. Download and install [git](https://git-for-windows.github.io/) if you haven't already.
-
-After you have installed all of the required dependencies, build the MXNet source code:
-
-1. Start ```cmd``` in windows.
-2. Download the MXNet source code from GitHub by using following command:
-```
-cd C:\
-git clone https://github.com/apache/incubator-mxnet.git --recursive
-```
-3. Verify that the `DCUDNN_INCLUDE` and `DCUDNN_LIBRARY` environment variables are pointing to the `include` folder and `cudnn.lib` file of your CUDA installed location, and `C:\incubator-mxnet` is the location of the source code you just cloned in the previous step.
-4. Create a build dir using the following command and go to the directory, for example:
-```
-mkdir C:\incubator-mxnet\build
-cd C:\incubator-mxnet\build
-```
-5. Compile the MXNet source code with `cmake` by using following command:
-```
-cmake -G "Visual Studio 15 2017 Win64" -T cuda=9.2,host=x64 -DUSE_CUDA=1 -DUSE_CUDNN=1 -DUSE_NVRTC=1 -DUSE_OPENCV=1 -DUSE_OPENMP=1 -DUSE_BLAS=open -DUSE_LAPACK=1 -DUSE_DIST_KVSTORE=0 -DCUDA_ARCH_LIST=Common -DCUDA_TOOLSET=9.2 -DCUDNN_INCLUDE=C:\cuda\include -DCUDNN_LIBRARY=C:\cuda\lib\x64\cudnn.lib "C:\incubator-mxnet"
-```
-* Make sure you set the environment variables correctly (OpenBLAS_HOME, OpenCV_DIR) and change the version of the Visual studio 2017 to v14.11 before enter above command.
-6. After the CMake successfully completed, compile the MXNet source code by using following command:
-```
-msbuild mxnet.sln /p:Configuration=Release;Platform=x64 /maxcpucount
-```
-
-
-**Option 2: Build with Visual Studio 2015**
-
-To build and install MXNet yourself using [Microsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/), you need the following dependencies. You may try a newer version of a particular dependency, but please open a pull request or [issue](https://github.com/apache/incubator-mxnet/issues/new) to update this guide if a newer version is validated.
-
-1. If [Microsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/) is not already installed, download and install it. You can download and install the free community edition. At least Update 3 of Microsoft Visual Studio 2015 is required to build MXNet from source. Upgrade via it's ```Tools -> Extensions and Updates... | Product Updates``` menu.
-2. Download and install [CMake](https://cmake.org/) if it is not already installed.
-3. Download and install [OpenCV](http://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.0.0/opencv-3.0.0.exe/download).
-4. Unzip the OpenCV package.
-5. Set the environment variable ```OpenCV_DIR``` to point to the ```OpenCV build directory``` (```C:\opencv\build\x64\vc14``` for example). Also, you need to add the OpenCV bin directory (```C:\opencv\build\x64\vc14\bin``` for example) to the ``PATH`` variable.
-6. If you don't have the Intel Math Kernel Library (MKL) installed, download and install [OpenBlas](http://sourceforge.net/projects/openblas/files/v0.2.14/).
-7. Set the environment variable ```OpenBLAS_HOME``` to point to the ```OpenBLAS``` directory that contains the ```include``` and ```lib``` directories. Typically, you can find the directory in ```C:\Program files (x86)\OpenBLAS\```.
-8. Download and install [CUDA](https://developer.nvidia.com/cuda-downloads?target_os=Windows&target_arch=x86_64) and [cuDNN](https://developer.nvidia.com/cudnn). To get access to the download link, register as an NVIDIA community user.
-9. Set the environment variable ```CUDACXX``` to point to the ```CUDA Compiler```(```C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.1\bin\nvcc.exe``` for example).
-10. Set the environment variable ```CUDNN_ROOT``` to point to the ```cuDNN``` directory that contains the ```include```,  ```lib``` and ```bin``` directories (```C:\Downloads\cudnn-9.1-windows7-x64-v7\cuda``` for example).
-
-After you have installed all of the required dependencies, build the MXNet source code:
-
-1. Download the MXNet source code from [GitHub](https://github.com/apache/incubator-mxnet) (make sure you also download third parties submodules e.g. ```git clone --recurse-submodules```).
-2. Use [CMake](https://cmake.org/) to create a Visual Studio solution in ```./build```.
-3. In Visual Studio, open the solution file,```.sln```, and compile it.
-These commands produce a library called ```mxnet.dll``` in the ```./build/Release/``` or ```./build/Debug``` folder.
-
-&nbsp;
-Next, we install ```graphviz``` library that we use for visualizing network graphs you build on MXNet. We will also install [Jupyter Notebook](http://jupyter.readthedocs.io/)  used for running MXNet tutorials and examples.
-- Install ```graphviz``` by downloading MSI installer from [Graphviz Download Page](https://graphviz.gitlab.io/_pages/Download/Download_windows.html).
-**Note** Make sure to add graphviz executable path to PATH environment variable. Refer [here for more details](http://stackoverflow.com/questions/35064304/runtimeerror-make-sure-the-graphviz-executables-are-on-your-systems-path-aft)
-- Install ```Jupyter``` by installing [Anaconda for Python 2.7](https://www.anaconda.com/download/)
-**Note** Do not install Anaconda for Python 3.5. MXNet has a few compatibility issues with Python 3.5.
-
-We have installed MXNet core library. Next, we will install MXNet interface package for programming language of your choice:
-- [Python](#install-the-mxnet-package-for-python)
-- [R](#install-mxnet-package-for-r)
-- [Julia](#install-the-mxnet-package-for-julia)
-- **Scala** is not yet available for Windows
-
-## Install the MXNet Package for Python
-
-These steps are required after building from source. If you already installed MXNet by using pip, you do not need to do these steps to use MXNet with Python.
-
-1. Install ```Python``` using windows installer available [here](https://www.python.org/downloads/release/python-2712/).
-2. Install ```Numpy``` using windows installer available [here](https://scipy.org/index.html).
-3. Start ```cmd``` and create a folder named ```common```(```mkdir C:\common```)
-4. Download the [mingw64_dll.zip](https://sourceforge.net/projects/openblas/files/v0.2.12/mingw64_dll.zip/download), unzip and copy three libraries (.dll files) that openblas.dll depends on to ```C:\common```.
-5. Copy the required .dll file to ```C:\common``` and make sure following libraries (.dll files) in the folder.
-```
-libgcc_s_seh-1.dll (in mingw64_dll)
-libgfortran-3.dll (in mingw64_dll)
-libquadmath-0.dll (in mingw64_dll)
-libopenblas.dll (in OpenBlas folder you download)
-opencv_world341.dll (in OpenCV folder you download)
-```
-6. Add ```C:\common``` to Environment Variables.
- * Type ```control sysdm.cpl``` on ```cmp```
- * Select the **Advanced tab** and click **Environment Variables**
- * Double click the **Path** and click **New**
- * Add ```C:\common``` and click OK
-7. Use setup.py to install the package.
-```bash
-    # Assuming you are in root mxnet source code folder
-    cd python
-    python setup.py install
-```
-
-Done! We have installed MXNet with Python interface.
-
-You can continue with using MXNet-Python, or if you want to try a different language API for MXNet, keep reading. Otherwise, jump ahead to [next steps](#next-steps).
-
-## Install the MXNet Package for R
-MXNet for R is available for both CPUs and GPUs.
-
-### Installing MXNet-R on a Computer with a CPU Processor
-
-To install MXNet on a computer with a CPU processor, choose from two options:
-
-* Use the prebuilt binary package
-* Build the library from source code
-
-#### Installing MXNet-R with the Prebuilt Binary Package(CPU)
-For Windows users, MXNet provides prebuilt binary packages.
-You can install the package directly in the R console.
-
-Note: packages for 3.6.x are not yet available.
-Install 3.5.x of R from [CRAN](https://cran.r-project.org/bin/windows/base/old/).
-
-For CPU-only package:
-
-```r
-  cran <- getOption("repos")
-  cran["dmlc"] <- "https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/R/CRAN/"
-  options(repos = cran)
-  install.packages("mxnet")
-```
-
-#### Building MXNet-R from Source Code(CPU)
-1. Clone the MXNet github repo.
-
-```sh
-git clone --recursive https://github.com/apache/incubator-mxnet
-```
-
-The `--recursive` is to clone all the submodules used by MXNet. You will be editing the ```"/mxnet/R-package"``` folder.
-
-2. Download prebuilt GPU-enabled MXNet libraries for Windows from [Windows release](https://github.com/yajiedesign/mxnet/releases). You will need `mxnet_x64_vc14_cpu.7z` and `prebuildbase_win10_x64_vc14.7z` where X stands for your CUDA toolkit version
-
-3. Create a folder called ```R-package/inst/libs/x64```. MXNet supports only 64-bit operating systems, so you need the x64 folder.
-
-4. Copy the following shared libraries (.dll files) into the ```R-package/inst/libs/x64``` folder:
-```
-libgcc_s_seh-1.dll
-libgfortran-3.dll
-libmxnet.dll
-libmxnet.lib
-libopenblas.dll
-libquadmath-0.dll
-mxnet.dll
-unzip.exe
-unzip32.dll
-vcomp140.dll
-wget.exe
-```
-These dlls can be found in `prebuildbase_win10_x64_vc14/3rdparty`, `mxnet_x64_vc14_cpu/build`, `mxnet_x64_vc14_cpu/lib`.
-
-5. Copy the header files from `dmlc`, `mxnet`, `mxshadow` and `nnvm` from mxnet_x64_vc14_cpu/include and mxnet_x64_vc14_cpu/nvnm/include into `./R-package/inst/include`. It should look like:
-
-```
-./R-package/inst
-└── include
-    ├── dmlc
-    ├── mxnet
-    ├── mshadow
-    └── nnvm
-
-```
-6. Make sure that R executable is added to your ```PATH``` in the environment variables. Running the ```where R``` command at the command prompt should return the location.
-7. Also make sure that Rtools is installed and the executable is added to your ```PATH``` in the environment variables.
-8. Temporary patch - im2rec currently results in crashes during the build. Remove the im2rec.h and im2rec.cc files in R-package/src/ from cloned repository and comment out the two im2rec lines in [R-package/src/mxnet.cc](https://github.com/apache/incubator-mxnet/blob/master/R-package/src/mxnet.cc) as shown below.
-```bat
-#include "./kvstore.h"
-#include "./export.h"
-//#include "./im2rec.h"
-......
-......
-  DataIterCreateFunction::InitRcppModule();
-  KVStore::InitRcppModule();
-  Exporter::InitRcppModule();
-//  IM2REC::InitRcppModule();
-}
-
-```
-
-9. Now open the Windows CMD with admin rights and change the directory to the `mxnet` folder(cloned repository). Then use the following commands
-to build R package:
-
-```bat
-echo import(Rcpp) > R-package\NAMESPACE
-echo import(methods) >> R-package\NAMESPACE
-Rscript -e "install.packages('devtools', repos = 'https://cloud.r-project.org')"
-cd R-package
-Rscript -e "library(devtools); library(methods); options(repos=c(CRAN='https://cloud.r-project.org')); install_deps(dependencies = TRUE)"
-cd ..
-
-R CMD INSTALL --no-multiarch R-package
-
-Rscript -e "require(mxnet); mxnet:::mxnet.export('R-package')"
-rm R-package/NAMESPACE
-Rscript -e "require(devtools); install_version('roxygen2', version = '5.0.1', repos = 'https://cloud.r-project.org/', quiet = TRUE)"
-Rscript -e "require(roxygen2); roxygen2::roxygenise('R-package')"
-
-R CMD INSTALL --build --no-multiarch R-package
-```
-
-
-### Installing MXNet-R on a Computer with a GPU Processor
-To install MXNet on a computer with a GPU processor, choose from two options:
-
-* Use the prebuilt binary package
-* Build the library from source code
-
-However, a few dependencies remain for both options.  You will need the following:
-* Install [Nvidia-drivers](http://www.nvidia.com/Download/index.aspx?lang=en-us) if not installed. Latest driver based on your system configuration is recommended.
-
-* Install [Microsoft Visual Studio](https://visualstudio.microsoft.com/downloads/) (VS2015 or VS2017 is required by CUDA)
-
-* Install  [NVidia CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit)(cu92 is recommended though we support cu80, cu90, cu91 and cu92)
-
-* Download and install [CuDNN](https://developer.nvidia.com/cudnn) (to provide a Deep Neural Network library). Latest version recommended.
-
-Note: A pre-requisite to above softwares is [Nvidia-drivers](http://www.nvidia.com/Download/index.aspx?lang=en-us) which we assume is installed.
-
-#### Installing MXNet-R with the Prebuilt Binary Package(GPU)
-For Windows users, MXNet provides prebuilt binary packages.
-You can install the package directly in the R console after you have the above software installed.
-
-Note: packages for 3.6.x are not yet available.
-Install 3.5.x of R from [CRAN](https://cran.r-project.org/bin/windows/base/old/).
-
-For GPU package:
-
-```r
-  cran <- getOption("repos")
-  cran["dmlc"] <- "https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/R/CRAN/GPU/cu92"
-  options(repos = cran)
-  install.packages("mxnet")
-```
-Change cu92 to cu80, cu90 or cu91 based on your CUDA toolkit version. Currently, MXNet supports these versions of CUDA.
-
-#### Building MXNet-R from Source Code(GPU)
-After you have installed above software, continue with the following steps to build MXNet-R:
-1. Clone the MXNet github repo.
-
-```sh
-git clone --recursive https://github.com/apache/incubator-mxnet
-```
-
-The `--recursive` is to clone all the submodules used by MXNet. You will be editing the ```"/mxnet/R-package"``` folder.
-
-2. Download prebuilt GPU-enabled MXNet libraries for Windows from https://github.com/yajiedesign/mxnet/releases. You will need `mxnet_x64_vc14_gpu_cuX.7z` and `prebuildbase_win10_x64_vc14.7z` where X stands for your CUDA toolkit version
-
-3. Create a folder called ```R-package/inst/libs/x64```. MXNet supports only 64-bit operating systems, so you need the x64 folder.
-
-4. Copy the following shared libraries (.dll files) into the ```R-package/inst/libs/x64``` folder:
-```
-libgcc_s_seh-1.dll
-libgfortran-3.dll
-libmxnet.dll
-libmxnet.lib
-libopenblas.dll
-libquadmath-0.dll
-mxnet.dll
-unzip.exe
-unzip32.dll
-vcomp140.dll
-wget.exe
-```
-These dlls can be found in `prebuildbase_win10_x64_vc14/3rdparty`, `mxnet_x64_vc14_gpu_cuX/build`, `mxnet_x64_vc14_gpu_cuX/lib`.
-
-5. Copy the header files from `dmlc`, `mxnet`, `mxshadow` and `nnvm` from mxnet_x64_vc14_gpuX/include and mxnet_x64_vc14_gpuX/nvnm/include into `./R-package/inst/include`. It should look like:
-
-```
-./R-package/inst
-└── include
-    ├── dmlc
-    ├── mxnet
-    ├── mshadow
-    └── nnvm
-
-```
-6. Make sure that R executable is added to your ```PATH``` in the environment variables. Running the ```where R``` command at the command prompt should return the location.
-7. Also make sure that Rtools is installed and the executable is added to your ```PATH``` in the environment variables.
-8. Temporary patch - im2rec currently results in crashes during the build. Remove the im2rec.h and im2rec.cc files in R-package/src/ from cloned repository and comment out the two im2rec lines in [R-package/src/mxnet.cc](https://github.com/apache/incubator-mxnet/blob/master/R-package/src/mxnet.cc) as shown below.
-```bat
-#include "./kvstore.h"
-#include "./export.h"
-//#include "./im2rec.h"
-......
-......
-  DataIterCreateFunction::InitRcppModule();
-  KVStore::InitRcppModule();
-  Exporter::InitRcppModule();
-//  IM2REC::InitRcppModule();
-}
-
-```
-9. Now open the Windows CMD with admin rights and change the directory to the `mxnet` folder(cloned repository). Then use the following commands
-to build R package:
-
-```bat
-echo import(Rcpp) > R-package\NAMESPACE
-echo import(methods) >> R-package\NAMESPACE
-Rscript -e "install.packages('devtools', repos = 'https://cloud.r-project.org')"
-cd R-package
-Rscript -e "library(devtools); library(methods); options(repos=c(CRAN='https://cloud.r-project.org')); install_deps(dependencies = TRUE)"
-cd ..
-
-R CMD INSTALL --no-multiarch R-package
-
-Rscript -e "require(mxnet); mxnet:::mxnet.export('R-package')"
-rm R-package/NAMESPACE
-Rscript -e "require(devtools); install_version('roxygen2', version = '5.0.1', repos = 'https://cloud.r-project.org/', quiet = TRUE)"
-Rscript -e "require(roxygen2); roxygen2::roxygenise('R-package')"
-
-R CMD INSTALL --build --no-multiarch R-package
-```
-
-**Note:** To maximize its portability, the MXNet library is built with the Rcpp end. Computers running Windows need [MSVC](https://en.wikipedia.org/wiki/Visual_C%2B%2B) (Microsoft Visual C++) to handle CUDA toolchain compatibilities.
-
-## Install the MXNet Package for Julia
-The MXNet package for Julia is hosted in a separate repository, MXNet.jl, which is available on [GitHub](https://github.com/dmlc/MXNet.jl). To use Julia binding it with an existing libmxnet installation, set the ```MXNET_HOME``` environment variable by running the following command:
-
-```bash
-export MXNET_HOME=/<path to>/libmxnet
-```
-
-The path to the existing libmxnet installation should be the root directory of libmxnet. In other words, you should be able to find the ```libmxnet.so``` file at ```$MXNET_HOME/lib```. For example, if the root directory of libmxnet is ```~```, you would run the following command:
-
-```bash
-	export MXNET_HOME=/~/libmxnet
-```
-
-You might want to add this command to your ```~/.bashrc``` file. If you do, you can install the Julia package in the Julia console using the following command:
-
-```julia
-	Pkg.add("MXNet")
-```
-
-For more details about installing and using MXNet with Julia, see the [MXNet Julia documentation](/api/julia/site/).
-
-
-## Installing the MXNet Package for Scala
-
-MXNet-Scala is not yet available for Windows.
-
-## Next Steps
-
-* [Tutorials](../tutorials/index.md)
-* [How To](../faq/index.md)
-* [Architecture](../architecture/index.md)
diff --git a/docs/model_zoo/index.md b/docs/model_zoo/index.md
deleted file mode 100644
index 779ddac74f1c..000000000000
--- a/docs/model_zoo/index.md
+++ /dev/null
@@ -1,120 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# MXNet Model Zoo
-
-MXNet features fast implementations of many state-of-the-art models reported in the academic literature. This Model Zoo is an
-ongoing project to collect complete models, with python scripts, pre-trained weights as well as instructions on how to build and fine tune these models.
-
-## How to Contribute a Pre-Trained Model (and what to include)
-
-The Model Zoo has good entries for CNNs but is seeking content in other areas.
-
-Issue a Pull Request containing the following:
-* Gist Log
-* .json model definition
-* Model parameter file
-* Readme file (details below)
-
-Readme file should contain:
-* Model Location, access instructions (wget)
-* Confirmation the trained model meets published accuracy from original paper
-* Step by step instructions on how to use the trained model
-* References to any other applicable docs or arxiv papers the model is based on
-
-## Convolutional Neural Networks (CNNs)
-
-Convolutional neural networks are the state-of-art architecture for many image and video processing problems. Some available datasets include:
-
-* [ImageNet](http://image-net.org/): a large corpus of 1 million natural images, divided into 1000 categories.
-* [CIFAR10](https://www.cs.toronto.edu/~kriz/cifar.html): 60,000 natural images (32 x 32 pixels) from 10 categories.
-* [PASCAL_VOC](http://host.robots.ox.ac.uk/pascal/VOC/): A subset of ImageNet images with object bounding boxes.
-* [UCF101](http://crcv.ucf.edu/data/UCF101.php): 13,320 videos from 101 action categories.
-* [Mini-Places2](http://6.869.csail.mit.edu/fa15/project.html): Subset of the Places2 dataset. Includes 100,000 images from 100 scene categories.
-* ImageNet 11k
-* [Places2](http://places2.csail.mit.edu/download.html): There are 1.6 million train images from 365 scene categories in the Places365-Standard, which are used to train the Places365 CNNs. There are 50 images per category in the validation set and 900 images per category in the testing set. Compared to the train set of Places365-Standard, the train set of Places365-Challenge has 6.2 million extra images, leading to totally 8 million train images for the Places365 challenge 2016. The validation set and testing set are the same as the Places365-Standard.
-* [Multimedia Commons](https://aws.amazon.com/public-datasets/multimedia-commons/): YFCC100M (99.2 million images and 0.8 million videos from Flickr) and supplemental material (pre-extracted features, additional annotations).
-
-For instructions on using these models, see [the python tutorial on using pre-trained ImageNet models](https://mxnet.incubator.apache.org/tutorials/python/predict_image.html).
-
-| Model Definition | Dataset | Model Weights | Research Basis | Contributors |
-| --- | --- | --- | --- | --- |
-| [CaffeNet](http://data.mxnet.io/mxnet/models/imagenet/caffenet/caffenet-symbol.json) | ImageNet | [Param File](http://data.mxnet.io/models/imagenet/caffenet/caffenet-0000.params) |   [Krizhevsky, 2012](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) | @jspisak |
-| [Network in Network (NiN)](http://data.mxnet.io/models/imagenet/nin/nin-symbol.json) | ImageNet | [Param File](http://data.mxnet.io/models/imagenet/nin/nin-0000.params) |  [Lin et al.., 2014](https://arxiv.org/pdf/1312.4400v3.pdf) | @jspisak |
-| [SqueezeNet v1.1](http://data.mxnet.io/models/imagenet/squeezenet/squeezenet_v1.1-symbol.json) | ImageNet | [Param File](http://data.mxnet.io/models/imagenet/squeezenet/squeezenet_v1.1-0000.params) | [Iandola et al.., 2016](https://arxiv.org/pdf/1602.07360v4.pdf) | @jspisak |
-| [VGG16](http://data.mxnet.io/models/imagenet/vgg/vgg16-symbol.json) | ImageNet | [Param File](http://data.mxnet.io/models/imagenet/vgg/vgg16-0000.params)| [Simonyan et al.., 2015](https://arxiv.org/pdf/1409.1556v6.pdf) | @jspisak |
-| [VGG19](http://data.mxnet.io/models/imagenet/vgg/vgg19-symbol.json) | ImageNet | [Param File](http://data.mxnet.io/models/imagenet/vgg/vgg19-0000.params) | [Simonyan et al.., 2015](https://arxiv.org/pdf/1409.1556v6.pdf) | @jspisak |
-| [Inception w/ BatchNorm](http://data.mxnet.io/models/imagenet/inception-bn/Inception-BN-symbol.json) | ImageNet | [Param File](http://data.mxnet.io/models/imagenet/inception-bn/Inception-BN-0126.params) | [Szegedy et al.., 2015](https://arxiv.org/pdf/1502.03167.pdf) | @jspisak |
-| [ResidualNet152](http://data.mxnet.io/models/imagenet/resnet/152-layers/resnet-152-symbol.json) | ImageNet | [Param File](http://data.mxnet.io/models/imagenet/resnet/152-layers/resnet-152-0000.params) | [He et al.., 2015](https://arxiv.org/pdf/1512.03385v1.pdf) | @jspisak |
-| [ResNext101-64x4d](http://data.mxnet.io/models/imagenet/resnext/101-layers/resnext-101-64x4d-symbol.json) | ImageNet | [Param File](http://data.mxnet.io/models/imagenet/resnext/101-layers/resnext-101-64x4d-0000.params) | [Xie et al.., 2016](https://arxiv.org/pdf/1611.05431.pdf) | @Jerryzcn |
-| Fast-RCNN | PASCAL VOC | [Param File] | [Girshick, 2015](https://arxiv.org/pdf/1504.08083v2.pdf) | |
-| Faster-RCNN | PASCAL VOC | [Param File] | [Ren et al..,2016](https://arxiv.org/pdf/1506.01497v3.pdf) | |
-| Single Shot Detection (SSD) | PASCAL VOC | [Param File] | [Liu et al.., 2016](https://arxiv.org/pdf/1512.02325v4.pdf) | |
-| [LocationNet](https://s3.amazonaws.com/mmcommons-tutorial/models/RN101-5k500-symbol.json) | [MultimediaCommons](https://aws.amazon.com/public-datasets/multimedia-commons/) | [Param File](https://s3.amazonaws.com/mmcommons-tutorial/models/RN101-5k500-0012.params) | [Weyand et al.., 2016](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45488.pdf) | @jychoi84 @kevinli7 |
-
-
-
-## Recurrent Neural Networks (RNNs) including LSTMs
-
-MXNet supports many types of recurrent neural networks (RNNs), including Long Short-Term Memory ([LSTM](http://www.bioinf.jku.at/publications/older/2604.pdf))
-and Gated Recurrent Units (GRU) networks. Some available datasets include:
-
-* [Sherlock Holmes](http://www.gutenberg.org/cache/epub/1661/pg1661.txt): Text corpus with ~1 million words.The task is predicting downstream words/characters.
-* [Penn Treebank (PTB)](https://catalog.ldc.upenn.edu/LDC95T7): Text corpus with ~1 million words. Vocabulary is limited to 10,000 words. The task is predicting downstream words/characters.
-* [Shakespeare](http://cs.stanford.edu/people/karpathy/char-rnn/): Complete text from Shakespeare's works.
-* [IMDB reviews](https://getsatisfaction.com/imdb/topics/imdb-data-now-available-in-amazon-s3): 25,000 movie reviews, labeled as positive or negative
-* [Facebook bAbI](https://research.facebook.com/researchers/1543934539189348): As a set of 20 question & answer tasks, each with 1,000 training examples.
-* [Flickr8k, COCO](http://mscoco.org/): Images with associated caption (sentences). Flickr8k consists of 8,092 images captioned by AmazonTurkers with ~40,000 captions. COCO has 328,000 images, each with 5 captions. The COCO images also come with labeled objects using segmentation algorithms.
-
-
-| Model Definition | Dataset | Model Weights | Research Basis | Contributors |
-| --- | --- | --- | --- | --- |
-| LSTM - Image Captioning | Flickr8k, MS COCO | | [Vinyals et al.., 2015](https://arxiv.org/pdf/1411.4555.pdf) | @... |
-| LSTM - Q&A System| bAbl | | [Weston et al.., 2015](https://arxiv.org/pdf/1502.05698v10.pdf) | |
-| LSTM - Sentiment Analysis| IMDB | | [Li et al.., 2015](http://arxiv.org/pdf/1503.00185v5.pdf) | |
-
-
-## Generative Adversarial Networks (GANs)
-
-[Generative Adversarial Networks](http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf) train a competing pair of
-neural networks: a generator network which transforms a latent vector into content like an image, and a discriminator
-network that tries to distinguish between generated content and supplied "real" training content.  When properly
-trained the two achieve a [Nash equilibrium](https://en.wikipedia.org/wiki/Nash_equilibrium).
-
-| Model Definition | Dataset | Model Weights | Research Basis | Contributors |
-| --- | --- | --- | --- | --- |
-| DCGANs | ImageNet | | [Radford et al..,2016](https://arxiv.org/pdf/1511.06434v2.pdf) | @... |
-| Text to Image Synthesis |MS COCO| | [Reed et al.., 2016](https://arxiv.org/pdf/1605.05396v2.pdf) | |
-| Deep Jazz	| | | [Deepjazz.io](https://deepjazz.io) | |
-
-
-
-## Other Models
-
-MXNet Supports a variety of model types beyond the canonical CNN and LSTM model types. These include deep reinforcement learning, linear models, etc.. Some available datasets and sources include:
-
-* [Google News](https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit): A text corpus with a vocabulary of 3 million words architected for word2vec.
-* [MovieLens 20M Dataset](http://grouplens.org/datasets/movielens/): 20 million ratings and 465,000 tag applications applied to 27,000 movies by 138,000 users. Includes tag genome data with 12 million relevance scores across 1,100 tags.
-* [Atari Video Game Emulator](http://stella.sourceforge.net/): Stella is a multi-platform Atari 2600 VCS emulator released under the GNU General Public License (GPL).
-
-
-| Model Definition | Dataset | Model Weights | Research Basis | Contributors |
-| --- | --- | --- | --- | --- |
-| Word2Vec | Google News | | [Mikolov et al.., 2013](https://arxiv.org/pdf/1310.4546v1.pdf) | @... |
-| Matrix Factorization | MovieLens 20M | | [Huang et al.., 2013](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/cikm2013_DSSM_fullversion.pdf) | |
-| Deep Q-Network | Atari video games | | [Minh et al.., 2015](http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html) | |
-| Asynchronous advantage actor-critic (A3C) | Atari video games | | [Minh et al.., 2016](https://arxiv.org/pdf/1602.01783.pdf) | |
diff --git a/docs/mxdoc.py b/docs/mxdoc.py
deleted file mode 100644
index 237b36a96606..000000000000
--- a/docs/mxdoc.py
+++ /dev/null
@@ -1,510 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-"""A sphnix-doc plugin to build mxnet docs"""
-from __future__ import print_function
-import subprocess
-import re
-import os
-import json
-import sys
-from recommonmark import transform
-import pypandoc
-import contextlib
-# Use six for Python 2 / 3 compatibility
-from six import StringIO
-from six.moves import configparser
-
-_BUILD_VER = os.getenv('BUILD_VER', 'default')
-print("Building version {}".format(_BUILD_VER))
-_DOC_SET = 'document_sets_' + _BUILD_VER
-
-parser = configparser.SafeConfigParser()
-parser.read('settings.ini')
-
-if _DOC_SET not in parser.sections():
-    _DOC_SET = 'document_sets_default'
-
-for section in [ _DOC_SET ]:
-    print("Document sets to generate:")
-    for candidate in [ 'scala_docs', 'java_docs', 'clojure_docs', 'doxygen_docs', 'julia_docs', 'r_docs' ]:
-        print('%-12s  : %s' % (candidate, parser.get(section, candidate)))
-
-_MXNET_DOCS_BUILD_MXNET = parser.getboolean('mxnet', 'build_mxnet')
-_SCALA_DOCS = parser.getboolean(_DOC_SET, 'scala_docs')
-_JAVA_DOCS = parser.getboolean(_DOC_SET, 'java_docs')
-_CLOJURE_DOCS = parser.getboolean(_DOC_SET, 'clojure_docs')
-_DOXYGEN_DOCS = parser.getboolean(_DOC_SET,  'doxygen_docs')
-_R_DOCS = parser.getboolean(_DOC_SET, 'r_docs')
-_JULIA_DOCS = parser.getboolean(_DOC_SET, 'julia_docs')
-_ARTIFACTS = parser.getboolean(_DOC_SET, 'artifacts')
-
-# white list to evaluate the code block output, such as ['tutorials/gluon']
-_EVAL_WHILTELIST = []
-
-# start or end of a code block
-_CODE_MARK = re.compile('^([ ]*)```([\w]*)')
-
-# language names and the according file extensions and comment symbol
-_LANGS = {'python' : ('py', '#'),
-          'r' : ('R','#'),
-          'scala' : ('scala', '//'),
-          'java' : ('java', '//'),
-          'julia' : ('jl', '#'),
-          'perl' : ('pl', '#'),
-          'cpp' : ('cc', '//'),
-          'bash' : ('sh', '#')}
-_LANG_SELECTION_MARK = 'INSERT SELECTION BUTTONS'
-_SRC_DOWNLOAD_MARK = 'INSERT SOURCE DOWNLOAD BUTTONS'
-
-def _run_cmd(cmds):
-    """Run commands, raise exception if failed"""
-    if not isinstance(cmds, str):
-        cmds = "".join(cmds)
-    print("Execute \"%s\"" % cmds)
-    try:
-        subprocess.check_call(cmds, shell=True)
-    except subprocess.CalledProcessError as err:
-        print(err)
-        raise err
-
-def generate_doxygen(app):
-    """Run the doxygen make commands"""
-    _run_cmd("cd %s/.. && make doxygen" % app.builder.srcdir)
-    _run_cmd("cp -rf doxygen/html %s/doxygen" % app.builder.outdir)
-
-def build_mxnet(app):
-    """Build mxnet .so lib"""
-    if not os.path.exists(os.path.join(app.builder.srcdir, '..', 'config.mk')):
-        _run_cmd("cd %s/.. && cp make/config.mk config.mk && make -j$(nproc) USE_MKLDNN=0 USE_CPP_PACKAGE=1 " %
-                app.builder.srcdir)
-    else:
-        _run_cmd("cd %s/.. && make -j$(nproc) USE_MKLDNN=0 USE_CPP_PACKAGE=1 " %
-                app.builder.srcdir)
-
-def build_julia_docs(app):
-    """build Julia docs"""
-    os.environ['MXNET_HOME'] = os.path.abspath(os.path.join(app.builder.srcdir, '..'))
-    print("Julia will check for MXNet in {}/lib".format(os.environ.get('MXNET_HOME')))
-    dest_path = app.builder.outdir + '/api/julia/site'
-    _run_cmd('cd {}/.. && make -C julia/docs'.format(app.builder.srcdir))
-    _run_cmd('mkdir -p {}'.format(dest_path))
-    _run_cmd('cd {}/.. && cp -a julia/docs/site/* {}'.format(app.builder.srcdir, dest_path))
-
-def build_r_docs(app):
-    """build r pdf"""
-    r_root = app.builder.srcdir + '/../R-package'
-    pdf_path = app.builder.srcdir + '/api/r/mxnet-r-reference-manual.pdf'
-    _run_cmd('cd ' + r_root +
-             '; R -e "roxygen2::roxygenize()"; R CMD Rd2pdf . --no-preview -o ' + pdf_path)
-    dest_path = app.builder.outdir + '/api/r/'
-    _run_cmd('mkdir -p ' + dest_path + '; mv ' + pdf_path + ' ' + dest_path)
-
-def build_scala(app):
-    """build scala for scala docs, java docs, and clojure docs to use"""
-    if any(v in _BUILD_VER for v in ['1.2.', '1.3.', '1.4.']):
-        _run_cmd("cd %s/.. && make scalapkg" % app.builder.srcdir)
-        _run_cmd("cd %s/.. && make scalainstall" % app.builder.srcdir)
-    else:
-        _run_cmd("cd %s/../scala-package && mvn -B install -DskipTests" % app.builder.srcdir)
-
-def build_scala_docs(app):
-    """build scala doc and then move the outdir"""
-    scala_path = app.builder.srcdir + '/../scala-package'
-    scala_doc_sources = 'find . -type f -name "*.scala" | egrep \"\.\/core|\.\/infer\" | egrep -v \"\/javaapi\"  | egrep -v \"Suite\"'
-    scala_doc_classpath = ':'.join([
-        '`find native -name "*.jar" | grep "target/lib/" | tr "\\n" ":" `',
-        '`find macros -name "*.jar" | tr "\\n" ":" `',
-        '`find core -name "*.jar" | tr "\\n" ":" `',
-        '`find infer -name "*.jar" | tr "\\n" ":" `'
-    ])
-    # There are unresolvable errors on mxnet 1.2.x. We are ignoring those errors while aborting the ci on newer versions
-    scala_ignore_errors = '; exit 0' if any(v in _BUILD_VER for v in ['1.2.', '1.3.']) else ''
-    _run_cmd('cd {}; scaladoc `{}` -classpath {} -feature -deprecation {}'
-             .format(scala_path, scala_doc_sources, scala_doc_classpath, scala_ignore_errors))
-    dest_path = app.builder.outdir + '/api/scala/docs'
-    _run_cmd('rm -rf ' + dest_path)
-    _run_cmd('mkdir -p ' + dest_path)
-    # 'index' and 'package.html' do not exist in later versions of scala; delete these after upgrading scala>2.12.x
-    scaladocs = ['index', 'index.html', 'org', 'lib', 'index.js', 'package.html']
-    for doc_file in scaladocs:
-        _run_cmd('cd ' + scala_path + ' && mv -f ' + doc_file + ' ' + dest_path + '; exit 0')
-
-def build_java_docs(app):
-    """build java docs and then move the outdir"""
-    java_path = app.builder.srcdir + '/../scala-package'
-    java_doc_sources = 'find . -type f -name "*.scala" | egrep \"\.\/core|\.\/infer\" | egrep \"\/javaapi\" | egrep -v \"Suite\"'
-    java_doc_classpath = ':'.join([
-        '`find native -name "*.jar" | grep "target/lib/" | tr "\\n" ":" `',
-        '`find macros -name "*.jar" | tr "\\n" ":" `',
-        '`find core -name "*.jar" | tr "\\n" ":" `',
-        '`find infer -name "*.jar" | tr "\\n" ":" `'
-    ])
-    _run_cmd('cd {}; scaladoc `{}` -classpath {} -feature -deprecation'
-             .format(java_path, java_doc_sources, java_doc_classpath))
-    dest_path = app.builder.outdir + '/api/java/docs'
-    _run_cmd('rm -rf ' + dest_path)
-    _run_cmd('mkdir -p ' + dest_path)
-    javadocs = ['index', 'index.html', 'org', 'lib', 'index.js', 'package.html']
-    for doc_file in javadocs:
-        _run_cmd('cd ' + java_path + ' && mv -f ' + doc_file + ' ' + dest_path + '; exit 0')
-
-def build_clojure_docs(app):
-    """build clojure doc and then move the outdir"""
-    clojure_path = app.builder.srcdir + '/../contrib/clojure-package'
-    _run_cmd('cd ' + clojure_path + '; lein codox')
-    dest_path = app.builder.outdir + '/api/clojure/docs'
-    _run_cmd('rm -rf ' + dest_path)
-    _run_cmd('mkdir -p ' + dest_path)
-    clojure_doc_path = app.builder.srcdir + '/../contrib/clojure-package/target/doc'
-    _run_cmd('cd ' + clojure_doc_path + ' && cp -r *  ' + dest_path + '; exit 0')
-
-def _convert_md_table_to_rst(table):
-    """Convert a markdown table to rst format"""
-    if len(table) < 3:
-        return ''
-    out = '```eval_rst\n.. list-table::\n   :header-rows: 1\n\n'
-    for i,l in enumerate(table):
-        cols = l.split('|')[1:-1]
-        if i == 0:
-            ncol = len(cols)
-        else:
-            if len(cols) != ncol:
-                return ''
-        if i == 1:
-            for c in cols:
-                if len(c) is not 0 and '---' not in c:
-                    return ''
-        else:
-            for j,c in enumerate(cols):
-                out += '   * - ' if j == 0 else '     - '
-                out += pypandoc.convert_text(
-                    c, 'rst', format='md').replace('\n', ' ').replace('\r', '') + '\n'
-    out += '```\n'
-    return out
-
-
-def convert_table(app, docname, source):
-    """Find tables in a markdown and then convert them into the rst format"""
-    num_tables = 0
-    for i,j in enumerate(source):
-        table = []
-        output = ''
-        in_table = False
-        for l in j.split('\n'):
-            r = l.strip()
-            if r.startswith('|'):
-                table.append(r)
-                in_table = True
-            else:
-                if in_table is True:
-                    converted = _convert_md_table_to_rst(table)
-                    if converted is '':
-                        print("Failed to convert the markdown table")
-                        print(table)
-                    else:
-                        num_tables += 1
-                    output += converted
-                    in_table = False
-                    table = []
-                output += l + '\n'
-        source[i] = output
-    if num_tables > 0:
-        print('Converted %d tables in %s' % (num_tables, docname))
-
-def _parse_code_lines(lines):
-    """A iterator that returns if a line is within a code block
-
-    Returns
-    -------
-    iterator of (str, bool, str, int)
-        - line: the line
-        - in_code: if this line is in a code block
-        - lang: the code block langunage
-        - indent: the code indent
-    """
-    in_code = False
-    lang = None
-    indent = None
-    for l in lines:
-        m = _CODE_MARK.match(l)
-        if m is not None:
-            if not in_code:
-                if m.groups()[1].lower() in _LANGS:
-                    lang = m.groups()[1].lower()
-                    indent = len(m.groups()[0])
-                    in_code = True
-                yield (l, in_code, lang, indent)
-            else:
-                yield (l, in_code, lang, indent)
-                lang = None
-                indent = None
-                in_code = False
-        else:
-            yield (l, in_code, lang, indent)
-
-def _get_lang_selection_btn(langs):
-    active = True
-    btngroup = '<div class="text-center">\n<div class="btn-group opt-group" role="group">'
-    for l in langs:
-        btngroup += '<button type="button" class="btn btn-default opt %s">%s</button>\n' % (
-            'active' if active else '', l[0].upper()+l[1:].lower())
-        active = False
-        btngroup += '</div>\n</div> <script type="text/javascript" src="../../_static/js/options.js"></script>'
-    return btngroup
-
-def _get_blocks(lines):
-    """split lines into code and non-code blocks
-
-    Returns
-    -------
-    iterator of (bool, str, list of str)
-      - if it is a code block
-      - source language
-      - lines of source
-    """
-    cur_block = []
-    pre_lang = None
-    pre_in_code = None
-    for (l, in_code, cur_lang, _) in _parse_code_lines(lines):
-        if in_code != pre_in_code:
-            if pre_in_code and len(cur_block) >= 2:
-                cur_block = cur_block[1:-1] # remove ```
-            # remove empty lines at head
-            while len(cur_block) > 0:
-                if len(cur_block[0]) == 0:
-                    cur_block.pop(0)
-                else:
-                    break
-            # remove empty lines at tail
-            while len(cur_block) > 0:
-                if len(cur_block[-1]) == 0:
-                    cur_block.pop()
-                else:
-                    break
-            if len(cur_block):
-                yield (pre_in_code, pre_lang, cur_block)
-            cur_block = []
-        cur_block.append(l)
-        pre_lang = cur_lang
-        pre_in_code = in_code
-    if len(cur_block):
-        yield (pre_in_code, pre_lang, cur_block)
-
-def _get_mk_code_block(src, lang):
-    """Return a markdown code block
-
-    E.g.
-    ```python
-    import mxnet
-    ````
-    """
-    if lang is None:
-        lang = ''
-    return '```'+lang+'\n'+src.rstrip()+'\n'+'```\n'
-
-@contextlib.contextmanager
-def _string_io():
-    oldout = sys.stdout
-    olderr = sys.stderr
-    strio = StringIO.StringIO()
-    sys.stdout = strio
-    sys.stderr = strio
-    yield strio
-    sys.stdout = oldout
-    sys.stderr = olderr
-
-def _get_python_block_output(src, global_dict, local_dict):
-    """Evaluate python source codes
-
-    Returns
-    (bool, str):
-      - True if success
-      - output
-    """
-    src = '\n'.join([l for l in src.split('\n')
-                     if not l.startswith('%') and not 'plt.show()' in l])
-    ret_status = True
-    err = ''
-    with _string_io() as s:
-        try:
-            exec(src, global_dict, global_dict)
-        except Exception as e:
-            err = str(e)
-            ret_status = False
-    return (ret_status, s.getvalue()+err)
-
-def _get_jupyter_notebook(lang, all_lines):
-    cells = []
-    # Exclude lines containing <!--notebook-skip-line-->
-    filtered_lines = [line for line in all_lines if "<!--notebook-skip-line-->" not in line]
-    for in_code, blk_lang, lines in _get_blocks(filtered_lines):
-        if blk_lang != lang:
-            in_code = False
-        src = '\n'.join(lines)
-        cell = {
-            "cell_type": "code" if in_code else "markdown",
-            "metadata": {},
-            "source":  src
-        }
-        if in_code:
-             cell.update({
-                 "outputs": [],
-                 "execution_count": None,
-             })
-        cells.append(cell)
-    ipynb = {"nbformat" : 4,
-             "nbformat_minor" : 2,
-             "metadata" : {"language":lang, "display_name":'', "name":''},
-             "cells" : cells}
-    return ipynb
-
-def _get_source(lang, lines):
-    cmt = _LANGS[lang][1] + ' '
-    out = []
-    for in_code, lines in _get_blocks(lang, lines):
-        if in_code:
-            out.append('')
-        for l in lines:
-            if in_code:
-                if '%matplotlib' not in l:
-                    out.append(l)
-            else:
-                if ('<div>' in l or '</div>' in l or
-                    '<script>' in l or '</script>' in l or
-                    '<!--' in l or '-->' in l or
-                    '%matplotlib' in l ):
-                    continue
-                out.append(cmt+l)
-        if in_code:
-            out.append('')
-
-    return out
-
-def _get_src_download_btn(out_prefix, langs, lines):
-    btn = '<div class="btn-group" role="group">\n'
-    for lang in langs:
-        ipynb = out_prefix
-        if lang == 'python':
-            ipynb += '.ipynb'
-        else:
-            ipynb += '_' + lang + '.ipynb'
-        with open(ipynb, 'w') as f:
-            json.dump(_get_jupyter_notebook(lang, lines), f)
-        f = ipynb.split('/')[-1]
-        btn += '<div class="download-btn"><a href="%s" download="%s">' \
-               '<span class="glyphicon glyphicon-download-alt"></span> %s</a></div>' % (f, f, f)
-    btn += '</div>\n'
-    return btn
-
-def add_buttons(app, docname, source):
-    out_prefix = app.builder.outdir + '/' + docname
-    dirname = os.path.dirname(out_prefix)
-    if not os.path.exists(dirname):
-        os.makedirs(dirname)
-
-    for i,j in enumerate(source):
-        local_dict = {}
-        global_dict = {}
-        lines = j.split('\n')
-        langs = set([l for (_, _, l, _) in _parse_code_lines(lines)
-                     if l is not None and l in _LANGS])
-        # first convert
-        for k,l in enumerate(lines):
-            if _SRC_DOWNLOAD_MARK in l:
-                lines[k] = _get_src_download_btn(
-                    out_prefix, langs, lines)
-        # # then add lang buttons
-        # for k,l in enumerate(lines):
-        #     if _LANG_SELECTION_MARK in l:
-        #         lines[k] = _get_lang_selection_btn(langs)
-
-        output = ''
-        for in_code, lang, lines in _get_blocks(lines):
-            src = '\n'.join(lines)+'\n'
-            if in_code:
-                output += _get_mk_code_block(src, lang)
-                if lang == 'python' and any([w in docname for w in _EVAL_WHILTELIST]):
-                    status, blk_out = _get_python_block_output(src, global_dict, local_dict)
-                    if len(blk_out):
-                        output += '<div class=\"cell-results-header\">Output:</div>\n\n'
-                        output += _get_mk_code_block(blk_out, 'results')
-            else:
-                output += src
-        source[i] = output
-
-        # source[i] = '\n'.join(lines)
-
-
-def copy_artifacts(app):
-    """Copies artifacts needed for website presentation"""
-    dest_path = app.builder.outdir + '/error'
-    source_path = app.builder.srcdir + '/build_version_doc/artifacts'
-    _run_cmd('cd ' + app.builder.srcdir)
-    _run_cmd('rm -rf ' + dest_path)
-    _run_cmd('mkdir -p ' + dest_path)
-    _run_cmd('cp ' + source_path + '/404.html ' + dest_path)
-    _run_cmd('cp ' + source_path + '/api.html ' + dest_path)
-    dest_path = app.builder.outdir + '/_static'
-    _run_cmd('rm -rf ' + dest_path)
-    _run_cmd('mkdir -p ' + dest_path)
-    _run_cmd('cp ' + app.builder.srcdir + '/_static/mxnet.css ' + dest_path)
-
-
-def setup(app):
-    # If MXNET_DOCS_BUILD_MXNET is set something different than 1
-    # Skip the build step
-    env_build_mxnet = os.getenv('MXNET_DOCS_BUILD_MXNET', default=None)
-    should_build_mxnet = _MXNET_DOCS_BUILD_MXNET
-    if env_build_mxnet is not None:
-        print("env MXNET_DOCS_BUILD_MXNET is set to " + str(env_build_mxnet))
-        should_build_mxnet = (env_build_mxnet == '1')
-
-    if should_build_mxnet:
-        print("Building MXNet!")
-        app.connect("builder-inited", build_mxnet)
-    if _DOXYGEN_DOCS:
-        print("Building Doxygen!")
-        app.connect("builder-inited", generate_doxygen)
-    if _SCALA_DOCS or _CLOJURE_DOCS:
-        print("Building Scala!")
-        app.connect("builder-inited", build_scala)
-    if _SCALA_DOCS:
-        print("Building Scala Docs!")
-        app.connect("builder-inited", build_scala_docs)
-    if _JAVA_DOCS:
-        print("Building Java Docs!")
-        app.connect("builder-inited", build_java_docs)
-    if _CLOJURE_DOCS:
-        print("Building Clojure Docs!")
-        app.connect("builder-inited", build_clojure_docs)
-    if _JULIA_DOCS:
-        print("Building Julia Docs!")
-        app.connect("builder-inited", build_julia_docs)
-    if _R_DOCS:
-        print("Building R Docs!")
-        app.connect("builder-inited", build_r_docs)
-    if _ARTIFACTS:
-        print("Copying Artifacts!")
-        app.connect("builder-inited", copy_artifacts)
-    app.connect('source-read', convert_table)
-    app.connect('source-read', add_buttons)
-    app.add_config_value('recommonmark_config', {
-        'url_resolver': lambda url: 'http://mxnet.io/' + url,
-        'enable_eval_rst': True,
-    }, True)
-    app.add_transform(transform.AutoStructify)
diff --git a/docs/tutorials/amp/amp_tutorial.md b/docs/python_docs/python/tutorials/performance/backend/amp.md
similarity index 94%
rename from docs/tutorials/amp/amp_tutorial.md
rename to docs/python_docs/python/tutorials/performance/backend/amp.md
index 2b747c6c82f6..cf3dcf76de25 100644
--- a/docs/tutorials/amp/amp_tutorial.md
+++ b/docs/python_docs/python/tutorials/performance/backend/amp.md
@@ -161,11 +161,14 @@ for epoch in range(1):
         btic = time.time()
 ```
 
-    INFO:root:[Epoch 0][Batch 49], Speed: 58.105 samples/sec, CrossEntropy=1.190, SmoothL1=0.688
-    INFO:root:[Epoch 0][Batch 99], Speed: 58.683 samples/sec, CrossEntropy=0.693, SmoothL1=0.536
-    INFO:root:[Epoch 0][Batch 149], Speed: 58.915 samples/sec, CrossEntropy=0.500, SmoothL1=0.453
-    INFO:root:[Epoch 0][Batch 199], Speed: 58.422 samples/sec, CrossEntropy=0.396, SmoothL1=0.399
+output 
 
+```text
+INFO:root:[Epoch 0][Batch 49], Speed: 58.105 samples/sec, CrossEntropy=1.190, SmoothL1=0.688
+INFO:root:[Epoch 0][Batch 99], Speed: 58.683 samples/sec, CrossEntropy=0.693, SmoothL1=0.536
+INFO:root:[Epoch 0][Batch 149], Speed: 58.915 samples/sec, CrossEntropy=0.500, SmoothL1=0.453
+INFO:root:[Epoch 0][Batch 199], Speed: 58.422 samples/sec, CrossEntropy=0.396, SmoothL1=0.399
+```
 
 ## Training with AMP
 
@@ -179,8 +182,10 @@ from mxnet.contrib import amp
 
 amp.init()
 ```
-
-    INFO:root:Using AMP
+output:
+```text
+INFO:root:Using AMP
+```
 
 
 After that, we can create the network exactly the same way we did in FP32 training.
@@ -244,11 +249,14 @@ for epoch in range(1):
         btic = time.time()
 ```
 
-    INFO:root:[Epoch 0][Batch 49], Speed: 93.585 samples/sec, CrossEntropy=1.166, SmoothL1=0.684
-    INFO:root:[Epoch 0][Batch 99], Speed: 93.773 samples/sec, CrossEntropy=0.682, SmoothL1=0.533
-    INFO:root:[Epoch 0][Batch 149], Speed: 93.399 samples/sec, CrossEntropy=0.493, SmoothL1=0.451
-    INFO:root:[Epoch 0][Batch 199], Speed: 93.674 samples/sec, CrossEntropy=0.391, SmoothL1=0.397
+output
 
+```bash
+INFO:root:[Epoch 0][Batch 49], Speed: 93.585 samples/sec, CrossEntropy=1.166, SmoothL1=0.684
+INFO:root:[Epoch 0][Batch 99], Speed: 93.773 samples/sec, CrossEntropy=0.682, SmoothL1=0.533
+INFO:root:[Epoch 0][Batch 149], Speed: 93.399 samples/sec, CrossEntropy=0.493, SmoothL1=0.451
+INFO:root:[Epoch 0][Batch 199], Speed: 93.674 samples/sec, CrossEntropy=0.391, SmoothL1=0.397
+```
 
 We got 60% speed increase from 3 additional lines of code!
 
@@ -340,6 +348,4 @@ with mx.Context(mx.gpu(0)):
 ## Current limitations of AMP
 
 - AMP's dynamic loss scaling currently supports only Gluon trainer with `update_on_kvstore=False` option set
-- Using `SoftmaxOutput`, `LinearRegressionOutput`, `LogisticRegressionOutput`, `MAERegressionOutput` with dynamic loss scaling does not work when training networks with multiple Gluon trainers and so multiple loss scales
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
+- Using `SoftmaxOutput`, `LinearRegressionOutput`, `LogisticRegressionOutput`, `MAERegressionOutput` with dynamic loss scaling does not work when training networks with multiple Gluon trainers and so multiple loss scales
\ No newline at end of file
diff --git a/docs/python_docs/python/tutorials/performance/backend/index.rst b/docs/python_docs/python/tutorials/performance/backend/index.rst
index fbe40c0ed684..f6ff5c8e3226 100644
--- a/docs/python_docs/python/tutorials/performance/backend/index.rst
+++ b/docs/python_docs/python/tutorials/performance/backend/index.rst
@@ -39,6 +39,12 @@ The following tutorials will help you learn how to use backend tools to boost pe
      :link: tvm.html
 
      How to use TVM to boost performance.
+
+  .. card::
+     :title: Automatic Mixed Precision (AMP)
+     :link: amp.html
+
+     How to use Automatic Mixed Precision to boost performance.
 ..
 
 .. toctree::
diff --git a/docs/settings.ini b/docs/settings.ini
index 22887f7a576b..9a57ac1292e8 100644
--- a/docs/settings.ini
+++ b/docs/settings.ini
@@ -16,7 +16,7 @@
 # under the License.
 
 [mxnet]
-build_mxnet = 1
+build_mxnet = 0
 
 [document_sets_tutorial]
 artifacts = 0
@@ -29,12 +29,12 @@ scala_docs = 0
 
 [document_sets_default]
 artifacts = 1
-clojure_docs = 1
-doxygen_docs = 1
-java_docs = 1
+clojure_docs = 0
+doxygen_docs = 0
+java_docs = 0
 r_docs = 0
-julia_docs = 1
-scala_docs = 1
+julia_docs = 0
+scala_docs = 0
 
 [document_sets_1.4.0]
 artifacts = 0
diff --git a/docs/static_site/src/pages/api/api.html b/docs/static_site/src/pages/api/api.html
index 098efda5e605..a1f4ae140701 100644
--- a/docs/static_site/src/pages/api/api.html
+++ b/docs/static_site/src/pages/api/api.html
@@ -1,19 +1,3 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
 ---
 layout: page
 title: Docs
@@ -87,6 +71,23 @@
   icon: /assets/img/perl_logo.svg
   tag: perl
 ---
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
 {%- for doc in page.docs -%}
   {%- if doc.tag == 'python' -%}
     <h2>Python API</h2>
diff --git a/docs/static_site/src/pages/api/architecture/exception_handling.md b/docs/static_site/src/pages/api/architecture/exception_handling.md
index b985b27b285e..b6323cf21629 100644
--- a/docs/static_site/src/pages/api/architecture/exception_handling.md
+++ b/docs/static_site/src/pages/api/architecture/exception_handling.md
@@ -1,3 +1,9 @@
+---
+layout: page_category
+title:  Exception Handling in MXNet
+category: architecture
+permalink: /api/architecture/exception_handling
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,12 +20,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_category
-title:  Exception Handling in MXNet
-category: architecture
-permalink: /api/architecture/exception_handling
----
 
 # Exception Handling in MXNet
 
diff --git a/docs/static_site/src/pages/api/architecture/note_data_loading.md b/docs/static_site/src/pages/api/architecture/note_data_loading.md
index 63d90024a80c..b93b6b601158 100644
--- a/docs/static_site/src/pages/api/architecture/note_data_loading.md
+++ b/docs/static_site/src/pages/api/architecture/note_data_loading.md
@@ -1,3 +1,9 @@
+---
+layout: page_category
+title:  Efficient Data Loaders
+category: architecture
+permalink: /api/architecture/note_data_loading
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,12 +20,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_category
-title:  Efficient Data Loaders
-category: architecture
-permalink: /api/architecture/note_data_loading
----
 
 # Designing Efficient Data Loaders for Deep Learning
 
diff --git a/docs/static_site/src/pages/api/architecture/note_engine.md b/docs/static_site/src/pages/api/architecture/note_engine.md
index cb626649e5ff..d339f45c9160 100644
--- a/docs/static_site/src/pages/api/architecture/note_engine.md
+++ b/docs/static_site/src/pages/api/architecture/note_engine.md
@@ -1,3 +1,9 @@
+---
+layout: page_category
+title:  Dependency Engine
+category: architecture
+permalink: /api/architecture/note_engine
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,12 +20,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_category
-title:  Dependency Engine
-category: architecture
-permalink: /api/architecture/note_engine
----
 
 # Dependency Engine for Deep Learning
 
diff --git a/docs/static_site/src/pages/api/architecture/note_memory.md b/docs/static_site/src/pages/api/architecture/note_memory.md
index 771d6a5ff012..58c76027aef9 100644
--- a/docs/static_site/src/pages/api/architecture/note_memory.md
+++ b/docs/static_site/src/pages/api/architecture/note_memory.md
@@ -1,3 +1,9 @@
+---
+layout: page_category
+title:  Memory Consumption
+category: architecture
+permalink: /api/architecture/note_memory
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,12 +20,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_category
-title:  Memory Consumption
-category: architecture
-permalink: /api/architecture/note_memory
----
 
 # Optimizing Memory Consumption in Deep Learning
 
diff --git a/docs/static_site/src/pages/api/architecture/overview.md b/docs/static_site/src/pages/api/architecture/overview.md
index d2ad51c87776..c0ef07c0d64e 100644
--- a/docs/static_site/src/pages/api/architecture/overview.md
+++ b/docs/static_site/src/pages/api/architecture/overview.md
@@ -1,3 +1,9 @@
+---
+layout: page_category
+title:  MXNet System Architecture
+category: architecture
+permalink: /api/architecture/overview
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,12 +20,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_category
-title:  MXNet System Architecture
-category: architecture
-permalink: /api/architecture/overview
----
 
 # MXNet System Architecture
 
diff --git a/docs/static_site/src/pages/api/architecture/program_model.md b/docs/static_site/src/pages/api/architecture/program_model.md
index be291d24b19e..14cfba70f281 100644
--- a/docs/static_site/src/pages/api/architecture/program_model.md
+++ b/docs/static_site/src/pages/api/architecture/program_model.md
@@ -1,3 +1,9 @@
+---
+layout: page_category
+title:  Deep Learning Programming Paradigm
+category: architecture
+permalink: /api/architecture/program_model
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,12 +20,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_category
-title:  Deep Learning Programming Paradigm
-category: architecture
-permalink: /api/architecture/program_model
----
 
 # Deep Learning Programming Paradigm
 
diff --git a/docs/static_site/src/pages/api/clojure/docs/tutorials/index.md b/docs/static_site/src/pages/api/clojure/docs/tutorials/index.md
index a424c87b335a..f59d789afd45 100644
--- a/docs/static_site/src/pages/api/clojure/docs/tutorials/index.md
+++ b/docs/static_site/src/pages/api/clojure/docs/tutorials/index.md
@@ -1,3 +1,10 @@
+---
+layout: page_landing_tutorials
+title:  Clojure Tutorials
+action: Get Started
+tag: clojure
+permalink: /api/clojure/docs/tutorials
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,10 +21,3 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_landing_tutorials
-title:  Clojure Tutorials
-action: Get Started
-tag: clojure
-permalink: /api/clojure/docs/tutorials
----
diff --git a/docs/static_site/src/pages/api/clojure/docs/tutorials/kvstore.md b/docs/static_site/src/pages/api/clojure/docs/tutorials/kvstore.md
index 96eb03abdeb7..243cb8a9e7db 100644
--- a/docs/static_site/src/pages/api/clojure/docs/tutorials/kvstore.md
+++ b/docs/static_site/src/pages/api/clojure/docs/tutorials/kvstore.md
@@ -1,3 +1,10 @@
+---
+layout: page_api
+title: KVStore API
+is_tutorial: true
+permalink: /api/clojure/docs/tutorials/kvstore
+tag: clojure
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: KVStore API
-is_tutorial: true
-permalink: /api/clojure/docs/tutorials/kvstore
-tag: clojure
----
 # KVStore API
 
 Topics:
diff --git a/docs/static_site/src/pages/api/clojure/docs/tutorials/module.md b/docs/static_site/src/pages/api/clojure/docs/tutorials/module.md
index 013e6ec44820..fdd739a7a333 100644
--- a/docs/static_site/src/pages/api/clojure/docs/tutorials/module.md
+++ b/docs/static_site/src/pages/api/clojure/docs/tutorials/module.md
@@ -1,3 +1,10 @@
+---
+layout: page_api
+title: Module API
+is_tutorial: true
+tag: clojure
+permalink: /api/clojure/docs/tutorials/module
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: Module API
-is_tutorial: true
-tag: clojure
-permalink: /api/clojure/docs/tutorials/module
----
 
 # Module API
 The module API provides an intermediate and high-level interface for performing computation with neural networks in MXNet. Module wraps a Symbol and one or more Executors. It has both a high level and intermediate level API.
diff --git a/docs/static_site/src/pages/api/clojure/docs/tutorials/ndarray.md b/docs/static_site/src/pages/api/clojure/docs/tutorials/ndarray.md
index a989d7482ecf..42d06c0d8802 100644
--- a/docs/static_site/src/pages/api/clojure/docs/tutorials/ndarray.md
+++ b/docs/static_site/src/pages/api/clojure/docs/tutorials/ndarray.md
@@ -1,3 +1,10 @@
+---
+layout: page_api
+title: NDArray
+is_tutorial: true
+tag: clojure
+permalink: /api/clojure/docs/tutorials/ndarray
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: NDArray
-is_tutorial: true
-tag: clojure
-permalink: /api/clojure/docs/tutorials/ndarray
----
 
 # NDArray API
 
diff --git a/docs/static_site/src/pages/api/clojure/docs/tutorials/symbol.md b/docs/static_site/src/pages/api/clojure/docs/tutorials/symbol.md
index 352b0406bd60..5d902629919f 100644
--- a/docs/static_site/src/pages/api/clojure/docs/tutorials/symbol.md
+++ b/docs/static_site/src/pages/api/clojure/docs/tutorials/symbol.md
@@ -1,3 +1,10 @@
+---
+layout: page_api
+title: Symbolic API
+is_tutorial: true
+tag: clojure
+permalink: /api/clojure/docs/tutorials/symbol
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: Symbolic API
-is_tutorial: true
-tag: clojure
-permalink: /api/clojure/docs/tutorials/symbol
----
 
 # MXNet Clojure Symbolic API
 
diff --git a/docs/static_site/src/pages/api/clojure/docs/tutorials/symbol_in_pictures.md b/docs/static_site/src/pages/api/clojure/docs/tutorials/symbol_in_pictures.md
index 09c9cfce02f7..705872191962 100644
--- a/docs/static_site/src/pages/api/clojure/docs/tutorials/symbol_in_pictures.md
+++ b/docs/static_site/src/pages/api/clojure/docs/tutorials/symbol_in_pictures.md
@@ -1,3 +1,10 @@
+---
+layout: page_api
+title: Symbolic API with Pictures
+is_tutorial: true
+tag: clojure
+permalink: /api/clojure/docs/tutorials/symbol_in_pictures
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: Symbolic API with Pictures
-is_tutorial: true
-tag: clojure
-permalink: /api/clojure/docs/tutorials/symbol_in_pictures
----
 
 # Symbolic Configuration and Execution in Pictures
 
diff --git a/docs/static_site/src/pages/api/clojure/index.md b/docs/static_site/src/pages/api/clojure/index.md
index af535c8614fa..39e90bf518a9 100644
--- a/docs/static_site/src/pages/api/clojure/index.md
+++ b/docs/static_site/src/pages/api/clojure/index.md
@@ -1,3 +1,11 @@
+---
+layout: page_api
+title: Clojure Guide
+action: Get Started
+action_url: /get_started
+permalink: /api/clojure
+tag: clojure
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,14 +22,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: Clojure Guide
-action: Get Started
-action_url: /get_started
-permalink: /api/clojure
-tag: clojure
----
 
 # MXNet - Clojure API
 
diff --git a/docs/static_site/src/pages/api/cpp/docs/tutorials/basics.md b/docs/static_site/src/pages/api/cpp/docs/tutorials/basics.md
index aec71d013ee6..00e07e5e96ac 100644
--- a/docs/static_site/src/pages/api/cpp/docs/tutorials/basics.md
+++ b/docs/static_site/src/pages/api/cpp/docs/tutorials/basics.md
@@ -1,3 +1,12 @@
+---
+layout: page_api
+title: Basics
+action: Get Started
+action_url: /get_started
+permalink: /api/cpp/docs/tutorials/basics
+is_tutorial: true
+tag: cpp
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,15 +23,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: Basics
-action: Get Started
-action_url: /get_started
-permalink: /api/cpp/docs/tutorials/basics
-is_tutorial: true
-tag: cpp
----
 
 Basics
 ======
diff --git a/docs/static_site/src/pages/api/cpp/docs/tutorials/index.md b/docs/static_site/src/pages/api/cpp/docs/tutorials/index.md
index 82a5330044a8..f020c8eb7960 100644
--- a/docs/static_site/src/pages/api/cpp/docs/tutorials/index.md
+++ b/docs/static_site/src/pages/api/cpp/docs/tutorials/index.md
@@ -1,3 +1,9 @@
+---
+layout: page_landing_tutorials
+title: C++ Tutorials
+permalink: /api/cpp/docs/tutorials/
+tag: cpp
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,9 +20,3 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_landing_tutorials
-title: C++ Tutorials
-permalink: /api/cpp/docs/tutorials/
-tag: cpp
----
diff --git a/docs/static_site/src/pages/api/cpp/docs/tutorials/mxnet_cpp_inference_tutorial.md b/docs/static_site/src/pages/api/cpp/docs/tutorials/mxnet_cpp_inference_tutorial.md
index 72a56240523a..c706e08a617d 100644
--- a/docs/static_site/src/pages/api/cpp/docs/tutorials/mxnet_cpp_inference_tutorial.md
+++ b/docs/static_site/src/pages/api/cpp/docs/tutorials/mxnet_cpp_inference_tutorial.md
@@ -1,3 +1,12 @@
+---
+layout: page_api
+title: C++ API inference tutorial
+action: Get Started
+action_url: /get_started
+permalink: /api/cpp/docs/tutorials/cpp_inference
+is_tutorial: true
+tag: cpp
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,15 +23,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: C++ API inference tutorial
-action: Get Started
-action_url: /get_started
-permalink: /api/cpp/docs/tutorials/cpp_inference
-is_tutorial: true
-tag: cpp
----
 
 # C++ API inference tutorial
 
diff --git a/docs/static_site/src/pages/api/cpp/docs/tutorials/subgraphAPI.md b/docs/static_site/src/pages/api/cpp/docs/tutorials/subgraphAPI.md
index bd5450e0bf7a..10d3956a92b4 100644
--- a/docs/static_site/src/pages/api/cpp/docs/tutorials/subgraphAPI.md
+++ b/docs/static_site/src/pages/api/cpp/docs/tutorials/subgraphAPI.md
@@ -1,3 +1,12 @@
+---
+layout: page_api
+title: Subgraph API
+action: Get Started
+action_url: /get_started
+permalink: /api/cpp/docs/tutorials/subgraph_api
+is_tutorial: true
+tag: cpp
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,15 +23,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: Subgraph API
-action: Get Started
-action_url: /get_started
-permalink: /api/cpp/docs/tutorials/subgraph_api
-is_tutorial: true
-tag: cpp
----
 
 ## Subgraph API
 
@@ -116,9 +116,11 @@ class SgProperty : public SubgraphProperty {
 ```
 {% endraw %}
 `SetAttr` is optional and developer can define their own attributes to control property behavior.
-There're 2 built-in attributes that used by MXNet executor.
+There're some built-in attributes that used by MXNet executor.
+
+`property_name`  : std::string, name of this property, used for diagnose.
 
-`property_name`  : std::string, name of this property.
+`disable` : bool, whther to disable this property.
 
 `inference_only` : bool, apply this property only for inference. Property will be skiped when need_grad=True. Default `false` if this attribute isn't defined.
 
diff --git a/docs/static_site/src/pages/api/cpp/index.md b/docs/static_site/src/pages/api/cpp/index.md
index f3b65a366078..594aa237a1e1 100644
--- a/docs/static_site/src/pages/api/cpp/index.md
+++ b/docs/static_site/src/pages/api/cpp/index.md
@@ -1,3 +1,11 @@
+---
+layout: page_api
+title: C++ Guide
+action: Get Started
+action_url: /get_started
+permalink: /api/cpp
+tag: cpp
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,14 +22,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: C++ Guide
-action: Get Started
-action_url: /get_started
-permalink: /api/cpp
-tag: cpp
----
 
 # MXNet - C++ API
 
diff --git a/docs/static_site/src/pages/api/faq/add_op_in_backend.md b/docs/static_site/src/pages/api/faq/add_op_in_backend.md
index f633e57ce645..65f1c635187d 100644
--- a/docs/static_site/src/pages/api/faq/add_op_in_backend.md
+++ b/docs/static_site/src/pages/api/faq/add_op_in_backend.md
@@ -1,3 +1,11 @@
+---
+layout: page_category
+title:  Exception Handling in MXNet
+category: faq
+faq_c: Extend and Contribute to MXNet
+question: How do I implement operators in MXNet backend?
+permalink: /api/faq/add_op_in_backend
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,14 +22,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_category
-title:  Exception Handling in MXNet
-category: faq
-faq_c: Extend and Contribute to MXNet
-question: How do I implement operators in MXNet backend?
-permalink: /api/faq/add_op_in_backend
----
 
 # A Beginner's Guide to Implementing Operators in MXNet Backend
 
diff --git a/docs/static_site/src/pages/api/faq/caffe.md b/docs/static_site/src/pages/api/faq/caffe.md
index 62b204e85dde..6728fa81d0b8 100644
--- a/docs/static_site/src/pages/api/faq/caffe.md
+++ b/docs/static_site/src/pages/api/faq/caffe.md
@@ -1,3 +1,11 @@
+---
+layout: page_category
+title:  Convert from Caffe to MXNet
+category: faq
+faq_c: Deployment Environments
+question: How to convert a Caffe model to MXNet?
+permalink: /api/faq/caffe
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,14 +22,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_category
-title:  Convert from Caffe to MXNet
-category: faq
-faq_c: Deployment Environments
-question: How to convert a Caffe model to MXNet?
-permalink: /api/faq/caffe
----
 # How to | Convert from Caffe to MXNet
 
 Key topics covered include the following:
diff --git a/docs/static_site/src/pages/api/faq/cloud.md b/docs/static_site/src/pages/api/faq/cloud.md
index 1782b3fc33a7..25c5841fef47 100644
--- a/docs/static_site/src/pages/api/faq/cloud.md
+++ b/docs/static_site/src/pages/api/faq/cloud.md
@@ -1,3 +1,11 @@
+---
+layout: page_category
+title:  MXNet on the Cloud
+category: faq
+faq_c: Deployment Environments
+question: How to run MXNet on AWS?
+permalink: /api/faq/cloud
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,14 +22,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_category
-title:  MXNet on the Cloud
-category: faq
-faq_c: Deployment Environments
-question: How to run MXNet on AWS?
-permalink: /api/faq/cloud
----
 
 # MXNet on the Cloud
 
diff --git a/docs/static_site/src/pages/api/faq/distributed_training.md b/docs/static_site/src/pages/api/faq/distributed_training.md
index 85e04d69cf30..b1629794d308 100644
--- a/docs/static_site/src/pages/api/faq/distributed_training.md
+++ b/docs/static_site/src/pages/api/faq/distributed_training.md
@@ -1,3 +1,11 @@
+---
+layout: page_category
+title:  Distributed Training in MXNet
+category: faq
+faq_c: Deployment Environments
+question: How to do distributed training using MXNet on AWS?
+permalink: /api/faq/distributed_training
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,14 +22,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_category
-title:  Distributed Training in MXNet
-category: faq
-faq_c: Deployment Environments
-question: How to do distributed training using MXNet on AWS?
-permalink: /api/faq/distributed_training
----
 
 # Distributed Training in MXNet
 MXNet supports distributed training enabling us to leverage multiple machines for faster training.
diff --git a/docs/static_site/src/pages/api/faq/env_var.md b/docs/static_site/src/pages/api/faq/env_var.md
index 2372a5478800..a5e3613229a9 100644
--- a/docs/static_site/src/pages/api/faq/env_var.md
+++ b/docs/static_site/src/pages/api/faq/env_var.md
@@ -1,3 +1,11 @@
+---
+layout: page_category
+title:  Environment Variables
+category: faq
+faq_c: Deployment Environments
+question: What are MXNet environment variables?
+permalink: /api/faq/env_var
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,14 +22,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_category
-title:  Environment Variables
-category: faq
-faq_c: Deployment Environments
-question: What are MXNet environment variables?
-permalink: /api/faq/env_var
----
 
 Environment Variables
 =====================
@@ -129,10 +129,10 @@ $env:MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
   - Values: Int ```(default=15)```
   - The maximum number of nodes in the subgraph executed in bulk during training (not inference). Setting this to a larger number may reduce the degree of parallelism for multi-GPU training.
 * MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN_FWD
-  - Values: Int ```(default=<value of MXNET_EXEC_BULK_MAX_NODE_TRAIN>)```
+  - Values: Int ```(default=<value of MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN>)```
   - The maximum number of nodes in the subgraph executed in bulk during training (not inference) in the forward pass.
 * MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN_BWD
-  - Values: Int ```(default=<value of MXNET_EXEC_BULK_MAX_NODE_TRAIN>)```
+  - Values: Int ```(default=<value of MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN>)```
   - The maximum number of nodes in the subgraph executed in bulk during training (not inference) in the backward pass.
 
 ## Control the Data Communication
diff --git a/docs/static_site/src/pages/api/faq/float16.md b/docs/static_site/src/pages/api/faq/float16.md
index d6b6210940eb..3938a8f1ca2d 100644
--- a/docs/static_site/src/pages/api/faq/float16.md
+++ b/docs/static_site/src/pages/api/faq/float16.md
@@ -1,3 +1,11 @@
+---
+layout: page_category
+title:  Float16
+category: faq
+faq_c: Speed
+question: How do I use mixed precision (float16) with MXNet or Gluon?
+permalink: /api/faq/floa16
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,14 +22,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_category
-title:  Float16
-category: faq
-faq_c: Speed
-question: How do I use mixed precision (float16) with MXNet or Gluon?
-permalink: /api/faq/floa16
----
 
 # Mixed precision training using float16
 
diff --git a/docs/static_site/src/pages/api/faq/gradient_compression.md b/docs/static_site/src/pages/api/faq/gradient_compression.md
index 74d35537508c..8a295c481aa3 100644
--- a/docs/static_site/src/pages/api/faq/gradient_compression.md
+++ b/docs/static_site/src/pages/api/faq/gradient_compression.md
@@ -1,3 +1,11 @@
+---
+layout: page_category
+title:  Gradient Compression
+category: Speed
+faq_c: Speed
+question: How do I use gradient compression with distributed training?
+permalink: /api/faq/gradient_compression
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,14 +22,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_category
-title:  Gradient Compression
-category: Speed
-faq_c: Speed
-question: How do I use gradient compression with distributed training?
-permalink: /api/faq/gradient_compression
----
 
 # Gradient Compression
 
diff --git a/docs/static_site/src/pages/api/faq/model_parallel_lstm.md b/docs/static_site/src/pages/api/faq/model_parallel_lstm.md
index 37a617042ae4..3263bfb9e9ca 100644
--- a/docs/static_site/src/pages/api/faq/model_parallel_lstm.md
+++ b/docs/static_site/src/pages/api/faq/model_parallel_lstm.md
@@ -1,3 +1,11 @@
+---
+layout: page_category
+title:  Model Parallel
+category: faq
+faq_c: Model
+question: How can I train using multiple GPUs with model parallelism?
+permalink: /api/faq/model_parallel_lstm
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,14 +22,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_category
-title:  Model Parallel
-category: faq
-faq_c: Model
-question: How can I train using multiple GPUs with model parallelism?
-permalink: /api/faq/model_parallel_lstm
----
 
 
 # Training with Multiple GPUs Using Model Parallelism
diff --git a/docs/static_site/src/pages/api/faq/new_op.md b/docs/static_site/src/pages/api/faq/new_op.md
index 360e7050efb5..8763cccc03a6 100644
--- a/docs/static_site/src/pages/api/faq/new_op.md
+++ b/docs/static_site/src/pages/api/faq/new_op.md
@@ -1,3 +1,11 @@
+---
+layout: page_category
+title:  Create New Operators
+category: faq
+faq_c: Extend and Contribute to MXNet
+question: How do I create new operators in MXNet with Python?
+permalink: /api/faq/new_op
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,14 +22,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_category
-title:  Create New Operators
-category: faq
-faq_c: Extend and Contribute to MXNet
-question: How do I create new operators in MXNet with Python?
-permalink: /api/faq/new_op
----
 
 # How to Create New Operators (Layers)
 
diff --git a/docs/static_site/src/pages/api/faq/nnpack.md b/docs/static_site/src/pages/api/faq/nnpack.md
index f03b08646f62..72950f86a410 100644
--- a/docs/static_site/src/pages/api/faq/nnpack.md
+++ b/docs/static_site/src/pages/api/faq/nnpack.md
@@ -1,3 +1,11 @@
+---
+layout: page_category
+title: NNPACK for Multi-Core CPU Support in MXNet
+category: faq
+faq_c: Speed
+question: Can I use nnpack to improve the CPU performance of MXNet?
+permalink: /api/faq/nnpack
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,14 +22,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_category
-title: NNPACK for Multi-Core CPU Support in MXNet
-category: faq
-faq_c: Speed
-question: Can I use nnpack to improve the CPU performance of MXNet?
-permalink: /api/faq/nnpack
----
 
 ### NNPACK for Multi-Core CPU Support in MXNet
 [NNPACK](https://github.com/Maratyszcza/NNPACK) is an acceleration package
diff --git a/docs/static_site/src/pages/api/faq/perf.md b/docs/static_site/src/pages/api/faq/perf.md
index 21eefb6c3fc7..1cf512d93da4 100644
--- a/docs/static_site/src/pages/api/faq/perf.md
+++ b/docs/static_site/src/pages/api/faq/perf.md
@@ -1,3 +1,11 @@
+---
+layout: page_category
+title: Some Tips for Improving MXNet Performance
+category: faq
+faq_c: Speed
+question: What are the best setup and data-handling tips and tricks for improving speed?
+permalink: /api/faq/perf
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,14 +22,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_category
-title: Some Tips for Improving MXNet Performance
-category: faq
-faq_c: Speed
-question: What are the best setup and data-handling tips and tricks for improving speed?
-permalink: /api/faq/perf
----
 
 
 # Some Tips for Improving MXNet Performance
diff --git a/docs/static_site/src/pages/api/faq/recordio.md b/docs/static_site/src/pages/api/faq/recordio.md
index 93389a1da400..d9770aca5886 100644
--- a/docs/static_site/src/pages/api/faq/recordio.md
+++ b/docs/static_site/src/pages/api/faq/recordio.md
@@ -1,3 +1,11 @@
+---
+layout: page_category
+title: Create a Dataset Using RecordIO
+category: faq
+faq_c: Speed
+question: How can I create a .rec dataset ?
+permalink: /api/faq/recordio
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,14 +22,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_category
-title: Create a Dataset Using RecordIO
-category: faq
-faq_c: Speed
-question: How can I create a .rec dataset ?
-permalink: /api/faq/recordio
----
 
 
 ## Create a Dataset Using RecordIO
diff --git a/docs/static_site/src/pages/api/faq/s3_integration.md b/docs/static_site/src/pages/api/faq/s3_integration.md
index 7c144c267c0a..d45942a31f0c 100644
--- a/docs/static_site/src/pages/api/faq/s3_integration.md
+++ b/docs/static_site/src/pages/api/faq/s3_integration.md
@@ -1,3 +1,11 @@
+---
+layout: page_category
+title: Use data from S3 for training
+category: faq
+faq_c: Deployment Environments
+question: How to use data from S3 for training?
+permalink: /api/faq/s3_integration
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,14 +22,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_category
-title: Use data from S3 for training
-category: faq
-faq_c: Deployment Environments
-question: How to use data from S3 for training?
-permalink: /api/faq/s3_integration
----
 
 
 # Use data from S3 for training
diff --git a/docs/static_site/src/pages/api/faq/security.md b/docs/static_site/src/pages/api/faq/security.md
index 6a47244ea9ef..ead7fa181d39 100644
--- a/docs/static_site/src/pages/api/faq/security.md
+++ b/docs/static_site/src/pages/api/faq/security.md
@@ -1,3 +1,11 @@
+---
+layout: page_category
+title: MXNet Security Best Practices
+category: faq
+faq_c: Security
+question: How to run MXNet securely?
+permalink: /api/faq/security
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,14 +22,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_category
-title: MXNet Security Best Practices
-category: faq
-faq_c: Security
-question: How to run MXNet securely?
-permalink: /api/faq/security
----
 
 # MXNet Security Best Practices
 
diff --git a/docs/static_site/src/pages/api/faq/smart_device.md b/docs/static_site/src/pages/api/faq/smart_device.md
index 5ed9ebc31bec..7fe8ddd6e477 100644
--- a/docs/static_site/src/pages/api/faq/smart_device.md
+++ b/docs/static_site/src/pages/api/faq/smart_device.md
@@ -1,3 +1,11 @@
+---
+layout: page_category
+title: Deep Learning at the Edge
+category: faq
+faq_c: Deployment Environments
+question: How to run MXNet securely?
+permalink: /api/faq/smart_device
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,14 +22,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_category
-title: Deep Learning at the Edge
-category: faq
-faq_c: Deployment Environments
-question: How to run MXNet securely?
-permalink: /api/faq/smart_device
----
 
 # Deep Learning in a Single File for Smart Devices
 
diff --git a/docs/static_site/src/pages/api/faq/visualize_graph.md b/docs/static_site/src/pages/api/faq/visualize_graph.md
index e520bc71d640..c9167a9d81a3 100644
--- a/docs/static_site/src/pages/api/faq/visualize_graph.md
+++ b/docs/static_site/src/pages/api/faq/visualize_graph.md
@@ -1,3 +1,11 @@
+---
+layout: page_category
+title: Visualize Neural Networks
+category: faq
+faq_c: Model
+question: How do I visualize neural networks as computation graphs?
+permalink: /api/faq/visualize_graph
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,14 +22,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_category
-title: Visualize Neural Networks
-category: faq
-faq_c: Model
-question: How do I visualize neural networks as computation graphs?
-permalink: /api/faq/visualize_graph
----
 # How to visualize Neural Networks as computation graph
 
 Here, we'll demonstrate how to use ```mx.viz.plot_network```
diff --git a/docs/static_site/src/pages/api/faq/why_mxnet.md b/docs/static_site/src/pages/api/faq/why_mxnet.md
index 779458ed0c9c..66ec6dc7e3a2 100644
--- a/docs/static_site/src/pages/api/faq/why_mxnet.md
+++ b/docs/static_site/src/pages/api/faq/why_mxnet.md
@@ -1,3 +1,11 @@
+---
+layout: page_category
+title: Why MXNet came to be?
+category: faq
+faq_c: Extend and Contribute to MXNet
+question: Why was MXNet developed in the first place ?
+permalink: /api/faq/why_mxnet
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,14 +22,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_category
-title: Why MXNet came to be?
-category: faq
-faq_c: Extend and Contribute to MXNet
-question: Why was MXNet developed in the first place ?
-permalink: /api/faq/why_mxnet
----
 
 # Why was MXNet developed in the first place ?
 
diff --git a/docs/static_site/src/pages/api/java/docs/tutorials/index.md b/docs/static_site/src/pages/api/java/docs/tutorials/index.md
index 00806e7911c9..a7f7114b4c0e 100644
--- a/docs/static_site/src/pages/api/java/docs/tutorials/index.md
+++ b/docs/static_site/src/pages/api/java/docs/tutorials/index.md
@@ -1,3 +1,9 @@
+---
+layout: page_landing_tutorials
+title: Java Tutorials
+permalink: /api/java/docs/tutorials
+tag: java
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,9 +20,3 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_landing_tutorials
-title: Java Tutorials
-permalink: /api/java/docs/tutorials
-tag: java
----
diff --git a/docs/static_site/src/pages/api/java/docs/tutorials/mxnet_java_on_intellij.md b/docs/static_site/src/pages/api/java/docs/tutorials/mxnet_java_on_intellij.md
index 4bfc8ab419bf..dd35ce033e07 100644
--- a/docs/static_site/src/pages/api/java/docs/tutorials/mxnet_java_on_intellij.md
+++ b/docs/static_site/src/pages/api/java/docs/tutorials/mxnet_java_on_intellij.md
@@ -1,3 +1,10 @@
+---
+layout: page_api
+title: Java with IntelliJ
+permalink: /api/java/docs/tutorials/mxnet_java_on_intellij
+is_tutorial: true
+tag: java
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: Java with IntelliJ
-permalink: /api/java/docs/tutorials/mxnet_java_on_intellij
-is_tutorial: true
-tag: java
----
 
 
 # Run MXNet Java Examples Using the IntelliJ IDE (macOS)
diff --git a/docs/static_site/src/pages/api/java/docs/tutorials/ssd_inference.md b/docs/static_site/src/pages/api/java/docs/tutorials/ssd_inference.md
index 3f30402628c0..6f8ee59b89b5 100644
--- a/docs/static_site/src/pages/api/java/docs/tutorials/ssd_inference.md
+++ b/docs/static_site/src/pages/api/java/docs/tutorials/ssd_inference.md
@@ -1,3 +1,10 @@
+---
+layout: page_api
+title: SSD Inference
+permalink: /api/java/docs/tutorials/ssd_inference
+is_tutorial: true
+tag: java
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: SSD Inference
-permalink: /api/java/docs/tutorials/ssd_inference
-is_tutorial: true
-tag: java
----
 
 # Multi Object Detection using pre-trained SSD Model via Java Inference APIs
 
diff --git a/docs/static_site/src/pages/api/java/index.md b/docs/static_site/src/pages/api/java/index.md
index 56d779b30703..c3e3d2a88b49 100644
--- a/docs/static_site/src/pages/api/java/index.md
+++ b/docs/static_site/src/pages/api/java/index.md
@@ -1,3 +1,11 @@
+---
+layout: page_api
+title: Java Guide
+action: Get Started
+action_url: /get_started
+permalink: /api/java
+tag: java
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,14 +22,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: Java Guide
-action: Get Started
-action_url: /get_started
-permalink: /api/java
-tag: java
----
 
 
 # MXNet - Java Inference API
diff --git a/docs/static_site/src/pages/api/julia/index.md b/docs/static_site/src/pages/api/julia/index.md
index 10c08da3ec90..28de72089aa0 100644
--- a/docs/static_site/src/pages/api/julia/index.md
+++ b/docs/static_site/src/pages/api/julia/index.md
@@ -1,3 +1,11 @@
+---
+layout: page_api
+title: Julia Guide
+action: Get Started
+action_url: /get_started
+permalink: /api/julia
+tag: julia
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,14 +22,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: Julia Guide
-action: Get Started
-action_url: /get_started
-permalink: /api/julia
-tag: julia
----
 
 
 # MXNet - Julia API
diff --git a/docs/static_site/src/pages/api/perl/docs/tutorials/index.md b/docs/static_site/src/pages/api/perl/docs/tutorials/index.md
index e98a3e4b4613..c1386bd4449b 100644
--- a/docs/static_site/src/pages/api/perl/docs/tutorials/index.md
+++ b/docs/static_site/src/pages/api/perl/docs/tutorials/index.md
@@ -1,3 +1,10 @@
+---
+layout: page_landing_tutorials
+title:  Perl Tutorials
+action: Get Started
+tag: perl
+permalink: /api/perl/docs/tutorials
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -13,11 +20,4 @@
 <!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
----
-layout: page_landing_tutorials
-title:  Perl Tutorials
-action: Get Started
-tag: perl
-permalink: /api/perl/docs/tutorials
----
+<!--- under the License. -->
\ No newline at end of file
diff --git a/docs/static_site/src/pages/api/perl/docs/tutorials/io.md b/docs/static_site/src/pages/api/perl/docs/tutorials/io.md
index b9ce200c5c2e..4e6078740007 100644
--- a/docs/static_site/src/pages/api/perl/docs/tutorials/io.md
+++ b/docs/static_site/src/pages/api/perl/docs/tutorials/io.md
@@ -1,3 +1,10 @@
+---
+layout: page_api
+title: Data Loading API
+is_tutorial: true
+tag: perl
+permalink: /api/perl/docs/tutorials/io
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: Data Loading API
-is_tutorial: true
-tag: perl
-permalink: /api/perl/docs/tutorials/io
----
 
 # Data Loading API
 
diff --git a/docs/static_site/src/pages/api/perl/docs/tutorials/kvstore.md b/docs/static_site/src/pages/api/perl/docs/tutorials/kvstore.md
index ea0343f7162f..559eb1fb219f 100644
--- a/docs/static_site/src/pages/api/perl/docs/tutorials/kvstore.md
+++ b/docs/static_site/src/pages/api/perl/docs/tutorials/kvstore.md
@@ -1,3 +1,10 @@
+---
+layout: page_api
+title: KVStore API
+is_tutorial: true
+tag: perl
+permalink: /api/perl/docs/tutorials/kvstore
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: KVStore API
-is_tutorial: true
-tag: perl
-permalink: /api/perl/docs/tutorials/kvstore
----
 
 
 # KVStore API
diff --git a/docs/static_site/src/pages/api/perl/docs/tutorials/module.md b/docs/static_site/src/pages/api/perl/docs/tutorials/module.md
index deb25ad92348..006bfbdd2441 100644
--- a/docs/static_site/src/pages/api/perl/docs/tutorials/module.md
+++ b/docs/static_site/src/pages/api/perl/docs/tutorials/module.md
@@ -1,3 +1,10 @@
+---
+layout: page_api
+title: Module API
+is_tutorial: true
+tag: perl
+permalink: /api/perl/docs/tutorials/module
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: Module API
-is_tutorial: true
-tag: perl
-permalink: /api/perl/docs/tutorials/module
----
 
 
 # Module API
diff --git a/docs/static_site/src/pages/api/perl/docs/tutorials/ndarray.md b/docs/static_site/src/pages/api/perl/docs/tutorials/ndarray.md
index 18ae72300c07..51c5556bbe23 100644
--- a/docs/static_site/src/pages/api/perl/docs/tutorials/ndarray.md
+++ b/docs/static_site/src/pages/api/perl/docs/tutorials/ndarray.md
@@ -1,3 +1,10 @@
+---
+layout: page_api
+title: NDArray API
+is_tutorial: true
+tag: perl
+permalink: /api/perl/docs/tutorials/ndarray
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: NDArray API
-is_tutorial: true
-tag: perl
-permalink: /api/perl/docs/tutorials/ndarray
----
 
 # NDArray API
 
diff --git a/docs/static_site/src/pages/api/perl/docs/tutorials/symbol.md b/docs/static_site/src/pages/api/perl/docs/tutorials/symbol.md
index 94d416a2d0b2..8f0ecccdb8f7 100644
--- a/docs/static_site/src/pages/api/perl/docs/tutorials/symbol.md
+++ b/docs/static_site/src/pages/api/perl/docs/tutorials/symbol.md
@@ -1,3 +1,10 @@
+---
+layout: page_api
+title: Symbol API
+is_tutorial: true
+tag: perl
+permalink: /api/perl/docs/tutorials/symbol
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: Symbol API
-is_tutorial: true
-tag: perl
-permalink: /api/perl/docs/tutorials/symbol
----
 
 # MXNet Perl Symbolic API
 
diff --git a/docs/static_site/src/pages/api/perl/index.md b/docs/static_site/src/pages/api/perl/index.md
index 79bc22dcb0c9..ae9969bd7f85 100644
--- a/docs/static_site/src/pages/api/perl/index.md
+++ b/docs/static_site/src/pages/api/perl/index.md
@@ -1,3 +1,11 @@
+---
+layout: page_api
+title: Perl Guide
+action: Get Started
+action_url: /get_started
+permalink: /api/perl
+tag: perl
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,14 +22,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: Perl Guide
-action: Get Started
-action_url: /get_started
-permalink: /api/perl
-tag: perl
----
 
 # MXNet - Perl API
 
diff --git a/docs/static_site/src/pages/api/python/index.md b/docs/static_site/src/pages/api/python/index.md
index bbd82e5c384f..aa9e360cd8a7 100644
--- a/docs/static_site/src/pages/api/python/index.md
+++ b/docs/static_site/src/pages/api/python/index.md
@@ -1,3 +1,11 @@
+---
+layout: page_api
+title: Python Guide
+action: Get Started
+action_url: /get_started
+permalink: /api/python
+tag: python
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,14 +22,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: Python Guide
-action: Get Started
-action_url: /get_started
-permalink: /api/python
-tag: python
----
 
 ## MXNet - Python API
 
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/callback_function.md b/docs/static_site/src/pages/api/r/docs/tutorials/callback_function.md
index e124483a3c5f..aa47eb8ee73d 100644
--- a/docs/static_site/src/pages/api/r/docs/tutorials/callback_function.md
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/callback_function.md
@@ -1,3 +1,10 @@
+---
+layout: page_api
+title: Callback Function
+is_tutorial: true
+tag: r
+permalink: /api/r/docs/tutorials/callback_function
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: Callback Function
-is_tutorial: true
-tag: r
-permalink: /api/r/docs/tutorials/callback_function
----
 
 Callback Function
 ======================================
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/char_rnn_model.md b/docs/static_site/src/pages/api/r/docs/tutorials/char_rnn_model.md
index 2c516608a1d2..065bba4b1274 100644
--- a/docs/static_site/src/pages/api/r/docs/tutorials/char_rnn_model.md
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/char_rnn_model.md
@@ -1,3 +1,10 @@
+---
+layout: page_api
+title: Char RNN Model
+is_tutorial: true
+tag: r
+permalink: /api/r/docs/tutorials/char_rnn_model
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: Char RNN Model
-is_tutorial: true
-tag: r
-permalink: /api/r/docs/tutorials/char_rnn_model
----
 
 
 # Character-level Language Model using RNN
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/classify_real_image_with_pretrained_model.md b/docs/static_site/src/pages/api/r/docs/tutorials/classify_real_image_with_pretrained_model.md
index 228ecb848a2d..4d8bec453988 100644
--- a/docs/static_site/src/pages/api/r/docs/tutorials/classify_real_image_with_pretrained_model.md
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/classify_real_image_with_pretrained_model.md
@@ -1,3 +1,10 @@
+---
+layout: page_api
+title: Classify Images with a PreTrained Model
+is_tutorial: true
+tag: r
+permalink: /api/r/docs/tutorials/classify_real_image_with_pretrained_model
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: Classify Images with a PreTrained Model
-is_tutorial: true
-tag: r
-permalink: /api/r/docs/tutorials/classify_real_image_with_pretrained_model
----
 
 Classify Images with a PreTrained Model
 =================================================
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/custom_iterator.md b/docs/static_site/src/pages/api/r/docs/tutorials/custom_iterator.md
index 5ecfb47b7ce9..fe63976483e3 100644
--- a/docs/static_site/src/pages/api/r/docs/tutorials/custom_iterator.md
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/custom_iterator.md
@@ -1,3 +1,10 @@
+---
+layout: page_api
+title: Custom Iterator Tutorial
+is_tutorial: true
+tag: r
+permalink: /api/r/docs/tutorials/custom_iterator
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: Custom Iterator Tutorial
-is_tutorial: true
-tag: r
-permalink: /api/r/docs/tutorials/custom_iterator
----
 
 
 Custom Iterator Tutorial
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/custom_loss_function.md b/docs/static_site/src/pages/api/r/docs/tutorials/custom_loss_function.md
index dbf1280462a9..d9da1aecc8e2 100644
--- a/docs/static_site/src/pages/api/r/docs/tutorials/custom_loss_function.md
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/custom_loss_function.md
@@ -1,3 +1,10 @@
+---
+layout: page_api
+title: Custom Loss Function
+is_tutorial: true
+tag: r
+permalink: /api/r/docs/tutorials/custom_loss_function
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: Custom Loss Function
-is_tutorial: true
-tag: r
-permalink: /api/r/docs/tutorials/custom_loss_function
----
 
 
 Customized loss function
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/five_minutes_neural_network.md b/docs/static_site/src/pages/api/r/docs/tutorials/five_minutes_neural_network.md
index 429240e2eda3..8323d9582ed2 100644
--- a/docs/static_site/src/pages/api/r/docs/tutorials/five_minutes_neural_network.md
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/five_minutes_neural_network.md
@@ -1,3 +1,10 @@
+---
+layout: page_api
+title: Five Minutes Neural Network
+is_tutorial: true
+tag: r
+permalink: /api/r/docs/tutorials/five_minutes_neural_network
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: Five Minutes Neural Network
-is_tutorial: true
-tag: r
-permalink: /api/r/docs/tutorials/five_minutes_neural_network
----
 
 Develop a Neural Network with MXNet in Five Minutes
 =============================================
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/index.md b/docs/static_site/src/pages/api/r/docs/tutorials/index.md
index 0e8293dd4b97..04b9abf1f86b 100644
--- a/docs/static_site/src/pages/api/r/docs/tutorials/index.md
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/index.md
@@ -1,3 +1,10 @@
+---
+layout: page_landing_tutorials
+title:  R Tutorials
+action: Get Started
+tag: r
+permalink: /api/r/docs/tutorials
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,10 +21,3 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_landing_tutorials
-title:  R Tutorials
-action: Get Started
-tag: r
-permalink: /api/r/docs/tutorials
----
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/mnist_competition.md b/docs/static_site/src/pages/api/r/docs/tutorials/mnist_competition.md
index d8aec3782524..db2e86a6d765 100644
--- a/docs/static_site/src/pages/api/r/docs/tutorials/mnist_competition.md
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/mnist_competition.md
@@ -1,3 +1,10 @@
+---
+layout: page_api
+title: MNIST Competition
+is_tutorial: true
+tag: r
+permalink: /api/r/docs/tutorials/mnist_competition
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: MNIST Competition
-is_tutorial: true
-tag: r
-permalink: /api/r/docs/tutorials/mnist_competition
----
 
 Handwritten Digits Classification Competition
 =============================================
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/multi_dim_lstm.md b/docs/static_site/src/pages/api/r/docs/tutorials/multi_dim_lstm.md
index 46c6f22ba849..fb4a653bdcd7 100644
--- a/docs/static_site/src/pages/api/r/docs/tutorials/multi_dim_lstm.md
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/multi_dim_lstm.md
@@ -1,3 +1,10 @@
+---
+layout: page_api
+title: LSTM Time Serie
+is_tutorial: true
+tag: r
+permalink: /api/r/docs/tutorials/multi_dim_lstm
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: LSTM Time Serie
-is_tutorial: true
-tag: r
-permalink: /api/r/docs/tutorials/multi_dim_lstm
----
 
 
 LSTM Time Series Example
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/ndarray.md b/docs/static_site/src/pages/api/r/docs/tutorials/ndarray.md
index 20098a04e341..2b655afeca8b 100644
--- a/docs/static_site/src/pages/api/r/docs/tutorials/ndarray.md
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/ndarray.md
@@ -1,3 +1,10 @@
+---
+layout: page_api
+title: NDArray
+is_tutorial: true
+tag: r
+permalink: /api/r/docs/tutorials/ndarray
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: NDArray
-is_tutorial: true
-tag: r
-permalink: /api/r/docs/tutorials/ndarray
----
 
 
 # NDArray: Vectorized Tensor Computations on CPUs and GPUs
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/symbol.md b/docs/static_site/src/pages/api/r/docs/tutorials/symbol.md
index c7c7b968b630..26bdcd8061e7 100644
--- a/docs/static_site/src/pages/api/r/docs/tutorials/symbol.md
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/symbol.md
@@ -1,3 +1,10 @@
+---
+layout: page_api
+title: NDArray
+is_tutorial: true
+tag: r
+permalink: /api/r/docs/tutorials/symbol
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: NDArray
-is_tutorial: true
-tag: r
-permalink: /api/r/docs/tutorials/symbol
----
 
 # Symbol and Automatic Differentiation
 
diff --git a/docs/static_site/src/pages/api/r/index.md b/docs/static_site/src/pages/api/r/index.md
index 8afdb29fde69..1f8ba603a4eb 100644
--- a/docs/static_site/src/pages/api/r/index.md
+++ b/docs/static_site/src/pages/api/r/index.md
@@ -1,3 +1,11 @@
+---
+layout: page_api
+title: R Guide
+action: Get Started
+action_url: /get_started
+permalink: /api/r
+tag: r
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,14 +22,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: R Guide
-action: Get Started
-action_url: /get_started
-permalink: /api/r
-tag: r
----
 
 
 # MXNet - R API
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/char_lstm.md b/docs/static_site/src/pages/api/scala/docs/tutorials/char_lstm.md
index 6125ad77f5b1..9b1b65fceedd 100644
--- a/docs/static_site/src/pages/api/scala/docs/tutorials/char_lstm.md
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/char_lstm.md
@@ -1,3 +1,10 @@
+---
+layout: page_api
+title: Char-LSTM
+is_tutorial: true
+tag: scala
+permalink: /api/scala/docs/tutorials/char_lstm
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: Char-LSTM
-is_tutorial: true
-tag: scala
-permalink: /api/scala/docs/tutorials/char_lstm
----
 
 # Developing a Character-level Language model
 
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/index.md b/docs/static_site/src/pages/api/scala/docs/tutorials/index.md
index f9b6e56c9d4f..2d6c4bbd5c9c 100644
--- a/docs/static_site/src/pages/api/scala/docs/tutorials/index.md
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/index.md
@@ -1,3 +1,9 @@
+---
+layout: page_landing_tutorials
+title: Scala Tutorials
+permalink: /api/scala/docs/tutorials
+tag: scala
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,9 +20,3 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_landing_tutorials
-title: Scala Tutorials
-permalink: /api/scala/docs/tutorials
-tag: scala
----
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/infer.md b/docs/static_site/src/pages/api/scala/docs/tutorials/infer.md
index daa996196393..9baeb7806dac 100644
--- a/docs/static_site/src/pages/api/scala/docs/tutorials/infer.md
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/infer.md
@@ -1,3 +1,10 @@
+---
+layout: page_api
+title: Infer API
+is_tutorial: true
+tag: scala
+permalink: /api/scala/docs/tutorials/infer
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: Infer API
-is_tutorial: true
-tag: scala
-permalink: /api/scala/docs/tutorials/infer
----
 
 # Infer API
 The MXNet Scala Infer API provides you with model loading and inference functionality using the MXNet Scala package.
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/io.md b/docs/static_site/src/pages/api/scala/docs/tutorials/io.md
index f9e9b462c4a2..c07526ef07c9 100644
--- a/docs/static_site/src/pages/api/scala/docs/tutorials/io.md
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/io.md
@@ -1,3 +1,10 @@
+---
+layout: page_api
+title: Data Loading API
+permalink: /api/scala/docs/tutorials/io
+is_tutorial: true
+tag: scala
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: Data Loading API
-permalink: /api/scala/docs/tutorials/io
-is_tutorial: true
-tag: scala
----
 
 # MXNet Scala Data Loading API
 This topic introduces the data input method for MXNet. MXNet uses an iterator to provide data to the neural network.  Iterators do some preprocessing and generate batches for the neural network.
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/kvstore.md b/docs/static_site/src/pages/api/scala/docs/tutorials/kvstore.md
index 08cb4cb50869..aa71705acd8d 100644
--- a/docs/static_site/src/pages/api/scala/docs/tutorials/kvstore.md
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/kvstore.md
@@ -1,3 +1,10 @@
+---
+layout: page_api
+title: KVStore API
+permalink: /api/scala/docs/tutorials/kvstore
+is_tutorial: true
+tag: scala
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: KVStore API
-permalink: /api/scala/docs/tutorials/kvstore
-is_tutorial: true
-tag: scala
----
 
 # KVStore API
 
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/mnist.md b/docs/static_site/src/pages/api/scala/docs/tutorials/mnist.md
index fbc6786b8e10..30c72e0339c4 100644
--- a/docs/static_site/src/pages/api/scala/docs/tutorials/mnist.md
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/mnist.md
@@ -1,3 +1,10 @@
+---
+layout: page_api
+title: MNIST Example
+is_tutorial: true
+tag: scala
+permalink: /api/scala/docs/tutorials/mnist
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: MNIST Example
-is_tutorial: true
-tag: scala
-permalink: /api/scala/docs/tutorials/mnist
----
 
 # Handwritten Digit Recognition
 
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/model.md b/docs/static_site/src/pages/api/scala/docs/tutorials/model.md
index 80fd24f0bca5..6423200e584f 100644
--- a/docs/static_site/src/pages/api/scala/docs/tutorials/model.md
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/model.md
@@ -1,3 +1,10 @@
+---
+layout: page_api
+title: Model API *Deprecated*
+permalink: /api/scala/docs/tutorials/model
+is_tutorial: true
+tag: scala
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: Model API *Deprecated*
-permalink: /api/scala/docs/tutorials/model
-is_tutorial: true
-tag: scala
----
 
 # MXNet Scala Model API
 
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/module.md b/docs/static_site/src/pages/api/scala/docs/tutorials/module.md
index 286baf58e1f0..fb58461fe751 100644
--- a/docs/static_site/src/pages/api/scala/docs/tutorials/module.md
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/module.md
@@ -1,3 +1,10 @@
+---
+layout: page_api
+title: Module API
+permalink: /api/scala/docs/tutorials/module
+is_tutorial: true
+tag: scala
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: Module API
-permalink: /api/scala/docs/tutorials/module
-is_tutorial: true
-tag: scala
----
 
 # Module API
 The module API provides an intermediate and high-level interface for performing computation with neural networks in MXNet. A *module* is an instance of subclasses of the `BaseModule`. The most widely used module class is called `Module`. Module wraps a `Symbol` and one or more `Executors`. For a full list of functions, see `BaseModule`.
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/mxnet_scala_on_intellij.md b/docs/static_site/src/pages/api/scala/docs/tutorials/mxnet_scala_on_intellij.md
index 252c0a044246..c934c3eaf4cc 100644
--- a/docs/static_site/src/pages/api/scala/docs/tutorials/mxnet_scala_on_intellij.md
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/mxnet_scala_on_intellij.md
@@ -1,3 +1,10 @@
+---
+layout: page_api
+title: Scala on IntelliJ
+is_tutorial: true
+tag: scala
+permalink: /api/scala/docs/tutorials/mxnet_scala_on_intellij
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: Scala on IntelliJ
-is_tutorial: true
-tag: scala
-permalink: /api/scala/docs/tutorials/mxnet_scala_on_intellij
----
 
 # Run MXNet Scala Examples Using the IntelliJ IDE (macOS)
 
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/ndarray.md b/docs/static_site/src/pages/api/scala/docs/tutorials/ndarray.md
index 04fbadf8367e..9a811c44ea8d 100644
--- a/docs/static_site/src/pages/api/scala/docs/tutorials/ndarray.md
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/ndarray.md
@@ -1,3 +1,10 @@
+---
+layout: page_api
+title: NDArray
+permalink: /api/scala/docs/tutorials/ndarray
+is_tutorial: true
+tag: scala
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: NDArray
-permalink: /api/scala/docs/tutorials/ndarray
-is_tutorial: true
-tag: scala
----
 
 # NDArray API
 
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/symbol.md b/docs/static_site/src/pages/api/scala/docs/tutorials/symbol.md
index d193200cb2cf..5136aed5b847 100644
--- a/docs/static_site/src/pages/api/scala/docs/tutorials/symbol.md
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/symbol.md
@@ -1,3 +1,10 @@
+---
+layout: page_api
+title: Symbol API
+permalink: /api/scala/docs/tutorials/symbol
+is_tutorial: true
+tag: scala
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: Symbol API
-permalink: /api/scala/docs/tutorials/symbol
-is_tutorial: true
-tag: scala
----
 
 # MXNet Scala Symbolic API
 
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/symbol_in_pictures.md b/docs/static_site/src/pages/api/scala/docs/tutorials/symbol_in_pictures.md
index fa6124849085..acbcbaf4ac73 100644
--- a/docs/static_site/src/pages/api/scala/docs/tutorials/symbol_in_pictures.md
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/symbol_in_pictures.md
@@ -1,3 +1,10 @@
+---
+layout: page_api
+title: Symbol in Pictures
+permalink: /api/scala/docs/tutorials/symbol_in_pictures
+is_tutorial: true
+tag: scala
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: Symbol in Pictures
-permalink: /api/scala/docs/tutorials/symbol_in_pictures
-is_tutorial: true
-tag: scala
----
 
 # Symbolic Configuration and Execution in Pictures
 
diff --git a/docs/static_site/src/pages/api/scala/index.md b/docs/static_site/src/pages/api/scala/index.md
index 62f6a58adb8b..b825f68f7751 100644
--- a/docs/static_site/src/pages/api/scala/index.md
+++ b/docs/static_site/src/pages/api/scala/index.md
@@ -1,3 +1,11 @@
+---
+layout: page_api
+title: Scala Guide
+action: Get Started
+action_url: /get_started
+permalink: /api/scala
+tag: scala
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,14 +22,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page_api
-title: Scala Guide
-action: Get Started
-action_url: /get_started
-permalink: /api/scala
-tag: scala
----
 
 
 # MXNet - Scala API
diff --git a/docs/static_site/src/pages/community/contribute.md b/docs/static_site/src/pages/community/contribute.md
index a48e1fe13e58..73937c7123a4 100644
--- a/docs/static_site/src/pages/community/contribute.md
+++ b/docs/static_site/src/pages/community/contribute.md
@@ -1,3 +1,11 @@
+---
+layout: page
+title: Contribute
+subtitle: Contribute to the Apache MXNet project
+action: Get Started
+action_url: /get_started
+permalink: /community/contribute
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,14 +22,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page
-title: Contribute
-subtitle: Contribute to the Apache MXNet project
-action: Get Started
-action_url: /get_started
-permalink: /community/contribute
----
 
 # Contributing to MXNet
 
diff --git a/docs/static_site/src/pages/get_started/build_from_source.md b/docs/static_site/src/pages/get_started/build_from_source.md
index af07c38e105a..6256e25f28d4 100644
--- a/docs/static_site/src/pages/get_started/build_from_source.md
+++ b/docs/static_site/src/pages/get_started/build_from_source.md
@@ -1,3 +1,10 @@
+---
+layout: page
+title: Building From Source
+action: Get Started
+action_url: /get_started
+permalink: /get_started/build_from_source
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page
-title: Building From Source
-action: Get Started
-action_url: /get_started
-permalink: /get_started/build_from_source
----
 
 
 # Build MXNet from Source
@@ -205,7 +205,7 @@ There is a configuration file for make,
 
 ``` bash
 echo "USE_NCCL=1" >> make/config.mk
-echo "USE_NCCP_PATH=path-to-nccl-installation-folder" >> make/config.mk
+echo "USE_NCCL_PATH=path-to-nccl-installation-folder" >> make/config.mk
 cp make/config.mk .
 ```
 - Run make command
diff --git a/docs/static_site/src/pages/get_started/c_plus_plus.md b/docs/static_site/src/pages/get_started/c_plus_plus.md
index edfb51b4ecf4..0559c7022710 100644
--- a/docs/static_site/src/pages/get_started/c_plus_plus.md
+++ b/docs/static_site/src/pages/get_started/c_plus_plus.md
@@ -1,3 +1,10 @@
+---
+layout: page
+title: C++ Setup
+action: Get Started
+action_url: /get_started
+permalink: /get_started/cpp_setup
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page
-title: C++ Setup
-action: Get Started
-action_url: /get_started
-permalink: /get_started/cpp_setup
----
 
 
 ## Build the C++ package
diff --git a/docs/static_site/src/pages/get_started/centos_setup.md b/docs/static_site/src/pages/get_started/centos_setup.md
index d3d203ffb7e6..315787d21f28 100644
--- a/docs/static_site/src/pages/get_started/centos_setup.md
+++ b/docs/static_site/src/pages/get_started/centos_setup.md
@@ -1,3 +1,10 @@
+---
+layout: page
+title: CentOS setup
+action: Get Started
+action_url: /get_started
+permalink: /get_started/centos_setup
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page
-title: CentOS setup
-action: Get Started
-action_url: /get_started
-permalink: /get_started/centos_setup
----
 
 
 # Installing MXNet on CentOS and other non-Ubuntu Linux systems
diff --git a/docs/static_site/src/pages/get_started/download.md b/docs/static_site/src/pages/get_started/download.md
index ec26e2227ef3..7d0176aef39c 100644
--- a/docs/static_site/src/pages/get_started/download.md
+++ b/docs/static_site/src/pages/get_started/download.md
@@ -1,3 +1,10 @@
+---
+layout: page
+title: Download Source Files
+action: Get Started
+action_url: /get_started
+permalink: /get_started/download
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page
-title: Download Source Files
-action: Get Started
-action_url: /get_started
-permalink: /get_started/download
----
 
 
 # Source Download
diff --git a/docs/static_site/src/pages/get_started/index.html b/docs/static_site/src/pages/get_started/index.html
index 1abf8b9e06d4..3e0e7601f387 100644
--- a/docs/static_site/src/pages/get_started/index.html
+++ b/docs/static_site/src/pages/get_started/index.html
@@ -1,3 +1,10 @@
+---
+layout: page
+title: Get Started
+action: MXNet Tutorials
+action_url: /api/python/docs/tutorials
+permalink: /get_started/index.html
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page
-title: Get Started
-action: MXNet Tutorials
-action_url: /api/python/docs/tutorials
-permalink: /get_started/index.html
----
 
 {% include /get_started/get_started.html %}
 
diff --git a/docs/static_site/src/pages/get_started/java_setup.md b/docs/static_site/src/pages/get_started/java_setup.md
index 52edde8cb383..8e789d79351c 100644
--- a/docs/static_site/src/pages/get_started/java_setup.md
+++ b/docs/static_site/src/pages/get_started/java_setup.md
@@ -1,3 +1,10 @@
+---
+layout: page
+title: Java Setup
+action: Get Started
+action_url: /get_started
+permalink: /get_started/java_setup
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page
-title: Java Setup
-action: Get Started
-action_url: /get_started
-permalink: /get_started/java_setup
----
 
 # Setup the MXNet Package for Java
 
diff --git a/docs/static_site/src/pages/get_started/jetson_setup.md b/docs/static_site/src/pages/get_started/jetson_setup.md
index 1bb52d30e137..2e58ad214795 100644
--- a/docs/static_site/src/pages/get_started/jetson_setup.md
+++ b/docs/static_site/src/pages/get_started/jetson_setup.md
@@ -1,3 +1,10 @@
+---
+layout: page
+title: Jetson Setup
+action: Get Started
+action_url: /get_started
+permalink: /get_started/jetson_setup
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page
-title: Jetson Setup
-action: Get Started
-action_url: /get_started
-permalink: /get_started/jetson_setup
----
 
 
 # Install MXNet on a Jetson
diff --git a/docs/static_site/src/pages/get_started/osx_setup.md b/docs/static_site/src/pages/get_started/osx_setup.md
index 26a63a1d13a3..abd278001146 100644
--- a/docs/static_site/src/pages/get_started/osx_setup.md
+++ b/docs/static_site/src/pages/get_started/osx_setup.md
@@ -1,3 +1,10 @@
+---
+layout: page
+title: OSX Setup
+action: Get Started
+action_url: /get_started
+permalink: /get_started/osx_setup
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page
-title: OSX Setup
-action: Get Started
-action_url: /get_started
-permalink: /get_started/osx_setup
----
 
 # Installing MXNet from source on OS X (Mac)
 
diff --git a/docs/static_site/src/pages/get_started/scala_setup.md b/docs/static_site/src/pages/get_started/scala_setup.md
index 0dc47aad4db6..5cf71ba451ce 100644
--- a/docs/static_site/src/pages/get_started/scala_setup.md
+++ b/docs/static_site/src/pages/get_started/scala_setup.md
@@ -1,3 +1,10 @@
+---
+layout: page
+title: Scala Setup
+action: Get Started
+action_url: /get_started
+permalink: /get_started/scala_setup
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page
-title: Scala Setup
-action: Get Started
-action_url: /get_started
-permalink: /get_started/scala_setup
----
 
 
 # Setup the MXNet Package for Scala
diff --git a/docs/static_site/src/pages/get_started/ubuntu_setup.md b/docs/static_site/src/pages/get_started/ubuntu_setup.md
index 5fc0e237d086..602f93d11608 100644
--- a/docs/static_site/src/pages/get_started/ubuntu_setup.md
+++ b/docs/static_site/src/pages/get_started/ubuntu_setup.md
@@ -1,3 +1,10 @@
+---
+layout: page
+title: Ubuntu Setup
+action: Get Started
+action_url: /get_started
+permalink: /get_started/ubuntu_setup
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page
-title: Ubuntu Setup
-action: Get Started
-action_url: /get_started
-permalink: /get_started/ubuntu_setup
----
 
 # Installing MXNet on Ubuntu
 
diff --git a/docs/static_site/src/pages/get_started/validate_mxnet.md b/docs/static_site/src/pages/get_started/validate_mxnet.md
index 8cdb05170903..d613c3bf0508 100644
--- a/docs/static_site/src/pages/get_started/validate_mxnet.md
+++ b/docs/static_site/src/pages/get_started/validate_mxnet.md
@@ -1,3 +1,10 @@
+---
+layout: page
+title: Validate MXNet
+action: Get Started
+action_url: /get_started
+permalink: /get_started/validate_mxnet
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page
-title: Validate MXNet
-action: Get Started
-action_url: /get_started
-permalink: /get_started/validate_mxnet
----
 
 # Validate Your MXNet Installation
 
diff --git a/docs/static_site/src/pages/get_started/windows_setup.md b/docs/static_site/src/pages/get_started/windows_setup.md
index a7a3aabfa75b..d0ae2a8abbe7 100644
--- a/docs/static_site/src/pages/get_started/windows_setup.md
+++ b/docs/static_site/src/pages/get_started/windows_setup.md
@@ -1,3 +1,10 @@
+---
+layout: page
+title: Windows Setup
+action: Get Started
+action_url: /get_started
+permalink: /get_started/windows_setup
+---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -14,13 +21,6 @@
 <!--- KIND, either express or implied.  See the License for the -->
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
----
-layout: page
-title: Windows Setup
-action: Get Started
-action_url: /get_started
-permalink: /get_started/windows_setup
----
 
 # Installing MXNet on Windows
 
diff --git a/docs/tutorials/amp/index.md b/docs/tutorials/amp/index.md
deleted file mode 100644
index faf6526fb824..000000000000
--- a/docs/tutorials/amp/index.md
+++ /dev/null
@@ -1,25 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Tutorials
-
-```eval_rst
-.. toctree::
-   :glob:
-
-   *
-```
diff --git a/docs/tutorials/basic/data.md b/docs/tutorials/basic/data.md
deleted file mode 100644
index 00c87d0c37c5..000000000000
--- a/docs/tutorials/basic/data.md
+++ /dev/null
@@ -1,474 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Iterators - Loading data
-In this tutorial, we focus on how to feed data into a training or inference program.
-Most training and inference modules in MXNet accept data iterators,
-which simplifies this procedure, especially when reading large datasets.
-Here we discuss the API conventions and several provided iterators.
-
-## Prerequisites
-
-To complete this tutorial, we need:  
-
-- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/install/index.html).  
-
-- [OpenCV Python library](http://opencv.org/opencv-3-2.html),  [Python Requests](http://docs.python-requests.org/en/master/), [Matplotlib](https://matplotlib.org/) and [Jupyter Notebook](http://jupyter.org/index.html).
-
-```
-$ pip install opencv-python requests matplotlib jupyter
-```
-
-## MXNet Data Iterator
-
-Data Iterators in *MXNet* are similar to Python iterator objects.
-In Python, the function `iter` allows fetching items sequentially by calling  `next()` on
- iterable objects such as a Python `list`.
-Iterators provide an abstract interface for traversing various types of iterable collections
- without needing to expose details about the underlying data source.
-
-In MXNet, data iterators return a batch of data as `DataBatch` on each call to `next`.
-A `DataBatch` often contains *n* training examples and their corresponding labels. Here *n* is the `batch_size` of the iterator. At the end of the data stream when there is no more data to read, the iterator raises ``StopIteration`` exception like Python `iter`. 
-The structure of `DataBatch` is defined [here](http://mxnet.io/api/python/io/io.html#mxnet.io.DataBatch).
-
-Information such as name, shape, type and layout on each training example and their corresponding label can be provided as `DataDesc` data descriptor objects via the `provide_data` and `provide_label` properties in `DataBatch`.
-The structure of `DataDesc` is defined [here](http://mxnet.io/api/python/io/io.html#mxnet.io.DataDesc).
-
-All IO in MXNet is handled via `mx.io.DataIter` and its subclasses. In this tutorial, we'll discuss a few commonly used iterators provided by MXNet.
-
-Before diving into the details let's setup the environment by importing some required packages:
-
-```python
-import mxnet as mx
-%matplotlib inline
-import os
-import sys
-import subprocess
-import numpy as np
-import matplotlib.pyplot as plt
-import tarfile
-
-import warnings
-warnings.filterwarnings("ignore", category=DeprecationWarning)
-```
-
-## Reading data in memory
-When data is stored in memory, backed by either an `NDArray` or ``numpy`` `ndarray`,
-we can use the [__`NDArrayIter`__](http://mxnet.io/api/python/io/io.html#mxnet.io.NDArrayIter) to read data as below:
-
-
-```python
-import numpy as np
-
-# fix the seed
-np.random.seed(42)
-mx.random.seed(42)
-
-data = np.random.rand(100,3)
-label = np.random.randint(0, 10, (100,))
-data_iter = mx.io.NDArrayIter(data=data, label=label, batch_size=30)
-for batch in data_iter:
-    print([batch.data, batch.label, batch.pad])
-```
-
-## Reading data from CSV files
-MXNet provides [`CSVIter`](http://mxnet.io/api/python/io/io.html#mxnet.io.CSVIter)
-to read from CSV files and can be used as below:
-
-```python
-#lets save `data` into a csv file first and try reading it back
-np.savetxt('data.csv', data, delimiter=',')
-data_iter = mx.io.CSVIter(data_csv='data.csv', data_shape=(3,), batch_size=30)
-for batch in data_iter:
-    print([batch.data, batch.pad])
-```
-
-## Custom Iterator
-When the built-in iterators do not suit your application needs,
-you can create your own custom data iterator.
-
-An iterator in _MXNet_ should
-1. Implement `next()` in ``Python2`` or `__next()__` in ``Python3``,
-   returning a `DataBatch` or raising a `StopIteration` exception if at the end of the data stream.
-2. Implement the `reset()` method to restart reading from the beginning.
-3. Have a `provide_data` attribute, consisting of a list of `DataDesc` objects that store the name, shape, type and layout information of the data (more info [here](http://mxnet.io/api/python/io/io.html#mxnet.io.DataBatch)).
-4. Have a `provide_label` attribute consisting of a list of `DataDesc` objects that store the name, shape, type and layout information of the label.
-
-When creating a new iterator, you can either start from scratch and define an iterator or reuse one of the existing iterators.
-For example, in the image captioning application, the input example is an image while the label is a sentence.
-Thus we can create a new iterator by:
-- creating a `image_iter` by using `ImageRecordIter` which provides multithreaded pre-fetch and augmentation.
-- creating a `caption_iter` by using `NDArrayIter` or the bucketing iterator provided in the *rnn* package.
-- `next()` returns the combined result of `image_iter.next()` and `caption_iter.next()`
-
-The example below shows how to create a Simple iterator.
-
-```python
-class SimpleIter(mx.io.DataIter):
-    def __init__(self, data_names, data_shapes, data_gen,
-                 label_names, label_shapes, label_gen, num_batches=10):
-        self._provide_data = list(zip(data_names, data_shapes))
-        self._provide_label = list(zip(label_names, label_shapes))
-        self.num_batches = num_batches
-        self.data_gen = data_gen
-        self.label_gen = label_gen
-        self.cur_batch = 0
-
-    def __iter__(self):
-        return self
-
-    def reset(self):
-        self.cur_batch = 0
-
-    def __next__(self):
-        return self.next()
-
-    @property
-    def provide_data(self):
-        return self._provide_data
-
-    @property
-    def provide_label(self):
-        return self._provide_label
-
-    def next(self):
-        if self.cur_batch < self.num_batches:
-            self.cur_batch += 1
-            data = [mx.nd.array(g(d[1])) for d,g in zip(self._provide_data, self.data_gen)]
-            label = [mx.nd.array(g(d[1])) for d,g in zip(self._provide_label, self.label_gen)]
-            return mx.io.DataBatch(data, label)
-        else:
-            raise StopIteration
-```
-
-We can use the above defined `SimpleIter` to train a simple MLP program below:
-
-
-```python
-import mxnet as mx
-num_classes = 10
-net = mx.sym.Variable('data')
-net = mx.sym.FullyConnected(data=net, name='fc1', num_hidden=64)
-net = mx.sym.Activation(data=net, name='relu1', act_type="relu")
-net = mx.sym.FullyConnected(data=net, name='fc2', num_hidden=num_classes)
-net = mx.sym.SoftmaxOutput(data=net, name='softmax')
-print(net.list_arguments())
-print(net.list_outputs())
-```
-
-Here, there are four variables that are learnable parameters:
-the *weights* and *biases* of FullyConnected layers *fc1* and *fc2*,
-two variables for input data: *data* for the training examples
-and *softmax_label* contains the respective labels and the *softmax_output*.
-
-The *data* variables are called free variables in MXNet's Symbol API.
-To execute a Symbol, they need to be bound with data.
-[Click here learn more about Symbol](http://mxnet.io/tutorials/basic/symbol.html).
-
-We use the data iterator to feed examples to a neural network via MXNet's `module` API.
-[Click here to learn more about Module](http://mxnet.io/tutorials/basic/module.html).
-
-
-```python
-import logging
-logging.basicConfig(level=logging.INFO)
-
-n = 32
-data_iter = SimpleIter(['data'], [(n, 100)],
-                  [lambda s: np.random.uniform(-1, 1, s)],
-                  ['softmax_label'], [(n,)],
-                  [lambda s: np.random.randint(0, num_classes, s)])
-
-mod = mx.mod.Module(symbol=net)
-mod.fit(data_iter, num_epoch=5)
-```
-
-A note on python 3 usage: Lot of the methods in mxnet use string for python2 and bytes for python3. 
-In order to keep this tutorial readable, we are going to define a utility function that converts
-string to bytes in python 3 environment
-
-```python
-def str_or_bytes(str):
-    """
-    A utility function for this tutorial that helps us convert string 
-    to bytes if we are using python3.
-
-    Parameters
-    ----------
-    str : string
-
-    Returns
-    -------
-    string (python2) or bytes (python3)
-    """
-    if sys.version_info[0] < 3:
-        return str
-    else:
-        return bytes(str, 'utf-8')
-```
-
-## Record IO
-Record IO is a file format used by MXNet for data IO.
-It compactly packs the data for efficient read and writes from distributed file system like Hadoop HDFS and AWS S3.
-You can learn more about the design of `RecordIO` [here](http://mxnet.io/architecture/note_data_loading.html).
-
-MXNet provides [__`MXRecordIO`__](http://mxnet.io/api/python/io/io.html#mxnet.recordio.MXRecordIO)
-and [__`MXIndexedRecordIO`__](http://mxnet.io/api/python/io/io.html#mxnet.recordio.MXIndexedRecordIO)
-for sequential access of data and random access of the data.
-
-### MXRecordIO
-First, let's look at an example on how to read and write sequentially
-using `MXRecordIO`. The files are named with a `.rec` extension.
-
-
-```python
-record = mx.recordio.MXRecordIO('tmp.rec', 'w')
-for i in range(5):
-    record.write(str_or_bytes('record_%d'%i))
-
-record.close()
-```
-
-We can read the data back by opening the file with an option `r` as below:
-
-```python
-record = mx.recordio.MXRecordIO('tmp.rec', 'r')
-while True:
-    item = record.read()
-    if not item:
-        break
-    print (item)
-record.close()
-```
-
-### MXIndexedRecordIO
-`MXIndexedRecordIO` supports random or indexed access to the data.
-We will create an indexed record file and a corresponding index file as below:
-
-
-```python
-record = mx.recordio.MXIndexedRecordIO('tmp.idx', 'tmp.rec', 'w')
-for i in range(5):
-    record.write_idx(i, str_or_bytes('record_%d'%i))
-
-record.close()
-```
-
-Now, we can access the individual records using the keys
-
-
-```python
-record = mx.recordio.MXIndexedRecordIO('tmp.idx', 'tmp.rec', 'r')
-record.read_idx(3)
-```
-
-You can also list all the keys in the file.
-
-
-```python
-record.keys
-```
-
-### Packing and Unpacking data
-
-Each record in a .rec file can contain arbitrary binary data. However, most deep learning tasks require data to be input in label/data format.
-The `mx.recordio` package provides a few utility functions for such operations, namely: `pack`, `unpack`, `pack_img`, and `unpack_img`.
-
-#### Packing/Unpacking Binary Data
-
-[__`pack`__](http://mxnet.io/api/python/io/io.html#mxnet.recordio.pack) and [__`unpack`__](http://mxnet.io/api/python/io/io.html#mxnet.recordio.unpack) are used for storing float (or 1d array of float) label and binary data. The data is packed along with a header. The header structure is defined [here](http://mxnet.io/api/python/io/io.html#mxnet.recordio.IRHeader).
-
-
-```python
-# pack
-data = 'data'
-label1 = 1.0
-header1 = mx.recordio.IRHeader(flag=0, label=label1, id=1, id2=0)
-s1 = mx.recordio.pack(header1, str_or_bytes(data))
-
-label2 = [1.0, 2.0, 3.0]
-header2 = mx.recordio.IRHeader(flag=3, label=label2, id=2, id2=0)
-s2 = mx.recordio.pack(header2, str_or_bytes(data))
-```
-
-```python
-# unpack
-print(mx.recordio.unpack(s1))
-print(mx.recordio.unpack(s2))
-```
-
-#### Packing/Unpacking Image Data
-
-MXNet provides [__`pack_img`__](http://mxnet.io/api/python/io/io.html#mxnet.recordio.pack_img) and [__`unpack_img`__](http://mxnet.io/api/python/io/io.html#mxnet.recordio.unpack_img) to pack/unpack image data.
-Records packed by `pack_img` can be loaded by `mx.io.ImageRecordIter`.
-
-
-```python
-data = np.ones((3,3,1), dtype=np.uint8)
-label = 1.0
-header = mx.recordio.IRHeader(flag=0, label=label, id=0, id2=0)
-s = mx.recordio.pack_img(header, data, quality=100, img_fmt='.jpg')
-```
-
-```python
-# unpack_img
-print(mx.recordio.unpack_img(s))
-```
-
-#### Using tools/im2rec.py
-You can also convert raw images into *RecordIO* format using the [__im2rec.py__](https://github.com/apache/incubator-mxnet/blob/master/tools/im2rec.py) utility script that is provided in the MXNet [src/tools](https://github.com/dmlc/mxnet/tree/master/tools) folder.
-An example of how to use the script for converting to *RecordIO* format is shown in the `Image IO` section below.
-
-* Note that there is a C++ API implementation of [im2rec](https://github.com/dmlc/mxnet/blob/master/tools/im2rec.cc), please refer to [RecordIO FAQ](https://mxnet.incubator.apache.org/faq/recordio.html) for more information.
-
-## Image IO
-
-In this section, we will learn how to preprocess and load image data in MXNet.
-
-There are 4 ways of loading image data in MXNet.
-   1. Using [__mx.image.imdecode__](http://mxnet.io/api/python/io/io.html#mxnet.image.imdecode) to load raw image files.
-   2. Using [__`mx.img.ImageIter`__](http://mxnet.io/api/python/io/io.html#mxnet.image.ImageIter) implemented in Python which is very flexible to customization. It can read from .rec(`RecordIO`) files and raw image files.
-   3. Using [__`mx.io.ImageRecordIter`__](http://mxnet.io/api/python/io/io.html#mxnet.io.ImageRecordIter) implemented on the MXNet backend in C++. This is less flexible to customization but provides various language bindings.
-   4. Creating a Custom iterator inheriting `mx.io.DataIter`
-
-
-### Preprocessing Images
-Images can be preprocessed in different ways. We list some of them below:
-- Using `mx.io.ImageRecordIter` which is fast but not very flexible. It is great for simple tasks like image recognition but won't work for more complex tasks like detection and segmentation.
-- Using `mx.recordio.unpack_img` (or `cv2.imread`, `skimage`, etc) + `numpy` is flexible but slow due to Python Global Interpreter Lock (GIL).
-- Using MXNet provided `mx.image` package. It stores images in [__`NDArray`__](http://mxnet.io/tutorials/basic/ndarray.html) format and leverages MXNet's [dependency engine](http://mxnet.io/architecture/note_engine.html) to automatically parallelize processing and circumvent GIL.
-
-Below, we demonstrate some of the frequently used preprocessing routines provided by the `mx.image` package.
-
-Let's download sample images that we can work with.
-
-
-```python
-fname = mx.test_utils.download(url='http://data.mxnet.io/data/test_images.tar.gz', dirname='data', overwrite=False)
-tar = tarfile.open(fname)
-tar.extractall(path=os.path.join('.','data'))
-tar.close()
-```
-
-#### Loading raw images
-`mx.image.imdecode` lets us load the images. `imdecode` provides a similar interface to ``OpenCV``.  
-
-**Note:** You will still need ``OpenCV``(not the CV2 Python library) installed to use `mx.image.imdecode`.
-
-```python
-img = mx.image.imdecode(open(os.path.join('data','test_images','ILSVRC2012_val_00000001.JPEG'), 'rb').read())
-plt.imshow(img.asnumpy()); plt.show()
-```
-
-#### Image Transformations
-
-
-```python
-# resize to w x h
-tmp = mx.image.imresize(img, 100, 70)
-plt.imshow(tmp.asnumpy()); plt.show()
-```
-
-```python
-# crop a random w x h region from image
-tmp, coord = mx.image.random_crop(img, (150, 200))
-print(coord)
-plt.imshow(tmp.asnumpy()); plt.show()
-```
-
-### Loading Data using Image Iterators
-
-Before we see how to read data using the two built-in Image iterators,
- lets get a sample __Caltech 101__ dataset
- that contains 101 classes of objects and converts them into record io format.
-Download and unzip
-
-
-```python
-fname = mx.test_utils.download(url='http://www.vision.caltech.edu/Image_Datasets/Caltech101/101_ObjectCategories.tar.gz', dirname='data', overwrite=False)
-tar = tarfile.open(fname)
-tar.extractall(path=os.path.join('.','data'))
-tar.close()
-```
-
-Let's take a look at the data. As you can see, under the root folder (./data/101_ObjectCategories) every category has a subfolder(./data/101_ObjectCategories/yin_yang).
-
-Now let's convert them into record io format using the `im2rec.py` utility script.
-First, we need to make a list that contains all the image files and their categories:
-
-```python
-im2rec_path = mx.test_utils.get_im2rec_path()
-data_path = os.path.join('data','101_ObjectCategories')
-prefix_path = os.path.join('data','caltech')
-
-with open(os.devnull, 'wb') as devnull:
-    subprocess.check_call(['python', im2rec_path, '--list', '--recursive', '--test-ratio=0.2', prefix_path, data_path],
-                          stdout=devnull)
-```
-
-The resulting list file (./data/caltech_train.lst) is in the format `index\t(one or more label)\tpath`. In this case, there is only one label for each image but you can modify the list to add in more for multi-label training.
-
-Then we can use this list to create our record io file:
-
-
-```python
-with open(os.devnull, 'wb') as devnull:
-    subprocess.check_call(['python', im2rec_path, '--num-thread=4', '--pass-through', '--test-ratio=0.2', prefix_path, data_path],
-                          stdout=devnull)
-```
-
-The record io files are now saved at here (./data)
-
-#### Using ImageRecordIter
-[__`ImageRecordIter`__](http://mxnet.io/api/python/io/io.html#mxnet.io.ImageRecordIter) can be used for loading image data saved in record io format. To use ImageRecordIter, simply create an instance by loading your record file:
-
-
-```python
-data_iter = mx.io.ImageRecordIter(
-    path_imgrec=os.path.join('.','data','caltech.rec'),
-    data_shape=(3, 227, 227), # output data shape. An 227x227 region will be cropped from the original image.
-    batch_size=4, # number of samples per batch
-    resize=256 # resize the shorter edge to 256 before cropping
-    # ... you can add more augmentation options as defined in ImageRecordIter.
-    )
-data_iter.reset()
-batch = data_iter.next()
-data = batch.data[0]
-for i in range(4):
-    plt.subplot(1,4,i+1)
-    plt.imshow(data[i].asnumpy().astype(np.uint8).transpose((1,2,0)))
-plt.show()
-```
-
-#### Using ImageIter
-[__ImageIter__](http://mxnet.io/api/python/io/io.html#mxnet.io.ImageIter) is a flexible interface that supports loading of images in both RecordIO and Raw format.
-
-
-```python
-data_iter = mx.image.ImageIter(batch_size=4, data_shape=(3, 227, 227),
-                              path_imgrec=os.path.join('.','data','caltech.rec'),
-                              path_imgidx=os.path.join('.','data','caltech.idx') )
-data_iter.reset()
-batch = data_iter.next()
-data = batch.data[0]
-for i in range(4):
-    plt.subplot(1,4,i+1)
-    plt.imshow(data[i].asnumpy().astype(np.uint8).transpose((1,2,0)))
-plt.show()
-```
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/basic/index.md b/docs/tutorials/basic/index.md
deleted file mode 100644
index faf6526fb824..000000000000
--- a/docs/tutorials/basic/index.md
+++ /dev/null
@@ -1,25 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Tutorials
-
-```eval_rst
-.. toctree::
-   :glob:
-
-   *
-```
diff --git a/docs/tutorials/basic/module.md b/docs/tutorials/basic/module.md
deleted file mode 100644
index 64a1e9482ea8..000000000000
--- a/docs/tutorials/basic/module.md
+++ /dev/null
@@ -1,364 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-
-# Module - Neural network training and inference
-
-Training a neural network involves quite a few steps. One need to specify how
-to feed input training data, initialize model parameters, perform forward and
-backward passes through the network, update weights based on computed gradients, do
-model checkpoints, etc. During prediction, one ends up repeating most of these
-steps. All this can be quite daunting to both newcomers as well as experienced
-developers.
-
-Luckily, MXNet modularizes commonly used code for training and inference in
-the `module` (`mod` for short) package. `Module` provides both high-level and
-intermediate-level interfaces for executing predefined networks. One can use
-both interfaces interchangeably. We will show the usage of both interfaces in
-this tutorial.
-
-## Prerequisites
-
-To complete this tutorial, we need:
-
-- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/install/index.html).  
-
-- [Jupyter Notebook](http://jupyter.org/index.html) and [Python Requests](http://docs.python-requests.org/en/master/) packages.
-```
-pip install jupyter requests
-```
-
-## Preliminary
-
-In this tutorial we will demonstrate `module` usage by training a
-[Multilayer Perceptron](https://en.wikipedia.org/wiki/Multilayer_perceptron) (MLP)
-on the [UCI letter recognition](https://archive.ics.uci.edu/ml/datasets/letter+recognition)
-dataset.
-
-The following code downloads the dataset and creates an 80:20 train:test
-split. It also initializes a training data iterator to return a batch of 32
-training examples each time. A separate iterator is also created for test data.
-
-
-```python
-import logging
-import random
-logging.getLogger().setLevel(logging.INFO)
-
-import mxnet as mx
-import numpy as np
-
-mx.random.seed(1234)
-np.random.seed(1234)
-random.seed(1234)
-
-fname = mx.test_utils.download('https://s3.us-east-2.amazonaws.com/mxnet-public/letter_recognition/letter-recognition.data')
-data = np.genfromtxt(fname, delimiter=',')[:,1:]
-label = np.array([ord(l.split(',')[0])-ord('A') for l in open(fname, 'r')])
-
-batch_size = 32
-ntrain = int(data.shape[0]*0.8)
-train_iter = mx.io.NDArrayIter(data[:ntrain, :], label[:ntrain], batch_size, shuffle=True)
-val_iter = mx.io.NDArrayIter(data[ntrain:, :], label[ntrain:], batch_size)
-```
-
-
-Next, we define the network.
-
-
-```python
-net = mx.sym.Variable('data')
-net = mx.sym.FullyConnected(net, name='fc1', num_hidden=64)
-net = mx.sym.Activation(net, name='relu1', act_type="relu")
-net = mx.sym.FullyConnected(net, name='fc2', num_hidden=26)
-net = mx.sym.SoftmaxOutput(net, name='softmax')
-mx.viz.plot_network(net, node_attrs={"shape":"oval","fixedsize":"false"})
-```
-
-
-
-
-![svg](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/basic/module/output_3_0.svg?sanitize=true)
-
-
-
-## Creating a Module
-
-Now we are ready to introduce module. The commonly used module class is
-`Module`. We can construct a module by specifying the following parameters:
-
-- `symbol`: the network definition
-- `context`: the device (or a list of devices) to use for execution
-- `data_names` : the list of input data variable names
-- `label_names` : the list of input label variable names
-
-For `net`, we have only one data named `data`, and one label named `softmax_label`,
-which is automatically named for us following the name `softmax` we specified for the `SoftmaxOutput` operator.
-
-
-```python
-mod = mx.mod.Module(symbol=net,
-                    context=mx.cpu(),
-                    data_names=['data'],
-                    label_names=['softmax_label'])
-```
-
-## Intermediate-level Interface
-
-We have created module. Now let us see how to run training and inference using module's intermediate-level APIs. These APIs give developers flexibility to do step-by-step
-computation by running `forward` and `backward` passes. It's also useful for debugging.
-
-To train a module, we need to perform following steps:
-
-- `bind` : Prepares environment for the computation by allocating memory.
-- `init_params` : Assigns and initializes parameters.
-- `init_optimizer` : Initializes optimizers. Defaults to `sgd`.
-- `metric.create` : Creates evaluation metric from input metric name.
-- `forward` : Forward computation.
-- `update_metric` : Evaluates and accumulates evaluation metric on outputs of the last forward computation.
-- `backward` : Backward computation.
-- `update` : Updates parameters according to the installed optimizer and the gradients computed in the previous forward-backward batch.
-
-This can be used as follows:
-
-
-```python
-# allocate memory given the input data and label shapes
-mod.bind(data_shapes=train_iter.provide_data, label_shapes=train_iter.provide_label)
-# initialize parameters by uniform random numbers
-mod.init_params(initializer=mx.init.Uniform(scale=.1))
-# use SGD with learning rate 0.1 to train
-mod.init_optimizer(optimizer='sgd', optimizer_params=(('learning_rate', 0.1), ))
-# use accuracy as the metric
-metric = mx.metric.create('acc')
-# train 5 epochs, i.e. going over the data iter one pass
-for epoch in range(5):
-    train_iter.reset()
-    metric.reset()
-    for batch in train_iter:
-        mod.forward(batch, is_train=True)       # compute predictions
-        mod.update_metric(metric, batch.label)  # accumulate prediction accuracy
-        mod.backward()                          # compute gradients
-        mod.update()                            # update parameters
-    print('Epoch %d, Training %s' % (epoch, metric.get()))
-```
-
-
-Expected output:
-
-
-```
-Epoch 0, Training ('accuracy', 0.434625)
-Epoch 1, Training ('accuracy', 0.6516875)
-Epoch 2, Training ('accuracy', 0.6968125)
-Epoch 3, Training ('accuracy', 0.7273125)
-Epoch 4, Training ('accuracy', 0.7575625)
-```
-
-
-To learn more about these APIs, visit [Module API](http://mxnet.io/api/python/module/module.html).
-
-## High-level Interface
-
-### Train
-
-Module also provides high-level APIs for training, predicting and evaluating for
-user convenience. Instead of doing all the steps mentioned in the above section,
-one can simply call [fit API](http://mxnet.io/api/python/module/module.html#mxnet.module.BaseModule.fit)
-and it internally executes the same steps.
-
-To fit a module, call the `fit` function as follows:
-
-
-```python
-# reset train_iter to the beginning
-train_iter.reset()
-
-# create a module
-mod = mx.mod.Module(symbol=net,
-                    context=mx.cpu(),
-                    data_names=['data'],
-                    label_names=['softmax_label'])
-
-# fit the module
-mod.fit(train_iter,
-        eval_data=val_iter,
-        optimizer='sgd',
-        optimizer_params={'learning_rate':0.1},
-        eval_metric='acc',
-        num_epoch=7)
-```
-
-
-Expected output:
-
-
-```
-INFO:root:Epoch[0] Train-accuracy=0.325437
-INFO:root:Epoch[0] Time cost=0.550
-INFO:root:Epoch[0] Validation-accuracy=0.568500
-INFO:root:Epoch[1] Train-accuracy=0.622188
-INFO:root:Epoch[1] Time cost=0.552
-INFO:root:Epoch[1] Validation-accuracy=0.656500
-INFO:root:Epoch[2] Train-accuracy=0.694375
-INFO:root:Epoch[2] Time cost=0.566
-INFO:root:Epoch[2] Validation-accuracy=0.703500
-INFO:root:Epoch[3] Train-accuracy=0.732187
-INFO:root:Epoch[3] Time cost=0.562
-INFO:root:Epoch[3] Validation-accuracy=0.748750
-INFO:root:Epoch[4] Train-accuracy=0.755375
-INFO:root:Epoch[4] Time cost=0.484
-INFO:root:Epoch[4] Validation-accuracy=0.761500
-INFO:root:Epoch[5] Train-accuracy=0.773188
-INFO:root:Epoch[5] Time cost=0.383
-INFO:root:Epoch[5] Validation-accuracy=0.715000
-INFO:root:Epoch[6] Train-accuracy=0.794687
-INFO:root:Epoch[6] Time cost=0.378
-INFO:root:Epoch[6] Validation-accuracy=0.802250
-```
-
-By default, `fit` function has `eval_metric` set to `accuracy`, `optimizer` to `sgd`
-and optimizer_params to `(('learning_rate', 0.01),)`.
-
-### Predict and Evaluate
-
-To predict with module, we can call `predict()`. It will collect and
-return all the prediction results.
-
-
-```python
-y = mod.predict(val_iter)
-assert y.shape == (4000, 26)
-```
-
-If we do not need the prediction outputs, but just need to evaluate on a test
-set, we can call the `score()` function. It runs prediction in the input validation
-dataset and evaluates the performance according to the given input metric.
-
-It can be used as follows:
-
-
-```python
-score = mod.score(val_iter, ['acc'])
-print("Accuracy score is %f" % (score[0][1]))
-assert score[0][1] > 0.76, "Achieved accuracy (%f) is less than expected (0.76)" % score[0][1]
-```
-
-
-Expected output:
-
-
-```
-Accuracy score is 0.802250
-```
-
-Some of the other metrics which can be used are `top_k_acc`(top-k-accuracy),
-`F1`, `RMSE`, `MSE`, `MAE`, `ce`(CrossEntropy). To learn more about the metrics,
-visit [Evaluation metric](http://mxnet.io/api/python/metric/metric.html).
-
-One can vary number of epochs, learning_rate, optimizer parameters to change the score
-and tune these parameters to get best score.
-
-### Save and Load
-
-We can save the module parameters after each training epoch by using a checkpoint callback.
-
-
-```python
-# construct a callback function to save checkpoints
-model_prefix = 'mx_mlp'
-checkpoint = mx.callback.do_checkpoint(model_prefix)
-
-mod = mx.mod.Module(symbol=net)
-mod.fit(train_iter, num_epoch=5, epoch_end_callback=checkpoint)
-```
-
-
-Expected output:
-
-
-```
-INFO:root:Epoch[0] Train-accuracy=0.098437
-INFO:root:Epoch[0] Time cost=0.421
-INFO:root:Saved checkpoint to "mx_mlp-0001.params"
-INFO:root:Epoch[1] Train-accuracy=0.257437
-INFO:root:Epoch[1] Time cost=0.520
-INFO:root:Saved checkpoint to "mx_mlp-0002.params"
-INFO:root:Epoch[2] Train-accuracy=0.457250
-INFO:root:Epoch[2] Time cost=0.562
-INFO:root:Saved checkpoint to "mx_mlp-0003.params"
-INFO:root:Epoch[3] Train-accuracy=0.558187
-INFO:root:Epoch[3] Time cost=0.434
-INFO:root:Saved checkpoint to "mx_mlp-0004.params"
-INFO:root:Epoch[4] Train-accuracy=0.617750
-INFO:root:Epoch[4] Time cost=0.414
-INFO:root:Saved checkpoint to "mx_mlp-0005.params"
-```
-
-To load the saved module parameters, call the `load_checkpoint` function. It
-loads the Symbol and the associated parameters. We can then set the loaded
-parameters into the module.
-
-
-```python
-sym, arg_params, aux_params = mx.model.load_checkpoint(model_prefix, 3)
-assert sym.tojson() == net.tojson()
-
-# assign the loaded parameters to the module
-mod.set_params(arg_params, aux_params)
-```
-
-Or if we just want to resume training from a saved checkpoint, instead of
-calling `set_params()`, we can directly call `fit()`, passing the loaded
-parameters, so that `fit()` knows to start from those parameters instead of
-initializing randomly from scratch. We also set the `begin_epoch` parameter so that
-`fit()` knows we are resuming from a previously saved epoch.
-
-
-```python
-mod = mx.mod.Module(symbol=sym)
-mod.fit(train_iter,
-        num_epoch=21,
-        arg_params=arg_params,
-        aux_params=aux_params,
-        begin_epoch=3)
-assert score[0][1] > 0.77, "Achieved accuracy (%f) is less than expected (0.77)" % score[0][1]        
-```
-
-
-Expected output:
-
-
-```
-INFO:root:Epoch[3] Train-accuracy=0.555438
-INFO:root:Epoch[3] Time cost=0.377
-INFO:root:Epoch[4] Train-accuracy=0.616625
-INFO:root:Epoch[4] Time cost=0.457
-INFO:root:Epoch[5] Train-accuracy=0.658438
-INFO:root:Epoch[5] Time cost=0.518
-...........................................
-INFO:root:Epoch[18] Train-accuracy=0.788687
-INFO:root:Epoch[18] Time cost=0.532
-INFO:root:Epoch[19] Train-accuracy=0.789562
-INFO:root:Epoch[19] Time cost=0.531
-INFO:root:Epoch[20] Train-accuracy=0.796250
-INFO:root:Epoch[20] Time cost=0.531
-```
-
-
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/basic/ndarray.md b/docs/tutorials/basic/ndarray.md
deleted file mode 100644
index be5dbcf29e88..000000000000
--- a/docs/tutorials/basic/ndarray.md
+++ /dev/null
@@ -1,491 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# NDArray - Imperative tensor operations on CPU/GPU
-
-In _MXNet_, `NDArray` is the core data structure for all mathematical
-computations.  An `NDArray` represents a multidimensional, fixed-size homogenous
-array.  If you're familiar with the scientific computing python package
-[NumPy](http://www.numpy.org/), you might notice that `mxnet.ndarray` is similar
-to `numpy.ndarray`.  Like the corresponding NumPy data structure, MXNet's
-`NDArray` enables imperative computation.
-
-So you might wonder, why not just use NumPy?  MXNet offers two compelling
-advantages.  First, MXNet's `NDArray` supports fast execution on a wide range of
-hardware configurations, including CPU, GPU, and multi-GPU machines.  _MXNet_
-also scales to distributed systems in the cloud.  Second, MXNet's `NDArray`
-executes code lazily, allowing it to automatically parallelize multiple
-operations across the available hardware.
-
-An `NDArray` is a multidimensional array of numbers with the same type.  We
-could represent the coordinates of a point in 3D space, e.g. `[2, 1, 6]` as a 1D
-array with shape (3).  Similarly, we could represent a 2D array.  Below, we
-present an array with length 2 along the first axis and length 3 along the
-second axis.
-```
-[[0, 1, 2]
- [3, 4, 5]]
-```
-Note that here the use of "dimension" is overloaded.  When we say a 2D array, we
-mean an array with 2 axes, not an array with two components.
-
-Each NDArray supports some important attributes that you'll often want to query:
-
-- **ndarray.shape**: The dimensions of the array. It is a tuple of integers
-  indicating the length of the array along each axis. For a matrix with `n` rows
-  and `m` columns, its `shape` will be `(n, m)`.
-- **ndarray.dtype**: A `numpy` _type_ object describing the type of its
-  elements.
-- **ndarray.size**: The total number of components in the array - equal to the
-  product of the components of its `shape`
-- **ndarray.context**: The device on which this array is stored, e.g. `cpu()` or
-  `gpu(1)`.
-
-## Prerequisites
-
-To complete this tutorial, we need:
-
-- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/install/index.html)
-- [Jupyter](http://jupyter.org/)
-    ```
-    pip install jupyter
-    ```
-- GPUs - A section of this tutorial uses GPUs. If you don't have GPUs on your
-machine, simply set the variable gpu_device (set in the GPUs section of this 
-tutorial) to mx.cpu().
-
-## Array Creation
-
-There are a few different ways to create an `NDArray`.
-
-* We can create an NDArray from a regular Python list or tuple by using the `array` function:
-
-```python
-import mxnet as mx
-# create a 1-dimensional array with a python list
-a = mx.nd.array([1,2,3])
-# create a 2-dimensional array with a nested python list
-b = mx.nd.array([[1,2,3], [2,3,4]])
-{'a.shape':a.shape, 'b.shape':b.shape}
-```
-
-* We can also create an MXNet NDArray from a `numpy.ndarray` object:
-
-```python
-import numpy as np
-import math
-c = np.arange(15).reshape(3,5)
-# create a 2-dimensional array from a numpy.ndarray object
-a = mx.nd.array(c)
-{'a.shape':a.shape}
-```
-
-We can specify the element type with the option `dtype`, which accepts a numpy
-type. By default, `float32` is used:
-
-```python
-# float32 is used by default
-a = mx.nd.array([1,2,3])
-# create an int32 array
-b = mx.nd.array([1,2,3], dtype=np.int32)
-# create a 16-bit float array
-c = mx.nd.array([1.2, 2.3], dtype=np.float16)
-(a.dtype, b.dtype, c.dtype)
-```
-
-If we know the size of the desired NDArray, but not the element values, MXNet
-offers several functions to create arrays with placeholder content:
-
-```python
-# create a 2-dimensional array full of zeros with shape (2,3)
-a = mx.nd.zeros((2,3))
-# create a same shape array full of ones
-b = mx.nd.ones((2,3))
-# create a same shape array with all elements set to 7
-c = mx.nd.full((2,3), 7)
-# create a same shape whose initial content is random and
-# depends on the state of the memory
-d = mx.nd.empty((2,3))
-```
-
-## Printing Arrays
-
-When inspecting the contents of an `NDArray`, it's often convenient to first
-extract its contents as a `numpy.ndarray` using the `asnumpy` function.  Numpy
-uses the following layout:
-
-- The last axis is printed from left to right,
-- The second-to-last is printed from top to bottom,
-- The rest are also printed from top to bottom, with each slice separated from
-  the next by an empty line.
-
-```python
-b = mx.nd.arange(18).reshape((3,2,3))
-b.asnumpy()
-```
-
-## Basic Operations
-
-When applied to NDArrays, the standard arithmetic operators apply *elementwise*
-calculations. The returned value is a new array whose content contains the
-result.
-
-```python
-a = mx.nd.ones((2,3))
-b = mx.nd.ones((2,3))
-# elementwise plus
-c = a + b
-# elementwise minus
-d = - c
-# elementwise pow and sin, and then transpose
-e = mx.nd.sin(c**2).T
-# elementwise max
-f = mx.nd.maximum(a, c)
-f.asnumpy()
-```
-
-As in `NumPy`, `*` represents element-wise multiplication. For matrix-matrix
-multiplication, use `dot`.
-
-```python
-a = mx.nd.arange(4).reshape((2,2))
-b = a * a
-c = mx.nd.dot(a,a)
-print("b: %s, \n c: %s" % (b.asnumpy(), c.asnumpy()))
-```
-
-The assignment operators such as `+=` and `*=` modify arrays in place, and thus
-don't allocate new memory to create a new array.
-
-```python
-a = mx.nd.ones((2,2))
-b = mx.nd.ones(a.shape)
-b += a
-b.asnumpy()
-```
-
-## Indexing and Slicing
-
-The slice operator `[]` applies on axis 0.
-
-```python
-a = mx.nd.array(np.arange(6).reshape(3,2))
-a[1:2] = 1
-a[:].asnumpy()
-```
-
-We can also slice a particular axis with the method `slice_axis`
-
-```python
-d = mx.nd.slice_axis(a, axis=1, begin=1, end=2)
-d.asnumpy()
-```
-
-## Shape Manipulation
-
-Using `reshape`, we can manipulate any arrays shape as long as the size remains
-unchanged.
-
-```python
-a = mx.nd.array(np.arange(24))
-b = a.reshape((2,3,4))
-b.asnumpy()
-```
-
-The `concat` method stacks multiple arrays along the first axis. Their
-shapes must be the same along the other axes.
-
-```python
-a = mx.nd.ones((2,3))
-b = mx.nd.ones((2,3))*2
-c = mx.nd.concat(a,b)
-c.asnumpy()
-```
-
-## Reduce
-
-Some functions, like `sum` and `mean` reduce arrays to scalars.
-
-```python
-a = mx.nd.ones((2,3))
-b = mx.nd.sum(a)
-b.asnumpy()
-```
-
-We can also reduce an array along a particular axis:
-
-```python
-c = mx.nd.sum_axis(a, axis=1)
-c.asnumpy()
-```
-
-## Broadcast
-
-We can also broadcast an array. Broadcasting operations, duplicate an array's
-value along an axis with length 1. The following code broadcasts along axis 1:
-
-```python
-a = mx.nd.array(np.arange(6).reshape(6,1))
-b = a.broadcast_to((6,4))  #
-b.asnumpy()
-```
-
-It's possible to simultaneously broadcast along multiple axes. In the following example, we broadcast along axes 1 and 2:
-
-```python
-c = a.reshape((2,1,1,3))
-d = c.broadcast_to((2,2,2,3))
-d.asnumpy()
-```
-
-Broadcasting can be applied automatically when executing some operations,
-e.g. `*` and `+` on arrays of different shapes.
-
-```python
-a = mx.nd.ones((3,2))
-b = mx.nd.ones((1,2))
-c = a + b
-c.asnumpy()
-```
-
-## Copies
-
-When assigning an NDArray to another Python variable, we copy a reference to the
-*same* NDArray. However, we often need to make a copy of the data, so that we
-can manipulate the new array without overwriting the original values.
-
-```python
-a = mx.nd.ones((2,2))
-b = a
-b is a # will be True
-```
-
-The `copy` method makes a deep copy of the array and its data:
-
-```python
-b = a.copy()
-b is a  # will be False
-```
-
-The above code allocates a new NDArray and then assigns to *b*. When we do not
-want to allocate additional memory, we can use the `copyto` method or the slice
-operator `[]` instead.
-
-```python
-b = mx.nd.ones(a.shape)
-c = b
-c[:] = a
-d = b
-a.copyto(d)
-(c is b, d is b)  # Both will be True
-```
-
-## Advanced Topics
-
-MXNet's NDArray offers some advanced features that differentiate it from the
-offerings you'll find in most other libraries.
-
-### GPU Support
-
-By default, NDArray operators are executed on CPU. But with MXNet, it's easy to
-switch to another computation resource, such as GPU, when available. Each
-NDArray's device information is stored in `ndarray.context`. When MXNet is
-compiled with flag `USE_CUDA=1` and the machine has at least one NVIDIA GPU, we
-can cause all computations to run on GPU 0 by using context `mx.gpu(0)`, or
-simply `mx.gpu()`. When we have access to two or more GPUs, the 2nd GPU is
-represented by `mx.gpu(1)`, etc.
-
-**Note** In order to execute the following section on a cpu set gpu_device to mx.cpu().
-```python
-gpu_device=mx.gpu() # Change this to mx.cpu() in absence of GPUs.
-
-
-def f():
-    a = mx.nd.ones((100,100))
-    b = mx.nd.ones((100,100))
-    c = a + b
-    print(c)
-# in default mx.cpu() is used
-f()
-# change the default context to the first GPU
-with mx.Context(gpu_device):
-    f()
-```
-
-We can also explicitly specify the context when creating an array:
-
-```python
-a = mx.nd.ones((100, 100), gpu_device)
-a
-```
-
-Currently, MXNet requires two arrays to sit on the same device for
-computation. There are several methods for copying data between devices.
-
-```python
-a = mx.nd.ones((100,100), mx.cpu())
-b = mx.nd.ones((100,100), gpu_device)
-c = mx.nd.ones((100,100), gpu_device)
-a.copyto(c)  # copy from CPU to GPU
-d = b + c
-e = b.as_in_context(c.context) + c  # same to above
-{'d':d, 'e':e}
-```
-
-### Serialize From/To (Distributed) Filesystems
-
-MXNet offers two simple ways to save (load) data to (from) disk. The first way
-is to use `pickle`, as you might with any other Python objects. `NDArray` is
-pickle-compatible.
-
-```python
-import pickle as pkl
-a = mx.nd.ones((2, 3))
-# pack and then dump into disk
-data = pkl.dumps(a)
-pkl.dump(data, open('tmp.pickle', 'wb'))
-# load from disk and then unpack
-data = pkl.load(open('tmp.pickle', 'rb'))
-b = pkl.loads(data)
-b.asnumpy()
-```
-
-The second way is to directly dump to disk in binary format by using the `save`
-and `load` methods. We can save/load a single NDArray, or a list of NDArrays:
-
-```python
-a = mx.nd.ones((2,3))
-b = mx.nd.ones((5,6))
-mx.nd.save("temp.ndarray", [a,b])
-c = mx.nd.load("temp.ndarray")
-c
-```
-
-It's also possible to save or load a dict of NDArrays in this fashion:
-
-```python
-d = {'a':a, 'b':b}
-mx.nd.save("temp.ndarray", d)
-c = mx.nd.load("temp.ndarray")
-c
-```
-
-The `load` and `save` methods are preferable to pickle in two respects
-
-1. When using these methods, you can save data from within the Python interface
-   and then use it later from another language's binding. For example, if we save
-   the data in Python:
-
-```python
-a = mx.nd.ones((2, 3))
-mx.nd.save("temp.ndarray", [a,])
-```
-
-we can later load it from R:
-```
-a <- mx.nd.load("temp.ndarray")
-as.array(a[[1]])
-##      [,1] [,2] [,3]
-## [1,]    1    1    1
-## [2,]    1    1    1
-```
-
-2. When a distributed filesystem such as Amazon S3 or Hadoop HDFS is set up, we
-   can directly save to and load from it.
-
-```
-mx.nd.save('s3://mybucket/mydata.ndarray', [a,])  # if compiled with USE_S3=1
-mx.nd.save('hdfs///users/myname/mydata.bin', [a,])  # if compiled with USE_HDFS=1
-```
-
-### Lazy Evaluation and Automatic Parallelization
-
-MXNet uses lazy evaluation to achieve superior performance.  When we run `a=b+1`
-in Python, the Python thread just pushes this operation into the backend engine
-and then returns.  There are two benefits to this approach:
-
-1. The main Python thread can continue to execute other computations once the
-   previous one is pushed. It is useful for frontend languages with heavy
-   overheads.
-2. It is easier for the backend engine to explore further optimization, such as
-   auto parallelization.
-
-The backend engine can resolve data dependencies and schedule the computations
-correctly. It is transparent to frontend users. We can explicitly call the
-method `wait_to_read` on the result array to wait until the computation
-finishes. Operations that copy data from an array to other packages, such as
-`asnumpy`, will implicitly call `wait_to_read`.
-
-
-```python
-import time
-def do(x, n):
-    """push computation into the backend engine"""
-    return [mx.nd.dot(x,x) for i in range(n)]
-def wait(x):
-    """wait until all results are available"""
-    for y in x:
-        y.wait_to_read()
-
-tic = time.time()
-a = mx.nd.ones((1000,1000))
-b = do(a, 50)
-print('time for all computations are pushed into the backend engine:\n %f sec' % (time.time() - tic))
-wait(b)
-print('time for all computations are finished:\n %f sec' % (time.time() - tic))
-```
-
-Besides analyzing data read and write dependencies, the backend engine is able
-to schedule computations with no dependency in parallel. For example, in the
-following code:
-
-```python
-a = mx.nd.ones((2,3))
-b = a + 1
-c = a + 2
-d = b * c
-```
-
-the second and third lines can be executed in parallel. The following example
-first runs on CPU and then on GPU:
-
-```python
-n = 10
-a = mx.nd.ones((1000,1000))
-b = mx.nd.ones((6000,6000), gpu_device)
-tic = time.time()
-c = do(a, n)
-wait(c)
-print('Time to finish the CPU workload: %f sec' % (time.time() - tic))
-d = do(b, n)
-wait(d)
-print('Time to finish both CPU/GPU workloads: %f sec' % (time.time() - tic))
-```
-
-Now we issue all workloads at the same time. The backend engine will try to
-parallel the CPU and GPU computations.
-
-```python
-tic = time.time()
-c = do(a, n)
-d = do(b, n)
-wait(c)
-wait(d)
-print('Both as finished in: %f sec' % (time.time() - tic))
-```
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/basic/ndarray_indexing.md b/docs/tutorials/basic/ndarray_indexing.md
deleted file mode 100644
index 00a231351d7d..000000000000
--- a/docs/tutorials/basic/ndarray_indexing.md
+++ /dev/null
@@ -1,394 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-
-# NDArray Indexing - Array indexing features
-
-MXNet's advanced indexing features are modeled after [NumPy's implementation and documentation](https://docs.scipy.org/doc/numpy-1.13.0/reference/arrays.indexing.html#combining-advanced-and-basic-indexing). You will see direct adaptations of many NumPy indexing features and examples which are close, if not identical, so we borrow much from their documentation.
-
-`NDArray`s can be indexed using the standard Python `x[obj]` syntax, where _x_ is the array and _obj_ the selection.
-
-There are two kinds of indexing available:
-
-1. basic slicing
-1. advanced indexing
-
-In MXNet, we support both basic and advanced indexing following the convention of indexing NumPy's `ndarray`.
-
-
-## Basic Slicing and Indexing
-
-Basic slicing extends Python's basic concept of slicing to N dimensions. For a quick review:
-
-```
-a[start:end] # items start through end-1
-a[start:]    # items start through the rest of the array
-a[:end]      # items from the beginning through end-1
-a[:]         # a copy of the whole array
-```
-
-
-```python
-from mxnet import nd
-```
-
-For some working examples of basic slicing we'll start simple.
-
-
-```python
-x = nd.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int32')
-x[5:]
-```
-
-
-
-
-
-    [5 6 7 8 9]
-    <NDArray 5 @cpu(0)>
-
-
-
-
-```python
-x = nd.array([0, 1, 2, 3])
-print('1D complete array, x=', x)
-s = x[1:3]
-print('slicing the 2nd and 3rd elements, s=', s)
-```
-
-    1D complete array, x=
-    [ 0.  1.  2.  3.]
-    <NDArray 4 @cpu(0)>
-    slicing the 2nd and 3rd elements, s=
-    [ 1.  2.]
-    <NDArray 2 @cpu(0)>
-
-
-Now let's try slicing the 2nd and 3rd elements of a multi-dimensional array.
-
-
-```python
-x = nd.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
-print('multi-D complete array, x=', x)
-s = x[1:3]
-print('slicing the 2nd and 3rd elements, s=', s)
-```
-
-    multi-D complete array, x=
-    [[  1.   2.   3.   4.]
-     [  5.   6.   7.   8.]
-     [  9.  10.  11.  12.]]
-    <NDArray 3x4 @cpu(0)>
-    slicing the 2nd and 3rd elements, s=
-    [[  5.   6.   7.   8.]
-     [  9.  10.  11.  12.]]
-    <NDArray 2x4 @cpu(0)>
-
-
-Now let's try writing to a specific element. We'll write `9` to element `2` using `x[2] = 9.0`, which will update the whole row.
-
-
-```python
-print('original x, x=', x)
-x[2] = 9.0
-print('replaced entire row with x[2] = 9.0, x=', x)
-```
-
-    original x, x=
-    [[  1.   2.   3.   4.]
-     [  5.   6.   7.   8.]
-     [  9.  10.  11.  12.]]
-    <NDArray 3x4 @cpu(0)>
-    replaced entire row with x[2] = 9.0, x=
-    [[ 1.  2.  3.  4.]
-     [ 5.  6.  7.  8.]
-     [ 9.  9.  9.  9.]]
-    <NDArray 3x4 @cpu(0)>
-
-
-We can target specific elements too. Let's replace the number `3` in the first row with the number `9` using `x[0, 2] = 9.0`.
-
-
-```python
-print('original x, x=', x)
-x[0, 2] = 9.0
-print('replaced specific element with x[0, 2] = 9.0, x=', x)
-```
-
-    original x, x=
-    [[ 1.  2.  3.  4.]
-     [ 5.  6.  7.  8.]
-     [ 9.  9.  9.  9.]]
-    <NDArray 3x4 @cpu(0)>
-    replaced specific element with x[0, 2] = 9.0, x=
-    [[ 1.  2.  9.  4.]
-     [ 5.  6.  7.  8.]
-     [ 9.  9.  9.  9.]]
-    <NDArray 3x4 @cpu(0)>
-
-
-Now lets target even more by selecting a couple of targets at the same time. We'll replace the `6` and the `7` with `x[1:2, 1:3] = 5.0`.
-
-
-```python
-print('original x, x=', x)
-x[1:2, 1:3] = 5.0
-print('replaced range of elements with x[1:2, 1:3] = 5.0, x=', x)
-```
-
-    original x, x=
-    [[ 1.  2.  9.  4.]
-     [ 5.  6.  7.  8.]
-     [ 9.  9.  9.  9.]]
-    <NDArray 3x4 @cpu(0)>
-    replaced range of elements with x[1:2, 1:3] = 5.0, x=
-    [[ 1.  2.  9.  4.]
-     [ 5.  5.  5.  8.]
-     [ 9.  9.  9.  9.]]
-    <NDArray 3x4 @cpu(0)>
-
-
-## New Indexing Features in v1.0
-
-### Step
-
-The basic slice syntax is `i:j:k` where _i_ is the starting index, _j_ is the stopping index, and _k_ is the step (_k_ must be nonzero).
-
-**Note**: Previously, MXNet supported basic slicing and indexing only with `step=1`. From release 1.0, arbitrary values of `step` are supported.
-
-
-```python
-x = nd.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int32')
-# Select elements 1 through 7, and use a step of 2
-x[1:7:2]
-```
-
-
-
-
-
-    [1 3 5]
-    <NDArray 3 @cpu(0)>
-
-
-
-## Negative Indices
-Negative _i_ and _j_ are interpreted as _n + i_ and _n + j_ where _n_ is the number of elements in the corresponding dimension. Negative _k_ makes stepping go towards smaller indices.
-
-
-```python
-x[-2:10]
-```
-
-
-
-
-
-    [8 9]
-    <NDArray 2 @cpu(0)>
-
-
-
-If the number of objects in the selection tuple is less than N , then : is assumed for any subsequent dimensions.
-
-
-```python
-x = nd.array([[[1],[2],[3]],
-              [[4],[5],[6]]], dtype='int32')
-x[1:2]
-```
-
-
-
-
-
-    [[[4]
-      [5]
-      [6]]]
-    <NDArray 1x3x1 @cpu(0)>
-
-
-
-You may use slicing to set values in the array, but (unlike lists) you can never grow the array. The size of the value to be set in `x[obj] = value` must be able to broadcast to the same shape as `x[obj]`.
-
-
-```python
-x = nd.arange(16, dtype='int32').reshape((4, 4))
-print(x)
-```
-
-
-    [[ 0  1  2  3]
-     [ 4  5  6  7]
-     [ 8  9 10 11]
-     [12 13 14 15]]
-    <NDArray 4x4 @cpu(0)>
-
-
-
-```python
-print(x[1:4:2, 3:0:-1])
-```
-
-
-    [[ 7  6  5]
-     [15 14 13]]
-    <NDArray 2x3 @cpu(0)>
-
-
-
-```python
-x[1:4:2, 3:0:-1] = [[16], [17]]
-print(x)
-```
-
-
-    [[ 0  1  2  3]
-     [ 4 16 16 16]
-     [ 8  9 10 11]
-     [12 17 17 17]]
-    <NDArray 4x4 @cpu(0)>
-
-
-## New Advanced Indexing Features in v1.0
-
-Advanced indexing is triggered when the selection object, obj, is a non-tuple sequence object (e.g. a Python list), a NumPy `ndarray` (of data type integer), an MXNet `NDArray`, or a tuple with at least one sequence object.
-
-Advanced indexing always returns a __copy__ of the data.
-
-**Note**:
-- When the selection object is a Python list, it must be a list of integers. MXNet does not support the selection object being a nested list. That is, `x[[1, 2]]` is supported, while `x[[1], [2]]` is not.
-- When the selection object is a NumPy `ndarray` or an MXNet `NDArray`, there is no dimension restrictions on the object.
-- When the selection object is a tuple containing Python list(s), both integer lists and nested lists are supported. That is, both `x[1:4, [1, 2]]` and `x[1:4, [[1], [2]]` are supported.
-
-### Purely Integer Array Indexing
-When the index consists of as many integer arrays as the array being indexed has dimensions, the indexing is straight forward, but different from slicing.
-
-Advanced indexes always are [broadcast](https://docs.scipy.org/doc/numpy-1.13.0/reference/ufuncs.html#ufuncs-broadcasting) and iterated as one:
-```
-result[i_1, ..., i_M] == x[ind_1[i_1, ..., i_M], ind_2[i_1, ..., i_M],
-                           ..., ind_N[i_1, ..., i_M]]
-```
-Note that the result shape is identical to the (broadcast) indexing array shapes `ind_1, ..., ind_N`.
-
-**Example:**
-From each row, a specific element should be selected. The row index is just [0, 1, 2] and the column index specifies the element to choose for the corresponding row, here [0, 1, 0]. Using both together the task can be solved using advanced indexing:
-
-
-```python
-x = nd.array([[1, 2],
-              [3, 4],
-              [5, 6]], dtype='int32')
-x[[0, 1, 2], [0, 1, 0]]
-```
-
-
-
-
-
-    [1 4 5]
-    <NDArray 3 @cpu(0)>
-
-
-
-To achieve a behavior similar to the basic slicing above, broadcasting can be used. This is best understood with an example.
-
-Example:
-From a 4x3 array the corner elements should be selected using advanced indexing. Thus all elements for which the column is one of `[0, 2]` and the row is one of `[0, 3]` need to be selected. To use advanced indexing one needs to select all elements explicitly. Using the method explained previously one could write:
-
-
-```python
-x = nd.array([[ 0,  1,  2],
-              [ 3,  4,  5],
-              [ 6,  7,  8],
-              [ 9, 10, 11]], dtype='int32')
-x[[[0, 0], [3, 3]],
-  [[0, 2], [0, 2]]]
-```
-
-
-
-
-
-    [[ 0  2]
-     [ 9 11]]
-    <NDArray 2x2 @cpu(0)>
-
-
-
-However, since the indexing arrays above just repeat themselves, broadcasting can be used.
-
-
-```python
-x[[[0], [3]],
-  [[0, 2]]]
-```
-
-
-
-
-
-    [[ 0  2]
-     [ 9 11]]
-    <NDArray 2x2 @cpu(0)>
-
-
-
-### Combining Advanced and Basic Indexing
-There are three situations we need to consider when mix advanced and basic indices in a single selection object. Let's look at examples to understand each one's behavior.
-
-- There is only one advanced index in the selection object. For example, `x` is an `NDArray` with `shape=(10, 20, 30, 40, 50)` and `result=x[:, :, ind]` has one advanced index `ind` with `shape=(2, 3, 4)` on the third axis. The `result` will have `shape=(10, 20, 2, 3, 4, 40, 50)` because the subspace of `x` in the third dimension is replaced by the subspace of `shape=(2, 3, 4)`. If we let _i_, _j_, _k_ loop over the (2, 3, 4)-shaped subspace, it is equivalent to `result[:, :, i, j, k, :, :] = x[:, :, ind[i, j, k], :, :]`.
-
-
-```python
-import numpy as np
-shape = (10, 20, 30, 40, 50)
-x = nd.arange(np.prod(shape), dtype='int32').reshape(shape)
-ind = nd.arange(24).reshape((2, 3, 4))
-print(x[:, :, ind].shape)
-```
-
-    (10, 20, 2, 3, 4, 40, 50)
-
-
-- There are at least two advanced indices in the selection object, and all the advanced indices are adjacent to each other. For example, `x` is an `NDArray` with `shape=(10, 20, 30, 40, 50)` and `result=x[:, :, ind1, ind2, :]` has two advanced indices with shapes that are broadcastable to `shape=(2, 3, 4)`. Then the `result` has `shape=(10, 20, 2, 3, 4, 50)` because `(30, 40)`-shaped subspace has been replaced with `(2, 3, 4)`-shaped subspace from the indices.
-
-
-```python
-ind1 = [0, 1, 2, 3]
-ind2 = [[[0], [1], [2]], [[3], [4], [5]]]
-print(x[:, :, ind1, ind2, :].shape)
-```
-
-    (10, 20, 2, 3, 4, 50)
-
-
-- There are at least two advanced indices in the selection object, and there is at least one advanced index separated from the others by basic indices. For example,  `x` is an `NDArray` with `shape=(10, 20, 30, 40, 50)` and `result=x[:, :, ind1, :, ind2]` has two advanced indices with shapes that are broadcastable to `shape=(2, 3, 4)`. Then the `result` has `shape=(2, 3, 4, 10, 20, 40)` because there is no unambiguous place to place the indexing subspace, hence it is prepended to the beginning.
-
-
-```python
-print(x[:, :, ind1, :, ind2].shape)
-```
-
-    (2, 3, 4, 10, 20, 40)
-
-## References
-
-[NumPy documentation](https://docs.scipy.org/doc/numpy-1.13.0/reference/arrays.indexing.html#combining-advanced-and-basic-indexing)
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/basic/reshape_transpose.md b/docs/tutorials/basic/reshape_transpose.md
deleted file mode 100644
index 40bdb38f3ec2..000000000000
--- a/docs/tutorials/basic/reshape_transpose.md
+++ /dev/null
@@ -1,214 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-## Difference between reshape and transpose operators
-
-What does it mean if MXNet gives you an error like the this?
-```
-Check failed: shape_.Size() == shape.Size() (127872 vs. 25088) NDArray.Reshape: target shape must have the same size as current shape when recording with autograd.
-```
-This error message tells you that the data being passed to your model or between layers in the model is not in the correct format. Modifying the shape of tensors is a very common operation in Deep Learning.
-For instance, when using pretrained neural networks it is often necessary to adjust the input data dimensions to correspond to what the network has been trained on, e.g. tensors of shape `[batch_size, channels, width, height]`.  This notebook discusses briefly the difference between the operators [Reshape](http://mxnet.incubator.apache.org/test/api/python/ndarray.html#mxnet.ndarray.NDArray.reshape) and [Transpose](http://mxnet.incubator.apache.org/test/api/python/ndarray.html#mxnet.ndarray.transpose). Both allow you to change the shape, however they are not the same and are commonly mistaken.
-
-```python
-import matplotlib.pyplot as plt
-import matplotlib.image as mpimg
-import mxnet as mx
-from mxnet import gluon
-import numpy as np
-```
-
-
-```python
-img_array = mpimg.imread('https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/basic/transpose_reshape/cat.png')
-plt.imshow(img_array)
-plt.axis("off")
-print (img_array.shape)
-```
-
-(157, 210, 3) <!--notebook-skip-line-->
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/basic/transpose_reshape/cat.png) <!--notebook-skip-line-->
-
-
-The color image has the following properties:
-* width: 210 pixels
-* height: 157 pixels
-* colors: 3 (RGB)
-
-Now let's reshape the image in order to exchange width and height dimensions.
-
-
-```python
-reshaped = img_array.reshape((210,157,3))
-print (reshaped.shape)
-plt.imshow(reshaped)
-plt.axis("off")
-```
-(210,157,3)<!--notebook-skip-line-->
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/basic/transpose_reshape/reshaped_image.png) <!--notebook-skip-line-->
-
-
-As we can see the first and second dimensions have changed. However the image can't be identified as cat any longer. In order to understand what happened, let's have a look at the image below.
-
-<img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/basic/transpose_reshape/reshape.png" style="width:700px;height:300px;">
-
-While the number of rows and columns changed, the layout of the underlying data did not. The pixel values that have been in one row are still in one row. This means for instance that pixel 10 in the upper right corner ends up in the middle of the image instead of the lower left corner. Consequently contextual information gets lost, because the relative position of pixel values is not the same anymore. As one can imagine a neural network would not be able to classify such an image as cat. 
-
-`Transpose` instead changes the layout of the underlying data.
-
-
-```python
-transposed = img_array.transpose((1,0,2))
-plt.imshow(transposed)
-plt.axis("off")
-```
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/basic/transpose_reshape/transposed_image.png) <!--notebook-skip-line-->
-
-
-As we can see width and height changed, by rotating pixel values by 90 degrees. Transpose does the following:
-
-<img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/basic/transpose_reshape/transpose.png" style="width:700px;height:300px;">
-
-As shown in the diagram, the axes have been flipped: pixel values that were in the first row are now in the first column.
-## When to transpose/reshape with MXNet
-In this chapter we discuss when transpose and reshape is used in MXNet. 
-#### Channel first for images
-Images are usually stored in the format height, wight, channel. When working with [convolutional](https://mxnet.incubator.apache.org/api/python/gluon/nn.html#mxnet.gluon.nn.Conv1D) layers, MXNet expects the layout to be `NCHW` (batch, channel, height, width). This is in contrast to Tensorflow, where image tensors are in the form `NHWC`. MXNet uses `NCHW` layout because of performance reasons on the GPU. When preprocessing the input images, you may have a function like the following:
-```python
-def transform(data, label): 
-     return data.astype(np.float32).transpose((2,0,1))/255.0, np.float32(label)
-```
-Images may also be stored as 1 dimensional vector for example in byte packed datasets. For instance, instead of `[28,28,1]` you may have `[784,1]`. In this situation you need to perform a reshape e.g. `ndarray.reshape((1,28,28))`
-
-
-#### TNC layout for RNN
-When working with [LSTM](https://mxnet.incubator.apache.org/api/python/gluon/rnn.html#mxnet.gluon.rnn.LSTM) or [GRU](https://mxnet.incubator.apache.org/api/python/gluon/rnn.html#mxnet.gluon.rnn.GRU) layers, the default layout for input and ouput tensors has to be `TNC` (sequence length, batch size, and feature dimensions). For instance in the following network the input goes into a 1 dimensional convolution layer and whose output goes into a GRU cell. Here the tensors would mismatch, because `Conv1D` takes data as `NCT`, but GRU  expects it to be `NTC`. To ensure that the forward pass does not crash, we need to do a tensor transpose. We can do this by defining a ```HybridLambda```.
-```python
-network = gluon.nn.HybridSequential()
-with network.name_scope():
-       network.add(gluon.nn.Conv1D(196, kernel_size=2, strides=1))
-       network.add(gluon.nn.HybridLambda(lambda F, x: F.transpose(x, (0, 2, 1))))
-       network.add(gluon.rnn.GRU(128))
-
-network.hybridize()
-network.initialize(mx.init.Xavier(), ctx=mx.cpu())
-a = mx.random.uniform(shape=(1,100,1000))
-network(a)
-output = network(a)
-print (output.shape)
-```
-(1, 999, 128) <!--notebook-skip-line-->
-#### Advanced reshaping with MXNet ndarrays
-It is sometimes useful to automatically infer the shape of tensors. Especially when you deal with very deep neural networks, it may not always be clear what the shape of a tensor is after a specific layer. For instance you may want the tensor to be two-dimensional where one dimension is the known batch_size. With ```mx.nd.array(-1, batch_size)``` the first dimension will be automatically inferred. Here is a simplified example:
-```python
-batch_size = 100
-input_data = mx.random.uniform(shape=(batch_size, 20,100))
-reshaped = input_data.reshape(batch_size, -1)
-print (input_data.shape, reshaped.shape) 
-```
-(100L, 20L, 100L), (100L, 2000L) <!--notebook-skip-line-->
-
-The reshape function of [MXNet's NDArray API](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html?highlight=reshape#mxnet.ndarray.NDArray.reshape) allows even more advanced transformations: For instance:0 copies the dimension from the input to the output shape,  -2 copies all/remainder of the input dimensions to the output shape. With -3 reshape uses the product of two consecutive dimensions of the input shape as the output dim.  With -4 reshape splits one dimension of the input into two dimensions passed subsequent to -4. Here an example:
-```python
-x = mx.nd.random.uniform(shape=(1, 3, 4, 64, 64))
-
-```
-Assume ```x```  with the shape ```[batch_size, channel, upscale, width, height]``` is the output of a model for image superresolution. Now we want to apply the upscale on width and height, to increase the 64x64 to an 128x128 image.
-To do so, we can use advanced reshaping, where we have to split the third dimension (upscale) and multiply it with width and height. We can do 
-```python
-x = x.reshape(1, 3, -4, 2, 2, 0, 0)
-print (x.shape)
-```
-
-(1L, 3L, 2L, 2L, 64L, 64L) <!--notebook-skip-line-->
-
-This splits up the third dimension into ```[2,2]```, so (1L, 3L, **4L** , 64L, 64L) becomes (1L, 3L, **2L** , **2L** , 64L, 64L)  The other dimensions remain unchanged. In order to multiply the new dimensions with width and height, we can do a transpose and then use reshape with -3.
-```python
-x = x.transpose((0, 1, 4, 2, 5, 3))
-print (x.shape)
-x = x.reshape(0, 0, -3, -3)
-print (x.shape)
-```
-
-(1L, 3L, 64L, 2L, 64L, 2L) <!--notebook-skip-line-->
-
-(1L, 3L, 128L, 128L) <!--notebook-skip-line-->
-
-Reshape -3 will calculate the dot product between the current and subsequent column. So (1L, 3L, **64L** , **2L** , ***64L, 2L*** ) becomes (1L, 3L, **128L** , ***128L*** )
-
-#### Most Common Pitfalls 
-In this section we want to show some of the most common pitfalls that happen when your input data is not correctly shaped.
-
-##### Forward Pass
-
-You execute the forward pass and get an error message followed by a very long stacktrace, for instance:
-
-```
-*** Error in `python': free(): invalid pointer: 0x00007fde5405a918 ***
-======= Backtrace: =========
-/lib/x86_64-linux-gnu/libc.so.6(+0x777e5)[0x7fdf475927e5]
-/lib/x86_64-linux-gnu/libc.so.6(+0x8037a)[0x7fdf4759b37a]
-/lib/x86_64-linux-gnu/libc.so.6(cfree+0x4c)[0x7fdf4759f53c]
-/home/ubuntu/anaconda3/lib/python3.6/site-packages/mxnet/libmxnet.so(MXExecutorReshape+0x1852)[0x7fdecef2c6e2]
-/home/ubuntu/anaconda3/lib/python3.6/lib-dynload/../../libffi.so.6(ffi_call_unix64+0x4c)[0x7fdf46332ec0]
-/home/ubuntu/anaconda3/lib/python3.6/lib-dynload/../../libffi.so.6(ffi_call+0x22d)[0x7fdf4633287d]
-/home/ubuntu/anaconda3/lib/python3.6/lib-dynload/_ctypes.cpython-36m-x86_64-linux-gnu.so(_ctypes_callproc+0x2ce)[0x7fdf46547e2e]
-/home/ubuntu/anaconda3/lib/python3.6/lib-dynload/_ctypes.cpython-36m-x86_64-linux-gnu.so(+0x12865)[0x7fdf46548865]
-python(_PyObject_FastCallDict+0x8b)[0x56457eba2d7b]
-python(+0x19e7ce)[0x56457ec327ce]
-python(_PyEval_EvalFrameDefault+0x2fa)[0x56457ec54cba]
-python(+0x197dae)[0x56457ec2bdae]
-[...]
-
-```
-This happens when you your data does not have the shape ```[batch_size, channel, width, height]``` e.g. your data may be a one-dimensional vector or when the color channel may be the last dimension instead of the second one.
-
-##### Backward Pass
-In other cases the forward pass may not fail, but the shape of the network output is not as expected. For instance in our previous RNN example, nothing is preventing us to skip the transpose. 
-
-```python
-network = gluon.nn.HybridSequential()
-with network.name_scope():
-       network.add(gluon.nn.Conv1D(196, kernel_size=2, strides=1))
-       #network.add(gluon.nn.HybridLambda(lambda F, x: F.transpose(x, (0, 2, 1))))
-       network.add(gluon.rnn.GRU(128))
-
-network.hybridize()
-network.initialize(mx.init.Xavier(), ctx=mx.cpu())
-a = mx.random.uniform(shape=(1,100,1000))
-output = network(a)
-print (output.shape)
-```
-(1, 196, 128)  <!--notebook-skip-line-->
-
-Instead of ```(1, 999, 128)``` the shape is now ```(1, 196, 128)```. But during the training loop, calculating the loss would crash because of shape mismatch between labels and output. You may get an error like the following:
-```
-mxnet.base.MXNetError: [10:56:29] src/ndarray/ndarray.cc:229: Check failed: shape_.Size() == shape.Size() (127872 vs. 25088) NDArray.Reshape: target shape must have the same size as current shape when recording with autograd.
-
-Stack trace returned 6 entries:
-[bt] (0) 0   libmxnet.so                         0x00000001126c0b90 libmxnet.so + 15248
-[bt] (1) 1   libmxnet.so                         0x00000001126c093f libmxnet.so + 14655
-[bt] (2) 2   libmxnet.so                         0x0000000113cd236d MXNDListFree + 1407789
-[bt] (3) 3   libmxnet.so                         0x0000000113b345ca MXNDArrayReshape64 + 970
-[bt] (4) 4   libffi.6.dylib                      0x000000010b399884 ffi_call_unix64 + 76
-[bt] (5) 5   ???                                 0x00007fff54cadf50 0x0 + 140734615969616
-
-```
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/basic/symbol.md b/docs/tutorials/basic/symbol.md
deleted file mode 100644
index 57e014f36f54..000000000000
--- a/docs/tutorials/basic/symbol.md
+++ /dev/null
@@ -1,460 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Symbol - Neural network graphs
-
-In the [previous tutorial](http://mxnet.io/tutorials/basic/ndarray.html), we introduced [`NDArray`](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html), the basic data structure for manipulating data in MXNet.
-Just using `NDArray` by itself, we can execute a wide range of mathematical operations. In fact, we could define and update a full neural network just by using `NDArray`.
-
-`NDArray` allows you to write programs for scientific computation in an imperative fashion, making full use of the native control of any front-end language. Gluon API uses this approach under the hood (before hybridization) to allow for flexible and debugable networks. So you might wonder, why don't we just use `NDArray` for all computation?
-
-MXNet also provides the `Symbol API`, an interface for symbolic programming. With symbolic programming, rather than executing operations step by step, we first define a [computation graph](https://mxnet.incubator.apache.org/versions/master/faq/visualize_graph.html). This graph contains placeholders for inputs and designated outputs. We can then compile the graph, yielding a function that can be bound to `NDArray`s and run. MXNet's `Symbol API` is similar to the network configurations used by [Caffe](http://caffe.berkeleyvision.org/) and the symbolic programming in [Theano](http://deeplearning.net/software/theano/). Gluon API takes advantage of this approach under the hood after the network has been hybridized.
-
-Another advantage conferred by symbolic approach is that we can optimize our functions before using them. For example, when we execute mathematical computations in imperative fashion, we don't know at the time that we run each operation, which values will be needed later on. But with symbolic programming, we declare the required outputs in advance. This means that we can recycle memory allocated in intermediate steps, as by performing operations in place. Symbolic API also uses less memory for the
-same network. Refer to [How To](http://mxnet.io/faq/index.html) and [Architecture](http://mxnet.io/architecture/index.html) section to know more.
-
-In our design notes, we present [a more thorough discussion on the comparative strengths of imperative and symbolic programing](http://mxnet.io/architecture/program_model.html). In this document, however, we'll focus on explaining how to use MXNet's `Symbol API`.
-
-In MXNet, we can compose Symbols from other Symbols, using operators, such as simple matrix operations (e.g. `+`), or whole neural network layers (e.g. convolution layer). Operator can take multiple input variables, can produce multiple output symbols and can maintain internal state symbols. For a visual explanation of these concepts, see [Symbolic Configuration and Execution in Pictures](http://mxnet.io/api/python/symbol_in_pictures/symbol_in_pictures.html).
-
-To make things concrete, let's take a hands-on look at the `Symbol API`. There are a few different ways to compose a [`Symbol`](http://mxnet.incubator.apache.org/api/python/symbol/symbol.html).
-
-## Prerequisites
-
-To complete this tutorial, we need:
-
-- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/install/index.html)
-- [Jupyter](http://jupyter.org/)
-    ```
-    pip install jupyter
-    ```
-- GPUs (optional). A section of this tutorial uses GPUs, if one is available. If not, the code will automatically switch to CPU.
-
-## Basic Symbol Composition
-
-### Basic Operators
-
-The following example builds a simple expression: `a + b`. First, we create two placeholders with `mx.sym.Variable`,
-giving them the names `a` and `b`. We then construct the desired symbol by using the operator `+`. We don't need to name our variables while creating them, MXNet will automatically generate a unique name for each. In the example below, `c` is assigned a unique name automatically.
-
-```python
-import mxnet as mx
-a = mx.sym.Variable('a')
-b = mx.sym.Variable('b')
-c = a + b
-(a, b, c)
-```
-
-Most operators supported by `NDArray` are also supported by `Symbol`, for example:
-
-```python
-# element-wise multiplication
-d = a * b
-# matrix multiplication
-e = mx.sym.dot(a, b)
-# reshape
-f = mx.sym.reshape(d+e, shape=(1,4))
-# broadcast
-g = mx.sym.broadcast_to(f, shape=(2,4))
-# plot
-mx.viz.plot_network(symbol=g,
-                    node_attrs={"shape": "oval", "fixedsize": "false"})
-```
-
-The computations declared in the above examples can be bound to the input data for evaluation by using `bind` method. We discuss this further in the [Symbol Manipulation](#symbol-manipulation) section.
-
-### Basic Neural Networks
-
-Besides the basic operators, `Symbol` also supports a rich set of neural network layers.
-The following example constructs a two layer fully connected neural network
-and then visualizes the structure of that network given the input data shape.
-
-```python
-net = mx.sym.Variable('data')
-net = mx.sym.FullyConnected(data=net, name='fc1', num_hidden=128)
-net = mx.sym.Activation(data=net, name='relu1', act_type="relu")
-net = mx.sym.FullyConnected(data=net, name='fc2', num_hidden=10)
-net = mx.sym.SoftmaxOutput(data=net, name='out')
-mx.viz.plot_network(net,
-                    shape={'data':(100, 200)},
-                    node_attrs={"shape": "oval", "fixedsize": "false"})
-```
-
-Each `Symbol` takes a unique string name. `NDArray` and `Symbol` both represent a single tensor. *Operators* represent the computation between tensors. Operators take `Symbol` or `NDArray` as inputs and might also additionally accept other hyperparameters such as the number of hidden neurons (`num_hidden`) or the activation type (`act_type`) and produce the output.
-
-We can view a `Symbol` simply as a function taking several arguments. And we can retrieve those arguments with the following method call:
-
-```python
-net.list_arguments()
-```
-
-These arguments are the parameters and inputs needed by each `Symbol`:
-
-- *data*: Input data needed by the variable *data*.
-- *fc1_weight* and *fc1_bias*: The weight and bias for the first fully connected layer *fc1*.
-- *fc2_weight* and *fc2_bias*: The weight and bias for the second fully connected layer *fc2*.
-- *out_label*: The label needed by the loss.
-
-We can also specify the names explicitly:
-
-```python
-net = mx.symbol.Variable('data')
-w = mx.symbol.Variable('myweight')
-net = mx.symbol.FullyConnected(data=net, weight=w, name='fc1', num_hidden=128)
-net.list_arguments()
-```
-
-In the above example, `FullyConnected` layer has 3 inputs: data, weight, bias. When any input is not specified, a variable will be automatically generated for it.
-
-## More Complicated Composition
-
-MXNet provides well-optimized Symbols for layers commonly used in deep learning (see [src/operator](https://github.com/dmlc/mxnet/tree/master/src/operator)). We can also define new operators in Python. The following example first
-performs an element-wise add between two Symbols, then feeds them to the fully connected operator:
-
-```python
-lhs = mx.symbol.Variable('data1')
-rhs = mx.symbol.Variable('data2')
-net = mx.symbol.FullyConnected(data=lhs + rhs, name='fc1', num_hidden=128)
-net.list_arguments()
-```
-
-We can also construct a `Symbol` in a more flexible way than the single forward composition depicted in the preceding example:
-
-```python
-data = mx.symbol.Variable('data')
-net1 = mx.symbol.FullyConnected(data=data, name='fc1', num_hidden=10)
-net1.list_arguments()
-net2 = mx.symbol.Variable('data2')
-net2 = mx.symbol.FullyConnected(data=net2, name='fc2', num_hidden=10)
-composed = net2(data2=net1, name='composed')
-composed.list_arguments()
-```
-
-In this example, `net2` is used as a function to apply to an existing `Symbol` `net1`, and the resulting *composed* `Symbol` will have all the attributes of `net1` and `net2`.
-
-Once you start building some bigger networks, you might want to name some symbols with a common prefix to outline the structure of your network. You can use the [Prefix class](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/name.py#L93) as follows:
-
-```python
-data = mx.sym.Variable("data")
-net = data
-n_layer = 2
-for i in range(n_layer):
-    with mx.name.Prefix("layer%d_" % (i + 1)):
-        net = mx.sym.FullyConnected(data=net, name="fc", num_hidden=100)
-net.list_arguments()
-```
-
-### Modularized Construction for Deep Networks
-
-Constructing a *deep* network layer by layer, (like the Google Inception network), can be tedious owing to the large number of layers. So, for such networks, we often modularize the construction.
-
-For example, in Google Inception network, we can first define a factory function which chains the convolution, batch normalization and rectified linear unit (ReLU) activation layers together.
-
-```python
-def ConvFactory(data, num_filter, kernel,
-                stride=(1, 1), pad=(0, 0), name=None, suffix=''):
-    conv = mx.sym.Convolution(data=data, num_filter=num_filter,
-                              kernel=kernel, stride=stride, pad=pad,
-                              name='conv_%s%s' % (name, suffix))
-
-    bn = mx.sym.BatchNorm(data=conv, name='bn_%s%s' % (name, suffix))
-
-    act = mx.sym.Activation(data=bn, act_type='relu',
-                            name='relu_%s%s' % (name, suffix))
-    return act
-
-prev = mx.sym.Variable(name="Previous Output")
-conv_comp = ConvFactory(data=prev, num_filter=64, kernel=(7, 7), stride=(2, 2))
-shape = {"Previous Output" : (128, 3, 28, 28)}
-mx.viz.plot_network(symbol=conv_comp, shape=shape,
-                    node_attrs={"shape": "oval", "fixedsize": "false"})
-```
-
-Then we can define a function that constructs an inception module based on factory function `ConvFactory`.
-
-```python
-def InceptionFactoryA(data, num_1x1, num_3x3red, num_3x3, num_d3x3red,
-                      num_d3x3, pool, proj, name):
-    # 1x1
-    c1x1 = ConvFactory(data=data, num_filter=num_1x1, kernel=(1, 1),
-                       name=('%s_1x1' % name))
-
-    # 3x3 reduce + 3x3
-    c3x3r = ConvFactory(data=data, num_filter=num_3x3red, kernel=(1, 1),
-                        name=('%s_3x3' % name), suffix='_reduce')
-    c3x3 = ConvFactory(data=c3x3r, num_filter=num_3x3, kernel=(3, 3),
-                       pad=(1, 1), name=('%s_3x3' % name))
-
-    # double 3x3 reduce + double 3x3
-    cd3x3r = ConvFactory(data=data, num_filter=num_d3x3red, kernel=(1, 1),
-                         name=('%s_double_3x3' % name), suffix='_reduce')
-    cd3x3 = ConvFactory(data=cd3x3r, num_filter=num_d3x3, kernel=(3, 3),
-                        pad=(1, 1), name=('%s_double_3x3_0' % name))
-    cd3x3 = ConvFactory(data=cd3x3, num_filter=num_d3x3, kernel=(3, 3),
-                        pad=(1, 1), name=('%s_double_3x3_1' % name))
-
-    # pool + proj
-    pooling = mx.sym.Pooling(data=data, kernel=(3, 3), stride=(1, 1),
-                             pad=(1, 1), pool_type=pool,
-                             name=('%s_pool_%s_pool' % (pool, name)))
-    cproj = ConvFactory(data=pooling, num_filter=proj, kernel=(1, 1),
-                        name=('%s_proj' %  name))
-
-    # concat
-    concat = mx.sym.Concat(*[c1x1, c3x3, cd3x3, cproj],
-                           name='ch_concat_%s_chconcat' % name)
-    return concat
-
-prev = mx.sym.Variable(name="Previous Output")
-in3a = InceptionFactoryA(prev, 64, 64, 64, 64, 96, "avg", 32, name="in3a")
-mx.viz.plot_network(symbol=in3a, shape=shape,
-                    node_attrs={"shape": "oval", "fixedsize": "false"})
-```
-
-Finally, we can obtain the whole network by chaining multiple inception modules. See a complete example [here](https://github.com/dmlc/mxnet/blob/master/example/image-classification/symbols/inception-bn.py).
-
-### Group Multiple Symbols
-
-To construct neural networks with multiple loss layers, we can use `mxnet.sym.Group` to group multiple Symbols together. The following example groups two outputs:
-
-```python
-net = mx.sym.Variable('data')
-fc1 = mx.sym.FullyConnected(data=net, name='fc1', num_hidden=128)
-net = mx.sym.Activation(data=fc1, name='relu1', act_type="relu")
-out1 = mx.sym.SoftmaxOutput(data=net, name='softmax')
-out2 = mx.sym.LinearRegressionOutput(data=net, name='regression')
-group = mx.sym.Group([out1, out2])
-group.list_outputs()
-```
-
-## Relations to NDArray
-
-As you can see now, both `Symbol` and `NDArray` provide multi-dimensional array operations, such as `c = a + b` in MXNet. We briefly clarify the differences here.
-
-The `NDArray` provides an imperative programming alike interface, in which the computations are evaluated sentence by sentence. While `Symbol` is closer to declarative programming, in which we first declare the computation and then evaluate with data. Examples in this category include regular expressions and SQL.
-
-The pros for `NDArray`:
-
-- Straightforward.
-- Easy to work with native language features (for loop, if-else condition, ..) and libraries (numpy, ..).
-- Easy step-by-step code debugging.
-
-The pros for `Symbol`:
-
-- Provides almost all functionalities of NDArray, such as `+`, `*`, `sin`, `reshape` etc.
-- Easy to save, load and visualize.
-- Easy for the backend to optimize the computation and memory usage.
-
-## Symbol Manipulation
-
-One important difference of `Symbol` compared to `NDArray` is that we first declare the computation and then bind the computation with data to run. In this section, we introduce the functions to manipulate a `Symbol` directly. But note that, most of them are wrapped by the high-level packages: [`Module`](https://mxnet.incubator.apache.org/api/python/module/module.html) and [`Gluon`](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html).
-
-### Shape and Type Inference
-
-For each `Symbol`, we can query its arguments, auxiliary states and outputs. We can also infer the output shape and type of the `Symbol` given the known input shape or type of some arguments, which facilitates memory allocation.
-
-```python
-arg_name = c.list_arguments()  # get the names of the inputs
-out_name = c.list_outputs()    # get the names of the outputs
-# infers output shape given the shape of input arguments
-arg_shape, out_shape, _ = c.infer_shape(a=(2, 3), b=(2, 3))
-# infers output type given the type of input arguments
-arg_type, out_type, _ = c.infer_type(a='float32', b='float32')
-{'input' : dict(zip(arg_name, arg_shape)),
- 'output' : dict(zip(out_name, out_shape))}
-{'input' : dict(zip(arg_name, arg_type)),
- 'output' : dict(zip(out_name, out_type))}
-```
-
-### Bind with Data and Evaluate
-
-The `Symbol` `c` constructed above declares what computation should be run. To evaluate it, we first need to feed the arguments, namely free variables, with data.
-
-We can do it by using the [`bind`](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.Symbol.bind) method, which accepts device context and a `dict` mapping free variable names to `NDArray`s as arguments and returns an [`Executor`](https://mxnet.incubator.apache.org/api/python/executor/executor.html#mxnet.executor.Executor). The `Executor` provides [`forward`](https://mxnet.incubator.apache.org/api/python/executor/executor.html#mxnet.executor.Executor.forward) method for evaluation and an attribute `outputs` to get all the results.
-
-```python
-ex = c.bind(ctx=mx.cpu(), args={'a' : mx.nd.ones([2,3]),
-                                'b' : mx.nd.ones([2,3])})
-ex.forward()
-print('number of outputs = %d\nthe first output = \n%s' % (
-           len(ex.outputs), ex.outputs[0].asnumpy()))
-```
-
-We can evaluate the same `Symbol` on GPU with different data.
-
-```python
-gpu_device = mx.gpu() if mx.test_utils.list_gpus() else mx.cpu()
-
-ex_gpu = c.bind(ctx=gpu_device, args={'a' : mx.nd.ones([3,4], gpu_device)*2,
-                                      'b' : mx.nd.ones([3,4], gpu_device)*3})
-ex_gpu.forward()
-ex_gpu.outputs[0].asnumpy()
-```
-
-We can also use [eval](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.Symbol.eval) method to evaluate the `Symbol`. It combines calls to `bind` and `forward` methods.
-
-```python
-ex = c.eval(ctx = mx.cpu(), a = mx.nd.ones([2,3]), b = mx.nd.ones([2,3]))
-print('number of outputs = %d\nthe first output = \n%s' % (
-            len(ex), ex[0].asnumpy()))
-```
-
-For neural nets, a more commonly used pattern is [simple_bind](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.Symbol.simple_bind), which creates all of the argument arrays for you. Then you can call `forward`, and [`backward`](https://mxnet.incubator.apache.org/api/python/executor/executor.html#mxnet.executor.Executor.backward) to get gradients if needed.
-
-### Load and Save
-
-Logically Symbols correspond to NDArrays. They both represent a tensor. They both are inputs/outputs of operators. We can either serialize a `Symbol` object by using `pickle`, or by using `save` and `load` methods directly as it is explained in [this NDArray tutorial](http://mxnet.io/tutorials/basic/ndarray.html#serialize-from-to-distributed-filesystems).
-
-When serializing `NDArray`, we serialize the tensor data in it and directly dump to disk in binary format. But `Symbol` uses a concept of graph. Graphs are composed by chaining operators. They are implicitly represented by output Symbols. So, when serializing a `Symbol`, we serialize the graph of which the `Symbol` is an output. While serialization, `Symbol` uses more readable `json` format for serialization. To convert `Symbol` to `json` string, use [`tojson`](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.Symbol.tojson) method.
-
-```python
-print(c.tojson())
-c.save('symbol-c.json')
-c2 = mx.sym.load('symbol-c.json')
-c.tojson() == c2.tojson()
-```
-
-## Customized Symbol
-
-Most operators such as [`mx.sym.Convolution`](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.Convolution) and [`mx.sym.Reshape`](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.reshape) are implemented in C++ for better performance. MXNet also allows users to write new operators using any front-end language such as Python. It often makes the developing and debugging much easier. To implement an operator in Python, refer to [How to create new operators](http://mxnet.io/faq/new_op.html).
-
-## Advanced Usages
-
-### Type Cast
-
-By default, MXNet uses 32-bit floats. But for better accuracy-performance, we can also use a lower precision data type.
-For example, The NVIDIA Tesla Pascal GPUs (e.g. P100) have improved 16-bit float performance, while GTX Pascal GPUs (e.g. GTX 1080) are fast on 8-bit integers.
-
-To convert the data type as per the requirements, we can use [`mx.sym.cast`](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.cast) operator as follows:
-
-```python
-a = mx.sym.Variable('data')
-b = mx.sym.cast(data=a, dtype='float16')
-arg, out, _ = b.infer_type(data='float32')
-print({'input':arg, 'output':out})
-
-c = mx.sym.cast(data=a, dtype='uint8')
-arg, out, _ = c.infer_type(data='int32')
-print({'input':arg, 'output':out})
-```
-
-### Variable Sharing
-
-To share the contents between several Symbols, we can bind these Symbols with the same array as follows:
-
-```python
-a = mx.sym.Variable('a')
-b = mx.sym.Variable('b')
-b = a + a * a
-
-data = mx.nd.ones((2,3)) * 2
-ex = b.bind(ctx=mx.cpu(), args={'a':data, 'b':data})
-ex.forward()
-ex.outputs[0].asnumpy()
-```
-
-### Weight tying
-
-You can use same principle to tie weights of different layers. In the example below two `FullyConnected` layers share same weights and biases, but process different data. Let's demonstrate how we can do it.
-
-In this example we first create training and evaluation datasets. Both of them consist of two individual `NDArray`s. We are using `NDArrayIter` to iterate over all of them.
-
-```python
-import numpy as np
-import mxnet as mx
-
-# Training data
-train_data_size = 4
-train_data1 = mx.random.uniform(shape=(train_data_size, 2))
-train_data2 = mx.random.uniform(shape=(train_data_size, 2))
-train_label = mx.nd.array([i % 2 for i in range(train_data_size)])
-batch_size = 3
-
-# Evaluation Data
-eval_data_size = 3
-eval_data1 = mx.random.uniform(shape=(eval_data_size, 2))
-eval_data2 = mx.random.uniform(shape=(eval_data_size, 2))
-eval_label = np.array([i % 2 for i in range(eval_data_size)])
-
-train_iter = mx.io.NDArrayIter({'inputs_left': train_data1,
-                                'inputs_right': train_data2},
-                               train_label, batch_size, shuffle=True,
-                               label_name='labels', last_batch_handle='pad')
-
-eval_iter = mx.io.NDArrayIter({'inputs_left': eval_data1,
-                               'inputs_right': eval_data2},
-                              eval_label, batch_size, shuffle=False, 
-                              label_name='labels',
-                              last_batch_handle='pad')
-```
-
-We define a `Symbol` for both `inputs_left` and `inputs_right` variables, and separate symbols for `shared_weight` and `shared_bias`. We use `shared_weight` and `shared_bias` symbols in both `FullyConnected` layers, making sure that they are using the same data underlying the symbols. This is where weight tying is happening.
-
-```python
-num_hidden_nodes = 2
-
-# Assume the left and right inputs have the same shape as each other
-inputs_left = mx.sym.var('inputs_left')
-inputs_right = mx.sym.var('inputs_right')
-labels = mx.symbol.Variable('labels')
-
-shared_weight = mx.symbol.Variable('shared_weight')
-shared_bias = mx.symbol.Variable('shared_bias')
-
-fc_left_sym = mx.sym.FullyConnected(data=inputs_left, weight=shared_weight,
-                                    bias=shared_bias,
-                                    num_hidden=num_hidden_nodes, name='fc_left')
-
-fc_right_sym = mx.sym.FullyConnected(data=inputs_right, weight=shared_weight,
-                                     bias=shared_bias,
-                                     num_hidden=num_hidden_nodes, name='fc_right')
-
-combined = mx.sym.concat(fc_left_sym, fc_right_sym)
-output = mx.sym.SoftmaxOutput(data=combined, label=labels, name='softmax')
-```
-
-In the next lines of the code, we use `Module API` to start the training. We first create a `Module` object and then call `fit` providing data iterators. To use trained model for prediction, we use `predict` method, providing evaluation data iterator.  
-
-```python
-model = mx.mod.Module(
-    symbol=output,
-    data_names=['inputs_left', 'inputs_right'],
-    label_names=['labels']
-)
-
-model.fit(train_iter, eval_iter,
-          optimizer_params={'learning_rate': 0.01, 'momentum': 0.9},
-          num_epoch=1,
-          eval_metric='acc')
-
-result = model.predict(eval_iter).asnumpy()
-print(result)
-```
-
-## Recommended Next Steps
-
-- Learn how to [use Module API to train neural network](https://mxnet.incubator.apache.org/versions/master/tutorials/basic/module.html).
-
-- Explore ways you can [load data using Data Iterators](https://mxnet.incubator.apache.org/versions/master/tutorials/basic/data.html).
-
-- [Use pretrained models](https://mxnet.incubator.apache.org/versions/master/tutorials/python/predict_image.html) for image object detection.
-
-- [Hybridize your models](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/hybrid.html) to get the best from both `Gluon` and `Symbol API`.
-
-- Convert your existing `Module API` code to `Gluon` as it is explained [here](https://mxnet.incubator.apache.org/versions/master/tutorials/python/module_to_gluon.html). 
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/c++/basics.md b/docs/tutorials/c++/basics.md
deleted file mode 100644
index f9fdb31dee34..000000000000
--- a/docs/tutorials/c++/basics.md
+++ /dev/null
@@ -1,213 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-Basics
-======
-
-This tutorial provides basic usages of the C++ package through the classical handwritten digits
-identification database--[MNIST](http://yann.lecun.com/exdb/mnist/).
-
-The following contents assume that the working directory is `/path/to/mxnet/cpp-package/example`.
-
-Load Data
---------
-Before going into codes, we need to fetch MNIST data. You can either use the script `/path/to/mxnet/cpp-package/example/get_data.sh`,
-or download mnist data by yourself from Lecun's [website](http://yann.lecun.com/exdb/mnist/)
-and decompress them into `data/mnist_data` folder.
-
-Except linking the MXNet shared library, the C++ package itself is a header-only package,
-which means all you need to do is to include the header files. Among the header files,
-`op.h` is special since it is generated dynamically. The generation should be done when
-[building the C++ package](http://mxnet.incubator.apache.org/versions/master/api/c++/index.html).
-It is important to note that you need to **copy the shared library** (`libmxnet.so` in Linux and MacOS,
-`libmxnet.dll` in Windows) from `/path/to/mxnet/lib` to the working directory.
-We do not recommend you to use pre-built binaries because MXNet is under heavy development,
-the operator definitions in `op.h` may be incompatible with the pre-built version.
-
-In order to use functionalities provides by the C++ package, first we include the general
-header file `MxNetCpp.h` and specify the namespaces.
-
-```cpp
-#include "mxnet-cpp/MxNetCpp.h"
-
-using namespace std;
-using namespace mxnet::cpp;
-```
-
-Next we can use the data iter to load MNIST data (separated to training sets and validation sets).
-The digits in MNIST are 2-dimension arrays, so we should set `flat` to true to flatten the data.
-
-```cpp
-auto train_iter = MXDataIter("MNISTIter")
-    .SetParam("image", "./data/mnist_data/train-images-idx3-ubyte")
-    .SetParam("label", "./data/mnist_data/train-labels-idx1-ubyte")
-    .SetParam("batch_size", batch_size)
-    .SetParam("flat", 1)
-    .CreateDataIter();
-auto val_iter = MXDataIter("MNISTIter")
-    .SetParam("image", "./data/mnist_data/t10k-images-idx3-ubyte")
-    .SetParam("label", "./data/mnist_data/t10k-labels-idx1-ubyte")
-    .SetParam("batch_size", batch_size)
-    .SetParam("flat", 1)
-    .CreateDataIter();
-```
-
-The data have been successfully loaded. We can now easily construct various models to identify
-the digits with the help of C++ package.
-
-
-Multilayer Perceptron
----------------------
-If you are not familiar with multilayer perceptron, you can get some basic information
-[here](http://mxnet.io/tutorials/python/mnist.html#multilayer-perceptron). We only focus on
-the implementation in this tutorial.
-
-Constructing multilayer perceptron model is straightforward, assume we store the hidden size
-for each layer in `layers`, and each layer uses
-[ReLu](https://en.wikipedia.org/wiki/Rectifier_(neural_networks)) function as activation.
-
-```cpp
-Symbol mlp(const vector<int> &layers) {
-  auto x = Symbol::Variable("X");
-  auto label = Symbol::Variable("label");
-
-  vector<Symbol> weights(layers.size());
-  vector<Symbol> biases(layers.size());
-  vector<Symbol> outputs(layers.size());
-
-  for (int i=0; i<layers.size(); ++i) {
-    weights[i] = Symbol::Variable("w" + to_string(i));
-    biases[i] = Symbol::Variable("b" + to_string(i));
-    Symbol fc = FullyConnected(
-      i == 0? x : outputs[i-1]
-      weights[i],
-      biases[i],
-      layers[i]
-    );
-    outputs[i] = i == layers.size()-1 ? fc : Activation(fc, ActivationActType::relu);
-  }
-
-  return SoftmaxOutput(outputs.back(), label);
-}
-```
-
-The above function defines a multilayer perceptron model where hidden sizes are specified
-by `layers`.
-
-We now create and initialize the parameters after the model is constructed. MXNet can help
- you to infer shapes of most of the parameters. Basically only the shape of data and label
- is needed.
-
-```cpp
-std::map<string, NDArray> args;
-args["X"] = NDArray(Shape(batch_size, image_size*image_size), ctx);
-args["label"] = NDArray(Shape(batch_size), ctx);
-// Let MXNet infer shapes other parameters such as weights
-net.InferArgsMap(ctx, &args, args);
-
-// Initialize all parameters with uniform distribution U(-0.01, 0.01)
-auto initializer = Uniform(0.01);
-for (auto& arg : args) {
-  // arg.first is parameter name, and arg.second is the value
-  initializer(arg.first, &arg.second);
-}
-```
-
-The rest is to train the model with an optimizer.
-```cpp
-// Create sgd optimizer
-Optimizer* opt = OptimizerRegistry::Find("sgd");
-opt->SetParam("rescale_grad", 1.0/batch_size);
-
-// Start training
-for (int iter = 0; iter < max_epoch; ++iter) {
-  train_iter.Reset();
-
-  while (train_iter.Next()) {
-    auto data_batch = train_iter.GetDataBatch();
-    // Set data and label
-    args["X"] = data_batch.data;
-    args["label"] = data_batch.label;
-
-    // Create executor by binding parameters to the model
-    auto *exec = net.SimpleBind(ctx, args);
-    // Compute gradients
-    exec->Forward(true);
-    exec->Backward();
-    // Update parameters
-    exec->UpdateAll(opt, learning_rate, weight_decay);
-    // Remember to free the memory
-    delete exec;
-  }
-}
-```
-
-We also want to see how our model performs. The C++ package provides convenient APIs for
-evaluating. Here we use accuracy as metric. The inference is almost the same as training,
- except that we don't need gradients.
-
-```cpp
-Accuracy acc;
-val_iter.Reset();
-while (val_iter.Next()) {
-  auto data_batch = val_iter.GetDataBatch();
-  args["X"] = data_batch.data;
-  args["label"] = data_batch.label;
-  auto *exec = net.SimpleBind(ctx, args);
-  // Forward pass is enough as no gradient is needed when evaluating
-  exec->Forward(false);
-  acc.Update(data_batch.label, exec->outputs[0]);
-  delete exec;
-}
-```
-
-You can find the complete code in `mlp_cpu.cpp`. Use `make mlp_cpu` to compile it,
- and `./mlp_cpu` to run it. If it complains that the shared library `libmxnet.so` is not found
- after typing `./mlp_cpu`, you will need to specify the path to the shared library in
- the environment variable `LD_LIBRARY_PATH` in Linux and `DYLD_LIBRARY_PATH`
- in MacOS. For example, if you are using MacOS, typing
- `DYLD_LIBRARY_PATH+=. ./mlp_cpu` would solve the problem. It basically tells the system
- to find the shared library under the current directory since we have just copied it here.
-
-GPU Support
------------
-It's worth noting that changing context from `Context::cpu()` to `Context::gpu()` is not enough,
-because the data read by data iter are stored in memory, we cannot assign it directly to the
-parameters. To bridge this gap, NDArray provides data synchronization functionalities between
-GPU and CPU. We will illustrate it by making the mlp code run on GPU.
-
-In the previous code, data are used like
-
-```cpp
-args["X"] = data_batch.data;
-args["label"] = data_batch.label;
-```
-
-It will be problematic if other parameters are created in the context of GPU. We can use
-`NDArray::CopyTo` to solve this problem.
-
-```cpp
-// Data provided by DataIter are stored in memory, should be copied to GPU first.
-data_batch.data.CopyTo(&args["X"]);
-data_batch.label.CopyTo(&args["label"]);
-// CopyTo is imperative, need to wait for it to complete.
-NDArray::WaitAll();
-```
-
-By replacing the former code to the latter one, we successfully port the code to GPU.
-You can find the complete code in `mlp_gpu.cpp`. Compilation is similar to the cpu version.
-Note that the shared library must be built with GPU support enabled.
diff --git a/docs/tutorials/c++/index.md b/docs/tutorials/c++/index.md
deleted file mode 100644
index faf6526fb824..000000000000
--- a/docs/tutorials/c++/index.md
+++ /dev/null
@@ -1,25 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Tutorials
-
-```eval_rst
-.. toctree::
-   :glob:
-
-   *
-```
diff --git a/docs/tutorials/c++/mxnet_cpp_inference_tutorial.md b/docs/tutorials/c++/mxnet_cpp_inference_tutorial.md
deleted file mode 100644
index ca4a75ef3b54..000000000000
--- a/docs/tutorials/c++/mxnet_cpp_inference_tutorial.md
+++ /dev/null
@@ -1,284 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# MXNet C++ API inference tutorial
-
-## Overview
-MXNet provides various useful tools and interfaces for deploying your model for inference. For example, you can use [MXNet Model Server](https://github.com/awslabs/mxnet-model-server) to start a service and host your trained model easily.
-Besides that, you can also use MXNet's different language APIs to integrate your model with your existing service. We provide [Python](https://mxnet.incubator.apache.org/api/python/module/module.html),    [Java](https://mxnet.incubator.apache.org/api/java/index.html), [Scala](https://mxnet.incubator.apache.org/api/scala/index.html), and [C++](https://mxnet.incubator.apache.org/api/c++/index.html) APIs.
-
-This tutorial is a continuation of the [Gluon end to end tutorial](https://mxnet.apache.org/versions/master/tutorials/gluon/gluon_from_experiment_to_deployment.html), we will focus on the MXNet C++ API. We have slightly modified the code in [C++ Inference Example](https://github.com/apache/incubator-mxnet/tree/master/cpp-package/example/inference) for our use case.
-
-## Prerequisites
-
-To complete this tutorial, you need:
-- Complete the training part of [Gluon end to end tutorial](https://mxnet.apache.org/versions/master/tutorials/gluon/gluon_from_experiment_to_deployment.html)
-- Learn the basics about [MXNet C++ API](https://github.com/apache/incubator-mxnet/tree/master/cpp-package)
-
-
-## Setup the MXNet C++ API
-To use the C++ API in MXNet, you need to build MXNet from source with C++ package. Please follow the [built from source guide](https://mxnet.incubator.apache.org/install/ubuntu_setup.html), and [C++ Package documentation](https://github.com/apache/incubator-mxnet/tree/master/cpp-package)
-to enable the C++ API.
-The summary of those two documents is that you need to build MXNet from source with `USE_CPP_PACKAGE` flag set to 1. For example: `make -j USE_CPP_PACKAGE=1`.
-
-## Load the model and run inference
-
-After you complete [the previous tutorial](https://mxnet.apache.org/versions/master/tutorials/gluon/gluon_from_experiment_to_deployment.html), you will get the following output files:
-1. Model Architecture stored in `flower-recognition-symbol.json`
-2. Model parameter values stored in `flower-recognition-0040.params` (`0040` is for 40 epochs we ran)
-3. Label names stored in `synset.txt`
-4. Mean and standard deviation values stored in `mean_std_224` for image normalization.
-
-
-Now we need to write the C++ code to load them and run prediction on a test image.
-The full code is available in the [C++ Inference Example](https://github.com/apache/incubator-mxnet/tree/master/cpp-package/example/inference), we will walk you through it and point out the necessary changes to make for our use case.
-
-
-
-### Write a predictor using the MXNet C++ API
-
-In general, the C++ inference code should follow the 4 steps below. We can do that using a Predictor class.
-1. Load the pre-trained model
-2. Load the parameters of pre-trained model
-3. Load the image to be classified in to NDArray and apply image transformation we did in training
-4. Run the forward pass and predict the class of the input image
-
-```cpp
-class Predictor {
- public:
-    Predictor() {}
-    Predictor(const std::string& model_json_file,
-              const std::string& model_params_file,
-              const Shape& input_shape,
-              bool gpu_context_type = false,
-              const std::string& synset_file = "",
-              const std::string& mean_image_file = "");
-    void PredictImage(const std::string& image_file);
-    ~Predictor();
-
- private:
-    void LoadModel(const std::string& model_json_file);
-    void LoadParameters(const std::string& model_parameters_file);
-    void LoadSynset(const std::string& synset_file);
-    NDArray LoadInputImage(const std::string& image_file);
-    void LoadMeanImageData();
-    void LoadDefaultMeanImageData();
-    void NormalizeInput(const std::string& mean_image_file);
-    inline bool FileExists(const std::string& name) {
-        struct stat buffer;
-        return (stat(name.c_str(), &buffer) == 0);
-    }
-    NDArray mean_img;
-    std::map<std::string, NDArray> args_map;
-    std::map<std::string, NDArray> aux_map;
-    std::vector<std::string> output_labels;
-    Symbol net;
-    Executor *executor;
-    Shape input_shape;
-    NDArray mean_image_data;
-    NDArray std_dev_image_data;
-    Context global_ctx = Context::cpu();
-    std::string mean_image_file;
-};
-```
-
-### Load the model, synset file, and normalization values
-
-In the Predictor constructor, you need to provide paths to saved json and param files. After that, add the following methods `LoadModel` and `LoadParameters` to load the network and its parameters. This part is the same as [the example](https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/inference/inception_inference.cpp).
-
-Next, we need to load synset file, and normalization values. We have made the following change since our synset file contains flower names and we used both mean and standard deviation for image normalization.
-
-```c++
-/*
- * The following function loads the synset file.
- * This information will be used later to report the label of input image.
- */
-void Predictor::LoadSynset(const std::string& synset_file) {
-  if (!FileExists(synset_file)) {
-    LG << "Synset file " << synset_file << " does not exist";
-    throw std::runtime_error("Synset file does not exist");
-  }
-  LG << "Loading the synset file.";
-  std::ifstream fi(synset_file.c_str());
-  if (!fi.is_open()) {
-    std::cerr << "Error opening synset file " << synset_file << std::endl;
-    throw std::runtime_error("Error in opening the synset file.");
-  }
-  std::string lemma;
-  while (getline(fi, lemma)) {
-    output_labels.push_back(lemma);
-  }
-  fi.close();
-}
-
-/*
- * The following function loads the mean and standard deviation values.
- * This data will be used for normalizing the image before running the forward
- * pass.
- * The output data has the same shape as that of the input image data.
- */
-void Predictor::LoadMeanImageData() {
-  LG << "Load the mean image data that will be used to normalize "
-     << "the image before running forward pass.";
-  mean_image_data = NDArray(input_shape, global_ctx, false);
-  mean_image_data.SyncCopyFromCPU(
-        NDArray::LoadToMap(mean_image_file)["mean_img"].GetData(),
-        input_shape.Size());
-  NDArray::WaitAll();
-   std_dev_image_data = NDArray(input_shape, global_ctx, false);
-   std_dev_image_data.SyncCopyFromCPU(
-       NDArray::LoadToMap(mean_image_file)["std_img"].GetData(),
-       input_shape.Size());
-    NDArray::WaitAll();
-}
-```
-
-
-
-### Load input image
-
-Now let's add a method to load the input image we want to predict and converts it to NDArray for prediction.
-```cpp
-NDArray Predictor::LoadInputImage(const std::string& image_file) {
-  if (!FileExists(image_file)) {
-    LG << "Image file " << image_file << " does not exist";
-    throw std::runtime_error("Image file does not exist");
-  }
-  LG << "Loading the image " << image_file << std::endl;
-  std::vector<float> array;
-  cv::Mat mat = cv::imread(image_file);
-  /*resize pictures to (224, 224) according to the pretrained model*/
-  int height = input_shape[2];
-  int width = input_shape[3];
-  int channels = input_shape[1];
-  cv::resize(mat, mat, cv::Size(height, width));
-  for (int c = 0; c < channels; ++c) {
-    for (int i = 0; i < height; ++i) {
-      for (int j = 0; j < width; ++j) {
-        array.push_back(static_cast<float>(mat.data[(i * height + j) * 3 + c]));
-      }
-    }
-  }
-  NDArray image_data = NDArray(input_shape, global_ctx, false);
-  image_data.SyncCopyFromCPU(array.data(), input_shape.Size());
-  NDArray::WaitAll();
-  return image_data;
-}
-```
-
-### Predict the image
-
-Finally, let's run the inference. It's basically using MXNet executor to do a forward pass. To run predictions on multiple images, you can load the images in a list of NDArrays and run prediction in batches. Note that the Predictor class may not be thread safe. Calling it in multi-threaded environments was not tested. To utilize multi-threaded prediction, you need to use the C predict API. Please follow the [C predict example](https://github.com/apache/incubator-mxnet/tree/master/example/image-classification/predict-cpp).
-
-An additional step is to normalize the image NDArrays values to `(0, 1)` and apply mean and standard deviation we just loaded. 
-
-```cpp
-/*
- * The following function runs the forward pass on the model.
- * The executor is created in the constructor.
- *
- */
-void Predictor::PredictImage(const std::string& image_file) {
-  // Load the input image
-  NDArray image_data = LoadInputImage(image_file);
-
-  // Normalize the image
-  image_data.Slice(0, 1) /= 255.0;
-  image_data -= mean_image_data;
-  image_data /= std_dev_image_data;
-
-  LG << "Running the forward pass on model to predict the image";
-  /*
-   * The executor->arg_arrays represent the arguments to the model.
-   *
-   * Copying the image_data that contains the NDArray of input image
-   * to the arg map of the executor. The input is stored with the key "data" in the map.
-   *
-   */
-  image_data.CopyTo(&(executor->arg_dict()["data"]));
-  NDArray::WaitAll();
-
-  // Run the forward pass.
-  executor->Forward(false);
-
-  // The output is available in executor->outputs.
-  auto array = executor->outputs[0].Copy(global_ctx);
-  NDArray::WaitAll();
-
-  /*
-   * Find out the maximum accuracy and the index associated with that accuracy.
-   * This is done by using the argmax operator on NDArray.
-   */
-  auto predicted = array.ArgmaxChannel();
-  NDArray::WaitAll();
-
-  int best_idx = predicted.At(0, 0);
-  float best_accuracy = array.At(0, best_idx);
-
-  if (output_labels.empty()) {
-    LG << "The model predicts the highest accuracy of " << best_accuracy << " at index "
-       << best_idx;
-  } else {
-    LG << "The model predicts the input image to be a [" << output_labels[best_idx]
-       << " ] with Accuracy = " << best_accuracy << std::endl;
-  }
-}
-```
-
-### Compile and run the inference code
-
-You can find the [full code for the inference example](https://github.com/apache/incubator-mxnet/tree/master/cpp-package/example/inference) in the `cpp-package` folder of the project
-, and to compile it use this [Makefile](https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/inference/Makefile).
-
-Make a copy of the example code, rename it to `flower_inference` and apply the changes we mentioned above. Now you will be able to compile and run inference. Run `make all`. Once this is complete, run inference with the following parameters. Remember to set your `LD_LIBRARY_PATH` to point to MXNet library if you have not done so.
-
-```bash
-make all
-export LD_LIBRARY_PATH=$LD_LIBRARY_PATH=:path/to/incubator-mxnet/lib
-./flower_inference --symbol flower-recognition-symbol.json --params flower-recognition-0040.params --synset synset.txt --mean mean_std_224.nd --image ./data/test/lotus/image_01832.jpg
-```
-
-Then it will predict your image:
-
-```bash
-[17:38:51] resnet.cpp:150: Loading the model from flower-recognition-symbol.json
-
-[17:38:51] resnet.cpp:163: Loading the model parameters from flower-recognition-0040.params
-
-[17:38:52] resnet.cpp:190: Loading the synset file.
-[17:38:52] resnet.cpp:211: Load the mean image data that will be used to normalize the image before running forward pass.
-[17:38:52] resnet.cpp:263: Loading the image ./data/test/lotus/image_01832.jpg
-
-[17:38:52] resnet.cpp:299: Running the forward pass on model to predict the image
-[17:38:52] resnet.cpp:331: The model predicts the input image to be a [lotus ] with Accuracy = 8.63046
-```
-
-
-
-## What's next
-
-Now you can explore more ways to run inference and deploy your models:
-1. [Java Inference examples](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer)
-2. [Scala Inference examples](https://mxnet.incubator.apache.org/tutorials/scala/)
-3. [ONNX model inference examples](https://mxnet.incubator.apache.org/tutorials/onnx/inference_on_onnx_model.html)
-4. [MXNet Model Server Examples](https://github.com/awslabs/mxnet-model-server/tree/master/examples)
-
-## References
-
-1. [Gluon end to end tutorial](https://mxnet.apache.org/versions/master/tutorials/gluon/gluon_from_experiment_to_deployment.html)
-2. [Gluon C++ inference example](https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/inference/)
-3. [Gluon C++ package](https://github.com/apache/incubator-mxnet/tree/master/cpp-package)
\ No newline at end of file
diff --git a/docs/tutorials/c++/subgraphAPI.md b/docs/tutorials/c++/subgraphAPI.md
deleted file mode 100644
index 91d24bd0319a..000000000000
--- a/docs/tutorials/c++/subgraphAPI.md
+++ /dev/null
@@ -1,166 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-## Subgraph API
-
-The subgraph API has been proposed and implemented as the default mechanism for integrating backend libraries to MXNet. The subgraph API is a very flexible interface. Although it was proposed as an integration mechanism, it has been used as a tool for manipulating NNVM graphs for graph-level optimizations, such as operator fusion.
-
-The subgraph API works as the following steps:
-
-* Search for particular patterns in a graph.
-* Group the operators/nodes with particular patterns into a subgraph and shrink the subgraph into a single node.
-* Replace the subgraph in the original graph with the subgraph node.
-
-The figure below illustrates the subgraph mechanism.
-
-![](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tutorials/subgraph/subgraph.png)
-
-The subgraph API allows the backend developers to customize the subgraph mechanism in two places:
-
-* Subgraph searching: define a subgraph selector to search for particular patterns in a computation graph.
-* Subgraph node creation: attach an operator to run the computation in the subgraph. We can potentially manipulate the subgraph here.
-
-
-The following is a demonstration of how the subgraph API can be applied to a simple task. Refer to the previous figure for an overview of the process. That is, replacing `Convolution` and `BatchNorm` with the conv_bn.
-
-The first step is to define a subgraph selector to find the required pattern. To find a pattern that has `Convolution` and `BatchNorm`, we can start the search on the node with `Convolution`. Then from the `Convolution` node, we search for `BatchNorm` along the outgoing edge.
-
-```C++
-class SgSelector : public SubgraphSelector {
- public:
-  SgSelector() {
-    find_bn = false;
-  }
-  bool Select(const nnvm::Node &n) override {
-    // Here we start on the Convolution node to search for a subgraph.
-    return n.op() && n.op()->name == "Convolution";
-  }
-  bool SelectInput(const nnvm::Node &n, const nnvm::Node &new_node) override {
-    // We don't need to search on the incoming edge.
-    return false;
-  }
-  bool SelectOutput(const nnvm::Node &n, const nnvm::Node &new_node) override {
-    // We search on the outgoing edge. Once we find a BatchNorm node, we won't
-    // accept any more BatchNorm nodes.
-    if (new_node.op() && new_node.op()->name == "BatchNorm" && !find_bn) {
-      find_bn = true;
-      return true;
-    } else {
-      return false;
-    }
-  }
-  std::vector<nnvm::Node *> Filter(const std::vector<nnvm::Node *> &candidates) override {
-    // We might have found a Convolution node, but we might have failed to find a BatchNorm
-    // node that uses the output of the Convolution node. If we failed, we should skip
-    // the Convolution node as well.
-    if (find_bn)
-      return candidates;
-    else
-      return std::vector<nnvm::Node *>();
-  }
- private:
-  bool find_bn;
-};
-```
-
-The second step is to define a subgraph property to use the subgraph selector above to customize the subgraph searching. By defining this class, we can also customize subgraph node creation. When customizing node creation, we can specify what operator to run the subgraph on the node. In this example, we use `CachedOp`, which itself is a graph executor, to run the subgraph with `Convolution` and `BatchNorm`. In practice, it's most likely that we use a single operator from a backend library to replace the two operators for execution.
-
-```C++
-class SgProperty : public SubgraphProperty {
- public:
-  static SubgraphPropertyPtr Create() {
-    return std::make_shared<SgProperty>();
-  }
-  nnvm::NodePtr CreateSubgraphNode(
-      const nnvm::Symbol &sym, const int subgraph_id = 0) const override {
-    // We can use CachedOp to execute the subgraph.
-    nnvm::NodePtr n = nnvm::Node::Create();
-    n->attrs.op = Op::Get("_CachedOp");
-    n->attrs.name = "ConvBN" + std::to_string(subgraph_id);
-    n->attrs.subgraphs.push_back(std::make_shared<nnvm::Symbol>(sym));
-    std::vector<std::pair<std::string, std::string> > flags{{"static_alloc", "true"}};
-    n->attrs.parsed = CachedOpPtr(new CachedOp(sym, flags));
-    return n;
-  }
-  SubgraphSelectorPtr CreateSubgraphSelector() const override {
-    auto property = std::make_shared<CreateSubgraphSelector>();
-    property->SetAttr<std::string>("property_name", "subgraph example pass"); // Optional, better to have it.
-    property->SetAttr<bool>("inference_only", true); // Optional, only for inference_only pass.
-    return property;
-  }
-};
-```
-`SetAttr` is optional and developer can define their own attributes to control property behavior.
-There're some built-in attributes that used by MXNet executor.
-
-`property_name`  : std::string, name of this property, used for diagnose.
-
-`disable` : bool, whther to disable this property.
-
-`inference_only` : bool, apply this property only for inference. Property will be skiped when need_grad=True. Default `false` if this attribute isn't defined.
-
-After defining the subgraph property, we need to register it under a backend in .cc file.
-
-Firstly, we need to register the backend
-
-```C++
-MXNET_REGISTER_SUBGRAPH_BACKEND(SgTest);
-```
-
-Then register the property under it.
-
-```C++
-MXNET_REGISTER_SUBGRAPH_PROPERTY(SgTest, SgProperty);
-```
-
-It's possible to register multiple properties for same backend. In practice, we recommend to put each property definition into .h file, and register backend in single .cc file. Property will be executed according to the register order.
-
-```C++
-#include "SgProperty.h" // Define SgProperty class
-#include "SgProperty2.h" // Define SgProperty2 class
-#include "SgProperty3.h" // Define SgProperty3 class
-
-MXNET_REGISTER_SUBGRAPH_BACKEND(SgTest);
-MXNET_REGISTER_SUBGRAPH_PROPERTY(SgTest, SgProperty);  // Execution order 1.
-MXNET_REGISTER_SUBGRAPH_PROPERTY(SgTest, SgProperty2); // Execution order 2.
-MXNET_REGISTER_SUBGRAPH_PROPERTY(SgTest, SgProperty3); // Execution order 3.
-```
-
-After compiling this subgraph mechanism into MXNet, we can use the environment variable `MXNET_SUBGRAPH_BACKEND` to activate it during symbol bind.
-
-```bash
-export MXNET_SUBGRAPH_BACKEND=SgTest
-```
-
-Or you can use python symbol API `get_backend_symbol` to run all properties registered for this backend and get returned symbol.
-
-```Python
-sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)
-sym = sym.get_backend_symbol('SgTest')
-```
-
-When `SgProperty` is activated, a message will be shown in terminal as
-
-```bash
-start to execute subgraph example pass.
-```
-
-This tutorial shows a simple example of how to use the subgraph API to search for patterns in an NNVM graph.
-Intested users can try different pattern matching rules (i.e., define their own `SubgraphSelector`) and
-attach different operators to execute the subgraphs.
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/control_flow/ControlFlowTutorial.md b/docs/tutorials/control_flow/ControlFlowTutorial.md
deleted file mode 100644
index 88aa383cb011..000000000000
--- a/docs/tutorials/control_flow/ControlFlowTutorial.md
+++ /dev/null
@@ -1,405 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Hybridize Gluon models with control flows.
-
-MXNet currently provides three control flow operators: `cond`, `foreach` and `while_loop`. Like other MXNet operators, they all have a version for NDArray and a version for Symbol. These two versions have exactly the same semantics. We can take advantage of this and use them in Gluon to hybridize models.
-
-In this tutorial, we use a few examples to demonstrate the use of control flow operators in Gluon and show how a model that requires control flow is hybridized.
-
-## Prepare running the code
-
-
-```python
-import mxnet as mx
-from mxnet.gluon import HybridBlock
-```
-
-## foreach
-`foreach` is a for loop that iterates over the first dimension of the input data (it can be an array or a list of arrays). It is defined with the following signature:
-
-```
-foreach(body, data, init_states, name) => (outputs, states)
-```
-
-It runs the Python function defined in `body` for every slice from the input arrays. The signature of the `body` function is defined as follows:
-
-```
-body(data, states) => (outputs, states)
-```
-
-The inputs of the `body` function have two parts: `data` is a slice of an array (if there is only one input array in `foreach`) or a list of slices (if there are a list of input arrays); `states` are the arrays from the previous iteration. The outputs of the `body` function also have two parts: `outputs` is an array or a list of arrays; `states` is the computation states of the current iteration. `outputs` from all iterations are concatenated as the outputs of `foreach`.
-
-The following pseudocode illustrates the execution of `foreach`.
-
-```python
-def foreach(body, data, init_states):
-    states = init_states
-    outs = []
-
-    for i in range(data.shape[0]):
-        s = data[i]
-        out, states = body(s, states)
-        outs.append(out)
-    outs = mx.nd.stack(*outs)
-    return outs, states
-```
-
-### Example 1: `foreach` works like map
-`foreach` can work like a map function of a functional language. In this case, the states of `foreach` can be an empty list, which means the computation doesn't carry computation states across iterations.
-
-In this example, we use `foreach` to increase each element's value of an array by one.
-
-
-```python
-data = mx.nd.arange(5)
-print(data)
-```
-
-    
-    [ 0.  1.  2.  3.  4.]
-    <NDArray 5 @cpu(0)>
-
-
-
-```python
-def add1(data, _):
-    return data + 1, []
-
-class Map(HybridBlock):
-    def hybrid_forward(self, F, data):
-        out, _ = F.contrib.foreach(add1, data, [])
-        return out
-    
-map_layer = Map()
-out = map_layer(data)
-print(out)
-```
-
-    
-    [[ 1.]
-     [ 2.]
-     [ 3.]
-     [ 4.]
-     [ 5.]]
-    <NDArray 5x1 @cpu(0)>
-
-
-We can hybridize the block and run the computation again. It should generate the same result.
-
-
-```python
-map_layer.hybridize()
-out = map_layer(data)
-print(out)
-```
-
-    
-    [[ 1.]
-     [ 2.]
-     [ 3.]
-     [ 4.]
-     [ 5.]]
-    <NDArray 5x1 @cpu(0)>
-
-
-### Example 2: `foreach` works like scan
-`foreach` can work like a scan function in a functional language. In this case, the outputs of the Python function is an empty list.
-
-
-```python
-def sum(data, state):
-    return [], state + data
-
-class Scan(HybridBlock):
-    def hybrid_forward(self, F, data):
-        _, state = F.contrib.foreach(sum, data, F.zeros((1)))
-        return state
-scan_layer = Scan()
-state = scan_layer(data)
-print(data)
-print(state)
-```
-
-    
-    [ 0.  1.  2.  3.  4.]
-    <NDArray 5 @cpu(0)>
-    
-    [ 10.]
-    <NDArray 1 @cpu(0)>
-
-
-
-```python
-scan_layer.hybridize()
-state = scan_layer(data)
-print(state)
-```
-
-    
-    [ 10.]
-    <NDArray 1 @cpu(0)>
-
-
-### Example 3: `foreach` with both outputs and states
-This is probably the most common use case of `foreach`. We extend the previous scan example and return both output and states.
-
-
-```python
-def sum(data, state):
-    return state + data, state + data
-
-class ScanV2(HybridBlock):
-    def hybrid_forward(self, F, data):
-        out, state = F.contrib.foreach(sum, data, F.zeros((1)))
-        return out, state
-scan_layer = ScanV2()
-out, state = scan_layer(data)
-print(out)
-print(state)
-```
-
-    
-    [[  0.]
-     [  1.]
-     [  3.]
-     [  6.]
-     [ 10.]]
-    <NDArray 5x1 @cpu(0)>
-    
-    [ 10.]
-    <NDArray 1 @cpu(0)>
-
-
-
-```python
-scan_layer.hybridize()
-out, state = scan_layer(data)
-print(out)
-print(state)
-```
-
-    
-    [[  0.]
-     [  1.]
-     [  3.]
-     [  6.]
-     [ 10.]]
-    <NDArray 5x1 @cpu(0)>
-    
-    [ 10.]
-    <NDArray 1 @cpu(0)>
-
-
-### Example 4: use `foreach` to run an RNN on a variable-length sequence
-Previous examples illustrate `foreach` with simple use cases. Here we show an example of processing variable-length sequences with `foreach`. The same idea is used by `dynamic_rnn` in TensorFlow for processing variable-length sequences.
-
-
-```python
-class DynamicRNNLayer(HybridBlock):
-    def __init__(self, cell, prefix=None, params=None):
-        super(DynamicRNNLayer, self).__init__(prefix=prefix, params=params)
-        self.cell = cell
-    def hybrid_forward(self, F, inputs, begin_state, valid_length):
-        states = begin_state
-        zeros = []
-        for s in states:
-            zeros.append(F.zeros_like(s))
-        # the last state is the iteration number.
-        states.append(F.zeros((1)))
-        def loop_body(inputs, states):
-            cell_states = states[:-1]
-            # Get the iteration number from the states.
-            iter_no = states[-1]
-            out, new_states = self.cell(inputs, cell_states)
-            # Copy the old state if we have reached the end of a sequence.
-            for i, state in enumerate(cell_states):
-                new_states[i] = F.where(F.broadcast_greater(valid_length, iter_no),
-                                        new_states[i], state)
-            new_states.append(iter_no + 1)
-            return out, new_states
-
-        outputs, states = F.contrib.foreach(loop_body, inputs, states)
-        outputs = F.SequenceMask(outputs, sequence_length=valid_length,
-                                 use_sequence_length=True, axis=0)
-        # the last state is the iteration number. We don't need it.
-        return outputs, states[:-1]
-
-
-seq_len = 10
-batch_size = 2
-input_size = 5
-hidden_size = 6
-
-rnn_data = mx.nd.normal(loc=0, scale=1, shape=(seq_len, batch_size, input_size))
-init_states = [mx.nd.normal(loc=0, scale=1, shape=(batch_size, hidden_size)) for i in range(2)]
-valid_length = mx.nd.round(mx.nd.random.uniform(low=1, high=10, shape=(batch_size))) 
-
-lstm = DynamicRNNLayer(mx.gluon.rnn.LSTMCell(hidden_size))
-lstm.initialize()
-res, states = lstm(rnn_data, [x for x in init_states], valid_length)
-
-lstm.hybridize()
-res, states = lstm(rnn_data, [x for x in init_states], valid_length)
-```
-
-## while_loop
-`while_loop` defines a while loop. It has the following signature:
-
-```
-while_loop(cond, body, loop_vars, max_iterations, name) => (outputs, states)
-```
-
-Instead of running over the first dimension of an array, `while_loop` checks a condition function in every iteration and runs a `body` function for computation. The signature of the `body` function is defined as follows:
-
-```
-body(state1, state2, ...) => (outputs, states)
-```
-
-The inputs of the `body` function in `while_loop` are a little different from the one in `foreach`. It has a variable number of input arguments. Each input argument is a loop variable and the number of arguments is determined by the number of loop variables. The outputs of the `body` function also have two parts: `outputs` is an array or a list of arrays; `states` are loop variables and will be passed to the next iteration as inputs of `body`. Like `foreach`, both `outputs` and `states` can be an empty list. `outputs` from all iterations are concatenated as the outputs of `while_loop`.
-
-### Example 5: scan with while_loop
-`while_loop` is more general than `foreach`. We can also use it to iterate over an array and sum all of its values together. In this example, instead of summing over the entire array, we only sum over the first 4 elements.
-
-**Note**: the output arrays of the current implementation of `while_loop` is determined by `max_iterations`. As such, even though the while loop in this example runs 4 iterations, it still outputs an array of 5 elements. The last element in the output array is actually filled with an arbitrary value.
-
-
-```python
-class ScanV2(HybridBlock):
-    def hybrid_forward(self, F, data):
-        def sum(state, i):
-            s = state + data[i]
-            return s, [s, i + 1]
-
-        def sum_cond(state, i):
-            return i < 4
-
-        out, state = F.contrib.while_loop(sum_cond, sum,
-                                          [F.zeros((1)), F.zeros((1))], max_iterations=5)
-        return out, state
-scan_layer = ScanV2()
-out, state = scan_layer(data)
-print(out)
-print(state)
-```
-
-    
-    [[ 0.]
-     [ 1.]
-     [ 3.]
-     [ 6.]
-     [ 0.]]
-    <NDArray 5x1 @cpu(0)>
-    [
-    [ 6.]
-    <NDArray 1 @cpu(0)>, 
-    [ 4.]
-    <NDArray 1 @cpu(0)>]
-
-
-## cond
-`cond` defines an if condition. It has the following signature:
-
-```
-cond(pred, then_func, else_func, name)
-```
-
-`cond` checks `pred`, which is a symbol or an NDArray with one element. If its value is true, it calls `then_func`. Otherwise, it calls `else_func`. The signature of `then_func` and `else_func` are as follows:
-
-```
-func() => [outputs]
-```
-
-`cond` requires all outputs from `then_func` and `else_func` have the same number of Symbols/NDArrays with the same shapes and data types.
-
-### Example 6: skip RNN computation with cond
-Example 4 shows how to process a batch with sequences of different lengths. It performs computation for all steps but discards some of the computation results.
-
-In this example, we show how to skip computation after we have reached the end of a sequence, whose length is indicated by `length`. The code below only works for a batch with one sequence.
-
-
-```python
-class SkipRNNCell(HybridBlock):
-    def __init__(self, cell, prefix=None, params=None):
-        super(SkipRNNCell, self).__init__(prefix=prefix, params=params)
-        self.cell = cell
-    def hybrid_forward(self, F, i, length, data, states):
-        def run_rnn():
-            return self.cell(data, states)
-
-        def copy_states():
-            return F.zeros_like(data), states
-        out, state = F.contrib.cond(i < length, run_rnn, copy_states)
-        return out, state
-
-class RNNLayer(HybridBlock):
-    def __init__(self, cell, prefix=None, params=None):
-        super(RNNLayer, self).__init__(prefix=prefix, params=params)
-        self.cell = SkipRNNCell(cell)
-    def hybrid_forward(self, F, length, data, init_states):
-        def body(data, states):
-            i = states[0]
-            out, states = self.cell(i, length, data, states[1])
-            return out, [i + 1, states]
-        print()
-        out, state = F.contrib.foreach(body, data, [F.zeros((1)), init_states])
-        return out, state
-
-
-seq_len = 5
-batch_size = 1
-input_size = 3
-hidden_size = 3
-
-rnn_data = mx.nd.normal(loc=0, scale=1, shape=(seq_len, batch_size, input_size))
-init_states = [mx.nd.normal(loc=0, scale=1, shape=(batch_size, hidden_size)) for i in range(2)]
-
-cell = mx.gluon.rnn.LSTMCell(hidden_size)
-layer = RNNLayer(cell)
-layer.initialize()
-
-out, states = layer(mx.nd.array([3]), rnn_data, init_states)
-print(rnn_data)
-print(out)
-```
-
-    ()
-    
-    [[[-1.25296438  0.387312   -0.41055229]]
-    
-     [[ 1.28453672  0.21001032 -0.08666432]]
-    
-     [[ 1.46422136 -1.30581355  0.9344402 ]]
-    
-     [[ 0.5380863  -0.16038011  0.84187603]]
-    
-     [[-1.00553632  3.13221502 -0.4358989 ]]]
-    <NDArray 5x1x3 @cpu(0)>
-    
-    [[[-0.02620504  0.1605694   0.29636264]]
-    
-     [[-0.00474182  0.08719197  0.17757624]]
-    
-     [[ 0.00631597  0.04674901  0.12468992]]
-    
-     [[ 0.          0.          0.        ]]
-    
-     [[ 0.          0.          0.        ]]]
-    <NDArray 5x1x3 @cpu(0)>
-
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/control_flow/index.md b/docs/tutorials/control_flow/index.md
deleted file mode 100644
index faf6526fb824..000000000000
--- a/docs/tutorials/control_flow/index.md
+++ /dev/null
@@ -1,25 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Tutorials
-
-```eval_rst
-.. toctree::
-   :glob:
-
-   *
-```
diff --git a/docs/tutorials/embedded/index.md b/docs/tutorials/embedded/index.md
deleted file mode 100644
index faf6526fb824..000000000000
--- a/docs/tutorials/embedded/index.md
+++ /dev/null
@@ -1,25 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Tutorials
-
-```eval_rst
-.. toctree::
-   :glob:
-
-   *
-```
diff --git a/docs/tutorials/embedded/wine_detector.md b/docs/tutorials/embedded/wine_detector.md
deleted file mode 100644
index a646de8bfe1d..000000000000
--- a/docs/tutorials/embedded/wine_detector.md
+++ /dev/null
@@ -1,405 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Real-time Object Detection with MXNet On The Raspberry Pi  
-
-This tutorial shows developers who work with the Raspberry Pi or similar embedded ARM-based devices how to compile MXNet for those devices and run a pretrained deep network model. It also shows how to use AWS IoT to manage and monitor MXNet models running on your devices.
-
-## What's In This Tutorial?
-
-This tutorial shows how to:
-
-1. Use MXNet to set up a real-time object classifier on a Raspberry Pi 3 device.
-
-2. Connect the local Raspberry Pi model to the AWS Cloud with AWS IoT to get real-time updates from the device.
-
-### Who's This Tutorial For?
-
-It assumes that you are familiar with the Raspbian operating system and the [Raspberry Pi ecosystem](https://www.raspberrypi.org/) and are somewhat familiar with machine learning, MXNet, and [AWS IoT](https://aws.amazon.com/iot/). All code is written in Python 2.7.
-
-### How to Use This Tutorial
-
-To follow this tutorial, you must set up your Pi as instructed (preferably from a fresh Raspbian install), and then create the files and run the bash commands described below. All instructions described are can be executed on the Raspberry Pi directly or via SSH.
-
-You will accomplish the following:
-
-- Build and Install MXNet with Python bindings on your Raspbian Based Raspberry Pi
-- Fetch and run a pre-trained MXNet model on your Pi
-- Create a real-time video analysis application for the Pi
-- Connect the application to the AWS IoT service
-
-## Prerequisites
-
-To complete this tutorial, you need:
-
-* Raspbian Wheezy or later, which can be downloaded [here](https://www.raspberrypi.org/downloads/raspbian/), loaded onto a 8GB+ micro SD card (with at least 4GB+ free)
-* A [Raspberry Pi 3](https://www.raspberrypi.org/blog/raspberry-pi-3-on-sale/) or equivalent Raspberry Pi with 1GB+ of RAM
-* A [Raspberry Pi Camera Module](https://www.raspberrypi.org/products/camera-module-v2/) [activated and running with the corresponding Python module](http://www.pyimagesearch.com/2015/02/23/install-opencv-and-python-on-your-raspberry-pi-2-and-b/) (for the real-time video analysis with the deep network model)
-* An AWS account With AWS IoT enabled and the [AWS IoT Python SDK](https://github.com/aws/aws-iot-device-sdk-python) (for remote, real-time managing and monitoring of the model running on the Pi)
-* The [cv2 Python library](http://www.pyimagesearch.com/2015/02/23/install-opencv-and-python-on-your-raspberry-pi-2-and-b/) for the Pi
-
-## Building MXNet for The Pi
-
-The first step is to get MXNet with the Python bindings running on your Raspberry Pi 3. There is a tutorial for that provided [here](http://mxnet.io/install/index.html). The linked tutorial walks you through downloading the dependencies, and building the full MXNet library for the Pi with the ARM specific compile flags. Be sure to build the library with open CV as we will be using a model that requires it to process images. Then you will register the Python bindings to MXNet. After this is done you should test that your installation works by opening a python REPL on your Pi and typing the following commands:
-
-
-```bash
-python
->>> import mxnet as mx
-```
-
-*Note: If you are getting memory allocation failed errors at this point (or at any point in this tutorial) it is likely because the full MXNet library takes up a large amount of RAM when loaded. You might want to kill the GUI and other processes that are occupying memory.*
-
-
-## Running A Pre-Trained Inception Model on The Pi
-
-We are now ready to load a pre-trained model and run inference on the Pi. We will be using a simple object recognition model trained on the ImageNet data set. The model is called batch normalized Inception network (or Inception_BN for short) and it is found in the MXNet model zoo.
-
-### Getting the Model
-
-The first step is to download, unzip, and set up the pre-trained deep network model files that we will be using to classify images. To do this run the following commands in your home directory:
-
-```bash
-curl --header 'Host: data.mxnet.io' --header 'User-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10.11; rv:45.0) Gecko/20100101 Firefox/45.0' --header 'Accept: text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8' --header 'Accept-Language: en-US,en;q=0.5' --header 'Referer: http://data.mxnet.io/models/imagenet/' --header 'Connection: keep-alive' 'http://data.mxnet.io/models/imagenet/inception-bn.tar.gz' -o 'inception-bn.tar.gz' -L
-
-tar -xvzf inception-bn.tar.gz
-
-mv Inception_BN-0039.params Inception_BN-0000.params
-```
-
-### Running the Model
-
-The next step is to create a python script to load the model, and run inference on local image files. To do this create a new file in your home directory called inception_predict.py and add the following code to it:
-
-
-```python
-# inception_predict.py
-
-import mxnet as mx
-import numpy as np
-import time
-import cv2, os, urllib
-from collections import namedtuple
-Batch = namedtuple('Batch', ['data'])
-
-# Load the symbols for the networks
-with open('synset.txt', 'r') as f:
-    synsets = [l.rstrip() for l in f]
-
-# Load the network parameters
-sym, arg_params, aux_params = mx.model.load_checkpoint('Inception-BN', 126)
-
-
-# Load the network into an MXNet module and bind the corresponding parameters
-mod = mx.mod.Module(symbol=sym, context=mx.cpu())
-mod.bind(for_training=False, data_shapes=[('data', (1,3,224,224))])
-mod.set_params(arg_params, aux_params)
-
-'''
-Function to predict objects by giving the model a pointer to an image file and running a forward pass through the model.
-
-inputs:
-filename = jpeg file of image to classify objects in
-mod = the module object representing the loaded model
-synsets = the list of symbols representing the model
-N = Optional parameter denoting how many predictions to return (default is top 5)
-
-outputs:
-python list of top N predicted objects and corresponding probabilities
-'''
-def predict(filename, mod, synsets, N=5):
-    tic = time.time()
-    img = cv2.cvtColor(cv2.imread(filename), cv2.COLOR_BGR2RGB)
-    if img is None:
-        return None
-    img = cv2.resize(img, (224, 224))
-    img = np.swapaxes(img, 0, 2)
-    img = np.swapaxes(img, 1, 2)
-    img = img[np.newaxis, :]
-    print "pre-processed image in "+str(time.time()-tic)
-
-    toc = time.time()
-    mod.forward(Batch([mx.nd.array(img)]))
-    prob = mod.get_outputs()[0].asnumpy()
-    prob = np.squeeze(prob)
-    print "forward pass in "+str(time.time()-toc)
-
-
-    topN = []
-    a = np.argsort(prob)[::-1]
-    for i in a[0:N]:
-        print('probability=%f, class=%s' %(prob[i], synsets[i]))
-        topN.append((prob[i], synsets[i]))
-    return topN
-
-
-# Code to download an image from the internet and run a prediction on it
-def predict_from_url(url, N=5):
-    filename = url.split("/")[-1]
-    urllib.urlretrieve(url, filename)
-    img = cv2.imread(filename)
-    if img is None:
-        print "Failed to download"
-    else:
-        return predict(filename, mod, synsets, N)
-
-# Code to predict on a local file
-def predict_from_local_file(filename, N=5):
-    return predict(filename, mod, synsets, N)
-```
-
-Now that we have defined inception_predict.py we can test that the model is running correctly. Open a Python REPL in your home directory and enter the following:
-
-```bash
-python
->>> from inception_predict import *
->>> predict_from_url("https://i.imgur.com/HzafyBA.jpg")
-```
-
-This should give a reasonable prediction for the fluffy cow in this [image](http://imgur.com/HzafyBA).
-
-```
-pre-processed image in 0.20366191864
-forward pass in 63.2164611816
-probability=0.718524, class=n02403003 ox
-probability=0.176381, class=n02389026 sorrel
-probability=0.095558, class=n03868242 oxcart
-probability=0.002765, class=n02408429 water buffalo, water ox, Asiatic buffalo, Bubalus bubalis
-probability=0.001262, class=n03935335 piggy bank, penny bank
-[(0.71852392, 'n02403003 ox'), (0.17638102, 'n02389026 sorrel'), (0.09555836, 'n03868242 oxcart'),
-(0.0027645244, 'n02408429 water buffalo, water ox, Asiatic buffalo, Bubalus bubalis'),
-(0.0012616422, 'n03935335 piggy bank, penny bank')]
-```
-
-
-## Running an Inception on Real-Time Video From PiCamera
-
-We can now move on to using this network for object detection in real-time video from the PiCamera.
-
-Doing this requires sending the images that the camera is capturing to the prediction code that we created in the previous step. To do this, create a new file in your home directory called camera_test.py and add the following code to it:
-
-
-```python
-# camera_test.py
-
-import picamera
-import inception_predict
-
-# Create camera interface
-camera = picamera.PiCamera()
-while True:
-    # Take the jpg image from camera
-    print "Capturing"
-    filename = '/home/pi/cap.jpg'
-    # Show quick preview of what's being captured
-    camera.start_preview()
-    camera.capture(filename)
-    camera.stop_preview()
-
-    # Run inception prediction on image
-    print "Predicting"
-    topn = inception_predict.predict_from_local_file(filename, N=5)
-
-    # Print the top N most likely objects in image (default set to 5, change this in the function call above)
-    print topn
-```
-
-You can then run this file by entering the following command:
-
-```bash
-python camera_test.py
-```
-
-If camera_test.py is working you should see a preview every few seconds of the image that is being captured and fed to the model, as well as predicted classes for objects in the image being written to the terminal.
-
-Try pointing the PiCamera at a few different objects and see what predictions the network comes out with.
-
-## Connecting Our Model To The AWS Cloud
-
-We can now move on to adding the code to send the predictions that this real-time model is making locally to the AWS cloud if certain conditions are met.
-
-The first step is to set up an AWS account if you don't have one yet. Then go to the [AWS IoT dashboard](https://us-west-2.console.aws.amazon.com/iotv2/home?region=us-west-2#/thinghub) and register a new device.
-
-After the device is registered, download and copy the corresponding rootCA, Certificate, and Private key to your home directory. Note the unique endpoint of your device shadow on the AWS IoT Dashboard.
-
-We will now build an application, based off the code in camera_test.py, which will send a message to the cloud whenever a wine bottle is detected in a frame by the PiCamera.
-
-To do this create a new file in your home directory called wine_alerter.py and add the following code to it:
-
-
-```python
-# wine_alerter.py
-
-import AWSIoTPythonSDK
-from AWSIoTPythonSDK.MQTTLib import AWSIoTMQTTClient
-import sys
-import logging
-import time
-import getopt
-import picamera
-import inception_predict
-
-# Custom MQTT message callback
-def customCallback(client, userdata, message):
-    print("Received a new message: ")
-    print(message.payload)
-    print("from topic: ")
-    print(message.topic)
-    print("--------------\n\n")
-
-# Usage
-usageInfo = """Usage:
-
-Use certificate based mutual authentication:
-python wine_alerter.py -e <endpoint> -r <rootCAFilePath> -c <certFilePath> -k <privateKeyFilePath>
-
-Use MQTT over WebSocket:
-python wine_alerter.py -e <endpoint> -r <rootCAFilePath> -w
-
-Type "python wine_alerter.py -h" for available options.
-"""
-
-# Help info
-helpInfo = """-e, --endpoint
-    Your AWS IoT custom endpoint
--r, --rootCA
-    Root CA file path
--c, --cert
-    Certificate file path
--k, --key
-    Private key file path
--w, --websocket
-    Use MQTT over WebSocket
--h, --help
-    Help information
-"""
-
-# Read in command-line parameters
-useWebsocket = False
-host = ""
-rootCAPath = ""
-certificatePath = ""
-privateKeyPath = ""
-try:
-    opts, args = getopt.getopt(sys.argv[1:], "hwe:k:c:r:", ["help", "endpoint=", "key=","cert=","rootCA=", "websocket"])
-    if len(opts) == 0:
-        raise getopt.GetoptError("No input parameters!")
-    for opt, arg in opts:
-        if opt in ("-h", "--help"):
-            print(helpInfo)
-            exit(0)
-        if opt in ("-e", "--endpoint"):
-            host = arg
-        if opt in ("-r", "--rootCA"):
-            rootCAPath = arg
-        if opt in ("-c", "--cert"):
-            certificatePath = arg
-        if opt in ("-k", "--key"):
-            privateKeyPath = arg
-        if opt in ("-w", "--websocket"):
-            useWebsocket = True
-except getopt.GetoptError:
-    print(usageInfo)
-    exit(1)
-
-# Missing configuration notification
-missingConfiguration = False
-if not host:
-    print("Missing '-e' or '--endpoint'")
-    missingConfiguration = True
-if not rootCAPath:
-    print("Missing '-r' or '--rootCA'")
-    missingConfiguration = True
-if not useWebsocket:
-    if not certificatePath:
-        print("Missing '-c' or '--cert'")
-        missingConfiguration = True
-    if not privateKeyPath:
-        print("Missing '-k' or '--key'")
-        missingConfiguration = True
-if missingConfiguration:
-    exit(2)
-
-
-# Configure logging
-logger = logging.getLogger("AWSIoTPythonSDK.core")
-logger.setLevel(logging.DEBUG)
-streamHandler = logging.StreamHandler()
-formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
-streamHandler.setFormatter(formatter)
-logger.addHandler(streamHandler)
-
-
-# Init AWSIoTMQTTClient For Publish/Subscribe Communication With Server
-myAWSIoTMQTTClient = None
-if useWebsocket:
-    myAWSIoTMQTTClient = AWSIoTMQTTClient("basicPubSub", useWebsocket=True)
-    myAWSIoTMQTTClient.configureEndpoint(host, 443)
-    myAWSIoTMQTTClient.configureCredentials(rootCAPath)
-else:
-    myAWSIoTMQTTClient = AWSIoTMQTTClient("basicPubSub")
-    myAWSIoTMQTTClient.configureEndpoint(host, 8883)
-    myAWSIoTMQTTClient.configureCredentials(rootCAPath, privateKeyPath, certificatePath)
-
-
-# AWSIoTMQTTClient connection configuration
-myAWSIoTMQTTClient.configureAutoReconnectBackoffTime(1, 32, 20)
-myAWSIoTMQTTClient.configureOfflinePublishQueueing(-1)  # Infinite offline Publish queueing
-myAWSIoTMQTTClient.configureDrainingFrequency(2)  # Draining: 2 Hz
-myAWSIoTMQTTClient.configureConnectDisconnectTimeout(10)  # 10 sec
-myAWSIoTMQTTClient.configureMQTTOperationTimeout(5)  # 5 sec
-
-
-# Connect and subscribe to AWS IoT
-myAWSIoTMQTTClient.connect()
-myAWSIoTMQTTClient.subscribe("sdk/test/Python", 1, customCallback)
-time.sleep(2)
-
-
-# Start the Camera and tell the Server we are alive
-print "Running camera"
-myAWSIoTMQTTClient.publish("sdk/test/Python", "New Message: Starting Camera", 0)
-camera = picamera.PiCamera()
-
-# Capture forever (this is a modified version of camera_test.py)
-while True:
-    filename = '/home/pi/cap.jpg'
-    camera.start_preview()
-    camera.capture(filename)
-    camera.stop_preview()
-    topn = inception_predict.predict_from_local_file(filename, N=5)
-
-    # Check if either of the top two predictions are wine related and publish a message if it is
-    # you can change 'wine' here to anything you want to alert the server about detecting
-    if 'wine' in topn[0][1] or 'wine' in topn[1][1]:
-        myAWSIoTMQTTClient.publish("sdk/test/Python", "New Message: WINE DETECTED!", 0)
-```
-
-You can then run this file by entering the following command
-
-```bash
-python wine_alerter.py -e <endpointURL> -r <rootCAFilePath> -c <certFilePath> -k <privateKeyFilePath>
-```
-
-If this is working you should see the same kind of image preview you did with camera_test.py every few seconds, however the console will only print a message now when a wine bottle is detected in the shot (you can edit the bottom lines in the wine_alerter.py code to make this alert for any object label from the [ImageNet-11k dataset](http://image-net.org/index) that you specify).
-
-You can open up the activity tab for the thing that you registered on the AWS IoT Dashboard and see the corresponding messages pushed to the server whenever a wine bottle is detected in a camera shot. Even if network connectivity periodically fails, AWS IoT will push updates out to the server when possible, allowing this system to reliably let you know when there is wine around.
-
-## Summary
-You now have a Raspberry Pi running a pre-trained MXNet model fully locally. This model is also linked to the cloud via AWS IoT and can reliably alert AWS whenever it sees a wine bottle.
-
-You can now extend this demo to create more interesting applications, such as using AWS IoT to push model updates to your Pi, loading different pre-trained models from the MXNet Model Zoo onto the Pi, or even training full end-to-end models on the Pi.
diff --git a/docs/tutorials/gluon/autograd.md b/docs/tutorials/gluon/autograd.md
deleted file mode 100644
index 4d8a042c9b03..000000000000
--- a/docs/tutorials/gluon/autograd.md
+++ /dev/null
@@ -1,72 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Automatic differentiation
-
-MXNet supports automatic differentiation with the `autograd` package.
-`autograd` allows you to differentiate a graph of NDArray operations
-with the chain rule.
-This is called define-by-run, i.e., the network is defined on-the-fly by
-running forward computation. You can define exotic network structures
-and differentiate them, and each iteration can have a totally different
-network structure.
-
-```python
-import mxnet as mx
-from mxnet import autograd
-```
-
-To use `autograd`, we must first mark variables that require gradient and
-attach gradient buffers to them:
-
-```python
-x = mx.nd.array([[1, 2], [3, 4]])
-x.attach_grad()
-```
-
-Now we can define the network while running forward computation by wrapping
-it inside a `record` (operations out of `record` does not define
-a graph and cannot be differentiated):
-
-```python
-with autograd.record():
-  y = x * 2
-  z = y * x
-```
-
-Let's backprop with `z.backward()`, which is equivalent to
-`z.backward(mx.nd.ones_like(z))`. When z has more than one entry, `z.backward()`
-is equivalent to `mx.nd.sum(z).backward()`:
-
-```python
-z.backward()
-print(x.grad)
-```
-
-Now, let's see if this is the expected output.
-
-Here, y = f(x), z = f(y) = f(g(x))
-which means y = 2 * x and z = 2 * x * x.
-
-After, doing backprop with `z.backward()`, we will get gradient dz/dx as follows:
-
-dy/dx = 2,
-dz/dx = 4 * x
-
-So, we should get x.grad as an array of [[4, 8],[12, 16]].
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/gluon/custom_layer.md b/docs/tutorials/gluon/custom_layer.md
deleted file mode 100644
index d2d1f56160b3..000000000000
--- a/docs/tutorials/gluon/custom_layer.md
+++ /dev/null
@@ -1,265 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-
-# How to write a custom layer in Apache MxNet Gluon API
-
-While Gluon API for Apache MxNet comes with [a decent number of pre-defined layers](https://mxnet.incubator.apache.org/api/python/gluon/nn.html), at some point one may find that a new layer is needed. Adding a new layer in Gluon API is straightforward, yet there are a few things that one needs to keep in mind.
-
-In this article, I will cover how to create a new layer from scratch, how to use it, what are possible pitfalls and how to avoid them.
-
-## The simplest custom layer
-
-To create a new layer in Gluon API, one must create a class that inherits from [Block](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/block.py#L123) class. This class provides the most basic functionality, and all pre-defined layers inherit from it directly or via other subclasses. Because each layer in Apache MxNet inherits from `Block`, words "layer" and "block" are used interchangeable inside of the Apache MxNet community.
-
-The only instance method needed to be implemented is [forward(self, x)](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/block.py#L415), which defines what exactly your layer is going to do during forward propagation. Notice, that it doesn't require to provide what the block should do during back propogation. Back propogation pass for blocks is done by Apache MxNet for you. 
-
-In the example below, we define a new layer and implement `forward()` method to normalize input data by fitting it into a range of [0, 1].
-
-
-```python
-# Do some initial imports used throughout this tutorial 
-from __future__ import print_function
-import mxnet as mx
-from mxnet import nd, gluon, autograd
-from mxnet.gluon.nn import Dense
-mx.random.seed(1)                      # Set seed for reproducable results
-```
-
-
-```python
-class NormalizationLayer(gluon.Block):
-    def __init__(self):
-        super(NormalizationLayer, self).__init__()
-
-    def forward(self, x):
-        return (x - nd.min(x)) / (nd.max(x) - nd.min(x))
-```
-
-The rest of methods of the `Block` class are already implemented, and majority of them are used to work with parameters of a block. There is one very special method named [hybridize()](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/block.py#L384), though, which I am going to cover before moving to a more complex example of a custom layer.
-
-## Hybridization and the difference between Block and HybridBlock
-
-Looking into implementation of [existing layers](https://mxnet.incubator.apache.org/api/python/gluon/nn.html), one may find that more often a block inherits from a [HybridBlock](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/block.py#L428), instead of directly inheriting from `Block`.
-
-The reason for that is that `HybridBlock` allows to write custom layers that can be used in imperative programming as well as in symbolic programming. It is convinient to support both ways, because the imperative programming eases the debugging of the code and the symbolic one provides faster execution speed. You can learn more about the difference between symbolic vs. imperative programming from [this article](https://mxnet.incubator.apache.org/architecture/program_model.html).
-
-Hybridization is a process that Apache MxNet uses to create a symbolic graph of a forward computation. This allows to increase computation performance by optimizing the computational symbolic graph. Once the symbolic graph is created, Apache MxNet caches and reuses it for subsequent computations.
-
-To simplify support of both imperative and symbolic programming, Apache MxNet introduce the `HybridBlock` class. Compare to the `Block` class, `HybridBlock` already has its [forward()](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.HybridBlock.forward) method implemented, but it defines a [hybrid_forward()](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.HybridBlock.hybrid_forward) method that needs to be implemented.
-
-The main difference between `forward()` and `hybrid_forward()` is an `F` argument. This argument sometimes is refered as a `backend` in the Apache MxNet community. Depending on if hybridization has been done or not, `F` can refer either to [mxnet.ndarray API](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html) or [mxnet.symbol API](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html). The former is used for imperative programming, and the latter for symbolic programming. 
-
-To support hybridization, it is important to use only methods avaible directly from `F` parameter. Usually, there are equivalent methods in both APIs, but sometimes there are mismatches or small variations. For example, by default, subtraction and division of NDArrays support broadcasting, while in Symbol API broadcasting is supported in a separate operators. 
-
-Knowing this, we can can rewrite our example layer, using HybridBlock:
-
-
-```python
-class NormalizationHybridLayer(gluon.HybridBlock):
-    def __init__(self):
-        super(NormalizationHybridLayer, self).__init__()
-
-    def hybrid_forward(self, F, x):
-        return F.broadcast_div(F.broadcast_sub(x, F.min(x)), (F.broadcast_sub(F.max(x), F.min(x))))
-```
-
-Thanks to inheriting from HybridBlock, one can easily do forward pass on a given ndarray, either on CPU or GPU:
-
-
-```python
-layer = NormalizationHybridLayer()
-layer(nd.array([1, 2, 3], ctx=mx.cpu()))
-```
-
-
-
-
-    
-    [0.  0.5 1. ]
-    <NDArray 3 @cpu(0)>
-
-
-
-As a rule of thumb, one should always implement custom layers by inheriting from `HybridBlock`. This allows to have more flexibility, and doesn't affect execution speed once hybridization is done. 
-
-Unfortunately, at the moment of writing this tutorial, NLP related layers such as [RNN](https://mxnet.incubator.apache.org/api/python/gluon/rnn.html#mxnet.gluon.rnn.RNN), [GRU](https://mxnet.incubator.apache.org/api/python/gluon/rnn.html#mxnet.gluon.rnn.GRU) and [LSTM](https://mxnet.incubator.apache.org/api/python/gluon/rnn.html#mxnet.gluon.rnn.LSTM) are directly inhereting from the `Block` class via common `_RNNLayer` class. That means that networks with such layers cannot be hybridized. But this might change in the future, so stay tuned.
-
-It is important to notice that hybridization has nothing to do with computation on GPU. One can train both hybridized and non-hybridized networks on both CPU and GPU, though hybridized networks would work faster. Though, it is hard to say in advance how much faster it is going to be.
-
-## Adding a custom layer to a network
-
-While it is possible, custom layers are rarely used separately. Most often they are used with predefined layers to create a neural network. Output of one layer is used as an input of another layer.
-
-Depending on which class you used as a base one, you can use either [Sequential](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.nn.Sequential) or [HybridSequential](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.nn.HybridSequential) container to form a sequential neural network. By adding layers one by one, one adds dependencies of one layer's input from another layer's output. It is worth noting, that both `Sequential` and `HybridSequential` containers inherit from `Block` and `HybridBlock` respectively. 
-
-Below is an example of how to create a simple neural network with a custom layer. In this example, `NormalizationHybridLayer` gets as an input the output from `Dense(5)` layer and pass its output as an input to `Dense(1)` layer.
-
-
-```python
-net = gluon.nn.HybridSequential()                         # Define a Neural Network as a sequence of hybrid blocks
-with net.name_scope():                                    # Used to disambiguate saving and loading net parameters
-    net.add(Dense(5))                                     # Add Dense layer with 5 neurons
-    net.add(NormalizationHybridLayer())                   # Add our custom layer
-    net.add(Dense(1))                                     # Add Dense layer with 1 neurons
-
-
-net.initialize(mx.init.Xavier(magnitude=2.24))            # Initialize parameters of all layers
-net.hybridize()                                           # Create, optimize and cache computational graph
-input = nd.random_uniform(low=-10, high=10, shape=(5, 2)) # Create 5 random examples with 2 feature each in range [-10, 10]
-net(input)
-```
-
-
-
-
-    
-    [[-0.13601446]
-     [ 0.26103732]
-     [-0.05046433]
-     [-1.2375476 ]
-     [-0.15506986]]
-    <NDArray 5x1 @cpu(0)>
-
-
-
-## Parameters of a custom layer
-
-Usually, a layer has a set of associated parameters, sometimes also referred as weights. This is an internal state of a layer. Most often, these parameters are the ones, that we want to learn during backpropogation step, but sometimes these parameters might be just constants we want to use during forward pass.
-
-All parameters of a block are stored and accessed via [ParameterDict](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/parameter.py#L508) class. This class helps with initialization, updating, saving and loading of the parameters. Each layer can have multiple set of parameters, and all of them can be stored in a single instance of the `ParameterDict` class. On a block level, the instance of the `ParameterDict` class is accessible via `self.params` field, and outside of a block one can access all parameters of the network via [collect_params()](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.Block.collect_params) method called on a `container`. `ParameterDict` uses [Parameter](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.Parameter) class to represent parameters inside of Apache MxNet neural network. If parameter doesn't exist, trying to get a parameter via `self.params` will create it automatically.
-
-
-```python
-class NormalizationHybridLayer(gluon.HybridBlock):
-    def __init__(self, hidden_units, scales):
-        super(NormalizationHybridLayer, self).__init__()
-
-        with self.name_scope():
-            self.weights = self.params.get('weights',
-                                           shape=(hidden_units, 0),
-                                           allow_deferred_init=True)
-
-            self.scales = self.params.get('scales',
-                                      shape=scales.shape,
-                                      init=mx.init.Constant(scales.asnumpy()), # Convert to regular list to make this object serializable
-                                      differentiable=False)
-            
-    def hybrid_forward(self, F, x, weights, scales):
-        normalized_data = F.broadcast_div(F.broadcast_sub(x, F.min(x)), (F.broadcast_sub(F.max(x), F.min(x))))
-        weighted_data = F.FullyConnected(normalized_data, weights, num_hidden=self.weights.shape[0], no_bias=True)
-        scaled_data = F.broadcast_mul(scales, weighted_data)
-        return scaled_data
-```
-
-In the example above 2 set of parameters are defined:
-1. Parameter `weights` is trainable. Its shape is unknown during construction phase and will be infered on the first run of forward propogation; 
-1. Parameter `scale` is a constant that doesn't change. Its shape is defined during construction.
-
-Notice a few aspects of this code:
-* `name_scope()` method is used to add a prefix to parameter names during saving and loading
-* Shape is not provided when creating `weights`. Instead it is going to be infered from the shape of the input
-* `Scales` parameter is initialized and marked as `differentiable=False`.
-* `F` backend is used for all calculations
-* The calculation of dot product is done using `F.FullyConnected()` method instead of `F.dot()` method. The one was chosen over another because the former supports automatic infering shapes of inputs while the latter doesn't. This is extremely important to know, if one doesn't want to hard code all the shapes. The best way to learn what operators supports automatic inference of input shapes at the moment is browsing C++ implementation of operators to see if one uses a method `SHAPE_ASSIGN_CHECK(*in_shape, fullc::kWeight, Shape2(param.num_hidden, num_input));`
-* `hybrid_forward()` method signature has changed. It accepts two new arguments: `weights` and `scales`.
-
-The last peculiarity is due to support of imperative and symbolic programming by `HybridBlock`. During training phase, parameters are passed to the layer by Apache MxNet framework as additional arguments to the method, because they might need to be converted to a `Symbol` depending on if the layer was hybridized. One shouldn't use `self.weights` and `self.scales` or `self.params.get` in `hybrid_forward` except to get shapes of parameters. 
-
-Running forward pass on this network is very similar to the previous example, so instead of just doing one forward pass, let's run whole training for a few epochs to show that `scales` parameter doesn't change during the training while `weights` parameter is changing.
-
-
-```python
-def print_params(title, net):
-    """
-    Helper function to print out the state of parameters of NormalizationHybridLayer
-    """
-    print(title)
-    hybridlayer_params = {k: v for k, v in net.collect_params().items() if 'normalizationhybridlayer' in k }
-    
-    for key, value in hybridlayer_params.items():
-        print('{} = {}\n'.format(key, value.data()))
-
-net = gluon.nn.HybridSequential()                             # Define a Neural Network as a sequence of hybrid blocks
-with net.name_scope():                                        # Used to disambiguate saving and loading net parameters
-    net.add(Dense(5))                                         # Add Dense layer with 5 neurons
-    net.add(NormalizationHybridLayer(hidden_units=5, 
-                                     scales = nd.array([2]))) # Add our custom layer
-    net.add(Dense(1))                                         # Add Dense layer with 1 neurons
-
-
-net.initialize(mx.init.Xavier(magnitude=2.24))                # Initialize parameters of all layers
-net.hybridize()                                               # Create, optimize and cache computational graph
-
-input = nd.random_uniform(low=-10, high=10, shape=(5, 2))     # Create 5 random examples with 2 feature each in range [-10, 10]
-label = nd.random_uniform(low=-1, high=1, shape=(5, 1))
-
-mse_loss = gluon.loss.L2Loss()                                # Mean squared error between output and label
-trainer = gluon.Trainer(net.collect_params(),                 # Init trainer with Stochastic Gradient Descent (sgd) optimization method and parameters for it
-                        'sgd', 
-                        {'learning_rate': 0.1, 'momentum': 0.9 })
-                        
-with autograd.record():                                       # Autograd records computations done on NDArrays inside "with" block 
-    output = net(input)                                       # Run forward propogation
-    
-    print_params("=========== Parameters after forward pass ===========\n", net)    
-    loss = mse_loss(output, label)                            # Calculate MSE
-    
-loss.backward()                                               # Backward computes gradients and stores them as a separate array within each NDArray in .grad field
-trainer.step(input.shape[0])                                  # Trainer updates parameters of every block, using .grad field using oprimization method (sgd in this example)
-                                                              # We provide batch size that is used as a divider in cost function formula
-print_params("=========== Parameters after backward pass ===========\n", net)
-
-```
-
-    =========== Parameters after forward pass ===========
-    
-    hybridsequential94_normalizationhybridlayer0_weights = 
-    [[-0.3983642  -0.505708   -0.02425683 -0.3133553  -0.35161012]
-     [ 0.6467543   0.3918715  -0.6154656  -0.20702496 -0.4243446 ]
-     [ 0.6077331   0.03922009  0.13425875  0.5729856  -0.14446527]
-     [-0.3572498   0.18545026 -0.09098256  0.5106366  -0.35151464]
-     [-0.39846328  0.22245121  0.13075739  0.33387476 -0.10088372]]
-    <NDArray 5x5 @cpu(0)>
-    
-    hybridsequential94_normalizationhybridlayer0_scales = 
-    [2.]
-    <NDArray 1 @cpu(0)>
-    
-    =========== Parameters after backward pass ===========
-    
-    hybridsequential94_normalizationhybridlayer0_weights = 
-    [[-0.29839832 -0.47213346  0.08348035 -0.2324698  -0.27368504]
-     [ 0.76268613  0.43080837 -0.49052125 -0.11322092 -0.3339738 ]
-     [ 0.48665082 -0.00144657  0.00376363  0.47501418 -0.23885089]
-     [-0.22626656  0.22944227  0.05018325  0.6166192  -0.24941102]
-     [-0.44946212  0.20532274  0.07579394  0.29261002 -0.14063817]]
-    <NDArray 5x5 @cpu(0)>
-    
-    hybridsequential94_normalizationhybridlayer0_scales = 
-    [2.]
-    <NDArray 1 @cpu(0)>
-    
-
-
-As it is seen from the output above, `weights` parameter has been changed by the training and `scales` not.
-
-## Conclusion
-
-One important quality of a Deep learning framework is extensibility. Empowered by flexible abstractions, like `Block` and `HybridBlock`, one can easily extend Apache MxNet functionality to match its needs.
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/gluon/customop.md b/docs/tutorials/gluon/customop.md
deleted file mode 100644
index 29ab21843114..000000000000
--- a/docs/tutorials/gluon/customop.md
+++ /dev/null
@@ -1,238 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-
-# Creating custom operators with numpy
-
-In this tutorial, we will learn how to build custom operators with numpy in python. We will go through two examples:
-- Custom operator without any `Parameter`s
-- Custom operator with `Parameter`s
-
-Custom operator in python is easy to develop and good for prototyping, but may hurt performance. If you find it to be a bottleneck, please consider moving to a C++ based implementation in the backend.
-
-
-
-```python
-import numpy as np
-import mxnet as mx
-from mxnet import gluon, autograd
-import os
-```
-
-## Parameter-less operators
-
-This operator implements the standard sigmoid activation function. This is only for illustration purposes, in real life you would use the built-in operator `mx.nd.relu`.
-
-### Forward & backward implementation
-
-First we implement the forward and backward computation by sub-classing `mx.operator.CustomOp`:
-
-
-```python
-class Sigmoid(mx.operator.CustomOp):
-    def forward(self, is_train, req, in_data, out_data, aux):
-        """Implements forward computation.
-
-        is_train : bool, whether forwarding for training or testing.
-        req : list of {'null', 'write', 'inplace', 'add'}, how to assign to out_data. 'null' means skip assignment, etc.
-        in_data : list of NDArray, input data.
-        out_data : list of NDArray, pre-allocated output buffers.
-        aux : list of NDArray, mutable auxiliary states. Usually not used.
-        """
-        x = in_data[0].asnumpy()
-        y = 1.0 / (1.0 + np.exp(-x))
-        self.assign(out_data[0], req[0], mx.nd.array(y))
-
-    def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
-        """Implements backward computation
-
-        req : list of {'null', 'write', 'inplace', 'add'}, how to assign to in_grad
-        out_grad : list of NDArray, gradient w.r.t. output data.
-        in_grad : list of NDArray, gradient w.r.t. input data. This is the output buffer.
-        """
-        y = out_data[0].asnumpy()
-        dy = out_grad[0].asnumpy()
-        dx = dy*(1.0 - y)*y
-        self.assign(in_grad[0], req[0], mx.nd.array(dx))
-```
-
-### Register custom operator
-
-Then we need to register the custom op and describe it's properties like input and output shapes so that mxnet can recognize it. This is done by sub-classing `mx.operator.CustomOpProp`:
-
-
-```python
-@mx.operator.register("sigmoid")  # register with name "sigmoid"
-class SigmoidProp(mx.operator.CustomOpProp):
-    def __init__(self):
-        super(SigmoidProp, self).__init__(True)
-
-    def list_arguments(self):
-        #  this can be omitted if you only have 1 input.
-        return ['data']
-
-    def list_outputs(self):
-        #  this can be omitted if you only have 1 output.
-        return ['output']
-
-    def infer_shape(self, in_shapes):
-        """Calculate output shapes from input shapes. This can be
-        omited if all your inputs and outputs have the same shape.
-
-        in_shapes : list of shape. Shape is described by a tuple of int.
-        """
-        data_shape = in_shapes[0]
-        output_shape = data_shape
-        # return 3 lists representing inputs shapes, outputs shapes, and aux data shapes.
-        return (data_shape,), (output_shape,), ()
-
-    def create_operator(self, ctx, in_shapes, in_dtypes):
-        #  create and return the CustomOp class.
-        return Sigmoid()
-```
-
-### Example Usage
-
-We can now use this operator by calling `mx.nd.Custom`:
-
-
-```python
-x = mx.nd.array([0, 1, 2, 3])
-# attach gradient buffer to x for autograd
-x.attach_grad()
-# forward in a record() section to save computation graph for backward
-# see autograd tutorial to learn more.
-with autograd.record():
-    y = mx.nd.Custom(x, op_type='sigmoid')
-print(y)
-```
-
-```python
-# call backward computation
-y.backward()
-# gradient is now saved to the grad buffer we attached previously
-print(x.grad)
-```
-
-## Parametrized Operator
-
-In the second use case we implement an operator with learnable weights. We implement the dense (or fully connected) layer that has one input, one output, and two learnable parameters: weight and bias.
-
-The dense operator performs a dot product between data and weight, then add bias to it.
-
-### Forward & backward implementation
-
-
-```python
-class Dense(mx.operator.CustomOp):
-    def __init__(self, bias):
-        self._bias = bias
-
-    def forward(self, is_train, req, in_data, out_data, aux):
-        x = in_data[0].asnumpy()
-        weight = in_data[1].asnumpy()
-        y = x.dot(weight.T) + self._bias
-        self.assign(out_data[0], req[0], mx.nd.array(y))
-
-    def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
-        x = in_data[0].asnumpy()
-        dy = out_grad[0].asnumpy()
-        dx = dy.T.dot(x)
-        self.assign(in_grad[0], req[0], mx.nd.array(dx))
-```
-
-### Registration
-
-
-```python
-@mx.operator.register("dense")  # register with name "sigmoid"
-class DenseProp(mx.operator.CustomOpProp):
-    def __init__(self, bias):
-        super(DenseProp, self).__init__(True)
-        # we use constant bias here to illustrate how to pass arguments
-        # to operators. All arguments are in string format so you need
-        # to convert them back to the type you want.
-        self._bias = float(bias)
-
-    def list_arguments(self):
-        return ['data', 'weight']
-
-    def list_outputs(self):
-        #  this can be omitted if you only have 1 output.
-        return ['output']
-
-    def infer_shape(self, in_shapes):
-        data_shape = in_shapes[0]
-        weight_shape = in_shapes[1]
-        output_shape = (data_shape[0], weight_shape[0])
-        # return 3 lists representing inputs shapes, outputs shapes, and aux data shapes.
-        return (data_shape, weight_shape), (output_shape,), ()
-
-    def create_operator(self, ctx, in_shapes, in_dtypes):
-        #  create and return the CustomOp class.
-        return Dense(self._bias)
-```
-
-### Use CustomOp together with Block
-
-Parameterized CustomOp are usually used together with Blocks, which holds the parameter.
-
-
-```python
-class DenseBlock(mx.gluon.Block):
-    def __init__(self, in_channels, channels, bias, **kwargs):
-        super(DenseBlock, self).__init__(**kwargs)
-        self._bias = bias
-        self.weight = self.params.get('weight', shape=(channels, in_channels))
-
-    def forward(self, x):
-        ctx = x.context
-        return mx.nd.Custom(x, self.weight.data(ctx), bias=self._bias, op_type='dense')
-```
-
-### Example usage
-
-
-```python
-dense = DenseBlock(3, 5, 0.1)
-dense.initialize()
-x = mx.nd.uniform(shape=(4, 3))
-y = dense(x)
-print(y)
-```
-
-## Using custom operators with fork
-In Linux systems, the default method in multiprocessing to create process is by using fork. If there are unfinished async custom operations when forking, the program will be blocked because of python GIL. Always use sync calls like `wait_to_read` or `waitall` before calling fork.
-
-```
-x = mx.nd.array([0, 1, 2, 3])
-y = mx.nd.Custom(x, op_type='sigmoid')
-# unfinished async sigmoid operation will cause blocking
-os.fork()
-```
-
-Correctly handling this will make mxnet depend upon libpython, so the workaround now is to ensure that all custom operations are executed before forking process.
-
-```
-x = mx.nd.array([0, 1, 2, 3])
-y = mx.nd.Custom(x, op_type='sigmoid')
-# force execution by reading y
-print(y.asnumpy())
-os.fork()
-```
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/gluon/data_augmentation.md b/docs/tutorials/gluon/data_augmentation.md
deleted file mode 100644
index 0e320fc2890e..000000000000
--- a/docs/tutorials/gluon/data_augmentation.md
+++ /dev/null
@@ -1,235 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Image Augmentation
-
-Augmentation is the process of randomly adjusting the dataset samples used for training. As a result, a greater diversity of samples will be seen by the network and it is therefore less likely to overfit the training dataset. Some of the spurious characteristics of the dataset can be reduced using this technique. One example would be a dataset of images from the same camera having the same color tint: it's unhelpful when you want to apply this model to images from other cameras. You can avoid this by randomly shifting the colours of each image slightly and training your network on these augmented images.
-
-Although this technique can be applied in a variety of domains, it's very common in Computer Vision, and we will focus on image augmentations in this tutorial. Some example image augmentations include random crops and flips, and adjustments to the brightness and contrast.
-
-#### What are the prerequisites?
-
-You should be familiar with the concept of a transform and how to apply it to a dataset before reading this tutorial. Check out the [Data Transforms tutorial]() if this is new to you or you need a quick refresher.
-
-#### Where can I find the augmentation transforms?
-
-You can find them in the `mxnet.gluon.data.vision.transforms` module, alongside the deterministic transforms we've seen previously, such as `ToTensor`, `Normalize`, `CenterCrop` and `Resize`. Augmentations involve an element of randomness and all the augmentation transforms are prefixed with `Random`, such as `RandomResizedCrop` and `RandomBrightness`. We'll start by importing MXNet and the `transforms`.
-
-
-```python
-import matplotlib.pyplot as plt
-import mxnet as mx
-from mxnet.gluon.data.vision import transforms
-```
-
-#### Sample Image
-
-So that we can see the effects of all the vision augmentations, we'll take a sample image of a giraffe and apply various augmentations to it. We can see what it looks like to begin with.
-
-
-```python
-image_url = 'https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/inputs/0.jpg'
-mx.test_utils.download(image_url, "giraffe.jpg")
-example_image = mx.image.imread("giraffe.jpg")
-plt.imshow(example_image.asnumpy())
-```
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/transforms/output_5_1.png)
-
-
-Since these augmentations are random, we'll apply the same augmentation a few times and plot all of the outputs. We define a few utility functions to help with this.
-
-
-```python
-def show_images(imgs, num_rows, num_cols, scale=2):
-    # show augmented images in a grid layout 
-    aspect_ratio = imgs[0].shape[0]/imgs[0].shape[1]
-    figsize = (num_cols * scale, num_rows * scale * aspect_ratio)
-    _, axes = plt.subplots(num_rows, num_cols, figsize=figsize)
-    for i in range(num_rows):
-        for j in range(num_cols):
-            axes[i][j].imshow(imgs[i * num_cols + j].asnumpy())
-            axes[i][j].axes.get_xaxis().set_visible(False)
-            axes[i][j].axes.get_yaxis().set_visible(False)
-    plt.subplots_adjust(hspace=0.1, wspace=0)
-    return axes
-
-def apply(img, aug, num_rows=2, num_cols=4, scale=3):
-    # apply augmentation multiple times to obtain different samples
-    Y = [aug(img) for _ in range(num_rows * num_cols)]
-    show_images(Y, num_rows, num_cols, scale)
-```
-
-# Spatial Augmentation
-
-One form of augmentation affects the spatial position of pixel values. Using combinations of slicing, scaling, translating, rotating and flipping the values of the original image can be shifted to create new images. Some operations (like scaling and rotation) require interpolation as pixels in the new image are combinations of pixels in the original image.
-
-### `RandomResizedCrop`
-
-Many Computer Visions tasks, such as image classification and object detection, should be robust to changes in the scale and position of objects in the image. You can incorporate this into the network using pooling layers, but an alternative method is to crop random regions of the original image. 
-
-As an example, we randomly (using a uniform distribution) crop a region of the image with:
-
-* an area of 10% to 100% of the original area
-* a ratio of width to height between 0.5 and 2
-
-And then we resize this cropped region to 200 by 200 pixels.
-
-
-```python
-shape_aug = transforms.RandomResizedCrop(size=(200, 200),
-                                         scale=(0.1, 1),
-                                         ratio=(0.5, 2))
-apply(example_image, shape_aug)
-```
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/transforms/output_12_0.png)
-
-
-### `RandomFlipLeftRight`
-
-A simple augmentation technique is flipping. Usually flipping horizontally doesn't change the category of object and results in an image that's still plausible in the real world. Using `RandomFlipLeftRight`, we randomly flip the image horizontally 50% of the time.
-
-
-```python
-apply(example_image, transforms.RandomFlipLeftRight())
-```
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/transforms/output_15_0.png)
-
-
-### `RandomFlipTopBottom`
-
-Although it's not as common as flipping left and right, you can flip the image vertically 50% of the time with `RandomFlipTopBottom`. With our giraffe example, we end up with less plausible samples that horizontal flipping, with the ground above the sky in some cases.
-
-
-```python
-apply(example_image, transforms.RandomFlipTopBottom())
-```
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/transforms/output_18_0.png)
-
-
-# Color Augmentation
-
-Usually, exact coloring doesn't play a significant role in the classification or detection of objects, so augmenting the colors of images is a good technique to make the network invariant to color shifts. Color properties that can be changed include brightness, contrast, saturation and hue.
-
-### `RandomBrightness`
-
-Use `RandomBrightness` to add a random brightness jitter to images. Use the `brightness` parameter to control the amount of jitter in brightness, with value from 0 (no change) to 1 (potentially large change). `brightness` doesn't specify whether the brightness of the augmented image will be lighter or darker, just the potential strength of the effect. Specifically the augmentation is given by:
-
-```
-alpha = 1.0 + random.uniform(-brightness, brightness)
-image *= alpha
-```
-
-So by setting this to 0.5 we randomly change the brightness of the image to a value between 50% ($1-0.5$) and 150% ($1+0.5$) of the original image.
-
-
-```python
-apply(example_image, transforms.RandomBrightness(0.5))
-```
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/transforms/output_23_0.png)
-
-
-### `RandomContrast`
-
-Use `RandomContrast` to add a random contrast jitter to an image. Contrast can be thought of as the degree to which light and dark colors in the image differ. Use the `contrast` parameter to control the amount of jitter in contrast, with value from 0 (no change) to 1 (potentially large change). `contrast` doesn't specify whether the contrast of the augmented image will be higher or lower, just the potential strength of the effect. Specifically, the augmentation is given by:
-
-```
-coef = nd.array([[[0.299, 0.587, 0.114]]])
-alpha = 1.0 + random.uniform(-contrast, contrast)
-gray = image * coef
-gray = (3.0 * (1.0 - alpha) / gray.size) * nd.sum(gray)
-image *= alpha
-image += gray
-```
-
-
-```python
-apply(example_image, transforms.RandomContrast(0.5))
-```
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/transforms/output_26_0.png)
-
-
-### `RandomSaturation`
-
-Use `RandomSaturation` to add a random saturation jitter to an image. Saturation can be thought of as the 'amount' of color in an image. Use the `saturation` parameter to control the amount of jitter in saturation, with value from 0 (no change) to 1 (potentially large change). `saturation` doesn't specify whether the saturation of the augmented image will be higher or lower, just the potential strength of the effect. Specifically the augmentation is using the method detailed [here](https://beesbuzz.biz/code/16-hsv-color-transforms).
-
-
-```python
-apply(example_image, transforms.RandomSaturation(0.5))
-```
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/transforms/output_29_0.png)
-
-
-### `RandomHue`
-
-Use `RandomHue` to add a random hue jitter to images. Hue can be thought of as the 'shade' of the colors in an image. Use the `hue` parameter to control the amount of jitter in hue, with value from 0 (no change) to 1 (potentially large change). `hue` doesn't specify whether the hue of the augmented image will be shifted one way or the other, just the potential strength of the effect. Specifically the augmentation is using the method detailed [here](https://beesbuzz.biz/code/16-hsv-color-transforms).
-
-
-```python
-apply(example_image, transforms.RandomHue(0.5))
-```
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/transforms/output_32_0.png)
-
-
-### `RandomColorJitter`
-
-`RandomColorJitter` is a convenience transform that can be used to perform multiple color augmentations at once. You can set the `brightness`, `contrast`, `saturation` and `hue` jitters, that function the same as above for their individual transforms.
-
-
-```python
-color_aug = transforms.RandomColorJitter(brightness=0.5,
-                                         contrast=0.5,
-                                         saturation=0.5,
-                                         hue=0.5)
-apply(example_image, color_aug)
-```
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/transforms/output_35_0.png)
-
-
-### `RandomLighting`
-
-Use `RandomLighting` for an AlexNet-style PCA-based noise augmentation.
-
-
-```python
-apply(example_image, transforms.RandomLighting(alpha=1))
-```
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/transforms/output_38_0.png)
-
-# Composed Augmentations
-
-In practice, we apply multiple augmentation techniques to an image to increase the variety of images in the dataset. Using the `Compose` transform that was introduced in the [Data Transforms tutorial](), we can apply 3 of the transforms we previously used above.
-
-
-```python
-augs = transforms.Compose([
-    transforms.RandomFlipLeftRight(), color_aug, shape_aug])
-apply(example_image, augs)
-```
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/transforms/output_41_0.png)
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
\ No newline at end of file
diff --git a/docs/tutorials/gluon/datasets.md b/docs/tutorials/gluon/datasets.md
deleted file mode 100644
index 6f645bb4384f..000000000000
--- a/docs/tutorials/gluon/datasets.md
+++ /dev/null
@@ -1,333 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-
-# Gluon `Dataset`s and `DataLoader`
-
-One of the most critical steps for model training and inference is loading the data: without data you can't do Machine Learning! In this tutorial we use the Gluon API to define a [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset) and use a [`DataLoader`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader) to iterate through the dataset in mini-batches.
-
-## Introduction to `Dataset`s
-
-[`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset) objects are used to represent collections of data, and include methods to load and parse the data (that is often stored on disk). Gluon has a number of different [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset) classes for working with image data straight out-of-the-box, but we'll use the [`ArrayDataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=arraydataset#mxnet.gluon.data.ArrayDataset) to introduce the idea of a [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset).
-
-We first start by generating random data `X` (with 3 variables) and corresponding random labels `y` to simulate a typical supervised learning task. We generate 10 samples and we pass them all to the [`ArrayDataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=arraydataset#mxnet.gluon.data.ArrayDataset).
-
-
-```python
-import mxnet as mx
-import os
-import tarfile
-
-mx.random.seed(42) # Fix the seed for reproducibility
-X = mx.random.uniform(shape=(10, 3))
-y = mx.random.uniform(shape=(10, 1))
-dataset = mx.gluon.data.dataset.ArrayDataset(X, y)
-```
-
-A key feature of a [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset) is the __*ability to retrieve a single sample given an index*__. Our random data and labels were generated in memory, so this [`ArrayDataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=arraydataset#mxnet.gluon.data.ArrayDataset) doesn't have to load anything from disk, but the interface is the same for all [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset)s.
-
-
-```python
-sample_idx = 4
-sample = dataset[sample_idx]
-
-assert len(sample) == 2
-assert sample[0].shape == (3, )
-assert sample[1].shape == (1, )
-print(sample)
-```
-
-    (
-     [ 0.4375872   0.29753461  0.89177299]
-     <NDArray 3 @cpu(0)>,
-     [ 0.83261985]
-     <NDArray 1 @cpu(0)>)
-
-
-We get a tuple of a data sample and its corresponding label, which makes sense because we passed the data `X` and the labels `y` in that order when we instantiated the [`ArrayDataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=arraydataset#mxnet.gluon.data.ArrayDataset). We don't usually retrieve individual samples from [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset) objects though (unless we're quality checking the output samples). Instead we use a [`DataLoader`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader).
-
-## Introduction to `DataLoader`
-
-A [`DataLoader`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader) is used to create mini-batches of samples from a [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset), and provides a convenient iterator interface for looping these batches. It's typically much more efficient to pass a mini-batch of data through a neural network than a single sample at a time, because the computation can be performed in parallel. A required parameter of [`DataLoader`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader) is the size of the mini-batches you want to create, called `batch_size`.
-
-Another benefit of using [`DataLoader`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader) is the ability to easily load data in parallel using [`multiprocessing`](https://docs.python.org/3.6/library/multiprocessing.html). You can set the `num_workers` parameter to the number of CPUs avalaible on your machine for maximum performance, or limit it to a lower number to spare resources.
-
-
-```python
-from multiprocessing import cpu_count
-CPU_COUNT = cpu_count()
-
-data_loader = mx.gluon.data.DataLoader(dataset, batch_size=5, num_workers=CPU_COUNT)
-
-for X_batch, y_batch in data_loader:
-    print("X_batch has shape {}, and y_batch has shape {}".format(X_batch.shape, y_batch.shape))
-```
-
-`X_batch has shape (5, 3), and y_batch has shape (5, 1)` <!--notebook-skip-line-->
-
-`X_batch has shape (5, 3), and y_batch has shape (5, 1)` <!--notebook-skip-line-->
-
-
-We can see 2 mini-batches of data (and labels), each with 5 samples, which makes sense given we started with a dataset of 10 samples. When comparing the shape of the batches to the samples returned by the [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset), we've gained an extra dimension at the start which is sometimes called the batch axis.
-
-Our `data_loader` loop will stop when every sample of `dataset` has been returned as part of a batch. Sometimes the dataset length isn't divisible by the mini-batch size, leaving a final batch with a smaller number of samples. [`DataLoader`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader)'s default behavior is to return this smaller mini-batch, but this can be changed by setting the `last_batch` parameter to `discard` (which ignores the last batch) or `rollover` (which starts the next epoch with the remaining samples).
-
-## Machine learning with `Dataset`s and `DataLoader`s
-
-You will often use a few different [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset) objects in your Machine Learning project. It's essential to separate your training dataset from testing dataset, and it's also good practice to have validation dataset (a.k.a. development dataset) that can be used for optimising hyperparameters.
-
-Using Gluon [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset) objects, we define the data to be included in each of these separate datasets. Common use cases for loading data are covered already (e.g. [`mxnet.gluon.data.vision.datasets.ImageFolderDataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=imagefolderdataset#mxnet.gluon.data.vision.datasets.ImageFolderDataset)), but it's simple to create your own custom [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset) classes for other types of data. You can even use included [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset) objects for common datasets if you want to experiment quickly; they download and parse the data for you! In this example we use the [Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset from Zalando Research.
-
-Many of the image [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset)s accept a function (via the optional `transform` parameter) which is applied to each sample returned by the [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset). It's useful for performing data augmentation, but can also be used for more simple data type conversion and pixel value scaling as seen below.
-
-
-```python
-def transform(data, label):
-    data = data.astype('float32')/255
-    return data, label
-
-train_dataset = mx.gluon.data.vision.datasets.FashionMNIST(train=True, transform=transform)
-valid_dataset = mx.gluon.data.vision.datasets.FashionMNIST(train=False, transform=transform)
-```
-
-
-```python
-%matplotlib inline
-from matplotlib.pylab import imshow
-
-sample_idx = 234
-sample = train_dataset[sample_idx]
-data = sample[0]
-label = sample[1]
-label_desc = {0:'T-shirt/top', 1:'Trouser', 2:'Pullover', 3:'Dress', 4:'Coat', 5:'Sandal', 6:'Shirt', 7:'Sneaker', 8:'Bag', 9:'Ankle boot'}
-
-imshow(data[:,:,0].asnumpy(), cmap='gray')
-print("Data type: {}".format(data.dtype))
-print("Label: {}".format(label))
-print("Label description: {}".format(label_desc[label]))
-```
-
-`Data type: <class 'numpy.float32'>`<!--notebook-skip-line-->
-
-`Label: 8`<!--notebook-skip-line-->
-
-`Label description: Bag`<!--notebook-skip-line-->
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/datasets/fashion_mnist_bag.png)<!--notebook-skip-line-->
-
-
-When training machine learning models it is important to shuffle the training samples every time you pass through the dataset (i.e. each epoch). Sometimes the order of your samples will have a spurious relationship with the target variable, and shuffling the samples helps remove this. With [`DataLoader`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader) it's as simple as adding `shuffle=True`. You don't need to shuffle the validation and testing data though.
-
-If you have more complex shuffling requirements (e.g. when handling sequential data), take a look at [`mxnet.gluon.data.BatchSampler`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=batchsampler#mxnet.gluon.data.BatchSampler) and pass this to your [`DataLoader`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader) instead.
-
-
-```python
-batch_size = 32
-train_data_loader = mx.gluon.data.DataLoader(train_dataset, batch_size, shuffle=True, num_workers=CPU_COUNT)
-valid_data_loader = mx.gluon.data.DataLoader(valid_dataset, batch_size, num_workers=CPU_COUNT)
-```
-
-With both `DataLoader`s defined, we can now train a model to classify each image and evaluate the validation loss at each epoch. Our Fashion MNIST dataset has 10 classes including shirt, dress, sneakers, etc. We define a simple fully connected network with a softmax output and use cross entropy as our loss.
-
-
-```python
-from mxnet import gluon, autograd, ndarray
-
-def construct_net():
-    net = gluon.nn.HybridSequential()
-    with net.name_scope():
-        net.add(gluon.nn.Dense(128, activation="relu"))
-        net.add(gluon.nn.Dense(64, activation="relu"))
-        net.add(gluon.nn.Dense(10))
-    return net
-
-# construct and initialize network.
-ctx =  mx.gpu() if mx.context.num_gpus() else mx.cpu()
-
-net = construct_net()
-net.hybridize()
-net.initialize(mx.init.Xavier(), ctx=ctx)
-# define loss and trainer.
-criterion = gluon.loss.SoftmaxCrossEntropyLoss()
-trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.1})
-```
-
-```python
-
-
-epochs = 5
-for epoch in range(epochs):
-    # training loop (with autograd and trainer steps, etc.)
-    cumulative_train_loss = mx.nd.zeros(1, ctx=ctx)
-    training_samples = 0
-    for batch_idx, (data, label) in enumerate(train_data_loader):
-        data = data.as_in_context(ctx).reshape((-1, 784)) # 28*28=784
-        label = label.as_in_context(ctx)
-        with autograd.record():
-            output = net(data)
-            loss = criterion(output, label)
-        loss.backward()
-        trainer.step(data.shape[0])
-        cumulative_train_loss += loss.sum()
-        training_samples += data.shape[0]
-    train_loss = cumulative_train_loss.asscalar()/training_samples
-
-    # validation loop
-    cumulative_valid_loss = mx.nd.zeros(1, ctx)
-    valid_samples = 0
-    for batch_idx, (data, label) in enumerate(valid_data_loader):
-        data = data.as_in_context(ctx).reshape((-1, 784)) # 28*28=784
-        label = label.as_in_context(ctx)
-        output = net(data)
-        loss = criterion(output, label)
-        cumulative_valid_loss += loss.sum()
-        valid_samples += data.shape[0]
-    valid_loss = cumulative_valid_loss.asscalar()/valid_samples
-
-    print("Epoch {}, training loss: {:.2f}, validation loss: {:.2f}".format(epoch, train_loss, valid_loss))
-```
-
-`Epoch 0, training loss: 0.54, validation loss: 0.45`<!--notebook-skip-line-->
-
-`...`<!--notebook-skip-line-->
-
-`Epoch 4, training loss: 0.32, validation loss: 0.33`<!--notebook-skip-line-->
-
-
-# Using own data with included `Dataset`s
-
-Gluon has a number of different [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset) classes for working with your own image data straight out-of-the-box. You can get started quickly using the [`mxnet.gluon.data.vision.datasets.ImageFolderDataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=imagefolderdataset#mxnet.gluon.data.vision.datasets.ImageFolderDataset) which loads images directly from a user-defined folder, and infers the label (i.e. class) from the folders.
-
-We will run through an example for image classification, but a similar process applies for other vision tasks. If you already have your own collection of images to work with you should partition your data into training and test sets, and place all objects of the same class into seperate folders. Similar to:
-```
-    ./images/train/car/abc.jpg
-    ./images/train/car/efg.jpg
-    ./images/train/bus/hij.jpg
-    ./images/train/bus/klm.jpg
-    ./images/test/car/xyz.jpg
-    ./images/test/bus/uvw.jpg
-```
-
-You can download the Caltech 101 dataset if you don't already have images to work with for this example, but please note the download is 126MB.
-
-```python
-
-data_folder = "data"
-dataset_name = "101_ObjectCategories"
-archive_file = "{}.tar.gz".format(dataset_name)
-archive_path = os.path.join(data_folder, archive_file)
-data_url = "https://s3.us-east-2.amazonaws.com/mxnet-public/"
-
-if not os.path.isfile(archive_path):
-    mx.test_utils.download("{}{}".format(data_url, archive_file), dirname = data_folder)
-    print('Extracting {} in {}...'.format(archive_file, data_folder))
-    tar = tarfile.open(archive_path, "r:gz")
-    tar.extractall(data_folder)
-    tar.close()
-    print('Data extracted.')
-```
-
-After downloading and extracting the data archive, we have two folders: `data/101_ObjectCategories` and `data/101_ObjectCategories_test`. We load the data into separate training and testing  [`ImageFolderDataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=imagefolderdataset#mxnet.gluon.data.vision.datasets.ImageFolderDataset)s.
-
-```python
-training_path = os.path.join(data_folder, dataset_name)
-testing_path = os.path.join(data_folder, "{}_test".format(dataset_name))
-```
-
-We instantiate the [`ImageFolderDataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=imagefolderdataset#mxnet.gluon.data.vision.datasets.ImageFolderDataset)s by providing the path to the data, and the folder structure will be traversed to determine which image classes are available and which images correspond to each class. You must take care to ensure the same classes are both the training and testing datasets, otherwise the label encodings can get muddled.
-
-Optionally, you can pass a `transform` parameter to these [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset)s as we've seen before.
-
-
-```python
-train_dataset = mx.gluon.data.vision.datasets.ImageFolderDataset(training_path)
-test_dataset = mx.gluon.data.vision.datasets.ImageFolderDataset(testing_path)
-```
-
-Samples from these datasets are tuples of data and label. Images are loaded from disk, decoded and optionally transformed when the `__getitem__(i)` method is called (equivalent to `train_dataset[i]`).
-
-As with the Fashion MNIST dataset the labels will be integer encoded. You can use the `synsets` property of the [`ImageFolderDataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=imagefolderdataset#mxnet.gluon.data.vision.datasets.ImageFolderDataset)s to retrieve the original descriptions (e.g. `train_dataset.synsets[i]`).
-
-
-```python
-sample_idx = 539
-sample = train_dataset[sample_idx]
-data = sample[0]
-label = sample[1]
-
-imshow(data.asnumpy(), cmap='gray')
-print("Data type: {}".format(data.dtype))
-print("Label: {}".format(label))
-print("Label description: {}".format(train_dataset.synsets[label]))
-assert label == 1
-```
-
-`Data type: <class 'numpy.uint8'>`<!--notebook-skip-line-->
-
-`Label: 1`<!--notebook-skip-line-->
-
-`Label description: Faces_easy` <!--notebook-skip-line-->
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/datasets/caltech101_face.png)<!--notebook-skip-line-->
-
-
-# Using own data with custom `Dataset`s
-
-Sometimes you have data that doesn't quite fit the format expected by the included [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset)s. You might be able to preprocess your data to fit the expected format, but it is easy to create your own dataset to do this.
-
-All you need to do is create a class that implements a `__getitem__` method, that returns a sample (i.e. a tuple of [`mx.nd.NDArray`](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray)s).
-
-See the [Data Augmentation with Masks](http://mxnet.incubator.apache.org/tutorials/python/data_augmentation_with_masks.html) tutorial for an example of this.
-
-# Appendix: Upgrading from Module `DataIter` to Gluon `DataLoader`
-
-Before Gluon's [`DataLoader`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader), MXNet used [`DataIter`](https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=dataiter#mxnet.io.DataIter) objects for loading data for training and testing. [`DataIter`](https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=dataiter#mxnet.io.DataIter) has a similar interface for iterating through data, but it isn't directly compatible with typical Gluon [`DataLoader`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader) loops. Unlike Gluon [`DataLoader`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader) which often returns a tuple of `(data, label)`, a [`DataIter`](https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=dataiter#mxnet.io.DataIter) returns a [`DataBatch`](https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=databatch#mxnet.io.DataBatch) object that has `data` and `label` properties. Switching to [`DataLoader`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader)s is highly recommended when using Gluon, but you'll need to take care of pre-processing steps such as augmentations in a `transform` function.
-
-So you can get up and running with Gluon quicker if you have already imlemented complex pre-processing steps using [`DataIter`](https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=dataiter#mxnet.io.DataIter), we have provided a simple class to wrap existing [`DataIter`](https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=dataiter#mxnet.io.DataIter) objects so they can be used in a typical Gluon training loop. You can use this class for `DataIter`s such as [`mxnet.image.ImageIter`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=imageiter#mxnet.image.ImageIter) and [`mxnet.io.ImageRecordIter`](https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=imagere#mxnet.io.ImageRecordIter) that have single data and label arrays.
-
-
-```python
-class DataIterLoader():
-    def __init__(self, data_iter):
-        self.data_iter = data_iter
-
-    def __iter__(self):
-        self.data_iter.reset()
-        return self
-
-    def __next__(self):
-        batch = self.data_iter.__next__()
-        assert len(batch.data) == len(batch.label) == 1
-        data = batch.data[0]
-        label = batch.label[0]
-        return data, label
-
-    def next(self):
-        return self.__next__() # for Python 2
-```
-
-
-```python
-data_iter = mx.io.NDArrayIter(data=X, label=y, batch_size=5)
-data_iter_loader = DataIterLoader(data_iter)
-for X_batch, y_batch in data_iter_loader:
-    assert X_batch.shape == (5, 3)
-    assert y_batch.shape == (5, 1)
-```
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
\ No newline at end of file
diff --git a/docs/tutorials/gluon/fit_api_tutorial.md b/docs/tutorials/gluon/fit_api_tutorial.md
deleted file mode 100644
index bc50690ac1a2..000000000000
--- a/docs/tutorials/gluon/fit_api_tutorial.md
+++ /dev/null
@@ -1,271 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-
-# MXNet Gluon Fit API
-
-In this tutorial, you will learn how to use the [Gluon Fit API](https://cwiki.apache.org/confluence/display/MXNET/Gluon+Fit+API+-+Tech+Design) which is the easiest way to train deep learning models using the [Gluon API](http://mxnet.incubator.apache.org/versions/master/gluon/index.html) in Apache MXNet. 
-
-With the Fit API, you can train a deep learning model with a minimal amount of code. Just specify the network, loss function and the data you want to train on. You don't need to worry about the boiler plate code to loop through the dataset in batches (often called as 'training loop'). Advanced users can train with bespoke training loops, and many of these use cases will be covered by the Fit API.
-
-To demonstrate the Fit API, you will train an image classification model using the [ResNet-18](https://arxiv.org/abs/1512.03385) neural network architecture. The model will be trained using the [Fashion-MNIST dataset](https://research.zalando.com/welcome/mission/research-projects/fashion-mnist/). 
-
-## Prerequisites
-
-To complete this tutorial, you will need:
-
-- [MXNet](https://mxnet.incubator.apache.org/install/#overview) (The version of MXNet will be >= 1.5.0, you can use `pip install mxnet` to get 1.5.0 release pip package or build from source with master, refer to [MXNet installation](http://mxnet.incubator.apache.org/versions/master/install/index.html?platform=Linux&language=Python&processor=CPU)
-- [Jupyter Notebook](https://jupyter.org/index.html) (For interactively running the provided .ipynb file)
-
-
-
-
-```python
-import mxnet as mx
-from mxnet import gluon
-from mxnet.gluon.model_zoo import vision
-from mxnet.gluon.contrib.estimator import estimator
-from mxnet.gluon.contrib.estimator.event_handler import TrainBegin, TrainEnd, EpochEnd, CheckpointHandler
-
-gpu_count = mx.context.num_gpus()
-ctx = [mx.gpu(i) for i in range(gpu_count)] if gpu_count > 0 else mx.cpu()
-```
-
-## Dataset
-
-[Fashion-MNIST](https://research.zalando.com/welcome/mission/research-projects/fashion-mnist/) dataset consists of fashion items divided into ten categories: t-shirt/top, trouser, pullover, dress, coat, sandal, shirt, sneaker, bag and ankle boot. 
-
-- It has 60,000 grayscale images of size 28 * 28 for training.  
-- It has 10,000 grayscale images of size 28 * 28 for testing/validation. 
-
-We will use the ```gluon.data.vision``` package to directly import the Fashion-MNIST dataset and perform pre-processing on it.
-
-
-```python
-# Get the training data 
-fashion_mnist_train = gluon.data.vision.FashionMNIST(train=True)
-
-# Get the validation data
-fashion_mnist_val = gluon.data.vision.FashionMNIST(train=False)
-```
-
-
-```python
-transforms = [gluon.data.vision.transforms.Resize(224), # We pick 224 as the model we use takes an input of size 224.
-                gluon.data.vision.transforms.ToTensor()]
-
-# Now we will stack all these together.
-transforms = gluon.data.vision.transforms.Compose(transforms)
-```
-
-
-```python
-# Apply the transformations
-fashion_mnist_train = fashion_mnist_train.transform_first(transforms)
-fashion_mnist_val = fashion_mnist_val.transform_first(transforms)
-```
-
-
-```python
-batch_size = 256 # Batch size of the images
-num_workers = 4 # The number of parallel workers for loading the data using Data Loaders.
-
-train_data_loader = gluon.data.DataLoader(fashion_mnist_train, batch_size=batch_size, 
-                                          shuffle=True, num_workers=num_workers)
-val_data_loader = gluon.data.DataLoader(fashion_mnist_val, batch_size=batch_size, 
-                                        shuffle=False, num_workers=num_workers)
-```
-
-## Model and Optimizers
-
-Let's load the resnet-18 model architecture from [Gluon Model Zoo](http://mxnet.apache.org/api/python/gluon/model_zoo.html) and initialize its parameters. The Gluon Model Zoo contains a repository of pre-trained models as well the model architecture definitions. We are using the model architecture from the model zoo in order to train it from scratch.
-
-
-```python
-resnet_18_v1 = vision.resnet18_v1(pretrained=False, classes = 10)
-resnet_18_v1.initialize(init = mx.init.Xavier(), ctx=ctx)
-```
-
-We will be using `SoftmaxCrossEntropyLoss` as the loss function since this is a multi-class classification problem. We will be using `sgd` (Stochastic Gradient Descent) as the optimizer. 
-You can experiment with a [different loss](http://mxnet.incubator.apache.org/versions/master/api/python/gluon/loss.html) or [optimizer](http://mxnet.incubator.apache.org/versions/master/api/python/optimization/optimization.html) as well. 
-
-
-```python
-loss_fn = gluon.loss.SoftmaxCrossEntropyLoss()
-```
-
-Let's define the trainer object for training the model.
-
-
-```python
-learning_rate = 0.04 # You can experiment with your own learning rate here
-num_epochs = 2 # You can run training for more epochs
-trainer = gluon.Trainer(resnet_18_v1.collect_params(), 
-                        'sgd', {'learning_rate': learning_rate})
-```
-
-## Train using Fit API
-
-As stated earlier, the Fit API greatly simplifies the boiler plate code and complexity for training using MXNet Gluon.
-
-In the basic usage example, with just 2 lines of code, we will set up our model for training.
-
-### Basic Usage
-
-
-```python
-train_acc = mx.metric.Accuracy() # Metric to monitor
-
-# Define the estimator, by passing to it the model, loss function, metrics, trainer object and context
-est = estimator.Estimator(net=resnet_18_v1, 
-                          loss=loss_fn, 
-                          metrics=train_acc, 
-                          trainer=trainer, 
-                          context=ctx)
-
-# ignore warnings for nightly test on CI only
-import warnings
-with warnings.catch_warnings():
-    warnings.simplefilter("ignore")
-    # Magic line
-    est.fit(train_data=train_data_loader,
-        epochs=num_epochs)
-```
-
-    Training begin: using optimizer SGD with current learning rate 0.0400 <!--notebook-skip-line-->
-    Train for 2 epochs. <!--notebook-skip-line-->
-    
-    [Epoch 0] finished in 25.110s: train_accuracy : 0.7877 train_softmaxcrossentropyloss0 : 0.5905 <!--notebook-skip-line-->
-    
-    [Epoch 1] finished in 23.595s: train_accuracy : 0.8823 train_softmaxcrossentropyloss0 : 0.3197 <!--notebook-skip-line-->
-    Train finished using total 48s at epoch 1. train_accuracy : 0.8823 train_softmaxcrossentropyloss0 : 0.3197 <!--notebook-skip-line-->
-
-
-### Advanced Usage
-
-The Fit API is also customizable with several `Event Handlers` which give a fine grained control over the steps in training and exposes callback methods that provide control over the stages involved in training. Available callback methods are: `train_begin`, `train_end`, `batch_begin`, `batch_end`, `epoch_begin` and `epoch_end`.
-
-You can use built-in event handlers such as `LoggingHandler`, `CheckpointHandler` or `EarlyStoppingHandler` to log and save the model at certain time-steps during training. You can also stop the training when the model's performance plateaus. 
-There are also some default utility handlers that will be added to your estimator by default. For example, `StoppingHandler` is used to control when the training ends, based on number of epochs or number of batches trained. 
-`MetricHandler` is used to calculate training metrics at end of each batch and epoch. 
-`ValidationHandler` is used to validate your model on test data at each epoch's end and then calculate validation metrics.
-You can create these utility handlers with different configurations and pass to estimator. This will override the default handler configuration.
-You can create a custom handler by inheriting one or multiple 
-[base event handlers](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/contrib/estimator/event_handler.py#L32)
- including: `TrainBegin`, `TrainEnd`, `EpochBegin`, `EpochEnd`, `BatchBegin`, `BatchEnd`.
-
-
-### Custom Event Handler
-
-Here we will showcase an example custom event handler the inherits features from a few base handler classes. 
-Our custom event handler is a simple one: record the loss values at the end of every epoch in our training phase.
-
-Note: For each of the method, the `Estimator` object is passed along, so you can access training metrics.
-
-```python
-class LossRecordHandler(TrainBegin, TrainEnd, EpochEnd):
-    def __init__(self):
-        super(LossRecordHandler, self).__init__()
-        self.loss_history = {}
-
-    def train_begin(self, estimator, *args, **kwargs):
-        print("Training begin")
-
-    def train_end(self, estimator, *args, **kwargs):
-        # Print all the losses at the end of training
-        print("Training ended")
-        for loss_name in self.loss_history:
-            for i, loss_val in enumerate(self.loss_history[loss_name]):
-                print("Epoch: {}, Loss name: {}, Loss value: {}".format(i, loss_name, loss_val))
-
-    def epoch_end(self, estimator, *args, **kwargs):
-        for metric in estimator.train_metrics:
-            # look for train Loss in training metrics
-            # we wrapped loss value as a metric to record it
-            if isinstance(metric, mx.metric.Loss):
-                loss_name, loss_val = metric.get()
-                # append loss value for this epoch
-                self.loss_history.setdefault(loss_name, []).append(loss_val)
-```
-
-
-```python
-# Let's reset the model, trainer and accuracy objects from above
-
-resnet_18_v1.initialize(force_reinit=True, init = mx.init.Xavier(), ctx=ctx)
-trainer = gluon.Trainer(resnet_18_v1.collect_params(), 
-                        'sgd', {'learning_rate': learning_rate})
-train_acc = mx.metric.Accuracy()
-```
-
-
-```python
-# Define the estimator, by passing to it the model, loss function, metrics, trainer object and context
-est = estimator.Estimator(net=resnet_18_v1,
-                          loss=loss_fn,
-                          metrics=train_acc,
-                          trainer=trainer, 
-                          context=ctx)
-
-# Define the handlers, let's say in built Checkpointhandler
-checkpoint_handler = CheckpointHandler(model_dir='./',
-                                       model_prefix='my_model',
-                                       monitor=train_acc,  # Monitors a metric
-                                       save_best=True)  # Save the best model in terms of
-# Let's instantiate another handler which we defined above 
-loss_record_handler = LossRecordHandler()
-# ignore warnings for nightly test on CI only
-import warnings
-with warnings.catch_warnings():
-    warnings.simplefilter("ignore")
-    # Magic line
-    est.fit(train_data=train_data_loader,
-            val_data=val_data_loader,
-            epochs=num_epochs,
-            event_handlers=[checkpoint_handler, loss_record_handler]) # Add the event handlers
-```
-
-    Training begin: using optimizer SGD with current learning rate 0.0400 <!--notebook-skip-line-->
-    Train for 2 epochs. <!--notebook-skip-line-->
-    
-    [Epoch 0] finished in 25.236s: train_accuracy : 0.7917 train_softmaxcrossentropyloss0 : 0.5741 val_accuracy : 0.6612 val_softmaxcrossentropyloss0 : 0.8627 <!--notebook-skip-line-->
-    
-    [Epoch 1] finished in 24.892s: train_accuracy : 0.8826 train_softmaxcrossentropyloss0 : 0.3229 val_accuracy : 0.8474 val_softmaxcrossentropyloss0 : 0.4262 <!--notebook-skip-line-->
-    
-    Train finished using total 50s at epoch 1. train_accuracy : 0.8826 train_softmaxcrossentropyloss0 : 0.3229 val_accuracy : 0.8474 val_softmaxcrossentropyloss0 : 0.4262 <!--notebook-skip-line-->
-
-    Training begin <!--notebook-skip-line-->
-    Epoch 1, loss 0.5741 <!--notebook-skip-line-->
-    Epoch 2, loss 0.3229 <!--notebook-skip-line-->
-
-You can load the saved model, by using the `load_parameters` API in Gluon. For more details refer to the [Loading model parameters from file tutorial](save_load_params.html#saving-model-parameters-to-file)
-
-
-```python
-resnet_18_v1 = vision.resnet18_v1(pretrained=False, classes=10)
-resnet_18_v1.load_parameters('./my_model-best.params', ctx=ctx)
-```
-
-## Summary
-
-- To learn more about deep learning with MXNeT, see [Dive Into Deep Learning](http://gluon.io)
-
-## Next Steps 
-
-- For more hands on learning about deep learning, check out [Dive into Deep Learning](https://d2l.ai)
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/gluon/gluon.md b/docs/tutorials/gluon/gluon.md
deleted file mode 100644
index d9a6e93af689..000000000000
--- a/docs/tutorials/gluon/gluon.md
+++ /dev/null
@@ -1,159 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Gluon - Neural network building blocks
-
-Gluon package is a high-level interface for MXNet designed to be easy to use while
-keeping most of the flexibility of low level API. Gluon supports both imperative
-and symbolic programming, making it easy to train complex models imperatively
-in Python and then deploy with symbolic graph in C++ and Scala.
-
-
-```python
-# import dependencies
-from __future__ import print_function
-import numpy as np
-import mxnet as mx
-import mxnet.ndarray as F
-import mxnet.gluon as gluon
-from mxnet.gluon import nn
-from mxnet import autograd
-```
-
-Neural networks (and other machine learning models) can be defined and trained
-with `gluon.nn` and `gluon.rnn` package. A typical training script has the following
-steps:
-
-- Define network
-- Initialize parameters
-- Loop over inputs
-- Forward input through network to get output
-- Compute loss with output and label
-- Backprop gradient
-- Update parameters with gradient descent.
-
-
-## Define Network
-
-`gluon.Block` is the basic building block of models. You can define networks by
-composing and inheriting `Block`:
-
-```python
-class Net(gluon.Block):
-    def __init__(self, **kwargs):
-        super(Net, self).__init__(**kwargs)
-        with self.name_scope():
-            # layers created in name_scope will inherit name space
-            # from parent layer.
-            self.conv1 = nn.Conv2D(6, kernel_size=5)
-            self.pool1 = nn.MaxPool2D(pool_size=(2,2))
-            self.conv2 = nn.Conv2D(16, kernel_size=5)
-            self.pool2 = nn.MaxPool2D(pool_size=(2,2))
-            self.fc1 = nn.Dense(120)
-            self.fc2 = nn.Dense(84)
-            self.fc3 = nn.Dense(10)
-
-    def forward(self, x):
-        x = self.pool1(F.relu(self.conv1(x)))
-        x = self.pool2(F.relu(self.conv2(x)))
-        # 0 means copy over size from corresponding dimension.
-        # -1 means infer size from the rest of dimensions.
-        x = x.reshape((0, -1))
-        x = F.relu(self.fc1(x))
-        x = F.relu(self.fc2(x))
-        x = self.fc3(x)
-        return x
-```
-
-## Initialize Parameters
-
-A network must be created and initialized before it can be used:
-
-```python
-net = Net()
-# Initialize on CPU. Replace with `mx.gpu(0)`, or `[mx.gpu(0), mx.gpu(1)]`,
-# etc to use one or more GPUs.
-net.initialize(mx.init.Xavier(), ctx=mx.cpu())
-```
-
-Note that because we didn't specify input size to layers in Net's constructor,
-the shape of parameters cannot be determined at this point. Actual initialization
-is deferred to the first forward pass, i.e. if you access `net.fc1.weight.data()`
-now an exception will be raised.
-
-You can actually initialize the weights by running a forward pass:
-
-```python
-data = mx.nd.random_normal(shape=(10, 1, 32, 32))  # dummy data
-output = net(data)
-```
-
-Or you can specify input size when creating layers, i.e. `nn.Dense(84, in_units=120)`
-instead of `nn.Dense(84)`.
-
-## Loss Functions
-
-Loss functions take (output, label) pairs and compute a scalar loss for each sample
-in the mini-batch. The scalars measure how far each output is from the label.
-
-There are many predefined loss functions in `gluon.loss`. Here we use
-`softmax_cross_entropy_loss` for digit classification.
-
-To compute loss and backprop for one iteration, we do:
-
-```python
-label = mx.nd.arange(10)  # dummy label
-with autograd.record():
-    output = net(data)
-    L = gluon.loss.SoftmaxCrossEntropyLoss()
-    loss = L(output, label)
-    loss.backward()
-print('loss:', loss)
-print('grad:', net.fc1.weight.grad())
-```
-
-## Updating the weights
-
-Now that gradient is computed, we just need to update the weights. This is usually
-done with formulas like `weight = weight - learning_rate * grad / batch_size`.
-Note we divide gradient by batch_size because gradient is aggregated over the
-entire batch. For example,
-
-```python
-lr = 0.01
-for p in net.collect_params().values():
-    p.data()[:] -= lr / data.shape[0] * p.grad()
-```
-
-But sometimes you want more fancy updating rules like momentum and Adam, and since
-this is a commonly used functionality, gluon provide a `Trainer` class for it:
-
-```python
-trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.01})
-
-with autograd.record():
-    output = net(data)
-    L = gluon.loss.SoftmaxCrossEntropyLoss()
-    loss = L(output, label)
-    loss.backward()
-
-# do the update. Trainer needs to know the batch size of data to normalize
-# the gradient by 1/batch_size.
-trainer.step(data.shape[0])
-```
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/gluon/gluon_from_experiment_to_deployment.md b/docs/tutorials/gluon/gluon_from_experiment_to_deployment.md
deleted file mode 100644
index f84a72ef1063..000000000000
--- a/docs/tutorials/gluon/gluon_from_experiment_to_deployment.md
+++ /dev/null
@@ -1,330 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-
-# Gluon: from experiment to deployment, an end to end tutorial
-
-## Overview
-MXNet Gluon API comes with a lot of great features, and it can provide you everything you need: from experimentation to deploying the model. In this tutorial, we will walk you through a common use case on how to build a model using gluon, train it on your data, and deploy it for inference.
-This tutorial covers training and inference in Python, please continue to [C++ inference part](https://mxnet.incubator.apache.org/versions/master/tutorials/c++/mxnet_cpp_inference_tutorial.html) after you finish.
-
-Let's say you need to build a service that provides flower species recognition. A common problem is that you don't have enough data to train a good model. In such cases, a technique called Transfer Learning can be used to make a more robust model.
-In Transfer Learning we make use of a pre-trained model that solves a related task, and was trained on a very large standard dataset, such as ImageNet. ImageNet is from a different domain, but we can utilize the knowledge in this pre-trained model to perform the new task at hand.
-
-Gluon provides State of the Art models for many of the standard tasks such as Classification, Object Detection, Segmentation, etc. In this tutorial we will use the pre-trained model [ResNet50 V2](https://arxiv.org/abs/1603.05027) trained on ImageNet dataset. This model achieves 77.11% top-1 accuracy on ImageNet. We seek to transfer as much knowledge as possible for our task of recognizing different species of flowers.
-
-
-
-
-## Prerequisites
-
-To complete this tutorial, you need:
-
-- [Build MXNet from source](https://mxnet.incubator.apache.org/install/ubuntu_setup.html#build-mxnet-from-source) with Python(Gluon) and C++ Packages
-- Learn the basics about Gluon with [A 60-minute Gluon Crash Course](https://gluon-crash-course.mxnet.io/)
-
-
-## The Data
-
-We will use the [Oxford 102 Category Flower Dataset](http://www.robots.ox.ac.uk/~vgg/data/flowers/102/) as an example to show you the steps.
-We have prepared a utility file to help you download and organize your data into train, test, and validation sets. Run the following Python code to download and prepare the data:
-
-
-```python
-import mxnet as mx
-data_util_file = "oxford_102_flower_dataset.py"
-base_url = "https://raw.githubusercontent.com/apache/incubator-mxnet/master/docs/tutorial_utils/data/{}?raw=true"
-mx.test_utils.download(base_url.format(data_util_file), fname=data_util_file)
-import oxford_102_flower_dataset
-
-# download and move data to train, test, valid folders
-path = './data'
-oxford_102_flower_dataset.get_data(path)
-```
-
-Now your data will be organized into train, test, and validation sets, images belong to the same class are moved to the same folder.
-
-## Training using Gluon
-
-### Define Hyper-parameters
-
-Now let's first import necessary packages:
-
-
-```python
-import math
-import os
-import time
-
-from mxnet import autograd
-from mxnet import gluon, init
-from mxnet.gluon import nn
-from mxnet.gluon.data.vision import transforms
-from mxnet.gluon.model_zoo.vision import resnet50_v2
-```
-
-Next, we define the hyper-parameters that we will use for fine-tuning. We will use the [MXNet learning rate scheduler](https://mxnet.incubator.apache.org/tutorials/gluon/learning_rate_schedules.html) to adjust learning rates during training.
-Here we set the `epochs` to 1 for quick demonstration, please change to 40 for actual training.
-
-```python
-classes = 102
-epochs = 1
-lr = 0.001
-per_device_batch_size = 32
-momentum = 0.9
-wd = 0.0001
-
-lr_factor = 0.75
-# learning rate change at following epochs
-lr_epochs = [10, 20, 30]
-
-num_gpus = mx.context.num_gpus()
-# you can replace num_workers with the number of cores on you device
-num_workers = 8
-ctx = [mx.gpu(i) for i in range(num_gpus)] if num_gpus > 0 else [mx.cpu()]
-batch_size = per_device_batch_size * max(num_gpus, 1)
-```
-
-Now we will apply data augmentations on training images. This makes minor alterations on the training images, and our model will consider them as distinct images. This can be very useful for fine-tuning on a relatively small dataset, and it will help improve the model. We can use the Gluon [DataSet API](https://mxnet.incubator.apache.org/tutorials/gluon/datasets.html), [DataLoader API](https://mxnet.incubator.apache.org/tutorials/gluon/datasets.html), and [Transform API](https://mxnet.incubator.apache.org/tutorials/gluon/data_augmentation.html) to load the images and apply the following data augmentations:
-1. Randomly crop the image and resize it to 224x224
-2. Randomly flip the image horizontally
-3. Randomly jitter color and add noise
-4. Transpose the data from `[height, width, num_channels]` to `[num_channels, height, width]`, and map values from [0, 255] to [0, 1]
-5. Normalize with the mean and standard deviation from the ImageNet dataset.
-
-For validation and inference, we only need to apply step 1, 4, and 5. We also need to save the mean and standard deviation values for [inference using C++](https://mxnet.incubator.apache.org/versions/master/tutorials/c++/mxnet_cpp_inference_tutorial.html).
-
-```python
-jitter_param = 0.4
-lighting_param = 0.1
-
-# mean and std for normalizing image value in range (0,1)
-mean = [0.485, 0.456, 0.406]
-std = [0.229, 0.224, 0.225]
-
-training_transformer = transforms.Compose([
-    transforms.RandomResizedCrop(224),
-    transforms.RandomFlipLeftRight(),
-    transforms.RandomColorJitter(brightness=jitter_param, contrast=jitter_param,
-                                 saturation=jitter_param),
-    transforms.RandomLighting(lighting_param),
-    transforms.ToTensor(),
-    transforms.Normalize(mean, std)
-])
-
-validation_transformer = transforms.Compose([
-    transforms.Resize(256),
-    transforms.CenterCrop(224),
-    transforms.ToTensor(),
-    transforms.Normalize(mean, std)
-])
-
-# save mean and std NDArray values for inference
-mean_img = mx.nd.stack(*[mx.nd.full((224, 224), m) for m in mean])
-std_img = mx.nd.stack(*[mx.nd.full((224, 224), s) for s in std])
-mx.nd.save('mean_std_224.nd', {"mean_img": mean_img, "std_img": std_img})
-
-train_path = os.path.join(path, 'train')
-val_path = os.path.join(path, 'valid')
-test_path = os.path.join(path, 'test')
-
-# loading the data and apply pre-processing(transforms) on images
-train_data = gluon.data.DataLoader(
-    gluon.data.vision.ImageFolderDataset(train_path).transform_first(training_transformer),
-    batch_size=batch_size, shuffle=True, num_workers=num_workers)
-
-val_data = gluon.data.DataLoader(
-    gluon.data.vision.ImageFolderDataset(val_path).transform_first(validation_transformer),
-    batch_size=batch_size, shuffle=False, num_workers=num_workers)
-
-test_data = gluon.data.DataLoader(
-    gluon.data.vision.ImageFolderDataset(test_path).transform_first(validation_transformer),
-    batch_size=batch_size, shuffle=False, num_workers=num_workers)
-```
-
-### Loading pre-trained model
-
-
-We will use pre-trained ResNet50_v2 model which was pre-trained on the [ImageNet Dataset](http://www.image-net.org/) with 1000 classes. To match the classes in the Flower dataset, we must redefine the last softmax (output) layer to be 102, then initialize the parameters.
-
-Before we go to training, one unique Gluon feature you should be aware of is hybridization. It allows you to convert your imperative code to a static symbolic graph, which is much more efficient to execute. There are two main benefits of hybridizing your model: better performance and easier serialization for deployment. The best part is that it's as simple as just calling `net.hybridize()`. To know more about Gluon hybridization, please follow the [hybridization tutorial](https://mxnet.incubator.apache.org/tutorials/gluon/hybrid.html).
-
-
-
-```python
-# load pre-trained resnet50_v2 from model zoo
-finetune_net = resnet50_v2(pretrained=True, ctx=ctx)
-
-# change last softmax layer since number of classes are different
-with finetune_net.name_scope():
-    finetune_net.output = nn.Dense(classes)
-finetune_net.output.initialize(init.Xavier(), ctx=ctx)
-# hybridize for better performance
-finetune_net.hybridize()
-
-num_batch = len(train_data)
-
-# setup learning rate scheduler
-iterations_per_epoch = math.ceil(num_batch)
-# learning rate change at following steps
-lr_steps = [epoch * iterations_per_epoch for epoch in lr_epochs]
-schedule = mx.lr_scheduler.MultiFactorScheduler(step=lr_steps, factor=lr_factor, base_lr=lr)
-
-# setup optimizer with learning rate scheduler, metric, and loss function
-sgd_optimizer = mx.optimizer.SGD(learning_rate=lr, lr_scheduler=schedule, momentum=momentum, wd=wd)
-metric = mx.metric.Accuracy()
-softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()
-```
-
-### Fine-tuning model on your custom dataset
-
-Now let's define the test metrics and start fine-tuning.
-
-
-
-```python
-def test(net, val_data, ctx):
-    metric = mx.metric.Accuracy()
-    for i, (data, label) in enumerate(val_data):
-        data = gluon.utils.split_and_load(data, ctx_list=ctx, even_split=False)
-        label = gluon.utils.split_and_load(label, ctx_list=ctx, even_split=False)
-        outputs = [net(x) for x in data]
-        metric.update(label, outputs)
-    return metric.get()
-
-trainer = gluon.Trainer(finetune_net.collect_params(), optimizer=sgd_optimizer)
-
-# start with epoch 1 for easier learning rate calculation
-for epoch in range(1, epochs + 1):
-
-    tic = time.time()
-    train_loss = 0
-    metric.reset()
-
-    for i, (data, label) in enumerate(train_data):
-        # get the images and labels
-        data = gluon.utils.split_and_load(data, ctx_list=ctx, even_split=False)
-        label = gluon.utils.split_and_load(label, ctx_list=ctx, even_split=False)
-        with autograd.record():
-            outputs = [finetune_net(x) for x in data]
-            loss = [softmax_cross_entropy(yhat, y) for yhat, y in zip(outputs, label)]
-        for l in loss:
-            l.backward()
-
-        trainer.step(batch_size)
-        train_loss += sum([l.mean().asscalar() for l in loss]) / len(loss)
-        metric.update(label, outputs)
-
-    _, train_acc = metric.get()
-    train_loss /= num_batch
-    _, val_acc = test(finetune_net, val_data, ctx)
-
-    print('[Epoch %d] Train-acc: %.3f, loss: %.3f | Val-acc: %.3f | learning-rate: %.3E | time: %.1f' %
-          (epoch, train_acc, train_loss, val_acc, trainer.learning_rate, time.time() - tic))
-
-_, test_acc = test(finetune_net, test_data, ctx)
-print('[Finished] Test-acc: %.3f' % (test_acc))
-```
-
-Following is the training result:
-```
-[Epoch 40] Train-acc: 0.945, loss: 0.354 | Val-acc: 0.955 | learning-rate: 4.219E-04 | time: 17.8
-[Finished] Test-acc: 0.952
-```
-In the previous example output, we trained the model using an [AWS p3.8xlarge instance](https://aws.amazon.com/ec2/instance-types/p3/) with 4 Tesla V100 GPUs. We were able to reach a test accuracy of 95.5% with 40 epochs in around 12 minutes. This was really fast because our model was pre-trained on a much larger dataset, ImageNet, with around 1.3 million images. It worked really well to capture features on our small dataset.
-
-
-### Save the fine-tuned model
-
-
-We now have a trained our custom model. This can be serialized into model files using the export function. The export function will export the model architecture into a `.json` file and model parameters into a `.params` file.
-
-
-
-```python
-finetune_net.export("flower-recognition", epoch=epochs)
-
-```
-
-`export` creates `flower-recognition-symbol.json` and `flower-recognition-0040.params` (`0040` is for 40 epochs we ran) in the current directory. These files can be used for model deployment in the next section.
-
-## Load the model and run inference using the MXNet Module API
-
-MXNet provides various useful tools and interfaces for deploying your model for inference. For example, you can use [MXNet Model Server](https://github.com/awslabs/mxnet-model-server) to start a service and host your trained model easily.
-Besides that, you can also use MXNet's different language APIs to integrate your model with your existing service. We provide [Python](https://mxnet.incubator.apache.org/api/python/module/module.html),    [Java](https://mxnet.incubator.apache.org/api/java/index.html), [Scala](https://mxnet.incubator.apache.org/api/scala/index.html), and [C++](https://mxnet.incubator.apache.org/api/c++/index.html) APIs.
-
-Here we will briefly introduce how to run inference using Module API in Python. There is more detailed explanation available in the [Predict Image Tutorial](https://mxnet.incubator.apache.org/tutorials/python/predict_image.html).
-In general, prediction consists of the following steps:
-1. Load the model architecture (symbol file) and trained parameter values (params file)
-2. Load the synset file for label names
-3. Load the image and apply the same transformation we did on validation dataset during training
-4. Run a forward pass on the image data
-5. Convert output probabilities to predicted label name
-
-```python
-import numpy as np
-from collections import namedtuple
-
-ctx = mx.cpu()
-# load model symbol and params
-sym, arg_params, aux_params = mx.model.load_checkpoint('flower-recognition', epochs)
-mod = mx.mod.Module(symbol=sym, context=ctx, label_names=None)
-mod.bind(for_training=False, data_shapes=[('data', (1, 3, 224, 224))], label_shapes=mod._label_shapes)
-mod.set_params(arg_params, aux_params, allow_missing=True)
-
-# load synset for label names
-with open('synset.txt', 'r') as f:
-    labels = [l.rstrip() for l in f]
-
-# load an image for prediction
-img = mx.image.imread('./data/test/lotus/image_01832.jpg')
-# apply transform we did during training
-img = validation_transformer(img)
-# batchify
-img = img.expand_dims(axis=0)
-Batch = namedtuple('Batch', ['data'])
-mod.forward(Batch([img]))
-prob = mod.get_outputs()[0].asnumpy()
-prob = np.squeeze(prob)
-idx = np.argmax(prob)
-print('probability=%f, class=%s' % (prob[idx], labels[idx]))
-```
-
-Following is the output, you can see the image has been classified as lotus correctly.
-```
-probability=9.798435, class=lotus
-```
-
-## What's next
-
-You can continue to the [next tutorial](https://mxnet.incubator.apache.org/versions/master/tutorials/c++/mxnet_cpp_inference_tutorial.html) on how to load the model we just trained and run inference using MXNet C++ API.
-
-You can also find more ways to run inference and deploy your models here:
-1. [Java Inference examples](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer)
-2. [Scala Inference examples](https://mxnet.incubator.apache.org/tutorials/scala/)
-3. [ONNX model inference examples](https://mxnet.incubator.apache.org/tutorials/onnx/inference_on_onnx_model.html)
-4. [MXNet Model Server Examples](https://github.com/awslabs/mxnet-model-server/tree/master/examples)
-
-## References
-
-1. [Transfer Learning for Oxford102 Flower Dataset](https://github.com/Arsey/keras-transfer-learning-for-oxford102)
-2. [Gluon book on fine-tuning](https://www.d2l.ai/chapter_computer-vision/fine-tuning.html)
-3. [Gluon CV transfer learning tutorial](https://gluon-cv.mxnet.io/build/examples_classification/transfer_learning_minc.html)
-4. [Gluon crash course](https://gluon-crash-course.mxnet.io/)
-5. [Gluon CPP inference example](https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/inference/)
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/gluon/gotchas_numpy_in_mxnet.md b/docs/tutorials/gluon/gotchas_numpy_in_mxnet.md
deleted file mode 100644
index 1c285a41d76b..000000000000
--- a/docs/tutorials/gluon/gotchas_numpy_in_mxnet.md
+++ /dev/null
@@ -1,185 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-
-# Gotchas using NumPy in Apache MXNet
-
-The goal of this tutorial is to explain some common misconceptions about using [NumPy](http://www.numpy.org/) arrays in Apache MXNet. We are going to explain why you need to minimize or completely remove usage of NumPy from your Apache MXNet code. We also going to show how to minimize NumPy performance impact, when you have to use NumPy.
-
-## Asynchronous and non-blocking nature of Apache MXNet
-
-Instead of using NumPy arrays Apache MXNet offers its own array implementation named [NDArray](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html). `NDArray API` was intentionally designed to be similar to `NumPy`, but there are differences.
-
-One key difference is in the way calculations are executed. Every `NDArray` manipulation in Apache MXNet is done in asynchronous, non-blocking way. That means, that when we write code like `c = a * b`, where both `a` and `b` are `NDArrays`, the function is pushed to the [Execution Engine](https://mxnet.incubator.apache.org/architecture/overview.html#execution-engine), which starts the calculation. The function immediately returns back, and the  user thread can continue execution, despite the fact that the calculation may not have been completed yet. 
-
-`Execution Engine` builds the computation graph which may reorder or combine some calculations, but it honors dependency order: if there are other manipulation with `c` done later in the code, the `Execution Engine` will start doing them once the result of `c` is available. We don't need to write callbacks to start execution of subsequent code - the `Execution Engine` is going to do it for us. 
-
-To get the result of the computation we only need to access the resulting variable, and the flow of the code will be blocked until the computation results are assigned to the resulting variable. This behavior allows to increase code performance while still supporting imperative programming mode. 
-
-Refer to the [intro tutorial to NDArray](https://mxnet.incubator.apache.org/tutorials/basic/ndarray.html), if you are new to Apache MXNet and would like to learn more how to manipulate NDArrays.
-
-## Converting NDArray to NumPy Array blocks calculation
-
-Many people are familiar with NumPy and flexible doing tensor manipulations using it. `NDArray API` offers  a convinient [.asnumpy() method](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.asnumpy) to cast `nd.array` to `np.array`. However, by doing this cast and using `np.array` for calculation, we cannot use all the goodness of `Execution Engine`. All manipulations done on `np.array` are blocking. Moreover, the cast to `np.array` itself is a blocking operation (same as [.asscalar()](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.asscalar), [.wait_to_read()](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.wait_to_read) and [.waitall()](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.waitall)). 
-
-That means that if we have a long computation graph and, at some point, we want to cast the result to `np.array`, it may feel like the casting takes a lot of time. But what really takes this time is `Execution Engine`, which finishes all the async calculations we have pushed into it to get the final result, which then will be converted to `np.array`.
-
-Because of the blocking nature of [.asnumpy() method](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.asnumpy), using it reduces the execution performance, especially if the calculations are done on GPU: Apache MXNet has to copy data from GPU to CPU to return `np.array`. 
-
-The best solution is to **make manipulations directly on NDArrays by methods provided in [NDArray API](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html)**.
-
-## NumPy operators vs. NDArray operators
-
-Despite the fact that [NDArray API](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html) was specifically designed to be similar to `NumPy`, sometimes it is not easy to replace existing `NumPy` computations. The main reason is that not all operators, that are available in `NumPy`, are available in `NDArray API`. The list of currently available operators is available on [NDArray class page](http://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#the-ndarray-class).
-
-If a required operator is missing from `NDArray API`, there are few things you can do.
-
-### Combine a higher level operator using a few lower level operators
-
-There are a situation, when you can assemble a higher level operator using existing operators. An example for that is the [np.full_like()](https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.full_like.html) operator. This operator doesn't exist in `NDArray API`, but can be easily replaced with a combination of existing operators.
-
-
-```python
-from mxnet import nd
-import numpy as np
-
-# NumPy has full_like() operator 
-np_y = np.full_like(a=np.arange(6, dtype=int), fill_value=10)
-
-# NDArray doesn't have it, but we can replace it with
-# creating an array of ones and then multiplying by fill_value
-nd_y = nd.ones(shape=(6,)) * 10
-
-# To compare results we had to convert NDArray to NumPy
-# But this is okay for that particular case
-np.array_equal(np_y, nd_y.asnumpy())
-```
-
-    True <!--notebook-skip-line-->
-
-### Find similar operator with different name and/or signature
-
-Some operators may have slightly different name, but are similar in terms of functionality. For example [nd.ravel_multi_index()](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.ravel_multi_index) is similar to [np.ravel()](https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.ma.ravel.html#numpy.ma.ravel). In other cases some operators may have similar names, but different signatures. For example [np.split()](https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.split.html#numpy.split) and [nd.split()](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.split) are similar, but the former works with indices and the latter requires the number of splits to be provided.
-
-One particular example of different input requirements is [nd.pad()](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.pad). The trick is that it can only work with 4-dimensional tensors. If your input has less dimensions, then you need to expand its number before using `nd.pad()` as it is shown in the code block below: 
-
-
-```python
-def pad_array(data, max_length):
-    # expand dimensions to 4, because nd.pad can work only with 4 dims
-    data_expanded = data.reshape(1, 1, 1, data.shape[0])
-    
-    # pad all 4 dimensions with constant value of 0
-    data_padded = nd.pad(data_expanded,
-                             mode='constant',
-                             pad_width=[0, 0, 0, 0, 0, 0, 0, max_length - data.shape[0]],
-                             constant_value=0)
-    
-    # remove temporary dimensions 
-    data_reshaped_back = data_padded.reshape(max_length)
-    return data_reshaped_back
-
-pad_array(nd.array([1, 2, 3]), max_length=10)
-```
-    
-    [ 1.  2.  3.  0.  0.  0.  0.  0.  0.  0.] <!--notebook-skip-line-->
-
-    <NDArray 10 @cpu(0)> <!--notebook-skip-line-->
-
-
-### Search for an operator on [Github](https://github.com/apache/incubator-mxnet/labels/Operator)
-
-Apache MXNet community is responsive to requests, and everyone is welcomed to contribute new operators. Have in mind, that there is always a lag between new operators being merged into the codebase and release of a next stable version. For example, [nd.diag()](https://github.com/apache/incubator-mxnet/pull/11643) operator was recently introduced to Apache MXNet, but on the moment of writing this tutorial, it is not in any stable release. You can always get all latest implementations by installing the [master version](https://mxnet.incubator.apache.org/install/index.html?version=master#) of Apache MXNet.
-
-## How to minimize the impact of blocking calls
-
-There are cases, when you have to use either `.asnumpy()` or `.asscalar()` methods. As it is explained before, this will force Apache MXNet to block the execution until the result can be retrieved. One common use case is printing a metric or a value of a loss function.
-
-You can minimize the impact of a blocking call by calling `.asnumpy()` or `.asscalar()` in the moment, when you think the calculation of this value is already done. In the example below, we introduce the `LossBuffer` class. It is used to cache the previous value of a loss function. By doing so, we delay printing by one iteration in hope that the `Execution Engine` would finish the previous iteration and blocking time would be minimized.
-
-
-```python
-from __future__ import print_function
-
-import mxnet as mx
-from mxnet import gluon, nd, autograd
-from mxnet.ndarray import NDArray
-from mxnet.gluon import HybridBlock
-import numpy as np
-
-class LossBuffer(object):
-    """
-    Simple buffer for storing loss value
-    """
-    def __init__(self):
-        self._loss = None
-
-    def new_loss(self, loss):
-        ret = self._loss
-        self._loss = loss
-        return ret
-
-    @property
-    def loss(self):
-        return self._loss
-
-
-net = gluon.nn.Dense(10)
-ce = gluon.loss.SoftmaxCELoss()
-net.initialize()
-
-data = nd.random.uniform(shape=(1024, 100))
-label = nd.array(np.random.randint(0, 10, (1024,)), dtype='int32')
-train_dataset = gluon.data.ArrayDataset(data, label)
-train_data = gluon.data.DataLoader(train_dataset, batch_size=128, shuffle=True, num_workers=2)
-
-trainer = gluon.Trainer(net.collect_params(), optimizer='sgd')
-loss_buffer = LossBuffer()
-
-for data, label in train_data:
-    with autograd.record():
-        out = net(data)
-        # This call saves new loss and returns previous loss
-        prev_loss = loss_buffer.new_loss(ce(out, label))
-        
-    loss_buffer.loss.backward()
-    trainer.step(data.shape[0])
-    
-    if prev_loss is not None:
-        print("Loss: {}".format(np.mean(prev_loss.asnumpy())))
-```
-
-    Loss: 2.310760974884033 <!--notebook-skip-line-->
-
-    Loss: 2.334498643875122 <!--notebook-skip-line-->
-
-    Loss: 2.3244147300720215 <!--notebook-skip-line-->
-
-    Loss: 2.332686424255371 <!--notebook-skip-line-->
-
-    Loss: 2.321366310119629 <!--notebook-skip-line-->
-
-    Loss: 2.3236165046691895 <!--notebook-skip-line-->
-
-    Loss: 2.3178648948669434 <!--notebook-skip-line-->
-
-
-## Conclusion
-
-For performance reasons, it is better to use native `NDArray API` methods and avoid using NumPy altogether. In case when you must use NumPy, you can use convenient method `.asnumpy()` on `NDArray` to get NumPy representation. By doing so, you block the whole computational process, and force data to be synced between CPU and GPU. If it is a necessary evil to do that, try to minimize the blocking time by calling `.asnumpy()` in time, when you expect the value to be already computed.
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
\ No newline at end of file
diff --git a/docs/tutorials/gluon/hybrid.md b/docs/tutorials/gluon/hybrid.md
deleted file mode 100644
index 9de9713403dc..000000000000
--- a/docs/tutorials/gluon/hybrid.md
+++ /dev/null
@@ -1,266 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Hybrid - Faster training and easy deployment
-
-*Related Content:*
-* [Fast, portable neural networks with Gluon HybridBlocks](https://gluon.mxnet.io/chapter07_distributed-learning/hybridize.html)
-* [A Hybrid of Imperative and Symbolic Programming
-](http://en.diveintodeeplearning.org/chapter_computational-performance/hybridize.html)
-
-Deep learning frameworks can be roughly divided into two categories: declarative
-and imperative. With declarative frameworks (including Tensorflow, Theano, etc)
-users first declare a fixed computation graph and then execute it end-to-end.
-The benefit of fixed computation graph is it's portable and runs more
-efficiently. However, it's less flexible because any logic must be encoded
-into the graph as special operators like `scan`, `while_loop` and `cond`.
-It's also hard to debug.
-
-Imperative frameworks (including PyTorch, Chainer, etc) are just the opposite:
-they execute commands one-by-one just like old fashioned Matlab and Numpy.
-This style is more flexible, easier to debug, but less efficient.
-
-`HybridBlock` seamlessly combines declarative programming and imperative programming
-to offer the benefit of both. Users can quickly develop and debug models with
-imperative programming and switch to efficient declarative execution by simply
-calling: `HybridBlock.hybridize()`.
-
-## HybridBlock
-
-`HybridBlock` is very similar to `Block` but has a few restrictions:
-
-- All children layers of `HybridBlock` must also be `HybridBlock`.
-- Only methods that are implemented for both `NDArray` and `Symbol` can be used.
-  For example you cannot use `.asnumpy()`, `.shape`, etc.
-- Operations cannot change from run to run. For example, you cannot do `if x:`
-  if `x` is different for each iteration.
-
-To use hybrid support, we subclass the `HybridBlock`:
-
-```python
-import mxnet as mx
-from mxnet import gluon
-from mxnet.gluon import nn
-
-mx.random.seed(42)
-
-class Net(gluon.HybridBlock):
-    def __init__(self, **kwargs):
-        super(Net, self).__init__(**kwargs)
-        with self.name_scope():
-            # layers created in name_scope will inherit name space
-            # from parent layer.
-            self.conv1 = nn.Conv2D(6, kernel_size=5)
-            self.pool1 = nn.MaxPool2D(pool_size=2)
-            self.conv2 = nn.Conv2D(16, kernel_size=5)
-            self.pool2 = nn.MaxPool2D(pool_size=2)
-            self.fc1 = nn.Dense(120)
-            self.fc2 = nn.Dense(84)
-            # You can use a Dense layer for fc3 but we do dot product manually
-            # here for illustration purposes.
-            self.fc3_weight = self.params.get('fc3_weight', shape=(10, 84))
-
-    def hybrid_forward(self, F, x, fc3_weight):
-        # Here `F` can be either mx.nd or mx.sym, x is the input data,
-        # and fc3_weight is either self.fc3_weight.data() or
-        # self.fc3_weight.var() depending on whether x is Symbol or NDArray
-        print(x)
-        x = self.pool1(F.relu(self.conv1(x)))
-        x = self.pool2(F.relu(self.conv2(x)))
-        # 0 means copy over size from corresponding dimension.
-        # -1 means infer size from the rest of dimensions.
-        x = x.reshape((0, -1))
-        x = F.relu(self.fc1(x))
-        x = F.relu(self.fc2(x))
-        x = F.dot(x, fc3_weight, transpose_b=True)
-        return x
-```
-
-## Hybridize
-
-By default, `HybridBlock` runs just like a standard `Block`. Each time a layer
-is called, its `hybrid_forward` will be run:
-
-```python
-net = Net()
-net.initialize()
-x = mx.nd.random_normal(shape=(16, 1, 28, 28))
-net(x)
-x = mx.nd.random_normal(shape=(16, 1, 28, 28))
-net(x)
-```
-
-Hybrid execution can be activated by simply calling `.hybridize()` on the top
-level layer. The first forward call after activation will try to build a
-computation graph from `hybrid_forward` and cache it. On subsequent forward
-calls the cached graph, instead of `hybrid_forward`, will be invoked:
-
-```python
-net.hybridize()
-x = mx.nd.random_normal(shape=(16, 1, 28, 28))
-net(x)
-x = mx.nd.random_normal(shape=(16, 1, 28, 28))
-net(x)
-```
-
-Note that before hybridize, `print(x)` printed out one NDArray for forward,
-but after hybridize, only the first forward printed out a Symbol. On subsequent
-forward `hybrid_forward` is not called so nothing was printed.
-
-Hybridize will speed up execution and save memory. If the top level layer is
-not a `HybridBlock`, you can still call `.hybridize()` on it and Gluon will try
-to hybridize its children layers instead.
-
-`hybridize` also accepts several options for performance tuning. For example, you
-can do
-
-```python
-net.hybridize(static_alloc=True)
-# or
-net.hybridize(static_alloc=True, static_shape=True)
-```
-
-Please refer to the [API manual](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html?highlight=hybridize#mxnet.gluon.Block.hybridize)
-for details.
-
-## Serializing trained model for deployment
-
-Models implemented as `HybridBlock` can be easily serialized. The serialized
-model can be loaded back later or used for deployment
-with other language front-ends like C, C++ and Scala. To this end, we simply
-use `export` and `SymbolBlock.imports`:
-
-```python
-net(x)
-net.export('model', epoch=1)
-```
-
-Two files `model-symbol.json` and `model-0001.params` are saved on disk.
-You can use other language bindings to load them. You can also load them back
-to gluon with `SymbolBlock`:
-
-```python
-import warnings
-
-with warnings.catch_warnings():
-    warnings.simplefilter("ignore")
-    net2 = gluon.SymbolBlock.imports('model-symbol.json', ['data'], 'model-0001.params')
-```
-
-## Operators that do not work with hybridize
-
-If you want to hybridize your model, you must use `F.some_operator` in your 'hybrid_forward' function.
-`F` will be `mxnet.nd` before you hybridize and `mxnet.sym` after hybridize. While most APIs are the same in NDArray and Symbol, there are some differences. Writing `F.some_operator` and call `hybridize` may not work all of the time.
-Here we list some frequently used NDArray APIs that can't be hybridized and provide you the work arounds.  
-
-### Element-wise Operators
-
-In NDArray APIs, the following arithmetic and comparison APIs are automatically broadcasted if the input NDArrays have different shapes.
-However, that's not the case in Symbol API. It's not automatically broadcasted, and you have to manually specify to use another set of broadcast operators for Symbols expected to have different shapes.
-
-
-| NDArray APIs  | Description  |
-|---|---|
-| [NDArray.\__add\__](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__add__) | x.\__add\__(y) <=> x+y <=> mx.nd.add(x, y)  |
-| [NDArray.\__sub\__](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__sub__) | x.\__sub\__(y) <=> x-y <=> mx.nd.subtract(x, y)  |
-| [NDArray.\__mul\__](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__mul__) | x.\__mul\__(y) <=> x*y <=> mx.nd.multiply(x, y)  |
-| [NDArray.\__div\__](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__div__) | x.\__div\__(y) <=> x/y <=> mx.nd.divide(x, y)  |
-| [NDArray.\__mod\__](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__mod__) | x.\__mod\__(y) <=> x%y <=> mx.nd.modulo(x, y)  |
-| [NDArray.\__lt\__](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__lt__) |  x.\__lt\__(y) <=> x<y <=> x mx.nd.lesser(x, y) |
-| [NDArray.\__le\__](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__le__) |  x.\__le\__(y) <=> x<=y <=> mx.nd.less_equal(x, y) |
-| [NDArray.\__gt\__](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__gt__) |  x.\__gt\__(y) <=> x>y <=> mx.nd.greater(x, y) |
-| [NDArray.\__ge\__](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__ge__) |  x.\__ge\__(y) <=> x>=y <=> mx.nd.greater_equal(x, y)|
-| [NDArray.\__eq\__](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__eq__) |  x.\__eq\__(y) <=> x==y <=> mx.nd.equal(x, y) |
-| [NDArray.\__ne\__](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__ne__) |  x.\__ne\__(y) <=> x!=y <=> mx.nd.not_equal(x, y) |
-
-The current workaround is to use corresponding broadcast operators for arithmetic and comparison to avoid potential hybridization failure when input shapes are different.
-
-| Symbol APIs  | Description  |
-|---|---|
-|[broadcast_add](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.broadcast_add) | Returns element-wise sum of the input arrays with broadcasting. |
-|[broadcast_sub](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.broadcast_sub) | Returns element-wise difference of the input arrays with broadcasting. |
-|[broadcast_mul](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.broadcast_mul) | Returns element-wise product of the input arrays with broadcasting. |
-|[broadcast_div](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.broadcast_div) | Returns element-wise division of the input arrays with broadcasting. |
-|[broadcast_mod](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.broadcast_mod) | Returns element-wise modulo of the input arrays with broadcasting. |
-|[broadcast_equal](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.broadcast_equal) | Returns the result of element-wise *equal to* (==) comparison operation with broadcasting. |
-|[broadcast_not_equal](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.broadcast_not_equal) | Returns the result of element-wise *not equal to* (!=) comparison operation with broadcasting. |
-|[broadcast_greater](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.broadcast_greater) | Returns the result of element-wise *greater than* (>) comparison operation with broadcasting. |
-|[broadcast_greater_equal](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.broadcast_greater_equal) | Returns the result of element-wise *greater than or equal to* (>=) comparison operation with broadcasting. |
-|[broadcast_lesser](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.broadcast_lesser) |	Returns the result of element-wise *lesser than* (<) comparison operation with broadcasting. |
-|[broadcast_lesser_equal](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.broadcast_lesser_equal) | Returns the result of element-wise *lesser than or equal to* (<=) comparison operation with broadcasting. |
-
-For example, if you want to add a NDarray to your input x, use `broadcast_add` instead of `+`:
-
-```python
-def hybrid_forward(self, F, x):
-    # avoid writing: return x + F.ones((1, 1))
-    return F.broadcast_add(x, F.ones((1, 1)))
-```
-
-If you used `+`, it would still work before hybridization, but will throw an error of shape missmtach after hybridization.
-
-### Shape
-
-Gluon's imperative interface is very flexible and allows you to print the shape of the NDArray. However, Symbol does not have shape attributes. As a result, you need to avoid printing shapes in `hybrid_forward`.
-Otherwise, you will get the following error:
-```
-AttributeError: 'Symbol' object has no attribute 'shape'
-```
-
-### Slice
-`[]` in NDArray is used to get a slice from the array. However, `[]` in Symbol is used to get an output from a grouped symbol.
-For example, you will get different results for the following method before and after hybridization.
-
-```python
-def hybrid_forward(self, F, x):
-    return x[0]
-```
-
-The current workaround is to explicitly call [`slice`](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.slice) or [`slice_axis`](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.slice_axis) operators in `hybrid_forward`.
-
-
-### Not implemented operators
-
-Some of the often used operators in NDArray are not implemented in Symbol, and will cause hybridization failure.
-
-#### NDArray.asnumpy
-Symbol does not support the `asnumpy` function. You need to avoid calling `asnumpy` in `hybrid_forward`.
-
-#### Array creation APIs
-
-`mx.nd.array()` is used a lot, but Symbol does not have the `array` API. The current workaround is to use `F.ones`, `F.zeros`, or `F.full`, which exist in both the NDArray and Symbol APIs.
-
-#### In-Place Arithmetic Operators
-
-In-place arithmetic operators may be used in Gluon imperative mode, however if you expect to hybridize, you should write these operations explicitly instead.
-For example, avoid writing `x += y` and use `x  = x + y`, otherwise you will get `NotImplementedError`. This applies to all the following operators:
-
-| NDArray in-place arithmetic operators | Description |
-|---|---|
-|[NDArray.\__iadd\__](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__iadd__) |	x.\__iadd\__(y) <=> x+=y |
-|[NDArray.\__isub\__](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__isub__) |	x.\__isub\__(y) <=> x-=y |
-|[NDArray.\__imul\__](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__imul__) |	x.\__imul\__(y) <=> x*=y |
-|[NDArray.\__idiv\__](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__idiv__) |	x.\__rdiv\__(y) <=> x/=y |
-|[NDArray.\__imod\__](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__imod__) |	x.\__rmod\__(y) <=> x%=y |
-
-
-
-## Summary
-
-The recommended practice is to utilize the flexibility of imperative NDArray API during experimentation. Once you finalized your model, make necessary changes mentioned above so you can call `hybridize` function to improve performance.
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/gluon/index.md b/docs/tutorials/gluon/index.md
deleted file mode 100644
index faf6526fb824..000000000000
--- a/docs/tutorials/gluon/index.md
+++ /dev/null
@@ -1,25 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Tutorials
-
-```eval_rst
-.. toctree::
-   :glob:
-
-   *
-```
diff --git a/docs/tutorials/gluon/info_gan.md b/docs/tutorials/gluon/info_gan.md
deleted file mode 100644
index c35df69fb6d3..000000000000
--- a/docs/tutorials/gluon/info_gan.md
+++ /dev/null
@@ -1,456 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-
-# Image similarity search with InfoGAN
-
-This notebook shows how to implement an InfoGAN based on Gluon. InfoGAN is an extension of GANs, where the generator input is split in 2 parts: random noise and a latent code (see [InfoGAN Paper](https://arxiv.org/pdf/1606.03657.pdf)). 
-The codes are made meaningful by maximizing the mutual information between code and generator output. InfoGAN learns a disentangled representation in a completely unsupervised manner. It can be used for many applications such as image similarity search. This notebook uses the DCGAN example from the [Straight Dope Book](https://gluon.mxnet.io/chapter14_generative-adversarial-networks/dcgan.html) and extends it to create an InfoGAN. 
-
-
-```python
-from __future__ import print_function
-from datetime import datetime
-import logging
-import multiprocessing
-import os
-import sys
-import tarfile
-import time
-
-import numpy as np
-from matplotlib import pyplot as plt
-from mxboard import SummaryWriter
-import mxnet as mx
-from mxnet import gluon
-from mxnet import ndarray as nd
-from mxnet.gluon import nn, utils
-from mxnet import autograd
-
-```
-
-The latent code vector can contain several variables, which can be categorical and/or continuous. We set `n_continuous` to 2 and `n_categories` to 10.
-
-
-```python
-batch_size   = 64
-z_dim        = 100
-n_continuous = 2
-n_categories = 10
-ctx = mx.gpu() if mx.context.num_gpus() else mx.cpu()
-```
-
-Some functions to load and normalize images.
-
-
-```python
-lfw_url = 'http://vis-www.cs.umass.edu/lfw/lfw-deepfunneled.tgz'
-data_path = 'lfw_dataset'
-if not os.path.exists(data_path):
-    os.makedirs(data_path)
-    data_file = utils.download(lfw_url)
-    with tarfile.open(data_file) as tar:
-        tar.extractall(path=data_path)
-
-```
-
-
-```python
-def transform(data, width=64, height=64):
-    data = mx.image.imresize(data, width, height)
-    data = nd.transpose(data, (2,0,1))
-    data = data.astype(np.float32)/127.5 - 1
-    if data.shape[0] == 1:
-        data = nd.tile(data, (3, 1, 1))
-    return data.reshape((1,) + data.shape)
-```
-
-
-```python
-def get_files(data_dir):
-    images    = []
-    filenames = []
-    for path, _, fnames in os.walk(data_dir):
-        for fname in fnames:
-            if not fname.endswith('.jpg'):
-                continue
-            img = os.path.join(path, fname)
-            img_arr = mx.image.imread(img)
-            img_arr = transform(img_arr)
-            images.append(img_arr)
-            filenames.append(path + "/" + fname)
-    return images, filenames        
-```
-
-Load the dataset `lfw_dataset` which contains images of celebrities.
-
-
-```python
-data_dir = 'lfw_dataset'
-images, filenames = get_files(data_dir)
-split = int(len(images)*0.8)
-test_images = images[split:]
-test_filenames = filenames[split:]
-train_images = images[:split]
-train_filenames = filenames[:split]
-
-train_data = gluon.data.ArrayDataset(nd.concatenate(train_images))
-train_dataloader = gluon.data.DataLoader(train_data, batch_size=batch_size, shuffle=True, last_batch='rollover', num_workers=multiprocessing.cpu_count()-1)
-```
-
-## Generator
-Define the Generator model. Architecture is taken from the DCGAN implementation in [Straight Dope Book](https://gluon.mxnet.io/chapter14_generative-adversarial-networks/dcgan.html). The Generator consist of  4 layers where each layer involves a strided convolution, batch normalization, and rectified nonlinearity. It takes as input random noise and the latent code and produces an `(64,64,3)` output image.
-
-
-```python
-class Generator(gluon.HybridBlock):
-    def __init__(self, **kwargs):
-        super(Generator, self).__init__(**kwargs)
-        with self.name_scope():
-            self.prev = nn.HybridSequential()
-            self.prev.add(nn.Dense(1024, use_bias=False), nn.BatchNorm(), nn.Activation(activation='relu'))
-            self.G = nn.HybridSequential()
-         
-            self.G.add(nn.Conv2DTranspose(64 * 8, 4, 1, 0, use_bias=False))
-            self.G.add(nn.BatchNorm())
-            self.G.add(nn.Activation('relu'))
-            self.G.add(nn.Conv2DTranspose(64 * 4, 4, 2, 1, use_bias=False))
-            self.G.add(nn.BatchNorm())
-            self.G.add(nn.Activation('relu'))
-            self.G.add(nn.Conv2DTranspose(64 * 2, 4, 2, 1, use_bias=False))
-            self.G.add(nn.BatchNorm())
-            self.G.add(nn.Activation('relu'))
-            self.G.add(nn.Conv2DTranspose(64, 4, 2, 1, use_bias=False))
-            self.G.add(nn.BatchNorm())
-            self.G.add(nn.Activation('relu'))
-            self.G.add(nn.Conv2DTranspose(3, 4, 2, 1, use_bias=False))
-            self.G.add(nn.Activation('tanh'))
-
-    def hybrid_forward(self, F, x):
-        x = self.prev(x)
-        x = F.reshape(x, (0, -1, 1, 1))
-        return self.G(x)
-```
-
-## Discriminator
-Define the Discriminator and Q model. The Q model shares many layers with the Discriminator. Its task is to estimate the code `c` for a given fake image.  It is used to maximize the lower bound to the mutual information.
-
-
-```python
-class Discriminator(gluon.HybridBlock):
-    def __init__(self, **kwargs):
-        super(Discriminator, self).__init__(**kwargs)
-        with self.name_scope():
-            self.D = nn.HybridSequential()
-            self.D.add(nn.Conv2D(64, 4, 2, 1, use_bias=False))
-            self.D.add(nn.LeakyReLU(0.2))
-            self.D.add(nn.Conv2D(64 * 2, 4, 2, 1, use_bias=False))
-            self.D.add(nn.BatchNorm())
-            self.D.add(nn.LeakyReLU(0.2))
-            self.D.add(nn.Conv2D(64 * 4, 4, 2, 1, use_bias=False))
-            self.D.add(nn.BatchNorm())
-            self.D.add(nn.LeakyReLU(0.2))
-            self.D.add(nn.Conv2D(64 * 8, 4, 2, 1, use_bias=False))
-            self.D.add(nn.BatchNorm())
-            self.D.add(nn.LeakyReLU(0.2))
-
-            self.D.add(nn.Dense(1024, use_bias=False), nn.BatchNorm(), nn.Activation(activation='relu'))
-       
-            self.prob = nn.Dense(1)
-            self.feat = nn.HybridSequential()
-            self.feat.add(nn.Dense(128, use_bias=False), nn.BatchNorm(), nn.Activation(activation='relu'))
-            self.category_prob = nn.Dense(n_categories)
-            self.continuous_mean = nn.Dense(n_continuous)
-            self.Q = nn.HybridSequential()
-            self.Q.add(self.feat, self.category_prob, self.continuous_mean)
-
-    def hybrid_forward(self, F, x):
-        x               = self.D(x)
-        prob            = self.prob(x)
-        feat            = self.feat(x)
-        category_prob   = self.category_prob(feat)
-        continuous_mean = self.continuous_mean(feat)
-        
-        return prob, category_prob, continuous_mean
-```
-
-The InfoGAN has the following layout.
-<img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/info_gan/InfoGAN.png" style="width:800px;height:250px;">
-
-Discriminator and Generator are the same as in the DCGAN example. On top of the Disciminator is the Q model, which is estimating the code `c` for given fake images. The Generator's input is random noise and the latent code `c`.  
-
-## Training Loop
-Initialize Generator and Discriminator and define correspoing trainer function.
-
-
-```python
-generator = Generator()
-generator.hybridize()
-generator.initialize(mx.init.Normal(0.002), ctx=ctx)
-
-discriminator = Discriminator()
-discriminator.hybridize()
-discriminator.initialize(mx.init.Normal(0.002), ctx=ctx)
-
-lr   = 0.0001
-beta = 0.5
-
-g_trainer = gluon.Trainer(generator.collect_params(), 'adam', {'learning_rate': lr, 'beta1': beta})
-d_trainer = gluon.Trainer(discriminator.collect_params(), 'adam', {'learning_rate': lr, 'beta1': beta})
-q_trainer = gluon.Trainer(discriminator.Q.collect_params(), 'adam', {'learning_rate': lr, 'beta1': beta})
-```
-
-Create vectors with real (=1) and fake labels (=0).
-
-
-```python
-real_label = nd.ones((batch_size,), ctx=ctx)
-fake_label = nd.zeros((batch_size,),ctx=ctx)
-```
-
-Load a pretrained model.
-
-
-```python
-if os.path.isfile('infogan_d_latest.params') and os.path.isfile('infogan_g_latest.params'):
-    discriminator.load_parameters('infogan_d_latest.params', ctx=ctx, allow_missing=True, ignore_extra=True)
-    generator.load_parameters('infogan_g_latest.params', ctx=ctx, allow_missing=True, ignore_extra=True)
-```
-There are 2 differences between InfoGAN and DCGAN: the extra latent code and the Q network to estimate the code.
-The latent code is part of the Generator input and it contains mutliple variables (continuous, categorical) that can represent different distributions. In order to make sure that the Generator uses the latent code, mutual information is introduced into the GAN loss term. Mutual information measures how much X is known given Y or vice versa. It is defined as:
-
-![gif](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/info_gan/entropy.gif) 
-
-The InfoGAN loss is:
-
-![gif](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/info_gan/loss.gif)
-
-where `V(D,G)` is the GAN loss and the mutual information `I(c, G(z, c))` goes in as regularization. The goal is to reach high mutual information, in order to learn meaningful codes for the data. 
-
-
-Define the loss functions. `SoftmaxCrossEntropyLoss` for the categorical code,  `L2Loss` for the continious code and `SigmoidBinaryCrossEntropyLoss` for the normal GAN loss.
-
-
-```python
-loss1 = gluon.loss.SigmoidBinaryCrossEntropyLoss()
-loss2 = gluon.loss.L2Loss()
-loss3 = gluon.loss.SoftmaxCrossEntropyLoss()
-```
-
-This function samples `c`, `z`, and concatenates them to create the generator input.
-
-
-```python
-def create_generator_input():
-    
-    #create random noise
-    z      = nd.random_normal(0, 1, shape=(batch_size, z_dim), ctx=ctx)
-    label  = nd.array(np.random.randint(n_categories, size=batch_size)).as_in_context(ctx)
-    c1     = nd.one_hot(label, depth=n_categories).as_in_context(ctx)
-    c2     = nd.random.uniform(-1, 1, shape=(batch_size, n_continuous)).as_in_context(ctx)
-
-    # concatenate random noise with c which will be the input of the generator
-    return nd.concat(z, c1, c2, dim=1), label, c2
-```
-
-Define the training loop. 
-1. The discriminator receives `real_data` and `loss1` measures how many real images have been identified as real
-2. The discriminator receives `fake_image` from the Generator and `loss1` measures how many fake images have been identified as fake
-3. Update Discriminator. Currently, it is updated every second iteration in order to avoid that the Discriminator becomes too strong. You may want to change that.
-4. The updated discriminator receives `fake_image` and `loss1` measures how many fake images have been been identified as real, `loss2` measures the difference between the sampled continuous latent code `c` and the output of the Q model and `loss3` measures the difference between the sampled categorical latent code `c` and the output of the Q model.
-4. Update Generator and Q
-
-
-```python
-with SummaryWriter(logdir='./logs/') as sw:
-    
-    epochs = 1
-    counter = 0
-    for epoch in range(epochs):
-        print("Epoch", epoch)
-        starttime = time.time()
-        
-        d_error_epoch = nd.zeros((1,), ctx=ctx)
-        g_error_epoch = nd.zeros((1,), ctx=ctx)
-        
-        for idx, data in enumerate(train_dataloader):
-                
-            #get real data and generator input
-            real_data = data.as_in_context(ctx)     
-            g_input, label, c2 = create_generator_input()
-
-            
-            #Update discriminator: Input real data and fake data
-            with autograd.record():
-                output_real,_,_ = discriminator(real_data)
-                d_error_real    = loss1(output_real, real_label)
-                
-                # create fake image and input it to discriminator
-                fake_image      = generator(g_input)
-                output_fake,_,_ = discriminator(fake_image.detach())
-                d_error_fake    = loss1(output_fake, fake_label)
-                
-                # total discriminator error
-                d_error         = d_error_real + d_error_fake
-
-            d_error_epoch += d_error.mean()
-            
-            #Update D every second iteration
-            if (counter+1) % 2 == 0:
-                d_error.backward()
-                d_trainer.step(batch_size)
-
-            #Update generator: Input random noise and latent code vector
-            with autograd.record():
-                fake_image = generator(g_input)
-                output_fake, category_prob, continuous_mean = discriminator(fake_image)
-                g_error = loss1(output_fake, real_label) + loss3(category_prob, label) + loss2(c2, continuous_mean)
-
-            g_error.backward()
-            g_error_epoch += g_error.mean()
-            
-            g_trainer.step(batch_size)
-            q_trainer.step(batch_size)
-
-            # logging
-            if idx % 10 == 0:
-                count = idx + 1
-                logging.info('speed: {} samples/s'.format(batch_size / (time.time() - starttime)))
-                logging.info('discriminator loss = %f, generator loss = %f at iter %d epoch %d'
-                         %(d_error_epoch.asscalar()/count,g_error_epoch.asscalar()/count, count, epoch))
-
-                g_input,_,_ = create_generator_input()
-                
-                # create some fake image for logging in MXBoard
-                fake_image = generator(g_input)
-
-                sw.add_scalar(tag='Loss_D', value={'test':d_error_epoch.asscalar()/count}, global_step=counter)
-                sw.add_scalar(tag='Loss_G', value={'test':g_error_epoch.asscalar()/count}, global_step=counter)
-                sw.add_image(tag='data_image', image=((fake_image[0]+ 1.0) * 127.5).astype(np.uint8)  , global_step=counter)
-                sw.flush()
-            
-            counter += 1
-        
-        discriminator.save_parameters("infogan_d_latest.params")
-        generator.save_parameters("infogan_g_latest.params")
-```
-
-## Image similarity
-Once the InfoGAN is trained, we can use the Discriminator to do an image similarity search. The idea is that the network learned meaningful features from the images based on the mutual information e.g. pose of people in an image.
-
-Load the trained discriminator and retrieve one of its last layers.
-
-
-```python
-discriminator = Discriminator()
-discriminator.load_parameters("infogan_d_latest.params", ctx=ctx, ignore_extra=True)
-
-discriminator = discriminator.D[:11]
-print (discriminator)
-
-discriminator.hybridize()
-```
-
-Nearest neighbor function, which takes a matrix of features and an input feature vector. It returns the 3 closest features.
-
-
-```python
-def get_knn(features, input_vector, k=3):
-    dist = (nd.square(features - input_vector).sum(axis=1))/features.shape[0]
-    indices = dist.asnumpy().argsort()[:k]
-    return [(index, dist[index].asscalar()) for index in indices]
-```
-
-A helper function to visualize image data.
-
-
-```python
-def visualize(img_array):
-    plt.imshow(((img_array.asnumpy().transpose(1, 2, 0) + 1.0) * 127.5).astype(np.uint8))
-    plt.axis('off')
-```
-
-Take some images from the test data, obtain its feature vector from `discriminator.D[:11]` and plot images of the corresponding closest vectors in the feature space.
-
-
-```python
-feature_size = 8192 
-
-features = nd.zeros((len(test_images), feature_size), ctx=ctx)
-
-for idx, image in enumerate(test_images):
-  
-    feature = discriminator(nd.array(image, ctx=ctx))
-    feature = feature.reshape(feature_size,)
-    features[idx,:] = feature.copyto(ctx)
-
-
-for image in test_images[:100]:
-
-    feature = discriminator(mx.nd.array(image, ctx=ctx))
-    feature = feature.reshape((feature_size,))
-    image   = image.reshape((3,64,64))
-
-
-    indices = get_knn(features, feature, k=10)
-    fig = plt.figure(figsize=(15,12))
-    plt.subplot(1,10,1)
-
-    visualize(image)
-    for i in range(2,9): 
-        if indices[i-1][1] < 1.5:
-            plt.subplot(1,10,i)
-            sim = test_images[indices[i-1][0]].reshape(3,64,64)
-            visualize(sim)
-    plt.show()
-    plt.clf()
-```
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/info_gan/output.png)<!--notebook-skip-line--> 
-
-## How the Generator learns
-We trained the Generator for a couple of epochs and stored a couple of fake images per epoch. Check the video.
-                    ![alt text](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/info_gan/infogan.gif)
-                                                        
-
-The following function computes the TSNE on the feature matrix and stores the result in a json-file. This file can be loaded with [TSNEViewer](https://ml4a.github.io/guides/ImageTSNEViewer/) 
-
-
-```python
-import json
-
-from sklearn.manifold import TSNE
-from scipy.spatial import distance
-
-tsne = TSNE(n_components=2, learning_rate=150, perplexity=30, verbose=2).fit_transform(features.asnumpy())
-
-# save data to json
-data = []
-counter = 0
-for i,f in enumerate(test_filenames):
-    
-    point = [float((tsne[i,k] - np.min(tsne[:,k]))/(np.max(tsne[:,k]) - np.min(tsne[:,k]))) for k in range(2) ]
-    data.append({"path": os.path.abspath(os.path.join(os.getcwd(),f)), "point": point})
-    
-with open("imagetsne.json", 'w') as outfile:
-    json.dump(data, outfile)
-```
-
-Load the file with TSNEViewer. You can now inspect whether similiar looking images are grouped nearby or not. 
-
-<img src="https://raw.githubusercontent.com/NRauschmayr/web-data/master/mxnet/doc/tutorials/info_gan/tsne.png" style="width:800px;height:600px;">
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/gluon/learning_rate_finder.md b/docs/tutorials/gluon/learning_rate_finder.md
deleted file mode 100644
index b580bee7c5fb..000000000000
--- a/docs/tutorials/gluon/learning_rate_finder.md
+++ /dev/null
@@ -1,332 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-
-# Learning Rate Finder
-
-Setting the learning rate for stochastic gradient descent (SGD) is crucially important when training neural network because it controls both the speed of convergence and the ultimate performance of the network. Set the learning too low and you could be twiddling your thumbs for quite some time as the parameters update very slowly. Set it too high and the updates will skip over optimal solutions, or worse the optimizer might not converge at all!
-
-Leslie Smith from the U.S. Naval Research Laboratory presented a method for finding a good learning rate in a paper called ["Cyclical Learning Rates for Training Neural Networks"](https://arxiv.org/abs/1506.01186). We implement this method in MXNet (with the Gluon API) and create a 'Learning Rate Finder' which you can use while training your own networks. We take a look at the central idea of the paper, cyclical learning rate schedules, in the ['Advanced Learning Rate Schedules'](https://mxnet.incubator.apache.org/tutorials/gluon/learning_rate_schedules_advanced.html) tutorial.
-
-## Simple Idea
-
-Given an initialized network, a defined loss and a training dataset we take the following steps:
-
-1. Train one batch at a time (a.k.a. an iteration)
-2. Start with a very small learning rate (e.g. 0.000001) and slowly increase it every iteration
-3. Record the training loss and continue until we see the training loss diverge
-
-We then analyse the results by plotting a graph of the learning rate against the training loss as seen below (taking note of the log scales).
-
-<img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/lr_finder/finder_plot_w_annotations.png" width="500px"/> <!--notebook-skip-line-->
-
-As expected, for very small learning rates we don't see much change in the loss as the parameter updates are negligible. At a learning rate of 0.001, we start to see the loss fall. Setting the initial learning rate here is reasonable, but we still have the potential to learn faster. We observe a drop in the loss up until 0.1 where the loss appears to diverge. We want to set the initial learning rate as high as possible before the loss becomes unstable, so we choose a learning rate of 0.05.
-
-## Epoch to Iteration
-
-Usually, our unit of work is an epoch (a full pass through the dataset) and the learning rate would typically be held constant throughout the epoch. With the Learning Rate Finder (and cyclical learning rate schedules) we are required to vary the learning rate every iteration. As such we structure our training code so that a single iteration can be run with a given learning rate. You can implement Learner as you wish. Just initialize the network, define the loss and trainer in `__init__` and keep your training logic for a single batch in `iteration`.
-
-
-```python
-import mxnet as mx
-
-# Set seed for reproducibility
-mx.random.seed(42)
-
-class Learner():
-    def __init__(self, net, data_loader, ctx):
-        """
-        :param net: network (mx.gluon.Block)
-        :param data_loader: training data loader (mx.gluon.data.DataLoader)
-        :param ctx: context (mx.gpu or mx.cpu)
-        """
-        self.net = net
-        self.data_loader = data_loader
-        self.ctx = ctx
-        # So we don't need to be in `for batch in data_loader` scope
-        # and can call for next batch in `iteration`
-        self.data_loader_iter = iter(self.data_loader)
-        self.net.initialize(mx.init.Xavier(), ctx=self.ctx)
-        self.loss_fn = mx.gluon.loss.SoftmaxCrossEntropyLoss()
-        self.trainer = mx.gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': .001})
-        
-    def iteration(self, lr=None, take_step=True):
-        """
-        :param lr: learning rate to use for iteration (float)
-        :param take_step: take trainer step to update weights (boolean)
-        :return: iteration loss (float)
-        """
-        # Update learning rate if different this iteration
-        if lr and (lr != self.trainer.learning_rate):
-            self.trainer.set_learning_rate(lr)
-        # Get next batch, and move context (e.g. to GPU if set)
-        data, label = next(self.data_loader_iter)
-        data = data.as_in_context(self.ctx)
-        label = label.as_in_context(self.ctx)
-        # Standard forward and backward pass
-        with mx.autograd.record():
-            output = self.net(data)
-            loss = self.loss_fn(output, label)
-        loss.backward()     
-        # Update parameters
-        if take_step: self.trainer.step(data.shape[0])  
-        # Set and return loss.
-        self.iteration_loss = mx.nd.mean(loss).asscalar()
-        return self.iteration_loss
-
-    def close(self):
-        # Close open iterator and associated workers
-        self.data_loader_iter.shutdown()
-```
-
-We also adjust our `DataLoader` so that it continuously provides batches of data and doesn't stop after a single epoch. We can then call `iteration` as many times as required for the loss to diverge as part of the Learning Rate Finder process. We implement a custom `BatchSampler` for this, that keeps returning random indices of samples to be included in the next batch. We use the CIFAR-10 dataset for image classification to test our Learning Rate Finder.
-
-
-```python
-from mxnet.gluon.data.vision import transforms
-
-transform = transforms.Compose([
-    # Switches HWC to CHW, and converts to `float32`
-    transforms.ToTensor(),
-    # Channel-wise, using pre-computed means and stds 
-    transforms.Normalize(mean=[0.4914, 0.4822, 0.4465],
-                         std=[0.2023, 0.1994, 0.2010])
-])
-
-dataset = mx.gluon.data.vision.datasets.CIFAR10(train=True).transform_first(transform)
-
-class ContinuousBatchSampler():
-    def __init__(self, sampler, batch_size):
-        self._sampler = sampler
-        self._batch_size = batch_size
-
-    def __iter__(self):
-        batch = []
-        while True:
-            for i in self._sampler:
-                batch.append(i)
-                if len(batch) == self._batch_size:
-                    yield batch
-                    batch = []
-
-sampler = mx.gluon.data.RandomSampler(len(dataset))
-batch_sampler = ContinuousBatchSampler(sampler, batch_size=128)
-data_loader = mx.gluon.data.DataLoader(dataset, batch_sampler=batch_sampler)
-```
-
-## Implementation
-
-With preparation complete, we're ready to write our Learning Rate Finder that wraps the `Learner` we defined above. We implement a `find` method for the procedure, and `plot` for the visualization. Starting with a very low learning rate as defined by `lr_start` we train one iteration at a time and keep multiplying the learning rate by `lr_multiplier`. We analyse the loss and continue until it diverges according to `LRFinderStoppingCriteria` (which is defined later on). You may also notice that we save the parameters and state of the optimizer before the process and restore afterwards. This is so the Learning Rate Finder process doesn't impact the state of the model, and can be used at any point during training.
-
-
-```python
-from matplotlib import pyplot as plt
-
-class LRFinder():
-    def __init__(self, learner):
-        """
-        :param learner: able to take single iteration with given learning rate and return loss
-           and save and load parameters of the network (Learner)
-        """
-        self.learner = learner
-        
-    def find(self, lr_start=1e-6, lr_multiplier=1.1, smoothing=0.3):
-        """
-        :param lr_start: learning rate to start search (float)
-        :param lr_multiplier: factor the learning rate is multiplied by at each step of search (float)
-        :param smoothing: amount of smoothing applied to loss for stopping criteria (float)
-        :return: learning rate and loss pairs (list of (float, float) tuples)
-        """
-        # Used to initialize weights; pass data, but don't take step.
-        # Would expect for new model with lazy weight initialization
-        self.learner.iteration(take_step=False)
-        # Used to initialize trainer (if no step has been taken)
-        if not self.learner.trainer._kv_initialized:
-            self.learner.trainer._init_kvstore()
-        # Store params and optimizer state for restore after lr_finder procedure
-        # Useful for applying the method partway through training, not just for initialization of lr.
-        self.learner.net.save_parameters("lr_finder.params")
-        self.learner.trainer.save_states("lr_finder.state")
-        lr = lr_start
-        self.results = [] # List of (lr, loss) tuples
-        stopping_criteria = LRFinderStoppingCriteria(smoothing)
-        while True:
-            # Run iteration, and block until loss is calculated.
-            loss = self.learner.iteration(lr)
-            self.results.append((lr, loss))
-            if stopping_criteria(loss):
-                break
-            lr = lr * lr_multiplier
-        # Restore params (as finder changed them)
-        self.learner.net.load_parameters("lr_finder.params", ctx=self.learner.ctx)
-        self.learner.trainer.load_states("lr_finder.state")
-        return self.results
-        
-    def plot(self):
-        lrs = [e[0] for e in self.results]
-        losses = [e[1] for e in self.results]
-        plt.figure(figsize=(6,8))
-        plt.scatter(lrs, losses)
-        plt.xlabel("Learning Rate")
-        plt.ylabel("Loss")
-        plt.xscale('log')
-        plt.yscale('log')
-        axes = plt.gca()
-        axes.set_xlim([lrs[0], lrs[-1]])
-        y_lower = min(losses) * 0.8
-        y_upper = losses[0] * 4
-        axes.set_ylim([y_lower, y_upper])
-        plt.show()
-```
-
-
-You can define the `LRFinderStoppingCriteria` as you wish, but empirical testing suggests using a smoothed average gives a more consistent stopping rule (see `smoothing`). We stop when the smoothed average of the loss exceeds twice the initial loss, assuming there have been a minimum number of iterations (see `min_iter`).
-
-
-```python
-class LRFinderStoppingCriteria():
-    def __init__(self, smoothing=0.3, min_iter=20):
-        """
-        :param smoothing: applied to running mean which is used for thresholding (float)
-        :param min_iter: minimum number of iterations before early stopping can occur (int)
-        """
-        self.smoothing = smoothing
-        self.min_iter = min_iter
-        self.first_loss = None
-        self.running_mean = None
-        self.counter = 0
-        
-    def __call__(self, loss):
-        """
-        :param loss: from single iteration (float)
-        :return: indicator to stop (boolean)
-        """
-        self.counter += 1
-        if self.first_loss is None:
-            self.first_loss = loss
-        if self.running_mean is None:
-            self.running_mean = loss
-        else:
-            self.running_mean = ((1 - self.smoothing) * loss) + (self.smoothing * self.running_mean)
-        return (self.running_mean > self.first_loss * 2) and (self.counter >= self.min_iter)
-```
-
-## Usage
-
-Using a Pre-activation ResNet-18 from the Gluon model zoo, we instantiate our Learner and fire up our Learning Rate Finder!
-
-
-```python
-ctx = mx.gpu() if mx.context.num_gpus() else mx.cpu()
-net = mx.gluon.model_zoo.vision.resnet18_v2(classes=10)
-learner = Learner(net=net, data_loader=data_loader, ctx=ctx)
-lr_finder = LRFinder(learner)
-lr_finder.find(lr_start=1e-6)
-lr_finder.plot()
-```
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/lr_finder/finder_plot.png) <!--notebook-skip-line-->
-
-
-As discussed before, we should select a learning rate where the loss is falling (i.e. from 0.001 to 0.05) but before the loss starts to diverge (i.e. 0.1). We prefer higher learning rates where possible, so we select an initial learning rate of 0.05. Just as a test, we will run 500 epochs using this learning rate and evaluate the loss on the final batch. As we're working with a single batch of 128 samples, the variance of the loss estimates will be reasonably high, but it will give us a general idea. We save the initialized parameters for a later comparison with other learning rates.
-
-
-```python
-learner.net.save_parameters("net.params")
-lr = 0.05
-
-for iter_idx in range(300):
-    learner.iteration(lr=lr)
-    if ((iter_idx % 100) == 0):
-        print("Iteration: {}, Loss: {:.5g}".format(iter_idx, learner.iteration_loss))
-print("Final Loss: {:.5g}".format(learner.iteration_loss))
-```
-
-Iteration: 0, Loss: 2.785 <!--notebook-skip-line-->
-
-Iteration: 100, Loss: 1.6653 <!--notebook-skip-line-->
-
-Iteration: 200, Loss: 1.4891 <!--notebook-skip-line-->
-
-
-Final Loss: 1.1812 <!--notebook-skip-line-->
-
-
-We see a sizable drop in the loss from approx. 2.7 to 1.2.
-
-And now we have a baseline, let's see what happens when we train with a learning rate that's higher than advisable at 0.5.
-
-
-```python
-net = mx.gluon.model_zoo.vision.resnet18_v2(classes=10)
-learner = Learner(net=net, data_loader=data_loader, ctx=ctx)
-learner.net.load_parameters("net.params", ctx=ctx)
-lr = 0.5
-
-for iter_idx in range(300):
-    learner.iteration(lr=lr)
-    if ((iter_idx % 100) == 0):
-        print("Iteration: {}, Loss: {:.5g}".format(iter_idx, learner.iteration_loss))
-print("Final Loss: {:.5g}".format(learner.iteration_loss))
-```
-
-Iteration: 0, Loss: 2.6469 <!--notebook-skip-line-->
-
-Iteration: 100, Loss: 1.9666 <!--notebook-skip-line-->
-
-Iteration: 200, Loss: 1.6919 <!--notebook-skip-line-->
-
-
-Final Loss: 1.366 <!--notebook-skip-line-->
-
-
-We still observe a fall in the loss but aren't able to reach as low as before.
-
-And lastly, we see how the model trains with a more conservative learning rate of 0.005.
-
-
-```python
-net = mx.gluon.model_zoo.vision.resnet18_v2(classes=10)
-learner = Learner(net=net, data_loader=data_loader, ctx=ctx)
-learner.net.load_parameters("net.params", ctx=ctx)
-lr = 0.005
-
-for iter_idx in range(300):
-    learner.iteration(lr=lr)
-    if ((iter_idx % 100) == 0):
-        print("Iteration: {}, Loss: {:.5g}".format(iter_idx, learner.iteration_loss))
-print("Final Loss: {:.5g}".format(learner.iteration_loss))
-```
-
-Iteration: 0, Loss: 2.605 <!--notebook-skip-line-->
-
-Iteration: 100, Loss: 1.8621 <!--notebook-skip-line-->
-
-Iteration: 200, Loss: 1.6316 <!--notebook-skip-line-->
-
-
-Final Loss: 1.2919 <!--notebook-skip-line-->
-
-
-Although we get quite similar results to when we set the learning rate at 0.05 (because we're still in the region of falling loss on the Learning Rate Finder plot), we can still optimize our network faster using a slightly higher rate.
-
-## Wrap Up
-
-Give Learning Rate Finder a try on your current projects, and experiment with the different learning rate schedules found in the tutorials [here](https://mxnet.incubator.apache.org/tutorials/gluon/learning_rate_schedules.html) and [here](https://mxnet.incubator.apache.org/tutorials/gluon/learning_rate_schedules_advanced.html).
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/gluon/learning_rate_schedules.md b/docs/tutorials/gluon/learning_rate_schedules.md
deleted file mode 100644
index 119677373577..000000000000
--- a/docs/tutorials/gluon/learning_rate_schedules.md
+++ /dev/null
@@ -1,345 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-
-# Learning Rate Schedules
-
-Setting the learning rate for stochastic gradient descent (SGD) is crucially important when training neural networks because it controls both the speed of convergence and the ultimate performance of the network. One of the simplest learning rate strategies is to have a fixed learning rate throughout the training process. Choosing a small learning rate allows the optimizer find good solutions, but this comes at the expense of limiting the initial speed of convergence. Changing the learning rate over time can overcome this tradeoff.
-
-Schedules define how the learning rate changes over time and are typically specified for each epoch or iteration (i.e. batch) of training. Schedules differ from adaptive methods (such as AdaDelta and Adam) because they:
-
-* change the global learning rate for the optimizer, rather than parameter-wise learning rates
-* don't take feedback from the training process and are specified beforehand
-
-In this tutorial, we visualize the schedules defined in `mx.lr_scheduler`, show how to implement custom schedules and see an example of using a schedule while training models. Since schedules are passed to `mx.optimizer.Optimizer` classes, these methods work with both Module and Gluon APIs.
-
-
-```python
-from __future__ import print_function
-import math
-import matplotlib.pyplot as plt
-import mxnet as mx
-from mxnet.gluon import nn
-from mxnet.gluon.data.vision import transforms
-import numpy as np
-%matplotlib inline
-```
-
-```python
-def plot_schedule(schedule_fn, iterations=1500):
-    # Iteration count starting at 1
-    iterations = [i+1 for i in range(iterations)]
-    lrs = [schedule_fn(i) for i in iterations]
-    plt.scatter(iterations, lrs)
-    plt.xlabel("Iteration")
-    plt.ylabel("Learning Rate")
-    plt.show()
-```
-
-## Schedules
-
-In this section, we take a look at the schedules in `mx.lr_scheduler`. All of these schedules define the learning rate for a given iteration, and it is expected that iterations start at 1 rather than 0. So to find the learning rate for the 100th iteration, you can call `schedule(100)`.
-
-### Stepwise Decay Schedule
-
-One of the most commonly used learning rate schedules is called stepwise decay, where the learning rate is reduced by a factor at certain intervals. MXNet implements a `FactorScheduler` for equally spaced intervals, and `MultiFactorScheduler` for greater control. We start with an example of halving the learning rate every 250 iterations. More precisely, the learning rate will be multiplied by `factor` _after_ the `step` index and multiples thereafter. So in the example below the learning rate of the 250th iteration will be 1 and the 251st iteration will be 0.5.
-
-
-```python
-schedule = mx.lr_scheduler.FactorScheduler(step=250, factor=0.5)
-schedule.base_lr = 1
-plot_schedule(schedule)
-```
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/lr_schedules/factor.png) <!--notebook-skip-line-->
-
-
-Note: the `base_lr` is used to determine the initial learning rate. It takes a default value of 0.01 since we inherit from `mx.lr_scheduler.LRScheduler`, but it can be set as a property of the schedule. We will see later in this tutorial that `base_lr` is set automatically when providing the `lr_schedule` to `Optimizer`. Also be aware that the schedules in `mx.lr_scheduler` have state (i.e. counters, etc) so calling the schedule out of order may give unexpected results.
-
-We can define non-uniform intervals with `MultiFactorScheduler` and in the example below we halve the learning rate _after_ the 250th, 750th (i.e. a step length of 500 iterations) and 900th (a step length of 150 iterations). As before, the learning rate of the 250th iteration will be 1 and the 251th iteration will be 0.5.
-
-
-```python
-schedule = mx.lr_scheduler.MultiFactorScheduler(step=[250, 750, 900], factor=0.5)
-schedule.base_lr = 1
-plot_schedule(schedule)
-```
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/lr_schedules/multifactor.png) <!--notebook-skip-line-->
-
-
-### Polynomial Schedule
-
-Stepwise schedules and the discontinuities they introduce may sometimes lead to instability in the optimization, so in some cases smoother schedules are preferred. `PolyScheduler` gives a smooth decay using a polynomial function and reaches a learning rate of 0 after `max_update` iterations. In the example below, we have a quadratic function (`pwr=2`) that falls from 0.998 at iteration 1 to 0 at iteration 1000. After this the learning rate stays at 0, so nothing will be learnt from `max_update` iterations onwards.
-
-
-```python
-schedule = mx.lr_scheduler.PolyScheduler(max_update=1000, base_lr=1, pwr=2)
-plot_schedule(schedule)
-```
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/lr_schedules/polynomial.png) <!--notebook-skip-line-->
-
-
-Note: unlike `FactorScheduler`, the `base_lr` is set as an argument when instantiating the schedule.
-
-And we don't evaluate at `iteration=0` (to get `base_lr`) since we are working with schedules starting at `iteration=1`.
-
-### Custom Schedules
-
-You can implement your own custom schedule with a function or callable class, that takes an integer denoting the iteration index (starting at 1) and returns a float representing the learning rate to be used for that iteration. We implement the Cosine Annealing Schedule in the example below as a callable class (see `__call__` method).
-
-
-```python
-class CosineAnnealingSchedule():
-    def __init__(self, min_lr, max_lr, cycle_length):
-        self.min_lr = min_lr
-        self.max_lr = max_lr
-        self.cycle_length = cycle_length
-        
-    def __call__(self, iteration):
-        if iteration <= self.cycle_length:
-            unit_cycle = (1 + math.cos(iteration * math.pi / self.cycle_length)) / 2
-            adjusted_cycle = (unit_cycle * (self.max_lr - self.min_lr)) + self.min_lr
-            return adjusted_cycle
-        else:
-            return self.min_lr
-
-
-schedule = CosineAnnealingSchedule(min_lr=0, max_lr=1, cycle_length=1000)
-plot_schedule(schedule)
-```
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/lr_schedules/cosine.png) <!--notebook-skip-line-->
-
-
-## Using Schedules
-
-While training a simple handwritten digit classifier on the MNIST dataset, we take a look at how to use a learning rate schedule during training. Our demonstration model is a basic convolutional neural network. We start by preparing our `DataLoader` and defining the network. 
-
-As discussed above, the schedule should return a learning rate given an (1-based) iteration index.
-
-
-```python
-# Use GPU if one exists, else use CPU
-ctx = mx.gpu() if mx.context.num_gpus() else mx.cpu()
-
-# MNIST images are 28x28. Total pixels in input layer is 28x28 = 784
-num_inputs = 784
-# Clasify the images into one of the 10 digits
-num_outputs = 10
-# 64 images in a batch
-batch_size = 64
-
-# Load the training data
-train_dataset = mx.gluon.data.vision.MNIST(train=True).transform_first(transforms.ToTensor())
-train_dataloader = mx.gluon.data.DataLoader(train_dataset, batch_size, shuffle=True, num_workers=5)
-
-# Build a simple convolutional network
-def build_cnn():
-    net = nn.HybridSequential()
-    with net.name_scope():
-        # First convolution
-        net.add(nn.Conv2D(channels=10, kernel_size=5, activation='relu'))
-        net.add(nn.MaxPool2D(pool_size=2, strides=2))
-        # Second convolution
-        net.add(nn.Conv2D(channels=20, kernel_size=5, activation='relu'))
-        net.add(nn.MaxPool2D(pool_size=2, strides=2))
-        # Flatten the output before the fully connected layers
-        net.add(nn.Flatten())
-        # First fully connected layers with 512 neurons
-        net.add(nn.Dense(512, activation="relu"))
-        # Second fully connected layer with as many neurons as the number of classes
-        net.add(nn.Dense(num_outputs))
-        return net
-    
-net = build_cnn()
-```
-
-We then initialize our network (technically deferred until we pass the first batch) and define the loss.
-
-
-```python
-# Initialize the parameters with Xavier initializer
-net.collect_params().initialize(mx.init.Xavier(), ctx=ctx)
-# Use cross entropy loss
-softmax_cross_entropy = mx.gluon.loss.SoftmaxCrossEntropyLoss()
-```
-
-We're now ready to create our schedule, and in this example we opt for a stepwise decay schedule using `MultiFactorScheduler`. Since we're only training a demonstration model for a limited number of epochs (10 in total) we will exaggerate the schedule and drop the learning rate by 90% after the 4th, 7th and 9th epochs. We call these steps, and the drop occurs _after_ the step index. Schedules are defined for iterations (i.e. training batches), so we must represent our steps in iterations too.
-
-
-```python
-steps_epochs = [4, 7, 9]
-# assuming we keep partial batches, see `last_batch` parameter of DataLoader
-iterations_per_epoch = math.ceil(len(train_dataset) / batch_size)
-# iterations just before starts of epochs (iterations are 1-indexed)
-steps_iterations = [s*iterations_per_epoch for s in steps_epochs]
-print("Learning rate drops after iterations: {}".format(steps_iterations))
-```
-
-    Learning rate drops after iterations: [3752, 6566, 8442]
-
-
-
-```python
-schedule = mx.lr_scheduler.MultiFactorScheduler(step=steps_iterations, factor=0.1)
-```
-
-**We create our `Optimizer` and pass the schedule via the `lr_scheduler` parameter.** In this example we're using Stochastic Gradient Descent.
-
-
-```python
-sgd_optimizer = mx.optimizer.SGD(learning_rate=0.03, lr_scheduler=schedule)
-```
-
-And we use this optimizer (with schedule) in our `Trainer` and train for 10 epochs. Alternatively, we could have set the `optimizer` to the string `sgd`, and pass a dictionary of the optimizer parameters directly to the trainer using `optimizer_params`.
-
-
-```python
-trainer = mx.gluon.Trainer(params=net.collect_params(), optimizer=sgd_optimizer)
-```
-
-
-```python
-num_epochs = 10
-# epoch and batch counts starting at 1
-for epoch in range(1, num_epochs+1):
-    # Iterate through the images and labels in the training data
-    for batch_num, (data, label) in enumerate(train_dataloader, start=1):
-        # get the images and labels
-        data = data.as_in_context(ctx)
-        label = label.as_in_context(ctx)
-        # Ask autograd to record the forward pass
-        with mx.autograd.record():
-            # Run the forward pass
-            output = net(data)
-            # Compute the loss
-            loss = softmax_cross_entropy(output, label)
-        # Compute gradients
-        loss.backward()
-        # Update parameters
-        trainer.step(data.shape[0])
-
-        # Show loss and learning rate after first iteration of epoch
-        if batch_num == 1:
-            curr_loss = mx.nd.mean(loss).asscalar()
-            curr_lr = trainer.learning_rate
-            print("Epoch: %d; Batch %d; Loss %f; LR %f" % (epoch, batch_num, curr_loss, curr_lr))
-```
-
-Epoch: 1; Batch 1; Loss 2.304071; LR 0.030000 <!--notebook-skip-line-->
-
-Epoch: 2; Batch 1; Loss 0.059640; LR 0.030000 <!--notebook-skip-line-->
-
-Epoch: 3; Batch 1; Loss 0.072601; LR 0.030000 <!--notebook-skip-line-->
-
-Epoch: 4; Batch 1; Loss 0.042228; LR 0.030000 <!--notebook-skip-line-->
-
-Epoch: 5; Batch 1; Loss 0.025745; LR 0.003000 <!--notebook-skip-line-->
-
-Epoch: 6; Batch 1; Loss 0.027391; LR 0.003000 <!--notebook-skip-line-->
-
-Epoch: 7; Batch 1; Loss 0.048237; LR 0.003000 <!--notebook-skip-line-->
-
-Epoch: 8; Batch 1; Loss 0.024213; LR 0.000300 <!--notebook-skip-line-->
-
-Epoch: 9; Batch 1; Loss 0.008892; LR 0.000300 <!--notebook-skip-line-->
-
-Epoch: 10; Batch 1; Loss 0.006875; LR 0.000030 <!--notebook-skip-line-->
-
-
-We see that the learning rate starts at 0.03, and falls to 0.00003 by the end of training as per the schedule we defined.
-
-### Manually setting the learning rate: Gluon API only
-
-When using the method above you don't need to manually keep track of iteration count and set the learning rate, so this is the recommended approach for most cases. Sometimes you might want more fine-grained control over setting the learning rate though, so Gluon's `Trainer` provides the `set_learning_rate` method for this.
-
-We replicate the example above, but now keep track of the `iteration_idx`, call the schedule and set the learning rate appropriately using `set_learning_rate`. We also use `schedule.base_lr` to set the initial learning rate for the schedule since we are calling the schedule directly and not using it as part of the `Optimizer`.
-
-
-```python
-net = build_cnn()
-net.collect_params().initialize(mx.init.Xavier(), ctx=ctx)
-
-schedule = mx.lr_scheduler.MultiFactorScheduler(step=steps_iterations, factor=0.1)
-schedule.base_lr = 0.03
-sgd_optimizer = mx.optimizer.SGD()
-trainer = mx.gluon.Trainer(params=net.collect_params(), optimizer=sgd_optimizer)
-
-iteration_idx = 1
-num_epochs = 10
-# epoch and batch counts starting at 1
-for epoch in range(1, num_epochs + 1):
-    # Iterate through the images and labels in the training data
-    for batch_num, (data, label) in enumerate(train_dataloader, start=1):
-        # get the images and labels
-        data = data.as_in_context(ctx)
-        label = label.as_in_context(ctx)
-        # Ask autograd to record the forward pass
-        with mx.autograd.record():
-            # Run the forward pass
-            output = net(data)
-            # Compute the loss
-            loss = softmax_cross_entropy(output, label)
-        # Compute gradients
-        loss.backward()
-        # Update the learning rate
-        lr = schedule(iteration_idx)
-        trainer.set_learning_rate(lr)
-        # Update parameters
-        trainer.step(data.shape[0])
-        # Show loss and learning rate after first iteration of epoch
-        if batch_num == 1:
-            curr_loss = mx.nd.mean(loss).asscalar()
-            curr_lr = trainer.learning_rate
-            print("Epoch: %d; Batch %d; Loss %f; LR %f" % (epoch, batch_num, curr_loss, curr_lr))
-        iteration_idx += 1
-```
-
-Epoch: 1; Batch 1; Loss 2.334119; LR 0.030000 <!--notebook-skip-line-->
-
-Epoch: 2; Batch 1; Loss 0.178930; LR 0.030000 <!--notebook-skip-line-->
-
-Epoch: 3; Batch 1; Loss 0.142640; LR 0.030000 <!--notebook-skip-line-->
-
-Epoch: 4; Batch 1; Loss 0.041116; LR 0.030000 <!--notebook-skip-line-->
-
-Epoch: 5; Batch 1; Loss 0.051049; LR 0.003000 <!--notebook-skip-line-->
-
-Epoch: 6; Batch 1; Loss 0.027170; LR 0.003000 <!--notebook-skip-line-->
-
-Epoch: 7; Batch 1; Loss 0.083776; LR 0.003000 <!--notebook-skip-line-->
-
-Epoch: 8; Batch 1; Loss 0.082553; LR 0.000300 <!--notebook-skip-line-->
-
-Epoch: 9; Batch 1; Loss 0.027984; LR 0.000300 <!--notebook-skip-line-->
-
-Epoch: 10; Batch 1; Loss 0.030896; LR 0.000030 <!--notebook-skip-line-->
-
-
-Once again, we see the learning rate start at 0.03, and fall to 0.00003 by the end of training as per the schedule we defined.
-
-## Advanced Schedules
-
-We have a related tutorial on Advanced Learning Rate Schedules that shows reference implementations of schedules that give state-of-the-art results. We look at cyclical schedules applied to a variety of cycle shapes, and many other techniques such as warm-up and cool-down.
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
\ No newline at end of file
diff --git a/docs/tutorials/gluon/learning_rate_schedules_advanced.md b/docs/tutorials/gluon/learning_rate_schedules_advanced.md
deleted file mode 100644
index 287f70d535e8..000000000000
--- a/docs/tutorials/gluon/learning_rate_schedules_advanced.md
+++ /dev/null
@@ -1,325 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-
- # Advanced Learning Rate Schedules
-
-Given the importance of learning rate and the learning rate schedule for training neural networks, there have been a number of research papers published recently on the subject. Although many practitioners are using simple learning rate schedules such as stepwise decay, research has shown that there are other strategies that work better in most situations. We implement a number of different schedule shapes in this tutorial and introduce cyclical schedules.
-
-See the "Learning Rate Schedules" tutorial for a more basic overview of learning rates, and an example of how to use them while training your own models.
-
-
-```python
-%matplotlib inline
-import copy
-import math
-import mxnet as mx
-import numpy as np
-import matplotlib.pyplot as plt
-```
-
-```python
-def plot_schedule(schedule_fn, iterations=1500):
-    # Iteration count starting at 1
-    iterations = [i+1 for i in range(iterations)]
-    lrs = [schedule_fn(i) for i in iterations]
-    plt.scatter(iterations, lrs)
-    plt.xlabel("Iteration")
-    plt.ylabel("Learning Rate")
-    plt.show()
-```
-
-## Custom Schedule Shapes
-
-### (Slanted) Triangular
-
-While trying to push the boundaries of batch size for faster training, [Priya Goyal et al. (2017)](https://arxiv.org/abs/1706.02677) found that having a smooth linear warm up in the learning rate at the start of training improved the stability of the optimizer and lead to better solutions. It was found that a smooth increases gave improved performance over stepwise increases.
-
-We look at "warm-up" in more detail later in the tutorial, but this could be viewed as a specific case of the **"triangular"** schedule that was proposed by [Leslie N. Smith (2015)](https://arxiv.org/abs/1506.01186). Quite simply, the schedule linearly increases then decreases between a lower and upper bound. Originally it was suggested this schedule be used as part of a cyclical schedule but more recently researchers have been using a single cycle.
-
-One adjustment proposed by [Jeremy Howard, Sebastian Ruder (2018)](https://arxiv.org/abs/1801.06146) was to change the ratio between the increasing and decreasing stages, instead of the 50:50 split. Changing the increasing fraction (`inc_fraction!=0.5`) leads to a **"slanted triangular"** schedule. Using `inc_fraction<0.5` tends to give better results.
-
-
-```python
-class TriangularSchedule():
-    def __init__(self, min_lr, max_lr, cycle_length, inc_fraction=0.5):     
-        """
-        min_lr: lower bound for learning rate (float)
-        max_lr: upper bound for learning rate (float)
-        cycle_length: iterations between start and finish (int)
-        inc_fraction: fraction of iterations spent in increasing stage (float)
-        """
-        self.min_lr = min_lr
-        self.max_lr = max_lr
-        self.cycle_length = cycle_length
-        self.inc_fraction = inc_fraction
-        
-    def __call__(self, iteration):
-        if iteration <= self.cycle_length*self.inc_fraction:
-            unit_cycle = iteration * 1 / (self.cycle_length * self.inc_fraction)
-        elif iteration <= self.cycle_length:
-            unit_cycle = (self.cycle_length - iteration) * 1 / (self.cycle_length * (1 - self.inc_fraction))
-        else:
-            unit_cycle = 0
-        adjusted_cycle = (unit_cycle * (self.max_lr - self.min_lr)) + self.min_lr
-        return adjusted_cycle
-```
-
-We look an example of a slanted triangular schedule that increases from a learning rate of 1 to 2, and back to 1 over 1000 iterations. Since we set `inc_fraction=0.2`, 200 iterations are used for the increasing stage, and 800 for the decreasing stage. After this, the schedule stays at the lower bound indefinitely.
-
-
-```python
-schedule = TriangularSchedule(min_lr=1, max_lr=2, cycle_length=1000, inc_fraction=0.2)
-plot_schedule(schedule)
-```
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/lr_schedules/adv_triangular.png) <!--notebook-skip-line-->
-
-
-### Cosine
-
-Continuing with the idea that smooth decay profiles give improved performance over stepwise decay, [Ilya Loshchilov, Frank Hutter (2016)](https://arxiv.org/abs/1608.03983) used **"cosine annealing"** schedules to good effect. As with triangular schedules, the original idea was that this should be used as part of a cyclical schedule, but we begin by implementing the cosine annealing component before the full Stochastic Gradient Descent with Warm Restarts (SGDR) method later in the tutorial.
-
-
-```python
-class CosineAnnealingSchedule():
-    def __init__(self, min_lr, max_lr, cycle_length):
-        """
-        min_lr: lower bound for learning rate (float)
-        max_lr: upper bound for learning rate (float)
-        cycle_length: iterations between start and finish (int)
-        """
-        self.min_lr = min_lr
-        self.max_lr = max_lr
-        self.cycle_length = cycle_length
-        
-    def __call__(self, iteration):
-        if iteration <= self.cycle_length:
-            unit_cycle = (1 + math.cos(iteration * math.pi / self.cycle_length)) / 2
-            adjusted_cycle = (unit_cycle * (self.max_lr - self.min_lr)) + self.min_lr
-            return adjusted_cycle
-        else:
-            return self.min_lr
-```
-
-We look at an example of a cosine annealing schedule that smoothing decreases from a learning rate of 2 to 1 across 1000 iterations. After this, the schedule stays at the lower bound indefinietly.
-
-
-```python
-schedule = CosineAnnealingSchedule(min_lr=1, max_lr=2, cycle_length=1000)
-plot_schedule(schedule)
-```
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/lr_schedules/adv_cosine.png) <!--notebook-skip-line-->
-
-
-## Custom Schedule Modifiers
-
-We now take a look some adjustments that can be made to existing schedules. We see how to add linear warm-up and its compliment linear cool-down, before using this to implement the "1-Cycle" schedule used by [Leslie N. Smith, Nicholay Topin (2017)](https://arxiv.org/abs/1708.07120) for "super-convergence". We then look at cyclical schedules and implement the original cyclical schedule from [Leslie N. Smith (2015)](https://arxiv.org/abs/1506.01186) before finishing with a look at ["SGDR: Stochastic Gradient Descent with Warm Restarts" by Ilya Loshchilov, Frank Hutter (2016)](https://arxiv.org/abs/1608.03983).
-
-Unlike the schedules above and those implemented in `mx.lr_scheduler`, these classes are designed to modify existing schedules so they take the argument `schedule` (for initialized schedules) or `schedule_class` when being initialized.
-
-### Warm-Up
-
-Using the idea of linear warm-up of the learning rate proposed in ["Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour" by Priya Goyal et al. (2017)](https://arxiv.org/abs/1706.02677), we implement a wrapper class that adds warm-up to an existing schedule. Going from `start_lr` to the initial learning rate of the `schedule` over `length` iterations, this adjustment is useful when training with large batch sizes.
-
-
-```python
-class LinearWarmUp():
-    def __init__(self, schedule, start_lr, length):
-        """
-        schedule: a pre-initialized schedule (e.g. TriangularSchedule(min_lr=0.5, max_lr=2, cycle_length=500))
-        start_lr: learning rate used at start of the warm-up (float)
-        length: number of iterations used for the warm-up (int)
-        """
-        self.schedule = schedule
-        self.start_lr = start_lr
-        # calling mx.lr_scheduler.LRScheduler effects state, so calling a copy
-        self.finish_lr = copy.copy(schedule)(0)
-        self.length = length
-    
-    def __call__(self, iteration):
-        if iteration <= self.length:
-            return iteration * (self.finish_lr - self.start_lr)/(self.length) + self.start_lr
-        else:
-            return self.schedule(iteration - self.length)
-```
-
-As an example, we add a linear warm-up of the learning rate (from 0 to 1 over 250 iterations) to a stepwise decay schedule. We first create the `MultiFactorScheduler` (and set the `base_lr`) and then pass it to `LinearWarmUp` to add the warm-up at the start. We can use `LinearWarmUp` with any other schedule including `CosineAnnealingSchedule`.
-
-
-```python
-schedule = mx.lr_scheduler.MultiFactorScheduler(step=[250, 750, 900], factor=0.5)
-schedule.base_lr = 1
-schedule = LinearWarmUp(schedule, start_lr=0, length=250)
-plot_schedule(schedule)
-```
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/lr_schedules/adv_warmup.png) <!--notebook-skip-line-->
-
-
-### Cool-Down
-
-Similarly, we could add a linear cool-down period to our schedule and this is used in the "1-Cycle" schedule proposed by [Leslie N. Smith, Nicholay Topin (2017)](https://arxiv.org/abs/1708.07120) to train neural networks very quickly in certain circumstances (coined "super-convergence"). We reduce the learning rate from its value at `start_idx` of `schedule` to `finish_lr` over a period of `length`, and then maintain `finish_lr` thereafter.
-
-
-```python
-class LinearCoolDown():
-    def __init__(self, schedule, finish_lr, start_idx, length):
-        """
-        schedule: a pre-initialized schedule (e.g. TriangularSchedule(min_lr=0.5, max_lr=2, cycle_length=500))
-        finish_lr: learning rate used at end of the cool-down (float)
-        start_idx: iteration to start the cool-down (int)
-        length: number of iterations used for the cool-down (int)
-        """
-        self.schedule = schedule
-        # calling mx.lr_scheduler.LRScheduler effects state, so calling a copy
-        self.start_lr = copy.copy(self.schedule)(start_idx)
-        self.finish_lr = finish_lr
-        self.start_idx = start_idx
-        self.finish_idx = start_idx + length
-        self.length = length
-    
-    def __call__(self, iteration):
-        if iteration <= self.start_idx:
-            return self.schedule(iteration)
-        elif iteration <= self.finish_idx:
-            return (iteration - self.start_idx) * (self.finish_lr - self.start_lr) / (self.length) + self.start_lr
-        else:
-            return self.finish_lr
-```
-
-As an example, we apply learning rate cool-down to a `MultiFactorScheduler`. Starting the cool-down at iteration 1000, we reduce the learning rate linearly from 0.125 to 0.001 over 500 iterations, and hold the learning rate at 0.001 after this.
-
-
-```python
-schedule = mx.lr_scheduler.MultiFactorScheduler(step=[250, 750, 900], factor=0.5)
-schedule.base_lr = 1
-schedule = LinearCoolDown(schedule, finish_lr=0.001, start_idx=1000, length=500)
-plot_schedule(schedule)
-```
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/lr_schedules/adv_cooldown.png) <!--notebook-skip-line-->
-
-
-#### 1-Cycle: for "Super-Convergence"
-
-So we can implement the "1-Cycle" schedule proposed by [Leslie N. Smith, Nicholay Topin (2017)](https://arxiv.org/abs/1708.07120) we use a single and symmetric cycle of the triangular schedule above (i.e. `inc_fraction=0.5`), followed by a cool-down period of `cooldown_length` iterations.
-
-
-```python
-class OneCycleSchedule():
-    def __init__(self, start_lr, max_lr, cycle_length, cooldown_length=0, finish_lr=None):
-        """
-        start_lr: lower bound for learning rate in triangular cycle (float)
-        max_lr: upper bound for learning rate in triangular cycle (float)
-        cycle_length: iterations between start and finish of triangular cycle: 2x 'stepsize' (int)
-        cooldown_length: number of iterations used for the cool-down (int)
-        finish_lr: learning rate used at end of the cool-down (float)
-        """
-        if (cooldown_length > 0) and (finish_lr is None):
-            raise ValueError("Must specify finish_lr when using cooldown_length > 0.")
-        if (cooldown_length == 0) and (finish_lr is not None):
-            raise ValueError("Must specify cooldown_length > 0 when using finish_lr.")
-            
-        finish_lr = finish_lr if (cooldown_length > 0) else start_lr
-        schedule = TriangularSchedule(min_lr=start_lr, max_lr=max_lr, cycle_length=cycle_length)
-        self.schedule = LinearCoolDown(schedule, finish_lr=finish_lr, start_idx=cycle_length, length=cooldown_length)
-        
-    def __call__(self, iteration):
-        return self.schedule(iteration)
-```
-
-As an example, we linearly increase and then decrease the learning rate from 0.1 to 0.5 and back over 500 iterations (i.e. single triangular cycle), before reducing the learning rate further to 0.001 over the next 750 iterations (i.e. cool-down).
-
-
-```python
-schedule = OneCycleSchedule(start_lr=0.1, max_lr=0.5, cycle_length=500, cooldown_length=750, finish_lr=0.001)
-plot_schedule(schedule)
-```
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/lr_schedules/adv_onecycle.png) <!--notebook-skip-line-->
-
-
-### Cyclical
-
-Originally proposed by [Leslie N. Smith (2015)](https://arxiv.org/abs/1506.01186), the idea of cyclically increasing and decreasing the learning rate has been shown to give faster convergence and more optimal solutions. We implement a wrapper class that loops existing cycle-based schedules such as `TriangularSchedule` and `CosineAnnealingSchedule` to provide infinitely repeating schedules. We pass the schedule class (rather than an instance) because one feature of the `CyclicalSchedule` is to vary the `cycle_length` over time as seen in [Ilya Loshchilov, Frank Hutter (2016)](https://arxiv.org/abs/1608.03983) using `cycle_length_decay`. Another feature is the ability to decay the cycle magnitude over time with `cycle_magnitude_decay`.
-
-
-```python
-class CyclicalSchedule():
-    def __init__(self, schedule_class, cycle_length, cycle_length_decay=1, cycle_magnitude_decay=1, **kwargs):
-        """
-        schedule_class: class of schedule, expected to take `cycle_length` argument
-        cycle_length: iterations used for initial cycle (int)
-        cycle_length_decay: factor multiplied to cycle_length each cycle (float)
-        cycle_magnitude_decay: factor multiplied learning rate magnitudes each cycle (float)
-        kwargs: passed to the schedule_class
-        """
-        self.schedule_class = schedule_class
-        self.length = cycle_length
-        self.length_decay = cycle_length_decay
-        self.magnitude_decay = cycle_magnitude_decay
-        self.kwargs = kwargs
-    
-    def __call__(self, iteration):
-        cycle_idx = 0
-        cycle_length = self.length
-        idx = self.length
-        while idx <= iteration:
-            cycle_length = math.ceil(cycle_length * self.length_decay)
-            cycle_idx += 1
-            idx += cycle_length
-        cycle_offset = iteration - idx + cycle_length
-        
-        schedule = self.schedule_class(cycle_length=cycle_length, **self.kwargs)
-        return schedule(cycle_offset) * self.magnitude_decay**cycle_idx
-```
-
-As an example, we implement the triangular cyclical schedule presented in ["Cyclical Learning Rates for Training Neural Networks" by Leslie N. Smith (2015)](https://arxiv.org/abs/1506.01186). We use slightly different terminology to the paper here because we use `cycle_length` that is twice the 'stepsize' used in the paper. We repeat cycles, each with a length of 500 iterations and lower and upper learning rate bounds of 0.5 and 2 respectively.
-
-
-```python
-schedule = CyclicalSchedule(TriangularSchedule, min_lr=0.5, max_lr=2, cycle_length=500)
-plot_schedule(schedule)
-```
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/lr_schedules/adv_cyclical.png) <!--notebook-skip-line-->
-
-
-And lastly, we implement the scheduled used in ["SGDR: Stochastic Gradient Descent with Warm Restarts" by Ilya Loshchilov, Frank Hutter (2016)](https://arxiv.org/abs/1608.03983). We repeat cosine annealing schedules, but each time we halve the magnitude and double the cycle length.
-
-
-```python
-schedule = CyclicalSchedule(CosineAnnealingSchedule, min_lr=0.01, max_lr=2,
-                            cycle_length=250, cycle_length_decay=2, cycle_magnitude_decay=0.5)
-plot_schedule(schedule)
-```
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/lr_schedules/adv_sgdr.png) <!--notebook-skip-line-->
-
-
-**_Want to learn more?_** Checkout the "Learning Rate Schedules" tutorial for a more basic overview of learning rates found in `mx.lr_scheduler`, and an example of how to use them while training your own models.
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
\ No newline at end of file
diff --git a/docs/tutorials/gluon/logistic_regression_explained.md b/docs/tutorials/gluon/logistic_regression_explained.md
deleted file mode 100644
index 57ed56eb8f4f..000000000000
--- a/docs/tutorials/gluon/logistic_regression_explained.md
+++ /dev/null
@@ -1,255 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-
-# Logistic regression using Gluon API explained
-
-Logistic Regression is one of the first models newcomers to Deep Learning are implementing. The focus of this tutorial is to show how to do logistic regression using Gluon API.
-
-Before anything else, let's import required packages for this tutorial.
-
-
-```python
-import numpy as np
-import mxnet as mx
-from mxnet import nd, autograd, gluon
-from mxnet.gluon import nn, Trainer
-from mxnet.gluon.data import DataLoader, ArrayDataset
-
-mx.random.seed(12345)  # Added for reproducibility
-```
-
-In this tutorial we will use fake dataset, which contains 10 features drawn from a normal distribution with mean equals to 0 and standard deviation equals to 1, and a class label, which can be either 0 or 1. The size of the dataset is an arbitrary value. The function below helps us to generate a dataset. Class label `y` is generated via a non-random logic, so the network would have a pattern to look for. Boundary of 3 is selected to make sure that number of positive examples smaller than negative, but not too small
-
-
-```python
-def get_random_data(size, ctx):
-    x = nd.normal(0, 1, shape=(size, 10), ctx=ctx)
-    y = x.sum(axis=1) > 3
-    return x, y
-```
-
-Also, let's define a set of hyperparameters, that we are going to use later. Since our model is simple and dataset is small, we are going to use CPU for calculations. Feel free to change it to GPU for a more advanced scenario.
-
-
-```python
-ctx = mx.cpu()
-train_data_size = 1000
-val_data_size = 100
-batch_size = 10
-```
-
-## Working with data
-
-To work with data, Apache MXNet provides [Dataset](https://mxnet.incubator.apache.org/api/python/gluon/data.html#mxnet.gluon.data.Dataset) and [DataLoader](https://mxnet.incubator.apache.org/api/python/gluon/data.html#mxnet.gluon.data.DataLoader) classes. The former is used to provide an indexed access to the data, the latter is used to shuffle and batchify the data. To learn more about working with data in Gluon, please refer to [Gluon Datasets and Dataloaders](https://mxnet.incubator.apache.org/tutorials/gluon/datasets.html) tutorial.
-
-Below we define training and validation datasets, which we are going to use in the tutorial.
-
-
-```python
-train_x, train_ground_truth_class = get_random_data(train_data_size, ctx)
-train_dataset = ArrayDataset(train_x, train_ground_truth_class)
-train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
-
-val_x, val_ground_truth_class = get_random_data(val_data_size, ctx)
-val_dataset = ArrayDataset(val_x, val_ground_truth_class)
-val_dataloader = DataLoader(val_dataset, batch_size=batch_size, shuffle=True)
-```
-
-## Defining and training the model
-
-The only requirement for the logistic regression is that the last layer of the network must be a single neuron. Apache MXNet allows us to do so by using [Dense](https://mxnet.incubator.apache.org/api/python/gluon/nn.html#mxnet.gluon.nn.Dense) layer and specifying the number of units to 1. The rest of the network can be arbitrarily complex.
-
-Below, we define a model which has an input layer of 10 neurons, a couple of inner layers of 10 neurons each, and output layer of 1 neuron. We stack the layers using [HybridSequential](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.nn.HybridSequential) block and initialize parameters of the network using [Xavier](https://mxnet.incubator.apache.org/api/python/optimization/optimization.html#mxnet.initializer.Xavier) initialization.
-
-
-```python
-net = nn.HybridSequential()
-
-with net.name_scope():
-    net.add(nn.Dense(units=10, activation='relu'))  # input layer
-    net.add(nn.Dense(units=10, activation='relu'))   # inner layer 1
-    net.add(nn.Dense(units=10, activation='relu'))   # inner layer 2
-    net.add(nn.Dense(units=1))   # output layer: notice, it must have only 1 neuron
-
-net.initialize(mx.init.Xavier())
-```
-
-After defining the model, we need to define a few more things: our loss, our trainer and our metric.
-
-Loss function is used to calculate how the output of the network differs from the ground truth. Because classes  of the logistic regression are either 0 or 1, we are using [SigmoidBinaryCrossEntropyLoss](https://mxnet.incubator.apache.org/api/python/gluon/loss.html#mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss). Notice that we do not specify `from_sigmoid` attribute in the code, which means that the output of the neuron doesn't need to go through sigmoid, but at inference we'd have to pass it through sigmoid. You can learn more about cross entropy on [wikipedia](https://en.wikipedia.org/wiki/Cross_entropy).
-
-Trainer object allows to specify the method of training to be used. For our tutorial we use [Stochastic Gradient Descent (SGD)](https://mxnet.incubator.apache.org/api/python/optimization/optimization.html#mxnet.optimizer.SGD). For more information on SGD refer to [the following tutorial](https://gluon.mxnet.io/chapter06_optimization/gd-sgd-scratch.html). We also need to parametrize it with learning rate value, which defines the weight updates, and weight decay, which is used for regularization.
-
-Metric helps us to estimate how good our model is in terms of a problem we are trying to solve. Where loss function has more importance for the training process, a metric is usually the thing we are trying to improve and reach maximum value. We also can use more than one metric, to measure various aspects of our model. In our example, we are using [Accuracy](https://mxnet.incubator.apache.org/api/python/metric/metric.html?highlight=metric.acc#mxnet.metric.Accuracy) and [F1 score](http://mxnet.incubator.apache.org/api/python/metric/metric.html?highlight=metric.f1#mxnet.metric.F1) as measurements of success of our model.
-
-Below we define these objects.
-
-
-```python
-loss = gluon.loss.SigmoidBinaryCrossEntropyLoss()
-trainer = Trainer(params=net.collect_params(), optimizer='sgd',
-                  optimizer_params={'learning_rate': 0.1})
-accuracy = mx.metric.Accuracy()
-f1 = mx.metric.F1()
-```
-
-The next step is to define the training function in which we iterate over all batches of training data, execute the forward pass on each batch and calculate training loss. On line 19, we sum losses of every batch per epoch into a single variable, because we calculate loss per single batch, but want to display it per epoch.
-
-
-```python
-def train_model():
-    cumulative_train_loss = 0
-
-    for i, (data, label) in enumerate(train_dataloader):
-        with autograd.record():
-            # Do forward pass on a batch of training data
-            output = net(data)
-
-            # Calculate loss for the training data batch
-            loss_result = loss(output, label)
-
-        # Calculate gradients
-        loss_result.backward()
-
-        # Update parameters of the network
-        trainer.step(batch_size)
-
-        # sum losses of every batch
-        cumulative_train_loss += nd.sum(loss_result).asscalar()
-
-    return cumulative_train_loss
-```
-
-## Validating the model
-
-Our validation function is very similar to the training one. The main difference is that we want to calculate accuracy of the model. We use [Accuracy metric](https://mxnet.incubator.apache.org/api/python/metric/metric.html?highlight=metric.acc#mxnet.metric.Accuracy) to do so. 
-
-`Accuracy` metric requires 2 arguments: 1) a vector of ground-truth classes and 2) A vector or matrix of predictions. When predictions are of the same shape as the vector of ground-truth classes, `Accuracy` class assumes that prediction vector contains predicted classes. So, it converts the vector to `Int32` and compare each item of ground-truth classes to prediction vector.
-
-Because of the behaviour above, you will get an unexpected result if you just apply [Sigmoid](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.sigmoid) function to the network result and pass it to `Accuracy` metric. As mentioned before, we need to apply `Sigmoid` function to the output of the neuron to get a probability of belonging to the class 1. But `Sigmoid` function produces output in range [0; 1], and all numbers in that range are going to be casted to 0, even if it is as high as 0.99. To avoid this we write a custom bit of code on line 12, that:
-
-1. Calculates sigmoid using `Sigmoid` function
-
-2. Subtracts a threshold from the original sigmoid output. Usually, the threshold is equal to 0.5, but it can be higher, if you want to increase certainty of an item to belong to class 1.
-
-3. Uses [mx.nd.ceil](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.ceil) function, which converts all negative values to 0 and all positive values to 1
-
-After these transformations we can pass the result to `Accuracy.update()` method and expect it to behave in a proper way.
-
-For `F1` metric to work, instead of one number per class, we must pass probabilities of belonging to both classes. Because of that, on lines 21-22 we:
-
-1. Reshape predictions to a single vector
-
-2. We stack together two vectors: probabilities of belonging to class 0 (1 - `prediction`) and probabilities of belonging to class 1.
-
-Then we pass this stacked matrix to `F1` score.
-
-
-```python
-def validate_model(threshold):
-    cumulative_val_loss = 0
-
-    for i, (val_data, val_ground_truth_class) in enumerate(val_dataloader):
-        # Do forward pass on a batch of validation data
-        output = net(val_data)
-
-        # Similar to cumulative training loss, calculate cumulative validation loss
-        cumulative_val_loss += nd.sum(loss(output, val_ground_truth_class)).asscalar()
-
-        # getting prediction as a sigmoid
-        prediction = net(val_data).sigmoid()
-
-        # Converting neuron outputs to classes
-        predicted_classes = mx.nd.ceil(prediction - threshold)
-
-        # Update validation accuracy
-        accuracy.update(val_ground_truth_class, predicted_classes.reshape(-1))
-
-        # calculate probabilities of belonging to different classes. F1 metric works only with this notation
-        prediction = prediction.reshape(-1)
-        probabilities = mx.nd.stack(1 - prediction, prediction, axis=1)
-
-        f1.update(val_ground_truth_class, probabilities)
-
-    return cumulative_val_loss
-```
-
-## Putting it all together
-
-By using the defined above functions, we can finally write our main training loop.
-
-
-```python
-epochs = 10
-threshold = 0.5
-
-for e in range(epochs):
-    avg_train_loss = train_model() / train_data_size
-    avg_val_loss = validate_model(threshold) / val_data_size
-
-    print("Epoch: %s, Training loss: %.2f, Validation loss: %.2f, Validation accuracy: %.2f, F1 score: %.2f" %
-          (e, avg_train_loss, avg_val_loss, accuracy.get()[1], f1.get()[1]))
-
-    # we reset accuracy, so the new epoch's accuracy would be calculated from the blank state
-    accuracy.reset()
-```
-
-    Epoch: 0, Training loss: 0.43, Validation loss: 0.36, Validation accuracy: 0.85, F1 score: 0.00 <!--notebook-skip-line-->
-
-    Epoch: 1, Training loss: 0.22, Validation loss: 0.14, Validation accuracy: 0.96, F1 score: 0.35 <!--notebook-skip-line-->
-
-    Epoch: 2, Training loss: 0.09, Validation loss: 0.11, Validation accuracy: 0.97, F1 score: 0.48 <!--notebook-skip-line-->
-
-    Epoch: 3, Training loss: 0.07, Validation loss: 0.09, Validation accuracy: 0.96, F1 score: 0.53 <!--notebook-skip-line-->
-
-    Epoch: 4, Training loss: 0.06, Validation loss: 0.09, Validation accuracy: 0.97, F1 score: 0.58 <!--notebook-skip-line-->
-
-    Epoch: 5, Training loss: 0.04, Validation loss: 0.12, Validation accuracy: 0.97, F1 score: 0.59 <!--notebook-skip-line-->
-
-    Epoch: 6, Training loss: 0.05, Validation loss: 0.09, Validation accuracy: 0.99, F1 score: 0.62 <!--notebook-skip-line-->
-
-    Epoch: 7, Training loss: 0.05, Validation loss: 0.10, Validation accuracy: 0.97, F1 score: 0.62 <!--notebook-skip-line-->
-
-    Epoch: 8, Training loss: 0.05, Validation loss: 0.12, Validation accuracy: 0.95, F1 score: 0.63 <!--notebook-skip-line-->
-
-    Epoch: 9, Training loss: 0.04, Validation loss: 0.09, Validation accuracy: 0.98, F1 score: 0.65 <!--notebook-skip-line-->
-
-
-In our case we hit the accuracy of 0.98 and F1 score of 0.65.
-
-## Tip 1: Use only one neuron in the output layer
-
-Despite that there are 2 classes, there should be only one output neuron, because `SigmoidBinaryCrossEntropyLoss` accepts only one feature as an input.
-
-## Tip 2: Encode classes as 0 and 1
-
-For `SigmoidBinaryCrossEntropyLoss` to work it is required that classes were encoded as 0 and 1. In some datasets the class encoding might be different, like -1 and 1 or 1 and 2. If this is how your dataset looks like, then you need to re-encode the data before using `SigmoidBinaryCrossEntropyLoss`.
-
-## Tip 3: Use SigmoidBinaryCrossEntropyLoss instead of LogisticRegressionOutput
-
-NDArray API has two options to calculate logistic regression loss: [SigmoidBinaryCrossEntropyLoss](https://mxnet.incubator.apache.org/api/python/gluon/loss.html#mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss) and [LogisticRegressionOutput](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.LogisticRegressionOutput). `LogisticRegressionOutput` is designed to be an output layer when using the Module API, and is not supposed to be used when using Gluon API.
-
-## Conclusion
-
-In this tutorial I explained some potential pitfalls to be aware of. When doing logistic regression using Gluon API remember to:
-1. Use only one neuron in the output layer
-1. Encode class labels as 0 or 1
-1. Use `SigmoidBinaryCrossEntropyLoss`
-1. Convert probabilities to classes before calculating Accuracy
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/gluon/mnist.md b/docs/tutorials/gluon/mnist.md
deleted file mode 100644
index 828bfd51f601..000000000000
--- a/docs/tutorials/gluon/mnist.md
+++ /dev/null
@@ -1,460 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Hand-written Digit Recognition
-
-In this tutorial, we'll give you a step-by-step walkthrough of building a hand-written digit classifier using the [MNIST](https://en.wikipedia.org/wiki/MNIST_database) dataset.
-
-MNIST is a widely used dataset for the hand-written digit classification task. It consists of 70,000 labeled grayscale images of hand-written digits, each 28x28 pixels in size. The dataset is split into 60,000 training images and 10,000 test images. There are 10 classes (one for each of the 10 digits). The task at hand is to train a model that can correctly classify the images into the digits they represent. The 60,000 training images are used to fit the model, and its performance in terms of classification accuracy is subsequently validated on the 10,000 test images.
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/mnist.png)
-
-**Figure 1:** Sample images from the MNIST dataset.
-
-This tutorial uses MXNet's high-level *Gluon* interface to implement neural networks in an imperative fashion. It is based on [the corresponding tutorial written with the symbolic approach](https://mxnet.incubator.apache.org/tutorials/python/mnist.html).
-
-## Prerequisites
-
-To complete this tutorial, you need:
-
-- MXNet. See the instructions for your operating system in [Setup and Installation](https://mxnet.incubator.apache.org/install/index.html).
-- The Python [`requests`](http://docs.python-requests.org/en/master/) library.
-- (Optional) The [Jupyter Notebook](https://jupyter.org/index.html) software for interactively running the provided `.ipynb` file.
-
-```
-$ pip install requests jupyter
-```
-
-## Loading Data
-
-The following code downloads the MNIST dataset to the default location (`.mxnet/datasets/mnist/` in your home directory) and creates `Dataset` objects `train_data` and `val_data` for training and validation, respectively.
-These objects can later be used to get one image or a batch of images at a time, together with their corresponding labels.
-
-We also immediately apply the `transform_first()` method and supply a function that moves the channel axis of the images to the beginning (`(28, 28, 1) -> (1, 28, 28)`), casts them to `float32` and rescales them from `[0, 255]` to `[0, 1]`.
-The name `transform_first` reflects the fact that these datasets contain images and labels, and that the transform should only be applied to the first of each `(image, label)` pair.
-
-```python
-import mxnet as mx
-
-# Select a fixed random seed for reproducibility
-mx.random.seed(42)
-
-def data_xform(data):
-    """Move channel axis to the beginning, cast to float32, and normalize to [0, 1]."""
-    return nd.moveaxis(data, 2, 0).astype('float32') / 255
-
-train_data = mx.gluon.data.vision.MNIST(train=True).transform_first(data_xform)
-val_data = mx.gluon.data.vision.MNIST(train=False).transform_first(data_xform)
-```
-
-Since the MNIST dataset is relatively small, the `MNIST` class loads it into memory all at once, but for larger datasets like ImageNet, this would no longer be possible.
-The Gluon `Dataset` class from which `MNIST` derives supports both cases.
-In general, `Dataset` and `DataLoader` (which we will encounter next) are the machinery in MXNet that provides a stream of input data to be consumed by a training algorithm, typically in batches of multiple data entities at once for better efficiency.
-In this tutorial, we will configure the data loader to feed examples in batches of 100.
-
-An image batch is commonly represented as a 4-D array with shape `(batch_size, num_channels, height, width)`.
-This convention is denoted by "NCHW", and it is the default in MXNet.
-For the MNIST dataset, each image has a size of 28x28 pixels and one color channel (grayscale), hence the shape of an input batch will be `(batch_size, 1, 28, 28)`.
-
-Another important consideration is the order of input samples.
-When feeding training examples, it is critical not feed samples with the same label in succession since doing so can slow down training progress.
-Data iterators, i.e., instances of [`DataLoader`](https://mxnet.incubator.apache.org/api/python/gluon/data.html#mxnet.gluon.data.DataLoader), take care of this issue by randomly shuffling the inputs.
-Note that we only need to shuffle the training data -- for validation data, the order does not matter.
-
-The following code initializes the data iterators for the MNIST dataset.
-
-```python
-batch_size = 100
-train_loader = mx.gluon.data.DataLoader(train_data, shuffle=True, batch_size=batch_size)
-val_loader = mx.gluon.data.DataLoader(val_data, shuffle=False, batch_size=batch_size)
-```
-
-## Approaches
-
-We will cover two approaches for performing the hand-written digit recognition task.
-In our first attempt, we will make use of a traditional neural network architecture called [Multilayer Perceptron (MLP)](https://en.wikipedia.org/wiki/Multilayer_perceptron).
-Although this architecture lets us achieve over 95 % accuracy on the validation set, we will recognize and discuss some of its drawbacks and use them as a motivation for using a different network.
-In the subsequent second attempt, we introduce the more advanced and very widely used [Convolutional Neural Network (CNN)](https://en.wikipedia.org/wiki/Convolutional_neural_network) architecture that has proven to work very well for image classification tasks.
-
-As a first step, we run some convenience imports of frequently used modules.
-
-```python
-from __future__ import print_function  # only relevant for Python 2
-import mxnet as mx
-from mxnet import nd, gluon, autograd
-from mxnet.gluon import nn
-```
-
-### Defining a network: Multilayer Perceptron (MLP)
-
-MLPs consist of several fully connected layers.
-In a fully connected (short: FC) layer, each neuron is connected to every neuron in its preceding layer.
-From a linear algebra perspective, an FC layer applies an [affine transform](https://en.wikipedia.org/wiki/Affine_transformation) *Y = X W + b* to an input matrix *X* of size (*n x m*) and outputs a matrix *Y* of size (*n x k*).
-The number *k*, also referred to as *hidden size*, corresponds to the number of neurons in the FC layer.
-An FC layer has two learnable parameters: the (*m x k*) weight matrix *W* and the (*1 x k*) bias vector *b*.
-
-In an MLP, the outputs of FC layers are typically fed into an activation function that applies an elementwise nonlinearity.
-This step is crucial since it gives neural networks the ability to classify inputs that are not linearly separable.
-Common choices for activation functions are [sigmoid](https://en.wikipedia.org/wiki/Sigmoid_function), [hyperbolic tangent ("tanh")](https://en.wikipedia.org/wiki/Hyperbolic_function#Definitions), and [rectified linear unit (ReLU)](https://en.wikipedia.org/wiki/Rectifier_(neural_networks)).
-In this example, we'll use the ReLU activation function since it has several nice properties that make it a good default choice.
-
-The following code snippet declares three fully connected (or *dense*) layers with 128, 64 and 10 neurons each, where the last number of neurons matches the number of output classes in our dataset.
-Note that the last layer uses no activation function since the [softmax](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.softmax) activation will be implicitly applied by the loss function later on.
-To build the neural network, we use a [`HybridSequential`](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.nn.HybridSequential) layer, which is a convenience class to build a linear stack of layers, often called a *feed-forward neural net*.
-
-The "Hybrid" part of name `HybridSequential` refers to the fact that such a layer can be used with both the Gluon API and the Symbol API.
-Using hybrid blocks over dynamic-only blocks (e.g. [`Sequential`](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.nn.Sequential)) has several advantages apart from being compatible with a wider range of existing code: for instance, the computation graph of the network can be visualized with `mxnet.viz.plot_network()` and inspected for errors.
-Unless a network requires non-static runtime elements like loops, conditionals or random layer selection in its forward pass, it is generally a good idea to err on the side of hybrid blocks.
-For details on the differences, see the documentation on [`Block`](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.Block) and [`HybridBlock`](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.HybridBlock).
-
-```python
-net = nn.HybridSequential(prefix='MLP_')
-with net.name_scope():
-    net.add(
-        nn.Flatten(),
-        nn.Dense(128, activation='relu'),
-        nn.Dense(64, activation='relu'),
-        nn.Dense(10, activation=None)  # loss function includes softmax already, see below
-    )
-```
-
-**Note**: using the `name_scope()` context manager is optional.
-It is, however, good practice since it uses a common prefix for the names of all layers generated in that scope, which can be very helpful during debugging.
-
-#### Initializing parameters and optimizer
-
-Before the network can be used, its parameters (weights and biases) need to be set to initial values that are sufficiently random while keeping the magnitude of gradients limited.
-The [Xavier](https://mxnet.incubator.apache.org/api/python/optimization/optimization.html#mxnet.initializer.Xavier) initializer is usually a good default choice.
-
-Since the `net.initialize()` method creates arrays for its parameters, it needs to know where to store the values: in CPU or GPU memory.
-Like many other functions and classes that deal with memory management in one way or another, the `initialize()` method takes an optional `ctx` (short for *context*) argument, where the return value of either `mx.cpu()` or `mx.gpu()` can be provided.
-
-```python
-ctx = mx.gpu(0) if mx.context.num_gpus() > 0 else mx.cpu(0)
-net.initialize(mx.init.Xavier(), ctx=ctx)
-```
-
-To train the network parameters, we will make use of the [stochastic gradient descent (SGD)](https://en.wikipedia.org/wiki/Stochastic_gradient_descent) optimizer.
-More specifically, we use mini-batch SGD in contrast to the classical SGD that processes one example at a time, which is very slow in practice.
-(Recall that we set the batch size to 100 in the ["Loading Data"](#loading-data) part.)
-
-Besides the batch size, the SGD algorithm has one important *hyperparameter*: the *learning rate*.
-It determines the size of steps that the algorithm takes in search of parameters that allow the network to optimally fit the training data.
-Therefore, this value has great influence on both the course of the training process and its final outcome.
-In general, hyperparameters refer to *non-learnable* values that need to be chosen before training and that have a potential effect on the outcome.
-In this example, further hyperparameters are the number of layers in the network, the number of neurons of the first two layers, the activation function and (later) the loss function.
-
-The SGD optimization method can be accessed in MXNet Gluon through the [`Trainer`](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#trainer) class.
-Internally, it makes use of the [`SGD`](https://mxnet.incubator.apache.org/api/python/optimization/optimization.html#mxnet.optimizer.SGD) optimizer class.
-
-```python
-trainer = gluon.Trainer(
-    params=net.collect_params(),
-    optimizer='sgd',
-    optimizer_params={'learning_rate': 0.04},
-)
-```
-
-#### Training
-
-Training the network requires a way to tell how well the network currently fits the training data.
-Following common practice in optimization, this quality of fit is expressed through a *loss value* (also referred to as badness-of-fit or data discrepancy), which the algorithm then tries to minimize by adjusting the weights of the model.
-
-Ideally, in a classification task, we would like to use the prediction inaccuracy, i.e., the fraction of incorrectly classified samples, to guide the training to a lower value.
-Unfortunately, inaccuracy is a poor choice for training since it contains almost no information that can be used to update the network parameters (its gradient is zero almost everywhere).
-As a better behaved proxy for inaccuracy, the [softmax cross-entropy loss](https://mxnet.incubator.apache.org/api/python/gluon/loss.html#mxnet.gluon.loss.SoftmaxCrossEntropyLoss) is a popular choice.
-It has the essential property of being minimal for the correct prediction, but at the same time, it is everywhere differentiable with nonzero gradient.
-The [accuracy](https://mxnet.incubator.apache.org/api/python/metric/metric.html#mxnet.metric.Accuracy) metric is still useful for monitoring the training progress, since it is more intuitively interpretable than a loss value.
-
-**Note:** `SoftmaxCrossEntropyLoss` combines the softmax activation and the cross entropy loss function in one layer, therefore the last layer in our network has no activation function.
-
-```python
-metric = mx.metric.Accuracy()
-loss_function = gluon.loss.SoftmaxCrossEntropyLoss()
-```
-
-Typically, the training is run until convergence, which means that further iterations will no longer lead to improvements of the loss function, and that the network has probably learned a good set of model parameters from the train data.
-For the purpose of this tutorial, we only loop 10 times over the entire dataset.
-One such pass over the data is usually called an *epoch*.
-
-The following steps are taken in each `epoch`:
-
-- Get a minibatch of `inputs` and `labels` from the `train_loader`.
-- Feed the `inputs` to the network, producing `outputs`.
-- Compute the minibatch `loss` value by comparing `outputs` to `labels`.
-- Use backpropagation to compute the gradients of the loss with respect to each of the network parameters by calling `loss.backward()`.
-- Update the parameters of the network according to the optimizer rule with `trainer.step(batch_size=inputs.shape[0])`.
-- Print the current accuracy over the training data, i.e., the fraction of correctly classified training examples.
-
-```python
-num_epochs = 10
-
-for epoch in range(num_epochs):
-    for inputs, labels in train_loader:
-        # Possibly copy inputs and labels to the GPU
-        inputs = inputs.as_in_context(ctx)
-        labels = labels.as_in_context(ctx)
-
-        # The forward pass and the loss computation need to be wrapped
-        # in a `record()` scope to make sure the computational graph is
-        # recorded in order to automatically compute the gradients
-        # during the backward pass.
-        with autograd.record():
-            outputs = net(inputs)
-            loss = loss_function(outputs, labels)
-
-        # Compute gradients by backpropagation and update the evaluation
-        # metric
-        loss.backward()
-        metric.update(labels, outputs)
-
-        # Update the parameters by stepping the trainer; the batch size
-        # is required to normalize the gradients by `1 / batch_size`.
-        trainer.step(batch_size=inputs.shape[0])
-
-    # Print the evaluation metric and reset it for the next epoch
-    name, acc = metric.get()
-    print('After epoch {}: {} = {}'.format(epoch + 1, name, acc))
-    metric.reset()
-```
-
-#### Validation
-
-When the above training has completed, we can evaluate the trained model by comparing predictions from the validation dataset with their respective correct labels.
-It is important to notice that the validation data was not used during training, i.e., the network has not seen the images and their true labels yet.
-Keeping a part of the data aside for validation is crucial for detecting *overfitting* of a network: If a neural network has enough parameters, it can simply memorize the training data and look up the true label for a given training image.
-While this results in 100 % training accuracy, such an overfit model would perform very poorly on new data.
-In other words, an overfit model does not generalize to a broader class of inputs than the training set, and such an outcome is almost always undesirable.
-Therefore, having a subset of "unseen" data for validation is an important part of good practice in machine learning.
-
-To validate our model on the validation data, we can run the following snippet of code:
-
-```python
-metric = mx.metric.Accuracy()
-for inputs, labels in val_loader:
-    # Possibly copy inputs and labels to the GPU
-    inputs = inputs.as_in_context(ctx)
-    labels = labels.as_in_context(ctx)
-    metric.update(labels, net(inputs))
-print('Validaton: {} = {}'.format(*metric.get()))
-assert metric.get()[1] > 0.96
-```
-
-If everything went well, we should see an accuracy value that is around 0.968, which means that we are able to accurately predict the digit in 97 % of test images.
-This is a pretty good result, but as we will see in the next part of this tutorial, we can do a lot better than that.
-
-That said, a single number only conveys very limited information on the performance of our neural network.
-It is always a good idea to actually look at the images on which the network performed poorly, and check for clues on how to improve the performance.
-We do that with the help of a small function that produces a list of the images which the network got wrong, together with the predicted and true labels.
-
-```python
-def get_mislabeled(loader):
-    """Return list of ``(input, pred_lbl, true_lbl)`` for mislabeled samples."""
-    mislabeled = []
-    for inputs, labels in loader:
-        inputs = inputs.as_in_context(ctx)
-        labels = labels.as_in_context(ctx)
-        outputs = net(inputs)
-        # Predicted label is the index is where the output is maximal
-        preds = nd.argmax(outputs, axis=1)
-        for i, p, l in zip(inputs, preds, labels):
-            p, l = int(p.asscalar()), int(l.asscalar())
-            if p != l:
-                mislabeled.append((i.asnumpy(), p, l))
-    return mislabeled
-```
-
-We can now get the mislabeled images in the training and validation sets and plot a selection of them:
-
-```python
-import numpy as np
-
-sample_size = 8
-wrong_train = get_mislabeled(train_loader)
-wrong_val = get_mislabeled(val_loader)
-wrong_train_sample = [wrong_train[i] for i in np.random.randint(0, len(wrong_train), size=sample_size)]
-wrong_val_sample = [wrong_val[i] for i in np.random.randint(0, len(wrong_val), size=sample_size)]
-
-import matplotlib.pyplot as plt
-
-fig, axs = plt.subplots(ncols=sample_size)
-for ax, (img, pred, lbl) in zip(axs, wrong_train_sample):
-    fig.set_size_inches(18, 4)
-    fig.suptitle("Sample of wrong predictions in the training set", fontsize=20)
-    ax.imshow(img[0], cmap="gray")
-    ax.set_title("Predicted: {}\nActual: {}".format(pred, lbl))
-    ax.xaxis.set_visible(False)
-    ax.yaxis.set_visible(False)
-
-fig, axs = plt.subplots(ncols=sample_size)
-for ax, (img, pred, lbl) in zip(axs, wrong_val_sample):
-    fig.set_size_inches(18, 4)
-    fig.suptitle("Sample of wrong predictions in the validation set", fontsize=20)
-    ax.imshow(img[0], cmap="gray")
-    ax.set_title("Predicted: {}\nActual: {}".format(pred, lbl))
-    ax.xaxis.set_visible(False)
-    ax.yaxis.set_visible(False)
-```
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/mnist_wrong_preds_train.png) <!--notebook-skip-line-->
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/mnist_wrong_preds_val.png) <!--notebook-skip-line-->
-
-In this case, it is rather obvious that our MLP network is either too simple or has not been trained long enough to perform really great on this dataset, as can be seen from the fact that some of the mislabeled examples are rather "easy" and should not be a challenge for our neural net.
-As it turns out, moving to the CNN architecture presented in the following section will give a big performance boost.
-
-### Convolutional Neural Network (CNN)
-
-A fundamental issue with the MLP network is that it requires the inputs to be flattened (in the non-batch axes) before they can be processed by the dense layers.
-This means in particular that the spatial structure of an image is largely discarded, and that the values describing it are just treated as a long vector.
-The network then has to figure out the neighborhood relations of pixels from scratch by adjusting its weights accordingly, which seems very wasteful.
-
-A CNN aims to address this problem by using a more structured weight representation.
-Instead of connecting all inputs to all outputs, the characteristic [convolution layer](https://mxnet.incubator.apache.org/api/python/gluon/nn.html#mxnet.gluon.nn.Conv2D) only considers a small neighborhood of a pixel to compute the value of the corresponding output pixel.
-In particular, the spatial structure of the image is preserved, i.e., one can speak of input and output pixels in the first place.
-Only the size of the image may change through convolutions.
-[This article](http://deeplearning.net/software/theano/tutorial/conv_arithmetic.html) gives a good and intuitive explanation of convolutions in the context of deep learning.
-
-The size of the neighborhood that a convolution layer considers for each pixel is usually referred to as *filter size* or *kernel size*.
-The array of weights -- which does not depend on the output pixel location, only on the position within such a neighborhood -- is called *filter* or *kernel*.
-Typical filter sizes range from *3 x 3* to *13 x 13*, which implies that a convolution layer has *far* fewer parameters than a dense layer.
-
-```python
-conv_layer = nn.Conv2D(kernel_size=(3, 3), channels=32, in_channels=16, activation='relu')
-print(conv_layer.params)
-```
-
-`Parameter conv0_weight (shape=(32, 16, 3, 3), dtype=<class 'numpy.float32'>)` <!--notebook-skip-line-->
-
-`Parameter conv0_bias (shape=(32,), dtype=<class 'numpy.float32'>)` <!--notebook-skip-line-->
-
-Filters can be thought of as little feature detectors: in early layers, they learn to detect small local structures like edges, whereas later layers become sensitive to more and more global structures.
-Since images often contain a rich set of such features, it is customary to have each convolution layer employ and learn many different filters in parallel, so as to detect many different image features on their respective scales.
-This stacking of filters, which directly translates to a stacking of output images, is referred to as output *channels* of the convolution layer.
-Likewise, the input can already have multiple channels.
-In the above example, the convolution layer takes an input image with 16 channels and maps it to an image with 32 channels by convolving each of the input channels with a different set of 32 filters and then summing over the 16 input channels.
-Therefore, the total number of filter parameters in the convolution layer is `channels * in_channels * prod(kernel_size)`, which amounts to 4608 in the above example.
-
-Another characteristic feature of CNNs is the usage of *pooling*, i.e., summarizing patches to a single number, to shrink the size of an image as it travels through the layers.
-This step lowers the computational burden of training the network, but the main motivation for pooling is the assumption that it makes the network less sensitive to small translations, rotations or deformations of the image.
-Popular pooling strategies are max-pooling and average-pooling, and they are usually performed after convolution.
-
-The following code defines a CNN architecture called *LeNet*.
-The LeNet architecture is a popular network known to work well on digit classification tasks.
-We will use a version that differs slightly from the original in the usage of `tanh` activations instead of `sigmoid`.
-
-```python
-lenet = nn.HybridSequential(prefix='LeNet_')
-with lenet.name_scope():
-    lenet.add(
-        nn.Conv2D(channels=20, kernel_size=(5, 5), activation='tanh'),
-        nn.MaxPool2D(pool_size=(2, 2), strides=(2, 2)),
-        nn.Conv2D(channels=50, kernel_size=(5, 5), activation='tanh'),
-        nn.MaxPool2D(pool_size=(2, 2), strides=(2, 2)),
-        nn.Flatten(),
-        nn.Dense(500, activation='tanh'),
-        nn.Dense(10, activation=None),
-    )
-```
-
-To get an overview of all intermediate sizes of arrays and the number of parameters in each layer, the `summary()` method can be a great help.
-It requires the network parameters to be initialized, and an input array to infer the sizes.
-
-```python
-lenet.initialize(mx.init.Xavier(), ctx=ctx)
-lenet.summary(nd.zeros((1, 1, 28, 28), ctx=ctx))
-```
-
-```
-Output:
-
---------------------------------------------------------------------------------
-        Layer (type)                                Output Shape         Param #
-================================================================================
-               Input                              (1, 1, 28, 28)               0
-        Activation-1                <Symbol eNet_conv0_tanh_fwd>               0
-        Activation-2                             (1, 20, 24, 24)               0
-            Conv2D-3                             (1, 20, 24, 24)             520
-         MaxPool2D-4                             (1, 20, 12, 12)               0
-        Activation-5                <Symbol eNet_conv1_tanh_fwd>               0
-        Activation-6                               (1, 50, 8, 8)               0
-            Conv2D-7                               (1, 50, 8, 8)           25050
-         MaxPool2D-8                               (1, 50, 4, 4)               0
-           Flatten-9                                    (1, 800)               0
-       Activation-10               <Symbol eNet_dense0_tanh_fwd>               0
-       Activation-11                                    (1, 500)               0
-            Dense-12                                    (1, 500)          400500
-            Dense-13                                     (1, 10)            5010
-================================================================================
-Parameters in forward computation graph, duplicate included
-   Total params: 431080
-   Trainable params: 431080
-   Non-trainable params: 0
-Shared params in forward computation graph: 0
-Unique parameters in model: 431080
---------------------------------------------------------------------------------
-```
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/conv_mnist.png)
-
-**Figure 3:** First conv + pooling layer in LeNet.
-
-Now we train LeNet with similar hyperparameters and procedure as before.
-Note that it is advisable to use a GPU if possible, since this model is significantly more computationally demanding to evaluate and train than the previous MLP.
-
-```python
-trainer = gluon.Trainer(
-    params=lenet.collect_params(),
-    optimizer='sgd',
-    optimizer_params={'learning_rate': 0.04},
-)
-metric = mx.metric.Accuracy()
-num_epochs = 10
-
-for epoch in range(num_epochs):
-    for inputs, labels in train_loader:
-        inputs = inputs.as_in_context(ctx)
-        labels = labels.as_in_context(ctx)
-
-        with autograd.record():
-            outputs = lenet(inputs)
-            loss = loss_function(outputs, labels)
-
-        loss.backward()
-        metric.update(labels, outputs)
-
-        trainer.step(batch_size=inputs.shape[0])
-
-    name, acc = metric.get()
-    print('After epoch {}: {} = {}'.format(epoch + 1, name, acc))
-    metric.reset()
-
-for inputs, labels in val_loader:
-    inputs = inputs.as_in_context(ctx)
-    labels = labels.as_in_context(ctx)
-    metric.update(labels, lenet(inputs))
-print('Validaton: {} = {}'.format(*metric.get()))
-assert metric.get()[1] > 0.985
-```
-
-If all went well, we should see a higher accuracy metric for predictions made using LeNet.
-With this CNN we should be able to correctly predict around 99% of all validation images.
-
-## Summary
-
-In this tutorial, we demonstrated how to use MXNet to solve a standard computer vision problem: classifying images of hand-written digits.
-We showed how to quickly build, train and evaluate models such as MLPs and CNNs with the MXNet Gluon package.
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/gluon/multi_gpu.md b/docs/tutorials/gluon/multi_gpu.md
deleted file mode 100644
index 9a8c06e1f2c5..000000000000
--- a/docs/tutorials/gluon/multi_gpu.md
+++ /dev/null
@@ -1,193 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Multiple GPUs training with Gluon API
-
-In this tutorial we will walk through how one can train deep learning neural networks on multiple GPUs within a single machine. This tutorial focuses on data parallelism as opposed to model parallelism. Data parallelism approach assumes, that you can fit whole your model in a GPU and only training data needs to be partitioned. This is different from model parallelism, where the model is so big, that it doesn't fit into a single GPU, so it needs to be partitioned as well. Model parallelism is not supported by Apache MXNet out of the box, and one has to manually route the data among different devices to achieve model parallelism. Check out [model parallelism tutorial](https://mxnet.incubator.apache.org/versions/master/faq/model_parallel_lstm.html) to learn more about it.
-Here we will focus on implementing data parallel training for a convolutional neural network called LeNet.
-
-## Prerequisites
-
-- Two or more GPUs 
-- CUDA 9 or higher
-- cuDNN v7 or higher
-- Knowledge of how to train a model using Gluon API
-
-## Storing data on GPU
-
-The basic primitive in Apache MXNet to specify a tensor is [NDArray](https://mxnet.incubator.apache.org/api/python/ndarray/sparse.html#module-mxnet.ndarray). When you create NDArray you have to provide the context - the device where this tensor is going to be stored. The context can be either CPU or GPU and both can be indexed: if your machine has multiple GPUs, you can provide an index to specify which GPU to use. By default, CPU context is used, and that means that the tensor will live in main RAM. Below is an example how to create two tensors where one is stored on the first GPU and the second is stored on the second GPU. Notice, that this example will work even when you have one or no GPUs at all. We use [mx.context.num_gpus](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/context.py#L262) to find the number of available GPUs.
-
-```python
-import mxnet as mx
-
-n_gpu = mx.context.num_gpus()
-context = [mx.gpu(0), mx.gpu(1)] if n_gpu >= 2 else \
-          [mx.gpu(), mx.gpu()] if n_gpu == 1 else \
-          [mx.cpu(), mx.cpu()]
-
-a = mx.nd.array([1, 2, 3], ctx=context[0])
-b = mx.nd.array([5, 6, 7], ctx=context[1])
-```
-
-The next step would be to do operations on these 2 NDArrays. But, unfortunately, if we try to do any operation involved both these arrays, Apache MXNet will return an error: `Check failed: e == cudaSuccess CUDA: an illegal memory access was encountered`. This error is returned because we tried to use arrays that are stored on different contexts: Apache MXNet wants users to explicitly control memory allocation and doesn't automatically copy data between GPUs. If we want to do an operation on these arrays we have to have them in the same GPU. The result of the operation is going to be also stored on that GPU as well.
-
-We can manually copy data between GPUs using [as_in_context method](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html?#mxnet.ndarray.NDArray.as_in_context). We can get the current context of an NDArray via [context property](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html?#mxnet.ndarray.NDArray.context).
-
-```python
-c = a + b.as_in_context(a.context)
-```
-
-Using this example, we have learnt that we can perform operations with NDArrays only if they are stored on the same GPU. So, how can we split the data between GPUs, but use the same model for training? We will answer this question in the next section.
-
-## Storing the network on multiple GPUs
-
-When you create a network using [Blocks](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.Block) the parameters of blocks are also stored in NDArrays. When you initialize your network, you have to specify which context you are going to use for the underlying NDArrays. The feature of the [initialize method](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.Block.initialize) is that it can accept the list of contexts, meaning that you can provide more than one context to store underlying parameters. In the example below, we create the LeNet network and initialize it to be stored on GPU(0) and GPU(1) simultaneously. Each GPU will receive its own copy of the parameters:
-
-```python
-from mxnet import init
-from mxnet.gluon import nn
-
-net = nn.Sequential()
-net.add(nn.Conv2D(channels=6, kernel_size=5, activation='relu'),
-        nn.MaxPool2D(pool_size=2, strides=2),
-        nn.Conv2D(channels=16, kernel_size=3, activation='relu'),
-        nn.MaxPool2D(pool_size=2, strides=2),
-        nn.Flatten(),
-        nn.Dense(120, activation="relu"),
-        nn.Dense(84, activation="relu"),
-        nn.Dense(10))
-
-net.initialize(init=init.Xavier(), ctx=context)
-```
-
-The actual initialization will happen once we do the first forward pass on the network, but at this stage Apache MXNet knows that we are expecting parameters of the network to be on both GPUs.
-
-## Multiple GPUs training schema
-
-At this moment, we have learnt how to define NDArrays in different contexts and that a network can be initialized on two GPUs at the same time.
-
-To do multiple GPU training with a given batch of the data, we divide the examples in the batch into number of portions equal to the number of GPUs we use and distribute one to each GPU. Then, each GPU will individually calculate the local gradient of the model parameters based on the batch subset it was assigned and the model parameters it maintains. Next, we sum together the local gradients on the GPUs to get the current batch stochastic gradient. After that, each GPU uses this batch stochastic gradient to update the complete set of model parameters that it maintains. Figure below depicts the batch stochastic gradient calculation using data parallelism and two GPUs.
-
-![data-parallel](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/data-parallel.svg)
-
-This approach allows us to avoid the limitation of doing operations on different GPUs - we move subsets of data to each GPU and the operations are happening inside each individual GPU only. After that we aggregate the resulting gradients and each GPU receives a copy of the gradients to do model parameters update.
-
-Using that approach, knowing a way to move data between contexts and how to initialize a model on multiple contexts, we already know everything that is needed to do multiple GPU training. But Apache MXNet also provides us a convenient method to distribute data between multiple GPUs, which we are going to cover in the section below.
-
-## Splitting data between GPUs
-
-Apache MXNet provides a utility method [gluon.utils.split_and_load](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.utils.split_and_load) to split the data between multiple contexts. The result of the method's call is a list of NDArrays each of which is stored on a separate context provided in the `ctx_list` argument. The code below demonstrates how to use the method:
-
-```python
-data = mx.random.uniform(shape=(100, 10))
-result = mx.gluon.utils.split_and_load(data, ctx_list=context)
-```
-
-If we explore the result, we will notice, that `split_and_load` method divided the data in two chunks of the same shape `(50, 10)`. If the number of elements is uneven, we have to specify `even_split=False` to instruct the method to do uneven split.
-
-At this point we are ready to assemble a complete multiple GPUs training example.
-
-## Multiple GPUs classification of MNIST images
-
-In the first step, we are going to load the MNIST images and use [ToTensor](https://mxnet.apache.org/api/python/gluon/data.html#mxnet.gluon.data.vision.transforms.ToTensor) to convert the format of the data from `height x width x channel` to `channel x height x width` and divide it by 255.
-
-```python
-train_data = mx.gluon.data.vision.MNIST(train=True).transform_first(mx.gluon.data.vision.transforms.ToTensor())
-val_data = mx.gluon.data.vision.MNIST(train=False).transform_first(mx.gluon.data.vision.transforms.ToTensor())
-```
-
-The next step is to create a [DataLoader](https://mxnet.incubator.apache.org/api/python/gluon/data.html#mxnet.gluon.data.DataLoader) which constructs batches from the dataset. We create one for the training and one for the validation datasets.
-
-```python
-batch_size = 128
-train_loader = mx.gluon.data.DataLoader(train_data, shuffle=True, batch_size=batch_size)
-val_loader = mx.gluon.data.DataLoader(val_data, shuffle=False, batch_size=batch_size)
-```
-
-After that we define the [Trainer](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#trainer) that defines the optimization algorithm to be used and hyperparameters as well as the [Loss](https://mxnet.incubator.apache.org/api/python/gluon/loss.html#mxnet.gluon.loss.SoftmaxCrossEntropyLoss) function and a [metric](https://mxnet.incubator.apache.org/api/python/metric/metric.html#mxnet.metric.Accuracy) to track:
-
-```python
-trainer = mx.gluon.Trainer(
-    params=net.collect_params(),
-    optimizer='sgd',
-    optimizer_params={'learning_rate': 0.04},
-)
-
-metric = mx.metric.Accuracy()
-loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss()
-```
-
-After these preparations we are ready to define the training loop. In the training loop we will split the data between GPUs, pass them all through the individual GPU, do the backward step on each loss to accumulate the gradients, and call [trainer.step](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.Trainer.step) to actually update the parameters of the model:
-
-```python
-num_epochs = 10
-
-for epoch in range(num_epochs):
-    for inputs, labels in train_loader:
-        actual_batch_size = inputs.shape[0]
-        # Split data among GPUs. Since split_and_load is a deterministic function
-        # inputs and labels are going to be split in the same way between GPUs.
-        inputs = mx.gluon.utils.split_and_load(inputs, ctx_list=context, even_split=False)
-        labels = mx.gluon.utils.split_and_load(labels, ctx_list=context, even_split=False)
-
-        # The forward pass and the loss computation need to be wrapped
-        # in a `record()` scope to make sure the computational graph is
-        # recorded in order to automatically compute the gradients
-        # during the backward pass.
-        with mx.autograd.record():
-            outputs = [net(input_slice) for input_slice in inputs]
-            losses = [loss_function(o, l) for o, l in zip(outputs, labels)]
-
-        # Iterate over losses to compute gradients for each input slice
-        for loss in losses:
-            loss.backward()
-
-        # update metric for each output
-        for l, o in zip(labels, outputs):
-            metric.update(l, o)
-
-        # Update the parameters by stepping the trainer; the batch size
-        # is required to normalize the gradients by `1 / batch_size`.
-        trainer.step(batch_size=actual_batch_size, ignore_stale_grad=True)
-
-    # Print the evaluation metric and reset it for the next epoch
-    name, acc = metric.get()
-    print('After epoch {}: {} = {}'.format(epoch + 1, name, acc))
-    metric.reset()
-```
-
-If you run this example and run `nvidia-smi` tool from NVIDIA, you will notice that both GPUs are used to perform calculations.
-
-## Advanced topic
-
-As we mentioned above, the gradients for each data split are calculated independently and then later summed together. We haven't mentioned yet where exactly this aggregation happens.
-
-Apache MXNet uses [KVStore](https://mxnet.incubator.apache.org/versions/master/api/scala/kvstore.html) - a virtual place for data sharing between different devices, including machines and GPUs. The KVStore is responsible for storing and, by default, aggregating the gradients of the model. The physical location of the KVStore is defined when we create a [Trainer](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/gluon.html#mxnet.gluon.Trainer) and by default is set to `device`, which mean it will aggregate gradients and update weights on GPUs. The actual data is distributed in round-robin fashion among available GPUs per block. This statement means two things, which are important to know from practical perspective.
-
-The first thing is there is an additional memory allocation that happens on GPUs that is not directly related to your data and your model to store auxiliary information for GPUs sync-up. Depending on the complexity of your model, the amount of required memory can be significant, and you may even experience CUDA out of memory exceptions. If that is the case, and you cannot decrease batch size anymore, you may want to consider switching `KVStore` storage to RAM by setting `kvstore` argument to `local` during instantiation of the `Trainer`. Often this decreases the wall-clock performance time of your model, because the gradients and parameters would need to be copied to RAM and back.
-
-The second thing is that since  the auxiliary information is distributed among GPUs in round-robin fashion on per block level, `KVStore` may use more memory on some GPUs and less on others. For example, if your model has a very big embedding layer, you may see that your first GPU uses 90% of your memory while others use only 50%. That affects how much data you actually can load in a single batch, because the data between devices is split evenly. If that is the case and you have to keep or increase your batch size, you may want to switch to the `local` mode.
-
-## Conclusion
-
-With Apache MXNet training using multiple GPUs doesn't need a lot of extra code. To do the multiple GPUs training you need to initialize a model on all GPUs, split the batches of data into separate splits where each is stored on a different GPU and run the model separately on every split. The synchronization of gradients and parameters between GPUs is done automatically by Apache MXNet.
-
-## Recommended Next Steps
-
-* Check out our two video tutorial on improving your code performance. In the [first video](https://www.youtube.com/watch?v=n8tN6pRZBdE) we explain how to visualize the performance, and in the [second video](https://www.youtube.com/watch?v=Cqo7FPftNyo) we show how to optimize it.
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
\ No newline at end of file
diff --git a/docs/tutorials/gluon/naming.md b/docs/tutorials/gluon/naming.md
deleted file mode 100644
index c2293a9644e1..000000000000
--- a/docs/tutorials/gluon/naming.md
+++ /dev/null
@@ -1,210 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Naming of Gluon Parameter and Blocks
-
-In Gluon, each [`Parameter`](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/gluon.html#mxnet.gluon.Parameter) has a name and each [`Block`](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/gluon.html#mxnet.gluon.Block) has a prefix. `Parameter` names are specified by users, and `Block` names can be either specified by users or automatically created.
-
-In this tutorial we talk about the best practices on naming. First, let's import MXNet and Gluon:
-
-```python
-from __future__ import print_function
-import mxnet as mx
-from mxnet import gluon
-```
-
-## Naming Blocks
-
-When you create a `Block`, you can assign it a prefix:
-
-```python
-mydense = gluon.nn.Dense(100, prefix='mydense_')
-print(mydense.prefix)
-print(mydense.name)
-```
-
-The prefix of the `Block` acts like its name. As you can see from the example above, the only difference is that the last `_` is removed from the `Block`'s name.
-
-When no prefix is given, `Gluon` will automatically generate one:
-
-```python
-dense0 = gluon.nn.Dense(100)
-dense0.prefix
-```
-
-When you create more Blocks of the same kind, they will be named with an incrementing indices to avoid naming collision. To illustrate that, let's create a new `Dense` layer without specifying the prefix:
-
-```python
-dense1 = gluon.nn.Dense(100)
-dense1.prefix
-```
-
-As we can see prefixes of `dense0` and `dense1` blocks are different.
-
-## Naming Parameters
-
-Parameters within a `Block` will be named by prepending the prefix of the `Block` to the name of the `Parameter`:
-
-```python
-dense0.collect_params()
-```
-
-As we can see, both `weight` and `bias` parameters of the `Dense` block have the same prefix `dense0_`.
-
-If you create a new `Parameter`, for example, when you [create a custom block](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/custom_layer.html#parameters-of-a-custom-layer), you have to specify its name as Gluon cannot set a default one.
-
-## Name scopes
-
-To manage the names of nested Blocks, each `Block` has a `name_scope` attached to it. All Blocks created within a name scope will have its parent `Block`'s prefix prepended to its name. We can retrieve the `name_scope` by using [`.name_scope()`](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/gluon.html#mxnet.gluon.Block.name_scope) method of the `Block`.
-
-Let's demonstrate this by first defining a simple neural network:
-
-```python
-class Model(gluon.Block):
-    def __init__(self, **kwargs):
-        super(Model, self).__init__(**kwargs)
-        with self.name_scope():
-            self.dense0 = gluon.nn.Dense(20)
-            self.dense1 = gluon.nn.Dense(20)
-            self.mydense = gluon.nn.Dense(20, prefix='mydense_')
-
-    def forward(self, x):
-        x = mx.nd.relu(self.dense0(x))
-        x = mx.nd.relu(self.dense1(x))
-        return mx.nd.relu(self.mydense(x))
-```
-
-Here we defined three `Dense` layers, out of which only the last one has a prefix. Now, let's create and initialize our network.
-
-```python
-model0 = Model()
-model0.initialize()
-model0(mx.nd.zeros((1, 20)))
-print(model0.prefix)
-print(model0.dense0.prefix)
-print(model0.dense1.prefix)
-print(model0.mydense.prefix)
-```
-
-- Note that `model0.dense0` is named as `model0_dense0_` instead of `dense0_`.
-
-- As before, to avoid naming collision, the next `Dense` layer got automatic prefix `dense1_`, and its full prefix is `model0_dense1_`.
-
-- Also note that although we specified `mydense_` as prefix for `model.mydense`, its parent's prefix is automatically prepended to generate the prefix `model0_mydense_`.
-
-If we create another instance of `Model`, it will be given a different name like shown before in the example with the `Dense` layer.
-
-```python
-model1 = Model()
-print(model1.prefix)
-print(model1.dense0.prefix)
-print(model1.dense1.prefix)
-print(model1.mydense.prefix)
-```
-
-Note that `model1.dense0` is still named as `dense0_` instead of `dense2_`, following `Dense` layers in previously created `model0`. This is because each instance of model's name scope is independent of each other.
-
-**It is recommended that you manually specify a prefix for the top level `Block`, i.e. `model = Model(prefix='mymodel_')`, to avoid potential confusion in naming.**
-
-The same principle applies to container blocks like [`Sequential`](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/gluon.html#mxnet.gluon.nn.Sequential). `name_scope` can be used inside as well as outside of `__init__`. It is recommended to use `name_scope` with container blocks as the naming of the parameters inside of it tends to be more clear.
-
-```python
-net = gluon.nn.Sequential()
-with net.name_scope():
-    net.add(gluon.nn.Dense(20))
-    net.add(gluon.nn.Dense(20))
-print(net.prefix)
-print(net[0].prefix)
-print(net[1].prefix)
-```
-
-Models loaded from [`gluon.model_zoo`](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/model_zoo.html#module-mxnet.gluon.model_zoo) behave in a similar way:
-
-```python
-net = gluon.nn.Sequential()
-with net.name_scope():
-    net.add(gluon.model_zoo.vision.alexnet(pretrained=True))
-    net.add(gluon.model_zoo.vision.alexnet(pretrained=True))
-print(net.prefix, net[0].prefix, net[1].prefix)
-```
-
-## Saving and loading of parameters
-
-Because `model0` and `model1` have different prefixes, their parameters also have different names:
-
-```python
-print(model0.collect_params(), '\n')
-print(model1.collect_params())
-```
-
-As a result, if you try to save parameters of `model0` and then load it into `model1`, you'll get an error due to unmatching names:
-
-```python
-model0.collect_params().save('model.params')
-try:
-    model1.collect_params().load('model.params', mx.cpu())
-except Exception as e:
-    print(e)
-```
-
-To solve this problem, we use `save_parameters`/`load_parameters` instead of `collect_params` and `save`/`load`. The `save_parameters` method uses model structure instead of parameter names to match parameters.
-
-```python
-model0.save_parameters('model.params')
-model1.load_parameters('model.params')
-print(mx.nd.load('model.params').keys())
-```
-
-## Replacing Blocks in networks and fine-tuning
-
-Sometimes you may want to load a pretrained model, and replace certain Blocks in it for fine-tuning. For example, the [`AlexNet`](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/model_zoo.html#vision) model in the model zoo has 1000 output dimensions, but maybe you have only 100 classes in your application. Let's see how to change the number of output dimensions from 1000 to 100. 
-
-The first step is to load a pretrained `AlexNet`:
-
-```python
-alexnet = gluon.model_zoo.vision.alexnet(pretrained=True)
-print(alexnet.output)
-print(alexnet.output.prefix)
-```
-
-- In Gluon Model Zoo, all image classification models follow the format where the feature extraction layers are named `features` while the output layer is named `output`.
-
-- Note that the output layer is a `Dense` block with 1000 dimension outputs.
-
-- Some networks, including `AlexNet`, provide a way to directly specify number of output classes by using `classes` argument. We will still show how to do it in a replacing block manner for demonstration purposes.
-
-To change the output to 100 dimension, we replace it with a new `Block` which has only 100 units.
-
-```python
-with alexnet.name_scope():
-    alexnet.output = gluon.nn.Dense(100)
-alexnet.output.initialize()
-print(alexnet.output)
-print(alexnet.output.prefix)
-```
-
-## Conclusion
-
-Each `Block` has a `prefix` and each `Parameter` has a `name`. Gluon can automatically generate unique prefixes for blocks, but it is up to you to specify Parameter's name if you create a `Parameter` yourself. Prefixes act like name spaces and create a hierarchy following the nesting of blocks in your model. To save and load model parameters, it is better to use `save_parameters`/`load_parameters` methods on the parent `Block`.
-
-## Recommended Next Steps
-
-- Learn more about [model serialization](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/save_load_params.html) in Gluon.
-
-- [Create custom blocks](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/custom_layer.html) with your own parameters.
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/gluon/ndarray.md b/docs/tutorials/gluon/ndarray.md
deleted file mode 100644
index f1503aec18db..000000000000
--- a/docs/tutorials/gluon/ndarray.md
+++ /dev/null
@@ -1,162 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# NDArray - Scientific computing on CPU and GPU
-
-NDArray is a tensor data structure similar to numpy's multi-dimensional array.
-In addition, it supports asynchronous computation on CPU and GPU.
-
-First, let's import MXNet:
-
-```python
-from __future__ import print_function
-import numpy as np
-import mxnet as mx
-```
-
-## Creating NDArray
-
-There are many ways to create NDArray.
-
-Construct from (nested) list:
-```python
-x = mx.nd.array([[1, 2, 3], [4, 5, 6]])
-print(x)
-```
-
-Construct from numpy array:
-```python
-x_numpy = np.ones((2, 3))
-x = mx.nd.array(x_numpy)
-print(x)
-```
-
-Array construction routines:
-```python
-# create an 2x3 array of ones
-x = mx.nd.ones((2, 3))
-print(x)
-# create an 2x3 array of zeros
-x = mx.nd.zeros((2, 3))
-print(x)
-# create an 1d-array of 0 to 5 and reshape to 2x3
-x = mx.nd.arange(6).reshape((2, 3))
-print(x)
-```
-
-You can convert an NDArray to numpy array to retrieve its data with `.asnumpy()`:
-```python
-z = x.asnumpy()
-print(z)
-```
-
-## Basic attributes
-
-NDArray has some basic attributes that you often want to query:
-
-**NDArray.shape**: The dimensions of the array. It is a tuple of integers
-indicating the length of the array along each axis. For a matrix with `n` rows
-and `m` columns, its `shape` will be `(n, m)`.
-
-```python
-print('x.shape:', x.shape)
-```
-
-**NDArray.dtype**: A `numpy` _type_ object describing the type of array
-elements.
-
-```python
-print('x.dtype:', x.dtype)
-```
-
-**NDArray.size**: the total number of components in the array - equals to the
-product of the components of its `shape`
-
-```python
-print('x.size:', x.size)
-```
-
-**NDArray.context**: The device on which this array is stored, e.g. `mx.cpu()`
-or `mx.gpu(1)`.
-
-```python
-print('x.context:', x.context)
-```
-
-## NDArray Operations
-
-NDArray supports a wide range of operations. Simple operations can be called
-with python syntax:
-
-```python
-x = mx.nd.array([[1, 2], [3, 4]])
-y = mx.nd.array([[4, 3], [2, 1]])
-print(x + y)
-```
-
-You can also call operators from the `mxnet.ndarray` (or `mx.nd` for short) name space:
-
-```python
-z = mx.nd.add(x, y)
-print(z)
-```
-
-You can also pass additional flags to operators:
-
-```python
-z = mx.nd.sum(x, axis=0)
-print('axis=0:', z)
-z = mx.nd.sum(x, axis=1)
-print('axis=1:', z)
-```
-
-## Using GPU
-
-Each NDArray lives on a `Context`. MXNet supports `mx.cpu()` for CPU and `mx.gpu(0)`,
-`mx.gpu(1)`, etc for GPU. You can specify context when creating NDArray:
-
-```python
-# creates on CPU (the default).
-# Replace mx.cpu() with mx.gpu(0) if you have a GPU.
-x = mx.nd.zeros((2, 2), ctx=mx.cpu())
-print(x)
-```
-
-```python
-x = mx.nd.array([[1, 2], [3, 4]], ctx=mx.cpu())
-print(x)
-```
-
-You can copy arrays between devices with `.copyto()`:
-
-```python
-# Copy x to cpu. Replace with mx.gpu(0) if you have GPU.
-y = x.copyto(mx.cpu())
-print(y)
-```
-
-```python
-# Copy x to another NDArray, possibly on another Context.
-y = mx.nd.zeros_like(x)
-x.copyto(y)
-print(y)
-```
-
-See the [Advanced NDArray tutorial](../basic/ndarray.md) for a more detailed
-introduction to NDArray API.
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/gluon/performance.md b/docs/tutorials/gluon/performance.md
deleted file mode 100644
index 27a7e9ee6517..000000000000
--- a/docs/tutorials/gluon/performance.md
+++ /dev/null
@@ -1,483 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Gluon Performance Tips & Tricks
-
-Compared to traditional machine learning methods, the field of deep-learning has increased model accuracy across a wide range of tasks, but it has also increased the amount of computation required for model training and inference. Specialised hardware chips, such as GPUs and FPGAs, can speed up the execution of networks, but it can sometimes be hard to write code that uses the hardware to its full potential. We will be looking at a few simple tips and trick in this tutorial that you can use to speed up training and ultimately save on training costs. You'll find most of these tips and tricks useful for inference too.
-
-We'll start by writing some code to train an image classification network for the CIFAR-10 dataset, and then benchmark the throughput of the network in terms of samples processed per second. After some performance analysis, we'll identify the bottlenecks (i.e. the components limiting throughput) and improve the training speed step-by-step. We'll bring together all the tips and tricks at the end and evaluate our performance gains.
-
-
-```python
-from __future__ import print_function
-import multiprocessing
-import time
-import mxnet as mx
-import numpy as np
-```
-
-An Amazon EC2 p3.2xlarge instance was used to benchmark the code in this tutorial. You are likely to get different results and find different bottlenecks on other hardware, but these tips and tricks should still help improve training speed for bottleneck components. A GPU is recommended for this tutorial.
-
-
-```python
-ctx = mx.gpu() if mx.test_utils.list_gpus() else mx.cpu()
-print("Using {} context.".format(ctx))
-```
-
-    Using gpu(0) context.
-
-
-We'll use the `CIFAR10` dataset provided out-of-the-box with Gluon.
-
-
-```python
-dataset = mx.gluon.data.vision.CIFAR10(train=True)
-print('{} samples'.format(len(dataset)))
-```
-
-    50000 samples
-
-
-So we can learn how to identify training bottlenecks, let's intentionally introduce a bottleneck by adding a short `sleep` into the data loading pipeline. We transform each 32x32 CIFAR-10 image to 224x224 so we can use it with the ResNet-50 network designed for ImageNet. [CIFAR-10 specific ResNet networks](https://gluon-cv.mxnet.io/api/model_zoo.html#gluoncv.model_zoo.get_cifar_resnet) exist but we use the more standard ImageNet variants in this example.
-
-
-```python
-def transform_fn(x):
-    time.sleep(0.01)  # artificial slow-down
-    image = mx.image.imresize(x, w=224, h=224)
-    return image.astype('float32').transpose((2, 0, 1))
-
-dataset = dataset.transform_first(transform_fn)
-```
-
-Setting our batch size to 16, we can create the `DataLoader`.
-
-
-```python
-batch_size = 16
-dataloader = mx.gluon.data.DataLoader(dataset,
-                                      batch_size=batch_size,
-                                      shuffle=True,
-                                      last_batch="discard")
-print('{} batches'.format(len(dataloader)))
-```
-
-    3125 batches
-
-
-Up next, we create all of the other components required for training, such as the network, the loss function, the evaluation metric and parameter trainer.
-
-
-```python
-net = mx.gluon.model_zoo.vision.resnet50_v2(pretrained=False, ctx=ctx)
-net.initialize(mx.init.Xavier(magnitude=2.3), ctx=ctx)
-loss_fn = mx.gluon.loss.SoftmaxCrossEntropyLoss()
-metric = mx.metric.Accuracy()
-learning_rate = 0.001
-trainer = mx.gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': learning_rate})
-```
-
-## Initial Benchmark
-
-As a starting point, let's benchmark the throughput of our training loop: calculating the average samples per second across 25 iterations, where each iteration is a batch of 16 samples. We'll run a single forward pass through the network before starting our benchmark timer to avoid including shape inference and lazy initialization in the throughput calculations.
-
-
-```python
-def single_forward(net, dataloader, dtype='float32'):
-    data, label = next(iter(dataloader))
-    data = data.astype(dtype)
-    data = data.as_in_context(ctx)
-    pred = net(data)
-    pred.wait_to_read()
-```
-
-
-```python
-single_forward(net, dataloader)
-iters = 25
-num_samples = 0
-num_iters = 0
-start_time = time.time()
-for iter_idx, (data, label) in enumerate(dataloader):
-    num_samples += data.shape[0]
-    num_iters += 1
-    data = data.as_in_context(ctx)
-    label = label.as_in_context(ctx)
-    with mx.autograd.record():
-        pred = net(data)
-        loss = loss_fn(pred, label)
-    loss.backward()
-    trainer.step(data.shape[0])
-    metric.update(label, pred)
-    print('.', end='')
-    if num_iters >= iters:
-        break
-mx.nd.waitall()
-end_time = time.time()
-total_time = end_time - start_time
-print('\n')
-print('average iterations/sec: {:.4f}'.format(num_iters/total_time))
-print('average samples/sec: {:.4f}'.format(num_samples/total_time))
-```
-
-    .........................
-    
-    average iterations/sec: 4.4936
-    average samples/sec: 71.8975
-
-
-Although ~70 samples per second might sound respectable, let's see if we can do any better by identifying the bottleneck in the training loop and optimizing that component. A significant amount of time can be wasted by optimizing components that aren't bottlenecks.
-
-## Identifying the bottleneck
-
-Monitoring the CPU (with `top`) and GPU utilization (with `nvidia-smi`) provide clues as to where potential bottlenecks lie. With the example above, when simultaneously running these monitoring tool, you might spot a single process on the CPU fixed at ~100% utilization while the GPU utilization behaves erratically and often falls to ~0%. Seeing behaviour like can indicate the CPU is struggling to process data and the GPU is being starved of data.
-
-MXNet's Profiler is another highly recommended tool for identifying bottlenecks, since it gives timing data for individual MXNet operations. Check out [this comprehensive tutorial](https://mxnet.incubator.apache.org/versions/master/tutorials/python/profiler.html) for more details. As a simpler form of analysis, we will split our training loop into two common components:
-
-1. Data Loading
-2. Network Execution (forward and backward passes)
-
-We define two function to independently benchmark these components: `benchmark_dataloader` and `benchmark_network`.
-
-
-```python
-def benchmark_dataloader(dataloader, iters=25):
-    num_samples = 0
-    num_iters = 0
-    start_time = time.time()
-    startup_time = None
-    for iter_idx, sample in enumerate(dataloader):
-        if startup_time is None:
-            startup_time = time.time()
-        num_samples += sample[0].shape[0]
-        num_iters += 1
-        if num_iters >= iters:
-            break
-        print('.', end='')
-    end_time = time.time()
-    total_time = end_time - start_time
-    total_startup_time = startup_time - start_time
-    total_iter_time = end_time - startup_time
-    print('\n')
-    print('total startup time: {:.4f}'.format(total_startup_time))
-    print('average iterations/sec: {:.4f}'.format(num_iters/total_iter_time))
-    print('average samples/sec: {:.4f}'.format(num_samples/total_iter_time))
-    
-    
-def benchmark_network(data, label, net, loss_fn, trainer, iters=25):
-    num_samples = 0
-    num_iters = 0
-    mx.nd.waitall()
-    start_time = time.time()
-    for iter_idx in range(iters):
-        num_samples += data.shape[0]
-        num_iters += 1
-        with mx.autograd.record():
-            pred = net(data)
-            loss = loss_fn(pred, label)
-        loss.backward()
-        trainer.step(data.shape[0])
-        mx.nd.waitall()
-        if num_iters >= iters:
-            break
-        print('.', end='')
-    end_time = time.time()
-    total_time = end_time - start_time
-    print('\n')
-    print('average iterations/sec: {:.4f}'.format(num_iters/total_time))
-    print('average samples/sec: {:.4f}'.format(num_samples/total_time))
-```
-
-Our `benchmark_dataloader` function just loops through the `DataLoader` for a given number of iterations: it doesn't transfer the data to the correct context or pass it to the network. Our `benchmark_network` function just performs a forward and backward pass on an identical (and pre-transferred) batch of data: it doesn't require new data to be loaded. We'll run both of these functions now.
-
-
-```python
-print('\n', '### benchmark_dataloader', '\n')
-benchmark_dataloader(dataloader)
-print('\n', '### benchmark_network', '\n')
-data, label = next(iter(dataloader))
-data = data.as_in_context(ctx)
-label = label.as_in_context(ctx)
-benchmark_network(data, label, net, loss_fn, trainer)
-```
-
-    
-     ### benchmark_dataloader 
-    
-    ........................
-    
-    total startup time: 0.1697
-    average iterations/sec: 6.2201
-    average samples/sec: 99.5217
-    
-     ### benchmark_network 
-    
-    ........................
-    
-    average iterations/sec: 15.1908
-    average samples/sec: 243.0525
-
-
-Our data loading pipeline appears to be the bottleneck for training: ~100 samples/second compared with ~250 samples/second for network execution. One limiting factor could be disk throughput when reading samples (using a SSD instead of HDD can help with this), but in this case we intentionally added a delay in data transformation. Augmentation can often be a bottleneck in training if the following trick isn't applied.
-
-## Tips & Tricks #1: Use multiple workers on `DataLoader`
-
-In the previous section, we established that the data loading component of the training loop was the bottleneck. Instead of simply removing the artificial delay, let's assume it was some pre-processing or augmentation step that couldn't be removed. We found that the CPU utilization was fixed at 100%, but this was just for a single core. Usually machines have multiple cores and with one easy trick we can leverage more CPU cores to pre-process the data. Setting `num_workers` on the `DataLoader` will result in multiple workers being used to preprocess the data. We can use `multiprocessing.cpu_count()` to find the number of CPU cores available on the machine, and we save 1 core for the main thread.
-
-
-```python
-num_workers = multiprocessing.cpu_count() - 1
-dataloader = mx.gluon.data.DataLoader(dataset,
-                                      batch_size=batch_size,
-                                      shuffle=True,
-                                      last_batch="discard",
-                                      num_workers=num_workers)
-print('Using {} workers for DataLoader.'.format(num_workers))
-```
-
-    Using 7 workers for DataLoader.
-
-
-We benchmark the two main components once again:
-
-
-```python
-print('\n', '### benchmark_dataloader', '\n')
-benchmark_dataloader(dataloader)
-print('\n', '### benchmark_network', '\n')
-data, label = next(iter(dataloader))
-data = data.as_in_context(ctx)
-label = label.as_in_context(ctx)
-benchmark_network(data, label, net, loss_fn, trainer, iters=10)
-```
-
-    
-     ### benchmark_dataloader 
-    
-    ........................
-    
-    total startup time: 0.1935
-    average iterations/sec: 46.2375
-    average samples/sec: 739.7994
-    
-     ### benchmark_network 
-    
-    .........
-    
-    average iterations/sec: 14.8614
-    average samples/sec: 237.7819
-
-
-Our data loading pipeline is no longer the bottleneck for training throughput: ~700 samples per second versus ~250 samples per second for network execution as before. Check out [NVIDIA DALI](https://docs.nvidia.com/deeplearning/sdk/dali-developer-guide/docs/examples/mxnet/gluon.html) if you need to futher optimize your data pipeline. We can now focus our attention on improving the network throughput.
-
-## Tips & Tricks #2: Hybridize the network
-
-Gluon networks run in imperative mode by default, executing `NDArray` operations as the lines of code are stepped through one by one. While in imperative mode, debugging is often simplified and more flexible networks can be defined (using Python control flow). But this comes at a slight cost in terms of throughput. Since the network doesn't know what line of code will be run next, the network operations cannot be optimized and additional memory allocations are required (which all takes time). Most networks though can be written as `HybridBlocks` and, with the `hybridize` method, can be converted to symbolic mode execution. We can expect throughput to increase slightly in this mode. Watch out though: debugging can get more complicated. Setting `static_alloc=True` and `static_shape=True` reduce the number of memory allocations required while training. Once again, we run `single_forward` to force the hybridization process to occur before benchmarking.
-
-
-```python
-net.hybridize(static_alloc=True, static_shape=True)
-single_forward(net, dataloader)
-```
-
-
-```python
-print('\n', '### benchmark_dataloader', '\n')
-benchmark_dataloader(dataloader)
-print('\n', '### benchmark_network', '\n')
-data, label = next(iter(dataloader))
-data = data.as_in_context(ctx)
-label = label.as_in_context(ctx)
-benchmark_network(data, label, net, loss_fn, trainer)
-```
-
-    
-     ### benchmark_dataloader 
-    
-    ........................
-    
-    total startup time: 0.1847
-    average iterations/sec: 46.3004
-    average samples/sec: 740.8072
-    
-     ### benchmark_network 
-    
-    ........................
-    
-    average iterations/sec: 16.5738
-    average samples/sec: 265.1812
-
-
-We can see quite a modest ~10% increase in throughput after hybridization. Gains can depend on a number of factors including the network architecture and the batch size used (a larger increase expected for smaller batch size). Our network execution is still the bottleneck in training so let's focus on that again.
-
-## Tips & Tricks #3: Increase the batch size
-
-GPUs are optimized for high throughput and they do this by performing many operations in parallel. Our NVIDIA Tesla V100 GPU utilization peaks at ~85% while running the last example. Although this is already quite high, there's still room improvement given this metric shows the percentage of time *at least one* kernel is running (over the last 1 second by default). Given we have enough memory available, the throughput of the network can be improved by increasing the batch size since more samples are processed in parallel. At this stage we're using approximately 1/4 of the available GPU memory, so let's increase out batch size by a factor of 4, from 16 to 64. Changing the batch size does have some side effects though. Using the same optimizer with same hyperparameters often leads to slower convergence. More gradients, from more samples, are averaged which leads to a smaller variance in the batch gradient overall. One simple trick to mitigate this is to increase the learning rate by the same factor: so in this case, from 0.001 to 0.004.
-
-
-```python
-batch_size = batch_size * 4
-print('batch_size: {}'.format(batch_size))
-learning_rate = learning_rate * 4
-print('learning_rate: {}'.format(learning_rate))
-dataloader = mx.gluon.data.DataLoader(dataset,
-                                            batch_size=batch_size,
-                                            shuffle=True,
-                                            last_batch="discard",
-                                            num_workers=num_workers)
-trainer = mx.gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': learning_rate})
-single_forward(net, dataloader)
-```
-
-    batch_size: 64
-    learning_rate: 0.004
-
-
-
-```python
-print('\n', '### benchmark_dataloader', '\n')
-benchmark_dataloader(dataloader)
-print('\n', '### benchmark_network', '\n')
-data, label = next(iter(dataloader))
-data = data.as_in_context(ctx)
-label = label.as_in_context(ctx)
-benchmark_network(data, label, net, loss_fn, trainer)
-```
-
-    
-     ### benchmark_dataloader 
-    
-    ........................
-    
-    total startup time: 0.7195
-    average iterations/sec: 11.7149
-    average samples/sec: 749.7521
-    
-     ### benchmark_network 
-    
-    ........................
-    
-    average iterations/sec: 5.5370
-    average samples/sec: 354.3710
-
-
-Once again, we see improvements in throughput. ~30% higher this time. Checking GPU memory usage, we still have room to increase the batch size higher than 64 (on NVIDIA Tesla V100). When the batch size starts to reach very large numbers (>512), simple tricks such as linear scaling of the learning rate might be insufficient for maintaining good convergence. Consider using a [warm-up learning rate schedule](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/learning_rate_schedules_advanced.html) and changing to specialized optimizers such as [LBSGD](https://mxnet.incubator.apache.org/api/python/optimization/optimization.html#mxnet.optimizer.LBSGD). 
-
-## Tips & Tricks #4: Using Mixed-Precision (`float32` and `float16`)
-
-Model execution is still our bottleneck, so let's try out a new trick called [mixed precision](https://mxnet.incubator.apache.org/versions/master/faq/float16.html) training. Some recent GPUs have cores that are optimized for 'half-precision' (i.e. `float16`) operations and they can be much faster than their 'full-precision' (i.e. `float32`) counterparts. Given all of the randomness already in neural network training, this reduction in precision doesn't significantly impact the model accuracy in many cases. Convergence is slightly better when you keep the network parameters at full-precision but forward and backward passes can be performed at half-precision: hence the term 'mixed-precision'. Also check out [Automatic Mixed Precision](https://mxnet.incubator.apache.org/versions/master/tutorials/amp/amp_tutorial.html) (AMP) for a more automated way of optimizing your network.
-
-We need to `cast` the network to `'float16'`, configure our optimizer to use `multi_precision` and convert our input data types to `'float16'` too.
-
-
-```python
-net.cast('float16')
-trainer = mx.gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': learning_rate,
-                                                         'multi_precision': True})
-single_forward(net, dataloader, dtype='float16')
-```
-
-
-```python
-print('\n', '### benchmark_dataloader', '\n')
-benchmark_dataloader(dataloader)
-print('\n', '### benchmark_network', '\n')
-data, label = next(iter(dataloader))
-data = data.astype('float16').as_in_context(ctx)
-label = label.astype('float16').as_in_context(ctx)
-benchmark_network(data, label, net, loss_fn, trainer)
-```
-
-    
-     ### benchmark_dataloader 
-    
-    ........................
-    
-    total startup time: 0.7006
-    average iterations/sec: 11.6537
-    average samples/sec: 745.8338
-    
-     ### benchmark_network 
-    
-    ........................
-    
-    average iterations/sec: 11.3614
-    average samples/sec: 727.1298
-
-
-Overall we see a substantial increase in training throughput: double compared to full-precision training.
-
-## Tips & Tricks #5: Others
-
-Many other tips and tricks exist for optimizing the throughput of training.
-
-One area we didn't explicitly benchmark in this tutorial is data transfer from CPU to GPU memory. Usually this isn't an issue, but for very large arrays this can become a bottleneck too. You might be able to compress your data significantly before transferring if your data is sparse (i.e. mostly zero values). Check out the [sparse array tutorial](https://mxnet.incubator.apache.org/versions/master/tutorials/index.html) for more details and an example of how this can impact training speed.
-
-Another useful trick if data pre-processing or data transfer is the bottleneck is pre-fetching batches. You can write your training loop to transfer the next batch of data to GPU before processing the current batch. Once again, this trick is memory permitting.
-
-And finally, if you are an advanced user, check out the various [environment variables](https://mxnet.incubator.apache.org/faq/env_var.html) that can be configured to change the behaviour of the MXNet backend.
-
-## Final Benchmark
-
-We will now combine all of the above tricks and tips in the complete training loop and compare to the initial benchmark.
-
-
-```python
-iters = 25
-num_samples = 0
-num_iters = 0
-start_time = time.time()
-for iter_idx, (data, label) in enumerate(dataloader):
-    num_samples += data.shape[0]
-    num_iters += 1
-    data = data.as_in_context(ctx).astype('float16')
-    label = label.as_in_context(ctx).astype('float16')
-    with mx.autograd.record():
-        pred = net(data)
-        loss = loss_fn(pred, label)
-    loss.backward()
-    trainer.step(data.shape[0])
-    metric.update(label, pred)
-    print('.', end='')
-    if num_iters >= iters:
-        break
-end_time = time.time()
-total_time = end_time - start_time
-print('\n')
-print('average iterations/sec: {:.4f}'.format(num_iters/total_time))
-print('average samples/sec: {:.4f}'.format(num_samples/total_time))
-```
-
-    .........................
-    
-    average iterations/sec: 7.9435
-    average samples/sec: 508.3821
-
-
-Using the above tips and tricks we managed to increase the throughput of training by ~600% from the initial benchmark! Our training throughput is less than the throughput of the individual components we tested, but there are additional overheads that we didn't previously measure (such as data transfer to GPU).
-
-## Conclusion
-
-We learned a number of tips and tricks to optimize the throughput of training, and they lead to a considerable increase compared to our initial baseline. As general rules, set `num_workers` on the `DataLoader` to >0, and hybridize your network if you're not debugging. You should increase `batch_size` where possible, but do this with care because of its potential effects on convergence. And finally, consider mixed precision training for substantial speed-ups.
-
-## Recommended Next Steps
-
-* Use the [MXNet Profiler](https://mxnet.incubator.apache.org/versions/master/tutorials/python/profiler.html) to identify additional bottlenecks and other areas for optimization.
-* Check out the [hybridization tutorial](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/hybrid.html) for more details on how to write custom `HybridBlock`s.
-* Consider using [multi-GPU](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/multi_gpu.html) and [multi-host](https://github.com/apache/incubator-mxnet/tree/master/example/distributed_training) training if you reach the limits of single GPU training.
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/gluon/pretrained_models.md b/docs/tutorials/gluon/pretrained_models.md
deleted file mode 100644
index 27ec1a95b8cd..000000000000
--- a/docs/tutorials/gluon/pretrained_models.md
+++ /dev/null
@@ -1,392 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-
-# Using pre-trained models in MXNet
-
-
-In this tutorial we will see how to use multiple pre-trained models with Apache MXNet. First, let's download three image classification models from the Apache MXNet [Gluon model zoo](https://mxnet.incubator.apache.org/api/python/gluon/model_zoo.html).
-* **DenseNet-121** ([research paper](https://arxiv.org/abs/1608.06993)), improved state of the art on [ImageNet dataset](http://image-net.org/challenges/LSVRC) in 2016.
-* **MobileNet** ([research paper](https://arxiv.org/abs/1704.04861)), MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks, suitable for mobile applications.
-* **ResNet-18** ([research paper](https://arxiv.org/abs/1512.03385v1)), the -152 version is the 2015 winner in multiple categories.
-
-Why would you want to try multiple models? Why not just pick the one with the best accuracy? As we will see later in the tutorial, even though these models have been trained on the same dataset and optimized for maximum accuracy, they do behave slightly differently on specific images. In addition, prediction speed and memory footprints can vary, and that is an important factor for many applications. By trying a few pretrained models, you have an opportunity to find a model that can be a good fit for solving your business problem.
-
-
-```python
-import json
-
-import matplotlib.pyplot as plt
-import mxnet as mx
-from mxnet import gluon, nd
-from mxnet.gluon.model_zoo import vision
-import numpy as np
-%matplotlib inline
-```
-
-## Loading the model
-
-The [Gluon Model Zoo](https://mxnet.incubator.apache.org/api/python/gluon/model_zoo.html) provides a collection of off-the-shelf models. You can get the ImageNet pre-trained model by using `pretrained=True`. 
-If you want to train on your own classification problem from scratch, you can get an untrained network with a specific number of classes using the `classes` parameter: for example `net = vision.resnet18_v1(classes=10)`. However note that you cannot use the `pretrained` and `classes` parameter at the same time. If you want to use pre-trained weights as initialization of your network except for the last layer, have a look at the last section of this tutorial.
-
-We can specify the *context* where we want to run the model: the default behavior is to use a CPU context. There are two reasons for this:
-* First, this will allow you to test the notebook even if your machine is not equipped with a GPU :)
-* Second, we're going to predict a single image and we don't have any specific performance requirements. For production applications where you'd want to predict large batches of images with the best possible throughput, a GPU could definitely be the way to go.
-* If you want to use a GPU, make sure you have pip installed the right version of mxnet, or you will get an error when using the `mx.gpu()` context. Refer to the [install instructions](http://mxnet.incubator.apache.org/install/index.html)
-
-
-```python
-# We set the context to CPU, you can switch to GPU if you have one and installed a compatible version of MXNet 
-ctx = mx.cpu() 
-```
-
-
-```python
-# We can load three the three models
-densenet121 = vision.densenet121(pretrained=True, ctx=ctx)
-mobileNet = vision.mobilenet0_5(pretrained=True, ctx=ctx)
-resnet18 = vision.resnet18_v1(pretrained=True, ctx=ctx)
-```
-
-We can look at the description of the MobileNet network for example, which has a relatively simple yet deep architecture
-
-
-```python
-print(mobileNet)
-```
-
-    MobileNet(
-      (features): HybridSequential(
-        (0): Conv2D(3 -> 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
-        (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=16)
-        (2): Activation(relu)
-        (3): Conv2D(1 -> 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=16, bias=False)
-        (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=16)
-        (5): Activation(relu)
-        (6): Conv2D(16 -> 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
-        (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=32)
-        (8): Activation(relu)
-        (9): Conv2D(1 -> 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=32, bias=False)
-        (10): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=32)
-        (11): Activation(relu)
-        (12): Conv2D(32 -> 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
-        (13): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=64)
-        (14): Activation(relu)
-        (15): Conv2D(1 -> 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=64, bias=False)
-        (16): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=64)
-        (17): Activation(relu)
-        (18): Conv2D(64 -> 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
-        (19): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=64)
-        (20): Activation(relu)
-        (21): Conv2D(1 -> 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=64, bias=False)
-        (22): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=64)
-        (23): Activation(relu)
-        (24): Conv2D(64 -> 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
-        (25): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=128)
-        (26): Activation(relu)
-        (27): Conv2D(1 -> 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=128, bias=False)
-        (28): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=128)
-        (29): Activation(relu)
-        (30): Conv2D(128 -> 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
-        (31): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=128)
-        (32): Activation(relu)
-        (33): Conv2D(1 -> 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=128, bias=False)
-        (34): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=128)
-        (35): Activation(relu)
-        (36): Conv2D(128 -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
-        (37): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256)
-        (38): Activation(relu)
-        (39): Conv2D(1 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256, bias=False)
-        (40): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256)
-        (41): Activation(relu)
-        (42): Conv2D(256 -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
-        (43): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256)
-        (44): Activation(relu)
-        (45): Conv2D(1 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256, bias=False)
-        (46): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256)
-        (47): Activation(relu)
-        (48): Conv2D(256 -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
-        (49): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256)
-        (50): Activation(relu)
-        (51): Conv2D(1 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256, bias=False)
-        (52): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256)
-        (53): Activation(relu)
-        (54): Conv2D(256 -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
-        (55): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256)
-        (56): Activation(relu)
-        (57): Conv2D(1 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256, bias=False)
-        (58): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256)
-        (59): Activation(relu)
-        (60): Conv2D(256 -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
-        (61): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256)
-        (62): Activation(relu)
-        (63): Conv2D(1 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256, bias=False)
-        (64): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256)
-        (65): Activation(relu)
-        (66): Conv2D(256 -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
-        (67): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256)
-        (68): Activation(relu)
-        (69): Conv2D(1 -> 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=256, bias=False)
-        (70): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256)
-        (71): Activation(relu)
-        (72): Conv2D(256 -> 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
-        (73): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512)
-        (74): Activation(relu)
-        (75): Conv2D(1 -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512, bias=False)
-        (76): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512)
-        (77): Activation(relu)
-        (78): Conv2D(512 -> 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
-        (79): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512)
-        (80): Activation(relu)
-        (81): GlobalAvgPool2D(size=(1, 1), stride=(1, 1), padding=(0, 0), ceil_mode=True)
-        (82): Flatten
-      )
-      (output): Dense(512 -> 1000, linear)
-    )
-
-
-Let's have a closer look at the first convolution layer:
-
-
-```python
-print(mobileNet.features[0].params)
-```
-
-`mobilenet1_conv0_ (Parameter mobilenet1_conv0_weight (shape=(16, 3, 3, 3), dtype=<class 'numpy.float32'>))`<!--notebook-skip-line-->
-
-
-The first layer applies **`16`** different convolutional masks, of size **`InputChannels x 3 x 3`**. For the first convolution, there are **`3`** input channels, the `R`, `G`, `B` channels of the input image. That gives us the weight matrix of shape **`16 x 3 x 3 x 3`**. There is no bias applied in this convolution.
-
-Let's have a look at the output layer now:
-
-
-```python
-print(mobileNet.output)
-```
-
-`Dense(512 -> 1000, linear)`<!--notebook-skip-line-->
-
-
-Did you notice the shape of layer? The weight matrix is **1000 x 512**. This layer contains 1,000 neurons: each of them will store an activation representative of the probability of the image belonging to a specific category. Each neuron is also fully connected to all 512 neurons in the previous layer.
-
-OK, enough exploring! Now let's use these models to classify our own images.
-
-## Loading the data
-All three models have been pre-trained on the ImageNet data set which includes over 1.2 million pictures of objects and animals sorted in 1,000 categories.
-We get the imageNet list of labels. That way we have the mapping so when the model predicts for example category index `4`, we know it is predicting `hammerhead, hammerhead shark`
-
-
-```python
-mx.test_utils.download('https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/onnx/image_net_labels.json')
-categories = np.array(json.load(open('image_net_labels.json', 'r')))
-print(categories[4])
-```
-
-`hammerhead, hammerhead shark` <!--notebook-skip-line-->
-
-
-Get a test image
-
-
-```python
-filename = mx.test_utils.download('https://github.com/dmlc/web-data/blob/master/mxnet/doc/tutorials/onnx/images/dog.jpg?raw=true', fname='dog.jpg')
-```
-
-If you want to use your own image for the test, copy the image to the same folder that contains the notebook and change the following line:
-
-
-```python
-filename = 'dog.jpg'
-```
-
-Load the image as a NDArray
-
-
-```python
-image = mx.image.imread(filename)
-plt.imshow(image.asnumpy())
-```
-
-![png](https://github.com/dmlc/web-data/blob/master/mxnet/doc/tutorials/onnx/images/dog.jpg?raw=true)<!--notebook-skip-line-->
-
-
-Neural network expects input in a specific format. Usually images comes in the `Width x Height x Channels` format. Where channels are the RGB channels.
-This network accepts images in the `BatchSize x 3 x 224 x 224`. `224 x 224` is the image resolution, that's how the model was trained. `3` is the number of channels : Red, Green and Blue (in this order). In this case we use a `BatchSize` of `1` since we are predicting one image at a time.
-Here are the transformation steps:
-* Read the image: this will return a NDArray shaped as (image height, image width, 3), with the three channels in RGB order.
-* Resize the shorter edge of the image 224.
-* Crop, using a size of 224x224 from the center of the image.
-* Shift the mean and standard deviation of our color channels to match the ones of the dataset the network has been trained on.
-* Transpose the array from (Height, Width, 3) to (3, Height, Width).
-* Add a fourth dimension, the batch dimension.
-
-
-
-```python
-def transform(image):
-    resized = mx.image.resize_short(image, 224) #minimum 224x224 images
-    cropped, crop_info = mx.image.center_crop(resized, (224, 224))
-    normalized = mx.image.color_normalize(cropped.astype(np.float32)/255,
-                                      mean=mx.nd.array([0.485, 0.456, 0.406]),
-                                      std=mx.nd.array([0.229, 0.224, 0.225])) 
-    # the network expect batches of the form (N,3,224,224)
-    transposed = normalized.transpose((2,0,1))  # Transposing from (224, 224, 3) to (3, 224, 224)
-    batchified = transposed.expand_dims(axis=0) # change the shape from (3, 224, 224) to (1, 3, 224, 224)
-    return batchified
-```
-
-## Testing the different networks
-We run the image through each pre-trained network. The models output a *NDArray* holding 1,000 activation values, which we convert to probabilities using the `softmax()` function, corresponding to the 1,000 categories it has been trained on. The output prediction NDArray has only one row since batch size is equal to 1
-
-
-```python
-predictions = resnet18(transform(image)).softmax()
-print(predictions.shape)
-```
-
-`(1, 1000)`<!--notebook-skip-line-->
-
-
-We then take the top `k` predictions for our image, here the top `3`.
-
-
-```python
-top_pred = predictions.topk(k=3)[0].asnumpy()
-```
-
-And we print the categories predicted with their corresponding probabilities:
-
-
-```python
-for index in top_pred:
-    probability = predictions[0][int(index)]
-    category = categories[int(index)]
-    print("{}: {:.2f}%".format(category, probability.asscalar()*100))
-```
-
-
-`boxer: 93.03%` <!--notebook-skip-line-->
-
-`bull mastiff: 5.73%`<!--notebook-skip-line-->
-
-`Staffordshire bullterrier, Staffordshire bull terrier: 0.58%` <!--notebook-skip-line-->
-
-
-Let's turn this into a function. Our parameters are an image, a model, a list of categories and the number of top categories we'd like to print. 
-
-
-```python
-def predict(model, image, categories, k):
-    predictions = model(transform(image)).softmax()
-    top_pred = predictions.topk(k=k)[0].asnumpy()
-    for index in top_pred:
-        probability = predictions[0][int(index)]
-        category = categories[int(index)]
-        print("{}: {:.2f}%".format(category, probability.asscalar()*100))
-    print('')
-```
-
-### DenseNet121
-
-
-```python
-%%time
-predict(densenet121, image, categories, 3)
-```
-
-`boxer: 94.77%`<!--notebook-skip-line-->
-
-`bull mastiff: 2.26%`<!--notebook-skip-line-->
-
-`American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier: 1.69%`<!--notebook-skip-line-->
-    
-
-`CPU times: user 360 ms, sys: 0 ns, total: 360 ms`<!--notebook-skip-line-->
-
-`Wall time: 165 ms`<!--notebook-skip-line-->
-
-
-### MobileNet
-
-
-```python
-%%time
-predict(mobileNet, image, categories, 3)
-```
-
-`boxer: 84.02%` <!--notebook-skip-line-->
-
-`bull mastiff: 13.63%` <!--notebook-skip-line-->
-
-`Rhodesian ridgeback: 0.66%` <!--notebook-skip-line-->
-
-
-`CPU times: user 72 ms, sys: 0 ns, total: 72 ms` <!--notebook-skip-line-->
-
-`Wall time: 31.2 ms` <!--notebook-skip-line-->
-
-
-### Resnet-18
-
-
-```python
-%%time
-predict(resnet18, image, categories, 3)
-```
-
-`boxer: 93.03%` <!--notebook-skip-line-->
-
-`bull mastiff: 5.73%` <!--notebook-skip-line-->
-
-`Staffordshire bullterrier, Staffordshire bull terrier: 0.58%` <!--notebook-skip-line-->
-
-
-`CPU times: user 156 ms, sys: 0 ns, total: 156 ms` <!--notebook-skip-line-->
-
-`Wall time: 77.1 ms` <!--notebook-skip-line-->
-
-
-As you can see, pre-trained networks produce slightly different predictions, and have different run-time. In this case, MobileNet is almost **5 times faster** than DenseNet!
-
-## Fine-tuning pre-trained models
-
-You can replace the output layer of your pre-trained model to fit the right number of classes for your own image classification task like this, for example for 10 classes:
-
-
-```python
-NUM_CLASSES = 10
-with resnet18.name_scope():
-    resnet18.output = gluon.nn.Dense(NUM_CLASSES)
-```
-
-
-```python
-print(resnet18.output)
-```
-
-`Dense(None -> 10, linear)` <!--notebook-skip-line-->
-
-
-Now you can train your model on your new data using the pre-trained weights as initialization. This is called transfer learning and it has proved to be very useful especially in the cases where you only have access to a small dataset. Your network will have already learned how to perform general pattern detection and feature extraction on the larger dataset.
-You can learn more about transfer learning and fine-tuning with MXNet in these tutorials:
-- [Transferring knowledge through fine-tuning](http://gluon.mxnet.io/chapter08_computer-vision/fine-tuning.html)
-- [Fine Tuning an ONNX Model](https://mxnet.incubator.apache.org/tutorials/onnx/fine_tuning_gluon.html)
-
-
-That's it! Explore the model zoo, have fun with pre-trained models!
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/gluon/save_load_params.md b/docs/tutorials/gluon/save_load_params.md
deleted file mode 100644
index c82ec5ac35a7..000000000000
--- a/docs/tutorials/gluon/save_load_params.md
+++ /dev/null
@@ -1,281 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Saving and Loading Gluon Models
-
-Training large models take a lot of time and it is a good idea to save the trained models to files to avoid training them again and again. There are a number of reasons to do this. For example, you might want to do inference on a machine that is different from the one where the model was trained. Sometimes model's performance on validation set decreases towards the end of the training because of overfitting. If you saved your model parameters after every epoch, at the end you can decide to use the model that performs best on the validation set. Another reason would be to train your model using one language (like Python that has a lot of tools for training) and run inference using a different language (like Scala probably because your application is built on Scala).
-
-In this tutorial, we will learn ways to save and load Gluon models. There are two ways to save/load Gluon models:
-
-**1. Save/load model parameters only**
-
-Parameters of any Gluon model can be saved using the `save_parameters` and `load_parameters` method. This does not save model architecture. This method is used to save parameters of dynamic (non-hybrid) models. Model architecture cannot be saved for dynamic models because model architecture changes during execution.
-
-**2. Save/load model parameters AND architecture**
-
-The Model architecture of `Hybrid` models stays static and don't change during execution. Therefore both model parameters AND architecture can be saved and loaded using `export`, `imports` methods.
-
-Let's look at the above methods in more detail. Let's start by importing the modules we'll need.
-
-```python
-from __future__ import print_function
-
-import mxnet as mx
-import mxnet.ndarray as nd
-from mxnet import nd, autograd, gluon
-from mxnet.gluon.data.vision import transforms
-
-import numpy as np
-```
-
-## Setup: build and train a simple model
-
-We need a trained model before we can save it to a file. So let's go ahead and build a very simple convolutional network and train it on MNIST data.
-
-Let's define a helper function to build a LeNet model and another helper to train LeNet with MNIST.
-
-```python
-# Use GPU if one exists, else use CPU
-ctx = mx.gpu() if mx.context.num_gpus() else mx.cpu()
-
-# MNIST images are 28x28. Total pixels in input layer is 28x28 = 784
-num_inputs = 784
-# Clasify the images into one of the 10 digits
-num_outputs = 10
-# 64 images in a batch
-batch_size = 64
-
-# Load the training data
-train_data = gluon.data.DataLoader(gluon.data.vision.MNIST(train=True).transform_first(transforms.ToTensor()),
-                                   batch_size, shuffle=True)
-
-# Build a simple convolutional network
-def build_lenet(net):    
-    with net.name_scope():
-        # First convolution
-        net.add(gluon.nn.Conv2D(channels=20, kernel_size=5, activation='relu'))
-        net.add(gluon.nn.MaxPool2D(pool_size=2, strides=2))
-        # Second convolution
-        net.add(gluon.nn.Conv2D(channels=50, kernel_size=5, activation='relu'))
-        net.add(gluon.nn.MaxPool2D(pool_size=2, strides=2))
-        # Flatten the output before the fully connected layers
-        net.add(gluon.nn.Flatten())
-        # First fully connected layers with 512 neurons
-        net.add(gluon.nn.Dense(512, activation="relu"))
-        # Second fully connected layer with as many neurons as the number of classes
-        net.add(gluon.nn.Dense(num_outputs))
-
-        return net
-
-# Train a given model using MNIST data
-def train_model(model):
-    # Initialize the parameters with Xavier initializer
-    model.collect_params().initialize(mx.init.Xavier(), ctx=ctx)
-    # Use cross entropy loss
-    softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()
-    # Use Adam optimizer
-    trainer = gluon.Trainer(model.collect_params(), 'adam', {'learning_rate': .001})
-
-    # Train for one epoch
-    for epoch in range(1):
-        # Iterate through the images and labels in the training data
-        for batch_num, (data, label) in enumerate(train_data):
-            # get the images and labels
-            data = data.as_in_context(ctx)
-            label = label.as_in_context(ctx)
-            # Ask autograd to record the forward pass
-            with autograd.record():
-                # Run the forward pass
-                output = model(data)
-                # Compute the loss
-                loss = softmax_cross_entropy(output, label)
-            # Compute gradients
-            loss.backward()
-            # Update parameters
-            trainer.step(data.shape[0])
-
-            # Print loss once in a while
-            if batch_num % 50 == 0:
-                curr_loss = nd.mean(loss).asscalar()
-                print("Epoch: %d; Batch %d; Loss %f" % (epoch, batch_num, curr_loss))
-```
-
-Let's build a model and train it. After training, we will save and restore this model from a file.
-
-```python
-net = build_lenet(gluon.nn.Sequential())
-train_model(net)
-```
-<pre>Epoch: 0; Batch 0; Loss 2.288904 <!--notebook-skip-line-->
-Epoch: 0; Batch 50; Loss 0.269372 <!--notebook-skip-line-->
-Epoch: 0; Batch 100; Loss 0.238990 <!--notebook-skip-line-->
-Epoch: 0; Batch 150; Loss 0.320592 <!--notebook-skip-line-->
-Epoch: 0; Batch 200; Loss 0.048619 <!--notebook-skip-line-->
-Epoch: 0; Batch 250; Loss 0.121555 <!--notebook-skip-line-->
-Epoch: 0; Batch 300; Loss 0.083645 <!--notebook-skip-line-->
-Epoch: 0; Batch 350; Loss 0.040627 <!--notebook-skip-line-->
-Epoch: 0; Batch 400; Loss 0.195946 <!--notebook-skip-line-->
-Epoch: 0; Batch 450; Loss 0.155514 <!--notebook-skip-line-->
-Epoch: 0; Batch 500; Loss 0.031762 <!--notebook-skip-line-->
-Epoch: 0; Batch 550; Loss 0.056516 <!--notebook-skip-line-->
-Epoch: 0; Batch 600; Loss 0.095174 <!--notebook-skip-line-->
-Epoch: 0; Batch 650; Loss 0.054901 <!--notebook-skip-line-->
-Epoch: 0; Batch 700; Loss 0.030067 <!--notebook-skip-line-->
-Epoch: 0; Batch 750; Loss 0.102611 <!--notebook-skip-line-->
-Epoch: 0; Batch 800; Loss 0.010036 <!--notebook-skip-line-->
-Epoch: 0; Batch 850; Loss 0.051853 <!--notebook-skip-line-->
-Epoch: 0; Batch 900; Loss 0.008402 <!--notebook-skip-line-->
-</pre> <!--notebook-skip-line-->
-
-## Saving model parameters to file
-
-Okay, we now have a model (`net`) that we can save to a file. Let's save the parameters of this model to a file using the `save_parameters` function.
-
-```python
-file_name = "net.params"
-net.save_parameters(file_name)
-```
-
-We have successfully saved the parameters of the model into a file.
-
-Note: `Block.collect_params().save()` is not a recommended way to save parameters of a Gluon network if you plan to load the parameters back into a Gluon network using `Block.load_parameters()`.
-
-## Loading model parameters from file
-
-Let's now create a network with the parameters we saved into the file. We build the network again using the helper first and then load the weights from the file we saved using the `load_parameters` function.
-
-```python
-new_net = build_lenet(gluon.nn.Sequential())
-new_net.load_parameters(file_name, ctx=ctx)
-```
-
-Note that to do this, we need the definition of the network as Python code. If we want to recreate this network on a different machine using the saved weights, we need the same Python code (`build_lenet`) that created the network to create the `new_net` object shown above. This means Python code needs to be copied over to any machine where we want to run this network.
-
-If our network is [Hybrid](https://mxnet.incubator.apache.org/tutorials/gluon/hybrid.html), we can even save the network architecture into files and we won't need the network definition in a Python file to load the network. We'll see how to do it in the next section.
-
-Let's test the model we just loaded from file.
-
-```python
-import matplotlib.pyplot as plt
-
-def verify_loaded_model(net):
-    """Run inference using ten random images.
-    Print both input and output of the model"""
-
-    def transform(data, label):
-        return data.astype(np.float32)/255, label.astype(np.float32)
-
-    # Load ten random images from the test dataset
-    sample_data = mx.gluon.data.DataLoader(mx.gluon.data.vision.MNIST(train=False, transform=transform),
-                                  10, shuffle=True)
-
-    for data, label in sample_data:
-
-        # Display the images
-        img = nd.transpose(data, (1,0,2,3))
-        img = nd.reshape(img, (28,10*28,1))
-        imtiles = nd.tile(img, (1,1,3))
-        plt.imshow(imtiles.asnumpy())
-        plt.show()
-
-        # Display the predictions
-        data = nd.transpose(data, (0, 3, 1, 2))
-        out = net(data.as_in_context(ctx))
-        predictions = nd.argmax(out, axis=1)
-        print('Model predictions: ', predictions.asnumpy())
-
-        break
-
-verify_loaded_model(new_net)
-```
-![Model inputs](https://raw.githubusercontent.com/indhub/web-data/4a9c100aa996df3dff0e7f493029d411c2b526c3/mxnet/tutorials/gluon/save_load_params/mnist_in_1.png) <!--notebook-skip-line-->
-
-Model predictions:  [1. 1. 4. 5. 0. 5. 7. 0. 3. 6.] <!--notebook-skip-line-->
-
-## Saving model parameters AND architecture to file
-
-[Hybrid](https://mxnet.incubator.apache.org/tutorials/gluon/hybrid.html) models can be serialized as JSON files using the `export` function. Once serialized, these models can be loaded from other language bindings like C++ or Scala for faster inference or inference in different environments.
-
-Note that the network we created above is not a Hybrid network and therefore cannot be serialized into a JSON file. So, let's create a Hybrid version of the same network and train it.
-
-```python
-net = build_lenet(gluon.nn.HybridSequential())
-net.hybridize()
-train_model(net)
-```
-
-<pre>Epoch: 0; Batch 0; Loss 2.323284 <!--notebook-skip-line-->
-Epoch: 0; Batch 50; Loss 0.444733 <!--notebook-skip-line-->
-Epoch: 0; Batch 100; Loss 0.103407 <!--notebook-skip-line-->
-Epoch: 0; Batch 150; Loss 0.166772 <!--notebook-skip-line-->
-Epoch: 0; Batch 200; Loss 0.227569 <!--notebook-skip-line-->
-Epoch: 0; Batch 250; Loss 0.069515 <!--notebook-skip-line-->
-Epoch: 0; Batch 300; Loss 0.074086 <!--notebook-skip-line-->
-Epoch: 0; Batch 350; Loss 0.074382 <!--notebook-skip-line-->
-Epoch: 0; Batch 400; Loss 0.026569 <!--notebook-skip-line-->
-Epoch: 0; Batch 450; Loss 0.097248 <!--notebook-skip-line-->
-Epoch: 0; Batch 500; Loss 0.059895 <!--notebook-skip-line-->
-Epoch: 0; Batch 550; Loss 0.053194 <!--notebook-skip-line-->
-Epoch: 0; Batch 600; Loss 0.076294 <!--notebook-skip-line-->
-Epoch: 0; Batch 650; Loss 0.047274 <!--notebook-skip-line-->
-Epoch: 0; Batch 700; Loss 0.007898 <!--notebook-skip-line-->
-Epoch: 0; Batch 750; Loss 0.039478 <!--notebook-skip-line-->
-Epoch: 0; Batch 800; Loss 0.031342 <!--notebook-skip-line-->
-Epoch: 0; Batch 850; Loss 0.059289 <!--notebook-skip-line-->
-Epoch: 0; Batch 900; Loss 0.037809 <!--notebook-skip-line-->
-</pre> <!--notebook-skip-line-->
-
-We now have a trained hybrid network. This can be exported into files using the `export` function. The `export` function will export the model architecture into a `.json` file and model parameters into a `.params` file.
-
-```python
-net.export("lenet", epoch=1)
-```
-
-`export` in this case creates `lenet-symbol.json` and `lenet-0001.params` in the current directory.
-
-## Loading model parameters AND architecture from file
-
-### From a different frontend
-
-One of the main reasons to serialize model architecture into a JSON file is to load it from a different frontend like C, C++ or Scala. Here is a couple of examples:
-1. [Loading serialized Hybrid networks from C](https://github.com/apache/incubator-mxnet/blob/master/example/image-classification/predict-cpp/image-classification-predict.cc)
-2. [Loading serialized Hybrid networks from Scala](https://github.com/apache/incubator-mxnet/blob/master/scala-package/infer/src/main/scala/org/apache/mxnet/infer/ImageClassifier.scala)
-
-### From Python
-
-Serialized Hybrid networks (saved as .JSON and .params file) can be loaded and used inside Python frontend using `gluon.nn.SymbolBlock`. To demonstrate that, let's load the network we serialized above.
-
-```python
-import warnings
-with warnings.catch_warnings():
-    warnings.simplefilter("ignore")
-    deserialized_net = gluon.nn.SymbolBlock.imports("lenet-symbol.json", ['data'], "lenet-0001.params", ctx=ctx)
-```
-
-`deserialized_net` now contains the network we deserialized from files. Let's test the deserialized network to make sure it works.
-
-```python
-verify_loaded_model(deserialized_net)
-```
-
-![Model inputs](https://raw.githubusercontent.com/indhub/web-data/4a9c100aa996df3dff0e7f493029d411c2b526c3/mxnet/tutorials/gluon/save_load_params/mnist_in_2.png) <!--notebook-skip-line-->
-
-Model predictions:  [4. 8. 0. 1. 5. 5. 8. 8. 1. 9.] <!--notebook-skip-line-->
-
-That's all! We learned how to save and load Gluon networks from files. Parameters of any Gluon network can be persisted into files. For hybrid networks, both the architecture of the network and the parameters can be saved to and loaded from files.
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/gluon/transforms.md b/docs/tutorials/gluon/transforms.md
deleted file mode 100644
index 10e0829299eb..000000000000
--- a/docs/tutorials/gluon/transforms.md
+++ /dev/null
@@ -1,173 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Data Transforms
-
-Creating a [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset) is the starting point of the data pipeline, but we usually need to change samples before passing them to the network. Gluon `transforms` provide us with a simple way to apply these changes. We can use out-of-the-box transforms or create our own.
-
-We'll demonstrate this by adjusting samples returned by the [`CIFAR10`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=cifar#mxnet.gluon.data.vision.datasets.CIFAR10) dataset and start by importing the relevant modules.
-
-
-```python
-import mxnet as mx
-from matplotlib import pyplot as plt
-from mxnet import image
-from mxnet.gluon import data as gdata, utils
-import numpy as np
-```
-
-After creating our [CIFAR-10 `Dataset`]([`CIFAR10`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=cifar#mxnet.gluon.data.vision.datasets.CIFAR10)), we can inspect a random sample.
-
-
-```python
-dataset = mx.gluon.data.vision.CIFAR10()
-```
-
-
-```python
-sample_idx = 42
-sample_data, sample_label = dataset[sample_idx]
-print("data shape: {}".format(sample_data.shape))
-print("data type: {}".format(sample_data.dtype))
-print("data range: {} to {}".format(sample_data.min().asscalar(),
-                                    sample_data.max().asscalar()))
-print("label: {}".format(sample_label))
-plt.imshow(sample_data.asnumpy())
-```
-
-Our sample looks fine, but we need to need to make a few changes before using this as an input to a neural network.
-
-### Using `ToTensor` and `.transform_first`
-
-Ordering of dimensions (sometimes called the data layout) is important for correct usage of a neural network. Currently our samples are ordered (height, width, channel) but we need to change this to (channel, height, width) before passing to our network. We also need to change our data type. Currently it's `uint8`, but we need to change this to `float32`.
-
-MXNet Gluon provides a number of useful transforms for common computer vision cases like this. We will use [`ToTensor`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=totens#mxnet.gluon.data.vision.transforms.ToTensor) to change the data layout and convert integers (between 0 and 255) to floats (between 0 and 1). We apply the transform to our `dataset` using the [`transform_first`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=transform_first#mxnet.gluon.data.Dataset.transform_first) method. We have 2 elements per sample here (i.e. data and label), so the transform is only applied to the first element (i.e. data).
-
-Advanced: `transform` (instead of [`transform_first`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=transform_first#mxnet.gluon.data.Dataset.transform_first)) can be used to transform all elements in the sample.
-
-
-```python
-transform_fn = mx.gluon.data.vision.transforms.ToTensor()
-dataset = dataset.transform_first(transform_fn)
-```
-
-
-```python
-sample_data, sample_label = dataset[sample_idx]
-print("data shape: {}".format(sample_data.shape))
-print("data type: {}".format(sample_data.dtype))
-print("data range: {} to {}".format(sample_data.min().asscalar(),
-                                    sample_data.max().asscalar()))
-print("label: {}".format(sample_label))
-```
-
-Our data has changed, while the label has been left untouched.
-
-### `Normalize`
-
-We scaled the values of our data samples between 0 and 1 as part of [`ToTensor`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=totens#mxnet.gluon.data.vision.transforms.ToTensor) but we may want or need to normalize our data instead: i.e. shift to zero-center and scale to unit variance. You can do this with the following steps:
-
-* **Step 1** is to calculate the mean and standard deviation of each channel for the entire training dataset.
-* **Step 2** is to use these statistics to normalize each sample for training and for inference too.
-
-When using pre-trained models, you need to use the same normalization statistics that were used for training. Models from the Gluon Model Zoo expect normalization statistics of `mean=[0.485, 0.456, 0.406]` and `std=[0.229, 0.224, 0.225]`.
-
-When training your own model from scratch, you can estimate the normalization statistics from the training dataset (not test dataset) using the code snippet found below. Models often benefit from input normalization because it prevents saturation of activations and prevents certain features from dominating due to differences in scale.
-
-
-```python
-# estimate channel mean and std
-sample_means = np.empty(shape=(len(dataset), 3))
-sample_stds = np.empty(shape=(len(dataset), 3))
-for idx in range(len(dataset)):
-    sample_data = dataset[idx][0].asnumpy()
-    sample_means[idx] = sample_data.mean(axis=(1,2))
-    sample_stds[idx] = sample_data.std(axis=(1,2))
-print("channel means: {}".format(sample_means.mean(axis=0)))
-print("channel stds: {}".format(sample_stds.mean(axis=0)))
-```
-
-We can create our `Normalize` transform using these statistics and apply it to our `dataset` as before.
-
-
-```python
-normalize_fn = mx.gluon.data.vision.transforms.Normalize(mean=[0.49139969, 0.48215842, 0.44653093],
-                                                         std=[0.20220212, 0.19931542, 0.20086347])
-dataset = dataset.transform_first(normalize_fn)
-```
-
-And now when we inspect the values for a single sample, we find that it's no longer bounded by 0 and 1.
-
-
-```python
-sample_data, sample_label = dataset[sample_idx]
-print("data range: {} to {}".format(sample_data.min().asscalar(),
-                                    sample_data.max().asscalar()))
-```
-
-### `Compose`
-
-We've now seen two examples of transforms: [`ToTensor`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=totens#mxnet.gluon.data.vision.transforms.ToTensor) and `Normalize`. We applied both transforms to our `dataset` through repeated calls to `transform_first`, but Gluon has a dedicated transform to stack other transforms that's preferred. With [`Compose`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=compose#mxnet.gluon.data.vision.transforms.Compose) we can choose and order the transforms we want to apply.
-
-Caution: ordering of transforms is important. e.g. [`ToTensor`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=totens#mxnet.gluon.data.vision.transforms.ToTensor) should be applied before `Normalize`, but after `Resize` and `CenterCrop`.
-
-
-```python
-dataset = mx.gluon.data.vision.CIFAR10()
-transform_fn = mx.gluon.data.vision.transforms.Compose([
-    mx.gluon.data.vision.transforms.ToTensor(),
-    mx.gluon.data.vision.transforms.Normalize(mean=[0.49139969, 0.48215842, 0.44653093],
-                                              std=[0.20220212, 0.19931542, 0.20086347]),
-])
-dataset = dataset.transform_first(transform_fn)
-```
-
-
-```python
-sample_data, sample_label = dataset[sample_idx]
-print("data range: {} to {}".format(sample_data.min().asscalar(),
-                                    sample_data.max().asscalar()))
-```
-
-As a sanity check, we get the same result as before.
-
-### `CenterCrop` and `Resize`
-
-Some networks require an input of a certain size (e.g. convolutional neural network with a final dense layer). Specifying the `size`, you can use `CenterCrop` and `Resize` to modify the spatial dimensions of an image. We show an example of downsampling to 10px by 10px here.
-
-
-```python
-dataset = mx.gluon.data.vision.CIFAR10()
-transform_fn = mx.gluon.data.vision.transforms.Resize(size=(10, 10))
-dataset = dataset.transform_first(transform_fn)
-sample_data, sample_label = dataset[sample_idx]
-plt.imshow(sample_data.asnumpy())
-```
-
-### Augmentation
-
-Augmenting samples is also common practice: that is, randomly permuting existing samples to make new samples to reduce issues of network overfitting. Using image samples as an example, you could crop to random regions, flip from left to right or even jitter the brightness of the image. Gluon has a number of random transforms that are covered in depth in the Data Augmentation tutorial.
-
-## Summary
-
-We've now seen how to use Gluon transforms to adjust samples returned by a [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset). You should aim to construct a `transform_fn` using [`Compose`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=compose#mxnet.gluon.data.vision.transforms.Compose), and then apply it to the `Dataset` using [`transform_first`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=transform_first#mxnet.gluon.data.Dataset.transform_first) or `transform`.
-
-### Additional Reading
-
-Check out the introduction to data tutorial for an overview of how `transforms` fit into the complete data pipeline. More information on data augmentation can be found here. And the [GluonNLP](https://gluon-nlp.mxnet.io/api/modules/data.html) and [GluonCV](https://gluon-cv.mxnet.io/api/data.transforms.html) toolkits provide a variety of domain specific transforms that you might find useful.
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md
deleted file mode 100644
index f773a79f63a7..000000000000
--- a/docs/tutorials/index.md
+++ /dev/null
@@ -1,257 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Tutorials
-
-```eval_rst
-.. toctree::
-   :hidden:
-
-   basic/index.md
-   c++/index.md
-   control_flow/index.md
-   embedded/index.md
-   gluon/index.md
-   java/index.md
-   mkldnn/index.md
-   nlp/index.md
-   onnx/index.md
-   python/index.md
-   r/index.md
-   scala/index.md
-   sparse/index.md
-   speech_recognition/index.md
-   tensorrt/index.md
-   unsupervised_learning/index.md
-   vision/index.md
-   amp/index.md
-```
-
-MXNet tutorials can be found in this section. A variety of language bindings are available for MXNet (including Python, Scala, Java, Clojure, C++ and R) and we have a different tutorial section for each language.
-
-Are you new to MXNet, and don't have a preference on language? We currently recommend starting with Python, and specifically the Gluon APIs (versus Module APIs) as they're more flexible and easier to debug.
-
-Another great resource for learning MXNet is our [examples section](https://github.com/apache/incubator-mxnet/tree/master/example) which includes a wide variety of models (from basic to state-of-the-art) for a wide variety of tasks including: object detection, style transfer, reinforcement learning, and many others.
-
-**Contents:**
-  * [Python Tutorials](#python-tutorials)
-  * [Scala Tutorials](#scala-tutorials)
-  * [Java Tutorials](#java-tutorials)
-  * [Clojure Tutorials](#clojure-tutorials)
-  * [C++ Tutorials](#c---tutorials)
-  * [R Tutorials](#r-tutorials)
-  * [Perl Tutorials](#perl-tutorials)
-  * [Contributing Tutorials](#contributing-tutorials)
-
-<hr>
-
-## Python Tutorials
-
-We have two types of API available for Python: Gluon APIs and Module APIs. [See here](/api/python/gluon/gluon.html) for a comparison.
-
-A comprehensive introduction to Gluon can be found at [Dive into Deep Learning](http://www.d2l.ai/). Structured like a book, it build up from first principles of deep learning and take a theoretical walkthrough of progressively more complex models using the Gluon API. Also check out the [60-Minute Gluon Crash Course](http://gluon-crash-course.mxnet.io/) if you're short on time or have used other deep learning frameworks before.
-
-Use the tutorial selector below to filter to the relevant tutorials. You might see a download link in the top right corner of some tutorials. Use this to download a Jupyter Notebook version of the tutorial, and re-run and adjust the code as you wish.
-
-<script type="text/javascript" src='../_static/js/options.js'></script>
-<!-- Gluon vs Module -->
-
-Select API:&nbsp;
-<div class="btn-group opt-group" role="group">
-  <button type="button" class="btn btn-default opt active" style="font-size:22px">Gluon</button>
-  <button type="button" class="btn btn-default opt"   style="font-size:22px">Module</button>
-</div>
-<!-- END - Main Menu -->
-<br>
-<div class="gluon">
-
-* Getting Started
-    * [60-Minute Gluon Crash Course](http://gluon-crash-course.mxnet.io/) <img src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg" alt="External link" height="15px" style="margin: 0px 0px 3px 3px;"/>
-    * [MNIST Handwritten Digit Classification](/tutorials/gluon/mnist.html)
-* Models
-    * [Model Zoo: using pre-trained models](/tutorials/gluon/pretrained_models.html)
-    * [Linear Regression](http://gluon.mxnet.io/chapter02_supervised-learning/linear-regression-gluon.html) <img src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg" alt="External link" height="15px" style="margin: 0px 0px 3px 3px;"/>
-    * [Logistic Regression](/tutorials/gluon/logistic_regression_explained.html)
-    * [Word-level text generation with RNN, LSTM and GRU](http://gluon.mxnet.io/chapter05_recurrent-neural-networks/rnns-gluon.html) <img src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg" alt="External link" height="15px" style="margin: 0px 0px 3px 3px;"/>
-    * [Visual Question Answering](http://gluon.mxnet.io/chapter08_computer-vision/visual-question-answer.html) <img src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg" alt="External link" height="15px" style="margin: 0px 0px 3px 3px;"/>
-   * [Image similiarity search with InfoGAN](/tutorials/gluon/info_gan.html)
-* Practitioner Guides
-    * [Gotchas using NumPy](/tutorials/gluon/gotchas_numpy_in_mxnet.html)
-    * [Gluon Performance Tips & Tricks](/tutorials/gluon/performance.html)
-    * [Multi-GPU training](/tutorials/gluon/multi_gpu.html)<span style="color:red"> (new!) </span>([Alternative](http://gluon.mxnet.io/chapter07_distributed-learning/multiple-gpus-gluon.html) <img src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg" alt="External link" height="15px" style="margin: 0px 0px 3px 3px;"/>)
-    * [Checkpointing and Model Serialization (a.k.a. saving and loading)](/tutorials/gluon/save_load_params.html) <img src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg" alt="External link" height="15px" style="margin: 0px 0px 3px 3px;"/> ([Alternative](http://gluon.mxnet.io/chapter03_deep-neural-networks/serialization.html))
-    * [Distributed Training](https://github.com/apache/incubator-mxnet/tree/master/example/distributed_training)
-    * [Inference using an ONNX model](/tutorials/onnx/inference_on_onnx_model.html)
-    * [Fine-tuning an ONNX model on Gluon](/tutorials/onnx/fine_tuning_gluon.html)
-    * [Visualizing Decisions of Convolutional Neural Networks](/tutorials/vision/cnn_visualization.html)
-    * [Learning Rate Finder](/tutorials/gluon/learning_rate_finder.html)
-    * [Learning Rate Schedules](/tutorials/gluon/learning_rate_schedules.html)
-    * [Advanced Learning Rate Schedules](/tutorials/gluon/learning_rate_schedules_advanced.html)
-    * [Profiling MXNet Models](/tutorials/python/profiler.html)
-    * [Module to Gluon API](/tutorials/python/module_to_gluon.html)
-    * [Gluon end to end from training to inference](/tutorials/gluon/gluon_from_experiment_to_deployment.html)
-    * [Automatic Mixed Precision in Gluon](/tutorials/amp/amp_tutorial.html)
-    * [How to build and install MXNet with MKL-DNN backend](/tutorials/mkldnn/MKLDNN_README.html)
-    * [How to quantize custom models with MKL-DNN backend](/tutorials/mkldnn/mkldnn_quantization.html)<span style="color:red"> (new!) </span>
-* API Guides
-    * Core APIs
-        * NDArray
-            * [NDArray API](/tutorials/gluon/ndarray.html) ([Alternative](http://gluon.mxnet.io/chapter01_crashcourse/ndarray.html) <img src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg" alt="External link" height="15px" style="margin: 0px 0px 3px 3px;"/>)
-            * [Advanced NDArray API](/tutorials/basic/ndarray.html)
-            * [Difference between reshape and transpose](/tutorials/basic/reshape_transpose.html)
-            * [NDArray Indexing](https://mxnet.incubator.apache.org/tutorials/basic/ndarray_indexing.html)
-            * Sparse NDArray
-                * [Introduction to Sparse NDArray](/tutorials/sparse/train_gluon.html)
-                * [Sparse Gradient Updates (RowSparseNDArray)](/tutorials/sparse/row_sparse.html)
-                * [Compressed Sparse Row Storage Format (CSRNDArray)](/tutorials/sparse/csr.html)
-        * Symbol
-            * [Symbol API](/tutorials/basic/symbol.html) (Caution: written before Gluon existed)
-        * KVStore
-            * [Key-Value Store API](/tutorials/python/kvstore.html)
-    * Gluon APIs
-        * Blocks and Operators
-            * [Blocks](/tutorials/gluon/gluon.html) ([Alternative](http://gluon.mxnet.io/chapter03_deep-neural-networks/plumbing.html) <img src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg" alt="External link" height="15px" style="margin: 0px 0px 3px 3px;"/>)
-            * [Custom Blocks](/tutorials/gluon/custom_layer.html) ([Alternative](http://gluon.mxnet.io/chapter03_deep-neural-networks/custom-layer.html) <img src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg" alt="External link" height="15px" style="margin: 0px 0px 3px 3px;"/>)
-            * [HybridBlocks](/tutorials/gluon/hybrid.html) ([Alternative](http://gluon.mxnet.io/chapter07_distributed-learning/hybridize.html) <img src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg" alt="External link" height="15px" style="margin: 0px 0px 3px 3px;"/>)
-            * [Block Naming](/tutorials/gluon/naming.html)
-            * [Custom Operators](/tutorials/gluon/customop.html)
-            * [Control Flow operators](/tutorials/control_flow/ControlFlowTutorial.html)
-        * Autograd
-            * [AutoGrad API](/tutorials/gluon/autograd.html)
-            * [AutoGrad API with chain rule](http://gluon.mxnet.io/chapter01_crashcourse/autograd.html) <img src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg" alt="External link" height="15px" style="margin: 0px 0px 3px 3px;"/>
-            * [AutoGrad API with Python control flow](http://gluon-crash-course.mxnet.io/autograd.html) <img src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg" alt="External link" height="15px" style="margin: 0px 0px 3px 3px;"/>
-        * Data
-            * [Datasets and DataLoaders](/tutorials/gluon/datasets.html)
-            * [Data Transforms](/tutorials/gluon/transforms.html)
-            * [Applying Data Augmentation](/tutorials/gluon/data_augmentation.html)
-            * [Data Augmentation with Masks (for Object Segmentation)](https://mxnet.incubator.apache.org/tutorials/python/data_augmentation_with_masks.html)
-        * Fit API
-            * [Using Fit API](/tutorials/gluon/fit_api_tutorial.html)
-</div> <!--end of gluon-->
-
-<div class="module">
-
-* Getting Started
-    * [Module API](/tutorials/basic/module.html)
-    * [MNIST Handwritten Digit Classification](/tutorials/python/mnist.html)
-* Models
-    * [Linear Regression](/tutorials/python/linear-regression.html)
-    * [Linear Regression with Sparse Symbols](/tutorials/sparse/train.html)
-    * [MNIST Handwritten Digit Classification](/tutorials/python/mnist.html)
-    * [Movie Review Classification using Convolutional Networks](/tutorials/nlp/cnn.html)
-    * [Generative Adversarial Networks (GANs)](/tutorials/unsupervised_learning/gan.html)
-    * [Speech Recognition with Connectionist Temporal Classification Loss](/tutorials/speech_recognition/ctc.html)
-* Practitioner Guides
-    * [Predicting on new images using a pre-trained ImageNet model](/tutorials/python/predict_image.html)
-    * [Fine-Tuning a pre-trained ImageNet model with a new dataset](/faq/finetune.html)
-    * [Large-Scale Multi-Host Multi-GPU Image Classification](/tutorials/vision/large_scale_classification.html)
-    * [Importing an ONNX model into MXNet](/tutorials/onnx/super_resolution.html)
-    * [Optimizing Deep Learning Computation Graphs with TensorRT](/tutorials/tensorrt/inference_with_trt.html)
-* API Guides
-    * Core APIs
-        * NDArray
-            * [NDArray API](/tutorials/gluon/ndarray.html)
-            * [Advanced NDArray API](/tutorials/basic/ndarray.html)
-            * [NDArray Indexing](/tutorials/basic/ndarray_indexing.html)
-            * Sparse NDArray
-                * [Sparse Gradient Updates (RowSparseNDArray)](/tutorials/sparse/row_sparse.html)
-                * [Compressed Sparse Row Storage Format (CSRNDArray)](/tutorials/sparse/csr.html)
-                * [Linear Regression with Sparse Symbols](/tutorials/sparse/train.html)
-        * Symbol
-            * [Symbol API](/tutorials/basic/symbol.html)
-        * KVStore
-            * [Key-Value Store API](/tutorials/python/kvstore.html)
-    * Module APIs
-        * [Module API](/tutorials/basic/module.html)
-        * Data
-            * [Data Iterators](/tutorials/basic/data.html)
-            * [Applying Data Augmentation](/tutorials/python/data_augmentation.html)
-            * [Types of Data Augmentation](/tutorials/python/types_of_data_augmentation.html)
-</div> <!--end of module-->
-
-<hr>
-
-## Scala Tutorials
-* [MXNet-Scala Tutorials Overview](/tutorials/scala/)
-* Getting Started
-    * [MXNet and JetBrain's IntelliJ](/tutorials/scala/mxnet_scala_on_intellij.html)
-* Models
-    * [MNIST Handwritten Digit Recognition with Fully Connected Network](/tutorials/scala/mnist.html)
-    * [Barack Obama speech generation with Character-level LSTM](/tutorials/scala/char_lstm.html)
-* [MXNet-Scala  Examples](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/scala/org/apache/mxnetexamples)
-<hr>
-
-## Java Tutorials
-* Getting Started
-    * [Developer Environment Setup on IntelliJ IDE](/tutorials/java/mxnet_java_on_intellij.html)
-* [Multi Object Detection using pre-trained Single Shot Detector (SSD) Model](/tutorials/java/ssd_inference.html)
-* [MXNet-Java  Examples](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer)
-<hr>
-
-## Clojure Tutorials
-* [How to use the NDArray API to perform vector/matrix/tensor operations](../api/clojure/ndarray.html)
-* [Multi-GPU and multi-host distributed training with the KVStore API](../api/clojure/kvstore.html)
-* [How to use the Symbol API to assemble neural networks from layers](../api/clojure/symbol.html)
-* [How to use the Module API](../api/clojure/module.html) (deprecated)
-<hr>
-
-## C++ Tutorials
-
-* Models
-    * [MNIST Handwritten Digit Recognition with Fully Connected Network](/tutorials/c%2B%2B/basics.html)
-
-* Backends
-    * [Subgraph API](/tutorials/c%2B%2B/subgraphAPI.html)
-
-* Inference
-    * [C++ Inference](/tutorials/c%2B%2B/mxnet_cpp_inference_tutorial.html)
-
-<hr>
-
-## R Tutorials
-
-* Getting Started
-    * [Basic Classification & Regression](/tutorials/r/fiveMinutesNeuralNetwork.html)
-    * [Using a pre-trained model for Image Classification](/tutorials/r/classifyRealImageWithPretrainedModel.html)
-* Models
-    * [MNIST Handwritten Digit Classification with Convolutional Network](/tutorials/r/mnistCompetition.html)
-    * [Shakespeare generation with Character-level RNN](/tutorials/r/charRnnModel.html)
-* API Guides
-    * [NDArray API](/tutorials/r/ndarray.html)
-    * [Symbol API](/tutorials/r/symbol.html)
-    * [Callbacks](/tutorials/r/CallbackFunction.html)
-    * [Custom Data Iterators](/tutorials/r/CustomIterator.html)
-    * [Custom Loss Functions](/tutorials/r/CustomLossFunction.html)
-
-<hr>
-
-## Perl Tutorials
-
-* Getting Started
-    * [Machine learning in Perl](http://blogs.perl.org/users/sergey_kolychev/2017/02/machine-learning-in-perl.html)
-    * [Calculator and Robo-Shakespeare](http://blogs.perl.org/users/sergey_kolychev/2017/04/machine-learning-in-perl-part2-a-calculator-handwritten-digits-and-roboshakespeare.html)
-* Gluon
-    * [DCGAN](http://blogs.perl.org/users/sergey_kolychev/2017/10/machine-learning-in-perl-part3-deep-convolutional-generative-adversarial-network.html)
-    * [Image classification and Style transfer](http://blogs.perl.org/users/sergey_kolychev/2018/07/machine-learning-in-perl-kyuubi-goes-to-a-modelzoo-during-the-starry-night.html)
-
-<hr>
-
-## Contributing Tutorials
-
-We really appreciate contributions, and tutorials are a great way to share your knowledge and help the community. After you have followed [these steps](https://github.com/apache/incubator-mxnet/tree/master/example#contributing), please submit a pull request on Github.
-
-And if you have any feedback on this section please raise an issue on Github.
diff --git a/docs/tutorials/java/index.md b/docs/tutorials/java/index.md
deleted file mode 100644
index faf6526fb824..000000000000
--- a/docs/tutorials/java/index.md
+++ /dev/null
@@ -1,25 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Tutorials
-
-```eval_rst
-.. toctree::
-   :glob:
-
-   *
-```
diff --git a/docs/tutorials/java/mxnet_java_on_intellij.md b/docs/tutorials/java/mxnet_java_on_intellij.md
deleted file mode 100644
index 82ac1e1b3f3c..000000000000
--- a/docs/tutorials/java/mxnet_java_on_intellij.md
+++ /dev/null
@@ -1,180 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Run MXNet Java Examples Using the IntelliJ IDE (macOS)
-
-This tutorial guides you through setting up a simple Java project in IntelliJ IDE on macOS and demonstrates usage of the MXNet Java APIs.
-
-## Prerequisites:
-To use this tutorial you need the following pre-requisites:
-
-- [Java 8 JDK](http://www.oracle.com/technetwork/java/javase/downloads/index.html)
-- [Maven](https://maven.apache.org/install.html)
-- [OpenCV](https://opencv.org/)
-- [IntelliJ IDEA](https://www.jetbrains.com/idea/) (One can download the community edition from [here](https://www.jetbrains.com/idea/download))
-
-### MacOS Prerequisites
-
-Run the following commands to install the prerequisites on MacOS.
-```
-/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
-brew update
-brew tap caskroom/versions
-brew cask install java8
-brew install maven
-brew install opencv
-```
-
-### Ubuntu Prerequisites
-
-Run the following commands to install the prerequisites on Ubuntu.
-
-```
-sudo apt-get install openjdk-8-jdk maven
-```
-
-
-## Set Up Your Project
-
-**Step 1.** Install and setup [IntelliJ IDEA](https://www.jetbrains.com/idea/)
-
-**Step 2.** Create a new Project:
-
-![intellij welcome](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/scala/intellij-welcome.png)
-
-From the IntelliJ welcome screen, select "Create New Project".
-
-Choose the Maven project type. 
-
-Select the checkbox for `Create from archetype`, then choose `org.apache.maven.archetypes:maven-archetype-quickstart` from the list below. More on this can be found on a Maven tutorial : [Maven in 5 Minutes](https://maven.apache.org/guides/getting-started/maven-in-five-minutes.html). 
-
-![maven project type - archetype](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tutorials/java/project-archetype.png)
-
-click `Next`.
-
-![project metadata](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tutorials/java/intellij-project-metadata.png)
-
-Set the project's metadata. For this tutorial, use the following:
-
-**GroupId**
-```
-mxnet
-```
-**ArtifactId**
-```
-javaMXNet
-```
-**Version**
-```
-1.0-SNAPSHOT
-```
-
-![project properties](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tutorials/java/intellij-project-properties.png)
-
-Review the project's properties. The settings can be left as their default.
-
-![project location](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tutorials/java/intellij-project-location.png)
-
-Set the project's location. The rest of the settings can be left as their default.
-
-![project 1](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tutorials/java/intellij-project-pom.png)
-
-After clicking Finish, you will be presented with the project's first view.
-The project's `pom.xml` will be open for editing.
-
-**Step 3.** The Java packages are currently available on Maven. Add the following under the `dependencies` tag :
-
-```html
-<dependency>
-  <groupId>org.apache.mxnet</groupId>
-  <artifactId>mxnet-full_2.11-osx-x86_64-cpu</artifactId>
-  <version>1.4.0</version>
-</dependency>
-```
-The official Java Packages have been released as part of MXNet 1.4 and are available on the [MXNet Maven package repository](https://search.maven.org/#search%7Cga%7C1%7Cg%3A%22org.apache.mxnet%22).
-
-Note :
-- Change the osx-x86_64 to linux-x86_64 if your platform is linux.
-- Change cpu into gpu if you have a gpu backed machine and want to use gpu.
-
-
-**Step 4.** Import dependencies with Maven:
-
-  - Note the prompt in the lower right corner that states "Maven projects need to be imported". If this is not visible, click on the little greed balloon that appears in the lower right corner.
-
-![import_dependencies](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tutorials/java/project-import-changes.png)
-
-Click "Import Changes" in this prompt.
-
-**Step 5.** Build the project:
-- To build the project, from the menu choose Build, and then choose Build Project.
-
-**Step 6.** Navigate to the App.java class in the project and paste the code in `main` method from HelloWorld.java from [Java Demo project](https://github.com/apache/incubator-mxnet/blob/java-api/scala-package/mxnet-demo/java-demo/src/main/java/mxnet/HelloWorld.java) on MXNet repository, overwriting the original hello world code.
-You can also grab the entire [Java Demo project](https://github.com/apache/incubator-mxnet/tree/java-api/scala-package/mxnet-demo/java-demo) and run it by following the instructions on the [README](https://github.com/apache/incubator-mxnet/blob/java-api/scala-package/mxnet-demo/java-demo/README.md).
-
-**Step 7.** Now run the App.java. 
-
-The result should be something similar to this:
-
-```
-Hello World!
-(1,2)
-Process finished with exit code 0
-```
-
-### Troubleshooting
-
-If you get an error, check the dependencies at the beginning of this tutorial. For example, you might see the following in the middle of the error messages, where `x.x` would the version it's looking for.
-
-```
-...
-Library not loaded: /usr/local/opt/opencv/lib/libopencv_calib3d.x.x.dylib
-...
-```
-
-This can be resolved be installing OpenCV.
-
-### Command Line Build Option
-
-- You can also compile the project by using the following command at the command line. Change directories to this project's root folder then run the following:
-
-```bash
-mvn clean install dependency:copy-dependencies
-```
-If the command succeeds, you should see a lot of info and some warning messages, followed by:
-
-```bash
-[INFO] ------------------------------------------------------------------------
-[INFO] BUILD SUCCESS
-[INFO] ------------------------------------------------------------------------
-[INFO] Total time: 3.475 s
-[INFO] Finished at: 2018-11-08T05:06:31-08:00
-[INFO] ------------------------------------------------------------------------
-```
-The build generates a new jar file in the `target` folder called `javaMXNet-1.0-SNAPSHOT.jar`.
-
-To run the App.java use the following command from the project's root folder and you should see the same output as we got when the project was run from IntelliJ.
-```bash
-java -cp "target/javaMXNet-1.0-SNAPSHOT.jar:target/dependency/*" mxnet.App
-```
-
-## Next Steps
-For more information about MXNet Java resources, see the following:
-
-* [Java Inference API](/api/java/index.html)
-* [Java Inference Examples](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer)
-* [MXNet Tutorials Index](http://mxnet.io/tutorials/index.html)
diff --git a/docs/tutorials/java/ssd_inference.md b/docs/tutorials/java/ssd_inference.md
deleted file mode 100644
index 38c29be23be4..000000000000
--- a/docs/tutorials/java/ssd_inference.md
+++ /dev/null
@@ -1,203 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Multi Object Detection using pre-trained SSD Model via Java Inference APIs
-
-This tutorial shows how to use MXNet Java Inference APIs to run inference on a pre-trained Single Shot Detector (SSD) Model.
-
-The SSD model is trained on the Pascal VOC 2012 dataset. The network is a SSD model built on Resnet50 as the base network to extract image features. The model is trained to detect the following entities (classes): ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']. For more details about the model, you can refer to the [MXNet SSD example](https://github.com/apache/incubator-mxnet/tree/master/example/ssd).
-
-## Prerequisites
-
-To complete this tutorial, you need the following:
-* [MXNet Java Setup on IntelliJ IDEA](mxnet_java_on_intellij.md) (Optional)
-* [wget](https://www.gnu.org/software/wget/) To download model artifacts
-* SSD Model artifacts
-    * Use the following script to get the SSD Model files :
-```bash
-data_path=/tmp/resnet50_ssd
-mkdir -p "$data_path"
-wget https://s3.amazonaws.com/model-server/models/resnet50_ssd/resnet50_ssd_model-symbol.json -P $data_path
-wget https://s3.amazonaws.com/model-server/models/resnet50_ssd/resnet50_ssd_model-0000.params -P $data_path
-wget https://s3.amazonaws.com/model-server/models/resnet50_ssd/synset.txt -P $data_path
-```
-* Test images  : A few sample images to run inference on.
-    * Use the following script to download sample images :
-```bash
-image_path=/tmp/resnet50_ssd/images
-mkdir -p "$image_path"
-cd $image_path
-wget https://cloud.githubusercontent.com/assets/3307514/20012567/cbb60336-a27d-11e6-93ff-cbc3f09f5c9e.jpg -O dog.jpg
-wget https://cloud.githubusercontent.com/assets/3307514/20012563/cbb41382-a27d-11e6-92a9-18dab4fd1ad3.jpg -O person.jpg
-```
-
-Alternately, you can get the entire SSD Model artifacts + images in one single script from the MXNet Repository by running [get_ssd_data.sh script](https://github.com/apache/incubator-mxnet/blob/master/scala-package/examples/scripts/infer/objectdetector/get_ssd_data.sh)  
-
-## Time to code!
-1\. Following the [MXNet Java Setup on IntelliJ IDEA](mxnet_java_on_intellij.md) tutorial, in the same project `JavaMXNet`, create a new empty class called : `ObjectDetectionTutorial.java`.
-
-2\. In the `main` function of `ObjectDetectionTutorial.java` define the downloaded model path and the image data paths. This is the same path where we downloaded the model artifacts and images in a previous step.
-
-```java
-String modelPathPrefix = "/tmp/resnet50_ssd/resnet50_ssd_model";
-String inputImagePath = "/tmp/resnet50_ssd/images/dog.jpg";
-```
-
-3\. We can run the inference code in this example on either CPU or GPU (if you have a GPU backed machine) by choosing the appropriate context.
-
-```java
-
-List<Context> context = getContext();
-...
-
-private static List<Context> getContext() {
-List<Context> ctx = new ArrayList<>();
-ctx.add(Context.cpu()); // Choosing CPU Context here
-
-return ctx;
-}
-```
-
-4\. To provide an input to the model, define the input shape to the model and the Input Data Descriptor (DataDesc) as shown below :
-
-```java
-Shape inputShape = new Shape(new int[] {1, 3, 512, 512});
-List<DataDesc> inputDescriptors = new ArrayList<DataDesc>();
-inputDescriptors.add(new DataDesc("data", inputShape, DType.Float32(), "NCHW"));
-```
-
-The input shape can be interpreted as follows : The input has a batch size of 1, with 3 RGB channels in the image, and the height and width of the image is 512 each.
-
-5\. To run an actual inference on the given image, add the following lines to the `ObjectDetectionTutorial.java` class :
-
-```java
-BufferedImage img = ObjectDetector.loadImageFromFile(inputImagePath);
-ObjectDetector objDet = new ObjectDetector(modelPathPrefix, inputDescriptors, context, 0);
-List<List<ObjectDetectorOutput>> output = objDet.imageObjectDetect(img, 3); // Top 3 objects detected will be returned
-```
-
-6\. Let's piece all of the above steps together by showing the final contents of the `ObjectDetectionTutorial.java`.
-
-```java
-package mxnet;
-
-import org.apache.mxnet.infer.javaapi.ObjectDetector;
-import org.apache.mxnet.infer.javaapi.ObjectDetectorOutput;
-import org.apache.mxnet.javaapi.Context;
-import org.apache.mxnet.javaapi.DType;
-import org.apache.mxnet.javaapi.DataDesc;
-import org.apache.mxnet.javaapi.Shape;
-
-import java.awt.image.BufferedImage;
-import java.util.ArrayList;
-import java.util.Arrays;
-import java.util.List;
-
-public class ObjectDetectionTutorial {
-
-    public static void main(String[] args) {
-
-        String modelPathPrefix = "/tmp/resnet50_ssd/resnet50_ssd_model";
-
-        String inputImagePath = "/tmp/resnet50_ssd/images/dog.jpg";
-
-        List<Context> context = getContext();
-
-        Shape inputShape = new Shape(new int[] {1, 3, 512, 512});
-
-        List<DataDesc> inputDescriptors = new ArrayList<DataDesc>();
-        inputDescriptors.add(new DataDesc("data", inputShape, DType.Float32(), "NCHW"));
-
-        BufferedImage img = ObjectDetector.loadImageFromFile(inputImagePath);
-        ObjectDetector objDet = new ObjectDetector(modelPathPrefix, inputDescriptors, context, 0);
-        List<List<ObjectDetectorOutput>> output = objDet.imageObjectDetect(img, 3);
-
-        printOutput(output, inputShape);
-    }
-
-
-    private static List<Context> getContext() {
-        List<Context> ctx = new ArrayList<>();
-        ctx.add(Context.cpu());
-
-        return ctx;
-    }
-
-    private static void printOutput(List<List<ObjectDetectorOutput>> output, Shape inputShape) {
-
-        StringBuilder outputStr = new StringBuilder();
-
-        int width = inputShape.get(3);
-        int height = inputShape.get(2);
-
-        for (List<ObjectDetectorOutput> ele : output) {
-            for (ObjectDetectorOutput i : ele) {
-                outputStr.append("Class: " + i.getClassName() + "\n");
-                outputStr.append("Probabilties: " + i.getProbability() + "\n");
-
-                List<Float> coord = Arrays.asList(i.getXMin() * width,
-                        i.getXMax() * height, i.getYMin() * width, i.getYMax() * height);
-                StringBuilder sb = new StringBuilder();
-                for (float c: coord) {
-                    sb.append(", ").append(c);
-                }
-                outputStr.append("Coord:" + sb.substring(2)+ "\n");
-            }
-        }
-        System.out.println(outputStr);
-
-    }
-}
-```
-
-7\. To compile and run this code, change directories to this project's root folder, then run the following:
-```bash
-mvn clean install dependency:copy-dependencies
-```
-
-The build generates a new jar file in the `target` folder called `javaMXNet-1.0-SNAPSHOT.jar`.
-
-To run the ObjectDetectionTutorial.java use the following command from the project's root folder.
-```bash
-java -cp "target/javaMXNet-1.0-SNAPSHOT.jar:target/dependency/*" mxnet.ObjectDetectionTutorial
-```
-
-You should see a similar output being generated for the dog image that we used:
-```bash
-Class: car
-Probabilties: 0.99847263
-Coord:312.21335, 72.02908, 456.01443, 150.66176
-Class: bicycle
-Probabilties: 0.9047381
-Coord:155.9581, 149.96365, 383.83694, 418.94516
-Class: dog
-Probabilties: 0.82268167
-Coord:83.82356, 179.14001, 206.63783, 476.78754
-```
-
-![dog_1](https://cloud.githubusercontent.com/assets/3307514/20012567/cbb60336-a27d-11e6-93ff-cbc3f09f5c9e.jpg)
-
-The results returned by the inference call translate into the regions in the image where the model detected objects.
-
-![dog_2](https://cloud.githubusercontent.com/assets/3307514/19171063/91ec2792-8be0-11e6-983c-773bd6868fa8.png)
-
-## Next Steps
-For more information about MXNet Java resources, see the following:
-
-* [Java Inference API](/api/java/index.md)
-* [Java Inference Examples](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer)
-* [MXNet Tutorials Index](/tutorials/index.md)
diff --git a/docs/tutorials/mkldnn/MKLDNN_README.md b/docs/tutorials/mkldnn/MKLDNN_README.md
deleted file mode 100644
index d8f78ecf71a7..000000000000
--- a/docs/tutorials/mkldnn/MKLDNN_README.md
+++ /dev/null
@@ -1,291 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Build/Install MXNet with MKL-DNN
-
-A better training and inference performance is expected to be achieved on Intel-Architecture CPUs with MXNet built with [Intel MKL-DNN](https://github.com/intel/mkl-dnn) on multiple operating system, including Linux, Windows and MacOS.
-In the following sections, you will find build instructions for MXNet with Intel MKL-DNN on Linux, MacOS and Windows.
-
-Please find MKL-DNN optimized operators and other features in the [MKL-DNN operator list](../mkldnn/operator_list.md).
-
-The detailed performance data collected on Intel Xeon CPU with MXNet built with Intel MKL-DNN can be found [here](https://mxnet.incubator.apache.org/faq/perf.html#intel-cpu).
-
-
-<h2 id="0">Contents</h2>
-
-* [1. Linux](#1)
-* [2. MacOS](#2)
-* [3. Windows](#3)
-* [4. Verify MXNet with python](#4)
-* [5. Enable MKL BLAS](#5)
-* [6. Enable graph optimization](#6)
-* [7. Quantization](#7)
-* [8. Support](#8)
-
-<h2 id="1">Linux</h2>
-
-### Prerequisites
-
-```
-sudo apt-get update
-sudo apt-get install -y build-essential git
-sudo apt-get install -y libopenblas-dev liblapack-dev
-sudo apt-get install -y libopencv-dev
-sudo apt-get install -y graphviz
-```
-
-### Clone MXNet sources
-
-```
-git clone --recursive https://github.com/apache/incubator-mxnet.git
-cd incubator-mxnet
-```
-
-### Build MXNet with MKL-DNN
-
-```
-make -j $(nproc) USE_OPENCV=1 USE_MKLDNN=1 USE_BLAS=mkl USE_INTEL_PATH=/opt/intel
-```
-
-If you don't have the full [MKL](https://software.intel.com/en-us/intel-mkl) library installation, you might use OpenBLAS as the blas library, by setting USE_BLAS=openblas.
-
-<h2 id="2">MacOS</h2>
-
-### Prerequisites
-
-Install the dependencies, required for MXNet, with the following commands:
-
-- [Homebrew](https://brew.sh/)
-- llvm (clang in macOS does not support OpenMP)
-- OpenCV (for computer vision operations)
-
-```
-# Paste this command in Mac terminal to install Homebrew
-/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
-
-# install dependency
-brew update
-brew install pkg-config
-brew install graphviz
-brew tap homebrew/core
-brew install opencv
-brew tap homebrew/versions
-brew install llvm
-```
-
-### Clone MXNet sources
-
-```
-git clone --recursive https://github.com/apache/incubator-mxnet.git
-cd incubator-mxnet
-```
-
-### Build MXNet with MKL-DNN
-
-```
-LIBRARY_PATH=$(brew --prefix llvm)/lib/ make -j $(sysctl -n hw.ncpu) CC=$(brew --prefix llvm)/bin/clang CXX=$(brew --prefix llvm)/bin/clang++ USE_OPENCV=1 USE_OPENMP=1 USE_MKLDNN=1 USE_BLAS=apple
-```
-
-<h2 id="3">Windows</h2>
-
-On Windows, you can use [Micrsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/) and [Microsoft Visual Studio 2017](https://www.visualstudio.com/downloads/) to compile MXNet with Intel MKL-DNN.
-[Micrsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/) is recommended.
-
-**Visual Studio 2015**
-
-To build and install MXNet yourself, you need the following dependencies. Install the required dependencies:
-
-1. If [Microsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/) is not already installed, download and install it. You can download and install the free community edition.
-2. Download and Install [CMake 3](https://cmake.org/files/v3.14/cmake-3.14.0-win64-x64.msi) if it is not already installed.
-3. Download [OpenCV 3](https://sourceforge.net/projects/opencvlibrary/files/3.4.5/opencv-3.4.5-vc14_vc15.exe/download), and unzip the OpenCV package, set the environment variable ```OpenCV_DIR``` to point to the ```OpenCV build directory``` (e.g.,```OpenCV_DIR = C:\opencv\build ```). Also, add the OpenCV bin directory (```C:\opencv\build\x64\vc14\bin``` for example) to the ``PATH`` variable.
-4. If you have Intel Math Kernel Library (Intel MKL) installed, set ```MKL_ROOT``` to point to ```MKL``` directory that contains the ```include``` and ```lib```. If you want to use MKL blas, you should set ```-DUSE_BLAS=mkl``` when cmake. Typically, you can find the directory in ```C:\Program Files (x86)\IntelSWTools\compilers_and_libraries\windows\mkl```.
-5. If you don't have the Intel Math Kernel Library (MKL) installed, download and install [OpenBLAS](http://sourceforge.net/projects/openblas/files/v0.2.14/), or build the latest version of OpenBLAS from source. Note that you should also download ```mingw64.dll.zip``` along with openBLAS and add them to PATH.
-6. Set the environment variable ```OpenBLAS_HOME``` to point to the ```OpenBLAS``` directory that contains the ```include``` and ```lib``` directories. Typically, you can find the directory in ```C:\Downloads\OpenBLAS\```.
-
-After you have installed all of the required dependencies, build the MXNet source code:
-
-1. Start a Visual Studio command prompt by click windows Start menu>>Visual Studio 2015>>VS2015 X64 Native Tools Command Prompt, and download the MXNet source code from [GitHub](https://github.com/apache/incubator-mxnet) by the command:
-```
-git clone --recursive https://github.com/apache/incubator-mxnet.git
-cd C:\incubator-mxent
-```
-2. Enable Intel MKL-DNN by -DUSE_MKLDNN=1. Use [CMake 3](https://cmake.org/) to create a Visual Studio solution in ```./build```. Make sure to specify the architecture in the
-command:
-```
->mkdir build
->cd build
->cmake -G "Visual Studio 14 Win64" .. -DUSE_CUDA=0 -DUSE_CUDNN=0 -DUSE_NVRTC=0 -DUSE_OPENCV=1 -DUSE_OPENMP=1 -DUSE_PROFILER=1 -DUSE_BLAS=open -DUSE_LAPACK=1 -DUSE_DIST_KVSTORE=0 -DCUDA_ARCH_NAME=All -DUSE_MKLDNN=1 -DCMAKE_BUILD_TYPE=Release
-```
-3. Enable Intel MKL-DNN and Intel MKL as BLAS library by the command:
-```
->"C:\Program Files (x86)\IntelSWTools\compilers_and_libraries\windows\mkl\bin\mklvars.bat" intel64
->cmake -G "Visual Studio 14 Win64" .. -DUSE_CUDA=0 -DUSE_CUDNN=0 -DUSE_NVRTC=0 -DUSE_OPENCV=1 -DUSE_OPENMP=1 -DUSE_PROFILER=1 -DUSE_BLAS=mkl -DUSE_LAPACK=1 -DUSE_DIST_KVSTORE=0 -DCUDA_ARCH_NAME=All -DUSE_MKLDNN=1 -DCMAKE_BUILD_TYPE=Release -DMKL_ROOT="C:\Program Files (x86)\IntelSWTools\compilers_and_libraries\windows\mkl"
-```
-4. After the CMake successfully completed, in Visual Studio, open the solution file ```.sln``` and compile it, or compile the MXNet source code by using following command:
-```r
-msbuild mxnet.sln /p:Configuration=Release;Platform=x64 /maxcpucount
-```
-   These commands produce mxnet library called ```libmxnet.dll``` in the ```./build/Release/``` or ```./build/Debug``` folder. Also ```libmkldnn.dll``` with be in the ```./build/3rdparty/mkldnn/src/Release/```
-
-5. Make sure that all the dll files used above(such as `libmkldnn.dll`, `libmklml*.dll`, `libiomp5.dll`, `libopenblas*.dll`, etc) are added to the system PATH. For convinence, you can put all of them to ```\windows\system32```. Or you will come across `Not Found Dependencies` when loading MXNet.
-
-**Visual Studio 2017**
-
-User can follow the same steps of Visual Studio 2015 to build MXNET with MKL-DNN, but change the version related command, for example,```C:\opencv\build\x64\vc15\bin``` and build command is as below:
-
-```
->cmake -G "Visual Studio 15 Win64" .. -DUSE_CUDA=0 -DUSE_CUDNN=0 -DUSE_NVRTC=0 -DUSE_OPENCV=1 -DUSE_OPENMP=1 -DUSE_PROFILER=1 -DUSE_BLAS=mkl -DUSE_LAPACK=1 -DUSE_DIST_KVSTORE=0 -DCUDA_ARCH_NAME=All -DUSE_MKLDNN=1 -DCMAKE_BUILD_TYPE=Release -DMKL_ROOT="C:\Program Files (x86)\IntelSWTools\compilers_and_libraries\windows\mkl"
-
-```
-
-<h2 id="4">Verify MXNet with python</h2>
-
-Preinstall python and some dependent modules:
-```
-pip install numpy graphviz
-set PYTHONPATH=[workdir]\incubator-mxnet\python
-```
-or install mxnet
-```
-cd python
-sudo python setup.py install
-python -c "import mxnet as mx;print((mx.nd.ones((2, 3))*2).asnumpy());"
-```
-Expected Output:
-```
-[[ 2.  2.  2.]
- [ 2.  2.  2.]]
-```
-### Verify whether MKL-DNN works
-
-After MXNet is installed, you can verify if MKL-DNN backend works well with a single Convolution layer.
-```
-import mxnet as mx
-import numpy as np
-
-num_filter = 32
-kernel = (3, 3)
-pad = (1, 1)
-shape = (32, 32, 256, 256)
-
-x = mx.sym.Variable('x')
-w = mx.sym.Variable('w')
-y = mx.sym.Convolution(data=x, weight=w, num_filter=num_filter, kernel=kernel, no_bias=True, pad=pad)
-exe = y.simple_bind(mx.cpu(), x=shape)
-
-exe.arg_arrays[0][:] = np.random.normal(size=exe.arg_arrays[0].shape)
-exe.arg_arrays[1][:] = np.random.normal(size=exe.arg_arrays[1].shape)
-
-exe.forward(is_train=False)
-o = exe.outputs[0]
-t = o.asnumpy()
-```
-
-More detailed debugging and profiling information can be logged by setting the environment variable 'MKLDNN_VERBOSE':
-```
-export MKLDNN_VERBOSE=1
-```
-For example, by running above code snippet, the following debugging logs providing more insights on MKL-DNN primitives `convolution` and `reorder`. That includes: Memory layout, infer shape and the time cost of primitive execution.
-```
-mkldnn_verbose,exec,reorder,jit:uni,undef,in:f32_nchw out:f32_nChw16c,num:1,32x32x256x256,6.47681
-mkldnn_verbose,exec,reorder,jit:uni,undef,in:f32_oihw out:f32_OIhw16i16o,num:1,32x32x3x3,0.0429688
-mkldnn_verbose,exec,convolution,jit:avx512_common,forward_inference,fsrc:nChw16c fwei:OIhw16i16o fbia:undef fdst:nChw16c,alg:convolution_direct,mb32_g1ic32oc32_ih256oh256kh3sh1dh0ph1_iw256ow256kw3sw1dw0pw1,9.98193
-mkldnn_verbose,exec,reorder,jit:uni,undef,in:f32_oihw out:f32_OIhw16i16o,num:1,32x32x3x3,0.0510254
-mkldnn_verbose,exec,reorder,jit:uni,undef,in:f32_nChw16c out:f32_nchw,num:1,32x32x256x256,20.4819
-```
-
-<h2 id="5">Enable MKL BLAS</h2>
-
-With MKL BLAS, the performace is expected to furtherly improved with variable range depending on the computation load of the models.
-You can redistribute not only dynamic libraries but also headers, examples and static libraries on accepting the license [Intel Simplified license](https://software.intel.com/en-us/license/intel-simplified-software-license).
-Installing the full MKL installation enables MKL support for all operators under the linalg namespace.
-
-  1. Download and install the latest full MKL version following instructions on the [intel website.](https://software.intel.com/en-us/mkl) You can also install MKL through [YUM](https://software.intel.com/en-us/articles/installing-intel-free-libs-and-python-yum-repo) or [APT](https://software.intel.com/en-us/articles/installing-intel-free-libs-and-python-apt-repo) Repository.
-
-  2. Run `make -j ${nproc} USE_BLAS=mkl`
-
-  3. Navigate into the python directory
-
-  4. Run `sudo python setup.py install`
-
-### Verify whether MKL works
-
-After MXNet is installed, you can verify if MKL BLAS works well with a single dot layer.
-
-```
-import mxnet as mx
-import numpy as np
-
-shape_x = (1, 10, 8)
-shape_w = (1, 12, 8)
-
-x_npy = np.random.normal(0, 1, shape_x)
-w_npy = np.random.normal(0, 1, shape_w)
-
-x = mx.sym.Variable('x')
-w = mx.sym.Variable('w')
-y = mx.sym.batch_dot(x, w, transpose_b=True)
-exe = y.simple_bind(mx.cpu(), x=x_npy.shape, w=w_npy.shape)
-
-exe.forward(is_train=False)
-o = exe.outputs[0]
-t = o.asnumpy()
-```
-
-You can open the `MKL_VERBOSE` flag by setting environment variable:
-```
-export MKL_VERBOSE=1
-```
-Then by running above code snippet, you probably will get the following output message which means `SGEMM` primitive from MKL are called. Layout information and primitive execution performance are also demonstrated in the log message.
-```
-Numpy + Intel(R) MKL: THREADING LAYER: (null)
-Numpy + Intel(R) MKL: setting Intel(R) MKL to use INTEL OpenMP runtime
-Numpy + Intel(R) MKL: preloading libiomp5.so runtime
-MKL_VERBOSE Intel(R) MKL 2019.0 Update 3 Product build 20190125 for Intel(R) 64 architecture Intel(R) Advanced Vector Extensions 512 (Intel(R) AVX-512) enabled processors, Lnx 2.40GHz lp64 intel_thread NMICDev:0
-MKL_VERBOSE SGEMM(T,N,12,10,8,0x7f7f927b1378,0x1bc2140,8,0x1ba8040,8,0x7f7f927b1380,0x7f7f7400a280,12) 8.93ms CNR:OFF Dyn:1 FastMM:1 TID:0  NThr:40 WDiv:HOST:+0.000
-```
-
-<h2 id="6">Enable graph optimization</h2>
-
-Graph optimization with subgraph is available and enabled by default in master branch. For MXNet release v1.5, you can manually enable it by:
-
-```
-export MXNET_SUBGRAPH_BACKEND=MKLDNN
-```
-
-This limitations of this experimental feature are:
-
-- Use this feature only for inference. When training, be sure to turn the feature off by unsetting the `MXNET_SUBGRAPH_BACKEND` environment variable.
-
-- This feature will only run on the CPU, even if you're using a GPU-enabled build of MXNet.
-
-
-<h2 id="7">Quantization and Inference with INT8</h2>
-
-Benefiting from Intel MKL-DNN, MXNet built with Intel MKL-DNN brings outstanding performance improvement on quantization and inference with INT8 Intel CPU Platform on Intel Xeon Scalable Platform.
-
-- [CNN Quantization Examples](https://github.com/apache/incubator-mxnet/tree/master/example/quantization).
-
-- [Model Quantization for Production-Level Neural Network Inference](https://cwiki.apache.org/confluence/display/MXNET/MXNet+Graph+Optimization+and+Quantization+based+on+subgraph+and+MKL-DNN).
-
-<h2 id="8">Next Steps and Support</h2>
-
-- For questions or support specific to MKL, visit the [Intel MKL](https://software.intel.com/en-us/mkl) website.
-
-- For questions or support specific to MKL, visit the [Intel MKLDNN](https://github.com/intel/mkl-dnn) website.
-
-- If you find bugs, please open an issue on GitHub for [MXNet with MKL](https://github.com/apache/incubator-mxnet/labels/MKL) or [MXNet with MKLDNN](https://github.com/apache/incubator-mxnet/labels/MKLDNN).
diff --git a/docs/tutorials/mkldnn/index.md b/docs/tutorials/mkldnn/index.md
deleted file mode 100644
index faf6526fb824..000000000000
--- a/docs/tutorials/mkldnn/index.md
+++ /dev/null
@@ -1,25 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Tutorials
-
-```eval_rst
-.. toctree::
-   :glob:
-
-   *
-```
diff --git a/docs/tutorials/mkldnn/mkldnn_quantization.md b/docs/tutorials/mkldnn/mkldnn_quantization.md
deleted file mode 100644
index a5547c02790c..000000000000
--- a/docs/tutorials/mkldnn/mkldnn_quantization.md
+++ /dev/null
@@ -1,259 +0,0 @@
-
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Quantize custom models with MKL-DNN backend
-
-This document is to introduce how to quantize the customer models from FP32 to INT8 with Apache/MXNet toolkit and APIs under Intel CPU.
-
-If you are not familiar with Apache/MXNet quantization flow, please reference [quantization blog](https://medium.com/apache-mxnet/model-quantization-for-production-level-neural-network-inference-f54462ebba05) first, and the performance data is shown in [Apache/MXNet C++ interface](https://github.com/apache/incubator-mxnet/tree/master/cpp-package/example/inference) and [GluonCV](https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html). 
-
-## Installation and Prerequisites
-
-Installing MXNet with MKLDNN backend is an easy and essential process. You can follow [How to build and install MXNet with MKL-DNN backend](https://mxnet.incubator.apache.org/tutorials/mkldnn/MKLDNN_README.html) to build and install MXNet from source. Also, you can install the release or nightly version via PyPi and pip directly by running:
-
-```
-# release version
-pip install mxnet-mkl
-# nightly version
-pip install mxnet-mkl --pre
-```
-
-## Image Classification Demo
-
-A quantization script [imagenet_gen_qsym_mkldnn.py](https://github.com/apache/incubator-mxnet/blob/master/example/quantization/imagenet_gen_qsym_mkldnn.py) has been designed to launch quantization for image-classification models. This script is  integrated with [Gluon-CV modelzoo](https://gluon-cv.mxnet.io/model_zoo/classification.html), so that all pre-trained models can be downloaded from Gluon-CV and then converted for quantization. For details, you can refer [Model Quantization with Calibration Examples](https://github.com/apache/incubator-mxnet/blob/master/example/quantization/README.md).
-
-## Integrate Quantization Flow to Your Project
-
-Quantization flow works for both symbolic and Gluon models. If you're using Gluon, you can first refer [Saving and Loading Gluon Models](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/save_load_params.html) to hybridize your computation graph and export it as a symbol before running quantization.
-
-In general, the quantization flow includes 4 steps. The user can get the acceptable accuracy from step 1 to 3 with minimum effort. Most of thing in this stage is out-of-box and the data scientists and researchers only need to focus on how to represent data and layers in their model. After a quantized model is generated, you may want to deploy it online and the performance will be the next key point. Thus, step 4, calibration, can improve the performance a lot by reducing lots of runtime calculation.
-
-![quantization flow](https://github.com/dmlc/web-data/raw/master/mxnet/tutorials/mkldnn/quantization/quantization.png)
-
-Now, we are going to take Gluon ResNet18 as an example to show how each step work.
-
-### Initialize Model
-
-```python
-import logging
-import mxnet as mx
-from mxnet.gluon.model_zoo import vision
-from mxnet.contrib.quantization import *
-
-logging.basicConfig()
-logger = logging.getLogger('logger')
-logger.setLevel(logging.INFO)
-
-batch_shape = (1, 3, 224, 224)
-resnet18 = vision.resnet18_v1(pretrained=True)
-resnet18.hybridize()
-resnet18.forward(mx.nd.zeros(batch_shape))
-resnet18.export('resnet18_v1')
-sym, arg_params, aux_params = mx.model.load_checkpoint('resnet18_v1', 0)
-# (optional) visualize float32 model
-mx.viz.plot_network(sym)
-```
-First, we download resnet18-v1 model from gluon modelzoo and export it as a symbol. You can visualize float32 model. Below is a raw residual block.
-
-![float32 model](https://github.com/dmlc/web-data/raw/master/mxnet/tutorials/mkldnn/quantization/fp32_raw.png)
-
-#### Model Fusion
-
-```python
-sym = sym.get_backend_symbol('MKLDNN_QUANTIZE')
-# (optional) visualize fused float32 model
-mx.viz.plot_network(sym)
-```
-It's important to add this line to enable graph fusion before quantization to get better performance. Below is a fused residual block. Batchnorm, Activation and elemwise_add are fused into Convolution.
-
-![float32 fused model](https://github.com/dmlc/web-data/raw/master/mxnet/tutorials/mkldnn/quantization/fp32_fusion.png)
-
-### Quantize Model
-
-A python interface `quantize_graph` is provided for the user. Thus, it is very flexible for the data scientist to construct the expected models based on different requirements in a real deployment.
-
-```python
-# quantize configs
-# set exclude layers
-excluded_names = []
-# set calib mode.
-calib_mode = 'none'
-# set calib_layer
-calib_layer = None
-# set quantized_dtype
-quantized_dtype = 'auto'
-logger.info('Quantizing FP32 model Resnet18-V1')
-qsym, qarg_params, aux_params, collector = quantize_graph(sym=sym, arg_params=arg_params, aux_params=aux_params,
-                                                          ctx=mx.cpu(), excluded_sym_names=excluded_names,
-                                                          calib_mode=calib_mode, calib_layer=calib_layer,
-                                                          quantized_dtype=quantized_dtype, logger=logger)
-# (optional) visualize quantized model
-mx.viz.plot_network(qsym)
-# save quantized model
-mx.model.save_checkpoint('quantized-resnet18_v1', 0, qsym, qarg_params, aux_params)
-```
-
-By applying `quantize_graph` to the symbolic model, a new quantized model can be generated, named `qsym` along with its parameters. We can see `_contrib_requantize` operators are inserted after `Convolution` to convert the INT32 output to FP32. 
-
-![none calibrated model](https://github.com/dmlc/web-data/raw/master/mxnet/tutorials/mkldnn/quantization/none_calib.png)
-
-Below table gives some descriptions.
-
-| param              | type            | description|
-|--------------------|-----------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| excluded_sym_names | list of strings | A list of strings representing the names of the symbols that users want to excluding from being quantized.|
-| calib_mode         | str             | If calib_mode='none', no calibration will be used and the thresholds for requantization after the corresponding layers will be calculated at runtime by calling min  and max operators. The quantized models generated in this mode are normally 10-20% slower than those with  calibrations during inference.<br>If calib_mode='naive', the min and max values of the layer outputs from a calibration dataset will be directly taken as the thresholds for quantization.<br>If calib_mode='entropy', the thresholds for quantization will be derived such that the KL divergence between the distributions of FP32 layer outputs and  quantized layer outputs is minimized based upon the calibration dataset. |
-| calib_layer        | function        | Given a layer's output name in string, return True or False for deciding whether to calibrate this layer.<br>If yes, the statistics of the layer's output will be collected; otherwise, no information of the layer's output will be collected.<br>If not provided, all the layers' outputs that need requantization will be collected.|
-| quantized_dtype    | str             | The quantized destination type for input data. Currently support 'int8', 'uint8' and 'auto'.<br>'auto' means automatically select output type according to calibration result.|
-
-### Evaluate & Tune
-
-Now, you get a pair of quantized symbol and params file for inference. For Gluon inference, only difference is to load model and params by a SymbolBlock as below example:
-
-```python
-quantized_net = mx.gluon.SymbolBlock.imports('quantized-resnet18_v1-symbol.json', 'data', 'quantized-resnet18_v1-0000.params')
-quantized_net.hybridize(static_shape=True, static_alloc=True)
-batch_size = 1
-data = mx.nd.ones((batch_size,3,224,224))
-quantized_net(data)
-```
-
-Now, you can get the accuracy from a quantized network. Furthermore, you can try to select different layers or OPs to be quantized by `excluded_sym_names` parameter and figure out an acceptable accuracy.
-
-### Calibrate Model (optional for performance)
-
-The quantized model generated in previous steps can be very slow during inference since it will calculate min and max at runtime. We recommend using offline calibration for better performance by setting `calib_mode` to `naive` or `entropy`. And then calling `set_monitor_callback` api to collect layer information with a subset of the validation datasets before int8 inference.
-
-```python
-# quantization configs
-# set exclude layers
-excluded_names = []
-# set calib mode.
-calib_mode = 'naive'
-# set calib_layer
-calib_layer = None
-# set quantized_dtype
-quantized_dtype = 'auto'
-logger.info('Quantizing FP32 model resnet18-V1')
-cqsym, cqarg_params, aux_params, collector = quantize_graph(sym=sym, arg_params=arg_params, aux_params=aux_params,
-                                                            ctx=mx.cpu(), excluded_sym_names=excluded_names,
-                                                            calib_mode=calib_mode, calib_layer=calib_layer,
-                                                            quantized_dtype=quantized_dtype, logger=logger)
-
-# download imagenet validation dataset
-mx.test_utils.download('http://data.mxnet.io/data/val_256_q90.rec', 'dataset.rec')
-# set rgb info for data
-mean_std = {'mean_r': 123.68, 'mean_g': 116.779, 'mean_b': 103.939, 'std_r': 58.393, 'std_g': 57.12, 'std_b': 57.375}
-# set batch size
-batch_size = 16
-# create DataIter
-data = mx.io.ImageRecordIter(path_imgrec='dataset.rec', batch_size=batch_size, data_shape=batch_shape[1:], rand_crop=False, rand_mirror=False, **mean_std)
-# create module
-mod = mx.mod.Module(symbol=sym, label_names=None, context=mx.cpu())
-mod.bind(for_training=False, data_shapes=data.provide_data, label_shapes=None)
-mod.set_params(arg_params, aux_params)
-
-# calibration configs
-# set num_calib_batches
-num_calib_batches = 5
-max_num_examples = num_calib_batches * batch_size
-# monitor FP32 Inference
-mod._exec_group.execs[0].set_monitor_callback(collector.collect, monitor_all=True)
-num_batches = 0
-num_examples = 0
-for batch in data:
-    mod.forward(data_batch=batch, is_train=False)
-    num_batches += 1
-    num_examples += batch_size
-    if num_examples >= max_num_examples:
-        break
-if logger is not None:
-    logger.info("Collected statistics from %d batches with batch_size=%d"
-                % (num_batches, batch_size))
-```
-
-After that, layer information will be filled into the `collector` returned by `quantize_graph` api. Then, you need to write the layer information into int8 model by calling `calib_graph` api.
-
-
-```python
-# write scaling factor into quantized symbol
-cqsym, cqarg_params, aux_params = calib_graph(qsym=cqsym, arg_params=arg_params, aux_params=aux_params,
-                                            collector=collector, calib_mode=calib_mode,
-                                            quantized_dtype=quantized_dtype, logger=logger)
-# (optional) visualize quantized model
-mx.viz.plot_network(cqsym)
-```
-
-Below is a quantized residual block with naive calibration. We can see `min_calib_range` and `max_calib_range` are written into `_contrib_requantize` operators.
-
-![naive calibrated model](https://github.com/dmlc/web-data/raw/master/mxnet/tutorials/mkldnn/quantization/naive_calib.png)
-
-When you get a quantized model with calibration, keeping sure to call fusion api again since this can fuse some `requantize` or `dequantize` operators for further performance improvement.
-
-```python
-# perform post-quantization fusion
-cqsym = cqsym.get_backend_symbol('MKLDNN_QUANTIZE')
-# (optional) visualize post-quantized model
-mx.viz.plot_network(cqsym)
-# save quantized model
-mx.model.save_checkpoint('quantized-resnet18_v1', 0, cqsym, cqarg_params, aux_params)
-```
-
-Below is a post-quantized residual block. We can see `_contrib_requantize` operators are fused into `Convolution` operators.
-
-![post-quantized model](https://github.com/dmlc/web-data/raw/master/mxnet/tutorials/mkldnn/quantization/post_quantize.png)
-
-BTW, You can also modify the `min_calib_range` and `max_calib_range` in the JSON file directly.
-
-```
-    {
-      "op": "_sg_mkldnn_conv", 
-      "name": "quantized_sg_mkldnn_conv_bn_act_6", 
-      "attrs": {
-        "max_calib_range": "3.562147", 
-        "min_calib_range": "0.000000", 
-        "quantized": "true", 
-        "with_act": "true", 
-        "with_bn": "true"
-      }, 
-......
-```
-
-### Tips for Model Calibration
-
-#### Accuracy Tuning
-
-- Try to use `entropy` calib mode;
-
-- Try to exclude some layers which may cause obvious accuracy drop;
-
-- Change calibration dataset by setting different `num_calib_batches` or shuffle your validation dataset;
-
-#### Performance Tuning
-
-- Keep sure to perform graph fusion before quantization;
-
-- If lots of `requantize` layers exist, keep sure to perform post-quantization fusion after calibration;
-
-- Compare the MXNet profile or `MKLDNN_VERBOSE` of float32 and int8 inference;
-
-## Deploy with Python/C++
-
-MXNet also supports deploy quantized models with C++. Refer [MXNet C++ Package](https://github.com/apache/incubator-mxnet/blob/master/cpp-package/README.md) for more details.
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/mkldnn/operator_list.md b/docs/tutorials/mkldnn/operator_list.md
deleted file mode 100644
index 0ef0f29f4cdc..000000000000
--- a/docs/tutorials/mkldnn/operator_list.md
+++ /dev/null
@@ -1,90 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# MKL-DNN Operator list
-
-MXNet MKL-DNN backend provides optimized implementations for various operators covering a broad range of applications including image classification, object detection, natural language processing. 
-
-To help users understanding MKL-DNN backend better, the following table summarizes the list of supported operators, data types and functionalities.  A subset of operators support faster training and inference by using a lower precision version. Refer to the following table's `INT8 Inference` column to see which operators are supported.
-
-| Operator           | Function                   | FP32 Training (backward) | FP32 Inference | INT8 Inference |
-| ---                | ---                        | ---                      | ---            | ---            |
-| **Convolution**    | 1D Convolution             | Y                        | Y              | N              |
-|                    | 2D Convolution             | Y                        | Y              | Y              |
-|                    | 3D Convolution             | Y                        | Y              | N              |
-| **Deconvolution**  | 2D Deconvolution           | Y                        | Y              | N              |
-|                    | 3D Deconvolution           | Y                        | Y              | N              |
-| **FullyConnected** | 1D-4D input, flatten=True  | N                        | Y              | Y              |
-|                    | 1D-4D input, flatten=False | N                        | Y              | Y              |
-| **Pooling**        | 2D max Pooling             | Y                        | Y              | Y              |
-|                    | 2D avg pooling             | Y                        | Y              | Y              |
-| **BatchNorm**      | 2D BatchNorm               | Y                        | Y              | N              |
-| **LRN**            | 2D LRN                     | Y                        | Y              | N              |
-| **Activation**     | ReLU                       | Y                        | Y              | Y              |
-|                    | Tanh                       | Y                        | Y              | N              |
-|                    | SoftReLU                   | Y                        | Y              | N              |
-|                    | Sigmoid                    | Y                        | Y              | N              |
-| **softmax**        | 1D-4D input                | Y                        | Y              | N              |
-| **Softmax_output** | 1D-4D input                | N                        | Y              | N              |
-| **Transpose**      | 1D-4D input                | N                        | Y              | N              |
-| **elemwise_add**   | 1D-4D input                | Y                        | Y              | Y              |
-| **Concat**         | 1D-4D input                | Y                        | Y              | Y              |
-| **slice**          | 1D-4D input                | N                        | Y              | N              |
-| **Reshape**        | 1D-4D input                | N                        | Y              | N              |
-| **Flatten**        | 1D-4D input                | N                        | Y              | N              |
-| **Quantization**   | 1D-4D input                | N                        | N              | Y              |
-| **Dequantization** | 1D-4D input                | N                        | N              | Y              |
-| **Requantization** | 1D-4D input                | N                        | N              | Y              |
-
-Besides direct operator optimizations, we also provide graph fusion passes listed in the table below. Users can choose to enable or disable these fusion patterns through environmental variables.
-
-For example, you can enable all FP32 fusion passes in the following table by:
-
-```
-export MXNET_SUBGRAPH_BACKEND=MKLDNN
-```
-
-And disable `Convolution + Activation` fusion by:
-
-```
-export MXNET_DISABLE_MKLDNN_FUSE_CONV_RELU=1
-```
-
-When generating the corresponding INT8 symbol, users can enable INT8 operator fusion passes as following:
-
-```
-# get qsym after model quantization
-qsym = qsym.get_backend_symbol('MKLDNN_QUANTIZE')
-qsym.save(symbol_name) # fused INT8 operators will be save into the symbol JSON file
-```
-
-| Fusion pattern                                            | Disable                             |
-| ---                                                       | ---                                 |
-| Convolution + Activation                                  | MXNET_DISABLE_MKLDNN_FUSE_CONV_RELU |
-| Convolution + elemwise_add                                | MXNET_DISABLE_MKLDNN_FUSE_CONV_SUM  |
-| Convolution + BatchNorm                                   | MXNET_DISABLE_MKLDNN_FUSE_CONV_BN   |
-| Convolution + Activation + elemwise_add                   |                                     |
-| Convolution + BatchNorm + Activation + elemwise_add       |                                     |
-| FullyConnected + Activation(ReLU)                         | MXNET_DISABLE_MKLDNN_FUSE_FC_RELU   |
-| Convolution (INT8) + re-quantization                      |                                     |
-| FullyConnected (INT8) + re-quantization                   |                                     |
-| FullyConnected (INT8) + re-quantization + de-quantization |                                     |
-
-
-To install MXNet MKL-DNN backend, please refer to [MKL-DNN backend readme](MKLDNN_README.md)
-
-For performance numbers, please refer to [performance on Intel CPU](../../faq/perf.md#intel-cpu)
diff --git a/docs/tutorials/nlp/cnn.md b/docs/tutorials/nlp/cnn.md
deleted file mode 100644
index 105bf03f9e2b..000000000000
--- a/docs/tutorials/nlp/cnn.md
+++ /dev/null
@@ -1,468 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Text Classification Using a Convolutional Neural Network on MXNet
-
-This tutorial is based of Yoon Kim's [paper](https://arxiv.org/abs/1408.5882) on using convolutional neural networks for sentence sentiment classification. The tutorial has been tested on MXNet 1.0 running under Python 2.7 and Python 3.6.
-
-For this tutorial, we will train a convolutional deep network model on movie review sentences from Rotten Tomatoes labeled with their sentiment. The result will be a model that can classify a sentence based on its sentiment (with 1 being a purely positive sentiment, 0 being a purely negative sentiment and 0.5 being neutral).
-
-Our first step will be to fetch the labeled training data of positive and negative sentiment sentences and process it into sets of vectors that are then randomly split into train and test sets.
-
-
-```python
-from __future__ import print_function
-
-from collections import Counter
-import itertools
-import numpy as np
-import re
-
-try:
-    # For Python 3.0 and later
-    from urllib.request import urlopen
-except ImportError:
-    # Fall back to Python 2's urllib2
-    from urllib2 import urlopen
-    
-def clean_str(string):
-    """
-    Tokenization/string cleaning.
-    Original from https://github.com/yoonkim/CNN_sentence/blob/master/process_data.py
-    """
-    string = re.sub(r"[^A-Za-z0-9(),!?\'\`]", " ", string)
-    string = re.sub(r"\'s", " \'s", string)
-    string = re.sub(r"\'ve", " \'ve", string)
-    string = re.sub(r"n\'t", " n\'t", string)
-    string = re.sub(r"\'re", " \'re", string)
-    string = re.sub(r"\'d", " \'d", string)
-    string = re.sub(r"\'ll", " \'ll", string)
-    string = re.sub(r",", " , ", string)
-    string = re.sub(r"!", " ! ", string)
-    string = re.sub(r"\(", " \( ", string)
-    string = re.sub(r"\)", " \) ", string)
-    string = re.sub(r"\?", " \? ", string)
-    string = re.sub(r"\s{2,}", " ", string)
-    
-    return string.strip().lower()
-
-def download_sentences(url):
-    """
-    Download sentences from specified URL. 
-    
-    Strip trailing newline, convert to Unicode.
-    """
-    
-    remote_file = urlopen(url)
-    return [line.decode('Latin1').strip() for line in remote_file.readlines()]
-    
-def load_data_and_labels():
-    """
-    Loads polarity data from files, splits the data into words and generates labels.
-    Returns split sentences and labels.
-    """
-
-    positive_examples = download_sentences('https://raw.githubusercontent.com/yoonkim/CNN_sentence/master/rt-polarity.pos')
-    negative_examples = download_sentences('https://raw.githubusercontent.com/yoonkim/CNN_sentence/master/rt-polarity.neg')
-    
-    # Tokenize
-    x_text = positive_examples + negative_examples
-    x_text = [clean_str(sent).split(" ") for sent in x_text]
-
-    # Generate labels
-    positive_labels = [1 for _ in positive_examples]
-    negative_labels = [0 for _ in negative_examples]
-    y = np.concatenate([positive_labels, negative_labels], 0)
-    return x_text, y
-
-
-def pad_sentences(sentences, padding_word="</s>"):
-    """
-    Pads all sentences to be the length of the longest sentence.
-    Returns padded sentences.
-    """
-    sequence_length = max(len(x) for x in sentences)
-    padded_sentences = []
-    for i in range(len(sentences)):
-        sentence = sentences[i]
-        num_padding = sequence_length - len(sentence)
-        new_sentence = sentence + [padding_word] * num_padding
-        padded_sentences.append(new_sentence)
-        
-    return padded_sentences
-
-
-def build_vocab(sentences):
-    """
-    Builds a vocabulary mapping from token to index based on the sentences.
-    Returns vocabulary mapping and inverse vocabulary mapping.
-    """
-    # Build vocabulary
-    word_counts = Counter(itertools.chain(*sentences))
-    
-    # Mapping from index to word
-    vocabulary_inv = [x[0] for x in word_counts.most_common()]
-    
-    # Mapping from word to index
-    vocabulary = {x: i for i, x in enumerate(vocabulary_inv)}
-    
-    return vocabulary, vocabulary_inv
-
-
-def build_input_data(sentences, labels, vocabulary):
-    """
-    Maps sentences and labels to vectors based on a vocabulary.
-    """
-    x = np.array([
-            [vocabulary[word] for word in sentence]
-            for sentence in sentences])
-    y = np.array(labels)
-    
-    return x, y
-
-"""
-Loads and preprocesses data for the MR dataset.
-Returns input vectors, labels, vocabulary, and inverse vocabulary.
-"""
-# Load and preprocess data
-sentences, labels = load_data_and_labels()
-sentences_padded = pad_sentences(sentences)
-vocabulary, vocabulary_inv = build_vocab(sentences_padded)
-x, y = build_input_data(sentences_padded, labels, vocabulary)
-
-vocab_size = len(vocabulary)
-
-# randomly shuffle data
-np.random.seed(10)
-shuffle_indices = np.random.permutation(np.arange(len(y)))
-x_shuffled = x[shuffle_indices]
-y_shuffled = y[shuffle_indices]
-
-# split train/dev set
-# there are a total of 10662 labeled examples to train on
-x_train, x_dev = x_shuffled[:-1000], x_shuffled[-1000:]
-y_train, y_dev = y_shuffled[:-1000], y_shuffled[-1000:]
-
-sentence_size = x_train.shape[1]
-
-print('Train/Dev split: %d/%d' % (len(y_train), len(y_dev)))
-print('train shape:', x_train.shape)
-print('dev shape:', x_dev.shape)
-print('vocab_size', vocab_size)
-print('sentence max words', sentence_size)
-```
-
-`Train/Dev split: 9662/1000`<!--notebook-skip-line-->
-
-`train shape: (9662, 56)`<!--notebook-skip-line-->
-
-`dev shape: (1000, 56)`<!--notebook-skip-line-->
-
-`vocab_size 18766`<!--notebook-skip-line-->
-
-`sentence max words 56`<!--notebook-skip-line-->
-
-
-Now that we prepared the training and test data by loading, vectorizing and shuffling it we can go on to defining the network architecture we want to train with the data.
-
-We will first set up some placeholders for the input and output of the network then define the first layer, an embedding layer, which learns to map word vectors into a lower dimensional vector space where distances between words correspond to how related they are (with respect to sentiment they convey).
-
-
-```python
-import mxnet as mx
-import sys,os
-
-'''
-Define batch size and the place holders for network inputs and outputs
-'''
-
-batch_size = 50
-print('batch size', batch_size)
-
-input_x = mx.sym.Variable('data') # placeholder for input data
-input_y = mx.sym.Variable('softmax_label') # placeholder for output label
-
-
-'''
-Define the first network layer (embedding)
-'''
-
-# create embedding layer to learn representation of words in a lower dimensional subspace (much like word2vec)
-num_embed = 300 # dimensions to embed words into
-print('embedding dimensions', num_embed)
-
-embed_layer = mx.sym.Embedding(data=input_x, input_dim=vocab_size, output_dim=num_embed, name='vocab_embed')
-
-# reshape embedded data for next layer
-conv_input = mx.sym.Reshape(data=embed_layer, shape=(batch_size, 1, sentence_size, num_embed))
-```
-
-`batch size 50` <!--notebook-skip-line-->
-
-`embedding dimensions 300` <!--notebook-skip-line-->
-
-
-The next layer in the network performs convolutions over the ordered embedded word vectors in a sentence using multiple filter sizes, sliding over 3, 4 or 5 words at a time. This is the equivalent of looking at all 3-grams, 4-grams and 5-grams in a sentence and will allow us to understand how words contribute to sentiment in the context of those around them.
-
-After each convolution, we add a max-pool layer to extract the most significant elements in each convolution and turn them into a feature vector.
-
-Because each convolution+pool filter produces tensors of different shapes we need to create a layer for each of them, and then concatenate the results of these layers into one big feature vector.
-
-
-```python
-# create convolution + (max) pooling layer for each filter operation
-filter_list=[3, 4, 5] # the size of filters to use
-print('convolution filters', filter_list)
-
-num_filter=100
-pooled_outputs = []
-for filter_size in filter_list:
-    convi = mx.sym.Convolution(data=conv_input, kernel=(filter_size, num_embed), num_filter=num_filter)
-    relui = mx.sym.Activation(data=convi, act_type='relu')
-    pooli = mx.sym.Pooling(data=relui, pool_type='max', kernel=(sentence_size - filter_size + 1, 1), stride=(1, 1))
-    pooled_outputs.append(pooli)
-
-# combine all pooled outputs
-total_filters = num_filter * len(filter_list)
-concat = mx.sym.Concat(*pooled_outputs, dim=1)
-
-# reshape for next layer
-h_pool = mx.sym.Reshape(data=concat, shape=(batch_size, total_filters))
-```
-
-`convolution filters [3, 4, 5]` <!--notebook-skip-line-->
-
-
-Next, we add dropout regularization, which will randomly disable a fraction of neurons in the layer (set to 50% here) to ensure that that model does not overfit. This prevents neurons from co-adapting and forces them to learn individually useful features.
-
-This is necessary for our model because the dataset has a vocabulary of size around 20k and only around 10k examples so since this data set is pretty small we're likely to overfit with a powerful model (like this neural net).
-
-
-```python
-# dropout layer
-dropout = 0.5
-print('dropout probability', dropout)
-
-if dropout > 0.0:
-    h_drop = mx.sym.Dropout(data=h_pool, p=dropout)
-else:
-    h_drop = h_pool
-
-```
-
-`dropout probability 0.5` <!--notebook-skip-line-->
-
-
-Finally, we add a fully connected layer to add non-linearity to the model. We then classify the resulting output of this layer using a softmax function, yielding a result between 0 (negative sentiment) and 1 (positive).
-
-
-```python
-# fully connected layer
-num_label = 2
-
-cls_weight = mx.sym.Variable('cls_weight')
-cls_bias = mx.sym.Variable('cls_bias')
-
-fc = mx.sym.FullyConnected(data=h_drop, weight=cls_weight, bias=cls_bias, num_hidden=num_label)
-
-# softmax output
-sm = mx.sym.SoftmaxOutput(data=fc, label=input_y, name='softmax')
-
-# set CNN pointer to the "back" of the network
-cnn = sm
-```
-
-Now that we have defined our CNN model we will define the device on our machine that we will train and execute this model on, as well as the datasets to train and test this model with.
-
-*If you are running this code be sure that you have a GPU on your machine if your ctx is set to mx.gpu(0) otherwise you can set your ctx to mx.cpu(0) which will run the training much slower*
-
-
-```python
-from collections import namedtuple
-import math
-import time
-
-# Define the structure of our CNN Model (as a named tuple)
-CNNModel = namedtuple("CNNModel", ['cnn_exec', 'symbol', 'data', 'label', 'param_blocks'])
-
-# Define what device to train/test on, use GPU if available
-ctx = mx.gpu() if mx.context.num_gpus() else mx.cpu()
-
-arg_names = cnn.list_arguments()
-
-input_shapes = {}
-input_shapes['data'] = (batch_size, sentence_size)
-
-arg_shape, out_shape, aux_shape = cnn.infer_shape(**input_shapes)
-arg_arrays = [mx.nd.zeros(s, ctx) for s in arg_shape]
-args_grad = {}
-for shape, name in zip(arg_shape, arg_names):
-    if name in ['softmax_label', 'data']: # input, output
-        continue
-    args_grad[name] = mx.nd.zeros(shape, ctx)
-
-cnn_exec = cnn.bind(ctx=ctx, args=arg_arrays, args_grad=args_grad, grad_req='add')
-
-param_blocks = []
-arg_dict = dict(zip(arg_names, cnn_exec.arg_arrays))
-initializer = mx.initializer.Uniform(0.1)
-for i, name in enumerate(arg_names):
-    if name in ['softmax_label', 'data']: # input, output
-        continue
-    initializer(mx.init.InitDesc(name), arg_dict[name])
-
-    param_blocks.append( (i, arg_dict[name], args_grad[name], name) )
-
-data = cnn_exec.arg_dict['data']
-label = cnn_exec.arg_dict['softmax_label']
-
-cnn_model= CNNModel(cnn_exec=cnn_exec, symbol=cnn, data=data, label=label, param_blocks=param_blocks)
-```
-
-We can now execute the training and testing of our network, which in-part mxnet automatically does for us with its forward and backward propagation methods, along with its automatic gradient calculations.
-
-
-```python
-'''
-Train the cnn_model using back prop
-'''
-
-optimizer = 'rmsprop'
-max_grad_norm = 5.0
-learning_rate = 0.0005
-epoch = 50
-
-print('optimizer', optimizer)
-print('maximum gradient', max_grad_norm)
-print('learning rate (step size)', learning_rate)
-print('epochs to train for', epoch)
-
-# create optimizer
-opt = mx.optimizer.create(optimizer)
-opt.lr = learning_rate
-
-updater = mx.optimizer.get_updater(opt)
-
-# For each training epoch
-for iteration in range(epoch):
-    tic = time.time()
-    num_correct = 0
-    num_total = 0
-
-    # Over each batch of training data
-    for begin in range(0, x_train.shape[0], batch_size):
-        batchX = x_train[begin:begin+batch_size]
-        batchY = y_train[begin:begin+batch_size]
-        if batchX.shape[0] != batch_size:
-            continue
-
-        cnn_model.data[:] = batchX
-        cnn_model.label[:] = batchY
-
-        # forward
-        cnn_model.cnn_exec.forward(is_train=True)
-
-        # backward
-        cnn_model.cnn_exec.backward()
-
-        # eval on training data
-        num_correct += sum(batchY == np.argmax(cnn_model.cnn_exec.outputs[0].asnumpy(), axis=1))
-        num_total += len(batchY)
-
-        # update weights
-        norm = 0
-        for idx, weight, grad, name in cnn_model.param_blocks:
-            grad /= batch_size
-            l2_norm = mx.nd.norm(grad).asscalar()
-            norm += l2_norm * l2_norm
-
-        norm = math.sqrt(norm)
-        for idx, weight, grad, name in cnn_model.param_blocks:
-            if norm > max_grad_norm:
-                grad *= (max_grad_norm / norm)
-
-            updater(idx, grad, weight)
-
-            # reset gradient to zero
-            grad[:] = 0.0
-
-    # Decay learning rate for this epoch to ensure we are not "overshooting" optima
-    if iteration % 50 == 0 and iteration > 0:
-        opt.lr *= 0.5
-        print('reset learning rate to %g' % opt.lr)
-
-    # End of training loop for this epoch
-    toc = time.time()
-    train_time = toc - tic
-    train_acc = num_correct * 100 / float(num_total)
-
-    # Saving checkpoint to disk
-    if (iteration + 1) % 10 == 0:
-        prefix = 'cnn'
-        cnn_model.symbol.save('./%s-symbol.json' % prefix)
-        save_dict = {('arg:%s' % k) : v  for k, v in cnn_model.cnn_exec.arg_dict.items()}
-        save_dict.update({('aux:%s' % k) : v for k, v in cnn_model.cnn_exec.aux_dict.items()})
-        param_name = './%s-%04d.params' % (prefix, iteration)
-        mx.nd.save(param_name, save_dict)
-        print('Saved checkpoint to %s' % param_name)
-
-
-    # Evaluate model after this epoch on dev (test) set
-    num_correct = 0
-    num_total = 0
-
-    # For each test batch
-    for begin in range(0, x_dev.shape[0], batch_size):
-        batchX = x_dev[begin:begin+batch_size]
-        batchY = y_dev[begin:begin+batch_size]
-
-        if batchX.shape[0] != batch_size:
-            continue
-
-        cnn_model.data[:] = batchX
-        cnn_model.cnn_exec.forward(is_train=False)
-
-        num_correct += sum(batchY == np.argmax(cnn_model.cnn_exec.outputs[0].asnumpy(), axis=1))
-        num_total += len(batchY)
-
-    dev_acc = num_correct * 100 / float(num_total)
-    print('Iter [%d] Train: Time: %.3fs, Training Accuracy: %.3f \
-            --- Dev Accuracy thus far: %.3f' % (iteration, train_time, train_acc, dev_acc))
-```
-
-
-    optimizer rmsprop
-    maximum gradient 5.0
-    learning rate (step size) 0.0005
-    epochs to train for 50
-    Iter [0] Train: Time: 3.903s, Training Accuracy: 56.290             --- Dev Accuracy thus far: 63.300
-    ...
-    Iter [8] Train: Time: 3.144s, Training Accuracy: 98.425             --- Dev Accuracy thus far: 78.000
-    Saved checkpoint to ./cnn-0009.params
-    ...
-
-Now that we have gone through the trouble of training the model, we have stored the learned parameters in the .params file in our local directory. We can now load this file whenever we want and predict the sentiment of new sentences by running them through a forward pass of the trained model.
-
-## References
-- ["Implementing a CNN for Text Classification in TensorFlow" blog post](http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/)
-- [Convolutional Neural Networks for Sentence Classification](https://arxiv.org/abs/1408.5882)
-
-## Next Steps
-* [MXNet tutorials index](http://mxnet.io/tutorials/index.html)
-
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/nlp/index.md b/docs/tutorials/nlp/index.md
deleted file mode 100644
index faf6526fb824..000000000000
--- a/docs/tutorials/nlp/index.md
+++ /dev/null
@@ -1,25 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Tutorials
-
-```eval_rst
-.. toctree::
-   :glob:
-
-   *
-```
diff --git a/docs/tutorials/onnx/export_mxnet_to_onnx.md b/docs/tutorials/onnx/export_mxnet_to_onnx.md
deleted file mode 100644
index c0f8d92901f6..000000000000
--- a/docs/tutorials/onnx/export_mxnet_to_onnx.md
+++ /dev/null
@@ -1,151 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-
-# Exporting MXNet model to ONNX format
-
-[Open Neural Network Exchange (ONNX)](https://github.com/onnx/onnx) provides an open source format for AI models. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types.
-
-In this tutorial, we will show how you can save MXNet models to the ONNX format.
-
-MXNet-ONNX operators coverage and features are updated regularly. Visit the [ONNX operator coverage](https://cwiki.apache.org/confluence/display/MXNET/ONNX+Operator+Coverage) page for the latest information.
-
-In this tutorial, we will learn how to use MXNet to ONNX exporter on pre-trained models.
-
-## Prerequisites
-
-To run the tutorial you will need to have installed the following python modules:
-- [MXNet >= 1.3.0](http://mxnet.incubator.apache.org/install/index.html)
-- [onnx]( https://github.com/onnx/onnx#installation) v1.2.1 (follow the install guide)
-
-*Note:* MXNet-ONNX importer and exporter follows version 7 of ONNX operator set which comes with ONNX v1.2.1.
-
-
-```python
-import mxnet as mx
-import numpy as np
-from mxnet.contrib import onnx as onnx_mxnet
-import logging
-logging.basicConfig(level=logging.INFO)
-```
-
-## Downloading a model from the MXNet model zoo
-
-We download the pre-trained ResNet-18 [ImageNet](http://www.image-net.org/) model from the [MXNet Model Zoo](http://data.mxnet.io/models/imagenet/).
-We will also download synset file to match labels.
-
-```python
-# Download pre-trained resnet model - json and params by running following code.
-path='http://data.mxnet.io/models/imagenet/'
-[mx.test_utils.download(path+'resnet/18-layers/resnet-18-0000.params'),
- mx.test_utils.download(path+'resnet/18-layers/resnet-18-symbol.json'),
- mx.test_utils.download(path+'synset.txt')]
-```
-
-Now, we have downloaded ResNet-18 symbol, params and synset file on the disk.
-
-## MXNet to ONNX exporter API
-
-Let us describe the MXNet's `export_model` API. 
-
-```python
-help(onnx_mxnet.export_model)
-```
-
-```
-Help on function export_model in module mxnet.contrib.onnx.mx2onnx.export_model:
-
-export_model(sym, params, input_shape, input_type=<type 'numpy.float32'>, onnx_file_path=u'model.onnx', verbose=False)
-    Exports the MXNet model file, passed as a parameter, into ONNX model.
-    Accepts both symbol,parameter objects as well as json and params filepaths as input.
-    Operator support and coverage - https://cwiki.apache.org/confluence/display/MXNET/MXNet-ONNX+Integration
-    
-    Parameters
-    ----------
-    sym : str or symbol object
-        Path to the json file or Symbol object
-    params : str or symbol object
-        Path to the params file or params dictionary. (Including both arg_params and aux_params)
-    input_shape : List of tuple
-        Input shape of the model e.g [(1,3,224,224)]
-    input_type : data type
-        Input data type e.g. np.float32
-    onnx_file_path : str
-        Path where to save the generated onnx file
-    verbose : Boolean
-        If true will print logs of the model conversion
-    
-    Returns
-    -------
-    onnx_file_path : str
-        Onnx file path
-```
-
-`export_model` API can accept the MXNet model in one of the following two ways.
-
-1. MXNet sym, params objects:
-    * This is useful if we are training a model. At the end of training, we just need to invoke the `export_model` function and provide sym and params objects as inputs with other attributes to save the model in ONNX format.
-2. MXNet's exported json and params files:
-    * This is useful if we have pre-trained models and we want to convert them to ONNX format.
-
-Since we have downloaded pre-trained model files, we will use the `export_model` API by passing the path for symbol and params files.
-
-## How to use MXNet to ONNX exporter API
-
-We will use the downloaded pre-trained model files (sym, params) and define input variables.
-
-```python
-# Downloaded input symbol and params files
-sym = './resnet-18-symbol.json'
-params = './resnet-18-0000.params'
-
-# Standard Imagenet input - 3 channels, 224*224
-input_shape = (1,3,224,224)
-
-# Path of the output file
-onnx_file = './mxnet_exported_resnet50.onnx'
-```
-
-We have defined the input parameters required for the `export_model` API. Now, we are ready to covert the MXNet model into ONNX format.
-
-```python
-# Invoke export model API. It returns path of the converted onnx model
-converted_model_path = onnx_mxnet.export_model(sym, params, [input_shape], np.float32, onnx_file)
-```
-
-This API returns path of the converted model which you can later use to import the model into other frameworks.
-
-## Check validity of ONNX model
-
-Now we can check validity of the converted ONNX model by using ONNX checker tool. The tool will validate the model by checking if the content contains valid protobuf:
-
-```python
-from onnx import checker
-import onnx
-
-# Load onnx model
-model_proto = onnx.load_model(converted_model_path)
-
-# Check if converted ONNX protobuf is valid
-checker.check_graph(model_proto.graph)
-```
-
-If the converted protobuf format doesn't qualify to ONNX proto specifications, the checker will throw errors, but in this case it successfully passes. 
-
-This method confirms exported model protobuf is valid. Now, the model is ready to be imported in other frameworks for inference!
-    
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/onnx/fine_tuning_gluon.md b/docs/tutorials/onnx/fine_tuning_gluon.md
deleted file mode 100644
index e7cef6809b10..000000000000
--- a/docs/tutorials/onnx/fine_tuning_gluon.md
+++ /dev/null
@@ -1,458 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-
-# Fine-tuning an ONNX model with MXNet/Gluon
-
-Fine-tuning is a common practice in Transfer Learning. One can take advantage of the pre-trained weights of a network, and use them as an initializer for their own task. Indeed, quite often it is difficult to gather a dataset large enough that it would allow training from scratch deep and complex networks such as ResNet152 or VGG16. For example in an image classification task, using a network trained on a large dataset like ImageNet gives a good base from which the weights can be slightly updated, or fine-tuned, to predict accurately the new classes. We will see in this tutorial that this can be achieved even with a relatively small number of new training examples.
-
-
-[Open Neural Network Exchange (ONNX)](https://github.com/onnx/onnx) provides an open source format for AI models. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types.
-
-In this tutorial we will:
-
-- learn how to pick a specific layer from a pre-trained .onnx model file
-- learn how to load this model in Gluon and fine-tune it on a different dataset
-
-## Pre-requisite
-
-To run the tutorial you will need to have installed the following python modules:
-- [MXNet > 1.1.0](http://mxnet.incubator.apache.org/install/index.html)
-- [onnx](https://github.com/onnx/onnx)
-- matplotlib
-
-We recommend that you have first followed this tutorial:
-- [Inference using an ONNX model on MXNet Gluon](https://mxnet.incubator.apache.org/tutorials/onnx/inference_on_onnx_model.html)
-
-
-```python
-import json
-import logging
-import multiprocessing
-import os
-import tarfile
-
-logging.basicConfig(level=logging.INFO)
-
-import matplotlib.pyplot as plt
-import mxnet as mx
-from mxnet import gluon, nd, autograd
-from mxnet.gluon.data.vision.datasets import ImageFolderDataset
-from mxnet.gluon.data import DataLoader
-import mxnet.contrib.onnx as onnx_mxnet
-import numpy as np
-
-%matplotlib inline
-```
-
-
-### Downloading supporting files
-These are images and a vizualisation script:
-
-
-```python
-image_folder = "images"
-utils_file = "utils.py" # contain utils function to plot nice visualization
-images = ['wrench.jpg', 'dolphin.jpg', 'lotus.jpg']
-base_url = "https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/onnx/{}?raw=true"
-
-
-for image in images:
-    mx.test_utils.download(base_url.format("{}/{}".format(image_folder, image)), fname=image,dirname=image_folder)
-mx.test_utils.download(base_url.format(utils_file), fname=utils_file)
-
-from utils import *
-```
-
-## Downloading a model from the ONNX model zoo
-
-We download a pre-trained model, in our case the [GoogleNet](https://arxiv.org/abs/1409.4842) model, trained on [ImageNet](http://www.image-net.org/) from the [ONNX model zoo](https://github.com/onnx/models). The model comes packaged in an archive `tar.gz` file containing an `model.onnx` model file.
-
-
-```python
-base_url = "https://s3.amazonaws.com/download.onnx/models/opset_3/"
-current_model = "bvlc_googlenet"
-model_folder = "model"
-archive_file = "{}.tar.gz".format(current_model)
-archive_path = os.path.join(model_folder, archive_file)
-url = "{}{}".format(base_url, archive_file)
-onnx_path = os.path.join(model_folder, current_model, 'model.onnx')
-
-# Download the zipped model
-mx.test_utils.download(url, dirname = model_folder)
-
-# Extract the model
-if not os.path.isdir(os.path.join(model_folder, current_model)):
-    print('Extracting {} in {}...'.format(archive_path, model_folder))
-    tar = tarfile.open(archive_path, "r:gz")
-    tar.extractall(model_folder)
-    tar.close()
-    print('Model extracted.')
-```
-
-## Downloading the Caltech101 dataset
-
-The [Caltech101 dataset](http://www.vision.caltech.edu/Image_Datasets/Caltech101/) is made of pictures of objects belonging to 101 categories. About 40 to 800 images per category. Most categories have about 50 images.
-
-*L. Fei-Fei, R. Fergus and P. Perona. Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. IEEE. CVPR 2004, Workshop on Generative-Model
-Based Vision. 2004*
-
-
-```python
-data_folder = "data"
-dataset_name = "101_ObjectCategories"
-archive_file = "{}.tar.gz".format(dataset_name)
-archive_path = os.path.join(data_folder, archive_file)
-data_url = "https://s3.us-east-2.amazonaws.com/mxnet-public/"
-
-if not os.path.isfile(archive_path):
-    mx.test_utils.download("{}{}".format(data_url, archive_file), dirname = data_folder)
-    print('Extracting {} in {}...'.format(archive_file, data_folder))
-    tar = tarfile.open(archive_path, "r:gz")
-    tar.extractall(data_folder)
-    tar.close()
-    print('Data extracted.')
-```
-
-
-```python
-training_path = os.path.join(data_folder, dataset_name)
-testing_path = os.path.join(data_folder, "{}_test".format(dataset_name))
-```
-
-### Load the data using an ImageFolderDataset and a DataLoader
-
-We need to transform the images to a format accepted by the network
-
-
-```python
-EDGE = 224
-SIZE = (EDGE, EDGE)
-BATCH_SIZE = 32
-NUM_WORKERS = 6
-```
-
-We transform the dataset images using the following operations:
-- resize the shorter edge to 224, the longer edge will be greater or equal to 224
-- center and crop an area of size (224,224)
-- transpose the channels to be (3,224,224)
-
-
-```python
-def transform(image, label):
-    resized = mx.image.resize_short(image, EDGE)
-    cropped, crop_info = mx.image.center_crop(resized, SIZE)
-    transposed = nd.transpose(cropped, (2,0,1))
-    return transposed, label
-```
-
-The train and test dataset are created automatically by passing the root of each folder. The labels are built using the sub-folders names as label.
-```
-train_root
-__label1
-____image1
-____image2
-__label2
-____image3
-____image4
-```
-
-
-```python
-dataset_train = ImageFolderDataset(root=training_path)
-dataset_test = ImageFolderDataset(root=testing_path)
-```
-
-We use several worker processes, which means the dataloading and pre-processing is going to be distributed across multiple processes. This will help preventing our GPU from starving and waiting for the data to be copied across
-
-
-```python
-dataloader_train = DataLoader(dataset_train.transform(transform, lazy=False), batch_size=BATCH_SIZE, last_batch='rollover',
-                              shuffle=True, num_workers=NUM_WORKERS)
-dataloader_test = DataLoader(dataset_test.transform(transform, lazy=False), batch_size=BATCH_SIZE, last_batch='rollover',
-                             shuffle=False, num_workers=NUM_WORKERS)
-print("Train dataset: {} images, Test dataset: {} images".format(len(dataset_train), len(dataset_test)))
-```
-
-
-`Train dataset: 6996 images, Test dataset: 1681 images`<!--notebook-skip-line-->
-
-
-
-```python
-categories = dataset_train.synsets
-NUM_CLASSES = len(categories)
-BATCH_SIZE = 32
-```
-
-Let's plot the 1000th image to test the dataset
-
-
-```python
-N = 1000
-plt.imshow((transform(dataset_train[N][0], 0)[0].asnumpy().transpose((1,2,0))))
-plt.axis('off')
-print(categories[dataset_train[N][1]])
-```
-
-
-`Motorbikes`<!--notebook-skip-line-->
-
-
-
-![png](https://github.com/dmlc/web-data/blob/master/mxnet/doc/tutorials/onnx/motorbike.png?raw=true)<!--notebook-skip-line-->
-
-
-## Fine-Tuning the ONNX model
-
-### Getting the last layer
-
-Load the ONNX model
-
-
-```python
-sym, arg_params, aux_params = onnx_mxnet.import_model(onnx_path)
-```
-
-This function get the output of a given layer
-
-
-```python
-def get_layer_output(symbol, arg_params, aux_params, layer_name):
-    all_layers = symbol.get_internals()
-    net = all_layers[layer_name+'_output']
-    net = mx.symbol.Flatten(data=net)
-    new_args = dict({k:arg_params[k] for k in arg_params if k in net.list_arguments()})
-    new_aux = dict({k:aux_params[k] for k in aux_params if k in net.list_arguments()})
-    return (net, new_args, new_aux)
-```
-
-Here we print the different layers of the network to make it easier to pick the right one
-
-
-```python
-sym.get_internals()
-```
-
-
-
-
-```<Symbol group [data_0, pad0, conv1/7x7_s2_w_0, conv1/7x7_s2_b_0, convolution0, relu0, pad1, pooling0, lrn0, pad2, conv2/3x3_reduce_w_0, conv2/3x3_reduce_b_0, convolution1, relu1, pad3, conv2/3x3_w_0, conv2/3x3_b_0, convolution2, relu2, lrn1, pad4, pooling1, pad5, inception_3a/1x1_w_0, inception_3a/1x1_b_0, convolution3, relu3, pad6, .................................................................................inception_5b/pool_proj_b_0, convolution56, relu56, concat8, pad70, pooling13, dropout0, flatten0, loss3/classifier_w_0, linalg_gemm20, loss3/classifier_b_0, _mulscalar0, broadcast_add0, softmax0]>```<!--notebook-skip-line-->
-
-
-
-We get the network until the output of the `flatten0` layer
-
-
-```python
-new_sym, new_arg_params, new_aux_params = get_layer_output(sym, arg_params, aux_params, 'flatten0')
-```
-
-### Fine-tuning in gluon
-
-
-We can now take advantage of the features and pattern detection knowledge that our network learnt training on ImageNet, and apply that to the new Caltech101 dataset.
-
-
-We pick a context, fine-tuning on CPU will be **WAY** slower.
-
-
-```python
-ctx = mx.gpu() if mx.context.num_gpus() > 0 else mx.cpu()
-```
-
-We create a symbol block that is going to hold all our pre-trained layers, and assign the weights of the different pre-trained layers to the newly created SymbolBlock
-
-
-```python
-import warnings
-with warnings.catch_warnings():
-    warnings.simplefilter("ignore")
-    pre_trained = gluon.nn.SymbolBlock(outputs=new_sym, inputs=mx.sym.var('data_0'))
-net_params = pre_trained.collect_params()
-for param in new_arg_params:
-    if param in net_params:
-        net_params[param]._load_init(new_arg_params[param], ctx=ctx)
-for param in new_aux_params:
-    if param in net_params:
-        net_params[param]._load_init(new_aux_params[param], ctx=ctx)
-
-```
-
-We create the new dense layer with the right new number of classes (101) and initialize the weights
-
-
-```python
-dense_layer = gluon.nn.Dense(NUM_CLASSES)
-dense_layer.initialize(mx.init.Xavier(magnitude=2.24), ctx=ctx)
-```
-
-We add the SymbolBlock and the new dense layer to a HybridSequential network
-
-
-```python
-net = gluon.nn.HybridSequential()
-with net.name_scope():
-    net.add(pre_trained)
-    net.add(dense_layer)
-```
-
-### Loss
-Softmax cross entropy for multi-class classification
-
-
-```python
-softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()
-```
-
-### Trainer
-Initialize trainer with common training parameters
-
-
-```python
-LEARNING_RATE = 0.0005
-WDECAY = 0.00001
-MOMENTUM = 0.9
-```
-
-The trainer will retrain and fine-tune the entire network. If we use `dense_layer` instead of `net` in the cell below, the gradient updates would only be applied to the new last dense layer. Essentially we would be using the pre-trained network as a featurizer.
-
-
-```python
-trainer = gluon.Trainer(net.collect_params(), 'sgd', 
-                        {'learning_rate': LEARNING_RATE,
-                         'wd':WDECAY,
-                         'momentum':MOMENTUM})
-```
-
-### Evaluation loop
-
-We measure the accuracy in a non-blocking way, using `nd.array` to take care of the parallelisation that MXNet and Gluon offers.
-
-
-```python
- def evaluate_accuracy_gluon(data_iterator, net):
-    num_instance = 0
-    sum_metric = nd.zeros(1,ctx=ctx, dtype=np.int32)
-    for i, (data, label) in enumerate(data_iterator):
-        data = data.astype(np.float32).as_in_context(ctx)
-        label = label.astype(np.int32).as_in_context(ctx)
-        output = net(data)
-        prediction = nd.argmax(output, axis=1).astype(np.int32)
-        num_instance += len(prediction)
-        sum_metric += (prediction==label).sum()
-    accuracy = (sum_metric.astype(np.float32)/num_instance)
-    return accuracy.asscalar()
-```
-
-
-```python
-%%time
-print("Untrained network Test Accuracy: {0:.4f}".format(evaluate_accuracy_gluon(dataloader_test, net)))
-```
-
-`Untrained network Test Accuracy: 0.0192`<!--notebook-skip-line-->
-
-
-
-### Training loop
-
-
-```python
-val_accuracy = 0
-for epoch in range(5):
-    for i, (data, label) in enumerate(dataloader_train):
-        data = data.astype(np.float32).as_in_context(ctx)
-        label = label.as_in_context(ctx)
-
-        if i%20==0 and i >0:
-            print('Batch [{0}] loss: {1:.4f}'.format(i, loss.mean().asscalar()))
-
-        with autograd.record():
-            output = net(data)
-            loss = softmax_cross_entropy(output, label)
-        loss.backward()
-        trainer.step(data.shape[0])
-
-    nd.waitall() # wait at the end of the epoch    
-    new_val_accuracy = evaluate_accuracy_gluon(dataloader_test, net)    
-    print("Epoch [{0}] Test Accuracy {1:.4f} ".format(epoch, new_val_accuracy))
-
-    # We perform early-stopping regularization, to prevent the model from overfitting
-    if val_accuracy > new_val_accuracy:
-        print('Validation accuracy is decreasing, stopping training')
-        break
-    val_accuracy = new_val_accuracy              
-```
-
-`Epoch 4, Test Accuracy 0.8942307829856873`<!--notebook-skip-line-->
-
-
-## Testing
-In the previous tutorial, we saw that the network trained on ImageNet couldn't classify correctly `wrench`, `dolphin`, `lotus` because these are not categories of the ImageNet dataset.
-
-Let's see if our network fine-tuned on Caltech101 is up for the task:
-
-
-```python
-# Number of predictions to show
-TOP_P = 3
-```
-
-
-```python
-# Convert img to format expected by the network
-def transform(img):
-    return nd.array(np.expand_dims(np.transpose(img, (2,0,1)),axis=0).astype(np.float32), ctx=ctx)
-```
-
-
-```python
-# Load and transform the test images
-caltech101_images_test = [plt.imread(os.path.join(image_folder, "{}".format(img))) for img in images]
-caltech101_images_transformed = [transform(img) for img in caltech101_images_test]
-```
-
-Helper function to run batches of data
-
-
-```python
-def run_batch(net, data):
-    results = []
-    for batch in data:
-        outputs = net(batch)
-        results.extend([o for o in outputs.asnumpy()])
-    return np.array(results)
-```
-
-
-```python
-result = run_batch(net, caltech101_images_transformed)
-```
-
-
-```python
-plot_predictions(caltech101_images_test, result, categories, TOP_P)
-```
-
-
-![png](https://github.com/dmlc/web-data/blob/master/mxnet/doc/tutorials/onnx/caltech101_correct.png?raw=true)<!--notebook-skip-line-->
-
-
-**Great!** The network classified these images correctly after being fine-tuned on a dataset that contains images of `wrench`, `dolphin` and `lotus`
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/onnx/index.md b/docs/tutorials/onnx/index.md
deleted file mode 100644
index faf6526fb824..000000000000
--- a/docs/tutorials/onnx/index.md
+++ /dev/null
@@ -1,25 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Tutorials
-
-```eval_rst
-.. toctree::
-   :glob:
-
-   *
-```
diff --git a/docs/tutorials/onnx/inference_on_onnx_model.md b/docs/tutorials/onnx/inference_on_onnx_model.md
deleted file mode 100644
index 1c946bffcc06..000000000000
--- a/docs/tutorials/onnx/inference_on_onnx_model.md
+++ /dev/null
@@ -1,256 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-
-# Running inference on MXNet/Gluon from an ONNX model
-
-[Open Neural Network Exchange (ONNX)](https://github.com/onnx/onnx) provides an open source format for AI models. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types.
-
-In this tutorial we will:
-    
-- learn how to load a pre-trained .onnx model file into MXNet/Gluon
-- learn how to test this model using the sample input/output
-- learn how to test the model on custom images
-
-## Pre-requisite
-
-To run the tutorial you will need to have installed the following python modules:
-- [MXNet > 1.1.0](http://mxnet.incubator.apache.org/install/index.html)
-- [onnx](https://github.com/onnx/onnx) (follow the install guide)
-- matplotlib
-
-
-```python
-import numpy as np
-import mxnet as mx
-from mxnet.contrib import onnx as onnx_mxnet
-from mxnet import gluon, nd
-%matplotlib inline
-import matplotlib.pyplot as plt
-import tarfile, os
-import json
-import logging
-logging.basicConfig(level=logging.INFO)
-```
-
-### Downloading supporting files
-These are images and a vizualisation script
-
-
-```python
-image_folder = "images"
-utils_file = "utils.py" # contain utils function to plot nice visualization
-image_net_labels_file = "image_net_labels.json"
-images = ['apron.jpg', 'hammerheadshark.jpg', 'dog.jpg', 'wrench.jpg', 'dolphin.jpg', 'lotus.jpg']
-base_url = "https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/onnx/{}?raw=true"
-
-for image in images:
-    mx.test_utils.download(base_url.format("{}/{}".format(image_folder, image)), fname=image,dirname=image_folder)
-mx.test_utils.download(base_url.format(utils_file), fname=utils_file)
-mx.test_utils.download(base_url.format(image_net_labels_file), fname=image_net_labels_file)
-
-from utils import * 
-```
-
-## Downloading a model from the ONNX model zoo
-
-We download a pre-trained model, in our case the [GoogleNet](https://arxiv.org/abs/1409.4842) model, trained on [ImageNet](http://www.image-net.org/) from the [ONNX model zoo](https://github.com/onnx/models). The model comes packaged in an archive `tar.gz` file containing an `model.onnx` model file.
-
-
-```python
-base_url = "https://s3.amazonaws.com/download.onnx/models/opset_3/" 
-current_model = "bvlc_googlenet"
-model_folder = "model"
-archive = "{}.tar.gz".format(current_model)
-archive_file = os.path.join(model_folder, archive)
-url = "{}{}".format(base_url, archive)
-```
-
-Download and extract pre-trained model
-
-
-```python
-mx.test_utils.download(url, dirname = model_folder)
-if not os.path.isdir(os.path.join(model_folder, current_model)):
-    print('Extracting model...')
-    tar = tarfile.open(archive_file, "r:gz")
-    tar.extractall(model_folder)
-    tar.close()
-    print('Extracted')
-```
-
-The models have been pre-trained on ImageNet, let's load the label mapping of the 1000 classes.
-
-
-```python
-categories = json.load(open(image_net_labels_file, 'r'))
-```
-
-## Loading the model into MXNet Gluon
-
-
-```python
-onnx_path = os.path.join(model_folder, current_model, "model.onnx")
-```
-
-We get the symbol and parameter objects
-
-
-```python
-sym, arg_params, aux_params = onnx_mxnet.import_model(onnx_path)
-```
-
-We pick a context, CPU is fine for inference, switch to mx.gpu() if you want to use your GPU.
-
-
-```python
-ctx = mx.cpu()
-```
-
-We obtain the data names of the inputs to the model by using the model metadata API: 
-
-```python
-model_metadata = onnx_mxnet.get_model_metadata(onnx_path)
-print(model_metadata)
-```
-
-```
-{'output_tensor_data': [(u'gpu_0/softmax_1', (1L, 1000L))],
- 'input_tensor_data': [(u'gpu_0/data_0', (1L, 3L, 224L, 224L))]}
-```
-
-```python
-data_names = [inputs[0] for inputs in model_metadata.get('input_tensor_data')]
-print(data_names)
-```
-
-
-```[u'data_0']```<!--notebook-skip-line-->
-
-And load them into a MXNet Gluon symbol block. 
-
-```python
-import warnings
-with warnings.catch_warnings():
-    warnings.simplefilter("ignore")
-    net = gluon.nn.SymbolBlock(outputs=sym, inputs=mx.sym.var('data_0'))
-net_params = net.collect_params()
-for param in arg_params:
-    if param in net_params:
-        net_params[param]._load_init(arg_params[param], ctx=ctx)
-for param in aux_params:
-    if param in net_params:
-        net_params[param]._load_init(aux_params[param], ctx=ctx)
-```
-
-We can now cache the computational graph through [hybridization](https://mxnet.incubator.apache.org/tutorials/gluon/hybrid.html) to gain some performance
-
-
-
-```python
-net.hybridize()
-```
-
-We can visualize the network (requires graphviz installed)
-
-
-```python
-mx.visualization.plot_network(sym,  node_attrs={"shape":"oval","fixedsize":"false"})
-```
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/onnx/network2.png?raw=true)<!--notebook-skip-line-->
-
-
-
-This is a helper function to run M batches of data of batch-size N through the net and collate the outputs into an array of shape (K, 1000) where K=MxN is the total number of examples (mumber of batches x batch-size) run through the network.
-
-
-```python
-def run_batch(net, data):
-    results = []
-    for batch in data:
-        outputs = net(batch)
-        results.extend([o for o in outputs.asnumpy()])
-    return np.array(results)
-```
-
-## Test using real images
-
-
-```python
-TOP_P = 3 # How many top guesses we show in the visualization
-```
-
-
-Transform function to set the data into the format the network expects, (N, 3, 224, 224) where N is the batch size.
-
-
-```python
-def transform(img):
-    return np.expand_dims(np.transpose(img, (2,0,1)),axis=0).astype(np.float32)
-```
-
-
-We load two sets of images in memory
-
-
-```python
-image_net_images = [plt.imread('{}/{}.jpg'.format(image_folder, path)) for path in ['apron', 'hammerheadshark','dog']]
-caltech101_images = [plt.imread('{}/{}.jpg'.format(image_folder, path)) for path in ['wrench', 'dolphin','lotus']]
-images = image_net_images + caltech101_images
-```
-
-And run them as a batch through the network to get the predictions
-
-```python
-batch = nd.array(np.concatenate([transform(img) for img in images], axis=0), ctx=ctx)
-result = run_batch(net, [batch])
-```
-
-
-```python
-plot_predictions(image_net_images, result[:3], categories, TOP_P)
-```
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/onnx/imagenet.png?raw=true)<!--notebook-skip-line-->
-
-
-**Well done!** Looks like it is doing a pretty good job at classifying pictures when the category is a ImageNet label
-
-Let's now see the results on the 3 other images
-
-
-```python
-plot_predictions(caltech101_images, result[3:7], categories, TOP_P)
-```
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/onnx/caltech101.png?raw=true)<!--notebook-skip-line-->
-
-
-**Hmm, not so good...**  Even though predictions are close, they are not accurate, which is due to the fact that the ImageNet dataset does not contain `wrench`, `dolphin`, or `lotus` categories and our network has been trained on ImageNet.
-
-Lucky for us, the [Caltech101 dataset](http://www.vision.caltech.edu/Image_Datasets/Caltech101/) has them, let's see how we can fine-tune our network to classify these categories correctly.
-
-We show that in our next tutorial:
-
-
-- [Fine-tuning an ONNX Model using the modern imperative MXNet/Gluon](http://mxnet.incubator.apache.org/tutorials/onnx/fine_tuning_gluon.html)
-    
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/onnx/super_resolution.md b/docs/tutorials/onnx/super_resolution.md
deleted file mode 100644
index db5932949f14..000000000000
--- a/docs/tutorials/onnx/super_resolution.md
+++ /dev/null
@@ -1,140 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Importing an ONNX model into MXNet
-
-In this tutorial we will:
-
-- learn how to load a pre-trained ONNX model file into MXNet.
-- run inference in MXNet.
-
-## Prerequisites
-This example assumes that the following python packages are installed:
-- [mxnet](http://mxnet.incubator.apache.org/install/index.html)
-- [onnx](https://github.com/onnx/onnx) (follow the install guide)
-- Pillow - A Python Image Processing package and is required for input pre-processing. It can be installed with ```pip install Pillow```.
-- matplotlib
-
-
-```python
-from PIL import Image
-import numpy as np
-import mxnet as mx
-import mxnet.contrib.onnx as onnx_mxnet
-from mxnet.test_utils import download
-from matplotlib.pyplot import imshow
-```
-
-### Fetching the required files
-
-
-```python
-img_url = 'https://s3.amazonaws.com/onnx-mxnet/examples/super_res_input.jpg'
-download(img_url, 'super_res_input.jpg')
-model_url = 'https://s3.amazonaws.com/onnx-mxnet/examples/super_resolution.onnx'
-onnx_model_file = download(model_url, 'super_resolution.onnx')
-```
-
-## Loading the model into MXNet
-
-To completely describe a pre-trained model in MXNet, we need two elements: a symbolic graph, containing the model's network definition, and a binary file containing the model weights. You can import the ONNX model and get the symbol and parameters objects using ``import_model`` API. The paameter object is split into argument parameters and auxilliary parameters.
-
-
-```python
-sym, arg, aux = onnx_mxnet.import_model(onnx_model_file)
-```
-
-We can now visualize the imported model (graphviz needs to be installed)
-
-
-```python
-mx.viz.plot_network(sym, node_attrs={"shape":"oval","fixedsize":"false"})
-```
-
-
-
-
-![svg](https://s3.amazonaws.com/onnx-mxnet/examples/super_res_mxnet_model.png) <!--notebook-skip-line-->
-
-
-
-## Input Pre-processing
-
-We will transform the previously downloaded input image into an input tensor.
-
-
-```python
-img = Image.open('super_res_input.jpg').resize((224, 224))
-img_ycbcr = img.convert("YCbCr")
-img_y, img_cb, img_cr = img_ycbcr.split()
-test_image = np.array(img_y)[np.newaxis, np.newaxis, :, :]
-```
-
-## Run Inference using MXNet's Module API
-
-We will use MXNet's Module API to run the inference. For this we will need to create the module, bind it to the input data and assign the loaded weights from the two parameter objects - argument parameters and auxilliary parameters.
-
-To obtain the input data names we run the following line, which picks all the inputs of the symbol graph excluding the argument and auxiliary parameters:
-
-```python
-data_names = [graph_input for graph_input in sym.list_inputs()
-                      if graph_input not in arg and graph_input not in aux]
-print(data_names)
-```
-
-```['1']```
-
-```python
-mod = mx.mod.Module(symbol=sym, data_names=data_names, context=mx.cpu(), label_names=None)
-mod.bind(for_training=False, data_shapes=[(data_names[0],test_image.shape)], label_shapes=None)
-mod.set_params(arg_params=arg, aux_params=aux, allow_missing=True, allow_extra=True)
-```
-
-Module API's forward method requires batch of data as input. We will prepare the data in that format and feed it to the forward method.
-
-
-```python
-from collections import namedtuple
-Batch = namedtuple('Batch', ['data'])
-
-# forward on the provided data batch
-mod.forward(Batch([mx.nd.array(test_image)]))
-```
-
-To get the output of previous forward computation, you use ``module.get_outputs()`` method.
-It returns an ``ndarray`` that we convert to a ``numpy`` array and then to Pillow's image format
-
-
-```python
-output = mod.get_outputs()[0][0][0]
-img_out_y = Image.fromarray(np.uint8((output.asnumpy().clip(0, 255)), mode='L'))
-result_img = Image.merge(
-"YCbCr", [
-                img_out_y,
-                img_cb.resize(img_out_y.size, Image.BICUBIC),
-                img_cr.resize(img_out_y.size, Image.BICUBIC)
-]).convert("RGB")
-result_img.save("super_res_output.jpg")
-```
-
-You can now compare the input image and the resulting output image. As you will notice, the model was able to increase the spatial resolution from ``256x256`` to ``672x672``.
-
-| Input Image | Output Image | <!--notebook-skip-line-->
-| ----------- | ------------ | <!--notebook-skip-line-->
-| ![input](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/onnx/images/super_res_input.jpg?raw=true) | ![output](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/onnx/images/super_res_output.jpg?raw=true) | <!--notebook-skip-line-->
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
\ No newline at end of file
diff --git a/docs/tutorials/python/data_augmentation.md b/docs/tutorials/python/data_augmentation.md
deleted file mode 100644
index 45a4e3d8c86d..000000000000
--- a/docs/tutorials/python/data_augmentation.md
+++ /dev/null
@@ -1,180 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Methods of applying data augmentation (Module API)
-
-Data Augmentation is a regularization technique that's used to avoid overfitting when training Machine Learning models. Although the technique can be applied in a variety of domains, it's very common in Computer Vision. Adjustments are made to the original images in the training dataset before being used in training. Some example adjustments include translating, cropping, scaling, rotating, changing brightness and contrast. We do this to reduce the dependence of the model on spurious characteristics; e.g. training data may only contain faces that fill 1/4 of the image, so the model trained without data augmentation might unhelpfully learn that faces can only be of this size.
-
-In this tutorial we discuss the different interfaces available in MXNet to perform data augmentation if you're using the Module API. We start by showing a complete example using Module's [`ImageIter`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=imageiter#mxnet.image.ImageIter), and then unpack the example to gain a greater understanding of the internals. In the process you'll learn about augmentation functions, [`mxnet.image.Augmenter`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=augmen#mxnet.image.Augmenter) classes and Augmenter lists.
-
-
-```python
-%matplotlib inline
-import mxnet as mx # used version '1.0.0' at time of writing
-import numpy as np
-from matplotlib.pyplot import imshow
-import multiprocessing
-import os
-
-mx.random.seed(42) # set seed for repeatability
-```
-
-We define a utility function below, that will be used for visualising the augmentations in the tutorial.
-
-
-```python
-def plot_mx_array(array):
-    """
-    Array expected to be height x width x 3 (channels), and values are floats between 0 and 255.
-    """
-    assert array.shape[2] == 3, "RGB Channel should be last"
-    imshow((array.clip(0, 255)/255).asnumpy())
-```
-
-```python
-image_dir = os.path.join("data", "images")
-mx.test_utils.download('https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/inputs/0.jpg', dirname=image_dir)
-example_image = mx.image.imread(os.path.join(image_dir,"0.jpg")).astype("float32")
-plot_mx_array(example_image)
-```
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/use/output_5_0.png)<!--notebook-skip-line-->
-
-
-## Quick start using [`ImageIter`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=imageiter#mxnet.image.ImageIter)
-
-One of the most convenient ways to augment your image data is via arguments of [`mxnet.image.ImageIter`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=imageiter#mxnet.image.ImageIter), but you'll need to reference the documentation of [`mxnet.image.CreateAugmenter`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=createaugmenter#mxnet.image.CreateAugmenter) to see a full list of available options. Under the hood, additional arguments passed to [`ImageIter`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=imageiter#mxnet.image.ImageIter) are collected as keyword arguments (`**kwargs`), and are passed to [`mxnet.image.CreateAugmenter`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=createaugmenter#mxnet.image.CreateAugmenter). We'll see this in more detail in the sections below, but [`mxnet.image.CreateAugmenter`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=createaugmenter#mxnet.image.CreateAugmenter) creates a list of  [`mxnet.image.Augmenter`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=augmen#mxnet.image.Augmenter)s corresponding to each type of augmentation (e.g. crop, flip, change of brightness, etc.), and this list will be iterated though and the augmentations applied in turn. Alternatively, you can create this list yourself and pass it to [`ImageIter`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=imageiter#mxnet.image.ImageIter) via the `aug_list` argument.
-
-
-We show a simple example of this below, after creating an `images.lst` file used by the [`ImageIter`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=imageiter#mxnet.image.ImageIter). Use [`tools/im2rec.py`](https://github.com/apache/incubator-mxnet/blob/master/tools/im2rec.py) to create the `images.lst` if you don't already have this for your data.
-
-```python
-path_to_image = os.path.join("images", "0.jpg")
-index = 0
-label = 0.
-list_file_content = "{0}\t{1:.5f}\t{2}".format(index, label, path_to_image)
-
-path_list_file = os.path.join(image_dir, "images.lst")
-with open(path_list_file, 'w') as f:
-    f.write(list_file_content)
-
-```
-
-```python
-training_iter = mx.image.ImageIter(batch_size = 1,
-                                   data_shape = (3, 300, 300),
-                                   path_root= 'data', path_imglist=path_list_file,
-                                   rand_crop=0.5, rand_mirror=True, inter_method=10,
-                                   brightness=0.125, contrast=0.125, saturation=0.125,
-                                   pca_noise=0.02
-                                   )
-```
-
-
-```python
-for batch in training_iter:
-    assert batch.data[0].shape == (1, 3, 300, 300)
-    assert batch.label[0].shape == (1,)
-    sample = batch.data[0][0].transpose(axes=[1,2,0])
-    plot_mx_array(sample)
-    break
-```
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/use/output_28_1.png)<!--notebook-skip-line-->
-
-[`mxnet.image.ImageDetIter`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=imagedetiter#mxnet.image.ImageDetIter) works similarly (with [`mxnet.image.CreateDetAugmenter`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=createdetaugmenter#mxnet.image.CreateDetAugmenter)), but [`mxnet.io.ImageRecordIter`](https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=imagerecorditer#mxnet.io.ImageRecordIter) has a slightly different interface, so reference the documentation [here](https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=imagerecorditer#mxnet.io.ImageRecordIter) if you're using Record IO data format.
-
-## Manual Augmentation
-
-Although the vast majority of cases will be covered using the augmentation arguments of [`mxnet.image.ImageIter`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=imageiter#mxnet.image.ImageIter) as we've seen above, sometime you'll want more fine grained control of augmentations. We will now dive into some of the lower level methods for image augmentation, that you can use to manually apply augmentations to images.
-
-### Augmentation Functions
-
-MXNet provides a small number of augmentation functions that are quick and easy to use, but they are limited to positional augmentations (such as [`mxnet.image.random_crop`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=random_crop#mxnet.image.random_crop) and [`mxnet.image.resize_short`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=random_crop#mxnet.image.resize_short) functions) as opposed to color augmentations (such as brightness jitter). Although these functions are easy to apply, the [`mxnet.image.Augmenter`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=augmen#mxnet.image.Augmenter) classes are much more comprehensive and just as easy to use, as we'll see in the next section.
-
-
-```python
-aug_image, crop_box = mx.image.random_crop(example_image, size=(100, 100))
-plot_mx_array(aug_image)
-assert aug_image.shape == (100, 100, 3)
-```
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/use/output_16_0.png)<!--notebook-skip-line-->
-
-
-### Augmenter Classes
-
-You can apply a wide variety of positional and color augmentations with [`mxnet.image.Augmenter`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=augmen#mxnet.image.Augmenter) classes, and using them is the recommended approach for applying augmentations manually. After creating an instance of an Augmenter with the required parameters, you can call the Augmenter just as you would a function. Under the hood a `__call__` method is defined which applies the augmentation. Augmenters with randomness are randomized each time the Augmenter is called, so calling the same Augmenter twice will give different results on the same input.
-
-
-```python
-aug = mx.image.RandomCropAug(size=(100, 100))
-aug_image = aug(example_image)
-plot_mx_array(aug_image)
-assert aug_image.shape == (100, 100, 3)
-```
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/use/output_19_0.png)<!--notebook-skip-line-->
-
-
-### Augmenter list
-
-Very often you'll want to apply many different types of augmentation to an image. Instead of nesting the calls of Augmenters, a natural structure for handling a large number of Augmenters is a list. You can construct this list manually, or you can use helper functions like [`mxnet.image.CreateAugmenter`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=createaugmenter#mxnet.image.CreateAugmenter) to create these lists automatically.
-
-Object detection tasks require the same positional augmentations to be applied to the data and the label, so you should use [`mxnet.image.CreateDetAugmenter`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=createdetaugmenter#mxnet.image.CreateDetAugmenter) which handles this case.
-
-
-```python
-# created manually
-aug_list = [mx.image.RandomCropAug(size=(100, 100)), mx.image.BrightnessJitterAug(brightness=1)]
-aug_image = example_image.copy()
-for aug in aug_list:
-    aug_image = aug(aug_image)
-plot_mx_array(aug_image)
-assert all([isinstance(a, mx.image.Augmenter) for a in aug_list])
-```
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/use/output_22_1.png)<!--notebook-skip-line-->
-
-
-
-```python
-# created automatically
-aug_list = mx.image.CreateAugmenter(data_shape=(3, 300, 300), rand_crop=0.5,
-        rand_mirror=True, mean=True, brightness=0.125, contrast=0.125,
-        saturation=0.125, pca_noise=0.05, inter_method=10)
-aug_image = example_image.copy()
-for aug in aug_list:
-    aug_image = aug(aug_image)
-plot_mx_array(aug_image)
-assert all([isinstance(a, mx.image.Augmenter) for a in aug_list])
-```
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/use/output_23_1.png)<!--notebook-skip-line-->
-
-
-__*Watch Out!*__ Check some examples that are output after applying all the augmentations. You may find that the augmentation steps are too severe and may actually prevent the model from learning. Some of the augmentation parameters used in this tutorial are set high for demonstration purposes (e.g. `brightness=1`); you might want to reduce them if your training error stays too high during training. Some examples of excessive augmentation are shown below:
-
-<img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/use//severe_aug.png" alt="Drawing" style="width: 700px;"/>
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
\ No newline at end of file
diff --git a/docs/tutorials/python/data_augmentation_with_masks.md b/docs/tutorials/python/data_augmentation_with_masks.md
deleted file mode 100644
index 66beece720a9..000000000000
--- a/docs/tutorials/python/data_augmentation_with_masks.md
+++ /dev/null
@@ -1,261 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-
-# Data Augmentation with Masks
-
-## Data Augmentation
-
-Data Augmentation is a regularization technique that's used to avoid overfitting when training Machine Learning models. Although the technique can be applied in a variety of domains, it's very common in Computer Vision, and this will be the focus of the tutorial. Adjustments are made to the original images in the training dataset before being used in training. Some example adjustments include translating, croping, scaling, rotating, changing brightness and contrast. We do this to reduce the dependence of the model on spurious characteristics; e.g. training data may only contain faces that fill 1/4 of the image, so the model trainied without data augmentation might unhelpfully learn that faces can only be of this size.
-
-<img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/with_mask/orig_vs_aug.png" alt="Drawing" style="width: 480px;"/>
-
-## Masks
-
-Certain Computer Vision tasks (like [Object Segmentation](https://arxiv.org/abs/1506.06204)) require the use of 'masks', and we have to take extra care when using these in conjunction with data augmentation techniques. Given an underlying base image (with 3 channels), a masking channel can be added to provide additional metadata to certain regions of the base image. Masking channels often contain binary values, and these can be used to label a single class, e.g. to label a dog in the foreground. Multi-class segmentation problems could use many binary masking channels (i.e. one binary channel per class), but it is more common to see RGB representations, where each class is a different color. We take an example from the [COCO dataset](http://cocodataset.org/).
-
-<img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/with_mask/masks.png" alt="Drawing" style="width: 700px;"/>
-
-## Data Augmentation with Masks
-
-When we adjust the position of the base image as part of data augmentation, we also need to apply exactly the same operation to the associated masks. An example would be after applying a horizontal flip to the base image, we'd need to also flip the mask, to preserve the corresponsence between the base image and mask.
-
-Color changes to the base image don't need to be applied to the segmentation masks though; and may even lead to errors with the masks. An example with a RGB mask, would be accidentally converting a region of green for dog to blue for cat.
-
-<img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/with_mask/aug_and_mask.png" alt="Drawing" style="width: 800px;"/>
-
-# Custom Dataset
-
-With Gluon it's easy to work with different types of data. You can write custom Datasets and plug them directly into a DataLoader which will handle batching. Segmentation tasks are structured in such a way that the data is the base image and the label is the mask, so we will create a custom Dataset for this. Our Dataset will return base images with their corresponsing masks.
-
-It will be based on the `mx.gluon.data.vision.ImageFolderDataset` for simplicity, and will load files from a single folder, containing images of the form `xyz.jpg` and their corresponsing mask `xyz_mask.png`.
-
-`__getitem__` must be implemented, as this will be used by the DataLoader.
-
-
-```python
-%matplotlib inline
-import collections
-import mxnet as mx # used version '1.0.0' at time of writing
-from mxnet.gluon.data import dataset
-import os
-import numpy as np
-from matplotlib.pyplot import imshow
-import matplotlib.pyplot as plt
-
-mx.random.seed(42) # set seed for repeatability
-
-
-class ImageWithMaskDataset(dataset.Dataset):
-    """
-    A dataset for loading images (with masks) stored as `xyz.jpg` and `xyz_mask.png`.
-
-    Parameters
-    ----------
-    root : str
-        Path to root directory.
-    transform : callable, default None
-        A function that takes data and label and transforms them:
-    ::
-        transform = lambda data, label: (data.astype(np.float32)/255, label)
-    """
-    def __init__(self, root, transform=None):
-        self._root = os.path.expanduser(root)
-        self._transform = transform
-        self._exts = ['.jpg', '.jpeg', '.png']
-        self._list_images(self._root)
-
-    def _list_images(self, root):
-        images = collections.defaultdict(dict)
-        for filename in sorted(os.listdir(root)):
-            name, ext = os.path.splitext(filename)
-            mask_flag = name.endswith("_mask")
-            if ext.lower() not in self._exts:
-                continue
-            if not mask_flag:
-                images[name]["base"] = filename
-            else:
-                name = name[:-5] # to remove '_mask'
-                images[name]["mask"] = filename
-        self._image_list = list(images.values())
-
-    def __getitem__(self, idx):
-        assert 'base' in self._image_list[idx], "Couldn't find base image for: " + image_list[idx]["mask"]
-        base_filepath = os.path.join(self._root, self._image_list[idx]["base"])
-        base = mx.image.imread(base_filepath)
-        assert 'mask' in self._image_list[idx], "Couldn't find mask image for: " + image_list[idx]["base"]
-        mask_filepath = os.path.join(self._root, self._image_list[idx]["mask"])
-        mask = mx.image.imread(mask_filepath)
-        if self._transform is not None:
-            return self._transform(base, mask)
-        else:
-            return base, mask
-
-    def __len__(self):
-        return len(self._image_list)
-```
-
-## Using our Dataset 
-
-Usually Datasets are used in conjunction with DataLoaders, but we'll sample a single base image and mask pair for testing purposes. Calling `dataset[0]` (which is equivalent to `dataset.__getitem__(0)`) returns the first base image and mask pair from the `_image_list`. At first download the sample images and then we'll load them without any augmentation.
-
-```python
-image_dir = os.path.join("data", "images")
-mx.test_utils.download('https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/inputs/0.jpg', dirname=image_dir)
-mx.test_utils.download('https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/inputs/0_mask.png', dirname=image_dir)
-dataset = ImageWithMaskDataset(root=image_dir)
-sample = dataset.__getitem__(0)
-sample_base = sample[0].astype('float32')
-sample_mask = sample[1].astype('float32')
-assert sample_base.shape == (427, 640, 3)
-assert sample_mask.shape == (427, 640, 3)
-```
-
-
-```python
-def plot_mx_arrays(arrays):
-    """
-    Array expected to be height x width x 3 (channels), and values are floats between 0 and 255.
-    """
-    plt.subplots(figsize=(12, 4))
-    for idx, array in enumerate(arrays):
-        assert array.shape[2] == 3, "RGB Channel should be last"
-        plt.subplot(1, 2, idx+1)
-        imshow((array.clip(0, 255)/255).asnumpy())
-```
-
-
-```python
-plot_mx_arrays([sample_base, sample_mask])
-```
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/with_mask/output_16_0.png)<!--notebook-skip-line-->
-
-
-# Implementing `transform` for Augmentation
-
-We now construct our augmentation pipeline by implementing a transform function. Given a data sample and its corresponding label, this function must also return data and a label. In our specific example, our transform function will take the base image and corresponding mask, and return the augmented base image and correctly augmented mask. We will provide this to the `ImageWithMaskDataset` via the `transform` argument, and it will be applied to each sample (i.e. each data and label pair).
-
-Our approach is to apply positional augmentations to the combined base image and mask, and then apply the color augmentations to the positionally augmented base image only. We concatenate the base image with the mask along the channels dimension. So if we have a 3 channel base image, and a 3 channel mask, the result will be a 6 channel array. After applying positional augmentations on this array, we split out the base image and mask once again. Our last step is to apply the colour augmentation to just the augmented base image.
-
-
-```python
-def positional_augmentation(joint):
-    # Random crop
-    crop_height = 200
-    crop_width = 200
-    aug = mx.image.RandomCropAug(size=(crop_width, crop_height)) # Watch out: weight before height in size param!
-    aug_joint = aug(joint)
-    # Deterministic resize
-    resize_size = 100
-    aug = mx.image.ResizeAug(resize_size)
-    aug_joint = aug(aug_joint)
-    # Add more translation/scale/rotation augmentations here...
-    return aug_joint
-
-
-def color_augmentation(base):
-    # Only applied to the base image, and not the mask layers.
-    aug = mx.image.BrightnessJitterAug(brightness=0.2)
-    aug_base = aug(base)
-    # Add more color augmentations here...
-    return aug_base
-
-
-def joint_transform(base, mask):
-    ### Convert types
-    base = base.astype('float32')/255
-    mask = mask.astype('float32')/255
-    
-    ### Join
-    # Concatinate on channels dim, to obtain an 6 channel image
-    # (3 channels for the base image, plus 3 channels for the mask)
-    base_channels = base.shape[2] # so we know where to split later on
-    joint = mx.nd.concat(base, mask, dim=2)
-
-    ### Augmentation Part 1: positional
-    aug_joint = positional_augmentation(joint)
-    
-    ### Split
-    aug_base = aug_joint[:, :, :base_channels]
-    aug_mask = aug_joint[:, :, base_channels:]
-    
-    ### Augmentation Part 2: color
-    aug_base = color_augmentation(aug_base)
-
-    return aug_base, aug_mask
-```
-
-# Using Augmentation 
-
-It's simple to use augmentation now that we have the `joint_transform` function defined. Simply set the `tranform` argument when defining the Dataset. You'll notice the alignment between the base image and the mask is preserved, and the mask colors are left unchanged.
-
-
-```python
-image_dir = os.path.join("data","images")
-ds = ImageWithMaskDataset(root=image_dir, transform=joint_transform)
-sample = ds.__getitem__(0)
-assert len(sample) == 2
-assert sample[0].shape == (100, 100, 3)
-assert sample[1].shape == (100, 100, 3)
-plot_mx_arrays([sample[0]*255, sample[1]*255])
-```
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/with_mask/output_22_0.png)<!--notebook-skip-line-->
-
-
-# Summary
-
-We've succesfully created a custom Dataset for images and corresponding masks, implemented an augmentation `transform` function that correctly handles masks, and applied it to each sample of the Dataset. You're now ready to train your own object segmentation models!
-
-# Appendix (COCO Dataset)
-
-[COCO dataset](http://cocodataset.org/) is a great resource for image segmentation data. It contains over 200k labelled images, with over 1.5 million object instances across 80 object categories. You can download the data using `gsutil` as per the instuctions below (from http://cocodataset.org/#download):
-
-### 1) Install `gsutil`
-
-```
-curl https://sdk.cloud.google.com | bash
-```
-
-### 2) Download Images
-
-We download the validation data from 2017 from `gs://images.cocodataset.org/val2017` as an example. It's a much more manageable size (~770MB) compared to the test and training data with are both > 5GB.
-
-```
-mkdir coco_data
-mkdir coco_data/images
-gsutil -m rsync gs://images.cocodataset.org/val2017 coco_data/images
-```
-
-### 3) Download Masks (a.k.a. pixel maps) 
-
-```
-gsutil -m cp gs://images.cocodataset.org/annotations/stuff_annotations_trainval2017.zip \
-    coco_data/stuff_annotations_trainval2017.zip
-unzip coco_data/stuff_annotations_trainval2017.zip
-rm coco_data/stuff_annotations_trainval2017.zip
-unzip annotations/stuff_val2017_pixelmaps.zip
-rm -r annotations
-mkdir coco_data/masks
-mv -v stuff_val2017_pixelmaps/* coco_data/masks/
-rm -r stuff_val2017_pixelmaps
-```
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
\ No newline at end of file
diff --git a/docs/tutorials/python/index.md b/docs/tutorials/python/index.md
deleted file mode 100644
index faf6526fb824..000000000000
--- a/docs/tutorials/python/index.md
+++ /dev/null
@@ -1,25 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Tutorials
-
-```eval_rst
-.. toctree::
-   :glob:
-
-   *
-```
diff --git a/docs/tutorials/python/kvstore.md b/docs/tutorials/python/kvstore.md
deleted file mode 100644
index ad7d6bf8c2c1..000000000000
--- a/docs/tutorials/python/kvstore.md
+++ /dev/null
@@ -1,171 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Distributed Key-Value Store
-
-KVStore is a place for data sharing. Think of it as a single object shared
-across different devices (GPUs and computers), where each device can push data in
-and pull data out.
-
-## Initialization
-
-Let's consider a simple example: initializing
-a (`int`, `NDArray`) pair into the store, and then pulling the value out:
-
-```python
-import mxnet as mx
-
-kv = mx.kv.create('local') # create a local kv store.
-shape = (2,3)
-kv.init(3, mx.nd.ones(shape)*2)
-a = mx.nd.zeros(shape)
-kv.pull(3, out = a)
-print(a.asnumpy())
-```
-
-`[[ 2.  2.  2.],[ 2.  2.  2.]]`<!--notebook-skip-line-->
-
-## Push, Aggregate, and Update
-
-For any key that has been initialized, you can push a new value with the same shape to the key:
-
-```python
-kv.push(3, mx.nd.ones(shape)*8)
-kv.pull(3, out = a) # pull out the value
-print(a.asnumpy())
-```
-
-`[[ 8.  8.  8.],[ 8.  8.  8.]]`<!--notebook-skip-line-->
-
-The data for pushing can be stored on any device. Furthermore, you can push multiple
-values into the same key, where KVStore will first sum all of these
-values and then push the aggregated value. Here we will just demonstrate pushing a list of values on CPU.
-Please note summation only happens if the value list is longer than one
-
-```python
-contexts = [mx.cpu(i) for i in range(4)]
-b = [mx.nd.ones(shape, ctx) for ctx in contexts]
-kv.push(3, b)
-kv.pull(3, out = a)
-print(a.asnumpy())
-```
-
-`[[ 4.  4.  4.],[ 4.  4.  4.]]`<!--notebook-skip-line-->
-
-For each push, KVStore combines the pushed value with the value stored using an
-`updater`. The default updater is `ASSIGN`. You can replace the default to
-control how data is merged:
-
-```python
-def update(key, input, stored):
-    print("update on key: %d" % key)
-    stored += input * 2
-kv._set_updater(update)
-kv.pull(3, out=a)
-print(a.asnumpy())
-```
-
-`[[ 4.  4.  4.],[ 4.  4.  4.]]`<!--notebook-skip-line-->
-
-```python
-kv.push(3, mx.nd.ones(shape))
-kv.pull(3, out=a)
-print(a.asnumpy())
-```
-
-`update on key: 3`<!--notebook-skip-line-->
-
-`[[ 6.  6.  6.],[ 6.  6.  6.]]`<!--notebook-skip-line-->
-
-
-## Pull
-
-You've already seen how to pull a single key-value pair. Similarly, to push, you can
-pull the value onto several devices with a single call:
-
-```python
-b = [mx.nd.ones(shape, ctx) for ctx in contexts]
-kv.pull(3, out = b)
-print(b[1].asnumpy())
-```
-
-`[ 6.  6.  6.]],[[ 6.  6.  6.]`<!--notebook-skip-line-->
-
-## Handle a List of Key-Value Pairs
-
-All operations introduced so far involve a single key. KVStore also provides
-an interface for a list of key-value pairs. 
-
-For a single device:
-
-```python
-keys = [5, 7, 9]
-kv.init(keys, [mx.nd.ones(shape)]*len(keys))
-kv.push(keys, [mx.nd.ones(shape)]*len(keys))
-b = [mx.nd.zeros(shape)]*len(keys)
-kv.pull(keys, out = b)
-print(b[1].asnumpy())
-```
-
-`update on key: 5`<!--notebook-skip-line-->
-
-`update on key: 7`<!--notebook-skip-line-->
-
-`update on key: 9`<!--notebook-skip-line-->
-
-`[[ 3.  3.  3.],[ 3.  3.  3.]]`<!--notebook-skip-line-->
-
-For multiple devices:
-
-```python
-b = [[mx.nd.ones(shape, ctx) for ctx in contexts]] * len(keys)
-kv.push(keys, b)
-kv.pull(keys, out = b)
-print(b[1][1].asnumpy())
-```
-
-`update on key: 5`<!--notebook-skip-line-->
-
-`update on key: 7`<!--notebook-skip-line-->
-
-`update on key: 9`<!--notebook-skip-line-->
-
-`[[ 11.  11.  11.],[ 11.  11.  11.]]`<!--notebook-skip-line-->
-
-## Run on Multiple Machines
-Based on parameter server, the `updater` runs on the server nodes.
-When the distributed version is ready, we will update this section.
-
-
-<!-- ## How to Choose Between APIs -->
-
-<!-- You can mix APIs as much as you like. Here are some guidelines -->
-<!-- * Use the Symbolic API and a coarse-grained operator to create  an established structure. -->
-<!-- * Use a fine-grained operator to extend parts of a more flexible symbolic graph. -->
-<!-- * Do some dynamic NDArray tricks, which are even more flexible, between the calls of forward and backward executors. -->
-
-<!-- Different approaches offer you different levels of flexibility and -->
-<!-- efficiency. Normally, you do not need to be flexible in all parts of the -->
-<!-- network, so use the parts optimized for speed, and compose it -->
-<!-- flexibly with a fine-grained operator or a dynamic NDArray. Such a -->
-<!-- mixture allows you to build the deep learning architecture both efficiently and -->
-<!-- flexibly as your choice.  -->
-
-## Next Steps
-* [MXNet tutorials index](http://mxnet.io/tutorials/index.html)
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/python/linear-regression.md b/docs/tutorials/python/linear-regression.md
deleted file mode 100644
index 47d1acae9a14..000000000000
--- a/docs/tutorials/python/linear-regression.md
+++ /dev/null
@@ -1,215 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Linear Regression
-
-In this tutorial we'll walk through how one can implement *linear regression* using MXNet APIs.
-
-The function we are trying to learn is: *y = x<sub>1</sub>  +  2x<sub>2</sub>*, where *(x<sub>1</sub>,x<sub>2</sub>)* are input features and *y* is the corresponding label.
-
-## Prerequisites
-
-To complete this tutorial, we need:
-
-- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/install/index.html).
-
-- [Jupyter Notebook](http://jupyter.org/index.html).
-
-```
-$ pip install jupyter
-```
-
-To begin, the following code imports the necessary packages we'll need for this exercise.
-
-```python
-import mxnet as mx
-import numpy as np
-
-# Fix the random seed
-mx.random.seed(42)
-
-import logging
-logging.getLogger().setLevel(logging.DEBUG)
-```
-
-## Preparing the Data
-
-In MXNet, data is input via **Data Iterators**. Here we will illustrate
-how to encode a dataset into an iterator that MXNet can use. The data used in the example is made up of 2D data points with corresponding integer labels.
-
-```python
-#Training data
-train_data = np.random.uniform(0, 1, [100, 2])
-train_label = np.array([train_data[i][0] + 2 * train_data[i][1] for i in range(100)])
-batch_size = 1
-
-#Evaluation Data
-eval_data = np.array([[7,2],[6,10],[12,2]])
-eval_label = np.array([11,26,16])
-```
-
-Once we have the data ready, we need to put it into an iterator and specify
-parameters such as `batch_size` and `shuffle`. `batch_size` specifies the number
-of examples shown to the model each time we update its parameters and `shuffle`
-tells the iterator to randomize the order in which examples are shown to the model.
-
-
-```python
-train_iter = mx.io.NDArrayIter(train_data, train_label, batch_size, shuffle=True, label_name='lin_reg_label')
-eval_iter = mx.io.NDArrayIter(eval_data, eval_label, batch_size, shuffle=False, label_name='lin_reg_label')
-```
-
-In the above example, we have made use of `NDArrayIter`, which is useful for iterating
-over both numpy ndarrays and MXNet NDArrays. In general, there are different types of iterators in
-MXNet and you can use one based on the type of data you are processing.
-Documentation for iterators can be found [here](http://mxnet.io/api/python/io/io.html).
-
-## MXNet Classes
-
-1. **IO:** The IO class as we already saw works on the data and carries out
-   operations such as feeding data in batches and shuffling.
-
-2. **Symbol:** The actual MXNet neural network is composed using symbols. MXNet has
-   different types of symbols, including variable placeholders for input data,
-   neural network layers, and operators that manipulate NDArrays.
-
-3. **Module:** The module class in MXNet is used to define the overall computation.
-	It is initialized with the model we want to train, the training inputs (data and labels)
-	and some additional parameters such as learning rate and the optimization
-	algorithm to use.
-
-## Defining the Model
-
-MXNet uses **Symbols** for defining a model. Symbols are the building blocks
-and make up various components of the model. Symbols are used to define:
-
-1. **Variables:** A variable is a placeholder for future data. This symbol is used
-   to define a spot which will be filled with training data/labels in the future
-   when we commence training.
-2. **Neural Network Layers:** The layers of a network or any other type of model are
-   also defined by Symbols. Such a symbol takes one or more previous symbols as
-   inputs, performs some transformations on them, and creates one or more outputs.
-   One such example is the `FullyConnected` symbol which specifies a fully connected
-   layer of a neural network.
-3. **Outputs:** Output symbols are MXNet's way of defining a loss. They are
-   suffixed with the word "Output" (eg. the `SoftmaxOutput` layer). You can also
-   [create your own loss function](https://github.com/dmlc/mxnet/blob/master/docs/tutorials/r/CustomLossFunction.md#how-to-use-your-own-loss-function).
-   Some examples of existing losses are: `LinearRegressionOutput`, which computes
-   the l2-loss between it's input symbol and the labels provided to it;
-   `SoftmaxOutput`, which computes the categorical cross-entropy.
-
-The ones described above and other symbols are chained together with the output of
-one symbol serving as input to the next to build the network topology. More information
-about the different types of symbols can be found [here](http://mxnet.io/api/python/symbol/symbol.html).
-
-```python
-X = mx.sym.Variable('data')
-Y = mx.symbol.Variable('lin_reg_label')
-fully_connected_layer  = mx.sym.FullyConnected(data=X, name='fc1', num_hidden = 1)
-lro = mx.sym.LinearRegressionOutput(data=fully_connected_layer, label=Y, name="lro")
-```
-
-The above network uses the following layers:
-
-1. `FullyConnected`: The fully connected symbol represents a fully connected layer
-   of a neural network (without any activation being applied), which in essence,
-   is just a linear regression on the input attributes. It takes the following
-   parameters:
-
-   - `data`: Input to the layer (specifies the symbol whose output should be fed here)
-   - `num_hidden`: Number of hidden neurons in the layer, which is same as the dimensionality
-     of the layer's output
-
-2. `LinearRegressionOutput`: Output layers in MXNet compute training loss, which is
-	the measure of inaccuracy in the model's predictions. The goal of training is to minimize the
-	training loss. In our example, the `LinearRegressionOutput` layer computes the *l2* loss against
-	its input and the labels provided to it. The parameters to this layer are:
-
-   - `data`: Input to this layer (specifies the symbol whose output should be fed here)
-   - `label`: The training labels against which we will compare the input to the layer for calculation of l2 loss
-
-**Note on naming convention:** the label variable's name should be the same as the
-`label_name` parameter passed to your training data iterator. The default value of
-this is `softmax_label`, but we have updated it to `lin_reg_label` in this
-tutorial as you can see in `Y = mx.symbol.Variable('lin_reg_label')` and
-`train_iter = mx.io.NDArrayIter(..., label_name='lin_reg_label')`.
-
-Finally, the network is input to a *Module*, where we specify the symbol
-whose output needs to be minimized (in our case, `lro` or the `lin_reg_output`), the
-learning rate to be used while optimization and the number of epochs we want to
-train our model for.
-
-```python
-model = mx.mod.Module(
-    symbol = lro ,
-    data_names=['data'],
-    label_names = ['lin_reg_label']# network structure
-)
-```
-
-We can visualize the network we created by plotting it:
-
-```python
-mx.viz.plot_network(symbol=lro, node_attrs={"shape":"oval","fixedsize":"false"})
-```
-
-## Training the model
-
-Once we have defined the model structure, the next step is to train the
-parameters of the model to fit the training data. This is accomplished using the
-`fit()` function of the `Module` class.
-
-```python
-model.fit(train_iter, eval_iter,
-            optimizer_params={'learning_rate':0.01, 'momentum': 0.9},
-            num_epoch=20,
-            eval_metric='mse',
-            batch_end_callback = mx.callback.Speedometer(batch_size, 2))
-```
-
-## Using a trained model: (Testing and Inference)
-
-Once we have a trained model, we can do a couple of things with it - we can either
-use it for inference or we can evaluate the trained model on test data. The latter is shown below:
-
-```python
-model.predict(eval_iter).asnumpy()
-```
-
-We can also evaluate our model according to some metric. In this example, we are
-evaluating our model's mean squared error (MSE) on the evaluation data.
-
-```python
-metric = mx.metric.MSE()
-mse = model.score(eval_iter, metric)
-print("Achieved {0:.6f} validation MSE".format(mse[0][1]))
-assert model.score(eval_iter, metric)[0][1] < 0.01001, "Achieved MSE (%f) is larger than expected (0.01001)" % model.score(eval_iter, metric)[0][1]
-```
-
-Let us try and add some noise to the evaluation data and see how the MSE changes:
-
-```python
-eval_data = np.array([[7,2],[6,10],[12,2]])
-eval_label = np.array([11.1,26.1,16.1]) #Adding 0.1 to each of the values
-eval_iter = mx.io.NDArrayIter(eval_data, eval_label, batch_size, shuffle=False, label_name='lin_reg_label')
-model.score(eval_iter, metric)
-```
-
-We can also create a custom metric and use it to evaluate a model. More
-information on metrics can be found in the [API documentation](http://mxnet.incubator.apache.org/api/python/metric/metric.html).
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/python/mnist.md b/docs/tutorials/python/mnist.md
deleted file mode 100644
index ac965617e347..000000000000
--- a/docs/tutorials/python/mnist.md
+++ /dev/null
@@ -1,222 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Handwritten Digit Recognition
-
-In this tutorial, we'll give you a step by step walk-through of how to build a hand-written digit classifier using the [MNIST](https://en.wikipedia.org/wiki/MNIST_database) dataset. For someone new to deep learning, this exercise is arguably the "Hello World" equivalent.
-
-MNIST is a widely used dataset for the hand-written digit classification task. It consists of 70,000 labeled 28x28 pixel grayscale images of hand-written digits. The dataset is split into 60,000 training images and 10,000 test images. There are 10 classes (one for each of the 10 digits). The task at hand is to train a model using the 60,000 training images and subsequently test its classification accuracy on the 10,000 test images.
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/mnist.png)
-
-**Figure 1:** Sample images from the MNIST dataset.
-
-## Prerequisites
-To complete this tutorial, we need:  
-
-- MXNet version 0.10 or later. See the installation instructions for your operating system in [Setup and Installation](http://mxnet.io/install/index.html).
-
-- [Python Requests](http://docs.python-requests.org/en/master/) and [Jupyter Notebook](http://jupyter.org/index.html).
-
-```
-$ pip install requests jupyter
-```
-
-## Loading Data
-
-Before we define the model, let's first fetch the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset.
-
-The following source code downloads and loads the images and the corresponding labels into memory.
-
-```python
-import mxnet as mx
-mnist = mx.test_utils.get_mnist()
-
-# Fix the seed
-mx.random.seed(42)
-
-# Set the compute context, GPU is available otherwise CPU
-ctx = mx.gpu() if mx.context.num_gpus() else mx.cpu()
-```
-
-After running the above source code, the entire MNIST dataset should be fully loaded into memory. Note that for large datasets it is not feasible to pre-load the entire dataset first like we did here. What is needed is a mechanism by which we can quickly and efficiently stream data directly from the source. MXNet Data iterators come to the rescue here by providing exactly that. Data iterator is the mechanism by which we feed input data into an MXNet training algorithm and they are very simple to initialize and use and are optimized for speed. During training, we typically process training samples in small batches and over the entire training lifetime will end up processing each training example multiple times. In this tutorial, we'll configure the data iterator to feed examples in batches of 100. Keep in mind that each example is a 28x28 grayscale image and the corresponding label.
-
-Image batches are commonly represented by a 4-D array with shape `(batch_size, num_channels, width, height)`. For the MNIST dataset, since the images are grayscale, there is only one color channel. Also, the images are 28x28 pixels, and so each image has width and height equal to 28. Therefore, the shape of input is `(batch_size, 1, 28, 28)`. Another important consideration is the order of input samples. When feeding training examples, it is critical that we don't feed samples with the same label in succession. Doing so can slow down training.
-Data iterators take care of this by randomly shuffling the inputs. Note that we only need to shuffle the training data. The order does not matter for test data.
-
-The following source code initializes the data iterators for the MNIST dataset. Note that we initialize two iterators: one for train data and one for test data.
-
-```python
-batch_size = 100
-train_iter = mx.io.NDArrayIter(mnist['train_data'], mnist['train_label'], batch_size, shuffle=True)
-val_iter = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], batch_size)
-```
-
-## Training
-We will cover a couple of approaches for performing the hand written digit recognition task. The first approach makes use of a traditional deep neural network architecture called Multilayer Perceptron (MLP). We'll discuss its drawbacks and use that as a motivation to introduce a second more advanced approach called Convolution Neural Network (CNN) that has proven to work very well for image classification tasks.
-
-### Multilayer Perceptron
-
-The first approach makes use of a [Multilayer Perceptron](https://en.wikipedia.org/wiki/Multilayer_perceptron) to solve this problem. We'll define the MLP using MXNet's symbolic interface. We begin by creating a place holder variable for the input data. When working with an MLP, we need to flatten our 28x28 images into a flat 1-D structure of 784 (28 * 28) raw pixel values. The order of pixel values in the flattened vector does not matter as long as we are being consistent about how we do this across all images.
-
-```python
-data = mx.sym.var('data')
-# Flatten the data from 4-D shape into 2-D (batch_size, num_channel*width*height)
-data = mx.sym.flatten(data=data)
-```
-One might wonder if we are discarding valuable information by flattening. That is indeed true and we'll cover this more when we talk about convolutional neural networks where we preserve the input shape. For now, we'll go ahead and work with flattened images.
-
-MLPs contains several fully connected layers. A fully connected layer or FC layer for short, is one where each neuron in the layer is connected to every neuron in its preceding layer. From a linear algebra perspective, an FC layer applies an [affine transform](https://en.wikipedia.org/wiki/Affine_transformation) to the *n x m* input matrix *X* and outputs a matrix *Y* of size *n x k*, where *k* is the number of neurons in the FC layer. *k* is also referred to as the hidden size. The output *Y* is computed according to the equation *Y = X W<sup>T</sup> + b*. The FC layer has two learnable parameters, the *k x m* weight matrix *W* and the *1 x k* bias vector *b*. The summation of bias vector follows the broadcasting rules explained in [`mxnet.sym.broadcast_to()`](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.broadcast_to). Conceptually, broadcasting replicates row elements of the bias vector to create an *n x k* matrix before summation.
-
-
-In an MLP, the outputs of most FC layers are fed into an activation function, which applies an element-wise non-linearity. This step is critical and it gives neural networks the ability to classify inputs that are not linearly separable. Common choices for activation functions are sigmoid, tanh, and [rectified linear unit](https://en.wikipedia.org/wiki/Rectifier_%28neural_networks%29) (ReLU). In this example, we'll use the ReLU activation function which has several desirable properties and is typically considered a default choice.
-
-The following code declares two fully connected layers with 128 and 64 neurons each. Furthermore, these FC layers are sandwiched between ReLU activation layers each one responsible for performing an element-wise ReLU transformation on the FC layer output.
-
-```python
-# The first fully-connected layer and the corresponding activation function
-fc1  = mx.sym.FullyConnected(data=data, num_hidden=128)
-act1 = mx.sym.Activation(data=fc1, act_type="relu")
-
-# The second fully-connected layer and the corresponding activation function
-fc2  = mx.sym.FullyConnected(data=act1, num_hidden = 64)
-act2 = mx.sym.Activation(data=fc2, act_type="relu")
-```
-
-The last fully connected layer often has its hidden size equal to the number of output classes in the dataset. The activation function for this layer will be the softmax function. The Softmax layer maps its input to a probability score for each class of output. During the training stage, a loss function computes the [cross entropy](https://en.wikipedia.org/wiki/Cross_entropy) between the probability distribution (softmax output) predicted by the network and the true probability distribution given by the label.
-
-The following source code declares the final fully connected layer of size 10. 10 incidentally is the total number of digits. The output from this layer is fed into a `SoftMaxOutput` layer that performs softmax and cross-entropy loss computation in one go. Note that loss computation only happens during training.
-
-```python
-# MNIST has 10 classes
-fc3  = mx.sym.FullyConnected(data=act2, num_hidden=10)
-# Softmax with cross entropy loss
-mlp  = mx.sym.SoftmaxOutput(data=fc3, name='softmax')
-```
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mlp_mnist.png)
-
-**Figure 2:** MLP network architecture for MNIST.
-
-Now that both the data iterator and neural network are defined, we can commence training. Here we'll employ the `module` feature in MXNet which provides a high-level abstraction for running training and inference on predefined networks. The module API allows the user to specify appropriate parameters that control how the training proceeds.
-
-The following source code initializes a module to train the MLP network we defined above. For our training, we will make use of the stochastic gradient descent (SGD) optimizer. In particular, we'll be using mini-batch SGD. Standard SGD processes train data one example at a time. In practice, this is very slow and one can speed up the process by processing examples in small batches. In this case, our batch size will be 100, which is a reasonable choice. Another parameter we select here is the learning rate, which controls the step size the optimizer takes in search of a solution. We'll pick a learning rate of 0.1, again a reasonable choice. Settings such as batch size and learning rate are what are usually referred to as hyper-parameters. What values we give them can have a great impact on training performance. For the purpose of this tutorial, we'll start with some reasonable and safe values. In other tutorials, we'll discuss how one might go about finding a combination of hyper-parameters for optimal model performance.
-
-Typically, one runs the training until convergence, which means that we have learned a good set of model parameters (weights + biases) from the train data. For the purpose of this tutorial, we'll run training for 10 epochs and stop. An epoch is one full pass over the entire train data.
-
-```python
-import logging
-logging.getLogger().setLevel(logging.DEBUG)  # logging to stdout
-# create a trainable module on compute context
-mlp_model = mx.mod.Module(symbol=mlp, context=ctx)
-mlp_model.fit(train_iter,  # train data
-              eval_data=val_iter,  # validation data
-              optimizer='sgd',  # use SGD to train
-              optimizer_params={'learning_rate':0.1},  # use fixed learning rate
-              eval_metric='acc',  # report accuracy during training
-              batch_end_callback = mx.callback.Speedometer(batch_size, 100), # output progress for each 100 data batches
-              num_epoch=10)  # train for at most 10 dataset passes
-```
-
-### Prediction
-
-After the above training completes, we can evaluate the trained model by running predictions on test data. The following source code computes the prediction probability scores for each test image. *prob[i][j]* is the probability that the *i*-th test image contains the *j*-th output class.
-
-```python
-test_iter = mx.io.NDArrayIter(mnist['test_data'], None, batch_size)
-prob = mlp_model.predict(test_iter)
-assert prob.shape == (10000, 10)
-```
-
-Since the dataset also has labels for all test images, we can compute the accuracy metric as follows:
-
-```python
-test_iter = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], batch_size)
-# predict accuracy of mlp
-acc = mx.metric.Accuracy()
-mlp_model.score(test_iter, acc)
-print(acc)
-assert acc.get()[1] > 0.96, "Achieved accuracy (%f) is lower than expected (0.96)" % acc.get()[1]
-```
-If everything went well, we should see an accuracy value that is around 0.96, which means that we are able to accurately predict the digit in 96% of test images. This is a pretty good result. But as we will see in the next part of this tutorial, we can do a lot better than that.
-
-### Convolutional Neural Network
-
-Earlier, we briefly touched on a drawback of MLP when we said we need to discard the input image's original shape and flatten it as a vector before we can feed it as input to the MLP's first fully connected layer. Turns out this is an important issue because we don't take advantage of the fact that pixels in the image have natural spatial correlation along the horizontal and vertical axes. A convolutional neural network (CNN) aims to address this problem by using a more structured weight representation. Instead of flattening the image and doing a simple matrix-matrix multiplication, it employs one or more convolutional layers that each performs a 2-D convolution on the input image.
-
-A single convolution layer consists of one or more filters that each play the role of a feature detector. During training, a CNN learns appropriate representations (parameters) for these filters. Similar to MLP, the output from the convolutional layer is transformed by applying a non-linearity. Besides the convolutional layer, another key aspect of a CNN is the pooling layer. A pooling layer serves to make the CNN translation invariant: a digit remains the same even when it is shifted left/right/up/down by a few pixels. A pooling layer reduces a *n x m* patch into a single value to make the network less sensitive to the spatial location. Pooling layer is always included after each conv (+ activation) layer in the CNN.
-
-The following source code defines a convolutional neural network architecture called LeNet. LeNet is a popular network known to work well on digit classification tasks. We will use a slightly different version from the original LeNet implementation, replacing the sigmoid activations with tanh activations for the neurons
-
-```python
-data = mx.sym.var('data')
-# first conv layer
-conv1 = mx.sym.Convolution(data=data, kernel=(5,5), num_filter=20)
-tanh1 = mx.sym.Activation(data=conv1, act_type="tanh")
-pool1 = mx.sym.Pooling(data=tanh1, pool_type="max", kernel=(2,2), stride=(2,2))
-# second conv layer
-conv2 = mx.sym.Convolution(data=pool1, kernel=(5,5), num_filter=50)
-tanh2 = mx.sym.Activation(data=conv2, act_type="tanh")
-pool2 = mx.sym.Pooling(data=tanh2, pool_type="max", kernel=(2,2), stride=(2,2))
-# first fullc layer
-flatten = mx.sym.flatten(data=pool2)
-fc1 = mx.symbol.FullyConnected(data=flatten, num_hidden=500)
-tanh3 = mx.sym.Activation(data=fc1, act_type="tanh")
-# second fullc
-fc2 = mx.sym.FullyConnected(data=tanh3, num_hidden=10)
-# softmax loss
-lenet = mx.sym.SoftmaxOutput(data=fc2, name='softmax')
-```
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/conv_mnist.png)
-
-**Figure 3:** First conv + pooling layer in LeNet.
-
-Now we train LeNet with the same hyper-parameters as before. Note that, if a GPU is available, we recommend using it. This greatly speeds up computation given that LeNet is more complex and compute-intensive than the previous multilayer perceptron. To do so, we only need to change `mx.cpu()` to `mx.gpu()` and MXNet takes care of the rest. Just like before, we'll stop training after 10 epochs.
-
-```python
-lenet_model = mx.mod.Module(symbol=lenet, context=ctx)
-# train with the same
-lenet_model.fit(train_iter,
-                eval_data=val_iter,
-                optimizer='sgd',
-                optimizer_params={'learning_rate':0.1},
-                eval_metric='acc',
-                batch_end_callback = mx.callback.Speedometer(batch_size, 100),
-                num_epoch=10)
-```
-
-### Prediction
-
-Finally, we'll use the trained LeNet model to generate predictions for the test data.
-
-```python
-test_iter = mx.io.NDArrayIter(mnist['test_data'], None, batch_size)
-prob = lenet_model.predict(test_iter)
-test_iter = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], batch_size)
-# predict accuracy for lenet
-acc = mx.metric.Accuracy()
-lenet_model.score(test_iter, acc)
-print(acc)
-assert acc.get()[1] > 0.98, "Achieved accuracy (%f) is lower than expected (0.98)" % acc.get()[1]
-```
-If all went well, we should see a higher accuracy metric for predictions made using LeNet. With CNN we should be able to correctly predict around 98% of all test images.
-
-## Summary
-
-In this tutorial, we have learned how to use MXNet to solve a standard computer vision problem: classifying images of hand written digits. You have seen how to quickly and easily build, train and evaluate models such as MLP and CNN with MXNet.
-
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/python/module_to_gluon.md b/docs/tutorials/python/module_to_gluon.md
deleted file mode 100644
index 838ad667836d..000000000000
--- a/docs/tutorials/python/module_to_gluon.md
+++ /dev/null
@@ -1,365 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Converting Module API code to the Gluon API
-
-Sometimes you find yourself in the situation where the model you want to use has been written using the symbolic Module API rather than the simpler, easier-to-debug, more flexible, imperative Gluon API. In this tutorial, we will give you a comprehensive guide for transforming Module code to Gluon code.
-
-The different steps to take into consideration are:
-
-I) Data loading
-
-II) Model definition
-
-III) Loss
-
-IV) Training Loop
-
-V) Exporting Models
-
-VI) Loading Models for Inference
-
-In the following section we will look at 1:1 mappings between the Module and the Gluon ways of training a neural network.
-
-## I - Data Loading
-
-In this section we will be looking at the difference in loading data between Module and Gluon.
-Let's first import a few Python modules.
-
-```python
-from collections import namedtuple
-import logging
-logging.basicConfig(level=logging.INFO)
-import random
-
-import numpy as np
-import mxnet as mx
-from mxnet.gluon.data import ArrayDataset, DataLoader
-from mxnet.gluon import nn
-from mxnet import gluon
-
-# parameters
-batch_size = 5
-dataset_length = 50
-
-# random seeds
-random.seed(1)
-np.random.seed(1)
-mx.random.seed(1)
-
-```
-
-#### Module
-
-When using the Module API we use a [`DataIter`](https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=dataiter#mxnet.io.DataIter), in addition to the data itself, the [`DataIter`](https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=dataiter#mxnet.io.DataIter) contains information about the name of the input symbols. 
-
-In the Module API, `DataIter`s are responsible for both holding the data and iterating through it. Some `DataIter`s support multi-threading like the [`ImageRecordIter`](https://mxnet.incubator.apache.org/api/python/io/io.html#mxnet.io.ImageRecordIter), while other don't, such as the [`NDArrayIter`](https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=ndarrayiter#mxnet.io.NDArrayIter).
-
-Let's create some random data, following the same format as grayscale 28x28 images.
-
-
-```python
-train_data = np.random.rand(dataset_length, 28,28).astype('float32')
-train_label = np.random.randint(0, 10, (dataset_length,)).astype('float32')
-```
-
-We can now wraps this data into an ArrayIterator that will create batches of data using the first dimension of the provided array as the batch dimension. 
-
-```python
-data_iter = mx.io.NDArrayIter(data=train_data, label=train_label, batch_size=batch_size, shuffle=False, data_name='data', label_name='softmax_label')
-for batch in data_iter:
-    print(batch.data[0].shape, batch.label[0])
-    break
-```
-
-    (5, 28, 28) 
-    [5. 0. 3. 4. 9.]
-    <NDArray 5 @cpu(0)>
-
-
-#### Gluon
-
-With Gluon, the preferred method is to use a [`DataLoader`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader) that makes use of a [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset) to asynchronously prefetch the data. 
-
-The Gluon API offers you the ability to efficiently fetch data and separate the concerns of loading versus holding data. The DataLoader role is to request certain indices of the dataset. The Dataset has ownership of the data.
-The `Dataset` data can be in or out of memory, and the `DataLoader` role is to request certain indices of the dataset, in the main thread or through multi-processing (or multi-threaded) workers and batch the data together. 
-
-```python
-dataset = ArrayDataset(train_data, train_label)
-dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False, num_workers=0)
-for data, label in dataloader:
-    print(data.shape, label)
-    break
-```
-
-    (5, 28, 28) 
-    [5. 0. 3. 4. 9.]
-    <NDArray 5 @cpu(0)>
-
-You can check the [`Dataset` and `DataLoader` tutorials](https://mxnet.incubator.apache.org/tutorials/gluon/datasets.html) out. You can either rewrite your code in order to use one of the provided [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset) class, like the [`ArrayDataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=arraydataset#mxnet.gluon.data.ArrayDataset) or the [`ImageFolderDataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=imagefolderdataset#mxnet.gluon.data.vision.datasets.ImageFolderDataset)
-
-
-## II - Model Definition
-
-Let's look at the model definition from the [MNIST Module Tutorial](https://mxnet.incubator.apache.org/tutorials/python/mnist.html):
-
-#### Module
-
-For the Module API, you define the data flow by setting `data` keyword argument of one layer to the next.
-You then bind the symbolic model to a specific compute context and specify the symbol names for the data and the label.
-
-```python
-
-# context
-ctx = mx.cpu()
-
-def get_module_network():
-    data = mx.sym.var('data')
-    data = mx.sym.flatten(data=data)
-    fc1  = mx.sym.FullyConnected(data=data, num_hidden=128)
-    act1 = mx.sym.Activation(data=fc1, act_type="relu")
-    fc2  = mx.sym.FullyConnected(data=act1, num_hidden = 64)
-    act2 = mx.sym.Activation(data=fc2, act_type="relu")
-    fc3  = mx.sym.FullyConnected(data=act2, num_hidden=10)
-    mlp  = mx.sym.SoftmaxOutput(data=fc3, name='softmax')
-    return mlp
-
-mlp = get_module_network()
-# Bind model to Module
-mlp_model = mx.mod.Module(symbol=mlp, context=ctx, data_names=['data'], label_names=['softmax_label'])
-```
-
-#### Gluon
-
-In Gluon, for the equivalent model, you would create a `Sequential` block, in that case a `HybridSequential` block to allow for future hybridization since we are only using [hybridizable blocks](https://mxnet.incubator.apache.org/tutorials/gluon/hybrid.html). The flow of the data will be automatically set from one layer to the next, since they are held in a `Sequential` block.
-Note that we don't need named symbols for the input, and we show how the loss is handled in Gluon in the next section.
-
-```python
-def get_gluon_network():
-    net = nn.HybridSequential()
-    with net.name_scope():
-        net.add(
-            nn.Flatten(),
-            nn.Dense(units=128, activation="relu"),
-            nn.Dense(units=64, activation="relu"),
-            nn.Dense(units=10)
-        )
-    return net
-
-net = get_gluon_network()
-```
-
-## III - Loss
-
-The loss, that you are trying to minimize using an optimization algorithm like SGD, is defined differently in the Module API than in Gluon.
-
-
-#### Module
-
-
-In the module API, the loss is part of the network. It has usually a forward pass result, that is the inference value, and a backward pass that is the gradient of the output with respect to that particular loss.
-
-For example, the [sym.SoftmaxOutput](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html?highlight=softmaxout#mxnet.symbol.SoftmaxOutput) is a softmax output in the forward pass and the gradient with respect to the cross-entropy loss in the backward pass.
-
-```python
-# Softmax with cross entropy loss, directly part of the network
-out = mx.sym.SoftmaxOutput(data=mlp, name='softmax')
-```
-
-#### Gluon
-
-
-In Gluon, it is a lot more transparent. Losses, like the [SoftmaxCrossEntropyLoss](https://mxnet.incubator.apache.org/api/python/gluon/loss.html?highlight=softmaxcross#mxnet.gluon.loss.SoftmaxCrossEntropyLoss), are only computing the actual value of the loss. You then call `.backward()` on the loss value to compute the gradient of the parameters with respect to that loss. At inference time, you simply call `.softmax()` on your output to get the output of your network normalized according to the softmax function.
-
-
-```python
-# We simply create a loss function we will use in our training loop
-loss_fn = gluon.loss.SoftmaxCrossEntropyLoss()
-```
-
-In the next section we will show how you use this loss function in Gluon to generate the loss value in the main training loop.
-
-## IV - Training Loop
-
-
-#### Module
-
-The Module API provides a [`.fit()`](https://mxnet.incubator.apache.org/api/python/module/module.html?highlight=.fit#mxnet.module.BaseModule.fit) function that takes care of fitting training data to your symbolic model. With Gluon, your execution flow controls the data flow, so you need to write your own loop. It might seems like it is more verbose, but you have a lot more control as to what is happening during the training. 
-With the [`.fit()`](https://mxnet.incubator.apache.org/api/python/module/module.html?highlight=.fit#mxnet.module.BaseModule.fit) function, you control the metric reporting, checkpointing or weights initialization through a lot of different keyword arguments (check the [docs](https://mxnet.incubator.apache.org/api/python/module/module.html?highlight=.fit#mxnet.module.BaseModule.fit)). That is where you define the optimizer for example.
-
-```python
-mlp_model.fit(data_iter,  # train data
-              eval_data=data_iter,  # validation data
-              optimizer='sgd',  # use SGD to train
-              force_init=True,
-              force_rebind=True,
-              optimizer_params={'learning_rate':0.1},  # use fixed learning rate
-              eval_metric='acc',  # report accuracy during training
-              num_epoch=5)  # train for 5 full dataset passes
-```
-
-```INFO:root:Epoch[4] Train-accuracy=0.070000```<!--notebook-skip-line-->
-
-```INFO:root:Epoch[4] Time cost=0.038```<!--notebook-skip-line-->
-
-```INFO:root:Epoch[4] Validation-accuracy=0.125000```<!--notebook-skip-line-->
-
-#### Gluon
-
-
-With Gluon, you do these operations directly in the training loop, and the optimizer is part of the [`Trainer`](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html?highlight=trainer#mxnet.gluon.Trainer) object that handles the weight updates of your parameters.
-
-Notice the `loss.backward()` we call before updating the weight as mentionned in the previous section
-
-```python
-net.initialize(mx.init.Xavier(magnitude=2.24), ctx=ctx) # Initialize network and trainer
-trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.1})
-
-metric = mx.metric.Accuracy() # Pick a metric
-
-for e in range(5): # start of epoch
-    
-    for data, label in dataloader: # start of mini-batch
-        data = data.as_in_context(ctx)
-        label = label.as_in_context(ctx)
-        
-        with mx.autograd.record():
-            output = net(data) # forward pass
-            loss = loss_fn(output, label) # get loss
-            
-        loss.backward() # compute gradients
-        trainer.step(data.shape[0]) # update weights with SGD
-        metric.update(label, output) # update the metrics # end of mini-batch
-
-    name, acc = metric.get()
-    print('training metrics at epoch %d: %s=%f'%(e, name, acc))
-    metric.reset() # end of epoch
-```
-
-```training metrics at epoch 3: accuracy=0.155000```<!--notebook-skip-line-->
-
-```training metrics at epoch 4: accuracy=0.145000```<!--notebook-skip-line-->
-
-The Gluon training code is more verbose than the simple `.fit` from Module. However that is also the main advantage, there is no black magic going on here, you have full control of your training loop. You can for example easily set breakpoints, modify a learning rate or print data during the training flow. This flexibility also makes easy to implement more complex use-case like gradient accumulation across batches.
-
-## V - Exporting Model
-
-The ultimate purpose of training a model is to be able to export it and share it, whether it is for deployment or simply reproducibility purposes.
-
-#### Module
-
-
-With the Module API, you can save model using the [`.save_checkpoint()`](https://mxnet.incubator.apache.org/api/python/module/module.html?highlight=save_chec#mxnet.module.Module.save_checkpoint) and get a `-symbol.json` and a `.params` file that represent your network. 
-
-
-```python
-mlp_model.save_checkpoint('module-model', epoch=5)
-# module-model-0005.params module-model-symbol.json
-```
-
-```INFO:root:Saved checkpoint to "module-model-0005.params"```<!--notebook-skip-line-->
-
-#### Gluon
-
-
-
-With Gluon, network parameters are associated with a `Block`, but the execution flow is controlled in python through the code in `.forward()` function. Hence only [hybridized networks]() can be exported with a `-symbol.json` and `.params` file using [`.export()`](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html?highlight=export#mxnet.gluon.HybridBlock.export), non-hybridized models can only have their parameters exported using [`.save_parameters()`](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html?highlight=save_pa#mxnet.gluon.Block.save_parameters). Check this great tutorial to learn more: [Saving and Loading Gluon Models](https://mxnet.incubator.apache.org/tutorials/gluon/save_load_params.html).
-
-
-Any models:
-
-```python
-# save only the parameters
-net.save_parameters('gluon-model.params')
-# gluon-model.params
-```
-
-Hybridized models:
-
-```python
-# save the parameters and the symbolic representation
-net.hybridize()
-net(mx.nd.ones((1,1,28,28), ctx))
-
-net.export('gluon-model-hybrid', epoch=5)
-# gluon-model-hybrid-symbol.json gluon-model-hybrid-0005.params
-```
-
-## VI - Loading Model for Inference
-
-
-#### Module
-
-
-For inference, in the Module API, you need to first load the parameters and symbol, bind the symbol to a module and load the corresponding parameters. You can then pass a batch of data through that module and request the output of the network.
-
-
-```python
-# Load the symbol and parameters
-sym, arg_params, aux_params = mx.model.load_checkpoint('module-model', 5)
-
-# Bind them in a module
-mod = mx.mod.Module(symbol=sym, context=ctx, label_names=None)
-mod.bind(for_training=False, data_shapes=[('data', (1,1,28,28))], 
-         label_shapes=mod._label_shapes)
-
-# Set the parameters
-mod.set_params(arg_params, aux_params, allow_missing=True)
-
-# Run the inference
-Batch = namedtuple('Batch', ['data'])
-mod.forward(Batch([mx.nd.ones((1,28,28))]))
-prob = mod.get_outputs()[0].asnumpy()
-print("Output probabilities: {}".format(prob))
-```
-
-`Output probabilities: [[0.05537598 0.03889056 0.06126577 0.08879893 0.12371024 0.05759033 0.1378248  0.26134694 0.07905186 0.09614458]]`<!--notebook-skip-line-->
-
-#### Gluon (Symbolic Model)
-
-For the Gluon API, it is a lot simpler. You can just load a serialized model in a [`SymbolBlock`](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html?highlight=symbolblo#mxnet.gluon.SymbolBlock) and run inference directly.
-
-```python
-import warnings
-with warnings.catch_warnings():
-    warnings.simplefilter("ignore")
-    net = gluon.SymbolBlock.imports('module-model-symbol.json', ['data', 'softmax_label'], 'module-model-0005.params')
-prob = net(mx.nd.ones((1,1,28,28)), mx.nd.ones(1)) # note the second argument here to account for the softmax_label symbol
-print("Output probabilities: {}".format(prob.asnumpy()))
-```
-
-`Output probabilities: [[0.05537598 0.03889056 0.06126577 0.08879893 0.12371024 0.05759033 0.1378248  0.26134694 0.07905186 0.09614458]]`<!--notebook-skip-line-->
-
-#### Gluon (Imperative Model)
-
-```python
-net = get_gluon_network()
-net.load_parameters('gluon-model.params')
-prob = net(mx.nd.ones((1,1,28,28))).softmax()
-print("Output probabilities: {}".format(prob.asnumpy()))
-```
-
-`Output probabilities: [[0.01298077 0.00173413 0.01661885 0.3362421  0.00536332 0.02099853 0.01413316 0.5528366  0.0133819  0.02571066]]`<!--notebook-skip-line-->
-
-## Conclusion
-
-This tutorial lead you through the steps necessary to train a deep learning model and showed you the differences between the symbolic approach of the Module API and the imperative one of the Gluon API. If you need more help converting your Module API code to the Gluon API, reach out to the community on the [discuss forum](https://discuss.mxnet.io)!
-You can also compare the scripts for training MNIST in [Gluon](https://mxnet.incubator.apache.org/tutorials/gluon/mnist.html) and [Module](https://mxnet.incubator.apache.org/tutorials/python/mnist.html).
-
-
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/python/predict_image.md b/docs/tutorials/python/predict_image.md
deleted file mode 100644
index 0721abd6a731..000000000000
--- a/docs/tutorials/python/predict_image.md
+++ /dev/null
@@ -1,171 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Predict with pre-trained models
-
-This tutorial explains how to recognize objects in an image with a pre-trained model, and how to perform feature extraction.
-
-## Prerequisites
-
-To complete this tutorial, we need:
-
-- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/install/index.html)
-
-- [Matplotlib](https://matplotlib.org/) and [Jupyter Notebook](http://jupyter.org/index.html).
-
-```
-$ pip install matplotlib
-```
-
-## Loading
-
-We first download a pre-trained ResNet 18 model that is trained on the ImageNet dataset with over 1 million images and one thousand classes. A pre-trained model contains two parts, a json file containing the model definition and a binary file containing the parameters. In addition, there may be a `synset.txt` text file for the labels.
-
-```python
-import mxnet as mx
-path='http://data.mxnet.io/models/imagenet/'
-[mx.test_utils.download(path+'resnet/18-layers/resnet-18-0000.params'),
- mx.test_utils.download(path+'resnet/18-layers/resnet-18-symbol.json'),
- mx.test_utils.download(path+'synset.txt')]
-```
-
-Next, we load the downloaded model. 
-
-```python
-# set the context on CPU, switch to GPU if there is one available
-ctx = mx.cpu()
-```
-
-```python
-sym, arg_params, aux_params = mx.model.load_checkpoint('resnet-18', 0)
-mod = mx.mod.Module(symbol=sym, context=ctx, label_names=None)
-mod.bind(for_training=False, data_shapes=[('data', (1,3,224,224))], 
-         label_shapes=mod._label_shapes)
-mod.set_params(arg_params, aux_params, allow_missing=True)
-with open('synset.txt', 'r') as f:
-    labels = [l.rstrip() for l in f]
-```
-
-## Predicting
-
-We first define helper functions for downloading an image and performing the
-prediction:
-
-```python
-%matplotlib inline
-import matplotlib.pyplot as plt
-import numpy as np
-# define a simple data batch
-from collections import namedtuple
-Batch = namedtuple('Batch', ['data'])
-
-def get_image(url, show=False):
-    # download and show the image. Remove query string from the file name.
-    fname = mx.test_utils.download(url, fname=url.split('/')[-1].split('?')[0])
-    img = mx.image.imread(fname)
-    if img is None:
-        return None
-    if show:
-        plt.imshow(img.asnumpy())
-        plt.axis('off')
-    # convert into format (batch, RGB, width, height)
-    img = mx.image.imresize(img, 224, 224) # resize
-    img = img.transpose((2, 0, 1)) # Channel first
-    img = img.expand_dims(axis=0) # batchify
-    img = img.astype('float32') # for gpu context
-    return img
-
-def predict(url):
-    img = get_image(url, show=True)
-    # compute the predict probabilities
-    mod.forward(Batch([img]))
-    prob = mod.get_outputs()[0].asnumpy()
-    # print the top-5
-    prob = np.squeeze(prob)
-    a = np.argsort(prob)[::-1]
-    for i in a[0:5]:
-        print('probability=%f, class=%s' %(prob[i], labels[i]))
-```
-
-Now, we can perform prediction with any downloadable URL:
-
-```python
-predict('https://github.com/dmlc/web-data/blob/master/mxnet/doc/tutorials/python/predict_image/cat.jpg?raw=true')
-```
-
-`probability=0.249607, class=n02119022 red fox, Vulpes vulpes` <!--notebook-skip-line-->
-
-`probability=0.172868, class=n02119789 kit fox, Vulpes macrotis` <!--notebook-skip-line-->
-
-![](https://github.com/dmlc/web-data/blob/master/mxnet/doc/tutorials/python/predict_image/cat.jpg?raw=true) <!--notebook-skip-line-->
-
-```python
-predict('https://github.com/dmlc/web-data/blob/master/mxnet/doc/tutorials/python/predict_image/dog.jpg?raw=true')
-```
-
-`probability=0.873920, class=n02110958 pug, pug-dog` <!--notebook-skip-line-->
-
-`probability=0.102659, class=n02108422 bull mastiff` <!--notebook-skip-line-->
-
-![](https://github.com/dmlc/web-data/blob/master/mxnet/doc/tutorials/python/predict_image/dog.jpg?raw=true) <!--notebook-skip-line-->
-
-## Feature extraction
-
-By feature extraction, we mean presenting the input images by the output of an internal layer rather than the last softmax layer. These outputs, which can be viewed as the feature of the raw input image, can then be used by other applications such as object detection.
-
-We can use the ``get_internals`` method to get all internal layers from a Symbol.
-
-```python
-# list the last 10 layers
-all_layers = sym.get_internals()
-all_layers.list_outputs()[-10:]
-```
-
-```
-['bn1_moving_var',
- 'bn1_output',
- 'relu1_output',
- 'pool1_output',
- 'flatten0_output',
- 'fc1_weight',
- 'fc1_bias',
- 'fc1_output',
- 'softmax_label',
- 'softmax_output']
- ```
-
-An often used layer for feature extraction is the one before the last fully connected layer. For ResNet, and also Inception, it is the flattened layer with name `flatten0` which reshapes the 4-D convolutional layer output into 2-D for the fully connected layer. The following source code extracts a new Symbol which outputs the flattened layer and creates a model.
-
-```python
-fe_sym = all_layers['flatten0_output']
-fe_mod = mx.mod.Module(symbol=fe_sym, context=ctx, label_names=None)
-fe_mod.bind(for_training=False, data_shapes=[('data', (1,3,224,224))])
-fe_mod.set_params(arg_params, aux_params)
-```
-
-We can now invoke `forward` to obtain the features:
-
-```python
-img = get_image('https://github.com/dmlc/web-data/blob/master/mxnet/doc/tutorials/python/predict_image/cat.jpg?raw=true')
-fe_mod.forward(Batch([img]))
-features = fe_mod.get_outputs()[0]
-print('Shape',features.shape)
-print(features.asnumpy())
-assert features.shape == (1, 512)
-```
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/python/types_of_data_augmentation.md b/docs/tutorials/python/types_of_data_augmentation.md
deleted file mode 100644
index 63ad468f85f6..000000000000
--- a/docs/tutorials/python/types_of_data_augmentation.md
+++ /dev/null
@@ -1,395 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-
-# Types of Data Augmentation
-
-Data Augmentation is a regularization technique that's used to avoid overfitting when training Machine Learning models. Although the technique can be applied in a variety of domains, it's very common in Computer Vision, and this will be the focus of the tutorial.
-
-Adjustments are made to the original images in the training dataset before being used in training. Some example adjustments include translating, croping, scaling, rotating, changing brightness and contrast. We do this to reduce the dependence of the model on spurious characteristics; e.g. training data may only contain faces that fill 1/4 of the image, so the model trainied without data augmentation might unhelpfully learn that faces can only be of this size.
-
-After defining some utility functions to visualise the example images, this tutorial details each different augmentation that can be used to adjust both the position and the colors of images. We discuss augmentations that are combined into single functions, and conclude with a FAQ section.
-
-
-```python
-%matplotlib inline
-from matplotlib.pyplot import imshow
-import mxnet as mx  # used version '1.0.0' at time of writing
-import numpy as np
-
-mx.random.seed(42) # set seed for repeatability
-```
-
-We define a utility function below, that will be used for visualising the augmentations in the tutorial.
-
-
-```python
-def plot_mx_array(array):
-    """
-    Array expected to be height x width x 3 (channels), and values are floats between 0 and 255.
-    """
-    assert array.shape[2] == 3, "RGB Channel should be last"
-    imshow((array.clip(0, 255)/255).asnumpy())
-```
-
-We load an example image, this will be the target for our augmentations in the tutorial. 
-
-```python
-mx.test_utils.download('https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/inputs/0.jpg')
-example_image = mx.image.imread("0.jpg")
-assert example_image.dtype == np.uint8
-```
-
-
-You'll notice that the image is loaded with with `numpy.int8` datatype. Some functions such as `swapaxes` don't work on `int` types, so we'll convert to `float32`, and visualize.
-
-
-```python
-example_image = example_image.astype("float32")    
-plot_mx_array(example_image)
-```
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_8_0.png)<!--notebook-skip-line-->
-
-
-# Position Augmentation
-
-One form of augmentation affects the position of pixel values. Using combinations of slicing, scaling, translating, rotating and fliping the values of the original image can be shifted to create new images. Some operations (like scaling and rotation) require interpolation as pixels in the new image are combinations of pixels in the original image.
-
-### Crop
-
-You can use [`mxnet.image.RandomCropAug`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=randomcropaug#mxnet.image.RandomCropAug) and [`mxnet.image.CenterCropAug`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=centercropaug#mxnet.image.CenterCropAug) to create instances of the Augmenter class, which can be called just like a function.
-
-It's worth noting that the randomisation for `RandomCropAug` happens when calling the Augmenter, and not at the point of instantiation. You'll end up with different images each time you call the Augmenter, so it can't be used to apply the same augmentation to another image. You can use [`mxnet.random.seed`](https://mxnet.incubator.apache.org/api/python/symbol/random.html?highlight=seed#mxnet.random.seed) for random but repeatable augmentations.
-
-`CenterCropAug` is determanistic and just takes the most central crop of given size.
-
-
-```python
-aug = mx.image.RandomCropAug(size=(100, 100))
-aug_image = aug(example_image)
-plot_mx_array(aug_image)
-
-assert example_image.shape == (427, 640, 3)
-assert aug_image.shape == (100, 100, 3)
-```
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_13_1.png)<!--notebook-skip-line-->
-
-
-__*Watch Out!*__ Crop are a great way of adding diversity to your training examples, but be careful not to take it to the extreme. An example of this would be cropping out an object of interest from the image completely. Visualise a few examples after cropping to determine if this will be an issue.
-
-If you're training object detection models, it's recommended that you use the [`mxnet.image.DetRandomCropAug`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=detrandomcropaug#mxnet.image.DetRandomCropAug) augmenter. Instead of the `size` parameter, it has parameters such as `min_object_covered` to prevent this issue.
-
-### Resize
-
-Use [`mxnet.image.ResizeAug`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=resizeaug#mxnet.image.ResizeAug) and [`mxnet.image.ForceResizeAug`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=resizeaug#mxnet.image.ForceResizeAug) for scaling the image.
-
-`mxnet.image.ResizeAug` scales the shortest edge to the defined size, and automatically adjusts the longest edge to maintain the aspect ratio of the input image.
-`mxnet.image.ForceResizeAug` scales both height and width to the defined size, even if this doesn't preserve the aspect ratio.
-
-
-```python
-aug = mx.image.ResizeAug(size=50)
-aug_image = aug(example_image)
-plot_mx_array(aug_image)
-
-assert example_image.shape == (427, 640, 3)
-assert aug_image.shape == (50, 74, 3)
-```
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_17_1.png)<!--notebook-skip-line-->
-
-
-__*Watch out!*__ `size` should be (width, height).
-
-
-```python
-aug = mx.image.ForceResizeAug(size=(100, 50))
-aug_image = aug(example_image)
-plot_mx_array(aug_image)
-
-assert example_image.shape == (427, 640, 3)
-assert aug_image.shape == (50, 100, 3)
-```
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_19_1.png)<!--notebook-skip-line-->
-
-
-### Horizontal Flip 
-
-Use [`mxnet.image.HorizontalFlipAug`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=horizontalflipaug#mxnet.image.HorizontalFlipAug) to flip the image horizontally, where `p` is the probability of a flip occurring.
-
-We set `p=1` here to demonstration the effect, but in practice you'd choose a float between 0 and 1 (e.g. 0.5).
-
-
-```python
-aug = mx.image.HorizontalFlipAug(p=1)
-aug_image = aug(example_image)
-plot_mx_array(aug_image)
-
-assert example_image.shape == (427, 640, 3)
-assert aug_image.shape == (427, 640, 3)
-```
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_22_1.png)<!--notebook-skip-line-->
-
-
-You can get a random vertical flip too using [`mxnet.NDArray.swapaxes`](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html?highlight=swapaxes#mxnet.ndarray.NDArray.swapaxes) (to switch height and width) before and after the random horizontal flip. Once again `p` will be the probability of a flip occurring, and is set to 1 for demonstration purposes.
-
-
-```python
-aug = mx.image.HorizontalFlipAug(p=1)
-aug_image = aug(example_image.swapaxes(0, 1)).swapaxes(0, 1)
-plot_mx_array(aug_image)
-```
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_24_0.png)<!--notebook-skip-line-->
-
-
-# Color Augmentation
-
-Another form of augmentation changes the pixel values, rather than the pixel positions. Since some of these functions don't have obvious implentations, code snippets have been provided for most augmenters.
-
-__*Watch Out!*__ Unlike with positional augmentation, the color augmentation functions edit the input images inplace.
-So you'll need to copy the image if you want to make augmentations on the same image independently.
-
-### Brightness
-
-Use [`mxnet.image.mx.image.BrightnessJitterAug`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=brightnessjitteraug#mxnet.image.BrightnessJitterAug) to add a random brightness jitter to images. Use the `brightness` parameter to control the amount of jitter in brightness, with value from 0 (no change) to 1 (potentially large change). `brightness` doesn't specify whether the brightness of the augmented image will be lighter or darker, just the potential strength of the effect. Specifically the augmentation is given by:
-
-    alpha = 1.0 + random.uniform(-brightness, brightness)
-    image *= alpha
-
-
-```python
-example_image_copy = example_image.copy()
-aug = mx.image.BrightnessJitterAug(brightness=1)
-aug_image = aug(example_image_copy)
-plot_mx_array(aug_image)
-```
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_29_0.png)<!--notebook-skip-line-->
-
-
-### Contrast
-
-Use [`mxnet.image.ContrastJitterAug`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=contrastjitteraug#mxnet.image.ContrastJitterAug) to add a random contrast jitter to an image. Contrast can be thought of as the degree to which light and dark colours in the image differ. Use the `contrast` parameter to control the amount of jitter in contrast, with value from 0 (no change) to 1 (potentially large change). `contrast` doesn't specify whether the constrast of the augmented image will be higher or lower, just the potential strength of the effect. Specifically the augmentation is given by:
-
-    coef = nd.array([[[0.299, 0.587, 0.114]]])
-    alpha = 1.0 + random.uniform(-contrast, contrast)
-    gray = image * coef
-    gray = (3.0 * (1.0 - alpha) / gray.size) * nd.sum(gray)
-    image *= alpha
-    image += gray
-
-
-```python
-example_image_copy = example_image.copy()
-aug = mx.image.ContrastJitterAug(contrast=1)
-aug_image = aug(example_image_copy)
-plot_mx_array(aug_image)
-```
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_32_0.png) <!--notebook-skip-line-->
-
-
-### Saturation 
-
-Use [`mxnet.image.SaturationJitterAug`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=image.saturationjitteraug#mxnet.image.SaturationJitterAug) to add a random saturation jitter to an image. Saturation can be thought of as the 'amount' of color in an image. Use the `saturation` parameter to control the amount of jitter in saturation, with value from 0 (no change) to 1 (potentially large change). `saturation` doesn't specify whether the saturation of the augmented image will be higher or lower, just the potential strength of the effect. Specifically the augmentation is using the method detailed [here](https://beesbuzz.biz/code/hsv_color_transforms.php).
-
-
-```python
-example_image_copy = example_image.copy()
-aug = mx.image.SaturationJitterAug(saturation=1)
-aug_image = aug(example_image_copy)
-plot_mx_array(aug_image)
-```
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_35_0.png)<!--notebook-skip-line-->
-
-
-### Hue
-
-Use [`mxnet.image.HueJitterAug`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=huejitteraug#mxnet.image.HueJitterAug) to add a random hue jitter to images. Hue can be thought of as the 'shade' of the colors in an image. Use the `hue` parameter to control the amount of jitter in hue, with value from 0 (no change) to 1 (potentially large change). `hue` doesn't specify whether the hue of the augmented image will be shifted one way or the other, just the potential strength of the effect. Specifically the augmentation is using the method detailed [here](https://beesbuzz.biz/code/hsv_color_transforms.php).
-
-
-```python
-example_image_copy = example_image.copy()
-aug = mx.image.HueJitterAug(hue=0.5)
-aug_image = aug(example_image_copy)
-plot_mx_array(aug_image)
-```
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_38_0.png)<!--notebook-skip-line-->
-
-
-### LightingAug
-
-Use [`mxnet.image.LightingAug`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=lightingaug#mxnet.image.LightingAug) to add PCA based noise, using the following method:
-
-    alpha = np.random.normal(0, alphastd, size=(3,))
-    rgb = np.dot(eigvec * alpha, eigval)
-    image += nd.array(rgb)
-
-
-```python
-example_image_copy = example_image.copy()
-aug = mx.image.LightingAug(alphastd=100, eigval=np.asarray([1,1,1]), eigvec=np.ones((3,3)))
-aug_image = aug(example_image_copy)
-plot_mx_array(aug_image)
-```
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_41_0.png)<!--notebook-skip-line-->
-
-
-### Color Normalization 
-
-Given a per channel mean and standard deviation, [`mxnet.image.ColorNormalizeAug`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=colornormalizeaug#mxnet.image.ColorNormalizeAug) will normalise the values on each channel, as given by:
-
-    if mean is not None:
-        image -= mean
-    if std is not None:
-        image /= std
-
-
-```python
-example_image_copy = example_image.copy()
-mean = [0, 10, 20]
-stdev = [1, 2, 3]
-```
-
-If you calculate the true mean of the image, each channel in the output will be 0 centered. And if you calculate the true standard deviation, each channel in the output will have unit variance. Some arbritary mean and stdev values have been picked for demonstration.
-
-
-```python
-aug = mx.image.ColorNormalizeAug(mean=mean, std=stdev)
-aug_image = aug(example_image_copy)
-plot_mx_array(aug_image)
-```
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_46_0.png)<!--notebook-skip-line-->
-
-
-###  Grayscale
-
-Use [`mxnet.image.RandomGrayAug`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=randomgrayaug#mxnet.image.RandomGrayAug) to randomly remove the color from the image. `p` is the probability of a greyscale occuring.
-
-We set `p=1` here to demonstration the effect, but in practice you'd choose a float between 0 and 1 (e.g. 0.5).
-
-
-```python
-example_image_copy = example_image.copy()
-aug = mx.image.RandomGrayAug(p=1)
-aug_image = aug(example_image_copy)
-plot_mx_array(aug_image)
-```
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_49_0.png)<!--notebook-skip-line-->
-
-
-# Combinations 
-
-Some shortcut functions are provided to perform multiple augmentation in a single function.
-
-
-```python
-# A random crop, with a random resizing, and random aspect ratio jitter
-example_image_copy = example_image.copy()
-aug = mx.image.RandomSizedCropAug(size=(100, 100), area=0.1, ratio=(1.0, 1.5))
-aug_image = aug(example_image_copy)
-plot_mx_array(aug_image)
-
-assert aug_image.shape == (100, 100, 3)
-```
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_52_0.png)<!--notebook-skip-line-->
-
-
-
-```python
-# A random brightness, contrast and saturation jitter in random order.
-example_image_copy = example_image.copy()
-aug = mx.image.ColorJitterAug(brightness=1, contrast=1, saturation=1)
-aug_image = aug(example_image_copy)
-plot_mx_array(aug_image)
-```
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_53_0.png)<!--notebook-skip-line-->
-
-
-And lastly, you can use [`mxnet.image.RandomOrderAug`](https://mxnet.incubator.apache.org/api/python/image/image.html#mxnet.image.RandomOrderAug) to apply multiple augmenters to an image, in a random order.
-
-
-```python
-example_image_copy = example_image.copy()
-aug_list = [
-    mx.image.RandomCropAug(size=(250, 250)),
-    mx.image.HorizontalFlipAug(p=1),
-    mx.image.BrightnessJitterAug(brightness=1),
-    mx.image.HueJitterAug(hue=0.5)
-]
-aug = mx.image.RandomOrderAug(aug_list)
-aug_image = aug(example_image_copy)
-plot_mx_array(aug_image)
-
-assert aug_image.shape == (250, 250, 3)
-```
-
-
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_55_0.png)<!--notebook-skip-line-->
-
-# FAQs
-
-#### 1) What shape should arrays/images be when using Augmenters?
-
-Augmenters work with arrays of shape `height` x `width` x `channel`.
-
-When working with `ImageIter`s such as [`mxnet.image.ImageIter`](https://mxnet.incubator.apache.org/api/python/image/image.html#mxnet.image.ImageIter), you should perform the augmentations while iterating, rather than on the output of the iterator. `ImageIter` return images of shape `3` x `height` x `width`, so you would have to use [`mxnet.NDArray.swapaxes`](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html?highlight=swapaxes#mxnet.ndarray.NDArray.swapaxes) before applying Augmenters; hence why this later approach is not recommended. You can instead specify the augmentation steps in the constructor of the `ImageIter`, using the `aug_list` argument or `**kwargs`, and the dimension ordering will be handled for you.
-
-__Note__: Some implicit augmentation may occur when using `ImageIter`. You must specify the `data_shape` in the constructor of the `ImageIter`, and if the height and width are different from the image source, it crops and/or scales. Scaling is applied equally in both height and width axes so image isn't streched.
-
-
-#### 2) What channel order should be used when using Augmenters?
-
-MXNet's default is Red, Green, Blue (RGB), while OpenCV's default is Blue, Green, Red (BGR).
-
-
-#### 3) What value ranges should be used when using Augmenters?
-
-Most Augmenters don't require values of a certain range. Some Augmenters (such as `mxnet.image.ResizeAug` and `mxnet.image.BrightnessJitterAug`) can output images with a larger value range than the input though. An example of this would be an image with values 0 to 255, where pixels already close to the range boundary might be shifted outside of the 0 to 255 range by a random brightness jitter. You'll notice that the `plot_mx_array` function clips values outside of the 0 to 255 range.
-
-#### 4) Are augmentations performed on GPU?
-
-Most of the augmenters contain a mixture of control logic and `NDArray` operations. Although `NDArray` operations can be run on GPU, the control logic will still run on CPU which may limit performance. With Gluon DataLoaders you can specify `num_workers` to perform loading and augmentation in parallel with multiprocessing workers.
-
-#### 4) Can I implement custom augmentations?
-
-Yes, you can implement your own class that inherits from [`Augmenter`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=augmenter#mxnet.image.Augmenter) and define the augmentation steps in the `__call__` method. You can also implement a `dumps` method which returns a string representation of the augmenter and its parameters: it's used when inspecting a list of `Augmenter`s.
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/r/CallbackFunction.md b/docs/tutorials/r/CallbackFunction.md
deleted file mode 100644
index 1258c527e456..000000000000
--- a/docs/tutorials/r/CallbackFunction.md
+++ /dev/null
@@ -1,271 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-Callback Function
-======================================
-
-This tutorial provides guidelines for using and writing callback functions,
-which can very useful in model training.
-
-Model Training Example
-----------
-
-Let's begin with a small example. We can build and train a model with the following code:
-
-
- ```r
-    library(mxnet)
-    data(BostonHousing, package="mlbench")
-    train.ind = seq(1, 506, 3)
-    train.x = data.matrix(BostonHousing[train.ind, -14])
-    train.y = BostonHousing[train.ind, 14]
-    test.x = data.matrix(BostonHousing[-train.ind, -14])
-    test.y = BostonHousing[-train.ind, 14]
-    data <- mx.symbol.Variable("data")
-    fc1 <- mx.symbol.FullyConnected(data, num_hidden=1)
-    lro <- mx.symbol.LinearRegressionOutput(fc1)
-    mx.set.seed(0)
-    model <- mx.model.FeedForward.create(
-      lro, X=train.x, y=train.y,
-      eval.data=list(data=test.x, label=test.y),
-      ctx=mx.cpu(), num.round=10, array.batch.size=20,
-      learning.rate=2e-6, momentum=0.9, eval.metric=mx.metric.rmse)
- ```
-
- ```
-    ## Auto detect layout of input matrix, use row major..
-    ## Start training with 1 devices
-    ## [1] Train-rmse=16.063282524034
-    ## [1] Validation-rmse=10.1766446093622
-    ## [2] Train-rmse=12.2792375712573
-    ## [2] Validation-rmse=12.4331776190813
-    ## [3] Train-rmse=11.1984634005885
-    ## [3] Validation-rmse=10.3303041888193
-    ## [4] Train-rmse=10.2645236892904
-    ## [4] Validation-rmse=8.42760407903415
-    ## [5] Train-rmse=9.49711005504284
-    ## [5] Validation-rmse=8.44557808483234
-    ## [6] Train-rmse=9.07733734175182
-    ## [6] Validation-rmse=8.33225500266177
-    ## [7] Train-rmse=9.07884450847991
-    ## [7] Validation-rmse=8.38827833418459
-    ## [8] Train-rmse=9.10463850277417
-    ## [8] Validation-rmse=8.37394452365264
-    ## [9] Train-rmse=9.03977049028532
-    ## [9] Validation-rmse=8.25927979725672
-    ## [10] Train-rmse=8.96870685004475
-    ## [10] Validation-rmse=8.19509291481822
- ```
-
-We also provide two optional parameters, `batch.end.callback` and `epoch.end.callback`, which can provide great flexibility in model training.
-
-How to Use Callback Functions
----------
-
-This package provides two callback functions:
-
-- `mx.callback.save.checkpoint` saves a checkpoint to files during each period iteration.
-
-```r
-         model <- mx.model.FeedForward.create(
-           lro, X=train.x, y=train.y,
-           eval.data=list(data=test.x, label=test.y),
-           ctx=mx.cpu(), num.round=10, array.batch.size=20,
-           learning.rate=2e-6, momentum=0.9, eval.metric=mx.metric.rmse,
-           epoch.end.callback = mx.callback.save.checkpoint("boston"))
-```
-
-```
-          ## Auto detect layout of input matrix, use row major..
-          ## Start training with 1 devices
-          ## [1] Train-rmse=19.1621424021617
-          ## [1] Validation-rmse=20.721515592165
-          ## Model checkpoint saved to boston-0001.params
-          ## [2] Train-rmse=13.5127391952367
-          ## [2] Validation-rmse=14.1822123675007
-          ## Model checkpoint saved to boston-0002.params
-```
-
-
-- `mx.callback.log.train.metric` logs a training metric each period. You can use it either as a `batch.end.callback` or an
-`epoch.end.callback`.
-
-
-```r
-         model <- mx.model.FeedForward.create(
-           lro, X=train.x, y=train.y,
-           eval.data=list(data=test.x, label=test.y),
-           ctx=mx.cpu(), num.round=10, array.batch.size=20,
-           learning.rate=2e-6, momentum=0.9, eval.metric=mx.metric.rmse,
-           batch.end.callback = mx.callback.log.train.metric(5))
- ```
-
-```
-         ## Auto detect layout of input matrix, use row major..
-         ## Start training with 1 devices
-         ## Batch [5] Train-rmse=17.6514558545416
-         ## [1] Train-rmse=15.2879610219001
-         ## [1] Validation-rmse=12.3332062820921
-         ## Batch [5] Train-rmse=11.939392828565
-         ## [2] Train-rmse=11.4382242547217
-         ## [2] Validation-rmse=9.91176550103181
-         ............
-```
-
-You also can save the training and evaluation errors for later use by passing a reference class:
-
-
- ```r
-    logger <- mx.metric.logger$new()
-    model <- mx.model.FeedForward.create(
-      lro, X=train.x, y=train.y,
-      eval.data=list(data=test.x, label=test.y),
-      ctx=mx.cpu(), num.round=10, array.batch.size=20,
-      learning.rate=2e-6, momentum=0.9, eval.metric=mx.metric.rmse,
-      epoch.end.callback = mx.callback.log.train.metric(5, logger))
- ```
-
- ```
-    ## Auto detect layout of input matrix, use row major..
-    ## Start training with 1 devices
-    ## [1] Train-rmse=19.1083228733256
-    ## [1] Validation-rmse=12.7150687428974
-    ## [2] Train-rmse=15.7684378116157
-    ## [2] Validation-rmse=14.8105319420491
-    ............
- ```
-
- ```r
-    head(logger$train)
- ```
-
- ```
-    ## [1] 19.108323 15.768438 13.531470 11.386050  9.555477  9.351324
- ```
-
- ```r
-    head(logger$eval)
- ```
-
- ```
-    ## [1] 12.715069 14.810532 15.840361 10.898733  9.349706  9.363087
- ```
-
-How to Write Your Own Callback Functions
-----------
-
-You can find the source code for the two callback functions on [GitHub](https://github.com/dmlc/mxnet/blob/master/R-package/R/callback.R) and use it as a template:
-
-Basically, all callback functions follow the following structure:
-
-
- ```r
-    mx.callback.fun <- function() {
-      function(iteration, nbatch, env) {
-      }
-    }
- ```
-
-The following `mx.callback.save.checkpoint` function is stateless. It gets the model from the environment and saves it:.
-
-
- ```r
-    mx.callback.save.checkpoint <- function(prefix, period=1) {
-      function(iteration, nbatch, env) {
-      if (iteration %% period == 0) {
-      mx.model.save(env$model, prefix, iteration)
-      cat(sprintf("Model checkpoint saved to %s-%04d.params\n", prefix, iteration))
-    }
-    return(TRUE)
-      }
-    }
- ```
-
-The `mx.callback.log.train.metric` is a little more complex. It holds a reference class and updates it during the training
-process:
-
-
- ```r
-    mx.callback.log.train.metric <- function(period, logger=NULL) {
-      function(iteration, nbatch, env) {
-    if (nbatch %% period == 0 && !is.null(env$metric)) {
-      result <- env$metric$get(env$train.metric)
-      if (nbatch != 0)
-        cat(paste0("Batch [", nbatch, "] Train-", result$name, "=", result$value, "\n"))
-      if (!is.null(logger)) {
-        if (class(logger) != "mx.metric.logger") {
-          stop("Invalid mx.metric.logger.")
-        }
-        logger$train <- c(logger$train, result$value)
-        if (!is.null(env$eval.metric)) {
-          result <- env$metric$get(env$eval.metric)
-          if (nbatch != 0)
-            cat(paste0("Batch [", nbatch, "] Validation-", result$name, "=", result$value, "\n"))
-          logger$eval <- c(logger$eval, result$value)
-        }
-      }
-    }
-    return(TRUE)
-      }
-    }
- ```
-
-Now you might be curious why both callback functions `return(TRUE)`.
-
-Can we `return(FALSE)`?
-
-Yes! You can stop the training early with `return(FALSE)`. See the following examples.
-
-
-  ```r
-     mx.callback.early.stop <- function(eval.metric) {
-      function(iteration, nbatch, env) {
-    if (!is.null(env$metric)) {
-      if (!is.null(eval.metric)) {
-        result <- env$metric$get(env$eval.metric)
-        if (result$value < eval.metric) {
-          return(FALSE)
-        }
-      }
-    }
-    return(TRUE)
-      }
-    }
-    model <- mx.model.FeedForward.create(
-      lro, X=train.x, y=train.y,
-      eval.data=list(data=test.x, label=test.y),
-      ctx=mx.cpu(), num.round=10, array.batch.size=20,
-      learning.rate=2e-6, momentum=0.9, eval.metric=mx.metric.rmse,
-      epoch.end.callback = mx.callback.early.stop(10))
- ```
-
- ```
-    ## Auto detect layout of input matrix, use row major..
-    ## Start training with 1 devices
-    ## [1] Train-rmse=18.5897984387033
-    ## [1] Validation-rmse=13.5555213820571
-    ## [2] Train-rmse=12.5867564040256
-    ## [2] Validation-rmse=9.76304967080928
- ```
-
-When the validation metric dips below the threshold we set, the training process stops.
-
-## Next Steps
-* [Neural Networks with MXNet in Five Minutes](http://mxnet.io/tutorials/r/fiveMinutesNeuralNetwork.html)
-* [Classify Real-World Images with a Pretrained Model](http://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
-* [Handwritten Digits Classification Competition](http://mxnet.io/tutorials/r/mnistCompetition.html)
-* [Character Language Model Using RNN](http://mxnet.io/tutorials/r/charRnnModel.html)
diff --git a/docs/tutorials/r/CustomIterator.md b/docs/tutorials/r/CustomIterator.md
deleted file mode 100644
index 377838d93e46..000000000000
--- a/docs/tutorials/r/CustomIterator.md
+++ /dev/null
@@ -1,219 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-Custom Iterator Tutorial
-======================================
-
-This tutorial provides a guideline on how to use and write custom iterators, which can very useful when having a dataset that does not fit into memory.
-
-Getting the data
-----------
-The data we are going to use is the [MNIST dataset](http://yann.lecun.com/exdb/mnist/) in CSV format, the data can be found in this [web](http://pjreddie.com/projects/mnist-in-csv/).
-
-To download the data:
-
-```bash
-wget http://pjreddie.com/media/files/mnist_train.csv
-wget http://pjreddie.com/media/files/mnist_test.csv
-```
-
-You'll get two files, `mnist_train.csv` that contains 60.000 examples of hand written numbers and `mxnist_test.csv` that contains 10.000 examples. The first element of each line in the CSV is the label, which is a number between 0 and 9. The rest of the line are 784 numbers between 0 and 255, corresponding to the levels of grey of a matrix of 28x28. Therefore, each line contains an image of 28x28 pixels of a hand written number and its true label.
-
-Custom CSV Iterator
-----------
-Next we are going to create a custom CSV Iterator based on the [C++ CSVIterator class](https://github.com/dmlc/mxnet/blob/master/src/io/iter_csv.cc).
-
-For that we are going to use the R function `mx.io.CSVIter` as a base class. This class has as parameters `data.csv, data.shape, batch.size` and two main functions, `iter.next()` that calls the iterator in the next batch of data and `value()` that returns the train data and the label.
-
-The R Custom Iterator needs to inherit from the C++ data iterator class, for that we used the class `Rcpp_MXArrayDataIter` extracted with RCPP. Also, it needs to have the same parameters: `data.csv, data.shape, batch.size`. Apart from that, we can also add the field `iter`, which is the CSV Iterator that we are going to expand.
-
-```r
-CustomCSVIter <- setRefClass("CustomCSVIter",
-								fields=c("iter", "data.csv", "data.shape", "batch.size"),
-								contains = "Rcpp_MXArrayDataIter",
-								#...
-                            )
-```
-
-The next step is to initialize the class. For that we call the base `mx.io.CSVIter` and fill the rest of the fields.
-
-```r
-CustomCSVIter <- setRefClass("CustomCSVIter",
-								fields=c("iter", "data.csv", "data.shape", "batch.size"),
-								contains = "Rcpp_MXArrayDataIter",
-								methods=list(
-	                             	initialize=function(iter, data.csv, data.shape, batch.size){
-										feature_len <- data.shape*data.shape + 1
-										csv_iter <- mx.io.CSVIter(data.csv=data.csv, data.shape=c(feature_len), batch.size=batch.size)
-										.self$iter <- csv_iter
-										.self$data.csv <- data.csv
-										.self$data.shape <- data.shape
-										.self$batch.size <- batch.size
-										.self
-	                               	},
-                             	#...
-                             	)
-                            )
-```
-
-So far there is no difference between the original class and the custom class. Let's implement the function `value()`. In this case what we are going to do is transform the data that comes from the original class as an array of 785 numbers into a matrix of 28x28 and a label. We will also normalize the training data to be between 0 and 1.
-
-```r
-CustomCSVIter <- setRefClass("CustomCSVIter",
-								fields=c("iter", "data.csv", "data.shape", "batch.size"),
-								contains = "Rcpp_MXArrayDataIter",
-								methods=list(
-	                             	initialize=function(iter, data.csv, data.shape, batch.size){
-										feature_len <- data.shape*data.shape + 1
-										csv_iter <- mx.io.CSVIter(data.csv=data.csv, data.shape=c(feature_len), batch.size=batch.size)
-										.self$iter <- csv_iter
-										.self$data.csv <- data.csv
-										.self$data.shape <- data.shape
-										.self$batch.size <- batch.size
-										.self
-	                               	},
-									value=function(){
-										val <- as.array(.self$iter$value()$data)
-										val.x <- val[-1,]
-										val.y <- val[1,]
-										val.x <- val.x/255
-										dim(val.x) <- c(data.shape, data.shape, 1, ncol(val.x))
-										val.x <- mx.nd.array(val.x)
-										val.y <- mx.nd.array(val.y)
-										list(data=val.x, label=val.y)
-									},
-                             	#...
-                             	)
-                            )
-```
-Finally we are going to add the rest of the functions needed for the training to work correctly. The final `CustomCSVIter` looks like this:
-
-```r
-CustomCSVIter <- setRefClass("CustomCSVIter",
-								fields=c("iter", "data.csv", "data.shape", "batch.size"),
-								contains = "Rcpp_MXArrayDataIter",
-								methods=list(
-	                             	initialize=function(iter, data.csv, data.shape, batch.size){
-										feature_len <- data.shape*data.shape + 1
-										csv_iter <- mx.io.CSVIter(data.csv=data.csv, data.shape=c(feature_len), batch.size=batch.size)
-										.self$iter <- csv_iter
-										.self$data.csv <- data.csv
-										.self$data.shape <- data.shape
-										.self$batch.size <- batch.size
-										.self
-	                               	},
-									value=function(){
-										val <- as.array(.self$iter$value()$data)
-										val.x <- val[-1,]
-										val.y <- val[1,]
-										val.x <- val.x/255
-										dim(val.x) <- c(data.shape, data.shape, 1, ncol(val.x))
-										val.x <- mx.nd.array(val.x)
-										val.y <- mx.nd.array(val.y)
-										list(data=val.x, label=val.y)
-									},
-									iter.next=function(){
-										.self$iter$iter.next()
-									},
-									reset=function(){
-										.self$iter$reset()
-									},
-									num.pad=function(){
-										.self$iter$num.pad()
-									},
-									finalize=function(){
-										.self$iter$finalize()
-									}
-                             	)
-                            )
-```
-
-To call the class we can just do:
-
-```r
-batch.size <- 100
-train.iter <- CustomCSVIter$new(iter = NULL, data.csv = "mnist_train.csv", data.shape = 28, batch.size = batch.size)
-```
-
-CNN Model
-----------
-
-For this tutorial we are going to use the known LeNet architecture:
-
-```r
-lenet.model <- function(){
-  data <- mx.symbol.Variable('data')
-  conv1 <- mx.symbol.Convolution(data=data, kernel=c(5,5), num_filter=20) #first conv
-  tanh1 <- mx.symbol.Activation(data=conv1, act_type="tanh")
-  pool1 <- mx.symbol.Pooling(data=tanh1, pool_type="max", kernel=c(2,2), stride=c(2,2))
-  conv2 <- mx.symbol.Convolution(data=pool1, kernel=c(5,5), num_filter=50)# second conv
-  tanh2 <- mx.symbol.Activation(data=conv2, act_type="tanh")
-  pool2 <- mx.symbol.Pooling(data=tanh2, pool_type="max", kernel=c(2,2), stride=c(2,2))
-  flatten <- mx.symbol.Flatten(data=pool2)
-  fc1 <- mx.symbol.FullyConnected(data=flatten, num_hidden=100) # first fullc
-  tanh3 <- mx.symbol.Activation(data=fc1, act_type="tanh")
-  fc2 <- mx.symbol.FullyConnected(data=tanh3, num_hidden=10) # second fullc
-  network <- mx.symbol.SoftmaxOutput(data=fc2) # loss
-  network
-}
-network <- lenet.model()
-```
-
-Training with the Custom Iterator
-----------
-Finally, we can directly add the custom iterator as the training data source.
-
-```r
-model <- mx.model.FeedForward.create(symbol=network,
-                                     X=train.iter,
-                                     ctx=mx.gpu(0),
-                                     num.round=10,
-                                     array.batch.size=batch.size,
-                                     learning.rate=0.1,
-                                     momentum=0.9,  
-                                     eval.metric=mx.metric.accuracy,
-                                     wd=0.00001,
-                                     batch.end.callback=mx.callback.log.speedometer(batch.size, frequency = 100)
-                                     )
-```
-
-The last 2 iterations with a K80 GPU looks like this:
-
-```bash
-[8] Train-accuracy=0.998866666666667
-Batch [100] Speed: 15413.0104454713 samples/sec Train-accuracy=0.999
-Batch [200] Speed: 16629.3412459049 samples/sec Train-accuracy=0.99935
-Batch [300] Speed: 18412.6900509319 samples/sec Train-accuracy=0.9995
-Batch [400] Speed: 16757.2882328335 samples/sec Train-accuracy=0.999425
-Batch [500] Speed: 17116.6529207406 samples/sec Train-accuracy=0.99946
-Batch [600] Speed: 19627.589505195 samples/sec Train-accuracy=0.99945
-[9] Train-accuracy=0.9991
-Batch [100] Speed: 18971.5745536982 samples/sec Train-accuracy=0.9992
-Batch [200] Speed: 15554.8822435383 samples/sec Train-accuracy=0.99955
-Batch [300] Speed: 18327.6950115053 samples/sec Train-accuracy=0.9997
-Batch [400] Speed: 17103.0705411788 samples/sec Train-accuracy=0.9997
-Batch [500] Speed: 15104.8656902394 samples/sec Train-accuracy=0.99974
-Batch [600] Speed: 13818.7899518255 samples/sec Train-accuracy=0.99975
-[10] Train-accuracy=0.99975
-```
-
-Conclusion
-----------
-
-We have shown how to create a custom CSV Iterator by extending the class `mx.io.CSVIter`. In our class, we iteratively read from a CSV file a batch of data that will be transformed and then processed in the stochastic gradient descent optimization. That way, we are able to manage CSV files that are bigger than the memory of the machine we are using.
-
-Based of this custom iterator, we can also create data loaders that internally transform or expand the data, allowing to manage files of any size.
diff --git a/docs/tutorials/r/CustomLossFunction.md b/docs/tutorials/r/CustomLossFunction.md
deleted file mode 100644
index 6b60886bceba..000000000000
--- a/docs/tutorials/r/CustomLossFunction.md
+++ /dev/null
@@ -1,223 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-Customized loss function
-======================================
-
-This tutorial provides guidelines for using customized loss function in network construction.
-
-Model Training Example
-----------------------
-
-Let's begin with a small regression example. We can build and train a regression model with the following code:
-
-``` r
-data(BostonHousing, package = "mlbench")
-BostonHousing[, sapply(BostonHousing, is.factor)] <-
-  as.numeric(as.character(BostonHousing[, sapply(BostonHousing, is.factor)]))
-BostonHousing <- data.frame(scale(BostonHousing))
-
-test.ind = seq(1, 506, 5)    # 1 pt in 5 used for testing
-train.x = data.matrix(BostonHousing[-test.ind,-14])
-train.y = BostonHousing[-test.ind, 14]
-test.x = data.matrix(BostonHousing[--test.ind,-14])
-test.y = BostonHousing[--test.ind, 14]
-
-require(mxnet)
-```
-
-    ## Loading required package: mxnet
-
-``` r
-data <- mx.symbol.Variable("data")
-label <- mx.symbol.Variable("label")
-fc1 <- mx.symbol.FullyConnected(data, num_hidden = 14, name = "fc1")
-tanh1 <- mx.symbol.Activation(fc1, act_type = "tanh", name = "tanh1")
-fc2 <- mx.symbol.FullyConnected(tanh1, num_hidden = 1, name = "fc2")
-lro <- mx.symbol.LinearRegressionOutput(fc2, name = "lro")
-
-mx.set.seed(0)
-model <- mx.model.FeedForward.create(lro, X = train.x, y = train.y,
-                                     ctx = mx.cpu(),
-                                     num.round = 5,
-                                     array.batch.size = 60,
-                                     optimizer = "rmsprop",
-                                     verbose = TRUE,
-                                     array.layout = "rowmajor",
-                                     batch.end.callback = NULL,
-                                     epoch.end.callback = NULL)
-```
-
-    ## Start training with 1 devices
-
-``` r
-pred <- predict(model, test.x)
-```
-
-    ## Warning in mx.model.select.layout.predict(X, model): Auto detect layout of input matrix, use rowmajor..
-
-``` r
-sum((test.y - pred[1,])^2) / length(test.y)
-```
-
-    ## [1] 0.2485236
-
-Besides the `LinearRegressionOutput`, we also provide `LogisticRegressionOutput` and `MAERegressionOutput`. However, this might not be enough for real-world models. You can provide your own loss function by using `mx.symbol.MakeLoss` when constructing the network.
-
-How to Use Your Own Loss Function
----------------------------------
-
-We still use our previous example, but this time we use `mx.symbol.MakeLoss` to minimize the `(pred-label)^2`
-
-``` r
-data <- mx.symbol.Variable("data")
-label <- mx.symbol.Variable("label")
-fc1 <- mx.symbol.FullyConnected(data, num_hidden = 14, name = "fc1")
-tanh1 <- mx.symbol.Activation(fc1, act_type = "tanh", name = "tanh1")
-fc2 <- mx.symbol.FullyConnected(tanh1, num_hidden = 1, name = "fc2")
-lro2 <- mx.symbol.MakeLoss(mx.symbol.square(mx.symbol.Reshape(fc2, shape = 0) - label), name="lro2")
-```
-
-Then we can train the network just as usual.
-
-``` r
-mx.set.seed(0)
-model2 <- mx.model.FeedForward.create(lro2, X = train.x, y = train.y,
-                                      ctx = mx.cpu(),
-                                      num.round = 5,
-                                      array.batch.size = 60,
-                                      optimizer = "rmsprop",
-                                      verbose = TRUE,
-                                      array.layout = "rowmajor",
-                                      batch.end.callback = NULL,
-                                      epoch.end.callback = NULL)
-```
-
-    ## Start training with 1 devices
-
-We should get very similar results because we are actually minimizing the same loss function. However, the result is quite different.
-
-``` r
-pred2 <- predict(model2, test.x)
-```
-
-    ## Warning in mx.model.select.layout.predict(X, model): Auto detect layout of input matrix, use rowmajor..
-
-``` r
-sum((test.y - pred2)^2) / length(test.y)
-```
-
-    ## [1] 1.234584
-
-This is because output of `mx.symbol.MakeLoss` is the gradient of loss with respect to the input data. We can get the real prediction as below.
-
-``` r
-internals = internals(model2$symbol)
-fc_symbol = internals[[match("fc2_output", outputs(internals))]]
-
-model3 <- list(symbol = fc_symbol,
-               arg.params = model2$arg.params,
-               aux.params = model2$aux.params)
-
-class(model3) <- "MXFeedForwardModel"
-
-pred3 <- predict(model3, test.x)
-```
-
-    ## Warning in mx.model.select.layout.predict(X, model): Auto detect layout of input matrix, use rowmajor..
-
-``` r
-sum((test.y - pred3[1,])^2) / length(test.y)
-```
-
-    ## [1] 0.248294
-
-We have provided many operations on the symbols. An example of `|pred-label|` can be found below.
-
-``` r
-lro_abs <- mx.symbol.MakeLoss(mx.symbol.abs(mx.symbol.Reshape(fc2, shape = 0) - label))
-mx.set.seed(0)
-model4 <- mx.model.FeedForward.create(lro_abs, X = train.x, y = train.y,
-                                      ctx = mx.cpu(),
-                                      num.round = 20,
-                                      array.batch.size = 60,
-                                      optimizer = "sgd",
-                                      learning.rate = 0.001,
-                                      verbose = TRUE,
-                                      array.layout = "rowmajor",
-                                      batch.end.callback = NULL,
-                                      epoch.end.callback = NULL)
-```
-
-    ## Start training with 1 devices
-
-``` r
-internals = internals(model4$symbol)
-fc_symbol = internals[[match("fc2_output", outputs(internals))]]
-
-model5 <- list(symbol = fc_symbol,
-               arg.params = model4$arg.params,
-               aux.params = model4$aux.params)
-
-class(model5) <- "MXFeedForwardModel"
-
-pred5 <- predict(model5, test.x)
-```
-
-    ## Warning in mx.model.select.layout.predict(X, model): Auto detect layout of input matrix, use rowmajor..
-
-``` r
-sum(abs(test.y - pred5[1,])) / length(test.y)
-```
-
-    ## [1] 0.7056902
-
-``` r
-lro_mae <- mx.symbol.MAERegressionOutput(fc2, name = "lro")
-mx.set.seed(0)
-model6 <- mx.model.FeedForward.create(lro_mae, X = train.x, y = train.y,
-                                      ctx = mx.cpu(),
-                                      num.round = 20,
-                                      array.batch.size = 60,
-                                      optimizer = "sgd",
-                                      learning.rate = 0.001,
-                                      verbose = TRUE,
-                                      array.layout = "rowmajor",
-                                      batch.end.callback = NULL,
-                                      epoch.end.callback = NULL)
-```
-
-    ## Start training with 1 devices
-
-``` r
-pred6 <- predict(model6, test.x)
-```
-
-    ## Warning in mx.model.select.layout.predict(X, model): Auto detect layout of input matrix, use rowmajor..
-
-``` r
-sum(abs(test.y - pred6[1,])) / length(test.y)
-```
-
-    ## [1] 0.7056902
-
-
-## Next Steps
-* [Neural Networks with MXNet in Five Minutes](http://mxnet.io/tutorials/r/fiveMinutesNeuralNetwork.html)
-* [Classify Real-World Images with a PreTrained Model](http://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
-* [Handwritten Digits Classification Competition](http://mxnet.io/tutorials/r/mnistCompetition.html)
-* [Character Language Model Using RNN](http://mxnet.io/tutorials/r/charRnnModel.html)
diff --git a/docs/tutorials/r/MultidimLstm.md b/docs/tutorials/r/MultidimLstm.md
deleted file mode 100644
index 5abd38d2e02c..000000000000
--- a/docs/tutorials/r/MultidimLstm.md
+++ /dev/null
@@ -1,319 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-LSTM time series example
-=============================================
-
-This tutorial shows how to use an LSTM model with multivariate data, and generate predictions from it. For demonstration purposes, we used an open source [pollution data](https://archive.ics.uci.edu/ml/datasets/Beijing+PM2.5+Data).
-The tutorial is an illustration of how to use LSTM models with MXNet-R. We are forecasting the air pollution with data recorded at the US embassy in Beijing, China for five years.
-
-Dataset Attribution:
-"PM2.5 data of US Embassy in Beijing" 
-We want to predict pollution levels(PM2.5 concentration) in the city given the above dataset.
-
-```r
-Dataset description:
-No: row number
-year: year of data in this row
-month: month of data in this row
-day: day of data in this row
-hour: hour of data in this row
-pm2.5: PM2.5 concentration
-DEWP: Dew Point
-TEMP: Temperature
-PRES: Pressure
-cbwd: Combined wind direction
-Iws: Cumulated wind speed
-Is: Cumulated hours of snow
-Ir: Cumulated hours of rain
-```
-
-We use past PM2.5 concentration, dew point, temperature, pressure, wind speed, snow and rain to predict
-PM2.5 concentration levels.
-
-Load and pre-process the data
----------
-The first step is to load in the data and preprocess it. It is assumed that the data has been downloaded in a .csv file: data.csv from the [pollution dataset](https://archive.ics.uci.edu/ml/datasets/Beijing+PM2.5+Data)
-
- ```r
-## Loading required packages
-library("readr")
-library("dplyr")
-library("mxnet")
-library("abind")
- ```
-
-
-
- ```r
-## Preprocessing steps
-Data <- read.csv(file = "/Users/khedia/Downloads/data.csv",
-                 header = TRUE,
-                 sep = ",")
-
-## Extracting specific features from the dataset as variables for time series We extract
-## pollution, temperature, pressue, windspeed, snowfall and rainfall information from dataset
-df <- data.frame(Data$pm2.5,
-                 Data$DEWP,
-                 Data$TEMP,
-                 Data$PRES,
-                 Data$Iws,
-                 Data$Is,
-                 Data$Ir)
-df[is.na(df)] <- 0
-
-## Now we normalise each of the feature set to a range(0,1)
-df <- matrix(as.matrix(df),
-             ncol = ncol(df),
-             dimnames = NULL)
-
-rangenorm <- function(x) {
-    (x - min(x))/(max(x) - min(x))
-}
-df <- apply(df, 2, rangenorm)
-df <- t(df)
-  ```
-For using multidimesional data with MXNet-R, we need to convert training data to the form
-(n_dim x seq_len x num_samples). For one-to-one RNN flavours labels should be of the form (seq_len x num_samples) while for many-to-one flavour, the labels should be of the form (1 x num_samples). Please note that MXNet-R currently supports only these two flavours of RNN.
-We have used n_dim = 7, seq_len = 100,  and num_samples = 430 because the dataset has 430 samples, each the length of 100 timestamps, we have seven time series as input features so each input has dimesnion of seven at each time step.
-
-
-```r
-n_dim <- 7
-seq_len <- 100
-num_samples <- 430
-
-## extract only required data from dataset
-trX <- df[1:n_dim, 25:(24 + (seq_len * num_samples))]
-
-## the label data(next PM2.5 concentration) should be one time step
-## ahead of the current PM2.5 concentration
-trY <- df[1, 26:(25 + (seq_len * num_samples))]
-
-## reshape the matrices in the format acceptable by MXNetR RNNs
-trainX <- trX
-dim(trainX) <- c(n_dim, seq_len, num_samples)
-trainY <- trY
-dim(trainY) <- c(seq_len, num_samples)
-```
-
-
-
-Defining and training the network
----------
-
-```r
-batch.size <- 32
-
-# take first 300 samples for training - remaining 100 for evaluation
-train_ids <- 1:300
-eval_ids <- 301:400
-
-## The number of samples used for training and evaluation is arbitrary.  I have kept aside few
-## samples for testing purposes create dataiterators
-train.data <- mx.io.arrayiter(data = trainX[, , train_ids, drop = F], 
-                              label = trainY[, train_ids],
-                              batch.size = batch.size, shuffle = TRUE)
-
-eval.data <- mx.io.arrayiter(data = trainX[, , eval_ids, drop = F], 
-                             label = trainY[, eval_ids],
-                             batch.size = batch.size, shuffle = FALSE)
-
-## Create the symbol for RNN
-symbol <- rnn.graph(num_rnn_layer = 1,
-                    num_hidden = 5,
-                    input_size = NULL,
-                    num_embed = NULL,
-                    num_decode = 1,
-                    masking = F, 
-                    loss_output = "linear",
-                    dropout = 0.2, 
-                    ignore_label = -1, 
-                    cell_type = "lstm", 
-                    output_last_state = T,
-                    config = "one-to-one")
-
-
-
-mx.metric.mse.seq <- mx.metric.custom("MSE", function(label, pred) {
-    label = mx.nd.reshape(label, shape = -1)
-    pred = mx.nd.reshape(pred, shape = -1)
-    res <- mx.nd.mean(mx.nd.square(label - pred))
-    return(as.array(res))
-})
-
-
-
-ctx <- mx.cpu()
-
-initializer <- mx.init.Xavier(rnd_type = "gaussian",
-                              factor_type = "avg", 
-                              magnitude = 3)
-
-optimizer <- mx.opt.create("adadelta",
-                           rho = 0.9, 
-                           eps = 1e-05, 
-                           wd = 1e-06, 
-                           clip_gradient = 1, 
-                           rescale.grad = 1/batch.size)
-
-logger <- mx.metric.logger()
-epoch.end.callback <- mx.callback.log.train.metric(period = 10, 
-                                                   logger = logger)
-
-## train the network
-system.time(model <- mx.model.buckets(symbol = symbol, 
-                                      train.data = train.data, 
-                                      eval.data = eval.data,
-                                      num.round = 100, 
-                                      ctx = ctx, 
-                                      verbose = TRUE, 
-                                      metric = mx.metric.mse.seq, 
-                                      initializer = initializer,
-                                      optimizer = optimizer, 
-                                      batch.end.callback = NULL, 
-                                      epoch.end.callback = epoch.end.callback))
-```
-Output:
-```
-Start training with 1 devices
-[1] Train-MSE=0.197570244409144
-[1] Validation-MSE=0.0153861071448773
-[2] Train-MSE=0.0152517843060195
-[2] Validation-MSE=0.0128299412317574
-[3] Train-MSE=0.0124418652616441
-[3] Validation-MSE=0.010827143676579
-[4] Train-MSE=0.0105128229130059
-[4] Validation-MSE=0.00940261723008007
-[5] Train-MSE=0.00914482437074184
-[5] Validation-MSE=0.00830172537826002
-[6] Train-MSE=0.00813581114634871
-[6] Validation-MSE=0.00747016374953091
-[7] Train-MSE=0.00735094994306564
-[7] Validation-MSE=0.00679832429159433
-[8] Train-MSE=0.00672049634158611
-[8] Validation-MSE=0.00623159145470709
-[9] Train-MSE=0.00620287149213254
-[9] Validation-MSE=0.00577476259786636
-[10] Train-MSE=0.00577280316501856
-[10] Validation-MSE=0.00539038667920977
-..........
-..........
-[91] Train-MSE=0.00177705133100972
-[91] Validation-MSE=0.00154715491225943
-[92] Train-MSE=0.00177639147732407
-[92] Validation-MSE=0.00154592350008897
-[93] Train-MSE=0.00177577760769054
-[93] Validation-MSE=0.00154474508599378
-[94] Train-MSE=0.0017752077546902
-[94] Validation-MSE=0.0015436161775142
-[95] Train-MSE=0.00177468206966296
-[95] Validation-MSE=0.00154253660002723
-[96] Train-MSE=0.00177419915562496
-[96] Validation-MSE=0.00154150440357625
-[97] Train-MSE=0.0017737578949891
-[97] Validation-MSE=0.00154051734716631
-[98] Train-MSE=0.00177335749613121
-[98] Validation-MSE=0.00153957353904843
-[99] Train-MSE=0.00177299699280411
-[99] Validation-MSE=0.00153867155313492
-[100] Train-MSE=0.00177267640829086
-[100] Validation-MSE=0.00153781197150238
-
-   user  system elapsed 
- 21.937   1.914  13.402 
-```
-We can see how mean squared error varies with epochs below.
-
-![png](https://github.com/dmlc/web-data/blob/master/mxnet/doc/tutorials/r/images/loss.png?raw=true)<!--notebook-skip-line-->
-
-Inference on the network
----------
-Now we have trained the network. Let's use it for inference.
-
-```r
-## We extract the state symbols for RNN
-internals <- model$symbol$get.internals()
-sym_state <- internals$get.output(which(internals$outputs %in% "RNN_state"))
-sym_state_cell <- internals$get.output(which(internals$outputs %in% "RNN_state_cell"))
-sym_output <- internals$get.output(which(internals$outputs %in% "loss_output"))
-symbol <- mx.symbol.Group(sym_output, sym_state, sym_state_cell)
-
-## We will predict 100 timestamps for 401st sample (first sample from the test samples)
-pred_length <- 100
-predicted <- numeric()
-
-## We pass the 400th sample through the network to get the weights and use it for predicting next
-## 100 time stamps.
-data <- mx.nd.array(trainX[, , 400, drop = F])
-label <- mx.nd.array(trainY[, 400, drop = F])
-
-
-## We create dataiterators for the input, please note that the label is required to create
-## iterator and will not be used in the inference. You can use dummy values too in the label.
-infer.data <- mx.io.arrayiter(data = data, 
-                              label = label, 
-                              batch.size = 1, 
-                              shuffle = FALSE)
-
-infer <- mx.infer.rnn.one(infer.data = infer.data, 
-                          symbol = symbol, 
-                          arg.params = model$arg.params,
-                          aux.params = model$aux.params, 
-                          input.params = NULL, 
-                          ctx = ctx)
-## Once we get the weights for the above time series, we try to predict the next 100 steps for
-## this time series, which is technically our 401st time series.
-
-actual <- trainY[, 401]
-
-## Now we iterate one by one to generate each of the next timestamp pollution values
-
-for (i in 1:pred_length) {
-
-    data <- mx.nd.array(trainX[, i, 401, drop = F])
-    label <- mx.nd.array(trainY[i, 401, drop = F])
-    infer.data <- mx.io.arrayiter(data = data, 
-                                  label = label, 
-                                  batch.size = 1, 
-                                  shuffle = FALSE)
-    ## note that we use rnn state values from previous iterations here
-    infer <- mx.infer.rnn.one(infer.data = infer.data,
-                              symbol = symbol,
-                              ctx = ctx, 
-                              arg.params = model$arg.params,
-                              aux.params = model$aux.params, 
-                              input.params = list(rnn.state = infer[[2]], 
-                                                  rnn.state.cell = infer[[3]]))
-
-    pred <- infer[[1]]
-    predicted <- c(predicted, as.numeric(as.array(pred)))
-
-}
-
-```
-Now predicted contains the predicted 100 values. We use ggplot to plot the actual and predicted values as shown below.
-
-![png](https://github.com/dmlc/web-data/blob/master/mxnet/doc/tutorials/r/images/sample_401.png?raw=true)<!--notebook-skip-line-->
-
-We also repeated the above experiments to generate the next 100 samples to 301st time series and we got the following results.
-
-![png](https://github.com/dmlc/web-data/blob/master/mxnet/doc/tutorials/r/images/sample_301.png?raw=true)<!--notebook-skip-line-->
-
-The above tutorial is just for demonstration purposes and has not been tuned extensively for accuracy.
-
-For more tutorials on MXNet-R, head on to [MXNet-R tutorials](https://mxnet.incubator.apache.org/tutorials/r/index.html)
\ No newline at end of file
diff --git a/docs/tutorials/r/charRnnModel.md b/docs/tutorials/r/charRnnModel.md
deleted file mode 100644
index ed873d49214d..000000000000
--- a/docs/tutorials/r/charRnnModel.md
+++ /dev/null
@@ -1,310 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-
-# Character-level Language Model using RNN
-
-This tutorial will demonstrate creating a language model using a character level RNN model using MXNet-R package. You will need the following R packages to run this tutorial -
- - readr
- - stringr
- - stringi
- - mxnet
-
-We will use the [tinyshakespeare](https://github.com/dmlc/web-data/tree/master/mxnet/tinyshakespeare) dataset to build this model.
-
-
-```R
-library("readr")
-library("stringr")
-library("stringi")
-library("mxnet")
-```
-
-## Preprocess and prepare the data
-
-Download the data:
-
-
-```R
-download.data <- function(data_dir) {
-    dir.create(data_dir, showWarnings = FALSE)
-    if (!file.exists(paste0(data_dir,'input.txt'))) {
-        download.file(url='https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tinyshakespeare/input.txt',
-                      destfile=paste0(data_dir,'input.txt'), method='wget')
-    }
-}
-```
-
-Next we transform the test into feature vectors that is fed into the RNN model. The `make_data` function reads the dataset, cleans it of any non-alphanumeric characters, splits it into individual characters and groups it into sequences of length `seq.len`.
-
-
-```R
-make_data <- function(path, seq.len = 32, dic=NULL) {
-  
-  text_vec <- read_file(file = path)
-  text_vec <- stri_enc_toascii(str = text_vec)
-  text_vec <- str_replace_all(string = text_vec, pattern = "[^[:print:]]", replacement = "")
-  text_vec <- strsplit(text_vec, '') %>% unlist
-  
-  if (is.null(dic)) {
-    char_keep <- sort(unique(text_vec))
-  } else char_keep <- names(dic)[!dic == 0]
-  
-  # Remove terms not part of dictionary
-  text_vec <- text_vec[text_vec %in% char_keep]
-  
-  # Build dictionary
-  dic <- 1:length(char_keep)
-  names(dic) <- char_keep
-  
-  # reverse dictionary
-  rev_dic <- names(dic)
-  names(rev_dic) <- dic
-  
-  # Adjust by -1 to have a 1-lag for labels
-  num.seq <- (length(text_vec) - 1) %/% seq.len
-  
-  features <- dic[text_vec[1:(seq.len * num.seq)]] 
-  labels <- dic[text_vec[1:(seq.len*num.seq) + 1]]
-  
-  features_array <- array(features, dim = c(seq.len, num.seq))
-  labels_array <- array(labels, dim = c(seq.len, num.seq))
-  
-  return (list(features_array = features_array, labels_array = labels_array, dic = dic, rev_dic = rev_dic))
-}
-
-
-seq.len <- 100
-data_prep <- make_data(path = "input.txt", seq.len = seq.len, dic=NULL)
-```
-
-Fetch the features and labels for training the model, and split the data into training and evaluation in 9:1 ratio.
-
-
-```R
-X <- data_prep$features_array
-Y <- data_prep$labels_array
-dic <- data_prep$dic
-rev_dic <- data_prep$rev_dic
-vocab <- length(dic)
-
-samples <- tail(dim(X), 1)
-train.val.fraction <- 0.9
-
-X.train.data <- X[, 1:as.integer(samples * train.val.fraction)]
-X.val.data <- X[, -(1:as.integer(samples * train.val.fraction))]
-
-X.train.label <- Y[, 1:as.integer(samples * train.val.fraction)]
-X.val.label <- Y[, -(1:as.integer(samples * train.val.fraction))]
-
-train_buckets <- list("100" = list(data = X.train.data, label = X.train.label))
-eval_buckets <- list("100" = list(data = X.val.data, label = X.val.label))
-
-train_buckets <- list(buckets = train_buckets, dic = dic, rev_dic = rev_dic)
-eval_buckets <- list(buckets = eval_buckets, dic = dic, rev_dic = rev_dic)
-```
-
-Create iterators for training and evaluation datasets.
-
-
-```R
-vocab <- length(eval_buckets$dic)
-
-batch.size <- 32
-
-train.data <- mx.io.bucket.iter(buckets = train_buckets$buckets, batch.size = batch.size, 
-                                data.mask.element = 0, shuffle = TRUE)
-
-eval.data <- mx.io.bucket.iter(buckets = eval_buckets$buckets, batch.size = batch.size,
-                               data.mask.element = 0, shuffle = FALSE)
-```
-
-## Train the Model
-
-
-This model is a multi-layer RNN for sampling from character-level language models. It has a one-to-one model configuration since for each character, we want to predict the next one. For a sequence of length 100, there are also 100 labels, corresponding the same sequence of characters but offset by a position of +1. The parameters output_last_state is set to TRUE in order to access the state of the RNN cells when performing inference.
-
-
-```R
-rnn_graph_one_one <- rnn.graph(num_rnn_layer = 3, 
-                               num_hidden = 96,
-                               input_size = vocab,
-                               num_embed = 64, 
-                               num_decode = vocab,
-                               dropout = 0.2, 
-                               ignore_label = 0,
-                               cell_type = "lstm",
-                               masking = F,
-                               output_last_state = T,
-                               loss_output = "softmax",
-                               config = "one-to-one")
-
-graph.viz(rnn_graph_one_one, type = "graph", direction = "LR", 
-          graph.height.px = 180, shape=c(100, 64))
-
-devices <- mx.cpu()
-
-initializer <- mx.init.Xavier(rnd_type = "gaussian", factor_type = "avg", magnitude = 3)
-
-optimizer <- mx.opt.create("adadelta", rho = 0.9, eps = 1e-5, wd = 1e-8,
-                           clip_gradient = 5, rescale.grad = 1/batch.size)
-
-logger <- mx.metric.logger()
-epoch.end.callback <- mx.callback.log.train.metric(period = 1, logger = logger)
-batch.end.callback <- mx.callback.log.train.metric(period = 50)
-
-mx.metric.custom_nd <- function(name, feval) {
-  init <- function() {
-    c(0, 0)
-  }
-  update <- function(label, pred, state) {
-    m <- feval(label, pred)
-    state <- c(state[[1]] + 1, state[[2]] + m)
-    return(state)
-  }
-  get <- function(state) {
-    list(name=name, value = (state[[2]] / state[[1]]))
-  }
-  ret <- (list(init = init, update = update, get = get))
-  class(ret) <- "mx.metric"
-  return(ret)
-}
-
-mx.metric.Perplexity <- mx.metric.custom_nd("Perplexity", function(label, pred) {
-  label <- mx.nd.reshape(label, shape = -1)
-  label_probs <- as.array(mx.nd.choose.element.0index(pred, label))
-  batch <- length(label_probs)
-  NLL <- -sum(log(pmax(1e-15, as.array(label_probs)))) / batch
-  Perplexity <- exp(NLL)
-  return(Perplexity)
-})
-
-model <- mx.model.buckets(symbol = rnn_graph_one_one,
-                          train.data = train.data, eval.data = eval.data, 
-                          num.round = 20, ctx = devices, verbose = TRUE,
-                          metric = mx.metric.Perplexity, 
-                          initializer = initializer, optimizer = optimizer, 
-                          batch.end.callback = NULL, 
-                          epoch.end.callback = epoch.end.callback)
-
-mx.model.save(model, prefix = "one_to_one_seq_model", iteration = 20)
-```
-
-    Start training with 1 devices
-    [1] Train-Perplexity=13.7040474322178
-    [1] Validation-Perplexity=7.94617194460922
-    [2] Train-Perplexity=6.57039815554525
-    [2] Validation-Perplexity=6.60806110658011
-    [3] Train-Perplexity=5.65360504501481
-    [3] Validation-Perplexity=6.18932770630876
-    [4] Train-Perplexity=5.32547285727298
-    [4] Validation-Perplexity=6.02198756798859
-    [5] Train-Perplexity=5.14373631472579
-    [5] Validation-Perplexity=5.8095658243407
-    [6] Train-Perplexity=5.03077673487379
-    [6] Validation-Perplexity=5.72582993567431
-    [7] Train-Perplexity=4.94453383291536
-    [7] Validation-Perplexity=5.6445258528126
-    [8] Train-Perplexity=4.88635290100261
-    [8] Validation-Perplexity=5.6730024536433
-    [9] Train-Perplexity=4.84205646230548
-    [9] Validation-Perplexity=5.50960780230982
-    [10] Train-Perplexity=4.80441673535513
-    [10] Validation-Perplexity=5.57002263750006
-    [11] Train-Perplexity=4.77763413242626
-    [11] Validation-Perplexity=5.55152143269169
-    [12] Train-Perplexity=4.74937775290777
-    [12] Validation-Perplexity=5.44968305351486
-    [13] Train-Perplexity=4.72824849541467
-    [13] Validation-Perplexity=5.50889348298234
-    [14] Train-Perplexity=4.70980846981694
-    [14] Validation-Perplexity=5.51473225859859
-    [15] Train-Perplexity=4.69685776886122
-    [15] Validation-Perplexity=5.45391985233811
-    [16] Train-Perplexity=4.67837107034824
-    [16] Validation-Perplexity=5.46636764997829
-    [17] Train-Perplexity=4.66866961934873
-    [17] Validation-Perplexity=5.44267086113492
-    [18] Train-Perplexity=4.65611469144194
-    [18] Validation-Perplexity=5.4290169469462
-    [19] Train-Perplexity=4.64614689879405
-    [19] Validation-Perplexity=5.44221549833917
-    [20] Train-Perplexity=4.63764001963654
-    [20] Validation-Perplexity=5.42114250842862
-
-
-## Inference on the Model
-
-We now use the saved model to do inference and sample text character by character that will look like the original training data.
-
-
-```R
-set.seed(0)
-model <- mx.model.load(prefix = "one_to_one_seq_model", iteration = 20)
-
-internals <- model$symbol$get.internals()
-sym_state <- internals$get.output(which(internals$outputs %in% "RNN_state"))
-sym_state_cell <- internals$get.output(which(internals$outputs %in% "RNN_state_cell"))
-sym_output <- internals$get.output(which(internals$outputs %in% "loss_output"))
-symbol <- mx.symbol.Group(sym_output, sym_state, sym_state_cell)
-
-infer_raw <- c("Thou ")
-infer_split <- dic[strsplit(infer_raw, '') %>% unlist]
-infer_length <- length(infer_split)
-
-infer.data <- mx.io.arrayiter(data = matrix(infer_split), label = matrix(infer_split),  
-                              batch.size = 1, shuffle = FALSE)
-
-infer <- mx.infer.rnn.one(infer.data = infer.data, 
-                          symbol = symbol,
-                          arg.params = model$arg.params,
-                          aux.params = model$aux.params,
-                          input.params = NULL, 
-                          ctx = devices)
-
-pred_prob <- as.numeric(as.array(mx.nd.slice.axis(
-    infer$loss_output, axis = 0, begin = infer_length-1, end = infer_length)))
-pred <- sample(length(pred_prob), prob = pred_prob, size = 1) - 1
-predict <- c(predict, pred)
-
-for (i in 1:200) {
-  
-  infer.data <- mx.io.arrayiter(data = as.matrix(pred), label = as.matrix(pred),  
-                                batch.size = 1, shuffle = FALSE)
-  
-  infer <- mx.infer.rnn.one(infer.data = infer.data, 
-                            symbol = symbol,
-                            arg.params = model$arg.params,
-                            aux.params = model$aux.params,
-                            input.params = list(rnn.state = infer[[2]], 
-                                                rnn.state.cell = infer[[3]]), 
-                            ctx = devices)
-  
-  pred_prob <- as.numeric(as.array(infer$loss_output))
-  pred <- sample(length(pred_prob), prob = pred_prob, size = 1, replace = T) - 1
-  predict <- c(predict, pred)
-}
-
-predict_txt <- paste0(rev_dic[as.character(predict)], collapse = "")
-predict_txt_tot <- paste0(infer_raw, predict_txt, collapse = "")
-print(predict_txt_tot)
-```
-
-    [1] "Thou NAknowledge thee my Comfort and his late she.FRIAR LAURENCE:Nothing a groats waterd forth. The lend he thank that;When she I am brother draw London: and not hear that know.BENVOLIO:How along, makes your "
-
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/r/classifyRealImageWithPretrainedModel.md b/docs/tutorials/r/classifyRealImageWithPretrainedModel.md
deleted file mode 100644
index 1344285dac59..000000000000
--- a/docs/tutorials/r/classifyRealImageWithPretrainedModel.md
+++ /dev/null
@@ -1,201 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-Classify Images with a PreTrained Model
-=================================================
-MXNet is a flexible and efficient deep learning framework. One of the interesting things that a deep learning
-algorithm can do is classify real world images.
-
-In this tutorial, we show how to use a pre-trained Inception-BatchNorm network to predict the class of an
-image. For information about the network architecture, see  [1].
-
-The pre-trained Inception-BatchNorm network is able to be downloaded from [this link](http://data.mxnet.io/mxnet/data/Inception.zip)
-This model gives the recent state-of-art prediction accuracy on image net dataset.
-
-Load the MXNet Package
----------------
-To get started, load the mxnet package:
-
- ```r
-    require(mxnet)
- ```
-
- ```
-    ## Loading required package: mxnet
-    ## Loading required package: methods
- ```
-
-Now load the imager package to load and preprocess the images in R:
-
-
- ```r
-    require(imager)
- ```
-
- ```
-    ## Loading required package: imager
-    ## Loading required package: plyr
-    ## Loading required package: magrittr
-    ## Loading required package: stringr
-    ## Loading required package: png
-    ## Loading required package: jpeg
-    ##
-    ## Attaching package: 'imager'
-    ##
-    ## The following object is masked from 'package:magrittr':
-    ##
-    ##     add
-    ##
-    ## The following object is masked from 'package:plyr':
-    ##
-    ##     liply
-    ##
-    ## The following objects are masked from 'package:stats':
-    ##
-    ##     convolve, spectrum
-    ##
-    ## The following object is masked from 'package:graphics':
-    ##
-    ##     frame
-    ##
-    ## The following object is masked from 'package:base':
-    ##
-    ##     save.image
- ```
-
-Load the PreTrained Model
--------------------------
-Make sure you unzip the pre-trained model in the current folder. Use the model
-loading function to load the model into R:
-
- ```r
-    model = mx.model.load("Inception/Inception_BN", iteration=39)
- ```
-
-Load in the mean image, which is used for preprocessing using:
-
-
- ```r
-    mean.img = as.array(mx.nd.load("Inception/mean_224.nd")[["mean_img"]])
- ```
-
-Load and Preprocess the Image
------------------------------
-Now, we are ready to classify a real image. In this example, we simply take the parrots image
-from the imager package. You can use another image, if   you prefer.
-
-Load and plot the image:
-
-
-```r
-    im <- load.image(system.file("extdata/parrots.png", package="imager"))
-    plot(im)
- ```
-
-![plot of chunk unnamed-chunk-5](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/knitr/classifyRealImageWithPretrainedModel-unnamed-chunk-5-1.png)
-
-Before feeding the image to the deep network, we need to perform some preprocessing
-to make the image meet the deep network input requirements. Preprocessing
-includes cropping  and subtracting the mean.
-Because MXNet is deeply integrated with R, we can do all the processing in an R function:
-
-
- ```r
-    preproc.image <- function(im, mean.image) {
-      # crop the image
-      shape <- dim(im)
-      short.edge <- min(shape[1:2])
-      xx <- floor((shape[1] - short.edge) / 2)
-      yy <- floor((shape[2] - short.edge) / 2)
-      cropped <- crop.borders(im, xx, yy)
-      # resize to 224 x 224, needed by input of the model.
-      resized <- resize(cropped, 224, 224)
-      # convert to array (x, y, channel)
-      arr <- as.array(resized) * 255
-      dim(arr) <- c(224, 224, 3)
-      # subtract the mean
-      normed <- arr - mean.img
-      # Reshape to format needed by mxnet (width, height, channel, num)
-      dim(normed) <- c(224, 224, 3, 1)
-      return(normed)
-    }
- ```
-
-Use the defined preprocessing function to get the normalized image:
-
-
- ```r
-    normed <- preproc.image(im, mean.img)
- ```
-
-Classify the Image
-------------------
-Now we are ready to classify the image! Use the ```predict``` function
-to get the probability over classes:
-
-
- ```r
-    prob <- predict(model, X=normed)
-    dim(prob)
- ```
-
- ```
-    ## [1] 1000    1
- ```
-
-As you can see, ```prob``` is a 1 times 1000 array, which gives the probability
-over the 1000 image classes of the input.
-
-Use the ```max.col``` on the transpose of ```prob``` to get the class index:
-
- ```r
-    max.idx <- max.col(t(prob))
-    max.idx
- ```
-
- ```
-    ## [1] 89
- ```
-
-The index doesn't make much sense, so let's see what it really means.
-Read the names of the classes from the following file:
-
-
- ```r
-    synsets <- readLines("Inception/synset.txt")
- ```
-
-Let's see what the image really is:
-
-
- ```r
-    print(paste0("Predicted Top-class: ", synsets  [[max.idx]]))
- ```
-
- ```
-    ## [1] "Predicted Top-class: n01818515 macaw"
- ```
-
-It's a macaw!
-
-Reference
----------
-[1] Ioffe, Sergey, and Christian Szegedy. "Batch normalization: Accelerating deep network training by reducing internal covariate shift." arXiv preprint arXiv:1502.03167 (2015).
-
-## Next Steps
-* [Handwritten Digits Classification Competition](http://mxnet.io/tutorials/r/mnistCompetition.html)
-* [Character Language Model using RNN](http://mxnet.io/tutorials/r/charRnnModel.html)
diff --git a/docs/tutorials/r/fiveMinutesNeuralNetwork.md b/docs/tutorials/r/fiveMinutesNeuralNetwork.md
deleted file mode 100644
index 39775b3ab640..000000000000
--- a/docs/tutorials/r/fiveMinutesNeuralNetwork.md
+++ /dev/null
@@ -1,334 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-Develop a Neural Network with MXNet in Five Minutes
-=============================================
-
-This tutorial is designed for new users of the `mxnet` package for R. It shows how to construct a neural network to do regression in 5 minutes. It shows how to perform classification and regression tasks, respectively. The data we use is in the `mlbench` package. Instructions to install R and MXNet's R package in different environments can be found [here](http://mxnet.incubator.apache.org/install/index.html?platform=Linux&language=R&processor=CPU).
-
-## Classification
-
- ```
-    ## Loading required package: mlbench
- ```
- ```r
-    if (!require(mlbench)) {
-      install.packages('mlbench')
-    }
- ```
-
- ```
-    ## Loading required package: mxnet
- ```
-
- ```r
-    require(mxnet)
- ```
-
- ```
-    ## Loading required datasets
- ```
-
- ```r
-    data(Sonar, package="mlbench")
-
-    Sonar[,61] = as.numeric(Sonar[,61])-1
-    train.ind = c(1:50, 100:150)
-    train.x = data.matrix(Sonar[train.ind, 1:60])
-    train.y = Sonar[train.ind, 61]
-    test.x = data.matrix(Sonar[-train.ind, 1:60])
-    test.y = Sonar[-train.ind, 61]
- ```
-
-We are going to use a multi-layer perceptron as our classifier. In `mxnet`, we have a function called `mx.mlp` for building a general multi-layer neural network to do classification or regression.
-
-`mx.mlp` requires the following parameters:
-
-- Training data and label
-- Number of hidden nodes in each hidden layer
-- Number of nodes in the output layer
-- Type of the activation
-- Type of the output loss
-- The device to train (GPU or CPU)
-- Other parameters for `mx.model.FeedForward.create`
-
-The following code shows an example usage of `mx.mlp`:
-
-
- ```r
-    mx.set.seed(0)
-    model <- mx.mlp(train.x, train.y, hidden_node=10, out_node=2, out_activation="softmax",
-                num.round=20, array.batch.size=15, learning.rate=0.07, momentum=0.9,
-                eval.metric=mx.metric.accuracy)
- ```
-
- ```
-    ## Auto detect layout of input matrix, use rowmajor..
-    ## Start training with 1 devices
-    ## [1] Train-accuracy=0.488888888888889
-    ## [2] Train-accuracy=0.514285714285714
-    ## [3] Train-accuracy=0.514285714285714
-    ## [4] Train-accuracy=0.514285714285714
-    ## [5] Train-accuracy=0.514285714285714
-    ## [6] Train-accuracy=0.523809523809524
-    ## [7] Train-accuracy=0.619047619047619
-    ## [8] Train-accuracy=0.695238095238095
-    ## [9] Train-accuracy=0.695238095238095
-    ## [10] Train-accuracy=0.761904761904762
-    ## [11] Train-accuracy=0.828571428571429
-    ## [12] Train-accuracy=0.771428571428571
-    ## [13] Train-accuracy=0.742857142857143
-    ## [14] Train-accuracy=0.733333333333333
-    ## [15] Train-accuracy=0.771428571428571
-    ## [16] Train-accuracy=0.847619047619048
-    ## [17] Train-accuracy=0.857142857142857
-    ## [18] Train-accuracy=0.838095238095238
-    ## [19] Train-accuracy=0.838095238095238
-    ## [20] Train-accuracy=0.838095238095238
- ```
-
-Note that `mx.set.seed` controls the random process in `mxnet`. You can see the accuracy in each round during training. It's also easy to make predictions and evaluate.
-
-To get an idea of what is happening, view the computation graph from R:
-
- ```r
-    graph.viz(model$symbol)
- ```
-
-[<img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/knitr/graph.computation.png">](https://github.com/dmlc/mxnet)
-
- ```r
-    preds = predict(model, test.x)
- ```
-
- ```
-    ## Auto detect layout of input matrix, use rowmajor.
- ```
-
- ```r
-    pred.label = max.col(t(preds))-1
-    table(pred.label, test.y)
- ```
-
- ```
-    ##           test.y
-    ## pred.label  0  1
-    ##          0 24 14
-    ##          1 36 33
- ```
-
-Note for that for multi-class predictions, mxnet outputs `nclass` x `nexamples`, with each row corresponding to the probability of the class.
-
-## Regression
-
-Again, let us preprocess the data:
-
-
- ```r
-    data(BostonHousing, package="mlbench")
-
-    train.ind = seq(1, 506, 3)
-    train.x = data.matrix(BostonHousing[train.ind, -14])
-    train.y = BostonHousing[train.ind, 14]
-    test.x = data.matrix(BostonHousing[-train.ind, -14])
-    test.y = BostonHousing[-train.ind, 14]
- ```
-
-Although we can use `mx.mlp` again to do regression by changing the `out_activation`, this time we are going to introduce a flexible way to configure neural networks in `mxnet`. Configuration is done by the "Symbol" system in `mxnet`. The Symbol system takes care of the links among nodes, activation, dropout ratio, etc. Configure a multi-layer neural network as follows:
-
-
- ```r
-    # Define the input data
-    data <- mx.symbol.Variable("data")
-    # A fully connected hidden layer
-    # data: input source
-    # num_hidden: number of neurons in this hidden layer
-    fc1 <- mx.symbol.FullyConnected(data, num_hidden=1)
-
-    # Use linear regression for the output layer
-    lro <- mx.symbol.LinearRegressionOutput(fc1)
- ```
-
-What matters for a regression task is mainly the last function. It enables the new network to optimize for squared loss. Now let's train on this simple data set. In this configuration, we dropped the hidden layer so that the input layer is directly connected to the output layer.
-
-Next, make prediction with this structure and other parameters with `mx.model.FeedForward.create`:
-
-
- ```r
-    mx.set.seed(0)
-    model <- mx.model.FeedForward.create(lro, X=train.x, y=train.y,
-                                         ctx=mx.cpu(),     num.round=50, array.batch.size=20,
-                                         learning.rate=2e-6, momentum=0.9,  eval.metric=mx.metric.rmse)
- ```
-
- ```
-    ## Auto detect layout of input matrix, use rowmajor.
-    ## Start training with 1 devices
-    ## [1] Train-rmse=16.063282524034
-    ## [2] Train-rmse=12.2792375712573
-    ## [3] Train-rmse=11.1984634005885
-    ## [4] Train-rmse=10.2645236892904
-    ## [5] Train-rmse=9.49711005504284
-    ## [6] Train-rmse=9.07733734175182
-    ## [7] Train-rmse=9.07884450847991
-    ## [8] Train-rmse=9.10463850277417
-    ## [9] Train-rmse=9.03977049028532
-    ## [10] Train-rmse=8.96870685004475
-    ## [11] Train-rmse=8.93113287361574
-    ## [12] Train-rmse=8.89937257821847
-    ## [13] Train-rmse=8.87182096922953
-    ## [14] Train-rmse=8.84476075083586
-    ## [15] Train-rmse=8.81464673014974
-    ## [16] Train-rmse=8.78672567900196
-    ## [17] Train-rmse=8.76265872846474
-    ## [18] Train-rmse=8.73946101419974
-    ## [19] Train-rmse=8.71651926303267
-    ## [20] Train-rmse=8.69457600919277
-    ## [21] Train-rmse=8.67354928674563
-    ## [22] Train-rmse=8.65328755392436
-    ## [23] Train-rmse=8.63378039680078
-    ## [24] Train-rmse=8.61488162586984
-    ## [25] Train-rmse=8.5965105183022
-    ## [26] Train-rmse=8.57868133563275
-    ## [27] Train-rmse=8.56135851937663
-    ## [28] Train-rmse=8.5444819772098
-    ## [29] Train-rmse=8.52802114610432
-    ## [30] Train-rmse=8.5119504512622
-    ## [31] Train-rmse=8.49624261719241
-    ## [32] Train-rmse=8.48087453238701
-    ## [33] Train-rmse=8.46582689119887
-    ## [34] Train-rmse=8.45107881002491
-    ## [35] Train-rmse=8.43661331401712
-    ## [36] Train-rmse=8.42241575909639
-    ## [37] Train-rmse=8.40847217331365
-    ## [38] Train-rmse=8.39476931796395
-    ## [39] Train-rmse=8.38129658373974
-    ## [40] Train-rmse=8.36804269059018
-    ## [41] Train-rmse=8.35499817678397
-    ## [42] Train-rmse=8.34215505742154
-    ## [43] Train-rmse=8.32950441908131
-    ## [44] Train-rmse=8.31703985777311
-    ## [45] Train-rmse=8.30475363906755
-    ## [46] Train-rmse=8.29264031506106
-    ## [47] Train-rmse=8.28069372820073
-    ## [48] Train-rmse=8.26890902770415
-    ## [49] Train-rmse=8.25728089053853
-    ## [50] Train-rmse=8.24580511500735
- ```
-
-It's also easy to make a prediction and evaluate it:
-
-
- ```r
-    preds = predict(model, test.x)
- ```
-
- ```
-    ## Auto detect layout of input matrix, use rowmajor..
- ```
-
- ```r
-    sqrt(mean((preds-test.y)^2))
- ```
-
- ```
-    ## [1] 7.800502
- ```
-
-Currently, we have four predefined metrics: "accuracy", "rmse", "mae", and "rmsle". MXNet provides the interface for defining your own metrics:
-
-
- ```r
-    demo.metric.mae <- mx.metric.custom("mae", function(label, pred) {
-      pred <- mx.nd.reshape(pred, shape = 0)
-      res <- mx.nd.mean(mx.nd.abs(label-pred))
-      return(res)
-    })
- ```
-
-This is an example of the mean absolute error metric. Simply plug it into the training function:
-
-
- ```r
-    mx.set.seed(0)
-    model <- mx.model.FeedForward.create(lro, X=train.x, y=train.y,
-                                         ctx=mx.cpu(),    num.round=50, array.batch.size=20,
-                                         learning.rate=2e-6, momentum=0.9, eval.metric=demo.metric.mae)
- ```
-
- ```
-    ## Auto detect layout of input matrix, use rowmajor.
-    ## Start training with 1 devices
-    ## [1] Train-mae=14.953625731998
-    ## [2] Train-mae=11.4802955521478
-    ## [3] Train-mae=8.50700579749213
-    ## [4] Train-mae=7.30591265360514
-    ## [5] Train-mae=7.38049803839789
-    ## [6] Train-mae=7.36036252975464
-    ## [7] Train-mae=7.06519222259521
-    ## [8] Train-mae=6.9962231847975
-    ## [9] Train-mae=6.96296903822157
-    ## [10] Train-mae=6.9046172036065
-    ## [11] Train-mae=6.87867620256212
-    ## [12] Train-mae=6.85872554779053
-    ## [13] Train-mae=6.81936407089233
-    ## [14] Train-mae=6.79135354359945
-    ## [15] Train-mae=6.77438741260105
-    ## [16] Train-mae=6.75365140702989
-    ## [17] Train-mae=6.73369296391805
-    ## [18] Train-mae=6.71600982877943
-    ## [19] Train-mae=6.69932826360067
-    ## [20] Train-mae=6.6852519777086
-    ## [21] Train-mae=6.67343420452542
-    ## [22] Train-mae=6.66315894656711
-    ## [23] Train-mae=6.65314838621351
-    ## [24] Train-mae=6.64388704299927
-    ## [25] Train-mae=6.63480265935262
-    ## [26] Train-mae=6.62583245171441
-    ## [27] Train-mae=6.61697626113892
-    ## [28] Train-mae=6.60842116673787
-    ## [29] Train-mae=6.60040124257406
-    ## [30] Train-mae=6.59264140658908
-    ## [31] Train-mae=6.58551020092434
-    ## [32] Train-mae=6.57864215638902
-    ## [33] Train-mae=6.57178926467896
-    ## [34] Train-mae=6.56495311525133
-    ## [35] Train-mae=6.55813185373942
-    ## [36] Train-mae=6.5513252152337
-    ## [37] Train-mae=6.54453214009603
-    ## [38] Train-mae=6.53775374094645
-    ## [39] Train-mae=6.53098879920112
-    ## [40] Train-mae=6.52423816257053
-    ## [41] Train-mae=6.51764053768582
-    ## [42] Train-mae=6.51121346155802
-    ## [43] Train-mae=6.5047902001275
-    ## [44] Train-mae=6.49837123023139
-    ## [45] Train-mae=6.49216641320123
-    ## [46] Train-mae=6.48598252402412
-    ## [47] Train-mae=6.4798010720147
-    ## [48] Train-mae=6.47362396452162
-    ## [49] Train-mae=6.46745183732775
-    ## [50] Train-mae=6.46128723356459
- ```
-
-Congratulations! You've learned the basics for using MXNet in R. To learn how to use MXNet's advanced features, see the other tutorials.
-
-
-## Next Steps
-* [Classify Real-World Images with Pre-trained Model](http://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
-* [Handwritten Digits Classification Competition](http://mxnet.io/tutorials/r/mnistCompetition.html)
-* [Character Language Model using RNN](http://mxnet.io/tutorials/r/charRnnModel.html)
diff --git a/docs/tutorials/r/index.md b/docs/tutorials/r/index.md
deleted file mode 100644
index 13db9694864d..000000000000
--- a/docs/tutorials/r/index.md
+++ /dev/null
@@ -1,31 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# R Tutorials
-
-These tutorials introduce a few fundamental concepts in deep learning and how to implement them in R using _MXNet_.
-
-```eval_rst
-.. toctree::
-   :glob:
-
-   *
-```
-
-<br>
-
-More tutorials and examples are available in the GitHub [repository](https://github.com/apache/incubator-mxnet/tree/master/example).
diff --git a/docs/tutorials/r/mnistCompetition.md b/docs/tutorials/r/mnistCompetition.md
deleted file mode 100644
index f59aabd77905..000000000000
--- a/docs/tutorials/r/mnistCompetition.md
+++ /dev/null
@@ -1,356 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-Handwritten Digits Classification Competition
-=============================================
-
-[MNIST](http://yann.lecun.com/exdb/mnist/) is a handwritten digits image data set created by Yann LeCun. Every digit is represented by a 28 x 28 pixel image. It's become a standard data set for testing classifiers on simple image input. A neural network is a strong model for image classification tasks. There's a [long-term hosted competition](https://www.kaggle.com/c/digit-recognizer) on Kaggle using this data set.
-This tutorial shows how to use [MXNet](https://github.com/dmlc/mxnet/tree/master/R-package) to compete in this challenge.
-
-## Loading the Data 
-
-First, let's download the data from [Kaggle](https://www.kaggle.com/c/digit-recognizer/data) and put it in the `data/` folder in your working directory.
-
-Now we can read it in R and convert it to matrices:
-
-
- ```r
-    require(mxnet)
- ```
-
- ```
-    ## Loading required package: mxnet
-    ## Loading required package: methods
- ```
-
- ```r
-    train <- read.csv('data/train.csv', header=TRUE)
-    test <- read.csv('data/test.csv', header=TRUE)
-    train <- data.matrix(train)
-    test <- data.matrix(test)
-
-    train.x <- train[,-1]
-    train.y <- train[,1]
- ```
-
-Every image is represented as a single row in train/test. The greyscale of each image falls in the range [0, 255]. Linearly transform it into [0,1] by using the following command:
-
-
- ```r
-    train.x <- t(train.x/255)
-    test <- t(test/255)
- ```
-Transpose the input matrix to npixel x nexamples, which is the major format for columns accepted by MXNet (and the convention of R).
-
-In the label section, the number of each digit is fairly evenly distributed:
-
-
- ```r
-    table(train.y)
- ```
-
- ```
-    ## train.y
-    ##    0    1    2    3    4    5    6    7    8    9
-    ## 4132 4684 4177 4351 4072 3795 4137 4401 4063 4188
-```
-
-## Configuring the Network
-
-Now that we have the data, let's configure the structure of our network:
-
-
- ```r
-    data <- mx.symbol.Variable("data")
-    fc1 <- mx.symbol.FullyConnected(data, name="fc1", num_hidden=128)
-    act1 <- mx.symbol.Activation(fc1, name="relu1", act_type="relu")
-    fc2 <- mx.symbol.FullyConnected(act1, name="fc2", num_hidden=64)
-    act2 <- mx.symbol.Activation(fc2, name="relu2", act_type="relu")
-    fc3 <- mx.symbol.FullyConnected(act2, name="fc3", num_hidden=10)
-    softmax <- mx.symbol.SoftmaxOutput(fc3, name="sm")
- ```
-
-1. In `mxnet`, we use the data type `symbol` to configure the network. `data <- mx.symbol.Variable("data")` uses `data` to represent the input data, i.e., the input layer.
-2. We set the first hidden layer with `fc1 <- mx.symbol.FullyConnected(data, name="fc1", num_hidden=128)`. This layer has `data` as the input, its name, and the number of hidden neurons.
-3. Activation is set with `act1 <- mx.symbol.Activation(fc1, name="relu1", act_type="relu")`. The activation function takes the output from the first hidden layer, `fc1`.
-4. The second hidden layer takes the result from `act1` as input, with its name as "fc2" and the number of hidden neurons as 64.
-5. The second activation is almost the same as `act1`, except we have a different input source and name.
-6. This generates the output layer. Because there are only 10 digits, we set the number of neurons to 10.
-7. Finally, we set the activation to softmax to get a probabilistic prediction.
-
-## Training
-
-We are almost ready for the training process. Before we start the computation, let's decide which device to use:
-
-
- ```r
-    devices <- mx.cpu()
- ```
-
-We assign CPU to `mxnet`. Now, you can run the following command to train the neural network! Note that `mx.set.seed` is the function that controls the random process in `mxnet`:
-
-
- ```r
-    mx.set.seed(0)
-    model <- mx.model.FeedForward.create(softmax, X=train.x, y=train.y,
-                                         ctx=devices, num.round=10, array.batch.size=100,
-                                         learning.rate=0.07, momentum=0.9,  eval.metric=mx.metric.accuracy,
-                                         initializer=mx.init.uniform(0.07),
-                                            epoch.end.callback=mx.callback.log.train.metric(100))
- ```
-
- ```
-    ## Start training with 1 devices
-    ## Batch [100] Train-accuracy=0.6563
-    ## Batch [200] Train-accuracy=0.777999999999999
-    ## Batch [300] Train-accuracy=0.827466666666665
-    ## Batch [400] Train-accuracy=0.855499999999999
-    ## [1] Train-accuracy=0.859832935560859
-    ## Batch [100] Train-accuracy=0.9529
-    ## Batch [200] Train-accuracy=0.953049999999999
-    ## Batch [300] Train-accuracy=0.955866666666666
-    ## Batch [400] Train-accuracy=0.957525000000001
-    ## [2] Train-accuracy=0.958309523809525
-    ## Batch [100] Train-accuracy=0.968
-    ## Batch [200] Train-accuracy=0.9677
-    ## Batch [300] Train-accuracy=0.9696
-    ## Batch [400] Train-accuracy=0.970650000000002
-    ## [3] Train-accuracy=0.970809523809526
-    ## Batch [100] Train-accuracy=0.973
-    ## Batch [200] Train-accuracy=0.974249999999999
-    ## Batch [300] Train-accuracy=0.976
-    ## Batch [400] Train-accuracy=0.977100000000003
-    ## [4] Train-accuracy=0.977452380952384
-    ## Batch [100] Train-accuracy=0.9834
-    ## Batch [200] Train-accuracy=0.981949999999999
-    ## Batch [300] Train-accuracy=0.981900000000001
-    ## Batch [400] Train-accuracy=0.982600000000003
-    ## [5] Train-accuracy=0.983000000000003
-    ## Batch [100] Train-accuracy=0.983399999999999
-    ## Batch [200] Train-accuracy=0.98405
-    ## Batch [300] Train-accuracy=0.985000000000001
-    ## Batch [400] Train-accuracy=0.985725000000003
-    ## [6] Train-accuracy=0.985952380952384
-    ## Batch [100] Train-accuracy=0.988999999999999
-    ## Batch [200] Train-accuracy=0.9876
-    ## Batch [300] Train-accuracy=0.988100000000001
-    ## Batch [400] Train-accuracy=0.988750000000003
-    ## [7] Train-accuracy=0.988880952380955
-    ## Batch [100] Train-accuracy=0.991999999999999
-    ## Batch [200] Train-accuracy=0.9912
-    ## Batch [300] Train-accuracy=0.990066666666668
-    ## Batch [400] Train-accuracy=0.990275000000003
-    ## [8] Train-accuracy=0.990452380952384
-    ## Batch [100] Train-accuracy=0.9937
-    ## Batch [200] Train-accuracy=0.99235
-    ## Batch [300] Train-accuracy=0.991966666666668
-    ## Batch [400] Train-accuracy=0.991425000000003
-    ## [9] Train-accuracy=0.991500000000003
-    ## Batch [100] Train-accuracy=0.9942
-    ## Batch [200] Train-accuracy=0.99245
-    ## Batch [300] Train-accuracy=0.992433333333334
-    ## Batch [400] Train-accuracy=0.992275000000002
-    ## [10] Train-accuracy=0.992380952380955
- ```
-
-## Making a Prediction and Submitting to the Competition
-
-To make a prediction, type:
-
-
- ```r
-    preds <- predict(model, test)
-    dim(preds)
- ```
-
- ```
-    ## [1]    10 28000
- ```
-
-It is a matrix with 28000 rows and 10 cols, containing the desired classification probabilities from the output layer. To extract the maximum label for each row, use `max.col`:
-
-
- ```r
-    pred.label <- max.col(t(preds)) - 1
-    table(pred.label)
- ```
-
- ```
-    ## pred.label
-    ##    0    1    2    3    4    5    6    7    8    9
-    ## 2818 3195 2744 2767 2683 2596 2798 2790 2784 2825
- ```
-
-With a little extra effort to modify the .csv format, our submission is ready for the competition!
-
-
- ```r
-    submission <- data.frame(ImageId=1:ncol(test), Label=pred.label)
-    write.csv(submission, file='submission.csv', row.names=FALSE,  quote=FALSE)
- ```
-
-## LeNet
-
-Now let's use a new network structure: [LeNet](http://yann.lecun.com/exdb/lenet/). It has been proposed by Yann LeCun for recognizing handwritten digits. We'll demonstrate how to construct and train a LeNet in `mxnet`.
-
-First, we construct the network:
-
-
-```r
-# input
-data <- mx.symbol.Variable('data')
-# first conv
-conv1 <- mx.symbol.Convolution(data=data, kernel=c(5,5), num_filter=20)
-tanh1 <- mx.symbol.Activation(data=conv1, act_type="tanh")
-pool1 <- mx.symbol.Pooling(data=tanh1, pool_type="max",
-                          kernel=c(2,2), stride=c(2,2))
-# second conv
-conv2 <- mx.symbol.Convolution(data=pool1, kernel=c(5,5), num_filter=50)
-tanh2 <- mx.symbol.Activation(data=conv2, act_type="tanh")
-pool2 <- mx.symbol.Pooling(data=tanh2, pool_type="max",
-                          kernel=c(2,2), stride=c(2,2))
-# first fullc
-flatten <- mx.symbol.Flatten(data=pool2)
-fc1 <- mx.symbol.FullyConnected(data=flatten, num_hidden=500)
-tanh3 <- mx.symbol.Activation(data=fc1, act_type="tanh")
-# second fullc
-fc2 <- mx.symbol.FullyConnected(data=tanh3, num_hidden=10)
-# loss
-lenet <- mx.symbol.SoftmaxOutput(data=fc2)
-```
-
-Then let's reshape the matrices into arrays:
-
-
-```r
-train.array <- train.x
-dim(train.array) <- c(28, 28, 1, ncol(train.x))
-test.array <- test
-dim(test.array) <- c(28, 28, 1, ncol(test))
-```
-
-We want to compare training speed on different devices, so define the devices:
-
-
-```r
-n.gpu <- 1
-device.cpu <- mx.cpu()
-device.gpu <- lapply(0:(n.gpu-1), function(i) {
-  mx.gpu(i)
-})
-```
-
-We can pass a list of devices to ask MXNet to train on multiple GPUs (you can do this for CPUs,
-but because internal computation of CPUs is already multi-threaded, there is less gain than with using GPUs).
-
-Start by training on the CPU first. Because this takes a bit time, we run it for just one iteration.
-
-
- ```r
-    mx.set.seed(0)
-    tic <- proc.time()
-    model <- mx.model.FeedForward.create(lenet, X=train.array, y=train.y,
-                                     ctx=device.cpu, num.round=1, array.batch.size=100,
-                                     learning.rate=0.05, momentum=0.9, wd=0.00001,
-                                     eval.metric=mx.metric.accuracy,
-                                       epoch.end.callback=mx.callback.log.train.metric(100))
- ```
-
- ```
-    ## Start training with 1 devices
-    ## Batch [100] Train-accuracy=0.1066
-    ## Batch [200] Train-accuracy=0.16495
-    ## Batch [300] Train-accuracy=0.401766666666667
-    ## Batch [400] Train-accuracy=0.537675
-    ## [1] Train-accuracy=0.557136038186157
- ```
-
- ```r
-    print(proc.time() - tic)
- ```
-
- ```
-    ##    user  system elapsed
-    ## 130.030 204.976  83.821
- ```
-
-Train on a GPU:
-
-
- ```r
-    mx.set.seed(0)
-    tic <- proc.time()
-    model <- mx.model.FeedForward.create(lenet, X=train.array, y=train.y,
-                                     ctx=device.gpu, num.round=5, array.batch.size=100,
-                                     learning.rate=0.05, momentum=0.9, wd=0.00001,
-                                     eval.metric=mx.metric.accuracy,
-                                       epoch.end.callback=mx.callback.log.train.metric(100))
- ```
-
- ```
-    ## Start training with 1 devices
-    ## Batch [100] Train-accuracy=0.1066
-    ## Batch [200] Train-accuracy=0.1596
-    ## Batch [300] Train-accuracy=0.3983
-    ## Batch [400] Train-accuracy=0.533975
-    ## [1] Train-accuracy=0.553532219570405
-    ## Batch [100] Train-accuracy=0.958
-    ## Batch [200] Train-accuracy=0.96155
-    ## Batch [300] Train-accuracy=0.966100000000001
-    ## Batch [400] Train-accuracy=0.968550000000003
-    ## [2] Train-accuracy=0.969071428571432
-    ## Batch [100] Train-accuracy=0.977
-    ## Batch [200] Train-accuracy=0.97715
-    ## Batch [300] Train-accuracy=0.979566666666668
-    ## Batch [400] Train-accuracy=0.980900000000003
-    ## [3] Train-accuracy=0.981309523809527
-    ## Batch [100] Train-accuracy=0.9853
-    ## Batch [200] Train-accuracy=0.985899999999999
-    ## Batch [300] Train-accuracy=0.986966666666668
-    ## Batch [400] Train-accuracy=0.988150000000002
-    ## [4] Train-accuracy=0.988452380952384
-    ## Batch [100] Train-accuracy=0.990199999999999
-    ## Batch [200] Train-accuracy=0.98995
-    ## Batch [300] Train-accuracy=0.990600000000001
-    ## Batch [400] Train-accuracy=0.991325000000002
-    ## [5] Train-accuracy=0.991523809523812
- ```
-
- ```r
-    print(proc.time() - tic)
- ```
-
- ```
-    ##    user  system elapsed
-    ##   9.288   1.680   6.889
- ```
-
-By using a GPU processor, we significantly speed up training!
-Now, we can submit the result to Kaggle to see the improvement of our ranking!
-
-
- ```r
-    preds <- predict(model, test.array)
-    pred.label <- max.col(t(preds)) - 1
-    submission <- data.frame(ImageId=1:ncol(test), Label=pred.label)
-    write.csv(submission, file='submission.csv', row.names=FALSE, quote=FALSE)
- ```
-
-![](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/knitr/mnistCompetition-kaggle-submission.png)
-
-##  Next Steps
-* [Character Language Model using RNN](http://mxnet.io/tutorials/r/charRnnModel.html)
diff --git a/docs/tutorials/r/ndarray.md b/docs/tutorials/r/ndarray.md
deleted file mode 100644
index e935a5052cf3..000000000000
--- a/docs/tutorials/r/ndarray.md
+++ /dev/null
@@ -1,223 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# NDArray: Vectorized Tensor Computations on CPUs and GPUs
-
-`NDArray` is the basic vectorized operation unit in MXNet for matrix and tensor computations.
-Users can perform usual calculations as on an R"s array, but with two additional features:
-
-
-
-- Multiple devices: All operations can be run on various devices including
-CPUs and GPUs.
-
-
-- Automatic parallelization: All operations are automatically executed in
-   parallel with each other.
-
-## Create and Initialize
-
-Let"s create `NDArray` on either a GPU or a CPU:
-
-
-```r
-require(mxnet)
-```
-
-```
-## Loading required package: mxnet
-## Loading required package: methods
-```
-
-```r
-a <- mx.nd.zeros(c(2, 3)) # create a 2-by-3 matrix on cpu
-b <- mx.nd.zeros(c(2, 3), mx.cpu()) # create a 2-by-3 matrix on cpu
-# c <- mx.nd.zeros(c(2, 3), mx.gpu(0)) # create a 2-by-3 matrix on gpu 0, if you have CUDA enabled.
-```
-
-Typically for CUDA-enabled devices, the device id of a GPU starts from 0.
-That's why we passed in 0 to the GPU id. 
-
-We can initialize an `NDArray` object in various ways:
-
-
-```r
-a <- mx.nd.ones(c(4, 4))
-b <- mx.rnorm(c(4, 5))
-c <- mx.nd.array(1:5)
-```
-
-To check the numbers in an `NDArray`, we can simply run:
-
-
-```r
-a <- mx.nd.ones(c(2, 3))
-b <- as.array(a)
-class(b)
-```
-
-```
-## [1] "matrix"
-```
-
-```r
-b
-```
-
-```
-##      [,1] [,2] [,3]
-## [1,]    1    1    1
-## [2,]    1    1    1
-```
-
-## Performing Basic Operations
-
-### Elemental-wise Operations
-
-You can perform elemental-wise operations on `NDArray` objects, as follows:
-
-
-```r
-a <- mx.nd.ones(c(2, 4)) * 2
-b <- mx.nd.ones(c(2, 4)) / 8
-as.array(a)
-```
-
-```
-##      [,1] [,2] [,3] [,4]
-## [1,]    2    2    2    2
-## [2,]    2    2    2    2
-```
-
-```r
-as.array(b)
-```
-
-```
-##       [,1]  [,2]  [,3]  [,4]
-## [1,] 0.125 0.125 0.125 0.125
-## [2,] 0.125 0.125 0.125 0.125
-```
-
-```r
-c <- a + b
-as.array(c)
-```
-
-```
-##       [,1]  [,2]  [,3]  [,4]
-## [1,] 2.125 2.125 2.125 2.125
-## [2,] 2.125 2.125 2.125 2.125
-```
-
-```r
-d <- c / a - 5
-as.array(d)
-```
-
-```
-##         [,1]    [,2]    [,3]    [,4]
-## [1,] -3.9375 -3.9375 -3.9375 -3.9375
-## [2,] -3.9375 -3.9375 -3.9375 -3.9375
-```
-
-If two `NDArray`s are located on different devices, we need to explicitly move them to the same one. For instance:
-
-
-```r
-a <- mx.nd.ones(c(2, 3)) * 2
-b <- mx.nd.ones(c(2, 3), mx.gpu()) / 8
-c <- mx.nd.copyto(a, mx.gpu()) * b
-as.array(c)
-```
-
-### Loading and Saving
-
-You can save a list of `NDArray` object to your disk with `mx.nd.save`:
-
-
-```r
-a <- mx.nd.ones(c(2, 3))
-mx.nd.save(list(a), "temp.ndarray")
-```
-
-You can load it back easily:
-
-
-```r
-a <- mx.nd.load("temp.ndarray")
-as.array(a[[1]])
-```
-
-```
-##      [,1] [,2] [,3]
-## [1,]    1    1    1
-## [2,]    1    1    1
-```
-
-We can directly save data to and load it from a distributed file system, such as Amazon S3 and HDFS:
-
-
-```r
-mx.nd.save(list(a), "s3://mybucket/mydata.bin")
-mx.nd.save(list(a), "hdfs///users/myname/mydata.bin")
-```
-
-## Automatic Parallelization
-
-`NDArray` can automatically execute operations in parallel. Automatic parallelization is useful when
-using multiple resources, such as CPU cards, GPU cards, and CPU-to-GPU memory bandwidth.
-
-For example, if we write `a <- a + 1` followed by `b <- b + 1`, and `a` is on a CPU and 
-`b` is on a GPU, executing them in parallel improves
-efficiency. Furthermore, because copying data between CPUs and GPUs are also expensive, running in parallel with other computations further increases efficiency.
-
-It's hard to find the code that can be executed in parallel by eye. In the
-following example, `a <- a + 1` and `c <- c * 3` can be executed in parallel, but `a <- a + 1` and
-`b <- b * 3` should be in sequential.
-
-
-```r
-a <- mx.nd.ones(c(2,3))
-b <- a
-c <- mx.nd.copyto(a, mx.cpu())
-a <- a + 1
-b <- b * 3
-c <- c * 3
-```
-
-Luckily, MXNet can automatically resolve the dependencies and
-execute operations in parallel accurately. This allows us to write our program assuming there is only a single thread. MXNet will
-automatically dispatch the program to multiple devices.
-
-MXNet achieves this with lazy evaluation. Each operation is issued to an
-internal engine, and then returned. For example, if we run `a <- a + 1`, it
-returns immediately after pushing the plus operator to the engine. This
-asynchronous processing allows us to push more operators to the engine. It determines
-the read and write dependencies and the best way to execute them in
-parallel.
-
-The actual computations are finished, allowing us to copy the results someplace else, such as `as.array(a)` or `mx.nd.save(a, "temp.dat")`. To write highly parallelized codes, we only need to postpone when we need
-the results.
-
-## Next Steps
-* [Symbol](http://mxnet.io/tutorials/r/symbol.html)
-* [Write and use callback functions](http://mxnet.io/tutorials/r/CallbackFunction.html)
-* [Neural Networks with MXNet in Five Minutes](http://mxnet.io/tutorials/r/fiveMinutesNeuralNetwork.html)
-* [Classify Real-World Images with Pre-trained Model](http://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
-* [Handwritten Digits Classification Competition](http://mxnet.io/tutorials/r/mnistCompetition.html)
-* [Character Language Model using RNN](http://mxnet.io/tutorials/r/charRnnModel.html)
\ No newline at end of file
diff --git a/docs/tutorials/r/symbol.md b/docs/tutorials/r/symbol.md
deleted file mode 100644
index d60c5fbfbc59..000000000000
--- a/docs/tutorials/r/symbol.md
+++ /dev/null
@@ -1,147 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Symbol and Automatic Differentiation
-
-The computational unit `NDArray` requires a way to construct neural networks. MXNet provides a symbolic interface, named Symbol, to do this. Symbol combines both flexibility and efficiency.
-
-## Basic Composition of Symbols
-
-The following code creates a two-layer perceptron network:
-
-
-```r
-require(mxnet)
-net <- mx.symbol.Variable("data")
-net <- mx.symbol.FullyConnected(data=net, name="fc1", num_hidden=128)
-net <- mx.symbol.Activation(data=net, name="relu1", act_type="relu")
-net <- mx.symbol.FullyConnected(data=net, name="fc2", num_hidden=64)
-net <- mx.symbol.Softmax(data=net, name="out")
-class(net)
-```
-
-```
-## [1] "Rcpp_MXSymbol"
-## attr(,"package")
-## [1] "mxnet"
-```
-
-Each symbol takes a (unique) string name. *Variable* often defines the inputs,
-or free variables. Other symbols take a symbol as the input (*data*),
-and may accept other hyper parameters, such as the number of hidden neurons (*num_hidden*)
-or the activation type (*act_type*).
-
-A symbol can be viewed as a function that takes several arguments, whose
-names are automatically generated and can be retrieved with the following command:
-
-
-```r
-arguments(net)
-```
-
-```
-## [1] "data"       "fc1_weight" "fc1_bias"   "fc2_weight" "fc2_bias"
-## [6] "out_label"
-```
-
-The arguments are the parameters need by each symbol:
-
-- *data*: Input data needed by the variable *data*
-- *fc1_weight* and *fc1_bias*: The weight and bias for the first fully connected layer, *fc1*
-- *fc2_weight* and *fc2_bias*: The weight and bias for the second fully connected layer, *fc2*
-- *out_label*: The label needed by the loss
-
-We can also specify the automatically generated names explicitly:
-
-
-```r
-data <- mx.symbol.Variable("data")
-w <- mx.symbol.Variable("myweight")
-net <- mx.symbol.FullyConnected(data=data, weight=w, name="fc1", num_hidden=128)
-arguments(net)
-```
-
-```
-## [1] "data"     "myweight" "fc1_bias"
-```
-
-## More Complicated Composition of Symbols
-
-MXNet provides well-optimized symbols for
-commonly used layers in deep learning. You can also define new operators
-in Python. The following example first performs an element-wise add between two
-symbols, then feeds them to the fully connected operator:
-
-
-```r
-lhs <- mx.symbol.Variable("data1")
-rhs <- mx.symbol.Variable("data2")
-net <- mx.symbol.FullyConnected(data=lhs + rhs, name="fc1", num_hidden=128)
-arguments(net)
-```
-
-```
-## [1] "data1"      "data2"      "fc1_weight" "fc1_bias"
-```
-
-We can construct a symbol more flexibly than by using the single
-forward composition, for example:
-
-
-```r
-net <- mx.symbol.Variable("data")
-net <- mx.symbol.FullyConnected(data=net, name="fc1", num_hidden=128)
-net2 <- mx.symbol.Variable("data2")
-net2 <- mx.symbol.FullyConnected(data=net2, name="net2", num_hidden=128)
-composed.net <- mx.apply(net, data=net2, name="compose")
-arguments(composed.net)
-```
-
-```
-## [1] "data2"       "net2_weight" "net2_bias"   "fc1_weight"  "fc1_bias"
-```
-
-In the example, *net* is used as a function to apply to an existing symbol
-*net*. The resulting *composed.net* will replace the original argument *data* with
-*net2* instead.
-
-## Training a Neural Net
-
-The [model API](https://github.com/apache/incubator-mxnet/blob/master/R-package/R/model.R) is a thin wrapper around the symbolic executors to support neural net training.
-
-We encourage you to read [Symbolic Configuration and Execution in Pictures for python package](../../api/python/symbol_in_pictures/symbol_in_pictures.md)for a detailed explanation of concepts in pictures.
-
-## How Efficient Is the Symbolic API?
-
-The Symbolic API brings the efficient C++
-operations in powerful toolkits, such as CXXNet and Caffe, together with the
-flexible dynamic NDArray operations. All of the memory and computation resources are
-allocated statically during bind operations, to maximize runtime performance and memory
-utilization.
-
-The coarse-grained operators are equivalent to CXXNet layers, which are
-extremely efficient.  We also provide fine-grained operators for more flexible
-composition. Because MXNet does more in-place memory allocation, it can
-be more memory efficient than CXXNet and gets to the same runtime with
-greater flexibility.
-
-## Next Steps
-* [Write and use callback functions](http://mxnet.io/tutorials/r/CallbackFunction.html)
-* [Neural Networks with MXNet in Five Minutes](http://mxnet.io/tutorials/r/fiveMinutesNeuralNetwork.html)
-* [Classify Real-World Images with Pre-trained Model](http://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
-* [Handwritten Digits Classification Competition](http://mxnet.io/tutorials/r/mnistCompetition.html)
-* [Character Language Model using RNN](http://mxnet.io/tutorials/r/charRnnModel.html)
diff --git a/docs/tutorials/scala/char_lstm.md b/docs/tutorials/scala/char_lstm.md
deleted file mode 100644
index aca08dc79920..000000000000
--- a/docs/tutorials/scala/char_lstm.md
+++ /dev/null
@@ -1,526 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Developing a Character-level Language model
-
-This tutorial shows how to train a character-level language model with a multilayer recurrent neural network (RNN) using Scala. This model takes one text file as input and trains an RNN that learns to predict the next character in the sequence. In this tutorial, you train a multilayer LSTM (Long Short-Term Memory) network that generates relevant text using Barack Obama's speech patterns.
-
-There are many documents that explain LSTM concepts. If you aren't familiar with LSTM, refer to the following before you proceed:
-- Christopher Olah's [Understanding LSTM blog post](http://colah.github.io/posts/2015-08-Understanding-LSTMs/)
-- [Training a LSTM char-rnn in Julia to Generate Random Sentences](http://dmlc.ml/mxnet/2015/11/15/char-lstm-in-julia.html)
-- [Bucketing in MXNet in Python](https://github.com/dmlc/mxnet-notebooks/blob/master/python/tutorials/char_lstm.ipynb)
-- [Bucketing in MXNet](http://mxnet.io/faq/bucketing.html)
-
-## How to Use This Tutorial
-
-There are three ways to use this tutorial:
-
-1) Run it by copying the provided code snippets and pasting them into the Scala command line, making the appropriate changes to the input file path.
-
-2) Reuse the code by making changes to relevant parameters and running it from command line.
-
-3) [Run the source code directly](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/scala/org/apache/mxnetexamples/rnn) by running the [provided scripts](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/scripts/rnn).
-
-To run the scripts:
-- Build and train the model with the [run_train_charrnn.sh script](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/scripts/rnn/run_train_charrnn.sh). Edit the script as follows:
-
-Edit the CLASS_PATH variable in the script to include your operating system-specific folder (e.g., linux-x86_64-cpu/linux-x86_64-gpu/osx-x86_64-cpu) in the path. Run the script with the following command:
-
-```bash
-
-    bash run_train_charrnn.sh <which GPU card to use; -1 means CPU> <input data path> <location to save the model>
-
-    e.g.,
-    bash run_train_charrnn.sh -1 ./datas/obama.txt ./models/obama
-
-```
-
-- Run inference with the [run_test_charrnn.sh script](https://github.com/dmlc/mxnet/blob/master/scala-package/examples/scripts/rnn/run_test_charrnn.sh). Edit the script as follows:
-
-Edit the CLASS_PATH variable in the script to include your operating system-specific folder (e.g., linux-x86_64-cpu/linux-x86_64-gpu/osx-x86_64-cpu) in the path. Run the script with the following command:
-
-```bash
-
-    bash run_test_charrnn.sh <input data path> <trained model from previous script>
-
-    e.g.,
-    bash run_test_charrnn.sh ./datas/obama.txt ./models/obama
-```
-
-In this tutorial, you will accomplish the following:
-
--	Build an LSTM network that learns speech patterns from Barack Obama's speeches at the character level. At each time interval, the input is a character.
--	Clean up the dataset.
--	Train a model.
--	Fit the model.
--	Build the inference model.
-
-## Prerequisites
-
-To complete this tutorial, setup and run the scala interpreter by following the [instructions](https://mxnet.incubator.apache.org/install/scala_setup.html#interpreter).
-
-## Download the Data
-
-First, download the data, which contains Barack Obama's speeches. The data is stored in a file called obama.txt and is available on [mxnet.io](http://data.mxnet.io/data/char_lstm.zip)
-
-To download the data which contains Barack Obama's speeches:
-
-1) Download the dataset with the following command:
-
-    ```bash
-        wget http://data.mxnet.io/data/char_lstm.zip
-    ```
-
-2) Unzip the dataset with the following command:
-
-    ```bash
-        unzip char_lstm.zip -d char_lstm/
-    ```
-
-3) The downloaded data contains President Obama's speeches. You can have sneak peek at the dataset with the following command:
-
-    ```bash
-        head -10 obama.txt
-    ```
-
-Output:
-```
-        Call to Renewal Keynote Address Call to Renewal Pt 1Call to Renewal Part 2 TOPIC: Our Past, Our Future & Vision for America June
-        28, 2006 Call to Renewal' Keynote Address Complete Text Good morning. I appreciate the opportunity to speak here at the Call to R
-        enewal's Building a Covenant for a New America conference. I've had the opportunity to take a look at your Covenant for a New Ame
-        rica. It is filled with outstanding policies and prescriptions for much of what ails this country. So I'd like to congratulate yo
-        u all on the thoughtful presentations you've given so far about poverty and justice in America, and for putting fire under the fe
-        et of the political leadership here in Washington.But today I'd like to talk about the connection between religion and politics a
-        nd perhaps offer some thoughts about how we can sort through some of the often bitter arguments that we've been seeing over the l
-        ast several years.I do so because, as you all know, we can affirm the importance of poverty in the Bible; and we can raise up and
-         pass out this Covenant for a New America. We can talk to the press, and we can discuss the religious call to address poverty and
-         environmental stewardship all we want, but it won't have an impact unless we tackle head-on the mutual suspicion that sometimes
-```
-
-## Prepare the Data
-
-To preprocess the dataset, define the following utility functions:
-
-* `readContent` - Reads data from the data file.
-* `buildVocab` - Maps each character to a unique Integer ID, i.e., a build a vocabulary
-* `text2Id` - Encodes each sentence with an Integer ID.
-
-Then, use these utility functions to generate vocabulary from the input text file (obama.txt).
-
-To prepare the data:
-
-1) Read the dataset with the following function:
-
-    ```scala
-        scala> import scala.io.Source
-
-        import scala.io.Source
-
-        scala> // Read file
-        scala> def readContent(path: String): String = Source.fromFile(path).mkString
-
-        readContent: (path: String)String
-
-    ```
-
-2) Build a vocabulary with the following function:
-
-    ```scala
-        scala> // Build  a vocabulary of what char we have in the content
-        scala> def buildVocab(path: String): Map[String, Int] = {
-                val content = readContent(path).split("\n")
-                var idx = 1 // 0 is left for zero padding
-                var theVocab = Map[String, Int]()
-                for (line <- content) {
-                 for (char <- line) {
-                   val key = s"$char"
-                   if (!theVocab.contains(key)) {
-                     theVocab = theVocab + (key -> idx)
-                     idx += 1
-                   }
-                 }
-                }
-                theVocab
-               }
-
-               buildVocab: (path: String)Map[String,Int]
-    ```
-
-3) To assign each character a unique numerical ID, use the following function:
-
-    ```scala
-        scala> def text2Id(sentence: String, theVocab: Map[String, Int]): Array[Int] = {
-                val words = for (char <- sentence) yield theVocab(s"$char")
-                words.toArray
-              }
-
-              text2Id: (sentence: String, theVocab: Map[String,Int])Array[Int]
-    ```
-
-4) Now, build a character vocabulary from the dataset (obama.txt). Change the input filepath (dataPath) to reflect your settings.   
-
-    ```scala
-        scala> // Give your system path to the "obama.txt" we have downloaded using previous steps.
-        scala> val dataPath = "obama.txt"
-        dataPath: String = obama.txt
-
-        scala> val vocab = buildVocab(dataPath)
-
-        scala> vocab.size
-        res23: Int = 82
-    ```
-
-
-## Build a Multi-layer LSTM model
-
-Now, create a multi-layer LSTM model.
-
-To create the model:
-
-1) Load the helper files (`Lstm.scala`, `BucketIo.scala` and `RnnModel.scala`).
-`Lstm.scala` contains the definition of the LSTM cell. `BucketIo.scala` creates a sentence iterator. `RnnModel.scala` is used for model inference. The helper files are available on the [MXNet site](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/scala/org/apache/mxnetexamples/rnn).
-To load them, at the Scala command prompt type:
-
-    ```scala
-        scala> :load ../../../scala-package/examples/src/main/scala/org/apache/mxnet/examples/rnn/Lstm.scala
-        scala> :load ../../../scala-package/examples/src/main/scala/org/apache/mxnet/examples/rnn/BucketIo.scala
-        scala> :load ../../../scala-package/examples/src/main/scala/org/apache/mxnet/examples/rnn/RnnModel.scala
-    ```
-
-2) Set the LSTM hyperparameters as follows:
-
-    ```scala
-        scala> // We can support various input lengths.
-        scala> // For this problem, we cut each input sentence to a length of 129 characters.
-        scala> // So we only need a fixed length bucket length.
-        scala> val buckets = Array(129)
-        buckets: Array[Int] = Array(129)
-
-        scala> // hidden unit in LSTM cell
-        scala> val numHidden = 512
-        numHidden: Int = 512
-
-        scala> // The embedding dimension, which maps a char to a 256 dim vector
-        scala> val numEmbed = 256
-        numEmbed: Int = 256
-
-        scala> // The number of lstm layers
-        scala> val numLstmLayer = 3
-        numLstmLayer: Int = 3
-
-        scala> // The batch size for training
-        scala> val batchSize = 32
-        batchSize: Int = 32
-    ```
-
-3) Now, construct the LSTM network as a symbolic computation graph. Type the following to create a graph in which the model is unrolled for a fixed length explicitly in time.
-
-    ```scala
-        scala> // generate symbol for a length
-        scala> def symGen(seqLen: Int): Symbol = {
-            Lstm.lstmUnroll(numLstmLayer, seqLen, vocab.size + 1,
-                        numHidden = numHidden, numEmbed = numEmbed,
-                        numLabel = vocab.size + 1, dropout = 0.2f)
-          }
-        symGen: (seqLen: Int)org.apache.mxnet.Symbol
-
-        scala> // create the network symbol
-        scala> val symbol = symGen(buckets(0))
-        symbol: org.apache.mxnet.Symbol = org.apache.mxnet.Symbol@3a589eed
-
-    ```      
-
-4) To train the model, initialize states for the LSTM and create a data iterator, which groups the data into buckets.
-Note: The BucketSentenceIter data iterator supports various length examples; however, we use only the fixed length version in this tutorial.
-
-    ```scala
-
-        scala> // initialize states for LSTM
-        scala> val initC = for (l <- 0 until numLstmLayer) yield (s"l${l}_init_c", (batchSize, numHidden))
-
-        initC: scala.collection.immutable.IndexedSeq[(String, (Int, Int))] = Vector((l0_init_c,(32,512)),
-        (l1_init_c,(32,512)), (l2_init_c,(32,512)))
-
-        scala> val initH = for (l <- 0 until numLstmLayer) yield (s"l${l}_init_h", (batchSize, numHidden))
-
-        initH: scala.collection.immutable.IndexedSeq[(String, (Int, Int))] = Vector((l0_init_h,(32,512)),
-        (l1_init_h,(32,512)), (l2_init_h,(32,512)))
-
-        scala> val initStates = initC ++ initH
-
-        initStates: scala.collection.immutable.IndexedSeq[(String, (Int, Int))] =
-        Vector((l0_init_c,(32,512)), (l1_init_c,(32,512)), (l2_init_c,(32,512)), (l0_init_h,(32,512)),
-        (l1_init_h,(32,512)), (l2_init_h,(32,512)))
-
-        scala> val dataTrain = new BucketIo.BucketSentenceIter(dataPath, vocab, buckets,
-                                              batchSize, initStates, seperateChar = "\n",
-                                              text2Id = text2Id, readContent = readContent)
-
-        dataTrain: BucketIo.BucketSentenceIter = non-empty iterator
-
-    ```
-
-5) You can set more than 100 epochs, but for this tutorial, specify 75 epochs. Each epoch can take as long as 4 minutes on a GPU. In this tutorial, you will use the [ADAM optimizer](http://mxnet.io/api/scala/docs/index.html#org.apache.mxnet.optimizer.Adam):
-
-    ```scala
-        scala> import org.apache.mxnet._
-        import org.apache.mxnet._
-
-        scala> import org.apache.mxnet.Callback.Speedometer
-        import org.apache.mxnet.Callback.Speedometer
-
-        scala> import org.apache.mxnet.optimizer.Adam
-        import org.apache.mxnet.optimizer.Adam
-
-        scala> // and we will see result by training 75 epochs
-        scala> val numEpoch = 75
-        numEpoch: Int = 75
-
-        scala> // learning rate
-        scala> val learningRate = 0.001f
-        learningRate: Float = 0.001
-
-    ```
-
-6) Define the perplexity utility function for the evaluation metric which is used to calculate the negative log-likelihood during training.
-
-    ```scala
-        scala> def perplexity(label: NDArray, pred: NDArray): Float = {
-                val shape = label.shape
-                val size = shape(0) * shape(1)
-                val labelT = {
-                  val tmp = label.toArray.grouped(shape(1)).toArray
-                  val result = Array.fill[Float](size)(0f)
-                  var idx = 0
-                  for (i <- 0 until shape(1)) {
-                    for (j <- 0 until shape(0)) {
-                      result(idx) = tmp(j)(i)
-                      idx += 1
-                    }
-                  }
-                  result
-                }
-                var loss = 0f
-                val predArray = pred.toArray.grouped(pred.shape(1)).toArray
-                for (i <- 0 until pred.shape(0)) {
-                  loss += -Math.log(Math.max(1e-10, predArray(i)(labelT(i).toInt)).toFloat).toFloat
-                }
-                loss / size
-                }
-
-        perplexity: (label: org.apache.mxnet.NDArray, pred: org.apache.mxnet.NDArray)Float
-
-        scala> def doCheckpoint(prefix: String): EpochEndCallback = new EpochEndCallback {
-                    override def invoke(epoch: Int, symbol: Symbol,
-                                        argParams: Map[String, NDArray],
-                                        auxStates: Map[String, NDArray]): Unit = {
-                      Model.saveCheckpoint(prefix, epoch + 1, symbol, argParams, auxStates)
-                    }
-                }
-
-        doCheckpoint: (prefix: String)org.apache.mxnet.EpochEndCallback
-
-    ```
-
-7) Define the initializer that is required for creating a model, as follows:
-
-    ```scala
-        scala> val initializer = new Xavier(factorType = "in", magnitude = 2.34f)
-
-        initializer: org.apache.mxnet.Xavier = org.apache.mxnet.Xavier@54e8f10a
-
-    ```
-
-8) Now, you have implemented all the supporting infrastructures for the char-lstm model. To train the model, use the standard [MXNet high-level API](http://mxnet.io/api/scala/docs/index.html#org.apache.mxnet.FeedForward). You can train the model on a single GPU or CPU from multiple GPUs or CPUs by changing ```scala .setContext(Array(Context.gpu(0),Context.gpu(1),Context.gpu(2),Context.gpu(3)))``` to ```scala .setContext(Array(Context.gpu(0)))```:
-
-    ```scala
-        scala> val model = FeedForward.newBuilder(symbol)
-                .setContext(Array(Context.gpu(0),Context.gpu(1),Context.gpu(2),Context.gpu(3)))
-                .setNumEpoch(numEpoch)
-                .setOptimizer(new Adam(learningRate = learningRate, wd = 0.00001f))
-                .setInitializer(initializer)
-                .setTrainData(dataTrain)
-                .setEvalMetric(new CustomMetric(perplexity, name = "perplexity"))
-                .setBatchEndCallback(new Speedometer(batchSize, 20))
-                .setEpochEndCallback(doCheckpoint("obama"))
-                .build()
-
-        model: org.apache.mxnet.FeedForward = org.apache.mxnet.FeedForward@4926f6c7
-    ```
-
-Now, you have an LSTM model and you've trained it. Use this model to create the inference.
-
-## Build the Inference Model
-
-You can now sample sentences from the trained model. The sampler works as follows:
-- Takes some fixed character set (e.g., "The United States") and feeds it into the LSTM as the starting input.
-- The LSTM produces an output distribution over the vocabulary and a state in the first time step then, samples a character from the output distribution and fixes it as the second character.
-- In the next time step, feeds the previously sampled character as input.
-- Continues running until it has sampled enough characters. Note we are running mini-batches, so several sentences could be sampled simultaneously.
-
-To build the inference model, define the following utility functions that help MXNet make inferences:
-
-* `makeRevertVocab` - Reverts the key value in the dictionary for easy access to characters while predicting
-* `makeInput` -  Uses a given character as input
-* `cdf`, `choice` - `cdf` is a helper function for the `choice` function, which is used to create random samples
-* `makeOutput` - Directs the model to use either random output or fixed output by choosing the option with the greatest probability.
-
-    ```scala
-        scala> import scala.util.Random
-
-        scala> // helper structure for prediction
-        scala> def makeRevertVocab(vocab: Map[String, Int]): Map[Int, String] = {
-                  var dic = Map[Int, String]()
-                  vocab.foreach { case (k, v) =>
-                    dic = dic + (v -> k)
-                  }
-                  dic
-                }
-
-      makeRevertVocab: (vocab: Map[String,Int])Map[Int,String]
-
-      scala> // make input from char
-      scala> def makeInput(char: Char, vocab: Map[String, Int], arr: NDArray): Unit = {
-              val idx = vocab(s"$char")
-              val tmp = NDArray.zeros(1)
-              tmp.set(idx)
-              arr.set(tmp)
-            }
-
-      makeInput: (char: Char, vocab: Map[String,Int], arr: org.apache.mxnet.NDArray)Unit
-
-      scala> // helper function for random sample
-      scala> def cdf(weights: Array[Float]): Array[Float] = {
-                val total = weights.sum
-                var result = Array[Float]()
-                var cumsum = 0f
-                for (w <- weights) {
-                  cumsum += w
-                  result = result :+ (cumsum / total)
-                }
-                result
-              }
-
-      cdf: (weights: Array[Float])Array[Float]
-
-      scala> def choice(population: Array[String], weights: Array[Float]): String = {
-              assert(population.length == weights.length)
-              val cdfVals = cdf(weights)
-              val x = Random.nextFloat()
-              var idx = 0
-              var found = false
-              for (i <- 0 until cdfVals.length) {
-                if (cdfVals(i) >= x && !found) {
-                  idx = i
-                  found = true
-                }
-              }
-              population(idx)
-            }
-
-      choice: (population: Array[String], weights: Array[Float])String
-
-      scala> // we can use random output or fixed output by choosing largest probability
-      scala> def makeOutput(prob: Array[Float], vocab: Map[Int, String],
-                              sample: Boolean = false, temperature: Float = 1f): String = {
-                 var idx = -1
-                 val char = if (sample == false) {
-                   idx = ((-1f, -1) /: prob.zipWithIndex) { (max, elem) =>
-                     if (max._1 < elem._1) elem else max
-                   }._2
-                   if (vocab.contains(idx)) vocab(idx)
-                   else ""
-                 } else {
-                   val fixDict = Array("") ++ (1 until vocab.size + 1).map(i => vocab(i))
-                   var scaleProb = prob.map(x => if (x < 1e-6) 1e-6 else if (x > 1 - 1e-6) 1 - 1e-6 else x)
-                   var rescale = scaleProb.map(x => Math.exp(Math.log(x) / temperature).toFloat)
-                   val sum = rescale.sum.toFloat
-                   rescale = rescale.map(_ / sum)
-                   choice(fixDict, rescale)
-                 }
-                 char
-               }
-
-      makeOutput: (prob: Array[Float], vocab: Map[Int,String], sample: Boolean, temperature: Float)String
-
-    ```
-
-1) Build the inference model:
-
-    ```scala
-        scala> // load from check-point
-        scala> val (_, argParams, _) = Model.loadCheckpoint("obama", 75)
-
-        scala> // build an inference model
-        scala> val model = new RnnModel.LSTMInferenceModel(numLstmLayer, vocab.size + 1, \
-                                   numHidden = numHidden, numEmbed = numEmbed, \
-                                   numLabel = vocab.size + 1, argParams = argParams, \
-                                   ctx = Context.cpu(), dropout = 0.2f)
-
-        model: RnnModel.LSTMInferenceModel = RnnModel$LSTMInferenceModel@2f0c0319
-    ```
-
-2) Now you can generate a sequence of 1200 characters (you can select any number of characters you want) starting with "The United States" as follows:
-
-    ```scala
-
-        scala> val seqLength = 1200
-        seqLength: Int = 1200
-
-        scala> val inputNdarray = NDArray.zeros(1)
-        inputNdarray: org.apache.mxnet.NDArray = org.apache.mxnet.NDArray@9c231a24
-
-        scala> val revertVocab = makeRevertVocab(vocab)
-
-        scala> // Feel free to change the starter sentence
-
-        scala> var output = "The United States"
-        output: String = The United States
-
-        scala> val randomSample = true
-        randomSample: Boolean = true
-
-        scala> var newSentence = true
-        newSentence: Boolean = true
-
-        scala> val ignoreLength = output.length()
-        ignoreLength: Int = 17
-
-        scala> for (i <- 0 until seqLength) {
-                if (i <= ignoreLength - 1) makeInput(output(i), vocab, inputNdarray)
-                else makeInput(output.takeRight(1)(0), vocab, inputNdarray)
-                val prob = model.forward(inputNdarray, newSentence)
-                newSentence = false
-                val nextChar = makeOutput(prob, revertVocab, randomSample)
-                if (nextChar == "") newSentence = true
-                if (i >= ignoreLength) output = output ++ nextChar
-              }
-
-        scala> output
-
-        res7: String = The United States who have been blessed no companies would be proud that the challenges we face, it's not as directly untelle are in my daughters - you can afford -- life-saving march care and poor information and receiving battle against other speeces and lead its people. After champions of 2006, and because Africa in America, separate has been conferenced by children ation of discrimination, we remember all of this, succeeded in any other feelings of a palently better political process - at lliims being disability payment. All across all different mights of a more just a few global personal morality and industrialized ready to succeed.One can afford when the earliest days of a pension you can add to the system be confructive despair. They have starting in the demand for...
-
-    ```
-
-
-You can see the output generated from Obama's speeches. All of the line breaks, punctuation, and uppercase and lowercase letters were produced by the sampler (no post-processing was performed).
-
-
-## Next Steps
-* [Scala API](http://mxnet.io/api/scala/)
-* [More Scala Examples](https://github.com/dmlc/mxnet/tree/master/scala-package/examples/)
-* [MXNet tutorials index](http://mxnet.io/tutorials/index.html)
diff --git a/docs/tutorials/scala/index.md b/docs/tutorials/scala/index.md
deleted file mode 100644
index 1e03345a1e45..000000000000
--- a/docs/tutorials/scala/index.md
+++ /dev/null
@@ -1,34 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# MXNet-Scala Tutorials
-
-## Installation & Setup
-
-Using MXNet-Scala is easiest with Maven. You have a couple of options for setting up that depend on your environment.
-
-**Note:** Windows is not yet supported.
-
-* [MXNet-Scala Setup Guide Using Maven](../../install/scala_setup.md)
-* [Setup Scala with MXNet and Create a MXNet-Scala Project with IntelliJ](mxnet_scala_on_intellij.md)
-
-## Tutorials
-
-* [MNIST with MXNet-Scala](mnist.md)
-* [Character-level Language Model with MXNet-Scala](char_lstm.md)
-
-See the [tutorials](http://mxnet.incubator.apache.org/tutorials/index.html#other-languages-api-tutorials) page on MXNet.io for more.
diff --git a/docs/tutorials/scala/mnist.md b/docs/tutorials/scala/mnist.md
deleted file mode 100644
index 89bc203231f7..000000000000
--- a/docs/tutorials/scala/mnist.md
+++ /dev/null
@@ -1,134 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Handwritten Digit Recognition
-
-This Scala tutorial guides you through a classic computer vision application: identifying hand written digits.
-
-Let's train a 3-layer network (i.e multilayer perceptron network) on the MNIST dataset to classify handwritten digits.
-
-## Prerequisites
-To complete this tutorial, we need:
-
-- to compile the latest MXNet version. See the MXNet installation instructions for your operating system in [Setup and Installation](http://mxnet.io/install/index.html).
-- to compile the Scala API. See Scala API build instructions in [Build](https://github.com/dmlc/mxnet/tree/master/scala-package).
-
-## Define the Network
-
-First, define the neural network's architecture using the Symbol API:
-
-```scala
-import org.apache.mxnet._
-import org.apache.mxnet.optimizer.SGD
-
-// model definition
-val data = Symbol.Variable("data")
-val fc1 = Symbol.api.FullyConnected(Some(data), num_hidden = 128, name = "fc1")
-val act1 = Symbol.api.Activation(Some(fc1), "relu", "relu1")
-val fc2 = Symbol.api.FullyConnected(Some(act1), num_hidden = 64, name = "fc2")
-val act2 = Symbol.api.Activation(Some(fc2), "relu", "relu2")
-val fc3 = Symbol.api.FullyConnected(Some(act2), num_hidden = 10, name = "fc3")
-val mlp = Symbol.api.SoftmaxOutput(Some(fc3), name = "sm")
-```
-
-## Load the Data
-
-Then, load the training and validation data using DataIterators.
-
-You can download the MNIST data using the [get_mnist_data script](https://github.com/dmlc/mxnet/blob/master/scala-package/core/scripts/get_mnist_data.sh). We've already written a DataIterator for the MNIST dataset:
-
-```scala
-// load MNIST dataset
-val trainDataIter = IO.MNISTIter(Map(
-  "image" -> "data/train-images-idx3-ubyte",
-  "label" -> "data/train-labels-idx1-ubyte",
-  "data_shape" -> "(1, 28, 28)",
-  "label_name" -> "sm_label",
-  "batch_size" -> "50",
-  "shuffle" -> "1",
-  "flat" -> "0",
-  "silent" -> "0",
-  "seed" -> "10"))
-
-val valDataIter = IO.MNISTIter(Map(
-  "image" -> "data/t10k-images-idx3-ubyte",
-  "label" -> "data/t10k-labels-idx1-ubyte",
-  "data_shape" -> "(1, 28, 28)",
-  "label_name" -> "sm_label",
-  "batch_size" -> "50",
-  "shuffle" -> "1",
-  "flat" -> "0", "silent" -> "0"))
-```
-
-## Train the model
-
-We can use the FeedForward builder to train our network:
-
-```scala
-// setup model and fit the training data
-val model = FeedForward.newBuilder(mlp)
-      .setContext(Context.cpu())
-      .setNumEpoch(10)
-      .setOptimizer(new SGD(learningRate = 0.1f, momentum = 0.9f, wd = 0.0001f))
-      .setTrainData(trainDataIter)
-      .setEvalData(valDataIter)
-      .build()
-```
-
-## Make predictions
-
-Finally, let's make predictions against the validation dataset and compare the predicted labels with the real labels.
-
-```scala
-val probArrays = model.predict(valDataIter)
-// in this case, we do not have multiple outputs
-require(probArrays.length == 1)
-val prob = probArrays(0)
-
-// get real labels
-import scala.collection.mutable.ListBuffer
-valDataIter.reset()
-val labels = ListBuffer.empty[NDArray]
-while (valDataIter.hasNext) {
-  val evalData = valDataIter.next()
-  labels += evalData.label(0).copy()
-}
-val y = NDArray.concatenate(labels)
-
-// get predicted labels
-val predictedY = NDArray.argmax_channel(prob)
-require(y.shape == predictedY.shape)
-
-// calculate accuracy
-var numCorrect = 0
-var numTotal = 0
-for ((labelElem, predElem) <- y.toArray zip predictedY.toArray) {
-  if (labelElem == predElem) {
-    numCorrect += 1
-  }
-  numTotal += 1
-}
-val acc = numCorrect.toFloat / numTotal
-println(s"Final accuracy = $acc")
-```
-
-Check out more MXNet Scala examples below.
-
-## Next Steps
-* [Scala API](http://mxnet.io/api/scala/)
-* [More Scala Examples](https://github.com/dmlc/mxnet/tree/master/scala-package/examples/)
-* [MXNet tutorials index](http://mxnet.io/tutorials/index.html)
diff --git a/docs/tutorials/scala/mxnet_scala_on_intellij.md b/docs/tutorials/scala/mxnet_scala_on_intellij.md
deleted file mode 100644
index b9dc7e6d5152..000000000000
--- a/docs/tutorials/scala/mxnet_scala_on_intellij.md
+++ /dev/null
@@ -1,449 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Run MXNet Scala Examples Using the IntelliJ IDE (macOS)
-
-This tutorial guides you through setting up a Scala project in the IntelliJ IDE on macOS, and shows how to use the MXNet package from your application.
-
-## Prerequisites:
-To use this tutorial you need the following software:
-
-- [Java 8 JDK](http://www.oracle.com/technetwork/java/javase/downloads/index.html)
-- [Maven](https://maven.apache.org/install.html)
-- [Scala](https://www.scala-lang.org/download/) - comes with IntelliJ, so you don't need to install it separately
-- [OpenCV](https://opencv.org/)
-- [IntelliJ IDE](https://www.jetbrains.com/idea/)
-
-The following instructions are provided for macOS and Ubuntu 16.04. Windows support is not yet available.
-
-### Mac Prerequisites Setup
-
-**Step 1.** Install brew:
-```
-/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
-```
-
-Or, if you already have brew, update it:
-```
-brew update
-```
-
-**Step 2.** Install Java 8 JDK:
-```
-brew tap caskroom/versions
-brew cask install java8
-```
-
-**Step 3.** Install maven:
-```
-brew install maven
-```
-
-**Step 4.** Install OpenCV:
-```
-brew install opencv
-```
-
-
-### Ubuntu Prerequisites Setup
-
-**Step 1.**: Download the MXNet source.
-
-```bash
-git clone --recursive https://github.com/apache/incubator-mxnet.git mxnet
-cd mxnet
-```
-
-**Step 2.**: Run the dependency installation scripts.
-
-```bash
-sudo ./ci/docker/install/ubuntu_core.sh
-sudo ./ci/docker/install/ubuntu_scala.sh
-```
-
-
-## Access the Maven Package for MXNet
-
-To view the latest Maven packages, you can check the Maven repository for the `org.apache.mxnet` packages.
-
-*  [MXNet Maven Packages](https://search.maven.org/#search%7Cga%7C1%7Cg%3A%22org.apache.mxnet%22)
-
-In the tutorial below, you will install the latest package at the time of this writing. You may find a different artifact for your specific operating system, or a newer one by reviewing what is currently available in the Maven repo.
-
-The configuration you should update is in the pom file's dependency for MXNet:
-
-```html
-<dependency>
-  <groupId>org.apache.mxnet</groupId>
-  <artifactId>mxnet-full_2.11-osx-x86_64-cpu</artifactId>
-</dependency>
-```
-
-## Set Up Your Project
-
-Now that you've installed your prerequisites, you are ready to setup IntelliJ and your first MXNet-Scala project!
-
-**Step 1.** Install and setup IntelliJ:
-    - When prompted for what to features to enable during IntelliJ's first startup, make sure you select Scala.
-
-    - Install the plugin for IntelliJ IDE by following these steps:
-   On **Menu**, choose **Preferences**, choose **Plugins**, type **Scala**, and then choose **Install**. For further plugin help and instructions, refer to [Scala plugin setup for IDE](https://www.jetbrains.com/help/idea/scala.html).
-
-**Step 2.** Create a new project:
-
-![intellij welcome](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/scala/intellij-welcome.png)
-
-From the IntelliJ welcome screen, select "Create New Project".
-
-![maven project type](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/scala/intellij-project-type.png)
-
-Choose the Maven project type.
-
-![maven project type - archetype](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/scala/intellij-project-type-archetype-check.png)
-
-Select the checkbox for `Create from archetype`.
-
-![maven project type - archetype](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/scala/intellij-project-type-archetype-add.png)
-
-Click the `Add Archetype` button, and add the following information to each field.
-
-**GroupId**
-```
-net.alchim31.maven
-```
-**ArtifactId**
-```
-scala-archetype-simple
-```
-**Version**
-```
-1.6
-```
-**Repository**
-```
-https://mvnrepository.com/artifact/net.alchim31.maven/scala-archetype-simple
-```
-
-![maven project type - archetype](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/scala/intellij-project-type-archetype-add-confirm.png)
-
-Click `Ok` to add the archetype, make sure it is selected from the list, and then click `Next`.
-
-![project metadata](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/scala/intellij-project-metadata.png)
-
-Set the project's metadata. For this tutorial, use the following:
-
-**GroupId**
-```
-mxnet
-```
-**ArtifactId**
-```
-scalaMXNet
-```
-**Version**
-```
-1.0-SNAPSHOT
-```
-
-![project properties](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/scala/intellij-project-properties.png)
-
-Review the project's properties. The settings can be left as their default.
-
-![project location](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/scala/intellij-project-location.png)
-
-Set the project's location. The rest of the settings can be left as their default.
-
-![project 1](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/scala/intellij-project-1.png)
-
-After clicking Finish, you will be presented with the project's first view.
-The project's `pom.xml` will be open for editing.
-
-**Step 3.** Replace the pom file's content with the following code. Changes include:
-  - Project properties: `scala.version`, upgrading from `2.11.5` to `2.11.8`
-  - Project dependencies: adding the MXNet package from Maven and updating the dependency for JUnitRunner (specs2-junit_) and logging
-  - Build options: removing '-make:transitive'
-
-
-```html
-<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">
-  <modelVersion>4.0.0</modelVersion>
-  <groupId>mxnet</groupId>
-  <artifactId>scalaMXNet</artifactId>
-  <version>1.0-SNAPSHOT</version>
-  <name>${project.artifactId}</name>
-  <description>My wonderful scala app</description>
-  <inceptionYear>2018</inceptionYear>
-  <licenses>
-    <license>
-      <name>Apache2</name>
-      <url>http://mxnet.apache.org</url>
-      <distribution>repo</distribution>
-    </license>
-  </licenses>
-  <profiles>
-    <profile>
-      <id>osx-x86_64-cpu</id>
-      <properties>
-        <platform>osx-x86_64-cpu</platform>
-      </properties>
-    </profile>
-    <profile>
-      <id>linux-x86_64-cpu</id>
-      <properties>
-        <platform>linux-x86_64-cpu</platform>
-      </properties>
-    </profile>
-    <profile>
-      <id>linux-x86_64-gpu</id>
-      <properties>
-        <platform>linux-x86_64-gpu</platform>
-      </properties>
-    </profile>
-  </profiles>
-  <properties>
-    <maven.compiler.source>1.6</maven.compiler.source>
-    <maven.compiler.target>1.6</maven.compiler.target>
-    <encoding>UTF-8</encoding>
-    <scala.version>2.11.8</scala.version>
-    <scala.compat.version>2.11</scala.compat.version>
-    <slf4jVersion>1.7.7</slf4jVersion>
-    <platform>osx-x86_64-cpu</platform>
-  </properties>
-
-  <dependencies>
-    <dependency>
-      <groupId>org.apache.mxnet</groupId>
-      <artifactId>mxnet-full_2.11-osx-x86_64-cpu</artifactId>
-      <version>[1.4.0,)</version>
-    </dependency>
-    <dependency>
-      <groupId>org.scala-lang</groupId>
-      <artifactId>scala-library</artifactId>
-      <version>${scala.version}</version>
-    </dependency>
-    <dependency>
-      <groupId>args4j</groupId>
-      <artifactId>args4j</artifactId>
-      <version>2.0.29</version>
-    </dependency>
-
-    <!-- Test -->
-    <dependency>
-      <groupId>junit</groupId>
-      <artifactId>junit</artifactId>
-      <version>4.11</version>
-      <scope>test</scope>
-    </dependency>
-    <dependency>
-      <groupId>org.specs2</groupId>
-      <artifactId>specs2-junit_${scala.compat.version}</artifactId>
-      <version>2.4.16</version>
-      <scope>test</scope>
-    </dependency>
-    <dependency>
-      <groupId>org.scalatest</groupId>
-      <artifactId>scalatest_${scala.compat.version}</artifactId>
-      <version>2.2.4</version>
-      <scope>test</scope>
-    </dependency>
-
-    <!-- Logging -->
-    <dependency>
-      <groupId>org.slf4j</groupId>
-      <artifactId>slf4j-api</artifactId>
-      <version>${slf4jVersion}</version>
-    </dependency>
-    <dependency>
-      <groupId>org.slf4j</groupId>
-      <artifactId>slf4j-log4j12</artifactId>
-      <version>${slf4jVersion}</version>
-    </dependency>
-  </dependencies>
-
-  <build>
-    <sourceDirectory>src/main/scala</sourceDirectory>
-    <testSourceDirectory>src/test/scala</testSourceDirectory>
-    <plugins>
-      <plugin>
-        <!-- see http://davidb.github.com/scala-maven-plugin -->
-        <groupId>net.alchim31.maven</groupId>
-        <artifactId>scala-maven-plugin</artifactId>
-        <version>3.2.0</version>
-        <executions>
-          <execution>
-            <goals>
-              <goal>compile</goal>
-              <goal>testCompile</goal>
-            </goals>
-            <configuration>
-              <args>
-                <!--arg>-make:transitive</arg-->
-                <arg>-dependencyfile</arg>
-                <arg>${project.build.directory}/.scala_dependencies</arg>
-              </args>
-            </configuration>
-          </execution>
-        </executions>
-      </plugin>
-      <plugin>
-        <groupId>org.apache.maven.plugins</groupId>
-        <artifactId>maven-surefire-plugin</artifactId>
-        <version>2.18.1</version>
-        <configuration>
-          <useFile>false</useFile>
-          <disableXmlReport>true</disableXmlReport>
-          <!-- If you have classpath issue like NoDefClassError,... -->
-          <!-- useManifestOnlyJar>false</useManifestOnlyJar -->
-          <includes>
-            <include>**/*Test.*</include>
-            <include>**/*Suite.*</include>
-          </includes>
-        </configuration>
-      </plugin>
-    </plugins>
-  </build>
-</project>
-```
-
-**Step 4.** Import dependencies with Maven:
-
-  - Note the prompt in the lower right corner that states "Maven projects need to be imported". If this is not visible, click on the little greed balloon that appears in the lower right corner.
-
-![import dependencies](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/scala/intellij-project-import-changes.gif)
-
-Click "Import Changes" in this prompt.
-
-**Step 5.** Setup log4j configuration
-
-Create a folder `src/main/resources` and a new file in it `src/main/resources/log4j.properties` with the contents:
-
-```
-log4j.rootLogger = info, stdout
-
-log4j.appender.stdout = org.apache.log4j.ConsoleAppender
-log4j.appender.stdout.Target = System.out
-log4j.appender.stdout.layout = org.apache.log4j.PatternLayout
-log4j.appender.stdout.layout.ConversionPattern=%d{yyyy-MM-dd HH:mm:ss,SSS} [%t] [%c] [%p] - %m%n
-```
-
-**Step 6.** Build the project:
-- To build the project, from the menu choose Build, and then choose Build Project.
-* Note that if you run into ```bad option: '-make:transitive'```, please remove ```<arg>-make:transitive</arg>``` from `pom.xml` and `<parameter value="-make:transitive" />` from  `.idea/scala_compiler`.
-
-**Step 7.** Run the Hello World App:
-
-![hello world app](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/scala/intellij-project-hello-world-app.png)
-
-Navigate to the App included with the project.
-
-![run hello world](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/scala/intellij-project-hello-world-run.png)
-
-Run the App by clicking the green arrow, and verify the Hello World output
-
-**Step 8.** Run Sample MXNet Code in the App:
-
-![run hello mxnet](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/scala/intellij-project-hello-mxnet.png)
-
-Paste the following code in the App, overwriting the original hello world code. Then click the green arrow to run it.
-
-```scala
-object App extends App {
-  import org.apache.mxnet._
-
-  private val a = NDArray.ones(2, 3)
-  println("Testing MXNet by generating an 2x3 NDArray...")
-  println("Shape is: ")
-  println(a.shape)
-}
-```
-
-The result should be this output:
-
-```
-Testing MXNet by generating an 2x3 NDArray...
-Shape is:
-(2,3)
-
-Process finished with exit code 0
-```
-
-
-### Troubleshooting
-
-If you get an error, check the dependencies at the beginning of this tutorial. For example, you might see the following in the middle of the error messages, where `x.x` would the version it's looking for.
-
-```
-...
-Library not loaded: /usr/local/opt/opencv/lib/libopencv_calib3d.x.x.dylib
-...
-```
-
-This can be resolved be installing OpenCV.
-
-
-### Using MXNet from source
-
-If you chose to "Build from Source" when following the [install instructions](https://mxnet.incubator.apache.org/install/index.html) (or the detailed [build from source instructions](https://mxnet.incubator.apache.org/install/build_from_source.html#installing-mxnet-language-bindings)), you can use your custom build instead of the build from maven.  Use your build by editing the `pom.xml` file and replacing the `org.apache.mxnet` dependency with the following:
-
-```
-      <groupId>org.apache.mxnet</groupId>
-      <artifactId>mxnet-core_${scala.version}-${platform}-sources</artifactId>
-      <scope>system</scope>
-      <systemPath>/PathToMXNetSource/incubator-mxnet/scala-package/assembly/osx-x86_64-cpu/target/mxnet-full_${scala.version}-osx-x86_64-cpu-1.6.0-SNAPSHOT-sources.jar</systemPath>
-    </dependency>
-
-    <dependency>
-      <groupId>org.apache.mxnet</groupId>
-      <artifactId>mxnet-full_${scala.version}-${platform}</artifactId>
-      <scope>system</scope>
-      <systemPath>/PathToMXNetSource/incubator-mxnet/scala-package/assembly/osx-x86_64-cpu/target/mxnet-full_${scala.version}-osx-x86_64-cpu-1.6.0-SNAPSHOT.jar</systemPath>
-    </dependency>
-```
-
-Note that you have to edit both of the `systemPath` properties to point to your generated jar files.
-
-
-### Command Line Build Option
-
-- You can also compile the project by using the following command at the command line. Change directories to this project's folder then run the following:
-
-```bash
-mvn clean package -e -P osx-x86_64-cpu
-```
-The `-P <platform>` parameter tells the build which platform to target.
-The `-e` will give you more details if the build fails. If it succeeds, you should see a lot of info and some warning messages, followed by:
-
-```bash
-[INFO] ------------------------------------------------------------------------
-[INFO] BUILD SUCCESS
-[INFO] ------------------------------------------------------------------------
-[INFO] Total time: 1.186 s
-[INFO] Finished at: 2018-03-06T15:17:36-08:00
-[INFO] Final Memory: 11M/155M
-[INFO] ------------------------------------------------------------------------
-```
-The build generates a new jar file in the `target` folder called `scalaInference-1.0-SNAPSHOT.jar`.
-
-
-
-## Next Steps
-For more information about MXNet Scala resources, see the following:
-
-* [Scala API](http://mxnet.io/api/scala/)
-* [Scala Inference API](https://mxnet.incubator.apache.org/api/scala/infer.html)
-* [Scala Examples](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/)
-* [MXNet Tutorials Index](http://mxnet.io/tutorials/index.html)
diff --git a/docs/tutorials/sparse/csr.md b/docs/tutorials/sparse/csr.md
deleted file mode 100644
index 25de1f716045..000000000000
--- a/docs/tutorials/sparse/csr.md
+++ /dev/null
@@ -1,552 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-
-# CSRNDArray - NDArray in Compressed Sparse Row Storage Format
-
-Many real world datasets deal with high dimensional sparse feature vectors. Take for instance a recommendation system where the number of categories and users is on the order of millions. The purchase data for each category by user would show that most users only make a few purchases, leading to a dataset with high sparsity (i.e. most of the elements are zeros).
-
-Storing and manipulating such large sparse matrices in the default dense structure results in wasted memory and processing on the zeros. To take advantage of the sparse structure of the matrix, the `CSRNDArray` in MXNet stores the matrix in [compressed sparse row (CSR)](https://en.wikipedia.org/wiki/Sparse_matrix#Compressed_sparse_row_.28CSR.2C_CRS_or_Yale_format.29) format and uses specialized algorithms in operators.
-**The format is designed for 2D matrices with a large number of columns,
-and each row is sparse (i.e. with only a few nonzeros).**
-
-## Advantages of Compressed Sparse Row NDArray (CSRNDArray)
-For matrices of high sparsity (e.g. ~1% non-zeros = ~1% density), there are two primary advantages of `CSRNDArray` over the existing `NDArray`:
-
-- memory consumption is reduced significantly
-- certain operations are much faster (e.g. matrix-vector multiplication)
-
-You may be familiar with the CSR storage format in [SciPy](https://www.scipy.org/) and will note the similarities in MXNet's implementation. However there are some additional competitive features in `CSRNDArray` inherited from `NDArray`, such as non-blocking asynchronous evaluation and automatic parallelization that are not available in SciPy's flavor of CSR. You can find further explanations for evaluation and parallelization strategy in MXNet in the [NDArray tutorial](https://mxnet.incubator.apache.org/tutorials/basic/ndarray.html#lazy-evaluation-and-automatic-parallelization).
-
-The introduction of `CSRNDArray` also brings a new attribute, `stype` as a holder for storage type info, to `NDArray`. You can query **ndarray.stype** now in addition to the oft-queried attributes such as **ndarray.shape**, **ndarray.dtype**, and **ndarray.context**. For a typical dense NDArray, the value of `stype` is **"default"**. For a `CSRNDArray`, the value of stype is **"csr"**.
-
-## Prerequisites
-
-To complete this tutorial, you will need:
-
-- MXNet. See the instructions for your operating system in [Setup and Installation](https://mxnet.io/install/index.html)
-- [Jupyter](http://jupyter.org/)
-    ```
-    pip install jupyter
-    ```
-- Basic knowledge of NDArray in MXNet. See the detailed tutorial for NDArray in [NDArray - Imperative tensor operations on CPU/GPU](https://mxnet.incubator.apache.org/tutorials/basic/ndarray.html).
-- SciPy - A section of this tutorial uses SciPy package in Python. If you don't have SciPy, the example in that section will be ignored.
-- GPUs - A section of this tutorial uses GPUs. If you don't have GPUs on your machine, simply set the variable `gpu_device` (set in the GPUs section of this tutorial) to `mx.cpu()`.
-
-## Compressed Sparse Row Matrix
-
-A CSRNDArray represents a 2D matrix as three separate 1D arrays: **data**, **indptr** and **indices**, where the column indices for row `i` are stored in `indices[indptr[i]:indptr[i+1]]` in ascending order, and their corresponding values are stored in `data[indptr[i]:indptr[i+1]]`.
-
-- **data**: CSR format data array of the matrix
-- **indices**: CSR format index array of the matrix
-- **indptr**: CSR format index pointer array of the matrix
-
-### Example Matrix Compression
-
-For example, given the matrix:
-```
-[[7, 0, 8, 0]
- [0, 0, 0, 0]
- [0, 9, 0, 0]]
-```
-
-We can compress this matrix using CSR, and to do so we need to calculate `data`, `indices`, and `indptr`.
-
-The `data` array holds all the non-zero entries of the matrix in row-major order. Put another way, you create a data array that has all of the zeros removed from the matrix, row by row, storing the numbers in that order. Your result:
-
-    data = [7, 8, 9]
-
-The `indices` array stores the column index for each non-zero element in `data`. As you cycle through the data array, starting with 7, you can see it is in column 0. Then looking at 8, you can see it is in column 2. Lastly 9 is in column 1. Your result:
-
-    indices = [0, 2, 1]
-
-The `indptr` array is what will help identify the rows where the data appears. It stores the offset into `data` of the first non-zero element number of each row of the matrix. This array always starts with 0 (reasons can be explored later), so indptr[0] is 0. Each subsequent value in the array is the aggregate number of non-zero elements up to that row. Looking at the first row of the matrix you can see two non-zero values, so indptr[1] is 2. The next row contains all zeros, so the aggregate is still 2, so indptr[2] is 2. Finally, you see the last row contains one non-zero element bring the aggregate to 3, so indptr[3] is 3. To reconstruct the dense matrix, you will use `data[0:2]` and `indices[0:2]` for the first row, `data[2:2]` and `indices[2:2]` for the second row (which contains all zeros), and `data[2:3]` and `indices[2:3]` for the third row. Your result:
-
-    indptr = [0, 2, 2, 3]
-
-Note that in MXNet, the column indices for a given row are always sorted in ascending order,
-and duplicated column indices for the same row are not allowed.
-
-## Array Creation
-
-There are a few different ways to create a `CSRNDArray`, but first let's recreate the matrix we just discussed using the `data`, `indices`, and `indptr` we calculated in the previous example.
-
-You can create a CSRNDArray with data, indices and indptr by using the `csr_matrix` function:
-
-
-```python
-import mxnet as mx
-# Create a CSRNDArray with python lists
-shape = (3, 4)
-data_list = [7, 8, 9]
-indices_list = [0, 2, 1]
-indptr_list = [0, 2, 2, 3]
-a = mx.nd.sparse.csr_matrix((data_list, indices_list, indptr_list), shape=shape)
-# Inspect the matrix
-a.asnumpy()
-```
-
-
-
-
-    array([[ 7.,  0.,  8.,  0.],
-           [ 0.,  0.,  0.,  0.],
-           [ 0.,  9.,  0.,  0.]], dtype=float32)
-
-
-
-
-```python
-import numpy as np
-# Create a CSRNDArray with numpy arrays
-data_np = np.array([7, 8, 9])
-indptr_np = np.array([0, 2, 2, 3])
-indices_np = np.array([0, 2, 1])
-b = mx.nd.sparse.csr_matrix((data_np, indices_np, indptr_np), shape=shape)
-b.asnumpy()
-```
-
-
-
-
-    array([[7, 0, 8, 0],
-           [0, 0, 0, 0],
-           [0, 9, 0, 0]])
-
-
-
-
-```python
-# Compare the two. They are exactly the same.
-{'a':a.asnumpy(), 'b':b.asnumpy()}
-```
-
-
-
-
-    {'a': array([[ 7.,  0.,  8.,  0.],
-            [ 0.,  0.,  0.,  0.],
-            [ 0.,  9.,  0.,  0.]], dtype=float32), 'b': array([[7, 0, 8, 0],
-            [0, 0, 0, 0],
-            [0, 9, 0, 0]])}
-
-
-
-You can create an MXNet CSRNDArray from a `scipy.sparse.csr.csr_matrix` object by using the `array` function:
-
-
-```python
-try:
-    import scipy.sparse as spsp
-    # generate a csr matrix in scipy
-    c = spsp.csr.csr_matrix((data_np, indices_np, indptr_np), shape=shape)
-    # create a CSRNDArray from a scipy csr object
-    d = mx.nd.sparse.array(c)
-    print('d:{}'.format(d.asnumpy()))
-except ImportError:
-    print("scipy package is required")
-```
-
-    d:[[7 0 8 0]
-     [0 0 0 0]
-     [0 9 0 0]]
-
-
-What if you have a big set of data and you haven't calculated indices or indptr yet? Let's try a simple CSRNDArray from an existing array of data and derive those values with some built-in functions. We can mockup a "big" dataset with a random amount of the data being non-zero, then compress it by using the `tostype` function, which is explained further in the [Storage Type Conversion](#storage-type-conversion) section:
-
-
-```python
-big_array = mx.nd.round(mx.nd.random.uniform(low=0, high=1, shape=(1000, 100)))
-print(big_array)
-big_array_csr = big_array.tostype('csr')
-# Access indices array
-indices = big_array_csr.indices
-# Access indptr array
-indptr = big_array_csr.indptr
-# Access data array
-data = big_array_csr.data
-# The total size of `data`, `indices` and `indptr` arrays is much lesser than the dense big_array!
-```
-
-    
-    [[ 1.  1.  0. ...,  0.  1.  1.]
-     [ 0.  0.  0. ...,  0.  0.  1.]
-     [ 1.  0.  0. ...,  1.  0.  0.]
-     ..., 
-     [ 0.  1.  1. ...,  0.  0.  0.]
-     [ 1.  1.  0. ...,  1.  0.  1.]
-     [ 1.  0.  1. ...,  1.  0.  0.]]
-    <NDArray 1000x100 @cpu(0)>
-
-
-You can also create a CSRNDArray from another using the `array` function specifying the element data type with the option `dtype`,
-which accepts a numpy type. By default, `float32` is used.
-
-
-```python
-# Float32 is used by default
-e = mx.nd.sparse.array(a)
-# Create a 16-bit float array
-f = mx.nd.array(a, dtype=np.float16)
-(e.dtype, f.dtype)
-```
-
-
-
-
-    (numpy.float32, numpy.float16)
-
-
-
-## Inspecting Arrays
-
-A variety of methods are available for you to use for inspecting CSR arrays:
-* **.asnumpy()**
-* **.data**
-* **.indices**
-* **.indptr**
-
-As you have seen already, we can inspect the contents of a `CSRNDArray` by filling
-its contents into a dense `numpy.ndarray` using the `asnumpy` function.
-
-
-```python
-a.asnumpy()
-```
-
-
-
-
-    array([[ 7.,  0.,  8.,  0.],
-           [ 0.,  0.,  0.,  0.],
-           [ 0.,  9.,  0.,  0.]], dtype=float32)
-
-
-
-You can also inspect the internal storage of a CSRNDArray by accessing attributes such as `indptr`, `indices` and `data`:
-
-
-```python
-# Access data array
-data = a.data
-# Access indices array
-indices = a.indices
-# Access indptr array
-indptr = a.indptr
-{'a.stype': a.stype, 'data':data, 'indices':indices, 'indptr':indptr}
-```
-
-
-
-
-    {'a.stype': 'csr', 'data': 
-     [ 7.  8.  9.]
-     <NDArray 3 @cpu(0)>, 'indices': 
-     [0 2 1]
-     <NDArray 3 @cpu(0)>, 'indptr': 
-     [0 2 2 3]
-     <NDArray 4 @cpu(0)>}
-
-
-
-## Storage Type Conversion
-
-You can also convert storage types with:
-* **tostype**
-* **cast_storage**
-
-To convert an NDArray to a CSRNDArray and vice versa by using the ``tostype`` function:
-
-
-```python
-# Create a dense NDArray
-ones = mx.nd.ones((2,2))
-# Cast the storage type from `default` to `csr`
-csr = ones.tostype('csr')
-# Cast the storage type from `csr` to `default`
-dense = csr.tostype('default')
-{'csr':csr, 'dense':dense}
-```
-
-
-
-
-    {'csr': 
-     <CSRNDArray 2x2 @cpu(0)>, 'dense': 
-     [[ 1.  1.]
-      [ 1.  1.]]
-     <NDArray 2x2 @cpu(0)>}
-
-
-
-To convert the storage type by using the `cast_storage` operator:
-
-
-```python
-# Create a dense NDArray
-ones = mx.nd.ones((2,2))
-# Cast the storage type to `csr`
-csr = mx.nd.sparse.cast_storage(ones, 'csr')
-# Cast the storage type to `default`
-dense = mx.nd.sparse.cast_storage(csr, 'default')
-{'csr':csr, 'dense':dense}
-```
-
-
-
-
-    {'csr': 
-     <CSRNDArray 2x2 @cpu(0)>, 'dense': 
-     [[ 1.  1.]
-      [ 1.  1.]]
-     <NDArray 2x2 @cpu(0)>}
-
-
-
-## Copies
-
-You can use the `copy` method which makes a deep copy of the array and its data, and returns a new array.
-You can also use the `copyto` method or the slice operator `[]` to deep copy to an existing array.
-
-
-```python
-a = mx.nd.ones((2,2)).tostype('csr')
-b = a.copy()
-c = mx.nd.sparse.zeros('csr', (2,2))
-c[:] = a
-d = mx.nd.sparse.zeros('csr', (2,2))
-a.copyto(d)
-{'b is a': b is a, 'b.asnumpy()':b.asnumpy(), 'c.asnumpy()':c.asnumpy(), 'd.asnumpy()':d.asnumpy()}
-```
-
-
-
-
-    {'b is a': False, 'b.asnumpy()': array([[ 1.,  1.],
-            [ 1.,  1.]], dtype=float32), 'c.asnumpy()': array([[ 1.,  1.],
-            [ 1.,  1.]], dtype=float32), 'd.asnumpy()': array([[ 1.,  1.],
-            [ 1.,  1.]], dtype=float32)}
-
-
-
-If the storage types of source array and destination array do not match,
-the storage type of destination array will not change when copying with `copyto` or
-the slice operator `[]`.
-
-
-```python
-e = mx.nd.sparse.zeros('csr', (2,2))
-f = mx.nd.sparse.zeros('csr', (2,2))
-g = mx.nd.ones(e.shape)
-e[:] = g
-g.copyto(f)
-{'e.stype':e.stype, 'f.stype':f.stype, 'g.stype':g.stype}
-```
-
-
-
-
-    {'e.stype': 'csr', 'f.stype': 'csr', 'g.stype': 'default'}
-
-
-
-## Indexing and Slicing
-You can slice a CSRNDArray on axis 0 with operator `[]`, which copies the slices and returns a new CSRNDArray.
-
-
-```python
-a = mx.nd.array(np.arange(6).reshape(3,2)).tostype('csr')
-b = a[1:2].asnumpy()
-c = a[:].asnumpy()
-{'a':a, 'b':b, 'c':c}
-```
-
-
-
-
-    {'a': 
-     <CSRNDArray 3x2 @cpu(0)>,
-     'b': array([[ 2.,  3.]], dtype=float32),
-     'c': array([[ 0.,  1.],
-            [ 2.,  3.],
-            [ 4.,  5.]], dtype=float32)}
-
-
-
-Note that multi-dimensional indexing or slicing along a particular axis is currently not supported for a CSRNDArray.
-
-## Sparse Operators and Storage Type Inference
-
-Operators that have specialized implementation for sparse arrays can be accessed in `mx.nd.sparse`. You can read the [mxnet.ndarray.sparse API documentation](https://mxnet.incubator.apache.org/versions/master/api/python/ndarray/sparse.html) to find what sparse operators are available.
-
-
-```python
-shape = (3, 4)
-data = [7, 8, 9]
-indptr = [0, 2, 2, 3]
-indices = [0, 2, 1]
-a = mx.nd.sparse.csr_matrix((data, indices, indptr), shape=shape) # a csr matrix as lhs
-rhs = mx.nd.ones((4, 1))      # a dense vector as rhs
-out = mx.nd.sparse.dot(a, rhs)  # invoke sparse dot operator specialized for dot(csr, dense)
-{'out':out}
-```
-
-
-
-
-    {'out': 
-     [[ 15.]
-      [  0.]
-      [  9.]]
-     <NDArray 3x1 @cpu(0)>}
-
-
-
-For any sparse operator, the storage type of output array is inferred based on inputs. You can either read the documentation or inspect the `stype` attribute of the output array to know what storage type is inferred:
-
-
-```python
-b = a * 2  # b will be a CSRNDArray since zero multiplied by 2 is still zero
-c = a + mx.nd.ones(shape=(3, 4))  # c will be a dense NDArray
-{'b.stype':b.stype, 'c.stype':c.stype}
-```
-
-
-
-
-    {'b.stype': 'csr', 'c.stype': 'default'}
-
-
-
-For operators that don't specialize in sparse arrays, we can still use them with sparse inputs with some performance penalty. In MXNet, dense operators require all inputs and outputs to be in the dense format.
-
-If sparse inputs are provided, MXNet will convert sparse inputs into dense ones temporarily, so that the dense operator can be used.
-
-If sparse outputs are provided, MXNet will convert the dense outputs generated by the dense operator into the provided sparse format.
-
-
-```python
-e = mx.nd.sparse.zeros('csr', a.shape)
-d = mx.nd.log(a) # dense operator with a sparse input
-e = mx.nd.log(a, out=e) # dense operator with a sparse output
-{'a.stype':a.stype, 'd.stype':d.stype, 'e.stype':e.stype} # stypes of a and e will be not changed
-```
-
-
-
-
-    {'a.stype': 'csr', 'd.stype': 'default', 'e.stype': 'csr'}
-
-
-
-Note that warning messages will be printed when such a storage fallback event happens. If you are using jupyter notebook, the warning message will be printed in your terminal console.
-
-## Data Loading
-
-You can load data in batches from a CSRNDArray using `mx.io.NDArrayIter`:
-
-
-```python
-# Create the source CSRNDArray
-data = mx.nd.array(np.arange(36).reshape((9,4))).tostype('csr')
-labels = np.ones([9, 1])
-batch_size = 3
-dataiter = mx.io.NDArrayIter(data, labels, batch_size, last_batch_handle='discard')
-# Inspect the data batches
-[batch.data[0] for batch in dataiter]
-```
-
-
-
-
-    [
-     <CSRNDArray 3x4 @cpu(0)>, 
-     <CSRNDArray 3x4 @cpu(0)>, 
-     <CSRNDArray 3x4 @cpu(0)>]
-
-
-
-You can also load data stored in the [libsvm file format](https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/) using `mx.io.LibSVMIter`, where the format is: ``<label> <col_idx1>:<value1> <col_idx2>:<value2> ... <col_idxN>:<valueN>``. Each line in the file records the label and the column indices and data for non-zero entries. For example, for a matrix with 6 columns, ``1 2:1.5 4:-3.5`` means the label is ``1``, the data is ``[[0, 0, 1,5, 0, -3.5, 0]]``. More detailed examples of `mx.io.LibSVMIter` are available in the [API documentation](https://mxnet.incubator.apache.org/versions/master/api/python/io/io.html#mxnet.io.LibSVMIter).
-
-
-```python
-# Create a sample libsvm file in current working directory
-import os
-cwd = os.getcwd()
-data_path = os.path.join(cwd, 'data.t')
-with open(data_path, 'w') as fout:
-    fout.write('1.0 0:1 2:2\n')
-    fout.write('1.0 0:3 5:4\n')
-    fout.write('1.0 2:5 8:6 9:7\n')
-    fout.write('1.0 3:8\n')
-    fout.write('-1 0:0.5 9:1.5\n')
-    fout.write('-2.0\n')
-    fout.write('-3.0 0:-0.6 1:2.25 2:1.25\n')
-    fout.write('-3.0 1:2 2:-1.25\n')
-    fout.write('4 2:-1.2\n')
-
-# Load CSRNDArrays from the file
-data_train = mx.io.LibSVMIter(data_libsvm=data_path, data_shape=(10,), label_shape=(1,), batch_size=3)
-for batch in data_train:
-    print(data_train.getdata())
-    print(data_train.getlabel())
-```
-
-    
-    <CSRNDArray 3x10 @cpu(0)>
-    
-    [ 1.  1.  1.]
-    <NDArray 3 @cpu(0)>
-    
-    <CSRNDArray 3x10 @cpu(0)>
-    
-    [ 1. -1. -2.]
-    <NDArray 3 @cpu(0)>
-    
-    <CSRNDArray 3x10 @cpu(0)>
-    
-    [-3. -3.  4.]
-    <NDArray 3 @cpu(0)>
-
-
-Note that in the file the column indices are expected to be sorted in ascending order per row, and be zero-based instead of one-based.
-
-## Advanced Topics
-
-### GPU Support
-
-By default, `CSRNDArray` operators are executed on CPU. To create a `CSRNDArray` on a GPU, we need to explicitly specify the context:
-
-**Note** If a GPU is not available, an error will be reported in the following section. In order to execute it a cpu, set `gpu_device` to `mx.cpu()`.
-
-
-```python
-import sys
-gpu_device=mx.gpu() # Change this to mx.cpu() in absence of GPUs.
-try:
-    a = mx.nd.sparse.zeros('csr', (100, 100), ctx=gpu_device)
-    a
-except mx.MXNetError as err:
-    sys.stderr.write(str(err))
-```
-
-## Next 
-
-[Train a Linear Regression Model with Sparse Symbols](http://mxnet.incubator.apache.org/tutorials/sparse/train.html)
-
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/sparse/index.md b/docs/tutorials/sparse/index.md
deleted file mode 100644
index faf6526fb824..000000000000
--- a/docs/tutorials/sparse/index.md
+++ /dev/null
@@ -1,25 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Tutorials
-
-```eval_rst
-.. toctree::
-   :glob:
-
-   *
-```
diff --git a/docs/tutorials/sparse/row_sparse.md b/docs/tutorials/sparse/row_sparse.md
deleted file mode 100644
index f89de3d5b75e..000000000000
--- a/docs/tutorials/sparse/row_sparse.md
+++ /dev/null
@@ -1,581 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-
-# RowSparseNDArray - NDArray for Sparse Gradient Updates
-
-## Motivation
-
-Many real world datasets deal with high dimensional sparse feature vectors. When learning
-the weights of models with sparse datasets, the derived gradients of the weights could be sparse.
-
-Let's say we perform a matrix multiplication of ``X``  and ``W``, where ``X`` is a 1x2 matrix, and ``W`` is a 2x3 matrix. Let ``Y`` be the matrix multiplication of the two matrices:
-
-
-```python
-import mxnet as mx
-X = mx.nd.array([[1,0]])
-W = mx.nd.array([[3,4,5], [6,7,8]])
-Y = mx.nd.dot(X, W)
-{'X': X, 'W': W, 'Y': Y}
-```
-
-    {'W': 
-     [[ 3.  4.  5.]
-      [ 6.  7.  8.]]
-     <NDArray 2x3 @cpu(0)>, 'X': 
-     [[ 1.  0.]]
-     <NDArray 1x2 @cpu(0)>, 'Y': 
-     [[ 3.  4.  5.]]
-     <NDArray 1x3 @cpu(0)>}
-
-
-As you can see,
-
-```
-Y[0][0] = X[0][0] * W[0][0] + X[0][1] * W[1][0] = 1 * 3 + 0 * 6 = 3
-Y[0][1] = X[0][0] * W[0][1] + X[0][1] * W[1][1] = 1 * 4 + 0 * 7 = 4
-Y[0][2] = X[0][0] * W[0][2] + X[0][1] * W[1][2] = 1 * 5 + 0 * 8 = 5
-```
-
-What about dY / dW, the gradient for ``W``? Let's call it ``grad_W``. To start with, the shape of ``grad_W`` is the same as that of ``W`` as we are taking the derivatives with respect to ``W``, which is 2x3. Then we calculate each entry in ``grad_W`` as follows:
-
-```
-grad_W[0][0] = X[0][0] = 1
-grad_W[0][1] = X[0][0] = 1
-grad_W[0][2] = X[0][0] = 1
-grad_W[1][0] = X[0][1] = 0
-grad_W[1][1] = X[0][1] = 0
-grad_W[1][2] = X[0][1] = 0
-```
-
-As a matter of fact, you can calculate ``grad_W`` by multiplying the transpose of ``X`` with a matrix of ones:
-
-
-```python
-grad_W = mx.nd.dot(X, mx.nd.ones_like(Y), transpose_a=True)
-grad_W
-```
-
-
-
-
-    
-    [[ 1.  1.  1.]
-     [ 0.  0.  0.]]
-    <NDArray 2x3 @cpu(0)>
-
-
-
-As you can see, row 0 of ``grad_W`` contains non-zero values while row 1 of ``grad_W`` does not. Why did that happen?
-If you look at how ``grad_W`` is calculated, notice that since column 1 of ``X`` is filled with zeros, row 1 of ``grad_W`` is filled with zeros too.
-
-In the real world, gradients for parameters that interact with sparse inputs ususally have gradients where many row slices are completely zeros. Storing and manipulating such sparse matrices with many row slices of all zeros in the default dense structure results in wasted memory and processing on the zeros. More importantly, many gradient based optimization methods such as SGD, [AdaGrad](https://stanford.edu/~jduchi/projects/DuchiHaSi10_colt.pdf) and [Adam](https://arxiv.org/pdf/1412.6980.pdf)
-take advantage of sparse gradients and prove to be efficient and effective. 
-**In MXNet, the ``RowSparseNDArray`` stores the matrix in ``row sparse`` format, which is designed for arrays of which most row slices are all zeros.**
-In this tutorial, we will describe what the row sparse format is and how to use RowSparseNDArray for sparse gradient updates in MXNet.
-
-## Prerequisites
-
-To complete this tutorial, we need:
-
-- MXNet. See the instructions for your operating system in [Setup and Installation](https://mxnet.incubator.apache.org/install/index.html)
-- [Jupyter](http://jupyter.org/)
-    ```
-    pip install jupyter
-    ```
-- Basic knowledge of NDArray in MXNet. See the detailed tutorial for NDArray in [NDArray - Imperative tensor operations on CPU/GPU](https://mxnet.incubator.apache.org/tutorials/basic/ndarray.html)
-- Understanding of [automatic differentiation with autograd](http://gluon.mxnet.io/chapter01_crashcourse/autograd.html)
-- GPUs - A section of this tutorial uses GPUs. If you don't have GPUs on your
-machine, simply set the variable `gpu_device` (set in the GPUs section of this
-tutorial) to `mx.cpu()`
-
-## Row Sparse Format
-
-A RowSparseNDArray represents a multidimensional NDArray of shape `[LARGE0, D1, .. , Dn]` using two separate 1D arrays:
-`data` and `indices`.
-
-- data: an NDArray of any dtype with shape `[D0, D1, ..., Dn]`.
-- indices: a 1D int64 NDArray with shape `[D0]` with values sorted in ascending order.
-
-The ``indices`` array stores the indices of the row slices with **non-zeros**,
-while the values are stored in ``data`` array. The corresponding NDArray `dense` represented by RowSparseNDArray `rsp` has
-
-``dense[rsp.indices[i], :, :, :, ...] = rsp.data[i, :, :, :, ...]``
-
-A RowSparseNDArray is typically used to represent non-zero row slices of a large NDArray of shape `[LARGE0, D1, .. , Dn]` where LARGE0 >> D0 and most row slices are zeros.
-
-Given this two-dimension matrix:
-
-
-```python
-[[ 1, 2, 3],
- [ 0, 0, 0],
- [ 4, 0, 5],
- [ 0, 0, 0],
- [ 0, 0, 0]]
-```
-
-The row sparse representation would be:
-- `data` array holds all the non-zero row slices of the array.
-- `indices` array stores the row index for each row slice with non-zero elements.
-
-
-```python
-data = [[1, 2, 3], [4, 0, 5]]
-indices = [0, 2]
-```
-
-`RowSparseNDArray` supports multidimensional arrays. Given this 3D tensor:
-
-
-```python
-[[[1, 0],
-  [0, 2],
-  [3, 4]],
-
- [[5, 0],
-  [6, 0],
-  [0, 0]],
-
- [[0, 0],
-  [0, 0],
-  [0, 0]]]
-```
-
-The row sparse representation would be (with `data` and `indices` defined the same as above):
-
-
-```python
-data = [[[1, 0], [0, 2], [3, 4]], [[5, 0], [6, 0], [0, 0]]]
-indices = [0, 1]
-```
-
-``RowSparseNDArray`` is a subclass of ``NDArray``. If you query **stype** of a RowSparseNDArray,
-the value will be **"row_sparse"**.
-
-## Array Creation
-
-You can create a `RowSparseNDArray` with data and indices by using the `row_sparse_array` function:
-
-
-```python
-import mxnet as mx
-import numpy as np
-# Create a RowSparseNDArray with python lists
-shape = (6, 2)
-data_list = [[1, 2], [3, 4]]
-indices_list = [1, 4]
-a = mx.nd.sparse.row_sparse_array((data_list, indices_list), shape=shape)
-# Create a RowSparseNDArray with numpy arrays
-data_np = np.array([[1, 2], [3, 4]])
-indices_np = np.array([1, 4])
-b = mx.nd.sparse.row_sparse_array((data_np, indices_np), shape=shape)
-{'a':a, 'b':b}
-```
-
-
-
-
-`{'a':  <RowSparseNDArray 6x2 @cpu(0)>, 'b':   <RowSparseNDArray 6x2 @cpu(0)>}`<!--notebook-skip-line-->
-
-
-
-## Function Overview
-
-Similar to `CSRNDArray`, the are several functions with `RowSparseNDArray` that behave the same way. In the code blocks below you can try out these common functions:
-
-- **.dtype** - to set the data type
-- **.asnumpy** - to cast as a numpy array for inspecting it
-- **.data** - to access the data array
-- **.indices** - to access the indices array
-- **.tostype** - to set the storage type
-- **.cast_storage** - to convert the storage type
-- **.copy** - to copy the array
-- **.copyto** - to copy to deep copy an existing array
-
-
-## Setting Type
-
-You can create a `RowSparseNDArray` from another specifying the element data type with the option `dtype`, which accepts a numpy type. By default, `float32` is used.
-
-
-```python
-# Float32 is used by default
-c = mx.nd.sparse.array(a)
-# Create a 16-bit float array
-d = mx.nd.array(a, dtype=np.float16)
-(c.dtype, d.dtype)
-```
-
-
-
-
-`(numpy.float32, numpy.float16)`<!--notebook-skip-line-->
-
-
-
-## Inspecting Arrays
-
-As with `CSRNDArray`, you can inspect the contents of a `RowSparseNDArray` by filling
-its contents into a dense `numpy.ndarray` using the `asnumpy` function.
-
-
-```python
-a.asnumpy()
-```
-
-
-
-
-    array([[ 0.,  0.],
-           [ 1.,  2.],
-           [ 0.,  0.],
-           [ 0.,  0.],
-           [ 3.,  4.],
-           [ 0.,  0.]], dtype=float32)
-
-
-
-You can inspect the internal storage of a RowSparseNDArray by accessing attributes such as `indices` and `data`:
-
-
-```python
-# Access data array
-data = a.data
-# Access indices array
-indices = a.indices
-{'a.stype': a.stype, 'data':data, 'indices':indices}
-```
-
-
-
-
-    {'a.stype': 'row_sparse', 'data': 
-     [[ 1.  2.]
-      [ 3.  4.]]
-     <NDArray 2x2 @cpu(0)>, 'indices': 
-     [1 4]
-     <NDArray 2 @cpu(0)>}
-
-
-
-## Storage Type Conversion
-
-You can convert an NDArray to a RowSparseNDArray and vice versa by using the `tostype` function:
-
-
-```python
-# Create a dense NDArray
-ones = mx.nd.ones((2,2))
-# Cast the storage type from `default` to `row_sparse`
-rsp = ones.tostype('row_sparse')
-# Cast the storage type from `row_sparse` to `default`
-dense = rsp.tostype('default')
-{'rsp':rsp, 'dense':dense}
-```
-
-
-
-
-    {'dense': 
-     [[ 1.  1.]
-      [ 1.  1.]]
-     <NDArray 2x2 @cpu(0)>, 'rsp': 
-     <RowSparseNDArray 2x2 @cpu(0)>}
-
-
-
-You can also convert the storage type by using the `cast_storage` operator:
-
-
-```python
-# Create a dense NDArray
-ones = mx.nd.ones((2,2))
-# Cast the storage type to `row_sparse`
-rsp = mx.nd.sparse.cast_storage(ones, 'row_sparse')
-# Cast the storage type to `default`
-dense = mx.nd.sparse.cast_storage(rsp, 'default')
-{'rsp':rsp, 'dense':dense}
-```
-
-
-
-
-    {'dense': 
-     [[ 1.  1.]
-      [ 1.  1.]]
-     <NDArray 2x2 @cpu(0)>, 'rsp': 
-     <RowSparseNDArray 2x2 @cpu(0)>}
-
-
-
-## Copies
-
-You can use the `copy` method which makes a deep copy of the array and its data, and returns a new array.
-We can also use the `copyto` method or the slice operator `[]` to deep copy to an existing array.
-
-
-```python
-a = mx.nd.ones((2,2)).tostype('row_sparse')
-b = a.copy()
-c = mx.nd.sparse.zeros('row_sparse', (2,2))
-c[:] = a
-d = mx.nd.sparse.zeros('row_sparse', (2,2))
-a.copyto(d)
-{'b is a': b is a, 'b.asnumpy()':b.asnumpy(), 'c.asnumpy()':c.asnumpy(), 'd.asnumpy()':d.asnumpy()}
-```
-
-
-
-
-    {'b is a': False, 'b.asnumpy()': array([[ 1.,  1.],
-            [ 1.,  1.]], dtype=float32), 'c.asnumpy()': array([[ 1.,  1.],
-            [ 1.,  1.]], dtype=float32), 'd.asnumpy()': array([[ 1.,  1.],
-            [ 1.,  1.]], dtype=float32)}
-
-
-
-If the storage types of source array and destination array do not match,
-the storage type of destination array will not change when copying with `copyto` or the slice operator `[]`. The source array will be temporarily converted to desired storage type before the copy.
-
-
-```python
-e = mx.nd.sparse.zeros('row_sparse', (2,2))
-f = mx.nd.sparse.zeros('row_sparse', (2,2))
-g = mx.nd.ones(e.shape)
-e[:] = g
-g.copyto(f)
-{'e.stype':e.stype, 'f.stype':f.stype, 'g.stype':g.stype}
-```
-
-
-
-
-`{'e.stype': 'row_sparse', 'f.stype': 'row_sparse', 'g.stype': 'default'}`<!--notebook-skip-line-->
-
-
-
-## Retain Row Slices
-
-You can retain a subset of row slices from a RowSparseNDArray specified by their row indices.
-
-
-```python
-data = [[1, 2], [3, 4], [5, 6]]
-indices = [0, 2, 3]
-rsp = mx.nd.sparse.row_sparse_array((data, indices), shape=(5, 2))
-# Retain row 0 and row 1
-rsp_retained = mx.nd.sparse.retain(rsp, mx.nd.array([0, 1]))
-{'rsp.asnumpy()': rsp.asnumpy(), 'rsp_retained': rsp_retained, 'rsp_retained.asnumpy()': rsp_retained.asnumpy()}
-```
-
-
-
-
-    {'rsp.asnumpy()': array([[ 1.,  2.],
-            [ 0.,  0.],
-            [ 3.,  4.],
-            [ 5.,  6.],
-            [ 0.,  0.]], dtype=float32), 'rsp_retained': 
-     <RowSparseNDArray 5x2 @cpu(0)>, 'rsp_retained.asnumpy()': array([[ 1.,  2.],
-            [ 0.,  0.],
-            [ 0.,  0.],
-            [ 0.,  0.],
-            [ 0.,  0.]], dtype=float32)}
-
-
-
-## Sparse Operators and Storage Type Inference
-
-Operators that have specialized implementation for sparse arrays can be accessed in ``mx.nd.sparse``. You can read the [mxnet.ndarray.sparse API documentation](http://mxnet.incubator.apache.org/api/python/ndarray/sparse.html) to find what sparse operators are available.
-
-
-```python
-shape = (3, 5)
-data = [7, 8, 9]
-indptr = [0, 2, 2, 3]
-indices = [0, 2, 1]
-# A csr matrix as lhs
-lhs = mx.nd.sparse.csr_matrix((data, indices, indptr), shape=shape)
-# A dense matrix as rhs
-rhs = mx.nd.ones((3, 2))
-# row_sparse result is inferred from sparse operator dot(csr.T, dense) based on input stypes
-transpose_dot = mx.nd.sparse.dot(lhs, rhs, transpose_a=True)
-{'transpose_dot': transpose_dot, 'transpose_dot.asnumpy()': transpose_dot.asnumpy()}
-```
-
-
-
-
-    {'transpose_dot': 
-     <RowSparseNDArray 5x2 @cpu(0)>, 'transpose_dot.asnumpy()': array([[ 7.,  7.],
-            [ 9.,  9.],
-            [ 8.,  8.],
-            [ 0.,  0.],
-            [ 0.,  0.]], dtype=float32)}
-
-
-
-For any sparse operator, the storage type of output array is inferred based on inputs. You can either read the documentation or inspect the `stype` attribute of output array to know what storage type is inferred:
-
-
-```python
-a = transpose_dot.copy()
-b = a * 2  # b will be a RowSparseNDArray since zero multiplied by 2 is still zero
-c = a + mx.nd.ones((5, 2))  # c will be a dense NDArray
-{'b.stype':b.stype, 'c.stype':c.stype}
-```
-
-
-
-
-`{'b.stype': 'row_sparse', 'c.stype': 'default'}`<!--notebook-skip-line-->
-
-
-
-For operators that don't specialize in sparse arrays, you can still use them with sparse inputs with some performance penalty.
-In MXNet, dense operators require all inputs and outputs to be in the dense format.
-
-If sparse inputs are provided, MXNet will convert sparse inputs into dense ones temporarily so that the dense operator can be used.
-
-If sparse outputs are provided, MXNet will convert the dense outputs generated by the dense operator into the provided sparse format.
-
-For operators that don't specialize in sparse arrays, you can still use them with sparse inputs with some performance penalty.
-
-
-```python
-e = mx.nd.sparse.zeros('row_sparse', a.shape)
-d = mx.nd.log(a) # dense operator with a sparse input
-e = mx.nd.log(a, out=e) # dense operator with a sparse output
-{'a.stype':a.stype, 'd.stype':d.stype, 'e.stype':e.stype} # stypes of a and e will be not changed
-```
-
-
-
-
-`{'a.stype': 'row_sparse', 'd.stype': 'default', 'e.stype': 'row_sparse'}` <!--notebook-skip-line-->
-
-
-
-Note that warning messages will be printed when such a storage fallback event happens. If you are using jupyter notebook, the warning message will be printed in your terminal console.
-
-## Sparse Optimizers
-
-In MXNet, sparse gradient updates are applied when gradient is in `row_sparse` storage and the optimizer is created with `lazy_update=True`.
-The sparse optimizers only update the row slices of the weight and the states whose indices appear
-in `gradient.indices`. For example, the default update rule for SGD optimizer is:
-
-```
-rescaled_grad = learning_rate * rescale_grad * clip(grad, clip_gradient) + weight_decay * weight
-state = momentum * state + rescaled_grad
-weight = weight - state
-```
-
-However, with sparse gradient the SGD optimizer uses the following lazy update by default:
-
-```
-for row in grad.indices:
-    rescaled_grad[row] = learning_rate * rescale_grad * clip(grad[row], clip_gradient) + weight_decay * weight[row]
-    state[row] = momentum[row] * state[row] + rescaled_grad[row]
-    weight[row] = weight[row] - state[row]
-```
-
-This means that the lazy update leads to different optimization results if `weight_decay` or `momentum` is non-zero.
-To disable lazy update, please set `lazy_update` to be False when creating the optimizer.
-
-
-```python
-# Create weight
-shape = (4, 2)
-weight = mx.nd.ones(shape).tostype('row_sparse')
-# Create gradient
-data = [[1, 2], [4, 5]]
-indices = [1, 2]
-grad = mx.nd.sparse.row_sparse_array((data, indices), shape=shape)
-sgd = mx.optimizer.SGD(learning_rate=0.01, momentum=0.01)
-# Create momentum
-momentum = sgd.create_state(0, weight)
-# Before the update
-{"grad.asnumpy()":grad.asnumpy(), "weight.asnumpy()":weight.asnumpy(), "momentum.asnumpy()":momentum.asnumpy()}
-```
-
-
-
-
-    {'grad.asnumpy()': array([[ 0.,  0.],
-            [ 1.,  2.],
-            [ 4.,  5.],
-            [ 0.,  0.]], dtype=float32), 'momentum.asnumpy()': array([[ 0.,  0.],
-            [ 0.,  0.],
-            [ 0.,  0.],
-            [ 0.,  0.]], dtype=float32), 'weight.asnumpy()': array([[ 1.,  1.],
-            [ 1.,  1.],
-            [ 1.,  1.],
-            [ 1.,  1.]], dtype=float32)}
-
-
-
-
-```python
-sgd.update(0, weight, grad, momentum)
-# Only row 0 and row 2 are updated for both weight and momentum
-{"weight.asnumpy()":weight.asnumpy(), "momentum.asnumpy()":momentum.asnumpy()}
-```
-
-
-
-
-    {'momentum.asnumpy()': array([[ 0.  ,  0.  ],
-            [-0.01, -0.02],
-            [-0.04, -0.05],
-            [ 0.  ,  0.  ]], dtype=float32),
-     'weight.asnumpy()': array([[ 1.        ,  1.        ],
-            [ 0.99000001,  0.98000002],
-            [ 0.95999998,  0.94999999],
-            [ 1.        ,  1.        ]], dtype=float32)}
-
-
-
-Note that only [mxnet.optimizer.SGD](https://mxnet.incubator.apache.org/api/python/optimization/optimization.html#mxnet.optimizer.SGD), [mxnet.optimizer.Adam](https://mxnet.incubator.apache.org/api/python/optimization/optimization.html#mxnet.optimizer.Adam), and
-[mxnet.optimizer.AdaGrad](https://mxnet.incubator.apache.org/api/python/optimization/optimization.html#mxnet.optimizer.AdaGrad) support sparse updates in MXNet.
-
-## Advanced Topics
-
-### GPU Support
-
-By default, RowSparseNDArray operators are executed on CPU. To create a RowSparseNDArray on gpu, we need to explicitly specify the context:
-
-**Note** If a GPU is not available, an error will be reported in the following section. In order to execute it on a cpu, set gpu_device to mx.cpu().
-
-
-```python
-import sys
-gpu_device=mx.gpu() # Change this to mx.cpu() in absence of GPUs.
-try:
-    a = mx.nd.sparse.zeros('row_sparse', (100, 100), ctx=gpu_device)
-    a
-except mx.MXNetError as err:
-    sys.stderr.write(str(err))
-```
-
-## Next 
-
-[Train a Linear Regression Model with Sparse Symbols](http://mxnet.incubator.apache.org/tutorials/sparse/train.html)
-
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/sparse/train.md b/docs/tutorials/sparse/train.md
deleted file mode 100644
index 5eec460c6630..000000000000
--- a/docs/tutorials/sparse/train.md
+++ /dev/null
@@ -1,338 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-
-# Train a Linear Regression Model with Sparse Symbols
-In previous tutorials, we introduced `CSRNDArray` and `RowSparseNDArray`,
-the basic data structures for manipulating sparse data.
-MXNet also provides `Sparse Symbol` API, which enables symbolic expressions that handle sparse arrays.
-In this tutorial, we first focus on how to compose a symbolic graph with sparse operators,
-then train a linear regression model using sparse symbols with the Module API.
-
-## Prerequisites
-
-To complete this tutorial, we need:
-
-- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/install/index.html).  
-
-- [Jupyter Notebook](http://jupyter.org/index.html) and [Python Requests](http://docs.python-requests.org/en/master/) packages.
-```
-pip install jupyter requests
-```
-
-- Basic knowledge of Symbol in MXNet. See the detailed tutorial for Symbol in [Symbol - Neural Network Graphs and Auto-differentiation](https://mxnet.incubator.apache.org/tutorials/basic/symbol.html).
-
-- Basic knowledge of CSRNDArray in MXNet. See the detailed tutorial for CSRNDArray in [CSRNDArray - NDArray in Compressed Sparse Row Storage Format](https://mxnet.incubator.apache.org/versions/master/tutorials/sparse/csr.html).
-
-- Basic knowledge of RowSparseNDArray in MXNet. See the detailed tutorial for RowSparseNDArray in [RowSparseNDArray - NDArray for Sparse Gradient Updates](https://mxnet.incubator.apache.org/versions/master/tutorials/sparse/row_sparse.html).
-
-## Variables
-
-Variables are placeholder for arrays. We can use them to hold sparse arrays too.
-
-### Variable Storage Types
-
-The `stype` attribute of a variable is used to indicate the storage type of the array.
-By default, the `stype` of a variable is "default" which indicates the default dense storage format.
-We can specify the `stype` of a variable as "csr" or "row_sparse" to hold sparse arrays.
-
-
-```python
-import mxnet as mx
-import numpy as np
-import random
-
-# set the seeds for repeatability
-random.seed(42)
-np.random.seed(42)
-mx.random.seed(42)
-
-# Create a variable to hold an NDArray
-a = mx.sym.Variable('a')
-# Create a variable to hold a CSRNDArray
-b = mx.sym.Variable('b', stype='csr')
-# Create a variable to hold a RowSparseNDArray
-c = mx.sym.Variable('c', stype='row_sparse')
-(a, b, c)
-```
-
-
-
-
-`(<Symbol a>, <Symbol b>, <Symbol c>)` <!--notebook-skip-line-->
-
-
-
-### Bind with Sparse Arrays
-
-The sparse symbols constructed above declare storage types of the arrays to hold.
-To evaluate them, we need to feed the free variables with sparse data.
-
-You can instantiate an executor from a sparse symbol by using the `simple_bind` method,
-which allocate zeros to all free variables according to their storage types.
-The executor provides `forward` method for evaluation and an attribute
-`outputs` to get all the results. Later, we will show the use of the `backward` method and other methods computing the gradients and updating parameters. A simple example first:
-
-
-```python
-shape = (2,2)
-# Instantiate an executor from sparse symbols
-b_exec = b.simple_bind(ctx=mx.cpu(), b=shape)
-c_exec = c.simple_bind(ctx=mx.cpu(), c=shape)
-b_exec.forward()
-c_exec.forward()
-# Sparse arrays of zeros are bound to b and c
-print(b_exec.outputs, c_exec.outputs)
-```
-
-    ([
-    <CSRNDArray 2x2 @cpu(0)>], [
-    <RowSparseNDArray 2x2 @cpu(0)>])
-
-
-You can update the array held by the variable by accessing executor's `arg_dict` and assigning new values.
-
-
-```python
-b_exec.arg_dict['b'][:] = mx.nd.ones(shape).tostype('csr')
-b_exec.forward()
-# The array `b` holds are updated to be ones
-eval_b = b_exec.outputs[0]
-{'eval_b': eval_b, 'eval_b.asnumpy()': eval_b.asnumpy()}
-```
-
-
-
-
-    {'eval_b':
-     <CSRNDArray 2x2 @cpu(0)>, 'eval_b.asnumpy()': array([[ 1.,  1.],
-            [ 1.,  1.]], dtype=float32)}
-
-
-
-## Symbol Composition and Storage Type Inference
-
-### Basic Symbol Composition
-
-The following example builds a simple element-wise addition expression with different storage types.
-The sparse symbols are available in the `mx.sym.sparse` package.
-
-
-```python
-# Element-wise addition of variables with "default" stype
-d = mx.sym.elemwise_add(a, a)
-# Element-wise addition of variables with "csr" stype
-e = mx.sym.sparse.negative(b)
-# Element-wise addition of variables with "row_sparse" stype
-f = mx.sym.sparse.elemwise_add(c, c)
-{'d':d, 'e':e, 'f':f}
-```
-
-
-
-
-    {'d': <Symbol elemwise_add0>,
-     'e': <Symbol negative0>,
-     'f': <Symbol elemwise_add1>}
-
-
-
-### Storage Type Inference
-
-What will be the output storage types of sparse symbols? In MXNet, for any sparse symbol, the result storage types are inferred based on storage types of inputs.
-You can read the [Sparse Symbol API](https://mxnet.incubator.apache.org/versions/master/api/python/symbol/sparse.html) documentation to find what output storage types are. In the example below we will try out the storage types introduced in the Row Sparse and Compressed Sparse Row tutorials: `default` (dense), `csr`, and `row_sparse`.
-
-
-```python
-add_exec = mx.sym.Group([d, e, f]).simple_bind(ctx=mx.cpu(), a=shape, b=shape, c=shape)
-add_exec.forward()
-dense_add = add_exec.outputs[0]
-# The output storage type of elemwise_add(csr, csr) will be inferred as "csr"
-csr_add = add_exec.outputs[1]
-# The output storage type of elemwise_add(row_sparse, row_sparse) will be inferred as "row_sparse"
-rsp_add = add_exec.outputs[2]
-{'dense_add.stype': dense_add.stype, 'csr_add.stype':csr_add.stype, 'rsp_add.stype': rsp_add.stype}
-```
-
-
-
-
-    {'csr_add.stype': 'csr',
-     'dense_add.stype': 'default',
-     'rsp_add.stype': 'row_sparse'}
-
-
-
-### Storage Type Fallback
-
-For operators that don't specialize in certain sparse arrays, you can still use them with sparse inputs with some performance penalty. In MXNet, dense operators require all inputs and outputs to be in the dense format. If sparse inputs are provided, MXNet will convert sparse inputs into dense ones temporarily so that the dense operator can be used. If sparse outputs are provided, MXNet will convert the dense outputs generated by the dense operator into the provided sparse format. Warning messages will be printed when such a storage fallback event happens.
-
-
-```python
-# `log` operator doesn't support sparse inputs at all, but we can fallback on the dense implementation
-csr_log = mx.sym.log(a)
-# `elemwise_add` operator doesn't support adding csr with row_sparse, but we can fallback on the dense implementation
-csr_rsp_add = mx.sym.elemwise_add(b, c)
-fallback_exec = mx.sym.Group([csr_rsp_add, csr_log]).simple_bind(ctx=mx.cpu(), a=shape, b=shape, c=shape)
-fallback_exec.forward()
-fallback_add = fallback_exec.outputs[0]
-fallback_log = fallback_exec.outputs[1]
-{'fallback_add': fallback_add, 'fallback_log': fallback_log}
-```
-
-
-
-
-    {'fallback_add':
-     [[ 0.  0.]
-      [ 0.  0.]]
-     <NDArray 2x2 @cpu(0)>, 'fallback_log':
-     [[-inf -inf]
-      [-inf -inf]]
-     <NDArray 2x2 @cpu(0)>}
-
-
-
-### Inspecting Storage Types of the Symbol Graph
-
-When the environment variable `MXNET_INFER_STORAGE_TYPE_VERBOSE_LOGGING` is set to `1`, MXNet will log the storage type information of
-operators' inputs and outputs in the computation graph. For example, we can inspect the storage types of
-a linear classification network with sparse operators. Uncomment the line below and inspect your console.:
-
-
-```python
-# Set logging level for executor
-import mxnet as mx
-import os
-#os.environ['MXNET_INFER_STORAGE_TYPE_VERBOSE_LOGGING'] = "1"
-# Data in csr format
-data = mx.sym.var('data', stype='csr', shape=(32, 10000))
-# Weight in row_sparse format
-weight = mx.sym.var('weight', stype='row_sparse', shape=(10000, 2))
-bias = mx.symbol.Variable("bias", shape=(2,))
-dot = mx.symbol.sparse.dot(data, weight)
-pred = mx.symbol.broadcast_add(dot, bias)
-y = mx.symbol.Variable("label")
-output = mx.symbol.SoftmaxOutput(data=pred, label=y, name="output")
-executor = output.simple_bind(ctx=mx.cpu())
-```
-
-## Training with Module APIs
-
-In the following section we'll walk through how one can implement **linear regression** using sparse symbols and sparse optimizers.
-
-The function you will explore is: *y = x<sub>1</sub>  +  2x<sub>2</sub> + ... 100x<sub>100*, where *(x<sub>1</sub>,x<sub>2</sub>, ..., x<sub>100</sub>)* are input features and *y* is the corresponding label.
-
-### Preparing the Data
-
-In MXNet, both [mx.io.LibSVMIter](https://mxnet.incubator.apache.org/versions/master/api/python/io/io.html#mxnet.io.LibSVMIter)
-and [mx.io.NDArrayIter](https://mxnet.incubator.apache.org/versions/master/api/python/io/io.html#mxnet.io.NDArrayIter)
-support loading sparse data in CSR format. In this example, we'll use the `NDArrayIter`.
-
-You may see some warnings from SciPy. You don't need to worry about those for this example.
-
-
-```python
-# Random training data
-feature_dimension = 100
-train_data = mx.test_utils.rand_ndarray((1000, feature_dimension), 'csr', 0.01)
-target_weight = mx.nd.arange(1, feature_dimension + 1).reshape((feature_dimension, 1))
-train_label = mx.nd.dot(train_data, target_weight)
-batch_size = 1
-train_iter = mx.io.NDArrayIter(train_data, train_label, batch_size, last_batch_handle='discard', label_name='label')
-```
-
-### Defining the Model
-
-Below is an example of a linear regression model specifying the storage type of the variables.
-
-
-```python
-initializer = mx.initializer.Normal(sigma=0.01)
-X = mx.sym.Variable('data', stype='csr')
-Y = mx.symbol.Variable('label')
-weight = mx.symbol.Variable('weight', stype='row_sparse', shape=(feature_dimension, 1), init=initializer)
-bias = mx.symbol.Variable('bias', shape=(1, ))
-pred = mx.sym.broadcast_add(mx.sym.sparse.dot(X, weight), bias)
-lro = mx.sym.LinearRegressionOutput(data=pred, label=Y, name="lro")
-```
-
-The above network uses the following symbols:
-
-1. `Variable X`: The placeholder for sparse data inputs. The `csr` stype indicates that the array to hold is in CSR format.
-
-2. `Variable Y`: The placeholder for dense labels.
-
-3. `Variable weight`: The placeholder for the weight to learn. The `stype` of weight is specified as `row_sparse` so that it is initialized as RowSparseNDArray,
-   and the optimizer will perform sparse update rules on it. The `init` attribute specifies what initializer to use for this variable.
-
-4. `Variable bias`: The placeholder for the bias to learn.
-
-5. `sparse.dot`: The dot product operation of `X` and `weight`. The sparse implementation will be invoked to handle `csr` and `row_sparse` inputs.
-
-6. `broadcast_add`: The broadcasting add operation to apply `bias`.
-
-7. `LinearRegressionOutput`: The output layer which computes *l2* loss against its input and the labels provided to it.
-
-### Training the model
-
-Once we have defined the model structure, the next step is to create a module and initialize the parameters and optimizer.
-
-
-```python
-# Create module
-mod = mx.mod.Module(symbol=lro, data_names=['data'], label_names=['label'])
-# Allocate memory by giving the input data and label shapes
-mod.bind(data_shapes=train_iter.provide_data, label_shapes=train_iter.provide_label)
-# Initialize parameters by random numbers
-mod.init_params(initializer=initializer)
-# Use SGD as the optimizer, which performs sparse update on "row_sparse" weight
-sgd = mx.optimizer.SGD(learning_rate=0.05, rescale_grad=1.0/batch_size, momentum=0.9)
-mod.init_optimizer(optimizer=sgd)
-```
-
-Finally, we train the parameters of the model to fit the training data by using the `forward`, `backward`, and `update` methods in Module.
-
-
-```python
-# Use mean square error as the metric
-metric = mx.metric.create('MSE')
-# Train 10 epochs
-for epoch in range(10):
-    train_iter.reset()
-    metric.reset()
-    for batch in train_iter:
-        mod.forward(batch, is_train=True)       # compute predictions
-        mod.update_metric(metric, batch.label)  # accumulate prediction accuracy
-        mod.backward()                          # compute gradients
-        mod.update()                            # update parameters
-    print('Epoch %d, Metric = %s' % (epoch, metric.get()))
-assert metric.get()[1] < 1, "Achieved MSE (%f) is larger than expected (1.0)" % metric.get()[1]    
-```
-
-`Epoch 9, Metric = ('mse', 0.35979430613957991)`<!--notebook-skip-line-->
-
-
-
-
-### Training the model with multiple machines or multiple devices
-
-Distributed training with `row_sparse` weights and gradients are supported in MXNet, which significantly reduces communication cost for large models. To train a sparse model with multiple machines, you need to call `prepare` before `forward`, or `save_checkpoint`.
-Please refer to the example in [mxnet/example/sparse/linear_classification](https://github.com/apache/incubator-mxnet/tree/master/example/sparse/linear_classification)
-for more details.
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/speech_recognition/ctc.md b/docs/tutorials/speech_recognition/ctc.md
deleted file mode 100644
index 948e681de52f..000000000000
--- a/docs/tutorials/speech_recognition/ctc.md
+++ /dev/null
@@ -1,43 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Connectionist Temporal Classification
-
-```python
-
-import mxnet as mx
-print(mx.__version__)
-```
-
-`1.1.0`<!--notebook-skip-line-->
-
-
-[Connectionist Temporal Classification](https://www.cs.toronto.edu/~graves/icml_2006.pdf) (CTC) is a cost function that is used to train Recurrent Neural Networks (RNNs) to label unsegmented input sequence data in supervised learning. For example, in a speech recognition application, using a typical cross-entropy loss, the input signal needs to be segmented into words or sub-words. However, using CTC-loss, it suffices to provide one label sequence for input sequence and the network learns both the alignment as well labeling. Baidu's warp-ctc page contains a more detailed [introduction to CTC-loss](https://github.com/baidu-research/warp-ctc#introduction).
-
-## CTC-loss in MXNet
-MXNet supports two CTC-loss layers in Symbol API:
-
-* `mxnet.symbol.contrib.ctc_loss` is implemented in MXNet and included as part of the standard package.
-* `mxnet.symbol.WarpCTC` uses Baidu's warp-ctc library and requires building warp-ctc library and mxnet library both from source.
-
-## LSTM OCR Example
-MXNet's example folder contains a [CTC example](https://github.com/apache/incubator-mxnet/tree/master/example/ctc) for using CTC loss with an LSTM network to perform Optical Character Recognition (OCR) prediction on CAPTCHA images. The example demonstrates use of both CTC loss options, as well as inference after training using network symbol and parameter checkpoints.
-
-## Next Steps
-* [MXNet tutorials index](http://mxnet.io/tutorials/index.html)
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
\ No newline at end of file
diff --git a/docs/tutorials/speech_recognition/index.md b/docs/tutorials/speech_recognition/index.md
deleted file mode 100644
index faf6526fb824..000000000000
--- a/docs/tutorials/speech_recognition/index.md
+++ /dev/null
@@ -1,25 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Tutorials
-
-```eval_rst
-.. toctree::
-   :glob:
-
-   *
-```
diff --git a/docs/tutorials/tensorrt/index.md b/docs/tutorials/tensorrt/index.md
deleted file mode 100644
index bc10874886ef..000000000000
--- a/docs/tutorials/tensorrt/index.md
+++ /dev/null
@@ -1,25 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Tutorials
-
-```eval_rst
-.. toctree::
-   :glob:
-
-   *
-```
\ No newline at end of file
diff --git a/docs/tutorials/tensorrt/inference_with_trt.md b/docs/tutorials/tensorrt/inference_with_trt.md
deleted file mode 100644
index 409c96e4ab2a..000000000000
--- a/docs/tutorials/tensorrt/inference_with_trt.md
+++ /dev/null
@@ -1,143 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Optimizing Deep Learning Computation Graphs with TensorRT
-
-NVIDIA's TensorRT is a deep learning library that has been shown to provide large speedups when used for network inference. MXNet 1.3.0 is shipping with experimental integrated support for TensorRT. This means MXNet users can noew make use of this acceleration library to efficiently run their networks. In this blog post we'll see how to install, enable and run TensorRT with MXNet.  We'll also give some insight into what is happening behind the scenes in MXNet to enable TensorRT graph execution.
-
-## Installation and Prerequisites
-Installing MXNet with TensorRT integration is an easy process. First ensure that you are running Ubuntu 16.04, that you have updated your video drivers, and you have installed CUDA 9.0 or 9.2.  You'll need a Pascal or newer generation NVIDIA gpu.  You'll also have to download and install TensorRT libraries [instructions here](https://docs.nvidia.com/deeplearning/sdk/tensorrt-install-guide/index.html).  Once your these prerequisites installed and up-to-date you can install a special build of MXNet with TensorRT support enabled via PyPi and pip.  Install the appropriate version by running:
-
-To install with CUDA 9.0:
-```
-pip install mxnet-tensorrt-cu90
-```
-
-To install with CUDA 9.2:
-```
-pip install mxnet-tensorrt-cu92
-```
-
-If you are running an operating system other than Ubuntu 16.04, or just prefer to use a docker image with all prerequisites installed you can instead run:
-```
-nvidia-docker run -ti mxnet/tensorrt python
-```
-
-## Sample Models
-### Resnet 18
-TensorRT is an inference only library, so for the purposes of this blog post we will be using a pre-trained network, in this case a Resnet 18.  Resnets are a computationally intensive model architecture that are often used as a backbone for various computer vision tasks. Resnets are also commonly used as a reference for benchmarking deep learning library performance.  In this section we'll use a pretrained Resnet 18 from the [Gluon Model Zoo](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/model_zoo.html) and compare its inference speed with TensorRT using MXNet with TensorRT integration turned off as a baseline.
-
-## Model Initialization
-```python
-import mxnet as mx
-from mxnet.gluon.model_zoo import vision
-import time
-import os
-
-batch_shape = (1, 3, 224, 224)
-resnet18 = vision.resnet18_v2(pretrained=True)
-resnet18.hybridize()
-resnet18.forward(mx.nd.zeros(batch_shape))
-resnet18.export('resnet18_v2')
-sym, arg_params, aux_params = mx.model.load_checkpoint('resnet18_v2', 0)
-```
-In our first section of code we import the modules needed to run MXNet, and to time our benchmark runs.  We then download a pretrained version of Resnet18, hybridize it, and load it symbolically.  It's important to note that the experimental version of TensorRT integration will only work with the symbolic MXNet API. If you're using Gluon, you must [hybridize](https://gluon.mxnet.io/chapter07_distributed-learning/hybridize.html) your computation graph and export it as a symbol before running inference.  This may be addressed in future releases of MXNet, but in general if you're concerned about getting the best inference performance possible from your models, it's a good practice to hybridize.
-
-## MXNet Baseline Performance
-```python
-# Create sample input
-input = mx.nd.zeros(batch_shape)
-
-# Execute with MXNet
-os.environ['MXNET_USE_TENSORRT'] = '0'
-executor = sym.simple_bind(ctx=mx.gpu(0), data=batch_shape, grad_req='null', force_rebind=True)
-executor.copy_params_from(arg_params, aux_params)
-
-# Warmup
-print('Warming up MXNet')
-for i in range(0, 10):
-    y_gen = executor.forward(is_train=False, data=input)
-    y_gen[0].wait_to_read()
-
-# Timing
-print('Starting MXNet timed run')
-start = time.process_time()
-for i in range(0, 10000):
-    y_gen = executor.forward(is_train=False, data=input)
-    y_gen[0].wait_to_read()
-end = time.time()
-print(time.process_time() - start)
-```
-
-For this experiment we are strictly interested in inference performance, so to simplify the benchmark we'll pass a tensor filled with zeros as an input.  We then bind a symbol as usual, returning a normal MXNet executor, and we run forward on this executor in a loop.  To help improve the accuracy of our benchmarks we run a small number of predictions as a warmup before running our timed loop.  This will ensure various lazy operations, which do not represent real-world usage, have completed before we measure relative performance improvement.  On a modern PC with a Titan V GPU the time taken for our MXNet baseline is **33.73s**.  Next we'll run the same model with TensorRT enabled, and see how the performance compares.
-
-While TensorRT integration remains experimental, we require users to set an environment variable to enable graph compilation.  You can see that at the start of this test we explicitly disabled TensorRT graph compilation support.  Next, we will run the same predictions using TensorRT.  This will require us to explicitly enable the MXNET_USE_TENSORRT environment variable, and we'll also use a slightly different API to bind our symbol.
-
-## MXNet with TensorRT Integration Performance
-```python
-# Execute with TensorRT
-print('Building TensorRT engine')
-os.environ['MXNET_USE_TENSORRT'] = '1'
-arg_params.update(aux_params)
-all_params = dict([(k, v.as_in_context(mx.gpu(0))) for k, v in arg_params.items()])
-executor = mx.contrib.tensorrt.tensorrt_bind(sym, ctx=mx.gpu(0), all_params=all_params,
-                                             data=batch_shape, grad_req='null', force_rebind=True)
-```
-
-Instead of calling simple_bind directly on our symbol to return an executor, we call an experimental API from the contrib module of MXNet. This call is meant to emulate the simple_bind call, and has many of the same arguments.  One difference to note is that this call takes params in the form of a single merged dictionary to assist with a tensor cleanup pass that we'll describe below.
-
-As TensorRT integration improves our goal is to gradually deprecate this tensorrt_bind call, and allow users to use TensorRT transparently (see the [Subgraph API](https://cwiki.apache.org/confluence/display/MXNET/MXNet+Graph+Optimization+and+Quantization+based+on+subgraph+and+MKL-DNN) for more information).  When this happens, the similarity between tensorrt_bind and simple_bind should make it easy to migrate your code.
-
-```
-#Warmup
-print('Warming up TensorRT')
-for i in range(0, 10):
-    y_gen = executor.forward(is_train=False, data=input)
-    y_gen[0].wait_to_read()
-
-# Timing
-print('Starting TensorRT timed run')
-start = time.process_time()
-for i in range(0, 10000):
-    y_gen = executor.forward(is_train=False, data=input)
-    y_gen[0].wait_to_read()
-end = time.time()
-print(time.process_time() - start)
-```
-We run timing with a warmup once more, and on the same machine, run in **18.99s**. A 1.8x speed improvement!  Speed improvements when using libraries like TensorRT can come from a variety of optimizations, but in this case our speedups are coming from a technique known as [operator fusion](http://dmlc.ml/2016/11/21/fusion-and-runtime-compilation-for-nnvm-and-tinyflow.html).
-
-## Operators and Subgraph Fusion
-
-Behind the scenes a number of interesting things are happening to make these optimizations possible, and most revolve around subgraphs and operator fusion.  As we can see in the images below, neural networks can be represented as computation graphs of operators (nodes in the graphs).  Operators can perform a variety of functions, but most run simple mathematics and linear algebra on tensors.  Often these operators run more efficiently if fused together into a large CUDA kernel that is executed on the GPU in a single call.  What the MXNet TensorRT integration enables is the ability to scan the entire computation graph, identify interesting subgraphs and optimize them with TensorRT.
-
-This means that when an MXNet computation graph is constructed, it will be parsed to determine if there are any sub-graphs that contain operator types that are supported by TensorRT.  If MXNet determines that there are one (or many) compatible subgraphs during the graph-parse, it will extract these graphs and replace them with special TensorRT nodes (visible in the diagrams below).  As the graph is executed, whenever a TensorRT node is reached the graph will make a library call to TensorRT.  TensorRT will then run its own implementation of the subgraph, potentially with many operators fused together into a single CUDA kernel.
-
-During this process MXNet will take care of passing along the input to the node and fetching the results.  MXNet will also attempt to remove any duplicated weights (parameters) during the graph initialization to keep memory usage low.  That is, if there are graph weights that are used only in the TensorRT sections of the graph, they will be removed from the MXNet set of parameters, and their memory will be freed.
-
-The examples below shows a Gluon implementation of a Wavenet before and after a TensorRT graph pass. You can see that for this network TensorRT supports a subset of the operators involved. This makes it an interesting example to visualize, as several subgraphs are extracted and replaced with special TensorRT nodes. The Resnet used as an example above would be less interesting to visualization. The entire Resnet graph is supported by TensorRT, and hence the optimized graph would be a single TensorRT node.  If your browser is unable to render svg files you can view the graphs in png format: [unoptimized](_static/tutorials/tensorrt/wavenet_unoptimized.png) and [optimized](_static/tutorials/tensorrt/wavenet_optimized.png).
-
-## Before
-![before](wavenet_unoptimized.svg)
-
-## After
-![after](wavenet_optimized.svg)
-
-## Future Work
-As mentioned above, MXNet developers are excited about the possibilities of [creating APIs](https://cwiki.apache.org/confluence/display/MXNET/MXNet+Graph+Optimization+and+Quantization+based+on+subgraph+and+MKL-DNN) that deal specifically with subgraphs.  As this work matures it will bring many improvements for TensorRT users.  We hope this will also be an opportunity for other acceleration libraries to integrate with MXNet.
-
-## Thanks
-Thank you to NVIDIA for contributing this feature, and specifically thanks to Marek Kolodziej and Clement Fuji-Tsang.  Thanks to Junyuan Xie and Jun Wu for the code reviews and design feedback, and to Aaron Markham for the copy review.
diff --git a/docs/tutorials/tensorrt/wavenet_optimized.svg b/docs/tutorials/tensorrt/wavenet_optimized.svg
deleted file mode 100644
index 22bdbcdf440b..000000000000
--- a/docs/tutorials/tensorrt/wavenet_optimized.svg
+++ /dev/null
@@ -1 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" id="export" class="graph" preserveAspectRatio="xMidYMid meet" style="" width="1460" height="3825"><rect id="background" fill="#fff" pointer-events="all" width="1460" height="3825"/><g id="origin" transform="translate(66.35292968750001, 66.35292968750001) scale(1)"><g id="clusters" class="clusters"/><g id="edge-paths" class="edge-paths"><defs><marker id="arrowhead-vee" viewBox="0 0 10 10" refX="9" refY="5" markerUnits="strokeWidth" markerWidth="8" markerHeight="6" orient="auto"><path d="M 0 0 L 10 5 L 0 10 L 4 5 z" style="stroke-width: 1; stroke-dasharray: 1, 0;"/></marker></defs><path class="edge-path" d="M650.9140625,22L650.9140625,28.166666666666668C650.9140625,34.333333333333336,650.9140625,46.666666666666664,650.9140625,59C650.9140625,71.33333333333333,650.9140625,83.66666666666667,650.9140625,89.83333333333333L650.9140625,96" marker-end="url(#arrowhead-vee)" id="edge-[0,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M650.9140625,170L650.9140625,176.16666666666666C650.9140625,182.33333333333334,650.9140625,194.66666666666666,650.9140625,207C650.9140625,219.33333333333334,650.9140625,231.66666666666666,650.9140625,237.83333333333334L650.9140625,244" marker-end="url(#arrowhead-vee)" id="edge-[2,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M682.9352692367504,296.00000381469727L690.530042072292,302.16667048136395C698.1248149078336,308.3333371480306,713.3143605789168,320.66667048136395,720.9091334144583,333.00000381469727C728.50390625,345.3333371480306,728.50390625,357.66667048136395,728.50390625,363.8333371480306L728.50390625,370.00000381469727" marker-end="url(#arrowhead-vee)" id="edge-[3,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M618.8928557632496,296.00000381469727L611.298082927708,302.16667048136395C603.7033100921664,308.3333371480306,588.5137644210832,320.66667048136395,580.9189915855417,338.25000381469727C573.32421875,355.8333371480306,573.32421875,378.66667048136395,573.32421875,401.50000381469727C573.32421875,424.3333371480306,573.32421875,447.16667048136395,583.2923583984375,464.75000381469727C593.260498046875,482.3333371480306,613.19677734375,494.66667048136395,623.1649169921875,500.8333371480306L633.133056640625,507.00000381469727" marker-end="url(#arrowhead-vee)" id="edge-[3,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M728.50390625,433.00000381469727L728.50390625,439.16667048136395C728.50390625,445.3333371480306,728.50390625,457.66667048136395,718.5357666015625,470.00000381469727C708.567626953125,482.3333371480306,688.63134765625,494.66667048136395,678.6632080078125,500.8333371480306L668.695068359375,507.00000381469727" marker-end="url(#arrowhead-vee)" id="edge-[4,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M686.3999837239584,529.0000038146973L706.2936062282987,535.1666704813639C726.187228732639,541.3333371480306,765.9744737413195,553.6666704813639,785.8680962456598,566.0000038146973C805.76171875,578.3333371480306,805.76171875,590.6666704813639,805.76171875,596.8333371480306L805.76171875,603.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[5,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M594.5234375,523.3265316031515L519.2278645833334,530.4387769717424C443.9322916666667,537.5510223403334,293.3411458333333,551.7755130775154,218.04557291666666,570.304425112773C142.75,588.8333371480306,142.75,611.6666704813639,142.75,634.5000038146973C142.75,657.3333371480306,142.75,680.1666704813639,230.921875,698.8127390216795C319.09375,717.458807561995,495.4375,731.9176113092925,583.609375,739.1470131829414L671.78125,746.3764150565901" marker-end="url(#arrowhead-vee)" id="edge-[5,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M594.5234375,522.5459402568208L504.6848958333333,529.7882841831336C414.8463541666667,537.0306281094463,235.16927083333334,551.5153159620718,145.33072916666666,570.1743265550512C55.4921875,588.8333371480306,55.4921875,611.6666704813639,55.4921875,634.5000038146973C55.4921875,657.3333371480306,55.4921875,680.1666704813639,55.4921875,699.5833371480306C55.4921875,719.0000038146973,55.4921875,735.0000038146973,55.4921875,751.0000038146973C55.4921875,767.0000038146973,55.4921875,783.0000038146973,55.4921875,802.4166704813639C55.4921875,821.8333371480306,55.4921875,844.6666704813639,55.4921875,867.5000038146973C55.4921875,890.3333371480306,55.4921875,913.1666704813639,55.4921875,932.5833371480306C55.4921875,952.0000038146973,55.4921875,968.0000038146973,55.4921875,984.0000038146973C55.4921875,1000.0000038146973,55.4921875,1016.0000038146973,55.4921875,1035.416670481364C55.4921875,1054.8333371480305,55.4921875,1077.666670481364,55.4921875,1100.5000038146973C55.4921875,1123.3333371480305,55.4921875,1146.166670481364,55.4921875,1165.5833371480305C55.4921875,1185.0000038146973,55.4921875,1201.0000038146973,55.4921875,1217.0000038146973C55.4921875,1233.0000038146973,55.4921875,1249.0000038146973,55.4921875,1268.416670481364C55.4921875,1287.8333371480305,55.4921875,1310.666670481364,55.4921875,1333.5000038146973C55.4921875,1356.3333371480305,55.4921875,1379.166670481364,55.4921875,1398.5833371480305C55.4921875,1418.0000038146973,55.4921875,1434.0000038146973,55.4921875,1450.0000038146973C55.4921875,1466.0000038146973,55.4921875,1482.0000038146973,55.4921875,1501.416670481364C55.4921875,1520.8333371480305,55.4921875,1543.666670481364,55.4921875,1566.5000038146973C55.4921875,1589.3333371480305,55.4921875,1612.166670481364,55.4921875,1631.5833371480305C55.4921875,1651.0000038146973,55.4921875,1667.0000038146973,55.4921875,1683.0000038146973C55.4921875,1699.0000038146973,55.4921875,1715.0000038146973,55.4921875,1734.416670481364C55.4921875,1753.8333371480305,55.4921875,1776.666670481364,55.4921875,1799.5000038146973C55.4921875,1822.3333371480305,55.4921875,1845.166670481364,55.4921875,1864.5833371480305C55.4921875,1884.0000038146973,55.4921875,1900.0000038146973,55.4921875,1916.0000038146973C55.4921875,1932.0000038146973,55.4921875,1948.0000038146973,55.4921875,1967.416670481364C55.4921875,1986.8333371480305,55.4921875,2009.666670481364,55.4921875,2032.5000038146973C55.4921875,2055.3333371480308,55.4921875,2078.166670481364,55.4921875,2095.7500038146973C55.4921875,2113.3333371480308,55.4921875,2125.666670481364,55.4921875,2131.8333371480308L55.4921875,2138.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[5,1]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M55.4921875,2201.0000038146973L55.4921875,2207.166670481364C55.4921875,2213.3333371480308,55.4921875,2225.666670481364,55.4921875,2239.8333371480308C55.4921875,2254.0000038146973,55.4921875,2270.0000038146973,55.4921875,2278.0000038146973L55.4921875,2286.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[6,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M55.4921875,2327.0000038146973L55.4921875,2335.0000038146973C55.4921875,2343.0000038146973,55.4921875,2359.0000038146973,132.40288628472223,2373.166670481364C209.31358506944446,2387.3333371480308,363.1349826388889,2399.666670481364,440.04568142361114,2405.8333371480308L516.9563802083334,2412.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[7,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M805.76171875,666.0000038146973L805.76171875,672.1666704813639C805.76171875,678.3333371480306,805.76171875,690.6666704813639,795.7935791015625,703.0000038146973C785.825439453125,715.3333371480306,765.88916015625,727.6666704813639,755.9210205078125,733.8333371480306L745.952880859375,740.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[8,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M762.9165852864584,762.0000038146973L782.3946804470487,768.1666704813639C801.872775607639,774.3333371480306,840.8289659288195,786.6666704813639,860.3070610894097,799.0000038146973C879.78515625,811.3333371480306,879.78515625,823.6666704813639,879.78515625,829.8333371480306L879.78515625,836.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[9,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M671.78125,757.2347347846451L608.8229166666666,764.1956129563205C545.8645833333334,771.1564911279958,419.9479166666667,785.0782474713466,356.9895833333333,803.4557923096886C294.03125,821.8333371480306,294.03125,844.6666704813639,294.03125,867.5000038146973C294.03125,890.3333371480306,294.03125,913.1666704813639,369.3268229166667,931.6955825166216C444.6223958333333,950.2244945518792,595.2135416666666,964.448985289061,670.5091145833334,971.5612306576521L745.8046875,978.6734760262431" marker-end="url(#arrowhead-vee)" id="edge-[9,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M671.78125,756.2237354392732L594.8190104166666,763.3531135018438C517.8567708333334,770.4824915644145,363.9322916666667,784.741247689556,286.9700520833333,803.2872924187932C210.0078125,821.8333371480306,210.0078125,844.6666704813639,210.0078125,867.5000038146973C210.0078125,890.3333371480306,210.0078125,913.1666704813639,210.0078125,932.5833371480306C210.0078125,952.0000038146973,210.0078125,968.0000038146973,210.0078125,984.0000038146973C210.0078125,1000.0000038146973,210.0078125,1016.0000038146973,210.0078125,1035.416670481364C210.0078125,1054.8333371480305,210.0078125,1077.666670481364,210.0078125,1100.5000038146973C210.0078125,1123.3333371480305,210.0078125,1146.166670481364,210.0078125,1165.5833371480305C210.0078125,1185.0000038146973,210.0078125,1201.0000038146973,210.0078125,1217.0000038146973C210.0078125,1233.0000038146973,210.0078125,1249.0000038146973,210.0078125,1268.416670481364C210.0078125,1287.8333371480305,210.0078125,1310.666670481364,210.0078125,1333.5000038146973C210.0078125,1356.3333371480305,210.0078125,1379.166670481364,210.0078125,1398.5833371480305C210.0078125,1418.0000038146973,210.0078125,1434.0000038146973,210.0078125,1450.0000038146973C210.0078125,1466.0000038146973,210.0078125,1482.0000038146973,210.0078125,1501.416670481364C210.0078125,1520.8333371480305,210.0078125,1543.666670481364,210.0078125,1566.5000038146973C210.0078125,1589.3333371480305,210.0078125,1612.166670481364,210.0078125,1631.5833371480305C210.0078125,1651.0000038146973,210.0078125,1667.0000038146973,210.0078125,1683.0000038146973C210.0078125,1699.0000038146973,210.0078125,1715.0000038146973,210.0078125,1734.416670481364C210.0078125,1753.8333371480305,210.0078125,1776.666670481364,210.0078125,1799.5000038146973C210.0078125,1822.3333371480305,210.0078125,1845.166670481364,210.0078125,1864.5833371480305C210.0078125,1884.0000038146973,210.0078125,1900.0000038146973,210.0078125,1916.0000038146973C210.0078125,1932.0000038146973,210.0078125,1948.0000038146973,210.0078125,1967.416670481364C210.0078125,1986.8333371480305,210.0078125,2009.666670481364,210.0078125,2032.5000038146973C210.0078125,2055.3333371480308,210.0078125,2078.166670481364,210.0078125,2101.0000038146973C210.0078125,2123.8333371480308,210.0078125,2146.666670481364,210.0078125,2169.5000038146973C210.0078125,2192.3333371480308,210.0078125,2215.166670481364,210.0078125,2232.7500038146973C210.0078125,2250.3333371480308,210.0078125,2262.666670481364,210.0078125,2268.8333371480308L210.0078125,2275.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[9,1]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M210.0078125,2338.0000038146973L210.0078125,2344.166670481364C210.0078125,2350.3333371480308,210.0078125,2362.666670481364,267.0675455729167,2375.0000038146973C324.1272786458333,2387.3333371480308,438.2467447916667,2399.666670481364,495.3064778645833,2405.8333371480308L552.3662109375,2412.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[10,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M879.78515625,899.0000038146973L879.78515625,905.1666704813639C879.78515625,911.3333371480306,879.78515625,923.6666704813639,869.8170166015625,936.0000038146973C859.848876953125,948.3333371480306,839.91259765625,960.6666704813639,829.9444580078125,966.8333371480306L819.976318359375,973.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[11,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M836.9400227864584,995.0000038146973L856.4181179470487,1001.1666704813639C875.896213107639,1007.3333371480306,914.8524034288195,1019.6666704813639,934.3304985894097,1032.0000038146973C953.80859375,1044.3333371480305,953.80859375,1056.666670481364,953.80859375,1062.8333371480305L953.80859375,1069.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[12,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M745.8046875,991.5163071013878L695.18359375,998.2635898869394C644.5625,1005.010872672491,543.3203125,1018.5054382435941,492.69921875,1036.6693876958122C442.078125,1054.8333371480305,442.078125,1077.666670481364,442.078125,1100.5000038146973C442.078125,1123.3333371480305,442.078125,1146.166670481364,505.0364583333333,1164.544215319706C567.9947916666666,1182.9217601580478,693.9114583333334,1196.8435165013987,756.8697916666666,1203.804394673074L819.828125,1210.7652728447495" marker-end="url(#arrowhead-vee)" id="edge-[12,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M745.8046875,990.0943573767025L681.1796875,997.0786317830349C616.5546875,1004.0629061893675,487.3046875,1018.0314550020324,422.6796875,1036.4323960750314C358.0546875,1054.8333371480305,358.0546875,1077.666670481364,358.0546875,1100.5000038146973C358.0546875,1123.3333371480305,358.0546875,1146.166670481364,358.0546875,1165.5833371480305C358.0546875,1185.0000038146973,358.0546875,1201.0000038146973,358.0546875,1217.0000038146973C358.0546875,1233.0000038146973,358.0546875,1249.0000038146973,358.0546875,1268.416670481364C358.0546875,1287.8333371480305,358.0546875,1310.666670481364,358.0546875,1333.5000038146973C358.0546875,1356.3333371480305,358.0546875,1379.166670481364,358.0546875,1398.5833371480305C358.0546875,1418.0000038146973,358.0546875,1434.0000038146973,358.0546875,1450.0000038146973C358.0546875,1466.0000038146973,358.0546875,1482.0000038146973,358.0546875,1501.416670481364C358.0546875,1520.8333371480305,358.0546875,1543.666670481364,358.0546875,1566.5000038146973C358.0546875,1589.3333371480305,358.0546875,1612.166670481364,358.0546875,1631.5833371480305C358.0546875,1651.0000038146973,358.0546875,1667.0000038146973,358.0546875,1683.0000038146973C358.0546875,1699.0000038146973,358.0546875,1715.0000038146973,358.0546875,1734.416670481364C358.0546875,1753.8333371480305,358.0546875,1776.666670481364,358.0546875,1799.5000038146973C358.0546875,1822.3333371480305,358.0546875,1845.166670481364,358.0546875,1864.5833371480305C358.0546875,1884.0000038146973,358.0546875,1900.0000038146973,358.0546875,1916.0000038146973C358.0546875,1932.0000038146973,358.0546875,1948.0000038146973,358.0546875,1967.416670481364C358.0546875,1986.8333371480305,358.0546875,2009.666670481364,358.0546875,2032.5000038146973C358.0546875,2055.3333371480308,358.0546875,2078.166670481364,358.0546875,2101.0000038146973C358.0546875,2123.8333371480308,358.0546875,2146.666670481364,358.0546875,2169.5000038146973C358.0546875,2192.3333371480308,358.0546875,2215.166670481364,358.0546875,2232.7500038146973C358.0546875,2250.3333371480308,358.0546875,2262.666670481364,358.0546875,2268.8333371480308L358.0546875,2275.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[12,1]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M358.0546875,2338.0000038146973L358.0546875,2344.166670481364C358.0546875,2350.3333371480308,358.0546875,2362.666670481364,396.0945095486111,2375.0000038146973C434.1343315972222,2387.3333371480308,510.2139756944444,2399.666670481364,548.2537977430555,2405.8333371480308L586.2936197916666,2412.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[13,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M953.80859375,1132.0000038146973L953.80859375,1138.166670481364C953.80859375,1144.3333371480305,953.80859375,1156.666670481364,943.8404541015625,1169.0000038146973C933.872314453125,1181.3333371480305,913.93603515625,1193.666670481364,903.9678955078125,1199.8333371480305L893.999755859375,1206.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[14,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M910.9634602864584,1228.0000038146973L930.4415554470487,1234.166670481364C949.919650607639,1240.3333371480305,988.8758409288195,1252.666670481364,1008.3539360894097,1265.0000038146973C1027.83203125,1277.3333371480305,1027.83203125,1289.666670481364,1027.83203125,1295.8333371480305L1027.83203125,1302.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[15,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M819.828125,1226.4610633450086L781.5442708333334,1232.8842200899567C743.2604166666666,1239.3073768349047,666.6927083333334,1252.153690324801,628.4088541666666,1269.9935137364157C590.125,1287.8333371480305,590.125,1310.666670481364,590.125,1333.5000038146973C590.125,1356.3333371480305,590.125,1379.166670481364,640.74609375,1397.330619933582C691.3671875,1415.4945693858006,792.609375,1428.9891349569036,843.23046875,1435.7364177424552L893.8515625,1442.4837005280067" marker-end="url(#arrowhead-vee)" id="edge-[15,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M819.828125,1224.3132280891036L767.5403645833334,1231.0943573767026C715.2526041666666,1237.8754866643014,610.6770833333334,1251.4377452394995,558.3893229166666,1269.635541193765C506.1015625,1287.8333371480305,506.1015625,1310.666670481364,506.1015625,1333.5000038146973C506.1015625,1356.3333371480305,506.1015625,1379.166670481364,506.1015625,1398.5833371480305C506.1015625,1418.0000038146973,506.1015625,1434.0000038146973,506.1015625,1450.0000038146973C506.1015625,1466.0000038146973,506.1015625,1482.0000038146973,506.1015625,1501.416670481364C506.1015625,1520.8333371480305,506.1015625,1543.666670481364,506.1015625,1566.5000038146973C506.1015625,1589.3333371480305,506.1015625,1612.166670481364,506.1015625,1631.5833371480305C506.1015625,1651.0000038146973,506.1015625,1667.0000038146973,506.1015625,1683.0000038146973C506.1015625,1699.0000038146973,506.1015625,1715.0000038146973,506.1015625,1734.416670481364C506.1015625,1753.8333371480305,506.1015625,1776.666670481364,506.1015625,1799.5000038146973C506.1015625,1822.3333371480305,506.1015625,1845.166670481364,506.1015625,1864.5833371480305C506.1015625,1884.0000038146973,506.1015625,1900.0000038146973,506.1015625,1916.0000038146973C506.1015625,1932.0000038146973,506.1015625,1948.0000038146973,506.1015625,1967.416670481364C506.1015625,1986.8333371480305,506.1015625,2009.666670481364,506.1015625,2032.5000038146973C506.1015625,2055.3333371480308,506.1015625,2078.166670481364,506.1015625,2101.0000038146973C506.1015625,2123.8333371480308,506.1015625,2146.666670481364,506.1015625,2169.5000038146973C506.1015625,2192.3333371480308,506.1015625,2215.166670481364,506.1015625,2232.7500038146973C506.1015625,2250.3333371480308,506.1015625,2262.666670481364,506.1015625,2268.8333371480308L506.1015625,2275.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[15,1]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M506.1015625,2338.0000038146973L506.1015625,2344.166670481364C506.1015625,2350.3333371480308,506.1015625,2362.666670481364,525.1214735243055,2375.0000038146973C544.1413845486112,2387.3333371480308,582.1812065972223,2399.666670481364,601.2011176215278,2405.8333371480308L620.2210286458334,2412.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[16,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1027.83203125,1365.0000038146973L1027.83203125,1371.166670481364C1027.83203125,1377.3333371480305,1027.83203125,1389.666670481364,1017.8638916015625,1402.0000038146973C1007.895751953125,1414.3333371480305,987.95947265625,1426.666670481364,977.9913330078125,1432.8333371480305L968.023193359375,1439.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[17,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M984.9868977864584,1461.0000038146973L1004.4649929470487,1467.166670481364C1023.943088107639,1473.3333371480305,1062.8992784288196,1485.666670481364,1082.3773735894097,1498.0000038146973C1101.85546875,1510.3333371480305,1101.85546875,1522.666670481364,1101.85546875,1528.8333371480305L1101.85546875,1535.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[18,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M901.6427408854166,1461.0000038146973L874.3975965711805,1467.166670481364C847.1524522569443,1473.3333371480305,792.6621636284722,1485.666670481364,765.417019314236,1503.2500038146973C738.171875,1520.8333371480305,738.171875,1543.666670481364,738.171875,1566.5000038146973C738.171875,1589.3333371480305,738.171875,1612.166670481364,776.4557291666666,1630.0064938929788C814.7395833333334,1647.8463173045936,891.3072916666666,1660.6926307944898,929.5911458333334,1667.115787539438L967.875,1673.538944284386" marker-end="url(#arrowhead-vee)" id="edge-[18,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M893.8515625,1459.141534157705L853.9010416666666,1465.6179457672035C813.9505208333334,1472.0943573767024,734.0494791666666,1485.0471805956997,694.0989583333334,1502.9402588718651C654.1484375,1520.8333371480305,654.1484375,1543.666670481364,654.1484375,1566.5000038146973C654.1484375,1589.3333371480305,654.1484375,1612.166670481364,654.1484375,1631.5833371480305C654.1484375,1651.0000038146973,654.1484375,1667.0000038146973,654.1484375,1683.0000038146973C654.1484375,1699.0000038146973,654.1484375,1715.0000038146973,654.1484375,1734.416670481364C654.1484375,1753.8333371480305,654.1484375,1776.666670481364,654.1484375,1799.5000038146973C654.1484375,1822.3333371480305,654.1484375,1845.166670481364,654.1484375,1864.5833371480305C654.1484375,1884.0000038146973,654.1484375,1900.0000038146973,654.1484375,1916.0000038146973C654.1484375,1932.0000038146973,654.1484375,1948.0000038146973,654.1484375,1967.416670481364C654.1484375,1986.8333371480305,654.1484375,2009.666670481364,654.1484375,2032.5000038146973C654.1484375,2055.3333371480308,654.1484375,2078.166670481364,654.1484375,2101.0000038146973C654.1484375,2123.8333371480308,654.1484375,2146.666670481364,654.1484375,2169.5000038146973C654.1484375,2192.3333371480308,654.1484375,2215.166670481364,654.1484375,2232.7500038146973C654.1484375,2250.3333371480308,654.1484375,2262.666670481364,654.1484375,2268.8333371480308L654.1484375,2275.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[18,1]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M654.1484375,2338.0000038146973L654.1484375,2344.166670481364C654.1484375,2350.3333371480308,654.1484375,2362.666670481364,654.1484375,2375.0000038146973C654.1484375,2387.3333371480308,654.1484375,2399.666670481364,654.1484375,2405.8333371480308L654.1484375,2412.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[19,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1101.85546875,1598.0000038146973L1101.85546875,1604.166670481364C1101.85546875,1610.3333371480305,1101.85546875,1622.666670481364,1091.8873291015625,1635.0000038146973C1081.919189453125,1647.3333371480305,1061.98291015625,1659.666670481364,1052.0147705078125,1665.8333371480305L1042.046630859375,1672.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[20,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1059.0103352864583,1694.0000038146973L1078.4884304470486,1700.166670481364C1097.966525607639,1706.3333371480305,1136.9227159288193,1718.666670481364,1156.4008110894097,1731.0000038146973C1175.87890625,1743.3333371480305,1175.87890625,1755.666670481364,1175.87890625,1761.8333371480305L1175.87890625,1768.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[21,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M992.6298828125,1694.0000038146973L974.8946940104166,1700.166670481364C957.1595052083334,1706.3333371480305,921.6891276041666,1718.666670481364,903.9539388020834,1736.2500038146973C886.21875,1753.8333371480305,886.21875,1776.666670481364,886.21875,1799.5000038146973C886.21875,1822.3333371480305,886.21875,1845.166670481364,913.4638943142362,1862.7500038146973C940.7090386284723,1880.3333371480305,995.1993272569445,1892.666670481364,1022.4444715711807,1898.8333371480305L1049.6896158854167,1905.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[21,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M973.37451171875,1694.0000038146973L944.8446451822916,1700.166670481364C916.3147786458334,1706.3333371480305,859.2550455729166,1718.666670481364,830.7251790364584,1736.2500038146973C802.1953125,1753.8333371480305,802.1953125,1776.666670481364,802.1953125,1799.5000038146973C802.1953125,1822.3333371480305,802.1953125,1845.166670481364,802.1953125,1864.5833371480305C802.1953125,1884.0000038146973,802.1953125,1900.0000038146973,802.1953125,1916.0000038146973C802.1953125,1932.0000038146973,802.1953125,1948.0000038146973,802.1953125,1967.416670481364C802.1953125,1986.8333371480305,802.1953125,2009.666670481364,802.1953125,2032.5000038146973C802.1953125,2055.3333371480308,802.1953125,2078.166670481364,802.1953125,2101.0000038146973C802.1953125,2123.8333371480308,802.1953125,2146.666670481364,802.1953125,2169.5000038146973C802.1953125,2192.3333371480308,802.1953125,2215.166670481364,802.1953125,2232.7500038146973C802.1953125,2250.3333371480308,802.1953125,2262.666670481364,802.1953125,2268.8333371480308L802.1953125,2275.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[21,1]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M802.1953125,2338.0000038146973L802.1953125,2344.166670481364C802.1953125,2350.3333371480308,802.1953125,2362.666670481364,783.1754014756945,2375.0000038146973C764.1554904513888,2387.3333371480308,726.1156684027777,2399.666670481364,707.0957573784722,2405.8333371480308L688.0758463541666,2412.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[22,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1175.87890625,1831.0000038146973L1175.87890625,1837.166670481364C1175.87890625,1843.3333371480305,1175.87890625,1855.666670481364,1165.9107666015625,1868.0000038146973C1155.942626953125,1880.3333371480305,1136.00634765625,1892.666670481364,1126.0382080078125,1898.8333371480305L1116.070068359375,1905.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[23,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1133.0337727864583,1927.0000038146973L1152.5118679470486,1933.166670481364C1171.989963107639,1939.3333371480305,1210.9461534288193,1951.666670481364,1230.4242485894097,1964.0000038146973C1249.90234375,1976.3333371480305,1249.90234375,1988.666670481364,1249.90234375,1994.8333371480305L1249.90234375,2001.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[24,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1089.4930013020833,1927.0000038146973L1084.5618760850693,1933.166670481364C1079.6307508680554,1939.3333371480305,1069.7685004340276,1951.666670481364,1064.837375217014,1969.2500038146973C1059.90625,1986.8333371480305,1059.90625,2009.666670481364,1059.90625,2032.5000038146973C1059.90625,2055.3333371480308,1059.90625,2078.166670481364,1075.632185218978,2099.166670481364C1091.3581204379561,2120.166670481364,1122.8099908759125,2139.3333371480308,1138.5359260948906,2148.916670481364L1154.2618613138686,2158.5000038146973" marker-end="url(#arrowhead-vee)" id="edge-[24,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1064.3616536458333,1927.0000038146973L1045.3417426215276,1933.166670481364C1026.3218315972222,1939.3333371480305,988.282009548611,1951.666670481364,969.2620985243055,1969.2500038146973C950.2421875,1986.8333371480305,950.2421875,2009.666670481364,950.2421875,2032.5000038146973C950.2421875,2055.3333371480308,950.2421875,2078.166670481364,950.2421875,2101.0000038146973C950.2421875,2123.8333371480308,950.2421875,2146.666670481364,950.2421875,2169.5000038146973C950.2421875,2192.3333371480308,950.2421875,2215.166670481364,950.2421875,2232.7500038146973C950.2421875,2250.3333371480308,950.2421875,2262.666670481364,950.2421875,2268.8333371480308L950.2421875,2275.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[24,1]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M950.2421875,2338.0000038146973L950.2421875,2344.166670481364C950.2421875,2350.3333371480308,950.2421875,2362.666670481364,912.2023654513888,2375.0000038146973C874.1625434027778,2387.3333371480308,798.0828993055557,2399.666670481364,760.0430772569445,2405.8333371480308L722.0032552083334,2412.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[25,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1249.90234375,2064.0000038146973L1249.90234375,2070.166670481364C1249.90234375,2076.3333371480308,1249.90234375,2088.666670481364,1239.0473169479926,2104.416670481364C1228.1922901459855,2120.166670481364,1206.4822365419707,2139.3333371480308,1195.6272097399635,2148.916670481364L1184.7721829379561,2158.5000038146973" marker-end="url(#arrowhead-vee)" id="edge-[26,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1184.1994753649635,2180.5000038146973L1194.5555523874696,2190.0833371480308C1204.9116294099756,2199.666670481364,1225.6237834549877,2218.8333371480308,1235.979860477494,2234.5833371480308C1246.3359375,2250.3333371480308,1246.3359375,2262.666670481364,1246.3359375,2268.8333371480308L1246.3359375,2275.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[27,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1160.4255246350365,2180.5000038146973L1150.0694476125304,2190.0833371480308C1139.7133705900244,2199.666670481364,1119.0012165450123,2218.8333371480308,1108.645139522506,2234.5833371480308C1098.2890625,2250.3333371480308,1098.2890625,2262.666670481364,1098.2890625,2268.8333371480308L1098.2890625,2275.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[27,1]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1098.2890625,2338.0000038146973L1098.2890625,2344.166670481364C1098.2890625,2350.3333371480308,1098.2890625,2362.666670481364,1041.2293294270833,2375.0000038146973C984.1695963541666,2387.3333371480308,870.0501302083334,2399.666670481364,812.9903971354166,2405.8333371480308L755.9306640625,2412.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[28,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1246.3359375,2338.0000038146973L1246.3359375,2344.166670481364C1246.3359375,2350.3333371480308,1246.3359375,2362.666670481364,1170.2562934027778,2375.0000038146973C1094.1766493055554,2387.3333371480308,942.017361111111,2399.666670481364,865.9377170138888,2405.8333371480308L789.8580729166666,2412.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[29,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M646.158935546875,2434.0000038146973L641.6799723307291,2440.166670481364C637.2010091145834,2446.3333371480308,628.2430826822916,2458.666670481364,623.7641194661459,2472.8333371480308C619.28515625,2487.0000038146973,619.28515625,2503.0000038146973,619.28515625,2519.0000038146973C619.28515625,2535.0000038146973,619.28515625,2551.0000038146973,619.28515625,2568.5833371480308C619.28515625,2586.166670481364,619.28515625,2605.3333371480308,619.28515625,2624.5000038146973C619.28515625,2643.666670481364,619.28515625,2662.8333371480308,619.28515625,2678.5833371480308C619.28515625,2694.3333371480308,619.28515625,2706.666670481364,619.28515625,2712.8333371480308L619.28515625,2719.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[30,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M684.6195475260416,2434.0000038146973L701.7018364800347,2440.166670481364C718.7841254340277,2446.3333371480308,752.9487033420138,2458.666670481364,770.0309922960068,2471.0000038146973C787.11328125,2483.3333371480308,787.11328125,2495.666670481364,787.11328125,2501.8333371480308L787.11328125,2508.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[30,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M623.6773274739584,2434.0000038146973L606.5950385199653,2440.166670481364C589.5127495659723,2446.3333371480308,555.3481716579862,2458.666670481364,538.265882703993,2472.8333371480308C521.18359375,2487.0000038146973,521.18359375,2503.0000038146973,521.18359375,2519.0000038146973C521.18359375,2535.0000038146973,521.18359375,2551.0000038146973,521.18359375,2568.5833371480308C521.18359375,2586.166670481364,521.18359375,2605.3333371480308,521.18359375,2624.5000038146973C521.18359375,2643.666670481364,521.18359375,2662.8333371480308,521.18359375,2682.0000038146973C521.18359375,2701.166670481364,521.18359375,2720.3333371480308,521.18359375,2739.5000038146973C521.18359375,2758.666670481364,521.18359375,2777.8333371480308,533.7869194878473,2793.5833371480308C546.3902452256945,2809.3333371480308,571.5968967013889,2821.666670481364,584.2002224392362,2827.8333371480308L596.8035481770834,2834.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[30,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M619.28515625,2760.0000038146973L619.28515625,2766.166670481364C619.28515625,2772.3333371480308,619.28515625,2784.666670481364,619.28515625,2797.0000038146973C619.28515625,2809.3333371480308,619.28515625,2821.666670481364,619.28515625,2827.8333371480308L619.28515625,2834.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[31,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M787.11328125,2530.0000038146973L787.11328125,2536.166670481364C787.11328125,2542.3333371480308,787.11328125,2554.666670481364,787.11328125,2567.0000038146973C787.11328125,2579.3333371480308,787.11328125,2591.666670481364,787.11328125,2597.8333371480308L787.11328125,2604.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[32,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M787.11328125,2645.0000038146973L787.11328125,2651.166670481364C787.11328125,2657.3333371480308,787.11328125,2669.666670481364,787.11328125,2682.0000038146973C787.11328125,2694.3333371480308,787.11328125,2706.666670481364,787.11328125,2712.8333371480308L787.11328125,2719.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[33,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M787.11328125,2760.0000038146973L787.11328125,2766.166670481364C787.11328125,2772.3333371480308,787.11328125,2784.666670481364,765.5520290798612,2797.0000038146973C743.9907769097222,2809.3333371480308,700.8682725694443,2821.666670481364,679.3070203993055,2827.8333371480308L657.7457682291666,2834.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[34,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M619.28515625,2856.0000038146973L619.28515625,2862.166670481364C619.28515625,2868.3333371480308,619.28515625,2880.666670481364,619.28515625,2893.0000038146973C619.28515625,2905.3333371480308,619.28515625,2917.666670481364,619.28515625,2923.8333371480308L619.28515625,2930.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[35,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M611.295654296875,2952.0000038146973L606.8166910807291,2958.166670481364C602.3377278645834,2964.3333371480308,593.3798014322916,2976.666670481364,588.9008382161459,2990.8333371480308C584.421875,3005.0000038146973,584.421875,3021.0000038146973,584.421875,3037.0000038146973C584.421875,3053.0000038146973,584.421875,3069.0000038146973,584.421875,3086.5833371480308C584.421875,3104.166670481364,584.421875,3123.3333371480308,584.421875,3142.5000038146973C584.421875,3161.666670481364,584.421875,3180.8333371480308,584.421875,3196.5833371480308C584.421875,3212.3333371480308,584.421875,3224.666670481364,584.421875,3230.8333371480308L584.421875,3237.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[36,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M649.7562662760416,2952.0000038146973L666.8385552300347,2958.166670481364C683.9208441840277,2964.3333371480308,718.0854220920138,2976.666670481364,735.1677110460068,2989.0000038146973C752.25,3001.3333371480308,752.25,3013.666670481364,752.25,3019.8333371480308L752.25,3026.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[36,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M588.8140462239584,2952.0000038146973L571.7317572699653,2958.166670481364C554.6494683159723,2964.3333371480308,520.4848904079862,2976.666670481364,503.4026014539931,2990.8333371480308C486.3203125,3005.0000038146973,486.3203125,3021.0000038146973,486.3203125,3037.0000038146973C486.3203125,3053.0000038146973,486.3203125,3069.0000038146973,486.3203125,3086.5833371480308C486.3203125,3104.166670481364,486.3203125,3123.3333371480308,486.3203125,3142.5000038146973C486.3203125,3161.666670481364,486.3203125,3180.8333371480308,486.3203125,3200.0000038146973C486.3203125,3219.166670481364,486.3203125,3238.3333371480308,486.3203125,3257.5000038146973C486.3203125,3276.666670481364,486.3203125,3295.8333371480308,498.92363823784723,3311.5833371480308C511.5269639756945,3327.3333371480308,536.7336154513889,3339.666670481364,549.3369411892362,3345.8333371480308L561.9402669270834,3352.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[36,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M584.421875,3278.0000038146973L584.421875,3284.166670481364C584.421875,3290.3333371480308,584.421875,3302.666670481364,584.421875,3315.0000038146973C584.421875,3327.3333371480308,584.421875,3339.666670481364,584.421875,3345.8333371480308L584.421875,3352.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[37,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M752.25,3048.0000038146973L752.25,3054.166670481364C752.25,3060.3333371480308,752.25,3072.666670481364,752.25,3085.0000038146973C752.25,3097.3333371480308,752.25,3109.666670481364,752.25,3115.8333371480308L752.25,3122.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[38,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M752.25,3163.0000038146973L752.25,3169.166670481364C752.25,3175.3333371480308,752.25,3187.666670481364,752.25,3200.0000038146973C752.25,3212.3333371480308,752.25,3224.666670481364,752.25,3230.8333371480308L752.25,3237.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[39,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M752.25,3278.0000038146973L752.25,3284.166670481364C752.25,3290.3333371480308,752.25,3302.666670481364,730.6887478298612,3315.0000038146973C709.1274956597222,3327.3333371480308,666.0049913194445,3339.666670481364,644.4437391493055,3345.8333371480308L622.8824869791666,3352.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[40,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M584.421875,3374.0000038146973L584.421875,3380.166670481364C584.421875,3386.3333371480308,584.421875,3398.666670481364,584.421875,3411.0000038146973C584.421875,3423.3333371480308,584.421875,3435.666670481364,584.421875,3441.8333371480308L584.421875,3448.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[41,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M584.421875,3470.0000038146973L584.421875,3476.166670481364C584.421875,3482.3333371480308,584.421875,3494.666670481364,584.421875,3507.0000038146973C584.421875,3519.3333371480308,584.421875,3531.666670481364,584.421875,3537.8333371480308L584.421875,3544.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[42,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M584.421875,3596.0000076293945L584.421875,3602.166674296061C584.421875,3608.333340962728,584.421875,3620.666674296061,584.421875,3633.0000076293945C584.421875,3645.333340962728,584.421875,3657.666674296061,584.421875,3663.833340962728L584.421875,3670.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[43,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/></g><g id="edge-labels" class="edge-labels"><g class="edge-label" transform="translate(668.140625,59)" style="opacity: 1;"><g transform="translate(-12.2265625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">input</tspan></text></g></g><g class="edge-label" transform="translate(691.4921875,207)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(769.08203125,333.00000381469727)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(613.90234375,401.50000381469727)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(762.66796875,470.00000381469727)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(846.33984375,566.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(183.328125,634.5000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(86.1328125,1333.5000038146973)" style="opacity: 1;"><g transform="translate(-25.640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">(1) ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(96.0703125,2238.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(96.0703125,2375.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(839.92578125,703.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(920.36328125,799.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(334.609375,867.5000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(240.6484375,1498.0000038146973)" style="opacity: 1;"><g transform="translate(-25.640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">(1) ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(244.171875,2375.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(913.94921875,936.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(994.38671875,1032.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(482.65625,1100.5000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(388.6953125,1635.0000038146973)" style="opacity: 1;"><g transform="translate(-25.640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">(1) ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(392.21875,2375.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (2)</tspan></text></g></g><g class="edge-label" transform="translate(987.97265625,1169.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(1068.41015625,1265.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(630.703125,1333.5000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(536.7421875,1731.0000038146973)" style="opacity: 1;"><g transform="translate(-25.640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">(1) ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(540.265625,2375.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (3)</tspan></text></g></g><g class="edge-label" transform="translate(1061.99609375,1402.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(1142.43359375,1498.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(778.75,1566.5000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(684.7890625,1868.0000038146973)" style="opacity: 1;"><g transform="translate(-25.640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">(1) ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(688.3125,2375.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (4)</tspan></text></g></g><g class="edge-label" transform="translate(1136.01953125,1635.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(1216.45703125,1731.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(926.796875,1799.5000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(832.8359375,1964.0000038146973)" style="opacity: 1;"><g transform="translate(-25.640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">(1) ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(836.359375,2375.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (5)</tspan></text></g></g><g class="edge-label" transform="translate(1210.04296875,1868.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(1290.48046875,1964.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1100.484375,2032.5000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(980.8828125,2101.0000038146973)" style="opacity: 1;"><g transform="translate(-25.640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">(1) ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(984.40625,2375.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (6)</tspan></text></g></g><g class="edge-label" transform="translate(1284.06640625,2101.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(1286.9140625,2238.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1128.9296875,2238.0000038146973)" style="opacity: 1;"><g transform="translate(-25.640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">(1) ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1132.453125,2375.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (7)</tspan></text></g></g><g class="edge-label" transform="translate(1280.5,2375.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (8)</tspan></text></g></g><g class="edge-label" transform="translate(659.86328125,2567.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(827.69140625,2471.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(555.34765625,2624.5000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(659.86328125,2797.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(827.69140625,2567.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(827.69140625,2682.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(821.27734375,2797.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (2)</tspan></text></g></g><g class="edge-label" transform="translate(659.86328125,2893.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(625,3085.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(792.828125,2989.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(520.484375,3142.5000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(625,3315.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(792.828125,3085.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(792.828125,3200.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(786.4140625,3315.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (2)</tspan></text></g></g><g class="edge-label" transform="translate(625,3411.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(625,3507.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(605.171875,3633.0000076293945)" style="opacity: 1;"><g transform="translate(-15.75,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output</tspan></text></g></g></g><g id="nodes" class="nodes"><g id="node-wavenet1_embedding0_fwd" class="node" transform="translate(650.9140625,133)" style="opacity: 1;"><g transform="translate(-77.234375,-37)"><g><g class="node-item node-item-operator node-item-operator-transform" transform="translate(0,0)"><path d="M5,0h63.28125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-68.28125a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 68); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Embedding</text><title>wavenet1_embedding0_fwd</title></g><g class="node-item node-item-input" transform="translate(68.28125,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_embedding0_weight" transform="translate(107.921875,0)"><path d="M0,0h41.546875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[512,24]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h154.46875a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-144.46875a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dtype</tspan><tspan> = float32</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">input_dim</tspan><tspan> = 512</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">output_dim</tspan><tspan> = 24</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">sparse_grad</tspan><tspan> = False</tspan></text></g><line class="node" x1="68.28125" y1="0" x2="68.28125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="107.921875" y1="0" x2="107.921875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="154.46875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h144.46875a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-144.46875a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_swapaxes0" class="node" transform="translate(650.9140625,270.00000190734863)" style="opacity: 1;"><g transform="translate(-49.5546875,-26.000001907348633)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h54.46875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-59.46875a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">SwapAxis</text><title>wavenet1_swapaxes0</title></g><g class="node-item node-item-input" transform="translate(59.46875,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h99.109375a0,0 0 0 1 0,0v25a5,5 0 0 1 -5,5h-89.109375a5,5 0 0 1 -5,-5v-25a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dim1</tspan><tspan> = 1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dim2</tspan><tspan> = 2</tspan></text></g><line class="node" x1="59.46875" y1="0" x2="59.46875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="99.109375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h89.109375a5,5 0 0 1 5,5v42a5,5 0 0 1 -5,5h-89.109375a5,5 0 0 1 -5,-5v-42a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue0_slice_axis0" class="node" transform="translate(728.50390625,401.50000381469727)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_causaldilatedresidue0_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 1</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-TRT_node10" class="node" transform="translate(650.9140625,518.0000038146973)" style="opacity: 1;"><g transform="translate(-56.390625,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h42.109375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-42.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_trt_op</text><title>TRT_node10</title></g><g class="node-item node-item-input" transform="translate(47.109375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(86.75,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><line class="node" x1="47.109375" y1="0" x2="47.109375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="86.75" y1="0" x2="86.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h102.78125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-102.78125a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis0" class="node" transform="translate(55.4921875,2169.5000038146973)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 510</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1__plusscalar0" class="node" transform="translate(55.4921875,2306.5000038146973)" style="opacity: 1;"><g transform="translate(-55.4921875,-20.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h66.34375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-71.34375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_plus_scalar</text><title>wavenet1__plusscalar0</title></g><g class="node-item node-item-input" transform="translate(71.34375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h110.984375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-100.984375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">scalar</tspan><tspan> = 0</tspan></text></g><line class="node" x1="71.34375" y1="0" x2="71.34375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="110.984375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h100.984375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-100.984375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue1_slice_axis0" class="node" transform="translate(805.76171875,634.5000038146973)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_causaldilatedresidue1_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 2</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-TRT_node9" class="node" transform="translate(728.171875,751.0000038146973)" style="opacity: 1;"><g transform="translate(-56.390625,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h42.109375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-42.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_trt_op</text><title>TRT_node9</title></g><g class="node-item node-item-input" transform="translate(47.109375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(86.75,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><line class="node" x1="47.109375" y1="0" x2="47.109375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="86.75" y1="0" x2="86.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h102.78125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-102.78125a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis1" class="node" transform="translate(210.0078125,2306.5000038146973)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis1</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 508</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue2_slice_axis0" class="node" transform="translate(879.78515625,867.5000038146973)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_causaldilatedresidue2_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 4</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-TRT_node8" class="node" transform="translate(802.1953125,984.0000038146973)" style="opacity: 1;"><g transform="translate(-56.390625,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h42.109375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-42.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_trt_op</text><title>TRT_node8</title></g><g class="node-item node-item-input" transform="translate(47.109375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(86.75,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><line class="node" x1="47.109375" y1="0" x2="47.109375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="86.75" y1="0" x2="86.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h102.78125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-102.78125a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis2" class="node" transform="translate(358.0546875,2306.5000038146973)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis2</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 504</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue3_slice_axis0" class="node" transform="translate(953.80859375,1100.5000038146973)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_causaldilatedresidue3_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 8</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-TRT_node7" class="node" transform="translate(876.21875,1217.0000038146973)" style="opacity: 1;"><g transform="translate(-56.390625,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h42.109375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-42.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_trt_op</text><title>TRT_node7</title></g><g class="node-item node-item-input" transform="translate(47.109375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(86.75,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><line class="node" x1="47.109375" y1="0" x2="47.109375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="86.75" y1="0" x2="86.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h102.78125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-102.78125a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis3" class="node" transform="translate(506.1015625,2306.5000038146973)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis3</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 496</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue4_slice_axis0" class="node" transform="translate(1027.83203125,1333.5000038146973)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_causaldilatedresidue4_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 16</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-TRT_node6" class="node" transform="translate(950.2421875,1450.0000038146973)" style="opacity: 1;"><g transform="translate(-56.390625,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h42.109375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-42.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_trt_op</text><title>TRT_node6</title></g><g class="node-item node-item-input" transform="translate(47.109375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(86.75,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><line class="node" x1="47.109375" y1="0" x2="47.109375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="86.75" y1="0" x2="86.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h102.78125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-102.78125a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis4" class="node" transform="translate(654.1484375,2306.5000038146973)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis4</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 480</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue5_slice_axis0" class="node" transform="translate(1101.85546875,1566.5000038146973)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_causaldilatedresidue5_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 32</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-TRT_node5" class="node" transform="translate(1024.265625,1683.0000038146973)" style="opacity: 1;"><g transform="translate(-56.390625,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h42.109375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-42.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_trt_op</text><title>TRT_node5</title></g><g class="node-item node-item-input" transform="translate(47.109375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(86.75,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><line class="node" x1="47.109375" y1="0" x2="47.109375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="86.75" y1="0" x2="86.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h102.78125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-102.78125a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis5" class="node" transform="translate(802.1953125,2306.5000038146973)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis5</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 448</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue6_slice_axis0" class="node" transform="translate(1175.87890625,1799.5000038146973)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_causaldilatedresidue6_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 64</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-TRT_node4" class="node" transform="translate(1098.2890625,1916.0000038146973)" style="opacity: 1;"><g transform="translate(-56.390625,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h42.109375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-42.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_trt_op</text><title>TRT_node4</title></g><g class="node-item node-item-input" transform="translate(47.109375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(86.75,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><line class="node" x1="47.109375" y1="0" x2="47.109375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="86.75" y1="0" x2="86.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h102.78125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-102.78125a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis6" class="node" transform="translate(950.2421875,2306.5000038146973)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis6</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 384</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue7_slice_axis0" class="node" transform="translate(1249.90234375,2032.5000038146973)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_causaldilatedresidue7_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 128</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-TRT_node3" class="node" transform="translate(1172.3125,2169.5000038146973)" style="opacity: 1;"><g transform="translate(-56.390625,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h42.109375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-42.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_trt_op</text><title>TRT_node3</title></g><g class="node-item node-item-input" transform="translate(47.109375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(86.75,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><line class="node" x1="47.109375" y1="0" x2="47.109375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="86.75" y1="0" x2="86.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h102.78125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-102.78125a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis7" class="node" transform="translate(1098.2890625,2306.5000038146973)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis7</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 256</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis8" class="node" transform="translate(1246.3359375,2306.5000038146973)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis8</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 0</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-TRT_node2" class="node" transform="translate(654.1484375,2423.0000038146973)" style="opacity: 1;"><g transform="translate(-147.5,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h42.109375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-42.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_trt_op</text><title>TRT_node2</title></g><g class="node-item node-item-input" transform="translate(47.109375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(86.75,0)"><path d="M0,0h26.03125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-26.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><g class="node-item node-item-input" transform="translate(112.78125,0)"><path d="M0,0h26.03125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-26.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(2)</text></g><g class="node-item node-item-input" transform="translate(138.8125,0)"><path d="M0,0h26.03125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-26.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(3)</text></g><g class="node-item node-item-input" transform="translate(164.84375,0)"><path d="M0,0h26.03125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-26.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(4)</text></g><g class="node-item node-item-input" transform="translate(190.875,0)"><path d="M0,0h26.03125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-26.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(5)</text></g><g class="node-item node-item-input" transform="translate(216.90625,0)"><path d="M0,0h26.03125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-26.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(6)</text></g><g class="node-item node-item-input" transform="translate(242.9375,0)"><path d="M0,0h26.03125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-26.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(7)</text></g><g class="node-item node-item-input" transform="translate(268.96875,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(8)</text></g><line class="node" x1="47.109375" y1="0" x2="47.109375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="86.75" y1="0" x2="86.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.78125" y1="0" x2="112.78125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="138.8125" y1="0" x2="138.8125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="164.84375" y1="0" x2="164.84375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="190.875" y1="0" x2="190.875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="216.90625" y1="0" x2="216.90625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="242.9375" y1="0" x2="242.9375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="268.96875" y1="0" x2="268.96875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h285a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-285a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0__greater_scalar0" class="node" transform="translate(619.28515625,2739.5000038146973)" style="opacity: 1;"><g transform="translate(-63.1015625,-20.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h81.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-86.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_greater_scalar</text><title>wavenet1_elu0__greater_scalar0</title></g><g class="node-item node-item-input" transform="translate(86.5625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h126.203125a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-116.203125a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">scalar</tspan><tspan> = 0</tspan></text></g><line class="node" x1="86.5625" y1="0" x2="86.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="126.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.203125a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-116.203125a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0_exp0" class="node" transform="translate(787.11328125,2519.0000038146973)" style="opacity: 1;"><g transform="translate(-35.5703125,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h26.5a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-26.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">exp</text><title>wavenet1_elu0_exp0</title></g><g class="node-item node-item-input" transform="translate(31.5,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-34.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><line class="node" x1="31.5" y1="0" x2="31.5" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h61.140625a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-61.140625a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0__minusscalar0" class="node" transform="translate(787.11328125,2624.5000038146973)" style="opacity: 1;"><g transform="translate(-60.3359375,-20.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h76.03125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-81.03125a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_minus_scalar</text><title>wavenet1_elu0__minusscalar0</title></g><g class="node-item node-item-input" transform="translate(81.03125,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h120.671875a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-110.671875a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">scalar</tspan><tspan> = 1.0</tspan></text></g><line class="node" x1="81.03125" y1="0" x2="81.03125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="120.671875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h110.671875a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-110.671875a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0__mulscalar0" class="node" transform="translate(787.11328125,2739.5000038146973)" style="opacity: 1;"><g transform="translate(-54.7265625,-20.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h64.8125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-69.8125a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_mul_scalar</text><title>wavenet1_elu0__mulscalar0</title></g><g class="node-item node-item-input" transform="translate(69.8125,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h109.453125a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-99.453125a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">scalar</tspan><tspan> = 1.0</tspan></text></g><line class="node" x1="69.8125" y1="0" x2="69.8125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="109.453125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h99.453125a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-99.453125a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0_where0" class="node" transform="translate(619.28515625,2845.0000038146973)" style="opacity: 1;"><g transform="translate(-68.0234375,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h39.34375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.34375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">where</text><title>wavenet1_elu0_where0</title></g><g class="node-item node-item-input" transform="translate(44.34375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(83.984375,0)"><path d="M0,0h26.03125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-26.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><g class="node-item node-item-input" transform="translate(110.015625,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(2)</text></g><line class="node" x1="44.34375" y1="0" x2="44.34375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="83.984375" y1="0" x2="83.984375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="110.015625" y1="0" x2="110.015625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h126.046875a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-126.046875a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-TRT_node1" class="node" transform="translate(619.28515625,2941.0000038146973)" style="opacity: 1;"><g transform="translate(-43.375,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h42.109375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-42.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_trt_op</text><title>TRT_node1</title></g><g class="node-item node-item-input" transform="translate(47.109375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-34.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><line class="node" x1="47.109375" y1="0" x2="47.109375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h76.75a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-76.75a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0__greater_scalar0" class="node" transform="translate(584.421875,3257.5000038146973)" style="opacity: 1;"><g transform="translate(-63.1015625,-20.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h81.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-86.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_greater_scalar</text><title>wavenet1_elu0__greater_scalar0</title></g><g class="node-item node-item-input" transform="translate(86.5625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h126.203125a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-116.203125a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">scalar</tspan><tspan> = 0</tspan></text></g><line class="node" x1="86.5625" y1="0" x2="86.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="126.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.203125a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-116.203125a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0_exp0" class="node" transform="translate(752.25,3037.0000038146973)" style="opacity: 1;"><g transform="translate(-35.5703125,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h26.5a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-26.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">exp</text><title>wavenet1_elu0_exp0</title></g><g class="node-item node-item-input" transform="translate(31.5,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-34.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><line class="node" x1="31.5" y1="0" x2="31.5" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h61.140625a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-61.140625a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0__minusscalar0" class="node" transform="translate(752.25,3142.5000038146973)" style="opacity: 1;"><g transform="translate(-60.3359375,-20.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h76.03125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-81.03125a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_minus_scalar</text><title>wavenet1_elu0__minusscalar0</title></g><g class="node-item node-item-input" transform="translate(81.03125,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h120.671875a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-110.671875a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">scalar</tspan><tspan> = 1.0</tspan></text></g><line class="node" x1="81.03125" y1="0" x2="81.03125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="120.671875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h110.671875a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-110.671875a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0__mulscalar0" class="node" transform="translate(752.25,3257.5000038146973)" style="opacity: 1;"><g transform="translate(-54.7265625,-20.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h64.8125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-69.8125a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_mul_scalar</text><title>wavenet1_elu0__mulscalar0</title></g><g class="node-item node-item-input" transform="translate(69.8125,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h109.453125a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-99.453125a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">scalar</tspan><tspan> = 1.0</tspan></text></g><line class="node" x1="69.8125" y1="0" x2="69.8125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="109.453125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h99.453125a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-99.453125a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0_where0" class="node" transform="translate(584.421875,3363.0000038146973)" style="opacity: 1;"><g transform="translate(-68.0234375,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h39.34375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.34375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">where</text><title>wavenet1_elu0_where0</title></g><g class="node-item node-item-input" transform="translate(44.34375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(83.984375,0)"><path d="M0,0h26.03125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-26.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><g class="node-item node-item-input" transform="translate(110.015625,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(2)</text></g><line class="node" x1="44.34375" y1="0" x2="44.34375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="83.984375" y1="0" x2="83.984375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="110.015625" y1="0" x2="110.015625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h126.046875a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-126.046875a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-TRT_node0" class="node" transform="translate(584.421875,3459.0000038146973)" style="opacity: 1;"><g transform="translate(-43.375,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h42.109375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-42.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_trt_op</text><title>TRT_node0</title></g><g class="node-item node-item-input" transform="translate(47.109375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-34.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><line class="node" x1="47.109375" y1="0" x2="47.109375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h76.75a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-76.75a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_swapaxes1" class="node" transform="translate(584.421875,3570.000005722046)" style="opacity: 1;"><g transform="translate(-49.5546875,-26.000001907348633)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h54.46875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-59.46875a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">SwapAxis</text><title>wavenet1_swapaxes1</title></g><g class="node-item node-item-input" transform="translate(59.46875,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h99.109375a0,0 0 0 1 0,0v25a5,5 0 0 1 -5,5h-89.109375a5,5 0 0 1 -5,-5v-25a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dim1</tspan><tspan> = 1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dim2</tspan><tspan> = 2</tspan></text></g><line class="node" x1="59.46875" y1="0" x2="59.46875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="99.109375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h89.109375a5,5 0 0 1 5,5v42a5,5 0 0 1 -5,5h-89.109375a5,5 0 0 1 -5,-5v-42a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g class="node graph-input" transform="translate(650.9140625,11)" style="opacity: 1;"><g transform="translate(-17.875,-11)"><g><g class="node-item graph-item-input" transform="translate(0,0)"><path d="M5,0h25.75a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-25.75a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">data</text></g><path class="node border" d="M5,0h25.75a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-25.75a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g class="node" transform="translate(584.421875,3681.0000076293945)" style="opacity: 1;"><g transform="translate(-58.914066314697266,-11)"><g><g class="node-item graph-item-output" transform="translate(0,0)"><path d="M5,0h107.828125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-107.828125a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">wavenet1_swapaxes1</text></g><path class="node border" d="M5,0h107.828125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-107.828125a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g></g></g></svg>
\ No newline at end of file
diff --git a/docs/tutorials/tensorrt/wavenet_unoptimized.svg b/docs/tutorials/tensorrt/wavenet_unoptimized.svg
deleted file mode 100644
index bb39e9ebbb7d..000000000000
--- a/docs/tutorials/tensorrt/wavenet_unoptimized.svg
+++ /dev/null
@@ -1,17 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" id="export" class="graph" preserveAspectRatio="xMidYMid meet" style="" width="1849" height="7587"><rect id="background" fill="#fff" pointer-events="all" width="1849" height="7587"/><g id="origin" transform="translate(84.00605468750001, 84.00605468750001) scale(1)"><g id="clusters" class="clusters"/><g id="edge-paths" class="edge-paths"><defs><marker id="arrowhead-vee" viewBox="0 0 10 10" refX="9" refY="5" markerUnits="strokeWidth" markerWidth="8" markerHeight="6" orient="auto"><path d="M 0 0 L 10 5 L 0 10 L 4 5 z" style="stroke-width: 1; stroke-dasharray: 1, 0;"/></marker></defs><path class="edge-path" d="M468.48828315734863,22L468.48828315734863,28.166666666666668C468.48828315734863,34.333333333333336,468.48828315734863,46.666666666666664,468.48828315734863,59C468.48828315734863,71.33333333333333,468.48828315734863,83.66666666666667,468.48828315734863,89.83333333333333L468.48828315734863,96" marker-end="url(#arrowhead-vee)" id="edge-[0,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M468.48828315734863,170L468.48828315734863,176.16666666666666C468.48828315734863,182.33333333333334,468.48828315734863,194.66666666666666,468.48828315734863,207C468.48828315734863,219.33333333333334,468.48828315734863,231.66666666666666,468.48828315734863,237.83333333333334L468.48828315734863,244" marker-end="url(#arrowhead-vee)" id="edge-[2,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M418.93359565734863,281.8539668475258L383.29817899068195,290.3783063420544C347.6627623240153,298.90264583658296,276.39192899068195,315.9513248256401,240.7565123240153,330.64233098683536C205.12109565734863,345.3333371480306,205.12109565734863,357.66667048136395,205.12109565734863,363.8333371480306L205.12109565734863,370.00000381469727" marker-end="url(#arrowhead-vee)" id="edge-[3,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M518.0429706573486,291.9625747074757L533.4752623240153,298.80214622534595C548.907553990682,305.64171774321625,579.7721373240153,319.3208607789567,595.204428990682,332.32709896349365C610.6367206573486,345.3333371480306,610.6367206573486,357.66667048136395,610.6367206573486,363.8333371480306L610.6367206573486,370.00000381469727" marker-end="url(#arrowhead-vee)" id="edge-[3,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M518.0429706573486,281.4203389111014L555.345053990682,290.01694972836736C592.6471373240153,298.61356054563333,667.251303990682,315.8067821801653,704.5533873240153,335.820059664098C741.8554706573486,355.8333371480306,741.8554706573486,378.66667048136395,741.8554706573486,401.50000381469727C741.8554706573486,424.3333371480306,741.8554706573486,447.16667048136395,741.8554706573486,468.16667048136395C741.8554706573486,489.16667048136395,741.8554706573486,508.3333371480306,741.8554706573486,527.5000038146973C741.8554706573486,546.6666704813639,741.8554706573486,565.8333371480306,741.8554706573486,583.4166704813639C741.8554706573486,601.0000038146973,741.8554706573486,617.0000038146973,741.8554706573486,633.0000038146973C741.8554706573486,649.0000038146973,741.8554706573486,665.0000038146973,741.8554706573486,679.1666704813639C741.8554706573486,693.3333371480306,741.8554706573486,705.6666704813639,741.8554706573486,711.8333371480306L741.8554706573486,718.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[3,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M205.12109565734863,433.00000381469727L205.12109565734863,439.16667048136395C205.12109565734863,445.3333371480306,205.12109565734863,457.66667048136395,205.12109565734863,470.00000381469727C205.12109565734863,482.3333371480306,205.12109565734863,494.66667048136395,205.12109565734863,500.8333371480306L205.12109565734863,507.00000381469727" marker-end="url(#arrowhead-vee)" id="edge-[6,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M205.12109565734863,548.0000038146973L205.12109565734863,554.1666704813639C205.12109565734863,560.3333371480306,205.12109565734863,572.6666704813639,205.12109565734863,585.0000038146973C205.12109565734863,597.3333371480306,205.12109565734863,609.6666704813639,205.12109565734863,615.8333371480306L205.12109565734863,622.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[7,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M610.6367206573486,433.00000381469727L610.6367206573486,439.16667048136395C610.6367206573486,445.3333371480306,610.6367206573486,457.66667048136395,610.6367206573486,470.00000381469727C610.6367206573486,482.3333371480306,610.6367206573486,494.66667048136395,610.6367206573486,500.8333371480306L610.6367206573486,507.00000381469727" marker-end="url(#arrowhead-vee)" id="edge-[10,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M610.6367206573486,548.0000038146973L610.6367206573486,554.1666704813639C610.6367206573486,560.3333371480306,610.6367206573486,572.6666704813639,555.3932310740153,585.3723885101082C500.14974149068195,598.0781065388525,389.6627623240153,611.1562092630077,334.41927274068195,617.6952606250853L279.17578315734863,624.2343119871629" marker-end="url(#arrowhead-vee)" id="edge-[11,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M180.1643081108729,644.0000038146973L166.17338175906073,650.1666704813639C152.1824554072486,656.3333371480306,124.20060270362428,668.6666704813639,110.20967635181215,686.2500038146973C96.21875,703.8333371480306,96.21875,726.6666704813639,96.21875,749.5000038146973C96.21875,772.3333371480306,96.21875,795.1666704813639,96.21875,814.5833373069763C96.21875,834.0000041325887,96.21875,850.0000044504801,96.21875,866.0000047683716C96.21875,882.0000050862631,96.21875,898.0000054041544,96.21875,918.3333388964335C96.21875,938.6666723887125,96.21875,963.3333390553793,96.21875,988.0000057220459C96.21875,1012.6666723887125,96.21875,1037.3333390553792,96.21875,1059.250005722046C96.21875,1081.1666723887126,96.21875,1100.3333390553792,96.21875,1119.500005722046C96.21875,1138.6666723887126,96.21875,1157.8333390553792,96.21875,1173.5833390553792C96.21875,1189.3333390553792,96.21875,1201.6666723887126,96.21875,1207.8333390553792L96.21875,1214.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[12,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M279.17578315734863,643.4090999232283L323.7486998240153,649.6742505718065C368.32161649068195,655.9394012203846,457.4674498240153,668.4697025175409,502.04036649068195,680.9015198327858C546.6132831573486,693.3333371480306,546.6132831573486,705.6666704813639,546.6132831573486,711.8333371480306L546.6132831573486,718.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[12,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M96.21875,1277.000005722046L96.21875,1283.1666723887126C96.21875,1289.3333390553792,96.21875,1301.6666723887126,96.21875,1314.000005722046C96.21875,1326.3333390553792,96.21875,1338.6666723887126,96.21875,1344.8333390553792L96.21875,1351.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[15,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M96.21875,1414.000005722046L96.21875,1420.1666723887126C96.21875,1426.3333390553792,96.21875,1438.6666723887126,96.21875,1452.8333390553792C96.21875,1467.000005722046,96.21875,1483.000005722046,96.21875,1491.000005722046L96.21875,1499.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[16,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M96.21875,1540.000005722046L96.21875,1548.000005722046C96.21875,1556.000005722046,96.21875,1572.000005722046,110.07039147106143,1590.5000055631C123.92203294212284,1609.0000054041545,151.62531588424568,1630.000005086263,165.4769573553071,1640.5000049273174L179.3285988263685,1651.0000047683716" marker-end="url(#arrowhead-vee)" id="edge-[17,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M546.6132831573486,781.0000038146973L546.6132831573486,787.1666704813639C546.6132831573486,793.3333371480306,546.6132831573486,805.6666704813639,546.6132831573486,818.0000038146973C546.6132831573486,830.3333371480306,546.6132831573486,842.6666704813639,546.6132831573486,848.8333371480306L546.6132831573486,855.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[20,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M741.8554706573486,781.0000038146973L741.8554706573486,787.1666704813639C741.8554706573486,793.3333371480306,741.8554706573486,805.6666704813639,716.7722734560542,818.0000038146973C691.6890762547597,830.3333371480306,641.5226818521709,842.6666704813639,616.4394846508765,848.8333371480306L591.356287449582,855.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[21,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M483.67301158690117,877.0000057220459L448.38831694741765,883.1666723887125C413.1036223079341,889.3333390553793,342.53423302896704,901.6666723887125,307.2495383894835,914.0000057220459C271.96484375,926.3333390553793,271.96484375,938.6666723887125,271.96484375,944.8333390553793L271.96484375,951.0000057220459" marker-end="url(#arrowhead-vee)" id="edge-[22,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M553.9036477907922,877.0000057220459L557.9906700339935,883.1666723887125C562.0776922771948,889.3333390553793,570.2517367635974,901.6666723887125,574.3387590067987,914.0000057220459C578.42578125,926.3333390553793,578.42578125,938.6666723887125,578.42578125,944.8333390553793L578.42578125,951.0000057220459" marker-end="url(#arrowhead-vee)" id="edge-[22,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M609.8632869720459,874.3310091665718L660.0585988362631,880.9425085924842C710.2539107004801,887.5540080183964,810.6445344289144,900.7770068702212,860.8398462931315,919.7218396294669C911.0351581573486,938.6666723887125,911.0351581573486,963.3333390553793,911.0351581573486,988.0000057220459C911.0351581573486,1012.6666723887125,911.0351581573486,1037.3333390553792,911.0351581573486,1059.250005722046C911.0351581573486,1081.1666723887126,911.0351581573486,1100.3333390553792,911.0351581573486,1119.500005722046C911.0351581573486,1138.6666723887126,911.0351581573486,1157.8333390553792,911.0351581573486,1178.8333390553792C911.0351581573486,1199.8333390553792,911.0351581573486,1222.6666723887126,911.0351581573486,1245.500005722046C911.0351581573486,1268.3333390553792,911.0351581573486,1291.1666723887126,911.0351581573486,1308.750005722046C911.0351581573486,1326.3333390553792,911.0351581573486,1338.6666723887126,911.0351581573486,1344.8333390553792L911.0351581573486,1351.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[22,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M271.96484375,1025.000005722046L271.96484375,1031.1666723887126C271.96484375,1037.3333390553792,271.96484375,1049.6666723887126,271.96484375,1062.000005722046C271.96484375,1074.3333390553792,271.96484375,1086.6666723887126,271.96484375,1092.8333390553792L271.96484375,1099.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[25,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M271.96484375,1140.000005722046L271.96484375,1146.1666723887126C271.96484375,1152.3333390553792,271.96484375,1164.6666723887126,293.4021964340024,1180.4166723887126C314.83954911800487,1196.1666723887126,357.7142544860097,1215.3333390553792,379.15160717001214,1224.9166723887126L400.5889598540146,1234.500005722046" marker-end="url(#arrowhead-vee)" id="edge-[26,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M578.42578125,1025.000005722046L578.42578125,1031.1666723887126C578.42578125,1037.3333390553792,578.42578125,1049.6666723887126,578.42578125,1062.000005722046C578.42578125,1074.3333390553792,578.42578125,1086.6666723887126,578.42578125,1092.8333390553792L578.42578125,1099.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[29,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M578.42578125,1140.000005722046L578.42578125,1146.1666723887126C578.42578125,1152.3333390553792,578.42578125,1164.6666723887126,556.9884285659975,1180.4166723887126C535.5510758819952,1196.1666723887126,492.6763705139903,1215.3333390553792,471.23901782998786,1224.9166723887126L449.8016651459854,1234.500005722046" marker-end="url(#arrowhead-vee)" id="edge-[30,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M405.72952782846716,1256.500005722046L388.77070027372264,1266.0833390553792C371.81187271897807,1275.6666723887126,337.89421760948903,1294.8333390553792,320.9353900547445,1310.5833390553792C303.9765625,1326.3333390553792,303.9765625,1338.6666723887126,303.9765625,1344.8333390553792L303.9765625,1351.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[31,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M444.66109717153284,1256.500005722046L461.61992472627736,1266.0833390553792C478.57875228102193,1275.6666723887126,512.496407390511,1294.8333390553792,529.4552349452555,1310.5833390553792C546.4140625,1326.3333390553792,546.4140625,1338.6666723887126,546.4140625,1344.8333390553792L546.4140625,1351.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[31,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M303.9765625,1414.000005722046L303.9765625,1420.1666723887126C303.9765625,1426.3333390553792,303.9765625,1438.6666723887126,303.9765625,1451.000005722046C303.9765625,1463.3333390553792,303.9765625,1475.6666723887126,303.9765625,1481.8333390553792L303.9765625,1488.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[34,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M303.9765625,1551.000005722046L303.9765625,1557.1666723887126C303.9765625,1563.3333390553792,303.9765625,1575.6666723887126,288.34905534636164,1592.3333388964336C272.7215481927232,1609.0000054041545,241.46653388544647,1630.000005086263,225.8390267318081,1640.5000049273174L210.2115195781697,1651.0000047683716" marker-end="url(#arrowhead-vee)" id="edge-[35,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M193.83984375,1673.0000066757202L193.83984375,1683.5000065167744C193.83984375,1694.0000063578289,193.83984375,1715.0000060399373,193.83984375,1735.083339214325C193.83984375,1755.1666723887126,193.83984375,1774.3333390553792,193.83984375,1793.500005722046C193.83984375,1812.6666723887126,193.83984375,1831.8333390553792,193.83984375,1849.4166723887126C193.83984375,1867.000005722046,193.83984375,1883.000005722046,193.83984375,1899.000005722046C193.83984375,1915.000005722046,193.83984375,1931.000005722046,193.83984375,1950.4166723887126C193.83984375,1969.8333390553792,193.83984375,1992.6666723887126,193.83984375,2015.500005722046C193.83984375,2038.3333390553792,193.83984375,2061.1666723887124,193.83984375,2084.000005722046C193.83984375,2106.8333390553794,193.83984375,2129.6666723887124,193.83984375,2152.500005722046C193.83984375,2175.3333390553794,193.83984375,2198.1666723887124,195.44056190181155,2220.0833388964334C197.04128005362307,2242.0000054041543,200.24271635724617,2263.000005086263,201.84343450905772,2273.500004927317L203.44415266086924,2284.0000047683716" marker-end="url(#arrowhead-vee)" id="edge-[36,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M546.4140625,1414.000005722046L546.4140625,1420.1666723887126C546.4140625,1426.3333390553792,546.4140625,1438.6666723887126,570.1106246837354,1454.4166722297668C593.8071868674707,1470.166672070821,641.2003112349414,1489.3333384195964,664.8968734186768,1498.916671593984L688.5934356024121,1508.5000047683716" marker-end="url(#arrowhead-vee)" id="edge-[39,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M911.0351581573486,1414.000005722046L911.0351581573486,1420.1666723887126C911.0351581573486,1426.3333390553792,911.0351581573486,1438.6666723887126,883.7202540331332,1454.4166722297668C856.4053499089177,1470.166672070821,801.7755416604869,1489.3333384195964,774.4606375362715,1498.916671593984L747.1457334120561,1508.5000047683716" marker-end="url(#arrowhead-vee)" id="edge-[40,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M685.9463097667933,1530.5000066757202L659.9435397068859,1540.083339850108C633.9407696469784,1549.6666730244954,581.9352295271635,1568.8333393732707,555.9324594672561,1584.583339214325C529.9296894073486,1600.3333390553792,529.9296894073486,1612.6666723887126,529.9296894073486,1618.8333390553792L529.9296894073486,1625.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[41,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M728.8673901302863,1530.5000066757202L740.2579816764634,1540.083339850108C751.6485732226405,1549.6666730244954,774.4297563149945,1568.8333393732707,785.8203478611716,1584.583339214325C797.2109394073486,1600.3333390553792,797.2109394073486,1612.6666723887126,797.2109394073486,1618.8333390553792L797.2109394073486,1625.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[41,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M774.3132768258126,1530.5000066757202L825.2968716310687,1540.083339850108C876.2804664363247,1549.6666730244954,978.2476560468367,1568.8333393732707,1029.2312508520927,1590.7500058809917C1080.2148456573486,1612.6666723887126,1080.2148456573486,1637.3333390553792,1080.2148456573486,1662.000005722046C1080.2148456573486,1686.6666723887126,1080.2148456573486,1711.3333390553792,1080.2148456573486,1733.250005722046C1080.2148456573486,1755.1666723887126,1080.2148456573486,1774.3333390553792,1080.2148456573486,1793.500005722046C1080.2148456573486,1812.6666723887126,1080.2148456573486,1831.8333390553792,1080.2148456573486,1849.4166723887126C1080.2148456573486,1867.000005722046,1080.2148456573486,1883.000005722046,1080.2148456573486,1899.000005722046C1080.2148456573486,1915.000005722046,1080.2148456573486,1931.000005722046,1080.2148456573486,1945.1666723887126C1080.2148456573486,1959.3333390553792,1080.2148456573486,1971.6666723887126,1080.2148456573486,1977.8333390553792L1080.2148456573486,1984.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[41,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M529.9296894073486,1699.000005722046L529.9296894073486,1705.1666723887126C529.9296894073486,1711.3333390553792,529.9296894073486,1723.6666723887126,529.9296894073486,1736.000005722046C529.9296894073486,1748.3333390553792,529.9296894073486,1760.6666723887126,529.9296894073486,1766.8333390553792L529.9296894073486,1773.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[44,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M529.9296894073486,1814.000005722046L529.9296894073486,1820.1666723887126C529.9296894073486,1826.3333390553792,529.9296894073486,1838.6666723887126,538.23471599155,1851.000005722046C546.5397425757515,1863.3333390553792,563.1497957441542,1875.6666723887126,571.4548223283556,1881.8333390553792L579.759848912557,1888.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[45,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M797.2109394073486,1699.000005722046L797.2109394073486,1705.1666723887126C797.2109394073486,1711.3333390553792,797.2109394073486,1723.6666723887126,797.2109394073486,1736.000005722046C797.2109394073486,1748.3333390553792,797.2109394073486,1760.6666723887126,797.2109394073486,1766.8333390553792L797.2109394073486,1773.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[48,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M797.2109394073486,1814.000005722046L797.2109394073486,1820.1666723887126C797.2109394073486,1826.3333390553792,797.2109394073486,1838.6666723887126,771.1777498457167,1851.000005722046C745.1445602840848,1863.3333390553792,693.0781811608209,1875.6666723887126,667.044991599189,1881.8333390553792L641.011802037557,1888.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[49,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M570.7937026421229,1910.000005722046L557.4622001184357,1916.1666723887126C544.1306975947485,1922.3333390553792,517.4676925473742,1934.6666723887126,504.1361900236871,1947.000005722046C490.8046875,1959.3333390553792,490.8046875,1971.6666723887126,490.8046875,1977.8333390553792L490.8046875,1984.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[50,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M635.318442662557,1910.000005722046L658.1599004533556,1916.1666723887126C681.0013582441542,1922.3333390553792,726.6842738257515,1934.6666723887126,749.5257316165502,1947.000005722046C772.3671894073486,1959.3333390553792,772.3671894073486,1971.6666723887126,772.3671894073486,1977.8333390553792L772.3671894073486,1984.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[50,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M490.8046875,2047.000005722046L490.8046875,2053.1666723887124C490.8046875,2059.3333390553794,490.8046875,2071.6666723887124,490.8046875,2084.000005722046C490.8046875,2096.3333390553794,490.8046875,2108.6666723887124,490.8046875,2114.8333390553794L490.8046875,2121.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[53,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M490.8046875,2184.000005722046L490.8046875,2190.1666723887124C490.8046875,2196.3333390553794,490.8046875,2208.6666723887124,450.2685027845992,2225.3333388964334C409.7323180691985,2242.0000054041543,328.659948638397,2263.000005086263,288.12376392299626,2273.500004927317L247.58757920759552,2284.0000047683716" marker-end="url(#arrowhead-vee)" id="edge-[54,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M205.12109565734863,2306.00000667572L205.12109565734863,2316.5000065167746C205.12109565734863,2327.0000063578286,205.12109565734863,2348.0000060399375,205.12109565734863,2368.083339214325C205.12109565734863,2388.1666723887124,205.12109565734863,2407.3333390553794,205.12109565734863,2426.500005722046C205.12109565734863,2445.6666723887124,205.12109565734863,2464.8333390553794,205.12109565734863,2482.4166723887124C205.12109565734863,2500.000005722046,205.12109565734863,2516.000005722046,205.12109565734863,2532.000005722046C205.12109565734863,2548.000005722046,205.12109565734863,2564.000005722046,205.12109565734863,2583.4166723887124C205.12109565734863,2602.8333390553794,205.12109565734863,2625.6666723887124,205.12109565734863,2648.500005722046C205.12109565734863,2671.3333390553794,205.12109565734863,2694.1666723887124,205.12109565734863,2717.000005722046C205.12109565734863,2739.8333390553794,205.12109565734863,2762.6666723887124,205.12109565734863,2785.500005722046C205.12109565734863,2808.3333390553794,205.12109565734863,2831.1666723887124,205.12109565734863,2853.0833388964334C205.12109565734863,2875.0000054041543,205.12109565734863,2896.000005086263,205.12109565734863,2906.500004927317L205.12109565734863,2917.0000047683716" marker-end="url(#arrowhead-vee)" id="edge-[55,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M772.3671894073486,2047.000005722046L772.3671894073486,2053.1666723887124C772.3671894073486,2059.3333390553794,772.3671894073486,2071.6666723887124,788.1209956013536,2087.416672229767C803.8748017953586,2103.1666720708213,835.3824141833687,2122.333338419596,851.1362203773737,2131.916671593984L866.8900265713787,2141.5000047683716" marker-end="url(#arrowhead-vee)" id="edge-[58,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1080.2148456573486,2047.000005722046L1080.2148456573486,2053.1666723887124C1080.2148456573486,2059.3333390553794,1080.2148456573486,2071.6666723887124,1052.8999415331332,2087.416672229767C1025.5850374089177,2103.1666720708213,970.9552291604869,2122.333338419596,943.6403250362714,2131.916671593984L916.3254209120561,2141.5000047683716" marker-end="url(#arrowhead-vee)" id="edge-[59,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M850.1855315099994,2163.50000667572L819.878568242891,2173.0833398501077C789.5716049757824,2182.6666730244956,728.9576784415655,2201.8333393732705,698.6507151744571,2217.583339214325C668.3437519073486,2233.3333390553794,668.3437519073486,2245.6666723887124,668.3437519073486,2251.8333390553794L668.3437519073486,2258.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[60,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M889.1171042283412,2163.50000667572L892.7277955081757,2173.0833398501077C896.3384867880104,2182.6666730244956,903.5598693476795,2201.8333393732705,907.1705606275141,2217.583339214325C910.7812519073486,2233.3333390553794,910.7812519073486,2245.6666723887124,910.7812519073486,2251.8333390553794L910.7812519073486,2258.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[60,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M943.4929643258126,2163.50000667572L994.4765591310687,2173.0833398501077C1045.4601539363246,2182.6666730244956,1147.4273435468367,2201.8333393732705,1198.4109383520927,2223.7500058809915C1249.3945331573486,2245.6666723887124,1249.3945331573486,2270.3333390553794,1249.3945331573486,2295.000005722046C1249.3945331573486,2319.6666723887124,1249.3945331573486,2344.3333390553794,1249.3945331573486,2366.250005722046C1249.3945331573486,2388.1666723887124,1249.3945331573486,2407.3333390553794,1249.3945331573486,2426.500005722046C1249.3945331573486,2445.6666723887124,1249.3945331573486,2464.8333390553794,1249.3945331573486,2482.4166723887124C1249.3945331573486,2500.000005722046,1249.3945331573486,2516.000005722046,1249.3945331573486,2532.000005722046C1249.3945331573486,2548.000005722046,1249.3945331573486,2564.000005722046,1249.3945331573486,2578.1666723887124C1249.3945331573486,2592.3333390553794,1249.3945331573486,2604.6666723887124,1249.3945331573486,2610.8333390553794L1249.3945331573486,2617.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[60,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M668.3437519073486,2332.000005722046L668.3437519073486,2338.1666723887124C668.3437519073486,2344.3333390553794,668.3437519073486,2356.6666723887124,668.3437519073486,2369.000005722046C668.3437519073486,2381.3333390553794,668.3437519073486,2393.6666723887124,668.3437519073486,2399.8333390553794L668.3437519073486,2406.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[63,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M668.3437519073486,2447.000005722046L668.3437519073486,2453.1666723887124C668.3437519073486,2459.3333390553794,668.3437519073486,2471.6666723887124,680.6013067033556,2484.000005722046C692.8588614993624,2496.3333390553794,717.3739710913763,2508.6666723887124,729.6315258873833,2514.8333390553794L741.8890806833903,2521.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[64,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M910.7812519073486,2332.000005722046L910.7812519073486,2338.1666723887124C910.7812519073486,2344.3333390553794,910.7812519073486,2356.6666723887124,910.7812519073486,2369.000005722046C910.7812519073486,2381.3333390553794,910.7812519073486,2393.6666723887124,910.7812519073486,2399.8333390553794L910.7812519073486,2406.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[67,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M910.7812519073486,2447.000005722046L910.7812519073486,2453.1666723887124C910.7812519073486,2459.3333390553794,910.7812519073486,2471.6666723887124,891.8923223283556,2484.000005722046C873.0033927493624,2496.3333390553794,835.2255335913763,2508.6666723887124,816.3366040123833,2514.8333390553794L797.4476744333903,2521.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[68,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M732.9229344129562,2543.000005722046L715.6389036774635,2549.1666723887124C698.3548729419708,2555.3333390553794,663.7868114709854,2567.6666723887124,646.5027807354927,2580.000005722046C629.21875,2592.3333390553794,629.21875,2604.6666723887124,629.21875,2610.8333390553794L629.21875,2617.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[69,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M808.4387226104736,2543.000005722046L833.4893004099528,2549.1666723887124C858.539878209432,2555.3333390553794,908.6410338083903,2567.6666723887124,933.6916116078695,2580.000005722046C958.7421894073486,2592.3333390553794,958.7421894073486,2604.6666723887124,958.7421894073486,2610.8333390553794L958.7421894073486,2617.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[69,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M629.21875,2680.000005722046L629.21875,2686.1666723887124C629.21875,2692.3333390553794,629.21875,2704.6666723887124,629.21875,2717.000005722046C629.21875,2729.3333390553794,629.21875,2741.6666723887124,629.21875,2747.8333390553794L629.21875,2754.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[72,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M629.21875,2817.000005722046L629.21875,2823.1666723887124C629.21875,2829.3333390553794,629.21875,2841.6666723887124,569.0427323893333,2858.3333388964334C508.8667147786667,2875.0000054041543,388.5146795573334,2896.000005086263,328.3386619466667,2906.500004927317L268.1626443360001,2917.0000047683716" marker-end="url(#arrowhead-vee)" id="edge-[73,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M205.12109565734863,2939.00000667572L205.12109565734863,2949.5000065167746C205.12109565734863,2960.0000063578286,205.12109565734863,2981.0000060399375,205.12109565734863,3001.083339214325C205.12109565734863,3021.1666723887124,205.12109565734863,3040.3333390553794,205.12109565734863,3059.500005722046C205.12109565734863,3078.6666723887124,205.12109565734863,3097.8333390553794,205.12109565734863,3115.4166723887124C205.12109565734863,3133.000005722046,205.12109565734863,3149.000005722046,205.12109565734863,3165.000005722046C205.12109565734863,3181.000005722046,205.12109565734863,3197.000005722046,205.12109565734863,3216.4166723887124C205.12109565734863,3235.8333390553794,205.12109565734863,3258.6666723887124,205.12109565734863,3281.500005722046C205.12109565734863,3304.3333390553794,205.12109565734863,3327.1666723887124,205.12109565734863,3350.000005722046C205.12109565734863,3372.8333390553794,205.12109565734863,3395.6666723887124,205.12109565734863,3418.500005722046C205.12109565734863,3441.3333390553794,205.12109565734863,3464.1666723887124,205.12109565734863,3486.0833388964334C205.12109565734863,3508.0000054041543,205.12109565734863,3529.000005086263,205.12109565734863,3539.500004927317L205.12109565734863,3550.0000047683716" marker-end="url(#arrowhead-vee)" id="edge-[74,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M958.7421894073486,2680.000005722046L958.7421894073486,2686.1666723887124C958.7421894073486,2692.3333390553794,958.7421894073486,2704.6666723887124,972.0903254009855,2720.416672229767C985.4384613946225,2736.1666720708213,1012.1347333818964,2755.333338419596,1025.4828693755333,2764.916671593984L1038.8310053691703,2774.5000047683716" marker-end="url(#arrowhead-vee)" id="edge-[77,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1249.3945331573486,2680.000005722046L1249.3945331573486,2686.1666723887124C1249.3945331573486,2692.3333390553794,1249.3945331573486,2704.6666723887124,1222.0796290331332,2720.416672229767C1194.7647249089177,2736.1666720708213,1140.1349166604869,2755.333338419596,1112.8200125362714,2764.916671593984L1085.505108412056,2774.5000047683716" marker-end="url(#arrowhead-vee)" id="edge-[78,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1010.4352456080544,2796.50000667572L972.3483820746034,2806.0833398501077C934.2615185411524,2815.6666730244956,858.0877914742505,2834.8333393732705,820.0009279407996,2850.583339214325C781.9140644073486,2866.3333390553794,781.9140644073486,2878.6666723887124,781.9140644073486,2884.8333390553794L781.9140644073486,2891.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[79,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1049.3668183263962,2796.50000667572L1045.1976093398882,2806.0833398501077C1041.0284003533804,2815.6666730244956,1032.6899823803644,2834.8333393732705,1028.5207733938566,2850.583339214325C1024.3515644073486,2866.3333390553794,1024.3515644073486,2878.6666723887124,1024.3515644073486,2884.8333390553794L1024.3515644073486,2891.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[79,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1112.6726518258126,2796.50000667572L1163.6562466310686,2806.0833398501077C1214.6398414363246,2815.6666730244956,1316.6070310468367,2834.8333393732705,1367.5906258520927,2856.7500058809915C1418.5742206573486,2878.6666723887124,1418.5742206573486,2903.3333390553794,1418.5742206573486,2928.000005722046C1418.5742206573486,2952.6666723887124,1418.5742206573486,2977.3333390553794,1418.5742206573486,2999.250005722046C1418.5742206573486,3021.1666723887124,1418.5742206573486,3040.3333390553794,1418.5742206573486,3059.500005722046C1418.5742206573486,3078.6666723887124,1418.5742206573486,3097.8333390553794,1418.5742206573486,3115.4166723887124C1418.5742206573486,3133.000005722046,1418.5742206573486,3149.000005722046,1418.5742206573486,3165.000005722046C1418.5742206573486,3181.000005722046,1418.5742206573486,3197.000005722046,1418.5742206573486,3211.1666723887124C1418.5742206573486,3225.3333390553794,1418.5742206573486,3237.6666723887124,1418.5742206573486,3243.8333390553794L1418.5742206573486,3250.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[79,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M781.9140644073486,2965.000005722046L781.9140644073486,2971.1666723887124C781.9140644073486,2977.3333390553794,781.9140644073486,2989.6666723887124,781.9140644073486,3002.000005722046C781.9140644073486,3014.3333390553794,781.9140644073486,3026.6666723887124,781.9140644073486,3032.8333390553794L781.9140644073486,3039.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[82,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M781.9140644073486,3080.000005722046L781.9140644073486,3086.1666723887124C781.9140644073486,3092.3333390553794,781.9140644073486,3104.6666723887124,797.4873065948486,3117.000005722046C813.0605487823486,3129.3333390553794,844.2070331573486,3141.6666723887124,859.7802753448486,3147.8333390553794L875.3535175323486,3154.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[83,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1024.3515644073486,2965.000005722046L1024.3515644073486,2971.1666723887124C1024.3515644073486,2977.3333390553794,1024.3515644073486,2989.6666723887124,1024.3515644073486,3002.000005722046C1024.3515644073486,3014.3333390553794,1024.3515644073486,3026.6666723887124,1024.3515644073486,3032.8333390553794L1024.3515644073486,3039.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[86,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1024.3515644073486,3080.000005722046L1024.3515644073486,3086.1666723887124C1024.3515644073486,3092.3333390553794,1024.3515644073486,3104.6666723887124,1008.7783222198486,3117.000005722046C993.2050800323486,3129.3333390553794,962.0585956573486,3141.6666723887124,946.4853534698486,3147.8333390553794L930.9121112823486,3154.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[87,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M866.3873712619146,3176.000005722046L845.7876531349289,3182.1666723887124C825.1879350079431,3188.3333390553794,783.9884987539716,3200.6666723887124,763.3887806269859,3213.000005722046C742.7890625,3225.3333390553794,742.7890625,3237.6666723887124,742.7890625,3243.8333390553794L742.7890625,3250.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[88,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M941.903159459432,3176.000005722046L963.6380498674181,3182.1666723887124C985.3729402754043,3188.3333390553794,1028.8427210913765,3200.6666723887124,1050.5776114993625,3213.000005722046C1072.3125019073486,3225.3333390553794,1072.3125019073486,3237.6666723887124,1072.3125019073486,3243.8333390553794L1072.3125019073486,3250.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[88,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M742.7890625,3313.000005722046L742.7890625,3319.1666723887124C742.7890625,3325.3333390553794,742.7890625,3337.6666723887124,742.7890625,3350.000005722046C742.7890625,3362.3333390553794,742.7890625,3374.6666723887124,742.7890625,3380.8333390553794L742.7890625,3387.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[91,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M742.7890625,3450.000005722046L742.7890625,3456.1666723887124C742.7890625,3462.3333390553794,742.7890625,3474.6666723887124,663.719401995341,3491.715807861741C584.649741490682,3508.7649433347683,426.51042048136395,3530.5298809474903,347.4407599767049,3541.4123497538517L268.3710994720459,3552.2948185602127" marker-end="url(#arrowhead-vee)" id="edge-[92,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M205.12109565734863,3572.00000667572L205.12109565734863,3582.5000065167746C205.12109565734863,3593.0000063578286,205.12109565734863,3614.0000060399375,205.12109565734863,3634.083339214325C205.12109565734863,3654.1666723887124,205.12109565734863,3673.3333390553794,205.12109565734863,3692.500005722046C205.12109565734863,3711.6666723887124,205.12109565734863,3730.8333390553794,205.12109565734863,3748.4166723887124C205.12109565734863,3766.000005722046,205.12109565734863,3782.000005722046,205.12109565734863,3798.000005722046C205.12109565734863,3814.000005722046,205.12109565734863,3830.000005722046,205.12109565734863,3849.4166723887124C205.12109565734863,3868.8333390553794,205.12109565734863,3891.6666723887124,205.12109565734863,3914.500005722046C205.12109565734863,3937.3333390553794,205.12109565734863,3960.1666723887124,205.12109565734863,3983.000005722046C205.12109565734863,4005.8333390553794,205.12109565734863,4028.6666723887124,205.12109565734863,4051.500005722046C205.12109565734863,4074.3333390553794,205.12109565734863,4097.166672388713,208.15348026479452,4119.083338896434C211.1858648722404,4141.000005404155,217.2506340871322,4162.000005086263,220.2830186945781,4172.500004927318L223.31540330202398,4183.000004768372" marker-end="url(#arrowhead-vee)" id="edge-[93,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1072.3125019073486,3313.000005722046L1072.3125019073486,3319.1666723887124C1072.3125019073486,3325.3333390553794,1072.3125019073486,3337.6666723887124,1093.440538167328,3353.416672229767C1114.5685744273076,3369.1666720708213,1156.8246469472663,3388.333338419596,1177.9526832072459,3397.916671593984L1199.0807194672252,3407.5000047683716" marker-end="url(#arrowhead-vee)" id="edge-[96,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1418.5742206573486,3313.000005722046L1418.5742206573486,3319.1666723887124C1418.5742206573486,3325.3333390553794,1418.5742206573486,3337.6666723887124,1391.2593165331334,3353.416672229767C1363.9444124089177,3369.1666720708213,1309.3146041604869,3388.333338419596,1281.9997000362714,3397.916671593984L1254.684795912056,3407.5000047683716" marker-end="url(#arrowhead-vee)" id="edge-[97,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1170.6849597061093,3429.50000667572L1124.8181959063159,3439.0833398501077C1078.9514321065224,3448.6666730244956,987.2179045069355,3467.8333393732705,941.351140707142,3483.583339214325C895.4843769073486,3499.3333390553794,895.4843769073486,3511.6666723887124,895.4843769073486,3517.8333390553794L895.4843769073486,3524.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[98,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1209.6165324244512,3429.50000667572L1197.6674231716008,3439.0833398501077C1185.7183139187503,3448.6666730244956,1161.8200954130496,3467.8333393732705,1149.870986160199,3483.583339214325C1137.9218769073486,3499.3333390553794,1137.9218769073486,3511.6666723887124,1137.9218769073486,3517.8333390553794L1137.9218769073486,3524.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[98,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1269.742678148196,3429.50000667572L1310.1761904413881,3439.0833398501077C1350.6097027345802,3448.6666730244956,1431.4767273209645,3467.8333393732705,1471.9102396141564,3489.7500058809915C1512.3437519073486,3511.6666723887124,1512.3437519073486,3536.3333390553794,1512.3437519073486,3561.000005722046C1512.3437519073486,3585.6666723887124,1512.3437519073486,3610.3333390553794,1512.3437519073486,3632.250005722046C1512.3437519073486,3654.1666723887124,1512.3437519073486,3673.3333390553794,1512.3437519073486,3692.500005722046C1512.3437519073486,3711.6666723887124,1512.3437519073486,3730.8333390553794,1512.3437519073486,3748.4166723887124C1512.3437519073486,3766.000005722046,1512.3437519073486,3782.000005722046,1512.3437519073486,3798.000005722046C1512.3437519073486,3814.000005722046,1512.3437519073486,3830.000005722046,1512.3437519073486,3844.1666723887124C1512.3437519073486,3858.3333390553794,1512.3437519073486,3870.6666723887124,1512.3437519073486,3876.8333390553794L1512.3437519073486,3883.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[98,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M895.4843769073486,3598.000005722046L895.4843769073486,3604.1666723887124C895.4843769073486,3610.3333390553794,895.4843769073486,3622.6666723887124,895.4843769073486,3635.000005722046C895.4843769073486,3647.3333390553794,895.4843769073486,3659.6666723887124,895.4843769073486,3665.8333390553794L895.4843769073486,3672.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[101,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M895.4843769073486,3713.000005722046L895.4843769073486,3719.1666723887124C895.4843769073486,3725.3333390553794,895.4843769073486,3737.6666723887124,911.0576190948486,3750.000005722046C926.6308612823486,3762.3333390553794,957.7773456573486,3774.6666723887124,973.3505878448486,3780.8333390553794L988.9238300323486,3787.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[102,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1137.9218769073486,3598.000005722046L1137.9218769073486,3604.1666723887124C1137.9218769073486,3610.3333390553794,1137.9218769073486,3622.6666723887124,1137.9218769073486,3635.000005722046C1137.9218769073486,3647.3333390553794,1137.9218769073486,3659.6666723887124,1137.9218769073486,3665.8333390553794L1137.9218769073486,3672.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[105,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1137.9218769073486,3713.000005722046L1137.9218769073486,3719.1666723887124C1137.9218769073486,3725.3333390553794,1137.9218769073486,3737.6666723887124,1122.3486347198486,3750.000005722046C1106.7753925323486,3762.3333390553794,1075.6289081573486,3774.6666723887124,1060.0556659698486,3780.8333390553794L1044.4824237823486,3787.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[106,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M979.9576837619146,3809.000005722046L959.3579656349289,3815.1666723887124C938.7582475079431,3821.3333390553794,897.5588112539716,3833.6666723887124,876.9590931269857,3846.000005722046C856.359375,3858.3333390553794,856.359375,3870.6666723887124,856.359375,3876.8333390553794L856.359375,3883.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[107,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1055.473471959432,3809.000005722046L1077.208362367418,3815.1666723887124C1098.943252775404,3821.3333390553794,1142.4130335913762,3833.6666723887124,1164.1479239993625,3846.000005722046C1185.8828144073486,3858.3333390553794,1185.8828144073486,3870.6666723887124,1185.8828144073486,3876.8333390553794L1185.8828144073486,3883.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[107,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M856.359375,3946.000005722046L856.359375,3952.1666723887124C856.359375,3958.3333390553794,856.359375,3970.6666723887124,856.359375,3983.000005722046C856.359375,3995.3333390553794,856.359375,4007.6666723887124,856.359375,4013.8333390553794L856.359375,4020.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[110,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M856.359375,4083.000005722046L856.359375,4089.1666723887124C856.359375,4095.3333390553794,856.359375,4107.666672388713,761.9231777191162,4124.928183710683C667.4869804382324,4142.189695032653,478.61458587646484,4164.3793843432595,384.17838859558105,4175.474228998563L289.74219131469727,4186.569073653866" marker-end="url(#arrowhead-vee)" id="edge-[111,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M226.4921875,4205.00000667572L226.4921875,4215.500006516774C226.4921875,4226.000006357829,226.4921875,4247.000006039937,226.4921875,4267.083339214325C226.4921875,4287.166672388713,226.4921875,4306.333339055379,226.4921875,4325.500005722046C226.4921875,4344.666672388713,226.4921875,4363.833339055379,226.4921875,4381.416672388713C226.4921875,4399.000005722046,226.4921875,4415.000005722046,226.4921875,4431.000005722046C226.4921875,4447.000005722046,226.4921875,4463.000005722046,226.4921875,4482.416672388713C226.4921875,4501.833339055379,226.4921875,4524.666672388713,226.4921875,4547.500005722046C226.4921875,4570.333339055379,226.4921875,4593.166672388713,226.4921875,4616.000005722046C226.4921875,4638.833339055379,226.4921875,4661.666672388713,226.4921875,4684.500005722046C226.4921875,4707.333339055379,226.4921875,4730.166672388713,235.80162570367122,4752.083338896434C245.11106390734244,4774.000005404155,263.72994031468494,4795.000005086263,273.03937851835616,4805.500004927318L282.3488167220274,4816.000004768372" marker-end="url(#arrowhead-vee)" id="edge-[112,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1185.8828144073486,3946.000005722046L1185.8828144073486,3952.1666723887124C1185.8828144073486,3958.3333390553794,1185.8828144073486,3970.6666723887124,1214.7907509336706,3986.416672229767C1243.6986874599925,4002.1666720708213,1301.5145605126365,4021.333338419596,1330.4224970389585,4030.916671593984L1359.3304335652804,4040.5000047683716" marker-end="url(#arrowhead-vee)" id="edge-[115,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1512.3437519073486,3946.000005722046L1512.3437519073486,3952.1666723887124C1512.3437519073486,3958.3333390553794,1512.3437519073486,3970.6666723887124,1495.578930295197,3986.416672229767C1478.8141086830456,4002.1666720708213,1445.2844654587425,4021.333338419596,1428.5196438465912,4030.916671593984L1411.7548222344396,4040.5000047683716" marker-end="url(#arrowhead-vee)" id="edge-[116,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1330.9346738041643,4062.50000667572L1277.2880097380282,4072.0833398501077C1223.6413456718924,4081.6666730244956,1116.3480175396205,4100.833339373271,1062.7013534734845,4116.583339214325C1009.0546894073486,4132.333339055379,1009.0546894073486,4144.666672388713,1009.0546894073486,4150.833339055379L1009.0546894073486,4157.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[117,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1369.8662465225061,4062.50000667572L1350.1372370033132,4072.0833398501077C1330.4082274841203,4081.6666730244956,1290.9502084457345,4100.833339373271,1271.2211989265415,4116.583339214325C1251.4921894073486,4132.333339055379,1251.4921894073486,4144.666672388713,1251.4921894073486,4150.833339055379L1251.4921894073486,4157.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[117,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1419.4565792890253,4062.50000667572L1442.9312643087458,4072.0833398501077C1466.4059493284665,4081.6666730244956,1513.3553193679074,4100.833339373271,1536.830004387628,4122.750005880992C1560.3046894073486,4144.666672388713,1560.3046894073486,4169.333339055379,1560.3046894073486,4194.000005722046C1560.3046894073486,4218.666672388713,1560.3046894073486,4243.333339055379,1560.3046894073486,4265.250005722046C1560.3046894073486,4287.166672388713,1560.3046894073486,4306.333339055379,1560.3046894073486,4325.500005722046C1560.3046894073486,4344.666672388713,1560.3046894073486,4363.833339055379,1560.3046894073486,4381.416672388713C1560.3046894073486,4399.000005722046,1560.3046894073486,4415.000005722046,1560.3046894073486,4431.000005722046C1560.3046894073486,4447.000005722046,1560.3046894073486,4463.000005722046,1560.3046894073486,4477.166672388713C1560.3046894073486,4491.333339055379,1560.3046894073486,4503.666672388713,1560.3046894073486,4509.833339055379L1560.3046894073486,4516.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[117,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1009.0546894073486,4231.000005722046L1009.0546894073486,4237.166672388713C1009.0546894073486,4243.333339055379,1009.0546894073486,4255.666672388713,1009.0546894073486,4268.000005722046C1009.0546894073486,4280.333339055379,1009.0546894073486,4292.666672388713,1009.0546894073486,4298.833339055379L1009.0546894073486,4305.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[120,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1009.0546894073486,4346.000005722046L1009.0546894073486,4352.166672388713C1009.0546894073486,4358.333339055379,1009.0546894073486,4370.666672388713,1024.6279315948486,4383.000005722046C1040.2011737823486,4395.333339055379,1071.3476581573486,4407.666672388713,1086.9209003448486,4413.833339055379L1102.4941425323486,4420.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[121,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1251.4921894073486,4231.000005722046L1251.4921894073486,4237.166672388713C1251.4921894073486,4243.333339055379,1251.4921894073486,4255.666672388713,1251.4921894073486,4268.000005722046C1251.4921894073486,4280.333339055379,1251.4921894073486,4292.666672388713,1251.4921894073486,4298.833339055379L1251.4921894073486,4305.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[124,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1251.4921894073486,4346.000005722046L1251.4921894073486,4352.166672388713C1251.4921894073486,4358.333339055379,1251.4921894073486,4370.666672388713,1235.9189472198486,4383.000005722046C1220.3457050323486,4395.333339055379,1189.1992206573486,4407.666672388713,1173.6259784698486,4413.833339055379L1158.0527362823486,4420.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[125,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1093.5279962619145,4442.000005722046L1072.9282781349286,4448.166672388713C1052.328560007943,4454.333339055379,1011.1291237539714,4466.666672388713,990.5294056269857,4479.000005722046C969.9296875,4491.333339055379,969.9296875,4503.666672388713,969.9296875,4509.833339055379L969.9296875,4516.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[126,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1169.043784459432,4442.000005722046L1190.778674867418,4448.166672388713C1212.513565275404,4454.333339055379,1255.9833460913762,4466.666672388713,1277.7182364993625,4479.000005722046C1299.4531269073486,4491.333339055379,1299.4531269073486,4503.666672388713,1299.4531269073486,4509.833339055379L1299.4531269073486,4516.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[126,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M969.9296875,4579.000005722046L969.9296875,4585.166672388713C969.9296875,4591.333339055379,969.9296875,4603.666672388713,969.9296875,4616.000005722046C969.9296875,4628.333339055379,969.9296875,4640.666672388713,969.9296875,4646.833339055379L969.9296875,4653.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[129,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M969.9296875,4716.000005722046L969.9296875,4722.166672388713C969.9296875,4728.333339055379,969.9296875,4740.666672388713,867.5000006357828,4758.015815185227C765.0703137715658,4775.364957981742,560.2109400431315,4797.729910241437,457.78125317891437,4808.912386371286L355.35156631469727,4820.094862501133" marker-end="url(#arrowhead-vee)" id="edge-[130,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M292.1015625,4838.00000667572L292.1015625,4848.500006516774C292.1015625,4859.000006357829,292.1015625,4880.000006039937,292.1015625,4900.083339214325C292.1015625,4920.166672388713,292.1015625,4939.333339055379,292.1015625,4958.500005722046C292.1015625,4977.666672388713,292.1015625,4996.833339055379,292.1015625,5014.416672388713C292.1015625,5032.000005722046,292.1015625,5048.000005722046,292.1015625,5064.000005722046C292.1015625,5080.000005722046,292.1015625,5096.000005722046,292.1015625,5115.416672388713C292.1015625,5134.833339055379,292.1015625,5157.666672388713,292.1015625,5180.500005722046C292.1015625,5203.333339055379,292.1015625,5226.166672388713,292.1015625,5249.000005722046C292.1015625,5271.833339055379,292.1015625,5294.666672388713,292.1015625,5317.500005722046C292.1015625,5340.333339055379,292.1015625,5363.166672388713,301.4110007036712,5385.083338896434C310.72043890734244,5407.000005404155,329.33931531468494,5428.000005086263,338.64875351835616,5438.500004927318L347.9581917220274,5449.000004768372" marker-end="url(#arrowhead-vee)" id="edge-[131,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1299.4531269073486,4579.000005722046L1299.4531269073486,4585.166672388713C1299.4531269073486,4591.333339055379,1299.4531269073486,4603.666672388713,1322.2895059765854,4619.416672229767C1345.125885045822,4635.166672070821,1390.7986431842955,4654.333338419597,1413.6350222535323,4663.916671593984L1436.471401322769,4673.500004768372" marker-end="url(#arrowhead-vee)" id="edge-[134,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1560.3046894073486,4579.000005722046L1560.3046894073486,4585.166672388713C1560.3046894073486,4591.333339055379,1560.3046894073486,4603.666672388713,1546.647237345241,4619.416672229767C1532.9897852831334,4635.166672070821,1505.6748811589177,4654.333338419597,1492.01742909681,4663.916671593984L1478.3599770347023,4673.500004768372" marker-end="url(#arrowhead-vee)" id="edge-[135,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1408.0756415616531,4695.50000667572L1360.5005349526025,4705.083339850108C1312.9254283435516,4714.666673024495,1217.7752151254501,4733.833339373271,1170.2001085163995,4749.583339214325C1122.6250019073486,4765.333339055379,1122.6250019073486,4777.666672388713,1122.6250019073486,4783.833339055379L1122.6250019073486,4790.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[136,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1447.007214279995,4695.50000667572L1433.3497622178872,4705.083339850108C1419.6923101557795,4714.666673024495,1392.3774060315639,4733.833339373271,1378.7199539694564,4749.583339214325C1365.0625019073486,4765.333339055379,1365.0625019073486,4777.666672388713,1365.0625019073486,4783.833339055379L1365.0625019073486,4790.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[136,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1478.3599770347023,4695.50000667572L1492.01742909681,4705.083339850108C1505.6748811589177,4714.666673024495,1532.9897852831334,4733.833339373271,1546.647237345241,4755.750005880992C1560.3046894073486,4777.666672388713,1560.3046894073486,4802.333339055379,1560.3046894073486,4827.000005722046C1560.3046894073486,4851.666672388713,1560.3046894073486,4876.333339055379,1560.3046894073486,4898.250005722046C1560.3046894073486,4920.166672388713,1560.3046894073486,4939.333339055379,1560.3046894073486,4958.500005722046C1560.3046894073486,4977.666672388713,1560.3046894073486,4996.833339055379,1560.3046894073486,5014.416672388713C1560.3046894073486,5032.000005722046,1560.3046894073486,5048.000005722046,1560.3046894073486,5064.000005722046C1560.3046894073486,5080.000005722046,1560.3046894073486,5096.000005722046,1560.3046894073486,5110.166672388713C1560.3046894073486,5124.333339055379,1560.3046894073486,5136.666672388713,1560.3046894073486,5142.833339055379L1560.3046894073486,5149.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[136,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1122.6250019073486,4864.000005722046L1122.6250019073486,4870.166672388713C1122.6250019073486,4876.333339055379,1122.6250019073486,4888.666672388713,1122.6250019073486,4901.000005722046C1122.6250019073486,4913.333339055379,1122.6250019073486,4925.666672388713,1122.6250019073486,4931.833339055379L1122.6250019073486,4938.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[139,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1122.6250019073486,4979.000005722046L1122.6250019073486,4985.166672388713C1122.6250019073486,4991.333339055379,1122.6250019073486,5003.666672388713,1138.1982440948486,5016.000005722046C1153.7714862823486,5028.333339055379,1184.9179706573486,5040.666672388713,1200.4912128448486,5046.833339055379L1216.0644550323486,5053.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[140,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1365.0625019073486,4864.000005722046L1365.0625019073486,4870.166672388713C1365.0625019073486,4876.333339055379,1365.0625019073486,4888.666672388713,1365.0625019073486,4901.000005722046C1365.0625019073486,4913.333339055379,1365.0625019073486,4925.666672388713,1365.0625019073486,4931.833339055379L1365.0625019073486,4938.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[143,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1365.0625019073486,4979.000005722046L1365.0625019073486,4985.166672388713C1365.0625019073486,4991.333339055379,1365.0625019073486,5003.666672388713,1349.4892597198486,5016.000005722046C1333.9160175323486,5028.333339055379,1302.7695331573486,5040.666672388713,1287.1962909698486,5046.833339055379L1271.6230487823486,5053.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[144,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1207.0983087619145,5075.000005722046L1186.4985906349286,5081.166672388713C1165.898872507943,5087.333339055379,1124.6994362539715,5099.666672388713,1104.0997181269856,5112.000005722046C1083.5,5124.333339055379,1083.5,5136.666672388713,1083.5,5142.833339055379L1083.5,5149.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[145,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1271.6230487823486,5075.000005722046L1287.1962909698486,5081.166672388713C1302.7695331573486,5087.333339055379,1333.9160175323486,5099.666672388713,1349.4892597198486,5112.000005722046C1365.0625019073486,5124.333339055379,1365.0625019073486,5136.666672388713,1365.0625019073486,5142.833339055379L1365.0625019073486,5149.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[145,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1083.5,5212.000005722046L1083.5,5218.166672388713C1083.5,5224.333339055379,1083.5,5236.666672388713,1083.5,5249.000005722046C1083.5,5261.333339055379,1083.5,5273.666672388713,1083.5,5279.833339055379L1083.5,5286.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[148,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1083.5,5349.000005722046L1083.5,5355.166672388713C1083.5,5361.333339055379,1083.5,5373.666672388713,973.0768235524496,5391.091865091818C862.653647104899,5408.517057794924,641.8072942097982,5431.034109867801,531.3841177622477,5442.29263590424L420.96094131469727,5453.551161940679" marker-end="url(#arrowhead-vee)" id="edge-[149,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M357.7109375,5471.00000667572L357.7109375,5481.500006516774C357.7109375,5492.000006357829,357.7109375,5513.000006039937,357.7109375,5533.083339214325C357.7109375,5553.166672388713,357.7109375,5572.333339055379,357.7109375,5591.500005722046C357.7109375,5610.666672388713,357.7109375,5629.833339055379,357.7109375,5647.416672388713C357.7109375,5665.000005722046,357.7109375,5681.000005722046,357.7109375,5697.000005722046C357.7109375,5713.000005722046,357.7109375,5729.000005722046,357.7109375,5748.416672388713C357.7109375,5767.833339055379,357.7109375,5790.666672388713,357.7109375,5813.500005722046C357.7109375,5836.333339055379,357.7109375,5859.166672388713,357.7109375,5882.000005722046C357.7109375,5904.833339055379,357.7109375,5927.666672388713,357.7109375,5950.500005722046C357.7109375,5973.333339055379,357.7109375,5996.166672388713,416.8398434321086,6014.372880678105C475.9687493642171,6032.579088967496,594.2265612284342,6046.158172212946,653.3554671605428,6052.9477138356715L712.4843730926514,6059.737255458397" marker-end="url(#arrowhead-vee)" id="edge-[150,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1365.0625019073486,5212.000005722046L1365.0625019073486,5218.166672388713C1365.0625019073486,5224.333339055379,1365.0625019073486,5236.666672388713,1378.7199539694564,5252.416672229767C1392.3774060315639,5268.166672070821,1419.6923101557795,5287.333338419597,1433.3497622178872,5296.916671593984L1447.007214279995,5306.500004768372" marker-end="url(#arrowhead-vee)" id="edge-[153,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1560.3046894073486,5212.000005722046L1560.3046894073486,5218.166672388713C1560.3046894073486,5224.333339055379,1560.3046894073486,5236.666672388713,1546.647237345241,5252.416672229767C1532.9897852831334,5268.166672070821,1505.6748811589177,5287.333338419597,1492.01742909681,5296.916671593984L1478.3599770347023,5306.500004768372" marker-end="url(#arrowhead-vee)" id="edge-[154,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1438.07724087805,5328.50000667572L1416.6398885495998,5338.083339850108C1395.2025362211496,5347.666673024495,1352.327831564249,5366.833339373271,1330.890479235799,5382.583339214325C1309.4531269073486,5398.333339055379,1309.4531269073486,5410.666672388713,1309.4531269073486,5416.833339055379L1309.4531269073486,5423.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[155,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1482.1493820165197,5328.50000667572L1499.1082092899912,5338.083339850108C1516.0670365634626,5347.666673024495,1549.9846911104057,5366.833339373271,1566.943518383877,5382.583339214325C1583.9023456573486,5398.333339055379,1583.9023456573486,5410.666672388713,1583.9023456573486,5416.833339055379L1583.9023456573486,5423.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[155,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1309.4531269073486,5497.000005722046L1309.4531269073486,5503.166672388713C1309.4531269073486,5509.333339055379,1309.4531269073486,5521.666672388713,1309.4531269073486,5534.000005722046C1309.4531269073486,5546.333339055379,1309.4531269073486,5558.666672388713,1309.4531269073486,5564.833339055379L1309.4531269073486,5571.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[158,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1309.4531269073486,5612.000005722046L1309.4531269073486,5618.166672388713C1309.4531269073486,5624.333339055379,1309.4531269073486,5636.666672388713,1329.138985739814,5649.000005722046C1348.8248445722793,5661.333339055379,1388.19656223721,5673.666672388713,1407.882421069675,5679.833339055379L1427.5682799021404,5686.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[159,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1583.9023456573486,5497.000005722046L1583.9023456573486,5503.166672388713C1583.9023456573486,5509.333339055379,1583.9023456573486,5521.666672388713,1583.9023456573486,5534.000005722046C1583.9023456573486,5546.333339055379,1583.9023456573486,5558.666672388713,1583.9023456573486,5564.833339055379L1583.9023456573486,5571.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[162,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1583.9023456573486,5612.000005722046L1583.9023456573486,5618.166672388713C1583.9023456573486,5624.333339055379,1583.9023456573486,5636.666672388713,1568.3291034698486,5649.000005722046C1552.7558612823486,5661.333339055379,1521.6093769073486,5673.666672388713,1506.0361347198486,5679.833339055379L1490.4628925323486,5686.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[163,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1462.6835956573486,5708.000005722046L1462.6835956573486,5714.166672388713C1462.6835956573486,5720.333339055379,1462.6835956573486,5732.666672388713,1462.6835956573486,5745.000005722046C1462.6835956573486,5757.333339055379,1462.6835956573486,5769.666672388713,1462.6835956573486,5775.833339055379L1462.6835956573486,5782.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[164,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1462.6835956573486,5845.000005722046L1462.6835956573486,5851.166672388713C1462.6835956573486,5857.333339055379,1462.6835956573486,5869.666672388713,1462.6835956573486,5882.000005722046C1462.6835956573486,5894.333339055379,1462.6835956573486,5906.666672388713,1462.6835956573486,5912.833339055379L1462.6835956573486,5919.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[167,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1462.6835956573486,5982.000005722046L1462.6835956573486,5988.166672388713C1462.6835956573486,5994.333339055379,1462.6835956573486,6006.666672388713,1358.7337265014648,6020.096749046144C1254.783857345581,6033.526825703575,1046.8841190338135,6048.053645685104,942.9342498779297,6055.31705567587L838.9843807220459,6062.580465666634" marker-end="url(#arrowhead-vee)" id="edge-[168,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M775.7343769073486,6078.0000076293945L775.7343769073486,6084.1666742960615C775.7343769073486,6090.333340962728,775.7343769073486,6102.6666742960615,775.7343769073486,6116.833340962728C775.7343769073486,6131.0000076293945,775.7343769073486,6147.0000076293945,775.7343769073486,6163.0000076293945C775.7343769073486,6179.0000076293945,775.7343769073486,6195.0000076293945,775.7343769073486,6212.583340962728C775.7343769073486,6230.1666742960615,775.7343769073486,6249.333340962728,775.7343769073486,6268.5000076293945C775.7343769073486,6287.6666742960615,775.7343769073486,6306.833340962728,775.7343769073486,6322.583340962728C775.7343769073486,6338.333340962728,775.7343769073486,6350.6666742960615,775.7343769073486,6356.833340962728L775.7343769073486,6363.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[169,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M814.1949914568168,6078.0000076293945L835.7562431985721,6084.1666742960615C857.3174949403274,6090.333340962728,900.439998423838,6102.6666742960615,922.0012501655933,6115.0000076293945C943.5625019073486,6127.333340962728,943.5625019073486,6139.6666742960615,943.5625019073486,6145.833340962728L943.5625019073486,6152.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[169,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M753.2527673319985,6078.0000076293945L740.6494418445568,6084.1666742960615C728.0461163571152,6090.333340962728,702.8394653822319,6102.6666742960615,690.2361398947902,6116.833340962728C677.6328144073486,6131.0000076293945,677.6328144073486,6147.0000076293945,677.6328144073486,6163.0000076293945C677.6328144073486,6179.0000076293945,677.6328144073486,6195.0000076293945,677.6328144073486,6212.583340962728C677.6328144073486,6230.1666742960615,677.6328144073486,6249.333340962728,677.6328144073486,6268.5000076293945C677.6328144073486,6287.6666742960615,677.6328144073486,6306.833340962728,677.6328144073486,6326.0000076293945C677.6328144073486,6345.1666742960615,677.6328144073486,6364.333340962728,677.6328144073486,6383.5000076293945C677.6328144073486,6402.6666742960615,677.6328144073486,6421.833340962728,694.7151033613417,6437.583340962728C711.7973923153348,6453.333340962728,745.9619702233209,6465.6666742960615,763.044259177314,6471.833340962728L780.126548131307,6478.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[169,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M775.7343769073486,6404.0000076293945L775.7343769073486,6410.1666742960615C775.7343769073486,6416.333340962728,775.7343769073486,6428.6666742960615,780.2133401234945,6441.0000076293945C784.6923033396403,6453.333340962728,793.650229771932,6465.6666742960615,798.1291929880778,6471.833340962728L802.6081562042236,6478.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[170,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M943.5625019073486,6174.0000076293945L943.5625019073486,6180.1666742960615C943.5625019073486,6186.333340962728,943.5625019073486,6198.6666742960615,943.5625019073486,6211.0000076293945C943.5625019073486,6223.333340962728,943.5625019073486,6235.6666742960615,943.5625019073486,6241.833340962728L943.5625019073486,6248.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[171,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M943.5625019073486,6289.0000076293945L943.5625019073486,6295.1666742960615C943.5625019073486,6301.333340962728,943.5625019073486,6313.6666742960615,943.5625019073486,6326.0000076293945C943.5625019073486,6338.333340962728,943.5625019073486,6350.6666742960615,943.5625019073486,6356.833340962728L943.5625019073486,6363.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[172,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M943.5625019073486,6404.0000076293945L943.5625019073486,6410.1666742960615C943.5625019073486,6416.333340962728,943.5625019073486,6428.6666742960615,926.4802129533556,6441.0000076293945C909.3979239993624,6453.333340962728,875.2333460913763,6465.6666742960615,858.1510571373833,6471.833340962728L841.0687681833903,6478.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[173,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M810.5976581573486,6500.0000076293945L810.5976581573486,6506.1666742960615C810.5976581573486,6512.333340962728,810.5976581573486,6524.6666742960615,810.5976581573486,6537.0000076293945C810.5976581573486,6549.333340962728,810.5976581573486,6561.6666742960615,810.5976581573486,6567.833340962728L810.5976581573486,6574.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[174,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M794.5656383124581,6637.0000076293945L791.4270947449398,6643.1666742960615C788.2885511774216,6649.333340962728,782.011464042385,6661.6666742960615,778.8729204748669,6675.833340962728C775.7343769073486,6690.0000076293945,775.7343769073486,6706.0000076293945,775.7343769073486,6722.0000076293945C775.7343769073486,6738.0000076293945,775.7343769073486,6754.0000076293945,775.7343769073486,6771.583340962728C775.7343769073486,6789.1666742960615,775.7343769073486,6808.333340962728,775.7343769073486,6827.5000076293945C775.7343769073486,6846.6666742960615,775.7343769073486,6865.833340962728,775.7343769073486,6881.583340962728C775.7343769073486,6897.333340962728,775.7343769073486,6909.6666742960615,775.7343769073486,6915.833340962728L775.7343769073486,6922.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[177,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M871.7420753562537,6637.0000076293945L883.7121464481029,6643.1666742960615C895.682217539952,6649.333340962728,919.6223597236503,6661.6666742960615,931.5924308154995,6674.0000076293945C943.5625019073486,6686.333340962728,943.5625019073486,6698.6666742960615,943.5625019073486,6704.833340962728L943.5625019073486,6711.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[177,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M749.4532409584435,6637.0000076293945L737.4831698665944,6643.1666742960615C725.5130987747452,6649.333340962728,701.5729565910469,6661.6666742960615,689.6028854991978,6675.833340962728C677.6328144073486,6690.0000076293945,677.6328144073486,6706.0000076293945,677.6328144073486,6722.0000076293945C677.6328144073486,6738.0000076293945,677.6328144073486,6754.0000076293945,677.6328144073486,6771.583340962728C677.6328144073486,6789.1666742960615,677.6328144073486,6808.333340962728,677.6328144073486,6827.5000076293945C677.6328144073486,6846.6666742960615,677.6328144073486,6865.833340962728,677.6328144073486,6885.0000076293945C677.6328144073486,6904.1666742960615,677.6328144073486,6923.333340962728,677.6328144073486,6942.5000076293945C677.6328144073486,6961.6666742960615,677.6328144073486,6980.833340962728,690.2361401451958,6996.583340962728C702.8394658830431,7012.333340962728,728.0461173587377,7024.6666742960615,740.6494430965848,7030.833340962728L753.252768834432,7037.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[177,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M775.7343769073486,6963.0000076293945L775.7343769073486,6969.1666742960615C775.7343769073486,6975.333340962728,775.7343769073486,6987.6666742960615,775.7343769073486,7000.0000076293945C775.7343769073486,7012.333340962728,775.7343769073486,7024.6666742960615,775.7343769073486,7030.833340962728L775.7343769073486,7037.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[178,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M943.5625019073486,6733.0000076293945L943.5625019073486,6739.1666742960615C943.5625019073486,6745.333340962728,943.5625019073486,6757.6666742960615,943.5625019073486,6770.0000076293945C943.5625019073486,6782.333340962728,943.5625019073486,6794.6666742960615,943.5625019073486,6800.833340962728L943.5625019073486,6807.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[179,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M943.5625019073486,6848.0000076293945L943.5625019073486,6854.1666742960615C943.5625019073486,6860.333340962728,943.5625019073486,6872.6666742960615,943.5625019073486,6885.0000076293945C943.5625019073486,6897.333340962728,943.5625019073486,6909.6666742960615,943.5625019073486,6915.833340962728L943.5625019073486,6922.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[180,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M943.5625019073486,6963.0000076293945L943.5625019073486,6969.1666742960615C943.5625019073486,6975.333340962728,943.5625019073486,6987.6666742960615,922.0012497372098,7000.0000076293945C900.4399975670708,7012.333340962728,857.317493226793,7024.6666742960615,835.7562410566542,7030.833340962728L814.1949888865153,7037.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[181,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M775.7343769073486,7059.0000076293945L775.7343769073486,7065.1666742960615C775.7343769073486,7071.333340962728,775.7343769073486,7083.6666742960615,775.7343769073486,7096.0000076293945C775.7343769073486,7108.333340962728,775.7343769073486,7120.6666742960615,775.7343769073486,7126.833340962728L775.7343769073486,7133.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[182,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M775.7343769073486,7196.0000076293945L775.7343769073486,7202.1666742960615C775.7343769073486,7208.333340962728,775.7343769073486,7220.6666742960615,775.7343769073486,7233.0000076293945C775.7343769073486,7245.333340962728,775.7343769073486,7257.6666742960615,775.7343769073486,7263.833340962728L775.7343769073486,7270.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[185,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M775.7343769073486,7322.000011444092L775.7343769073486,7328.166678110759C775.7343769073486,7334.333344777425,775.7343769073486,7346.666678110759,775.7343769073486,7359.000011444092C775.7343769073486,7371.333344777425,775.7343769073486,7383.666678110759,775.7343769073486,7389.833344777425L775.7343769073486,7396.000011444092" marker-end="url(#arrowhead-vee)" id="edge-[186,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/></g><g id="edge-labels" class="edge-labels"><g class="edge-label" transform="translate(485.71484565734863,59)" style="opacity: 1;"><g transform="translate(-12.2265625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">input</tspan></text></g></g><g class="edge-label" transform="translate(509.06640815734863,207)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(245.69922065734863,333.00000381469727)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(651.2148456573486,333.00000381469727)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(782.4335956573486,527.5000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(245.69922065734863,470.00000381469727)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(245.69922065734863,585.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(651.2148456573486,470.00000381469727)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(644.8007831573486,585.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(136.796875,914.0000057220459)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(587.1914081573486,681.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(136.796875,1314.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(136.796875,1451.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(139.1796875,1588.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(589.5742206573486,818.0000038146973)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(784.8164081573486,818.0000038146973)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(312.54296875,914.0000057220459)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(619.00390625,914.0000057220459)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(951.6132831573486,1119.500005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(312.54296875,1062.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(312.54296875,1177.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(619.00390625,1062.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(612.58984375,1177.000005722046)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(344.5546875,1314.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(586.9921875,1314.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(344.5546875,1451.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(346.9375,1588.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(236.80078125,1947.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(589.375,1451.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(953.9960956573486,1451.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(570.5078144073486,1588.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(837.7890644073486,1588.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1120.7929706573486,1793.500005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(570.5078144073486,1736.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(570.5078144073486,1851.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(837.7890644073486,1736.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(831.3750019073486,1851.000005722046)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(531.3828125,1947.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(812.9453144073486,1947.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(531.3828125,2084.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(533.765625,2221.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(248.08203315734863,2580.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(815.3281269073486,2084.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(1123.1757831573486,2084.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(708.9218769073486,2221.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(951.3593769073486,2221.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1289.9726581573486,2426.500005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(708.9218769073486,2369.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(708.9218769073486,2484.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(951.3593769073486,2369.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(944.9453144073486,2484.000005722046)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(669.796875,2580.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(999.3203144073486,2580.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(669.796875,2717.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(672.1796875,2854.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(248.08203315734863,3213.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(1001.7031269073486,2717.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(1292.3554706573486,2717.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(822.4921894073486,2854.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1064.9296894073486,2854.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1459.1523456573486,3059.500005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(822.4921894073486,3002.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(822.4921894073486,3117.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1064.9296894073486,3002.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1058.5156269073486,3117.000005722046)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(783.3671875,3213.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1112.8906269073486,3213.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(783.3671875,3350.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(785.75,3487.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(248.08203315734863,3846.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(1115.2734394073486,3350.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(1461.5351581573486,3350.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(936.0625019073486,3487.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1178.5000019073486,3487.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1552.9218769073486,3692.500005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(936.0625019073486,3635.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(936.0625019073486,3750.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1178.5000019073486,3635.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1172.0859394073486,3750.000005722046)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(896.9375,3846.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1226.4609394073486,3846.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(896.9375,3983.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(899.3203125,4120.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(269.453125,4479.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(1228.8437519073486,3983.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(1555.3046894073486,3983.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(1049.6328144073486,4120.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1292.0703144073486,4120.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1600.8828144073486,4325.500005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1049.6328144073486,4268.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1049.6328144073486,4383.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1292.0703144073486,4268.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1285.6562519073486,4383.000005722046)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(1010.5078125,4479.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1340.0312519073486,4479.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1010.5078125,4616.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1012.890625,4753.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(335.0625,5112.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(1342.4140644073486,4616.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(1603.2656269073486,4616.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(1163.2031269073486,4753.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1405.6406269073486,4753.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1600.8828144073486,4958.500005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1163.2031269073486,4901.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1163.2031269073486,5016.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1405.6406269073486,4901.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1399.2265644073486,5016.000005722046)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(1124.078125,5112.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1405.6406269073486,5112.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1124.078125,5249.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1126.4609375,5386.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(400.671875,5745.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(1408.0234394073486,5249.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(1603.2656269073486,5249.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(1350.0312519073486,5386.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1624.4804706573486,5386.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1350.0312519073486,5534.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1350.0312519073486,5649.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1624.4804706573486,5534.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1618.0664081573486,5649.000005722046)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(1503.2617206573486,5745.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1503.2617206573486,5882.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1505.6445331573486,6019.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(816.3125019073486,6211.0000076293945)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(984.1406269073486,6115.0000076293945)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(711.7968769073486,6268.5000076293945)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(816.3125019073486,6441.0000076293945)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(984.1406269073486,6211.0000076293945)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(984.1406269073486,6326.0000076293945)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(977.7265644073486,6441.0000076293945)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (2)</tspan></text></g></g><g class="edge-label" transform="translate(851.1757831573486,6537.0000076293945)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(816.3125019073486,6770.0000076293945)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(984.1406269073486,6674.0000076293945)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(711.7968769073486,6827.5000076293945)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(816.3125019073486,7000.0000076293945)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(984.1406269073486,6770.0000076293945)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(984.1406269073486,6885.0000076293945)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(977.7265644073486,7000.0000076293945)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (2)</tspan></text></g></g><g class="edge-label" transform="translate(816.3125019073486,7096.0000076293945)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(816.3125019073486,7233.0000076293945)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(796.4843769073486,7359.000011444092)" style="opacity: 1;"><g transform="translate(-15.75,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output</tspan></text></g></g></g><g id="nodes" class="nodes"><g id="node-wavenet1_embedding0_fwd" class="node" transform="translate(468.48828315734863,133)" style="opacity: 1;"><g transform="translate(-77.234375,-37)"><g><g class="node-item node-item-operator node-item-operator-transform" transform="translate(0,0)"><path d="M5,0h63.28125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-68.28125a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 68); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Embedding</text><title>wavenet1_embedding0_fwd</title></g><g class="node-item node-item-input" transform="translate(68.28125,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_embedding0_weight" transform="translate(107.921875,0)"><path d="M0,0h41.546875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[512,24]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h154.46875a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-144.46875a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dtype</tspan><tspan> = float32</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">input_dim</tspan><tspan> = 512</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">output_dim</tspan><tspan> = 24</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">sparse_grad</tspan><tspan> = False</tspan></text></g><line class="node" x1="68.28125" y1="0" x2="68.28125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="107.921875" y1="0" x2="107.921875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="154.46875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h144.46875a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-144.46875a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_swapaxes0" class="node" transform="translate(468.48828315734863,270.00000190734863)" style="opacity: 1;"><g transform="translate(-49.5546875,-26.000001907348633)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h54.46875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-59.46875a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">SwapAxis</text><title>wavenet1_swapaxes0</title></g><g class="node-item node-item-input" transform="translate(59.46875,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h99.109375a0,0 0 0 1 0,0v25a5,5 0 0 1 -5,5h-89.109375a5,5 0 0 1 -5,-5v-25a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dim1</tspan><tspan> = 1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dim2</tspan><tspan> = 2</tspan></text></g><line class="node" x1="59.46875" y1="0" x2="59.46875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="99.109375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h89.109375a5,5 0 0 1 5,5v42a5,5 0 0 1 -5,5h-89.109375a5,5 0 0 1 -5,-5v-42a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue0_conv0_fwd" class="node" transform="translate(205.12109565734863,401.50000381469727)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue0_conv0_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue0_conv0_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue0_conv0_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue0_conv0_sigmoid_fwd" class="node" transform="translate(205.12109565734863,527.5000038146973)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue0_conv0_sigmoid_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = sigmoid</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue0_conv1_fwd" class="node" transform="translate(610.6367206573486,401.50000381469727)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue0_conv1_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue0_conv1_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue0_conv1_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue0_conv1_tanh_fwd" class="node" transform="translate(610.6367206573486,527.5000038146973)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue0_conv1_tanh_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = tanh</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue0__mul0" class="node" transform="translate(205.12109565734863,633.0000038146973)" style="opacity: 1;"><g transform="translate(-74.0546875,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77.4375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77.4375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_mul</text><title>wavenet1_causaldilatedresidue0__mul0</title></g><g class="node-item node-item-input" transform="translate(82.4375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(122.078125,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><line class="node" x1="82.4375" y1="0" x2="82.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="122.078125" y1="0" x2="122.078125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h138.109375a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-138.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue0_conv2_fwd" class="node" transform="translate(96.21875,1245.500005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue0_conv2_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue0_conv2_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[64,24,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue0_conv2_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[64,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 64</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis0" class="node" transform="translate(96.21875,1382.500005722046)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 510</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1__plusscalar0" class="node" transform="translate(96.21875,1519.500005722046)" style="opacity: 1;"><g transform="translate(-55.4921875,-20.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h66.34375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-71.34375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_plus_scalar</text><title>wavenet1__plusscalar0</title></g><g class="node-item node-item-input" transform="translate(71.34375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h110.984375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-100.984375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">scalar</tspan><tspan> = 0</tspan></text></g><line class="node" x1="71.34375" y1="0" x2="71.34375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="110.984375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h100.984375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-100.984375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue0_conv3_fwd" class="node" transform="translate(546.6132831573486,749.5000038146973)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue0_conv3_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue0_conv3_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue0_conv3_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue0_slice_axis0" class="node" transform="translate(741.8554706573486,749.5000038146973)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_causaldilatedresidue0_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 1</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue0__plus0" class="node" transform="translate(546.6132831573486,866.0000047683716)" style="opacity: 1;"><g transform="translate(-63.250003814697266,-11.000000953674316)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_add</text><title>wavenet1_causaldilatedresidue0__plus0</title></g><g class="node-item node-item-input" transform="translate(82,0)"><path d="M0,0h39.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-39.5a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">inputs</text><title>
-</title></g><line class="node" x1="82" y1="0" x2="82" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-116.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue1_conv0_fwd" class="node" transform="translate(271.96484375,988.0000057220459)" style="opacity: 1;"><g transform="translate(-96.21875,-37)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue1_conv0_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue1_conv0_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue1_conv0_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dilate</tspan><tspan> = (2,)</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue1_conv0_sigmoid_fwd" class="node" transform="translate(271.96484375,1119.500005722046)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue1_conv0_sigmoid_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = sigmoid</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue1_conv1_fwd" class="node" transform="translate(578.42578125,988.0000057220459)" style="opacity: 1;"><g transform="translate(-96.21875,-37)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue1_conv1_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue1_conv1_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue1_conv1_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dilate</tspan><tspan> = (2,)</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue1_conv1_tanh_fwd" class="node" transform="translate(578.42578125,1119.500005722046)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue1_conv1_tanh_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = tanh</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue1__mul0" class="node" transform="translate(425.1953125,1245.500005722046)" style="opacity: 1;"><g transform="translate(-74.0546875,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77.4375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77.4375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_mul</text><title>wavenet1_causaldilatedresidue1__mul0</title></g><g class="node-item node-item-input" transform="translate(82.4375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(122.078125,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><line class="node" x1="82.4375" y1="0" x2="82.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="122.078125" y1="0" x2="122.078125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h138.109375a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-138.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue1_conv2_fwd" class="node" transform="translate(303.9765625,1382.500005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue1_conv2_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue1_conv2_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[64,24,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue1_conv2_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[64,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 64</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis1" class="node" transform="translate(303.9765625,1519.500005722046)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis1</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 508</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1__plus0" class="node" transform="translate(193.83984375,1662.000005722046)" style="opacity: 1;"><g transform="translate(-63.250003814697266,-11.000000953674316)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_add</text><title>wavenet1__plus0</title></g><g class="node-item node-item-input" transform="translate(82,0)"><path d="M0,0h39.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-39.5a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">inputs</text><title>
-</title></g><line class="node" x1="82" y1="0" x2="82" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-116.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue1_conv3_fwd" class="node" transform="translate(546.4140625,1382.500005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue1_conv3_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue1_conv3_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue1_conv3_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue1_slice_axis0" class="node" transform="translate(911.0351581573486,1382.500005722046)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_causaldilatedresidue1_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 2</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue1__plus0" class="node" transform="translate(715.7929706573486,1519.500005722046)" style="opacity: 1;"><g transform="translate(-63.250003814697266,-11.000000953674316)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_add</text><title>wavenet1_causaldilatedresidue1__plus0</title></g><g class="node-item node-item-input" transform="translate(82,0)"><path d="M0,0h39.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-39.5a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">inputs</text><title>
-</title></g><line class="node" x1="82" y1="0" x2="82" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-116.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue2_conv0_fwd" class="node" transform="translate(529.9296894073486,1662.000005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-37)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue2_conv0_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue2_conv0_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue2_conv0_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dilate</tspan><tspan> = (4,)</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue2_conv0_sigmoid_fwd" class="node" transform="translate(529.9296894073486,1793.500005722046)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue2_conv0_sigmoid_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = sigmoid</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue2_conv1_fwd" class="node" transform="translate(797.2109394073486,1662.000005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-37)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue2_conv1_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue2_conv1_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue2_conv1_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dilate</tspan><tspan> = (4,)</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue2_conv1_tanh_fwd" class="node" transform="translate(797.2109394073486,1793.500005722046)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue2_conv1_tanh_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = tanh</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue2__mul0" class="node" transform="translate(594.5742206573486,1899.000005722046)" style="opacity: 1;"><g transform="translate(-74.0546875,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77.4375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77.4375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_mul</text><title>wavenet1_causaldilatedresidue2__mul0</title></g><g class="node-item node-item-input" transform="translate(82.4375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(122.078125,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><line class="node" x1="82.4375" y1="0" x2="82.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="122.078125" y1="0" x2="122.078125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h138.109375a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-138.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue2_conv2_fwd" class="node" transform="translate(490.8046875,2015.500005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue2_conv2_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue2_conv2_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[64,24,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue2_conv2_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[64,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 64</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis2" class="node" transform="translate(490.8046875,2152.500005722046)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis2</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 504</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1__plus1" class="node" transform="translate(205.12109565734863,2295.000005722046)" style="opacity: 1;"><g transform="translate(-63.250003814697266,-11.000000953674316)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_add</text><title>wavenet1__plus1</title></g><g class="node-item node-item-input" transform="translate(82,0)"><path d="M0,0h39.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-39.5a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">inputs</text><title>
-</title></g><line class="node" x1="82" y1="0" x2="82" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-116.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue2_conv3_fwd" class="node" transform="translate(772.3671894073486,2015.500005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue2_conv3_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue2_conv3_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue2_conv3_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue2_slice_axis0" class="node" transform="translate(1080.2148456573486,2015.500005722046)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_causaldilatedresidue2_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 4</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue2__plus0" class="node" transform="translate(884.9726581573486,2152.500005722046)" style="opacity: 1;"><g transform="translate(-63.250003814697266,-11.000000953674316)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_add</text><title>wavenet1_causaldilatedresidue2__plus0</title></g><g class="node-item node-item-input" transform="translate(82,0)"><path d="M0,0h39.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-39.5a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">inputs</text><title>
-</title></g><line class="node" x1="82" y1="0" x2="82" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-116.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue3_conv0_fwd" class="node" transform="translate(668.3437519073486,2295.000005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-37)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue3_conv0_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue3_conv0_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue3_conv0_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dilate</tspan><tspan> = (8,)</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue3_conv0_sigmoid_fwd" class="node" transform="translate(668.3437519073486,2426.500005722046)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue3_conv0_sigmoid_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = sigmoid</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue3_conv1_fwd" class="node" transform="translate(910.7812519073486,2295.000005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-37)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue3_conv1_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue3_conv1_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue3_conv1_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dilate</tspan><tspan> = (8,)</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue3_conv1_tanh_fwd" class="node" transform="translate(910.7812519073486,2426.500005722046)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue3_conv1_tanh_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = tanh</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue3__mul0" class="node" transform="translate(763.7539081573486,2532.000005722046)" style="opacity: 1;"><g transform="translate(-74.0546875,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77.4375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77.4375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_mul</text><title>wavenet1_causaldilatedresidue3__mul0</title></g><g class="node-item node-item-input" transform="translate(82.4375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(122.078125,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><line class="node" x1="82.4375" y1="0" x2="82.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="122.078125" y1="0" x2="122.078125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h138.109375a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-138.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue3_conv2_fwd" class="node" transform="translate(629.21875,2648.500005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue3_conv2_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue3_conv2_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[64,24,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue3_conv2_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[64,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 64</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis3" class="node" transform="translate(629.21875,2785.500005722046)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis3</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 496</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1__plus2" class="node" transform="translate(205.12109565734863,2928.000005722046)" style="opacity: 1;"><g transform="translate(-63.250003814697266,-11.000000953674316)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_add</text><title>wavenet1__plus2</title></g><g class="node-item node-item-input" transform="translate(82,0)"><path d="M0,0h39.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-39.5a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">inputs</text><title>
-</title></g><line class="node" x1="82" y1="0" x2="82" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-116.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue3_conv3_fwd" class="node" transform="translate(958.7421894073486,2648.500005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue3_conv3_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue3_conv3_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue3_conv3_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue3_slice_axis0" class="node" transform="translate(1249.3945331573486,2648.500005722046)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_causaldilatedresidue3_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 8</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue3__plus0" class="node" transform="translate(1054.1523456573486,2785.500005722046)" style="opacity: 1;"><g transform="translate(-63.250003814697266,-11.000000953674316)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_add</text><title>wavenet1_causaldilatedresidue3__plus0</title></g><g class="node-item node-item-input" transform="translate(82,0)"><path d="M0,0h39.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-39.5a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">inputs</text><title>
-</title></g><line class="node" x1="82" y1="0" x2="82" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-116.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue4_conv0_fwd" class="node" transform="translate(781.9140644073486,2928.000005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-37)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue4_conv0_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue4_conv0_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue4_conv0_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dilate</tspan><tspan> = (16,)</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue4_conv0_sigmoid_fwd" class="node" transform="translate(781.9140644073486,3059.500005722046)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue4_conv0_sigmoid_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = sigmoid</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue4_conv1_fwd" class="node" transform="translate(1024.3515644073486,2928.000005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-37)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue4_conv1_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue4_conv1_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue4_conv1_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dilate</tspan><tspan> = (16,)</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue4_conv1_tanh_fwd" class="node" transform="translate(1024.3515644073486,3059.500005722046)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue4_conv1_tanh_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = tanh</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue4__mul0" class="node" transform="translate(903.1328144073486,3165.000005722046)" style="opacity: 1;"><g transform="translate(-74.0546875,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77.4375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77.4375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_mul</text><title>wavenet1_causaldilatedresidue4__mul0</title></g><g class="node-item node-item-input" transform="translate(82.4375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(122.078125,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><line class="node" x1="82.4375" y1="0" x2="82.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="122.078125" y1="0" x2="122.078125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h138.109375a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-138.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue4_conv2_fwd" class="node" transform="translate(742.7890625,3281.500005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue4_conv2_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue4_conv2_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[64,24,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue4_conv2_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[64,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 64</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis4" class="node" transform="translate(742.7890625,3418.500005722046)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis4</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 480</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1__plus3" class="node" transform="translate(205.12109565734863,3561.000005722046)" style="opacity: 1;"><g transform="translate(-63.250003814697266,-11.000000953674316)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_add</text><title>wavenet1__plus3</title></g><g class="node-item node-item-input" transform="translate(82,0)"><path d="M0,0h39.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-39.5a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">inputs</text><title>
-</title></g><line class="node" x1="82" y1="0" x2="82" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-116.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue4_conv3_fwd" class="node" transform="translate(1072.3125019073486,3281.500005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue4_conv3_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue4_conv3_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue4_conv3_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue4_slice_axis0" class="node" transform="translate(1418.5742206573486,3281.500005722046)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_causaldilatedresidue4_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 16</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue4__plus0" class="node" transform="translate(1223.3320331573486,3418.500005722046)" style="opacity: 1;"><g transform="translate(-63.250003814697266,-11.000000953674316)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_add</text><title>wavenet1_causaldilatedresidue4__plus0</title></g><g class="node-item node-item-input" transform="translate(82,0)"><path d="M0,0h39.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-39.5a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">inputs</text><title>
-</title></g><line class="node" x1="82" y1="0" x2="82" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-116.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue5_conv0_fwd" class="node" transform="translate(895.4843769073486,3561.000005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-37)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue5_conv0_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue5_conv0_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue5_conv0_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dilate</tspan><tspan> = (32,)</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue5_conv0_sigmoid_fwd" class="node" transform="translate(895.4843769073486,3692.500005722046)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue5_conv0_sigmoid_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = sigmoid</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue5_conv1_fwd" class="node" transform="translate(1137.9218769073486,3561.000005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-37)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue5_conv1_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue5_conv1_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue5_conv1_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dilate</tspan><tspan> = (32,)</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue5_conv1_tanh_fwd" class="node" transform="translate(1137.9218769073486,3692.500005722046)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue5_conv1_tanh_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = tanh</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue5__mul0" class="node" transform="translate(1016.7031269073486,3798.000005722046)" style="opacity: 1;"><g transform="translate(-74.0546875,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77.4375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77.4375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_mul</text><title>wavenet1_causaldilatedresidue5__mul0</title></g><g class="node-item node-item-input" transform="translate(82.4375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(122.078125,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><line class="node" x1="82.4375" y1="0" x2="82.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="122.078125" y1="0" x2="122.078125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h138.109375a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-138.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue5_conv2_fwd" class="node" transform="translate(856.359375,3914.500005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue5_conv2_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue5_conv2_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[64,24,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue5_conv2_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[64,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 64</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis5" class="node" transform="translate(856.359375,4051.500005722046)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis5</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 448</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1__plus4" class="node" transform="translate(226.4921875,4194.000005722046)" style="opacity: 1;"><g transform="translate(-63.250003814697266,-11.000000953674316)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_add</text><title>wavenet1__plus4</title></g><g class="node-item node-item-input" transform="translate(82,0)"><path d="M0,0h39.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-39.5a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">inputs</text><title>
-</title></g><line class="node" x1="82" y1="0" x2="82" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-116.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue5_conv3_fwd" class="node" transform="translate(1185.8828144073486,3914.500005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue5_conv3_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue5_conv3_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue5_conv3_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue5_slice_axis0" class="node" transform="translate(1512.3437519073486,3914.500005722046)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_causaldilatedresidue5_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 32</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue5__plus0" class="node" transform="translate(1392.5117206573486,4051.500005722046)" style="opacity: 1;"><g transform="translate(-63.250003814697266,-11.000000953674316)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_add</text><title>wavenet1_causaldilatedresidue5__plus0</title></g><g class="node-item node-item-input" transform="translate(82,0)"><path d="M0,0h39.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-39.5a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">inputs</text><title>
-</title></g><line class="node" x1="82" y1="0" x2="82" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-116.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue6_conv0_fwd" class="node" transform="translate(1009.0546894073486,4194.000005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-37)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue6_conv0_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue6_conv0_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue6_conv0_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dilate</tspan><tspan> = (64,)</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue6_conv0_sigmoid_fwd" class="node" transform="translate(1009.0546894073486,4325.500005722046)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue6_conv0_sigmoid_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = sigmoid</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue6_conv1_fwd" class="node" transform="translate(1251.4921894073486,4194.000005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-37)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue6_conv1_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue6_conv1_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue6_conv1_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dilate</tspan><tspan> = (64,)</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue6_conv1_tanh_fwd" class="node" transform="translate(1251.4921894073486,4325.500005722046)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue6_conv1_tanh_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = tanh</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue6__mul0" class="node" transform="translate(1130.2734394073486,4431.000005722046)" style="opacity: 1;"><g transform="translate(-74.0546875,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77.4375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77.4375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_mul</text><title>wavenet1_causaldilatedresidue6__mul0</title></g><g class="node-item node-item-input" transform="translate(82.4375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(122.078125,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><line class="node" x1="82.4375" y1="0" x2="82.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="122.078125" y1="0" x2="122.078125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h138.109375a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-138.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue6_conv2_fwd" class="node" transform="translate(969.9296875,4547.500005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue6_conv2_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue6_conv2_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[64,24,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue6_conv2_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[64,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 64</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis6" class="node" transform="translate(969.9296875,4684.500005722046)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis6</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 384</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1__plus5" class="node" transform="translate(292.1015625,4827.000005722046)" style="opacity: 1;"><g transform="translate(-63.250003814697266,-11.000000953674316)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_add</text><title>wavenet1__plus5</title></g><g class="node-item node-item-input" transform="translate(82,0)"><path d="M0,0h39.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-39.5a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">inputs</text><title>
-</title></g><line class="node" x1="82" y1="0" x2="82" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-116.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue6_conv3_fwd" class="node" transform="translate(1299.4531269073486,4547.500005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue6_conv3_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue6_conv3_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue6_conv3_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue6_slice_axis0" class="node" transform="translate(1560.3046894073486,4547.500005722046)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_causaldilatedresidue6_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 64</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue6__plus0" class="node" transform="translate(1462.6835956573486,4684.500005722046)" style="opacity: 1;"><g transform="translate(-63.250003814697266,-11.000000953674316)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_add</text><title>wavenet1_causaldilatedresidue6__plus0</title></g><g class="node-item node-item-input" transform="translate(82,0)"><path d="M0,0h39.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-39.5a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">inputs</text><title>
-</title></g><line class="node" x1="82" y1="0" x2="82" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-116.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue7_conv0_fwd" class="node" transform="translate(1122.6250019073486,4827.000005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-37)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue7_conv0_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue7_conv0_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue7_conv0_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dilate</tspan><tspan> = (128,)</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue7_conv0_sigmoid_fwd" class="node" transform="translate(1122.6250019073486,4958.500005722046)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue7_conv0_sigmoid_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = sigmoid</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue7_conv1_fwd" class="node" transform="translate(1365.0625019073486,4827.000005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-37)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue7_conv1_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue7_conv1_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue7_conv1_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dilate</tspan><tspan> = (128,)</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue7_conv1_tanh_fwd" class="node" transform="translate(1365.0625019073486,4958.500005722046)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue7_conv1_tanh_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = tanh</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue7__mul0" class="node" transform="translate(1243.8437519073486,5064.000005722046)" style="opacity: 1;"><g transform="translate(-74.0546875,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77.4375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77.4375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_mul</text><title>wavenet1_causaldilatedresidue7__mul0</title></g><g class="node-item node-item-input" transform="translate(82.4375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(122.078125,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><line class="node" x1="82.4375" y1="0" x2="82.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="122.078125" y1="0" x2="122.078125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h138.109375a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-138.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue7_conv2_fwd" class="node" transform="translate(1083.5,5180.500005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue7_conv2_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue7_conv2_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[64,24,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue7_conv2_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[64,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 64</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis7" class="node" transform="translate(1083.5,5317.500005722046)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis7</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 256</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1__plus6" class="node" transform="translate(357.7109375,5460.000005722046)" style="opacity: 1;"><g transform="translate(-63.250003814697266,-11.000000953674316)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_add</text><title>wavenet1__plus6</title></g><g class="node-item node-item-input" transform="translate(82,0)"><path d="M0,0h39.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-39.5a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">inputs</text><title>
-</title></g><line class="node" x1="82" y1="0" x2="82" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-116.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue7_conv3_fwd" class="node" transform="translate(1365.0625019073486,5180.500005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue7_conv3_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue7_conv3_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue7_conv3_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue7_slice_axis0" class="node" transform="translate(1560.3046894073486,5180.500005722046)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_causaldilatedresidue7_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 128</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue7__plus0" class="node" transform="translate(1462.6835956573486,5317.500005722046)" style="opacity: 1;"><g transform="translate(-63.250003814697266,-11.000000953674316)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_add</text><title>wavenet1_causaldilatedresidue7__plus0</title></g><g class="node-item node-item-input" transform="translate(82,0)"><path d="M0,0h39.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-39.5a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">inputs</text><title>
-</title></g><line class="node" x1="82" y1="0" x2="82" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-116.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue8_conv0_fwd" class="node" transform="translate(1309.4531269073486,5460.000005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-37)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue8_conv0_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue8_conv0_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue8_conv0_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dilate</tspan><tspan> = (256,)</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue8_conv0_sigmoid_fwd" class="node" transform="translate(1309.4531269073486,5591.500005722046)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue8_conv0_sigmoid_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = sigmoid</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue8_conv1_fwd" class="node" transform="translate(1583.9023456573486,5460.000005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-37)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue8_conv1_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue8_conv1_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue8_conv1_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dilate</tspan><tspan> = (256,)</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue8_conv1_tanh_fwd" class="node" transform="translate(1583.9023456573486,5591.500005722046)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue8_conv1_tanh_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = tanh</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue8__mul0" class="node" transform="translate(1462.6835956573486,5697.000005722046)" style="opacity: 1;"><g transform="translate(-74.0546875,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77.4375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77.4375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_mul</text><title>wavenet1_causaldilatedresidue8__mul0</title></g><g class="node-item node-item-input" transform="translate(82.4375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(122.078125,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><line class="node" x1="82.4375" y1="0" x2="82.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="122.078125" y1="0" x2="122.078125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h138.109375a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-138.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue8_conv2_fwd" class="node" transform="translate(1462.6835956573486,5813.500005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue8_conv2_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue8_conv2_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[64,24,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue8_conv2_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[64,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 64</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis8" class="node" transform="translate(1462.6835956573486,5950.500005722046)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis8</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 0</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1__plus7" class="node" transform="translate(775.7343769073486,6067.00000667572)" style="opacity: 1;"><g transform="translate(-63.250003814697266,-11.000000953674316)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_add</text><title>wavenet1__plus7</title></g><g class="node-item node-item-input" transform="translate(82,0)"><path d="M0,0h39.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-39.5a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">inputs</text><title>
-</title></g><line class="node" x1="82" y1="0" x2="82" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-116.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0__greater_scalar0" class="node" transform="translate(775.7343769073486,6383.5000076293945)" style="opacity: 1;"><g transform="translate(-63.1015625,-20.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h81.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-86.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_greater_scalar</text><title>wavenet1_elu0__greater_scalar0</title></g><g class="node-item node-item-input" transform="translate(86.5625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h126.203125a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-116.203125a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">scalar</tspan><tspan> = 0</tspan></text></g><line class="node" x1="86.5625" y1="0" x2="86.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="126.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.203125a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-116.203125a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0_exp0" class="node" transform="translate(943.5625019073486,6163.0000076293945)" style="opacity: 1;"><g transform="translate(-35.5703125,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h26.5a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-26.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">exp</text><title>wavenet1_elu0_exp0</title></g><g class="node-item node-item-input" transform="translate(31.5,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-34.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><line class="node" x1="31.5" y1="0" x2="31.5" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h61.140625a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-61.140625a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0__minusscalar0" class="node" transform="translate(943.5625019073486,6268.5000076293945)" style="opacity: 1;"><g transform="translate(-60.3359375,-20.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h76.03125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-81.03125a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_minus_scalar</text><title>wavenet1_elu0__minusscalar0</title></g><g class="node-item node-item-input" transform="translate(81.03125,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h120.671875a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-110.671875a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">scalar</tspan><tspan> = 1.0</tspan></text></g><line class="node" x1="81.03125" y1="0" x2="81.03125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="120.671875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h110.671875a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-110.671875a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0__mulscalar0" class="node" transform="translate(943.5625019073486,6383.5000076293945)" style="opacity: 1;"><g transform="translate(-54.7265625,-20.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h64.8125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-69.8125a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_mul_scalar</text><title>wavenet1_elu0__mulscalar0</title></g><g class="node-item node-item-input" transform="translate(69.8125,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h109.453125a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-99.453125a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">scalar</tspan><tspan> = 1.0</tspan></text></g><line class="node" x1="69.8125" y1="0" x2="69.8125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="109.453125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h99.453125a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-99.453125a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0_where0" class="node" transform="translate(810.5976581573486,6489.0000076293945)" style="opacity: 1;"><g transform="translate(-68.0234375,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h39.34375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.34375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">where</text><title>wavenet1_elu0_where0</title></g><g class="node-item node-item-input" transform="translate(44.34375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(83.984375,0)"><path d="M0,0h26.03125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-26.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><g class="node-item node-item-input" transform="translate(110.015625,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(2)</text></g><line class="node" x1="44.34375" y1="0" x2="44.34375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="83.984375" y1="0" x2="83.984375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="110.015625" y1="0" x2="110.015625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h126.046875a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-126.046875a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_conv0_fwd" class="node" transform="translate(810.5976581573486,6605.5000076293945)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_conv0_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_conv0_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[64,64,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_conv0_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[64,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 64</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0__greater_scalar0" class="node" transform="translate(775.7343769073486,6942.5000076293945)" style="opacity: 1;"><g transform="translate(-63.1015625,-20.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h81.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-86.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_greater_scalar</text><title>wavenet1_elu0__greater_scalar0</title></g><g class="node-item node-item-input" transform="translate(86.5625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h126.203125a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-116.203125a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">scalar</tspan><tspan> = 0</tspan></text></g><line class="node" x1="86.5625" y1="0" x2="86.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="126.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.203125a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-116.203125a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0_exp0" class="node" transform="translate(943.5625019073486,6722.0000076293945)" style="opacity: 1;"><g transform="translate(-35.5703125,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h26.5a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-26.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">exp</text><title>wavenet1_elu0_exp0</title></g><g class="node-item node-item-input" transform="translate(31.5,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-34.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><line class="node" x1="31.5" y1="0" x2="31.5" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h61.140625a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-61.140625a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0__minusscalar0" class="node" transform="translate(943.5625019073486,6827.5000076293945)" style="opacity: 1;"><g transform="translate(-60.3359375,-20.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h76.03125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-81.03125a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_minus_scalar</text><title>wavenet1_elu0__minusscalar0</title></g><g class="node-item node-item-input" transform="translate(81.03125,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h120.671875a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-110.671875a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">scalar</tspan><tspan> = 1.0</tspan></text></g><line class="node" x1="81.03125" y1="0" x2="81.03125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="120.671875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h110.671875a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-110.671875a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0__mulscalar0" class="node" transform="translate(943.5625019073486,6942.5000076293945)" style="opacity: 1;"><g transform="translate(-54.7265625,-20.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h64.8125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-69.8125a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_mul_scalar</text><title>wavenet1_elu0__mulscalar0</title></g><g class="node-item node-item-input" transform="translate(69.8125,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h109.453125a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-99.453125a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">scalar</tspan><tspan> = 1.0</tspan></text></g><line class="node" x1="69.8125" y1="0" x2="69.8125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="109.453125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h99.453125a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-99.453125a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0_where0" class="node" transform="translate(775.7343769073486,7048.0000076293945)" style="opacity: 1;"><g transform="translate(-68.0234375,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h39.34375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.34375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">where</text><title>wavenet1_elu0_where0</title></g><g class="node-item node-item-input" transform="translate(44.34375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(83.984375,0)"><path d="M0,0h26.03125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-26.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><g class="node-item node-item-input" transform="translate(110.015625,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(2)</text></g><line class="node" x1="44.34375" y1="0" x2="44.34375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="83.984375" y1="0" x2="83.984375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="110.015625" y1="0" x2="110.015625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h126.046875a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-126.046875a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_conv1_fwd" class="node" transform="translate(775.7343769073486,7164.5000076293945)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_conv1_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_conv1_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[512,64,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_conv1_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[512,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 512</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_swapaxes1" class="node" transform="translate(775.7343769073486,7296.000009536743)" style="opacity: 1;"><g transform="translate(-49.5546875,-26.000001907348633)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h54.46875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-59.46875a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">SwapAxis</text><title>wavenet1_swapaxes1</title></g><g class="node-item node-item-input" transform="translate(59.46875,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h99.109375a0,0 0 0 1 0,0v25a5,5 0 0 1 -5,5h-89.109375a5,5 0 0 1 -5,-5v-25a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dim1</tspan><tspan> = 1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dim2</tspan><tspan> = 2</tspan></text></g><line class="node" x1="59.46875" y1="0" x2="59.46875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="99.109375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h89.109375a5,5 0 0 1 5,5v42a5,5 0 0 1 -5,5h-89.109375a5,5 0 0 1 -5,-5v-42a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g class="node graph-input" transform="translate(468.48828315734863,11)" style="opacity: 1;"><g transform="translate(-17.875,-11)"><g><g class="node-item graph-item-input" transform="translate(0,0)"><path d="M5,0h25.75a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-25.75a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">data</text></g><path class="node border" d="M5,0h25.75a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-25.75a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g class="node" transform="translate(775.7343769073486,7407.000011444092)" style="opacity: 1;"><g transform="translate(-58.914066314697266,-11)"><g><g class="node-item graph-item-output" transform="translate(0,0)"><path d="M5,0h107.828125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-107.828125a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">wavenet1_swapaxes1</text></g><path class="node border" d="M5,0h107.828125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-107.828125a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g></g></g></svg>
\ No newline at end of file
diff --git a/docs/tutorials/unsupervised_learning/gan.md b/docs/tutorials/unsupervised_learning/gan.md
deleted file mode 100644
index 0416593b6c1e..000000000000
--- a/docs/tutorials/unsupervised_learning/gan.md
+++ /dev/null
@@ -1,418 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-
-# Generative Adversarial Network (GAN)
-
-Generative Adversarial Networks (GANs) are a class of algorithms used in unsupervised learning - you don't need labels for your dataset in order to train a GAN.
-
-The GAN framework is composed of two neural networks: a Generator network and a Discriminator network.
-
-The Generator's job is to take a set of random numbers and produce the data (such as images or text).
-
-The Discriminator then takes in that data as well as samples of that data from a dataset and tries to determine if it is "fake" (created by the Generator network) or "real" (from the original dataset).
-
-During training, the two networks play a game against each other. The Generator tries to create realistic data, so that it can fool the Discriminator into thinking that the data it generated is from the original dataset. At the same time, the Discriminator tries to not be fooled - it learns to become better at determining if data is real or fake.
-
-Since the two networks are fighting in this game, they can be seen as as adversaries, which is where the term "Generative Adversarial Network" comes from.
-
-## Deep Convolutional Generative Adversarial Networks
-
-This tutorial takes a look at Deep Convolutional Generative Adversarial Networks (DCGAN), which combines Convolutional Neural Networks (CNNs) and GANs.
-
-We will create a DCGAN that is able to create images of handwritten digits from random numbers. The tutorial uses the neural net architecture and guidelines outlined in [this paper](https://arxiv.org/abs/1511.06434), and the MNIST dataset.
-
-## How to Use This Tutorial
-You can use this tutorial by executing each snippet of python code in order as it appears in the tutorial.
-
-
-1. The first net is the "Generator" and creates images of handwritten digits from random numbers.
-2. The second net is the "Discriminator" and determines if the image created by the Generator is real (a realistic looking image of handwritten digits) or fake (an image that does not look like it is from the original dataset).
-
-Apart from creating a DCGAN, you'll also learn:
-
-- How to manipulate and iterate through batches of image data that you can feed into your neural network.
-
-- How to create a custom MXNet data iterator that generates random numbers from a normal distribution.
-
-- How to create a custom training process in MXNet, using lower level functions from the MXNet Module API such as .bind() .forward() and .backward(). The training process for a DCGAN is more complex than many other neural networks, so we need to use these functions instead of using the higher level .fit() function.
-
-- How to visualize images as they are going through the training process
-
-## Prerequisites
-
-This tutorial assumes you are familiar with the concepts of CNNs and have implemented one in MXNet. You should also be familiar with the concept of logistic regression. Having a basic understanding of MXNet data iterators helps, since we will create a custom data iterator to iterate though random numbers as inputs to the Generator network.
-
-This example is designed to be trained on a single GPU. Training this network on CPU can be slow, so it's recommended that you use a GPU for training.
-
-To complete this tutorial, you need:
-
-- MXNet
-- Python, and the following libraries for Python:
-    - Numpy - for matrix math
-    - OpenCV - for image manipulation
-    - Matplotlib - to visualize the output
-
-## The Data
-We need two pieces of data to train the DCGAN:
-    1. Images of handwritten digits from the MNIST dataset
-    2. Random numbers from a normal distribution
-
-The Generator network will use the random numbers as the input to produce the images of handwritten digits, and the Discriminator network will use images of handwritten digits from the MNIST dataset to determine if images produced by the Generator are realistic.
-
-The MNIST dataset contains 70,000 images of handwritten digits. Each image is 28x28 pixels in size. To create random numbers, we're going to create a custom MXNet data iterator, which will returns random numbers from a normal distribution as we need then.
-
-## Prepare the Data
-
-### 1. Preparing the MNSIT dataset
-
-Let us start by preparing the handwritten digits from the MNIST dataset.
-```python
-import mxnet as mx
-import numpy as np
-
-mnist_train = mx.gluon.data.vision.datasets.MNIST(train=True)
-mnist_test = mx.gluon.data.vision.datasets.MNIST(train=False)
-```
-
-```python
-# The downloaded data is of type `Dataset` which are
-# Well suited to work with the new Gluon interface but less
-# With the older symbol API, used in this tutorial.
-# Therefore we convert them to numpy array first
-X = np.zeros((70000, 28, 28))
-for i, (data, label) in enumerate(mnist_train):
-    X[i] = data.asnumpy()[:,:,0]
-for i, (data, label) in enumerate(mnist_test):
-    X[len(mnist_train)+i] = data.asnumpy()[:,:,0]
-```
-
-Next, we will randomize the handwritten digits by using numpy to create random permutations on the dataset on the rows (images). Every image in the dataset is arranged into a 28x28 grid, where each cell in the grid represents 1 pixel of the image.
-
-```python
-#Use a seed so that we get the same random permutation each time
-np.random.seed(1)
-p = np.random.permutation(X.shape[0])
-X = X[p]
-```
-Since the DCGAN that we're creating takes in a 64x64 image as the input, we will use OpenCV to resize the each 28x28 image to 64x64 images:
-```python
-import cv2
-X = np.asarray([cv2.resize(x, (64,64)) for x in X])
-```
-Each pixel in the 64x64 image is represented by a number between 0-255, that represents the intensity of the pixel. However, we want to input numbers between -1 and 1 into the DCGAN, as suggested by the [research paper](https://arxiv.org/abs/1511.06434). To rescale the pixel values, we will divide it by (255/2). This changes the scale to 0-2. We then subtract by 1 to get them in the range of -1 to 1.
-
-```python
-X = X.astype(np.float32, copy=False)/(255.0/2) - 1.0
-```
-Ultimately, images are fed into the neural net through a 70000x3x64x64 array but they are currently in a 70000x64x64 array. We need to add 3 channels to the images. Typically, when we are working with the images, the 3 channels represent the red, green, and blue (RGB) components of each image. Since the MNIST dataset is grayscale, we only need 1 channel to represent the dataset. We will pad the other channels with 0's:
-
-```python
-X = X.reshape((70000, 1, 64, 64))
-X = np.tile(X, (1, 3, 1, 1))
-```
-Finally, we will put the images into MXNet's NDArrayIter, which will allow MXNet to easily iterate through the images during training. We will also split them up into batches of 64 images each. Every time we iterate, we will get a 4 dimensional array with size (64, 3, 64, 64), representing a batch of 64 images.
-```python
-import mxnet as mx
-batch_size = 64
-image_iter = mx.io.NDArrayIter(X, batch_size=batch_size)
-```
-### 2. Preparing Random Numbers
-
-We need to input random numbers from a normal distribution to the Generator network, so we will create an MXNet DataIter that produces random numbers for each training batch. The DataIter is the base class of MXNet's Data Loading API. Below, we create a class called RandIter which is a subclass of DataIter. We use MXNet's built-in mx.random.normal function to return the random numbers from a normal distribution during the iteration.
-
-```python
-class RandIter(mx.io.DataIter):
-    def __init__(self, batch_size, ndim):
-        self.batch_size = batch_size
-        self.ndim = ndim
-        self.provide_data = [('rand', (batch_size, ndim, 1, 1))]
-        self.provide_label = []
-
-    def iter_next(self):
-        return True
-
-    def getdata(self):
-        #Returns random numbers from a gaussian (normal) distribution
-        #with mean=0 and standard deviation = 1
-        return [mx.random.normal(0, 1.0, shape=(self.batch_size, self.ndim, 1, 1))]
-```
-When we initialize the RandIter, we need to provide two numbers: the batch size and how many random numbers we want in order to produce a single image from. This number is referred to as Z, and we will set this to 100. This value comes from the research paper on the topic. Every time we iterate and get a batch of random numbers, we will get a 4 dimensional array with shape: (batch_size, Z, 1, 1), which in the example is (64, 100, 1, 1).
-```python
-Z = 100
-rand_iter = RandIter(batch_size, Z)
-```
-## Create the Model
-
-The model has two networks that we will train together - the Generator network and the Discriminator network.
-
-### The Generator
-
-Let us start off by defining the Generator network, which uses Deconvolution layers (also called as fractionally strided layers) to generate an image form random numbers :
-```python
-no_bias = True
-fix_gamma = True
-epsilon = 1e-5 + 1e-12
-
-rand = mx.sym.Variable('rand')
-
-g1 = mx.sym.Deconvolution(rand, name='g1', kernel=(4,4), num_filter=1024, no_bias=no_bias)
-gbn1 = mx.sym.BatchNorm(g1, name='gbn1', fix_gamma=fix_gamma, eps=epsilon)
-gact1 = mx.sym.Activation(gbn1, name='gact1', act_type='relu')
-
-g2 = mx.sym.Deconvolution(gact1, name='g2', kernel=(4,4), stride=(2,2), pad=(1,1), num_filter=512, no_bias=no_bias)
-gbn2 = mx.sym.BatchNorm(g2, name='gbn2', fix_gamma=fix_gamma, eps=epsilon)
-gact2 = mx.sym.Activation(gbn2, name='gact2', act_type='relu')
-
-g3 = mx.sym.Deconvolution(gact2, name='g3', kernel=(4,4), stride=(2,2), pad=(1,1), num_filter=256, no_bias=no_bias)
-gbn3 = mx.sym.BatchNorm(g3, name='gbn3', fix_gamma=fix_gamma, eps=epsilon)
-gact3 = mx.sym.Activation(gbn3, name='gact3', act_type='relu')
-
-g4 = mx.sym.Deconvolution(gact3, name='g4', kernel=(4,4), stride=(2,2), pad=(1,1), num_filter=128, no_bias=no_bias)
-gbn4 = mx.sym.BatchNorm(g4, name='gbn4', fix_gamma=fix_gamma, eps=epsilon)
-gact4 = mx.sym.Activation(gbn4, name='gact4', act_type='relu')
-
-g5 = mx.sym.Deconvolution(gact4, name='g5', kernel=(4,4), stride=(2,2), pad=(1,1), num_filter=3, no_bias=no_bias)
-generatorSymbol = mx.sym.Activation(g5, name='gact5', act_type='tanh')
-```
-
-The Generator image starts with random numbers that will be obtained from the RandIter we created earlier, so we created the rand variable for this input.
-We then start creating the model starting with a Deconvolution layer (sometimes called 'fractionally strided layer'). We apply batch normalization and ReLU activation after the Deconvolution layer.
-
-We repeat this process 4 times, applying a (2,2) stride and (1,1) pad at each Deconvolution layer, which doubles the size of the image at each layer. By creating these layers, the Generator network will have to learn to upsample the input vector of random numbers, Z at each layer, so that network output a final image. We also reduce by half the number of filters at each layer, reducing dimensionality at each layer. Ultimately, the output layer is a 64x64x3 layer, representing the size and channels of the image. We use tanh activation instead of relu on the last layer, as recommended by the research on DCGANs. The output of neurons in the final gout layer represent the pixels of generated image.
-
-Notice we used 3 parameters to help us create the model: no_bias, fixed_gamma, and epsilon. Neurons in the network won't have a bias added to them, this seems to work better in practice for the DCGAN. In the batch norm layer, we set fixed_gamma=True, which means gamma=1 for all of the batch norm layers. epsilon is a small number that gets added to the batch norm so that we don't end up dividing by zero. By default, CuDNN requires that this number is greater than 1e-5, so we add a small number to this value, ensuring this values stays small.
-
-### The Discriminator
-
-Let us now create the Discriminator network, which will take in images of handwritten digits from the MNIST dataset and images created by the Generator network:
-```python
-data = mx.sym.Variable('data')
-
-d1 = mx.sym.Convolution(data, name='d1', kernel=(4,4), stride=(2,2), pad=(1,1), num_filter=128, no_bias=no_bias)
-dact1 = mx.sym.LeakyReLU(d1, name='dact1', act_type='leaky', slope=0.2)
-
-d2 = mx.sym.Convolution(dact1, name='d2', kernel=(4,4), stride=(2,2), pad=(1,1), num_filter=256, no_bias=no_bias)
-dbn2 = mx.sym.BatchNorm(d2, name='dbn2', fix_gamma=fix_gamma, eps=epsilon)
-dact2 = mx.sym.LeakyReLU(dbn2, name='dact2', act_type='leaky', slope=0.2)
-
-d3 = mx.sym.Convolution(dact2, name='d3', kernel=(4,4), stride=(2,2), pad=(1,1), num_filter=512, no_bias=no_bias)
-dbn3 = mx.sym.BatchNorm(d3, name='dbn3', fix_gamma=fix_gamma, eps=epsilon)
-dact3 = mx.sym.LeakyReLU(dbn3, name='dact3', act_type='leaky', slope=0.2)
-
-d4 = mx.sym.Convolution(dact3, name='d4', kernel=(4,4), stride=(2,2), pad=(1,1), num_filter=1024, no_bias=no_bias)
-dbn4 = mx.sym.BatchNorm(d4, name='dbn4', fix_gamma=fix_gamma, eps=epsilon)
-dact4 = mx.sym.LeakyReLU(dbn4, name='dact4', act_type='leaky', slope=0.2)
-
-d5 = mx.sym.Convolution(dact4, name='d5', kernel=(4,4), num_filter=1, no_bias=no_bias)
-d5 = mx.sym.Flatten(d5)
-
-label = mx.sym.Variable('label')
-discriminatorSymbol = mx.sym.LogisticRegressionOutput(data=d5, label=label, name='dloss')
-```
-
-We start off by creating the data variable, which is used to hold the input images to the Discriminator.
-
-The Discriminator then goes through a series of 5 convolutional layers, each with a 4x4 kernel, 2x2 stride, and 1x1 pad. These layers half the size of the image (which starts at 64x64) at each convolutional layer. The model also increases dimensionality at each layer by doubling the number of filters per convolutional layer, starting at 128 filters and ending at 1024 filters before we flatten the output.
-
-At the final convolution, we flatten the neural net to get one number as the final output of Discriminator network. This number is the probability that the image is real, as determined by the Discriminator. We use logistic regression to determine this probability. When we pass in "real" images from the MNIST dataset, we can label these as 1 and we can label the "fake" images from the Generator net as 0 to perform logistic regression on the Discriminator network.
-
-### Prepare the models using the Module API
-
-So far we have defined a MXNet Symbol for both the Generator and the Discriminator network. Before we can train the model, we need to bind these symbols using the Module API, which creates the computation graph for the models. It also allows us to decide how we want to initialize the model and what type of optimizer we want to use. Let us set up the Module for both the networks:
-```python
-#Hyper-parameters
-sigma = 0.02
-lr = 0.0002
-beta1 = 0.5
-# Define the compute context, use GPU if available
-ctx = mx.gpu() if mx.context.num_gpus() else mx.cpu()
-
-#=============Generator Module=============
-generator = mx.mod.Module(symbol=generatorSymbol, data_names=('rand',), label_names=None, context=ctx)
-generator.bind(data_shapes=rand_iter.provide_data)
-generator.init_params(initializer=mx.init.Normal(sigma))
-generator.init_optimizer(
-    optimizer='adam',
-    optimizer_params={
-        'learning_rate': lr,
-        'beta1': beta1,
-    })
-mods = [generator]
-
-# =============Discriminator Module=============
-discriminator = mx.mod.Module(symbol=discriminatorSymbol, data_names=('data',), label_names=('label',), context=ctx)
-discriminator.bind(data_shapes=image_iter.provide_data,
-          label_shapes=[('label', (batch_size,))],
-          inputs_need_grad=True)
-discriminator.init_params(initializer=mx.init.Normal(sigma))
-discriminator.init_optimizer(
-    optimizer='adam',
-    optimizer_params={
-        'learning_rate': lr,
-        'beta1': beta1,
-    })
-mods.append(discriminator)
-```
-First, we create Modules for the networks and then bind the symbols that we've created in the previous steps to the modules.
-We use rand_iter.provide_data as the  data_shape to bind the Generator network. This means that as we iterate though batches of the data on the Generator Module, the RandIter will provide us with random numbers to feed the Module using it's provide_data function.
-
-Similarly, we bind the Discriminator Module to image_iter.provide_data, which gives us images from MNIST from the NDArrayIter we had set up earlier, called image_iter.
-
-Notice that we are using the Normal Initialization, with the hyperparameter sigma=0.02. This means the weight initializations for the neurons in the networks will be random numbers from a Gaussian (normal) distribution with a mean of 0 and a standard deviation of 0.02.
-
-We also use the Adam optimizer for gradient decent. We've set up two hyperparameters, lr and beta1 based on the values used in the DCGAN paper. We're using a single gpu, gpu(0) for training. Set the context to cpu() if you do not have a GPU on your machine.
-
-### Visualizing The Training
-Before we train the model, let us set up some helper functions that will help visualize what the Generator is producing, compared to what the real image is:
-```python
-from matplotlib import pyplot as plt
-
-#Takes the images in the batch and arranges them in an array so that they can be
-#Plotted using matplotlib
-def fill_buf(buf, num_images, img, shape):
-    width = buf.shape[0]/shape[1]
-    height = buf.shape[1]/shape[0]
-    img_width = int(num_images%width)*shape[0]
-    img_hight = int(num_images/height)*shape[1]
-    buf[img_hight:img_hight+shape[1], img_width:img_width+shape[0], :] = img
-
-#Plots two images side by side using matplotlib
-def visualize(fake, real):
-    #64x3x64x64 to 64x64x64x3
-    fake = fake.transpose((0, 2, 3, 1))
-    #Pixel values from 0-255
-    fake = np.clip((fake+1.0)*(255.0/2.0), 0, 255).astype(np.uint8)
-    #Repeat for real image
-    real = real.transpose((0, 2, 3, 1))
-    real = np.clip((real+1.0)*(255.0/2.0), 0, 255).astype(np.uint8)
-
-    #Create buffer array that will hold all the images in the batch
-    #Fill the buffer so to arrange all images in the batch onto the buffer array
-    n = np.ceil(np.sqrt(fake.shape[0]))
-    fbuff = np.zeros((int(n*fake.shape[1]), int(n*fake.shape[2]), int(fake.shape[3])), dtype=np.uint8)
-    for i, img in enumerate(fake):
-        fill_buf(fbuff, i, img, fake.shape[1:3])
-    rbuff = np.zeros((int(n*real.shape[1]), int(n*real.shape[2]), int(real.shape[3])), dtype=np.uint8)
-    for i, img in enumerate(real):
-        fill_buf(rbuff, i, img, real.shape[1:3])
-
-    #Create a matplotlib figure with two subplots: one for the real and the other for the fake
-    #fill each plot with the buffer array, which creates the image
-    fig = plt.figure()
-    ax1 = fig.add_subplot(2,2,1)
-    ax1.imshow(fbuff)
-    ax2 = fig.add_subplot(2,2,2)
-    ax2.imshow(rbuff)
-    plt.show()
-```
-
-## Fit the Model
-Training the DCGAN is a complex process that requires multiple steps.
-To fit the model, for every batch of data in the MNIST dataset:
-
-1. Use the Z vector, which contains the random numbers to do a forward pass through the Generator network. This outputs the "fake" image, since it is created from the Generator.
-
-2. Use the fake image as the input to do a forward and backward pass through the Discriminator network. We set the labels for logistic regression to 0 to represent that this is a fake image. This trains the Discriminator to learn what a fake image looks like. We save the gradient produced in backpropagation for the next step.
-
-3. Do a forward and backward pass through the Discriminator using a real image from the MNIST dataset. The label for logistic regression will now be 1 to represent the real images, so the Discriminator can learn to recognize a real image.
-
-4. Update the Discriminator by adding the result of the gradient generated during backpropagation on the fake image with the gradient from backpropagation on the real image.
-
-5. Now that the Discriminator has been updated for the this data batch, we still need to update the Generator. First, do a forward and backwards pass with the same data batch on the updated Discriminator, to produce a new gradient. Use the new gradient to do a backwards pass
-
-Here is the main training loop for the DCGAN:
-
-```python
-# =============train===============
-print('Training...')
-for epoch in range(1):
-    image_iter.reset()
-    for i, batch in enumerate(image_iter):
-        #Get a batch of random numbers to generate an image from the generator
-        rbatch = rand_iter.next()
-        #Forward pass on training batch
-        generator.forward(rbatch, is_train=True)
-        #Output of training batch is the 64x64x3 image
-        outG = generator.get_outputs()
-
-        #Pass the generated (fake) image through the discriminator, and save the gradient
-        #Label (for logistic regression) is an array of 0's since this image is fake
-        label = mx.nd.zeros((batch_size,), ctx=ctx)
-        #Forward pass on the output of the discriminator network
-        discriminator.forward(mx.io.DataBatch(outG, [label]), is_train=True)
-        #Do the backward pass and save the gradient
-        discriminator.backward()
-        gradD = [[grad.copyto(grad.context) for grad in grads] for grads in discriminator._exec_group.grad_arrays]
-
-        #Pass a batch of real images from MNIST through the discriminator
-        #Set the label to be an array of 1's because these are the real images
-        label[:] = 1
-        batch.label = [label]
-        #Forward pass on a batch of MNIST images
-        discriminator.forward(batch, is_train=True)
-        #Do the backward pass and add the saved gradient from the fake images to the gradient
-        #generated by this backwards pass on the real images
-        discriminator.backward()
-        for gradsr, gradsf in zip(discriminator._exec_group.grad_arrays, gradD):
-            for gradr, gradf in zip(gradsr, gradsf):
-                gradr += gradf
-        #Update gradient on the discriminator
-        discriminator.update()
-
-        #Now that we've updated the discriminator, let's update the generator
-        #First do a forward pass and backwards pass on the newly updated discriminator
-        #With the current batch
-        discriminator.forward(mx.io.DataBatch(outG, [label]), is_train=True)
-        discriminator.backward()
-        #Get the input gradient from the backwards pass on the discriminator,
-        #and use it to do the backwards pass on the generator
-        diffD = discriminator.get_input_grads()
-        generator.backward(diffD)
-        #Update the gradients on the generator
-        generator.update()
-
-        #Increment to the next batch, printing every 50 batches
-        i += 1
-        if i % 50 == 0:
-            print('epoch:', epoch, 'iter:', i)
-            print
-            print("   From generator:        From MNIST:")
-
-            visualize(outG[0].asnumpy(), batch.data[0].asnumpy())
-```
-
-This will train the GAN network and visualize the progress that we are making as the networks are trained. After every 25 iterations, we are calling the visualize function that we created earlier, which plots the intermediate results.
-
-The plot on the left will represent what the Generator created (the fake image) in the most recent iteration. The plot on the right will represent the Original (real) image from the MNIST dataset that was inputted to the Discriminator on the same iteration.
-
-As the training goes on, the Generator becomes better at generating realistic images. You can see this happening since the images on the left becomes closer to the original dataset with each iteration.
-
-## Summary
-
-We have now successfully used Apache MXNet to train a Deep Convolutional Generative Adversarial Neural Networks (DCGAN) using the MNIST dataset.
-
-As a result, we have created two neural nets: a Generator, which is able to create images of handwritten digits from random numbers, and a Discriminator, which is able to take an image and determine if it is an image of handwritten digits.
-
-Along the way, we have learned how to do the image manipulation and visualization that is associated with the training of deep neural nets. We have also learned how to use MXNet's Module APIs to perform advanced model training functionality to fit the model.
-
-## Acknowledgements
-
-This tutorial is based on [MXNet DCGAN codebase](https://github.com/apache/incubator-mxnet/blob/master/example/gluon/dc_gan/dcgan.py),
-[The original paper on GANs](https://arxiv.org/abs/1406.2661), as well as [this paper on deep convolutional GANs](https://arxiv.org/abs/1511.06434).
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/unsupervised_learning/index.md b/docs/tutorials/unsupervised_learning/index.md
deleted file mode 100644
index faf6526fb824..000000000000
--- a/docs/tutorials/unsupervised_learning/index.md
+++ /dev/null
@@ -1,25 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Tutorials
-
-```eval_rst
-.. toctree::
-   :glob:
-
-   *
-```
diff --git a/docs/tutorials/vision/cnn_visualization.md b/docs/tutorials/vision/cnn_visualization.md
deleted file mode 100644
index 1eb89e104264..000000000000
--- a/docs/tutorials/vision/cnn_visualization.md
+++ /dev/null
@@ -1,275 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Visualizing Decisions of Convolutional Neural Networks
-
-Convolutional Neural Networks have made a lot of progress in Computer Vision. Their accuracy is as good as humans in some tasks. However, it remains difficult to explain the predictions of convolutional neural networks, as they lack the interpretability offered by other models such as decision trees.
-
-It is often helpful to be able to explain why a model made the prediction it made. For example, when a model misclassifies an image, without visualizing the network's decision, it is hard to say why the misclassification was made.
-
-<img align="right" src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/cnn_visualization/volcano_barn_spider.png" alt="Explaining the misclassification of volcano as spider" width=500px/>
-
-Visualizations can also build confidence about the predictions of a model. For example, even if a model correctly predicts birds as birds, we would want to confirm that the model bases its decision on the features of bird and not on the features of some other object that might occur together with birds in the dataset (like leaves).
-
-In this tutorial we show how to visualize the predictions made by convolutional neural networks using [Gradient-weighted Class Activation Mapping](https://arxiv.org/abs/1610.02391). Unlike many other visualization methods, Grad-CAM can be used on a wide variety of CNN model families - CNNs with fully connected layers, CNNs used for structural outputs (e.g. captioning), CNNs used in tasks with multi-model input (e.g. VQA) or reinforcement learning without architectural changes or re-training.
-
-In the rest of this notebook, we will explain how to visualize predictions made by [VGG-16](https://arxiv.org/abs/1409.1556). We begin by importing the required dependencies. 
-
-## Prerequesites
-* OpenCV is required by `gradcam` (below) and can be installed with pip using `pip opencv-python`.
-
-* the `gradcam` module contains the implementation of visualization techniques used in this notebook. `gradcam` can be installed to a temporary directory by executing the following code block.
-
-```python
-from __future__ import print_function
-
-import mxnet as mx
-from mxnet import gluon
-
-from matplotlib import pyplot as plt
-import numpy as np
-
-gradcam_file = "gradcam.py" 
-base_url = "https://raw.githubusercontent.com/apache/incubator-mxnet/master/docs/tutorial_utils/vision/cnn_visualization/{}?raw=true"
-mx.test_utils.download(base_url.format(gradcam_file), fname=gradcam_file)
-import gradcam
-```
-
-## Building the network to visualize
-
-Next, we build the network we want to visualize. For this example, we will use the [VGG-16](https://arxiv.org/abs/1409.1556) network. This code was taken from the Gluon [model zoo](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/model_zoo/vision/alexnet.py) and refactored to make it easy to switch between `gradcam`'s and Gluon's implementation of ReLU and Conv2D. Same code can be used for both training and visualization with a minor (one line) change.
-
-Notice that we import ReLU and Conv2D from `gradcam` module instead of mxnet.gluon.nn.
-- We use a modified ReLU because we use guided backpropagation for visualization and guided backprop requires ReLU layer to block the backward flow of negative gradients corresponding to the neurons which decrease the activation of the higher layer unit we aim to visualize. Check [this](https://arxiv.org/abs/1412.6806) paper to learn more about guided backprop.
-- We use a modified Conv2D (a wrapper on top of Gluon's Conv2D) because we want to capture the output of a given convolutional layer and its gradients. This is needed to implement Grad-CAM. Check [this](https://arxiv.org/abs/1610.02391) paper to learn more about Grad-CAM.
-
-When you train the network, you could just import `Activation` and `Conv2D` from `gluon.nn` instead. No other part of the code needs any change to switch between training and visualization.
-
-```python
-import os
-from mxnet.gluon.model_zoo import model_store
-
-from mxnet.initializer import Xavier
-from mxnet.gluon.nn import MaxPool2D, Flatten, Dense, Dropout, BatchNorm
-from gradcam import Activation, Conv2D
-
-class VGG(mx.gluon.HybridBlock):
-    def __init__(self, layers, filters, classes=1000, **kwargs):
-        super(VGG, self).__init__(**kwargs)
-        assert len(layers) == len(filters)
-        with self.name_scope():
-            self.features = self._make_features(layers, filters)
-            self.features.add(Dense(4096, activation='relu',
-                                       weight_initializer='normal',
-                                       bias_initializer='zeros'))
-            self.features.add(Dropout(rate=0.5))
-            self.features.add(Dense(4096, activation='relu',
-                                       weight_initializer='normal',
-                                       bias_initializer='zeros'))
-            self.features.add(Dropout(rate=0.5))
-            self.output = Dense(classes,
-                                   weight_initializer='normal',
-                                   bias_initializer='zeros')
-
-    def _make_features(self, layers, filters):
-        featurizer = mx.gluon.nn.HybridSequential(prefix='')
-        for i, num in enumerate(layers):
-            for _ in range(num):
-                featurizer.add(Conv2D(filters[i], kernel_size=3, padding=1,
-                                         weight_initializer=Xavier(rnd_type='gaussian',
-                                                                   factor_type='out',
-                                                                   magnitude=2),
-                                         bias_initializer='zeros'))
-                featurizer.add(Activation('relu'))
-            featurizer.add(MaxPool2D(strides=2))
-        return featurizer
-
-    def hybrid_forward(self, F, x):
-        x = self.features(x)
-        x = self.output(x)
-        return x
-```
-
-## Loading pretrained weights
-
-We'll use pre-trained weights (trained on ImageNet) from model zoo instead of training the model from scratch.
-
-```python
-# Number of convolution layers and number of filters for each VGG configuration.
-# Check the VGG [paper](https://arxiv.org/abs/1409.1556) for more details on the different architectures.
-vgg_spec = {11: ([1, 1, 2, 2, 2], [64, 128, 256, 512, 512]),
-            13: ([2, 2, 2, 2, 2], [64, 128, 256, 512, 512]),
-            16: ([2, 2, 3, 3, 3], [64, 128, 256, 512, 512]),
-            19: ([2, 2, 4, 4, 4], [64, 128, 256, 512, 512])}
-
-def get_vgg(num_layers, ctx=mx.cpu(), root=os.path.join('~', '.mxnet', 'models'), **kwargs):
-
-    # Get the number of convolution layers and filters
-    layers, filters = vgg_spec[num_layers]
-
-    # Build the modified VGG network
-    net = VGG(layers, filters, **kwargs)
-    net.initialize(ctx=ctx)
-    
-    # Get the pretrained model
-    vgg = mx.gluon.model_zoo.vision.get_vgg(num_layers, pretrained=True, ctx=ctx)
-    
-    # Set the parameters in the new network
-    params = vgg.collect_params()
-    for key in params:
-        param = params[key]
-        net.collect_params()[net.prefix+key.replace(vgg.prefix, '')].set_data(param.data())
-
-    return net
-
-def vgg16(**kwargs):
-    return get_vgg(16, **kwargs)
-```
-
-## Preprocessing and other helpers
-
-We'll resize the input image to 224x224 before feeding it to the network. We normalize the images using the same parameters ImageNet dataset was normalised using to create the pretrained model. These parameters are published [here](https://mxnet.incubator.apache.org/api/python/gluon/model_zoo.html). We use `transpose` to convert the image to channel-last format.
-
-Note that we do not hybridize the network. This is because we want `gradcam.Activation` and `gradcam.Conv2D` to behave differently at different times during the execution. For example, `gradcam.Activation` will do the regular backpropagation while computing the gradient of the topmost convolutional layer but will do guided backpropagation when computing the gradient of the image.
-
-```python
-image_sz = (224, 224)
-
-def preprocess(data):
-    data = mx.image.imresize(data, image_sz[0], image_sz[1])
-    data = data.astype(np.float32)
-    data = data/255
-    data = mx.image.color_normalize(data,
-                                    mean=mx.nd.array([0.485, 0.456, 0.406]),
-                                    std=mx.nd.array([0.229, 0.224, 0.225]))
-    data = mx.nd.transpose(data, (2,0,1))
-    return data
-
-network = vgg16(ctx=mx.cpu())
-```
-
-We define a helper to display multiple images in a row in Jupyter notebook.
-
-```python
-def show_images(pred_str, images):
-    titles = [pred_str, 'Grad-CAM', 'Guided Grad-CAM', 'Saliency Map']
-    num_images = len(images)
-    fig=plt.figure(figsize=(15,15))
-    rows, cols = 1, num_images
-    for i in range(num_images):
-        fig.add_subplot(rows, cols, i+1)
-        plt.xlabel(titles[i])
-        img = images[i].astype(np.uint8)
-        plt.imshow(img, cmap='gray' if i==num_images-1 else None)
-    plt.show()
-```
-
-Given an image, the network predicts a probability distribution over all categories. The most probable category can be found by applying the `argmax` operation. This gives an integer corresponding to the category. We still need to convert this to a human readable category name to know what category the network predicted. [Synset](http://data.mxnet.io/models/imagenet/synset.txt) file contains the mapping between Imagenet category index and category name. We'll download the synset file, load it in a list to convert category index to human readable category names.
-
-```python
-synset_url = "http://data.mxnet.io/models/imagenet/synset.txt"
-synset_file_name = "synset.txt"
-mx.test_utils.download(synset_url, fname=synset_file_name)
-synset = []
-with open('synset.txt', 'r') as f:
-    synset = [l.rstrip().split(' ', 1)[1].split(',')[0] for l in f]
-    
-def get_class_name(cls_id):
-    return "%s (%d)" % (synset[cls_id], cls_id)
-
-def run_inference(net, data):
-    out = net(data)
-    return out.argmax(axis=1).asnumpy()[0].astype(int)
-```
-
-## Visualizing CNN decisions
-
-Next, we'll write a method to get an image, preprocess it, predict category and visualize the prediction. We'll use `gradcam.visualize()` to create the visualizations. `gradcam.visualize` returns a tuple with the following visualizations:
-
-1. **Grad-CAM:** This is a heatmap superimposed on the input image showing which part(s) of the image contributed most to the CNN's decision.
-2. **Guided Grad-CAM:** Guided Grad-CAM shows which exact pixels contributed the most to the CNN's decision.
-3. **Saliency map:** Saliency map is a monochrome image showing which pixels contributed the most to the CNN's decision. Sometimes, it is easier to see the areas in the image that most influence the output in a monochrome image than in a color image.
-
-
-```python
-def visualize(net, img_path, conv_layer_name):
-    orig_img = mx.img.imread(img_path)
-    preprocessed_img = preprocess(orig_img)
-    preprocessed_img = preprocessed_img.expand_dims(axis=0)
-    
-    pred_str = get_class_name(run_inference(net, preprocessed_img))
-    
-    orig_img = mx.image.imresize(orig_img, image_sz[0], image_sz[1]).asnumpy()
-    vizs = gradcam.visualize(net, preprocessed_img, orig_img, conv_layer_name)
-    return (pred_str, (orig_img, *vizs))
-```
-
-Next, we need to get the name of the last convolutional layer that extracts features from the image. We use the gradient information flowing into the last convolutional layer of the CNN to understand the importance of each neuron for a decision of interest. We are interested in the last convolutional layer because convolutional features naturally retain spatial information which is lost in fully connected layers. So, we expect the last convolutional layer to have the best compromise between high level semantics and detailed spacial information. The neurons in this layer look for semantic class specific information in the image (like object parts).
-
-In our network, feature extractors are added to a HybridSequential block named features. You can list the layers in that block by just printing `network.features`. You can see that the topmost convolutional layer is at index 28. `network.features[28]._name` will give the name of the layer.
-
-```python
-last_conv_layer_name = network.features[28]._name
-print(last_conv_layer_name)
-```
-vgg0_conv2d12<!--notebook-skip-line-->
-
-Let's download some images we can use for visualization.
-
-```python
-images = ["hummingbird.jpg", "jellyfish.jpg", "snow_leopard.jpg", "volcano.jpg"]
-base_url = "https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/cnn_visualization/{}?raw=true"
-for image in images:
-    mx.test_utils.download(base_url.format(image), fname=image)
-```
-
-We now have everything we need to start visualizing. Let's visualize the CNN decision for the images we downloaded.
-
-```python
-show_images(*visualize(network, "hummingbird.jpg", last_conv_layer_name))
-```
-
-<img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/cnn_visualization/hummingbird.png" alt="Visualizing CNN decision"/><!--notebook-skip-line-->
-
-```python
-show_images(*visualize(network, "jellyfish.jpg", last_conv_layer_name))
-```
-
-<img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/cnn_visualization/jellyfish.png" alt="Visualizing CNN decision"/><!--notebook-skip-line-->
-
-```python
-show_images(*visualize(network, "snow_leopard.jpg", last_conv_layer_name))
-```
-
-<img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/cnn_visualization/snow_leopard.png" alt="Visualizing CNN decision"/><!--notebook-skip-line-->
-
-Shown above are some images the network was able to predict correctly. We can see that the network is basing its decision on the appropriate features. Now, let's look at an example that the network gets the prediction wrong and visualize why it gets the prediction wrong.
-
-```python
-show_images(*visualize(network, "volcano.jpg", last_conv_layer_name))
-```
-
-<img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/cnn_visualization/volcano.png" alt="Visualizing CNN decision"/><!--notebook-skip-line-->
-
-While it is not immediately evident why the network thinks this volcano is a spider, after looking at the Grad-CAM visualization, it is hard to look at the volcano and not see the spider!
-
-Being able to visualize why a CNN predicts specific classes is a powerful tool to diagnose prediction failures. Even when the network is making correct predictions, visualizing activations is an important step to verify that the network is making its decisions based on the right features and not some correlation which happens to exist in the training data.
-
-The visualization method demonstrated in this tutorial applies to a wide variety of network architectures and a wide variety of tasks beyond classification - like VQA and image captioning. Any type of differentiable output can be used to create the visualizations shown above. Visualization techniques like these solve (at least partially) the long standing problem of interpretability of neural networks.
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
-
diff --git a/docs/tutorials/vision/index.md b/docs/tutorials/vision/index.md
deleted file mode 100644
index faf6526fb824..000000000000
--- a/docs/tutorials/vision/index.md
+++ /dev/null
@@ -1,25 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Tutorials
-
-```eval_rst
-.. toctree::
-   :glob:
-
-   *
-```
diff --git a/docs/tutorials/vision/large_scale_classification.md b/docs/tutorials/vision/large_scale_classification.md
deleted file mode 100644
index 36a0ff6c9daa..000000000000
--- a/docs/tutorials/vision/large_scale_classification.md
+++ /dev/null
@@ -1,282 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Large Scale Image Classification
-
-Training a neural network with a large number of images presents several challenges. Even with the latest GPUs, it is not possible to train large networks using a large number of images in a reasonable amount of time using a single GPU. This problem can be somewhat mitigated by using multiple GPUs in a single machine. But there is a limit to the number of GPUs that can be attached to one machine (typically 8 or 16). This tutorial explains how to train large networks with terabytes of data using multiple machines each containing multiple GPUs.
-
-## Prerequisites
-- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/install/index.html).  
-
-- [OpenCV Python library](http://opencv.org/opencv-3-2.html)
-
-```
-$ pip install opencv-python
-```
-
-```python
-import mxnet as mx
-print(mx.__version__)
-```
-
-`1.1.0`<!--notebook-skip-line-->
-
-
-## Preprocessing
-
-### Disk space
-The first step in training with large data is downloading the data and preprocessing it. For this tutorial, we will be using the full ImageNet dataset. Note that, at least 2 TB of disk space is required to download and preprocess this data. It is strongly recommended to use SSD instead of HDD. SSD is much better at dealing with a large number of small image files. After the preprocessing completes and images are packed into recordIO files, HDD should be fine for training.
-
-In this tutorial, we will use an AWS storage instance for data preprocessing. The storage instance `i3.4xlarge` has 3.8 TB of disk space across two NVMe SSD disks. We will use software RAID to combine them into one disk and mount it at `~/data`.
-
-```
-sudo mdadm --create --verbose /dev/md0 --level=stripe --raid-devices=2 \
-	/dev/nvme0n1 /dev/nvme1n1
-sudo mkfs /dev/md0
-sudo mkdir ~/data
-sudo mount /dev/md0 ~/data
-sudo chown ${whoami} ~/data
-```
-
-We now have sufficient disk space to download and preprocess the data.
-
-### Download ImageNet
-
-In this tutorial, we will be using the full ImageNet dataset which can be downloaded from http://www.image-net.org/download-images. `fall11_whole.tar` contains all the images. This file is 1.2 TB in size and could take a long time to download.
-
-After downloading, untar the file.
-```
-export ROOT=full
-mkdir $ROOT
-tar -xvf fall11_whole.tar -C $ROOT
-```
-
-That should give you a collection of tar files. Each tar file represents a category and contains all images belonging to that category. We can unzip each tar file and copy the images into a folder named after the name of the tar file.
-
-```
-for i in $ROOT/*.tar; do j=${i%.*}; echo $j;  mkdir -p $j; tar -xf $i -C $j; done
-rm $ROOT/*.tar
-
-ls $ROOT | head
-n00004475
-n00005787
-n00006024
-n00006484
-n00007846
-n00015388
-n00017222
-n00021265
-n00021939
-n00120010
-```
-
-### Remove uncommon classes for transfer learning (optional)
-A common reason to train a network on ImageNet data is to use it for transfer learning (including feature extraction or fine-tuning other models). According to [this](https://arxiv.org/pdf/1608.08614v1.pdf) study, classes with too few images don't help in transfer learning. So, we could remove classes with fewer than a certain number of images. The following code will remove classes with less than 500 images.
-
-```
-BAK=${ROOT}_filtered
-mkdir -p ${BAK}
-for c in ${ROOT}/n*; do
-    count=`ls $c/*.JPEG | wc -l`
-    if [ "$count" -gt "500" ]; then
-        echo "keep $c, count = $count"
-    else
-        echo "remove $c, $count"
-        mv $c ${BAK}/
-    fi
-done
-```
-
-### Generate a validation set
-To ensure we don't overfit the data, we will create a validation set separate from the training set. During training, we will monitor loss on the validation set frequently. We create the validation set by picking fifty random images from each class and moving them to the validation set.
-```
-VAL_ROOT=${ROOT}_val
-mkdir -p ${VAL_ROOT}
-for i in ${ROOT}/n*; do
-    c=`basename $i`
-    echo $c
-    mkdir -p ${VAL_ROOT}/$c
-    for j in `ls $i/*.JPEG | shuf | head -n 50`; do
-        mv $j ${VAL_ROOT}/$c/
-    done
-done
-```
-
-### Pack images into record files
-While MXNet can read image files directly, it is recommended to pack the image files into a recordIO file for increased performance. MXNet provides a tool (tools/im2rec.py) to do this. To use this tool, MXNet and OpenCV's python module needs to be installed in the system.
-
-Set the environment variable `MXNET` to point to the MXNet installation directory and `NAME` to the name of the dataset. Here, we assume MXNet is installed at `~/mxnet`
-
-```
-MXNET=~/mxnet
-NAME=full_imagenet_500_filtered
-```
-
-To create the recordIO files, we first create a list of images we want in the recordIO files and then use `im2rec` to pack images in the list into recordIO files. We create this list in `train_meta`. Training data is around 1TB. We split it into 8 parts, with each part roughly 100 GB in size.
-
-```
-mkdir -p train_meta
-python ${MXNET}/tools/im2rec.py --list --chunks 8 --recursive \
-train_meta/${NAME} ${ROOT}
-```
-
-We then resize the images such that the short edge is 480 pixels long and pack the images into recordIO files. Since most of the work is disk I/O, we use multiple (16) threads to get the work done faster.
-
-```
-python ${MXNET}/tools/im2rec.py --resize 480 --quality 90 \
---num-thread 16 train_meta/${NAME} ${ROOT}
-```
-
-Once done, we move the rec files into a folder named `train`.
-
-```
-mkdir -p train
-mv train_meta/*.rec train/
-```
-
-We do similar preprocessing for the validation set.
-
-```
-mkdir -p val_meta
-python ${MXNET}/tools/im2rec.py --list --recursive \
-val_meta/${NAME} ${VAL_ROOT}
-python ${MXNET}/tools/im2rec.py --resize 480 --quality 90 \
---num-thread 16 val_meta/${NAME} ${VAL_ROOT}
-mkdir -p val
-mv val_meta/*.rec val/
-```
-
-We now have all training and validation images in recordIO format in `train` and `val` directories respectively. We can now use these `.rec` files for training.
-
-## Training
-
-[ResNet](https://arxiv.org/abs/1512.03385) has shown its effectiveness on ImageNet competition. Our experiments also [reproduced](https://github.com/tornadomeet/ResNet) the results reported in the paper. As we increase the number of layers from 18 to 152, we see steady improvement in validation accuracy. Given this is a huge dataset, we will use Resnet with 152 layers.
-
-Due to the huge computational complexity, even the fastest GPU needs more than one day for a single pass of the data. We often need tens of epochs before the training converges to good validation accuracy. While we can use multiple GPUs in a machine, the number of GPUs in a machine is often limited to 8 or 16. For faster training, in this tutorial, we will use multiple machines each containing multiple GPUs to train the model.
-
-### Setup
-
-We will use 16 machines (P2.16x instances), each containing 16 GPUs (Tesla K80). These machines are interconnected via 20 Gbps ethernet.
-
-AWS CloudFormation makes it very easy to create deep learning clusters. We follow instructions from [this](https://aws.amazon.com/blogs/compute/distributed-deep-learning-made-easy/) page and create a deep learning cluster with 16 P2.16x instances.
-
-We load the data and code in the first machine (we'll refer to this machine as master). We share both the data and code to other machines using EFS.
-
-If you are setting up your cluster manually, without using AWS CloudFormation, remember to do the following:
-1. Compile MXNet using `USE_DIST_KVSTORE=1` to enable distributed training.
-2. Create a hosts file in the master that contains the host names of all the machines in the cluster. For example,
-   ```
-   $ head -3 hosts
-   deeplearning-worker1
-   deeplearning-worker2
-   deeplearning-worker3
-   ```
-   It should be possible to ssh into any of these machines from the master by invoking `ssh` with just a hostname from the file. For example,
-   ```
-   $ ssh deeplearning-worker2
-   ===================================
-   Deep Learning AMI for Ubuntu
-   ===================================
-   ...
-   ubuntu@ip-10-0-1-199:~$
-   ```
-   One way to do this is to use ssh agent forwarding. Please check [this](https://aws.amazon.com/blogs/security/securely-connect-to-linux-instances-running-in-a-private-amazon-vpc/) page to learn how to set this up. In short, you'll configure all machines to login using a particular certificate (mycert.pem) which is present on your local machine. You then login to the master using the certificate and the `-A` switch to enable agent forwarding. Now, from the master, you should be able to login to any other machine in the cluster by providing just the hostname (example: `ssh deeplearning-worker2`).
-
-### Run Training
-After the cluster is setup, login to master and run the following command from ${MXNET}/example/image-classification
-
-```
-../../tools/launch.py -n 16 -H $DEEPLEARNING_WORKERS_PATH python train_imagenet.py --network resnet \
---num-layers 152 --data-train ~/data/train --data-val ~/data/val/ --gpus 0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15 \
---batch-size 8192 --model ~/data/model/resnet152 --num-epochs 1 --kv-store dist_sync
-```
-
-launch.py launches the command it is provided in all the machine in the cluster. List of machines in the cluster must be provided to launch.py using the `-H` switch. Here is description of options used for launch.py.
-
-|Option | Description|
-|-------|------------|
-|n      | specifies the number of worker jobs to run on each machine. We run 16 workers since we have 16 machines in the cluster.|
-|H      | specifies the path to a file that has a list of hostnames of machines in the cluster. Since we created the cluster using the AWS deep learning CloudFormation template, the environment variable `$DEEPLEARNING_WORKERS_PATH` points to the required file.|
-
-train_imagenet.py trains the network provided by the `--network` option using the data provided by the `--data-train` and `--data-val` options. Here is description of the options used with train_imagenet.py.
-
-|Option     | Description|
-|-----------|------------|
-|network    | The network to train. Could be any of the network available in `${MXNET}/example/image-classification`. For this tutorial, we use Resnet.|
-|num-layers | Number of layers to use in the network. We use 152 layer Resnet.|
-|data-train | Directory containing the training images. We point to the EFS location (`~/data/train/`) where we stored the training images.|
-|data-val   | Directory containing the validation images. We point to the EFS location (`~/data/val`) where we stored the validation images.|
-|gpus       | Comma separated list of gpu indices to use for training on each machine. We use all 16 GPUs.|
-|batch-size | Batch size across all GPUs. This is equal to batch size per GPU * total number of GPUs. We use a batch size of 32 images per GPU. So, effective batch size is 32 * 16 * 16 = 8192.|
-|model      | Path prefix for the model file created by the training.|
-|num-epochs | Number of epochs to train.|
-|kv-store   | Key/Value store for parameter synchronization. We use distributed kv store since we are doing distributed training.|
-
-After training is complete, trained models are available in the directory specified by the `--model` option. Models are saved in two parts: model-symbol.json for the network definition and model-n.params for the parameters saved after the n'th epoch.
-
-## Scalability
-One common concern using large number of machines for training is the scalability. We have benchmarked scalability running several popular networks on clusters with up to 256 GPUs and the speedup is very close to ideal.
-
-This scalability test was run on sixteen P2.16xl instances with 256 GPUs in total. We used AWS deep learning AMI with CUDA 7.5 and CUDNN 5.1 installed.
-
-We fixed the batch size per GPU constant and doubled the number of GPUs for every subsequent test. Synchronized SGD (--kv-store dist_device_sync) was used. The CNNs used are located [here](https://github.com/dmlc/mxnet/tree/master/example/image-classification/symbols).
-
-|                     | alexnet | inception-v3 | resnet-152|
-|---------------------|---------|--------------|-----------|
-|batch size per GPU   | 512     | 32           | 32        |
-|model size (MB)      | 203     | 95           | 240       |
-
- Number of images processed per second is shown in the following table:
-
- |Number of GPUs  | Alexnet | Inception-v3 | Resnet-152|
- |----------------|---------|--------------|-----------|
- |1               |457.07   |30.4          |20.8       |
- |2               |870.43   |59.61         |38.76      |
- |4               |1514.8   |117.9         |77.01      |
- |8               |2852.5   |233.39        |153.07     |
- |16              |4244.18  |447.61        |298.03     |
- |32              |7945.57  |882.57        |595.53     |
- |64              |15840.52 |1761.24       |1179.86    |
- |128             |31334.88 |3416.2        |2333.47    |
- |256             |61938.36 |6660.98       |4630.42    |
-
- The following figure shows speedup against the number of GPUs used and compares it with ideal speedup.
-
- ![Speedup Graph](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/speedup-p2.png)
-
-## Troubleshooting guidelines
-
-### Validation accuracy
-It is often straightforward to achieve a reasonable validation accuracy, but achieving the state-of-the-art numbers reported in papers can sometimes be very hard. Here are few things you can try to improve validation accuracy.
-- Adding more data augmentations often reduces the gap between training and validation accuracy. Data augmentation could be reduced in epochs closer to the end.
-- Start with a large learning rate and keep it large for a long time. For example, in CIFAR10, you could keep the learning rate at 0.1 for the first 200 epochs and then reduce it to 0.01.
-- Do not use a batch size that is too large, especially batch size >> number of classes.
-
-### Speed
-- Distributed training improves speed when computation cost of a batch is high. So, make sure your workload is not too small (like LeNet on MNIST). Make sure batch size is reasonably large.
-- Make sure data-read and preprocessing is not the bottleneck. Use the `--test-io 1` flag to check how many images can be pre-processed per second.
-- Increase --data-nthreads (default is 4) to use more threads for data preprocessing.
-- Data preprocessing is done by opencv. If opencv is compiled from source code, check if it is configured correctly.
-- Use `--benchmark 1` to use randomly generated data rather than real data to narrow down where the bottleneck is.
-- Check [this](http://mxnet.io/faq/perf.html) page for more details.
-
-### Memory
-If the batch size is too big, it can exhaust GPU memory. If this happens, you'll see the error message "cudaMalloc failed: out of memory" or something similar. There are a couple of ways to fix this:
-- Reduce the batch size.
-- Set the environment variable `MXNET_BACKWARD_DO_MIRROR` to 1. It reduces the memory consumption by trading off speed. For example, with batch size 64, inception-v3 uses 10G memory and trains 30 image/sec on a single K80 GPU. When mirroring is enabled, with 10G GPU memory consumption, we can run inception-v3 using batch size of 128. The cost is that, the speed reduces to 27 images/sec.
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
\ No newline at end of file

From 6af6570b065e1d4886621763777297eedb2fde84 Mon Sep 17 00:00:00 2001
From: Lin Yuan <apeforest@gmail.com>
Date: Thu, 19 Sep 2019 13:05:59 -0700
Subject: [PATCH 343/813] fix flaky test (#16191)

---
 tests/python/unittest/test_profiler.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/tests/python/unittest/test_profiler.py b/tests/python/unittest/test_profiler.py
index d58c08b5f26e..8e8209f5235b 100644
--- a/tests/python/unittest/test_profiler.py
+++ b/tests/python/unittest/test_profiler.py
@@ -112,7 +112,6 @@ def get_log():
     var.asnumpy()
     stop = time.time()
     task.stop()
-    assert stop > start
     profiler.set_state('stop')
 
 

From 6247dc820445c7bd48489352ddef5c9838ab8e51 Mon Sep 17 00:00:00 2001
From: Haozheng Fan <fhztc1997618@gmail.com>
Date: Fri, 20 Sep 2019 05:56:41 +0800
Subject: [PATCH 344/813] [Numpy] Differentiable svd (#15795)

* use (m, m) temp space

* add technical report citation

* add comments for the tricky block matrix multiplication

* differentiable svd
---
 python/mxnet/_numpy_op_doc.py            |  66 +++++
 src/operator/c_lapack_api.cc             |  12 +
 src/operator/c_lapack_api.h              |  57 +++++
 src/operator/linalg.h                    |  21 ++
 src/operator/linalg_impl.h               | 122 +++++++++
 src/operator/numpy/linalg/np_gesvd-inl.h | 303 +++++++++++++++++++++++
 src/operator/numpy/linalg/np_gesvd.cc    | 119 +++++++++
 src/operator/numpy/linalg/np_gesvd.cu    |  43 ++++
 tests/python/unittest/test_numpy_op.py   |  96 ++++++-
 9 files changed, 838 insertions(+), 1 deletion(-)
 create mode 100644 src/operator/numpy/linalg/np_gesvd-inl.h
 create mode 100644 src/operator/numpy/linalg/np_gesvd.cc
 create mode 100644 src/operator/numpy/linalg/np_gesvd.cu

diff --git a/python/mxnet/_numpy_op_doc.py b/python/mxnet/_numpy_op_doc.py
index c95752a00cb6..ae215803a524 100644
--- a/python/mxnet/_numpy_op_doc.py
+++ b/python/mxnet/_numpy_op_doc.py
@@ -449,4 +449,70 @@ def _np_reshape(a, newshape, order='C', out=None):
     --------
     ndarray.reshape : Equivalent method.
     """
+
+
+def _np__linalg_svd(a):
+    r"""
+    svd(a)
+
+    Singular Value Decomposition.
+
+    When `a` is a 2D array, it is factorized as ``ut @ np.diag(s) @ v``,
+    where `ut` and `v` are 2D orthonormal arrays and `s` is a 1D
+    array of `a`'s singular values. When `a` is higher-dimensional, SVD is
+    applied in stacked mode as explained below.
+
+    Parameters
+    ----------
+    a : (..., M, N) ndarray 
+        A real or complex array with ``a.ndim >= 2`` and ``M <= N``.
+
+    Returns
+    -------
+    ut: (..., M, M) ndarray
+        Orthonormal array(s). The first ``a.ndim - 2`` dimensions have the same
+        size as those of the input `a`.
+    s : (..., M) ndarray
+        Vector(s) with the singular values, within each vector sorted in
+        descending order. The first ``a.ndim - 2`` dimensions have the same
+        size as those of the input `a`.
+    v : (..., M, N) ndarray
+        Orthonormal array(s). The first ``a.ndim - 2`` dimensions have the same
+        size as those of the input `a`.
+
+    Notes
+    -----
+
+    The decomposition is performed using LAPACK routine ``_gesvd``.
+
+    SVD is usually described for the factorization of a 2D matrix :math:`A`.
+    The higher-dimensional case will be discussed below. In the 2D case, SVD is
+    written as :math:`A = U^T S V`, where :math:`A = a`, :math:`U^T = ut`,
+    :math:`S= \mathtt{np.diag}(s)` and :math:`V = v`. The 1D array `s`
+    contains the singular values of `a` and `ut` and `v` are orthonormal. The rows
+    of `v` are the eigenvectors of :math:`A^T A` and the columns of `ut` are
+    the eigenvectors of :math:`A A^T`. In both cases the corresponding
+    (possibly non-zero) eigenvalues are given by ``s**2``.
+
+    If `a` has more than two dimensions, then broadcasting rules apply.
+    This means that SVD is working in "stacked" mode: it iterates over 
+    all indices of the first ``a.ndim - 2`` dimensions and for each
+    combination SVD is applied to the last two indices. The matrix `a` 
+    can be reconstructed from the decomposition with either 
+    ``(ut * s[..., None, :]) @ v`` or
+    ``ut @ (s[..., None] * v)``. (The ``@`` operator denotes batch matrix multiplication)
+
+    Examples
+    --------
+    >>> a = np.arange(54).reshape(6, 9)
+    >>> ut, s, v = np.linalg.svd(a)
+    >>> ut.shape, s.shape, v.shape
+    ((6, 6), (6,), (6, 9))
+    >>> s = s.reshape(6, 1)
+    >>> ret = np.dot(ut, s * v)
+    >>> (ret - a > 1e-3).sum()
+    array(0.)
+    >>> (ret - a < -1e-3).sum()
+    array(0.)
+    """
     pass
diff --git a/src/operator/c_lapack_api.cc b/src/operator/c_lapack_api.cc
index 33a5b0816c8d..e7a97848700d 100644
--- a/src/operator/c_lapack_api.cc
+++ b/src/operator/c_lapack_api.cc
@@ -63,6 +63,14 @@
     return 1; \
   }
 
+  #define MXNET_LAPACK_CWRAPPER6(func, dtype) \
+  int MXNET_LAPACK_##func(int matrix_layout, int m, int n, dtype* ut, \
+                          int ldut, dtype* s, dtype* v, int ldv, \
+                          dtype* work, int lwork) { \
+    LOG(FATAL) << "MXNet build without lapack. Function " << #func << " is not available."; \
+    return 1; \
+  }
+
   #define MXNET_LAPACK_UNAVAILABLE(func) \
   int mxnet_lapack_##func(...) { \
     LOG(FATAL) << "MXNet build without lapack. Function " << #func << " is not available."; \
@@ -89,4 +97,8 @@
 
   MXNET_LAPACK_CWRAPPER5(sgetri, float)
   MXNET_LAPACK_CWRAPPER5(dgetri, double)
+
+  MXNET_LAPACK_CWRAPPER6(sgesvd, float)
+  MXNET_LAPACK_CWRAPPER6(dgesvd, double)
+
 #endif  // MSHADOW_USE_MKL == 0
diff --git a/src/operator/c_lapack_api.h b/src/operator/c_lapack_api.h
index c63229c06798..a47bbd0b5857 100644
--- a/src/operator/c_lapack_api.h
+++ b/src/operator/c_lapack_api.h
@@ -120,6 +120,13 @@ extern "C" {
   MXNET_LAPACK_FSIG_SYEVD(ssyevd, float)
   MXNET_LAPACK_FSIG_SYEVD(dsyevd, double)
 
+  #define MXNET_LAPACK_FSIG_GESVD(func, dtype) \
+    void func##_(char *jobu, char *jobvt, int *m, int *n, dtype *a, int *lda, dtype *s, \
+                 dtype* u, int *ldu, dtype *vt, int *ldvt, dtype *work, int *lwork, int *info);
+
+  MXNET_LAPACK_FSIG_GESVD(sgesvd, float)
+  MXNET_LAPACK_FSIG_GESVD(dgesvd, double)
+
   #ifdef __ANDROID__
     #define MXNET_LAPACK_FSIG_GETRF(func, dtype) \
       int func##_(int *m, int *n, dtype *a, int *lda, int *ipiv, int *info);
@@ -242,6 +249,24 @@ inline void flip(int m, int n, DType *b, int ldb, DType *a, int lda) {
   #define MXNET_LAPACK_sgetrf LAPACKE_sgetrf
   #define MXNET_LAPACK_dgetrf LAPACKE_dgetrf
 
+  // Internally A is factorized as U * L * VT, and (according to the tech report)
+  // we want to factorize it as UT * L * V, so we pass ut as u and v as vt.
+  // We also have to allocate at least m - 1 DType elements as workspace as the internal
+  // LAPACKE function needs it to store `superb`. (see MKL documentation)
+  #define MXNET_LAPACK_CWRAP_GESVD(prefix, dtype) \
+  inline int MXNET_LAPACK_##prefix##gesvd(int matrix_layout, int m, int n, dtype* ut, \
+                                          int ldut, dtype* s, dtype* v, int ldv, \
+                                          dtype* work, int lwork) { \
+    if (lwork != -1) { \
+      return LAPACKE_##prefix##gesvd(matrix_layout, 'S', 'O', m, n, v, ldv, s, ut, ldut, \
+                                     v, ldv, work); \
+    } \
+    *work = m - 1; \
+    return 0; \
+  }
+  MXNET_LAPACK_CWRAP_GESVD(s, float)
+  MXNET_LAPACK_CWRAP_GESVD(d, double)
+
   #define MXNET_LAPACK_CWRAP_GETRI(prefix, dtype) \
   inline int MXNET_LAPACK_##prefix##getri(int matrix_layout, int n, dtype *a, int lda, \
                                           int *ipiv, dtype *work, int lwork) { \
@@ -361,6 +386,28 @@ inline void flip(int m, int n, DType *b, int ldb, DType *a, int lda) {
   MXNET_LAPACK_CWRAP_SYEVD(ssyevd, float)
   MXNET_LAPACK_CWRAP_SYEVD(dsyevd, double)
 
+  // Note: Supports row-major format only. Internally, column-major is used, so all
+  // inputs/outputs are flipped and transposed. m and n are flipped as well.
+  #define MXNET_LAPACK_CWRAP_GESVD(func, dtype) \
+  inline int MXNET_LAPACK_##func(int matrix_layout, int m, int n, dtype* ut, \
+                                 int ldut, dtype* s, dtype* v, int ldv, \
+                                 dtype* work, int lwork) { \
+    if (matrix_layout == MXNET_LAPACK_ROW_MAJOR) { \
+      int info(0); \
+      char jobu('O'); \
+      char jobvt('S'); \
+      func##_(&jobu, &jobvt, &n, &m, v, &ldv, s, v, &ldv, ut, &ldut, work, &lwork, &info); \
+      return info; \
+    } else { \
+      CHECK(false) << "MXNET_LAPACK_" << #func << " implemented for row-major layout only"; \
+      return 1; \
+    } \
+  }
+  MXNET_LAPACK_CWRAP_GESVD(sgesvd, float)
+  MXNET_LAPACK_CWRAP_GESVD(dgesvd, double)
+
+  #define MXNET_LAPACK
+
   // Note: Both MXNET_LAPACK_*getrf, MXNET_LAPACK_*getri can only be called with col-major format
   // (MXNet) for performance.
   #define MXNET_LAPACK_CWRAP_GETRF(prefix, dtype) \
@@ -421,6 +468,12 @@ inline void flip(int m, int n, DType *b, int ldb, DType *a, int lda) {
   int MXNET_LAPACK_##func(int matrix_layout, int n, dtype *a, int lda, \
                           int *ipiv, dtype *work, int lwork);
 
+  #define MXNET_LAPACK_CWRAPPER6(func, dtype) \
+  int MXNET_LAPACK_##func(int matrix_layout, int m, int n, dtype* ut, \
+                          int ldut, dtype* s, dtype* v, int ldv, \
+                          dtype* work, int lwork);
+
+
   #define MXNET_LAPACK_UNAVAILABLE(func) \
   int mxnet_lapack_##func(...);
   MXNET_LAPACK_CWRAPPER1(spotrf, float)
@@ -444,6 +497,10 @@ inline void flip(int m, int n, DType *b, int ldb, DType *a, int lda) {
 
   MXNET_LAPACK_CWRAPPER5(sgetri, float)
   MXNET_LAPACK_CWRAPPER5(dgetri, double)
+
+  MXNET_LAPACK_CWRAPPER6(sgesvd, float)
+  MXNET_LAPACK_CWRAPPER6(dgesvd, double)
+
   #undef MXNET_LAPACK_CWRAPPER1
   #undef MXNET_LAPACK_CWRAPPER2
   #undef MXNET_LAPACK_CWRAPPER3
diff --git a/src/operator/linalg.h b/src/operator/linalg.h
index 8f1eedae03ac..c74adb5b6372 100644
--- a/src/operator/linalg.h
+++ b/src/operator/linalg.h
@@ -191,6 +191,27 @@ int linalg_syevd_workspace_query(const Tensor<xpu, 2, DType>& A,
                                  const Tensor<xpu, 1, DType>& L,
                                  Stream<xpu> *s = 0);
 
+//////////////////////////////// GESVD ////////////////////////////////////////////
+
+// CPU/GPU-versions of LAPACK function "gesvd". Please refer to the
+// LAPACK documentation for further details.
+// Note: V is input and output parameter (it overwrites A)
+
+template<typename xpu, typename DType>
+void linalg_gesvd(const Tensor<xpu, 2, DType>& UT,
+                  const Tensor<xpu, 1, DType>& L,
+                  const Tensor<xpu, 2, DType>& V,
+                  const Tensor<xpu, 1, DType>& work,
+                  Stream<xpu>* s = 0);
+
+// This function determines the amount of workspace needed for linalg_gesvd
+// which is returned as number of elements of type DType.
+template<typename xpu, typename DType>
+int linalg_gesvd_workspace_query(const Tensor<xpu, 2, DType>& UT,
+                                 const Tensor<xpu, 1, DType>& L,
+                                 const Tensor<xpu, 2, DType>& V,
+                                 Stream<xpu>* s = 0);
+
 //////////////////////////////// GETRF ////////////////////////////////////////////
 
 // CPU/GPU-versions of LAPACK function "getrf". Please refer to the
diff --git a/src/operator/linalg_impl.h b/src/operator/linalg_impl.h
index 958e95555502..97d40099d4fd 100644
--- a/src/operator/linalg_impl.h
+++ b/src/operator/linalg_impl.h
@@ -1234,6 +1234,128 @@ LINALG_GPU_SYEVD_WORKSPACE_QUERY(DnDsyevd, double)
 
 #endif  // __CUDACC__
 
+//////////////////////////////// GESVD ////////////////////////////////////////////
+
+// CPU/GPU-versions of LAPACK function "gesvd"
+
+template<typename xpu, typename DType> inline
+void check_gesvd(const Tensor<xpu, 2, DType>& UT,
+                 const Tensor<xpu, 1, DType>& L,
+                 const Tensor<xpu, 2, DType>& V) {
+  // Any checking that helps user debug potential problems.
+  CHECK_LE(V.size(0), V.size(1))
+    << "The second to last dimension of A must be less or equal to the "
+    << "last dimension";
+  CHECK_EQ(UT.size(0), UT.size(1))
+    << "UT must be square matrix";
+  CHECK_EQ(V.size(0), L.size(0))
+    << "V, L have incompatible sizes";
+  CHECK_EQ(V.size(0), UT.size(0))
+    << "V, UT must have compatible sizes";
+}
+
+#define LINALG_CPU_GESVD(fname, DType) \
+template<> inline \
+void linalg_gesvd<cpu, DType>(const Tensor<cpu, 2, DType>& UT, \
+                              const Tensor<cpu, 1, DType>& L, \
+                              const Tensor<cpu, 2, DType>& V, \
+                              const Tensor<cpu, 1, DType>& work, \
+                              Stream<cpu> *s) { \
+  check_gesvd(UT, L, V); \
+  int lwork(work.size(0)); \
+  int ret(MXNET_LAPACK_##fname(MXNET_LAPACK_ROW_MAJOR, V.size(0), V.size(1), \
+                               UT.dptr_, UT.stride_, L.dptr_, V.dptr_, V.stride_, \
+                               work.dptr_, lwork)); \
+  CHECK_EQ(ret, 0) << #fname << " failed in lapack on cpu."; \
+}
+
+LINALG_CPU_GESVD(sgesvd, float)
+LINALG_CPU_GESVD(dgesvd, double)
+
+#define LINALG_CPU_GESVD_WORKSPACE_QUERY(func, DType) \
+template<> inline \
+int linalg_gesvd_workspace_query<cpu, DType>(const Tensor<cpu, 2, DType>& UT, \
+                                             const Tensor<cpu, 1, DType>& L, \
+                                             const Tensor<cpu, 2, DType>& V, \
+                                             Stream<cpu> *s) { \
+  DType work(0.0); \
+  MXNET_LAPACK_##func(MXNET_LAPACK_ROW_MAJOR, V.size(0), V.size(1), \
+                      UT.dptr_, UT.stride_, L.dptr_, V.dptr_, V.stride_, \
+                      &work, -1); \
+  return static_cast<int>(work); \
+}
+LINALG_CPU_GESVD_WORKSPACE_QUERY(sgesvd, float)
+LINALG_CPU_GESVD_WORKSPACE_QUERY(dgesvd, double)
+
+#ifdef __CUDACC__
+
+// GESVD only available with cuda8 or higher.
+#if CUDA_VERSION >= 8000
+
+#define LINALG_GPU_GESVD(fname, DType) \
+template<> inline \
+void linalg_gesvd<gpu, DType>(const Tensor<gpu, 2, DType>& UT, \
+                              const Tensor<gpu, 1, DType>& L, \
+                              const Tensor<gpu, 2, DType>& V, \
+                              const Tensor<gpu, 1, DType>& work, \
+                              Stream<gpu> *s) { \
+  using namespace mxnet; \
+  using mshadow::gpu; \
+  check_gesvd(UT, L, V); \
+  Storage::Handle info = Storage::Get()->Alloc(sizeof(int), Context::GPU()); \
+  CUSOLVER_CALL(cusolver##fname(Stream<gpu>::GetSolverHandle(s), \
+                'O', 'S', V.size(1), V.size(0), V.dptr_, V.stride_, L.dptr_, V.dptr_, V.stride_, \
+                UT.dptr_, UT.stride_, work.dptr_, work.size(0), \
+                V.dptr_, static_cast<int *>(info.dptr))); \
+  Storage::Get()->Free(info); \
+}
+
+#define LINALG_GPU_GESVD_WORKSPACE_QUERY(fname, DType) \
+template<> inline \
+int linalg_gesvd_workspace_query<gpu, DType>(const Tensor<gpu, 2, DType>& UT, \
+                                             const Tensor<gpu, 1, DType>& L, \
+                                             const Tensor<gpu, 2, DType>& V, \
+                                             Stream<gpu> *s) { \
+  using namespace mxnet; \
+  using mshadow::gpu; \
+  int lwork(0); \
+  CUSOLVER_CALL(cusolver##fname##_bufferSize(Stream<gpu>::GetSolverHandle(s), \
+                                             V.size(1), V.size(0), &lwork)); \
+  return lwork; \
+}
+
+#else
+
+#define LINALG_GPU_GESVD(fname, DType) \
+template<> inline \
+void linalg_gesvd<gpu, DType>(const Tensor<gpu, 2, DType>& UT, \
+                              const Tensor<gpu, 1, DType>& L, \
+                              const Tensor<gpu, 2, DType>& V, \
+                              const Tensor<gpu, 1, DType>& work, \
+                              Stream<gpu> *s) { \
+  LOG(FATAL) << "gesvd requires CUDA version >= 8.0!"; \
+}
+
+#define LINALG_GPU_GESVD_WORKSPACE_QUERY(fname, DType) \
+template<> inline \
+int linalg_gesvd_workspace_query<gpu, DType>(const Tensor<gpu, 2, DType>& UT, \
+                                             const Tensor<gpu, 1, DType>& L, \
+                                             const Tensor<gpu, 2, DType>& V, \
+                                             Stream<gpu> *s) { \
+  LOG(FATAL) << "gesvd requires CUDA version >= 8.0!"; \
+  return 0; \
+}
+
+#endif  // CUDA_VERSION >= 8000
+
+LINALG_GPU_GESVD(DnSgesvd, float)
+LINALG_GPU_GESVD(DnDgesvd, double)
+
+LINALG_GPU_GESVD_WORKSPACE_QUERY(DnSgesvd, float)
+LINALG_GPU_GESVD_WORKSPACE_QUERY(DnDgesvd, double)
+
+#endif  // __CUDACC__
+
 //////////////////////////////// GETRF ////////////////////////////////////////////
 
 // CPU/GPU-versions of LAPACK function "getrf"
diff --git a/src/operator/numpy/linalg/np_gesvd-inl.h b/src/operator/numpy/linalg/np_gesvd-inl.h
new file mode 100644
index 000000000000..7ce3078dae66
--- /dev/null
+++ b/src/operator/numpy/linalg/np_gesvd-inl.h
@@ -0,0 +1,303 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2017 by Contributors
+ * \file np_gesvd-inl.h
+ * \brief Function definition of the SVD Operator.
+ */
+#ifndef MXNET_OPERATOR_NUMPY_LINALG_NP_GESVD_INL_H_
+#define MXNET_OPERATOR_NUMPY_LINALG_NP_GESVD_INL_H_
+
+#include <mxnet/operator_util.h>
+#include <vector>
+#include "../../tensor/la_op.h"
+#include "../../tensor/la_op-inl.h"
+
+namespace mxnet {
+namespace op {
+
+struct GesvdVecSign {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int i, int m, int n, DType* UT,
+                                  DType* V, int ldut, int ldv) {
+    DType* vrow(V + i * ldv);
+    DType maxval(fabs(vrow[0])), vval(0.0);
+    int maxind(0);
+    for (int i = 1; i < n; ++i) {
+      vval = fabs(vrow[i]);
+      if (vval > maxval) {
+        maxval = vval;
+        maxind = i;
+      }
+    }
+    if (vrow[maxind] < 0) {
+      DType* utcol(UT + i % m + (i / m) * ldut * m);
+      for (int i = 0; i < n; ++i) {
+        vrow[i] = -vrow[i];
+        if (i < m) {
+          utcol[i * ldut] = -utcol[i * ldut];
+        }
+      }
+    }
+  }
+};
+
+// (UT, L, V) = gesvd(A) [singular value decomposition]
+// - V can overwrite A
+// - Needs workspace (DType), size of which is determined by a workspace query
+struct gesvd {
+  template<typename xpu, typename DType>
+  static void op(const Tensor<xpu, 3, DType>& A,
+                 const Tensor<xpu, 3, DType>& UT,
+                 const Tensor<xpu, 2, DType>& L,
+                 const Tensor<xpu, 3, DType>& V,
+                 const OpContext& ctx,
+                 const nnvm::NodeAttrs& attrs) {
+    Stream<xpu> *s = ctx.get_stream<xpu>();
+    if (A.dptr_ != V.dptr_) Copy(V, A, s);
+    // From here on, we work on V only
+    // Reserve workspace (size determined by query)
+    int lwork(linalg_gesvd_workspace_query(UT[0], L[0], V[0], s));
+    Tensor<xpu, 1, DType> work = ctx.requested[0]
+      .get_space_typed<xpu, 1, DType>(Shape1(lwork), s);
+    // Loop over items in batch
+    for (index_t i = 0; i < UT.size(0); ++i) {
+      linalg_gesvd(UT[i], L[i], V[i], work, s);
+    }
+    // Set signs in a deterministic way
+    using namespace mxnet_op;
+    Kernel<GesvdVecSign, xpu>::Launch
+      (s, V.size(0) * V.size(1), V.size(1), V.size(2),
+       UT.dptr_, V.dptr_, UT.stride_, V.stride_);
+  }
+};
+
+// (A) => (UT, L, V)
+template<typename xpu, typename laop>
+void NumpyLaGesvdForward(const nnvm::NodeAttrs& attrs,
+                         const OpContext& ctx,
+                         const std::vector<TBlob>& inputs,
+                         const std::vector<OpReqType>& req,
+                         const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  CHECK_EQ(inputs.size(), 1);
+  CHECK_EQ(outputs.size(), 3);
+  if (inputs[0].shape_.Size() == 0) {
+    return;
+  }
+  MSHADOW_SGL_DBL_TYPE_SWITCH(outputs[0].type_flag_, OType, {
+    mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+    laop::op(inputs[0].FlatToKD<xpu, 3, OType>(s),
+             outputs[0].FlatToKD<xpu, 3, OType>(s),
+             outputs[1].FlatToKD<xpu, 2, OType>(s),
+             outputs[2].FlatToKD<xpu, 3, OType>(s), ctx, attrs);
+  });
+}
+
+// Helper for gesvd_backward. See technical report
+// `Auto-Differentiating Linear Algebra` for details
+// on https://arxiv.org/pdf/1710.08717.pdf
+template<typename DType>
+DType gesvd_back_helper_eps(DType* X);
+
+template<>
+MSHADOW_XINLINE float gesvd_back_helper_eps(float* X) {
+  return 1e-30;
+}
+
+template<>
+MSHADOW_XINLINE double gesvd_back_helper_eps(double* X) {
+  return 1e-100;
+}
+
+// dA overwritten by L^-1 dA
+struct GesvdBackHelper_dV {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int k, int m, int n, DType* L, int ldl,
+                                  DType* dA, int ldda) {
+    const int offl(k * ldl);
+    const int offda(k * m * ldda);
+    DType denom(0.0);
+    const DType eps(gesvd_back_helper_eps(dA));
+    for (int i = 0; i < m; ++i) {
+      denom = L[offl + i];
+      if (denom < eps) denom = eps;
+      for (int j = 0; j < n; ++j) {
+        dA[offda + i * ldda + j] /= denom;
+      }
+    }
+  }
+};
+
+// X (square) overwritten by X L
+// Y overwritten by the diagonal of X
+struct GesvdBackHelper_G1 {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int k, int m, int n, DType* X, int ldx,
+                                  DType* L, int ldl, DType* Y, int ldy) {
+    const int offl(k * ldl);
+    const int offy(k * ldy);
+    const int offx(k * m * ldx);
+    DType numer(0.0);
+    for (int i = 0; i < m; ++i) {
+      Y[offy + i] = X[offx + i * ldx + i];
+      for (int j = 0; j < m; ++j) {
+        numer = L[offl + j];
+        X[offx + i * ldx + j] *= numer;
+      }
+    }
+  }
+};
+
+struct GesvdBackHelper_G2 {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int k, int m, int n, DType* X, int ldx,
+                                  DType* L, int ldl, DType* dL, int lddl,
+                                  DType* Y, int ldy) {
+    const int offx(k * m * ldx);
+    const int offl(k * ldl);
+    const int offdl(k * lddl);
+    const int offy(k * ldy);
+    const DType eps(gesvd_back_helper_eps(X));
+    DType denom1(0.0), denom2(0.0), elem(0.0);
+
+    for (int i = 0; i < m; ++i) {
+      for (int j = i + 1; j < m; ++j) {
+        denom1 = L[offl + i] - L[offl + j];
+        denom2 = L[offl + i] + L[offl + j];
+        if (denom1 < eps) denom1 = eps;
+        if (denom2 < eps) denom2 = eps;
+        elem = (X[offx + i * ldx + j] - X[offx + j * ldx + i]) / denom1 / denom2;
+        X[offx + i * ldx + j] = elem * L[offl + j];
+        X[offx + j * ldx + i] = elem * L[offl + i];
+      }
+      X[offx + i * ldx + i] = -Y[offy + i] + dL[offdl + i];
+    }
+  }
+};
+
+struct gesvd_backward {
+  template<typename xpu, typename DType>
+  static void op(const Tensor<xpu, 3, DType>& dUT,
+                 const Tensor<xpu, 2, DType>& dL,
+                 const Tensor<xpu, 3, DType>& dV,
+                 const Tensor<xpu, 3, DType>& UT,
+                 const Tensor<xpu, 2, DType>& L,
+                 const Tensor<xpu, 3, DType>& V,
+                 const Tensor<xpu, 3, DType>& dA,
+                 const Tensor<xpu, 3, DType>& tempM,
+                 const Tensor<xpu, 2, DType>& tempMd,
+                 Stream<xpu>* s, const nnvm::NodeAttrs& attrs) {
+    // Backward of (UT, L, V) = gesvd(A)
+    using namespace mxnet_op;
+    if (dA.dptr_ != dV.dptr_) {
+      Copy(dA, dV, s);
+    }
+    // From here on, we work on dA only
+    int k = dA.size(0), m = dA.size(1), n = dA.size(2);
+
+    // Need temporal space, same shape as dUT
+    // invdV:
+    Kernel<GesvdBackHelper_dV, xpu>::Launch
+      (s, k, m, n, L.dptr_, L.stride_, dA.dptr_, dA.stride_);
+
+    // G1:
+    // This is just to make sure there are no invalid values (NaN, infinity) in tempM and tempMd
+    tempM.FlatTo1D() = 0;
+    tempMd.FlatTo1D() = 0;
+    gemm::op(dA, V, tempM, DType(1.0), DType(0.0), false, true, s);
+    Kernel<GesvdBackHelper_G1, xpu>::Launch
+      (s, k, m, n, tempM.dptr_, tempM.stride_,
+       L.dptr_, L.stride_, tempMd.dptr_, tempMd.stride_);
+    gemm::op(dUT, UT, tempM, DType(1.0), DType(1.0), true, false, s);
+
+    // G2:
+    Kernel<GesvdBackHelper_G2, xpu>::Launch
+      (s, k, m, n, tempM.dptr_, tempM.stride_,
+       L.dptr_, L.stride_, dL.dptr_, dL.stride_,
+       tempMd.dptr_, tempMd.stride_);
+
+    // G3:
+    gemm::op(tempM, V, dA, DType(1.0), DType(1.0), false, false, s);
+    // dA <- dot(UT, dA). Loop over (k, m, m) blocks to avoid large temporary memory
+    for (int i = 0; i < n; i += m) {
+      int ncols = n - i < m ? n - i : m;
+      Tensor<xpu, 3, DType> t = Tensor<xpu, 3, DType>(dA.dptr_ + i,
+        Shape3(k, m, ncols), dA.stride_, dA.stream_);
+      Tensor<xpu, 3, DType> out = Tensor<xpu, 3, DType>(tempM.dptr_,
+        Shape3(k, m, ncols), tempM.stride_, tempM.stream_);
+      gemm::op(UT, t, out, DType(1.0), DType(0.0), false, false, s);
+      Copy(t, out, s);
+    }
+  }
+};
+
+// (dUT, dL, dV, UT, L, V) => (dA)
+template<typename xpu, typename laop>
+void NumpyLaGesvdBackward(const nnvm::NodeAttrs& attrs,
+                          const OpContext& ctx,
+                          const std::vector<TBlob>& inputs,
+                          const std::vector<OpReqType>& req,
+                          const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  CHECK_EQ(inputs.size(), 6);
+  CHECK_EQ(outputs.size(), 1);
+  if (outputs[0].shape_.Size() == 0) {
+    return;
+  }
+  MSHADOW_SGL_DBL_TYPE_SWITCH(outputs[0].type_flag_, OType, {
+    TBlob tspace(outputs[0]);
+    TBlob tempM, tempMd;
+    int kmn = outputs[0].shape_.Size();
+    int kmm = inputs[0].shape_.Size();
+    int km = inputs[1].shape_.Size();
+    if (req[0] == kAddTo) {
+      Tensor<xpu, 1, OType> tempspace = ctx.requested[0]
+        .get_space_typed<xpu, 1, OType>(Shape1(kmn + kmm + km), s);
+      tspace = TBlob(tempspace.Slice(0, kmn)).reshape(outputs[0].shape_);
+      tempM = TBlob(tempspace.Slice(kmn, kmn + kmm)).reshape(inputs[0].shape_);
+      tempMd = TBlob(tempspace.Slice(kmn + kmm, kmn + kmm + km)).reshape(inputs[1].shape_);
+    } else {
+      Tensor<xpu, 1, OType> tempspace = ctx.requested[0]
+        .get_space_typed<xpu, 1, OType>(Shape1(kmm + km), s);
+      tempM = TBlob(tempspace.Slice(0, kmm)).reshape(inputs[0].shape_);
+      tempMd = TBlob(tempspace.Slice(kmm, kmm + km)).reshape(inputs[1].shape_);
+    }
+    laop::op(inputs[0].FlatToKD<xpu, 3, OType>(s),  // dUT
+             inputs[1].FlatToKD<xpu, 2, OType>(s),  // dL
+             inputs[2].FlatToKD<xpu, 3, OType>(s),  // dV
+             inputs[3].FlatToKD<xpu, 3, OType>(s),  // UT
+             inputs[4].FlatToKD<xpu, 2, OType>(s),  // L
+             inputs[5].FlatToKD<xpu, 3, OType>(s),  // V
+             tspace.FlatToKD<xpu, 3, OType>(s),  // dA
+             tempM.FlatToKD<xpu, 3, OType>(s),  // tempM
+             tempMd.FlatToKD<xpu, 2, OType>(s),  // tempMd
+             s, attrs);
+    if (req[0] == kAddTo) {
+      Tensor<xpu, 1, OType> out = outputs[0].FlatTo1D<xpu, OType>(s);
+      out += tspace.FlatTo1D<xpu, OType>(s);
+    }
+  });
+}
+
+}  // namespace op
+}  // namespace mxnet
+#endif  // MXNET_OPERATOR_NUMPY_LINALG_NP_GESVD_INL_H_
diff --git a/src/operator/numpy/linalg/np_gesvd.cc b/src/operator/numpy/linalg/np_gesvd.cc
new file mode 100644
index 000000000000..2be9c778decf
--- /dev/null
+++ b/src/operator/numpy/linalg/np_gesvd.cc
@@ -0,0 +1,119 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_svd.cc
+ * \brief CPU implementation of the SVD Operator
+ */
+#include <mxnet/operator_util.h>
+#include <vector>
+#include "./np_gesvd-inl.h"
+#include "../../mshadow_op.h"
+#include "../../mxnet_op.h"
+#include "../../operator_common.h"
+#include "../../elemwise_op_common.h"
+
+namespace mxnet {
+namespace op {
+
+// Shape inference function for gesvd
+// Inputs: A. Outputs: UT, L, V
+inline bool NumpyLaGesvdShape(const nnvm::NodeAttrs& attrs,
+                           mxnet::ShapeVector* in_attrs,
+                           mxnet::ShapeVector* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1);
+  CHECK_EQ(out_attrs->size(), 3);
+  const mxnet::TShape& in_a = (*in_attrs)[0];
+  const mxnet::TShape& out_ut = (*out_attrs)[0];
+  const mxnet::TShape& out_l = (*out_attrs)[1];
+  const mxnet::TShape& out_v = (*out_attrs)[2];
+  if (in_a.ndim() >= 2) {
+    // Forward shape inference.
+    const int ndim(in_a.ndim());
+    CHECK_LE(in_a[ndim - 2], in_a[ndim - 1])
+      << "Input A shape wrong: The second to last dimension must be less than or"
+      << "equal to the last dimension";
+    // V must have same shape as A
+    SHAPE_ASSIGN_CHECK(*out_attrs, 2, in_a);
+    std::vector<int> oshape_l(ndim - 1);
+    for (int i = 0; i < ndim - 1; ++i) {
+      oshape_l[i] = in_a[i];
+    }
+    mxnet::TShape tshape_l(oshape_l.begin(), oshape_l.end());
+    SHAPE_ASSIGN_CHECK(*out_attrs, 1, tshape_l);
+    std::vector<int> oshape_ut(ndim);
+    for (int i = 0; i < ndim - 1; ++i) {
+      oshape_ut[i] = in_a[i];
+    }
+    oshape_ut[ndim - 1] = in_a[ndim - 2];
+    mxnet::TShape tshape_ut(oshape_ut.begin(), oshape_ut.end());
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, tshape_ut);
+    return true;
+  }
+  if (out_ut.ndim() >= 2 && out_ut.ndim() == out_l.ndim()+1 &&
+      out_v.ndim() == out_ut.ndim()) {
+    // Backward shape inference.
+    const int ndim(out_ut.ndim());
+    for (int i = 0; i < ndim - 1; ++i) {
+      CHECK_EQ(out_ut[i], out_l[i])
+        << "Outputs UT, L must have same dimensions except for last";
+      CHECK_EQ(out_v[i], out_l[i])
+        << "Outputs V, L must have same dimensions except for last";
+    }
+    CHECK_EQ(out_ut[ndim - 2], out_ut[ndim - 1])
+      << "Output UT shape wrong: Last two dimensions must be equal";
+    CHECK_LE(out_v[ndim-2], out_v[ndim-1])
+      << "Output V shape wrong: The second to last dimension must be less than or"
+      << "equal to the last dimension";
+    // A must have same shape as V
+    SHAPE_ASSIGN_CHECK(*in_attrs, 0, out_v);
+    return true;
+  }
+  return false;
+}
+
+NNVM_REGISTER_OP(_np__linalg_svd)
+.describe(R"code()code" ADD_FILELINE)
+.set_num_inputs(1)
+.set_num_outputs(3)
+.set_attr<nnvm::FListInputNames>("FListInputNames", [](const NodeAttrs& attrs) {
+  return std::vector<std::string>{"A"}; })
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyLaGesvdShape)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 3>)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption", [](const NodeAttrs& attrs) {
+  return std::vector<std::pair<int, int>>{{0, 2}}; })
+.set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& attrs) {
+  return std::vector<ResourceRequest>{ResourceRequest::kTempSpace}; })
+.set_attr<FCompute>("FCompute<cpu>", NumpyLaGesvdForward<cpu, gesvd>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{"_backward_np_linalg_svd"})
+.add_argument("A", "NDArray-or-Symbol", "Input matrices to be factorized");
+
+NNVM_REGISTER_OP(_backward_np_linalg_svd)
+.set_num_inputs(6)
+.set_num_outputs(1)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption", [](const NodeAttrs& attrs) {
+  return std::vector<std::pair<int, int> >{{2, 0}}; })
+.set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& attrs) {
+  return std::vector<ResourceRequest>{ResourceRequest::kTempSpace}; })
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FCompute>("FCompute<cpu>", NumpyLaGesvdBackward<cpu, gesvd_backward>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/linalg/np_gesvd.cu b/src/operator/numpy/linalg/np_gesvd.cu
new file mode 100644
index 000000000000..ee1546360447
--- /dev/null
+++ b/src/operator/numpy/linalg/np_gesvd.cu
@@ -0,0 +1,43 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file np_gesvd.cu
+ * \brief GPU implementation of the SVD Operator
+ */
+
+#include <mxnet/operator_util.h>
+#include <vector>
+#include "./np_gesvd-inl.h"
+
+namespace mxnet {
+namespace op {
+
+#if MXNET_USE_CUSOLVER == 1
+
+NNVM_REGISTER_OP(_np__linalg_svd)
+.set_attr<FCompute>("FCompute<gpu>", NumpyLaGesvdForward<gpu, gesvd>);
+
+NNVM_REGISTER_OP(_backward_np_linalg_svd)
+.set_attr<FCompute>("FCompute<gpu>", NumpyLaGesvdBackward<gpu, gesvd_backward>);
+
+#endif
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 07bd2864cfb8..2ce0fa4fd9cb 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -30,7 +30,6 @@
 from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf
 import platform
 
-
 @with_seed()
 @use_np
 def test_np_tensordot():
@@ -1981,6 +1980,101 @@ def get_grad_right(a1, a2):
             assert_almost_equal(x.grad.asnumpy(), x_grad, rtol=rtol, atol=atol)
 
 
+@with_seed()
+@use_np
+def test_np_svd():
+    class TestSVD(HybridBlock):
+        def __init__(self):
+            super(TestSVD, self).__init__()
+
+        def hybrid_forward(self, F, data):
+            return F.np.linalg.svd(data)
+
+    def get_grad(UT, L, V):
+        m = V.shape[-2]
+        n = V.shape[-1]
+        E = _np.zeros_like(UT)
+        dUT = _np.ones_like(UT)
+        dV = _np.ones_like(V)
+        for i in range(m):
+            for j in range(i + 1, m):
+                denom1 = _np.maximum(L[..., i] - L[..., j], 1e-20)
+                denom2 = _np.maximum(L[..., i] + L[..., j], 1e-20)
+                E[..., i, j] = 1.0 / denom1 / denom2
+                E[..., j, i] = -E[..., i, j]
+            E[..., i, i] = 0
+        G1 = _np.matmul(1.0 / L[..., None] * dV, _np.swapaxes(V, -2, -1)) * L[..., None, :]
+        G1 = G1 + _np.matmul(_np.swapaxes(dUT, -2, -1), UT)
+        X = G1 * E
+        G2 = _np.eye(m) + (X + _np.swapaxes(X, -2, -1)) * L[..., None, :] - 1.0 / L[..., None] * _np.matmul(dV, _np.swapaxes(V, -2, -1)) * _np.eye(m)
+        dA = _np.matmul(UT, _np.matmul(G2, V) + 1.0 / L[..., None] * dV)
+        return dA
+
+    shapes = [
+        (3, 3),
+        (3, 5),
+        (4, 4),
+        (4, 5),
+        (5, 5),
+        (5, 6),
+        (6, 6),
+        (0, 1),
+        (6, 5, 6),
+        (2, 3, 3, 4),
+        (4, 2, 1, 2),
+        (0, 5, 3, 3),
+        (5, 0, 3, 3),
+        (3, 3, 0, 0),
+    ]
+    dtypes = ['float32', 'float64']
+    for hybridize in [True, False]:
+        for dtype in dtypes:
+            for shape in shapes:
+                rtol = atol = 0.01
+                test_svd = TestSVD()
+                if hybridize:
+                    test_svd.hybridize()
+                data_np = _np.random.uniform(-10.0, 10.0, shape)
+                data_np = _np.array(data_np, dtype=dtype)
+                data = np.array(data_np, dtype=dtype)
+                data.attach_grad()
+                with mx.autograd.record():
+                    ret = test_svd(data)
+                UT = ret[0].asnumpy()
+                L = ret[1].asnumpy()
+                V = ret[2].asnumpy()
+                # check UT @ L @ V == A
+                t = _np.matmul(UT * L[..., None, :], V)
+                assert t.shape == data_np.shape
+                assert_almost_equal(t, data_np, rtol=rtol, atol=atol)
+                # check UT @ U == I
+                I = _np.matmul(UT, _np.swapaxes(UT, -2, -1))
+                I_np = _np.ones_like(UT) * _np.eye(shape[-2])
+                assert I.shape == I_np.shape
+                assert_almost_equal(I, I_np, rtol=rtol, atol=atol)
+                # check U @ UT == I
+                I = _np.matmul(_np.swapaxes(UT, -2, -1), UT)
+                I_np = _np.ones_like(UT) * _np.eye(shape[-2])
+                assert I.shape == I_np.shape
+                assert_almost_equal(I, I_np, rtol=rtol, atol=atol)
+                # check V @ VT == I
+                I = _np.matmul(V, _np.swapaxes(V, -2, -1))
+                I_np = _np.ones_like(UT) * _np.eye(shape[-2])
+                assert I.shape == I_np.shape
+                assert_almost_equal(I, I_np, rtol=rtol, atol=atol)
+                # check descending singular values
+                s = [L[..., i] - L[..., i + 1] for i in range(L.shape[-1] - 1)]
+                s = _np.array(s)
+                assert (s >= -1e-5).all()
+                if L.size > 0:
+                    assert (L[..., -1] >= -1e-5).all()
+                # check backward
+                mx.autograd.backward(ret)
+                if ((s > 1e-5).all() and (L.size == 0 or (L > 1e-5).all())):
+                    backward_expected = get_grad(ret[0].asnumpy(), ret[1].asnumpy(), ret[2].asnumpy())
+                    assert_almost_equal(data.grad.asnumpy(), backward_expected, rtol=rtol, atol=atol)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From a783d8119260fa370c8b12d3154ebc27e8e7284d Mon Sep 17 00:00:00 2001
From: Xi Wang <xidulu@gmail.com>
Date: Fri, 20 Sep 2019 11:41:14 +0800
Subject: [PATCH 345/813] add epsilon to sum(pvalue) upperbound (#16211)

---
 src/operator/numpy/random/np_multinomial_op.cu | 2 +-
 src/operator/numpy/random/np_multinomial_op.h  | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/operator/numpy/random/np_multinomial_op.cu b/src/operator/numpy/random/np_multinomial_op.cu
index bf05ede55ac7..6aa163978774 100644
--- a/src/operator/numpy/random/np_multinomial_op.cu
+++ b/src/operator/numpy/random/np_multinomial_op.cu
@@ -35,7 +35,7 @@ void CheckPvalGPU(DType* input, int prob_length) {
   DType sum = DType(0.0);
   for (int i = 0; i < prob_length; ++i) {
     sum += pvals_[i];
-    CHECK(sum <= DType(1.0))
+    CHECK(sum <= DType(1.0 + 1e-12))
       << "sum(pvals[:-1]) > 1.0";
   }
 }
diff --git a/src/operator/numpy/random/np_multinomial_op.h b/src/operator/numpy/random/np_multinomial_op.h
index 2a54fba9b1cc..2350d208e43c 100644
--- a/src/operator/numpy/random/np_multinomial_op.h
+++ b/src/operator/numpy/random/np_multinomial_op.h
@@ -107,7 +107,7 @@ void CheckPval(DType* input, int prob_length) {
   DType sum = DType(0.0);
   for (int i = 0; i < prob_length; ++i) {
     sum += input[i];
-    CHECK_LE(sum, 1.0)
+    CHECK_LE(sum, 1.0 + 1e-12)
       << "sum(pvals[:-1]) > 1.0";
   }
 }
@@ -178,7 +178,7 @@ void NumpyMultinomialForward(const nnvm::NodeAttrs& attrs,
         sum += param.pvals.value()[i];
         // copy the tuple to data for later kernel usage
         pvals_[i] = param.pvals.value()[i];
-        CHECK_LE(sum, 1.0)
+        CHECK_LE(sum, 1.0 + 1e-12)
           << "sum(pvals[:-1]) > 1.0";
     }
     Kernel<multinomial_kernel, xpu>::Launch(

From 7126438911f0335cdcdf48e76b2aa0f758a131be Mon Sep 17 00:00:00 2001
From: Thomas Delteil <thomas.delteil1@gmail.com>
Date: Thu, 19 Sep 2019 22:17:04 -0700
Subject: [PATCH 346/813] New Website: New Pipeline [3/3] (#15883)

Merging new website pipelines and fix to content
---
 Makefile                                      |   20 +-
 R-package/DESCRIPTION                         |    1 +
 R-package/src/export.cc                       |   13 +
 ci/Jenkinsfile_utils.groovy                   |   24 +-
 ci/docker/Dockerfile.build.test.arm_qemu      |    1 +
 ci/docker/Dockerfile.build.ubuntu_blc         |    1 +
 ci/docker/Dockerfile.build.ubuntu_build_cuda  |    1 +
 ci/docker/Dockerfile.build.ubuntu_cpu         |    2 +-
 .../docker/Dockerfile.build.ubuntu_cpu_c      |   31 +-
 ci/docker/Dockerfile.build.ubuntu_cpu_jekyll  |   68 +
 ci/docker/Dockerfile.build.ubuntu_cpu_julia   |   81 +
 ci/docker/Dockerfile.build.ubuntu_cpu_lite    |   45 +
 ci/docker/Dockerfile.build.ubuntu_cpu_python  |   49 +
 ci/docker/Dockerfile.build.ubuntu_cpu_r       |   43 +
 ci/docker/Dockerfile.build.ubuntu_cpu_scala   |   50 +
 ci/docker/Dockerfile.build.ubuntu_gpu_cu100   |    3 +-
 ci/docker/Dockerfile.build.ubuntu_gpu_cu101   |    3 +-
 ci/docker/Dockerfile.build.ubuntu_gpu_cu80    |    3 +-
 ci/docker/Dockerfile.build.ubuntu_gpu_cu90    |    3 +-
 ci/docker/Dockerfile.build.ubuntu_gpu_cu92    |    3 +-
 .../Dockerfile.build.ubuntu_gpu_tensorrt      |    1 +
 ci/docker/Dockerfile.build.ubuntu_nightly_cpu |    3 +-
 ci/docker/Dockerfile.build.ubuntu_nightly_gpu |    2 +-
 .../{docs_requirements => requirements}       |    9 +-
 ci/docker/install/ubuntu_clojure.sh           |    2 +-
 ci/docker/install/ubuntu_docs.sh              |    6 +-
 ci/docker/install/ubuntu_python.sh            |    4 +-
 ci/docker/install/ubuntu_r.sh                 |    5 +-
 ci/docker/runtime_functions.sh                |  355 ++-
 ci/jenkins/Jenkins_steps.groovy               |  255 +-
 .../jenkins/Jenkinsfile_website_c_docs        |   30 +-
 ci/jenkins/Jenkinsfile_website_clojure_docs   |   53 +
 ci/jenkins/Jenkinsfile_website_full           |   67 +
 ci/jenkins/Jenkinsfile_website_full_pr        |   62 +
 ci/jenkins/Jenkinsfile_website_java_docs      |   53 +
 ci/jenkins/Jenkinsfile_website_jekyll_docs    |   49 +
 ci/jenkins/Jenkinsfile_website_julia_docs     |   53 +
 .../jenkins/Jenkinsfile_website_mxnet_build   |   28 +-
 ci/jenkins/Jenkinsfile_website_python_docs    |   53 +
 ci/jenkins/Jenkinsfile_website_r_docs         |   53 +
 ci/jenkins/Jenkinsfile_website_scala_docs     |   53 +
 ci/other/ci_deploy_doc.sh                     |    2 +-
 ci/publish/website/README.md                  |   20 +
 ci/publish/website/beta-deploy.sh             |   34 +
 ci/publish/website/deploy.sh                  |   99 +
 ci/publish/website/publish_artifacts.sh       |   34 +
 docs/README.md                                |    2 +
 docs/python_docs/_static/mxnet.css            |    2 +-
 .../tutorials/deploy/run-on-aws/cloud.rst     |    5 +-
 .../tutorials/deploy/run-on-aws/index.rst     |    8 +-
 .../python/tutorials/extend/custom_layer.md   |   87 +-
 .../packages/gluon/custom_layer_beginners.md  |    2 +-
 .../gluon_from_experiment_to_deployment.md    |  327 ++
 .../python/tutorials/packages/gluon/index.rst |    2 +-
 .../tutorials/performance/backend/profiler.md |    3 +
 .../themes/mx-theme/mxtheme/drawer.html       |    2 +-
 .../static/sphinx_materialdesign_theme.css    |    2 +-
 .../sphinx_materialdesign_theme.css.map       |    2 +-
 .../static/sphinx_materialdesign_theme.js     |    2 +-
 .../static/sphinx_materialdesign_theme.js.map |    2 +-
 .../mxtheme/static/webfonts/fa-solid-900.eot  |  Bin 0 -> 197172 bytes
 .../mxtheme/static/webfonts/fa-solid-900.svg  | 2618 +++++++++++++++++
 .../mxtheme/static/webfonts/fa-solid-900.ttf  |  Bin 0 -> 196952 bytes
 .../mxtheme/static/webfonts/fa-solid-900.woff |  Bin 0 -> 95436 bytes
 .../static/webfonts/fa-solid-900.woff2        |  Bin 0 -> 73852 bytes
 .../src/js/sphinx_materialdesign_theme.js     |    3 +-
 .../mx-theme/src/scss/layout/_layout.scss     |    3 +-
 docs/settings.ini                             |  172 --
 .../src/pages/api/architecture/note_engine.md |    7 +-
 .../src/pages/api/architecture/note_memory.md |    3 +-
 .../src/pages/api/architecture/overview.md    |    4 +-
 .../pages/api/architecture/program_model.md   |   11 +-
 .../api/clojure/docs/tutorials/kvstore.md     |    2 +-
 .../api/clojure/docs/tutorials/module.md      |   13 +-
 .../api/clojure/docs/tutorials/ndarray.md     |    6 +-
 .../api/clojure/docs/tutorials/symbol.md      |   11 +-
 .../docs/tutorials/symbol_in_pictures.md      |    4 +-
 .../pages/api/cpp/docs/tutorials/basics.md    |    2 +-
 .../tutorials/mxnet_cpp_inference_tutorial.md |   20 +-
 .../src/pages/api/faq/add_op_in_backend.md    |    9 +-
 docs/static_site/src/pages/api/faq/caffe.md   |    2 +-
 docs/static_site/src/pages/api/faq/cloud.md   |   14 +-
 .../src/pages/api/faq/distributed_training.md |   12 +-
 docs/static_site/src/pages/api/faq/env_var.md |    6 +-
 docs/static_site/src/pages/api/faq/float16.md |   14 +-
 .../src/pages/api/faq/model_parallel_lstm.md  |    4 +-
 docs/static_site/src/pages/api/faq/new_op.md  |    4 +-
 docs/static_site/src/pages/api/faq/nnpack.md  |    2 +-
 docs/static_site/src/pages/api/faq/perf.md    |    4 +-
 .../src/pages/api/faq/s3_integration.md       |    2 +-
 .../src/pages/api/faq/visualize_graph.md      |    6 +-
 .../src/pages/api/faq/why_mxnet.md            |    2 +-
 .../docs/tutorials/mxnet_java_on_intellij.md  |    8 +-
 .../api/java/docs/tutorials/ssd_inference.md  |    8 +-
 docs/static_site/src/pages/api/java/index.md  |    9 +-
 docs/static_site/src/pages/api/julia/index.md |    2 +-
 .../pages/api/perl/docs/tutorials/symbol.md   |    2 +-
 .../api/scala/docs/tutorials/char_lstm.md     |  571 ++--
 .../pages/api/scala/docs/tutorials/infer.md   |    9 +-
 .../src/pages/api/scala/docs/tutorials/io.md  |  153 +-
 .../pages/api/scala/docs/tutorials/kvstore.md |    2 +-
 .../pages/api/scala/docs/tutorials/mnist.md   |    6 +-
 .../pages/api/scala/docs/tutorials/model.md   |   16 +-
 .../pages/api/scala/docs/tutorials/module.md  |   14 +-
 .../docs/tutorials/mxnet_scala_on_intellij.md |    7 +-
 .../pages/api/scala/docs/tutorials/ndarray.md |    4 +-
 .../pages/api/scala/docs/tutorials/symbol.md  |   18 +-
 .../docs/tutorials/symbol_in_pictures.md      |    4 +-
 .../pages/get_started/build_from_source.md    |   40 +-
 .../src/pages/get_started/c_plus_plus.md      |    4 +-
 .../src/pages/get_started/java_setup.md       |   10 +-
 .../src/pages/get_started/osx_setup.md        |   17 +-
 .../src/pages/get_started/scala_setup.md      |   10 +-
 .../src/pages/get_started/ubuntu_setup.md     |   18 +-
 .../src/pages/get_started/windows_setup.md    |   18 +-
 docs/tutorials/python/profiler.md             |  336 ---
 docs/tutorials/sparse/train_gluon.md          |  470 ---
 tests/nightly/JenkinsfileForBinaries          |   16 -
 tests/tutorials/test_tutorials.py             |    1 +
 tools/dependencies/README.md                  |    2 +-
 120 files changed, 5269 insertions(+), 1744 deletions(-)
 rename docs/api/r/Makefile => ci/docker/Dockerfile.build.ubuntu_cpu_c (56%)
 create mode 100644 ci/docker/Dockerfile.build.ubuntu_cpu_jekyll
 create mode 100644 ci/docker/Dockerfile.build.ubuntu_cpu_julia
 create mode 100644 ci/docker/Dockerfile.build.ubuntu_cpu_lite
 create mode 100644 ci/docker/Dockerfile.build.ubuntu_cpu_python
 create mode 100644 ci/docker/Dockerfile.build.ubuntu_cpu_r
 create mode 100644 ci/docker/Dockerfile.build.ubuntu_cpu_scala
 rename ci/docker/install/{docs_requirements => requirements} (91%)
 rename docs/Jenkinsfile-dev => ci/jenkins/Jenkinsfile_website_c_docs (66%)
 create mode 100644 ci/jenkins/Jenkinsfile_website_clojure_docs
 create mode 100644 ci/jenkins/Jenkinsfile_website_full
 create mode 100644 ci/jenkins/Jenkinsfile_website_full_pr
 create mode 100644 ci/jenkins/Jenkinsfile_website_java_docs
 create mode 100644 ci/jenkins/Jenkinsfile_website_jekyll_docs
 create mode 100644 ci/jenkins/Jenkinsfile_website_julia_docs
 rename docs/Jenkinsfile => ci/jenkins/Jenkinsfile_website_mxnet_build (56%)
 create mode 100644 ci/jenkins/Jenkinsfile_website_python_docs
 create mode 100644 ci/jenkins/Jenkinsfile_website_r_docs
 create mode 100644 ci/jenkins/Jenkinsfile_website_scala_docs
 create mode 100644 ci/publish/website/README.md
 create mode 100755 ci/publish/website/beta-deploy.sh
 create mode 100644 ci/publish/website/deploy.sh
 create mode 100755 ci/publish/website/publish_artifacts.sh
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/gluon_from_experiment_to_deployment.md
 create mode 100644 docs/python_docs/themes/mx-theme/mxtheme/static/webfonts/fa-solid-900.eot
 create mode 100644 docs/python_docs/themes/mx-theme/mxtheme/static/webfonts/fa-solid-900.svg
 create mode 100644 docs/python_docs/themes/mx-theme/mxtheme/static/webfonts/fa-solid-900.ttf
 create mode 100644 docs/python_docs/themes/mx-theme/mxtheme/static/webfonts/fa-solid-900.woff
 create mode 100644 docs/python_docs/themes/mx-theme/mxtheme/static/webfonts/fa-solid-900.woff2
 delete mode 100644 docs/settings.ini
 delete mode 100644 docs/tutorials/python/profiler.md
 delete mode 100644 docs/tutorials/sparse/train_gluon.md

diff --git a/Makefile b/Makefile
index fcef885bee0a..19aab2885e68 100644
--- a/Makefile
+++ b/Makefile
@@ -453,7 +453,7 @@ ifeq ($(USE_DIST_KVSTORE), 1)
 	LDFLAGS += $(PS_LDFLAGS_A)
 endif
 
-.PHONY: clean all extra-packages test lint docs clean_all rcpplint rcppexport roxygen\
+.PHONY: clean all extra-packages test lint clean_all rcpplint rcppexport roxygen\
 	cython2 cython3 cython cyclean
 
 all: lib/libmxnet.a lib/libmxnet.so $(BIN) extra-packages sample_lib
@@ -664,20 +664,6 @@ cpplint:
 pylint:
 	python3 -m pylint --rcfile=$(ROOTDIR)/ci/other/pylintrc --ignore-patterns=".*\.so$$,.*\.dll$$,.*\.dylib$$" python/mxnet tools/caffe_converter/*.py
 
-sample_lib:
-	$(CXX) -shared -fPIC example/lib_api/mylib.cc -o libsample_lib.so -I include/mxnet
-
-doc: docs
-
-docs:
-	make -C docs html
-
-clean_docs:
-	make -C docs clean
-
-doxygen:
-	doxygen docs/Doxyfile
-
 # Cython build
 cython:
 	cd python; $(PYTHON) setup.py build_ext --inplace --with-cython
@@ -727,6 +713,10 @@ rpkgtest:
 	Rscript -e 'require(testthat);res<-test_dir("R-package/tests/testthat");if(!testthat:::all_passed(res)){stop("Test failures", call. = FALSE)}'
 	Rscript -e 'res<-covr:::package_coverage("R-package");fileConn<-file(paste("r-package_coverage_",toString(runif(1)),".json"));writeLines(covr:::to_codecov(res), fileConn);close(fileConn)'
 
+
+sample_lib:
+	$(CXX) -shared -fPIC example/lib_api/mylib.cc -o libsample_lib.so -I include/mxnet
+
 scalaclean:
 	(cd $(ROOTDIR)/scala-package && mvn clean)
 
diff --git a/R-package/DESCRIPTION b/R-package/DESCRIPTION
index b124877c8d3c..06a9bb03b6ae 100644
--- a/R-package/DESCRIPTION
+++ b/R-package/DESCRIPTION
@@ -33,3 +33,4 @@ Depends:
 LinkingTo: Rcpp
 VignetteBuilder: knitr
 RoxygenNote: 6.1.1
+Encoding: UTF-8
diff --git a/R-package/src/export.cc b/R-package/src/export.cc
index e1a8be5536a1..ae64a1761966 100644
--- a/R-package/src/export.cc
+++ b/R-package/src/export.cc
@@ -76,6 +76,15 @@ std::string ExportDocString(const std::string& docstring) {
   return os.str();
 }
 
+std::string ReplaceAll(std::string str, const std::string& from, const std::string& to) {
+    size_t start_pos = 0;
+    while ((start_pos = str.find(from, start_pos)) != std::string::npos) {
+        str.replace(start_pos, from.length(), to);
+        start_pos += to.length();  // Handles case where 'to' is a substring of 'from'
+    }
+    return str;
+}
+
 void ExportVArgFunction(std::ostream& os,  // NOLINT(*)
                         const std::string& func_name,
                         const std::string& docstr) {
@@ -118,6 +127,10 @@ void Exporter::Export(const std::string& path) {
       || fname == "mx.varg.symbol.min") continue;
     Rcpp::List func_info(scope->get_function(fname));
     std::string docstr = Rcpp::as<std::string>(func_info[2]);
+
+    docstr = ReplaceAll(docstr, std::string("\a"), std::string("\\a"));
+    docstr = ReplaceAll(docstr, std::string("\b"), std::string("\\b"));
+
     if (docstr.find("@export") == std::string::npos) continue;
     if (fname.find("mx.varg.") == 0) {
       ExportVArgFunction(script, fname, docstr);
diff --git a/ci/Jenkinsfile_utils.groovy b/ci/Jenkinsfile_utils.groovy
index a5fe0c8a04b8..7fbf3692f6bc 100644
--- a/ci/Jenkinsfile_utils.groovy
+++ b/ci/Jenkinsfile_utils.groovy
@@ -67,7 +67,7 @@ def pack_lib(name, libs, include_gcov_data = false) {
   sh returnStatus: true, script: """
 set +e
 echo "Packing ${libs} into ${name}"
-for i in \$(echo ${libs} | sed -e 's/,/ /g'); do md5sum \$i; done
+for i in \$(echo ${libs} | sed -e 's/,/ /g'); do md5sum \$i; ls -lh \$i; done
 return 0
 """
   stash includes: libs, name: name
@@ -114,7 +114,7 @@ def get_git_commit_hash() {
 def publish_test_coverage() {
     // CodeCovs auto detection has trouble with our CIs PR validation due the merging strategy
     git_commit_hash = get_git_commit_hash()
-   
+
     if (env.CHANGE_ID) {
       // PR execution
       codecovArgs = "-B ${env.CHANGE_TARGET} -C ${git_commit_hash} -P ${env.CHANGE_ID}"
@@ -168,9 +168,9 @@ def get_repo_url() {
 def update_github_commit_status(state, message) {
   node(NODE_UTILITY) {
     // NOTE: https://issues.jenkins-ci.org/browse/JENKINS-39482
-    //The GitHubCommitStatusSetter requires that the Git Server is defined under 
-    //*Manage Jenkins > Configure System > GitHub > GitHub Servers*. 
-    //Otherwise the GitHubCommitStatusSetter is not able to resolve the repository name 
+    //The GitHubCommitStatusSetter requires that the Git Server is defined under
+    //*Manage Jenkins > Configure System > GitHub > GitHub Servers*.
+    //Otherwise the GitHubCommitStatusSetter is not able to resolve the repository name
     //properly and you would see an empty list of repos:
     //[Set GitHub commit status (universal)] PENDING on repos [] (sha:xxxxxxx) with context:test/mycontext
     //See https://cwiki.apache.org/confluence/display/MXNET/Troubleshooting#Troubleshooting-GitHubcommit/PRstatusdoesnotgetpublished
@@ -182,7 +182,7 @@ def update_github_commit_status(state, message) {
 
     commitSha = get_git_commit_hash()
     echo "commitSha=${commitSha}"
-    
+
     context = get_github_context()
     echo "context=${context}"
 
@@ -216,29 +216,29 @@ def update_github_commit_status(state, message) {
 def get_github_context() {
   // Since we use multi-branch pipelines, Jenkins appends the branch name to the job name
   if (env.BRANCH_NAME) {
-    short_job_name = JOB_NAME.substring(0, JOB_NAME.lastIndexOf('/')) 
+    short_job_name = JOB_NAME.substring(0, JOB_NAME.lastIndexOf('/'))
   } else {
     short_job_name = JOB_NAME
   }
-  
+
   return "ci/jenkins/${short_job_name}"
 }
 
 def parallel_stage(stage_name, steps) {
     // Allow to pass an array of steps that will be executed in parallel in a stage
     new_map = [:]
-    
+
     for (def step in steps) {
         new_map = new_map << step
     }
-    
+
     stage(stage_name) {
       parallel new_map
     }
 }
 
 def assign_node_labels(args) {
-  // This function allows to assign instance labels to the generalized placeholders. 
+  // This function allows to assign instance labels to the generalized placeholders.
   // This serves two purposes:
   // 1. Allow generalized placeholders (e.g. NODE_WINDOWS_CPU) in the job definition
   //    in order to abstract away the underlying node label. This allows to schedule a job
@@ -263,7 +263,7 @@ def main_wrapper(args) {
   // args:
   // - core_logic: Jenkins pipeline containing core execution logic
   // - failure_handler: Failure handler
-  
+
   // assign any caught errors here
   err = null
   try {
diff --git a/ci/docker/Dockerfile.build.test.arm_qemu b/ci/docker/Dockerfile.build.test.arm_qemu
index 68891a72c284..5dc610a524b0 100644
--- a/ci/docker/Dockerfile.build.test.arm_qemu
+++ b/ci/docker/Dockerfile.build.test.arm_qemu
@@ -24,6 +24,7 @@ WORKDIR /work
 
 RUN apt-get update
 COPY install/ubuntu_python.sh /work/
+COPY install/requirements /work/
 RUN /work/ubuntu_python.sh
 
 COPY install/ubuntu_arm_qemu.sh /work
diff --git a/ci/docker/Dockerfile.build.ubuntu_blc b/ci/docker/Dockerfile.build.ubuntu_blc
index 294740ce1392..a413648366b2 100644
--- a/ci/docker/Dockerfile.build.ubuntu_blc
+++ b/ci/docker/Dockerfile.build.ubuntu_blc
@@ -25,6 +25,7 @@ WORKDIR /work/deps
 COPY install/ubuntu_core.sh /work/
 RUN /work/ubuntu_core.sh
 COPY install/ubuntu_python.sh /work/
+COPY install/requirements /work/
 RUN /work/ubuntu_python.sh
 COPY install/ubuntu_npm_blc.sh /work/
 RUN /work/ubuntu_npm_blc.sh
diff --git a/ci/docker/Dockerfile.build.ubuntu_build_cuda b/ci/docker/Dockerfile.build.ubuntu_build_cuda
index 47f1d1f9ca58..e085c2dc09a0 100644
--- a/ci/docker/Dockerfile.build.ubuntu_build_cuda
+++ b/ci/docker/Dockerfile.build.ubuntu_build_cuda
@@ -30,6 +30,7 @@ RUN /work/ubuntu_core.sh
 COPY install/deb_ubuntu_ccache.sh /work/
 RUN /work/deb_ubuntu_ccache.sh
 COPY install/ubuntu_python.sh /work/
+COPY install/requirements /work/
 RUN /work/ubuntu_python.sh
 COPY install/ubuntu_scala.sh /work/
 COPY install/sbt.gpg /work/
diff --git a/ci/docker/Dockerfile.build.ubuntu_cpu b/ci/docker/Dockerfile.build.ubuntu_cpu
index 35dcf3ed7410..f41d629289a7 100644
--- a/ci/docker/Dockerfile.build.ubuntu_cpu
+++ b/ci/docker/Dockerfile.build.ubuntu_cpu
@@ -29,6 +29,7 @@ COPY install/deb_ubuntu_ccache.sh /work/
 RUN /work/deb_ubuntu_ccache.sh
 
 COPY install/ubuntu_python.sh /work/
+COPY install/requirements /work/
 RUN /work/ubuntu_python.sh
 
 COPY install/ubuntu_scala.sh /work/
@@ -67,7 +68,6 @@ COPY install/ubuntu_onnx.sh /work/
 RUN /work/ubuntu_onnx.sh
 
 COPY install/ubuntu_docs.sh /work/
-COPY install/docs_requirements /work/
 RUN /work/ubuntu_docs.sh
 
 # Always last
diff --git a/docs/api/r/Makefile b/ci/docker/Dockerfile.build.ubuntu_cpu_c
similarity index 56%
rename from docs/api/r/Makefile
rename to ci/docker/Dockerfile.build.ubuntu_cpu_c
index 435345daa3d3..c7969da1bb1d 100644
--- a/docs/api/r/Makefile
+++ b/ci/docker/Dockerfile.build.ubuntu_cpu_c
@@ -1,3 +1,4 @@
+# -*- mode: dockerfile -*-
 # Licensed to the Apache Software Foundation (ASF) under one
 # or more contributor license agreements.  See the NOTICE file
 # distributed with this work for additional information
@@ -14,21 +15,21 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
+#
+# Dockerfile to build and run MXNet on Ubuntu 16.04 for CPU
+
+FROM ubuntu:16.04
+
+WORKDIR /work/deps
+
+COPY install/ubuntu_core.sh /work/
+RUN /work/ubuntu_core.sh
+
+COPY install/deb_ubuntu_ccache.sh /work/
+RUN /work/deb_ubuntu_ccache.sh
 
-# This is the makefile for compiling Rmarkdown files into the md file with results.
-PKGROOT=../../R-package
+RUN apt-get update && apt-get install -y doxygen graphviz
 
-# ADD The Markdown to be built here, with suffix md
-classifyRealImageWithPretrainedModel.md:
-mnistCompetition.md:
-ndarrayAndSymbolTutorial.md:
-fiveMinutesNeuralNetwork.md:
+COPY runtime_functions.sh /work/
 
-# General Rules for build rmarkdowns, need knitr
-%.md: $(PKGROOT)/vignettes/%.Rmd
-	rm -rf "../../web-data/mxnet/knitr/$(basename $@)-"*;
-	Rscript -e \
-	"require(knitr);"\
-	"knitr::opts_knit\$$set(root.dir=\".\");"\
-	"knitr::opts_chunk\$$set(fig.path=\"../../web-data/mxnet/knitr/$(basename $@)-\");"\
-	"knitr::knit(\"$+\")"
+WORKDIR /work/mxnet
\ No newline at end of file
diff --git a/ci/docker/Dockerfile.build.ubuntu_cpu_jekyll b/ci/docker/Dockerfile.build.ubuntu_cpu_jekyll
new file mode 100644
index 000000000000..62db71e2429e
--- /dev/null
+++ b/ci/docker/Dockerfile.build.ubuntu_cpu_jekyll
@@ -0,0 +1,68 @@
+# -*- mode: dockerfile -*-
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+# Dockerfile to build and run MXNet on Ubuntu 16.04 for CPU
+
+FROM ubuntu:16.04
+
+WORKDIR /work/deps
+
+SHELL ["/bin/bash", "-l", "-c" ]
+
+RUN apt-get update && apt-get install -y \
+      build-essential \
+      git \
+      zlib1g-dev \
+      gnupg2 \
+      curl
+
+# Always last, except here to prevent conflicts with rvm
+ARG USER_ID=0
+ARG GROUP_ID=0
+COPY install/ubuntu_adduser.sh /work/
+RUN /work/ubuntu_adduser.sh
+
+RUN curl -sSL https://rvm.io/mpapis.asc | gpg2 --import - && \
+    curl -sSL https://rvm.io/pkuczynski.asc | gpg2 --import - && \
+    curl -sSL https://get.rvm.io | bash -s stable
+
+RUN source /etc/profile.d/rvm.sh && \
+    rvm requirements && \
+    rvm install 2.6 && \
+    rvm use 2.6.3 --default
+
+ENV BUNDLE_HOME=/work/deps/bundle
+ENV BUNDLE_APP_CONFIG=/work/deps/bundle
+ENV BUNDLE_BIN=/work/deps/bundle/bin
+ENV GEM_BIN=/work/deps/gem/bin
+ENV GEM_HOME=/work/deps/gem
+
+RUN echo "gem: --no-ri --no-rdoc" > ~/.gemrc
+RUN yes | gem update --system
+RUN yes | gem install --force bundler
+RUN gem install jekyll
+
+ENV PATH=$BUNDLE_BIN:$GEM_BIN:$PATH
+
+COPY runtime_functions.sh /work/
+
+RUN chown -R jenkins_slave /work/ && \
+    chown -R jenkins_slave /usr/local/bin && \
+    chown -R jenkins_slave /usr/local/rvm
+
+WORKDIR /work/mxnet
\ No newline at end of file
diff --git a/ci/docker/Dockerfile.build.ubuntu_cpu_julia b/ci/docker/Dockerfile.build.ubuntu_cpu_julia
new file mode 100644
index 000000000000..108869b680cd
--- /dev/null
+++ b/ci/docker/Dockerfile.build.ubuntu_cpu_julia
@@ -0,0 +1,81 @@
+# -*- mode: dockerfile -*-
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+# Dockerfile to build and run MXNet on Ubuntu 16.04 for CPU
+
+FROM ubuntu:16.04
+
+WORKDIR /work/deps
+
+COPY install/ubuntu_core.sh /work/
+RUN /work/ubuntu_core.sh
+
+COPY install/deb_ubuntu_ccache.sh /work/
+RUN /work/deb_ubuntu_ccache.sh
+
+COPY install/ubuntu_python.sh /work/
+COPY install/requirements /work/
+RUN /work/ubuntu_python.sh
+
+COPY install/ubuntu_scala.sh /work/
+COPY install/sbt.gpg /work/
+RUN /work/ubuntu_scala.sh
+
+COPY install/ubuntu_clojure.sh /work/
+RUN /work/ubuntu_clojure.sh
+
+COPY install/ubuntu_r.sh /work/
+COPY install/r.gpg /work/
+RUN /work/ubuntu_r.sh
+
+COPY install/ubuntu_perl.sh /work/
+RUN /work/ubuntu_perl.sh
+
+COPY install/ubuntu_julia.sh /work/
+RUN /work/ubuntu_julia.sh
+
+COPY install/ubuntu_clang.sh /work/
+RUN /work/ubuntu_clang.sh
+
+COPY install/ubuntu_gcc8.sh /work/
+RUN /work/ubuntu_gcc8.sh
+
+COPY install/ubuntu_mkl.sh /work/
+RUN /work/ubuntu_mkl.sh
+
+COPY install/ubuntu_mklml.sh /work/
+RUN /work/ubuntu_mklml.sh
+
+COPY install/ubuntu_caffe.sh /work/
+RUN /work/ubuntu_caffe.sh
+
+COPY install/ubuntu_onnx.sh /work/
+RUN /work/ubuntu_onnx.sh
+
+COPY install/ubuntu_docs.sh /work/
+RUN /work/ubuntu_docs.sh
+
+# Always last
+ARG USER_ID=0
+ARG GROUP_ID=0
+COPY install/ubuntu_adduser.sh /work/
+RUN /work/ubuntu_adduser.sh
+
+COPY runtime_functions.sh /work/
+
+WORKDIR /work/mxnet
\ No newline at end of file
diff --git a/ci/docker/Dockerfile.build.ubuntu_cpu_lite b/ci/docker/Dockerfile.build.ubuntu_cpu_lite
new file mode 100644
index 000000000000..ca5618ac1cd7
--- /dev/null
+++ b/ci/docker/Dockerfile.build.ubuntu_cpu_lite
@@ -0,0 +1,45 @@
+# -*- mode: dockerfile -*-
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+# Dockerfile to build and run MXNet on Ubuntu 16.04 for CPU
+
+FROM ubuntu:16.04
+
+WORKDIR /work/deps
+
+COPY install/ubuntu_core.sh /work/
+RUN /work/ubuntu_core.sh
+
+COPY install/deb_ubuntu_ccache.sh /work/
+RUN /work/deb_ubuntu_ccache.sh
+
+COPY install/ubuntu_clang.sh /work/
+RUN /work/ubuntu_clang.sh
+
+COPY install/ubuntu_gcc8.sh /work/
+RUN /work/ubuntu_gcc8.sh
+
+# Always last
+ARG USER_ID=0
+ARG GROUP_ID=0
+COPY install/ubuntu_adduser.sh /work/
+RUN /work/ubuntu_adduser.sh
+
+COPY runtime_functions.sh /work/
+
+WORKDIR /work/mxnet
\ No newline at end of file
diff --git a/ci/docker/Dockerfile.build.ubuntu_cpu_python b/ci/docker/Dockerfile.build.ubuntu_cpu_python
new file mode 100644
index 000000000000..6b217d4d341d
--- /dev/null
+++ b/ci/docker/Dockerfile.build.ubuntu_cpu_python
@@ -0,0 +1,49 @@
+# -*- mode: dockerfile -*-
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+# Dockerfile to build and run MXNet on Ubuntu 16.04 for CPU
+
+FROM ubuntu:16.04
+
+WORKDIR /work/deps
+
+COPY install/ubuntu_core.sh /work/
+RUN /work/ubuntu_core.sh
+
+COPY install/deb_ubuntu_ccache.sh /work/
+RUN /work/deb_ubuntu_ccache.sh
+
+COPY install/ubuntu_python.sh /work/
+COPY install/requirements /work/
+RUN /work/ubuntu_python.sh
+
+COPY install/ubuntu_onnx.sh /work/
+RUN /work/ubuntu_onnx.sh
+
+COPY install/ubuntu_docs.sh /work/
+RUN /work/ubuntu_docs.sh
+
+# Always last
+ARG USER_ID=0
+ARG GROUP_ID=0
+COPY install/ubuntu_adduser.sh /work/
+RUN /work/ubuntu_adduser.sh
+
+COPY runtime_functions.sh /work/
+
+WORKDIR /work/mxnet
\ No newline at end of file
diff --git a/ci/docker/Dockerfile.build.ubuntu_cpu_r b/ci/docker/Dockerfile.build.ubuntu_cpu_r
new file mode 100644
index 000000000000..264d34cd6422
--- /dev/null
+++ b/ci/docker/Dockerfile.build.ubuntu_cpu_r
@@ -0,0 +1,43 @@
+# -*- mode: dockerfile -*-
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+# Dockerfile to build and run MXNet on Ubuntu 16.04 for CPU
+
+FROM ubuntu:16.04
+
+WORKDIR /work/deps
+
+COPY install/ubuntu_core.sh /work/
+RUN /work/ubuntu_core.sh
+
+COPY install/deb_ubuntu_ccache.sh /work/
+RUN /work/deb_ubuntu_ccache.sh
+
+COPY install/ubuntu_r.sh /work/
+COPY install/r.gpg /work/
+RUN /work/ubuntu_r.sh
+
+# Always last
+ARG USER_ID=0
+ARG GROUP_ID=0
+COPY install/ubuntu_adduser.sh /work/
+RUN /work/ubuntu_adduser.sh
+
+COPY runtime_functions.sh /work/
+
+WORKDIR /work/mxnet
diff --git a/ci/docker/Dockerfile.build.ubuntu_cpu_scala b/ci/docker/Dockerfile.build.ubuntu_cpu_scala
new file mode 100644
index 000000000000..38874d290e1d
--- /dev/null
+++ b/ci/docker/Dockerfile.build.ubuntu_cpu_scala
@@ -0,0 +1,50 @@
+# -*- mode: dockerfile -*-
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+# Dockerfile to build and run MXNet on Ubuntu 16.04 for CPU
+
+FROM ubuntu:16.04
+
+WORKDIR /work/deps
+
+COPY install/ubuntu_core.sh /work/
+RUN /work/ubuntu_core.sh
+
+COPY install/deb_ubuntu_ccache.sh /work/
+RUN /work/deb_ubuntu_ccache.sh
+
+COPY install/ubuntu_python.sh /work/
+COPY install/requirements /work/
+RUN /work/ubuntu_python.sh
+
+COPY install/ubuntu_scala.sh /work/
+COPY install/sbt.gpg /work/
+RUN /work/ubuntu_scala.sh
+
+COPY install/ubuntu_clojure.sh /work/
+RUN /work/ubuntu_clojure.sh
+
+# Always last
+ARG USER_ID=0
+ARG GROUP_ID=0
+COPY install/ubuntu_adduser.sh /work/
+RUN /work/ubuntu_adduser.sh
+
+COPY runtime_functions.sh /work/
+
+WORKDIR /work/mxnet
\ No newline at end of file
diff --git a/ci/docker/Dockerfile.build.ubuntu_gpu_cu100 b/ci/docker/Dockerfile.build.ubuntu_gpu_cu100
index 46d27e35022b..894930bc6303 100644
--- a/ci/docker/Dockerfile.build.ubuntu_gpu_cu100
+++ b/ci/docker/Dockerfile.build.ubuntu_gpu_cu100
@@ -29,6 +29,7 @@ COPY install/deb_ubuntu_ccache.sh /work/
 RUN /work/deb_ubuntu_ccache.sh
 
 COPY install/ubuntu_python.sh /work/
+COPY install/requirements /work/
 RUN /work/ubuntu_python.sh
 
 COPY install/ubuntu_scala.sh /work/
@@ -61,7 +62,7 @@ COPY install/ubuntu_onnx.sh /work/
 RUN /work/ubuntu_onnx.sh
 
 COPY install/ubuntu_docs.sh /work/
-COPY install/docs_requirements /work/
+COPY install/requirements /work/
 RUN /work/ubuntu_docs.sh
 
 COPY install/ubuntu_tutorials.sh /work/
diff --git a/ci/docker/Dockerfile.build.ubuntu_gpu_cu101 b/ci/docker/Dockerfile.build.ubuntu_gpu_cu101
index 21a938f8f84a..9699b37aa45f 100644
--- a/ci/docker/Dockerfile.build.ubuntu_gpu_cu101
+++ b/ci/docker/Dockerfile.build.ubuntu_gpu_cu101
@@ -29,6 +29,7 @@ COPY install/deb_ubuntu_ccache.sh /work/
 RUN /work/deb_ubuntu_ccache.sh
 
 COPY install/ubuntu_python.sh /work/
+COPY install/requirements /work/
 RUN /work/ubuntu_python.sh
 
 COPY install/ubuntu_scala.sh /work/
@@ -61,7 +62,7 @@ COPY install/ubuntu_onnx.sh /work/
 RUN /work/ubuntu_onnx.sh
 
 COPY install/ubuntu_docs.sh /work/
-COPY install/docs_requirements /work/
+COPY install/requirements /work/
 RUN /work/ubuntu_docs.sh
 
 COPY install/ubuntu_tutorials.sh /work/
diff --git a/ci/docker/Dockerfile.build.ubuntu_gpu_cu80 b/ci/docker/Dockerfile.build.ubuntu_gpu_cu80
index 9c7a8084b093..a1031af811cf 100644
--- a/ci/docker/Dockerfile.build.ubuntu_gpu_cu80
+++ b/ci/docker/Dockerfile.build.ubuntu_gpu_cu80
@@ -29,6 +29,7 @@ COPY install/deb_ubuntu_ccache.sh /work/
 RUN /work/deb_ubuntu_ccache.sh
 
 COPY install/ubuntu_python.sh /work/
+COPY install/requirements /work/
 RUN /work/ubuntu_python.sh
 
 COPY install/ubuntu_scala.sh /work/
@@ -61,7 +62,7 @@ COPY install/ubuntu_onnx.sh /work/
 RUN /work/ubuntu_onnx.sh
 
 COPY install/ubuntu_docs.sh /work/
-COPY install/docs_requirements /work/
+COPY install/requirements /work/
 RUN /work/ubuntu_docs.sh
 
 COPY install/ubuntu_tutorials.sh /work/
diff --git a/ci/docker/Dockerfile.build.ubuntu_gpu_cu90 b/ci/docker/Dockerfile.build.ubuntu_gpu_cu90
index 19530a212424..56ebd55c94e0 100644
--- a/ci/docker/Dockerfile.build.ubuntu_gpu_cu90
+++ b/ci/docker/Dockerfile.build.ubuntu_gpu_cu90
@@ -29,6 +29,7 @@ COPY install/deb_ubuntu_ccache.sh /work/
 RUN /work/deb_ubuntu_ccache.sh
 
 COPY install/ubuntu_python.sh /work/
+COPY install/requirements /work/
 RUN /work/ubuntu_python.sh
 
 COPY install/ubuntu_scala.sh /work/
@@ -61,7 +62,7 @@ COPY install/ubuntu_onnx.sh /work/
 RUN /work/ubuntu_onnx.sh
 
 COPY install/ubuntu_docs.sh /work/
-COPY install/docs_requirements /work/
+COPY install/requirements /work/
 RUN /work/ubuntu_docs.sh
 
 COPY install/ubuntu_tutorials.sh /work/
diff --git a/ci/docker/Dockerfile.build.ubuntu_gpu_cu92 b/ci/docker/Dockerfile.build.ubuntu_gpu_cu92
index f239eec4af27..f6008a5c09ca 100644
--- a/ci/docker/Dockerfile.build.ubuntu_gpu_cu92
+++ b/ci/docker/Dockerfile.build.ubuntu_gpu_cu92
@@ -29,6 +29,7 @@ COPY install/deb_ubuntu_ccache.sh /work/
 RUN /work/deb_ubuntu_ccache.sh
 
 COPY install/ubuntu_python.sh /work/
+COPY install/requirements /work/
 RUN /work/ubuntu_python.sh
 
 COPY install/ubuntu_scala.sh /work/
@@ -61,7 +62,7 @@ COPY install/ubuntu_onnx.sh /work/
 RUN /work/ubuntu_onnx.sh
 
 COPY install/ubuntu_docs.sh /work/
-COPY install/docs_requirements /work/
+COPY install/requirements /work/
 RUN /work/ubuntu_docs.sh
 
 COPY install/ubuntu_tutorials.sh /work/
diff --git a/ci/docker/Dockerfile.build.ubuntu_gpu_tensorrt b/ci/docker/Dockerfile.build.ubuntu_gpu_tensorrt
index 09591ee0b9c9..90bd772ecb17 100644
--- a/ci/docker/Dockerfile.build.ubuntu_gpu_tensorrt
+++ b/ci/docker/Dockerfile.build.ubuntu_gpu_tensorrt
@@ -27,6 +27,7 @@ RUN /work/ubuntu_core.sh
 COPY install/deb_ubuntu_ccache.sh /work/
 RUN /work/deb_ubuntu_ccache.sh
 COPY install/ubuntu_python.sh /work/
+COPY install/requirements /work/
 RUN /work/ubuntu_python.sh
 COPY install/tensorrt.sh /work
 RUN /work/tensorrt.sh
diff --git a/ci/docker/Dockerfile.build.ubuntu_nightly_cpu b/ci/docker/Dockerfile.build.ubuntu_nightly_cpu
index 1b126c18be47..8e36a74cbde7 100644
--- a/ci/docker/Dockerfile.build.ubuntu_nightly_cpu
+++ b/ci/docker/Dockerfile.build.ubuntu_nightly_cpu
@@ -29,6 +29,7 @@ COPY install/deb_ubuntu_ccache.sh /work/
 RUN /work/deb_ubuntu_ccache.sh
 
 COPY install/ubuntu_python.sh /work/
+COPY install/requirements /work/
 RUN /work/ubuntu_python.sh
 
 COPY install/ubuntu_scala.sh /work/
@@ -55,7 +56,7 @@ COPY install/ubuntu_onnx.sh /work/
 RUN /work/ubuntu_onnx.sh
 
 COPY install/ubuntu_docs.sh /work/
-COPY install/docs_requirements /work/
+COPY install/requirements /work/
 RUN /work/ubuntu_docs.sh
 
 COPY install/ubuntu_nightly_tests.sh /work/
diff --git a/ci/docker/Dockerfile.build.ubuntu_nightly_gpu b/ci/docker/Dockerfile.build.ubuntu_nightly_gpu
index 275a5a54fc66..ec811e5202ab 100644
--- a/ci/docker/Dockerfile.build.ubuntu_nightly_gpu
+++ b/ci/docker/Dockerfile.build.ubuntu_nightly_gpu
@@ -61,7 +61,7 @@ COPY install/ubuntu_onnx.sh /work/
 RUN /work/ubuntu_onnx.sh
 
 COPY install/ubuntu_docs.sh /work/
-COPY install/docs_requirements /work/
+COPY install/requirements /work/
 RUN /work/ubuntu_docs.sh
 
 COPY install/ubuntu_tutorials.sh /work/
diff --git a/ci/docker/install/docs_requirements b/ci/docker/install/requirements
similarity index 91%
rename from ci/docker/install/docs_requirements
rename to ci/docker/install/requirements
index f78dca2bc655..cbfc521e2c08 100644
--- a/ci/docker/install/docs_requirements
+++ b/ci/docker/install/requirements
@@ -18,19 +18,16 @@
 # build and install are separated so changes to build don't invalidate
 # the whole docker cache for the image
 
-beautifulsoup4==4.6.3
-breathe==4.10.0
+boto3==1.9.229
 cpplint==1.3.0
-CommonMark==0.5.4
+Cython==0.29.7
+decorator==4.4.0
 h5py==2.8.0rc1
 mock==2.0.0
 nose==1.3.7
 nose-timer==0.7.3
 numpy>1.16.0,<2.0.0
 pylint==2.3.1; python_version >= '3.0'
-pypandoc==1.4
-recommonmark==0.4.0
 requests<2.19.0,>=2.18.4
 scipy==1.0.1
 six==1.11.0
-sphinx==1.5.6
diff --git a/ci/docker/install/ubuntu_clojure.sh b/ci/docker/install/ubuntu_clojure.sh
index b20e35898552..dcaae6ba048d 100755
--- a/ci/docker/install/ubuntu_clojure.sh
+++ b/ci/docker/install/ubuntu_clojure.sh
@@ -24,7 +24,7 @@ set -ex
 # install libraries for mxnet's clojure package on ubuntu
 echo 'Installing Clojure...'
 
-wget https://raw.githubusercontent.com/technomancy/leiningen/stable/bin/lein
+wget -q https://raw.githubusercontent.com/technomancy/leiningen/stable/bin/lein
 chmod 775 lein
 sudo cp lein /usr/local/bin
 echo "Y" | sudo lein downgrade 2.8.3
diff --git a/ci/docker/install/ubuntu_docs.sh b/ci/docker/install/ubuntu_docs.sh
index 5dc201c15b52..10a0016a4125 100755
--- a/ci/docker/install/ubuntu_docs.sh
+++ b/ci/docker/install/ubuntu_docs.sh
@@ -28,7 +28,9 @@ apt-get install -y \
     doxygen \
     pandoc
 
-pip3 install -r /work/docs_requirements
-pip2 install -r /work/docs_requirements
+# Can probably delete these and docs_requirements
+wget -q https://repo.anaconda.com/miniconda/Miniconda2-latest-Linux-x86_64.sh
+chmod +x Miniconda2-latest-Linux-x86_64.sh
+./Miniconda2-latest-Linux-x86_64.sh -b -p /work/miniconda
 
 echo 'Dependency installation complete.'
diff --git a/ci/docker/install/ubuntu_python.sh b/ci/docker/install/ubuntu_python.sh
index b8626d3037d5..12149121ba0b 100755
--- a/ci/docker/install/ubuntu_python.sh
+++ b/ci/docker/install/ubuntu_python.sh
@@ -30,5 +30,5 @@ wget -nv https://bootstrap.pypa.io/get-pip.py
 python3 get-pip.py
 python2 get-pip.py
 
-pip2 install nose cpplint==1.3.0 'numpy>1.16.0,<2.0.0' nose-timer 'requests<2.19.0,>=2.18.4' h5py==2.8.0rc1 scipy==1.0.1 boto3 Cython==0.29.7
-pip3 install nose cpplint==1.3.0 pylint==2.3.1 'numpy>1.16.0,<2.0.0' nose-timer 'requests<2.19.0,>=2.18.4' h5py==2.8.0rc1 scipy==1.0.1 boto3 Cython==0.29.7 decorator
+pip3 install -r /work/requirements
+pip2 install -r /work/requirements
diff --git a/ci/docker/install/ubuntu_r.sh b/ci/docker/install/ubuntu_r.sh
index cefc4172f245..548b6c00b0eb 100755
--- a/ci/docker/install/ubuntu_r.sh
+++ b/ci/docker/install/ubuntu_r.sh
@@ -41,4 +41,7 @@ apt-get install -y --allow-unauthenticated \
     libxml2-dev \
     libxt-dev \
     r-base \
-    r-base-dev
+    r-base-dev \
+    texinfo \
+    texlive \
+    texlive-fonts-extra 
diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index de753a56be7f..b2a50f30af4e 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -1350,34 +1350,6 @@ test_ubuntu_cpu_python3() {
     popd
 }
 
-build_docs() {
-    set -ex
-    pushd .
-
-    # Setup environment for Julia docs
-    export PATH="/work/julia10/bin:$PATH"
-    export MXNET_HOME='/work/mxnet'
-    export JULIA_DEPOT_PATH='/work/julia-depot'
-
-    julia -e 'using InteractiveUtils; versioninfo()'
-    export LD_PRELOAD='/usr/lib/x86_64-linux-gnu/libjemalloc.so'
-    export LD_LIBRARY_PATH=/work/mxnet/lib:$LD_LIBRARY_PATH
-
-    cd /work/mxnet/docs/build_version_doc
-    # Parameters are set in the Jenkins pipeline: restricted-website-build
-    # $1: the list of branches/tags to build
-    # $2: the list of tags to display
-    # So you can build from the 1.2.0 branch, but display 1.2.1 on the site
-    # $3: the fork URL
-    ./build_all_version.sh $1 $2 $3
-    # $4: the default version tag for the website
-    # $5: the base URL
-    ./update_all_version.sh $2 $4 $5
-    cd VersionedWeb
-    tar -zcvf ../artifacts.tgz .
-    popd
-}
-
 # Functions that run the nightly Tests:
 
 #Runs Apache RAT Check on MXNet Source for License Headers
@@ -1560,8 +1532,7 @@ nightly_estimator() {
     nosetests test_sentiment_rnn.py
 }
 
-# Deploy
-
+# For testing PRs
 deploy_docs() {
     set -ex
     pushd .
@@ -1580,9 +1551,331 @@ deploy_docs() {
 
     export CC="ccache gcc"
     export CXX="ccache g++"
-    make docs SPHINXOPTS=-W USE_MKLDNN=0
 
+    build_python_docs
+
+    popd
+}
+
+
+build_docs_setup() {
+    build_folder="docs/_build"
+    mxnetlib_folder="/work/mxnet/lib"
+
+    mkdir -p $build_folder
+    mkdir -p $mxnetlib_folder
+}
+
+build_ubuntu_cpu_docs() {
+    set -ex
+    export CC="gcc"
+    export CXX="g++"
+    build_ccache_wrappers
+    make \
+        DEV=1                         \
+        USE_CPP_PACKAGE=1             \
+        USE_BLAS=openblas             \
+        USE_MKLDNN=0                  \
+        USE_DIST_KVSTORE=1            \
+        USE_LIBJPEG_TURBO=1           \
+        USE_SIGNAL_HANDLER=1          \
+        -j$(nproc)
+}
+
+
+build_jekyll_docs() {
+    set -ex
+    source /etc/profile.d/rvm.sh
+
+    pushd .
+    build_docs_setup
+    pushd docs/static_site
+    make clean
+    make html
     popd
+
+    GZIP=-9 tar zcvf jekyll-artifacts.tgz -C docs/static_site/build html
+    mv jekyll-artifacts.tgz docs/_build/
+    popd
+}
+
+
+build_python_docs() {
+   set -ex
+   pushd .
+
+   build_docs_setup
+
+   pushd docs/python_docs
+   eval "$(/work/miniconda/bin/conda shell.bash hook)"
+   conda env create -f environment.yml -p /work/conda_env
+   conda activate /work/conda_env
+   pip install themes/mx-theme
+   pip install -e /work/mxnet/python --user
+
+   pushd python
+   make clean
+   make html EVAL=0
+
+   GZIP=-9 tar zcvf python-artifacts.tgz -C build/_build/html .
+   popd
+
+   mv python/python-artifacts.tgz /work/mxnet/docs/_build/
+   popd
+
+   popd
+}
+
+
+build_c_docs() {
+    set -ex
+    pushd .
+
+    build_docs_setup
+    doc_path="docs/cpp_docs"
+    pushd $doc_path
+
+    make clean
+    make html
+
+    doc_artifact="c-artifacts.tgz"
+    GZIP=-9 tar zcvf $doc_artifact -C build/html/html .
+    popd
+
+    mv $doc_path/$doc_artifact docs/_build/
+
+    popd
+}
+
+
+build_r_docs() {
+    set -ex
+    pushd .
+
+    build_docs_setup
+    r_root='R-package'
+    r_pdf='mxnet-r-reference-manual.pdf'
+    r_build='build'
+    docs_build_path="$r_root/$r_build/$r_pdf"
+    artifacts_path='docs/_build/r-artifacts.tgz'
+
+    mkdir -p $r_root/$r_build
+
+    unittest_ubuntu_minimal_R
+
+    pushd $r_root
+
+    R_LIBS=/tmp/r-site-library R CMD Rd2pdf . --no-preview --encoding=utf8 -o $r_build/$r_pdf
+
+    popd
+
+    GZIP=-9 tar zcvf $artifacts_path $docs_build_path
+
+    popd
+}
+
+
+build_scala() {
+   set -ex
+   pushd .
+
+   cd scala-package
+   mvn -B install -DskipTests
+
+   popd
+}
+
+
+build_scala_docs() {
+    set -ex
+    pushd .
+    build_docs_setup
+    build_scala
+
+    scala_path='scala-package'
+    docs_build_path='scala-package/docs/build/docs/scala'
+    artifacts_path='docs/_build/scala-artifacts.tgz'
+
+    pushd $scala_path
+
+    scala_doc_sources=`find . -type f -name "*.scala" | egrep "./core|./infer" | egrep -v "/javaapi"  | egrep -v "Suite" | egrep -v "/mxnetexamples"`
+    jar_native=`find native -name "*.jar" | grep "target/lib/" | tr "\\n" ":" `
+    jar_macros=`find macros -name "*.jar" | tr "\\n" ":" `
+    jar_core=`find core -name "*.jar" | tr "\\n" ":" `
+    jar_infer=`find infer -name "*.jar" | tr "\\n" ":" `
+    scala_doc_classpath=$jar_native:$jar_macros:$jar_core:$jar_infer
+
+    scala_ignore_errors=''
+    legacy_ver=".*1.2|1.3.*"
+    # BUILD_VER needs to be pull from environment vars
+    if [[ $_BUILD_VER =~ $legacy_ver ]]
+    then
+      # There are unresolvable errors on mxnet 1.2.x. We are ignoring those
+      # errors while aborting the ci on newer versions
+      echo "We will ignoring unresolvable errors on MXNet 1.2/1.3."
+      scala_ignore_errors='; exit 0'
+    fi
+
+    scaladoc $scala_doc_sources -classpath $scala_doc_classpath $scala_ignore_errors -doc-title MXNet
+    popd
+
+    # Clean-up old artifacts
+    rm -rf $docs_build_path
+    mkdir -p $docs_build_path
+
+    for doc_file in index index.html org lib index.js package.html; do
+        mv $scala_path/$doc_file $docs_build_path
+    done
+
+    GZIP=-9 tar -zcvf $artifacts_path -C $docs_build_path .
+
+    popd
+}
+
+
+build_julia_docs() {
+   set -ex
+   pushd .
+
+   build_docs_setup
+   # Setup environment for Julia docs
+   export PATH="/work/julia10/bin:$PATH"
+   export MXNET_HOME='/work/mxnet'
+   export JULIA_DEPOT_PATH='/work/julia-depot'
+   export LD_PRELOAD='/usr/lib/x86_64-linux-gnu/libjemalloc.so'
+   export LD_LIBRARY_PATH=/work/mxnet/lib:$LD_LIBRARY_PATH
+
+   julia_doc_path='julia/docs/site/'
+   julia_doc_artifact='docs/_build/julia-artifacts.tgz'
+
+   echo "Julia will check for MXNet in $MXNET_HOME/lib"
+
+   make -C julia/docs
+
+   GZIP=-9 tar -zcvf $julia_doc_artifact -C $julia_doc_path .
+
+   popd
+}
+
+
+build_java_docs() {
+    set -ex
+    pushd .
+
+    build_docs_setup
+    build_scala
+
+    # Re-use scala-package build artifacts.
+    java_path='scala-package'
+    docs_build_path='docs/scala-package/build/docs/java'
+    artifacts_path='docs/_build/java-artifacts.tgz'
+
+    pushd $java_path
+
+    java_doc_sources=`find . -type f -name "*.scala" | egrep "./core|./infer"  | egrep "/javaapi"  | egrep -v "Suite" | egrep -v "/mxnetexamples"`
+    jar_native=`find native -name "*.jar" | grep "target/lib/" | tr "\\n" ":" `
+    jar_macros=`find macros -name "*.jar" | tr "\\n" ":" `
+    jar_core=`find core -name "*.jar" | tr "\\n" ":" `
+    jar_infer=`find infer -name "*.jar" | tr "\\n" ":" `
+    java_doc_classpath=$jar_native:$jar_macros:$jar_core:$jar_infer
+
+    scaladoc $java_doc_sources -classpath $java_doc_classpath -feature -deprecation -doc-title MXNet
+    popd
+
+    # Clean-up old artifacts
+    rm -rf $docs_build_path
+    mkdir -p $docs_build_path
+
+    for doc_file in index index.html org lib index.js package.html; do
+        mv $java_path/$doc_file $docs_build_path
+    done
+
+    GZIP=-9 tar -zcvf $artifacts_path -C $docs_build_path .
+
+    popd
+}
+
+
+build_clojure_docs() {
+    set -ex
+    pushd .
+
+    build_docs_setup
+    build_scala
+
+    clojure_path='contrib/clojure-package'
+    clojure_doc_path='contrib/clojure-package/target/doc'
+    clojure_doc_artifact='docs/_build/clojure-artifacts.tgz'
+
+    pushd $clojure_path
+    lein codox
+    popd
+
+    GZIP=-9 tar -zcvf $clojure_doc_artifact -C $clojure_doc_path .
+
+    popd
+}
+
+build_docs() {
+    pushd docs/_build
+    tar -xzf jekyll-artifacts.tgz
+    api_folder='html/api'
+    # Python has it's own landing page/site so we don't put it in /docs/api
+    mkdir -p $api_folder/python/docs && tar -xzf python-artifacts.tgz --directory $api_folder/python/docs
+    mkdir -p $api_folder/cpp/docs/api && tar -xzf c-artifacts.tgz --directory $api_folder/cpp/docs/api
+    mkdir -p $api_folder/r/docs/api && tar -xzf r-artifacts.tgz --directory $api_folder/r/docs/api
+    mkdir -p $api_folder/julia/docs/api && tar -xzf julia-artifacts.tgz --directory $api_folder/julia/docs/api
+    mkdir -p $api_folder/scala/docs/api && tar -xzf scala-artifacts.tgz --directory $api_folder/scala/docs/api
+    mkdir -p $api_folder/java/docs/api && tar -xzf java-artifacts.tgz --directory $api_folder/java/docs/api
+    mkdir -p $api_folder/clojure/docs/api && tar -xzf clojure-artifacts.tgz --directory $api_folder/clojure/docs/api
+    GZIP=-9 tar -zcvf full_website.tgz -C html .
+    popd
+}
+
+build_docs_small() {
+    pushd docs/_build
+    tar -xzf jekyll-artifacts.tgz
+    api_folder='html/api'
+    mkdir -p $api_folder/python/docs && tar -xzf python-artifacts.tgz --directory $api_folder/python/docs
+    # The folder to be published is now in /docs/_build/html
+    popd
+}
+
+create_repo() {
+   repo_folder=$1
+   mxnet_url=$2
+   git clone $mxnet_url $repo_folder --recursive
+   echo "Adding MXNet upstream repo..."
+   cd $repo_folder
+   git remote add upstream https://github.com/apache/incubator-mxnet
+   cd ..
+}
+
+
+refresh_branches() {
+   repo_folder=$1
+   cd $repo_folder
+   git fetch
+   git fetch upstream
+   cd ..
+}
+
+checkout() {
+   repo_folder=$1
+   cd $repo_folder
+   # Overriding configs later will cause a conflict here, so stashing...
+   git stash
+   # Fails to checkout if not available locally, so try upstream
+   git checkout "$repo_folder" || git branch $repo_folder "upstream/$repo_folder" && git checkout "$repo_folder" || exit 1
+   if [ $tag == 'master' ]; then
+      git pull
+      # master gets warnings as errors for Sphinx builds
+      OPTS="-W"
+      else
+      OPTS=
+   fi
+   git submodule update --init --recursive
+   cd ..
 }
 
 build_static_libmxnet() {
diff --git a/ci/jenkins/Jenkins_steps.groovy b/ci/jenkins/Jenkins_steps.groovy
index eda1d349c9e1..30db32252e66 100644
--- a/ci/jenkins/Jenkins_steps.groovy
+++ b/ci/jenkins/Jenkins_steps.groovy
@@ -1369,16 +1369,20 @@ def test_qemu_armv7_cpu() {
     }]
 }
 
+// This is for running on PRs
 def docs_website() {
     return ['Docs': {
       node(NODE_LINUX_CPU) {
         ws('workspace/docs') {
           timeout(time: max_time, unit: 'MINUTES') {
-            utils.init_git()
-            utils.docker_run('ubuntu_cpu', 'deploy_docs', false)
+
+            unstash 'jekyll-artifacts'
+            unstash 'python-artifacts'
+            utils.docker_run('ubuntu_cpu_jekyll', 'build_docs_small', false)
 
             master_url = utils.get_jenkins_master_url()
             if ( master_url == 'jenkins.mxnet-ci.amazon-ml.com') {
+                // TODO: Make sure this scripts publish the website from the right folder
                 sh "ci/other/ci_deploy_doc.sh ${env.BRANCH_NAME} ${env.BUILD_NUMBER}"
             } else {
                 print "Skipping staging documentation publishing since we are not running in prod. Host: {$master_url}"
@@ -1389,6 +1393,253 @@ def docs_website() {
     }]
 }
 
+
+// This creates the MXNet binary needed for generating different docs sets
+def compile_unix_lite() {
+    return ['MXNet lib': {
+      node(NODE_LINUX_CPU) {
+        ws('workspace/docs') {
+          timeout(time: max_time, unit: 'MINUTES') {
+            utils.init_git()
+            utils.docker_run('ubuntu_cpu_lite', 'build_ubuntu_cpu_docs', false)
+            utils.pack_lib('libmxnet', 'lib/libmxnet.so', false)
+          }
+        }
+      }
+    }]
+}
+
+
+def should_pack_website() {
+  if (env.BRANCH_NAME) {
+    if (env.BRANCH_NAME == "master" || env.BRANCH_NAME.startsWith("new_")) {
+      return true
+    }
+  } else {
+    return true 
+  }
+  return false
+}
+  
+// Each of the docs_{lang} functions will build the docs...
+// Stashing is only needed for master for website publishing or for testing "new_"
+
+// Call this function from Jenkins to generate just the Python API microsite artifacts.
+def docs_python() {
+    return ['Python Docs': {
+      node(NODE_LINUX_CPU) {
+        ws('workspace/docs') {
+          timeout(time: max_time, unit: 'MINUTES') {
+            utils.unpack_and_init('libmxnet', mx_lib, false)
+            utils.docker_run('ubuntu_cpu_python', 'build_python_docs', false)
+            if (should_pack_website()) {
+              utils.pack_lib('python-artifacts', 'docs/_build/python-artifacts.tgz', false)
+            }
+          }
+        }
+      }
+    }]
+}
+
+
+// Call this function from Jenkins to generate just the C and C++ API microsite artifacts.
+def docs_c() {
+    return ['C Docs': {
+      node(NODE_LINUX_CPU) {
+        ws('workspace/docs') {
+          timeout(time: max_time, unit: 'MINUTES') {
+            utils.unpack_and_init('libmxnet', 'lib/libmxnet.so', false)
+            utils.docker_run('ubuntu_cpu_c', 'build_c_docs', false)
+            if (should_pack_website()) {
+              utils.pack_lib('c-artifacts', 'docs/_build/c-artifacts.tgz', false)
+            }
+          }
+        }
+      }
+    }]
+}
+
+
+// Call this function from Jenkins to generate just the Julia API microsite artifacts.
+def docs_julia() {
+    return ['Julia Docs': {
+      node(NODE_LINUX_CPU) {
+        ws('workspace/docs') {
+          timeout(time: max_time, unit: 'MINUTES') {
+            utils.unpack_and_init('libmxnet', mx_lib, false)
+            utils.docker_run('ubuntu_cpu_julia', 'build_julia_docs', false)
+            if (should_pack_website()) {
+              utils.pack_lib('julia-artifacts', 'docs/_build/julia-artifacts.tgz', false)
+            }
+          }
+        }
+      }
+    }]
+}
+
+
+// Call this function from Jenkins to generate just the R API PDF artifact.
+def docs_r() {
+    return ['R Docs': {
+      node(NODE_LINUX_CPU) {
+        ws('workspace/docs') {
+          timeout(time: max_time, unit: 'MINUTES') {
+            utils.unpack_and_init('libmxnet', mx_lib, false)
+            utils.docker_run('ubuntu_cpu_r', 'build_r_docs', false)
+            if (should_pack_website()) {
+              utils.pack_lib('r-artifacts', 'docs/_build/r-artifacts.tgz', false)
+            }
+          }
+        }
+      }
+    }]
+}
+
+
+// Call this function from Jenkins to generate just the Scala API microsite artifacts.
+// It will also generate the Scala package.
+def docs_scala() {
+    return ['Scala Docs': {
+      node(NODE_LINUX_CPU) {
+        ws('workspace/docs') {
+          timeout(time: max_time, unit: 'MINUTES') {
+            utils.unpack_and_init('libmxnet', mx_lib, false)
+            utils.docker_run('ubuntu_cpu_scala', 'build_scala_docs', false)
+            if (should_pack_website()) {
+              utils.pack_lib('scala-artifacts', 'docs/_build/scala-artifacts.tgz', false)
+            }
+          }
+        }
+      }
+    }]
+}
+
+
+// Call this function from Jenkins to generate just the Java API microsite artifacts.
+// It will also generate the Scala package.
+def docs_java() {
+    return ['Java Docs': {
+      node(NODE_LINUX_CPU) {
+        ws('workspace/docs') {
+          timeout(time: max_time, unit: 'MINUTES') {
+            utils.unpack_and_init('libmxnet', mx_lib, false)
+            utils.docker_run('ubuntu_cpu_scala', 'build_java_docs', false)
+            if (should_pack_website()) {
+              utils.pack_lib('java-artifacts', 'docs/_build/java-artifacts.tgz', false)
+            }
+          }
+        }
+      }
+    }]
+}
+
+
+// Call this function from Jenkins to generate just the Clojure API microsite artifacts.
+// It will also generate the Scala package.
+def docs_clojure() {
+    return ['Clojure Docs': {
+      node(NODE_LINUX_CPU) {
+        ws('workspace/docs') {
+          timeout(time: max_time, unit: 'MINUTES') {
+            utils.unpack_and_init('libmxnet', mx_lib, false)
+            utils.docker_run('ubuntu_cpu_scala', 'build_clojure_docs', false)
+            if (should_pack_website()) {
+              utils.pack_lib('clojure-artifacts', 'docs/_build/clojure-artifacts.tgz', false)
+            }
+          }
+        }
+      }
+    }]
+}
+
+
+// Call this function from Jenkins to generate just the main website artifacts.
+def docs_jekyll() {
+    return ['Main Jekyll Website': {
+      node(NODE_LINUX_CPU) {
+        ws('workspace/docs') {
+          timeout(time: max_time, unit: 'MINUTES') {
+            utils.init_git()
+            utils.docker_run('ubuntu_cpu_jekyll', 'build_jekyll_docs', false)
+            if (should_pack_website()) {
+              utils.pack_lib('jekyll-artifacts', 'docs/_build/jekyll-artifacts.tgz', false)
+            }
+          }
+        }
+      }
+    }]
+}
+
+
+// This is for publishing the full website
+// Assumes you have run all of the docs generation functions
+// Called from Jenkins_website_full and Jenkins_website_full_pr
+def docs_prepare() {
+    return ['Prepare for publication of the full website': {
+      node(NODE_LINUX_CPU) {
+        ws('workspace/docs') {
+          timeout(time: max_time, unit: 'MINUTES') {
+            utils.init_git()
+
+            unstash 'jekyll-artifacts'
+            unstash 'c-artifacts'
+            unstash 'python-artifacts'
+            unstash 'r-artifacts'
+            unstash 'julia-artifacts'
+            unstash 'scala-artifacts'
+            unstash 'java-artifacts'
+            unstash 'clojure-artifacts'
+
+            utils.docker_run('ubuntu_cpu_jekyll', 'build_docs', false)
+
+            // only stash if we're going to unstash later
+            // utils.pack_lib('full_website', 'docs/_build/full_website.tgz', false)
+
+            // archive so the publish pipeline can access the artifact
+            archiveArtifacts 'docs/_build/full_website.tgz'
+          }
+        }
+      }
+    }]
+}
+
+
+def docs_archive() {
+    return ['Archive the full website': {
+      node(NODE_LINUX_CPU) {
+        ws('workspace/docs') {
+          timeout(time: max_time, unit: 'MINUTES') {
+            archiveArtifacts 'docs/_build/full_website.tgz'
+          }
+        }
+      }
+    }]
+}
+
+
+// This is for the full website
+def docs_publish() {
+    return ['Publish the full website': {
+      node(NODE_LINUX_CPU) {
+        ws('workspace/docs') {
+          timeout(time: max_time, unit: 'MINUTES') {
+            // If used stashed files, you can retrieve them here
+            //unstash 'full_website'
+            //sh 'tar -xzf docs/_build/full_website.tgz --directory .'
+            try {
+              build 'restricted-website-publish-master'
+            }
+            catch (Exception e) {
+               println(e.getMessage())
+            }
+          }
+        }
+      }
+    }]
+}
+
+
+
 def misc_asan_cpu() {
     return ['CPU ASAN': {
       node(NODE_LINUX_CPU) {
diff --git a/docs/Jenkinsfile-dev b/ci/jenkins/Jenkinsfile_website_c_docs
similarity index 66%
rename from docs/Jenkinsfile-dev
rename to ci/jenkins/Jenkinsfile_website_c_docs
index 760a2f953061..e678920e3a4f 100644
--- a/docs/Jenkinsfile-dev
+++ b/ci/jenkins/Jenkinsfile_website_c_docs
@@ -16,39 +16,37 @@
 // KIND, either express or implied.  See the License for the
 // specific language governing permissions and limitations
 // under the License.
-
+//
 // Jenkins pipeline
 // See documents at https://jenkins.io/doc/book/pipeline/jenkinsfile/
 
 // timeout in minutes
-max_time = 120
+max_time = 20
 
 node('utility') {
   // Loading the utilities requires a node context unfortunately
   checkout scm
   utils = load('ci/Jenkinsfile_utils.groovy')
+  custom_steps = load('ci/jenkins/Jenkins_steps.groovy')
 }
 utils.assign_node_labels(utility: 'utility', linux_cpu: 'mxnetlinux-cpu')
 
 utils.main_wrapper(
 core_logic: {
-  stage('Build Docs') {
-    node(NODE_LINUX_CPU) {
-      ws('workspace/docs') {
-        utils.init_git()
-        timeout(time: max_time, unit: 'MINUTES') {
-            sh "ci/build.py -p ubuntu_cpu --docker-registry ${env.DOCKER_CACHE_REGISTRY} --docker-build-retries 3 /work/runtime_functions.sh build_docs ${params.tags_to_build} ${params.tag_list} ${params.fork} ${params.tag_default} ${params.domain}"
-            archiveArtifacts 'docs/build_version_doc/artifacts.tgz'
-            build 'test-website-publish'
-        }
-      }
-    }
-  }
+  utils.parallel_stage('Build', [
+    custom_steps.compile_unix_lite()
+  ])
+  
+  utils.parallel_stage('C Docs', [
+    custom_steps.docs_c()
+  ])
+
 }
 ,
 failure_handler: {
-  if (currentBuild.result == "FAILURE") {
-    // Do nothing.
+  // Only send email if master or release branches failed
+  if (currentBuild.result == "FAILURE" && (env.BRANCH_NAME == "master" || env.BRANCH_NAME.startsWith("v"))) {
+    emailext body: 'Build for MXNet branch ${BRANCH_NAME} has broken. Please view the build at ${BUILD_URL}', replyTo: '${EMAIL}', subject: '[BUILD FAILED] Branch ${BRANCH_NAME} build ${BUILD_NUMBER}', to: '${EMAIL}'
   }
 }
 )
diff --git a/ci/jenkins/Jenkinsfile_website_clojure_docs b/ci/jenkins/Jenkinsfile_website_clojure_docs
new file mode 100644
index 000000000000..697dca48e58b
--- /dev/null
+++ b/ci/jenkins/Jenkinsfile_website_clojure_docs
@@ -0,0 +1,53 @@
+// -*- mode: groovy -*-
+
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+//
+// Jenkins pipeline
+// See documents at https://jenkins.io/doc/book/pipeline/jenkinsfile/
+
+// timeout in minutes
+max_time = 20
+
+node('utility') {
+  // Loading the utilities requires a node context unfortunately
+  checkout scm
+  utils = load('ci/Jenkinsfile_utils.groovy')
+  custom_steps = load('ci/jenkins/Jenkins_steps.groovy')
+}
+utils.assign_node_labels(utility: 'utility', linux_cpu: 'mxnetlinux-cpu')
+
+utils.main_wrapper(
+core_logic: {
+  utils.parallel_stage('Build', [
+    custom_steps.compile_unix_lite()
+  ])
+  
+  utils.parallel_stage('Clojure Docs', [
+    custom_steps.docs_clojure()
+
+  ])
+
+}
+,
+failure_handler: {
+  // Only send email if master or release branches failed
+  if (currentBuild.result == "FAILURE" && (env.BRANCH_NAME == "master" || env.BRANCH_NAME.startsWith("v"))) {
+    emailext body: 'Build for MXNet branch ${BRANCH_NAME} has broken. Please view the build at ${BUILD_URL}', replyTo: '${EMAIL}', subject: '[BUILD FAILED] Branch ${BRANCH_NAME} build ${BUILD_NUMBER}', to: '${EMAIL}'
+  }
+}
+)
diff --git a/ci/jenkins/Jenkinsfile_website_full b/ci/jenkins/Jenkinsfile_website_full
new file mode 100644
index 000000000000..39cc6f4e2dc9
--- /dev/null
+++ b/ci/jenkins/Jenkinsfile_website_full
@@ -0,0 +1,67 @@
+// -*- mode: groovy -*-
+
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+//
+// Jenkins pipeline
+// See documents at https://jenkins.io/doc/book/pipeline/jenkinsfile/
+
+// timeout in minutes
+max_time = 180
+
+node('restricted-utility') {
+  // Loading the utilities requires a node context unfortunately
+  checkout scm
+  utils = load('ci/Jenkinsfile_utils.groovy')
+  custom_steps = load('ci/jenkins/Jenkins_steps.groovy')
+}
+
+utils.assign_node_labels(utility: 'restricted-utility', linux_cpu: 'restricted-mxnetlinux-cpu', linux_gpu: 'restricted-mxnetlinux-gpu', linux_gpu_p3: 'restricted-mxnetlinux-gpu-p3', windows_cpu: 'restricted-mxnetwindows-cpu', windows_gpu: 'restricted-mxnetwindows-gpu')
+
+utils.main_wrapper(
+core_logic: {
+  utils.parallel_stage('Build', [
+    custom_steps.compile_unix_lite()
+  ])
+
+  utils.parallel_stage('Build Docs', [
+    custom_steps.docs_jekyll(),
+    custom_steps.docs_c(),
+    custom_steps.docs_python(),
+    custom_steps.docs_julia(),
+    custom_steps.docs_r(),
+    custom_steps.docs_scala(),
+    custom_steps.docs_java(),
+    custom_steps.docs_clojure()
+  ])
+
+  utils.parallel_stage('Prepare', [
+    custom_steps.docs_prepare()
+  ])
+
+  utils.parallel_stage('Publish', [
+    custom_steps.docs_publish()
+  ])
+}
+,
+failure_handler: {
+  // Only send email if master or release branches failed
+  if (currentBuild.result == "FAILURE" && (env.BRANCH_NAME == "master" || env.BRANCH_NAME.startsWith("v"))) {
+    emailext body: 'Build for MXNet branch ${BRANCH_NAME} has broken. Please view the build at ${BUILD_URL}', replyTo: '${EMAIL}', subject: '[BUILD FAILED] Branch ${BRANCH_NAME} build ${BUILD_NUMBER}', to: '${EMAIL}'
+  }
+}
+)
diff --git a/ci/jenkins/Jenkinsfile_website_full_pr b/ci/jenkins/Jenkinsfile_website_full_pr
new file mode 100644
index 000000000000..133c6c204964
--- /dev/null
+++ b/ci/jenkins/Jenkinsfile_website_full_pr
@@ -0,0 +1,62 @@
+// -*- mode: groovy -*-
+
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+//
+// Jenkins pipeline
+// See documents at https://jenkins.io/doc/book/pipeline/jenkinsfile/
+
+// timeout in minutes
+max_time = 180
+
+node('utility') {
+  // Loading the utilities requires a node context unfortunately
+  checkout scm
+  utils = load('ci/Jenkinsfile_utils.groovy')
+  custom_steps = load('ci/jenkins/Jenkins_steps.groovy')
+}
+utils.assign_node_labels(utility: 'utility', linux_cpu: 'mxnetlinux-cpu')
+
+utils.main_wrapper(
+core_logic: {
+  utils.parallel_stage('Build', [
+    custom_steps.compile_unix_lite()
+  ])
+
+  utils.parallel_stage('Build Docs', [
+    // Optimization would be to flag these not to stash if not previewing them
+    custom_steps.docs_jekyll(),
+    custom_steps.docs_c(),
+    custom_steps.docs_python(),
+    custom_steps.docs_julia(),
+    custom_steps.docs_r(),
+    custom_steps.docs_scala(),
+    custom_steps.docs_java(),
+    custom_steps.docs_clojure()
+  ])
+
+  // TODO: add a website preview function
+
+}
+,
+failure_handler: {
+  // Only send email if master or release branches failed
+  if (currentBuild.result == "FAILURE" && (env.BRANCH_NAME == "master" || env.BRANCH_NAME.startsWith("v"))) {
+    emailext body: 'Build for MXNet branch ${BRANCH_NAME} has broken. Please view the build at ${BUILD_URL}', replyTo: '${EMAIL}', subject: '[BUILD FAILED] Branch ${BRANCH_NAME} build ${BUILD_NUMBER}', to: '${EMAIL}'
+  }
+}
+)
diff --git a/ci/jenkins/Jenkinsfile_website_java_docs b/ci/jenkins/Jenkinsfile_website_java_docs
new file mode 100644
index 000000000000..4453b444039b
--- /dev/null
+++ b/ci/jenkins/Jenkinsfile_website_java_docs
@@ -0,0 +1,53 @@
+// -*- mode: groovy -*-
+
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+//
+// Jenkins pipeline
+// See documents at https://jenkins.io/doc/book/pipeline/jenkinsfile/
+
+// timeout in minutes
+max_time = 20
+
+node('utility') {
+  // Loading the utilities requires a node context unfortunately
+  checkout scm
+  utils = load('ci/Jenkinsfile_utils.groovy')
+  custom_steps = load('ci/jenkins/Jenkins_steps.groovy')
+}
+utils.assign_node_labels(utility: 'utility', linux_cpu: 'mxnetlinux-cpu')
+
+utils.main_wrapper(
+core_logic: {
+  utils.parallel_stage('Build', [
+    custom_steps.compile_unix_lite()
+  ])
+  
+  utils.parallel_stage('Java Docs', [
+    custom_steps.docs_java()
+
+  ])
+
+}
+,
+failure_handler: {
+  // Only send email if master or release branches failed
+  if (currentBuild.result == "FAILURE" && (env.BRANCH_NAME == "master" || env.BRANCH_NAME.startsWith("v"))) {
+    emailext body: 'Build for MXNet branch ${BRANCH_NAME} has broken. Please view the build at ${BUILD_URL}', replyTo: '${EMAIL}', subject: '[BUILD FAILED] Branch ${BRANCH_NAME} build ${BUILD_NUMBER}', to: '${EMAIL}'
+  }
+}
+)
diff --git a/ci/jenkins/Jenkinsfile_website_jekyll_docs b/ci/jenkins/Jenkinsfile_website_jekyll_docs
new file mode 100644
index 000000000000..c4c5ff89dd86
--- /dev/null
+++ b/ci/jenkins/Jenkinsfile_website_jekyll_docs
@@ -0,0 +1,49 @@
+// -*- mode: groovy -*-
+
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+//
+// Jenkins pipeline
+// See documents at https://jenkins.io/doc/book/pipeline/jenkinsfile/
+
+// timeout in minutes
+max_time = 20
+
+node('utility') {
+  // Loading the utilities requires a node context unfortunately
+  checkout scm
+  utils = load('ci/Jenkinsfile_utils.groovy')
+  custom_steps = load('ci/jenkins/Jenkins_steps.groovy')
+}
+utils.assign_node_labels(utility: 'utility', linux_cpu: 'mxnetlinux-cpu')
+
+utils.main_wrapper(
+core_logic: {
+  utils.parallel_stage('Jekyll Website Docs', [
+    custom_steps.docs_jekyll()
+
+  ])
+
+}
+,
+failure_handler: {
+  // Only send email if master or release branches failed
+  if (currentBuild.result == "FAILURE" && (env.BRANCH_NAME == "master" || env.BRANCH_NAME.startsWith("v"))) {
+    emailext body: 'Build for MXNet branch ${BRANCH_NAME} has broken. Please view the build at ${BUILD_URL}', replyTo: '${EMAIL}', subject: '[BUILD FAILED] Branch ${BRANCH_NAME} build ${BUILD_NUMBER}', to: '${EMAIL}'
+  }
+}
+)
diff --git a/ci/jenkins/Jenkinsfile_website_julia_docs b/ci/jenkins/Jenkinsfile_website_julia_docs
new file mode 100644
index 000000000000..e1e9aaa983fb
--- /dev/null
+++ b/ci/jenkins/Jenkinsfile_website_julia_docs
@@ -0,0 +1,53 @@
+// -*- mode: groovy -*-
+
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+//
+// Jenkins pipeline
+// See documents at https://jenkins.io/doc/book/pipeline/jenkinsfile/
+
+// timeout in minutes
+max_time = 60
+
+node('utility') {
+  // Loading the utilities requires a node context unfortunately
+  checkout scm
+  utils = load('ci/Jenkinsfile_utils.groovy')
+  custom_steps = load('ci/jenkins/Jenkins_steps.groovy')
+}
+utils.assign_node_labels(utility: 'utility', linux_cpu: 'mxnetlinux-cpu')
+
+utils.main_wrapper(
+core_logic: {
+  utils.parallel_stage('Build', [
+    custom_steps.compile_unix_lite()
+  ])
+  
+  utils.parallel_stage('Julia Docs', [
+    custom_steps.docs_julia()
+
+  ])
+
+}
+,
+failure_handler: {
+  // Only send email if master or release branches failed
+  if (currentBuild.result == "FAILURE" && (env.BRANCH_NAME == "master" || env.BRANCH_NAME.startsWith("v"))) {
+    emailext body: 'Build for MXNet branch ${BRANCH_NAME} has broken. Please view the build at ${BUILD_URL}', replyTo: '${EMAIL}', subject: '[BUILD FAILED] Branch ${BRANCH_NAME} build ${BUILD_NUMBER}', to: '${EMAIL}'
+  }
+}
+)
diff --git a/docs/Jenkinsfile b/ci/jenkins/Jenkinsfile_website_mxnet_build
similarity index 56%
rename from docs/Jenkinsfile
rename to ci/jenkins/Jenkinsfile_website_mxnet_build
index 676204291893..a3c3330d0349 100644
--- a/docs/Jenkinsfile
+++ b/ci/jenkins/Jenkinsfile_website_mxnet_build
@@ -16,39 +16,33 @@
 // KIND, either express or implied.  See the License for the
 // specific language governing permissions and limitations
 // under the License.
-
+//
 // Jenkins pipeline
 // See documents at https://jenkins.io/doc/book/pipeline/jenkinsfile/
 
 // timeout in minutes
 max_time = 180
 
-node('restricted-utility') {
+node('utility') {
   // Loading the utilities requires a node context unfortunately
   checkout scm
   utils = load('ci/Jenkinsfile_utils.groovy')
+  custom_steps = load('ci/jenkins/Jenkins_steps.groovy')
 }
-utils.assign_node_labels(utility: 'restricted-utility', linux_cpu: 'restricted-mxnetlinux-cpu')
+utils.assign_node_labels(utility: 'utility', linux_cpu: 'mxnetlinux-cpu')
 
 utils.main_wrapper(
 core_logic: {
-  stage('Build Docs') {
-    node(NODE_LINUX_CPU) {
-      ws('workspace/docs') {
-        utils.init_git()
-        timeout(time: max_time, unit: 'MINUTES') {
-            sh "ci/build.py -p ubuntu_cpu --docker-registry ${env.DOCKER_CACHE_REGISTRY} --docker-build-retries 3 /work/runtime_functions.sh build_docs ${params.tags_to_build} ${params.tag_list} ${params.fork} ${params.tag_default} ${params.domain}"
-            archiveArtifacts 'docs/build_version_doc/artifacts.tgz'
-            build 'restricted-website-publish'
-        }
-      }
-    }
-  }
+  utils.parallel_stage('Build', [
+    custom_steps.compile_unix_lite()
+  ])
+
 }
 ,
 failure_handler: {
-  if (currentBuild.result == "FAILURE") {
-    emailext body: 'Generating the website has failed. Please view the build at ${BUILD_URL}', replyTo: '${EMAIL}', subject: '[WEBSITE FAILED] Build ${BUILD_NUMBER}', to: '${EMAIL}'
+  // Only send email if master or release branches failed
+  if (currentBuild.result == "FAILURE" && (env.BRANCH_NAME == "master" || env.BRANCH_NAME.startsWith("v"))) {
+    emailext body: 'Build for MXNet branch ${BRANCH_NAME} has broken. Please view the build at ${BUILD_URL}', replyTo: '${EMAIL}', subject: '[BUILD FAILED] Branch ${BRANCH_NAME} build ${BUILD_NUMBER}', to: '${EMAIL}'
   }
 }
 )
diff --git a/ci/jenkins/Jenkinsfile_website_python_docs b/ci/jenkins/Jenkinsfile_website_python_docs
new file mode 100644
index 000000000000..baaf4519541f
--- /dev/null
+++ b/ci/jenkins/Jenkinsfile_website_python_docs
@@ -0,0 +1,53 @@
+// -*- mode: groovy -*-
+
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+//
+// Jenkins pipeline
+// See documents at https://jenkins.io/doc/book/pipeline/jenkinsfile/
+
+// timeout in minutes
+max_time = 60
+
+node('utility') {
+  // Loading the utilities requires a node context unfortunately
+  checkout scm
+  utils = load('ci/Jenkinsfile_utils.groovy')
+  custom_steps = load('ci/jenkins/Jenkins_steps.groovy')
+}
+utils.assign_node_labels(utility: 'utility', linux_cpu: 'mxnetlinux-cpu')
+
+utils.main_wrapper(
+core_logic: {
+  utils.parallel_stage('Build', [
+    custom_steps.compile_unix_lite()
+  ])
+  
+  utils.parallel_stage('Python Docs', [
+    custom_steps.docs_python()
+
+  ])
+
+}
+,
+failure_handler: {
+  // Only send email if master or release branches failed
+  if (currentBuild.result == "FAILURE" && (env.BRANCH_NAME == "master" || env.BRANCH_NAME.startsWith("v"))) {
+    emailext body: 'Build for MXNet branch ${BRANCH_NAME} has broken. Please view the build at ${BUILD_URL}', replyTo: '${EMAIL}', subject: '[BUILD FAILED] Branch ${BRANCH_NAME} build ${BUILD_NUMBER}', to: '${EMAIL}'
+  }
+}
+)
diff --git a/ci/jenkins/Jenkinsfile_website_r_docs b/ci/jenkins/Jenkinsfile_website_r_docs
new file mode 100644
index 000000000000..5b9f6630563f
--- /dev/null
+++ b/ci/jenkins/Jenkinsfile_website_r_docs
@@ -0,0 +1,53 @@
+// -*- mode: groovy -*-
+
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+//
+// Jenkins pipeline
+// See documents at https://jenkins.io/doc/book/pipeline/jenkinsfile/
+
+// timeout in minutes
+max_time = 60
+
+node('utility') {
+  // Loading the utilities requires a node context unfortunately
+  checkout scm
+  utils = load('ci/Jenkinsfile_utils.groovy')
+  custom_steps = load('ci/jenkins/Jenkins_steps.groovy')
+}
+utils.assign_node_labels(utility: 'utility', linux_cpu: 'mxnetlinux-cpu')
+
+utils.main_wrapper(
+core_logic: {
+  utils.parallel_stage('Build', [
+    custom_steps.compile_unix_lite()
+  ])
+
+  utils.parallel_stage('R Docs', [
+    custom_steps.docs_r()
+
+  ])
+
+}
+,
+failure_handler: {
+  // Only send email if master or release branches failed
+  if (currentBuild.result == "FAILURE" && (env.BRANCH_NAME == "master" || env.BRANCH_NAME.startsWith("v"))) {
+    emailext body: 'Build for MXNet branch ${BRANCH_NAME} has broken. Please view the build at ${BUILD_URL}', replyTo: '${EMAIL}', subject: '[BUILD FAILED] Branch ${BRANCH_NAME} build ${BUILD_NUMBER}', to: '${EMAIL}'
+  }
+}
+)
diff --git a/ci/jenkins/Jenkinsfile_website_scala_docs b/ci/jenkins/Jenkinsfile_website_scala_docs
new file mode 100644
index 000000000000..6a083dae7957
--- /dev/null
+++ b/ci/jenkins/Jenkinsfile_website_scala_docs
@@ -0,0 +1,53 @@
+// -*- mode: groovy -*-
+
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+//
+// Jenkins pipeline
+// See documents at https://jenkins.io/doc/book/pipeline/jenkinsfile/
+
+// timeout in minutes
+max_time = 20
+
+node('utility') {
+  // Loading the utilities requires a node context unfortunately
+  checkout scm
+  utils = load('ci/Jenkinsfile_utils.groovy')
+  custom_steps = load('ci/jenkins/Jenkins_steps.groovy')
+}
+utils.assign_node_labels(utility: 'utility', linux_cpu: 'mxnetlinux-cpu')
+
+utils.main_wrapper(
+core_logic: {
+  utils.parallel_stage('Build', [
+    custom_steps.compile_unix_lite()
+  ])
+  
+  utils.parallel_stage('Scala Docs', [
+    custom_steps.docs_scala()
+
+  ])
+
+}
+,
+failure_handler: {
+  // Only send email if master or release branches failed
+  if (currentBuild.result == "FAILURE" && (env.BRANCH_NAME == "master" || env.BRANCH_NAME.startsWith("v"))) {
+    emailext body: 'Build for MXNet branch ${BRANCH_NAME} has broken. Please view the build at ${BUILD_URL}', replyTo: '${EMAIL}', subject: '[BUILD FAILED] Branch ${BRANCH_NAME} build ${BUILD_NUMBER}', to: '${EMAIL}'
+  }
+}
+)
diff --git a/ci/other/ci_deploy_doc.sh b/ci/other/ci_deploy_doc.sh
index a300794b55da..3d5c85fb794e 100755
--- a/ci/other/ci_deploy_doc.sh
+++ b/ci/other/ci_deploy_doc.sh
@@ -28,5 +28,5 @@
 #
 set -ex
 
-aws s3 sync --delete docs/_build/html/ s3://mxnet-ci-doc/$1/$2 \
+aws s3 sync --delete . s3://mxnet-ci-doc/$1/$2 \
     && echo "Doc is hosted at http://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/$1/$2/index.html"
diff --git a/ci/publish/website/README.md b/ci/publish/website/README.md
new file mode 100644
index 000000000000..967dbe67ea16
--- /dev/null
+++ b/ci/publish/website/README.md
@@ -0,0 +1,20 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Website Deployment
+
+Refer to the [MXNet Developer Wiki](https://cwiki.apache.org/confluence/display/MXNET/Building+the+New+Website).
diff --git a/ci/publish/website/beta-deploy.sh b/ci/publish/website/beta-deploy.sh
new file mode 100755
index 000000000000..88b19927480f
--- /dev/null
+++ b/ci/publish/website/beta-deploy.sh
@@ -0,0 +1,34 @@
+#!/bin/bash
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+# A yaml file is written to trigger a staging deployment.
+# This file must be placed in the root of the site repo.
+# profile: sets custom the url using the pattern 'mxnet-PROFILE'
+# Example using 'beta': https://mxnet-beta.staged.apache.org/
+# Documentation: https://www.staged.apache.org/
+
+set -ex
+
+if [ ! -f ./.asf.yaml ]; then
+  echo -e "\nGenerating .asf.yaml file"
+  cat > ./.asf.yaml <<EOL
+staging:
+  profile: beta
+EOL
+fi
diff --git a/ci/publish/website/deploy.sh b/ci/publish/website/deploy.sh
new file mode 100644
index 000000000000..3309d852f77f
--- /dev/null
+++ b/ci/publish/website/deploy.sh
@@ -0,0 +1,99 @@
+#!/bin/bash
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+# build and install are separated so changes to build don't invalidate
+# the whole docker cache for the image
+
+# This script requires that APACHE_PASSWORD and APACHE_USERNAME are set
+# environment variables. Also, artifacts must be previously uploaded to S3
+# in the MXNet public bucket (mxnet-public.s3.us-east-2.amazonaws.com).
+
+set -ex
+
+
+# Managed by Jenkins; set these env vars if running locally
+# export APACHE_USERNAME=
+# export APACHE_PASSWORD=
+
+# Configuration for artifacts
+version=$2
+api_list=("cpp" "clojure" "java" "julia" "python" "r" "scala")
+jekyll_fork=ThomasDelteil
+
+
+setup_mxnet_site_repo() {
+   fork=$1
+   if [ ! -d "incubator-mxnet-site" ]; then
+     git clone https://$APACHE_USERNAME:$APACHE_PASSWORD@github.com/aaronmarkham/incubator-mxnet-site.git
+   fi
+
+   cd incubator-mxnet-site
+   git checkout asf-site
+   rm -rf *
+   git rm -r *
+   cd ..
+}
+
+
+setup_mxnet_site_repo()
+
+
+setup_jekyll_repo() {
+   fork=$1
+   if [ ! -d "mxnet.io-v2" ]; then
+     git clone https://github.com/$fork/mxnet.io-v2.git
+   fi
+}
+
+
+setup_jekyll_repo() $jekyll_fork
+
+# Copy in the main jekyll website artifacts
+web_artifacts=mxnet.io-v2/release
+web_dir=incubator-mxnet-site
+cp -a $web_artifacts/* $web_dir
+
+
+fetch_artifacts() {
+    api=$1
+    artifacts=https://mxnet-public.s3.us-east-2.amazonaws.com/docs/$version/$api-artifacts.tgz
+    dir=incubator-mxnet-site/api/
+    wget -q $artifacts
+    mkdir -p $dir
+    tar xf $api-artifacts.tgz -C $dir
+}
+
+# Download and untar each of the API artifacts
+for i in "${api_list[@]}"
+do
+    fetch_artifacts $i
+done
+
+# Commit the updates
+cd incubator-mxnet-site
+pwd
+git branch
+git add .
+git commit -m "Nightly build"
+git push origin asf-site
+# bump the site to force replication
+date > date.txt
+git add date.txt
+git commit -m "Bump the publish timestamp."
+git push origin asf-site
diff --git a/ci/publish/website/publish_artifacts.sh b/ci/publish/website/publish_artifacts.sh
new file mode 100755
index 000000000000..015a1bcfb843
--- /dev/null
+++ b/ci/publish/website/publish_artifacts.sh
@@ -0,0 +1,34 @@
+#!/bin/bash
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+# build and install are separated so changes to build don't invalidate
+# the whole docker cache for the image
+
+# This script requires that APACHE_PASSWORD and APACHE_USERNAME are set
+# environment variables. Also, artifacts must be previously uploaded to S3
+# in the MXNet public bucket (mxnet-public.s3.us-east-2.amazonaws.com).
+
+set -ex
+
+api_list=("cpp" "clojure" "java" "julia" "python" "r" "scala")
+version=v1.5.0
+for i in "${api_list[@]}"
+do
+    tar cvf $i-artifacts.tgz $i && aws s3 cp $i-artifacts.tgz s3://mxnet-public/docs/$version/$i-artifacts.tgz
+done
diff --git a/docs/README.md b/docs/README.md
index b98d15eecb49..7993d07f026f 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -30,6 +30,8 @@ Each language documentation is built in a modular way, so that if you are a cont
 
 You can also use the project's CI tools to emulate any changes with Docker. You can use these tools to install dependencies and run the parts of the build you want to test.
 
+Refer to the [MXNet Developer Wiki](https://cwiki.apache.org/confluence/display/MXNET/Building+the+New+Website) for instructions on building the docs locally.
+
 If you plan to contribute changes to the documentation or website, please submit a pull request. Contributions are welcome!
 
 ## Python Docs
diff --git a/docs/python_docs/_static/mxnet.css b/docs/python_docs/_static/mxnet.css
index 7f6380bf6733..3909aedc06fa 100644
--- a/docs/python_docs/_static/mxnet.css
+++ b/docs/python_docs/_static/mxnet.css
@@ -15,7 +15,7 @@
     }
 }
 
-.mdl-layout__container {
+.mdl-layout {
     visibility: hidden;
 }
 
diff --git a/docs/python_docs/python/tutorials/deploy/run-on-aws/cloud.rst b/docs/python_docs/python/tutorials/deploy/run-on-aws/cloud.rst
index 9562cea2b95a..20f69a80e118 100644
--- a/docs/python_docs/python/tutorials/deploy/run-on-aws/cloud.rst
+++ b/docs/python_docs/python/tutorials/deploy/run-on-aws/cloud.rst
@@ -101,6 +101,5 @@ credentials:
 http://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSGettingStartedGuide/AWSCredentials.html
 .. \_s3cmd: http://s3tools.org/s3cmd .. *Deep Learning AMI:
 https://aws.amazon.com/marketplace/pp/B01M0AXXQB?qid=1475211685369&sr=0-1&ref*\ =srh\_res\_product\_title
-.. \_MXNet-v0.9.3 tag: https://github.com/dmlc/mxnet .. \_Jupyter:
-http://jupyter.org .. \_good tutorial:
-https://github.com/dmlc/mxnet-notebooks
+.. \_MXNet-v0.9.3 tag: https://github.com/apache/incubator-mxnet .. \_Jupyter:
+http://jupyter.org
diff --git a/docs/python_docs/python/tutorials/deploy/run-on-aws/index.rst b/docs/python_docs/python/tutorials/deploy/run-on-aws/index.rst
index d59c1a1fca16..c22b7b68d189 100644
--- a/docs/python_docs/python/tutorials/deploy/run-on-aws/index.rst
+++ b/docs/python_docs/python/tutorials/deploy/run-on-aws/index.rst
@@ -34,7 +34,13 @@ The following tutorials will help you learn how to deploy MXNet on various AWS p
 
       How to run MXNet using Amazon SageMaker.
 
-    .. card::
+   .. card::
+      :title: MXNet on the Cloud
+      :link: cloud.html
+
+      How to run MXNet in the cloud.
+
+   .. card::
       :title: MXNet on the Cloud
       :link: cloud.html
 
diff --git a/docs/python_docs/python/tutorials/extend/custom_layer.md b/docs/python_docs/python/tutorials/extend/custom_layer.md
index 402752d067dc..875961acc226 100644
--- a/docs/python_docs/python/tutorials/extend/custom_layer.md
+++ b/docs/python_docs/python/tutorials/extend/custom_layer.md
@@ -26,6 +26,7 @@ In this article, I will cover how to create a new layer from scratch, how to use
 
 To create a new layer in Gluon API, one must create a class that inherits from [Block](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/block.py#L123) class. This class provides the most basic functionality, and all pre-defined layers inherit from it directly or via other subclasses. Because each layer in Apache MxNet inherits from `Block`, words "layer" and "block" are used interchangeable inside of the Apache MxNet community.
 
+- MXNet [7b24137](https://github.com/apache/incubator-mxnet/commit/7b24137ed45df605defa4ce72ec91554f6e445f0). See Instructions in [Setup and Installation]({{'/get_started'|relative_url}}). 
 The only instance method needed to be implemented is [forward(self, x)](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/block.py#L415), which defines what exactly your layer is going to do during forward propagation. Notice, that it doesn't require to provide what the block should do during back propogation. Back propogation pass for blocks is done by Apache MxNet for you. 
 
 In the example below, we define a new layer and implement `forward()` method to normalize input data by fitting it into a range of [0, 1].
@@ -86,12 +87,11 @@ layer = NormalizationHybridLayer()
 layer(nd.array([1, 2, 3], ctx=mx.cpu()))
 ```
 
+Output:
 
-
-
-```text
-    [0.  0.5 1. ]
-    <NDArray 3 @cpu(0)>
+```bash
+[0.  0.5 1. ]
+<NDArray 3 @cpu(0)>
 ```
 
 
@@ -125,15 +125,15 @@ net(input)
 ```
 
 
+Output:
 
-
-```text
-    [[-0.13601446]
-     [ 0.26103732]
-     [-0.05046433]
-     [-1.2375476 ]
-     [-0.15506986]]
-    <NDArray 5x1 @cpu(0)>
+```bash
+[[-0.13601446]
+ [ 0.26103732]
+ [-0.05046433]
+ [-1.2375476 ]
+ [-0.15506986]]
+<NDArray 5x1 @cpu(0)>
 ```
 
 
@@ -223,37 +223,38 @@ loss.backward()                                               # Backward compute
 trainer.step(input.shape[0])                                  # Trainer updates parameters of every block, using .grad field using oprimization method (sgd in this example)
                                                               # We provide batch size that is used as a divider in cost function formula
 print_params("=========== Parameters after backward pass ===========\n", net)
-
 ```
 
-```text
-    =========== Parameters after forward pass ===========
-    
-    hybridsequential94_normalizationhybridlayer0_weights = 
-    [[-0.3983642  -0.505708   -0.02425683 -0.3133553  -0.35161012]
-     [ 0.6467543   0.3918715  -0.6154656  -0.20702496 -0.4243446 ]
-     [ 0.6077331   0.03922009  0.13425875  0.5729856  -0.14446527]
-     [-0.3572498   0.18545026 -0.09098256  0.5106366  -0.35151464]
-     [-0.39846328  0.22245121  0.13075739  0.33387476 -0.10088372]]
-    <NDArray 5x5 @cpu(0)>
-    
-    hybridsequential94_normalizationhybridlayer0_scales = 
-    [2.]
-    <NDArray 1 @cpu(0)>
-    
-    =========== Parameters after backward pass ===========
-    
-    hybridsequential94_normalizationhybridlayer0_weights = 
-    [[-0.29839832 -0.47213346  0.08348035 -0.2324698  -0.27368504]
-     [ 0.76268613  0.43080837 -0.49052125 -0.11322092 -0.3339738 ]
-     [ 0.48665082 -0.00144657  0.00376363  0.47501418 -0.23885089]
-     [-0.22626656  0.22944227  0.05018325  0.6166192  -0.24941102]
-     [-0.44946212  0.20532274  0.07579394  0.29261002 -0.14063817]]
-    <NDArray 5x5 @cpu(0)>
-    
-    hybridsequential94_normalizationhybridlayer0_scales = 
-    [2.]
-    <NDArray 1 @cpu(0)>
+Output:
+
+```bash
+=========== Parameters after forward pass ===========
+
+hybridsequential94_normalizationhybridlayer0_weights = 
+[[-0.3983642  -0.505708   -0.02425683 -0.3133553  -0.35161012]
+ [ 0.6467543   0.3918715  -0.6154656  -0.20702496 -0.4243446 ]
+ [ 0.6077331   0.03922009  0.13425875  0.5729856  -0.14446527]
+ [-0.3572498   0.18545026 -0.09098256  0.5106366  -0.35151464]
+ [-0.39846328  0.22245121  0.13075739  0.33387476 -0.10088372]]
+<NDArray 5x5 @cpu(0)>
+
+hybridsequential94_normalizationhybridlayer0_scales = 
+[2.]
+<NDArray 1 @cpu(0)>
+
+=========== Parameters after backward pass ===========
+
+hybridsequential94_normalizationhybridlayer0_weights = 
+[[-0.29839832 -0.47213346  0.08348035 -0.2324698  -0.27368504]
+ [ 0.76268613  0.43080837 -0.49052125 -0.11322092 -0.3339738 ]
+ [ 0.48665082 -0.00144657  0.00376363  0.47501418 -0.23885089]
+ [-0.22626656  0.22944227  0.05018325  0.6166192  -0.24941102]
+ [-0.44946212  0.20532274  0.07579394  0.29261002 -0.14063817]]
+<NDArray 5x5 @cpu(0)>
+
+hybridsequential94_normalizationhybridlayer0_scales = 
+[2.]
+<NDArray 1 @cpu(0)>
 ``` 
 
 
@@ -262,5 +263,3 @@ As it is seen from the output above, `weights` parameter has been changed by the
 ## Conclusion
 
 One important quality of a Deep learning framework is extensibility. Empowered by flexible abstractions, like `Block` and `HybridBlock`, one can easily extend Apache MxNet functionality to match its needs.
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
\ No newline at end of file
diff --git a/docs/python_docs/python/tutorials/packages/gluon/custom_layer_beginners.md b/docs/python_docs/python/tutorials/packages/gluon/custom_layer_beginners.md
index 958736b99090..cbc35c07ad7b 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/custom_layer_beginners.md
+++ b/docs/python_docs/python/tutorials/packages/gluon/custom_layer_beginners.md
@@ -141,7 +141,7 @@ class NormalizationHybridLayer(gluon.HybridBlock):
 
             self.scales = self.params.get('scales',
                                       shape=scales.shape,
-                                      init=mx.init.Constant(scales.asnumpy().tolist()), # Convert to regular list to make this object serializable
+                                      init=mx.init.Constant(scales.asnumpy()),
                                       differentiable=False)
             
     def hybrid_forward(self, F, x, weights, scales):
diff --git a/docs/python_docs/python/tutorials/packages/gluon/gluon_from_experiment_to_deployment.md b/docs/python_docs/python/tutorials/packages/gluon/gluon_from_experiment_to_deployment.md
new file mode 100644
index 000000000000..f0111240188d
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/gluon_from_experiment_to_deployment.md
@@ -0,0 +1,327 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+
+# Gluon: from experiment to deployment, an end to end tutorial
+
+## Overview
+MXNet Gluon API comes with a lot of great features, and it can provide you everything you need: from experimentation to deploying the model. In this tutorial, we will walk you through a common use case on how to build a model using gluon, train it on your data, and deploy it for inference.
+This tutorial covers training and inference in Python, please continue to [C++ inference part](https://mxnet.incubator.apache.org/versions/master/tutorials/c++/mxnet_cpp_inference_tutorial.html) after you finish.
+
+Let's say you need to build a service that provides flower species recognition. A common problem is that you don't have enough data to train a good model. In such cases, a technique called Transfer Learning can be used to make a more robust model.
+In Transfer Learning we make use of a pre-trained model that solves a related task, and was trained on a very large standard dataset, such as ImageNet. ImageNet is from a different domain, but we can utilize the knowledge in this pre-trained model to perform the new task at hand.
+
+Gluon provides State of the Art models for many of the standard tasks such as Classification, Object Detection, Segmentation, etc. In this tutorial we will use the pre-trained model [ResNet50 V2](https://arxiv.org/abs/1603.05027) trained on ImageNet dataset. This model achieves 77.11% top-1 accuracy on ImageNet. We seek to transfer as much knowledge as possible for our task of recognizing different species of flowers.
+
+
+
+
+## Prerequisites
+
+To complete this tutorial, you need:
+
+- [Build MXNet from source](https://mxnet.incubator.apache.org/install/ubuntu_setup.html#build-mxnet-from-source) with Python(Gluon) and C++ Packages
+- Learn the basics about Gluon with [A 60-minute Gluon Crash Course](https://gluon-crash-course.mxnet.io/)
+
+
+## The Data
+
+We will use the [Oxford 102 Category Flower Dataset](http://www.robots.ox.ac.uk/~vgg/data/flowers/102/) as an example to show you the steps.
+We have prepared a utility file to help you download and organize your data into train, test, and validation sets. Run the following Python code to download and prepare the data:
+
+
+```python
+import mxnet as mx
+data_util_file = "oxford_102_flower_dataset.py"
+base_url = "https://raw.githubusercontent.com/apache/incubator-mxnet/master/docs/tutorial_utils/data/{}?raw=true"
+mx.test_utils.download(base_url.format(data_util_file), fname=data_util_file)
+import oxford_102_flower_dataset
+
+# download and move data to train, test, valid folders
+path = './data'
+oxford_102_flower_dataset.get_data(path)
+```
+
+Now your data will be organized into train, test, and validation sets, images belong to the same class are moved to the same folder.
+
+## Training using Gluon
+
+### Define Hyper-parameters
+
+Now let's first import necessary packages:
+
+
+```python
+import math
+import os
+import time
+
+from mxnet import autograd
+from mxnet import gluon, init
+from mxnet.gluon import nn
+from mxnet.gluon.data.vision import transforms
+from mxnet.gluon.model_zoo.vision import resnet50_v2
+```
+
+Next, we define the hyper-parameters that we will use for fine-tuning. We will use the [MXNet learning rate scheduler](https://mxnet.incubator.apache.org/tutorials/gluon/learning_rate_schedules.html) to adjust learning rates during training.
+Here we set the `epochs` to 1 for quick demonstration, please change to 40 for actual training.
+
+```python
+classes = 102
+epochs = 1
+lr = 0.001
+per_device_batch_size = 32
+momentum = 0.9
+wd = 0.0001
+
+lr_factor = 0.75
+# learning rate change at following epochs
+lr_epochs = [10, 20, 30]
+
+num_gpus = mx.context.num_gpus()
+# you can replace num_workers with the number of cores on you device
+num_workers = 8
+ctx = [mx.gpu(i) for i in range(num_gpus)] if num_gpus > 0 else [mx.cpu()]
+batch_size = per_device_batch_size * max(num_gpus, 1)
+```
+
+Now we will apply data augmentations on training images. This makes minor alterations on the training images, and our model will consider them as distinct images. This can be very useful for fine-tuning on a relatively small dataset, and it will help improve the model. We can use the Gluon [DataSet API](https://mxnet.incubator.apache.org/tutorials/gluon/datasets.html), [DataLoader API](https://mxnet.incubator.apache.org/tutorials/gluon/datasets.html), and [Transform API](https://mxnet.incubator.apache.org/tutorials/gluon/data_augmentation.html) to load the images and apply the following data augmentations:
+1. Randomly crop the image and resize it to 224x224
+2. Randomly flip the image horizontally
+3. Randomly jitter color and add noise
+4. Transpose the data from `[height, width, num_channels]` to `[num_channels, height, width]`, and map values from [0, 255] to [0, 1]
+5. Normalize with the mean and standard deviation from the ImageNet dataset.
+
+For validation and inference, we only need to apply step 1, 4, and 5. We also need to save the mean and standard deviation values for [inference using C++](https://mxnet.incubator.apache.org/versions/master/tutorials/c++/mxnet_cpp_inference_tutorial.html).
+
+```python
+jitter_param = 0.4
+lighting_param = 0.1
+
+# mean and std for normalizing image value in range (0,1)
+mean = [0.485, 0.456, 0.406]
+std = [0.229, 0.224, 0.225]
+
+training_transformer = transforms.Compose([
+    transforms.RandomResizedCrop(224),
+    transforms.RandomFlipLeftRight(),
+    transforms.RandomColorJitter(brightness=jitter_param, contrast=jitter_param,
+                                 saturation=jitter_param),
+    transforms.RandomLighting(lighting_param),
+    transforms.ToTensor(),
+    transforms.Normalize(mean, std)
+])
+
+validation_transformer = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(mean, std)
+])
+
+# save mean and std NDArray values for inference
+mean_img = mx.nd.stack(*[mx.nd.full((224, 224), m) for m in mean])
+std_img = mx.nd.stack(*[mx.nd.full((224, 224), s) for s in std])
+mx.nd.save('mean_std_224.nd', {"mean_img": mean_img, "std_img": std_img})
+
+train_path = os.path.join(path, 'train')
+val_path = os.path.join(path, 'valid')
+test_path = os.path.join(path, 'test')
+
+# loading the data and apply pre-processing(transforms) on images
+train_data = gluon.data.DataLoader(
+    gluon.data.vision.ImageFolderDataset(train_path).transform_first(training_transformer),
+    batch_size=batch_size, shuffle=True, num_workers=num_workers)
+
+val_data = gluon.data.DataLoader(
+    gluon.data.vision.ImageFolderDataset(val_path).transform_first(validation_transformer),
+    batch_size=batch_size, shuffle=False, num_workers=num_workers)
+
+test_data = gluon.data.DataLoader(
+    gluon.data.vision.ImageFolderDataset(test_path).transform_first(validation_transformer),
+    batch_size=batch_size, shuffle=False, num_workers=num_workers)
+```
+
+### Loading pre-trained model
+
+
+We will use pre-trained ResNet50_v2 model which was pre-trained on the [ImageNet Dataset](http://www.image-net.org/) with 1000 classes. To match the classes in the Flower dataset, we must redefine the last softmax (output) layer to be 102, then initialize the parameters.
+
+Before we go to training, one unique Gluon feature you should be aware of is hybridization. It allows you to convert your imperative code to a static symbolic graph, which is much more efficient to execute. There are two main benefits of hybridizing your model: better performance and easier serialization for deployment. The best part is that it's as simple as just calling `net.hybridize()`. To know more about Gluon hybridization, please follow the [hybridization tutorial](https://mxnet.incubator.apache.org/tutorials/gluon/hybrid.html).
+
+
+
+```python
+# load pre-trained resnet50_v2 from model zoo
+finetune_net = resnet50_v2(pretrained=True, ctx=ctx)
+
+# change last softmax layer since number of classes are different
+with finetune_net.name_scope():
+    finetune_net.output = nn.Dense(classes)
+finetune_net.output.initialize(init.Xavier(), ctx=ctx)
+# hybridize for better performance
+finetune_net.hybridize()
+
+num_batch = len(train_data)
+
+# setup learning rate scheduler
+iterations_per_epoch = math.ceil(num_batch)
+# learning rate change at following steps
+lr_steps = [epoch * iterations_per_epoch for epoch in lr_epochs]
+schedule = mx.lr_scheduler.MultiFactorScheduler(step=lr_steps, factor=lr_factor, base_lr=lr)
+
+# setup optimizer with learning rate scheduler, metric, and loss function
+sgd_optimizer = mx.optimizer.SGD(learning_rate=lr, lr_scheduler=schedule, momentum=momentum, wd=wd)
+metric = mx.metric.Accuracy()
+softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()
+```
+
+### Fine-tuning model on your custom dataset
+
+Now let's define the test metrics and start fine-tuning.
+
+
+
+```python
+def test(net, val_data, ctx):
+    metric = mx.metric.Accuracy()
+    for i, (data, label) in enumerate(val_data):
+        data = gluon.utils.split_and_load(data, ctx_list=ctx, even_split=False)
+        label = gluon.utils.split_and_load(label, ctx_list=ctx, even_split=False)
+        outputs = [net(x) for x in data]
+        metric.update(label, outputs)
+    return metric.get()
+
+trainer = gluon.Trainer(finetune_net.collect_params(), optimizer=sgd_optimizer)
+
+# start with epoch 1 for easier learning rate calculation
+for epoch in range(1, epochs + 1):
+
+    tic = time.time()
+    train_loss = 0
+    metric.reset()
+
+    for i, (data, label) in enumerate(train_data):
+        # get the images and labels
+        data = gluon.utils.split_and_load(data, ctx_list=ctx, even_split=False)
+        label = gluon.utils.split_and_load(label, ctx_list=ctx, even_split=False)
+        with autograd.record():
+            outputs = [finetune_net(x) for x in data]
+            loss = [softmax_cross_entropy(yhat, y) for yhat, y in zip(outputs, label)]
+        for l in loss:
+            l.backward()
+
+        trainer.step(batch_size)
+        train_loss += sum([l.mean().asscalar() for l in loss]) / len(loss)
+        metric.update(label, outputs)
+
+    _, train_acc = metric.get()
+    train_loss /= num_batch
+    _, val_acc = test(finetune_net, val_data, ctx)
+
+    print('[Epoch %d] Train-acc: %.3f, loss: %.3f | Val-acc: %.3f | learning-rate: %.3E | time: %.1f' %
+          (epoch, train_acc, train_loss, val_acc, trainer.learning_rate, time.time() - tic))
+
+_, test_acc = test(finetune_net, test_data, ctx)
+print('[Finished] Test-acc: %.3f' % (test_acc))
+```
+
+Following is the training result:
+```text
+[Epoch 40] Train-acc: 0.945, loss: 0.354 | Val-acc: 0.955 | learning-rate: 4.219E-04 | time: 17.8
+[Finished] Test-acc: 0.952
+```
+In the previous example output, we trained the model using an [AWS p3.8xlarge instance](https://aws.amazon.com/ec2/instance-types/p3/) with 4 Tesla V100 GPUs. We were able to reach a test accuracy of 95.5% with 40 epochs in around 12 minutes. This was really fast because our model was pre-trained on a much larger dataset, ImageNet, with around 1.3 million images. It worked really well to capture features on our small dataset.
+
+
+### Save the fine-tuned model
+
+
+We now have a trained our custom model. This can be serialized into model files using the export function. The export function will export the model architecture into a `.json` file and model parameters into a `.params` file.
+
+
+
+```python
+finetune_net.export("flower-recognition", epoch=epochs)
+
+```
+
+`export` creates `flower-recognition-symbol.json` and `flower-recognition-0040.params` (`0040` is for 40 epochs we ran) in the current directory. These files can be used for model deployment in the next section.
+
+## Load the model and run inference using the MXNet Module API
+
+MXNet provides various useful tools and interfaces for deploying your model for inference. For example, you can use [MXNet Model Server](https://github.com/awslabs/mxnet-model-server) to start a service and host your trained model easily.
+Besides that, you can also use MXNet's different language APIs to integrate your model with your existing service. We provide [Python](https://mxnet.incubator.apache.org/api/python/module/module.html),    [Java](https://mxnet.incubator.apache.org/api/java/index.html), [Scala](https://mxnet.incubator.apache.org/api/scala/index.html), and [C++](https://mxnet.incubator.apache.org/api/c++/index.html) APIs.
+
+Here we will briefly introduce how to run inference using Module API in Python. There is more detailed explanation available in the [Predict Image Tutorial](https://mxnet.incubator.apache.org/tutorials/python/predict_image.html).
+In general, prediction consists of the following steps:
+1. Load the model architecture (symbol file) and trained parameter values (params file)
+2. Load the synset file for label names
+3. Load the image and apply the same transformation we did on validation dataset during training
+4. Run a forward pass on the image data
+5. Convert output probabilities to predicted label name
+
+```python
+import numpy as np
+from collections import namedtuple
+
+ctx = mx.cpu()
+# load model symbol and params
+sym, arg_params, aux_params = mx.model.load_checkpoint('flower-recognition', epochs)
+mod = mx.mod.Module(symbol=sym, context=ctx, label_names=None)
+mod.bind(for_training=False, data_shapes=[('data', (1, 3, 224, 224))], label_shapes=mod._label_shapes)
+mod.set_params(arg_params, aux_params, allow_missing=True)
+
+# load synset for label names
+with open('synset.txt', 'r') as f:
+    labels = [l.rstrip() for l in f]
+
+# load an image for prediction
+img = mx.image.imread('./data/test/lotus/image_01832.jpg')
+# apply transform we did during training
+img = validation_transformer(img)
+# batchify
+img = img.expand_dims(axis=0)
+Batch = namedtuple('Batch', ['data'])
+mod.forward(Batch([img]))
+prob = mod.get_outputs()[0].asnumpy()
+prob = np.squeeze(prob)
+idx = np.argmax(prob)
+print('probability=%f, class=%s' % (prob[idx], labels[idx]))
+```
+
+Following is the output, you can see the image has been classified as lotus correctly.
+```text
+probability=9.798435, class=lotus
+```
+
+## What's next
+
+You can continue to the [next tutorial](https://mxnet.incubator.apache.org/versions/master/tutorials/c++/mxnet_cpp_inference_tutorial.html) on how to load the model we just trained and run inference using MXNet C++ API.
+
+You can also find more ways to run inference and deploy your models here:
+1. [Java Inference examples](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer)
+2. [Scala Inference examples](https://mxnet.incubator.apache.org/tutorials/scala/)
+4. [MXNet Model Server Examples](https://github.com/awslabs/mxnet-model-server/tree/master/examples)
+
+## References
+
+1. [Transfer Learning for Oxford102 Flower Dataset](https://github.com/Arsey/keras-transfer-learning-for-oxford102)
+2. [Gluon book on fine-tuning](https://www.d2l.ai/chapter_computer-vision/fine-tuning.html)
+3. [Gluon CV transfer learning tutorial](https://gluon-cv.mxnet.io/build/examples_classification/transfer_learning_minc.html)
+4. [Gluon crash course](https://gluon-crash-course.mxnet.io/)
+5. [Gluon CPP inference example](https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/inference/)
\ No newline at end of file
diff --git a/docs/python_docs/python/tutorials/packages/gluon/index.rst b/docs/python_docs/python/tutorials/packages/gluon/index.rst
index 85bb04faad42..01f2d7c398d5 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/index.rst
+++ b/docs/python_docs/python/tutorials/packages/gluon/index.rst
@@ -37,7 +37,7 @@ Getting started
 
    .. card::
       :title: Gluon: from experiment to deployment
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/gluon_from_experiment_to_deployment.html
+      :link: gluon_from_experiment_to_deployment.html
 
       An end to end tutorial on working with the MXNet Gluon API.
 
diff --git a/docs/python_docs/python/tutorials/performance/backend/profiler.md b/docs/python_docs/python/tutorials/performance/backend/profiler.md
index 91a74e4f49cf..d7abd1b4eb1c 100644
--- a/docs/python_docs/python/tutorials/performance/backend/profiler.md
+++ b/docs/python_docs/python/tutorials/performance/backend/profiler.md
@@ -57,6 +57,7 @@ from mxnet import profiler
 
 profiler.set_config(profile_all=True,
                     aggregate_stats=True,
+                    continuous_dump=True,
                     filename='profile_output.json')
 ```
 
@@ -185,6 +186,8 @@ There are a few ways to view the information collected by the profiler. You can
 You can use the `profiler.dumps()` method to view the information collected by the profiler in the console. The collected information contains time taken by each operator, time taken by each C API and memory consumed in both CPU and GPU.
 
 ```python
+profiler.set_state('run')
+profiler.set_state('stop')
 print(profiler.dumps())
 ```
 
diff --git a/docs/python_docs/themes/mx-theme/mxtheme/drawer.html b/docs/python_docs/themes/mx-theme/mxtheme/drawer.html
index f5f4a016c18c..ea0280159820 100644
--- a/docs/python_docs/themes/mx-theme/mxtheme/drawer.html
+++ b/docs/python_docs/themes/mx-theme/mxtheme/drawer.html
@@ -2,7 +2,7 @@
     {% block menu %}
       <div class="globaltoc">
         <span class="mdl-layout-title toc">{{ _('Table Of Contents') }}</span>
-        {% set toctree = toctree(maxdepth=3, collapse=False, includehidden=True, titles_only=True) %}
+        {% set toctree = toctree(maxdepth=6, collapse=False, includehidden=True, titles_only=True) %}
         {% if toctree %}
             {% set lines = toctree.split('\n') %}
             <nav class="mdl-navigation">
diff --git a/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.css b/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.css
index 5ca19ef5d437..3f4b6434c28b 100644
--- a/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.css
+++ b/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.css
@@ -1,2 +1,2 @@
-.admonition,.mdl-shadow--2dp,.page-content pre:hover,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list){box-shadow:0 2px 2px 0 rgba(0,0,0,.14),0 3px 1px -2px rgba(0,0,0,.2),0 1px 5px 0 rgba(0,0,0,.12)}.mdl-shadow--3dp{box-shadow:0 3px 4px 0 rgba(0,0,0,.14),0 3px 3px -2px rgba(0,0,0,.2),0 1px 8px 0 rgba(0,0,0,.12)}.mdl-shadow--4dp{box-shadow:0 4px 5px 0 rgba(0,0,0,.14),0 1px 10px 0 rgba(0,0,0,.12),0 2px 4px -1px rgba(0,0,0,.2)}.mdl-shadow--6dp{box-shadow:0 6px 10px 0 rgba(0,0,0,.14),0 1px 18px 0 rgba(0,0,0,.12),0 3px 5px -1px rgba(0,0,0,.2)}.mdl-shadow--8dp{box-shadow:0 8px 10px 1px rgba(0,0,0,.14),0 3px 14px 2px rgba(0,0,0,.12),0 5px 5px -3px rgba(0,0,0,.2)}.mdl-shadow--16dp{box-shadow:0 16px 24px 2px rgba(0,0,0,.14),0 6px 30px 5px rgba(0,0,0,.12),0 8px 10px -5px rgba(0,0,0,.2)}.mdl-shadow--24dp{box-shadow:0 9px 46px 8px rgba(0,0,0,.14),0 11px 15px -7px rgba(0,0,0,.12),0 24px 38px 3px rgba(0,0,0,.2)}.mdl-data-table,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list){position:relative;border:1px solid rgba(0,0,0,.12);border-collapse:collapse;white-space:nowrap;font-size:13px;background-color:#fff}.mdl-data-table thead,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) thead{padding-bottom:3px}.mdl-data-table thead .mdl-data-table__select,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) thead .mdl-data-table__select{margin-top:0}.mdl-data-table tbody tr,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) tbody tr{position:relative;height:48px;transition-duration:.28s;transition-timing-function:cubic-bezier(.4,0,.2,1);transition-property:background-color}.mdl-data-table tbody tr.is-selected,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) tbody tr.is-selected{background-color:#e0e0e0}.mdl-data-table tbody tr:hover,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) tbody tr:hover{background-color:#eee}.mdl-data-table td,.mdl-data-table th,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) td,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th{padding:0 18px 12px;text-align:right}.mdl-data-table td:first-of-type,.mdl-data-table th:first-of-type,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) td:first-of-type,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th:first-of-type{padding-left:24px}.mdl-data-table td:last-of-type,.mdl-data-table th:last-of-type,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) td:last-of-type,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th:last-of-type{padding-right:24px}.mdl-data-table td,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) td{position:relative;vertical-align:middle;height:48px;border-top:1px solid rgba(0,0,0,.12);border-bottom:1px solid rgba(0,0,0,.12);padding-top:12px;box-sizing:border-box}.mdl-data-table td .mdl-data-table__select,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) td .mdl-data-table__select{vertical-align:middle}.mdl-data-table th,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th{position:relative;vertical-align:bottom;text-overflow:ellipsis;font-size:14px;font-weight:700;line-height:24px;letter-spacing:0;height:48px;font-size:12px;color:rgba(0,0,0,.54);padding-bottom:8px;box-sizing:border-box}.mdl-data-table th.mdl-data-table__header--sorted-ascending,.mdl-data-table th.mdl-data-table__header--sorted-descending,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-ascending,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-descending{color:rgba(0,0,0,.87)}.mdl-data-table th.mdl-data-table__header--sorted-ascending:before,.mdl-data-table th.mdl-data-table__header--sorted-descending:before,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-ascending:before,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-descending:before{font-family:Material Icons;font-weight:400;font-style:normal;font-size:24px;line-height:1;letter-spacing:normal;text-transform:none;display:inline-block;word-wrap:normal;font-feature-settings:"liga";-webkit-font-feature-settings:"liga";-webkit-font-smoothing:antialiased;font-size:16px;content:"\e5d8";margin-right:5px;vertical-align:sub}.mdl-data-table th.mdl-data-table__header--sorted-ascending:hover,.mdl-data-table th.mdl-data-table__header--sorted-descending:hover,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-ascending:hover,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-descending:hover{cursor:pointer}.mdl-data-table th.mdl-data-table__header--sorted-ascending:hover:before,.mdl-data-table th.mdl-data-table__header--sorted-descending:hover:before,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-ascending:hover:before,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-descending:hover:before{color:rgba(0,0,0,.26)}.mdl-data-table th.mdl-data-table__header--sorted-descending:before,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-descending:before{content:"\e5db"}.mdl-data-table__select{width:16px}.mdl-data-table__cell--non-numeric.mdl-data-table__cell--non-numeric,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) td,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th{text-align:left}.mdl-mini-footer{display:flex;flex-flow:row wrap;justify-content:space-between;padding:32px 16px;color:#9e9e9e;background-color:#424242}.mdl-mini-footer:after{content:"";display:block}.mdl-mini-footer .mdl-logo{line-height:36px}.mdl-mini-footer--link-list,.mdl-mini-footer__link-list,footer.mdl-mini-footer>div.mdl-mini-footer__left-section ul{display:flex;flex-flow:row nowrap;list-style:none;margin:0;padding:0}.mdl-mini-footer--link-list li,.mdl-mini-footer__link-list li,footer.mdl-mini-footer>div.mdl-mini-footer__left-section ul li{margin-bottom:0;margin-right:16px}@media screen and (min-width:760px){.mdl-mini-footer--link-list li,.mdl-mini-footer__link-list li,footer.mdl-mini-footer>div.mdl-mini-footer__left-section ul li{line-height:36px}}.mdl-mini-footer--link-list a,.mdl-mini-footer__link-list a,footer.mdl-mini-footer>div.mdl-mini-footer__left-section ul a{color:inherit;text-decoration:none;white-space:nowrap}.mdl-mini-footer--left-section,.mdl-mini-footer__left-section{display:inline-block;order:0}.mdl-mini-footer--right-section,.mdl-mini-footer__right-section{display:inline-block;order:1}.mdl-mini-footer--social-btn,.mdl-mini-footer__social-btn{width:36px;height:36px;padding:0;margin:0;background-color:#9e9e9e;border:none}.mdl-card{display:flex;flex-direction:column;font-size:16px;font-weight:400;min-height:200px;overflow:hidden;width:330px;z-index:1;position:relative;background:#fff;border-radius:2px;box-sizing:border-box}.mdl-card__media{background-color:#ff6e40;background-repeat:repeat;background-position:50% 50%;background-size:cover;background-origin:padding-box;background-attachment:scroll;box-sizing:border-box}.mdl-card__title{align-items:center;color:#000;display:block;display:flex;justify-content:stretch;line-height:normal;padding:16px;perspective-origin:165px 56px;transform-origin:165px 56px;box-sizing:border-box}.mdl-card__title.mdl-card--border{border-bottom:1px solid rgba(0,0,0,.1)}.mdl-card__title-text{align-self:flex-end;color:inherit;display:block;display:flex;font-size:24px;font-weight:300;line-height:normal;overflow:hidden;transform-origin:149px 48px;margin:0}.mdl-card__subtitle-text{font-size:14px;color:rgba(0,0,0,.54);margin:0}.mdl-card__supporting-text{color:rgba(0,0,0,.54);font-size:1rem;line-height:18px;overflow:hidden;padding:16px;width:90%}.mdl-card__supporting-text.mdl-card--border{border-bottom:1px solid rgba(0,0,0,.1)}.mdl-card__actions{font-size:16px;line-height:normal;width:100%;background-color:transparent;padding:8px;box-sizing:border-box}.mdl-card__actions.mdl-card--border{border-top:1px solid rgba(0,0,0,.1)}.mdl-card--expand{flex-grow:1}.mdl-card__menu{position:absolute;right:16px;top:16px}.mdl-button{background:transparent;border:none;border-radius:2px;color:#000;position:relative;height:36px;margin:0;min-width:64px;padding:0 16px;display:inline-block;font-family:Roboto,Helvetica,Arial,sans-serif;font-size:14px;font-weight:500;text-transform:uppercase;line-height:1;letter-spacing:0;overflow:hidden;will-change:box-shadow;transition:box-shadow .2s cubic-bezier(.4,0,1,1),background-color .2s cubic-bezier(.4,0,.2,1),color .2s cubic-bezier(.4,0,.2,1);outline:none;cursor:pointer;text-decoration:none;text-align:center;line-height:36px;vertical-align:middle}.mdl-button::-moz-focus-inner{border:0}.mdl-button:hover{background-color:hsla(0,0%,62%,.2)}.mdl-button:focus:not(:active){background-color:rgba(0,0,0,.12)}.mdl-button:active{background-color:hsla(0,0%,62%,.4)}.mdl-button.mdl-button--colored{color:#2196f3}.mdl-button.mdl-button--colored:focus:not(:active){background-color:rgba(0,0,0,.12)}input.mdl-button[type=submit]{-webkit-appearance:none}.mdl-button--raised{background:hsla(0,0%,62%,.2);box-shadow:0 2px 2px 0 rgba(0,0,0,.14),0 3px 1px -2px rgba(0,0,0,.2),0 1px 5px 0 rgba(0,0,0,.12)}.mdl-button--raised:active{box-shadow:0 4px 5px 0 rgba(0,0,0,.14),0 1px 10px 0 rgba(0,0,0,.12),0 2px 4px -1px rgba(0,0,0,.2);background-color:hsla(0,0%,62%,.4)}.mdl-button--raised:focus:not(:active){box-shadow:0 0 8px rgba(0,0,0,.18),0 8px 16px rgba(0,0,0,.36);background-color:hsla(0,0%,62%,.4)}.mdl-button--raised.mdl-button--colored{background:#2196f3;color:#fff}.mdl-button--raised.mdl-button--colored:active,.mdl-button--raised.mdl-button--colored:focus:not(:active),.mdl-button--raised.mdl-button--colored:hover{background-color:#2196f3}.mdl-button--raised.mdl-button--colored .mdl-ripple{background:#fff}.mdl-button--fab{border-radius:50%;font-size:24px;height:56px;margin:auto;min-width:56px;width:56px;padding:0;overflow:hidden;background:hsla(0,0%,62%,.2);box-shadow:0 1px 1.5px 0 rgba(0,0,0,.12),0 1px 1px 0 rgba(0,0,0,.24);position:relative;line-height:normal}.admonition.attention .mdl-button--fab .admonition-title:before,.admonition.caution .mdl-button--fab .admonition-title:before,.admonition.danger .mdl-button--fab .admonition-title:before,.admonition.error .mdl-button--fab .admonition-title:before,.admonition.hint .mdl-button--fab .admonition-title:before,.admonition.important .mdl-button--fab .admonition-title:before,.admonition.note .mdl-button--fab .admonition-title:before,.admonition.seealso .mdl-button--fab .admonition-title:before,.admonition.tip .mdl-button--fab .admonition-title:before,.admonition.warning .mdl-button--fab .admonition-title:before,.mdl-button--fab .admonition.attention .admonition-title:before,.mdl-button--fab .admonition.caution .admonition-title:before,.mdl-button--fab .admonition.danger .admonition-title:before,.mdl-button--fab .admonition.error .admonition-title:before,.mdl-button--fab .admonition.hint .admonition-title:before,.mdl-button--fab .admonition.important .admonition-title:before,.mdl-button--fab .admonition.note .admonition-title:before,.mdl-button--fab .admonition.seealso .admonition-title:before,.mdl-button--fab .admonition.tip .admonition-title:before,.mdl-button--fab .admonition.warning .admonition-title:before,.mdl-button--fab .material-icons,.mdl-button--fab a.download:before{position:absolute;top:50%;left:50%;transform:translate(-12px,-12px);line-height:24px;width:24px}.mdl-button--fab.mdl-button--mini-fab{height:40px;min-width:40px;width:40px}.mdl-button--fab .mdl-button__ripple-container{border-radius:50%;-webkit-mask-image:-webkit-radial-gradient(circle,#fff,#000)}.mdl-button--fab:active{box-shadow:0 4px 5px 0 rgba(0,0,0,.14),0 1px 10px 0 rgba(0,0,0,.12),0 2px 4px -1px rgba(0,0,0,.2);background-color:hsla(0,0%,62%,.4)}.mdl-button--fab:focus:not(:active){box-shadow:0 0 8px rgba(0,0,0,.18),0 8px 16px rgba(0,0,0,.36);background-color:hsla(0,0%,62%,.4)}.mdl-button--fab.mdl-button--colored{background:#ff6e40;color:#fff}.mdl-button--fab.mdl-button--colored:active,.mdl-button--fab.mdl-button--colored:focus:not(:active),.mdl-button--fab.mdl-button--colored:hover{background-color:#ff6e40}.mdl-button--fab.mdl-button--colored .mdl-ripple{background:#fff}.mdl-button--icon{border-radius:50%;font-size:24px;height:32px;margin-left:0;margin-right:0;min-width:32px;width:32px;padding:0;overflow:hidden;color:inherit;line-height:normal}.admonition.attention .mdl-button--icon .admonition-title:before,.admonition.caution .mdl-button--icon .admonition-title:before,.admonition.danger .mdl-button--icon .admonition-title:before,.admonition.error .mdl-button--icon .admonition-title:before,.admonition.hint .mdl-button--icon .admonition-title:before,.admonition.important .mdl-button--icon .admonition-title:before,.admonition.note .mdl-button--icon .admonition-title:before,.admonition.seealso .mdl-button--icon .admonition-title:before,.admonition.tip .mdl-button--icon .admonition-title:before,.admonition.warning .mdl-button--icon .admonition-title:before,.mdl-button--icon .admonition.attention .admonition-title:before,.mdl-button--icon .admonition.caution .admonition-title:before,.mdl-button--icon .admonition.danger .admonition-title:before,.mdl-button--icon .admonition.error .admonition-title:before,.mdl-button--icon .admonition.hint .admonition-title:before,.mdl-button--icon .admonition.important .admonition-title:before,.mdl-button--icon .admonition.note .admonition-title:before,.mdl-button--icon .admonition.seealso .admonition-title:before,.mdl-button--icon .admonition.tip .admonition-title:before,.mdl-button--icon .admonition.warning .admonition-title:before,.mdl-button--icon .material-icons,.mdl-button--icon a.download:before{position:absolute;top:50%;left:50%;transform:translate(-12px,-12px);line-height:24px;width:24px}.mdl-button--icon.mdl-button--mini-icon{height:24px;min-width:24px;width:24px}.admonition.attention .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.caution .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.danger .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.error .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.hint .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.important .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.note .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.seealso .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.tip .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.warning .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.attention .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.caution .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.danger .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.error .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.hint .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.important .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.note .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.seealso .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.tip .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.warning .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .material-icons,.mdl-button--icon.mdl-button--mini-icon a.download:before{top:0;left:0}.mdl-button--icon .mdl-button__ripple-container{border-radius:50%;-webkit-mask-image:-webkit-radial-gradient(circle,#fff,#000)}.mdl-button__ripple-container{display:block;height:100%;left:0;position:absolute;top:0;width:100%;z-index:0;overflow:hidden}.mdl-button.mdl-button--disabled .mdl-button__ripple-container .mdl-ripple,.mdl-button[disabled] .mdl-button__ripple-container .mdl-ripple{background-color:transparent}.mdl-button--primary.mdl-button--primary{color:#2196f3}.mdl-button--primary.mdl-button--primary .mdl-ripple{background:#fff}.mdl-button--primary.mdl-button--primary.mdl-button--fab,.mdl-button--primary.mdl-button--primary.mdl-button--raised{color:#fff;background-color:#2196f3}.mdl-button--accent.mdl-button--accent{color:#ff6e40}.mdl-button--accent.mdl-button--accent .mdl-ripple{background:#fff}.mdl-button--accent.mdl-button--accent.mdl-button--fab,.mdl-button--accent.mdl-button--accent.mdl-button--raised{color:#fff;background-color:#ff6e40}.mdl-button.mdl-button--disabled.mdl-button--disabled,.mdl-button[disabled][disabled]{color:rgba(0,0,0,.26);cursor:default;background-color:transparent}.mdl-button--fab.mdl-button--disabled.mdl-button--disabled,.mdl-button--fab[disabled][disabled]{background-color:rgba(0,0,0,.12);color:rgba(0,0,0,.26)}.mdl-button--raised.mdl-button--disabled.mdl-button--disabled,.mdl-button--raised[disabled][disabled]{background-color:rgba(0,0,0,.12);color:rgba(0,0,0,.26);box-shadow:none}.mdl-button--colored.mdl-button--disabled.mdl-button--disabled,.mdl-button--colored[disabled][disabled]{color:rgba(0,0,0,.26)}.admonition.attention .mdl-button .admonition-title:before,.admonition.caution .mdl-button .admonition-title:before,.admonition.danger .mdl-button .admonition-title:before,.admonition.error .mdl-button .admonition-title:before,.admonition.hint .mdl-button .admonition-title:before,.admonition.important .mdl-button .admonition-title:before,.admonition.note .mdl-button .admonition-title:before,.admonition.seealso .mdl-button .admonition-title:before,.admonition.tip .mdl-button .admonition-title:before,.admonition.warning .mdl-button .admonition-title:before,.mdl-button .admonition.attention .admonition-title:before,.mdl-button .admonition.caution .admonition-title:before,.mdl-button .admonition.danger .admonition-title:before,.mdl-button .admonition.error .admonition-title:before,.mdl-button .admonition.hint .admonition-title:before,.mdl-button .admonition.important .admonition-title:before,.mdl-button .admonition.note .admonition-title:before,.mdl-button .admonition.seealso .admonition-title:before,.mdl-button .admonition.tip .admonition-title:before,.mdl-button .admonition.warning .admonition-title:before,.mdl-button .material-icons,.mdl-button a.download:before{vertical-align:middle}.font-light{font-weight:300}.font-regular{font-weight:400}.font-heavy{font-weight:700}.left{text-align:left}.right{text-align:right}.center{text-align:center;margin-left:auto;margin-right:auto}.justify{text-align:justify}.hidden-sm{display:none}.container{width:100%;margin-left:auto;margin-right:auto}@media only screen and (min-width:33.75em){.container{width:80%}}@media only screen and (min-width:60em){.container{width:75%;max-width:60rem}}.row{position:relative;width:100%}.row [class^=col]{float:left;margin:.5rem 1%;min-height:.125rem}.row:after{content:"";display:table;clear:both}.col-1,.col-2,.col-3,.col-4,.col-5,.col-6,.col-7,.col-8,.col-9,.col-10,.col-11,.col-12{width:98%}.col-1-sm{width:6.33333%}.col-2-sm{width:14.66667%}.col-3-sm{width:23%}.col-4-sm{width:31.33333%}.col-5-sm{width:39.66667%}.col-6-sm{width:48%}.col-7-sm{width:56.33333%}.col-8-sm{width:64.66667%}.col-9-sm{width:73%}.col-10-sm{width:81.33333%}.col-11-sm{width:89.66667%}.col-12-sm{width:98%}@media only screen and (min-width:45em){.col-1{width:6.33333%}.col-2{width:14.66667%}.col-3{width:23%}.col-4{width:31.33333%}.col-5{width:39.66667%}.col-6{width:48%}.col-7{width:56.33333%}.col-8{width:64.66667%}.col-9{width:73%}.col-10{width:81.33333%}.col-11{width:89.66667%}.col-12{width:98%}.hidden-sm{display:block}}.row{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;flex-wrap:wrap}.row>[class*=col-]{display:flex;flex-direction:column}.admonition.attention .admonition-title:before,.admonition.caution .admonition-title:before,.admonition.danger .admonition-title:before,.admonition.error .admonition-title:before,.admonition.hint .admonition-title:before,.admonition.important .admonition-title:before,.admonition.note .admonition-title:before,.admonition.seealso .admonition-title:before,.admonition.tip .admonition-title:before,.admonition.warning .admonition-title:before,.material-icons,a.download:before{font-family:Material Icons;font-weight:400;font-style:normal;font-size:24px;display:inline-block;line-height:1;text-transform:none;letter-spacing:normal;word-wrap:normal;white-space:nowrap;direction:ltr;-webkit-font-smoothing:antialiased;text-rendering:optimizeLegibility;-moz-osx-font-smoothing:grayscale;font-feature-settings:"liga"}html{font-size:16px}body{display:block!important;background-color:#fafafa;font-size:1rem;line-height:1.5rem;font-family:-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol}.mdl-layout__content:focus{outline:none}a.download>code.download,blockquote,h1,h2,h3,h4,h5,h6,span.mdl-layout-title{font-family:-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol}.contents,.contents a,.globaltoc a.current,.toc-backref,.toctree-wrapper,.toctree-wrapper a,h1,h2,h3,h4,h5,h6{color:#048ccc!important}a{text-decoration:none}.page-content,.page-content dd,.page-content dl,.page-content dt,.page-content ol,.page-content p,.page-content table,.page-content td,.page-content th,.page-content ul{font-size:1rem}.brand{color:inherit;text-decoration:none}.section{overflow-x:auto}img{max-width:100%;display:block;margin-left:auto;margin-right:auto}div.figure p.caption{text-align:center;margin-top:.75rem}div.figure p.caption span.caption-number{font-style:normal}div.figure p.caption .caption-number:after{content:"\00a0"}.svg-icon{width:16px;height:16px;display:inline-block;fill:#f5f5f5;padding-right:5px;padding-top:4px;vertical-align:text-top}.admonition.attention a.download>i.admonition-title:before,.admonition.caution a.download>i.admonition-title:before,.admonition.danger a.download>i.admonition-title:before,.admonition.error a.download>i.admonition-title:before,.admonition.hint a.download>i.admonition-title:before,.admonition.important a.download>i.admonition-title:before,.admonition.note a.download>i.admonition-title:before,.admonition.seealso a.download>i.admonition-title:before,.admonition.tip a.download>i.admonition-title:before,.admonition.warning a.download>i.admonition-title:before,a.download>i.material-icons{position:relative;top:5px}a.download{text-decoration:none}.wrapper:after{content:"";display:table;clear:both}.wrapper{max-width:1090px;margin-right:auto;margin-left:auto;padding-right:45px;padding-left:30px}@media screen and (max-width:1024px){.wrapper{max-width:1120px;padding-right:15px;padding-left:15px}}.mdl-layout__container{margin-top:76px;height:calc(100% - 76px)}.document{width:100%;margin:0 auto;display:flex}@media (min-width:1795px){.document{width:1400px}}.document .page-content{width:100%;margin:0 auto;padding:0 12px}@media (min-width:992px){.document .page-content{width:90%;padding:0 5%}}@media (min-width:1200px){.document .page-content{width:calc(90% - 230px);padding:0 5%}}.document .side-doc-outline{width:230px}@media (max-width:1199px){.document .side-doc-outline{display:none}}.document .side-doc-outline--content{position:fixed;overflow-x:auto;overflow-y:auto;width:inherit}.document .side-doc-outline--content::-webkit-scrollbar{width:6px}.document .side-doc-outline--content::-webkit-scrollbar-track{border-radius:6px}.document .side-doc-outline--content::-webkit-scrollbar-thumb{background-color:rgba(0,0,0,.3);border-radius:6px;box-shadow:0 0 0 1px hsla(0,0%,100%,.3)}@keyframes float-in{0%{transform:translateY(.5rem);opacity:0}to{transform:translateY(0);opacity:1}}@keyframes float-out{0%{transform:translateY(0);opacity:1}to{transform:translateY(.5rem);opacity:0}}.page-content .headerlink{display:inline-block;text-decoration:none;margin-left:.8rem;color:inherit;opacity:0}.page-content .headerlink:hover{animation:float-in .2s cubic-bezier(.4,0,.2,1) 0s forwards}.page-content h1 .toc-backref,.page-content h2 .toc-backref,.page-content h3 .toc-backref,.page-content h4 .toc-backref,.page-content h5 .toc-backref,.page-content h6 .toc-backref{text-decoration:none}.page-content h1:hover .headerlink,.page-content h2:hover .headerlink,.page-content h3:hover .headerlink,.page-content h4:hover .headerlink,.page-content h5:hover .headerlink,.page-content h6:hover .headerlink{animation:float-in .2s cubic-bezier(.4,0,.2,1) 0s forwards}.page-content h1{font-size:2rem;line-height:2.25rem}.page-content h2{font-size:1.75rem;line-height:2rem;padding-top:1.5rem;margin-top:0;margin-bottom:1rem}.page-content h3{font-size:1.5rem;line-height:1.75rem;padding-top:1rem;margin-top:0;margin-bottom:.75rem}.page-content h4{font-size:1.25rem;line-height:1.5rem;padding-top:.75rem;margin-top:0;margin-bottom:.5rem}.page-content div.page-content h5{font-size:1.1rem;line-height:1.5rem;padding-top:2rem;margin-top:0;margin-bottom:1rem}.page-content div.page-content h6{font-size:1rem;line-height:1.5rem;padding-top:2rem;margin-top:0;margin-bottom:1rem}.admonition{padding:12px 20px;margin-top:10px;margin-bottom:10px}.admonition p.last{margin:16px}.admonition .admonition-title{font-size:16px;font-weight:700;color:#555;text-transform:uppercase;margin-top:7px}.admonition.note{border-left:4px solid #00bcd4;background-color:rgba(0,188,212,.1)}.admonition.note .admonition-title{font-size:16px;font-weight:700;color:#00bcd4;margin-top:4px;margin-bottom:8px}.admonition.note .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"info_outline";font-size:18px}.admonition.seealso{border-left:4px solid #00bcd4;background-color:rgba(0,188,212,.1)}.admonition.seealso .admonition-title{font-size:16px;font-weight:700;color:#00bcd4;margin-top:4px;margin-bottom:8px}.admonition.seealso .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"search";font-size:18px}.admonition.hint{border-left:4px solid #00bcd4;background-color:rgba(0,188,212,.1)}.admonition.hint .admonition-title{font-size:16px;font-weight:700;color:#00bcd4;margin-top:4px;margin-bottom:8px}.admonition.hint .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"help_outline";font-size:18px}.admonition.warning{border-left:4px solid #ffc107;background-color:rgba(255,193,7,.1)}.admonition.warning .admonition-title{font-size:16px;font-weight:700;color:#ffc107;margin-top:4px;margin-bottom:8px}.admonition.warning .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"warning";font-size:18px}.admonition.attention{border-left:4px solid #ffc107;background-color:rgba(255,193,7,.1)}.admonition.attention .admonition-title{font-size:16px;font-weight:700;color:#ffc107;margin-top:4px;margin-bottom:8px}.admonition.attention .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"warning";font-size:18px}.admonition.tip{border-left:4px solid #8bc34a;background-color:rgba(139,195,74,.1)}.admonition.tip .admonition-title{font-size:16px;font-weight:700;color:#8bc34a;margin-top:4px;margin-bottom:8px}.admonition.tip .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"lightbulb_outline";font-size:18px}.admonition.important{border-left:4px solid #8bc34a;background-color:rgba(139,195,74,.1)}.admonition.important .admonition-title{font-size:16px;font-weight:700;color:#8bc34a;margin-top:4px;margin-bottom:8px}.admonition.important .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"check_circle";font-size:18px}.admonition.error{border-left:4px solid #f44336;background-color:rgba(244,67,54,.1)}.admonition.error .admonition-title{font-size:16px;font-weight:700;color:#f44336;margin-top:4px;margin-bottom:8px}.admonition.error .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"error_outline";font-size:18px}.admonition.caution{border-left:4px solid #f44336;background-color:rgba(244,67,54,.1)}.admonition.caution .admonition-title{font-size:16px;font-weight:700;color:#f44336;margin-top:4px;margin-bottom:8px}.admonition.caution .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"error_outline";font-size:18px}.admonition.danger{border-left:4px solid #f44336;background-color:rgba(244,67,54,.1)}.admonition.danger .admonition-title{font-size:16px;font-weight:700;color:#f44336;margin-top:4px;margin-bottom:8px}.admonition.danger .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"error_outline";font-size:18px}.page-content .highlight{margin:1px 0}.page-content .highlight pre{background:rgba(0,0,0,.05);color:rgba(0,0,0,.87);font-family:Menlo,DejaVu Sans Mono,Liberation Mono,Consolas,Ubuntu Mono,Courier New,andale mono,lucida console,monospace;padding:.75rem;overflow:auto;overflow-y:hidden}.page-content .highlight pre .nd,.page-content .highlight pre .o{color:rgba(0,0,0,.87)}.page-content div.highlight-console div.highlight{background:none}.page-content .output .highlight pre{color:rgba(0,0,0,.87);background:#fafafa;border:1px solid #999;padding:.75rem}.page-content .code,.page-content code:not(.download){margin:0;border-radius:2px}.page-content .code,.page-content .code span.pre,.page-content code:not(.download),.page-content code:not(.download) span.pre{font-family:Menlo,DejaVu Sans Mono,Liberation Mono,Consolas,Ubuntu Mono,Courier New,andale mono,lucida console,monospace}.page-content .viewcode-link{padding-left:2em;font-size:80%}.page-content .class>dt,.page-content .function>dt,.page-content .method>dt,.page-content .rubric{display:table;margin:10px 0;font-size:100%;line-height:normal;background:#e7f2fa;color:#2b98f0;border-top:3px solid #55adf3;padding:10px;position:relative}.page-content .class>dt .descclassname,.page-content .class>dt .descname,.page-content .function>dt .descclassname,.page-content .function>dt .descname,.page-content .method>dt .descclassname,.page-content .method>dt .descname,.page-content .rubric .descclassname,.page-content .rubric .descname{color:rgba(0,0,0,.87);background:#e7f2fa;padding:3px}.page-content .class>dt em,.page-content .function>dt em,.page-content .method>dt em,.page-content .rubric em{padding:0 2px}.page-content .rubric{margin:30px 0 10px}.page-content .field-body{padding-left:40px}.page-content .field-body ul{padding:0 0 0 16px;margin:0}.page-content .seealso .docutils>dt{float:left;clear:left;padding:0 6px}.page-content .seealso .docutils>dd{padding-left:6em}.page-content .nblast{padding-bottom:1em}.page-content pre{font-size:90%;background:#eee;color:#455a64;padding:16px 32px;width:auto;border-radius:4px;word-wrap:break-word}.page-content pre:hover:before{font-family:-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol;padding:0 .5rem;content:attr(click-to-copy);color:rgba(0,0,0,.5);border-radius:4px;position:relative;float:right;top:-.5rem;right:-.5rem;background:#c8c8c8;font-size:.8rem;cursor:pointer}.page-content blockquote{font-size:1rem;padding:0 1rem;border-left:3px solid rgba(0,0,0,.05)}.page-content blockquote:after{content:""!important;margin-left:0}.page-content blockquote:before{content:""!important}.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list){margin:1.5rem 0;table-layout:fixed;max-width:100%;min-width:70%}.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) td,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th{white-space:normal;overflow-wrap:break-word}.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) caption{font-size:16px;margin:1rem 0 .8rem;white-space:normal}.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) caption .caption-number{font-style:normal}.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) caption .caption-number:after{content:"\00a0"}.globaltoc .caption,.globaltoc .toc{display:none}.globaltoc ul{list-style-type:none;padding:0;margin:0}.globaltoc ul li{min-height:36px}.globaltoc ul li .link-wrapper{display:flex;justify-content:space-between}.globaltoc ul li .link-wrapper>a{padding:8px 0;display:block;width:100%;font-size:1rem;text-decoration:none;color:#757575}.globaltoc ul li .link-wrapper>a.current{font-weight:700}.globaltoc .nav-toggle{padding:0;float:right;display:flex;align-items:center;justify-content:center;height:36px}.globaltoc .nav-toggle>a{padding:0;margin-left:0;margin-right:4px;cursor:pointer}.globaltoc .nav-toggle>a>i{font-size:18px}.globaltoc .nav-toggle.show{transform:rotate(180deg)}.globaltoc .nav-toggle.show>a{margin-right:0;margin-left:4px}.globaltoc nav>ul>li>span.link-wrapper{padding-left:8px}.globaltoc nav>ul>li>ul>li>span.link-wrapper{padding-left:16px}.globaltoc nav>ul>li>ul>li>ul>li>span.link-wrapper{padding-left:24px}.globaltoc nav>ul>li>ul>li>ul>li>ul>li>span.link-wrapper{padding-left:32px}.globaltoc nav>ul>li>ul>li>ul>li>ul>li>ul>li>span.link-wrapper{padding-left:40px}.globaltoc nav>ul>li>ul>li>ul>li>ul>li>ul>li>ul>li>span.link-wrapper{padding-left:48px}.localtoc{font-size:.75rem;padding-top:1rem}.localtoc .caption{padding-left:12px}.localtoc .caption-text{font-size:.9rem;font-weight:700}.localtoc>ul>li>a{display:none}.localtoc ul{padding:0;list-style-type:none}.localtoc li{padding-left:6px}.localtoc a{display:block;text-decoration:none;color:inherit;margin-top:8px;padding-left:8px;line-height:1.1rem}.localtoc a.current{padding-left:5px;border-left:3px solid;font-weight:700}.contents.topic,.toctree-wrapper{border-left:5px solid}.contents.topic>p.topic-title,.toctree-wrapper>p.caption{color:#757575;font-size:1rem;padding-left:14px}.contents.topic ul,.toctree-wrapper ul{padding-left:14px;list-style:none;line-height:30px}.contents.topic a,.toctree-wrapper a{font-size:1.2rem;text-decoration:none}.contents.topic a .pre,.toctree-wrapper a .pre{font-size:1rem}.contents.topic>ul>li>a,.toctree-wrapper>ul>li>a{font-size:1.3rem}.contents.topic>ul>li>a .pre,.toctree-wrapper>ul>li>a .pre{font-size:1.1rem}.page-content ul li{margin:.3rem 0}.page-content ul li p{margin:0}.page-content .option-list .option{font-family:Menlo,DejaVu Sans Mono,Liberation Mono,Consolas,Ubuntu Mono,Courier New,andale mono,lucida console,monospace}.page-content .option-list td{padding:.5rem;border:none}.mdl-layout__drawer{background-color:#fff}.mdl-layout__drawer::-webkit-scrollbar{width:6px}.mdl-layout__drawer::-webkit-scrollbar-track{border-radius:6px}.mdl-layout__drawer::-webkit-scrollbar-thumb{background-color:rgba(0,0,0,.3);border-radius:6px;box-shadow:0 0 0 1px hsla(0,0%,100%,.3)}.mdl-layout__drawer>.mdl-layout-title{font-weight:700;text-align:right;margin:0;padding:0;line-height:32px;border-bottom:1px solid rgba(0,0,0,.1);min-height:64px}.mdl-layout__drawer>.mdl-layout-title .title{color:inherit;display:block;height:100%;width:100%;text-decoration:none}.mdl-layout__drawer>.mdl-layout-title .title>img.logo{width:100%;margin:0;padding:0}.mdl-layout__drawer>.mdl-layout-title .title-text{font-weight:700;text-align:right;padding:0 10px;margin:16px 0 8px;line-height:32px;font-family:-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol;color:inherit;display:block}nav.breadcrumb>a.mdl-navigation__link{padding:0 8px;font-size:18px}@media (max-width:1199px){nav.breadcrumb{width:calc(100% - 64px)}nav.breadcrumb a.mdl-navigation__link.is-active{overflow-x:hidden;width:100%;overflow:hidden;white-space:nowrap;text-overflow:ellipsis}.admonition.attention nav.breadcrumb i.admonition-title:before,.admonition.caution nav.breadcrumb i.admonition-title:before,.admonition.danger nav.breadcrumb i.admonition-title:before,.admonition.error nav.breadcrumb i.admonition-title:before,.admonition.hint nav.breadcrumb i.admonition-title:before,.admonition.important nav.breadcrumb i.admonition-title:before,.admonition.note nav.breadcrumb i.admonition-title:before,.admonition.seealso nav.breadcrumb i.admonition-title:before,.admonition.tip nav.breadcrumb i.admonition-title:before,.admonition.warning nav.breadcrumb i.admonition-title:before,nav.breadcrumb .admonition.attention i.admonition-title:before,nav.breadcrumb .admonition.caution i.admonition-title:before,nav.breadcrumb .admonition.danger i.admonition-title:before,nav.breadcrumb .admonition.error i.admonition-title:before,nav.breadcrumb .admonition.hint i.admonition-title:before,nav.breadcrumb .admonition.important i.admonition-title:before,nav.breadcrumb .admonition.note i.admonition-title:before,nav.breadcrumb .admonition.seealso i.admonition-title:before,nav.breadcrumb .admonition.tip i.admonition-title:before,nav.breadcrumb .admonition.warning i.admonition-title:before,nav.breadcrumb a.mdl-navigation__link:not(.is-active),nav.breadcrumb i.material-icons{display:none}}div.mdl-layout__header{margin-top:77px}.mdl-layout__drawer-button{top:13px!important}div.mdl-layout__header-row.header-links{background:hsla(0,0%,100%,.2);width:100%;overflow-x:auto;overflow-y:hidden}div.mdl-layout__header-row.header-links a.mdl-navigation__link{font-size:1rem}div.mdl-layout__header-row.header-links a.mdl-navigation__link i{font-size:1.2rem;margin:0 8px;position:relative;bottom:-.1rem}div.mdl-layout__header-row.header-links a.mdl-navigation__link:hover{background-color:#2196f3;color:#eee}div.mdl-layout__header-row.header-links a.mdl-navigation__link[href="#"]{background-color:#2196f3;opacity:1;color:#fff}.site-title{font-weight:300!important;line-height:57px;letter-spacing:-1px;margin-bottom:0;float:left;color:#fff}.site-title,.site-title:visited{color:#424242}.site-header{position:fixed;top:0;width:100%;min-height:55px;padding-top:10px;padding-bottom:10px;background-color:#048ccc;z-index:10;font-weight:300;font-size:17px;border-bottom:1px solid #fff}.site-header-logo{width:120px;display:initial}.site-nav{float:right;line-height:57px}.site-nav .menu-icon,.site-nav .nav-trigger{display:none}.site-nav .page-link{color:#fff;line-height:1.5;font-weight:300}.site-nav .page-link:not(:last-child){margin-right:40px}.site-nav .page-link:hover{color:#fff;text-shadow:-.06ex 0 #fff,.06ex 0 #fff}.site-nav .page-link.page-current{color:#fff;text-decoration:underline}@media screen and (max-width:1024px){.site-nav{position:absolute;top:9px;right:15px;background-color:#178dc9;border-radius:2px;text-align:right}.site-nav label[for=nav-trigger]{display:block;float:right;width:36px;height:36px;z-index:2;cursor:pointer}.site-nav .menu-icon{display:block;float:right;width:36px;height:26px;line-height:0;padding-top:20px;text-align:center}.site-nav .menu-icon>svg{fill:#fff}.site-nav input~.trigger{clear:both;display:none}.site-nav input:checked~.trigger{display:block;padding-bottom:5px}.site-nav .page-link{padding:5px 10px;display:block;margin-left:20px}.site-nav .page-link:not(:last-child){margin-right:0}}footer.mdl-mini-footer{background-color:#212121}footer.mdl-mini-footer>div.mdl-mini-footer__left-section{margin-bottom:20px;display:flex;flex-direction:column}footer.mdl-mini-footer>div.mdl-mini-footer__left-section .mdl-logo{font-size:1.1rem}footer.mdl-mini-footer>div.mdl-mini-footer__right-section{font-size:.9rem;display:flex;flex-direction:column;justify-content:flex-end}footer.mdl-mini-footer>div.mdl-mini-footer__right-section a{color:inherit;font-weight:700;text-decoration:none}footer.mdl-mini-footer p.caption{display:none}.pagenation{width:100%;margin-top:80px;height:92px;background-color:#424242;display:flex}.pagenation #button-next,.pagenation #button-prev,.pagenation .button-common{text-transform:none;padding:0;height:92px;display:flex;justify-content:center;align-items:center;color:#fff}.pagenation #button-prev{margin-right:auto}.pagenation #button-prev .pagenation-text{text-align:left}.pagenation #button-next{margin-left:auto;flex-direction:row-reverse}.pagenation #button-next .pagenation-text{text-align:right}.pagenation-arrow-L{margin-right:20px}.pagenation-arrow-R{margin-left:20px}.pagenation-text{line-height:30px;font-size:20px}.pagenation-direction{opacity:.7;font-size:18px}@media screen and (max-width:1024px){.pagenation #button-prev{width:20%}.pagenation #button-next{width:80%}.pagenation #button-prev .pagenation-text{display:none}}@media screen and (min-width:1025px){.pagenation #button-next,.pagenation #button-prev{width:50%}.pagenation #button-prev .pagenation-text{display:block}}.site-footer{border-top:1px solid #f5f5f5;padding:30px 0;background-color:#424242}.site-footer .footer-category-title{color:#048ccc}.site-footer a,.site-footer a:visited{color:#f5f5f5!important}.site-footer2{background-color:#424242;padding-top:40px;padding-bottom:10px}.footer-heading{margin-bottom:15px}.contact-list,.social-media-list{list-style:none;margin-left:0}.footer-bottom-warning{font-size:80%;color:#fff;float:left}.footer-logo{width:200px;margin-bottom:30px;margin-top:30px}.footer-col{float:left;margin-bottom:15px;padding-left:15px}.footer-text{color:#f5f5f5}#waterfall-exp::-webkit-input-placeholder{color:#ccc}#waterfall-exp:-ms-input-placeholder{color:#ccc}#waterfall-exp::-moz-placeholder{color:#ccc}ul.search span.highlighted{font-weight:700}ul.search>li{margin-bottom:24px}#search-results ul{list-style:none;padding:0}#search-results ul li>a{text-decoration:none;font-size:1.2rem}a.download:before{content:"file_download";position:relative;top:5px;margin-right:5px}button.download{position:sticky;margin-left:1em}.mdl-card{margin:1em 1.5em 1em 0;display:inline-block;width:250px;min-height:140px;padding:18px}.mdl-card:hover{box-shadow:0 10px 20px rgba(0,0,0,.25),0 6px 6px rgba(0,0,0,.22);color:#000;cursor:pointer}.mdl-card__title{padding:0 0 1em;font-size:18px;color:#444}.mdl-card__supporting-text{line-height:1.5rem;padding:0;width:100%}.head-card.mdl-card{width:auto;display:block;max-width:800px;padding:24px}.head-card>.mdl-card__title{padding-bottom:0;height:60px;font-weight:700;text-transform:uppercase}.head-card>.mdl-card__menu{color:#fff}.head-card>.mdl-card__actions{padding:0}.cards{display:flex;flex-direction:row;flex-wrap:wrap}
+.admonition,.mdl-shadow--2dp,.page-content pre:hover,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list){box-shadow:0 2px 2px 0 rgba(0,0,0,.14),0 3px 1px -2px rgba(0,0,0,.2),0 1px 5px 0 rgba(0,0,0,.12)}.mdl-shadow--3dp{box-shadow:0 3px 4px 0 rgba(0,0,0,.14),0 3px 3px -2px rgba(0,0,0,.2),0 1px 8px 0 rgba(0,0,0,.12)}.mdl-shadow--4dp{box-shadow:0 4px 5px 0 rgba(0,0,0,.14),0 1px 10px 0 rgba(0,0,0,.12),0 2px 4px -1px rgba(0,0,0,.2)}.mdl-shadow--6dp{box-shadow:0 6px 10px 0 rgba(0,0,0,.14),0 1px 18px 0 rgba(0,0,0,.12),0 3px 5px -1px rgba(0,0,0,.2)}.mdl-shadow--8dp{box-shadow:0 8px 10px 1px rgba(0,0,0,.14),0 3px 14px 2px rgba(0,0,0,.12),0 5px 5px -3px rgba(0,0,0,.2)}.mdl-shadow--16dp{box-shadow:0 16px 24px 2px rgba(0,0,0,.14),0 6px 30px 5px rgba(0,0,0,.12),0 8px 10px -5px rgba(0,0,0,.2)}.mdl-shadow--24dp{box-shadow:0 9px 46px 8px rgba(0,0,0,.14),0 11px 15px -7px rgba(0,0,0,.12),0 24px 38px 3px rgba(0,0,0,.2)}.mdl-data-table,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list){position:relative;border:1px solid rgba(0,0,0,.12);border-collapse:collapse;white-space:nowrap;font-size:13px;background-color:#fff}.mdl-data-table thead,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) thead{padding-bottom:3px}.mdl-data-table thead .mdl-data-table__select,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) thead .mdl-data-table__select{margin-top:0}.mdl-data-table tbody tr,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) tbody tr{position:relative;height:48px;transition-duration:.28s;transition-timing-function:cubic-bezier(.4,0,.2,1);transition-property:background-color}.mdl-data-table tbody tr.is-selected,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) tbody tr.is-selected{background-color:#e0e0e0}.mdl-data-table tbody tr:hover,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) tbody tr:hover{background-color:#eee}.mdl-data-table td,.mdl-data-table th,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) td,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th{padding:0 18px 12px;text-align:right}.mdl-data-table td:first-of-type,.mdl-data-table th:first-of-type,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) td:first-of-type,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th:first-of-type{padding-left:24px}.mdl-data-table td:last-of-type,.mdl-data-table th:last-of-type,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) td:last-of-type,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th:last-of-type{padding-right:24px}.mdl-data-table td,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) td{position:relative;vertical-align:middle;height:48px;border-top:1px solid rgba(0,0,0,.12);border-bottom:1px solid rgba(0,0,0,.12);padding-top:12px;box-sizing:border-box}.mdl-data-table td .mdl-data-table__select,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) td .mdl-data-table__select{vertical-align:middle}.mdl-data-table th,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th{position:relative;vertical-align:bottom;text-overflow:ellipsis;font-size:14px;font-weight:700;line-height:24px;letter-spacing:0;height:48px;font-size:12px;color:rgba(0,0,0,.54);padding-bottom:8px;box-sizing:border-box}.mdl-data-table th.mdl-data-table__header--sorted-ascending,.mdl-data-table th.mdl-data-table__header--sorted-descending,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-ascending,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-descending{color:rgba(0,0,0,.87)}.mdl-data-table th.mdl-data-table__header--sorted-ascending:before,.mdl-data-table th.mdl-data-table__header--sorted-descending:before,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-ascending:before,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-descending:before{font-family:Material Icons;font-weight:400;font-style:normal;font-size:24px;line-height:1;letter-spacing:normal;text-transform:none;display:inline-block;word-wrap:normal;font-feature-settings:"liga";-webkit-font-feature-settings:"liga";-webkit-font-smoothing:antialiased;font-size:16px;content:"\e5d8";margin-right:5px;vertical-align:sub}.mdl-data-table th.mdl-data-table__header--sorted-ascending:hover,.mdl-data-table th.mdl-data-table__header--sorted-descending:hover,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-ascending:hover,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-descending:hover{cursor:pointer}.mdl-data-table th.mdl-data-table__header--sorted-ascending:hover:before,.mdl-data-table th.mdl-data-table__header--sorted-descending:hover:before,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-ascending:hover:before,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-descending:hover:before{color:rgba(0,0,0,.26)}.mdl-data-table th.mdl-data-table__header--sorted-descending:before,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-descending:before{content:"\e5db"}.mdl-data-table__select{width:16px}.mdl-data-table__cell--non-numeric.mdl-data-table__cell--non-numeric,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) td,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th{text-align:left}.mdl-mini-footer{display:flex;flex-flow:row wrap;justify-content:space-between;padding:32px 16px;color:#9e9e9e;background-color:#424242}.mdl-mini-footer:after{content:"";display:block}.mdl-mini-footer .mdl-logo{line-height:36px}.mdl-mini-footer--link-list,.mdl-mini-footer__link-list,footer.mdl-mini-footer>div.mdl-mini-footer__left-section ul{display:flex;flex-flow:row nowrap;list-style:none;margin:0;padding:0}.mdl-mini-footer--link-list li,.mdl-mini-footer__link-list li,footer.mdl-mini-footer>div.mdl-mini-footer__left-section ul li{margin-bottom:0;margin-right:16px}@media screen and (min-width:760px){.mdl-mini-footer--link-list li,.mdl-mini-footer__link-list li,footer.mdl-mini-footer>div.mdl-mini-footer__left-section ul li{line-height:36px}}.mdl-mini-footer--link-list a,.mdl-mini-footer__link-list a,footer.mdl-mini-footer>div.mdl-mini-footer__left-section ul a{color:inherit;text-decoration:none;white-space:nowrap}.mdl-mini-footer--left-section,.mdl-mini-footer__left-section{display:inline-block;order:0}.mdl-mini-footer--right-section,.mdl-mini-footer__right-section{display:inline-block;order:1}.mdl-mini-footer--social-btn,.mdl-mini-footer__social-btn{width:36px;height:36px;padding:0;margin:0;background-color:#9e9e9e;border:none}.mdl-card{display:flex;flex-direction:column;font-size:16px;font-weight:400;min-height:200px;overflow:hidden;width:330px;z-index:1;position:relative;background:#fff;border-radius:2px;box-sizing:border-box}.mdl-card__media{background-color:#ff6e40;background-repeat:repeat;background-position:50% 50%;background-size:cover;background-origin:padding-box;background-attachment:scroll;box-sizing:border-box}.mdl-card__title{align-items:center;color:#000;display:block;display:flex;justify-content:stretch;line-height:normal;padding:16px;perspective-origin:165px 56px;transform-origin:165px 56px;box-sizing:border-box}.mdl-card__title.mdl-card--border{border-bottom:1px solid rgba(0,0,0,.1)}.mdl-card__title-text{align-self:flex-end;color:inherit;display:block;display:flex;font-size:24px;font-weight:300;line-height:normal;overflow:hidden;transform-origin:149px 48px;margin:0}.mdl-card__subtitle-text{font-size:14px;color:rgba(0,0,0,.54);margin:0}.mdl-card__supporting-text{color:rgba(0,0,0,.54);font-size:1rem;line-height:18px;overflow:hidden;padding:16px;width:90%}.mdl-card__supporting-text.mdl-card--border{border-bottom:1px solid rgba(0,0,0,.1)}.mdl-card__actions{font-size:16px;line-height:normal;width:100%;background-color:transparent;padding:8px;box-sizing:border-box}.mdl-card__actions.mdl-card--border{border-top:1px solid rgba(0,0,0,.1)}.mdl-card--expand{flex-grow:1}.mdl-card__menu{position:absolute;right:16px;top:16px}.mdl-button{background:transparent;border:none;border-radius:2px;color:#000;position:relative;height:36px;margin:0;min-width:64px;padding:0 16px;display:inline-block;font-family:Roboto,Helvetica,Arial,sans-serif;font-size:14px;font-weight:500;text-transform:uppercase;line-height:1;letter-spacing:0;overflow:hidden;will-change:box-shadow;transition:box-shadow .2s cubic-bezier(.4,0,1,1),background-color .2s cubic-bezier(.4,0,.2,1),color .2s cubic-bezier(.4,0,.2,1);outline:none;cursor:pointer;text-decoration:none;text-align:center;line-height:36px;vertical-align:middle}.mdl-button::-moz-focus-inner{border:0}.mdl-button:hover{background-color:hsla(0,0%,62%,.2)}.mdl-button:focus:not(:active){background-color:rgba(0,0,0,.12)}.mdl-button:active{background-color:hsla(0,0%,62%,.4)}.mdl-button.mdl-button--colored{color:#2196f3}.mdl-button.mdl-button--colored:focus:not(:active){background-color:rgba(0,0,0,.12)}input.mdl-button[type=submit]{-webkit-appearance:none}.mdl-button--raised{background:hsla(0,0%,62%,.2);box-shadow:0 2px 2px 0 rgba(0,0,0,.14),0 3px 1px -2px rgba(0,0,0,.2),0 1px 5px 0 rgba(0,0,0,.12)}.mdl-button--raised:active{box-shadow:0 4px 5px 0 rgba(0,0,0,.14),0 1px 10px 0 rgba(0,0,0,.12),0 2px 4px -1px rgba(0,0,0,.2);background-color:hsla(0,0%,62%,.4)}.mdl-button--raised:focus:not(:active){box-shadow:0 0 8px rgba(0,0,0,.18),0 8px 16px rgba(0,0,0,.36);background-color:hsla(0,0%,62%,.4)}.mdl-button--raised.mdl-button--colored{background:#2196f3;color:#fff}.mdl-button--raised.mdl-button--colored:active,.mdl-button--raised.mdl-button--colored:focus:not(:active),.mdl-button--raised.mdl-button--colored:hover{background-color:#2196f3}.mdl-button--raised.mdl-button--colored .mdl-ripple{background:#fff}.mdl-button--fab{border-radius:50%;font-size:24px;height:56px;margin:auto;min-width:56px;width:56px;padding:0;overflow:hidden;background:hsla(0,0%,62%,.2);box-shadow:0 1px 1.5px 0 rgba(0,0,0,.12),0 1px 1px 0 rgba(0,0,0,.24);position:relative;line-height:normal}.admonition.attention .mdl-button--fab .admonition-title:before,.admonition.caution .mdl-button--fab .admonition-title:before,.admonition.danger .mdl-button--fab .admonition-title:before,.admonition.error .mdl-button--fab .admonition-title:before,.admonition.hint .mdl-button--fab .admonition-title:before,.admonition.important .mdl-button--fab .admonition-title:before,.admonition.note .mdl-button--fab .admonition-title:before,.admonition.seealso .mdl-button--fab .admonition-title:before,.admonition.tip .mdl-button--fab .admonition-title:before,.admonition.warning .mdl-button--fab .admonition-title:before,.mdl-button--fab .admonition.attention .admonition-title:before,.mdl-button--fab .admonition.caution .admonition-title:before,.mdl-button--fab .admonition.danger .admonition-title:before,.mdl-button--fab .admonition.error .admonition-title:before,.mdl-button--fab .admonition.hint .admonition-title:before,.mdl-button--fab .admonition.important .admonition-title:before,.mdl-button--fab .admonition.note .admonition-title:before,.mdl-button--fab .admonition.seealso .admonition-title:before,.mdl-button--fab .admonition.tip .admonition-title:before,.mdl-button--fab .admonition.warning .admonition-title:before,.mdl-button--fab .material-icons,.mdl-button--fab a.download:before{position:absolute;top:50%;left:50%;transform:translate(-12px,-12px);line-height:24px;width:24px}.mdl-button--fab.mdl-button--mini-fab{height:40px;min-width:40px;width:40px}.mdl-button--fab .mdl-button__ripple-container{border-radius:50%;-webkit-mask-image:-webkit-radial-gradient(circle,#fff,#000)}.mdl-button--fab:active{box-shadow:0 4px 5px 0 rgba(0,0,0,.14),0 1px 10px 0 rgba(0,0,0,.12),0 2px 4px -1px rgba(0,0,0,.2);background-color:hsla(0,0%,62%,.4)}.mdl-button--fab:focus:not(:active){box-shadow:0 0 8px rgba(0,0,0,.18),0 8px 16px rgba(0,0,0,.36);background-color:hsla(0,0%,62%,.4)}.mdl-button--fab.mdl-button--colored{background:#ff6e40;color:#fff}.mdl-button--fab.mdl-button--colored:active,.mdl-button--fab.mdl-button--colored:focus:not(:active),.mdl-button--fab.mdl-button--colored:hover{background-color:#ff6e40}.mdl-button--fab.mdl-button--colored .mdl-ripple{background:#fff}.mdl-button--icon{border-radius:50%;font-size:24px;height:32px;margin-left:0;margin-right:0;min-width:32px;width:32px;padding:0;overflow:hidden;color:inherit;line-height:normal}.admonition.attention .mdl-button--icon .admonition-title:before,.admonition.caution .mdl-button--icon .admonition-title:before,.admonition.danger .mdl-button--icon .admonition-title:before,.admonition.error .mdl-button--icon .admonition-title:before,.admonition.hint .mdl-button--icon .admonition-title:before,.admonition.important .mdl-button--icon .admonition-title:before,.admonition.note .mdl-button--icon .admonition-title:before,.admonition.seealso .mdl-button--icon .admonition-title:before,.admonition.tip .mdl-button--icon .admonition-title:before,.admonition.warning .mdl-button--icon .admonition-title:before,.mdl-button--icon .admonition.attention .admonition-title:before,.mdl-button--icon .admonition.caution .admonition-title:before,.mdl-button--icon .admonition.danger .admonition-title:before,.mdl-button--icon .admonition.error .admonition-title:before,.mdl-button--icon .admonition.hint .admonition-title:before,.mdl-button--icon .admonition.important .admonition-title:before,.mdl-button--icon .admonition.note .admonition-title:before,.mdl-button--icon .admonition.seealso .admonition-title:before,.mdl-button--icon .admonition.tip .admonition-title:before,.mdl-button--icon .admonition.warning .admonition-title:before,.mdl-button--icon .material-icons,.mdl-button--icon a.download:before{position:absolute;top:50%;left:50%;transform:translate(-12px,-12px);line-height:24px;width:24px}.mdl-button--icon.mdl-button--mini-icon{height:24px;min-width:24px;width:24px}.admonition.attention .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.caution .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.danger .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.error .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.hint .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.important .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.note .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.seealso .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.tip .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.warning .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.attention .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.caution .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.danger .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.error .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.hint .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.important .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.note .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.seealso .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.tip .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.warning .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .material-icons,.mdl-button--icon.mdl-button--mini-icon a.download:before{top:0;left:0}.mdl-button--icon .mdl-button__ripple-container{border-radius:50%;-webkit-mask-image:-webkit-radial-gradient(circle,#fff,#000)}.mdl-button__ripple-container{display:block;height:100%;left:0;position:absolute;top:0;width:100%;z-index:0;overflow:hidden}.mdl-button.mdl-button--disabled .mdl-button__ripple-container .mdl-ripple,.mdl-button[disabled] .mdl-button__ripple-container .mdl-ripple{background-color:transparent}.mdl-button--primary.mdl-button--primary{color:#2196f3}.mdl-button--primary.mdl-button--primary .mdl-ripple{background:#fff}.mdl-button--primary.mdl-button--primary.mdl-button--fab,.mdl-button--primary.mdl-button--primary.mdl-button--raised{color:#fff;background-color:#2196f3}.mdl-button--accent.mdl-button--accent{color:#ff6e40}.mdl-button--accent.mdl-button--accent .mdl-ripple{background:#fff}.mdl-button--accent.mdl-button--accent.mdl-button--fab,.mdl-button--accent.mdl-button--accent.mdl-button--raised{color:#fff;background-color:#ff6e40}.mdl-button.mdl-button--disabled.mdl-button--disabled,.mdl-button[disabled][disabled]{color:rgba(0,0,0,.26);cursor:default;background-color:transparent}.mdl-button--fab.mdl-button--disabled.mdl-button--disabled,.mdl-button--fab[disabled][disabled]{background-color:rgba(0,0,0,.12);color:rgba(0,0,0,.26)}.mdl-button--raised.mdl-button--disabled.mdl-button--disabled,.mdl-button--raised[disabled][disabled]{background-color:rgba(0,0,0,.12);color:rgba(0,0,0,.26);box-shadow:none}.mdl-button--colored.mdl-button--disabled.mdl-button--disabled,.mdl-button--colored[disabled][disabled]{color:rgba(0,0,0,.26)}.admonition.attention .mdl-button .admonition-title:before,.admonition.caution .mdl-button .admonition-title:before,.admonition.danger .mdl-button .admonition-title:before,.admonition.error .mdl-button .admonition-title:before,.admonition.hint .mdl-button .admonition-title:before,.admonition.important .mdl-button .admonition-title:before,.admonition.note .mdl-button .admonition-title:before,.admonition.seealso .mdl-button .admonition-title:before,.admonition.tip .mdl-button .admonition-title:before,.admonition.warning .mdl-button .admonition-title:before,.mdl-button .admonition.attention .admonition-title:before,.mdl-button .admonition.caution .admonition-title:before,.mdl-button .admonition.danger .admonition-title:before,.mdl-button .admonition.error .admonition-title:before,.mdl-button .admonition.hint .admonition-title:before,.mdl-button .admonition.important .admonition-title:before,.mdl-button .admonition.note .admonition-title:before,.mdl-button .admonition.seealso .admonition-title:before,.mdl-button .admonition.tip .admonition-title:before,.mdl-button .admonition.warning .admonition-title:before,.mdl-button .material-icons,.mdl-button a.download:before{vertical-align:middle}.font-light{font-weight:300}.font-regular{font-weight:400}.font-heavy{font-weight:700}.left{text-align:left}.right{text-align:right}.center{text-align:center;margin-left:auto;margin-right:auto}.justify{text-align:justify}.hidden-sm{display:none}.container{width:100%;margin-left:auto;margin-right:auto}@media only screen and (min-width:33.75em){.container{width:80%}}@media only screen and (min-width:60em){.container{width:75%;max-width:60rem}}.row{position:relative;width:100%}.row [class^=col]{float:left;margin:.5rem 1%;min-height:.125rem}.row:after{content:"";display:table;clear:both}.col-1,.col-2,.col-3,.col-4,.col-5,.col-6,.col-7,.col-8,.col-9,.col-10,.col-11,.col-12{width:98%}.col-1-sm{width:6.33333%}.col-2-sm{width:14.66667%}.col-3-sm{width:23%}.col-4-sm{width:31.33333%}.col-5-sm{width:39.66667%}.col-6-sm{width:48%}.col-7-sm{width:56.33333%}.col-8-sm{width:64.66667%}.col-9-sm{width:73%}.col-10-sm{width:81.33333%}.col-11-sm{width:89.66667%}.col-12-sm{width:98%}@media only screen and (min-width:45em){.col-1{width:6.33333%}.col-2{width:14.66667%}.col-3{width:23%}.col-4{width:31.33333%}.col-5{width:39.66667%}.col-6{width:48%}.col-7{width:56.33333%}.col-8{width:64.66667%}.col-9{width:73%}.col-10{width:81.33333%}.col-11{width:89.66667%}.col-12{width:98%}.hidden-sm{display:block}}.row{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;flex-wrap:wrap}.row>[class*=col-]{display:flex;flex-direction:column}.admonition.attention .admonition-title:before,.admonition.caution .admonition-title:before,.admonition.danger .admonition-title:before,.admonition.error .admonition-title:before,.admonition.hint .admonition-title:before,.admonition.important .admonition-title:before,.admonition.note .admonition-title:before,.admonition.seealso .admonition-title:before,.admonition.tip .admonition-title:before,.admonition.warning .admonition-title:before,.material-icons,a.download:before{font-family:Material Icons;font-weight:400;font-style:normal;font-size:24px;display:inline-block;line-height:1;text-transform:none;letter-spacing:normal;word-wrap:normal;white-space:nowrap;direction:ltr;-webkit-font-smoothing:antialiased;text-rendering:optimizeLegibility;-moz-osx-font-smoothing:grayscale;font-feature-settings:"liga"}html{font-size:16px}body{display:block!important;background-color:#fafafa;font-size:1rem;line-height:1.5rem;font-family:-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol}.mdl-layout__content:focus{outline:none}a.download>code.download,blockquote,h1,h2,h3,h4,h5,h6,span.mdl-layout-title{font-family:-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol}.contents,.contents a,.globaltoc a.current,.toc-backref,.toctree-wrapper,.toctree-wrapper a,h1,h2,h3,h4,h5,h6{color:#048ccc!important}a{text-decoration:none}.page-content,.page-content dd,.page-content dl,.page-content dt,.page-content ol,.page-content p,.page-content table,.page-content td,.page-content th,.page-content ul{font-size:1rem}.brand{color:inherit;text-decoration:none}.section{overflow-x:auto}img{max-width:100%;display:block;margin-left:auto;margin-right:auto}div.figure p.caption{text-align:center;margin-top:.75rem}div.figure p.caption span.caption-number{font-style:normal}div.figure p.caption .caption-number:after{content:"\00a0"}.svg-icon{width:16px;height:16px;display:inline-block;fill:#f5f5f5;padding-right:5px;padding-top:4px;vertical-align:text-top}.admonition.attention a.download>i.admonition-title:before,.admonition.caution a.download>i.admonition-title:before,.admonition.danger a.download>i.admonition-title:before,.admonition.error a.download>i.admonition-title:before,.admonition.hint a.download>i.admonition-title:before,.admonition.important a.download>i.admonition-title:before,.admonition.note a.download>i.admonition-title:before,.admonition.seealso a.download>i.admonition-title:before,.admonition.tip a.download>i.admonition-title:before,.admonition.warning a.download>i.admonition-title:before,a.download>i.material-icons{position:relative;top:5px}a.download{text-decoration:none}.wrapper:after{content:"";display:table;clear:both}.wrapper{max-width:1090px;margin-right:auto;margin-left:auto;padding-right:45px;padding-left:30px}@media screen and (max-width:1024px){.wrapper{max-width:1120px;padding-right:15px;padding-left:15px}}.mdl-layout{margin-top:76px}.document{width:100%;margin:0 auto;display:flex}@media (min-width:1795px){.document{width:1400px}}.document .page-content{width:100%;margin:0 auto;padding:0 12px}@media (min-width:992px){.document .page-content{width:90%;padding:0 5%}}@media (min-width:1200px){.document .page-content{width:calc(90% - 230px);padding:0 5%}}.document .side-doc-outline{width:230px}@media (max-width:1199px){.document .side-doc-outline{display:none}}.document .side-doc-outline--content{position:fixed;overflow-x:auto;overflow-y:auto;width:inherit}.document .side-doc-outline--content::-webkit-scrollbar{width:6px}.document .side-doc-outline--content::-webkit-scrollbar-track{border-radius:6px}.document .side-doc-outline--content::-webkit-scrollbar-thumb{background-color:rgba(0,0,0,.3);border-radius:6px;box-shadow:0 0 0 1px hsla(0,0%,100%,.3)}@keyframes float-in{0%{transform:translateY(.5rem);opacity:0}to{transform:translateY(0);opacity:1}}@keyframes float-out{0%{transform:translateY(0);opacity:1}to{transform:translateY(.5rem);opacity:0}}.page-content .headerlink{display:inline-block;text-decoration:none;margin-left:.8rem;color:inherit;opacity:0}.page-content .headerlink:hover{animation:float-in .2s cubic-bezier(.4,0,.2,1) 0s forwards}.page-content h1 .toc-backref,.page-content h2 .toc-backref,.page-content h3 .toc-backref,.page-content h4 .toc-backref,.page-content h5 .toc-backref,.page-content h6 .toc-backref{text-decoration:none}.page-content h1:hover .headerlink,.page-content h2:hover .headerlink,.page-content h3:hover .headerlink,.page-content h4:hover .headerlink,.page-content h5:hover .headerlink,.page-content h6:hover .headerlink{animation:float-in .2s cubic-bezier(.4,0,.2,1) 0s forwards}.page-content h1{font-size:2rem;line-height:2.25rem}.page-content h2{font-size:1.75rem;line-height:2rem;padding-top:1.5rem;margin-top:0;margin-bottom:1rem}.page-content h3{font-size:1.5rem;line-height:1.75rem;padding-top:1rem;margin-top:0;margin-bottom:.75rem}.page-content h4{font-size:1.25rem;line-height:1.5rem;padding-top:.75rem;margin-top:0;margin-bottom:.5rem}.page-content div.page-content h5{font-size:1.1rem;line-height:1.5rem;padding-top:2rem;margin-top:0;margin-bottom:1rem}.page-content div.page-content h6{font-size:1rem;line-height:1.5rem;padding-top:2rem;margin-top:0;margin-bottom:1rem}.admonition{padding:12px 20px;margin-top:10px;margin-bottom:10px}.admonition p.last{margin:16px}.admonition .admonition-title{font-size:16px;font-weight:700;color:#555;text-transform:uppercase;margin-top:7px}.admonition.note{border-left:4px solid #00bcd4;background-color:rgba(0,188,212,.1)}.admonition.note .admonition-title{font-size:16px;font-weight:700;color:#00bcd4;margin-top:4px;margin-bottom:8px}.admonition.note .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"info_outline";font-size:18px}.admonition.seealso{border-left:4px solid #00bcd4;background-color:rgba(0,188,212,.1)}.admonition.seealso .admonition-title{font-size:16px;font-weight:700;color:#00bcd4;margin-top:4px;margin-bottom:8px}.admonition.seealso .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"search";font-size:18px}.admonition.hint{border-left:4px solid #00bcd4;background-color:rgba(0,188,212,.1)}.admonition.hint .admonition-title{font-size:16px;font-weight:700;color:#00bcd4;margin-top:4px;margin-bottom:8px}.admonition.hint .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"help_outline";font-size:18px}.admonition.warning{border-left:4px solid #ffc107;background-color:rgba(255,193,7,.1)}.admonition.warning .admonition-title{font-size:16px;font-weight:700;color:#ffc107;margin-top:4px;margin-bottom:8px}.admonition.warning .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"warning";font-size:18px}.admonition.attention{border-left:4px solid #ffc107;background-color:rgba(255,193,7,.1)}.admonition.attention .admonition-title{font-size:16px;font-weight:700;color:#ffc107;margin-top:4px;margin-bottom:8px}.admonition.attention .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"warning";font-size:18px}.admonition.tip{border-left:4px solid #8bc34a;background-color:rgba(139,195,74,.1)}.admonition.tip .admonition-title{font-size:16px;font-weight:700;color:#8bc34a;margin-top:4px;margin-bottom:8px}.admonition.tip .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"lightbulb_outline";font-size:18px}.admonition.important{border-left:4px solid #8bc34a;background-color:rgba(139,195,74,.1)}.admonition.important .admonition-title{font-size:16px;font-weight:700;color:#8bc34a;margin-top:4px;margin-bottom:8px}.admonition.important .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"check_circle";font-size:18px}.admonition.error{border-left:4px solid #f44336;background-color:rgba(244,67,54,.1)}.admonition.error .admonition-title{font-size:16px;font-weight:700;color:#f44336;margin-top:4px;margin-bottom:8px}.admonition.error .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"error_outline";font-size:18px}.admonition.caution{border-left:4px solid #f44336;background-color:rgba(244,67,54,.1)}.admonition.caution .admonition-title{font-size:16px;font-weight:700;color:#f44336;margin-top:4px;margin-bottom:8px}.admonition.caution .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"error_outline";font-size:18px}.admonition.danger{border-left:4px solid #f44336;background-color:rgba(244,67,54,.1)}.admonition.danger .admonition-title{font-size:16px;font-weight:700;color:#f44336;margin-top:4px;margin-bottom:8px}.admonition.danger .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"error_outline";font-size:18px}.page-content .highlight{margin:1px 0}.page-content .highlight pre{background:rgba(0,0,0,.05);color:rgba(0,0,0,.87);font-family:Menlo,DejaVu Sans Mono,Liberation Mono,Consolas,Ubuntu Mono,Courier New,andale mono,lucida console,monospace;padding:.75rem;overflow:auto;overflow-y:hidden}.page-content .highlight pre .nd,.page-content .highlight pre .o{color:rgba(0,0,0,.87)}.page-content div.highlight-console div.highlight{background:none}.page-content .output .highlight pre{color:rgba(0,0,0,.87);background:#fafafa;border:1px solid #999;padding:.75rem}.page-content .code,.page-content code:not(.download){margin:0;border-radius:2px}.page-content .code,.page-content .code span.pre,.page-content code:not(.download),.page-content code:not(.download) span.pre{font-family:Menlo,DejaVu Sans Mono,Liberation Mono,Consolas,Ubuntu Mono,Courier New,andale mono,lucida console,monospace}.page-content .viewcode-link{padding-left:2em;font-size:80%}.page-content .class>dt,.page-content .function>dt,.page-content .method>dt,.page-content .rubric{display:table;margin:10px 0;font-size:100%;line-height:normal;background:#e7f2fa;color:#2b98f0;border-top:3px solid #55adf3;padding:10px;position:relative}.page-content .class>dt .descclassname,.page-content .class>dt .descname,.page-content .function>dt .descclassname,.page-content .function>dt .descname,.page-content .method>dt .descclassname,.page-content .method>dt .descname,.page-content .rubric .descclassname,.page-content .rubric .descname{color:rgba(0,0,0,.87);background:#e7f2fa;padding:3px}.page-content .class>dt em,.page-content .function>dt em,.page-content .method>dt em,.page-content .rubric em{padding:0 2px}.page-content .rubric{margin:30px 0 10px}.page-content .field-body{padding-left:40px}.page-content .field-body ul{padding:0 0 0 16px;margin:0}.page-content .seealso .docutils>dt{float:left;clear:left;padding:0 6px}.page-content .seealso .docutils>dd{padding-left:6em}.page-content .nblast{padding-bottom:1em}.page-content pre{font-size:90%;background:#eee;color:#455a64;padding:16px 32px;width:auto;border-radius:4px;word-wrap:break-word}.page-content pre:hover:before{font-family:-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol;padding:0 .5rem;content:attr(click-to-copy);color:rgba(0,0,0,.5);border-radius:4px;position:relative;float:right;top:-.5rem;right:-.5rem;background:#c8c8c8;font-size:.8rem;cursor:pointer}.page-content blockquote{font-size:1rem;padding:0 1rem;border-left:3px solid rgba(0,0,0,.05)}.page-content blockquote:after{content:""!important;margin-left:0}.page-content blockquote:before{content:""!important}.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list){margin:1.5rem 0;table-layout:fixed;max-width:100%;min-width:70%}.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) td,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th{white-space:normal;overflow-wrap:break-word}.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) caption{font-size:16px;margin:1rem 0 .8rem;white-space:normal}.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) caption .caption-number{font-style:normal}.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) caption .caption-number:after{content:"\00a0"}.globaltoc .caption,.globaltoc .toc{display:none}.globaltoc ul{list-style-type:none;padding:0;margin:0}.globaltoc ul li{min-height:36px}.globaltoc ul li .link-wrapper{display:flex;justify-content:space-between}.globaltoc ul li .link-wrapper>a{padding:8px 0;display:block;width:100%;font-size:1rem;text-decoration:none;color:#757575}.globaltoc ul li .link-wrapper>a.current{font-weight:700}.globaltoc .nav-toggle{padding:0;float:right;display:flex;align-items:center;justify-content:center;height:36px}.globaltoc .nav-toggle>a{padding:0;margin-left:0;margin-right:4px;cursor:pointer}.globaltoc .nav-toggle>a>i{font-size:18px}.globaltoc .nav-toggle.show{transform:rotate(180deg)}.globaltoc .nav-toggle.show>a{margin-right:0;margin-left:4px}.globaltoc nav>ul>li>span.link-wrapper{padding-left:8px}.globaltoc nav>ul>li>ul>li>span.link-wrapper{padding-left:16px}.globaltoc nav>ul>li>ul>li>ul>li>span.link-wrapper{padding-left:24px}.globaltoc nav>ul>li>ul>li>ul>li>ul>li>span.link-wrapper{padding-left:32px}.globaltoc nav>ul>li>ul>li>ul>li>ul>li>ul>li>span.link-wrapper{padding-left:40px}.globaltoc nav>ul>li>ul>li>ul>li>ul>li>ul>li>ul>li>span.link-wrapper{padding-left:48px}.localtoc{font-size:.75rem;padding-top:1rem}.localtoc .caption{padding-left:12px}.localtoc .caption-text{font-size:.9rem;font-weight:700}.localtoc>ul>li>a{display:none}.localtoc ul{padding:0;list-style-type:none}.localtoc li{padding-left:6px}.localtoc a{display:block;text-decoration:none;color:inherit;margin-top:8px;padding-left:8px;line-height:1.1rem}.localtoc a.current{padding-left:5px;border-left:3px solid;font-weight:700}.contents.topic,.toctree-wrapper{border-left:5px solid}.contents.topic>p.topic-title,.toctree-wrapper>p.caption{color:#757575;font-size:1rem;padding-left:14px}.contents.topic ul,.toctree-wrapper ul{padding-left:14px;list-style:none;line-height:30px}.contents.topic a,.toctree-wrapper a{font-size:1.2rem;text-decoration:none}.contents.topic a .pre,.toctree-wrapper a .pre{font-size:1rem}.contents.topic>ul>li>a,.toctree-wrapper>ul>li>a{font-size:1.3rem}.contents.topic>ul>li>a .pre,.toctree-wrapper>ul>li>a .pre{font-size:1.1rem}.page-content ul li{margin:.3rem 0}.page-content ul li p{margin:0}.page-content .option-list .option{font-family:Menlo,DejaVu Sans Mono,Liberation Mono,Consolas,Ubuntu Mono,Courier New,andale mono,lucida console,monospace}.page-content .option-list td{padding:.5rem;border:none}.mdl-layout__drawer{background-color:#fff}.mdl-layout__drawer::-webkit-scrollbar{width:6px}.mdl-layout__drawer::-webkit-scrollbar-track{border-radius:6px}.mdl-layout__drawer::-webkit-scrollbar-thumb{background-color:rgba(0,0,0,.3);border-radius:6px;box-shadow:0 0 0 1px hsla(0,0%,100%,.3)}.mdl-layout__drawer>.mdl-layout-title{font-weight:700;text-align:right;margin:0;padding:0;line-height:32px;border-bottom:1px solid rgba(0,0,0,.1);min-height:64px}.mdl-layout__drawer>.mdl-layout-title .title{color:inherit;display:block;height:100%;width:100%;text-decoration:none}.mdl-layout__drawer>.mdl-layout-title .title>img.logo{width:100%;margin:0;padding:0}.mdl-layout__drawer>.mdl-layout-title .title-text{font-weight:700;text-align:right;padding:0 10px;margin:16px 0 8px;line-height:32px;font-family:-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol;color:inherit;display:block}nav.breadcrumb>a.mdl-navigation__link{padding:0 8px;font-size:18px}@media (max-width:1199px){nav.breadcrumb{width:calc(100% - 64px)}nav.breadcrumb a.mdl-navigation__link.is-active{overflow-x:hidden;width:100%;overflow:hidden;white-space:nowrap;text-overflow:ellipsis}.admonition.attention nav.breadcrumb i.admonition-title:before,.admonition.caution nav.breadcrumb i.admonition-title:before,.admonition.danger nav.breadcrumb i.admonition-title:before,.admonition.error nav.breadcrumb i.admonition-title:before,.admonition.hint nav.breadcrumb i.admonition-title:before,.admonition.important nav.breadcrumb i.admonition-title:before,.admonition.note nav.breadcrumb i.admonition-title:before,.admonition.seealso nav.breadcrumb i.admonition-title:before,.admonition.tip nav.breadcrumb i.admonition-title:before,.admonition.warning nav.breadcrumb i.admonition-title:before,nav.breadcrumb .admonition.attention i.admonition-title:before,nav.breadcrumb .admonition.caution i.admonition-title:before,nav.breadcrumb .admonition.danger i.admonition-title:before,nav.breadcrumb .admonition.error i.admonition-title:before,nav.breadcrumb .admonition.hint i.admonition-title:before,nav.breadcrumb .admonition.important i.admonition-title:before,nav.breadcrumb .admonition.note i.admonition-title:before,nav.breadcrumb .admonition.seealso i.admonition-title:before,nav.breadcrumb .admonition.tip i.admonition-title:before,nav.breadcrumb .admonition.warning i.admonition-title:before,nav.breadcrumb a.mdl-navigation__link:not(.is-active),nav.breadcrumb i.material-icons{display:none}}div.mdl-layout__header{margin-top:77px}.mdl-layout__drawer-button{top:13px!important}div.mdl-layout__header-row.header-links{background:hsla(0,0%,100%,.2);width:100%;overflow-x:auto;overflow-y:hidden}div.mdl-layout__header-row.header-links a.mdl-navigation__link{font-size:1rem}div.mdl-layout__header-row.header-links a.mdl-navigation__link i{font-size:1.2rem;margin:0 8px;position:relative;bottom:-.1rem}div.mdl-layout__header-row.header-links a.mdl-navigation__link:hover{background-color:#2196f3;color:#eee}div.mdl-layout__header-row.header-links a.mdl-navigation__link[href="#"]{background-color:#2196f3;opacity:1;color:#fff}.site-title{font-weight:300!important;line-height:57px;letter-spacing:-1px;margin-bottom:0;float:left;color:#fff}.site-title,.site-title:visited{color:#424242}.site-header{position:fixed;top:0;width:100%;min-height:55px;padding-top:10px;padding-bottom:10px;background-color:#048ccc;z-index:10;font-weight:300;font-size:17px;border-bottom:1px solid #fff}.site-header-logo{width:120px;display:initial}.site-nav{float:right;line-height:57px}.site-nav .menu-icon,.site-nav .nav-trigger{display:none}.site-nav .page-link{color:#fff;line-height:1.5;font-weight:300}.site-nav .page-link:not(:last-child){margin-right:40px}.site-nav .page-link:hover{color:#fff;text-shadow:-.06ex 0 #fff,.06ex 0 #fff}.site-nav .page-link.page-current{color:#fff;text-decoration:underline}@media screen and (max-width:1024px){.site-nav{position:absolute;top:9px;right:15px;background-color:#178dc9;border-radius:2px;text-align:right}.site-nav label[for=nav-trigger]{display:block;float:right;width:36px;height:36px;z-index:2;cursor:pointer}.site-nav .menu-icon{display:block;float:right;width:36px;height:26px;line-height:0;padding-top:20px;text-align:center}.site-nav .menu-icon>svg{fill:#fff}.site-nav input~.trigger{clear:both;display:none}.site-nav input:checked~.trigger{display:block;padding-bottom:5px}.site-nav .page-link{padding:5px 10px;display:block;margin-left:20px}.site-nav .page-link:not(:last-child){margin-right:0}}footer.mdl-mini-footer{background-color:#212121}footer.mdl-mini-footer>div.mdl-mini-footer__left-section{margin-bottom:20px;display:flex;flex-direction:column}footer.mdl-mini-footer>div.mdl-mini-footer__left-section .mdl-logo{font-size:1.1rem}footer.mdl-mini-footer>div.mdl-mini-footer__right-section{font-size:.9rem;display:flex;flex-direction:column;justify-content:flex-end}footer.mdl-mini-footer>div.mdl-mini-footer__right-section a{color:inherit;font-weight:700;text-decoration:none}footer.mdl-mini-footer p.caption{display:none}.pagenation{width:100%;margin-top:80px;height:92px;background-color:#424242;display:flex}.pagenation #button-next,.pagenation #button-prev,.pagenation .button-common{text-transform:none;padding:0;height:92px;display:flex;justify-content:center;align-items:center;color:#fff}.pagenation #button-prev{margin-right:auto}.pagenation #button-prev .pagenation-text{text-align:left}.pagenation #button-next{margin-left:auto;flex-direction:row-reverse}.pagenation #button-next .pagenation-text{text-align:right}.pagenation-arrow-L{margin-right:20px}.pagenation-arrow-R{margin-left:20px}.pagenation-text{line-height:30px;font-size:20px}.pagenation-direction{opacity:.7;font-size:18px}@media screen and (max-width:1024px){.pagenation #button-prev{width:20%}.pagenation #button-next{width:80%}.pagenation #button-prev .pagenation-text{display:none}}@media screen and (min-width:1025px){.pagenation #button-next,.pagenation #button-prev{width:50%}.pagenation #button-prev .pagenation-text{display:block}}.site-footer{border-top:1px solid #f5f5f5;padding:30px 0;background-color:#424242}.site-footer .footer-category-title{color:#048ccc}.site-footer a,.site-footer a:visited{color:#f5f5f5!important}.site-footer2{background-color:#424242;padding-top:40px;padding-bottom:10px}.footer-heading{margin-bottom:15px}.contact-list,.social-media-list{list-style:none;margin-left:0}.footer-bottom-warning{font-size:80%;color:#fff;float:left}.footer-logo{width:200px;margin-bottom:30px;margin-top:30px}.footer-col{float:left;margin-bottom:15px;padding-left:15px}.footer-text{color:#f5f5f5}#waterfall-exp::-webkit-input-placeholder{color:#ccc}#waterfall-exp:-ms-input-placeholder{color:#ccc}#waterfall-exp::-moz-placeholder{color:#ccc}ul.search span.highlighted{font-weight:700}ul.search>li{margin-bottom:24px}#search-results ul{list-style:none;padding:0}#search-results ul li>a{text-decoration:none;font-size:1.2rem}a.download:before{content:"file_download";position:relative;top:5px;margin-right:5px}button.download{position:sticky;margin-left:1em}.mdl-card{margin:1em 1.5em 1em 0;display:inline-block;width:250px;min-height:140px;padding:18px}.mdl-card:hover{box-shadow:0 10px 20px rgba(0,0,0,.25),0 6px 6px rgba(0,0,0,.22);color:#000;cursor:pointer}.mdl-card__title{padding:0 0 1em;font-size:18px;color:#444}.mdl-card__supporting-text{line-height:1.5rem;padding:0;width:100%}.head-card.mdl-card{width:auto;display:block;max-width:800px;padding:24px}.head-card>.mdl-card__title{padding-bottom:0;height:60px;font-weight:700;text-transform:uppercase}.head-card>.mdl-card__menu{color:#fff}.head-card>.mdl-card__actions{padding:0}.cards{display:flex;flex-direction:row;flex-wrap:wrap}
 /*# sourceMappingURL=/sphinx_materialdesign_theme.css.map */
\ No newline at end of file
diff --git a/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.css.map b/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.css.map
index 270e9379f5cc..a5856367972c 100644
--- a/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.css.map
+++ b/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.css.map
@@ -1 +1 @@
-{"version":3,"sources":["../../node_modules/material-design-lite/src/shadow/_shadow.scss","../../node_modules/material-design-lite/src/_mixins.scss","../../node_modules/material-design-lite/src/data-table/_data-table.scss","../../node_modules/material-design-lite/src/_variables.scss","../../node_modules/material-design-lite/src/footer/_mini_footer.scss","../../node_modules/material-design-lite/src/card/_card.scss","../../node_modules/material-design-lite/src/button/_button.scss","../scss/grid/_simplegrid.scss","../scss/fonts/_material-icons.scss","../scss/_root.scss","../scss/_variables.scss","../scss/layout/_layout.scss","../scss/headerings/_headerings.scss","../scss/admonitions/_admonitions.scss","../scss/code/_code.scss","../scss/blockquote/_blockquote.scss","../scss/tables/_tables.scss","../scss/toc/_globaltoc.scss","../scss/toc/_localtoc.scss","../scss/toc/_toctree.scss","../scss/lists/_lists.scss","../scss/drawer/_drawer.scss","../scss/header/_header.scss","../scss/footer/_footer.scss","../scss/search/_search.scss","../scss/downloadlink/_downloadlink.scss","../scss/card/_card.scss"],"names":[],"mappings":"AAmBA,wJCoNE,gGAEqE,CDlNvE,iBCqNE,gGAEqE,CDnNvE,iBCsNE,iGAEmE,CDpNrE,iBCuNE,kGAEmE,CDrNrE,iBCwNE,sGAEmE,CDtNrE,kBC0NE,wGAEqE,CDxNvE,kBC4NE,yGAEqE,CCtPvE,mHACE,iBAAkB,CAClB,gCCohBkC,CDnhBlC,wBAAyB,CACzB,kBAAmB,CACnB,cC0gByB,CDzgBzB,qBAAiD,CANnD,+HASI,kBAAmB,CATvB,+KAYM,YAAa,CAZnB,qIAkBM,iBAAkB,CAClB,WC0gBsB,CFlR1B,wBCvP6C,CDwP7C,kDEkN6D,CDzczD,oCAAqC,CArB3C,6JAwBQ,wBCigB4B,CDzhBpC,iJA4BQ,qBC4fwB,CDxhBhC,kPAkCI,mBCggBsD,CD/ftD,gBAAiB,CAnCrB,0SAsCM,iBAAkB,CAtCxB,sSA0CM,kBAAmB,CA1CzB,yHA+CI,iBAAkB,CAClB,qBAAsB,CACtB,WC4ewB,CD3exB,oCCoegC,CDnehC,uCCmegC,CDlehC,gBCof8C,CDnf9C,qBAAsB,CArD1B,yKAwDM,qBAAsB,CAxD5B,yHA6DI,iBAAkB,CAClB,qBAAsB,CACtB,sBAAuB,CDsCzB,cAAe,CAIb,eAAiB,CAEnB,gBAAiB,CACjB,gBAAiB,CC3Cf,WC4dwB,CD3dxB,cC8c8B,CD7c9B,qBCgd+B,CD/c/B,kBAAmB,CACnB,qBAAsB,CArE1B,wZAyEM,qBC2coC,CDphB1C,obD8LE,0BAA6B,CAC7B,eAAmB,CACnB,iBAAkB,CAClB,cAAe,CACf,aAAc,CACd,qBAAsB,CACtB,mBAAoB,CACpB,oBAAqB,CACrB,gBAAiB,CACjB,4BAA6B,CAC7B,oCAAqC,CACrC,kCAAmC,CC7H7B,cCqc+B,CDpc/B,eAAgB,CAChB,gBAAiB,CACjB,kBAAmB,CA/E3B,gbAkFQ,cAAe,CAlFvB,4cAoFU,qBCic2C,CDrhBrD,2NAyFM,eAAgB,CAKtB,wBACE,UAAW,CAGb,iRACE,eAAgB,CEpGlB,iBACE,YAAa,CACb,kBAAmB,CACnB,6BAA8B,CAE9B,iBDoZY,CClZZ,aDwRiD,CCvRjD,wBDsRoD,CC9RtD,uBAWI,UAAW,CACX,aAAc,CAZlB,2BAgBI,gBDsYkB,CClYtB,oHAEE,YAAa,CACb,oBAAqB,CAErB,eAAgB,CAEhB,QAAS,CACT,SAAU,CARZ,6HAWI,eAAgB,CAChB,iBDyXU,CCvXV,oCAdJ,6HAeM,gBDmXgB,CCjXnB,CAjBH,0HAoBI,aAAc,CACd,oBAAqB,CACrB,kBAAmB,CAIvB,8DAEE,oBAAqB,CACrB,OAAQ,CAGV,gEAEE,oBAAqB,CACrB,OAAQ,CAGV,0DAEE,UD0VoB,CCzVpB,WDyVoB,CCvVpB,SAAU,CACV,QAAS,CAET,wBD6NiD,CC3NjD,WAAY,CCpEd,UACE,YAAa,CACb,qBAAsB,CACtB,cF2amB,CE1anB,eAAgB,CAChB,gBFwaiB,CEvajB,eAAgB,CAChB,WFqagB,CEpahB,SF2bc,CE1bd,iBAAkB,CAClB,eFiOqD,CEhOrD,iBAAkB,CAClB,qBAAsB,CAGxB,iBACE,wBF6N6D,CE5N7D,wBAAyB,CACzB,2BAA4B,CAC5B,qBAAsB,CACtB,6BAA8B,CAC9B,4BAA6B,CAC7B,qBAAsB,CAGxB,iBACE,kBAAmB,CACnB,UFiN+C,CEhN/C,aAAc,CACd,YAAa,CACb,uBAAwB,CACxB,kBAAmB,CACnB,YFiZ4B,CEhZ5B,6BFoZoC,CEnZpC,2BFsZkC,CErZlC,qBAAsB,CAVxB,kCAaI,sCFyM+B,CErMnC,sBACE,mBAAoB,CACpB,aAAc,CACd,aAAc,CACd,YAAa,CACb,cFgYyB,CE/XzB,eFkZ+B,CEjZ/B,kBAAmB,CACnB,eAAgB,CAChB,2BFwYuC,CEvYvC,QAAS,CAGX,yBACE,cFwX4B,CEvX5B,qBFuL0D,CEtL1D,QAAS,CAGX,2BACE,qBFgLsE,CE/KtE,cF8XmC,CE7XnC,gBF8XqC,CE7XrC,eAAgB,CAChB,YF+W4B,CE9W5B,SAAU,CANZ,4CASI,sCFyK+B,CErKnC,mBACE,cFqX2B,CEpX3B,kBAAmB,CACnB,UAAW,CACX,4BAA+B,CAC/B,WAAY,CACZ,qBAAsB,CANxB,oCASI,mCF4J+B,CExJnC,kBACE,WAAY,CAId,gBACE,iBAAkB,CAClB,UAAW,CACX,QAAS,CC7FX,YACE,sBAAuB,CACvB,WAAY,CACZ,iBH+cwB,CG9cxB,UHgHsD,CG/GtD,iBAAkB,CAClB,WHyckB,CGxclB,QAAS,CACT,cHscqB,CGrcrB,cHucmB,CGtcnB,oBAAqB,CLVnB,6CE8CuD,CFmIzD,cAAe,CACf,eAAgB,CAChB,wBAAyB,CACzB,aAAc,CACd,gBAAiB,CKzKjB,eAAgB,CAChB,sBAAuB,CACvB,+HH+c6D,CG5c7D,YAAa,CACb,cAAe,CACf,oBAAqB,CACrB,iBAAkB,CAClB,gBH0bkB,CGzblB,qBAAsB,CAtBxB,8BAyBI,QAAS,CAzBb,kBA6BI,kCHsF8D,CGnHlE,+BAiCI,gCHsFuD,CGvH3D,mBAqCI,kCHiF6D,CGtHjE,gCAyCI,aHiFwD,CG1H5D,mDA4CM,gCH2EqD,CGtE3D,8BACE,uBAAuB,CAIvB,oBACE,4BH4D8D,CFgGhE,gGAEqE,CK/JrE,2BLuKA,iGAEmE,CKnK/D,kCH0D2D,CGhE/D,uCLyJA,6DAA8D,CK9I1D,kCHqD2D,CGhE/D,wCAeI,kBHqDsD,CGpDtD,UHqDiE,CGrErE,wJA2BM,wBH4CmD,CGvEzD,oDA+BM,eH4C4D,CGrClE,iBACE,iBAAkB,CAClB,cHwXuB,CGvXvB,WHqXkB,CGpXlB,WAAY,CACZ,cHmXkB,CGlXlB,UHkXkB,CGjXlB,SAAU,CACV,eAAgB,CAChB,4BHc8D,CGb9D,oEAAwE,CACxE,iBAAkB,CAClB,kBAAmB,CAZrB,0wCAeI,iBAAkB,CAClB,OAAQ,CACR,QAAS,CACT,gCAA8E,CAC9E,gBHuWqB,CGtWrB,UHsWqB,CG1XzB,sCAwBI,WHiWqB,CGhWrB,cHgWqB,CG/VrB,UH+VqB,CGzXzB,+CA8BI,iBAAkB,CAElB,4DAAiE,CAhCrE,wBLiIA,iGAEmE,CK9F/D,kCHX2D,CG1B/D,oCLmHA,6DAA8D,CKzE1D,kCHhB2D,CG1B/D,qCA8CI,kBHFiD,CGGjD,UHA+D,CG/CnE,+IA0DM,wBHZsD,CG9C5D,iDA8DM,eHd+D,CGqBrE,kBACE,iBAAkB,CAClB,cHmTuB,CGlTvB,WHoTmB,CGnTnB,aAAc,CACd,cAAe,CACf,cHiTmB,CGhTnB,UHgTmB,CG/SnB,SAAU,CACV,eAAgB,CAChB,aAAc,CACd,kBAAmB,CAXrB,gyCAcI,iBAAkB,CAClB,OAAQ,CACR,QAAS,CACT,gCAA8E,CAC9E,gBHmSqB,CGlSrB,UHkSqB,CGrTzB,wCAuBI,WHiSsB,CGhStB,cHgSsB,CG/RtB,UH+RsB,CGxT1B,owDA4BM,KAAyD,CACzD,MAA0D,CA7BhE,gDAkCI,iBAAkB,CAElB,4DAAiE,CAMrE,8BACE,aAAc,CACd,WAAY,CACZ,MAAS,CACT,iBAAkB,CAClB,KAAQ,CACR,UAAW,CACX,SAAU,CACV,eAAgB,CAEhB,2IAEE,4BAA6B,CAMnC,yCACE,aHpG0D,CGmG5D,qDAGI,eHrGmE,CGkGvE,qHAMI,UHxGmE,CGyGnE,wBH1GwD,CG8G5D,uCACE,aHjGqD,CGgGvD,mDAGI,eHhGiE,CG6FrE,iHAMI,UHnGiE,CGoGjE,wBHvGmD,CG6GvD,sFAII,qBHpHoE,CGqHpE,cAAe,CACf,4BAA6B,CAG9B,gGAIG,gCH9HgE,CG+HhE,qBH9HkE,CGkIrE,sGAIG,gCHvIgE,CGwIhE,qBHvIkE,CGwIlE,eAAgB,CAGnB,wGAIG,qBH/IkE,CGqJxE,4pCACE,qBAAsB,CClSxB,YACE,eAVqB,CAavB,cACE,eAbuB,CAgBzB,YACE,eAhBqB,CAqBvB,MACE,eAAgB,CAGlB,OACE,gBAAiB,CAGnB,QACE,iBAAkB,CAClB,gBAAiB,CACjB,iBAAkB,CAGpB,SACE,kBAAmB,CAGrB,WACE,YAAa,CAWf,WACE,UAAW,CACX,gBAAiB,CACjB,iBAAkB,CAElB,2CALF,WAMI,SAAU,CAOb,CAJC,wCATF,WAUI,SAAU,CACV,eAAgB,CAEnB,CAED,KACE,iBAAkB,CAClB,UAAW,CAGb,kBACE,UAAW,CACX,eAAiB,CACjB,kBAAoB,CAGtB,WACE,UAAW,CACX,aAAc,CACd,UAAW,CAGb,uFAYE,SAlDS,CAqDX,UACE,cAA0C,CAG5C,UACE,eAAyC,CAG3C,UACE,SAAwC,CAG1C,UACE,eAAwC,CAG1C,UACE,eAA+C,CAGjD,UACE,SAAwC,CAG1C,UACE,eAA+C,CAGjD,UACE,eAA+C,CAGjD,UACE,SAA+C,CAGjD,WACE,eAAgD,CAGlD,WACE,eAAgD,CAGlD,WACE,SAlGS,CAqGX,wCACE,OACE,cAA0C,CAE5C,OACE,eAAyC,CAE3C,OACE,SAAwC,CAE1C,OACE,eAAwC,CAE1C,OACE,eAA+C,CAEjD,OACE,SAAwC,CAE1C,OACE,eAA+C,CAEjD,OACE,eAA+C,CAEjD,OACE,SAA+C,CAEjD,QACE,eAAgD,CAElD,QACE,eAAgD,CAElD,QACE,SAxIO,CANX,WAkJI,aAAc,CACf,CAxHH,KA4HE,mBAAoB,CACpB,oBAAqB,CACrB,mBAAoB,CACpB,YAAa,CACb,cAAe,CAGjB,mBACE,YAAa,CACb,qBAAsB,CCxMxB,2dACI,0BAA6B,CAC7B,eAAmB,CACnB,iBAAkB,CAClB,cAAe,CACf,oBAAqB,CACrB,aAAc,CACd,mBAAoB,CACpB,qBAAsB,CACtB,gBAAiB,CACjB,kBAAmB,CACnB,aAAc,CAGd,kCAAmC,CAEnC,iCAAkC,CAGlC,iCAAkC,CAGlC,4BAA6B,CC3BjC,KACI,cCEY,CDChB,KACI,uBAAyB,CACzB,wBCDsB,CDEtB,cAAe,CACf,kBAAmB,CACnB,6ICA0J,CDG9J,2BACI,YAAa,CAGjB,4EAEI,6ICT0J,CDY9J,8GACI,uBAA8B,CAGlC,EACI,oBAAqB,CAGzB,yKAGQ,cAAe,CAIvB,OACI,aAAc,CACd,oBAAqB,CAGzB,SACI,eAAgB,CAOnB,IACG,cAAe,CACf,aAAc,CACd,gBAAiB,CACjB,iBAAkB,CAGtB,qBAEQ,iBAAkB,CAClB,iBAAkB,CAH1B,yCAMY,iBAAkB,CAN9B,2CASY,eAAgB,CAK5B,UACE,UAAW,CACX,WAAY,CACZ,oBAAqB,CACrB,YC3C0C,CD4C1C,iBAAkB,CAClB,eAAgB,CAChB,uBAAwB,CAM1B,6kBACI,iBAAkB,CAClB,OAAQ,CAGZ,WACI,oBAAqB,CAGzB,eACE,UAAW,CACX,aAAc,CACd,UAAW,CAGb,SAEE,gBAA2D,CAC3D,iBAAkB,CAClB,gBAAiB,CACjB,kBAA0C,CAC1C,iBC9EqB,CDiFrB,qCATF,SAWI,gBAAuD,CACvD,kBAAgC,CAChC,iBAA+B,CAElC,CEtFD,uBACI,eAAgB,CAChB,wBAAyB,CAI7B,UACI,UAAW,CACX,aAAc,CACd,YAAa,CAEb,0BALJ,UAMQ,YAjCiB,CA8ExB,CAnDD,wBASQ,UAAW,CACX,aAAc,CACd,cAAe,CAEf,yBAbR,wBAcY,SA9BU,CA+BV,YA9Ba,CAqCpB,CAJG,0BAlBR,wBAmBY,uBA/B0B,CAgC1B,YA/Ba,CAiCpB,CAtBL,4BAyBQ,WA7CY,CA+CZ,0BA3BR,4BA4BY,YAAa,CAqBpB,CAjDL,qCA+BY,cAAe,CACf,eAAgB,CAChB,eAAgB,CAChB,aAAc,CAlC1B,wDAoCgB,SAAU,CApC1B,8DAwCgB,iBAAkB,CAxClC,8DA4CgB,+BAAmC,CACnC,iBAAkB,CAClB,uCAA4C,CC/E5D,oBACI,GACI,2BAA6B,CAC7B,SAAU,CAEjB,GACC,uBAAwB,CACxB,SAAU,CAAA,CAIZ,qBACI,GACI,uBAAwB,CACxB,SAAU,CAEjB,GACC,2BAA6B,CAC7B,SAAU,CAAA,CAIZ,0BAEQ,oBAAqB,CACrB,oBAAqB,CACrB,iBAAmB,CACnB,aAAc,CACd,SAAU,CANlB,gCAQY,0DAAsE,CARlF,oLAcY,oBAAqB,CAdjC,kNAkBgB,0DAAsE,CAlBtF,iBAwBQ,cAAe,CACf,mBAAoB,CAzB5B,iBA6BQ,iBAAkB,CAClB,gBAAiB,CACjB,kBAAmB,CACnB,YAAa,CACb,kBAAmB,CAjC3B,iBAqCQ,gBAAiB,CACjB,mBAAoB,CACpB,gBAAiB,CACjB,YAAe,CACf,oBAAqB,CAzC7B,iBA6CQ,iBAAkB,CAClB,kBAAmB,CACnB,kBAAmB,CACnB,YAAe,CACf,mBAAoB,CAjD5B,kCAqDQ,gBAAiB,CACjB,kBAAmB,CACnB,gBAAiB,CACjB,YAAe,CACf,kBAAmB,CAzD3B,kCA6DQ,cAAe,CACf,kBAAmB,CACnB,gBAAiB,CACjB,YAAe,CACf,kBAAmB,CCT3B,YAGI,iBAAkB,CAClB,eAAgB,CAChB,kBAAmB,CALvB,mBAOQ,WAAY,CAPpB,8BAUQ,cAAe,CACf,eAAiB,CACjB,UAAW,CACX,wBAAyB,CACzB,cAAe,CAdvB,iBApBI,6BA/CgC,CAgDhC,mCA/C4C,CAgD5C,mCACI,cAAe,CACf,eAAiB,CACjB,aApD4B,CAsD5B,cAAe,CACf,iBAAkB,CAClB,0CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,sBA3DwB,CA4DxB,cAAe,CAK3B,oBApBI,6BA1CgC,CA2ChC,mCA1C4C,CA2C5C,sCACI,cAAe,CACf,eAAiB,CACjB,aA/C4B,CAiD5B,cAAe,CACf,iBAAkB,CAClB,6CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,gBAtDkB,CAuDlB,cAAe,CAK3B,iBApBI,6BApDgC,CAqDhC,mCApD4C,CAqD5C,mCACI,cAAe,CACf,eAAiB,CACjB,aAzD4B,CA2D5B,cAAe,CACf,iBAAkB,CAClB,0CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,sBAhEwB,CAiExB,cAAe,CAK3B,oBApBI,6BArCgC,CAsChC,mCArC4C,CAsC5C,sCACI,cAAe,CACf,eAAiB,CACjB,aA1C4B,CA4C5B,cAAe,CACf,iBAAkB,CAClB,6CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,iBAjDmB,CAkDnB,cAAe,CAK3B,sBApBI,6BAhCgC,CAiChC,mCAhC4C,CAiC5C,wCACI,cAAe,CACf,eAAiB,CACjB,aArC4B,CAuC5B,cAAe,CACf,iBAAkB,CAClB,+CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,iBA5CmB,CA6CnB,cAAe,CAK3B,gBApBI,6BA3BiC,CA4BjC,oCA3B6C,CA4B7C,kCACI,cAAe,CACf,eAAiB,CACjB,aAhC6B,CAkC7B,cAAe,CACf,iBAAkB,CAClB,yCAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,2BAvC6B,CAwC7B,cAAe,CAK3B,sBApBI,6BAtBiC,CAuBjC,oCAtB6C,CAuB7C,wCACI,cAAe,CACf,eAAiB,CACjB,aA3B6B,CA6B7B,cAAe,CACf,iBAAkB,CAClB,+CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,sBAlCyB,CAmCzB,cAAe,CAK3B,kBApBI,6BAjBgC,CAkBhC,mCAjB4C,CAkB5C,oCACI,cAAe,CACf,eAAiB,CACjB,aAtB4B,CAwB5B,cAAe,CACf,iBAAkB,CAClB,2CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,uBA7ByB,CA8BzB,cAAe,CAK3B,oBApBI,6BAZgC,CAahC,mCAZ4C,CAa5C,sCACI,cAAe,CACf,eAAiB,CACjB,aAjB4B,CAmB5B,cAAe,CACf,iBAAkB,CAClB,6CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,uBAxByB,CAyBzB,cAAe,CAK3B,mBApBI,6BAPgC,CAQhC,mCAP4C,CAQ5C,qCACI,cAAe,CACf,eAAiB,CACjB,aAZ4B,CAc5B,cAAe,CACf,iBAAkB,CAClB,4CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,uBAnByB,CAoBzB,cAAe,CCzE3B,yBAEQ,YAAa,CAFrB,6BAIY,0BJEqB,CIDrB,qBAAsB,CACtB,wHJE2I,CID3I,cAAgB,CAChB,aAAc,CACd,iBAAkB,CAT9B,iEAWgB,qBAAsB,CAXtC,kDAiBQ,eAAgB,CAjBxB,qCAwBgB,qBAAsB,CACtB,kBJpBU,CIuBV,qBAAmB,CACnB,cAAgB,CA7BhC,sDAmCQ,QAAW,CAEX,iBAAkB,CArC1B,8HAoCQ,wHJ5B+I,CIRvJ,6BA4CQ,gBAAiB,CACjB,aAAc,CA7CtB,kGAiDQ,aAAc,CACd,aAAc,CACd,cAAe,CACf,kBAAmB,CACnB,kBAAmB,CACnB,aAAc,CACd,4BAA6B,CAC7B,YAAa,CACb,iBAAkB,CAzD1B,wSA2DY,qBAAsB,CACtB,kBAAmB,CACnB,WAAY,CA7DxB,8GAgEY,aAAc,CAhE1B,sBAsEQ,kBAAqB,CAtE7B,0BA2EQ,iBAAkB,CA3E1B,6BA6EY,kBAAmB,CACnB,QAAS,CA9ErB,oCA6FO,UAAW,CACX,UAAW,CACX,aAAc,CA/FrB,oCAmGO,gBAAiB,CAnGxB,sBAsGQ,kBAAmB,CAtG3B,kBA0GQ,aAAc,CACd,eAAgB,CAChB,aAAc,CACd,iBAAkB,CAClB,UAAW,CACX,iBAAkB,CAClB,oBAAqB,CAhH7B,+BAsHgB,6IJ7G8I,CI8G9I,eAAiB,CACjB,2BAA4B,CAC5B,oBAAyB,CACzB,iBAAkB,CAClB,iBAAkB,CAClB,WAAY,CACZ,UAAY,CACZ,YAAc,CACd,kBAA8B,CAC9B,eAAiB,CACjB,cAAe,CC9H9B,yBAEO,cAAe,CACf,cAAe,CACf,qCLDyB,CKHhC,+BAOW,oBAAsB,CACtB,aAAc,CARzB,gCAWW,oBAAsB,CCdlC,mGAKQ,eAAgB,CAChB,kBAAmB,CACnB,cAAe,CACf,aAAc,CARtB,4MAYY,kBAAmB,CACnB,wBAAyB,CAbrC,2GAiBY,cNdI,CMeJ,mBAAuB,CACvB,kBAAmB,CAnB/B,2HAqBgB,iBAAkB,CArBlC,iIAwBgB,eAAgB,CCxBhC,oCAGQ,YAAa,CAHrB,cAQQ,oBAAqB,CACrB,SAAU,CACV,QAAS,CAVjB,iBAaY,eAAgB,CAb5B,+BAegB,YAAa,CACb,6BAA8B,CAhB9C,iCAkBoB,aAAc,CACd,aAAc,CACd,UAAW,CACX,cAAe,CACf,oBAAqB,CACrB,adyKoB,CchMxC,yCAyBwB,eAAiB,CAzBzC,uBAiCQ,SAAU,CACV,WAAY,CACZ,YAAa,CACb,kBAAmB,CACnB,sBAAuB,CACvB,WAAY,CAtCpB,yBAwCY,SAAU,CACV,aAAc,CACd,gBAAiB,CACjB,cAAe,CA3C3B,2BA6CgB,cAAe,CA7C/B,4BAiDY,wBAA0B,CAjDtC,8BAmDgB,cAAe,CACf,eAAgB,CApDhC,uCA2DY,gBAAiB,CA3D7B,6CA8DY,iBAAkB,CA9D9B,mDAiEY,iBAAkB,CAjE9B,yDAoEY,iBAAkB,CApE9B,+DAuEY,iBAAkB,CAvE9B,qEA0EY,iBAAkB,CC1E9B,UACI,gBAAkB,CAClB,gBAAiB,CAFrB,mBAKQ,iBAAkB,CAL1B,wBAOY,eAAiB,CACjB,eAAgB,CAR5B,kBAaQ,YAAa,CAbrB,aAiBQ,SAAU,CACV,oBAAqB,CAlB7B,aAsBQ,gBAAiB,CAtBzB,YA0BQ,aAAc,CACd,oBAAqB,CACrB,aAAc,CACd,cAAe,CACf,gBAAiB,CACjB,kBAAmB,CA/B3B,oBAkCY,gBAAiB,CACjB,qBAAsB,CACtB,eAAiB,CCjC5B,iCAEI,qBAAsB,CAG1B,yDAEI,aAAyB,CACzB,cAAe,CACf,iBAAkB,CAGtB,uCAEI,iBAAkB,CAClB,eAAgB,CAChB,gBAAiB,CAGrB,qCAEI,gBAAiB,CACjB,oBAAqB,CAHzB,+CAKQ,cAAe,CAIvB,iDAEI,gBAAiB,CAFrB,2DAIQ,gBAAiB,CCnC1B,oBAGY,cAAe,CAH3B,sBAKgB,QAAS,CALzB,mCAWY,wHVH2I,CURvJ,8BAcY,aAAe,CACf,WAAY,CCXpB,oBACI,qBAAsB,CADzB,uCAIO,SAAU,CAJjB,6CAQO,iBAAkB,CARzB,6CAYO,+BAAmC,CACnC,iBAAkB,CAClB,uCAA4C,CAdnD,sCAkBO,eAAiB,CACjB,gBAAiB,CACjB,QAAS,CACT,SAAU,CACV,gBAAiB,CACjB,sCAAuC,CACvC,eAAgB,CAxBvB,6CA0BW,aAAc,CACd,aAAc,CACd,WAAY,CACZ,UAAW,CACX,oBAAqB,CA9BhC,sDAgCe,UAAW,CACX,QAAS,CACT,SAAU,CAlCzB,kDAsCe,eAAiB,CACjB,gBAAiB,CACjB,cAAe,CACf,iBAAoB,CACpB,gBAAiB,CACjB,6IXtC0I,CWuC1I,aAAc,CACd,aAAc,CC7ClC,sCAEQ,aAAc,CACd,cAAe,CAEnB,0BALJ,eAMQ,uBAA0B,CANlC,gDAQY,iBAAkB,CAClB,UAAW,CACX,eAAgB,CAChB,kBAAmB,CACnB,sBAAuB,CAZnC,wwCAgBY,YAAa,CAChB,CAIT,uBACI,eAAgB,CAGpB,2BACI,kBAAoB,CAGxB,wCACI,6BAAiC,CACjC,UAAW,CACX,eAAgB,CAChB,iBAAkB,CAJtB,+DAOQ,cAAe,CAPvB,iEASY,gBAAiB,CACjB,YAAa,CACb,iBAAkB,CAClB,aAAe,CAZ3B,qEAiBQ,wBAAmD,CACnD,UAAc,CAlBtB,yEAqBQ,wBAAmD,CACnD,SAAU,CACV,UAAc,CAOtB,YACE,yBAA2B,CAC3B,gBAAiB,CACjB,mBAAoB,CACpB,eAAgB,CAChB,UAAW,CACX,UAAY,CANd,gCAUI,aZzCuC,CY8C3C,aACE,cAAe,CACf,KAAM,CACN,UAAW,CACX,eAAgB,CAChB,gBAAiB,CACjB,mBAAoB,CACpB,wBZzD0B,CY0D1B,UAAW,CACX,eAAgB,CAChB,cAAe,CACf,4BAA8B,CAGhC,kBACE,WAAY,CACZ,eAAgB,CAGlB,UACE,WAAY,CACZ,gBAAiB,CAFnB,4CASI,YAAa,CATjB,qBAaI,UAAY,CACZ,eAAgB,CAChB,eAAgB,CAfpB,sCAkBM,iBAAkB,CAlBxB,2BAsBM,UAAY,CACZ,sCAA4C,CAvBlD,kCA4BI,UAAY,CACZ,yBAA0B,CAG5B,qCAhCF,UAiCI,iBAAkB,CAClB,OAAQ,CACR,UAAW,CACX,wBAAiC,CACjC,iBAAkB,CAClB,gBAAiB,CAtCrB,iCAyCM,aAAc,CACd,WAAY,CACZ,UAAW,CACX,WAAY,CACZ,SAAU,CACV,cAAe,CA9CrB,qBAkDM,aAAc,CACd,WAAY,CACZ,UAAW,CACX,WAAY,CACZ,aAAc,CACd,gBAAiB,CACjB,iBAAkB,CAxDxB,yBA2DQ,SAAW,CA3DnB,yBAgEM,UAAW,CACX,YAAa,CAjEnB,iCAqEM,aAAc,CACd,kBAAmB,CAtEzB,qBA0EM,gBAAiB,CACjB,aAAc,CAMd,gBAAiB,CAjFvB,sCA8EQ,cAAe,CAChB,CC7KP,uBACI,wBAAyB,CAD7B,yDAGQ,kBAAmB,CACnB,YAAa,CACb,qBAAsB,CAL9B,mEAOY,gBAAiB,CAP7B,0DAcQ,eAAiB,CACjB,YAAa,CACb,qBAAsB,CACtB,wBAAyB,CAjBjC,4DAoBY,aAAc,CACd,eAAiB,CACjB,oBAAqB,CAtBjC,iCA0BQ,YAAa,CAOpB,YACG,UAAW,CACX,eAAgB,CAChB,WAAY,CACZ,wBAAyB,CACzB,YAAa,CALhB,6EAQO,mBAAoB,CACpB,SAAU,CACV,WAAY,CACZ,YAAa,CACb,sBAAuB,CACvB,kBAAmB,CACnB,UAAc,CAdrB,yBAkBO,iBAAkB,CAlBzB,0CAoBW,eAAgB,CApB3B,yBA0BO,gBAAiB,CACjB,0BAA2B,CA3BlC,0CA6BW,gBAAiB,CAKrB,oBACI,iBAAkB,CAEtB,oBACI,gBAAiB,CAIzB,iBACI,gBAAiB,CACjB,cAAe,CAGnB,sBACI,UAAY,CACZ,cAAe,CAEnB,qCAnDH,yBAqDW,SAAU,CArDrB,yBAyDW,SAAU,CAzDrB,0CA6DW,YAAa,CAChB,CAEL,qCAhEH,kDAmEW,SAAU,CAnErB,0CAuEW,aAAc,CACjB,CAST,aACE,4BbvF0C,CawF1C,cAAwB,CACxB,wBAAyB,CAH3B,oCAKI,ab9FwB,CayF5B,sCAWM,uBAAmC,CAMzC,cACE,wBAAyB,CACzB,gBAAiB,CACjB,mBAAoB,CAGtB,gBACE,kBAAgC,CAGlC,iCAEE,eAAgB,CAChB,aAAc,CAIhB,uBACE,aAAc,CACd,UAAY,CACZ,UAAW,CAGb,aACE,WAAY,CACZ,kBAAmB,CACnB,eAAgB,CAGlB,YACE,UAAW,CACX,kBAAgC,CAChC,iBAA+B,CAGjC,aACE,ab3I0C,Cc5B5C,0CACI,UAAW,CAEf,qCACI,UAAW,CAEf,iCACI,UAAW,CAGf,2BACI,eAAiB,CAGrB,aACI,kBAAmB,CAGvB,mBAEQ,eAAgB,CAChB,SAAU,CAHlB,wBAMgB,oBAAqB,CACrB,gBAAiB,CC5BjC,kBAGQ,uBAAwB,CACxB,iBAAkB,CAClB,OAAQ,CACR,gBAAiB,CAIzB,gBACI,eAAgB,CAChB,eAAgB,CpBMpB,UqBjBI,sBAAuB,CACvB,oBAAqB,CACrB,WAAY,CACZ,gBAAiB,CACjB,YAAa,CAEjB,gBACI,gEAAoE,CACpE,UAAW,CACX,cAAe,CrBiCnB,iBqB9BI,eAAkB,CAClB,cAAe,CACf,UAAW,CrBgEf,2BqB5DI,kBAAmB,CACnB,SAAY,CACZ,UAAW,CAGf,oBACI,UAAW,CACX,aAAc,CACd,eAAgB,CAChB,YAAa,CAGjB,4BACI,gBAAmB,CACnB,WAAY,CACZ,eAAgB,CAChB,wBAAyB,CAE7B,2BACI,UAAW,CAEf,8BACI,SAAU,CAEd,OACI,YAAa,CACb,kBAAmB,CACnB,cAAe","file":"sphinx_materialdesign_theme.css","sourceRoot":"../../src/js","sourcesContent":["/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n@import \"../variables\";\n@import \"../mixins\";\n\n.mdl-shadow--2dp {\n  @include shadow-2dp();\n}\n\n.mdl-shadow--3dp {\n  @include shadow-3dp();\n}\n\n.mdl-shadow--4dp {\n  @include shadow-4dp();\n}\n\n.mdl-shadow--6dp {\n  @include shadow-6dp();\n}\n\n.mdl-shadow--8dp {\n  @include shadow-8dp();\n}\n\n.mdl-shadow--16dp {\n  @include shadow-16dp();\n}\n\n.mdl-shadow--24dp {\n  @include shadow-24dp();\n}\n","/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n/* Typography */\n\n@mixin typo-preferred-font($usePreferred: true) {\n  @if $usePreferred {\n    font-family: $preferred_font;\n  }\n}\n\n@mixin typo-display-4($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 112px;\n  font-weight: 300;\n  line-height: 1;\n  letter-spacing: -0.04em;\n\n  @if $colorContrast {\n    opacity: 0.54;\n  }\n}\n\n@mixin typo-display-3($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 56px;\n  font-weight: 400;\n  line-height: 1.35;\n  letter-spacing: -0.02em;\n\n  @if $colorContrast {\n    opacity: 0.54;\n  }\n}\n\n@mixin typo-display-2($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 45px;\n  font-weight: 400;\n  line-height: 48px;\n\n  @if $colorContrast {\n    opacity: 0.54;\n  }\n}\n\n@mixin typo-display-1($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 34px;\n  font-weight: 400;\n  line-height: 40px;\n\n  @if $colorContrast {\n    opacity: 0.54;\n  }\n}\n\n@mixin typo-headline($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 24px;\n  font-weight: 400;\n  line-height: 32px;\n  -moz-osx-font-smoothing: grayscale;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-title($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 20px;\n  font-weight: 500;\n  line-height: 1;\n  letter-spacing: 0.02em;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-subhead($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 16px;\n  font-weight: 400;\n  line-height: 24px;\n  letter-spacing: 0.04em;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-subhead-2($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 16px;\n  font-weight: 400;\n  line-height: 28px;\n  letter-spacing: 0.04em;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-body-2($colorContrast: false, $usePreferred: false) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 14px;\n  @if $usePreferred {\n    font-weight: 500;\n  } @else {\n    font-weight: bold;\n  }\n  line-height: 24px;\n  letter-spacing: 0;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-body-1($colorContrast: false, $usePreferred: false) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 14px;\n  font-weight: 400;\n  line-height: 24px;\n  letter-spacing: 0;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-caption($colorContrast: false, $usePreferred: false) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 12px;\n  font-weight: 400;\n  line-height: 1;\n  letter-spacing: 0;\n\n  @if $colorContrast {\n    opacity: 0.54;\n  }\n}\n\n@mixin typo-blockquote($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  position: relative;\n  font-size: 24px;\n  font-weight: 300;\n  font-style: italic;\n  line-height: 1.35;\n  letter-spacing: 0.08em;\n\n  &:before {\n    position: absolute;\n    left: -0.5em;\n    content: '“';\n  }\n\n  &:after {\n    content: '”';\n    margin-left: -0.05em;\n  }\n\n  @if $colorContrast {\n    opacity: 0.54;\n  }\n}\n\n@mixin typo-menu($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 14px;\n  font-weight: 500;\n  line-height: 1;\n  letter-spacing: 0;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-button($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 14px;\n  font-weight: 500;\n  text-transform: uppercase;\n  line-height: 1;\n  letter-spacing: 0;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-icon() {\n  font-family: 'Material Icons';\n  font-weight: normal;\n  font-style: normal;\n  font-size: 24px;\n  line-height: 1;\n  letter-spacing: normal;\n  text-transform: none;\n  display: inline-block;\n  word-wrap: normal;\n  font-feature-settings: 'liga';\n  -webkit-font-feature-settings: 'liga';\n  -webkit-font-smoothing: antialiased;\n}\n\n/* Shadows */\n\n// Focus shadow mixin.\n@mixin focus-shadow() {\n  box-shadow: 0 0 8px rgba(0,0,0,.18),0 8px 16px rgba(0,0,0,.36);\n}\n\n@mixin shadow-2dp() {\n  box-shadow: 0 2px 2px 0 rgba(0, 0, 0, $shadow-key-penumbra-opacity),\n              0 3px 1px -2px rgba(0, 0, 0, $shadow-key-umbra-opacity),\n              0 1px 5px 0 rgba(0, 0, 0, $shadow-ambient-shadow-opacity);\n}\n@mixin shadow-3dp() {\n  box-shadow: 0 3px 4px 0 rgba(0, 0, 0, $shadow-key-penumbra-opacity),\n              0 3px 3px -2px rgba(0, 0, 0, $shadow-key-umbra-opacity),\n              0 1px 8px 0 rgba(0, 0, 0, $shadow-ambient-shadow-opacity);\n}\n@mixin shadow-4dp() {\n  box-shadow: 0 4px 5px 0 rgba(0, 0, 0, $shadow-key-penumbra-opacity),\n              0 1px 10px 0 rgba(0, 0, 0, $shadow-ambient-shadow-opacity),\n              0 2px 4px -1px rgba(0, 0, 0, $shadow-key-umbra-opacity);\n}\n@mixin shadow-6dp() {\n  box-shadow: 0 6px 10px 0 rgba(0, 0, 0, $shadow-key-penumbra-opacity),\n              0 1px 18px 0 rgba(0, 0, 0, $shadow-ambient-shadow-opacity),\n              0 3px 5px -1px rgba(0, 0, 0, $shadow-key-umbra-opacity);\n}\n@mixin shadow-8dp() {\n  box-shadow: 0 8px 10px 1px rgba(0, 0, 0, $shadow-key-penumbra-opacity),\n              0 3px 14px 2px rgba(0, 0, 0, $shadow-ambient-shadow-opacity),\n              0 5px 5px -3px rgba(0, 0, 0, $shadow-key-umbra-opacity);\n}\n\n@mixin shadow-16dp() {\n  box-shadow: 0 16px 24px 2px rgba(0, 0, 0, $shadow-key-penumbra-opacity),\n              0  6px 30px 5px rgba(0, 0, 0, $shadow-ambient-shadow-opacity),\n              0  8px 10px -5px rgba(0, 0, 0, $shadow-key-umbra-opacity);\n}\n\n@mixin shadow-24dp() {\n  box-shadow: 0  9px 46px  8px rgba(0, 0, 0, $shadow-key-penumbra-opacity),\n              0 11px 15px -7px rgba(0, 0, 0, $shadow-ambient-shadow-opacity),\n              0 24px 38px  3px rgba(0, 0, 0, $shadow-key-umbra-opacity);\n}\n\n/* Animations */\n\n@mixin material-animation-fast-out-slow-in($duration:0.2s) {\n  transition-duration: $duration;\n  transition-timing-function: $animation-curve-fast-out-slow-in;\n}\n\n@mixin material-animation-linear-out-slow-in($duration:0.2s) {\n  transition-duration: $duration;\n  transition-timing-function: $animation-curve-linear-out-slow-in;\n}\n\n@mixin material-animation-fast-out-linear-in($duration:0.2s) {\n  transition-duration: $duration;\n  transition-timing-function: $animation-curve-fast-out-linear-in;\n}\n\n@mixin material-animation-default($duration:0.2s) {\n  transition-duration: $duration;\n  transition-timing-function: $animation-curve-default;\n}\n\n/* Dialog */\n\n@mixin dialog-width($units:5) {\n  @if(type_of($units) != 'number') {\n    @error \"The unit given to dialog-width should be a number.\";\n  }\n  // 56dp is the base unit width for Dialogs.\n  // With 5 units being the number of units for a mobile device.\n  // https://goo.gl/sK2O5o\n  width: $units * 56px;\n}\n","/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n@import \"../variables\";\n@import \"../mixins\";\n\n.mdl-data-table {\n  position: relative;\n  border: $data-table-dividers;\n  border-collapse: collapse;\n  white-space: nowrap;\n  font-size: $data-table-font-size;\n  background-color: unquote(\"rgb(#{$color-white})\");\n\n  thead {\n    padding-bottom: 3px;\n\n    .mdl-data-table__select {\n      margin-top: 0;\n    }\n  }\n\n  tbody {\n    tr {\n      position: relative;\n      height: $data-table-row-height;\n      @include material-animation-default(0.28s);\n      transition-property: background-color;\n\n      &.is-selected {\n        background-color: $data-table-selection-color;\n      }\n\n      &:hover {\n        background-color: $data-table-hover-color;\n      }\n    }\n  }\n\n  td, th {\n    padding: 0 $data-table-column-padding 12px $data-table-column-padding;\n    text-align: right;\n\n    &:first-of-type {\n      padding-left: 24px;\n    }\n\n    &:last-of-type {\n      padding-right: 24px;\n    }\n  }\n\n  td {\n    position: relative;\n    vertical-align: middle;\n    height: $data-table-row-height;\n    border-top: $data-table-dividers;\n    border-bottom: $data-table-dividers;\n    padding-top: $data-table-cell-top;\n    box-sizing: border-box;\n\n    .mdl-data-table__select {\n      vertical-align: middle;\n    }\n  }\n\n  th {\n    position: relative;\n    vertical-align: bottom;\n    text-overflow: ellipsis;\n    @include typo-body-2();\n    height: $data-table-row-height;\n    font-size: $data-table-header-font-size;\n    color: $data-table-header-color;\n    padding-bottom: 8px;\n    box-sizing: border-box;\n\n    &.mdl-data-table__header--sorted-ascending,\n    &.mdl-data-table__header--sorted-descending {\n      color: $data-table-header-sorted-color;\n      &:before {\n        @include typo-icon;\n        font-size: $data-table-header-sort-icon-size;\n        content: \"\\e5d8\";\n        margin-right: 5px;\n        vertical-align: sub;\n      }\n      &:hover {\n        cursor: pointer;\n        &:before {\n          color: $data-table-header-sorted-icon-hover-color;\n        }\n      }\n    }\n    &.mdl-data-table__header--sorted-descending:before {\n      content: \"\\e5db\";\n    }\n  }\n}\n\n.mdl-data-table__select {\n  width: 16px;\n}\n\n.mdl-data-table__cell--non-numeric.mdl-data-table__cell--non-numeric {\n  text-align: left;\n}\n","/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n/*------------------------------------*\\\n    $CONTENTS\n\\*------------------------------------*/\n/**\n * STYLE GUIDE VARIABLES------------------Declarations of Sass variables\n * -----Typography\n * -----Colors\n * -----Textfield\n * -----Switch\n * -----Spinner\n * -----Radio\n * -----Menu\n * -----List\n * -----Layout\n * -----Icon toggles\n * -----Footer\n * -----Column\n * -----Checkbox\n * -----Card\n * -----Button\n * -----Animation\n * -----Progress\n * -----Badge\n * -----Shadows\n * -----Grid\n * -----Data table\n * -----Dialog\n * -----Snackbar\n * -----Tooltip\n * -----Chip\n *\n * Even though all variables have the `!default` directive, most of them\n * should not be changed as they are dependent one another. This can cause\n * visual distortions (like alignment issues) that are hard to track down\n * and fix.\n */\n\n\n/* ==========  TYPOGRAPHY  ========== */\n\n/* We're splitting fonts into \"preferred\" and \"performance\" in order to optimize\n   page loading. For important text, such as the body, we want it to load\n   immediately and not wait for the web font load, whereas for other sections,\n   such as headers and titles, we're OK with things taking a bit longer to load.\n   We do have some optional classes and parameters in the mixins, in case you\n   definitely want to make sure you're using the preferred font and don't mind\n   the performance hit.\n   We should be able to improve on this once CSS Font Loading L3 becomes more\n   widely available.\n*/\n$preferred_font: 'Roboto', 'Helvetica', 'Arial', sans-serif !default;\n$performance_font: 'Helvetica', 'Arial', sans-serif !default;\n\n/* ==========  COLORS  ========== */\n\n/**\n*\n* Material design color palettes.\n* @see http://www.google.com/design/spec/style/color.html\n*\n**/\n\n@import \"color-definitions\";\n@import \"functions\";\n\n/* ==========  IMAGES  ========== */\n$image_path: '/images' !default;\n\n/* ==========  Color & Themes  ========== */\n\n// Define whether individual color palette items should have classes created.\n// Setting this to true will remove individual color classes for each color in the palettes.\n// To improve overall performance (assuming they aren't used) by:\n// * Saving server bandwidth sending the extra classes\n// * Save client computation against the classes\n// it is RECOMMENDED you set this to true.\n$trim-color-classes: false !default;\n\n// Use color primarily for emphasis. Choose colors that fit with\n// your brand and provide good contrast between visual components.\n$color-primary: $palette-indigo-500 !default;\n$color-primary-dark: $palette-indigo-700 !default;\n$color-accent: $palette-pink-A200 !default;\n\n// Our primary is dark, so use $color-dark-contrast for overlaid text.\n$color-primary-contrast: $color-dark-contrast !default;\n// Our accent is dark, so use $color-dark-contrast for overlaid text.\n$color-accent-contrast: $color-dark-contrast !default;\n\n// Replace all colors with placeholders if we're generating a template.\n@if $styleguide-generate-template == true {\n  $color-primary: '$color-primary';\n  $color-primary-dark: '$color-primary-dark';\n  $color-accent: '$color-accent';\n  $color-primary-contrast: '$color-primary-contrast';\n  $color-accent-contrast: '$color-accent-contrast';\n}\n\n/* ==========  Typography  ========== */\n\n// We use the following default color styles: text-color-primary and\n// text-color-secondary. For light themes, use text-color-primary-inverse\n// and text-color-secondary-inverse.\n\n$text-color-primary: unquote(\"rgba(#{$color-black}, 0.87)\") !default;\n$text-link-color: unquote(\"rgb(#{$color-accent})\") !default;\n\n// Define whether to target elements directly for typographic enhancements.\n// Turning this off means you need to use mdl-* classes more often.\n// Other components may also fail to adhere to MD without these rules.\n// It is strongly recommended you leave this as true.\n\n$target-elements-directly: true !default;\n\n/* ==========  Components  ========== */\n\n/* ==========  Standard Buttons  ========== */\n\n// Default button colors.\n$button-primary-color: unquote(\"rgba(#{$palette-grey-500}, 0.20)\") !default;\n$button-secondary-color: unquote(\"rgb(#{$color-black})\") !default;\n$button-hover-color: $button-primary-color !default;\n$button-active-color: unquote(\"rgba(#{$palette-grey-500}, 0.40)\") !default;\n$button-focus-color: unquote(\"rgba(#{$color-black}, 0.12)\") !default;\n\n// Colored button colors.\n$button-primary-color-alt: unquote(\"rgb(#{$color-primary})\") !default;\n$button-secondary-color-alt: unquote(\"rgb(#{$color-primary-contrast})\") !default;\n$button-hover-color-alt: unquote(\"rgb(#{$color-primary})\") !default;\n$button-active-color-alt: unquote(\"rgb(#{$color-primary})\") !default;\n$button-focus-color-alt: $button-focus-color !default;\n\n// Ripple color for colored raised buttons.\n$button-ripple-color-alt: unquote(\"rgb(#{$color-primary-contrast})\") !default;\n\n// Disabled button colors.\n$button-primary-color-disabled: unquote(\"rgba(#{$color-black}, 0.12)\") !default;\n$button-secondary-color-disabled: unquote(\"rgba(#{$color-black}, 0.26)\") !default;\n\n// FAB colors and sizes.\n$button-fab-color-alt: unquote(\"rgb(#{$color-accent})\") !default;\n$button-fab-hover-color-alt: unquote(\"rgb(#{$color-accent})\") !default;\n$button-fab-active-color-alt: unquote(\"rgb(#{$color-accent})\") !default;\n$button-fab-text-color-alt: unquote(\"rgb(#{$color-accent-contrast})\") !default;\n$button-fab-ripple-color-alt: unquote(\"rgb(#{$color-accent-contrast})\") !default;\n\n// Icon button colors and sizes.\n$button-icon-color: unquote(\"rgb(#{$palette-grey-700})\") !default;\n$button-icon-focus-color: $button-focus-color !default;\n\n/* ==========  Icon Toggles  ========== */\n\n$icon-toggle-color: unquote(\"rgb(#{$palette-grey-700})\") !default;\n$icon-toggle-focus-color: $button-focus-color !default;\n$icon-toggle-checked-color: unquote(\"rgb(#{$color-primary})\") !default;\n$icon-toggle-checked-focus-color: unquote(\"rgba(#{$color-primary}, 0.26)\") !default;\n$icon-toggle-disabled-color: unquote(\"rgba(#{$color-black}, 0.26)\") !default;\n\n/* ==========  Radio Buttons  ========== */\n\n$radio-color: unquote(\"rgb(#{$color-primary})\") !default;\n$radio-off-color: unquote(\"rgba(#{$color-black}, 0.54)\") !default;\n$radio-disabled-color: unquote(\"rgba(#{$color-black}, 0.26)\") !default;\n\n/* ==========  Ripple effect  ========== */\n\n$ripple-bg-color: unquote(\"rgb(#{$color-light-contrast})\") !default;\n\n/* ==========  Layout  ========== */\n\n$layout-nav-color: unquote(\"rgb(#{$palette-grey-300})\") !default;\n\n// Drawer\n$layout-drawer-bg-color: unquote(\"rgb(#{$palette-grey-50})\") !default;\n$layout-drawer-border-color: unquote(\"rgb(#{$palette-grey-300})\") !default;\n$layout-text-color: unquote(\"rgb(#{$palette-grey-800})\") !default;\n$layout-drawer-navigation-color: #757575 !default;\n$layout-drawer-navigation-link-active-background: unquote(\"rgb(#{$palette-grey-300})\") !default;\n$layout-drawer-navigation-link-active-color: unquote(\"rgb(#{$color-light-contrast})\") !default;\n\n// Header\n$layout-header-bg-color: unquote(\"rgb(#{$color-primary})\") !default;\n$layout-header-text-color: unquote(\"rgb(#{$color-primary-contrast})\") !default;\n$layout-header-nav-hover-color: unquote(\"rgba(#{$palette-grey-700}, 0.6)\") !default;\n$layout-header-tab-text-color: unquote(\"rgba(#{$color-primary-contrast}, 0.6)\") !default;\n\n// Tabs\n$layout-header-tab-highlight: unquote(\"rgb(#{$color-accent})\") !default;\n\n/* ==========  Content Tabs  ========== */\n\n$tab-highlight-color: unquote(\"rgb(#{$color-primary})\") !default;\n$tab-text-color: unquote(\"rgba(#{$color-black}, 0.54)\") !default;\n$tab-active-text-color: unquote(\"rgba(#{$color-black}, 0.87)\") !default;\n$tab-border-color: unquote(\"rgb(#{$palette-grey-300})\") !default;\n\n/* ==========  Checkboxes  ========== */\n\n$checkbox-color: unquote(\"rgb(#{$color-primary})\") !default;\n$checkbox-off-color: unquote(\"rgba(#{$color-black}, 0.54)\") !default;\n$checkbox-disabled-color: unquote(\"rgba(#{$color-black}, 0.26)\") !default;\n$checkbox-focus-color: unquote(\"rgba(#{$color-primary}, 0.26)\") !default;\n$checkbox-image-path: $image_path;\n\n/* ==========  Switches  ========== */\n\n$switch-color: unquote(\"rgb(#{$color-primary})\") !default;\n$switch-faded-color: unquote(\"rgba(#{$color-primary}, 0.26)\") !default;\n$switch-thumb-color: $switch-color !default;\n$switch-track-color: unquote(\"rgba(#{$color-primary}, 0.5)\") !default;\n\n$switch-off-thumb-color: unquote(\"rgb(#{$palette-grey-50})\") !default;\n$switch-off-track-color: unquote(\"rgba(#{$color-black}, 0.26)\") !default;\n$switch-disabled-thumb-color: unquote(\"rgb(#{$palette-grey-400})\") !default;\n$switch-disabled-track-color: unquote(\"rgba(#{$color-black}, 0.12)\") !default;\n\n/* ==========  Spinner  ========== */\n\n$spinner-color-1: unquote(\"rgb(#{$palette-blue-400})\") !default;\n$spinner-color-2: unquote(\"rgb(#{$palette-red-500})\") !default;\n$spinner-color-3: unquote(\"rgb(#{$palette-yellow-600})\") !default;\n$spinner-color-4: unquote(\"rgb(#{$palette-green-500})\") !default;\n\n$spinner-single-color: unquote(\"rgb(#{$color-primary})\") !default;\n\n/* ==========  Text fields  ========== */\n\n$input-text-background-color: transparent !default;\n$input-text-label-color: unquote(\"rgba(#{$color-black}, 0.26)\") !default;\n$input-text-bottom-border-color: unquote(\"rgba(#{$color-black}, 0.12)\") !default;\n$input-text-highlight-color: unquote(\"rgb(#{$color-primary})\") !default;\n$input-text-disabled-color: $input-text-bottom-border-color !default;\n$input-text-disabled-text-color: $input-text-label-color !default;\n$input-text-error-color: unquote(\"rgb(#{$palette-red-A700})\") !default;\n\n/* ==========  Card  ========== */\n\n$card-background-color: unquote(\"rgb(#{$color-white})\") !default;\n$card-text-color: unquote(\"rgb(#{$color-black})\") !default;\n$card-image-placeholder-color: unquote(\"rgb(#{$color-accent})\") !default;\n$card-supporting-text-text-color: unquote(\"rgba(#{$color-black}, 0.54)\") !default;\n$card-border-color: rgba(0,0,0,0.1) !default;\n$card-subtitle-color: unquote(\"rgba(#{$color-black}, 0.54)\") !default;\n\n/* ==========  Sliders ========== */\n\n$range-bg-color: unquote(\"rgba(#{$color-black}, 0.26)\") !default;\n$range-color: unquote(\"rgb(#{$color-primary})\") !default;\n$range-faded-color: unquote(\"rgba(#{$color-primary}, 0.26)\") !default;\n$range-bg-focus-color: unquote(\"rgba(#{$color-black}, 0.12)\") !default;\n\n/* ========== Progress ========== */\n$progress-main-color: unquote(\"rgb(#{$color-primary})\") !default;\n$progress-secondary-color: unquote(\"rgba(#{$color-primary-contrast}, 0.7)\") !default;\n$progress-fallback-buffer-color: unquote(\"rgba(#{$color-primary-contrast}, 0.9)\") !default;\n$progress-image-path: $image_path;\n\n/* ==========  List ========== */\n\n$list-main-text-text-color: unquote(\"rgba(#{$color-black}, 0.87)\") !default;\n$list-supporting-text-text-color: unquote(\"rgba(#{$color-black}, 0.54)\") !default;\n$list-icon-color: unquote(\"rgb(#{$palette-grey-600})\") !default;\n$list-avatar-color: white !default;\n\n/* ==========  Item ========== */\n\n// Default Item Colors\n$default-item-text-color: unquote(\"rgba(#{$color-black}, 0.87)\") !default;\n$default-item-outline-color: unquote(\"rgb(#{$palette-grey-400})\") !default;\n$default-item-hover-bg-color: unquote(\"rgb(#{$palette-grey-200})\") !default;\n$default-item-focus-bg-color: unquote(\"rgb(#{$palette-grey-200})\") !default;\n$default-item-active-bg-color: unquote(\"rgb(#{$palette-grey-300})\") !default;\n$default-item-divider-color: unquote(\"rgba(#{$color-black}, 0.12)\") !default;\n\n// Disabled Button Colors\n$disabled-item-text-color: unquote(\"rgb(#{$palette-grey-400})\") !default;\n\n/* ==========  Dropdown menu ========== */\n\n$default-dropdown-bg-color: unquote(\"rgb(#{$color-white})\") !default;\n\n/* ==========  Tooltips  ========== */\n\n$tooltip-text-color: unquote(\"rgb(#{$color-white})\") !default;\n$tooltip-background-color: unquote(\"rgba(#{$palette-grey-700}, 0.9)\") !default;\n\n/* ==========  Footer  ========== */\n\n$footer-bg-color: unquote(\"rgb(#{$palette-grey-800})\") !default;\n$footer-color: unquote(\"rgb(#{$palette-grey-500})\") !default;\n$footer-heading-color: unquote(\"rgb(#{$palette-grey-300})\") !default;\n$footer-button-fill-color: $footer-color !default;\n$footer-underline-color: $footer-color !default;\n\n\n/* TEXTFIELD */\n\n$input-text-font-size: 16px !default;\n$input-text-width: 100% !default;\n$input-text-padding: 4px !default;\n$input-text-vertical-spacing: 20px !default;\n\n$input-text-button-size: 32px !default;\n$input-text-floating-label-fontsize: 12px !default;\n$input-text-expandable-icon-top: 16px !default;\n\n\n/* SWITCH */\n\n$switch-label-font-size: 16px !default;\n$switch-label-height: 24px !default;\n$switch-track-height: 14px !default;\n$switch-track-length: 36px !default;\n$switch-thumb-size: 20px !default;\n$switch-track-top: ($switch-label-height - $switch-track-height) / 2 !default;\n$switch-thumb-top: ($switch-label-height - $switch-thumb-size) / 2 !default;\n$switch-ripple-size: $switch-label-height * 2 !default;\n$switch-helper-size: 8px !default;\n\n/* SPINNER */\n\n$spinner-size: 28px !default;\n$spinner-stroke-width: 3px !default;\n\n// Amount of circle the arc takes up.\n$spinner-arc-size: 270deg !default;\n// Time it takes to expand and contract arc.\n$spinner-arc-time: 1333ms !default;\n// How much the start location of the arc should rotate each time.\n$spinner-arc-start-rot: 216deg !default;\n\n$spinner-duration: 360 * $spinner-arc-time / (\n    strip-units($spinner-arc-start-rot + (360deg - $spinner-arc-size)));\n\n\n/* RADIO */\n\n$radio-label-font-size: 16px !default;\n$radio-label-height: 24px !default;\n$radio-button-size: 16px !default;\n$radio-inner-margin: $radio-button-size / 4;\n$radio-padding: 8px !default;\n$radio-top-offset: ($radio-label-height - $radio-button-size) / 2;\n$radio-ripple-size: 42px !default;\n\n\n/* MENU */\n\n$menu-expand-duration: 0.3s !default;\n$menu-fade-duration: 0.2s !default;\n\n/* LIST */\n\n$list-border: 8px !default;\n$list-min-height: 48px !default;\n$list-min-padding: 16px !default;\n$list-bottom-padding: 20px !default;\n$list-avatar-text-left-distance: 72px !default;\n$list-icon-text-left-distance: 72px !default;\n\n$list-avatar-size: 40px !default;\n$list-icon-size: 24px !default;\n\n$list-two-line-height: 72px !default;\n$list-three-line-height: 88px !default;\n\n/* LAYOUT */\n\n$layout-drawer-narrow: 240px !default;\n$layout-drawer-wide: 456px !default;\n$layout-drawer-width: $layout-drawer-narrow !default;\n\n$layout-header-icon-size: 32px !default;\n$layout-screen-size-threshold: 1024px !default;\n$layout-header-icon-margin: 24px !default;\n$layout-drawer-button-mobile-size: 32px !default;\n$layout-drawer-button-desktop-size: 48px !default;\n\n$layout-header-mobile-row-height: 56px !default;\n$layout-mobile-header-height: $layout-header-mobile-row-height;\n$layout-header-desktop-row-height: 64px !default;\n$layout-desktop-header-height: $layout-header-desktop-row-height;\n\n$layout-header-desktop-baseline: 80px !default;\n$layout-header-mobile-baseline: 72px !default;\n$layout-header-mobile-indent: 16px !default;\n$layout-header-desktop-indent: 40px !default;\n\n$layout-tab-font-size: 14px !default;\n$layout-tab-bar-height: 48px !default;\n$layout-tab-mobile-padding: 12px !default;\n$layout-tab-desktop-padding: 24px !default;\n$layout-tab-highlight-thickness: 2px !default;\n\n\n/* ICON TOGGLE */\n\n$icon-toggle-size: 32px !default;\n$icon-toggle-font-size: 24px !default;\n$icon-toggle-ripple-size: 36px !default;\n\n/* FOOTER */\n\n/*mega-footer*/\n$footer-min-padding: 16px !default;\n$footer-padding-sides: 40px !default;\n$footer-heading-font-size: 14px !default;\n$footer-heading-line-height: (1.7 * $footer-heading-font-size) !default;\n$footer-btn-size: 36px  !default;\n\n/*mini-footer*/\n$padding: 16px !default;\n$footer-heading-font-size: 24px !default;\n$footer-heading-line-height: (1.5 * $footer-heading-font-size) !default;\n$footer-btn-size: 36px !default;\n\n/* CHECKBOX */\n\n$checkbox-label-font-size: 16px !default;\n$checkbox-label-height: 24px !default;\n$checkbox-button-size: 16px !default;\n$checkbox-inner-margin: 2px !default;\n$checkbox-padding: 8px !default;\n$checkbox-top-offset:\n($checkbox-label-height - $checkbox-button-size - $checkbox-inner-margin) / 2;\n$checkbox-ripple-size: $checkbox-label-height * 1.5;\n\n/* CARD */\n\n/* Card dimensions */\n$card-width: 330px !default;\n$card-height: 200px !default;\n$card-font-size: 16px !default;\n$card-title-font-size: 24px !default;\n$card-subtitle-font-size: 14px !default;\n$card-horizontal-padding: 16px !default;\n$card-vertical-padding: 16px !default;\n\n$card-title-perspective-origin-x: 165px !default;\n$card-title-perspective-origin-y: 56px !default;\n\n$card-title-transform-origin-x: 165px !default;\n$card-title-transform-origin-y: 56px !default;\n\n$card-title-text-transform-origin-x: 149px !default;\n$card-title-text-transform-origin-y: 48px !default;\n\n$card-supporting-text-font-size: 1rem !default;\n$card-supporting-text-line-height: 18px !default;\n\n$card-actions-font-size: 16px !default;\n\n$card-title-text-font-weight: 300 !default;\n$card-z-index: 1 !default;\n\n/* Cover image */\n$card-cover-image-height: 186px !default;\n$card-background-image-url: '' !default;\n\n\n/* BUTTON */\n/**\n *\n * Dimensions\n *\n */\n$button-min-width: 64px !default;\n$button-height: 36px !default;\n$button-padding: 16px !default;\n$button-margin: 4px !default;\n$button-border-radius: 2px !default;\n\n$button-fab-size: 56px !default;\n$button-fab-size-mini: 40px !default;\n$button-fab-font-size: 24px !default;\n\n$button-icon-size: 32px !default;\n$button-icon-size-mini: 24px !default;\n\n\n/* ANIMATION */\n$animation-curve-fast-out-slow-in: cubic-bezier(0.4, 0, 0.2, 1) !default;\n$animation-curve-linear-out-slow-in: cubic-bezier(0, 0, 0.2, 1) !default;\n$animation-curve-fast-out-linear-in: cubic-bezier(0.4, 0, 1, 1) !default;\n\n$animation-curve-default: $animation-curve-fast-out-slow-in !default;\n\n\n/* PROGRESS */\n$bar-height: 4px !default;\n\n/* BADGE */\n$badge-font-size: 12px !default;\n$badge-color: unquote(\"rgb(#{$color-accent-contrast})\") !default;\n$badge-color-inverse: unquote(\"rgb(#{$color-accent})\") !default;\n$badge-background: unquote(\"rgb(#{$color-accent})\") !default;\n$badge-background-inverse: unquote(\"rgba(#{$color-accent-contrast},0.2)\") !default;\n$badge-size : 22px !default;\n$badge-padding: 2px !default;\n$badge-overlap: 12px !default;\n\n/* SHADOWS */\n\n$shadow-key-umbra-opacity: 0.2 !default;\n$shadow-key-penumbra-opacity: 0.14 !default;\n$shadow-ambient-shadow-opacity: 0.12 !default;\n\n/* GRID */\n\n$grid-desktop-columns: 12 !default;\n$grid-desktop-gutter: 16px !default;\n$grid-desktop-margin: 16px !default;\n\n$grid-desktop-breakpoint: 840px !default;\n\n$grid-tablet-columns: 8 !default;\n$grid-tablet-gutter: $grid-desktop-gutter !default;\n$grid-tablet-margin: $grid-desktop-margin !default;\n\n$grid-tablet-breakpoint: 480px !default;\n\n$grid-phone-columns: 4 !default;\n$grid-phone-gutter: $grid-desktop-gutter !default;\n$grid-phone-margin: $grid-desktop-margin !default;\n\n$grid-cell-default-columns: $grid-phone-columns !default;\n$grid-max-columns: $grid-desktop-columns !default;\n\n/* DATA TABLE */\n\n$data-table-font-size: 13px !default;\n$data-table-header-font-size: 12px !default;\n$data-table-header-sort-icon-size: 16px !default;\n\n$data-table-header-color: rgba(#000, 0.54) !default;\n$data-table-header-sorted-color: rgba(#000, 0.87) !default;\n$data-table-header-sorted-icon-hover-color: rgba(#000, 0.26) !default;\n$data-table-divider-color: rgba(#000, 0.12) !default;\n\n$data-table-hover-color: #eeeeee !default;\n$data-table-selection-color: #e0e0e0 !default;\n\n$data-table-dividers: 1px solid $data-table-divider-color !default;\n\n$data-table-row-height: 48px !default;\n$data-table-last-row-height: 56px !default;\n$data-table-header-height: 56px !default;\n\n$data-table-column-spacing: 36px !default;\n$data-table-column-padding: $data-table-column-spacing / 2;\n\n$data-table-card-header-height: 64px !default;\n$data-table-card-title-top: 20px !default;\n$data-table-card-padding: 24px !default;\n$data-table-button-padding-right: 16px !default;\n$data-table-cell-top: $data-table-card-padding / 2;\n\n/* DIALOG */\n$dialog-content-color: $card-supporting-text-text-color;\n\n/* SNACKBAR */\n\n// Hard coded since the color is not present in any palette.\n$snackbar-background-color: #323232 !default;\n$snackbar-tablet-breakpoint: $grid-tablet-breakpoint;\n$snackbar-action-color: unquote(\"rgb(#{$color-accent})\") !default;\n\n/* TOOLTIP */\n$tooltip-font-size: 10px !default;\n$tooltip-font-size-large: 14px !default;\n\n/* CHIP */\n$chip-bg-color: rgb(222, 222, 222) !default;\n$chip-bg-active-color: rgb(214, 214, 214) !default;\n$chip-height: 32px !default;\n$chip-font-size: 13px !default; \n","/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n@import \"../variables\";\n\n.mdl-mini-footer {\n  display: flex;\n  flex-flow: row wrap;\n  justify-content: space-between;\n\n  padding: ($padding * 2) $padding;\n\n  color: $footer-color;\n  background-color: $footer-bg-color;\n\n  &:after {\n    content: '';\n    display: block;\n  }\n\n  & .mdl-logo {\n    line-height: $footer-btn-size;\n  }\n}\n\n.mdl-mini-footer--link-list,\n.mdl-mini-footer__link-list {\n  display: flex;\n  flex-flow: row nowrap;\n\n  list-style: none;\n\n  margin: 0;\n  padding: 0;\n\n  & li {\n    margin-bottom: 0;\n    margin-right: $padding;\n\n    @media screen and (min-width: 760px) {\n      line-height: $footer-btn-size;\n    }\n  }\n\n  & a {\n    color: inherit;\n    text-decoration: none;\n    white-space: nowrap;\n  }\n}\n\n.mdl-mini-footer--left-section,\n.mdl-mini-footer__left-section {\n  display: inline-block;\n  order: 0;\n}\n\n.mdl-mini-footer--right-section,\n.mdl-mini-footer__right-section {\n  display: inline-block;\n  order: 1;\n}\n\n.mdl-mini-footer--social-btn,\n.mdl-mini-footer__social-btn {\n  width: $footer-btn-size;\n  height: $footer-btn-size;\n\n  padding: 0;\n  margin: 0;\n\n  background-color: $footer-button-fill-color;\n\n  border: none;\n}\n","/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n@import \"../variables\";\n\n.mdl-card {\n  display: flex;\n  flex-direction: column;\n  font-size: $card-font-size;\n  font-weight: 400;\n  min-height: $card-height;\n  overflow: hidden;\n  width: $card-width;\n  z-index: $card-z-index;\n  position: relative;\n  background: $card-background-color;\n  border-radius: 2px;\n  box-sizing: border-box;\n}\n\n.mdl-card__media {\n  background-color: $card-image-placeholder-color;\n  background-repeat: repeat;\n  background-position: 50% 50%;\n  background-size: cover;\n  background-origin: padding-box;\n  background-attachment: scroll;\n  box-sizing: border-box;\n}\n\n.mdl-card__title {\n  align-items: center;\n  color: $card-text-color;\n  display: block;\n  display: flex;\n  justify-content: stretch;\n  line-height: normal;\n  padding: $card-vertical-padding $card-horizontal-padding;\n  perspective-origin: $card-title-perspective-origin-x $card-title-perspective-origin-y;\n  transform-origin: $card-title-transform-origin-x $card-title-transform-origin-y;\n  box-sizing: border-box;\n\n  &.mdl-card--border {\n    border-bottom: 1px solid $card-border-color;\n  }\n}\n\n.mdl-card__title-text {\n  align-self: flex-end;\n  color: inherit;\n  display: block;\n  display: flex;\n  font-size: $card-title-font-size;\n  font-weight: $card-title-text-font-weight;\n  line-height: normal;\n  overflow: hidden;\n  transform-origin: $card-title-text-transform-origin-x $card-title-text-transform-origin-y;\n  margin: 0;\n}\n\n.mdl-card__subtitle-text {\n  font-size: $card-subtitle-font-size;\n  color: $card-subtitle-color;\n  margin: 0;\n}\n\n.mdl-card__supporting-text {\n  color: $card-supporting-text-text-color;\n  font-size: $card-supporting-text-font-size;\n  line-height: $card-supporting-text-line-height;\n  overflow: hidden;\n  padding: $card-vertical-padding $card-horizontal-padding;\n  width: 90%;\n\n  &.mdl-card--border {\n    border-bottom: 1px solid $card-border-color;\n  }\n}\n\n.mdl-card__actions {\n  font-size: $card-actions-font-size;\n  line-height: normal;\n  width: 100%;\n  background-color: rgba(0,0,0,0);\n  padding: 8px;\n  box-sizing: border-box;\n\n  &.mdl-card--border {\n    border-top: 1px solid $card-border-color;\n  }\n}\n\n.mdl-card--expand {\n  flex-grow: 1;\n}\n\n\n.mdl-card__menu {\n  position: absolute;\n  right: 16px;\n  top: 16px;\n}\n","/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n@import \"../variables\";\n@import \"../mixins\";\n\n// The button component. Defaults to a flat button.\n.mdl-button {\n  background: transparent;\n  border: none;\n  border-radius: $button-border-radius;\n  color: $button-secondary-color;\n  position: relative;\n  height: $button-height;\n  margin: 0;\n  min-width: $button-min-width;\n  padding: 0 $button-padding;\n  display: inline-block;\n  @include typo-button();\n  overflow: hidden;\n  will-change: box-shadow;\n  transition: box-shadow 0.2s $animation-curve-fast-out-linear-in,\n              background-color 0.2s $animation-curve-default,\n              color 0.2s $animation-curve-default;\n  outline: none;\n  cursor: pointer;\n  text-decoration: none;\n  text-align: center;\n  line-height: $button-height;\n  vertical-align: middle;\n\n  &::-moz-focus-inner {\n    border: 0;\n  }\n\n  &:hover {\n    background-color: $button-hover-color;\n  }\n\n  &:focus:not(:active) {\n    background-color: $button-focus-color;\n  }\n\n  &:active {\n    background-color: $button-active-color;\n  }\n\n  &.mdl-button--colored {\n    color: $button-primary-color-alt;\n\n    &:focus:not(:active) {\n      background-color: $button-focus-color-alt;\n    }\n  }\n}\n\ninput.mdl-button[type=\"submit\"] {\n  -webkit-appearance:none;\n}\n\n  // Raised buttons\n  .mdl-button--raised {\n    background: $button-primary-color;\n    @include shadow-2dp();\n\n    &:active {\n      @include shadow-4dp();\n      background-color: $button-active-color;\n    }\n\n    &:focus:not(:active) {\n      @include focus-shadow();\n      background-color: $button-active-color;\n    }\n\n    &.mdl-button--colored {\n      background: $button-primary-color-alt;\n      color: $button-secondary-color-alt;\n\n      &:hover {\n        background-color: $button-hover-color-alt;\n      }\n\n      &:active {\n        background-color: $button-active-color-alt;\n      }\n\n      &:focus:not(:active) {\n        background-color: $button-active-color-alt;\n      }\n\n      & .mdl-ripple {\n        background: $button-ripple-color-alt;\n      }\n    }\n  }\n\n\n  // FABs\n  .mdl-button--fab {\n    border-radius: 50%;\n    font-size: $button-fab-font-size;\n    height: $button-fab-size;\n    margin: auto;\n    min-width: $button-fab-size;\n    width: $button-fab-size;\n    padding: 0;\n    overflow: hidden;\n    background: $button-primary-color;\n    box-shadow: 0 1px 1.5px 0 rgba(0,0,0,0.12), 0 1px 1px 0 rgba(0,0,0,0.24);\n    position: relative;\n    line-height: normal;\n\n    & .material-icons {\n      position: absolute;\n      top: 50%;\n      left: 50%;\n      transform: translate(- $button-fab-font-size / 2, - $button-fab-font-size / 2);\n      line-height: $button-fab-font-size;\n      width: $button-fab-font-size;\n    }\n\n    &.mdl-button--mini-fab {\n      height: $button-fab-size-mini;\n      min-width: $button-fab-size-mini;\n      width: $button-fab-size-mini;\n    }\n\n    & .mdl-button__ripple-container {\n      border-radius: 50%;\n      // Fixes clipping bug in Safari.\n      -webkit-mask-image: -webkit-radial-gradient(circle, white, black);\n    }\n\n    &:active {\n      @include shadow-4dp();\n      background-color: $button-active-color;\n    }\n\n    &:focus:not(:active) {\n      @include focus-shadow();\n      background-color: $button-active-color;\n    }\n\n    &.mdl-button--colored {\n      background: $button-fab-color-alt;\n      color: $button-fab-text-color-alt;\n\n      &:hover {\n        background-color: $button-fab-hover-color-alt;\n      }\n\n      &:focus:not(:active) {\n        background-color: $button-fab-active-color-alt;\n      }\n\n      &:active {\n        background-color: $button-fab-active-color-alt;\n      }\n\n      & .mdl-ripple {\n        background: $button-fab-ripple-color-alt;\n      }\n    }\n  }\n\n\n  // Icon buttons\n  .mdl-button--icon {\n    border-radius: 50%;\n    font-size: $button-fab-font-size;\n    height: $button-icon-size;\n    margin-left: 0;\n    margin-right: 0;\n    min-width: $button-icon-size;\n    width: $button-icon-size;\n    padding: 0;\n    overflow: hidden;\n    color: inherit;\n    line-height: normal;\n\n    & .material-icons {\n      position: absolute;\n      top: 50%;\n      left: 50%;\n      transform: translate(- $button-fab-font-size / 2, - $button-fab-font-size / 2);\n      line-height: $button-fab-font-size;\n      width: $button-fab-font-size;\n    }\n\n    &.mdl-button--mini-icon {\n      height: $button-icon-size-mini;\n      min-width: $button-icon-size-mini;\n      width: $button-icon-size-mini;\n\n      & .material-icons {\n        top: ($button-icon-size-mini - $button-fab-font-size) / 2;\n        left: ($button-icon-size-mini - $button-fab-font-size) / 2;\n      }\n    }\n\n    & .mdl-button__ripple-container {\n      border-radius: 50%;\n      // Fixes clipping bug in Safari.\n      -webkit-mask-image: -webkit-radial-gradient(circle, white, black);\n    }\n  }\n\n\n  // Ripples\n  .mdl-button__ripple-container {\n    display: block;\n    height: 100%;\n    left: 0px;\n    position: absolute;\n    top: 0px;\n    width: 100%;\n    z-index: 0;\n    overflow: hidden;\n\n    .mdl-button[disabled] & .mdl-ripple,\n    .mdl-button.mdl-button--disabled & .mdl-ripple {\n      background-color: transparent;\n    }\n  }\n\n// Colorized buttons\n\n.mdl-button--primary.mdl-button--primary {\n  color: $button-primary-color-alt;\n  & .mdl-ripple {\n    background: $button-secondary-color-alt;\n  }\n  &.mdl-button--raised, &.mdl-button--fab {\n    color: $button-secondary-color-alt;\n    background-color: $button-primary-color-alt;\n  }\n}\n\n.mdl-button--accent.mdl-button--accent {\n  color: $button-fab-color-alt;\n  & .mdl-ripple {\n    background: $button-fab-text-color-alt;\n  }\n  &.mdl-button--raised, &.mdl-button--fab {\n    color: $button-fab-text-color-alt;\n    background-color: $button-fab-color-alt;\n  }\n}\n\n// Disabled buttons\n\n.mdl-button {\n  // Bump up specificity by using [disabled] twice.\n  &[disabled][disabled],\n  &.mdl-button--disabled.mdl-button--disabled {\n    color: $button-secondary-color-disabled;\n    cursor: default;\n    background-color: transparent;\n  }\n\n  &--fab {\n    // Bump up specificity by using [disabled] twice.\n    &[disabled][disabled],\n    &.mdl-button--disabled.mdl-button--disabled {\n      background-color: $button-primary-color-disabled;\n      color: $button-secondary-color-disabled;\n    }\n  }\n\n  &--raised {\n    // Bump up specificity by using [disabled] twice.\n    &[disabled][disabled],\n    &.mdl-button--disabled.mdl-button--disabled {\n      background-color: $button-primary-color-disabled;\n      color: $button-secondary-color-disabled;\n      box-shadow: none;\n    }\n  }\n  &--colored {\n    // Bump up specificity by using [disabled] twice.\n    &[disabled][disabled],\n    &.mdl-button--disabled.mdl-button--disabled {\n      color: $button-secondary-color-disabled;\n    }\n  }\n}\n\n// Align icons inside buttons with text\n.mdl-button .material-icons {\n  vertical-align: middle;\n}\n","// SIMPLE GRID - SASS/SCSS\n\n\n// fonts\n$font-weight-light: 300;\n$font-weight-regular: 400;\n$font-weight-heavy: 700;\n\n// colors\n$dark-grey: #333447;\n$dark-gray: #333447; // for the Americans\n\n\n.font-light {\n  font-weight: $font-weight-light;\n}\n\n.font-regular {\n  font-weight: $font-weight-regular;\n}\n\n.font-heavy {\n  font-weight: $font-weight-heavy;\n}\n\n// utility\n\n.left {\n  text-align: left;\n}\n\n.right {\n  text-align: right;\n}\n\n.center {\n  text-align: center;\n  margin-left: auto;\n  margin-right: auto;\n}\n\n.justify {\n  text-align: justify;\n}\n\n.hidden-sm {\n  display: none;\n}\n\n// grid\n\n$width: 98%;\n$gutter: 2%;\n$breakpoint-small: 33.75em; // 540px\n$breakpoint-med: 45em; // 720px\n$breakpoint-large: 60em; // 960px\n\n.container {\n  width: 100%;\n  margin-left: auto;\n  margin-right: auto;\n\n  @media only screen and (min-width: $breakpoint-small) {\n    width: 80%;\n  }\n\n  @media only screen and (min-width: $breakpoint-large) {\n    width: 75%;\n    max-width: 60rem;\n  }\n}\n\n.row {\n  position: relative;\n  width: 100%;\n}\n\n.row [class^=\"col\"] {\n  float: left;\n  margin: 0.5rem 1%;\n  min-height: 0.125rem;\n}\n\n.row::after {\n  content: \"\";\n  display: table;\n  clear: both;\n}\n\n.col-1,\n.col-2,\n.col-3,\n.col-4,\n.col-5,\n.col-6,\n.col-7,\n.col-8,\n.col-9,\n.col-10,\n.col-11,\n.col-12 {\n  width: $width;\n}\n\n.col-1-sm {\n  width: ($width / 12) - ($gutter * 11 / 12);\n}\n\n.col-2-sm {\n  width: ($width / 6) - ($gutter * 10 / 12);\n}\n\n.col-3-sm {\n  width: ($width / 4) - ($gutter * 9 / 12);\n}\n\n.col-4-sm {\n  width: ($width / 3) - ($gutter * 8 / 12);\n}\n\n.col-5-sm {\n  width: ($width / (12 / 5)) - ($gutter * 7 / 12);\n}\n\n.col-6-sm {\n  width: ($width / 2) - ($gutter * 6 / 12);\n}\n\n.col-7-sm {\n  width: ($width / (12 / 7)) - ($gutter * 5 / 12);\n}\n\n.col-8-sm {\n  width: ($width / (12 / 8)) - ($gutter * 4 / 12);\n}\n\n.col-9-sm {\n  width: ($width / (12 / 9)) - ($gutter * 3 / 12);\n}\n\n.col-10-sm {\n  width: ($width / (12 / 10)) - ($gutter * 2 / 12);\n}\n\n.col-11-sm {\n  width: ($width / (12 / 11)) - ($gutter * 1 / 12);\n}\n\n.col-12-sm {\n  width: $width;\n}\n\n@media only screen and (min-width: $breakpoint-med) {\n  .col-1 {\n    width: ($width / 12) - ($gutter * 11 / 12);\n  }\n  .col-2 {\n    width: ($width / 6) - ($gutter * 10 / 12);\n  }\n  .col-3 {\n    width: ($width / 4) - ($gutter * 9 / 12);\n  }\n  .col-4 {\n    width: ($width / 3) - ($gutter * 8 / 12);\n  }\n  .col-5 {\n    width: ($width / (12 / 5)) - ($gutter * 7 / 12);\n  }\n  .col-6 {\n    width: ($width / 2) - ($gutter * 6 / 12);\n  }\n  .col-7 {\n    width: ($width / (12 / 7)) - ($gutter * 5 / 12);\n  }\n  .col-8 {\n    width: ($width / (12 / 8)) - ($gutter * 4 / 12);\n  }\n  .col-9 {\n    width: ($width / (12 / 9)) - ($gutter * 3 / 12);\n  }\n  .col-10 {\n    width: ($width / (12 / 10)) - ($gutter * 2 / 12);\n  }\n  .col-11 {\n    width: ($width / (12 / 11)) - ($gutter * 1 / 12);\n  }\n  .col-12 {\n    width: $width;\n  }\n\n  .hidden-sm {\n    display: block;\n  }\n}\n\n.row {\n  display: -webkit-box;\n  display: -webkit-flex;\n  display: -ms-flexbox;\n  display: flex;\n  flex-wrap: wrap;\n}\n\n.row > [class*='col-'] {\n  display: flex;\n  flex-direction: column;\n}\n","\n/*\nMaterial Icons\n*/\n\n.material-icons {\n    font-family: 'Material Icons';\n    font-weight: normal;\n    font-style: normal;\n    font-size: 24px;  /* Preferred icon size */\n    display: inline-block;\n    line-height: 1;\n    text-transform: none;\n    letter-spacing: normal;\n    word-wrap: normal;\n    white-space: nowrap;\n    direction: ltr;\n  \n    /* Support for all WebKit browsers. */\n    -webkit-font-smoothing: antialiased;\n    /* Support for Safari and Chrome. */\n    text-rendering: optimizeLegibility;\n  \n    /* Support for Firefox. */\n    -moz-osx-font-smoothing: grayscale;\n  \n    /* Support for IE. */\n    font-feature-settings: 'liga';\n  }","html {\n    font-size: $font_size;\n}\n\nbody {\n    display: block !important;\n    background-color: $background_color;\n    font-size: 1rem;\n    line-height: 1.5rem;\n    font-family: $body_font_family;\n}\n\n.mdl-layout__content:focus {\n    outline: none;\n }\n\nh1, h2, h3, h4, h5, h6, blockquote, span.mdl-layout-title,\na.download > code.download {\n    font-family: $body_font_family;\n}\n\nh1, h2, h3, h4, h5, h6, .toc-backref, .contents, .toctree-wrapper, .contents a, .toctree-wrapper a, .globaltoc a.current {\n    color: $color-mxnet !important;\n}\n\na {\n    text-decoration: none;\n}\n\n.page-content {\n    font-size: 1rem;\n    p, ul, ol, dl, dd, dt, table, th, td {\n        font-size: 1rem;\n    }\n}\n\n.brand {\n    color: inherit;\n    text-decoration: none;\n}\n\n.section {\n    overflow-x: auto;\n}\n\n\n/*\n *  Figure Directive Styles\n */\n img {\n    max-width: 100%;\n    display: block;\n    margin-left: auto;\n    margin-right: auto;\n }\n\ndiv.figure {\n    p.caption {\n        text-align: center;\n        margin-top: .75rem;\n\n        span.caption-number {\n            font-style: normal;\n        }\n        .caption-number::after {\n            content: \"\\00a0\";\n        }\n    }\n}\n\n.svg-icon {\n  width: 16px;\n  height: 16px;\n  display: inline-block;\n  fill: $grey-color-light;\n  padding-right: 5px;\n  padding-top: 4px;\n  vertical-align: text-top;\n}\n\n/*\n * Download Link Styles\n */\na.download > i.material-icons {\n    position: relative;\n    top: 5px;\n}\n\na.download {\n    text-decoration: none;\n}\n\n%clearfix:after {\n  content: \"\";\n  display: table;\n  clear: both;\n}\n\n.wrapper {\n  max-width: -webkit-calc(#{$content-width} - (#{$spacing-unit} * 2));\n  max-width: calc(#{$content-width} - (#{$spacing-unit} * 2));\n  margin-right: auto;\n  margin-left: auto;\n  padding-right: calc(#{$spacing-unit}+15px);\n  padding-left: $spacing-unit;\n  @extend %clearfix;\n\n  @media screen and (max-width: $on-laptop) {\n    max-width: -webkit-calc(#{$content-width} - (#{$spacing-unit}));\n    max-width: calc(#{$content-width} - (#{$spacing-unit}));\n    padding-right: $spacing-unit / 2;\n    padding-left: $spacing-unit / 2;\n  }\n}\n\n","/*\nVariables\n*/\n$font_size: 16px;\n\n$background_color: #fafafa;\n$code_background: rgba(0,0,0,.05);\n\n$code_font_family: \"Menlo\", \"DejaVu Sans Mono\", \"Liberation Mono\", \"Consolas\", \"Ubuntu Mono\", \"Courier New\", \"andale mono\", \"lucida console\", monospace !default;\n$body_font_family: -apple-system,BlinkMacSystemFont,\"Segoe UI\",Roboto,\"Helvetica Neue\",Arial,sans-serif,\"Apple Color Emoji\",\"Segoe UI Emoji\",\"Segoe UI Symbol\" !default;\n$base-font-size:   17px !default;\n\n$xl-breakpoint: 1795px;\n$lg-breakpoint: 1200px;\n$md-breakpoint: 992px;\n$sm-breakpoint: 768px;\n$xs-breakpoint: 576px;\n\n$color-primary: $palette-blue-500;\n$color-primary-dark: $palette-blue-700 !default;\n$color-accent: $palette-deep-orange-A200 !default;\n$color-primary-contrast: $color-white !default;\n$color-accent-contrast: $color-white !default;\n\n\n$base-line-height: 1.5 !default;\n$spacing-unit:     30px !default;\n\n$color-mxnet: rgb(4,140,204);\n$color-mxnet-dark: rgb(4,60,110);\n$grey-color:       #828282 !default;\n$grey-color-light: lighten($grey-color, 45%) !default;\n$grey-color-dark:  darken($grey-color, 25%) !default;\n\n$table-text-align: left !default;\n\n// Width of the content area\n$content-width:    1150px !default;\n\n$on-palm:          600px !default;\n$on-palm:          900px !default;\n$on-laptop:        1024px !default;","/**\n * Layout Styles\n */\n $layout: (\n    document: (\n        xl: (\n            width: 1400px,\n        )\n    ),\n    drawer-container: (\n        width: $layout-drawer-width,\n    ),\n    side-doc-outline: (\n        width: 230px,\n    ),\n    page-content: (\n        md: (\n            width: 90%,\n            padding: 0 5%\n        ),\n        lg: (\n            width: calc( 90% - 230px ),\n            padding: 0 5%\n        )\n    )\n);\n\n.mdl-layout__container {\n    margin-top: 76px;\n    height: calc(100% - 76px);   \n}\n\n\n.document {\n    width: 100%;\n    margin: 0 auto;\n    display: flex;\n\n    @media (min-width: $xl-breakpoint) {\n        width: map-get(map-get(map-get($layout, document), xl), width);\n    }\n    .page-content {\n        width: 100%;\n        margin: 0 auto;\n        padding: 0 12px;\n\n        @media (min-width: $md-breakpoint) {\n            width: map-get(map-get(map-get($layout, page-content), md), width);\n            padding: map-get(map-get(map-get($layout, page-content), md), padding);\n        }\n\n        @media (min-width: $lg-breakpoint) {\n            width: map-get(map-get(map-get($layout, page-content), lg), width);\n            padding: map-get(map-get(map-get($layout, page-content), lg), padding);\n        }\n    }\n\n    .side-doc-outline {\n        width: map-get(map-get($layout, side-doc-outline), width);\n\n        @media (max-width: $lg-breakpoint - 1) {\n            display: none;\n        } \n        &--content {\n            position: fixed;\n            overflow-x: auto;\n            overflow-y: auto;\n            width: inherit;\n            &::-webkit-scrollbar {\n                width: 6px;\n            }\n    \n            &::-webkit-scrollbar-track {\n                border-radius: 6px;\n            }\n    \n            &::-webkit-scrollbar-thumb {\n                background-color: rgba(0, 0, 0, .3);\n                border-radius: 6px;\n                box-shadow:0 0 0 1px rgba(255, 255, 255, .3);\n            }\n        }\n    }\n\n}","@keyframes float-in {\n    0% {\n        transform: translateY(0.5rem);\n        opacity: 0;\n    }\n\t100% {\n\t\ttransform: translateY(0);\n\t\topacity: 1;\n\t}\n}\n\n@keyframes float-out {\n    0% {\n        transform: translateY(0);\n        opacity: 1;\n    }\n\t100% {\n\t\ttransform: translateY(0.5rem);\n\t\topacity: 0;\n\t}\n}\n\n.page-content {\n    .headerlink {\n        display: inline-block;\n        text-decoration: none;\n        margin-left: 0.8rem;\n        color: inherit;\n        opacity: 0;\n        &:hover {\n            animation: float-in 0.2s $animation-curve-fast-out-slow-in 0s forwards;\n        }\n    }\n\n    h1, h2, h3, h4, h5, h6 {\n        .toc-backref {\n            text-decoration: none;\n        }\n        &:hover {\n            .headerlink {\n                animation: float-in 0.2s $animation-curve-fast-out-slow-in 0s forwards;\n            }\n        }\n    }\n\n    h1 {\n        font-size: 2rem;\n        line-height: 2.25rem;\n    }\n\n    h2 {\n        font-size: 1.75rem;\n        line-height: 2rem;\n        padding-top: 1.5rem;\n        margin-top: 0;\n        margin-bottom: 1rem;\n    }\n\n    h3 {\n        font-size: 1.5rem;\n        line-height: 1.75rem;\n        padding-top: 1rem;\n        margin-top: 0px;\n        margin-bottom: .75rem;\n    }\n\n    h4 {\n        font-size: 1.25rem;\n        line-height: 1.5rem;\n        padding-top: .75rem;\n        margin-top: 0px;\n        margin-bottom: .5rem;\n    }\n\n    div.page-content h5 {\n        font-size: 1.1rem;\n        line-height: 1.5rem;\n        padding-top: 2rem;\n        margin-top: 0px;\n        margin-bottom: 1rem;\n    }\n\n    div.page-content h6 {\n        font-size: 1rem;\n        line-height: 1.5rem;\n        padding-top: 2rem;\n        margin-top: 0px;\n        margin-bottom: 1rem;\n    }\n\n\n}\n","\n/*\n * Admonition Styles\n */\n $admonitions: (\n    hint: (\n        font-color: rgb(0, 188, 212),\n        background-color: rgba(0, 188, 212, 0.1),\n        icon-content: \"help_outline\"\n    ),\n    note: (\n        font-color: rgb(0, 188, 212),\n        background-color: rgba(0, 188, 212, 0.1),\n        icon-content: \"info_outline\"\n    ),\n    seealso: (\n        font-color: rgb(0, 188, 212),\n        background-color: rgba(0, 188, 212, 0.1),\n        icon-content: \"search\"\n    ),\n    warning: (\n        font-color: rgb(255, 193, 7),\n        background-color: rgba(255, 193, 7, 0.1),\n        icon-content: \"warning\"\n    ),\n    attention: (\n        font-color: rgb(255, 193, 7),\n        background-color: rgba(255, 193, 7, 0.1),\n        icon-content: \"warning\"\n    ),\n    tip: (\n        font-color: rgb(139, 195, 74),\n        background-color: rgba(139, 195, 74, 0.1),\n        icon-content: \"lightbulb_outline\"\n    ),\n    important: (\n        font-color: rgb(139, 195, 74),\n        background-color: rgba(139, 195, 74, 0.1),\n        icon-content:  \"check_circle\"\n    ),\n    error: (\n        font-color: rgb(244, 67, 54),\n        background-color: rgba(244, 67, 54, 0.1),\n        icon-content: \"error_outline\"\n    ),\n    caution: (\n        font-color: rgb(244, 67, 54),\n        background-color: rgba(244, 67, 54, 0.1),\n        icon-content: \"error_outline\"\n    ),\n    danger: (\n        font-color: rgb(244, 67, 54),\n        background-color: rgba(244, 67, 54, 0.1),\n        icon-content: \"error_outline\"\n    )\n);\n\n @mixin admonition-style($type) {\n    border-left: solid 4px map-get(map-get($admonitions, $type), font-color);\n    background-color: map-get(map-get($admonitions, $type), background-color);\n    .admonition-title {\n        font-size: 16px;\n        font-weight: bold;\n        color: map-get(map-get($admonitions, $type), font-color);\n\n        margin-top: 4px;\n        margin-bottom: 8px;\n        &::before {\n            @extend .material-icons;\n            position: relative;\n            margin-right: 5px;\n            top: 3px;\n            content: map-get(map-get($admonitions, $type), icon-content);\n            font-size: 18px;\n        }\n    }\n}\n\n.admonition {\n    @extend .mdl-shadow--2dp;\n\n    padding: 12px 20px;\n    margin-top: 10px;\n    margin-bottom: 10px;\n    p.last {\n        margin: 16px;\n    }\n    .admonition-title {\n        font-size: 16px;\n        font-weight: bold;\n        color: #555;\n        text-transform: uppercase;\n        margin-top: 7px;\n    }\n\n    @each $type in (note, seealso, hint, warning, attention, tip, important, error, caution, danger) {\n        &.#{$type} {\n            @include admonition-style($type);\n        }\n    }\n}\n",".page-content {\n    .highlight {\n        margin: 1px 0;\n        pre {\n            background: $code_background;\n            color: rgba(0,0,0,.87);\n            font-family: $code_font_family;\n            padding: 0.75rem;\n            overflow: auto;\n            overflow-y: hidden;\n            .o, .nd {\n                color: rgba(0,0,0,.87);\n            }\n        }\n    }\n\n    div.highlight-console div.highlight {\n        background: none;\n    }\n\n    // for jupyter notebook output cell\n    .output {\n        .highlight {\n            pre {\n                color: rgba(0,0,0,.87);\n                background: $background_color;\n                border-width: 1px;\n                border-color: #999;\n                border-style: solid;\n                padding: 0.75rem;\n            }\n        }\n     }\n\n    .code, code:not(.download) {\n        margin: 0 0;\n        font-family: $code_font_family;\n        border-radius: 2px;\n        span.pre {\n            font-family: $code_font_family;\n        }\n    }\n\n    .viewcode-link {\n        padding-left: 2em;\n        font-size: 80%;\n    }\n\n    .rubric, .method > dt, .function > dt, .class > dt {\n        display: table;\n        margin: 10px 0;\n        font-size: 100%;\n        line-height: normal;\n        background: #e7f2fa;\n        color: #2B98F0;\n        border-top: solid 3px #55ADF3;\n        padding: 10px;\n        position: relative;\n        .descname, .descclassname {\n            color: rgba(0,0,0,.87);\n            background: #e7f2fa;\n            padding: 3px;\n        }\n        em {\n            padding: 0 2px;\n        }\n    }\n\n\n    .rubric {\n        margin: 30px 0 10px 0;\n     }\n\n\n    .field-body {\n        padding-left: 40px;\n        ul {\n            padding: 0 0 0 16px;\n            margin: 0;\n        }\n    }\n\n     // .docutils > dt {\n    //     padding: 6px;\n    //     display: table;\n    //     margin-bottom: 6px;\n    //     border: none;\n    //     border-left: solid 3px #ccc;\n    //     background: #f0f0f0;\n    //     color: #555;\n    // }\n\n    .seealso .docutils > dt {\n       float: left;\n       clear: left;\n       padding: 0 6px;\n     }\n\n    .seealso .docutils > dd {\n       padding-left: 6em;\n    }\n    .nblast {\n        padding-bottom: 1em;\n    }\n\n    pre {\n        font-size: 90%;\n        background: #eee;\n        color: #455A64;\n        padding: 16px 32px;\n        width: auto;\n        border-radius: 4px;\n        word-wrap: break-word;\n\n        &:hover {\n            @extend .mdl-shadow--2dp;\n\n            &:before {\n                font-family: $body_font_family;\n                padding: 0 0.5rem;\n                content: attr(click-to-copy);\n                color: rgba(0, 0, 0, 0.5);\n                border-radius: 4px;\n                position: relative;\n                float: right;\n                top: -0.5rem;\n                right: -0.5rem;\n                background: rgb(200, 200, 200);\n                font-size: 0.8rem;\n                cursor: pointer;\n            }\n        }\n    }\n}\n","/*\n * Quotation Block Styles\n */\n .page-content {\n    blockquote {\n        font-size: 1rem;\n        padding: 0 1rem;\n        border-left: 3px solid $code_background;\n\n        &:after {\n            content: \"\" !important;\n            margin-left: 0;\n        }\n        &:before {\n            content: \"\" !important;\n        }\n    }\n }\n",".page-content {\n    table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) {\n        @extend .mdl-data-table;\n        @extend .mdl-shadow--2dp;\n\n        margin: 1.5rem 0;\n        table-layout: fixed;\n        max-width: 100%;\n        min-width: 70%;\n\n        th, td {\n            @extend .mdl-data-table__cell--non-numeric;\n            white-space: normal;\n            overflow-wrap: break-word;\n        }\n\n        caption {\n            font-size: $font_size;\n            margin: 1rem 0 0.8rem 0;\n            white-space: normal;\n            .caption-number {\n                font-style: normal;\n            }\n            .caption-number::after {\n                content: \"\\00a0\";\n            }\n        }\n\n    }\n}\n",".globaltoc {\n    \n    .caption, .toc {\n        display: none;\n    }\n\n    ul {\n\n        list-style-type: none;\n        padding: 0;\n        margin: 0;\n\n        li {\n            min-height: 36px;\n            .link-wrapper {\n                display: flex;\n                justify-content: space-between;\n                > a {\n                    padding: 8px 0;\n                    display: block;\n                    width: 100%;\n                    font-size: 1rem;\n                    text-decoration: none;\n                    color: $layout-drawer-navigation-color;\n                    &.current {\n                        font-weight: bold;\n                    }\n                }\n            }\n        }\n    }\n\n    .nav-toggle {\n        padding: 0;\n        float: right;\n        display: flex;\n        align-items: center;\n        justify-content: center;\n        height: 36px;\n        > a {\n            padding: 0;\n            margin-left: 0;\n            margin-right: 4px;\n            cursor: pointer;\n            > i {\n                font-size: 18px;\n            }\n        }\n        &.show {\n            transform: rotateZ(180deg);\n            > a {\n                margin-right: 0;\n                margin-left: 4px;\n            }\n        }\n    }\n\n    nav {\n        > ul > li > span.link-wrapper {\n            padding-left: 8px;\n        }\n        > ul > li > ul > li > span.link-wrapper {\n            padding-left: 16px;\n        }\n        > ul > li > ul > li > ul > li > span.link-wrapper {\n            padding-left: 24px;\n        }\n        > ul > li > ul > li > ul > li > ul> li > span.link-wrapper {\n            padding-left: 32px;\n        }\n        > ul > li > ul > li > ul > li > ul > li > ul> li > span.link-wrapper {\n            padding-left: 40px;\n        }\n        > ul > li > ul > li > ul > li > ul > li > ul > li > ul> li > span.link-wrapper {\n            padding-left: 48px;\n        }\n    }\n}\n",".localtoc {\n    font-size: 0.75rem;\n    padding-top: 1rem;\n\n    .caption {\n        padding-left: 12px;\n        &-text {\n            font-size: 0.9rem;\n            font-weight: 700;\n        }\n    }\n\n    > ul > li > a {\n        display: none;\n    }\n\n    ul {\n        padding: 0;\n        list-style-type: none;\n    }\n\n    li {\n        padding-left: 6px;\n    }\n\n    a {\n        display: block;\n        text-decoration: none;\n        color: inherit;\n        margin-top: 8px;\n        padding-left: 8px;\n        line-height: 1.1rem;\n    \n        &.current {\n            padding-left: 5px;\n            border-left: 3px solid;\n            font-weight: bold;\n        }\n    }\n}","/*\r\n *  Toctree and Contents Directive Styles\r\n */\r\n .toctree-wrapper,\r\n .contents.topic {\r\n     border-left: 5px solid;\r\n }\r\n\r\n .toctree-wrapper > p.caption,\r\n .contents.topic > p.topic-title {\r\n     color: rgb(117, 117, 117);\r\n     font-size: 1rem;\r\n     padding-left: 14px;\r\n }\r\n\r\n .toctree-wrapper ul,\r\n .contents.topic ul{\r\n     padding-left: 14px;\r\n     list-style: none;\r\n     line-height: 30px;\r\n }\r\n\r\n .toctree-wrapper a,\r\n .contents.topic a {\r\n     font-size: 1.2rem;\r\n     text-decoration: none;\r\n     .pre {\r\n         font-size: 1rem;\r\n     }\r\n }\r\n\r\n .toctree-wrapper > ul > li > a,\r\n .contents.topic > ul > li > a {\r\n     font-size: 1.3rem;\r\n     .pre {\r\n         font-size: 1.1rem;\r\n     }\r\n }\r\n",".page-content {\n    ul {\n        li {\n            margin: .3rem 0;\n            p {\n                margin: 0;\n            }\n        }\n    }\n    .option-list {\n        .option {\n            font-family: $code_font_family;\n        }\n        td {\n            padding: 0.5rem;\n            border: none;\n        }\n    }\n}\n","/*\r\n * Drawer Styles\r\n */\r\n.mdl-layout {\r\n    &__drawer {\r\n        background-color: #fff;\r\n\r\n        &::-webkit-scrollbar {\r\n            width: 6px;\r\n        }\r\n\r\n        &::-webkit-scrollbar-track {\r\n            border-radius: 6px;\r\n        }\r\n\r\n        &::-webkit-scrollbar-thumb {\r\n            background-color: rgba(0, 0, 0, .3);\r\n            border-radius: 6px;\r\n            box-shadow:0 0 0 1px rgba(255, 255, 255, .3);\r\n        }\r\n\r\n        > .mdl-layout-title {\r\n            font-weight: bold;\r\n            text-align: right;\r\n            margin: 0;\r\n            padding: 0;\r\n            line-height: 32px;\r\n            border-bottom: 1px solid rgba(0,0,0,.1);\r\n            min-height: 64px;\r\n            .title {\r\n                color: inherit;\r\n                display: block;\r\n                height: 100%;\r\n                width: 100%;\r\n                text-decoration: none;\r\n                > img.logo {\r\n                    width: 100%;\r\n                    margin: 0;\r\n                    padding: 0;\r\n                }\r\n\r\n                &-text {\r\n                    font-weight: bold;\r\n                    text-align: right;\r\n                    padding: 0 10px;\r\n                    margin: 16px 0 8px 0;\r\n                    line-height: 32px;\r\n                    font-family: $body_font_family;\r\n                    color: inherit;\r\n                    display: block;\r\n                }\r\n            }\r\n        }\r\n    }\r\n}\r\n","/*\r\n * Header Styles\r\n */\r\n\r\nnav.breadcrumb {\r\n    > a.mdl-navigation__link {\r\n        padding: 0 8px;\r\n        font-size: 18px;\r\n    }\r\n    @media (max-width: $lg-breakpoint - 1) {\r\n        width: calc( 100% - 64px );\r\n        a.mdl-navigation__link.is-active {\r\n            overflow-x: hidden;\r\n            width: 100%;\r\n            overflow: hidden;\r\n            white-space: nowrap;\r\n            text-overflow: ellipsis;\r\n        }\r\n        a.mdl-navigation__link:not(.is-active),\r\n        i.material-icons {\r\n            display: none;\r\n        }\r\n    }\r\n}\r\n\r\ndiv.mdl-layout__header {\r\n    margin-top: 77px;\r\n}\r\n\r\n.mdl-layout__drawer-button {\r\n    top: 13px !important;\r\n}\r\n\r\ndiv.mdl-layout__header-row.header-links {\r\n    background: rgba(255,255,255,0.2);\r\n    width: 100%;\r\n    overflow-x: auto;\r\n    overflow-y: hidden;\r\n\r\n    a.mdl-navigation__link  {\r\n        font-size: 1rem;\r\n        i {\r\n            font-size: 1.2rem;\r\n            margin: 0 8px;\r\n            position: relative;\r\n            bottom: -0.1rem;\r\n        }\r\n    };\r\n\r\n    a.mdl-navigation__link:hover  {\r\n        background-color: unquote(\"rgb(#{$color-primary})\");\r\n        color: #eeeeee;\r\n    };\r\n    a.mdl-navigation__link[href=\"#\"]  {\r\n        background-color: unquote(\"rgb(#{$color-primary})\");\r\n        opacity: 1;\r\n        color: #ffffff;\r\n    };\r\n}\r\n\r\n/* mxnet-header */\r\n\r\n\r\n.site-title {\r\n  font-weight: 300 !important;\r\n  line-height: 57px;\r\n  letter-spacing: -1px;\r\n  margin-bottom: 0;\r\n  float: left;\r\n  color: white;\r\n\r\n  &,\r\n  &:visited {\r\n    color: $grey-color-dark;\r\n  }\r\n}\r\n\r\n\r\n.site-header {\r\n  position: fixed;\r\n  top: 0;\r\n  width: 100%;\r\n  min-height: 55px;\r\n  padding-top: 10px;\r\n  padding-bottom: 10px;\r\n  background-color: $color-mxnet;\r\n  z-index: 10;\r\n  font-weight: 300;\r\n  font-size: 17px;\r\n  border-bottom: 1px solid white;\r\n}\r\n\r\n.site-header-logo {\r\n  width: 120px;\r\n  display: initial;\r\n}\r\n\r\n.site-nav {\r\n  float: right;\r\n  line-height: 57px;\r\n\r\n  .nav-trigger {\r\n    display: none;\r\n  }\r\n\r\n  .menu-icon {\r\n    display: none;\r\n  }\r\n\r\n  .page-link {\r\n    color: white;\r\n    line-height: 1.5;\r\n    font-weight: 300;\r\n    // Gaps between nav items, but not on the last one\r\n    &:not(:last-child) {\r\n      margin-right: 40px;\r\n    }\r\n\r\n    &:hover {\r\n      color: white;\r\n      text-shadow: -0.06ex 0 white, 0.06ex 0 white;\r\n    }\r\n  }\r\n\r\n  .page-link.page-current {\r\n    color: white;\r\n    text-decoration: underline;\r\n  }\r\n\r\n  @media screen and (max-width: $on-laptop) {\r\n    position: absolute;\r\n    top: 9px;\r\n    right: 15px;\r\n    background-color: rgb(23,141,201);\r\n    border-radius: 2px;\r\n    text-align: right;\r\n\r\n    label[for=\"nav-trigger\"] {\r\n      display: block;\r\n      float: right;\r\n      width: 36px;\r\n      height: 36px;\r\n      z-index: 2;\r\n      cursor: pointer;\r\n    }\r\n\r\n    .menu-icon {\r\n      display: block;\r\n      float: right;\r\n      width: 36px;\r\n      height: 26px;\r\n      line-height: 0;\r\n      padding-top: 20px;\r\n      text-align: center;\r\n\r\n      > svg {\r\n        fill: white;\r\n      }\r\n    }\r\n\r\n    input ~ .trigger {\r\n      clear: both;\r\n      display: none;\r\n    }\r\n\r\n    input:checked ~ .trigger {\r\n      display: block;\r\n      padding-bottom: 5px;\r\n    }\r\n\r\n    .page-link {\r\n      padding: 5px 10px;\r\n      display: block;\r\n\r\n      &:not(:last-child) {\r\n        margin-right: 0;\r\n      }\r\n\r\n      margin-left: 20px;\r\n    }\r\n  }\r\n}","/*\r\n * Footer Styles\r\n */\r\nfooter.mdl-mini-footer {\r\n    background-color: #212121;\r\n    > div.mdl-mini-footer__left-section {\r\n        margin-bottom: 20px;\r\n        display: flex;\r\n        flex-direction: column;\r\n        .mdl-logo {\r\n            font-size: 1.1rem;\r\n        }\r\n        ul {\r\n            @extend .mdl-mini-footer__link-list;\r\n        }\r\n    }\r\n    > div.mdl-mini-footer__right-section {\r\n        font-size: 0.9rem;\r\n        display: flex;\r\n        flex-direction: column;\r\n        justify-content: flex-end;\r\n\r\n        a {\r\n            color: inherit;\r\n            font-weight: bold;\r\n            text-decoration: none;\r\n        }\r\n    }\r\n    p.caption {\r\n        display: none;\r\n    }\r\n}\r\n\r\n/*\r\n * Pagenation Block Styles\r\n */\r\n .pagenation {\r\n    width: 100%;\r\n    margin-top: 80px;\r\n    height: 92px;\r\n    background-color: #424242;\r\n    display: flex;\r\n\r\n    .button-common {\r\n        text-transform: none;\r\n        padding: 0;\r\n        height: 92px;\r\n        display: flex;\r\n        justify-content: center;\r\n        align-items: center;\r\n        color: #ffffff;\r\n    }\r\n    #button-prev {\r\n        @extend .button-common;\r\n        margin-right: auto;\r\n        .pagenation-text {\r\n            text-align: left;\r\n        }\r\n        \r\n    }\r\n    #button-next {\r\n        @extend .button-common;\r\n        margin-left: auto;\r\n        flex-direction: row-reverse;\r\n        .pagenation-text {\r\n            text-align: right;\r\n        }\r\n    }\r\n\r\n    &-arrow {\r\n        &-L {\r\n            margin-right: 20px;\r\n        }\r\n        &-R {\r\n            margin-left: 20px;\r\n        }\r\n    }\r\n\r\n    &-text {\r\n        line-height: 30px;\r\n        font-size: 20px;\r\n    }\r\n\r\n    &-direction {\r\n        opacity: 0.7;\r\n        font-size: 18px;\r\n    }\r\n    @media screen and (max-width: 1024px) {\r\n        #button-prev {\r\n            width: 20%;\r\n        }\r\n        \r\n        #button-next {\r\n            width: 80%;\r\n        }\r\n    \r\n        #button-prev .pagenation-text {\r\n            display: none;\r\n        }\r\n    }\r\n    @media screen and (min-width: 1025px) {\r\n        #button-prev,\r\n        #button-next {\r\n            width: 50%;\r\n        }\r\n    \r\n        #button-prev .pagenation-text {\r\n            display: block;\r\n        }\r\n    }\r\n}\r\n\r\n\r\n\r\n/**\r\n * Site footer\r\n */\r\n.site-footer {\r\n  border-top: 1px solid $grey-color-light;\r\n  padding: $spacing-unit 0;\r\n  background-color: #424242;\r\n  .footer-category-title {\r\n    color: $color-mxnet;\r\n  }\r\n  a {\r\n    color: $grey-color-light !important;\r\n\r\n    &:visited {\r\n      color: $grey-color-light !important;\r\n    }\r\n  }\r\n\r\n}\r\n\r\n.site-footer2 {\r\n  background-color: #424242;\r\n  padding-top: 40px;\r\n  padding-bottom: 10px;\r\n}\r\n\r\n.footer-heading {\r\n  margin-bottom: $spacing-unit / 2;\r\n}\r\n\r\n.contact-list,\r\n.social-media-list {\r\n  list-style: none;\r\n  margin-left: 0;\r\n}\r\n\r\n\r\n.footer-bottom-warning {\r\n  font-size: 80%;\r\n  color: white;\r\n  float: left;\r\n}\r\n\r\n.footer-logo {\r\n  width: 200px;\r\n  margin-bottom: 30px;\r\n  margin-top: 30px;\r\n}\r\n\r\n.footer-col {\r\n  float: left;\r\n  margin-bottom: $spacing-unit / 2;\r\n  padding-left: $spacing-unit / 2;\r\n}\r\n\r\n.footer-text {\r\n  color: $grey-color-light;\r\n}\r\n\r\n"," /*\r\n  * Search Styles\r\n  */\r\n#waterfall-exp::-webkit-input-placeholder {\r\n    color: #ccc;\r\n}\r\n#waterfall-exp:-ms-input-placeholder {\r\n    color: #ccc;\r\n}\r\n#waterfall-exp::-moz-placeholder {\r\n    color: #ccc;\r\n}\r\n\r\nul.search span.highlighted {\r\n    font-weight: bold;\r\n}\r\n\r\nul.search > li {\r\n    margin-bottom: 24px;\r\n}\r\n\r\n#search-results {\r\n    ul {\r\n        list-style: none;\r\n        padding: 0;\r\n        li {\r\n            > a {\r\n                text-decoration: none;\r\n                font-size: 1.2rem;\r\n            }\r\n        }\r\n    }\r\n}\r\n","a.download {\n    &:before {\n        @extend .material-icons;\n        content: \"file_download\";\n        position: relative;\n        top: 5px;\n        margin-right: 5px;\n    }\n}\n\nbutton.download {\n    position: sticky;\n    margin-left: 1em;\n}\n",".mdl-card {\n    margin: 1em 1.5em 1em 0;\n    display: inline-block;\n    width: 250px;\n    min-height: 140px;\n    padding: 18px;\n}\n.mdl-card:hover {\n    box-shadow: 0 10px 20px rgba(0,0,0,0.25), 0 6px 6px rgba(0,0,0,0.22);\n    color: #000;\n    cursor: pointer;\n}\n.mdl-card__title {\n    padding: 0 0 1em 0;\n    font-size: 18px;\n    color: #444;\n}\n\n.mdl-card__supporting-text {\n    line-height: 1.5rem;\n    padding: 0px;\n    width: 100%;\n}\n\n.head-card.mdl-card {\n    width: auto;\n    display: block;\n    max-width: 800px;\n    padding: 24px;\n}\n\n.head-card > .mdl-card__title {\n    padding-bottom: 0px;\n    height: 60px;\n    font-weight: 700;\n    text-transform: uppercase;\n}\n.head-card > .mdl-card__menu {\n    color: #fff;\n}\n.head-card > .mdl-card__actions {\n    padding: 0;\n}\n.cards {\n    display: flex;\n    flex-direction: row;\n    flex-wrap: wrap;\n}\n"]}
\ No newline at end of file
+{"version":3,"sources":["../../node_modules/material-design-lite/src/shadow/_shadow.scss","../../node_modules/material-design-lite/src/_mixins.scss","../../node_modules/material-design-lite/src/data-table/_data-table.scss","../../node_modules/material-design-lite/src/_variables.scss","../../node_modules/material-design-lite/src/footer/_mini_footer.scss","../../node_modules/material-design-lite/src/card/_card.scss","../../node_modules/material-design-lite/src/button/_button.scss","../scss/grid/_simplegrid.scss","../scss/fonts/_material-icons.scss","../scss/_root.scss","../scss/_variables.scss","../scss/layout/_layout.scss","../scss/headerings/_headerings.scss","../scss/admonitions/_admonitions.scss","../scss/code/_code.scss","../scss/blockquote/_blockquote.scss","../scss/tables/_tables.scss","../scss/toc/_globaltoc.scss","../scss/toc/_localtoc.scss","../scss/toc/_toctree.scss","../scss/lists/_lists.scss","../scss/drawer/_drawer.scss","../scss/header/_header.scss","../scss/footer/_footer.scss","../scss/search/_search.scss","../scss/downloadlink/_downloadlink.scss","../scss/card/_card.scss"],"names":[],"mappings":"AAmBA,wJCoNE,gGAEqE,CDlNvE,iBCqNE,gGAEqE,CDnNvE,iBCsNE,iGAEmE,CDpNrE,iBCuNE,kGAEmE,CDrNrE,iBCwNE,sGAEmE,CDtNrE,kBC0NE,wGAEqE,CDxNvE,kBC4NE,yGAEqE,CCtPvE,mHACE,iBAAkB,CAClB,gCCohBkC,CDnhBlC,wBAAyB,CACzB,kBAAmB,CACnB,cC0gByB,CDzgBzB,qBAAiD,CANnD,+HASI,kBAAmB,CATvB,+KAYM,YAAa,CAZnB,qIAkBM,iBAAkB,CAClB,WC0gBsB,CFlR1B,wBCvP6C,CDwP7C,kDEkN6D,CDzczD,oCAAqC,CArB3C,6JAwBQ,wBCigB4B,CDzhBpC,iJA4BQ,qBC4fwB,CDxhBhC,kPAkCI,mBCggBsD,CD/ftD,gBAAiB,CAnCrB,0SAsCM,iBAAkB,CAtCxB,sSA0CM,kBAAmB,CA1CzB,yHA+CI,iBAAkB,CAClB,qBAAsB,CACtB,WC4ewB,CD3exB,oCCoegC,CDnehC,uCCmegC,CDlehC,gBCof8C,CDnf9C,qBAAsB,CArD1B,yKAwDM,qBAAsB,CAxD5B,yHA6DI,iBAAkB,CAClB,qBAAsB,CACtB,sBAAuB,CDsCzB,cAAe,CAIb,eAAiB,CAEnB,gBAAiB,CACjB,gBAAiB,CC3Cf,WC4dwB,CD3dxB,cC8c8B,CD7c9B,qBCgd+B,CD/c/B,kBAAmB,CACnB,qBAAsB,CArE1B,wZAyEM,qBC2coC,CDphB1C,obD8LE,0BAA6B,CAC7B,eAAmB,CACnB,iBAAkB,CAClB,cAAe,CACf,aAAc,CACd,qBAAsB,CACtB,mBAAoB,CACpB,oBAAqB,CACrB,gBAAiB,CACjB,4BAA6B,CAC7B,oCAAqC,CACrC,kCAAmC,CC7H7B,cCqc+B,CDpc/B,eAAgB,CAChB,gBAAiB,CACjB,kBAAmB,CA/E3B,gbAkFQ,cAAe,CAlFvB,4cAoFU,qBCic2C,CDrhBrD,2NAyFM,eAAgB,CAKtB,wBACE,UAAW,CAGb,iRACE,eAAgB,CEpGlB,iBACE,YAAa,CACb,kBAAmB,CACnB,6BAA8B,CAE9B,iBDoZY,CClZZ,aDwRiD,CCvRjD,wBDsRoD,CC9RtD,uBAWI,UAAW,CACX,aAAc,CAZlB,2BAgBI,gBDsYkB,CClYtB,oHAEE,YAAa,CACb,oBAAqB,CAErB,eAAgB,CAEhB,QAAS,CACT,SAAU,CARZ,6HAWI,eAAgB,CAChB,iBDyXU,CCvXV,oCAdJ,6HAeM,gBDmXgB,CCjXnB,CAjBH,0HAoBI,aAAc,CACd,oBAAqB,CACrB,kBAAmB,CAIvB,8DAEE,oBAAqB,CACrB,OAAQ,CAGV,gEAEE,oBAAqB,CACrB,OAAQ,CAGV,0DAEE,UD0VoB,CCzVpB,WDyVoB,CCvVpB,SAAU,CACV,QAAS,CAET,wBD6NiD,CC3NjD,WAAY,CCpEd,UACE,YAAa,CACb,qBAAsB,CACtB,cF2amB,CE1anB,eAAgB,CAChB,gBFwaiB,CEvajB,eAAgB,CAChB,WFqagB,CEpahB,SF2bc,CE1bd,iBAAkB,CAClB,eFiOqD,CEhOrD,iBAAkB,CAClB,qBAAsB,CAGxB,iBACE,wBF6N6D,CE5N7D,wBAAyB,CACzB,2BAA4B,CAC5B,qBAAsB,CACtB,6BAA8B,CAC9B,4BAA6B,CAC7B,qBAAsB,CAGxB,iBACE,kBAAmB,CACnB,UFiN+C,CEhN/C,aAAc,CACd,YAAa,CACb,uBAAwB,CACxB,kBAAmB,CACnB,YFiZ4B,CEhZ5B,6BFoZoC,CEnZpC,2BFsZkC,CErZlC,qBAAsB,CAVxB,kCAaI,sCFyM+B,CErMnC,sBACE,mBAAoB,CACpB,aAAc,CACd,aAAc,CACd,YAAa,CACb,cFgYyB,CE/XzB,eFkZ+B,CEjZ/B,kBAAmB,CACnB,eAAgB,CAChB,2BFwYuC,CEvYvC,QAAS,CAGX,yBACE,cFwX4B,CEvX5B,qBFuL0D,CEtL1D,QAAS,CAGX,2BACE,qBFgLsE,CE/KtE,cF8XmC,CE7XnC,gBF8XqC,CE7XrC,eAAgB,CAChB,YF+W4B,CE9W5B,SAAU,CANZ,4CASI,sCFyK+B,CErKnC,mBACE,cFqX2B,CEpX3B,kBAAmB,CACnB,UAAW,CACX,4BAA+B,CAC/B,WAAY,CACZ,qBAAsB,CANxB,oCASI,mCF4J+B,CExJnC,kBACE,WAAY,CAId,gBACE,iBAAkB,CAClB,UAAW,CACX,QAAS,CC7FX,YACE,sBAAuB,CACvB,WAAY,CACZ,iBH+cwB,CG9cxB,UHgHsD,CG/GtD,iBAAkB,CAClB,WHyckB,CGxclB,QAAS,CACT,cHscqB,CGrcrB,cHucmB,CGtcnB,oBAAqB,CLVnB,6CE8CuD,CFmIzD,cAAe,CACf,eAAgB,CAChB,wBAAyB,CACzB,aAAc,CACd,gBAAiB,CKzKjB,eAAgB,CAChB,sBAAuB,CACvB,+HH+c6D,CG5c7D,YAAa,CACb,cAAe,CACf,oBAAqB,CACrB,iBAAkB,CAClB,gBH0bkB,CGzblB,qBAAsB,CAtBxB,8BAyBI,QAAS,CAzBb,kBA6BI,kCHsF8D,CGnHlE,+BAiCI,gCHsFuD,CGvH3D,mBAqCI,kCHiF6D,CGtHjE,gCAyCI,aHiFwD,CG1H5D,mDA4CM,gCH2EqD,CGtE3D,8BACE,uBAAuB,CAIvB,oBACE,4BH4D8D,CFgGhE,gGAEqE,CK/JrE,2BLuKA,iGAEmE,CKnK/D,kCH0D2D,CGhE/D,uCLyJA,6DAA8D,CK9I1D,kCHqD2D,CGhE/D,wCAeI,kBHqDsD,CGpDtD,UHqDiE,CGrErE,wJA2BM,wBH4CmD,CGvEzD,oDA+BM,eH4C4D,CGrClE,iBACE,iBAAkB,CAClB,cHwXuB,CGvXvB,WHqXkB,CGpXlB,WAAY,CACZ,cHmXkB,CGlXlB,UHkXkB,CGjXlB,SAAU,CACV,eAAgB,CAChB,4BHc8D,CGb9D,oEAAwE,CACxE,iBAAkB,CAClB,kBAAmB,CAZrB,0wCAeI,iBAAkB,CAClB,OAAQ,CACR,QAAS,CACT,gCAA8E,CAC9E,gBHuWqB,CGtWrB,UHsWqB,CG1XzB,sCAwBI,WHiWqB,CGhWrB,cHgWqB,CG/VrB,UH+VqB,CGzXzB,+CA8BI,iBAAkB,CAElB,4DAAiE,CAhCrE,wBLiIA,iGAEmE,CK9F/D,kCHX2D,CG1B/D,oCLmHA,6DAA8D,CKzE1D,kCHhB2D,CG1B/D,qCA8CI,kBHFiD,CGGjD,UHA+D,CG/CnE,+IA0DM,wBHZsD,CG9C5D,iDA8DM,eHd+D,CGqBrE,kBACE,iBAAkB,CAClB,cHmTuB,CGlTvB,WHoTmB,CGnTnB,aAAc,CACd,cAAe,CACf,cHiTmB,CGhTnB,UHgTmB,CG/SnB,SAAU,CACV,eAAgB,CAChB,aAAc,CACd,kBAAmB,CAXrB,gyCAcI,iBAAkB,CAClB,OAAQ,CACR,QAAS,CACT,gCAA8E,CAC9E,gBHmSqB,CGlSrB,UHkSqB,CGrTzB,wCAuBI,WHiSsB,CGhStB,cHgSsB,CG/RtB,UH+RsB,CGxT1B,owDA4BM,KAAyD,CACzD,MAA0D,CA7BhE,gDAkCI,iBAAkB,CAElB,4DAAiE,CAMrE,8BACE,aAAc,CACd,WAAY,CACZ,MAAS,CACT,iBAAkB,CAClB,KAAQ,CACR,UAAW,CACX,SAAU,CACV,eAAgB,CAEhB,2IAEE,4BAA6B,CAMnC,yCACE,aHpG0D,CGmG5D,qDAGI,eHrGmE,CGkGvE,qHAMI,UHxGmE,CGyGnE,wBH1GwD,CG8G5D,uCACE,aHjGqD,CGgGvD,mDAGI,eHhGiE,CG6FrE,iHAMI,UHnGiE,CGoGjE,wBHvGmD,CG6GvD,sFAII,qBHpHoE,CGqHpE,cAAe,CACf,4BAA6B,CAG9B,gGAIG,gCH9HgE,CG+HhE,qBH9HkE,CGkIrE,sGAIG,gCHvIgE,CGwIhE,qBHvIkE,CGwIlE,eAAgB,CAGnB,wGAIG,qBH/IkE,CGqJxE,4pCACE,qBAAsB,CClSxB,YACE,eAVqB,CAavB,cACE,eAbuB,CAgBzB,YACE,eAhBqB,CAqBvB,MACE,eAAgB,CAGlB,OACE,gBAAiB,CAGnB,QACE,iBAAkB,CAClB,gBAAiB,CACjB,iBAAkB,CAGpB,SACE,kBAAmB,CAGrB,WACE,YAAa,CAWf,WACE,UAAW,CACX,gBAAiB,CACjB,iBAAkB,CAElB,2CALF,WAMI,SAAU,CAOb,CAJC,wCATF,WAUI,SAAU,CACV,eAAgB,CAEnB,CAED,KACE,iBAAkB,CAClB,UAAW,CAGb,kBACE,UAAW,CACX,eAAiB,CACjB,kBAAoB,CAGtB,WACE,UAAW,CACX,aAAc,CACd,UAAW,CAGb,uFAYE,SAlDS,CAqDX,UACE,cAA0C,CAG5C,UACE,eAAyC,CAG3C,UACE,SAAwC,CAG1C,UACE,eAAwC,CAG1C,UACE,eAA+C,CAGjD,UACE,SAAwC,CAG1C,UACE,eAA+C,CAGjD,UACE,eAA+C,CAGjD,UACE,SAA+C,CAGjD,WACE,eAAgD,CAGlD,WACE,eAAgD,CAGlD,WACE,SAlGS,CAqGX,wCACE,OACE,cAA0C,CAE5C,OACE,eAAyC,CAE3C,OACE,SAAwC,CAE1C,OACE,eAAwC,CAE1C,OACE,eAA+C,CAEjD,OACE,SAAwC,CAE1C,OACE,eAA+C,CAEjD,OACE,eAA+C,CAEjD,OACE,SAA+C,CAEjD,QACE,eAAgD,CAElD,QACE,eAAgD,CAElD,QACE,SAxIO,CANX,WAkJI,aAAc,CACf,CAxHH,KA4HE,mBAAoB,CACpB,oBAAqB,CACrB,mBAAoB,CACpB,YAAa,CACb,cAAe,CAGjB,mBACE,YAAa,CACb,qBAAsB,CCxMxB,2dACI,0BAA6B,CAC7B,eAAmB,CACnB,iBAAkB,CAClB,cAAe,CACf,oBAAqB,CACrB,aAAc,CACd,mBAAoB,CACpB,qBAAsB,CACtB,gBAAiB,CACjB,kBAAmB,CACnB,aAAc,CAGd,kCAAmC,CAEnC,iCAAkC,CAGlC,iCAAkC,CAGlC,4BAA6B,CC3BjC,KACI,cCEY,CDChB,KACI,uBAAyB,CACzB,wBCDsB,CDEtB,cAAe,CACf,kBAAmB,CACnB,6ICA0J,CDG9J,2BACI,YAAa,CAGjB,4EAEI,6ICT0J,CDY9J,8GACI,uBAA8B,CAGlC,EACI,oBAAqB,CAGzB,yKAGQ,cAAe,CAIvB,OACI,aAAc,CACd,oBAAqB,CAGzB,SACI,eAAgB,CAOnB,IACG,cAAe,CACf,aAAc,CACd,gBAAiB,CACjB,iBAAkB,CAGtB,qBAEQ,iBAAkB,CAClB,iBAAkB,CAH1B,yCAMY,iBAAkB,CAN9B,2CASY,eAAgB,CAK5B,UACE,UAAW,CACX,WAAY,CACZ,oBAAqB,CACrB,YC3C0C,CD4C1C,iBAAkB,CAClB,eAAgB,CAChB,uBAAwB,CAM1B,6kBACI,iBAAkB,CAClB,OAAQ,CAGZ,WACI,oBAAqB,CAGzB,eACE,UAAW,CACX,aAAc,CACd,UAAW,CAGb,SAEE,gBAA2D,CAC3D,iBAAkB,CAClB,gBAAiB,CACjB,kBAA0C,CAC1C,iBC9EqB,CDiFrB,qCATF,SAWI,gBAAuD,CACvD,kBAAgC,CAChC,iBAA+B,CAElC,CEtFD,YACI,eAAgB,CAIpB,UACI,UAAW,CACX,aAAc,CACd,YAAa,CAEb,0BALJ,UAMQ,YAhCiB,CA6ExB,CAnDD,wBASQ,UAAW,CACX,aAAc,CACd,cAAe,CAEf,yBAbR,wBAcY,SA7BU,CA8BV,YA7Ba,CAoCpB,CAJG,0BAlBR,wBAmBY,uBA9B0B,CA+B1B,YA9Ba,CAgCpB,CAtBL,4BAyBQ,WA5CY,CA8CZ,0BA3BR,4BA4BY,YAAa,CAqBpB,CAjDL,qCA+BY,cAAe,CACf,eAAgB,CAChB,eAAgB,CAChB,aAAc,CAlC1B,wDAoCgB,SAAU,CApC1B,8DAwCgB,iBAAkB,CAxClC,8DA4CgB,+BAAmC,CACnC,iBAAkB,CAClB,uCAA4C,CC9E5D,oBACI,GACI,2BAA6B,CAC7B,SAAU,CAEjB,GACC,uBAAwB,CACxB,SAAU,CAAA,CAIZ,qBACI,GACI,uBAAwB,CACxB,SAAU,CAEjB,GACC,2BAA6B,CAC7B,SAAU,CAAA,CAIZ,0BAEQ,oBAAqB,CACrB,oBAAqB,CACrB,iBAAmB,CACnB,aAAc,CACd,SAAU,CANlB,gCAQY,0DAAsE,CARlF,oLAcY,oBAAqB,CAdjC,kNAkBgB,0DAAsE,CAlBtF,iBAwBQ,cAAe,CACf,mBAAoB,CAzB5B,iBA6BQ,iBAAkB,CAClB,gBAAiB,CACjB,kBAAmB,CACnB,YAAa,CACb,kBAAmB,CAjC3B,iBAqCQ,gBAAiB,CACjB,mBAAoB,CACpB,gBAAiB,CACjB,YAAe,CACf,oBAAqB,CAzC7B,iBA6CQ,iBAAkB,CAClB,kBAAmB,CACnB,kBAAmB,CACnB,YAAe,CACf,mBAAoB,CAjD5B,kCAqDQ,gBAAiB,CACjB,kBAAmB,CACnB,gBAAiB,CACjB,YAAe,CACf,kBAAmB,CAzD3B,kCA6DQ,cAAe,CACf,kBAAmB,CACnB,gBAAiB,CACjB,YAAe,CACf,kBAAmB,CCT3B,YAGI,iBAAkB,CAClB,eAAgB,CAChB,kBAAmB,CALvB,mBAOQ,WAAY,CAPpB,8BAUQ,cAAe,CACf,eAAiB,CACjB,UAAW,CACX,wBAAyB,CACzB,cAAe,CAdvB,iBApBI,6BA/CgC,CAgDhC,mCA/C4C,CAgD5C,mCACI,cAAe,CACf,eAAiB,CACjB,aApD4B,CAsD5B,cAAe,CACf,iBAAkB,CAClB,0CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,sBA3DwB,CA4DxB,cAAe,CAK3B,oBApBI,6BA1CgC,CA2ChC,mCA1C4C,CA2C5C,sCACI,cAAe,CACf,eAAiB,CACjB,aA/C4B,CAiD5B,cAAe,CACf,iBAAkB,CAClB,6CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,gBAtDkB,CAuDlB,cAAe,CAK3B,iBApBI,6BApDgC,CAqDhC,mCApD4C,CAqD5C,mCACI,cAAe,CACf,eAAiB,CACjB,aAzD4B,CA2D5B,cAAe,CACf,iBAAkB,CAClB,0CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,sBAhEwB,CAiExB,cAAe,CAK3B,oBApBI,6BArCgC,CAsChC,mCArC4C,CAsC5C,sCACI,cAAe,CACf,eAAiB,CACjB,aA1C4B,CA4C5B,cAAe,CACf,iBAAkB,CAClB,6CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,iBAjDmB,CAkDnB,cAAe,CAK3B,sBApBI,6BAhCgC,CAiChC,mCAhC4C,CAiC5C,wCACI,cAAe,CACf,eAAiB,CACjB,aArC4B,CAuC5B,cAAe,CACf,iBAAkB,CAClB,+CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,iBA5CmB,CA6CnB,cAAe,CAK3B,gBApBI,6BA3BiC,CA4BjC,oCA3B6C,CA4B7C,kCACI,cAAe,CACf,eAAiB,CACjB,aAhC6B,CAkC7B,cAAe,CACf,iBAAkB,CAClB,yCAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,2BAvC6B,CAwC7B,cAAe,CAK3B,sBApBI,6BAtBiC,CAuBjC,oCAtB6C,CAuB7C,wCACI,cAAe,CACf,eAAiB,CACjB,aA3B6B,CA6B7B,cAAe,CACf,iBAAkB,CAClB,+CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,sBAlCyB,CAmCzB,cAAe,CAK3B,kBApBI,6BAjBgC,CAkBhC,mCAjB4C,CAkB5C,oCACI,cAAe,CACf,eAAiB,CACjB,aAtB4B,CAwB5B,cAAe,CACf,iBAAkB,CAClB,2CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,uBA7ByB,CA8BzB,cAAe,CAK3B,oBApBI,6BAZgC,CAahC,mCAZ4C,CAa5C,sCACI,cAAe,CACf,eAAiB,CACjB,aAjB4B,CAmB5B,cAAe,CACf,iBAAkB,CAClB,6CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,uBAxByB,CAyBzB,cAAe,CAK3B,mBApBI,6BAPgC,CAQhC,mCAP4C,CAQ5C,qCACI,cAAe,CACf,eAAiB,CACjB,aAZ4B,CAc5B,cAAe,CACf,iBAAkB,CAClB,4CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,uBAnByB,CAoBzB,cAAe,CCzE3B,yBAEQ,YAAa,CAFrB,6BAIY,0BJEqB,CIDrB,qBAAsB,CACtB,wHJE2I,CID3I,cAAgB,CAChB,aAAc,CACd,iBAAkB,CAT9B,iEAWgB,qBAAsB,CAXtC,kDAiBQ,eAAgB,CAjBxB,qCAwBgB,qBAAsB,CACtB,kBJpBU,CIuBV,qBAAmB,CACnB,cAAgB,CA7BhC,sDAmCQ,QAAW,CAEX,iBAAkB,CArC1B,8HAoCQ,wHJ5B+I,CIRvJ,6BA4CQ,gBAAiB,CACjB,aAAc,CA7CtB,kGAiDQ,aAAc,CACd,aAAc,CACd,cAAe,CACf,kBAAmB,CACnB,kBAAmB,CACnB,aAAc,CACd,4BAA6B,CAC7B,YAAa,CACb,iBAAkB,CAzD1B,wSA2DY,qBAAsB,CACtB,kBAAmB,CACnB,WAAY,CA7DxB,8GAgEY,aAAc,CAhE1B,sBAsEQ,kBAAqB,CAtE7B,0BA2EQ,iBAAkB,CA3E1B,6BA6EY,kBAAmB,CACnB,QAAS,CA9ErB,oCA6FO,UAAW,CACX,UAAW,CACX,aAAc,CA/FrB,oCAmGO,gBAAiB,CAnGxB,sBAsGQ,kBAAmB,CAtG3B,kBA0GQ,aAAc,CACd,eAAgB,CAChB,aAAc,CACd,iBAAkB,CAClB,UAAW,CACX,iBAAkB,CAClB,oBAAqB,CAhH7B,+BAsHgB,6IJ7G8I,CI8G9I,eAAiB,CACjB,2BAA4B,CAC5B,oBAAyB,CACzB,iBAAkB,CAClB,iBAAkB,CAClB,WAAY,CACZ,UAAY,CACZ,YAAc,CACd,kBAA8B,CAC9B,eAAiB,CACjB,cAAe,CC9H9B,yBAEO,cAAe,CACf,cAAe,CACf,qCLDyB,CKHhC,+BAOW,oBAAsB,CACtB,aAAc,CARzB,gCAWW,oBAAsB,CCdlC,mGAKQ,eAAgB,CAChB,kBAAmB,CACnB,cAAe,CACf,aAAc,CARtB,4MAYY,kBAAmB,CACnB,wBAAyB,CAbrC,2GAiBY,cNdI,CMeJ,mBAAuB,CACvB,kBAAmB,CAnB/B,2HAqBgB,iBAAkB,CArBlC,iIAwBgB,eAAgB,CCxBhC,oCAGQ,YAAa,CAHrB,cAQQ,oBAAqB,CACrB,SAAU,CACV,QAAS,CAVjB,iBAaY,eAAgB,CAb5B,+BAegB,YAAa,CACb,6BAA8B,CAhB9C,iCAkBoB,aAAc,CACd,aAAc,CACd,UAAW,CACX,cAAe,CACf,oBAAqB,CACrB,adyKoB,CchMxC,yCAyBwB,eAAiB,CAzBzC,uBAiCQ,SAAU,CACV,WAAY,CACZ,YAAa,CACb,kBAAmB,CACnB,sBAAuB,CACvB,WAAY,CAtCpB,yBAwCY,SAAU,CACV,aAAc,CACd,gBAAiB,CACjB,cAAe,CA3C3B,2BA6CgB,cAAe,CA7C/B,4BAiDY,wBAA0B,CAjDtC,8BAmDgB,cAAe,CACf,eAAgB,CApDhC,uCA2DY,gBAAiB,CA3D7B,6CA8DY,iBAAkB,CA9D9B,mDAiEY,iBAAkB,CAjE9B,yDAoEY,iBAAkB,CApE9B,+DAuEY,iBAAkB,CAvE9B,qEA0EY,iBAAkB,CC1E9B,UACI,gBAAkB,CAClB,gBAAiB,CAFrB,mBAKQ,iBAAkB,CAL1B,wBAOY,eAAiB,CACjB,eAAgB,CAR5B,kBAaQ,YAAa,CAbrB,aAiBQ,SAAU,CACV,oBAAqB,CAlB7B,aAsBQ,gBAAiB,CAtBzB,YA0BQ,aAAc,CACd,oBAAqB,CACrB,aAAc,CACd,cAAe,CACf,gBAAiB,CACjB,kBAAmB,CA/B3B,oBAkCY,gBAAiB,CACjB,qBAAsB,CACtB,eAAiB,CCjC5B,iCAEI,qBAAsB,CAG1B,yDAEI,aAAyB,CACzB,cAAe,CACf,iBAAkB,CAGtB,uCAEI,iBAAkB,CAClB,eAAgB,CAChB,gBAAiB,CAGrB,qCAEI,gBAAiB,CACjB,oBAAqB,CAHzB,+CAKQ,cAAe,CAIvB,iDAEI,gBAAiB,CAFrB,2DAIQ,gBAAiB,CCnC1B,oBAGY,cAAe,CAH3B,sBAKgB,QAAS,CALzB,mCAWY,wHVH2I,CURvJ,8BAcY,aAAe,CACf,WAAY,CCXpB,oBACI,qBAAsB,CADzB,uCAIO,SAAU,CAJjB,6CAQO,iBAAkB,CARzB,6CAYO,+BAAmC,CACnC,iBAAkB,CAClB,uCAA4C,CAdnD,sCAkBO,eAAiB,CACjB,gBAAiB,CACjB,QAAS,CACT,SAAU,CACV,gBAAiB,CACjB,sCAAuC,CACvC,eAAgB,CAxBvB,6CA0BW,aAAc,CACd,aAAc,CACd,WAAY,CACZ,UAAW,CACX,oBAAqB,CA9BhC,sDAgCe,UAAW,CACX,QAAS,CACT,SAAU,CAlCzB,kDAsCe,eAAiB,CACjB,gBAAiB,CACjB,cAAe,CACf,iBAAoB,CACpB,gBAAiB,CACjB,6IXtC0I,CWuC1I,aAAc,CACd,aAAc,CC7ClC,sCAEQ,aAAc,CACd,cAAe,CAEnB,0BALJ,eAMQ,uBAA0B,CANlC,gDAQY,iBAAkB,CAClB,UAAW,CACX,eAAgB,CAChB,kBAAmB,CACnB,sBAAuB,CAZnC,wwCAgBY,YAAa,CAChB,CAIT,uBACI,eAAgB,CAGpB,2BACI,kBAAoB,CAGxB,wCACI,6BAAiC,CACjC,UAAW,CACX,eAAgB,CAChB,iBAAkB,CAJtB,+DAOQ,cAAe,CAPvB,iEASY,gBAAiB,CACjB,YAAa,CACb,iBAAkB,CAClB,aAAe,CAZ3B,qEAiBQ,wBAAmD,CACnD,UAAc,CAlBtB,yEAqBQ,wBAAmD,CACnD,SAAU,CACV,UAAc,CAOtB,YACE,yBAA2B,CAC3B,gBAAiB,CACjB,mBAAoB,CACpB,eAAgB,CAChB,UAAW,CACX,UAAY,CANd,gCAUI,aZzCuC,CY8C3C,aACE,cAAe,CACf,KAAM,CACN,UAAW,CACX,eAAgB,CAChB,gBAAiB,CACjB,mBAAoB,CACpB,wBZzD0B,CY0D1B,UAAW,CACX,eAAgB,CAChB,cAAe,CACf,4BAA8B,CAGhC,kBACE,WAAY,CACZ,eAAgB,CAGlB,UACE,WAAY,CACZ,gBAAiB,CAFnB,4CASI,YAAa,CATjB,qBAaI,UAAY,CACZ,eAAgB,CAChB,eAAgB,CAfpB,sCAkBM,iBAAkB,CAlBxB,2BAsBM,UAAY,CACZ,sCAA4C,CAvBlD,kCA4BI,UAAY,CACZ,yBAA0B,CAG5B,qCAhCF,UAiCI,iBAAkB,CAClB,OAAQ,CACR,UAAW,CACX,wBAAiC,CACjC,iBAAkB,CAClB,gBAAiB,CAtCrB,iCAyCM,aAAc,CACd,WAAY,CACZ,UAAW,CACX,WAAY,CACZ,SAAU,CACV,cAAe,CA9CrB,qBAkDM,aAAc,CACd,WAAY,CACZ,UAAW,CACX,WAAY,CACZ,aAAc,CACd,gBAAiB,CACjB,iBAAkB,CAxDxB,yBA2DQ,SAAW,CA3DnB,yBAgEM,UAAW,CACX,YAAa,CAjEnB,iCAqEM,aAAc,CACd,kBAAmB,CAtEzB,qBA0EM,gBAAiB,CACjB,aAAc,CAMd,gBAAiB,CAjFvB,sCA8EQ,cAAe,CAChB,CC7KP,uBACI,wBAAyB,CAD7B,yDAGQ,kBAAmB,CACnB,YAAa,CACb,qBAAsB,CAL9B,mEAOY,gBAAiB,CAP7B,0DAcQ,eAAiB,CACjB,YAAa,CACb,qBAAsB,CACtB,wBAAyB,CAjBjC,4DAoBY,aAAc,CACd,eAAiB,CACjB,oBAAqB,CAtBjC,iCA0BQ,YAAa,CAOpB,YACG,UAAW,CACX,eAAgB,CAChB,WAAY,CACZ,wBAAyB,CACzB,YAAa,CALhB,6EAQO,mBAAoB,CACpB,SAAU,CACV,WAAY,CACZ,YAAa,CACb,sBAAuB,CACvB,kBAAmB,CACnB,UAAc,CAdrB,yBAkBO,iBAAkB,CAlBzB,0CAoBW,eAAgB,CApB3B,yBA0BO,gBAAiB,CACjB,0BAA2B,CA3BlC,0CA6BW,gBAAiB,CAKrB,oBACI,iBAAkB,CAEtB,oBACI,gBAAiB,CAIzB,iBACI,gBAAiB,CACjB,cAAe,CAGnB,sBACI,UAAY,CACZ,cAAe,CAEnB,qCAnDH,yBAqDW,SAAU,CArDrB,yBAyDW,SAAU,CAzDrB,0CA6DW,YAAa,CAChB,CAEL,qCAhEH,kDAmEW,SAAU,CAnErB,0CAuEW,aAAc,CACjB,CAST,aACE,4BbvF0C,CawF1C,cAAwB,CACxB,wBAAyB,CAH3B,oCAKI,ab9FwB,CayF5B,sCAWM,uBAAmC,CAMzC,cACE,wBAAyB,CACzB,gBAAiB,CACjB,mBAAoB,CAGtB,gBACE,kBAAgC,CAGlC,iCAEE,eAAgB,CAChB,aAAc,CAIhB,uBACE,aAAc,CACd,UAAY,CACZ,UAAW,CAGb,aACE,WAAY,CACZ,kBAAmB,CACnB,eAAgB,CAGlB,YACE,UAAW,CACX,kBAAgC,CAChC,iBAA+B,CAGjC,aACE,ab3I0C,Cc5B5C,0CACI,UAAW,CAEf,qCACI,UAAW,CAEf,iCACI,UAAW,CAGf,2BACI,eAAiB,CAGrB,aACI,kBAAmB,CAGvB,mBAEQ,eAAgB,CAChB,SAAU,CAHlB,wBAMgB,oBAAqB,CACrB,gBAAiB,CC5BjC,kBAGQ,uBAAwB,CACxB,iBAAkB,CAClB,OAAQ,CACR,gBAAiB,CAIzB,gBACI,eAAgB,CAChB,eAAgB,CpBMpB,UqBjBI,sBAAuB,CACvB,oBAAqB,CACrB,WAAY,CACZ,gBAAiB,CACjB,YAAa,CAEjB,gBACI,gEAAoE,CACpE,UAAW,CACX,cAAe,CrBiCnB,iBqB9BI,eAAkB,CAClB,cAAe,CACf,UAAW,CrBgEf,2BqB5DI,kBAAmB,CACnB,SAAY,CACZ,UAAW,CAGf,oBACI,UAAW,CACX,aAAc,CACd,eAAgB,CAChB,YAAa,CAGjB,4BACI,gBAAmB,CACnB,WAAY,CACZ,eAAgB,CAChB,wBAAyB,CAE7B,2BACI,UAAW,CAEf,8BACI,SAAU,CAEd,OACI,YAAa,CACb,kBAAmB,CACnB,cAAe","file":"sphinx_materialdesign_theme.css","sourceRoot":"../../src/js","sourcesContent":["/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n@import \"../variables\";\n@import \"../mixins\";\n\n.mdl-shadow--2dp {\n  @include shadow-2dp();\n}\n\n.mdl-shadow--3dp {\n  @include shadow-3dp();\n}\n\n.mdl-shadow--4dp {\n  @include shadow-4dp();\n}\n\n.mdl-shadow--6dp {\n  @include shadow-6dp();\n}\n\n.mdl-shadow--8dp {\n  @include shadow-8dp();\n}\n\n.mdl-shadow--16dp {\n  @include shadow-16dp();\n}\n\n.mdl-shadow--24dp {\n  @include shadow-24dp();\n}\n","/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n/* Typography */\n\n@mixin typo-preferred-font($usePreferred: true) {\n  @if $usePreferred {\n    font-family: $preferred_font;\n  }\n}\n\n@mixin typo-display-4($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 112px;\n  font-weight: 300;\n  line-height: 1;\n  letter-spacing: -0.04em;\n\n  @if $colorContrast {\n    opacity: 0.54;\n  }\n}\n\n@mixin typo-display-3($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 56px;\n  font-weight: 400;\n  line-height: 1.35;\n  letter-spacing: -0.02em;\n\n  @if $colorContrast {\n    opacity: 0.54;\n  }\n}\n\n@mixin typo-display-2($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 45px;\n  font-weight: 400;\n  line-height: 48px;\n\n  @if $colorContrast {\n    opacity: 0.54;\n  }\n}\n\n@mixin typo-display-1($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 34px;\n  font-weight: 400;\n  line-height: 40px;\n\n  @if $colorContrast {\n    opacity: 0.54;\n  }\n}\n\n@mixin typo-headline($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 24px;\n  font-weight: 400;\n  line-height: 32px;\n  -moz-osx-font-smoothing: grayscale;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-title($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 20px;\n  font-weight: 500;\n  line-height: 1;\n  letter-spacing: 0.02em;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-subhead($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 16px;\n  font-weight: 400;\n  line-height: 24px;\n  letter-spacing: 0.04em;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-subhead-2($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 16px;\n  font-weight: 400;\n  line-height: 28px;\n  letter-spacing: 0.04em;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-body-2($colorContrast: false, $usePreferred: false) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 14px;\n  @if $usePreferred {\n    font-weight: 500;\n  } @else {\n    font-weight: bold;\n  }\n  line-height: 24px;\n  letter-spacing: 0;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-body-1($colorContrast: false, $usePreferred: false) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 14px;\n  font-weight: 400;\n  line-height: 24px;\n  letter-spacing: 0;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-caption($colorContrast: false, $usePreferred: false) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 12px;\n  font-weight: 400;\n  line-height: 1;\n  letter-spacing: 0;\n\n  @if $colorContrast {\n    opacity: 0.54;\n  }\n}\n\n@mixin typo-blockquote($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  position: relative;\n  font-size: 24px;\n  font-weight: 300;\n  font-style: italic;\n  line-height: 1.35;\n  letter-spacing: 0.08em;\n\n  &:before {\n    position: absolute;\n    left: -0.5em;\n    content: '“';\n  }\n\n  &:after {\n    content: '”';\n    margin-left: -0.05em;\n  }\n\n  @if $colorContrast {\n    opacity: 0.54;\n  }\n}\n\n@mixin typo-menu($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 14px;\n  font-weight: 500;\n  line-height: 1;\n  letter-spacing: 0;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-button($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 14px;\n  font-weight: 500;\n  text-transform: uppercase;\n  line-height: 1;\n  letter-spacing: 0;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-icon() {\n  font-family: 'Material Icons';\n  font-weight: normal;\n  font-style: normal;\n  font-size: 24px;\n  line-height: 1;\n  letter-spacing: normal;\n  text-transform: none;\n  display: inline-block;\n  word-wrap: normal;\n  font-feature-settings: 'liga';\n  -webkit-font-feature-settings: 'liga';\n  -webkit-font-smoothing: antialiased;\n}\n\n/* Shadows */\n\n// Focus shadow mixin.\n@mixin focus-shadow() {\n  box-shadow: 0 0 8px rgba(0,0,0,.18),0 8px 16px rgba(0,0,0,.36);\n}\n\n@mixin shadow-2dp() {\n  box-shadow: 0 2px 2px 0 rgba(0, 0, 0, $shadow-key-penumbra-opacity),\n              0 3px 1px -2px rgba(0, 0, 0, $shadow-key-umbra-opacity),\n              0 1px 5px 0 rgba(0, 0, 0, $shadow-ambient-shadow-opacity);\n}\n@mixin shadow-3dp() {\n  box-shadow: 0 3px 4px 0 rgba(0, 0, 0, $shadow-key-penumbra-opacity),\n              0 3px 3px -2px rgba(0, 0, 0, $shadow-key-umbra-opacity),\n              0 1px 8px 0 rgba(0, 0, 0, $shadow-ambient-shadow-opacity);\n}\n@mixin shadow-4dp() {\n  box-shadow: 0 4px 5px 0 rgba(0, 0, 0, $shadow-key-penumbra-opacity),\n              0 1px 10px 0 rgba(0, 0, 0, $shadow-ambient-shadow-opacity),\n              0 2px 4px -1px rgba(0, 0, 0, $shadow-key-umbra-opacity);\n}\n@mixin shadow-6dp() {\n  box-shadow: 0 6px 10px 0 rgba(0, 0, 0, $shadow-key-penumbra-opacity),\n              0 1px 18px 0 rgba(0, 0, 0, $shadow-ambient-shadow-opacity),\n              0 3px 5px -1px rgba(0, 0, 0, $shadow-key-umbra-opacity);\n}\n@mixin shadow-8dp() {\n  box-shadow: 0 8px 10px 1px rgba(0, 0, 0, $shadow-key-penumbra-opacity),\n              0 3px 14px 2px rgba(0, 0, 0, $shadow-ambient-shadow-opacity),\n              0 5px 5px -3px rgba(0, 0, 0, $shadow-key-umbra-opacity);\n}\n\n@mixin shadow-16dp() {\n  box-shadow: 0 16px 24px 2px rgba(0, 0, 0, $shadow-key-penumbra-opacity),\n              0  6px 30px 5px rgba(0, 0, 0, $shadow-ambient-shadow-opacity),\n              0  8px 10px -5px rgba(0, 0, 0, $shadow-key-umbra-opacity);\n}\n\n@mixin shadow-24dp() {\n  box-shadow: 0  9px 46px  8px rgba(0, 0, 0, $shadow-key-penumbra-opacity),\n              0 11px 15px -7px rgba(0, 0, 0, $shadow-ambient-shadow-opacity),\n              0 24px 38px  3px rgba(0, 0, 0, $shadow-key-umbra-opacity);\n}\n\n/* Animations */\n\n@mixin material-animation-fast-out-slow-in($duration:0.2s) {\n  transition-duration: $duration;\n  transition-timing-function: $animation-curve-fast-out-slow-in;\n}\n\n@mixin material-animation-linear-out-slow-in($duration:0.2s) {\n  transition-duration: $duration;\n  transition-timing-function: $animation-curve-linear-out-slow-in;\n}\n\n@mixin material-animation-fast-out-linear-in($duration:0.2s) {\n  transition-duration: $duration;\n  transition-timing-function: $animation-curve-fast-out-linear-in;\n}\n\n@mixin material-animation-default($duration:0.2s) {\n  transition-duration: $duration;\n  transition-timing-function: $animation-curve-default;\n}\n\n/* Dialog */\n\n@mixin dialog-width($units:5) {\n  @if(type_of($units) != 'number') {\n    @error \"The unit given to dialog-width should be a number.\";\n  }\n  // 56dp is the base unit width for Dialogs.\n  // With 5 units being the number of units for a mobile device.\n  // https://goo.gl/sK2O5o\n  width: $units * 56px;\n}\n","/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n@import \"../variables\";\n@import \"../mixins\";\n\n.mdl-data-table {\n  position: relative;\n  border: $data-table-dividers;\n  border-collapse: collapse;\n  white-space: nowrap;\n  font-size: $data-table-font-size;\n  background-color: unquote(\"rgb(#{$color-white})\");\n\n  thead {\n    padding-bottom: 3px;\n\n    .mdl-data-table__select {\n      margin-top: 0;\n    }\n  }\n\n  tbody {\n    tr {\n      position: relative;\n      height: $data-table-row-height;\n      @include material-animation-default(0.28s);\n      transition-property: background-color;\n\n      &.is-selected {\n        background-color: $data-table-selection-color;\n      }\n\n      &:hover {\n        background-color: $data-table-hover-color;\n      }\n    }\n  }\n\n  td, th {\n    padding: 0 $data-table-column-padding 12px $data-table-column-padding;\n    text-align: right;\n\n    &:first-of-type {\n      padding-left: 24px;\n    }\n\n    &:last-of-type {\n      padding-right: 24px;\n    }\n  }\n\n  td {\n    position: relative;\n    vertical-align: middle;\n    height: $data-table-row-height;\n    border-top: $data-table-dividers;\n    border-bottom: $data-table-dividers;\n    padding-top: $data-table-cell-top;\n    box-sizing: border-box;\n\n    .mdl-data-table__select {\n      vertical-align: middle;\n    }\n  }\n\n  th {\n    position: relative;\n    vertical-align: bottom;\n    text-overflow: ellipsis;\n    @include typo-body-2();\n    height: $data-table-row-height;\n    font-size: $data-table-header-font-size;\n    color: $data-table-header-color;\n    padding-bottom: 8px;\n    box-sizing: border-box;\n\n    &.mdl-data-table__header--sorted-ascending,\n    &.mdl-data-table__header--sorted-descending {\n      color: $data-table-header-sorted-color;\n      &:before {\n        @include typo-icon;\n        font-size: $data-table-header-sort-icon-size;\n        content: \"\\e5d8\";\n        margin-right: 5px;\n        vertical-align: sub;\n      }\n      &:hover {\n        cursor: pointer;\n        &:before {\n          color: $data-table-header-sorted-icon-hover-color;\n        }\n      }\n    }\n    &.mdl-data-table__header--sorted-descending:before {\n      content: \"\\e5db\";\n    }\n  }\n}\n\n.mdl-data-table__select {\n  width: 16px;\n}\n\n.mdl-data-table__cell--non-numeric.mdl-data-table__cell--non-numeric {\n  text-align: left;\n}\n","/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n/*------------------------------------*\\\n    $CONTENTS\n\\*------------------------------------*/\n/**\n * STYLE GUIDE VARIABLES------------------Declarations of Sass variables\n * -----Typography\n * -----Colors\n * -----Textfield\n * -----Switch\n * -----Spinner\n * -----Radio\n * -----Menu\n * -----List\n * -----Layout\n * -----Icon toggles\n * -----Footer\n * -----Column\n * -----Checkbox\n * -----Card\n * -----Button\n * -----Animation\n * -----Progress\n * -----Badge\n * -----Shadows\n * -----Grid\n * -----Data table\n * -----Dialog\n * -----Snackbar\n * -----Tooltip\n * -----Chip\n *\n * Even though all variables have the `!default` directive, most of them\n * should not be changed as they are dependent one another. This can cause\n * visual distortions (like alignment issues) that are hard to track down\n * and fix.\n */\n\n\n/* ==========  TYPOGRAPHY  ========== */\n\n/* We're splitting fonts into \"preferred\" and \"performance\" in order to optimize\n   page loading. For important text, such as the body, we want it to load\n   immediately and not wait for the web font load, whereas for other sections,\n   such as headers and titles, we're OK with things taking a bit longer to load.\n   We do have some optional classes and parameters in the mixins, in case you\n   definitely want to make sure you're using the preferred font and don't mind\n   the performance hit.\n   We should be able to improve on this once CSS Font Loading L3 becomes more\n   widely available.\n*/\n$preferred_font: 'Roboto', 'Helvetica', 'Arial', sans-serif !default;\n$performance_font: 'Helvetica', 'Arial', sans-serif !default;\n\n/* ==========  COLORS  ========== */\n\n/**\n*\n* Material design color palettes.\n* @see http://www.google.com/design/spec/style/color.html\n*\n**/\n\n@import \"color-definitions\";\n@import \"functions\";\n\n/* ==========  IMAGES  ========== */\n$image_path: '/images' !default;\n\n/* ==========  Color & Themes  ========== */\n\n// Define whether individual color palette items should have classes created.\n// Setting this to true will remove individual color classes for each color in the palettes.\n// To improve overall performance (assuming they aren't used) by:\n// * Saving server bandwidth sending the extra classes\n// * Save client computation against the classes\n// it is RECOMMENDED you set this to true.\n$trim-color-classes: false !default;\n\n// Use color primarily for emphasis. Choose colors that fit with\n// your brand and provide good contrast between visual components.\n$color-primary: $palette-indigo-500 !default;\n$color-primary-dark: $palette-indigo-700 !default;\n$color-accent: $palette-pink-A200 !default;\n\n// Our primary is dark, so use $color-dark-contrast for overlaid text.\n$color-primary-contrast: $color-dark-contrast !default;\n// Our accent is dark, so use $color-dark-contrast for overlaid text.\n$color-accent-contrast: $color-dark-contrast !default;\n\n// Replace all colors with placeholders if we're generating a template.\n@if $styleguide-generate-template == true {\n  $color-primary: '$color-primary';\n  $color-primary-dark: '$color-primary-dark';\n  $color-accent: '$color-accent';\n  $color-primary-contrast: '$color-primary-contrast';\n  $color-accent-contrast: '$color-accent-contrast';\n}\n\n/* ==========  Typography  ========== */\n\n// We use the following default color styles: text-color-primary and\n// text-color-secondary. For light themes, use text-color-primary-inverse\n// and text-color-secondary-inverse.\n\n$text-color-primary: unquote(\"rgba(#{$color-black}, 0.87)\") !default;\n$text-link-color: unquote(\"rgb(#{$color-accent})\") !default;\n\n// Define whether to target elements directly for typographic enhancements.\n// Turning this off means you need to use mdl-* classes more often.\n// Other components may also fail to adhere to MD without these rules.\n// It is strongly recommended you leave this as true.\n\n$target-elements-directly: true !default;\n\n/* ==========  Components  ========== */\n\n/* ==========  Standard Buttons  ========== */\n\n// Default button colors.\n$button-primary-color: unquote(\"rgba(#{$palette-grey-500}, 0.20)\") !default;\n$button-secondary-color: unquote(\"rgb(#{$color-black})\") !default;\n$button-hover-color: $button-primary-color !default;\n$button-active-color: unquote(\"rgba(#{$palette-grey-500}, 0.40)\") !default;\n$button-focus-color: unquote(\"rgba(#{$color-black}, 0.12)\") !default;\n\n// Colored button colors.\n$button-primary-color-alt: unquote(\"rgb(#{$color-primary})\") !default;\n$button-secondary-color-alt: unquote(\"rgb(#{$color-primary-contrast})\") !default;\n$button-hover-color-alt: unquote(\"rgb(#{$color-primary})\") !default;\n$button-active-color-alt: unquote(\"rgb(#{$color-primary})\") !default;\n$button-focus-color-alt: $button-focus-color !default;\n\n// Ripple color for colored raised buttons.\n$button-ripple-color-alt: unquote(\"rgb(#{$color-primary-contrast})\") !default;\n\n// Disabled button colors.\n$button-primary-color-disabled: unquote(\"rgba(#{$color-black}, 0.12)\") !default;\n$button-secondary-color-disabled: unquote(\"rgba(#{$color-black}, 0.26)\") !default;\n\n// FAB colors and sizes.\n$button-fab-color-alt: unquote(\"rgb(#{$color-accent})\") !default;\n$button-fab-hover-color-alt: unquote(\"rgb(#{$color-accent})\") !default;\n$button-fab-active-color-alt: unquote(\"rgb(#{$color-accent})\") !default;\n$button-fab-text-color-alt: unquote(\"rgb(#{$color-accent-contrast})\") !default;\n$button-fab-ripple-color-alt: unquote(\"rgb(#{$color-accent-contrast})\") !default;\n\n// Icon button colors and sizes.\n$button-icon-color: unquote(\"rgb(#{$palette-grey-700})\") !default;\n$button-icon-focus-color: $button-focus-color !default;\n\n/* ==========  Icon Toggles  ========== */\n\n$icon-toggle-color: unquote(\"rgb(#{$palette-grey-700})\") !default;\n$icon-toggle-focus-color: $button-focus-color !default;\n$icon-toggle-checked-color: unquote(\"rgb(#{$color-primary})\") !default;\n$icon-toggle-checked-focus-color: unquote(\"rgba(#{$color-primary}, 0.26)\") !default;\n$icon-toggle-disabled-color: unquote(\"rgba(#{$color-black}, 0.26)\") !default;\n\n/* ==========  Radio Buttons  ========== */\n\n$radio-color: unquote(\"rgb(#{$color-primary})\") !default;\n$radio-off-color: unquote(\"rgba(#{$color-black}, 0.54)\") !default;\n$radio-disabled-color: unquote(\"rgba(#{$color-black}, 0.26)\") !default;\n\n/* ==========  Ripple effect  ========== */\n\n$ripple-bg-color: unquote(\"rgb(#{$color-light-contrast})\") !default;\n\n/* ==========  Layout  ========== */\n\n$layout-nav-color: unquote(\"rgb(#{$palette-grey-300})\") !default;\n\n// Drawer\n$layout-drawer-bg-color: unquote(\"rgb(#{$palette-grey-50})\") !default;\n$layout-drawer-border-color: unquote(\"rgb(#{$palette-grey-300})\") !default;\n$layout-text-color: unquote(\"rgb(#{$palette-grey-800})\") !default;\n$layout-drawer-navigation-color: #757575 !default;\n$layout-drawer-navigation-link-active-background: unquote(\"rgb(#{$palette-grey-300})\") !default;\n$layout-drawer-navigation-link-active-color: unquote(\"rgb(#{$color-light-contrast})\") !default;\n\n// Header\n$layout-header-bg-color: unquote(\"rgb(#{$color-primary})\") !default;\n$layout-header-text-color: unquote(\"rgb(#{$color-primary-contrast})\") !default;\n$layout-header-nav-hover-color: unquote(\"rgba(#{$palette-grey-700}, 0.6)\") !default;\n$layout-header-tab-text-color: unquote(\"rgba(#{$color-primary-contrast}, 0.6)\") !default;\n\n// Tabs\n$layout-header-tab-highlight: unquote(\"rgb(#{$color-accent})\") !default;\n\n/* ==========  Content Tabs  ========== */\n\n$tab-highlight-color: unquote(\"rgb(#{$color-primary})\") !default;\n$tab-text-color: unquote(\"rgba(#{$color-black}, 0.54)\") !default;\n$tab-active-text-color: unquote(\"rgba(#{$color-black}, 0.87)\") !default;\n$tab-border-color: unquote(\"rgb(#{$palette-grey-300})\") !default;\n\n/* ==========  Checkboxes  ========== */\n\n$checkbox-color: unquote(\"rgb(#{$color-primary})\") !default;\n$checkbox-off-color: unquote(\"rgba(#{$color-black}, 0.54)\") !default;\n$checkbox-disabled-color: unquote(\"rgba(#{$color-black}, 0.26)\") !default;\n$checkbox-focus-color: unquote(\"rgba(#{$color-primary}, 0.26)\") !default;\n$checkbox-image-path: $image_path;\n\n/* ==========  Switches  ========== */\n\n$switch-color: unquote(\"rgb(#{$color-primary})\") !default;\n$switch-faded-color: unquote(\"rgba(#{$color-primary}, 0.26)\") !default;\n$switch-thumb-color: $switch-color !default;\n$switch-track-color: unquote(\"rgba(#{$color-primary}, 0.5)\") !default;\n\n$switch-off-thumb-color: unquote(\"rgb(#{$palette-grey-50})\") !default;\n$switch-off-track-color: unquote(\"rgba(#{$color-black}, 0.26)\") !default;\n$switch-disabled-thumb-color: unquote(\"rgb(#{$palette-grey-400})\") !default;\n$switch-disabled-track-color: unquote(\"rgba(#{$color-black}, 0.12)\") !default;\n\n/* ==========  Spinner  ========== */\n\n$spinner-color-1: unquote(\"rgb(#{$palette-blue-400})\") !default;\n$spinner-color-2: unquote(\"rgb(#{$palette-red-500})\") !default;\n$spinner-color-3: unquote(\"rgb(#{$palette-yellow-600})\") !default;\n$spinner-color-4: unquote(\"rgb(#{$palette-green-500})\") !default;\n\n$spinner-single-color: unquote(\"rgb(#{$color-primary})\") !default;\n\n/* ==========  Text fields  ========== */\n\n$input-text-background-color: transparent !default;\n$input-text-label-color: unquote(\"rgba(#{$color-black}, 0.26)\") !default;\n$input-text-bottom-border-color: unquote(\"rgba(#{$color-black}, 0.12)\") !default;\n$input-text-highlight-color: unquote(\"rgb(#{$color-primary})\") !default;\n$input-text-disabled-color: $input-text-bottom-border-color !default;\n$input-text-disabled-text-color: $input-text-label-color !default;\n$input-text-error-color: unquote(\"rgb(#{$palette-red-A700})\") !default;\n\n/* ==========  Card  ========== */\n\n$card-background-color: unquote(\"rgb(#{$color-white})\") !default;\n$card-text-color: unquote(\"rgb(#{$color-black})\") !default;\n$card-image-placeholder-color: unquote(\"rgb(#{$color-accent})\") !default;\n$card-supporting-text-text-color: unquote(\"rgba(#{$color-black}, 0.54)\") !default;\n$card-border-color: rgba(0,0,0,0.1) !default;\n$card-subtitle-color: unquote(\"rgba(#{$color-black}, 0.54)\") !default;\n\n/* ==========  Sliders ========== */\n\n$range-bg-color: unquote(\"rgba(#{$color-black}, 0.26)\") !default;\n$range-color: unquote(\"rgb(#{$color-primary})\") !default;\n$range-faded-color: unquote(\"rgba(#{$color-primary}, 0.26)\") !default;\n$range-bg-focus-color: unquote(\"rgba(#{$color-black}, 0.12)\") !default;\n\n/* ========== Progress ========== */\n$progress-main-color: unquote(\"rgb(#{$color-primary})\") !default;\n$progress-secondary-color: unquote(\"rgba(#{$color-primary-contrast}, 0.7)\") !default;\n$progress-fallback-buffer-color: unquote(\"rgba(#{$color-primary-contrast}, 0.9)\") !default;\n$progress-image-path: $image_path;\n\n/* ==========  List ========== */\n\n$list-main-text-text-color: unquote(\"rgba(#{$color-black}, 0.87)\") !default;\n$list-supporting-text-text-color: unquote(\"rgba(#{$color-black}, 0.54)\") !default;\n$list-icon-color: unquote(\"rgb(#{$palette-grey-600})\") !default;\n$list-avatar-color: white !default;\n\n/* ==========  Item ========== */\n\n// Default Item Colors\n$default-item-text-color: unquote(\"rgba(#{$color-black}, 0.87)\") !default;\n$default-item-outline-color: unquote(\"rgb(#{$palette-grey-400})\") !default;\n$default-item-hover-bg-color: unquote(\"rgb(#{$palette-grey-200})\") !default;\n$default-item-focus-bg-color: unquote(\"rgb(#{$palette-grey-200})\") !default;\n$default-item-active-bg-color: unquote(\"rgb(#{$palette-grey-300})\") !default;\n$default-item-divider-color: unquote(\"rgba(#{$color-black}, 0.12)\") !default;\n\n// Disabled Button Colors\n$disabled-item-text-color: unquote(\"rgb(#{$palette-grey-400})\") !default;\n\n/* ==========  Dropdown menu ========== */\n\n$default-dropdown-bg-color: unquote(\"rgb(#{$color-white})\") !default;\n\n/* ==========  Tooltips  ========== */\n\n$tooltip-text-color: unquote(\"rgb(#{$color-white})\") !default;\n$tooltip-background-color: unquote(\"rgba(#{$palette-grey-700}, 0.9)\") !default;\n\n/* ==========  Footer  ========== */\n\n$footer-bg-color: unquote(\"rgb(#{$palette-grey-800})\") !default;\n$footer-color: unquote(\"rgb(#{$palette-grey-500})\") !default;\n$footer-heading-color: unquote(\"rgb(#{$palette-grey-300})\") !default;\n$footer-button-fill-color: $footer-color !default;\n$footer-underline-color: $footer-color !default;\n\n\n/* TEXTFIELD */\n\n$input-text-font-size: 16px !default;\n$input-text-width: 100% !default;\n$input-text-padding: 4px !default;\n$input-text-vertical-spacing: 20px !default;\n\n$input-text-button-size: 32px !default;\n$input-text-floating-label-fontsize: 12px !default;\n$input-text-expandable-icon-top: 16px !default;\n\n\n/* SWITCH */\n\n$switch-label-font-size: 16px !default;\n$switch-label-height: 24px !default;\n$switch-track-height: 14px !default;\n$switch-track-length: 36px !default;\n$switch-thumb-size: 20px !default;\n$switch-track-top: ($switch-label-height - $switch-track-height) / 2 !default;\n$switch-thumb-top: ($switch-label-height - $switch-thumb-size) / 2 !default;\n$switch-ripple-size: $switch-label-height * 2 !default;\n$switch-helper-size: 8px !default;\n\n/* SPINNER */\n\n$spinner-size: 28px !default;\n$spinner-stroke-width: 3px !default;\n\n// Amount of circle the arc takes up.\n$spinner-arc-size: 270deg !default;\n// Time it takes to expand and contract arc.\n$spinner-arc-time: 1333ms !default;\n// How much the start location of the arc should rotate each time.\n$spinner-arc-start-rot: 216deg !default;\n\n$spinner-duration: 360 * $spinner-arc-time / (\n    strip-units($spinner-arc-start-rot + (360deg - $spinner-arc-size)));\n\n\n/* RADIO */\n\n$radio-label-font-size: 16px !default;\n$radio-label-height: 24px !default;\n$radio-button-size: 16px !default;\n$radio-inner-margin: $radio-button-size / 4;\n$radio-padding: 8px !default;\n$radio-top-offset: ($radio-label-height - $radio-button-size) / 2;\n$radio-ripple-size: 42px !default;\n\n\n/* MENU */\n\n$menu-expand-duration: 0.3s !default;\n$menu-fade-duration: 0.2s !default;\n\n/* LIST */\n\n$list-border: 8px !default;\n$list-min-height: 48px !default;\n$list-min-padding: 16px !default;\n$list-bottom-padding: 20px !default;\n$list-avatar-text-left-distance: 72px !default;\n$list-icon-text-left-distance: 72px !default;\n\n$list-avatar-size: 40px !default;\n$list-icon-size: 24px !default;\n\n$list-two-line-height: 72px !default;\n$list-three-line-height: 88px !default;\n\n/* LAYOUT */\n\n$layout-drawer-narrow: 240px !default;\n$layout-drawer-wide: 456px !default;\n$layout-drawer-width: $layout-drawer-narrow !default;\n\n$layout-header-icon-size: 32px !default;\n$layout-screen-size-threshold: 1024px !default;\n$layout-header-icon-margin: 24px !default;\n$layout-drawer-button-mobile-size: 32px !default;\n$layout-drawer-button-desktop-size: 48px !default;\n\n$layout-header-mobile-row-height: 56px !default;\n$layout-mobile-header-height: $layout-header-mobile-row-height;\n$layout-header-desktop-row-height: 64px !default;\n$layout-desktop-header-height: $layout-header-desktop-row-height;\n\n$layout-header-desktop-baseline: 80px !default;\n$layout-header-mobile-baseline: 72px !default;\n$layout-header-mobile-indent: 16px !default;\n$layout-header-desktop-indent: 40px !default;\n\n$layout-tab-font-size: 14px !default;\n$layout-tab-bar-height: 48px !default;\n$layout-tab-mobile-padding: 12px !default;\n$layout-tab-desktop-padding: 24px !default;\n$layout-tab-highlight-thickness: 2px !default;\n\n\n/* ICON TOGGLE */\n\n$icon-toggle-size: 32px !default;\n$icon-toggle-font-size: 24px !default;\n$icon-toggle-ripple-size: 36px !default;\n\n/* FOOTER */\n\n/*mega-footer*/\n$footer-min-padding: 16px !default;\n$footer-padding-sides: 40px !default;\n$footer-heading-font-size: 14px !default;\n$footer-heading-line-height: (1.7 * $footer-heading-font-size) !default;\n$footer-btn-size: 36px  !default;\n\n/*mini-footer*/\n$padding: 16px !default;\n$footer-heading-font-size: 24px !default;\n$footer-heading-line-height: (1.5 * $footer-heading-font-size) !default;\n$footer-btn-size: 36px !default;\n\n/* CHECKBOX */\n\n$checkbox-label-font-size: 16px !default;\n$checkbox-label-height: 24px !default;\n$checkbox-button-size: 16px !default;\n$checkbox-inner-margin: 2px !default;\n$checkbox-padding: 8px !default;\n$checkbox-top-offset:\n($checkbox-label-height - $checkbox-button-size - $checkbox-inner-margin) / 2;\n$checkbox-ripple-size: $checkbox-label-height * 1.5;\n\n/* CARD */\n\n/* Card dimensions */\n$card-width: 330px !default;\n$card-height: 200px !default;\n$card-font-size: 16px !default;\n$card-title-font-size: 24px !default;\n$card-subtitle-font-size: 14px !default;\n$card-horizontal-padding: 16px !default;\n$card-vertical-padding: 16px !default;\n\n$card-title-perspective-origin-x: 165px !default;\n$card-title-perspective-origin-y: 56px !default;\n\n$card-title-transform-origin-x: 165px !default;\n$card-title-transform-origin-y: 56px !default;\n\n$card-title-text-transform-origin-x: 149px !default;\n$card-title-text-transform-origin-y: 48px !default;\n\n$card-supporting-text-font-size: 1rem !default;\n$card-supporting-text-line-height: 18px !default;\n\n$card-actions-font-size: 16px !default;\n\n$card-title-text-font-weight: 300 !default;\n$card-z-index: 1 !default;\n\n/* Cover image */\n$card-cover-image-height: 186px !default;\n$card-background-image-url: '' !default;\n\n\n/* BUTTON */\n/**\n *\n * Dimensions\n *\n */\n$button-min-width: 64px !default;\n$button-height: 36px !default;\n$button-padding: 16px !default;\n$button-margin: 4px !default;\n$button-border-radius: 2px !default;\n\n$button-fab-size: 56px !default;\n$button-fab-size-mini: 40px !default;\n$button-fab-font-size: 24px !default;\n\n$button-icon-size: 32px !default;\n$button-icon-size-mini: 24px !default;\n\n\n/* ANIMATION */\n$animation-curve-fast-out-slow-in: cubic-bezier(0.4, 0, 0.2, 1) !default;\n$animation-curve-linear-out-slow-in: cubic-bezier(0, 0, 0.2, 1) !default;\n$animation-curve-fast-out-linear-in: cubic-bezier(0.4, 0, 1, 1) !default;\n\n$animation-curve-default: $animation-curve-fast-out-slow-in !default;\n\n\n/* PROGRESS */\n$bar-height: 4px !default;\n\n/* BADGE */\n$badge-font-size: 12px !default;\n$badge-color: unquote(\"rgb(#{$color-accent-contrast})\") !default;\n$badge-color-inverse: unquote(\"rgb(#{$color-accent})\") !default;\n$badge-background: unquote(\"rgb(#{$color-accent})\") !default;\n$badge-background-inverse: unquote(\"rgba(#{$color-accent-contrast},0.2)\") !default;\n$badge-size : 22px !default;\n$badge-padding: 2px !default;\n$badge-overlap: 12px !default;\n\n/* SHADOWS */\n\n$shadow-key-umbra-opacity: 0.2 !default;\n$shadow-key-penumbra-opacity: 0.14 !default;\n$shadow-ambient-shadow-opacity: 0.12 !default;\n\n/* GRID */\n\n$grid-desktop-columns: 12 !default;\n$grid-desktop-gutter: 16px !default;\n$grid-desktop-margin: 16px !default;\n\n$grid-desktop-breakpoint: 840px !default;\n\n$grid-tablet-columns: 8 !default;\n$grid-tablet-gutter: $grid-desktop-gutter !default;\n$grid-tablet-margin: $grid-desktop-margin !default;\n\n$grid-tablet-breakpoint: 480px !default;\n\n$grid-phone-columns: 4 !default;\n$grid-phone-gutter: $grid-desktop-gutter !default;\n$grid-phone-margin: $grid-desktop-margin !default;\n\n$grid-cell-default-columns: $grid-phone-columns !default;\n$grid-max-columns: $grid-desktop-columns !default;\n\n/* DATA TABLE */\n\n$data-table-font-size: 13px !default;\n$data-table-header-font-size: 12px !default;\n$data-table-header-sort-icon-size: 16px !default;\n\n$data-table-header-color: rgba(#000, 0.54) !default;\n$data-table-header-sorted-color: rgba(#000, 0.87) !default;\n$data-table-header-sorted-icon-hover-color: rgba(#000, 0.26) !default;\n$data-table-divider-color: rgba(#000, 0.12) !default;\n\n$data-table-hover-color: #eeeeee !default;\n$data-table-selection-color: #e0e0e0 !default;\n\n$data-table-dividers: 1px solid $data-table-divider-color !default;\n\n$data-table-row-height: 48px !default;\n$data-table-last-row-height: 56px !default;\n$data-table-header-height: 56px !default;\n\n$data-table-column-spacing: 36px !default;\n$data-table-column-padding: $data-table-column-spacing / 2;\n\n$data-table-card-header-height: 64px !default;\n$data-table-card-title-top: 20px !default;\n$data-table-card-padding: 24px !default;\n$data-table-button-padding-right: 16px !default;\n$data-table-cell-top: $data-table-card-padding / 2;\n\n/* DIALOG */\n$dialog-content-color: $card-supporting-text-text-color;\n\n/* SNACKBAR */\n\n// Hard coded since the color is not present in any palette.\n$snackbar-background-color: #323232 !default;\n$snackbar-tablet-breakpoint: $grid-tablet-breakpoint;\n$snackbar-action-color: unquote(\"rgb(#{$color-accent})\") !default;\n\n/* TOOLTIP */\n$tooltip-font-size: 10px !default;\n$tooltip-font-size-large: 14px !default;\n\n/* CHIP */\n$chip-bg-color: rgb(222, 222, 222) !default;\n$chip-bg-active-color: rgb(214, 214, 214) !default;\n$chip-height: 32px !default;\n$chip-font-size: 13px !default; \n","/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n@import \"../variables\";\n\n.mdl-mini-footer {\n  display: flex;\n  flex-flow: row wrap;\n  justify-content: space-between;\n\n  padding: ($padding * 2) $padding;\n\n  color: $footer-color;\n  background-color: $footer-bg-color;\n\n  &:after {\n    content: '';\n    display: block;\n  }\n\n  & .mdl-logo {\n    line-height: $footer-btn-size;\n  }\n}\n\n.mdl-mini-footer--link-list,\n.mdl-mini-footer__link-list {\n  display: flex;\n  flex-flow: row nowrap;\n\n  list-style: none;\n\n  margin: 0;\n  padding: 0;\n\n  & li {\n    margin-bottom: 0;\n    margin-right: $padding;\n\n    @media screen and (min-width: 760px) {\n      line-height: $footer-btn-size;\n    }\n  }\n\n  & a {\n    color: inherit;\n    text-decoration: none;\n    white-space: nowrap;\n  }\n}\n\n.mdl-mini-footer--left-section,\n.mdl-mini-footer__left-section {\n  display: inline-block;\n  order: 0;\n}\n\n.mdl-mini-footer--right-section,\n.mdl-mini-footer__right-section {\n  display: inline-block;\n  order: 1;\n}\n\n.mdl-mini-footer--social-btn,\n.mdl-mini-footer__social-btn {\n  width: $footer-btn-size;\n  height: $footer-btn-size;\n\n  padding: 0;\n  margin: 0;\n\n  background-color: $footer-button-fill-color;\n\n  border: none;\n}\n","/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n@import \"../variables\";\n\n.mdl-card {\n  display: flex;\n  flex-direction: column;\n  font-size: $card-font-size;\n  font-weight: 400;\n  min-height: $card-height;\n  overflow: hidden;\n  width: $card-width;\n  z-index: $card-z-index;\n  position: relative;\n  background: $card-background-color;\n  border-radius: 2px;\n  box-sizing: border-box;\n}\n\n.mdl-card__media {\n  background-color: $card-image-placeholder-color;\n  background-repeat: repeat;\n  background-position: 50% 50%;\n  background-size: cover;\n  background-origin: padding-box;\n  background-attachment: scroll;\n  box-sizing: border-box;\n}\n\n.mdl-card__title {\n  align-items: center;\n  color: $card-text-color;\n  display: block;\n  display: flex;\n  justify-content: stretch;\n  line-height: normal;\n  padding: $card-vertical-padding $card-horizontal-padding;\n  perspective-origin: $card-title-perspective-origin-x $card-title-perspective-origin-y;\n  transform-origin: $card-title-transform-origin-x $card-title-transform-origin-y;\n  box-sizing: border-box;\n\n  &.mdl-card--border {\n    border-bottom: 1px solid $card-border-color;\n  }\n}\n\n.mdl-card__title-text {\n  align-self: flex-end;\n  color: inherit;\n  display: block;\n  display: flex;\n  font-size: $card-title-font-size;\n  font-weight: $card-title-text-font-weight;\n  line-height: normal;\n  overflow: hidden;\n  transform-origin: $card-title-text-transform-origin-x $card-title-text-transform-origin-y;\n  margin: 0;\n}\n\n.mdl-card__subtitle-text {\n  font-size: $card-subtitle-font-size;\n  color: $card-subtitle-color;\n  margin: 0;\n}\n\n.mdl-card__supporting-text {\n  color: $card-supporting-text-text-color;\n  font-size: $card-supporting-text-font-size;\n  line-height: $card-supporting-text-line-height;\n  overflow: hidden;\n  padding: $card-vertical-padding $card-horizontal-padding;\n  width: 90%;\n\n  &.mdl-card--border {\n    border-bottom: 1px solid $card-border-color;\n  }\n}\n\n.mdl-card__actions {\n  font-size: $card-actions-font-size;\n  line-height: normal;\n  width: 100%;\n  background-color: rgba(0,0,0,0);\n  padding: 8px;\n  box-sizing: border-box;\n\n  &.mdl-card--border {\n    border-top: 1px solid $card-border-color;\n  }\n}\n\n.mdl-card--expand {\n  flex-grow: 1;\n}\n\n\n.mdl-card__menu {\n  position: absolute;\n  right: 16px;\n  top: 16px;\n}\n","/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n@import \"../variables\";\n@import \"../mixins\";\n\n// The button component. Defaults to a flat button.\n.mdl-button {\n  background: transparent;\n  border: none;\n  border-radius: $button-border-radius;\n  color: $button-secondary-color;\n  position: relative;\n  height: $button-height;\n  margin: 0;\n  min-width: $button-min-width;\n  padding: 0 $button-padding;\n  display: inline-block;\n  @include typo-button();\n  overflow: hidden;\n  will-change: box-shadow;\n  transition: box-shadow 0.2s $animation-curve-fast-out-linear-in,\n              background-color 0.2s $animation-curve-default,\n              color 0.2s $animation-curve-default;\n  outline: none;\n  cursor: pointer;\n  text-decoration: none;\n  text-align: center;\n  line-height: $button-height;\n  vertical-align: middle;\n\n  &::-moz-focus-inner {\n    border: 0;\n  }\n\n  &:hover {\n    background-color: $button-hover-color;\n  }\n\n  &:focus:not(:active) {\n    background-color: $button-focus-color;\n  }\n\n  &:active {\n    background-color: $button-active-color;\n  }\n\n  &.mdl-button--colored {\n    color: $button-primary-color-alt;\n\n    &:focus:not(:active) {\n      background-color: $button-focus-color-alt;\n    }\n  }\n}\n\ninput.mdl-button[type=\"submit\"] {\n  -webkit-appearance:none;\n}\n\n  // Raised buttons\n  .mdl-button--raised {\n    background: $button-primary-color;\n    @include shadow-2dp();\n\n    &:active {\n      @include shadow-4dp();\n      background-color: $button-active-color;\n    }\n\n    &:focus:not(:active) {\n      @include focus-shadow();\n      background-color: $button-active-color;\n    }\n\n    &.mdl-button--colored {\n      background: $button-primary-color-alt;\n      color: $button-secondary-color-alt;\n\n      &:hover {\n        background-color: $button-hover-color-alt;\n      }\n\n      &:active {\n        background-color: $button-active-color-alt;\n      }\n\n      &:focus:not(:active) {\n        background-color: $button-active-color-alt;\n      }\n\n      & .mdl-ripple {\n        background: $button-ripple-color-alt;\n      }\n    }\n  }\n\n\n  // FABs\n  .mdl-button--fab {\n    border-radius: 50%;\n    font-size: $button-fab-font-size;\n    height: $button-fab-size;\n    margin: auto;\n    min-width: $button-fab-size;\n    width: $button-fab-size;\n    padding: 0;\n    overflow: hidden;\n    background: $button-primary-color;\n    box-shadow: 0 1px 1.5px 0 rgba(0,0,0,0.12), 0 1px 1px 0 rgba(0,0,0,0.24);\n    position: relative;\n    line-height: normal;\n\n    & .material-icons {\n      position: absolute;\n      top: 50%;\n      left: 50%;\n      transform: translate(- $button-fab-font-size / 2, - $button-fab-font-size / 2);\n      line-height: $button-fab-font-size;\n      width: $button-fab-font-size;\n    }\n\n    &.mdl-button--mini-fab {\n      height: $button-fab-size-mini;\n      min-width: $button-fab-size-mini;\n      width: $button-fab-size-mini;\n    }\n\n    & .mdl-button__ripple-container {\n      border-radius: 50%;\n      // Fixes clipping bug in Safari.\n      -webkit-mask-image: -webkit-radial-gradient(circle, white, black);\n    }\n\n    &:active {\n      @include shadow-4dp();\n      background-color: $button-active-color;\n    }\n\n    &:focus:not(:active) {\n      @include focus-shadow();\n      background-color: $button-active-color;\n    }\n\n    &.mdl-button--colored {\n      background: $button-fab-color-alt;\n      color: $button-fab-text-color-alt;\n\n      &:hover {\n        background-color: $button-fab-hover-color-alt;\n      }\n\n      &:focus:not(:active) {\n        background-color: $button-fab-active-color-alt;\n      }\n\n      &:active {\n        background-color: $button-fab-active-color-alt;\n      }\n\n      & .mdl-ripple {\n        background: $button-fab-ripple-color-alt;\n      }\n    }\n  }\n\n\n  // Icon buttons\n  .mdl-button--icon {\n    border-radius: 50%;\n    font-size: $button-fab-font-size;\n    height: $button-icon-size;\n    margin-left: 0;\n    margin-right: 0;\n    min-width: $button-icon-size;\n    width: $button-icon-size;\n    padding: 0;\n    overflow: hidden;\n    color: inherit;\n    line-height: normal;\n\n    & .material-icons {\n      position: absolute;\n      top: 50%;\n      left: 50%;\n      transform: translate(- $button-fab-font-size / 2, - $button-fab-font-size / 2);\n      line-height: $button-fab-font-size;\n      width: $button-fab-font-size;\n    }\n\n    &.mdl-button--mini-icon {\n      height: $button-icon-size-mini;\n      min-width: $button-icon-size-mini;\n      width: $button-icon-size-mini;\n\n      & .material-icons {\n        top: ($button-icon-size-mini - $button-fab-font-size) / 2;\n        left: ($button-icon-size-mini - $button-fab-font-size) / 2;\n      }\n    }\n\n    & .mdl-button__ripple-container {\n      border-radius: 50%;\n      // Fixes clipping bug in Safari.\n      -webkit-mask-image: -webkit-radial-gradient(circle, white, black);\n    }\n  }\n\n\n  // Ripples\n  .mdl-button__ripple-container {\n    display: block;\n    height: 100%;\n    left: 0px;\n    position: absolute;\n    top: 0px;\n    width: 100%;\n    z-index: 0;\n    overflow: hidden;\n\n    .mdl-button[disabled] & .mdl-ripple,\n    .mdl-button.mdl-button--disabled & .mdl-ripple {\n      background-color: transparent;\n    }\n  }\n\n// Colorized buttons\n\n.mdl-button--primary.mdl-button--primary {\n  color: $button-primary-color-alt;\n  & .mdl-ripple {\n    background: $button-secondary-color-alt;\n  }\n  &.mdl-button--raised, &.mdl-button--fab {\n    color: $button-secondary-color-alt;\n    background-color: $button-primary-color-alt;\n  }\n}\n\n.mdl-button--accent.mdl-button--accent {\n  color: $button-fab-color-alt;\n  & .mdl-ripple {\n    background: $button-fab-text-color-alt;\n  }\n  &.mdl-button--raised, &.mdl-button--fab {\n    color: $button-fab-text-color-alt;\n    background-color: $button-fab-color-alt;\n  }\n}\n\n// Disabled buttons\n\n.mdl-button {\n  // Bump up specificity by using [disabled] twice.\n  &[disabled][disabled],\n  &.mdl-button--disabled.mdl-button--disabled {\n    color: $button-secondary-color-disabled;\n    cursor: default;\n    background-color: transparent;\n  }\n\n  &--fab {\n    // Bump up specificity by using [disabled] twice.\n    &[disabled][disabled],\n    &.mdl-button--disabled.mdl-button--disabled {\n      background-color: $button-primary-color-disabled;\n      color: $button-secondary-color-disabled;\n    }\n  }\n\n  &--raised {\n    // Bump up specificity by using [disabled] twice.\n    &[disabled][disabled],\n    &.mdl-button--disabled.mdl-button--disabled {\n      background-color: $button-primary-color-disabled;\n      color: $button-secondary-color-disabled;\n      box-shadow: none;\n    }\n  }\n  &--colored {\n    // Bump up specificity by using [disabled] twice.\n    &[disabled][disabled],\n    &.mdl-button--disabled.mdl-button--disabled {\n      color: $button-secondary-color-disabled;\n    }\n  }\n}\n\n// Align icons inside buttons with text\n.mdl-button .material-icons {\n  vertical-align: middle;\n}\n","// SIMPLE GRID - SASS/SCSS\n\n\n// fonts\n$font-weight-light: 300;\n$font-weight-regular: 400;\n$font-weight-heavy: 700;\n\n// colors\n$dark-grey: #333447;\n$dark-gray: #333447; // for the Americans\n\n\n.font-light {\n  font-weight: $font-weight-light;\n}\n\n.font-regular {\n  font-weight: $font-weight-regular;\n}\n\n.font-heavy {\n  font-weight: $font-weight-heavy;\n}\n\n// utility\n\n.left {\n  text-align: left;\n}\n\n.right {\n  text-align: right;\n}\n\n.center {\n  text-align: center;\n  margin-left: auto;\n  margin-right: auto;\n}\n\n.justify {\n  text-align: justify;\n}\n\n.hidden-sm {\n  display: none;\n}\n\n// grid\n\n$width: 98%;\n$gutter: 2%;\n$breakpoint-small: 33.75em; // 540px\n$breakpoint-med: 45em; // 720px\n$breakpoint-large: 60em; // 960px\n\n.container {\n  width: 100%;\n  margin-left: auto;\n  margin-right: auto;\n\n  @media only screen and (min-width: $breakpoint-small) {\n    width: 80%;\n  }\n\n  @media only screen and (min-width: $breakpoint-large) {\n    width: 75%;\n    max-width: 60rem;\n  }\n}\n\n.row {\n  position: relative;\n  width: 100%;\n}\n\n.row [class^=\"col\"] {\n  float: left;\n  margin: 0.5rem 1%;\n  min-height: 0.125rem;\n}\n\n.row::after {\n  content: \"\";\n  display: table;\n  clear: both;\n}\n\n.col-1,\n.col-2,\n.col-3,\n.col-4,\n.col-5,\n.col-6,\n.col-7,\n.col-8,\n.col-9,\n.col-10,\n.col-11,\n.col-12 {\n  width: $width;\n}\n\n.col-1-sm {\n  width: ($width / 12) - ($gutter * 11 / 12);\n}\n\n.col-2-sm {\n  width: ($width / 6) - ($gutter * 10 / 12);\n}\n\n.col-3-sm {\n  width: ($width / 4) - ($gutter * 9 / 12);\n}\n\n.col-4-sm {\n  width: ($width / 3) - ($gutter * 8 / 12);\n}\n\n.col-5-sm {\n  width: ($width / (12 / 5)) - ($gutter * 7 / 12);\n}\n\n.col-6-sm {\n  width: ($width / 2) - ($gutter * 6 / 12);\n}\n\n.col-7-sm {\n  width: ($width / (12 / 7)) - ($gutter * 5 / 12);\n}\n\n.col-8-sm {\n  width: ($width / (12 / 8)) - ($gutter * 4 / 12);\n}\n\n.col-9-sm {\n  width: ($width / (12 / 9)) - ($gutter * 3 / 12);\n}\n\n.col-10-sm {\n  width: ($width / (12 / 10)) - ($gutter * 2 / 12);\n}\n\n.col-11-sm {\n  width: ($width / (12 / 11)) - ($gutter * 1 / 12);\n}\n\n.col-12-sm {\n  width: $width;\n}\n\n@media only screen and (min-width: $breakpoint-med) {\n  .col-1 {\n    width: ($width / 12) - ($gutter * 11 / 12);\n  }\n  .col-2 {\n    width: ($width / 6) - ($gutter * 10 / 12);\n  }\n  .col-3 {\n    width: ($width / 4) - ($gutter * 9 / 12);\n  }\n  .col-4 {\n    width: ($width / 3) - ($gutter * 8 / 12);\n  }\n  .col-5 {\n    width: ($width / (12 / 5)) - ($gutter * 7 / 12);\n  }\n  .col-6 {\n    width: ($width / 2) - ($gutter * 6 / 12);\n  }\n  .col-7 {\n    width: ($width / (12 / 7)) - ($gutter * 5 / 12);\n  }\n  .col-8 {\n    width: ($width / (12 / 8)) - ($gutter * 4 / 12);\n  }\n  .col-9 {\n    width: ($width / (12 / 9)) - ($gutter * 3 / 12);\n  }\n  .col-10 {\n    width: ($width / (12 / 10)) - ($gutter * 2 / 12);\n  }\n  .col-11 {\n    width: ($width / (12 / 11)) - ($gutter * 1 / 12);\n  }\n  .col-12 {\n    width: $width;\n  }\n\n  .hidden-sm {\n    display: block;\n  }\n}\n\n.row {\n  display: -webkit-box;\n  display: -webkit-flex;\n  display: -ms-flexbox;\n  display: flex;\n  flex-wrap: wrap;\n}\n\n.row > [class*='col-'] {\n  display: flex;\n  flex-direction: column;\n}\n","\n/*\nMaterial Icons\n*/\n\n.material-icons {\n    font-family: 'Material Icons';\n    font-weight: normal;\n    font-style: normal;\n    font-size: 24px;  /* Preferred icon size */\n    display: inline-block;\n    line-height: 1;\n    text-transform: none;\n    letter-spacing: normal;\n    word-wrap: normal;\n    white-space: nowrap;\n    direction: ltr;\n  \n    /* Support for all WebKit browsers. */\n    -webkit-font-smoothing: antialiased;\n    /* Support for Safari and Chrome. */\n    text-rendering: optimizeLegibility;\n  \n    /* Support for Firefox. */\n    -moz-osx-font-smoothing: grayscale;\n  \n    /* Support for IE. */\n    font-feature-settings: 'liga';\n  }","html {\n    font-size: $font_size;\n}\n\nbody {\n    display: block !important;\n    background-color: $background_color;\n    font-size: 1rem;\n    line-height: 1.5rem;\n    font-family: $body_font_family;\n}\n\n.mdl-layout__content:focus {\n    outline: none;\n }\n\nh1, h2, h3, h4, h5, h6, blockquote, span.mdl-layout-title,\na.download > code.download {\n    font-family: $body_font_family;\n}\n\nh1, h2, h3, h4, h5, h6, .toc-backref, .contents, .toctree-wrapper, .contents a, .toctree-wrapper a, .globaltoc a.current {\n    color: $color-mxnet !important;\n}\n\na {\n    text-decoration: none;\n}\n\n.page-content {\n    font-size: 1rem;\n    p, ul, ol, dl, dd, dt, table, th, td {\n        font-size: 1rem;\n    }\n}\n\n.brand {\n    color: inherit;\n    text-decoration: none;\n}\n\n.section {\n    overflow-x: auto;\n}\n\n\n/*\n *  Figure Directive Styles\n */\n img {\n    max-width: 100%;\n    display: block;\n    margin-left: auto;\n    margin-right: auto;\n }\n\ndiv.figure {\n    p.caption {\n        text-align: center;\n        margin-top: .75rem;\n\n        span.caption-number {\n            font-style: normal;\n        }\n        .caption-number::after {\n            content: \"\\00a0\";\n        }\n    }\n}\n\n.svg-icon {\n  width: 16px;\n  height: 16px;\n  display: inline-block;\n  fill: $grey-color-light;\n  padding-right: 5px;\n  padding-top: 4px;\n  vertical-align: text-top;\n}\n\n/*\n * Download Link Styles\n */\na.download > i.material-icons {\n    position: relative;\n    top: 5px;\n}\n\na.download {\n    text-decoration: none;\n}\n\n%clearfix:after {\n  content: \"\";\n  display: table;\n  clear: both;\n}\n\n.wrapper {\n  max-width: -webkit-calc(#{$content-width} - (#{$spacing-unit} * 2));\n  max-width: calc(#{$content-width} - (#{$spacing-unit} * 2));\n  margin-right: auto;\n  margin-left: auto;\n  padding-right: calc(#{$spacing-unit}+15px);\n  padding-left: $spacing-unit;\n  @extend %clearfix;\n\n  @media screen and (max-width: $on-laptop) {\n    max-width: -webkit-calc(#{$content-width} - (#{$spacing-unit}));\n    max-width: calc(#{$content-width} - (#{$spacing-unit}));\n    padding-right: $spacing-unit / 2;\n    padding-left: $spacing-unit / 2;\n  }\n}\n\n","/*\nVariables\n*/\n$font_size: 16px;\n\n$background_color: #fafafa;\n$code_background: rgba(0,0,0,.05);\n\n$code_font_family: \"Menlo\", \"DejaVu Sans Mono\", \"Liberation Mono\", \"Consolas\", \"Ubuntu Mono\", \"Courier New\", \"andale mono\", \"lucida console\", monospace !default;\n$body_font_family: -apple-system,BlinkMacSystemFont,\"Segoe UI\",Roboto,\"Helvetica Neue\",Arial,sans-serif,\"Apple Color Emoji\",\"Segoe UI Emoji\",\"Segoe UI Symbol\" !default;\n$base-font-size:   17px !default;\n\n$xl-breakpoint: 1795px;\n$lg-breakpoint: 1200px;\n$md-breakpoint: 992px;\n$sm-breakpoint: 768px;\n$xs-breakpoint: 576px;\n\n$color-primary: $palette-blue-500;\n$color-primary-dark: $palette-blue-700 !default;\n$color-accent: $palette-deep-orange-A200 !default;\n$color-primary-contrast: $color-white !default;\n$color-accent-contrast: $color-white !default;\n\n\n$base-line-height: 1.5 !default;\n$spacing-unit:     30px !default;\n\n$color-mxnet: rgb(4,140,204);\n$color-mxnet-dark: rgb(4,60,110);\n$grey-color:       #828282 !default;\n$grey-color-light: lighten($grey-color, 45%) !default;\n$grey-color-dark:  darken($grey-color, 25%) !default;\n\n$table-text-align: left !default;\n\n// Width of the content area\n$content-width:    1150px !default;\n\n$on-palm:          600px !default;\n$on-palm:          900px !default;\n$on-laptop:        1024px !default;","/**\n * Layout Styles\n */\n $layout: (\n    document: (\n        xl: (\n            width: 1400px,\n        )\n    ),\n    drawer-container: (\n        width: $layout-drawer-width,\n    ),\n    side-doc-outline: (\n        width: 230px,\n    ),\n    page-content: (\n        md: (\n            width: 90%,\n            padding: 0 5%\n        ),\n        lg: (\n            width: calc( 90% - 230px ),\n            padding: 0 5%\n        )\n    )\n);\n\n.mdl-layout {\n    margin-top: 76px;\n}\n\n\n.document {\n    width: 100%;\n    margin: 0 auto;\n    display: flex;\n\n    @media (min-width: $xl-breakpoint) {\n        width: map-get(map-get(map-get($layout, document), xl), width);\n    }\n    .page-content {\n        width: 100%;\n        margin: 0 auto;\n        padding: 0 12px;\n\n        @media (min-width: $md-breakpoint) {\n            width: map-get(map-get(map-get($layout, page-content), md), width);\n            padding: map-get(map-get(map-get($layout, page-content), md), padding);\n        }\n\n        @media (min-width: $lg-breakpoint) {\n            width: map-get(map-get(map-get($layout, page-content), lg), width);\n            padding: map-get(map-get(map-get($layout, page-content), lg), padding);\n        }\n    }\n\n    .side-doc-outline {\n        width: map-get(map-get($layout, side-doc-outline), width);\n\n        @media (max-width: $lg-breakpoint - 1) {\n            display: none;\n        } \n        &--content {\n            position: fixed;\n            overflow-x: auto;\n            overflow-y: auto;\n            width: inherit;\n            &::-webkit-scrollbar {\n                width: 6px;\n            }\n    \n            &::-webkit-scrollbar-track {\n                border-radius: 6px;\n            }\n    \n            &::-webkit-scrollbar-thumb {\n                background-color: rgba(0, 0, 0, .3);\n                border-radius: 6px;\n                box-shadow:0 0 0 1px rgba(255, 255, 255, .3);\n            }\n        }\n    }\n\n}","@keyframes float-in {\n    0% {\n        transform: translateY(0.5rem);\n        opacity: 0;\n    }\n\t100% {\n\t\ttransform: translateY(0);\n\t\topacity: 1;\n\t}\n}\n\n@keyframes float-out {\n    0% {\n        transform: translateY(0);\n        opacity: 1;\n    }\n\t100% {\n\t\ttransform: translateY(0.5rem);\n\t\topacity: 0;\n\t}\n}\n\n.page-content {\n    .headerlink {\n        display: inline-block;\n        text-decoration: none;\n        margin-left: 0.8rem;\n        color: inherit;\n        opacity: 0;\n        &:hover {\n            animation: float-in 0.2s $animation-curve-fast-out-slow-in 0s forwards;\n        }\n    }\n\n    h1, h2, h3, h4, h5, h6 {\n        .toc-backref {\n            text-decoration: none;\n        }\n        &:hover {\n            .headerlink {\n                animation: float-in 0.2s $animation-curve-fast-out-slow-in 0s forwards;\n            }\n        }\n    }\n\n    h1 {\n        font-size: 2rem;\n        line-height: 2.25rem;\n    }\n\n    h2 {\n        font-size: 1.75rem;\n        line-height: 2rem;\n        padding-top: 1.5rem;\n        margin-top: 0;\n        margin-bottom: 1rem;\n    }\n\n    h3 {\n        font-size: 1.5rem;\n        line-height: 1.75rem;\n        padding-top: 1rem;\n        margin-top: 0px;\n        margin-bottom: .75rem;\n    }\n\n    h4 {\n        font-size: 1.25rem;\n        line-height: 1.5rem;\n        padding-top: .75rem;\n        margin-top: 0px;\n        margin-bottom: .5rem;\n    }\n\n    div.page-content h5 {\n        font-size: 1.1rem;\n        line-height: 1.5rem;\n        padding-top: 2rem;\n        margin-top: 0px;\n        margin-bottom: 1rem;\n    }\n\n    div.page-content h6 {\n        font-size: 1rem;\n        line-height: 1.5rem;\n        padding-top: 2rem;\n        margin-top: 0px;\n        margin-bottom: 1rem;\n    }\n\n\n}\n","\n/*\n * Admonition Styles\n */\n $admonitions: (\n    hint: (\n        font-color: rgb(0, 188, 212),\n        background-color: rgba(0, 188, 212, 0.1),\n        icon-content: \"help_outline\"\n    ),\n    note: (\n        font-color: rgb(0, 188, 212),\n        background-color: rgba(0, 188, 212, 0.1),\n        icon-content: \"info_outline\"\n    ),\n    seealso: (\n        font-color: rgb(0, 188, 212),\n        background-color: rgba(0, 188, 212, 0.1),\n        icon-content: \"search\"\n    ),\n    warning: (\n        font-color: rgb(255, 193, 7),\n        background-color: rgba(255, 193, 7, 0.1),\n        icon-content: \"warning\"\n    ),\n    attention: (\n        font-color: rgb(255, 193, 7),\n        background-color: rgba(255, 193, 7, 0.1),\n        icon-content: \"warning\"\n    ),\n    tip: (\n        font-color: rgb(139, 195, 74),\n        background-color: rgba(139, 195, 74, 0.1),\n        icon-content: \"lightbulb_outline\"\n    ),\n    important: (\n        font-color: rgb(139, 195, 74),\n        background-color: rgba(139, 195, 74, 0.1),\n        icon-content:  \"check_circle\"\n    ),\n    error: (\n        font-color: rgb(244, 67, 54),\n        background-color: rgba(244, 67, 54, 0.1),\n        icon-content: \"error_outline\"\n    ),\n    caution: (\n        font-color: rgb(244, 67, 54),\n        background-color: rgba(244, 67, 54, 0.1),\n        icon-content: \"error_outline\"\n    ),\n    danger: (\n        font-color: rgb(244, 67, 54),\n        background-color: rgba(244, 67, 54, 0.1),\n        icon-content: \"error_outline\"\n    )\n);\n\n @mixin admonition-style($type) {\n    border-left: solid 4px map-get(map-get($admonitions, $type), font-color);\n    background-color: map-get(map-get($admonitions, $type), background-color);\n    .admonition-title {\n        font-size: 16px;\n        font-weight: bold;\n        color: map-get(map-get($admonitions, $type), font-color);\n\n        margin-top: 4px;\n        margin-bottom: 8px;\n        &::before {\n            @extend .material-icons;\n            position: relative;\n            margin-right: 5px;\n            top: 3px;\n            content: map-get(map-get($admonitions, $type), icon-content);\n            font-size: 18px;\n        }\n    }\n}\n\n.admonition {\n    @extend .mdl-shadow--2dp;\n\n    padding: 12px 20px;\n    margin-top: 10px;\n    margin-bottom: 10px;\n    p.last {\n        margin: 16px;\n    }\n    .admonition-title {\n        font-size: 16px;\n        font-weight: bold;\n        color: #555;\n        text-transform: uppercase;\n        margin-top: 7px;\n    }\n\n    @each $type in (note, seealso, hint, warning, attention, tip, important, error, caution, danger) {\n        &.#{$type} {\n            @include admonition-style($type);\n        }\n    }\n}\n",".page-content {\n    .highlight {\n        margin: 1px 0;\n        pre {\n            background: $code_background;\n            color: rgba(0,0,0,.87);\n            font-family: $code_font_family;\n            padding: 0.75rem;\n            overflow: auto;\n            overflow-y: hidden;\n            .o, .nd {\n                color: rgba(0,0,0,.87);\n            }\n        }\n    }\n\n    div.highlight-console div.highlight {\n        background: none;\n    }\n\n    // for jupyter notebook output cell\n    .output {\n        .highlight {\n            pre {\n                color: rgba(0,0,0,.87);\n                background: $background_color;\n                border-width: 1px;\n                border-color: #999;\n                border-style: solid;\n                padding: 0.75rem;\n            }\n        }\n     }\n\n    .code, code:not(.download) {\n        margin: 0 0;\n        font-family: $code_font_family;\n        border-radius: 2px;\n        span.pre {\n            font-family: $code_font_family;\n        }\n    }\n\n    .viewcode-link {\n        padding-left: 2em;\n        font-size: 80%;\n    }\n\n    .rubric, .method > dt, .function > dt, .class > dt {\n        display: table;\n        margin: 10px 0;\n        font-size: 100%;\n        line-height: normal;\n        background: #e7f2fa;\n        color: #2B98F0;\n        border-top: solid 3px #55ADF3;\n        padding: 10px;\n        position: relative;\n        .descname, .descclassname {\n            color: rgba(0,0,0,.87);\n            background: #e7f2fa;\n            padding: 3px;\n        }\n        em {\n            padding: 0 2px;\n        }\n    }\n\n\n    .rubric {\n        margin: 30px 0 10px 0;\n     }\n\n\n    .field-body {\n        padding-left: 40px;\n        ul {\n            padding: 0 0 0 16px;\n            margin: 0;\n        }\n    }\n\n     // .docutils > dt {\n    //     padding: 6px;\n    //     display: table;\n    //     margin-bottom: 6px;\n    //     border: none;\n    //     border-left: solid 3px #ccc;\n    //     background: #f0f0f0;\n    //     color: #555;\n    // }\n\n    .seealso .docutils > dt {\n       float: left;\n       clear: left;\n       padding: 0 6px;\n     }\n\n    .seealso .docutils > dd {\n       padding-left: 6em;\n    }\n    .nblast {\n        padding-bottom: 1em;\n    }\n\n    pre {\n        font-size: 90%;\n        background: #eee;\n        color: #455A64;\n        padding: 16px 32px;\n        width: auto;\n        border-radius: 4px;\n        word-wrap: break-word;\n\n        &:hover {\n            @extend .mdl-shadow--2dp;\n\n            &:before {\n                font-family: $body_font_family;\n                padding: 0 0.5rem;\n                content: attr(click-to-copy);\n                color: rgba(0, 0, 0, 0.5);\n                border-radius: 4px;\n                position: relative;\n                float: right;\n                top: -0.5rem;\n                right: -0.5rem;\n                background: rgb(200, 200, 200);\n                font-size: 0.8rem;\n                cursor: pointer;\n            }\n        }\n    }\n}\n","/*\n * Quotation Block Styles\n */\n .page-content {\n    blockquote {\n        font-size: 1rem;\n        padding: 0 1rem;\n        border-left: 3px solid $code_background;\n\n        &:after {\n            content: \"\" !important;\n            margin-left: 0;\n        }\n        &:before {\n            content: \"\" !important;\n        }\n    }\n }\n",".page-content {\n    table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) {\n        @extend .mdl-data-table;\n        @extend .mdl-shadow--2dp;\n\n        margin: 1.5rem 0;\n        table-layout: fixed;\n        max-width: 100%;\n        min-width: 70%;\n\n        th, td {\n            @extend .mdl-data-table__cell--non-numeric;\n            white-space: normal;\n            overflow-wrap: break-word;\n        }\n\n        caption {\n            font-size: $font_size;\n            margin: 1rem 0 0.8rem 0;\n            white-space: normal;\n            .caption-number {\n                font-style: normal;\n            }\n            .caption-number::after {\n                content: \"\\00a0\";\n            }\n        }\n\n    }\n}\n",".globaltoc {\n    \n    .caption, .toc {\n        display: none;\n    }\n\n    ul {\n\n        list-style-type: none;\n        padding: 0;\n        margin: 0;\n\n        li {\n            min-height: 36px;\n            .link-wrapper {\n                display: flex;\n                justify-content: space-between;\n                > a {\n                    padding: 8px 0;\n                    display: block;\n                    width: 100%;\n                    font-size: 1rem;\n                    text-decoration: none;\n                    color: $layout-drawer-navigation-color;\n                    &.current {\n                        font-weight: bold;\n                    }\n                }\n            }\n        }\n    }\n\n    .nav-toggle {\n        padding: 0;\n        float: right;\n        display: flex;\n        align-items: center;\n        justify-content: center;\n        height: 36px;\n        > a {\n            padding: 0;\n            margin-left: 0;\n            margin-right: 4px;\n            cursor: pointer;\n            > i {\n                font-size: 18px;\n            }\n        }\n        &.show {\n            transform: rotateZ(180deg);\n            > a {\n                margin-right: 0;\n                margin-left: 4px;\n            }\n        }\n    }\n\n    nav {\n        > ul > li > span.link-wrapper {\n            padding-left: 8px;\n        }\n        > ul > li > ul > li > span.link-wrapper {\n            padding-left: 16px;\n        }\n        > ul > li > ul > li > ul > li > span.link-wrapper {\n            padding-left: 24px;\n        }\n        > ul > li > ul > li > ul > li > ul> li > span.link-wrapper {\n            padding-left: 32px;\n        }\n        > ul > li > ul > li > ul > li > ul > li > ul> li > span.link-wrapper {\n            padding-left: 40px;\n        }\n        > ul > li > ul > li > ul > li > ul > li > ul > li > ul> li > span.link-wrapper {\n            padding-left: 48px;\n        }\n    }\n}\n",".localtoc {\n    font-size: 0.75rem;\n    padding-top: 1rem;\n\n    .caption {\n        padding-left: 12px;\n        &-text {\n            font-size: 0.9rem;\n            font-weight: 700;\n        }\n    }\n\n    > ul > li > a {\n        display: none;\n    }\n\n    ul {\n        padding: 0;\n        list-style-type: none;\n    }\n\n    li {\n        padding-left: 6px;\n    }\n\n    a {\n        display: block;\n        text-decoration: none;\n        color: inherit;\n        margin-top: 8px;\n        padding-left: 8px;\n        line-height: 1.1rem;\n    \n        &.current {\n            padding-left: 5px;\n            border-left: 3px solid;\n            font-weight: bold;\n        }\n    }\n}","/*\r\n *  Toctree and Contents Directive Styles\r\n */\r\n .toctree-wrapper,\r\n .contents.topic {\r\n     border-left: 5px solid;\r\n }\r\n\r\n .toctree-wrapper > p.caption,\r\n .contents.topic > p.topic-title {\r\n     color: rgb(117, 117, 117);\r\n     font-size: 1rem;\r\n     padding-left: 14px;\r\n }\r\n\r\n .toctree-wrapper ul,\r\n .contents.topic ul{\r\n     padding-left: 14px;\r\n     list-style: none;\r\n     line-height: 30px;\r\n }\r\n\r\n .toctree-wrapper a,\r\n .contents.topic a {\r\n     font-size: 1.2rem;\r\n     text-decoration: none;\r\n     .pre {\r\n         font-size: 1rem;\r\n     }\r\n }\r\n\r\n .toctree-wrapper > ul > li > a,\r\n .contents.topic > ul > li > a {\r\n     font-size: 1.3rem;\r\n     .pre {\r\n         font-size: 1.1rem;\r\n     }\r\n }\r\n",".page-content {\n    ul {\n        li {\n            margin: .3rem 0;\n            p {\n                margin: 0;\n            }\n        }\n    }\n    .option-list {\n        .option {\n            font-family: $code_font_family;\n        }\n        td {\n            padding: 0.5rem;\n            border: none;\n        }\n    }\n}\n","/*\r\n * Drawer Styles\r\n */\r\n.mdl-layout {\r\n    &__drawer {\r\n        background-color: #fff;\r\n\r\n        &::-webkit-scrollbar {\r\n            width: 6px;\r\n        }\r\n\r\n        &::-webkit-scrollbar-track {\r\n            border-radius: 6px;\r\n        }\r\n\r\n        &::-webkit-scrollbar-thumb {\r\n            background-color: rgba(0, 0, 0, .3);\r\n            border-radius: 6px;\r\n            box-shadow:0 0 0 1px rgba(255, 255, 255, .3);\r\n        }\r\n\r\n        > .mdl-layout-title {\r\n            font-weight: bold;\r\n            text-align: right;\r\n            margin: 0;\r\n            padding: 0;\r\n            line-height: 32px;\r\n            border-bottom: 1px solid rgba(0,0,0,.1);\r\n            min-height: 64px;\r\n            .title {\r\n                color: inherit;\r\n                display: block;\r\n                height: 100%;\r\n                width: 100%;\r\n                text-decoration: none;\r\n                > img.logo {\r\n                    width: 100%;\r\n                    margin: 0;\r\n                    padding: 0;\r\n                }\r\n\r\n                &-text {\r\n                    font-weight: bold;\r\n                    text-align: right;\r\n                    padding: 0 10px;\r\n                    margin: 16px 0 8px 0;\r\n                    line-height: 32px;\r\n                    font-family: $body_font_family;\r\n                    color: inherit;\r\n                    display: block;\r\n                }\r\n            }\r\n        }\r\n    }\r\n}\r\n","/*\r\n * Header Styles\r\n */\r\n\r\nnav.breadcrumb {\r\n    > a.mdl-navigation__link {\r\n        padding: 0 8px;\r\n        font-size: 18px;\r\n    }\r\n    @media (max-width: $lg-breakpoint - 1) {\r\n        width: calc( 100% - 64px );\r\n        a.mdl-navigation__link.is-active {\r\n            overflow-x: hidden;\r\n            width: 100%;\r\n            overflow: hidden;\r\n            white-space: nowrap;\r\n            text-overflow: ellipsis;\r\n        }\r\n        a.mdl-navigation__link:not(.is-active),\r\n        i.material-icons {\r\n            display: none;\r\n        }\r\n    }\r\n}\r\n\r\ndiv.mdl-layout__header {\r\n    margin-top: 77px;\r\n}\r\n\r\n.mdl-layout__drawer-button {\r\n    top: 13px !important;\r\n}\r\n\r\ndiv.mdl-layout__header-row.header-links {\r\n    background: rgba(255,255,255,0.2);\r\n    width: 100%;\r\n    overflow-x: auto;\r\n    overflow-y: hidden;\r\n\r\n    a.mdl-navigation__link  {\r\n        font-size: 1rem;\r\n        i {\r\n            font-size: 1.2rem;\r\n            margin: 0 8px;\r\n            position: relative;\r\n            bottom: -0.1rem;\r\n        }\r\n    };\r\n\r\n    a.mdl-navigation__link:hover  {\r\n        background-color: unquote(\"rgb(#{$color-primary})\");\r\n        color: #eeeeee;\r\n    };\r\n    a.mdl-navigation__link[href=\"#\"]  {\r\n        background-color: unquote(\"rgb(#{$color-primary})\");\r\n        opacity: 1;\r\n        color: #ffffff;\r\n    };\r\n}\r\n\r\n/* mxnet-header */\r\n\r\n\r\n.site-title {\r\n  font-weight: 300 !important;\r\n  line-height: 57px;\r\n  letter-spacing: -1px;\r\n  margin-bottom: 0;\r\n  float: left;\r\n  color: white;\r\n\r\n  &,\r\n  &:visited {\r\n    color: $grey-color-dark;\r\n  }\r\n}\r\n\r\n\r\n.site-header {\r\n  position: fixed;\r\n  top: 0;\r\n  width: 100%;\r\n  min-height: 55px;\r\n  padding-top: 10px;\r\n  padding-bottom: 10px;\r\n  background-color: $color-mxnet;\r\n  z-index: 10;\r\n  font-weight: 300;\r\n  font-size: 17px;\r\n  border-bottom: 1px solid white;\r\n}\r\n\r\n.site-header-logo {\r\n  width: 120px;\r\n  display: initial;\r\n}\r\n\r\n.site-nav {\r\n  float: right;\r\n  line-height: 57px;\r\n\r\n  .nav-trigger {\r\n    display: none;\r\n  }\r\n\r\n  .menu-icon {\r\n    display: none;\r\n  }\r\n\r\n  .page-link {\r\n    color: white;\r\n    line-height: 1.5;\r\n    font-weight: 300;\r\n    // Gaps between nav items, but not on the last one\r\n    &:not(:last-child) {\r\n      margin-right: 40px;\r\n    }\r\n\r\n    &:hover {\r\n      color: white;\r\n      text-shadow: -0.06ex 0 white, 0.06ex 0 white;\r\n    }\r\n  }\r\n\r\n  .page-link.page-current {\r\n    color: white;\r\n    text-decoration: underline;\r\n  }\r\n\r\n  @media screen and (max-width: $on-laptop) {\r\n    position: absolute;\r\n    top: 9px;\r\n    right: 15px;\r\n    background-color: rgb(23,141,201);\r\n    border-radius: 2px;\r\n    text-align: right;\r\n\r\n    label[for=\"nav-trigger\"] {\r\n      display: block;\r\n      float: right;\r\n      width: 36px;\r\n      height: 36px;\r\n      z-index: 2;\r\n      cursor: pointer;\r\n    }\r\n\r\n    .menu-icon {\r\n      display: block;\r\n      float: right;\r\n      width: 36px;\r\n      height: 26px;\r\n      line-height: 0;\r\n      padding-top: 20px;\r\n      text-align: center;\r\n\r\n      > svg {\r\n        fill: white;\r\n      }\r\n    }\r\n\r\n    input ~ .trigger {\r\n      clear: both;\r\n      display: none;\r\n    }\r\n\r\n    input:checked ~ .trigger {\r\n      display: block;\r\n      padding-bottom: 5px;\r\n    }\r\n\r\n    .page-link {\r\n      padding: 5px 10px;\r\n      display: block;\r\n\r\n      &:not(:last-child) {\r\n        margin-right: 0;\r\n      }\r\n\r\n      margin-left: 20px;\r\n    }\r\n  }\r\n}","/*\r\n * Footer Styles\r\n */\r\nfooter.mdl-mini-footer {\r\n    background-color: #212121;\r\n    > div.mdl-mini-footer__left-section {\r\n        margin-bottom: 20px;\r\n        display: flex;\r\n        flex-direction: column;\r\n        .mdl-logo {\r\n            font-size: 1.1rem;\r\n        }\r\n        ul {\r\n            @extend .mdl-mini-footer__link-list;\r\n        }\r\n    }\r\n    > div.mdl-mini-footer__right-section {\r\n        font-size: 0.9rem;\r\n        display: flex;\r\n        flex-direction: column;\r\n        justify-content: flex-end;\r\n\r\n        a {\r\n            color: inherit;\r\n            font-weight: bold;\r\n            text-decoration: none;\r\n        }\r\n    }\r\n    p.caption {\r\n        display: none;\r\n    }\r\n}\r\n\r\n/*\r\n * Pagenation Block Styles\r\n */\r\n .pagenation {\r\n    width: 100%;\r\n    margin-top: 80px;\r\n    height: 92px;\r\n    background-color: #424242;\r\n    display: flex;\r\n\r\n    .button-common {\r\n        text-transform: none;\r\n        padding: 0;\r\n        height: 92px;\r\n        display: flex;\r\n        justify-content: center;\r\n        align-items: center;\r\n        color: #ffffff;\r\n    }\r\n    #button-prev {\r\n        @extend .button-common;\r\n        margin-right: auto;\r\n        .pagenation-text {\r\n            text-align: left;\r\n        }\r\n        \r\n    }\r\n    #button-next {\r\n        @extend .button-common;\r\n        margin-left: auto;\r\n        flex-direction: row-reverse;\r\n        .pagenation-text {\r\n            text-align: right;\r\n        }\r\n    }\r\n\r\n    &-arrow {\r\n        &-L {\r\n            margin-right: 20px;\r\n        }\r\n        &-R {\r\n            margin-left: 20px;\r\n        }\r\n    }\r\n\r\n    &-text {\r\n        line-height: 30px;\r\n        font-size: 20px;\r\n    }\r\n\r\n    &-direction {\r\n        opacity: 0.7;\r\n        font-size: 18px;\r\n    }\r\n    @media screen and (max-width: 1024px) {\r\n        #button-prev {\r\n            width: 20%;\r\n        }\r\n        \r\n        #button-next {\r\n            width: 80%;\r\n        }\r\n    \r\n        #button-prev .pagenation-text {\r\n            display: none;\r\n        }\r\n    }\r\n    @media screen and (min-width: 1025px) {\r\n        #button-prev,\r\n        #button-next {\r\n            width: 50%;\r\n        }\r\n    \r\n        #button-prev .pagenation-text {\r\n            display: block;\r\n        }\r\n    }\r\n}\r\n\r\n\r\n\r\n/**\r\n * Site footer\r\n */\r\n.site-footer {\r\n  border-top: 1px solid $grey-color-light;\r\n  padding: $spacing-unit 0;\r\n  background-color: #424242;\r\n  .footer-category-title {\r\n    color: $color-mxnet;\r\n  }\r\n  a {\r\n    color: $grey-color-light !important;\r\n\r\n    &:visited {\r\n      color: $grey-color-light !important;\r\n    }\r\n  }\r\n\r\n}\r\n\r\n.site-footer2 {\r\n  background-color: #424242;\r\n  padding-top: 40px;\r\n  padding-bottom: 10px;\r\n}\r\n\r\n.footer-heading {\r\n  margin-bottom: $spacing-unit / 2;\r\n}\r\n\r\n.contact-list,\r\n.social-media-list {\r\n  list-style: none;\r\n  margin-left: 0;\r\n}\r\n\r\n\r\n.footer-bottom-warning {\r\n  font-size: 80%;\r\n  color: white;\r\n  float: left;\r\n}\r\n\r\n.footer-logo {\r\n  width: 200px;\r\n  margin-bottom: 30px;\r\n  margin-top: 30px;\r\n}\r\n\r\n.footer-col {\r\n  float: left;\r\n  margin-bottom: $spacing-unit / 2;\r\n  padding-left: $spacing-unit / 2;\r\n}\r\n\r\n.footer-text {\r\n  color: $grey-color-light;\r\n}\r\n\r\n"," /*\r\n  * Search Styles\r\n  */\r\n#waterfall-exp::-webkit-input-placeholder {\r\n    color: #ccc;\r\n}\r\n#waterfall-exp:-ms-input-placeholder {\r\n    color: #ccc;\r\n}\r\n#waterfall-exp::-moz-placeholder {\r\n    color: #ccc;\r\n}\r\n\r\nul.search span.highlighted {\r\n    font-weight: bold;\r\n}\r\n\r\nul.search > li {\r\n    margin-bottom: 24px;\r\n}\r\n\r\n#search-results {\r\n    ul {\r\n        list-style: none;\r\n        padding: 0;\r\n        li {\r\n            > a {\r\n                text-decoration: none;\r\n                font-size: 1.2rem;\r\n            }\r\n        }\r\n    }\r\n}\r\n","a.download {\n    &:before {\n        @extend .material-icons;\n        content: \"file_download\";\n        position: relative;\n        top: 5px;\n        margin-right: 5px;\n    }\n}\n\nbutton.download {\n    position: sticky;\n    margin-left: 1em;\n}\n",".mdl-card {\n    margin: 1em 1.5em 1em 0;\n    display: inline-block;\n    width: 250px;\n    min-height: 140px;\n    padding: 18px;\n}\n.mdl-card:hover {\n    box-shadow: 0 10px 20px rgba(0,0,0,0.25), 0 6px 6px rgba(0,0,0,0.22);\n    color: #000;\n    cursor: pointer;\n}\n.mdl-card__title {\n    padding: 0 0 1em 0;\n    font-size: 18px;\n    color: #444;\n}\n\n.mdl-card__supporting-text {\n    line-height: 1.5rem;\n    padding: 0px;\n    width: 100%;\n}\n\n.head-card.mdl-card {\n    width: auto;\n    display: block;\n    max-width: 800px;\n    padding: 24px;\n}\n\n.head-card > .mdl-card__title {\n    padding-bottom: 0px;\n    height: 60px;\n    font-weight: 700;\n    text-transform: uppercase;\n}\n.head-card > .mdl-card__menu {\n    color: #fff;\n}\n.head-card > .mdl-card__actions {\n    padding: 0;\n}\n.cards {\n    display: flex;\n    flex-direction: row;\n    flex-wrap: wrap;\n}\n"]}
\ No newline at end of file
diff --git a/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.js b/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.js
index 6a18c4558a2b..6b3ab86993a7 100644
--- a/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.js
+++ b/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.js
@@ -700,6 +700,6 @@ var e=arguments[3];if(require("core-js/shim"),require("regenerator-runtime/runti
 },{}],"j3Sc":[function(require,module,exports) {
 "use strict";function t(t,e){if(!(t instanceof e))throw new TypeError("Cannot call a class as a function")}function e(t,e){for(var i=0;i<e.length;i++){var n=e[i];n.enumerable=n.enumerable||!1,n.configurable=!0,"value"in n&&(n.writable=!0),Object.defineProperty(t,n.key,n)}}function i(t,i,n){return i&&e(t.prototype,i),n&&e(t,n),t}Object.defineProperty(exports,"__esModule",{value:!0}),exports.default=void 0;var n=function(){function e(){t(this,e),this.header=$("header.mdl-layout__header"),this.pagenation=$("div.pagenation"),this.footer=$("footer.mdl-mini-footer"),this.win=$(window),this.scrollElement=$("main"),this.content=$(".page-content"),this.outline=$(".side-doc-outline--content"),this.attachEvent(),this.adjust()}return i(e,[{key:"adjust",value:function(){this.setPageContentMinHeight(),this.setLocaltocHeight()}},{key:"setPageContentMinHeight",value:function(){this.win.innerHeight();var t=this.header.outerHeight(),e=this.footer.outerHeight(!0)+this.pagenation.outerHeight(!0);this.content.css("min-height",this.win.innerHeight()-t-e)}},{key:"setLocaltocHeight",value:function(){var t=this.pagenation.position().top+parseInt(this.pagenation.css("margin-top"),10),e=this.scrollElement.scrollTop()+this.content.outerHeight(),i=this.win.innerHeight()-this.header.outerHeight(),n=0;n=(n=e>t?t:e)>i?i:n,this.outline.css("height",n)}},{key:"attachEvent",value:function(){var t,e,i=this;this.scrollElement.on("scroll",function(){t&&clearTimeout(t),t=setTimeout(function(){i.adjust()},1)}),this.win.on("resize",function(){e&&clearTimeout(e),e=setTimeout(function(){i.adjust()},1)})}}]),e}();exports.default=n;
 },{}],"brfV":[function(require,module,exports) {
-"use strict";require("../scss/sphinx_materialdesign_theme.scss"),require("material-design-lite"),require("babel-polyfill");var a=e(require("./scrollspy")),t=e(require("./adjust-height"));function e(a){return a&&a.__esModule?a:{default:a}}$(function(){var t,e;t=$(".mdl-layout__drawer nav").find("li"),$.each(t,function(a,t){var e=$(t),n=$('<span class="link-wrapper"></span>'),l=e.children("a");e.append(n.append(l));var i=e.hasClass("current")&&!l.hasClass("current"),o=e.children("ul");if(o.length){var d="globalnav-".concat(a);o.attr("id",d),o.addClass("collapse");var s=$('<span class="nav-toggle"></span>');i?(o.addClass("show"),s.addClass("show")):o.hide(),e.append(n.append(s.append($('<a class="mdl-button mdl-js-button mdl-button--icon" data-toggle="#'.concat(d,'"><span style="color: #888"><i class="material-icons">keyboard_arrow_down</i></span></span>'))))).append(o)}}),$(".mdl-layout__drawer nav .nav-toggle a").click(function(){var a=$(this),t=a.attr("data-toggle");$("ul".concat(t)).toggleClass("show").animate({height:"toggle",opacity:"toggle"}),a.parent().toggleClass("show")}),e=$(".breadcrumb"),$("#waterfall-exp").focus(function(){$(window).width()<=1024&&e.hide()}).blur(function(){$(window).width()<=1024&&e.show()});new a.default({contentSelector:".page-content .section",navSelector:".localtoc a",scrollSelector:"main",className:"current",offsetTop:64});$(".mdl-layout__content").focus(),$(".mx-card").each(function(){$(this).addClass("mdl-card mdl-shadow--2dp")}),$(".mx-card .mx-card-title").each(function(){$(this).addClass("mdl-card__title")}),$(".mx-card .mx-card-text").each(function(){$(this).addClass("mdl-card__supporting-text")}),$(".mx-card-link").each(function(){$(this).hide()}),$(".mdl-card").each(function(){$(this).click(function(){var a=$(this).find(".mx-card-link").text();return a&&(window.location=a),!0})}),$("a.download").each(function(){var a=document.createElement("button");a.className="download mdl-button mdl-js-button mdl-button--fab mdl-js-ripple-effect";var t=document.createElement("i");t.className="material-icons";var e=document.createTextNode("file_download");t.appendChild(e),a.appendChild(t);var n=$(this).attr("href");a.onclick=function(){window.location=n};var l=n.split("/").slice(-1).pop();a.id=l?l.replace(".","-"):"download-button-"+$(this).index();var i=document.createElement("div");i.className="mdl-tooltip",i.setAttribute("data-mdl-for",a.id);var o=$(this).find("span.pre").map(function(){return $(this).text()}).get().join(" ");i.innerHTML=o,componentHandler.upgradeElement(a),$(this).remove();var d=$(".section h1").first();d.append(a),d.append(i)}),$(".mdl-layout__container").css("visibility","visible")});
+"use strict";require("../scss/sphinx_materialdesign_theme.scss"),require("material-design-lite"),require("babel-polyfill");var a=e(require("./scrollspy")),t=e(require("./adjust-height"));function e(a){return a&&a.__esModule?a:{default:a}}$(function(){var t,e;t=$(".mdl-layout__drawer nav").find("li"),$.each(t,function(a,t){var e=$(t),n=$('<span class="link-wrapper"></span>'),l=e.children("a");e.append(n.append(l));var i=e.hasClass("current")&&!l.hasClass("current"),o=e.children("ul");if(o.length){var d="globalnav-".concat(a);o.attr("id",d),o.addClass("collapse");var s=$('<span class="nav-toggle"></span>');i?(o.addClass("show"),s.addClass("show")):o.hide(),e.append(n.append(s.append($('<a class="mdl-button mdl-js-button mdl-button--icon" data-toggle="#'.concat(d,'"><span style="color: #888"><i class="material-icons">keyboard_arrow_down</i></span></span>'))))).append(o)}}),$(".mdl-layout__drawer nav .nav-toggle a").click(function(){var a=$(this),t=a.attr("data-toggle");$("ul".concat(t)).toggleClass("show").animate({height:"toggle",opacity:"toggle"}),a.parent().toggleClass("show")}),e=$(".breadcrumb"),$("#waterfall-exp").focus(function(){$(window).width()<=1024&&e.hide()}).blur(function(){$(window).width()<=1024&&e.show()});new a.default({contentSelector:".page-content .section",navSelector:".localtoc a",scrollSelector:"main",className:"current",offsetTop:64});$(".mdl-layout__content").focus(),$(".mx-card").each(function(){$(this).addClass("mdl-card mdl-shadow--2dp")}),$(".mx-card .mx-card-title").each(function(){$(this).addClass("mdl-card__title")}),$(".mx-card .mx-card-text").each(function(){$(this).addClass("mdl-card__supporting-text")}),$(".mx-card-link").each(function(){$(this).hide()}),$(".mdl-card").each(function(){$(this).click(function(){var a=$(this).find(".mx-card-link").text();return a&&(window.location=a),!0})}),$("a.download").each(function(){var a=document.createElement("button");a.className="download mdl-button mdl-js-button mdl-button--fab mdl-js-ripple-effect";var t=document.createElement("i");t.className="material-icons";var e=document.createTextNode("file_download");t.appendChild(e),a.appendChild(t);var n=$(this).attr("href");a.onclick=function(){window.location=n};var l=n.split("/").slice(-1).pop();a.id=l?l.replace(".","-"):"download-button-"+$(this).index();var i=document.createElement("div");i.className="mdl-tooltip",i.setAttribute("data-mdl-for",a.id);var o=$(this).find("span.pre").map(function(){return $(this).text()}).get().join(" ");i.innerHTML=o,componentHandler.upgradeElement(a),$(this).remove();var d=$(".section h1").first();d.append(a),d.append(i)}),$(".mdl-layout").css("visibility","visible")});
 },{"../scss/sphinx_materialdesign_theme.scss":"BS4D","material-design-lite":"vKy7","babel-polyfill":"0zUF","./scrollspy":"sKvN","./adjust-height":"j3Sc"}]},{},["brfV"], null)
 //# sourceMappingURL=/sphinx_materialdesign_theme.js.map
\ No newline at end of file
diff --git a/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.js.map b/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.js.map
index 59d6b8b77d7f..4c4c70d29921 100644
--- a/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.js.map
+++ b/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.js.map
@@ -1 +1 @@
-{"version":3,"sources":["ripple.js","tabs.js","layout.js","mdlComponentHandler.js","rAF.js","button.js","checkbox.js","icon-toggle.js","menu.js","progress.js","radio.js","slider.js","snackbar.js","spinner.js","switch.js","textfield.js","tooltip.js","data-table.js","../../node_modules/core-js/modules/_global.js","../../node_modules/core-js/modules/_has.js","../../node_modules/core-js/modules/_fails.js","../../node_modules/core-js/modules/_descriptors.js","../../node_modules/core-js/modules/_core.js","../../node_modules/core-js/modules/_is-object.js","../../node_modules/core-js/modules/_an-object.js","../../node_modules/core-js/modules/_dom-create.js","../../node_modules/core-js/modules/_ie8-dom-define.js","../../node_modules/core-js/modules/_to-primitive.js","../../node_modules/core-js/modules/_object-dp.js","../../node_modules/core-js/modules/_property-desc.js","../../node_modules/core-js/modules/_hide.js","../../node_modules/core-js/modules/_uid.js","../../node_modules/core-js/modules/_library.js","../../node_modules/core-js/modules/_shared.js","../../node_modules/core-js/modules/_function-to-string.js","../../node_modules/core-js/modules/_redefine.js","../../node_modules/core-js/modules/_a-function.js","../../node_modules/core-js/modules/_ctx.js","../../node_modules/core-js/modules/_export.js","../../node_modules/core-js/modules/_meta.js","../../node_modules/core-js/modules/_wks.js","../../node_modules/core-js/modules/_set-to-string-tag.js","../../node_modules/core-js/modules/_wks-ext.js","../../node_modules/core-js/modules/_wks-define.js","../../node_modules/core-js/modules/_cof.js","../../node_modules/core-js/modules/_iobject.js","../../node_modules/core-js/modules/_defined.js","../../node_modules/core-js/modules/_to-iobject.js","../../node_modules/core-js/modules/_to-integer.js","../../node_modules/core-js/modules/_to-length.js","../../node_modules/core-js/modules/_to-absolute-index.js","../../node_modules/core-js/modules/_array-includes.js","../../node_modules/core-js/modules/_shared-key.js","../../node_modules/core-js/modules/_object-keys-internal.js","../../node_modules/core-js/modules/_enum-bug-keys.js","../../node_modules/core-js/modules/_object-keys.js","../../node_modules/core-js/modules/_object-gops.js","../../node_modules/core-js/modules/_object-pie.js","../../node_modules/core-js/modules/_enum-keys.js","../../node_modules/core-js/modules/_is-array.js","../../node_modules/core-js/modules/_to-object.js","../../node_modules/core-js/modules/_object-dps.js","../../node_modules/core-js/modules/_html.js","../../node_modules/core-js/modules/_object-create.js","../../node_modules/core-js/modules/_object-gopn.js","../../node_modules/core-js/modules/_object-gopn-ext.js","../../node_modules/core-js/modules/_object-gopd.js","../../node_modules/core-js/modules/es6.symbol.js","../../node_modules/core-js/modules/es6.object.create.js","../../node_modules/core-js/modules/es6.object.define-property.js","../../node_modules/core-js/modules/es6.object.define-properties.js","../../node_modules/core-js/modules/_object-sap.js","../../node_modules/core-js/modules/es6.object.get-own-property-descriptor.js","../../node_modules/core-js/modules/_object-gpo.js","../../node_modules/core-js/modules/es6.object.get-prototype-of.js","../../node_modules/core-js/modules/es6.object.keys.js","../../node_modules/core-js/modules/es6.object.get-own-property-names.js","../../node_modules/core-js/modules/es6.object.freeze.js","../../node_modules/core-js/modules/es6.object.seal.js","../../node_modules/core-js/modules/es6.object.prevent-extensions.js","../../node_modules/core-js/modules/es6.object.is-frozen.js","../../node_modules/core-js/modules/es6.object.is-sealed.js","../../node_modules/core-js/modules/es6.object.is-extensible.js","../../node_modules/core-js/modules/_object-assign.js","../../node_modules/core-js/modules/es6.object.assign.js","../../node_modules/core-js/modules/_same-value.js","../../node_modules/core-js/modules/es6.object.is.js","../../node_modules/core-js/modules/_set-proto.js","../../node_modules/core-js/modules/es6.object.set-prototype-of.js","../../node_modules/core-js/modules/_classof.js","../../node_modules/core-js/modules/es6.object.to-string.js","../../node_modules/core-js/modules/_invoke.js","../../node_modules/core-js/modules/_bind.js","../../node_modules/core-js/modules/es6.function.bind.js","../../node_modules/core-js/modules/es6.function.name.js","../../node_modules/core-js/modules/es6.function.has-instance.js","../../node_modules/core-js/modules/_string-ws.js","../../node_modules/core-js/modules/_string-trim.js","../../node_modules/core-js/modules/_parse-int.js","../../node_modules/core-js/modules/es6.parse-int.js","../../node_modules/core-js/modules/_parse-float.js","../../node_modules/core-js/modules/es6.parse-float.js","../../node_modules/core-js/modules/_inherit-if-required.js","../../node_modules/core-js/modules/es6.number.constructor.js","../../node_modules/core-js/modules/_a-number-value.js","../../node_modules/core-js/modules/_string-repeat.js","../../node_modules/core-js/modules/es6.number.to-fixed.js","../../node_modules/core-js/modules/es6.number.to-precision.js","../../node_modules/core-js/modules/es6.number.epsilon.js","../../node_modules/core-js/modules/es6.number.is-finite.js","../../node_modules/core-js/modules/_is-integer.js","../../node_modules/core-js/modules/es6.number.is-integer.js","../../node_modules/core-js/modules/es6.number.is-nan.js","../../node_modules/core-js/modules/es6.number.is-safe-integer.js","../../node_modules/core-js/modules/es6.number.max-safe-integer.js","../../node_modules/core-js/modules/es6.number.min-safe-integer.js","../../node_modules/core-js/modules/es6.number.parse-float.js","../../node_modules/core-js/modules/es6.number.parse-int.js","../../node_modules/core-js/modules/_math-log1p.js","../../node_modules/core-js/modules/es6.math.acosh.js","../../node_modules/core-js/modules/es6.math.asinh.js","../../node_modules/core-js/modules/es6.math.atanh.js","../../node_modules/core-js/modules/_math-sign.js","../../node_modules/core-js/modules/es6.math.cbrt.js","../../node_modules/core-js/modules/es6.math.clz32.js","../../node_modules/core-js/modules/es6.math.cosh.js","../../node_modules/core-js/modules/_math-expm1.js","../../node_modules/core-js/modules/es6.math.expm1.js","../../node_modules/core-js/modules/_math-fround.js","../../node_modules/core-js/modules/es6.math.fround.js","../../node_modules/core-js/modules/es6.math.hypot.js","../../node_modules/core-js/modules/es6.math.imul.js","../../node_modules/core-js/modules/es6.math.log10.js","../../node_modules/core-js/modules/es6.math.log1p.js","../../node_modules/core-js/modules/es6.math.log2.js","../../node_modules/core-js/modules/es6.math.sign.js","../../node_modules/core-js/modules/es6.math.sinh.js","../../node_modules/core-js/modules/es6.math.tanh.js","../../node_modules/core-js/modules/es6.math.trunc.js","../../node_modules/core-js/modules/es6.string.from-code-point.js","../../node_modules/core-js/modules/es6.string.raw.js","../../node_modules/core-js/modules/es6.string.trim.js","../../node_modules/core-js/modules/_string-at.js","../../node_modules/core-js/modules/_iterators.js","../../node_modules/core-js/modules/_iter-create.js","../../node_modules/core-js/modules/_iter-define.js","../../node_modules/core-js/modules/es6.string.iterator.js","../../node_modules/core-js/modules/es6.string.code-point-at.js","../../node_modules/core-js/modules/_is-regexp.js","../../node_modules/core-js/modules/_string-context.js","../../node_modules/core-js/modules/_fails-is-regexp.js","../../node_modules/core-js/modules/es6.string.ends-with.js","../../node_modules/core-js/modules/es6.string.includes.js","../../node_modules/core-js/modules/es6.string.repeat.js","../../node_modules/core-js/modules/es6.string.starts-with.js","../../node_modules/core-js/modules/_string-html.js","../../node_modules/core-js/modules/es6.string.anchor.js","../../node_modules/core-js/modules/es6.string.big.js","../../node_modules/core-js/modules/es6.string.blink.js","../../node_modules/core-js/modules/es6.string.bold.js","../../node_modules/core-js/modules/es6.string.fixed.js","../../node_modules/core-js/modules/es6.string.fontcolor.js","../../node_modules/core-js/modules/es6.string.fontsize.js","../../node_modules/core-js/modules/es6.string.italics.js","../../node_modules/core-js/modules/es6.string.link.js","../../node_modules/core-js/modules/es6.string.small.js","../../node_modules/core-js/modules/es6.string.strike.js","../../node_modules/core-js/modules/es6.string.sub.js","../../node_modules/core-js/modules/es6.string.sup.js","../../node_modules/core-js/modules/es6.date.now.js","../../node_modules/core-js/modules/es6.date.to-json.js","../../node_modules/core-js/modules/_date-to-iso-string.js","../../node_modules/core-js/modules/es6.date.to-iso-string.js","../../node_modules/core-js/modules/es6.date.to-string.js","../../node_modules/core-js/modules/_date-to-primitive.js","../../node_modules/core-js/modules/es6.date.to-primitive.js","../../node_modules/core-js/modules/es6.array.is-array.js","../../node_modules/core-js/modules/_iter-call.js","../../node_modules/core-js/modules/_is-array-iter.js","../../node_modules/core-js/modules/_create-property.js","../../node_modules/core-js/modules/core.get-iterator-method.js","../../node_modules/core-js/modules/_iter-detect.js","../../node_modules/core-js/modules/es6.array.from.js","../../node_modules/core-js/modules/es6.array.of.js","../../node_modules/core-js/modules/_strict-method.js","../../node_modules/core-js/modules/es6.array.join.js","../../node_modules/core-js/modules/es6.array.slice.js","../../node_modules/core-js/modules/es6.array.sort.js","../../node_modules/core-js/modules/_array-species-constructor.js","../../node_modules/core-js/modules/_array-species-create.js","../../node_modules/core-js/modules/_array-methods.js","../../node_modules/core-js/modules/es6.array.for-each.js","../../node_modules/core-js/modules/es6.array.map.js","../../node_modules/core-js/modules/es6.array.filter.js","../../node_modules/core-js/modules/es6.array.some.js","../../node_modules/core-js/modules/es6.array.every.js","../../node_modules/core-js/modules/_array-reduce.js","../../node_modules/core-js/modules/es6.array.reduce.js","../../node_modules/core-js/modules/es6.array.reduce-right.js","../../node_modules/core-js/modules/es6.array.index-of.js","../../node_modules/core-js/modules/es6.array.last-index-of.js","../../node_modules/core-js/modules/_array-copy-within.js","../../node_modules/core-js/modules/_add-to-unscopables.js","../../node_modules/core-js/modules/es6.array.copy-within.js","../../node_modules/core-js/modules/_array-fill.js","../../node_modules/core-js/modules/es6.array.fill.js","../../node_modules/core-js/modules/es6.array.find.js","../../node_modules/core-js/modules/es6.array.find-index.js","../../node_modules/core-js/modules/_set-species.js","../../node_modules/core-js/modules/es6.array.species.js","../../node_modules/core-js/modules/_iter-step.js","../../node_modules/core-js/modules/es6.array.iterator.js","../../node_modules/core-js/modules/_flags.js","../../node_modules/core-js/modules/es6.regexp.constructor.js","../../node_modules/core-js/modules/_regexp-exec.js","../../node_modules/core-js/modules/es6.regexp.exec.js","../../node_modules/core-js/modules/es6.regexp.flags.js","../../node_modules/core-js/modules/es6.regexp.to-string.js","../../node_modules/core-js/modules/_advance-string-index.js","../../node_modules/core-js/modules/_regexp-exec-abstract.js","../../node_modules/core-js/modules/_fix-re-wks.js","../../node_modules/core-js/modules/es6.regexp.match.js","../../node_modules/core-js/modules/es6.regexp.replace.js","../../node_modules/core-js/modules/es6.regexp.search.js","../../node_modules/core-js/modules/_species-constructor.js","../../node_modules/core-js/modules/es6.regexp.split.js","../../node_modules/core-js/modules/_an-instance.js","../../node_modules/core-js/modules/_for-of.js","../../node_modules/core-js/modules/_task.js","../../node_modules/core-js/modules/_microtask.js","../../node_modules/core-js/modules/_new-promise-capability.js","../../node_modules/core-js/modules/_perform.js","../../node_modules/core-js/modules/_user-agent.js","../../node_modules/core-js/modules/_promise-resolve.js","../../node_modules/core-js/modules/_redefine-all.js","../../node_modules/core-js/modules/es6.promise.js","../../node_modules/core-js/modules/_validate-collection.js","../../node_modules/core-js/modules/_collection-strong.js","../../node_modules/core-js/modules/_collection.js","../../node_modules/core-js/modules/es6.map.js","../../node_modules/core-js/modules/es6.set.js","../../node_modules/core-js/modules/_collection-weak.js","../../node_modules/core-js/modules/es6.weak-map.js","../../node_modules/core-js/modules/es6.weak-set.js","../../node_modules/core-js/modules/_typed.js","../../node_modules/core-js/modules/_to-index.js","../../node_modules/core-js/modules/_typed-buffer.js","../../node_modules/core-js/modules/es6.typed.array-buffer.js","../../node_modules/core-js/modules/es6.typed.data-view.js","../../node_modules/core-js/modules/_typed-array.js","../../node_modules/core-js/modules/es6.typed.int8-array.js","../../node_modules/core-js/modules/es6.typed.uint8-array.js","../../node_modules/core-js/modules/es6.typed.uint8-clamped-array.js","../../node_modules/core-js/modules/es6.typed.int16-array.js","../../node_modules/core-js/modules/es6.typed.uint16-array.js","../../node_modules/core-js/modules/es6.typed.int32-array.js","../../node_modules/core-js/modules/es6.typed.uint32-array.js","../../node_modules/core-js/modules/es6.typed.float32-array.js","../../node_modules/core-js/modules/es6.typed.float64-array.js","../../node_modules/core-js/modules/es6.reflect.apply.js","../../node_modules/core-js/modules/es6.reflect.construct.js","../../node_modules/core-js/modules/es6.reflect.define-property.js","../../node_modules/core-js/modules/es6.reflect.delete-property.js","../../node_modules/core-js/modules/es6.reflect.enumerate.js","../../node_modules/core-js/modules/es6.reflect.get.js","../../node_modules/core-js/modules/es6.reflect.get-own-property-descriptor.js","../../node_modules/core-js/modules/es6.reflect.get-prototype-of.js","../../node_modules/core-js/modules/es6.reflect.has.js","../../node_modules/core-js/modules/es6.reflect.is-extensible.js","../../node_modules/core-js/modules/_own-keys.js","../../node_modules/core-js/modules/es6.reflect.own-keys.js","../../node_modules/core-js/modules/es6.reflect.prevent-extensions.js","../../node_modules/core-js/modules/es6.reflect.set.js","../../node_modules/core-js/modules/es6.reflect.set-prototype-of.js","../../node_modules/core-js/modules/es7.array.includes.js","../../node_modules/core-js/modules/_flatten-into-array.js","../../node_modules/core-js/modules/es7.array.flat-map.js","../../node_modules/core-js/modules/es7.array.flatten.js","../../node_modules/core-js/modules/es7.string.at.js","../../node_modules/core-js/modules/_string-pad.js","../../node_modules/core-js/modules/es7.string.pad-start.js","../../node_modules/core-js/modules/es7.string.pad-end.js","../../node_modules/core-js/modules/es7.string.trim-left.js","../../node_modules/core-js/modules/es7.string.trim-right.js","../../node_modules/core-js/modules/es7.string.match-all.js","../../node_modules/core-js/modules/es7.symbol.async-iterator.js","../../node_modules/core-js/modules/es7.symbol.observable.js","../../node_modules/core-js/modules/es7.object.get-own-property-descriptors.js","../../node_modules/core-js/modules/_object-to-array.js","../../node_modules/core-js/modules/es7.object.values.js","../../node_modules/core-js/modules/es7.object.entries.js","../../node_modules/core-js/modules/_object-forced-pam.js","../../node_modules/core-js/modules/es7.object.define-getter.js","../../node_modules/core-js/modules/es7.object.define-setter.js","../../node_modules/core-js/modules/es7.object.lookup-getter.js","../../node_modules/core-js/modules/es7.object.lookup-setter.js","../../node_modules/core-js/modules/_array-from-iterable.js","../../node_modules/core-js/modules/_collection-to-json.js","../../node_modules/core-js/modules/es7.map.to-json.js","../../node_modules/core-js/modules/es7.set.to-json.js","../../node_modules/core-js/modules/_set-collection-of.js","../../node_modules/core-js/modules/es7.map.of.js","../../node_modules/core-js/modules/es7.set.of.js","../../node_modules/core-js/modules/es7.weak-map.of.js","../../node_modules/core-js/modules/es7.weak-set.of.js","../../node_modules/core-js/modules/_set-collection-from.js","../../node_modules/core-js/modules/es7.map.from.js","../../node_modules/core-js/modules/es7.set.from.js","../../node_modules/core-js/modules/es7.weak-map.from.js","../../node_modules/core-js/modules/es7.weak-set.from.js","../../node_modules/core-js/modules/es7.global.js","../../node_modules/core-js/modules/es7.system.global.js","../../node_modules/core-js/modules/es7.error.is-error.js","../../node_modules/core-js/modules/es7.math.clamp.js","../../node_modules/core-js/modules/es7.math.deg-per-rad.js","../../node_modules/core-js/modules/es7.math.degrees.js","../../node_modules/core-js/modules/_math-scale.js","../../node_modules/core-js/modules/es7.math.fscale.js","../../node_modules/core-js/modules/es7.math.iaddh.js","../../node_modules/core-js/modules/es7.math.isubh.js","../../node_modules/core-js/modules/es7.math.imulh.js","../../node_modules/core-js/modules/es7.math.rad-per-deg.js","../../node_modules/core-js/modules/es7.math.radians.js","../../node_modules/core-js/modules/es7.math.scale.js","../../node_modules/core-js/modules/es7.math.umulh.js","../../node_modules/core-js/modules/es7.math.signbit.js","../../node_modules/core-js/modules/es7.promise.finally.js","../../node_modules/core-js/modules/es7.promise.try.js","../../node_modules/core-js/modules/_metadata.js","../../node_modules/core-js/modules/es7.reflect.define-metadata.js","../../node_modules/core-js/modules/es7.reflect.delete-metadata.js","../../node_modules/core-js/modules/es7.reflect.get-metadata.js","../../node_modules/core-js/modules/es7.reflect.get-metadata-keys.js","../../node_modules/core-js/modules/es7.reflect.get-own-metadata.js","../../node_modules/core-js/modules/es7.reflect.get-own-metadata-keys.js","../../node_modules/core-js/modules/es7.reflect.has-metadata.js","../../node_modules/core-js/modules/es7.reflect.has-own-metadata.js","../../node_modules/core-js/modules/es7.reflect.metadata.js","../../node_modules/core-js/modules/es7.asap.js","../../node_modules/core-js/modules/es7.observable.js","../../node_modules/core-js/modules/web.timers.js","../../node_modules/core-js/modules/web.immediate.js","../../node_modules/core-js/modules/web.dom.iterable.js","../../node_modules/core-js/shim.js","../../node_modules/regenerator-runtime/runtime.js","../../node_modules/core-js/modules/_replacer.js","../../node_modules/core-js/modules/core.regexp.escape.js","../../node_modules/core-js/fn/regexp/escape.js","../../node_modules/babel-polyfill/lib/index.js","scrollspy.js","adjust-height.js","sphinx_materialdesign_theme.js"],"names":["MaterialTab","tab","ctx","element_","classList","contains","CssClasses_","MDL_JS_RIPPLE_EFFECT","rippleContainer","document","createElement","add","MDL_RIPPLE_CONTAINER","ripple","MDL_RIPPLE","appendChild","addEventListener","e","getAttribute","charAt","preventDefault","href","split","panel","querySelector","resetTabState_","resetPanelState_","ACTIVE_CLASS","MaterialLayoutTab","tabs","panels","layout","selectTab","content_","IS_ACTIVE","tabBar_","JS_RIPPLE_EFFECT","RIPPLE_CONTAINER","RIPPLE","TAB_MANUAL_SWITCH","show","componentHandler","upgradeDom","optJsClass","optCssClass","upgradeElement","element","upgradeElements","elements","upgradeAllRegistered","registerUpgradedCallback","jsClass","callback","register","config","downgradeElements","nodes","findRegisteredClass_","name","optReplace","i","registeredComponents_","length","className","getUpgradedListOfElement_","dataUpgraded","isElementUpgraded_","upgradedList","indexOf","createEvent_","eventType","bubbles","cancelable","window","CustomEvent","ev","createEvent","initEvent","upgradeDomInternal","cssClass","registeredClass","querySelectorAll","n","upgradeElementInternal","Element","Error","upgradingEv","dispatchEvent","defaultPrevented","classesToUpgrade","push","forEach","component","setAttribute","join","instance","classConstructor","componentConfigProperty_","createdComponents_","j","m","callbacks","widget","upgradedEv","deconstructComponentInternal","componentIndex","splice","upgrades","componentPlace","classAsString","upgradeElementsInternal","Array","isArray","prototype","slice","call","HTMLElement","children","upgradeAllRegisteredInternal","registerUpgradedCallbackInternal","regClass","registerInternal","widgetMissing","newConfig","constructor","item","hasOwnProperty","downgradeNodesInternal","downgradeNode","node","filter","NodeList","Node","ComponentConfigPublic","ComponentConfig","Component","documentElement","Date","now","getTime","vendors","requestAnimationFrame","vp","cancelAnimationFrame","test","navigator","userAgent","lastTime","nextTime","Math","max","setTimeout","clearTimeout","MaterialButton","this","init","Constant_","RIPPLE_EFFECT","blurHandler_","event","blur","disable","disabled","enable","rippleElement_","boundRippleBlurHandler","bind","boundButtonBlurHandler","MaterialCheckbox","TINY_TIMEOUT","INPUT","BOX_OUTLINE","FOCUS_HELPER","TICK_OUTLINE","RIPPLE_IGNORE_EVENTS","RIPPLE_CENTER","IS_FOCUSED","IS_DISABLED","IS_CHECKED","IS_UPGRADED","onChange_","updateClasses_","onFocus_","onBlur_","remove","onMouseUp_","blur_","checkDisabled","checkToggleState","inputElement_","checked","check","uncheck","boxOutline","tickContainer","tickOutline","rippleContainerElement_","boundRippleMouseUp","boundInputOnChange","boundInputOnFocus","boundInputOnBlur","boundElementMouseUp","MaterialIconToggle","boundElementOnMouseUp","MaterialMenu","TRANSITION_DURATION_SECONDS","TRANSITION_DURATION_FRACTION","CLOSE_TIMEOUT","Keycodes_","ENTER","ESCAPE","SPACE","UP_ARROW","DOWN_ARROW","CONTAINER","OUTLINE","ITEM","ITEM_RIPPLE_CONTAINER","IS_VISIBLE","IS_ANIMATING","BOTTOM_LEFT","BOTTOM_RIGHT","TOP_LEFT","TOP_RIGHT","UNALIGNED","container","parentElement","insertBefore","removeChild","container_","outline","outline_","forElId","forEl","getElementById","forElement_","handleForClick_","handleForKeyboardEvent_","items","boundItemKeydown_","handleItemKeyboardEvent_","boundItemClick_","handleItemClick_","tabIndex","evt","rect","getBoundingClientRect","forRect","style","right","top","offsetTop","offsetHeight","left","offsetLeft","bottom","toggle","keyCode","focus","currentIndex","target","MouseEvent","click","hide","hasAttribute","stopPropagation","closing_","applyClip_","height","width","clip","removeAnimationEndListener_","addAnimationEndListener_","transitionDuration","itemDelay","transitionDelay","parentNode","removeEventListener","removeProperty","MaterialProgress","INDETERMINATE_CLASS","setProgress","p","progressbar_","setBuffer","bufferbar_","auxbar_","el","MaterialRadio","JS_RADIO","RADIO_BTN","RADIO_OUTER_CIRCLE","RADIO_INNER_CIRCLE","radios","getElementsByClassName","btnElement_","onMouseup_","boundChangeHandler_","boundFocusHandler_","boundBlurHandler_","boundMouseUpHandler_","outerCircle","innerCircle","MaterialSlider","isIE_","msPointerEnabled","IE_CONTAINER","SLIDER_CONTAINER","BACKGROUND_FLEX","BACKGROUND_LOWER","BACKGROUND_UPPER","IS_LOWEST_VALUE","onInput_","updateValueStyles_","onContainerMouseDown_","newEvent","buttons","clientX","clientY","y","fraction","value","min","backgroundLower_","flex","webkitFlex","backgroundUpper_","change","containerIE","backgroundFlex","boundInputHandler","boundChangeHandler","boundMouseUpHandler","boundContainerMouseDownHandler","MaterialSnackbar","textElement_","cssClasses_","MESSAGE","actionElement_","ACTION","active","actionHandler_","undefined","message_","actionText_","queuedNotifications_","setActionHidden_","ANIMATION_LENGTH","SNACKBAR","ACTIVE","displaySnackbar_","textContent","cleanup_","timeout_","showSnackbar","data","checkQueue_","shift","Boolean","removeAttribute","MaterialSpinner","MDL_SPINNER_LAYER_COUNT","MDL_SPINNER_LAYER","MDL_SPINNER_CIRCLE_CLIPPER","MDL_SPINNER_CIRCLE","MDL_SPINNER_GAP_PATCH","MDL_SPINNER_LEFT","MDL_SPINNER_RIGHT","createLayer","index","layer","leftClipper","gapPatch","rightClipper","circleOwners","circle","stop","start","MaterialSwitch","TRACK","THUMB","on","off","track","thumb","focusHelper","boundFocusHandler","boundBlurHandler","MaterialTabs","TAB_CLASS","PANEL_CLASS","UPGRADED_CLASS","MDL_JS_RIPPLE_EFFECT_IGNORE_EVENTS","initTabs_","tabs_","panels_","k","MaterialTextfield","maxRows","NO_MAX_ROWS","MAX_ROWS_ATTRIBUTE","LABEL","IS_DIRTY","IS_INVALID","HAS_PLACEHOLDER","onKeyDown_","currentRowCount","onReset_","checkValidity","checkDirty","checkFocus","input_","validity","valid","label_","parseInt","isNaN","boundUpdateClassesHandler","boundResetHandler","boundKeyDownHandler","invalid","MaterialTooltip","BOTTOM","LEFT","RIGHT","TOP","handleMouseEnter_","props","marginLeft","offsetWidth","marginTop","hideTooltip_","boundMouseEnterHandler","boundMouseLeaveAndScrollHandler","MaterialLayout","MAX_WIDTH","TAB_SCROLL_PIXELS","RESIZE_TIMEOUT","MENU_ICON","CHEVRON_LEFT","CHEVRON_RIGHT","Mode_","STANDARD","SEAMED","WATERFALL","SCROLL","HEADER","DRAWER","CONTENT","DRAWER_BTN","ICON","HEADER_SEAMED","HEADER_WATERFALL","HEADER_SCROLL","FIXED_HEADER","OBFUSCATOR","TAB_BAR","TAB_CONTAINER","TAB","TAB_BAR_BUTTON","TAB_BAR_LEFT_BUTTON","TAB_BAR_RIGHT_BUTTON","PANEL","HAS_DRAWER","HAS_TABS","HAS_SCROLLING_HEADER","CASTING_SHADOW","IS_COMPACT","IS_SMALL_SCREEN","IS_DRAWER_OPEN","ON_LARGE_SCREEN","ON_SMALL_SCREEN","contentScrollHandler_","header_","headerVisible","scrollTop","keyboardEventHandler_","drawer_","toggleDrawer","screenSizeHandler_","screenSizeMediaQuery_","matches","obfuscator_","drawerToggleHandler_","type","headerTransitionEndHandler_","headerClickHandler_","tabBar","drawerButton","focusedElement","directChildren","childNodes","numChildren","c","child","persisted","overflowY","mode","drawerButtonIcon","innerHTML","firstChild","obfuscator","matchMedia","addListener","tabContainer","leftButton","leftButtonIcon","scrollLeft","rightButton","rightButtonIcon","tabUpdateHandler","scrollWidth","windowResizeHandler","resizeTimeoutId_","MaterialDataTable","DATA_TABLE","SELECTABLE","SELECT_ELEMENT","IS_SELECTED","selectRow_","checkbox","row","opt_rows","createCheckbox_","label","labelClasses","firstHeader","bodyRows","footRows","rows","concat","th","headerCheckbox","firstCell","td","nodeName","toUpperCase","rowCheckbox","MaterialRipple","INITIAL_SCALE","INITIAL_SIZE","INITIAL_OPACITY","FINAL_OPACITY","FINAL_SCALE","RIPPLE_EFFECT_IGNORE_EVENTS","downHandler_","boundHeight","boundWidth","rippleSize_","sqrt","ignoringMouseDown_","frameCount","getFrameCount","setFrameCount","x","bound","currentTarget","round","touches","setRippleXY","setRippleStyles","animFrameHandler","upHandler_","detail","recentering","frameCount_","x_","y_","boundDownHandler","boundUpHandler","fC","getRippleElement","newX","newY","transformString","scale","offset","webkitTransform","msTransform","transform","ScrollSpy","args","doc","nav","navSelector","win","winHeight","innerHeight","scrollElement","scrollSelector","contents","getContents","contentSelector","attachEvent","scrollTimer","resizeTimer","spy","targets","getViewState","toggleNavClass","elementListInView","current","isView","calcBotom","elementTop","elementBottom","maxDepth","maxDepthElement","$","tempDepth","getTagDepth","navElement","id","find","get","tagName","AdjustHeight","header","pagenation","footer","content","adjust","setPageContentMinHeight","setLocaltocHeight","winH","headerHeight","outerHeight","footerHeight","css","pagenationPotisionTop","position","outlineBottom","reconstructionDrawerGlobalToc","$lists","$breadcrumb","each","li","$li","$linkWrapper","$link","append","isCurrent","hasClass","$ul","ulId","attr","addClass","$toggleWrapper","$toggle","toggleClass","animate","opacity","parent","url","text","location","button","icon","createTextNode","link","onclick","fileName","pop","replace","hint","hintText","map","first"],"mappings":";;;AAgOA,SAAA,EAAA,GAAA,OAAA,EAAA,mBAAA,QAAA,iBAAA,OAAA,SAAA,SAAA,GAAA,cAAA,GAAA,SAAA,GAAA,OAAA,GAAA,mBAAA,QAAA,EAAA,cAAA,QAAA,IAAA,OAAA,UAAA,gBAAA,IAAA,IAAA,WAAA,aCnHAA,SAAAA,EAAAC,EAAAC,GACAD,GAAAA,EAAA,CACAC,GAAAA,EAAAC,SAAAC,UAAAC,SAAAH,EAAAI,YAAAC,sBAAA,CACAC,IAAAA,EAAAC,SAAAC,cAAA,QACAF,EAAAJ,UAAAO,IAAAT,EAAAI,YAAAM,sBACAJ,EAAAJ,UAAAO,IAAAT,EAAAI,YAAAC,sBACAM,IAAAA,EAAAJ,SAAAC,cAAA,QACAG,EAAAT,UAAAO,IAAAT,EAAAI,YAAAQ,YACAN,EAAAO,YAAAF,GACAZ,EAAAc,YAAAP,GAEAP,EAAAe,iBAAA,QAAA,SAAAC,GACA,GAAA,MAAAhB,EAAAiB,aAAA,QAAAC,OAAA,GAAA,CACAF,EAAAG,iBACAC,IAAAA,EAAApB,EAAAoB,KAAAC,MAAA,KAAA,GACAC,EAAArB,EAAAC,SAAAqB,cAAA,IAAAH,GACAnB,EAAAuB,iBACAvB,EAAAwB,mBACAzB,EAAAG,UAAAO,IAAAT,EAAAI,YAAAqB,cACAJ,EAAAnB,UAAAO,IAAAT,EAAAI,YAAAqB,kBCwTAC,SAAAA,EAAA3B,EAAA4B,EAAAC,EAAAC,GAIAC,SAAAA,IACAX,IAAAA,EAAApB,EAAAoB,KAAAC,MAAA,KAAA,GACAC,EAAAQ,EAAAE,SAAAT,cAAA,IAAAH,GACAU,EAAAN,eAAAI,GACAE,EAAAL,iBAAAI,GACA7B,EAAAG,UAAAO,IAAAoB,EAAAzB,YAAA4B,WACAX,EAAAnB,UAAAO,IAAAoB,EAAAzB,YAAA4B,WAEAH,GAAAA,EAAAI,QAAA/B,UAAAC,SAAA0B,EAAAzB,YAAA8B,kBAAA,CACA5B,IAAAA,EAAAC,SAAAC,cAAA,QACAF,EAAAJ,UAAAO,IAAAoB,EAAAzB,YAAA+B,kBACA7B,EAAAJ,UAAAO,IAAAoB,EAAAzB,YAAA8B,kBACAvB,IAAAA,EAAAJ,SAAAC,cAAA,QACAG,EAAAT,UAAAO,IAAAoB,EAAAzB,YAAAgC,QACA9B,EAAAO,YAAAF,GACAZ,EAAAc,YAAAP,GAEAuB,EAAAI,QAAA/B,UAAAC,SAAA0B,EAAAzB,YAAAiC,oBACAtC,EAAAe,iBAAA,QAAA,SAAAC,GACAhB,MAAAA,EAAAiB,aAAA,QAAAC,OAAA,KACAF,EAAAG,iBACAY,OAIA/B,EAAAuC,KAAAR,ECzbAS,IAAAA,EAAAA,CAUAC,WAAA,SAAAC,EAAAC,KAQAC,eAAA,SAAAC,EAAAH,KAOAI,gBAAA,SAAAC,KAKAC,qBAAA,aAWAC,yBAAA,SAAAC,EAAAC,KAMAC,SAAA,SAAAC,KAMAC,kBAAA,SAAAC,OAGAf,EAAA,WAoBAgB,SAAAA,EAAAC,EAAAC,GACA,IAAA,IAAAC,EAAA,EAAAA,EAAAC,EAAAC,OAAAF,IACA,GAAAC,EAAAD,GAAAG,YAAAL,EAIA,YAHA,IAAAC,IACAE,EAAAD,GAAAD,GAEAE,EAAAD,GAGA,OAAA,EAUAI,SAAAA,EAAAlB,GACAmB,IAAAA,EAAAnB,EAAA5B,aAAA,iBAEA,OAAA,OAAA+C,EAAAA,CAAA,IAAAA,EAAA3C,MAAA,KAYA4C,SAAAA,EAAApB,EAAAK,GAEAgB,OAAA,IADAH,EAAAlB,GACAsB,QAAAjB,GAWAkB,SAAAA,EAAAC,EAAAC,EAAAC,GACA,GAAA,gBAAAC,QAAA,mBAAAA,OAAAC,YACA,OAAA,IAAAA,YAAAJ,EAAAA,CACAC,QAAAA,EACAC,WAAAA,IAGAG,IAAAA,EAAAlE,SAAAmE,YAAA,UACAD,OAAAA,EAAAE,UAAAP,EAAAC,EAAAC,GACAG,EAaAG,SAAAA,EAAAnC,EAAAC,GACA,QAAA,IAAAD,QACA,IAAAC,EACA,IAAA,IAAAgB,EAAA,EAAAA,EAAAC,EAAAC,OAAAF,IACAkB,EAAAjB,EAAAD,GAAAG,UACAF,EAAAD,GAAAmB,cAEA,CACA5B,IAAAA,EAAA,EACA,QAAA,IAAAP,EAAA,CACAoC,IAAAA,EAAAvB,EAAAN,GACA6B,IACApC,EAAAoC,EAAAD,UAKA,IAAA,IADA/B,EAAAvC,SAAAwE,iBAAA,IAAArC,GACAsC,EAAA,EAAAA,EAAAlC,EAAAc,OAAAoB,IACAC,EAAAnC,EAAAkC,GAAA/B,IAYAgC,SAAAA,EAAArC,EAAAH,GAEA,KAAA,UAAAG,EAAAA,IAAAA,aAAAsC,SACA,MAAA,IAAAC,MAAA,qDAGAC,IAAAA,EAAAjB,EAAA,0BAAA,GAAA,GACAvB,GAAAA,EAAAyC,cAAAD,IACAA,EAAAE,iBAAA,CAIArB,IAAAA,EAAAH,EAAAlB,GACA2C,EAAAA,GAGA9C,GAAAA,EAUAuB,EAAApB,EAAAH,IACA8C,EAAAC,KAAAjC,EAAAd,QAXA,CACAvC,IAAAA,EAAA0C,EAAA1C,UACAyD,EAAA8B,QAAA,SAAAC,GAEAxF,EAAAC,SAAAuF,EAAAb,YACA,IAAAU,EAAArB,QAAAwB,KACA1B,EAAApB,EAAA8C,EAAA7B,YACA0B,EAAAC,KAAAE,KAQA,IAAA,IAAAZ,EAAApB,EAAA,EAAAsB,EAAAO,EAAA3B,OAAAF,EAAAsB,EAAAtB,IAAA,CACAoB,KAAAA,EAAAS,EAAA7B,IAkBA,MAAA,IAAAyB,MACA,8DAhBAlB,EAAAuB,KAAAV,EAAAjB,WACAjB,EAAA+C,aAAA,gBAAA1B,EAAA2B,KAAA,MACAC,IAAAA,EAAA,IAAAf,EAAAgB,iBAAAlD,GACAiD,EAAAE,GAAAjB,EACAkB,EAAAR,KAAAK,GAEA,IAAA,IAAAI,EAAA,EAAAC,EAAApB,EAAAqB,UAAAvC,OAAAqC,EAAAC,EAAAD,IACAnB,EAAAqB,UAAAF,GAAArD,GAGAkC,EAAAsB,SAEAxD,EAAAkC,EAAAjB,WAAAgC,GAOAQ,IAAAA,EAAAlC,EAAA,yBAAA,GAAA,GACAvB,EAAAyC,cAAAgB,KAgHAC,SAAAA,EAAAZ,GACAA,GAAAA,EAAA,CACAa,IAAAA,EAAAP,EAAA9B,QAAAwB,GACAM,EAAAQ,OAAAD,EAAA,GAEAE,IAAAA,EAAAf,EAAAzF,SAAAe,aAAA,iBAAAI,MAAA,KACAsF,EAAAD,EAAAvC,QAAAwB,EAAAK,GAAAY,eACAF,EAAAD,OAAAE,EAAA,GACAhB,EAAAzF,SAAA0F,aAAA,gBAAAc,EAAAb,KAAA,MAEAnB,IAAAA,EAAAN,EAAA,2BAAA,GAAA,GACAuB,EAAAzF,SAAAoF,cAAAZ,IArSAd,IAAAA,EAAAA,GAGAqC,EAAAA,GAEAD,EAAA,8BAgUA,MAAA,CACAvD,WAAAoC,EACAjC,eAAAsC,EACApC,gBApJA+D,SAAAA,EAAA9D,GACA+D,MAAAC,QAAAhE,KAEAA,EADAA,aAAAoC,QAAAA,CACApC,GAEA+D,MAAAE,UAAAC,MAAAC,KAAAnE,IAGA,IAAA,IAAAF,EAAAc,EAAA,EAAAsB,EAAAlC,EAAAc,OAAAF,EAAAsB,EAAAtB,KACAd,EAAAE,EAAAY,cACAwD,cACAjC,EAAArC,GACAA,EAAAuE,SAAAvD,OAAA,GACAgD,EAAAhE,EAAAuE,YAwIApE,qBA5DAqE,WACA,IAAA,IAAApC,EAAA,EAAAA,EAAArB,EAAAC,OAAAoB,IACAJ,EAAAjB,EAAAqB,GAAAnB,YA2DAb,yBAxEAqE,SAAApE,EAAAC,GACAoE,IAAAA,EAAA/D,EAAAN,GACAqE,GACAA,EAAAnB,UAAAX,KAAAtC,IAsEAC,SA/HAoE,SAAAnE,GAKAoE,IAEApB,GAAAA,OAFA,IAAAhD,EAAAgD,aACA,IAAAhD,EAAA,SAIAgD,EAAAhD,EAAAgD,QAAAhD,EAAA,QAGAqE,IAAAA,EAAAA,CACA3B,iBAAA1C,EAAAsE,aAAAtE,EAAA,YACAS,UAAAT,EAAAuD,eAAAvD,EAAA,cACAyB,SAAAzB,EAAAyB,UAAAzB,EAAA,SACAgD,OAAAA,EACAD,UAAAA,IAGAxC,GAAAA,EAAA8B,QAAA,SAAAkC,GACAA,GAAAA,EAAA9C,WAAA4C,EAAA5C,SACA,MAAA,IAAAM,MAAA,sDAAAwC,EAAA9C,UAEA8C,GAAAA,EAAA9D,YAAA4D,EAAA5D,UACA,MAAA,IAAAsB,MAAA,wDAIA/B,EAAAsE,YAAAX,UACAa,eAAA7B,GACA,MAAA,IAAAZ,MACA,uCAAAY,EACA,2BAGAxC,EAAAH,EAAAuD,cAAAc,IAGA9D,EAAA6B,KAAAiC,IAwFApE,kBA9BAwE,SAAAvE,GAKAwE,IAAAA,EAAA,SAAAC,GACA/B,EAAAgC,OAAA,SAAAL,GACAA,OAAAA,EAAA1H,WAAA8H,IACAtC,QAAAa,IAEAhD,GAAAA,aAAAuD,OAAAvD,aAAA2E,SACA,IAAA,IAAAjD,EAAA,EAAAA,EAAA1B,EAAAM,OAAAoB,IACA8C,EAAAxE,EAAA0B,QAEA,CAAA,KAAA1B,aAAA4E,MAGA,MAAA,IAAA/C,MAAA,qDAFA2C,EAAAxE,MAjUA,IA+VA6E,sBAcA5F,EAAA6F,gBAcA7F,EAAA8F,UAIA9F,EAAA,WAAAA,EAAAC,WACAD,EAAA,eAAAA,EAAAI,eACAJ,EAAA,gBAAAA,EAAAM,gBACAN,EAAA,qBACAA,EAAAQ,qBACAR,EAAA,yBACAA,EAAAS,yBACAT,EAAA,SAAAA,EAAAY,SACAZ,EAAA,kBAAAA,EAAAc,kBACAkB,OAAAhC,iBAAAA,EACAgC,OAAA,iBAAAhC,EAEAgC,OAAAzD,iBAAA,OAAA,WAQAP,cAAAA,SAAAC,cAAA,QACA,kBAAAD,UACA,qBAAAgE,QAAAsC,MAAAE,UAAAtB,SACAlF,SAAA+H,gBAAApI,UAAAO,IAAA,UACA8B,EAAAQ,yBAKAR,EAAAI,eAAA,aAIAJ,EAAAY,SAAA,gBC7eAoF,KAAAC,MAKAD,KAAAC,IAAA,WACA,OAAA,IAAAD,MAAAE,WAEAF,KAAA,IAAAA,KAAAC,KAMA,IAAA,IAJAE,EAAAA,CACA,SACA,OAEAhF,EAAA,EAAAA,EAAAgF,EAAA9E,SAAAW,OAAAoE,wBAAAjF,EAAA,CACAkF,IAAAA,EAAAF,EAAAhF,GACAa,OAAAoE,sBAAApE,OAAAqE,EAAA,yBACArE,OAAAsE,qBAAAtE,OAAAqE,EAAA,yBAAArE,OAAAqE,EAAA,+BACArE,OAAA,sBAAAA,OAAAoE,sBACApE,OAAA,qBAAAA,OAAAsE,qBAEA,GAAA,uBAAAC,KAAAvE,OAAAwE,UAAAC,aAAAzE,OAAAoE,wBAAApE,OAAAsE,qBAAA,CACAI,IAAAA,EAAA,EAKA1E,OAAAoE,sBAAA,SAAAzF,GACAsF,IAAAA,EAAAD,KAAAC,MACAU,EAAAC,KAAAC,IAAAH,EAAA,GAAAT,GACAa,OAAAA,WAAA,WACAnG,EAAA+F,EAAAC,IACAA,EAAAV,IAEAjE,OAAAsE,qBAAAS,aACA/E,OAAA,sBAAAA,OAAAoE,sBACApE,OAAA,qBAAAA,OAAAsE,qBCpBAU,IAAAA,EAAA,SAAA3G,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,eAAAgF,EAOAA,EAAAxC,UAAA2C,UAAAA,GASAH,EAAAxC,UAAA3G,YAAAA,CACAuJ,cAAA,uBACAxH,iBAAA,+BACAC,OAAA,cAQAmH,EAAAxC,UAAA6C,aAAA,SAAAC,GACAA,GACAL,KAAAvJ,SAAA6J,QASAP,EAAAxC,UAAAgD,QAAA,WACA9J,KAAAA,SAAA+J,UAAAA,GAEAT,EAAAxC,UAAA,QAAAwC,EAAAxC,UAAAgD,QAMAR,EAAAxC,UAAAkD,OAAA,WACAhK,KAAAA,SAAA+J,UAAAA,GAEAT,EAAAxC,UAAA,OAAAwC,EAAAxC,UAAAkD,OAIAV,EAAAxC,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACAuJ,GAAAA,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAuJ,eAAA,CACArJ,IAAAA,EAAAC,SAAAC,cAAA,QACAF,EAAAJ,UAAAO,IAAA+I,KAAApJ,YAAA+B,kBACAqH,KAAAU,eAAA3J,SAAAC,cAAA,QACAgJ,KAAAU,eAAAhK,UAAAO,IAAA+I,KAAApJ,YAAAgC,QACA9B,EAAAO,YAAA2I,KAAAU,gBACAV,KAAAW,uBAAAX,KAAAI,aAAAQ,KAAAZ,MACAA,KAAAU,eAAApJ,iBAAA,UAAA0I,KAAAW,wBACAX,KAAAvJ,SAAAY,YAAAP,GAEA+J,KAAAA,uBAAAb,KAAAI,aAAAQ,KAAAZ,MACAA,KAAAvJ,SAAAa,iBAAA,UAAA0I,KAAAa,wBACAb,KAAAvJ,SAAAa,iBAAA,aAAA0I,KAAAa,0BAKA9H,EAAAY,SAAAA,CACAuE,YAAA6B,EACA5C,cAAA,iBACA9B,SAAA,gBACAuB,QAAAA,ICjFAkE,IAAAA,EAAA,SAAA1H,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,iBAAA+F,EAOAA,EAAAvD,UAAA2C,UAAAA,CAAAa,aAAA,MASAD,EAAAvD,UAAA3G,YAAAA,CACAoK,MAAA,sBACAC,YAAA,4BACAC,aAAA,6BACAC,aAAA,6BACAhB,cAAA,uBACAiB,qBAAA,sCACAzI,iBAAA,iCACA0I,cAAA,qBACAzI,OAAA,aACA0I,WAAA,aACAC,YAAA,cACAC,WAAA,aACAC,YAAA,eAQAX,EAAAvD,UAAAmE,UAAA,SAAArB,GACAsB,KAAAA,kBAQAb,EAAAvD,UAAAqE,SAAA,SAAAvB,GACA5J,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA0K,aAQAR,EAAAvD,UAAAsE,QAAA,SAAAxB,GACA5J,KAAAA,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA0K,aAQAR,EAAAvD,UAAAwE,WAAA,SAAA1B,GACA2B,KAAAA,SAOAlB,EAAAvD,UAAAoE,eAAA,WACAM,KAAAA,gBACAjC,KAAAkC,oBAOApB,EAAAvD,UAAAyE,MAAA,WAGAjH,OAAA8E,WAAA,WACAsC,KAAAA,cAAA7B,QACAM,KAAAZ,MAAAA,KAAAE,UAAAa,eAQAD,EAAAvD,UAAA2E,iBAAA,WACAC,KAAAA,cAAAC,QACApC,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA4K,YAEAxB,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA4K,aAGAV,EAAAvD,UAAA,iBAAAuD,EAAAvD,UAAA2E,iBAMApB,EAAAvD,UAAA0E,cAAA,WACAE,KAAAA,cAAA3B,SACAR,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA2K,aAEAvB,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA2K,cAGAT,EAAAvD,UAAA,cAAAuD,EAAAvD,UAAA0E,cAMAnB,EAAAvD,UAAAgD,QAAA,WACA4B,KAAAA,cAAA3B,UAAAA,EACAR,KAAA2B,kBAEAb,EAAAvD,UAAA,QAAAuD,EAAAvD,UAAAgD,QAMAO,EAAAvD,UAAAkD,OAAA,WACA0B,KAAAA,cAAA3B,UAAAA,EACAR,KAAA2B,kBAEAb,EAAAvD,UAAA,OAAAuD,EAAAvD,UAAAkD,OAMAK,EAAAvD,UAAA8E,MAAA,WACAF,KAAAA,cAAAC,SAAAA,EACApC,KAAA2B,kBAEAb,EAAAvD,UAAA,MAAAuD,EAAAvD,UAAA8E,MAMAvB,EAAAvD,UAAA+E,QAAA,WACAH,KAAAA,cAAAC,SAAAA,EACApC,KAAA2B,kBAEAb,EAAAvD,UAAA,QAAAuD,EAAAvD,UAAA+E,QAIAxB,EAAAvD,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACA0L,KAAAA,cAAAnC,KAAAvJ,SAAAqB,cAAA,IAAAkI,KAAApJ,YAAAoK,OACAuB,IAAAA,EAAAxL,SAAAC,cAAA,QACAuL,EAAA7L,UAAAO,IAAA+I,KAAApJ,YAAAqK,aACAuB,IAAAA,EAAAzL,SAAAC,cAAA,QACAwL,EAAA9L,UAAAO,IAAA+I,KAAApJ,YAAAsK,cACAuB,IAAAA,EAAA1L,SAAAC,cAAA,QACAyL,GAAAA,EAAA/L,UAAAO,IAAA+I,KAAApJ,YAAAuK,cACAoB,EAAAlL,YAAAoL,GACAzC,KAAAvJ,SAAAY,YAAAmL,GACAxC,KAAAvJ,SAAAY,YAAAkL,GACAvC,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAuJ,eAAA,CACA1J,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAAwK,sBACApB,KAAA0C,wBAAA3L,SAAAC,cAAA,QACAgJ,KAAA0C,wBAAAhM,UAAAO,IAAA+I,KAAApJ,YAAA+B,kBACAqH,KAAA0C,wBAAAhM,UAAAO,IAAA+I,KAAApJ,YAAAuJ,eACAH,KAAA0C,wBAAAhM,UAAAO,IAAA+I,KAAApJ,YAAAyK,eACArB,KAAA2C,mBAAA3C,KAAA+B,WAAAnB,KAAAZ,MACAA,KAAA0C,wBAAApL,iBAAA,UAAA0I,KAAA2C,oBACAxL,IAAAA,EAAAJ,SAAAC,cAAA,QACAG,EAAAT,UAAAO,IAAA+I,KAAApJ,YAAAgC,QACAoH,KAAA0C,wBAAArL,YAAAF,GACA6I,KAAAvJ,SAAAY,YAAA2I,KAAA0C,yBAEAE,KAAAA,mBAAA5C,KAAA0B,UAAAd,KAAAZ,MACAA,KAAA6C,kBAAA7C,KAAA4B,SAAAhB,KAAAZ,MACAA,KAAA8C,iBAAA9C,KAAA6B,QAAAjB,KAAAZ,MACAA,KAAA+C,oBAAA/C,KAAA+B,WAAAnB,KAAAZ,MACAA,KAAAmC,cAAA7K,iBAAA,SAAA0I,KAAA4C,oBACA5C,KAAAmC,cAAA7K,iBAAA,QAAA0I,KAAA6C,mBACA7C,KAAAmC,cAAA7K,iBAAA,OAAA0I,KAAA8C,kBACA9C,KAAAvJ,SAAAa,iBAAA,UAAA0I,KAAA+C,qBACA/C,KAAA2B,iBACA3B,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA6K,eAKA1I,EAAAY,SAAAA,CACAuE,YAAA4C,EACA3D,cAAA,mBACA9B,SAAA,kBACAuB,QAAAA,IC9MAoG,IAAAA,EAAA,SAAA5J,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,mBAAAiI,EAOAA,EAAAzF,UAAA2C,UAAAA,CAAAa,aAAA,MASAiC,EAAAzF,UAAA3G,YAAAA,CACAoK,MAAA,yBACAtI,iBAAA,uBACA0I,qBAAA,sCACAzI,iBAAA,oCACA0I,cAAA,qBACAzI,OAAA,aACA0I,WAAA,aACAC,YAAA,cACAC,WAAA,cAQAwB,EAAAzF,UAAAmE,UAAA,SAAArB,GACAsB,KAAAA,kBAQAqB,EAAAzF,UAAAqE,SAAA,SAAAvB,GACA5J,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA0K,aAQA0B,EAAAzF,UAAAsE,QAAA,SAAAxB,GACA5J,KAAAA,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA0K,aAQA0B,EAAAzF,UAAAwE,WAAA,SAAA1B,GACA2B,KAAAA,SAOAgB,EAAAzF,UAAAoE,eAAA,WACAM,KAAAA,gBACAjC,KAAAkC,oBAOAc,EAAAzF,UAAAyE,MAAA,WAGAjH,OAAA8E,WAAA,WACAsC,KAAAA,cAAA7B,QACAM,KAAAZ,MAAAA,KAAAE,UAAAa,eAQAiC,EAAAzF,UAAA2E,iBAAA,WACAC,KAAAA,cAAAC,QACApC,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA4K,YAEAxB,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA4K,aAGAwB,EAAAzF,UAAA,iBAAAyF,EAAAzF,UAAA2E,iBAMAc,EAAAzF,UAAA0E,cAAA,WACAE,KAAAA,cAAA3B,SACAR,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA2K,aAEAvB,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA2K,cAGAyB,EAAAzF,UAAA,cAAAyF,EAAAzF,UAAA0E,cAMAe,EAAAzF,UAAAgD,QAAA,WACA4B,KAAAA,cAAA3B,UAAAA,EACAR,KAAA2B,kBAEAqB,EAAAzF,UAAA,QAAAyF,EAAAzF,UAAAgD,QAMAyC,EAAAzF,UAAAkD,OAAA,WACA0B,KAAAA,cAAA3B,UAAAA,EACAR,KAAA2B,kBAEAqB,EAAAzF,UAAA,OAAAyF,EAAAzF,UAAAkD,OAMAuC,EAAAzF,UAAA8E,MAAA,WACAF,KAAAA,cAAAC,SAAAA,EACApC,KAAA2B,kBAEAqB,EAAAzF,UAAA,MAAAyF,EAAAzF,UAAA8E,MAMAW,EAAAzF,UAAA+E,QAAA,WACAH,KAAAA,cAAAC,SAAAA,EACApC,KAAA2B,kBAEAqB,EAAAzF,UAAA,QAAAyF,EAAAzF,UAAA+E,QAIAU,EAAAzF,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACAuJ,GAAAA,KAAAmC,cAAAnC,KAAAvJ,SAAAqB,cAAA,IAAAkI,KAAApJ,YAAAoK,OACAhB,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAA8B,kBAAA,CACAjC,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAAwK,sBACApB,KAAA0C,wBAAA3L,SAAAC,cAAA,QACAgJ,KAAA0C,wBAAAhM,UAAAO,IAAA+I,KAAApJ,YAAA+B,kBACAqH,KAAA0C,wBAAAhM,UAAAO,IAAA+I,KAAApJ,YAAA8B,kBACAsH,KAAA0C,wBAAAhM,UAAAO,IAAA+I,KAAApJ,YAAAyK,eACArB,KAAA2C,mBAAA3C,KAAA+B,WAAAnB,KAAAZ,MACAA,KAAA0C,wBAAApL,iBAAA,UAAA0I,KAAA2C,oBACAxL,IAAAA,EAAAJ,SAAAC,cAAA,QACAG,EAAAT,UAAAO,IAAA+I,KAAApJ,YAAAgC,QACAoH,KAAA0C,wBAAArL,YAAAF,GACA6I,KAAAvJ,SAAAY,YAAA2I,KAAA0C,yBAEAE,KAAAA,mBAAA5C,KAAA0B,UAAAd,KAAAZ,MACAA,KAAA6C,kBAAA7C,KAAA4B,SAAAhB,KAAAZ,MACAA,KAAA8C,iBAAA9C,KAAA6B,QAAAjB,KAAAZ,MACAA,KAAAiD,sBAAAjD,KAAA+B,WAAAnB,KAAAZ,MACAA,KAAAmC,cAAA7K,iBAAA,SAAA0I,KAAA4C,oBACA5C,KAAAmC,cAAA7K,iBAAA,QAAA0I,KAAA6C,mBACA7C,KAAAmC,cAAA7K,iBAAA,OAAA0I,KAAA8C,kBACA9C,KAAAvJ,SAAAa,iBAAA,UAAA0I,KAAAiD,uBACAjD,KAAA2B,iBACA3B,KAAAvJ,SAAAC,UAAAO,IAAA,iBAKA8B,EAAAY,SAAAA,CACAuE,YAAA8E,EACA7F,cAAA,qBACA9B,SAAA,qBACAuB,QAAAA,ICjMAsG,IAAAA,EAAA,SAAA9J,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,aAAAmI,EAOAA,EAAA3F,UAAA2C,UAAAA,CAEAiD,4BAAA,GAEAC,6BAAA,GAGAC,cAAA,KAQAH,EAAA3F,UAAA+F,UAAAA,CACAC,MAAA,GACAC,OAAA,GACAC,MAAA,GACAC,SAAA,GACAC,WAAA,IAUAT,EAAA3F,UAAA3G,YAAAA,CACAgN,UAAA,sBACAC,QAAA,oBACAC,KAAA,iBACAC,sBAAA,kCACA5D,cAAA,uBACAiB,qBAAA,sCACAxI,OAAA,aAEA6I,YAAA,cACAuC,WAAA,aACAC,aAAA,eAEAC,YAAA,wBAEAC,aAAA,yBACAC,SAAA,qBACAC,UAAA,sBACAC,UAAA,uBAKApB,EAAA3F,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CAEA8N,IAAAA,EAAAxN,SAAAC,cAAA,OACAuN,EAAA7N,UAAAO,IAAA+I,KAAApJ,YAAAgN,WACA5D,KAAAvJ,SAAA+N,cAAAC,aAAAF,EAAAvE,KAAAvJ,UACAuJ,KAAAvJ,SAAA+N,cAAAE,YAAA1E,KAAAvJ,UACA8N,EAAAlN,YAAA2I,KAAAvJ,UACAuJ,KAAA2E,WAAAJ,EAEAK,IAAAA,EAAA7N,SAAAC,cAAA,OACA4N,EAAAlO,UAAAO,IAAA+I,KAAApJ,YAAAiN,SACA7D,KAAA6E,SAAAD,EACAL,EAAAE,aAAAG,EAAA5E,KAAAvJ,UAEAqO,IAAAA,EAAA9E,KAAAvJ,SAAAe,aAAA,QAAAwI,KAAAvJ,SAAAe,aAAA,gBACAuN,EAAA,KACAD,KACAC,EAAAhO,SAAAiO,eAAAF,MAEA9E,KAAAiF,YAAAF,EACAA,EAAAzN,iBAAA,QAAA0I,KAAAkF,gBAAAtE,KAAAZ,OACA+E,EAAAzN,iBAAA,UAAA0I,KAAAmF,wBAAAvE,KAAAZ,SAGAoF,IAAAA,EAAApF,KAAAvJ,SAAA8E,iBAAA,IAAAyE,KAAApJ,YAAAkN,MACAuB,KAAAA,kBAAArF,KAAAsF,yBAAA1E,KAAAZ,MACAA,KAAAuF,gBAAAvF,KAAAwF,iBAAA5E,KAAAZ,MACA,IAAA,IAAA9F,EAAA,EAAAA,EAAAkL,EAAAhL,OAAAF,IAEAkL,EAAAlL,GAAA5C,iBAAA,QAAA0I,KAAAuF,iBAEAH,EAAAlL,GAAAuL,SAAA,KAEAL,EAAAlL,GAAA5C,iBAAA,UAAA0I,KAAAqF,mBAGArF,GAAAA,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAuJ,eAEA,IADAH,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAAwK,sBACAlH,EAAA,EAAAA,EAAAkL,EAAAhL,OAAAF,IAAA,CACAiE,IAAAA,EAAAiH,EAAAlL,GACApD,EAAAC,SAAAC,cAAA,QACAF,EAAAJ,UAAAO,IAAA+I,KAAApJ,YAAAmN,uBACA5M,IAAAA,EAAAJ,SAAAC,cAAA,QACAG,EAAAT,UAAAO,IAAA+I,KAAApJ,YAAAgC,QACA9B,EAAAO,YAAAF,GACAgH,EAAA9G,YAAAP,GACAqH,EAAAzH,UAAAO,IAAA+I,KAAApJ,YAAAuJ,eAIA1J,KAAAA,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAsN,cACAlE,KAAA6E,SAAAnO,UAAAO,IAAA+I,KAAApJ,YAAAsN,aAEAlE,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAuN,eACAnE,KAAA6E,SAAAnO,UAAAO,IAAA+I,KAAApJ,YAAAuN,cAEAnE,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAwN,WACApE,KAAA6E,SAAAnO,UAAAO,IAAA+I,KAAApJ,YAAAwN,UAEApE,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAyN,YACArE,KAAA6E,SAAAnO,UAAAO,IAAA+I,KAAApJ,YAAAyN,WAEArE,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAA0N,YACAtE,KAAA6E,SAAAnO,UAAAO,IAAA+I,KAAApJ,YAAA0N,WAEAC,EAAA7N,UAAAO,IAAA+I,KAAApJ,YAAA6K,eAUAyB,EAAA3F,UAAA2H,gBAAA,SAAAQ,GACA1F,GAAAA,KAAAvJ,UAAAuJ,KAAAiF,YAAA,CACAU,IAAAA,EAAA3F,KAAAiF,YAAAW,wBACAC,EAAA7F,KAAAiF,YAAAT,cAAAoB,wBACAnP,KAAAA,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAA0N,aACAtE,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAuN,eAEAnE,KAAA2E,WAAAmB,MAAAC,MAAAF,EAAAE,MAAAJ,EAAAI,MAAA,KACA/F,KAAA2E,WAAAmB,MAAAE,IAAAhG,KAAAiF,YAAAgB,UAAAjG,KAAAiF,YAAAiB,aAAA,MACAlG,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAwN,WAEApE,KAAA2E,WAAAmB,MAAAK,KAAAnG,KAAAiF,YAAAmB,WAAA,KACApG,KAAA2E,WAAAmB,MAAAO,OAAAR,EAAAQ,OAAAV,EAAAK,IAAA,MACAhG,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAyN,YAEArE,KAAA2E,WAAAmB,MAAAC,MAAAF,EAAAE,MAAAJ,EAAAI,MAAA,KACA/F,KAAA2E,WAAAmB,MAAAO,OAAAR,EAAAQ,OAAAV,EAAAK,IAAA,OAGAhG,KAAA2E,WAAAmB,MAAAK,KAAAnG,KAAAiF,YAAAmB,WAAA,KACApG,KAAA2E,WAAAmB,MAAAE,IAAAhG,KAAAiF,YAAAgB,UAAAjG,KAAAiF,YAAAiB,aAAA,OAGAI,KAAAA,OAAAZ,IAQAxC,EAAA3F,UAAA4H,wBAAA,SAAAO,GACA1F,GAAAA,KAAAvJ,UAAAuJ,KAAA2E,YAAA3E,KAAAiF,YAAA,CACAG,IAAAA,EAAApF,KAAAvJ,SAAA8E,iBAAA,IAAAyE,KAAApJ,YAAAkN,KAAA,oBACAsB,GAAAA,EAAAhL,OAAA,GAAA4F,KAAA2E,WAAAjO,UAAAC,SAAAqJ,KAAApJ,YAAAoN,cACA0B,EAAAa,UAAAvG,KAAAsD,UAAAI,UACAgC,EAAAhO,iBACA0N,EAAAA,EAAAhL,OAAA,GAAAoM,SACAd,EAAAa,UAAAvG,KAAAsD,UAAAK,aACA+B,EAAAhO,iBACA0N,EAAA,GAAAoB,YAWAtD,EAAA3F,UAAA+H,yBAAA,SAAAI,GACA1F,GAAAA,KAAAvJ,UAAAuJ,KAAA2E,WAAA,CACAS,IAAAA,EAAApF,KAAAvJ,SAAA8E,iBAAA,IAAAyE,KAAApJ,YAAAkN,KAAA,oBACAsB,GAAAA,GAAAA,EAAAhL,OAAA,GAAA4F,KAAA2E,WAAAjO,UAAAC,SAAAqJ,KAAApJ,YAAAoN,YAAA,CACAyC,IAAAA,EAAApJ,MAAAE,UAAAC,MAAAC,KAAA2H,GAAA1K,QAAAgL,EAAAgB,QACAhB,GAAAA,EAAAa,UAAAvG,KAAAsD,UAAAI,SACAgC,EAAAhO,iBACA+O,EAAA,EACArB,EAAAqB,EAAA,GAAAD,QAEApB,EAAAA,EAAAhL,OAAA,GAAAoM,aAEA,GAAAd,EAAAa,UAAAvG,KAAAsD,UAAAK,WACA+B,EAAAhO,iBACA0N,EAAAhL,OAAAqM,EAAA,EACArB,EAAAqB,EAAA,GAAAD,QAEApB,EAAA,GAAAoB,aAEA,GAAAd,EAAAa,UAAAvG,KAAAsD,UAAAG,OAAAiC,EAAAa,UAAAvG,KAAAsD,UAAAC,MAAA,CACAmC,EAAAhO,iBAEAH,IAAAA,EAAA,IAAAoP,WAAA,aACAjB,EAAAgB,OAAA7K,cAAAtE,GACAA,EAAA,IAAAoP,WAAA,WACAjB,EAAAgB,OAAA7K,cAAAtE,GAEAmO,EAAAgB,OAAAE,aACAlB,EAAAa,UAAAvG,KAAAsD,UAAAE,SACAkC,EAAAhO,iBACAsI,KAAA6G,WAWA3D,EAAA3F,UAAAiI,iBAAA,SAAAE,GACAA,EAAAgB,OAAAI,aAAA,YACApB,EAAAqB,mBAGA/G,KAAAgH,UAAAA,EACAjM,OAAA8E,WAAA,SAAA6F,GACAmB,KAAAA,OACA7G,KAAAgH,UAAAA,GACApG,KAAAZ,MAAAA,KAAAE,UAAAmD,iBAYAH,EAAA3F,UAAA0J,WAAA,SAAAC,EAAAC,GACA1Q,KAAAA,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAA0N,WAEAtE,KAAAvJ,SAAAqP,MAAAsB,KAAA,GACApH,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAuN,cAEAnE,KAAAvJ,SAAAqP,MAAAsB,KAAA,UAAAD,EAAA,QAAAA,EAAA,MACAnH,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAwN,UAEApE,KAAAvJ,SAAAqP,MAAAsB,KAAA,QAAAF,EAAA,QAAAA,EAAA,QACAlH,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAyN,WAEArE,KAAAvJ,SAAAqP,MAAAsB,KAAA,QAAAF,EAAA,MAAAC,EAAA,MAAAD,EAAA,MAAAC,EAAA,MAGAnH,KAAAvJ,SAAAqP,MAAAsB,KAAA,IASAlE,EAAA3F,UAAA8J,4BAAA,SAAA3B,GACAA,EAAAgB,OAAAhQ,UAAAoL,OAAAoB,EAAA3F,UAAA3G,YAAAqN,eAOAf,EAAA3F,UAAA+J,yBAAA,WACA7Q,KAAAA,SAAAa,iBAAA,gBAAA0I,KAAAqH,6BACArH,KAAAvJ,SAAAa,iBAAA,sBAAA0I,KAAAqH,8BAOAnE,EAAA3F,UAAAzE,KAAA,SAAA4M,GACA1F,GAAAA,KAAAvJ,UAAAuJ,KAAA2E,YAAA3E,KAAA6E,SAAA,CAEAqC,IAAAA,EAAAlH,KAAAvJ,SAAAmP,wBAAAsB,OACAC,EAAAnH,KAAAvJ,SAAAmP,wBAAAuB,MAEAxC,KAAAA,WAAAmB,MAAAqB,MAAAA,EAAA,KACAnH,KAAA2E,WAAAmB,MAAAoB,OAAAA,EAAA,KACAlH,KAAA6E,SAAAiB,MAAAqB,MAAAA,EAAA,KACAnH,KAAA6E,SAAAiB,MAAAoB,OAAAA,EAAA,KAKA,IAAA,IAJAK,EAAAvH,KAAAE,UAAAiD,4BAAAnD,KAAAE,UAAAkD,6BAGAgC,EAAApF,KAAAvJ,SAAA8E,iBAAA,IAAAyE,KAAApJ,YAAAkN,MACA5J,EAAA,EAAAA,EAAAkL,EAAAhL,OAAAF,IAAA,CACAsN,IAAAA,EAEAA,EADAxH,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAwN,WAAApE,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAyN,YACA6C,EAAA9B,EAAAlL,GAAA+L,UAAAb,EAAAlL,GAAAgM,cAAAgB,EAAAK,EAAA,IAEAnC,EAAAlL,GAAA+L,UAAAiB,EAAAK,EAAA,IAEAnC,EAAAlL,GAAA4L,MAAA2B,gBAAAD,EAGAP,KAAAA,WAAAC,EAAAC,GAGApM,OAAAoE,sBAAA,WACA1I,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAAqN,cACAjE,KAAAvJ,SAAAqP,MAAAsB,KAAA,UAAAD,EAAA,MAAAD,EAAA,QACAlH,KAAA2E,WAAAjO,UAAAO,IAAA+I,KAAApJ,YAAAoN,aACApD,KAAAZ,OAEAA,KAAAsH,2BAEA5N,IAAAA,EAAA,SAAAnC,GAOAA,IAAAmO,GAAA1F,KAAAgH,UAAAzP,EAAAmP,OAAAgB,aAAA1H,KAAAvJ,WACAM,SAAA4Q,oBAAA,QAAAjO,GACAsG,KAAA6G,SAEAjG,KAAAZ,MACAjJ,SAAAO,iBAAA,QAAAoC,KAGAwJ,EAAA3F,UAAA,KAAA2F,EAAA3F,UAAAzE,KAMAoK,EAAA3F,UAAAsJ,KAAA,WACA7G,GAAAA,KAAAvJ,UAAAuJ,KAAA2E,YAAA3E,KAAA6E,SAAA,CAGA,IAAA,IAFAO,EAAApF,KAAAvJ,SAAA8E,iBAAA,IAAAyE,KAAApJ,YAAAkN,MAEA5J,EAAA,EAAAA,EAAAkL,EAAAhL,OAAAF,IACAkL,EAAAlL,GAAA4L,MAAA8B,eAAA,oBAGAjC,IAAAA,EAAA3F,KAAAvJ,SAAAmP,wBACAsB,EAAAvB,EAAAuB,OACAC,EAAAxB,EAAAwB,MAGA1Q,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAAqN,cACAjE,KAAAiH,WAAAC,EAAAC,GACAnH,KAAA2E,WAAAjO,UAAAoL,OAAA9B,KAAApJ,YAAAoN,YAEAhE,KAAAsH,6BAGApE,EAAA3F,UAAA,KAAA2F,EAAA3F,UAAAsJ,KAMA3D,EAAA3F,UAAA+I,OAAA,SAAAZ,GACAf,KAAAA,WAAAjO,UAAAC,SAAAqJ,KAAApJ,YAAAoN,YACAhE,KAAA6G,OAEA7G,KAAAlH,KAAA4M,IAGAxC,EAAA3F,UAAA,OAAA2F,EAAA3F,UAAA+I,OAGAvN,EAAAY,SAAAA,CACAuE,YAAAgF,EACA/F,cAAA,eACA9B,SAAA,cACAuB,QAAAA,ICvYAiL,IAAAA,EAAA,SAAAzO,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,iBAAA8M,EAOAA,EAAAtK,UAAA2C,UAAAA,GASA2H,EAAAtK,UAAA3G,YAAAA,CAAAkR,oBAAA,+BAOAD,EAAAtK,UAAAwK,YAAA,SAAAC,GACAvR,KAAAA,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAkR,uBAGA9H,KAAAiI,aAAAnC,MAAAqB,MAAAa,EAAA,MAEAH,EAAAtK,UAAA,YAAAsK,EAAAtK,UAAAwK,YAOAF,EAAAtK,UAAA2K,UAAA,SAAAF,GACAG,KAAAA,WAAArC,MAAAqB,MAAAa,EAAA,IACAhI,KAAAoI,QAAAtC,MAAAqB,MAAA,IAAAa,EAAA,KAEAH,EAAAtK,UAAA,UAAAsK,EAAAtK,UAAA2K,UAIAL,EAAAtK,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACA4R,IAAAA,EAAAtR,SAAAC,cAAA,OACAqR,EAAAhO,UAAA,uBACA2F,KAAAvJ,SAAAY,YAAAgR,GACArI,KAAAiI,aAAAI,GACAA,EAAAtR,SAAAC,cAAA,QACAqD,UAAA,qBACA2F,KAAAvJ,SAAAY,YAAAgR,GACArI,KAAAmI,WAAAE,GACAA,EAAAtR,SAAAC,cAAA,QACAqD,UAAA,kBACA2F,KAAAvJ,SAAAY,YAAAgR,GACArI,KAAAoI,QAAAC,EACArI,KAAAiI,aAAAnC,MAAAqB,MAAA,KACAnH,KAAAmI,WAAArC,MAAAqB,MAAA,OACAnH,KAAAoI,QAAAtC,MAAAqB,MAAA,KACAnH,KAAAvJ,SAAAC,UAAAO,IAAA,iBAKA8B,EAAAY,SAAAA,CACAuE,YAAA2J,EACA1K,cAAA,mBACA9B,SAAA,kBACAuB,QAAAA,IC3EA0L,IAAAA,EAAA,SAAAlP,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,cAAAuN,EAOAA,EAAA/K,UAAA2C,UAAAA,CAAAa,aAAA,MASAuH,EAAA/K,UAAA3G,YAAAA,CACA0K,WAAA,aACAC,YAAA,cACAC,WAAA,aACAC,YAAA,cACA8G,SAAA,eACAC,UAAA,oBACAC,mBAAA,0BACAC,mBAAA,0BACAvI,cAAA,uBACAiB,qBAAA,sCACAzI,iBAAA,8BACA0I,cAAA,qBACAzI,OAAA,cAQA0P,EAAA/K,UAAAmE,UAAA,SAAArB,GAIA,IAAA,IADAsI,EAAA5R,SAAA6R,uBAAA5I,KAAApJ,YAAA2R,UACArO,EAAA,EAAAA,EAAAyO,EAAAvO,OAAAF,IAAA,CACAyO,EAAAzO,GAAApC,cAAA,IAAAkI,KAAApJ,YAAA4R,WAEAhR,aAAA,UAAAwI,KAAA6I,YAAArR,aAAA,cACA,IAAAmR,EAAAzO,GAAA,eACAyO,EAAAzO,GAAA,cAAAyH,mBAWA2G,EAAA/K,UAAAqE,SAAA,SAAAvB,GACA5J,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA0K,aAQAgH,EAAA/K,UAAAsE,QAAA,SAAAxB,GACA5J,KAAAA,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA0K,aAQAgH,EAAA/K,UAAAuL,WAAA,SAAAzI,GACA2B,KAAAA,SAOAsG,EAAA/K,UAAAoE,eAAA,WACAM,KAAAA,gBACAjC,KAAAkC,oBAOAoG,EAAA/K,UAAAyE,MAAA,WAGAjH,OAAA8E,WAAA,WACAgJ,KAAAA,YAAAvI,QACAM,KAAAZ,MAAAA,KAAAE,UAAAa,eAQAuH,EAAA/K,UAAA0E,cAAA,WACA4G,KAAAA,YAAArI,SACAR,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA2K,aAEAvB,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA2K,cAGA+G,EAAA/K,UAAA,cAAA+K,EAAA/K,UAAA0E,cAMAqG,EAAA/K,UAAA2E,iBAAA,WACA2G,KAAAA,YAAAzG,QACApC,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA4K,YAEAxB,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA4K,aAGA8G,EAAA/K,UAAA,iBAAA+K,EAAA/K,UAAA2E,iBAMAoG,EAAA/K,UAAAgD,QAAA,WACAsI,KAAAA,YAAArI,UAAAA,EACAR,KAAA2B,kBAEA2G,EAAA/K,UAAA,QAAA+K,EAAA/K,UAAAgD,QAMA+H,EAAA/K,UAAAkD,OAAA,WACAoI,KAAAA,YAAArI,UAAAA,EACAR,KAAA2B,kBAEA2G,EAAA/K,UAAA,OAAA+K,EAAA/K,UAAAkD,OAMA6H,EAAA/K,UAAA8E,MAAA,WACAwG,KAAAA,YAAAzG,SAAAA,EACApC,KAAA0B,UAAA,OAEA4G,EAAA/K,UAAA,MAAA+K,EAAA/K,UAAA8E,MAMAiG,EAAA/K,UAAA+E,QAAA,WACAuG,KAAAA,YAAAzG,SAAAA,EACApC,KAAA0B,UAAA,OAEA4G,EAAA/K,UAAA,QAAA+K,EAAA/K,UAAA+E,QAIAgG,EAAA/K,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACAoS,KAAAA,YAAA7I,KAAAvJ,SAAAqB,cAAA,IAAAkI,KAAApJ,YAAA4R,WACAxI,KAAA+I,oBAAA/I,KAAA0B,UAAAd,KAAAZ,MACAA,KAAAgJ,mBAAAhJ,KAAA0B,UAAAd,KAAAZ,MACAA,KAAAiJ,kBAAAjJ,KAAA6B,QAAAjB,KAAAZ,MACAA,KAAAkJ,qBAAAlJ,KAAA8I,WAAAlI,KAAAZ,MACAmJ,IAAAA,EAAApS,SAAAC,cAAA,QACAmS,EAAAzS,UAAAO,IAAA+I,KAAApJ,YAAA6R,oBACAW,IAIAtS,EAJAsS,EAAArS,SAAAC,cAAA,QAKAgJ,GAJAoJ,EAAA1S,UAAAO,IAAA+I,KAAApJ,YAAA8R,oBACA1I,KAAAvJ,SAAAY,YAAA8R,GACAnJ,KAAAvJ,SAAAY,YAAA+R,GAEApJ,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAuJ,eAAA,CACA1J,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAAwK,uBACAtK,EAAAC,SAAAC,cAAA,SACAN,UAAAO,IAAA+I,KAAApJ,YAAA+B,kBACA7B,EAAAJ,UAAAO,IAAA+I,KAAApJ,YAAAuJ,eACArJ,EAAAJ,UAAAO,IAAA+I,KAAApJ,YAAAyK,eACAvK,EAAAQ,iBAAA,UAAA0I,KAAAkJ,sBACA/R,IAAAA,EAAAJ,SAAAC,cAAA,QACAG,EAAAT,UAAAO,IAAA+I,KAAApJ,YAAAgC,QACA9B,EAAAO,YAAAF,GACA6I,KAAAvJ,SAAAY,YAAAP,GAEA+R,KAAAA,YAAAvR,iBAAA,SAAA0I,KAAA+I,qBACA/I,KAAA6I,YAAAvR,iBAAA,QAAA0I,KAAAgJ,oBACAhJ,KAAA6I,YAAAvR,iBAAA,OAAA0I,KAAAiJ,mBACAjJ,KAAAvJ,SAAAa,iBAAA,UAAA0I,KAAAkJ,sBACAlJ,KAAA2B,iBACA3B,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA6K,eAKA1I,EAAAY,SAAAA,CACAuE,YAAAoK,EACAnL,cAAA,gBACA9B,SAAA,eACAuB,QAAAA,ICtNAyM,IAAAA,EAAA,SAAAjQ,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAsJ,MAAAvO,OAAAwE,UAAAgK,iBAEAvJ,KAAAC,QAEAlF,OAAA,eAAAsO,EAOAA,EAAA9L,UAAA2C,UAAAA,GASAmJ,EAAA9L,UAAA3G,YAAAA,CACA4S,aAAA,2BACAC,iBAAA,wBACAC,gBAAA,8BACAC,iBAAA,+BACAC,iBAAA,+BACAC,gBAAA,kBACApI,YAAA,eAQA4H,EAAA9L,UAAAuM,SAAA,SAAAzJ,GACA0J,KAAAA,sBAQAV,EAAA9L,UAAAmE,UAAA,SAAArB,GACA0J,KAAAA,sBAQAV,EAAA9L,UAAAwE,WAAA,SAAA1B,GACAA,EAAAqG,OAAApG,QAYA+I,EAAA9L,UAAAyM,sBAAA,SAAA3J,GAGAA,GAAAA,EAAAqG,SAAA1G,KAAAvJ,SAAA+N,cAAA,CAKAnE,EAAA3I,iBACAuS,IAAAA,EAAA,IAAAtD,WAAA,YAAA,CACAD,OAAArG,EAAAqG,OACAwD,QAAA7J,EAAA6J,QACAC,QAAA9J,EAAA8J,QACAC,QAAApK,KAAAvJ,SAAAmP,wBAAAyE,IAEA5T,KAAAA,SAAAoF,cAAAoO,KAOAZ,EAAA9L,UAAAwM,mBAAA,WAEAO,IAAAA,GAAAtK,KAAAvJ,SAAA8T,MAAAvK,KAAAvJ,SAAA+T,MAAAxK,KAAAvJ,SAAAmJ,IAAAI,KAAAvJ,SAAA+T,KACAF,IAAAA,EACAtK,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAAiT,iBAEA7J,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAAiT,iBAEA7J,KAAAsJ,QACAtJ,KAAAyK,iBAAA3E,MAAA4E,KAAAJ,EACAtK,KAAAyK,iBAAA3E,MAAA6E,WAAAL,EACAtK,KAAA4K,iBAAA9E,MAAA4E,KAAA,EAAAJ,EACAtK,KAAA4K,iBAAA9E,MAAA6E,WAAA,EAAAL,IASAjB,EAAA9L,UAAAgD,QAAA,WACA9J,KAAAA,SAAA+J,UAAAA,GAEA6I,EAAA9L,UAAA,QAAA8L,EAAA9L,UAAAgD,QAMA8I,EAAA9L,UAAAkD,OAAA,WACAhK,KAAAA,SAAA+J,UAAAA,GAEA6I,EAAA9L,UAAA,OAAA8L,EAAA9L,UAAAkD,OAOA4I,EAAA9L,UAAAsN,OAAA,SAAAN,QACA,IAAAA,IACAvK,KAAAvJ,SAAA8T,MAAAA,GAEAvK,KAAA+J,sBAEAV,EAAA9L,UAAA,OAAA8L,EAAA9L,UAAAsN,OAIAxB,EAAA9L,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACAuJ,GAAAA,KAAAsJ,MAAA,CAIAwB,IAAAA,EAAA/T,SAAAC,cAAA,OACA8T,EAAApU,UAAAO,IAAA+I,KAAApJ,YAAA4S,cACAxJ,KAAAvJ,SAAA+N,cAAAC,aAAAqG,EAAA9K,KAAAvJ,UACAuJ,KAAAvJ,SAAA+N,cAAAE,YAAA1E,KAAAvJ,UACAqU,EAAAzT,YAAA2I,KAAAvJ,cACA,CAIA8N,IAAAA,EAAAxN,SAAAC,cAAA,OACAuN,EAAA7N,UAAAO,IAAA+I,KAAApJ,YAAA6S,kBACAzJ,KAAAvJ,SAAA+N,cAAAC,aAAAF,EAAAvE,KAAAvJ,UACAuJ,KAAAvJ,SAAA+N,cAAAE,YAAA1E,KAAAvJ,UACA8N,EAAAlN,YAAA2I,KAAAvJ,UACAsU,IAAAA,EAAAhU,SAAAC,cAAA,OACA+T,EAAArU,UAAAO,IAAA+I,KAAApJ,YAAA8S,iBACAnF,EAAAlN,YAAA0T,GACA/K,KAAAyK,iBAAA1T,SAAAC,cAAA,OACAgJ,KAAAyK,iBAAA/T,UAAAO,IAAA+I,KAAApJ,YAAA+S,kBACAoB,EAAA1T,YAAA2I,KAAAyK,kBACAzK,KAAA4K,iBAAA7T,SAAAC,cAAA,OACAgJ,KAAA4K,iBAAAlU,UAAAO,IAAA+I,KAAApJ,YAAAgT,kBACAmB,EAAA1T,YAAA2I,KAAA4K,kBAEAI,KAAAA,kBAAAhL,KAAA8J,SAAAlJ,KAAAZ,MACAA,KAAAiL,mBAAAjL,KAAA0B,UAAAd,KAAAZ,MACAA,KAAAkL,oBAAAlL,KAAA+B,WAAAnB,KAAAZ,MACAA,KAAAmL,+BAAAnL,KAAAgK,sBAAApJ,KAAAZ,MACAA,KAAAvJ,SAAAa,iBAAA,QAAA0I,KAAAgL,mBACAhL,KAAAvJ,SAAAa,iBAAA,SAAA0I,KAAAiL,oBACAjL,KAAAvJ,SAAAa,iBAAA,UAAA0I,KAAAkL,qBACAlL,KAAAvJ,SAAA+N,cAAAlN,iBAAA,YAAA0I,KAAAmL,gCACAnL,KAAA+J,qBACA/J,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA6K,eAKA1I,EAAAY,SAAAA,CACAuE,YAAAmL,EACAlM,cAAA,iBACA9B,SAAA,gBACAuB,QAAAA,IC9LAwO,IAAAA,EAAA,SAAAhS,GACA4G,GAAAA,KAAAvJ,SAAA2C,EACA4G,KAAAqL,aAAArL,KAAAvJ,SAAAqB,cAAA,IAAAkI,KAAAsL,YAAAC,SACAvL,KAAAwL,eAAAxL,KAAAvJ,SAAAqB,cAAA,IAAAkI,KAAAsL,YAAAG,SACAzL,KAAAqL,aACA,MAAA,IAAA1P,MAAA,mDAEA,IAAAqE,KAAAwL,eACA,MAAA,IAAA7P,MAAA,mDAEA+P,KAAAA,QAAAA,EACA1L,KAAA2L,oBAAAC,EACA5L,KAAA6L,cAAAD,EACA5L,KAAA8L,iBAAAF,EACA5L,KAAA+L,qBAAAA,GACA/L,KAAAgM,kBAAAA,IAEAjR,OAAA,iBAAAqQ,EAOAA,EAAA7N,UAAA2C,UAAAA,CAEA+L,iBAAA,KAUAb,EAAA7N,UAAA+N,YAAAA,CACAY,SAAA,eACAX,QAAA,qBACAE,OAAA,uBACAU,OAAA,wBAOAf,EAAA7N,UAAA6O,iBAAA,WACA3V,KAAAA,SAAA0F,aAAA,cAAA,QACA6D,KAAA2L,iBACA3L,KAAAwL,eAAAa,YAAArM,KAAA8L,YACA9L,KAAAwL,eAAAlU,iBAAA,QAAA0I,KAAA2L,gBACA3L,KAAAgM,kBAAAA,IAEAhM,KAAAqL,aAAAgB,YAAArM,KAAA6L,SACA7L,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAAsL,YAAAa,QACAnM,KAAAvJ,SAAA0F,aAAA,cAAA,SACA0D,WAAAG,KAAAsM,SAAA1L,KAAAZ,MAAAA,KAAAuM,WAQAnB,EAAA7N,UAAAiP,aAAA,SAAAC,GACAb,QAAAA,IAAAa,EACA,MAAA,IAAA9Q,MAAA,oEAEAiQ,QAAAA,IAAAa,EAAA,QACA,MAAA,IAAA9Q,MAAA,6CAEA8Q,GAAAA,EAAA,gBAAAA,EAAA,WACA,MAAA,IAAA9Q,MAAA,gDAEA+P,KAAAA,OACA1L,KAAA+L,qBAAA/P,KAAAyQ,IAEAzM,KAAA0L,QAAAA,EACA1L,KAAA6L,SAAAY,EAAA,QACAA,EAAA,QACAzM,KAAAuM,SAAAE,EAAA,QAEAzM,KAAAuM,SAAA,KAEAE,EAAA,gBACAzM,KAAA2L,eAAAc,EAAA,eAEAA,EAAA,aACAzM,KAAA8L,YAAAW,EAAA,YAEAzM,KAAAoM,qBAGAhB,EAAA7N,UAAA,aAAA6N,EAAA7N,UAAAiP,aAOApB,EAAA7N,UAAAmP,YAAA,WACAX,KAAAA,qBAAA3R,OAAA,GACA4F,KAAAwM,aAAAxM,KAAA+L,qBAAAY,UAQAvB,EAAA7N,UAAA+O,SAAA,WACA7V,KAAAA,SAAAC,UAAAoL,OAAA9B,KAAAsL,YAAAa,QACAtM,WAAA,WACApJ,KAAAA,SAAA0F,aAAA,cAAA,QACA6D,KAAAqL,aAAAgB,YAAA,GACAO,QAAA5M,KAAAwL,eAAAhU,aAAA,kBACAwI,KAAAgM,kBAAAA,GACAhM,KAAAwL,eAAAa,YAAA,GACArM,KAAAwL,eAAA7D,oBAAA,QAAA3H,KAAA2L,iBAEA3L,KAAA2L,oBAAAC,EACA5L,KAAA6L,cAAAD,EACA5L,KAAA8L,iBAAAF,EACA5L,KAAA0L,QAAAA,EACA1L,KAAA0M,eACA9L,KAAAZ,MAAAA,KAAAE,UAAA+L,mBAQAb,EAAA7N,UAAAyO,iBAAA,SAAAzB,GACAA,EACAvK,KAAAwL,eAAArP,aAAA,cAAA,QAEA6D,KAAAwL,eAAAqB,gBAAA,gBAKA9T,EAAAY,SAAAA,CACAuE,YAAAkN,EACAjO,cAAA,mBACA9B,SAAA,kBACAuB,QAAAA,IClJAkQ,IAAAA,EAAA,SAAA1T,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,gBAAA+R,EAOAA,EAAAvP,UAAA2C,UAAAA,CAAA6M,wBAAA,GASAD,EAAAvP,UAAA3G,YAAAA,CACAoW,kBAAA,qBACAC,2BAAA,8BACAC,mBAAA,sBACAC,sBAAA,yBACAC,iBAAA,oBACAC,kBAAA,sBAQAP,EAAAvP,UAAA+P,YAAA,SAAAC,GACAC,IAAAA,EAAAzW,SAAAC,cAAA,OACAwW,EAAA9W,UAAAO,IAAA+I,KAAApJ,YAAAoW,mBACAQ,EAAA9W,UAAAO,IAAA+I,KAAApJ,YAAAoW,kBAAA,IAAAO,GACAE,IAAAA,EAAA1W,SAAAC,cAAA,OACAyW,EAAA/W,UAAAO,IAAA+I,KAAApJ,YAAAqW,4BACAQ,EAAA/W,UAAAO,IAAA+I,KAAApJ,YAAAwW,kBACAM,IAAAA,EAAA3W,SAAAC,cAAA,OACA0W,EAAAhX,UAAAO,IAAA+I,KAAApJ,YAAAuW,uBACAQ,IAAAA,EAAA5W,SAAAC,cAAA,OACA2W,EAAAjX,UAAAO,IAAA+I,KAAApJ,YAAAqW,4BACAU,EAAAjX,UAAAO,IAAA+I,KAAApJ,YAAAyW,mBAMA,IAAA,IALAO,EAAAA,CACAH,EACAC,EACAC,GAEAzT,EAAA,EAAAA,EAAA0T,EAAAxT,OAAAF,IAAA,CACA2T,IAAAA,EAAA9W,SAAAC,cAAA,OACA6W,EAAAnX,UAAAO,IAAA+I,KAAApJ,YAAAsW,oBACAU,EAAA1T,GAAA7C,YAAAwW,GAEAL,EAAAnW,YAAAoW,GACAD,EAAAnW,YAAAqW,GACAF,EAAAnW,YAAAsW,GACA3N,KAAAvJ,SAAAY,YAAAmW,IAEAV,EAAAvP,UAAA,YAAAuP,EAAAvP,UAAA+P,YAOAR,EAAAvP,UAAAuQ,KAAA,WACArX,KAAAA,SAAAC,UAAAoL,OAAA,cAEAgL,EAAAvP,UAAA,KAAAuP,EAAAvP,UAAAuQ,KAQAhB,EAAAvP,UAAAwQ,MAAA,WACAtX,KAAAA,SAAAC,UAAAO,IAAA,cAEA6V,EAAAvP,UAAA,MAAAuP,EAAAvP,UAAAwQ,MAIAjB,EAAAvP,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACA,IAAA,IAAAyD,EAAA,EAAAA,GAAA8F,KAAAE,UAAA6M,wBAAA7S,IACA8F,KAAAsN,YAAApT,GAEAzD,KAAAA,SAAAC,UAAAO,IAAA,iBAKA8B,EAAAY,SAAAA,CACAuE,YAAA4O,EACA3P,cAAA,kBACA9B,SAAA,iBACAuB,QAAAA,ICrGAoR,IAAAA,EAAA,SAAA5U,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,eAAAiT,EAOAA,EAAAzQ,UAAA2C,UAAAA,CAAAa,aAAA,MASAiN,EAAAzQ,UAAA3G,YAAAA,CACAoK,MAAA,oBACAiN,MAAA,oBACAC,MAAA,oBACAhN,aAAA,2BACAf,cAAA,uBACAiB,qBAAA,sCACAzI,iBAAA,+BACA0I,cAAA,qBACAzI,OAAA,aACA0I,WAAA,aACAC,YAAA,cACAC,WAAA,cAQAwM,EAAAzQ,UAAAmE,UAAA,SAAArB,GACAsB,KAAAA,kBAQAqM,EAAAzQ,UAAAqE,SAAA,SAAAvB,GACA5J,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA0K,aAQA0M,EAAAzQ,UAAAsE,QAAA,SAAAxB,GACA5J,KAAAA,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA0K,aAQA0M,EAAAzQ,UAAAwE,WAAA,SAAA1B,GACA2B,KAAAA,SAOAgM,EAAAzQ,UAAAoE,eAAA,WACAM,KAAAA,gBACAjC,KAAAkC,oBAOA8L,EAAAzQ,UAAAyE,MAAA,WAGAjH,OAAA8E,WAAA,WACAsC,KAAAA,cAAA7B,QACAM,KAAAZ,MAAAA,KAAAE,UAAAa,eAQAiN,EAAAzQ,UAAA0E,cAAA,WACAE,KAAAA,cAAA3B,SACAR,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA2K,aAEAvB,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA2K,cAGAyM,EAAAzQ,UAAA,cAAAyQ,EAAAzQ,UAAA0E,cAMA+L,EAAAzQ,UAAA2E,iBAAA,WACAC,KAAAA,cAAAC,QACApC,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA4K,YAEAxB,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA4K,aAGAwM,EAAAzQ,UAAA,iBAAAyQ,EAAAzQ,UAAA2E,iBAMA8L,EAAAzQ,UAAAgD,QAAA,WACA4B,KAAAA,cAAA3B,UAAAA,EACAR,KAAA2B,kBAEAqM,EAAAzQ,UAAA,QAAAyQ,EAAAzQ,UAAAgD,QAMAyN,EAAAzQ,UAAAkD,OAAA,WACA0B,KAAAA,cAAA3B,UAAAA,EACAR,KAAA2B,kBAEAqM,EAAAzQ,UAAA,OAAAyQ,EAAAzQ,UAAAkD,OAMAuN,EAAAzQ,UAAA4Q,GAAA,WACAhM,KAAAA,cAAAC,SAAAA,EACApC,KAAA2B,kBAEAqM,EAAAzQ,UAAA,GAAAyQ,EAAAzQ,UAAA4Q,GAMAH,EAAAzQ,UAAA6Q,IAAA,WACAjM,KAAAA,cAAAC,SAAAA,EACApC,KAAA2B,kBAEAqM,EAAAzQ,UAAA,IAAAyQ,EAAAzQ,UAAA6Q,IAIAJ,EAAAzQ,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACA0L,KAAAA,cAAAnC,KAAAvJ,SAAAqB,cAAA,IAAAkI,KAAApJ,YAAAoK,OACAqN,IAAAA,EAAAtX,SAAAC,cAAA,OACAqX,EAAA3X,UAAAO,IAAA+I,KAAApJ,YAAAqX,OACAK,IAAAA,EAAAvX,SAAAC,cAAA,OACAsX,EAAA5X,UAAAO,IAAA+I,KAAApJ,YAAAsX,OACAK,IAAAA,EAAAxX,SAAAC,cAAA,QACAuX,GAAAA,EAAA7X,UAAAO,IAAA+I,KAAApJ,YAAAsK,cACAoN,EAAAjX,YAAAkX,GACAvO,KAAAvJ,SAAAY,YAAAgX,GACArO,KAAAvJ,SAAAY,YAAAiX,GACAtO,KAAAkL,oBAAAlL,KAAA+B,WAAAnB,KAAAZ,MACAA,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAuJ,eAAA,CACA1J,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAAwK,sBACApB,KAAA0C,wBAAA3L,SAAAC,cAAA,QACAgJ,KAAA0C,wBAAAhM,UAAAO,IAAA+I,KAAApJ,YAAA+B,kBACAqH,KAAA0C,wBAAAhM,UAAAO,IAAA+I,KAAApJ,YAAAuJ,eACAH,KAAA0C,wBAAAhM,UAAAO,IAAA+I,KAAApJ,YAAAyK,eACArB,KAAA0C,wBAAApL,iBAAA,UAAA0I,KAAAkL,qBACA/T,IAAAA,EAAAJ,SAAAC,cAAA,QACAG,EAAAT,UAAAO,IAAA+I,KAAApJ,YAAAgC,QACAoH,KAAA0C,wBAAArL,YAAAF,GACA6I,KAAAvJ,SAAAY,YAAA2I,KAAA0C,yBAEAuI,KAAAA,mBAAAjL,KAAA0B,UAAAd,KAAAZ,MACAA,KAAAwO,kBAAAxO,KAAA4B,SAAAhB,KAAAZ,MACAA,KAAAyO,iBAAAzO,KAAA6B,QAAAjB,KAAAZ,MACAA,KAAAmC,cAAA7K,iBAAA,SAAA0I,KAAAiL,oBACAjL,KAAAmC,cAAA7K,iBAAA,QAAA0I,KAAAwO,mBACAxO,KAAAmC,cAAA7K,iBAAA,OAAA0I,KAAAyO,kBACAzO,KAAAvJ,SAAAa,iBAAA,UAAA0I,KAAAkL,qBACAlL,KAAA2B,iBACA3B,KAAAvJ,SAAAC,UAAAO,IAAA,iBAKA8B,EAAAY,SAAAA,CACAuE,YAAA8P,EACA7Q,cAAA,iBACA9B,SAAA,gBACAuB,QAAAA,Ib5MA8R,IAAAA,EAAA,SAAAtV,GAEA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,aAAA2T,EAOAA,EAAAnR,UAAA2C,UAAAA,GASAwO,EAAAnR,UAAA3G,YAAAA,CACA+X,UAAA,gBACAC,YAAA,kBACA3W,aAAA,YACA4W,eAAA,cACAhY,qBAAA,uBACAK,qBAAA,6BACAE,WAAA,aACA0X,mCAAA,uCAOAJ,EAAAnR,UAAAwR,UAAA,WACAtY,KAAAA,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAC,uBACAmJ,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAAkY,oCAGA9O,KAAAgP,MAAAhP,KAAAvJ,SAAA8E,iBAAA,IAAAyE,KAAApJ,YAAA+X,WACA3O,KAAAiP,QAAAjP,KAAAvJ,SAAA8E,iBAAA,IAAAyE,KAAApJ,YAAAgY,aAEA,IAAA,IAAA1U,EAAA,EAAAA,EAAA8F,KAAAgP,MAAA5U,OAAAF,IACA,IAAA5D,EAAA0J,KAAAgP,MAAA9U,GAAA8F,MAEAvJ,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAAiY,iBAOAH,EAAAnR,UAAAxF,eAAA,WACA,IAAA,IAAAmX,EAAA,EAAAA,EAAAlP,KAAAgP,MAAA5U,OAAA8U,IACAlP,KAAAgP,MAAAE,GAAAxY,UAAAoL,OAAA9B,KAAApJ,YAAAqB,eAQAyW,EAAAnR,UAAAvF,iBAAA,WACA,IAAA,IAAAyE,EAAA,EAAAA,EAAAuD,KAAAiP,QAAA7U,OAAAqC,IACAuD,KAAAiP,QAAAxS,GAAA/F,UAAAoL,OAAA9B,KAAApJ,YAAAqB,eAMAyW,EAAAnR,UAAA0C,KAAA,WACAxJ,KAAAA,UACAuJ,KAAA+O,aAoCAhW,EAAAY,SAAAA,CACAuE,YAAAwQ,EACAvR,cAAA,eACA9B,SAAA,gBclHA8T,IAAAA,EAAA,SAAA/V,GACA3C,KAAAA,SAAA2C,EACA4G,KAAAoP,QAAApP,KAAAE,UAAAmP,YAEArP,KAAAC,QAEAlF,OAAA,kBAAAoU,EAOAA,EAAA5R,UAAA2C,UAAAA,CACAmP,aAAA,EACAC,mBAAA,WAUAH,EAAA5R,UAAA3G,YAAAA,CACA2Y,MAAA,uBACAvO,MAAA,uBACAwO,SAAA,WACAlO,WAAA,aACAC,YAAA,cACAkO,WAAA,aACAhO,YAAA,cACAiO,gBAAA,mBAQAP,EAAA5R,UAAAoS,WAAA,SAAAtP,GACAuP,IAAAA,EAAAvP,EAAAqG,OAAA6D,MAAA3S,MAAA,MAAAwC,OACAiG,KAAAA,EAAAkG,SACAqJ,GAAA5P,KAAAoP,SACA/O,EAAA3I,kBAUAyX,EAAA5R,UAAAqE,SAAA,SAAAvB,GACA5J,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA0K,aAQA6N,EAAA5R,UAAAsE,QAAA,SAAAxB,GACA5J,KAAAA,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA0K,aAQA6N,EAAA5R,UAAAsS,SAAA,SAAAxP,GACAsB,KAAAA,kBAOAwN,EAAA5R,UAAAoE,eAAA,WACAM,KAAAA,gBACAjC,KAAA8P,gBACA9P,KAAA+P,aACA/P,KAAAgQ,cAQAb,EAAA5R,UAAA0E,cAAA,WACAgO,KAAAA,OAAAzP,SACAR,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA2K,aAEAvB,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA2K,cAGA4N,EAAA5R,UAAA,cAAA4R,EAAA5R,UAAA0E,cAMAkN,EAAA5R,UAAAyS,WAAA,WACApD,QAAA5M,KAAAvJ,SAAAqB,cAAA,WACAkI,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA0K,YAEAtB,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA0K,aAGA6N,EAAA5R,UAAA,WAAA4R,EAAA5R,UAAAyS,WAMAb,EAAA5R,UAAAuS,cAAA,WACAG,KAAAA,OAAAC,WACAlQ,KAAAiQ,OAAAC,SAAAC,MACAnQ,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA6Y,YAEAzP,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA6Y,cAIAN,EAAA5R,UAAA,cAAA4R,EAAA5R,UAAAuS,cAMAX,EAAA5R,UAAAwS,WAAA,WACAE,KAAAA,OAAA1F,OAAAvK,KAAAiQ,OAAA1F,MAAAnQ,OAAA,EACA4F,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA4Y,UAEAxP,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA4Y,WAGAL,EAAA5R,UAAA,WAAA4R,EAAA5R,UAAAwS,WAMAZ,EAAA5R,UAAAgD,QAAA,WACA0P,KAAAA,OAAAzP,UAAAA,EACAR,KAAA2B,kBAEAwN,EAAA5R,UAAA,QAAA4R,EAAA5R,UAAAgD,QAMA4O,EAAA5R,UAAAkD,OAAA,WACAwP,KAAAA,OAAAzP,UAAAA,EACAR,KAAA2B,kBAEAwN,EAAA5R,UAAA,OAAA4R,EAAA5R,UAAAkD,OAOA0O,EAAA5R,UAAAsN,OAAA,SAAAN,GACA0F,KAAAA,OAAA1F,MAAAA,GAAA,GACAvK,KAAA2B,kBAEAwN,EAAA5R,UAAA,OAAA4R,EAAA5R,UAAAsN,OAIAsE,EAAA5R,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,WACAuJ,KAAAoQ,OAAApQ,KAAAvJ,SAAAqB,cAAA,IAAAkI,KAAApJ,YAAA2Y,OACAvP,KAAAiQ,OAAAjQ,KAAAvJ,SAAAqB,cAAA,IAAAkI,KAAApJ,YAAAoK,OACAhB,KAAAiQ,QAAA,CACAA,KAAAA,OAAAnJ,aAAA9G,KAAAE,UAAAoP,sBACAtP,KAAAoP,QAAAiB,SAAArQ,KAAAiQ,OAAAzY,aAAAwI,KAAAE,UAAAoP,oBAAA,IACAgB,MAAAtQ,KAAAoP,WACApP,KAAAoP,QAAApP,KAAAE,UAAAmP,cAGArP,KAAAiQ,OAAAnJ,aAAA,gBACA9G,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA8Y,iBAEA1P,KAAAuQ,0BAAAvQ,KAAA2B,eAAAf,KAAAZ,MACAA,KAAAwO,kBAAAxO,KAAA4B,SAAAhB,KAAAZ,MACAA,KAAAyO,iBAAAzO,KAAA6B,QAAAjB,KAAAZ,MACAA,KAAAwQ,kBAAAxQ,KAAA6P,SAAAjP,KAAAZ,MACAA,KAAAiQ,OAAA3Y,iBAAA,QAAA0I,KAAAuQ,2BACAvQ,KAAAiQ,OAAA3Y,iBAAA,QAAA0I,KAAAwO,mBACAxO,KAAAiQ,OAAA3Y,iBAAA,OAAA0I,KAAAyO,kBACAzO,KAAAiQ,OAAA3Y,iBAAA,QAAA0I,KAAAwQ,mBACAxQ,KAAAoP,UAAApP,KAAAE,UAAAmP,cAGArP,KAAAyQ,oBAAAzQ,KAAA2P,WAAA/O,KAAAZ,MACAA,KAAAiQ,OAAA3Y,iBAAA,UAAA0I,KAAAyQ,sBAEAC,IAAAA,EAAA1Q,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAA6Y,YACA9N,KAAAA,iBACA3B,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA6K,aACAiP,GACA1Q,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA6Y,YAEAzP,KAAAiQ,OAAAnJ,aAAA,eACA9G,KAAAvJ,SAAA+P,QACAxG,KAAAgQ,gBAOAjX,EAAAY,SAAAA,CACAuE,YAAAiR,EACAhS,cAAA,oBACA9B,SAAA,mBACAuB,QAAAA,IC/NA+T,IAAAA,EAAA,SAAAvX,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,gBAAA4V,EAOAA,EAAApT,UAAA2C,UAAAA,GASAyQ,EAAApT,UAAA3G,YAAAA,CACA4B,UAAA,YACAoY,OAAA,sBACAC,KAAA,oBACAC,MAAA,qBACAC,IAAA,oBAQAJ,EAAApT,UAAAyT,kBAAA,SAAA3Q,GACA4Q,IAAAA,EAAA5Q,EAAAqG,OAAAd,wBACAO,EAAA8K,EAAA9K,KAAA8K,EAAA9J,MAAA,EACAnB,EAAAiL,EAAAjL,IAAAiL,EAAA/J,OAAA,EACAgK,EAAAlR,KAAAvJ,SAAA0a,YAAA,GAAA,EACAC,EAAApR,KAAAvJ,SAAAyP,aAAA,GAAA,EACAzP,KAAAA,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAia,OAAA7Q,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAka,QACA3K,EAAA8K,EAAA9J,MAAA,EACAnB,EAAAoL,EAAA,GACApR,KAAAvJ,SAAAqP,MAAAE,IAAA,IACAhG,KAAAvJ,SAAAqP,MAAAsL,UAAA,MAEApR,KAAAvJ,SAAAqP,MAAAE,IAAAA,EAAA,KACAhG,KAAAvJ,SAAAqP,MAAAsL,UAAAA,EAAA,OAGAjL,EAAA+K,EAAA,GACAlR,KAAAvJ,SAAAqP,MAAAK,KAAA,IACAnG,KAAAvJ,SAAAqP,MAAAoL,WAAA,MAEAlR,KAAAvJ,SAAAqP,MAAAK,KAAAA,EAAA,KACAnG,KAAAvJ,SAAAqP,MAAAoL,WAAAA,EAAA,MAGAlR,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAma,KACA/Q,KAAAvJ,SAAAqP,MAAAE,IAAAiL,EAAAjL,IAAAhG,KAAAvJ,SAAAyP,aAAA,GAAA,KACAlG,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAka,OACA9Q,KAAAvJ,SAAAqP,MAAAK,KAAA8K,EAAA9K,KAAA8K,EAAA9J,MAAA,GAAA,KACAnH,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAia,MACA7Q,KAAAvJ,SAAAqP,MAAAK,KAAA8K,EAAA9K,KAAAnG,KAAAvJ,SAAA0a,YAAA,GAAA,KAEAnR,KAAAvJ,SAAAqP,MAAAE,IAAAiL,EAAAjL,IAAAiL,EAAA/J,OAAA,GAAA,KAEAlH,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA4B,YAOAmY,EAAApT,UAAA8T,aAAA,WACA5a,KAAAA,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA4B,YAKAmY,EAAApT,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACAqO,IAAAA,EAAA9E,KAAAvJ,SAAAe,aAAA,QAAAwI,KAAAvJ,SAAAe,aAAA,gBACAsN,IACA9E,KAAAiF,YAAAlO,SAAAiO,eAAAF,IAEA9E,KAAAiF,cAEAjF,KAAAiF,YAAA6B,aAAA,aACA9G,KAAAiF,YAAA9I,aAAA,WAAA,KAEA6D,KAAAsR,uBAAAtR,KAAAgR,kBAAApQ,KAAAZ,MACAA,KAAAuR,gCAAAvR,KAAAqR,aAAAzQ,KAAAZ,MACAA,KAAAiF,YAAA3N,iBAAA,aAAA0I,KAAAsR,wBAAAA,GACAtR,KAAAiF,YAAA3N,iBAAA,WAAA0I,KAAAsR,wBAAAA,GACAtR,KAAAiF,YAAA3N,iBAAA,aAAA0I,KAAAuR,iCAAAA,GACAxW,OAAAzD,iBAAA,SAAA0I,KAAAuR,iCAAAA,GACAxW,OAAAzD,iBAAA,aAAA0I,KAAAuR,oCAMAxY,EAAAY,SAAAA,CACAuE,YAAAyS,EACAxT,cAAA,kBACA9B,SAAA,gBd1GAmW,IAAAA,EAAA,SAAApY,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,eAAAyW,EAOAA,EAAAjU,UAAA2C,UAAAA,CACAuR,UAAA,sBACAC,kBAAA,IACAC,eAAA,IACAC,UAAA,WACAC,aAAA,eACAC,cAAA,iBAQAN,EAAAjU,UAAA+F,UAAAA,CACAC,MAAA,GACAC,OAAA,GACAC,MAAA,IAQA+N,EAAAjU,UAAAwU,MAAAA,CACAC,SAAA,EACAC,OAAA,EACAC,UAAA,EACAC,OAAA,GAUAX,EAAAjU,UAAA3G,YAAAA,CACAgN,UAAA,wBACAwO,OAAA,qBACAC,OAAA,qBACAC,QAAA,sBACAC,WAAA,4BACAC,KAAA,iBACA9Z,iBAAA,uBACAC,iBAAA,mCACAC,OAAA,aACAwI,qBAAA,sCACAqR,cAAA,6BACAC,iBAAA,gCACAC,cAAA,6BACAC,aAAA,2BACAC,WAAA,yBACAC,QAAA,sBACAC,cAAA,gCACAC,IAAA,kBACAC,eAAA,6BACAC,oBAAA,kCACAC,qBAAA,mCACAta,kBAAA,gCACAua,MAAA,wBACAC,WAAA,aACAC,SAAA,WACAC,qBAAA,uBACAC,eAAA,oBACAC,WAAA,aACAC,gBAAA,kBACAC,eAAA,aACAnb,UAAA,YACAiJ,YAAA,cACAwC,aAAA,eACA2P,gBAAA,gCACAC,gBAAA,iCAOArC,EAAAjU,UAAAuW,sBAAA,WACA,IAAA9T,KAAA+T,QAAArd,UAAAC,SAAAqJ,KAAApJ,YAAAqN,cAAA,CAGA+P,IAAAA,GAAAhU,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAA8c,kBAAA1T,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAgc,cACAra,KAAAA,SAAA0b,UAAA,IAAAjU,KAAA+T,QAAArd,UAAAC,SAAAqJ,KAAApJ,YAAA6c,aACAzT,KAAA+T,QAAArd,UAAAO,IAAA+I,KAAApJ,YAAA4c,gBACAxT,KAAA+T,QAAArd,UAAAO,IAAA+I,KAAApJ,YAAA6c,YACAO,GACAhU,KAAA+T,QAAArd,UAAAO,IAAA+I,KAAApJ,YAAAqN,eAEAjE,KAAAzH,SAAA0b,WAAA,GAAAjU,KAAA+T,QAAArd,UAAAC,SAAAqJ,KAAApJ,YAAA6c,cACAzT,KAAA+T,QAAArd,UAAAoL,OAAA9B,KAAApJ,YAAA4c,gBACAxT,KAAA+T,QAAArd,UAAAoL,OAAA9B,KAAApJ,YAAA6c,YACAO,GACAhU,KAAA+T,QAAArd,UAAAO,IAAA+I,KAAApJ,YAAAqN,iBAUAuN,EAAAjU,UAAA2W,sBAAA,SAAAxO,GAEAA,EAAAa,UAAAvG,KAAAsD,UAAAE,QAAAxD,KAAAmU,QAAAzd,UAAAC,SAAAqJ,KAAApJ,YAAA+c,iBACA3T,KAAAoU,gBAQA5C,EAAAjU,UAAA8W,mBAAA,WACAC,KAAAA,sBAAAC,QACAvU,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA8c,kBAEA1T,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA8c,iBAEA1T,KAAAmU,UACAnU,KAAAmU,QAAAzd,UAAAoL,OAAA9B,KAAApJ,YAAA+c,gBACA3T,KAAAwU,YAAA9d,UAAAoL,OAAA9B,KAAApJ,YAAA+c,mBAUAnC,EAAAjU,UAAAkX,qBAAA,SAAA/O,GACAA,GAAAA,GAAA,YAAAA,EAAAgP,KAAA,CACAhP,GAAAA,EAAAa,UAAAvG,KAAAsD,UAAAG,OAAAiC,EAAAa,UAAAvG,KAAAsD,UAAAC,MAKA,OAHAmC,EAAAhO,iBAMA0c,KAAAA,gBAOA5C,EAAAjU,UAAAoX,4BAAA,WACAZ,KAAAA,QAAArd,UAAAoL,OAAA9B,KAAApJ,YAAAqN,eAOAuN,EAAAjU,UAAAqX,oBAAA,WACAb,KAAAA,QAAArd,UAAAC,SAAAqJ,KAAApJ,YAAA6c,cACAzT,KAAA+T,QAAArd,UAAAoL,OAAA9B,KAAApJ,YAAA6c,YACAzT,KAAA+T,QAAArd,UAAAO,IAAA+I,KAAApJ,YAAAqN,gBAQAuN,EAAAjU,UAAAxF,eAAA,SAAA8c,GACA,IAAA,IAAA3F,EAAA,EAAAA,EAAA2F,EAAAza,OAAA8U,IACA2F,EAAA3F,GAAAxY,UAAAoL,OAAA9B,KAAApJ,YAAA4B,YAQAgZ,EAAAjU,UAAAvF,iBAAA,SAAAI,GACA,IAAA,IAAAqE,EAAA,EAAAA,EAAArE,EAAAgC,OAAAqC,IACArE,EAAAqE,GAAA/F,UAAAoL,OAAA9B,KAAApJ,YAAA4B,YAQAgZ,EAAAjU,UAAA6W,aAAA,WACAU,IAAAA,EAAA9U,KAAAvJ,SAAAqB,cAAA,IAAAkI,KAAApJ,YAAA2b,YACA4B,KAAAA,QAAAzd,UAAA4P,OAAAtG,KAAApJ,YAAA+c,gBACA3T,KAAAwU,YAAA9d,UAAA4P,OAAAtG,KAAApJ,YAAA+c,gBAEA3T,KAAAmU,QAAAzd,UAAAC,SAAAqJ,KAAApJ,YAAA+c,iBACA3T,KAAAmU,QAAAhY,aAAA,cAAA,SACA2Y,EAAA3Y,aAAA,gBAAA,UAEA6D,KAAAmU,QAAAhY,aAAA,cAAA,QACA2Y,EAAA3Y,aAAA,gBAAA,WAGAqV,EAAAjU,UAAA,aAAAiU,EAAAjU,UAAA6W,aAIA5C,EAAAjU,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACA8N,IAAAA,EAAAxN,SAAAC,cAAA,OACAuN,EAAA7N,UAAAO,IAAA+I,KAAApJ,YAAAgN,WACAmR,IAAAA,EAAA/U,KAAAvJ,SAAAqB,cAAA,UACArB,KAAAA,SAAA+N,cAAAC,aAAAF,EAAAvE,KAAAvJ,UACAuJ,KAAAvJ,SAAA+N,cAAAE,YAAA1E,KAAAvJ,UACA8N,EAAAlN,YAAA2I,KAAAvJ,UACAse,GACAA,EAAAvO,QAIA,IAAA,IAFAwO,EAAAhV,KAAAvJ,SAAAwe,WACAC,EAAAF,EAAA5a,OACA+a,EAAA,EAAAA,EAAAD,EAAAC,IAAA,CACAC,IAAAA,EAAAJ,EAAAG,GACAC,EAAA1e,WAAA0e,EAAA1e,UAAAC,SAAAqJ,KAAApJ,YAAAwb,UACApS,KAAA+T,QAAAqB,GAEAA,EAAA1e,WAAA0e,EAAA1e,UAAAC,SAAAqJ,KAAApJ,YAAAyb,UACArS,KAAAmU,QAAAiB,GAEAA,EAAA1e,WAAA0e,EAAA1e,UAAAC,SAAAqJ,KAAApJ,YAAA0b,WACAtS,KAAAzH,SAAA6c,GAGAra,OAAAzD,iBAAA,WAAA,SAAAC,GACAA,EAAA8d,YAGArV,KAAAvJ,SAAAqP,MAAAwP,UAAA,SACAnW,sBAAA,WACA1I,KAAAA,SAAAqP,MAAAwP,UAAA,IACA1U,KAAAZ,SAEAY,KAAAZ,OAAAA,GACAA,KAAA+T,UACA/T,KAAAvH,QAAAuH,KAAA+T,QAAAjc,cAAA,IAAAkI,KAAApJ,YAAAkc,UAEAyC,IAAAA,EAAAvV,KAAA+R,MAAAC,SACAhS,GAAAA,KAAA+T,UACA/T,KAAA+T,QAAArd,UAAAC,SAAAqJ,KAAApJ,YAAA6b,eACA8C,EAAAvV,KAAA+R,MAAAE,OACAjS,KAAA+T,QAAArd,UAAAC,SAAAqJ,KAAApJ,YAAA8b,mBACA6C,EAAAvV,KAAA+R,MAAAG,UACAlS,KAAA+T,QAAAzc,iBAAA,gBAAA0I,KAAA2U,4BAAA/T,KAAAZ,OACAA,KAAA+T,QAAAzc,iBAAA,QAAA0I,KAAA4U,oBAAAhU,KAAAZ,QACAA,KAAA+T,QAAArd,UAAAC,SAAAqJ,KAAApJ,YAAA+b,iBACA4C,EAAAvV,KAAA+R,MAAAI,OACA5N,EAAA7N,UAAAO,IAAA+I,KAAApJ,YAAA2c,uBAEAgC,IAAAvV,KAAA+R,MAAAC,UACAhS,KAAA+T,QAAArd,UAAAO,IAAA+I,KAAApJ,YAAA4c,gBACAxT,KAAAvH,SACAuH,KAAAvH,QAAA/B,UAAAO,IAAA+I,KAAApJ,YAAA4c,iBAEA+B,IAAAvV,KAAA+R,MAAAE,QAAAsD,IAAAvV,KAAA+R,MAAAI,QACAnS,KAAA+T,QAAArd,UAAAoL,OAAA9B,KAAApJ,YAAA4c,gBACAxT,KAAAvH,SACAuH,KAAAvH,QAAA/B,UAAAoL,OAAA9B,KAAApJ,YAAA4c,iBAEA+B,IAAAvV,KAAA+R,MAAAG,YAIAlS,KAAAzH,SAAAjB,iBAAA,SAAA0I,KAAA8T,sBAAAlT,KAAAZ,OACAA,KAAA8T,0BAIA9T,KAAAmU,QAAA,CACAW,IAAAA,EAAA9U,KAAAvJ,SAAAqB,cAAA,IAAAkI,KAAApJ,YAAA2b,YACA,IAAAuC,EAAA,EACAA,EAAA/d,SAAAC,cAAA,QACAmF,aAAA,gBAAA,SACA2Y,EAAA3Y,aAAA,OAAA,UACA2Y,EAAA3Y,aAAA,WAAA,KACA2Y,EAAApe,UAAAO,IAAA+I,KAAApJ,YAAA2b,YACAiD,IAAAA,EAAAze,SAAAC,cAAA,KACAwe,EAAA9e,UAAAO,IAAA+I,KAAApJ,YAAA4b,MACAgD,EAAAC,UAAAzV,KAAAE,UAAA0R,UACAkD,EAAAzd,YAAAme,GAEArB,KAAAA,QAAAzd,UAAAC,SAAAqJ,KAAApJ,YAAAgd,iBAEAkB,EAAApe,UAAAO,IAAA+I,KAAApJ,YAAAgd,iBACA5T,KAAAmU,QAAAzd,UAAAC,SAAAqJ,KAAApJ,YAAAid,kBAEAiB,EAAApe,UAAAO,IAAA+I,KAAApJ,YAAAid,iBAEAiB,EAAAxd,iBAAA,QAAA0I,KAAAyU,qBAAA7T,KAAAZ,OACA8U,EAAAxd,iBAAA,UAAA0I,KAAAyU,qBAAA7T,KAAAZ,OAIAA,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAAyc,YAGArT,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAgc,cACA5S,KAAA+T,QAAAtP,aAAAqQ,EAAA9U,KAAA+T,QAAA2B,YAEA1V,KAAAvJ,SAAAgO,aAAAqQ,EAAA9U,KAAAzH,UAEAod,IAAAA,EAAA5e,SAAAC,cAAA,OACA2e,EAAAjf,UAAAO,IAAA+I,KAAApJ,YAAAic,YACA7S,KAAAvJ,SAAAY,YAAAse,GACAA,EAAAre,iBAAA,QAAA0I,KAAAyU,qBAAA7T,KAAAZ,OACAA,KAAAwU,YAAAmB,EACA3V,KAAAmU,QAAA7c,iBAAA,UAAA0I,KAAAkU,sBAAAtT,KAAAZ,OACAA,KAAAmU,QAAAhY,aAAA,cAAA,QAIA6D,GAAAA,KAAAsU,sBAAAvZ,OAAA6a,WAAA5V,KAAAE,UAAAuR,WACAzR,KAAAsU,sBAAAuB,YAAA7V,KAAAqU,mBAAAzT,KAAAZ,OACAA,KAAAqU,qBAEArU,KAAA+T,SAAA/T,KAAAvH,QAAA,CACAhC,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA0c,UACAwC,IAAAA,EAAA/e,SAAAC,cAAA,OACA8e,EAAApf,UAAAO,IAAA+I,KAAApJ,YAAAmc,eACA/S,KAAA+T,QAAAtP,aAAAqR,EAAA9V,KAAAvH,SACAuH,KAAA+T,QAAArP,YAAA1E,KAAAvH,SACAsd,IAAAA,EAAAhf,SAAAC,cAAA,OACA+e,EAAArf,UAAAO,IAAA+I,KAAApJ,YAAAqc,gBACA8C,EAAArf,UAAAO,IAAA+I,KAAApJ,YAAAsc,qBACA8C,IAAAA,EAAAjf,SAAAC,cAAA,KACAgf,EAAAtf,UAAAO,IAAA+I,KAAApJ,YAAA4b,MACAwD,EAAA3J,YAAArM,KAAAE,UAAA2R,aACAkE,EAAA1e,YAAA2e,GACAD,EAAAze,iBAAA,QAAA,WACAmB,KAAAA,QAAAwd,YAAAjW,KAAAE,UAAAwR,mBACA9Q,KAAAZ,OACAkW,IAAAA,EAAAnf,SAAAC,cAAA,OACAkf,EAAAxf,UAAAO,IAAA+I,KAAApJ,YAAAqc,gBACAiD,EAAAxf,UAAAO,IAAA+I,KAAApJ,YAAAuc,sBACAgD,IAAAA,EAAApf,SAAAC,cAAA,KACAmf,EAAAzf,UAAAO,IAAA+I,KAAApJ,YAAA4b,MACA2D,EAAA9J,YAAArM,KAAAE,UAAA4R,cACAoE,EAAA7e,YAAA8e,GACAD,EAAA5e,iBAAA,QAAA,WACAmB,KAAAA,QAAAwd,YAAAjW,KAAAE,UAAAwR,mBACA9Q,KAAAZ,OACA8V,EAAAze,YAAA0e,GACAD,EAAAze,YAAA2I,KAAAvH,SACAqd,EAAAze,YAAA6e,GAGAE,IAAAA,EAAA,WACA3d,KAAAA,QAAAwd,WAAA,EACAF,EAAArf,UAAAO,IAAA+I,KAAApJ,YAAA4B,WAEAud,EAAArf,UAAAoL,OAAA9B,KAAApJ,YAAA4B,WAEAwH,KAAAvH,QAAAwd,WAAAjW,KAAAvH,QAAA4d,YAAArW,KAAAvH,QAAA0Y,YACA+E,EAAAxf,UAAAO,IAAA+I,KAAApJ,YAAA4B,WAEA0d,EAAAxf,UAAAoL,OAAA9B,KAAApJ,YAAA4B,YAEAoI,KAAAZ,MACAvH,KAAAA,QAAAnB,iBAAA,SAAA8e,GACAA,IAEAE,IAAAA,EAAA,WAEAC,KAAAA,kBACAzW,aAAAE,KAAAuW,kBAEAvW,KAAAuW,iBAAA1W,WAAA,WACAuW,IACApW,KAAAuW,iBAAA,MACA3V,KAAAZ,MAAAA,KAAAE,UAAAyR,iBACA/Q,KAAAZ,MACAjF,OAAAzD,iBAAA,SAAAgf,GACAtW,KAAAvH,QAAA/B,UAAAC,SAAAqJ,KAAApJ,YAAA8B,mBACAsH,KAAAvH,QAAA/B,UAAAO,IAAA+I,KAAApJ,YAAAwK,sBAMA,IAAA,IAHAjJ,EAAA6H,KAAAvH,QAAA8C,iBAAA,IAAAyE,KAAApJ,YAAAoc,KACA5a,EAAA4H,KAAAzH,SAAAgD,iBAAA,IAAAyE,KAAApJ,YAAAwc,OAEAlZ,EAAA,EAAAA,EAAA/B,EAAAiC,OAAAF,IACA,IAAAhC,EAAAC,EAAA+B,GAAA/B,EAAAC,EAAA4H,MAGAvJ,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA6K,eA2CA1G,OAAA,kBAAA7C,EAGAa,EAAAY,SAAAA,CACAuE,YAAAsT,EACArU,cAAA,iBACA9B,SAAA,kBercAmb,IAAAA,EAAA,SAAApd,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,kBAAAyb,EAOAA,EAAAjZ,UAAA2C,UAAAA,GASAsW,EAAAjZ,UAAA3G,YAAAA,CACA6f,WAAA,iBACAC,WAAA,6BACAC,eAAA,yBACAC,YAAA,cACAnV,YAAA,eAWA+U,EAAAjZ,UAAAsZ,WAAA,SAAAC,EAAAC,EAAAC,GACAD,OAAAA,EACA,WACAD,EAAA1U,QACA2U,EAAArgB,UAAAO,IAAA+I,KAAApJ,YAAAggB,aAEAG,EAAArgB,UAAAoL,OAAA9B,KAAApJ,YAAAggB,cAEAhW,KAAAZ,MAEAgX,EACA,WACA9c,IAAAA,EAEA4c,GAAAA,EAAA1U,QACA,IAAAlI,EAAA,EAAAA,EAAA8c,EAAA5c,OAAAF,IACA8c,EAAA9c,GAAApC,cAAA,MAAAA,cAAA,iBACA,iBAAAuK,QACA2U,EAAA9c,GAAAxD,UAAAO,IAAA+I,KAAApJ,YAAAggB,kBAGA,IAAA1c,EAAA,EAAAA,EAAA8c,EAAA5c,OAAAF,IACA8c,EAAA9c,GAAApC,cAAA,MAAAA,cAAA,iBACA,iBAAAwK,UACA0U,EAAA9c,GAAAxD,UAAAoL,OAAA9B,KAAApJ,YAAAggB,cAGAhW,KAAAZ,WAjBA,GA4BAwW,EAAAjZ,UAAA0Z,gBAAA,SAAAF,EAAAC,GACAE,IAAAA,EAAAngB,SAAAC,cAAA,SACAmgB,EAAAA,CACA,eACA,kBACA,uBACAnX,KAAApJ,YAAA+f,gBAEAO,EAAA7c,UAAA8c,EAAA/a,KAAA,KACA0a,IAAAA,EAAA/f,SAAAC,cAAA,SACA8f,OAAAA,EAAApC,KAAA,WACAoC,EAAApgB,UAAAO,IAAA,uBACA8f,GACAD,EAAA1U,QAAA2U,EAAArgB,UAAAC,SAAAqJ,KAAApJ,YAAAggB,aACAE,EAAAxf,iBAAA,SAAA0I,KAAA6W,WAAAC,EAAAC,KACAC,GACAF,EAAAxf,iBAAA,SAAA0I,KAAA6W,WAAAC,EAAA,KAAAE,IAEAE,EAAA7f,YAAAyf,GACA/d,EAAAI,eAAA+d,EAAA,oBACAA,GAKAV,EAAAjZ,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACA2gB,IAAAA,EAAApX,KAAAvJ,SAAAqB,cAAA,MACAuf,EAAAha,MAAAE,UAAAC,MAAAC,KAAAuC,KAAAvJ,SAAA8E,iBAAA,aACA+b,EAAAja,MAAAE,UAAAC,MAAAC,KAAAuC,KAAAvJ,SAAA8E,iBAAA,aACAgc,EAAAF,EAAAG,OAAAF,GACAtX,GAAAA,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAA8f,YAAA,CACAe,IAAAA,EAAA1gB,SAAAC,cAAA,MACA0gB,EAAA1X,KAAAiX,gBAAA,KAAAM,GACAE,EAAApgB,YAAAqgB,GACAN,EAAA5S,cAAAC,aAAAgT,EAAAL,GACA,IAAA,IAAAld,EAAA,EAAAA,EAAAqd,EAAAnd,OAAAF,IAAA,CACAyd,IAAAA,EAAAJ,EAAArd,GAAApC,cAAA,MACA6f,GAAAA,EAAA,CACAC,IAAAA,EAAA7gB,SAAAC,cAAA,MACA,GAAA,UAAAugB,EAAArd,GAAAwN,WAAAmQ,SAAAC,cAAA,CACAC,IAAAA,EAAA/X,KAAAiX,gBAAAM,EAAArd,IACA0d,EAAAvgB,YAAA0gB,GAEAR,EAAArd,GAAAuK,aAAAmT,EAAAD,IAGAlhB,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA6K,gBAMA1I,EAAAY,SAAAA,CACAuE,YAAAsY,EACArZ,cAAA,oBACA9B,SAAA,sBjBnIA2c,IAAAA,EAAA,SAAA5e,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,eAAAid,EAOAA,EAAAza,UAAA2C,UAAAA,CACA+X,cAAA,wBACAC,aAAA,MACAC,gBAAA,MACAC,cAAA,IACAC,YAAA,IAUAL,EAAAza,UAAA3G,YAAAA,CACAyK,cAAA,qBACAiX,4BAAA,sCACA1f,OAAA,aACAqL,aAAA,eACAD,WAAA,cAQAgU,EAAAza,UAAAgb,aAAA,SAAAlY,GACA,IAAAL,KAAAU,eAAAoF,MAAAqB,QAAAnH,KAAAU,eAAAoF,MAAAoB,OAAA,CACAvB,IAAAA,EAAA3F,KAAAvJ,SAAAmP,wBACA4S,KAAAA,YAAA7S,EAAAuB,OACAlH,KAAAyY,WAAA9S,EAAAwB,MACAnH,KAAA0Y,YAAA,EAAA/Y,KAAAgZ,KAAAhT,EAAAwB,MAAAxB,EAAAwB,MAAAxB,EAAAuB,OAAAvB,EAAAuB,QAAA,EACAlH,KAAAU,eAAAoF,MAAAqB,MAAAnH,KAAA0Y,YAAA,KACA1Y,KAAAU,eAAAoF,MAAAoB,OAAAlH,KAAA0Y,YAAA,KAEA1Y,GAAAA,KAAAU,eAAAhK,UAAAO,IAAA+I,KAAApJ,YAAAoN,YACA,cAAA3D,EAAAqU,MAAA1U,KAAA4Y,mBACA5Y,KAAA4Y,oBAAAA,MACA,CAKAC,GAJAxY,eAAAA,EAAAqU,OACA1U,KAAA4Y,oBAAAA,GAEA5Y,KAAA8Y,gBACA,EACA,OAEAC,KAAAA,cAAA,GAEAC,IAAAA,EACA3O,EAFA4O,EAAA5Y,EAAA6Y,cAAAtT,wBAIA,GAAA,IAAAvF,EAAA8J,SAAA,IAAA9J,EAAA+J,QACA4O,EAAArZ,KAAAwZ,MAAAF,EAAA9R,MAAA,GACAkD,EAAA1K,KAAAwZ,MAAAF,EAAA/R,OAAA,OACA,CACAiD,IAAAA,OAAAyB,IAAAvL,EAAA8J,QAAA9J,EAAA8J,QAAA9J,EAAA+Y,QAAA,GAAAjP,QACAC,OAAAwB,IAAAvL,EAAA+J,QAAA/J,EAAA+J,QAAA/J,EAAA+Y,QAAA,GAAAhP,QACA4O,EAAArZ,KAAAwZ,MAAAhP,EAAA8O,EAAA9S,MACAkE,EAAA1K,KAAAwZ,MAAA/O,EAAA6O,EAAAjT,KAEAqT,KAAAA,YAAAL,EAAA3O,GACArK,KAAAsZ,iBAAAA,GACAve,OAAAoE,sBAAAa,KAAAuZ,iBAAA3Y,KAAAZ,SASAgY,EAAAza,UAAAic,WAAA,SAAAnZ,GAEAA,GAAA,IAAAA,EAAAoZ,QAIA1e,OAAA8E,WAAA,WACAa,KAAAA,eAAAhK,UAAAoL,OAAA9B,KAAApJ,YAAAoN,aACApD,KAAAZ,MAAA,IAMAgY,EAAAza,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACAijB,IAAAA,EAAA1Z,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAyK,eACA5K,KAAAA,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAA0hB,+BACAtY,KAAAU,eAAAV,KAAAvJ,SAAAqB,cAAA,IAAAkI,KAAApJ,YAAAgC,QACAoH,KAAA2Z,YAAA,EACA3Z,KAAA0Y,YAAA,EACA1Y,KAAA4Z,GAAA,EACA5Z,KAAA6Z,GAAA,EAIA7Z,KAAA4Y,oBAAAA,EACA5Y,KAAA8Z,iBAAA9Z,KAAAuY,aAAA3X,KAAAZ,MACAA,KAAAvJ,SAAAa,iBAAA,YAAA0I,KAAA8Z,kBACA9Z,KAAAvJ,SAAAa,iBAAA,aAAA0I,KAAA8Z,kBACA9Z,KAAA+Z,eAAA/Z,KAAAwZ,WAAA5Y,KAAAZ,MACAA,KAAAvJ,SAAAa,iBAAA,UAAA0I,KAAA+Z,gBACA/Z,KAAAvJ,SAAAa,iBAAA,aAAA0I,KAAA+Z,gBACA/Z,KAAAvJ,SAAAa,iBAAA,WAAA0I,KAAA+Z,gBACA/Z,KAAAvJ,SAAAa,iBAAA,OAAA0I,KAAA+Z,gBAKA/Z,KAAA8Y,cAAA,WACA9Y,OAAAA,KAAA2Z,aAMA3Z,KAAA+Y,cAAA,SAAAiB,GACAL,KAAAA,YAAAK,GAMAha,KAAAia,iBAAA,WACAja,OAAAA,KAAAU,gBAOAV,KAAAqZ,YAAA,SAAAa,EAAAC,GACAP,KAAAA,GAAAM,EACAla,KAAA6Z,GAAAM,GAMAna,KAAAsZ,gBAAA,SAAAvL,GACA,GAAA,OAAA/N,KAAAU,eAAA,CACA0Z,IAAAA,EACAC,EAEAC,EAAA,aAAAta,KAAA4Z,GAAA,OAAA5Z,KAAA6Z,GAAA,MACA9L,GACAsM,EAAAra,KAAAE,UAAA+X,cACAjY,KAAAE,UAAAgY,eAEAmC,EAAAra,KAAAE,UAAAmY,YACArY,KAAA0Y,YAAA,KACAgB,IACAY,EAAA,aAAAta,KAAAyY,WAAA,EAAA,OAAAzY,KAAAwY,YAAA,EAAA,QAGA4B,EAAA,yBAAAE,EAAAD,EACAra,KAAAU,eAAAoF,MAAAyU,gBAAAH,EACApa,KAAAU,eAAAoF,MAAA0U,YAAAJ,EACApa,KAAAU,eAAAoF,MAAA2U,UAAAL,EACArM,EACA/N,KAAAU,eAAAhK,UAAAoL,OAAA9B,KAAApJ,YAAAqN,cAEAjE,KAAAU,eAAAhK,UAAAO,IAAA+I,KAAApJ,YAAAqN,gBAOAjE,KAAAuZ,iBAAA,WACAI,KAAAA,eAAA,EACA5e,OAAAoE,sBAAAa,KAAAuZ,iBAAA3Y,KAAAZ,OAEAA,KAAAsZ,iBAAAA,OAQAvgB,EAAAY,SAAAA,CACAuE,YAAA8Z,EACA7a,cAAA,iBACA9B,SAAA,uBACAuB,QAAAA,IAAA;;;AkB/NA,IAAA,EAAA,OAAA,QAAA,oBAAA,QAAA,OAAA,MAAA,KACA,OAAA,oBAAA,MAAA,KAAA,MAAA,KAAA,KAEA,SAAA,cAAA,GACA,iBAAA,MAAA,IAAA;;ACLA,IAAA,EAAA,GAAA,eACA,OAAA,QAAA,SAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA;;ACFA,OAAA,QAAA,SAAA,GACA,IACA,QAAA,IACA,MAAA,GACA,OAAA;;ACHA,OAAA,SAAA,QAAA,WAAA,CAAA,WACA,OAAA,GAAA,OAAA,eAAA,GAAA,IAAA,CAAA,IAAA,WAAA,OAAA,KAAA;;ACFA,IAAA,EAAA,OAAA,QAAA,CAAA,QAAA,SACA,iBAAA,MAAA,IAAA;;ACDA,OAAA,QAAA,SAAA,GACA,MAAA,iBAAA,EAAA,OAAA,EAAA,mBAAA;;ACDA,IAAA,EAAA,QAAA,gBACA,OAAA,QAAA,SAAA,GACA,IAAA,EAAA,GAAA,MAAA,UAAA,EAAA,sBACA,OAAA;;ACHA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,aAAA,SAEA,EAAA,EAAA,IAAA,EAAA,EAAA,eACA,OAAA,QAAA,SAAA,GACA,OAAA,EAAA,EAAA,cAAA,GAAA;;ACLA,OAAA,SAAA,QAAA,oBAAA,QAAA,WAAA,CAAA,WACA,OAAA,GAAA,OAAA,eAAA,QAAA,gBAAA,CAAA,OAAA,IAAA,CAAA,IAAA,WAAA,OAAA,KAAA;;ACAA,IAAA,EAAA,QAAA,gBAGA,OAAA,QAAA,SAAA,EAAA,GACA,IAAA,EAAA,GAAA,OAAA,EACA,IAAA,EAAA,EACA,GAAA,GAAA,mBAAA,EAAA,EAAA,YAAA,EAAA,EAAA,EAAA,KAAA,IAAA,OAAA,EACA,GAAA,mBAAA,EAAA,EAAA,WAAA,EAAA,EAAA,EAAA,KAAA,IAAA,OAAA,EACA,IAAA,GAAA,mBAAA,EAAA,EAAA,YAAA,EAAA,EAAA,EAAA,KAAA,IAAA,OAAA,EACA,MAAA,UAAA;;ACVA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,qBACA,EAAA,QAAA,mBACA,EAAA,OAAA,eAEA,QAAA,EAAA,QAAA,kBAAA,OAAA,eAAA,SAAA,EAAA,EAAA,GAIA,GAHA,EAAA,GACA,EAAA,EAAA,GAAA,GACA,EAAA,GACA,EAAA,IACA,OAAA,EAAA,EAAA,EAAA,GACA,MAAA,IACA,GAAA,QAAA,GAAA,QAAA,EAAA,MAAA,UAAA,4BAEA,MADA,UAAA,IAAA,EAAA,GAAA,EAAA,OACA;;ACdA,OAAA,QAAA,SAAA,EAAA,GACA,MAAA,CACA,aAAA,EAAA,GACA,eAAA,EAAA,GACA,WAAA,EAAA,GACA,MAAA;;ACLA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,oBACA,OAAA,QAAA,QAAA,kBAAA,SAAA,EAAA,EAAA,GACA,OAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,KACA,SAAA,EAAA,EAAA,GAEA,OADA,EAAA,GAAA,EACA;;ACNA,IAAA,EAAA,EACA,EAAA,KAAA,SACA,OAAA,QAAA,SAAA,GACA,MAAA,UAAA,YAAA,IAAA,EAAA,GAAA,EAAA,QAAA,EAAA,GAAA,SAAA;;ACHA,OAAA,SAAA;;;ACAA,IAAA,EAAA,QAAA,WACA,EAAA,QAAA,aACA,EAAA,qBACA,EAAA,EAAA,KAAA,EAAA,GAAA,KAEA,OAAA,QAAA,SAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,QAAA,IAAA,EAAA,EAAA,MACA,WAAA,IAAA,KAAA,CACA,QAAA,EAAA,QACA,KAAA,QAAA,cAAA,OAAA,SACA,UAAA;;ACVA,OAAA,QAAA,QAAA,YAAA,CAAA,4BAAA,SAAA;;;ACAA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,WACA,EAAA,QAAA,UACA,EAAA,QAAA,SAAA,CAAA,OACA,EAAA,QAAA,yBACA,EAAA,WACA,GAAA,GAAA,GAAA,MAAA,GAEA,QAAA,WAAA,cAAA,SAAA,GACA,OAAA,EAAA,KAAA,KAGA,OAAA,QAAA,SAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,mBAAA,EACA,IAAA,EAAA,EAAA,SAAA,EAAA,EAAA,OAAA,IACA,EAAA,KAAA,IACA,IAAA,EAAA,EAAA,IAAA,EAAA,EAAA,EAAA,EAAA,GAAA,GAAA,EAAA,GAAA,EAAA,KAAA,OAAA,MACA,IAAA,EACA,EAAA,GAAA,EACA,EAGA,EAAA,GACA,EAAA,GAAA,EAEA,EAAA,EAAA,EAAA,WALA,EAAA,GACA,EAAA,EAAA,EAAA,OAOA,SAAA,UAAA,EAAA,WACA,MAAA,mBAAA,MAAA,KAAA,IAAA,EAAA,KAAA;;AC7BA,OAAA,QAAA,SAAA,GACA,GAAA,mBAAA,EAAA,MAAA,UAAA,EAAA,uBACA,OAAA;;ACDA,IAAA,EAAA,QAAA,iBACA,OAAA,QAAA,SAAA,EAAA,EAAA,GAEA,GADA,EAAA,QACA,IAAA,EAAA,OAAA,EACA,OAAA,GACA,KAAA,EAAA,OAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,IAEA,KAAA,EAAA,OAAA,SAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,EAAA,IAEA,KAAA,EAAA,OAAA,SAAA,EAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,EAAA,EAAA,IAGA,OAAA,WACA,OAAA,EAAA,MAAA,EAAA;;;ACjBA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,WACA,EAAA,QAAA,WACA,EAAA,QAAA,eACA,EAAA,QAAA,UACA,EAAA,YAEA,EAAA,SAAA,EAAA,EAAA,GACA,IAQA,EAAA,EAAA,EAAA,EARA,EAAA,EAAA,EAAA,EACA,EAAA,EAAA,EAAA,EACA,EAAA,EAAA,EAAA,EACA,EAAA,EAAA,EAAA,EACA,EAAA,EAAA,EAAA,EACA,EAAA,EAAA,EAAA,EAAA,EAAA,KAAA,EAAA,GAAA,KAAA,EAAA,IAAA,IAAA,GACA,EAAA,EAAA,EAAA,EAAA,KAAA,EAAA,GAAA,IACA,EAAA,EAAA,KAAA,EAAA,GAAA,IAGA,IAAA,KADA,IAAA,EAAA,GACA,EAIA,IAFA,GAAA,GAAA,QAAA,IAAA,EAAA,IAEA,EAAA,GAAA,GAEA,EAAA,GAAA,EAAA,EAAA,EAAA,GAAA,GAAA,mBAAA,EAAA,EAAA,SAAA,KAAA,GAAA,EAEA,GAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAEA,EAAA,IAAA,GAAA,EAAA,EAAA,EAAA,GACA,GAAA,EAAA,IAAA,IAAA,EAAA,GAAA,IAGA,EAAA,KAAA,EAEA,EAAA,EAAA,EACA,EAAA,EAAA,EACA,EAAA,EAAA,EACA,EAAA,EAAA,EACA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,EAAA,IACA,OAAA,QAAA;;AC1CA,IAAA,EAAA,QAAA,SAAA,CAAA,QACA,EAAA,QAAA,gBACA,EAAA,QAAA,UACA,EAAA,QAAA,gBAAA,EACA,EAAA,EACA,EAAA,OAAA,cAAA,WACA,OAAA,GAEA,GAAA,QAAA,WAAA,CAAA,WACA,OAAA,EAAA,OAAA,kBAAA,OAEA,EAAA,SAAA,GACA,EAAA,EAAA,EAAA,CAAA,MAAA,CACA,EAAA,OAAA,EACA,EAAA,OAGA,EAAA,SAAA,EAAA,GAEA,IAAA,EAAA,GAAA,MAAA,iBAAA,EAAA,GAAA,iBAAA,EAAA,IAAA,KAAA,EACA,IAAA,EAAA,EAAA,GAAA,CAEA,IAAA,EAAA,GAAA,MAAA,IAEA,IAAA,EAAA,MAAA,IAEA,EAAA,GAEA,OAAA,EAAA,GAAA,GAEA,EAAA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,GAAA,CAEA,IAAA,EAAA,GAAA,OAAA,EAEA,IAAA,EAAA,OAAA,EAEA,EAAA,GAEA,OAAA,EAAA,GAAA,GAGA,EAAA,SAAA,GAEA,OADA,GAAA,EAAA,MAAA,EAAA,KAAA,EAAA,EAAA,IAAA,EAAA,GACA,GAEA,EAAA,OAAA,QAAA,CACA,IAAA,EACA,MAAA,EACA,QAAA,EACA,QAAA,EACA,SAAA;;ACnDA,IAAA,EAAA,QAAA,YAAA,CAAA,OACA,EAAA,QAAA,UACA,EAAA,QAAA,aAAA,OACA,EAAA,mBAAA,EAEA,EAAA,OAAA,QAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,GACA,GAAA,EAAA,KAAA,EAAA,EAAA,GAAA,UAAA,KAGA,EAAA,MAAA;;ACVA,IAAA,EAAA,QAAA,gBAAA,EACA,EAAA,QAAA,UACA,EAAA,QAAA,SAAA,CAAA,eAEA,OAAA,QAAA,SAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,EAAA,EAAA,EAAA,UAAA,IAAA,EAAA,EAAA,EAAA,CAAA,cAAA,EAAA,MAAA;;ACLA,QAAA,EAAA,QAAA;;;ACAA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,WACA,EAAA,QAAA,cACA,EAAA,QAAA,cACA,EAAA,QAAA,gBAAA,EACA,OAAA,QAAA,SAAA,GACA,IAAA,EAAA,EAAA,SAAA,EAAA,OAAA,EAAA,GAAA,EAAA,QAAA,IACA,KAAA,EAAA,OAAA,IAAA,KAAA,GAAA,EAAA,EAAA,EAAA,CAAA,MAAA,EAAA,EAAA;;ACPA,IAAA,EAAA,GAAA,SAEA,OAAA,QAAA,SAAA,GACA,OAAA,EAAA,KAAA,GAAA,MAAA,GAAA;;ACFA,IAAA,EAAA,QAAA,UAEA,OAAA,QAAA,OAAA,KAAA,qBAAA,GAAA,OAAA,SAAA,GACA,MAAA,UAAA,EAAA,GAAA,EAAA,MAAA,IAAA,OAAA;;ACHA,OAAA,QAAA,SAAA,GACA,GAAA,MAAA,EAAA,MAAA,UAAA,yBAAA,GACA,OAAA;;ACFA,IAAA,EAAA,QAAA,cACA,EAAA,QAAA,cACA,OAAA,QAAA,SAAA,GACA,OAAA,EAAA,EAAA;;ACHA,IAAA,EAAA,KAAA,KACA,EAAA,KAAA,MACA,OAAA,QAAA,SAAA,GACA,OAAA,MAAA,GAAA,GAAA,GAAA,EAAA,EAAA,EAAA,GAAA;;ACHA,IAAA,EAAA,QAAA,iBACA,EAAA,KAAA,IACA,OAAA,QAAA,SAAA,GACA,OAAA,EAAA,EAAA,EAAA,EAAA,GAAA,kBAAA;;ACJA,IAAA,EAAA,QAAA,iBACA,EAAA,KAAA,IACA,EAAA,KAAA,IACA,OAAA,QAAA,SAAA,EAAA,GAEA,OADA,EAAA,EAAA,IACA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,EAAA;;ACHA,IAAA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,EAAA,QAAA,wBACA,OAAA,QAAA,SAAA,GACA,OAAA,SAAA,EAAA,EAAA,GACA,IAGA,EAHA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,QACA,EAAA,EAAA,EAAA,GAIA,GAAA,GAAA,GAAA,GAAA,KAAA,EAAA,GAGA,IAFA,EAAA,EAAA,OAEA,EAAA,OAAA,OAEA,KAAA,EAAA,EAAA,IAAA,IAAA,GAAA,KAAA,IACA,EAAA,KAAA,EAAA,OAAA,GAAA,GAAA,EACA,OAAA,IAAA;;ACpBA,IAAA,EAAA,QAAA,YAAA,CAAA,QACA,EAAA,QAAA,UACA,OAAA,QAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,GAAA,EAAA;;ACHA,IAAA,EAAA,QAAA,UACA,EAAA,QAAA,iBACA,EAAA,QAAA,oBAAA,EAAA,GACA,EAAA,QAAA,gBAAA,CAAA,YAEA,OAAA,QAAA,SAAA,EAAA,GACA,IAGA,EAHA,EAAA,EAAA,GACA,EAAA,EACA,EAAA,GAEA,IAAA,KAAA,EAAA,GAAA,GAAA,EAAA,EAAA,IAAA,EAAA,KAAA,GAEA,KAAA,EAAA,OAAA,GAAA,EAAA,EAAA,EAAA,EAAA,SACA,EAAA,EAAA,IAAA,EAAA,KAAA,IAEA,OAAA;;ACdA,OAAA,QAAA,gGAEA,MAAA;;ACFA,IAAA,EAAA,QAAA,2BACA,EAAA,QAAA,oBAEA,OAAA,QAAA,OAAA,MAAA,SAAA,GACA,OAAA,EAAA,EAAA;;ACLA,QAAA,EAAA,OAAA;;ACAA,QAAA,EAAA,GAAA;;ACCA,IAAA,EAAA,QAAA,kBACA,EAAA,QAAA,kBACA,EAAA,QAAA,iBACA,OAAA,QAAA,SAAA,GACA,IAAA,EAAA,EAAA,GACA,EAAA,EAAA,EACA,GAAA,EAKA,IAJA,IAGA,EAHA,EAAA,EAAA,GACA,EAAA,EAAA,EACA,EAAA,EAEA,EAAA,OAAA,GAAA,EAAA,KAAA,EAAA,EAAA,EAAA,OAAA,EAAA,KAAA,GACA,OAAA;;ACZA,IAAA,EAAA,QAAA,UACA,OAAA,QAAA,MAAA,SAAA,SAAA,GACA,MAAA,SAAA,EAAA;;ACFA,IAAA,EAAA,QAAA,cACA,OAAA,QAAA,SAAA,GACA,OAAA,OAAA,EAAA;;ACHA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,kBAEA,OAAA,QAAA,QAAA,kBAAA,OAAA,iBAAA,SAAA,EAAA,GACA,EAAA,GAKA,IAJA,IAGA,EAHA,EAAA,EAAA,GACA,EAAA,EAAA,OACA,EAAA,EAEA,EAAA,GAAA,EAAA,EAAA,EAAA,EAAA,EAAA,KAAA,EAAA,IACA,OAAA;;ACXA,IAAA,EAAA,QAAA,aAAA,SACA,OAAA,QAAA,GAAA,EAAA;;ACAA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,QAAA,oBACA,EAAA,QAAA,gBAAA,CAAA,YACA,EAAA,aACA,EAAA,YAGA,EAAA,WAEA,IAIA,EAJA,EAAA,QAAA,gBAAA,CAAA,UACA,EAAA,EAAA,OAcA,IAVA,EAAA,MAAA,QAAA,OACA,QAAA,WAAA,YAAA,GACA,EAAA,IAAA,eAGA,EAAA,EAAA,cAAA,UACA,OACA,EAAA,MAAA,uCACA,EAAA,QACA,EAAA,EAAA,EACA,YAAA,EAAA,GAAA,EAAA,IACA,OAAA,KAGA,OAAA,QAAA,OAAA,QAAA,SAAA,EAAA,GACA,IAAA,EAQA,OAPA,OAAA,GACA,EAAA,GAAA,EAAA,GACA,EAAA,IAAA,EACA,EAAA,GAAA,KAEA,EAAA,GAAA,GACA,EAAA,SACA,IAAA,EAAA,EAAA,EAAA,EAAA;;ACtCA,IAAA,EAAA,QAAA,2BACA,EAAA,QAAA,oBAAA,OAAA,SAAA,aAEA,QAAA,EAAA,OAAA,qBAAA,SAAA,GACA,OAAA,EAAA,EAAA;;ACJA,IAAA,EAAA,QAAA,iBACA,EAAA,QAAA,kBAAA,EACA,EAAA,GAAA,SAEA,EAAA,iBAAA,QAAA,QAAA,OAAA,oBACA,OAAA,oBAAA,QAAA,GAEA,EAAA,SAAA,GACA,IACA,OAAA,EAAA,GACA,MAAA,GACA,OAAA,EAAA,UAIA,OAAA,QAAA,EAAA,SAAA,GACA,OAAA,GAAA,mBAAA,EAAA,KAAA,GAAA,EAAA,GAAA,EAAA,EAAA;;ACjBA,IAAA,EAAA,QAAA,iBACA,EAAA,QAAA,oBACA,EAAA,QAAA,iBACA,EAAA,QAAA,mBACA,EAAA,QAAA,UACA,EAAA,QAAA,qBACA,EAAA,OAAA,yBAEA,QAAA,EAAA,QAAA,kBAAA,EAAA,SAAA,EAAA,GAGA,GAFA,EAAA,EAAA,GACA,EAAA,EAAA,GAAA,GACA,EAAA,IACA,OAAA,EAAA,EAAA,GACA,MAAA,IACA,GAAA,EAAA,EAAA,GAAA,OAAA,GAAA,EAAA,EAAA,KAAA,EAAA,GAAA,EAAA;;;ACdA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,UACA,EAAA,QAAA,kBACA,EAAA,QAAA,aACA,EAAA,QAAA,eACA,EAAA,QAAA,WAAA,IACA,EAAA,QAAA,YACA,EAAA,QAAA,aACA,EAAA,QAAA,wBACA,EAAA,QAAA,UACA,EAAA,QAAA,UACA,EAAA,QAAA,cACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,EAAA,QAAA,eACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,QAAA,mBACA,EAAA,QAAA,oBACA,EAAA,QAAA,oBACA,EAAA,QAAA,sBACA,EAAA,QAAA,kBACA,EAAA,QAAA,kBACA,EAAA,QAAA,gBACA,EAAA,QAAA,kBACA,EAAA,EAAA,EACA,EAAA,EAAA,EACA,EAAA,EAAA,EACA,EAAA,EAAA,OACA,EAAA,EAAA,KACA,EAAA,GAAA,EAAA,UACA,EAAA,YACA,EAAA,EAAA,WACA,EAAA,EAAA,eACA,EAAA,GAAA,qBACA,EAAA,EAAA,mBACA,EAAA,EAAA,WACA,EAAA,EAAA,cACA,EAAA,OAAA,GACA,EAAA,mBAAA,KAAA,EAAA,EACA,EAAA,EAAA,QAEA,GAAA,IAAA,EAAA,KAAA,EAAA,GAAA,UAGA,EAAA,GAAA,EAAA,WACA,OAEA,GAFA,EAAA,EAAA,GAAA,IAAA,CACA,IAAA,WAAA,OAAA,EAAA,KAAA,IAAA,CAAA,MAAA,IAAA,MACA,IACA,SAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,EAAA,GACA,UAAA,EAAA,GACA,EAAA,EAAA,EAAA,GACA,GAAA,IAAA,GAAA,EAAA,EAAA,EAAA,IACA,EAEA,EAAA,SAAA,GACA,IAAA,EAAA,EAAA,GAAA,EAAA,EAAA,IAEA,OADA,EAAA,GAAA,EACA,GAGA,EAAA,GAAA,iBAAA,EAAA,SAAA,SAAA,GACA,MAAA,iBAAA,GACA,SAAA,GACA,OAAA,aAAA,GAGA,EAAA,SAAA,EAAA,EAAA,GAKA,OAJA,IAAA,GAAA,EAAA,EAAA,EAAA,GACA,EAAA,GACA,EAAA,EAAA,GAAA,GACA,EAAA,GACA,EAAA,EAAA,IACA,EAAA,YAIA,EAAA,EAAA,IAAA,EAAA,GAAA,KAAA,EAAA,GAAA,IAAA,GACA,EAAA,EAAA,EAAA,CAAA,WAAA,EAAA,GAAA,OAJA,EAAA,EAAA,IAAA,EAAA,EAAA,EAAA,EAAA,EAAA,KACA,EAAA,GAAA,IAAA,GAIA,EAAA,EAAA,EAAA,IACA,EAAA,EAAA,EAAA,IAEA,EAAA,SAAA,EAAA,GACA,EAAA,GAKA,IAJA,IAGA,EAHA,EAAA,EAAA,EAAA,EAAA,IACA,EAAA,EACA,EAAA,EAAA,OAEA,EAAA,GAAA,EAAA,EAAA,EAAA,EAAA,KAAA,EAAA,IACA,OAAA,GAEA,EAAA,SAAA,EAAA,GACA,YAAA,IAAA,EAAA,EAAA,GAAA,EAAA,EAAA,GAAA,IAEA,EAAA,SAAA,GACA,IAAA,EAAA,EAAA,KAAA,KAAA,EAAA,EAAA,GAAA,IACA,QAAA,OAAA,GAAA,EAAA,EAAA,KAAA,EAAA,EAAA,QACA,IAAA,EAAA,KAAA,KAAA,EAAA,EAAA,IAAA,EAAA,KAAA,IAAA,KAAA,GAAA,KAAA,IAEA,EAAA,SAAA,EAAA,GAGA,GAFA,EAAA,EAAA,GACA,EAAA,EAAA,GAAA,GACA,IAAA,IAAA,EAAA,EAAA,IAAA,EAAA,EAAA,GAAA,CACA,IAAA,EAAA,EAAA,EAAA,GAEA,OADA,IAAA,EAAA,EAAA,IAAA,EAAA,EAAA,IAAA,EAAA,GAAA,KAAA,EAAA,YAAA,GACA,IAEA,EAAA,SAAA,GAKA,IAJA,IAGA,EAHA,EAAA,EAAA,EAAA,IACA,EAAA,GACA,EAAA,EAEA,EAAA,OAAA,GACA,EAAA,EAAA,EAAA,EAAA,OAAA,GAAA,GAAA,GAAA,GAAA,EAAA,KAAA,GACA,OAAA,GAEA,EAAA,SAAA,GAMA,IALA,IAIA,EAJA,EAAA,IAAA,EACA,EAAA,EAAA,EAAA,EAAA,EAAA,IACA,EAAA,GACA,EAAA,EAEA,EAAA,OAAA,IACA,EAAA,EAAA,EAAA,EAAA,OAAA,IAAA,EAAA,EAAA,IAAA,EAAA,KAAA,EAAA,IACA,OAAA,GAIA,IAYA,GAXA,EAAA,WACA,GAAA,gBAAA,EAAA,MAAA,UAAA,gCACA,IAAA,EAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,GACA,EAAA,SAAA,GACA,OAAA,GAAA,EAAA,KAAA,EAAA,GACA,EAAA,KAAA,IAAA,EAAA,KAAA,GAAA,KAAA,KAAA,GAAA,IAAA,GACA,EAAA,KAAA,EAAA,EAAA,EAAA,KAGA,OADA,GAAA,GAAA,EAAA,EAAA,EAAA,CAAA,cAAA,EAAA,IAAA,IACA,EAAA,KAEA,GAAA,WAAA,WACA,OAAA,KAAA,KAGA,EAAA,EAAA,EACA,EAAA,EAAA,EACA,QAAA,kBAAA,EAAA,EAAA,EAAA,EACA,QAAA,iBAAA,EAAA,EACA,EAAA,EAAA,EAEA,IAAA,QAAA,eACA,EAAA,EAAA,uBAAA,GAAA,GAGA,EAAA,EAAA,SAAA,GACA,OAAA,EAAA,EAAA,MAIA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,CAAA,OAAA,IAEA,IAAA,IAAA,GAAA,iHAGA,MAAA,KAAA,GAAA,EAAA,GAAA,OAAA,IAAA,EAAA,GAAA,OAEA,IAAA,IAAA,GAAA,EAAA,EAAA,OAAA,GAAA,EAAA,GAAA,OAAA,IAAA,EAAA,GAAA,OAEA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,SAAA,CAEA,IAAA,SAAA,GACA,OAAA,EAAA,EAAA,GAAA,IACA,EAAA,GACA,EAAA,GAAA,EAAA,IAGA,OAAA,SAAA,GACA,IAAA,EAAA,GAAA,MAAA,UAAA,EAAA,qBACA,IAAA,IAAA,KAAA,EAAA,GAAA,EAAA,KAAA,EAAA,OAAA,GAEA,UAAA,WAAA,GAAA,GACA,UAAA,WAAA,GAAA,KAGA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,SAAA,CAEA,OAAA,EAEA,eAAA,EAEA,iBAAA,EAEA,yBAAA,EAEA,oBAAA,EAEA,sBAAA,IAKA,IAAA,GAAA,EAAA,WAAA,EAAA,EAAA,KAEA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,SAAA,CACA,sBAAA,SAAA,GACA,OAAA,EAAA,EAAA,EAAA,OAKA,GAAA,EAAA,EAAA,EAAA,EAAA,IAAA,GAAA,EAAA,WACA,IAAA,EAAA,IAIA,MAAA,UAAA,EAAA,CAAA,KAAA,MAAA,EAAA,CAAA,EAAA,KAAA,MAAA,EAAA,OAAA,OACA,OAAA,CACA,UAAA,SAAA,GAIA,IAHA,IAEA,EAAA,EAFA,EAAA,CAAA,GACA,EAAA,EAEA,UAAA,OAAA,GAAA,EAAA,KAAA,UAAA,MAEA,GADA,EAAA,EAAA,EAAA,IACA,EAAA,SAAA,IAAA,KAAA,EAAA,GAMA,OALA,EAAA,KAAA,EAAA,SAAA,EAAA,GAEA,GADA,mBAAA,IAAA,EAAA,EAAA,KAAA,KAAA,EAAA,KACA,EAAA,GAAA,OAAA,IAEA,EAAA,GAAA,EACA,EAAA,MAAA,EAAA,MAKA,EAAA,GAAA,IAAA,QAAA,UAAA,CAAA,EAAA,GAAA,EAAA,EAAA,GAAA,SAEA,EAAA,EAAA,UAEA,EAAA,KAAA,QAAA,GAEA,EAAA,EAAA,KAAA,QAAA;;ACrPA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,SAAA,CAAA,OAAA,QAAA;;ACFA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,kBAAA,SAAA,CAAA,eAAA,QAAA,gBAAA;;ACFA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,kBAAA,SAAA,CAAA,iBAAA,QAAA;;ACDA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,WACA,EAAA,QAAA,YACA,OAAA,QAAA,SAAA,EAAA,GACA,IAAA,GAAA,EAAA,QAAA,IAAA,IAAA,OAAA,GACA,EAAA,GACA,EAAA,GAAA,EAAA,GACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,WAAA,EAAA,KAAA,SAAA;;ACPA,IAAA,EAAA,QAAA,iBACA,EAAA,QAAA,kBAAA,EAEA,QAAA,gBAAA,CAAA,2BAAA,WACA,OAAA,SAAA,EAAA,GACA,OAAA,EAAA,EAAA,GAAA;;ACLA,IAAA,EAAA,QAAA,UACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBAAA,CAAA,YACA,EAAA,OAAA,UAEA,OAAA,QAAA,OAAA,gBAAA,SAAA,GAEA,OADA,EAAA,EAAA,GACA,EAAA,EAAA,GAAA,EAAA,GACA,mBAAA,EAAA,aAAA,aAAA,EAAA,YACA,EAAA,YAAA,UACA,aAAA,OAAA,EAAA;;ACVA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,iBAEA,QAAA,gBAAA,CAAA,iBAAA,WACA,OAAA,SAAA,GACA,OAAA,EAAA,EAAA;;ACLA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,kBAEA,QAAA,gBAAA,CAAA,OAAA,WACA,OAAA,SAAA,GACA,OAAA,EAAA,EAAA;;ACLA,QAAA,gBAAA,CAAA,sBAAA,WACA,OAAA,QAAA,sBAAA;;ACDA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,WAAA,SAEA,QAAA,gBAAA,CAAA,SAAA,SAAA,GACA,OAAA,SAAA,GACA,OAAA,GAAA,EAAA,GAAA,EAAA,EAAA,IAAA;;ACLA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,WAAA,SAEA,QAAA,gBAAA,CAAA,OAAA,SAAA,GACA,OAAA,SAAA,GACA,OAAA,GAAA,EAAA,GAAA,EAAA,EAAA,IAAA;;ACLA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,WAAA,SAEA,QAAA,gBAAA,CAAA,oBAAA,SAAA,GACA,OAAA,SAAA,GACA,OAAA,GAAA,EAAA,GAAA,EAAA,EAAA,IAAA;;ACLA,IAAA,EAAA,QAAA,gBAEA,QAAA,gBAAA,CAAA,WAAA,SAAA,GACA,OAAA,SAAA,GACA,OAAA,EAAA,MAAA,GAAA,EAAA;;ACJA,IAAA,EAAA,QAAA,gBAEA,QAAA,gBAAA,CAAA,WAAA,SAAA,GACA,OAAA,SAAA,GACA,OAAA,EAAA,MAAA,GAAA,EAAA;;ACJA,IAAA,EAAA,QAAA,gBAEA,QAAA,gBAAA,CAAA,eAAA,SAAA,GACA,OAAA,SAAA,GACA,QAAA,EAAA,MAAA,GAAA,EAAA;;ACLA,aAEA,IAAA,EAAA,QAAA,kBACA,EAAA,QAAA,kBACA,EAAA,QAAA,kBACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,EAAA,QAAA,cACA,EAAA,OAAA,OAGA,OAAA,SAAA,GAAA,QAAA,WAAA,CAAA,WACA,IAAA,EAAA,GACA,EAAA,GAEA,EAAA,SACA,EAAA,uBAGA,OAFA,EAAA,GAAA,EACA,EAAA,MAAA,IAAA,QAAA,SAAA,GAAA,EAAA,GAAA,IACA,GAAA,EAAA,GAAA,GAAA,IAAA,OAAA,KAAA,EAAA,GAAA,IAAA,KAAA,KAAA,IACA,SAAA,EAAA,GAMA,IALA,IAAA,EAAA,EAAA,GACA,EAAA,UAAA,OACA,EAAA,EACA,EAAA,EAAA,EACA,EAAA,EAAA,EACA,EAAA,GAMA,IALA,IAIA,EAJA,EAAA,EAAA,UAAA,MACA,EAAA,EAAA,EAAA,GAAA,OAAA,EAAA,IAAA,EAAA,GACA,EAAA,EAAA,OACA,EAAA,EAEA,EAAA,GACA,EAAA,EAAA,KACA,IAAA,EAAA,KAAA,EAAA,KAAA,EAAA,GAAA,EAAA,IAEA,OAAA,GACA;;ACpCA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,EAAA,EAAA,SAAA,CAAA,OAAA,QAAA;;ACFA,OAAA,QAAA,OAAA,IAAA,SAAA,EAAA,GAEA,OAAA,IAAA,EAAA,IAAA,GAAA,EAAA,GAAA,EAAA,EAAA,GAAA,GAAA,GAAA;;ACFA,IAAA,EAAA,QAAA,aACA,EAAA,EAAA,EAAA,SAAA,CAAA,GAAA,QAAA;;ACAA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,SAAA,EAAA,GAEA,GADA,EAAA,IACA,EAAA,IAAA,OAAA,EAAA,MAAA,UAAA,EAAA,8BAEA,OAAA,QAAA,CACA,IAAA,OAAA,iBAAA,aAAA,GACA,SAAA,EAAA,EAAA,GACA,KACA,EAAA,QAAA,SAAA,CAAA,SAAA,KAAA,QAAA,kBAAA,EAAA,OAAA,UAAA,aAAA,IAAA,IACA,EAAA,IACA,IAAA,aAAA,OACA,MAAA,GAAA,GAAA,EACA,OAAA,SAAA,EAAA,GAIA,OAHA,EAAA,EAAA,GACA,EAAA,EAAA,UAAA,EACA,EAAA,EAAA,GACA,GAVA,CAYA,IAAA,QAAA,GACA,MAAA;;ACtBA,IAAA,EAAA,QAAA,aACA,EAAA,EAAA,EAAA,SAAA,CAAA,eAAA,QAAA,gBAAA;;ACDA,IAAA,EAAA,QAAA,UACA,EAAA,QAAA,SAAA,CAAA,eAEA,EAAA,aAAA,EAAA,WAAA,OAAA,UAAA,IAGA,EAAA,SAAA,EAAA,GACA,IACA,OAAA,EAAA,GACA,MAAA,MAGA,OAAA,QAAA,SAAA,GACA,IAAA,EAAA,EAAA,EACA,YAAA,IAAA,EAAA,YAAA,OAAA,EAAA,OAEA,iBAAA,EAAA,EAAA,EAAA,OAAA,GAAA,IAAA,EAEA,EAAA,EAAA,GAEA,WAAA,EAAA,EAAA,KAAA,mBAAA,EAAA,OAAA,YAAA;;ACrBA,aAEA,IAAA,EAAA,QAAA,cACA,EAAA,GACA,EAAA,QAAA,SAAA,CAAA,gBAAA,IACA,EAAA,IAAA,cACA,QAAA,cAAA,CAAA,OAAA,UAAA,WAAA,WACA,MAAA,WAAA,EAAA,MAAA,MACA;;ACPA,OAAA,QAAA,SAAA,EAAA,EAAA,GACA,IAAA,OAAA,IAAA,EACA,OAAA,EAAA,QACA,KAAA,EAAA,OAAA,EAAA,IACA,EAAA,KAAA,GACA,KAAA,EAAA,OAAA,EAAA,EAAA,EAAA,IACA,EAAA,KAAA,EAAA,EAAA,IACA,KAAA,EAAA,OAAA,EAAA,EAAA,EAAA,GAAA,EAAA,IACA,EAAA,KAAA,EAAA,EAAA,GAAA,EAAA,IACA,KAAA,EAAA,OAAA,EAAA,EAAA,EAAA,GAAA,EAAA,GAAA,EAAA,IACA,EAAA,KAAA,EAAA,EAAA,GAAA,EAAA,GAAA,EAAA,IACA,KAAA,EAAA,OAAA,EAAA,EAAA,EAAA,GAAA,EAAA,GAAA,EAAA,GAAA,EAAA,IACA,EAAA,KAAA,EAAA,EAAA,GAAA,EAAA,GAAA,EAAA,GAAA,EAAA,IACA,OAAA,EAAA,MAAA,EAAA;;ACdA,aACA,IAAA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,EAAA,QAAA,aACA,EAAA,GAAA,MACA,EAAA,GAEA,EAAA,SAAA,EAAA,EAAA,GACA,KAAA,KAAA,GAAA,CACA,IAAA,IAAA,EAAA,GAAA,EAAA,EAAA,EAAA,EAAA,IAAA,EAAA,GAAA,KAAA,EAAA,IAEA,EAAA,GAAA,SAAA,MAAA,gBAAA,EAAA,KAAA,KAAA,KACA,OAAA,EAAA,GAAA,EAAA,IAGA,OAAA,QAAA,SAAA,MAAA,SAAA,GACA,IAAA,EAAA,EAAA,MACA,EAAA,EAAA,KAAA,UAAA,GACA,EAAA,WACA,IAAA,EAAA,EAAA,OAAA,EAAA,KAAA,YACA,OAAA,gBAAA,EAAA,EAAA,EAAA,EAAA,OAAA,GAAA,EAAA,EAAA,EAAA,IAGA,OADA,EAAA,EAAA,aAAA,EAAA,UAAA,EAAA,WACA;;ACtBA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,WAAA,CAAA,KAAA,QAAA;;ACHA,IAAA,EAAA,QAAA,gBAAA,EACA,EAAA,SAAA,UACA,EAAA,wBACA,EAAA,OAGA,KAAA,GAAA,QAAA,mBAAA,EAAA,EAAA,EAAA,CACA,cAAA,EACA,IAAA,WACA,IACA,OAAA,GAAA,MAAA,MAAA,GAAA,GACA,MAAA,GACA,MAAA;;ACZA,aACA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,QAAA,SAAA,CAAA,eACA,EAAA,SAAA,UAEA,KAAA,GAAA,QAAA,gBAAA,EAAA,EAAA,EAAA,CAAA,MAAA,SAAA,GACA,GAAA,mBAAA,OAAA,EAAA,GAAA,OAAA,EACA,IAAA,EAAA,KAAA,WAAA,OAAA,aAAA,KAEA,KAAA,EAAA,EAAA,IAAA,GAAA,KAAA,YAAA,EAAA,OAAA,EACA,OAAA;;ACXA,OAAA,QAAA;;ACAA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,cACA,EAAA,QAAA,YACA,EAAA,QAAA,gBACA,EAAA,IAAA,EAAA,IACA,EAAA,KACA,EAAA,OAAA,IAAA,EAAA,EAAA,KACA,EAAA,OAAA,EAAA,EAAA,MAEA,EAAA,SAAA,EAAA,EAAA,GACA,IAAA,EAAA,GACA,EAAA,EAAA,WACA,QAAA,EAAA,MAAA,EAAA,MAAA,IAEA,EAAA,EAAA,GAAA,EAAA,EAAA,GAAA,EAAA,GACA,IAAA,EAAA,GAAA,GACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,SAAA,IAMA,EAAA,EAAA,KAAA,SAAA,EAAA,GAIA,OAHA,EAAA,OAAA,EAAA,IACA,EAAA,IAAA,EAAA,EAAA,QAAA,EAAA,KACA,EAAA,IAAA,EAAA,EAAA,QAAA,EAAA,KACA,GAGA,OAAA,QAAA;;AC7BA,IAAA,EAAA,QAAA,aAAA,SACA,EAAA,QAAA,kBAAA,KACA,EAAA,QAAA,gBACA,EAAA,cAEA,OAAA,QAAA,IAAA,EAAA,EAAA,OAAA,KAAA,EAAA,EAAA,QAAA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,OAAA,GAAA,GACA,OAAA,EAAA,EAAA,IAAA,IAAA,EAAA,KAAA,GAAA,GAAA,MACA;;ACRA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBAEA,EAAA,EAAA,EAAA,EAAA,GAAA,UAAA,GAAA,CAAA,SAAA;;ACHA,IAAA,EAAA,QAAA,aAAA,WACA,EAAA,QAAA,kBAAA,KAEA,OAAA,QAAA,EAAA,EAAA,QAAA,gBAAA,QAAA,EAAA,EAAA,SAAA,GACA,IAAA,EAAA,EAAA,OAAA,GAAA,GACA,EAAA,EAAA,GACA,OAAA,IAAA,GAAA,KAAA,EAAA,OAAA,IAAA,EAAA,GACA;;ACPA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,kBAEA,EAAA,EAAA,EAAA,EAAA,GAAA,YAAA,GAAA,CAAA,WAAA;;ACHA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,gBAAA,IACA,OAAA,QAAA,SAAA,EAAA,EAAA,GACA,IACA,EADA,EAAA,EAAA,YAIA,OAFA,IAAA,GAAA,mBAAA,IAAA,EAAA,EAAA,aAAA,EAAA,WAAA,EAAA,IAAA,GACA,EAAA,EAAA,GACA;;;ACPA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,UACA,EAAA,QAAA,UACA,EAAA,QAAA,0BACA,EAAA,QAAA,mBACA,EAAA,QAAA,YACA,EAAA,QAAA,kBAAA,EACA,EAAA,QAAA,kBAAA,EACA,EAAA,QAAA,gBAAA,EACA,EAAA,QAAA,kBAAA,KACA,EAAA,SACA,EAAA,EAAA,GACA,EAAA,EACA,EAAA,EAAA,UAEA,EAAA,EAAA,QAAA,mBAAA,CAAA,KAAA,EACA,EAAA,SAAA,OAAA,UAGA,EAAA,SAAA,GACA,IAAA,EAAA,EAAA,GAAA,GACA,GAAA,iBAAA,GAAA,EAAA,OAAA,EAAA,CAEA,IACA,EAAA,EAAA,EADA,GADA,EAAA,EAAA,EAAA,OAAA,EAAA,EAAA,IACA,WAAA,GAEA,GAAA,KAAA,GAAA,KAAA,GAEA,GAAA,MADA,EAAA,EAAA,WAAA,KACA,MAAA,EAAA,OAAA,SACA,GAAA,KAAA,EAAA,CACA,OAAA,EAAA,WAAA,IACA,KAAA,GAAA,KAAA,GAAA,EAAA,EAAA,EAAA,GAAA,MACA,KAAA,GAAA,KAAA,IAAA,EAAA,EAAA,EAAA,GAAA,MACA,QAAA,OAAA,EAEA,IAAA,IAAA,EAAA,EAAA,EAAA,MAAA,GAAA,EAAA,EAAA,EAAA,EAAA,OAAA,EAAA,EAAA,IAIA,IAHA,EAAA,EAAA,WAAA,IAGA,IAAA,EAAA,EAAA,OAAA,IACA,OAAA,SAAA,EAAA,IAEA,OAAA,GAGA,IAAA,EAAA,UAAA,EAAA,QAAA,EAAA,QAAA,CACA,EAAA,SAAA,GACA,IAAA,EAAA,UAAA,OAAA,EAAA,EAAA,EACA,EAAA,KACA,OAAA,aAAA,IAEA,EAAA,EAAA,WAAA,EAAA,QAAA,KAAA,KAAA,EAAA,IAAA,GACA,EAAA,IAAA,EAAA,EAAA,IAAA,EAAA,GAAA,EAAA,IAEA,IAAA,IAMA,EANA,EAAA,QAAA,kBAAA,EAAA,GAAA,6KAMA,MAAA,KAAA,EAAA,EAAA,EAAA,OAAA,EAAA,IACA,EAAA,EAAA,EAAA,EAAA,MAAA,EAAA,EAAA,IACA,EAAA,EAAA,EAAA,EAAA,EAAA,IAGA,EAAA,UAAA,EACA,EAAA,YAAA,EACA,QAAA,cAAA,CAAA,EAAA,EAAA;;ACnEA,IAAA,EAAA,QAAA,UACA,OAAA,QAAA,SAAA,EAAA,GACA,GAAA,iBAAA,GAAA,UAAA,EAAA,GAAA,MAAA,UAAA,GACA,OAAA;;ACHA,aACA,IAAA,EAAA,QAAA,iBACA,EAAA,QAAA,cAEA,OAAA,QAAA,SAAA,GACA,IAAA,EAAA,OAAA,EAAA,OACA,EAAA,GACA,EAAA,EAAA,GACA,GAAA,EAAA,GAAA,GAAA,EAAA,EAAA,MAAA,WAAA,2BACA,KAAA,EAAA,GAAA,KAAA,KAAA,GAAA,GAAA,EAAA,IAAA,GAAA,GACA,OAAA;;ACVA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,QAAA,qBACA,EAAA,QAAA,oBACA,EAAA,GAAA,QACA,EAAA,KAAA,MACA,EAAA,CAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GACA,EAAA,wCACA,EAAA,IAEA,EAAA,SAAA,EAAA,GAGA,IAFA,IAAA,GAAA,EACA,EAAA,IACA,EAAA,GACA,GAAA,EAAA,EAAA,GACA,EAAA,GAAA,EAAA,IACA,EAAA,EAAA,EAAA,MAGA,EAAA,SAAA,GAGA,IAFA,IAAA,EAAA,EACA,EAAA,IACA,GAAA,GACA,GAAA,EAAA,GACA,EAAA,GAAA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,KAGA,EAAA,WAGA,IAFA,IAAA,EAAA,EACA,EAAA,KACA,GAAA,GACA,GAAA,KAAA,GAAA,IAAA,GAAA,IAAA,EAAA,GAAA,CACA,IAAA,EAAA,OAAA,EAAA,IACA,EAAA,KAAA,EAAA,EAAA,EAAA,EAAA,KAAA,EAAA,EAAA,EAAA,QAAA,EAEA,OAAA,GAEA,EAAA,SAAA,EAAA,EAAA,GACA,OAAA,IAAA,EAAA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,EAAA,EAAA,IAEA,EAAA,SAAA,GAGA,IAFA,IAAA,EAAA,EACA,EAAA,EACA,GAAA,MACA,GAAA,GACA,GAAA,KAEA,KAAA,GAAA,GACA,GAAA,EACA,GAAA,EACA,OAAA,GAGA,EAAA,EAAA,EAAA,EAAA,KAAA,IACA,UAAA,KAAA,QAAA,IACA,MAAA,GAAA,QAAA,IACA,SAAA,MAAA,QAAA,IACA,yBAAA,mBAAA,QAAA,MACA,QAAA,WAAA,CAAA,WAEA,EAAA,KAAA,OACA,SAAA,CACA,QAAA,SAAA,GACA,IAIA,EAAA,EAAA,EAAA,EAJA,EAAA,EAAA,KAAA,GACA,EAAA,EAAA,GACA,EAAA,GACA,EAAA,EAEA,GAAA,EAAA,GAAA,EAAA,GAAA,MAAA,WAAA,GAEA,GAAA,GAAA,EAAA,MAAA,MACA,GAAA,IAAA,MAAA,GAAA,KAAA,OAAA,OAAA,GAKA,GAJA,EAAA,IACA,EAAA,IACA,GAAA,GAEA,EAAA,MAKA,GAHA,GADA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,IAAA,IACA,EAAA,EAAA,EAAA,GAAA,EAAA,GAAA,EAAA,EAAA,EAAA,EAAA,GACA,GAAA,kBACA,EAAA,GAAA,GACA,EAAA,CAGA,IAFA,EAAA,EAAA,GACA,EAAA,EACA,GAAA,GACA,EAAA,IAAA,GACA,GAAA,EAIA,IAFA,EAAA,EAAA,GAAA,EAAA,GAAA,GACA,EAAA,EAAA,EACA,GAAA,IACA,EAAA,GAAA,IACA,GAAA,GAEA,EAAA,GAAA,GACA,EAAA,EAAA,GACA,EAAA,GACA,EAAA,SAEA,EAAA,EAAA,GACA,EAAA,IAAA,EAAA,GACA,EAAA,IAAA,EAAA,KAAA,EAAA,GAQA,OAHA,EAFA,EAAA,EAEA,IADA,EAAA,EAAA,SACA,EAAA,KAAA,EAAA,KAAA,EAAA,EAAA,GAAA,EAAA,EAAA,MAAA,EAAA,EAAA,GAAA,IAAA,EAAA,MAAA,EAAA,IAEA,EAAA;;AC9GA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,YACA,EAAA,QAAA,qBACA,EAAA,GAAA,YAEA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,WAEA,MAAA,MAAA,EAAA,KAAA,OAAA,OACA,EAAA,WAEA,EAAA,KAAA,OACA,SAAA,CACA,YAAA,SAAA,GACA,IAAA,EAAA,EAAA,KAAA,6CACA,YAAA,IAAA,EAAA,EAAA,KAAA,GAAA,EAAA,KAAA,EAAA;;ACdA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,SAAA,CAAA,QAAA,KAAA,IAAA,GAAA;;ACFA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,aAAA,SAEA,EAAA,EAAA,EAAA,SAAA,CACA,SAAA,SAAA,GACA,MAAA,iBAAA,GAAA,EAAA;;ACLA,IAAA,EAAA,QAAA,gBACA,EAAA,KAAA,MACA,OAAA,QAAA,SAAA,GACA,OAAA,EAAA,IAAA,SAAA,IAAA,EAAA,KAAA;;ACHA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,SAAA,CAAA,UAAA,QAAA;;ACFA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,SAAA,CACA,MAAA,SAAA,GAEA,OAAA,GAAA;;ACLA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,KAAA,IAEA,EAAA,EAAA,EAAA,SAAA,CACA,cAAA,SAAA,GACA,OAAA,EAAA,IAAA,EAAA,IAAA;;ACNA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,SAAA,CAAA,iBAAA;;ACFA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,SAAA,CAAA,kBAAA;;ACHA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,kBAEA,EAAA,EAAA,EAAA,EAAA,GAAA,OAAA,YAAA,GAAA,SAAA,CAAA,WAAA;;ACHA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBAEA,EAAA,EAAA,EAAA,EAAA,GAAA,OAAA,UAAA,GAAA,SAAA,CAAA,SAAA;;ACFA,OAAA,QAAA,KAAA,OAAA,SAAA,GACA,OAAA,GAAA,IAAA,MAAA,EAAA,KAAA,EAAA,EAAA,EAAA,EAAA,KAAA,IAAA,EAAA;;ACDA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,KAAA,KACA,EAAA,KAAA,MAEA,EAAA,EAAA,EAAA,EAAA,IAAA,GAEA,KAAA,KAAA,MAAA,EAAA,OAAA,aAEA,EAAA,EAAA,IAAA,EAAA,GACA,OAAA,CACA,MAAA,SAAA,GACA,OAAA,GAAA,GAAA,EAAA,IAAA,EAAA,kBACA,KAAA,IAAA,GAAA,KAAA,IACA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,EAAA;;ACdA,IAAA,EAAA,QAAA,aACA,EAAA,KAAA,MAEA,SAAA,EAAA,GACA,OAAA,SAAA,GAAA,IAAA,GAAA,EAAA,EAAA,GAAA,GAAA,GAAA,KAAA,IAAA,EAAA,KAAA,KAAA,EAAA,EAAA,IAAA,EAIA,EAAA,EAAA,EAAA,EAAA,IAAA,GAAA,EAAA,EAAA,GAAA,GAAA,OAAA,CAAA,MAAA;;ACRA,IAAA,EAAA,QAAA,aACA,EAAA,KAAA,MAGA,EAAA,EAAA,EAAA,EAAA,IAAA,GAAA,EAAA,GAAA,GAAA,GAAA,OAAA,CACA,MAAA,SAAA,GACA,OAAA,IAAA,GAAA,GAAA,EAAA,KAAA,KAAA,EAAA,IAAA,EAAA,IAAA;;ACNA,OAAA,QAAA,KAAA,MAAA,SAAA,GAEA,OAAA,IAAA,GAAA,IAAA,GAAA,EAAA,EAAA,EAAA,GAAA,EAAA;;ACFA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBAEA,EAAA,EAAA,EAAA,OAAA,CACA,KAAA,SAAA,GACA,OAAA,EAAA,GAAA,GAAA,KAAA,IAAA,KAAA,IAAA,GAAA,EAAA;;ACLA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CACA,MAAA,SAAA,GACA,OAAA,KAAA,GAAA,GAAA,KAAA,MAAA,KAAA,IAAA,EAAA,IAAA,KAAA,OAAA;;ACJA,IAAA,EAAA,QAAA,aACA,EAAA,KAAA,IAEA,EAAA,EAAA,EAAA,OAAA,CACA,KAAA,SAAA,GACA,OAAA,EAAA,GAAA,GAAA,GAAA,IAAA;;ACLA,IAAA,EAAA,KAAA,MACA,OAAA,SAAA,GAEA,EAAA,IAAA,oBAAA,EAAA,IAAA,qBAEA,OAAA,GAAA,OACA,SAAA,GACA,OAAA,IAAA,GAAA,GAAA,EAAA,GAAA,MAAA,EAAA,KAAA,EAAA,EAAA,EAAA,EAAA,KAAA,IAAA,GAAA,GACA;;ACRA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBAEA,EAAA,EAAA,EAAA,EAAA,GAAA,GAAA,KAAA,OAAA,OAAA,CAAA,MAAA;;ACHA,IAAA,EAAA,QAAA,gBACA,EAAA,KAAA,IACA,EAAA,EAAA,GAAA,IACA,EAAA,EAAA,GAAA,IACA,EAAA,EAAA,EAAA,MAAA,EAAA,GACA,EAAA,EAAA,GAAA,KAEA,EAAA,SAAA,GACA,OAAA,EAAA,EAAA,EAAA,EAAA,GAGA,OAAA,QAAA,KAAA,QAAA,SAAA,GACA,IAEA,EAAA,EAFA,EAAA,KAAA,IAAA,GACA,EAAA,EAAA,GAEA,OAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,GAEA,GADA,GAAA,EAAA,EAAA,GAAA,IACA,EAAA,IAEA,GAAA,GAAA,EAAA,GAAA,EAAA,GACA,EAAA;;ACpBA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CAAA,OAAA,QAAA;;ACFA,IAAA,EAAA,QAAA,aACA,EAAA,KAAA,IAEA,EAAA,EAAA,EAAA,OAAA,CACA,MAAA,SAAA,EAAA,GAMA,IALA,IAIA,EAAA,EAJA,EAAA,EACA,EAAA,EACA,EAAA,UAAA,OACA,EAAA,EAEA,EAAA,GAEA,GADA,EAAA,EAAA,UAAA,QAGA,EAAA,GADA,EAAA,EAAA,GACA,EAAA,EACA,EAAA,GAGA,GAFA,EAAA,GACA,EAAA,EAAA,GACA,EACA,EAEA,OAAA,IAAA,EAAA,EAAA,EAAA,EAAA,EAAA,KAAA,KAAA;;ACrBA,IAAA,EAAA,QAAA,aACA,EAAA,KAAA,KAGA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,WAAA,CAAA,WACA,OAAA,GAAA,EAAA,WAAA,IAAA,GAAA,EAAA,SACA,OAAA,CACA,KAAA,SAAA,EAAA,GACA,IACA,GAAA,EACA,GAAA,EACA,EAHA,MAGA,EACA,EAJA,MAIA,EACA,OAAA,EAAA,EAAA,IALA,MAKA,IAAA,IAAA,EAAA,GALA,MAKA,IAAA,KAAA,KAAA;;ACbA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CACA,MAAA,SAAA,GACA,OAAA,KAAA,IAAA,GAAA,KAAA;;ACJA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CAAA,MAAA,QAAA;;ACFA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CACA,KAAA,SAAA,GACA,OAAA,KAAA,IAAA,GAAA,KAAA;;ACJA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CAAA,KAAA,QAAA;;ACFA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,KAAA,IAGA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,WAAA,CAAA,WACA,OAAA,QAAA,KAAA,MAAA,SACA,OAAA,CACA,KAAA,SAAA,GACA,OAAA,KAAA,IAAA,GAAA,GAAA,GACA,EAAA,GAAA,GAAA,IAAA,GACA,EAAA,EAAA,GAAA,GAAA,EAAA,KAAA,KAAA,EAAA;;ACXA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,KAAA,IAEA,EAAA,EAAA,EAAA,OAAA,CACA,KAAA,SAAA,GACA,IAAA,EAAA,EAAA,GAAA,GACA,EAAA,GAAA,GACA,OAAA,GAAA,EAAA,EAAA,EAAA,GAAA,EAAA,GAAA,GAAA,EAAA,IAAA,EAAA,GAAA,GAAA;;ACRA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CACA,MAAA,SAAA,GACA,OAAA,EAAA,EAAA,KAAA,MAAA,KAAA,MAAA;;ACLA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,wBACA,EAAA,OAAA,aACA,EAAA,OAAA,cAGA,EAAA,EAAA,EAAA,EAAA,KAAA,GAAA,GAAA,EAAA,QAAA,SAAA,CAEA,cAAA,SAAA,GAKA,IAJA,IAGA,EAHA,EAAA,GACA,EAAA,UAAA,OACA,EAAA,EAEA,EAAA,GAAA,CAEA,GADA,GAAA,UAAA,KACA,EAAA,EAAA,WAAA,EAAA,MAAA,WAAA,EAAA,8BACA,EAAA,KAAA,EAAA,MACA,EAAA,GACA,EAAA,QAAA,GAAA,QAAA,IAAA,EAAA,KAAA,QAEA,OAAA,EAAA,KAAA;;ACpBA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBAEA,EAAA,EAAA,EAAA,SAAA,CAEA,IAAA,SAAA,GAMA,IALA,IAAA,EAAA,EAAA,EAAA,KACA,EAAA,EAAA,EAAA,QACA,EAAA,UAAA,OACA,EAAA,GACA,EAAA,EACA,EAAA,GACA,EAAA,KAAA,OAAA,EAAA,OACA,EAAA,GAAA,EAAA,KAAA,OAAA,UAAA,KACA,OAAA,EAAA,KAAA;;ACfA,aAEA,QAAA,iBAAA,CAAA,OAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA;;ACJA,IAAA,EAAA,QAAA,iBACA,EAAA,QAAA,cAGA,OAAA,QAAA,SAAA,GACA,OAAA,SAAA,EAAA,GACA,IAGA,EAAA,EAHA,EAAA,OAAA,EAAA,IACA,EAAA,EAAA,GACA,EAAA,EAAA,OAEA,OAAA,EAAA,GAAA,GAAA,EAAA,EAAA,QAAA,GACA,EAAA,EAAA,WAAA,IACA,OAAA,EAAA,OAAA,EAAA,IAAA,IAAA,EAAA,EAAA,WAAA,EAAA,IAAA,OAAA,EAAA,MACA,EAAA,EAAA,OAAA,GAAA,EACA,EAAA,EAAA,MAAA,EAAA,EAAA,GAAA,EAAA,OAAA,EAAA,OAAA,IAAA;;ACdA,OAAA,QAAA;;ACAA,aACA,IAAA,EAAA,QAAA,oBACA,EAAA,QAAA,oBACA,EAAA,QAAA,wBACA,EAAA,GAGA,QAAA,UAAA,CAAA,EAAA,QAAA,SAAA,CAAA,YAAA,WAAA,OAAA,OAEA,OAAA,QAAA,SAAA,EAAA,EAAA,GACA,EAAA,UAAA,EAAA,EAAA,CAAA,KAAA,EAAA,EAAA,KACA,EAAA,EAAA,EAAA;;ACXA,aACA,IAAA,EAAA,QAAA,cACA,EAAA,QAAA,aACA,EAAA,QAAA,eACA,EAAA,QAAA,WACA,EAAA,QAAA,gBACA,EAAA,QAAA,kBACA,EAAA,QAAA,wBACA,EAAA,QAAA,iBACA,EAAA,QAAA,SAAA,CAAA,YACA,IAAA,GAAA,MAAA,QAAA,GAAA,QACA,EAAA,aACA,EAAA,OACA,EAAA,SAEA,EAAA,WAAA,OAAA,MAEA,OAAA,QAAA,SAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,GACA,IAeA,EAAA,EAAA,EAfA,EAAA,SAAA,GACA,IAAA,GAAA,KAAA,EAAA,OAAA,EAAA,GACA,OAAA,GACA,KAAA,EACA,KAAA,EAAA,OAAA,WAAA,OAAA,IAAA,EAAA,KAAA,IACA,OAAA,WAAA,OAAA,IAAA,EAAA,KAAA,KAEA,EAAA,EAAA,YACA,EAAA,GAAA,EACA,GAAA,EACA,EAAA,EAAA,UACA,EAAA,EAAA,IAAA,EAAA,IAAA,GAAA,EAAA,GACA,EAAA,GAAA,EAAA,GACA,EAAA,EAAA,EAAA,EAAA,WAAA,OAAA,EACA,EAAA,SAAA,GAAA,EAAA,SAAA,EAwBA,GArBA,IACA,EAAA,EAAA,EAAA,KAAA,IAAA,OACA,OAAA,WAAA,EAAA,OAEA,EAAA,EAAA,GAAA,GAEA,GAAA,mBAAA,EAAA,IAAA,EAAA,EAAA,EAAA,IAIA,GAAA,GAAA,EAAA,OAAA,IACA,GAAA,EACA,EAAA,WAAA,OAAA,EAAA,KAAA,QAGA,IAAA,IAAA,IAAA,GAAA,EAAA,IACA,EAAA,EAAA,EAAA,GAGA,EAAA,GAAA,EACA,EAAA,GAAA,EACA,EAMA,GALA,EAAA,CACA,OAAA,EAAA,EAAA,EAAA,GACA,KAAA,EAAA,EAAA,EAAA,GACA,QAAA,GAEA,EAAA,IAAA,KAAA,EACA,KAAA,GAAA,EAAA,EAAA,EAAA,EAAA,SACA,EAAA,EAAA,EAAA,EAAA,GAAA,GAAA,GAAA,EAAA,GAEA,OAAA;;ACnEA,aACA,IAAA,EAAA,QAAA,eAAA,EAAA,GAGA,QAAA,iBAAA,CAAA,OAAA,SAAA,SAAA,GACA,KAAA,GAAA,OAAA,GACA,KAAA,GAAA,GAEA,WACA,IAEA,EAFA,EAAA,KAAA,GACA,EAAA,KAAA,GAEA,OAAA,GAAA,EAAA,OAAA,CAAA,WAAA,EAAA,MAAA,IACA,EAAA,EAAA,EAAA,GACA,KAAA,IAAA,EAAA,OACA,CAAA,MAAA,EAAA,MAAA;;ACfA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,eAAA,EAAA,GACA,EAAA,EAAA,EAAA,SAAA,CAEA,YAAA,SAAA,GACA,OAAA,EAAA,KAAA;;ACLA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,UACA,EAAA,QAAA,SAAA,CAAA,SACA,OAAA,QAAA,SAAA,GACA,IAAA,EACA,OAAA,EAAA,UAAA,KAAA,EAAA,EAAA,MAAA,EAAA,UAAA,EAAA;;ACLA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,cAEA,OAAA,QAAA,SAAA,EAAA,EAAA,GACA,GAAA,EAAA,GAAA,MAAA,UAAA,UAAA,EAAA,0BACA,OAAA,OAAA,EAAA;;ACNA,IAAA,EAAA,QAAA,SAAA,CAAA,SACA,OAAA,QAAA,SAAA,GACA,IAAA,EAAA,IACA,IACA,MAAA,GAAA,GACA,MAAA,GACA,IAEA,OADA,EAAA,IAAA,GACA,MAAA,GAAA,GACA,MAAA,KACA,OAAA;;ACTA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,qBACA,EAAA,WACA,EAAA,GAAA,GAEA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,qBAAA,CAAA,GAAA,SAAA,CACA,SAAA,SAAA,GACA,IAAA,EAAA,EAAA,KAAA,EAAA,GACA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,EACA,EAAA,EAAA,EAAA,QACA,OAAA,IAAA,EAAA,EAAA,KAAA,IAAA,EAAA,GAAA,GACA,EAAA,OAAA,GACA,OAAA,EACA,EAAA,KAAA,EAAA,EAAA,GACA,EAAA,MAAA,EAAA,EAAA,OAAA,KAAA;;AChBA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,qBACA,EAAA,WAEA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,qBAAA,CAAA,GAAA,SAAA,CACA,SAAA,SAAA,GACA,SAAA,EAAA,KAAA,EAAA,GACA,QAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA;;ACTA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,SAAA,CAEA,OAAA,QAAA;;ACHA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,qBACA,EAAA,aACA,EAAA,GAAA,GAEA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,qBAAA,CAAA,GAAA,SAAA,CACA,WAAA,SAAA,GACA,IAAA,EAAA,EAAA,KAAA,EAAA,GACA,EAAA,EAAA,KAAA,IAAA,UAAA,OAAA,EAAA,UAAA,QAAA,EAAA,EAAA,SACA,EAAA,OAAA,GACA,OAAA,EACA,EAAA,KAAA,EAAA,EAAA,GACA,EAAA,MAAA,EAAA,EAAA,EAAA,UAAA;;ACfA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,YACA,EAAA,QAAA,cACA,EAAA,KAEA,EAAA,SAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,OAAA,EAAA,IACA,EAAA,IAAA,EAEA,MADA,KAAA,IAAA,GAAA,IAAA,EAAA,KAAA,OAAA,GAAA,QAAA,EAAA,UAAA,KACA,EAAA,IAAA,EAAA,KAAA,EAAA,KAEA,OAAA,QAAA,SAAA,EAAA,GACA,IAAA,EAAA,GACA,EAAA,GAAA,EAAA,GACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,WACA,IAAA,EAAA,GAAA,GAAA,KACA,OAAA,IAAA,EAAA,eAAA,EAAA,MAAA,KAAA,OAAA,IACA,SAAA;;ACjBA,aAEA,QAAA,iBAAA,CAAA,SAAA,SAAA,GACA,OAAA,SAAA,GACA,OAAA,EAAA,KAAA,IAAA,OAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,MAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,MAAA,GAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,QAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,QAAA,GAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,OAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,IAAA,GAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,QAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,KAAA,GAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,YAAA,SAAA,GACA,OAAA,SAAA,GACA,OAAA,EAAA,KAAA,OAAA,QAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,WAAA,SAAA,GACA,OAAA,SAAA,GACA,OAAA,EAAA,KAAA,OAAA,OAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,UAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,IAAA,GAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,OAAA,SAAA,GACA,OAAA,SAAA,GACA,OAAA,EAAA,KAAA,IAAA,OAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,QAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,QAAA,GAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,SAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,SAAA,GAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,MAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,MAAA,GAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,MAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,MAAA,GAAA;;ACHA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CAAA,IAAA,WAAA,OAAA,IAAA,MAAA;;ACHA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,mBAEA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,WAAA,CAAA,WACA,OAAA,OAAA,IAAA,KAAA,KAAA,UACA,IAAA,KAAA,UAAA,OAAA,KAAA,CAAA,YAAA,WAAA,OAAA,OACA,OAAA,CAEA,OAAA,SAAA,GACA,IAAA,EAAA,EAAA,MACA,EAAA,EAAA,GACA,MAAA,iBAAA,GAAA,SAAA,GAAA,EAAA,cAAA;;ACbA,aAEA,IAAA,EAAA,QAAA,YACA,EAAA,KAAA,UAAA,QACA,EAAA,KAAA,UAAA,YAEA,EAAA,SAAA,GACA,OAAA,EAAA,EAAA,EAAA,IAAA,GAIA,OAAA,QAAA,EAAA,WACA,MAAA,4BAAA,EAAA,KAAA,IAAA,MAAA,KAAA,QACA,EAAA,WACA,EAAA,KAAA,IAAA,KAAA,QACA,WACA,IAAA,SAAA,EAAA,KAAA,OAAA,MAAA,WAAA,sBACA,IAAA,EAAA,KACA,EAAA,EAAA,iBACA,EAAA,EAAA,qBACA,EAAA,EAAA,EAAA,IAAA,EAAA,KAAA,IAAA,GACA,OAAA,GAAA,QAAA,KAAA,IAAA,IAAA,MAAA,GAAA,GAAA,GACA,IAAA,EAAA,EAAA,cAAA,GAAA,IAAA,EAAA,EAAA,cACA,IAAA,EAAA,EAAA,eAAA,IAAA,EAAA,EAAA,iBACA,IAAA,EAAA,EAAA,iBAAA,KAAA,EAAA,GAAA,EAAA,IAAA,EAAA,IAAA,KACA;;ACxBA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,yBAGA,EAAA,EAAA,EAAA,EAAA,GAAA,KAAA,UAAA,cAAA,GAAA,OAAA,CACA,YAAA;;ACNA,IAAA,EAAA,KAAA,UACA,EAAA,eACA,EAAA,WACA,EAAA,EAAA,GACA,EAAA,EAAA,QACA,IAAA,KAAA,KAAA,IAAA,GACA,QAAA,cAAA,CAAA,EAAA,EAAA,WACA,IAAA,EAAA,EAAA,KAAA,MAEA,OAAA,GAAA,EAAA,EAAA,KAAA,MAAA;;ACTA,aACA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,mBACA,EAAA,SAEA,OAAA,QAAA,SAAA,GACA,GAAA,WAAA,GAAA,IAAA,GAAA,YAAA,EAAA,MAAA,UAAA,kBACA,OAAA,EAAA,EAAA,MAAA,GAAA;;ACPA,IAAA,EAAA,QAAA,SAAA,CAAA,eACA,EAAA,KAAA,UAEA,KAAA,GAAA,QAAA,UAAA,CAAA,EAAA,EAAA,QAAA;;ACFA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,QAAA,CAAA,QAAA,QAAA;;ACFA,IAAA,EAAA,QAAA,gBACA,OAAA,QAAA,SAAA,EAAA,EAAA,EAAA,GACA,IACA,OAAA,EAAA,EAAA,EAAA,GAAA,GAAA,EAAA,IAAA,EAAA,GAEA,MAAA,GACA,IAAA,EAAA,EAAA,OAEA,WADA,IAAA,GAAA,EAAA,EAAA,KAAA,IACA;;ACRA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,SAAA,CAAA,YACA,EAAA,MAAA,UAEA,OAAA,QAAA,SAAA,GACA,YAAA,IAAA,IAAA,EAAA,QAAA,GAAA,EAAA,KAAA;;ACNA,aACA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,oBAEA,OAAA,QAAA,SAAA,EAAA,EAAA,GACA,KAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,IACA,EAAA,GAAA;;ACNA,IAAA,EAAA,QAAA,cACA,EAAA,QAAA,SAAA,CAAA,YACA,EAAA,QAAA,gBACA,OAAA,QAAA,QAAA,WAAA,kBAAA,SAAA,GACA,GAAA,MAAA,EAAA,OAAA,EAAA,IACA,EAAA,eACA,EAAA,EAAA;;ACNA,IAAA,EAAA,QAAA,SAAA,CAAA,YACA,GAAA,EAEA,IACA,IAAA,EAAA,CAAA,GAAA,KACA,EAAA,OAAA,WAAA,GAAA,GAEA,MAAA,KAAA,EAAA,WAAA,MAAA,IACA,MAAA,IAEA,OAAA,QAAA,SAAA,EAAA,GACA,IAAA,IAAA,EAAA,OAAA,EACA,IAAA,GAAA,EACA,IACA,IAAA,EAAA,CAAA,GACA,EAAA,EAAA,KACA,EAAA,KAAA,WAAA,MAAA,CAAA,KAAA,GAAA,IACA,EAAA,GAAA,WAAA,OAAA,GACA,EAAA,GACA,MAAA,IACA,OAAA;;ACpBA,aACA,IAAA,EAAA,QAAA,UACA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,oBACA,EAAA,QAAA,gBACA,EAAA,QAAA,sBACA,EAAA,QAAA,8BAEA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,iBAAA,CAAA,SAAA,GAAA,MAAA,KAAA,KAAA,QAAA,CAEA,KAAA,SAAA,GACA,IAOA,EAAA,EAAA,EAAA,EAPA,EAAA,EAAA,GACA,EAAA,mBAAA,KAAA,KAAA,MACA,EAAA,UAAA,OACA,EAAA,EAAA,EAAA,UAAA,QAAA,EACA,OAAA,IAAA,EACA,EAAA,EACA,EAAA,EAAA,GAIA,GAFA,IAAA,EAAA,EAAA,EAAA,EAAA,EAAA,UAAA,QAAA,EAAA,IAEA,MAAA,GAAA,GAAA,OAAA,EAAA,GAMA,IAAA,EAAA,IAAA,EADA,EAAA,EAAA,EAAA,SACA,EAAA,EAAA,IACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,GAAA,EAAA,SANA,IAAA,EAAA,EAAA,KAAA,GAAA,EAAA,IAAA,IAAA,EAAA,EAAA,QAAA,KAAA,IACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,CAAA,EAAA,MAAA,IAAA,GAAA,EAAA,OASA,OADA,EAAA,OAAA,EACA;;AClCA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,sBAGA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,WAAA,CAAA,WACA,SAAA,KACA,QAAA,MAAA,GAAA,KAAA,aAAA,KACA,QAAA,CAEA,GAAA,WAIA,IAHA,IAAA,EAAA,EACA,EAAA,UAAA,OACA,EAAA,IAAA,mBAAA,KAAA,KAAA,OAAA,GACA,EAAA,GAAA,EAAA,EAAA,EAAA,UAAA,MAEA,OADA,EAAA,OAAA,EACA;;AChBA,aACA,IAAA,EAAA,QAAA,YAEA,OAAA,QAAA,SAAA,EAAA,GACA,QAAA,GAAA,EAAA,WAEA,EAAA,EAAA,KAAA,KAAA,aAAA,GAAA,EAAA,KAAA;;ACNA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,GAAA,KAGA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,eAAA,SAAA,QAAA,mBAAA,CAAA,IAAA,QAAA,CACA,KAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,WAAA,IAAA,EAAA,IAAA;;ACTA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,WACA,EAAA,QAAA,UACA,EAAA,QAAA,wBACA,EAAA,QAAA,gBACA,EAAA,GAAA,MAGA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,WAAA,CAAA,WACA,GAAA,EAAA,KAAA,KACA,QAAA,CACA,MAAA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,KAAA,QACA,EAAA,EAAA,MAEA,GADA,OAAA,IAAA,EAAA,EAAA,EACA,SAAA,EAAA,OAAA,EAAA,KAAA,KAAA,EAAA,GAMA,IALA,IAAA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,GACA,EAAA,IAAA,MAAA,GACA,EAAA,EACA,EAAA,EAAA,IAAA,EAAA,GAAA,UAAA,EACA,KAAA,OAAA,EAAA,GACA,KAAA,EAAA,GACA,OAAA;;ACzBA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,EAAA,QAAA,YACA,EAAA,GAAA,KACA,EAAA,CAAA,EAAA,EAAA,GAEA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,WAEA,EAAA,UAAA,OACA,EAAA,WAEA,EAAA,KAAA,UAEA,QAAA,mBAAA,CAAA,IAAA,QAAA,CAEA,KAAA,SAAA,GACA,YAAA,IAAA,EACA,EAAA,KAAA,EAAA,OACA,EAAA,KAAA,EAAA,MAAA,EAAA;;ACpBA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,eACA,EAAA,QAAA,SAAA,CAAA,WAEA,OAAA,QAAA,SAAA,GACA,IAAA,EASA,OARA,EAAA,KAGA,mBAFA,EAAA,EAAA,cAEA,IAAA,QAAA,EAAA,EAAA,aAAA,OAAA,GACA,EAAA,IAEA,QADA,EAAA,EAAA,MACA,OAAA,SAEA,IAAA,EAAA,MAAA;;ACbA,IAAA,EAAA,QAAA,gCAEA,OAAA,QAAA,SAAA,EAAA,GACA,OAAA,IAAA,EAAA,GAAA,CAAA;;ACGA,IAAA,EAAA,QAAA,UACA,EAAA,QAAA,cACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,2BACA,OAAA,QAAA,SAAA,EAAA,GACA,IAAA,EAAA,GAAA,EACA,EAAA,GAAA,EACA,EAAA,GAAA,EACA,EAAA,GAAA,EACA,EAAA,GAAA,EACA,EAAA,GAAA,GAAA,EACA,EAAA,GAAA,EACA,OAAA,SAAA,EAAA,EAAA,GAQA,IAPA,IAMA,EAAA,EANA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,QACA,EAAA,EACA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,QAAA,EAEA,EAAA,EAAA,IAAA,IAAA,GAAA,KAAA,KAEA,EAAA,EADA,EAAA,EAAA,GACA,EAAA,GACA,GACA,GAAA,EAAA,EAAA,GAAA,OACA,GAAA,EAAA,OAAA,GACA,KAAA,EAAA,OAAA,EACA,KAAA,EAAA,OAAA,EACA,KAAA,EAAA,OAAA,EACA,KAAA,EAAA,EAAA,KAAA,QACA,GAAA,EAAA,OAAA,EAGA,OAAA,GAAA,EAAA,GAAA,EAAA,EAAA;;ACzCA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,mBAAA,CAAA,GACA,EAAA,QAAA,mBAAA,CAAA,GAAA,SAAA,GAEA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,QAAA,CAEA,QAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA;;ACRA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,mBAAA,CAAA,GAEA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,mBAAA,CAAA,GAAA,KAAA,GAAA,QAAA,CAEA,IAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA;;ACPA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,mBAAA,CAAA,GAEA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,mBAAA,CAAA,GAAA,QAAA,GAAA,QAAA,CAEA,OAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA;;ACPA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,mBAAA,CAAA,GAEA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,mBAAA,CAAA,GAAA,MAAA,GAAA,QAAA,CAEA,KAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA;;ACPA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,mBAAA,CAAA,GAEA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,mBAAA,CAAA,GAAA,OAAA,GAAA,QAAA,CAEA,MAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA;;ACPA,IAAA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,EAAA,QAAA,cACA,EAAA,QAAA,gBAEA,OAAA,QAAA,SAAA,EAAA,EAAA,EAAA,EAAA,GACA,EAAA,GACA,IAAA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,QACA,EAAA,EAAA,EAAA,EAAA,EACA,EAAA,GAAA,EAAA,EACA,GAAA,EAAA,EAAA,OAAA,CACA,GAAA,KAAA,EAAA,CACA,EAAA,EAAA,GACA,GAAA,EACA,MAGA,GADA,GAAA,EACA,EAAA,EAAA,EAAA,GAAA,EACA,MAAA,UAAA,+CAGA,KAAA,EAAA,GAAA,EAAA,EAAA,EAAA,GAAA,EAAA,KAAA,IACA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,IAEA,OAAA;;AC1BA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,mBAEA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,mBAAA,CAAA,GAAA,QAAA,GAAA,QAAA,CAEA,OAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA,OAAA,UAAA,IAAA;;ACPA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,mBAEA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,mBAAA,CAAA,GAAA,aAAA,GAAA,QAAA,CAEA,YAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA,OAAA,UAAA,IAAA;;ACPA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,oBAAA,EAAA,GACA,EAAA,GAAA,QACA,IAAA,GAAA,EAAA,CAAA,GAAA,QAAA,GAAA,GAAA,EAEA,EAAA,EAAA,EAAA,EAAA,GAAA,IAAA,QAAA,mBAAA,CAAA,IAAA,QAAA,CAEA,QAAA,SAAA,GACA,OAAA,EAEA,EAAA,MAAA,KAAA,YAAA,EACA,EAAA,KAAA,EAAA,UAAA;;ACZA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,EAAA,GAAA,YACA,IAAA,GAAA,EAAA,CAAA,GAAA,YAAA,GAAA,GAAA,EAEA,EAAA,EAAA,EAAA,EAAA,GAAA,IAAA,QAAA,mBAAA,CAAA,IAAA,QAAA,CAEA,YAAA,SAAA,GAEA,GAAA,EAAA,OAAA,EAAA,MAAA,KAAA,YAAA,EACA,IAAA,EAAA,EAAA,MACA,EAAA,EAAA,EAAA,QACA,EAAA,EAAA,EAGA,IAFA,UAAA,OAAA,IAAA,EAAA,KAAA,IAAA,EAAA,EAAA,UAAA,MACA,EAAA,IAAA,EAAA,EAAA,GACA,GAAA,EAAA,IAAA,GAAA,KAAA,GAAA,EAAA,KAAA,EAAA,OAAA,GAAA,EACA,OAAA;;AClBA,aACA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,wBACA,EAAA,QAAA,gBAEA,OAAA,QAAA,GAAA,YAAA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,MACA,EAAA,EAAA,EAAA,QACA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,GACA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,EACA,EAAA,KAAA,UAAA,IAAA,EAAA,EAAA,EAAA,EAAA,IAAA,EAAA,EAAA,GACA,EAAA,EAMA,IALA,EAAA,GAAA,EAAA,EAAA,IACA,GAAA,EACA,GAAA,EAAA,EACA,GAAA,EAAA,GAEA,KAAA,GACA,KAAA,EAAA,EAAA,GAAA,EAAA,UACA,EAAA,GACA,GAAA,EACA,GAAA,EACA,OAAA;;ACvBA,IAAA,EAAA,QAAA,SAAA,CAAA,eACA,EAAA,MAAA,UACA,MAAA,EAAA,IAAA,QAAA,UAAA,CAAA,EAAA,EAAA,IACA,OAAA,QAAA,SAAA,GACA,EAAA,GAAA,IAAA;;ACJA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,QAAA,CAAA,WAAA,QAAA,0BAEA,QAAA,wBAAA,CAAA;;ACJA,aACA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,wBACA,EAAA,QAAA,gBACA,OAAA,QAAA,SAAA,GAOA,IANA,IAAA,EAAA,EAAA,MACA,EAAA,EAAA,EAAA,QACA,EAAA,UAAA,OACA,EAAA,EAAA,EAAA,EAAA,UAAA,QAAA,EAAA,GACA,EAAA,EAAA,EAAA,UAAA,QAAA,EACA,OAAA,IAAA,EAAA,EAAA,EAAA,EAAA,GACA,EAAA,GAAA,EAAA,KAAA,EACA,OAAA;;ACZA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,QAAA,CAAA,KAAA,QAAA,mBAEA,QAAA,wBAAA,CAAA;;ACLA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,mBAAA,CAAA,GACA,EAAA,OACA,GAAA,EAEA,IAAA,IAAA,MAAA,GAAA,GAAA,WAAA,GAAA,IACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,CACA,KAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,MAGA,QAAA,wBAAA,CAAA;;ACbA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,mBAAA,CAAA,GACA,EAAA,YACA,GAAA,EAEA,IAAA,IAAA,MAAA,GAAA,GAAA,WAAA,GAAA,IACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,CACA,UAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,MAGA,QAAA,wBAAA,CAAA;;;ACbA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,kBACA,EAAA,QAAA,SAAA,CAAA,WAEA,OAAA,QAAA,SAAA,GACA,IAAA,EAAA,EAAA,GACA,GAAA,IAAA,EAAA,IAAA,EAAA,EAAA,EAAA,EAAA,CACA,cAAA,EACA,IAAA,WAAA,OAAA;;ACVA,QAAA,iBAAA,CAAA;;ACAA,OAAA,QAAA,SAAA,EAAA,GACA,MAAA,CAAA,MAAA,EAAA,OAAA;;ACDA,aACA,IAAA,EAAA,QAAA,yBACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBAMA,OAAA,QAAA,QAAA,iBAAA,CAAA,MAAA,QAAA,SAAA,EAAA,GACA,KAAA,GAAA,EAAA,GACA,KAAA,GAAA,EACA,KAAA,GAAA,GAEA,WACA,IAAA,EAAA,KAAA,GACA,EAAA,KAAA,GACA,EAAA,KAAA,KACA,OAAA,GAAA,GAAA,EAAA,QACA,KAAA,QAAA,EACA,EAAA,IAEA,EAAA,EAAA,QAAA,EAAA,EACA,UAAA,EAAA,EAAA,GACA,CAAA,EAAA,EAAA,MACA,UAGA,EAAA,UAAA,EAAA,MAEA,EAAA,QACA,EAAA,UACA,EAAA;;ACjCA,aAEA,IAAA,EAAA,QAAA,gBACA,OAAA,QAAA,WACA,IAAA,EAAA,EAAA,MACA,EAAA,GAMA,OALA,EAAA,SAAA,GAAA,KACA,EAAA,aAAA,GAAA,KACA,EAAA,YAAA,GAAA,KACA,EAAA,UAAA,GAAA,KACA,EAAA,SAAA,GAAA,KACA;;;ACXA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,0BACA,EAAA,QAAA,gBAAA,EACA,EAAA,QAAA,kBAAA,EACA,EAAA,QAAA,gBACA,EAAA,QAAA,YACA,EAAA,EAAA,OACA,EAAA,EACA,EAAA,EAAA,UACA,EAAA,KACA,EAAA,KAEA,EAAA,IAAA,EAAA,KAAA,EAEA,GAAA,QAAA,qBAAA,GAAA,QAAA,WAAA,CAAA,WAGA,OAFA,EAAA,QAAA,SAAA,CAAA,WAAA,EAEA,EAAA,IAAA,GAAA,EAAA,IAAA,GAAA,QAAA,EAAA,EAAA,QACA,CACA,EAAA,SAAA,EAAA,GACA,IAAA,EAAA,gBAAA,EACA,EAAA,EAAA,GACA,OAAA,IAAA,EACA,OAAA,GAAA,GAAA,EAAA,cAAA,GAAA,EAAA,EACA,EAAA,EACA,IAAA,EAAA,IAAA,EAAA,EAAA,OAAA,EAAA,GACA,GAAA,EAAA,aAAA,GAAA,EAAA,OAAA,EAAA,GAAA,EAAA,EAAA,KAAA,GAAA,GACA,EAAA,KAAA,EAAA,IASA,IAPA,IAAA,EAAA,SAAA,GACA,KAAA,GAAA,EAAA,EAAA,EAAA,CACA,cAAA,EACA,IAAA,WAAA,OAAA,EAAA,IACA,IAAA,SAAA,GAAA,EAAA,GAAA,MAGA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,OAAA,GAAA,EAAA,EAAA,MACA,EAAA,YAAA,EACA,EAAA,UAAA,EACA,QAAA,cAAA,CAAA,EAAA,SAAA,GAGA,QAAA,iBAAA,CAAA;;AC1CA,aAEA,IAAA,EAAA,QAAA,YAEA,EAAA,OAAA,UAAA,KAIA,EAAA,OAAA,UAAA,QAEA,EAAA,EAEA,EAAA,YAEA,EAAA,WACA,IAAA,EAAA,IACA,EAAA,MAGA,OAFA,EAAA,KAAA,EAAA,KACA,EAAA,KAAA,EAAA,KACA,IAAA,EAAA,IAAA,IAAA,EAAA,GALA,GASA,OAAA,IAAA,OAAA,KAAA,IAAA,GAEA,EAAA,GAAA,EAEA,IACA,EAAA,SAAA,GACA,IACA,EAAA,EAAA,EAAA,EADA,EAAA,KAwBA,OArBA,IACA,EAAA,IAAA,OAAA,IAAA,EAAA,OAAA,WAAA,EAAA,KAAA,KAEA,IAAA,EAAA,EAAA,IAEA,EAAA,EAAA,KAAA,EAAA,GAEA,GAAA,IACA,EAAA,GAAA,EAAA,OAAA,EAAA,MAAA,EAAA,GAAA,OAAA,GAEA,GAAA,GAAA,EAAA,OAAA,GAIA,EAAA,KAAA,EAAA,GAAA,EAAA,WACA,IAAA,EAAA,EAAA,EAAA,UAAA,OAAA,EAAA,SACA,IAAA,UAAA,KAAA,EAAA,QAAA,KAKA,IAIA,OAAA,QAAA;;ACzDA,aACA,IAAA,EAAA,QAAA,kBACA,QAAA,YAAA,CAAA,CACA,OAAA,SACA,OAAA,EACA,OAAA,IAAA,IAAA,MACA,CACA,KAAA;;ACNA,QAAA,mBAAA,KAAA,KAAA,OAAA,QAAA,gBAAA,EAAA,OAAA,UAAA,QAAA,CACA,cAAA,EACA,IAAA,QAAA;;;ACHA,aACA,QAAA,sBACA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,YACA,EAAA,QAAA,kBACA,EAAA,WACA,EAAA,IAAA,GAEA,EAAA,SAAA,GACA,QAAA,cAAA,CAAA,OAAA,UAAA,EAAA,GAAA,IAIA,QAAA,WAAA,CAAA,WAAA,MAAA,QAAA,EAAA,KAAA,CAAA,OAAA,IAAA,MAAA,QACA,EAAA,WACA,IAAA,EAAA,EAAA,MACA,MAAA,IAAA,OAAA,EAAA,OAAA,IACA,UAAA,EAAA,EAAA,OAAA,GAAA,aAAA,OAAA,EAAA,KAAA,QAAA,KAGA,EAAA,MAAA,GACA,EAAA,WACA,OAAA,EAAA,KAAA;;ACtBA,aACA,IAAA,EAAA,QAAA,eAAA,EAAA,GAIA,OAAA,QAAA,SAAA,EAAA,EAAA,GACA,OAAA,GAAA,EAAA,EAAA,EAAA,GAAA,OAAA;;ACNA,aAEA,IAAA,EAAA,QAAA,cACA,EAAA,OAAA,UAAA,KAIA,OAAA,QAAA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,KACA,GAAA,mBAAA,EAAA,CACA,IAAA,EAAA,EAAA,KAAA,EAAA,GACA,GAAA,iBAAA,EACA,MAAA,IAAA,UAAA,sEAEA,OAAA,EAEA,GAAA,WAAA,EAAA,GACA,MAAA,IAAA,UAAA,+CAEA,OAAA,EAAA,KAAA,EAAA;;ACnBA,aACA,QAAA,qBACA,IAAA,EAAA,QAAA,eACA,EAAA,QAAA,WACA,EAAA,QAAA,YACA,EAAA,QAAA,cACA,EAAA,QAAA,UACA,EAAA,QAAA,kBAEA,EAAA,EAAA,WAEA,GAAA,EAAA,WAIA,IAAA,EAAA,IAMA,OALA,EAAA,KAAA,WACA,IAAA,EAAA,GAEA,OADA,EAAA,OAAA,CAAA,EAAA,KACA,GAEA,MAAA,GAAA,QAAA,EAAA,UAGA,EAAA,WAEA,IAAA,EAAA,OACA,EAAA,EAAA,KACA,EAAA,KAAA,WAAA,OAAA,EAAA,MAAA,KAAA,YACA,IAAA,EAAA,KAAA,MAAA,GACA,OAAA,IAAA,EAAA,QAAA,MAAA,EAAA,IAAA,MAAA,EAAA,GANA,GASA,OAAA,QAAA,SAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,GAEA,GAAA,EAAA,WAEA,IAAA,EAAA,GAEA,OADA,EAAA,GAAA,WAAA,OAAA,GACA,GAAA,GAAA,GAAA,KAGA,EAAA,GAAA,EAAA,WAEA,IAAA,GAAA,EACA,EAAA,IASA,OARA,EAAA,KAAA,WAAA,OAAA,GAAA,EAAA,MACA,UAAA,IAGA,EAAA,YAAA,GACA,EAAA,YAAA,GAAA,WAAA,OAAA,IAEA,EAAA,GAAA,KACA,SACA,EAEA,IACA,IACA,GACA,YAAA,IAAA,GACA,UAAA,IAAA,EACA,CACA,IAAA,EAAA,IAAA,GACA,EAAA,EACA,EACA,EACA,GAAA,GACA,SAAA,EAAA,EAAA,EAAA,EAAA,GACA,OAAA,EAAA,OAAA,EACA,IAAA,EAIA,CAAA,MAAA,EAAA,MAAA,EAAA,KAAA,EAAA,EAAA,IAEA,CAAA,MAAA,EAAA,MAAA,EAAA,KAAA,EAAA,EAAA,IAEA,CAAA,MAAA,KAGA,EAAA,EAAA,GACA,EAAA,EAAA,GAEA,EAAA,OAAA,UAAA,EAAA,GACA,EAAA,OAAA,UAAA,EAAA,GAAA,EAGA,SAAA,EAAA,GAAA,OAAA,EAAA,KAAA,EAAA,KAAA,IAGA,SAAA,GAAA,OAAA,EAAA,KAAA,EAAA;;AC5FA,aAEA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,2BACA,EAAA,QAAA,2BAGA,QAAA,gBAAA,CAAA,QAAA,EAAA,SAAA,EAAA,EAAA,EAAA,GACA,MAAA,CAGA,SAAA,GACA,IAAA,EAAA,EAAA,MACA,EAAA,MAAA,OAAA,EAAA,EAAA,GACA,YAAA,IAAA,EAAA,EAAA,KAAA,EAAA,GAAA,IAAA,OAAA,GAAA,GAAA,OAAA,KAIA,SAAA,GACA,IAAA,EAAA,EAAA,EAAA,EAAA,MACA,GAAA,EAAA,KAAA,OAAA,EAAA,MACA,IAAA,EAAA,EAAA,GACA,EAAA,OAAA,MACA,IAAA,EAAA,OAAA,OAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,QACA,EAAA,UAAA,EAIA,IAHA,IAEA,EAFA,EAAA,GACA,EAAA,EAEA,QAAA,EAAA,EAAA,EAAA,KAAA,CACA,IAAA,EAAA,OAAA,EAAA,IACA,EAAA,GAAA,EACA,KAAA,IAAA,EAAA,UAAA,EAAA,EAAA,EAAA,EAAA,WAAA,IACA,IAEA,OAAA,IAAA,EAAA,KAAA;;;ACkFA,IAAA,EAAA,UAAA,GApHA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,QAAA,2BACA,EAAA,QAAA,2BACA,EAAA,KAAA,IACA,EAAA,KAAA,IACA,EAAA,KAAA,MACA,EAAA,4BACA,EAAA,oBAEA,EAAA,SAAA,GACA,YAAA,IAAA,EAAA,EAAA,OAAA,IAIA,QAAA,gBAAA,CAAA,UAAA,EAAA,SAAA,EAAA,EAAA,EAAA,GACA,MAAA,CAGA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,MACA,EAAA,MAAA,OAAA,EAAA,EAAA,GACA,YAAA,IAAA,EACA,EAAA,KAAA,EAAA,EAAA,GACA,EAAA,KAAA,OAAA,GAAA,EAAA,IAIA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,EAAA,EAAA,KAAA,GACA,GAAA,EAAA,KAAA,OAAA,EAAA,MAEA,IAAA,EAAA,EAAA,GACA,EAAA,OAAA,MACA,EAAA,mBAAA,EACA,IAAA,EAAA,OAAA,IACA,IAAA,EAAA,EAAA,OACA,GAAA,EAAA,CACA,IAAA,EAAA,EAAA,QACA,EAAA,UAAA,EAGA,IADA,IAAA,EAAA,KACA,CACA,IAAA,EAAA,EAAA,EAAA,GACA,GAAA,OAAA,EAAA,MAEA,GADA,EAAA,KAAA,IACA,EAAA,MAEA,KADA,OAAA,EAAA,MACA,EAAA,UAAA,EAAA,EAAA,EAAA,EAAA,WAAA,IAIA,IAFA,IAAA,EAAA,GACA,EAAA,EACA,EAAA,EAAA,EAAA,EAAA,OAAA,IAAA,CACA,EAAA,EAAA,GASA,IARA,IAAA,EAAA,OAAA,EAAA,IACA,EAAA,EAAA,EAAA,EAAA,EAAA,OAAA,EAAA,QAAA,GACA,EAAA,GAMA,EAAA,EAAA,EAAA,EAAA,OAAA,IAAA,EAAA,KAAA,EAAA,EAAA,KACA,IAAA,EAAA,EAAA,OACA,GAAA,EAAA,CACA,IAAA,EAAA,CAAA,GAAA,OAAA,EAAA,EAAA,QACA,IAAA,GAAA,EAAA,KAAA,GACA,IAAA,EAAA,OAAA,EAAA,WAAA,EAAA,SAEA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAEA,GAAA,IACA,GAAA,EAAA,MAAA,EAAA,GAAA,EACA,EAAA,EAAA,EAAA,QAGA,OAAA,EAAA,EAAA,MAAA,KAKA,SAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,EAAA,OACA,EAAA,EAAA,OACA,EAAA,EAKA,YAJA,IAAA,IACA,EAAA,EAAA,GACA,EAAA,GAEA,EAAA,KAAA,EAAA,EAAA,SAAA,EAAA,GACA,IAAA,EACA,OAAA,EAAA,OAAA,IACA,IAAA,IAAA,MAAA,IACA,IAAA,IAAA,OAAA,EACA,IAAA,IAAA,OAAA,EAAA,MAAA,EAAA,GACA,IAAA,IAAA,OAAA,EAAA,MAAA,GACA,IAAA,IACA,EAAA,EAAA,EAAA,MAAA,GAAA,IACA,MACA,QACA,IAAA,GAAA,EACA,GAAA,IAAA,EAAA,OAAA,EACA,GAAA,EAAA,EAAA,CACA,IAAA,EAAA,EAAA,EAAA,IACA,OAAA,IAAA,EAAA,EACA,GAAA,OAAA,IAAA,EAAA,EAAA,GAAA,EAAA,OAAA,GAAA,EAAA,EAAA,GAAA,EAAA,OAAA,GACA,EAEA,EAAA,EAAA,EAAA,GAEA,YAAA,IAAA,EAAA,GAAA;;AClHA,aAEA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,QAAA,2BAGA,QAAA,gBAAA,CAAA,SAAA,EAAA,SAAA,EAAA,EAAA,EAAA,GACA,MAAA,CAGA,SAAA,GACA,IAAA,EAAA,EAAA,MACA,EAAA,MAAA,OAAA,EAAA,EAAA,GACA,YAAA,IAAA,EAAA,EAAA,KAAA,EAAA,GAAA,IAAA,OAAA,GAAA,GAAA,OAAA,KAIA,SAAA,GACA,IAAA,EAAA,EAAA,EAAA,EAAA,MACA,GAAA,EAAA,KAAA,OAAA,EAAA,MACA,IAAA,EAAA,EAAA,GACA,EAAA,OAAA,MACA,EAAA,EAAA,UACA,EAAA,EAAA,KAAA,EAAA,UAAA,GACA,IAAA,EAAA,EAAA,EAAA,GAEA,OADA,EAAA,EAAA,UAAA,KAAA,EAAA,UAAA,GACA,OAAA,GAAA,EAAA,EAAA;;AC1BA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,QAAA,SAAA,CAAA,WACA,OAAA,QAAA,SAAA,EAAA,GACA,IACA,EADA,EAAA,EAAA,GAAA,YAEA,YAAA,IAAA,GAAA,OAAA,EAAA,EAAA,GAAA,IAAA,EAAA,EAAA;;ACPA,aAEA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,0BACA,EAAA,QAAA,2BACA,EAAA,QAAA,gBACA,EAAA,QAAA,2BACA,EAAA,QAAA,kBACA,EAAA,QAAA,YACA,EAAA,KAAA,IACA,EAAA,GAAA,KACA,EAAA,QACA,EAAA,SACA,EAAA,YACA,EAAA,WAGA,GAAA,EAAA,WAAA,OAAA,EAAA,OAGA,QAAA,gBAAA,CAAA,QAAA,EAAA,SAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAkDA,OAxCA,EARA,KAAA,OAAA,GAAA,QAAA,IACA,GAAA,OAAA,GAAA,QAAA,GAAA,IACA,GAAA,KAAA,GAAA,WAAA,IACA,GAAA,IAAA,GAAA,YAAA,IACA,IAAA,GAAA,QAAA,GAAA,GACA,GAAA,GAAA,MAAA,GAGA,SAAA,EAAA,GACA,IAAA,EAAA,OAAA,MACA,QAAA,IAAA,GAAA,IAAA,EAAA,MAAA,GAEA,IAAA,EAAA,GAAA,OAAA,EAAA,KAAA,EAAA,EAAA,GAWA,IAVA,IASA,EAAA,EAAA,EATA,EAAA,GACA,GAAA,EAAA,WAAA,IAAA,KACA,EAAA,UAAA,IAAA,KACA,EAAA,QAAA,IAAA,KACA,EAAA,OAAA,IAAA,IACA,EAAA,EACA,OAAA,IAAA,EAAA,EAAA,IAAA,EAEA,EAAA,IAAA,OAAA,EAAA,OAAA,EAAA,MAEA,EAAA,EAAA,KAAA,EAAA,QACA,EAAA,EAAA,IACA,IACA,EAAA,KAAA,EAAA,MAAA,EAAA,EAAA,QACA,EAAA,GAAA,GAAA,EAAA,MAAA,EAAA,IAAA,EAAA,MAAA,EAAA,EAAA,MAAA,IACA,EAAA,EAAA,GAAA,GACA,EAAA,EACA,EAAA,IAAA,KAEA,EAAA,KAAA,EAAA,OAAA,EAAA,KAKA,OAHA,IAAA,EAAA,IACA,GAAA,EAAA,KAAA,KAAA,EAAA,KAAA,IACA,EAAA,KAAA,EAAA,MAAA,IACA,EAAA,GAAA,EAAA,EAAA,MAAA,EAAA,GAAA,GAGA,IAAA,QAAA,EAAA,GAAA,GACA,SAAA,EAAA,GACA,YAAA,IAAA,GAAA,IAAA,EAAA,GAAA,EAAA,KAAA,KAAA,EAAA,IAGA,EAGA,CAGA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,MACA,EAAA,MAAA,OAAA,EAAA,EAAA,GACA,YAAA,IAAA,EACA,EAAA,KAAA,EAAA,EAAA,GACA,EAAA,KAAA,OAAA,GAAA,EAAA,IAOA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,EAAA,EAAA,KAAA,EAAA,IAAA,GACA,GAAA,EAAA,KAAA,OAAA,EAAA,MAEA,IAAA,EAAA,EAAA,GACA,EAAA,OAAA,MACA,EAAA,EAAA,EAAA,QAEA,EAAA,EAAA,QACA,GAAA,EAAA,WAAA,IAAA,KACA,EAAA,UAAA,IAAA,KACA,EAAA,QAAA,IAAA,KACA,EAAA,IAAA,KAIA,EAAA,IAAA,EAAA,EAAA,EAAA,OAAA,EAAA,OAAA,IAAA,GACA,OAAA,IAAA,EAAA,EAAA,IAAA,EACA,GAAA,IAAA,EAAA,MAAA,GACA,GAAA,IAAA,EAAA,OAAA,OAAA,OAAA,EAAA,EAAA,GAAA,CAAA,GAAA,GAIA,IAHA,IAAA,EAAA,EACA,EAAA,EACA,EAAA,GACA,EAAA,EAAA,QAAA,CACA,EAAA,UAAA,EAAA,EAAA,EACA,IACA,EADA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,MAAA,IAEA,GACA,OAAA,IACA,EAAA,EAAA,EAAA,EAAA,WAAA,EAAA,EAAA,IAAA,EAAA,WAAA,EAEA,EAAA,EAAA,EAAA,EAAA,OACA,CAEA,GADA,EAAA,KAAA,EAAA,MAAA,EAAA,IACA,EAAA,SAAA,EAAA,OAAA,EACA,IAAA,IAAA,EAAA,EAAA,GAAA,EAAA,OAAA,EAAA,IAEA,GADA,EAAA,KAAA,EAAA,IACA,EAAA,SAAA,EAAA,OAAA,EAEA,EAAA,EAAA,GAIA,OADA,EAAA,KAAA,EAAA,MAAA,IACA;;AClIA,OAAA,QAAA,SAAA,EAAA,EAAA,EAAA,GACA,KAAA,aAAA,SAAA,IAAA,GAAA,KAAA,EACA,MAAA,UAAA,EAAA,2BACA,OAAA;;ACHA,IAAA,EAAA,QAAA,UACA,EAAA,QAAA,gBACA,EAAA,QAAA,oBACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,8BACA,EAAA,GACA,EAAA,GACA,EAAA,OAAA,QAAA,SAAA,EAAA,EAAA,EAAA,EAAA,GACA,IAGA,EAAA,EAAA,EAAA,EAHA,EAAA,EAAA,WAAA,OAAA,GAAA,EAAA,GACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GACA,EAAA,EAEA,GAAA,mBAAA,EAAA,MAAA,UAAA,EAAA,qBAEA,GAAA,EAAA,IAAA,IAAA,EAAA,EAAA,EAAA,QAAA,EAAA,EAAA,IAEA,IADA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,IAAA,GAAA,EAAA,IAAA,EAAA,EAAA,OACA,GAAA,IAAA,EAAA,OAAA,OACA,IAAA,EAAA,EAAA,KAAA,KAAA,EAAA,EAAA,QAAA,MAEA,IADA,EAAA,EAAA,EAAA,EAAA,EAAA,MAAA,MACA,GAAA,IAAA,EAAA,OAAA,GAGA,EAAA,MAAA,EACA,EAAA,OAAA;;;;ACxBA,IAaA,EAAA,EAAA,EAbA,EAAA,QAAA,UACA,EAAA,QAAA,aACA,EAAA,QAAA,WACA,EAAA,QAAA,iBACA,EAAA,QAAA,aACA,EAAA,EAAA,QACA,EAAA,EAAA,aACA,EAAA,EAAA,eACA,EAAA,EAAA,eACA,EAAA,EAAA,SACA,EAAA,EACA,EAAA,GACA,EAAA,qBAEA,EAAA,WACA,IAAA,GAAA,KAEA,GAAA,EAAA,eAAA,GAAA,CACA,IAAA,EAAA,EAAA,UACA,EAAA,GACA,MAGA,EAAA,SAAA,GACA,EAAA,KAAA,EAAA,OAGA,GAAA,IACA,EAAA,SAAA,GAGA,IAFA,IAAA,EAAA,GACA,EAAA,EACA,UAAA,OAAA,GAAA,EAAA,KAAA,UAAA,MAMA,OALA,IAAA,GAAA,WAEA,EAAA,mBAAA,EAAA,EAAA,SAAA,GAAA,IAEA,EAAA,GACA,GAEA,EAAA,SAAA,UACA,EAAA,IAGA,WAAA,QAAA,SAAA,CAAA,GACA,EAAA,SAAA,GACA,EAAA,SAAA,EAAA,EAAA,EAAA,KAGA,GAAA,EAAA,IACA,EAAA,SAAA,GACA,EAAA,IAAA,EAAA,EAAA,EAAA,KAGA,GAEA,GADA,EAAA,IAAA,GACA,MACA,EAAA,MAAA,UAAA,EACA,EAAA,EAAA,EAAA,YAAA,EAAA,IAGA,EAAA,kBAAA,mBAAA,cAAA,EAAA,eACA,EAAA,SAAA,GACA,EAAA,YAAA,EAAA,GAAA,MAEA,EAAA,iBAAA,UAAA,GAAA,IAGA,EADA,KAAA,EAAA,UACA,SAAA,GACA,EAAA,YAAA,EAAA,WAAA,GAAA,WACA,EAAA,YAAA,MACA,EAAA,KAAA,KAKA,SAAA,GACA,WAAA,EAAA,EAAA,EAAA,GAAA,KAIA,OAAA,QAAA,CACA,IAAA,EACA,MAAA;;;;AClFA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,WAAA,IACA,EAAA,EAAA,kBAAA,EAAA,uBACA,EAAA,EAAA,QACA,EAAA,EAAA,QACA,EAAA,WAAA,QAAA,SAAA,CAAA,GAEA,OAAA,QAAA,WACA,IAAA,EAAA,EAAA,EAEA,EAAA,WACA,IAAA,EAAA,EAEA,IADA,IAAA,EAAA,EAAA,SAAA,EAAA,OACA,GAAA,CACA,EAAA,EAAA,GACA,EAAA,EAAA,KACA,IACA,IACA,MAAA,GAGA,MAFA,EAAA,IACA,OAAA,EACA,GAEA,OAAA,EACA,GAAA,EAAA,SAIA,GAAA,EACA,EAAA,WACA,EAAA,SAAA,SAGA,IAAA,GAAA,EAAA,WAAA,EAAA,UAAA,WAQA,GAAA,GAAA,EAAA,QAAA,CAEA,IAAA,EAAA,EAAA,aAAA,GACA,EAAA,WACA,EAAA,KAAA,SASA,EAAA,WAEA,EAAA,KAAA,EAAA,QAvBA,CACA,IAAA,GAAA,EACA,EAAA,SAAA,eAAA,IACA,IAAA,EAAA,GAAA,QAAA,EAAA,CAAA,eAAA,IACA,EAAA,WACA,EAAA,KAAA,GAAA,GAsBA,OAAA,SAAA,GACA,IAAA,EAAA,CAAA,GAAA,EAAA,UAAA,GACA,IAAA,EAAA,KAAA,GACA,IACA,EAAA,EACA,KACA,EAAA;;AClEA,aAEA,IAAA,EAAA,QAAA,iBAEA,SAAA,EAAA,GACA,IAAA,EAAA,EACA,KAAA,QAAA,IAAA,EAAA,SAAA,EAAA,GACA,QAAA,IAAA,QAAA,IAAA,EAAA,MAAA,UAAA,2BACA,EAAA,EACA,EAAA,IAEA,KAAA,QAAA,EAAA,GACA,KAAA,OAAA,EAAA,GAGA,OAAA,QAAA,EAAA,SAAA,GACA,OAAA,IAAA,EAAA;;AChBA,OAAA,QAAA,SAAA,GACA,IACA,MAAA,CAAA,GAAA,EAAA,EAAA,KACA,MAAA,GACA,MAAA,CAAA,GAAA,EAAA,EAAA;;;ACJA,IAAA,EAAA,QAAA,aACA,EAAA,EAAA,UAEA,OAAA,QAAA,GAAA,EAAA,WAAA;;ACHA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,6BAEA,OAAA,QAAA,SAAA,EAAA,GAEA,GADA,EAAA,GACA,EAAA,IAAA,EAAA,cAAA,EAAA,OAAA,EACA,IAAA,EAAA,EAAA,EAAA,GAGA,OADA,EADA,EAAA,SACA,GACA,EAAA;;ACVA,IAAA,EAAA,QAAA,eACA,OAAA,QAAA,SAAA,EAAA,EAAA,GACA,IAAA,IAAA,KAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,GACA,OAAA;;;;ACHA,aACA,IAwBA,EAAA,EAAA,EAAA,EAxBA,EAAA,QAAA,cACA,EAAA,QAAA,aACA,EAAA,QAAA,UACA,EAAA,QAAA,cACA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,QAAA,kBACA,EAAA,QAAA,aACA,EAAA,QAAA,0BACA,EAAA,QAAA,WAAA,IACA,EAAA,QAAA,eAAA,GACA,EAAA,QAAA,6BACA,EAAA,QAAA,cACA,EAAA,QAAA,iBACA,EAAA,QAAA,sBACA,EAAA,UACA,EAAA,EAAA,UACA,EAAA,EAAA,QACA,EAAA,GAAA,EAAA,SACA,EAAA,GAAA,EAAA,IAAA,GACA,EAAA,EAAA,GACA,EAAA,WAAA,EAAA,GACA,EAAA,aAEA,EAAA,EAAA,EAAA,EAEA,IAAA,WACA,IAEA,IAAA,EAAA,EAAA,QAAA,GACA,GAAA,EAAA,YAAA,IAAA,QAAA,SAAA,CAAA,YAAA,SAAA,GACA,EAAA,EAAA,IAGA,OAAA,GAAA,mBAAA,wBACA,EAAA,KAAA,aAAA,GAIA,IAAA,EAAA,QAAA,SACA,IAAA,EAAA,QAAA,aACA,MAAA,KAfA,GAmBA,EAAA,SAAA,GACA,IAAA,EACA,SAAA,EAAA,IAAA,mBAAA,EAAA,EAAA,QAAA,GAEA,EAAA,SAAA,EAAA,GACA,IAAA,EAAA,GAAA,CACA,EAAA,IAAA,EACA,IAAA,EAAA,EAAA,GACA,EAAA,WAoCA,IAnCA,IAAA,EAAA,EAAA,GACA,EAAA,GAAA,EAAA,GACA,EAAA,EACA,EAAA,SAAA,GACA,IAIA,EAAA,EAAA,EAJA,EAAA,EAAA,EAAA,GAAA,EAAA,KACA,EAAA,EAAA,QACA,EAAA,EAAA,OACA,EAAA,EAAA,OAEA,IACA,GACA,IACA,GAAA,EAAA,IAAA,EAAA,GACA,EAAA,GAAA,IAEA,IAAA,EAAA,EAAA,GAEA,GAAA,EAAA,QACA,EAAA,EAAA,GACA,IACA,EAAA,OACA,GAAA,IAGA,IAAA,EAAA,QACA,EAAA,EAAA,yBACA,EAAA,EAAA,IACA,EAAA,KAAA,EAAA,EAAA,GACA,EAAA,IACA,EAAA,GACA,MAAA,GACA,IAAA,GAAA,EAAA,OACA,EAAA,KAGA,EAAA,OAAA,GAAA,EAAA,EAAA,MACA,EAAA,GAAA,GACA,EAAA,IAAA,EACA,IAAA,EAAA,IAAA,EAAA,OAGA,EAAA,SAAA,GACA,EAAA,KAAA,EAAA,WACA,IAEA,EAAA,EAAA,EAFA,EAAA,EAAA,GACA,EAAA,EAAA,GAeA,GAbA,IACA,EAAA,EAAA,WACA,EACA,EAAA,KAAA,qBAAA,EAAA,IACA,EAAA,EAAA,sBACA,EAAA,CAAA,QAAA,EAAA,OAAA,KACA,EAAA,EAAA,UAAA,EAAA,OACA,EAAA,MAAA,8BAAA,KAIA,EAAA,GAAA,GAAA,EAAA,GAAA,EAAA,GACA,EAAA,QAAA,EACA,GAAA,EAAA,EAAA,MAAA,EAAA,KAGA,EAAA,SAAA,GACA,OAAA,IAAA,EAAA,IAAA,KAAA,EAAA,IAAA,EAAA,IAAA,QAEA,EAAA,SAAA,GACA,EAAA,KAAA,EAAA,WACA,IAAA,EACA,EACA,EAAA,KAAA,mBAAA,IACA,EAAA,EAAA,qBACA,EAAA,CAAA,QAAA,EAAA,OAAA,EAAA,QAIA,EAAA,SAAA,GACA,IAAA,EAAA,KACA,EAAA,KACA,EAAA,IAAA,GACA,EAAA,EAAA,IAAA,GACA,GAAA,EACA,EAAA,GAAA,EACA,EAAA,KAAA,EAAA,GAAA,EAAA,GAAA,SACA,EAAA,GAAA,KAEA,EAAA,SAAA,GACA,IACA,EADA,EAAA,KAEA,IAAA,EAAA,GAAA,CACA,EAAA,IAAA,EACA,EAAA,EAAA,IAAA,EACA,IACA,GAAA,IAAA,EAAA,MAAA,EAAA,qCACA,EAAA,EAAA,IACA,EAAA,WACA,IAAA,EAAA,CAAA,GAAA,EAAA,IAAA,GACA,IACA,EAAA,KAAA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,IACA,MAAA,GACA,EAAA,KAAA,EAAA,OAIA,EAAA,GAAA,EACA,EAAA,GAAA,EACA,EAAA,GAAA,IAEA,MAAA,GACA,EAAA,KAAA,CAAA,GAAA,EAAA,IAAA,GAAA,MAKA,IAEA,EAAA,SAAA,GACA,EAAA,KAAA,EAAA,EAAA,MACA,EAAA,GACA,EAAA,KAAA,MACA,IACA,EAAA,EAAA,EAAA,KAAA,GAAA,EAAA,EAAA,KAAA,IACA,MAAA,GACA,EAAA,KAAA,KAAA,MAIA,EAAA,SAAA,GACA,KAAA,GAAA,GACA,KAAA,QAAA,EACA,KAAA,GAAA,EACA,KAAA,IAAA,EACA,KAAA,QAAA,EACA,KAAA,GAAA,EACA,KAAA,IAAA,IAEA,UAAA,QAAA,kBAAA,CAAA,EAAA,UAAA,CAEA,KAAA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,EAAA,KAAA,IAOA,OANA,EAAA,GAAA,mBAAA,GAAA,EACA,EAAA,KAAA,mBAAA,GAAA,EACA,EAAA,OAAA,EAAA,EAAA,YAAA,EACA,KAAA,GAAA,KAAA,GACA,KAAA,IAAA,KAAA,GAAA,KAAA,GACA,KAAA,IAAA,EAAA,MAAA,GACA,EAAA,SAGA,MAAA,SAAA,GACA,OAAA,KAAA,UAAA,EAAA,MAGA,EAAA,WACA,IAAA,EAAA,IAAA,EACA,KAAA,QAAA,EACA,KAAA,QAAA,EAAA,EAAA,EAAA,GACA,KAAA,OAAA,EAAA,EAAA,EAAA,IAEA,EAAA,EAAA,EAAA,SAAA,GACA,OAAA,IAAA,GAAA,IAAA,EACA,IAAA,EAAA,GACA,EAAA,KAIA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,CAAA,QAAA,IACA,QAAA,uBAAA,CAAA,EAAA,GACA,QAAA,iBAAA,CAAA,GACA,EAAA,QAAA,WAAA,GAGA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,EAAA,CAEA,OAAA,SAAA,GACA,IAAA,EAAA,EAAA,MAGA,OADA,EADA,EAAA,QACA,GACA,EAAA,WAGA,EAAA,EAAA,EAAA,EAAA,GAAA,IAAA,GAAA,EAAA,CAEA,QAAA,SAAA,GACA,OAAA,EAAA,GAAA,OAAA,EAAA,EAAA,KAAA,MAGA,EAAA,EAAA,EAAA,EAAA,IAAA,GAAA,QAAA,iBAAA,CAAA,SAAA,GACA,EAAA,IAAA,GAAA,MAAA,MACA,EAAA,CAEA,IAAA,SAAA,GACA,IAAA,EAAA,KACA,EAAA,EAAA,GACA,EAAA,EAAA,QACA,EAAA,EAAA,OACA,EAAA,EAAA,WACA,IAAA,EAAA,GACA,EAAA,EACA,EAAA,EACA,EAAA,GAAA,EAAA,SAAA,GACA,IAAA,EAAA,IACA,GAAA,EACA,EAAA,UAAA,GACA,IACA,EAAA,QAAA,GAAA,KAAA,SAAA,GACA,IACA,GAAA,EACA,EAAA,GAAA,IACA,GAAA,EAAA,KACA,OAEA,GAAA,EAAA,KAGA,OADA,EAAA,GAAA,EAAA,EAAA,GACA,EAAA,SAGA,KAAA,SAAA,GACA,IAAA,EAAA,KACA,EAAA,EAAA,GACA,EAAA,EAAA,OACA,EAAA,EAAA,WACA,EAAA,GAAA,EAAA,SAAA,GACA,EAAA,QAAA,GAAA,KAAA,EAAA,QAAA,OAIA,OADA,EAAA,GAAA,EAAA,EAAA,GACA,EAAA;;AC3RA,IAAA,EAAA,QAAA,gBACA,OAAA,QAAA,SAAA,EAAA,GACA,IAAA,EAAA,IAAA,EAAA,KAAA,EAAA,MAAA,UAAA,0BAAA,EAAA,cACA,OAAA;;ACHA,aACA,IAAA,EAAA,QAAA,gBAAA,EACA,EAAA,QAAA,oBACA,EAAA,QAAA,mBACA,EAAA,QAAA,UACA,EAAA,QAAA,kBACA,EAAA,QAAA,aACA,EAAA,QAAA,kBACA,EAAA,QAAA,gBACA,EAAA,QAAA,kBACA,EAAA,QAAA,kBACA,EAAA,QAAA,WAAA,QACA,EAAA,QAAA,0BACA,EAAA,EAAA,KAAA,OAEA,EAAA,SAAA,EAAA,GAEA,IACA,EADA,EAAA,EAAA,GAEA,GAAA,MAAA,EAAA,OAAA,EAAA,GAAA,GAEA,IAAA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,EACA,GAAA,EAAA,GAAA,EAAA,OAAA,GAIA,OAAA,QAAA,CACA,eAAA,SAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,SAAA,EAAA,GACA,EAAA,EAAA,EAAA,EAAA,MACA,EAAA,GAAA,EACA,EAAA,GAAA,EAAA,MACA,EAAA,QAAA,EACA,EAAA,QAAA,EACA,EAAA,GAAA,EACA,MAAA,GAAA,EAAA,EAAA,EAAA,EAAA,GAAA,KAsDA,OApDA,EAAA,EAAA,UAAA,CAGA,MAAA,WACA,IAAA,IAAA,EAAA,EAAA,KAAA,GAAA,EAAA,EAAA,GAAA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,EACA,EAAA,GAAA,EACA,EAAA,IAAA,EAAA,EAAA,EAAA,EAAA,OAAA,UACA,EAAA,EAAA,GAEA,EAAA,GAAA,EAAA,QAAA,EACA,EAAA,GAAA,GAIA,OAAA,SAAA,GACA,IAAA,EAAA,EAAA,KAAA,GACA,EAAA,EAAA,EAAA,GACA,GAAA,EAAA,CACA,IAAA,EAAA,EAAA,EACA,EAAA,EAAA,SACA,EAAA,GAAA,EAAA,GACA,EAAA,GAAA,EACA,IAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,GACA,EAAA,IAAA,IAAA,EAAA,GAAA,GACA,EAAA,IAAA,IAAA,EAAA,GAAA,GACA,EAAA,KACA,QAAA,GAIA,QAAA,SAAA,GACA,EAAA,KAAA,GAGA,IAFA,IACA,EADA,EAAA,EAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,EAAA,GAEA,EAAA,EAAA,EAAA,EAAA,KAAA,IAGA,IAFA,EAAA,EAAA,EAAA,EAAA,EAAA,MAEA,GAAA,EAAA,GAAA,EAAA,EAAA,GAKA,IAAA,SAAA,GACA,QAAA,EAAA,EAAA,KAAA,GAAA,MAGA,GAAA,EAAA,EAAA,UAAA,OAAA,CACA,IAAA,WACA,OAAA,EAAA,KAAA,GAAA,MAGA,GAEA,IAAA,SAAA,EAAA,EAAA,GACA,IACA,EAAA,EADA,EAAA,EAAA,EAAA,GAoBA,OAjBA,EACA,EAAA,EAAA,GAGA,EAAA,GAAA,EAAA,CACA,EAAA,EAAA,EAAA,GAAA,GACA,EAAA,EACA,EAAA,EACA,EAAA,EAAA,EAAA,GACA,OAAA,EACA,GAAA,GAEA,EAAA,KAAA,EAAA,GAAA,GACA,IAAA,EAAA,EAAA,GACA,EAAA,KAEA,MAAA,IAAA,EAAA,GAAA,GAAA,IACA,GAEA,SAAA,EACA,UAAA,SAAA,EAAA,EAAA,GAGA,EAAA,EAAA,EAAA,SAAA,EAAA,GACA,KAAA,GAAA,EAAA,EAAA,GACA,KAAA,GAAA,EACA,KAAA,QAAA,GACA,WAKA,IAJA,IACA,EADA,KACA,GACA,EAFA,KAEA,GAEA,GAAA,EAAA,GAAA,EAAA,EAAA,EAEA,OANA,KAMA,KANA,KAMA,GAAA,EAAA,EAAA,EAAA,EANA,KAMA,GAAA,IAMA,EAAA,EAAA,QAAA,EAAA,EAAA,EACA,UAAA,EAAA,EAAA,EACA,CAAA,EAAA,EAAA,EAAA,KAdA,KAQA,QAAA,EACA,EAAA,KAMA,EAAA,UAAA,UAAA,GAAA,GAGA,EAAA;;;AC7IA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,aACA,EAAA,QAAA,eACA,EAAA,QAAA,mBACA,EAAA,QAAA,WACA,EAAA,QAAA,aACA,EAAA,QAAA,kBACA,EAAA,QAAA,gBACA,EAAA,QAAA,YACA,EAAA,QAAA,kBACA,EAAA,QAAA,wBACA,EAAA,QAAA,0BAEA,OAAA,QAAA,SAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,GACA,EAAA,EACA,EAAA,EAAA,MAAA,MACA,EAAA,GAAA,EAAA,UACA,EAAA,GACA,EAAA,SAAA,GACA,IAAA,EAAA,EAAA,GACA,EAAA,EAAA,EACA,UAAA,EAAA,SAAA,GACA,QAAA,IAAA,EAAA,KAAA,EAAA,KAAA,KAAA,IAAA,EAAA,EAAA,IACA,OAAA,EAAA,SAAA,GACA,QAAA,IAAA,EAAA,KAAA,EAAA,KAAA,KAAA,IAAA,EAAA,EAAA,IACA,OAAA,EAAA,SAAA,GACA,OAAA,IAAA,EAAA,QAAA,EAAA,EAAA,KAAA,KAAA,IAAA,EAAA,EAAA,IACA,OAAA,EAAA,SAAA,GAAA,OAAA,EAAA,KAAA,KAAA,IAAA,EAAA,EAAA,GAAA,MACA,SAAA,EAAA,GAAA,OAAA,EAAA,KAAA,KAAA,IAAA,EAAA,EAAA,EAAA,GAAA,QAGA,GAAA,mBAAA,IAAA,GAAA,EAAA,UAAA,EAAA,YACA,IAAA,GAAA,UAAA,UAMA,CACA,IAAA,EAAA,IAAA,EAEA,EAAA,EAAA,GAAA,EAAA,IAAA,EAAA,IAAA,EAEA,EAAA,EAAA,WAAA,EAAA,IAAA,KAEA,EAAA,EAAA,SAAA,GAAA,IAAA,EAAA,KAEA,GAAA,GAAA,EAAA,WAIA,IAFA,IAAA,EAAA,IAAA,EACA,EAAA,EACA,KAAA,EAAA,GAAA,EAAA,GACA,OAAA,EAAA,KAAA,KAEA,KACA,EAAA,EAAA,SAAA,EAAA,GACA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,IAAA,EAAA,EAAA,GAEA,OADA,MAAA,GAAA,EAAA,EAAA,EAAA,EAAA,GAAA,GACA,KAEA,UAAA,EACA,EAAA,YAAA,IAEA,GAAA,KACA,EAAA,UACA,EAAA,OACA,GAAA,EAAA,SAEA,GAAA,IAAA,EAAA,GAEA,GAAA,EAAA,cAAA,EAAA,WApCA,EAAA,EAAA,eAAA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,UAAA,GACA,EAAA,MAAA,EA4CA,OAPA,EAAA,EAAA,GAEA,EAAA,GAAA,EACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,GAAA,GAAA,GAEA,GAAA,EAAA,UAAA,EAAA,EAAA,GAEA;;ACnFA,aACA,IAAA,EAAA,QAAA,wBACA,EAAA,QAAA,0BACA,EAAA,MAGA,OAAA,QAAA,QAAA,gBAAA,CAAA,EAAA,SAAA,GACA,OAAA,WAAA,OAAA,EAAA,KAAA,UAAA,OAAA,EAAA,UAAA,QAAA,KACA,CAEA,IAAA,SAAA,GACA,IAAA,EAAA,EAAA,SAAA,EAAA,KAAA,GAAA,GACA,OAAA,GAAA,EAAA,GAGA,IAAA,SAAA,EAAA,GACA,OAAA,EAAA,IAAA,EAAA,KAAA,GAAA,IAAA,EAAA,EAAA,EAAA,KAEA,GAAA;;AClBA,aACA,IAAA,EAAA,QAAA,wBACA,EAAA,QAAA,0BACA,EAAA,MAGA,OAAA,QAAA,QAAA,gBAAA,CAAA,EAAA,SAAA,GACA,OAAA,WAAA,OAAA,EAAA,KAAA,UAAA,OAAA,EAAA,UAAA,QAAA,KACA,CAEA,IAAA,SAAA,GACA,OAAA,EAAA,IAAA,EAAA,KAAA,GAAA,EAAA,IAAA,EAAA,EAAA,EAAA,KAEA;;ACbA,aACA,IAAA,EAAA,QAAA,mBACA,EAAA,QAAA,WAAA,QACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,kBACA,EAAA,QAAA,aACA,EAAA,QAAA,oBACA,EAAA,QAAA,UACA,EAAA,QAAA,0BACA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,EAGA,EAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,GAAA,IAAA,IAEA,EAAA,WACA,KAAA,EAAA,IAEA,EAAA,SAAA,EAAA,GACA,OAAA,EAAA,EAAA,EAAA,SAAA,GACA,OAAA,EAAA,KAAA,KAGA,EAAA,UAAA,CACA,IAAA,SAAA,GACA,IAAA,EAAA,EAAA,KAAA,GACA,GAAA,EAAA,OAAA,EAAA,IAEA,IAAA,SAAA,GACA,QAAA,EAAA,KAAA,IAEA,IAAA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,KAAA,GACA,EAAA,EAAA,GAAA,EACA,KAAA,EAAA,KAAA,CAAA,EAAA,KAEA,OAAA,SAAA,GACA,IAAA,EAAA,EAAA,KAAA,EAAA,SAAA,GACA,OAAA,EAAA,KAAA,IAGA,OADA,GAAA,KAAA,EAAA,OAAA,EAAA,MACA,IAIA,OAAA,QAAA,CACA,eAAA,SAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,SAAA,EAAA,GACA,EAAA,EAAA,EAAA,EAAA,MACA,EAAA,GAAA,EACA,EAAA,GAAA,IACA,EAAA,QAAA,EACA,MAAA,GAAA,EAAA,EAAA,EAAA,EAAA,GAAA,KAoBA,OAlBA,EAAA,EAAA,UAAA,CAGA,OAAA,SAAA,GACA,IAAA,EAAA,GAAA,OAAA,EACA,IAAA,EAAA,EAAA,GACA,OAAA,IAAA,EAAA,EAAA,EAAA,KAAA,IAAA,OAAA,GACA,GAAA,EAAA,EAAA,KAAA,YAAA,EAAA,KAAA,KAIA,IAAA,SAAA,GACA,IAAA,EAAA,GAAA,OAAA,EACA,IAAA,EAAA,EAAA,GACA,OAAA,IAAA,EAAA,EAAA,EAAA,KAAA,IAAA,IAAA,GACA,GAAA,EAAA,EAAA,KAAA,OAGA,GAEA,IAAA,SAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,EAAA,IAAA,GAGA,OAFA,IAAA,EAAA,EAAA,GAAA,IAAA,EAAA,GACA,EAAA,EAAA,IAAA,EACA,GAEA,QAAA;;;ACnFA,aACA,IAcA,EAdA,EAAA,QAAA,aACA,EAAA,QAAA,mBAAA,CAAA,GACA,EAAA,QAAA,eACA,EAAA,QAAA,WACA,EAAA,QAAA,oBACA,EAAA,QAAA,sBACA,EAAA,QAAA,gBACA,EAAA,QAAA,0BACA,EAAA,QAAA,0BACA,GAAA,EAAA,eAAA,kBAAA,EACA,EAAA,UACA,EAAA,EAAA,QACA,EAAA,OAAA,aACA,EAAA,EAAA,QAGA,EAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,UAAA,OAAA,EAAA,UAAA,QAAA,KAIA,EAAA,CAEA,IAAA,SAAA,GACA,GAAA,EAAA,GAAA,CACA,IAAA,EAAA,EAAA,GACA,OAAA,IAAA,EAAA,EAAA,EAAA,KAAA,IAAA,IAAA,GACA,EAAA,EAAA,KAAA,SAAA,IAIA,IAAA,SAAA,EAAA,GACA,OAAA,EAAA,IAAA,EAAA,KAAA,GAAA,EAAA,KAKA,EAAA,OAAA,QAAA,QAAA,gBAAA,CAAA,EAAA,EAAA,EAAA,GAAA,GAAA,GAGA,GAAA,IAEA,GADA,EAAA,EAAA,eAAA,EAAA,IACA,UAAA,GACA,EAAA,MAAA,EACA,EAAA,CAAA,SAAA,MAAA,MAAA,OAAA,SAAA,GACA,IAAA,EAAA,EAAA,UACA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,SAAA,EAAA,GAEA,GAAA,EAAA,KAAA,EAAA,GAAA,CACA,KAAA,KAAA,KAAA,GAAA,IAAA,GACA,IAAA,EAAA,KAAA,GAAA,GAAA,EAAA,GACA,MAAA,OAAA,EAAA,KAAA,EAEA,OAAA,EAAA,KAAA,KAAA,EAAA;;ACxDA,aACA,IAAA,EAAA,QAAA,sBACA,EAAA,QAAA,0BACA,EAAA,UAGA,QAAA,gBAAA,CAAA,EAAA,SAAA,GACA,OAAA,WAAA,OAAA,EAAA,KAAA,UAAA,OAAA,EAAA,UAAA,QAAA,KACA,CAEA,IAAA,SAAA,GACA,OAAA,EAAA,IAAA,EAAA,KAAA,GAAA,GAAA,KAEA,GAAA,GAAA;;;ACEA,IAfA,IASA,EATA,EAAA,QAAA,aACA,EAAA,QAAA,WACA,EAAA,QAAA,UACA,EAAA,EAAA,eACA,EAAA,EAAA,QACA,KAAA,EAAA,cAAA,EAAA,UACA,EAAA,EACA,EAAA,EACA,EAAA,EAGA,EAAA,iHAEA,MAAA,KAEA,EAAA,IACA,EAAA,EAAA,EAAA,QACA,EAAA,EAAA,UAAA,GAAA,GACA,EAAA,EAAA,UAAA,GAAA,IACA,GAAA,EAGA,OAAA,QAAA,CACA,IAAA,EACA,OAAA,EACA,MAAA,EACA,KAAA;;ACzBA,IAAA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,OAAA,QAAA,SAAA,GACA,QAAA,IAAA,EAAA,OAAA,EACA,IAAA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,GAAA,IAAA,EAAA,MAAA,WAAA,iBACA,OAAA;;;ACRA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,kBACA,EAAA,QAAA,cACA,EAAA,QAAA,YACA,EAAA,QAAA,WACA,EAAA,QAAA,mBACA,EAAA,QAAA,YACA,EAAA,QAAA,kBACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,EAAA,QAAA,eACA,EAAA,QAAA,kBAAA,EACA,EAAA,QAAA,gBAAA,EACA,EAAA,QAAA,iBACA,EAAA,QAAA,wBACA,EAAA,cACA,EAAA,WACA,EAAA,YACA,EAAA,gBACA,EAAA,eACA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,EAAA,KACA,EAAA,EAAA,WAEA,EAAA,EAAA,SACA,EAAA,EACA,EAAA,EAAA,IACA,EAAA,EAAA,IACA,EAAA,EAAA,MACA,EAAA,EAAA,IACA,EAAA,EAAA,IACA,EAAA,SACA,EAAA,aACA,EAAA,aACA,EAAA,EAAA,KAAA,EACA,EAAA,EAAA,KAAA,EACA,EAAA,EAAA,KAAA,EAGA,SAAA,EAAA,EAAA,EAAA,GACA,IAOA,EAAA,EAAA,EAPA,EAAA,IAAA,MAAA,GACA,EAAA,EAAA,EAAA,EAAA,EACA,GAAA,GAAA,GAAA,EACA,EAAA,GAAA,EACA,EAAA,KAAA,EAAA,EAAA,GAAA,IAAA,EAAA,GAAA,IAAA,EACA,EAAA,EACA,EAAA,EAAA,GAAA,IAAA,GAAA,EAAA,EAAA,EAAA,EAAA,EAkCA,KAhCA,EAAA,EAAA,KAEA,GAAA,IAAA,GAEA,EAAA,GAAA,EAAA,EAAA,EACA,EAAA,IAEA,EAAA,EAAA,EAAA,GAAA,GACA,GAAA,EAAA,EAAA,GAAA,IAAA,IACA,IACA,GAAA,IAGA,GADA,EAAA,GAAA,EACA,EAAA,EAEA,EAAA,EAAA,EAAA,EAAA,IAEA,GAAA,IACA,IACA,GAAA,GAEA,EAAA,GAAA,GACA,EAAA,EACA,EAAA,GACA,EAAA,GAAA,GACA,GAAA,EAAA,EAAA,GAAA,EAAA,EAAA,GACA,GAAA,IAEA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,EAAA,GACA,EAAA,IAGA,GAAA,EAAA,EAAA,KAAA,IAAA,EAAA,GAAA,IAAA,GAAA,GAGA,IAFA,EAAA,GAAA,EAAA,EACA,GAAA,EACA,EAAA,EAAA,EAAA,KAAA,IAAA,EAAA,GAAA,IAAA,GAAA,GAEA,OADA,IAAA,IAAA,IAAA,EACA,EAEA,SAAA,EAAA,EAAA,EAAA,GACA,IAOA,EAPA,EAAA,EAAA,EAAA,EAAA,EACA,GAAA,GAAA,GAAA,EACA,EAAA,GAAA,EACA,EAAA,EAAA,EACA,EAAA,EAAA,EACA,EAAA,EAAA,KACA,EAAA,IAAA,EAGA,IADA,IAAA,EACA,EAAA,EAAA,EAAA,IAAA,EAAA,EAAA,GAAA,IAAA,GAAA,GAIA,IAHA,EAAA,GAAA,IAAA,GAAA,EACA,KAAA,EACA,GAAA,EACA,EAAA,EAAA,EAAA,IAAA,EAAA,EAAA,GAAA,IAAA,GAAA,GACA,GAAA,IAAA,EACA,EAAA,EAAA,MACA,CAAA,GAAA,IAAA,EACA,OAAA,EAAA,IAAA,GAAA,EAAA,EAEA,GAAA,EAAA,EAAA,GACA,GAAA,EACA,OAAA,GAAA,EAAA,GAAA,EAAA,EAAA,EAAA,EAAA,GAGA,SAAA,EAAA,GACA,OAAA,EAAA,IAAA,GAAA,EAAA,IAAA,GAAA,EAAA,IAAA,EAAA,EAAA,GAEA,SAAA,EAAA,GACA,MAAA,CAAA,IAAA,GAEA,SAAA,EAAA,GACA,MAAA,CAAA,IAAA,EAAA,GAAA,EAAA,KAEA,SAAA,EAAA,GACA,MAAA,CAAA,IAAA,EAAA,GAAA,EAAA,IAAA,GAAA,GAAA,IAAA,GAAA,GAAA,KAEA,SAAA,EAAA,GACA,OAAA,EAAA,EAAA,GAAA,GAEA,SAAA,EAAA,GACA,OAAA,EAAA,EAAA,GAAA,GAGA,SAAA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,GAAA,EAAA,CAAA,IAAA,WAAA,OAAA,KAAA,MAGA,SAAA,EAAA,EAAA,EAAA,EAAA,GACA,IACA,EAAA,GADA,GAEA,GAAA,EAAA,EAAA,EAAA,GAAA,MAAA,EAAA,GACA,IAAA,EAAA,EAAA,GAAA,GACA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,MAAA,EAAA,EAAA,GACA,OAAA,EAAA,EAAA,EAAA,UAEA,SAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GACA,IACA,EAAA,GADA,GAEA,GAAA,EAAA,EAAA,EAAA,GAAA,MAAA,EAAA,GAIA,IAHA,IAAA,EAAA,EAAA,GAAA,GACA,EAAA,EAAA,EAAA,GACA,EAAA,GAAA,GACA,EAAA,EAAA,EAAA,EAAA,IAAA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAGA,GAAA,EAAA,IAgFA,CACA,IAAA,EAAA,WACA,EAAA,OACA,EAAA,WACA,IAAA,GAAA,MACA,EAAA,WAIA,OAHA,IAAA,EACA,IAAA,EAAA,KACA,IAAA,EAAA,KACA,EAAA,MAAA,IACA,CAMA,IADA,IACA,EADA,GAJA,EAAA,SAAA,GAEA,OADA,EAAA,KAAA,GACA,IAAA,EAAA,EAAA,MAEA,GAAA,EAAA,GACA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,OAAA,IACA,EAAA,EAAA,QAAA,GAAA,EAAA,EAAA,EAAA,EAAA,IAEA,IAAA,EAAA,YAAA,GAGA,IAAA,EAAA,IAAA,EAAA,IAAA,EAAA,IACA,EAAA,EAAA,GAAA,QACA,EAAA,QAAA,EAAA,YACA,EAAA,QAAA,EAAA,aACA,EAAA,QAAA,IAAA,EAAA,QAAA,IAAA,EAAA,EAAA,GAAA,CACA,QAAA,SAAA,EAAA,GACA,EAAA,KAAA,KAAA,EAAA,GAAA,IAAA,KAEA,SAAA,SAAA,EAAA,GACA,EAAA,KAAA,KAAA,EAAA,GAAA,IAAA,OAEA,QAhHA,EAAA,SAAA,GACA,EAAA,KAAA,EAAA,GACA,IAAA,EAAA,EAAA,GACA,KAAA,GAAA,EAAA,KAAA,IAAA,MAAA,GAAA,GACA,KAAA,GAAA,GAGA,EAAA,SAAA,EAAA,EAAA,GACA,EAAA,KAAA,EAAA,GACA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,GAAA,EAAA,GAAA,EAAA,EAAA,MAAA,EAAA,iBAEA,GAAA,GADA,OAAA,IAAA,EAAA,EAAA,EAAA,EAAA,IACA,EAAA,MAAA,EAAA,GACA,KAAA,GAAA,EACA,KAAA,GAAA,EACA,KAAA,GAAA,GAGA,IACA,EAAA,EAAA,EAAA,MACA,EAAA,EAAA,EAAA,MACA,EAAA,EAAA,EAAA,MACA,EAAA,EAAA,EAAA,OAGA,EAAA,EAAA,GAAA,CACA,QAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,GAAA,IAAA,IAAA,IAEA,SAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,GAAA,IAEA,SAAA,SAAA,GACA,IAAA,EAAA,EAAA,KAAA,EAAA,EAAA,UAAA,IACA,OAAA,EAAA,IAAA,EAAA,EAAA,KAAA,IAAA,IAEA,UAAA,SAAA,GACA,IAAA,EAAA,EAAA,KAAA,EAAA,EAAA,UAAA,IACA,OAAA,EAAA,IAAA,EAAA,EAAA,IAEA,SAAA,SAAA,GACA,OAAA,EAAA,EAAA,KAAA,EAAA,EAAA,UAAA,MAEA,UAAA,SAAA,GACA,OAAA,EAAA,EAAA,KAAA,EAAA,EAAA,UAAA,OAAA,GAEA,WAAA,SAAA,GACA,OAAA,EAAA,EAAA,KAAA,EAAA,EAAA,UAAA,IAAA,GAAA,IAEA,WAAA,SAAA,GACA,OAAA,EAAA,EAAA,KAAA,EAAA,EAAA,UAAA,IAAA,GAAA,IAEA,QAAA,SAAA,EAAA,GACA,EAAA,KAAA,EAAA,EAAA,EAAA,IAEA,SAAA,SAAA,EAAA,GACA,EAAA,KAAA,EAAA,EAAA,EAAA,IAEA,SAAA,SAAA,EAAA,GACA,EAAA,KAAA,EAAA,EAAA,EAAA,EAAA,UAAA,KAEA,UAAA,SAAA,EAAA,GACA,EAAA,KAAA,EAAA,EAAA,EAAA,EAAA,UAAA,KAEA,SAAA,SAAA,EAAA,GACA,EAAA,KAAA,EAAA,EAAA,EAAA,EAAA,UAAA,KAEA,UAAA,SAAA,EAAA,GACA,EAAA,KAAA,EAAA,EAAA,EAAA,EAAA,UAAA,KAEA,WAAA,SAAA,EAAA,GACA,EAAA,KAAA,EAAA,EAAA,EAAA,EAAA,UAAA,KAEA,WAAA,SAAA,EAAA,GACA,EAAA,KAAA,EAAA,EAAA,EAAA,EAAA,UAAA,OAsCA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,EAAA,GAAA,EAAA,MAAA,GACA,QAAA,GAAA,EACA,QAAA,GAAA;;ACnRA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,YACA,EAAA,QAAA,mBACA,EAAA,QAAA,gBACA,EAAA,QAAA,wBACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,aAAA,YACA,EAAA,QAAA,0BACA,EAAA,EAAA,YACA,EAAA,EAAA,SACA,EAAA,EAAA,KAAA,EAAA,OACA,EAAA,EAAA,UAAA,MACA,EAAA,EAAA,KACA,EAAA,cAEA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,IAAA,GAAA,CAAA,YAAA,IAEA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,OAAA,EAAA,CAEA,OAAA,SAAA,GACA,OAAA,GAAA,EAAA,IAAA,EAAA,IAAA,KAAA,KAIA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,WAAA,CAAA,WACA,OAAA,IAAA,EAAA,GAAA,MAAA,OAAA,GAAA,aACA,EAAA,CAEA,MAAA,SAAA,EAAA,GACA,QAAA,IAAA,QAAA,IAAA,EAAA,OAAA,EAAA,KAAA,EAAA,MAAA,GAQA,IAPA,IAAA,EAAA,EAAA,MAAA,WACA,EAAA,EAAA,EAAA,GACA,EAAA,OAAA,IAAA,EAAA,EAAA,EAAA,GACA,EAAA,IAAA,EAAA,KAAA,GAAA,CAAA,EAAA,EAAA,IACA,EAAA,IAAA,EAAA,MACA,EAAA,IAAA,EAAA,GACA,EAAA,EACA,EAAA,GACA,EAAA,SAAA,IAAA,EAAA,SAAA,MACA,OAAA,KAIA,QAAA,iBAAA,CAAA;;AC7CA,IAAA,EAAA,QAAA,aACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,YAAA,IAAA,CACA,SAAA,QAAA,mBAAA;;;AC8dA,IAAA,EAAA,UAAA,GA/dA,GAAA,QAAA,kBAAA,CACA,IAAA,EAAA,QAAA,cAEA,GADA,EAAA,QAAA,aACA,QAAA,aACA,EAAA,QAAA,aACA,EAAA,QAAA,YACA,EAAA,QAAA,mBACA,EAAA,QAAA,UACA,EAAA,QAAA,kBACA,EAAA,QAAA,oBACA,EAAA,QAAA,WACA,EAAA,QAAA,mBACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,EAAA,QAAA,eACA,EAAA,QAAA,wBACA,EAAA,QAAA,mBACA,EAAA,QAAA,UACA,EAAA,QAAA,cACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,oBACA,EAAA,QAAA,oBACA,EAAA,QAAA,iBACA,EAAA,QAAA,kBAAA,EACA,EAAA,QAAA,8BACA,EAAA,QAAA,UACA,EAAA,QAAA,UACA,EAAA,QAAA,oBACA,EAAA,QAAA,qBACA,EAAA,QAAA,0BACA,EAAA,QAAA,wBACA,EAAA,QAAA,gBACA,EAAA,QAAA,kBACA,EAAA,QAAA,kBACA,EAAA,QAAA,iBACA,EAAA,QAAA,wBACA,EAAA,QAAA,gBACA,EAAA,QAAA,kBACA,EAAA,EAAA,EACA,EAAA,EAAA,EACA,EAAA,EAAA,WACA,EAAA,EAAA,UACA,EAAA,EAAA,WACA,EAAA,cACA,EAAA,SAAA,EACA,EAAA,oBACA,EAAA,YACA,EAAA,MAAA,GACA,EAAA,EAAA,YACA,EAAA,EAAA,SACA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,GAAA,EAAA,GACA,GAAA,EAAA,GACA,GAAA,GAAA,GACA,GAAA,GAAA,GACA,GAAA,EAAA,OACA,GAAA,EAAA,KACA,GAAA,EAAA,QACA,GAAA,EAAA,YACA,GAAA,EAAA,OACA,GAAA,EAAA,YACA,GAAA,EAAA,KACA,GAAA,EAAA,KACA,GAAA,EAAA,MACA,GAAA,EAAA,SACA,GAAA,EAAA,eACA,GAAA,EAAA,YACA,GAAA,EAAA,eACA,GAAA,EAAA,qBACA,GAAA,EAAA,mBACA,GAAA,EAAA,OACA,GAAA,EAAA,MACA,GAAA,EAAA,KACA,GAAA,gBAEA,GAAA,EAAA,EAAA,SAAA,EAAA,GACA,OAAA,GAAA,EAAA,EAAA,EAAA,KAAA,KAGA,GAAA,EAAA,WAEA,OAAA,IAAA,IAAA,EAAA,IAAA,YAAA,CAAA,IAAA,QAAA,KAGA,KAAA,KAAA,EAAA,GAAA,KAAA,EAAA,WACA,IAAA,EAAA,GAAA,IAAA,MAGA,GAAA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,GACA,GAAA,EAAA,GAAA,EAAA,EAAA,MAAA,EAAA,iBACA,OAAA,GAGA,GAAA,SAAA,GACA,GAAA,EAAA,IAAA,MAAA,EAAA,OAAA,EACA,MAAA,EAAA,EAAA,2BAGA,GAAA,SAAA,EAAA,GACA,KAAA,EAAA,IAAA,MAAA,GACA,MAAA,EAAA,wCACA,OAAA,IAAA,EAAA,IAGA,GAAA,SAAA,EAAA,GACA,OAAA,GAAA,EAAA,EAAA,EAAA,KAAA,IAGA,GAAA,SAAA,EAAA,GAIA,IAHA,IAAA,EAAA,EACA,EAAA,EAAA,OACA,EAAA,GAAA,EAAA,GACA,EAAA,GAAA,EAAA,GAAA,EAAA,KACA,OAAA,GAGA,GAAA,SAAA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,CAAA,IAAA,WAAA,OAAA,KAAA,GAAA,OAGA,GAAA,SAAA,GACA,IAKA,EAAA,EAAA,EAAA,EAAA,EAAA,EALA,EAAA,EAAA,GACA,EAAA,UAAA,OACA,EAAA,EAAA,EAAA,UAAA,QAAA,EACA,OAAA,IAAA,EACA,EAAA,EAAA,GAEA,GAAA,MAAA,IAAA,EAAA,GAAA,CACA,IAAA,EAAA,EAAA,KAAA,GAAA,EAAA,GAAA,EAAA,IAAA,EAAA,EAAA,QAAA,KAAA,IACA,EAAA,KAAA,EAAA,OACA,EAAA,EAGA,IADA,GAAA,EAAA,IAAA,EAAA,EAAA,EAAA,UAAA,GAAA,IACA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,EAAA,GAAA,KAAA,GAAA,EAAA,EAAA,IACA,EAAA,GAAA,EAAA,EAAA,EAAA,GAAA,GAAA,EAAA,GAEA,OAAA,GAGA,GAAA,WAIA,IAHA,IAAA,EAAA,EACA,EAAA,UAAA,OACA,EAAA,GAAA,KAAA,GACA,EAAA,GAAA,EAAA,GAAA,UAAA,KACA,OAAA,GAIA,KAAA,GAAA,EAAA,WAAA,GAAA,KAAA,IAAA,EAAA,MAEA,GAAA,WACA,OAAA,GAAA,MAAA,GAAA,GAAA,KAAA,GAAA,OAAA,GAAA,MAAA,YAGA,GAAA,CACA,WAAA,SAAA,EAAA,GACA,OAAA,EAAA,KAAA,GAAA,MAAA,EAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,IAEA,MAAA,SAAA,GACA,OAAA,EAAA,GAAA,MAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,IAEA,KAAA,SAAA,GACA,OAAA,EAAA,MAAA,GAAA,MAAA,YAEA,OAAA,SAAA,GACA,OAAA,GAAA,KAAA,EAAA,GAAA,MAAA,EACA,UAAA,OAAA,EAAA,UAAA,QAAA,KAEA,KAAA,SAAA,GACA,OAAA,GAAA,GAAA,MAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,IAEA,UAAA,SAAA,GACA,OAAA,GAAA,GAAA,MAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,IAEA,QAAA,SAAA,GACA,EAAA,GAAA,MAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,IAEA,QAAA,SAAA,GACA,OAAA,GAAA,GAAA,MAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,IAEA,SAAA,SAAA,GACA,OAAA,GAAA,GAAA,MAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,IAEA,KAAA,SAAA,GACA,OAAA,GAAA,MAAA,GAAA,MAAA,YAEA,YAAA,SAAA,GACA,OAAA,GAAA,MAAA,GAAA,MAAA,YAEA,IAAA,SAAA,GACA,OAAA,GAAA,GAAA,MAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,IAEA,OAAA,SAAA,GACA,OAAA,GAAA,MAAA,GAAA,MAAA,YAEA,YAAA,SAAA,GACA,OAAA,GAAA,MAAA,GAAA,MAAA,YAEA,QAAA,WAMA,IALA,IAIA,EAHA,EAAA,GADA,MACA,OACA,EAAA,KAAA,MAAA,EAAA,GACA,EAAA,EAEA,EAAA,GACA,EANA,KAMA,GANA,KAOA,KAPA,OAOA,GAPA,KAQA,GAAA,EACA,OATA,MAWA,KAAA,SAAA,GACA,OAAA,EAAA,GAAA,MAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,IAEA,KAAA,SAAA,GACA,OAAA,GAAA,KAAA,GAAA,MAAA,IAEA,SAAA,SAAA,EAAA,GACA,IAAA,EAAA,GAAA,MACA,EAAA,EAAA,OACA,EAAA,EAAA,EAAA,GACA,OAAA,IAAA,EAAA,EAAA,EAAA,KAAA,CACA,EAAA,OACA,EAAA,WAAA,EAAA,EAAA,kBACA,QAAA,IAAA,EAAA,EAAA,EAAA,EAAA,IAAA,MAKA,GAAA,SAAA,EAAA,GACA,OAAA,GAAA,KAAA,GAAA,KAAA,GAAA,MAAA,EAAA,KAGA,GAAA,SAAA,GACA,GAAA,MACA,IAAA,EAAA,GAAA,UAAA,GAAA,GACA,EAAA,KAAA,OACA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,QACA,EAAA,EACA,GAAA,EAAA,EAAA,EAAA,MAAA,EAAA,IACA,KAAA,EAAA,GAAA,KAAA,EAAA,GAAA,EAAA,MAGA,GAAA,CACA,QAAA,WACA,OAAA,GAAA,KAAA,GAAA,QAEA,KAAA,WACA,OAAA,GAAA,KAAA,GAAA,QAEA,OAAA,WACA,OAAA,GAAA,KAAA,GAAA,SAIA,GAAA,SAAA,EAAA,GACA,OAAA,EAAA,IACA,EAAA,KACA,iBAAA,GACA,KAAA,GACA,QAAA,IAAA,OAAA,IAEA,GAAA,SAAA,EAAA,GACA,OAAA,GAAA,EAAA,EAAA,EAAA,GAAA,IACA,EAAA,EAAA,EAAA,IACA,EAAA,EAAA,IAEA,GAAA,SAAA,EAAA,EAAA,GACA,QAAA,GAAA,EAAA,EAAA,EAAA,GAAA,KACA,EAAA,IACA,EAAA,EAAA,WACA,EAAA,EAAA,QACA,EAAA,EAAA,QAEA,EAAA,cACA,EAAA,EAAA,cAAA,EAAA,UACA,EAAA,EAAA,gBAAA,EAAA,WAIA,EAAA,EAAA,EAAA,IAFA,EAAA,GAAA,EAAA,MACA,IAIA,KACA,EAAA,EAAA,GACA,EAAA,EAAA,IAGA,EAAA,EAAA,EAAA,EAAA,GAAA,GAAA,SAAA,CACA,yBAAA,GACA,eAAA,KAGA,EAAA,WAAA,GAAA,KAAA,QACA,GAAA,GAAA,WACA,OAAA,GAAA,KAAA,QAIA,IAAA,GAAA,EAAA,GAAA,IACA,EAAA,GAAA,IACA,EAAA,GAAA,GAAA,GAAA,QACA,EAAA,GAAA,CACA,MAAA,GACA,IAAA,GACA,YAAA,aACA,SAAA,GACA,eAAA,KAEA,GAAA,GAAA,SAAA,KACA,GAAA,GAAA,aAAA,KACA,GAAA,GAAA,aAAA,KACA,GAAA,GAAA,SAAA,KACA,EAAA,GAAA,GAAA,CACA,IAAA,WAAA,OAAA,KAAA,OAIA,OAAA,QAAA,SAAA,EAAA,EAAA,EAAA,GAEA,IAAA,EAAA,IADA,IAAA,GACA,UAAA,IAAA,QACA,EAAA,MAAA,EACA,EAAA,MAAA,EACA,EAAA,EAAA,GACA,EAAA,GAAA,GACA,EAAA,GAAA,EAAA,GACA,GAAA,IAAA,EAAA,IACA,EAAA,GACA,EAAA,GAAA,EAAA,GAUA,EAAA,SAAA,EAAA,GACA,EAAA,EAAA,EAAA,CACA,IAAA,WACA,OAZA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,GACA,OAAA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,EAAA,IAUA,CAAA,KAAA,IAEA,IAAA,SAAA,GACA,OAXA,SAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,GACA,IAAA,GAAA,EAAA,KAAA,MAAA,IAAA,EAAA,EAAA,EAAA,IAAA,IAAA,IAAA,GACA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,EAAA,EAAA,IAQA,CAAA,KAAA,EAAA,IAEA,YAAA,KAGA,GACA,EAAA,EAAA,SAAA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,EAAA,MACA,IAEA,EAAA,EAAA,EAAA,EAFA,EAAA,EACA,EAAA,EAEA,GAAA,EAAA,GAIA,CAAA,KAAA,aAAA,IAAA,EAAA,EAAA,KAAA,GAAA,GAAA,GAaA,OAAA,MAAA,EACA,GAAA,EAAA,GAEA,GAAA,KAAA,EAAA,GAfA,EAAA,EACA,EAAA,GAAA,EAAA,GACA,IAAA,EAAA,EAAA,WACA,QAAA,IAAA,EAAA,CACA,GAAA,EAAA,EAAA,MAAA,EAAA,IAEA,IADA,EAAA,EAAA,GACA,EAAA,MAAA,EAAA,SAGA,IADA,EAAA,EAAA,GAAA,GACA,EAAA,EAAA,MAAA,EAAA,IAEA,EAAA,EAAA,OAfA,EAAA,EAAA,GAEA,EAAA,IAAA,EADA,EAAA,EAAA,GA2BA,IAPA,EAAA,EAAA,KAAA,CACA,EAAA,EACA,EAAA,EACA,EAAA,EACA,EAAA,EACA,EAAA,IAAA,EAAA,KAEA,EAAA,GAAA,EAAA,EAAA,OAEA,EAAA,EAAA,GAAA,EAAA,IACA,EAAA,EAAA,cAAA,IACA,EAAA,WACA,EAAA,MACA,EAAA,WACA,IAAA,GAAA,MACA,EAAA,SAAA,GACA,IAAA,EACA,IAAA,EAAA,MACA,IAAA,EAAA,KACA,IAAA,EAAA,KACA,KACA,EAAA,EAAA,SAAA,EAAA,EAAA,EAAA,GAEA,IAAA,EAGA,OAJA,EAAA,EAAA,EAAA,GAIA,EAAA,GACA,aAAA,IAAA,EAAA,EAAA,KAAA,GAAA,GAAA,OACA,IAAA,EACA,IAAA,EAAA,EAAA,GAAA,EAAA,GAAA,QACA,IAAA,EACA,IAAA,EAAA,EAAA,GAAA,EAAA,IACA,IAAA,EAAA,GAEA,MAAA,EAAA,GAAA,EAAA,GACA,GAAA,KAAA,EAAA,GATA,IAAA,EAAA,EAAA,MAWA,EAAA,IAAA,SAAA,UAAA,EAAA,GAAA,OAAA,EAAA,IAAA,EAAA,GAAA,SAAA,GACA,KAAA,GAAA,EAAA,EAAA,EAAA,EAAA,MAEA,EAAA,GAAA,EACA,IAAA,EAAA,YAAA,IAEA,IAAA,EAAA,EAAA,IACA,IAAA,IACA,UAAA,EAAA,MAAA,MAAA,EAAA,MACA,EAAA,GAAA,OACA,EAAA,EAAA,IAAA,GACA,EAAA,EAAA,GAAA,GACA,EAAA,EAAA,IAAA,GACA,EAAA,EAAA,GAAA,IAEA,EAAA,IAAA,EAAA,GAAA,KAAA,EAAA,MAAA,IACA,EAAA,EAAA,GAAA,CACA,IAAA,WAAA,OAAA,KAIA,EAAA,GAAA,EAEA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,GAAA,GAAA,GAEA,EAAA,EAAA,EAAA,EAAA,CACA,kBAAA,IAGA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,WAAA,EAAA,GAAA,KAAA,EAAA,KAAA,EAAA,CACA,KAAA,GACA,GAAA,KAGA,KAAA,GAAA,EAAA,EAAA,EAAA,GAEA,EAAA,EAAA,EAAA,EAAA,IAEA,EAAA,GAEA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,CAAA,IAAA,KAEA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,EAAA,IAEA,GAAA,EAAA,UAAA,KAAA,EAAA,SAAA,IAEA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,WACA,IAAA,EAAA,GAAA,UACA,EAAA,CAAA,MAAA,KAEA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,WACA,MAAA,CAAA,EAAA,GAAA,kBAAA,IAAA,EAAA,CAAA,EAAA,IAAA,qBACA,EAAA,WACA,EAAA,eAAA,KAAA,CAAA,EAAA,OACA,EAAA,CAAA,eAAA,KAEA,EAAA,GAAA,EAAA,EAAA,EACA,GAAA,GAAA,EAAA,EAAA,GAAA,SAEA,OAAA,QAAA;;AC/dA,QAAA,iBAAA,CAAA,OAAA,EAAA,SAAA,GACA,OAAA,SAAA,EAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,EAAA;;ACFA,QAAA,iBAAA,CAAA,QAAA,EAAA,SAAA,GACA,OAAA,SAAA,EAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,EAAA;;ACFA,QAAA,iBAAA,CAAA,QAAA,EAAA,SAAA,GACA,OAAA,SAAA,EAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,EAAA,MAEA;;ACJA,QAAA,iBAAA,CAAA,QAAA,EAAA,SAAA,GACA,OAAA,SAAA,EAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,EAAA;;ACFA,QAAA,iBAAA,CAAA,SAAA,EAAA,SAAA,GACA,OAAA,SAAA,EAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,EAAA;;ACFA,QAAA,iBAAA,CAAA,QAAA,EAAA,SAAA,GACA,OAAA,SAAA,EAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,EAAA;;ACFA,QAAA,iBAAA,CAAA,SAAA,EAAA,SAAA,GACA,OAAA,SAAA,EAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,EAAA;;ACFA,QAAA,iBAAA,CAAA,UAAA,EAAA,SAAA,GACA,OAAA,SAAA,EAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,EAAA;;ACFA,QAAA,iBAAA,CAAA,UAAA,EAAA,SAAA,GACA,OAAA,SAAA,EAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,EAAA;;ACDA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,GAAA,QAAA,aAAA,SAAA,IAAA,MACA,EAAA,SAAA,MAEA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,WAAA,CAAA,WACA,EAAA,gBACA,UAAA,CACA,MAAA,SAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,OAAA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,KAAA,EAAA,EAAA;;ACZA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,oBACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,YACA,EAAA,QAAA,WACA,GAAA,QAAA,aAAA,SAAA,IAAA,UAIA,EAAA,EAAA,WACA,SAAA,KACA,QAAA,EAAA,aAAA,GAAA,aAAA,KAEA,GAAA,EAAA,WACA,EAAA,gBAGA,EAAA,EAAA,EAAA,EAAA,GAAA,GAAA,GAAA,UAAA,CACA,UAAA,SAAA,EAAA,GACA,EAAA,GACA,EAAA,GACA,IAAA,EAAA,UAAA,OAAA,EAAA,EAAA,EAAA,UAAA,IACA,GAAA,IAAA,EAAA,OAAA,EAAA,EAAA,EAAA,GACA,GAAA,GAAA,EAAA,CAEA,OAAA,EAAA,QACA,KAAA,EAAA,OAAA,IAAA,EACA,KAAA,EAAA,OAAA,IAAA,EAAA,EAAA,IACA,KAAA,EAAA,OAAA,IAAA,EAAA,EAAA,GAAA,EAAA,IACA,KAAA,EAAA,OAAA,IAAA,EAAA,EAAA,GAAA,EAAA,GAAA,EAAA,IACA,KAAA,EAAA,OAAA,IAAA,EAAA,EAAA,GAAA,EAAA,GAAA,EAAA,GAAA,EAAA,IAGA,IAAA,EAAA,CAAA,MAEA,OADA,EAAA,KAAA,MAAA,EAAA,GACA,IAAA,EAAA,MAAA,EAAA,IAGA,IAAA,EAAA,EAAA,UACA,EAAA,EAAA,EAAA,GAAA,EAAA,OAAA,WACA,EAAA,SAAA,MAAA,KAAA,EAAA,EAAA,GACA,OAAA,EAAA,GAAA,EAAA;;AC3CA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,mBAGA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,WAAA,CAAA,WAEA,QAAA,eAAA,EAAA,EAAA,GAAA,EAAA,CAAA,MAAA,IAAA,EAAA,CAAA,MAAA,MACA,UAAA,CACA,eAAA,SAAA,EAAA,EAAA,GACA,EAAA,GACA,EAAA,EAAA,GAAA,GACA,EAAA,GACA,IAEA,OADA,EAAA,EAAA,EAAA,EAAA,IACA,EACA,MAAA,GACA,OAAA;;AClBA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,kBAAA,EACA,EAAA,QAAA,gBAEA,EAAA,EAAA,EAAA,UAAA,CACA,eAAA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,EAAA,GAAA,GACA,QAAA,IAAA,EAAA,sBAAA,EAAA;;ACRA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,SAAA,GACA,KAAA,GAAA,EAAA,GACA,KAAA,GAAA,EACA,IACA,EADA,EAAA,KAAA,GAAA,GAEA,IAAA,KAAA,EAAA,EAAA,KAAA,IAEA,QAAA,iBAAA,CAAA,EAAA,SAAA,WACA,IAEA,EADA,EADA,KACA,GAEA,GACA,GAJA,KAIA,IAAA,EAAA,OAAA,MAAA,CAAA,WAAA,EAAA,MAAA,YACA,EAAA,EALA,KAKA,SALA,KAKA,KACA,MAAA,CAAA,MAAA,EAAA,MAAA,KAGA,EAAA,EAAA,EAAA,UAAA,CACA,UAAA,SAAA,GACA,OAAA,IAAA,EAAA;;ACtBA,IAAA,EAAA,QAAA,kBACA,EAAA,QAAA,iBACA,EAAA,QAAA,UACA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBAEA,SAAA,EAAA,EAAA,GACA,IACA,EAAA,EADA,EAAA,UAAA,OAAA,EAAA,EAAA,UAAA,GAEA,OAAA,EAAA,KAAA,EAAA,EAAA,IACA,EAAA,EAAA,EAAA,EAAA,IAAA,EAAA,EAAA,SACA,EAAA,WACA,IAAA,EAAA,IACA,EAAA,IAAA,KAAA,QACA,EACA,EAAA,EAAA,EAAA,IAAA,EAAA,EAAA,EAAA,QAAA,EAGA,EAAA,EAAA,EAAA,UAAA,CAAA,IAAA;;ACnBA,IAAA,EAAA,QAAA,kBACA,EAAA,QAAA,aACA,EAAA,QAAA,gBAEA,EAAA,EAAA,EAAA,UAAA,CACA,yBAAA,SAAA,EAAA,GACA,OAAA,EAAA,EAAA,EAAA,GAAA;;ACNA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBAEA,EAAA,EAAA,EAAA,UAAA,CACA,eAAA,SAAA,GACA,OAAA,EAAA,EAAA;;ACNA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,UAAA,CACA,IAAA,SAAA,EAAA,GACA,OAAA,KAAA;;ACJA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,OAAA,aAEA,EAAA,EAAA,EAAA,UAAA,CACA,aAAA,SAAA,GAEA,OADA,EAAA,IACA,GAAA,EAAA;;ACPA,IAAA,EAAA,QAAA,kBACA,EAAA,QAAA,kBACA,EAAA,QAAA,gBACA,EAAA,QAAA,aAAA,QACA,OAAA,QAAA,GAAA,EAAA,SAAA,SAAA,GACA,IAAA,EAAA,EAAA,EAAA,EAAA,IACA,EAAA,EAAA,EACA,OAAA,EAAA,EAAA,OAAA,EAAA,IAAA;;ACPA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,UAAA,CAAA,QAAA,QAAA;;ACFA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,OAAA,kBAEA,EAAA,EAAA,EAAA,UAAA,CACA,kBAAA,SAAA,GACA,EAAA,GACA,IAEA,OADA,GAAA,EAAA,IACA,EACA,MAAA,GACA,OAAA;;ACXA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,kBACA,EAAA,QAAA,iBACA,EAAA,QAAA,UACA,EAAA,QAAA,aACA,EAAA,QAAA,oBACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBAEA,SAAA,EAAA,EAAA,EAAA,GACA,IAEA,EAAA,EAFA,EAAA,UAAA,OAAA,EAAA,EAAA,UAAA,GACA,EAAA,EAAA,EAAA,EAAA,GAAA,GAEA,IAAA,EAAA,CACA,GAAA,EAAA,EAAA,EAAA,IACA,OAAA,EAAA,EAAA,EAAA,EAAA,GAEA,EAAA,EAAA,GAEA,GAAA,EAAA,EAAA,SAAA,CACA,IAAA,IAAA,EAAA,WAAA,EAAA,GAAA,OAAA,EACA,GAAA,EAAA,EAAA,EAAA,EAAA,GAAA,CACA,GAAA,EAAA,KAAA,EAAA,MAAA,IAAA,EAAA,SAAA,OAAA,EACA,EAAA,MAAA,EACA,EAAA,EAAA,EAAA,EAAA,QACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,IACA,OAAA,EAEA,YAAA,IAAA,EAAA,MAAA,EAAA,IAAA,KAAA,EAAA,IAAA,GAGA,EAAA,EAAA,EAAA,UAAA,CAAA,IAAA;;AC/BA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBAEA,GAAA,EAAA,EAAA,EAAA,UAAA,CACA,eAAA,SAAA,EAAA,GACA,EAAA,MAAA,EAAA,GACA,IAEA,OADA,EAAA,IAAA,EAAA,IACA,EACA,MAAA,GACA,OAAA;;ACXA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,oBAAA,EAAA,GAEA,EAAA,EAAA,EAAA,QAAA,CACA,SAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,MAIA,QAAA,wBAAA,CAAA;;ACXA,aAEA,IAAA,EAAA,QAAA,eACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,UACA,EAAA,QAAA,SAAA,CAAA,sBAEA,SAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAMA,IALA,IAGA,EAAA,EAHA,EAAA,EACA,EAAA,EACA,IAAA,GAAA,EAAA,EAAA,EAAA,GAGA,EAAA,GAAA,CACA,GAAA,KAAA,EAAA,CASA,GARA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,GAAA,EAAA,GAEA,GAAA,EACA,EAAA,KAEA,OAAA,KADA,EAAA,EAAA,MACA,EAAA,EAAA,IAGA,GAAA,EAAA,EACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,EAAA,EAAA,GAAA,MACA,CACA,GAAA,GAAA,iBAAA,MAAA,YACA,EAAA,GAAA,EAGA,IAEA,IAEA,OAAA,EAGA,OAAA,QAAA;;ACtCA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,yBACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,QAAA,2BAEA,EAAA,EAAA,EAAA,QAAA,CACA,QAAA,SAAA,GACA,IACA,EAAA,EADA,EAAA,EAAA,MAMA,OAJA,EAAA,GACA,EAAA,EAAA,EAAA,QACA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,UAAA,IACA,KAIA,QAAA,wBAAA,CAAA;;ACrBA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,yBACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,QAAA,2BAEA,EAAA,EAAA,EAAA,QAAA,CACA,QAAA,WACA,IAAA,EAAA,UAAA,GACA,EAAA,EAAA,MACA,EAAA,EAAA,EAAA,QACA,EAAA,EAAA,EAAA,GAEA,OADA,EAAA,EAAA,EAAA,EAAA,EAAA,OAAA,IAAA,EAAA,EAAA,EAAA,IACA,KAIA,QAAA,wBAAA,CAAA;;ACpBA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,eAAA,EAAA,GAEA,EAAA,EAAA,EAAA,SAAA,CACA,GAAA,SAAA,GACA,OAAA,EAAA,KAAA;;ACNA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,oBACA,EAAA,QAAA,cAEA,OAAA,QAAA,SAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,OAAA,EAAA,IACA,EAAA,EAAA,OACA,OAAA,IAAA,EAAA,IAAA,OAAA,GACA,EAAA,EAAA,GACA,GAAA,GAAA,GAAA,IAAA,EAAA,OAAA,EACA,IAAA,EAAA,EAAA,EACA,EAAA,EAAA,KAAA,EAAA,KAAA,KAAA,EAAA,EAAA,SAEA,OADA,EAAA,OAAA,IAAA,EAAA,EAAA,MAAA,EAAA,IACA,EAAA,EAAA,EAAA,EAAA;;ACdA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,QAAA,iBAGA,EAAA,mDAAA,KAAA,GAEA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,SAAA,CACA,SAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,GAAA;;ACXA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,QAAA,iBAGA,EAAA,mDAAA,KAAA,GAEA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,SAAA,CACA,OAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,GAAA;;ACXA,aAEA,QAAA,iBAAA,CAAA,WAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,KAEA;;ACNA,aAEA,QAAA,iBAAA,CAAA,YAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,KAEA;;ACNA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,cACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,YACA,EAAA,OAAA,UAEA,EAAA,SAAA,EAAA,GACA,KAAA,GAAA,EACA,KAAA,GAAA,GAGA,QAAA,iBAAA,CAAA,EAAA,gBAAA,WACA,IAAA,EAAA,KAAA,GAAA,KAAA,KAAA,IACA,MAAA,CAAA,MAAA,EAAA,KAAA,OAAA,KAGA,EAAA,EAAA,EAAA,SAAA,CACA,SAAA,SAAA,GAEA,GADA,EAAA,OACA,EAAA,GAAA,MAAA,UAAA,EAAA,qBACA,IAAA,EAAA,OAAA,MACA,EAAA,UAAA,EAAA,OAAA,EAAA,OAAA,EAAA,KAAA,GACA,EAAA,IAAA,OAAA,EAAA,QAAA,EAAA,QAAA,KAAA,EAAA,IAAA,GAEA,OADA,EAAA,UAAA,EAAA,EAAA,WACA,IAAA,EAAA,EAAA;;AC3BA,QAAA,gBAAA,CAAA;;ACAA,QAAA,gBAAA,CAAA;;ACCA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,eACA,EAAA,QAAA,iBACA,EAAA,QAAA,kBACA,EAAA,QAAA,sBAEA,EAAA,EAAA,EAAA,SAAA,CACA,0BAAA,SAAA,GAOA,IANA,IAKA,EAAA,EALA,EAAA,EAAA,GACA,EAAA,EAAA,EACA,EAAA,EAAA,GACA,EAAA,GACA,EAAA,EAEA,EAAA,OAAA,QAEA,KADA,EAAA,EAAA,EAAA,EAAA,EAAA,QACA,EAAA,EAAA,EAAA,GAEA,OAAA;;ACnBA,IAAA,EAAA,QAAA,kBACA,EAAA,QAAA,kBACA,EAAA,QAAA,iBACA,EAAA,QAAA,iBAAA,EACA,OAAA,QAAA,SAAA,GACA,OAAA,SAAA,GAOA,IANA,IAKA,EALA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,EAAA,OACA,EAAA,EACA,EAAA,GAEA,EAAA,GACA,EAAA,EAAA,KACA,IAAA,EAAA,KAAA,EAAA,IACA,EAAA,KAAA,EAAA,CAAA,EAAA,EAAA,IAAA,EAAA,IAGA,OAAA;;ACjBA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,qBAAA,EAAA,GAEA,EAAA,EAAA,EAAA,SAAA,CACA,OAAA,SAAA,GACA,OAAA,EAAA;;ACLA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,qBAAA,EAAA,GAEA,EAAA,EAAA,EAAA,SAAA,CACA,QAAA,SAAA,GACA,OAAA,EAAA;;ACNA,aAEA,OAAA,QAAA,QAAA,gBAAA,QAAA,WAAA,CAAA,WACA,IAAA,EAAA,KAAA,SAGA,iBAAA,KAAA,KAAA,EAAA,qBACA,QAAA,aAAA;;ACPA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBAGA,QAAA,mBAAA,EAAA,EAAA,EAAA,QAAA,wBAAA,SAAA,CACA,iBAAA,SAAA,EAAA,GACA,EAAA,EAAA,EAAA,MAAA,EAAA,CAAA,IAAA,EAAA,GAAA,YAAA,EAAA,cAAA;;ACTA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBAGA,QAAA,mBAAA,EAAA,EAAA,EAAA,QAAA,wBAAA,SAAA,CACA,iBAAA,SAAA,EAAA,GACA,EAAA,EAAA,EAAA,MAAA,EAAA,CAAA,IAAA,EAAA,GAAA,YAAA,EAAA,cAAA;;ACTA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,mBACA,EAAA,QAAA,iBACA,EAAA,QAAA,kBAAA,EAGA,QAAA,mBAAA,EAAA,EAAA,EAAA,QAAA,wBAAA,SAAA,CACA,iBAAA,SAAA,GACA,IAEA,EAFA,EAAA,EAAA,MACA,EAAA,EAAA,GAAA,GAEA,GACA,GAAA,EAAA,EAAA,EAAA,GAAA,OAAA,EAAA,UACA,EAAA,EAAA;;ACfA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,mBACA,EAAA,QAAA,iBACA,EAAA,QAAA,kBAAA,EAGA,QAAA,mBAAA,EAAA,EAAA,EAAA,QAAA,wBAAA,SAAA,CACA,iBAAA,SAAA,GACA,IAEA,EAFA,EAAA,EAAA,MACA,EAAA,EAAA,GAAA,GAEA,GACA,GAAA,EAAA,EAAA,EAAA,GAAA,OAAA,EAAA,UACA,EAAA,EAAA;;ACfA,IAAA,EAAA,QAAA,aAEA,OAAA,QAAA,SAAA,EAAA,GACA,IAAA,EAAA,GAEA,OADA,EAAA,GAAA,EAAA,EAAA,KAAA,EAAA,GACA;;ACJA,IAAA,EAAA,QAAA,cACA,EAAA,QAAA,0BACA,OAAA,QAAA,SAAA,GACA,OAAA,WACA,GAAA,EAAA,OAAA,EAAA,MAAA,UAAA,EAAA,yBACA,OAAA,EAAA;;ACLA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,EAAA,EAAA,MAAA,CAAA,OAAA,QAAA,wBAAA,CAAA;;ACFA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,EAAA,EAAA,MAAA,CAAA,OAAA,QAAA,wBAAA,CAAA;;ACHA,aAEA,IAAA,EAAA,QAAA,aAEA,OAAA,QAAA,SAAA,GACA,EAAA,EAAA,EAAA,EAAA,CAAA,GAAA,WAGA,IAFA,IAAA,EAAA,UAAA,OACA,EAAA,IAAA,MAAA,GACA,KAAA,EAAA,GAAA,UAAA,GACA,OAAA,IAAA,KAAA;;ACRA,QAAA,uBAAA,CAAA;;ACAA,QAAA,uBAAA,CAAA;;ACAA,QAAA,uBAAA,CAAA;;ACAA,QAAA,uBAAA,CAAA;;ACDA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,QAAA,UACA,EAAA,QAAA,aAEA,OAAA,QAAA,SAAA,GACA,EAAA,EAAA,EAAA,EAAA,CAAA,KAAA,SAAA,GACA,IACA,EAAA,EAAA,EAAA,EADA,EAAA,UAAA,GAKA,OAHA,EAAA,OACA,OAAA,IAAA,IACA,EAAA,GACA,MAAA,EAAA,IAAA,MACA,EAAA,GACA,GACA,EAAA,EACA,EAAA,EAAA,EAAA,UAAA,GAAA,GACA,EAAA,GAAA,EAAA,SAAA,GACA,EAAA,KAAA,EAAA,EAAA,SAGA,EAAA,GAAA,EAAA,EAAA,KAAA,GAEA,IAAA,KAAA;;ACxBA,QAAA,yBAAA,CAAA;;ACAA,QAAA,yBAAA,CAAA;;ACAA,QAAA,yBAAA,CAAA;;ACAA,QAAA,yBAAA,CAAA;;ACAA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,CAAA,OAAA,QAAA;;ACFA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,SAAA,CAAA,OAAA,QAAA;;ACFA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,UAEA,EAAA,EAAA,EAAA,QAAA,CACA,QAAA,SAAA,GACA,MAAA,UAAA,EAAA;;ACLA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CACA,MAAA,SAAA,EAAA,EAAA,GACA,OAAA,KAAA,IAAA,EAAA,KAAA,IAAA,EAAA;;ACJA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CAAA,YAAA,KAAA,GAAA;;ACFA,IAAA,EAAA,QAAA,aACA,EAAA,IAAA,KAAA,GAEA,EAAA,EAAA,EAAA,OAAA,CACA,QAAA,SAAA,GACA,OAAA,EAAA;;ACLA,OAAA,QAAA,KAAA,OAAA,SAAA,EAAA,EAAA,EAAA,EAAA,GACA,OACA,IAAA,UAAA,QAEA,GAAA,GAEA,GAAA,GAEA,GAAA,GAEA,GAAA,GAEA,GAAA,EACA,IACA,IAAA,EAAA,GAAA,KAAA,EAAA,EAAA,GACA,EAAA,IAAA,EAAA,IAAA,EAAA,GAAA;;ACfA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,QAAA,kBAEA,EAAA,EAAA,EAAA,OAAA,CACA,OAAA,SAAA,EAAA,EAAA,EAAA,EAAA,GACA,OAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA;;ACNA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CACA,MAAA,SAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,IAAA,EAEA,EAAA,IAAA,EACA,OAFA,IAAA,IAEA,IAAA,KAAA,EAAA,GAAA,EAAA,KAAA,EAAA,IAAA,MAAA,IAAA;;ACPA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CACA,MAAA,SAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,IAAA,EAEA,EAAA,IAAA,EACA,OAFA,IAAA,IAEA,IAAA,MAAA,EAAA,IAAA,EAAA,GAAA,EAAA,IAAA,KAAA,IAAA;;ACPA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CACA,MAAA,SAAA,EAAA,GACA,IACA,GAAA,EACA,GAAA,EACA,EAHA,MAGA,EACA,EAJA,MAIA,EACA,EAAA,GAAA,GACA,EAAA,GAAA,GACA,GAAA,EAAA,IAAA,IAAA,EAAA,IAAA,IACA,OAAA,EAAA,GAAA,GAAA,MAAA,EAAA,IAAA,IARA,MAQA,IAAA;;ACZA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CAAA,YAAA,IAAA,KAAA;;ACFA,IAAA,EAAA,QAAA,aACA,EAAA,KAAA,GAAA,IAEA,EAAA,EAAA,EAAA,OAAA,CACA,QAAA,SAAA,GACA,OAAA,EAAA;;ACLA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CAAA,MAAA,QAAA;;ACFA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CACA,MAAA,SAAA,EAAA,GACA,IACA,GAAA,EACA,GAAA,EACA,EAHA,MAGA,EACA,EAJA,MAIA,EACA,EAAA,IAAA,GACA,EAAA,IAAA,GACA,GAAA,EAAA,IAAA,IAAA,EAAA,IAAA,IACA,OAAA,EAAA,GAAA,IAAA,MAAA,EAAA,IAAA,IARA,MAQA,KAAA;;ACZA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CAAA,QAAA,SAAA,GAEA,OAAA,GAAA,IAAA,EAAA,EAAA,GAAA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA;;;ACJA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,WACA,EAAA,QAAA,aACA,EAAA,QAAA,0BACA,EAAA,QAAA,sBAEA,EAAA,EAAA,EAAA,EAAA,EAAA,UAAA,CAAA,QAAA,SAAA,GACA,IAAA,EAAA,EAAA,KAAA,EAAA,SAAA,EAAA,SACA,EAAA,mBAAA,EACA,OAAA,KAAA,KACA,EAAA,SAAA,GACA,OAAA,EAAA,EAAA,KAAA,KAAA,WAAA,OAAA,KACA,EACA,EAAA,SAAA,GACA,OAAA,EAAA,EAAA,KAAA,KAAA,WAAA,MAAA,KACA;;ACjBA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,6BACA,EAAA,QAAA,cAEA,EAAA,EAAA,EAAA,UAAA,CAAA,IAAA,SAAA,GACA,IAAA,EAAA,EAAA,EAAA,MACA,EAAA,EAAA,GAEA,OADA,EAAA,EAAA,EAAA,OAAA,EAAA,SAAA,EAAA,GACA,EAAA;;ACVA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,aACA,EAAA,QAAA,YAAA,CAAA,YACA,EAAA,EAAA,QAAA,EAAA,MAAA,IAAA,QAAA,o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iBAAA,EAAA,EAAA,OAAA,IAEA,EAAA,SAAA,GACA,EAAA,EAAA,EAAA,UAAA,IAGA,OAAA,QAAA,CACA,MAAA,EACA,IAAA,EACA,IAAA,EACA,IAAA,EACA,IAAA,EACA,KAAA,EACA,IAAA,EACA,IAAA;;ACjDA,IAAA,EAAA,QAAA,eACA,EAAA,QAAA,gBACA,EAAA,EAAA,IACA,EAAA,EAAA,IAEA,EAAA,IAAA,CAAA,eAAA,SAAA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA;;ACNA,IAAA,EAAA,QAAA,eACA,EAAA,QAAA,gBACA,EAAA,EAAA,IACA,EAAA,EAAA,IACA,EAAA,EAAA,MAEA,EAAA,IAAA,CAAA,eAAA,SAAA,EAAA,GACA,IAAA,EAAA,UAAA,OAAA,OAAA,EAAA,EAAA,UAAA,IACA,EAAA,EAAA,EAAA,GAAA,GAAA,GACA,QAAA,IAAA,IAAA,EAAA,OAAA,GAAA,OAAA,EACA,GAAA,EAAA,KAAA,OAAA,EACA,IAAA,EAAA,EAAA,IAAA,GAEA,OADA,EAAA,OAAA,KACA,EAAA,MAAA,EAAA,OAAA;;ACbA,IAAA,EAAA,QAAA,eACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,EAAA,IACA,EAAA,EAAA,IACA,EAAA,EAAA,IAEA,EAAA,SAAA,EAAA,EAAA,GAEA,GADA,EAAA,EAAA,EAAA,GACA,OAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,GACA,OAAA,OAAA,EAAA,EAAA,EAAA,EAAA,QAAA,GAGA,EAAA,IAAA,CAAA,YAAA,SAAA,EAAA,GACA,OAAA,EAAA,EAAA,EAAA,GAAA,UAAA,OAAA,OAAA,EAAA,EAAA,UAAA;;ACfA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,0BACA,EAAA,QAAA,eACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,EAAA,KACA,EAAA,EAAA,IAEA,EAAA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,GAAA,OAAA,EAAA,OAAA,EACA,IAAA,EAAA,EAAA,EAAA,GACA,OAAA,EAAA,OAAA,EAAA,OAAA,EAAA,IAAA,EAAA,EAAA,OAAA,KAAA,EAAA,GAGA,EAAA,IAAA,CAAA,gBAAA,SAAA,GACA,OAAA,EAAA,EAAA,GAAA,UAAA,OAAA,OAAA,EAAA,EAAA,UAAA;;ACjBA,IAAA,EAAA,QAAA,eACA,EAAA,QAAA,gBACA,EAAA,EAAA,IACA,EAAA,EAAA,IAEA,EAAA,IAAA,CAAA,eAAA,SAAA,EAAA,GACA,OAAA,EAAA,EAAA,EAAA,GACA,UAAA,OAAA,OAAA,EAAA,EAAA,UAAA;;ACPA,IAAA,EAAA,QAAA,eACA,EAAA,QAAA,gBACA,EAAA,EAAA,KACA,EAAA,EAAA,IAEA,EAAA,IAAA,CAAA,mBAAA,SAAA,GACA,OAAA,EAAA,EAAA,GAAA,UAAA,OAAA,OAAA,EAAA,EAAA,UAAA;;ACNA,IAAA,EAAA,QAAA,eACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,EAAA,IACA,EAAA,EAAA,IAEA,EAAA,SAAA,EAAA,EAAA,GAEA,GADA,EAAA,EAAA,EAAA,GACA,OAAA,EACA,IAAA,EAAA,EAAA,GACA,OAAA,OAAA,GAAA,EAAA,EAAA,EAAA,IAGA,EAAA,IAAA,CAAA,YAAA,SAAA,EAAA,GACA,OAAA,EAAA,EAAA,EAAA,GAAA,UAAA,OAAA,OAAA,EAAA,EAAA,UAAA;;ACdA,IAAA,EAAA,QAAA,eACA,EAAA,QAAA,gBACA,EAAA,EAAA,IACA,EAAA,EAAA,IAEA,EAAA,IAAA,CAAA,eAAA,SAAA,EAAA,GACA,OAAA,EAAA,EAAA,EAAA,GACA,UAAA,OAAA,OAAA,EAAA,EAAA,UAAA;;ACPA,IAAA,EAAA,QAAA,eACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,EAAA,IACA,EAAA,EAAA,IAEA,EAAA,IAAA,CAAA,SAAA,SAAA,EAAA,GACA,OAAA,SAAA,EAAA,GACA,EACA,EAAA,QACA,IAAA,EAAA,EAAA,GAAA,GACA,EAAA;;;ACVA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,eAAA,GACA,EAAA,QAAA,aAAA,QACA,EAAA,WAAA,QAAA,SAAA,CAAA,GAEA,EAAA,EAAA,EAAA,CACA,KAAA,SAAA,GACA,IAAA,EAAA,GAAA,EAAA,OACA,EAAA,EAAA,EAAA,KAAA,GAAA;;;ACTA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,aACA,EAAA,QAAA,WACA,EAAA,QAAA,eAAA,GACA,EAAA,QAAA,SAAA,CAAA,cACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,EAAA,QAAA,kBACA,EAAA,QAAA,mBACA,EAAA,QAAA,WACA,EAAA,QAAA,aACA,EAAA,EAAA,OAEA,EAAA,SAAA,GACA,OAAA,MAAA,OAAA,EAAA,EAAA,IAGA,EAAA,SAAA,GACA,IAAA,EAAA,EAAA,GACA,IACA,EAAA,QAAA,EACA,MAIA,EAAA,SAAA,GACA,YAAA,IAAA,EAAA,IAGA,EAAA,SAAA,GACA,EAAA,KACA,EAAA,QAAA,EACA,EAAA,KAIA,EAAA,SAAA,EAAA,GACA,EAAA,GACA,KAAA,QAAA,EACA,KAAA,GAAA,EACA,EAAA,IAAA,EAAA,MACA,IACA,IAAA,EAAA,EAAA,GACA,EAAA,EACA,MAAA,IACA,mBAAA,EAAA,YAAA,EAAA,WAAA,EAAA,eACA,EAAA,GACA,KAAA,GAAA,GAEA,MAAA,GAEA,YADA,EAAA,MAAA,GAEA,EAAA,OAAA,EAAA,OAGA,EAAA,UAAA,EAAA,GAAA,CACA,YAAA,WAAA,EAAA,SAGA,IAAA,EAAA,SAAA,GACA,KAAA,GAAA,GAGA,EAAA,UAAA,EAAA,GAAA,CACA,KAAA,SAAA,GACA,IAAA,EAAA,KAAA,GACA,IAAA,EAAA,GAAA,CACA,IAAA,EAAA,EAAA,GACA,IACA,IAAA,EAAA,EAAA,EAAA,MACA,GAAA,EAAA,OAAA,EAAA,KAAA,EAAA,GACA,MAAA,GACA,IACA,EAAA,GACA,QACA,MAAA,MAKA,MAAA,SAAA,GACA,IAAA,EAAA,KAAA,GACA,GAAA,EAAA,GAAA,MAAA,EACA,IAAA,EAAA,EAAA,GACA,EAAA,QAAA,EACA,IACA,IAAA,EAAA,EAAA,EAAA,OACA,IAAA,EAAA,MAAA,EACA,EAAA,EAAA,KAAA,EAAA,GACA,MAAA,GACA,IACA,EAAA,GACA,QACA,MAAA,GAGA,OADA,EAAA,GACA,GAEA,SAAA,SAAA,GACA,IAAA,EAAA,KAAA,GACA,IAAA,EAAA,GAAA,CACA,IAAA,EAAA,EAAA,GACA,EAAA,QAAA,EACA,IACA,IAAA,EAAA,EAAA,EAAA,UACA,EAAA,EAAA,EAAA,KAAA,EAAA,QAAA,EACA,MAAA,GACA,IACA,EAAA,GACA,QACA,MAAA,GAGA,OADA,EAAA,GACA,MAKA,IAAA,EAAA,SAAA,GACA,EAAA,KAAA,EAAA,aAAA,MAAA,GAAA,EAAA,IAGA,EAAA,EAAA,UAAA,CACA,UAAA,SAAA,GACA,OAAA,IAAA,EAAA,EAAA,KAAA,KAEA,QAAA,SAAA,GACA,IAAA,EAAA,KACA,OAAA,IAAA,EAAA,SAAA,EAAA,SAAA,SAAA,EAAA,GACA,EAAA,GACA,IAAA,EAAA,EAAA,UAAA,CACA,KAAA,SAAA,GACA,IACA,OAAA,EAAA,GACA,MAAA,GACA,EAAA,GACA,EAAA,gBAGA,MAAA,EACA,SAAA,SAMA,EAAA,EAAA,CACA,KAAA,SAAA,GACA,IAAA,EAAA,mBAAA,KAAA,KAAA,EACA,EAAA,EAAA,EAAA,GAAA,IACA,GAAA,EAAA,CACA,IAAA,EAAA,EAAA,EAAA,KAAA,IACA,OAAA,EAAA,cAAA,EAAA,EAAA,IAAA,EAAA,SAAA,GACA,OAAA,EAAA,UAAA,KAGA,OAAA,IAAA,EAAA,SAAA,GACA,IAAA,GAAA,EAeA,OAdA,EAAA,WACA,IAAA,EAAA,CACA,IACA,GAAA,EAAA,GAAA,EAAA,SAAA,GAEA,GADA,EAAA,KAAA,GACA,EAAA,OAAA,MACA,EAAA,OACA,MAAA,GACA,GAAA,EAAA,MAAA,EAEA,YADA,EAAA,MAAA,GAEA,EAAA,cAGA,WAAA,GAAA,MAGA,GAAA,WACA,IAAA,IAAA,EAAA,EAAA,EAAA,UAAA,OAAA,EAAA,IAAA,MAAA,GAAA,EAAA,GAAA,EAAA,GAAA,UAAA,KACA,OAAA,IAAA,mBAAA,KAAA,KAAA,GAAA,SAAA,GACA,IAAA,GAAA,EASA,OARA,EAAA,WACA,IAAA,EAAA,CACA,IAAA,IAAA,EAAA,EAAA,EAAA,EAAA,SAAA,EAEA,GADA,EAAA,KAAA,EAAA,IACA,EAAA,OACA,EAAA,cAGA,WAAA,GAAA,QAKA,EAAA,EAAA,UAAA,EAAA,WAAA,OAAA,OAEA,EAAA,EAAA,EAAA,CAAA,WAAA,IAEA,QAAA,iBAAA,CAAA;;;ACrMA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,GAAA,MACA,EAAA,WAAA,KAAA,GACA,EAAA,SAAA,GACA,OAAA,SAAA,EAAA,GACA,IAAA,EAAA,UAAA,OAAA,EACA,IAAA,GAAA,EAAA,KAAA,UAAA,GACA,OAAA,EAAA,EAAA,YAEA,mBAAA,EAAA,EAAA,SAAA,IAAA,MAAA,KAAA,IACA,EAAA,KAGA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,CACA,WAAA,EAAA,EAAA,YACA,YAAA,EAAA,EAAA;;AClBA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,WACA,EAAA,EAAA,EAAA,EAAA,EAAA,CACA,aAAA,EAAA,IACA,eAAA,EAAA;;;ACyCA,IA7CA,IAAA,EAAA,QAAA,wBACA,EAAA,QAAA,kBACA,EAAA,QAAA,eACA,EAAA,QAAA,aACA,EAAA,QAAA,WACA,EAAA,QAAA,gBACA,EAAA,QAAA,UACA,EAAA,EAAA,YACA,EAAA,EAAA,eACA,EAAA,EAAA,MAEA,EAAA,CACA,aAAA,EACA,qBAAA,EACA,cAAA,EACA,gBAAA,EACA,aAAA,EACA,eAAA,EACA,cAAA,EACA,sBAAA,EACA,UAAA,EACA,mBAAA,EACA,gBAAA,EACA,iBAAA,EACA,mBAAA,EACA,WAAA,EACA,eAAA,EACA,cAAA,EACA,UAAA,EACA,kBAAA,EACA,QAAA,EACA,aAAA,EACA,eAAA,EACA,eAAA,EACA,gBAAA,EACA,cAAA,EACA,eAAA,EACA,kBAAA,EACA,kBAAA,EACA,gBAAA,EACA,kBAAA,EACA,eAAA,EACA,WAAA,GAGA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,EAAA,OAAA,IAAA,CACA,IAIA,EAJA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,GAAA,EAAA,UAEA,GAAA,IACA,EAAA,IAAA,EAAA,EAAA,EAAA,GACA,EAAA,IAAA,EAAA,EAAA,EAAA,GACA,EAAA,GAAA,EACA,GAAA,IAAA,KAAA,EAAA,EAAA,IAAA,EAAA,EAAA,EAAA,EAAA,IAAA;;ACvDA,QAAA,wBACA,QAAA,+BACA,QAAA,wCACA,QAAA,0CACA,QAAA,oDACA,QAAA,yCACA,QAAA,6BACA,QAAA,+CACA,QAAA,+BACA,QAAA,6BACA,QAAA,2CACA,QAAA,kCACA,QAAA,kCACA,QAAA,sCACA,QAAA,+BACA,QAAA,2BACA,QAAA,yCACA,QAAA,kCACA,QAAA,+BACA,QAAA,+BACA,QAAA,uCACA,QAAA,2BACA,QAAA,6BACA,QAAA,oCACA,QAAA,iCACA,QAAA,qCACA,QAAA,gCACA,QAAA,kCACA,QAAA,mCACA,QAAA,+BACA,QAAA,wCACA,QAAA,yCACA,QAAA,yCACA,QAAA,oCACA,QAAA,kCACA,QAAA,4BACA,QAAA,4BACA,QAAA,4BACA,QAAA,2BACA,QAAA,4BACA,QAAA,2BACA,QAAA,4BACA,QAAA,6BACA,QAAA,4BACA,QAAA,2BACA,QAAA,4BACA,QAAA,4BACA,QAAA,2BACA,QAAA,2BACA,QAAA,2BACA,QAAA,2BACA,QAAA,4BACA,QAAA,wCACA,QAAA,4BACA,QAAA,6BACA,QAAA,iCACA,QAAA,sCACA,QAAA,kCACA,QAAA,iCACA,QAAA,+BACA,QAAA,oCACA,QAAA,+BACA,QAAA,4BACA,QAAA,8BACA,QAAA,6BACA,QAAA,8BACA,QAAA,kCACA,QAAA,iCACA,QAAA,gCACA,QAAA,6BACA,QAAA,8BACA,QAAA,+BACA,QAAA,4BACA,QAAA,4BACA,QAAA,0BACA,QAAA,8BACA,QAAA,oCACA,QAAA,gCACA,QAAA,mCACA,QAAA,gCACA,QAAA,4BACA,QAAA,0BACA,QAAA,4BACA,QAAA,6BACA,QAAA,4BACA,QAAA,gCACA,QAAA,2BACA,QAAA,8BACA,QAAA,4BACA,QAAA,6BACA,QAAA,8BACA,QAAA,oCACA,QAAA,gCACA,QAAA,qCACA,QAAA,mCACA,QAAA,4BACA,QAAA,4BACA,QAAA,kCACA,QAAA,+BACA,QAAA,gCACA,QAAA,oCACA,QAAA,6BACA,QAAA,kCACA,QAAA,8BACA,QAAA,8BACA,QAAA,gCACA,QAAA,+BACA,QAAA,8BACA,QAAA,yBACA,QAAA,qBACA,QAAA,qBACA,QAAA,0BACA,QAAA,0BACA,QAAA,oCACA,QAAA,iCACA,QAAA,kCACA,QAAA,mCACA,QAAA,2CACA,QAAA,mCACA,QAAA,oCACA,QAAA,mCACA,QAAA,oCACA,QAAA,qCACA,QAAA,qCACA,QAAA,+BACA,QAAA,mCACA,QAAA,yCACA,QAAA,yCACA,QAAA,mCACA,QAAA,6BACA,QAAA,qDACA,QAAA,0CACA,QAAA,6BACA,QAAA,uCACA,QAAA,kCACA,QAAA,4CACA,QAAA,6BACA,QAAA,0CACA,QAAA,gCACA,QAAA,gCACA,QAAA,+BACA,QAAA,2BACA,QAAA,kCACA,QAAA,gCACA,QAAA,kCACA,QAAA,mCACA,QAAA,kCACA,QAAA,uCACA,QAAA,mCACA,QAAA,qDACA,QAAA,+BACA,QAAA,gCACA,QAAA,sCACA,QAAA,sCACA,QAAA,sCACA,QAAA,sCACA,QAAA,6BACA,QAAA,6BACA,QAAA,wBACA,QAAA,wBACA,QAAA,6BACA,QAAA,6BACA,QAAA,0BACA,QAAA,0BACA,QAAA,+BACA,QAAA,+BACA,QAAA,wBACA,QAAA,+BACA,QAAA,gCACA,QAAA,4BACA,QAAA,kCACA,QAAA,8BACA,QAAA,6BACA,QAAA,4BACA,QAAA,4BACA,QAAA,4BACA,QAAA,kCACA,QAAA,8BACA,QAAA,4BACA,QAAA,4BACA,QAAA,8BACA,QAAA,iCACA,QAAA,6BACA,QAAA,yCACA,QAAA,yCACA,QAAA,sCACA,QAAA,2CACA,QAAA,0CACA,QAAA,+CACA,QAAA,sCACA,QAAA,0CACA,QAAA,kCACA,QAAA,sBACA,QAAA,4BACA,QAAA,wBACA,QAAA,2BACA,QAAA,8BACA,OAAA,QAAA,QAAA;;;AC2hBA,IAAA,EAAA,UAAA,IAttBA,SAAA,GACA,aAEA,IAEA,EAFA,EAAA,OAAA,UACA,EAAA,EAAA,eAEA,EAAA,mBAAA,OAAA,OAAA,GACA,EAAA,EAAA,UAAA,aACA,EAAA,EAAA,eAAA,kBACA,EAAA,EAAA,aAAA,gBAEA,EAAA,iBAAA,OACA,EAAA,EAAA,mBACA,GAAA,EACA,IAGA,OAAA,QAAA,OAJA,EAaA,EAAA,EAAA,mBAAA,EAAA,OAAA,QAAA,IAcA,KAAA,EAoBA,IAAA,EAAA,iBACA,EAAA,iBACA,EAAA,YACA,EAAA,YAIA,EAAA,GAYA,EAAA,GACA,EAAA,GAAA,WACA,OAAA,MAGA,IAAA,EAAA,OAAA,eACA,EAAA,GAAA,EAAA,EAAA,EAAA,MACA,GACA,IAAA,GACA,EAAA,KAAA,EAAA,KAGA,EAAA,GAGA,IAAA,EAAA,EAAA,UACA,EAAA,UAAA,OAAA,OAAA,GACA,EAAA,UAAA,EAAA,YAAA,EACA,EAAA,YAAA,EACA,EAAA,GACA,EAAA,YAAA,oBAYA,EAAA,oBAAA,SAAA,GACA,IAAA,EAAA,mBAAA,GAAA,EAAA,YACA,QAAA,IACA,IAAA,GAGA,uBAAA,EAAA,aAAA,EAAA,QAIA,EAAA,KAAA,SAAA,GAUA,OATA,OAAA,eACA,OAAA,eAAA,EAAA,IAEA,EAAA,UAAA,EACA,KAAA,IACA,EAAA,GAAA,sBAGA,EAAA,UAAA,OAAA,OAAA,GACA,GAOA,EAAA,MAAA,SAAA,GACA,MAAA,CAAA,QAAA,IAkFA,EAAA,EAAA,WACA,EAAA,UAAA,GAAA,WACA,OAAA,MAEA,EAAA,cAAA,EAKA,EAAA,MAAA,SAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,IAAA,EACA,EAAA,EAAA,EAAA,EAAA,IAGA,OAAA,EAAA,oBAAA,GACA,EACA,EAAA,OAAA,KAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,MAAA,EAAA,UAsKA,EAAA,GAEA,EAAA,GAAA,YAOA,EAAA,GAAA,WACA,OAAA,MAGA,EAAA,SAAA,WACA,MAAA,sBAkCA,EAAA,KAAA,SAAA,GACA,IAAA,EAAA,GACA,IAAA,IAAA,KAAA,EACA,EAAA,KAAA,GAMA,OAJA,EAAA,UAIA,SAAA,IACA,KAAA,EAAA,QAAA,CACA,IAAA,EAAA,EAAA,MACA,GAAA,KAAA,EAGA,OAFA,EAAA,MAAA,EACA,EAAA,MAAA,EACA,EAQA,OADA,EAAA,MAAA,EACA,IAsCA,EAAA,OAAA,EAMA,EAAA,UAAA,CACA,YAAA,EAEA,MAAA,SAAA,GAcA,GAbA,KAAA,KAAA,EACA,KAAA,KAAA,EAGA,KAAA,KAAA,KAAA,MAAA,EACA,KAAA,MAAA,EACA,KAAA,SAAA,KAEA,KAAA,OAAA,OACA,KAAA,IAAA,EAEA,KAAA,WAAA,QAAA,IAEA,EACA,IAAA,IAAA,KAAA,KAEA,MAAA,EAAA,OAAA,IACA,EAAA,KAAA,KAAA,KACA,OAAA,EAAA,MAAA,MACA,KAAA,GAAA,IAMA,KAAA,WACA,KAAA,MAAA,EAEA,IACA,EADA,KAAA,WAAA,GACA,WACA,GAAA,UAAA,EAAA,KACA,MAAA,EAAA,IAGA,OAAA,KAAA,MAGA,kBAAA,SAAA,GACA,GAAA,KAAA,KACA,MAAA,EAGA,IAAA,EAAA,KACA,SAAA,EAAA,EAAA,GAYA,OAXA,EAAA,KAAA,QACA,EAAA,IAAA,EACA,EAAA,KAAA,EAEA,IAGA,EAAA,OAAA,OACA,EAAA,IAAA,KAGA,EAGA,IAAA,IAAA,EAAA,KAAA,WAAA,OAAA,EAAA,GAAA,IAAA,EAAA,CACA,IAAA,EAAA,KAAA,WAAA,GACA,EAAA,EAAA,WAEA,GAAA,SAAA,EAAA,OAIA,OAAA,EAAA,OAGA,GAAA,EAAA,QAAA,KAAA,KAAA,CACA,IAAA,EAAA,EAAA,KAAA,EAAA,YACA,EAAA,EAAA,KAAA,EAAA,cAEA,GAAA,GAAA,EAAA,CACA,GAAA,KAAA,KAAA,EAAA,SACA,OAAA,EAAA,EAAA,UAAA,GACA,GAAA,KAAA,KAAA,EAAA,WACA,OAAA,EAAA,EAAA,iBAGA,GAAA,GACA,GAAA,KAAA,KAAA,EAAA,SACA,OAAA,EAAA,EAAA,UAAA,OAGA,CAAA,IAAA,EAMA,MAAA,IAAA,MAAA,0CALA,GAAA,KAAA,KAAA,EAAA,WACA,OAAA,EAAA,EAAA,gBAUA,OAAA,SAAA,EAAA,GACA,IAAA,IAAA,EAAA,KAAA,WAAA,OAAA,EAAA,GAAA,IAAA,EAAA,CACA,IAAA,EAAA,KAAA,WAAA,GACA,GAAA,EAAA,QAAA,KAAA,MACA,EAAA,KAAA,EAAA,eACA,KAAA,KAAA,EAAA,WAAA,CACA,IAAA,EAAA,EACA,OAIA,IACA,UAAA,GACA,aAAA,IACA,EAAA,QAAA,GACA,GAAA,EAAA,aAGA,EAAA,MAGA,IAAA,EAAA,EAAA,EAAA,WAAA,GAIA,OAHA,EAAA,KAAA,EACA,EAAA,IAAA,EAEA,GACA,KAAA,OAAA,OACA,KAAA,KAAA,EAAA,WACA,GAGA,KAAA,SAAA,IAGA,SAAA,SAAA,EAAA,GACA,GAAA,UAAA,EAAA,KACA,MAAA,EAAA,IAcA,MAXA,UAAA,EAAA,MACA,aAAA,EAAA,KACA,KAAA,KAAA,EAAA,IACA,WAAA,EAAA,MACA,KAAA,KAAA,KAAA,IAAA,EAAA,IACA,KAAA,OAAA,SACA,KAAA,KAAA,OACA,WAAA,EAAA,MAAA,IACA,KAAA,KAAA,GAGA,GAGA,OAAA,SAAA,GACA,IAAA,IAAA,EAAA,KAAA,WAAA,OAAA,EAAA,GAAA,IAAA,EAAA,CACA,IAAA,EAAA,KAAA,WAAA,GACA,GAAA,EAAA,aAAA,EAGA,OAFA,KAAA,SAAA,EAAA,WAAA,EAAA,UACA,EAAA,GACA,IAKA,MAAA,SAAA,GACA,IAAA,IAAA,EAAA,KAAA,WAAA,OAAA,EAAA,GAAA,IAAA,EAAA,CACA,IAAA,EAAA,KAAA,WAAA,GACA,GAAA,EAAA,SAAA,EAAA,CACA,IAAA,EAAA,EAAA,WACA,GAAA,UAAA,EAAA,KAAA,CACA,IAAA,EAAA,EAAA,IACA,EAAA,GAEA,OAAA,GAMA,MAAA,IAAA,MAAA,0BAGA,cAAA,SAAA,EAAA,EAAA,GAaA,OAZA,KAAA,SAAA,CACA,SAAA,EAAA,GACA,WAAA,EACA,QAAA,GAGA,SAAA,KAAA,SAGA,KAAA,IAAA,GAGA,IA/qBA,SAAA,EAAA,EAAA,EAAA,EAAA,GAEA,IAAA,EAAA,GAAA,EAAA,qBAAA,EAAA,EAAA,EACA,EAAA,OAAA,OAAA,EAAA,WACA,EAAA,IAAA,EAAA,GAAA,IAMA,OAFA,EAAA,QA8MA,SAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAEA,OAAA,SAAA,EAAA,GACA,GAAA,IAAA,EACA,MAAA,IAAA,MAAA,gCAGA,GAAA,IAAA,EAAA,CACA,GAAA,UAAA,EACA,MAAA,EAKA,OAAA,IAMA,IAHA,EAAA,OAAA,EACA,EAAA,IAAA,IAEA,CACA,IAAA,EAAA,EAAA,SACA,GAAA,EAAA,CACA,IAAA,EAAA,EAAA,EAAA,GACA,GAAA,EAAA,CACA,GAAA,IAAA,EAAA,SACA,OAAA,GAIA,GAAA,SAAA,EAAA,OAGA,EAAA,KAAA,EAAA,MAAA,EAAA,SAEA,GAAA,UAAA,EAAA,OAAA,CACA,GAAA,IAAA,EAEA,MADA,EAAA,EACA,EAAA,IAGA,EAAA,kBAAA,EAAA,SAEA,WAAA,EAAA,QACA,EAAA,OAAA,SAAA,EAAA,KAGA,EAAA,EAEA,IAAA,EAAA,EAAA,EAAA,EAAA,GACA,GAAA,WAAA,EAAA,KAAA,CAOA,GAJA,EAAA,EAAA,KACA,EACA,EAEA,EAAA,MAAA,EACA,SAGA,MAAA,CACA,MAAA,EAAA,IACA,KAAA,EAAA,MAGA,UAAA,EAAA,OACA,EAAA,EAGA,EAAA,OAAA,QACA,EAAA,IAAA,EAAA,OAtRA,CAAA,EAAA,EAAA,GAEA,EAcA,SAAA,EAAA,EAAA,EAAA,GACA,IACA,MAAA,CAAA,KAAA,SAAA,IAAA,EAAA,KAAA,EAAA,IACA,MAAA,GACA,MAAA,CAAA,KAAA,QAAA,IAAA,IAiBA,SAAA,KACA,SAAA,KACA,SAAA,KA4BA,SAAA,EAAA,GACA,CAAA,OAAA,QAAA,UAAA,QAAA,SAAA,GACA,EAAA,GAAA,SAAA,GACA,OAAA,KAAA,QAAA,EAAA,MAoCA,SAAA,EAAA,GACA,SAAA,EAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,EAAA,GAAA,EAAA,GACA,GAAA,UAAA,EAAA,KAEA,CACA,IAAA,EAAA,EAAA,IACA,EAAA,EAAA,MACA,OAAA,GACA,iBAAA,GACA,EAAA,KAAA,EAAA,WACA,QAAA,QAAA,EAAA,SAAA,KAAA,SAAA,GACA,EAAA,OAAA,EAAA,EAAA,IACA,SAAA,GACA,EAAA,QAAA,EAAA,EAAA,KAIA,QAAA,QAAA,GAAA,KAAA,SAAA,GAgBA,EAAA,MAAA,EACA,EAAA,IACA,GAhCA,EAAA,EAAA,KAwCA,IAAA,EAJA,iBAAA,EAAA,SAAA,EAAA,QAAA,SACA,EAAA,EAAA,QAAA,OAAA,KAAA,IAmCA,KAAA,QA9BA,SAAA,EAAA,GACA,SAAA,IACA,OAAA,IAAA,QAAA,SAAA,EAAA,GACA,EAAA,EAAA,EAAA,EAAA,KAIA,OAAA,EAaA,EAAA,EAAA,KACA,EAGA,GACA,KA+GA,SAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,SAAA,EAAA,QACA,GAAA,IAAA,EAAA,CAKA,GAFA,EAAA,SAAA,KAEA,UAAA,EAAA,OAAA,CACA,GAAA,EAAA,SAAA,SAGA,EAAA,OAAA,SACA,EAAA,IAAA,EACA,EAAA,EAAA,GAEA,UAAA,EAAA,QAGA,OAAA,EAIA,EAAA,OAAA,QACA,EAAA,IAAA,IAAA,UACA,kDAGA,OAAA,EAGA,IAAA,EAAA,EAAA,EAAA,EAAA,SAAA,EAAA,KAEA,GAAA,UAAA,EAAA,KAIA,OAHA,EAAA,OAAA,QACA,EAAA,IAAA,EAAA,IACA,EAAA,SAAA,KACA,EAGA,IAAA,EAAA,EAAA,IAEA,OAAA,EAOA,EAAA,MAGA,EAAA,EAAA,YAAA,EAAA,MAGA,EAAA,KAAA,EAAA,QAQA,WAAA,EAAA,SACA,EAAA,OAAA,OACA,EAAA,IAAA,GAUA,EAAA,SAAA,KACA,GANA,GA3BA,EAAA,OAAA,QACA,EAAA,IAAA,IAAA,UAAA,oCACA,EAAA,SAAA,KACA,GAoDA,SAAA,EAAA,GACA,IAAA,EAAA,CAAA,OAAA,EAAA,IAEA,KAAA,IACA,EAAA,SAAA,EAAA,IAGA,KAAA,IACA,EAAA,WAAA,EAAA,GACA,EAAA,SAAA,EAAA,IAGA,KAAA,WAAA,KAAA,GAGA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,YAAA,GACA,EAAA,KAAA,gBACA,EAAA,IACA,EAAA,WAAA,EAGA,SAAA,EAAA,GAIA,KAAA,WAAA,CAAA,CAAA,OAAA,SACA,EAAA,QAAA,EAAA,MACA,KAAA,OAAA,GA8BA,SAAA,EAAA,GACA,GAAA,EAAA,CACA,IAAA,EAAA,EAAA,GACA,GAAA,EACA,OAAA,EAAA,KAAA,GAGA,GAAA,mBAAA,EAAA,KACA,OAAA,EAGA,IAAA,MAAA,EAAA,QAAA,CACA,IAAA,GAAA,EAAA,EAAA,SAAA,IACA,OAAA,EAAA,EAAA,QACA,GAAA,EAAA,KAAA,EAAA,GAGA,OAFA,EAAA,MAAA,EAAA,GACA,EAAA,MAAA,EACA,EAOA,OAHA,EAAA,MAAA,EACA,EAAA,MAAA,EAEA,GAGA,OAAA,EAAA,KAAA,GAKA,MAAA,CAAA,KAAA,GAIA,SAAA,IACA,MAAA,CAAA,MAAA,EAAA,MAAA,IApgBA,CAktBA,iBAAA,EAAA,EACA,iBAAA,OAAA,OACA,iBAAA,KAAA,KAAA;;AC9tBA,OAAA,QAAA,SAAA,EAAA,GACA,IAAA,EAAA,IAAA,OAAA,GAAA,SAAA,GACA,OAAA,EAAA,IACA,EACA,OAAA,SAAA,GACA,OAAA,OAAA,GAAA,QAAA,EAAA;;ACJA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,cAAA,CAAA,sBAAA,QAEA,EAAA,EAAA,EAAA,SAAA,CAAA,OAAA,SAAA,GAAA,OAAA,EAAA;;ACJA,QAAA,oCACA,OAAA,QAAA,QAAA,uBAAA,OAAA;;;;AC0BA,IAAA,EAAA,UAAA,GAnBA,GANA,QAAA,gBAEA,QAAA,+BAEA,QAAA,4BAEA,EAAA,eACA,MAAA,IAAA,MAAA,kDAEA,EAAA,gBAAA,EAEA,IAAA,EAAA,iBACA,SAAA,EAAA,EAAA,EAAA,GACA,EAAA,IAAA,OAAA,GAAA,EAAA,EAAA,CACA,UAAA,EACA,cAAA,EACA,MAAA,IAIA,EAAA,OAAA,UAAA,UAAA,GAAA,UACA,EAAA,OAAA,UAAA,WAAA,GAAA,QAEA,gMAAA,MAAA,KAAA,QAAA,SAAA,GACA,GAAA,IAAA,EAAA,MAAA,EAAA,SAAA,KAAA,KAAA,GAAA;;ACmFK,aAAA,SAAA,EAAA,EAAA,GAAA,KAAA,aAAA,GAAA,MAAA,IAAA,UAAA,qCAAA,SAAA,EAAA,EAAA,GAAA,IAAA,IAAA,EAAA,EAAA,EAAA,EAAA,OAAA,IAAA,CAAA,IAAA,EAAA,EAAA,GAAA,EAAA,WAAA,EAAA,aAAA,EAAA,EAAA,cAAA,EAAA,UAAA,IAAA,EAAA,UAAA,GAAA,OAAA,eAAA,EAAA,EAAA,IAAA,IAAA,SAAA,EAAA,EAAA,EAAA,GAAA,OAAA,GAAA,EAAA,EAAA,UAAA,GAAA,GAAA,EAAA,EAAA,GAAA,EAAA,OAAA,eAAA,QAAA,aAAA,CAAA,OAAA,IAAA,QAAA,aAAA,EA7GgB8d,IAAAA,EA6GhB,WA5GWC,SAAAA,EAAAA,GAAM,EAAA,KAAA,GAETC,KAAAA,IAAM7jB,SACN8jB,KAAAA,IAAM,KAAKD,IAAIrf,iBAAiBof,EAAKG,aAElB,KAApB,KAAKD,IAAIzgB,SAER2gB,KAAAA,IAAMhgB,OACNigB,KAAAA,UAAY,KAAKD,IAAIE,YAErBC,KAAAA,cAAgB,KAAKN,IAAI9iB,cAAc6iB,EAAKQ,gBAC5C9gB,KAAAA,UAAYsgB,EAAKtgB,UACjB4L,KAAAA,UAAY0U,EAAK1U,WAAa,EAE9BmV,KAAAA,SAAW,GACXA,KAAAA,SAAW,KAAKC,YAAYV,EAAKW,iBAEjCC,KAAAA,eA2FR,OAAA,EAAA,EAAA,CAAA,CAAA,IAAA,cAxFa,MAAA,WAAA,IACNC,EAWAC,EAZM,EAAA,KAELP,KAAAA,cAAc5jB,iBAAiB,SAAU,WACtCkkB,GACA1b,aAAa0b,GAGjBA,EAAc3b,WAAW,WACrB,EAAK6b,OACN,KAIFR,KAAAA,cAAc5jB,iBAAiB,SAAU,WACtCmkB,GACA3b,aAAa2b,GAGjBA,EAAc5b,WAAW,WACrB,EAAK6b,OACN,OAoEV,CAAA,IAAA,cA/DWJ,MAAAA,SAAAA,GAEH,IADCK,IAAAA,EAAU,GACPzhB,EAAI,EAAG0F,EAAM,KAAKib,IAAIzgB,OAAQF,EAAI0F,EAAK1F,IAAK,CAC3CvC,IAAAA,EAAO,KAAKkjB,IAAI3gB,GAAGvC,KACzBgkB,EAAQ3f,KAAK,KAAK4e,IAAI5V,eAAerN,EAAKC,MAAM,KAAK,KAElD+jB,OAAAA,IAyDV,CAAA,IAAA,MAtDK,MAAA,WACEriB,IAAAA,EAAW,KAAKsiB,eACfC,KAAAA,eAAeviB,KAoDvB,CAAA,IAAA,eAjDc,MAAA,WAEN,IADCwiB,IAAAA,EAAoB,GACjB5hB,EAAI,EAAG0F,EAAM,KAAKwb,SAAShhB,OAAQF,EAAI0F,EAAK1F,IAAK,CAChD6hB,IAAAA,EAAU,KAAKX,SAASlhB,GAC1B6hB,GAAW,KAAKC,OAAOD,IACvBD,EAAkB9f,KAAK+f,GAIxBD,OAAAA,IAwCV,CAAA,IAAA,SArCM1iB,MAAAA,SAAAA,GACG6a,IAAAA,EAAY,KAAKiH,cAAcjH,UAC/BgI,EAAYhI,EAAY,KAAKhO,UAE7BiW,EADO9iB,EAAQwM,wBACGI,IAAMiO,EACxBkI,EAAgBD,EAAa9iB,EAAQ8M,aAEpCgW,OAAAA,GAAcD,EAAY,KAAKhW,WAAakW,EAAgBlI,EAAY,KAAKhO,YA8BvF,CAAA,IAAA,iBA3Bc3M,MAAAA,SAAAA,GAIN,IAHD8iB,IAAAA,EAAW,EACXC,EAAkBC,IAEbpiB,EAAI,EAAG0F,EAAMtG,EAASc,OAAQF,EAAI0F,EAAK1F,IAAK,CAC3CmO,IAAAA,EAAK/O,EAASY,GACdqiB,EAAY,KAAKC,YAAYnU,GAC/B+T,EAAWG,IACXH,EAAWG,EACXF,EAAkBhU,GAIrB,IAAA,IAAInO,EAAI,EAAG0F,EAAM,KAAKib,IAAIzgB,OAAQF,EAAI0F,EAAK1F,IAAK,CAC3CuiB,IAAAA,EAAa,KAAK5B,IAAI3gB,GACxBuiB,EAAW9kB,KAAKC,MAAM,KAAK,KAAOykB,EAAgBK,IAClDD,EAAW/lB,UAAUO,IAAI,KAAKoD,WAC9BoiB,EAAW/lB,UAAUO,IAAI,6BAEzBwlB,EAAW/lB,UAAUoL,OAAO,KAAKzH,WACjCoiB,EAAW/lB,UAAUoL,OAAO,+BAOvC,CAAA,IAAA,cAFW1I,MAAAA,SAAAA,GACDiX,OAAAA,SAASiM,EAAEljB,GAASujB,KAAK,qBAAqBC,IAAI,GAAGC,QAAQjlB,MAAM,KAAK,QAClF,EAAA,GAAA,QAAA,QAAA;;AChDA,aAAA,SAAA,EAAA,EAAA,GAAA,KAAA,aAAA,GAAA,MAAA,IAAA,UAAA,qCAAA,SAAA,EAAA,EAAA,GAAA,IAAA,IAAA,EAAA,EAAA,EAAA,EAAA,OAAA,IAAA,CAAA,IAAA,EAAA,EAAA,GAAA,EAAA,WAAA,EAAA,aAAA,EAAA,EAAA,cAAA,EAAA,UAAA,IAAA,EAAA,UAAA,GAAA,OAAA,eAAA,EAAA,EAAA,IAAA,IAAA,SAAA,EAAA,EAAA,EAAA,GAAA,OAAA,GAAA,EAAA,EAAA,UAAA,GAAA,GAAA,EAAA,EAAA,GAAA,EAAA,OAAA,eAAA,QAAA,aAAA,CAAA,OAAA,IAAA,QAAA,aAAA,EA7DgBklB,IAAAA,EA6DhB,WA3Da,SAAA,IAAA,EAAA,KAAA,GACLC,KAAAA,OAAST,EAAE,6BACXU,KAAAA,WAAaV,EAAE,kBACfW,KAAAA,OAASX,EAAE,0BACXvB,KAAAA,IAAMuB,EAAEvhB,QACRmgB,KAAAA,cAAgBoB,EAAE,QAClBY,KAAAA,QAAUZ,EAAE,iBACZ1X,KAAAA,QAAU0X,EAAE,8BAEZf,KAAAA,cACA4B,KAAAA,SAiDR,OAAA,EAAA,EAAA,CAAA,CAAA,IAAA,SA9CQ,MAAA,WACAC,KAAAA,0BACAC,KAAAA,sBA4CR,CAAA,IAAA,0BAzCyB,MAAA,WACT,KAAKtC,IAAIE,cAAhBqC,IACAC,EAAe,KAAKR,OAAOS,cAC3BC,EAAe,KAAKR,OAAOO,aAAY,GAAQ,KAAKR,WAAWQ,aAAY,GAE5EN,KAAAA,QAAQQ,IAAI,aAAc,KAAK3C,IAAIE,cAAgBsC,EAAeE,KAoC1E,CAAA,IAAA,oBAjCmB,MAAA,WACVE,IAAAA,EAAwB,KAAKX,WAAWY,WAAW5X,IAAMqK,SAAS,KAAK2M,WAAWU,IAAI,cAAe,IACrGG,EAAgB,KAAK3C,cAAcjH,YAAc,KAAKiJ,QAAQM,cAC9DF,EAAO,KAAKvC,IAAIE,cAAgB,KAAK8B,OAAOS,cAE9ChT,EAAM,EAEVA,GADAA,EAAMqT,EAAgBF,EAAwBA,EAAwBE,GAC1DP,EAAOA,EAAO9S,EACrB5F,KAAAA,QAAQ8Y,IAAI,SAAUlT,KAyB9B,CAAA,IAAA,cAtBa,MAAA,WAAA,IACNgR,EAWAC,EAZM,EAAA,KAELP,KAAAA,cAAc/M,GAAG,SAAU,WACxBqN,GACA1b,aAAa0b,GAGjBA,EAAc3b,WAAW,WACrB,EAAKsd,UACN,KAIFpC,KAAAA,IAAI5M,GAAG,SAAU,WACdsN,GACA3b,aAAa2b,GAGjBA,EAAc5b,WAAW,WACrB,EAAKsd,UACN,SAEV,EAAA,GAAA,QAAA,QAAA;;ACvDL,aANA,QAAA,4CACA,QAAA,wBACA,QAAA,kBACA,IAAA,EAAA,EAAA,QAAA,gBACA,EAAA,EAAA,QAAA,oBAEA,SAAA,EAAA,GAAA,OAAA,GAAA,EAAA,WAAA,EAAA,CAAA,QAAA,GAAAb,EAAE,WAEWwB,IAECC,EAuEAC,EAvEAD,EADazB,EAAE,2BACKK,KAAK,MAC/BL,EAAE2B,KAAKF,EAAQ,SAASxQ,EAAO2Q,GACrBC,IAAAA,EAAM7B,EAAE4B,GACRE,EAAe9B,EAAE,sCACjB+B,EAAQF,EAAIxgB,SAAS,KAC3BwgB,EAAIG,OAAOF,EAAaE,OAAOD,IAEzBE,IAAAA,EAAYJ,EAAIK,SAAS,aAAeH,EAAMG,SAAS,WACvDC,EAAMN,EAAIxgB,SAAS,MACrB8gB,GAAAA,EAAIrkB,OAAQ,CACNskB,IAAAA,EAAoBnR,aAAAA,OAAAA,GAC1BkR,EAAIE,KAAK,KAAMD,GACfD,EAAIG,SAAS,YACPC,IAAAA,EAAiBvC,EAAE,oCACrBiC,GACAE,EAAIG,SAAS,QACbC,EAAeD,SAAS,SAExBH,EAAI5X,OAGRsX,EAAIG,OACAF,EAAaE,OACTO,EAAeP,OACXhC,EAAwEoC,sEAAAA,OAAAA,EAD5E,mGAINJ,OAAOG,MAMjBnC,EAAE,yCAAyC1V,MAAM,WACvCkY,IAAAA,EAAUxC,EAAE,MACZI,EAAKoC,EAAQH,KAAK,eACxBrC,EAAOI,KAAAA,OAAAA,IAAMqC,YAAY,QAAQC,QAAQ,CAAC9X,OAAQ,SAAU+X,QAAS,WACrEH,EAAQI,SAASH,YAAY,UAkC3Bf,EAAc1B,EAAE,eAEtBA,EAAE,kBAAkB9V,MAAM,WAClB8V,EAAEvhB,QAAQoM,SAAW,MACrB6W,EAAYnX,SAEjBvG,KAAK,WACAgc,EAAEvhB,QAAQoM,SAAW,MACrB6W,EAAYllB,SAYZ,IAAI4hB,EAAJ,QAAc,CACtBY,gBAAiB,yBACjBR,YAAa,cACbK,eAAgB,OAChB9gB,UAAW,UACX4L,UAAW,KAEfqW,EAAE,wBAAwB9V,QAE1B8V,EAAE,YAAY2B,KAAK,WACf3B,EAAE,MAAMsC,SAAS,8BAErBtC,EAAE,2BAA2B2B,KAAK,WAC9B3B,EAAE,MAAMsC,SAAS,qBAErBtC,EAAE,0BAA0B2B,KAAK,WAC7B3B,EAAE,MAAMsC,SAAS,+BAErBtC,EAAE,iBAAiB2B,KAAK,WACpB3B,EAAE,MAAMzV,SAEZyV,EAAE,aAAa2B,KAAK,WAChB3B,EAAE,MAAM1V,MAAM,WACNuY,IAAAA,EAAM7C,EAAE,MAAMK,KAAK,iBAAiByC,OAIjC,OAHHD,IACApkB,OAAOskB,SAAWF,IAEf,MAIf7C,EAAE,cAAc2B,KAAK,WAEbqB,IAAAA,EAASvoB,SAASC,cAAc,UACpCsoB,EAAOjlB,UAAY,yEAGfklB,IAAAA,EAAOxoB,SAASC,cAAc,KAClCuoB,EAAKllB,UAAY,iBACb+kB,IAAAA,EAAOroB,SAASyoB,eAAe,iBACnCD,EAAKloB,YAAY+nB,GACjBE,EAAOjoB,YAAYkoB,GAGfE,IAAAA,EAAOnD,EAAE,MAAMqC,KAAK,QACxBW,EAAOI,QAAU,WACb3kB,OAAOskB,SAAWI,GAElBE,IAAAA,EAAWF,EAAK7nB,MAAM,KAAK4F,OAAO,GAAGoiB,MAErCN,EAAO5C,GADPiD,EACYA,EAASE,QAAQ,IAAK,KAEtB,mBAAqBvD,EAAE,MAAM/O,QAIzCuS,IAAAA,EAAO/oB,SAASC,cAAc,OAClC8oB,EAAKzlB,UAAY,cACjBylB,EAAK3jB,aAAa,eAAgBmjB,EAAO5C,IACrCqD,IAAAA,EAAWzD,EAAE,MAAMK,KAAK,YAAYqD,IAAI,WACjC1D,OAAAA,EAAE,MAAM8C,SAChBxC,MAAMxgB,KAAK,KACd0jB,EAAKrK,UAAYsK,EAEjBhnB,iBAAiBI,eAAemmB,GAChChD,EAAE,MAAMxa,SACJib,IAAAA,EAAST,EAAE,eAAe2D,QAC9BlD,EAAOuB,OAAOgB,GACdvC,EAAOuB,OAAOwB,KAGlBxD,EAAE,0BAA0BoB,IAAI,aAAc","file":"sphinx_materialdesign_theme.js","sourceRoot":"../../src/js","sourcesContent":["/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Ripple MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialRipple = function MaterialRipple(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialRipple'] = MaterialRipple;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialRipple.prototype.Constant_ = {\n    INITIAL_SCALE: 'scale(0.0001, 0.0001)',\n    INITIAL_SIZE: '1px',\n    INITIAL_OPACITY: '0.4',\n    FINAL_OPACITY: '0',\n    FINAL_SCALE: ''\n};\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialRipple.prototype.CssClasses_ = {\n    RIPPLE_CENTER: 'mdl-ripple--center',\n    RIPPLE_EFFECT_IGNORE_EVENTS: 'mdl-js-ripple-effect--ignore-events',\n    RIPPLE: 'mdl-ripple',\n    IS_ANIMATING: 'is-animating',\n    IS_VISIBLE: 'is-visible'\n};\n/**\n   * Handle mouse / finger down on element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialRipple.prototype.downHandler_ = function (event) {\n    if (!this.rippleElement_.style.width && !this.rippleElement_.style.height) {\n        var rect = this.element_.getBoundingClientRect();\n        this.boundHeight = rect.height;\n        this.boundWidth = rect.width;\n        this.rippleSize_ = Math.sqrt(rect.width * rect.width + rect.height * rect.height) * 2 + 2;\n        this.rippleElement_.style.width = this.rippleSize_ + 'px';\n        this.rippleElement_.style.height = this.rippleSize_ + 'px';\n    }\n    this.rippleElement_.classList.add(this.CssClasses_.IS_VISIBLE);\n    if (event.type === 'mousedown' && this.ignoringMouseDown_) {\n        this.ignoringMouseDown_ = false;\n    } else {\n        if (event.type === 'touchstart') {\n            this.ignoringMouseDown_ = true;\n        }\n        var frameCount = this.getFrameCount();\n        if (frameCount > 0) {\n            return;\n        }\n        this.setFrameCount(1);\n        var bound = event.currentTarget.getBoundingClientRect();\n        var x;\n        var y;\n        // Check if we are handling a keyboard click.\n        if (event.clientX === 0 && event.clientY === 0) {\n            x = Math.round(bound.width / 2);\n            y = Math.round(bound.height / 2);\n        } else {\n            var clientX = event.clientX !== undefined ? event.clientX : event.touches[0].clientX;\n            var clientY = event.clientY !== undefined ? event.clientY : event.touches[0].clientY;\n            x = Math.round(clientX - bound.left);\n            y = Math.round(clientY - bound.top);\n        }\n        this.setRippleXY(x, y);\n        this.setRippleStyles(true);\n        window.requestAnimationFrame(this.animFrameHandler.bind(this));\n    }\n};\n/**\n   * Handle mouse / finger up on element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialRipple.prototype.upHandler_ = function (event) {\n    // Don't fire for the artificial \"mouseup\" generated by a double-click.\n    if (event && event.detail !== 2) {\n        // Allow a repaint to occur before removing this class, so the animation\n        // shows for tap events, which seem to trigger a mouseup too soon after\n        // mousedown.\n        window.setTimeout(function () {\n            this.rippleElement_.classList.remove(this.CssClasses_.IS_VISIBLE);\n        }.bind(this), 0);\n    }\n};\n/**\n   * Initialize element.\n   */\nMaterialRipple.prototype.init = function () {\n    if (this.element_) {\n        var recentering = this.element_.classList.contains(this.CssClasses_.RIPPLE_CENTER);\n        if (!this.element_.classList.contains(this.CssClasses_.RIPPLE_EFFECT_IGNORE_EVENTS)) {\n            this.rippleElement_ = this.element_.querySelector('.' + this.CssClasses_.RIPPLE);\n            this.frameCount_ = 0;\n            this.rippleSize_ = 0;\n            this.x_ = 0;\n            this.y_ = 0;\n            // Touch start produces a compat mouse down event, which would cause a\n            // second ripples. To avoid that, we use this property to ignore the first\n            // mouse down after a touch start.\n            this.ignoringMouseDown_ = false;\n            this.boundDownHandler = this.downHandler_.bind(this);\n            this.element_.addEventListener('mousedown', this.boundDownHandler);\n            this.element_.addEventListener('touchstart', this.boundDownHandler);\n            this.boundUpHandler = this.upHandler_.bind(this);\n            this.element_.addEventListener('mouseup', this.boundUpHandler);\n            this.element_.addEventListener('mouseleave', this.boundUpHandler);\n            this.element_.addEventListener('touchend', this.boundUpHandler);\n            this.element_.addEventListener('blur', this.boundUpHandler);\n            /**\n         * Getter for frameCount_.\n         * @return {number} the frame count.\n         */\n            this.getFrameCount = function () {\n                return this.frameCount_;\n            };\n            /**\n         * Setter for frameCount_.\n         * @param {number} fC the frame count.\n         */\n            this.setFrameCount = function (fC) {\n                this.frameCount_ = fC;\n            };\n            /**\n         * Getter for rippleElement_.\n         * @return {Element} the ripple element.\n         */\n            this.getRippleElement = function () {\n                return this.rippleElement_;\n            };\n            /**\n         * Sets the ripple X and Y coordinates.\n         * @param  {number} newX the new X coordinate\n         * @param  {number} newY the new Y coordinate\n         */\n            this.setRippleXY = function (newX, newY) {\n                this.x_ = newX;\n                this.y_ = newY;\n            };\n            /**\n         * Sets the ripple styles.\n         * @param  {boolean} start whether or not this is the start frame.\n         */\n            this.setRippleStyles = function (start) {\n                if (this.rippleElement_ !== null) {\n                    var transformString;\n                    var scale;\n                    var size;\n                    var offset = 'translate(' + this.x_ + 'px, ' + this.y_ + 'px)';\n                    if (start) {\n                        scale = this.Constant_.INITIAL_SCALE;\n                        size = this.Constant_.INITIAL_SIZE;\n                    } else {\n                        scale = this.Constant_.FINAL_SCALE;\n                        size = this.rippleSize_ + 'px';\n                        if (recentering) {\n                            offset = 'translate(' + this.boundWidth / 2 + 'px, ' + this.boundHeight / 2 + 'px)';\n                        }\n                    }\n                    transformString = 'translate(-50%, -50%) ' + offset + scale;\n                    this.rippleElement_.style.webkitTransform = transformString;\n                    this.rippleElement_.style.msTransform = transformString;\n                    this.rippleElement_.style.transform = transformString;\n                    if (start) {\n                        this.rippleElement_.classList.remove(this.CssClasses_.IS_ANIMATING);\n                    } else {\n                        this.rippleElement_.classList.add(this.CssClasses_.IS_ANIMATING);\n                    }\n                }\n            };\n            /**\n         * Handles an animation frame.\n         */\n            this.animFrameHandler = function () {\n                if (this.frameCount_-- > 0) {\n                    window.requestAnimationFrame(this.animFrameHandler.bind(this));\n                } else {\n                    this.setRippleStyles(false);\n                }\n            };\n        }\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialRipple,\n    classAsString: 'MaterialRipple',\n    cssClass: 'mdl-js-ripple-effect',\n    widget: false\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Tabs MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {Element} element The element that will be upgraded.\n   */\nvar MaterialTabs = function MaterialTabs(element) {\n    // Stores the HTML element.\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialTabs'] = MaterialTabs;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialTabs.prototype.Constant_ = {};\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialTabs.prototype.CssClasses_ = {\n    TAB_CLASS: 'mdl-tabs__tab',\n    PANEL_CLASS: 'mdl-tabs__panel',\n    ACTIVE_CLASS: 'is-active',\n    UPGRADED_CLASS: 'is-upgraded',\n    MDL_JS_RIPPLE_EFFECT: 'mdl-js-ripple-effect',\n    MDL_RIPPLE_CONTAINER: 'mdl-tabs__ripple-container',\n    MDL_RIPPLE: 'mdl-ripple',\n    MDL_JS_RIPPLE_EFFECT_IGNORE_EVENTS: 'mdl-js-ripple-effect--ignore-events'\n};\n/**\n   * Handle clicks to a tabs component\n   *\n   * @private\n   */\nMaterialTabs.prototype.initTabs_ = function () {\n    if (this.element_.classList.contains(this.CssClasses_.MDL_JS_RIPPLE_EFFECT)) {\n        this.element_.classList.add(this.CssClasses_.MDL_JS_RIPPLE_EFFECT_IGNORE_EVENTS);\n    }\n    // Select element tabs, document panels\n    this.tabs_ = this.element_.querySelectorAll('.' + this.CssClasses_.TAB_CLASS);\n    this.panels_ = this.element_.querySelectorAll('.' + this.CssClasses_.PANEL_CLASS);\n    // Create new tabs for each tab element\n    for (var i = 0; i < this.tabs_.length; i++) {\n        new MaterialTab(this.tabs_[i], this);\n    }\n    this.element_.classList.add(this.CssClasses_.UPGRADED_CLASS);\n};\n/**\n   * Reset tab state, dropping active classes\n   *\n   * @private\n   */\nMaterialTabs.prototype.resetTabState_ = function () {\n    for (var k = 0; k < this.tabs_.length; k++) {\n        this.tabs_[k].classList.remove(this.CssClasses_.ACTIVE_CLASS);\n    }\n};\n/**\n   * Reset panel state, droping active classes\n   *\n   * @private\n   */\nMaterialTabs.prototype.resetPanelState_ = function () {\n    for (var j = 0; j < this.panels_.length; j++) {\n        this.panels_[j].classList.remove(this.CssClasses_.ACTIVE_CLASS);\n    }\n};\n/**\n   * Initialize element.\n   */\nMaterialTabs.prototype.init = function () {\n    if (this.element_) {\n        this.initTabs_();\n    }\n};\n/**\n   * Constructor for an individual tab.\n   *\n   * @constructor\n   * @param {Element} tab The HTML element for the tab.\n   * @param {MaterialTabs} ctx The MaterialTabs object that owns the tab.\n   */\nfunction MaterialTab(tab, ctx) {\n    if (tab) {\n        if (ctx.element_.classList.contains(ctx.CssClasses_.MDL_JS_RIPPLE_EFFECT)) {\n            var rippleContainer = document.createElement('span');\n            rippleContainer.classList.add(ctx.CssClasses_.MDL_RIPPLE_CONTAINER);\n            rippleContainer.classList.add(ctx.CssClasses_.MDL_JS_RIPPLE_EFFECT);\n            var ripple = document.createElement('span');\n            ripple.classList.add(ctx.CssClasses_.MDL_RIPPLE);\n            rippleContainer.appendChild(ripple);\n            tab.appendChild(rippleContainer);\n        }\n        tab.addEventListener('click', function (e) {\n            if (tab.getAttribute('href').charAt(0) === '#') {\n                e.preventDefault();\n                var href = tab.href.split('#')[1];\n                var panel = ctx.element_.querySelector('#' + href);\n                ctx.resetTabState_();\n                ctx.resetPanelState_();\n                tab.classList.add(ctx.CssClasses_.ACTIVE_CLASS);\n                panel.classList.add(ctx.CssClasses_.ACTIVE_CLASS);\n            }\n        });\n    }\n}\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialTabs,\n    classAsString: 'MaterialTabs',\n    cssClass: 'mdl-js-tabs'\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Layout MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialLayout = function MaterialLayout(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialLayout'] = MaterialLayout;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialLayout.prototype.Constant_ = {\n    MAX_WIDTH: '(max-width: 1024px)',\n    TAB_SCROLL_PIXELS: 100,\n    RESIZE_TIMEOUT: 100,\n    MENU_ICON: '&#xE5D2;',\n    CHEVRON_LEFT: 'chevron_left',\n    CHEVRON_RIGHT: 'chevron_right'\n};\n/**\n   * Keycodes, for code readability.\n   *\n   * @enum {number}\n   * @private\n   */\nMaterialLayout.prototype.Keycodes_ = {\n    ENTER: 13,\n    ESCAPE: 27,\n    SPACE: 32\n};\n/**\n   * Modes.\n   *\n   * @enum {number}\n   * @private\n   */\nMaterialLayout.prototype.Mode_ = {\n    STANDARD: 0,\n    SEAMED: 1,\n    WATERFALL: 2,\n    SCROLL: 3\n};\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialLayout.prototype.CssClasses_ = {\n    CONTAINER: 'mdl-layout__container',\n    HEADER: 'mdl-layout__header',\n    DRAWER: 'mdl-layout__drawer',\n    CONTENT: 'mdl-layout__content',\n    DRAWER_BTN: 'mdl-layout__drawer-button',\n    ICON: 'material-icons',\n    JS_RIPPLE_EFFECT: 'mdl-js-ripple-effect',\n    RIPPLE_CONTAINER: 'mdl-layout__tab-ripple-container',\n    RIPPLE: 'mdl-ripple',\n    RIPPLE_IGNORE_EVENTS: 'mdl-js-ripple-effect--ignore-events',\n    HEADER_SEAMED: 'mdl-layout__header--seamed',\n    HEADER_WATERFALL: 'mdl-layout__header--waterfall',\n    HEADER_SCROLL: 'mdl-layout__header--scroll',\n    FIXED_HEADER: 'mdl-layout--fixed-header',\n    OBFUSCATOR: 'mdl-layout__obfuscator',\n    TAB_BAR: 'mdl-layout__tab-bar',\n    TAB_CONTAINER: 'mdl-layout__tab-bar-container',\n    TAB: 'mdl-layout__tab',\n    TAB_BAR_BUTTON: 'mdl-layout__tab-bar-button',\n    TAB_BAR_LEFT_BUTTON: 'mdl-layout__tab-bar-left-button',\n    TAB_BAR_RIGHT_BUTTON: 'mdl-layout__tab-bar-right-button',\n    TAB_MANUAL_SWITCH: 'mdl-layout__tab-manual-switch',\n    PANEL: 'mdl-layout__tab-panel',\n    HAS_DRAWER: 'has-drawer',\n    HAS_TABS: 'has-tabs',\n    HAS_SCROLLING_HEADER: 'has-scrolling-header',\n    CASTING_SHADOW: 'is-casting-shadow',\n    IS_COMPACT: 'is-compact',\n    IS_SMALL_SCREEN: 'is-small-screen',\n    IS_DRAWER_OPEN: 'is-visible',\n    IS_ACTIVE: 'is-active',\n    IS_UPGRADED: 'is-upgraded',\n    IS_ANIMATING: 'is-animating',\n    ON_LARGE_SCREEN: 'mdl-layout--large-screen-only',\n    ON_SMALL_SCREEN: 'mdl-layout--small-screen-only'\n};\n/**\n   * Handles scrolling on the content.\n   *\n   * @private\n   */\nMaterialLayout.prototype.contentScrollHandler_ = function () {\n    if (this.header_.classList.contains(this.CssClasses_.IS_ANIMATING)) {\n        return;\n    }\n    var headerVisible = !this.element_.classList.contains(this.CssClasses_.IS_SMALL_SCREEN) || this.element_.classList.contains(this.CssClasses_.FIXED_HEADER);\n    if (this.content_.scrollTop > 0 && !this.header_.classList.contains(this.CssClasses_.IS_COMPACT)) {\n        this.header_.classList.add(this.CssClasses_.CASTING_SHADOW);\n        this.header_.classList.add(this.CssClasses_.IS_COMPACT);\n        if (headerVisible) {\n            this.header_.classList.add(this.CssClasses_.IS_ANIMATING);\n        }\n    } else if (this.content_.scrollTop <= 0 && this.header_.classList.contains(this.CssClasses_.IS_COMPACT)) {\n        this.header_.classList.remove(this.CssClasses_.CASTING_SHADOW);\n        this.header_.classList.remove(this.CssClasses_.IS_COMPACT);\n        if (headerVisible) {\n            this.header_.classList.add(this.CssClasses_.IS_ANIMATING);\n        }\n    }\n};\n/**\n   * Handles a keyboard event on the drawer.\n   *\n   * @param {Event} evt The event that fired.\n   * @private\n   */\nMaterialLayout.prototype.keyboardEventHandler_ = function (evt) {\n    // Only react when the drawer is open.\n    if (evt.keyCode === this.Keycodes_.ESCAPE && this.drawer_.classList.contains(this.CssClasses_.IS_DRAWER_OPEN)) {\n        this.toggleDrawer();\n    }\n};\n/**\n   * Handles changes in screen size.\n   *\n   * @private\n   */\nMaterialLayout.prototype.screenSizeHandler_ = function () {\n    if (this.screenSizeMediaQuery_.matches) {\n        this.element_.classList.add(this.CssClasses_.IS_SMALL_SCREEN);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_SMALL_SCREEN);\n        // Collapse drawer (if any) when moving to a large screen size.\n        if (this.drawer_) {\n            this.drawer_.classList.remove(this.CssClasses_.IS_DRAWER_OPEN);\n            this.obfuscator_.classList.remove(this.CssClasses_.IS_DRAWER_OPEN);\n        }\n    }\n};\n/**\n   * Handles events of drawer button.\n   *\n   * @param {Event} evt The event that fired.\n   * @private\n   */\nMaterialLayout.prototype.drawerToggleHandler_ = function (evt) {\n    if (evt && evt.type === 'keydown') {\n        if (evt.keyCode === this.Keycodes_.SPACE || evt.keyCode === this.Keycodes_.ENTER) {\n            // prevent scrolling in drawer nav\n            evt.preventDefault();\n        } else {\n            // prevent other keys\n            return;\n        }\n    }\n    this.toggleDrawer();\n};\n/**\n   * Handles (un)setting the `is-animating` class\n   *\n   * @private\n   */\nMaterialLayout.prototype.headerTransitionEndHandler_ = function () {\n    this.header_.classList.remove(this.CssClasses_.IS_ANIMATING);\n};\n/**\n   * Handles expanding the header on click\n   *\n   * @private\n   */\nMaterialLayout.prototype.headerClickHandler_ = function () {\n    if (this.header_.classList.contains(this.CssClasses_.IS_COMPACT)) {\n        this.header_.classList.remove(this.CssClasses_.IS_COMPACT);\n        this.header_.classList.add(this.CssClasses_.IS_ANIMATING);\n    }\n};\n/**\n   * Reset tab state, dropping active classes\n   *\n   * @private\n   */\nMaterialLayout.prototype.resetTabState_ = function (tabBar) {\n    for (var k = 0; k < tabBar.length; k++) {\n        tabBar[k].classList.remove(this.CssClasses_.IS_ACTIVE);\n    }\n};\n/**\n   * Reset panel state, droping active classes\n   *\n   * @private\n   */\nMaterialLayout.prototype.resetPanelState_ = function (panels) {\n    for (var j = 0; j < panels.length; j++) {\n        panels[j].classList.remove(this.CssClasses_.IS_ACTIVE);\n    }\n};\n/**\n  * Toggle drawer state\n  *\n  * @public\n  */\nMaterialLayout.prototype.toggleDrawer = function () {\n    var drawerButton = this.element_.querySelector('.' + this.CssClasses_.DRAWER_BTN);\n    this.drawer_.classList.toggle(this.CssClasses_.IS_DRAWER_OPEN);\n    this.obfuscator_.classList.toggle(this.CssClasses_.IS_DRAWER_OPEN);\n    // Set accessibility properties.\n    if (this.drawer_.classList.contains(this.CssClasses_.IS_DRAWER_OPEN)) {\n        this.drawer_.setAttribute('aria-hidden', 'false');\n        drawerButton.setAttribute('aria-expanded', 'true');\n    } else {\n        this.drawer_.setAttribute('aria-hidden', 'true');\n        drawerButton.setAttribute('aria-expanded', 'false');\n    }\n};\nMaterialLayout.prototype['toggleDrawer'] = MaterialLayout.prototype.toggleDrawer;\n/**\n   * Initialize element.\n   */\nMaterialLayout.prototype.init = function () {\n    if (this.element_) {\n        var container = document.createElement('div');\n        container.classList.add(this.CssClasses_.CONTAINER);\n        var focusedElement = this.element_.querySelector(':focus');\n        this.element_.parentElement.insertBefore(container, this.element_);\n        this.element_.parentElement.removeChild(this.element_);\n        container.appendChild(this.element_);\n        if (focusedElement) {\n            focusedElement.focus();\n        }\n        var directChildren = this.element_.childNodes;\n        var numChildren = directChildren.length;\n        for (var c = 0; c < numChildren; c++) {\n            var child = directChildren[c];\n            if (child.classList && child.classList.contains(this.CssClasses_.HEADER)) {\n                this.header_ = child;\n            }\n            if (child.classList && child.classList.contains(this.CssClasses_.DRAWER)) {\n                this.drawer_ = child;\n            }\n            if (child.classList && child.classList.contains(this.CssClasses_.CONTENT)) {\n                this.content_ = child;\n            }\n        }\n        window.addEventListener('pageshow', function (e) {\n            if (e.persisted) {\n                // when page is loaded from back/forward cache\n                // trigger repaint to let layout scroll in safari\n                this.element_.style.overflowY = 'hidden';\n                requestAnimationFrame(function () {\n                    this.element_.style.overflowY = '';\n                }.bind(this));\n            }\n        }.bind(this), false);\n        if (this.header_) {\n            this.tabBar_ = this.header_.querySelector('.' + this.CssClasses_.TAB_BAR);\n        }\n        var mode = this.Mode_.STANDARD;\n        if (this.header_) {\n            if (this.header_.classList.contains(this.CssClasses_.HEADER_SEAMED)) {\n                mode = this.Mode_.SEAMED;\n            } else if (this.header_.classList.contains(this.CssClasses_.HEADER_WATERFALL)) {\n                mode = this.Mode_.WATERFALL;\n                this.header_.addEventListener('transitionend', this.headerTransitionEndHandler_.bind(this));\n                this.header_.addEventListener('click', this.headerClickHandler_.bind(this));\n            } else if (this.header_.classList.contains(this.CssClasses_.HEADER_SCROLL)) {\n                mode = this.Mode_.SCROLL;\n                container.classList.add(this.CssClasses_.HAS_SCROLLING_HEADER);\n            }\n            if (mode === this.Mode_.STANDARD) {\n                this.header_.classList.add(this.CssClasses_.CASTING_SHADOW);\n                if (this.tabBar_) {\n                    this.tabBar_.classList.add(this.CssClasses_.CASTING_SHADOW);\n                }\n            } else if (mode === this.Mode_.SEAMED || mode === this.Mode_.SCROLL) {\n                this.header_.classList.remove(this.CssClasses_.CASTING_SHADOW);\n                if (this.tabBar_) {\n                    this.tabBar_.classList.remove(this.CssClasses_.CASTING_SHADOW);\n                }\n            } else if (mode === this.Mode_.WATERFALL) {\n                // Add and remove shadows depending on scroll position.\n                // Also add/remove auxiliary class for styling of the compact version of\n                // the header.\n                this.content_.addEventListener('scroll', this.contentScrollHandler_.bind(this));\n                this.contentScrollHandler_();\n            }\n        }\n        // Add drawer toggling button to our layout, if we have an openable drawer.\n        if (this.drawer_) {\n            var drawerButton = this.element_.querySelector('.' + this.CssClasses_.DRAWER_BTN);\n            if (!drawerButton) {\n                drawerButton = document.createElement('div');\n                drawerButton.setAttribute('aria-expanded', 'false');\n                drawerButton.setAttribute('role', 'button');\n                drawerButton.setAttribute('tabindex', '0');\n                drawerButton.classList.add(this.CssClasses_.DRAWER_BTN);\n                var drawerButtonIcon = document.createElement('i');\n                drawerButtonIcon.classList.add(this.CssClasses_.ICON);\n                drawerButtonIcon.innerHTML = this.Constant_.MENU_ICON;\n                drawerButton.appendChild(drawerButtonIcon);\n            }\n            if (this.drawer_.classList.contains(this.CssClasses_.ON_LARGE_SCREEN)) {\n                //If drawer has ON_LARGE_SCREEN class then add it to the drawer toggle button as well.\n                drawerButton.classList.add(this.CssClasses_.ON_LARGE_SCREEN);\n            } else if (this.drawer_.classList.contains(this.CssClasses_.ON_SMALL_SCREEN)) {\n                //If drawer has ON_SMALL_SCREEN class then add it to the drawer toggle button as well.\n                drawerButton.classList.add(this.CssClasses_.ON_SMALL_SCREEN);\n            }\n            drawerButton.addEventListener('click', this.drawerToggleHandler_.bind(this));\n            drawerButton.addEventListener('keydown', this.drawerToggleHandler_.bind(this));\n            // Add a class if the layout has a drawer, for altering the left padding.\n            // Adds the HAS_DRAWER to the elements since this.header_ may or may\n            // not be present.\n            this.element_.classList.add(this.CssClasses_.HAS_DRAWER);\n            // If we have a fixed header, add the button to the header rather than\n            // the layout.\n            if (this.element_.classList.contains(this.CssClasses_.FIXED_HEADER)) {\n                this.header_.insertBefore(drawerButton, this.header_.firstChild);\n            } else {\n                this.element_.insertBefore(drawerButton, this.content_);\n            }\n            var obfuscator = document.createElement('div');\n            obfuscator.classList.add(this.CssClasses_.OBFUSCATOR);\n            this.element_.appendChild(obfuscator);\n            obfuscator.addEventListener('click', this.drawerToggleHandler_.bind(this));\n            this.obfuscator_ = obfuscator;\n            this.drawer_.addEventListener('keydown', this.keyboardEventHandler_.bind(this));\n            this.drawer_.setAttribute('aria-hidden', 'true');\n        }\n        // Keep an eye on screen size, and add/remove auxiliary class for styling\n        // of small screens.\n        this.screenSizeMediaQuery_ = window.matchMedia(this.Constant_.MAX_WIDTH);\n        this.screenSizeMediaQuery_.addListener(this.screenSizeHandler_.bind(this));\n        this.screenSizeHandler_();\n        // Initialize tabs, if any.\n        if (this.header_ && this.tabBar_) {\n            this.element_.classList.add(this.CssClasses_.HAS_TABS);\n            var tabContainer = document.createElement('div');\n            tabContainer.classList.add(this.CssClasses_.TAB_CONTAINER);\n            this.header_.insertBefore(tabContainer, this.tabBar_);\n            this.header_.removeChild(this.tabBar_);\n            var leftButton = document.createElement('div');\n            leftButton.classList.add(this.CssClasses_.TAB_BAR_BUTTON);\n            leftButton.classList.add(this.CssClasses_.TAB_BAR_LEFT_BUTTON);\n            var leftButtonIcon = document.createElement('i');\n            leftButtonIcon.classList.add(this.CssClasses_.ICON);\n            leftButtonIcon.textContent = this.Constant_.CHEVRON_LEFT;\n            leftButton.appendChild(leftButtonIcon);\n            leftButton.addEventListener('click', function () {\n                this.tabBar_.scrollLeft -= this.Constant_.TAB_SCROLL_PIXELS;\n            }.bind(this));\n            var rightButton = document.createElement('div');\n            rightButton.classList.add(this.CssClasses_.TAB_BAR_BUTTON);\n            rightButton.classList.add(this.CssClasses_.TAB_BAR_RIGHT_BUTTON);\n            var rightButtonIcon = document.createElement('i');\n            rightButtonIcon.classList.add(this.CssClasses_.ICON);\n            rightButtonIcon.textContent = this.Constant_.CHEVRON_RIGHT;\n            rightButton.appendChild(rightButtonIcon);\n            rightButton.addEventListener('click', function () {\n                this.tabBar_.scrollLeft += this.Constant_.TAB_SCROLL_PIXELS;\n            }.bind(this));\n            tabContainer.appendChild(leftButton);\n            tabContainer.appendChild(this.tabBar_);\n            tabContainer.appendChild(rightButton);\n            // Add and remove tab buttons depending on scroll position and total\n            // window size.\n            var tabUpdateHandler = function () {\n                if (this.tabBar_.scrollLeft > 0) {\n                    leftButton.classList.add(this.CssClasses_.IS_ACTIVE);\n                } else {\n                    leftButton.classList.remove(this.CssClasses_.IS_ACTIVE);\n                }\n                if (this.tabBar_.scrollLeft < this.tabBar_.scrollWidth - this.tabBar_.offsetWidth) {\n                    rightButton.classList.add(this.CssClasses_.IS_ACTIVE);\n                } else {\n                    rightButton.classList.remove(this.CssClasses_.IS_ACTIVE);\n                }\n            }.bind(this);\n            this.tabBar_.addEventListener('scroll', tabUpdateHandler);\n            tabUpdateHandler();\n            // Update tabs when the window resizes.\n            var windowResizeHandler = function () {\n                // Use timeouts to make sure it doesn't happen too often.\n                if (this.resizeTimeoutId_) {\n                    clearTimeout(this.resizeTimeoutId_);\n                }\n                this.resizeTimeoutId_ = setTimeout(function () {\n                    tabUpdateHandler();\n                    this.resizeTimeoutId_ = null;\n                }.bind(this), this.Constant_.RESIZE_TIMEOUT);\n            }.bind(this);\n            window.addEventListener('resize', windowResizeHandler);\n            if (this.tabBar_.classList.contains(this.CssClasses_.JS_RIPPLE_EFFECT)) {\n                this.tabBar_.classList.add(this.CssClasses_.RIPPLE_IGNORE_EVENTS);\n            }\n            // Select element tabs, document panels\n            var tabs = this.tabBar_.querySelectorAll('.' + this.CssClasses_.TAB);\n            var panels = this.content_.querySelectorAll('.' + this.CssClasses_.PANEL);\n            // Create new tabs for each tab element\n            for (var i = 0; i < tabs.length; i++) {\n                new MaterialLayoutTab(tabs[i], tabs, panels, this);\n            }\n        }\n        this.element_.classList.add(this.CssClasses_.IS_UPGRADED);\n    }\n};\n/**\n   * Constructor for an individual tab.\n   *\n   * @constructor\n   * @param {HTMLElement} tab The HTML element for the tab.\n   * @param {!Array<HTMLElement>} tabs Array with HTML elements for all tabs.\n   * @param {!Array<HTMLElement>} panels Array with HTML elements for all panels.\n   * @param {MaterialLayout} layout The MaterialLayout object that owns the tab.\n   */\nfunction MaterialLayoutTab(tab, tabs, panels, layout) {\n    /**\n     * Auxiliary method to programmatically select a tab in the UI.\n     */\n    function selectTab() {\n        var href = tab.href.split('#')[1];\n        var panel = layout.content_.querySelector('#' + href);\n        layout.resetTabState_(tabs);\n        layout.resetPanelState_(panels);\n        tab.classList.add(layout.CssClasses_.IS_ACTIVE);\n        panel.classList.add(layout.CssClasses_.IS_ACTIVE);\n    }\n    if (layout.tabBar_.classList.contains(layout.CssClasses_.JS_RIPPLE_EFFECT)) {\n        var rippleContainer = document.createElement('span');\n        rippleContainer.classList.add(layout.CssClasses_.RIPPLE_CONTAINER);\n        rippleContainer.classList.add(layout.CssClasses_.JS_RIPPLE_EFFECT);\n        var ripple = document.createElement('span');\n        ripple.classList.add(layout.CssClasses_.RIPPLE);\n        rippleContainer.appendChild(ripple);\n        tab.appendChild(rippleContainer);\n    }\n    if (!layout.tabBar_.classList.contains(layout.CssClasses_.TAB_MANUAL_SWITCH)) {\n        tab.addEventListener('click', function (e) {\n            if (tab.getAttribute('href').charAt(0) === '#') {\n                e.preventDefault();\n                selectTab();\n            }\n        });\n    }\n    tab.show = selectTab;\n}\nwindow['MaterialLayoutTab'] = MaterialLayoutTab;\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialLayout,\n    classAsString: 'MaterialLayout',\n    cssClass: 'mdl-js-layout'\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n/**\n * A component handler interface using the revealing module design pattern.\n * More details on this design pattern here:\n * https://github.com/jasonmayes/mdl-component-design-pattern\n *\n * @author Jason Mayes.\n */\n/* exported componentHandler */\n\n// Pre-defining the componentHandler interface, for closure documentation and\n// static verification.\nvar componentHandler = {\n  /**\n   * Searches existing DOM for elements of our component type and upgrades them\n   * if they have not already been upgraded.\n   *\n   * @param {string=} optJsClass the programatic name of the element class we\n   * need to create a new instance of.\n   * @param {string=} optCssClass the name of the CSS class elements of this\n   * type will have.\n   */\n  upgradeDom: function(optJsClass, optCssClass) {},\n  /**\n   * Upgrades a specific element rather than all in the DOM.\n   *\n   * @param {!Element} element The element we wish to upgrade.\n   * @param {string=} optJsClass Optional name of the class we want to upgrade\n   * the element to.\n   */\n  upgradeElement: function(element, optJsClass) {},\n  /**\n   * Upgrades a specific list of elements rather than all in the DOM.\n   *\n   * @param {!Element|!Array<!Element>|!NodeList|!HTMLCollection} elements\n   * The elements we wish to upgrade.\n   */\n  upgradeElements: function(elements) {},\n  /**\n   * Upgrades all registered components found in the current DOM. This is\n   * automatically called on window load.\n   */\n  upgradeAllRegistered: function() {},\n  /**\n   * Allows user to be alerted to any upgrades that are performed for a given\n   * component type\n   *\n   * @param {string} jsClass The class name of the MDL component we wish\n   * to hook into for any upgrades performed.\n   * @param {function(!HTMLElement)} callback The function to call upon an\n   * upgrade. This function should expect 1 parameter - the HTMLElement which\n   * got upgraded.\n   */\n  registerUpgradedCallback: function(jsClass, callback) {},\n  /**\n   * Registers a class for future use and attempts to upgrade existing DOM.\n   *\n   * @param {componentHandler.ComponentConfigPublic} config the registration configuration\n   */\n  register: function(config) {},\n  /**\n   * Downgrade either a given node, an array of nodes, or a NodeList.\n   *\n   * @param {!Node|!Array<!Node>|!NodeList} nodes\n   */\n  downgradeElements: function(nodes) {}\n};\n\ncomponentHandler = (function() {\n  'use strict';\n\n  /** @type {!Array<componentHandler.ComponentConfig>} */\n  var registeredComponents_ = [];\n\n  /** @type {!Array<componentHandler.Component>} */\n  var createdComponents_ = [];\n\n  var componentConfigProperty_ = 'mdlComponentConfigInternal_';\n\n  /**\n   * Searches registered components for a class we are interested in using.\n   * Optionally replaces a match with passed object if specified.\n   *\n   * @param {string} name The name of a class we want to use.\n   * @param {componentHandler.ComponentConfig=} optReplace Optional object to replace match with.\n   * @return {!Object|boolean}\n   * @private\n   */\n  function findRegisteredClass_(name, optReplace) {\n    for (var i = 0; i < registeredComponents_.length; i++) {\n      if (registeredComponents_[i].className === name) {\n        if (typeof optReplace !== 'undefined') {\n          registeredComponents_[i] = optReplace;\n        }\n        return registeredComponents_[i];\n      }\n    }\n    return false;\n  }\n\n  /**\n   * Returns an array of the classNames of the upgraded classes on the element.\n   *\n   * @param {!Element} element The element to fetch data from.\n   * @return {!Array<string>}\n   * @private\n   */\n  function getUpgradedListOfElement_(element) {\n    var dataUpgraded = element.getAttribute('data-upgraded');\n    // Use `['']` as default value to conform the `,name,name...` style.\n    return dataUpgraded === null ? [''] : dataUpgraded.split(',');\n  }\n\n  /**\n   * Returns true if the given element has already been upgraded for the given\n   * class.\n   *\n   * @param {!Element} element The element we want to check.\n   * @param {string} jsClass The class to check for.\n   * @returns {boolean}\n   * @private\n   */\n  function isElementUpgraded_(element, jsClass) {\n    var upgradedList = getUpgradedListOfElement_(element);\n    return upgradedList.indexOf(jsClass) !== -1;\n  }\n\n  /**\n   * Create an event object.\n   *\n   * @param {string} eventType The type name of the event.\n   * @param {boolean} bubbles Whether the event should bubble up the DOM.\n   * @param {boolean} cancelable Whether the event can be canceled.\n   * @returns {!Event}\n   */\n  function createEvent_(eventType, bubbles, cancelable) {\n    if ('CustomEvent' in window && typeof window.CustomEvent === 'function') {\n      return new CustomEvent(eventType, {\n        bubbles: bubbles,\n        cancelable: cancelable\n      });\n    } else {\n      var ev = document.createEvent('Events');\n      ev.initEvent(eventType, bubbles, cancelable);\n      return ev;\n    }\n  }\n\n  /**\n   * Searches existing DOM for elements of our component type and upgrades them\n   * if they have not already been upgraded.\n   *\n   * @param {string=} optJsClass the programatic name of the element class we\n   * need to create a new instance of.\n   * @param {string=} optCssClass the name of the CSS class elements of this\n   * type will have.\n   */\n  function upgradeDomInternal(optJsClass, optCssClass) {\n    if (typeof optJsClass === 'undefined' &&\n        typeof optCssClass === 'undefined') {\n      for (var i = 0; i < registeredComponents_.length; i++) {\n        upgradeDomInternal(registeredComponents_[i].className,\n            registeredComponents_[i].cssClass);\n      }\n    } else {\n      var jsClass = /** @type {string} */ (optJsClass);\n      if (typeof optCssClass === 'undefined') {\n        var registeredClass = findRegisteredClass_(jsClass);\n        if (registeredClass) {\n          optCssClass = registeredClass.cssClass;\n        }\n      }\n\n      var elements = document.querySelectorAll('.' + optCssClass);\n      for (var n = 0; n < elements.length; n++) {\n        upgradeElementInternal(elements[n], jsClass);\n      }\n    }\n  }\n\n  /**\n   * Upgrades a specific element rather than all in the DOM.\n   *\n   * @param {!Element} element The element we wish to upgrade.\n   * @param {string=} optJsClass Optional name of the class we want to upgrade\n   * the element to.\n   */\n  function upgradeElementInternal(element, optJsClass) {\n    // Verify argument type.\n    if (!(typeof element === 'object' && element instanceof Element)) {\n      throw new Error('Invalid argument provided to upgrade MDL element.');\n    }\n    // Allow upgrade to be canceled by canceling emitted event.\n    var upgradingEv = createEvent_('mdl-componentupgrading', true, true);\n    element.dispatchEvent(upgradingEv);\n    if (upgradingEv.defaultPrevented) {\n      return;\n    }\n\n    var upgradedList = getUpgradedListOfElement_(element);\n    var classesToUpgrade = [];\n    // If jsClass is not provided scan the registered components to find the\n    // ones matching the element's CSS classList.\n    if (!optJsClass) {\n      var classList = element.classList;\n      registeredComponents_.forEach(function(component) {\n        // Match CSS & Not to be upgraded & Not upgraded.\n        if (classList.contains(component.cssClass) &&\n            classesToUpgrade.indexOf(component) === -1 &&\n            !isElementUpgraded_(element, component.className)) {\n          classesToUpgrade.push(component);\n        }\n      });\n    } else if (!isElementUpgraded_(element, optJsClass)) {\n      classesToUpgrade.push(findRegisteredClass_(optJsClass));\n    }\n\n    // Upgrade the element for each classes.\n    for (var i = 0, n = classesToUpgrade.length, registeredClass; i < n; i++) {\n      registeredClass = classesToUpgrade[i];\n      if (registeredClass) {\n        // Mark element as upgraded.\n        upgradedList.push(registeredClass.className);\n        element.setAttribute('data-upgraded', upgradedList.join(','));\n        var instance = new registeredClass.classConstructor(element);\n        instance[componentConfigProperty_] = registeredClass;\n        createdComponents_.push(instance);\n        // Call any callbacks the user has registered with this component type.\n        for (var j = 0, m = registeredClass.callbacks.length; j < m; j++) {\n          registeredClass.callbacks[j](element);\n        }\n\n        if (registeredClass.widget) {\n          // Assign per element instance for control over API\n          element[registeredClass.className] = instance;\n        }\n      } else {\n        throw new Error(\n          'Unable to find a registered component for the given class.');\n      }\n\n      var upgradedEv = createEvent_('mdl-componentupgraded', true, false);\n      element.dispatchEvent(upgradedEv);\n    }\n  }\n\n  /**\n   * Upgrades a specific list of elements rather than all in the DOM.\n   *\n   * @param {!Element|!Array<!Element>|!NodeList|!HTMLCollection} elements\n   * The elements we wish to upgrade.\n   */\n  function upgradeElementsInternal(elements) {\n    if (!Array.isArray(elements)) {\n      if (elements instanceof Element) {\n        elements = [elements];\n      } else {\n        elements = Array.prototype.slice.call(elements);\n      }\n    }\n    for (var i = 0, n = elements.length, element; i < n; i++) {\n      element = elements[i];\n      if (element instanceof HTMLElement) {\n        upgradeElementInternal(element);\n        if (element.children.length > 0) {\n          upgradeElementsInternal(element.children);\n        }\n      }\n    }\n  }\n\n  /**\n   * Registers a class for future use and attempts to upgrade existing DOM.\n   *\n   * @param {componentHandler.ComponentConfigPublic} config\n   */\n  function registerInternal(config) {\n    // In order to support both Closure-compiled and uncompiled code accessing\n    // this method, we need to allow for both the dot and array syntax for\n    // property access. You'll therefore see the `foo.bar || foo['bar']`\n    // pattern repeated across this method.\n    var widgetMissing = (typeof config.widget === 'undefined' &&\n        typeof config['widget'] === 'undefined');\n    var widget = true;\n\n    if (!widgetMissing) {\n      widget = config.widget || config['widget'];\n    }\n\n    var newConfig = /** @type {componentHandler.ComponentConfig} */ ({\n      classConstructor: config.constructor || config['constructor'],\n      className: config.classAsString || config['classAsString'],\n      cssClass: config.cssClass || config['cssClass'],\n      widget: widget,\n      callbacks: []\n    });\n\n    registeredComponents_.forEach(function(item) {\n      if (item.cssClass === newConfig.cssClass) {\n        throw new Error('The provided cssClass has already been registered: ' + item.cssClass);\n      }\n      if (item.className === newConfig.className) {\n        throw new Error('The provided className has already been registered');\n      }\n    });\n\n    if (config.constructor.prototype\n        .hasOwnProperty(componentConfigProperty_)) {\n      throw new Error(\n          'MDL component classes must not have ' + componentConfigProperty_ +\n          ' defined as a property.');\n    }\n\n    var found = findRegisteredClass_(config.classAsString, newConfig);\n\n    if (!found) {\n      registeredComponents_.push(newConfig);\n    }\n  }\n\n  /**\n   * Allows user to be alerted to any upgrades that are performed for a given\n   * component type\n   *\n   * @param {string} jsClass The class name of the MDL component we wish\n   * to hook into for any upgrades performed.\n   * @param {function(!HTMLElement)} callback The function to call upon an\n   * upgrade. This function should expect 1 parameter - the HTMLElement which\n   * got upgraded.\n   */\n  function registerUpgradedCallbackInternal(jsClass, callback) {\n    var regClass = findRegisteredClass_(jsClass);\n    if (regClass) {\n      regClass.callbacks.push(callback);\n    }\n  }\n\n  /**\n   * Upgrades all registered components found in the current DOM. This is\n   * automatically called on window load.\n   */\n  function upgradeAllRegisteredInternal() {\n    for (var n = 0; n < registeredComponents_.length; n++) {\n      upgradeDomInternal(registeredComponents_[n].className);\n    }\n  }\n\n  /**\n   * Check the component for the downgrade method.\n   * Execute if found.\n   * Remove component from createdComponents list.\n   *\n   * @param {?componentHandler.Component} component\n   */\n  function deconstructComponentInternal(component) {\n    if (component) {\n      var componentIndex = createdComponents_.indexOf(component);\n      createdComponents_.splice(componentIndex, 1);\n\n      var upgrades = component.element_.getAttribute('data-upgraded').split(',');\n      var componentPlace = upgrades.indexOf(component[componentConfigProperty_].classAsString);\n      upgrades.splice(componentPlace, 1);\n      component.element_.setAttribute('data-upgraded', upgrades.join(','));\n\n      var ev = createEvent_('mdl-componentdowngraded', true, false);\n      component.element_.dispatchEvent(ev);\n    }\n  }\n\n  /**\n   * Downgrade either a given node, an array of nodes, or a NodeList.\n   *\n   * @param {!Node|!Array<!Node>|!NodeList} nodes\n   */\n  function downgradeNodesInternal(nodes) {\n    /**\n     * Auxiliary function to downgrade a single node.\n     * @param  {!Node} node the node to be downgraded\n     */\n    var downgradeNode = function(node) {\n      createdComponents_.filter(function(item) {\n        return item.element_ === node;\n      }).forEach(deconstructComponentInternal);\n    };\n    if (nodes instanceof Array || nodes instanceof NodeList) {\n      for (var n = 0; n < nodes.length; n++) {\n        downgradeNode(nodes[n]);\n      }\n    } else if (nodes instanceof Node) {\n      downgradeNode(nodes);\n    } else {\n      throw new Error('Invalid argument provided to downgrade MDL nodes.');\n    }\n  }\n\n  // Now return the functions that should be made public with their publicly\n  // facing names...\n  return {\n    upgradeDom: upgradeDomInternal,\n    upgradeElement: upgradeElementInternal,\n    upgradeElements: upgradeElementsInternal,\n    upgradeAllRegistered: upgradeAllRegisteredInternal,\n    registerUpgradedCallback: registerUpgradedCallbackInternal,\n    register: registerInternal,\n    downgradeElements: downgradeNodesInternal\n  };\n})();\n\n/**\n * Describes the type of a registered component type managed by\n * componentHandler. Provided for benefit of the Closure compiler.\n *\n * @typedef {{\n *   constructor: Function,\n *   classAsString: string,\n *   cssClass: string,\n *   widget: (string|boolean|undefined)\n * }}\n */\ncomponentHandler.ComponentConfigPublic;  // jshint ignore:line\n\n/**\n * Describes the type of a registered component type managed by\n * componentHandler. Provided for benefit of the Closure compiler.\n *\n * @typedef {{\n *   constructor: !Function,\n *   className: string,\n *   cssClass: string,\n *   widget: (string|boolean),\n *   callbacks: !Array<function(!HTMLElement)>\n * }}\n */\ncomponentHandler.ComponentConfig;  // jshint ignore:line\n\n/**\n * Created component (i.e., upgraded element) type as managed by\n * componentHandler. Provided for benefit of the Closure compiler.\n *\n * @typedef {{\n *   element_: !HTMLElement,\n *   className: string,\n *   classAsString: string,\n *   cssClass: string,\n *   widget: string\n * }}\n */\ncomponentHandler.Component;  // jshint ignore:line\n\n// Export all symbols, for the benefit of Closure compiler.\n// No effect on uncompiled code.\ncomponentHandler['upgradeDom'] = componentHandler.upgradeDom;\ncomponentHandler['upgradeElement'] = componentHandler.upgradeElement;\ncomponentHandler['upgradeElements'] = componentHandler.upgradeElements;\ncomponentHandler['upgradeAllRegistered'] =\n    componentHandler.upgradeAllRegistered;\ncomponentHandler['registerUpgradedCallback'] =\n    componentHandler.registerUpgradedCallback;\ncomponentHandler['register'] = componentHandler.register;\ncomponentHandler['downgradeElements'] = componentHandler.downgradeElements;\nwindow.componentHandler = componentHandler;\nwindow['componentHandler'] = componentHandler;\n\nwindow.addEventListener('load', function() {\n  'use strict';\n\n  /**\n   * Performs a \"Cutting the mustard\" test. If the browser supports the features\n   * tested, adds a mdl-js class to the <html> element. It then upgrades all MDL\n   * components requiring JavaScript.\n   */\n  if ('classList' in document.createElement('div') &&\n      'querySelector' in document &&\n      'addEventListener' in window && Array.prototype.forEach) {\n    document.documentElement.classList.add('mdl-js');\n    componentHandler.upgradeAllRegistered();\n  } else {\n    /**\n     * Dummy function to avoid JS errors.\n     */\n    componentHandler.upgradeElement = function() {};\n    /**\n     * Dummy function to avoid JS errors.\n     */\n    componentHandler.register = function() {};\n  }\n});\n","// Source: https://github.com/darius/requestAnimationFrame/blob/master/requestAnimationFrame.js\n// Adapted from https://gist.github.com/paulirish/1579671 which derived from\n// http://paulirish.com/2011/requestanimationframe-for-smart-animating/\n// http://my.opera.com/emoller/blog/2011/12/20/requestanimationframe-for-smart-er-animating\n// requestAnimationFrame polyfill by Erik Möller.\n// Fixes from Paul Irish, Tino Zijdel, Andrew Mao, Klemen Slavič, Darius Bacon\n// MIT license\nif (!Date.now) {\n    /**\n     * Date.now polyfill.\n     * @return {number} the current Date\n     */\n    Date.now = function () {\n        return new Date().getTime();\n    };\n    Date['now'] = Date.now;\n}\nvar vendors = [\n    'webkit',\n    'moz'\n];\nfor (var i = 0; i < vendors.length && !window.requestAnimationFrame; ++i) {\n    var vp = vendors[i];\n    window.requestAnimationFrame = window[vp + 'RequestAnimationFrame'];\n    window.cancelAnimationFrame = window[vp + 'CancelAnimationFrame'] || window[vp + 'CancelRequestAnimationFrame'];\n    window['requestAnimationFrame'] = window.requestAnimationFrame;\n    window['cancelAnimationFrame'] = window.cancelAnimationFrame;\n}\nif (/iP(ad|hone|od).*OS 6/.test(window.navigator.userAgent) || !window.requestAnimationFrame || !window.cancelAnimationFrame) {\n    var lastTime = 0;\n    /**\n     * requestAnimationFrame polyfill.\n     * @param  {!Function} callback the callback function.\n     */\n    window.requestAnimationFrame = function (callback) {\n        var now = Date.now();\n        var nextTime = Math.max(lastTime + 16, now);\n        return setTimeout(function () {\n            callback(lastTime = nextTime);\n        }, nextTime - now);\n    };\n    window.cancelAnimationFrame = clearTimeout;\n    window['requestAnimationFrame'] = window.requestAnimationFrame;\n    window['cancelAnimationFrame'] = window.cancelAnimationFrame;\n}","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Button MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialButton = function MaterialButton(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialButton'] = MaterialButton;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialButton.prototype.Constant_ = {};\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialButton.prototype.CssClasses_ = {\n    RIPPLE_EFFECT: 'mdl-js-ripple-effect',\n    RIPPLE_CONTAINER: 'mdl-button__ripple-container',\n    RIPPLE: 'mdl-ripple'\n};\n/**\n   * Handle blur of element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialButton.prototype.blurHandler_ = function (event) {\n    if (event) {\n        this.element_.blur();\n    }\n};\n// Public methods.\n/**\n   * Disable button.\n   *\n   * @public\n   */\nMaterialButton.prototype.disable = function () {\n    this.element_.disabled = true;\n};\nMaterialButton.prototype['disable'] = MaterialButton.prototype.disable;\n/**\n   * Enable button.\n   *\n   * @public\n   */\nMaterialButton.prototype.enable = function () {\n    this.element_.disabled = false;\n};\nMaterialButton.prototype['enable'] = MaterialButton.prototype.enable;\n/**\n   * Initialize element.\n   */\nMaterialButton.prototype.init = function () {\n    if (this.element_) {\n        if (this.element_.classList.contains(this.CssClasses_.RIPPLE_EFFECT)) {\n            var rippleContainer = document.createElement('span');\n            rippleContainer.classList.add(this.CssClasses_.RIPPLE_CONTAINER);\n            this.rippleElement_ = document.createElement('span');\n            this.rippleElement_.classList.add(this.CssClasses_.RIPPLE);\n            rippleContainer.appendChild(this.rippleElement_);\n            this.boundRippleBlurHandler = this.blurHandler_.bind(this);\n            this.rippleElement_.addEventListener('mouseup', this.boundRippleBlurHandler);\n            this.element_.appendChild(rippleContainer);\n        }\n        this.boundButtonBlurHandler = this.blurHandler_.bind(this);\n        this.element_.addEventListener('mouseup', this.boundButtonBlurHandler);\n        this.element_.addEventListener('mouseleave', this.boundButtonBlurHandler);\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialButton,\n    classAsString: 'MaterialButton',\n    cssClass: 'mdl-js-button',\n    widget: true\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Checkbox MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialCheckbox = function MaterialCheckbox(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialCheckbox'] = MaterialCheckbox;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialCheckbox.prototype.Constant_ = { TINY_TIMEOUT: 0.001 };\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialCheckbox.prototype.CssClasses_ = {\n    INPUT: 'mdl-checkbox__input',\n    BOX_OUTLINE: 'mdl-checkbox__box-outline',\n    FOCUS_HELPER: 'mdl-checkbox__focus-helper',\n    TICK_OUTLINE: 'mdl-checkbox__tick-outline',\n    RIPPLE_EFFECT: 'mdl-js-ripple-effect',\n    RIPPLE_IGNORE_EVENTS: 'mdl-js-ripple-effect--ignore-events',\n    RIPPLE_CONTAINER: 'mdl-checkbox__ripple-container',\n    RIPPLE_CENTER: 'mdl-ripple--center',\n    RIPPLE: 'mdl-ripple',\n    IS_FOCUSED: 'is-focused',\n    IS_DISABLED: 'is-disabled',\n    IS_CHECKED: 'is-checked',\n    IS_UPGRADED: 'is-upgraded'\n};\n/**\n   * Handle change of state.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialCheckbox.prototype.onChange_ = function (event) {\n    this.updateClasses_();\n};\n/**\n   * Handle focus of element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialCheckbox.prototype.onFocus_ = function (event) {\n    this.element_.classList.add(this.CssClasses_.IS_FOCUSED);\n};\n/**\n   * Handle lost focus of element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialCheckbox.prototype.onBlur_ = function (event) {\n    this.element_.classList.remove(this.CssClasses_.IS_FOCUSED);\n};\n/**\n   * Handle mouseup.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialCheckbox.prototype.onMouseUp_ = function (event) {\n    this.blur_();\n};\n/**\n   * Handle class updates.\n   *\n   * @private\n   */\nMaterialCheckbox.prototype.updateClasses_ = function () {\n    this.checkDisabled();\n    this.checkToggleState();\n};\n/**\n   * Add blur.\n   *\n   * @private\n   */\nMaterialCheckbox.prototype.blur_ = function () {\n    // TODO: figure out why there's a focus event being fired after our blur,\n    // so that we can avoid this hack.\n    window.setTimeout(function () {\n        this.inputElement_.blur();\n    }.bind(this), this.Constant_.TINY_TIMEOUT);\n};\n// Public methods.\n/**\n   * Check the inputs toggle state and update display.\n   *\n   * @public\n   */\nMaterialCheckbox.prototype.checkToggleState = function () {\n    if (this.inputElement_.checked) {\n        this.element_.classList.add(this.CssClasses_.IS_CHECKED);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_CHECKED);\n    }\n};\nMaterialCheckbox.prototype['checkToggleState'] = MaterialCheckbox.prototype.checkToggleState;\n/**\n   * Check the inputs disabled state and update display.\n   *\n   * @public\n   */\nMaterialCheckbox.prototype.checkDisabled = function () {\n    if (this.inputElement_.disabled) {\n        this.element_.classList.add(this.CssClasses_.IS_DISABLED);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_DISABLED);\n    }\n};\nMaterialCheckbox.prototype['checkDisabled'] = MaterialCheckbox.prototype.checkDisabled;\n/**\n   * Disable checkbox.\n   *\n   * @public\n   */\nMaterialCheckbox.prototype.disable = function () {\n    this.inputElement_.disabled = true;\n    this.updateClasses_();\n};\nMaterialCheckbox.prototype['disable'] = MaterialCheckbox.prototype.disable;\n/**\n   * Enable checkbox.\n   *\n   * @public\n   */\nMaterialCheckbox.prototype.enable = function () {\n    this.inputElement_.disabled = false;\n    this.updateClasses_();\n};\nMaterialCheckbox.prototype['enable'] = MaterialCheckbox.prototype.enable;\n/**\n   * Check checkbox.\n   *\n   * @public\n   */\nMaterialCheckbox.prototype.check = function () {\n    this.inputElement_.checked = true;\n    this.updateClasses_();\n};\nMaterialCheckbox.prototype['check'] = MaterialCheckbox.prototype.check;\n/**\n   * Uncheck checkbox.\n   *\n   * @public\n   */\nMaterialCheckbox.prototype.uncheck = function () {\n    this.inputElement_.checked = false;\n    this.updateClasses_();\n};\nMaterialCheckbox.prototype['uncheck'] = MaterialCheckbox.prototype.uncheck;\n/**\n   * Initialize element.\n   */\nMaterialCheckbox.prototype.init = function () {\n    if (this.element_) {\n        this.inputElement_ = this.element_.querySelector('.' + this.CssClasses_.INPUT);\n        var boxOutline = document.createElement('span');\n        boxOutline.classList.add(this.CssClasses_.BOX_OUTLINE);\n        var tickContainer = document.createElement('span');\n        tickContainer.classList.add(this.CssClasses_.FOCUS_HELPER);\n        var tickOutline = document.createElement('span');\n        tickOutline.classList.add(this.CssClasses_.TICK_OUTLINE);\n        boxOutline.appendChild(tickOutline);\n        this.element_.appendChild(tickContainer);\n        this.element_.appendChild(boxOutline);\n        if (this.element_.classList.contains(this.CssClasses_.RIPPLE_EFFECT)) {\n            this.element_.classList.add(this.CssClasses_.RIPPLE_IGNORE_EVENTS);\n            this.rippleContainerElement_ = document.createElement('span');\n            this.rippleContainerElement_.classList.add(this.CssClasses_.RIPPLE_CONTAINER);\n            this.rippleContainerElement_.classList.add(this.CssClasses_.RIPPLE_EFFECT);\n            this.rippleContainerElement_.classList.add(this.CssClasses_.RIPPLE_CENTER);\n            this.boundRippleMouseUp = this.onMouseUp_.bind(this);\n            this.rippleContainerElement_.addEventListener('mouseup', this.boundRippleMouseUp);\n            var ripple = document.createElement('span');\n            ripple.classList.add(this.CssClasses_.RIPPLE);\n            this.rippleContainerElement_.appendChild(ripple);\n            this.element_.appendChild(this.rippleContainerElement_);\n        }\n        this.boundInputOnChange = this.onChange_.bind(this);\n        this.boundInputOnFocus = this.onFocus_.bind(this);\n        this.boundInputOnBlur = this.onBlur_.bind(this);\n        this.boundElementMouseUp = this.onMouseUp_.bind(this);\n        this.inputElement_.addEventListener('change', this.boundInputOnChange);\n        this.inputElement_.addEventListener('focus', this.boundInputOnFocus);\n        this.inputElement_.addEventListener('blur', this.boundInputOnBlur);\n        this.element_.addEventListener('mouseup', this.boundElementMouseUp);\n        this.updateClasses_();\n        this.element_.classList.add(this.CssClasses_.IS_UPGRADED);\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialCheckbox,\n    classAsString: 'MaterialCheckbox',\n    cssClass: 'mdl-js-checkbox',\n    widget: true\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for icon toggle MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialIconToggle = function MaterialIconToggle(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialIconToggle'] = MaterialIconToggle;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialIconToggle.prototype.Constant_ = { TINY_TIMEOUT: 0.001 };\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialIconToggle.prototype.CssClasses_ = {\n    INPUT: 'mdl-icon-toggle__input',\n    JS_RIPPLE_EFFECT: 'mdl-js-ripple-effect',\n    RIPPLE_IGNORE_EVENTS: 'mdl-js-ripple-effect--ignore-events',\n    RIPPLE_CONTAINER: 'mdl-icon-toggle__ripple-container',\n    RIPPLE_CENTER: 'mdl-ripple--center',\n    RIPPLE: 'mdl-ripple',\n    IS_FOCUSED: 'is-focused',\n    IS_DISABLED: 'is-disabled',\n    IS_CHECKED: 'is-checked'\n};\n/**\n   * Handle change of state.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialIconToggle.prototype.onChange_ = function (event) {\n    this.updateClasses_();\n};\n/**\n   * Handle focus of element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialIconToggle.prototype.onFocus_ = function (event) {\n    this.element_.classList.add(this.CssClasses_.IS_FOCUSED);\n};\n/**\n   * Handle lost focus of element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialIconToggle.prototype.onBlur_ = function (event) {\n    this.element_.classList.remove(this.CssClasses_.IS_FOCUSED);\n};\n/**\n   * Handle mouseup.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialIconToggle.prototype.onMouseUp_ = function (event) {\n    this.blur_();\n};\n/**\n   * Handle class updates.\n   *\n   * @private\n   */\nMaterialIconToggle.prototype.updateClasses_ = function () {\n    this.checkDisabled();\n    this.checkToggleState();\n};\n/**\n   * Add blur.\n   *\n   * @private\n   */\nMaterialIconToggle.prototype.blur_ = function () {\n    // TODO: figure out why there's a focus event being fired after our blur,\n    // so that we can avoid this hack.\n    window.setTimeout(function () {\n        this.inputElement_.blur();\n    }.bind(this), this.Constant_.TINY_TIMEOUT);\n};\n// Public methods.\n/**\n   * Check the inputs toggle state and update display.\n   *\n   * @public\n   */\nMaterialIconToggle.prototype.checkToggleState = function () {\n    if (this.inputElement_.checked) {\n        this.element_.classList.add(this.CssClasses_.IS_CHECKED);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_CHECKED);\n    }\n};\nMaterialIconToggle.prototype['checkToggleState'] = MaterialIconToggle.prototype.checkToggleState;\n/**\n   * Check the inputs disabled state and update display.\n   *\n   * @public\n   */\nMaterialIconToggle.prototype.checkDisabled = function () {\n    if (this.inputElement_.disabled) {\n        this.element_.classList.add(this.CssClasses_.IS_DISABLED);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_DISABLED);\n    }\n};\nMaterialIconToggle.prototype['checkDisabled'] = MaterialIconToggle.prototype.checkDisabled;\n/**\n   * Disable icon toggle.\n   *\n   * @public\n   */\nMaterialIconToggle.prototype.disable = function () {\n    this.inputElement_.disabled = true;\n    this.updateClasses_();\n};\nMaterialIconToggle.prototype['disable'] = MaterialIconToggle.prototype.disable;\n/**\n   * Enable icon toggle.\n   *\n   * @public\n   */\nMaterialIconToggle.prototype.enable = function () {\n    this.inputElement_.disabled = false;\n    this.updateClasses_();\n};\nMaterialIconToggle.prototype['enable'] = MaterialIconToggle.prototype.enable;\n/**\n   * Check icon toggle.\n   *\n   * @public\n   */\nMaterialIconToggle.prototype.check = function () {\n    this.inputElement_.checked = true;\n    this.updateClasses_();\n};\nMaterialIconToggle.prototype['check'] = MaterialIconToggle.prototype.check;\n/**\n   * Uncheck icon toggle.\n   *\n   * @public\n   */\nMaterialIconToggle.prototype.uncheck = function () {\n    this.inputElement_.checked = false;\n    this.updateClasses_();\n};\nMaterialIconToggle.prototype['uncheck'] = MaterialIconToggle.prototype.uncheck;\n/**\n   * Initialize element.\n   */\nMaterialIconToggle.prototype.init = function () {\n    if (this.element_) {\n        this.inputElement_ = this.element_.querySelector('.' + this.CssClasses_.INPUT);\n        if (this.element_.classList.contains(this.CssClasses_.JS_RIPPLE_EFFECT)) {\n            this.element_.classList.add(this.CssClasses_.RIPPLE_IGNORE_EVENTS);\n            this.rippleContainerElement_ = document.createElement('span');\n            this.rippleContainerElement_.classList.add(this.CssClasses_.RIPPLE_CONTAINER);\n            this.rippleContainerElement_.classList.add(this.CssClasses_.JS_RIPPLE_EFFECT);\n            this.rippleContainerElement_.classList.add(this.CssClasses_.RIPPLE_CENTER);\n            this.boundRippleMouseUp = this.onMouseUp_.bind(this);\n            this.rippleContainerElement_.addEventListener('mouseup', this.boundRippleMouseUp);\n            var ripple = document.createElement('span');\n            ripple.classList.add(this.CssClasses_.RIPPLE);\n            this.rippleContainerElement_.appendChild(ripple);\n            this.element_.appendChild(this.rippleContainerElement_);\n        }\n        this.boundInputOnChange = this.onChange_.bind(this);\n        this.boundInputOnFocus = this.onFocus_.bind(this);\n        this.boundInputOnBlur = this.onBlur_.bind(this);\n        this.boundElementOnMouseUp = this.onMouseUp_.bind(this);\n        this.inputElement_.addEventListener('change', this.boundInputOnChange);\n        this.inputElement_.addEventListener('focus', this.boundInputOnFocus);\n        this.inputElement_.addEventListener('blur', this.boundInputOnBlur);\n        this.element_.addEventListener('mouseup', this.boundElementOnMouseUp);\n        this.updateClasses_();\n        this.element_.classList.add('is-upgraded');\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialIconToggle,\n    classAsString: 'MaterialIconToggle',\n    cssClass: 'mdl-js-icon-toggle',\n    widget: true\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for dropdown MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialMenu = function MaterialMenu(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialMenu'] = MaterialMenu;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialMenu.prototype.Constant_ = {\n    // Total duration of the menu animation.\n    TRANSITION_DURATION_SECONDS: 0.3,\n    // The fraction of the total duration we want to use for menu item animations.\n    TRANSITION_DURATION_FRACTION: 0.8,\n    // How long the menu stays open after choosing an option (so the user can see\n    // the ripple).\n    CLOSE_TIMEOUT: 150\n};\n/**\n   * Keycodes, for code readability.\n   *\n   * @enum {number}\n   * @private\n   */\nMaterialMenu.prototype.Keycodes_ = {\n    ENTER: 13,\n    ESCAPE: 27,\n    SPACE: 32,\n    UP_ARROW: 38,\n    DOWN_ARROW: 40\n};\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialMenu.prototype.CssClasses_ = {\n    CONTAINER: 'mdl-menu__container',\n    OUTLINE: 'mdl-menu__outline',\n    ITEM: 'mdl-menu__item',\n    ITEM_RIPPLE_CONTAINER: 'mdl-menu__item-ripple-container',\n    RIPPLE_EFFECT: 'mdl-js-ripple-effect',\n    RIPPLE_IGNORE_EVENTS: 'mdl-js-ripple-effect--ignore-events',\n    RIPPLE: 'mdl-ripple',\n    // Statuses\n    IS_UPGRADED: 'is-upgraded',\n    IS_VISIBLE: 'is-visible',\n    IS_ANIMATING: 'is-animating',\n    // Alignment options\n    BOTTOM_LEFT: 'mdl-menu--bottom-left',\n    // This is the default.\n    BOTTOM_RIGHT: 'mdl-menu--bottom-right',\n    TOP_LEFT: 'mdl-menu--top-left',\n    TOP_RIGHT: 'mdl-menu--top-right',\n    UNALIGNED: 'mdl-menu--unaligned'\n};\n/**\n   * Initialize element.\n   */\nMaterialMenu.prototype.init = function () {\n    if (this.element_) {\n        // Create container for the menu.\n        var container = document.createElement('div');\n        container.classList.add(this.CssClasses_.CONTAINER);\n        this.element_.parentElement.insertBefore(container, this.element_);\n        this.element_.parentElement.removeChild(this.element_);\n        container.appendChild(this.element_);\n        this.container_ = container;\n        // Create outline for the menu (shadow and background).\n        var outline = document.createElement('div');\n        outline.classList.add(this.CssClasses_.OUTLINE);\n        this.outline_ = outline;\n        container.insertBefore(outline, this.element_);\n        // Find the \"for\" element and bind events to it.\n        var forElId = this.element_.getAttribute('for') || this.element_.getAttribute('data-mdl-for');\n        var forEl = null;\n        if (forElId) {\n            forEl = document.getElementById(forElId);\n            if (forEl) {\n                this.forElement_ = forEl;\n                forEl.addEventListener('click', this.handleForClick_.bind(this));\n                forEl.addEventListener('keydown', this.handleForKeyboardEvent_.bind(this));\n            }\n        }\n        var items = this.element_.querySelectorAll('.' + this.CssClasses_.ITEM);\n        this.boundItemKeydown_ = this.handleItemKeyboardEvent_.bind(this);\n        this.boundItemClick_ = this.handleItemClick_.bind(this);\n        for (var i = 0; i < items.length; i++) {\n            // Add a listener to each menu item.\n            items[i].addEventListener('click', this.boundItemClick_);\n            // Add a tab index to each menu item.\n            items[i].tabIndex = '-1';\n            // Add a keyboard listener to each menu item.\n            items[i].addEventListener('keydown', this.boundItemKeydown_);\n        }\n        // Add ripple classes to each item, if the user has enabled ripples.\n        if (this.element_.classList.contains(this.CssClasses_.RIPPLE_EFFECT)) {\n            this.element_.classList.add(this.CssClasses_.RIPPLE_IGNORE_EVENTS);\n            for (i = 0; i < items.length; i++) {\n                var item = items[i];\n                var rippleContainer = document.createElement('span');\n                rippleContainer.classList.add(this.CssClasses_.ITEM_RIPPLE_CONTAINER);\n                var ripple = document.createElement('span');\n                ripple.classList.add(this.CssClasses_.RIPPLE);\n                rippleContainer.appendChild(ripple);\n                item.appendChild(rippleContainer);\n                item.classList.add(this.CssClasses_.RIPPLE_EFFECT);\n            }\n        }\n        // Copy alignment classes to the container, so the outline can use them.\n        if (this.element_.classList.contains(this.CssClasses_.BOTTOM_LEFT)) {\n            this.outline_.classList.add(this.CssClasses_.BOTTOM_LEFT);\n        }\n        if (this.element_.classList.contains(this.CssClasses_.BOTTOM_RIGHT)) {\n            this.outline_.classList.add(this.CssClasses_.BOTTOM_RIGHT);\n        }\n        if (this.element_.classList.contains(this.CssClasses_.TOP_LEFT)) {\n            this.outline_.classList.add(this.CssClasses_.TOP_LEFT);\n        }\n        if (this.element_.classList.contains(this.CssClasses_.TOP_RIGHT)) {\n            this.outline_.classList.add(this.CssClasses_.TOP_RIGHT);\n        }\n        if (this.element_.classList.contains(this.CssClasses_.UNALIGNED)) {\n            this.outline_.classList.add(this.CssClasses_.UNALIGNED);\n        }\n        container.classList.add(this.CssClasses_.IS_UPGRADED);\n    }\n};\n/**\n   * Handles a click on the \"for\" element, by positioning the menu and then\n   * toggling it.\n   *\n   * @param {Event} evt The event that fired.\n   * @private\n   */\nMaterialMenu.prototype.handleForClick_ = function (evt) {\n    if (this.element_ && this.forElement_) {\n        var rect = this.forElement_.getBoundingClientRect();\n        var forRect = this.forElement_.parentElement.getBoundingClientRect();\n        if (this.element_.classList.contains(this.CssClasses_.UNALIGNED)) {\n        } else if (this.element_.classList.contains(this.CssClasses_.BOTTOM_RIGHT)) {\n            // Position below the \"for\" element, aligned to its right.\n            this.container_.style.right = forRect.right - rect.right + 'px';\n            this.container_.style.top = this.forElement_.offsetTop + this.forElement_.offsetHeight + 'px';\n        } else if (this.element_.classList.contains(this.CssClasses_.TOP_LEFT)) {\n            // Position above the \"for\" element, aligned to its left.\n            this.container_.style.left = this.forElement_.offsetLeft + 'px';\n            this.container_.style.bottom = forRect.bottom - rect.top + 'px';\n        } else if (this.element_.classList.contains(this.CssClasses_.TOP_RIGHT)) {\n            // Position above the \"for\" element, aligned to its right.\n            this.container_.style.right = forRect.right - rect.right + 'px';\n            this.container_.style.bottom = forRect.bottom - rect.top + 'px';\n        } else {\n            // Default: position below the \"for\" element, aligned to its left.\n            this.container_.style.left = this.forElement_.offsetLeft + 'px';\n            this.container_.style.top = this.forElement_.offsetTop + this.forElement_.offsetHeight + 'px';\n        }\n    }\n    this.toggle(evt);\n};\n/**\n   * Handles a keyboard event on the \"for\" element.\n   *\n   * @param {Event} evt The event that fired.\n   * @private\n   */\nMaterialMenu.prototype.handleForKeyboardEvent_ = function (evt) {\n    if (this.element_ && this.container_ && this.forElement_) {\n        var items = this.element_.querySelectorAll('.' + this.CssClasses_.ITEM + ':not([disabled])');\n        if (items && items.length > 0 && this.container_.classList.contains(this.CssClasses_.IS_VISIBLE)) {\n            if (evt.keyCode === this.Keycodes_.UP_ARROW) {\n                evt.preventDefault();\n                items[items.length - 1].focus();\n            } else if (evt.keyCode === this.Keycodes_.DOWN_ARROW) {\n                evt.preventDefault();\n                items[0].focus();\n            }\n        }\n    }\n};\n/**\n   * Handles a keyboard event on an item.\n   *\n   * @param {Event} evt The event that fired.\n   * @private\n   */\nMaterialMenu.prototype.handleItemKeyboardEvent_ = function (evt) {\n    if (this.element_ && this.container_) {\n        var items = this.element_.querySelectorAll('.' + this.CssClasses_.ITEM + ':not([disabled])');\n        if (items && items.length > 0 && this.container_.classList.contains(this.CssClasses_.IS_VISIBLE)) {\n            var currentIndex = Array.prototype.slice.call(items).indexOf(evt.target);\n            if (evt.keyCode === this.Keycodes_.UP_ARROW) {\n                evt.preventDefault();\n                if (currentIndex > 0) {\n                    items[currentIndex - 1].focus();\n                } else {\n                    items[items.length - 1].focus();\n                }\n            } else if (evt.keyCode === this.Keycodes_.DOWN_ARROW) {\n                evt.preventDefault();\n                if (items.length > currentIndex + 1) {\n                    items[currentIndex + 1].focus();\n                } else {\n                    items[0].focus();\n                }\n            } else if (evt.keyCode === this.Keycodes_.SPACE || evt.keyCode === this.Keycodes_.ENTER) {\n                evt.preventDefault();\n                // Send mousedown and mouseup to trigger ripple.\n                var e = new MouseEvent('mousedown');\n                evt.target.dispatchEvent(e);\n                e = new MouseEvent('mouseup');\n                evt.target.dispatchEvent(e);\n                // Send click.\n                evt.target.click();\n            } else if (evt.keyCode === this.Keycodes_.ESCAPE) {\n                evt.preventDefault();\n                this.hide();\n            }\n        }\n    }\n};\n/**\n   * Handles a click event on an item.\n   *\n   * @param {Event} evt The event that fired.\n   * @private\n   */\nMaterialMenu.prototype.handleItemClick_ = function (evt) {\n    if (evt.target.hasAttribute('disabled')) {\n        evt.stopPropagation();\n    } else {\n        // Wait some time before closing menu, so the user can see the ripple.\n        this.closing_ = true;\n        window.setTimeout(function (evt) {\n            this.hide();\n            this.closing_ = false;\n        }.bind(this), this.Constant_.CLOSE_TIMEOUT);\n    }\n};\n/**\n   * Calculates the initial clip (for opening the menu) or final clip (for closing\n   * it), and applies it. This allows us to animate from or to the correct point,\n   * that is, the point it's aligned to in the \"for\" element.\n   *\n   * @param {number} height Height of the clip rectangle\n   * @param {number} width Width of the clip rectangle\n   * @private\n   */\nMaterialMenu.prototype.applyClip_ = function (height, width) {\n    if (this.element_.classList.contains(this.CssClasses_.UNALIGNED)) {\n        // Do not clip.\n        this.element_.style.clip = '';\n    } else if (this.element_.classList.contains(this.CssClasses_.BOTTOM_RIGHT)) {\n        // Clip to the top right corner of the menu.\n        this.element_.style.clip = 'rect(0 ' + width + 'px ' + '0 ' + width + 'px)';\n    } else if (this.element_.classList.contains(this.CssClasses_.TOP_LEFT)) {\n        // Clip to the bottom left corner of the menu.\n        this.element_.style.clip = 'rect(' + height + 'px 0 ' + height + 'px 0)';\n    } else if (this.element_.classList.contains(this.CssClasses_.TOP_RIGHT)) {\n        // Clip to the bottom right corner of the menu.\n        this.element_.style.clip = 'rect(' + height + 'px ' + width + 'px ' + height + 'px ' + width + 'px)';\n    } else {\n        // Default: do not clip (same as clipping to the top left corner).\n        this.element_.style.clip = '';\n    }\n};\n/**\n   * Cleanup function to remove animation listeners.\n   *\n   * @param {Event} evt\n   * @private\n   */\nMaterialMenu.prototype.removeAnimationEndListener_ = function (evt) {\n    evt.target.classList.remove(MaterialMenu.prototype.CssClasses_.IS_ANIMATING);\n};\n/**\n   * Adds an event listener to clean up after the animation ends.\n   *\n   * @private\n   */\nMaterialMenu.prototype.addAnimationEndListener_ = function () {\n    this.element_.addEventListener('transitionend', this.removeAnimationEndListener_);\n    this.element_.addEventListener('webkitTransitionEnd', this.removeAnimationEndListener_);\n};\n/**\n   * Displays the menu.\n   *\n   * @public\n   */\nMaterialMenu.prototype.show = function (evt) {\n    if (this.element_ && this.container_ && this.outline_) {\n        // Measure the inner element.\n        var height = this.element_.getBoundingClientRect().height;\n        var width = this.element_.getBoundingClientRect().width;\n        // Apply the inner element's size to the container and outline.\n        this.container_.style.width = width + 'px';\n        this.container_.style.height = height + 'px';\n        this.outline_.style.width = width + 'px';\n        this.outline_.style.height = height + 'px';\n        var transitionDuration = this.Constant_.TRANSITION_DURATION_SECONDS * this.Constant_.TRANSITION_DURATION_FRACTION;\n        // Calculate transition delays for individual menu items, so that they fade\n        // in one at a time.\n        var items = this.element_.querySelectorAll('.' + this.CssClasses_.ITEM);\n        for (var i = 0; i < items.length; i++) {\n            var itemDelay = null;\n            if (this.element_.classList.contains(this.CssClasses_.TOP_LEFT) || this.element_.classList.contains(this.CssClasses_.TOP_RIGHT)) {\n                itemDelay = (height - items[i].offsetTop - items[i].offsetHeight) / height * transitionDuration + 's';\n            } else {\n                itemDelay = items[i].offsetTop / height * transitionDuration + 's';\n            }\n            items[i].style.transitionDelay = itemDelay;\n        }\n        // Apply the initial clip to the text before we start animating.\n        this.applyClip_(height, width);\n        // Wait for the next frame, turn on animation, and apply the final clip.\n        // Also make it visible. This triggers the transitions.\n        window.requestAnimationFrame(function () {\n            this.element_.classList.add(this.CssClasses_.IS_ANIMATING);\n            this.element_.style.clip = 'rect(0 ' + width + 'px ' + height + 'px 0)';\n            this.container_.classList.add(this.CssClasses_.IS_VISIBLE);\n        }.bind(this));\n        // Clean up after the animation is complete.\n        this.addAnimationEndListener_();\n        // Add a click listener to the document, to close the menu.\n        var callback = function (e) {\n            // Check to see if the document is processing the same event that\n            // displayed the menu in the first place. If so, do nothing.\n            // Also check to see if the menu is in the process of closing itself, and\n            // do nothing in that case.\n            // Also check if the clicked element is a menu item\n            // if so, do nothing.\n            if (e !== evt && !this.closing_ && e.target.parentNode !== this.element_) {\n                document.removeEventListener('click', callback);\n                this.hide();\n            }\n        }.bind(this);\n        document.addEventListener('click', callback);\n    }\n};\nMaterialMenu.prototype['show'] = MaterialMenu.prototype.show;\n/**\n   * Hides the menu.\n   *\n   * @public\n   */\nMaterialMenu.prototype.hide = function () {\n    if (this.element_ && this.container_ && this.outline_) {\n        var items = this.element_.querySelectorAll('.' + this.CssClasses_.ITEM);\n        // Remove all transition delays; menu items fade out concurrently.\n        for (var i = 0; i < items.length; i++) {\n            items[i].style.removeProperty('transition-delay');\n        }\n        // Measure the inner element.\n        var rect = this.element_.getBoundingClientRect();\n        var height = rect.height;\n        var width = rect.width;\n        // Turn on animation, and apply the final clip. Also make invisible.\n        // This triggers the transitions.\n        this.element_.classList.add(this.CssClasses_.IS_ANIMATING);\n        this.applyClip_(height, width);\n        this.container_.classList.remove(this.CssClasses_.IS_VISIBLE);\n        // Clean up after the animation is complete.\n        this.addAnimationEndListener_();\n    }\n};\nMaterialMenu.prototype['hide'] = MaterialMenu.prototype.hide;\n/**\n   * Displays or hides the menu, depending on current state.\n   *\n   * @public\n   */\nMaterialMenu.prototype.toggle = function (evt) {\n    if (this.container_.classList.contains(this.CssClasses_.IS_VISIBLE)) {\n        this.hide();\n    } else {\n        this.show(evt);\n    }\n};\nMaterialMenu.prototype['toggle'] = MaterialMenu.prototype.toggle;\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialMenu,\n    classAsString: 'MaterialMenu',\n    cssClass: 'mdl-js-menu',\n    widget: true\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Progress MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialProgress = function MaterialProgress(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialProgress'] = MaterialProgress;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialProgress.prototype.Constant_ = {};\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialProgress.prototype.CssClasses_ = { INDETERMINATE_CLASS: 'mdl-progress__indeterminate' };\n/**\n   * Set the current progress of the progressbar.\n   *\n   * @param {number} p Percentage of the progress (0-100)\n   * @public\n   */\nMaterialProgress.prototype.setProgress = function (p) {\n    if (this.element_.classList.contains(this.CssClasses_.INDETERMINATE_CLASS)) {\n        return;\n    }\n    this.progressbar_.style.width = p + '%';\n};\nMaterialProgress.prototype['setProgress'] = MaterialProgress.prototype.setProgress;\n/**\n   * Set the current progress of the buffer.\n   *\n   * @param {number} p Percentage of the buffer (0-100)\n   * @public\n   */\nMaterialProgress.prototype.setBuffer = function (p) {\n    this.bufferbar_.style.width = p + '%';\n    this.auxbar_.style.width = 100 - p + '%';\n};\nMaterialProgress.prototype['setBuffer'] = MaterialProgress.prototype.setBuffer;\n/**\n   * Initialize element.\n   */\nMaterialProgress.prototype.init = function () {\n    if (this.element_) {\n        var el = document.createElement('div');\n        el.className = 'progressbar bar bar1';\n        this.element_.appendChild(el);\n        this.progressbar_ = el;\n        el = document.createElement('div');\n        el.className = 'bufferbar bar bar2';\n        this.element_.appendChild(el);\n        this.bufferbar_ = el;\n        el = document.createElement('div');\n        el.className = 'auxbar bar bar3';\n        this.element_.appendChild(el);\n        this.auxbar_ = el;\n        this.progressbar_.style.width = '0%';\n        this.bufferbar_.style.width = '100%';\n        this.auxbar_.style.width = '0%';\n        this.element_.classList.add('is-upgraded');\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialProgress,\n    classAsString: 'MaterialProgress',\n    cssClass: 'mdl-js-progress',\n    widget: true\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Radio MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialRadio = function MaterialRadio(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialRadio'] = MaterialRadio;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialRadio.prototype.Constant_ = { TINY_TIMEOUT: 0.001 };\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialRadio.prototype.CssClasses_ = {\n    IS_FOCUSED: 'is-focused',\n    IS_DISABLED: 'is-disabled',\n    IS_CHECKED: 'is-checked',\n    IS_UPGRADED: 'is-upgraded',\n    JS_RADIO: 'mdl-js-radio',\n    RADIO_BTN: 'mdl-radio__button',\n    RADIO_OUTER_CIRCLE: 'mdl-radio__outer-circle',\n    RADIO_INNER_CIRCLE: 'mdl-radio__inner-circle',\n    RIPPLE_EFFECT: 'mdl-js-ripple-effect',\n    RIPPLE_IGNORE_EVENTS: 'mdl-js-ripple-effect--ignore-events',\n    RIPPLE_CONTAINER: 'mdl-radio__ripple-container',\n    RIPPLE_CENTER: 'mdl-ripple--center',\n    RIPPLE: 'mdl-ripple'\n};\n/**\n   * Handle change of state.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialRadio.prototype.onChange_ = function (event) {\n    // Since other radio buttons don't get change events, we need to look for\n    // them to update their classes.\n    var radios = document.getElementsByClassName(this.CssClasses_.JS_RADIO);\n    for (var i = 0; i < radios.length; i++) {\n        var button = radios[i].querySelector('.' + this.CssClasses_.RADIO_BTN);\n        // Different name == different group, so no point updating those.\n        if (button.getAttribute('name') === this.btnElement_.getAttribute('name')) {\n            if (typeof radios[i]['MaterialRadio'] !== 'undefined') {\n                radios[i]['MaterialRadio'].updateClasses_();\n            }\n        }\n    }\n};\n/**\n   * Handle focus.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialRadio.prototype.onFocus_ = function (event) {\n    this.element_.classList.add(this.CssClasses_.IS_FOCUSED);\n};\n/**\n   * Handle lost focus.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialRadio.prototype.onBlur_ = function (event) {\n    this.element_.classList.remove(this.CssClasses_.IS_FOCUSED);\n};\n/**\n   * Handle mouseup.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialRadio.prototype.onMouseup_ = function (event) {\n    this.blur_();\n};\n/**\n   * Update classes.\n   *\n   * @private\n   */\nMaterialRadio.prototype.updateClasses_ = function () {\n    this.checkDisabled();\n    this.checkToggleState();\n};\n/**\n   * Add blur.\n   *\n   * @private\n   */\nMaterialRadio.prototype.blur_ = function () {\n    // TODO: figure out why there's a focus event being fired after our blur,\n    // so that we can avoid this hack.\n    window.setTimeout(function () {\n        this.btnElement_.blur();\n    }.bind(this), this.Constant_.TINY_TIMEOUT);\n};\n// Public methods.\n/**\n   * Check the components disabled state.\n   *\n   * @public\n   */\nMaterialRadio.prototype.checkDisabled = function () {\n    if (this.btnElement_.disabled) {\n        this.element_.classList.add(this.CssClasses_.IS_DISABLED);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_DISABLED);\n    }\n};\nMaterialRadio.prototype['checkDisabled'] = MaterialRadio.prototype.checkDisabled;\n/**\n   * Check the components toggled state.\n   *\n   * @public\n   */\nMaterialRadio.prototype.checkToggleState = function () {\n    if (this.btnElement_.checked) {\n        this.element_.classList.add(this.CssClasses_.IS_CHECKED);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_CHECKED);\n    }\n};\nMaterialRadio.prototype['checkToggleState'] = MaterialRadio.prototype.checkToggleState;\n/**\n   * Disable radio.\n   *\n   * @public\n   */\nMaterialRadio.prototype.disable = function () {\n    this.btnElement_.disabled = true;\n    this.updateClasses_();\n};\nMaterialRadio.prototype['disable'] = MaterialRadio.prototype.disable;\n/**\n   * Enable radio.\n   *\n   * @public\n   */\nMaterialRadio.prototype.enable = function () {\n    this.btnElement_.disabled = false;\n    this.updateClasses_();\n};\nMaterialRadio.prototype['enable'] = MaterialRadio.prototype.enable;\n/**\n   * Check radio.\n   *\n   * @public\n   */\nMaterialRadio.prototype.check = function () {\n    this.btnElement_.checked = true;\n    this.onChange_(null);\n};\nMaterialRadio.prototype['check'] = MaterialRadio.prototype.check;\n/**\n   * Uncheck radio.\n   *\n   * @public\n   */\nMaterialRadio.prototype.uncheck = function () {\n    this.btnElement_.checked = false;\n    this.onChange_(null);\n};\nMaterialRadio.prototype['uncheck'] = MaterialRadio.prototype.uncheck;\n/**\n   * Initialize element.\n   */\nMaterialRadio.prototype.init = function () {\n    if (this.element_) {\n        this.btnElement_ = this.element_.querySelector('.' + this.CssClasses_.RADIO_BTN);\n        this.boundChangeHandler_ = this.onChange_.bind(this);\n        this.boundFocusHandler_ = this.onChange_.bind(this);\n        this.boundBlurHandler_ = this.onBlur_.bind(this);\n        this.boundMouseUpHandler_ = this.onMouseup_.bind(this);\n        var outerCircle = document.createElement('span');\n        outerCircle.classList.add(this.CssClasses_.RADIO_OUTER_CIRCLE);\n        var innerCircle = document.createElement('span');\n        innerCircle.classList.add(this.CssClasses_.RADIO_INNER_CIRCLE);\n        this.element_.appendChild(outerCircle);\n        this.element_.appendChild(innerCircle);\n        var rippleContainer;\n        if (this.element_.classList.contains(this.CssClasses_.RIPPLE_EFFECT)) {\n            this.element_.classList.add(this.CssClasses_.RIPPLE_IGNORE_EVENTS);\n            rippleContainer = document.createElement('span');\n            rippleContainer.classList.add(this.CssClasses_.RIPPLE_CONTAINER);\n            rippleContainer.classList.add(this.CssClasses_.RIPPLE_EFFECT);\n            rippleContainer.classList.add(this.CssClasses_.RIPPLE_CENTER);\n            rippleContainer.addEventListener('mouseup', this.boundMouseUpHandler_);\n            var ripple = document.createElement('span');\n            ripple.classList.add(this.CssClasses_.RIPPLE);\n            rippleContainer.appendChild(ripple);\n            this.element_.appendChild(rippleContainer);\n        }\n        this.btnElement_.addEventListener('change', this.boundChangeHandler_);\n        this.btnElement_.addEventListener('focus', this.boundFocusHandler_);\n        this.btnElement_.addEventListener('blur', this.boundBlurHandler_);\n        this.element_.addEventListener('mouseup', this.boundMouseUpHandler_);\n        this.updateClasses_();\n        this.element_.classList.add(this.CssClasses_.IS_UPGRADED);\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialRadio,\n    classAsString: 'MaterialRadio',\n    cssClass: 'mdl-js-radio',\n    widget: true\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Slider MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialSlider = function MaterialSlider(element) {\n    this.element_ = element;\n    // Browser feature detection.\n    this.isIE_ = window.navigator.msPointerEnabled;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialSlider'] = MaterialSlider;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialSlider.prototype.Constant_ = {};\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialSlider.prototype.CssClasses_ = {\n    IE_CONTAINER: 'mdl-slider__ie-container',\n    SLIDER_CONTAINER: 'mdl-slider__container',\n    BACKGROUND_FLEX: 'mdl-slider__background-flex',\n    BACKGROUND_LOWER: 'mdl-slider__background-lower',\n    BACKGROUND_UPPER: 'mdl-slider__background-upper',\n    IS_LOWEST_VALUE: 'is-lowest-value',\n    IS_UPGRADED: 'is-upgraded'\n};\n/**\n   * Handle input on element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialSlider.prototype.onInput_ = function (event) {\n    this.updateValueStyles_();\n};\n/**\n   * Handle change on element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialSlider.prototype.onChange_ = function (event) {\n    this.updateValueStyles_();\n};\n/**\n   * Handle mouseup on element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialSlider.prototype.onMouseUp_ = function (event) {\n    event.target.blur();\n};\n/**\n   * Handle mousedown on container element.\n   * This handler is purpose is to not require the use to click\n   * exactly on the 2px slider element, as FireFox seems to be very\n   * strict about this.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   * @suppress {missingProperties}\n   */\nMaterialSlider.prototype.onContainerMouseDown_ = function (event) {\n    // If this click is not on the parent element (but rather some child)\n    // ignore. It may still bubble up.\n    if (event.target !== this.element_.parentElement) {\n        return;\n    }\n    // Discard the original event and create a new event that\n    // is on the slider element.\n    event.preventDefault();\n    var newEvent = new MouseEvent('mousedown', {\n        target: event.target,\n        buttons: event.buttons,\n        clientX: event.clientX,\n        clientY: this.element_.getBoundingClientRect().y\n    });\n    this.element_.dispatchEvent(newEvent);\n};\n/**\n   * Handle updating of values.\n   *\n   * @private\n   */\nMaterialSlider.prototype.updateValueStyles_ = function () {\n    // Calculate and apply percentages to div structure behind slider.\n    var fraction = (this.element_.value - this.element_.min) / (this.element_.max - this.element_.min);\n    if (fraction === 0) {\n        this.element_.classList.add(this.CssClasses_.IS_LOWEST_VALUE);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_LOWEST_VALUE);\n    }\n    if (!this.isIE_) {\n        this.backgroundLower_.style.flex = fraction;\n        this.backgroundLower_.style.webkitFlex = fraction;\n        this.backgroundUpper_.style.flex = 1 - fraction;\n        this.backgroundUpper_.style.webkitFlex = 1 - fraction;\n    }\n};\n// Public methods.\n/**\n   * Disable slider.\n   *\n   * @public\n   */\nMaterialSlider.prototype.disable = function () {\n    this.element_.disabled = true;\n};\nMaterialSlider.prototype['disable'] = MaterialSlider.prototype.disable;\n/**\n   * Enable slider.\n   *\n   * @public\n   */\nMaterialSlider.prototype.enable = function () {\n    this.element_.disabled = false;\n};\nMaterialSlider.prototype['enable'] = MaterialSlider.prototype.enable;\n/**\n   * Update slider value.\n   *\n   * @param {number} value The value to which to set the control (optional).\n   * @public\n   */\nMaterialSlider.prototype.change = function (value) {\n    if (typeof value !== 'undefined') {\n        this.element_.value = value;\n    }\n    this.updateValueStyles_();\n};\nMaterialSlider.prototype['change'] = MaterialSlider.prototype.change;\n/**\n   * Initialize element.\n   */\nMaterialSlider.prototype.init = function () {\n    if (this.element_) {\n        if (this.isIE_) {\n            // Since we need to specify a very large height in IE due to\n            // implementation limitations, we add a parent here that trims it down to\n            // a reasonable size.\n            var containerIE = document.createElement('div');\n            containerIE.classList.add(this.CssClasses_.IE_CONTAINER);\n            this.element_.parentElement.insertBefore(containerIE, this.element_);\n            this.element_.parentElement.removeChild(this.element_);\n            containerIE.appendChild(this.element_);\n        } else {\n            // For non-IE browsers, we need a div structure that sits behind the\n            // slider and allows us to style the left and right sides of it with\n            // different colors.\n            var container = document.createElement('div');\n            container.classList.add(this.CssClasses_.SLIDER_CONTAINER);\n            this.element_.parentElement.insertBefore(container, this.element_);\n            this.element_.parentElement.removeChild(this.element_);\n            container.appendChild(this.element_);\n            var backgroundFlex = document.createElement('div');\n            backgroundFlex.classList.add(this.CssClasses_.BACKGROUND_FLEX);\n            container.appendChild(backgroundFlex);\n            this.backgroundLower_ = document.createElement('div');\n            this.backgroundLower_.classList.add(this.CssClasses_.BACKGROUND_LOWER);\n            backgroundFlex.appendChild(this.backgroundLower_);\n            this.backgroundUpper_ = document.createElement('div');\n            this.backgroundUpper_.classList.add(this.CssClasses_.BACKGROUND_UPPER);\n            backgroundFlex.appendChild(this.backgroundUpper_);\n        }\n        this.boundInputHandler = this.onInput_.bind(this);\n        this.boundChangeHandler = this.onChange_.bind(this);\n        this.boundMouseUpHandler = this.onMouseUp_.bind(this);\n        this.boundContainerMouseDownHandler = this.onContainerMouseDown_.bind(this);\n        this.element_.addEventListener('input', this.boundInputHandler);\n        this.element_.addEventListener('change', this.boundChangeHandler);\n        this.element_.addEventListener('mouseup', this.boundMouseUpHandler);\n        this.element_.parentElement.addEventListener('mousedown', this.boundContainerMouseDownHandler);\n        this.updateValueStyles_();\n        this.element_.classList.add(this.CssClasses_.IS_UPGRADED);\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialSlider,\n    classAsString: 'MaterialSlider',\n    cssClass: 'mdl-js-slider',\n    widget: true\n});","/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Snackbar MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialSnackbar = function MaterialSnackbar(element) {\n    this.element_ = element;\n    this.textElement_ = this.element_.querySelector('.' + this.cssClasses_.MESSAGE);\n    this.actionElement_ = this.element_.querySelector('.' + this.cssClasses_.ACTION);\n    if (!this.textElement_) {\n        throw new Error('There must be a message element for a snackbar.');\n    }\n    if (!this.actionElement_) {\n        throw new Error('There must be an action element for a snackbar.');\n    }\n    this.active = false;\n    this.actionHandler_ = undefined;\n    this.message_ = undefined;\n    this.actionText_ = undefined;\n    this.queuedNotifications_ = [];\n    this.setActionHidden_(true);\n};\nwindow['MaterialSnackbar'] = MaterialSnackbar;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialSnackbar.prototype.Constant_ = {\n    // The duration of the snackbar show/hide animation, in ms.\n    ANIMATION_LENGTH: 250\n};\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialSnackbar.prototype.cssClasses_ = {\n    SNACKBAR: 'mdl-snackbar',\n    MESSAGE: 'mdl-snackbar__text',\n    ACTION: 'mdl-snackbar__action',\n    ACTIVE: 'mdl-snackbar--active'\n};\n/**\n   * Display the snackbar.\n   *\n   * @private\n   */\nMaterialSnackbar.prototype.displaySnackbar_ = function () {\n    this.element_.setAttribute('aria-hidden', 'true');\n    if (this.actionHandler_) {\n        this.actionElement_.textContent = this.actionText_;\n        this.actionElement_.addEventListener('click', this.actionHandler_);\n        this.setActionHidden_(false);\n    }\n    this.textElement_.textContent = this.message_;\n    this.element_.classList.add(this.cssClasses_.ACTIVE);\n    this.element_.setAttribute('aria-hidden', 'false');\n    setTimeout(this.cleanup_.bind(this), this.timeout_);\n};\n/**\n   * Show the snackbar.\n   *\n   * @param {Object} data The data for the notification.\n   * @public\n   */\nMaterialSnackbar.prototype.showSnackbar = function (data) {\n    if (data === undefined) {\n        throw new Error('Please provide a data object with at least a message to display.');\n    }\n    if (data['message'] === undefined) {\n        throw new Error('Please provide a message to be displayed.');\n    }\n    if (data['actionHandler'] && !data['actionText']) {\n        throw new Error('Please provide action text with the handler.');\n    }\n    if (this.active) {\n        this.queuedNotifications_.push(data);\n    } else {\n        this.active = true;\n        this.message_ = data['message'];\n        if (data['timeout']) {\n            this.timeout_ = data['timeout'];\n        } else {\n            this.timeout_ = 2750;\n        }\n        if (data['actionHandler']) {\n            this.actionHandler_ = data['actionHandler'];\n        }\n        if (data['actionText']) {\n            this.actionText_ = data['actionText'];\n        }\n        this.displaySnackbar_();\n    }\n};\nMaterialSnackbar.prototype['showSnackbar'] = MaterialSnackbar.prototype.showSnackbar;\n/**\n   * Check if the queue has items within it.\n   * If it does, display the next entry.\n   *\n   * @private\n   */\nMaterialSnackbar.prototype.checkQueue_ = function () {\n    if (this.queuedNotifications_.length > 0) {\n        this.showSnackbar(this.queuedNotifications_.shift());\n    }\n};\n/**\n   * Cleanup the snackbar event listeners and accessiblity attributes.\n   *\n   * @private\n   */\nMaterialSnackbar.prototype.cleanup_ = function () {\n    this.element_.classList.remove(this.cssClasses_.ACTIVE);\n    setTimeout(function () {\n        this.element_.setAttribute('aria-hidden', 'true');\n        this.textElement_.textContent = '';\n        if (!Boolean(this.actionElement_.getAttribute('aria-hidden'))) {\n            this.setActionHidden_(true);\n            this.actionElement_.textContent = '';\n            this.actionElement_.removeEventListener('click', this.actionHandler_);\n        }\n        this.actionHandler_ = undefined;\n        this.message_ = undefined;\n        this.actionText_ = undefined;\n        this.active = false;\n        this.checkQueue_();\n    }.bind(this), this.Constant_.ANIMATION_LENGTH);\n};\n/**\n   * Set the action handler hidden state.\n   *\n   * @param {boolean} value\n   * @private\n   */\nMaterialSnackbar.prototype.setActionHidden_ = function (value) {\n    if (value) {\n        this.actionElement_.setAttribute('aria-hidden', 'true');\n    } else {\n        this.actionElement_.removeAttribute('aria-hidden');\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialSnackbar,\n    classAsString: 'MaterialSnackbar',\n    cssClass: 'mdl-js-snackbar',\n    widget: true\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Spinner MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @param {HTMLElement} element The element that will be upgraded.\n   * @constructor\n   */\nvar MaterialSpinner = function MaterialSpinner(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialSpinner'] = MaterialSpinner;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialSpinner.prototype.Constant_ = { MDL_SPINNER_LAYER_COUNT: 4 };\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialSpinner.prototype.CssClasses_ = {\n    MDL_SPINNER_LAYER: 'mdl-spinner__layer',\n    MDL_SPINNER_CIRCLE_CLIPPER: 'mdl-spinner__circle-clipper',\n    MDL_SPINNER_CIRCLE: 'mdl-spinner__circle',\n    MDL_SPINNER_GAP_PATCH: 'mdl-spinner__gap-patch',\n    MDL_SPINNER_LEFT: 'mdl-spinner__left',\n    MDL_SPINNER_RIGHT: 'mdl-spinner__right'\n};\n/**\n   * Auxiliary method to create a spinner layer.\n   *\n   * @param {number} index Index of the layer to be created.\n   * @public\n   */\nMaterialSpinner.prototype.createLayer = function (index) {\n    var layer = document.createElement('div');\n    layer.classList.add(this.CssClasses_.MDL_SPINNER_LAYER);\n    layer.classList.add(this.CssClasses_.MDL_SPINNER_LAYER + '-' + index);\n    var leftClipper = document.createElement('div');\n    leftClipper.classList.add(this.CssClasses_.MDL_SPINNER_CIRCLE_CLIPPER);\n    leftClipper.classList.add(this.CssClasses_.MDL_SPINNER_LEFT);\n    var gapPatch = document.createElement('div');\n    gapPatch.classList.add(this.CssClasses_.MDL_SPINNER_GAP_PATCH);\n    var rightClipper = document.createElement('div');\n    rightClipper.classList.add(this.CssClasses_.MDL_SPINNER_CIRCLE_CLIPPER);\n    rightClipper.classList.add(this.CssClasses_.MDL_SPINNER_RIGHT);\n    var circleOwners = [\n        leftClipper,\n        gapPatch,\n        rightClipper\n    ];\n    for (var i = 0; i < circleOwners.length; i++) {\n        var circle = document.createElement('div');\n        circle.classList.add(this.CssClasses_.MDL_SPINNER_CIRCLE);\n        circleOwners[i].appendChild(circle);\n    }\n    layer.appendChild(leftClipper);\n    layer.appendChild(gapPatch);\n    layer.appendChild(rightClipper);\n    this.element_.appendChild(layer);\n};\nMaterialSpinner.prototype['createLayer'] = MaterialSpinner.prototype.createLayer;\n/**\n   * Stops the spinner animation.\n   * Public method for users who need to stop the spinner for any reason.\n   *\n   * @public\n   */\nMaterialSpinner.prototype.stop = function () {\n    this.element_.classList.remove('is-active');\n};\nMaterialSpinner.prototype['stop'] = MaterialSpinner.prototype.stop;\n/**\n   * Starts the spinner animation.\n   * Public method for users who need to manually start the spinner for any reason\n   * (instead of just adding the 'is-active' class to their markup).\n   *\n   * @public\n   */\nMaterialSpinner.prototype.start = function () {\n    this.element_.classList.add('is-active');\n};\nMaterialSpinner.prototype['start'] = MaterialSpinner.prototype.start;\n/**\n   * Initialize element.\n   */\nMaterialSpinner.prototype.init = function () {\n    if (this.element_) {\n        for (var i = 1; i <= this.Constant_.MDL_SPINNER_LAYER_COUNT; i++) {\n            this.createLayer(i);\n        }\n        this.element_.classList.add('is-upgraded');\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialSpinner,\n    classAsString: 'MaterialSpinner',\n    cssClass: 'mdl-js-spinner',\n    widget: true\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Checkbox MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialSwitch = function MaterialSwitch(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialSwitch'] = MaterialSwitch;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialSwitch.prototype.Constant_ = { TINY_TIMEOUT: 0.001 };\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialSwitch.prototype.CssClasses_ = {\n    INPUT: 'mdl-switch__input',\n    TRACK: 'mdl-switch__track',\n    THUMB: 'mdl-switch__thumb',\n    FOCUS_HELPER: 'mdl-switch__focus-helper',\n    RIPPLE_EFFECT: 'mdl-js-ripple-effect',\n    RIPPLE_IGNORE_EVENTS: 'mdl-js-ripple-effect--ignore-events',\n    RIPPLE_CONTAINER: 'mdl-switch__ripple-container',\n    RIPPLE_CENTER: 'mdl-ripple--center',\n    RIPPLE: 'mdl-ripple',\n    IS_FOCUSED: 'is-focused',\n    IS_DISABLED: 'is-disabled',\n    IS_CHECKED: 'is-checked'\n};\n/**\n   * Handle change of state.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialSwitch.prototype.onChange_ = function (event) {\n    this.updateClasses_();\n};\n/**\n   * Handle focus of element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialSwitch.prototype.onFocus_ = function (event) {\n    this.element_.classList.add(this.CssClasses_.IS_FOCUSED);\n};\n/**\n   * Handle lost focus of element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialSwitch.prototype.onBlur_ = function (event) {\n    this.element_.classList.remove(this.CssClasses_.IS_FOCUSED);\n};\n/**\n   * Handle mouseup.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialSwitch.prototype.onMouseUp_ = function (event) {\n    this.blur_();\n};\n/**\n   * Handle class updates.\n   *\n   * @private\n   */\nMaterialSwitch.prototype.updateClasses_ = function () {\n    this.checkDisabled();\n    this.checkToggleState();\n};\n/**\n   * Add blur.\n   *\n   * @private\n   */\nMaterialSwitch.prototype.blur_ = function () {\n    // TODO: figure out why there's a focus event being fired after our blur,\n    // so that we can avoid this hack.\n    window.setTimeout(function () {\n        this.inputElement_.blur();\n    }.bind(this), this.Constant_.TINY_TIMEOUT);\n};\n// Public methods.\n/**\n   * Check the components disabled state.\n   *\n   * @public\n   */\nMaterialSwitch.prototype.checkDisabled = function () {\n    if (this.inputElement_.disabled) {\n        this.element_.classList.add(this.CssClasses_.IS_DISABLED);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_DISABLED);\n    }\n};\nMaterialSwitch.prototype['checkDisabled'] = MaterialSwitch.prototype.checkDisabled;\n/**\n   * Check the components toggled state.\n   *\n   * @public\n   */\nMaterialSwitch.prototype.checkToggleState = function () {\n    if (this.inputElement_.checked) {\n        this.element_.classList.add(this.CssClasses_.IS_CHECKED);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_CHECKED);\n    }\n};\nMaterialSwitch.prototype['checkToggleState'] = MaterialSwitch.prototype.checkToggleState;\n/**\n   * Disable switch.\n   *\n   * @public\n   */\nMaterialSwitch.prototype.disable = function () {\n    this.inputElement_.disabled = true;\n    this.updateClasses_();\n};\nMaterialSwitch.prototype['disable'] = MaterialSwitch.prototype.disable;\n/**\n   * Enable switch.\n   *\n   * @public\n   */\nMaterialSwitch.prototype.enable = function () {\n    this.inputElement_.disabled = false;\n    this.updateClasses_();\n};\nMaterialSwitch.prototype['enable'] = MaterialSwitch.prototype.enable;\n/**\n   * Activate switch.\n   *\n   * @public\n   */\nMaterialSwitch.prototype.on = function () {\n    this.inputElement_.checked = true;\n    this.updateClasses_();\n};\nMaterialSwitch.prototype['on'] = MaterialSwitch.prototype.on;\n/**\n   * Deactivate switch.\n   *\n   * @public\n   */\nMaterialSwitch.prototype.off = function () {\n    this.inputElement_.checked = false;\n    this.updateClasses_();\n};\nMaterialSwitch.prototype['off'] = MaterialSwitch.prototype.off;\n/**\n   * Initialize element.\n   */\nMaterialSwitch.prototype.init = function () {\n    if (this.element_) {\n        this.inputElement_ = this.element_.querySelector('.' + this.CssClasses_.INPUT);\n        var track = document.createElement('div');\n        track.classList.add(this.CssClasses_.TRACK);\n        var thumb = document.createElement('div');\n        thumb.classList.add(this.CssClasses_.THUMB);\n        var focusHelper = document.createElement('span');\n        focusHelper.classList.add(this.CssClasses_.FOCUS_HELPER);\n        thumb.appendChild(focusHelper);\n        this.element_.appendChild(track);\n        this.element_.appendChild(thumb);\n        this.boundMouseUpHandler = this.onMouseUp_.bind(this);\n        if (this.element_.classList.contains(this.CssClasses_.RIPPLE_EFFECT)) {\n            this.element_.classList.add(this.CssClasses_.RIPPLE_IGNORE_EVENTS);\n            this.rippleContainerElement_ = document.createElement('span');\n            this.rippleContainerElement_.classList.add(this.CssClasses_.RIPPLE_CONTAINER);\n            this.rippleContainerElement_.classList.add(this.CssClasses_.RIPPLE_EFFECT);\n            this.rippleContainerElement_.classList.add(this.CssClasses_.RIPPLE_CENTER);\n            this.rippleContainerElement_.addEventListener('mouseup', this.boundMouseUpHandler);\n            var ripple = document.createElement('span');\n            ripple.classList.add(this.CssClasses_.RIPPLE);\n            this.rippleContainerElement_.appendChild(ripple);\n            this.element_.appendChild(this.rippleContainerElement_);\n        }\n        this.boundChangeHandler = this.onChange_.bind(this);\n        this.boundFocusHandler = this.onFocus_.bind(this);\n        this.boundBlurHandler = this.onBlur_.bind(this);\n        this.inputElement_.addEventListener('change', this.boundChangeHandler);\n        this.inputElement_.addEventListener('focus', this.boundFocusHandler);\n        this.inputElement_.addEventListener('blur', this.boundBlurHandler);\n        this.element_.addEventListener('mouseup', this.boundMouseUpHandler);\n        this.updateClasses_();\n        this.element_.classList.add('is-upgraded');\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialSwitch,\n    classAsString: 'MaterialSwitch',\n    cssClass: 'mdl-js-switch',\n    widget: true\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Textfield MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialTextfield = function MaterialTextfield(element) {\n    this.element_ = element;\n    this.maxRows = this.Constant_.NO_MAX_ROWS;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialTextfield'] = MaterialTextfield;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialTextfield.prototype.Constant_ = {\n    NO_MAX_ROWS: -1,\n    MAX_ROWS_ATTRIBUTE: 'maxrows'\n};\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialTextfield.prototype.CssClasses_ = {\n    LABEL: 'mdl-textfield__label',\n    INPUT: 'mdl-textfield__input',\n    IS_DIRTY: 'is-dirty',\n    IS_FOCUSED: 'is-focused',\n    IS_DISABLED: 'is-disabled',\n    IS_INVALID: 'is-invalid',\n    IS_UPGRADED: 'is-upgraded',\n    HAS_PLACEHOLDER: 'has-placeholder'\n};\n/**\n   * Handle input being entered.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialTextfield.prototype.onKeyDown_ = function (event) {\n    var currentRowCount = event.target.value.split('\\n').length;\n    if (event.keyCode === 13) {\n        if (currentRowCount >= this.maxRows) {\n            event.preventDefault();\n        }\n    }\n};\n/**\n   * Handle focus.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialTextfield.prototype.onFocus_ = function (event) {\n    this.element_.classList.add(this.CssClasses_.IS_FOCUSED);\n};\n/**\n   * Handle lost focus.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialTextfield.prototype.onBlur_ = function (event) {\n    this.element_.classList.remove(this.CssClasses_.IS_FOCUSED);\n};\n/**\n   * Handle reset event from out side.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialTextfield.prototype.onReset_ = function (event) {\n    this.updateClasses_();\n};\n/**\n   * Handle class updates.\n   *\n   * @private\n   */\nMaterialTextfield.prototype.updateClasses_ = function () {\n    this.checkDisabled();\n    this.checkValidity();\n    this.checkDirty();\n    this.checkFocus();\n};\n// Public methods.\n/**\n   * Check the disabled state and update field accordingly.\n   *\n   * @public\n   */\nMaterialTextfield.prototype.checkDisabled = function () {\n    if (this.input_.disabled) {\n        this.element_.classList.add(this.CssClasses_.IS_DISABLED);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_DISABLED);\n    }\n};\nMaterialTextfield.prototype['checkDisabled'] = MaterialTextfield.prototype.checkDisabled;\n/**\n  * Check the focus state and update field accordingly.\n  *\n  * @public\n  */\nMaterialTextfield.prototype.checkFocus = function () {\n    if (Boolean(this.element_.querySelector(':focus'))) {\n        this.element_.classList.add(this.CssClasses_.IS_FOCUSED);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_FOCUSED);\n    }\n};\nMaterialTextfield.prototype['checkFocus'] = MaterialTextfield.prototype.checkFocus;\n/**\n   * Check the validity state and update field accordingly.\n   *\n   * @public\n   */\nMaterialTextfield.prototype.checkValidity = function () {\n    if (this.input_.validity) {\n        if (this.input_.validity.valid) {\n            this.element_.classList.remove(this.CssClasses_.IS_INVALID);\n        } else {\n            this.element_.classList.add(this.CssClasses_.IS_INVALID);\n        }\n    }\n};\nMaterialTextfield.prototype['checkValidity'] = MaterialTextfield.prototype.checkValidity;\n/**\n   * Check the dirty state and update field accordingly.\n   *\n   * @public\n   */\nMaterialTextfield.prototype.checkDirty = function () {\n    if (this.input_.value && this.input_.value.length > 0) {\n        this.element_.classList.add(this.CssClasses_.IS_DIRTY);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_DIRTY);\n    }\n};\nMaterialTextfield.prototype['checkDirty'] = MaterialTextfield.prototype.checkDirty;\n/**\n   * Disable text field.\n   *\n   * @public\n   */\nMaterialTextfield.prototype.disable = function () {\n    this.input_.disabled = true;\n    this.updateClasses_();\n};\nMaterialTextfield.prototype['disable'] = MaterialTextfield.prototype.disable;\n/**\n   * Enable text field.\n   *\n   * @public\n   */\nMaterialTextfield.prototype.enable = function () {\n    this.input_.disabled = false;\n    this.updateClasses_();\n};\nMaterialTextfield.prototype['enable'] = MaterialTextfield.prototype.enable;\n/**\n   * Update text field value.\n   *\n   * @param {string} value The value to which to set the control (optional).\n   * @public\n   */\nMaterialTextfield.prototype.change = function (value) {\n    this.input_.value = value || '';\n    this.updateClasses_();\n};\nMaterialTextfield.prototype['change'] = MaterialTextfield.prototype.change;\n/**\n   * Initialize element.\n   */\nMaterialTextfield.prototype.init = function () {\n    if (this.element_) {\n        this.label_ = this.element_.querySelector('.' + this.CssClasses_.LABEL);\n        this.input_ = this.element_.querySelector('.' + this.CssClasses_.INPUT);\n        if (this.input_) {\n            if (this.input_.hasAttribute(this.Constant_.MAX_ROWS_ATTRIBUTE)) {\n                this.maxRows = parseInt(this.input_.getAttribute(this.Constant_.MAX_ROWS_ATTRIBUTE), 10);\n                if (isNaN(this.maxRows)) {\n                    this.maxRows = this.Constant_.NO_MAX_ROWS;\n                }\n            }\n            if (this.input_.hasAttribute('placeholder')) {\n                this.element_.classList.add(this.CssClasses_.HAS_PLACEHOLDER);\n            }\n            this.boundUpdateClassesHandler = this.updateClasses_.bind(this);\n            this.boundFocusHandler = this.onFocus_.bind(this);\n            this.boundBlurHandler = this.onBlur_.bind(this);\n            this.boundResetHandler = this.onReset_.bind(this);\n            this.input_.addEventListener('input', this.boundUpdateClassesHandler);\n            this.input_.addEventListener('focus', this.boundFocusHandler);\n            this.input_.addEventListener('blur', this.boundBlurHandler);\n            this.input_.addEventListener('reset', this.boundResetHandler);\n            if (this.maxRows !== this.Constant_.NO_MAX_ROWS) {\n                // TODO: This should handle pasting multi line text.\n                // Currently doesn't.\n                this.boundKeyDownHandler = this.onKeyDown_.bind(this);\n                this.input_.addEventListener('keydown', this.boundKeyDownHandler);\n            }\n            var invalid = this.element_.classList.contains(this.CssClasses_.IS_INVALID);\n            this.updateClasses_();\n            this.element_.classList.add(this.CssClasses_.IS_UPGRADED);\n            if (invalid) {\n                this.element_.classList.add(this.CssClasses_.IS_INVALID);\n            }\n            if (this.input_.hasAttribute('autofocus')) {\n                this.element_.focus();\n                this.checkFocus();\n            }\n        }\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialTextfield,\n    classAsString: 'MaterialTextfield',\n    cssClass: 'mdl-js-textfield',\n    widget: true\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Tooltip MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialTooltip = function MaterialTooltip(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialTooltip'] = MaterialTooltip;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialTooltip.prototype.Constant_ = {};\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialTooltip.prototype.CssClasses_ = {\n    IS_ACTIVE: 'is-active',\n    BOTTOM: 'mdl-tooltip--bottom',\n    LEFT: 'mdl-tooltip--left',\n    RIGHT: 'mdl-tooltip--right',\n    TOP: 'mdl-tooltip--top'\n};\n/**\n   * Handle mouseenter for tooltip.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialTooltip.prototype.handleMouseEnter_ = function (event) {\n    var props = event.target.getBoundingClientRect();\n    var left = props.left + props.width / 2;\n    var top = props.top + props.height / 2;\n    var marginLeft = -1 * (this.element_.offsetWidth / 2);\n    var marginTop = -1 * (this.element_.offsetHeight / 2);\n    if (this.element_.classList.contains(this.CssClasses_.LEFT) || this.element_.classList.contains(this.CssClasses_.RIGHT)) {\n        left = props.width / 2;\n        if (top + marginTop < 0) {\n            this.element_.style.top = '0';\n            this.element_.style.marginTop = '0';\n        } else {\n            this.element_.style.top = top + 'px';\n            this.element_.style.marginTop = marginTop + 'px';\n        }\n    } else {\n        if (left + marginLeft < 0) {\n            this.element_.style.left = '0';\n            this.element_.style.marginLeft = '0';\n        } else {\n            this.element_.style.left = left + 'px';\n            this.element_.style.marginLeft = marginLeft + 'px';\n        }\n    }\n    if (this.element_.classList.contains(this.CssClasses_.TOP)) {\n        this.element_.style.top = props.top - this.element_.offsetHeight - 10 + 'px';\n    } else if (this.element_.classList.contains(this.CssClasses_.RIGHT)) {\n        this.element_.style.left = props.left + props.width + 10 + 'px';\n    } else if (this.element_.classList.contains(this.CssClasses_.LEFT)) {\n        this.element_.style.left = props.left - this.element_.offsetWidth - 10 + 'px';\n    } else {\n        this.element_.style.top = props.top + props.height + 10 + 'px';\n    }\n    this.element_.classList.add(this.CssClasses_.IS_ACTIVE);\n};\n/**\n   * Hide tooltip on mouseleave or scroll\n   *\n   * @private\n   */\nMaterialTooltip.prototype.hideTooltip_ = function () {\n    this.element_.classList.remove(this.CssClasses_.IS_ACTIVE);\n};\n/**\n   * Initialize element.\n   */\nMaterialTooltip.prototype.init = function () {\n    if (this.element_) {\n        var forElId = this.element_.getAttribute('for') || this.element_.getAttribute('data-mdl-for');\n        if (forElId) {\n            this.forElement_ = document.getElementById(forElId);\n        }\n        if (this.forElement_) {\n            // It's left here because it prevents accidental text selection on Android\n            if (!this.forElement_.hasAttribute('tabindex')) {\n                this.forElement_.setAttribute('tabindex', '0');\n            }\n            this.boundMouseEnterHandler = this.handleMouseEnter_.bind(this);\n            this.boundMouseLeaveAndScrollHandler = this.hideTooltip_.bind(this);\n            this.forElement_.addEventListener('mouseenter', this.boundMouseEnterHandler, false);\n            this.forElement_.addEventListener('touchend', this.boundMouseEnterHandler, false);\n            this.forElement_.addEventListener('mouseleave', this.boundMouseLeaveAndScrollHandler, false);\n            window.addEventListener('scroll', this.boundMouseLeaveAndScrollHandler, true);\n            window.addEventListener('touchstart', this.boundMouseLeaveAndScrollHandler);\n        }\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialTooltip,\n    classAsString: 'MaterialTooltip',\n    cssClass: 'mdl-tooltip'\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Data Table Card MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {Element} element The element that will be upgraded.\n   */\nvar MaterialDataTable = function MaterialDataTable(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialDataTable'] = MaterialDataTable;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialDataTable.prototype.Constant_ = {};\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialDataTable.prototype.CssClasses_ = {\n    DATA_TABLE: 'mdl-data-table',\n    SELECTABLE: 'mdl-data-table--selectable',\n    SELECT_ELEMENT: 'mdl-data-table__select',\n    IS_SELECTED: 'is-selected',\n    IS_UPGRADED: 'is-upgraded'\n};\n/**\n   * Generates and returns a function that toggles the selection state of a\n   * single row (or multiple rows).\n   *\n   * @param {Element} checkbox Checkbox that toggles the selection state.\n   * @param {Element} row Row to toggle when checkbox changes.\n   * @param {(Array<Object>|NodeList)=} opt_rows Rows to toggle when checkbox changes.\n   * @private\n   */\nMaterialDataTable.prototype.selectRow_ = function (checkbox, row, opt_rows) {\n    if (row) {\n        return function () {\n            if (checkbox.checked) {\n                row.classList.add(this.CssClasses_.IS_SELECTED);\n            } else {\n                row.classList.remove(this.CssClasses_.IS_SELECTED);\n            }\n        }.bind(this);\n    }\n    if (opt_rows) {\n        return function () {\n            var i;\n            var el;\n            if (checkbox.checked) {\n                for (i = 0; i < opt_rows.length; i++) {\n                    el = opt_rows[i].querySelector('td').querySelector('.mdl-checkbox');\n                    el['MaterialCheckbox'].check();\n                    opt_rows[i].classList.add(this.CssClasses_.IS_SELECTED);\n                }\n            } else {\n                for (i = 0; i < opt_rows.length; i++) {\n                    el = opt_rows[i].querySelector('td').querySelector('.mdl-checkbox');\n                    el['MaterialCheckbox'].uncheck();\n                    opt_rows[i].classList.remove(this.CssClasses_.IS_SELECTED);\n                }\n            }\n        }.bind(this);\n    }\n};\n/**\n   * Creates a checkbox for a single or or multiple rows and hooks up the\n   * event handling.\n   *\n   * @param {Element} row Row to toggle when checkbox changes.\n   * @param {(Array<Object>|NodeList)=} opt_rows Rows to toggle when checkbox changes.\n   * @private\n   */\nMaterialDataTable.prototype.createCheckbox_ = function (row, opt_rows) {\n    var label = document.createElement('label');\n    var labelClasses = [\n        'mdl-checkbox',\n        'mdl-js-checkbox',\n        'mdl-js-ripple-effect',\n        this.CssClasses_.SELECT_ELEMENT\n    ];\n    label.className = labelClasses.join(' ');\n    var checkbox = document.createElement('input');\n    checkbox.type = 'checkbox';\n    checkbox.classList.add('mdl-checkbox__input');\n    if (row) {\n        checkbox.checked = row.classList.contains(this.CssClasses_.IS_SELECTED);\n        checkbox.addEventListener('change', this.selectRow_(checkbox, row));\n    } else if (opt_rows) {\n        checkbox.addEventListener('change', this.selectRow_(checkbox, null, opt_rows));\n    }\n    label.appendChild(checkbox);\n    componentHandler.upgradeElement(label, 'MaterialCheckbox');\n    return label;\n};\n/**\n   * Initialize element.\n   */\nMaterialDataTable.prototype.init = function () {\n    if (this.element_) {\n        var firstHeader = this.element_.querySelector('th');\n        var bodyRows = Array.prototype.slice.call(this.element_.querySelectorAll('tbody tr'));\n        var footRows = Array.prototype.slice.call(this.element_.querySelectorAll('tfoot tr'));\n        var rows = bodyRows.concat(footRows);\n        if (this.element_.classList.contains(this.CssClasses_.SELECTABLE)) {\n            var th = document.createElement('th');\n            var headerCheckbox = this.createCheckbox_(null, rows);\n            th.appendChild(headerCheckbox);\n            firstHeader.parentElement.insertBefore(th, firstHeader);\n            for (var i = 0; i < rows.length; i++) {\n                var firstCell = rows[i].querySelector('td');\n                if (firstCell) {\n                    var td = document.createElement('td');\n                    if (rows[i].parentNode.nodeName.toUpperCase() === 'TBODY') {\n                        var rowCheckbox = this.createCheckbox_(rows[i]);\n                        td.appendChild(rowCheckbox);\n                    }\n                    rows[i].insertBefore(td, firstCell);\n                }\n            }\n            this.element_.classList.add(this.CssClasses_.IS_UPGRADED);\n        }\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialDataTable,\n    classAsString: 'MaterialDataTable',\n    cssClass: 'mdl-js-data-table'\n});","// https://github.com/zloirock/core-js/issues/86#issuecomment-115759028\nvar global = module.exports = typeof window != 'undefined' && window.Math == Math\n  ? window : typeof self != 'undefined' && self.Math == Math ? self\n  // eslint-disable-next-line no-new-func\n  : Function('return this')();\nif (typeof __g == 'number') __g = global; // eslint-disable-line no-undef\n","var hasOwnProperty = {}.hasOwnProperty;\nmodule.exports = function (it, key) {\n  return hasOwnProperty.call(it, key);\n};\n","module.exports = function (exec) {\n  try {\n    return !!exec();\n  } catch (e) {\n    return true;\n  }\n};\n","// Thank's IE8 for his funny defineProperty\nmodule.exports = !require('./_fails')(function () {\n  return Object.defineProperty({}, 'a', { get: function () { return 7; } }).a != 7;\n});\n","var core = module.exports = { version: '2.6.9' };\nif (typeof __e == 'number') __e = core; // eslint-disable-line no-undef\n","module.exports = function (it) {\n  return typeof it === 'object' ? it !== null : typeof it === 'function';\n};\n","var isObject = require('./_is-object');\nmodule.exports = function (it) {\n  if (!isObject(it)) throw TypeError(it + ' is not an object!');\n  return it;\n};\n","var isObject = require('./_is-object');\nvar document = require('./_global').document;\n// typeof document.createElement is 'object' in old IE\nvar is = isObject(document) && isObject(document.createElement);\nmodule.exports = function (it) {\n  return is ? document.createElement(it) : {};\n};\n","module.exports = !require('./_descriptors') && !require('./_fails')(function () {\n  return Object.defineProperty(require('./_dom-create')('div'), 'a', { get: function () { return 7; } }).a != 7;\n});\n","// 7.1.1 ToPrimitive(input [, PreferredType])\nvar isObject = require('./_is-object');\n// instead of the ES6 spec version, we didn't implement @@toPrimitive case\n// and the second argument - flag - preferred type is a string\nmodule.exports = function (it, S) {\n  if (!isObject(it)) return it;\n  var fn, val;\n  if (S && typeof (fn = it.toString) == 'function' && !isObject(val = fn.call(it))) return val;\n  if (typeof (fn = it.valueOf) == 'function' && !isObject(val = fn.call(it))) return val;\n  if (!S && typeof (fn = it.toString) == 'function' && !isObject(val = fn.call(it))) return val;\n  throw TypeError(\"Can't convert object to primitive value\");\n};\n","var anObject = require('./_an-object');\nvar IE8_DOM_DEFINE = require('./_ie8-dom-define');\nvar toPrimitive = require('./_to-primitive');\nvar dP = Object.defineProperty;\n\nexports.f = require('./_descriptors') ? Object.defineProperty : function defineProperty(O, P, Attributes) {\n  anObject(O);\n  P = toPrimitive(P, true);\n  anObject(Attributes);\n  if (IE8_DOM_DEFINE) try {\n    return dP(O, P, Attributes);\n  } catch (e) { /* empty */ }\n  if ('get' in Attributes || 'set' in Attributes) throw TypeError('Accessors not supported!');\n  if ('value' in Attributes) O[P] = Attributes.value;\n  return O;\n};\n","module.exports = function (bitmap, value) {\n  return {\n    enumerable: !(bitmap & 1),\n    configurable: !(bitmap & 2),\n    writable: !(bitmap & 4),\n    value: value\n  };\n};\n","var dP = require('./_object-dp');\nvar createDesc = require('./_property-desc');\nmodule.exports = require('./_descriptors') ? function (object, key, value) {\n  return dP.f(object, key, createDesc(1, value));\n} : function (object, key, value) {\n  object[key] = value;\n  return object;\n};\n","var id = 0;\nvar px = Math.random();\nmodule.exports = function (key) {\n  return 'Symbol('.concat(key === undefined ? '' : key, ')_', (++id + px).toString(36));\n};\n","module.exports = false;\n","var core = require('./_core');\nvar global = require('./_global');\nvar SHARED = '__core-js_shared__';\nvar store = global[SHARED] || (global[SHARED] = {});\n\n(module.exports = function (key, value) {\n  return store[key] || (store[key] = value !== undefined ? value : {});\n})('versions', []).push({\n  version: core.version,\n  mode: require('./_library') ? 'pure' : 'global',\n  copyright: '© 2019 Denis Pushkarev (zloirock.ru)'\n});\n","module.exports = require('./_shared')('native-function-to-string', Function.toString);\n","var global = require('./_global');\nvar hide = require('./_hide');\nvar has = require('./_has');\nvar SRC = require('./_uid')('src');\nvar $toString = require('./_function-to-string');\nvar TO_STRING = 'toString';\nvar TPL = ('' + $toString).split(TO_STRING);\n\nrequire('./_core').inspectSource = function (it) {\n  return $toString.call(it);\n};\n\n(module.exports = function (O, key, val, safe) {\n  var isFunction = typeof val == 'function';\n  if (isFunction) has(val, 'name') || hide(val, 'name', key);\n  if (O[key] === val) return;\n  if (isFunction) has(val, SRC) || hide(val, SRC, O[key] ? '' + O[key] : TPL.join(String(key)));\n  if (O === global) {\n    O[key] = val;\n  } else if (!safe) {\n    delete O[key];\n    hide(O, key, val);\n  } else if (O[key]) {\n    O[key] = val;\n  } else {\n    hide(O, key, val);\n  }\n// add fake Function#toString for correct work wrapped methods / constructors with methods like LoDash isNative\n})(Function.prototype, TO_STRING, function toString() {\n  return typeof this == 'function' && this[SRC] || $toString.call(this);\n});\n","module.exports = function (it) {\n  if (typeof it != 'function') throw TypeError(it + ' is not a function!');\n  return it;\n};\n","// optional / simple context binding\nvar aFunction = require('./_a-function');\nmodule.exports = function (fn, that, length) {\n  aFunction(fn);\n  if (that === undefined) return fn;\n  switch (length) {\n    case 1: return function (a) {\n      return fn.call(that, a);\n    };\n    case 2: return function (a, b) {\n      return fn.call(that, a, b);\n    };\n    case 3: return function (a, b, c) {\n      return fn.call(that, a, b, c);\n    };\n  }\n  return function (/* ...args */) {\n    return fn.apply(that, arguments);\n  };\n};\n","var global = require('./_global');\nvar core = require('./_core');\nvar hide = require('./_hide');\nvar redefine = require('./_redefine');\nvar ctx = require('./_ctx');\nvar PROTOTYPE = 'prototype';\n\nvar $export = function (type, name, source) {\n  var IS_FORCED = type & $export.F;\n  var IS_GLOBAL = type & $export.G;\n  var IS_STATIC = type & $export.S;\n  var IS_PROTO = type & $export.P;\n  var IS_BIND = type & $export.B;\n  var target = IS_GLOBAL ? global : IS_STATIC ? global[name] || (global[name] = {}) : (global[name] || {})[PROTOTYPE];\n  var exports = IS_GLOBAL ? core : core[name] || (core[name] = {});\n  var expProto = exports[PROTOTYPE] || (exports[PROTOTYPE] = {});\n  var key, own, out, exp;\n  if (IS_GLOBAL) source = name;\n  for (key in source) {\n    // contains in native\n    own = !IS_FORCED && target && target[key] !== undefined;\n    // export native or passed\n    out = (own ? target : source)[key];\n    // bind timers to global for call from export context\n    exp = IS_BIND && own ? ctx(out, global) : IS_PROTO && typeof out == 'function' ? ctx(Function.call, out) : out;\n    // extend global\n    if (target) redefine(target, key, out, type & $export.U);\n    // export\n    if (exports[key] != out) hide(exports, key, exp);\n    if (IS_PROTO && expProto[key] != out) expProto[key] = out;\n  }\n};\nglobal.core = core;\n// type bitmap\n$export.F = 1;   // forced\n$export.G = 2;   // global\n$export.S = 4;   // static\n$export.P = 8;   // proto\n$export.B = 16;  // bind\n$export.W = 32;  // wrap\n$export.U = 64;  // safe\n$export.R = 128; // real proto method for `library`\nmodule.exports = $export;\n","var META = require('./_uid')('meta');\nvar isObject = require('./_is-object');\nvar has = require('./_has');\nvar setDesc = require('./_object-dp').f;\nvar id = 0;\nvar isExtensible = Object.isExtensible || function () {\n  return true;\n};\nvar FREEZE = !require('./_fails')(function () {\n  return isExtensible(Object.preventExtensions({}));\n});\nvar setMeta = function (it) {\n  setDesc(it, META, { value: {\n    i: 'O' + ++id, // object ID\n    w: {}          // weak collections IDs\n  } });\n};\nvar fastKey = function (it, create) {\n  // return primitive with prefix\n  if (!isObject(it)) return typeof it == 'symbol' ? it : (typeof it == 'string' ? 'S' : 'P') + it;\n  if (!has(it, META)) {\n    // can't set metadata to uncaught frozen object\n    if (!isExtensible(it)) return 'F';\n    // not necessary to add metadata\n    if (!create) return 'E';\n    // add missing metadata\n    setMeta(it);\n  // return object ID\n  } return it[META].i;\n};\nvar getWeak = function (it, create) {\n  if (!has(it, META)) {\n    // can't set metadata to uncaught frozen object\n    if (!isExtensible(it)) return true;\n    // not necessary to add metadata\n    if (!create) return false;\n    // add missing metadata\n    setMeta(it);\n  // return hash weak collections IDs\n  } return it[META].w;\n};\n// add metadata on freeze-family methods calling\nvar onFreeze = function (it) {\n  if (FREEZE && meta.NEED && isExtensible(it) && !has(it, META)) setMeta(it);\n  return it;\n};\nvar meta = module.exports = {\n  KEY: META,\n  NEED: false,\n  fastKey: fastKey,\n  getWeak: getWeak,\n  onFreeze: onFreeze\n};\n","var store = require('./_shared')('wks');\nvar uid = require('./_uid');\nvar Symbol = require('./_global').Symbol;\nvar USE_SYMBOL = typeof Symbol == 'function';\n\nvar $exports = module.exports = function (name) {\n  return store[name] || (store[name] =\n    USE_SYMBOL && Symbol[name] || (USE_SYMBOL ? Symbol : uid)('Symbol.' + name));\n};\n\n$exports.store = store;\n","var def = require('./_object-dp').f;\nvar has = require('./_has');\nvar TAG = require('./_wks')('toStringTag');\n\nmodule.exports = function (it, tag, stat) {\n  if (it && !has(it = stat ? it : it.prototype, TAG)) def(it, TAG, { configurable: true, value: tag });\n};\n","exports.f = require('./_wks');\n","var global = require('./_global');\nvar core = require('./_core');\nvar LIBRARY = require('./_library');\nvar wksExt = require('./_wks-ext');\nvar defineProperty = require('./_object-dp').f;\nmodule.exports = function (name) {\n  var $Symbol = core.Symbol || (core.Symbol = LIBRARY ? {} : global.Symbol || {});\n  if (name.charAt(0) != '_' && !(name in $Symbol)) defineProperty($Symbol, name, { value: wksExt.f(name) });\n};\n","var toString = {}.toString;\n\nmodule.exports = function (it) {\n  return toString.call(it).slice(8, -1);\n};\n","// fallback for non-array-like ES3 and non-enumerable old V8 strings\nvar cof = require('./_cof');\n// eslint-disable-next-line no-prototype-builtins\nmodule.exports = Object('z').propertyIsEnumerable(0) ? Object : function (it) {\n  return cof(it) == 'String' ? it.split('') : Object(it);\n};\n","// 7.2.1 RequireObjectCoercible(argument)\nmodule.exports = function (it) {\n  if (it == undefined) throw TypeError(\"Can't call method on  \" + it);\n  return it;\n};\n","// to indexed object, toObject with fallback for non-array-like ES3 strings\nvar IObject = require('./_iobject');\nvar defined = require('./_defined');\nmodule.exports = function (it) {\n  return IObject(defined(it));\n};\n","// 7.1.4 ToInteger\nvar ceil = Math.ceil;\nvar floor = Math.floor;\nmodule.exports = function (it) {\n  return isNaN(it = +it) ? 0 : (it > 0 ? floor : ceil)(it);\n};\n","// 7.1.15 ToLength\nvar toInteger = require('./_to-integer');\nvar min = Math.min;\nmodule.exports = function (it) {\n  return it > 0 ? min(toInteger(it), 0x1fffffffffffff) : 0; // pow(2, 53) - 1 == 9007199254740991\n};\n","var toInteger = require('./_to-integer');\nvar max = Math.max;\nvar min = Math.min;\nmodule.exports = function (index, length) {\n  index = toInteger(index);\n  return index < 0 ? max(index + length, 0) : min(index, length);\n};\n","// false -> Array#indexOf\n// true  -> Array#includes\nvar toIObject = require('./_to-iobject');\nvar toLength = require('./_to-length');\nvar toAbsoluteIndex = require('./_to-absolute-index');\nmodule.exports = function (IS_INCLUDES) {\n  return function ($this, el, fromIndex) {\n    var O = toIObject($this);\n    var length = toLength(O.length);\n    var index = toAbsoluteIndex(fromIndex, length);\n    var value;\n    // Array#includes uses SameValueZero equality algorithm\n    // eslint-disable-next-line no-self-compare\n    if (IS_INCLUDES && el != el) while (length > index) {\n      value = O[index++];\n      // eslint-disable-next-line no-self-compare\n      if (value != value) return true;\n    // Array#indexOf ignores holes, Array#includes - not\n    } else for (;length > index; index++) if (IS_INCLUDES || index in O) {\n      if (O[index] === el) return IS_INCLUDES || index || 0;\n    } return !IS_INCLUDES && -1;\n  };\n};\n","var shared = require('./_shared')('keys');\nvar uid = require('./_uid');\nmodule.exports = function (key) {\n  return shared[key] || (shared[key] = uid(key));\n};\n","var has = require('./_has');\nvar toIObject = require('./_to-iobject');\nvar arrayIndexOf = require('./_array-includes')(false);\nvar IE_PROTO = require('./_shared-key')('IE_PROTO');\n\nmodule.exports = function (object, names) {\n  var O = toIObject(object);\n  var i = 0;\n  var result = [];\n  var key;\n  for (key in O) if (key != IE_PROTO) has(O, key) && result.push(key);\n  // Don't enum bug & hidden keys\n  while (names.length > i) if (has(O, key = names[i++])) {\n    ~arrayIndexOf(result, key) || result.push(key);\n  }\n  return result;\n};\n","// IE 8- don't enum bug keys\nmodule.exports = (\n  'constructor,hasOwnProperty,isPrototypeOf,propertyIsEnumerable,toLocaleString,toString,valueOf'\n).split(',');\n","// 19.1.2.14 / 15.2.3.14 Object.keys(O)\nvar $keys = require('./_object-keys-internal');\nvar enumBugKeys = require('./_enum-bug-keys');\n\nmodule.exports = Object.keys || function keys(O) {\n  return $keys(O, enumBugKeys);\n};\n","exports.f = Object.getOwnPropertySymbols;\n","exports.f = {}.propertyIsEnumerable;\n","// all enumerable object keys, includes symbols\nvar getKeys = require('./_object-keys');\nvar gOPS = require('./_object-gops');\nvar pIE = require('./_object-pie');\nmodule.exports = function (it) {\n  var result = getKeys(it);\n  var getSymbols = gOPS.f;\n  if (getSymbols) {\n    var symbols = getSymbols(it);\n    var isEnum = pIE.f;\n    var i = 0;\n    var key;\n    while (symbols.length > i) if (isEnum.call(it, key = symbols[i++])) result.push(key);\n  } return result;\n};\n","// 7.2.2 IsArray(argument)\nvar cof = require('./_cof');\nmodule.exports = Array.isArray || function isArray(arg) {\n  return cof(arg) == 'Array';\n};\n","// 7.1.13 ToObject(argument)\nvar defined = require('./_defined');\nmodule.exports = function (it) {\n  return Object(defined(it));\n};\n","var dP = require('./_object-dp');\nvar anObject = require('./_an-object');\nvar getKeys = require('./_object-keys');\n\nmodule.exports = require('./_descriptors') ? Object.defineProperties : function defineProperties(O, Properties) {\n  anObject(O);\n  var keys = getKeys(Properties);\n  var length = keys.length;\n  var i = 0;\n  var P;\n  while (length > i) dP.f(O, P = keys[i++], Properties[P]);\n  return O;\n};\n","var document = require('./_global').document;\nmodule.exports = document && document.documentElement;\n","// 19.1.2.2 / 15.2.3.5 Object.create(O [, Properties])\nvar anObject = require('./_an-object');\nvar dPs = require('./_object-dps');\nvar enumBugKeys = require('./_enum-bug-keys');\nvar IE_PROTO = require('./_shared-key')('IE_PROTO');\nvar Empty = function () { /* empty */ };\nvar PROTOTYPE = 'prototype';\n\n// Create object with fake `null` prototype: use iframe Object with cleared prototype\nvar createDict = function () {\n  // Thrash, waste and sodomy: IE GC bug\n  var iframe = require('./_dom-create')('iframe');\n  var i = enumBugKeys.length;\n  var lt = '<';\n  var gt = '>';\n  var iframeDocument;\n  iframe.style.display = 'none';\n  require('./_html').appendChild(iframe);\n  iframe.src = 'javascript:'; // eslint-disable-line no-script-url\n  // createDict = iframe.contentWindow.Object;\n  // html.removeChild(iframe);\n  iframeDocument = iframe.contentWindow.document;\n  iframeDocument.open();\n  iframeDocument.write(lt + 'script' + gt + 'document.F=Object' + lt + '/script' + gt);\n  iframeDocument.close();\n  createDict = iframeDocument.F;\n  while (i--) delete createDict[PROTOTYPE][enumBugKeys[i]];\n  return createDict();\n};\n\nmodule.exports = Object.create || function create(O, Properties) {\n  var result;\n  if (O !== null) {\n    Empty[PROTOTYPE] = anObject(O);\n    result = new Empty();\n    Empty[PROTOTYPE] = null;\n    // add \"__proto__\" for Object.getPrototypeOf polyfill\n    result[IE_PROTO] = O;\n  } else result = createDict();\n  return Properties === undefined ? result : dPs(result, Properties);\n};\n","// 19.1.2.7 / 15.2.3.4 Object.getOwnPropertyNames(O)\nvar $keys = require('./_object-keys-internal');\nvar hiddenKeys = require('./_enum-bug-keys').concat('length', 'prototype');\n\nexports.f = Object.getOwnPropertyNames || function getOwnPropertyNames(O) {\n  return $keys(O, hiddenKeys);\n};\n","// fallback for IE11 buggy Object.getOwnPropertyNames with iframe and window\nvar toIObject = require('./_to-iobject');\nvar gOPN = require('./_object-gopn').f;\nvar toString = {}.toString;\n\nvar windowNames = typeof window == 'object' && window && Object.getOwnPropertyNames\n  ? Object.getOwnPropertyNames(window) : [];\n\nvar getWindowNames = function (it) {\n  try {\n    return gOPN(it);\n  } catch (e) {\n    return windowNames.slice();\n  }\n};\n\nmodule.exports.f = function getOwnPropertyNames(it) {\n  return windowNames && toString.call(it) == '[object Window]' ? getWindowNames(it) : gOPN(toIObject(it));\n};\n","var pIE = require('./_object-pie');\nvar createDesc = require('./_property-desc');\nvar toIObject = require('./_to-iobject');\nvar toPrimitive = require('./_to-primitive');\nvar has = require('./_has');\nvar IE8_DOM_DEFINE = require('./_ie8-dom-define');\nvar gOPD = Object.getOwnPropertyDescriptor;\n\nexports.f = require('./_descriptors') ? gOPD : function getOwnPropertyDescriptor(O, P) {\n  O = toIObject(O);\n  P = toPrimitive(P, true);\n  if (IE8_DOM_DEFINE) try {\n    return gOPD(O, P);\n  } catch (e) { /* empty */ }\n  if (has(O, P)) return createDesc(!pIE.f.call(O, P), O[P]);\n};\n","'use strict';\n// ECMAScript 6 symbols shim\nvar global = require('./_global');\nvar has = require('./_has');\nvar DESCRIPTORS = require('./_descriptors');\nvar $export = require('./_export');\nvar redefine = require('./_redefine');\nvar META = require('./_meta').KEY;\nvar $fails = require('./_fails');\nvar shared = require('./_shared');\nvar setToStringTag = require('./_set-to-string-tag');\nvar uid = require('./_uid');\nvar wks = require('./_wks');\nvar wksExt = require('./_wks-ext');\nvar wksDefine = require('./_wks-define');\nvar enumKeys = require('./_enum-keys');\nvar isArray = require('./_is-array');\nvar anObject = require('./_an-object');\nvar isObject = require('./_is-object');\nvar toObject = require('./_to-object');\nvar toIObject = require('./_to-iobject');\nvar toPrimitive = require('./_to-primitive');\nvar createDesc = require('./_property-desc');\nvar _create = require('./_object-create');\nvar gOPNExt = require('./_object-gopn-ext');\nvar $GOPD = require('./_object-gopd');\nvar $GOPS = require('./_object-gops');\nvar $DP = require('./_object-dp');\nvar $keys = require('./_object-keys');\nvar gOPD = $GOPD.f;\nvar dP = $DP.f;\nvar gOPN = gOPNExt.f;\nvar $Symbol = global.Symbol;\nvar $JSON = global.JSON;\nvar _stringify = $JSON && $JSON.stringify;\nvar PROTOTYPE = 'prototype';\nvar HIDDEN = wks('_hidden');\nvar TO_PRIMITIVE = wks('toPrimitive');\nvar isEnum = {}.propertyIsEnumerable;\nvar SymbolRegistry = shared('symbol-registry');\nvar AllSymbols = shared('symbols');\nvar OPSymbols = shared('op-symbols');\nvar ObjectProto = Object[PROTOTYPE];\nvar USE_NATIVE = typeof $Symbol == 'function' && !!$GOPS.f;\nvar QObject = global.QObject;\n// Don't use setters in Qt Script, https://github.com/zloirock/core-js/issues/173\nvar setter = !QObject || !QObject[PROTOTYPE] || !QObject[PROTOTYPE].findChild;\n\n// fallback for old Android, https://code.google.com/p/v8/issues/detail?id=687\nvar setSymbolDesc = DESCRIPTORS && $fails(function () {\n  return _create(dP({}, 'a', {\n    get: function () { return dP(this, 'a', { value: 7 }).a; }\n  })).a != 7;\n}) ? function (it, key, D) {\n  var protoDesc = gOPD(ObjectProto, key);\n  if (protoDesc) delete ObjectProto[key];\n  dP(it, key, D);\n  if (protoDesc && it !== ObjectProto) dP(ObjectProto, key, protoDesc);\n} : dP;\n\nvar wrap = function (tag) {\n  var sym = AllSymbols[tag] = _create($Symbol[PROTOTYPE]);\n  sym._k = tag;\n  return sym;\n};\n\nvar isSymbol = USE_NATIVE && typeof $Symbol.iterator == 'symbol' ? function (it) {\n  return typeof it == 'symbol';\n} : function (it) {\n  return it instanceof $Symbol;\n};\n\nvar $defineProperty = function defineProperty(it, key, D) {\n  if (it === ObjectProto) $defineProperty(OPSymbols, key, D);\n  anObject(it);\n  key = toPrimitive(key, true);\n  anObject(D);\n  if (has(AllSymbols, key)) {\n    if (!D.enumerable) {\n      if (!has(it, HIDDEN)) dP(it, HIDDEN, createDesc(1, {}));\n      it[HIDDEN][key] = true;\n    } else {\n      if (has(it, HIDDEN) && it[HIDDEN][key]) it[HIDDEN][key] = false;\n      D = _create(D, { enumerable: createDesc(0, false) });\n    } return setSymbolDesc(it, key, D);\n  } return dP(it, key, D);\n};\nvar $defineProperties = function defineProperties(it, P) {\n  anObject(it);\n  var keys = enumKeys(P = toIObject(P));\n  var i = 0;\n  var l = keys.length;\n  var key;\n  while (l > i) $defineProperty(it, key = keys[i++], P[key]);\n  return it;\n};\nvar $create = function create(it, P) {\n  return P === undefined ? _create(it) : $defineProperties(_create(it), P);\n};\nvar $propertyIsEnumerable = function propertyIsEnumerable(key) {\n  var E = isEnum.call(this, key = toPrimitive(key, true));\n  if (this === ObjectProto && has(AllSymbols, key) && !has(OPSymbols, key)) return false;\n  return E || !has(this, key) || !has(AllSymbols, key) || has(this, HIDDEN) && this[HIDDEN][key] ? E : true;\n};\nvar $getOwnPropertyDescriptor = function getOwnPropertyDescriptor(it, key) {\n  it = toIObject(it);\n  key = toPrimitive(key, true);\n  if (it === ObjectProto && has(AllSymbols, key) && !has(OPSymbols, key)) return;\n  var D = gOPD(it, key);\n  if (D && has(AllSymbols, key) && !(has(it, HIDDEN) && it[HIDDEN][key])) D.enumerable = true;\n  return D;\n};\nvar $getOwnPropertyNames = function getOwnPropertyNames(it) {\n  var names = gOPN(toIObject(it));\n  var result = [];\n  var i = 0;\n  var key;\n  while (names.length > i) {\n    if (!has(AllSymbols, key = names[i++]) && key != HIDDEN && key != META) result.push(key);\n  } return result;\n};\nvar $getOwnPropertySymbols = function getOwnPropertySymbols(it) {\n  var IS_OP = it === ObjectProto;\n  var names = gOPN(IS_OP ? OPSymbols : toIObject(it));\n  var result = [];\n  var i = 0;\n  var key;\n  while (names.length > i) {\n    if (has(AllSymbols, key = names[i++]) && (IS_OP ? has(ObjectProto, key) : true)) result.push(AllSymbols[key]);\n  } return result;\n};\n\n// 19.4.1.1 Symbol([description])\nif (!USE_NATIVE) {\n  $Symbol = function Symbol() {\n    if (this instanceof $Symbol) throw TypeError('Symbol is not a constructor!');\n    var tag = uid(arguments.length > 0 ? arguments[0] : undefined);\n    var $set = function (value) {\n      if (this === ObjectProto) $set.call(OPSymbols, value);\n      if (has(this, HIDDEN) && has(this[HIDDEN], tag)) this[HIDDEN][tag] = false;\n      setSymbolDesc(this, tag, createDesc(1, value));\n    };\n    if (DESCRIPTORS && setter) setSymbolDesc(ObjectProto, tag, { configurable: true, set: $set });\n    return wrap(tag);\n  };\n  redefine($Symbol[PROTOTYPE], 'toString', function toString() {\n    return this._k;\n  });\n\n  $GOPD.f = $getOwnPropertyDescriptor;\n  $DP.f = $defineProperty;\n  require('./_object-gopn').f = gOPNExt.f = $getOwnPropertyNames;\n  require('./_object-pie').f = $propertyIsEnumerable;\n  $GOPS.f = $getOwnPropertySymbols;\n\n  if (DESCRIPTORS && !require('./_library')) {\n    redefine(ObjectProto, 'propertyIsEnumerable', $propertyIsEnumerable, true);\n  }\n\n  wksExt.f = function (name) {\n    return wrap(wks(name));\n  };\n}\n\n$export($export.G + $export.W + $export.F * !USE_NATIVE, { Symbol: $Symbol });\n\nfor (var es6Symbols = (\n  // 19.4.2.2, 19.4.2.3, 19.4.2.4, 19.4.2.6, 19.4.2.8, 19.4.2.9, 19.4.2.10, 19.4.2.11, 19.4.2.12, 19.4.2.13, 19.4.2.14\n  'hasInstance,isConcatSpreadable,iterator,match,replace,search,species,split,toPrimitive,toStringTag,unscopables'\n).split(','), j = 0; es6Symbols.length > j;)wks(es6Symbols[j++]);\n\nfor (var wellKnownSymbols = $keys(wks.store), k = 0; wellKnownSymbols.length > k;) wksDefine(wellKnownSymbols[k++]);\n\n$export($export.S + $export.F * !USE_NATIVE, 'Symbol', {\n  // 19.4.2.1 Symbol.for(key)\n  'for': function (key) {\n    return has(SymbolRegistry, key += '')\n      ? SymbolRegistry[key]\n      : SymbolRegistry[key] = $Symbol(key);\n  },\n  // 19.4.2.5 Symbol.keyFor(sym)\n  keyFor: function keyFor(sym) {\n    if (!isSymbol(sym)) throw TypeError(sym + ' is not a symbol!');\n    for (var key in SymbolRegistry) if (SymbolRegistry[key] === sym) return key;\n  },\n  useSetter: function () { setter = true; },\n  useSimple: function () { setter = false; }\n});\n\n$export($export.S + $export.F * !USE_NATIVE, 'Object', {\n  // 19.1.2.2 Object.create(O [, Properties])\n  create: $create,\n  // 19.1.2.4 Object.defineProperty(O, P, Attributes)\n  defineProperty: $defineProperty,\n  // 19.1.2.3 Object.defineProperties(O, Properties)\n  defineProperties: $defineProperties,\n  // 19.1.2.6 Object.getOwnPropertyDescriptor(O, P)\n  getOwnPropertyDescriptor: $getOwnPropertyDescriptor,\n  // 19.1.2.7 Object.getOwnPropertyNames(O)\n  getOwnPropertyNames: $getOwnPropertyNames,\n  // 19.1.2.8 Object.getOwnPropertySymbols(O)\n  getOwnPropertySymbols: $getOwnPropertySymbols\n});\n\n// Chrome 38 and 39 `Object.getOwnPropertySymbols` fails on primitives\n// https://bugs.chromium.org/p/v8/issues/detail?id=3443\nvar FAILS_ON_PRIMITIVES = $fails(function () { $GOPS.f(1); });\n\n$export($export.S + $export.F * FAILS_ON_PRIMITIVES, 'Object', {\n  getOwnPropertySymbols: function getOwnPropertySymbols(it) {\n    return $GOPS.f(toObject(it));\n  }\n});\n\n// 24.3.2 JSON.stringify(value [, replacer [, space]])\n$JSON && $export($export.S + $export.F * (!USE_NATIVE || $fails(function () {\n  var S = $Symbol();\n  // MS Edge converts symbol values to JSON as {}\n  // WebKit converts symbol values to JSON as null\n  // V8 throws on boxed symbols\n  return _stringify([S]) != '[null]' || _stringify({ a: S }) != '{}' || _stringify(Object(S)) != '{}';\n})), 'JSON', {\n  stringify: function stringify(it) {\n    var args = [it];\n    var i = 1;\n    var replacer, $replacer;\n    while (arguments.length > i) args.push(arguments[i++]);\n    $replacer = replacer = args[1];\n    if (!isObject(replacer) && it === undefined || isSymbol(it)) return; // IE8 returns string on undefined\n    if (!isArray(replacer)) replacer = function (key, value) {\n      if (typeof $replacer == 'function') value = $replacer.call(this, key, value);\n      if (!isSymbol(value)) return value;\n    };\n    args[1] = replacer;\n    return _stringify.apply($JSON, args);\n  }\n});\n\n// 19.4.3.4 Symbol.prototype[@@toPrimitive](hint)\n$Symbol[PROTOTYPE][TO_PRIMITIVE] || require('./_hide')($Symbol[PROTOTYPE], TO_PRIMITIVE, $Symbol[PROTOTYPE].valueOf);\n// 19.4.3.5 Symbol.prototype[@@toStringTag]\nsetToStringTag($Symbol, 'Symbol');\n// 20.2.1.9 Math[@@toStringTag]\nsetToStringTag(Math, 'Math', true);\n// 24.3.3 JSON[@@toStringTag]\nsetToStringTag(global.JSON, 'JSON', true);\n","var $export = require('./_export');\n// 19.1.2.2 / 15.2.3.5 Object.create(O [, Properties])\n$export($export.S, 'Object', { create: require('./_object-create') });\n","var $export = require('./_export');\n// 19.1.2.4 / 15.2.3.6 Object.defineProperty(O, P, Attributes)\n$export($export.S + $export.F * !require('./_descriptors'), 'Object', { defineProperty: require('./_object-dp').f });\n","var $export = require('./_export');\n// 19.1.2.3 / 15.2.3.7 Object.defineProperties(O, Properties)\n$export($export.S + $export.F * !require('./_descriptors'), 'Object', { defineProperties: require('./_object-dps') });\n","// most Object methods by ES6 should accept primitives\nvar $export = require('./_export');\nvar core = require('./_core');\nvar fails = require('./_fails');\nmodule.exports = function (KEY, exec) {\n  var fn = (core.Object || {})[KEY] || Object[KEY];\n  var exp = {};\n  exp[KEY] = exec(fn);\n  $export($export.S + $export.F * fails(function () { fn(1); }), 'Object', exp);\n};\n","// 19.1.2.6 Object.getOwnPropertyDescriptor(O, P)\nvar toIObject = require('./_to-iobject');\nvar $getOwnPropertyDescriptor = require('./_object-gopd').f;\n\nrequire('./_object-sap')('getOwnPropertyDescriptor', function () {\n  return function getOwnPropertyDescriptor(it, key) {\n    return $getOwnPropertyDescriptor(toIObject(it), key);\n  };\n});\n","// 19.1.2.9 / 15.2.3.2 Object.getPrototypeOf(O)\nvar has = require('./_has');\nvar toObject = require('./_to-object');\nvar IE_PROTO = require('./_shared-key')('IE_PROTO');\nvar ObjectProto = Object.prototype;\n\nmodule.exports = Object.getPrototypeOf || function (O) {\n  O = toObject(O);\n  if (has(O, IE_PROTO)) return O[IE_PROTO];\n  if (typeof O.constructor == 'function' && O instanceof O.constructor) {\n    return O.constructor.prototype;\n  } return O instanceof Object ? ObjectProto : null;\n};\n","// 19.1.2.9 Object.getPrototypeOf(O)\nvar toObject = require('./_to-object');\nvar $getPrototypeOf = require('./_object-gpo');\n\nrequire('./_object-sap')('getPrototypeOf', function () {\n  return function getPrototypeOf(it) {\n    return $getPrototypeOf(toObject(it));\n  };\n});\n","// 19.1.2.14 Object.keys(O)\nvar toObject = require('./_to-object');\nvar $keys = require('./_object-keys');\n\nrequire('./_object-sap')('keys', function () {\n  return function keys(it) {\n    return $keys(toObject(it));\n  };\n});\n","// 19.1.2.7 Object.getOwnPropertyNames(O)\nrequire('./_object-sap')('getOwnPropertyNames', function () {\n  return require('./_object-gopn-ext').f;\n});\n","// 19.1.2.5 Object.freeze(O)\nvar isObject = require('./_is-object');\nvar meta = require('./_meta').onFreeze;\n\nrequire('./_object-sap')('freeze', function ($freeze) {\n  return function freeze(it) {\n    return $freeze && isObject(it) ? $freeze(meta(it)) : it;\n  };\n});\n","// 19.1.2.17 Object.seal(O)\nvar isObject = require('./_is-object');\nvar meta = require('./_meta').onFreeze;\n\nrequire('./_object-sap')('seal', function ($seal) {\n  return function seal(it) {\n    return $seal && isObject(it) ? $seal(meta(it)) : it;\n  };\n});\n","// 19.1.2.15 Object.preventExtensions(O)\nvar isObject = require('./_is-object');\nvar meta = require('./_meta').onFreeze;\n\nrequire('./_object-sap')('preventExtensions', function ($preventExtensions) {\n  return function preventExtensions(it) {\n    return $preventExtensions && isObject(it) ? $preventExtensions(meta(it)) : it;\n  };\n});\n","// 19.1.2.12 Object.isFrozen(O)\nvar isObject = require('./_is-object');\n\nrequire('./_object-sap')('isFrozen', function ($isFrozen) {\n  return function isFrozen(it) {\n    return isObject(it) ? $isFrozen ? $isFrozen(it) : false : true;\n  };\n});\n","// 19.1.2.13 Object.isSealed(O)\nvar isObject = require('./_is-object');\n\nrequire('./_object-sap')('isSealed', function ($isSealed) {\n  return function isSealed(it) {\n    return isObject(it) ? $isSealed ? $isSealed(it) : false : true;\n  };\n});\n","// 19.1.2.11 Object.isExtensible(O)\nvar isObject = require('./_is-object');\n\nrequire('./_object-sap')('isExtensible', function ($isExtensible) {\n  return function isExtensible(it) {\n    return isObject(it) ? $isExtensible ? $isExtensible(it) : true : false;\n  };\n});\n","'use strict';\n// 19.1.2.1 Object.assign(target, source, ...)\nvar DESCRIPTORS = require('./_descriptors');\nvar getKeys = require('./_object-keys');\nvar gOPS = require('./_object-gops');\nvar pIE = require('./_object-pie');\nvar toObject = require('./_to-object');\nvar IObject = require('./_iobject');\nvar $assign = Object.assign;\n\n// should work with symbols and should have deterministic property order (V8 bug)\nmodule.exports = !$assign || require('./_fails')(function () {\n  var A = {};\n  var B = {};\n  // eslint-disable-next-line no-undef\n  var S = Symbol();\n  var K = 'abcdefghijklmnopqrst';\n  A[S] = 7;\n  K.split('').forEach(function (k) { B[k] = k; });\n  return $assign({}, A)[S] != 7 || Object.keys($assign({}, B)).join('') != K;\n}) ? function assign(target, source) { // eslint-disable-line no-unused-vars\n  var T = toObject(target);\n  var aLen = arguments.length;\n  var index = 1;\n  var getSymbols = gOPS.f;\n  var isEnum = pIE.f;\n  while (aLen > index) {\n    var S = IObject(arguments[index++]);\n    var keys = getSymbols ? getKeys(S).concat(getSymbols(S)) : getKeys(S);\n    var length = keys.length;\n    var j = 0;\n    var key;\n    while (length > j) {\n      key = keys[j++];\n      if (!DESCRIPTORS || isEnum.call(S, key)) T[key] = S[key];\n    }\n  } return T;\n} : $assign;\n","// 19.1.3.1 Object.assign(target, source)\nvar $export = require('./_export');\n\n$export($export.S + $export.F, 'Object', { assign: require('./_object-assign') });\n","// 7.2.9 SameValue(x, y)\nmodule.exports = Object.is || function is(x, y) {\n  // eslint-disable-next-line no-self-compare\n  return x === y ? x !== 0 || 1 / x === 1 / y : x != x && y != y;\n};\n","// 19.1.3.10 Object.is(value1, value2)\nvar $export = require('./_export');\n$export($export.S, 'Object', { is: require('./_same-value') });\n","// Works with __proto__ only. Old v8 can't work with null proto objects.\n/* eslint-disable no-proto */\nvar isObject = require('./_is-object');\nvar anObject = require('./_an-object');\nvar check = function (O, proto) {\n  anObject(O);\n  if (!isObject(proto) && proto !== null) throw TypeError(proto + \": can't set as prototype!\");\n};\nmodule.exports = {\n  set: Object.setPrototypeOf || ('__proto__' in {} ? // eslint-disable-line\n    function (test, buggy, set) {\n      try {\n        set = require('./_ctx')(Function.call, require('./_object-gopd').f(Object.prototype, '__proto__').set, 2);\n        set(test, []);\n        buggy = !(test instanceof Array);\n      } catch (e) { buggy = true; }\n      return function setPrototypeOf(O, proto) {\n        check(O, proto);\n        if (buggy) O.__proto__ = proto;\n        else set(O, proto);\n        return O;\n      };\n    }({}, false) : undefined),\n  check: check\n};\n","// 19.1.3.19 Object.setPrototypeOf(O, proto)\nvar $export = require('./_export');\n$export($export.S, 'Object', { setPrototypeOf: require('./_set-proto').set });\n","// getting tag from 19.1.3.6 Object.prototype.toString()\nvar cof = require('./_cof');\nvar TAG = require('./_wks')('toStringTag');\n// ES3 wrong here\nvar ARG = cof(function () { return arguments; }()) == 'Arguments';\n\n// fallback for IE11 Script Access Denied error\nvar tryGet = function (it, key) {\n  try {\n    return it[key];\n  } catch (e) { /* empty */ }\n};\n\nmodule.exports = function (it) {\n  var O, T, B;\n  return it === undefined ? 'Undefined' : it === null ? 'Null'\n    // @@toStringTag case\n    : typeof (T = tryGet(O = Object(it), TAG)) == 'string' ? T\n    // builtinTag case\n    : ARG ? cof(O)\n    // ES3 arguments fallback\n    : (B = cof(O)) == 'Object' && typeof O.callee == 'function' ? 'Arguments' : B;\n};\n","'use strict';\n// 19.1.3.6 Object.prototype.toString()\nvar classof = require('./_classof');\nvar test = {};\ntest[require('./_wks')('toStringTag')] = 'z';\nif (test + '' != '[object z]') {\n  require('./_redefine')(Object.prototype, 'toString', function toString() {\n    return '[object ' + classof(this) + ']';\n  }, true);\n}\n","// fast apply, http://jsperf.lnkit.com/fast-apply/5\nmodule.exports = function (fn, args, that) {\n  var un = that === undefined;\n  switch (args.length) {\n    case 0: return un ? fn()\n                      : fn.call(that);\n    case 1: return un ? fn(args[0])\n                      : fn.call(that, args[0]);\n    case 2: return un ? fn(args[0], args[1])\n                      : fn.call(that, args[0], args[1]);\n    case 3: return un ? fn(args[0], args[1], args[2])\n                      : fn.call(that, args[0], args[1], args[2]);\n    case 4: return un ? fn(args[0], args[1], args[2], args[3])\n                      : fn.call(that, args[0], args[1], args[2], args[3]);\n  } return fn.apply(that, args);\n};\n","'use strict';\nvar aFunction = require('./_a-function');\nvar isObject = require('./_is-object');\nvar invoke = require('./_invoke');\nvar arraySlice = [].slice;\nvar factories = {};\n\nvar construct = function (F, len, args) {\n  if (!(len in factories)) {\n    for (var n = [], i = 0; i < len; i++) n[i] = 'a[' + i + ']';\n    // eslint-disable-next-line no-new-func\n    factories[len] = Function('F,a', 'return new F(' + n.join(',') + ')');\n  } return factories[len](F, args);\n};\n\nmodule.exports = Function.bind || function bind(that /* , ...args */) {\n  var fn = aFunction(this);\n  var partArgs = arraySlice.call(arguments, 1);\n  var bound = function (/* args... */) {\n    var args = partArgs.concat(arraySlice.call(arguments));\n    return this instanceof bound ? construct(fn, args.length, args) : invoke(fn, args, that);\n  };\n  if (isObject(fn.prototype)) bound.prototype = fn.prototype;\n  return bound;\n};\n","// 19.2.3.2 / 15.3.4.5 Function.prototype.bind(thisArg, args...)\nvar $export = require('./_export');\n\n$export($export.P, 'Function', { bind: require('./_bind') });\n","var dP = require('./_object-dp').f;\nvar FProto = Function.prototype;\nvar nameRE = /^\\s*function ([^ (]*)/;\nvar NAME = 'name';\n\n// 19.2.4.2 name\nNAME in FProto || require('./_descriptors') && dP(FProto, NAME, {\n  configurable: true,\n  get: function () {\n    try {\n      return ('' + this).match(nameRE)[1];\n    } catch (e) {\n      return '';\n    }\n  }\n});\n","'use strict';\nvar isObject = require('./_is-object');\nvar getPrototypeOf = require('./_object-gpo');\nvar HAS_INSTANCE = require('./_wks')('hasInstance');\nvar FunctionProto = Function.prototype;\n// 19.2.3.6 Function.prototype[@@hasInstance](V)\nif (!(HAS_INSTANCE in FunctionProto)) require('./_object-dp').f(FunctionProto, HAS_INSTANCE, { value: function (O) {\n  if (typeof this != 'function' || !isObject(O)) return false;\n  if (!isObject(this.prototype)) return O instanceof this;\n  // for environment w/o native `@@hasInstance` logic enough `instanceof`, but add this:\n  while (O = getPrototypeOf(O)) if (this.prototype === O) return true;\n  return false;\n} });\n","module.exports = '\\x09\\x0A\\x0B\\x0C\\x0D\\x20\\xA0\\u1680\\u180E\\u2000\\u2001\\u2002\\u2003' +\n  '\\u2004\\u2005\\u2006\\u2007\\u2008\\u2009\\u200A\\u202F\\u205F\\u3000\\u2028\\u2029\\uFEFF';\n","var $export = require('./_export');\nvar defined = require('./_defined');\nvar fails = require('./_fails');\nvar spaces = require('./_string-ws');\nvar space = '[' + spaces + ']';\nvar non = '\\u200b\\u0085';\nvar ltrim = RegExp('^' + space + space + '*');\nvar rtrim = RegExp(space + space + '*$');\n\nvar exporter = function (KEY, exec, ALIAS) {\n  var exp = {};\n  var FORCE = fails(function () {\n    return !!spaces[KEY]() || non[KEY]() != non;\n  });\n  var fn = exp[KEY] = FORCE ? exec(trim) : spaces[KEY];\n  if (ALIAS) exp[ALIAS] = fn;\n  $export($export.P + $export.F * FORCE, 'String', exp);\n};\n\n// 1 -> String#trimLeft\n// 2 -> String#trimRight\n// 3 -> String#trim\nvar trim = exporter.trim = function (string, TYPE) {\n  string = String(defined(string));\n  if (TYPE & 1) string = string.replace(ltrim, '');\n  if (TYPE & 2) string = string.replace(rtrim, '');\n  return string;\n};\n\nmodule.exports = exporter;\n","var $parseInt = require('./_global').parseInt;\nvar $trim = require('./_string-trim').trim;\nvar ws = require('./_string-ws');\nvar hex = /^[-+]?0[xX]/;\n\nmodule.exports = $parseInt(ws + '08') !== 8 || $parseInt(ws + '0x16') !== 22 ? function parseInt(str, radix) {\n  var string = $trim(String(str), 3);\n  return $parseInt(string, (radix >>> 0) || (hex.test(string) ? 16 : 10));\n} : $parseInt;\n","var $export = require('./_export');\nvar $parseInt = require('./_parse-int');\n// 18.2.5 parseInt(string, radix)\n$export($export.G + $export.F * (parseInt != $parseInt), { parseInt: $parseInt });\n","var $parseFloat = require('./_global').parseFloat;\nvar $trim = require('./_string-trim').trim;\n\nmodule.exports = 1 / $parseFloat(require('./_string-ws') + '-0') !== -Infinity ? function parseFloat(str) {\n  var string = $trim(String(str), 3);\n  var result = $parseFloat(string);\n  return result === 0 && string.charAt(0) == '-' ? -0 : result;\n} : $parseFloat;\n","var $export = require('./_export');\nvar $parseFloat = require('./_parse-float');\n// 18.2.4 parseFloat(string)\n$export($export.G + $export.F * (parseFloat != $parseFloat), { parseFloat: $parseFloat });\n","var isObject = require('./_is-object');\nvar setPrototypeOf = require('./_set-proto').set;\nmodule.exports = function (that, target, C) {\n  var S = target.constructor;\n  var P;\n  if (S !== C && typeof S == 'function' && (P = S.prototype) !== C.prototype && isObject(P) && setPrototypeOf) {\n    setPrototypeOf(that, P);\n  } return that;\n};\n","'use strict';\nvar global = require('./_global');\nvar has = require('./_has');\nvar cof = require('./_cof');\nvar inheritIfRequired = require('./_inherit-if-required');\nvar toPrimitive = require('./_to-primitive');\nvar fails = require('./_fails');\nvar gOPN = require('./_object-gopn').f;\nvar gOPD = require('./_object-gopd').f;\nvar dP = require('./_object-dp').f;\nvar $trim = require('./_string-trim').trim;\nvar NUMBER = 'Number';\nvar $Number = global[NUMBER];\nvar Base = $Number;\nvar proto = $Number.prototype;\n// Opera ~12 has broken Object#toString\nvar BROKEN_COF = cof(require('./_object-create')(proto)) == NUMBER;\nvar TRIM = 'trim' in String.prototype;\n\n// 7.1.3 ToNumber(argument)\nvar toNumber = function (argument) {\n  var it = toPrimitive(argument, false);\n  if (typeof it == 'string' && it.length > 2) {\n    it = TRIM ? it.trim() : $trim(it, 3);\n    var first = it.charCodeAt(0);\n    var third, radix, maxCode;\n    if (first === 43 || first === 45) {\n      third = it.charCodeAt(2);\n      if (third === 88 || third === 120) return NaN; // Number('+0x1') should be NaN, old V8 fix\n    } else if (first === 48) {\n      switch (it.charCodeAt(1)) {\n        case 66: case 98: radix = 2; maxCode = 49; break; // fast equal /^0b[01]+$/i\n        case 79: case 111: radix = 8; maxCode = 55; break; // fast equal /^0o[0-7]+$/i\n        default: return +it;\n      }\n      for (var digits = it.slice(2), i = 0, l = digits.length, code; i < l; i++) {\n        code = digits.charCodeAt(i);\n        // parseInt parses a string to a first unavailable symbol\n        // but ToNumber should return NaN if a string contains unavailable symbols\n        if (code < 48 || code > maxCode) return NaN;\n      } return parseInt(digits, radix);\n    }\n  } return +it;\n};\n\nif (!$Number(' 0o1') || !$Number('0b1') || $Number('+0x1')) {\n  $Number = function Number(value) {\n    var it = arguments.length < 1 ? 0 : value;\n    var that = this;\n    return that instanceof $Number\n      // check on 1..constructor(foo) case\n      && (BROKEN_COF ? fails(function () { proto.valueOf.call(that); }) : cof(that) != NUMBER)\n        ? inheritIfRequired(new Base(toNumber(it)), that, $Number) : toNumber(it);\n  };\n  for (var keys = require('./_descriptors') ? gOPN(Base) : (\n    // ES3:\n    'MAX_VALUE,MIN_VALUE,NaN,NEGATIVE_INFINITY,POSITIVE_INFINITY,' +\n    // ES6 (in case, if modules with ES6 Number statics required before):\n    'EPSILON,isFinite,isInteger,isNaN,isSafeInteger,MAX_SAFE_INTEGER,' +\n    'MIN_SAFE_INTEGER,parseFloat,parseInt,isInteger'\n  ).split(','), j = 0, key; keys.length > j; j++) {\n    if (has(Base, key = keys[j]) && !has($Number, key)) {\n      dP($Number, key, gOPD(Base, key));\n    }\n  }\n  $Number.prototype = proto;\n  proto.constructor = $Number;\n  require('./_redefine')(global, NUMBER, $Number);\n}\n","var cof = require('./_cof');\nmodule.exports = function (it, msg) {\n  if (typeof it != 'number' && cof(it) != 'Number') throw TypeError(msg);\n  return +it;\n};\n","'use strict';\nvar toInteger = require('./_to-integer');\nvar defined = require('./_defined');\n\nmodule.exports = function repeat(count) {\n  var str = String(defined(this));\n  var res = '';\n  var n = toInteger(count);\n  if (n < 0 || n == Infinity) throw RangeError(\"Count can't be negative\");\n  for (;n > 0; (n >>>= 1) && (str += str)) if (n & 1) res += str;\n  return res;\n};\n","'use strict';\nvar $export = require('./_export');\nvar toInteger = require('./_to-integer');\nvar aNumberValue = require('./_a-number-value');\nvar repeat = require('./_string-repeat');\nvar $toFixed = 1.0.toFixed;\nvar floor = Math.floor;\nvar data = [0, 0, 0, 0, 0, 0];\nvar ERROR = 'Number.toFixed: incorrect invocation!';\nvar ZERO = '0';\n\nvar multiply = function (n, c) {\n  var i = -1;\n  var c2 = c;\n  while (++i < 6) {\n    c2 += n * data[i];\n    data[i] = c2 % 1e7;\n    c2 = floor(c2 / 1e7);\n  }\n};\nvar divide = function (n) {\n  var i = 6;\n  var c = 0;\n  while (--i >= 0) {\n    c += data[i];\n    data[i] = floor(c / n);\n    c = (c % n) * 1e7;\n  }\n};\nvar numToString = function () {\n  var i = 6;\n  var s = '';\n  while (--i >= 0) {\n    if (s !== '' || i === 0 || data[i] !== 0) {\n      var t = String(data[i]);\n      s = s === '' ? t : s + repeat.call(ZERO, 7 - t.length) + t;\n    }\n  } return s;\n};\nvar pow = function (x, n, acc) {\n  return n === 0 ? acc : n % 2 === 1 ? pow(x, n - 1, acc * x) : pow(x * x, n / 2, acc);\n};\nvar log = function (x) {\n  var n = 0;\n  var x2 = x;\n  while (x2 >= 4096) {\n    n += 12;\n    x2 /= 4096;\n  }\n  while (x2 >= 2) {\n    n += 1;\n    x2 /= 2;\n  } return n;\n};\n\n$export($export.P + $export.F * (!!$toFixed && (\n  0.00008.toFixed(3) !== '0.000' ||\n  0.9.toFixed(0) !== '1' ||\n  1.255.toFixed(2) !== '1.25' ||\n  1000000000000000128.0.toFixed(0) !== '1000000000000000128'\n) || !require('./_fails')(function () {\n  // V8 ~ Android 4.3-\n  $toFixed.call({});\n})), 'Number', {\n  toFixed: function toFixed(fractionDigits) {\n    var x = aNumberValue(this, ERROR);\n    var f = toInteger(fractionDigits);\n    var s = '';\n    var m = ZERO;\n    var e, z, j, k;\n    if (f < 0 || f > 20) throw RangeError(ERROR);\n    // eslint-disable-next-line no-self-compare\n    if (x != x) return 'NaN';\n    if (x <= -1e21 || x >= 1e21) return String(x);\n    if (x < 0) {\n      s = '-';\n      x = -x;\n    }\n    if (x > 1e-21) {\n      e = log(x * pow(2, 69, 1)) - 69;\n      z = e < 0 ? x * pow(2, -e, 1) : x / pow(2, e, 1);\n      z *= 0x10000000000000;\n      e = 52 - e;\n      if (e > 0) {\n        multiply(0, z);\n        j = f;\n        while (j >= 7) {\n          multiply(1e7, 0);\n          j -= 7;\n        }\n        multiply(pow(10, j, 1), 0);\n        j = e - 1;\n        while (j >= 23) {\n          divide(1 << 23);\n          j -= 23;\n        }\n        divide(1 << j);\n        multiply(1, 1);\n        divide(2);\n        m = numToString();\n      } else {\n        multiply(0, z);\n        multiply(1 << -e, 0);\n        m = numToString() + repeat.call(ZERO, f);\n      }\n    }\n    if (f > 0) {\n      k = m.length;\n      m = s + (k <= f ? '0.' + repeat.call(ZERO, f - k) + m : m.slice(0, k - f) + '.' + m.slice(k - f));\n    } else {\n      m = s + m;\n    } return m;\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar $fails = require('./_fails');\nvar aNumberValue = require('./_a-number-value');\nvar $toPrecision = 1.0.toPrecision;\n\n$export($export.P + $export.F * ($fails(function () {\n  // IE7-\n  return $toPrecision.call(1, undefined) !== '1';\n}) || !$fails(function () {\n  // V8 ~ Android 4.3-\n  $toPrecision.call({});\n})), 'Number', {\n  toPrecision: function toPrecision(precision) {\n    var that = aNumberValue(this, 'Number#toPrecision: incorrect invocation!');\n    return precision === undefined ? $toPrecision.call(that) : $toPrecision.call(that, precision);\n  }\n});\n","// 20.1.2.1 Number.EPSILON\nvar $export = require('./_export');\n\n$export($export.S, 'Number', { EPSILON: Math.pow(2, -52) });\n","// 20.1.2.2 Number.isFinite(number)\nvar $export = require('./_export');\nvar _isFinite = require('./_global').isFinite;\n\n$export($export.S, 'Number', {\n  isFinite: function isFinite(it) {\n    return typeof it == 'number' && _isFinite(it);\n  }\n});\n","// 20.1.2.3 Number.isInteger(number)\nvar isObject = require('./_is-object');\nvar floor = Math.floor;\nmodule.exports = function isInteger(it) {\n  return !isObject(it) && isFinite(it) && floor(it) === it;\n};\n","// 20.1.2.3 Number.isInteger(number)\nvar $export = require('./_export');\n\n$export($export.S, 'Number', { isInteger: require('./_is-integer') });\n","// 20.1.2.4 Number.isNaN(number)\nvar $export = require('./_export');\n\n$export($export.S, 'Number', {\n  isNaN: function isNaN(number) {\n    // eslint-disable-next-line no-self-compare\n    return number != number;\n  }\n});\n","// 20.1.2.5 Number.isSafeInteger(number)\nvar $export = require('./_export');\nvar isInteger = require('./_is-integer');\nvar abs = Math.abs;\n\n$export($export.S, 'Number', {\n  isSafeInteger: function isSafeInteger(number) {\n    return isInteger(number) && abs(number) <= 0x1fffffffffffff;\n  }\n});\n","// 20.1.2.6 Number.MAX_SAFE_INTEGER\nvar $export = require('./_export');\n\n$export($export.S, 'Number', { MAX_SAFE_INTEGER: 0x1fffffffffffff });\n","// 20.1.2.10 Number.MIN_SAFE_INTEGER\nvar $export = require('./_export');\n\n$export($export.S, 'Number', { MIN_SAFE_INTEGER: -0x1fffffffffffff });\n","var $export = require('./_export');\nvar $parseFloat = require('./_parse-float');\n// 20.1.2.12 Number.parseFloat(string)\n$export($export.S + $export.F * (Number.parseFloat != $parseFloat), 'Number', { parseFloat: $parseFloat });\n","var $export = require('./_export');\nvar $parseInt = require('./_parse-int');\n// 20.1.2.13 Number.parseInt(string, radix)\n$export($export.S + $export.F * (Number.parseInt != $parseInt), 'Number', { parseInt: $parseInt });\n","// 20.2.2.20 Math.log1p(x)\nmodule.exports = Math.log1p || function log1p(x) {\n  return (x = +x) > -1e-8 && x < 1e-8 ? x - x * x / 2 : Math.log(1 + x);\n};\n","// 20.2.2.3 Math.acosh(x)\nvar $export = require('./_export');\nvar log1p = require('./_math-log1p');\nvar sqrt = Math.sqrt;\nvar $acosh = Math.acosh;\n\n$export($export.S + $export.F * !($acosh\n  // V8 bug: https://code.google.com/p/v8/issues/detail?id=3509\n  && Math.floor($acosh(Number.MAX_VALUE)) == 710\n  // Tor Browser bug: Math.acosh(Infinity) -> NaN\n  && $acosh(Infinity) == Infinity\n), 'Math', {\n  acosh: function acosh(x) {\n    return (x = +x) < 1 ? NaN : x > 94906265.62425156\n      ? Math.log(x) + Math.LN2\n      : log1p(x - 1 + sqrt(x - 1) * sqrt(x + 1));\n  }\n});\n","// 20.2.2.5 Math.asinh(x)\nvar $export = require('./_export');\nvar $asinh = Math.asinh;\n\nfunction asinh(x) {\n  return !isFinite(x = +x) || x == 0 ? x : x < 0 ? -asinh(-x) : Math.log(x + Math.sqrt(x * x + 1));\n}\n\n// Tor Browser bug: Math.asinh(0) -> -0\n$export($export.S + $export.F * !($asinh && 1 / $asinh(0) > 0), 'Math', { asinh: asinh });\n","// 20.2.2.7 Math.atanh(x)\nvar $export = require('./_export');\nvar $atanh = Math.atanh;\n\n// Tor Browser bug: Math.atanh(-0) -> 0\n$export($export.S + $export.F * !($atanh && 1 / $atanh(-0) < 0), 'Math', {\n  atanh: function atanh(x) {\n    return (x = +x) == 0 ? x : Math.log((1 + x) / (1 - x)) / 2;\n  }\n});\n","// 20.2.2.28 Math.sign(x)\nmodule.exports = Math.sign || function sign(x) {\n  // eslint-disable-next-line no-self-compare\n  return (x = +x) == 0 || x != x ? x : x < 0 ? -1 : 1;\n};\n","// 20.2.2.9 Math.cbrt(x)\nvar $export = require('./_export');\nvar sign = require('./_math-sign');\n\n$export($export.S, 'Math', {\n  cbrt: function cbrt(x) {\n    return sign(x = +x) * Math.pow(Math.abs(x), 1 / 3);\n  }\n});\n","// 20.2.2.11 Math.clz32(x)\nvar $export = require('./_export');\n\n$export($export.S, 'Math', {\n  clz32: function clz32(x) {\n    return (x >>>= 0) ? 31 - Math.floor(Math.log(x + 0.5) * Math.LOG2E) : 32;\n  }\n});\n","// 20.2.2.12 Math.cosh(x)\nvar $export = require('./_export');\nvar exp = Math.exp;\n\n$export($export.S, 'Math', {\n  cosh: function cosh(x) {\n    return (exp(x = +x) + exp(-x)) / 2;\n  }\n});\n","// 20.2.2.14 Math.expm1(x)\nvar $expm1 = Math.expm1;\nmodule.exports = (!$expm1\n  // Old FF bug\n  || $expm1(10) > 22025.465794806719 || $expm1(10) < 22025.4657948067165168\n  // Tor Browser bug\n  || $expm1(-2e-17) != -2e-17\n) ? function expm1(x) {\n  return (x = +x) == 0 ? x : x > -1e-6 && x < 1e-6 ? x + x * x / 2 : Math.exp(x) - 1;\n} : $expm1;\n","// 20.2.2.14 Math.expm1(x)\nvar $export = require('./_export');\nvar $expm1 = require('./_math-expm1');\n\n$export($export.S + $export.F * ($expm1 != Math.expm1), 'Math', { expm1: $expm1 });\n","// 20.2.2.16 Math.fround(x)\nvar sign = require('./_math-sign');\nvar pow = Math.pow;\nvar EPSILON = pow(2, -52);\nvar EPSILON32 = pow(2, -23);\nvar MAX32 = pow(2, 127) * (2 - EPSILON32);\nvar MIN32 = pow(2, -126);\n\nvar roundTiesToEven = function (n) {\n  return n + 1 / EPSILON - 1 / EPSILON;\n};\n\nmodule.exports = Math.fround || function fround(x) {\n  var $abs = Math.abs(x);\n  var $sign = sign(x);\n  var a, result;\n  if ($abs < MIN32) return $sign * roundTiesToEven($abs / MIN32 / EPSILON32) * MIN32 * EPSILON32;\n  a = (1 + EPSILON32 / EPSILON) * $abs;\n  result = a - (a - $abs);\n  // eslint-disable-next-line no-self-compare\n  if (result > MAX32 || result != result) return $sign * Infinity;\n  return $sign * result;\n};\n","// 20.2.2.16 Math.fround(x)\nvar $export = require('./_export');\n\n$export($export.S, 'Math', { fround: require('./_math-fround') });\n","// 20.2.2.17 Math.hypot([value1[, value2[, … ]]])\nvar $export = require('./_export');\nvar abs = Math.abs;\n\n$export($export.S, 'Math', {\n  hypot: function hypot(value1, value2) { // eslint-disable-line no-unused-vars\n    var sum = 0;\n    var i = 0;\n    var aLen = arguments.length;\n    var larg = 0;\n    var arg, div;\n    while (i < aLen) {\n      arg = abs(arguments[i++]);\n      if (larg < arg) {\n        div = larg / arg;\n        sum = sum * div * div + 1;\n        larg = arg;\n      } else if (arg > 0) {\n        div = arg / larg;\n        sum += div * div;\n      } else sum += arg;\n    }\n    return larg === Infinity ? Infinity : larg * Math.sqrt(sum);\n  }\n});\n","// 20.2.2.18 Math.imul(x, y)\nvar $export = require('./_export');\nvar $imul = Math.imul;\n\n// some WebKit versions fails with big numbers, some has wrong arity\n$export($export.S + $export.F * require('./_fails')(function () {\n  return $imul(0xffffffff, 5) != -5 || $imul.length != 2;\n}), 'Math', {\n  imul: function imul(x, y) {\n    var UINT16 = 0xffff;\n    var xn = +x;\n    var yn = +y;\n    var xl = UINT16 & xn;\n    var yl = UINT16 & yn;\n    return 0 | xl * yl + ((UINT16 & xn >>> 16) * yl + xl * (UINT16 & yn >>> 16) << 16 >>> 0);\n  }\n});\n","// 20.2.2.21 Math.log10(x)\nvar $export = require('./_export');\n\n$export($export.S, 'Math', {\n  log10: function log10(x) {\n    return Math.log(x) * Math.LOG10E;\n  }\n});\n","// 20.2.2.20 Math.log1p(x)\nvar $export = require('./_export');\n\n$export($export.S, 'Math', { log1p: require('./_math-log1p') });\n","// 20.2.2.22 Math.log2(x)\nvar $export = require('./_export');\n\n$export($export.S, 'Math', {\n  log2: function log2(x) {\n    return Math.log(x) / Math.LN2;\n  }\n});\n","// 20.2.2.28 Math.sign(x)\nvar $export = require('./_export');\n\n$export($export.S, 'Math', { sign: require('./_math-sign') });\n","// 20.2.2.30 Math.sinh(x)\nvar $export = require('./_export');\nvar expm1 = require('./_math-expm1');\nvar exp = Math.exp;\n\n// V8 near Chromium 38 has a problem with very small numbers\n$export($export.S + $export.F * require('./_fails')(function () {\n  return !Math.sinh(-2e-17) != -2e-17;\n}), 'Math', {\n  sinh: function sinh(x) {\n    return Math.abs(x = +x) < 1\n      ? (expm1(x) - expm1(-x)) / 2\n      : (exp(x - 1) - exp(-x - 1)) * (Math.E / 2);\n  }\n});\n","// 20.2.2.33 Math.tanh(x)\nvar $export = require('./_export');\nvar expm1 = require('./_math-expm1');\nvar exp = Math.exp;\n\n$export($export.S, 'Math', {\n  tanh: function tanh(x) {\n    var a = expm1(x = +x);\n    var b = expm1(-x);\n    return a == Infinity ? 1 : b == Infinity ? -1 : (a - b) / (exp(x) + exp(-x));\n  }\n});\n","// 20.2.2.34 Math.trunc(x)\nvar $export = require('./_export');\n\n$export($export.S, 'Math', {\n  trunc: function trunc(it) {\n    return (it > 0 ? Math.floor : Math.ceil)(it);\n  }\n});\n","var $export = require('./_export');\nvar toAbsoluteIndex = require('./_to-absolute-index');\nvar fromCharCode = String.fromCharCode;\nvar $fromCodePoint = String.fromCodePoint;\n\n// length should be 1, old FF problem\n$export($export.S + $export.F * (!!$fromCodePoint && $fromCodePoint.length != 1), 'String', {\n  // 21.1.2.2 String.fromCodePoint(...codePoints)\n  fromCodePoint: function fromCodePoint(x) { // eslint-disable-line no-unused-vars\n    var res = [];\n    var aLen = arguments.length;\n    var i = 0;\n    var code;\n    while (aLen > i) {\n      code = +arguments[i++];\n      if (toAbsoluteIndex(code, 0x10ffff) !== code) throw RangeError(code + ' is not a valid code point');\n      res.push(code < 0x10000\n        ? fromCharCode(code)\n        : fromCharCode(((code -= 0x10000) >> 10) + 0xd800, code % 0x400 + 0xdc00)\n      );\n    } return res.join('');\n  }\n});\n","var $export = require('./_export');\nvar toIObject = require('./_to-iobject');\nvar toLength = require('./_to-length');\n\n$export($export.S, 'String', {\n  // 21.1.2.4 String.raw(callSite, ...substitutions)\n  raw: function raw(callSite) {\n    var tpl = toIObject(callSite.raw);\n    var len = toLength(tpl.length);\n    var aLen = arguments.length;\n    var res = [];\n    var i = 0;\n    while (len > i) {\n      res.push(String(tpl[i++]));\n      if (i < aLen) res.push(String(arguments[i]));\n    } return res.join('');\n  }\n});\n","'use strict';\n// 21.1.3.25 String.prototype.trim()\nrequire('./_string-trim')('trim', function ($trim) {\n  return function trim() {\n    return $trim(this, 3);\n  };\n});\n","var toInteger = require('./_to-integer');\nvar defined = require('./_defined');\n// true  -> String#at\n// false -> String#codePointAt\nmodule.exports = function (TO_STRING) {\n  return function (that, pos) {\n    var s = String(defined(that));\n    var i = toInteger(pos);\n    var l = s.length;\n    var a, b;\n    if (i < 0 || i >= l) return TO_STRING ? '' : undefined;\n    a = s.charCodeAt(i);\n    return a < 0xd800 || a > 0xdbff || i + 1 === l || (b = s.charCodeAt(i + 1)) < 0xdc00 || b > 0xdfff\n      ? TO_STRING ? s.charAt(i) : a\n      : TO_STRING ? s.slice(i, i + 2) : (a - 0xd800 << 10) + (b - 0xdc00) + 0x10000;\n  };\n};\n","module.exports = {};\n","'use strict';\nvar create = require('./_object-create');\nvar descriptor = require('./_property-desc');\nvar setToStringTag = require('./_set-to-string-tag');\nvar IteratorPrototype = {};\n\n// 25.1.2.1.1 %IteratorPrototype%[@@iterator]()\nrequire('./_hide')(IteratorPrototype, require('./_wks')('iterator'), function () { return this; });\n\nmodule.exports = function (Constructor, NAME, next) {\n  Constructor.prototype = create(IteratorPrototype, { next: descriptor(1, next) });\n  setToStringTag(Constructor, NAME + ' Iterator');\n};\n","'use strict';\nvar LIBRARY = require('./_library');\nvar $export = require('./_export');\nvar redefine = require('./_redefine');\nvar hide = require('./_hide');\nvar Iterators = require('./_iterators');\nvar $iterCreate = require('./_iter-create');\nvar setToStringTag = require('./_set-to-string-tag');\nvar getPrototypeOf = require('./_object-gpo');\nvar ITERATOR = require('./_wks')('iterator');\nvar BUGGY = !([].keys && 'next' in [].keys()); // Safari has buggy iterators w/o `next`\nvar FF_ITERATOR = '@@iterator';\nvar KEYS = 'keys';\nvar VALUES = 'values';\n\nvar returnThis = function () { return this; };\n\nmodule.exports = function (Base, NAME, Constructor, next, DEFAULT, IS_SET, FORCED) {\n  $iterCreate(Constructor, NAME, next);\n  var getMethod = function (kind) {\n    if (!BUGGY && kind in proto) return proto[kind];\n    switch (kind) {\n      case KEYS: return function keys() { return new Constructor(this, kind); };\n      case VALUES: return function values() { return new Constructor(this, kind); };\n    } return function entries() { return new Constructor(this, kind); };\n  };\n  var TAG = NAME + ' Iterator';\n  var DEF_VALUES = DEFAULT == VALUES;\n  var VALUES_BUG = false;\n  var proto = Base.prototype;\n  var $native = proto[ITERATOR] || proto[FF_ITERATOR] || DEFAULT && proto[DEFAULT];\n  var $default = $native || getMethod(DEFAULT);\n  var $entries = DEFAULT ? !DEF_VALUES ? $default : getMethod('entries') : undefined;\n  var $anyNative = NAME == 'Array' ? proto.entries || $native : $native;\n  var methods, key, IteratorPrototype;\n  // Fix native\n  if ($anyNative) {\n    IteratorPrototype = getPrototypeOf($anyNative.call(new Base()));\n    if (IteratorPrototype !== Object.prototype && IteratorPrototype.next) {\n      // Set @@toStringTag to native iterators\n      setToStringTag(IteratorPrototype, TAG, true);\n      // fix for some old engines\n      if (!LIBRARY && typeof IteratorPrototype[ITERATOR] != 'function') hide(IteratorPrototype, ITERATOR, returnThis);\n    }\n  }\n  // fix Array#{values, @@iterator}.name in V8 / FF\n  if (DEF_VALUES && $native && $native.name !== VALUES) {\n    VALUES_BUG = true;\n    $default = function values() { return $native.call(this); };\n  }\n  // Define iterator\n  if ((!LIBRARY || FORCED) && (BUGGY || VALUES_BUG || !proto[ITERATOR])) {\n    hide(proto, ITERATOR, $default);\n  }\n  // Plug for library\n  Iterators[NAME] = $default;\n  Iterators[TAG] = returnThis;\n  if (DEFAULT) {\n    methods = {\n      values: DEF_VALUES ? $default : getMethod(VALUES),\n      keys: IS_SET ? $default : getMethod(KEYS),\n      entries: $entries\n    };\n    if (FORCED) for (key in methods) {\n      if (!(key in proto)) redefine(proto, key, methods[key]);\n    } else $export($export.P + $export.F * (BUGGY || VALUES_BUG), NAME, methods);\n  }\n  return methods;\n};\n","'use strict';\nvar $at = require('./_string-at')(true);\n\n// 21.1.3.27 String.prototype[@@iterator]()\nrequire('./_iter-define')(String, 'String', function (iterated) {\n  this._t = String(iterated); // target\n  this._i = 0;                // next index\n// 21.1.5.2.1 %StringIteratorPrototype%.next()\n}, function () {\n  var O = this._t;\n  var index = this._i;\n  var point;\n  if (index >= O.length) return { value: undefined, done: true };\n  point = $at(O, index);\n  this._i += point.length;\n  return { value: point, done: false };\n});\n","'use strict';\nvar $export = require('./_export');\nvar $at = require('./_string-at')(false);\n$export($export.P, 'String', {\n  // 21.1.3.3 String.prototype.codePointAt(pos)\n  codePointAt: function codePointAt(pos) {\n    return $at(this, pos);\n  }\n});\n","// 7.2.8 IsRegExp(argument)\nvar isObject = require('./_is-object');\nvar cof = require('./_cof');\nvar MATCH = require('./_wks')('match');\nmodule.exports = function (it) {\n  var isRegExp;\n  return isObject(it) && ((isRegExp = it[MATCH]) !== undefined ? !!isRegExp : cof(it) == 'RegExp');\n};\n","// helper for String#{startsWith, endsWith, includes}\nvar isRegExp = require('./_is-regexp');\nvar defined = require('./_defined');\n\nmodule.exports = function (that, searchString, NAME) {\n  if (isRegExp(searchString)) throw TypeError('String#' + NAME + \" doesn't accept regex!\");\n  return String(defined(that));\n};\n","var MATCH = require('./_wks')('match');\nmodule.exports = function (KEY) {\n  var re = /./;\n  try {\n    '/./'[KEY](re);\n  } catch (e) {\n    try {\n      re[MATCH] = false;\n      return !'/./'[KEY](re);\n    } catch (f) { /* empty */ }\n  } return true;\n};\n","// 21.1.3.6 String.prototype.endsWith(searchString [, endPosition])\n'use strict';\nvar $export = require('./_export');\nvar toLength = require('./_to-length');\nvar context = require('./_string-context');\nvar ENDS_WITH = 'endsWith';\nvar $endsWith = ''[ENDS_WITH];\n\n$export($export.P + $export.F * require('./_fails-is-regexp')(ENDS_WITH), 'String', {\n  endsWith: function endsWith(searchString /* , endPosition = @length */) {\n    var that = context(this, searchString, ENDS_WITH);\n    var endPosition = arguments.length > 1 ? arguments[1] : undefined;\n    var len = toLength(that.length);\n    var end = endPosition === undefined ? len : Math.min(toLength(endPosition), len);\n    var search = String(searchString);\n    return $endsWith\n      ? $endsWith.call(that, search, end)\n      : that.slice(end - search.length, end) === search;\n  }\n});\n","// 21.1.3.7 String.prototype.includes(searchString, position = 0)\n'use strict';\nvar $export = require('./_export');\nvar context = require('./_string-context');\nvar INCLUDES = 'includes';\n\n$export($export.P + $export.F * require('./_fails-is-regexp')(INCLUDES), 'String', {\n  includes: function includes(searchString /* , position = 0 */) {\n    return !!~context(this, searchString, INCLUDES)\n      .indexOf(searchString, arguments.length > 1 ? arguments[1] : undefined);\n  }\n});\n","var $export = require('./_export');\n\n$export($export.P, 'String', {\n  // 21.1.3.13 String.prototype.repeat(count)\n  repeat: require('./_string-repeat')\n});\n","// 21.1.3.18 String.prototype.startsWith(searchString [, position ])\n'use strict';\nvar $export = require('./_export');\nvar toLength = require('./_to-length');\nvar context = require('./_string-context');\nvar STARTS_WITH = 'startsWith';\nvar $startsWith = ''[STARTS_WITH];\n\n$export($export.P + $export.F * require('./_fails-is-regexp')(STARTS_WITH), 'String', {\n  startsWith: function startsWith(searchString /* , position = 0 */) {\n    var that = context(this, searchString, STARTS_WITH);\n    var index = toLength(Math.min(arguments.length > 1 ? arguments[1] : undefined, that.length));\n    var search = String(searchString);\n    return $startsWith\n      ? $startsWith.call(that, search, index)\n      : that.slice(index, index + search.length) === search;\n  }\n});\n","var $export = require('./_export');\nvar fails = require('./_fails');\nvar defined = require('./_defined');\nvar quot = /\"/g;\n// B.2.3.2.1 CreateHTML(string, tag, attribute, value)\nvar createHTML = function (string, tag, attribute, value) {\n  var S = String(defined(string));\n  var p1 = '<' + tag;\n  if (attribute !== '') p1 += ' ' + attribute + '=\"' + String(value).replace(quot, '&quot;') + '\"';\n  return p1 + '>' + S + '</' + tag + '>';\n};\nmodule.exports = function (NAME, exec) {\n  var O = {};\n  O[NAME] = exec(createHTML);\n  $export($export.P + $export.F * fails(function () {\n    var test = ''[NAME]('\"');\n    return test !== test.toLowerCase() || test.split('\"').length > 3;\n  }), 'String', O);\n};\n","'use strict';\n// B.2.3.2 String.prototype.anchor(name)\nrequire('./_string-html')('anchor', function (createHTML) {\n  return function anchor(name) {\n    return createHTML(this, 'a', 'name', name);\n  };\n});\n","'use strict';\n// B.2.3.3 String.prototype.big()\nrequire('./_string-html')('big', function (createHTML) {\n  return function big() {\n    return createHTML(this, 'big', '', '');\n  };\n});\n","'use strict';\n// B.2.3.4 String.prototype.blink()\nrequire('./_string-html')('blink', function (createHTML) {\n  return function blink() {\n    return createHTML(this, 'blink', '', '');\n  };\n});\n","'use strict';\n// B.2.3.5 String.prototype.bold()\nrequire('./_string-html')('bold', function (createHTML) {\n  return function bold() {\n    return createHTML(this, 'b', '', '');\n  };\n});\n","'use strict';\n// B.2.3.6 String.prototype.fixed()\nrequire('./_string-html')('fixed', function (createHTML) {\n  return function fixed() {\n    return createHTML(this, 'tt', '', '');\n  };\n});\n","'use strict';\n// B.2.3.7 String.prototype.fontcolor(color)\nrequire('./_string-html')('fontcolor', function (createHTML) {\n  return function fontcolor(color) {\n    return createHTML(this, 'font', 'color', color);\n  };\n});\n","'use strict';\n// B.2.3.8 String.prototype.fontsize(size)\nrequire('./_string-html')('fontsize', function (createHTML) {\n  return function fontsize(size) {\n    return createHTML(this, 'font', 'size', size);\n  };\n});\n","'use strict';\n// B.2.3.9 String.prototype.italics()\nrequire('./_string-html')('italics', function (createHTML) {\n  return function italics() {\n    return createHTML(this, 'i', '', '');\n  };\n});\n","'use strict';\n// B.2.3.10 String.prototype.link(url)\nrequire('./_string-html')('link', function (createHTML) {\n  return function link(url) {\n    return createHTML(this, 'a', 'href', url);\n  };\n});\n","'use strict';\n// B.2.3.11 String.prototype.small()\nrequire('./_string-html')('small', function (createHTML) {\n  return function small() {\n    return createHTML(this, 'small', '', '');\n  };\n});\n","'use strict';\n// B.2.3.12 String.prototype.strike()\nrequire('./_string-html')('strike', function (createHTML) {\n  return function strike() {\n    return createHTML(this, 'strike', '', '');\n  };\n});\n","'use strict';\n// B.2.3.13 String.prototype.sub()\nrequire('./_string-html')('sub', function (createHTML) {\n  return function sub() {\n    return createHTML(this, 'sub', '', '');\n  };\n});\n","'use strict';\n// B.2.3.14 String.prototype.sup()\nrequire('./_string-html')('sup', function (createHTML) {\n  return function sup() {\n    return createHTML(this, 'sup', '', '');\n  };\n});\n","// 20.3.3.1 / 15.9.4.4 Date.now()\nvar $export = require('./_export');\n\n$export($export.S, 'Date', { now: function () { return new Date().getTime(); } });\n","'use strict';\nvar $export = require('./_export');\nvar toObject = require('./_to-object');\nvar toPrimitive = require('./_to-primitive');\n\n$export($export.P + $export.F * require('./_fails')(function () {\n  return new Date(NaN).toJSON() !== null\n    || Date.prototype.toJSON.call({ toISOString: function () { return 1; } }) !== 1;\n}), 'Date', {\n  // eslint-disable-next-line no-unused-vars\n  toJSON: function toJSON(key) {\n    var O = toObject(this);\n    var pv = toPrimitive(O);\n    return typeof pv == 'number' && !isFinite(pv) ? null : O.toISOString();\n  }\n});\n","'use strict';\n// 20.3.4.36 / 15.9.5.43 Date.prototype.toISOString()\nvar fails = require('./_fails');\nvar getTime = Date.prototype.getTime;\nvar $toISOString = Date.prototype.toISOString;\n\nvar lz = function (num) {\n  return num > 9 ? num : '0' + num;\n};\n\n// PhantomJS / old WebKit has a broken implementations\nmodule.exports = (fails(function () {\n  return $toISOString.call(new Date(-5e13 - 1)) != '0385-07-25T07:06:39.999Z';\n}) || !fails(function () {\n  $toISOString.call(new Date(NaN));\n})) ? function toISOString() {\n  if (!isFinite(getTime.call(this))) throw RangeError('Invalid time value');\n  var d = this;\n  var y = d.getUTCFullYear();\n  var m = d.getUTCMilliseconds();\n  var s = y < 0 ? '-' : y > 9999 ? '+' : '';\n  return s + ('00000' + Math.abs(y)).slice(s ? -6 : -4) +\n    '-' + lz(d.getUTCMonth() + 1) + '-' + lz(d.getUTCDate()) +\n    'T' + lz(d.getUTCHours()) + ':' + lz(d.getUTCMinutes()) +\n    ':' + lz(d.getUTCSeconds()) + '.' + (m > 99 ? m : '0' + lz(m)) + 'Z';\n} : $toISOString;\n","// 20.3.4.36 / 15.9.5.43 Date.prototype.toISOString()\nvar $export = require('./_export');\nvar toISOString = require('./_date-to-iso-string');\n\n// PhantomJS / old WebKit has a broken implementations\n$export($export.P + $export.F * (Date.prototype.toISOString !== toISOString), 'Date', {\n  toISOString: toISOString\n});\n","var DateProto = Date.prototype;\nvar INVALID_DATE = 'Invalid Date';\nvar TO_STRING = 'toString';\nvar $toString = DateProto[TO_STRING];\nvar getTime = DateProto.getTime;\nif (new Date(NaN) + '' != INVALID_DATE) {\n  require('./_redefine')(DateProto, TO_STRING, function toString() {\n    var value = getTime.call(this);\n    // eslint-disable-next-line no-self-compare\n    return value === value ? $toString.call(this) : INVALID_DATE;\n  });\n}\n","'use strict';\nvar anObject = require('./_an-object');\nvar toPrimitive = require('./_to-primitive');\nvar NUMBER = 'number';\n\nmodule.exports = function (hint) {\n  if (hint !== 'string' && hint !== NUMBER && hint !== 'default') throw TypeError('Incorrect hint');\n  return toPrimitive(anObject(this), hint != NUMBER);\n};\n","var TO_PRIMITIVE = require('./_wks')('toPrimitive');\nvar proto = Date.prototype;\n\nif (!(TO_PRIMITIVE in proto)) require('./_hide')(proto, TO_PRIMITIVE, require('./_date-to-primitive'));\n","// 22.1.2.2 / 15.4.3.2 Array.isArray(arg)\nvar $export = require('./_export');\n\n$export($export.S, 'Array', { isArray: require('./_is-array') });\n","// call something on iterator step with safe closing on error\nvar anObject = require('./_an-object');\nmodule.exports = function (iterator, fn, value, entries) {\n  try {\n    return entries ? fn(anObject(value)[0], value[1]) : fn(value);\n  // 7.4.6 IteratorClose(iterator, completion)\n  } catch (e) {\n    var ret = iterator['return'];\n    if (ret !== undefined) anObject(ret.call(iterator));\n    throw e;\n  }\n};\n","// check on default Array iterator\nvar Iterators = require('./_iterators');\nvar ITERATOR = require('./_wks')('iterator');\nvar ArrayProto = Array.prototype;\n\nmodule.exports = function (it) {\n  return it !== undefined && (Iterators.Array === it || ArrayProto[ITERATOR] === it);\n};\n","'use strict';\nvar $defineProperty = require('./_object-dp');\nvar createDesc = require('./_property-desc');\n\nmodule.exports = function (object, index, value) {\n  if (index in object) $defineProperty.f(object, index, createDesc(0, value));\n  else object[index] = value;\n};\n","var classof = require('./_classof');\nvar ITERATOR = require('./_wks')('iterator');\nvar Iterators = require('./_iterators');\nmodule.exports = require('./_core').getIteratorMethod = function (it) {\n  if (it != undefined) return it[ITERATOR]\n    || it['@@iterator']\n    || Iterators[classof(it)];\n};\n","var ITERATOR = require('./_wks')('iterator');\nvar SAFE_CLOSING = false;\n\ntry {\n  var riter = [7][ITERATOR]();\n  riter['return'] = function () { SAFE_CLOSING = true; };\n  // eslint-disable-next-line no-throw-literal\n  Array.from(riter, function () { throw 2; });\n} catch (e) { /* empty */ }\n\nmodule.exports = function (exec, skipClosing) {\n  if (!skipClosing && !SAFE_CLOSING) return false;\n  var safe = false;\n  try {\n    var arr = [7];\n    var iter = arr[ITERATOR]();\n    iter.next = function () { return { done: safe = true }; };\n    arr[ITERATOR] = function () { return iter; };\n    exec(arr);\n  } catch (e) { /* empty */ }\n  return safe;\n};\n","'use strict';\nvar ctx = require('./_ctx');\nvar $export = require('./_export');\nvar toObject = require('./_to-object');\nvar call = require('./_iter-call');\nvar isArrayIter = require('./_is-array-iter');\nvar toLength = require('./_to-length');\nvar createProperty = require('./_create-property');\nvar getIterFn = require('./core.get-iterator-method');\n\n$export($export.S + $export.F * !require('./_iter-detect')(function (iter) { Array.from(iter); }), 'Array', {\n  // 22.1.2.1 Array.from(arrayLike, mapfn = undefined, thisArg = undefined)\n  from: function from(arrayLike /* , mapfn = undefined, thisArg = undefined */) {\n    var O = toObject(arrayLike);\n    var C = typeof this == 'function' ? this : Array;\n    var aLen = arguments.length;\n    var mapfn = aLen > 1 ? arguments[1] : undefined;\n    var mapping = mapfn !== undefined;\n    var index = 0;\n    var iterFn = getIterFn(O);\n    var length, result, step, iterator;\n    if (mapping) mapfn = ctx(mapfn, aLen > 2 ? arguments[2] : undefined, 2);\n    // if object isn't iterable or it's array with default iterator - use simple case\n    if (iterFn != undefined && !(C == Array && isArrayIter(iterFn))) {\n      for (iterator = iterFn.call(O), result = new C(); !(step = iterator.next()).done; index++) {\n        createProperty(result, index, mapping ? call(iterator, mapfn, [step.value, index], true) : step.value);\n      }\n    } else {\n      length = toLength(O.length);\n      for (result = new C(length); length > index; index++) {\n        createProperty(result, index, mapping ? mapfn(O[index], index) : O[index]);\n      }\n    }\n    result.length = index;\n    return result;\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar createProperty = require('./_create-property');\n\n// WebKit Array.of isn't generic\n$export($export.S + $export.F * require('./_fails')(function () {\n  function F() { /* empty */ }\n  return !(Array.of.call(F) instanceof F);\n}), 'Array', {\n  // 22.1.2.3 Array.of( ...items)\n  of: function of(/* ...args */) {\n    var index = 0;\n    var aLen = arguments.length;\n    var result = new (typeof this == 'function' ? this : Array)(aLen);\n    while (aLen > index) createProperty(result, index, arguments[index++]);\n    result.length = aLen;\n    return result;\n  }\n});\n","'use strict';\nvar fails = require('./_fails');\n\nmodule.exports = function (method, arg) {\n  return !!method && fails(function () {\n    // eslint-disable-next-line no-useless-call\n    arg ? method.call(null, function () { /* empty */ }, 1) : method.call(null);\n  });\n};\n","'use strict';\n// 22.1.3.13 Array.prototype.join(separator)\nvar $export = require('./_export');\nvar toIObject = require('./_to-iobject');\nvar arrayJoin = [].join;\n\n// fallback for not array-like strings\n$export($export.P + $export.F * (require('./_iobject') != Object || !require('./_strict-method')(arrayJoin)), 'Array', {\n  join: function join(separator) {\n    return arrayJoin.call(toIObject(this), separator === undefined ? ',' : separator);\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar html = require('./_html');\nvar cof = require('./_cof');\nvar toAbsoluteIndex = require('./_to-absolute-index');\nvar toLength = require('./_to-length');\nvar arraySlice = [].slice;\n\n// fallback for not array-like ES3 strings and DOM objects\n$export($export.P + $export.F * require('./_fails')(function () {\n  if (html) arraySlice.call(html);\n}), 'Array', {\n  slice: function slice(begin, end) {\n    var len = toLength(this.length);\n    var klass = cof(this);\n    end = end === undefined ? len : end;\n    if (klass == 'Array') return arraySlice.call(this, begin, end);\n    var start = toAbsoluteIndex(begin, len);\n    var upTo = toAbsoluteIndex(end, len);\n    var size = toLength(upTo - start);\n    var cloned = new Array(size);\n    var i = 0;\n    for (; i < size; i++) cloned[i] = klass == 'String'\n      ? this.charAt(start + i)\n      : this[start + i];\n    return cloned;\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar aFunction = require('./_a-function');\nvar toObject = require('./_to-object');\nvar fails = require('./_fails');\nvar $sort = [].sort;\nvar test = [1, 2, 3];\n\n$export($export.P + $export.F * (fails(function () {\n  // IE8-\n  test.sort(undefined);\n}) || !fails(function () {\n  // V8 bug\n  test.sort(null);\n  // Old WebKit\n}) || !require('./_strict-method')($sort)), 'Array', {\n  // 22.1.3.25 Array.prototype.sort(comparefn)\n  sort: function sort(comparefn) {\n    return comparefn === undefined\n      ? $sort.call(toObject(this))\n      : $sort.call(toObject(this), aFunction(comparefn));\n  }\n});\n","var isObject = require('./_is-object');\nvar isArray = require('./_is-array');\nvar SPECIES = require('./_wks')('species');\n\nmodule.exports = function (original) {\n  var C;\n  if (isArray(original)) {\n    C = original.constructor;\n    // cross-realm fallback\n    if (typeof C == 'function' && (C === Array || isArray(C.prototype))) C = undefined;\n    if (isObject(C)) {\n      C = C[SPECIES];\n      if (C === null) C = undefined;\n    }\n  } return C === undefined ? Array : C;\n};\n","// 9.4.2.3 ArraySpeciesCreate(originalArray, length)\nvar speciesConstructor = require('./_array-species-constructor');\n\nmodule.exports = function (original, length) {\n  return new (speciesConstructor(original))(length);\n};\n","// 0 -> Array#forEach\n// 1 -> Array#map\n// 2 -> Array#filter\n// 3 -> Array#some\n// 4 -> Array#every\n// 5 -> Array#find\n// 6 -> Array#findIndex\nvar ctx = require('./_ctx');\nvar IObject = require('./_iobject');\nvar toObject = require('./_to-object');\nvar toLength = require('./_to-length');\nvar asc = require('./_array-species-create');\nmodule.exports = function (TYPE, $create) {\n  var IS_MAP = TYPE == 1;\n  var IS_FILTER = TYPE == 2;\n  var IS_SOME = TYPE == 3;\n  var IS_EVERY = TYPE == 4;\n  var IS_FIND_INDEX = TYPE == 6;\n  var NO_HOLES = TYPE == 5 || IS_FIND_INDEX;\n  var create = $create || asc;\n  return function ($this, callbackfn, that) {\n    var O = toObject($this);\n    var self = IObject(O);\n    var f = ctx(callbackfn, that, 3);\n    var length = toLength(self.length);\n    var index = 0;\n    var result = IS_MAP ? create($this, length) : IS_FILTER ? create($this, 0) : undefined;\n    var val, res;\n    for (;length > index; index++) if (NO_HOLES || index in self) {\n      val = self[index];\n      res = f(val, index, O);\n      if (TYPE) {\n        if (IS_MAP) result[index] = res;   // map\n        else if (res) switch (TYPE) {\n          case 3: return true;             // some\n          case 5: return val;              // find\n          case 6: return index;            // findIndex\n          case 2: result.push(val);        // filter\n        } else if (IS_EVERY) return false; // every\n      }\n    }\n    return IS_FIND_INDEX ? -1 : IS_SOME || IS_EVERY ? IS_EVERY : result;\n  };\n};\n","'use strict';\nvar $export = require('./_export');\nvar $forEach = require('./_array-methods')(0);\nvar STRICT = require('./_strict-method')([].forEach, true);\n\n$export($export.P + $export.F * !STRICT, 'Array', {\n  // 22.1.3.10 / 15.4.4.18 Array.prototype.forEach(callbackfn [, thisArg])\n  forEach: function forEach(callbackfn /* , thisArg */) {\n    return $forEach(this, callbackfn, arguments[1]);\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar $map = require('./_array-methods')(1);\n\n$export($export.P + $export.F * !require('./_strict-method')([].map, true), 'Array', {\n  // 22.1.3.15 / 15.4.4.19 Array.prototype.map(callbackfn [, thisArg])\n  map: function map(callbackfn /* , thisArg */) {\n    return $map(this, callbackfn, arguments[1]);\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar $filter = require('./_array-methods')(2);\n\n$export($export.P + $export.F * !require('./_strict-method')([].filter, true), 'Array', {\n  // 22.1.3.7 / 15.4.4.20 Array.prototype.filter(callbackfn [, thisArg])\n  filter: function filter(callbackfn /* , thisArg */) {\n    return $filter(this, callbackfn, arguments[1]);\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar $some = require('./_array-methods')(3);\n\n$export($export.P + $export.F * !require('./_strict-method')([].some, true), 'Array', {\n  // 22.1.3.23 / 15.4.4.17 Array.prototype.some(callbackfn [, thisArg])\n  some: function some(callbackfn /* , thisArg */) {\n    return $some(this, callbackfn, arguments[1]);\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar $every = require('./_array-methods')(4);\n\n$export($export.P + $export.F * !require('./_strict-method')([].every, true), 'Array', {\n  // 22.1.3.5 / 15.4.4.16 Array.prototype.every(callbackfn [, thisArg])\n  every: function every(callbackfn /* , thisArg */) {\n    return $every(this, callbackfn, arguments[1]);\n  }\n});\n","var aFunction = require('./_a-function');\nvar toObject = require('./_to-object');\nvar IObject = require('./_iobject');\nvar toLength = require('./_to-length');\n\nmodule.exports = function (that, callbackfn, aLen, memo, isRight) {\n  aFunction(callbackfn);\n  var O = toObject(that);\n  var self = IObject(O);\n  var length = toLength(O.length);\n  var index = isRight ? length - 1 : 0;\n  var i = isRight ? -1 : 1;\n  if (aLen < 2) for (;;) {\n    if (index in self) {\n      memo = self[index];\n      index += i;\n      break;\n    }\n    index += i;\n    if (isRight ? index < 0 : length <= index) {\n      throw TypeError('Reduce of empty array with no initial value');\n    }\n  }\n  for (;isRight ? index >= 0 : length > index; index += i) if (index in self) {\n    memo = callbackfn(memo, self[index], index, O);\n  }\n  return memo;\n};\n","'use strict';\nvar $export = require('./_export');\nvar $reduce = require('./_array-reduce');\n\n$export($export.P + $export.F * !require('./_strict-method')([].reduce, true), 'Array', {\n  // 22.1.3.18 / 15.4.4.21 Array.prototype.reduce(callbackfn [, initialValue])\n  reduce: function reduce(callbackfn /* , initialValue */) {\n    return $reduce(this, callbackfn, arguments.length, arguments[1], false);\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar $reduce = require('./_array-reduce');\n\n$export($export.P + $export.F * !require('./_strict-method')([].reduceRight, true), 'Array', {\n  // 22.1.3.19 / 15.4.4.22 Array.prototype.reduceRight(callbackfn [, initialValue])\n  reduceRight: function reduceRight(callbackfn /* , initialValue */) {\n    return $reduce(this, callbackfn, arguments.length, arguments[1], true);\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar $indexOf = require('./_array-includes')(false);\nvar $native = [].indexOf;\nvar NEGATIVE_ZERO = !!$native && 1 / [1].indexOf(1, -0) < 0;\n\n$export($export.P + $export.F * (NEGATIVE_ZERO || !require('./_strict-method')($native)), 'Array', {\n  // 22.1.3.11 / 15.4.4.14 Array.prototype.indexOf(searchElement [, fromIndex])\n  indexOf: function indexOf(searchElement /* , fromIndex = 0 */) {\n    return NEGATIVE_ZERO\n      // convert -0 to +0\n      ? $native.apply(this, arguments) || 0\n      : $indexOf(this, searchElement, arguments[1]);\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar toIObject = require('./_to-iobject');\nvar toInteger = require('./_to-integer');\nvar toLength = require('./_to-length');\nvar $native = [].lastIndexOf;\nvar NEGATIVE_ZERO = !!$native && 1 / [1].lastIndexOf(1, -0) < 0;\n\n$export($export.P + $export.F * (NEGATIVE_ZERO || !require('./_strict-method')($native)), 'Array', {\n  // 22.1.3.14 / 15.4.4.15 Array.prototype.lastIndexOf(searchElement [, fromIndex])\n  lastIndexOf: function lastIndexOf(searchElement /* , fromIndex = @[*-1] */) {\n    // convert -0 to +0\n    if (NEGATIVE_ZERO) return $native.apply(this, arguments) || 0;\n    var O = toIObject(this);\n    var length = toLength(O.length);\n    var index = length - 1;\n    if (arguments.length > 1) index = Math.min(index, toInteger(arguments[1]));\n    if (index < 0) index = length + index;\n    for (;index >= 0; index--) if (index in O) if (O[index] === searchElement) return index || 0;\n    return -1;\n  }\n});\n","// 22.1.3.3 Array.prototype.copyWithin(target, start, end = this.length)\n'use strict';\nvar toObject = require('./_to-object');\nvar toAbsoluteIndex = require('./_to-absolute-index');\nvar toLength = require('./_to-length');\n\nmodule.exports = [].copyWithin || function copyWithin(target /* = 0 */, start /* = 0, end = @length */) {\n  var O = toObject(this);\n  var len = toLength(O.length);\n  var to = toAbsoluteIndex(target, len);\n  var from = toAbsoluteIndex(start, len);\n  var end = arguments.length > 2 ? arguments[2] : undefined;\n  var count = Math.min((end === undefined ? len : toAbsoluteIndex(end, len)) - from, len - to);\n  var inc = 1;\n  if (from < to && to < from + count) {\n    inc = -1;\n    from += count - 1;\n    to += count - 1;\n  }\n  while (count-- > 0) {\n    if (from in O) O[to] = O[from];\n    else delete O[to];\n    to += inc;\n    from += inc;\n  } return O;\n};\n","// 22.1.3.31 Array.prototype[@@unscopables]\nvar UNSCOPABLES = require('./_wks')('unscopables');\nvar ArrayProto = Array.prototype;\nif (ArrayProto[UNSCOPABLES] == undefined) require('./_hide')(ArrayProto, UNSCOPABLES, {});\nmodule.exports = function (key) {\n  ArrayProto[UNSCOPABLES][key] = true;\n};\n","// 22.1.3.3 Array.prototype.copyWithin(target, start, end = this.length)\nvar $export = require('./_export');\n\n$export($export.P, 'Array', { copyWithin: require('./_array-copy-within') });\n\nrequire('./_add-to-unscopables')('copyWithin');\n","// 22.1.3.6 Array.prototype.fill(value, start = 0, end = this.length)\n'use strict';\nvar toObject = require('./_to-object');\nvar toAbsoluteIndex = require('./_to-absolute-index');\nvar toLength = require('./_to-length');\nmodule.exports = function fill(value /* , start = 0, end = @length */) {\n  var O = toObject(this);\n  var length = toLength(O.length);\n  var aLen = arguments.length;\n  var index = toAbsoluteIndex(aLen > 1 ? arguments[1] : undefined, length);\n  var end = aLen > 2 ? arguments[2] : undefined;\n  var endPos = end === undefined ? length : toAbsoluteIndex(end, length);\n  while (endPos > index) O[index++] = value;\n  return O;\n};\n","// 22.1.3.6 Array.prototype.fill(value, start = 0, end = this.length)\nvar $export = require('./_export');\n\n$export($export.P, 'Array', { fill: require('./_array-fill') });\n\nrequire('./_add-to-unscopables')('fill');\n","'use strict';\n// 22.1.3.8 Array.prototype.find(predicate, thisArg = undefined)\nvar $export = require('./_export');\nvar $find = require('./_array-methods')(5);\nvar KEY = 'find';\nvar forced = true;\n// Shouldn't skip holes\nif (KEY in []) Array(1)[KEY](function () { forced = false; });\n$export($export.P + $export.F * forced, 'Array', {\n  find: function find(callbackfn /* , that = undefined */) {\n    return $find(this, callbackfn, arguments.length > 1 ? arguments[1] : undefined);\n  }\n});\nrequire('./_add-to-unscopables')(KEY);\n","'use strict';\n// 22.1.3.9 Array.prototype.findIndex(predicate, thisArg = undefined)\nvar $export = require('./_export');\nvar $find = require('./_array-methods')(6);\nvar KEY = 'findIndex';\nvar forced = true;\n// Shouldn't skip holes\nif (KEY in []) Array(1)[KEY](function () { forced = false; });\n$export($export.P + $export.F * forced, 'Array', {\n  findIndex: function findIndex(callbackfn /* , that = undefined */) {\n    return $find(this, callbackfn, arguments.length > 1 ? arguments[1] : undefined);\n  }\n});\nrequire('./_add-to-unscopables')(KEY);\n","'use strict';\nvar global = require('./_global');\nvar dP = require('./_object-dp');\nvar DESCRIPTORS = require('./_descriptors');\nvar SPECIES = require('./_wks')('species');\n\nmodule.exports = function (KEY) {\n  var C = global[KEY];\n  if (DESCRIPTORS && C && !C[SPECIES]) dP.f(C, SPECIES, {\n    configurable: true,\n    get: function () { return this; }\n  });\n};\n","require('./_set-species')('Array');\n","module.exports = function (done, value) {\n  return { value: value, done: !!done };\n};\n","'use strict';\nvar addToUnscopables = require('./_add-to-unscopables');\nvar step = require('./_iter-step');\nvar Iterators = require('./_iterators');\nvar toIObject = require('./_to-iobject');\n\n// 22.1.3.4 Array.prototype.entries()\n// 22.1.3.13 Array.prototype.keys()\n// 22.1.3.29 Array.prototype.values()\n// 22.1.3.30 Array.prototype[@@iterator]()\nmodule.exports = require('./_iter-define')(Array, 'Array', function (iterated, kind) {\n  this._t = toIObject(iterated); // target\n  this._i = 0;                   // next index\n  this._k = kind;                // kind\n// 22.1.5.2.1 %ArrayIteratorPrototype%.next()\n}, function () {\n  var O = this._t;\n  var kind = this._k;\n  var index = this._i++;\n  if (!O || index >= O.length) {\n    this._t = undefined;\n    return step(1);\n  }\n  if (kind == 'keys') return step(0, index);\n  if (kind == 'values') return step(0, O[index]);\n  return step(0, [index, O[index]]);\n}, 'values');\n\n// argumentsList[@@iterator] is %ArrayProto_values% (9.4.4.6, 9.4.4.7)\nIterators.Arguments = Iterators.Array;\n\naddToUnscopables('keys');\naddToUnscopables('values');\naddToUnscopables('entries');\n","'use strict';\n// 21.2.5.3 get RegExp.prototype.flags\nvar anObject = require('./_an-object');\nmodule.exports = function () {\n  var that = anObject(this);\n  var result = '';\n  if (that.global) result += 'g';\n  if (that.ignoreCase) result += 'i';\n  if (that.multiline) result += 'm';\n  if (that.unicode) result += 'u';\n  if (that.sticky) result += 'y';\n  return result;\n};\n","var global = require('./_global');\nvar inheritIfRequired = require('./_inherit-if-required');\nvar dP = require('./_object-dp').f;\nvar gOPN = require('./_object-gopn').f;\nvar isRegExp = require('./_is-regexp');\nvar $flags = require('./_flags');\nvar $RegExp = global.RegExp;\nvar Base = $RegExp;\nvar proto = $RegExp.prototype;\nvar re1 = /a/g;\nvar re2 = /a/g;\n// \"new\" creates a new object, old webkit buggy here\nvar CORRECT_NEW = new $RegExp(re1) !== re1;\n\nif (require('./_descriptors') && (!CORRECT_NEW || require('./_fails')(function () {\n  re2[require('./_wks')('match')] = false;\n  // RegExp constructor can alter flags and IsRegExp works correct with @@match\n  return $RegExp(re1) != re1 || $RegExp(re2) == re2 || $RegExp(re1, 'i') != '/a/i';\n}))) {\n  $RegExp = function RegExp(p, f) {\n    var tiRE = this instanceof $RegExp;\n    var piRE = isRegExp(p);\n    var fiU = f === undefined;\n    return !tiRE && piRE && p.constructor === $RegExp && fiU ? p\n      : inheritIfRequired(CORRECT_NEW\n        ? new Base(piRE && !fiU ? p.source : p, f)\n        : Base((piRE = p instanceof $RegExp) ? p.source : p, piRE && fiU ? $flags.call(p) : f)\n      , tiRE ? this : proto, $RegExp);\n  };\n  var proxy = function (key) {\n    key in $RegExp || dP($RegExp, key, {\n      configurable: true,\n      get: function () { return Base[key]; },\n      set: function (it) { Base[key] = it; }\n    });\n  };\n  for (var keys = gOPN(Base), i = 0; keys.length > i;) proxy(keys[i++]);\n  proto.constructor = $RegExp;\n  $RegExp.prototype = proto;\n  require('./_redefine')(global, 'RegExp', $RegExp);\n}\n\nrequire('./_set-species')('RegExp');\n","'use strict';\n\nvar regexpFlags = require('./_flags');\n\nvar nativeExec = RegExp.prototype.exec;\n// This always refers to the native implementation, because the\n// String#replace polyfill uses ./fix-regexp-well-known-symbol-logic.js,\n// which loads this file before patching the method.\nvar nativeReplace = String.prototype.replace;\n\nvar patchedExec = nativeExec;\n\nvar LAST_INDEX = 'lastIndex';\n\nvar UPDATES_LAST_INDEX_WRONG = (function () {\n  var re1 = /a/,\n      re2 = /b*/g;\n  nativeExec.call(re1, 'a');\n  nativeExec.call(re2, 'a');\n  return re1[LAST_INDEX] !== 0 || re2[LAST_INDEX] !== 0;\n})();\n\n// nonparticipating capturing group, copied from es5-shim's String#split patch.\nvar NPCG_INCLUDED = /()??/.exec('')[1] !== undefined;\n\nvar PATCH = UPDATES_LAST_INDEX_WRONG || NPCG_INCLUDED;\n\nif (PATCH) {\n  patchedExec = function exec(str) {\n    var re = this;\n    var lastIndex, reCopy, match, i;\n\n    if (NPCG_INCLUDED) {\n      reCopy = new RegExp('^' + re.source + '$(?!\\\\s)', regexpFlags.call(re));\n    }\n    if (UPDATES_LAST_INDEX_WRONG) lastIndex = re[LAST_INDEX];\n\n    match = nativeExec.call(re, str);\n\n    if (UPDATES_LAST_INDEX_WRONG && match) {\n      re[LAST_INDEX] = re.global ? match.index + match[0].length : lastIndex;\n    }\n    if (NPCG_INCLUDED && match && match.length > 1) {\n      // Fix browsers whose `exec` methods don't consistently return `undefined`\n      // for NPCG, like IE8. NOTE: This doesn' work for /(.?)?/\n      // eslint-disable-next-line no-loop-func\n      nativeReplace.call(match[0], reCopy, function () {\n        for (i = 1; i < arguments.length - 2; i++) {\n          if (arguments[i] === undefined) match[i] = undefined;\n        }\n      });\n    }\n\n    return match;\n  };\n}\n\nmodule.exports = patchedExec;\n","'use strict';\nvar regexpExec = require('./_regexp-exec');\nrequire('./_export')({\n  target: 'RegExp',\n  proto: true,\n  forced: regexpExec !== /./.exec\n}, {\n  exec: regexpExec\n});\n","// 21.2.5.3 get RegExp.prototype.flags()\nif (require('./_descriptors') && /./g.flags != 'g') require('./_object-dp').f(RegExp.prototype, 'flags', {\n  configurable: true,\n  get: require('./_flags')\n});\n","'use strict';\nrequire('./es6.regexp.flags');\nvar anObject = require('./_an-object');\nvar $flags = require('./_flags');\nvar DESCRIPTORS = require('./_descriptors');\nvar TO_STRING = 'toString';\nvar $toString = /./[TO_STRING];\n\nvar define = function (fn) {\n  require('./_redefine')(RegExp.prototype, TO_STRING, fn, true);\n};\n\n// 21.2.5.14 RegExp.prototype.toString()\nif (require('./_fails')(function () { return $toString.call({ source: 'a', flags: 'b' }) != '/a/b'; })) {\n  define(function toString() {\n    var R = anObject(this);\n    return '/'.concat(R.source, '/',\n      'flags' in R ? R.flags : !DESCRIPTORS && R instanceof RegExp ? $flags.call(R) : undefined);\n  });\n// FF44- RegExp#toString has a wrong name\n} else if ($toString.name != TO_STRING) {\n  define(function toString() {\n    return $toString.call(this);\n  });\n}\n","'use strict';\nvar at = require('./_string-at')(true);\n\n // `AdvanceStringIndex` abstract operation\n// https://tc39.github.io/ecma262/#sec-advancestringindex\nmodule.exports = function (S, index, unicode) {\n  return index + (unicode ? at(S, index).length : 1);\n};\n","'use strict';\n\nvar classof = require('./_classof');\nvar builtinExec = RegExp.prototype.exec;\n\n // `RegExpExec` abstract operation\n// https://tc39.github.io/ecma262/#sec-regexpexec\nmodule.exports = function (R, S) {\n  var exec = R.exec;\n  if (typeof exec === 'function') {\n    var result = exec.call(R, S);\n    if (typeof result !== 'object') {\n      throw new TypeError('RegExp exec method returned something other than an Object or null');\n    }\n    return result;\n  }\n  if (classof(R) !== 'RegExp') {\n    throw new TypeError('RegExp#exec called on incompatible receiver');\n  }\n  return builtinExec.call(R, S);\n};\n","'use strict';\nrequire('./es6.regexp.exec');\nvar redefine = require('./_redefine');\nvar hide = require('./_hide');\nvar fails = require('./_fails');\nvar defined = require('./_defined');\nvar wks = require('./_wks');\nvar regexpExec = require('./_regexp-exec');\n\nvar SPECIES = wks('species');\n\nvar REPLACE_SUPPORTS_NAMED_GROUPS = !fails(function () {\n  // #replace needs built-in support for named groups.\n  // #match works fine because it just return the exec results, even if it has\n  // a \"grops\" property.\n  var re = /./;\n  re.exec = function () {\n    var result = [];\n    result.groups = { a: '7' };\n    return result;\n  };\n  return ''.replace(re, '$<a>') !== '7';\n});\n\nvar SPLIT_WORKS_WITH_OVERWRITTEN_EXEC = (function () {\n  // Chrome 51 has a buggy \"split\" implementation when RegExp#exec !== nativeExec\n  var re = /(?:)/;\n  var originalExec = re.exec;\n  re.exec = function () { return originalExec.apply(this, arguments); };\n  var result = 'ab'.split(re);\n  return result.length === 2 && result[0] === 'a' && result[1] === 'b';\n})();\n\nmodule.exports = function (KEY, length, exec) {\n  var SYMBOL = wks(KEY);\n\n  var DELEGATES_TO_SYMBOL = !fails(function () {\n    // String methods call symbol-named RegEp methods\n    var O = {};\n    O[SYMBOL] = function () { return 7; };\n    return ''[KEY](O) != 7;\n  });\n\n  var DELEGATES_TO_EXEC = DELEGATES_TO_SYMBOL ? !fails(function () {\n    // Symbol-named RegExp methods call .exec\n    var execCalled = false;\n    var re = /a/;\n    re.exec = function () { execCalled = true; return null; };\n    if (KEY === 'split') {\n      // RegExp[@@split] doesn't call the regex's exec method, but first creates\n      // a new one. We need to return the patched regex when creating the new one.\n      re.constructor = {};\n      re.constructor[SPECIES] = function () { return re; };\n    }\n    re[SYMBOL]('');\n    return !execCalled;\n  }) : undefined;\n\n  if (\n    !DELEGATES_TO_SYMBOL ||\n    !DELEGATES_TO_EXEC ||\n    (KEY === 'replace' && !REPLACE_SUPPORTS_NAMED_GROUPS) ||\n    (KEY === 'split' && !SPLIT_WORKS_WITH_OVERWRITTEN_EXEC)\n  ) {\n    var nativeRegExpMethod = /./[SYMBOL];\n    var fns = exec(\n      defined,\n      SYMBOL,\n      ''[KEY],\n      function maybeCallNative(nativeMethod, regexp, str, arg2, forceStringMethod) {\n        if (regexp.exec === regexpExec) {\n          if (DELEGATES_TO_SYMBOL && !forceStringMethod) {\n            // The native String method already delegates to @@method (this\n            // polyfilled function), leasing to infinite recursion.\n            // We avoid it by directly calling the native @@method method.\n            return { done: true, value: nativeRegExpMethod.call(regexp, str, arg2) };\n          }\n          return { done: true, value: nativeMethod.call(str, regexp, arg2) };\n        }\n        return { done: false };\n      }\n    );\n    var strfn = fns[0];\n    var rxfn = fns[1];\n\n    redefine(String.prototype, KEY, strfn);\n    hide(RegExp.prototype, SYMBOL, length == 2\n      // 21.2.5.8 RegExp.prototype[@@replace](string, replaceValue)\n      // 21.2.5.11 RegExp.prototype[@@split](string, limit)\n      ? function (string, arg) { return rxfn.call(string, this, arg); }\n      // 21.2.5.6 RegExp.prototype[@@match](string)\n      // 21.2.5.9 RegExp.prototype[@@search](string)\n      : function (string) { return rxfn.call(string, this); }\n    );\n  }\n};\n","'use strict';\n\nvar anObject = require('./_an-object');\nvar toLength = require('./_to-length');\nvar advanceStringIndex = require('./_advance-string-index');\nvar regExpExec = require('./_regexp-exec-abstract');\n\n// @@match logic\nrequire('./_fix-re-wks')('match', 1, function (defined, MATCH, $match, maybeCallNative) {\n  return [\n    // `String.prototype.match` method\n    // https://tc39.github.io/ecma262/#sec-string.prototype.match\n    function match(regexp) {\n      var O = defined(this);\n      var fn = regexp == undefined ? undefined : regexp[MATCH];\n      return fn !== undefined ? fn.call(regexp, O) : new RegExp(regexp)[MATCH](String(O));\n    },\n    // `RegExp.prototype[@@match]` method\n    // https://tc39.github.io/ecma262/#sec-regexp.prototype-@@match\n    function (regexp) {\n      var res = maybeCallNative($match, regexp, this);\n      if (res.done) return res.value;\n      var rx = anObject(regexp);\n      var S = String(this);\n      if (!rx.global) return regExpExec(rx, S);\n      var fullUnicode = rx.unicode;\n      rx.lastIndex = 0;\n      var A = [];\n      var n = 0;\n      var result;\n      while ((result = regExpExec(rx, S)) !== null) {\n        var matchStr = String(result[0]);\n        A[n] = matchStr;\n        if (matchStr === '') rx.lastIndex = advanceStringIndex(S, toLength(rx.lastIndex), fullUnicode);\n        n++;\n      }\n      return n === 0 ? null : A;\n    }\n  ];\n});\n","'use strict';\n\nvar anObject = require('./_an-object');\nvar toObject = require('./_to-object');\nvar toLength = require('./_to-length');\nvar toInteger = require('./_to-integer');\nvar advanceStringIndex = require('./_advance-string-index');\nvar regExpExec = require('./_regexp-exec-abstract');\nvar max = Math.max;\nvar min = Math.min;\nvar floor = Math.floor;\nvar SUBSTITUTION_SYMBOLS = /\\$([$&`']|\\d\\d?|<[^>]*>)/g;\nvar SUBSTITUTION_SYMBOLS_NO_NAMED = /\\$([$&`']|\\d\\d?)/g;\n\nvar maybeToString = function (it) {\n  return it === undefined ? it : String(it);\n};\n\n// @@replace logic\nrequire('./_fix-re-wks')('replace', 2, function (defined, REPLACE, $replace, maybeCallNative) {\n  return [\n    // `String.prototype.replace` method\n    // https://tc39.github.io/ecma262/#sec-string.prototype.replace\n    function replace(searchValue, replaceValue) {\n      var O = defined(this);\n      var fn = searchValue == undefined ? undefined : searchValue[REPLACE];\n      return fn !== undefined\n        ? fn.call(searchValue, O, replaceValue)\n        : $replace.call(String(O), searchValue, replaceValue);\n    },\n    // `RegExp.prototype[@@replace]` method\n    // https://tc39.github.io/ecma262/#sec-regexp.prototype-@@replace\n    function (regexp, replaceValue) {\n      var res = maybeCallNative($replace, regexp, this, replaceValue);\n      if (res.done) return res.value;\n\n      var rx = anObject(regexp);\n      var S = String(this);\n      var functionalReplace = typeof replaceValue === 'function';\n      if (!functionalReplace) replaceValue = String(replaceValue);\n      var global = rx.global;\n      if (global) {\n        var fullUnicode = rx.unicode;\n        rx.lastIndex = 0;\n      }\n      var results = [];\n      while (true) {\n        var result = regExpExec(rx, S);\n        if (result === null) break;\n        results.push(result);\n        if (!global) break;\n        var matchStr = String(result[0]);\n        if (matchStr === '') rx.lastIndex = advanceStringIndex(S, toLength(rx.lastIndex), fullUnicode);\n      }\n      var accumulatedResult = '';\n      var nextSourcePosition = 0;\n      for (var i = 0; i < results.length; i++) {\n        result = results[i];\n        var matched = String(result[0]);\n        var position = max(min(toInteger(result.index), S.length), 0);\n        var captures = [];\n        // NOTE: This is equivalent to\n        //   captures = result.slice(1).map(maybeToString)\n        // but for some reason `nativeSlice.call(result, 1, result.length)` (called in\n        // the slice polyfill when slicing native arrays) \"doesn't work\" in safari 9 and\n        // causes a crash (https://pastebin.com/N21QzeQA) when trying to debug it.\n        for (var j = 1; j < result.length; j++) captures.push(maybeToString(result[j]));\n        var namedCaptures = result.groups;\n        if (functionalReplace) {\n          var replacerArgs = [matched].concat(captures, position, S);\n          if (namedCaptures !== undefined) replacerArgs.push(namedCaptures);\n          var replacement = String(replaceValue.apply(undefined, replacerArgs));\n        } else {\n          replacement = getSubstitution(matched, S, position, captures, namedCaptures, replaceValue);\n        }\n        if (position >= nextSourcePosition) {\n          accumulatedResult += S.slice(nextSourcePosition, position) + replacement;\n          nextSourcePosition = position + matched.length;\n        }\n      }\n      return accumulatedResult + S.slice(nextSourcePosition);\n    }\n  ];\n\n    // https://tc39.github.io/ecma262/#sec-getsubstitution\n  function getSubstitution(matched, str, position, captures, namedCaptures, replacement) {\n    var tailPos = position + matched.length;\n    var m = captures.length;\n    var symbols = SUBSTITUTION_SYMBOLS_NO_NAMED;\n    if (namedCaptures !== undefined) {\n      namedCaptures = toObject(namedCaptures);\n      symbols = SUBSTITUTION_SYMBOLS;\n    }\n    return $replace.call(replacement, symbols, function (match, ch) {\n      var capture;\n      switch (ch.charAt(0)) {\n        case '$': return '$';\n        case '&': return matched;\n        case '`': return str.slice(0, position);\n        case \"'\": return str.slice(tailPos);\n        case '<':\n          capture = namedCaptures[ch.slice(1, -1)];\n          break;\n        default: // \\d\\d?\n          var n = +ch;\n          if (n === 0) return match;\n          if (n > m) {\n            var f = floor(n / 10);\n            if (f === 0) return match;\n            if (f <= m) return captures[f - 1] === undefined ? ch.charAt(1) : captures[f - 1] + ch.charAt(1);\n            return match;\n          }\n          capture = captures[n - 1];\n      }\n      return capture === undefined ? '' : capture;\n    });\n  }\n});\n","'use strict';\n\nvar anObject = require('./_an-object');\nvar sameValue = require('./_same-value');\nvar regExpExec = require('./_regexp-exec-abstract');\n\n// @@search logic\nrequire('./_fix-re-wks')('search', 1, function (defined, SEARCH, $search, maybeCallNative) {\n  return [\n    // `String.prototype.search` method\n    // https://tc39.github.io/ecma262/#sec-string.prototype.search\n    function search(regexp) {\n      var O = defined(this);\n      var fn = regexp == undefined ? undefined : regexp[SEARCH];\n      return fn !== undefined ? fn.call(regexp, O) : new RegExp(regexp)[SEARCH](String(O));\n    },\n    // `RegExp.prototype[@@search]` method\n    // https://tc39.github.io/ecma262/#sec-regexp.prototype-@@search\n    function (regexp) {\n      var res = maybeCallNative($search, regexp, this);\n      if (res.done) return res.value;\n      var rx = anObject(regexp);\n      var S = String(this);\n      var previousLastIndex = rx.lastIndex;\n      if (!sameValue(previousLastIndex, 0)) rx.lastIndex = 0;\n      var result = regExpExec(rx, S);\n      if (!sameValue(rx.lastIndex, previousLastIndex)) rx.lastIndex = previousLastIndex;\n      return result === null ? -1 : result.index;\n    }\n  ];\n});\n","// 7.3.20 SpeciesConstructor(O, defaultConstructor)\nvar anObject = require('./_an-object');\nvar aFunction = require('./_a-function');\nvar SPECIES = require('./_wks')('species');\nmodule.exports = function (O, D) {\n  var C = anObject(O).constructor;\n  var S;\n  return C === undefined || (S = anObject(C)[SPECIES]) == undefined ? D : aFunction(S);\n};\n","'use strict';\n\nvar isRegExp = require('./_is-regexp');\nvar anObject = require('./_an-object');\nvar speciesConstructor = require('./_species-constructor');\nvar advanceStringIndex = require('./_advance-string-index');\nvar toLength = require('./_to-length');\nvar callRegExpExec = require('./_regexp-exec-abstract');\nvar regexpExec = require('./_regexp-exec');\nvar fails = require('./_fails');\nvar $min = Math.min;\nvar $push = [].push;\nvar $SPLIT = 'split';\nvar LENGTH = 'length';\nvar LAST_INDEX = 'lastIndex';\nvar MAX_UINT32 = 0xffffffff;\n\n// babel-minify transpiles RegExp('x', 'y') -> /x/y and it causes SyntaxError\nvar SUPPORTS_Y = !fails(function () { RegExp(MAX_UINT32, 'y'); });\n\n// @@split logic\nrequire('./_fix-re-wks')('split', 2, function (defined, SPLIT, $split, maybeCallNative) {\n  var internalSplit;\n  if (\n    'abbc'[$SPLIT](/(b)*/)[1] == 'c' ||\n    'test'[$SPLIT](/(?:)/, -1)[LENGTH] != 4 ||\n    'ab'[$SPLIT](/(?:ab)*/)[LENGTH] != 2 ||\n    '.'[$SPLIT](/(.?)(.?)/)[LENGTH] != 4 ||\n    '.'[$SPLIT](/()()/)[LENGTH] > 1 ||\n    ''[$SPLIT](/.?/)[LENGTH]\n  ) {\n    // based on es5-shim implementation, need to rework it\n    internalSplit = function (separator, limit) {\n      var string = String(this);\n      if (separator === undefined && limit === 0) return [];\n      // If `separator` is not a regex, use native split\n      if (!isRegExp(separator)) return $split.call(string, separator, limit);\n      var output = [];\n      var flags = (separator.ignoreCase ? 'i' : '') +\n                  (separator.multiline ? 'm' : '') +\n                  (separator.unicode ? 'u' : '') +\n                  (separator.sticky ? 'y' : '');\n      var lastLastIndex = 0;\n      var splitLimit = limit === undefined ? MAX_UINT32 : limit >>> 0;\n      // Make `global` and avoid `lastIndex` issues by working with a copy\n      var separatorCopy = new RegExp(separator.source, flags + 'g');\n      var match, lastIndex, lastLength;\n      while (match = regexpExec.call(separatorCopy, string)) {\n        lastIndex = separatorCopy[LAST_INDEX];\n        if (lastIndex > lastLastIndex) {\n          output.push(string.slice(lastLastIndex, match.index));\n          if (match[LENGTH] > 1 && match.index < string[LENGTH]) $push.apply(output, match.slice(1));\n          lastLength = match[0][LENGTH];\n          lastLastIndex = lastIndex;\n          if (output[LENGTH] >= splitLimit) break;\n        }\n        if (separatorCopy[LAST_INDEX] === match.index) separatorCopy[LAST_INDEX]++; // Avoid an infinite loop\n      }\n      if (lastLastIndex === string[LENGTH]) {\n        if (lastLength || !separatorCopy.test('')) output.push('');\n      } else output.push(string.slice(lastLastIndex));\n      return output[LENGTH] > splitLimit ? output.slice(0, splitLimit) : output;\n    };\n  // Chakra, V8\n  } else if ('0'[$SPLIT](undefined, 0)[LENGTH]) {\n    internalSplit = function (separator, limit) {\n      return separator === undefined && limit === 0 ? [] : $split.call(this, separator, limit);\n    };\n  } else {\n    internalSplit = $split;\n  }\n\n  return [\n    // `String.prototype.split` method\n    // https://tc39.github.io/ecma262/#sec-string.prototype.split\n    function split(separator, limit) {\n      var O = defined(this);\n      var splitter = separator == undefined ? undefined : separator[SPLIT];\n      return splitter !== undefined\n        ? splitter.call(separator, O, limit)\n        : internalSplit.call(String(O), separator, limit);\n    },\n    // `RegExp.prototype[@@split]` method\n    // https://tc39.github.io/ecma262/#sec-regexp.prototype-@@split\n    //\n    // NOTE: This cannot be properly polyfilled in engines that don't support\n    // the 'y' flag.\n    function (regexp, limit) {\n      var res = maybeCallNative(internalSplit, regexp, this, limit, internalSplit !== $split);\n      if (res.done) return res.value;\n\n      var rx = anObject(regexp);\n      var S = String(this);\n      var C = speciesConstructor(rx, RegExp);\n\n      var unicodeMatching = rx.unicode;\n      var flags = (rx.ignoreCase ? 'i' : '') +\n                  (rx.multiline ? 'm' : '') +\n                  (rx.unicode ? 'u' : '') +\n                  (SUPPORTS_Y ? 'y' : 'g');\n\n      // ^(? + rx + ) is needed, in combination with some S slicing, to\n      // simulate the 'y' flag.\n      var splitter = new C(SUPPORTS_Y ? rx : '^(?:' + rx.source + ')', flags);\n      var lim = limit === undefined ? MAX_UINT32 : limit >>> 0;\n      if (lim === 0) return [];\n      if (S.length === 0) return callRegExpExec(splitter, S) === null ? [S] : [];\n      var p = 0;\n      var q = 0;\n      var A = [];\n      while (q < S.length) {\n        splitter.lastIndex = SUPPORTS_Y ? q : 0;\n        var z = callRegExpExec(splitter, SUPPORTS_Y ? S : S.slice(q));\n        var e;\n        if (\n          z === null ||\n          (e = $min(toLength(splitter.lastIndex + (SUPPORTS_Y ? 0 : q)), S.length)) === p\n        ) {\n          q = advanceStringIndex(S, q, unicodeMatching);\n        } else {\n          A.push(S.slice(p, q));\n          if (A.length === lim) return A;\n          for (var i = 1; i <= z.length - 1; i++) {\n            A.push(z[i]);\n            if (A.length === lim) return A;\n          }\n          q = p = e;\n        }\n      }\n      A.push(S.slice(p));\n      return A;\n    }\n  ];\n});\n","module.exports = function (it, Constructor, name, forbiddenField) {\n  if (!(it instanceof Constructor) || (forbiddenField !== undefined && forbiddenField in it)) {\n    throw TypeError(name + ': incorrect invocation!');\n  } return it;\n};\n","var ctx = require('./_ctx');\nvar call = require('./_iter-call');\nvar isArrayIter = require('./_is-array-iter');\nvar anObject = require('./_an-object');\nvar toLength = require('./_to-length');\nvar getIterFn = require('./core.get-iterator-method');\nvar BREAK = {};\nvar RETURN = {};\nvar exports = module.exports = function (iterable, entries, fn, that, ITERATOR) {\n  var iterFn = ITERATOR ? function () { return iterable; } : getIterFn(iterable);\n  var f = ctx(fn, that, entries ? 2 : 1);\n  var index = 0;\n  var length, step, iterator, result;\n  if (typeof iterFn != 'function') throw TypeError(iterable + ' is not iterable!');\n  // fast case for arrays with default iterator\n  if (isArrayIter(iterFn)) for (length = toLength(iterable.length); length > index; index++) {\n    result = entries ? f(anObject(step = iterable[index])[0], step[1]) : f(iterable[index]);\n    if (result === BREAK || result === RETURN) return result;\n  } else for (iterator = iterFn.call(iterable); !(step = iterator.next()).done;) {\n    result = call(iterator, f, step.value, entries);\n    if (result === BREAK || result === RETURN) return result;\n  }\n};\nexports.BREAK = BREAK;\nexports.RETURN = RETURN;\n","var ctx = require('./_ctx');\nvar invoke = require('./_invoke');\nvar html = require('./_html');\nvar cel = require('./_dom-create');\nvar global = require('./_global');\nvar process = global.process;\nvar setTask = global.setImmediate;\nvar clearTask = global.clearImmediate;\nvar MessageChannel = global.MessageChannel;\nvar Dispatch = global.Dispatch;\nvar counter = 0;\nvar queue = {};\nvar ONREADYSTATECHANGE = 'onreadystatechange';\nvar defer, channel, port;\nvar run = function () {\n  var id = +this;\n  // eslint-disable-next-line no-prototype-builtins\n  if (queue.hasOwnProperty(id)) {\n    var fn = queue[id];\n    delete queue[id];\n    fn();\n  }\n};\nvar listener = function (event) {\n  run.call(event.data);\n};\n// Node.js 0.9+ & IE10+ has setImmediate, otherwise:\nif (!setTask || !clearTask) {\n  setTask = function setImmediate(fn) {\n    var args = [];\n    var i = 1;\n    while (arguments.length > i) args.push(arguments[i++]);\n    queue[++counter] = function () {\n      // eslint-disable-next-line no-new-func\n      invoke(typeof fn == 'function' ? fn : Function(fn), args);\n    };\n    defer(counter);\n    return counter;\n  };\n  clearTask = function clearImmediate(id) {\n    delete queue[id];\n  };\n  // Node.js 0.8-\n  if (require('./_cof')(process) == 'process') {\n    defer = function (id) {\n      process.nextTick(ctx(run, id, 1));\n    };\n  // Sphere (JS game engine) Dispatch API\n  } else if (Dispatch && Dispatch.now) {\n    defer = function (id) {\n      Dispatch.now(ctx(run, id, 1));\n    };\n  // Browsers with MessageChannel, includes WebWorkers\n  } else if (MessageChannel) {\n    channel = new MessageChannel();\n    port = channel.port2;\n    channel.port1.onmessage = listener;\n    defer = ctx(port.postMessage, port, 1);\n  // Browsers with postMessage, skip WebWorkers\n  // IE8 has postMessage, but it's sync & typeof its postMessage is 'object'\n  } else if (global.addEventListener && typeof postMessage == 'function' && !global.importScripts) {\n    defer = function (id) {\n      global.postMessage(id + '', '*');\n    };\n    global.addEventListener('message', listener, false);\n  // IE8-\n  } else if (ONREADYSTATECHANGE in cel('script')) {\n    defer = function (id) {\n      html.appendChild(cel('script'))[ONREADYSTATECHANGE] = function () {\n        html.removeChild(this);\n        run.call(id);\n      };\n    };\n  // Rest old browsers\n  } else {\n    defer = function (id) {\n      setTimeout(ctx(run, id, 1), 0);\n    };\n  }\n}\nmodule.exports = {\n  set: setTask,\n  clear: clearTask\n};\n","var global = require('./_global');\nvar macrotask = require('./_task').set;\nvar Observer = global.MutationObserver || global.WebKitMutationObserver;\nvar process = global.process;\nvar Promise = global.Promise;\nvar isNode = require('./_cof')(process) == 'process';\n\nmodule.exports = function () {\n  var head, last, notify;\n\n  var flush = function () {\n    var parent, fn;\n    if (isNode && (parent = process.domain)) parent.exit();\n    while (head) {\n      fn = head.fn;\n      head = head.next;\n      try {\n        fn();\n      } catch (e) {\n        if (head) notify();\n        else last = undefined;\n        throw e;\n      }\n    } last = undefined;\n    if (parent) parent.enter();\n  };\n\n  // Node.js\n  if (isNode) {\n    notify = function () {\n      process.nextTick(flush);\n    };\n  // browsers with MutationObserver, except iOS Safari - https://github.com/zloirock/core-js/issues/339\n  } else if (Observer && !(global.navigator && global.navigator.standalone)) {\n    var toggle = true;\n    var node = document.createTextNode('');\n    new Observer(flush).observe(node, { characterData: true }); // eslint-disable-line no-new\n    notify = function () {\n      node.data = toggle = !toggle;\n    };\n  // environments with maybe non-completely correct, but existent Promise\n  } else if (Promise && Promise.resolve) {\n    // Promise.resolve without an argument throws an error in LG WebOS 2\n    var promise = Promise.resolve(undefined);\n    notify = function () {\n      promise.then(flush);\n    };\n  // for other environments - macrotask based on:\n  // - setImmediate\n  // - MessageChannel\n  // - window.postMessag\n  // - onreadystatechange\n  // - setTimeout\n  } else {\n    notify = function () {\n      // strange IE + webpack dev server bug - use .call(global)\n      macrotask.call(global, flush);\n    };\n  }\n\n  return function (fn) {\n    var task = { fn: fn, next: undefined };\n    if (last) last.next = task;\n    if (!head) {\n      head = task;\n      notify();\n    } last = task;\n  };\n};\n","'use strict';\n// 25.4.1.5 NewPromiseCapability(C)\nvar aFunction = require('./_a-function');\n\nfunction PromiseCapability(C) {\n  var resolve, reject;\n  this.promise = new C(function ($$resolve, $$reject) {\n    if (resolve !== undefined || reject !== undefined) throw TypeError('Bad Promise constructor');\n    resolve = $$resolve;\n    reject = $$reject;\n  });\n  this.resolve = aFunction(resolve);\n  this.reject = aFunction(reject);\n}\n\nmodule.exports.f = function (C) {\n  return new PromiseCapability(C);\n};\n","module.exports = function (exec) {\n  try {\n    return { e: false, v: exec() };\n  } catch (e) {\n    return { e: true, v: e };\n  }\n};\n","var global = require('./_global');\nvar navigator = global.navigator;\n\nmodule.exports = navigator && navigator.userAgent || '';\n","var anObject = require('./_an-object');\nvar isObject = require('./_is-object');\nvar newPromiseCapability = require('./_new-promise-capability');\n\nmodule.exports = function (C, x) {\n  anObject(C);\n  if (isObject(x) && x.constructor === C) return x;\n  var promiseCapability = newPromiseCapability.f(C);\n  var resolve = promiseCapability.resolve;\n  resolve(x);\n  return promiseCapability.promise;\n};\n","var redefine = require('./_redefine');\nmodule.exports = function (target, src, safe) {\n  for (var key in src) redefine(target, key, src[key], safe);\n  return target;\n};\n","'use strict';\nvar LIBRARY = require('./_library');\nvar global = require('./_global');\nvar ctx = require('./_ctx');\nvar classof = require('./_classof');\nvar $export = require('./_export');\nvar isObject = require('./_is-object');\nvar aFunction = require('./_a-function');\nvar anInstance = require('./_an-instance');\nvar forOf = require('./_for-of');\nvar speciesConstructor = require('./_species-constructor');\nvar task = require('./_task').set;\nvar microtask = require('./_microtask')();\nvar newPromiseCapabilityModule = require('./_new-promise-capability');\nvar perform = require('./_perform');\nvar userAgent = require('./_user-agent');\nvar promiseResolve = require('./_promise-resolve');\nvar PROMISE = 'Promise';\nvar TypeError = global.TypeError;\nvar process = global.process;\nvar versions = process && process.versions;\nvar v8 = versions && versions.v8 || '';\nvar $Promise = global[PROMISE];\nvar isNode = classof(process) == 'process';\nvar empty = function () { /* empty */ };\nvar Internal, newGenericPromiseCapability, OwnPromiseCapability, Wrapper;\nvar newPromiseCapability = newGenericPromiseCapability = newPromiseCapabilityModule.f;\n\nvar USE_NATIVE = !!function () {\n  try {\n    // correct subclassing with @@species support\n    var promise = $Promise.resolve(1);\n    var FakePromise = (promise.constructor = {})[require('./_wks')('species')] = function (exec) {\n      exec(empty, empty);\n    };\n    // unhandled rejections tracking support, NodeJS Promise without it fails @@species test\n    return (isNode || typeof PromiseRejectionEvent == 'function')\n      && promise.then(empty) instanceof FakePromise\n      // v8 6.6 (Node 10 and Chrome 66) have a bug with resolving custom thenables\n      // https://bugs.chromium.org/p/chromium/issues/detail?id=830565\n      // we can't detect it synchronously, so just check versions\n      && v8.indexOf('6.6') !== 0\n      && userAgent.indexOf('Chrome/66') === -1;\n  } catch (e) { /* empty */ }\n}();\n\n// helpers\nvar isThenable = function (it) {\n  var then;\n  return isObject(it) && typeof (then = it.then) == 'function' ? then : false;\n};\nvar notify = function (promise, isReject) {\n  if (promise._n) return;\n  promise._n = true;\n  var chain = promise._c;\n  microtask(function () {\n    var value = promise._v;\n    var ok = promise._s == 1;\n    var i = 0;\n    var run = function (reaction) {\n      var handler = ok ? reaction.ok : reaction.fail;\n      var resolve = reaction.resolve;\n      var reject = reaction.reject;\n      var domain = reaction.domain;\n      var result, then, exited;\n      try {\n        if (handler) {\n          if (!ok) {\n            if (promise._h == 2) onHandleUnhandled(promise);\n            promise._h = 1;\n          }\n          if (handler === true) result = value;\n          else {\n            if (domain) domain.enter();\n            result = handler(value); // may throw\n            if (domain) {\n              domain.exit();\n              exited = true;\n            }\n          }\n          if (result === reaction.promise) {\n            reject(TypeError('Promise-chain cycle'));\n          } else if (then = isThenable(result)) {\n            then.call(result, resolve, reject);\n          } else resolve(result);\n        } else reject(value);\n      } catch (e) {\n        if (domain && !exited) domain.exit();\n        reject(e);\n      }\n    };\n    while (chain.length > i) run(chain[i++]); // variable length - can't use forEach\n    promise._c = [];\n    promise._n = false;\n    if (isReject && !promise._h) onUnhandled(promise);\n  });\n};\nvar onUnhandled = function (promise) {\n  task.call(global, function () {\n    var value = promise._v;\n    var unhandled = isUnhandled(promise);\n    var result, handler, console;\n    if (unhandled) {\n      result = perform(function () {\n        if (isNode) {\n          process.emit('unhandledRejection', value, promise);\n        } else if (handler = global.onunhandledrejection) {\n          handler({ promise: promise, reason: value });\n        } else if ((console = global.console) && console.error) {\n          console.error('Unhandled promise rejection', value);\n        }\n      });\n      // Browsers should not trigger `rejectionHandled` event if it was handled here, NodeJS - should\n      promise._h = isNode || isUnhandled(promise) ? 2 : 1;\n    } promise._a = undefined;\n    if (unhandled && result.e) throw result.v;\n  });\n};\nvar isUnhandled = function (promise) {\n  return promise._h !== 1 && (promise._a || promise._c).length === 0;\n};\nvar onHandleUnhandled = function (promise) {\n  task.call(global, function () {\n    var handler;\n    if (isNode) {\n      process.emit('rejectionHandled', promise);\n    } else if (handler = global.onrejectionhandled) {\n      handler({ promise: promise, reason: promise._v });\n    }\n  });\n};\nvar $reject = function (value) {\n  var promise = this;\n  if (promise._d) return;\n  promise._d = true;\n  promise = promise._w || promise; // unwrap\n  promise._v = value;\n  promise._s = 2;\n  if (!promise._a) promise._a = promise._c.slice();\n  notify(promise, true);\n};\nvar $resolve = function (value) {\n  var promise = this;\n  var then;\n  if (promise._d) return;\n  promise._d = true;\n  promise = promise._w || promise; // unwrap\n  try {\n    if (promise === value) throw TypeError(\"Promise can't be resolved itself\");\n    if (then = isThenable(value)) {\n      microtask(function () {\n        var wrapper = { _w: promise, _d: false }; // wrap\n        try {\n          then.call(value, ctx($resolve, wrapper, 1), ctx($reject, wrapper, 1));\n        } catch (e) {\n          $reject.call(wrapper, e);\n        }\n      });\n    } else {\n      promise._v = value;\n      promise._s = 1;\n      notify(promise, false);\n    }\n  } catch (e) {\n    $reject.call({ _w: promise, _d: false }, e); // wrap\n  }\n};\n\n// constructor polyfill\nif (!USE_NATIVE) {\n  // 25.4.3.1 Promise(executor)\n  $Promise = function Promise(executor) {\n    anInstance(this, $Promise, PROMISE, '_h');\n    aFunction(executor);\n    Internal.call(this);\n    try {\n      executor(ctx($resolve, this, 1), ctx($reject, this, 1));\n    } catch (err) {\n      $reject.call(this, err);\n    }\n  };\n  // eslint-disable-next-line no-unused-vars\n  Internal = function Promise(executor) {\n    this._c = [];             // <- awaiting reactions\n    this._a = undefined;      // <- checked in isUnhandled reactions\n    this._s = 0;              // <- state\n    this._d = false;          // <- done\n    this._v = undefined;      // <- value\n    this._h = 0;              // <- rejection state, 0 - default, 1 - handled, 2 - unhandled\n    this._n = false;          // <- notify\n  };\n  Internal.prototype = require('./_redefine-all')($Promise.prototype, {\n    // 25.4.5.3 Promise.prototype.then(onFulfilled, onRejected)\n    then: function then(onFulfilled, onRejected) {\n      var reaction = newPromiseCapability(speciesConstructor(this, $Promise));\n      reaction.ok = typeof onFulfilled == 'function' ? onFulfilled : true;\n      reaction.fail = typeof onRejected == 'function' && onRejected;\n      reaction.domain = isNode ? process.domain : undefined;\n      this._c.push(reaction);\n      if (this._a) this._a.push(reaction);\n      if (this._s) notify(this, false);\n      return reaction.promise;\n    },\n    // 25.4.5.1 Promise.prototype.catch(onRejected)\n    'catch': function (onRejected) {\n      return this.then(undefined, onRejected);\n    }\n  });\n  OwnPromiseCapability = function () {\n    var promise = new Internal();\n    this.promise = promise;\n    this.resolve = ctx($resolve, promise, 1);\n    this.reject = ctx($reject, promise, 1);\n  };\n  newPromiseCapabilityModule.f = newPromiseCapability = function (C) {\n    return C === $Promise || C === Wrapper\n      ? new OwnPromiseCapability(C)\n      : newGenericPromiseCapability(C);\n  };\n}\n\n$export($export.G + $export.W + $export.F * !USE_NATIVE, { Promise: $Promise });\nrequire('./_set-to-string-tag')($Promise, PROMISE);\nrequire('./_set-species')(PROMISE);\nWrapper = require('./_core')[PROMISE];\n\n// statics\n$export($export.S + $export.F * !USE_NATIVE, PROMISE, {\n  // 25.4.4.5 Promise.reject(r)\n  reject: function reject(r) {\n    var capability = newPromiseCapability(this);\n    var $$reject = capability.reject;\n    $$reject(r);\n    return capability.promise;\n  }\n});\n$export($export.S + $export.F * (LIBRARY || !USE_NATIVE), PROMISE, {\n  // 25.4.4.6 Promise.resolve(x)\n  resolve: function resolve(x) {\n    return promiseResolve(LIBRARY && this === Wrapper ? $Promise : this, x);\n  }\n});\n$export($export.S + $export.F * !(USE_NATIVE && require('./_iter-detect')(function (iter) {\n  $Promise.all(iter)['catch'](empty);\n})), PROMISE, {\n  // 25.4.4.1 Promise.all(iterable)\n  all: function all(iterable) {\n    var C = this;\n    var capability = newPromiseCapability(C);\n    var resolve = capability.resolve;\n    var reject = capability.reject;\n    var result = perform(function () {\n      var values = [];\n      var index = 0;\n      var remaining = 1;\n      forOf(iterable, false, function (promise) {\n        var $index = index++;\n        var alreadyCalled = false;\n        values.push(undefined);\n        remaining++;\n        C.resolve(promise).then(function (value) {\n          if (alreadyCalled) return;\n          alreadyCalled = true;\n          values[$index] = value;\n          --remaining || resolve(values);\n        }, reject);\n      });\n      --remaining || resolve(values);\n    });\n    if (result.e) reject(result.v);\n    return capability.promise;\n  },\n  // 25.4.4.4 Promise.race(iterable)\n  race: function race(iterable) {\n    var C = this;\n    var capability = newPromiseCapability(C);\n    var reject = capability.reject;\n    var result = perform(function () {\n      forOf(iterable, false, function (promise) {\n        C.resolve(promise).then(capability.resolve, reject);\n      });\n    });\n    if (result.e) reject(result.v);\n    return capability.promise;\n  }\n});\n","var isObject = require('./_is-object');\nmodule.exports = function (it, TYPE) {\n  if (!isObject(it) || it._t !== TYPE) throw TypeError('Incompatible receiver, ' + TYPE + ' required!');\n  return it;\n};\n","'use strict';\nvar dP = require('./_object-dp').f;\nvar create = require('./_object-create');\nvar redefineAll = require('./_redefine-all');\nvar ctx = require('./_ctx');\nvar anInstance = require('./_an-instance');\nvar forOf = require('./_for-of');\nvar $iterDefine = require('./_iter-define');\nvar step = require('./_iter-step');\nvar setSpecies = require('./_set-species');\nvar DESCRIPTORS = require('./_descriptors');\nvar fastKey = require('./_meta').fastKey;\nvar validate = require('./_validate-collection');\nvar SIZE = DESCRIPTORS ? '_s' : 'size';\n\nvar getEntry = function (that, key) {\n  // fast case\n  var index = fastKey(key);\n  var entry;\n  if (index !== 'F') return that._i[index];\n  // frozen object case\n  for (entry = that._f; entry; entry = entry.n) {\n    if (entry.k == key) return entry;\n  }\n};\n\nmodule.exports = {\n  getConstructor: function (wrapper, NAME, IS_MAP, ADDER) {\n    var C = wrapper(function (that, iterable) {\n      anInstance(that, C, NAME, '_i');\n      that._t = NAME;         // collection type\n      that._i = create(null); // index\n      that._f = undefined;    // first entry\n      that._l = undefined;    // last entry\n      that[SIZE] = 0;         // size\n      if (iterable != undefined) forOf(iterable, IS_MAP, that[ADDER], that);\n    });\n    redefineAll(C.prototype, {\n      // 23.1.3.1 Map.prototype.clear()\n      // 23.2.3.2 Set.prototype.clear()\n      clear: function clear() {\n        for (var that = validate(this, NAME), data = that._i, entry = that._f; entry; entry = entry.n) {\n          entry.r = true;\n          if (entry.p) entry.p = entry.p.n = undefined;\n          delete data[entry.i];\n        }\n        that._f = that._l = undefined;\n        that[SIZE] = 0;\n      },\n      // 23.1.3.3 Map.prototype.delete(key)\n      // 23.2.3.4 Set.prototype.delete(value)\n      'delete': function (key) {\n        var that = validate(this, NAME);\n        var entry = getEntry(that, key);\n        if (entry) {\n          var next = entry.n;\n          var prev = entry.p;\n          delete that._i[entry.i];\n          entry.r = true;\n          if (prev) prev.n = next;\n          if (next) next.p = prev;\n          if (that._f == entry) that._f = next;\n          if (that._l == entry) that._l = prev;\n          that[SIZE]--;\n        } return !!entry;\n      },\n      // 23.2.3.6 Set.prototype.forEach(callbackfn, thisArg = undefined)\n      // 23.1.3.5 Map.prototype.forEach(callbackfn, thisArg = undefined)\n      forEach: function forEach(callbackfn /* , that = undefined */) {\n        validate(this, NAME);\n        var f = ctx(callbackfn, arguments.length > 1 ? arguments[1] : undefined, 3);\n        var entry;\n        while (entry = entry ? entry.n : this._f) {\n          f(entry.v, entry.k, this);\n          // revert to the last existing entry\n          while (entry && entry.r) entry = entry.p;\n        }\n      },\n      // 23.1.3.7 Map.prototype.has(key)\n      // 23.2.3.7 Set.prototype.has(value)\n      has: function has(key) {\n        return !!getEntry(validate(this, NAME), key);\n      }\n    });\n    if (DESCRIPTORS) dP(C.prototype, 'size', {\n      get: function () {\n        return validate(this, NAME)[SIZE];\n      }\n    });\n    return C;\n  },\n  def: function (that, key, value) {\n    var entry = getEntry(that, key);\n    var prev, index;\n    // change existing entry\n    if (entry) {\n      entry.v = value;\n    // create new entry\n    } else {\n      that._l = entry = {\n        i: index = fastKey(key, true), // <- index\n        k: key,                        // <- key\n        v: value,                      // <- value\n        p: prev = that._l,             // <- previous entry\n        n: undefined,                  // <- next entry\n        r: false                       // <- removed\n      };\n      if (!that._f) that._f = entry;\n      if (prev) prev.n = entry;\n      that[SIZE]++;\n      // add to index\n      if (index !== 'F') that._i[index] = entry;\n    } return that;\n  },\n  getEntry: getEntry,\n  setStrong: function (C, NAME, IS_MAP) {\n    // add .keys, .values, .entries, [@@iterator]\n    // 23.1.3.4, 23.1.3.8, 23.1.3.11, 23.1.3.12, 23.2.3.5, 23.2.3.8, 23.2.3.10, 23.2.3.11\n    $iterDefine(C, NAME, function (iterated, kind) {\n      this._t = validate(iterated, NAME); // target\n      this._k = kind;                     // kind\n      this._l = undefined;                // previous\n    }, function () {\n      var that = this;\n      var kind = that._k;\n      var entry = that._l;\n      // revert to the last existing entry\n      while (entry && entry.r) entry = entry.p;\n      // get next entry\n      if (!that._t || !(that._l = entry = entry ? entry.n : that._t._f)) {\n        // or finish the iteration\n        that._t = undefined;\n        return step(1);\n      }\n      // return step by kind\n      if (kind == 'keys') return step(0, entry.k);\n      if (kind == 'values') return step(0, entry.v);\n      return step(0, [entry.k, entry.v]);\n    }, IS_MAP ? 'entries' : 'values', !IS_MAP, true);\n\n    // add [@@species], 23.1.2.2, 23.2.2.2\n    setSpecies(NAME);\n  }\n};\n","'use strict';\nvar global = require('./_global');\nvar $export = require('./_export');\nvar redefine = require('./_redefine');\nvar redefineAll = require('./_redefine-all');\nvar meta = require('./_meta');\nvar forOf = require('./_for-of');\nvar anInstance = require('./_an-instance');\nvar isObject = require('./_is-object');\nvar fails = require('./_fails');\nvar $iterDetect = require('./_iter-detect');\nvar setToStringTag = require('./_set-to-string-tag');\nvar inheritIfRequired = require('./_inherit-if-required');\n\nmodule.exports = function (NAME, wrapper, methods, common, IS_MAP, IS_WEAK) {\n  var Base = global[NAME];\n  var C = Base;\n  var ADDER = IS_MAP ? 'set' : 'add';\n  var proto = C && C.prototype;\n  var O = {};\n  var fixMethod = function (KEY) {\n    var fn = proto[KEY];\n    redefine(proto, KEY,\n      KEY == 'delete' ? function (a) {\n        return IS_WEAK && !isObject(a) ? false : fn.call(this, a === 0 ? 0 : a);\n      } : KEY == 'has' ? function has(a) {\n        return IS_WEAK && !isObject(a) ? false : fn.call(this, a === 0 ? 0 : a);\n      } : KEY == 'get' ? function get(a) {\n        return IS_WEAK && !isObject(a) ? undefined : fn.call(this, a === 0 ? 0 : a);\n      } : KEY == 'add' ? function add(a) { fn.call(this, a === 0 ? 0 : a); return this; }\n        : function set(a, b) { fn.call(this, a === 0 ? 0 : a, b); return this; }\n    );\n  };\n  if (typeof C != 'function' || !(IS_WEAK || proto.forEach && !fails(function () {\n    new C().entries().next();\n  }))) {\n    // create collection constructor\n    C = common.getConstructor(wrapper, NAME, IS_MAP, ADDER);\n    redefineAll(C.prototype, methods);\n    meta.NEED = true;\n  } else {\n    var instance = new C();\n    // early implementations not supports chaining\n    var HASNT_CHAINING = instance[ADDER](IS_WEAK ? {} : -0, 1) != instance;\n    // V8 ~  Chromium 40- weak-collections throws on primitives, but should return false\n    var THROWS_ON_PRIMITIVES = fails(function () { instance.has(1); });\n    // most early implementations doesn't supports iterables, most modern - not close it correctly\n    var ACCEPT_ITERABLES = $iterDetect(function (iter) { new C(iter); }); // eslint-disable-line no-new\n    // for early implementations -0 and +0 not the same\n    var BUGGY_ZERO = !IS_WEAK && fails(function () {\n      // V8 ~ Chromium 42- fails only with 5+ elements\n      var $instance = new C();\n      var index = 5;\n      while (index--) $instance[ADDER](index, index);\n      return !$instance.has(-0);\n    });\n    if (!ACCEPT_ITERABLES) {\n      C = wrapper(function (target, iterable) {\n        anInstance(target, C, NAME);\n        var that = inheritIfRequired(new Base(), target, C);\n        if (iterable != undefined) forOf(iterable, IS_MAP, that[ADDER], that);\n        return that;\n      });\n      C.prototype = proto;\n      proto.constructor = C;\n    }\n    if (THROWS_ON_PRIMITIVES || BUGGY_ZERO) {\n      fixMethod('delete');\n      fixMethod('has');\n      IS_MAP && fixMethod('get');\n    }\n    if (BUGGY_ZERO || HASNT_CHAINING) fixMethod(ADDER);\n    // weak collections should not contains .clear method\n    if (IS_WEAK && proto.clear) delete proto.clear;\n  }\n\n  setToStringTag(C, NAME);\n\n  O[NAME] = C;\n  $export($export.G + $export.W + $export.F * (C != Base), O);\n\n  if (!IS_WEAK) common.setStrong(C, NAME, IS_MAP);\n\n  return C;\n};\n","'use strict';\nvar strong = require('./_collection-strong');\nvar validate = require('./_validate-collection');\nvar MAP = 'Map';\n\n// 23.1 Map Objects\nmodule.exports = require('./_collection')(MAP, function (get) {\n  return function Map() { return get(this, arguments.length > 0 ? arguments[0] : undefined); };\n}, {\n  // 23.1.3.6 Map.prototype.get(key)\n  get: function get(key) {\n    var entry = strong.getEntry(validate(this, MAP), key);\n    return entry && entry.v;\n  },\n  // 23.1.3.9 Map.prototype.set(key, value)\n  set: function set(key, value) {\n    return strong.def(validate(this, MAP), key === 0 ? 0 : key, value);\n  }\n}, strong, true);\n","'use strict';\nvar strong = require('./_collection-strong');\nvar validate = require('./_validate-collection');\nvar SET = 'Set';\n\n// 23.2 Set Objects\nmodule.exports = require('./_collection')(SET, function (get) {\n  return function Set() { return get(this, arguments.length > 0 ? arguments[0] : undefined); };\n}, {\n  // 23.2.3.1 Set.prototype.add(value)\n  add: function add(value) {\n    return strong.def(validate(this, SET), value = value === 0 ? 0 : value, value);\n  }\n}, strong);\n","'use strict';\nvar redefineAll = require('./_redefine-all');\nvar getWeak = require('./_meta').getWeak;\nvar anObject = require('./_an-object');\nvar isObject = require('./_is-object');\nvar anInstance = require('./_an-instance');\nvar forOf = require('./_for-of');\nvar createArrayMethod = require('./_array-methods');\nvar $has = require('./_has');\nvar validate = require('./_validate-collection');\nvar arrayFind = createArrayMethod(5);\nvar arrayFindIndex = createArrayMethod(6);\nvar id = 0;\n\n// fallback for uncaught frozen keys\nvar uncaughtFrozenStore = function (that) {\n  return that._l || (that._l = new UncaughtFrozenStore());\n};\nvar UncaughtFrozenStore = function () {\n  this.a = [];\n};\nvar findUncaughtFrozen = function (store, key) {\n  return arrayFind(store.a, function (it) {\n    return it[0] === key;\n  });\n};\nUncaughtFrozenStore.prototype = {\n  get: function (key) {\n    var entry = findUncaughtFrozen(this, key);\n    if (entry) return entry[1];\n  },\n  has: function (key) {\n    return !!findUncaughtFrozen(this, key);\n  },\n  set: function (key, value) {\n    var entry = findUncaughtFrozen(this, key);\n    if (entry) entry[1] = value;\n    else this.a.push([key, value]);\n  },\n  'delete': function (key) {\n    var index = arrayFindIndex(this.a, function (it) {\n      return it[0] === key;\n    });\n    if (~index) this.a.splice(index, 1);\n    return !!~index;\n  }\n};\n\nmodule.exports = {\n  getConstructor: function (wrapper, NAME, IS_MAP, ADDER) {\n    var C = wrapper(function (that, iterable) {\n      anInstance(that, C, NAME, '_i');\n      that._t = NAME;      // collection type\n      that._i = id++;      // collection id\n      that._l = undefined; // leak store for uncaught frozen objects\n      if (iterable != undefined) forOf(iterable, IS_MAP, that[ADDER], that);\n    });\n    redefineAll(C.prototype, {\n      // 23.3.3.2 WeakMap.prototype.delete(key)\n      // 23.4.3.3 WeakSet.prototype.delete(value)\n      'delete': function (key) {\n        if (!isObject(key)) return false;\n        var data = getWeak(key);\n        if (data === true) return uncaughtFrozenStore(validate(this, NAME))['delete'](key);\n        return data && $has(data, this._i) && delete data[this._i];\n      },\n      // 23.3.3.4 WeakMap.prototype.has(key)\n      // 23.4.3.4 WeakSet.prototype.has(value)\n      has: function has(key) {\n        if (!isObject(key)) return false;\n        var data = getWeak(key);\n        if (data === true) return uncaughtFrozenStore(validate(this, NAME)).has(key);\n        return data && $has(data, this._i);\n      }\n    });\n    return C;\n  },\n  def: function (that, key, value) {\n    var data = getWeak(anObject(key), true);\n    if (data === true) uncaughtFrozenStore(that).set(key, value);\n    else data[that._i] = value;\n    return that;\n  },\n  ufstore: uncaughtFrozenStore\n};\n","'use strict';\nvar global = require('./_global');\nvar each = require('./_array-methods')(0);\nvar redefine = require('./_redefine');\nvar meta = require('./_meta');\nvar assign = require('./_object-assign');\nvar weak = require('./_collection-weak');\nvar isObject = require('./_is-object');\nvar validate = require('./_validate-collection');\nvar NATIVE_WEAK_MAP = require('./_validate-collection');\nvar IS_IE11 = !global.ActiveXObject && 'ActiveXObject' in global;\nvar WEAK_MAP = 'WeakMap';\nvar getWeak = meta.getWeak;\nvar isExtensible = Object.isExtensible;\nvar uncaughtFrozenStore = weak.ufstore;\nvar InternalMap;\n\nvar wrapper = function (get) {\n  return function WeakMap() {\n    return get(this, arguments.length > 0 ? arguments[0] : undefined);\n  };\n};\n\nvar methods = {\n  // 23.3.3.3 WeakMap.prototype.get(key)\n  get: function get(key) {\n    if (isObject(key)) {\n      var data = getWeak(key);\n      if (data === true) return uncaughtFrozenStore(validate(this, WEAK_MAP)).get(key);\n      return data ? data[this._i] : undefined;\n    }\n  },\n  // 23.3.3.5 WeakMap.prototype.set(key, value)\n  set: function set(key, value) {\n    return weak.def(validate(this, WEAK_MAP), key, value);\n  }\n};\n\n// 23.3 WeakMap Objects\nvar $WeakMap = module.exports = require('./_collection')(WEAK_MAP, wrapper, methods, weak, true, true);\n\n// IE11 WeakMap frozen keys fix\nif (NATIVE_WEAK_MAP && IS_IE11) {\n  InternalMap = weak.getConstructor(wrapper, WEAK_MAP);\n  assign(InternalMap.prototype, methods);\n  meta.NEED = true;\n  each(['delete', 'has', 'get', 'set'], function (key) {\n    var proto = $WeakMap.prototype;\n    var method = proto[key];\n    redefine(proto, key, function (a, b) {\n      // store frozen objects on internal weakmap shim\n      if (isObject(a) && !isExtensible(a)) {\n        if (!this._f) this._f = new InternalMap();\n        var result = this._f[key](a, b);\n        return key == 'set' ? this : result;\n      // store all the rest on native weakmap\n      } return method.call(this, a, b);\n    });\n  });\n}\n","'use strict';\nvar weak = require('./_collection-weak');\nvar validate = require('./_validate-collection');\nvar WEAK_SET = 'WeakSet';\n\n// 23.4 WeakSet Objects\nrequire('./_collection')(WEAK_SET, function (get) {\n  return function WeakSet() { return get(this, arguments.length > 0 ? arguments[0] : undefined); };\n}, {\n  // 23.4.3.1 WeakSet.prototype.add(value)\n  add: function add(value) {\n    return weak.def(validate(this, WEAK_SET), value, true);\n  }\n}, weak, false, true);\n","var global = require('./_global');\nvar hide = require('./_hide');\nvar uid = require('./_uid');\nvar TYPED = uid('typed_array');\nvar VIEW = uid('view');\nvar ABV = !!(global.ArrayBuffer && global.DataView);\nvar CONSTR = ABV;\nvar i = 0;\nvar l = 9;\nvar Typed;\n\nvar TypedArrayConstructors = (\n  'Int8Array,Uint8Array,Uint8ClampedArray,Int16Array,Uint16Array,Int32Array,Uint32Array,Float32Array,Float64Array'\n).split(',');\n\nwhile (i < l) {\n  if (Typed = global[TypedArrayConstructors[i++]]) {\n    hide(Typed.prototype, TYPED, true);\n    hide(Typed.prototype, VIEW, true);\n  } else CONSTR = false;\n}\n\nmodule.exports = {\n  ABV: ABV,\n  CONSTR: CONSTR,\n  TYPED: TYPED,\n  VIEW: VIEW\n};\n","// https://tc39.github.io/ecma262/#sec-toindex\nvar toInteger = require('./_to-integer');\nvar toLength = require('./_to-length');\nmodule.exports = function (it) {\n  if (it === undefined) return 0;\n  var number = toInteger(it);\n  var length = toLength(number);\n  if (number !== length) throw RangeError('Wrong length!');\n  return length;\n};\n","'use strict';\nvar global = require('./_global');\nvar DESCRIPTORS = require('./_descriptors');\nvar LIBRARY = require('./_library');\nvar $typed = require('./_typed');\nvar hide = require('./_hide');\nvar redefineAll = require('./_redefine-all');\nvar fails = require('./_fails');\nvar anInstance = require('./_an-instance');\nvar toInteger = require('./_to-integer');\nvar toLength = require('./_to-length');\nvar toIndex = require('./_to-index');\nvar gOPN = require('./_object-gopn').f;\nvar dP = require('./_object-dp').f;\nvar arrayFill = require('./_array-fill');\nvar setToStringTag = require('./_set-to-string-tag');\nvar ARRAY_BUFFER = 'ArrayBuffer';\nvar DATA_VIEW = 'DataView';\nvar PROTOTYPE = 'prototype';\nvar WRONG_LENGTH = 'Wrong length!';\nvar WRONG_INDEX = 'Wrong index!';\nvar $ArrayBuffer = global[ARRAY_BUFFER];\nvar $DataView = global[DATA_VIEW];\nvar Math = global.Math;\nvar RangeError = global.RangeError;\n// eslint-disable-next-line no-shadow-restricted-names\nvar Infinity = global.Infinity;\nvar BaseBuffer = $ArrayBuffer;\nvar abs = Math.abs;\nvar pow = Math.pow;\nvar floor = Math.floor;\nvar log = Math.log;\nvar LN2 = Math.LN2;\nvar BUFFER = 'buffer';\nvar BYTE_LENGTH = 'byteLength';\nvar BYTE_OFFSET = 'byteOffset';\nvar $BUFFER = DESCRIPTORS ? '_b' : BUFFER;\nvar $LENGTH = DESCRIPTORS ? '_l' : BYTE_LENGTH;\nvar $OFFSET = DESCRIPTORS ? '_o' : BYTE_OFFSET;\n\n// IEEE754 conversions based on https://github.com/feross/ieee754\nfunction packIEEE754(value, mLen, nBytes) {\n  var buffer = new Array(nBytes);\n  var eLen = nBytes * 8 - mLen - 1;\n  var eMax = (1 << eLen) - 1;\n  var eBias = eMax >> 1;\n  var rt = mLen === 23 ? pow(2, -24) - pow(2, -77) : 0;\n  var i = 0;\n  var s = value < 0 || value === 0 && 1 / value < 0 ? 1 : 0;\n  var e, m, c;\n  value = abs(value);\n  // eslint-disable-next-line no-self-compare\n  if (value != value || value === Infinity) {\n    // eslint-disable-next-line no-self-compare\n    m = value != value ? 1 : 0;\n    e = eMax;\n  } else {\n    e = floor(log(value) / LN2);\n    if (value * (c = pow(2, -e)) < 1) {\n      e--;\n      c *= 2;\n    }\n    if (e + eBias >= 1) {\n      value += rt / c;\n    } else {\n      value += rt * pow(2, 1 - eBias);\n    }\n    if (value * c >= 2) {\n      e++;\n      c /= 2;\n    }\n    if (e + eBias >= eMax) {\n      m = 0;\n      e = eMax;\n    } else if (e + eBias >= 1) {\n      m = (value * c - 1) * pow(2, mLen);\n      e = e + eBias;\n    } else {\n      m = value * pow(2, eBias - 1) * pow(2, mLen);\n      e = 0;\n    }\n  }\n  for (; mLen >= 8; buffer[i++] = m & 255, m /= 256, mLen -= 8);\n  e = e << mLen | m;\n  eLen += mLen;\n  for (; eLen > 0; buffer[i++] = e & 255, e /= 256, eLen -= 8);\n  buffer[--i] |= s * 128;\n  return buffer;\n}\nfunction unpackIEEE754(buffer, mLen, nBytes) {\n  var eLen = nBytes * 8 - mLen - 1;\n  var eMax = (1 << eLen) - 1;\n  var eBias = eMax >> 1;\n  var nBits = eLen - 7;\n  var i = nBytes - 1;\n  var s = buffer[i--];\n  var e = s & 127;\n  var m;\n  s >>= 7;\n  for (; nBits > 0; e = e * 256 + buffer[i], i--, nBits -= 8);\n  m = e & (1 << -nBits) - 1;\n  e >>= -nBits;\n  nBits += mLen;\n  for (; nBits > 0; m = m * 256 + buffer[i], i--, nBits -= 8);\n  if (e === 0) {\n    e = 1 - eBias;\n  } else if (e === eMax) {\n    return m ? NaN : s ? -Infinity : Infinity;\n  } else {\n    m = m + pow(2, mLen);\n    e = e - eBias;\n  } return (s ? -1 : 1) * m * pow(2, e - mLen);\n}\n\nfunction unpackI32(bytes) {\n  return bytes[3] << 24 | bytes[2] << 16 | bytes[1] << 8 | bytes[0];\n}\nfunction packI8(it) {\n  return [it & 0xff];\n}\nfunction packI16(it) {\n  return [it & 0xff, it >> 8 & 0xff];\n}\nfunction packI32(it) {\n  return [it & 0xff, it >> 8 & 0xff, it >> 16 & 0xff, it >> 24 & 0xff];\n}\nfunction packF64(it) {\n  return packIEEE754(it, 52, 8);\n}\nfunction packF32(it) {\n  return packIEEE754(it, 23, 4);\n}\n\nfunction addGetter(C, key, internal) {\n  dP(C[PROTOTYPE], key, { get: function () { return this[internal]; } });\n}\n\nfunction get(view, bytes, index, isLittleEndian) {\n  var numIndex = +index;\n  var intIndex = toIndex(numIndex);\n  if (intIndex + bytes > view[$LENGTH]) throw RangeError(WRONG_INDEX);\n  var store = view[$BUFFER]._b;\n  var start = intIndex + view[$OFFSET];\n  var pack = store.slice(start, start + bytes);\n  return isLittleEndian ? pack : pack.reverse();\n}\nfunction set(view, bytes, index, conversion, value, isLittleEndian) {\n  var numIndex = +index;\n  var intIndex = toIndex(numIndex);\n  if (intIndex + bytes > view[$LENGTH]) throw RangeError(WRONG_INDEX);\n  var store = view[$BUFFER]._b;\n  var start = intIndex + view[$OFFSET];\n  var pack = conversion(+value);\n  for (var i = 0; i < bytes; i++) store[start + i] = pack[isLittleEndian ? i : bytes - i - 1];\n}\n\nif (!$typed.ABV) {\n  $ArrayBuffer = function ArrayBuffer(length) {\n    anInstance(this, $ArrayBuffer, ARRAY_BUFFER);\n    var byteLength = toIndex(length);\n    this._b = arrayFill.call(new Array(byteLength), 0);\n    this[$LENGTH] = byteLength;\n  };\n\n  $DataView = function DataView(buffer, byteOffset, byteLength) {\n    anInstance(this, $DataView, DATA_VIEW);\n    anInstance(buffer, $ArrayBuffer, DATA_VIEW);\n    var bufferLength = buffer[$LENGTH];\n    var offset = toInteger(byteOffset);\n    if (offset < 0 || offset > bufferLength) throw RangeError('Wrong offset!');\n    byteLength = byteLength === undefined ? bufferLength - offset : toLength(byteLength);\n    if (offset + byteLength > bufferLength) throw RangeError(WRONG_LENGTH);\n    this[$BUFFER] = buffer;\n    this[$OFFSET] = offset;\n    this[$LENGTH] = byteLength;\n  };\n\n  if (DESCRIPTORS) {\n    addGetter($ArrayBuffer, BYTE_LENGTH, '_l');\n    addGetter($DataView, BUFFER, '_b');\n    addGetter($DataView, BYTE_LENGTH, '_l');\n    addGetter($DataView, BYTE_OFFSET, '_o');\n  }\n\n  redefineAll($DataView[PROTOTYPE], {\n    getInt8: function getInt8(byteOffset) {\n      return get(this, 1, byteOffset)[0] << 24 >> 24;\n    },\n    getUint8: function getUint8(byteOffset) {\n      return get(this, 1, byteOffset)[0];\n    },\n    getInt16: function getInt16(byteOffset /* , littleEndian */) {\n      var bytes = get(this, 2, byteOffset, arguments[1]);\n      return (bytes[1] << 8 | bytes[0]) << 16 >> 16;\n    },\n    getUint16: function getUint16(byteOffset /* , littleEndian */) {\n      var bytes = get(this, 2, byteOffset, arguments[1]);\n      return bytes[1] << 8 | bytes[0];\n    },\n    getInt32: function getInt32(byteOffset /* , littleEndian */) {\n      return unpackI32(get(this, 4, byteOffset, arguments[1]));\n    },\n    getUint32: function getUint32(byteOffset /* , littleEndian */) {\n      return unpackI32(get(this, 4, byteOffset, arguments[1])) >>> 0;\n    },\n    getFloat32: function getFloat32(byteOffset /* , littleEndian */) {\n      return unpackIEEE754(get(this, 4, byteOffset, arguments[1]), 23, 4);\n    },\n    getFloat64: function getFloat64(byteOffset /* , littleEndian */) {\n      return unpackIEEE754(get(this, 8, byteOffset, arguments[1]), 52, 8);\n    },\n    setInt8: function setInt8(byteOffset, value) {\n      set(this, 1, byteOffset, packI8, value);\n    },\n    setUint8: function setUint8(byteOffset, value) {\n      set(this, 1, byteOffset, packI8, value);\n    },\n    setInt16: function setInt16(byteOffset, value /* , littleEndian */) {\n      set(this, 2, byteOffset, packI16, value, arguments[2]);\n    },\n    setUint16: function setUint16(byteOffset, value /* , littleEndian */) {\n      set(this, 2, byteOffset, packI16, value, arguments[2]);\n    },\n    setInt32: function setInt32(byteOffset, value /* , littleEndian */) {\n      set(this, 4, byteOffset, packI32, value, arguments[2]);\n    },\n    setUint32: function setUint32(byteOffset, value /* , littleEndian */) {\n      set(this, 4, byteOffset, packI32, value, arguments[2]);\n    },\n    setFloat32: function setFloat32(byteOffset, value /* , littleEndian */) {\n      set(this, 4, byteOffset, packF32, value, arguments[2]);\n    },\n    setFloat64: function setFloat64(byteOffset, value /* , littleEndian */) {\n      set(this, 8, byteOffset, packF64, value, arguments[2]);\n    }\n  });\n} else {\n  if (!fails(function () {\n    $ArrayBuffer(1);\n  }) || !fails(function () {\n    new $ArrayBuffer(-1); // eslint-disable-line no-new\n  }) || fails(function () {\n    new $ArrayBuffer(); // eslint-disable-line no-new\n    new $ArrayBuffer(1.5); // eslint-disable-line no-new\n    new $ArrayBuffer(NaN); // eslint-disable-line no-new\n    return $ArrayBuffer.name != ARRAY_BUFFER;\n  })) {\n    $ArrayBuffer = function ArrayBuffer(length) {\n      anInstance(this, $ArrayBuffer);\n      return new BaseBuffer(toIndex(length));\n    };\n    var ArrayBufferProto = $ArrayBuffer[PROTOTYPE] = BaseBuffer[PROTOTYPE];\n    for (var keys = gOPN(BaseBuffer), j = 0, key; keys.length > j;) {\n      if (!((key = keys[j++]) in $ArrayBuffer)) hide($ArrayBuffer, key, BaseBuffer[key]);\n    }\n    if (!LIBRARY) ArrayBufferProto.constructor = $ArrayBuffer;\n  }\n  // iOS Safari 7.x bug\n  var view = new $DataView(new $ArrayBuffer(2));\n  var $setInt8 = $DataView[PROTOTYPE].setInt8;\n  view.setInt8(0, 2147483648);\n  view.setInt8(1, 2147483649);\n  if (view.getInt8(0) || !view.getInt8(1)) redefineAll($DataView[PROTOTYPE], {\n    setInt8: function setInt8(byteOffset, value) {\n      $setInt8.call(this, byteOffset, value << 24 >> 24);\n    },\n    setUint8: function setUint8(byteOffset, value) {\n      $setInt8.call(this, byteOffset, value << 24 >> 24);\n    }\n  }, true);\n}\nsetToStringTag($ArrayBuffer, ARRAY_BUFFER);\nsetToStringTag($DataView, DATA_VIEW);\nhide($DataView[PROTOTYPE], $typed.VIEW, true);\nexports[ARRAY_BUFFER] = $ArrayBuffer;\nexports[DATA_VIEW] = $DataView;\n","'use strict';\nvar $export = require('./_export');\nvar $typed = require('./_typed');\nvar buffer = require('./_typed-buffer');\nvar anObject = require('./_an-object');\nvar toAbsoluteIndex = require('./_to-absolute-index');\nvar toLength = require('./_to-length');\nvar isObject = require('./_is-object');\nvar ArrayBuffer = require('./_global').ArrayBuffer;\nvar speciesConstructor = require('./_species-constructor');\nvar $ArrayBuffer = buffer.ArrayBuffer;\nvar $DataView = buffer.DataView;\nvar $isView = $typed.ABV && ArrayBuffer.isView;\nvar $slice = $ArrayBuffer.prototype.slice;\nvar VIEW = $typed.VIEW;\nvar ARRAY_BUFFER = 'ArrayBuffer';\n\n$export($export.G + $export.W + $export.F * (ArrayBuffer !== $ArrayBuffer), { ArrayBuffer: $ArrayBuffer });\n\n$export($export.S + $export.F * !$typed.CONSTR, ARRAY_BUFFER, {\n  // 24.1.3.1 ArrayBuffer.isView(arg)\n  isView: function isView(it) {\n    return $isView && $isView(it) || isObject(it) && VIEW in it;\n  }\n});\n\n$export($export.P + $export.U + $export.F * require('./_fails')(function () {\n  return !new $ArrayBuffer(2).slice(1, undefined).byteLength;\n}), ARRAY_BUFFER, {\n  // 24.1.4.3 ArrayBuffer.prototype.slice(start, end)\n  slice: function slice(start, end) {\n    if ($slice !== undefined && end === undefined) return $slice.call(anObject(this), start); // FF fix\n    var len = anObject(this).byteLength;\n    var first = toAbsoluteIndex(start, len);\n    var fin = toAbsoluteIndex(end === undefined ? len : end, len);\n    var result = new (speciesConstructor(this, $ArrayBuffer))(toLength(fin - first));\n    var viewS = new $DataView(this);\n    var viewT = new $DataView(result);\n    var index = 0;\n    while (first < fin) {\n      viewT.setUint8(index++, viewS.getUint8(first++));\n    } return result;\n  }\n});\n\nrequire('./_set-species')(ARRAY_BUFFER);\n","var $export = require('./_export');\n$export($export.G + $export.W + $export.F * !require('./_typed').ABV, {\n  DataView: require('./_typed-buffer').DataView\n});\n","'use strict';\nif (require('./_descriptors')) {\n  var LIBRARY = require('./_library');\n  var global = require('./_global');\n  var fails = require('./_fails');\n  var $export = require('./_export');\n  var $typed = require('./_typed');\n  var $buffer = require('./_typed-buffer');\n  var ctx = require('./_ctx');\n  var anInstance = require('./_an-instance');\n  var propertyDesc = require('./_property-desc');\n  var hide = require('./_hide');\n  var redefineAll = require('./_redefine-all');\n  var toInteger = require('./_to-integer');\n  var toLength = require('./_to-length');\n  var toIndex = require('./_to-index');\n  var toAbsoluteIndex = require('./_to-absolute-index');\n  var toPrimitive = require('./_to-primitive');\n  var has = require('./_has');\n  var classof = require('./_classof');\n  var isObject = require('./_is-object');\n  var toObject = require('./_to-object');\n  var isArrayIter = require('./_is-array-iter');\n  var create = require('./_object-create');\n  var getPrototypeOf = require('./_object-gpo');\n  var gOPN = require('./_object-gopn').f;\n  var getIterFn = require('./core.get-iterator-method');\n  var uid = require('./_uid');\n  var wks = require('./_wks');\n  var createArrayMethod = require('./_array-methods');\n  var createArrayIncludes = require('./_array-includes');\n  var speciesConstructor = require('./_species-constructor');\n  var ArrayIterators = require('./es6.array.iterator');\n  var Iterators = require('./_iterators');\n  var $iterDetect = require('./_iter-detect');\n  var setSpecies = require('./_set-species');\n  var arrayFill = require('./_array-fill');\n  var arrayCopyWithin = require('./_array-copy-within');\n  var $DP = require('./_object-dp');\n  var $GOPD = require('./_object-gopd');\n  var dP = $DP.f;\n  var gOPD = $GOPD.f;\n  var RangeError = global.RangeError;\n  var TypeError = global.TypeError;\n  var Uint8Array = global.Uint8Array;\n  var ARRAY_BUFFER = 'ArrayBuffer';\n  var SHARED_BUFFER = 'Shared' + ARRAY_BUFFER;\n  var BYTES_PER_ELEMENT = 'BYTES_PER_ELEMENT';\n  var PROTOTYPE = 'prototype';\n  var ArrayProto = Array[PROTOTYPE];\n  var $ArrayBuffer = $buffer.ArrayBuffer;\n  var $DataView = $buffer.DataView;\n  var arrayForEach = createArrayMethod(0);\n  var arrayFilter = createArrayMethod(2);\n  var arraySome = createArrayMethod(3);\n  var arrayEvery = createArrayMethod(4);\n  var arrayFind = createArrayMethod(5);\n  var arrayFindIndex = createArrayMethod(6);\n  var arrayIncludes = createArrayIncludes(true);\n  var arrayIndexOf = createArrayIncludes(false);\n  var arrayValues = ArrayIterators.values;\n  var arrayKeys = ArrayIterators.keys;\n  var arrayEntries = ArrayIterators.entries;\n  var arrayLastIndexOf = ArrayProto.lastIndexOf;\n  var arrayReduce = ArrayProto.reduce;\n  var arrayReduceRight = ArrayProto.reduceRight;\n  var arrayJoin = ArrayProto.join;\n  var arraySort = ArrayProto.sort;\n  var arraySlice = ArrayProto.slice;\n  var arrayToString = ArrayProto.toString;\n  var arrayToLocaleString = ArrayProto.toLocaleString;\n  var ITERATOR = wks('iterator');\n  var TAG = wks('toStringTag');\n  var TYPED_CONSTRUCTOR = uid('typed_constructor');\n  var DEF_CONSTRUCTOR = uid('def_constructor');\n  var ALL_CONSTRUCTORS = $typed.CONSTR;\n  var TYPED_ARRAY = $typed.TYPED;\n  var VIEW = $typed.VIEW;\n  var WRONG_LENGTH = 'Wrong length!';\n\n  var $map = createArrayMethod(1, function (O, length) {\n    return allocate(speciesConstructor(O, O[DEF_CONSTRUCTOR]), length);\n  });\n\n  var LITTLE_ENDIAN = fails(function () {\n    // eslint-disable-next-line no-undef\n    return new Uint8Array(new Uint16Array([1]).buffer)[0] === 1;\n  });\n\n  var FORCED_SET = !!Uint8Array && !!Uint8Array[PROTOTYPE].set && fails(function () {\n    new Uint8Array(1).set({});\n  });\n\n  var toOffset = function (it, BYTES) {\n    var offset = toInteger(it);\n    if (offset < 0 || offset % BYTES) throw RangeError('Wrong offset!');\n    return offset;\n  };\n\n  var validate = function (it) {\n    if (isObject(it) && TYPED_ARRAY in it) return it;\n    throw TypeError(it + ' is not a typed array!');\n  };\n\n  var allocate = function (C, length) {\n    if (!(isObject(C) && TYPED_CONSTRUCTOR in C)) {\n      throw TypeError('It is not a typed array constructor!');\n    } return new C(length);\n  };\n\n  var speciesFromList = function (O, list) {\n    return fromList(speciesConstructor(O, O[DEF_CONSTRUCTOR]), list);\n  };\n\n  var fromList = function (C, list) {\n    var index = 0;\n    var length = list.length;\n    var result = allocate(C, length);\n    while (length > index) result[index] = list[index++];\n    return result;\n  };\n\n  var addGetter = function (it, key, internal) {\n    dP(it, key, { get: function () { return this._d[internal]; } });\n  };\n\n  var $from = function from(source /* , mapfn, thisArg */) {\n    var O = toObject(source);\n    var aLen = arguments.length;\n    var mapfn = aLen > 1 ? arguments[1] : undefined;\n    var mapping = mapfn !== undefined;\n    var iterFn = getIterFn(O);\n    var i, length, values, result, step, iterator;\n    if (iterFn != undefined && !isArrayIter(iterFn)) {\n      for (iterator = iterFn.call(O), values = [], i = 0; !(step = iterator.next()).done; i++) {\n        values.push(step.value);\n      } O = values;\n    }\n    if (mapping && aLen > 2) mapfn = ctx(mapfn, arguments[2], 2);\n    for (i = 0, length = toLength(O.length), result = allocate(this, length); length > i; i++) {\n      result[i] = mapping ? mapfn(O[i], i) : O[i];\n    }\n    return result;\n  };\n\n  var $of = function of(/* ...items */) {\n    var index = 0;\n    var length = arguments.length;\n    var result = allocate(this, length);\n    while (length > index) result[index] = arguments[index++];\n    return result;\n  };\n\n  // iOS Safari 6.x fails here\n  var TO_LOCALE_BUG = !!Uint8Array && fails(function () { arrayToLocaleString.call(new Uint8Array(1)); });\n\n  var $toLocaleString = function toLocaleString() {\n    return arrayToLocaleString.apply(TO_LOCALE_BUG ? arraySlice.call(validate(this)) : validate(this), arguments);\n  };\n\n  var proto = {\n    copyWithin: function copyWithin(target, start /* , end */) {\n      return arrayCopyWithin.call(validate(this), target, start, arguments.length > 2 ? arguments[2] : undefined);\n    },\n    every: function every(callbackfn /* , thisArg */) {\n      return arrayEvery(validate(this), callbackfn, arguments.length > 1 ? arguments[1] : undefined);\n    },\n    fill: function fill(value /* , start, end */) { // eslint-disable-line no-unused-vars\n      return arrayFill.apply(validate(this), arguments);\n    },\n    filter: function filter(callbackfn /* , thisArg */) {\n      return speciesFromList(this, arrayFilter(validate(this), callbackfn,\n        arguments.length > 1 ? arguments[1] : undefined));\n    },\n    find: function find(predicate /* , thisArg */) {\n      return arrayFind(validate(this), predicate, arguments.length > 1 ? arguments[1] : undefined);\n    },\n    findIndex: function findIndex(predicate /* , thisArg */) {\n      return arrayFindIndex(validate(this), predicate, arguments.length > 1 ? arguments[1] : undefined);\n    },\n    forEach: function forEach(callbackfn /* , thisArg */) {\n      arrayForEach(validate(this), callbackfn, arguments.length > 1 ? arguments[1] : undefined);\n    },\n    indexOf: function indexOf(searchElement /* , fromIndex */) {\n      return arrayIndexOf(validate(this), searchElement, arguments.length > 1 ? arguments[1] : undefined);\n    },\n    includes: function includes(searchElement /* , fromIndex */) {\n      return arrayIncludes(validate(this), searchElement, arguments.length > 1 ? arguments[1] : undefined);\n    },\n    join: function join(separator) { // eslint-disable-line no-unused-vars\n      return arrayJoin.apply(validate(this), arguments);\n    },\n    lastIndexOf: function lastIndexOf(searchElement /* , fromIndex */) { // eslint-disable-line no-unused-vars\n      return arrayLastIndexOf.apply(validate(this), arguments);\n    },\n    map: function map(mapfn /* , thisArg */) {\n      return $map(validate(this), mapfn, arguments.length > 1 ? arguments[1] : undefined);\n    },\n    reduce: function reduce(callbackfn /* , initialValue */) { // eslint-disable-line no-unused-vars\n      return arrayReduce.apply(validate(this), arguments);\n    },\n    reduceRight: function reduceRight(callbackfn /* , initialValue */) { // eslint-disable-line no-unused-vars\n      return arrayReduceRight.apply(validate(this), arguments);\n    },\n    reverse: function reverse() {\n      var that = this;\n      var length = validate(that).length;\n      var middle = Math.floor(length / 2);\n      var index = 0;\n      var value;\n      while (index < middle) {\n        value = that[index];\n        that[index++] = that[--length];\n        that[length] = value;\n      } return that;\n    },\n    some: function some(callbackfn /* , thisArg */) {\n      return arraySome(validate(this), callbackfn, arguments.length > 1 ? arguments[1] : undefined);\n    },\n    sort: function sort(comparefn) {\n      return arraySort.call(validate(this), comparefn);\n    },\n    subarray: function subarray(begin, end) {\n      var O = validate(this);\n      var length = O.length;\n      var $begin = toAbsoluteIndex(begin, length);\n      return new (speciesConstructor(O, O[DEF_CONSTRUCTOR]))(\n        O.buffer,\n        O.byteOffset + $begin * O.BYTES_PER_ELEMENT,\n        toLength((end === undefined ? length : toAbsoluteIndex(end, length)) - $begin)\n      );\n    }\n  };\n\n  var $slice = function slice(start, end) {\n    return speciesFromList(this, arraySlice.call(validate(this), start, end));\n  };\n\n  var $set = function set(arrayLike /* , offset */) {\n    validate(this);\n    var offset = toOffset(arguments[1], 1);\n    var length = this.length;\n    var src = toObject(arrayLike);\n    var len = toLength(src.length);\n    var index = 0;\n    if (len + offset > length) throw RangeError(WRONG_LENGTH);\n    while (index < len) this[offset + index] = src[index++];\n  };\n\n  var $iterators = {\n    entries: function entries() {\n      return arrayEntries.call(validate(this));\n    },\n    keys: function keys() {\n      return arrayKeys.call(validate(this));\n    },\n    values: function values() {\n      return arrayValues.call(validate(this));\n    }\n  };\n\n  var isTAIndex = function (target, key) {\n    return isObject(target)\n      && target[TYPED_ARRAY]\n      && typeof key != 'symbol'\n      && key in target\n      && String(+key) == String(key);\n  };\n  var $getDesc = function getOwnPropertyDescriptor(target, key) {\n    return isTAIndex(target, key = toPrimitive(key, true))\n      ? propertyDesc(2, target[key])\n      : gOPD(target, key);\n  };\n  var $setDesc = function defineProperty(target, key, desc) {\n    if (isTAIndex(target, key = toPrimitive(key, true))\n      && isObject(desc)\n      && has(desc, 'value')\n      && !has(desc, 'get')\n      && !has(desc, 'set')\n      // TODO: add validation descriptor w/o calling accessors\n      && !desc.configurable\n      && (!has(desc, 'writable') || desc.writable)\n      && (!has(desc, 'enumerable') || desc.enumerable)\n    ) {\n      target[key] = desc.value;\n      return target;\n    } return dP(target, key, desc);\n  };\n\n  if (!ALL_CONSTRUCTORS) {\n    $GOPD.f = $getDesc;\n    $DP.f = $setDesc;\n  }\n\n  $export($export.S + $export.F * !ALL_CONSTRUCTORS, 'Object', {\n    getOwnPropertyDescriptor: $getDesc,\n    defineProperty: $setDesc\n  });\n\n  if (fails(function () { arrayToString.call({}); })) {\n    arrayToString = arrayToLocaleString = function toString() {\n      return arrayJoin.call(this);\n    };\n  }\n\n  var $TypedArrayPrototype$ = redefineAll({}, proto);\n  redefineAll($TypedArrayPrototype$, $iterators);\n  hide($TypedArrayPrototype$, ITERATOR, $iterators.values);\n  redefineAll($TypedArrayPrototype$, {\n    slice: $slice,\n    set: $set,\n    constructor: function () { /* noop */ },\n    toString: arrayToString,\n    toLocaleString: $toLocaleString\n  });\n  addGetter($TypedArrayPrototype$, 'buffer', 'b');\n  addGetter($TypedArrayPrototype$, 'byteOffset', 'o');\n  addGetter($TypedArrayPrototype$, 'byteLength', 'l');\n  addGetter($TypedArrayPrototype$, 'length', 'e');\n  dP($TypedArrayPrototype$, TAG, {\n    get: function () { return this[TYPED_ARRAY]; }\n  });\n\n  // eslint-disable-next-line max-statements\n  module.exports = function (KEY, BYTES, wrapper, CLAMPED) {\n    CLAMPED = !!CLAMPED;\n    var NAME = KEY + (CLAMPED ? 'Clamped' : '') + 'Array';\n    var GETTER = 'get' + KEY;\n    var SETTER = 'set' + KEY;\n    var TypedArray = global[NAME];\n    var Base = TypedArray || {};\n    var TAC = TypedArray && getPrototypeOf(TypedArray);\n    var FORCED = !TypedArray || !$typed.ABV;\n    var O = {};\n    var TypedArrayPrototype = TypedArray && TypedArray[PROTOTYPE];\n    var getter = function (that, index) {\n      var data = that._d;\n      return data.v[GETTER](index * BYTES + data.o, LITTLE_ENDIAN);\n    };\n    var setter = function (that, index, value) {\n      var data = that._d;\n      if (CLAMPED) value = (value = Math.round(value)) < 0 ? 0 : value > 0xff ? 0xff : value & 0xff;\n      data.v[SETTER](index * BYTES + data.o, value, LITTLE_ENDIAN);\n    };\n    var addElement = function (that, index) {\n      dP(that, index, {\n        get: function () {\n          return getter(this, index);\n        },\n        set: function (value) {\n          return setter(this, index, value);\n        },\n        enumerable: true\n      });\n    };\n    if (FORCED) {\n      TypedArray = wrapper(function (that, data, $offset, $length) {\n        anInstance(that, TypedArray, NAME, '_d');\n        var index = 0;\n        var offset = 0;\n        var buffer, byteLength, length, klass;\n        if (!isObject(data)) {\n          length = toIndex(data);\n          byteLength = length * BYTES;\n          buffer = new $ArrayBuffer(byteLength);\n        } else if (data instanceof $ArrayBuffer || (klass = classof(data)) == ARRAY_BUFFER || klass == SHARED_BUFFER) {\n          buffer = data;\n          offset = toOffset($offset, BYTES);\n          var $len = data.byteLength;\n          if ($length === undefined) {\n            if ($len % BYTES) throw RangeError(WRONG_LENGTH);\n            byteLength = $len - offset;\n            if (byteLength < 0) throw RangeError(WRONG_LENGTH);\n          } else {\n            byteLength = toLength($length) * BYTES;\n            if (byteLength + offset > $len) throw RangeError(WRONG_LENGTH);\n          }\n          length = byteLength / BYTES;\n        } else if (TYPED_ARRAY in data) {\n          return fromList(TypedArray, data);\n        } else {\n          return $from.call(TypedArray, data);\n        }\n        hide(that, '_d', {\n          b: buffer,\n          o: offset,\n          l: byteLength,\n          e: length,\n          v: new $DataView(buffer)\n        });\n        while (index < length) addElement(that, index++);\n      });\n      TypedArrayPrototype = TypedArray[PROTOTYPE] = create($TypedArrayPrototype$);\n      hide(TypedArrayPrototype, 'constructor', TypedArray);\n    } else if (!fails(function () {\n      TypedArray(1);\n    }) || !fails(function () {\n      new TypedArray(-1); // eslint-disable-line no-new\n    }) || !$iterDetect(function (iter) {\n      new TypedArray(); // eslint-disable-line no-new\n      new TypedArray(null); // eslint-disable-line no-new\n      new TypedArray(1.5); // eslint-disable-line no-new\n      new TypedArray(iter); // eslint-disable-line no-new\n    }, true)) {\n      TypedArray = wrapper(function (that, data, $offset, $length) {\n        anInstance(that, TypedArray, NAME);\n        var klass;\n        // `ws` module bug, temporarily remove validation length for Uint8Array\n        // https://github.com/websockets/ws/pull/645\n        if (!isObject(data)) return new Base(toIndex(data));\n        if (data instanceof $ArrayBuffer || (klass = classof(data)) == ARRAY_BUFFER || klass == SHARED_BUFFER) {\n          return $length !== undefined\n            ? new Base(data, toOffset($offset, BYTES), $length)\n            : $offset !== undefined\n              ? new Base(data, toOffset($offset, BYTES))\n              : new Base(data);\n        }\n        if (TYPED_ARRAY in data) return fromList(TypedArray, data);\n        return $from.call(TypedArray, data);\n      });\n      arrayForEach(TAC !== Function.prototype ? gOPN(Base).concat(gOPN(TAC)) : gOPN(Base), function (key) {\n        if (!(key in TypedArray)) hide(TypedArray, key, Base[key]);\n      });\n      TypedArray[PROTOTYPE] = TypedArrayPrototype;\n      if (!LIBRARY) TypedArrayPrototype.constructor = TypedArray;\n    }\n    var $nativeIterator = TypedArrayPrototype[ITERATOR];\n    var CORRECT_ITER_NAME = !!$nativeIterator\n      && ($nativeIterator.name == 'values' || $nativeIterator.name == undefined);\n    var $iterator = $iterators.values;\n    hide(TypedArray, TYPED_CONSTRUCTOR, true);\n    hide(TypedArrayPrototype, TYPED_ARRAY, NAME);\n    hide(TypedArrayPrototype, VIEW, true);\n    hide(TypedArrayPrototype, DEF_CONSTRUCTOR, TypedArray);\n\n    if (CLAMPED ? new TypedArray(1)[TAG] != NAME : !(TAG in TypedArrayPrototype)) {\n      dP(TypedArrayPrototype, TAG, {\n        get: function () { return NAME; }\n      });\n    }\n\n    O[NAME] = TypedArray;\n\n    $export($export.G + $export.W + $export.F * (TypedArray != Base), O);\n\n    $export($export.S, NAME, {\n      BYTES_PER_ELEMENT: BYTES\n    });\n\n    $export($export.S + $export.F * fails(function () { Base.of.call(TypedArray, 1); }), NAME, {\n      from: $from,\n      of: $of\n    });\n\n    if (!(BYTES_PER_ELEMENT in TypedArrayPrototype)) hide(TypedArrayPrototype, BYTES_PER_ELEMENT, BYTES);\n\n    $export($export.P, NAME, proto);\n\n    setSpecies(NAME);\n\n    $export($export.P + $export.F * FORCED_SET, NAME, { set: $set });\n\n    $export($export.P + $export.F * !CORRECT_ITER_NAME, NAME, $iterators);\n\n    if (!LIBRARY && TypedArrayPrototype.toString != arrayToString) TypedArrayPrototype.toString = arrayToString;\n\n    $export($export.P + $export.F * fails(function () {\n      new TypedArray(1).slice();\n    }), NAME, { slice: $slice });\n\n    $export($export.P + $export.F * (fails(function () {\n      return [1, 2].toLocaleString() != new TypedArray([1, 2]).toLocaleString();\n    }) || !fails(function () {\n      TypedArrayPrototype.toLocaleString.call([1, 2]);\n    })), NAME, { toLocaleString: $toLocaleString });\n\n    Iterators[NAME] = CORRECT_ITER_NAME ? $nativeIterator : $iterator;\n    if (!LIBRARY && !CORRECT_ITER_NAME) hide(TypedArrayPrototype, ITERATOR, $iterator);\n  };\n} else module.exports = function () { /* empty */ };\n","require('./_typed-array')('Int8', 1, function (init) {\n  return function Int8Array(data, byteOffset, length) {\n    return init(this, data, byteOffset, length);\n  };\n});\n","require('./_typed-array')('Uint8', 1, function (init) {\n  return function Uint8Array(data, byteOffset, length) {\n    return init(this, data, byteOffset, length);\n  };\n});\n","require('./_typed-array')('Uint8', 1, function (init) {\n  return function Uint8ClampedArray(data, byteOffset, length) {\n    return init(this, data, byteOffset, length);\n  };\n}, true);\n","require('./_typed-array')('Int16', 2, function (init) {\n  return function Int16Array(data, byteOffset, length) {\n    return init(this, data, byteOffset, length);\n  };\n});\n","require('./_typed-array')('Uint16', 2, function (init) {\n  return function Uint16Array(data, byteOffset, length) {\n    return init(this, data, byteOffset, length);\n  };\n});\n","require('./_typed-array')('Int32', 4, function (init) {\n  return function Int32Array(data, byteOffset, length) {\n    return init(this, data, byteOffset, length);\n  };\n});\n","require('./_typed-array')('Uint32', 4, function (init) {\n  return function Uint32Array(data, byteOffset, length) {\n    return init(this, data, byteOffset, length);\n  };\n});\n","require('./_typed-array')('Float32', 4, function (init) {\n  return function Float32Array(data, byteOffset, length) {\n    return init(this, data, byteOffset, length);\n  };\n});\n","require('./_typed-array')('Float64', 8, function (init) {\n  return function Float64Array(data, byteOffset, length) {\n    return init(this, data, byteOffset, length);\n  };\n});\n","// 26.1.1 Reflect.apply(target, thisArgument, argumentsList)\nvar $export = require('./_export');\nvar aFunction = require('./_a-function');\nvar anObject = require('./_an-object');\nvar rApply = (require('./_global').Reflect || {}).apply;\nvar fApply = Function.apply;\n// MS Edge argumentsList argument is optional\n$export($export.S + $export.F * !require('./_fails')(function () {\n  rApply(function () { /* empty */ });\n}), 'Reflect', {\n  apply: function apply(target, thisArgument, argumentsList) {\n    var T = aFunction(target);\n    var L = anObject(argumentsList);\n    return rApply ? rApply(T, thisArgument, L) : fApply.call(T, thisArgument, L);\n  }\n});\n","// 26.1.2 Reflect.construct(target, argumentsList [, newTarget])\nvar $export = require('./_export');\nvar create = require('./_object-create');\nvar aFunction = require('./_a-function');\nvar anObject = require('./_an-object');\nvar isObject = require('./_is-object');\nvar fails = require('./_fails');\nvar bind = require('./_bind');\nvar rConstruct = (require('./_global').Reflect || {}).construct;\n\n// MS Edge supports only 2 arguments and argumentsList argument is optional\n// FF Nightly sets third argument as `new.target`, but does not create `this` from it\nvar NEW_TARGET_BUG = fails(function () {\n  function F() { /* empty */ }\n  return !(rConstruct(function () { /* empty */ }, [], F) instanceof F);\n});\nvar ARGS_BUG = !fails(function () {\n  rConstruct(function () { /* empty */ });\n});\n\n$export($export.S + $export.F * (NEW_TARGET_BUG || ARGS_BUG), 'Reflect', {\n  construct: function construct(Target, args /* , newTarget */) {\n    aFunction(Target);\n    anObject(args);\n    var newTarget = arguments.length < 3 ? Target : aFunction(arguments[2]);\n    if (ARGS_BUG && !NEW_TARGET_BUG) return rConstruct(Target, args, newTarget);\n    if (Target == newTarget) {\n      // w/o altered newTarget, optimization for 0-4 arguments\n      switch (args.length) {\n        case 0: return new Target();\n        case 1: return new Target(args[0]);\n        case 2: return new Target(args[0], args[1]);\n        case 3: return new Target(args[0], args[1], args[2]);\n        case 4: return new Target(args[0], args[1], args[2], args[3]);\n      }\n      // w/o altered newTarget, lot of arguments case\n      var $args = [null];\n      $args.push.apply($args, args);\n      return new (bind.apply(Target, $args))();\n    }\n    // with altered newTarget, not support built-in constructors\n    var proto = newTarget.prototype;\n    var instance = create(isObject(proto) ? proto : Object.prototype);\n    var result = Function.apply.call(Target, instance, args);\n    return isObject(result) ? result : instance;\n  }\n});\n","// 26.1.3 Reflect.defineProperty(target, propertyKey, attributes)\nvar dP = require('./_object-dp');\nvar $export = require('./_export');\nvar anObject = require('./_an-object');\nvar toPrimitive = require('./_to-primitive');\n\n// MS Edge has broken Reflect.defineProperty - throwing instead of returning false\n$export($export.S + $export.F * require('./_fails')(function () {\n  // eslint-disable-next-line no-undef\n  Reflect.defineProperty(dP.f({}, 1, { value: 1 }), 1, { value: 2 });\n}), 'Reflect', {\n  defineProperty: function defineProperty(target, propertyKey, attributes) {\n    anObject(target);\n    propertyKey = toPrimitive(propertyKey, true);\n    anObject(attributes);\n    try {\n      dP.f(target, propertyKey, attributes);\n      return true;\n    } catch (e) {\n      return false;\n    }\n  }\n});\n","// 26.1.4 Reflect.deleteProperty(target, propertyKey)\nvar $export = require('./_export');\nvar gOPD = require('./_object-gopd').f;\nvar anObject = require('./_an-object');\n\n$export($export.S, 'Reflect', {\n  deleteProperty: function deleteProperty(target, propertyKey) {\n    var desc = gOPD(anObject(target), propertyKey);\n    return desc && !desc.configurable ? false : delete target[propertyKey];\n  }\n});\n","'use strict';\n// 26.1.5 Reflect.enumerate(target)\nvar $export = require('./_export');\nvar anObject = require('./_an-object');\nvar Enumerate = function (iterated) {\n  this._t = anObject(iterated); // target\n  this._i = 0;                  // next index\n  var keys = this._k = [];      // keys\n  var key;\n  for (key in iterated) keys.push(key);\n};\nrequire('./_iter-create')(Enumerate, 'Object', function () {\n  var that = this;\n  var keys = that._k;\n  var key;\n  do {\n    if (that._i >= keys.length) return { value: undefined, done: true };\n  } while (!((key = keys[that._i++]) in that._t));\n  return { value: key, done: false };\n});\n\n$export($export.S, 'Reflect', {\n  enumerate: function enumerate(target) {\n    return new Enumerate(target);\n  }\n});\n","// 26.1.6 Reflect.get(target, propertyKey [, receiver])\nvar gOPD = require('./_object-gopd');\nvar getPrototypeOf = require('./_object-gpo');\nvar has = require('./_has');\nvar $export = require('./_export');\nvar isObject = require('./_is-object');\nvar anObject = require('./_an-object');\n\nfunction get(target, propertyKey /* , receiver */) {\n  var receiver = arguments.length < 3 ? target : arguments[2];\n  var desc, proto;\n  if (anObject(target) === receiver) return target[propertyKey];\n  if (desc = gOPD.f(target, propertyKey)) return has(desc, 'value')\n    ? desc.value\n    : desc.get !== undefined\n      ? desc.get.call(receiver)\n      : undefined;\n  if (isObject(proto = getPrototypeOf(target))) return get(proto, propertyKey, receiver);\n}\n\n$export($export.S, 'Reflect', { get: get });\n","// 26.1.7 Reflect.getOwnPropertyDescriptor(target, propertyKey)\nvar gOPD = require('./_object-gopd');\nvar $export = require('./_export');\nvar anObject = require('./_an-object');\n\n$export($export.S, 'Reflect', {\n  getOwnPropertyDescriptor: function getOwnPropertyDescriptor(target, propertyKey) {\n    return gOPD.f(anObject(target), propertyKey);\n  }\n});\n","// 26.1.8 Reflect.getPrototypeOf(target)\nvar $export = require('./_export');\nvar getProto = require('./_object-gpo');\nvar anObject = require('./_an-object');\n\n$export($export.S, 'Reflect', {\n  getPrototypeOf: function getPrototypeOf(target) {\n    return getProto(anObject(target));\n  }\n});\n","// 26.1.9 Reflect.has(target, propertyKey)\nvar $export = require('./_export');\n\n$export($export.S, 'Reflect', {\n  has: function has(target, propertyKey) {\n    return propertyKey in target;\n  }\n});\n","// 26.1.10 Reflect.isExtensible(target)\nvar $export = require('./_export');\nvar anObject = require('./_an-object');\nvar $isExtensible = Object.isExtensible;\n\n$export($export.S, 'Reflect', {\n  isExtensible: function isExtensible(target) {\n    anObject(target);\n    return $isExtensible ? $isExtensible(target) : true;\n  }\n});\n","// all object keys, includes non-enumerable and symbols\nvar gOPN = require('./_object-gopn');\nvar gOPS = require('./_object-gops');\nvar anObject = require('./_an-object');\nvar Reflect = require('./_global').Reflect;\nmodule.exports = Reflect && Reflect.ownKeys || function ownKeys(it) {\n  var keys = gOPN.f(anObject(it));\n  var getSymbols = gOPS.f;\n  return getSymbols ? keys.concat(getSymbols(it)) : keys;\n};\n","// 26.1.11 Reflect.ownKeys(target)\nvar $export = require('./_export');\n\n$export($export.S, 'Reflect', { ownKeys: require('./_own-keys') });\n","// 26.1.12 Reflect.preventExtensions(target)\nvar $export = require('./_export');\nvar anObject = require('./_an-object');\nvar $preventExtensions = Object.preventExtensions;\n\n$export($export.S, 'Reflect', {\n  preventExtensions: function preventExtensions(target) {\n    anObject(target);\n    try {\n      if ($preventExtensions) $preventExtensions(target);\n      return true;\n    } catch (e) {\n      return false;\n    }\n  }\n});\n","// 26.1.13 Reflect.set(target, propertyKey, V [, receiver])\nvar dP = require('./_object-dp');\nvar gOPD = require('./_object-gopd');\nvar getPrototypeOf = require('./_object-gpo');\nvar has = require('./_has');\nvar $export = require('./_export');\nvar createDesc = require('./_property-desc');\nvar anObject = require('./_an-object');\nvar isObject = require('./_is-object');\n\nfunction set(target, propertyKey, V /* , receiver */) {\n  var receiver = arguments.length < 4 ? target : arguments[3];\n  var ownDesc = gOPD.f(anObject(target), propertyKey);\n  var existingDescriptor, proto;\n  if (!ownDesc) {\n    if (isObject(proto = getPrototypeOf(target))) {\n      return set(proto, propertyKey, V, receiver);\n    }\n    ownDesc = createDesc(0);\n  }\n  if (has(ownDesc, 'value')) {\n    if (ownDesc.writable === false || !isObject(receiver)) return false;\n    if (existingDescriptor = gOPD.f(receiver, propertyKey)) {\n      if (existingDescriptor.get || existingDescriptor.set || existingDescriptor.writable === false) return false;\n      existingDescriptor.value = V;\n      dP.f(receiver, propertyKey, existingDescriptor);\n    } else dP.f(receiver, propertyKey, createDesc(0, V));\n    return true;\n  }\n  return ownDesc.set === undefined ? false : (ownDesc.set.call(receiver, V), true);\n}\n\n$export($export.S, 'Reflect', { set: set });\n","// 26.1.14 Reflect.setPrototypeOf(target, proto)\nvar $export = require('./_export');\nvar setProto = require('./_set-proto');\n\nif (setProto) $export($export.S, 'Reflect', {\n  setPrototypeOf: function setPrototypeOf(target, proto) {\n    setProto.check(target, proto);\n    try {\n      setProto.set(target, proto);\n      return true;\n    } catch (e) {\n      return false;\n    }\n  }\n});\n","'use strict';\n// https://github.com/tc39/Array.prototype.includes\nvar $export = require('./_export');\nvar $includes = require('./_array-includes')(true);\n\n$export($export.P, 'Array', {\n  includes: function includes(el /* , fromIndex = 0 */) {\n    return $includes(this, el, arguments.length > 1 ? arguments[1] : undefined);\n  }\n});\n\nrequire('./_add-to-unscopables')('includes');\n","'use strict';\n// https://tc39.github.io/proposal-flatMap/#sec-FlattenIntoArray\nvar isArray = require('./_is-array');\nvar isObject = require('./_is-object');\nvar toLength = require('./_to-length');\nvar ctx = require('./_ctx');\nvar IS_CONCAT_SPREADABLE = require('./_wks')('isConcatSpreadable');\n\nfunction flattenIntoArray(target, original, source, sourceLen, start, depth, mapper, thisArg) {\n  var targetIndex = start;\n  var sourceIndex = 0;\n  var mapFn = mapper ? ctx(mapper, thisArg, 3) : false;\n  var element, spreadable;\n\n  while (sourceIndex < sourceLen) {\n    if (sourceIndex in source) {\n      element = mapFn ? mapFn(source[sourceIndex], sourceIndex, original) : source[sourceIndex];\n\n      spreadable = false;\n      if (isObject(element)) {\n        spreadable = element[IS_CONCAT_SPREADABLE];\n        spreadable = spreadable !== undefined ? !!spreadable : isArray(element);\n      }\n\n      if (spreadable && depth > 0) {\n        targetIndex = flattenIntoArray(target, original, element, toLength(element.length), targetIndex, depth - 1) - 1;\n      } else {\n        if (targetIndex >= 0x1fffffffffffff) throw TypeError();\n        target[targetIndex] = element;\n      }\n\n      targetIndex++;\n    }\n    sourceIndex++;\n  }\n  return targetIndex;\n}\n\nmodule.exports = flattenIntoArray;\n","'use strict';\n// https://tc39.github.io/proposal-flatMap/#sec-Array.prototype.flatMap\nvar $export = require('./_export');\nvar flattenIntoArray = require('./_flatten-into-array');\nvar toObject = require('./_to-object');\nvar toLength = require('./_to-length');\nvar aFunction = require('./_a-function');\nvar arraySpeciesCreate = require('./_array-species-create');\n\n$export($export.P, 'Array', {\n  flatMap: function flatMap(callbackfn /* , thisArg */) {\n    var O = toObject(this);\n    var sourceLen, A;\n    aFunction(callbackfn);\n    sourceLen = toLength(O.length);\n    A = arraySpeciesCreate(O, 0);\n    flattenIntoArray(A, O, O, sourceLen, 0, 1, callbackfn, arguments[1]);\n    return A;\n  }\n});\n\nrequire('./_add-to-unscopables')('flatMap');\n","'use strict';\n// https://tc39.github.io/proposal-flatMap/#sec-Array.prototype.flatten\nvar $export = require('./_export');\nvar flattenIntoArray = require('./_flatten-into-array');\nvar toObject = require('./_to-object');\nvar toLength = require('./_to-length');\nvar toInteger = require('./_to-integer');\nvar arraySpeciesCreate = require('./_array-species-create');\n\n$export($export.P, 'Array', {\n  flatten: function flatten(/* depthArg = 1 */) {\n    var depthArg = arguments[0];\n    var O = toObject(this);\n    var sourceLen = toLength(O.length);\n    var A = arraySpeciesCreate(O, 0);\n    flattenIntoArray(A, O, O, sourceLen, 0, depthArg === undefined ? 1 : toInteger(depthArg));\n    return A;\n  }\n});\n\nrequire('./_add-to-unscopables')('flatten');\n","'use strict';\n// https://github.com/mathiasbynens/String.prototype.at\nvar $export = require('./_export');\nvar $at = require('./_string-at')(true);\n\n$export($export.P, 'String', {\n  at: function at(pos) {\n    return $at(this, pos);\n  }\n});\n","// https://github.com/tc39/proposal-string-pad-start-end\nvar toLength = require('./_to-length');\nvar repeat = require('./_string-repeat');\nvar defined = require('./_defined');\n\nmodule.exports = function (that, maxLength, fillString, left) {\n  var S = String(defined(that));\n  var stringLength = S.length;\n  var fillStr = fillString === undefined ? ' ' : String(fillString);\n  var intMaxLength = toLength(maxLength);\n  if (intMaxLength <= stringLength || fillStr == '') return S;\n  var fillLen = intMaxLength - stringLength;\n  var stringFiller = repeat.call(fillStr, Math.ceil(fillLen / fillStr.length));\n  if (stringFiller.length > fillLen) stringFiller = stringFiller.slice(0, fillLen);\n  return left ? stringFiller + S : S + stringFiller;\n};\n","'use strict';\n// https://github.com/tc39/proposal-string-pad-start-end\nvar $export = require('./_export');\nvar $pad = require('./_string-pad');\nvar userAgent = require('./_user-agent');\n\n// https://github.com/zloirock/core-js/issues/280\nvar WEBKIT_BUG = /Version\\/10\\.\\d+(\\.\\d+)?( Mobile\\/\\w+)? Safari\\//.test(userAgent);\n\n$export($export.P + $export.F * WEBKIT_BUG, 'String', {\n  padStart: function padStart(maxLength /* , fillString = ' ' */) {\n    return $pad(this, maxLength, arguments.length > 1 ? arguments[1] : undefined, true);\n  }\n});\n","'use strict';\n// https://github.com/tc39/proposal-string-pad-start-end\nvar $export = require('./_export');\nvar $pad = require('./_string-pad');\nvar userAgent = require('./_user-agent');\n\n// https://github.com/zloirock/core-js/issues/280\nvar WEBKIT_BUG = /Version\\/10\\.\\d+(\\.\\d+)?( Mobile\\/\\w+)? Safari\\//.test(userAgent);\n\n$export($export.P + $export.F * WEBKIT_BUG, 'String', {\n  padEnd: function padEnd(maxLength /* , fillString = ' ' */) {\n    return $pad(this, maxLength, arguments.length > 1 ? arguments[1] : undefined, false);\n  }\n});\n","'use strict';\n// https://github.com/sebmarkbage/ecmascript-string-left-right-trim\nrequire('./_string-trim')('trimLeft', function ($trim) {\n  return function trimLeft() {\n    return $trim(this, 1);\n  };\n}, 'trimStart');\n","'use strict';\n// https://github.com/sebmarkbage/ecmascript-string-left-right-trim\nrequire('./_string-trim')('trimRight', function ($trim) {\n  return function trimRight() {\n    return $trim(this, 2);\n  };\n}, 'trimEnd');\n","'use strict';\n// https://tc39.github.io/String.prototype.matchAll/\nvar $export = require('./_export');\nvar defined = require('./_defined');\nvar toLength = require('./_to-length');\nvar isRegExp = require('./_is-regexp');\nvar getFlags = require('./_flags');\nvar RegExpProto = RegExp.prototype;\n\nvar $RegExpStringIterator = function (regexp, string) {\n  this._r = regexp;\n  this._s = string;\n};\n\nrequire('./_iter-create')($RegExpStringIterator, 'RegExp String', function next() {\n  var match = this._r.exec(this._s);\n  return { value: match, done: match === null };\n});\n\n$export($export.P, 'String', {\n  matchAll: function matchAll(regexp) {\n    defined(this);\n    if (!isRegExp(regexp)) throw TypeError(regexp + ' is not a regexp!');\n    var S = String(this);\n    var flags = 'flags' in RegExpProto ? String(regexp.flags) : getFlags.call(regexp);\n    var rx = new RegExp(regexp.source, ~flags.indexOf('g') ? flags : 'g' + flags);\n    rx.lastIndex = toLength(regexp.lastIndex);\n    return new $RegExpStringIterator(rx, S);\n  }\n});\n","require('./_wks-define')('asyncIterator');\n","require('./_wks-define')('observable');\n","// https://github.com/tc39/proposal-object-getownpropertydescriptors\nvar $export = require('./_export');\nvar ownKeys = require('./_own-keys');\nvar toIObject = require('./_to-iobject');\nvar gOPD = require('./_object-gopd');\nvar createProperty = require('./_create-property');\n\n$export($export.S, 'Object', {\n  getOwnPropertyDescriptors: function getOwnPropertyDescriptors(object) {\n    var O = toIObject(object);\n    var getDesc = gOPD.f;\n    var keys = ownKeys(O);\n    var result = {};\n    var i = 0;\n    var key, desc;\n    while (keys.length > i) {\n      desc = getDesc(O, key = keys[i++]);\n      if (desc !== undefined) createProperty(result, key, desc);\n    }\n    return result;\n  }\n});\n","var DESCRIPTORS = require('./_descriptors');\nvar getKeys = require('./_object-keys');\nvar toIObject = require('./_to-iobject');\nvar isEnum = require('./_object-pie').f;\nmodule.exports = function (isEntries) {\n  return function (it) {\n    var O = toIObject(it);\n    var keys = getKeys(O);\n    var length = keys.length;\n    var i = 0;\n    var result = [];\n    var key;\n    while (length > i) {\n      key = keys[i++];\n      if (!DESCRIPTORS || isEnum.call(O, key)) {\n        result.push(isEntries ? [key, O[key]] : O[key]);\n      }\n    }\n    return result;\n  };\n};\n","// https://github.com/tc39/proposal-object-values-entries\nvar $export = require('./_export');\nvar $values = require('./_object-to-array')(false);\n\n$export($export.S, 'Object', {\n  values: function values(it) {\n    return $values(it);\n  }\n});\n","// https://github.com/tc39/proposal-object-values-entries\nvar $export = require('./_export');\nvar $entries = require('./_object-to-array')(true);\n\n$export($export.S, 'Object', {\n  entries: function entries(it) {\n    return $entries(it);\n  }\n});\n","'use strict';\n// Forced replacement prototype accessors methods\nmodule.exports = require('./_library') || !require('./_fails')(function () {\n  var K = Math.random();\n  // In FF throws only define methods\n  // eslint-disable-next-line no-undef, no-useless-call\n  __defineSetter__.call(null, K, function () { /* empty */ });\n  delete require('./_global')[K];\n});\n","'use strict';\nvar $export = require('./_export');\nvar toObject = require('./_to-object');\nvar aFunction = require('./_a-function');\nvar $defineProperty = require('./_object-dp');\n\n// B.2.2.2 Object.prototype.__defineGetter__(P, getter)\nrequire('./_descriptors') && $export($export.P + require('./_object-forced-pam'), 'Object', {\n  __defineGetter__: function __defineGetter__(P, getter) {\n    $defineProperty.f(toObject(this), P, { get: aFunction(getter), enumerable: true, configurable: true });\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar toObject = require('./_to-object');\nvar aFunction = require('./_a-function');\nvar $defineProperty = require('./_object-dp');\n\n// B.2.2.3 Object.prototype.__defineSetter__(P, setter)\nrequire('./_descriptors') && $export($export.P + require('./_object-forced-pam'), 'Object', {\n  __defineSetter__: function __defineSetter__(P, setter) {\n    $defineProperty.f(toObject(this), P, { set: aFunction(setter), enumerable: true, configurable: true });\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar toObject = require('./_to-object');\nvar toPrimitive = require('./_to-primitive');\nvar getPrototypeOf = require('./_object-gpo');\nvar getOwnPropertyDescriptor = require('./_object-gopd').f;\n\n// B.2.2.4 Object.prototype.__lookupGetter__(P)\nrequire('./_descriptors') && $export($export.P + require('./_object-forced-pam'), 'Object', {\n  __lookupGetter__: function __lookupGetter__(P) {\n    var O = toObject(this);\n    var K = toPrimitive(P, true);\n    var D;\n    do {\n      if (D = getOwnPropertyDescriptor(O, K)) return D.get;\n    } while (O = getPrototypeOf(O));\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar toObject = require('./_to-object');\nvar toPrimitive = require('./_to-primitive');\nvar getPrototypeOf = require('./_object-gpo');\nvar getOwnPropertyDescriptor = require('./_object-gopd').f;\n\n// B.2.2.5 Object.prototype.__lookupSetter__(P)\nrequire('./_descriptors') && $export($export.P + require('./_object-forced-pam'), 'Object', {\n  __lookupSetter__: function __lookupSetter__(P) {\n    var O = toObject(this);\n    var K = toPrimitive(P, true);\n    var D;\n    do {\n      if (D = getOwnPropertyDescriptor(O, K)) return D.set;\n    } while (O = getPrototypeOf(O));\n  }\n});\n","var forOf = require('./_for-of');\n\nmodule.exports = function (iter, ITERATOR) {\n  var result = [];\n  forOf(iter, false, result.push, result, ITERATOR);\n  return result;\n};\n","// https://github.com/DavidBruant/Map-Set.prototype.toJSON\nvar classof = require('./_classof');\nvar from = require('./_array-from-iterable');\nmodule.exports = function (NAME) {\n  return function toJSON() {\n    if (classof(this) != NAME) throw TypeError(NAME + \"#toJSON isn't generic\");\n    return from(this);\n  };\n};\n","// https://github.com/DavidBruant/Map-Set.prototype.toJSON\nvar $export = require('./_export');\n\n$export($export.P + $export.R, 'Map', { toJSON: require('./_collection-to-json')('Map') });\n","// https://github.com/DavidBruant/Map-Set.prototype.toJSON\nvar $export = require('./_export');\n\n$export($export.P + $export.R, 'Set', { toJSON: require('./_collection-to-json')('Set') });\n","'use strict';\n// https://tc39.github.io/proposal-setmap-offrom/\nvar $export = require('./_export');\n\nmodule.exports = function (COLLECTION) {\n  $export($export.S, COLLECTION, { of: function of() {\n    var length = arguments.length;\n    var A = new Array(length);\n    while (length--) A[length] = arguments[length];\n    return new this(A);\n  } });\n};\n","// https://tc39.github.io/proposal-setmap-offrom/#sec-map.of\nrequire('./_set-collection-of')('Map');\n","// https://tc39.github.io/proposal-setmap-offrom/#sec-set.of\nrequire('./_set-collection-of')('Set');\n","// https://tc39.github.io/proposal-setmap-offrom/#sec-weakmap.of\nrequire('./_set-collection-of')('WeakMap');\n","// https://tc39.github.io/proposal-setmap-offrom/#sec-weakset.of\nrequire('./_set-collection-of')('WeakSet');\n","'use strict';\n// https://tc39.github.io/proposal-setmap-offrom/\nvar $export = require('./_export');\nvar aFunction = require('./_a-function');\nvar ctx = require('./_ctx');\nvar forOf = require('./_for-of');\n\nmodule.exports = function (COLLECTION) {\n  $export($export.S, COLLECTION, { from: function from(source /* , mapFn, thisArg */) {\n    var mapFn = arguments[1];\n    var mapping, A, n, cb;\n    aFunction(this);\n    mapping = mapFn !== undefined;\n    if (mapping) aFunction(mapFn);\n    if (source == undefined) return new this();\n    A = [];\n    if (mapping) {\n      n = 0;\n      cb = ctx(mapFn, arguments[2], 2);\n      forOf(source, false, function (nextItem) {\n        A.push(cb(nextItem, n++));\n      });\n    } else {\n      forOf(source, false, A.push, A);\n    }\n    return new this(A);\n  } });\n};\n","// https://tc39.github.io/proposal-setmap-offrom/#sec-map.from\nrequire('./_set-collection-from')('Map');\n","// https://tc39.github.io/proposal-setmap-offrom/#sec-set.from\nrequire('./_set-collection-from')('Set');\n","// https://tc39.github.io/proposal-setmap-offrom/#sec-weakmap.from\nrequire('./_set-collection-from')('WeakMap');\n","// https://tc39.github.io/proposal-setmap-offrom/#sec-weakset.from\nrequire('./_set-collection-from')('WeakSet');\n","// https://github.com/tc39/proposal-global\nvar $export = require('./_export');\n\n$export($export.G, { global: require('./_global') });\n","// https://github.com/tc39/proposal-global\nvar $export = require('./_export');\n\n$export($export.S, 'System', { global: require('./_global') });\n","// https://github.com/ljharb/proposal-is-error\nvar $export = require('./_export');\nvar cof = require('./_cof');\n\n$export($export.S, 'Error', {\n  isError: function isError(it) {\n    return cof(it) === 'Error';\n  }\n});\n","// https://rwaldron.github.io/proposal-math-extensions/\nvar $export = require('./_export');\n\n$export($export.S, 'Math', {\n  clamp: function clamp(x, lower, upper) {\n    return Math.min(upper, Math.max(lower, x));\n  }\n});\n","// https://rwaldron.github.io/proposal-math-extensions/\nvar $export = require('./_export');\n\n$export($export.S, 'Math', { DEG_PER_RAD: Math.PI / 180 });\n","// https://rwaldron.github.io/proposal-math-extensions/\nvar $export = require('./_export');\nvar RAD_PER_DEG = 180 / Math.PI;\n\n$export($export.S, 'Math', {\n  degrees: function degrees(radians) {\n    return radians * RAD_PER_DEG;\n  }\n});\n","// https://rwaldron.github.io/proposal-math-extensions/\nmodule.exports = Math.scale || function scale(x, inLow, inHigh, outLow, outHigh) {\n  if (\n    arguments.length === 0\n      // eslint-disable-next-line no-self-compare\n      || x != x\n      // eslint-disable-next-line no-self-compare\n      || inLow != inLow\n      // eslint-disable-next-line no-self-compare\n      || inHigh != inHigh\n      // eslint-disable-next-line no-self-compare\n      || outLow != outLow\n      // eslint-disable-next-line no-self-compare\n      || outHigh != outHigh\n  ) return NaN;\n  if (x === Infinity || x === -Infinity) return x;\n  return (x - inLow) * (outHigh - outLow) / (inHigh - inLow) + outLow;\n};\n","// https://rwaldron.github.io/proposal-math-extensions/\nvar $export = require('./_export');\nvar scale = require('./_math-scale');\nvar fround = require('./_math-fround');\n\n$export($export.S, 'Math', {\n  fscale: function fscale(x, inLow, inHigh, outLow, outHigh) {\n    return fround(scale(x, inLow, inHigh, outLow, outHigh));\n  }\n});\n","// https://gist.github.com/BrendanEich/4294d5c212a6d2254703\nvar $export = require('./_export');\n\n$export($export.S, 'Math', {\n  iaddh: function iaddh(x0, x1, y0, y1) {\n    var $x0 = x0 >>> 0;\n    var $x1 = x1 >>> 0;\n    var $y0 = y0 >>> 0;\n    return $x1 + (y1 >>> 0) + (($x0 & $y0 | ($x0 | $y0) & ~($x0 + $y0 >>> 0)) >>> 31) | 0;\n  }\n});\n","// https://gist.github.com/BrendanEich/4294d5c212a6d2254703\nvar $export = require('./_export');\n\n$export($export.S, 'Math', {\n  isubh: function isubh(x0, x1, y0, y1) {\n    var $x0 = x0 >>> 0;\n    var $x1 = x1 >>> 0;\n    var $y0 = y0 >>> 0;\n    return $x1 - (y1 >>> 0) - ((~$x0 & $y0 | ~($x0 ^ $y0) & $x0 - $y0 >>> 0) >>> 31) | 0;\n  }\n});\n","// https://gist.github.com/BrendanEich/4294d5c212a6d2254703\nvar $export = require('./_export');\n\n$export($export.S, 'Math', {\n  imulh: function imulh(u, v) {\n    var UINT16 = 0xffff;\n    var $u = +u;\n    var $v = +v;\n    var u0 = $u & UINT16;\n    var v0 = $v & UINT16;\n    var u1 = $u >> 16;\n    var v1 = $v >> 16;\n    var t = (u1 * v0 >>> 0) + (u0 * v0 >>> 16);\n    return u1 * v1 + (t >> 16) + ((u0 * v1 >>> 0) + (t & UINT16) >> 16);\n  }\n});\n","// https://rwaldron.github.io/proposal-math-extensions/\nvar $export = require('./_export');\n\n$export($export.S, 'Math', { RAD_PER_DEG: 180 / Math.PI });\n","// https://rwaldron.github.io/proposal-math-extensions/\nvar $export = require('./_export');\nvar DEG_PER_RAD = Math.PI / 180;\n\n$export($export.S, 'Math', {\n  radians: function radians(degrees) {\n    return degrees * DEG_PER_RAD;\n  }\n});\n","// https://rwaldron.github.io/proposal-math-extensions/\nvar $export = require('./_export');\n\n$export($export.S, 'Math', { scale: require('./_math-scale') });\n","// https://gist.github.com/BrendanEich/4294d5c212a6d2254703\nvar $export = require('./_export');\n\n$export($export.S, 'Math', {\n  umulh: function umulh(u, v) {\n    var UINT16 = 0xffff;\n    var $u = +u;\n    var $v = +v;\n    var u0 = $u & UINT16;\n    var v0 = $v & UINT16;\n    var u1 = $u >>> 16;\n    var v1 = $v >>> 16;\n    var t = (u1 * v0 >>> 0) + (u0 * v0 >>> 16);\n    return u1 * v1 + (t >>> 16) + ((u0 * v1 >>> 0) + (t & UINT16) >>> 16);\n  }\n});\n","// http://jfbastien.github.io/papers/Math.signbit.html\nvar $export = require('./_export');\n\n$export($export.S, 'Math', { signbit: function signbit(x) {\n  // eslint-disable-next-line no-self-compare\n  return (x = +x) != x ? x : x == 0 ? 1 / x == Infinity : x > 0;\n} });\n","// https://github.com/tc39/proposal-promise-finally\n'use strict';\nvar $export = require('./_export');\nvar core = require('./_core');\nvar global = require('./_global');\nvar speciesConstructor = require('./_species-constructor');\nvar promiseResolve = require('./_promise-resolve');\n\n$export($export.P + $export.R, 'Promise', { 'finally': function (onFinally) {\n  var C = speciesConstructor(this, core.Promise || global.Promise);\n  var isFunction = typeof onFinally == 'function';\n  return this.then(\n    isFunction ? function (x) {\n      return promiseResolve(C, onFinally()).then(function () { return x; });\n    } : onFinally,\n    isFunction ? function (e) {\n      return promiseResolve(C, onFinally()).then(function () { throw e; });\n    } : onFinally\n  );\n} });\n","'use strict';\n// https://github.com/tc39/proposal-promise-try\nvar $export = require('./_export');\nvar newPromiseCapability = require('./_new-promise-capability');\nvar perform = require('./_perform');\n\n$export($export.S, 'Promise', { 'try': function (callbackfn) {\n  var promiseCapability = newPromiseCapability.f(this);\n  var result = perform(callbackfn);\n  (result.e ? promiseCapability.reject : promiseCapability.resolve)(result.v);\n  return promiseCapability.promise;\n} });\n","var Map = require('./es6.map');\nvar $export = require('./_export');\nvar shared = require('./_shared')('metadata');\nvar store = shared.store || (shared.store = new (require('./es6.weak-map'))());\n\nvar getOrCreateMetadataMap = function (target, targetKey, create) {\n  var targetMetadata = store.get(target);\n  if (!targetMetadata) {\n    if (!create) return undefined;\n    store.set(target, targetMetadata = new Map());\n  }\n  var keyMetadata = targetMetadata.get(targetKey);\n  if (!keyMetadata) {\n    if (!create) return undefined;\n    targetMetadata.set(targetKey, keyMetadata = new Map());\n  } return keyMetadata;\n};\nvar ordinaryHasOwnMetadata = function (MetadataKey, O, P) {\n  var metadataMap = getOrCreateMetadataMap(O, P, false);\n  return metadataMap === undefined ? false : metadataMap.has(MetadataKey);\n};\nvar ordinaryGetOwnMetadata = function (MetadataKey, O, P) {\n  var metadataMap = getOrCreateMetadataMap(O, P, false);\n  return metadataMap === undefined ? undefined : metadataMap.get(MetadataKey);\n};\nvar ordinaryDefineOwnMetadata = function (MetadataKey, MetadataValue, O, P) {\n  getOrCreateMetadataMap(O, P, true).set(MetadataKey, MetadataValue);\n};\nvar ordinaryOwnMetadataKeys = function (target, targetKey) {\n  var metadataMap = getOrCreateMetadataMap(target, targetKey, false);\n  var keys = [];\n  if (metadataMap) metadataMap.forEach(function (_, key) { keys.push(key); });\n  return keys;\n};\nvar toMetaKey = function (it) {\n  return it === undefined || typeof it == 'symbol' ? it : String(it);\n};\nvar exp = function (O) {\n  $export($export.S, 'Reflect', O);\n};\n\nmodule.exports = {\n  store: store,\n  map: getOrCreateMetadataMap,\n  has: ordinaryHasOwnMetadata,\n  get: ordinaryGetOwnMetadata,\n  set: ordinaryDefineOwnMetadata,\n  keys: ordinaryOwnMetadataKeys,\n  key: toMetaKey,\n  exp: exp\n};\n","var metadata = require('./_metadata');\nvar anObject = require('./_an-object');\nvar toMetaKey = metadata.key;\nvar ordinaryDefineOwnMetadata = metadata.set;\n\nmetadata.exp({ defineMetadata: function defineMetadata(metadataKey, metadataValue, target, targetKey) {\n  ordinaryDefineOwnMetadata(metadataKey, metadataValue, anObject(target), toMetaKey(targetKey));\n} });\n","var metadata = require('./_metadata');\nvar anObject = require('./_an-object');\nvar toMetaKey = metadata.key;\nvar getOrCreateMetadataMap = metadata.map;\nvar store = metadata.store;\n\nmetadata.exp({ deleteMetadata: function deleteMetadata(metadataKey, target /* , targetKey */) {\n  var targetKey = arguments.length < 3 ? undefined : toMetaKey(arguments[2]);\n  var metadataMap = getOrCreateMetadataMap(anObject(target), targetKey, false);\n  if (metadataMap === undefined || !metadataMap['delete'](metadataKey)) return false;\n  if (metadataMap.size) return true;\n  var targetMetadata = store.get(target);\n  targetMetadata['delete'](targetKey);\n  return !!targetMetadata.size || store['delete'](target);\n} });\n","var metadata = require('./_metadata');\nvar anObject = require('./_an-object');\nvar getPrototypeOf = require('./_object-gpo');\nvar ordinaryHasOwnMetadata = metadata.has;\nvar ordinaryGetOwnMetadata = metadata.get;\nvar toMetaKey = metadata.key;\n\nvar ordinaryGetMetadata = function (MetadataKey, O, P) {\n  var hasOwn = ordinaryHasOwnMetadata(MetadataKey, O, P);\n  if (hasOwn) return ordinaryGetOwnMetadata(MetadataKey, O, P);\n  var parent = getPrototypeOf(O);\n  return parent !== null ? ordinaryGetMetadata(MetadataKey, parent, P) : undefined;\n};\n\nmetadata.exp({ getMetadata: function getMetadata(metadataKey, target /* , targetKey */) {\n  return ordinaryGetMetadata(metadataKey, anObject(target), arguments.length < 3 ? undefined : toMetaKey(arguments[2]));\n} });\n","var Set = require('./es6.set');\nvar from = require('./_array-from-iterable');\nvar metadata = require('./_metadata');\nvar anObject = require('./_an-object');\nvar getPrototypeOf = require('./_object-gpo');\nvar ordinaryOwnMetadataKeys = metadata.keys;\nvar toMetaKey = metadata.key;\n\nvar ordinaryMetadataKeys = function (O, P) {\n  var oKeys = ordinaryOwnMetadataKeys(O, P);\n  var parent = getPrototypeOf(O);\n  if (parent === null) return oKeys;\n  var pKeys = ordinaryMetadataKeys(parent, P);\n  return pKeys.length ? oKeys.length ? from(new Set(oKeys.concat(pKeys))) : pKeys : oKeys;\n};\n\nmetadata.exp({ getMetadataKeys: function getMetadataKeys(target /* , targetKey */) {\n  return ordinaryMetadataKeys(anObject(target), arguments.length < 2 ? undefined : toMetaKey(arguments[1]));\n} });\n","var metadata = require('./_metadata');\nvar anObject = require('./_an-object');\nvar ordinaryGetOwnMetadata = metadata.get;\nvar toMetaKey = metadata.key;\n\nmetadata.exp({ getOwnMetadata: function getOwnMetadata(metadataKey, target /* , targetKey */) {\n  return ordinaryGetOwnMetadata(metadataKey, anObject(target)\n    , arguments.length < 3 ? undefined : toMetaKey(arguments[2]));\n} });\n","var metadata = require('./_metadata');\nvar anObject = require('./_an-object');\nvar ordinaryOwnMetadataKeys = metadata.keys;\nvar toMetaKey = metadata.key;\n\nmetadata.exp({ getOwnMetadataKeys: function getOwnMetadataKeys(target /* , targetKey */) {\n  return ordinaryOwnMetadataKeys(anObject(target), arguments.length < 2 ? undefined : toMetaKey(arguments[1]));\n} });\n","var metadata = require('./_metadata');\nvar anObject = require('./_an-object');\nvar getPrototypeOf = require('./_object-gpo');\nvar ordinaryHasOwnMetadata = metadata.has;\nvar toMetaKey = metadata.key;\n\nvar ordinaryHasMetadata = function (MetadataKey, O, P) {\n  var hasOwn = ordinaryHasOwnMetadata(MetadataKey, O, P);\n  if (hasOwn) return true;\n  var parent = getPrototypeOf(O);\n  return parent !== null ? ordinaryHasMetadata(MetadataKey, parent, P) : false;\n};\n\nmetadata.exp({ hasMetadata: function hasMetadata(metadataKey, target /* , targetKey */) {\n  return ordinaryHasMetadata(metadataKey, anObject(target), arguments.length < 3 ? undefined : toMetaKey(arguments[2]));\n} });\n","var metadata = require('./_metadata');\nvar anObject = require('./_an-object');\nvar ordinaryHasOwnMetadata = metadata.has;\nvar toMetaKey = metadata.key;\n\nmetadata.exp({ hasOwnMetadata: function hasOwnMetadata(metadataKey, target /* , targetKey */) {\n  return ordinaryHasOwnMetadata(metadataKey, anObject(target)\n    , arguments.length < 3 ? undefined : toMetaKey(arguments[2]));\n} });\n","var $metadata = require('./_metadata');\nvar anObject = require('./_an-object');\nvar aFunction = require('./_a-function');\nvar toMetaKey = $metadata.key;\nvar ordinaryDefineOwnMetadata = $metadata.set;\n\n$metadata.exp({ metadata: function metadata(metadataKey, metadataValue) {\n  return function decorator(target, targetKey) {\n    ordinaryDefineOwnMetadata(\n      metadataKey, metadataValue,\n      (targetKey !== undefined ? anObject : aFunction)(target),\n      toMetaKey(targetKey)\n    );\n  };\n} });\n","// https://github.com/rwaldron/tc39-notes/blob/master/es6/2014-09/sept-25.md#510-globalasap-for-enqueuing-a-microtask\nvar $export = require('./_export');\nvar microtask = require('./_microtask')();\nvar process = require('./_global').process;\nvar isNode = require('./_cof')(process) == 'process';\n\n$export($export.G, {\n  asap: function asap(fn) {\n    var domain = isNode && process.domain;\n    microtask(domain ? domain.bind(fn) : fn);\n  }\n});\n","'use strict';\n// https://github.com/zenparsing/es-observable\nvar $export = require('./_export');\nvar global = require('./_global');\nvar core = require('./_core');\nvar microtask = require('./_microtask')();\nvar OBSERVABLE = require('./_wks')('observable');\nvar aFunction = require('./_a-function');\nvar anObject = require('./_an-object');\nvar anInstance = require('./_an-instance');\nvar redefineAll = require('./_redefine-all');\nvar hide = require('./_hide');\nvar forOf = require('./_for-of');\nvar RETURN = forOf.RETURN;\n\nvar getMethod = function (fn) {\n  return fn == null ? undefined : aFunction(fn);\n};\n\nvar cleanupSubscription = function (subscription) {\n  var cleanup = subscription._c;\n  if (cleanup) {\n    subscription._c = undefined;\n    cleanup();\n  }\n};\n\nvar subscriptionClosed = function (subscription) {\n  return subscription._o === undefined;\n};\n\nvar closeSubscription = function (subscription) {\n  if (!subscriptionClosed(subscription)) {\n    subscription._o = undefined;\n    cleanupSubscription(subscription);\n  }\n};\n\nvar Subscription = function (observer, subscriber) {\n  anObject(observer);\n  this._c = undefined;\n  this._o = observer;\n  observer = new SubscriptionObserver(this);\n  try {\n    var cleanup = subscriber(observer);\n    var subscription = cleanup;\n    if (cleanup != null) {\n      if (typeof cleanup.unsubscribe === 'function') cleanup = function () { subscription.unsubscribe(); };\n      else aFunction(cleanup);\n      this._c = cleanup;\n    }\n  } catch (e) {\n    observer.error(e);\n    return;\n  } if (subscriptionClosed(this)) cleanupSubscription(this);\n};\n\nSubscription.prototype = redefineAll({}, {\n  unsubscribe: function unsubscribe() { closeSubscription(this); }\n});\n\nvar SubscriptionObserver = function (subscription) {\n  this._s = subscription;\n};\n\nSubscriptionObserver.prototype = redefineAll({}, {\n  next: function next(value) {\n    var subscription = this._s;\n    if (!subscriptionClosed(subscription)) {\n      var observer = subscription._o;\n      try {\n        var m = getMethod(observer.next);\n        if (m) return m.call(observer, value);\n      } catch (e) {\n        try {\n          closeSubscription(subscription);\n        } finally {\n          throw e;\n        }\n      }\n    }\n  },\n  error: function error(value) {\n    var subscription = this._s;\n    if (subscriptionClosed(subscription)) throw value;\n    var observer = subscription._o;\n    subscription._o = undefined;\n    try {\n      var m = getMethod(observer.error);\n      if (!m) throw value;\n      value = m.call(observer, value);\n    } catch (e) {\n      try {\n        cleanupSubscription(subscription);\n      } finally {\n        throw e;\n      }\n    } cleanupSubscription(subscription);\n    return value;\n  },\n  complete: function complete(value) {\n    var subscription = this._s;\n    if (!subscriptionClosed(subscription)) {\n      var observer = subscription._o;\n      subscription._o = undefined;\n      try {\n        var m = getMethod(observer.complete);\n        value = m ? m.call(observer, value) : undefined;\n      } catch (e) {\n        try {\n          cleanupSubscription(subscription);\n        } finally {\n          throw e;\n        }\n      } cleanupSubscription(subscription);\n      return value;\n    }\n  }\n});\n\nvar $Observable = function Observable(subscriber) {\n  anInstance(this, $Observable, 'Observable', '_f')._f = aFunction(subscriber);\n};\n\nredefineAll($Observable.prototype, {\n  subscribe: function subscribe(observer) {\n    return new Subscription(observer, this._f);\n  },\n  forEach: function forEach(fn) {\n    var that = this;\n    return new (core.Promise || global.Promise)(function (resolve, reject) {\n      aFunction(fn);\n      var subscription = that.subscribe({\n        next: function (value) {\n          try {\n            return fn(value);\n          } catch (e) {\n            reject(e);\n            subscription.unsubscribe();\n          }\n        },\n        error: reject,\n        complete: resolve\n      });\n    });\n  }\n});\n\nredefineAll($Observable, {\n  from: function from(x) {\n    var C = typeof this === 'function' ? this : $Observable;\n    var method = getMethod(anObject(x)[OBSERVABLE]);\n    if (method) {\n      var observable = anObject(method.call(x));\n      return observable.constructor === C ? observable : new C(function (observer) {\n        return observable.subscribe(observer);\n      });\n    }\n    return new C(function (observer) {\n      var done = false;\n      microtask(function () {\n        if (!done) {\n          try {\n            if (forOf(x, false, function (it) {\n              observer.next(it);\n              if (done) return RETURN;\n            }) === RETURN) return;\n          } catch (e) {\n            if (done) throw e;\n            observer.error(e);\n            return;\n          } observer.complete();\n        }\n      });\n      return function () { done = true; };\n    });\n  },\n  of: function of() {\n    for (var i = 0, l = arguments.length, items = new Array(l); i < l;) items[i] = arguments[i++];\n    return new (typeof this === 'function' ? this : $Observable)(function (observer) {\n      var done = false;\n      microtask(function () {\n        if (!done) {\n          for (var j = 0; j < items.length; ++j) {\n            observer.next(items[j]);\n            if (done) return;\n          } observer.complete();\n        }\n      });\n      return function () { done = true; };\n    });\n  }\n});\n\nhide($Observable.prototype, OBSERVABLE, function () { return this; });\n\n$export($export.G, { Observable: $Observable });\n\nrequire('./_set-species')('Observable');\n","// ie9- setTimeout & setInterval additional parameters fix\nvar global = require('./_global');\nvar $export = require('./_export');\nvar userAgent = require('./_user-agent');\nvar slice = [].slice;\nvar MSIE = /MSIE .\\./.test(userAgent); // <- dirty ie9- check\nvar wrap = function (set) {\n  return function (fn, time /* , ...args */) {\n    var boundArgs = arguments.length > 2;\n    var args = boundArgs ? slice.call(arguments, 2) : false;\n    return set(boundArgs ? function () {\n      // eslint-disable-next-line no-new-func\n      (typeof fn == 'function' ? fn : Function(fn)).apply(this, args);\n    } : fn, time);\n  };\n};\n$export($export.G + $export.B + $export.F * MSIE, {\n  setTimeout: wrap(global.setTimeout),\n  setInterval: wrap(global.setInterval)\n});\n","var $export = require('./_export');\nvar $task = require('./_task');\n$export($export.G + $export.B, {\n  setImmediate: $task.set,\n  clearImmediate: $task.clear\n});\n","var $iterators = require('./es6.array.iterator');\nvar getKeys = require('./_object-keys');\nvar redefine = require('./_redefine');\nvar global = require('./_global');\nvar hide = require('./_hide');\nvar Iterators = require('./_iterators');\nvar wks = require('./_wks');\nvar ITERATOR = wks('iterator');\nvar TO_STRING_TAG = wks('toStringTag');\nvar ArrayValues = Iterators.Array;\n\nvar DOMIterables = {\n  CSSRuleList: true, // TODO: Not spec compliant, should be false.\n  CSSStyleDeclaration: false,\n  CSSValueList: false,\n  ClientRectList: false,\n  DOMRectList: false,\n  DOMStringList: false,\n  DOMTokenList: true,\n  DataTransferItemList: false,\n  FileList: false,\n  HTMLAllCollection: false,\n  HTMLCollection: false,\n  HTMLFormElement: false,\n  HTMLSelectElement: false,\n  MediaList: true, // TODO: Not spec compliant, should be false.\n  MimeTypeArray: false,\n  NamedNodeMap: false,\n  NodeList: true,\n  PaintRequestList: false,\n  Plugin: false,\n  PluginArray: false,\n  SVGLengthList: false,\n  SVGNumberList: false,\n  SVGPathSegList: false,\n  SVGPointList: false,\n  SVGStringList: false,\n  SVGTransformList: false,\n  SourceBufferList: false,\n  StyleSheetList: true, // TODO: Not spec compliant, should be false.\n  TextTrackCueList: false,\n  TextTrackList: false,\n  TouchList: false\n};\n\nfor (var collections = getKeys(DOMIterables), i = 0; i < collections.length; i++) {\n  var NAME = collections[i];\n  var explicit = DOMIterables[NAME];\n  var Collection = global[NAME];\n  var proto = Collection && Collection.prototype;\n  var key;\n  if (proto) {\n    if (!proto[ITERATOR]) hide(proto, ITERATOR, ArrayValues);\n    if (!proto[TO_STRING_TAG]) hide(proto, TO_STRING_TAG, NAME);\n    Iterators[NAME] = ArrayValues;\n    if (explicit) for (key in $iterators) if (!proto[key]) redefine(proto, key, $iterators[key], true);\n  }\n}\n","require('./modules/es6.symbol');\nrequire('./modules/es6.object.create');\nrequire('./modules/es6.object.define-property');\nrequire('./modules/es6.object.define-properties');\nrequire('./modules/es6.object.get-own-property-descriptor');\nrequire('./modules/es6.object.get-prototype-of');\nrequire('./modules/es6.object.keys');\nrequire('./modules/es6.object.get-own-property-names');\nrequire('./modules/es6.object.freeze');\nrequire('./modules/es6.object.seal');\nrequire('./modules/es6.object.prevent-extensions');\nrequire('./modules/es6.object.is-frozen');\nrequire('./modules/es6.object.is-sealed');\nrequire('./modules/es6.object.is-extensible');\nrequire('./modules/es6.object.assign');\nrequire('./modules/es6.object.is');\nrequire('./modules/es6.object.set-prototype-of');\nrequire('./modules/es6.object.to-string');\nrequire('./modules/es6.function.bind');\nrequire('./modules/es6.function.name');\nrequire('./modules/es6.function.has-instance');\nrequire('./modules/es6.parse-int');\nrequire('./modules/es6.parse-float');\nrequire('./modules/es6.number.constructor');\nrequire('./modules/es6.number.to-fixed');\nrequire('./modules/es6.number.to-precision');\nrequire('./modules/es6.number.epsilon');\nrequire('./modules/es6.number.is-finite');\nrequire('./modules/es6.number.is-integer');\nrequire('./modules/es6.number.is-nan');\nrequire('./modules/es6.number.is-safe-integer');\nrequire('./modules/es6.number.max-safe-integer');\nrequire('./modules/es6.number.min-safe-integer');\nrequire('./modules/es6.number.parse-float');\nrequire('./modules/es6.number.parse-int');\nrequire('./modules/es6.math.acosh');\nrequire('./modules/es6.math.asinh');\nrequire('./modules/es6.math.atanh');\nrequire('./modules/es6.math.cbrt');\nrequire('./modules/es6.math.clz32');\nrequire('./modules/es6.math.cosh');\nrequire('./modules/es6.math.expm1');\nrequire('./modules/es6.math.fround');\nrequire('./modules/es6.math.hypot');\nrequire('./modules/es6.math.imul');\nrequire('./modules/es6.math.log10');\nrequire('./modules/es6.math.log1p');\nrequire('./modules/es6.math.log2');\nrequire('./modules/es6.math.sign');\nrequire('./modules/es6.math.sinh');\nrequire('./modules/es6.math.tanh');\nrequire('./modules/es6.math.trunc');\nrequire('./modules/es6.string.from-code-point');\nrequire('./modules/es6.string.raw');\nrequire('./modules/es6.string.trim');\nrequire('./modules/es6.string.iterator');\nrequire('./modules/es6.string.code-point-at');\nrequire('./modules/es6.string.ends-with');\nrequire('./modules/es6.string.includes');\nrequire('./modules/es6.string.repeat');\nrequire('./modules/es6.string.starts-with');\nrequire('./modules/es6.string.anchor');\nrequire('./modules/es6.string.big');\nrequire('./modules/es6.string.blink');\nrequire('./modules/es6.string.bold');\nrequire('./modules/es6.string.fixed');\nrequire('./modules/es6.string.fontcolor');\nrequire('./modules/es6.string.fontsize');\nrequire('./modules/es6.string.italics');\nrequire('./modules/es6.string.link');\nrequire('./modules/es6.string.small');\nrequire('./modules/es6.string.strike');\nrequire('./modules/es6.string.sub');\nrequire('./modules/es6.string.sup');\nrequire('./modules/es6.date.now');\nrequire('./modules/es6.date.to-json');\nrequire('./modules/es6.date.to-iso-string');\nrequire('./modules/es6.date.to-string');\nrequire('./modules/es6.date.to-primitive');\nrequire('./modules/es6.array.is-array');\nrequire('./modules/es6.array.from');\nrequire('./modules/es6.array.of');\nrequire('./modules/es6.array.join');\nrequire('./modules/es6.array.slice');\nrequire('./modules/es6.array.sort');\nrequire('./modules/es6.array.for-each');\nrequire('./modules/es6.array.map');\nrequire('./modules/es6.array.filter');\nrequire('./modules/es6.array.some');\nrequire('./modules/es6.array.every');\nrequire('./modules/es6.array.reduce');\nrequire('./modules/es6.array.reduce-right');\nrequire('./modules/es6.array.index-of');\nrequire('./modules/es6.array.last-index-of');\nrequire('./modules/es6.array.copy-within');\nrequire('./modules/es6.array.fill');\nrequire('./modules/es6.array.find');\nrequire('./modules/es6.array.find-index');\nrequire('./modules/es6.array.species');\nrequire('./modules/es6.array.iterator');\nrequire('./modules/es6.regexp.constructor');\nrequire('./modules/es6.regexp.exec');\nrequire('./modules/es6.regexp.to-string');\nrequire('./modules/es6.regexp.flags');\nrequire('./modules/es6.regexp.match');\nrequire('./modules/es6.regexp.replace');\nrequire('./modules/es6.regexp.search');\nrequire('./modules/es6.regexp.split');\nrequire('./modules/es6.promise');\nrequire('./modules/es6.map');\nrequire('./modules/es6.set');\nrequire('./modules/es6.weak-map');\nrequire('./modules/es6.weak-set');\nrequire('./modules/es6.typed.array-buffer');\nrequire('./modules/es6.typed.data-view');\nrequire('./modules/es6.typed.int8-array');\nrequire('./modules/es6.typed.uint8-array');\nrequire('./modules/es6.typed.uint8-clamped-array');\nrequire('./modules/es6.typed.int16-array');\nrequire('./modules/es6.typed.uint16-array');\nrequire('./modules/es6.typed.int32-array');\nrequire('./modules/es6.typed.uint32-array');\nrequire('./modules/es6.typed.float32-array');\nrequire('./modules/es6.typed.float64-array');\nrequire('./modules/es6.reflect.apply');\nrequire('./modules/es6.reflect.construct');\nrequire('./modules/es6.reflect.define-property');\nrequire('./modules/es6.reflect.delete-property');\nrequire('./modules/es6.reflect.enumerate');\nrequire('./modules/es6.reflect.get');\nrequire('./modules/es6.reflect.get-own-property-descriptor');\nrequire('./modules/es6.reflect.get-prototype-of');\nrequire('./modules/es6.reflect.has');\nrequire('./modules/es6.reflect.is-extensible');\nrequire('./modules/es6.reflect.own-keys');\nrequire('./modules/es6.reflect.prevent-extensions');\nrequire('./modules/es6.reflect.set');\nrequire('./modules/es6.reflect.set-prototype-of');\nrequire('./modules/es7.array.includes');\nrequire('./modules/es7.array.flat-map');\nrequire('./modules/es7.array.flatten');\nrequire('./modules/es7.string.at');\nrequire('./modules/es7.string.pad-start');\nrequire('./modules/es7.string.pad-end');\nrequire('./modules/es7.string.trim-left');\nrequire('./modules/es7.string.trim-right');\nrequire('./modules/es7.string.match-all');\nrequire('./modules/es7.symbol.async-iterator');\nrequire('./modules/es7.symbol.observable');\nrequire('./modules/es7.object.get-own-property-descriptors');\nrequire('./modules/es7.object.values');\nrequire('./modules/es7.object.entries');\nrequire('./modules/es7.object.define-getter');\nrequire('./modules/es7.object.define-setter');\nrequire('./modules/es7.object.lookup-getter');\nrequire('./modules/es7.object.lookup-setter');\nrequire('./modules/es7.map.to-json');\nrequire('./modules/es7.set.to-json');\nrequire('./modules/es7.map.of');\nrequire('./modules/es7.set.of');\nrequire('./modules/es7.weak-map.of');\nrequire('./modules/es7.weak-set.of');\nrequire('./modules/es7.map.from');\nrequire('./modules/es7.set.from');\nrequire('./modules/es7.weak-map.from');\nrequire('./modules/es7.weak-set.from');\nrequire('./modules/es7.global');\nrequire('./modules/es7.system.global');\nrequire('./modules/es7.error.is-error');\nrequire('./modules/es7.math.clamp');\nrequire('./modules/es7.math.deg-per-rad');\nrequire('./modules/es7.math.degrees');\nrequire('./modules/es7.math.fscale');\nrequire('./modules/es7.math.iaddh');\nrequire('./modules/es7.math.isubh');\nrequire('./modules/es7.math.imulh');\nrequire('./modules/es7.math.rad-per-deg');\nrequire('./modules/es7.math.radians');\nrequire('./modules/es7.math.scale');\nrequire('./modules/es7.math.umulh');\nrequire('./modules/es7.math.signbit');\nrequire('./modules/es7.promise.finally');\nrequire('./modules/es7.promise.try');\nrequire('./modules/es7.reflect.define-metadata');\nrequire('./modules/es7.reflect.delete-metadata');\nrequire('./modules/es7.reflect.get-metadata');\nrequire('./modules/es7.reflect.get-metadata-keys');\nrequire('./modules/es7.reflect.get-own-metadata');\nrequire('./modules/es7.reflect.get-own-metadata-keys');\nrequire('./modules/es7.reflect.has-metadata');\nrequire('./modules/es7.reflect.has-own-metadata');\nrequire('./modules/es7.reflect.metadata');\nrequire('./modules/es7.asap');\nrequire('./modules/es7.observable');\nrequire('./modules/web.timers');\nrequire('./modules/web.immediate');\nrequire('./modules/web.dom.iterable');\nmodule.exports = require('./modules/_core');\n","/**\n * Copyright (c) 2014, Facebook, Inc.\n * All rights reserved.\n *\n * This source code is licensed under the BSD-style license found in the\n * https://raw.github.com/facebook/regenerator/master/LICENSE file. An\n * additional grant of patent rights can be found in the PATENTS file in\n * the same directory.\n */\n\n!(function(global) {\n  \"use strict\";\n\n  var Op = Object.prototype;\n  var hasOwn = Op.hasOwnProperty;\n  var undefined; // More compressible than void 0.\n  var $Symbol = typeof Symbol === \"function\" ? Symbol : {};\n  var iteratorSymbol = $Symbol.iterator || \"@@iterator\";\n  var asyncIteratorSymbol = $Symbol.asyncIterator || \"@@asyncIterator\";\n  var toStringTagSymbol = $Symbol.toStringTag || \"@@toStringTag\";\n\n  var inModule = typeof module === \"object\";\n  var runtime = global.regeneratorRuntime;\n  if (runtime) {\n    if (inModule) {\n      // If regeneratorRuntime is defined globally and we're in a module,\n      // make the exports object identical to regeneratorRuntime.\n      module.exports = runtime;\n    }\n    // Don't bother evaluating the rest of this file if the runtime was\n    // already defined globally.\n    return;\n  }\n\n  // Define the runtime globally (as expected by generated code) as either\n  // module.exports (if we're in a module) or a new, empty object.\n  runtime = global.regeneratorRuntime = inModule ? module.exports : {};\n\n  function wrap(innerFn, outerFn, self, tryLocsList) {\n    // If outerFn provided and outerFn.prototype is a Generator, then outerFn.prototype instanceof Generator.\n    var protoGenerator = outerFn && outerFn.prototype instanceof Generator ? outerFn : Generator;\n    var generator = Object.create(protoGenerator.prototype);\n    var context = new Context(tryLocsList || []);\n\n    // The ._invoke method unifies the implementations of the .next,\n    // .throw, and .return methods.\n    generator._invoke = makeInvokeMethod(innerFn, self, context);\n\n    return generator;\n  }\n  runtime.wrap = wrap;\n\n  // Try/catch helper to minimize deoptimizations. Returns a completion\n  // record like context.tryEntries[i].completion. This interface could\n  // have been (and was previously) designed to take a closure to be\n  // invoked without arguments, but in all the cases we care about we\n  // already have an existing method we want to call, so there's no need\n  // to create a new function object. We can even get away with assuming\n  // the method takes exactly one argument, since that happens to be true\n  // in every case, so we don't have to touch the arguments object. The\n  // only additional allocation required is the completion record, which\n  // has a stable shape and so hopefully should be cheap to allocate.\n  function tryCatch(fn, obj, arg) {\n    try {\n      return { type: \"normal\", arg: fn.call(obj, arg) };\n    } catch (err) {\n      return { type: \"throw\", arg: err };\n    }\n  }\n\n  var GenStateSuspendedStart = \"suspendedStart\";\n  var GenStateSuspendedYield = \"suspendedYield\";\n  var GenStateExecuting = \"executing\";\n  var GenStateCompleted = \"completed\";\n\n  // Returning this object from the innerFn has the same effect as\n  // breaking out of the dispatch switch statement.\n  var ContinueSentinel = {};\n\n  // Dummy constructor functions that we use as the .constructor and\n  // .constructor.prototype properties for functions that return Generator\n  // objects. For full spec compliance, you may wish to configure your\n  // minifier not to mangle the names of these two functions.\n  function Generator() {}\n  function GeneratorFunction() {}\n  function GeneratorFunctionPrototype() {}\n\n  // This is a polyfill for %IteratorPrototype% for environments that\n  // don't natively support it.\n  var IteratorPrototype = {};\n  IteratorPrototype[iteratorSymbol] = function () {\n    return this;\n  };\n\n  var getProto = Object.getPrototypeOf;\n  var NativeIteratorPrototype = getProto && getProto(getProto(values([])));\n  if (NativeIteratorPrototype &&\n      NativeIteratorPrototype !== Op &&\n      hasOwn.call(NativeIteratorPrototype, iteratorSymbol)) {\n    // This environment has a native %IteratorPrototype%; use it instead\n    // of the polyfill.\n    IteratorPrototype = NativeIteratorPrototype;\n  }\n\n  var Gp = GeneratorFunctionPrototype.prototype =\n    Generator.prototype = Object.create(IteratorPrototype);\n  GeneratorFunction.prototype = Gp.constructor = GeneratorFunctionPrototype;\n  GeneratorFunctionPrototype.constructor = GeneratorFunction;\n  GeneratorFunctionPrototype[toStringTagSymbol] =\n    GeneratorFunction.displayName = \"GeneratorFunction\";\n\n  // Helper for defining the .next, .throw, and .return methods of the\n  // Iterator interface in terms of a single ._invoke method.\n  function defineIteratorMethods(prototype) {\n    [\"next\", \"throw\", \"return\"].forEach(function(method) {\n      prototype[method] = function(arg) {\n        return this._invoke(method, arg);\n      };\n    });\n  }\n\n  runtime.isGeneratorFunction = function(genFun) {\n    var ctor = typeof genFun === \"function\" && genFun.constructor;\n    return ctor\n      ? ctor === GeneratorFunction ||\n        // For the native GeneratorFunction constructor, the best we can\n        // do is to check its .name property.\n        (ctor.displayName || ctor.name) === \"GeneratorFunction\"\n      : false;\n  };\n\n  runtime.mark = function(genFun) {\n    if (Object.setPrototypeOf) {\n      Object.setPrototypeOf(genFun, GeneratorFunctionPrototype);\n    } else {\n      genFun.__proto__ = GeneratorFunctionPrototype;\n      if (!(toStringTagSymbol in genFun)) {\n        genFun[toStringTagSymbol] = \"GeneratorFunction\";\n      }\n    }\n    genFun.prototype = Object.create(Gp);\n    return genFun;\n  };\n\n  // Within the body of any async function, `await x` is transformed to\n  // `yield regeneratorRuntime.awrap(x)`, so that the runtime can test\n  // `hasOwn.call(value, \"__await\")` to determine if the yielded value is\n  // meant to be awaited.\n  runtime.awrap = function(arg) {\n    return { __await: arg };\n  };\n\n  function AsyncIterator(generator) {\n    function invoke(method, arg, resolve, reject) {\n      var record = tryCatch(generator[method], generator, arg);\n      if (record.type === \"throw\") {\n        reject(record.arg);\n      } else {\n        var result = record.arg;\n        var value = result.value;\n        if (value &&\n            typeof value === \"object\" &&\n            hasOwn.call(value, \"__await\")) {\n          return Promise.resolve(value.__await).then(function(value) {\n            invoke(\"next\", value, resolve, reject);\n          }, function(err) {\n            invoke(\"throw\", err, resolve, reject);\n          });\n        }\n\n        return Promise.resolve(value).then(function(unwrapped) {\n          // When a yielded Promise is resolved, its final value becomes\n          // the .value of the Promise<{value,done}> result for the\n          // current iteration. If the Promise is rejected, however, the\n          // result for this iteration will be rejected with the same\n          // reason. Note that rejections of yielded Promises are not\n          // thrown back into the generator function, as is the case\n          // when an awaited Promise is rejected. This difference in\n          // behavior between yield and await is important, because it\n          // allows the consumer to decide what to do with the yielded\n          // rejection (swallow it and continue, manually .throw it back\n          // into the generator, abandon iteration, whatever). With\n          // await, by contrast, there is no opportunity to examine the\n          // rejection reason outside the generator function, so the\n          // only option is to throw it from the await expression, and\n          // let the generator function handle the exception.\n          result.value = unwrapped;\n          resolve(result);\n        }, reject);\n      }\n    }\n\n    if (typeof global.process === \"object\" && global.process.domain) {\n      invoke = global.process.domain.bind(invoke);\n    }\n\n    var previousPromise;\n\n    function enqueue(method, arg) {\n      function callInvokeWithMethodAndArg() {\n        return new Promise(function(resolve, reject) {\n          invoke(method, arg, resolve, reject);\n        });\n      }\n\n      return previousPromise =\n        // If enqueue has been called before, then we want to wait until\n        // all previous Promises have been resolved before calling invoke,\n        // so that results are always delivered in the correct order. If\n        // enqueue has not been called before, then it is important to\n        // call invoke immediately, without waiting on a callback to fire,\n        // so that the async generator function has the opportunity to do\n        // any necessary setup in a predictable way. This predictability\n        // is why the Promise constructor synchronously invokes its\n        // executor callback, and why async functions synchronously\n        // execute code before the first await. Since we implement simple\n        // async functions in terms of async generators, it is especially\n        // important to get this right, even though it requires care.\n        previousPromise ? previousPromise.then(\n          callInvokeWithMethodAndArg,\n          // Avoid propagating failures to Promises returned by later\n          // invocations of the iterator.\n          callInvokeWithMethodAndArg\n        ) : callInvokeWithMethodAndArg();\n    }\n\n    // Define the unified helper method that is used to implement .next,\n    // .throw, and .return (see defineIteratorMethods).\n    this._invoke = enqueue;\n  }\n\n  defineIteratorMethods(AsyncIterator.prototype);\n  AsyncIterator.prototype[asyncIteratorSymbol] = function () {\n    return this;\n  };\n  runtime.AsyncIterator = AsyncIterator;\n\n  // Note that simple async functions are implemented on top of\n  // AsyncIterator objects; they just return a Promise for the value of\n  // the final result produced by the iterator.\n  runtime.async = function(innerFn, outerFn, self, tryLocsList) {\n    var iter = new AsyncIterator(\n      wrap(innerFn, outerFn, self, tryLocsList)\n    );\n\n    return runtime.isGeneratorFunction(outerFn)\n      ? iter // If outerFn is a generator, return the full iterator.\n      : iter.next().then(function(result) {\n          return result.done ? result.value : iter.next();\n        });\n  };\n\n  function makeInvokeMethod(innerFn, self, context) {\n    var state = GenStateSuspendedStart;\n\n    return function invoke(method, arg) {\n      if (state === GenStateExecuting) {\n        throw new Error(\"Generator is already running\");\n      }\n\n      if (state === GenStateCompleted) {\n        if (method === \"throw\") {\n          throw arg;\n        }\n\n        // Be forgiving, per 25.3.3.3.3 of the spec:\n        // https://people.mozilla.org/~jorendorff/es6-draft.html#sec-generatorresume\n        return doneResult();\n      }\n\n      context.method = method;\n      context.arg = arg;\n\n      while (true) {\n        var delegate = context.delegate;\n        if (delegate) {\n          var delegateResult = maybeInvokeDelegate(delegate, context);\n          if (delegateResult) {\n            if (delegateResult === ContinueSentinel) continue;\n            return delegateResult;\n          }\n        }\n\n        if (context.method === \"next\") {\n          // Setting context._sent for legacy support of Babel's\n          // function.sent implementation.\n          context.sent = context._sent = context.arg;\n\n        } else if (context.method === \"throw\") {\n          if (state === GenStateSuspendedStart) {\n            state = GenStateCompleted;\n            throw context.arg;\n          }\n\n          context.dispatchException(context.arg);\n\n        } else if (context.method === \"return\") {\n          context.abrupt(\"return\", context.arg);\n        }\n\n        state = GenStateExecuting;\n\n        var record = tryCatch(innerFn, self, context);\n        if (record.type === \"normal\") {\n          // If an exception is thrown from innerFn, we leave state ===\n          // GenStateExecuting and loop back for another invocation.\n          state = context.done\n            ? GenStateCompleted\n            : GenStateSuspendedYield;\n\n          if (record.arg === ContinueSentinel) {\n            continue;\n          }\n\n          return {\n            value: record.arg,\n            done: context.done\n          };\n\n        } else if (record.type === \"throw\") {\n          state = GenStateCompleted;\n          // Dispatch the exception by looping back around to the\n          // context.dispatchException(context.arg) call above.\n          context.method = \"throw\";\n          context.arg = record.arg;\n        }\n      }\n    };\n  }\n\n  // Call delegate.iterator[context.method](context.arg) and handle the\n  // result, either by returning a { value, done } result from the\n  // delegate iterator, or by modifying context.method and context.arg,\n  // setting context.delegate to null, and returning the ContinueSentinel.\n  function maybeInvokeDelegate(delegate, context) {\n    var method = delegate.iterator[context.method];\n    if (method === undefined) {\n      // A .throw or .return when the delegate iterator has no .throw\n      // method always terminates the yield* loop.\n      context.delegate = null;\n\n      if (context.method === \"throw\") {\n        if (delegate.iterator.return) {\n          // If the delegate iterator has a return method, give it a\n          // chance to clean up.\n          context.method = \"return\";\n          context.arg = undefined;\n          maybeInvokeDelegate(delegate, context);\n\n          if (context.method === \"throw\") {\n            // If maybeInvokeDelegate(context) changed context.method from\n            // \"return\" to \"throw\", let that override the TypeError below.\n            return ContinueSentinel;\n          }\n        }\n\n        context.method = \"throw\";\n        context.arg = new TypeError(\n          \"The iterator does not provide a 'throw' method\");\n      }\n\n      return ContinueSentinel;\n    }\n\n    var record = tryCatch(method, delegate.iterator, context.arg);\n\n    if (record.type === \"throw\") {\n      context.method = \"throw\";\n      context.arg = record.arg;\n      context.delegate = null;\n      return ContinueSentinel;\n    }\n\n    var info = record.arg;\n\n    if (! info) {\n      context.method = \"throw\";\n      context.arg = new TypeError(\"iterator result is not an object\");\n      context.delegate = null;\n      return ContinueSentinel;\n    }\n\n    if (info.done) {\n      // Assign the result of the finished delegate to the temporary\n      // variable specified by delegate.resultName (see delegateYield).\n      context[delegate.resultName] = info.value;\n\n      // Resume execution at the desired location (see delegateYield).\n      context.next = delegate.nextLoc;\n\n      // If context.method was \"throw\" but the delegate handled the\n      // exception, let the outer generator proceed normally. If\n      // context.method was \"next\", forget context.arg since it has been\n      // \"consumed\" by the delegate iterator. If context.method was\n      // \"return\", allow the original .return call to continue in the\n      // outer generator.\n      if (context.method !== \"return\") {\n        context.method = \"next\";\n        context.arg = undefined;\n      }\n\n    } else {\n      // Re-yield the result returned by the delegate method.\n      return info;\n    }\n\n    // The delegate iterator is finished, so forget it and continue with\n    // the outer generator.\n    context.delegate = null;\n    return ContinueSentinel;\n  }\n\n  // Define Generator.prototype.{next,throw,return} in terms of the\n  // unified ._invoke helper method.\n  defineIteratorMethods(Gp);\n\n  Gp[toStringTagSymbol] = \"Generator\";\n\n  // A Generator should always return itself as the iterator object when the\n  // @@iterator function is called on it. Some browsers' implementations of the\n  // iterator prototype chain incorrectly implement this, causing the Generator\n  // object to not be returned from this call. This ensures that doesn't happen.\n  // See https://github.com/facebook/regenerator/issues/274 for more details.\n  Gp[iteratorSymbol] = function() {\n    return this;\n  };\n\n  Gp.toString = function() {\n    return \"[object Generator]\";\n  };\n\n  function pushTryEntry(locs) {\n    var entry = { tryLoc: locs[0] };\n\n    if (1 in locs) {\n      entry.catchLoc = locs[1];\n    }\n\n    if (2 in locs) {\n      entry.finallyLoc = locs[2];\n      entry.afterLoc = locs[3];\n    }\n\n    this.tryEntries.push(entry);\n  }\n\n  function resetTryEntry(entry) {\n    var record = entry.completion || {};\n    record.type = \"normal\";\n    delete record.arg;\n    entry.completion = record;\n  }\n\n  function Context(tryLocsList) {\n    // The root entry object (effectively a try statement without a catch\n    // or a finally block) gives us a place to store values thrown from\n    // locations where there is no enclosing try statement.\n    this.tryEntries = [{ tryLoc: \"root\" }];\n    tryLocsList.forEach(pushTryEntry, this);\n    this.reset(true);\n  }\n\n  runtime.keys = function(object) {\n    var keys = [];\n    for (var key in object) {\n      keys.push(key);\n    }\n    keys.reverse();\n\n    // Rather than returning an object with a next method, we keep\n    // things simple and return the next function itself.\n    return function next() {\n      while (keys.length) {\n        var key = keys.pop();\n        if (key in object) {\n          next.value = key;\n          next.done = false;\n          return next;\n        }\n      }\n\n      // To avoid creating an additional object, we just hang the .value\n      // and .done properties off the next function object itself. This\n      // also ensures that the minifier will not anonymize the function.\n      next.done = true;\n      return next;\n    };\n  };\n\n  function values(iterable) {\n    if (iterable) {\n      var iteratorMethod = iterable[iteratorSymbol];\n      if (iteratorMethod) {\n        return iteratorMethod.call(iterable);\n      }\n\n      if (typeof iterable.next === \"function\") {\n        return iterable;\n      }\n\n      if (!isNaN(iterable.length)) {\n        var i = -1, next = function next() {\n          while (++i < iterable.length) {\n            if (hasOwn.call(iterable, i)) {\n              next.value = iterable[i];\n              next.done = false;\n              return next;\n            }\n          }\n\n          next.value = undefined;\n          next.done = true;\n\n          return next;\n        };\n\n        return next.next = next;\n      }\n    }\n\n    // Return an iterator with no values.\n    return { next: doneResult };\n  }\n  runtime.values = values;\n\n  function doneResult() {\n    return { value: undefined, done: true };\n  }\n\n  Context.prototype = {\n    constructor: Context,\n\n    reset: function(skipTempReset) {\n      this.prev = 0;\n      this.next = 0;\n      // Resetting context._sent for legacy support of Babel's\n      // function.sent implementation.\n      this.sent = this._sent = undefined;\n      this.done = false;\n      this.delegate = null;\n\n      this.method = \"next\";\n      this.arg = undefined;\n\n      this.tryEntries.forEach(resetTryEntry);\n\n      if (!skipTempReset) {\n        for (var name in this) {\n          // Not sure about the optimal order of these conditions:\n          if (name.charAt(0) === \"t\" &&\n              hasOwn.call(this, name) &&\n              !isNaN(+name.slice(1))) {\n            this[name] = undefined;\n          }\n        }\n      }\n    },\n\n    stop: function() {\n      this.done = true;\n\n      var rootEntry = this.tryEntries[0];\n      var rootRecord = rootEntry.completion;\n      if (rootRecord.type === \"throw\") {\n        throw rootRecord.arg;\n      }\n\n      return this.rval;\n    },\n\n    dispatchException: function(exception) {\n      if (this.done) {\n        throw exception;\n      }\n\n      var context = this;\n      function handle(loc, caught) {\n        record.type = \"throw\";\n        record.arg = exception;\n        context.next = loc;\n\n        if (caught) {\n          // If the dispatched exception was caught by a catch block,\n          // then let that catch block handle the exception normally.\n          context.method = \"next\";\n          context.arg = undefined;\n        }\n\n        return !! caught;\n      }\n\n      for (var i = this.tryEntries.length - 1; i >= 0; --i) {\n        var entry = this.tryEntries[i];\n        var record = entry.completion;\n\n        if (entry.tryLoc === \"root\") {\n          // Exception thrown outside of any try block that could handle\n          // it, so set the completion value of the entire function to\n          // throw the exception.\n          return handle(\"end\");\n        }\n\n        if (entry.tryLoc <= this.prev) {\n          var hasCatch = hasOwn.call(entry, \"catchLoc\");\n          var hasFinally = hasOwn.call(entry, \"finallyLoc\");\n\n          if (hasCatch && hasFinally) {\n            if (this.prev < entry.catchLoc) {\n              return handle(entry.catchLoc, true);\n            } else if (this.prev < entry.finallyLoc) {\n              return handle(entry.finallyLoc);\n            }\n\n          } else if (hasCatch) {\n            if (this.prev < entry.catchLoc) {\n              return handle(entry.catchLoc, true);\n            }\n\n          } else if (hasFinally) {\n            if (this.prev < entry.finallyLoc) {\n              return handle(entry.finallyLoc);\n            }\n\n          } else {\n            throw new Error(\"try statement without catch or finally\");\n          }\n        }\n      }\n    },\n\n    abrupt: function(type, arg) {\n      for (var i = this.tryEntries.length - 1; i >= 0; --i) {\n        var entry = this.tryEntries[i];\n        if (entry.tryLoc <= this.prev &&\n            hasOwn.call(entry, \"finallyLoc\") &&\n            this.prev < entry.finallyLoc) {\n          var finallyEntry = entry;\n          break;\n        }\n      }\n\n      if (finallyEntry &&\n          (type === \"break\" ||\n           type === \"continue\") &&\n          finallyEntry.tryLoc <= arg &&\n          arg <= finallyEntry.finallyLoc) {\n        // Ignore the finally entry if control is not jumping to a\n        // location outside the try/catch block.\n        finallyEntry = null;\n      }\n\n      var record = finallyEntry ? finallyEntry.completion : {};\n      record.type = type;\n      record.arg = arg;\n\n      if (finallyEntry) {\n        this.method = \"next\";\n        this.next = finallyEntry.finallyLoc;\n        return ContinueSentinel;\n      }\n\n      return this.complete(record);\n    },\n\n    complete: function(record, afterLoc) {\n      if (record.type === \"throw\") {\n        throw record.arg;\n      }\n\n      if (record.type === \"break\" ||\n          record.type === \"continue\") {\n        this.next = record.arg;\n      } else if (record.type === \"return\") {\n        this.rval = this.arg = record.arg;\n        this.method = \"return\";\n        this.next = \"end\";\n      } else if (record.type === \"normal\" && afterLoc) {\n        this.next = afterLoc;\n      }\n\n      return ContinueSentinel;\n    },\n\n    finish: function(finallyLoc) {\n      for (var i = this.tryEntries.length - 1; i >= 0; --i) {\n        var entry = this.tryEntries[i];\n        if (entry.finallyLoc === finallyLoc) {\n          this.complete(entry.completion, entry.afterLoc);\n          resetTryEntry(entry);\n          return ContinueSentinel;\n        }\n      }\n    },\n\n    \"catch\": function(tryLoc) {\n      for (var i = this.tryEntries.length - 1; i >= 0; --i) {\n        var entry = this.tryEntries[i];\n        if (entry.tryLoc === tryLoc) {\n          var record = entry.completion;\n          if (record.type === \"throw\") {\n            var thrown = record.arg;\n            resetTryEntry(entry);\n          }\n          return thrown;\n        }\n      }\n\n      // The context.catch method must only be called with a location\n      // argument that corresponds to a known catch block.\n      throw new Error(\"illegal catch attempt\");\n    },\n\n    delegateYield: function(iterable, resultName, nextLoc) {\n      this.delegate = {\n        iterator: values(iterable),\n        resultName: resultName,\n        nextLoc: nextLoc\n      };\n\n      if (this.method === \"next\") {\n        // Deliberately forget the last sent value so that we don't\n        // accidentally pass it on to the delegate.\n        this.arg = undefined;\n      }\n\n      return ContinueSentinel;\n    }\n  };\n})(\n  // Among the various tricks for obtaining a reference to the global\n  // object, this seems to be the most reliable technique that does not\n  // use indirect eval (which violates Content Security Policy).\n  typeof global === \"object\" ? global :\n  typeof window === \"object\" ? window :\n  typeof self === \"object\" ? self : this\n);\n","module.exports = function (regExp, replace) {\n  var replacer = replace === Object(replace) ? function (part) {\n    return replace[part];\n  } : replace;\n  return function (it) {\n    return String(it).replace(regExp, replacer);\n  };\n};\n","// https://github.com/benjamingr/RexExp.escape\nvar $export = require('./_export');\nvar $re = require('./_replacer')(/[\\\\^$*+?.()|[\\]{}]/g, '\\\\$&');\n\n$export($export.S, 'RegExp', { escape: function escape(it) { return $re(it); } });\n","require('../../modules/core.regexp.escape');\nmodule.exports = require('../../modules/_core').RegExp.escape;\n","\"use strict\";\n\nrequire(\"core-js/shim\");\n\nrequire(\"regenerator-runtime/runtime\");\n\nrequire(\"core-js/fn/regexp/escape\");\n\nif (global._babelPolyfill) {\n  throw new Error(\"only one instance of babel-polyfill is allowed\");\n}\nglobal._babelPolyfill = true;\n\nvar DEFINE_PROPERTY = \"defineProperty\";\nfunction define(O, key, value) {\n  O[key] || Object[DEFINE_PROPERTY](O, key, {\n    writable: true,\n    configurable: true,\n    value: value\n  });\n}\n\ndefine(String.prototype, \"padLeft\", \"\".padStart);\ndefine(String.prototype, \"padRight\", \"\".padEnd);\n\n\"pop,reverse,shift,keys,values,entries,indexOf,every,some,forEach,map,filter,find,findIndex,includes,join,slice,concat,push,splice,unshift,sort,lastIndexOf,reduce,reduceRight,copyWithin,fill\".split(\",\").forEach(function (key) {\n  [][key] && define(Array, key, Function.call.bind([][key]));\n});","export default class ScrollSpy {\n    constructor(args) {\n\n        this.doc = document;\n        this.nav = this.doc.querySelectorAll(args.navSelector);\n\n        if(!this.nav.length === 0) { return }\n\n        this.win = window;\n        this.winHeight = this.win.innerHeight;\n\n        this.scrollElement = this.doc.querySelector(args.scrollSelector);\n        this.className = args.className;\n        this.offsetTop = args.offsetTop || 0;\n\n        this.contents = [];\n        this.contents = this.getContents(args.contentSelector);\n\n        this.attachEvent();\n    }\n\n    attachEvent() {\n        let scrollTimer;\n        this.scrollElement.addEventListener('scroll', () => {\n            if (scrollTimer) {\n                clearTimeout(scrollTimer);\n            }\n\n            scrollTimer = setTimeout(() => {\n                this.spy();\n            }, 1);\n        });\n\n        let resizeTimer;\n        this.scrollElement.addEventListener('resize', () => {\n            if (resizeTimer) {\n                clearTimeout(resizeTimer);\n            }\n\n            resizeTimer = setTimeout(() => {\n                this.spy();\n            }, 1);\n        });\n\n    }\n\n    getContents(contentSelector) {\n        const targets = [];\n        for (let i = 0, max = this.nav.length; i < max; i++) {\n            const href = this.nav[i].href;\n            targets.push(this.doc.getElementById(href.split('#')[1]));\n        }\n        return targets;\n    }\n\n    spy() {\n        let elements = this.getViewState();\n        this.toggleNavClass(elements);\n    }\n\n    getViewState() {\n        const elementListInView = [];\n        for (let i = 0, max = this.contents.length; i < max; i++) {\n            const current = this.contents[i];\n            if (current && this.isView(current)) {\n                elementListInView.push(current);\n            }\n        }\n\n        return elementListInView;\n    }\n\n    isView(element) {\n        const scrollTop = this.scrollElement.scrollTop;\n        const calcBotom = scrollTop + this.offsetTop;\n        const rect = element.getBoundingClientRect();\n        const elementTop = rect.top + scrollTop;\n        const elementBottom = elementTop + element.offsetHeight;\n\n        return elementTop <= calcBotom + this.offsetTop && elementBottom > scrollTop + this.offsetTop;\n    }\n\n    toggleNavClass(elements) {\n        let maxDepth = 0;\n        let maxDepthElement = $();\n\n        for (let i = 0, max = elements.length; i < max; i++) {\n            const el = elements[i];\n            const tempDepth = this.getTagDepth(el);\n            if (maxDepth < tempDepth) {\n                maxDepth = tempDepth;\n                maxDepthElement = el;\n            }\n        }\n\n        for (let i = 0, max = this.nav.length; i < max; i++) {\n            const navElement = this.nav[i];\n            if (navElement.href.split('#')[1] === maxDepthElement.id) {\n                navElement.classList.add(this.className);\n                navElement.classList.add('mdl-color-text--primary');\n            } else {\n                navElement.classList.remove(this.className);\n                navElement.classList.remove('mdl-color-text--primary');\n            }\n        }\n    }\n\n    getTagDepth(element) {\n        return parseInt($(element).find('h1,h2,h3,h4,h5,h6').get(0).tagName.split('H')[1]);\n    }\n}\n","export default class AdjustHeight {\n    \n    constructor() {\n        this.header = $('header.mdl-layout__header');\n        this.pagenation = $('div.pagenation');\n        this.footer = $('footer.mdl-mini-footer');\n        this.win = $(window);\n        this.scrollElement = $('main');\n        this.content = $('.page-content');\n        this.outline = $('.side-doc-outline--content');\n\n        this.attachEvent();\n        this.adjust();\n    }\n\n    adjust() {\n        this.setPageContentMinHeight();\n        this.setLocaltocHeight();\n    }\n\n    setPageContentMinHeight() {\n        const winH = this.win.innerHeight();\n        const headerHeight = this.header.outerHeight();\n        const footerHeight = this.footer.outerHeight(true) + this.pagenation.outerHeight(true);\n\n        this.content.css('min-height', this.win.innerHeight() - headerHeight - footerHeight);\n    }\n\n    setLocaltocHeight() {\n        const pagenationPotisionTop = this.pagenation.position().top + parseInt(this.pagenation.css('margin-top'), 10);\n        const outlineBottom = this.scrollElement.scrollTop() + this.content.outerHeight();\n        const winH = this.win.innerHeight() - this.header.outerHeight();\n\n        let min = 0;\n        min = outlineBottom > pagenationPotisionTop ? pagenationPotisionTop : outlineBottom;\n        min = min > winH ? winH : min;\n        this.outline.css('height', min);\n    }\n\n    attachEvent() {\n        let scrollTimer;\n        this.scrollElement.on('scroll', () => {\n            if (scrollTimer) {\n                clearTimeout(scrollTimer);\n            }\n\n            scrollTimer = setTimeout(() => {\n                this.adjust();\n            }, 1);\n        });\n\n        let resizeTimer;\n        this.win.on('resize', () => {\n            if (resizeTimer) {\n                clearTimeout(resizeTimer);\n            }\n\n            resizeTimer = setTimeout(() => {\n                this.adjust();\n            }, 1);\n        });\n    }\n}","import \"../scss/sphinx_materialdesign_theme.scss\";\r\nimport \"material-design-lite\";\r\nimport \"babel-polyfill\";\r\nimport ScrollSpy from \"./scrollspy\";\r\nimport AdjustHeight from \"./adjust-height\";\r\n\r\n$(function() {\r\n\r\n    function reconstructionDrawerGlobalToc() {\r\n        const $globaltoc = $('.mdl-layout__drawer nav');\r\n        const $lists = $globaltoc.find('li');\r\n        $.each($lists, function(index, li) {\r\n            const $li = $(li);\r\n            const $linkWrapper = $('<span class=\"link-wrapper\"></span>');\r\n            const $link = $li.children('a');\r\n            $li.append($linkWrapper.append($link));\r\n\r\n            const isCurrent = $li.hasClass('current') && !$link.hasClass('current');\r\n            const $ul = $li.children('ul');\r\n            if ($ul.length) {\r\n                const ulId = `globalnav-${index}`;\r\n                $ul.attr('id', ulId);\r\n                $ul.addClass('collapse');\r\n                const $toggleWrapper = $('<span class=\"nav-toggle\"></span>');\r\n                if (isCurrent) {\r\n                    $ul.addClass('show');\r\n                    $toggleWrapper.addClass('show');\r\n                } else {\r\n                    $ul.hide();\r\n                }\r\n\r\n                $li.append(\r\n                    $linkWrapper.append(\r\n                        $toggleWrapper.append(\r\n                            $(`<a class=\"mdl-button mdl-js-button mdl-button--icon\" data-toggle=\"#${ulId}\"><span style=\"color: #888\"><i class=\"material-icons\">keyboard_arrow_down</i></span></span>`)\r\n                        )\r\n                    )\r\n                ).append($ul);\r\n            }\r\n        });\r\n    }\r\n\r\n    function collapse() {\r\n        $('.mdl-layout__drawer nav .nav-toggle a').click(function() {\r\n            const $toggle = $(this);\r\n            const id = $toggle.attr('data-toggle');\r\n            $(`ul${id}`).toggleClass('show').animate({height: \"toggle\", opacity: \"toggle\"});\r\n            $toggle.parent().toggleClass('show');\r\n        });\r\n    }\r\n\r\n    function styleMdlCodeBlock() {\r\n        $('pre').hover(function() {\r\n            $(this).attr('click-to-copy', 'click to copy...');\r\n        });\r\n        $('pre').click(function(){\r\n            var result = copyClipboard(this);\r\n            if (result) {\r\n                $(this).attr('click-to-copy', 'copied!');\r\n            }\r\n        });\r\n    }\r\n\r\n    function copyClipboard(selector) {\r\n        var body = document.body;\r\n        if(!body) return false;\r\n\r\n        var $target = $(selector);\r\n        if ($target.length === 0) { return false; }\r\n\r\n        var text = $target.text();\r\n        var textarea = document.createElement('textarea');\r\n        textarea.value = text;\r\n        document.body.appendChild(textarea);\r\n        textarea.select();\r\n        var result = document.execCommand('copy');\r\n        document.body.removeChild(textarea);\r\n        return result;\r\n    }\r\n\r\n    function quickSearchClickEvent() {\r\n        const $breadcrumb = $('.breadcrumb');\r\n\r\n        $('#waterfall-exp').focus(() => {\r\n            if ($(window).width() <= 1024) {\r\n                $breadcrumb.hide();\r\n            }\r\n        }).blur(() => {\r\n            if ($(window).width() <= 1024) {\r\n                $breadcrumb.show();\r\n            }\r\n        });\r\n    }\r\n\r\n    // styleMdlCodeBlock();\r\n\r\n    reconstructionDrawerGlobalToc();\r\n    collapse();\r\n    quickSearchClickEvent();\r\n\r\n\r\n    const spy = new ScrollSpy({\r\n        contentSelector: '.page-content .section',\r\n        navSelector: '.localtoc a',\r\n        scrollSelector: 'main' ,\r\n        className: 'current',\r\n        offsetTop: 64});\r\n\r\n    $('.mdl-layout__content').focus();\r\n\r\n    $('.mx-card').each(function(){\r\n        $(this).addClass('mdl-card mdl-shadow--2dp');\r\n    });\r\n    $('.mx-card .mx-card-title').each(function(){\r\n        $(this).addClass('mdl-card__title');\r\n    });\r\n    $('.mx-card .mx-card-text').each(function(){\r\n        $(this).addClass('mdl-card__supporting-text');\r\n    });\r\n    $('.mx-card-link').each(function(){\r\n        $(this).hide();\r\n    });\r\n    $('.mdl-card').each(function(){\r\n        $(this).click(function() {\r\n            var url = $(this).find('.mx-card-link').text();\r\n            if (url) {\r\n                window.location = url;\r\n            }\r\n            return true;\r\n        });\r\n    });\r\n\r\n    $('a.download').each(function() {\r\n        // button\r\n        var button = document.createElement('button');\r\n        button.className = 'download mdl-button mdl-js-button mdl-button--fab mdl-js-ripple-effect';\r\n\r\n        // icon\r\n        var icon = document.createElement('i');\r\n        icon.className = 'material-icons';\r\n        var text = document.createTextNode('file_download');\r\n        icon.appendChild(text);\r\n        button.appendChild(icon);\r\n\r\n        // link\r\n        var link = $(this).attr('href');\r\n        button.onclick = function() {\r\n            window.location = link;\r\n        };\r\n        var fileName = link.split(\"/\").slice(-1).pop();\r\n        if (fileName) {\r\n            button.id = fileName.replace('.', '-');\r\n        } else {\r\n            button.id = 'download-button-' + $(this).index();\r\n        }\r\n\r\n        // hint\r\n        var hint = document.createElement('div');\r\n        hint.className = 'mdl-tooltip';\r\n        hint.setAttribute('data-mdl-for', button.id);\r\n        var hintText = $(this).find('span.pre').map(function() {\r\n            return $(this).text();\r\n        }).get().join(' ');\r\n        hint.innerHTML = hintText;\r\n\r\n        componentHandler.upgradeElement(button);\r\n        $(this).remove();\r\n        var header = $('.section h1').first();\r\n        header.append(button);\r\n        header.append(hint);\r\n    });\r\n\r\n    $('.mdl-layout__container').css('visibility', 'visible');\r\n});\r\n"]}
\ No newline at end of file
+{"version":3,"sources":["ripple.js","tabs.js","layout.js","mdlComponentHandler.js","rAF.js","button.js","checkbox.js","icon-toggle.js","menu.js","progress.js","radio.js","slider.js","snackbar.js","spinner.js","switch.js","textfield.js","tooltip.js","data-table.js","../../node_modules/core-js/modules/_global.js","../../node_modules/core-js/modules/_has.js","../../node_modules/core-js/modules/_fails.js","../../node_modules/core-js/modules/_descriptors.js","../../node_modules/core-js/modules/_core.js","../../node_modules/core-js/modules/_is-object.js","../../node_modules/core-js/modules/_an-object.js","../../node_modules/core-js/modules/_dom-create.js","../../node_modules/core-js/modules/_ie8-dom-define.js","../../node_modules/core-js/modules/_to-primitive.js","../../node_modules/core-js/modules/_object-dp.js","../../node_modules/core-js/modules/_property-desc.js","../../node_modules/core-js/modules/_hide.js","../../node_modules/core-js/modules/_uid.js","../../node_modules/core-js/modules/_library.js","../../node_modules/core-js/modules/_shared.js","../../node_modules/core-js/modules/_function-to-string.js","../../node_modules/core-js/modules/_redefine.js","../../node_modules/core-js/modules/_a-function.js","../../node_modules/core-js/modules/_ctx.js","../../node_modules/core-js/modules/_export.js","../../node_modules/core-js/modules/_meta.js","../../node_modules/core-js/modules/_wks.js","../../node_modules/core-js/modules/_set-to-string-tag.js","../../node_modules/core-js/modules/_wks-ext.js","../../node_modules/core-js/modules/_wks-define.js","../../node_modules/core-js/modules/_cof.js","../../node_modules/core-js/modules/_iobject.js","../../node_modules/core-js/modules/_defined.js","../../node_modules/core-js/modules/_to-iobject.js","../../node_modules/core-js/modules/_to-integer.js","../../node_modules/core-js/modules/_to-length.js","../../node_modules/core-js/modules/_to-absolute-index.js","../../node_modules/core-js/modules/_array-includes.js","../../node_modules/core-js/modules/_shared-key.js","../../node_modules/core-js/modules/_object-keys-internal.js","../../node_modules/core-js/modules/_enum-bug-keys.js","../../node_modules/core-js/modules/_object-keys.js","../../node_modules/core-js/modules/_object-gops.js","../../node_modules/core-js/modules/_object-pie.js","../../node_modules/core-js/modules/_enum-keys.js","../../node_modules/core-js/modules/_is-array.js","../../node_modules/core-js/modules/_to-object.js","../../node_modules/core-js/modules/_object-dps.js","../../node_modules/core-js/modules/_html.js","../../node_modules/core-js/modules/_object-create.js","../../node_modules/core-js/modules/_object-gopn.js","../../node_modules/core-js/modules/_object-gopn-ext.js","../../node_modules/core-js/modules/_object-gopd.js","../../node_modules/core-js/modules/es6.symbol.js","../../node_modules/core-js/modules/es6.object.create.js","../../node_modules/core-js/modules/es6.object.define-property.js","../../node_modules/core-js/modules/es6.object.define-properties.js","../../node_modules/core-js/modules/_object-sap.js","../../node_modules/core-js/modules/es6.object.get-own-property-descriptor.js","../../node_modules/core-js/modules/_object-gpo.js","../../node_modules/core-js/modules/es6.object.get-prototype-of.js","../../node_modules/core-js/modules/es6.object.keys.js","../../node_modules/core-js/modules/es6.object.get-own-property-names.js","../../node_modules/core-js/modules/es6.object.freeze.js","../../node_modules/core-js/modules/es6.object.seal.js","../../node_modules/core-js/modules/es6.object.prevent-extensions.js","../../node_modules/core-js/modules/es6.object.is-frozen.js","../../node_modules/core-js/modules/es6.object.is-sealed.js","../../node_modules/core-js/modules/es6.object.is-extensible.js","../../node_modules/core-js/modules/_object-assign.js","../../node_modules/core-js/modules/es6.object.assign.js","../../node_modules/core-js/modules/_same-value.js","../../node_modules/core-js/modules/es6.object.is.js","../../node_modules/core-js/modules/_set-proto.js","../../node_modules/core-js/modules/es6.object.set-prototype-of.js","../../node_modules/core-js/modules/_classof.js","../../node_modules/core-js/modules/es6.object.to-string.js","../../node_modules/core-js/modules/_invoke.js","../../node_modules/core-js/modules/_bind.js","../../node_modules/core-js/modules/es6.function.bind.js","../../node_modules/core-js/modules/es6.function.name.js","../../node_modules/core-js/modules/es6.function.has-instance.js","../../node_modules/core-js/modules/_string-ws.js","../../node_modules/core-js/modules/_string-trim.js","../../node_modules/core-js/modules/_parse-int.js","../../node_modules/core-js/modules/es6.parse-int.js","../../node_modules/core-js/modules/_parse-float.js","../../node_modules/core-js/modules/es6.parse-float.js","../../node_modules/core-js/modules/_inherit-if-required.js","../../node_modules/core-js/modules/es6.number.constructor.js","../../node_modules/core-js/modules/_a-number-value.js","../../node_modules/core-js/modules/_string-repeat.js","../../node_modules/core-js/modules/es6.number.to-fixed.js","../../node_modules/core-js/modules/es6.number.to-precision.js","../../node_modules/core-js/modules/es6.number.epsilon.js","../../node_modules/core-js/modules/es6.number.is-finite.js","../../node_modules/core-js/modules/_is-integer.js","../../node_modules/core-js/modules/es6.number.is-integer.js","../../node_modules/core-js/modules/es6.number.is-nan.js","../../node_modules/core-js/modules/es6.number.is-safe-integer.js","../../node_modules/core-js/modules/es6.number.max-safe-integer.js","../../node_modules/core-js/modules/es6.number.min-safe-integer.js","../../node_modules/core-js/modules/es6.number.parse-float.js","../../node_modules/core-js/modules/es6.number.parse-int.js","../../node_modules/core-js/modules/_math-log1p.js","../../node_modules/core-js/modules/es6.math.acosh.js","../../node_modules/core-js/modules/es6.math.asinh.js","../../node_modules/core-js/modules/es6.math.atanh.js","../../node_modules/core-js/modules/_math-sign.js","../../node_modules/core-js/modules/es6.math.cbrt.js","../../node_modules/core-js/modules/es6.math.clz32.js","../../node_modules/core-js/modules/es6.math.cosh.js","../../node_modules/core-js/modules/_math-expm1.js","../../node_modules/core-js/modules/es6.math.expm1.js","../../node_modules/core-js/modules/_math-fround.js","../../node_modules/core-js/modules/es6.math.fround.js","../../node_modules/core-js/modules/es6.math.hypot.js","../../node_modules/core-js/modules/es6.math.imul.js","../../node_modules/core-js/modules/es6.math.log10.js","../../node_modules/core-js/modules/es6.math.log1p.js","../../node_modules/core-js/modules/es6.math.log2.js","../../node_modules/core-js/modules/es6.math.sign.js","../../node_modules/core-js/modules/es6.math.sinh.js","../../node_modules/core-js/modules/es6.math.tanh.js","../../node_modules/core-js/modules/es6.math.trunc.js","../../node_modules/core-js/modules/es6.string.from-code-point.js","../../node_modules/core-js/modules/es6.string.raw.js","../../node_modules/core-js/modules/es6.string.trim.js","../../node_modules/core-js/modules/_string-at.js","../../node_modules/core-js/modules/_iterators.js","../../node_modules/core-js/modules/_iter-create.js","../../node_modules/core-js/modules/_iter-define.js","../../node_modules/core-js/modules/es6.string.iterator.js","../../node_modules/core-js/modules/es6.string.code-point-at.js","../../node_modules/core-js/modules/_is-regexp.js","../../node_modules/core-js/modules/_string-context.js","../../node_modules/core-js/modules/_fails-is-regexp.js","../../node_modules/core-js/modules/es6.string.ends-with.js","../../node_modules/core-js/modules/es6.string.includes.js","../../node_modules/core-js/modules/es6.string.repeat.js","../../node_modules/core-js/modules/es6.string.starts-with.js","../../node_modules/core-js/modules/_string-html.js","../../node_modules/core-js/modules/es6.string.anchor.js","../../node_modules/core-js/modules/es6.string.big.js","../../node_modules/core-js/modules/es6.string.blink.js","../../node_modules/core-js/modules/es6.string.bold.js","../../node_modules/core-js/modules/es6.string.fixed.js","../../node_modules/core-js/modules/es6.string.fontcolor.js","../../node_modules/core-js/modules/es6.string.fontsize.js","../../node_modules/core-js/modules/es6.string.italics.js","../../node_modules/core-js/modules/es6.string.link.js","../../node_modules/core-js/modules/es6.string.small.js","../../node_modules/core-js/modules/es6.string.strike.js","../../node_modules/core-js/modules/es6.string.sub.js","../../node_modules/core-js/modules/es6.string.sup.js","../../node_modules/core-js/modules/es6.date.now.js","../../node_modules/core-js/modules/es6.date.to-json.js","../../node_modules/core-js/modules/_date-to-iso-string.js","../../node_modules/core-js/modules/es6.date.to-iso-string.js","../../node_modules/core-js/modules/es6.date.to-string.js","../../node_modules/core-js/modules/_date-to-primitive.js","../../node_modules/core-js/modules/es6.date.to-primitive.js","../../node_modules/core-js/modules/es6.array.is-array.js","../../node_modules/core-js/modules/_iter-call.js","../../node_modules/core-js/modules/_is-array-iter.js","../../node_modules/core-js/modules/_create-property.js","../../node_modules/core-js/modules/core.get-iterator-method.js","../../node_modules/core-js/modules/_iter-detect.js","../../node_modules/core-js/modules/es6.array.from.js","../../node_modules/core-js/modules/es6.array.of.js","../../node_modules/core-js/modules/_strict-method.js","../../node_modules/core-js/modules/es6.array.join.js","../../node_modules/core-js/modules/es6.array.slice.js","../../node_modules/core-js/modules/es6.array.sort.js","../../node_modules/core-js/modules/_array-species-constructor.js","../../node_modules/core-js/modules/_array-species-create.js","../../node_modules/core-js/modules/_array-methods.js","../../node_modules/core-js/modules/es6.array.for-each.js","../../node_modules/core-js/modules/es6.array.map.js","../../node_modules/core-js/modules/es6.array.filter.js","../../node_modules/core-js/modules/es6.array.some.js","../../node_modules/core-js/modules/es6.array.every.js","../../node_modules/core-js/modules/_array-reduce.js","../../node_modules/core-js/modules/es6.array.reduce.js","../../node_modules/core-js/modules/es6.array.reduce-right.js","../../node_modules/core-js/modules/es6.array.index-of.js","../../node_modules/core-js/modules/es6.array.last-index-of.js","../../node_modules/core-js/modules/_array-copy-within.js","../../node_modules/core-js/modules/_add-to-unscopables.js","../../node_modules/core-js/modules/es6.array.copy-within.js","../../node_modules/core-js/modules/_array-fill.js","../../node_modules/core-js/modules/es6.array.fill.js","../../node_modules/core-js/modules/es6.array.find.js","../../node_modules/core-js/modules/es6.array.find-index.js","../../node_modules/core-js/modules/_set-species.js","../../node_modules/core-js/modules/es6.array.species.js","../../node_modules/core-js/modules/_iter-step.js","../../node_modules/core-js/modules/es6.array.iterator.js","../../node_modules/core-js/modules/_flags.js","../../node_modules/core-js/modules/es6.regexp.constructor.js","../../node_modules/core-js/modules/_regexp-exec.js","../../node_modules/core-js/modules/es6.regexp.exec.js","../../node_modules/core-js/modules/es6.regexp.flags.js","../../node_modules/core-js/modules/es6.regexp.to-string.js","../../node_modules/core-js/modules/_advance-string-index.js","../../node_modules/core-js/modules/_regexp-exec-abstract.js","../../node_modules/core-js/modules/_fix-re-wks.js","../../node_modules/core-js/modules/es6.regexp.match.js","../../node_modules/core-js/modules/es6.regexp.replace.js","../../node_modules/core-js/modules/es6.regexp.search.js","../../node_modules/core-js/modules/_species-constructor.js","../../node_modules/core-js/modules/es6.regexp.split.js","../../node_modules/core-js/modules/_an-instance.js","../../node_modules/core-js/modules/_for-of.js","../../node_modules/core-js/modules/_task.js","../../node_modules/core-js/modules/_microtask.js","../../node_modules/core-js/modules/_new-promise-capability.js","../../node_modules/core-js/modules/_perform.js","../../node_modules/core-js/modules/_user-agent.js","../../node_modules/core-js/modules/_promise-resolve.js","../../node_modules/core-js/modules/_redefine-all.js","../../node_modules/core-js/modules/es6.promise.js","../../node_modules/core-js/modules/_validate-collection.js","../../node_modules/core-js/modules/_collection-strong.js","../../node_modules/core-js/modules/_collection.js","../../node_modules/core-js/modules/es6.map.js","../../node_modules/core-js/modules/es6.set.js","../../node_modules/core-js/modules/_collection-weak.js","../../node_modules/core-js/modules/es6.weak-map.js","../../node_modules/core-js/modules/es6.weak-set.js","../../node_modules/core-js/modules/_typed.js","../../node_modules/core-js/modules/_to-index.js","../../node_modules/core-js/modules/_typed-buffer.js","../../node_modules/core-js/modules/es6.typed.array-buffer.js","../../node_modules/core-js/modules/es6.typed.data-view.js","../../node_modules/core-js/modules/_typed-array.js","../../node_modules/core-js/modules/es6.typed.int8-array.js","../../node_modules/core-js/modules/es6.typed.uint8-array.js","../../node_modules/core-js/modules/es6.typed.uint8-clamped-array.js","../../node_modules/core-js/modules/es6.typed.int16-array.js","../../node_modules/core-js/modules/es6.typed.uint16-array.js","../../node_modules/core-js/modules/es6.typed.int32-array.js","../../node_modules/core-js/modules/es6.typed.uint32-array.js","../../node_modules/core-js/modules/es6.typed.float32-array.js","../../node_modules/core-js/modules/es6.typed.float64-array.js","../../node_modules/core-js/modules/es6.reflect.apply.js","../../node_modules/core-js/modules/es6.reflect.construct.js","../../node_modules/core-js/modules/es6.reflect.define-property.js","../../node_modules/core-js/modules/es6.reflect.delete-property.js","../../node_modules/core-js/modules/es6.reflect.enumerate.js","../../node_modules/core-js/modules/es6.reflect.get.js","../../node_modules/core-js/modules/es6.reflect.get-own-property-descriptor.js","../../node_modules/core-js/modules/es6.reflect.get-prototype-of.js","../../node_modules/core-js/modules/es6.reflect.has.js","../../node_modules/core-js/modules/es6.reflect.is-extensible.js","../../node_modules/core-js/modules/_own-keys.js","../../node_modules/core-js/modules/es6.reflect.own-keys.js","../../node_modules/core-js/modules/es6.reflect.prevent-extensions.js","../../node_modules/core-js/modules/es6.reflect.set.js","../../node_modules/core-js/modules/es6.reflect.set-prototype-of.js","../../node_modules/core-js/modules/es7.array.includes.js","../../node_modules/core-js/modules/_flatten-into-array.js","../../node_modules/core-js/modules/es7.array.flat-map.js","../../node_modules/core-js/modules/es7.array.flatten.js","../../node_modules/core-js/modules/es7.string.at.js","../../node_modules/core-js/modules/_string-pad.js","../../node_modules/core-js/modules/es7.string.pad-start.js","../../node_modules/core-js/modules/es7.string.pad-end.js","../../node_modules/core-js/modules/es7.string.trim-left.js","../../node_modules/core-js/modules/es7.string.trim-right.js","../../node_modules/core-js/modules/es7.string.match-all.js","../../node_modules/core-js/modules/es7.symbol.async-iterator.js","../../node_modules/core-js/modules/es7.symbol.observable.js","../../node_modules/core-js/modules/es7.object.get-own-property-descriptors.js","../../node_modules/core-js/modules/_object-to-array.js","../../node_modules/core-js/modules/es7.object.values.js","../../node_modules/core-js/modules/es7.object.entries.js","../../node_modules/core-js/modules/_object-forced-pam.js","../../node_modules/core-js/modules/es7.object.define-getter.js","../../node_modules/core-js/modules/es7.object.define-setter.js","../../node_modules/core-js/modules/es7.object.lookup-getter.js","../../node_modules/core-js/modules/es7.object.lookup-setter.js","../../node_modules/core-js/modules/_array-from-iterable.js","../../node_modules/core-js/modules/_collection-to-json.js","../../node_modules/core-js/modules/es7.map.to-json.js","../../node_modules/core-js/modules/es7.set.to-json.js","../../node_modules/core-js/modules/_set-collection-of.js","../../node_modules/core-js/modules/es7.map.of.js","../../node_modules/core-js/modules/es7.set.of.js","../../node_modules/core-js/modules/es7.weak-map.of.js","../../node_modules/core-js/modules/es7.weak-set.of.js","../../node_modules/core-js/modules/_set-collection-from.js","../../node_modules/core-js/modules/es7.map.from.js","../../node_modules/core-js/modules/es7.set.from.js","../../node_modules/core-js/modules/es7.weak-map.from.js","../../node_modules/core-js/modules/es7.weak-set.from.js","../../node_modules/core-js/modules/es7.global.js","../../node_modules/core-js/modules/es7.system.global.js","../../node_modules/core-js/modules/es7.error.is-error.js","../../node_modules/core-js/modules/es7.math.clamp.js","../../node_modules/core-js/modules/es7.math.deg-per-rad.js","../../node_modules/core-js/modules/es7.math.degrees.js","../../node_modules/core-js/modules/_math-scale.js","../../node_modules/core-js/modules/es7.math.fscale.js","../../node_modules/core-js/modules/es7.math.iaddh.js","../../node_modules/core-js/modules/es7.math.isubh.js","../../node_modules/core-js/modules/es7.math.imulh.js","../../node_modules/core-js/modules/es7.math.rad-per-deg.js","../../node_modules/core-js/modules/es7.math.radians.js","../../node_modules/core-js/modules/es7.math.scale.js","../../node_modules/core-js/modules/es7.math.umulh.js","../../node_modules/core-js/modules/es7.math.signbit.js","../../node_modules/core-js/modules/es7.promise.finally.js","../../node_modules/core-js/modules/es7.promise.try.js","../../node_modules/core-js/modules/_metadata.js","../../node_modules/core-js/modules/es7.reflect.define-metadata.js","../../node_modules/core-js/modules/es7.reflect.delete-metadata.js","../../node_modules/core-js/modules/es7.reflect.get-metadata.js","../../node_modules/core-js/modules/es7.reflect.get-metadata-keys.js","../../node_modules/core-js/modules/es7.reflect.get-own-metadata.js","../../node_modules/core-js/modules/es7.reflect.get-own-metadata-keys.js","../../node_modules/core-js/modules/es7.reflect.has-metadata.js","../../node_modules/core-js/modules/es7.reflect.has-own-metadata.js","../../node_modules/core-js/modules/es7.reflect.metadata.js","../../node_modules/core-js/modules/es7.asap.js","../../node_modules/core-js/modules/es7.observable.js","../../node_modules/core-js/modules/web.timers.js","../../node_modules/core-js/modules/web.immediate.js","../../node_modules/core-js/modules/web.dom.iterable.js","../../node_modules/core-js/shim.js","../../node_modules/regenerator-runtime/runtime.js","../../node_modules/core-js/modules/_replacer.js","../../node_modules/core-js/modules/core.regexp.escape.js","../../node_modules/core-js/fn/regexp/escape.js","../../node_modules/babel-polyfill/lib/index.js","scrollspy.js","adjust-height.js","sphinx_materialdesign_theme.js"],"names":["MaterialTab","tab","ctx","element_","classList","contains","CssClasses_","MDL_JS_RIPPLE_EFFECT","rippleContainer","document","createElement","add","MDL_RIPPLE_CONTAINER","ripple","MDL_RIPPLE","appendChild","addEventListener","e","getAttribute","charAt","preventDefault","href","split","panel","querySelector","resetTabState_","resetPanelState_","ACTIVE_CLASS","MaterialLayoutTab","tabs","panels","layout","selectTab","content_","IS_ACTIVE","tabBar_","JS_RIPPLE_EFFECT","RIPPLE_CONTAINER","RIPPLE","TAB_MANUAL_SWITCH","show","componentHandler","upgradeDom","optJsClass","optCssClass","upgradeElement","element","upgradeElements","elements","upgradeAllRegistered","registerUpgradedCallback","jsClass","callback","register","config","downgradeElements","nodes","findRegisteredClass_","name","optReplace","i","registeredComponents_","length","className","getUpgradedListOfElement_","dataUpgraded","isElementUpgraded_","upgradedList","indexOf","createEvent_","eventType","bubbles","cancelable","window","CustomEvent","ev","createEvent","initEvent","upgradeDomInternal","cssClass","registeredClass","querySelectorAll","n","upgradeElementInternal","Element","Error","upgradingEv","dispatchEvent","defaultPrevented","classesToUpgrade","push","forEach","component","setAttribute","join","instance","classConstructor","componentConfigProperty_","createdComponents_","j","m","callbacks","widget","upgradedEv","deconstructComponentInternal","componentIndex","splice","upgrades","componentPlace","classAsString","upgradeElementsInternal","Array","isArray","prototype","slice","call","HTMLElement","children","upgradeAllRegisteredInternal","registerUpgradedCallbackInternal","regClass","registerInternal","widgetMissing","newConfig","constructor","item","hasOwnProperty","downgradeNodesInternal","downgradeNode","node","filter","NodeList","Node","ComponentConfigPublic","ComponentConfig","Component","documentElement","Date","now","getTime","vendors","requestAnimationFrame","vp","cancelAnimationFrame","test","navigator","userAgent","lastTime","nextTime","Math","max","setTimeout","clearTimeout","MaterialButton","this","init","Constant_","RIPPLE_EFFECT","blurHandler_","event","blur","disable","disabled","enable","rippleElement_","boundRippleBlurHandler","bind","boundButtonBlurHandler","MaterialCheckbox","TINY_TIMEOUT","INPUT","BOX_OUTLINE","FOCUS_HELPER","TICK_OUTLINE","RIPPLE_IGNORE_EVENTS","RIPPLE_CENTER","IS_FOCUSED","IS_DISABLED","IS_CHECKED","IS_UPGRADED","onChange_","updateClasses_","onFocus_","onBlur_","remove","onMouseUp_","blur_","checkDisabled","checkToggleState","inputElement_","checked","check","uncheck","boxOutline","tickContainer","tickOutline","rippleContainerElement_","boundRippleMouseUp","boundInputOnChange","boundInputOnFocus","boundInputOnBlur","boundElementMouseUp","MaterialIconToggle","boundElementOnMouseUp","MaterialMenu","TRANSITION_DURATION_SECONDS","TRANSITION_DURATION_FRACTION","CLOSE_TIMEOUT","Keycodes_","ENTER","ESCAPE","SPACE","UP_ARROW","DOWN_ARROW","CONTAINER","OUTLINE","ITEM","ITEM_RIPPLE_CONTAINER","IS_VISIBLE","IS_ANIMATING","BOTTOM_LEFT","BOTTOM_RIGHT","TOP_LEFT","TOP_RIGHT","UNALIGNED","container","parentElement","insertBefore","removeChild","container_","outline","outline_","forElId","forEl","getElementById","forElement_","handleForClick_","handleForKeyboardEvent_","items","boundItemKeydown_","handleItemKeyboardEvent_","boundItemClick_","handleItemClick_","tabIndex","evt","rect","getBoundingClientRect","forRect","style","right","top","offsetTop","offsetHeight","left","offsetLeft","bottom","toggle","keyCode","focus","currentIndex","target","MouseEvent","click","hide","hasAttribute","stopPropagation","closing_","applyClip_","height","width","clip","removeAnimationEndListener_","addAnimationEndListener_","transitionDuration","itemDelay","transitionDelay","parentNode","removeEventListener","removeProperty","MaterialProgress","INDETERMINATE_CLASS","setProgress","p","progressbar_","setBuffer","bufferbar_","auxbar_","el","MaterialRadio","JS_RADIO","RADIO_BTN","RADIO_OUTER_CIRCLE","RADIO_INNER_CIRCLE","radios","getElementsByClassName","btnElement_","onMouseup_","boundChangeHandler_","boundFocusHandler_","boundBlurHandler_","boundMouseUpHandler_","outerCircle","innerCircle","MaterialSlider","isIE_","msPointerEnabled","IE_CONTAINER","SLIDER_CONTAINER","BACKGROUND_FLEX","BACKGROUND_LOWER","BACKGROUND_UPPER","IS_LOWEST_VALUE","onInput_","updateValueStyles_","onContainerMouseDown_","newEvent","buttons","clientX","clientY","y","fraction","value","min","backgroundLower_","flex","webkitFlex","backgroundUpper_","change","containerIE","backgroundFlex","boundInputHandler","boundChangeHandler","boundMouseUpHandler","boundContainerMouseDownHandler","MaterialSnackbar","textElement_","cssClasses_","MESSAGE","actionElement_","ACTION","active","actionHandler_","undefined","message_","actionText_","queuedNotifications_","setActionHidden_","ANIMATION_LENGTH","SNACKBAR","ACTIVE","displaySnackbar_","textContent","cleanup_","timeout_","showSnackbar","data","checkQueue_","shift","Boolean","removeAttribute","MaterialSpinner","MDL_SPINNER_LAYER_COUNT","MDL_SPINNER_LAYER","MDL_SPINNER_CIRCLE_CLIPPER","MDL_SPINNER_CIRCLE","MDL_SPINNER_GAP_PATCH","MDL_SPINNER_LEFT","MDL_SPINNER_RIGHT","createLayer","index","layer","leftClipper","gapPatch","rightClipper","circleOwners","circle","stop","start","MaterialSwitch","TRACK","THUMB","on","off","track","thumb","focusHelper","boundFocusHandler","boundBlurHandler","MaterialTabs","TAB_CLASS","PANEL_CLASS","UPGRADED_CLASS","MDL_JS_RIPPLE_EFFECT_IGNORE_EVENTS","initTabs_","tabs_","panels_","k","MaterialTextfield","maxRows","NO_MAX_ROWS","MAX_ROWS_ATTRIBUTE","LABEL","IS_DIRTY","IS_INVALID","HAS_PLACEHOLDER","onKeyDown_","currentRowCount","onReset_","checkValidity","checkDirty","checkFocus","input_","validity","valid","label_","parseInt","isNaN","boundUpdateClassesHandler","boundResetHandler","boundKeyDownHandler","invalid","MaterialTooltip","BOTTOM","LEFT","RIGHT","TOP","handleMouseEnter_","props","marginLeft","offsetWidth","marginTop","hideTooltip_","boundMouseEnterHandler","boundMouseLeaveAndScrollHandler","MaterialLayout","MAX_WIDTH","TAB_SCROLL_PIXELS","RESIZE_TIMEOUT","MENU_ICON","CHEVRON_LEFT","CHEVRON_RIGHT","Mode_","STANDARD","SEAMED","WATERFALL","SCROLL","HEADER","DRAWER","CONTENT","DRAWER_BTN","ICON","HEADER_SEAMED","HEADER_WATERFALL","HEADER_SCROLL","FIXED_HEADER","OBFUSCATOR","TAB_BAR","TAB_CONTAINER","TAB","TAB_BAR_BUTTON","TAB_BAR_LEFT_BUTTON","TAB_BAR_RIGHT_BUTTON","PANEL","HAS_DRAWER","HAS_TABS","HAS_SCROLLING_HEADER","CASTING_SHADOW","IS_COMPACT","IS_SMALL_SCREEN","IS_DRAWER_OPEN","ON_LARGE_SCREEN","ON_SMALL_SCREEN","contentScrollHandler_","header_","headerVisible","scrollTop","keyboardEventHandler_","drawer_","toggleDrawer","screenSizeHandler_","screenSizeMediaQuery_","matches","obfuscator_","drawerToggleHandler_","type","headerTransitionEndHandler_","headerClickHandler_","tabBar","drawerButton","focusedElement","directChildren","childNodes","numChildren","c","child","persisted","overflowY","mode","drawerButtonIcon","innerHTML","firstChild","obfuscator","matchMedia","addListener","tabContainer","leftButton","leftButtonIcon","scrollLeft","rightButton","rightButtonIcon","tabUpdateHandler","scrollWidth","windowResizeHandler","resizeTimeoutId_","MaterialDataTable","DATA_TABLE","SELECTABLE","SELECT_ELEMENT","IS_SELECTED","selectRow_","checkbox","row","opt_rows","createCheckbox_","label","labelClasses","firstHeader","bodyRows","footRows","rows","concat","th","headerCheckbox","firstCell","td","nodeName","toUpperCase","rowCheckbox","MaterialRipple","INITIAL_SCALE","INITIAL_SIZE","INITIAL_OPACITY","FINAL_OPACITY","FINAL_SCALE","RIPPLE_EFFECT_IGNORE_EVENTS","downHandler_","boundHeight","boundWidth","rippleSize_","sqrt","ignoringMouseDown_","frameCount","getFrameCount","setFrameCount","x","bound","currentTarget","round","touches","setRippleXY","setRippleStyles","animFrameHandler","upHandler_","detail","recentering","frameCount_","x_","y_","boundDownHandler","boundUpHandler","fC","getRippleElement","newX","newY","transformString","scale","offset","webkitTransform","msTransform","transform","ScrollSpy","args","doc","nav","navSelector","win","winHeight","innerHeight","scrollElement","scrollSelector","contents","getContents","contentSelector","attachEvent","scrollTimer","resizeTimer","spy","targets","getViewState","toggleNavClass","elementListInView","current","isView","calcBotom","elementTop","elementBottom","maxDepth","maxDepthElement","$","tempDepth","getTagDepth","navElement","id","find","get","tagName","AdjustHeight","header","pagenation","footer","content","adjust","setPageContentMinHeight","setLocaltocHeight","winH","headerHeight","outerHeight","footerHeight","css","pagenationPotisionTop","position","outlineBottom","reconstructionDrawerGlobalToc","$lists","$breadcrumb","each","li","$li","$linkWrapper","$link","append","isCurrent","hasClass","$ul","ulId","attr","addClass","$toggleWrapper","$toggle","toggleClass","animate","opacity","parent","url","text","location","button","icon","createTextNode","link","onclick","fileName","pop","replace","hint","hintText","map","first"],"mappings":";;;AAgOA,SAAA,EAAA,GAAA,OAAA,EAAA,mBAAA,QAAA,iBAAA,OAAA,SAAA,SAAA,GAAA,cAAA,GAAA,SAAA,GAAA,OAAA,GAAA,mBAAA,QAAA,EAAA,cAAA,QAAA,IAAA,OAAA,UAAA,gBAAA,IAAA,IAAA,WAAA,aCnHAA,SAAAA,EAAAC,EAAAC,GACAD,GAAAA,EAAA,CACAC,GAAAA,EAAAC,SAAAC,UAAAC,SAAAH,EAAAI,YAAAC,sBAAA,CACAC,IAAAA,EAAAC,SAAAC,cAAA,QACAF,EAAAJ,UAAAO,IAAAT,EAAAI,YAAAM,sBACAJ,EAAAJ,UAAAO,IAAAT,EAAAI,YAAAC,sBACAM,IAAAA,EAAAJ,SAAAC,cAAA,QACAG,EAAAT,UAAAO,IAAAT,EAAAI,YAAAQ,YACAN,EAAAO,YAAAF,GACAZ,EAAAc,YAAAP,GAEAP,EAAAe,iBAAA,QAAA,SAAAC,GACA,GAAA,MAAAhB,EAAAiB,aAAA,QAAAC,OAAA,GAAA,CACAF,EAAAG,iBACAC,IAAAA,EAAApB,EAAAoB,KAAAC,MAAA,KAAA,GACAC,EAAArB,EAAAC,SAAAqB,cAAA,IAAAH,GACAnB,EAAAuB,iBACAvB,EAAAwB,mBACAzB,EAAAG,UAAAO,IAAAT,EAAAI,YAAAqB,cACAJ,EAAAnB,UAAAO,IAAAT,EAAAI,YAAAqB,kBCwTAC,SAAAA,EAAA3B,EAAA4B,EAAAC,EAAAC,GAIAC,SAAAA,IACAX,IAAAA,EAAApB,EAAAoB,KAAAC,MAAA,KAAA,GACAC,EAAAQ,EAAAE,SAAAT,cAAA,IAAAH,GACAU,EAAAN,eAAAI,GACAE,EAAAL,iBAAAI,GACA7B,EAAAG,UAAAO,IAAAoB,EAAAzB,YAAA4B,WACAX,EAAAnB,UAAAO,IAAAoB,EAAAzB,YAAA4B,WAEAH,GAAAA,EAAAI,QAAA/B,UAAAC,SAAA0B,EAAAzB,YAAA8B,kBAAA,CACA5B,IAAAA,EAAAC,SAAAC,cAAA,QACAF,EAAAJ,UAAAO,IAAAoB,EAAAzB,YAAA+B,kBACA7B,EAAAJ,UAAAO,IAAAoB,EAAAzB,YAAA8B,kBACAvB,IAAAA,EAAAJ,SAAAC,cAAA,QACAG,EAAAT,UAAAO,IAAAoB,EAAAzB,YAAAgC,QACA9B,EAAAO,YAAAF,GACAZ,EAAAc,YAAAP,GAEAuB,EAAAI,QAAA/B,UAAAC,SAAA0B,EAAAzB,YAAAiC,oBACAtC,EAAAe,iBAAA,QAAA,SAAAC,GACAhB,MAAAA,EAAAiB,aAAA,QAAAC,OAAA,KACAF,EAAAG,iBACAY,OAIA/B,EAAAuC,KAAAR,ECzbAS,IAAAA,EAAAA,CAUAC,WAAA,SAAAC,EAAAC,KAQAC,eAAA,SAAAC,EAAAH,KAOAI,gBAAA,SAAAC,KAKAC,qBAAA,aAWAC,yBAAA,SAAAC,EAAAC,KAMAC,SAAA,SAAAC,KAMAC,kBAAA,SAAAC,OAGAf,EAAA,WAoBAgB,SAAAA,EAAAC,EAAAC,GACA,IAAA,IAAAC,EAAA,EAAAA,EAAAC,EAAAC,OAAAF,IACA,GAAAC,EAAAD,GAAAG,YAAAL,EAIA,YAHA,IAAAC,IACAE,EAAAD,GAAAD,GAEAE,EAAAD,GAGA,OAAA,EAUAI,SAAAA,EAAAlB,GACAmB,IAAAA,EAAAnB,EAAA5B,aAAA,iBAEA,OAAA,OAAA+C,EAAAA,CAAA,IAAAA,EAAA3C,MAAA,KAYA4C,SAAAA,EAAApB,EAAAK,GAEAgB,OAAA,IADAH,EAAAlB,GACAsB,QAAAjB,GAWAkB,SAAAA,EAAAC,EAAAC,EAAAC,GACA,GAAA,gBAAAC,QAAA,mBAAAA,OAAAC,YACA,OAAA,IAAAA,YAAAJ,EAAAA,CACAC,QAAAA,EACAC,WAAAA,IAGAG,IAAAA,EAAAlE,SAAAmE,YAAA,UACAD,OAAAA,EAAAE,UAAAP,EAAAC,EAAAC,GACAG,EAaAG,SAAAA,EAAAnC,EAAAC,GACA,QAAA,IAAAD,QACA,IAAAC,EACA,IAAA,IAAAgB,EAAA,EAAAA,EAAAC,EAAAC,OAAAF,IACAkB,EAAAjB,EAAAD,GAAAG,UACAF,EAAAD,GAAAmB,cAEA,CACA5B,IAAAA,EAAA,EACA,QAAA,IAAAP,EAAA,CACAoC,IAAAA,EAAAvB,EAAAN,GACA6B,IACApC,EAAAoC,EAAAD,UAKA,IAAA,IADA/B,EAAAvC,SAAAwE,iBAAA,IAAArC,GACAsC,EAAA,EAAAA,EAAAlC,EAAAc,OAAAoB,IACAC,EAAAnC,EAAAkC,GAAA/B,IAYAgC,SAAAA,EAAArC,EAAAH,GAEA,KAAA,UAAAG,EAAAA,IAAAA,aAAAsC,SACA,MAAA,IAAAC,MAAA,qDAGAC,IAAAA,EAAAjB,EAAA,0BAAA,GAAA,GACAvB,GAAAA,EAAAyC,cAAAD,IACAA,EAAAE,iBAAA,CAIArB,IAAAA,EAAAH,EAAAlB,GACA2C,EAAAA,GAGA9C,GAAAA,EAUAuB,EAAApB,EAAAH,IACA8C,EAAAC,KAAAjC,EAAAd,QAXA,CACAvC,IAAAA,EAAA0C,EAAA1C,UACAyD,EAAA8B,QAAA,SAAAC,GAEAxF,EAAAC,SAAAuF,EAAAb,YACA,IAAAU,EAAArB,QAAAwB,KACA1B,EAAApB,EAAA8C,EAAA7B,YACA0B,EAAAC,KAAAE,KAQA,IAAA,IAAAZ,EAAApB,EAAA,EAAAsB,EAAAO,EAAA3B,OAAAF,EAAAsB,EAAAtB,IAAA,CACAoB,KAAAA,EAAAS,EAAA7B,IAkBA,MAAA,IAAAyB,MACA,8DAhBAlB,EAAAuB,KAAAV,EAAAjB,WACAjB,EAAA+C,aAAA,gBAAA1B,EAAA2B,KAAA,MACAC,IAAAA,EAAA,IAAAf,EAAAgB,iBAAAlD,GACAiD,EAAAE,GAAAjB,EACAkB,EAAAR,KAAAK,GAEA,IAAA,IAAAI,EAAA,EAAAC,EAAApB,EAAAqB,UAAAvC,OAAAqC,EAAAC,EAAAD,IACAnB,EAAAqB,UAAAF,GAAArD,GAGAkC,EAAAsB,SAEAxD,EAAAkC,EAAAjB,WAAAgC,GAOAQ,IAAAA,EAAAlC,EAAA,yBAAA,GAAA,GACAvB,EAAAyC,cAAAgB,KAgHAC,SAAAA,EAAAZ,GACAA,GAAAA,EAAA,CACAa,IAAAA,EAAAP,EAAA9B,QAAAwB,GACAM,EAAAQ,OAAAD,EAAA,GAEAE,IAAAA,EAAAf,EAAAzF,SAAAe,aAAA,iBAAAI,MAAA,KACAsF,EAAAD,EAAAvC,QAAAwB,EAAAK,GAAAY,eACAF,EAAAD,OAAAE,EAAA,GACAhB,EAAAzF,SAAA0F,aAAA,gBAAAc,EAAAb,KAAA,MAEAnB,IAAAA,EAAAN,EAAA,2BAAA,GAAA,GACAuB,EAAAzF,SAAAoF,cAAAZ,IArSAd,IAAAA,EAAAA,GAGAqC,EAAAA,GAEAD,EAAA,8BAgUA,MAAA,CACAvD,WAAAoC,EACAjC,eAAAsC,EACApC,gBApJA+D,SAAAA,EAAA9D,GACA+D,MAAAC,QAAAhE,KAEAA,EADAA,aAAAoC,QAAAA,CACApC,GAEA+D,MAAAE,UAAAC,MAAAC,KAAAnE,IAGA,IAAA,IAAAF,EAAAc,EAAA,EAAAsB,EAAAlC,EAAAc,OAAAF,EAAAsB,EAAAtB,KACAd,EAAAE,EAAAY,cACAwD,cACAjC,EAAArC,GACAA,EAAAuE,SAAAvD,OAAA,GACAgD,EAAAhE,EAAAuE,YAwIApE,qBA5DAqE,WACA,IAAA,IAAApC,EAAA,EAAAA,EAAArB,EAAAC,OAAAoB,IACAJ,EAAAjB,EAAAqB,GAAAnB,YA2DAb,yBAxEAqE,SAAApE,EAAAC,GACAoE,IAAAA,EAAA/D,EAAAN,GACAqE,GACAA,EAAAnB,UAAAX,KAAAtC,IAsEAC,SA/HAoE,SAAAnE,GAKAoE,IAEApB,GAAAA,OAFA,IAAAhD,EAAAgD,aACA,IAAAhD,EAAA,SAIAgD,EAAAhD,EAAAgD,QAAAhD,EAAA,QAGAqE,IAAAA,EAAAA,CACA3B,iBAAA1C,EAAAsE,aAAAtE,EAAA,YACAS,UAAAT,EAAAuD,eAAAvD,EAAA,cACAyB,SAAAzB,EAAAyB,UAAAzB,EAAA,SACAgD,OAAAA,EACAD,UAAAA,IAGAxC,GAAAA,EAAA8B,QAAA,SAAAkC,GACAA,GAAAA,EAAA9C,WAAA4C,EAAA5C,SACA,MAAA,IAAAM,MAAA,sDAAAwC,EAAA9C,UAEA8C,GAAAA,EAAA9D,YAAA4D,EAAA5D,UACA,MAAA,IAAAsB,MAAA,wDAIA/B,EAAAsE,YAAAX,UACAa,eAAA7B,GACA,MAAA,IAAAZ,MACA,uCAAAY,EACA,2BAGAxC,EAAAH,EAAAuD,cAAAc,IAGA9D,EAAA6B,KAAAiC,IAwFApE,kBA9BAwE,SAAAvE,GAKAwE,IAAAA,EAAA,SAAAC,GACA/B,EAAAgC,OAAA,SAAAL,GACAA,OAAAA,EAAA1H,WAAA8H,IACAtC,QAAAa,IAEAhD,GAAAA,aAAAuD,OAAAvD,aAAA2E,SACA,IAAA,IAAAjD,EAAA,EAAAA,EAAA1B,EAAAM,OAAAoB,IACA8C,EAAAxE,EAAA0B,QAEA,CAAA,KAAA1B,aAAA4E,MAGA,MAAA,IAAA/C,MAAA,qDAFA2C,EAAAxE,MAjUA,IA+VA6E,sBAcA5F,EAAA6F,gBAcA7F,EAAA8F,UAIA9F,EAAA,WAAAA,EAAAC,WACAD,EAAA,eAAAA,EAAAI,eACAJ,EAAA,gBAAAA,EAAAM,gBACAN,EAAA,qBACAA,EAAAQ,qBACAR,EAAA,yBACAA,EAAAS,yBACAT,EAAA,SAAAA,EAAAY,SACAZ,EAAA,kBAAAA,EAAAc,kBACAkB,OAAAhC,iBAAAA,EACAgC,OAAA,iBAAAhC,EAEAgC,OAAAzD,iBAAA,OAAA,WAQAP,cAAAA,SAAAC,cAAA,QACA,kBAAAD,UACA,qBAAAgE,QAAAsC,MAAAE,UAAAtB,SACAlF,SAAA+H,gBAAApI,UAAAO,IAAA,UACA8B,EAAAQ,yBAKAR,EAAAI,eAAA,aAIAJ,EAAAY,SAAA,gBC7eAoF,KAAAC,MAKAD,KAAAC,IAAA,WACA,OAAA,IAAAD,MAAAE,WAEAF,KAAA,IAAAA,KAAAC,KAMA,IAAA,IAJAE,EAAAA,CACA,SACA,OAEAhF,EAAA,EAAAA,EAAAgF,EAAA9E,SAAAW,OAAAoE,wBAAAjF,EAAA,CACAkF,IAAAA,EAAAF,EAAAhF,GACAa,OAAAoE,sBAAApE,OAAAqE,EAAA,yBACArE,OAAAsE,qBAAAtE,OAAAqE,EAAA,yBAAArE,OAAAqE,EAAA,+BACArE,OAAA,sBAAAA,OAAAoE,sBACApE,OAAA,qBAAAA,OAAAsE,qBAEA,GAAA,uBAAAC,KAAAvE,OAAAwE,UAAAC,aAAAzE,OAAAoE,wBAAApE,OAAAsE,qBAAA,CACAI,IAAAA,EAAA,EAKA1E,OAAAoE,sBAAA,SAAAzF,GACAsF,IAAAA,EAAAD,KAAAC,MACAU,EAAAC,KAAAC,IAAAH,EAAA,GAAAT,GACAa,OAAAA,WAAA,WACAnG,EAAA+F,EAAAC,IACAA,EAAAV,IAEAjE,OAAAsE,qBAAAS,aACA/E,OAAA,sBAAAA,OAAAoE,sBACApE,OAAA,qBAAAA,OAAAsE,qBCpBAU,IAAAA,EAAA,SAAA3G,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,eAAAgF,EAOAA,EAAAxC,UAAA2C,UAAAA,GASAH,EAAAxC,UAAA3G,YAAAA,CACAuJ,cAAA,uBACAxH,iBAAA,+BACAC,OAAA,cAQAmH,EAAAxC,UAAA6C,aAAA,SAAAC,GACAA,GACAL,KAAAvJ,SAAA6J,QASAP,EAAAxC,UAAAgD,QAAA,WACA9J,KAAAA,SAAA+J,UAAAA,GAEAT,EAAAxC,UAAA,QAAAwC,EAAAxC,UAAAgD,QAMAR,EAAAxC,UAAAkD,OAAA,WACAhK,KAAAA,SAAA+J,UAAAA,GAEAT,EAAAxC,UAAA,OAAAwC,EAAAxC,UAAAkD,OAIAV,EAAAxC,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACAuJ,GAAAA,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAuJ,eAAA,CACArJ,IAAAA,EAAAC,SAAAC,cAAA,QACAF,EAAAJ,UAAAO,IAAA+I,KAAApJ,YAAA+B,kBACAqH,KAAAU,eAAA3J,SAAAC,cAAA,QACAgJ,KAAAU,eAAAhK,UAAAO,IAAA+I,KAAApJ,YAAAgC,QACA9B,EAAAO,YAAA2I,KAAAU,gBACAV,KAAAW,uBAAAX,KAAAI,aAAAQ,KAAAZ,MACAA,KAAAU,eAAApJ,iBAAA,UAAA0I,KAAAW,wBACAX,KAAAvJ,SAAAY,YAAAP,GAEA+J,KAAAA,uBAAAb,KAAAI,aAAAQ,KAAAZ,MACAA,KAAAvJ,SAAAa,iBAAA,UAAA0I,KAAAa,wBACAb,KAAAvJ,SAAAa,iBAAA,aAAA0I,KAAAa,0BAKA9H,EAAAY,SAAAA,CACAuE,YAAA6B,EACA5C,cAAA,iBACA9B,SAAA,gBACAuB,QAAAA,ICjFAkE,IAAAA,EAAA,SAAA1H,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,iBAAA+F,EAOAA,EAAAvD,UAAA2C,UAAAA,CAAAa,aAAA,MASAD,EAAAvD,UAAA3G,YAAAA,CACAoK,MAAA,sBACAC,YAAA,4BACAC,aAAA,6BACAC,aAAA,6BACAhB,cAAA,uBACAiB,qBAAA,sCACAzI,iBAAA,iCACA0I,cAAA,qBACAzI,OAAA,aACA0I,WAAA,aACAC,YAAA,cACAC,WAAA,aACAC,YAAA,eAQAX,EAAAvD,UAAAmE,UAAA,SAAArB,GACAsB,KAAAA,kBAQAb,EAAAvD,UAAAqE,SAAA,SAAAvB,GACA5J,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA0K,aAQAR,EAAAvD,UAAAsE,QAAA,SAAAxB,GACA5J,KAAAA,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA0K,aAQAR,EAAAvD,UAAAwE,WAAA,SAAA1B,GACA2B,KAAAA,SAOAlB,EAAAvD,UAAAoE,eAAA,WACAM,KAAAA,gBACAjC,KAAAkC,oBAOApB,EAAAvD,UAAAyE,MAAA,WAGAjH,OAAA8E,WAAA,WACAsC,KAAAA,cAAA7B,QACAM,KAAAZ,MAAAA,KAAAE,UAAAa,eAQAD,EAAAvD,UAAA2E,iBAAA,WACAC,KAAAA,cAAAC,QACApC,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA4K,YAEAxB,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA4K,aAGAV,EAAAvD,UAAA,iBAAAuD,EAAAvD,UAAA2E,iBAMApB,EAAAvD,UAAA0E,cAAA,WACAE,KAAAA,cAAA3B,SACAR,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA2K,aAEAvB,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA2K,cAGAT,EAAAvD,UAAA,cAAAuD,EAAAvD,UAAA0E,cAMAnB,EAAAvD,UAAAgD,QAAA,WACA4B,KAAAA,cAAA3B,UAAAA,EACAR,KAAA2B,kBAEAb,EAAAvD,UAAA,QAAAuD,EAAAvD,UAAAgD,QAMAO,EAAAvD,UAAAkD,OAAA,WACA0B,KAAAA,cAAA3B,UAAAA,EACAR,KAAA2B,kBAEAb,EAAAvD,UAAA,OAAAuD,EAAAvD,UAAAkD,OAMAK,EAAAvD,UAAA8E,MAAA,WACAF,KAAAA,cAAAC,SAAAA,EACApC,KAAA2B,kBAEAb,EAAAvD,UAAA,MAAAuD,EAAAvD,UAAA8E,MAMAvB,EAAAvD,UAAA+E,QAAA,WACAH,KAAAA,cAAAC,SAAAA,EACApC,KAAA2B,kBAEAb,EAAAvD,UAAA,QAAAuD,EAAAvD,UAAA+E,QAIAxB,EAAAvD,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACA0L,KAAAA,cAAAnC,KAAAvJ,SAAAqB,cAAA,IAAAkI,KAAApJ,YAAAoK,OACAuB,IAAAA,EAAAxL,SAAAC,cAAA,QACAuL,EAAA7L,UAAAO,IAAA+I,KAAApJ,YAAAqK,aACAuB,IAAAA,EAAAzL,SAAAC,cAAA,QACAwL,EAAA9L,UAAAO,IAAA+I,KAAApJ,YAAAsK,cACAuB,IAAAA,EAAA1L,SAAAC,cAAA,QACAyL,GAAAA,EAAA/L,UAAAO,IAAA+I,KAAApJ,YAAAuK,cACAoB,EAAAlL,YAAAoL,GACAzC,KAAAvJ,SAAAY,YAAAmL,GACAxC,KAAAvJ,SAAAY,YAAAkL,GACAvC,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAuJ,eAAA,CACA1J,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAAwK,sBACApB,KAAA0C,wBAAA3L,SAAAC,cAAA,QACAgJ,KAAA0C,wBAAAhM,UAAAO,IAAA+I,KAAApJ,YAAA+B,kBACAqH,KAAA0C,wBAAAhM,UAAAO,IAAA+I,KAAApJ,YAAAuJ,eACAH,KAAA0C,wBAAAhM,UAAAO,IAAA+I,KAAApJ,YAAAyK,eACArB,KAAA2C,mBAAA3C,KAAA+B,WAAAnB,KAAAZ,MACAA,KAAA0C,wBAAApL,iBAAA,UAAA0I,KAAA2C,oBACAxL,IAAAA,EAAAJ,SAAAC,cAAA,QACAG,EAAAT,UAAAO,IAAA+I,KAAApJ,YAAAgC,QACAoH,KAAA0C,wBAAArL,YAAAF,GACA6I,KAAAvJ,SAAAY,YAAA2I,KAAA0C,yBAEAE,KAAAA,mBAAA5C,KAAA0B,UAAAd,KAAAZ,MACAA,KAAA6C,kBAAA7C,KAAA4B,SAAAhB,KAAAZ,MACAA,KAAA8C,iBAAA9C,KAAA6B,QAAAjB,KAAAZ,MACAA,KAAA+C,oBAAA/C,KAAA+B,WAAAnB,KAAAZ,MACAA,KAAAmC,cAAA7K,iBAAA,SAAA0I,KAAA4C,oBACA5C,KAAAmC,cAAA7K,iBAAA,QAAA0I,KAAA6C,mBACA7C,KAAAmC,cAAA7K,iBAAA,OAAA0I,KAAA8C,kBACA9C,KAAAvJ,SAAAa,iBAAA,UAAA0I,KAAA+C,qBACA/C,KAAA2B,iBACA3B,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA6K,eAKA1I,EAAAY,SAAAA,CACAuE,YAAA4C,EACA3D,cAAA,mBACA9B,SAAA,kBACAuB,QAAAA,IC9MAoG,IAAAA,EAAA,SAAA5J,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,mBAAAiI,EAOAA,EAAAzF,UAAA2C,UAAAA,CAAAa,aAAA,MASAiC,EAAAzF,UAAA3G,YAAAA,CACAoK,MAAA,yBACAtI,iBAAA,uBACA0I,qBAAA,sCACAzI,iBAAA,oCACA0I,cAAA,qBACAzI,OAAA,aACA0I,WAAA,aACAC,YAAA,cACAC,WAAA,cAQAwB,EAAAzF,UAAAmE,UAAA,SAAArB,GACAsB,KAAAA,kBAQAqB,EAAAzF,UAAAqE,SAAA,SAAAvB,GACA5J,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA0K,aAQA0B,EAAAzF,UAAAsE,QAAA,SAAAxB,GACA5J,KAAAA,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA0K,aAQA0B,EAAAzF,UAAAwE,WAAA,SAAA1B,GACA2B,KAAAA,SAOAgB,EAAAzF,UAAAoE,eAAA,WACAM,KAAAA,gBACAjC,KAAAkC,oBAOAc,EAAAzF,UAAAyE,MAAA,WAGAjH,OAAA8E,WAAA,WACAsC,KAAAA,cAAA7B,QACAM,KAAAZ,MAAAA,KAAAE,UAAAa,eAQAiC,EAAAzF,UAAA2E,iBAAA,WACAC,KAAAA,cAAAC,QACApC,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA4K,YAEAxB,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA4K,aAGAwB,EAAAzF,UAAA,iBAAAyF,EAAAzF,UAAA2E,iBAMAc,EAAAzF,UAAA0E,cAAA,WACAE,KAAAA,cAAA3B,SACAR,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA2K,aAEAvB,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA2K,cAGAyB,EAAAzF,UAAA,cAAAyF,EAAAzF,UAAA0E,cAMAe,EAAAzF,UAAAgD,QAAA,WACA4B,KAAAA,cAAA3B,UAAAA,EACAR,KAAA2B,kBAEAqB,EAAAzF,UAAA,QAAAyF,EAAAzF,UAAAgD,QAMAyC,EAAAzF,UAAAkD,OAAA,WACA0B,KAAAA,cAAA3B,UAAAA,EACAR,KAAA2B,kBAEAqB,EAAAzF,UAAA,OAAAyF,EAAAzF,UAAAkD,OAMAuC,EAAAzF,UAAA8E,MAAA,WACAF,KAAAA,cAAAC,SAAAA,EACApC,KAAA2B,kBAEAqB,EAAAzF,UAAA,MAAAyF,EAAAzF,UAAA8E,MAMAW,EAAAzF,UAAA+E,QAAA,WACAH,KAAAA,cAAAC,SAAAA,EACApC,KAAA2B,kBAEAqB,EAAAzF,UAAA,QAAAyF,EAAAzF,UAAA+E,QAIAU,EAAAzF,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACAuJ,GAAAA,KAAAmC,cAAAnC,KAAAvJ,SAAAqB,cAAA,IAAAkI,KAAApJ,YAAAoK,OACAhB,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAA8B,kBAAA,CACAjC,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAAwK,sBACApB,KAAA0C,wBAAA3L,SAAAC,cAAA,QACAgJ,KAAA0C,wBAAAhM,UAAAO,IAAA+I,KAAApJ,YAAA+B,kBACAqH,KAAA0C,wBAAAhM,UAAAO,IAAA+I,KAAApJ,YAAA8B,kBACAsH,KAAA0C,wBAAAhM,UAAAO,IAAA+I,KAAApJ,YAAAyK,eACArB,KAAA2C,mBAAA3C,KAAA+B,WAAAnB,KAAAZ,MACAA,KAAA0C,wBAAApL,iBAAA,UAAA0I,KAAA2C,oBACAxL,IAAAA,EAAAJ,SAAAC,cAAA,QACAG,EAAAT,UAAAO,IAAA+I,KAAApJ,YAAAgC,QACAoH,KAAA0C,wBAAArL,YAAAF,GACA6I,KAAAvJ,SAAAY,YAAA2I,KAAA0C,yBAEAE,KAAAA,mBAAA5C,KAAA0B,UAAAd,KAAAZ,MACAA,KAAA6C,kBAAA7C,KAAA4B,SAAAhB,KAAAZ,MACAA,KAAA8C,iBAAA9C,KAAA6B,QAAAjB,KAAAZ,MACAA,KAAAiD,sBAAAjD,KAAA+B,WAAAnB,KAAAZ,MACAA,KAAAmC,cAAA7K,iBAAA,SAAA0I,KAAA4C,oBACA5C,KAAAmC,cAAA7K,iBAAA,QAAA0I,KAAA6C,mBACA7C,KAAAmC,cAAA7K,iBAAA,OAAA0I,KAAA8C,kBACA9C,KAAAvJ,SAAAa,iBAAA,UAAA0I,KAAAiD,uBACAjD,KAAA2B,iBACA3B,KAAAvJ,SAAAC,UAAAO,IAAA,iBAKA8B,EAAAY,SAAAA,CACAuE,YAAA8E,EACA7F,cAAA,qBACA9B,SAAA,qBACAuB,QAAAA,ICjMAsG,IAAAA,EAAA,SAAA9J,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,aAAAmI,EAOAA,EAAA3F,UAAA2C,UAAAA,CAEAiD,4BAAA,GAEAC,6BAAA,GAGAC,cAAA,KAQAH,EAAA3F,UAAA+F,UAAAA,CACAC,MAAA,GACAC,OAAA,GACAC,MAAA,GACAC,SAAA,GACAC,WAAA,IAUAT,EAAA3F,UAAA3G,YAAAA,CACAgN,UAAA,sBACAC,QAAA,oBACAC,KAAA,iBACAC,sBAAA,kCACA5D,cAAA,uBACAiB,qBAAA,sCACAxI,OAAA,aAEA6I,YAAA,cACAuC,WAAA,aACAC,aAAA,eAEAC,YAAA,wBAEAC,aAAA,yBACAC,SAAA,qBACAC,UAAA,sBACAC,UAAA,uBAKApB,EAAA3F,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CAEA8N,IAAAA,EAAAxN,SAAAC,cAAA,OACAuN,EAAA7N,UAAAO,IAAA+I,KAAApJ,YAAAgN,WACA5D,KAAAvJ,SAAA+N,cAAAC,aAAAF,EAAAvE,KAAAvJ,UACAuJ,KAAAvJ,SAAA+N,cAAAE,YAAA1E,KAAAvJ,UACA8N,EAAAlN,YAAA2I,KAAAvJ,UACAuJ,KAAA2E,WAAAJ,EAEAK,IAAAA,EAAA7N,SAAAC,cAAA,OACA4N,EAAAlO,UAAAO,IAAA+I,KAAApJ,YAAAiN,SACA7D,KAAA6E,SAAAD,EACAL,EAAAE,aAAAG,EAAA5E,KAAAvJ,UAEAqO,IAAAA,EAAA9E,KAAAvJ,SAAAe,aAAA,QAAAwI,KAAAvJ,SAAAe,aAAA,gBACAuN,EAAA,KACAD,KACAC,EAAAhO,SAAAiO,eAAAF,MAEA9E,KAAAiF,YAAAF,EACAA,EAAAzN,iBAAA,QAAA0I,KAAAkF,gBAAAtE,KAAAZ,OACA+E,EAAAzN,iBAAA,UAAA0I,KAAAmF,wBAAAvE,KAAAZ,SAGAoF,IAAAA,EAAApF,KAAAvJ,SAAA8E,iBAAA,IAAAyE,KAAApJ,YAAAkN,MACAuB,KAAAA,kBAAArF,KAAAsF,yBAAA1E,KAAAZ,MACAA,KAAAuF,gBAAAvF,KAAAwF,iBAAA5E,KAAAZ,MACA,IAAA,IAAA9F,EAAA,EAAAA,EAAAkL,EAAAhL,OAAAF,IAEAkL,EAAAlL,GAAA5C,iBAAA,QAAA0I,KAAAuF,iBAEAH,EAAAlL,GAAAuL,SAAA,KAEAL,EAAAlL,GAAA5C,iBAAA,UAAA0I,KAAAqF,mBAGArF,GAAAA,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAuJ,eAEA,IADAH,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAAwK,sBACAlH,EAAA,EAAAA,EAAAkL,EAAAhL,OAAAF,IAAA,CACAiE,IAAAA,EAAAiH,EAAAlL,GACApD,EAAAC,SAAAC,cAAA,QACAF,EAAAJ,UAAAO,IAAA+I,KAAApJ,YAAAmN,uBACA5M,IAAAA,EAAAJ,SAAAC,cAAA,QACAG,EAAAT,UAAAO,IAAA+I,KAAApJ,YAAAgC,QACA9B,EAAAO,YAAAF,GACAgH,EAAA9G,YAAAP,GACAqH,EAAAzH,UAAAO,IAAA+I,KAAApJ,YAAAuJ,eAIA1J,KAAAA,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAsN,cACAlE,KAAA6E,SAAAnO,UAAAO,IAAA+I,KAAApJ,YAAAsN,aAEAlE,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAuN,eACAnE,KAAA6E,SAAAnO,UAAAO,IAAA+I,KAAApJ,YAAAuN,cAEAnE,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAwN,WACApE,KAAA6E,SAAAnO,UAAAO,IAAA+I,KAAApJ,YAAAwN,UAEApE,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAyN,YACArE,KAAA6E,SAAAnO,UAAAO,IAAA+I,KAAApJ,YAAAyN,WAEArE,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAA0N,YACAtE,KAAA6E,SAAAnO,UAAAO,IAAA+I,KAAApJ,YAAA0N,WAEAC,EAAA7N,UAAAO,IAAA+I,KAAApJ,YAAA6K,eAUAyB,EAAA3F,UAAA2H,gBAAA,SAAAQ,GACA1F,GAAAA,KAAAvJ,UAAAuJ,KAAAiF,YAAA,CACAU,IAAAA,EAAA3F,KAAAiF,YAAAW,wBACAC,EAAA7F,KAAAiF,YAAAT,cAAAoB,wBACAnP,KAAAA,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAA0N,aACAtE,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAuN,eAEAnE,KAAA2E,WAAAmB,MAAAC,MAAAF,EAAAE,MAAAJ,EAAAI,MAAA,KACA/F,KAAA2E,WAAAmB,MAAAE,IAAAhG,KAAAiF,YAAAgB,UAAAjG,KAAAiF,YAAAiB,aAAA,MACAlG,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAwN,WAEApE,KAAA2E,WAAAmB,MAAAK,KAAAnG,KAAAiF,YAAAmB,WAAA,KACApG,KAAA2E,WAAAmB,MAAAO,OAAAR,EAAAQ,OAAAV,EAAAK,IAAA,MACAhG,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAyN,YAEArE,KAAA2E,WAAAmB,MAAAC,MAAAF,EAAAE,MAAAJ,EAAAI,MAAA,KACA/F,KAAA2E,WAAAmB,MAAAO,OAAAR,EAAAQ,OAAAV,EAAAK,IAAA,OAGAhG,KAAA2E,WAAAmB,MAAAK,KAAAnG,KAAAiF,YAAAmB,WAAA,KACApG,KAAA2E,WAAAmB,MAAAE,IAAAhG,KAAAiF,YAAAgB,UAAAjG,KAAAiF,YAAAiB,aAAA,OAGAI,KAAAA,OAAAZ,IAQAxC,EAAA3F,UAAA4H,wBAAA,SAAAO,GACA1F,GAAAA,KAAAvJ,UAAAuJ,KAAA2E,YAAA3E,KAAAiF,YAAA,CACAG,IAAAA,EAAApF,KAAAvJ,SAAA8E,iBAAA,IAAAyE,KAAApJ,YAAAkN,KAAA,oBACAsB,GAAAA,EAAAhL,OAAA,GAAA4F,KAAA2E,WAAAjO,UAAAC,SAAAqJ,KAAApJ,YAAAoN,cACA0B,EAAAa,UAAAvG,KAAAsD,UAAAI,UACAgC,EAAAhO,iBACA0N,EAAAA,EAAAhL,OAAA,GAAAoM,SACAd,EAAAa,UAAAvG,KAAAsD,UAAAK,aACA+B,EAAAhO,iBACA0N,EAAA,GAAAoB,YAWAtD,EAAA3F,UAAA+H,yBAAA,SAAAI,GACA1F,GAAAA,KAAAvJ,UAAAuJ,KAAA2E,WAAA,CACAS,IAAAA,EAAApF,KAAAvJ,SAAA8E,iBAAA,IAAAyE,KAAApJ,YAAAkN,KAAA,oBACAsB,GAAAA,GAAAA,EAAAhL,OAAA,GAAA4F,KAAA2E,WAAAjO,UAAAC,SAAAqJ,KAAApJ,YAAAoN,YAAA,CACAyC,IAAAA,EAAApJ,MAAAE,UAAAC,MAAAC,KAAA2H,GAAA1K,QAAAgL,EAAAgB,QACAhB,GAAAA,EAAAa,UAAAvG,KAAAsD,UAAAI,SACAgC,EAAAhO,iBACA+O,EAAA,EACArB,EAAAqB,EAAA,GAAAD,QAEApB,EAAAA,EAAAhL,OAAA,GAAAoM,aAEA,GAAAd,EAAAa,UAAAvG,KAAAsD,UAAAK,WACA+B,EAAAhO,iBACA0N,EAAAhL,OAAAqM,EAAA,EACArB,EAAAqB,EAAA,GAAAD,QAEApB,EAAA,GAAAoB,aAEA,GAAAd,EAAAa,UAAAvG,KAAAsD,UAAAG,OAAAiC,EAAAa,UAAAvG,KAAAsD,UAAAC,MAAA,CACAmC,EAAAhO,iBAEAH,IAAAA,EAAA,IAAAoP,WAAA,aACAjB,EAAAgB,OAAA7K,cAAAtE,GACAA,EAAA,IAAAoP,WAAA,WACAjB,EAAAgB,OAAA7K,cAAAtE,GAEAmO,EAAAgB,OAAAE,aACAlB,EAAAa,UAAAvG,KAAAsD,UAAAE,SACAkC,EAAAhO,iBACAsI,KAAA6G,WAWA3D,EAAA3F,UAAAiI,iBAAA,SAAAE,GACAA,EAAAgB,OAAAI,aAAA,YACApB,EAAAqB,mBAGA/G,KAAAgH,UAAAA,EACAjM,OAAA8E,WAAA,SAAA6F,GACAmB,KAAAA,OACA7G,KAAAgH,UAAAA,GACApG,KAAAZ,MAAAA,KAAAE,UAAAmD,iBAYAH,EAAA3F,UAAA0J,WAAA,SAAAC,EAAAC,GACA1Q,KAAAA,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAA0N,WAEAtE,KAAAvJ,SAAAqP,MAAAsB,KAAA,GACApH,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAuN,cAEAnE,KAAAvJ,SAAAqP,MAAAsB,KAAA,UAAAD,EAAA,QAAAA,EAAA,MACAnH,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAwN,UAEApE,KAAAvJ,SAAAqP,MAAAsB,KAAA,QAAAF,EAAA,QAAAA,EAAA,QACAlH,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAyN,WAEArE,KAAAvJ,SAAAqP,MAAAsB,KAAA,QAAAF,EAAA,MAAAC,EAAA,MAAAD,EAAA,MAAAC,EAAA,MAGAnH,KAAAvJ,SAAAqP,MAAAsB,KAAA,IASAlE,EAAA3F,UAAA8J,4BAAA,SAAA3B,GACAA,EAAAgB,OAAAhQ,UAAAoL,OAAAoB,EAAA3F,UAAA3G,YAAAqN,eAOAf,EAAA3F,UAAA+J,yBAAA,WACA7Q,KAAAA,SAAAa,iBAAA,gBAAA0I,KAAAqH,6BACArH,KAAAvJ,SAAAa,iBAAA,sBAAA0I,KAAAqH,8BAOAnE,EAAA3F,UAAAzE,KAAA,SAAA4M,GACA1F,GAAAA,KAAAvJ,UAAAuJ,KAAA2E,YAAA3E,KAAA6E,SAAA,CAEAqC,IAAAA,EAAAlH,KAAAvJ,SAAAmP,wBAAAsB,OACAC,EAAAnH,KAAAvJ,SAAAmP,wBAAAuB,MAEAxC,KAAAA,WAAAmB,MAAAqB,MAAAA,EAAA,KACAnH,KAAA2E,WAAAmB,MAAAoB,OAAAA,EAAA,KACAlH,KAAA6E,SAAAiB,MAAAqB,MAAAA,EAAA,KACAnH,KAAA6E,SAAAiB,MAAAoB,OAAAA,EAAA,KAKA,IAAA,IAJAK,EAAAvH,KAAAE,UAAAiD,4BAAAnD,KAAAE,UAAAkD,6BAGAgC,EAAApF,KAAAvJ,SAAA8E,iBAAA,IAAAyE,KAAApJ,YAAAkN,MACA5J,EAAA,EAAAA,EAAAkL,EAAAhL,OAAAF,IAAA,CACAsN,IAAAA,EAEAA,EADAxH,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAwN,WAAApE,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAyN,YACA6C,EAAA9B,EAAAlL,GAAA+L,UAAAb,EAAAlL,GAAAgM,cAAAgB,EAAAK,EAAA,IAEAnC,EAAAlL,GAAA+L,UAAAiB,EAAAK,EAAA,IAEAnC,EAAAlL,GAAA4L,MAAA2B,gBAAAD,EAGAP,KAAAA,WAAAC,EAAAC,GAGApM,OAAAoE,sBAAA,WACA1I,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAAqN,cACAjE,KAAAvJ,SAAAqP,MAAAsB,KAAA,UAAAD,EAAA,MAAAD,EAAA,QACAlH,KAAA2E,WAAAjO,UAAAO,IAAA+I,KAAApJ,YAAAoN,aACApD,KAAAZ,OAEAA,KAAAsH,2BAEA5N,IAAAA,EAAA,SAAAnC,GAOAA,IAAAmO,GAAA1F,KAAAgH,UAAAzP,EAAAmP,OAAAgB,aAAA1H,KAAAvJ,WACAM,SAAA4Q,oBAAA,QAAAjO,GACAsG,KAAA6G,SAEAjG,KAAAZ,MACAjJ,SAAAO,iBAAA,QAAAoC,KAGAwJ,EAAA3F,UAAA,KAAA2F,EAAA3F,UAAAzE,KAMAoK,EAAA3F,UAAAsJ,KAAA,WACA7G,GAAAA,KAAAvJ,UAAAuJ,KAAA2E,YAAA3E,KAAA6E,SAAA,CAGA,IAAA,IAFAO,EAAApF,KAAAvJ,SAAA8E,iBAAA,IAAAyE,KAAApJ,YAAAkN,MAEA5J,EAAA,EAAAA,EAAAkL,EAAAhL,OAAAF,IACAkL,EAAAlL,GAAA4L,MAAA8B,eAAA,oBAGAjC,IAAAA,EAAA3F,KAAAvJ,SAAAmP,wBACAsB,EAAAvB,EAAAuB,OACAC,EAAAxB,EAAAwB,MAGA1Q,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAAqN,cACAjE,KAAAiH,WAAAC,EAAAC,GACAnH,KAAA2E,WAAAjO,UAAAoL,OAAA9B,KAAApJ,YAAAoN,YAEAhE,KAAAsH,6BAGApE,EAAA3F,UAAA,KAAA2F,EAAA3F,UAAAsJ,KAMA3D,EAAA3F,UAAA+I,OAAA,SAAAZ,GACAf,KAAAA,WAAAjO,UAAAC,SAAAqJ,KAAApJ,YAAAoN,YACAhE,KAAA6G,OAEA7G,KAAAlH,KAAA4M,IAGAxC,EAAA3F,UAAA,OAAA2F,EAAA3F,UAAA+I,OAGAvN,EAAAY,SAAAA,CACAuE,YAAAgF,EACA/F,cAAA,eACA9B,SAAA,cACAuB,QAAAA,ICvYAiL,IAAAA,EAAA,SAAAzO,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,iBAAA8M,EAOAA,EAAAtK,UAAA2C,UAAAA,GASA2H,EAAAtK,UAAA3G,YAAAA,CAAAkR,oBAAA,+BAOAD,EAAAtK,UAAAwK,YAAA,SAAAC,GACAvR,KAAAA,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAkR,uBAGA9H,KAAAiI,aAAAnC,MAAAqB,MAAAa,EAAA,MAEAH,EAAAtK,UAAA,YAAAsK,EAAAtK,UAAAwK,YAOAF,EAAAtK,UAAA2K,UAAA,SAAAF,GACAG,KAAAA,WAAArC,MAAAqB,MAAAa,EAAA,IACAhI,KAAAoI,QAAAtC,MAAAqB,MAAA,IAAAa,EAAA,KAEAH,EAAAtK,UAAA,UAAAsK,EAAAtK,UAAA2K,UAIAL,EAAAtK,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACA4R,IAAAA,EAAAtR,SAAAC,cAAA,OACAqR,EAAAhO,UAAA,uBACA2F,KAAAvJ,SAAAY,YAAAgR,GACArI,KAAAiI,aAAAI,GACAA,EAAAtR,SAAAC,cAAA,QACAqD,UAAA,qBACA2F,KAAAvJ,SAAAY,YAAAgR,GACArI,KAAAmI,WAAAE,GACAA,EAAAtR,SAAAC,cAAA,QACAqD,UAAA,kBACA2F,KAAAvJ,SAAAY,YAAAgR,GACArI,KAAAoI,QAAAC,EACArI,KAAAiI,aAAAnC,MAAAqB,MAAA,KACAnH,KAAAmI,WAAArC,MAAAqB,MAAA,OACAnH,KAAAoI,QAAAtC,MAAAqB,MAAA,KACAnH,KAAAvJ,SAAAC,UAAAO,IAAA,iBAKA8B,EAAAY,SAAAA,CACAuE,YAAA2J,EACA1K,cAAA,mBACA9B,SAAA,kBACAuB,QAAAA,IC3EA0L,IAAAA,EAAA,SAAAlP,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,cAAAuN,EAOAA,EAAA/K,UAAA2C,UAAAA,CAAAa,aAAA,MASAuH,EAAA/K,UAAA3G,YAAAA,CACA0K,WAAA,aACAC,YAAA,cACAC,WAAA,aACAC,YAAA,cACA8G,SAAA,eACAC,UAAA,oBACAC,mBAAA,0BACAC,mBAAA,0BACAvI,cAAA,uBACAiB,qBAAA,sCACAzI,iBAAA,8BACA0I,cAAA,qBACAzI,OAAA,cAQA0P,EAAA/K,UAAAmE,UAAA,SAAArB,GAIA,IAAA,IADAsI,EAAA5R,SAAA6R,uBAAA5I,KAAApJ,YAAA2R,UACArO,EAAA,EAAAA,EAAAyO,EAAAvO,OAAAF,IAAA,CACAyO,EAAAzO,GAAApC,cAAA,IAAAkI,KAAApJ,YAAA4R,WAEAhR,aAAA,UAAAwI,KAAA6I,YAAArR,aAAA,cACA,IAAAmR,EAAAzO,GAAA,eACAyO,EAAAzO,GAAA,cAAAyH,mBAWA2G,EAAA/K,UAAAqE,SAAA,SAAAvB,GACA5J,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA0K,aAQAgH,EAAA/K,UAAAsE,QAAA,SAAAxB,GACA5J,KAAAA,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA0K,aAQAgH,EAAA/K,UAAAuL,WAAA,SAAAzI,GACA2B,KAAAA,SAOAsG,EAAA/K,UAAAoE,eAAA,WACAM,KAAAA,gBACAjC,KAAAkC,oBAOAoG,EAAA/K,UAAAyE,MAAA,WAGAjH,OAAA8E,WAAA,WACAgJ,KAAAA,YAAAvI,QACAM,KAAAZ,MAAAA,KAAAE,UAAAa,eAQAuH,EAAA/K,UAAA0E,cAAA,WACA4G,KAAAA,YAAArI,SACAR,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA2K,aAEAvB,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA2K,cAGA+G,EAAA/K,UAAA,cAAA+K,EAAA/K,UAAA0E,cAMAqG,EAAA/K,UAAA2E,iBAAA,WACA2G,KAAAA,YAAAzG,QACApC,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA4K,YAEAxB,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA4K,aAGA8G,EAAA/K,UAAA,iBAAA+K,EAAA/K,UAAA2E,iBAMAoG,EAAA/K,UAAAgD,QAAA,WACAsI,KAAAA,YAAArI,UAAAA,EACAR,KAAA2B,kBAEA2G,EAAA/K,UAAA,QAAA+K,EAAA/K,UAAAgD,QAMA+H,EAAA/K,UAAAkD,OAAA,WACAoI,KAAAA,YAAArI,UAAAA,EACAR,KAAA2B,kBAEA2G,EAAA/K,UAAA,OAAA+K,EAAA/K,UAAAkD,OAMA6H,EAAA/K,UAAA8E,MAAA,WACAwG,KAAAA,YAAAzG,SAAAA,EACApC,KAAA0B,UAAA,OAEA4G,EAAA/K,UAAA,MAAA+K,EAAA/K,UAAA8E,MAMAiG,EAAA/K,UAAA+E,QAAA,WACAuG,KAAAA,YAAAzG,SAAAA,EACApC,KAAA0B,UAAA,OAEA4G,EAAA/K,UAAA,QAAA+K,EAAA/K,UAAA+E,QAIAgG,EAAA/K,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACAoS,KAAAA,YAAA7I,KAAAvJ,SAAAqB,cAAA,IAAAkI,KAAApJ,YAAA4R,WACAxI,KAAA+I,oBAAA/I,KAAA0B,UAAAd,KAAAZ,MACAA,KAAAgJ,mBAAAhJ,KAAA0B,UAAAd,KAAAZ,MACAA,KAAAiJ,kBAAAjJ,KAAA6B,QAAAjB,KAAAZ,MACAA,KAAAkJ,qBAAAlJ,KAAA8I,WAAAlI,KAAAZ,MACAmJ,IAAAA,EAAApS,SAAAC,cAAA,QACAmS,EAAAzS,UAAAO,IAAA+I,KAAApJ,YAAA6R,oBACAW,IAIAtS,EAJAsS,EAAArS,SAAAC,cAAA,QAKAgJ,GAJAoJ,EAAA1S,UAAAO,IAAA+I,KAAApJ,YAAA8R,oBACA1I,KAAAvJ,SAAAY,YAAA8R,GACAnJ,KAAAvJ,SAAAY,YAAA+R,GAEApJ,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAuJ,eAAA,CACA1J,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAAwK,uBACAtK,EAAAC,SAAAC,cAAA,SACAN,UAAAO,IAAA+I,KAAApJ,YAAA+B,kBACA7B,EAAAJ,UAAAO,IAAA+I,KAAApJ,YAAAuJ,eACArJ,EAAAJ,UAAAO,IAAA+I,KAAApJ,YAAAyK,eACAvK,EAAAQ,iBAAA,UAAA0I,KAAAkJ,sBACA/R,IAAAA,EAAAJ,SAAAC,cAAA,QACAG,EAAAT,UAAAO,IAAA+I,KAAApJ,YAAAgC,QACA9B,EAAAO,YAAAF,GACA6I,KAAAvJ,SAAAY,YAAAP,GAEA+R,KAAAA,YAAAvR,iBAAA,SAAA0I,KAAA+I,qBACA/I,KAAA6I,YAAAvR,iBAAA,QAAA0I,KAAAgJ,oBACAhJ,KAAA6I,YAAAvR,iBAAA,OAAA0I,KAAAiJ,mBACAjJ,KAAAvJ,SAAAa,iBAAA,UAAA0I,KAAAkJ,sBACAlJ,KAAA2B,iBACA3B,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA6K,eAKA1I,EAAAY,SAAAA,CACAuE,YAAAoK,EACAnL,cAAA,gBACA9B,SAAA,eACAuB,QAAAA,ICtNAyM,IAAAA,EAAA,SAAAjQ,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAsJ,MAAAvO,OAAAwE,UAAAgK,iBAEAvJ,KAAAC,QAEAlF,OAAA,eAAAsO,EAOAA,EAAA9L,UAAA2C,UAAAA,GASAmJ,EAAA9L,UAAA3G,YAAAA,CACA4S,aAAA,2BACAC,iBAAA,wBACAC,gBAAA,8BACAC,iBAAA,+BACAC,iBAAA,+BACAC,gBAAA,kBACApI,YAAA,eAQA4H,EAAA9L,UAAAuM,SAAA,SAAAzJ,GACA0J,KAAAA,sBAQAV,EAAA9L,UAAAmE,UAAA,SAAArB,GACA0J,KAAAA,sBAQAV,EAAA9L,UAAAwE,WAAA,SAAA1B,GACAA,EAAAqG,OAAApG,QAYA+I,EAAA9L,UAAAyM,sBAAA,SAAA3J,GAGAA,GAAAA,EAAAqG,SAAA1G,KAAAvJ,SAAA+N,cAAA,CAKAnE,EAAA3I,iBACAuS,IAAAA,EAAA,IAAAtD,WAAA,YAAA,CACAD,OAAArG,EAAAqG,OACAwD,QAAA7J,EAAA6J,QACAC,QAAA9J,EAAA8J,QACAC,QAAApK,KAAAvJ,SAAAmP,wBAAAyE,IAEA5T,KAAAA,SAAAoF,cAAAoO,KAOAZ,EAAA9L,UAAAwM,mBAAA,WAEAO,IAAAA,GAAAtK,KAAAvJ,SAAA8T,MAAAvK,KAAAvJ,SAAA+T,MAAAxK,KAAAvJ,SAAAmJ,IAAAI,KAAAvJ,SAAA+T,KACAF,IAAAA,EACAtK,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAAiT,iBAEA7J,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAAiT,iBAEA7J,KAAAsJ,QACAtJ,KAAAyK,iBAAA3E,MAAA4E,KAAAJ,EACAtK,KAAAyK,iBAAA3E,MAAA6E,WAAAL,EACAtK,KAAA4K,iBAAA9E,MAAA4E,KAAA,EAAAJ,EACAtK,KAAA4K,iBAAA9E,MAAA6E,WAAA,EAAAL,IASAjB,EAAA9L,UAAAgD,QAAA,WACA9J,KAAAA,SAAA+J,UAAAA,GAEA6I,EAAA9L,UAAA,QAAA8L,EAAA9L,UAAAgD,QAMA8I,EAAA9L,UAAAkD,OAAA,WACAhK,KAAAA,SAAA+J,UAAAA,GAEA6I,EAAA9L,UAAA,OAAA8L,EAAA9L,UAAAkD,OAOA4I,EAAA9L,UAAAsN,OAAA,SAAAN,QACA,IAAAA,IACAvK,KAAAvJ,SAAA8T,MAAAA,GAEAvK,KAAA+J,sBAEAV,EAAA9L,UAAA,OAAA8L,EAAA9L,UAAAsN,OAIAxB,EAAA9L,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACAuJ,GAAAA,KAAAsJ,MAAA,CAIAwB,IAAAA,EAAA/T,SAAAC,cAAA,OACA8T,EAAApU,UAAAO,IAAA+I,KAAApJ,YAAA4S,cACAxJ,KAAAvJ,SAAA+N,cAAAC,aAAAqG,EAAA9K,KAAAvJ,UACAuJ,KAAAvJ,SAAA+N,cAAAE,YAAA1E,KAAAvJ,UACAqU,EAAAzT,YAAA2I,KAAAvJ,cACA,CAIA8N,IAAAA,EAAAxN,SAAAC,cAAA,OACAuN,EAAA7N,UAAAO,IAAA+I,KAAApJ,YAAA6S,kBACAzJ,KAAAvJ,SAAA+N,cAAAC,aAAAF,EAAAvE,KAAAvJ,UACAuJ,KAAAvJ,SAAA+N,cAAAE,YAAA1E,KAAAvJ,UACA8N,EAAAlN,YAAA2I,KAAAvJ,UACAsU,IAAAA,EAAAhU,SAAAC,cAAA,OACA+T,EAAArU,UAAAO,IAAA+I,KAAApJ,YAAA8S,iBACAnF,EAAAlN,YAAA0T,GACA/K,KAAAyK,iBAAA1T,SAAAC,cAAA,OACAgJ,KAAAyK,iBAAA/T,UAAAO,IAAA+I,KAAApJ,YAAA+S,kBACAoB,EAAA1T,YAAA2I,KAAAyK,kBACAzK,KAAA4K,iBAAA7T,SAAAC,cAAA,OACAgJ,KAAA4K,iBAAAlU,UAAAO,IAAA+I,KAAApJ,YAAAgT,kBACAmB,EAAA1T,YAAA2I,KAAA4K,kBAEAI,KAAAA,kBAAAhL,KAAA8J,SAAAlJ,KAAAZ,MACAA,KAAAiL,mBAAAjL,KAAA0B,UAAAd,KAAAZ,MACAA,KAAAkL,oBAAAlL,KAAA+B,WAAAnB,KAAAZ,MACAA,KAAAmL,+BAAAnL,KAAAgK,sBAAApJ,KAAAZ,MACAA,KAAAvJ,SAAAa,iBAAA,QAAA0I,KAAAgL,mBACAhL,KAAAvJ,SAAAa,iBAAA,SAAA0I,KAAAiL,oBACAjL,KAAAvJ,SAAAa,iBAAA,UAAA0I,KAAAkL,qBACAlL,KAAAvJ,SAAA+N,cAAAlN,iBAAA,YAAA0I,KAAAmL,gCACAnL,KAAA+J,qBACA/J,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA6K,eAKA1I,EAAAY,SAAAA,CACAuE,YAAAmL,EACAlM,cAAA,iBACA9B,SAAA,gBACAuB,QAAAA,IC9LAwO,IAAAA,EAAA,SAAAhS,GACA4G,GAAAA,KAAAvJ,SAAA2C,EACA4G,KAAAqL,aAAArL,KAAAvJ,SAAAqB,cAAA,IAAAkI,KAAAsL,YAAAC,SACAvL,KAAAwL,eAAAxL,KAAAvJ,SAAAqB,cAAA,IAAAkI,KAAAsL,YAAAG,SACAzL,KAAAqL,aACA,MAAA,IAAA1P,MAAA,mDAEA,IAAAqE,KAAAwL,eACA,MAAA,IAAA7P,MAAA,mDAEA+P,KAAAA,QAAAA,EACA1L,KAAA2L,oBAAAC,EACA5L,KAAA6L,cAAAD,EACA5L,KAAA8L,iBAAAF,EACA5L,KAAA+L,qBAAAA,GACA/L,KAAAgM,kBAAAA,IAEAjR,OAAA,iBAAAqQ,EAOAA,EAAA7N,UAAA2C,UAAAA,CAEA+L,iBAAA,KAUAb,EAAA7N,UAAA+N,YAAAA,CACAY,SAAA,eACAX,QAAA,qBACAE,OAAA,uBACAU,OAAA,wBAOAf,EAAA7N,UAAA6O,iBAAA,WACA3V,KAAAA,SAAA0F,aAAA,cAAA,QACA6D,KAAA2L,iBACA3L,KAAAwL,eAAAa,YAAArM,KAAA8L,YACA9L,KAAAwL,eAAAlU,iBAAA,QAAA0I,KAAA2L,gBACA3L,KAAAgM,kBAAAA,IAEAhM,KAAAqL,aAAAgB,YAAArM,KAAA6L,SACA7L,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAAsL,YAAAa,QACAnM,KAAAvJ,SAAA0F,aAAA,cAAA,SACA0D,WAAAG,KAAAsM,SAAA1L,KAAAZ,MAAAA,KAAAuM,WAQAnB,EAAA7N,UAAAiP,aAAA,SAAAC,GACAb,QAAAA,IAAAa,EACA,MAAA,IAAA9Q,MAAA,oEAEAiQ,QAAAA,IAAAa,EAAA,QACA,MAAA,IAAA9Q,MAAA,6CAEA8Q,GAAAA,EAAA,gBAAAA,EAAA,WACA,MAAA,IAAA9Q,MAAA,gDAEA+P,KAAAA,OACA1L,KAAA+L,qBAAA/P,KAAAyQ,IAEAzM,KAAA0L,QAAAA,EACA1L,KAAA6L,SAAAY,EAAA,QACAA,EAAA,QACAzM,KAAAuM,SAAAE,EAAA,QAEAzM,KAAAuM,SAAA,KAEAE,EAAA,gBACAzM,KAAA2L,eAAAc,EAAA,eAEAA,EAAA,aACAzM,KAAA8L,YAAAW,EAAA,YAEAzM,KAAAoM,qBAGAhB,EAAA7N,UAAA,aAAA6N,EAAA7N,UAAAiP,aAOApB,EAAA7N,UAAAmP,YAAA,WACAX,KAAAA,qBAAA3R,OAAA,GACA4F,KAAAwM,aAAAxM,KAAA+L,qBAAAY,UAQAvB,EAAA7N,UAAA+O,SAAA,WACA7V,KAAAA,SAAAC,UAAAoL,OAAA9B,KAAAsL,YAAAa,QACAtM,WAAA,WACApJ,KAAAA,SAAA0F,aAAA,cAAA,QACA6D,KAAAqL,aAAAgB,YAAA,GACAO,QAAA5M,KAAAwL,eAAAhU,aAAA,kBACAwI,KAAAgM,kBAAAA,GACAhM,KAAAwL,eAAAa,YAAA,GACArM,KAAAwL,eAAA7D,oBAAA,QAAA3H,KAAA2L,iBAEA3L,KAAA2L,oBAAAC,EACA5L,KAAA6L,cAAAD,EACA5L,KAAA8L,iBAAAF,EACA5L,KAAA0L,QAAAA,EACA1L,KAAA0M,eACA9L,KAAAZ,MAAAA,KAAAE,UAAA+L,mBAQAb,EAAA7N,UAAAyO,iBAAA,SAAAzB,GACAA,EACAvK,KAAAwL,eAAArP,aAAA,cAAA,QAEA6D,KAAAwL,eAAAqB,gBAAA,gBAKA9T,EAAAY,SAAAA,CACAuE,YAAAkN,EACAjO,cAAA,mBACA9B,SAAA,kBACAuB,QAAAA,IClJAkQ,IAAAA,EAAA,SAAA1T,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,gBAAA+R,EAOAA,EAAAvP,UAAA2C,UAAAA,CAAA6M,wBAAA,GASAD,EAAAvP,UAAA3G,YAAAA,CACAoW,kBAAA,qBACAC,2BAAA,8BACAC,mBAAA,sBACAC,sBAAA,yBACAC,iBAAA,oBACAC,kBAAA,sBAQAP,EAAAvP,UAAA+P,YAAA,SAAAC,GACAC,IAAAA,EAAAzW,SAAAC,cAAA,OACAwW,EAAA9W,UAAAO,IAAA+I,KAAApJ,YAAAoW,mBACAQ,EAAA9W,UAAAO,IAAA+I,KAAApJ,YAAAoW,kBAAA,IAAAO,GACAE,IAAAA,EAAA1W,SAAAC,cAAA,OACAyW,EAAA/W,UAAAO,IAAA+I,KAAApJ,YAAAqW,4BACAQ,EAAA/W,UAAAO,IAAA+I,KAAApJ,YAAAwW,kBACAM,IAAAA,EAAA3W,SAAAC,cAAA,OACA0W,EAAAhX,UAAAO,IAAA+I,KAAApJ,YAAAuW,uBACAQ,IAAAA,EAAA5W,SAAAC,cAAA,OACA2W,EAAAjX,UAAAO,IAAA+I,KAAApJ,YAAAqW,4BACAU,EAAAjX,UAAAO,IAAA+I,KAAApJ,YAAAyW,mBAMA,IAAA,IALAO,EAAAA,CACAH,EACAC,EACAC,GAEAzT,EAAA,EAAAA,EAAA0T,EAAAxT,OAAAF,IAAA,CACA2T,IAAAA,EAAA9W,SAAAC,cAAA,OACA6W,EAAAnX,UAAAO,IAAA+I,KAAApJ,YAAAsW,oBACAU,EAAA1T,GAAA7C,YAAAwW,GAEAL,EAAAnW,YAAAoW,GACAD,EAAAnW,YAAAqW,GACAF,EAAAnW,YAAAsW,GACA3N,KAAAvJ,SAAAY,YAAAmW,IAEAV,EAAAvP,UAAA,YAAAuP,EAAAvP,UAAA+P,YAOAR,EAAAvP,UAAAuQ,KAAA,WACArX,KAAAA,SAAAC,UAAAoL,OAAA,cAEAgL,EAAAvP,UAAA,KAAAuP,EAAAvP,UAAAuQ,KAQAhB,EAAAvP,UAAAwQ,MAAA,WACAtX,KAAAA,SAAAC,UAAAO,IAAA,cAEA6V,EAAAvP,UAAA,MAAAuP,EAAAvP,UAAAwQ,MAIAjB,EAAAvP,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACA,IAAA,IAAAyD,EAAA,EAAAA,GAAA8F,KAAAE,UAAA6M,wBAAA7S,IACA8F,KAAAsN,YAAApT,GAEAzD,KAAAA,SAAAC,UAAAO,IAAA,iBAKA8B,EAAAY,SAAAA,CACAuE,YAAA4O,EACA3P,cAAA,kBACA9B,SAAA,iBACAuB,QAAAA,ICrGAoR,IAAAA,EAAA,SAAA5U,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,eAAAiT,EAOAA,EAAAzQ,UAAA2C,UAAAA,CAAAa,aAAA,MASAiN,EAAAzQ,UAAA3G,YAAAA,CACAoK,MAAA,oBACAiN,MAAA,oBACAC,MAAA,oBACAhN,aAAA,2BACAf,cAAA,uBACAiB,qBAAA,sCACAzI,iBAAA,+BACA0I,cAAA,qBACAzI,OAAA,aACA0I,WAAA,aACAC,YAAA,cACAC,WAAA,cAQAwM,EAAAzQ,UAAAmE,UAAA,SAAArB,GACAsB,KAAAA,kBAQAqM,EAAAzQ,UAAAqE,SAAA,SAAAvB,GACA5J,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA0K,aAQA0M,EAAAzQ,UAAAsE,QAAA,SAAAxB,GACA5J,KAAAA,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA0K,aAQA0M,EAAAzQ,UAAAwE,WAAA,SAAA1B,GACA2B,KAAAA,SAOAgM,EAAAzQ,UAAAoE,eAAA,WACAM,KAAAA,gBACAjC,KAAAkC,oBAOA8L,EAAAzQ,UAAAyE,MAAA,WAGAjH,OAAA8E,WAAA,WACAsC,KAAAA,cAAA7B,QACAM,KAAAZ,MAAAA,KAAAE,UAAAa,eAQAiN,EAAAzQ,UAAA0E,cAAA,WACAE,KAAAA,cAAA3B,SACAR,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA2K,aAEAvB,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA2K,cAGAyM,EAAAzQ,UAAA,cAAAyQ,EAAAzQ,UAAA0E,cAMA+L,EAAAzQ,UAAA2E,iBAAA,WACAC,KAAAA,cAAAC,QACApC,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA4K,YAEAxB,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA4K,aAGAwM,EAAAzQ,UAAA,iBAAAyQ,EAAAzQ,UAAA2E,iBAMA8L,EAAAzQ,UAAAgD,QAAA,WACA4B,KAAAA,cAAA3B,UAAAA,EACAR,KAAA2B,kBAEAqM,EAAAzQ,UAAA,QAAAyQ,EAAAzQ,UAAAgD,QAMAyN,EAAAzQ,UAAAkD,OAAA,WACA0B,KAAAA,cAAA3B,UAAAA,EACAR,KAAA2B,kBAEAqM,EAAAzQ,UAAA,OAAAyQ,EAAAzQ,UAAAkD,OAMAuN,EAAAzQ,UAAA4Q,GAAA,WACAhM,KAAAA,cAAAC,SAAAA,EACApC,KAAA2B,kBAEAqM,EAAAzQ,UAAA,GAAAyQ,EAAAzQ,UAAA4Q,GAMAH,EAAAzQ,UAAA6Q,IAAA,WACAjM,KAAAA,cAAAC,SAAAA,EACApC,KAAA2B,kBAEAqM,EAAAzQ,UAAA,IAAAyQ,EAAAzQ,UAAA6Q,IAIAJ,EAAAzQ,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACA0L,KAAAA,cAAAnC,KAAAvJ,SAAAqB,cAAA,IAAAkI,KAAApJ,YAAAoK,OACAqN,IAAAA,EAAAtX,SAAAC,cAAA,OACAqX,EAAA3X,UAAAO,IAAA+I,KAAApJ,YAAAqX,OACAK,IAAAA,EAAAvX,SAAAC,cAAA,OACAsX,EAAA5X,UAAAO,IAAA+I,KAAApJ,YAAAsX,OACAK,IAAAA,EAAAxX,SAAAC,cAAA,QACAuX,GAAAA,EAAA7X,UAAAO,IAAA+I,KAAApJ,YAAAsK,cACAoN,EAAAjX,YAAAkX,GACAvO,KAAAvJ,SAAAY,YAAAgX,GACArO,KAAAvJ,SAAAY,YAAAiX,GACAtO,KAAAkL,oBAAAlL,KAAA+B,WAAAnB,KAAAZ,MACAA,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAuJ,eAAA,CACA1J,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAAwK,sBACApB,KAAA0C,wBAAA3L,SAAAC,cAAA,QACAgJ,KAAA0C,wBAAAhM,UAAAO,IAAA+I,KAAApJ,YAAA+B,kBACAqH,KAAA0C,wBAAAhM,UAAAO,IAAA+I,KAAApJ,YAAAuJ,eACAH,KAAA0C,wBAAAhM,UAAAO,IAAA+I,KAAApJ,YAAAyK,eACArB,KAAA0C,wBAAApL,iBAAA,UAAA0I,KAAAkL,qBACA/T,IAAAA,EAAAJ,SAAAC,cAAA,QACAG,EAAAT,UAAAO,IAAA+I,KAAApJ,YAAAgC,QACAoH,KAAA0C,wBAAArL,YAAAF,GACA6I,KAAAvJ,SAAAY,YAAA2I,KAAA0C,yBAEAuI,KAAAA,mBAAAjL,KAAA0B,UAAAd,KAAAZ,MACAA,KAAAwO,kBAAAxO,KAAA4B,SAAAhB,KAAAZ,MACAA,KAAAyO,iBAAAzO,KAAA6B,QAAAjB,KAAAZ,MACAA,KAAAmC,cAAA7K,iBAAA,SAAA0I,KAAAiL,oBACAjL,KAAAmC,cAAA7K,iBAAA,QAAA0I,KAAAwO,mBACAxO,KAAAmC,cAAA7K,iBAAA,OAAA0I,KAAAyO,kBACAzO,KAAAvJ,SAAAa,iBAAA,UAAA0I,KAAAkL,qBACAlL,KAAA2B,iBACA3B,KAAAvJ,SAAAC,UAAAO,IAAA,iBAKA8B,EAAAY,SAAAA,CACAuE,YAAA8P,EACA7Q,cAAA,iBACA9B,SAAA,gBACAuB,QAAAA,Ib5MA8R,IAAAA,EAAA,SAAAtV,GAEA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,aAAA2T,EAOAA,EAAAnR,UAAA2C,UAAAA,GASAwO,EAAAnR,UAAA3G,YAAAA,CACA+X,UAAA,gBACAC,YAAA,kBACA3W,aAAA,YACA4W,eAAA,cACAhY,qBAAA,uBACAK,qBAAA,6BACAE,WAAA,aACA0X,mCAAA,uCAOAJ,EAAAnR,UAAAwR,UAAA,WACAtY,KAAAA,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAC,uBACAmJ,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAAkY,oCAGA9O,KAAAgP,MAAAhP,KAAAvJ,SAAA8E,iBAAA,IAAAyE,KAAApJ,YAAA+X,WACA3O,KAAAiP,QAAAjP,KAAAvJ,SAAA8E,iBAAA,IAAAyE,KAAApJ,YAAAgY,aAEA,IAAA,IAAA1U,EAAA,EAAAA,EAAA8F,KAAAgP,MAAA5U,OAAAF,IACA,IAAA5D,EAAA0J,KAAAgP,MAAA9U,GAAA8F,MAEAvJ,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAAiY,iBAOAH,EAAAnR,UAAAxF,eAAA,WACA,IAAA,IAAAmX,EAAA,EAAAA,EAAAlP,KAAAgP,MAAA5U,OAAA8U,IACAlP,KAAAgP,MAAAE,GAAAxY,UAAAoL,OAAA9B,KAAApJ,YAAAqB,eAQAyW,EAAAnR,UAAAvF,iBAAA,WACA,IAAA,IAAAyE,EAAA,EAAAA,EAAAuD,KAAAiP,QAAA7U,OAAAqC,IACAuD,KAAAiP,QAAAxS,GAAA/F,UAAAoL,OAAA9B,KAAApJ,YAAAqB,eAMAyW,EAAAnR,UAAA0C,KAAA,WACAxJ,KAAAA,UACAuJ,KAAA+O,aAoCAhW,EAAAY,SAAAA,CACAuE,YAAAwQ,EACAvR,cAAA,eACA9B,SAAA,gBclHA8T,IAAAA,EAAA,SAAA/V,GACA3C,KAAAA,SAAA2C,EACA4G,KAAAoP,QAAApP,KAAAE,UAAAmP,YAEArP,KAAAC,QAEAlF,OAAA,kBAAAoU,EAOAA,EAAA5R,UAAA2C,UAAAA,CACAmP,aAAA,EACAC,mBAAA,WAUAH,EAAA5R,UAAA3G,YAAAA,CACA2Y,MAAA,uBACAvO,MAAA,uBACAwO,SAAA,WACAlO,WAAA,aACAC,YAAA,cACAkO,WAAA,aACAhO,YAAA,cACAiO,gBAAA,mBAQAP,EAAA5R,UAAAoS,WAAA,SAAAtP,GACAuP,IAAAA,EAAAvP,EAAAqG,OAAA6D,MAAA3S,MAAA,MAAAwC,OACAiG,KAAAA,EAAAkG,SACAqJ,GAAA5P,KAAAoP,SACA/O,EAAA3I,kBAUAyX,EAAA5R,UAAAqE,SAAA,SAAAvB,GACA5J,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA0K,aAQA6N,EAAA5R,UAAAsE,QAAA,SAAAxB,GACA5J,KAAAA,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA0K,aAQA6N,EAAA5R,UAAAsS,SAAA,SAAAxP,GACAsB,KAAAA,kBAOAwN,EAAA5R,UAAAoE,eAAA,WACAM,KAAAA,gBACAjC,KAAA8P,gBACA9P,KAAA+P,aACA/P,KAAAgQ,cAQAb,EAAA5R,UAAA0E,cAAA,WACAgO,KAAAA,OAAAzP,SACAR,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA2K,aAEAvB,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA2K,cAGA4N,EAAA5R,UAAA,cAAA4R,EAAA5R,UAAA0E,cAMAkN,EAAA5R,UAAAyS,WAAA,WACApD,QAAA5M,KAAAvJ,SAAAqB,cAAA,WACAkI,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA0K,YAEAtB,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA0K,aAGA6N,EAAA5R,UAAA,WAAA4R,EAAA5R,UAAAyS,WAMAb,EAAA5R,UAAAuS,cAAA,WACAG,KAAAA,OAAAC,WACAlQ,KAAAiQ,OAAAC,SAAAC,MACAnQ,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA6Y,YAEAzP,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA6Y,cAIAN,EAAA5R,UAAA,cAAA4R,EAAA5R,UAAAuS,cAMAX,EAAA5R,UAAAwS,WAAA,WACAE,KAAAA,OAAA1F,OAAAvK,KAAAiQ,OAAA1F,MAAAnQ,OAAA,EACA4F,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA4Y,UAEAxP,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA4Y,WAGAL,EAAA5R,UAAA,WAAA4R,EAAA5R,UAAAwS,WAMAZ,EAAA5R,UAAAgD,QAAA,WACA0P,KAAAA,OAAAzP,UAAAA,EACAR,KAAA2B,kBAEAwN,EAAA5R,UAAA,QAAA4R,EAAA5R,UAAAgD,QAMA4O,EAAA5R,UAAAkD,OAAA,WACAwP,KAAAA,OAAAzP,UAAAA,EACAR,KAAA2B,kBAEAwN,EAAA5R,UAAA,OAAA4R,EAAA5R,UAAAkD,OAOA0O,EAAA5R,UAAAsN,OAAA,SAAAN,GACA0F,KAAAA,OAAA1F,MAAAA,GAAA,GACAvK,KAAA2B,kBAEAwN,EAAA5R,UAAA,OAAA4R,EAAA5R,UAAAsN,OAIAsE,EAAA5R,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,WACAuJ,KAAAoQ,OAAApQ,KAAAvJ,SAAAqB,cAAA,IAAAkI,KAAApJ,YAAA2Y,OACAvP,KAAAiQ,OAAAjQ,KAAAvJ,SAAAqB,cAAA,IAAAkI,KAAApJ,YAAAoK,OACAhB,KAAAiQ,QAAA,CACAA,KAAAA,OAAAnJ,aAAA9G,KAAAE,UAAAoP,sBACAtP,KAAAoP,QAAAiB,SAAArQ,KAAAiQ,OAAAzY,aAAAwI,KAAAE,UAAAoP,oBAAA,IACAgB,MAAAtQ,KAAAoP,WACApP,KAAAoP,QAAApP,KAAAE,UAAAmP,cAGArP,KAAAiQ,OAAAnJ,aAAA,gBACA9G,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA8Y,iBAEA1P,KAAAuQ,0BAAAvQ,KAAA2B,eAAAf,KAAAZ,MACAA,KAAAwO,kBAAAxO,KAAA4B,SAAAhB,KAAAZ,MACAA,KAAAyO,iBAAAzO,KAAA6B,QAAAjB,KAAAZ,MACAA,KAAAwQ,kBAAAxQ,KAAA6P,SAAAjP,KAAAZ,MACAA,KAAAiQ,OAAA3Y,iBAAA,QAAA0I,KAAAuQ,2BACAvQ,KAAAiQ,OAAA3Y,iBAAA,QAAA0I,KAAAwO,mBACAxO,KAAAiQ,OAAA3Y,iBAAA,OAAA0I,KAAAyO,kBACAzO,KAAAiQ,OAAA3Y,iBAAA,QAAA0I,KAAAwQ,mBACAxQ,KAAAoP,UAAApP,KAAAE,UAAAmP,cAGArP,KAAAyQ,oBAAAzQ,KAAA2P,WAAA/O,KAAAZ,MACAA,KAAAiQ,OAAA3Y,iBAAA,UAAA0I,KAAAyQ,sBAEAC,IAAAA,EAAA1Q,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAA6Y,YACA9N,KAAAA,iBACA3B,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA6K,aACAiP,GACA1Q,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA6Y,YAEAzP,KAAAiQ,OAAAnJ,aAAA,eACA9G,KAAAvJ,SAAA+P,QACAxG,KAAAgQ,gBAOAjX,EAAAY,SAAAA,CACAuE,YAAAiR,EACAhS,cAAA,oBACA9B,SAAA,mBACAuB,QAAAA,IC/NA+T,IAAAA,EAAA,SAAAvX,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,gBAAA4V,EAOAA,EAAApT,UAAA2C,UAAAA,GASAyQ,EAAApT,UAAA3G,YAAAA,CACA4B,UAAA,YACAoY,OAAA,sBACAC,KAAA,oBACAC,MAAA,qBACAC,IAAA,oBAQAJ,EAAApT,UAAAyT,kBAAA,SAAA3Q,GACA4Q,IAAAA,EAAA5Q,EAAAqG,OAAAd,wBACAO,EAAA8K,EAAA9K,KAAA8K,EAAA9J,MAAA,EACAnB,EAAAiL,EAAAjL,IAAAiL,EAAA/J,OAAA,EACAgK,EAAAlR,KAAAvJ,SAAA0a,YAAA,GAAA,EACAC,EAAApR,KAAAvJ,SAAAyP,aAAA,GAAA,EACAzP,KAAAA,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAia,OAAA7Q,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAka,QACA3K,EAAA8K,EAAA9J,MAAA,EACAnB,EAAAoL,EAAA,GACApR,KAAAvJ,SAAAqP,MAAAE,IAAA,IACAhG,KAAAvJ,SAAAqP,MAAAsL,UAAA,MAEApR,KAAAvJ,SAAAqP,MAAAE,IAAAA,EAAA,KACAhG,KAAAvJ,SAAAqP,MAAAsL,UAAAA,EAAA,OAGAjL,EAAA+K,EAAA,GACAlR,KAAAvJ,SAAAqP,MAAAK,KAAA,IACAnG,KAAAvJ,SAAAqP,MAAAoL,WAAA,MAEAlR,KAAAvJ,SAAAqP,MAAAK,KAAAA,EAAA,KACAnG,KAAAvJ,SAAAqP,MAAAoL,WAAAA,EAAA,MAGAlR,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAma,KACA/Q,KAAAvJ,SAAAqP,MAAAE,IAAAiL,EAAAjL,IAAAhG,KAAAvJ,SAAAyP,aAAA,GAAA,KACAlG,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAka,OACA9Q,KAAAvJ,SAAAqP,MAAAK,KAAA8K,EAAA9K,KAAA8K,EAAA9J,MAAA,GAAA,KACAnH,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAia,MACA7Q,KAAAvJ,SAAAqP,MAAAK,KAAA8K,EAAA9K,KAAAnG,KAAAvJ,SAAA0a,YAAA,GAAA,KAEAnR,KAAAvJ,SAAAqP,MAAAE,IAAAiL,EAAAjL,IAAAiL,EAAA/J,OAAA,GAAA,KAEAlH,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA4B,YAOAmY,EAAApT,UAAA8T,aAAA,WACA5a,KAAAA,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA4B,YAKAmY,EAAApT,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACAqO,IAAAA,EAAA9E,KAAAvJ,SAAAe,aAAA,QAAAwI,KAAAvJ,SAAAe,aAAA,gBACAsN,IACA9E,KAAAiF,YAAAlO,SAAAiO,eAAAF,IAEA9E,KAAAiF,cAEAjF,KAAAiF,YAAA6B,aAAA,aACA9G,KAAAiF,YAAA9I,aAAA,WAAA,KAEA6D,KAAAsR,uBAAAtR,KAAAgR,kBAAApQ,KAAAZ,MACAA,KAAAuR,gCAAAvR,KAAAqR,aAAAzQ,KAAAZ,MACAA,KAAAiF,YAAA3N,iBAAA,aAAA0I,KAAAsR,wBAAAA,GACAtR,KAAAiF,YAAA3N,iBAAA,WAAA0I,KAAAsR,wBAAAA,GACAtR,KAAAiF,YAAA3N,iBAAA,aAAA0I,KAAAuR,iCAAAA,GACAxW,OAAAzD,iBAAA,SAAA0I,KAAAuR,iCAAAA,GACAxW,OAAAzD,iBAAA,aAAA0I,KAAAuR,oCAMAxY,EAAAY,SAAAA,CACAuE,YAAAyS,EACAxT,cAAA,kBACA9B,SAAA,gBd1GAmW,IAAAA,EAAA,SAAApY,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,eAAAyW,EAOAA,EAAAjU,UAAA2C,UAAAA,CACAuR,UAAA,sBACAC,kBAAA,IACAC,eAAA,IACAC,UAAA,WACAC,aAAA,eACAC,cAAA,iBAQAN,EAAAjU,UAAA+F,UAAAA,CACAC,MAAA,GACAC,OAAA,GACAC,MAAA,IAQA+N,EAAAjU,UAAAwU,MAAAA,CACAC,SAAA,EACAC,OAAA,EACAC,UAAA,EACAC,OAAA,GAUAX,EAAAjU,UAAA3G,YAAAA,CACAgN,UAAA,wBACAwO,OAAA,qBACAC,OAAA,qBACAC,QAAA,sBACAC,WAAA,4BACAC,KAAA,iBACA9Z,iBAAA,uBACAC,iBAAA,mCACAC,OAAA,aACAwI,qBAAA,sCACAqR,cAAA,6BACAC,iBAAA,gCACAC,cAAA,6BACAC,aAAA,2BACAC,WAAA,yBACAC,QAAA,sBACAC,cAAA,gCACAC,IAAA,kBACAC,eAAA,6BACAC,oBAAA,kCACAC,qBAAA,mCACAta,kBAAA,gCACAua,MAAA,wBACAC,WAAA,aACAC,SAAA,WACAC,qBAAA,uBACAC,eAAA,oBACAC,WAAA,aACAC,gBAAA,kBACAC,eAAA,aACAnb,UAAA,YACAiJ,YAAA,cACAwC,aAAA,eACA2P,gBAAA,gCACAC,gBAAA,iCAOArC,EAAAjU,UAAAuW,sBAAA,WACA,IAAA9T,KAAA+T,QAAArd,UAAAC,SAAAqJ,KAAApJ,YAAAqN,cAAA,CAGA+P,IAAAA,GAAAhU,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAA8c,kBAAA1T,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAgc,cACAra,KAAAA,SAAA0b,UAAA,IAAAjU,KAAA+T,QAAArd,UAAAC,SAAAqJ,KAAApJ,YAAA6c,aACAzT,KAAA+T,QAAArd,UAAAO,IAAA+I,KAAApJ,YAAA4c,gBACAxT,KAAA+T,QAAArd,UAAAO,IAAA+I,KAAApJ,YAAA6c,YACAO,GACAhU,KAAA+T,QAAArd,UAAAO,IAAA+I,KAAApJ,YAAAqN,eAEAjE,KAAAzH,SAAA0b,WAAA,GAAAjU,KAAA+T,QAAArd,UAAAC,SAAAqJ,KAAApJ,YAAA6c,cACAzT,KAAA+T,QAAArd,UAAAoL,OAAA9B,KAAApJ,YAAA4c,gBACAxT,KAAA+T,QAAArd,UAAAoL,OAAA9B,KAAApJ,YAAA6c,YACAO,GACAhU,KAAA+T,QAAArd,UAAAO,IAAA+I,KAAApJ,YAAAqN,iBAUAuN,EAAAjU,UAAA2W,sBAAA,SAAAxO,GAEAA,EAAAa,UAAAvG,KAAAsD,UAAAE,QAAAxD,KAAAmU,QAAAzd,UAAAC,SAAAqJ,KAAApJ,YAAA+c,iBACA3T,KAAAoU,gBAQA5C,EAAAjU,UAAA8W,mBAAA,WACAC,KAAAA,sBAAAC,QACAvU,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA8c,kBAEA1T,KAAAvJ,SAAAC,UAAAoL,OAAA9B,KAAApJ,YAAA8c,iBAEA1T,KAAAmU,UACAnU,KAAAmU,QAAAzd,UAAAoL,OAAA9B,KAAApJ,YAAA+c,gBACA3T,KAAAwU,YAAA9d,UAAAoL,OAAA9B,KAAApJ,YAAA+c,mBAUAnC,EAAAjU,UAAAkX,qBAAA,SAAA/O,GACAA,GAAAA,GAAA,YAAAA,EAAAgP,KAAA,CACAhP,GAAAA,EAAAa,UAAAvG,KAAAsD,UAAAG,OAAAiC,EAAAa,UAAAvG,KAAAsD,UAAAC,MAKA,OAHAmC,EAAAhO,iBAMA0c,KAAAA,gBAOA5C,EAAAjU,UAAAoX,4BAAA,WACAZ,KAAAA,QAAArd,UAAAoL,OAAA9B,KAAApJ,YAAAqN,eAOAuN,EAAAjU,UAAAqX,oBAAA,WACAb,KAAAA,QAAArd,UAAAC,SAAAqJ,KAAApJ,YAAA6c,cACAzT,KAAA+T,QAAArd,UAAAoL,OAAA9B,KAAApJ,YAAA6c,YACAzT,KAAA+T,QAAArd,UAAAO,IAAA+I,KAAApJ,YAAAqN,gBAQAuN,EAAAjU,UAAAxF,eAAA,SAAA8c,GACA,IAAA,IAAA3F,EAAA,EAAAA,EAAA2F,EAAAza,OAAA8U,IACA2F,EAAA3F,GAAAxY,UAAAoL,OAAA9B,KAAApJ,YAAA4B,YAQAgZ,EAAAjU,UAAAvF,iBAAA,SAAAI,GACA,IAAA,IAAAqE,EAAA,EAAAA,EAAArE,EAAAgC,OAAAqC,IACArE,EAAAqE,GAAA/F,UAAAoL,OAAA9B,KAAApJ,YAAA4B,YAQAgZ,EAAAjU,UAAA6W,aAAA,WACAU,IAAAA,EAAA9U,KAAAvJ,SAAAqB,cAAA,IAAAkI,KAAApJ,YAAA2b,YACA4B,KAAAA,QAAAzd,UAAA4P,OAAAtG,KAAApJ,YAAA+c,gBACA3T,KAAAwU,YAAA9d,UAAA4P,OAAAtG,KAAApJ,YAAA+c,gBAEA3T,KAAAmU,QAAAzd,UAAAC,SAAAqJ,KAAApJ,YAAA+c,iBACA3T,KAAAmU,QAAAhY,aAAA,cAAA,SACA2Y,EAAA3Y,aAAA,gBAAA,UAEA6D,KAAAmU,QAAAhY,aAAA,cAAA,QACA2Y,EAAA3Y,aAAA,gBAAA,WAGAqV,EAAAjU,UAAA,aAAAiU,EAAAjU,UAAA6W,aAIA5C,EAAAjU,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACA8N,IAAAA,EAAAxN,SAAAC,cAAA,OACAuN,EAAA7N,UAAAO,IAAA+I,KAAApJ,YAAAgN,WACAmR,IAAAA,EAAA/U,KAAAvJ,SAAAqB,cAAA,UACArB,KAAAA,SAAA+N,cAAAC,aAAAF,EAAAvE,KAAAvJ,UACAuJ,KAAAvJ,SAAA+N,cAAAE,YAAA1E,KAAAvJ,UACA8N,EAAAlN,YAAA2I,KAAAvJ,UACAse,GACAA,EAAAvO,QAIA,IAAA,IAFAwO,EAAAhV,KAAAvJ,SAAAwe,WACAC,EAAAF,EAAA5a,OACA+a,EAAA,EAAAA,EAAAD,EAAAC,IAAA,CACAC,IAAAA,EAAAJ,EAAAG,GACAC,EAAA1e,WAAA0e,EAAA1e,UAAAC,SAAAqJ,KAAApJ,YAAAwb,UACApS,KAAA+T,QAAAqB,GAEAA,EAAA1e,WAAA0e,EAAA1e,UAAAC,SAAAqJ,KAAApJ,YAAAyb,UACArS,KAAAmU,QAAAiB,GAEAA,EAAA1e,WAAA0e,EAAA1e,UAAAC,SAAAqJ,KAAApJ,YAAA0b,WACAtS,KAAAzH,SAAA6c,GAGAra,OAAAzD,iBAAA,WAAA,SAAAC,GACAA,EAAA8d,YAGArV,KAAAvJ,SAAAqP,MAAAwP,UAAA,SACAnW,sBAAA,WACA1I,KAAAA,SAAAqP,MAAAwP,UAAA,IACA1U,KAAAZ,SAEAY,KAAAZ,OAAAA,GACAA,KAAA+T,UACA/T,KAAAvH,QAAAuH,KAAA+T,QAAAjc,cAAA,IAAAkI,KAAApJ,YAAAkc,UAEAyC,IAAAA,EAAAvV,KAAA+R,MAAAC,SACAhS,GAAAA,KAAA+T,UACA/T,KAAA+T,QAAArd,UAAAC,SAAAqJ,KAAApJ,YAAA6b,eACA8C,EAAAvV,KAAA+R,MAAAE,OACAjS,KAAA+T,QAAArd,UAAAC,SAAAqJ,KAAApJ,YAAA8b,mBACA6C,EAAAvV,KAAA+R,MAAAG,UACAlS,KAAA+T,QAAAzc,iBAAA,gBAAA0I,KAAA2U,4BAAA/T,KAAAZ,OACAA,KAAA+T,QAAAzc,iBAAA,QAAA0I,KAAA4U,oBAAAhU,KAAAZ,QACAA,KAAA+T,QAAArd,UAAAC,SAAAqJ,KAAApJ,YAAA+b,iBACA4C,EAAAvV,KAAA+R,MAAAI,OACA5N,EAAA7N,UAAAO,IAAA+I,KAAApJ,YAAA2c,uBAEAgC,IAAAvV,KAAA+R,MAAAC,UACAhS,KAAA+T,QAAArd,UAAAO,IAAA+I,KAAApJ,YAAA4c,gBACAxT,KAAAvH,SACAuH,KAAAvH,QAAA/B,UAAAO,IAAA+I,KAAApJ,YAAA4c,iBAEA+B,IAAAvV,KAAA+R,MAAAE,QAAAsD,IAAAvV,KAAA+R,MAAAI,QACAnS,KAAA+T,QAAArd,UAAAoL,OAAA9B,KAAApJ,YAAA4c,gBACAxT,KAAAvH,SACAuH,KAAAvH,QAAA/B,UAAAoL,OAAA9B,KAAApJ,YAAA4c,iBAEA+B,IAAAvV,KAAA+R,MAAAG,YAIAlS,KAAAzH,SAAAjB,iBAAA,SAAA0I,KAAA8T,sBAAAlT,KAAAZ,OACAA,KAAA8T,0BAIA9T,KAAAmU,QAAA,CACAW,IAAAA,EAAA9U,KAAAvJ,SAAAqB,cAAA,IAAAkI,KAAApJ,YAAA2b,YACA,IAAAuC,EAAA,EACAA,EAAA/d,SAAAC,cAAA,QACAmF,aAAA,gBAAA,SACA2Y,EAAA3Y,aAAA,OAAA,UACA2Y,EAAA3Y,aAAA,WAAA,KACA2Y,EAAApe,UAAAO,IAAA+I,KAAApJ,YAAA2b,YACAiD,IAAAA,EAAAze,SAAAC,cAAA,KACAwe,EAAA9e,UAAAO,IAAA+I,KAAApJ,YAAA4b,MACAgD,EAAAC,UAAAzV,KAAAE,UAAA0R,UACAkD,EAAAzd,YAAAme,GAEArB,KAAAA,QAAAzd,UAAAC,SAAAqJ,KAAApJ,YAAAgd,iBAEAkB,EAAApe,UAAAO,IAAA+I,KAAApJ,YAAAgd,iBACA5T,KAAAmU,QAAAzd,UAAAC,SAAAqJ,KAAApJ,YAAAid,kBAEAiB,EAAApe,UAAAO,IAAA+I,KAAApJ,YAAAid,iBAEAiB,EAAAxd,iBAAA,QAAA0I,KAAAyU,qBAAA7T,KAAAZ,OACA8U,EAAAxd,iBAAA,UAAA0I,KAAAyU,qBAAA7T,KAAAZ,OAIAA,KAAAvJ,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAAyc,YAGArT,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAgc,cACA5S,KAAA+T,QAAAtP,aAAAqQ,EAAA9U,KAAA+T,QAAA2B,YAEA1V,KAAAvJ,SAAAgO,aAAAqQ,EAAA9U,KAAAzH,UAEAod,IAAAA,EAAA5e,SAAAC,cAAA,OACA2e,EAAAjf,UAAAO,IAAA+I,KAAApJ,YAAAic,YACA7S,KAAAvJ,SAAAY,YAAAse,GACAA,EAAAre,iBAAA,QAAA0I,KAAAyU,qBAAA7T,KAAAZ,OACAA,KAAAwU,YAAAmB,EACA3V,KAAAmU,QAAA7c,iBAAA,UAAA0I,KAAAkU,sBAAAtT,KAAAZ,OACAA,KAAAmU,QAAAhY,aAAA,cAAA,QAIA6D,GAAAA,KAAAsU,sBAAAvZ,OAAA6a,WAAA5V,KAAAE,UAAAuR,WACAzR,KAAAsU,sBAAAuB,YAAA7V,KAAAqU,mBAAAzT,KAAAZ,OACAA,KAAAqU,qBAEArU,KAAA+T,SAAA/T,KAAAvH,QAAA,CACAhC,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA0c,UACAwC,IAAAA,EAAA/e,SAAAC,cAAA,OACA8e,EAAApf,UAAAO,IAAA+I,KAAApJ,YAAAmc,eACA/S,KAAA+T,QAAAtP,aAAAqR,EAAA9V,KAAAvH,SACAuH,KAAA+T,QAAArP,YAAA1E,KAAAvH,SACAsd,IAAAA,EAAAhf,SAAAC,cAAA,OACA+e,EAAArf,UAAAO,IAAA+I,KAAApJ,YAAAqc,gBACA8C,EAAArf,UAAAO,IAAA+I,KAAApJ,YAAAsc,qBACA8C,IAAAA,EAAAjf,SAAAC,cAAA,KACAgf,EAAAtf,UAAAO,IAAA+I,KAAApJ,YAAA4b,MACAwD,EAAA3J,YAAArM,KAAAE,UAAA2R,aACAkE,EAAA1e,YAAA2e,GACAD,EAAAze,iBAAA,QAAA,WACAmB,KAAAA,QAAAwd,YAAAjW,KAAAE,UAAAwR,mBACA9Q,KAAAZ,OACAkW,IAAAA,EAAAnf,SAAAC,cAAA,OACAkf,EAAAxf,UAAAO,IAAA+I,KAAApJ,YAAAqc,gBACAiD,EAAAxf,UAAAO,IAAA+I,KAAApJ,YAAAuc,sBACAgD,IAAAA,EAAApf,SAAAC,cAAA,KACAmf,EAAAzf,UAAAO,IAAA+I,KAAApJ,YAAA4b,MACA2D,EAAA9J,YAAArM,KAAAE,UAAA4R,cACAoE,EAAA7e,YAAA8e,GACAD,EAAA5e,iBAAA,QAAA,WACAmB,KAAAA,QAAAwd,YAAAjW,KAAAE,UAAAwR,mBACA9Q,KAAAZ,OACA8V,EAAAze,YAAA0e,GACAD,EAAAze,YAAA2I,KAAAvH,SACAqd,EAAAze,YAAA6e,GAGAE,IAAAA,EAAA,WACA3d,KAAAA,QAAAwd,WAAA,EACAF,EAAArf,UAAAO,IAAA+I,KAAApJ,YAAA4B,WAEAud,EAAArf,UAAAoL,OAAA9B,KAAApJ,YAAA4B,WAEAwH,KAAAvH,QAAAwd,WAAAjW,KAAAvH,QAAA4d,YAAArW,KAAAvH,QAAA0Y,YACA+E,EAAAxf,UAAAO,IAAA+I,KAAApJ,YAAA4B,WAEA0d,EAAAxf,UAAAoL,OAAA9B,KAAApJ,YAAA4B,YAEAoI,KAAAZ,MACAvH,KAAAA,QAAAnB,iBAAA,SAAA8e,GACAA,IAEAE,IAAAA,EAAA,WAEAC,KAAAA,kBACAzW,aAAAE,KAAAuW,kBAEAvW,KAAAuW,iBAAA1W,WAAA,WACAuW,IACApW,KAAAuW,iBAAA,MACA3V,KAAAZ,MAAAA,KAAAE,UAAAyR,iBACA/Q,KAAAZ,MACAjF,OAAAzD,iBAAA,SAAAgf,GACAtW,KAAAvH,QAAA/B,UAAAC,SAAAqJ,KAAApJ,YAAA8B,mBACAsH,KAAAvH,QAAA/B,UAAAO,IAAA+I,KAAApJ,YAAAwK,sBAMA,IAAA,IAHAjJ,EAAA6H,KAAAvH,QAAA8C,iBAAA,IAAAyE,KAAApJ,YAAAoc,KACA5a,EAAA4H,KAAAzH,SAAAgD,iBAAA,IAAAyE,KAAApJ,YAAAwc,OAEAlZ,EAAA,EAAAA,EAAA/B,EAAAiC,OAAAF,IACA,IAAAhC,EAAAC,EAAA+B,GAAA/B,EAAAC,EAAA4H,MAGAvJ,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA6K,eA2CA1G,OAAA,kBAAA7C,EAGAa,EAAAY,SAAAA,CACAuE,YAAAsT,EACArU,cAAA,iBACA9B,SAAA,kBercAmb,IAAAA,EAAA,SAAApd,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,kBAAAyb,EAOAA,EAAAjZ,UAAA2C,UAAAA,GASAsW,EAAAjZ,UAAA3G,YAAAA,CACA6f,WAAA,iBACAC,WAAA,6BACAC,eAAA,yBACAC,YAAA,cACAnV,YAAA,eAWA+U,EAAAjZ,UAAAsZ,WAAA,SAAAC,EAAAC,EAAAC,GACAD,OAAAA,EACA,WACAD,EAAA1U,QACA2U,EAAArgB,UAAAO,IAAA+I,KAAApJ,YAAAggB,aAEAG,EAAArgB,UAAAoL,OAAA9B,KAAApJ,YAAAggB,cAEAhW,KAAAZ,MAEAgX,EACA,WACA9c,IAAAA,EAEA4c,GAAAA,EAAA1U,QACA,IAAAlI,EAAA,EAAAA,EAAA8c,EAAA5c,OAAAF,IACA8c,EAAA9c,GAAApC,cAAA,MAAAA,cAAA,iBACA,iBAAAuK,QACA2U,EAAA9c,GAAAxD,UAAAO,IAAA+I,KAAApJ,YAAAggB,kBAGA,IAAA1c,EAAA,EAAAA,EAAA8c,EAAA5c,OAAAF,IACA8c,EAAA9c,GAAApC,cAAA,MAAAA,cAAA,iBACA,iBAAAwK,UACA0U,EAAA9c,GAAAxD,UAAAoL,OAAA9B,KAAApJ,YAAAggB,cAGAhW,KAAAZ,WAjBA,GA4BAwW,EAAAjZ,UAAA0Z,gBAAA,SAAAF,EAAAC,GACAE,IAAAA,EAAAngB,SAAAC,cAAA,SACAmgB,EAAAA,CACA,eACA,kBACA,uBACAnX,KAAApJ,YAAA+f,gBAEAO,EAAA7c,UAAA8c,EAAA/a,KAAA,KACA0a,IAAAA,EAAA/f,SAAAC,cAAA,SACA8f,OAAAA,EAAApC,KAAA,WACAoC,EAAApgB,UAAAO,IAAA,uBACA8f,GACAD,EAAA1U,QAAA2U,EAAArgB,UAAAC,SAAAqJ,KAAApJ,YAAAggB,aACAE,EAAAxf,iBAAA,SAAA0I,KAAA6W,WAAAC,EAAAC,KACAC,GACAF,EAAAxf,iBAAA,SAAA0I,KAAA6W,WAAAC,EAAA,KAAAE,IAEAE,EAAA7f,YAAAyf,GACA/d,EAAAI,eAAA+d,EAAA,oBACAA,GAKAV,EAAAjZ,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACA2gB,IAAAA,EAAApX,KAAAvJ,SAAAqB,cAAA,MACAuf,EAAAha,MAAAE,UAAAC,MAAAC,KAAAuC,KAAAvJ,SAAA8E,iBAAA,aACA+b,EAAAja,MAAAE,UAAAC,MAAAC,KAAAuC,KAAAvJ,SAAA8E,iBAAA,aACAgc,EAAAF,EAAAG,OAAAF,GACAtX,GAAAA,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAA8f,YAAA,CACAe,IAAAA,EAAA1gB,SAAAC,cAAA,MACA0gB,EAAA1X,KAAAiX,gBAAA,KAAAM,GACAE,EAAApgB,YAAAqgB,GACAN,EAAA5S,cAAAC,aAAAgT,EAAAL,GACA,IAAA,IAAAld,EAAA,EAAAA,EAAAqd,EAAAnd,OAAAF,IAAA,CACAyd,IAAAA,EAAAJ,EAAArd,GAAApC,cAAA,MACA6f,GAAAA,EAAA,CACAC,IAAAA,EAAA7gB,SAAAC,cAAA,MACA,GAAA,UAAAugB,EAAArd,GAAAwN,WAAAmQ,SAAAC,cAAA,CACAC,IAAAA,EAAA/X,KAAAiX,gBAAAM,EAAArd,IACA0d,EAAAvgB,YAAA0gB,GAEAR,EAAArd,GAAAuK,aAAAmT,EAAAD,IAGAlhB,KAAAA,SAAAC,UAAAO,IAAA+I,KAAApJ,YAAA6K,gBAMA1I,EAAAY,SAAAA,CACAuE,YAAAsY,EACArZ,cAAA,oBACA9B,SAAA,sBjBnIA2c,IAAAA,EAAA,SAAA5e,GACA3C,KAAAA,SAAA2C,EAEA4G,KAAAC,QAEAlF,OAAA,eAAAid,EAOAA,EAAAza,UAAA2C,UAAAA,CACA+X,cAAA,wBACAC,aAAA,MACAC,gBAAA,MACAC,cAAA,IACAC,YAAA,IAUAL,EAAAza,UAAA3G,YAAAA,CACAyK,cAAA,qBACAiX,4BAAA,sCACA1f,OAAA,aACAqL,aAAA,eACAD,WAAA,cAQAgU,EAAAza,UAAAgb,aAAA,SAAAlY,GACA,IAAAL,KAAAU,eAAAoF,MAAAqB,QAAAnH,KAAAU,eAAAoF,MAAAoB,OAAA,CACAvB,IAAAA,EAAA3F,KAAAvJ,SAAAmP,wBACA4S,KAAAA,YAAA7S,EAAAuB,OACAlH,KAAAyY,WAAA9S,EAAAwB,MACAnH,KAAA0Y,YAAA,EAAA/Y,KAAAgZ,KAAAhT,EAAAwB,MAAAxB,EAAAwB,MAAAxB,EAAAuB,OAAAvB,EAAAuB,QAAA,EACAlH,KAAAU,eAAAoF,MAAAqB,MAAAnH,KAAA0Y,YAAA,KACA1Y,KAAAU,eAAAoF,MAAAoB,OAAAlH,KAAA0Y,YAAA,KAEA1Y,GAAAA,KAAAU,eAAAhK,UAAAO,IAAA+I,KAAApJ,YAAAoN,YACA,cAAA3D,EAAAqU,MAAA1U,KAAA4Y,mBACA5Y,KAAA4Y,oBAAAA,MACA,CAKAC,GAJAxY,eAAAA,EAAAqU,OACA1U,KAAA4Y,oBAAAA,GAEA5Y,KAAA8Y,gBACA,EACA,OAEAC,KAAAA,cAAA,GAEAC,IAAAA,EACA3O,EAFA4O,EAAA5Y,EAAA6Y,cAAAtT,wBAIA,GAAA,IAAAvF,EAAA8J,SAAA,IAAA9J,EAAA+J,QACA4O,EAAArZ,KAAAwZ,MAAAF,EAAA9R,MAAA,GACAkD,EAAA1K,KAAAwZ,MAAAF,EAAA/R,OAAA,OACA,CACAiD,IAAAA,OAAAyB,IAAAvL,EAAA8J,QAAA9J,EAAA8J,QAAA9J,EAAA+Y,QAAA,GAAAjP,QACAC,OAAAwB,IAAAvL,EAAA+J,QAAA/J,EAAA+J,QAAA/J,EAAA+Y,QAAA,GAAAhP,QACA4O,EAAArZ,KAAAwZ,MAAAhP,EAAA8O,EAAA9S,MACAkE,EAAA1K,KAAAwZ,MAAA/O,EAAA6O,EAAAjT,KAEAqT,KAAAA,YAAAL,EAAA3O,GACArK,KAAAsZ,iBAAAA,GACAve,OAAAoE,sBAAAa,KAAAuZ,iBAAA3Y,KAAAZ,SASAgY,EAAAza,UAAAic,WAAA,SAAAnZ,GAEAA,GAAA,IAAAA,EAAAoZ,QAIA1e,OAAA8E,WAAA,WACAa,KAAAA,eAAAhK,UAAAoL,OAAA9B,KAAApJ,YAAAoN,aACApD,KAAAZ,MAAA,IAMAgY,EAAAza,UAAA0C,KAAA,WACAD,GAAAA,KAAAvJ,SAAA,CACAijB,IAAAA,EAAA1Z,KAAAvJ,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAAyK,eACA5K,KAAAA,SAAAC,UAAAC,SAAAqJ,KAAApJ,YAAA0hB,+BACAtY,KAAAU,eAAAV,KAAAvJ,SAAAqB,cAAA,IAAAkI,KAAApJ,YAAAgC,QACAoH,KAAA2Z,YAAA,EACA3Z,KAAA0Y,YAAA,EACA1Y,KAAA4Z,GAAA,EACA5Z,KAAA6Z,GAAA,EAIA7Z,KAAA4Y,oBAAAA,EACA5Y,KAAA8Z,iBAAA9Z,KAAAuY,aAAA3X,KAAAZ,MACAA,KAAAvJ,SAAAa,iBAAA,YAAA0I,KAAA8Z,kBACA9Z,KAAAvJ,SAAAa,iBAAA,aAAA0I,KAAA8Z,kBACA9Z,KAAA+Z,eAAA/Z,KAAAwZ,WAAA5Y,KAAAZ,MACAA,KAAAvJ,SAAAa,iBAAA,UAAA0I,KAAA+Z,gBACA/Z,KAAAvJ,SAAAa,iBAAA,aAAA0I,KAAA+Z,gBACA/Z,KAAAvJ,SAAAa,iBAAA,WAAA0I,KAAA+Z,gBACA/Z,KAAAvJ,SAAAa,iBAAA,OAAA0I,KAAA+Z,gBAKA/Z,KAAA8Y,cAAA,WACA9Y,OAAAA,KAAA2Z,aAMA3Z,KAAA+Y,cAAA,SAAAiB,GACAL,KAAAA,YAAAK,GAMAha,KAAAia,iBAAA,WACAja,OAAAA,KAAAU,gBAOAV,KAAAqZ,YAAA,SAAAa,EAAAC,GACAP,KAAAA,GAAAM,EACAla,KAAA6Z,GAAAM,GAMAna,KAAAsZ,gBAAA,SAAAvL,GACA,GAAA,OAAA/N,KAAAU,eAAA,CACA0Z,IAAAA,EACAC,EAEAC,EAAA,aAAAta,KAAA4Z,GAAA,OAAA5Z,KAAA6Z,GAAA,MACA9L,GACAsM,EAAAra,KAAAE,UAAA+X,cACAjY,KAAAE,UAAAgY,eAEAmC,EAAAra,KAAAE,UAAAmY,YACArY,KAAA0Y,YAAA,KACAgB,IACAY,EAAA,aAAAta,KAAAyY,WAAA,EAAA,OAAAzY,KAAAwY,YAAA,EAAA,QAGA4B,EAAA,yBAAAE,EAAAD,EACAra,KAAAU,eAAAoF,MAAAyU,gBAAAH,EACApa,KAAAU,eAAAoF,MAAA0U,YAAAJ,EACApa,KAAAU,eAAAoF,MAAA2U,UAAAL,EACArM,EACA/N,KAAAU,eAAAhK,UAAAoL,OAAA9B,KAAApJ,YAAAqN,cAEAjE,KAAAU,eAAAhK,UAAAO,IAAA+I,KAAApJ,YAAAqN,gBAOAjE,KAAAuZ,iBAAA,WACAI,KAAAA,eAAA,EACA5e,OAAAoE,sBAAAa,KAAAuZ,iBAAA3Y,KAAAZ,OAEAA,KAAAsZ,iBAAAA,OAQAvgB,EAAAY,SAAAA,CACAuE,YAAA8Z,EACA7a,cAAA,iBACA9B,SAAA,uBACAuB,QAAAA,IAAA;;;AkB/NA,IAAA,EAAA,OAAA,QAAA,oBAAA,QAAA,OAAA,MAAA,KACA,OAAA,oBAAA,MAAA,KAAA,MAAA,KAAA,KAEA,SAAA,cAAA,GACA,iBAAA,MAAA,IAAA;;ACLA,IAAA,EAAA,GAAA,eACA,OAAA,QAAA,SAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA;;ACFA,OAAA,QAAA,SAAA,GACA,IACA,QAAA,IACA,MAAA,GACA,OAAA;;ACHA,OAAA,SAAA,QAAA,WAAA,CAAA,WACA,OAAA,GAAA,OAAA,eAAA,GAAA,IAAA,CAAA,IAAA,WAAA,OAAA,KAAA;;ACFA,IAAA,EAAA,OAAA,QAAA,CAAA,QAAA,SACA,iBAAA,MAAA,IAAA;;ACDA,OAAA,QAAA,SAAA,GACA,MAAA,iBAAA,EAAA,OAAA,EAAA,mBAAA;;ACDA,IAAA,EAAA,QAAA,gBACA,OAAA,QAAA,SAAA,GACA,IAAA,EAAA,GAAA,MAAA,UAAA,EAAA,sBACA,OAAA;;ACHA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,aAAA,SAEA,EAAA,EAAA,IAAA,EAAA,EAAA,eACA,OAAA,QAAA,SAAA,GACA,OAAA,EAAA,EAAA,cAAA,GAAA;;ACLA,OAAA,SAAA,QAAA,oBAAA,QAAA,WAAA,CAAA,WACA,OAAA,GAAA,OAAA,eAAA,QAAA,gBAAA,CAAA,OAAA,IAAA,CAAA,IAAA,WAAA,OAAA,KAAA;;ACAA,IAAA,EAAA,QAAA,gBAGA,OAAA,QAAA,SAAA,EAAA,GACA,IAAA,EAAA,GAAA,OAAA,EACA,IAAA,EAAA,EACA,GAAA,GAAA,mBAAA,EAAA,EAAA,YAAA,EAAA,EAAA,EAAA,KAAA,IAAA,OAAA,EACA,GAAA,mBAAA,EAAA,EAAA,WAAA,EAAA,EAAA,EAAA,KAAA,IAAA,OAAA,EACA,IAAA,GAAA,mBAAA,EAAA,EAAA,YAAA,EAAA,EAAA,EAAA,KAAA,IAAA,OAAA,EACA,MAAA,UAAA;;ACVA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,qBACA,EAAA,QAAA,mBACA,EAAA,OAAA,eAEA,QAAA,EAAA,QAAA,kBAAA,OAAA,eAAA,SAAA,EAAA,EAAA,GAIA,GAHA,EAAA,GACA,EAAA,EAAA,GAAA,GACA,EAAA,GACA,EAAA,IACA,OAAA,EAAA,EAAA,EAAA,GACA,MAAA,IACA,GAAA,QAAA,GAAA,QAAA,EAAA,MAAA,UAAA,4BAEA,MADA,UAAA,IAAA,EAAA,GAAA,EAAA,OACA;;ACdA,OAAA,QAAA,SAAA,EAAA,GACA,MAAA,CACA,aAAA,EAAA,GACA,eAAA,EAAA,GACA,WAAA,EAAA,GACA,MAAA;;ACLA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,oBACA,OAAA,QAAA,QAAA,kBAAA,SAAA,EAAA,EAAA,GACA,OAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,KACA,SAAA,EAAA,EAAA,GAEA,OADA,EAAA,GAAA,EACA;;ACNA,IAAA,EAAA,EACA,EAAA,KAAA,SACA,OAAA,QAAA,SAAA,GACA,MAAA,UAAA,YAAA,IAAA,EAAA,GAAA,EAAA,QAAA,EAAA,GAAA,SAAA;;ACHA,OAAA,SAAA;;;ACAA,IAAA,EAAA,QAAA,WACA,EAAA,QAAA,aACA,EAAA,qBACA,EAAA,EAAA,KAAA,EAAA,GAAA,KAEA,OAAA,QAAA,SAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,QAAA,IAAA,EAAA,EAAA,MACA,WAAA,IAAA,KAAA,CACA,QAAA,EAAA,QACA,KAAA,QAAA,cAAA,OAAA,SACA,UAAA;;ACVA,OAAA,QAAA,QAAA,YAAA,CAAA,4BAAA,SAAA;;;ACAA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,WACA,EAAA,QAAA,UACA,EAAA,QAAA,SAAA,CAAA,OACA,EAAA,QAAA,yBACA,EAAA,WACA,GAAA,GAAA,GAAA,MAAA,GAEA,QAAA,WAAA,cAAA,SAAA,GACA,OAAA,EAAA,KAAA,KAGA,OAAA,QAAA,SAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,mBAAA,EACA,IAAA,EAAA,EAAA,SAAA,EAAA,EAAA,OAAA,IACA,EAAA,KAAA,IACA,IAAA,EAAA,EAAA,IAAA,EAAA,EAAA,EAAA,EAAA,GAAA,GAAA,EAAA,GAAA,EAAA,KAAA,OAAA,MACA,IAAA,EACA,EAAA,GAAA,EACA,EAGA,EAAA,GACA,EAAA,GAAA,EAEA,EAAA,EAAA,EAAA,WALA,EAAA,GACA,EAAA,EAAA,EAAA,OAOA,SAAA,UAAA,EAAA,WACA,MAAA,mBAAA,MAAA,KAAA,IAAA,EAAA,KAAA;;AC7BA,OAAA,QAAA,SAAA,GACA,GAAA,mBAAA,EAAA,MAAA,UAAA,EAAA,uBACA,OAAA;;ACDA,IAAA,EAAA,QAAA,iBACA,OAAA,QAAA,SAAA,EAAA,EAAA,GAEA,GADA,EAAA,QACA,IAAA,EAAA,OAAA,EACA,OAAA,GACA,KAAA,EAAA,OAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,IAEA,KAAA,EAAA,OAAA,SAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,EAAA,IAEA,KAAA,EAAA,OAAA,SAAA,EAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,EAAA,EAAA,IAGA,OAAA,WACA,OAAA,EAAA,MAAA,EAAA;;;ACjBA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,WACA,EAAA,QAAA,WACA,EAAA,QAAA,eACA,EAAA,QAAA,UACA,EAAA,YAEA,EAAA,SAAA,EAAA,EAAA,GACA,IAQA,EAAA,EAAA,EAAA,EARA,EAAA,EAAA,EAAA,EACA,EAAA,EAAA,EAAA,EACA,EAAA,EAAA,EAAA,EACA,EAAA,EAAA,EAAA,EACA,EAAA,EAAA,EAAA,EACA,EAAA,EAAA,EAAA,EAAA,EAAA,KAAA,EAAA,GAAA,KAAA,EAAA,IAAA,IAAA,GACA,EAAA,EAAA,EAAA,EAAA,KAAA,EAAA,GAAA,IACA,EAAA,EAAA,KAAA,EAAA,GAAA,IAGA,IAAA,KADA,IAAA,EAAA,GACA,EAIA,IAFA,GAAA,GAAA,QAAA,IAAA,EAAA,IAEA,EAAA,GAAA,GAEA,EAAA,GAAA,EAAA,EAAA,EAAA,GAAA,GAAA,mBAAA,EAAA,EAAA,SAAA,KAAA,GAAA,EAEA,GAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAEA,EAAA,IAAA,GAAA,EAAA,EAAA,EAAA,GACA,GAAA,EAAA,IAAA,IAAA,EAAA,GAAA,IAGA,EAAA,KAAA,EAEA,EAAA,EAAA,EACA,EAAA,EAAA,EACA,EAAA,EAAA,EACA,EAAA,EAAA,EACA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,EAAA,IACA,OAAA,QAAA;;AC1CA,IAAA,EAAA,QAAA,SAAA,CAAA,QACA,EAAA,QAAA,gBACA,EAAA,QAAA,UACA,EAAA,QAAA,gBAAA,EACA,EAAA,EACA,EAAA,OAAA,cAAA,WACA,OAAA,GAEA,GAAA,QAAA,WAAA,CAAA,WACA,OAAA,EAAA,OAAA,kBAAA,OAEA,EAAA,SAAA,GACA,EAAA,EAAA,EAAA,CAAA,MAAA,CACA,EAAA,OAAA,EACA,EAAA,OAGA,EAAA,SAAA,EAAA,GAEA,IAAA,EAAA,GAAA,MAAA,iBAAA,EAAA,GAAA,iBAAA,EAAA,IAAA,KAAA,EACA,IAAA,EAAA,EAAA,GAAA,CAEA,IAAA,EAAA,GAAA,MAAA,IAEA,IAAA,EAAA,MAAA,IAEA,EAAA,GAEA,OAAA,EAAA,GAAA,GAEA,EAAA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,GAAA,CAEA,IAAA,EAAA,GAAA,OAAA,EAEA,IAAA,EAAA,OAAA,EAEA,EAAA,GAEA,OAAA,EAAA,GAAA,GAGA,EAAA,SAAA,GAEA,OADA,GAAA,EAAA,MAAA,EAAA,KAAA,EAAA,EAAA,IAAA,EAAA,GACA,GAEA,EAAA,OAAA,QAAA,CACA,IAAA,EACA,MAAA,EACA,QAAA,EACA,QAAA,EACA,SAAA;;ACnDA,IAAA,EAAA,QAAA,YAAA,CAAA,OACA,EAAA,QAAA,UACA,EAAA,QAAA,aAAA,OACA,EAAA,mBAAA,EAEA,EAAA,OAAA,QAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,GACA,GAAA,EAAA,KAAA,EAAA,EAAA,GAAA,UAAA,KAGA,EAAA,MAAA;;ACVA,IAAA,EAAA,QAAA,gBAAA,EACA,EAAA,QAAA,UACA,EAAA,QAAA,SAAA,CAAA,eAEA,OAAA,QAAA,SAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,EAAA,EAAA,EAAA,UAAA,IAAA,EAAA,EAAA,EAAA,CAAA,cAAA,EAAA,MAAA;;ACLA,QAAA,EAAA,QAAA;;;ACAA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,WACA,EAAA,QAAA,cACA,EAAA,QAAA,cACA,EAAA,QAAA,gBAAA,EACA,OAAA,QAAA,SAAA,GACA,IAAA,EAAA,EAAA,SAAA,EAAA,OAAA,EAAA,GAAA,EAAA,QAAA,IACA,KAAA,EAAA,OAAA,IAAA,KAAA,GAAA,EAAA,EAAA,EAAA,CAAA,MAAA,EAAA,EAAA;;ACPA,IAAA,EAAA,GAAA,SAEA,OAAA,QAAA,SAAA,GACA,OAAA,EAAA,KAAA,GAAA,MAAA,GAAA;;ACFA,IAAA,EAAA,QAAA,UAEA,OAAA,QAAA,OAAA,KAAA,qBAAA,GAAA,OAAA,SAAA,GACA,MAAA,UAAA,EAAA,GAAA,EAAA,MAAA,IAAA,OAAA;;ACHA,OAAA,QAAA,SAAA,GACA,GAAA,MAAA,EAAA,MAAA,UAAA,yBAAA,GACA,OAAA;;ACFA,IAAA,EAAA,QAAA,cACA,EAAA,QAAA,cACA,OAAA,QAAA,SAAA,GACA,OAAA,EAAA,EAAA;;ACHA,IAAA,EAAA,KAAA,KACA,EAAA,KAAA,MACA,OAAA,QAAA,SAAA,GACA,OAAA,MAAA,GAAA,GAAA,GAAA,EAAA,EAAA,EAAA,GAAA;;ACHA,IAAA,EAAA,QAAA,iBACA,EAAA,KAAA,IACA,OAAA,QAAA,SAAA,GACA,OAAA,EAAA,EAAA,EAAA,EAAA,GAAA,kBAAA;;ACJA,IAAA,EAAA,QAAA,iBACA,EAAA,KAAA,IACA,EAAA,KAAA,IACA,OAAA,QAAA,SAAA,EAAA,GAEA,OADA,EAAA,EAAA,IACA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,EAAA;;ACHA,IAAA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,EAAA,QAAA,wBACA,OAAA,QAAA,SAAA,GACA,OAAA,SAAA,EAAA,EAAA,GACA,IAGA,EAHA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,QACA,EAAA,EAAA,EAAA,GAIA,GAAA,GAAA,GAAA,GAAA,KAAA,EAAA,GAGA,IAFA,EAAA,EAAA,OAEA,EAAA,OAAA,OAEA,KAAA,EAAA,EAAA,IAAA,IAAA,GAAA,KAAA,IACA,EAAA,KAAA,EAAA,OAAA,GAAA,GAAA,EACA,OAAA,IAAA;;ACpBA,IAAA,EAAA,QAAA,YAAA,CAAA,QACA,EAAA,QAAA,UACA,OAAA,QAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,GAAA,EAAA;;ACHA,IAAA,EAAA,QAAA,UACA,EAAA,QAAA,iBACA,EAAA,QAAA,oBAAA,EAAA,GACA,EAAA,QAAA,gBAAA,CAAA,YAEA,OAAA,QAAA,SAAA,EAAA,GACA,IAGA,EAHA,EAAA,EAAA,GACA,EAAA,EACA,EAAA,GAEA,IAAA,KAAA,EAAA,GAAA,GAAA,EAAA,EAAA,IAAA,EAAA,KAAA,GAEA,KAAA,EAAA,OAAA,GAAA,EAAA,EAAA,EAAA,EAAA,SACA,EAAA,EAAA,IAAA,EAAA,KAAA,IAEA,OAAA;;ACdA,OAAA,QAAA,gGAEA,MAAA;;ACFA,IAAA,EAAA,QAAA,2BACA,EAAA,QAAA,oBAEA,OAAA,QAAA,OAAA,MAAA,SAAA,GACA,OAAA,EAAA,EAAA;;ACLA,QAAA,EAAA,OAAA;;ACAA,QAAA,EAAA,GAAA;;ACCA,IAAA,EAAA,QAAA,kBACA,EAAA,QAAA,kBACA,EAAA,QAAA,iBACA,OAAA,QAAA,SAAA,GACA,IAAA,EAAA,EAAA,GACA,EAAA,EAAA,EACA,GAAA,EAKA,IAJA,IAGA,EAHA,EAAA,EAAA,GACA,EAAA,EAAA,EACA,EAAA,EAEA,EAAA,OAAA,GAAA,EAAA,KAAA,EAAA,EAAA,EAAA,OAAA,EAAA,KAAA,GACA,OAAA;;ACZA,IAAA,EAAA,QAAA,UACA,OAAA,QAAA,MAAA,SAAA,SAAA,GACA,MAAA,SAAA,EAAA;;ACFA,IAAA,EAAA,QAAA,cACA,OAAA,QAAA,SAAA,GACA,OAAA,OAAA,EAAA;;ACHA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,kBAEA,OAAA,QAAA,QAAA,kBAAA,OAAA,iBAAA,SAAA,EAAA,GACA,EAAA,GAKA,IAJA,IAGA,EAHA,EAAA,EAAA,GACA,EAAA,EAAA,OACA,EAAA,EAEA,EAAA,GAAA,EAAA,EAAA,EAAA,EAAA,EAAA,KAAA,EAAA,IACA,OAAA;;ACXA,IAAA,EAAA,QAAA,aAAA,SACA,OAAA,QAAA,GAAA,EAAA;;ACAA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,QAAA,oBACA,EAAA,QAAA,gBAAA,CAAA,YACA,EAAA,aACA,EAAA,YAGA,EAAA,WAEA,IAIA,EAJA,EAAA,QAAA,gBAAA,CAAA,UACA,EAAA,EAAA,OAcA,IAVA,EAAA,MAAA,QAAA,OACA,QAAA,WAAA,YAAA,GACA,EAAA,IAAA,eAGA,EAAA,EAAA,cAAA,UACA,OACA,EAAA,MAAA,uCACA,EAAA,QACA,EAAA,EAAA,EACA,YAAA,EAAA,GAAA,EAAA,IACA,OAAA,KAGA,OAAA,QAAA,OAAA,QAAA,SAAA,EAAA,GACA,IAAA,EAQA,OAPA,OAAA,GACA,EAAA,GAAA,EAAA,GACA,EAAA,IAAA,EACA,EAAA,GAAA,KAEA,EAAA,GAAA,GACA,EAAA,SACA,IAAA,EAAA,EAAA,EAAA,EAAA;;ACtCA,IAAA,EAAA,QAAA,2BACA,EAAA,QAAA,oBAAA,OAAA,SAAA,aAEA,QAAA,EAAA,OAAA,qBAAA,SAAA,GACA,OAAA,EAAA,EAAA;;ACJA,IAAA,EAAA,QAAA,iBACA,EAAA,QAAA,kBAAA,EACA,EAAA,GAAA,SAEA,EAAA,iBAAA,QAAA,QAAA,OAAA,oBACA,OAAA,oBAAA,QAAA,GAEA,EAAA,SAAA,GACA,IACA,OAAA,EAAA,GACA,MAAA,GACA,OAAA,EAAA,UAIA,OAAA,QAAA,EAAA,SAAA,GACA,OAAA,GAAA,mBAAA,EAAA,KAAA,GAAA,EAAA,GAAA,EAAA,EAAA;;ACjBA,IAAA,EAAA,QAAA,iBACA,EAAA,QAAA,oBACA,EAAA,QAAA,iBACA,EAAA,QAAA,mBACA,EAAA,QAAA,UACA,EAAA,QAAA,qBACA,EAAA,OAAA,yBAEA,QAAA,EAAA,QAAA,kBAAA,EAAA,SAAA,EAAA,GAGA,GAFA,EAAA,EAAA,GACA,EAAA,EAAA,GAAA,GACA,EAAA,IACA,OAAA,EAAA,EAAA,GACA,MAAA,IACA,GAAA,EAAA,EAAA,GAAA,OAAA,GAAA,EAAA,EAAA,KAAA,EAAA,GAAA,EAAA;;;ACdA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,UACA,EAAA,QAAA,kBACA,EAAA,QAAA,aACA,EAAA,QAAA,eACA,EAAA,QAAA,WAAA,IACA,EAAA,QAAA,YACA,EAAA,QAAA,aACA,EAAA,QAAA,wBACA,EAAA,QAAA,UACA,EAAA,QAAA,UACA,EAAA,QAAA,cACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,EAAA,QAAA,eACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,QAAA,mBACA,EAAA,QAAA,oBACA,EAAA,QAAA,oBACA,EAAA,QAAA,sBACA,EAAA,QAAA,kBACA,EAAA,QAAA,kBACA,EAAA,QAAA,gBACA,EAAA,QAAA,kBACA,EAAA,EAAA,EACA,EAAA,EAAA,EACA,EAAA,EAAA,EACA,EAAA,EAAA,OACA,EAAA,EAAA,KACA,EAAA,GAAA,EAAA,UACA,EAAA,YACA,EAAA,EAAA,WACA,EAAA,EAAA,eACA,EAAA,GAAA,qBACA,EAAA,EAAA,mBACA,EAAA,EAAA,WACA,EAAA,EAAA,cACA,EAAA,OAAA,GACA,EAAA,mBAAA,KAAA,EAAA,EACA,EAAA,EAAA,QAEA,GAAA,IAAA,EAAA,KAAA,EAAA,GAAA,UAGA,EAAA,GAAA,EAAA,WACA,OAEA,GAFA,EAAA,EAAA,GAAA,IAAA,CACA,IAAA,WAAA,OAAA,EAAA,KAAA,IAAA,CAAA,MAAA,IAAA,MACA,IACA,SAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,EAAA,GACA,UAAA,EAAA,GACA,EAAA,EAAA,EAAA,GACA,GAAA,IAAA,GAAA,EAAA,EAAA,EAAA,IACA,EAEA,EAAA,SAAA,GACA,IAAA,EAAA,EAAA,GAAA,EAAA,EAAA,IAEA,OADA,EAAA,GAAA,EACA,GAGA,EAAA,GAAA,iBAAA,EAAA,SAAA,SAAA,GACA,MAAA,iBAAA,GACA,SAAA,GACA,OAAA,aAAA,GAGA,EAAA,SAAA,EAAA,EAAA,GAKA,OAJA,IAAA,GAAA,EAAA,EAAA,EAAA,GACA,EAAA,GACA,EAAA,EAAA,GAAA,GACA,EAAA,GACA,EAAA,EAAA,IACA,EAAA,YAIA,EAAA,EAAA,IAAA,EAAA,GAAA,KAAA,EAAA,GAAA,IAAA,GACA,EAAA,EAAA,EAAA,CAAA,WAAA,EAAA,GAAA,OAJA,EAAA,EAAA,IAAA,EAAA,EAAA,EAAA,EAAA,EAAA,KACA,EAAA,GAAA,IAAA,GAIA,EAAA,EAAA,EAAA,IACA,EAAA,EAAA,EAAA,IAEA,EAAA,SAAA,EAAA,GACA,EAAA,GAKA,IAJA,IAGA,EAHA,EAAA,EAAA,EAAA,EAAA,IACA,EAAA,EACA,EAAA,EAAA,OAEA,EAAA,GAAA,EAAA,EAAA,EAAA,EAAA,KAAA,EAAA,IACA,OAAA,GAEA,EAAA,SAAA,EAAA,GACA,YAAA,IAAA,EAAA,EAAA,GAAA,EAAA,EAAA,GAAA,IAEA,EAAA,SAAA,GACA,IAAA,EAAA,EAAA,KAAA,KAAA,EAAA,EAAA,GAAA,IACA,QAAA,OAAA,GAAA,EAAA,EAAA,KAAA,EAAA,EAAA,QACA,IAAA,EAAA,KAAA,KAAA,EAAA,EAAA,IAAA,EAAA,KAAA,IAAA,KAAA,GAAA,KAAA,IAEA,EAAA,SAAA,EAAA,GAGA,GAFA,EAAA,EAAA,GACA,EAAA,EAAA,GAAA,GACA,IAAA,IAAA,EAAA,EAAA,IAAA,EAAA,EAAA,GAAA,CACA,IAAA,EAAA,EAAA,EAAA,GAEA,OADA,IAAA,EAAA,EAAA,IAAA,EAAA,EAAA,IAAA,EAAA,GAAA,KAAA,EAAA,YAAA,GACA,IAEA,EAAA,SAAA,GAKA,IAJA,IAGA,EAHA,EAAA,EAAA,EAAA,IACA,EAAA,GACA,EAAA,EAEA,EAAA,OAAA,GACA,EAAA,EAAA,EAAA,EAAA,OAAA,GAAA,GAAA,GAAA,GAAA,EAAA,KAAA,GACA,OAAA,GAEA,EAAA,SAAA,GAMA,IALA,IAIA,EAJA,EAAA,IAAA,EACA,EAAA,EAAA,EAAA,EAAA,EAAA,IACA,EAAA,GACA,EAAA,EAEA,EAAA,OAAA,IACA,EAAA,EAAA,EAAA,EAAA,OAAA,IAAA,EAAA,EAAA,IAAA,EAAA,KAAA,EAAA,IACA,OAAA,GAIA,IAYA,GAXA,EAAA,WACA,GAAA,gBAAA,EAAA,MAAA,UAAA,gCACA,IAAA,EAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,GACA,EAAA,SAAA,GACA,OAAA,GAAA,EAAA,KAAA,EAAA,GACA,EAAA,KAAA,IAAA,EAAA,KAAA,GAAA,KAAA,KAAA,GAAA,IAAA,GACA,EAAA,KAAA,EAAA,EAAA,EAAA,KAGA,OADA,GAAA,GAAA,EAAA,EAAA,EAAA,CAAA,cAAA,EAAA,IAAA,IACA,EAAA,KAEA,GAAA,WAAA,WACA,OAAA,KAAA,KAGA,EAAA,EAAA,EACA,EAAA,EAAA,EACA,QAAA,kBAAA,EAAA,EAAA,EAAA,EACA,QAAA,iBAAA,EAAA,EACA,EAAA,EAAA,EAEA,IAAA,QAAA,eACA,EAAA,EAAA,uBAAA,GAAA,GAGA,EAAA,EAAA,SAAA,GACA,OAAA,EAAA,EAAA,MAIA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,CAAA,OAAA,IAEA,IAAA,IAAA,GAAA,iHAGA,MAAA,KAAA,GAAA,EAAA,GAAA,OAAA,IAAA,EAAA,GAAA,OAEA,IAAA,IAAA,GAAA,EAAA,EAAA,OAAA,GAAA,EAAA,GAAA,OAAA,IAAA,EAAA,GAAA,OAEA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,SAAA,CAEA,IAAA,SAAA,GACA,OAAA,EAAA,EAAA,GAAA,IACA,EAAA,GACA,EAAA,GAAA,EAAA,IAGA,OAAA,SAAA,GACA,IAAA,EAAA,GAAA,MAAA,UAAA,EAAA,qBACA,IAAA,IAAA,KAAA,EAAA,GAAA,EAAA,KAAA,EAAA,OAAA,GAEA,UAAA,WAAA,GAAA,GACA,UAAA,WAAA,GAAA,KAGA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,SAAA,CAEA,OAAA,EAEA,eAAA,EAEA,iBAAA,EAEA,yBAAA,EAEA,oBAAA,EAEA,sBAAA,IAKA,IAAA,GAAA,EAAA,WAAA,EAAA,EAAA,KAEA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,SAAA,CACA,sBAAA,SAAA,GACA,OAAA,EAAA,EAAA,EAAA,OAKA,GAAA,EAAA,EAAA,EAAA,EAAA,IAAA,GAAA,EAAA,WACA,IAAA,EAAA,IAIA,MAAA,UAAA,EAAA,CAAA,KAAA,MAAA,EAAA,CAAA,EAAA,KAAA,MAAA,EAAA,OAAA,OACA,OAAA,CACA,UAAA,SAAA,GAIA,IAHA,IAEA,EAAA,EAFA,EAAA,CAAA,GACA,EAAA,EAEA,UAAA,OAAA,GAAA,EAAA,KAAA,UAAA,MAEA,GADA,EAAA,EAAA,EAAA,IACA,EAAA,SAAA,IAAA,KAAA,EAAA,GAMA,OALA,EAAA,KAAA,EAAA,SAAA,EAAA,GAEA,GADA,mBAAA,IAAA,EAAA,EAAA,KAAA,KAAA,EAAA,KACA,EAAA,GAAA,OAAA,IAEA,EAAA,GAAA,EACA,EAAA,MAAA,EAAA,MAKA,EAAA,GAAA,IAAA,QAAA,UAAA,CAAA,EAAA,GAAA,EAAA,EAAA,GAAA,SAEA,EAAA,EAAA,UAEA,EAAA,KAAA,QAAA,GAEA,EAAA,EAAA,KAAA,QAAA;;ACrPA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,SAAA,CAAA,OAAA,QAAA;;ACFA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,kBAAA,SAAA,CAAA,eAAA,QAAA,gBAAA;;ACFA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,kBAAA,SAAA,CAAA,iBAAA,QAAA;;ACDA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,WACA,EAAA,QAAA,YACA,OAAA,QAAA,SAAA,EAAA,GACA,IAAA,GAAA,EAAA,QAAA,IAAA,IAAA,OAAA,GACA,EAAA,GACA,EAAA,GAAA,EAAA,GACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,WAAA,EAAA,KAAA,SAAA;;ACPA,IAAA,EAAA,QAAA,iBACA,EAAA,QAAA,kBAAA,EAEA,QAAA,gBAAA,CAAA,2BAAA,WACA,OAAA,SAAA,EAAA,GACA,OAAA,EAAA,EAAA,GAAA;;ACLA,IAAA,EAAA,QAAA,UACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBAAA,CAAA,YACA,EAAA,OAAA,UAEA,OAAA,QAAA,OAAA,gBAAA,SAAA,GAEA,OADA,EAAA,EAAA,GACA,EAAA,EAAA,GAAA,EAAA,GACA,mBAAA,EAAA,aAAA,aAAA,EAAA,YACA,EAAA,YAAA,UACA,aAAA,OAAA,EAAA;;ACVA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,iBAEA,QAAA,gBAAA,CAAA,iBAAA,WACA,OAAA,SAAA,GACA,OAAA,EAAA,EAAA;;ACLA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,kBAEA,QAAA,gBAAA,CAAA,OAAA,WACA,OAAA,SAAA,GACA,OAAA,EAAA,EAAA;;ACLA,QAAA,gBAAA,CAAA,sBAAA,WACA,OAAA,QAAA,sBAAA;;ACDA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,WAAA,SAEA,QAAA,gBAAA,CAAA,SAAA,SAAA,GACA,OAAA,SAAA,GACA,OAAA,GAAA,EAAA,GAAA,EAAA,EAAA,IAAA;;ACLA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,WAAA,SAEA,QAAA,gBAAA,CAAA,OAAA,SAAA,GACA,OAAA,SAAA,GACA,OAAA,GAAA,EAAA,GAAA,EAAA,EAAA,IAAA;;ACLA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,WAAA,SAEA,QAAA,gBAAA,CAAA,oBAAA,SAAA,GACA,OAAA,SAAA,GACA,OAAA,GAAA,EAAA,GAAA,EAAA,EAAA,IAAA;;ACLA,IAAA,EAAA,QAAA,gBAEA,QAAA,gBAAA,CAAA,WAAA,SAAA,GACA,OAAA,SAAA,GACA,OAAA,EAAA,MAAA,GAAA,EAAA;;ACJA,IAAA,EAAA,QAAA,gBAEA,QAAA,gBAAA,CAAA,WAAA,SAAA,GACA,OAAA,SAAA,GACA,OAAA,EAAA,MAAA,GAAA,EAAA;;ACJA,IAAA,EAAA,QAAA,gBAEA,QAAA,gBAAA,CAAA,eAAA,SAAA,GACA,OAAA,SAAA,GACA,QAAA,EAAA,MAAA,GAAA,EAAA;;ACLA,aAEA,IAAA,EAAA,QAAA,kBACA,EAAA,QAAA,kBACA,EAAA,QAAA,kBACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,EAAA,QAAA,cACA,EAAA,OAAA,OAGA,OAAA,SAAA,GAAA,QAAA,WAAA,CAAA,WACA,IAAA,EAAA,GACA,EAAA,GAEA,EAAA,SACA,EAAA,uBAGA,OAFA,EAAA,GAAA,EACA,EAAA,MAAA,IAAA,QAAA,SAAA,GAAA,EAAA,GAAA,IACA,GAAA,EAAA,GAAA,GAAA,IAAA,OAAA,KAAA,EAAA,GAAA,IAAA,KAAA,KAAA,IACA,SAAA,EAAA,GAMA,IALA,IAAA,EAAA,EAAA,GACA,EAAA,UAAA,OACA,EAAA,EACA,EAAA,EAAA,EACA,EAAA,EAAA,EACA,EAAA,GAMA,IALA,IAIA,EAJA,EAAA,EAAA,UAAA,MACA,EAAA,EAAA,EAAA,GAAA,OAAA,EAAA,IAAA,EAAA,GACA,EAAA,EAAA,OACA,EAAA,EAEA,EAAA,GACA,EAAA,EAAA,KACA,IAAA,EAAA,KAAA,EAAA,KAAA,EAAA,GAAA,EAAA,IAEA,OAAA,GACA;;ACpCA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,EAAA,EAAA,SAAA,CAAA,OAAA,QAAA;;ACFA,OAAA,QAAA,OAAA,IAAA,SAAA,EAAA,GAEA,OAAA,IAAA,EAAA,IAAA,GAAA,EAAA,GAAA,EAAA,EAAA,GAAA,GAAA,GAAA;;ACFA,IAAA,EAAA,QAAA,aACA,EAAA,EAAA,EAAA,SAAA,CAAA,GAAA,QAAA;;ACAA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,SAAA,EAAA,GAEA,GADA,EAAA,IACA,EAAA,IAAA,OAAA,EAAA,MAAA,UAAA,EAAA,8BAEA,OAAA,QAAA,CACA,IAAA,OAAA,iBAAA,aAAA,GACA,SAAA,EAAA,EAAA,GACA,KACA,EAAA,QAAA,SAAA,CAAA,SAAA,KAAA,QAAA,kBAAA,EAAA,OAAA,UAAA,aAAA,IAAA,IACA,EAAA,IACA,IAAA,aAAA,OACA,MAAA,GAAA,GAAA,EACA,OAAA,SAAA,EAAA,GAIA,OAHA,EAAA,EAAA,GACA,EAAA,EAAA,UAAA,EACA,EAAA,EAAA,GACA,GAVA,CAYA,IAAA,QAAA,GACA,MAAA;;ACtBA,IAAA,EAAA,QAAA,aACA,EAAA,EAAA,EAAA,SAAA,CAAA,eAAA,QAAA,gBAAA;;ACDA,IAAA,EAAA,QAAA,UACA,EAAA,QAAA,SAAA,CAAA,eAEA,EAAA,aAAA,EAAA,WAAA,OAAA,UAAA,IAGA,EAAA,SAAA,EAAA,GACA,IACA,OAAA,EAAA,GACA,MAAA,MAGA,OAAA,QAAA,SAAA,GACA,IAAA,EAAA,EAAA,EACA,YAAA,IAAA,EAAA,YAAA,OAAA,EAAA,OAEA,iBAAA,EAAA,EAAA,EAAA,OAAA,GAAA,IAAA,EAEA,EAAA,EAAA,GAEA,WAAA,EAAA,EAAA,KAAA,mBAAA,EAAA,OAAA,YAAA;;ACrBA,aAEA,IAAA,EAAA,QAAA,cACA,EAAA,GACA,EAAA,QAAA,SAAA,CAAA,gBAAA,IACA,EAAA,IAAA,cACA,QAAA,cAAA,CAAA,OAAA,UAAA,WAAA,WACA,MAAA,WAAA,EAAA,MAAA,MACA;;ACPA,OAAA,QAAA,SAAA,EAAA,EAAA,GACA,IAAA,OAAA,IAAA,EACA,OAAA,EAAA,QACA,KAAA,EAAA,OAAA,EAAA,IACA,EAAA,KAAA,GACA,KAAA,EAAA,OAAA,EAAA,EAAA,EAAA,IACA,EAAA,KAAA,EAAA,EAAA,IACA,KAAA,EAAA,OAAA,EAAA,EAAA,EAAA,GAAA,EAAA,IACA,EAAA,KAAA,EAAA,EAAA,GAAA,EAAA,IACA,KAAA,EAAA,OAAA,EAAA,EAAA,EAAA,GAAA,EAAA,GAAA,EAAA,IACA,EAAA,KAAA,EAAA,EAAA,GAAA,EAAA,GAAA,EAAA,IACA,KAAA,EAAA,OAAA,EAAA,EAAA,EAAA,GAAA,EAAA,GAAA,EAAA,GAAA,EAAA,IACA,EAAA,KAAA,EAAA,EAAA,GAAA,EAAA,GAAA,EAAA,GAAA,EAAA,IACA,OAAA,EAAA,MAAA,EAAA;;ACdA,aACA,IAAA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,EAAA,QAAA,aACA,EAAA,GAAA,MACA,EAAA,GAEA,EAAA,SAAA,EAAA,EAAA,GACA,KAAA,KAAA,GAAA,CACA,IAAA,IAAA,EAAA,GAAA,EAAA,EAAA,EAAA,EAAA,IAAA,EAAA,GAAA,KAAA,EAAA,IAEA,EAAA,GAAA,SAAA,MAAA,gBAAA,EAAA,KAAA,KAAA,KACA,OAAA,EAAA,GAAA,EAAA,IAGA,OAAA,QAAA,SAAA,MAAA,SAAA,GACA,IAAA,EAAA,EAAA,MACA,EAAA,EAAA,KAAA,UAAA,GACA,EAAA,WACA,IAAA,EAAA,EAAA,OAAA,EAAA,KAAA,YACA,OAAA,gBAAA,EAAA,EAAA,EAAA,EAAA,OAAA,GAAA,EAAA,EAAA,EAAA,IAGA,OADA,EAAA,EAAA,aAAA,EAAA,UAAA,EAAA,WACA;;ACtBA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,WAAA,CAAA,KAAA,QAAA;;ACHA,IAAA,EAAA,QAAA,gBAAA,EACA,EAAA,SAAA,UACA,EAAA,wBACA,EAAA,OAGA,KAAA,GAAA,QAAA,mBAAA,EAAA,EAAA,EAAA,CACA,cAAA,EACA,IAAA,WACA,IACA,OAAA,GAAA,MAAA,MAAA,GAAA,GACA,MAAA,GACA,MAAA;;ACZA,aACA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,QAAA,SAAA,CAAA,eACA,EAAA,SAAA,UAEA,KAAA,GAAA,QAAA,gBAAA,EAAA,EAAA,EAAA,CAAA,MAAA,SAAA,GACA,GAAA,mBAAA,OAAA,EAAA,GAAA,OAAA,EACA,IAAA,EAAA,KAAA,WAAA,OAAA,aAAA,KAEA,KAAA,EAAA,EAAA,IAAA,GAAA,KAAA,YAAA,EAAA,OAAA,EACA,OAAA;;ACXA,OAAA,QAAA;;ACAA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,cACA,EAAA,QAAA,YACA,EAAA,QAAA,gBACA,EAAA,IAAA,EAAA,IACA,EAAA,KACA,EAAA,OAAA,IAAA,EAAA,EAAA,KACA,EAAA,OAAA,EAAA,EAAA,MAEA,EAAA,SAAA,EAAA,EAAA,GACA,IAAA,EAAA,GACA,EAAA,EAAA,WACA,QAAA,EAAA,MAAA,EAAA,MAAA,IAEA,EAAA,EAAA,GAAA,EAAA,EAAA,GAAA,EAAA,GACA,IAAA,EAAA,GAAA,GACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,SAAA,IAMA,EAAA,EAAA,KAAA,SAAA,EAAA,GAIA,OAHA,EAAA,OAAA,EAAA,IACA,EAAA,IAAA,EAAA,EAAA,QAAA,EAAA,KACA,EAAA,IAAA,EAAA,EAAA,QAAA,EAAA,KACA,GAGA,OAAA,QAAA;;AC7BA,IAAA,EAAA,QAAA,aAAA,SACA,EAAA,QAAA,kBAAA,KACA,EAAA,QAAA,gBACA,EAAA,cAEA,OAAA,QAAA,IAAA,EAAA,EAAA,OAAA,KAAA,EAAA,EAAA,QAAA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,OAAA,GAAA,GACA,OAAA,EAAA,EAAA,IAAA,IAAA,EAAA,KAAA,GAAA,GAAA,MACA;;ACRA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBAEA,EAAA,EAAA,EAAA,EAAA,GAAA,UAAA,GAAA,CAAA,SAAA;;ACHA,IAAA,EAAA,QAAA,aAAA,WACA,EAAA,QAAA,kBAAA,KAEA,OAAA,QAAA,EAAA,EAAA,QAAA,gBAAA,QAAA,EAAA,EAAA,SAAA,GACA,IAAA,EAAA,EAAA,OAAA,GAAA,GACA,EAAA,EAAA,GACA,OAAA,IAAA,GAAA,KAAA,EAAA,OAAA,IAAA,EAAA,GACA;;ACPA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,kBAEA,EAAA,EAAA,EAAA,EAAA,GAAA,YAAA,GAAA,CAAA,WAAA;;ACHA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,gBAAA,IACA,OAAA,QAAA,SAAA,EAAA,EAAA,GACA,IACA,EADA,EAAA,EAAA,YAIA,OAFA,IAAA,GAAA,mBAAA,IAAA,EAAA,EAAA,aAAA,EAAA,WAAA,EAAA,IAAA,GACA,EAAA,EAAA,GACA;;;ACPA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,UACA,EAAA,QAAA,UACA,EAAA,QAAA,0BACA,EAAA,QAAA,mBACA,EAAA,QAAA,YACA,EAAA,QAAA,kBAAA,EACA,EAAA,QAAA,kBAAA,EACA,EAAA,QAAA,gBAAA,EACA,EAAA,QAAA,kBAAA,KACA,EAAA,SACA,EAAA,EAAA,GACA,EAAA,EACA,EAAA,EAAA,UAEA,EAAA,EAAA,QAAA,mBAAA,CAAA,KAAA,EACA,EAAA,SAAA,OAAA,UAGA,EAAA,SAAA,GACA,IAAA,EAAA,EAAA,GAAA,GACA,GAAA,iBAAA,GAAA,EAAA,OAAA,EAAA,CAEA,IACA,EAAA,EAAA,EADA,GADA,EAAA,EAAA,EAAA,OAAA,EAAA,EAAA,IACA,WAAA,GAEA,GAAA,KAAA,GAAA,KAAA,GAEA,GAAA,MADA,EAAA,EAAA,WAAA,KACA,MAAA,EAAA,OAAA,SACA,GAAA,KAAA,EAAA,CACA,OAAA,EAAA,WAAA,IACA,KAAA,GAAA,KAAA,GAAA,EAAA,EAAA,EAAA,GAAA,MACA,KAAA,GAAA,KAAA,IAAA,EAAA,EAAA,EAAA,GAAA,MACA,QAAA,OAAA,EAEA,IAAA,IAAA,EAAA,EAAA,EAAA,MAAA,GAAA,EAAA,EAAA,EAAA,EAAA,OAAA,EAAA,EAAA,IAIA,IAHA,EAAA,EAAA,WAAA,IAGA,IAAA,EAAA,EAAA,OAAA,IACA,OAAA,SAAA,EAAA,IAEA,OAAA,GAGA,IAAA,EAAA,UAAA,EAAA,QAAA,EAAA,QAAA,CACA,EAAA,SAAA,GACA,IAAA,EAAA,UAAA,OAAA,EAAA,EAAA,EACA,EAAA,KACA,OAAA,aAAA,IAEA,EAAA,EAAA,WAAA,EAAA,QAAA,KAAA,KAAA,EAAA,IAAA,GACA,EAAA,IAAA,EAAA,EAAA,IAAA,EAAA,GAAA,EAAA,IAEA,IAAA,IAMA,EANA,EAAA,QAAA,kBAAA,EAAA,GAAA,6KAMA,MAAA,KAAA,EAAA,EAAA,EAAA,OAAA,EAAA,IACA,EAAA,EAAA,EAAA,EAAA,MAAA,EAAA,EAAA,IACA,EAAA,EAAA,EAAA,EAAA,EAAA,IAGA,EAAA,UAAA,EACA,EAAA,YAAA,EACA,QAAA,cAAA,CAAA,EAAA,EAAA;;ACnEA,IAAA,EAAA,QAAA,UACA,OAAA,QAAA,SAAA,EAAA,GACA,GAAA,iBAAA,GAAA,UAAA,EAAA,GAAA,MAAA,UAAA,GACA,OAAA;;ACHA,aACA,IAAA,EAAA,QAAA,iBACA,EAAA,QAAA,cAEA,OAAA,QAAA,SAAA,GACA,IAAA,EAAA,OAAA,EAAA,OACA,EAAA,GACA,EAAA,EAAA,GACA,GAAA,EAAA,GAAA,GAAA,EAAA,EAAA,MAAA,WAAA,2BACA,KAAA,EAAA,GAAA,KAAA,KAAA,GAAA,GAAA,EAAA,IAAA,GAAA,GACA,OAAA;;ACVA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,QAAA,qBACA,EAAA,QAAA,oBACA,EAAA,GAAA,QACA,EAAA,KAAA,MACA,EAAA,CAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GACA,EAAA,wCACA,EAAA,IAEA,EAAA,SAAA,EAAA,GAGA,IAFA,IAAA,GAAA,EACA,EAAA,IACA,EAAA,GACA,GAAA,EAAA,EAAA,GACA,EAAA,GAAA,EAAA,IACA,EAAA,EAAA,EAAA,MAGA,EAAA,SAAA,GAGA,IAFA,IAAA,EAAA,EACA,EAAA,IACA,GAAA,GACA,GAAA,EAAA,GACA,EAAA,GAAA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,KAGA,EAAA,WAGA,IAFA,IAAA,EAAA,EACA,EAAA,KACA,GAAA,GACA,GAAA,KAAA,GAAA,IAAA,GAAA,IAAA,EAAA,GAAA,CACA,IAAA,EAAA,OAAA,EAAA,IACA,EAAA,KAAA,EAAA,EAAA,EAAA,EAAA,KAAA,EAAA,EAAA,EAAA,QAAA,EAEA,OAAA,GAEA,EAAA,SAAA,EAAA,EAAA,GACA,OAAA,IAAA,EAAA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,EAAA,EAAA,IAEA,EAAA,SAAA,GAGA,IAFA,IAAA,EAAA,EACA,EAAA,EACA,GAAA,MACA,GAAA,GACA,GAAA,KAEA,KAAA,GAAA,GACA,GAAA,EACA,GAAA,EACA,OAAA,GAGA,EAAA,EAAA,EAAA,EAAA,KAAA,IACA,UAAA,KAAA,QAAA,IACA,MAAA,GAAA,QAAA,IACA,SAAA,MAAA,QAAA,IACA,yBAAA,mBAAA,QAAA,MACA,QAAA,WAAA,CAAA,WAEA,EAAA,KAAA,OACA,SAAA,CACA,QAAA,SAAA,GACA,IAIA,EAAA,EAAA,EAAA,EAJA,EAAA,EAAA,KAAA,GACA,EAAA,EAAA,GACA,EAAA,GACA,EAAA,EAEA,GAAA,EAAA,GAAA,EAAA,GAAA,MAAA,WAAA,GAEA,GAAA,GAAA,EAAA,MAAA,MACA,GAAA,IAAA,MAAA,GAAA,KAAA,OAAA,OAAA,GAKA,GAJA,EAAA,IACA,EAAA,IACA,GAAA,GAEA,EAAA,MAKA,GAHA,GADA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,IAAA,IACA,EAAA,EAAA,EAAA,GAAA,EAAA,GAAA,EAAA,EAAA,EAAA,EAAA,GACA,GAAA,kBACA,EAAA,GAAA,GACA,EAAA,CAGA,IAFA,EAAA,EAAA,GACA,EAAA,EACA,GAAA,GACA,EAAA,IAAA,GACA,GAAA,EAIA,IAFA,EAAA,EAAA,GAAA,EAAA,GAAA,GACA,EAAA,EAAA,EACA,GAAA,IACA,EAAA,GAAA,IACA,GAAA,GAEA,EAAA,GAAA,GACA,EAAA,EAAA,GACA,EAAA,GACA,EAAA,SAEA,EAAA,EAAA,GACA,EAAA,IAAA,EAAA,GACA,EAAA,IAAA,EAAA,KAAA,EAAA,GAQA,OAHA,EAFA,EAAA,EAEA,IADA,EAAA,EAAA,SACA,EAAA,KAAA,EAAA,KAAA,EAAA,EAAA,GAAA,EAAA,EAAA,MAAA,EAAA,EAAA,GAAA,IAAA,EAAA,MAAA,EAAA,IAEA,EAAA;;AC9GA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,YACA,EAAA,QAAA,qBACA,EAAA,GAAA,YAEA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,WAEA,MAAA,MAAA,EAAA,KAAA,OAAA,OACA,EAAA,WAEA,EAAA,KAAA,OACA,SAAA,CACA,YAAA,SAAA,GACA,IAAA,EAAA,EAAA,KAAA,6CACA,YAAA,IAAA,EAAA,EAAA,KAAA,GAAA,EAAA,KAAA,EAAA;;ACdA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,SAAA,CAAA,QAAA,KAAA,IAAA,GAAA;;ACFA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,aAAA,SAEA,EAAA,EAAA,EAAA,SAAA,CACA,SAAA,SAAA,GACA,MAAA,iBAAA,GAAA,EAAA;;ACLA,IAAA,EAAA,QAAA,gBACA,EAAA,KAAA,MACA,OAAA,QAAA,SAAA,GACA,OAAA,EAAA,IAAA,SAAA,IAAA,EAAA,KAAA;;ACHA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,SAAA,CAAA,UAAA,QAAA;;ACFA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,SAAA,CACA,MAAA,SAAA,GAEA,OAAA,GAAA;;ACLA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,KAAA,IAEA,EAAA,EAAA,EAAA,SAAA,CACA,cAAA,SAAA,GACA,OAAA,EAAA,IAAA,EAAA,IAAA;;ACNA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,SAAA,CAAA,iBAAA;;ACFA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,SAAA,CAAA,kBAAA;;ACHA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,kBAEA,EAAA,EAAA,EAAA,EAAA,GAAA,OAAA,YAAA,GAAA,SAAA,CAAA,WAAA;;ACHA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBAEA,EAAA,EAAA,EAAA,EAAA,GAAA,OAAA,UAAA,GAAA,SAAA,CAAA,SAAA;;ACFA,OAAA,QAAA,KAAA,OAAA,SAAA,GACA,OAAA,GAAA,IAAA,MAAA,EAAA,KAAA,EAAA,EAAA,EAAA,EAAA,KAAA,IAAA,EAAA;;ACDA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,KAAA,KACA,EAAA,KAAA,MAEA,EAAA,EAAA,EAAA,EAAA,IAAA,GAEA,KAAA,KAAA,MAAA,EAAA,OAAA,aAEA,EAAA,EAAA,IAAA,EAAA,GACA,OAAA,CACA,MAAA,SAAA,GACA,OAAA,GAAA,GAAA,EAAA,IAAA,EAAA,kBACA,KAAA,IAAA,GAAA,KAAA,IACA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,EAAA;;ACdA,IAAA,EAAA,QAAA,aACA,EAAA,KAAA,MAEA,SAAA,EAAA,GACA,OAAA,SAAA,GAAA,IAAA,GAAA,EAAA,EAAA,GAAA,GAAA,GAAA,KAAA,IAAA,EAAA,KAAA,KAAA,EAAA,EAAA,IAAA,EAIA,EAAA,EAAA,EAAA,EAAA,IAAA,GAAA,EAAA,EAAA,GAAA,GAAA,OAAA,CAAA,MAAA;;ACRA,IAAA,EAAA,QAAA,aACA,EAAA,KAAA,MAGA,EAAA,EAAA,EAAA,EAAA,IAAA,GAAA,EAAA,GAAA,GAAA,GAAA,OAAA,CACA,MAAA,SAAA,GACA,OAAA,IAAA,GAAA,GAAA,EAAA,KAAA,KAAA,EAAA,IAAA,EAAA,IAAA;;ACNA,OAAA,QAAA,KAAA,MAAA,SAAA,GAEA,OAAA,IAAA,GAAA,IAAA,GAAA,EAAA,EAAA,EAAA,GAAA,EAAA;;ACFA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBAEA,EAAA,EAAA,EAAA,OAAA,CACA,KAAA,SAAA,GACA,OAAA,EAAA,GAAA,GAAA,KAAA,IAAA,KAAA,IAAA,GAAA,EAAA;;ACLA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CACA,MAAA,SAAA,GACA,OAAA,KAAA,GAAA,GAAA,KAAA,MAAA,KAAA,IAAA,EAAA,IAAA,KAAA,OAAA;;ACJA,IAAA,EAAA,QAAA,aACA,EAAA,KAAA,IAEA,EAAA,EAAA,EAAA,OAAA,CACA,KAAA,SAAA,GACA,OAAA,EAAA,GAAA,GAAA,GAAA,IAAA;;ACLA,IAAA,EAAA,KAAA,MACA,OAAA,SAAA,GAEA,EAAA,IAAA,oBAAA,EAAA,IAAA,qBAEA,OAAA,GAAA,OACA,SAAA,GACA,OAAA,IAAA,GAAA,GAAA,EAAA,GAAA,MAAA,EAAA,KAAA,EAAA,EAAA,EAAA,EAAA,KAAA,IAAA,GAAA,GACA;;ACRA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBAEA,EAAA,EAAA,EAAA,EAAA,GAAA,GAAA,KAAA,OAAA,OAAA,CAAA,MAAA;;ACHA,IAAA,EAAA,QAAA,gBACA,EAAA,KAAA,IACA,EAAA,EAAA,GAAA,IACA,EAAA,EAAA,GAAA,IACA,EAAA,EAAA,EAAA,MAAA,EAAA,GACA,EAAA,EAAA,GAAA,KAEA,EAAA,SAAA,GACA,OAAA,EAAA,EAAA,EAAA,EAAA,GAGA,OAAA,QAAA,KAAA,QAAA,SAAA,GACA,IAEA,EAAA,EAFA,EAAA,KAAA,IAAA,GACA,EAAA,EAAA,GAEA,OAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,GAEA,GADA,GAAA,EAAA,EAAA,GAAA,IACA,EAAA,IAEA,GAAA,GAAA,EAAA,GAAA,EAAA,GACA,EAAA;;ACpBA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CAAA,OAAA,QAAA;;ACFA,IAAA,EAAA,QAAA,aACA,EAAA,KAAA,IAEA,EAAA,EAAA,EAAA,OAAA,CACA,MAAA,SAAA,EAAA,GAMA,IALA,IAIA,EAAA,EAJA,EAAA,EACA,EAAA,EACA,EAAA,UAAA,OACA,EAAA,EAEA,EAAA,GAEA,GADA,EAAA,EAAA,UAAA,QAGA,EAAA,GADA,EAAA,EAAA,GACA,EAAA,EACA,EAAA,GAGA,GAFA,EAAA,GACA,EAAA,EAAA,GACA,EACA,EAEA,OAAA,IAAA,EAAA,EAAA,EAAA,EAAA,EAAA,KAAA,KAAA;;ACrBA,IAAA,EAAA,QAAA,aACA,EAAA,KAAA,KAGA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,WAAA,CAAA,WACA,OAAA,GAAA,EAAA,WAAA,IAAA,GAAA,EAAA,SACA,OAAA,CACA,KAAA,SAAA,EAAA,GACA,IACA,GAAA,EACA,GAAA,EACA,EAHA,MAGA,EACA,EAJA,MAIA,EACA,OAAA,EAAA,EAAA,IALA,MAKA,IAAA,IAAA,EAAA,GALA,MAKA,IAAA,KAAA,KAAA;;ACbA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CACA,MAAA,SAAA,GACA,OAAA,KAAA,IAAA,GAAA,KAAA;;ACJA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CAAA,MAAA,QAAA;;ACFA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CACA,KAAA,SAAA,GACA,OAAA,KAAA,IAAA,GAAA,KAAA;;ACJA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CAAA,KAAA,QAAA;;ACFA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,KAAA,IAGA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,WAAA,CAAA,WACA,OAAA,QAAA,KAAA,MAAA,SACA,OAAA,CACA,KAAA,SAAA,GACA,OAAA,KAAA,IAAA,GAAA,GAAA,GACA,EAAA,GAAA,GAAA,IAAA,GACA,EAAA,EAAA,GAAA,GAAA,EAAA,KAAA,KAAA,EAAA;;ACXA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,KAAA,IAEA,EAAA,EAAA,EAAA,OAAA,CACA,KAAA,SAAA,GACA,IAAA,EAAA,EAAA,GAAA,GACA,EAAA,GAAA,GACA,OAAA,GAAA,EAAA,EAAA,EAAA,GAAA,EAAA,GAAA,GAAA,EAAA,IAAA,EAAA,GAAA,GAAA;;ACRA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CACA,MAAA,SAAA,GACA,OAAA,EAAA,EAAA,KAAA,MAAA,KAAA,MAAA;;ACLA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,wBACA,EAAA,OAAA,aACA,EAAA,OAAA,cAGA,EAAA,EAAA,EAAA,EAAA,KAAA,GAAA,GAAA,EAAA,QAAA,SAAA,CAEA,cAAA,SAAA,GAKA,IAJA,IAGA,EAHA,EAAA,GACA,EAAA,UAAA,OACA,EAAA,EAEA,EAAA,GAAA,CAEA,GADA,GAAA,UAAA,KACA,EAAA,EAAA,WAAA,EAAA,MAAA,WAAA,EAAA,8BACA,EAAA,KAAA,EAAA,MACA,EAAA,GACA,EAAA,QAAA,GAAA,QAAA,IAAA,EAAA,KAAA,QAEA,OAAA,EAAA,KAAA;;ACpBA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBAEA,EAAA,EAAA,EAAA,SAAA,CAEA,IAAA,SAAA,GAMA,IALA,IAAA,EAAA,EAAA,EAAA,KACA,EAAA,EAAA,EAAA,QACA,EAAA,UAAA,OACA,EAAA,GACA,EAAA,EACA,EAAA,GACA,EAAA,KAAA,OAAA,EAAA,OACA,EAAA,GAAA,EAAA,KAAA,OAAA,UAAA,KACA,OAAA,EAAA,KAAA;;ACfA,aAEA,QAAA,iBAAA,CAAA,OAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA;;ACJA,IAAA,EAAA,QAAA,iBACA,EAAA,QAAA,cAGA,OAAA,QAAA,SAAA,GACA,OAAA,SAAA,EAAA,GACA,IAGA,EAAA,EAHA,EAAA,OAAA,EAAA,IACA,EAAA,EAAA,GACA,EAAA,EAAA,OAEA,OAAA,EAAA,GAAA,GAAA,EAAA,EAAA,QAAA,GACA,EAAA,EAAA,WAAA,IACA,OAAA,EAAA,OAAA,EAAA,IAAA,IAAA,EAAA,EAAA,WAAA,EAAA,IAAA,OAAA,EAAA,MACA,EAAA,EAAA,OAAA,GAAA,EACA,EAAA,EAAA,MAAA,EAAA,EAAA,GAAA,EAAA,OAAA,EAAA,OAAA,IAAA;;ACdA,OAAA,QAAA;;ACAA,aACA,IAAA,EAAA,QAAA,oBACA,EAAA,QAAA,oBACA,EAAA,QAAA,wBACA,EAAA,GAGA,QAAA,UAAA,CAAA,EAAA,QAAA,SAAA,CAAA,YAAA,WAAA,OAAA,OAEA,OAAA,QAAA,SAAA,EAAA,EAAA,GACA,EAAA,UAAA,EAAA,EAAA,CAAA,KAAA,EAAA,EAAA,KACA,EAAA,EAAA,EAAA;;ACXA,aACA,IAAA,EAAA,QAAA,cACA,EAAA,QAAA,aACA,EAAA,QAAA,eACA,EAAA,QAAA,WACA,EAAA,QAAA,gBACA,EAAA,QAAA,kBACA,EAAA,QAAA,wBACA,EAAA,QAAA,iBACA,EAAA,QAAA,SAAA,CAAA,YACA,IAAA,GAAA,MAAA,QAAA,GAAA,QACA,EAAA,aACA,EAAA,OACA,EAAA,SAEA,EAAA,WAAA,OAAA,MAEA,OAAA,QAAA,SAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,GACA,IAeA,EAAA,EAAA,EAfA,EAAA,SAAA,GACA,IAAA,GAAA,KAAA,EAAA,OAAA,EAAA,GACA,OAAA,GACA,KAAA,EACA,KAAA,EAAA,OAAA,WAAA,OAAA,IAAA,EAAA,KAAA,IACA,OAAA,WAAA,OAAA,IAAA,EAAA,KAAA,KAEA,EAAA,EAAA,YACA,EAAA,GAAA,EACA,GAAA,EACA,EAAA,EAAA,UACA,EAAA,EAAA,IAAA,EAAA,IAAA,GAAA,EAAA,GACA,EAAA,GAAA,EAAA,GACA,EAAA,EAAA,EAAA,EAAA,WAAA,OAAA,EACA,EAAA,SAAA,GAAA,EAAA,SAAA,EAwBA,GArBA,IACA,EAAA,EAAA,EAAA,KAAA,IAAA,OACA,OAAA,WAAA,EAAA,OAEA,EAAA,EAAA,GAAA,GAEA,GAAA,mBAAA,EAAA,IAAA,EAAA,EAAA,EAAA,IAIA,GAAA,GAAA,EAAA,OAAA,IACA,GAAA,EACA,EAAA,WAAA,OAAA,EAAA,KAAA,QAGA,IAAA,IAAA,IAAA,GAAA,EAAA,IACA,EAAA,EAAA,EAAA,GAGA,EAAA,GAAA,EACA,EAAA,GAAA,EACA,EAMA,GALA,EAAA,CACA,OAAA,EAAA,EAAA,EAAA,GACA,KAAA,EAAA,EAAA,EAAA,GACA,QAAA,GAEA,EAAA,IAAA,KAAA,EACA,KAAA,GAAA,EAAA,EAAA,EAAA,EAAA,SACA,EAAA,EAAA,EAAA,EAAA,GAAA,GAAA,GAAA,EAAA,GAEA,OAAA;;ACnEA,aACA,IAAA,EAAA,QAAA,eAAA,EAAA,GAGA,QAAA,iBAAA,CAAA,OAAA,SAAA,SAAA,GACA,KAAA,GAAA,OAAA,GACA,KAAA,GAAA,GAEA,WACA,IAEA,EAFA,EAAA,KAAA,GACA,EAAA,KAAA,GAEA,OAAA,GAAA,EAAA,OAAA,CAAA,WAAA,EAAA,MAAA,IACA,EAAA,EAAA,EAAA,GACA,KAAA,IAAA,EAAA,OACA,CAAA,MAAA,EAAA,MAAA;;ACfA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,eAAA,EAAA,GACA,EAAA,EAAA,EAAA,SAAA,CAEA,YAAA,SAAA,GACA,OAAA,EAAA,KAAA;;ACLA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,UACA,EAAA,QAAA,SAAA,CAAA,SACA,OAAA,QAAA,SAAA,GACA,IAAA,EACA,OAAA,EAAA,UAAA,KAAA,EAAA,EAAA,MAAA,EAAA,UAAA,EAAA;;ACLA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,cAEA,OAAA,QAAA,SAAA,EAAA,EAAA,GACA,GAAA,EAAA,GAAA,MAAA,UAAA,UAAA,EAAA,0BACA,OAAA,OAAA,EAAA;;ACNA,IAAA,EAAA,QAAA,SAAA,CAAA,SACA,OAAA,QAAA,SAAA,GACA,IAAA,EAAA,IACA,IACA,MAAA,GAAA,GACA,MAAA,GACA,IAEA,OADA,EAAA,IAAA,GACA,MAAA,GAAA,GACA,MAAA,KACA,OAAA;;ACTA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,qBACA,EAAA,WACA,EAAA,GAAA,GAEA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,qBAAA,CAAA,GAAA,SAAA,CACA,SAAA,SAAA,GACA,IAAA,EAAA,EAAA,KAAA,EAAA,GACA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,EACA,EAAA,EAAA,EAAA,QACA,OAAA,IAAA,EAAA,EAAA,KAAA,IAAA,EAAA,GAAA,GACA,EAAA,OAAA,GACA,OAAA,EACA,EAAA,KAAA,EAAA,EAAA,GACA,EAAA,MAAA,EAAA,EAAA,OAAA,KAAA;;AChBA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,qBACA,EAAA,WAEA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,qBAAA,CAAA,GAAA,SAAA,CACA,SAAA,SAAA,GACA,SAAA,EAAA,KAAA,EAAA,GACA,QAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA;;ACTA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,SAAA,CAEA,OAAA,QAAA;;ACHA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,qBACA,EAAA,aACA,EAAA,GAAA,GAEA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,qBAAA,CAAA,GAAA,SAAA,CACA,WAAA,SAAA,GACA,IAAA,EAAA,EAAA,KAAA,EAAA,GACA,EAAA,EAAA,KAAA,IAAA,UAAA,OAAA,EAAA,UAAA,QAAA,EAAA,EAAA,SACA,EAAA,OAAA,GACA,OAAA,EACA,EAAA,KAAA,EAAA,EAAA,GACA,EAAA,MAAA,EAAA,EAAA,EAAA,UAAA;;ACfA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,YACA,EAAA,QAAA,cACA,EAAA,KAEA,EAAA,SAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,OAAA,EAAA,IACA,EAAA,IAAA,EAEA,MADA,KAAA,IAAA,GAAA,IAAA,EAAA,KAAA,OAAA,GAAA,QAAA,EAAA,UAAA,KACA,EAAA,IAAA,EAAA,KAAA,EAAA,KAEA,OAAA,QAAA,SAAA,EAAA,GACA,IAAA,EAAA,GACA,EAAA,GAAA,EAAA,GACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,WACA,IAAA,EAAA,GAAA,GAAA,KACA,OAAA,IAAA,EAAA,eAAA,EAAA,MAAA,KAAA,OAAA,IACA,SAAA;;ACjBA,aAEA,QAAA,iBAAA,CAAA,SAAA,SAAA,GACA,OAAA,SAAA,GACA,OAAA,EAAA,KAAA,IAAA,OAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,MAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,MAAA,GAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,QAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,QAAA,GAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,OAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,IAAA,GAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,QAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,KAAA,GAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,YAAA,SAAA,GACA,OAAA,SAAA,GACA,OAAA,EAAA,KAAA,OAAA,QAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,WAAA,SAAA,GACA,OAAA,SAAA,GACA,OAAA,EAAA,KAAA,OAAA,OAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,UAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,IAAA,GAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,OAAA,SAAA,GACA,OAAA,SAAA,GACA,OAAA,EAAA,KAAA,IAAA,OAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,QAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,QAAA,GAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,SAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,SAAA,GAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,MAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,MAAA,GAAA;;ACJA,aAEA,QAAA,iBAAA,CAAA,MAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,MAAA,GAAA;;ACHA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CAAA,IAAA,WAAA,OAAA,IAAA,MAAA;;ACHA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,mBAEA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,WAAA,CAAA,WACA,OAAA,OAAA,IAAA,KAAA,KAAA,UACA,IAAA,KAAA,UAAA,OAAA,KAAA,CAAA,YAAA,WAAA,OAAA,OACA,OAAA,CAEA,OAAA,SAAA,GACA,IAAA,EAAA,EAAA,MACA,EAAA,EAAA,GACA,MAAA,iBAAA,GAAA,SAAA,GAAA,EAAA,cAAA;;ACbA,aAEA,IAAA,EAAA,QAAA,YACA,EAAA,KAAA,UAAA,QACA,EAAA,KAAA,UAAA,YAEA,EAAA,SAAA,GACA,OAAA,EAAA,EAAA,EAAA,IAAA,GAIA,OAAA,QAAA,EAAA,WACA,MAAA,4BAAA,EAAA,KAAA,IAAA,MAAA,KAAA,QACA,EAAA,WACA,EAAA,KAAA,IAAA,KAAA,QACA,WACA,IAAA,SAAA,EAAA,KAAA,OAAA,MAAA,WAAA,sBACA,IAAA,EAAA,KACA,EAAA,EAAA,iBACA,EAAA,EAAA,qBACA,EAAA,EAAA,EAAA,IAAA,EAAA,KAAA,IAAA,GACA,OAAA,GAAA,QAAA,KAAA,IAAA,IAAA,MAAA,GAAA,GAAA,GACA,IAAA,EAAA,EAAA,cAAA,GAAA,IAAA,EAAA,EAAA,cACA,IAAA,EAAA,EAAA,eAAA,IAAA,EAAA,EAAA,iBACA,IAAA,EAAA,EAAA,iBAAA,KAAA,EAAA,GAAA,EAAA,IAAA,EAAA,IAAA,KACA;;ACxBA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,yBAGA,EAAA,EAAA,EAAA,EAAA,GAAA,KAAA,UAAA,cAAA,GAAA,OAAA,CACA,YAAA;;ACNA,IAAA,EAAA,KAAA,UACA,EAAA,eACA,EAAA,WACA,EAAA,EAAA,GACA,EAAA,EAAA,QACA,IAAA,KAAA,KAAA,IAAA,GACA,QAAA,cAAA,CAAA,EAAA,EAAA,WACA,IAAA,EAAA,EAAA,KAAA,MAEA,OAAA,GAAA,EAAA,EAAA,KAAA,MAAA;;ACTA,aACA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,mBACA,EAAA,SAEA,OAAA,QAAA,SAAA,GACA,GAAA,WAAA,GAAA,IAAA,GAAA,YAAA,EAAA,MAAA,UAAA,kBACA,OAAA,EAAA,EAAA,MAAA,GAAA;;ACPA,IAAA,EAAA,QAAA,SAAA,CAAA,eACA,EAAA,KAAA,UAEA,KAAA,GAAA,QAAA,UAAA,CAAA,EAAA,EAAA,QAAA;;ACFA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,QAAA,CAAA,QAAA,QAAA;;ACFA,IAAA,EAAA,QAAA,gBACA,OAAA,QAAA,SAAA,EAAA,EAAA,EAAA,GACA,IACA,OAAA,EAAA,EAAA,EAAA,GAAA,GAAA,EAAA,IAAA,EAAA,GAEA,MAAA,GACA,IAAA,EAAA,EAAA,OAEA,WADA,IAAA,GAAA,EAAA,EAAA,KAAA,IACA;;ACRA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,SAAA,CAAA,YACA,EAAA,MAAA,UAEA,OAAA,QAAA,SAAA,GACA,YAAA,IAAA,IAAA,EAAA,QAAA,GAAA,EAAA,KAAA;;ACNA,aACA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,oBAEA,OAAA,QAAA,SAAA,EAAA,EAAA,GACA,KAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,IACA,EAAA,GAAA;;ACNA,IAAA,EAAA,QAAA,cACA,EAAA,QAAA,SAAA,CAAA,YACA,EAAA,QAAA,gBACA,OAAA,QAAA,QAAA,WAAA,kBAAA,SAAA,GACA,GAAA,MAAA,EAAA,OAAA,EAAA,IACA,EAAA,eACA,EAAA,EAAA;;ACNA,IAAA,EAAA,QAAA,SAAA,CAAA,YACA,GAAA,EAEA,IACA,IAAA,EAAA,CAAA,GAAA,KACA,EAAA,OAAA,WAAA,GAAA,GAEA,MAAA,KAAA,EAAA,WAAA,MAAA,IACA,MAAA,IAEA,OAAA,QAAA,SAAA,EAAA,GACA,IAAA,IAAA,EAAA,OAAA,EACA,IAAA,GAAA,EACA,IACA,IAAA,EAAA,CAAA,GACA,EAAA,EAAA,KACA,EAAA,KAAA,WAAA,MAAA,CAAA,KAAA,GAAA,IACA,EAAA,GAAA,WAAA,OAAA,GACA,EAAA,GACA,MAAA,IACA,OAAA;;ACpBA,aACA,IAAA,EAAA,QAAA,UACA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,oBACA,EAAA,QAAA,gBACA,EAAA,QAAA,sBACA,EAAA,QAAA,8BAEA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,iBAAA,CAAA,SAAA,GAAA,MAAA,KAAA,KAAA,QAAA,CAEA,KAAA,SAAA,GACA,IAOA,EAAA,EAAA,EAAA,EAPA,EAAA,EAAA,GACA,EAAA,mBAAA,KAAA,KAAA,MACA,EAAA,UAAA,OACA,EAAA,EAAA,EAAA,UAAA,QAAA,EACA,OAAA,IAAA,EACA,EAAA,EACA,EAAA,EAAA,GAIA,GAFA,IAAA,EAAA,EAAA,EAAA,EAAA,EAAA,UAAA,QAAA,EAAA,IAEA,MAAA,GAAA,GAAA,OAAA,EAAA,GAMA,IAAA,EAAA,IAAA,EADA,EAAA,EAAA,EAAA,SACA,EAAA,EAAA,IACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,GAAA,EAAA,SANA,IAAA,EAAA,EAAA,KAAA,GAAA,EAAA,IAAA,IAAA,EAAA,EAAA,QAAA,KAAA,IACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,CAAA,EAAA,MAAA,IAAA,GAAA,EAAA,OASA,OADA,EAAA,OAAA,EACA;;AClCA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,sBAGA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,WAAA,CAAA,WACA,SAAA,KACA,QAAA,MAAA,GAAA,KAAA,aAAA,KACA,QAAA,CAEA,GAAA,WAIA,IAHA,IAAA,EAAA,EACA,EAAA,UAAA,OACA,EAAA,IAAA,mBAAA,KAAA,KAAA,OAAA,GACA,EAAA,GAAA,EAAA,EAAA,EAAA,UAAA,MAEA,OADA,EAAA,OAAA,EACA;;AChBA,aACA,IAAA,EAAA,QAAA,YAEA,OAAA,QAAA,SAAA,EAAA,GACA,QAAA,GAAA,EAAA,WAEA,EAAA,EAAA,KAAA,KAAA,aAAA,GAAA,EAAA,KAAA;;ACNA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,GAAA,KAGA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,eAAA,SAAA,QAAA,mBAAA,CAAA,IAAA,QAAA,CACA,KAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,WAAA,IAAA,EAAA,IAAA;;ACTA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,WACA,EAAA,QAAA,UACA,EAAA,QAAA,wBACA,EAAA,QAAA,gBACA,EAAA,GAAA,MAGA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,WAAA,CAAA,WACA,GAAA,EAAA,KAAA,KACA,QAAA,CACA,MAAA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,KAAA,QACA,EAAA,EAAA,MAEA,GADA,OAAA,IAAA,EAAA,EAAA,EACA,SAAA,EAAA,OAAA,EAAA,KAAA,KAAA,EAAA,GAMA,IALA,IAAA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,GACA,EAAA,IAAA,MAAA,GACA,EAAA,EACA,EAAA,EAAA,IAAA,EAAA,GAAA,UAAA,EACA,KAAA,OAAA,EAAA,GACA,KAAA,EAAA,GACA,OAAA;;ACzBA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,EAAA,QAAA,YACA,EAAA,GAAA,KACA,EAAA,CAAA,EAAA,EAAA,GAEA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,WAEA,EAAA,UAAA,OACA,EAAA,WAEA,EAAA,KAAA,UAEA,QAAA,mBAAA,CAAA,IAAA,QAAA,CAEA,KAAA,SAAA,GACA,YAAA,IAAA,EACA,EAAA,KAAA,EAAA,OACA,EAAA,KAAA,EAAA,MAAA,EAAA;;ACpBA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,eACA,EAAA,QAAA,SAAA,CAAA,WAEA,OAAA,QAAA,SAAA,GACA,IAAA,EASA,OARA,EAAA,KAGA,mBAFA,EAAA,EAAA,cAEA,IAAA,QAAA,EAAA,EAAA,aAAA,OAAA,GACA,EAAA,IAEA,QADA,EAAA,EAAA,MACA,OAAA,SAEA,IAAA,EAAA,MAAA;;ACbA,IAAA,EAAA,QAAA,gCAEA,OAAA,QAAA,SAAA,EAAA,GACA,OAAA,IAAA,EAAA,GAAA,CAAA;;ACGA,IAAA,EAAA,QAAA,UACA,EAAA,QAAA,cACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,2BACA,OAAA,QAAA,SAAA,EAAA,GACA,IAAA,EAAA,GAAA,EACA,EAAA,GAAA,EACA,EAAA,GAAA,EACA,EAAA,GAAA,EACA,EAAA,GAAA,EACA,EAAA,GAAA,GAAA,EACA,EAAA,GAAA,EACA,OAAA,SAAA,EAAA,EAAA,GAQA,IAPA,IAMA,EAAA,EANA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,QACA,EAAA,EACA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,QAAA,EAEA,EAAA,EAAA,IAAA,IAAA,GAAA,KAAA,KAEA,EAAA,EADA,EAAA,EAAA,GACA,EAAA,GACA,GACA,GAAA,EAAA,EAAA,GAAA,OACA,GAAA,EAAA,OAAA,GACA,KAAA,EAAA,OAAA,EACA,KAAA,EAAA,OAAA,EACA,KAAA,EAAA,OAAA,EACA,KAAA,EAAA,EAAA,KAAA,QACA,GAAA,EAAA,OAAA,EAGA,OAAA,GAAA,EAAA,GAAA,EAAA,EAAA;;ACzCA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,mBAAA,CAAA,GACA,EAAA,QAAA,mBAAA,CAAA,GAAA,SAAA,GAEA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,QAAA,CAEA,QAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA;;ACRA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,mBAAA,CAAA,GAEA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,mBAAA,CAAA,GAAA,KAAA,GAAA,QAAA,CAEA,IAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA;;ACPA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,mBAAA,CAAA,GAEA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,mBAAA,CAAA,GAAA,QAAA,GAAA,QAAA,CAEA,OAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA;;ACPA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,mBAAA,CAAA,GAEA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,mBAAA,CAAA,GAAA,MAAA,GAAA,QAAA,CAEA,KAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA;;ACPA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,mBAAA,CAAA,GAEA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,mBAAA,CAAA,GAAA,OAAA,GAAA,QAAA,CAEA,MAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA;;ACPA,IAAA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,EAAA,QAAA,cACA,EAAA,QAAA,gBAEA,OAAA,QAAA,SAAA,EAAA,EAAA,EAAA,EAAA,GACA,EAAA,GACA,IAAA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,QACA,EAAA,EAAA,EAAA,EAAA,EACA,EAAA,GAAA,EAAA,EACA,GAAA,EAAA,EAAA,OAAA,CACA,GAAA,KAAA,EAAA,CACA,EAAA,EAAA,GACA,GAAA,EACA,MAGA,GADA,GAAA,EACA,EAAA,EAAA,EAAA,GAAA,EACA,MAAA,UAAA,+CAGA,KAAA,EAAA,GAAA,EAAA,EAAA,EAAA,GAAA,EAAA,KAAA,IACA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,IAEA,OAAA;;AC1BA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,mBAEA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,mBAAA,CAAA,GAAA,QAAA,GAAA,QAAA,CAEA,OAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA,OAAA,UAAA,IAAA;;ACPA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,mBAEA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,mBAAA,CAAA,GAAA,aAAA,GAAA,QAAA,CAEA,YAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA,OAAA,UAAA,IAAA;;ACPA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,oBAAA,EAAA,GACA,EAAA,GAAA,QACA,IAAA,GAAA,EAAA,CAAA,GAAA,QAAA,GAAA,GAAA,EAEA,EAAA,EAAA,EAAA,EAAA,GAAA,IAAA,QAAA,mBAAA,CAAA,IAAA,QAAA,CAEA,QAAA,SAAA,GACA,OAAA,EAEA,EAAA,MAAA,KAAA,YAAA,EACA,EAAA,KAAA,EAAA,UAAA;;ACZA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,EAAA,GAAA,YACA,IAAA,GAAA,EAAA,CAAA,GAAA,YAAA,GAAA,GAAA,EAEA,EAAA,EAAA,EAAA,EAAA,GAAA,IAAA,QAAA,mBAAA,CAAA,IAAA,QAAA,CAEA,YAAA,SAAA,GAEA,GAAA,EAAA,OAAA,EAAA,MAAA,KAAA,YAAA,EACA,IAAA,EAAA,EAAA,MACA,EAAA,EAAA,EAAA,QACA,EAAA,EAAA,EAGA,IAFA,UAAA,OAAA,IAAA,EAAA,KAAA,IAAA,EAAA,EAAA,UAAA,MACA,EAAA,IAAA,EAAA,EAAA,GACA,GAAA,EAAA,IAAA,GAAA,KAAA,GAAA,EAAA,KAAA,EAAA,OAAA,GAAA,EACA,OAAA;;AClBA,aACA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,wBACA,EAAA,QAAA,gBAEA,OAAA,QAAA,GAAA,YAAA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,MACA,EAAA,EAAA,EAAA,QACA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,GACA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,EACA,EAAA,KAAA,UAAA,IAAA,EAAA,EAAA,EAAA,EAAA,IAAA,EAAA,EAAA,GACA,EAAA,EAMA,IALA,EAAA,GAAA,EAAA,EAAA,IACA,GAAA,EACA,GAAA,EAAA,EACA,GAAA,EAAA,GAEA,KAAA,GACA,KAAA,EAAA,EAAA,GAAA,EAAA,UACA,EAAA,GACA,GAAA,EACA,GAAA,EACA,OAAA;;ACvBA,IAAA,EAAA,QAAA,SAAA,CAAA,eACA,EAAA,MAAA,UACA,MAAA,EAAA,IAAA,QAAA,UAAA,CAAA,EAAA,EAAA,IACA,OAAA,QAAA,SAAA,GACA,EAAA,GAAA,IAAA;;ACJA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,QAAA,CAAA,WAAA,QAAA,0BAEA,QAAA,wBAAA,CAAA;;ACJA,aACA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,wBACA,EAAA,QAAA,gBACA,OAAA,QAAA,SAAA,GAOA,IANA,IAAA,EAAA,EAAA,MACA,EAAA,EAAA,EAAA,QACA,EAAA,UAAA,OACA,EAAA,EAAA,EAAA,EAAA,UAAA,QAAA,EAAA,GACA,EAAA,EAAA,EAAA,UAAA,QAAA,EACA,OAAA,IAAA,EAAA,EAAA,EAAA,EAAA,GACA,EAAA,GAAA,EAAA,KAAA,EACA,OAAA;;ACZA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,QAAA,CAAA,KAAA,QAAA,mBAEA,QAAA,wBAAA,CAAA;;ACLA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,mBAAA,CAAA,GACA,EAAA,OACA,GAAA,EAEA,IAAA,IAAA,MAAA,GAAA,GAAA,WAAA,GAAA,IACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,CACA,KAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,MAGA,QAAA,wBAAA,CAAA;;ACbA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,mBAAA,CAAA,GACA,EAAA,YACA,GAAA,EAEA,IAAA,IAAA,MAAA,GAAA,GAAA,WAAA,GAAA,IACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,CACA,UAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,MAGA,QAAA,wBAAA,CAAA;;;ACbA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,kBACA,EAAA,QAAA,SAAA,CAAA,WAEA,OAAA,QAAA,SAAA,GACA,IAAA,EAAA,EAAA,GACA,GAAA,IAAA,EAAA,IAAA,EAAA,EAAA,EAAA,EAAA,CACA,cAAA,EACA,IAAA,WAAA,OAAA;;ACVA,QAAA,iBAAA,CAAA;;ACAA,OAAA,QAAA,SAAA,EAAA,GACA,MAAA,CAAA,MAAA,EAAA,OAAA;;ACDA,aACA,IAAA,EAAA,QAAA,yBACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBAMA,OAAA,QAAA,QAAA,iBAAA,CAAA,MAAA,QAAA,SAAA,EAAA,GACA,KAAA,GAAA,EAAA,GACA,KAAA,GAAA,EACA,KAAA,GAAA,GAEA,WACA,IAAA,EAAA,KAAA,GACA,EAAA,KAAA,GACA,EAAA,KAAA,KACA,OAAA,GAAA,GAAA,EAAA,QACA,KAAA,QAAA,EACA,EAAA,IAEA,EAAA,EAAA,QAAA,EAAA,EACA,UAAA,EAAA,EAAA,GACA,CAAA,EAAA,EAAA,MACA,UAGA,EAAA,UAAA,EAAA,MAEA,EAAA,QACA,EAAA,UACA,EAAA;;ACjCA,aAEA,IAAA,EAAA,QAAA,gBACA,OAAA,QAAA,WACA,IAAA,EAAA,EAAA,MACA,EAAA,GAMA,OALA,EAAA,SAAA,GAAA,KACA,EAAA,aAAA,GAAA,KACA,EAAA,YAAA,GAAA,KACA,EAAA,UAAA,GAAA,KACA,EAAA,SAAA,GAAA,KACA;;;ACXA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,0BACA,EAAA,QAAA,gBAAA,EACA,EAAA,QAAA,kBAAA,EACA,EAAA,QAAA,gBACA,EAAA,QAAA,YACA,EAAA,EAAA,OACA,EAAA,EACA,EAAA,EAAA,UACA,EAAA,KACA,EAAA,KAEA,EAAA,IAAA,EAAA,KAAA,EAEA,GAAA,QAAA,qBAAA,GAAA,QAAA,WAAA,CAAA,WAGA,OAFA,EAAA,QAAA,SAAA,CAAA,WAAA,EAEA,EAAA,IAAA,GAAA,EAAA,IAAA,GAAA,QAAA,EAAA,EAAA,QACA,CACA,EAAA,SAAA,EAAA,GACA,IAAA,EAAA,gBAAA,EACA,EAAA,EAAA,GACA,OAAA,IAAA,EACA,OAAA,GAAA,GAAA,EAAA,cAAA,GAAA,EAAA,EACA,EAAA,EACA,IAAA,EAAA,IAAA,EAAA,EAAA,OAAA,EAAA,GACA,GAAA,EAAA,aAAA,GAAA,EAAA,OAAA,EAAA,GAAA,EAAA,EAAA,KAAA,GAAA,GACA,EAAA,KAAA,EAAA,IASA,IAPA,IAAA,EAAA,SAAA,GACA,KAAA,GAAA,EAAA,EAAA,EAAA,CACA,cAAA,EACA,IAAA,WAAA,OAAA,EAAA,IACA,IAAA,SAAA,GAAA,EAAA,GAAA,MAGA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,OAAA,GAAA,EAAA,EAAA,MACA,EAAA,YAAA,EACA,EAAA,UAAA,EACA,QAAA,cAAA,CAAA,EAAA,SAAA,GAGA,QAAA,iBAAA,CAAA;;AC1CA,aAEA,IAAA,EAAA,QAAA,YAEA,EAAA,OAAA,UAAA,KAIA,EAAA,OAAA,UAAA,QAEA,EAAA,EAEA,EAAA,YAEA,EAAA,WACA,IAAA,EAAA,IACA,EAAA,MAGA,OAFA,EAAA,KAAA,EAAA,KACA,EAAA,KAAA,EAAA,KACA,IAAA,EAAA,IAAA,IAAA,EAAA,GALA,GASA,OAAA,IAAA,OAAA,KAAA,IAAA,GAEA,EAAA,GAAA,EAEA,IACA,EAAA,SAAA,GACA,IACA,EAAA,EAAA,EAAA,EADA,EAAA,KAwBA,OArBA,IACA,EAAA,IAAA,OAAA,IAAA,EAAA,OAAA,WAAA,EAAA,KAAA,KAEA,IAAA,EAAA,EAAA,IAEA,EAAA,EAAA,KAAA,EAAA,GAEA,GAAA,IACA,EAAA,GAAA,EAAA,OAAA,EAAA,MAAA,EAAA,GAAA,OAAA,GAEA,GAAA,GAAA,EAAA,OAAA,GAIA,EAAA,KAAA,EAAA,GAAA,EAAA,WACA,IAAA,EAAA,EAAA,EAAA,UAAA,OAAA,EAAA,SACA,IAAA,UAAA,KAAA,EAAA,QAAA,KAKA,IAIA,OAAA,QAAA;;ACzDA,aACA,IAAA,EAAA,QAAA,kBACA,QAAA,YAAA,CAAA,CACA,OAAA,SACA,OAAA,EACA,OAAA,IAAA,IAAA,MACA,CACA,KAAA;;ACNA,QAAA,mBAAA,KAAA,KAAA,OAAA,QAAA,gBAAA,EAAA,OAAA,UAAA,QAAA,CACA,cAAA,EACA,IAAA,QAAA;;;ACHA,aACA,QAAA,sBACA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,YACA,EAAA,QAAA,kBACA,EAAA,WACA,EAAA,IAAA,GAEA,EAAA,SAAA,GACA,QAAA,cAAA,CAAA,OAAA,UAAA,EAAA,GAAA,IAIA,QAAA,WAAA,CAAA,WAAA,MAAA,QAAA,EAAA,KAAA,CAAA,OAAA,IAAA,MAAA,QACA,EAAA,WACA,IAAA,EAAA,EAAA,MACA,MAAA,IAAA,OAAA,EAAA,OAAA,IACA,UAAA,EAAA,EAAA,OAAA,GAAA,aAAA,OAAA,EAAA,KAAA,QAAA,KAGA,EAAA,MAAA,GACA,EAAA,WACA,OAAA,EAAA,KAAA;;ACtBA,aACA,IAAA,EAAA,QAAA,eAAA,EAAA,GAIA,OAAA,QAAA,SAAA,EAAA,EAAA,GACA,OAAA,GAAA,EAAA,EAAA,EAAA,GAAA,OAAA;;ACNA,aAEA,IAAA,EAAA,QAAA,cACA,EAAA,OAAA,UAAA,KAIA,OAAA,QAAA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,KACA,GAAA,mBAAA,EAAA,CACA,IAAA,EAAA,EAAA,KAAA,EAAA,GACA,GAAA,iBAAA,EACA,MAAA,IAAA,UAAA,sEAEA,OAAA,EAEA,GAAA,WAAA,EAAA,GACA,MAAA,IAAA,UAAA,+CAEA,OAAA,EAAA,KAAA,EAAA;;ACnBA,aACA,QAAA,qBACA,IAAA,EAAA,QAAA,eACA,EAAA,QAAA,WACA,EAAA,QAAA,YACA,EAAA,QAAA,cACA,EAAA,QAAA,UACA,EAAA,QAAA,kBAEA,EAAA,EAAA,WAEA,GAAA,EAAA,WAIA,IAAA,EAAA,IAMA,OALA,EAAA,KAAA,WACA,IAAA,EAAA,GAEA,OADA,EAAA,OAAA,CAAA,EAAA,KACA,GAEA,MAAA,GAAA,QAAA,EAAA,UAGA,EAAA,WAEA,IAAA,EAAA,OACA,EAAA,EAAA,KACA,EAAA,KAAA,WAAA,OAAA,EAAA,MAAA,KAAA,YACA,IAAA,EAAA,KAAA,MAAA,GACA,OAAA,IAAA,EAAA,QAAA,MAAA,EAAA,IAAA,MAAA,EAAA,GANA,GASA,OAAA,QAAA,SAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,GAEA,GAAA,EAAA,WAEA,IAAA,EAAA,GAEA,OADA,EAAA,GAAA,WAAA,OAAA,GACA,GAAA,GAAA,GAAA,KAGA,EAAA,GAAA,EAAA,WAEA,IAAA,GAAA,EACA,EAAA,IASA,OARA,EAAA,KAAA,WAAA,OAAA,GAAA,EAAA,MACA,UAAA,IAGA,EAAA,YAAA,GACA,EAAA,YAAA,GAAA,WAAA,OAAA,IAEA,EAAA,GAAA,KACA,SACA,EAEA,IACA,IACA,GACA,YAAA,IAAA,GACA,UAAA,IAAA,EACA,CACA,IAAA,EAAA,IAAA,GACA,EAAA,EACA,EACA,EACA,GAAA,GACA,SAAA,EAAA,EAAA,EAAA,EAAA,GACA,OAAA,EAAA,OAAA,EACA,IAAA,EAIA,CAAA,MAAA,EAAA,MAAA,EAAA,KAAA,EAAA,EAAA,IAEA,CAAA,MAAA,EAAA,MAAA,EAAA,KAAA,EAAA,EAAA,IAEA,CAAA,MAAA,KAGA,EAAA,EAAA,GACA,EAAA,EAAA,GAEA,EAAA,OAAA,UAAA,EAAA,GACA,EAAA,OAAA,UAAA,EAAA,GAAA,EAGA,SAAA,EAAA,GAAA,OAAA,EAAA,KAAA,EAAA,KAAA,IAGA,SAAA,GAAA,OAAA,EAAA,KAAA,EAAA;;AC5FA,aAEA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,2BACA,EAAA,QAAA,2BAGA,QAAA,gBAAA,CAAA,QAAA,EAAA,SAAA,EAAA,EAAA,EAAA,GACA,MAAA,CAGA,SAAA,GACA,IAAA,EAAA,EAAA,MACA,EAAA,MAAA,OAAA,EAAA,EAAA,GACA,YAAA,IAAA,EAAA,EAAA,KAAA,EAAA,GAAA,IAAA,OAAA,GAAA,GAAA,OAAA,KAIA,SAAA,GACA,IAAA,EAAA,EAAA,EAAA,EAAA,MACA,GAAA,EAAA,KAAA,OAAA,EAAA,MACA,IAAA,EAAA,EAAA,GACA,EAAA,OAAA,MACA,IAAA,EAAA,OAAA,OAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,QACA,EAAA,UAAA,EAIA,IAHA,IAEA,EAFA,EAAA,GACA,EAAA,EAEA,QAAA,EAAA,EAAA,EAAA,KAAA,CACA,IAAA,EAAA,OAAA,EAAA,IACA,EAAA,GAAA,EACA,KAAA,IAAA,EAAA,UAAA,EAAA,EAAA,EAAA,EAAA,WAAA,IACA,IAEA,OAAA,IAAA,EAAA,KAAA;;;ACkFA,IAAA,EAAA,UAAA,GApHA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,QAAA,2BACA,EAAA,QAAA,2BACA,EAAA,KAAA,IACA,EAAA,KAAA,IACA,EAAA,KAAA,MACA,EAAA,4BACA,EAAA,oBAEA,EAAA,SAAA,GACA,YAAA,IAAA,EAAA,EAAA,OAAA,IAIA,QAAA,gBAAA,CAAA,UAAA,EAAA,SAAA,EAAA,EAAA,EAAA,GACA,MAAA,CAGA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,MACA,EAAA,MAAA,OAAA,EAAA,EAAA,GACA,YAAA,IAAA,EACA,EAAA,KAAA,EAAA,EAAA,GACA,EAAA,KAAA,OAAA,GAAA,EAAA,IAIA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,EAAA,EAAA,KAAA,GACA,GAAA,EAAA,KAAA,OAAA,EAAA,MAEA,IAAA,EAAA,EAAA,GACA,EAAA,OAAA,MACA,EAAA,mBAAA,EACA,IAAA,EAAA,OAAA,IACA,IAAA,EAAA,EAAA,OACA,GAAA,EAAA,CACA,IAAA,EAAA,EAAA,QACA,EAAA,UAAA,EAGA,IADA,IAAA,EAAA,KACA,CACA,IAAA,EAAA,EAAA,EAAA,GACA,GAAA,OAAA,EAAA,MAEA,GADA,EAAA,KAAA,IACA,EAAA,MAEA,KADA,OAAA,EAAA,MACA,EAAA,UAAA,EAAA,EAAA,EAAA,EAAA,WAAA,IAIA,IAFA,IAAA,EAAA,GACA,EAAA,EACA,EAAA,EAAA,EAAA,EAAA,OAAA,IAAA,CACA,EAAA,EAAA,GASA,IARA,IAAA,EAAA,OAAA,EAAA,IACA,EAAA,EAAA,EAAA,EAAA,EAAA,OAAA,EAAA,QAAA,GACA,EAAA,GAMA,EAAA,EAAA,EAAA,EAAA,OAAA,IAAA,EAAA,KAAA,EAAA,EAAA,KACA,IAAA,EAAA,EAAA,OACA,GAAA,EAAA,CACA,IAAA,EAAA,CAAA,GAAA,OAAA,EAAA,EAAA,QACA,IAAA,GAAA,EAAA,KAAA,GACA,IAAA,EAAA,OAAA,EAAA,WAAA,EAAA,SAEA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAEA,GAAA,IACA,GAAA,EAAA,MAAA,EAAA,GAAA,EACA,EAAA,EAAA,EAAA,QAGA,OAAA,EAAA,EAAA,MAAA,KAKA,SAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,EAAA,OACA,EAAA,EAAA,OACA,EAAA,EAKA,YAJA,IAAA,IACA,EAAA,EAAA,GACA,EAAA,GAEA,EAAA,KAAA,EAAA,EAAA,SAAA,EAAA,GACA,IAAA,EACA,OAAA,EAAA,OAAA,IACA,IAAA,IAAA,MAAA,IACA,IAAA,IAAA,OAAA,EACA,IAAA,IAAA,OAAA,EAAA,MAAA,EAAA,GACA,IAAA,IAAA,OAAA,EAAA,MAAA,GACA,IAAA,IACA,EAAA,EAAA,EAAA,MAAA,GAAA,IACA,MACA,QACA,IAAA,GAAA,EACA,GAAA,IAAA,EAAA,OAAA,EACA,GAAA,EAAA,EAAA,CACA,IAAA,EAAA,EAAA,EAAA,IACA,OAAA,IAAA,EAAA,EACA,GAAA,OAAA,IAAA,EAAA,EAAA,GAAA,EAAA,OAAA,GAAA,EAAA,EAAA,GAAA,EAAA,OAAA,GACA,EAEA,EAAA,EAAA,EAAA,GAEA,YAAA,IAAA,EAAA,GAAA;;AClHA,aAEA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,QAAA,2BAGA,QAAA,gBAAA,CAAA,SAAA,EAAA,SAAA,EAAA,EAAA,EAAA,GACA,MAAA,CAGA,SAAA,GACA,IAAA,EAAA,EAAA,MACA,EAAA,MAAA,OAAA,EAAA,EAAA,GACA,YAAA,IAAA,EAAA,EAAA,KAAA,EAAA,GAAA,IAAA,OAAA,GAAA,GAAA,OAAA,KAIA,SAAA,GACA,IAAA,EAAA,EAAA,EAAA,EAAA,MACA,GAAA,EAAA,KAAA,OAAA,EAAA,MACA,IAAA,EAAA,EAAA,GACA,EAAA,OAAA,MACA,EAAA,EAAA,UACA,EAAA,EAAA,KAAA,EAAA,UAAA,GACA,IAAA,EAAA,EAAA,EAAA,GAEA,OADA,EAAA,EAAA,UAAA,KAAA,EAAA,UAAA,GACA,OAAA,GAAA,EAAA,EAAA;;AC1BA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,QAAA,SAAA,CAAA,WACA,OAAA,QAAA,SAAA,EAAA,GACA,IACA,EADA,EAAA,EAAA,GAAA,YAEA,YAAA,IAAA,GAAA,OAAA,EAAA,EAAA,GAAA,IAAA,EAAA,EAAA;;ACPA,aAEA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,0BACA,EAAA,QAAA,2BACA,EAAA,QAAA,gBACA,EAAA,QAAA,2BACA,EAAA,QAAA,kBACA,EAAA,QAAA,YACA,EAAA,KAAA,IACA,EAAA,GAAA,KACA,EAAA,QACA,EAAA,SACA,EAAA,YACA,EAAA,WAGA,GAAA,EAAA,WAAA,OAAA,EAAA,OAGA,QAAA,gBAAA,CAAA,QAAA,EAAA,SAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAkDA,OAxCA,EARA,KAAA,OAAA,GAAA,QAAA,IACA,GAAA,OAAA,GAAA,QAAA,GAAA,IACA,GAAA,KAAA,GAAA,WAAA,IACA,GAAA,IAAA,GAAA,YAAA,IACA,IAAA,GAAA,QAAA,GAAA,GACA,GAAA,GAAA,MAAA,GAGA,SAAA,EAAA,GACA,IAAA,EAAA,OAAA,MACA,QAAA,IAAA,GAAA,IAAA,EAAA,MAAA,GAEA,IAAA,EAAA,GAAA,OAAA,EAAA,KAAA,EAAA,EAAA,GAWA,IAVA,IASA,EAAA,EAAA,EATA,EAAA,GACA,GAAA,EAAA,WAAA,IAAA,KACA,EAAA,UAAA,IAAA,KACA,EAAA,QAAA,IAAA,KACA,EAAA,OAAA,IAAA,IACA,EAAA,EACA,OAAA,IAAA,EAAA,EAAA,IAAA,EAEA,EAAA,IAAA,OAAA,EAAA,OAAA,EAAA,MAEA,EAAA,EAAA,KAAA,EAAA,QACA,EAAA,EAAA,IACA,IACA,EAAA,KAAA,EAAA,MAAA,EAAA,EAAA,QACA,EAAA,GAAA,GAAA,EAAA,MAAA,EAAA,IAAA,EAAA,MAAA,EAAA,EAAA,MAAA,IACA,EAAA,EAAA,GAAA,GACA,EAAA,EACA,EAAA,IAAA,KAEA,EAAA,KAAA,EAAA,OAAA,EAAA,KAKA,OAHA,IAAA,EAAA,IACA,GAAA,EAAA,KAAA,KAAA,EAAA,KAAA,IACA,EAAA,KAAA,EAAA,MAAA,IACA,EAAA,GAAA,EAAA,EAAA,MAAA,EAAA,GAAA,GAGA,IAAA,QAAA,EAAA,GAAA,GACA,SAAA,EAAA,GACA,YAAA,IAAA,GAAA,IAAA,EAAA,GAAA,EAAA,KAAA,KAAA,EAAA,IAGA,EAGA,CAGA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,MACA,EAAA,MAAA,OAAA,EAAA,EAAA,GACA,YAAA,IAAA,EACA,EAAA,KAAA,EAAA,EAAA,GACA,EAAA,KAAA,OAAA,GAAA,EAAA,IAOA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,EAAA,EAAA,KAAA,EAAA,IAAA,GACA,GAAA,EAAA,KAAA,OAAA,EAAA,MAEA,IAAA,EAAA,EAAA,GACA,EAAA,OAAA,MACA,EAAA,EAAA,EAAA,QAEA,EAAA,EAAA,QACA,GAAA,EAAA,WAAA,IAAA,KACA,EAAA,UAAA,IAAA,KACA,EAAA,QAAA,IAAA,KACA,EAAA,IAAA,KAIA,EAAA,IAAA,EAAA,EAAA,EAAA,OAAA,EAAA,OAAA,IAAA,GACA,OAAA,IAAA,EAAA,EAAA,IAAA,EACA,GAAA,IAAA,EAAA,MAAA,GACA,GAAA,IAAA,EAAA,OAAA,OAAA,OAAA,EAAA,EAAA,GAAA,CAAA,GAAA,GAIA,IAHA,IAAA,EAAA,EACA,EAAA,EACA,EAAA,GACA,EAAA,EAAA,QAAA,CACA,EAAA,UAAA,EAAA,EAAA,EACA,IACA,EADA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,MAAA,IAEA,GACA,OAAA,IACA,EAAA,EAAA,EAAA,EAAA,WAAA,EAAA,EAAA,IAAA,EAAA,WAAA,EAEA,EAAA,EAAA,EAAA,EAAA,OACA,CAEA,GADA,EAAA,KAAA,EAAA,MAAA,EAAA,IACA,EAAA,SAAA,EAAA,OAAA,EACA,IAAA,IAAA,EAAA,EAAA,GAAA,EAAA,OAAA,EAAA,IAEA,GADA,EAAA,KAAA,EAAA,IACA,EAAA,SAAA,EAAA,OAAA,EAEA,EAAA,EAAA,GAIA,OADA,EAAA,KAAA,EAAA,MAAA,IACA;;AClIA,OAAA,QAAA,SAAA,EAAA,EAAA,EAAA,GACA,KAAA,aAAA,SAAA,IAAA,GAAA,KAAA,EACA,MAAA,UAAA,EAAA,2BACA,OAAA;;ACHA,IAAA,EAAA,QAAA,UACA,EAAA,QAAA,gBACA,EAAA,QAAA,oBACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,8BACA,EAAA,GACA,EAAA,GACA,EAAA,OAAA,QAAA,SAAA,EAAA,EAAA,EAAA,EAAA,GACA,IAGA,EAAA,EAAA,EAAA,EAHA,EAAA,EAAA,WAAA,OAAA,GAAA,EAAA,GACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GACA,EAAA,EAEA,GAAA,mBAAA,EAAA,MAAA,UAAA,EAAA,qBAEA,GAAA,EAAA,IAAA,IAAA,EAAA,EAAA,EAAA,QAAA,EAAA,EAAA,IAEA,IADA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,IAAA,GAAA,EAAA,IAAA,EAAA,EAAA,OACA,GAAA,IAAA,EAAA,OAAA,OACA,IAAA,EAAA,EAAA,KAAA,KAAA,EAAA,EAAA,QAAA,MAEA,IADA,EAAA,EAAA,EAAA,EAAA,EAAA,MAAA,MACA,GAAA,IAAA,EAAA,OAAA,GAGA,EAAA,MAAA,EACA,EAAA,OAAA;;;;ACxBA,IAaA,EAAA,EAAA,EAbA,EAAA,QAAA,UACA,EAAA,QAAA,aACA,EAAA,QAAA,WACA,EAAA,QAAA,iBACA,EAAA,QAAA,aACA,EAAA,EAAA,QACA,EAAA,EAAA,aACA,EAAA,EAAA,eACA,EAAA,EAAA,eACA,EAAA,EAAA,SACA,EAAA,EACA,EAAA,GACA,EAAA,qBAEA,EAAA,WACA,IAAA,GAAA,KAEA,GAAA,EAAA,eAAA,GAAA,CACA,IAAA,EAAA,EAAA,UACA,EAAA,GACA,MAGA,EAAA,SAAA,GACA,EAAA,KAAA,EAAA,OAGA,GAAA,IACA,EAAA,SAAA,GAGA,IAFA,IAAA,EAAA,GACA,EAAA,EACA,UAAA,OAAA,GAAA,EAAA,KAAA,UAAA,MAMA,OALA,IAAA,GAAA,WAEA,EAAA,mBAAA,EAAA,EAAA,SAAA,GAAA,IAEA,EAAA,GACA,GAEA,EAAA,SAAA,UACA,EAAA,IAGA,WAAA,QAAA,SAAA,CAAA,GACA,EAAA,SAAA,GACA,EAAA,SAAA,EAAA,EAAA,EAAA,KAGA,GAAA,EAAA,IACA,EAAA,SAAA,GACA,EAAA,IAAA,EAAA,EAAA,EAAA,KAGA,GAEA,GADA,EAAA,IAAA,GACA,MACA,EAAA,MAAA,UAAA,EACA,EAAA,EAAA,EAAA,YAAA,EAAA,IAGA,EAAA,kBAAA,mBAAA,cAAA,EAAA,eACA,EAAA,SAAA,GACA,EAAA,YAAA,EAAA,GAAA,MAEA,EAAA,iBAAA,UAAA,GAAA,IAGA,EADA,KAAA,EAAA,UACA,SAAA,GACA,EAAA,YAAA,EAAA,WAAA,GAAA,WACA,EAAA,YAAA,MACA,EAAA,KAAA,KAKA,SAAA,GACA,WAAA,EAAA,EAAA,EAAA,GAAA,KAIA,OAAA,QAAA,CACA,IAAA,EACA,MAAA;;;;AClFA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,WAAA,IACA,EAAA,EAAA,kBAAA,EAAA,uBACA,EAAA,EAAA,QACA,EAAA,EAAA,QACA,EAAA,WAAA,QAAA,SAAA,CAAA,GAEA,OAAA,QAAA,WACA,IAAA,EAAA,EAAA,EAEA,EAAA,WACA,IAAA,EAAA,EAEA,IADA,IAAA,EAAA,EAAA,SAAA,EAAA,OACA,GAAA,CACA,EAAA,EAAA,GACA,EAAA,EAAA,KACA,IACA,IACA,MAAA,GAGA,MAFA,EAAA,IACA,OAAA,EACA,GAEA,OAAA,EACA,GAAA,EAAA,SAIA,GAAA,EACA,EAAA,WACA,EAAA,SAAA,SAGA,IAAA,GAAA,EAAA,WAAA,EAAA,UAAA,WAQA,GAAA,GAAA,EAAA,QAAA,CAEA,IAAA,EAAA,EAAA,aAAA,GACA,EAAA,WACA,EAAA,KAAA,SASA,EAAA,WAEA,EAAA,KAAA,EAAA,QAvBA,CACA,IAAA,GAAA,EACA,EAAA,SAAA,eAAA,IACA,IAAA,EAAA,GAAA,QAAA,EAAA,CAAA,eAAA,IACA,EAAA,WACA,EAAA,KAAA,GAAA,GAsBA,OAAA,SAAA,GACA,IAAA,EAAA,CAAA,GAAA,EAAA,UAAA,GACA,IAAA,EAAA,KAAA,GACA,IACA,EAAA,EACA,KACA,EAAA;;AClEA,aAEA,IAAA,EAAA,QAAA,iBAEA,SAAA,EAAA,GACA,IAAA,EAAA,EACA,KAAA,QAAA,IAAA,EAAA,SAAA,EAAA,GACA,QAAA,IAAA,QAAA,IAAA,EAAA,MAAA,UAAA,2BACA,EAAA,EACA,EAAA,IAEA,KAAA,QAAA,EAAA,GACA,KAAA,OAAA,EAAA,GAGA,OAAA,QAAA,EAAA,SAAA,GACA,OAAA,IAAA,EAAA;;AChBA,OAAA,QAAA,SAAA,GACA,IACA,MAAA,CAAA,GAAA,EAAA,EAAA,KACA,MAAA,GACA,MAAA,CAAA,GAAA,EAAA,EAAA;;;ACJA,IAAA,EAAA,QAAA,aACA,EAAA,EAAA,UAEA,OAAA,QAAA,GAAA,EAAA,WAAA;;ACHA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,6BAEA,OAAA,QAAA,SAAA,EAAA,GAEA,GADA,EAAA,GACA,EAAA,IAAA,EAAA,cAAA,EAAA,OAAA,EACA,IAAA,EAAA,EAAA,EAAA,GAGA,OADA,EADA,EAAA,SACA,GACA,EAAA;;ACVA,IAAA,EAAA,QAAA,eACA,OAAA,QAAA,SAAA,EAAA,EAAA,GACA,IAAA,IAAA,KAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,GACA,OAAA;;;;ACHA,aACA,IAwBA,EAAA,EAAA,EAAA,EAxBA,EAAA,QAAA,cACA,EAAA,QAAA,aACA,EAAA,QAAA,UACA,EAAA,QAAA,cACA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,QAAA,kBACA,EAAA,QAAA,aACA,EAAA,QAAA,0BACA,EAAA,QAAA,WAAA,IACA,EAAA,QAAA,eAAA,GACA,EAAA,QAAA,6BACA,EAAA,QAAA,cACA,EAAA,QAAA,iBACA,EAAA,QAAA,sBACA,EAAA,UACA,EAAA,EAAA,UACA,EAAA,EAAA,QACA,EAAA,GAAA,EAAA,SACA,EAAA,GAAA,EAAA,IAAA,GACA,EAAA,EAAA,GACA,EAAA,WAAA,EAAA,GACA,EAAA,aAEA,EAAA,EAAA,EAAA,EAEA,IAAA,WACA,IAEA,IAAA,EAAA,EAAA,QAAA,GACA,GAAA,EAAA,YAAA,IAAA,QAAA,SAAA,CAAA,YAAA,SAAA,GACA,EAAA,EAAA,IAGA,OAAA,GAAA,mBAAA,wBACA,EAAA,KAAA,aAAA,GAIA,IAAA,EAAA,QAAA,SACA,IAAA,EAAA,QAAA,aACA,MAAA,KAfA,GAmBA,EAAA,SAAA,GACA,IAAA,EACA,SAAA,EAAA,IAAA,mBAAA,EAAA,EAAA,QAAA,GAEA,EAAA,SAAA,EAAA,GACA,IAAA,EAAA,GAAA,CACA,EAAA,IAAA,EACA,IAAA,EAAA,EAAA,GACA,EAAA,WAoCA,IAnCA,IAAA,EAAA,EAAA,GACA,EAAA,GAAA,EAAA,GACA,EAAA,EACA,EAAA,SAAA,GACA,IAIA,EAAA,EAAA,EAJA,EAAA,EAAA,EAAA,GAAA,EAAA,KACA,EAAA,EAAA,QACA,EAAA,EAAA,OACA,EAAA,EAAA,OAEA,IACA,GACA,IACA,GAAA,EAAA,IAAA,EAAA,GACA,EAAA,GAAA,IAEA,IAAA,EAAA,EAAA,GAEA,GAAA,EAAA,QACA,EAAA,EAAA,GACA,IACA,EAAA,OACA,GAAA,IAGA,IAAA,EAAA,QACA,EAAA,EAAA,yBACA,EAAA,EAAA,IACA,EAAA,KAAA,EAAA,EAAA,GACA,EAAA,IACA,EAAA,GACA,MAAA,GACA,IAAA,GAAA,EAAA,OACA,EAAA,KAGA,EAAA,OAAA,GAAA,EAAA,EAAA,MACA,EAAA,GAAA,GACA,EAAA,IAAA,EACA,IAAA,EAAA,IAAA,EAAA,OAGA,EAAA,SAAA,GACA,EAAA,KAAA,EAAA,WACA,IAEA,EAAA,EAAA,EAFA,EAAA,EAAA,GACA,EAAA,EAAA,GAeA,GAbA,IACA,EAAA,EAAA,WACA,EACA,EAAA,KAAA,qBAAA,EAAA,IACA,EAAA,EAAA,sBACA,EAAA,CAAA,QAAA,EAAA,OAAA,KACA,EAAA,EAAA,UAAA,EAAA,OACA,EAAA,MAAA,8BAAA,KAIA,EAAA,GAAA,GAAA,EAAA,GAAA,EAAA,GACA,EAAA,QAAA,EACA,GAAA,EAAA,EAAA,MAAA,EAAA,KAGA,EAAA,SAAA,GACA,OAAA,IAAA,EAAA,IAAA,KAAA,EAAA,IAAA,EAAA,IAAA,QAEA,EAAA,SAAA,GACA,EAAA,KAAA,EAAA,WACA,IAAA,EACA,EACA,EAAA,KAAA,mBAAA,IACA,EAAA,EAAA,qBACA,EAAA,CAAA,QAAA,EAAA,OAAA,EAAA,QAIA,EAAA,SAAA,GACA,IAAA,EAAA,KACA,EAAA,KACA,EAAA,IAAA,GACA,EAAA,EAAA,IAAA,GACA,GAAA,EACA,EAAA,GAAA,EACA,EAAA,KAAA,EAAA,GAAA,EAAA,GAAA,SACA,EAAA,GAAA,KAEA,EAAA,SAAA,GACA,IACA,EADA,EAAA,KAEA,IAAA,EAAA,GAAA,CACA,EAAA,IAAA,EACA,EAAA,EAAA,IAAA,EACA,IACA,GAAA,IAAA,EAAA,MAAA,EAAA,qCACA,EAAA,EAAA,IACA,EAAA,WACA,IAAA,EAAA,CAAA,GAAA,EAAA,IAAA,GACA,IACA,EAAA,KAAA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,IACA,MAAA,GACA,EAAA,KAAA,EAAA,OAIA,EAAA,GAAA,EACA,EAAA,GAAA,EACA,EAAA,GAAA,IAEA,MAAA,GACA,EAAA,KAAA,CAAA,GAAA,EAAA,IAAA,GAAA,MAKA,IAEA,EAAA,SAAA,GACA,EAAA,KAAA,EAAA,EAAA,MACA,EAAA,GACA,EAAA,KAAA,MACA,IACA,EAAA,EAAA,EAAA,KAAA,GAAA,EAAA,EAAA,KAAA,IACA,MAAA,GACA,EAAA,KAAA,KAAA,MAIA,EAAA,SAAA,GACA,KAAA,GAAA,GACA,KAAA,QAAA,EACA,KAAA,GAAA,EACA,KAAA,IAAA,EACA,KAAA,QAAA,EACA,KAAA,GAAA,EACA,KAAA,IAAA,IAEA,UAAA,QAAA,kBAAA,CAAA,EAAA,UAAA,CAEA,KAAA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,EAAA,KAAA,IAOA,OANA,EAAA,GAAA,mBAAA,GAAA,EACA,EAAA,KAAA,mBAAA,GAAA,EACA,EAAA,OAAA,EAAA,EAAA,YAAA,EACA,KAAA,GAAA,KAAA,GACA,KAAA,IAAA,KAAA,GAAA,KAAA,GACA,KAAA,IAAA,EAAA,MAAA,GACA,EAAA,SAGA,MAAA,SAAA,GACA,OAAA,KAAA,UAAA,EAAA,MAGA,EAAA,WACA,IAAA,EAAA,IAAA,EACA,KAAA,QAAA,EACA,KAAA,QAAA,EAAA,EAAA,EAAA,GACA,KAAA,OAAA,EAAA,EAAA,EAAA,IAEA,EAAA,EAAA,EAAA,SAAA,GACA,OAAA,IAAA,GAAA,IAAA,EACA,IAAA,EAAA,GACA,EAAA,KAIA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,CAAA,QAAA,IACA,QAAA,uBAAA,CAAA,EAAA,GACA,QAAA,iBAAA,CAAA,GACA,EAAA,QAAA,WAAA,GAGA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,EAAA,CAEA,OAAA,SAAA,GACA,IAAA,EAAA,EAAA,MAGA,OADA,EADA,EAAA,QACA,GACA,EAAA,WAGA,EAAA,EAAA,EAAA,EAAA,GAAA,IAAA,GAAA,EAAA,CAEA,QAAA,SAAA,GACA,OAAA,EAAA,GAAA,OAAA,EAAA,EAAA,KAAA,MAGA,EAAA,EAAA,EAAA,EAAA,IAAA,GAAA,QAAA,iBAAA,CAAA,SAAA,GACA,EAAA,IAAA,GAAA,MAAA,MACA,EAAA,CAEA,IAAA,SAAA,GACA,IAAA,EAAA,KACA,EAAA,EAAA,GACA,EAAA,EAAA,QACA,EAAA,EAAA,OACA,EAAA,EAAA,WACA,IAAA,EAAA,GACA,EAAA,EACA,EAAA,EACA,EAAA,GAAA,EAAA,SAAA,GACA,IAAA,EAAA,IACA,GAAA,EACA,EAAA,UAAA,GACA,IACA,EAAA,QAAA,GAAA,KAAA,SAAA,GACA,IACA,GAAA,EACA,EAAA,GAAA,IACA,GAAA,EAAA,KACA,OAEA,GAAA,EAAA,KAGA,OADA,EAAA,GAAA,EAAA,EAAA,GACA,EAAA,SAGA,KAAA,SAAA,GACA,IAAA,EAAA,KACA,EAAA,EAAA,GACA,EAAA,EAAA,OACA,EAAA,EAAA,WACA,EAAA,GAAA,EAAA,SAAA,GACA,EAAA,QAAA,GAAA,KAAA,EAAA,QAAA,OAIA,OADA,EAAA,GAAA,EAAA,EAAA,GACA,EAAA;;AC3RA,IAAA,EAAA,QAAA,gBACA,OAAA,QAAA,SAAA,EAAA,GACA,IAAA,EAAA,IAAA,EAAA,KAAA,EAAA,MAAA,UAAA,0BAAA,EAAA,cACA,OAAA;;ACHA,aACA,IAAA,EAAA,QAAA,gBAAA,EACA,EAAA,QAAA,oBACA,EAAA,QAAA,mBACA,EAAA,QAAA,UACA,EAAA,QAAA,kBACA,EAAA,QAAA,aACA,EAAA,QAAA,kBACA,EAAA,QAAA,gBACA,EAAA,QAAA,kBACA,EAAA,QAAA,kBACA,EAAA,QAAA,WAAA,QACA,EAAA,QAAA,0BACA,EAAA,EAAA,KAAA,OAEA,EAAA,SAAA,EAAA,GAEA,IACA,EADA,EAAA,EAAA,GAEA,GAAA,MAAA,EAAA,OAAA,EAAA,GAAA,GAEA,IAAA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,EACA,GAAA,EAAA,GAAA,EAAA,OAAA,GAIA,OAAA,QAAA,CACA,eAAA,SAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,SAAA,EAAA,GACA,EAAA,EAAA,EAAA,EAAA,MACA,EAAA,GAAA,EACA,EAAA,GAAA,EAAA,MACA,EAAA,QAAA,EACA,EAAA,QAAA,EACA,EAAA,GAAA,EACA,MAAA,GAAA,EAAA,EAAA,EAAA,EAAA,GAAA,KAsDA,OApDA,EAAA,EAAA,UAAA,CAGA,MAAA,WACA,IAAA,IAAA,EAAA,EAAA,KAAA,GAAA,EAAA,EAAA,GAAA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,EACA,EAAA,GAAA,EACA,EAAA,IAAA,EAAA,EAAA,EAAA,EAAA,OAAA,UACA,EAAA,EAAA,GAEA,EAAA,GAAA,EAAA,QAAA,EACA,EAAA,GAAA,GAIA,OAAA,SAAA,GACA,IAAA,EAAA,EAAA,KAAA,GACA,EAAA,EAAA,EAAA,GACA,GAAA,EAAA,CACA,IAAA,EAAA,EAAA,EACA,EAAA,EAAA,SACA,EAAA,GAAA,EAAA,GACA,EAAA,GAAA,EACA,IAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,GACA,EAAA,IAAA,IAAA,EAAA,GAAA,GACA,EAAA,IAAA,IAAA,EAAA,GAAA,GACA,EAAA,KACA,QAAA,GAIA,QAAA,SAAA,GACA,EAAA,KAAA,GAGA,IAFA,IACA,EADA,EAAA,EAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,EAAA,GAEA,EAAA,EAAA,EAAA,EAAA,KAAA,IAGA,IAFA,EAAA,EAAA,EAAA,EAAA,EAAA,MAEA,GAAA,EAAA,GAAA,EAAA,EAAA,GAKA,IAAA,SAAA,GACA,QAAA,EAAA,EAAA,KAAA,GAAA,MAGA,GAAA,EAAA,EAAA,UAAA,OAAA,CACA,IAAA,WACA,OAAA,EAAA,KAAA,GAAA,MAGA,GAEA,IAAA,SAAA,EAAA,EAAA,GACA,IACA,EAAA,EADA,EAAA,EAAA,EAAA,GAoBA,OAjBA,EACA,EAAA,EAAA,GAGA,EAAA,GAAA,EAAA,CACA,EAAA,EAAA,EAAA,GAAA,GACA,EAAA,EACA,EAAA,EACA,EAAA,EAAA,EAAA,GACA,OAAA,EACA,GAAA,GAEA,EAAA,KAAA,EAAA,GAAA,GACA,IAAA,EAAA,EAAA,GACA,EAAA,KAEA,MAAA,IAAA,EAAA,GAAA,GAAA,IACA,GAEA,SAAA,EACA,UAAA,SAAA,EAAA,EAAA,GAGA,EAAA,EAAA,EAAA,SAAA,EAAA,GACA,KAAA,GAAA,EAAA,EAAA,GACA,KAAA,GAAA,EACA,KAAA,QAAA,GACA,WAKA,IAJA,IACA,EADA,KACA,GACA,EAFA,KAEA,GAEA,GAAA,EAAA,GAAA,EAAA,EAAA,EAEA,OANA,KAMA,KANA,KAMA,GAAA,EAAA,EAAA,EAAA,EANA,KAMA,GAAA,IAMA,EAAA,EAAA,QAAA,EAAA,EAAA,EACA,UAAA,EAAA,EAAA,EACA,CAAA,EAAA,EAAA,EAAA,KAdA,KAQA,QAAA,EACA,EAAA,KAMA,EAAA,UAAA,UAAA,GAAA,GAGA,EAAA;;;AC7IA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,aACA,EAAA,QAAA,eACA,EAAA,QAAA,mBACA,EAAA,QAAA,WACA,EAAA,QAAA,aACA,EAAA,QAAA,kBACA,EAAA,QAAA,gBACA,EAAA,QAAA,YACA,EAAA,QAAA,kBACA,EAAA,QAAA,wBACA,EAAA,QAAA,0BAEA,OAAA,QAAA,SAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,GACA,EAAA,EACA,EAAA,EAAA,MAAA,MACA,EAAA,GAAA,EAAA,UACA,EAAA,GACA,EAAA,SAAA,GACA,IAAA,EAAA,EAAA,GACA,EAAA,EAAA,EACA,UAAA,EAAA,SAAA,GACA,QAAA,IAAA,EAAA,KAAA,EAAA,KAAA,KAAA,IAAA,EAAA,EAAA,IACA,OAAA,EAAA,SAAA,GACA,QAAA,IAAA,EAAA,KAAA,EAAA,KAAA,KAAA,IAAA,EAAA,EAAA,IACA,OAAA,EAAA,SAAA,GACA,OAAA,IAAA,EAAA,QAAA,EAAA,EAAA,KAAA,KAAA,IAAA,EAAA,EAAA,IACA,OAAA,EAAA,SAAA,GAAA,OAAA,EAAA,KAAA,KAAA,IAAA,EAAA,EAAA,GAAA,MACA,SAAA,EAAA,GAAA,OAAA,EAAA,KAAA,KAAA,IAAA,EAAA,EAAA,EAAA,GAAA,QAGA,GAAA,mBAAA,IAAA,GAAA,EAAA,UAAA,EAAA,YACA,IAAA,GAAA,UAAA,UAMA,CACA,IAAA,EAAA,IAAA,EAEA,EAAA,EAAA,GAAA,EAAA,IAAA,EAAA,IAAA,EAEA,EAAA,EAAA,WAAA,EAAA,IAAA,KAEA,EAAA,EAAA,SAAA,GAAA,IAAA,EAAA,KAEA,GAAA,GAAA,EAAA,WAIA,IAFA,IAAA,EAAA,IAAA,EACA,EAAA,EACA,KAAA,EAAA,GAAA,EAAA,GACA,OAAA,EAAA,KAAA,KAEA,KACA,EAAA,EAAA,SAAA,EAAA,GACA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,IAAA,EAAA,EAAA,GAEA,OADA,MAAA,GAAA,EAAA,EAAA,EAAA,EAAA,GAAA,GACA,KAEA,UAAA,EACA,EAAA,YAAA,IAEA,GAAA,KACA,EAAA,UACA,EAAA,OACA,GAAA,EAAA,SAEA,GAAA,IAAA,EAAA,GAEA,GAAA,EAAA,cAAA,EAAA,WApCA,EAAA,EAAA,eAAA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,UAAA,GACA,EAAA,MAAA,EA4CA,OAPA,EAAA,EAAA,GAEA,EAAA,GAAA,EACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,GAAA,GAAA,GAEA,GAAA,EAAA,UAAA,EAAA,EAAA,GAEA;;ACnFA,aACA,IAAA,EAAA,QAAA,wBACA,EAAA,QAAA,0BACA,EAAA,MAGA,OAAA,QAAA,QAAA,gBAAA,CAAA,EAAA,SAAA,GACA,OAAA,WAAA,OAAA,EAAA,KAAA,UAAA,OAAA,EAAA,UAAA,QAAA,KACA,CAEA,IAAA,SAAA,GACA,IAAA,EAAA,EAAA,SAAA,EAAA,KAAA,GAAA,GACA,OAAA,GAAA,EAAA,GAGA,IAAA,SAAA,EAAA,GACA,OAAA,EAAA,IAAA,EAAA,KAAA,GAAA,IAAA,EAAA,EAAA,EAAA,KAEA,GAAA;;AClBA,aACA,IAAA,EAAA,QAAA,wBACA,EAAA,QAAA,0BACA,EAAA,MAGA,OAAA,QAAA,QAAA,gBAAA,CAAA,EAAA,SAAA,GACA,OAAA,WAAA,OAAA,EAAA,KAAA,UAAA,OAAA,EAAA,UAAA,QAAA,KACA,CAEA,IAAA,SAAA,GACA,OAAA,EAAA,IAAA,EAAA,KAAA,GAAA,EAAA,IAAA,EAAA,EAAA,EAAA,KAEA;;ACbA,aACA,IAAA,EAAA,QAAA,mBACA,EAAA,QAAA,WAAA,QACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,kBACA,EAAA,QAAA,aACA,EAAA,QAAA,oBACA,EAAA,QAAA,UACA,EAAA,QAAA,0BACA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,EAGA,EAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,GAAA,IAAA,IAEA,EAAA,WACA,KAAA,EAAA,IAEA,EAAA,SAAA,EAAA,GACA,OAAA,EAAA,EAAA,EAAA,SAAA,GACA,OAAA,EAAA,KAAA,KAGA,EAAA,UAAA,CACA,IAAA,SAAA,GACA,IAAA,EAAA,EAAA,KAAA,GACA,GAAA,EAAA,OAAA,EAAA,IAEA,IAAA,SAAA,GACA,QAAA,EAAA,KAAA,IAEA,IAAA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,KAAA,GACA,EAAA,EAAA,GAAA,EACA,KAAA,EAAA,KAAA,CAAA,EAAA,KAEA,OAAA,SAAA,GACA,IAAA,EAAA,EAAA,KAAA,EAAA,SAAA,GACA,OAAA,EAAA,KAAA,IAGA,OADA,GAAA,KAAA,EAAA,OAAA,EAAA,MACA,IAIA,OAAA,QAAA,CACA,eAAA,SAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,SAAA,EAAA,GACA,EAAA,EAAA,EAAA,EAAA,MACA,EAAA,GAAA,EACA,EAAA,GAAA,IACA,EAAA,QAAA,EACA,MAAA,GAAA,EAAA,EAAA,EAAA,EAAA,GAAA,KAoBA,OAlBA,EAAA,EAAA,UAAA,CAGA,OAAA,SAAA,GACA,IAAA,EAAA,GAAA,OAAA,EACA,IAAA,EAAA,EAAA,GACA,OAAA,IAAA,EAAA,EAAA,EAAA,KAAA,IAAA,OAAA,GACA,GAAA,EAAA,EAAA,KAAA,YAAA,EAAA,KAAA,KAIA,IAAA,SAAA,GACA,IAAA,EAAA,GAAA,OAAA,EACA,IAAA,EAAA,EAAA,GACA,OAAA,IAAA,EAAA,EAAA,EAAA,KAAA,IAAA,IAAA,GACA,GAAA,EAAA,EAAA,KAAA,OAGA,GAEA,IAAA,SAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,EAAA,IAAA,GAGA,OAFA,IAAA,EAAA,EAAA,GAAA,IAAA,EAAA,GACA,EAAA,EAAA,IAAA,EACA,GAEA,QAAA;;;ACnFA,aACA,IAcA,EAdA,EAAA,QAAA,aACA,EAAA,QAAA,mBAAA,CAAA,GACA,EAAA,QAAA,eACA,EAAA,QAAA,WACA,EAAA,QAAA,oBACA,EAAA,QAAA,sBACA,EAAA,QAAA,gBACA,EAAA,QAAA,0BACA,EAAA,QAAA,0BACA,GAAA,EAAA,eAAA,kBAAA,EACA,EAAA,UACA,EAAA,EAAA,QACA,EAAA,OAAA,aACA,EAAA,EAAA,QAGA,EAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,UAAA,OAAA,EAAA,UAAA,QAAA,KAIA,EAAA,CAEA,IAAA,SAAA,GACA,GAAA,EAAA,GAAA,CACA,IAAA,EAAA,EAAA,GACA,OAAA,IAAA,EAAA,EAAA,EAAA,KAAA,IAAA,IAAA,GACA,EAAA,EAAA,KAAA,SAAA,IAIA,IAAA,SAAA,EAAA,GACA,OAAA,EAAA,IAAA,EAAA,KAAA,GAAA,EAAA,KAKA,EAAA,OAAA,QAAA,QAAA,gBAAA,CAAA,EAAA,EAAA,EAAA,GAAA,GAAA,GAGA,GAAA,IAEA,GADA,EAAA,EAAA,eAAA,EAAA,IACA,UAAA,GACA,EAAA,MAAA,EACA,EAAA,CAAA,SAAA,MAAA,MAAA,OAAA,SAAA,GACA,IAAA,EAAA,EAAA,UACA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,SAAA,EAAA,GAEA,GAAA,EAAA,KAAA,EAAA,GAAA,CACA,KAAA,KAAA,KAAA,GAAA,IAAA,GACA,IAAA,EAAA,KAAA,GAAA,GAAA,EAAA,GACA,MAAA,OAAA,EAAA,KAAA,EAEA,OAAA,EAAA,KAAA,KAAA,EAAA;;ACxDA,aACA,IAAA,EAAA,QAAA,sBACA,EAAA,QAAA,0BACA,EAAA,UAGA,QAAA,gBAAA,CAAA,EAAA,SAAA,GACA,OAAA,WAAA,OAAA,EAAA,KAAA,UAAA,OAAA,EAAA,UAAA,QAAA,KACA,CAEA,IAAA,SAAA,GACA,OAAA,EAAA,IAAA,EAAA,KAAA,GAAA,GAAA,KAEA,GAAA,GAAA;;;ACEA,IAfA,IASA,EATA,EAAA,QAAA,aACA,EAAA,QAAA,WACA,EAAA,QAAA,UACA,EAAA,EAAA,eACA,EAAA,EAAA,QACA,KAAA,EAAA,cAAA,EAAA,UACA,EAAA,EACA,EAAA,EACA,EAAA,EAGA,EAAA,iHAEA,MAAA,KAEA,EAAA,IACA,EAAA,EAAA,EAAA,QACA,EAAA,EAAA,UAAA,GAAA,GACA,EAAA,EAAA,UAAA,GAAA,IACA,GAAA,EAGA,OAAA,QAAA,CACA,IAAA,EACA,OAAA,EACA,MAAA,EACA,KAAA;;ACzBA,IAAA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,OAAA,QAAA,SAAA,GACA,QAAA,IAAA,EAAA,OAAA,EACA,IAAA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,GAAA,IAAA,EAAA,MAAA,WAAA,iBACA,OAAA;;;ACRA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,kBACA,EAAA,QAAA,cACA,EAAA,QAAA,YACA,EAAA,QAAA,WACA,EAAA,QAAA,mBACA,EAAA,QAAA,YACA,EAAA,QAAA,kBACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,EAAA,QAAA,eACA,EAAA,QAAA,kBAAA,EACA,EAAA,QAAA,gBAAA,EACA,EAAA,QAAA,iBACA,EAAA,QAAA,wBACA,EAAA,cACA,EAAA,WACA,EAAA,YACA,EAAA,gBACA,EAAA,eACA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,EAAA,KACA,EAAA,EAAA,WAEA,EAAA,EAAA,SACA,EAAA,EACA,EAAA,EAAA,IACA,EAAA,EAAA,IACA,EAAA,EAAA,MACA,EAAA,EAAA,IACA,EAAA,EAAA,IACA,EAAA,SACA,EAAA,aACA,EAAA,aACA,EAAA,EAAA,KAAA,EACA,EAAA,EAAA,KAAA,EACA,EAAA,EAAA,KAAA,EAGA,SAAA,EAAA,EAAA,EAAA,GACA,IAOA,EAAA,EAAA,EAPA,EAAA,IAAA,MAAA,GACA,EAAA,EAAA,EAAA,EAAA,EACA,GAAA,GAAA,GAAA,EACA,EAAA,GAAA,EACA,EAAA,KAAA,EAAA,EAAA,GAAA,IAAA,EAAA,GAAA,IAAA,EACA,EAAA,EACA,EAAA,EAAA,GAAA,IAAA,GAAA,EAAA,EAAA,EAAA,EAAA,EAkCA,KAhCA,EAAA,EAAA,KAEA,GAAA,IAAA,GAEA,EAAA,GAAA,EAAA,EAAA,EACA,EAAA,IAEA,EAAA,EAAA,EAAA,GAAA,GACA,GAAA,EAAA,EAAA,GAAA,IAAA,IACA,IACA,GAAA,IAGA,GADA,EAAA,GAAA,EACA,EAAA,EAEA,EAAA,EAAA,EAAA,EAAA,IAEA,GAAA,IACA,IACA,GAAA,GAEA,EAAA,GAAA,GACA,EAAA,EACA,EAAA,GACA,EAAA,GAAA,GACA,GAAA,EAAA,EAAA,GAAA,EAAA,EAAA,GACA,GAAA,IAEA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,EAAA,GACA,EAAA,IAGA,GAAA,EAAA,EAAA,KAAA,IAAA,EAAA,GAAA,IAAA,GAAA,GAGA,IAFA,EAAA,GAAA,EAAA,EACA,GAAA,EACA,EAAA,EAAA,EAAA,KAAA,IAAA,EAAA,GAAA,IAAA,GAAA,GAEA,OADA,IAAA,IAAA,IAAA,EACA,EAEA,SAAA,EAAA,EAAA,EAAA,GACA,IAOA,EAPA,EAAA,EAAA,EAAA,EAAA,EACA,GAAA,GAAA,GAAA,EACA,EAAA,GAAA,EACA,EAAA,EAAA,EACA,EAAA,EAAA,EACA,EAAA,EAAA,KACA,EAAA,IAAA,EAGA,IADA,IAAA,EACA,EAAA,EAAA,EAAA,IAAA,EAAA,EAAA,GAAA,IAAA,GAAA,GAIA,IAHA,EAAA,GAAA,IAAA,GAAA,EACA,KAAA,EACA,GAAA,EACA,EAAA,EAAA,EAAA,IAAA,EAAA,EAAA,GAAA,IAAA,GAAA,GACA,GAAA,IAAA,EACA,EAAA,EAAA,MACA,CAAA,GAAA,IAAA,EACA,OAAA,EAAA,IAAA,GAAA,EAAA,EAEA,GAAA,EAAA,EAAA,GACA,GAAA,EACA,OAAA,GAAA,EAAA,GAAA,EAAA,EAAA,EAAA,EAAA,GAGA,SAAA,EAAA,GACA,OAAA,EAAA,IAAA,GAAA,EAAA,IAAA,GAAA,EAAA,IAAA,EAAA,EAAA,GAEA,SAAA,EAAA,GACA,MAAA,CAAA,IAAA,GAEA,SAAA,EAAA,GACA,MAAA,CAAA,IAAA,EAAA,GAAA,EAAA,KAEA,SAAA,EAAA,GACA,MAAA,CAAA,IAAA,EAAA,GAAA,EAAA,IAAA,GAAA,GAAA,IAAA,GAAA,GAAA,KAEA,SAAA,EAAA,GACA,OAAA,EAAA,EAAA,GAAA,GAEA,SAAA,EAAA,GACA,OAAA,EAAA,EAAA,GAAA,GAGA,SAAA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,GAAA,EAAA,CAAA,IAAA,WAAA,OAAA,KAAA,MAGA,SAAA,EAAA,EAAA,EAAA,EAAA,GACA,IACA,EAAA,GADA,GAEA,GAAA,EAAA,EAAA,EAAA,GAAA,MAAA,EAAA,GACA,IAAA,EAAA,EAAA,GAAA,GACA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,MAAA,EAAA,EAAA,GACA,OAAA,EAAA,EAAA,EAAA,UAEA,SAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GACA,IACA,EAAA,GADA,GAEA,GAAA,EAAA,EAAA,EAAA,GAAA,MAAA,EAAA,GAIA,IAHA,IAAA,EAAA,EAAA,GAAA,GACA,EAAA,EAAA,EAAA,GACA,EAAA,GAAA,GACA,EAAA,EAAA,EAAA,EAAA,IAAA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAGA,GAAA,EAAA,IAgFA,CACA,IAAA,EAAA,WACA,EAAA,OACA,EAAA,WACA,IAAA,GAAA,MACA,EAAA,WAIA,OAHA,IAAA,EACA,IAAA,EAAA,KACA,IAAA,EAAA,KACA,EAAA,MAAA,IACA,CAMA,IADA,IACA,EADA,GAJA,EAAA,SAAA,GAEA,OADA,EAAA,KAAA,GACA,IAAA,EAAA,EAAA,MAEA,GAAA,EAAA,GACA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,OAAA,IACA,EAAA,EAAA,QAAA,GAAA,EAAA,EAAA,EAAA,EAAA,IAEA,IAAA,EAAA,YAAA,GAGA,IAAA,EAAA,IAAA,EAAA,IAAA,EAAA,IACA,EAAA,EAAA,GAAA,QACA,EAAA,QAAA,EAAA,YACA,EAAA,QAAA,EAAA,aACA,EAAA,QAAA,IAAA,EAAA,QAAA,IAAA,EAAA,EAAA,GAAA,CACA,QAAA,SAAA,EAAA,GACA,EAAA,KAAA,KAAA,EAAA,GAAA,IAAA,KAEA,SAAA,SAAA,EAAA,GACA,EAAA,KAAA,KAAA,EAAA,GAAA,IAAA,OAEA,QAhHA,EAAA,SAAA,GACA,EAAA,KAAA,EAAA,GACA,IAAA,EAAA,EAAA,GACA,KAAA,GAAA,EAAA,KAAA,IAAA,MAAA,GAAA,GACA,KAAA,GAAA,GAGA,EAAA,SAAA,EAAA,EAAA,GACA,EAAA,KAAA,EAAA,GACA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,GAAA,EAAA,GAAA,EAAA,EAAA,MAAA,EAAA,iBAEA,GAAA,GADA,OAAA,IAAA,EAAA,EAAA,EAAA,EAAA,IACA,EAAA,MAAA,EAAA,GACA,KAAA,GAAA,EACA,KAAA,GAAA,EACA,KAAA,GAAA,GAGA,IACA,EAAA,EAAA,EAAA,MACA,EAAA,EAAA,EAAA,MACA,EAAA,EAAA,EAAA,MACA,EAAA,EAAA,EAAA,OAGA,EAAA,EAAA,GAAA,CACA,QAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,GAAA,IAAA,IAAA,IAEA,SAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,GAAA,IAEA,SAAA,SAAA,GACA,IAAA,EAAA,EAAA,KAAA,EAAA,EAAA,UAAA,IACA,OAAA,EAAA,IAAA,EAAA,EAAA,KAAA,IAAA,IAEA,UAAA,SAAA,GACA,IAAA,EAAA,EAAA,KAAA,EAAA,EAAA,UAAA,IACA,OAAA,EAAA,IAAA,EAAA,EAAA,IAEA,SAAA,SAAA,GACA,OAAA,EAAA,EAAA,KAAA,EAAA,EAAA,UAAA,MAEA,UAAA,SAAA,GACA,OAAA,EAAA,EAAA,KAAA,EAAA,EAAA,UAAA,OAAA,GAEA,WAAA,SAAA,GACA,OAAA,EAAA,EAAA,KAAA,EAAA,EAAA,UAAA,IAAA,GAAA,IAEA,WAAA,SAAA,GACA,OAAA,EAAA,EAAA,KAAA,EAAA,EAAA,UAAA,IAAA,GAAA,IAEA,QAAA,SAAA,EAAA,GACA,EAAA,KAAA,EAAA,EAAA,EAAA,IAEA,SAAA,SAAA,EAAA,GACA,EAAA,KAAA,EAAA,EAAA,EAAA,IAEA,SAAA,SAAA,EAAA,GACA,EAAA,KAAA,EAAA,EAAA,EAAA,EAAA,UAAA,KAEA,UAAA,SAAA,EAAA,GACA,EAAA,KAAA,EAAA,EAAA,EAAA,EAAA,UAAA,KAEA,SAAA,SAAA,EAAA,GACA,EAAA,KAAA,EAAA,EAAA,EAAA,EAAA,UAAA,KAEA,UAAA,SAAA,EAAA,GACA,EAAA,KAAA,EAAA,EAAA,EAAA,EAAA,UAAA,KAEA,WAAA,SAAA,EAAA,GACA,EAAA,KAAA,EAAA,EAAA,EAAA,EAAA,UAAA,KAEA,WAAA,SAAA,EAAA,GACA,EAAA,KAAA,EAAA,EAAA,EAAA,EAAA,UAAA,OAsCA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,EAAA,GAAA,EAAA,MAAA,GACA,QAAA,GAAA,EACA,QAAA,GAAA;;ACnRA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,YACA,EAAA,QAAA,mBACA,EAAA,QAAA,gBACA,EAAA,QAAA,wBACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,aAAA,YACA,EAAA,QAAA,0BACA,EAAA,EAAA,YACA,EAAA,EAAA,SACA,EAAA,EAAA,KAAA,EAAA,OACA,EAAA,EAAA,UAAA,MACA,EAAA,EAAA,KACA,EAAA,cAEA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,IAAA,GAAA,CAAA,YAAA,IAEA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,OAAA,EAAA,CAEA,OAAA,SAAA,GACA,OAAA,GAAA,EAAA,IAAA,EAAA,IAAA,KAAA,KAIA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,WAAA,CAAA,WACA,OAAA,IAAA,EAAA,GAAA,MAAA,OAAA,GAAA,aACA,EAAA,CAEA,MAAA,SAAA,EAAA,GACA,QAAA,IAAA,QAAA,IAAA,EAAA,OAAA,EAAA,KAAA,EAAA,MAAA,GAQA,IAPA,IAAA,EAAA,EAAA,MAAA,WACA,EAAA,EAAA,EAAA,GACA,EAAA,OAAA,IAAA,EAAA,EAAA,EAAA,GACA,EAAA,IAAA,EAAA,KAAA,GAAA,CAAA,EAAA,EAAA,IACA,EAAA,IAAA,EAAA,MACA,EAAA,IAAA,EAAA,GACA,EAAA,EACA,EAAA,GACA,EAAA,SAAA,IAAA,EAAA,SAAA,MACA,OAAA,KAIA,QAAA,iBAAA,CAAA;;AC7CA,IAAA,EAAA,QAAA,aACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,YAAA,IAAA,CACA,SAAA,QAAA,mBAAA;;;AC8dA,IAAA,EAAA,UAAA,GA/dA,GAAA,QAAA,kBAAA,CACA,IAAA,EAAA,QAAA,cAEA,GADA,EAAA,QAAA,aACA,QAAA,aACA,EAAA,QAAA,aACA,EAAA,QAAA,YACA,EAAA,QAAA,mBACA,EAAA,QAAA,UACA,EAAA,QAAA,kBACA,EAAA,QAAA,oBACA,EAAA,QAAA,WACA,EAAA,QAAA,mBACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,EAAA,QAAA,eACA,EAAA,QAAA,wBACA,EAAA,QAAA,mBACA,EAAA,QAAA,UACA,EAAA,QAAA,cACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,oBACA,EAAA,QAAA,oBACA,EAAA,QAAA,iBACA,EAAA,QAAA,kBAAA,EACA,EAAA,QAAA,8BACA,EAAA,QAAA,UACA,EAAA,QAAA,UACA,EAAA,QAAA,oBACA,EAAA,QAAA,qBACA,EAAA,QAAA,0BACA,EAAA,QAAA,wBACA,EAAA,QAAA,gBACA,EAAA,QAAA,kBACA,EAAA,QAAA,kBACA,EAAA,QAAA,iBACA,EAAA,QAAA,wBACA,EAAA,QAAA,gBACA,EAAA,QAAA,kBACA,EAAA,EAAA,EACA,EAAA,EAAA,EACA,EAAA,EAAA,WACA,EAAA,EAAA,UACA,EAAA,EAAA,WACA,EAAA,cACA,EAAA,SAAA,EACA,EAAA,oBACA,EAAA,YACA,EAAA,MAAA,GACA,EAAA,EAAA,YACA,EAAA,EAAA,SACA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,GAAA,EAAA,GACA,GAAA,EAAA,GACA,GAAA,GAAA,GACA,GAAA,GAAA,GACA,GAAA,EAAA,OACA,GAAA,EAAA,KACA,GAAA,EAAA,QACA,GAAA,EAAA,YACA,GAAA,EAAA,OACA,GAAA,EAAA,YACA,GAAA,EAAA,KACA,GAAA,EAAA,KACA,GAAA,EAAA,MACA,GAAA,EAAA,SACA,GAAA,EAAA,eACA,GAAA,EAAA,YACA,GAAA,EAAA,eACA,GAAA,EAAA,qBACA,GAAA,EAAA,mBACA,GAAA,EAAA,OACA,GAAA,EAAA,MACA,GAAA,EAAA,KACA,GAAA,gBAEA,GAAA,EAAA,EAAA,SAAA,EAAA,GACA,OAAA,GAAA,EAAA,EAAA,EAAA,KAAA,KAGA,GAAA,EAAA,WAEA,OAAA,IAAA,IAAA,EAAA,IAAA,YAAA,CAAA,IAAA,QAAA,KAGA,KAAA,KAAA,EAAA,GAAA,KAAA,EAAA,WACA,IAAA,EAAA,GAAA,IAAA,MAGA,GAAA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,GACA,GAAA,EAAA,GAAA,EAAA,EAAA,MAAA,EAAA,iBACA,OAAA,GAGA,GAAA,SAAA,GACA,GAAA,EAAA,IAAA,MAAA,EAAA,OAAA,EACA,MAAA,EAAA,EAAA,2BAGA,GAAA,SAAA,EAAA,GACA,KAAA,EAAA,IAAA,MAAA,GACA,MAAA,EAAA,wCACA,OAAA,IAAA,EAAA,IAGA,GAAA,SAAA,EAAA,GACA,OAAA,GAAA,EAAA,EAAA,EAAA,KAAA,IAGA,GAAA,SAAA,EAAA,GAIA,IAHA,IAAA,EAAA,EACA,EAAA,EAAA,OACA,EAAA,GAAA,EAAA,GACA,EAAA,GAAA,EAAA,GAAA,EAAA,KACA,OAAA,GAGA,GAAA,SAAA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,CAAA,IAAA,WAAA,OAAA,KAAA,GAAA,OAGA,GAAA,SAAA,GACA,IAKA,EAAA,EAAA,EAAA,EAAA,EAAA,EALA,EAAA,EAAA,GACA,EAAA,UAAA,OACA,EAAA,EAAA,EAAA,UAAA,QAAA,EACA,OAAA,IAAA,EACA,EAAA,EAAA,GAEA,GAAA,MAAA,IAAA,EAAA,GAAA,CACA,IAAA,EAAA,EAAA,KAAA,GAAA,EAAA,GAAA,EAAA,IAAA,EAAA,EAAA,QAAA,KAAA,IACA,EAAA,KAAA,EAAA,OACA,EAAA,EAGA,IADA,GAAA,EAAA,IAAA,EAAA,EAAA,EAAA,UAAA,GAAA,IACA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,EAAA,GAAA,KAAA,GAAA,EAAA,EAAA,IACA,EAAA,GAAA,EAAA,EAAA,EAAA,GAAA,GAAA,EAAA,GAEA,OAAA,GAGA,GAAA,WAIA,IAHA,IAAA,EAAA,EACA,EAAA,UAAA,OACA,EAAA,GAAA,KAAA,GACA,EAAA,GAAA,EAAA,GAAA,UAAA,KACA,OAAA,GAIA,KAAA,GAAA,EAAA,WAAA,GAAA,KAAA,IAAA,EAAA,MAEA,GAAA,WACA,OAAA,GAAA,MAAA,GAAA,GAAA,KAAA,GAAA,OAAA,GAAA,MAAA,YAGA,GAAA,CACA,WAAA,SAAA,EAAA,GACA,OAAA,EAAA,KAAA,GAAA,MAAA,EAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,IAEA,MAAA,SAAA,GACA,OAAA,EAAA,GAAA,MAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,IAEA,KAAA,SAAA,GACA,OAAA,EAAA,MAAA,GAAA,MAAA,YAEA,OAAA,SAAA,GACA,OAAA,GAAA,KAAA,EAAA,GAAA,MAAA,EACA,UAAA,OAAA,EAAA,UAAA,QAAA,KAEA,KAAA,SAAA,GACA,OAAA,GAAA,GAAA,MAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,IAEA,UAAA,SAAA,GACA,OAAA,GAAA,GAAA,MAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,IAEA,QAAA,SAAA,GACA,EAAA,GAAA,MAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,IAEA,QAAA,SAAA,GACA,OAAA,GAAA,GAAA,MAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,IAEA,SAAA,SAAA,GACA,OAAA,GAAA,GAAA,MAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,IAEA,KAAA,SAAA,GACA,OAAA,GAAA,MAAA,GAAA,MAAA,YAEA,YAAA,SAAA,GACA,OAAA,GAAA,MAAA,GAAA,MAAA,YAEA,IAAA,SAAA,GACA,OAAA,GAAA,GAAA,MAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,IAEA,OAAA,SAAA,GACA,OAAA,GAAA,MAAA,GAAA,MAAA,YAEA,YAAA,SAAA,GACA,OAAA,GAAA,MAAA,GAAA,MAAA,YAEA,QAAA,WAMA,IALA,IAIA,EAHA,EAAA,GADA,MACA,OACA,EAAA,KAAA,MAAA,EAAA,GACA,EAAA,EAEA,EAAA,GACA,EANA,KAMA,GANA,KAOA,KAPA,OAOA,GAPA,KAQA,GAAA,EACA,OATA,MAWA,KAAA,SAAA,GACA,OAAA,EAAA,GAAA,MAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,IAEA,KAAA,SAAA,GACA,OAAA,GAAA,KAAA,GAAA,MAAA,IAEA,SAAA,SAAA,EAAA,GACA,IAAA,EAAA,GAAA,MACA,EAAA,EAAA,OACA,EAAA,EAAA,EAAA,GACA,OAAA,IAAA,EAAA,EAAA,EAAA,KAAA,CACA,EAAA,OACA,EAAA,WAAA,EAAA,EAAA,kBACA,QAAA,IAAA,EAAA,EAAA,EAAA,EAAA,IAAA,MAKA,GAAA,SAAA,EAAA,GACA,OAAA,GAAA,KAAA,GAAA,KAAA,GAAA,MAAA,EAAA,KAGA,GAAA,SAAA,GACA,GAAA,MACA,IAAA,EAAA,GAAA,UAAA,GAAA,GACA,EAAA,KAAA,OACA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,QACA,EAAA,EACA,GAAA,EAAA,EAAA,EAAA,MAAA,EAAA,IACA,KAAA,EAAA,GAAA,KAAA,EAAA,GAAA,EAAA,MAGA,GAAA,CACA,QAAA,WACA,OAAA,GAAA,KAAA,GAAA,QAEA,KAAA,WACA,OAAA,GAAA,KAAA,GAAA,QAEA,OAAA,WACA,OAAA,GAAA,KAAA,GAAA,SAIA,GAAA,SAAA,EAAA,GACA,OAAA,EAAA,IACA,EAAA,KACA,iBAAA,GACA,KAAA,GACA,QAAA,IAAA,OAAA,IAEA,GAAA,SAAA,EAAA,GACA,OAAA,GAAA,EAAA,EAAA,EAAA,GAAA,IACA,EAAA,EAAA,EAAA,IACA,EAAA,EAAA,IAEA,GAAA,SAAA,EAAA,EAAA,GACA,QAAA,GAAA,EAAA,EAAA,EAAA,GAAA,KACA,EAAA,IACA,EAAA,EAAA,WACA,EAAA,EAAA,QACA,EAAA,EAAA,QAEA,EAAA,cACA,EAAA,EAAA,cAAA,EAAA,UACA,EAAA,EAAA,gBAAA,EAAA,WAIA,EAAA,EAAA,EAAA,IAFA,EAAA,GAAA,EAAA,MACA,IAIA,KACA,EAAA,EAAA,GACA,EAAA,EAAA,IAGA,EAAA,EAAA,EAAA,EAAA,GAAA,GAAA,SAAA,CACA,yBAAA,GACA,eAAA,KAGA,EAAA,WAAA,GAAA,KAAA,QACA,GAAA,GAAA,WACA,OAAA,GAAA,KAAA,QAIA,IAAA,GAAA,EAAA,GAAA,IACA,EAAA,GAAA,IACA,EAAA,GAAA,GAAA,GAAA,QACA,EAAA,GAAA,CACA,MAAA,GACA,IAAA,GACA,YAAA,aACA,SAAA,GACA,eAAA,KAEA,GAAA,GAAA,SAAA,KACA,GAAA,GAAA,aAAA,KACA,GAAA,GAAA,aAAA,KACA,GAAA,GAAA,SAAA,KACA,EAAA,GAAA,GAAA,CACA,IAAA,WAAA,OAAA,KAAA,OAIA,OAAA,QAAA,SAAA,EAAA,EAAA,EAAA,GAEA,IAAA,EAAA,IADA,IAAA,GACA,UAAA,IAAA,QACA,EAAA,MAAA,EACA,EAAA,MAAA,EACA,EAAA,EAAA,GACA,EAAA,GAAA,GACA,EAAA,GAAA,EAAA,GACA,GAAA,IAAA,EAAA,IACA,EAAA,GACA,EAAA,GAAA,EAAA,GAUA,EAAA,SAAA,EAAA,GACA,EAAA,EAAA,EAAA,CACA,IAAA,WACA,OAZA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,GACA,OAAA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,EAAA,IAUA,CAAA,KAAA,IAEA,IAAA,SAAA,GACA,OAXA,SAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,GACA,IAAA,GAAA,EAAA,KAAA,MAAA,IAAA,EAAA,EAAA,EAAA,IAAA,IAAA,IAAA,GACA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,EAAA,EAAA,IAQA,CAAA,KAAA,EAAA,IAEA,YAAA,KAGA,GACA,EAAA,EAAA,SAAA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,EAAA,MACA,IAEA,EAAA,EAAA,EAAA,EAFA,EAAA,EACA,EAAA,EAEA,GAAA,EAAA,GAIA,CAAA,KAAA,aAAA,IAAA,EAAA,EAAA,KAAA,GAAA,GAAA,GAaA,OAAA,MAAA,EACA,GAAA,EAAA,GAEA,GAAA,KAAA,EAAA,GAfA,EAAA,EACA,EAAA,GAAA,EAAA,GACA,IAAA,EAAA,EAAA,WACA,QAAA,IAAA,EAAA,CACA,GAAA,EAAA,EAAA,MAAA,EAAA,IAEA,IADA,EAAA,EAAA,GACA,EAAA,MAAA,EAAA,SAGA,IADA,EAAA,EAAA,GAAA,GACA,EAAA,EAAA,MAAA,EAAA,IAEA,EAAA,EAAA,OAfA,EAAA,EAAA,GAEA,EAAA,IAAA,EADA,EAAA,EAAA,GA2BA,IAPA,EAAA,EAAA,KAAA,CACA,EAAA,EACA,EAAA,EACA,EAAA,EACA,EAAA,EACA,EAAA,IAAA,EAAA,KAEA,EAAA,GAAA,EAAA,EAAA,OAEA,EAAA,EAAA,GAAA,EAAA,IACA,EAAA,EAAA,cAAA,IACA,EAAA,WACA,EAAA,MACA,EAAA,WACA,IAAA,GAAA,MACA,EAAA,SAAA,GACA,IAAA,EACA,IAAA,EAAA,MACA,IAAA,EAAA,KACA,IAAA,EAAA,KACA,KACA,EAAA,EAAA,SAAA,EAAA,EAAA,EAAA,GAEA,IAAA,EAGA,OAJA,EAAA,EAAA,EAAA,GAIA,EAAA,GACA,aAAA,IAAA,EAAA,EAAA,KAAA,GAAA,GAAA,OACA,IAAA,EACA,IAAA,EAAA,EAAA,GAAA,EAAA,GAAA,QACA,IAAA,EACA,IAAA,EAAA,EAAA,GAAA,EAAA,IACA,IAAA,EAAA,GAEA,MAAA,EAAA,GAAA,EAAA,GACA,GAAA,KAAA,EAAA,GATA,IAAA,EAAA,EAAA,MAWA,EAAA,IAAA,SAAA,UAAA,EAAA,GAAA,OAAA,EAAA,IAAA,EAAA,GAAA,SAAA,GACA,KAAA,GAAA,EAAA,EAAA,EAAA,EAAA,MAEA,EAAA,GAAA,EACA,IAAA,EAAA,YAAA,IAEA,IAAA,EAAA,EAAA,IACA,IAAA,IACA,UAAA,EAAA,MAAA,MAAA,EAAA,MACA,EAAA,GAAA,OACA,EAAA,EAAA,IAAA,GACA,EAAA,EAAA,GAAA,GACA,EAAA,EAAA,IAAA,GACA,EAAA,EAAA,GAAA,IAEA,EAAA,IAAA,EAAA,GAAA,KAAA,EAAA,MAAA,IACA,EAAA,EAAA,GAAA,CACA,IAAA,WAAA,OAAA,KAIA,EAAA,GAAA,EAEA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,GAAA,GAAA,GAEA,EAAA,EAAA,EAAA,EAAA,CACA,kBAAA,IAGA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,WAAA,EAAA,GAAA,KAAA,EAAA,KAAA,EAAA,CACA,KAAA,GACA,GAAA,KAGA,KAAA,GAAA,EAAA,EAAA,EAAA,GAEA,EAAA,EAAA,EAAA,EAAA,IAEA,EAAA,GAEA,EAAA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,CAAA,IAAA,KAEA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,EAAA,IAEA,GAAA,EAAA,UAAA,KAAA,EAAA,SAAA,IAEA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,WACA,IAAA,EAAA,GAAA,UACA,EAAA,CAAA,MAAA,KAEA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,WACA,MAAA,CAAA,EAAA,GAAA,kBAAA,IAAA,EAAA,CAAA,EAAA,IAAA,qBACA,EAAA,WACA,EAAA,eAAA,KAAA,CAAA,EAAA,OACA,EAAA,CAAA,eAAA,KAEA,EAAA,GAAA,EAAA,EAAA,EACA,GAAA,GAAA,EAAA,EAAA,GAAA,SAEA,OAAA,QAAA;;AC/dA,QAAA,iBAAA,CAAA,OAAA,EAAA,SAAA,GACA,OAAA,SAAA,EAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,EAAA;;ACFA,QAAA,iBAAA,CAAA,QAAA,EAAA,SAAA,GACA,OAAA,SAAA,EAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,EAAA;;ACFA,QAAA,iBAAA,CAAA,QAAA,EAAA,SAAA,GACA,OAAA,SAAA,EAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,EAAA,MAEA;;ACJA,QAAA,iBAAA,CAAA,QAAA,EAAA,SAAA,GACA,OAAA,SAAA,EAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,EAAA;;ACFA,QAAA,iBAAA,CAAA,SAAA,EAAA,SAAA,GACA,OAAA,SAAA,EAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,EAAA;;ACFA,QAAA,iBAAA,CAAA,QAAA,EAAA,SAAA,GACA,OAAA,SAAA,EAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,EAAA;;ACFA,QAAA,iBAAA,CAAA,SAAA,EAAA,SAAA,GACA,OAAA,SAAA,EAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,EAAA;;ACFA,QAAA,iBAAA,CAAA,UAAA,EAAA,SAAA,GACA,OAAA,SAAA,EAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,EAAA;;ACFA,QAAA,iBAAA,CAAA,UAAA,EAAA,SAAA,GACA,OAAA,SAAA,EAAA,EAAA,GACA,OAAA,EAAA,KAAA,EAAA,EAAA;;ACDA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,GAAA,QAAA,aAAA,SAAA,IAAA,MACA,EAAA,SAAA,MAEA,EAAA,EAAA,EAAA,EAAA,GAAA,QAAA,WAAA,CAAA,WACA,EAAA,gBACA,UAAA,CACA,MAAA,SAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,OAAA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,KAAA,EAAA,EAAA;;ACZA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,oBACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,YACA,EAAA,QAAA,WACA,GAAA,QAAA,aAAA,SAAA,IAAA,UAIA,EAAA,EAAA,WACA,SAAA,KACA,QAAA,EAAA,aAAA,GAAA,aAAA,KAEA,GAAA,EAAA,WACA,EAAA,gBAGA,EAAA,EAAA,EAAA,EAAA,GAAA,GAAA,GAAA,UAAA,CACA,UAAA,SAAA,EAAA,GACA,EAAA,GACA,EAAA,GACA,IAAA,EAAA,UAAA,OAAA,EAAA,EAAA,EAAA,UAAA,IACA,GAAA,IAAA,EAAA,OAAA,EAAA,EAAA,EAAA,GACA,GAAA,GAAA,EAAA,CAEA,OAAA,EAAA,QACA,KAAA,EAAA,OAAA,IAAA,EACA,KAAA,EAAA,OAAA,IAAA,EAAA,EAAA,IACA,KAAA,EAAA,OAAA,IAAA,EAAA,EAAA,GAAA,EAAA,IACA,KAAA,EAAA,OAAA,IAAA,EAAA,EAAA,GAAA,EAAA,GAAA,EAAA,IACA,KAAA,EAAA,OAAA,IAAA,EAAA,EAAA,GAAA,EAAA,GAAA,EAAA,GAAA,EAAA,IAGA,IAAA,EAAA,CAAA,MAEA,OADA,EAAA,KAAA,MAAA,EAAA,GACA,IAAA,EAAA,MAAA,EAAA,IAGA,IAAA,EAAA,EAAA,UACA,EAAA,EAAA,EAAA,GAAA,EAAA,OAAA,WACA,EAAA,SAAA,MAAA,KAAA,EAAA,EAAA,GACA,OAAA,EAAA,GAAA,EAAA;;AC3CA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,mBAGA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,WAAA,CAAA,WAEA,QAAA,eAAA,EAAA,EAAA,GAAA,EAAA,CAAA,MAAA,IAAA,EAAA,CAAA,MAAA,MACA,UAAA,CACA,eAAA,SAAA,EAAA,EAAA,GACA,EAAA,GACA,EAAA,EAAA,GAAA,GACA,EAAA,GACA,IAEA,OADA,EAAA,EAAA,EAAA,EAAA,IACA,EACA,MAAA,GACA,OAAA;;AClBA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,kBAAA,EACA,EAAA,QAAA,gBAEA,EAAA,EAAA,EAAA,UAAA,CACA,eAAA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,EAAA,GAAA,GACA,QAAA,IAAA,EAAA,sBAAA,EAAA;;ACRA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,SAAA,GACA,KAAA,GAAA,EAAA,GACA,KAAA,GAAA,EACA,IACA,EADA,EAAA,KAAA,GAAA,GAEA,IAAA,KAAA,EAAA,EAAA,KAAA,IAEA,QAAA,iBAAA,CAAA,EAAA,SAAA,WACA,IAEA,EADA,EADA,KACA,GAEA,GACA,GAJA,KAIA,IAAA,EAAA,OAAA,MAAA,CAAA,WAAA,EAAA,MAAA,YACA,EAAA,EALA,KAKA,SALA,KAKA,KACA,MAAA,CAAA,MAAA,EAAA,MAAA,KAGA,EAAA,EAAA,EAAA,UAAA,CACA,UAAA,SAAA,GACA,OAAA,IAAA,EAAA;;ACtBA,IAAA,EAAA,QAAA,kBACA,EAAA,QAAA,iBACA,EAAA,QAAA,UACA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBAEA,SAAA,EAAA,EAAA,GACA,IACA,EAAA,EADA,EAAA,UAAA,OAAA,EAAA,EAAA,UAAA,GAEA,OAAA,EAAA,KAAA,EAAA,EAAA,IACA,EAAA,EAAA,EAAA,EAAA,IAAA,EAAA,EAAA,SACA,EAAA,WACA,IAAA,EAAA,IACA,EAAA,IAAA,KAAA,QACA,EACA,EAAA,EAAA,EAAA,IAAA,EAAA,EAAA,EAAA,QAAA,EAGA,EAAA,EAAA,EAAA,UAAA,CAAA,IAAA;;ACnBA,IAAA,EAAA,QAAA,kBACA,EAAA,QAAA,aACA,EAAA,QAAA,gBAEA,EAAA,EAAA,EAAA,UAAA,CACA,yBAAA,SAAA,EAAA,GACA,OAAA,EAAA,EAAA,EAAA,GAAA;;ACNA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBAEA,EAAA,EAAA,EAAA,UAAA,CACA,eAAA,SAAA,GACA,OAAA,EAAA,EAAA;;ACNA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,UAAA,CACA,IAAA,SAAA,EAAA,GACA,OAAA,KAAA;;ACJA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,OAAA,aAEA,EAAA,EAAA,EAAA,UAAA,CACA,aAAA,SAAA,GAEA,OADA,EAAA,IACA,GAAA,EAAA;;ACPA,IAAA,EAAA,QAAA,kBACA,EAAA,QAAA,kBACA,EAAA,QAAA,gBACA,EAAA,QAAA,aAAA,QACA,OAAA,QAAA,GAAA,EAAA,SAAA,SAAA,GACA,IAAA,EAAA,EAAA,EAAA,EAAA,IACA,EAAA,EAAA,EACA,OAAA,EAAA,EAAA,OAAA,EAAA,IAAA;;ACPA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,UAAA,CAAA,QAAA,QAAA;;ACFA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,OAAA,kBAEA,EAAA,EAAA,EAAA,UAAA,CACA,kBAAA,SAAA,GACA,EAAA,GACA,IAEA,OADA,GAAA,EAAA,IACA,EACA,MAAA,GACA,OAAA;;ACXA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,kBACA,EAAA,QAAA,iBACA,EAAA,QAAA,UACA,EAAA,QAAA,aACA,EAAA,QAAA,oBACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBAEA,SAAA,EAAA,EAAA,EAAA,GACA,IAEA,EAAA,EAFA,EAAA,UAAA,OAAA,EAAA,EAAA,UAAA,GACA,EAAA,EAAA,EAAA,EAAA,GAAA,GAEA,IAAA,EAAA,CACA,GAAA,EAAA,EAAA,EAAA,IACA,OAAA,EAAA,EAAA,EAAA,EAAA,GAEA,EAAA,EAAA,GAEA,GAAA,EAAA,EAAA,SAAA,CACA,IAAA,IAAA,EAAA,WAAA,EAAA,GAAA,OAAA,EACA,GAAA,EAAA,EAAA,EAAA,EAAA,GAAA,CACA,GAAA,EAAA,KAAA,EAAA,MAAA,IAAA,EAAA,SAAA,OAAA,EACA,EAAA,MAAA,EACA,EAAA,EAAA,EAAA,EAAA,QACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,IACA,OAAA,EAEA,YAAA,IAAA,EAAA,MAAA,EAAA,IAAA,KAAA,EAAA,IAAA,GAGA,EAAA,EAAA,EAAA,UAAA,CAAA,IAAA;;AC/BA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBAEA,GAAA,EAAA,EAAA,EAAA,UAAA,CACA,eAAA,SAAA,EAAA,GACA,EAAA,MAAA,EAAA,GACA,IAEA,OADA,EAAA,IAAA,EAAA,IACA,EACA,MAAA,GACA,OAAA;;ACXA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,oBAAA,EAAA,GAEA,EAAA,EAAA,EAAA,QAAA,CACA,SAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,MAIA,QAAA,wBAAA,CAAA;;ACXA,aAEA,IAAA,EAAA,QAAA,eACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,UACA,EAAA,QAAA,SAAA,CAAA,sBAEA,SAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,GAMA,IALA,IAGA,EAAA,EAHA,EAAA,EACA,EAAA,EACA,IAAA,GAAA,EAAA,EAAA,EAAA,GAGA,EAAA,GAAA,CACA,GAAA,KAAA,EAAA,CASA,GARA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA,GAAA,EAAA,GAEA,GAAA,EACA,EAAA,KAEA,OAAA,KADA,EAAA,EAAA,MACA,EAAA,EAAA,IAGA,GAAA,EAAA,EACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,QAAA,EAAA,EAAA,GAAA,MACA,CACA,GAAA,GAAA,iBAAA,MAAA,YACA,EAAA,GAAA,EAGA,IAEA,IAEA,OAAA,EAGA,OAAA,QAAA;;ACtCA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,yBACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,QAAA,2BAEA,EAAA,EAAA,EAAA,QAAA,CACA,QAAA,SAAA,GACA,IACA,EAAA,EADA,EAAA,EAAA,MAMA,OAJA,EAAA,GACA,EAAA,EAAA,EAAA,QACA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,UAAA,IACA,KAIA,QAAA,wBAAA,CAAA;;ACrBA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,yBACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,QAAA,2BAEA,EAAA,EAAA,EAAA,QAAA,CACA,QAAA,WACA,IAAA,EAAA,UAAA,GACA,EAAA,EAAA,MACA,EAAA,EAAA,EAAA,QACA,EAAA,EAAA,EAAA,GAEA,OADA,EAAA,EAAA,EAAA,EAAA,EAAA,OAAA,IAAA,EAAA,EAAA,EAAA,IACA,KAIA,QAAA,wBAAA,CAAA;;ACpBA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,eAAA,EAAA,GAEA,EAAA,EAAA,EAAA,SAAA,CACA,GAAA,SAAA,GACA,OAAA,EAAA,KAAA;;ACNA,IAAA,EAAA,QAAA,gBACA,EAAA,QAAA,oBACA,EAAA,QAAA,cAEA,OAAA,QAAA,SAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,OAAA,EAAA,IACA,EAAA,EAAA,OACA,OAAA,IAAA,EAAA,IAAA,OAAA,GACA,EAAA,EAAA,GACA,GAAA,GAAA,GAAA,IAAA,EAAA,OAAA,EACA,IAAA,EAAA,EAAA,EACA,EAAA,EAAA,KAAA,EAAA,KAAA,KAAA,EAAA,EAAA,SAEA,OADA,EAAA,OAAA,IAAA,EAAA,EAAA,MAAA,EAAA,IACA,EAAA,EAAA,EAAA,EAAA;;ACdA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,QAAA,iBAGA,EAAA,mDAAA,KAAA,GAEA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,SAAA,CACA,SAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,GAAA;;ACXA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,QAAA,iBAGA,EAAA,mDAAA,KAAA,GAEA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,SAAA,CACA,OAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,UAAA,OAAA,EAAA,UAAA,QAAA,GAAA;;ACXA,aAEA,QAAA,iBAAA,CAAA,WAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,KAEA;;ACNA,aAEA,QAAA,iBAAA,CAAA,YAAA,SAAA,GACA,OAAA,WACA,OAAA,EAAA,KAAA,KAEA;;ACNA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,cACA,EAAA,QAAA,gBACA,EAAA,QAAA,gBACA,EAAA,QAAA,YACA,EAAA,OAAA,UAEA,EAAA,SAAA,EAAA,GACA,KAAA,GAAA,EACA,KAAA,GAAA,GAGA,QAAA,iBAAA,CAAA,EAAA,gBAAA,WACA,IAAA,EAAA,KAAA,GAAA,KAAA,KAAA,IACA,MAAA,CAAA,MAAA,EAAA,KAAA,OAAA,KAGA,EAAA,EAAA,EAAA,SAAA,CACA,SAAA,SAAA,GAEA,GADA,EAAA,OACA,EAAA,GAAA,MAAA,UAAA,EAAA,qBACA,IAAA,EAAA,OAAA,MACA,EAAA,UAAA,EAAA,OAAA,EAAA,OAAA,EAAA,KAAA,GACA,EAAA,IAAA,OAAA,EAAA,QAAA,EAAA,QAAA,KAAA,EAAA,IAAA,GAEA,OADA,EAAA,UAAA,EAAA,EAAA,WACA,IAAA,EAAA,EAAA;;AC3BA,QAAA,gBAAA,CAAA;;ACAA,QAAA,gBAAA,CAAA;;ACCA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,eACA,EAAA,QAAA,iBACA,EAAA,QAAA,kBACA,EAAA,QAAA,sBAEA,EAAA,EAAA,EAAA,SAAA,CACA,0BAAA,SAAA,GAOA,IANA,IAKA,EAAA,EALA,EAAA,EAAA,GACA,EAAA,EAAA,EACA,EAAA,EAAA,GACA,EAAA,GACA,EAAA,EAEA,EAAA,OAAA,QAEA,KADA,EAAA,EAAA,EAAA,EAAA,EAAA,QACA,EAAA,EAAA,EAAA,GAEA,OAAA;;ACnBA,IAAA,EAAA,QAAA,kBACA,EAAA,QAAA,kBACA,EAAA,QAAA,iBACA,EAAA,QAAA,iBAAA,EACA,OAAA,QAAA,SAAA,GACA,OAAA,SAAA,GAOA,IANA,IAKA,EALA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,EAAA,OACA,EAAA,EACA,EAAA,GAEA,EAAA,GACA,EAAA,EAAA,KACA,IAAA,EAAA,KAAA,EAAA,IACA,EAAA,KAAA,EAAA,CAAA,EAAA,EAAA,IAAA,EAAA,IAGA,OAAA;;ACjBA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,qBAAA,EAAA,GAEA,EAAA,EAAA,EAAA,SAAA,CACA,OAAA,SAAA,GACA,OAAA,EAAA;;ACLA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,qBAAA,EAAA,GAEA,EAAA,EAAA,EAAA,SAAA,CACA,QAAA,SAAA,GACA,OAAA,EAAA;;ACNA,aAEA,OAAA,QAAA,QAAA,gBAAA,QAAA,WAAA,CAAA,WACA,IAAA,EAAA,KAAA,SAGA,iBAAA,KAAA,KAAA,EAAA,qBACA,QAAA,aAAA;;ACPA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBAGA,QAAA,mBAAA,EAAA,EAAA,EAAA,QAAA,wBAAA,SAAA,CACA,iBAAA,SAAA,EAAA,GACA,EAAA,EAAA,EAAA,MAAA,EAAA,CAAA,IAAA,EAAA,GAAA,YAAA,EAAA,cAAA;;ACTA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBAGA,QAAA,mBAAA,EAAA,EAAA,EAAA,QAAA,wBAAA,SAAA,CACA,iBAAA,SAAA,EAAA,GACA,EAAA,EAAA,EAAA,MAAA,EAAA,CAAA,IAAA,EAAA,GAAA,YAAA,EAAA,cAAA;;ACTA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,mBACA,EAAA,QAAA,iBACA,EAAA,QAAA,kBAAA,EAGA,QAAA,mBAAA,EAAA,EAAA,EAAA,QAAA,wBAAA,SAAA,CACA,iBAAA,SAAA,GACA,IAEA,EAFA,EAAA,EAAA,MACA,EAAA,EAAA,GAAA,GAEA,GACA,GAAA,EAAA,EAAA,EAAA,GAAA,OAAA,EAAA,UACA,EAAA,EAAA;;ACfA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,gBACA,EAAA,QAAA,mBACA,EAAA,QAAA,iBACA,EAAA,QAAA,kBAAA,EAGA,QAAA,mBAAA,EAAA,EAAA,EAAA,QAAA,wBAAA,SAAA,CACA,iBAAA,SAAA,GACA,IAEA,EAFA,EAAA,EAAA,MACA,EAAA,EAAA,GAAA,GAEA,GACA,GAAA,EAAA,EAAA,EAAA,GAAA,OAAA,EAAA,UACA,EAAA,EAAA;;ACfA,IAAA,EAAA,QAAA,aAEA,OAAA,QAAA,SAAA,EAAA,GACA,IAAA,EAAA,GAEA,OADA,EAAA,GAAA,EAAA,EAAA,KAAA,EAAA,GACA;;ACJA,IAAA,EAAA,QAAA,cACA,EAAA,QAAA,0BACA,OAAA,QAAA,SAAA,GACA,OAAA,WACA,GAAA,EAAA,OAAA,EAAA,MAAA,UAAA,EAAA,yBACA,OAAA,EAAA;;ACLA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,EAAA,EAAA,MAAA,CAAA,OAAA,QAAA,wBAAA,CAAA;;ACFA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,EAAA,EAAA,MAAA,CAAA,OAAA,QAAA,wBAAA,CAAA;;ACHA,aAEA,IAAA,EAAA,QAAA,aAEA,OAAA,QAAA,SAAA,GACA,EAAA,EAAA,EAAA,EAAA,CAAA,GAAA,WAGA,IAFA,IAAA,EAAA,UAAA,OACA,EAAA,IAAA,MAAA,GACA,KAAA,EAAA,GAAA,UAAA,GACA,OAAA,IAAA,KAAA;;ACRA,QAAA,uBAAA,CAAA;;ACAA,QAAA,uBAAA,CAAA;;ACAA,QAAA,uBAAA,CAAA;;ACAA,QAAA,uBAAA,CAAA;;ACDA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,QAAA,UACA,EAAA,QAAA,aAEA,OAAA,QAAA,SAAA,GACA,EAAA,EAAA,EAAA,EAAA,CAAA,KAAA,SAAA,GACA,IACA,EAAA,EAAA,EAAA,EADA,EAAA,UAAA,GAKA,OAHA,EAAA,OACA,OAAA,IAAA,IACA,EAAA,GACA,MAAA,EAAA,IAAA,MACA,EAAA,GACA,GACA,EAAA,EACA,EAAA,EAAA,EAAA,UAAA,GAAA,GACA,EAAA,GAAA,EAAA,SAAA,GACA,EAAA,KAAA,EAAA,EAAA,SAGA,EAAA,GAAA,EAAA,EAAA,KAAA,GAEA,IAAA,KAAA;;ACxBA,QAAA,yBAAA,CAAA;;ACAA,QAAA,yBAAA,CAAA;;ACAA,QAAA,yBAAA,CAAA;;ACAA,QAAA,yBAAA,CAAA;;ACAA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,CAAA,OAAA,QAAA;;ACFA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,SAAA,CAAA,OAAA,QAAA;;ACFA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,UAEA,EAAA,EAAA,EAAA,QAAA,CACA,QAAA,SAAA,GACA,MAAA,UAAA,EAAA;;ACLA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CACA,MAAA,SAAA,EAAA,EAAA,GACA,OAAA,KAAA,IAAA,EAAA,KAAA,IAAA,EAAA;;ACJA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CAAA,YAAA,KAAA,GAAA;;ACFA,IAAA,EAAA,QAAA,aACA,EAAA,IAAA,KAAA,GAEA,EAAA,EAAA,EAAA,OAAA,CACA,QAAA,SAAA,GACA,OAAA,EAAA;;ACLA,OAAA,QAAA,KAAA,OAAA,SAAA,EAAA,EAAA,EAAA,EAAA,GACA,OACA,IAAA,UAAA,QAEA,GAAA,GAEA,GAAA,GAEA,GAAA,GAEA,GAAA,GAEA,GAAA,EACA,IACA,IAAA,EAAA,GAAA,KAAA,EAAA,EAAA,GACA,EAAA,IAAA,EAAA,IAAA,EAAA,GAAA;;ACfA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,QAAA,kBAEA,EAAA,EAAA,EAAA,OAAA,CACA,OAAA,SAAA,EAAA,EAAA,EAAA,EAAA,GACA,OAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA;;ACNA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CACA,MAAA,SAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,IAAA,EAEA,EAAA,IAAA,EACA,OAFA,IAAA,IAEA,IAAA,KAAA,EAAA,GAAA,EAAA,KAAA,EAAA,IAAA,MAAA,IAAA;;ACPA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CACA,MAAA,SAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,IAAA,EAEA,EAAA,IAAA,EACA,OAFA,IAAA,IAEA,IAAA,MAAA,EAAA,IAAA,EAAA,GAAA,EAAA,IAAA,KAAA,IAAA;;ACPA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CACA,MAAA,SAAA,EAAA,GACA,IACA,GAAA,EACA,GAAA,EACA,EAHA,MAGA,EACA,EAJA,MAIA,EACA,EAAA,GAAA,GACA,EAAA,GAAA,GACA,GAAA,EAAA,IAAA,IAAA,EAAA,IAAA,IACA,OAAA,EAAA,GAAA,GAAA,MAAA,EAAA,IAAA,IARA,MAQA,IAAA;;ACZA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CAAA,YAAA,IAAA,KAAA;;ACFA,IAAA,EAAA,QAAA,aACA,EAAA,KAAA,GAAA,IAEA,EAAA,EAAA,EAAA,OAAA,CACA,QAAA,SAAA,GACA,OAAA,EAAA;;ACLA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CAAA,MAAA,QAAA;;ACFA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CACA,MAAA,SAAA,EAAA,GACA,IACA,GAAA,EACA,GAAA,EACA,EAHA,MAGA,EACA,EAJA,MAIA,EACA,EAAA,IAAA,GACA,EAAA,IAAA,GACA,GAAA,EAAA,IAAA,IAAA,EAAA,IAAA,IACA,OAAA,EAAA,GAAA,IAAA,MAAA,EAAA,IAAA,IARA,MAQA,KAAA;;ACZA,IAAA,EAAA,QAAA,aAEA,EAAA,EAAA,EAAA,OAAA,CAAA,QAAA,SAAA,GAEA,OAAA,GAAA,IAAA,EAAA,EAAA,GAAA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA;;;ACJA,aACA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,WACA,EAAA,QAAA,aACA,EAAA,QAAA,0BACA,EAAA,QAAA,sBAEA,EAAA,EAAA,EAAA,EAAA,EAAA,UAAA,CAAA,QAAA,SAAA,GACA,IAAA,EAAA,EAAA,KAAA,EAAA,SAAA,EAAA,SACA,EAAA,mBAAA,EACA,OAAA,KAAA,KACA,EAAA,SAAA,GACA,OAAA,EAAA,EAAA,KAAA,KAAA,WAAA,OAAA,KACA,EACA,EAAA,SAAA,GACA,OAAA,EAAA,EAAA,KAAA,KAAA,WAAA,MAAA,KACA;;ACjBA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,6BACA,EAAA,QAAA,cAEA,EAAA,EAAA,EAAA,UAAA,CAAA,IAAA,SAAA,GACA,IAAA,EAAA,EAAA,EAAA,MACA,EAAA,EAAA,GAEA,OADA,EAAA,EAAA,EAAA,OAAA,EAAA,SAAA,EAAA,GACA,EAAA;;ACVA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,aACA,EAAA,QAAA,YAAA,CAAA,YACA,EAAA,EAAA,QAAA,EAAA,MAAA,IAAA,QAAA,o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iBAAA,EAAA,EAAA,OAAA,IAEA,EAAA,SAAA,GACA,EAAA,EAAA,EAAA,UAAA,IAGA,OAAA,QAAA,CACA,MAAA,EACA,IAAA,EACA,IAAA,EACA,IAAA,EACA,IAAA,EACA,KAAA,EACA,IAAA,EACA,IAAA;;ACjDA,IAAA,EAAA,QAAA,eACA,EAAA,QAAA,gBACA,EAAA,EAAA,IACA,EAAA,EAAA,IAEA,EAAA,IAAA,CAAA,eAAA,SAAA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,EAAA,EAAA,GAAA,EAAA;;ACNA,IAAA,EAAA,QAAA,eACA,EAAA,QAAA,gBACA,EAAA,EAAA,IACA,EAAA,EAAA,IACA,EAAA,EAAA,MAEA,EAAA,IAAA,CAAA,eAAA,SAAA,EAAA,GACA,IAAA,EAAA,UAAA,OAAA,OAAA,EAAA,EAAA,UAAA,IACA,EAAA,EAAA,EAAA,GAAA,GAAA,GACA,QAAA,IAAA,IAAA,EAAA,OAAA,GAAA,OAAA,EACA,GAAA,EAAA,KAAA,OAAA,EACA,IAAA,EAAA,EAAA,IAAA,GAEA,OADA,EAAA,OAAA,KACA,EAAA,MAAA,EAAA,OAAA;;ACbA,IAAA,EAAA,QAAA,eACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,EAAA,IACA,EAAA,EAAA,IACA,EAAA,EAAA,IAEA,EAAA,SAAA,EAAA,EAAA,GAEA,GADA,EAAA,EAAA,EAAA,GACA,OAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,GACA,OAAA,OAAA,EAAA,EAAA,EAAA,EAAA,QAAA,GAGA,EAAA,IAAA,CAAA,YAAA,SAAA,EAAA,GACA,OAAA,EAAA,EAAA,EAAA,GAAA,UAAA,OAAA,OAAA,EAAA,EAAA,UAAA;;ACfA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,0BACA,EAAA,QAAA,eACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,EAAA,KACA,EAAA,EAAA,IAEA,EAAA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,GAAA,OAAA,EAAA,OAAA,EACA,IAAA,EAAA,EAAA,EAAA,GACA,OAAA,EAAA,OAAA,EAAA,OAAA,EAAA,IAAA,EAAA,EAAA,OAAA,KAAA,EAAA,GAGA,EAAA,IAAA,CAAA,gBAAA,SAAA,GACA,OAAA,EAAA,EAAA,GAAA,UAAA,OAAA,OAAA,EAAA,EAAA,UAAA;;ACjBA,IAAA,EAAA,QAAA,eACA,EAAA,QAAA,gBACA,EAAA,EAAA,IACA,EAAA,EAAA,IAEA,EAAA,IAAA,CAAA,eAAA,SAAA,EAAA,GACA,OAAA,EAAA,EAAA,EAAA,GACA,UAAA,OAAA,OAAA,EAAA,EAAA,UAAA;;ACPA,IAAA,EAAA,QAAA,eACA,EAAA,QAAA,gBACA,EAAA,EAAA,KACA,EAAA,EAAA,IAEA,EAAA,IAAA,CAAA,mBAAA,SAAA,GACA,OAAA,EAAA,EAAA,GAAA,UAAA,OAAA,OAAA,EAAA,EAAA,UAAA;;ACNA,IAAA,EAAA,QAAA,eACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,EAAA,IACA,EAAA,EAAA,IAEA,EAAA,SAAA,EAAA,EAAA,GAEA,GADA,EAAA,EAAA,EAAA,GACA,OAAA,EACA,IAAA,EAAA,EAAA,GACA,OAAA,OAAA,GAAA,EAAA,EAAA,EAAA,IAGA,EAAA,IAAA,CAAA,YAAA,SAAA,EAAA,GACA,OAAA,EAAA,EAAA,EAAA,GAAA,UAAA,OAAA,OAAA,EAAA,EAAA,UAAA;;ACdA,IAAA,EAAA,QAAA,eACA,EAAA,QAAA,gBACA,EAAA,EAAA,IACA,EAAA,EAAA,IAEA,EAAA,IAAA,CAAA,eAAA,SAAA,EAAA,GACA,OAAA,EAAA,EAAA,EAAA,GACA,UAAA,OAAA,OAAA,EAAA,EAAA,UAAA;;ACPA,IAAA,EAAA,QAAA,eACA,EAAA,QAAA,gBACA,EAAA,QAAA,iBACA,EAAA,EAAA,IACA,EAAA,EAAA,IAEA,EAAA,IAAA,CAAA,SAAA,SAAA,EAAA,GACA,OAAA,SAAA,EAAA,GACA,EACA,EAAA,QACA,IAAA,EAAA,EAAA,GAAA,GACA,EAAA;;;ACVA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,eAAA,GACA,EAAA,QAAA,aAAA,QACA,EAAA,WAAA,QAAA,SAAA,CAAA,GAEA,EAAA,EAAA,EAAA,CACA,KAAA,SAAA,GACA,IAAA,EAAA,GAAA,EAAA,OACA,EAAA,EAAA,EAAA,KAAA,GAAA;;;ACTA,aAEA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,aACA,EAAA,QAAA,WACA,EAAA,QAAA,eAAA,GACA,EAAA,QAAA,SAAA,CAAA,cACA,EAAA,QAAA,iBACA,EAAA,QAAA,gBACA,EAAA,QAAA,kBACA,EAAA,QAAA,mBACA,EAAA,QAAA,WACA,EAAA,QAAA,aACA,EAAA,EAAA,OAEA,EAAA,SAAA,GACA,OAAA,MAAA,OAAA,EAAA,EAAA,IAGA,EAAA,SAAA,GACA,IAAA,EAAA,EAAA,GACA,IACA,EAAA,QAAA,EACA,MAIA,EAAA,SAAA,GACA,YAAA,IAAA,EAAA,IAGA,EAAA,SAAA,GACA,EAAA,KACA,EAAA,QAAA,EACA,EAAA,KAIA,EAAA,SAAA,EAAA,GACA,EAAA,GACA,KAAA,QAAA,EACA,KAAA,GAAA,EACA,EAAA,IAAA,EAAA,MACA,IACA,IAAA,EAAA,EAAA,GACA,EAAA,EACA,MAAA,IACA,mBAAA,EAAA,YAAA,EAAA,WAAA,EAAA,eACA,EAAA,GACA,KAAA,GAAA,GAEA,MAAA,GAEA,YADA,EAAA,MAAA,GAEA,EAAA,OAAA,EAAA,OAGA,EAAA,UAAA,EAAA,GAAA,CACA,YAAA,WAAA,EAAA,SAGA,IAAA,EAAA,SAAA,GACA,KAAA,GAAA,GAGA,EAAA,UAAA,EAAA,GAAA,CACA,KAAA,SAAA,GACA,IAAA,EAAA,KAAA,GACA,IAAA,EAAA,GAAA,CACA,IAAA,EAAA,EAAA,GACA,IACA,IAAA,EAAA,EAAA,EAAA,MACA,GAAA,EAAA,OAAA,EAAA,KAAA,EAAA,GACA,MAAA,GACA,IACA,EAAA,GACA,QACA,MAAA,MAKA,MAAA,SAAA,GACA,IAAA,EAAA,KAAA,GACA,GAAA,EAAA,GAAA,MAAA,EACA,IAAA,EAAA,EAAA,GACA,EAAA,QAAA,EACA,IACA,IAAA,EAAA,EAAA,EAAA,OACA,IAAA,EAAA,MAAA,EACA,EAAA,EAAA,KAAA,EAAA,GACA,MAAA,GACA,IACA,EAAA,GACA,QACA,MAAA,GAGA,OADA,EAAA,GACA,GAEA,SAAA,SAAA,GACA,IAAA,EAAA,KAAA,GACA,IAAA,EAAA,GAAA,CACA,IAAA,EAAA,EAAA,GACA,EAAA,QAAA,EACA,IACA,IAAA,EAAA,EAAA,EAAA,UACA,EAAA,EAAA,EAAA,KAAA,EAAA,QAAA,EACA,MAAA,GACA,IACA,EAAA,GACA,QACA,MAAA,GAGA,OADA,EAAA,GACA,MAKA,IAAA,EAAA,SAAA,GACA,EAAA,KAAA,EAAA,aAAA,MAAA,GAAA,EAAA,IAGA,EAAA,EAAA,UAAA,CACA,UAAA,SAAA,GACA,OAAA,IAAA,EAAA,EAAA,KAAA,KAEA,QAAA,SAAA,GACA,IAAA,EAAA,KACA,OAAA,IAAA,EAAA,SAAA,EAAA,SAAA,SAAA,EAAA,GACA,EAAA,GACA,IAAA,EAAA,EAAA,UAAA,CACA,KAAA,SAAA,GACA,IACA,OAAA,EAAA,GACA,MAAA,GACA,EAAA,GACA,EAAA,gBAGA,MAAA,EACA,SAAA,SAMA,EAAA,EAAA,CACA,KAAA,SAAA,GACA,IAAA,EAAA,mBAAA,KAAA,KAAA,EACA,EAAA,EAAA,EAAA,GAAA,IACA,GAAA,EAAA,CACA,IAAA,EAAA,EAAA,EAAA,KAAA,IACA,OAAA,EAAA,cAAA,EAAA,EAAA,IAAA,EAAA,SAAA,GACA,OAAA,EAAA,UAAA,KAGA,OAAA,IAAA,EAAA,SAAA,GACA,IAAA,GAAA,EAeA,OAdA,EAAA,WACA,IAAA,EAAA,CACA,IACA,GAAA,EAAA,GAAA,EAAA,SAAA,GAEA,GADA,EAAA,KAAA,GACA,EAAA,OAAA,MACA,EAAA,OACA,MAAA,GACA,GAAA,EAAA,MAAA,EAEA,YADA,EAAA,MAAA,GAEA,EAAA,cAGA,WAAA,GAAA,MAGA,GAAA,WACA,IAAA,IAAA,EAAA,EAAA,EAAA,UAAA,OAAA,EAAA,IAAA,MAAA,GAAA,EAAA,GAAA,EAAA,GAAA,UAAA,KACA,OAAA,IAAA,mBAAA,KAAA,KAAA,GAAA,SAAA,GACA,IAAA,GAAA,EASA,OARA,EAAA,WACA,IAAA,EAAA,CACA,IAAA,IAAA,EAAA,EAAA,EAAA,EAAA,SAAA,EAEA,GADA,EAAA,KAAA,EAAA,IACA,EAAA,OACA,EAAA,cAGA,WAAA,GAAA,QAKA,EAAA,EAAA,UAAA,EAAA,WAAA,OAAA,OAEA,EAAA,EAAA,EAAA,CAAA,WAAA,IAEA,QAAA,iBAAA,CAAA;;;ACrMA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,aACA,EAAA,QAAA,iBACA,EAAA,GAAA,MACA,EAAA,WAAA,KAAA,GACA,EAAA,SAAA,GACA,OAAA,SAAA,EAAA,GACA,IAAA,EAAA,UAAA,OAAA,EACA,IAAA,GAAA,EAAA,KAAA,UAAA,GACA,OAAA,EAAA,EAAA,YAEA,mBAAA,EAAA,EAAA,SAAA,IAAA,MAAA,KAAA,IACA,EAAA,KAGA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,EAAA,CACA,WAAA,EAAA,EAAA,YACA,YAAA,EAAA,EAAA;;AClBA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,WACA,EAAA,EAAA,EAAA,EAAA,EAAA,CACA,aAAA,EAAA,IACA,eAAA,EAAA;;;ACyCA,IA7CA,IAAA,EAAA,QAAA,wBACA,EAAA,QAAA,kBACA,EAAA,QAAA,eACA,EAAA,QAAA,aACA,EAAA,QAAA,WACA,EAAA,QAAA,gBACA,EAAA,QAAA,UACA,EAAA,EAAA,YACA,EAAA,EAAA,eACA,EAAA,EAAA,MAEA,EAAA,CACA,aAAA,EACA,qBAAA,EACA,cAAA,EACA,gBAAA,EACA,aAAA,EACA,eAAA,EACA,cAAA,EACA,sBAAA,EACA,UAAA,EACA,mBAAA,EACA,gBAAA,EACA,iBAAA,EACA,mBAAA,EACA,WAAA,EACA,eAAA,EACA,cAAA,EACA,UAAA,EACA,kBAAA,EACA,QAAA,EACA,aAAA,EACA,eAAA,EACA,eAAA,EACA,gBAAA,EACA,cAAA,EACA,eAAA,EACA,kBAAA,EACA,kBAAA,EACA,gBAAA,EACA,kBAAA,EACA,eAAA,EACA,WAAA,GAGA,EAAA,EAAA,GAAA,EAAA,EAAA,EAAA,EAAA,OAAA,IAAA,CACA,IAIA,EAJA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,EAAA,GACA,EAAA,GAAA,EAAA,UAEA,GAAA,IACA,EAAA,IAAA,EAAA,EAAA,EAAA,GACA,EAAA,IAAA,EAAA,EAAA,EAAA,GACA,EAAA,GAAA,EACA,GAAA,IAAA,KAAA,EAAA,EAAA,IAAA,EAAA,EAAA,EAAA,EAAA,IAAA;;ACvDA,QAAA,wBACA,QAAA,+BACA,QAAA,wCACA,QAAA,0CACA,QAAA,oDACA,QAAA,yCACA,QAAA,6BACA,QAAA,+CACA,QAAA,+BACA,QAAA,6BACA,QAAA,2CACA,QAAA,kCACA,QAAA,kCACA,QAAA,sCACA,QAAA,+BACA,QAAA,2BACA,QAAA,yCACA,QAAA,kCACA,QAAA,+BACA,QAAA,+BACA,QAAA,uCACA,QAAA,2BACA,QAAA,6BACA,QAAA,oCACA,QAAA,iCACA,QAAA,qCACA,QAAA,gCACA,QAAA,kCACA,QAAA,mCACA,QAAA,+BACA,QAAA,wCACA,QAAA,yCACA,QAAA,yCACA,QAAA,oCACA,QAAA,kCACA,QAAA,4BACA,QAAA,4BACA,QAAA,4BACA,QAAA,2BACA,QAAA,4BACA,QAAA,2BACA,QAAA,4BACA,QAAA,6BACA,QAAA,4BACA,QAAA,2BACA,QAAA,4BACA,QAAA,4BACA,QAAA,2BACA,QAAA,2BACA,QAAA,2BACA,QAAA,2BACA,QAAA,4BACA,QAAA,wCACA,QAAA,4BACA,QAAA,6BACA,QAAA,iCACA,QAAA,sCACA,QAAA,kCACA,QAAA,iCACA,QAAA,+BACA,QAAA,oCACA,QAAA,+BACA,QAAA,4BACA,QAAA,8BACA,QAAA,6BACA,QAAA,8BACA,QAAA,kCACA,QAAA,iCACA,QAAA,gCACA,QAAA,6BACA,QAAA,8BACA,QAAA,+BACA,QAAA,4BACA,QAAA,4BACA,QAAA,0BACA,QAAA,8BACA,QAAA,oCACA,QAAA,gCACA,QAAA,mCACA,QAAA,gCACA,QAAA,4BACA,QAAA,0BACA,QAAA,4BACA,QAAA,6BACA,QAAA,4BACA,QAAA,gCACA,QAAA,2BACA,QAAA,8BACA,QAAA,4BACA,QAAA,6BACA,QAAA,8BACA,QAAA,oCACA,QAAA,gCACA,QAAA,qCACA,QAAA,mCACA,QAAA,4BACA,QAAA,4BACA,QAAA,kCACA,QAAA,+BACA,QAAA,gCACA,QAAA,oCACA,QAAA,6BACA,QAAA,kCACA,QAAA,8BACA,QAAA,8BACA,QAAA,gCACA,QAAA,+BACA,QAAA,8BACA,QAAA,yBACA,QAAA,qBACA,QAAA,qBACA,QAAA,0BACA,QAAA,0BACA,QAAA,oCACA,QAAA,iCACA,QAAA,kCACA,QAAA,mCACA,QAAA,2CACA,QAAA,mCACA,QAAA,oCACA,QAAA,mCACA,QAAA,oCACA,QAAA,qCACA,QAAA,qCACA,QAAA,+BACA,QAAA,mCACA,QAAA,yCACA,QAAA,yCACA,QAAA,mCACA,QAAA,6BACA,QAAA,qDACA,QAAA,0CACA,QAAA,6BACA,QAAA,uCACA,QAAA,kCACA,QAAA,4CACA,QAAA,6BACA,QAAA,0CACA,QAAA,gCACA,QAAA,gCACA,QAAA,+BACA,QAAA,2BACA,QAAA,kCACA,QAAA,gCACA,QAAA,kCACA,QAAA,mCACA,QAAA,kCACA,QAAA,uCACA,QAAA,mCACA,QAAA,qDACA,QAAA,+BACA,QAAA,gCACA,QAAA,sCACA,QAAA,sCACA,QAAA,sCACA,QAAA,sCACA,QAAA,6BACA,QAAA,6BACA,QAAA,wBACA,QAAA,wBACA,QAAA,6BACA,QAAA,6BACA,QAAA,0BACA,QAAA,0BACA,QAAA,+BACA,QAAA,+BACA,QAAA,wBACA,QAAA,+BACA,QAAA,gCACA,QAAA,4BACA,QAAA,kCACA,QAAA,8BACA,QAAA,6BACA,QAAA,4BACA,QAAA,4BACA,QAAA,4BACA,QAAA,kCACA,QAAA,8BACA,QAAA,4BACA,QAAA,4BACA,QAAA,8BACA,QAAA,iCACA,QAAA,6BACA,QAAA,yCACA,QAAA,yCACA,QAAA,sCACA,QAAA,2CACA,QAAA,0CACA,QAAA,+CACA,QAAA,sCACA,QAAA,0CACA,QAAA,kCACA,QAAA,sBACA,QAAA,4BACA,QAAA,wBACA,QAAA,2BACA,QAAA,8BACA,OAAA,QAAA,QAAA;;;AC2hBA,IAAA,EAAA,UAAA,IAttBA,SAAA,GACA,aAEA,IAEA,EAFA,EAAA,OAAA,UACA,EAAA,EAAA,eAEA,EAAA,mBAAA,OAAA,OAAA,GACA,EAAA,EAAA,UAAA,aACA,EAAA,EAAA,eAAA,kBACA,EAAA,EAAA,aAAA,gBAEA,EAAA,iBAAA,OACA,EAAA,EAAA,mBACA,GAAA,EACA,IAGA,OAAA,QAAA,OAJA,EAaA,EAAA,EAAA,mBAAA,EAAA,OAAA,QAAA,IAcA,KAAA,EAoBA,IAAA,EAAA,iBACA,EAAA,iBACA,EAAA,YACA,EAAA,YAIA,EAAA,GAYA,EAAA,GACA,EAAA,GAAA,WACA,OAAA,MAGA,IAAA,EAAA,OAAA,eACA,EAAA,GAAA,EAAA,EAAA,EAAA,MACA,GACA,IAAA,GACA,EAAA,KAAA,EAAA,KAGA,EAAA,GAGA,IAAA,EAAA,EAAA,UACA,EAAA,UAAA,OAAA,OAAA,GACA,EAAA,UAAA,EAAA,YAAA,EACA,EAAA,YAAA,EACA,EAAA,GACA,EAAA,YAAA,oBAYA,EAAA,oBAAA,SAAA,GACA,IAAA,EAAA,mBAAA,GAAA,EAAA,YACA,QAAA,IACA,IAAA,GAGA,uBAAA,EAAA,aAAA,EAAA,QAIA,EAAA,KAAA,SAAA,GAUA,OATA,OAAA,eACA,OAAA,eAAA,EAAA,IAEA,EAAA,UAAA,EACA,KAAA,IACA,EAAA,GAAA,sBAGA,EAAA,UAAA,OAAA,OAAA,GACA,GAOA,EAAA,MAAA,SAAA,GACA,MAAA,CAAA,QAAA,IAkFA,EAAA,EAAA,WACA,EAAA,UAAA,GAAA,WACA,OAAA,MAEA,EAAA,cAAA,EAKA,EAAA,MAAA,SAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,IAAA,EACA,EAAA,EAAA,EAAA,EAAA,IAGA,OAAA,EAAA,oBAAA,GACA,EACA,EAAA,OAAA,KAAA,SAAA,GACA,OAAA,EAAA,KAAA,EAAA,MAAA,EAAA,UAsKA,EAAA,GAEA,EAAA,GAAA,YAOA,EAAA,GAAA,WACA,OAAA,MAGA,EAAA,SAAA,WACA,MAAA,sBAkCA,EAAA,KAAA,SAAA,GACA,IAAA,EAAA,GACA,IAAA,IAAA,KAAA,EACA,EAAA,KAAA,GAMA,OAJA,EAAA,UAIA,SAAA,IACA,KAAA,EAAA,QAAA,CACA,IAAA,EAAA,EAAA,MACA,GAAA,KAAA,EAGA,OAFA,EAAA,MAAA,EACA,EAAA,MAAA,EACA,EAQA,OADA,EAAA,MAAA,EACA,IAsCA,EAAA,OAAA,EAMA,EAAA,UAAA,CACA,YAAA,EAEA,MAAA,SAAA,GAcA,GAbA,KAAA,KAAA,EACA,KAAA,KAAA,EAGA,KAAA,KAAA,KAAA,MAAA,EACA,KAAA,MAAA,EACA,KAAA,SAAA,KAEA,KAAA,OAAA,OACA,KAAA,IAAA,EAEA,KAAA,WAAA,QAAA,IAEA,EACA,IAAA,IAAA,KAAA,KAEA,MAAA,EAAA,OAAA,IACA,EAAA,KAAA,KAAA,KACA,OAAA,EAAA,MAAA,MACA,KAAA,GAAA,IAMA,KAAA,WACA,KAAA,MAAA,EAEA,IACA,EADA,KAAA,WAAA,GACA,WACA,GAAA,UAAA,EAAA,KACA,MAAA,EAAA,IAGA,OAAA,KAAA,MAGA,kBAAA,SAAA,GACA,GAAA,KAAA,KACA,MAAA,EAGA,IAAA,EAAA,KACA,SAAA,EAAA,EAAA,GAYA,OAXA,EAAA,KAAA,QACA,EAAA,IAAA,EACA,EAAA,KAAA,EAEA,IAGA,EAAA,OAAA,OACA,EAAA,IAAA,KAGA,EAGA,IAAA,IAAA,EAAA,KAAA,WAAA,OAAA,EAAA,GAAA,IAAA,EAAA,CACA,IAAA,EAAA,KAAA,WAAA,GACA,EAAA,EAAA,WAEA,GAAA,SAAA,EAAA,OAIA,OAAA,EAAA,OAGA,GAAA,EAAA,QAAA,KAAA,KAAA,CACA,IAAA,EAAA,EAAA,KAAA,EAAA,YACA,EAAA,EAAA,KAAA,EAAA,cAEA,GAAA,GAAA,EAAA,CACA,GAAA,KAAA,KAAA,EAAA,SACA,OAAA,EAAA,EAAA,UAAA,GACA,GAAA,KAAA,KAAA,EAAA,WACA,OAAA,EAAA,EAAA,iBAGA,GAAA,GACA,GAAA,KAAA,KAAA,EAAA,SACA,OAAA,EAAA,EAAA,UAAA,OAGA,CAAA,IAAA,EAMA,MAAA,IAAA,MAAA,0CALA,GAAA,KAAA,KAAA,EAAA,WACA,OAAA,EAAA,EAAA,gBAUA,OAAA,SAAA,EAAA,GACA,IAAA,IAAA,EAAA,KAAA,WAAA,OAAA,EAAA,GAAA,IAAA,EAAA,CACA,IAAA,EAAA,KAAA,WAAA,GACA,GAAA,EAAA,QAAA,KAAA,MACA,EAAA,KAAA,EAAA,eACA,KAAA,KAAA,EAAA,WAAA,CACA,IAAA,EAAA,EACA,OAIA,IACA,UAAA,GACA,aAAA,IACA,EAAA,QAAA,GACA,GAAA,EAAA,aAGA,EAAA,MAGA,IAAA,EAAA,EAAA,EAAA,WAAA,GAIA,OAHA,EAAA,KAAA,EACA,EAAA,IAAA,EAEA,GACA,KAAA,OAAA,OACA,KAAA,KAAA,EAAA,WACA,GAGA,KAAA,SAAA,IAGA,SAAA,SAAA,EAAA,GACA,GAAA,UAAA,EAAA,KACA,MAAA,EAAA,IAcA,MAXA,UAAA,EAAA,MACA,aAAA,EAAA,KACA,KAAA,KAAA,EAAA,IACA,WAAA,EAAA,MACA,KAAA,KAAA,KAAA,IAAA,EAAA,IACA,KAAA,OAAA,SACA,KAAA,KAAA,OACA,WAAA,EAAA,MAAA,IACA,KAAA,KAAA,GAGA,GAGA,OAAA,SAAA,GACA,IAAA,IAAA,EAAA,KAAA,WAAA,OAAA,EAAA,GAAA,IAAA,EAAA,CACA,IAAA,EAAA,KAAA,WAAA,GACA,GAAA,EAAA,aAAA,EAGA,OAFA,KAAA,SAAA,EAAA,WAAA,EAAA,UACA,EAAA,GACA,IAKA,MAAA,SAAA,GACA,IAAA,IAAA,EAAA,KAAA,WAAA,OAAA,EAAA,GAAA,IAAA,EAAA,CACA,IAAA,EAAA,KAAA,WAAA,GACA,GAAA,EAAA,SAAA,EAAA,CACA,IAAA,EAAA,EAAA,WACA,GAAA,UAAA,EAAA,KAAA,CACA,IAAA,EAAA,EAAA,IACA,EAAA,GAEA,OAAA,GAMA,MAAA,IAAA,MAAA,0BAGA,cAAA,SAAA,EAAA,EAAA,GAaA,OAZA,KAAA,SAAA,CACA,SAAA,EAAA,GACA,WAAA,EACA,QAAA,GAGA,SAAA,KAAA,SAGA,KAAA,IAAA,GAGA,IA/qBA,SAAA,EAAA,EAAA,EAAA,EAAA,GAEA,IAAA,EAAA,GAAA,EAAA,qBAAA,EAAA,EAAA,EACA,EAAA,OAAA,OAAA,EAAA,WACA,EAAA,IAAA,EAAA,GAAA,IAMA,OAFA,EAAA,QA8MA,SAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAEA,OAAA,SAAA,EAAA,GACA,GAAA,IAAA,EACA,MAAA,IAAA,MAAA,gCAGA,GAAA,IAAA,EAAA,CACA,GAAA,UAAA,EACA,MAAA,EAKA,OAAA,IAMA,IAHA,EAAA,OAAA,EACA,EAAA,IAAA,IAEA,CACA,IAAA,EAAA,EAAA,SACA,GAAA,EAAA,CACA,IAAA,EAAA,EAAA,EAAA,GACA,GAAA,EAAA,CACA,GAAA,IAAA,EAAA,SACA,OAAA,GAIA,GAAA,SAAA,EAAA,OAGA,EAAA,KAAA,EAAA,MAAA,EAAA,SAEA,GAAA,UAAA,EAAA,OAAA,CACA,GAAA,IAAA,EAEA,MADA,EAAA,EACA,EAAA,IAGA,EAAA,kBAAA,EAAA,SAEA,WAAA,EAAA,QACA,EAAA,OAAA,SAAA,EAAA,KAGA,EAAA,EAEA,IAAA,EAAA,EAAA,EAAA,EAAA,GACA,GAAA,WAAA,EAAA,KAAA,CAOA,GAJA,EAAA,EAAA,KACA,EACA,EAEA,EAAA,MAAA,EACA,SAGA,MAAA,CACA,MAAA,EAAA,IACA,KAAA,EAAA,MAGA,UAAA,EAAA,OACA,EAAA,EAGA,EAAA,OAAA,QACA,EAAA,IAAA,EAAA,OAtRA,CAAA,EAAA,EAAA,GAEA,EAcA,SAAA,EAAA,EAAA,EAAA,GACA,IACA,MAAA,CAAA,KAAA,SAAA,IAAA,EAAA,KAAA,EAAA,IACA,MAAA,GACA,MAAA,CAAA,KAAA,QAAA,IAAA,IAiBA,SAAA,KACA,SAAA,KACA,SAAA,KA4BA,SAAA,EAAA,GACA,CAAA,OAAA,QAAA,UAAA,QAAA,SAAA,GACA,EAAA,GAAA,SAAA,GACA,OAAA,KAAA,QAAA,EAAA,MAoCA,SAAA,EAAA,GACA,SAAA,EAAA,EAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,EAAA,GAAA,EAAA,GACA,GAAA,UAAA,EAAA,KAEA,CACA,IAAA,EAAA,EAAA,IACA,EAAA,EAAA,MACA,OAAA,GACA,iBAAA,GACA,EAAA,KAAA,EAAA,WACA,QAAA,QAAA,EAAA,SAAA,KAAA,SAAA,GACA,EAAA,OAAA,EAAA,EAAA,IACA,SAAA,GACA,EAAA,QAAA,EAAA,EAAA,KAIA,QAAA,QAAA,GAAA,KAAA,SAAA,GAgBA,EAAA,MAAA,EACA,EAAA,IACA,GAhCA,EAAA,EAAA,KAwCA,IAAA,EAJA,iBAAA,EAAA,SAAA,EAAA,QAAA,SACA,EAAA,EAAA,QAAA,OAAA,KAAA,IAmCA,KAAA,QA9BA,SAAA,EAAA,GACA,SAAA,IACA,OAAA,IAAA,QAAA,SAAA,EAAA,GACA,EAAA,EAAA,EAAA,EAAA,KAIA,OAAA,EAaA,EAAA,EAAA,KACA,EAGA,GACA,KA+GA,SAAA,EAAA,EAAA,GACA,IAAA,EAAA,EAAA,SAAA,EAAA,QACA,GAAA,IAAA,EAAA,CAKA,GAFA,EAAA,SAAA,KAEA,UAAA,EAAA,OAAA,CACA,GAAA,EAAA,SAAA,SAGA,EAAA,OAAA,SACA,EAAA,IAAA,EACA,EAAA,EAAA,GAEA,UAAA,EAAA,QAGA,OAAA,EAIA,EAAA,OAAA,QACA,EAAA,IAAA,IAAA,UACA,kDAGA,OAAA,EAGA,IAAA,EAAA,EAAA,EAAA,EAAA,SAAA,EAAA,KAEA,GAAA,UAAA,EAAA,KAIA,OAHA,EAAA,OAAA,QACA,EAAA,IAAA,EAAA,IACA,EAAA,SAAA,KACA,EAGA,IAAA,EAAA,EAAA,IAEA,OAAA,EAOA,EAAA,MAGA,EAAA,EAAA,YAAA,EAAA,MAGA,EAAA,KAAA,EAAA,QAQA,WAAA,EAAA,SACA,EAAA,OAAA,OACA,EAAA,IAAA,GAUA,EAAA,SAAA,KACA,GANA,GA3BA,EAAA,OAAA,QACA,EAAA,IAAA,IAAA,UAAA,oCACA,EAAA,SAAA,KACA,GAoDA,SAAA,EAAA,GACA,IAAA,EAAA,CAAA,OAAA,EAAA,IAEA,KAAA,IACA,EAAA,SAAA,EAAA,IAGA,KAAA,IACA,EAAA,WAAA,EAAA,GACA,EAAA,SAAA,EAAA,IAGA,KAAA,WAAA,KAAA,GAGA,SAAA,EAAA,GACA,IAAA,EAAA,EAAA,YAAA,GACA,EAAA,KAAA,gBACA,EAAA,IACA,EAAA,WAAA,EAGA,SAAA,EAAA,GAIA,KAAA,WAAA,CAAA,CAAA,OAAA,SACA,EAAA,QAAA,EAAA,MACA,KAAA,OAAA,GA8BA,SAAA,EAAA,GACA,GAAA,EAAA,CACA,IAAA,EAAA,EAAA,GACA,GAAA,EACA,OAAA,EAAA,KAAA,GAGA,GAAA,mBAAA,EAAA,KACA,OAAA,EAGA,IAAA,MAAA,EAAA,QAAA,CACA,IAAA,GAAA,EAAA,EAAA,SAAA,IACA,OAAA,EAAA,EAAA,QACA,GAAA,EAAA,KAAA,EAAA,GAGA,OAFA,EAAA,MAAA,EAAA,GACA,EAAA,MAAA,EACA,EAOA,OAHA,EAAA,MAAA,EACA,EAAA,MAAA,EAEA,GAGA,OAAA,EAAA,KAAA,GAKA,MAAA,CAAA,KAAA,GAIA,SAAA,IACA,MAAA,CAAA,MAAA,EAAA,MAAA,IApgBA,CAktBA,iBAAA,EAAA,EACA,iBAAA,OAAA,OACA,iBAAA,KAAA,KAAA;;AC9tBA,OAAA,QAAA,SAAA,EAAA,GACA,IAAA,EAAA,IAAA,OAAA,GAAA,SAAA,GACA,OAAA,EAAA,IACA,EACA,OAAA,SAAA,GACA,OAAA,OAAA,GAAA,QAAA,EAAA;;ACJA,IAAA,EAAA,QAAA,aACA,EAAA,QAAA,cAAA,CAAA,sBAAA,QAEA,EAAA,EAAA,EAAA,SAAA,CAAA,OAAA,SAAA,GAAA,OAAA,EAAA;;ACJA,QAAA,oCACA,OAAA,QAAA,QAAA,uBAAA,OAAA;;;;AC0BA,IAAA,EAAA,UAAA,GAnBA,GANA,QAAA,gBAEA,QAAA,+BAEA,QAAA,4BAEA,EAAA,eACA,MAAA,IAAA,MAAA,kDAEA,EAAA,gBAAA,EAEA,IAAA,EAAA,iBACA,SAAA,EAAA,EAAA,EAAA,GACA,EAAA,IAAA,OAAA,GAAA,EAAA,EAAA,CACA,UAAA,EACA,cAAA,EACA,MAAA,IAIA,EAAA,OAAA,UAAA,UAAA,GAAA,UACA,EAAA,OAAA,UAAA,WAAA,GAAA,QAEA,gMAAA,MAAA,KAAA,QAAA,SAAA,GACA,GAAA,IAAA,EAAA,MAAA,EAAA,SAAA,KAAA,KAAA,GAAA;;ACmFK,aAAA,SAAA,EAAA,EAAA,GAAA,KAAA,aAAA,GAAA,MAAA,IAAA,UAAA,qCAAA,SAAA,EAAA,EAAA,GAAA,IAAA,IAAA,EAAA,EAAA,EAAA,EAAA,OAAA,IAAA,CAAA,IAAA,EAAA,EAAA,GAAA,EAAA,WAAA,EAAA,aAAA,EAAA,EAAA,cAAA,EAAA,UAAA,IAAA,EAAA,UAAA,GAAA,OAAA,eAAA,EAAA,EAAA,IAAA,IAAA,SAAA,EAAA,EAAA,EAAA,GAAA,OAAA,GAAA,EAAA,EAAA,UAAA,GAAA,GAAA,EAAA,EAAA,GAAA,EAAA,OAAA,eAAA,QAAA,aAAA,CAAA,OAAA,IAAA,QAAA,aAAA,EA7GgB8d,IAAAA,EA6GhB,WA5GWC,SAAAA,EAAAA,GAAM,EAAA,KAAA,GAETC,KAAAA,IAAM7jB,SACN8jB,KAAAA,IAAM,KAAKD,IAAIrf,iBAAiBof,EAAKG,aAElB,KAApB,KAAKD,IAAIzgB,SAER2gB,KAAAA,IAAMhgB,OACNigB,KAAAA,UAAY,KAAKD,IAAIE,YAErBC,KAAAA,cAAgB,KAAKN,IAAI9iB,cAAc6iB,EAAKQ,gBAC5C9gB,KAAAA,UAAYsgB,EAAKtgB,UACjB4L,KAAAA,UAAY0U,EAAK1U,WAAa,EAE9BmV,KAAAA,SAAW,GACXA,KAAAA,SAAW,KAAKC,YAAYV,EAAKW,iBAEjCC,KAAAA,eA2FR,OAAA,EAAA,EAAA,CAAA,CAAA,IAAA,cAxFa,MAAA,WAAA,IACNC,EAWAC,EAZM,EAAA,KAELP,KAAAA,cAAc5jB,iBAAiB,SAAU,WACtCkkB,GACA1b,aAAa0b,GAGjBA,EAAc3b,WAAW,WACrB,EAAK6b,OACN,KAIFR,KAAAA,cAAc5jB,iBAAiB,SAAU,WACtCmkB,GACA3b,aAAa2b,GAGjBA,EAAc5b,WAAW,WACrB,EAAK6b,OACN,OAoEV,CAAA,IAAA,cA/DWJ,MAAAA,SAAAA,GAEH,IADCK,IAAAA,EAAU,GACPzhB,EAAI,EAAG0F,EAAM,KAAKib,IAAIzgB,OAAQF,EAAI0F,EAAK1F,IAAK,CAC3CvC,IAAAA,EAAO,KAAKkjB,IAAI3gB,GAAGvC,KACzBgkB,EAAQ3f,KAAK,KAAK4e,IAAI5V,eAAerN,EAAKC,MAAM,KAAK,KAElD+jB,OAAAA,IAyDV,CAAA,IAAA,MAtDK,MAAA,WACEriB,IAAAA,EAAW,KAAKsiB,eACfC,KAAAA,eAAeviB,KAoDvB,CAAA,IAAA,eAjDc,MAAA,WAEN,IADCwiB,IAAAA,EAAoB,GACjB5hB,EAAI,EAAG0F,EAAM,KAAKwb,SAAShhB,OAAQF,EAAI0F,EAAK1F,IAAK,CAChD6hB,IAAAA,EAAU,KAAKX,SAASlhB,GAC1B6hB,GAAW,KAAKC,OAAOD,IACvBD,EAAkB9f,KAAK+f,GAIxBD,OAAAA,IAwCV,CAAA,IAAA,SArCM1iB,MAAAA,SAAAA,GACG6a,IAAAA,EAAY,KAAKiH,cAAcjH,UAC/BgI,EAAYhI,EAAY,KAAKhO,UAE7BiW,EADO9iB,EAAQwM,wBACGI,IAAMiO,EACxBkI,EAAgBD,EAAa9iB,EAAQ8M,aAEpCgW,OAAAA,GAAcD,EAAY,KAAKhW,WAAakW,EAAgBlI,EAAY,KAAKhO,YA8BvF,CAAA,IAAA,iBA3Bc3M,MAAAA,SAAAA,GAIN,IAHD8iB,IAAAA,EAAW,EACXC,EAAkBC,IAEbpiB,EAAI,EAAG0F,EAAMtG,EAASc,OAAQF,EAAI0F,EAAK1F,IAAK,CAC3CmO,IAAAA,EAAK/O,EAASY,GACdqiB,EAAY,KAAKC,YAAYnU,GAC/B+T,EAAWG,IACXH,EAAWG,EACXF,EAAkBhU,GAIrB,IAAA,IAAInO,EAAI,EAAG0F,EAAM,KAAKib,IAAIzgB,OAAQF,EAAI0F,EAAK1F,IAAK,CAC3CuiB,IAAAA,EAAa,KAAK5B,IAAI3gB,GACxBuiB,EAAW9kB,KAAKC,MAAM,KAAK,KAAOykB,EAAgBK,IAClDD,EAAW/lB,UAAUO,IAAI,KAAKoD,WAC9BoiB,EAAW/lB,UAAUO,IAAI,6BAEzBwlB,EAAW/lB,UAAUoL,OAAO,KAAKzH,WACjCoiB,EAAW/lB,UAAUoL,OAAO,+BAOvC,CAAA,IAAA,cAFW1I,MAAAA,SAAAA,GACDiX,OAAAA,SAASiM,EAAEljB,GAASujB,KAAK,qBAAqBC,IAAI,GAAGC,QAAQjlB,MAAM,KAAK,QAClF,EAAA,GAAA,QAAA,QAAA;;AChDA,aAAA,SAAA,EAAA,EAAA,GAAA,KAAA,aAAA,GAAA,MAAA,IAAA,UAAA,qCAAA,SAAA,EAAA,EAAA,GAAA,IAAA,IAAA,EAAA,EAAA,EAAA,EAAA,OAAA,IAAA,CAAA,IAAA,EAAA,EAAA,GAAA,EAAA,WAAA,EAAA,aAAA,EAAA,EAAA,cAAA,EAAA,UAAA,IAAA,EAAA,UAAA,GAAA,OAAA,eAAA,EAAA,EAAA,IAAA,IAAA,SAAA,EAAA,EAAA,EAAA,GAAA,OAAA,GAAA,EAAA,EAAA,UAAA,GAAA,GAAA,EAAA,EAAA,GAAA,EAAA,OAAA,eAAA,QAAA,aAAA,CAAA,OAAA,IAAA,QAAA,aAAA,EA7DgBklB,IAAAA,EA6DhB,WA3Da,SAAA,IAAA,EAAA,KAAA,GACLC,KAAAA,OAAST,EAAE,6BACXU,KAAAA,WAAaV,EAAE,kBACfW,KAAAA,OAASX,EAAE,0BACXvB,KAAAA,IAAMuB,EAAEvhB,QACRmgB,KAAAA,cAAgBoB,EAAE,QAClBY,KAAAA,QAAUZ,EAAE,iBACZ1X,KAAAA,QAAU0X,EAAE,8BAEZf,KAAAA,cACA4B,KAAAA,SAiDR,OAAA,EAAA,EAAA,CAAA,CAAA,IAAA,SA9CQ,MAAA,WACAC,KAAAA,0BACAC,KAAAA,sBA4CR,CAAA,IAAA,0BAzCyB,MAAA,WACT,KAAKtC,IAAIE,cAAhBqC,IACAC,EAAe,KAAKR,OAAOS,cAC3BC,EAAe,KAAKR,OAAOO,aAAY,GAAQ,KAAKR,WAAWQ,aAAY,GAE5EN,KAAAA,QAAQQ,IAAI,aAAc,KAAK3C,IAAIE,cAAgBsC,EAAeE,KAoC1E,CAAA,IAAA,oBAjCmB,MAAA,WACVE,IAAAA,EAAwB,KAAKX,WAAWY,WAAW5X,IAAMqK,SAAS,KAAK2M,WAAWU,IAAI,cAAe,IACrGG,EAAgB,KAAK3C,cAAcjH,YAAc,KAAKiJ,QAAQM,cAC9DF,EAAO,KAAKvC,IAAIE,cAAgB,KAAK8B,OAAOS,cAE9ChT,EAAM,EAEVA,GADAA,EAAMqT,EAAgBF,EAAwBA,EAAwBE,GAC1DP,EAAOA,EAAO9S,EACrB5F,KAAAA,QAAQ8Y,IAAI,SAAUlT,KAyB9B,CAAA,IAAA,cAtBa,MAAA,WAAA,IACNgR,EAWAC,EAZM,EAAA,KAELP,KAAAA,cAAc/M,GAAG,SAAU,WACxBqN,GACA1b,aAAa0b,GAGjBA,EAAc3b,WAAW,WACrB,EAAKsd,UACN,KAIFpC,KAAAA,IAAI5M,GAAG,SAAU,WACdsN,GACA3b,aAAa2b,GAGjBA,EAAc5b,WAAW,WACrB,EAAKsd,UACN,SAEV,EAAA,GAAA,QAAA,QAAA;;ACvDL,aANA,QAAA,4CACA,QAAA,wBACA,QAAA,kBACA,IAAA,EAAA,EAAA,QAAA,gBACA,EAAA,EAAA,QAAA,oBAEA,SAAA,EAAA,GAAA,OAAA,GAAA,EAAA,WAAA,EAAA,CAAA,QAAA,GAAAb,EAAE,WAEWwB,IAECC,EAuEAC,EAvEAD,EADazB,EAAE,2BACKK,KAAK,MAC/BL,EAAE2B,KAAKF,EAAQ,SAASxQ,EAAO2Q,GACrBC,IAAAA,EAAM7B,EAAE4B,GACRE,EAAe9B,EAAE,sCACjB+B,EAAQF,EAAIxgB,SAAS,KAC3BwgB,EAAIG,OAAOF,EAAaE,OAAOD,IAEzBE,IAAAA,EAAYJ,EAAIK,SAAS,aAAeH,EAAMG,SAAS,WACvDC,EAAMN,EAAIxgB,SAAS,MACrB8gB,GAAAA,EAAIrkB,OAAQ,CACNskB,IAAAA,EAAoBnR,aAAAA,OAAAA,GAC1BkR,EAAIE,KAAK,KAAMD,GACfD,EAAIG,SAAS,YACPC,IAAAA,EAAiBvC,EAAE,oCACrBiC,GACAE,EAAIG,SAAS,QACbC,EAAeD,SAAS,SAExBH,EAAI5X,OAGRsX,EAAIG,OACAF,EAAaE,OACTO,EAAeP,OACXhC,EAAwEoC,sEAAAA,OAAAA,EAD5E,mGAINJ,OAAOG,MAMjBnC,EAAE,yCAAyC1V,MAAM,WACvCkY,IAAAA,EAAUxC,EAAE,MACZI,EAAKoC,EAAQH,KAAK,eACxBrC,EAAOI,KAAAA,OAAAA,IAAMqC,YAAY,QAAQC,QAAQ,CAAC9X,OAAQ,SAAU+X,QAAS,WACrEH,EAAQI,SAASH,YAAY,UAkC3Bf,EAAc1B,EAAE,eAEtBA,EAAE,kBAAkB9V,MAAM,WAClB8V,EAAEvhB,QAAQoM,SAAW,MACrB6W,EAAYnX,SAEjBvG,KAAK,WACAgc,EAAEvhB,QAAQoM,SAAW,MACrB6W,EAAYllB,SAYZ,IAAI4hB,EAAJ,QAAc,CACtBY,gBAAiB,yBACjBR,YAAa,cACbK,eAAgB,OAChB9gB,UAAW,UACX4L,UAAW,KAEfqW,EAAE,wBAAwB9V,QAE1B8V,EAAE,YAAY2B,KAAK,WACf3B,EAAE,MAAMsC,SAAS,8BAErBtC,EAAE,2BAA2B2B,KAAK,WAC9B3B,EAAE,MAAMsC,SAAS,qBAErBtC,EAAE,0BAA0B2B,KAAK,WAC7B3B,EAAE,MAAMsC,SAAS,+BAErBtC,EAAE,iBAAiB2B,KAAK,WACpB3B,EAAE,MAAMzV,SAEZyV,EAAE,aAAa2B,KAAK,WAChB3B,EAAE,MAAM1V,MAAM,WACNuY,IAAAA,EAAM7C,EAAE,MAAMK,KAAK,iBAAiByC,OAIjC,OAHHD,IACApkB,OAAOskB,SAAWF,IAEf,MAIf7C,EAAE,cAAc2B,KAAK,WAEbqB,IAAAA,EAASvoB,SAASC,cAAc,UACpCsoB,EAAOjlB,UAAY,yEAGfklB,IAAAA,EAAOxoB,SAASC,cAAc,KAClCuoB,EAAKllB,UAAY,iBACb+kB,IAAAA,EAAOroB,SAASyoB,eAAe,iBACnCD,EAAKloB,YAAY+nB,GACjBE,EAAOjoB,YAAYkoB,GAGfE,IAAAA,EAAOnD,EAAE,MAAMqC,KAAK,QACxBW,EAAOI,QAAU,WACb3kB,OAAOskB,SAAWI,GAElBE,IAAAA,EAAWF,EAAK7nB,MAAM,KAAK4F,OAAO,GAAGoiB,MAErCN,EAAO5C,GADPiD,EACYA,EAASE,QAAQ,IAAK,KAEtB,mBAAqBvD,EAAE,MAAM/O,QAIzCuS,IAAAA,EAAO/oB,SAASC,cAAc,OAClC8oB,EAAKzlB,UAAY,cACjBylB,EAAK3jB,aAAa,eAAgBmjB,EAAO5C,IACrCqD,IAAAA,EAAWzD,EAAE,MAAMK,KAAK,YAAYqD,IAAI,WACjC1D,OAAAA,EAAE,MAAM8C,SAChBxC,MAAMxgB,KAAK,KACd0jB,EAAKrK,UAAYsK,EAEjBhnB,iBAAiBI,eAAemmB,GAChChD,EAAE,MAAMxa,SACJib,IAAAA,EAAST,EAAE,eAAe2D,QAC9BlD,EAAOuB,OAAOgB,GACdvC,EAAOuB,OAAOwB,KAGlBxD,EAAE,eAAeoB,IAAI,aAAc","file":"sphinx_materialdesign_theme.js","sourceRoot":"../../src/js","sourcesContent":["/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Ripple MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialRipple = function MaterialRipple(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialRipple'] = MaterialRipple;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialRipple.prototype.Constant_ = {\n    INITIAL_SCALE: 'scale(0.0001, 0.0001)',\n    INITIAL_SIZE: '1px',\n    INITIAL_OPACITY: '0.4',\n    FINAL_OPACITY: '0',\n    FINAL_SCALE: ''\n};\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialRipple.prototype.CssClasses_ = {\n    RIPPLE_CENTER: 'mdl-ripple--center',\n    RIPPLE_EFFECT_IGNORE_EVENTS: 'mdl-js-ripple-effect--ignore-events',\n    RIPPLE: 'mdl-ripple',\n    IS_ANIMATING: 'is-animating',\n    IS_VISIBLE: 'is-visible'\n};\n/**\n   * Handle mouse / finger down on element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialRipple.prototype.downHandler_ = function (event) {\n    if (!this.rippleElement_.style.width && !this.rippleElement_.style.height) {\n        var rect = this.element_.getBoundingClientRect();\n        this.boundHeight = rect.height;\n        this.boundWidth = rect.width;\n        this.rippleSize_ = Math.sqrt(rect.width * rect.width + rect.height * rect.height) * 2 + 2;\n        this.rippleElement_.style.width = this.rippleSize_ + 'px';\n        this.rippleElement_.style.height = this.rippleSize_ + 'px';\n    }\n    this.rippleElement_.classList.add(this.CssClasses_.IS_VISIBLE);\n    if (event.type === 'mousedown' && this.ignoringMouseDown_) {\n        this.ignoringMouseDown_ = false;\n    } else {\n        if (event.type === 'touchstart') {\n            this.ignoringMouseDown_ = true;\n        }\n        var frameCount = this.getFrameCount();\n        if (frameCount > 0) {\n            return;\n        }\n        this.setFrameCount(1);\n        var bound = event.currentTarget.getBoundingClientRect();\n        var x;\n        var y;\n        // Check if we are handling a keyboard click.\n        if (event.clientX === 0 && event.clientY === 0) {\n            x = Math.round(bound.width / 2);\n            y = Math.round(bound.height / 2);\n        } else {\n            var clientX = event.clientX !== undefined ? event.clientX : event.touches[0].clientX;\n            var clientY = event.clientY !== undefined ? event.clientY : event.touches[0].clientY;\n            x = Math.round(clientX - bound.left);\n            y = Math.round(clientY - bound.top);\n        }\n        this.setRippleXY(x, y);\n        this.setRippleStyles(true);\n        window.requestAnimationFrame(this.animFrameHandler.bind(this));\n    }\n};\n/**\n   * Handle mouse / finger up on element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialRipple.prototype.upHandler_ = function (event) {\n    // Don't fire for the artificial \"mouseup\" generated by a double-click.\n    if (event && event.detail !== 2) {\n        // Allow a repaint to occur before removing this class, so the animation\n        // shows for tap events, which seem to trigger a mouseup too soon after\n        // mousedown.\n        window.setTimeout(function () {\n            this.rippleElement_.classList.remove(this.CssClasses_.IS_VISIBLE);\n        }.bind(this), 0);\n    }\n};\n/**\n   * Initialize element.\n   */\nMaterialRipple.prototype.init = function () {\n    if (this.element_) {\n        var recentering = this.element_.classList.contains(this.CssClasses_.RIPPLE_CENTER);\n        if (!this.element_.classList.contains(this.CssClasses_.RIPPLE_EFFECT_IGNORE_EVENTS)) {\n            this.rippleElement_ = this.element_.querySelector('.' + this.CssClasses_.RIPPLE);\n            this.frameCount_ = 0;\n            this.rippleSize_ = 0;\n            this.x_ = 0;\n            this.y_ = 0;\n            // Touch start produces a compat mouse down event, which would cause a\n            // second ripples. To avoid that, we use this property to ignore the first\n            // mouse down after a touch start.\n            this.ignoringMouseDown_ = false;\n            this.boundDownHandler = this.downHandler_.bind(this);\n            this.element_.addEventListener('mousedown', this.boundDownHandler);\n            this.element_.addEventListener('touchstart', this.boundDownHandler);\n            this.boundUpHandler = this.upHandler_.bind(this);\n            this.element_.addEventListener('mouseup', this.boundUpHandler);\n            this.element_.addEventListener('mouseleave', this.boundUpHandler);\n            this.element_.addEventListener('touchend', this.boundUpHandler);\n            this.element_.addEventListener('blur', this.boundUpHandler);\n            /**\n         * Getter for frameCount_.\n         * @return {number} the frame count.\n         */\n            this.getFrameCount = function () {\n                return this.frameCount_;\n            };\n            /**\n         * Setter for frameCount_.\n         * @param {number} fC the frame count.\n         */\n            this.setFrameCount = function (fC) {\n                this.frameCount_ = fC;\n            };\n            /**\n         * Getter for rippleElement_.\n         * @return {Element} the ripple element.\n         */\n            this.getRippleElement = function () {\n                return this.rippleElement_;\n            };\n            /**\n         * Sets the ripple X and Y coordinates.\n         * @param  {number} newX the new X coordinate\n         * @param  {number} newY the new Y coordinate\n         */\n            this.setRippleXY = function (newX, newY) {\n                this.x_ = newX;\n                this.y_ = newY;\n            };\n            /**\n         * Sets the ripple styles.\n         * @param  {boolean} start whether or not this is the start frame.\n         */\n            this.setRippleStyles = function (start) {\n                if (this.rippleElement_ !== null) {\n                    var transformString;\n                    var scale;\n                    var size;\n                    var offset = 'translate(' + this.x_ + 'px, ' + this.y_ + 'px)';\n                    if (start) {\n                        scale = this.Constant_.INITIAL_SCALE;\n                        size = this.Constant_.INITIAL_SIZE;\n                    } else {\n                        scale = this.Constant_.FINAL_SCALE;\n                        size = this.rippleSize_ + 'px';\n                        if (recentering) {\n                            offset = 'translate(' + this.boundWidth / 2 + 'px, ' + this.boundHeight / 2 + 'px)';\n                        }\n                    }\n                    transformString = 'translate(-50%, -50%) ' + offset + scale;\n                    this.rippleElement_.style.webkitTransform = transformString;\n                    this.rippleElement_.style.msTransform = transformString;\n                    this.rippleElement_.style.transform = transformString;\n                    if (start) {\n                        this.rippleElement_.classList.remove(this.CssClasses_.IS_ANIMATING);\n                    } else {\n                        this.rippleElement_.classList.add(this.CssClasses_.IS_ANIMATING);\n                    }\n                }\n            };\n            /**\n         * Handles an animation frame.\n         */\n            this.animFrameHandler = function () {\n                if (this.frameCount_-- > 0) {\n                    window.requestAnimationFrame(this.animFrameHandler.bind(this));\n                } else {\n                    this.setRippleStyles(false);\n                }\n            };\n        }\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialRipple,\n    classAsString: 'MaterialRipple',\n    cssClass: 'mdl-js-ripple-effect',\n    widget: false\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Tabs MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {Element} element The element that will be upgraded.\n   */\nvar MaterialTabs = function MaterialTabs(element) {\n    // Stores the HTML element.\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialTabs'] = MaterialTabs;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialTabs.prototype.Constant_ = {};\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialTabs.prototype.CssClasses_ = {\n    TAB_CLASS: 'mdl-tabs__tab',\n    PANEL_CLASS: 'mdl-tabs__panel',\n    ACTIVE_CLASS: 'is-active',\n    UPGRADED_CLASS: 'is-upgraded',\n    MDL_JS_RIPPLE_EFFECT: 'mdl-js-ripple-effect',\n    MDL_RIPPLE_CONTAINER: 'mdl-tabs__ripple-container',\n    MDL_RIPPLE: 'mdl-ripple',\n    MDL_JS_RIPPLE_EFFECT_IGNORE_EVENTS: 'mdl-js-ripple-effect--ignore-events'\n};\n/**\n   * Handle clicks to a tabs component\n   *\n   * @private\n   */\nMaterialTabs.prototype.initTabs_ = function () {\n    if (this.element_.classList.contains(this.CssClasses_.MDL_JS_RIPPLE_EFFECT)) {\n        this.element_.classList.add(this.CssClasses_.MDL_JS_RIPPLE_EFFECT_IGNORE_EVENTS);\n    }\n    // Select element tabs, document panels\n    this.tabs_ = this.element_.querySelectorAll('.' + this.CssClasses_.TAB_CLASS);\n    this.panels_ = this.element_.querySelectorAll('.' + this.CssClasses_.PANEL_CLASS);\n    // Create new tabs for each tab element\n    for (var i = 0; i < this.tabs_.length; i++) {\n        new MaterialTab(this.tabs_[i], this);\n    }\n    this.element_.classList.add(this.CssClasses_.UPGRADED_CLASS);\n};\n/**\n   * Reset tab state, dropping active classes\n   *\n   * @private\n   */\nMaterialTabs.prototype.resetTabState_ = function () {\n    for (var k = 0; k < this.tabs_.length; k++) {\n        this.tabs_[k].classList.remove(this.CssClasses_.ACTIVE_CLASS);\n    }\n};\n/**\n   * Reset panel state, droping active classes\n   *\n   * @private\n   */\nMaterialTabs.prototype.resetPanelState_ = function () {\n    for (var j = 0; j < this.panels_.length; j++) {\n        this.panels_[j].classList.remove(this.CssClasses_.ACTIVE_CLASS);\n    }\n};\n/**\n   * Initialize element.\n   */\nMaterialTabs.prototype.init = function () {\n    if (this.element_) {\n        this.initTabs_();\n    }\n};\n/**\n   * Constructor for an individual tab.\n   *\n   * @constructor\n   * @param {Element} tab The HTML element for the tab.\n   * @param {MaterialTabs} ctx The MaterialTabs object that owns the tab.\n   */\nfunction MaterialTab(tab, ctx) {\n    if (tab) {\n        if (ctx.element_.classList.contains(ctx.CssClasses_.MDL_JS_RIPPLE_EFFECT)) {\n            var rippleContainer = document.createElement('span');\n            rippleContainer.classList.add(ctx.CssClasses_.MDL_RIPPLE_CONTAINER);\n            rippleContainer.classList.add(ctx.CssClasses_.MDL_JS_RIPPLE_EFFECT);\n            var ripple = document.createElement('span');\n            ripple.classList.add(ctx.CssClasses_.MDL_RIPPLE);\n            rippleContainer.appendChild(ripple);\n            tab.appendChild(rippleContainer);\n        }\n        tab.addEventListener('click', function (e) {\n            if (tab.getAttribute('href').charAt(0) === '#') {\n                e.preventDefault();\n                var href = tab.href.split('#')[1];\n                var panel = ctx.element_.querySelector('#' + href);\n                ctx.resetTabState_();\n                ctx.resetPanelState_();\n                tab.classList.add(ctx.CssClasses_.ACTIVE_CLASS);\n                panel.classList.add(ctx.CssClasses_.ACTIVE_CLASS);\n            }\n        });\n    }\n}\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialTabs,\n    classAsString: 'MaterialTabs',\n    cssClass: 'mdl-js-tabs'\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Layout MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialLayout = function MaterialLayout(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialLayout'] = MaterialLayout;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialLayout.prototype.Constant_ = {\n    MAX_WIDTH: '(max-width: 1024px)',\n    TAB_SCROLL_PIXELS: 100,\n    RESIZE_TIMEOUT: 100,\n    MENU_ICON: '&#xE5D2;',\n    CHEVRON_LEFT: 'chevron_left',\n    CHEVRON_RIGHT: 'chevron_right'\n};\n/**\n   * Keycodes, for code readability.\n   *\n   * @enum {number}\n   * @private\n   */\nMaterialLayout.prototype.Keycodes_ = {\n    ENTER: 13,\n    ESCAPE: 27,\n    SPACE: 32\n};\n/**\n   * Modes.\n   *\n   * @enum {number}\n   * @private\n   */\nMaterialLayout.prototype.Mode_ = {\n    STANDARD: 0,\n    SEAMED: 1,\n    WATERFALL: 2,\n    SCROLL: 3\n};\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialLayout.prototype.CssClasses_ = {\n    CONTAINER: 'mdl-layout__container',\n    HEADER: 'mdl-layout__header',\n    DRAWER: 'mdl-layout__drawer',\n    CONTENT: 'mdl-layout__content',\n    DRAWER_BTN: 'mdl-layout__drawer-button',\n    ICON: 'material-icons',\n    JS_RIPPLE_EFFECT: 'mdl-js-ripple-effect',\n    RIPPLE_CONTAINER: 'mdl-layout__tab-ripple-container',\n    RIPPLE: 'mdl-ripple',\n    RIPPLE_IGNORE_EVENTS: 'mdl-js-ripple-effect--ignore-events',\n    HEADER_SEAMED: 'mdl-layout__header--seamed',\n    HEADER_WATERFALL: 'mdl-layout__header--waterfall',\n    HEADER_SCROLL: 'mdl-layout__header--scroll',\n    FIXED_HEADER: 'mdl-layout--fixed-header',\n    OBFUSCATOR: 'mdl-layout__obfuscator',\n    TAB_BAR: 'mdl-layout__tab-bar',\n    TAB_CONTAINER: 'mdl-layout__tab-bar-container',\n    TAB: 'mdl-layout__tab',\n    TAB_BAR_BUTTON: 'mdl-layout__tab-bar-button',\n    TAB_BAR_LEFT_BUTTON: 'mdl-layout__tab-bar-left-button',\n    TAB_BAR_RIGHT_BUTTON: 'mdl-layout__tab-bar-right-button',\n    TAB_MANUAL_SWITCH: 'mdl-layout__tab-manual-switch',\n    PANEL: 'mdl-layout__tab-panel',\n    HAS_DRAWER: 'has-drawer',\n    HAS_TABS: 'has-tabs',\n    HAS_SCROLLING_HEADER: 'has-scrolling-header',\n    CASTING_SHADOW: 'is-casting-shadow',\n    IS_COMPACT: 'is-compact',\n    IS_SMALL_SCREEN: 'is-small-screen',\n    IS_DRAWER_OPEN: 'is-visible',\n    IS_ACTIVE: 'is-active',\n    IS_UPGRADED: 'is-upgraded',\n    IS_ANIMATING: 'is-animating',\n    ON_LARGE_SCREEN: 'mdl-layout--large-screen-only',\n    ON_SMALL_SCREEN: 'mdl-layout--small-screen-only'\n};\n/**\n   * Handles scrolling on the content.\n   *\n   * @private\n   */\nMaterialLayout.prototype.contentScrollHandler_ = function () {\n    if (this.header_.classList.contains(this.CssClasses_.IS_ANIMATING)) {\n        return;\n    }\n    var headerVisible = !this.element_.classList.contains(this.CssClasses_.IS_SMALL_SCREEN) || this.element_.classList.contains(this.CssClasses_.FIXED_HEADER);\n    if (this.content_.scrollTop > 0 && !this.header_.classList.contains(this.CssClasses_.IS_COMPACT)) {\n        this.header_.classList.add(this.CssClasses_.CASTING_SHADOW);\n        this.header_.classList.add(this.CssClasses_.IS_COMPACT);\n        if (headerVisible) {\n            this.header_.classList.add(this.CssClasses_.IS_ANIMATING);\n        }\n    } else if (this.content_.scrollTop <= 0 && this.header_.classList.contains(this.CssClasses_.IS_COMPACT)) {\n        this.header_.classList.remove(this.CssClasses_.CASTING_SHADOW);\n        this.header_.classList.remove(this.CssClasses_.IS_COMPACT);\n        if (headerVisible) {\n            this.header_.classList.add(this.CssClasses_.IS_ANIMATING);\n        }\n    }\n};\n/**\n   * Handles a keyboard event on the drawer.\n   *\n   * @param {Event} evt The event that fired.\n   * @private\n   */\nMaterialLayout.prototype.keyboardEventHandler_ = function (evt) {\n    // Only react when the drawer is open.\n    if (evt.keyCode === this.Keycodes_.ESCAPE && this.drawer_.classList.contains(this.CssClasses_.IS_DRAWER_OPEN)) {\n        this.toggleDrawer();\n    }\n};\n/**\n   * Handles changes in screen size.\n   *\n   * @private\n   */\nMaterialLayout.prototype.screenSizeHandler_ = function () {\n    if (this.screenSizeMediaQuery_.matches) {\n        this.element_.classList.add(this.CssClasses_.IS_SMALL_SCREEN);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_SMALL_SCREEN);\n        // Collapse drawer (if any) when moving to a large screen size.\n        if (this.drawer_) {\n            this.drawer_.classList.remove(this.CssClasses_.IS_DRAWER_OPEN);\n            this.obfuscator_.classList.remove(this.CssClasses_.IS_DRAWER_OPEN);\n        }\n    }\n};\n/**\n   * Handles events of drawer button.\n   *\n   * @param {Event} evt The event that fired.\n   * @private\n   */\nMaterialLayout.prototype.drawerToggleHandler_ = function (evt) {\n    if (evt && evt.type === 'keydown') {\n        if (evt.keyCode === this.Keycodes_.SPACE || evt.keyCode === this.Keycodes_.ENTER) {\n            // prevent scrolling in drawer nav\n            evt.preventDefault();\n        } else {\n            // prevent other keys\n            return;\n        }\n    }\n    this.toggleDrawer();\n};\n/**\n   * Handles (un)setting the `is-animating` class\n   *\n   * @private\n   */\nMaterialLayout.prototype.headerTransitionEndHandler_ = function () {\n    this.header_.classList.remove(this.CssClasses_.IS_ANIMATING);\n};\n/**\n   * Handles expanding the header on click\n   *\n   * @private\n   */\nMaterialLayout.prototype.headerClickHandler_ = function () {\n    if (this.header_.classList.contains(this.CssClasses_.IS_COMPACT)) {\n        this.header_.classList.remove(this.CssClasses_.IS_COMPACT);\n        this.header_.classList.add(this.CssClasses_.IS_ANIMATING);\n    }\n};\n/**\n   * Reset tab state, dropping active classes\n   *\n   * @private\n   */\nMaterialLayout.prototype.resetTabState_ = function (tabBar) {\n    for (var k = 0; k < tabBar.length; k++) {\n        tabBar[k].classList.remove(this.CssClasses_.IS_ACTIVE);\n    }\n};\n/**\n   * Reset panel state, droping active classes\n   *\n   * @private\n   */\nMaterialLayout.prototype.resetPanelState_ = function (panels) {\n    for (var j = 0; j < panels.length; j++) {\n        panels[j].classList.remove(this.CssClasses_.IS_ACTIVE);\n    }\n};\n/**\n  * Toggle drawer state\n  *\n  * @public\n  */\nMaterialLayout.prototype.toggleDrawer = function () {\n    var drawerButton = this.element_.querySelector('.' + this.CssClasses_.DRAWER_BTN);\n    this.drawer_.classList.toggle(this.CssClasses_.IS_DRAWER_OPEN);\n    this.obfuscator_.classList.toggle(this.CssClasses_.IS_DRAWER_OPEN);\n    // Set accessibility properties.\n    if (this.drawer_.classList.contains(this.CssClasses_.IS_DRAWER_OPEN)) {\n        this.drawer_.setAttribute('aria-hidden', 'false');\n        drawerButton.setAttribute('aria-expanded', 'true');\n    } else {\n        this.drawer_.setAttribute('aria-hidden', 'true');\n        drawerButton.setAttribute('aria-expanded', 'false');\n    }\n};\nMaterialLayout.prototype['toggleDrawer'] = MaterialLayout.prototype.toggleDrawer;\n/**\n   * Initialize element.\n   */\nMaterialLayout.prototype.init = function () {\n    if (this.element_) {\n        var container = document.createElement('div');\n        container.classList.add(this.CssClasses_.CONTAINER);\n        var focusedElement = this.element_.querySelector(':focus');\n        this.element_.parentElement.insertBefore(container, this.element_);\n        this.element_.parentElement.removeChild(this.element_);\n        container.appendChild(this.element_);\n        if (focusedElement) {\n            focusedElement.focus();\n        }\n        var directChildren = this.element_.childNodes;\n        var numChildren = directChildren.length;\n        for (var c = 0; c < numChildren; c++) {\n            var child = directChildren[c];\n            if (child.classList && child.classList.contains(this.CssClasses_.HEADER)) {\n                this.header_ = child;\n            }\n            if (child.classList && child.classList.contains(this.CssClasses_.DRAWER)) {\n                this.drawer_ = child;\n            }\n            if (child.classList && child.classList.contains(this.CssClasses_.CONTENT)) {\n                this.content_ = child;\n            }\n        }\n        window.addEventListener('pageshow', function (e) {\n            if (e.persisted) {\n                // when page is loaded from back/forward cache\n                // trigger repaint to let layout scroll in safari\n                this.element_.style.overflowY = 'hidden';\n                requestAnimationFrame(function () {\n                    this.element_.style.overflowY = '';\n                }.bind(this));\n            }\n        }.bind(this), false);\n        if (this.header_) {\n            this.tabBar_ = this.header_.querySelector('.' + this.CssClasses_.TAB_BAR);\n        }\n        var mode = this.Mode_.STANDARD;\n        if (this.header_) {\n            if (this.header_.classList.contains(this.CssClasses_.HEADER_SEAMED)) {\n                mode = this.Mode_.SEAMED;\n            } else if (this.header_.classList.contains(this.CssClasses_.HEADER_WATERFALL)) {\n                mode = this.Mode_.WATERFALL;\n                this.header_.addEventListener('transitionend', this.headerTransitionEndHandler_.bind(this));\n                this.header_.addEventListener('click', this.headerClickHandler_.bind(this));\n            } else if (this.header_.classList.contains(this.CssClasses_.HEADER_SCROLL)) {\n                mode = this.Mode_.SCROLL;\n                container.classList.add(this.CssClasses_.HAS_SCROLLING_HEADER);\n            }\n            if (mode === this.Mode_.STANDARD) {\n                this.header_.classList.add(this.CssClasses_.CASTING_SHADOW);\n                if (this.tabBar_) {\n                    this.tabBar_.classList.add(this.CssClasses_.CASTING_SHADOW);\n                }\n            } else if (mode === this.Mode_.SEAMED || mode === this.Mode_.SCROLL) {\n                this.header_.classList.remove(this.CssClasses_.CASTING_SHADOW);\n                if (this.tabBar_) {\n                    this.tabBar_.classList.remove(this.CssClasses_.CASTING_SHADOW);\n                }\n            } else if (mode === this.Mode_.WATERFALL) {\n                // Add and remove shadows depending on scroll position.\n                // Also add/remove auxiliary class for styling of the compact version of\n                // the header.\n                this.content_.addEventListener('scroll', this.contentScrollHandler_.bind(this));\n                this.contentScrollHandler_();\n            }\n        }\n        // Add drawer toggling button to our layout, if we have an openable drawer.\n        if (this.drawer_) {\n            var drawerButton = this.element_.querySelector('.' + this.CssClasses_.DRAWER_BTN);\n            if (!drawerButton) {\n                drawerButton = document.createElement('div');\n                drawerButton.setAttribute('aria-expanded', 'false');\n                drawerButton.setAttribute('role', 'button');\n                drawerButton.setAttribute('tabindex', '0');\n                drawerButton.classList.add(this.CssClasses_.DRAWER_BTN);\n                var drawerButtonIcon = document.createElement('i');\n                drawerButtonIcon.classList.add(this.CssClasses_.ICON);\n                drawerButtonIcon.innerHTML = this.Constant_.MENU_ICON;\n                drawerButton.appendChild(drawerButtonIcon);\n            }\n            if (this.drawer_.classList.contains(this.CssClasses_.ON_LARGE_SCREEN)) {\n                //If drawer has ON_LARGE_SCREEN class then add it to the drawer toggle button as well.\n                drawerButton.classList.add(this.CssClasses_.ON_LARGE_SCREEN);\n            } else if (this.drawer_.classList.contains(this.CssClasses_.ON_SMALL_SCREEN)) {\n                //If drawer has ON_SMALL_SCREEN class then add it to the drawer toggle button as well.\n                drawerButton.classList.add(this.CssClasses_.ON_SMALL_SCREEN);\n            }\n            drawerButton.addEventListener('click', this.drawerToggleHandler_.bind(this));\n            drawerButton.addEventListener('keydown', this.drawerToggleHandler_.bind(this));\n            // Add a class if the layout has a drawer, for altering the left padding.\n            // Adds the HAS_DRAWER to the elements since this.header_ may or may\n            // not be present.\n            this.element_.classList.add(this.CssClasses_.HAS_DRAWER);\n            // If we have a fixed header, add the button to the header rather than\n            // the layout.\n            if (this.element_.classList.contains(this.CssClasses_.FIXED_HEADER)) {\n                this.header_.insertBefore(drawerButton, this.header_.firstChild);\n            } else {\n                this.element_.insertBefore(drawerButton, this.content_);\n            }\n            var obfuscator = document.createElement('div');\n            obfuscator.classList.add(this.CssClasses_.OBFUSCATOR);\n            this.element_.appendChild(obfuscator);\n            obfuscator.addEventListener('click', this.drawerToggleHandler_.bind(this));\n            this.obfuscator_ = obfuscator;\n            this.drawer_.addEventListener('keydown', this.keyboardEventHandler_.bind(this));\n            this.drawer_.setAttribute('aria-hidden', 'true');\n        }\n        // Keep an eye on screen size, and add/remove auxiliary class for styling\n        // of small screens.\n        this.screenSizeMediaQuery_ = window.matchMedia(this.Constant_.MAX_WIDTH);\n        this.screenSizeMediaQuery_.addListener(this.screenSizeHandler_.bind(this));\n        this.screenSizeHandler_();\n        // Initialize tabs, if any.\n        if (this.header_ && this.tabBar_) {\n            this.element_.classList.add(this.CssClasses_.HAS_TABS);\n            var tabContainer = document.createElement('div');\n            tabContainer.classList.add(this.CssClasses_.TAB_CONTAINER);\n            this.header_.insertBefore(tabContainer, this.tabBar_);\n            this.header_.removeChild(this.tabBar_);\n            var leftButton = document.createElement('div');\n            leftButton.classList.add(this.CssClasses_.TAB_BAR_BUTTON);\n            leftButton.classList.add(this.CssClasses_.TAB_BAR_LEFT_BUTTON);\n            var leftButtonIcon = document.createElement('i');\n            leftButtonIcon.classList.add(this.CssClasses_.ICON);\n            leftButtonIcon.textContent = this.Constant_.CHEVRON_LEFT;\n            leftButton.appendChild(leftButtonIcon);\n            leftButton.addEventListener('click', function () {\n                this.tabBar_.scrollLeft -= this.Constant_.TAB_SCROLL_PIXELS;\n            }.bind(this));\n            var rightButton = document.createElement('div');\n            rightButton.classList.add(this.CssClasses_.TAB_BAR_BUTTON);\n            rightButton.classList.add(this.CssClasses_.TAB_BAR_RIGHT_BUTTON);\n            var rightButtonIcon = document.createElement('i');\n            rightButtonIcon.classList.add(this.CssClasses_.ICON);\n            rightButtonIcon.textContent = this.Constant_.CHEVRON_RIGHT;\n            rightButton.appendChild(rightButtonIcon);\n            rightButton.addEventListener('click', function () {\n                this.tabBar_.scrollLeft += this.Constant_.TAB_SCROLL_PIXELS;\n            }.bind(this));\n            tabContainer.appendChild(leftButton);\n            tabContainer.appendChild(this.tabBar_);\n            tabContainer.appendChild(rightButton);\n            // Add and remove tab buttons depending on scroll position and total\n            // window size.\n            var tabUpdateHandler = function () {\n                if (this.tabBar_.scrollLeft > 0) {\n                    leftButton.classList.add(this.CssClasses_.IS_ACTIVE);\n                } else {\n                    leftButton.classList.remove(this.CssClasses_.IS_ACTIVE);\n                }\n                if (this.tabBar_.scrollLeft < this.tabBar_.scrollWidth - this.tabBar_.offsetWidth) {\n                    rightButton.classList.add(this.CssClasses_.IS_ACTIVE);\n                } else {\n                    rightButton.classList.remove(this.CssClasses_.IS_ACTIVE);\n                }\n            }.bind(this);\n            this.tabBar_.addEventListener('scroll', tabUpdateHandler);\n            tabUpdateHandler();\n            // Update tabs when the window resizes.\n            var windowResizeHandler = function () {\n                // Use timeouts to make sure it doesn't happen too often.\n                if (this.resizeTimeoutId_) {\n                    clearTimeout(this.resizeTimeoutId_);\n                }\n                this.resizeTimeoutId_ = setTimeout(function () {\n                    tabUpdateHandler();\n                    this.resizeTimeoutId_ = null;\n                }.bind(this), this.Constant_.RESIZE_TIMEOUT);\n            }.bind(this);\n            window.addEventListener('resize', windowResizeHandler);\n            if (this.tabBar_.classList.contains(this.CssClasses_.JS_RIPPLE_EFFECT)) {\n                this.tabBar_.classList.add(this.CssClasses_.RIPPLE_IGNORE_EVENTS);\n            }\n            // Select element tabs, document panels\n            var tabs = this.tabBar_.querySelectorAll('.' + this.CssClasses_.TAB);\n            var panels = this.content_.querySelectorAll('.' + this.CssClasses_.PANEL);\n            // Create new tabs for each tab element\n            for (var i = 0; i < tabs.length; i++) {\n                new MaterialLayoutTab(tabs[i], tabs, panels, this);\n            }\n        }\n        this.element_.classList.add(this.CssClasses_.IS_UPGRADED);\n    }\n};\n/**\n   * Constructor for an individual tab.\n   *\n   * @constructor\n   * @param {HTMLElement} tab The HTML element for the tab.\n   * @param {!Array<HTMLElement>} tabs Array with HTML elements for all tabs.\n   * @param {!Array<HTMLElement>} panels Array with HTML elements for all panels.\n   * @param {MaterialLayout} layout The MaterialLayout object that owns the tab.\n   */\nfunction MaterialLayoutTab(tab, tabs, panels, layout) {\n    /**\n     * Auxiliary method to programmatically select a tab in the UI.\n     */\n    function selectTab() {\n        var href = tab.href.split('#')[1];\n        var panel = layout.content_.querySelector('#' + href);\n        layout.resetTabState_(tabs);\n        layout.resetPanelState_(panels);\n        tab.classList.add(layout.CssClasses_.IS_ACTIVE);\n        panel.classList.add(layout.CssClasses_.IS_ACTIVE);\n    }\n    if (layout.tabBar_.classList.contains(layout.CssClasses_.JS_RIPPLE_EFFECT)) {\n        var rippleContainer = document.createElement('span');\n        rippleContainer.classList.add(layout.CssClasses_.RIPPLE_CONTAINER);\n        rippleContainer.classList.add(layout.CssClasses_.JS_RIPPLE_EFFECT);\n        var ripple = document.createElement('span');\n        ripple.classList.add(layout.CssClasses_.RIPPLE);\n        rippleContainer.appendChild(ripple);\n        tab.appendChild(rippleContainer);\n    }\n    if (!layout.tabBar_.classList.contains(layout.CssClasses_.TAB_MANUAL_SWITCH)) {\n        tab.addEventListener('click', function (e) {\n            if (tab.getAttribute('href').charAt(0) === '#') {\n                e.preventDefault();\n                selectTab();\n            }\n        });\n    }\n    tab.show = selectTab;\n}\nwindow['MaterialLayoutTab'] = MaterialLayoutTab;\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialLayout,\n    classAsString: 'MaterialLayout',\n    cssClass: 'mdl-js-layout'\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n/**\n * A component handler interface using the revealing module design pattern.\n * More details on this design pattern here:\n * https://github.com/jasonmayes/mdl-component-design-pattern\n *\n * @author Jason Mayes.\n */\n/* exported componentHandler */\n\n// Pre-defining the componentHandler interface, for closure documentation and\n// static verification.\nvar componentHandler = {\n  /**\n   * Searches existing DOM for elements of our component type and upgrades them\n   * if they have not already been upgraded.\n   *\n   * @param {string=} optJsClass the programatic name of the element class we\n   * need to create a new instance of.\n   * @param {string=} optCssClass the name of the CSS class elements of this\n   * type will have.\n   */\n  upgradeDom: function(optJsClass, optCssClass) {},\n  /**\n   * Upgrades a specific element rather than all in the DOM.\n   *\n   * @param {!Element} element The element we wish to upgrade.\n   * @param {string=} optJsClass Optional name of the class we want to upgrade\n   * the element to.\n   */\n  upgradeElement: function(element, optJsClass) {},\n  /**\n   * Upgrades a specific list of elements rather than all in the DOM.\n   *\n   * @param {!Element|!Array<!Element>|!NodeList|!HTMLCollection} elements\n   * The elements we wish to upgrade.\n   */\n  upgradeElements: function(elements) {},\n  /**\n   * Upgrades all registered components found in the current DOM. This is\n   * automatically called on window load.\n   */\n  upgradeAllRegistered: function() {},\n  /**\n   * Allows user to be alerted to any upgrades that are performed for a given\n   * component type\n   *\n   * @param {string} jsClass The class name of the MDL component we wish\n   * to hook into for any upgrades performed.\n   * @param {function(!HTMLElement)} callback The function to call upon an\n   * upgrade. This function should expect 1 parameter - the HTMLElement which\n   * got upgraded.\n   */\n  registerUpgradedCallback: function(jsClass, callback) {},\n  /**\n   * Registers a class for future use and attempts to upgrade existing DOM.\n   *\n   * @param {componentHandler.ComponentConfigPublic} config the registration configuration\n   */\n  register: function(config) {},\n  /**\n   * Downgrade either a given node, an array of nodes, or a NodeList.\n   *\n   * @param {!Node|!Array<!Node>|!NodeList} nodes\n   */\n  downgradeElements: function(nodes) {}\n};\n\ncomponentHandler = (function() {\n  'use strict';\n\n  /** @type {!Array<componentHandler.ComponentConfig>} */\n  var registeredComponents_ = [];\n\n  /** @type {!Array<componentHandler.Component>} */\n  var createdComponents_ = [];\n\n  var componentConfigProperty_ = 'mdlComponentConfigInternal_';\n\n  /**\n   * Searches registered components for a class we are interested in using.\n   * Optionally replaces a match with passed object if specified.\n   *\n   * @param {string} name The name of a class we want to use.\n   * @param {componentHandler.ComponentConfig=} optReplace Optional object to replace match with.\n   * @return {!Object|boolean}\n   * @private\n   */\n  function findRegisteredClass_(name, optReplace) {\n    for (var i = 0; i < registeredComponents_.length; i++) {\n      if (registeredComponents_[i].className === name) {\n        if (typeof optReplace !== 'undefined') {\n          registeredComponents_[i] = optReplace;\n        }\n        return registeredComponents_[i];\n      }\n    }\n    return false;\n  }\n\n  /**\n   * Returns an array of the classNames of the upgraded classes on the element.\n   *\n   * @param {!Element} element The element to fetch data from.\n   * @return {!Array<string>}\n   * @private\n   */\n  function getUpgradedListOfElement_(element) {\n    var dataUpgraded = element.getAttribute('data-upgraded');\n    // Use `['']` as default value to conform the `,name,name...` style.\n    return dataUpgraded === null ? [''] : dataUpgraded.split(',');\n  }\n\n  /**\n   * Returns true if the given element has already been upgraded for the given\n   * class.\n   *\n   * @param {!Element} element The element we want to check.\n   * @param {string} jsClass The class to check for.\n   * @returns {boolean}\n   * @private\n   */\n  function isElementUpgraded_(element, jsClass) {\n    var upgradedList = getUpgradedListOfElement_(element);\n    return upgradedList.indexOf(jsClass) !== -1;\n  }\n\n  /**\n   * Create an event object.\n   *\n   * @param {string} eventType The type name of the event.\n   * @param {boolean} bubbles Whether the event should bubble up the DOM.\n   * @param {boolean} cancelable Whether the event can be canceled.\n   * @returns {!Event}\n   */\n  function createEvent_(eventType, bubbles, cancelable) {\n    if ('CustomEvent' in window && typeof window.CustomEvent === 'function') {\n      return new CustomEvent(eventType, {\n        bubbles: bubbles,\n        cancelable: cancelable\n      });\n    } else {\n      var ev = document.createEvent('Events');\n      ev.initEvent(eventType, bubbles, cancelable);\n      return ev;\n    }\n  }\n\n  /**\n   * Searches existing DOM for elements of our component type and upgrades them\n   * if they have not already been upgraded.\n   *\n   * @param {string=} optJsClass the programatic name of the element class we\n   * need to create a new instance of.\n   * @param {string=} optCssClass the name of the CSS class elements of this\n   * type will have.\n   */\n  function upgradeDomInternal(optJsClass, optCssClass) {\n    if (typeof optJsClass === 'undefined' &&\n        typeof optCssClass === 'undefined') {\n      for (var i = 0; i < registeredComponents_.length; i++) {\n        upgradeDomInternal(registeredComponents_[i].className,\n            registeredComponents_[i].cssClass);\n      }\n    } else {\n      var jsClass = /** @type {string} */ (optJsClass);\n      if (typeof optCssClass === 'undefined') {\n        var registeredClass = findRegisteredClass_(jsClass);\n        if (registeredClass) {\n          optCssClass = registeredClass.cssClass;\n        }\n      }\n\n      var elements = document.querySelectorAll('.' + optCssClass);\n      for (var n = 0; n < elements.length; n++) {\n        upgradeElementInternal(elements[n], jsClass);\n      }\n    }\n  }\n\n  /**\n   * Upgrades a specific element rather than all in the DOM.\n   *\n   * @param {!Element} element The element we wish to upgrade.\n   * @param {string=} optJsClass Optional name of the class we want to upgrade\n   * the element to.\n   */\n  function upgradeElementInternal(element, optJsClass) {\n    // Verify argument type.\n    if (!(typeof element === 'object' && element instanceof Element)) {\n      throw new Error('Invalid argument provided to upgrade MDL element.');\n    }\n    // Allow upgrade to be canceled by canceling emitted event.\n    var upgradingEv = createEvent_('mdl-componentupgrading', true, true);\n    element.dispatchEvent(upgradingEv);\n    if (upgradingEv.defaultPrevented) {\n      return;\n    }\n\n    var upgradedList = getUpgradedListOfElement_(element);\n    var classesToUpgrade = [];\n    // If jsClass is not provided scan the registered components to find the\n    // ones matching the element's CSS classList.\n    if (!optJsClass) {\n      var classList = element.classList;\n      registeredComponents_.forEach(function(component) {\n        // Match CSS & Not to be upgraded & Not upgraded.\n        if (classList.contains(component.cssClass) &&\n            classesToUpgrade.indexOf(component) === -1 &&\n            !isElementUpgraded_(element, component.className)) {\n          classesToUpgrade.push(component);\n        }\n      });\n    } else if (!isElementUpgraded_(element, optJsClass)) {\n      classesToUpgrade.push(findRegisteredClass_(optJsClass));\n    }\n\n    // Upgrade the element for each classes.\n    for (var i = 0, n = classesToUpgrade.length, registeredClass; i < n; i++) {\n      registeredClass = classesToUpgrade[i];\n      if (registeredClass) {\n        // Mark element as upgraded.\n        upgradedList.push(registeredClass.className);\n        element.setAttribute('data-upgraded', upgradedList.join(','));\n        var instance = new registeredClass.classConstructor(element);\n        instance[componentConfigProperty_] = registeredClass;\n        createdComponents_.push(instance);\n        // Call any callbacks the user has registered with this component type.\n        for (var j = 0, m = registeredClass.callbacks.length; j < m; j++) {\n          registeredClass.callbacks[j](element);\n        }\n\n        if (registeredClass.widget) {\n          // Assign per element instance for control over API\n          element[registeredClass.className] = instance;\n        }\n      } else {\n        throw new Error(\n          'Unable to find a registered component for the given class.');\n      }\n\n      var upgradedEv = createEvent_('mdl-componentupgraded', true, false);\n      element.dispatchEvent(upgradedEv);\n    }\n  }\n\n  /**\n   * Upgrades a specific list of elements rather than all in the DOM.\n   *\n   * @param {!Element|!Array<!Element>|!NodeList|!HTMLCollection} elements\n   * The elements we wish to upgrade.\n   */\n  function upgradeElementsInternal(elements) {\n    if (!Array.isArray(elements)) {\n      if (elements instanceof Element) {\n        elements = [elements];\n      } else {\n        elements = Array.prototype.slice.call(elements);\n      }\n    }\n    for (var i = 0, n = elements.length, element; i < n; i++) {\n      element = elements[i];\n      if (element instanceof HTMLElement) {\n        upgradeElementInternal(element);\n        if (element.children.length > 0) {\n          upgradeElementsInternal(element.children);\n        }\n      }\n    }\n  }\n\n  /**\n   * Registers a class for future use and attempts to upgrade existing DOM.\n   *\n   * @param {componentHandler.ComponentConfigPublic} config\n   */\n  function registerInternal(config) {\n    // In order to support both Closure-compiled and uncompiled code accessing\n    // this method, we need to allow for both the dot and array syntax for\n    // property access. You'll therefore see the `foo.bar || foo['bar']`\n    // pattern repeated across this method.\n    var widgetMissing = (typeof config.widget === 'undefined' &&\n        typeof config['widget'] === 'undefined');\n    var widget = true;\n\n    if (!widgetMissing) {\n      widget = config.widget || config['widget'];\n    }\n\n    var newConfig = /** @type {componentHandler.ComponentConfig} */ ({\n      classConstructor: config.constructor || config['constructor'],\n      className: config.classAsString || config['classAsString'],\n      cssClass: config.cssClass || config['cssClass'],\n      widget: widget,\n      callbacks: []\n    });\n\n    registeredComponents_.forEach(function(item) {\n      if (item.cssClass === newConfig.cssClass) {\n        throw new Error('The provided cssClass has already been registered: ' + item.cssClass);\n      }\n      if (item.className === newConfig.className) {\n        throw new Error('The provided className has already been registered');\n      }\n    });\n\n    if (config.constructor.prototype\n        .hasOwnProperty(componentConfigProperty_)) {\n      throw new Error(\n          'MDL component classes must not have ' + componentConfigProperty_ +\n          ' defined as a property.');\n    }\n\n    var found = findRegisteredClass_(config.classAsString, newConfig);\n\n    if (!found) {\n      registeredComponents_.push(newConfig);\n    }\n  }\n\n  /**\n   * Allows user to be alerted to any upgrades that are performed for a given\n   * component type\n   *\n   * @param {string} jsClass The class name of the MDL component we wish\n   * to hook into for any upgrades performed.\n   * @param {function(!HTMLElement)} callback The function to call upon an\n   * upgrade. This function should expect 1 parameter - the HTMLElement which\n   * got upgraded.\n   */\n  function registerUpgradedCallbackInternal(jsClass, callback) {\n    var regClass = findRegisteredClass_(jsClass);\n    if (regClass) {\n      regClass.callbacks.push(callback);\n    }\n  }\n\n  /**\n   * Upgrades all registered components found in the current DOM. This is\n   * automatically called on window load.\n   */\n  function upgradeAllRegisteredInternal() {\n    for (var n = 0; n < registeredComponents_.length; n++) {\n      upgradeDomInternal(registeredComponents_[n].className);\n    }\n  }\n\n  /**\n   * Check the component for the downgrade method.\n   * Execute if found.\n   * Remove component from createdComponents list.\n   *\n   * @param {?componentHandler.Component} component\n   */\n  function deconstructComponentInternal(component) {\n    if (component) {\n      var componentIndex = createdComponents_.indexOf(component);\n      createdComponents_.splice(componentIndex, 1);\n\n      var upgrades = component.element_.getAttribute('data-upgraded').split(',');\n      var componentPlace = upgrades.indexOf(component[componentConfigProperty_].classAsString);\n      upgrades.splice(componentPlace, 1);\n      component.element_.setAttribute('data-upgraded', upgrades.join(','));\n\n      var ev = createEvent_('mdl-componentdowngraded', true, false);\n      component.element_.dispatchEvent(ev);\n    }\n  }\n\n  /**\n   * Downgrade either a given node, an array of nodes, or a NodeList.\n   *\n   * @param {!Node|!Array<!Node>|!NodeList} nodes\n   */\n  function downgradeNodesInternal(nodes) {\n    /**\n     * Auxiliary function to downgrade a single node.\n     * @param  {!Node} node the node to be downgraded\n     */\n    var downgradeNode = function(node) {\n      createdComponents_.filter(function(item) {\n        return item.element_ === node;\n      }).forEach(deconstructComponentInternal);\n    };\n    if (nodes instanceof Array || nodes instanceof NodeList) {\n      for (var n = 0; n < nodes.length; n++) {\n        downgradeNode(nodes[n]);\n      }\n    } else if (nodes instanceof Node) {\n      downgradeNode(nodes);\n    } else {\n      throw new Error('Invalid argument provided to downgrade MDL nodes.');\n    }\n  }\n\n  // Now return the functions that should be made public with their publicly\n  // facing names...\n  return {\n    upgradeDom: upgradeDomInternal,\n    upgradeElement: upgradeElementInternal,\n    upgradeElements: upgradeElementsInternal,\n    upgradeAllRegistered: upgradeAllRegisteredInternal,\n    registerUpgradedCallback: registerUpgradedCallbackInternal,\n    register: registerInternal,\n    downgradeElements: downgradeNodesInternal\n  };\n})();\n\n/**\n * Describes the type of a registered component type managed by\n * componentHandler. Provided for benefit of the Closure compiler.\n *\n * @typedef {{\n *   constructor: Function,\n *   classAsString: string,\n *   cssClass: string,\n *   widget: (string|boolean|undefined)\n * }}\n */\ncomponentHandler.ComponentConfigPublic;  // jshint ignore:line\n\n/**\n * Describes the type of a registered component type managed by\n * componentHandler. Provided for benefit of the Closure compiler.\n *\n * @typedef {{\n *   constructor: !Function,\n *   className: string,\n *   cssClass: string,\n *   widget: (string|boolean),\n *   callbacks: !Array<function(!HTMLElement)>\n * }}\n */\ncomponentHandler.ComponentConfig;  // jshint ignore:line\n\n/**\n * Created component (i.e., upgraded element) type as managed by\n * componentHandler. Provided for benefit of the Closure compiler.\n *\n * @typedef {{\n *   element_: !HTMLElement,\n *   className: string,\n *   classAsString: string,\n *   cssClass: string,\n *   widget: string\n * }}\n */\ncomponentHandler.Component;  // jshint ignore:line\n\n// Export all symbols, for the benefit of Closure compiler.\n// No effect on uncompiled code.\ncomponentHandler['upgradeDom'] = componentHandler.upgradeDom;\ncomponentHandler['upgradeElement'] = componentHandler.upgradeElement;\ncomponentHandler['upgradeElements'] = componentHandler.upgradeElements;\ncomponentHandler['upgradeAllRegistered'] =\n    componentHandler.upgradeAllRegistered;\ncomponentHandler['registerUpgradedCallback'] =\n    componentHandler.registerUpgradedCallback;\ncomponentHandler['register'] = componentHandler.register;\ncomponentHandler['downgradeElements'] = componentHandler.downgradeElements;\nwindow.componentHandler = componentHandler;\nwindow['componentHandler'] = componentHandler;\n\nwindow.addEventListener('load', function() {\n  'use strict';\n\n  /**\n   * Performs a \"Cutting the mustard\" test. If the browser supports the features\n   * tested, adds a mdl-js class to the <html> element. It then upgrades all MDL\n   * components requiring JavaScript.\n   */\n  if ('classList' in document.createElement('div') &&\n      'querySelector' in document &&\n      'addEventListener' in window && Array.prototype.forEach) {\n    document.documentElement.classList.add('mdl-js');\n    componentHandler.upgradeAllRegistered();\n  } else {\n    /**\n     * Dummy function to avoid JS errors.\n     */\n    componentHandler.upgradeElement = function() {};\n    /**\n     * Dummy function to avoid JS errors.\n     */\n    componentHandler.register = function() {};\n  }\n});\n","// Source: https://github.com/darius/requestAnimationFrame/blob/master/requestAnimationFrame.js\n// Adapted from https://gist.github.com/paulirish/1579671 which derived from\n// http://paulirish.com/2011/requestanimationframe-for-smart-animating/\n// http://my.opera.com/emoller/blog/2011/12/20/requestanimationframe-for-smart-er-animating\n// requestAnimationFrame polyfill by Erik Möller.\n// Fixes from Paul Irish, Tino Zijdel, Andrew Mao, Klemen Slavič, Darius Bacon\n// MIT license\nif (!Date.now) {\n    /**\n     * Date.now polyfill.\n     * @return {number} the current Date\n     */\n    Date.now = function () {\n        return new Date().getTime();\n    };\n    Date['now'] = Date.now;\n}\nvar vendors = [\n    'webkit',\n    'moz'\n];\nfor (var i = 0; i < vendors.length && !window.requestAnimationFrame; ++i) {\n    var vp = vendors[i];\n    window.requestAnimationFrame = window[vp + 'RequestAnimationFrame'];\n    window.cancelAnimationFrame = window[vp + 'CancelAnimationFrame'] || window[vp + 'CancelRequestAnimationFrame'];\n    window['requestAnimationFrame'] = window.requestAnimationFrame;\n    window['cancelAnimationFrame'] = window.cancelAnimationFrame;\n}\nif (/iP(ad|hone|od).*OS 6/.test(window.navigator.userAgent) || !window.requestAnimationFrame || !window.cancelAnimationFrame) {\n    var lastTime = 0;\n    /**\n     * requestAnimationFrame polyfill.\n     * @param  {!Function} callback the callback function.\n     */\n    window.requestAnimationFrame = function (callback) {\n        var now = Date.now();\n        var nextTime = Math.max(lastTime + 16, now);\n        return setTimeout(function () {\n            callback(lastTime = nextTime);\n        }, nextTime - now);\n    };\n    window.cancelAnimationFrame = clearTimeout;\n    window['requestAnimationFrame'] = window.requestAnimationFrame;\n    window['cancelAnimationFrame'] = window.cancelAnimationFrame;\n}","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Button MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialButton = function MaterialButton(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialButton'] = MaterialButton;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialButton.prototype.Constant_ = {};\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialButton.prototype.CssClasses_ = {\n    RIPPLE_EFFECT: 'mdl-js-ripple-effect',\n    RIPPLE_CONTAINER: 'mdl-button__ripple-container',\n    RIPPLE: 'mdl-ripple'\n};\n/**\n   * Handle blur of element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialButton.prototype.blurHandler_ = function (event) {\n    if (event) {\n        this.element_.blur();\n    }\n};\n// Public methods.\n/**\n   * Disable button.\n   *\n   * @public\n   */\nMaterialButton.prototype.disable = function () {\n    this.element_.disabled = true;\n};\nMaterialButton.prototype['disable'] = MaterialButton.prototype.disable;\n/**\n   * Enable button.\n   *\n   * @public\n   */\nMaterialButton.prototype.enable = function () {\n    this.element_.disabled = false;\n};\nMaterialButton.prototype['enable'] = MaterialButton.prototype.enable;\n/**\n   * Initialize element.\n   */\nMaterialButton.prototype.init = function () {\n    if (this.element_) {\n        if (this.element_.classList.contains(this.CssClasses_.RIPPLE_EFFECT)) {\n            var rippleContainer = document.createElement('span');\n            rippleContainer.classList.add(this.CssClasses_.RIPPLE_CONTAINER);\n            this.rippleElement_ = document.createElement('span');\n            this.rippleElement_.classList.add(this.CssClasses_.RIPPLE);\n            rippleContainer.appendChild(this.rippleElement_);\n            this.boundRippleBlurHandler = this.blurHandler_.bind(this);\n            this.rippleElement_.addEventListener('mouseup', this.boundRippleBlurHandler);\n            this.element_.appendChild(rippleContainer);\n        }\n        this.boundButtonBlurHandler = this.blurHandler_.bind(this);\n        this.element_.addEventListener('mouseup', this.boundButtonBlurHandler);\n        this.element_.addEventListener('mouseleave', this.boundButtonBlurHandler);\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialButton,\n    classAsString: 'MaterialButton',\n    cssClass: 'mdl-js-button',\n    widget: true\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Checkbox MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialCheckbox = function MaterialCheckbox(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialCheckbox'] = MaterialCheckbox;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialCheckbox.prototype.Constant_ = { TINY_TIMEOUT: 0.001 };\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialCheckbox.prototype.CssClasses_ = {\n    INPUT: 'mdl-checkbox__input',\n    BOX_OUTLINE: 'mdl-checkbox__box-outline',\n    FOCUS_HELPER: 'mdl-checkbox__focus-helper',\n    TICK_OUTLINE: 'mdl-checkbox__tick-outline',\n    RIPPLE_EFFECT: 'mdl-js-ripple-effect',\n    RIPPLE_IGNORE_EVENTS: 'mdl-js-ripple-effect--ignore-events',\n    RIPPLE_CONTAINER: 'mdl-checkbox__ripple-container',\n    RIPPLE_CENTER: 'mdl-ripple--center',\n    RIPPLE: 'mdl-ripple',\n    IS_FOCUSED: 'is-focused',\n    IS_DISABLED: 'is-disabled',\n    IS_CHECKED: 'is-checked',\n    IS_UPGRADED: 'is-upgraded'\n};\n/**\n   * Handle change of state.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialCheckbox.prototype.onChange_ = function (event) {\n    this.updateClasses_();\n};\n/**\n   * Handle focus of element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialCheckbox.prototype.onFocus_ = function (event) {\n    this.element_.classList.add(this.CssClasses_.IS_FOCUSED);\n};\n/**\n   * Handle lost focus of element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialCheckbox.prototype.onBlur_ = function (event) {\n    this.element_.classList.remove(this.CssClasses_.IS_FOCUSED);\n};\n/**\n   * Handle mouseup.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialCheckbox.prototype.onMouseUp_ = function (event) {\n    this.blur_();\n};\n/**\n   * Handle class updates.\n   *\n   * @private\n   */\nMaterialCheckbox.prototype.updateClasses_ = function () {\n    this.checkDisabled();\n    this.checkToggleState();\n};\n/**\n   * Add blur.\n   *\n   * @private\n   */\nMaterialCheckbox.prototype.blur_ = function () {\n    // TODO: figure out why there's a focus event being fired after our blur,\n    // so that we can avoid this hack.\n    window.setTimeout(function () {\n        this.inputElement_.blur();\n    }.bind(this), this.Constant_.TINY_TIMEOUT);\n};\n// Public methods.\n/**\n   * Check the inputs toggle state and update display.\n   *\n   * @public\n   */\nMaterialCheckbox.prototype.checkToggleState = function () {\n    if (this.inputElement_.checked) {\n        this.element_.classList.add(this.CssClasses_.IS_CHECKED);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_CHECKED);\n    }\n};\nMaterialCheckbox.prototype['checkToggleState'] = MaterialCheckbox.prototype.checkToggleState;\n/**\n   * Check the inputs disabled state and update display.\n   *\n   * @public\n   */\nMaterialCheckbox.prototype.checkDisabled = function () {\n    if (this.inputElement_.disabled) {\n        this.element_.classList.add(this.CssClasses_.IS_DISABLED);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_DISABLED);\n    }\n};\nMaterialCheckbox.prototype['checkDisabled'] = MaterialCheckbox.prototype.checkDisabled;\n/**\n   * Disable checkbox.\n   *\n   * @public\n   */\nMaterialCheckbox.prototype.disable = function () {\n    this.inputElement_.disabled = true;\n    this.updateClasses_();\n};\nMaterialCheckbox.prototype['disable'] = MaterialCheckbox.prototype.disable;\n/**\n   * Enable checkbox.\n   *\n   * @public\n   */\nMaterialCheckbox.prototype.enable = function () {\n    this.inputElement_.disabled = false;\n    this.updateClasses_();\n};\nMaterialCheckbox.prototype['enable'] = MaterialCheckbox.prototype.enable;\n/**\n   * Check checkbox.\n   *\n   * @public\n   */\nMaterialCheckbox.prototype.check = function () {\n    this.inputElement_.checked = true;\n    this.updateClasses_();\n};\nMaterialCheckbox.prototype['check'] = MaterialCheckbox.prototype.check;\n/**\n   * Uncheck checkbox.\n   *\n   * @public\n   */\nMaterialCheckbox.prototype.uncheck = function () {\n    this.inputElement_.checked = false;\n    this.updateClasses_();\n};\nMaterialCheckbox.prototype['uncheck'] = MaterialCheckbox.prototype.uncheck;\n/**\n   * Initialize element.\n   */\nMaterialCheckbox.prototype.init = function () {\n    if (this.element_) {\n        this.inputElement_ = this.element_.querySelector('.' + this.CssClasses_.INPUT);\n        var boxOutline = document.createElement('span');\n        boxOutline.classList.add(this.CssClasses_.BOX_OUTLINE);\n        var tickContainer = document.createElement('span');\n        tickContainer.classList.add(this.CssClasses_.FOCUS_HELPER);\n        var tickOutline = document.createElement('span');\n        tickOutline.classList.add(this.CssClasses_.TICK_OUTLINE);\n        boxOutline.appendChild(tickOutline);\n        this.element_.appendChild(tickContainer);\n        this.element_.appendChild(boxOutline);\n        if (this.element_.classList.contains(this.CssClasses_.RIPPLE_EFFECT)) {\n            this.element_.classList.add(this.CssClasses_.RIPPLE_IGNORE_EVENTS);\n            this.rippleContainerElement_ = document.createElement('span');\n            this.rippleContainerElement_.classList.add(this.CssClasses_.RIPPLE_CONTAINER);\n            this.rippleContainerElement_.classList.add(this.CssClasses_.RIPPLE_EFFECT);\n            this.rippleContainerElement_.classList.add(this.CssClasses_.RIPPLE_CENTER);\n            this.boundRippleMouseUp = this.onMouseUp_.bind(this);\n            this.rippleContainerElement_.addEventListener('mouseup', this.boundRippleMouseUp);\n            var ripple = document.createElement('span');\n            ripple.classList.add(this.CssClasses_.RIPPLE);\n            this.rippleContainerElement_.appendChild(ripple);\n            this.element_.appendChild(this.rippleContainerElement_);\n        }\n        this.boundInputOnChange = this.onChange_.bind(this);\n        this.boundInputOnFocus = this.onFocus_.bind(this);\n        this.boundInputOnBlur = this.onBlur_.bind(this);\n        this.boundElementMouseUp = this.onMouseUp_.bind(this);\n        this.inputElement_.addEventListener('change', this.boundInputOnChange);\n        this.inputElement_.addEventListener('focus', this.boundInputOnFocus);\n        this.inputElement_.addEventListener('blur', this.boundInputOnBlur);\n        this.element_.addEventListener('mouseup', this.boundElementMouseUp);\n        this.updateClasses_();\n        this.element_.classList.add(this.CssClasses_.IS_UPGRADED);\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialCheckbox,\n    classAsString: 'MaterialCheckbox',\n    cssClass: 'mdl-js-checkbox',\n    widget: true\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for icon toggle MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialIconToggle = function MaterialIconToggle(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialIconToggle'] = MaterialIconToggle;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialIconToggle.prototype.Constant_ = { TINY_TIMEOUT: 0.001 };\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialIconToggle.prototype.CssClasses_ = {\n    INPUT: 'mdl-icon-toggle__input',\n    JS_RIPPLE_EFFECT: 'mdl-js-ripple-effect',\n    RIPPLE_IGNORE_EVENTS: 'mdl-js-ripple-effect--ignore-events',\n    RIPPLE_CONTAINER: 'mdl-icon-toggle__ripple-container',\n    RIPPLE_CENTER: 'mdl-ripple--center',\n    RIPPLE: 'mdl-ripple',\n    IS_FOCUSED: 'is-focused',\n    IS_DISABLED: 'is-disabled',\n    IS_CHECKED: 'is-checked'\n};\n/**\n   * Handle change of state.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialIconToggle.prototype.onChange_ = function (event) {\n    this.updateClasses_();\n};\n/**\n   * Handle focus of element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialIconToggle.prototype.onFocus_ = function (event) {\n    this.element_.classList.add(this.CssClasses_.IS_FOCUSED);\n};\n/**\n   * Handle lost focus of element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialIconToggle.prototype.onBlur_ = function (event) {\n    this.element_.classList.remove(this.CssClasses_.IS_FOCUSED);\n};\n/**\n   * Handle mouseup.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialIconToggle.prototype.onMouseUp_ = function (event) {\n    this.blur_();\n};\n/**\n   * Handle class updates.\n   *\n   * @private\n   */\nMaterialIconToggle.prototype.updateClasses_ = function () {\n    this.checkDisabled();\n    this.checkToggleState();\n};\n/**\n   * Add blur.\n   *\n   * @private\n   */\nMaterialIconToggle.prototype.blur_ = function () {\n    // TODO: figure out why there's a focus event being fired after our blur,\n    // so that we can avoid this hack.\n    window.setTimeout(function () {\n        this.inputElement_.blur();\n    }.bind(this), this.Constant_.TINY_TIMEOUT);\n};\n// Public methods.\n/**\n   * Check the inputs toggle state and update display.\n   *\n   * @public\n   */\nMaterialIconToggle.prototype.checkToggleState = function () {\n    if (this.inputElement_.checked) {\n        this.element_.classList.add(this.CssClasses_.IS_CHECKED);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_CHECKED);\n    }\n};\nMaterialIconToggle.prototype['checkToggleState'] = MaterialIconToggle.prototype.checkToggleState;\n/**\n   * Check the inputs disabled state and update display.\n   *\n   * @public\n   */\nMaterialIconToggle.prototype.checkDisabled = function () {\n    if (this.inputElement_.disabled) {\n        this.element_.classList.add(this.CssClasses_.IS_DISABLED);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_DISABLED);\n    }\n};\nMaterialIconToggle.prototype['checkDisabled'] = MaterialIconToggle.prototype.checkDisabled;\n/**\n   * Disable icon toggle.\n   *\n   * @public\n   */\nMaterialIconToggle.prototype.disable = function () {\n    this.inputElement_.disabled = true;\n    this.updateClasses_();\n};\nMaterialIconToggle.prototype['disable'] = MaterialIconToggle.prototype.disable;\n/**\n   * Enable icon toggle.\n   *\n   * @public\n   */\nMaterialIconToggle.prototype.enable = function () {\n    this.inputElement_.disabled = false;\n    this.updateClasses_();\n};\nMaterialIconToggle.prototype['enable'] = MaterialIconToggle.prototype.enable;\n/**\n   * Check icon toggle.\n   *\n   * @public\n   */\nMaterialIconToggle.prototype.check = function () {\n    this.inputElement_.checked = true;\n    this.updateClasses_();\n};\nMaterialIconToggle.prototype['check'] = MaterialIconToggle.prototype.check;\n/**\n   * Uncheck icon toggle.\n   *\n   * @public\n   */\nMaterialIconToggle.prototype.uncheck = function () {\n    this.inputElement_.checked = false;\n    this.updateClasses_();\n};\nMaterialIconToggle.prototype['uncheck'] = MaterialIconToggle.prototype.uncheck;\n/**\n   * Initialize element.\n   */\nMaterialIconToggle.prototype.init = function () {\n    if (this.element_) {\n        this.inputElement_ = this.element_.querySelector('.' + this.CssClasses_.INPUT);\n        if (this.element_.classList.contains(this.CssClasses_.JS_RIPPLE_EFFECT)) {\n            this.element_.classList.add(this.CssClasses_.RIPPLE_IGNORE_EVENTS);\n            this.rippleContainerElement_ = document.createElement('span');\n            this.rippleContainerElement_.classList.add(this.CssClasses_.RIPPLE_CONTAINER);\n            this.rippleContainerElement_.classList.add(this.CssClasses_.JS_RIPPLE_EFFECT);\n            this.rippleContainerElement_.classList.add(this.CssClasses_.RIPPLE_CENTER);\n            this.boundRippleMouseUp = this.onMouseUp_.bind(this);\n            this.rippleContainerElement_.addEventListener('mouseup', this.boundRippleMouseUp);\n            var ripple = document.createElement('span');\n            ripple.classList.add(this.CssClasses_.RIPPLE);\n            this.rippleContainerElement_.appendChild(ripple);\n            this.element_.appendChild(this.rippleContainerElement_);\n        }\n        this.boundInputOnChange = this.onChange_.bind(this);\n        this.boundInputOnFocus = this.onFocus_.bind(this);\n        this.boundInputOnBlur = this.onBlur_.bind(this);\n        this.boundElementOnMouseUp = this.onMouseUp_.bind(this);\n        this.inputElement_.addEventListener('change', this.boundInputOnChange);\n        this.inputElement_.addEventListener('focus', this.boundInputOnFocus);\n        this.inputElement_.addEventListener('blur', this.boundInputOnBlur);\n        this.element_.addEventListener('mouseup', this.boundElementOnMouseUp);\n        this.updateClasses_();\n        this.element_.classList.add('is-upgraded');\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialIconToggle,\n    classAsString: 'MaterialIconToggle',\n    cssClass: 'mdl-js-icon-toggle',\n    widget: true\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for dropdown MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialMenu = function MaterialMenu(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialMenu'] = MaterialMenu;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialMenu.prototype.Constant_ = {\n    // Total duration of the menu animation.\n    TRANSITION_DURATION_SECONDS: 0.3,\n    // The fraction of the total duration we want to use for menu item animations.\n    TRANSITION_DURATION_FRACTION: 0.8,\n    // How long the menu stays open after choosing an option (so the user can see\n    // the ripple).\n    CLOSE_TIMEOUT: 150\n};\n/**\n   * Keycodes, for code readability.\n   *\n   * @enum {number}\n   * @private\n   */\nMaterialMenu.prototype.Keycodes_ = {\n    ENTER: 13,\n    ESCAPE: 27,\n    SPACE: 32,\n    UP_ARROW: 38,\n    DOWN_ARROW: 40\n};\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialMenu.prototype.CssClasses_ = {\n    CONTAINER: 'mdl-menu__container',\n    OUTLINE: 'mdl-menu__outline',\n    ITEM: 'mdl-menu__item',\n    ITEM_RIPPLE_CONTAINER: 'mdl-menu__item-ripple-container',\n    RIPPLE_EFFECT: 'mdl-js-ripple-effect',\n    RIPPLE_IGNORE_EVENTS: 'mdl-js-ripple-effect--ignore-events',\n    RIPPLE: 'mdl-ripple',\n    // Statuses\n    IS_UPGRADED: 'is-upgraded',\n    IS_VISIBLE: 'is-visible',\n    IS_ANIMATING: 'is-animating',\n    // Alignment options\n    BOTTOM_LEFT: 'mdl-menu--bottom-left',\n    // This is the default.\n    BOTTOM_RIGHT: 'mdl-menu--bottom-right',\n    TOP_LEFT: 'mdl-menu--top-left',\n    TOP_RIGHT: 'mdl-menu--top-right',\n    UNALIGNED: 'mdl-menu--unaligned'\n};\n/**\n   * Initialize element.\n   */\nMaterialMenu.prototype.init = function () {\n    if (this.element_) {\n        // Create container for the menu.\n        var container = document.createElement('div');\n        container.classList.add(this.CssClasses_.CONTAINER);\n        this.element_.parentElement.insertBefore(container, this.element_);\n        this.element_.parentElement.removeChild(this.element_);\n        container.appendChild(this.element_);\n        this.container_ = container;\n        // Create outline for the menu (shadow and background).\n        var outline = document.createElement('div');\n        outline.classList.add(this.CssClasses_.OUTLINE);\n        this.outline_ = outline;\n        container.insertBefore(outline, this.element_);\n        // Find the \"for\" element and bind events to it.\n        var forElId = this.element_.getAttribute('for') || this.element_.getAttribute('data-mdl-for');\n        var forEl = null;\n        if (forElId) {\n            forEl = document.getElementById(forElId);\n            if (forEl) {\n                this.forElement_ = forEl;\n                forEl.addEventListener('click', this.handleForClick_.bind(this));\n                forEl.addEventListener('keydown', this.handleForKeyboardEvent_.bind(this));\n            }\n        }\n        var items = this.element_.querySelectorAll('.' + this.CssClasses_.ITEM);\n        this.boundItemKeydown_ = this.handleItemKeyboardEvent_.bind(this);\n        this.boundItemClick_ = this.handleItemClick_.bind(this);\n        for (var i = 0; i < items.length; i++) {\n            // Add a listener to each menu item.\n            items[i].addEventListener('click', this.boundItemClick_);\n            // Add a tab index to each menu item.\n            items[i].tabIndex = '-1';\n            // Add a keyboard listener to each menu item.\n            items[i].addEventListener('keydown', this.boundItemKeydown_);\n        }\n        // Add ripple classes to each item, if the user has enabled ripples.\n        if (this.element_.classList.contains(this.CssClasses_.RIPPLE_EFFECT)) {\n            this.element_.classList.add(this.CssClasses_.RIPPLE_IGNORE_EVENTS);\n            for (i = 0; i < items.length; i++) {\n                var item = items[i];\n                var rippleContainer = document.createElement('span');\n                rippleContainer.classList.add(this.CssClasses_.ITEM_RIPPLE_CONTAINER);\n                var ripple = document.createElement('span');\n                ripple.classList.add(this.CssClasses_.RIPPLE);\n                rippleContainer.appendChild(ripple);\n                item.appendChild(rippleContainer);\n                item.classList.add(this.CssClasses_.RIPPLE_EFFECT);\n            }\n        }\n        // Copy alignment classes to the container, so the outline can use them.\n        if (this.element_.classList.contains(this.CssClasses_.BOTTOM_LEFT)) {\n            this.outline_.classList.add(this.CssClasses_.BOTTOM_LEFT);\n        }\n        if (this.element_.classList.contains(this.CssClasses_.BOTTOM_RIGHT)) {\n            this.outline_.classList.add(this.CssClasses_.BOTTOM_RIGHT);\n        }\n        if (this.element_.classList.contains(this.CssClasses_.TOP_LEFT)) {\n            this.outline_.classList.add(this.CssClasses_.TOP_LEFT);\n        }\n        if (this.element_.classList.contains(this.CssClasses_.TOP_RIGHT)) {\n            this.outline_.classList.add(this.CssClasses_.TOP_RIGHT);\n        }\n        if (this.element_.classList.contains(this.CssClasses_.UNALIGNED)) {\n            this.outline_.classList.add(this.CssClasses_.UNALIGNED);\n        }\n        container.classList.add(this.CssClasses_.IS_UPGRADED);\n    }\n};\n/**\n   * Handles a click on the \"for\" element, by positioning the menu and then\n   * toggling it.\n   *\n   * @param {Event} evt The event that fired.\n   * @private\n   */\nMaterialMenu.prototype.handleForClick_ = function (evt) {\n    if (this.element_ && this.forElement_) {\n        var rect = this.forElement_.getBoundingClientRect();\n        var forRect = this.forElement_.parentElement.getBoundingClientRect();\n        if (this.element_.classList.contains(this.CssClasses_.UNALIGNED)) {\n        } else if (this.element_.classList.contains(this.CssClasses_.BOTTOM_RIGHT)) {\n            // Position below the \"for\" element, aligned to its right.\n            this.container_.style.right = forRect.right - rect.right + 'px';\n            this.container_.style.top = this.forElement_.offsetTop + this.forElement_.offsetHeight + 'px';\n        } else if (this.element_.classList.contains(this.CssClasses_.TOP_LEFT)) {\n            // Position above the \"for\" element, aligned to its left.\n            this.container_.style.left = this.forElement_.offsetLeft + 'px';\n            this.container_.style.bottom = forRect.bottom - rect.top + 'px';\n        } else if (this.element_.classList.contains(this.CssClasses_.TOP_RIGHT)) {\n            // Position above the \"for\" element, aligned to its right.\n            this.container_.style.right = forRect.right - rect.right + 'px';\n            this.container_.style.bottom = forRect.bottom - rect.top + 'px';\n        } else {\n            // Default: position below the \"for\" element, aligned to its left.\n            this.container_.style.left = this.forElement_.offsetLeft + 'px';\n            this.container_.style.top = this.forElement_.offsetTop + this.forElement_.offsetHeight + 'px';\n        }\n    }\n    this.toggle(evt);\n};\n/**\n   * Handles a keyboard event on the \"for\" element.\n   *\n   * @param {Event} evt The event that fired.\n   * @private\n   */\nMaterialMenu.prototype.handleForKeyboardEvent_ = function (evt) {\n    if (this.element_ && this.container_ && this.forElement_) {\n        var items = this.element_.querySelectorAll('.' + this.CssClasses_.ITEM + ':not([disabled])');\n        if (items && items.length > 0 && this.container_.classList.contains(this.CssClasses_.IS_VISIBLE)) {\n            if (evt.keyCode === this.Keycodes_.UP_ARROW) {\n                evt.preventDefault();\n                items[items.length - 1].focus();\n            } else if (evt.keyCode === this.Keycodes_.DOWN_ARROW) {\n                evt.preventDefault();\n                items[0].focus();\n            }\n        }\n    }\n};\n/**\n   * Handles a keyboard event on an item.\n   *\n   * @param {Event} evt The event that fired.\n   * @private\n   */\nMaterialMenu.prototype.handleItemKeyboardEvent_ = function (evt) {\n    if (this.element_ && this.container_) {\n        var items = this.element_.querySelectorAll('.' + this.CssClasses_.ITEM + ':not([disabled])');\n        if (items && items.length > 0 && this.container_.classList.contains(this.CssClasses_.IS_VISIBLE)) {\n            var currentIndex = Array.prototype.slice.call(items).indexOf(evt.target);\n            if (evt.keyCode === this.Keycodes_.UP_ARROW) {\n                evt.preventDefault();\n                if (currentIndex > 0) {\n                    items[currentIndex - 1].focus();\n                } else {\n                    items[items.length - 1].focus();\n                }\n            } else if (evt.keyCode === this.Keycodes_.DOWN_ARROW) {\n                evt.preventDefault();\n                if (items.length > currentIndex + 1) {\n                    items[currentIndex + 1].focus();\n                } else {\n                    items[0].focus();\n                }\n            } else if (evt.keyCode === this.Keycodes_.SPACE || evt.keyCode === this.Keycodes_.ENTER) {\n                evt.preventDefault();\n                // Send mousedown and mouseup to trigger ripple.\n                var e = new MouseEvent('mousedown');\n                evt.target.dispatchEvent(e);\n                e = new MouseEvent('mouseup');\n                evt.target.dispatchEvent(e);\n                // Send click.\n                evt.target.click();\n            } else if (evt.keyCode === this.Keycodes_.ESCAPE) {\n                evt.preventDefault();\n                this.hide();\n            }\n        }\n    }\n};\n/**\n   * Handles a click event on an item.\n   *\n   * @param {Event} evt The event that fired.\n   * @private\n   */\nMaterialMenu.prototype.handleItemClick_ = function (evt) {\n    if (evt.target.hasAttribute('disabled')) {\n        evt.stopPropagation();\n    } else {\n        // Wait some time before closing menu, so the user can see the ripple.\n        this.closing_ = true;\n        window.setTimeout(function (evt) {\n            this.hide();\n            this.closing_ = false;\n        }.bind(this), this.Constant_.CLOSE_TIMEOUT);\n    }\n};\n/**\n   * Calculates the initial clip (for opening the menu) or final clip (for closing\n   * it), and applies it. This allows us to animate from or to the correct point,\n   * that is, the point it's aligned to in the \"for\" element.\n   *\n   * @param {number} height Height of the clip rectangle\n   * @param {number} width Width of the clip rectangle\n   * @private\n   */\nMaterialMenu.prototype.applyClip_ = function (height, width) {\n    if (this.element_.classList.contains(this.CssClasses_.UNALIGNED)) {\n        // Do not clip.\n        this.element_.style.clip = '';\n    } else if (this.element_.classList.contains(this.CssClasses_.BOTTOM_RIGHT)) {\n        // Clip to the top right corner of the menu.\n        this.element_.style.clip = 'rect(0 ' + width + 'px ' + '0 ' + width + 'px)';\n    } else if (this.element_.classList.contains(this.CssClasses_.TOP_LEFT)) {\n        // Clip to the bottom left corner of the menu.\n        this.element_.style.clip = 'rect(' + height + 'px 0 ' + height + 'px 0)';\n    } else if (this.element_.classList.contains(this.CssClasses_.TOP_RIGHT)) {\n        // Clip to the bottom right corner of the menu.\n        this.element_.style.clip = 'rect(' + height + 'px ' + width + 'px ' + height + 'px ' + width + 'px)';\n    } else {\n        // Default: do not clip (same as clipping to the top left corner).\n        this.element_.style.clip = '';\n    }\n};\n/**\n   * Cleanup function to remove animation listeners.\n   *\n   * @param {Event} evt\n   * @private\n   */\nMaterialMenu.prototype.removeAnimationEndListener_ = function (evt) {\n    evt.target.classList.remove(MaterialMenu.prototype.CssClasses_.IS_ANIMATING);\n};\n/**\n   * Adds an event listener to clean up after the animation ends.\n   *\n   * @private\n   */\nMaterialMenu.prototype.addAnimationEndListener_ = function () {\n    this.element_.addEventListener('transitionend', this.removeAnimationEndListener_);\n    this.element_.addEventListener('webkitTransitionEnd', this.removeAnimationEndListener_);\n};\n/**\n   * Displays the menu.\n   *\n   * @public\n   */\nMaterialMenu.prototype.show = function (evt) {\n    if (this.element_ && this.container_ && this.outline_) {\n        // Measure the inner element.\n        var height = this.element_.getBoundingClientRect().height;\n        var width = this.element_.getBoundingClientRect().width;\n        // Apply the inner element's size to the container and outline.\n        this.container_.style.width = width + 'px';\n        this.container_.style.height = height + 'px';\n        this.outline_.style.width = width + 'px';\n        this.outline_.style.height = height + 'px';\n        var transitionDuration = this.Constant_.TRANSITION_DURATION_SECONDS * this.Constant_.TRANSITION_DURATION_FRACTION;\n        // Calculate transition delays for individual menu items, so that they fade\n        // in one at a time.\n        var items = this.element_.querySelectorAll('.' + this.CssClasses_.ITEM);\n        for (var i = 0; i < items.length; i++) {\n            var itemDelay = null;\n            if (this.element_.classList.contains(this.CssClasses_.TOP_LEFT) || this.element_.classList.contains(this.CssClasses_.TOP_RIGHT)) {\n                itemDelay = (height - items[i].offsetTop - items[i].offsetHeight) / height * transitionDuration + 's';\n            } else {\n                itemDelay = items[i].offsetTop / height * transitionDuration + 's';\n            }\n            items[i].style.transitionDelay = itemDelay;\n        }\n        // Apply the initial clip to the text before we start animating.\n        this.applyClip_(height, width);\n        // Wait for the next frame, turn on animation, and apply the final clip.\n        // Also make it visible. This triggers the transitions.\n        window.requestAnimationFrame(function () {\n            this.element_.classList.add(this.CssClasses_.IS_ANIMATING);\n            this.element_.style.clip = 'rect(0 ' + width + 'px ' + height + 'px 0)';\n            this.container_.classList.add(this.CssClasses_.IS_VISIBLE);\n        }.bind(this));\n        // Clean up after the animation is complete.\n        this.addAnimationEndListener_();\n        // Add a click listener to the document, to close the menu.\n        var callback = function (e) {\n            // Check to see if the document is processing the same event that\n            // displayed the menu in the first place. If so, do nothing.\n            // Also check to see if the menu is in the process of closing itself, and\n            // do nothing in that case.\n            // Also check if the clicked element is a menu item\n            // if so, do nothing.\n            if (e !== evt && !this.closing_ && e.target.parentNode !== this.element_) {\n                document.removeEventListener('click', callback);\n                this.hide();\n            }\n        }.bind(this);\n        document.addEventListener('click', callback);\n    }\n};\nMaterialMenu.prototype['show'] = MaterialMenu.prototype.show;\n/**\n   * Hides the menu.\n   *\n   * @public\n   */\nMaterialMenu.prototype.hide = function () {\n    if (this.element_ && this.container_ && this.outline_) {\n        var items = this.element_.querySelectorAll('.' + this.CssClasses_.ITEM);\n        // Remove all transition delays; menu items fade out concurrently.\n        for (var i = 0; i < items.length; i++) {\n            items[i].style.removeProperty('transition-delay');\n        }\n        // Measure the inner element.\n        var rect = this.element_.getBoundingClientRect();\n        var height = rect.height;\n        var width = rect.width;\n        // Turn on animation, and apply the final clip. Also make invisible.\n        // This triggers the transitions.\n        this.element_.classList.add(this.CssClasses_.IS_ANIMATING);\n        this.applyClip_(height, width);\n        this.container_.classList.remove(this.CssClasses_.IS_VISIBLE);\n        // Clean up after the animation is complete.\n        this.addAnimationEndListener_();\n    }\n};\nMaterialMenu.prototype['hide'] = MaterialMenu.prototype.hide;\n/**\n   * Displays or hides the menu, depending on current state.\n   *\n   * @public\n   */\nMaterialMenu.prototype.toggle = function (evt) {\n    if (this.container_.classList.contains(this.CssClasses_.IS_VISIBLE)) {\n        this.hide();\n    } else {\n        this.show(evt);\n    }\n};\nMaterialMenu.prototype['toggle'] = MaterialMenu.prototype.toggle;\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialMenu,\n    classAsString: 'MaterialMenu',\n    cssClass: 'mdl-js-menu',\n    widget: true\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Progress MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialProgress = function MaterialProgress(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialProgress'] = MaterialProgress;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialProgress.prototype.Constant_ = {};\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialProgress.prototype.CssClasses_ = { INDETERMINATE_CLASS: 'mdl-progress__indeterminate' };\n/**\n   * Set the current progress of the progressbar.\n   *\n   * @param {number} p Percentage of the progress (0-100)\n   * @public\n   */\nMaterialProgress.prototype.setProgress = function (p) {\n    if (this.element_.classList.contains(this.CssClasses_.INDETERMINATE_CLASS)) {\n        return;\n    }\n    this.progressbar_.style.width = p + '%';\n};\nMaterialProgress.prototype['setProgress'] = MaterialProgress.prototype.setProgress;\n/**\n   * Set the current progress of the buffer.\n   *\n   * @param {number} p Percentage of the buffer (0-100)\n   * @public\n   */\nMaterialProgress.prototype.setBuffer = function (p) {\n    this.bufferbar_.style.width = p + '%';\n    this.auxbar_.style.width = 100 - p + '%';\n};\nMaterialProgress.prototype['setBuffer'] = MaterialProgress.prototype.setBuffer;\n/**\n   * Initialize element.\n   */\nMaterialProgress.prototype.init = function () {\n    if (this.element_) {\n        var el = document.createElement('div');\n        el.className = 'progressbar bar bar1';\n        this.element_.appendChild(el);\n        this.progressbar_ = el;\n        el = document.createElement('div');\n        el.className = 'bufferbar bar bar2';\n        this.element_.appendChild(el);\n        this.bufferbar_ = el;\n        el = document.createElement('div');\n        el.className = 'auxbar bar bar3';\n        this.element_.appendChild(el);\n        this.auxbar_ = el;\n        this.progressbar_.style.width = '0%';\n        this.bufferbar_.style.width = '100%';\n        this.auxbar_.style.width = '0%';\n        this.element_.classList.add('is-upgraded');\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialProgress,\n    classAsString: 'MaterialProgress',\n    cssClass: 'mdl-js-progress',\n    widget: true\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Radio MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialRadio = function MaterialRadio(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialRadio'] = MaterialRadio;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialRadio.prototype.Constant_ = { TINY_TIMEOUT: 0.001 };\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialRadio.prototype.CssClasses_ = {\n    IS_FOCUSED: 'is-focused',\n    IS_DISABLED: 'is-disabled',\n    IS_CHECKED: 'is-checked',\n    IS_UPGRADED: 'is-upgraded',\n    JS_RADIO: 'mdl-js-radio',\n    RADIO_BTN: 'mdl-radio__button',\n    RADIO_OUTER_CIRCLE: 'mdl-radio__outer-circle',\n    RADIO_INNER_CIRCLE: 'mdl-radio__inner-circle',\n    RIPPLE_EFFECT: 'mdl-js-ripple-effect',\n    RIPPLE_IGNORE_EVENTS: 'mdl-js-ripple-effect--ignore-events',\n    RIPPLE_CONTAINER: 'mdl-radio__ripple-container',\n    RIPPLE_CENTER: 'mdl-ripple--center',\n    RIPPLE: 'mdl-ripple'\n};\n/**\n   * Handle change of state.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialRadio.prototype.onChange_ = function (event) {\n    // Since other radio buttons don't get change events, we need to look for\n    // them to update their classes.\n    var radios = document.getElementsByClassName(this.CssClasses_.JS_RADIO);\n    for (var i = 0; i < radios.length; i++) {\n        var button = radios[i].querySelector('.' + this.CssClasses_.RADIO_BTN);\n        // Different name == different group, so no point updating those.\n        if (button.getAttribute('name') === this.btnElement_.getAttribute('name')) {\n            if (typeof radios[i]['MaterialRadio'] !== 'undefined') {\n                radios[i]['MaterialRadio'].updateClasses_();\n            }\n        }\n    }\n};\n/**\n   * Handle focus.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialRadio.prototype.onFocus_ = function (event) {\n    this.element_.classList.add(this.CssClasses_.IS_FOCUSED);\n};\n/**\n   * Handle lost focus.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialRadio.prototype.onBlur_ = function (event) {\n    this.element_.classList.remove(this.CssClasses_.IS_FOCUSED);\n};\n/**\n   * Handle mouseup.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialRadio.prototype.onMouseup_ = function (event) {\n    this.blur_();\n};\n/**\n   * Update classes.\n   *\n   * @private\n   */\nMaterialRadio.prototype.updateClasses_ = function () {\n    this.checkDisabled();\n    this.checkToggleState();\n};\n/**\n   * Add blur.\n   *\n   * @private\n   */\nMaterialRadio.prototype.blur_ = function () {\n    // TODO: figure out why there's a focus event being fired after our blur,\n    // so that we can avoid this hack.\n    window.setTimeout(function () {\n        this.btnElement_.blur();\n    }.bind(this), this.Constant_.TINY_TIMEOUT);\n};\n// Public methods.\n/**\n   * Check the components disabled state.\n   *\n   * @public\n   */\nMaterialRadio.prototype.checkDisabled = function () {\n    if (this.btnElement_.disabled) {\n        this.element_.classList.add(this.CssClasses_.IS_DISABLED);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_DISABLED);\n    }\n};\nMaterialRadio.prototype['checkDisabled'] = MaterialRadio.prototype.checkDisabled;\n/**\n   * Check the components toggled state.\n   *\n   * @public\n   */\nMaterialRadio.prototype.checkToggleState = function () {\n    if (this.btnElement_.checked) {\n        this.element_.classList.add(this.CssClasses_.IS_CHECKED);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_CHECKED);\n    }\n};\nMaterialRadio.prototype['checkToggleState'] = MaterialRadio.prototype.checkToggleState;\n/**\n   * Disable radio.\n   *\n   * @public\n   */\nMaterialRadio.prototype.disable = function () {\n    this.btnElement_.disabled = true;\n    this.updateClasses_();\n};\nMaterialRadio.prototype['disable'] = MaterialRadio.prototype.disable;\n/**\n   * Enable radio.\n   *\n   * @public\n   */\nMaterialRadio.prototype.enable = function () {\n    this.btnElement_.disabled = false;\n    this.updateClasses_();\n};\nMaterialRadio.prototype['enable'] = MaterialRadio.prototype.enable;\n/**\n   * Check radio.\n   *\n   * @public\n   */\nMaterialRadio.prototype.check = function () {\n    this.btnElement_.checked = true;\n    this.onChange_(null);\n};\nMaterialRadio.prototype['check'] = MaterialRadio.prototype.check;\n/**\n   * Uncheck radio.\n   *\n   * @public\n   */\nMaterialRadio.prototype.uncheck = function () {\n    this.btnElement_.checked = false;\n    this.onChange_(null);\n};\nMaterialRadio.prototype['uncheck'] = MaterialRadio.prototype.uncheck;\n/**\n   * Initialize element.\n   */\nMaterialRadio.prototype.init = function () {\n    if (this.element_) {\n        this.btnElement_ = this.element_.querySelector('.' + this.CssClasses_.RADIO_BTN);\n        this.boundChangeHandler_ = this.onChange_.bind(this);\n        this.boundFocusHandler_ = this.onChange_.bind(this);\n        this.boundBlurHandler_ = this.onBlur_.bind(this);\n        this.boundMouseUpHandler_ = this.onMouseup_.bind(this);\n        var outerCircle = document.createElement('span');\n        outerCircle.classList.add(this.CssClasses_.RADIO_OUTER_CIRCLE);\n        var innerCircle = document.createElement('span');\n        innerCircle.classList.add(this.CssClasses_.RADIO_INNER_CIRCLE);\n        this.element_.appendChild(outerCircle);\n        this.element_.appendChild(innerCircle);\n        var rippleContainer;\n        if (this.element_.classList.contains(this.CssClasses_.RIPPLE_EFFECT)) {\n            this.element_.classList.add(this.CssClasses_.RIPPLE_IGNORE_EVENTS);\n            rippleContainer = document.createElement('span');\n            rippleContainer.classList.add(this.CssClasses_.RIPPLE_CONTAINER);\n            rippleContainer.classList.add(this.CssClasses_.RIPPLE_EFFECT);\n            rippleContainer.classList.add(this.CssClasses_.RIPPLE_CENTER);\n            rippleContainer.addEventListener('mouseup', this.boundMouseUpHandler_);\n            var ripple = document.createElement('span');\n            ripple.classList.add(this.CssClasses_.RIPPLE);\n            rippleContainer.appendChild(ripple);\n            this.element_.appendChild(rippleContainer);\n        }\n        this.btnElement_.addEventListener('change', this.boundChangeHandler_);\n        this.btnElement_.addEventListener('focus', this.boundFocusHandler_);\n        this.btnElement_.addEventListener('blur', this.boundBlurHandler_);\n        this.element_.addEventListener('mouseup', this.boundMouseUpHandler_);\n        this.updateClasses_();\n        this.element_.classList.add(this.CssClasses_.IS_UPGRADED);\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialRadio,\n    classAsString: 'MaterialRadio',\n    cssClass: 'mdl-js-radio',\n    widget: true\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Slider MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialSlider = function MaterialSlider(element) {\n    this.element_ = element;\n    // Browser feature detection.\n    this.isIE_ = window.navigator.msPointerEnabled;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialSlider'] = MaterialSlider;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialSlider.prototype.Constant_ = {};\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialSlider.prototype.CssClasses_ = {\n    IE_CONTAINER: 'mdl-slider__ie-container',\n    SLIDER_CONTAINER: 'mdl-slider__container',\n    BACKGROUND_FLEX: 'mdl-slider__background-flex',\n    BACKGROUND_LOWER: 'mdl-slider__background-lower',\n    BACKGROUND_UPPER: 'mdl-slider__background-upper',\n    IS_LOWEST_VALUE: 'is-lowest-value',\n    IS_UPGRADED: 'is-upgraded'\n};\n/**\n   * Handle input on element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialSlider.prototype.onInput_ = function (event) {\n    this.updateValueStyles_();\n};\n/**\n   * Handle change on element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialSlider.prototype.onChange_ = function (event) {\n    this.updateValueStyles_();\n};\n/**\n   * Handle mouseup on element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialSlider.prototype.onMouseUp_ = function (event) {\n    event.target.blur();\n};\n/**\n   * Handle mousedown on container element.\n   * This handler is purpose is to not require the use to click\n   * exactly on the 2px slider element, as FireFox seems to be very\n   * strict about this.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   * @suppress {missingProperties}\n   */\nMaterialSlider.prototype.onContainerMouseDown_ = function (event) {\n    // If this click is not on the parent element (but rather some child)\n    // ignore. It may still bubble up.\n    if (event.target !== this.element_.parentElement) {\n        return;\n    }\n    // Discard the original event and create a new event that\n    // is on the slider element.\n    event.preventDefault();\n    var newEvent = new MouseEvent('mousedown', {\n        target: event.target,\n        buttons: event.buttons,\n        clientX: event.clientX,\n        clientY: this.element_.getBoundingClientRect().y\n    });\n    this.element_.dispatchEvent(newEvent);\n};\n/**\n   * Handle updating of values.\n   *\n   * @private\n   */\nMaterialSlider.prototype.updateValueStyles_ = function () {\n    // Calculate and apply percentages to div structure behind slider.\n    var fraction = (this.element_.value - this.element_.min) / (this.element_.max - this.element_.min);\n    if (fraction === 0) {\n        this.element_.classList.add(this.CssClasses_.IS_LOWEST_VALUE);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_LOWEST_VALUE);\n    }\n    if (!this.isIE_) {\n        this.backgroundLower_.style.flex = fraction;\n        this.backgroundLower_.style.webkitFlex = fraction;\n        this.backgroundUpper_.style.flex = 1 - fraction;\n        this.backgroundUpper_.style.webkitFlex = 1 - fraction;\n    }\n};\n// Public methods.\n/**\n   * Disable slider.\n   *\n   * @public\n   */\nMaterialSlider.prototype.disable = function () {\n    this.element_.disabled = true;\n};\nMaterialSlider.prototype['disable'] = MaterialSlider.prototype.disable;\n/**\n   * Enable slider.\n   *\n   * @public\n   */\nMaterialSlider.prototype.enable = function () {\n    this.element_.disabled = false;\n};\nMaterialSlider.prototype['enable'] = MaterialSlider.prototype.enable;\n/**\n   * Update slider value.\n   *\n   * @param {number} value The value to which to set the control (optional).\n   * @public\n   */\nMaterialSlider.prototype.change = function (value) {\n    if (typeof value !== 'undefined') {\n        this.element_.value = value;\n    }\n    this.updateValueStyles_();\n};\nMaterialSlider.prototype['change'] = MaterialSlider.prototype.change;\n/**\n   * Initialize element.\n   */\nMaterialSlider.prototype.init = function () {\n    if (this.element_) {\n        if (this.isIE_) {\n            // Since we need to specify a very large height in IE due to\n            // implementation limitations, we add a parent here that trims it down to\n            // a reasonable size.\n            var containerIE = document.createElement('div');\n            containerIE.classList.add(this.CssClasses_.IE_CONTAINER);\n            this.element_.parentElement.insertBefore(containerIE, this.element_);\n            this.element_.parentElement.removeChild(this.element_);\n            containerIE.appendChild(this.element_);\n        } else {\n            // For non-IE browsers, we need a div structure that sits behind the\n            // slider and allows us to style the left and right sides of it with\n            // different colors.\n            var container = document.createElement('div');\n            container.classList.add(this.CssClasses_.SLIDER_CONTAINER);\n            this.element_.parentElement.insertBefore(container, this.element_);\n            this.element_.parentElement.removeChild(this.element_);\n            container.appendChild(this.element_);\n            var backgroundFlex = document.createElement('div');\n            backgroundFlex.classList.add(this.CssClasses_.BACKGROUND_FLEX);\n            container.appendChild(backgroundFlex);\n            this.backgroundLower_ = document.createElement('div');\n            this.backgroundLower_.classList.add(this.CssClasses_.BACKGROUND_LOWER);\n            backgroundFlex.appendChild(this.backgroundLower_);\n            this.backgroundUpper_ = document.createElement('div');\n            this.backgroundUpper_.classList.add(this.CssClasses_.BACKGROUND_UPPER);\n            backgroundFlex.appendChild(this.backgroundUpper_);\n        }\n        this.boundInputHandler = this.onInput_.bind(this);\n        this.boundChangeHandler = this.onChange_.bind(this);\n        this.boundMouseUpHandler = this.onMouseUp_.bind(this);\n        this.boundContainerMouseDownHandler = this.onContainerMouseDown_.bind(this);\n        this.element_.addEventListener('input', this.boundInputHandler);\n        this.element_.addEventListener('change', this.boundChangeHandler);\n        this.element_.addEventListener('mouseup', this.boundMouseUpHandler);\n        this.element_.parentElement.addEventListener('mousedown', this.boundContainerMouseDownHandler);\n        this.updateValueStyles_();\n        this.element_.classList.add(this.CssClasses_.IS_UPGRADED);\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialSlider,\n    classAsString: 'MaterialSlider',\n    cssClass: 'mdl-js-slider',\n    widget: true\n});","/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Snackbar MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialSnackbar = function MaterialSnackbar(element) {\n    this.element_ = element;\n    this.textElement_ = this.element_.querySelector('.' + this.cssClasses_.MESSAGE);\n    this.actionElement_ = this.element_.querySelector('.' + this.cssClasses_.ACTION);\n    if (!this.textElement_) {\n        throw new Error('There must be a message element for a snackbar.');\n    }\n    if (!this.actionElement_) {\n        throw new Error('There must be an action element for a snackbar.');\n    }\n    this.active = false;\n    this.actionHandler_ = undefined;\n    this.message_ = undefined;\n    this.actionText_ = undefined;\n    this.queuedNotifications_ = [];\n    this.setActionHidden_(true);\n};\nwindow['MaterialSnackbar'] = MaterialSnackbar;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialSnackbar.prototype.Constant_ = {\n    // The duration of the snackbar show/hide animation, in ms.\n    ANIMATION_LENGTH: 250\n};\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialSnackbar.prototype.cssClasses_ = {\n    SNACKBAR: 'mdl-snackbar',\n    MESSAGE: 'mdl-snackbar__text',\n    ACTION: 'mdl-snackbar__action',\n    ACTIVE: 'mdl-snackbar--active'\n};\n/**\n   * Display the snackbar.\n   *\n   * @private\n   */\nMaterialSnackbar.prototype.displaySnackbar_ = function () {\n    this.element_.setAttribute('aria-hidden', 'true');\n    if (this.actionHandler_) {\n        this.actionElement_.textContent = this.actionText_;\n        this.actionElement_.addEventListener('click', this.actionHandler_);\n        this.setActionHidden_(false);\n    }\n    this.textElement_.textContent = this.message_;\n    this.element_.classList.add(this.cssClasses_.ACTIVE);\n    this.element_.setAttribute('aria-hidden', 'false');\n    setTimeout(this.cleanup_.bind(this), this.timeout_);\n};\n/**\n   * Show the snackbar.\n   *\n   * @param {Object} data The data for the notification.\n   * @public\n   */\nMaterialSnackbar.prototype.showSnackbar = function (data) {\n    if (data === undefined) {\n        throw new Error('Please provide a data object with at least a message to display.');\n    }\n    if (data['message'] === undefined) {\n        throw new Error('Please provide a message to be displayed.');\n    }\n    if (data['actionHandler'] && !data['actionText']) {\n        throw new Error('Please provide action text with the handler.');\n    }\n    if (this.active) {\n        this.queuedNotifications_.push(data);\n    } else {\n        this.active = true;\n        this.message_ = data['message'];\n        if (data['timeout']) {\n            this.timeout_ = data['timeout'];\n        } else {\n            this.timeout_ = 2750;\n        }\n        if (data['actionHandler']) {\n            this.actionHandler_ = data['actionHandler'];\n        }\n        if (data['actionText']) {\n            this.actionText_ = data['actionText'];\n        }\n        this.displaySnackbar_();\n    }\n};\nMaterialSnackbar.prototype['showSnackbar'] = MaterialSnackbar.prototype.showSnackbar;\n/**\n   * Check if the queue has items within it.\n   * If it does, display the next entry.\n   *\n   * @private\n   */\nMaterialSnackbar.prototype.checkQueue_ = function () {\n    if (this.queuedNotifications_.length > 0) {\n        this.showSnackbar(this.queuedNotifications_.shift());\n    }\n};\n/**\n   * Cleanup the snackbar event listeners and accessiblity attributes.\n   *\n   * @private\n   */\nMaterialSnackbar.prototype.cleanup_ = function () {\n    this.element_.classList.remove(this.cssClasses_.ACTIVE);\n    setTimeout(function () {\n        this.element_.setAttribute('aria-hidden', 'true');\n        this.textElement_.textContent = '';\n        if (!Boolean(this.actionElement_.getAttribute('aria-hidden'))) {\n            this.setActionHidden_(true);\n            this.actionElement_.textContent = '';\n            this.actionElement_.removeEventListener('click', this.actionHandler_);\n        }\n        this.actionHandler_ = undefined;\n        this.message_ = undefined;\n        this.actionText_ = undefined;\n        this.active = false;\n        this.checkQueue_();\n    }.bind(this), this.Constant_.ANIMATION_LENGTH);\n};\n/**\n   * Set the action handler hidden state.\n   *\n   * @param {boolean} value\n   * @private\n   */\nMaterialSnackbar.prototype.setActionHidden_ = function (value) {\n    if (value) {\n        this.actionElement_.setAttribute('aria-hidden', 'true');\n    } else {\n        this.actionElement_.removeAttribute('aria-hidden');\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialSnackbar,\n    classAsString: 'MaterialSnackbar',\n    cssClass: 'mdl-js-snackbar',\n    widget: true\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Spinner MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @param {HTMLElement} element The element that will be upgraded.\n   * @constructor\n   */\nvar MaterialSpinner = function MaterialSpinner(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialSpinner'] = MaterialSpinner;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialSpinner.prototype.Constant_ = { MDL_SPINNER_LAYER_COUNT: 4 };\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialSpinner.prototype.CssClasses_ = {\n    MDL_SPINNER_LAYER: 'mdl-spinner__layer',\n    MDL_SPINNER_CIRCLE_CLIPPER: 'mdl-spinner__circle-clipper',\n    MDL_SPINNER_CIRCLE: 'mdl-spinner__circle',\n    MDL_SPINNER_GAP_PATCH: 'mdl-spinner__gap-patch',\n    MDL_SPINNER_LEFT: 'mdl-spinner__left',\n    MDL_SPINNER_RIGHT: 'mdl-spinner__right'\n};\n/**\n   * Auxiliary method to create a spinner layer.\n   *\n   * @param {number} index Index of the layer to be created.\n   * @public\n   */\nMaterialSpinner.prototype.createLayer = function (index) {\n    var layer = document.createElement('div');\n    layer.classList.add(this.CssClasses_.MDL_SPINNER_LAYER);\n    layer.classList.add(this.CssClasses_.MDL_SPINNER_LAYER + '-' + index);\n    var leftClipper = document.createElement('div');\n    leftClipper.classList.add(this.CssClasses_.MDL_SPINNER_CIRCLE_CLIPPER);\n    leftClipper.classList.add(this.CssClasses_.MDL_SPINNER_LEFT);\n    var gapPatch = document.createElement('div');\n    gapPatch.classList.add(this.CssClasses_.MDL_SPINNER_GAP_PATCH);\n    var rightClipper = document.createElement('div');\n    rightClipper.classList.add(this.CssClasses_.MDL_SPINNER_CIRCLE_CLIPPER);\n    rightClipper.classList.add(this.CssClasses_.MDL_SPINNER_RIGHT);\n    var circleOwners = [\n        leftClipper,\n        gapPatch,\n        rightClipper\n    ];\n    for (var i = 0; i < circleOwners.length; i++) {\n        var circle = document.createElement('div');\n        circle.classList.add(this.CssClasses_.MDL_SPINNER_CIRCLE);\n        circleOwners[i].appendChild(circle);\n    }\n    layer.appendChild(leftClipper);\n    layer.appendChild(gapPatch);\n    layer.appendChild(rightClipper);\n    this.element_.appendChild(layer);\n};\nMaterialSpinner.prototype['createLayer'] = MaterialSpinner.prototype.createLayer;\n/**\n   * Stops the spinner animation.\n   * Public method for users who need to stop the spinner for any reason.\n   *\n   * @public\n   */\nMaterialSpinner.prototype.stop = function () {\n    this.element_.classList.remove('is-active');\n};\nMaterialSpinner.prototype['stop'] = MaterialSpinner.prototype.stop;\n/**\n   * Starts the spinner animation.\n   * Public method for users who need to manually start the spinner for any reason\n   * (instead of just adding the 'is-active' class to their markup).\n   *\n   * @public\n   */\nMaterialSpinner.prototype.start = function () {\n    this.element_.classList.add('is-active');\n};\nMaterialSpinner.prototype['start'] = MaterialSpinner.prototype.start;\n/**\n   * Initialize element.\n   */\nMaterialSpinner.prototype.init = function () {\n    if (this.element_) {\n        for (var i = 1; i <= this.Constant_.MDL_SPINNER_LAYER_COUNT; i++) {\n            this.createLayer(i);\n        }\n        this.element_.classList.add('is-upgraded');\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialSpinner,\n    classAsString: 'MaterialSpinner',\n    cssClass: 'mdl-js-spinner',\n    widget: true\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Checkbox MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialSwitch = function MaterialSwitch(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialSwitch'] = MaterialSwitch;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialSwitch.prototype.Constant_ = { TINY_TIMEOUT: 0.001 };\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialSwitch.prototype.CssClasses_ = {\n    INPUT: 'mdl-switch__input',\n    TRACK: 'mdl-switch__track',\n    THUMB: 'mdl-switch__thumb',\n    FOCUS_HELPER: 'mdl-switch__focus-helper',\n    RIPPLE_EFFECT: 'mdl-js-ripple-effect',\n    RIPPLE_IGNORE_EVENTS: 'mdl-js-ripple-effect--ignore-events',\n    RIPPLE_CONTAINER: 'mdl-switch__ripple-container',\n    RIPPLE_CENTER: 'mdl-ripple--center',\n    RIPPLE: 'mdl-ripple',\n    IS_FOCUSED: 'is-focused',\n    IS_DISABLED: 'is-disabled',\n    IS_CHECKED: 'is-checked'\n};\n/**\n   * Handle change of state.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialSwitch.prototype.onChange_ = function (event) {\n    this.updateClasses_();\n};\n/**\n   * Handle focus of element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialSwitch.prototype.onFocus_ = function (event) {\n    this.element_.classList.add(this.CssClasses_.IS_FOCUSED);\n};\n/**\n   * Handle lost focus of element.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialSwitch.prototype.onBlur_ = function (event) {\n    this.element_.classList.remove(this.CssClasses_.IS_FOCUSED);\n};\n/**\n   * Handle mouseup.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialSwitch.prototype.onMouseUp_ = function (event) {\n    this.blur_();\n};\n/**\n   * Handle class updates.\n   *\n   * @private\n   */\nMaterialSwitch.prototype.updateClasses_ = function () {\n    this.checkDisabled();\n    this.checkToggleState();\n};\n/**\n   * Add blur.\n   *\n   * @private\n   */\nMaterialSwitch.prototype.blur_ = function () {\n    // TODO: figure out why there's a focus event being fired after our blur,\n    // so that we can avoid this hack.\n    window.setTimeout(function () {\n        this.inputElement_.blur();\n    }.bind(this), this.Constant_.TINY_TIMEOUT);\n};\n// Public methods.\n/**\n   * Check the components disabled state.\n   *\n   * @public\n   */\nMaterialSwitch.prototype.checkDisabled = function () {\n    if (this.inputElement_.disabled) {\n        this.element_.classList.add(this.CssClasses_.IS_DISABLED);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_DISABLED);\n    }\n};\nMaterialSwitch.prototype['checkDisabled'] = MaterialSwitch.prototype.checkDisabled;\n/**\n   * Check the components toggled state.\n   *\n   * @public\n   */\nMaterialSwitch.prototype.checkToggleState = function () {\n    if (this.inputElement_.checked) {\n        this.element_.classList.add(this.CssClasses_.IS_CHECKED);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_CHECKED);\n    }\n};\nMaterialSwitch.prototype['checkToggleState'] = MaterialSwitch.prototype.checkToggleState;\n/**\n   * Disable switch.\n   *\n   * @public\n   */\nMaterialSwitch.prototype.disable = function () {\n    this.inputElement_.disabled = true;\n    this.updateClasses_();\n};\nMaterialSwitch.prototype['disable'] = MaterialSwitch.prototype.disable;\n/**\n   * Enable switch.\n   *\n   * @public\n   */\nMaterialSwitch.prototype.enable = function () {\n    this.inputElement_.disabled = false;\n    this.updateClasses_();\n};\nMaterialSwitch.prototype['enable'] = MaterialSwitch.prototype.enable;\n/**\n   * Activate switch.\n   *\n   * @public\n   */\nMaterialSwitch.prototype.on = function () {\n    this.inputElement_.checked = true;\n    this.updateClasses_();\n};\nMaterialSwitch.prototype['on'] = MaterialSwitch.prototype.on;\n/**\n   * Deactivate switch.\n   *\n   * @public\n   */\nMaterialSwitch.prototype.off = function () {\n    this.inputElement_.checked = false;\n    this.updateClasses_();\n};\nMaterialSwitch.prototype['off'] = MaterialSwitch.prototype.off;\n/**\n   * Initialize element.\n   */\nMaterialSwitch.prototype.init = function () {\n    if (this.element_) {\n        this.inputElement_ = this.element_.querySelector('.' + this.CssClasses_.INPUT);\n        var track = document.createElement('div');\n        track.classList.add(this.CssClasses_.TRACK);\n        var thumb = document.createElement('div');\n        thumb.classList.add(this.CssClasses_.THUMB);\n        var focusHelper = document.createElement('span');\n        focusHelper.classList.add(this.CssClasses_.FOCUS_HELPER);\n        thumb.appendChild(focusHelper);\n        this.element_.appendChild(track);\n        this.element_.appendChild(thumb);\n        this.boundMouseUpHandler = this.onMouseUp_.bind(this);\n        if (this.element_.classList.contains(this.CssClasses_.RIPPLE_EFFECT)) {\n            this.element_.classList.add(this.CssClasses_.RIPPLE_IGNORE_EVENTS);\n            this.rippleContainerElement_ = document.createElement('span');\n            this.rippleContainerElement_.classList.add(this.CssClasses_.RIPPLE_CONTAINER);\n            this.rippleContainerElement_.classList.add(this.CssClasses_.RIPPLE_EFFECT);\n            this.rippleContainerElement_.classList.add(this.CssClasses_.RIPPLE_CENTER);\n            this.rippleContainerElement_.addEventListener('mouseup', this.boundMouseUpHandler);\n            var ripple = document.createElement('span');\n            ripple.classList.add(this.CssClasses_.RIPPLE);\n            this.rippleContainerElement_.appendChild(ripple);\n            this.element_.appendChild(this.rippleContainerElement_);\n        }\n        this.boundChangeHandler = this.onChange_.bind(this);\n        this.boundFocusHandler = this.onFocus_.bind(this);\n        this.boundBlurHandler = this.onBlur_.bind(this);\n        this.inputElement_.addEventListener('change', this.boundChangeHandler);\n        this.inputElement_.addEventListener('focus', this.boundFocusHandler);\n        this.inputElement_.addEventListener('blur', this.boundBlurHandler);\n        this.element_.addEventListener('mouseup', this.boundMouseUpHandler);\n        this.updateClasses_();\n        this.element_.classList.add('is-upgraded');\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialSwitch,\n    classAsString: 'MaterialSwitch',\n    cssClass: 'mdl-js-switch',\n    widget: true\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Textfield MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialTextfield = function MaterialTextfield(element) {\n    this.element_ = element;\n    this.maxRows = this.Constant_.NO_MAX_ROWS;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialTextfield'] = MaterialTextfield;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialTextfield.prototype.Constant_ = {\n    NO_MAX_ROWS: -1,\n    MAX_ROWS_ATTRIBUTE: 'maxrows'\n};\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialTextfield.prototype.CssClasses_ = {\n    LABEL: 'mdl-textfield__label',\n    INPUT: 'mdl-textfield__input',\n    IS_DIRTY: 'is-dirty',\n    IS_FOCUSED: 'is-focused',\n    IS_DISABLED: 'is-disabled',\n    IS_INVALID: 'is-invalid',\n    IS_UPGRADED: 'is-upgraded',\n    HAS_PLACEHOLDER: 'has-placeholder'\n};\n/**\n   * Handle input being entered.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialTextfield.prototype.onKeyDown_ = function (event) {\n    var currentRowCount = event.target.value.split('\\n').length;\n    if (event.keyCode === 13) {\n        if (currentRowCount >= this.maxRows) {\n            event.preventDefault();\n        }\n    }\n};\n/**\n   * Handle focus.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialTextfield.prototype.onFocus_ = function (event) {\n    this.element_.classList.add(this.CssClasses_.IS_FOCUSED);\n};\n/**\n   * Handle lost focus.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialTextfield.prototype.onBlur_ = function (event) {\n    this.element_.classList.remove(this.CssClasses_.IS_FOCUSED);\n};\n/**\n   * Handle reset event from out side.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialTextfield.prototype.onReset_ = function (event) {\n    this.updateClasses_();\n};\n/**\n   * Handle class updates.\n   *\n   * @private\n   */\nMaterialTextfield.prototype.updateClasses_ = function () {\n    this.checkDisabled();\n    this.checkValidity();\n    this.checkDirty();\n    this.checkFocus();\n};\n// Public methods.\n/**\n   * Check the disabled state and update field accordingly.\n   *\n   * @public\n   */\nMaterialTextfield.prototype.checkDisabled = function () {\n    if (this.input_.disabled) {\n        this.element_.classList.add(this.CssClasses_.IS_DISABLED);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_DISABLED);\n    }\n};\nMaterialTextfield.prototype['checkDisabled'] = MaterialTextfield.prototype.checkDisabled;\n/**\n  * Check the focus state and update field accordingly.\n  *\n  * @public\n  */\nMaterialTextfield.prototype.checkFocus = function () {\n    if (Boolean(this.element_.querySelector(':focus'))) {\n        this.element_.classList.add(this.CssClasses_.IS_FOCUSED);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_FOCUSED);\n    }\n};\nMaterialTextfield.prototype['checkFocus'] = MaterialTextfield.prototype.checkFocus;\n/**\n   * Check the validity state and update field accordingly.\n   *\n   * @public\n   */\nMaterialTextfield.prototype.checkValidity = function () {\n    if (this.input_.validity) {\n        if (this.input_.validity.valid) {\n            this.element_.classList.remove(this.CssClasses_.IS_INVALID);\n        } else {\n            this.element_.classList.add(this.CssClasses_.IS_INVALID);\n        }\n    }\n};\nMaterialTextfield.prototype['checkValidity'] = MaterialTextfield.prototype.checkValidity;\n/**\n   * Check the dirty state and update field accordingly.\n   *\n   * @public\n   */\nMaterialTextfield.prototype.checkDirty = function () {\n    if (this.input_.value && this.input_.value.length > 0) {\n        this.element_.classList.add(this.CssClasses_.IS_DIRTY);\n    } else {\n        this.element_.classList.remove(this.CssClasses_.IS_DIRTY);\n    }\n};\nMaterialTextfield.prototype['checkDirty'] = MaterialTextfield.prototype.checkDirty;\n/**\n   * Disable text field.\n   *\n   * @public\n   */\nMaterialTextfield.prototype.disable = function () {\n    this.input_.disabled = true;\n    this.updateClasses_();\n};\nMaterialTextfield.prototype['disable'] = MaterialTextfield.prototype.disable;\n/**\n   * Enable text field.\n   *\n   * @public\n   */\nMaterialTextfield.prototype.enable = function () {\n    this.input_.disabled = false;\n    this.updateClasses_();\n};\nMaterialTextfield.prototype['enable'] = MaterialTextfield.prototype.enable;\n/**\n   * Update text field value.\n   *\n   * @param {string} value The value to which to set the control (optional).\n   * @public\n   */\nMaterialTextfield.prototype.change = function (value) {\n    this.input_.value = value || '';\n    this.updateClasses_();\n};\nMaterialTextfield.prototype['change'] = MaterialTextfield.prototype.change;\n/**\n   * Initialize element.\n   */\nMaterialTextfield.prototype.init = function () {\n    if (this.element_) {\n        this.label_ = this.element_.querySelector('.' + this.CssClasses_.LABEL);\n        this.input_ = this.element_.querySelector('.' + this.CssClasses_.INPUT);\n        if (this.input_) {\n            if (this.input_.hasAttribute(this.Constant_.MAX_ROWS_ATTRIBUTE)) {\n                this.maxRows = parseInt(this.input_.getAttribute(this.Constant_.MAX_ROWS_ATTRIBUTE), 10);\n                if (isNaN(this.maxRows)) {\n                    this.maxRows = this.Constant_.NO_MAX_ROWS;\n                }\n            }\n            if (this.input_.hasAttribute('placeholder')) {\n                this.element_.classList.add(this.CssClasses_.HAS_PLACEHOLDER);\n            }\n            this.boundUpdateClassesHandler = this.updateClasses_.bind(this);\n            this.boundFocusHandler = this.onFocus_.bind(this);\n            this.boundBlurHandler = this.onBlur_.bind(this);\n            this.boundResetHandler = this.onReset_.bind(this);\n            this.input_.addEventListener('input', this.boundUpdateClassesHandler);\n            this.input_.addEventListener('focus', this.boundFocusHandler);\n            this.input_.addEventListener('blur', this.boundBlurHandler);\n            this.input_.addEventListener('reset', this.boundResetHandler);\n            if (this.maxRows !== this.Constant_.NO_MAX_ROWS) {\n                // TODO: This should handle pasting multi line text.\n                // Currently doesn't.\n                this.boundKeyDownHandler = this.onKeyDown_.bind(this);\n                this.input_.addEventListener('keydown', this.boundKeyDownHandler);\n            }\n            var invalid = this.element_.classList.contains(this.CssClasses_.IS_INVALID);\n            this.updateClasses_();\n            this.element_.classList.add(this.CssClasses_.IS_UPGRADED);\n            if (invalid) {\n                this.element_.classList.add(this.CssClasses_.IS_INVALID);\n            }\n            if (this.input_.hasAttribute('autofocus')) {\n                this.element_.focus();\n                this.checkFocus();\n            }\n        }\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialTextfield,\n    classAsString: 'MaterialTextfield',\n    cssClass: 'mdl-js-textfield',\n    widget: true\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Tooltip MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {HTMLElement} element The element that will be upgraded.\n   */\nvar MaterialTooltip = function MaterialTooltip(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialTooltip'] = MaterialTooltip;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialTooltip.prototype.Constant_ = {};\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialTooltip.prototype.CssClasses_ = {\n    IS_ACTIVE: 'is-active',\n    BOTTOM: 'mdl-tooltip--bottom',\n    LEFT: 'mdl-tooltip--left',\n    RIGHT: 'mdl-tooltip--right',\n    TOP: 'mdl-tooltip--top'\n};\n/**\n   * Handle mouseenter for tooltip.\n   *\n   * @param {Event} event The event that fired.\n   * @private\n   */\nMaterialTooltip.prototype.handleMouseEnter_ = function (event) {\n    var props = event.target.getBoundingClientRect();\n    var left = props.left + props.width / 2;\n    var top = props.top + props.height / 2;\n    var marginLeft = -1 * (this.element_.offsetWidth / 2);\n    var marginTop = -1 * (this.element_.offsetHeight / 2);\n    if (this.element_.classList.contains(this.CssClasses_.LEFT) || this.element_.classList.contains(this.CssClasses_.RIGHT)) {\n        left = props.width / 2;\n        if (top + marginTop < 0) {\n            this.element_.style.top = '0';\n            this.element_.style.marginTop = '0';\n        } else {\n            this.element_.style.top = top + 'px';\n            this.element_.style.marginTop = marginTop + 'px';\n        }\n    } else {\n        if (left + marginLeft < 0) {\n            this.element_.style.left = '0';\n            this.element_.style.marginLeft = '0';\n        } else {\n            this.element_.style.left = left + 'px';\n            this.element_.style.marginLeft = marginLeft + 'px';\n        }\n    }\n    if (this.element_.classList.contains(this.CssClasses_.TOP)) {\n        this.element_.style.top = props.top - this.element_.offsetHeight - 10 + 'px';\n    } else if (this.element_.classList.contains(this.CssClasses_.RIGHT)) {\n        this.element_.style.left = props.left + props.width + 10 + 'px';\n    } else if (this.element_.classList.contains(this.CssClasses_.LEFT)) {\n        this.element_.style.left = props.left - this.element_.offsetWidth - 10 + 'px';\n    } else {\n        this.element_.style.top = props.top + props.height + 10 + 'px';\n    }\n    this.element_.classList.add(this.CssClasses_.IS_ACTIVE);\n};\n/**\n   * Hide tooltip on mouseleave or scroll\n   *\n   * @private\n   */\nMaterialTooltip.prototype.hideTooltip_ = function () {\n    this.element_.classList.remove(this.CssClasses_.IS_ACTIVE);\n};\n/**\n   * Initialize element.\n   */\nMaterialTooltip.prototype.init = function () {\n    if (this.element_) {\n        var forElId = this.element_.getAttribute('for') || this.element_.getAttribute('data-mdl-for');\n        if (forElId) {\n            this.forElement_ = document.getElementById(forElId);\n        }\n        if (this.forElement_) {\n            // It's left here because it prevents accidental text selection on Android\n            if (!this.forElement_.hasAttribute('tabindex')) {\n                this.forElement_.setAttribute('tabindex', '0');\n            }\n            this.boundMouseEnterHandler = this.handleMouseEnter_.bind(this);\n            this.boundMouseLeaveAndScrollHandler = this.hideTooltip_.bind(this);\n            this.forElement_.addEventListener('mouseenter', this.boundMouseEnterHandler, false);\n            this.forElement_.addEventListener('touchend', this.boundMouseEnterHandler, false);\n            this.forElement_.addEventListener('mouseleave', this.boundMouseLeaveAndScrollHandler, false);\n            window.addEventListener('scroll', this.boundMouseLeaveAndScrollHandler, true);\n            window.addEventListener('touchstart', this.boundMouseLeaveAndScrollHandler);\n        }\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialTooltip,\n    classAsString: 'MaterialTooltip',\n    cssClass: 'mdl-tooltip'\n});","/**\n * @license\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n   * Class constructor for Data Table Card MDL component.\n   * Implements MDL component design pattern defined at:\n   * https://github.com/jasonmayes/mdl-component-design-pattern\n   *\n   * @constructor\n   * @param {Element} element The element that will be upgraded.\n   */\nvar MaterialDataTable = function MaterialDataTable(element) {\n    this.element_ = element;\n    // Initialize instance.\n    this.init();\n};\nwindow['MaterialDataTable'] = MaterialDataTable;\n/**\n   * Store constants in one place so they can be updated easily.\n   *\n   * @enum {string | number}\n   * @private\n   */\nMaterialDataTable.prototype.Constant_ = {};\n/**\n   * Store strings for class names defined by this component that are used in\n   * JavaScript. This allows us to simply change it in one place should we\n   * decide to modify at a later date.\n   *\n   * @enum {string}\n   * @private\n   */\nMaterialDataTable.prototype.CssClasses_ = {\n    DATA_TABLE: 'mdl-data-table',\n    SELECTABLE: 'mdl-data-table--selectable',\n    SELECT_ELEMENT: 'mdl-data-table__select',\n    IS_SELECTED: 'is-selected',\n    IS_UPGRADED: 'is-upgraded'\n};\n/**\n   * Generates and returns a function that toggles the selection state of a\n   * single row (or multiple rows).\n   *\n   * @param {Element} checkbox Checkbox that toggles the selection state.\n   * @param {Element} row Row to toggle when checkbox changes.\n   * @param {(Array<Object>|NodeList)=} opt_rows Rows to toggle when checkbox changes.\n   * @private\n   */\nMaterialDataTable.prototype.selectRow_ = function (checkbox, row, opt_rows) {\n    if (row) {\n        return function () {\n            if (checkbox.checked) {\n                row.classList.add(this.CssClasses_.IS_SELECTED);\n            } else {\n                row.classList.remove(this.CssClasses_.IS_SELECTED);\n            }\n        }.bind(this);\n    }\n    if (opt_rows) {\n        return function () {\n            var i;\n            var el;\n            if (checkbox.checked) {\n                for (i = 0; i < opt_rows.length; i++) {\n                    el = opt_rows[i].querySelector('td').querySelector('.mdl-checkbox');\n                    el['MaterialCheckbox'].check();\n                    opt_rows[i].classList.add(this.CssClasses_.IS_SELECTED);\n                }\n            } else {\n                for (i = 0; i < opt_rows.length; i++) {\n                    el = opt_rows[i].querySelector('td').querySelector('.mdl-checkbox');\n                    el['MaterialCheckbox'].uncheck();\n                    opt_rows[i].classList.remove(this.CssClasses_.IS_SELECTED);\n                }\n            }\n        }.bind(this);\n    }\n};\n/**\n   * Creates a checkbox for a single or or multiple rows and hooks up the\n   * event handling.\n   *\n   * @param {Element} row Row to toggle when checkbox changes.\n   * @param {(Array<Object>|NodeList)=} opt_rows Rows to toggle when checkbox changes.\n   * @private\n   */\nMaterialDataTable.prototype.createCheckbox_ = function (row, opt_rows) {\n    var label = document.createElement('label');\n    var labelClasses = [\n        'mdl-checkbox',\n        'mdl-js-checkbox',\n        'mdl-js-ripple-effect',\n        this.CssClasses_.SELECT_ELEMENT\n    ];\n    label.className = labelClasses.join(' ');\n    var checkbox = document.createElement('input');\n    checkbox.type = 'checkbox';\n    checkbox.classList.add('mdl-checkbox__input');\n    if (row) {\n        checkbox.checked = row.classList.contains(this.CssClasses_.IS_SELECTED);\n        checkbox.addEventListener('change', this.selectRow_(checkbox, row));\n    } else if (opt_rows) {\n        checkbox.addEventListener('change', this.selectRow_(checkbox, null, opt_rows));\n    }\n    label.appendChild(checkbox);\n    componentHandler.upgradeElement(label, 'MaterialCheckbox');\n    return label;\n};\n/**\n   * Initialize element.\n   */\nMaterialDataTable.prototype.init = function () {\n    if (this.element_) {\n        var firstHeader = this.element_.querySelector('th');\n        var bodyRows = Array.prototype.slice.call(this.element_.querySelectorAll('tbody tr'));\n        var footRows = Array.prototype.slice.call(this.element_.querySelectorAll('tfoot tr'));\n        var rows = bodyRows.concat(footRows);\n        if (this.element_.classList.contains(this.CssClasses_.SELECTABLE)) {\n            var th = document.createElement('th');\n            var headerCheckbox = this.createCheckbox_(null, rows);\n            th.appendChild(headerCheckbox);\n            firstHeader.parentElement.insertBefore(th, firstHeader);\n            for (var i = 0; i < rows.length; i++) {\n                var firstCell = rows[i].querySelector('td');\n                if (firstCell) {\n                    var td = document.createElement('td');\n                    if (rows[i].parentNode.nodeName.toUpperCase() === 'TBODY') {\n                        var rowCheckbox = this.createCheckbox_(rows[i]);\n                        td.appendChild(rowCheckbox);\n                    }\n                    rows[i].insertBefore(td, firstCell);\n                }\n            }\n            this.element_.classList.add(this.CssClasses_.IS_UPGRADED);\n        }\n    }\n};\n// The component registers itself. It can assume componentHandler is available\n// in the global scope.\ncomponentHandler.register({\n    constructor: MaterialDataTable,\n    classAsString: 'MaterialDataTable',\n    cssClass: 'mdl-js-data-table'\n});","// https://github.com/zloirock/core-js/issues/86#issuecomment-115759028\nvar global = module.exports = typeof window != 'undefined' && window.Math == Math\n  ? window : typeof self != 'undefined' && self.Math == Math ? self\n  // eslint-disable-next-line no-new-func\n  : Function('return this')();\nif (typeof __g == 'number') __g = global; // eslint-disable-line no-undef\n","var hasOwnProperty = {}.hasOwnProperty;\nmodule.exports = function (it, key) {\n  return hasOwnProperty.call(it, key);\n};\n","module.exports = function (exec) {\n  try {\n    return !!exec();\n  } catch (e) {\n    return true;\n  }\n};\n","// Thank's IE8 for his funny defineProperty\nmodule.exports = !require('./_fails')(function () {\n  return Object.defineProperty({}, 'a', { get: function () { return 7; } }).a != 7;\n});\n","var core = module.exports = { version: '2.6.9' };\nif (typeof __e == 'number') __e = core; // eslint-disable-line no-undef\n","module.exports = function (it) {\n  return typeof it === 'object' ? it !== null : typeof it === 'function';\n};\n","var isObject = require('./_is-object');\nmodule.exports = function (it) {\n  if (!isObject(it)) throw TypeError(it + ' is not an object!');\n  return it;\n};\n","var isObject = require('./_is-object');\nvar document = require('./_global').document;\n// typeof document.createElement is 'object' in old IE\nvar is = isObject(document) && isObject(document.createElement);\nmodule.exports = function (it) {\n  return is ? document.createElement(it) : {};\n};\n","module.exports = !require('./_descriptors') && !require('./_fails')(function () {\n  return Object.defineProperty(require('./_dom-create')('div'), 'a', { get: function () { return 7; } }).a != 7;\n});\n","// 7.1.1 ToPrimitive(input [, PreferredType])\nvar isObject = require('./_is-object');\n// instead of the ES6 spec version, we didn't implement @@toPrimitive case\n// and the second argument - flag - preferred type is a string\nmodule.exports = function (it, S) {\n  if (!isObject(it)) return it;\n  var fn, val;\n  if (S && typeof (fn = it.toString) == 'function' && !isObject(val = fn.call(it))) return val;\n  if (typeof (fn = it.valueOf) == 'function' && !isObject(val = fn.call(it))) return val;\n  if (!S && typeof (fn = it.toString) == 'function' && !isObject(val = fn.call(it))) return val;\n  throw TypeError(\"Can't convert object to primitive value\");\n};\n","var anObject = require('./_an-object');\nvar IE8_DOM_DEFINE = require('./_ie8-dom-define');\nvar toPrimitive = require('./_to-primitive');\nvar dP = Object.defineProperty;\n\nexports.f = require('./_descriptors') ? Object.defineProperty : function defineProperty(O, P, Attributes) {\n  anObject(O);\n  P = toPrimitive(P, true);\n  anObject(Attributes);\n  if (IE8_DOM_DEFINE) try {\n    return dP(O, P, Attributes);\n  } catch (e) { /* empty */ }\n  if ('get' in Attributes || 'set' in Attributes) throw TypeError('Accessors not supported!');\n  if ('value' in Attributes) O[P] = Attributes.value;\n  return O;\n};\n","module.exports = function (bitmap, value) {\n  return {\n    enumerable: !(bitmap & 1),\n    configurable: !(bitmap & 2),\n    writable: !(bitmap & 4),\n    value: value\n  };\n};\n","var dP = require('./_object-dp');\nvar createDesc = require('./_property-desc');\nmodule.exports = require('./_descriptors') ? function (object, key, value) {\n  return dP.f(object, key, createDesc(1, value));\n} : function (object, key, value) {\n  object[key] = value;\n  return object;\n};\n","var id = 0;\nvar px = Math.random();\nmodule.exports = function (key) {\n  return 'Symbol('.concat(key === undefined ? '' : key, ')_', (++id + px).toString(36));\n};\n","module.exports = false;\n","var core = require('./_core');\nvar global = require('./_global');\nvar SHARED = '__core-js_shared__';\nvar store = global[SHARED] || (global[SHARED] = {});\n\n(module.exports = function (key, value) {\n  return store[key] || (store[key] = value !== undefined ? value : {});\n})('versions', []).push({\n  version: core.version,\n  mode: require('./_library') ? 'pure' : 'global',\n  copyright: '© 2019 Denis Pushkarev (zloirock.ru)'\n});\n","module.exports = require('./_shared')('native-function-to-string', Function.toString);\n","var global = require('./_global');\nvar hide = require('./_hide');\nvar has = require('./_has');\nvar SRC = require('./_uid')('src');\nvar $toString = require('./_function-to-string');\nvar TO_STRING = 'toString';\nvar TPL = ('' + $toString).split(TO_STRING);\n\nrequire('./_core').inspectSource = function (it) {\n  return $toString.call(it);\n};\n\n(module.exports = function (O, key, val, safe) {\n  var isFunction = typeof val == 'function';\n  if (isFunction) has(val, 'name') || hide(val, 'name', key);\n  if (O[key] === val) return;\n  if (isFunction) has(val, SRC) || hide(val, SRC, O[key] ? '' + O[key] : TPL.join(String(key)));\n  if (O === global) {\n    O[key] = val;\n  } else if (!safe) {\n    delete O[key];\n    hide(O, key, val);\n  } else if (O[key]) {\n    O[key] = val;\n  } else {\n    hide(O, key, val);\n  }\n// add fake Function#toString for correct work wrapped methods / constructors with methods like LoDash isNative\n})(Function.prototype, TO_STRING, function toString() {\n  return typeof this == 'function' && this[SRC] || $toString.call(this);\n});\n","module.exports = function (it) {\n  if (typeof it != 'function') throw TypeError(it + ' is not a function!');\n  return it;\n};\n","// optional / simple context binding\nvar aFunction = require('./_a-function');\nmodule.exports = function (fn, that, length) {\n  aFunction(fn);\n  if (that === undefined) return fn;\n  switch (length) {\n    case 1: return function (a) {\n      return fn.call(that, a);\n    };\n    case 2: return function (a, b) {\n      return fn.call(that, a, b);\n    };\n    case 3: return function (a, b, c) {\n      return fn.call(that, a, b, c);\n    };\n  }\n  return function (/* ...args */) {\n    return fn.apply(that, arguments);\n  };\n};\n","var global = require('./_global');\nvar core = require('./_core');\nvar hide = require('./_hide');\nvar redefine = require('./_redefine');\nvar ctx = require('./_ctx');\nvar PROTOTYPE = 'prototype';\n\nvar $export = function (type, name, source) {\n  var IS_FORCED = type & $export.F;\n  var IS_GLOBAL = type & $export.G;\n  var IS_STATIC = type & $export.S;\n  var IS_PROTO = type & $export.P;\n  var IS_BIND = type & $export.B;\n  var target = IS_GLOBAL ? global : IS_STATIC ? global[name] || (global[name] = {}) : (global[name] || {})[PROTOTYPE];\n  var exports = IS_GLOBAL ? core : core[name] || (core[name] = {});\n  var expProto = exports[PROTOTYPE] || (exports[PROTOTYPE] = {});\n  var key, own, out, exp;\n  if (IS_GLOBAL) source = name;\n  for (key in source) {\n    // contains in native\n    own = !IS_FORCED && target && target[key] !== undefined;\n    // export native or passed\n    out = (own ? target : source)[key];\n    // bind timers to global for call from export context\n    exp = IS_BIND && own ? ctx(out, global) : IS_PROTO && typeof out == 'function' ? ctx(Function.call, out) : out;\n    // extend global\n    if (target) redefine(target, key, out, type & $export.U);\n    // export\n    if (exports[key] != out) hide(exports, key, exp);\n    if (IS_PROTO && expProto[key] != out) expProto[key] = out;\n  }\n};\nglobal.core = core;\n// type bitmap\n$export.F = 1;   // forced\n$export.G = 2;   // global\n$export.S = 4;   // static\n$export.P = 8;   // proto\n$export.B = 16;  // bind\n$export.W = 32;  // wrap\n$export.U = 64;  // safe\n$export.R = 128; // real proto method for `library`\nmodule.exports = $export;\n","var META = require('./_uid')('meta');\nvar isObject = require('./_is-object');\nvar has = require('./_has');\nvar setDesc = require('./_object-dp').f;\nvar id = 0;\nvar isExtensible = Object.isExtensible || function () {\n  return true;\n};\nvar FREEZE = !require('./_fails')(function () {\n  return isExtensible(Object.preventExtensions({}));\n});\nvar setMeta = function (it) {\n  setDesc(it, META, { value: {\n    i: 'O' + ++id, // object ID\n    w: {}          // weak collections IDs\n  } });\n};\nvar fastKey = function (it, create) {\n  // return primitive with prefix\n  if (!isObject(it)) return typeof it == 'symbol' ? it : (typeof it == 'string' ? 'S' : 'P') + it;\n  if (!has(it, META)) {\n    // can't set metadata to uncaught frozen object\n    if (!isExtensible(it)) return 'F';\n    // not necessary to add metadata\n    if (!create) return 'E';\n    // add missing metadata\n    setMeta(it);\n  // return object ID\n  } return it[META].i;\n};\nvar getWeak = function (it, create) {\n  if (!has(it, META)) {\n    // can't set metadata to uncaught frozen object\n    if (!isExtensible(it)) return true;\n    // not necessary to add metadata\n    if (!create) return false;\n    // add missing metadata\n    setMeta(it);\n  // return hash weak collections IDs\n  } return it[META].w;\n};\n// add metadata on freeze-family methods calling\nvar onFreeze = function (it) {\n  if (FREEZE && meta.NEED && isExtensible(it) && !has(it, META)) setMeta(it);\n  return it;\n};\nvar meta = module.exports = {\n  KEY: META,\n  NEED: false,\n  fastKey: fastKey,\n  getWeak: getWeak,\n  onFreeze: onFreeze\n};\n","var store = require('./_shared')('wks');\nvar uid = require('./_uid');\nvar Symbol = require('./_global').Symbol;\nvar USE_SYMBOL = typeof Symbol == 'function';\n\nvar $exports = module.exports = function (name) {\n  return store[name] || (store[name] =\n    USE_SYMBOL && Symbol[name] || (USE_SYMBOL ? Symbol : uid)('Symbol.' + name));\n};\n\n$exports.store = store;\n","var def = require('./_object-dp').f;\nvar has = require('./_has');\nvar TAG = require('./_wks')('toStringTag');\n\nmodule.exports = function (it, tag, stat) {\n  if (it && !has(it = stat ? it : it.prototype, TAG)) def(it, TAG, { configurable: true, value: tag });\n};\n","exports.f = require('./_wks');\n","var global = require('./_global');\nvar core = require('./_core');\nvar LIBRARY = require('./_library');\nvar wksExt = require('./_wks-ext');\nvar defineProperty = require('./_object-dp').f;\nmodule.exports = function (name) {\n  var $Symbol = core.Symbol || (core.Symbol = LIBRARY ? {} : global.Symbol || {});\n  if (name.charAt(0) != '_' && !(name in $Symbol)) defineProperty($Symbol, name, { value: wksExt.f(name) });\n};\n","var toString = {}.toString;\n\nmodule.exports = function (it) {\n  return toString.call(it).slice(8, -1);\n};\n","// fallback for non-array-like ES3 and non-enumerable old V8 strings\nvar cof = require('./_cof');\n// eslint-disable-next-line no-prototype-builtins\nmodule.exports = Object('z').propertyIsEnumerable(0) ? Object : function (it) {\n  return cof(it) == 'String' ? it.split('') : Object(it);\n};\n","// 7.2.1 RequireObjectCoercible(argument)\nmodule.exports = function (it) {\n  if (it == undefined) throw TypeError(\"Can't call method on  \" + it);\n  return it;\n};\n","// to indexed object, toObject with fallback for non-array-like ES3 strings\nvar IObject = require('./_iobject');\nvar defined = require('./_defined');\nmodule.exports = function (it) {\n  return IObject(defined(it));\n};\n","// 7.1.4 ToInteger\nvar ceil = Math.ceil;\nvar floor = Math.floor;\nmodule.exports = function (it) {\n  return isNaN(it = +it) ? 0 : (it > 0 ? floor : ceil)(it);\n};\n","// 7.1.15 ToLength\nvar toInteger = require('./_to-integer');\nvar min = Math.min;\nmodule.exports = function (it) {\n  return it > 0 ? min(toInteger(it), 0x1fffffffffffff) : 0; // pow(2, 53) - 1 == 9007199254740991\n};\n","var toInteger = require('./_to-integer');\nvar max = Math.max;\nvar min = Math.min;\nmodule.exports = function (index, length) {\n  index = toInteger(index);\n  return index < 0 ? max(index + length, 0) : min(index, length);\n};\n","// false -> Array#indexOf\n// true  -> Array#includes\nvar toIObject = require('./_to-iobject');\nvar toLength = require('./_to-length');\nvar toAbsoluteIndex = require('./_to-absolute-index');\nmodule.exports = function (IS_INCLUDES) {\n  return function ($this, el, fromIndex) {\n    var O = toIObject($this);\n    var length = toLength(O.length);\n    var index = toAbsoluteIndex(fromIndex, length);\n    var value;\n    // Array#includes uses SameValueZero equality algorithm\n    // eslint-disable-next-line no-self-compare\n    if (IS_INCLUDES && el != el) while (length > index) {\n      value = O[index++];\n      // eslint-disable-next-line no-self-compare\n      if (value != value) return true;\n    // Array#indexOf ignores holes, Array#includes - not\n    } else for (;length > index; index++) if (IS_INCLUDES || index in O) {\n      if (O[index] === el) return IS_INCLUDES || index || 0;\n    } return !IS_INCLUDES && -1;\n  };\n};\n","var shared = require('./_shared')('keys');\nvar uid = require('./_uid');\nmodule.exports = function (key) {\n  return shared[key] || (shared[key] = uid(key));\n};\n","var has = require('./_has');\nvar toIObject = require('./_to-iobject');\nvar arrayIndexOf = require('./_array-includes')(false);\nvar IE_PROTO = require('./_shared-key')('IE_PROTO');\n\nmodule.exports = function (object, names) {\n  var O = toIObject(object);\n  var i = 0;\n  var result = [];\n  var key;\n  for (key in O) if (key != IE_PROTO) has(O, key) && result.push(key);\n  // Don't enum bug & hidden keys\n  while (names.length > i) if (has(O, key = names[i++])) {\n    ~arrayIndexOf(result, key) || result.push(key);\n  }\n  return result;\n};\n","// IE 8- don't enum bug keys\nmodule.exports = (\n  'constructor,hasOwnProperty,isPrototypeOf,propertyIsEnumerable,toLocaleString,toString,valueOf'\n).split(',');\n","// 19.1.2.14 / 15.2.3.14 Object.keys(O)\nvar $keys = require('./_object-keys-internal');\nvar enumBugKeys = require('./_enum-bug-keys');\n\nmodule.exports = Object.keys || function keys(O) {\n  return $keys(O, enumBugKeys);\n};\n","exports.f = Object.getOwnPropertySymbols;\n","exports.f = {}.propertyIsEnumerable;\n","// all enumerable object keys, includes symbols\nvar getKeys = require('./_object-keys');\nvar gOPS = require('./_object-gops');\nvar pIE = require('./_object-pie');\nmodule.exports = function (it) {\n  var result = getKeys(it);\n  var getSymbols = gOPS.f;\n  if (getSymbols) {\n    var symbols = getSymbols(it);\n    var isEnum = pIE.f;\n    var i = 0;\n    var key;\n    while (symbols.length > i) if (isEnum.call(it, key = symbols[i++])) result.push(key);\n  } return result;\n};\n","// 7.2.2 IsArray(argument)\nvar cof = require('./_cof');\nmodule.exports = Array.isArray || function isArray(arg) {\n  return cof(arg) == 'Array';\n};\n","// 7.1.13 ToObject(argument)\nvar defined = require('./_defined');\nmodule.exports = function (it) {\n  return Object(defined(it));\n};\n","var dP = require('./_object-dp');\nvar anObject = require('./_an-object');\nvar getKeys = require('./_object-keys');\n\nmodule.exports = require('./_descriptors') ? Object.defineProperties : function defineProperties(O, Properties) {\n  anObject(O);\n  var keys = getKeys(Properties);\n  var length = keys.length;\n  var i = 0;\n  var P;\n  while (length > i) dP.f(O, P = keys[i++], Properties[P]);\n  return O;\n};\n","var document = require('./_global').document;\nmodule.exports = document && document.documentElement;\n","// 19.1.2.2 / 15.2.3.5 Object.create(O [, Properties])\nvar anObject = require('./_an-object');\nvar dPs = require('./_object-dps');\nvar enumBugKeys = require('./_enum-bug-keys');\nvar IE_PROTO = require('./_shared-key')('IE_PROTO');\nvar Empty = function () { /* empty */ };\nvar PROTOTYPE = 'prototype';\n\n// Create object with fake `null` prototype: use iframe Object with cleared prototype\nvar createDict = function () {\n  // Thrash, waste and sodomy: IE GC bug\n  var iframe = require('./_dom-create')('iframe');\n  var i = enumBugKeys.length;\n  var lt = '<';\n  var gt = '>';\n  var iframeDocument;\n  iframe.style.display = 'none';\n  require('./_html').appendChild(iframe);\n  iframe.src = 'javascript:'; // eslint-disable-line no-script-url\n  // createDict = iframe.contentWindow.Object;\n  // html.removeChild(iframe);\n  iframeDocument = iframe.contentWindow.document;\n  iframeDocument.open();\n  iframeDocument.write(lt + 'script' + gt + 'document.F=Object' + lt + '/script' + gt);\n  iframeDocument.close();\n  createDict = iframeDocument.F;\n  while (i--) delete createDict[PROTOTYPE][enumBugKeys[i]];\n  return createDict();\n};\n\nmodule.exports = Object.create || function create(O, Properties) {\n  var result;\n  if (O !== null) {\n    Empty[PROTOTYPE] = anObject(O);\n    result = new Empty();\n    Empty[PROTOTYPE] = null;\n    // add \"__proto__\" for Object.getPrototypeOf polyfill\n    result[IE_PROTO] = O;\n  } else result = createDict();\n  return Properties === undefined ? result : dPs(result, Properties);\n};\n","// 19.1.2.7 / 15.2.3.4 Object.getOwnPropertyNames(O)\nvar $keys = require('./_object-keys-internal');\nvar hiddenKeys = require('./_enum-bug-keys').concat('length', 'prototype');\n\nexports.f = Object.getOwnPropertyNames || function getOwnPropertyNames(O) {\n  return $keys(O, hiddenKeys);\n};\n","// fallback for IE11 buggy Object.getOwnPropertyNames with iframe and window\nvar toIObject = require('./_to-iobject');\nvar gOPN = require('./_object-gopn').f;\nvar toString = {}.toString;\n\nvar windowNames = typeof window == 'object' && window && Object.getOwnPropertyNames\n  ? Object.getOwnPropertyNames(window) : [];\n\nvar getWindowNames = function (it) {\n  try {\n    return gOPN(it);\n  } catch (e) {\n    return windowNames.slice();\n  }\n};\n\nmodule.exports.f = function getOwnPropertyNames(it) {\n  return windowNames && toString.call(it) == '[object Window]' ? getWindowNames(it) : gOPN(toIObject(it));\n};\n","var pIE = require('./_object-pie');\nvar createDesc = require('./_property-desc');\nvar toIObject = require('./_to-iobject');\nvar toPrimitive = require('./_to-primitive');\nvar has = require('./_has');\nvar IE8_DOM_DEFINE = require('./_ie8-dom-define');\nvar gOPD = Object.getOwnPropertyDescriptor;\n\nexports.f = require('./_descriptors') ? gOPD : function getOwnPropertyDescriptor(O, P) {\n  O = toIObject(O);\n  P = toPrimitive(P, true);\n  if (IE8_DOM_DEFINE) try {\n    return gOPD(O, P);\n  } catch (e) { /* empty */ }\n  if (has(O, P)) return createDesc(!pIE.f.call(O, P), O[P]);\n};\n","'use strict';\n// ECMAScript 6 symbols shim\nvar global = require('./_global');\nvar has = require('./_has');\nvar DESCRIPTORS = require('./_descriptors');\nvar $export = require('./_export');\nvar redefine = require('./_redefine');\nvar META = require('./_meta').KEY;\nvar $fails = require('./_fails');\nvar shared = require('./_shared');\nvar setToStringTag = require('./_set-to-string-tag');\nvar uid = require('./_uid');\nvar wks = require('./_wks');\nvar wksExt = require('./_wks-ext');\nvar wksDefine = require('./_wks-define');\nvar enumKeys = require('./_enum-keys');\nvar isArray = require('./_is-array');\nvar anObject = require('./_an-object');\nvar isObject = require('./_is-object');\nvar toObject = require('./_to-object');\nvar toIObject = require('./_to-iobject');\nvar toPrimitive = require('./_to-primitive');\nvar createDesc = require('./_property-desc');\nvar _create = require('./_object-create');\nvar gOPNExt = require('./_object-gopn-ext');\nvar $GOPD = require('./_object-gopd');\nvar $GOPS = require('./_object-gops');\nvar $DP = require('./_object-dp');\nvar $keys = require('./_object-keys');\nvar gOPD = $GOPD.f;\nvar dP = $DP.f;\nvar gOPN = gOPNExt.f;\nvar $Symbol = global.Symbol;\nvar $JSON = global.JSON;\nvar _stringify = $JSON && $JSON.stringify;\nvar PROTOTYPE = 'prototype';\nvar HIDDEN = wks('_hidden');\nvar TO_PRIMITIVE = wks('toPrimitive');\nvar isEnum = {}.propertyIsEnumerable;\nvar SymbolRegistry = shared('symbol-registry');\nvar AllSymbols = shared('symbols');\nvar OPSymbols = shared('op-symbols');\nvar ObjectProto = Object[PROTOTYPE];\nvar USE_NATIVE = typeof $Symbol == 'function' && !!$GOPS.f;\nvar QObject = global.QObject;\n// Don't use setters in Qt Script, https://github.com/zloirock/core-js/issues/173\nvar setter = !QObject || !QObject[PROTOTYPE] || !QObject[PROTOTYPE].findChild;\n\n// fallback for old Android, https://code.google.com/p/v8/issues/detail?id=687\nvar setSymbolDesc = DESCRIPTORS && $fails(function () {\n  return _create(dP({}, 'a', {\n    get: function () { return dP(this, 'a', { value: 7 }).a; }\n  })).a != 7;\n}) ? function (it, key, D) {\n  var protoDesc = gOPD(ObjectProto, key);\n  if (protoDesc) delete ObjectProto[key];\n  dP(it, key, D);\n  if (protoDesc && it !== ObjectProto) dP(ObjectProto, key, protoDesc);\n} : dP;\n\nvar wrap = function (tag) {\n  var sym = AllSymbols[tag] = _create($Symbol[PROTOTYPE]);\n  sym._k = tag;\n  return sym;\n};\n\nvar isSymbol = USE_NATIVE && typeof $Symbol.iterator == 'symbol' ? function (it) {\n  return typeof it == 'symbol';\n} : function (it) {\n  return it instanceof $Symbol;\n};\n\nvar $defineProperty = function defineProperty(it, key, D) {\n  if (it === ObjectProto) $defineProperty(OPSymbols, key, D);\n  anObject(it);\n  key = toPrimitive(key, true);\n  anObject(D);\n  if (has(AllSymbols, key)) {\n    if (!D.enumerable) {\n      if (!has(it, HIDDEN)) dP(it, HIDDEN, createDesc(1, {}));\n      it[HIDDEN][key] = true;\n    } else {\n      if (has(it, HIDDEN) && it[HIDDEN][key]) it[HIDDEN][key] = false;\n      D = _create(D, { enumerable: createDesc(0, false) });\n    } return setSymbolDesc(it, key, D);\n  } return dP(it, key, D);\n};\nvar $defineProperties = function defineProperties(it, P) {\n  anObject(it);\n  var keys = enumKeys(P = toIObject(P));\n  var i = 0;\n  var l = keys.length;\n  var key;\n  while (l > i) $defineProperty(it, key = keys[i++], P[key]);\n  return it;\n};\nvar $create = function create(it, P) {\n  return P === undefined ? _create(it) : $defineProperties(_create(it), P);\n};\nvar $propertyIsEnumerable = function propertyIsEnumerable(key) {\n  var E = isEnum.call(this, key = toPrimitive(key, true));\n  if (this === ObjectProto && has(AllSymbols, key) && !has(OPSymbols, key)) return false;\n  return E || !has(this, key) || !has(AllSymbols, key) || has(this, HIDDEN) && this[HIDDEN][key] ? E : true;\n};\nvar $getOwnPropertyDescriptor = function getOwnPropertyDescriptor(it, key) {\n  it = toIObject(it);\n  key = toPrimitive(key, true);\n  if (it === ObjectProto && has(AllSymbols, key) && !has(OPSymbols, key)) return;\n  var D = gOPD(it, key);\n  if (D && has(AllSymbols, key) && !(has(it, HIDDEN) && it[HIDDEN][key])) D.enumerable = true;\n  return D;\n};\nvar $getOwnPropertyNames = function getOwnPropertyNames(it) {\n  var names = gOPN(toIObject(it));\n  var result = [];\n  var i = 0;\n  var key;\n  while (names.length > i) {\n    if (!has(AllSymbols, key = names[i++]) && key != HIDDEN && key != META) result.push(key);\n  } return result;\n};\nvar $getOwnPropertySymbols = function getOwnPropertySymbols(it) {\n  var IS_OP = it === ObjectProto;\n  var names = gOPN(IS_OP ? OPSymbols : toIObject(it));\n  var result = [];\n  var i = 0;\n  var key;\n  while (names.length > i) {\n    if (has(AllSymbols, key = names[i++]) && (IS_OP ? has(ObjectProto, key) : true)) result.push(AllSymbols[key]);\n  } return result;\n};\n\n// 19.4.1.1 Symbol([description])\nif (!USE_NATIVE) {\n  $Symbol = function Symbol() {\n    if (this instanceof $Symbol) throw TypeError('Symbol is not a constructor!');\n    var tag = uid(arguments.length > 0 ? arguments[0] : undefined);\n    var $set = function (value) {\n      if (this === ObjectProto) $set.call(OPSymbols, value);\n      if (has(this, HIDDEN) && has(this[HIDDEN], tag)) this[HIDDEN][tag] = false;\n      setSymbolDesc(this, tag, createDesc(1, value));\n    };\n    if (DESCRIPTORS && setter) setSymbolDesc(ObjectProto, tag, { configurable: true, set: $set });\n    return wrap(tag);\n  };\n  redefine($Symbol[PROTOTYPE], 'toString', function toString() {\n    return this._k;\n  });\n\n  $GOPD.f = $getOwnPropertyDescriptor;\n  $DP.f = $defineProperty;\n  require('./_object-gopn').f = gOPNExt.f = $getOwnPropertyNames;\n  require('./_object-pie').f = $propertyIsEnumerable;\n  $GOPS.f = $getOwnPropertySymbols;\n\n  if (DESCRIPTORS && !require('./_library')) {\n    redefine(ObjectProto, 'propertyIsEnumerable', $propertyIsEnumerable, true);\n  }\n\n  wksExt.f = function (name) {\n    return wrap(wks(name));\n  };\n}\n\n$export($export.G + $export.W + $export.F * !USE_NATIVE, { Symbol: $Symbol });\n\nfor (var es6Symbols = (\n  // 19.4.2.2, 19.4.2.3, 19.4.2.4, 19.4.2.6, 19.4.2.8, 19.4.2.9, 19.4.2.10, 19.4.2.11, 19.4.2.12, 19.4.2.13, 19.4.2.14\n  'hasInstance,isConcatSpreadable,iterator,match,replace,search,species,split,toPrimitive,toStringTag,unscopables'\n).split(','), j = 0; es6Symbols.length > j;)wks(es6Symbols[j++]);\n\nfor (var wellKnownSymbols = $keys(wks.store), k = 0; wellKnownSymbols.length > k;) wksDefine(wellKnownSymbols[k++]);\n\n$export($export.S + $export.F * !USE_NATIVE, 'Symbol', {\n  // 19.4.2.1 Symbol.for(key)\n  'for': function (key) {\n    return has(SymbolRegistry, key += '')\n      ? SymbolRegistry[key]\n      : SymbolRegistry[key] = $Symbol(key);\n  },\n  // 19.4.2.5 Symbol.keyFor(sym)\n  keyFor: function keyFor(sym) {\n    if (!isSymbol(sym)) throw TypeError(sym + ' is not a symbol!');\n    for (var key in SymbolRegistry) if (SymbolRegistry[key] === sym) return key;\n  },\n  useSetter: function () { setter = true; },\n  useSimple: function () { setter = false; }\n});\n\n$export($export.S + $export.F * !USE_NATIVE, 'Object', {\n  // 19.1.2.2 Object.create(O [, Properties])\n  create: $create,\n  // 19.1.2.4 Object.defineProperty(O, P, Attributes)\n  defineProperty: $defineProperty,\n  // 19.1.2.3 Object.defineProperties(O, Properties)\n  defineProperties: $defineProperties,\n  // 19.1.2.6 Object.getOwnPropertyDescriptor(O, P)\n  getOwnPropertyDescriptor: $getOwnPropertyDescriptor,\n  // 19.1.2.7 Object.getOwnPropertyNames(O)\n  getOwnPropertyNames: $getOwnPropertyNames,\n  // 19.1.2.8 Object.getOwnPropertySymbols(O)\n  getOwnPropertySymbols: $getOwnPropertySymbols\n});\n\n// Chrome 38 and 39 `Object.getOwnPropertySymbols` fails on primitives\n// https://bugs.chromium.org/p/v8/issues/detail?id=3443\nvar FAILS_ON_PRIMITIVES = $fails(function () { $GOPS.f(1); });\n\n$export($export.S + $export.F * FAILS_ON_PRIMITIVES, 'Object', {\n  getOwnPropertySymbols: function getOwnPropertySymbols(it) {\n    return $GOPS.f(toObject(it));\n  }\n});\n\n// 24.3.2 JSON.stringify(value [, replacer [, space]])\n$JSON && $export($export.S + $export.F * (!USE_NATIVE || $fails(function () {\n  var S = $Symbol();\n  // MS Edge converts symbol values to JSON as {}\n  // WebKit converts symbol values to JSON as null\n  // V8 throws on boxed symbols\n  return _stringify([S]) != '[null]' || _stringify({ a: S }) != '{}' || _stringify(Object(S)) != '{}';\n})), 'JSON', {\n  stringify: function stringify(it) {\n    var args = [it];\n    var i = 1;\n    var replacer, $replacer;\n    while (arguments.length > i) args.push(arguments[i++]);\n    $replacer = replacer = args[1];\n    if (!isObject(replacer) && it === undefined || isSymbol(it)) return; // IE8 returns string on undefined\n    if (!isArray(replacer)) replacer = function (key, value) {\n      if (typeof $replacer == 'function') value = $replacer.call(this, key, value);\n      if (!isSymbol(value)) return value;\n    };\n    args[1] = replacer;\n    return _stringify.apply($JSON, args);\n  }\n});\n\n// 19.4.3.4 Symbol.prototype[@@toPrimitive](hint)\n$Symbol[PROTOTYPE][TO_PRIMITIVE] || require('./_hide')($Symbol[PROTOTYPE], TO_PRIMITIVE, $Symbol[PROTOTYPE].valueOf);\n// 19.4.3.5 Symbol.prototype[@@toStringTag]\nsetToStringTag($Symbol, 'Symbol');\n// 20.2.1.9 Math[@@toStringTag]\nsetToStringTag(Math, 'Math', true);\n// 24.3.3 JSON[@@toStringTag]\nsetToStringTag(global.JSON, 'JSON', true);\n","var $export = require('./_export');\n// 19.1.2.2 / 15.2.3.5 Object.create(O [, Properties])\n$export($export.S, 'Object', { create: require('./_object-create') });\n","var $export = require('./_export');\n// 19.1.2.4 / 15.2.3.6 Object.defineProperty(O, P, Attributes)\n$export($export.S + $export.F * !require('./_descriptors'), 'Object', { defineProperty: require('./_object-dp').f });\n","var $export = require('./_export');\n// 19.1.2.3 / 15.2.3.7 Object.defineProperties(O, Properties)\n$export($export.S + $export.F * !require('./_descriptors'), 'Object', { defineProperties: require('./_object-dps') });\n","// most Object methods by ES6 should accept primitives\nvar $export = require('./_export');\nvar core = require('./_core');\nvar fails = require('./_fails');\nmodule.exports = function (KEY, exec) {\n  var fn = (core.Object || {})[KEY] || Object[KEY];\n  var exp = {};\n  exp[KEY] = exec(fn);\n  $export($export.S + $export.F * fails(function () { fn(1); }), 'Object', exp);\n};\n","// 19.1.2.6 Object.getOwnPropertyDescriptor(O, P)\nvar toIObject = require('./_to-iobject');\nvar $getOwnPropertyDescriptor = require('./_object-gopd').f;\n\nrequire('./_object-sap')('getOwnPropertyDescriptor', function () {\n  return function getOwnPropertyDescriptor(it, key) {\n    return $getOwnPropertyDescriptor(toIObject(it), key);\n  };\n});\n","// 19.1.2.9 / 15.2.3.2 Object.getPrototypeOf(O)\nvar has = require('./_has');\nvar toObject = require('./_to-object');\nvar IE_PROTO = require('./_shared-key')('IE_PROTO');\nvar ObjectProto = Object.prototype;\n\nmodule.exports = Object.getPrototypeOf || function (O) {\n  O = toObject(O);\n  if (has(O, IE_PROTO)) return O[IE_PROTO];\n  if (typeof O.constructor == 'function' && O instanceof O.constructor) {\n    return O.constructor.prototype;\n  } return O instanceof Object ? ObjectProto : null;\n};\n","// 19.1.2.9 Object.getPrototypeOf(O)\nvar toObject = require('./_to-object');\nvar $getPrototypeOf = require('./_object-gpo');\n\nrequire('./_object-sap')('getPrototypeOf', function () {\n  return function getPrototypeOf(it) {\n    return $getPrototypeOf(toObject(it));\n  };\n});\n","// 19.1.2.14 Object.keys(O)\nvar toObject = require('./_to-object');\nvar $keys = require('./_object-keys');\n\nrequire('./_object-sap')('keys', function () {\n  return function keys(it) {\n    return $keys(toObject(it));\n  };\n});\n","// 19.1.2.7 Object.getOwnPropertyNames(O)\nrequire('./_object-sap')('getOwnPropertyNames', function () {\n  return require('./_object-gopn-ext').f;\n});\n","// 19.1.2.5 Object.freeze(O)\nvar isObject = require('./_is-object');\nvar meta = require('./_meta').onFreeze;\n\nrequire('./_object-sap')('freeze', function ($freeze) {\n  return function freeze(it) {\n    return $freeze && isObject(it) ? $freeze(meta(it)) : it;\n  };\n});\n","// 19.1.2.17 Object.seal(O)\nvar isObject = require('./_is-object');\nvar meta = require('./_meta').onFreeze;\n\nrequire('./_object-sap')('seal', function ($seal) {\n  return function seal(it) {\n    return $seal && isObject(it) ? $seal(meta(it)) : it;\n  };\n});\n","// 19.1.2.15 Object.preventExtensions(O)\nvar isObject = require('./_is-object');\nvar meta = require('./_meta').onFreeze;\n\nrequire('./_object-sap')('preventExtensions', function ($preventExtensions) {\n  return function preventExtensions(it) {\n    return $preventExtensions && isObject(it) ? $preventExtensions(meta(it)) : it;\n  };\n});\n","// 19.1.2.12 Object.isFrozen(O)\nvar isObject = require('./_is-object');\n\nrequire('./_object-sap')('isFrozen', function ($isFrozen) {\n  return function isFrozen(it) {\n    return isObject(it) ? $isFrozen ? $isFrozen(it) : false : true;\n  };\n});\n","// 19.1.2.13 Object.isSealed(O)\nvar isObject = require('./_is-object');\n\nrequire('./_object-sap')('isSealed', function ($isSealed) {\n  return function isSealed(it) {\n    return isObject(it) ? $isSealed ? $isSealed(it) : false : true;\n  };\n});\n","// 19.1.2.11 Object.isExtensible(O)\nvar isObject = require('./_is-object');\n\nrequire('./_object-sap')('isExtensible', function ($isExtensible) {\n  return function isExtensible(it) {\n    return isObject(it) ? $isExtensible ? $isExtensible(it) : true : false;\n  };\n});\n","'use strict';\n// 19.1.2.1 Object.assign(target, source, ...)\nvar DESCRIPTORS = require('./_descriptors');\nvar getKeys = require('./_object-keys');\nvar gOPS = require('./_object-gops');\nvar pIE = require('./_object-pie');\nvar toObject = require('./_to-object');\nvar IObject = require('./_iobject');\nvar $assign = Object.assign;\n\n// should work with symbols and should have deterministic property order (V8 bug)\nmodule.exports = !$assign || require('./_fails')(function () {\n  var A = {};\n  var B = {};\n  // eslint-disable-next-line no-undef\n  var S = Symbol();\n  var K = 'abcdefghijklmnopqrst';\n  A[S] = 7;\n  K.split('').forEach(function (k) { B[k] = k; });\n  return $assign({}, A)[S] != 7 || Object.keys($assign({}, B)).join('') != K;\n}) ? function assign(target, source) { // eslint-disable-line no-unused-vars\n  var T = toObject(target);\n  var aLen = arguments.length;\n  var index = 1;\n  var getSymbols = gOPS.f;\n  var isEnum = pIE.f;\n  while (aLen > index) {\n    var S = IObject(arguments[index++]);\n    var keys = getSymbols ? getKeys(S).concat(getSymbols(S)) : getKeys(S);\n    var length = keys.length;\n    var j = 0;\n    var key;\n    while (length > j) {\n      key = keys[j++];\n      if (!DESCRIPTORS || isEnum.call(S, key)) T[key] = S[key];\n    }\n  } return T;\n} : $assign;\n","// 19.1.3.1 Object.assign(target, source)\nvar $export = require('./_export');\n\n$export($export.S + $export.F, 'Object', { assign: require('./_object-assign') });\n","// 7.2.9 SameValue(x, y)\nmodule.exports = Object.is || function is(x, y) {\n  // eslint-disable-next-line no-self-compare\n  return x === y ? x !== 0 || 1 / x === 1 / y : x != x && y != y;\n};\n","// 19.1.3.10 Object.is(value1, value2)\nvar $export = require('./_export');\n$export($export.S, 'Object', { is: require('./_same-value') });\n","// Works with __proto__ only. Old v8 can't work with null proto objects.\n/* eslint-disable no-proto */\nvar isObject = require('./_is-object');\nvar anObject = require('./_an-object');\nvar check = function (O, proto) {\n  anObject(O);\n  if (!isObject(proto) && proto !== null) throw TypeError(proto + \": can't set as prototype!\");\n};\nmodule.exports = {\n  set: Object.setPrototypeOf || ('__proto__' in {} ? // eslint-disable-line\n    function (test, buggy, set) {\n      try {\n        set = require('./_ctx')(Function.call, require('./_object-gopd').f(Object.prototype, '__proto__').set, 2);\n        set(test, []);\n        buggy = !(test instanceof Array);\n      } catch (e) { buggy = true; }\n      return function setPrototypeOf(O, proto) {\n        check(O, proto);\n        if (buggy) O.__proto__ = proto;\n        else set(O, proto);\n        return O;\n      };\n    }({}, false) : undefined),\n  check: check\n};\n","// 19.1.3.19 Object.setPrototypeOf(O, proto)\nvar $export = require('./_export');\n$export($export.S, 'Object', { setPrototypeOf: require('./_set-proto').set });\n","// getting tag from 19.1.3.6 Object.prototype.toString()\nvar cof = require('./_cof');\nvar TAG = require('./_wks')('toStringTag');\n// ES3 wrong here\nvar ARG = cof(function () { return arguments; }()) == 'Arguments';\n\n// fallback for IE11 Script Access Denied error\nvar tryGet = function (it, key) {\n  try {\n    return it[key];\n  } catch (e) { /* empty */ }\n};\n\nmodule.exports = function (it) {\n  var O, T, B;\n  return it === undefined ? 'Undefined' : it === null ? 'Null'\n    // @@toStringTag case\n    : typeof (T = tryGet(O = Object(it), TAG)) == 'string' ? T\n    // builtinTag case\n    : ARG ? cof(O)\n    // ES3 arguments fallback\n    : (B = cof(O)) == 'Object' && typeof O.callee == 'function' ? 'Arguments' : B;\n};\n","'use strict';\n// 19.1.3.6 Object.prototype.toString()\nvar classof = require('./_classof');\nvar test = {};\ntest[require('./_wks')('toStringTag')] = 'z';\nif (test + '' != '[object z]') {\n  require('./_redefine')(Object.prototype, 'toString', function toString() {\n    return '[object ' + classof(this) + ']';\n  }, true);\n}\n","// fast apply, http://jsperf.lnkit.com/fast-apply/5\nmodule.exports = function (fn, args, that) {\n  var un = that === undefined;\n  switch (args.length) {\n    case 0: return un ? fn()\n                      : fn.call(that);\n    case 1: return un ? fn(args[0])\n                      : fn.call(that, args[0]);\n    case 2: return un ? fn(args[0], args[1])\n                      : fn.call(that, args[0], args[1]);\n    case 3: return un ? fn(args[0], args[1], args[2])\n                      : fn.call(that, args[0], args[1], args[2]);\n    case 4: return un ? fn(args[0], args[1], args[2], args[3])\n                      : fn.call(that, args[0], args[1], args[2], args[3]);\n  } return fn.apply(that, args);\n};\n","'use strict';\nvar aFunction = require('./_a-function');\nvar isObject = require('./_is-object');\nvar invoke = require('./_invoke');\nvar arraySlice = [].slice;\nvar factories = {};\n\nvar construct = function (F, len, args) {\n  if (!(len in factories)) {\n    for (var n = [], i = 0; i < len; i++) n[i] = 'a[' + i + ']';\n    // eslint-disable-next-line no-new-func\n    factories[len] = Function('F,a', 'return new F(' + n.join(',') + ')');\n  } return factories[len](F, args);\n};\n\nmodule.exports = Function.bind || function bind(that /* , ...args */) {\n  var fn = aFunction(this);\n  var partArgs = arraySlice.call(arguments, 1);\n  var bound = function (/* args... */) {\n    var args = partArgs.concat(arraySlice.call(arguments));\n    return this instanceof bound ? construct(fn, args.length, args) : invoke(fn, args, that);\n  };\n  if (isObject(fn.prototype)) bound.prototype = fn.prototype;\n  return bound;\n};\n","// 19.2.3.2 / 15.3.4.5 Function.prototype.bind(thisArg, args...)\nvar $export = require('./_export');\n\n$export($export.P, 'Function', { bind: require('./_bind') });\n","var dP = require('./_object-dp').f;\nvar FProto = Function.prototype;\nvar nameRE = /^\\s*function ([^ (]*)/;\nvar NAME = 'name';\n\n// 19.2.4.2 name\nNAME in FProto || require('./_descriptors') && dP(FProto, NAME, {\n  configurable: true,\n  get: function () {\n    try {\n      return ('' + this).match(nameRE)[1];\n    } catch (e) {\n      return '';\n    }\n  }\n});\n","'use strict';\nvar isObject = require('./_is-object');\nvar getPrototypeOf = require('./_object-gpo');\nvar HAS_INSTANCE = require('./_wks')('hasInstance');\nvar FunctionProto = Function.prototype;\n// 19.2.3.6 Function.prototype[@@hasInstance](V)\nif (!(HAS_INSTANCE in FunctionProto)) require('./_object-dp').f(FunctionProto, HAS_INSTANCE, { value: function (O) {\n  if (typeof this != 'function' || !isObject(O)) return false;\n  if (!isObject(this.prototype)) return O instanceof this;\n  // for environment w/o native `@@hasInstance` logic enough `instanceof`, but add this:\n  while (O = getPrototypeOf(O)) if (this.prototype === O) return true;\n  return false;\n} });\n","module.exports = '\\x09\\x0A\\x0B\\x0C\\x0D\\x20\\xA0\\u1680\\u180E\\u2000\\u2001\\u2002\\u2003' +\n  '\\u2004\\u2005\\u2006\\u2007\\u2008\\u2009\\u200A\\u202F\\u205F\\u3000\\u2028\\u2029\\uFEFF';\n","var $export = require('./_export');\nvar defined = require('./_defined');\nvar fails = require('./_fails');\nvar spaces = require('./_string-ws');\nvar space = '[' + spaces + ']';\nvar non = '\\u200b\\u0085';\nvar ltrim = RegExp('^' + space + space + '*');\nvar rtrim = RegExp(space + space + '*$');\n\nvar exporter = function (KEY, exec, ALIAS) {\n  var exp = {};\n  var FORCE = fails(function () {\n    return !!spaces[KEY]() || non[KEY]() != non;\n  });\n  var fn = exp[KEY] = FORCE ? exec(trim) : spaces[KEY];\n  if (ALIAS) exp[ALIAS] = fn;\n  $export($export.P + $export.F * FORCE, 'String', exp);\n};\n\n// 1 -> String#trimLeft\n// 2 -> String#trimRight\n// 3 -> String#trim\nvar trim = exporter.trim = function (string, TYPE) {\n  string = String(defined(string));\n  if (TYPE & 1) string = string.replace(ltrim, '');\n  if (TYPE & 2) string = string.replace(rtrim, '');\n  return string;\n};\n\nmodule.exports = exporter;\n","var $parseInt = require('./_global').parseInt;\nvar $trim = require('./_string-trim').trim;\nvar ws = require('./_string-ws');\nvar hex = /^[-+]?0[xX]/;\n\nmodule.exports = $parseInt(ws + '08') !== 8 || $parseInt(ws + '0x16') !== 22 ? function parseInt(str, radix) {\n  var string = $trim(String(str), 3);\n  return $parseInt(string, (radix >>> 0) || (hex.test(string) ? 16 : 10));\n} : $parseInt;\n","var $export = require('./_export');\nvar $parseInt = require('./_parse-int');\n// 18.2.5 parseInt(string, radix)\n$export($export.G + $export.F * (parseInt != $parseInt), { parseInt: $parseInt });\n","var $parseFloat = require('./_global').parseFloat;\nvar $trim = require('./_string-trim').trim;\n\nmodule.exports = 1 / $parseFloat(require('./_string-ws') + '-0') !== -Infinity ? function parseFloat(str) {\n  var string = $trim(String(str), 3);\n  var result = $parseFloat(string);\n  return result === 0 && string.charAt(0) == '-' ? -0 : result;\n} : $parseFloat;\n","var $export = require('./_export');\nvar $parseFloat = require('./_parse-float');\n// 18.2.4 parseFloat(string)\n$export($export.G + $export.F * (parseFloat != $parseFloat), { parseFloat: $parseFloat });\n","var isObject = require('./_is-object');\nvar setPrototypeOf = require('./_set-proto').set;\nmodule.exports = function (that, target, C) {\n  var S = target.constructor;\n  var P;\n  if (S !== C && typeof S == 'function' && (P = S.prototype) !== C.prototype && isObject(P) && setPrototypeOf) {\n    setPrototypeOf(that, P);\n  } return that;\n};\n","'use strict';\nvar global = require('./_global');\nvar has = require('./_has');\nvar cof = require('./_cof');\nvar inheritIfRequired = require('./_inherit-if-required');\nvar toPrimitive = require('./_to-primitive');\nvar fails = require('./_fails');\nvar gOPN = require('./_object-gopn').f;\nvar gOPD = require('./_object-gopd').f;\nvar dP = require('./_object-dp').f;\nvar $trim = require('./_string-trim').trim;\nvar NUMBER = 'Number';\nvar $Number = global[NUMBER];\nvar Base = $Number;\nvar proto = $Number.prototype;\n// Opera ~12 has broken Object#toString\nvar BROKEN_COF = cof(require('./_object-create')(proto)) == NUMBER;\nvar TRIM = 'trim' in String.prototype;\n\n// 7.1.3 ToNumber(argument)\nvar toNumber = function (argument) {\n  var it = toPrimitive(argument, false);\n  if (typeof it == 'string' && it.length > 2) {\n    it = TRIM ? it.trim() : $trim(it, 3);\n    var first = it.charCodeAt(0);\n    var third, radix, maxCode;\n    if (first === 43 || first === 45) {\n      third = it.charCodeAt(2);\n      if (third === 88 || third === 120) return NaN; // Number('+0x1') should be NaN, old V8 fix\n    } else if (first === 48) {\n      switch (it.charCodeAt(1)) {\n        case 66: case 98: radix = 2; maxCode = 49; break; // fast equal /^0b[01]+$/i\n        case 79: case 111: radix = 8; maxCode = 55; break; // fast equal /^0o[0-7]+$/i\n        default: return +it;\n      }\n      for (var digits = it.slice(2), i = 0, l = digits.length, code; i < l; i++) {\n        code = digits.charCodeAt(i);\n        // parseInt parses a string to a first unavailable symbol\n        // but ToNumber should return NaN if a string contains unavailable symbols\n        if (code < 48 || code > maxCode) return NaN;\n      } return parseInt(digits, radix);\n    }\n  } return +it;\n};\n\nif (!$Number(' 0o1') || !$Number('0b1') || $Number('+0x1')) {\n  $Number = function Number(value) {\n    var it = arguments.length < 1 ? 0 : value;\n    var that = this;\n    return that instanceof $Number\n      // check on 1..constructor(foo) case\n      && (BROKEN_COF ? fails(function () { proto.valueOf.call(that); }) : cof(that) != NUMBER)\n        ? inheritIfRequired(new Base(toNumber(it)), that, $Number) : toNumber(it);\n  };\n  for (var keys = require('./_descriptors') ? gOPN(Base) : (\n    // ES3:\n    'MAX_VALUE,MIN_VALUE,NaN,NEGATIVE_INFINITY,POSITIVE_INFINITY,' +\n    // ES6 (in case, if modules with ES6 Number statics required before):\n    'EPSILON,isFinite,isInteger,isNaN,isSafeInteger,MAX_SAFE_INTEGER,' +\n    'MIN_SAFE_INTEGER,parseFloat,parseInt,isInteger'\n  ).split(','), j = 0, key; keys.length > j; j++) {\n    if (has(Base, key = keys[j]) && !has($Number, key)) {\n      dP($Number, key, gOPD(Base, key));\n    }\n  }\n  $Number.prototype = proto;\n  proto.constructor = $Number;\n  require('./_redefine')(global, NUMBER, $Number);\n}\n","var cof = require('./_cof');\nmodule.exports = function (it, msg) {\n  if (typeof it != 'number' && cof(it) != 'Number') throw TypeError(msg);\n  return +it;\n};\n","'use strict';\nvar toInteger = require('./_to-integer');\nvar defined = require('./_defined');\n\nmodule.exports = function repeat(count) {\n  var str = String(defined(this));\n  var res = '';\n  var n = toInteger(count);\n  if (n < 0 || n == Infinity) throw RangeError(\"Count can't be negative\");\n  for (;n > 0; (n >>>= 1) && (str += str)) if (n & 1) res += str;\n  return res;\n};\n","'use strict';\nvar $export = require('./_export');\nvar toInteger = require('./_to-integer');\nvar aNumberValue = require('./_a-number-value');\nvar repeat = require('./_string-repeat');\nvar $toFixed = 1.0.toFixed;\nvar floor = Math.floor;\nvar data = [0, 0, 0, 0, 0, 0];\nvar ERROR = 'Number.toFixed: incorrect invocation!';\nvar ZERO = '0';\n\nvar multiply = function (n, c) {\n  var i = -1;\n  var c2 = c;\n  while (++i < 6) {\n    c2 += n * data[i];\n    data[i] = c2 % 1e7;\n    c2 = floor(c2 / 1e7);\n  }\n};\nvar divide = function (n) {\n  var i = 6;\n  var c = 0;\n  while (--i >= 0) {\n    c += data[i];\n    data[i] = floor(c / n);\n    c = (c % n) * 1e7;\n  }\n};\nvar numToString = function () {\n  var i = 6;\n  var s = '';\n  while (--i >= 0) {\n    if (s !== '' || i === 0 || data[i] !== 0) {\n      var t = String(data[i]);\n      s = s === '' ? t : s + repeat.call(ZERO, 7 - t.length) + t;\n    }\n  } return s;\n};\nvar pow = function (x, n, acc) {\n  return n === 0 ? acc : n % 2 === 1 ? pow(x, n - 1, acc * x) : pow(x * x, n / 2, acc);\n};\nvar log = function (x) {\n  var n = 0;\n  var x2 = x;\n  while (x2 >= 4096) {\n    n += 12;\n    x2 /= 4096;\n  }\n  while (x2 >= 2) {\n    n += 1;\n    x2 /= 2;\n  } return n;\n};\n\n$export($export.P + $export.F * (!!$toFixed && (\n  0.00008.toFixed(3) !== '0.000' ||\n  0.9.toFixed(0) !== '1' ||\n  1.255.toFixed(2) !== '1.25' ||\n  1000000000000000128.0.toFixed(0) !== '1000000000000000128'\n) || !require('./_fails')(function () {\n  // V8 ~ Android 4.3-\n  $toFixed.call({});\n})), 'Number', {\n  toFixed: function toFixed(fractionDigits) {\n    var x = aNumberValue(this, ERROR);\n    var f = toInteger(fractionDigits);\n    var s = '';\n    var m = ZERO;\n    var e, z, j, k;\n    if (f < 0 || f > 20) throw RangeError(ERROR);\n    // eslint-disable-next-line no-self-compare\n    if (x != x) return 'NaN';\n    if (x <= -1e21 || x >= 1e21) return String(x);\n    if (x < 0) {\n      s = '-';\n      x = -x;\n    }\n    if (x > 1e-21) {\n      e = log(x * pow(2, 69, 1)) - 69;\n      z = e < 0 ? x * pow(2, -e, 1) : x / pow(2, e, 1);\n      z *= 0x10000000000000;\n      e = 52 - e;\n      if (e > 0) {\n        multiply(0, z);\n        j = f;\n        while (j >= 7) {\n          multiply(1e7, 0);\n          j -= 7;\n        }\n        multiply(pow(10, j, 1), 0);\n        j = e - 1;\n        while (j >= 23) {\n          divide(1 << 23);\n          j -= 23;\n        }\n        divide(1 << j);\n        multiply(1, 1);\n        divide(2);\n        m = numToString();\n      } else {\n        multiply(0, z);\n        multiply(1 << -e, 0);\n        m = numToString() + repeat.call(ZERO, f);\n      }\n    }\n    if (f > 0) {\n      k = m.length;\n      m = s + (k <= f ? '0.' + repeat.call(ZERO, f - k) + m : m.slice(0, k - f) + '.' + m.slice(k - f));\n    } else {\n      m = s + m;\n    } return m;\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar $fails = require('./_fails');\nvar aNumberValue = require('./_a-number-value');\nvar $toPrecision = 1.0.toPrecision;\n\n$export($export.P + $export.F * ($fails(function () {\n  // IE7-\n  return $toPrecision.call(1, undefined) !== '1';\n}) || !$fails(function () {\n  // V8 ~ Android 4.3-\n  $toPrecision.call({});\n})), 'Number', {\n  toPrecision: function toPrecision(precision) {\n    var that = aNumberValue(this, 'Number#toPrecision: incorrect invocation!');\n    return precision === undefined ? $toPrecision.call(that) : $toPrecision.call(that, precision);\n  }\n});\n","// 20.1.2.1 Number.EPSILON\nvar $export = require('./_export');\n\n$export($export.S, 'Number', { EPSILON: Math.pow(2, -52) });\n","// 20.1.2.2 Number.isFinite(number)\nvar $export = require('./_export');\nvar _isFinite = require('./_global').isFinite;\n\n$export($export.S, 'Number', {\n  isFinite: function isFinite(it) {\n    return typeof it == 'number' && _isFinite(it);\n  }\n});\n","// 20.1.2.3 Number.isInteger(number)\nvar isObject = require('./_is-object');\nvar floor = Math.floor;\nmodule.exports = function isInteger(it) {\n  return !isObject(it) && isFinite(it) && floor(it) === it;\n};\n","// 20.1.2.3 Number.isInteger(number)\nvar $export = require('./_export');\n\n$export($export.S, 'Number', { isInteger: require('./_is-integer') });\n","// 20.1.2.4 Number.isNaN(number)\nvar $export = require('./_export');\n\n$export($export.S, 'Number', {\n  isNaN: function isNaN(number) {\n    // eslint-disable-next-line no-self-compare\n    return number != number;\n  }\n});\n","// 20.1.2.5 Number.isSafeInteger(number)\nvar $export = require('./_export');\nvar isInteger = require('./_is-integer');\nvar abs = Math.abs;\n\n$export($export.S, 'Number', {\n  isSafeInteger: function isSafeInteger(number) {\n    return isInteger(number) && abs(number) <= 0x1fffffffffffff;\n  }\n});\n","// 20.1.2.6 Number.MAX_SAFE_INTEGER\nvar $export = require('./_export');\n\n$export($export.S, 'Number', { MAX_SAFE_INTEGER: 0x1fffffffffffff });\n","// 20.1.2.10 Number.MIN_SAFE_INTEGER\nvar $export = require('./_export');\n\n$export($export.S, 'Number', { MIN_SAFE_INTEGER: -0x1fffffffffffff });\n","var $export = require('./_export');\nvar $parseFloat = require('./_parse-float');\n// 20.1.2.12 Number.parseFloat(string)\n$export($export.S + $export.F * (Number.parseFloat != $parseFloat), 'Number', { parseFloat: $parseFloat });\n","var $export = require('./_export');\nvar $parseInt = require('./_parse-int');\n// 20.1.2.13 Number.parseInt(string, radix)\n$export($export.S + $export.F * (Number.parseInt != $parseInt), 'Number', { parseInt: $parseInt });\n","// 20.2.2.20 Math.log1p(x)\nmodule.exports = Math.log1p || function log1p(x) {\n  return (x = +x) > -1e-8 && x < 1e-8 ? x - x * x / 2 : Math.log(1 + x);\n};\n","// 20.2.2.3 Math.acosh(x)\nvar $export = require('./_export');\nvar log1p = require('./_math-log1p');\nvar sqrt = Math.sqrt;\nvar $acosh = Math.acosh;\n\n$export($export.S + $export.F * !($acosh\n  // V8 bug: https://code.google.com/p/v8/issues/detail?id=3509\n  && Math.floor($acosh(Number.MAX_VALUE)) == 710\n  // Tor Browser bug: Math.acosh(Infinity) -> NaN\n  && $acosh(Infinity) == Infinity\n), 'Math', {\n  acosh: function acosh(x) {\n    return (x = +x) < 1 ? NaN : x > 94906265.62425156\n      ? Math.log(x) + Math.LN2\n      : log1p(x - 1 + sqrt(x - 1) * sqrt(x + 1));\n  }\n});\n","// 20.2.2.5 Math.asinh(x)\nvar $export = require('./_export');\nvar $asinh = Math.asinh;\n\nfunction asinh(x) {\n  return !isFinite(x = +x) || x == 0 ? x : x < 0 ? -asinh(-x) : Math.log(x + Math.sqrt(x * x + 1));\n}\n\n// Tor Browser bug: Math.asinh(0) -> -0\n$export($export.S + $export.F * !($asinh && 1 / $asinh(0) > 0), 'Math', { asinh: asinh });\n","// 20.2.2.7 Math.atanh(x)\nvar $export = require('./_export');\nvar $atanh = Math.atanh;\n\n// Tor Browser bug: Math.atanh(-0) -> 0\n$export($export.S + $export.F * !($atanh && 1 / $atanh(-0) < 0), 'Math', {\n  atanh: function atanh(x) {\n    return (x = +x) == 0 ? x : Math.log((1 + x) / (1 - x)) / 2;\n  }\n});\n","// 20.2.2.28 Math.sign(x)\nmodule.exports = Math.sign || function sign(x) {\n  // eslint-disable-next-line no-self-compare\n  return (x = +x) == 0 || x != x ? x : x < 0 ? -1 : 1;\n};\n","// 20.2.2.9 Math.cbrt(x)\nvar $export = require('./_export');\nvar sign = require('./_math-sign');\n\n$export($export.S, 'Math', {\n  cbrt: function cbrt(x) {\n    return sign(x = +x) * Math.pow(Math.abs(x), 1 / 3);\n  }\n});\n","// 20.2.2.11 Math.clz32(x)\nvar $export = require('./_export');\n\n$export($export.S, 'Math', {\n  clz32: function clz32(x) {\n    return (x >>>= 0) ? 31 - Math.floor(Math.log(x + 0.5) * Math.LOG2E) : 32;\n  }\n});\n","// 20.2.2.12 Math.cosh(x)\nvar $export = require('./_export');\nvar exp = Math.exp;\n\n$export($export.S, 'Math', {\n  cosh: function cosh(x) {\n    return (exp(x = +x) + exp(-x)) / 2;\n  }\n});\n","// 20.2.2.14 Math.expm1(x)\nvar $expm1 = Math.expm1;\nmodule.exports = (!$expm1\n  // Old FF bug\n  || $expm1(10) > 22025.465794806719 || $expm1(10) < 22025.4657948067165168\n  // Tor Browser bug\n  || $expm1(-2e-17) != -2e-17\n) ? function expm1(x) {\n  return (x = +x) == 0 ? x : x > -1e-6 && x < 1e-6 ? x + x * x / 2 : Math.exp(x) - 1;\n} : $expm1;\n","// 20.2.2.14 Math.expm1(x)\nvar $export = require('./_export');\nvar $expm1 = require('./_math-expm1');\n\n$export($export.S + $export.F * ($expm1 != Math.expm1), 'Math', { expm1: $expm1 });\n","// 20.2.2.16 Math.fround(x)\nvar sign = require('./_math-sign');\nvar pow = Math.pow;\nvar EPSILON = pow(2, -52);\nvar EPSILON32 = pow(2, -23);\nvar MAX32 = pow(2, 127) * (2 - EPSILON32);\nvar MIN32 = pow(2, -126);\n\nvar roundTiesToEven = function (n) {\n  return n + 1 / EPSILON - 1 / EPSILON;\n};\n\nmodule.exports = Math.fround || function fround(x) {\n  var $abs = Math.abs(x);\n  var $sign = sign(x);\n  var a, result;\n  if ($abs < MIN32) return $sign * roundTiesToEven($abs / MIN32 / EPSILON32) * MIN32 * EPSILON32;\n  a = (1 + EPSILON32 / EPSILON) * $abs;\n  result = a - (a - $abs);\n  // eslint-disable-next-line no-self-compare\n  if (result > MAX32 || result != result) return $sign * Infinity;\n  return $sign * result;\n};\n","// 20.2.2.16 Math.fround(x)\nvar $export = require('./_export');\n\n$export($export.S, 'Math', { fround: require('./_math-fround') });\n","// 20.2.2.17 Math.hypot([value1[, value2[, … ]]])\nvar $export = require('./_export');\nvar abs = Math.abs;\n\n$export($export.S, 'Math', {\n  hypot: function hypot(value1, value2) { // eslint-disable-line no-unused-vars\n    var sum = 0;\n    var i = 0;\n    var aLen = arguments.length;\n    var larg = 0;\n    var arg, div;\n    while (i < aLen) {\n      arg = abs(arguments[i++]);\n      if (larg < arg) {\n        div = larg / arg;\n        sum = sum * div * div + 1;\n        larg = arg;\n      } else if (arg > 0) {\n        div = arg / larg;\n        sum += div * div;\n      } else sum += arg;\n    }\n    return larg === Infinity ? Infinity : larg * Math.sqrt(sum);\n  }\n});\n","// 20.2.2.18 Math.imul(x, y)\nvar $export = require('./_export');\nvar $imul = Math.imul;\n\n// some WebKit versions fails with big numbers, some has wrong arity\n$export($export.S + $export.F * require('./_fails')(function () {\n  return $imul(0xffffffff, 5) != -5 || $imul.length != 2;\n}), 'Math', {\n  imul: function imul(x, y) {\n    var UINT16 = 0xffff;\n    var xn = +x;\n    var yn = +y;\n    var xl = UINT16 & xn;\n    var yl = UINT16 & yn;\n    return 0 | xl * yl + ((UINT16 & xn >>> 16) * yl + xl * (UINT16 & yn >>> 16) << 16 >>> 0);\n  }\n});\n","// 20.2.2.21 Math.log10(x)\nvar $export = require('./_export');\n\n$export($export.S, 'Math', {\n  log10: function log10(x) {\n    return Math.log(x) * Math.LOG10E;\n  }\n});\n","// 20.2.2.20 Math.log1p(x)\nvar $export = require('./_export');\n\n$export($export.S, 'Math', { log1p: require('./_math-log1p') });\n","// 20.2.2.22 Math.log2(x)\nvar $export = require('./_export');\n\n$export($export.S, 'Math', {\n  log2: function log2(x) {\n    return Math.log(x) / Math.LN2;\n  }\n});\n","// 20.2.2.28 Math.sign(x)\nvar $export = require('./_export');\n\n$export($export.S, 'Math', { sign: require('./_math-sign') });\n","// 20.2.2.30 Math.sinh(x)\nvar $export = require('./_export');\nvar expm1 = require('./_math-expm1');\nvar exp = Math.exp;\n\n// V8 near Chromium 38 has a problem with very small numbers\n$export($export.S + $export.F * require('./_fails')(function () {\n  return !Math.sinh(-2e-17) != -2e-17;\n}), 'Math', {\n  sinh: function sinh(x) {\n    return Math.abs(x = +x) < 1\n      ? (expm1(x) - expm1(-x)) / 2\n      : (exp(x - 1) - exp(-x - 1)) * (Math.E / 2);\n  }\n});\n","// 20.2.2.33 Math.tanh(x)\nvar $export = require('./_export');\nvar expm1 = require('./_math-expm1');\nvar exp = Math.exp;\n\n$export($export.S, 'Math', {\n  tanh: function tanh(x) {\n    var a = expm1(x = +x);\n    var b = expm1(-x);\n    return a == Infinity ? 1 : b == Infinity ? -1 : (a - b) / (exp(x) + exp(-x));\n  }\n});\n","// 20.2.2.34 Math.trunc(x)\nvar $export = require('./_export');\n\n$export($export.S, 'Math', {\n  trunc: function trunc(it) {\n    return (it > 0 ? Math.floor : Math.ceil)(it);\n  }\n});\n","var $export = require('./_export');\nvar toAbsoluteIndex = require('./_to-absolute-index');\nvar fromCharCode = String.fromCharCode;\nvar $fromCodePoint = String.fromCodePoint;\n\n// length should be 1, old FF problem\n$export($export.S + $export.F * (!!$fromCodePoint && $fromCodePoint.length != 1), 'String', {\n  // 21.1.2.2 String.fromCodePoint(...codePoints)\n  fromCodePoint: function fromCodePoint(x) { // eslint-disable-line no-unused-vars\n    var res = [];\n    var aLen = arguments.length;\n    var i = 0;\n    var code;\n    while (aLen > i) {\n      code = +arguments[i++];\n      if (toAbsoluteIndex(code, 0x10ffff) !== code) throw RangeError(code + ' is not a valid code point');\n      res.push(code < 0x10000\n        ? fromCharCode(code)\n        : fromCharCode(((code -= 0x10000) >> 10) + 0xd800, code % 0x400 + 0xdc00)\n      );\n    } return res.join('');\n  }\n});\n","var $export = require('./_export');\nvar toIObject = require('./_to-iobject');\nvar toLength = require('./_to-length');\n\n$export($export.S, 'String', {\n  // 21.1.2.4 String.raw(callSite, ...substitutions)\n  raw: function raw(callSite) {\n    var tpl = toIObject(callSite.raw);\n    var len = toLength(tpl.length);\n    var aLen = arguments.length;\n    var res = [];\n    var i = 0;\n    while (len > i) {\n      res.push(String(tpl[i++]));\n      if (i < aLen) res.push(String(arguments[i]));\n    } return res.join('');\n  }\n});\n","'use strict';\n// 21.1.3.25 String.prototype.trim()\nrequire('./_string-trim')('trim', function ($trim) {\n  return function trim() {\n    return $trim(this, 3);\n  };\n});\n","var toInteger = require('./_to-integer');\nvar defined = require('./_defined');\n// true  -> String#at\n// false -> String#codePointAt\nmodule.exports = function (TO_STRING) {\n  return function (that, pos) {\n    var s = String(defined(that));\n    var i = toInteger(pos);\n    var l = s.length;\n    var a, b;\n    if (i < 0 || i >= l) return TO_STRING ? '' : undefined;\n    a = s.charCodeAt(i);\n    return a < 0xd800 || a > 0xdbff || i + 1 === l || (b = s.charCodeAt(i + 1)) < 0xdc00 || b > 0xdfff\n      ? TO_STRING ? s.charAt(i) : a\n      : TO_STRING ? s.slice(i, i + 2) : (a - 0xd800 << 10) + (b - 0xdc00) + 0x10000;\n  };\n};\n","module.exports = {};\n","'use strict';\nvar create = require('./_object-create');\nvar descriptor = require('./_property-desc');\nvar setToStringTag = require('./_set-to-string-tag');\nvar IteratorPrototype = {};\n\n// 25.1.2.1.1 %IteratorPrototype%[@@iterator]()\nrequire('./_hide')(IteratorPrototype, require('./_wks')('iterator'), function () { return this; });\n\nmodule.exports = function (Constructor, NAME, next) {\n  Constructor.prototype = create(IteratorPrototype, { next: descriptor(1, next) });\n  setToStringTag(Constructor, NAME + ' Iterator');\n};\n","'use strict';\nvar LIBRARY = require('./_library');\nvar $export = require('./_export');\nvar redefine = require('./_redefine');\nvar hide = require('./_hide');\nvar Iterators = require('./_iterators');\nvar $iterCreate = require('./_iter-create');\nvar setToStringTag = require('./_set-to-string-tag');\nvar getPrototypeOf = require('./_object-gpo');\nvar ITERATOR = require('./_wks')('iterator');\nvar BUGGY = !([].keys && 'next' in [].keys()); // Safari has buggy iterators w/o `next`\nvar FF_ITERATOR = '@@iterator';\nvar KEYS = 'keys';\nvar VALUES = 'values';\n\nvar returnThis = function () { return this; };\n\nmodule.exports = function (Base, NAME, Constructor, next, DEFAULT, IS_SET, FORCED) {\n  $iterCreate(Constructor, NAME, next);\n  var getMethod = function (kind) {\n    if (!BUGGY && kind in proto) return proto[kind];\n    switch (kind) {\n      case KEYS: return function keys() { return new Constructor(this, kind); };\n      case VALUES: return function values() { return new Constructor(this, kind); };\n    } return function entries() { return new Constructor(this, kind); };\n  };\n  var TAG = NAME + ' Iterator';\n  var DEF_VALUES = DEFAULT == VALUES;\n  var VALUES_BUG = false;\n  var proto = Base.prototype;\n  var $native = proto[ITERATOR] || proto[FF_ITERATOR] || DEFAULT && proto[DEFAULT];\n  var $default = $native || getMethod(DEFAULT);\n  var $entries = DEFAULT ? !DEF_VALUES ? $default : getMethod('entries') : undefined;\n  var $anyNative = NAME == 'Array' ? proto.entries || $native : $native;\n  var methods, key, IteratorPrototype;\n  // Fix native\n  if ($anyNative) {\n    IteratorPrototype = getPrototypeOf($anyNative.call(new Base()));\n    if (IteratorPrototype !== Object.prototype && IteratorPrototype.next) {\n      // Set @@toStringTag to native iterators\n      setToStringTag(IteratorPrototype, TAG, true);\n      // fix for some old engines\n      if (!LIBRARY && typeof IteratorPrototype[ITERATOR] != 'function') hide(IteratorPrototype, ITERATOR, returnThis);\n    }\n  }\n  // fix Array#{values, @@iterator}.name in V8 / FF\n  if (DEF_VALUES && $native && $native.name !== VALUES) {\n    VALUES_BUG = true;\n    $default = function values() { return $native.call(this); };\n  }\n  // Define iterator\n  if ((!LIBRARY || FORCED) && (BUGGY || VALUES_BUG || !proto[ITERATOR])) {\n    hide(proto, ITERATOR, $default);\n  }\n  // Plug for library\n  Iterators[NAME] = $default;\n  Iterators[TAG] = returnThis;\n  if (DEFAULT) {\n    methods = {\n      values: DEF_VALUES ? $default : getMethod(VALUES),\n      keys: IS_SET ? $default : getMethod(KEYS),\n      entries: $entries\n    };\n    if (FORCED) for (key in methods) {\n      if (!(key in proto)) redefine(proto, key, methods[key]);\n    } else $export($export.P + $export.F * (BUGGY || VALUES_BUG), NAME, methods);\n  }\n  return methods;\n};\n","'use strict';\nvar $at = require('./_string-at')(true);\n\n// 21.1.3.27 String.prototype[@@iterator]()\nrequire('./_iter-define')(String, 'String', function (iterated) {\n  this._t = String(iterated); // target\n  this._i = 0;                // next index\n// 21.1.5.2.1 %StringIteratorPrototype%.next()\n}, function () {\n  var O = this._t;\n  var index = this._i;\n  var point;\n  if (index >= O.length) return { value: undefined, done: true };\n  point = $at(O, index);\n  this._i += point.length;\n  return { value: point, done: false };\n});\n","'use strict';\nvar $export = require('./_export');\nvar $at = require('./_string-at')(false);\n$export($export.P, 'String', {\n  // 21.1.3.3 String.prototype.codePointAt(pos)\n  codePointAt: function codePointAt(pos) {\n    return $at(this, pos);\n  }\n});\n","// 7.2.8 IsRegExp(argument)\nvar isObject = require('./_is-object');\nvar cof = require('./_cof');\nvar MATCH = require('./_wks')('match');\nmodule.exports = function (it) {\n  var isRegExp;\n  return isObject(it) && ((isRegExp = it[MATCH]) !== undefined ? !!isRegExp : cof(it) == 'RegExp');\n};\n","// helper for String#{startsWith, endsWith, includes}\nvar isRegExp = require('./_is-regexp');\nvar defined = require('./_defined');\n\nmodule.exports = function (that, searchString, NAME) {\n  if (isRegExp(searchString)) throw TypeError('String#' + NAME + \" doesn't accept regex!\");\n  return String(defined(that));\n};\n","var MATCH = require('./_wks')('match');\nmodule.exports = function (KEY) {\n  var re = /./;\n  try {\n    '/./'[KEY](re);\n  } catch (e) {\n    try {\n      re[MATCH] = false;\n      return !'/./'[KEY](re);\n    } catch (f) { /* empty */ }\n  } return true;\n};\n","// 21.1.3.6 String.prototype.endsWith(searchString [, endPosition])\n'use strict';\nvar $export = require('./_export');\nvar toLength = require('./_to-length');\nvar context = require('./_string-context');\nvar ENDS_WITH = 'endsWith';\nvar $endsWith = ''[ENDS_WITH];\n\n$export($export.P + $export.F * require('./_fails-is-regexp')(ENDS_WITH), 'String', {\n  endsWith: function endsWith(searchString /* , endPosition = @length */) {\n    var that = context(this, searchString, ENDS_WITH);\n    var endPosition = arguments.length > 1 ? arguments[1] : undefined;\n    var len = toLength(that.length);\n    var end = endPosition === undefined ? len : Math.min(toLength(endPosition), len);\n    var search = String(searchString);\n    return $endsWith\n      ? $endsWith.call(that, search, end)\n      : that.slice(end - search.length, end) === search;\n  }\n});\n","// 21.1.3.7 String.prototype.includes(searchString, position = 0)\n'use strict';\nvar $export = require('./_export');\nvar context = require('./_string-context');\nvar INCLUDES = 'includes';\n\n$export($export.P + $export.F * require('./_fails-is-regexp')(INCLUDES), 'String', {\n  includes: function includes(searchString /* , position = 0 */) {\n    return !!~context(this, searchString, INCLUDES)\n      .indexOf(searchString, arguments.length > 1 ? arguments[1] : undefined);\n  }\n});\n","var $export = require('./_export');\n\n$export($export.P, 'String', {\n  // 21.1.3.13 String.prototype.repeat(count)\n  repeat: require('./_string-repeat')\n});\n","// 21.1.3.18 String.prototype.startsWith(searchString [, position ])\n'use strict';\nvar $export = require('./_export');\nvar toLength = require('./_to-length');\nvar context = require('./_string-context');\nvar STARTS_WITH = 'startsWith';\nvar $startsWith = ''[STARTS_WITH];\n\n$export($export.P + $export.F * require('./_fails-is-regexp')(STARTS_WITH), 'String', {\n  startsWith: function startsWith(searchString /* , position = 0 */) {\n    var that = context(this, searchString, STARTS_WITH);\n    var index = toLength(Math.min(arguments.length > 1 ? arguments[1] : undefined, that.length));\n    var search = String(searchString);\n    return $startsWith\n      ? $startsWith.call(that, search, index)\n      : that.slice(index, index + search.length) === search;\n  }\n});\n","var $export = require('./_export');\nvar fails = require('./_fails');\nvar defined = require('./_defined');\nvar quot = /\"/g;\n// B.2.3.2.1 CreateHTML(string, tag, attribute, value)\nvar createHTML = function (string, tag, attribute, value) {\n  var S = String(defined(string));\n  var p1 = '<' + tag;\n  if (attribute !== '') p1 += ' ' + attribute + '=\"' + String(value).replace(quot, '&quot;') + '\"';\n  return p1 + '>' + S + '</' + tag + '>';\n};\nmodule.exports = function (NAME, exec) {\n  var O = {};\n  O[NAME] = exec(createHTML);\n  $export($export.P + $export.F * fails(function () {\n    var test = ''[NAME]('\"');\n    return test !== test.toLowerCase() || test.split('\"').length > 3;\n  }), 'String', O);\n};\n","'use strict';\n// B.2.3.2 String.prototype.anchor(name)\nrequire('./_string-html')('anchor', function (createHTML) {\n  return function anchor(name) {\n    return createHTML(this, 'a', 'name', name);\n  };\n});\n","'use strict';\n// B.2.3.3 String.prototype.big()\nrequire('./_string-html')('big', function (createHTML) {\n  return function big() {\n    return createHTML(this, 'big', '', '');\n  };\n});\n","'use strict';\n// B.2.3.4 String.prototype.blink()\nrequire('./_string-html')('blink', function (createHTML) {\n  return function blink() {\n    return createHTML(this, 'blink', '', '');\n  };\n});\n","'use strict';\n// B.2.3.5 String.prototype.bold()\nrequire('./_string-html')('bold', function (createHTML) {\n  return function bold() {\n    return createHTML(this, 'b', '', '');\n  };\n});\n","'use strict';\n// B.2.3.6 String.prototype.fixed()\nrequire('./_string-html')('fixed', function (createHTML) {\n  return function fixed() {\n    return createHTML(this, 'tt', '', '');\n  };\n});\n","'use strict';\n// B.2.3.7 String.prototype.fontcolor(color)\nrequire('./_string-html')('fontcolor', function (createHTML) {\n  return function fontcolor(color) {\n    return createHTML(this, 'font', 'color', color);\n  };\n});\n","'use strict';\n// B.2.3.8 String.prototype.fontsize(size)\nrequire('./_string-html')('fontsize', function (createHTML) {\n  return function fontsize(size) {\n    return createHTML(this, 'font', 'size', size);\n  };\n});\n","'use strict';\n// B.2.3.9 String.prototype.italics()\nrequire('./_string-html')('italics', function (createHTML) {\n  return function italics() {\n    return createHTML(this, 'i', '', '');\n  };\n});\n","'use strict';\n// B.2.3.10 String.prototype.link(url)\nrequire('./_string-html')('link', function (createHTML) {\n  return function link(url) {\n    return createHTML(this, 'a', 'href', url);\n  };\n});\n","'use strict';\n// B.2.3.11 String.prototype.small()\nrequire('./_string-html')('small', function (createHTML) {\n  return function small() {\n    return createHTML(this, 'small', '', '');\n  };\n});\n","'use strict';\n// B.2.3.12 String.prototype.strike()\nrequire('./_string-html')('strike', function (createHTML) {\n  return function strike() {\n    return createHTML(this, 'strike', '', '');\n  };\n});\n","'use strict';\n// B.2.3.13 String.prototype.sub()\nrequire('./_string-html')('sub', function (createHTML) {\n  return function sub() {\n    return createHTML(this, 'sub', '', '');\n  };\n});\n","'use strict';\n// B.2.3.14 String.prototype.sup()\nrequire('./_string-html')('sup', function (createHTML) {\n  return function sup() {\n    return createHTML(this, 'sup', '', '');\n  };\n});\n","// 20.3.3.1 / 15.9.4.4 Date.now()\nvar $export = require('./_export');\n\n$export($export.S, 'Date', { now: function () { return new Date().getTime(); } });\n","'use strict';\nvar $export = require('./_export');\nvar toObject = require('./_to-object');\nvar toPrimitive = require('./_to-primitive');\n\n$export($export.P + $export.F * require('./_fails')(function () {\n  return new Date(NaN).toJSON() !== null\n    || Date.prototype.toJSON.call({ toISOString: function () { return 1; } }) !== 1;\n}), 'Date', {\n  // eslint-disable-next-line no-unused-vars\n  toJSON: function toJSON(key) {\n    var O = toObject(this);\n    var pv = toPrimitive(O);\n    return typeof pv == 'number' && !isFinite(pv) ? null : O.toISOString();\n  }\n});\n","'use strict';\n// 20.3.4.36 / 15.9.5.43 Date.prototype.toISOString()\nvar fails = require('./_fails');\nvar getTime = Date.prototype.getTime;\nvar $toISOString = Date.prototype.toISOString;\n\nvar lz = function (num) {\n  return num > 9 ? num : '0' + num;\n};\n\n// PhantomJS / old WebKit has a broken implementations\nmodule.exports = (fails(function () {\n  return $toISOString.call(new Date(-5e13 - 1)) != '0385-07-25T07:06:39.999Z';\n}) || !fails(function () {\n  $toISOString.call(new Date(NaN));\n})) ? function toISOString() {\n  if (!isFinite(getTime.call(this))) throw RangeError('Invalid time value');\n  var d = this;\n  var y = d.getUTCFullYear();\n  var m = d.getUTCMilliseconds();\n  var s = y < 0 ? '-' : y > 9999 ? '+' : '';\n  return s + ('00000' + Math.abs(y)).slice(s ? -6 : -4) +\n    '-' + lz(d.getUTCMonth() + 1) + '-' + lz(d.getUTCDate()) +\n    'T' + lz(d.getUTCHours()) + ':' + lz(d.getUTCMinutes()) +\n    ':' + lz(d.getUTCSeconds()) + '.' + (m > 99 ? m : '0' + lz(m)) + 'Z';\n} : $toISOString;\n","// 20.3.4.36 / 15.9.5.43 Date.prototype.toISOString()\nvar $export = require('./_export');\nvar toISOString = require('./_date-to-iso-string');\n\n// PhantomJS / old WebKit has a broken implementations\n$export($export.P + $export.F * (Date.prototype.toISOString !== toISOString), 'Date', {\n  toISOString: toISOString\n});\n","var DateProto = Date.prototype;\nvar INVALID_DATE = 'Invalid Date';\nvar TO_STRING = 'toString';\nvar $toString = DateProto[TO_STRING];\nvar getTime = DateProto.getTime;\nif (new Date(NaN) + '' != INVALID_DATE) {\n  require('./_redefine')(DateProto, TO_STRING, function toString() {\n    var value = getTime.call(this);\n    // eslint-disable-next-line no-self-compare\n    return value === value ? $toString.call(this) : INVALID_DATE;\n  });\n}\n","'use strict';\nvar anObject = require('./_an-object');\nvar toPrimitive = require('./_to-primitive');\nvar NUMBER = 'number';\n\nmodule.exports = function (hint) {\n  if (hint !== 'string' && hint !== NUMBER && hint !== 'default') throw TypeError('Incorrect hint');\n  return toPrimitive(anObject(this), hint != NUMBER);\n};\n","var TO_PRIMITIVE = require('./_wks')('toPrimitive');\nvar proto = Date.prototype;\n\nif (!(TO_PRIMITIVE in proto)) require('./_hide')(proto, TO_PRIMITIVE, require('./_date-to-primitive'));\n","// 22.1.2.2 / 15.4.3.2 Array.isArray(arg)\nvar $export = require('./_export');\n\n$export($export.S, 'Array', { isArray: require('./_is-array') });\n","// call something on iterator step with safe closing on error\nvar anObject = require('./_an-object');\nmodule.exports = function (iterator, fn, value, entries) {\n  try {\n    return entries ? fn(anObject(value)[0], value[1]) : fn(value);\n  // 7.4.6 IteratorClose(iterator, completion)\n  } catch (e) {\n    var ret = iterator['return'];\n    if (ret !== undefined) anObject(ret.call(iterator));\n    throw e;\n  }\n};\n","// check on default Array iterator\nvar Iterators = require('./_iterators');\nvar ITERATOR = require('./_wks')('iterator');\nvar ArrayProto = Array.prototype;\n\nmodule.exports = function (it) {\n  return it !== undefined && (Iterators.Array === it || ArrayProto[ITERATOR] === it);\n};\n","'use strict';\nvar $defineProperty = require('./_object-dp');\nvar createDesc = require('./_property-desc');\n\nmodule.exports = function (object, index, value) {\n  if (index in object) $defineProperty.f(object, index, createDesc(0, value));\n  else object[index] = value;\n};\n","var classof = require('./_classof');\nvar ITERATOR = require('./_wks')('iterator');\nvar Iterators = require('./_iterators');\nmodule.exports = require('./_core').getIteratorMethod = function (it) {\n  if (it != undefined) return it[ITERATOR]\n    || it['@@iterator']\n    || Iterators[classof(it)];\n};\n","var ITERATOR = require('./_wks')('iterator');\nvar SAFE_CLOSING = false;\n\ntry {\n  var riter = [7][ITERATOR]();\n  riter['return'] = function () { SAFE_CLOSING = true; };\n  // eslint-disable-next-line no-throw-literal\n  Array.from(riter, function () { throw 2; });\n} catch (e) { /* empty */ }\n\nmodule.exports = function (exec, skipClosing) {\n  if (!skipClosing && !SAFE_CLOSING) return false;\n  var safe = false;\n  try {\n    var arr = [7];\n    var iter = arr[ITERATOR]();\n    iter.next = function () { return { done: safe = true }; };\n    arr[ITERATOR] = function () { return iter; };\n    exec(arr);\n  } catch (e) { /* empty */ }\n  return safe;\n};\n","'use strict';\nvar ctx = require('./_ctx');\nvar $export = require('./_export');\nvar toObject = require('./_to-object');\nvar call = require('./_iter-call');\nvar isArrayIter = require('./_is-array-iter');\nvar toLength = require('./_to-length');\nvar createProperty = require('./_create-property');\nvar getIterFn = require('./core.get-iterator-method');\n\n$export($export.S + $export.F * !require('./_iter-detect')(function (iter) { Array.from(iter); }), 'Array', {\n  // 22.1.2.1 Array.from(arrayLike, mapfn = undefined, thisArg = undefined)\n  from: function from(arrayLike /* , mapfn = undefined, thisArg = undefined */) {\n    var O = toObject(arrayLike);\n    var C = typeof this == 'function' ? this : Array;\n    var aLen = arguments.length;\n    var mapfn = aLen > 1 ? arguments[1] : undefined;\n    var mapping = mapfn !== undefined;\n    var index = 0;\n    var iterFn = getIterFn(O);\n    var length, result, step, iterator;\n    if (mapping) mapfn = ctx(mapfn, aLen > 2 ? arguments[2] : undefined, 2);\n    // if object isn't iterable or it's array with default iterator - use simple case\n    if (iterFn != undefined && !(C == Array && isArrayIter(iterFn))) {\n      for (iterator = iterFn.call(O), result = new C(); !(step = iterator.next()).done; index++) {\n        createProperty(result, index, mapping ? call(iterator, mapfn, [step.value, index], true) : step.value);\n      }\n    } else {\n      length = toLength(O.length);\n      for (result = new C(length); length > index; index++) {\n        createProperty(result, index, mapping ? mapfn(O[index], index) : O[index]);\n      }\n    }\n    result.length = index;\n    return result;\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar createProperty = require('./_create-property');\n\n// WebKit Array.of isn't generic\n$export($export.S + $export.F * require('./_fails')(function () {\n  function F() { /* empty */ }\n  return !(Array.of.call(F) instanceof F);\n}), 'Array', {\n  // 22.1.2.3 Array.of( ...items)\n  of: function of(/* ...args */) {\n    var index = 0;\n    var aLen = arguments.length;\n    var result = new (typeof this == 'function' ? this : Array)(aLen);\n    while (aLen > index) createProperty(result, index, arguments[index++]);\n    result.length = aLen;\n    return result;\n  }\n});\n","'use strict';\nvar fails = require('./_fails');\n\nmodule.exports = function (method, arg) {\n  return !!method && fails(function () {\n    // eslint-disable-next-line no-useless-call\n    arg ? method.call(null, function () { /* empty */ }, 1) : method.call(null);\n  });\n};\n","'use strict';\n// 22.1.3.13 Array.prototype.join(separator)\nvar $export = require('./_export');\nvar toIObject = require('./_to-iobject');\nvar arrayJoin = [].join;\n\n// fallback for not array-like strings\n$export($export.P + $export.F * (require('./_iobject') != Object || !require('./_strict-method')(arrayJoin)), 'Array', {\n  join: function join(separator) {\n    return arrayJoin.call(toIObject(this), separator === undefined ? ',' : separator);\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar html = require('./_html');\nvar cof = require('./_cof');\nvar toAbsoluteIndex = require('./_to-absolute-index');\nvar toLength = require('./_to-length');\nvar arraySlice = [].slice;\n\n// fallback for not array-like ES3 strings and DOM objects\n$export($export.P + $export.F * require('./_fails')(function () {\n  if (html) arraySlice.call(html);\n}), 'Array', {\n  slice: function slice(begin, end) {\n    var len = toLength(this.length);\n    var klass = cof(this);\n    end = end === undefined ? len : end;\n    if (klass == 'Array') return arraySlice.call(this, begin, end);\n    var start = toAbsoluteIndex(begin, len);\n    var upTo = toAbsoluteIndex(end, len);\n    var size = toLength(upTo - start);\n    var cloned = new Array(size);\n    var i = 0;\n    for (; i < size; i++) cloned[i] = klass == 'String'\n      ? this.charAt(start + i)\n      : this[start + i];\n    return cloned;\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar aFunction = require('./_a-function');\nvar toObject = require('./_to-object');\nvar fails = require('./_fails');\nvar $sort = [].sort;\nvar test = [1, 2, 3];\n\n$export($export.P + $export.F * (fails(function () {\n  // IE8-\n  test.sort(undefined);\n}) || !fails(function () {\n  // V8 bug\n  test.sort(null);\n  // Old WebKit\n}) || !require('./_strict-method')($sort)), 'Array', {\n  // 22.1.3.25 Array.prototype.sort(comparefn)\n  sort: function sort(comparefn) {\n    return comparefn === undefined\n      ? $sort.call(toObject(this))\n      : $sort.call(toObject(this), aFunction(comparefn));\n  }\n});\n","var isObject = require('./_is-object');\nvar isArray = require('./_is-array');\nvar SPECIES = require('./_wks')('species');\n\nmodule.exports = function (original) {\n  var C;\n  if (isArray(original)) {\n    C = original.constructor;\n    // cross-realm fallback\n    if (typeof C == 'function' && (C === Array || isArray(C.prototype))) C = undefined;\n    if (isObject(C)) {\n      C = C[SPECIES];\n      if (C === null) C = undefined;\n    }\n  } return C === undefined ? Array : C;\n};\n","// 9.4.2.3 ArraySpeciesCreate(originalArray, length)\nvar speciesConstructor = require('./_array-species-constructor');\n\nmodule.exports = function (original, length) {\n  return new (speciesConstructor(original))(length);\n};\n","// 0 -> Array#forEach\n// 1 -> Array#map\n// 2 -> Array#filter\n// 3 -> Array#some\n// 4 -> Array#every\n// 5 -> Array#find\n// 6 -> Array#findIndex\nvar ctx = require('./_ctx');\nvar IObject = require('./_iobject');\nvar toObject = require('./_to-object');\nvar toLength = require('./_to-length');\nvar asc = require('./_array-species-create');\nmodule.exports = function (TYPE, $create) {\n  var IS_MAP = TYPE == 1;\n  var IS_FILTER = TYPE == 2;\n  var IS_SOME = TYPE == 3;\n  var IS_EVERY = TYPE == 4;\n  var IS_FIND_INDEX = TYPE == 6;\n  var NO_HOLES = TYPE == 5 || IS_FIND_INDEX;\n  var create = $create || asc;\n  return function ($this, callbackfn, that) {\n    var O = toObject($this);\n    var self = IObject(O);\n    var f = ctx(callbackfn, that, 3);\n    var length = toLength(self.length);\n    var index = 0;\n    var result = IS_MAP ? create($this, length) : IS_FILTER ? create($this, 0) : undefined;\n    var val, res;\n    for (;length > index; index++) if (NO_HOLES || index in self) {\n      val = self[index];\n      res = f(val, index, O);\n      if (TYPE) {\n        if (IS_MAP) result[index] = res;   // map\n        else if (res) switch (TYPE) {\n          case 3: return true;             // some\n          case 5: return val;              // find\n          case 6: return index;            // findIndex\n          case 2: result.push(val);        // filter\n        } else if (IS_EVERY) return false; // every\n      }\n    }\n    return IS_FIND_INDEX ? -1 : IS_SOME || IS_EVERY ? IS_EVERY : result;\n  };\n};\n","'use strict';\nvar $export = require('./_export');\nvar $forEach = require('./_array-methods')(0);\nvar STRICT = require('./_strict-method')([].forEach, true);\n\n$export($export.P + $export.F * !STRICT, 'Array', {\n  // 22.1.3.10 / 15.4.4.18 Array.prototype.forEach(callbackfn [, thisArg])\n  forEach: function forEach(callbackfn /* , thisArg */) {\n    return $forEach(this, callbackfn, arguments[1]);\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar $map = require('./_array-methods')(1);\n\n$export($export.P + $export.F * !require('./_strict-method')([].map, true), 'Array', {\n  // 22.1.3.15 / 15.4.4.19 Array.prototype.map(callbackfn [, thisArg])\n  map: function map(callbackfn /* , thisArg */) {\n    return $map(this, callbackfn, arguments[1]);\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar $filter = require('./_array-methods')(2);\n\n$export($export.P + $export.F * !require('./_strict-method')([].filter, true), 'Array', {\n  // 22.1.3.7 / 15.4.4.20 Array.prototype.filter(callbackfn [, thisArg])\n  filter: function filter(callbackfn /* , thisArg */) {\n    return $filter(this, callbackfn, arguments[1]);\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar $some = require('./_array-methods')(3);\n\n$export($export.P + $export.F * !require('./_strict-method')([].some, true), 'Array', {\n  // 22.1.3.23 / 15.4.4.17 Array.prototype.some(callbackfn [, thisArg])\n  some: function some(callbackfn /* , thisArg */) {\n    return $some(this, callbackfn, arguments[1]);\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar $every = require('./_array-methods')(4);\n\n$export($export.P + $export.F * !require('./_strict-method')([].every, true), 'Array', {\n  // 22.1.3.5 / 15.4.4.16 Array.prototype.every(callbackfn [, thisArg])\n  every: function every(callbackfn /* , thisArg */) {\n    return $every(this, callbackfn, arguments[1]);\n  }\n});\n","var aFunction = require('./_a-function');\nvar toObject = require('./_to-object');\nvar IObject = require('./_iobject');\nvar toLength = require('./_to-length');\n\nmodule.exports = function (that, callbackfn, aLen, memo, isRight) {\n  aFunction(callbackfn);\n  var O = toObject(that);\n  var self = IObject(O);\n  var length = toLength(O.length);\n  var index = isRight ? length - 1 : 0;\n  var i = isRight ? -1 : 1;\n  if (aLen < 2) for (;;) {\n    if (index in self) {\n      memo = self[index];\n      index += i;\n      break;\n    }\n    index += i;\n    if (isRight ? index < 0 : length <= index) {\n      throw TypeError('Reduce of empty array with no initial value');\n    }\n  }\n  for (;isRight ? index >= 0 : length > index; index += i) if (index in self) {\n    memo = callbackfn(memo, self[index], index, O);\n  }\n  return memo;\n};\n","'use strict';\nvar $export = require('./_export');\nvar $reduce = require('./_array-reduce');\n\n$export($export.P + $export.F * !require('./_strict-method')([].reduce, true), 'Array', {\n  // 22.1.3.18 / 15.4.4.21 Array.prototype.reduce(callbackfn [, initialValue])\n  reduce: function reduce(callbackfn /* , initialValue */) {\n    return $reduce(this, callbackfn, arguments.length, arguments[1], false);\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar $reduce = require('./_array-reduce');\n\n$export($export.P + $export.F * !require('./_strict-method')([].reduceRight, true), 'Array', {\n  // 22.1.3.19 / 15.4.4.22 Array.prototype.reduceRight(callbackfn [, initialValue])\n  reduceRight: function reduceRight(callbackfn /* , initialValue */) {\n    return $reduce(this, callbackfn, arguments.length, arguments[1], true);\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar $indexOf = require('./_array-includes')(false);\nvar $native = [].indexOf;\nvar NEGATIVE_ZERO = !!$native && 1 / [1].indexOf(1, -0) < 0;\n\n$export($export.P + $export.F * (NEGATIVE_ZERO || !require('./_strict-method')($native)), 'Array', {\n  // 22.1.3.11 / 15.4.4.14 Array.prototype.indexOf(searchElement [, fromIndex])\n  indexOf: function indexOf(searchElement /* , fromIndex = 0 */) {\n    return NEGATIVE_ZERO\n      // convert -0 to +0\n      ? $native.apply(this, arguments) || 0\n      : $indexOf(this, searchElement, arguments[1]);\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar toIObject = require('./_to-iobject');\nvar toInteger = require('./_to-integer');\nvar toLength = require('./_to-length');\nvar $native = [].lastIndexOf;\nvar NEGATIVE_ZERO = !!$native && 1 / [1].lastIndexOf(1, -0) < 0;\n\n$export($export.P + $export.F * (NEGATIVE_ZERO || !require('./_strict-method')($native)), 'Array', {\n  // 22.1.3.14 / 15.4.4.15 Array.prototype.lastIndexOf(searchElement [, fromIndex])\n  lastIndexOf: function lastIndexOf(searchElement /* , fromIndex = @[*-1] */) {\n    // convert -0 to +0\n    if (NEGATIVE_ZERO) return $native.apply(this, arguments) || 0;\n    var O = toIObject(this);\n    var length = toLength(O.length);\n    var index = length - 1;\n    if (arguments.length > 1) index = Math.min(index, toInteger(arguments[1]));\n    if (index < 0) index = length + index;\n    for (;index >= 0; index--) if (index in O) if (O[index] === searchElement) return index || 0;\n    return -1;\n  }\n});\n","// 22.1.3.3 Array.prototype.copyWithin(target, start, end = this.length)\n'use strict';\nvar toObject = require('./_to-object');\nvar toAbsoluteIndex = require('./_to-absolute-index');\nvar toLength = require('./_to-length');\n\nmodule.exports = [].copyWithin || function copyWithin(target /* = 0 */, start /* = 0, end = @length */) {\n  var O = toObject(this);\n  var len = toLength(O.length);\n  var to = toAbsoluteIndex(target, len);\n  var from = toAbsoluteIndex(start, len);\n  var end = arguments.length > 2 ? arguments[2] : undefined;\n  var count = Math.min((end === undefined ? len : toAbsoluteIndex(end, len)) - from, len - to);\n  var inc = 1;\n  if (from < to && to < from + count) {\n    inc = -1;\n    from += count - 1;\n    to += count - 1;\n  }\n  while (count-- > 0) {\n    if (from in O) O[to] = O[from];\n    else delete O[to];\n    to += inc;\n    from += inc;\n  } return O;\n};\n","// 22.1.3.31 Array.prototype[@@unscopables]\nvar UNSCOPABLES = require('./_wks')('unscopables');\nvar ArrayProto = Array.prototype;\nif (ArrayProto[UNSCOPABLES] == undefined) require('./_hide')(ArrayProto, UNSCOPABLES, {});\nmodule.exports = function (key) {\n  ArrayProto[UNSCOPABLES][key] = true;\n};\n","// 22.1.3.3 Array.prototype.copyWithin(target, start, end = this.length)\nvar $export = require('./_export');\n\n$export($export.P, 'Array', { copyWithin: require('./_array-copy-within') });\n\nrequire('./_add-to-unscopables')('copyWithin');\n","// 22.1.3.6 Array.prototype.fill(value, start = 0, end = this.length)\n'use strict';\nvar toObject = require('./_to-object');\nvar toAbsoluteIndex = require('./_to-absolute-index');\nvar toLength = require('./_to-length');\nmodule.exports = function fill(value /* , start = 0, end = @length */) {\n  var O = toObject(this);\n  var length = toLength(O.length);\n  var aLen = arguments.length;\n  var index = toAbsoluteIndex(aLen > 1 ? arguments[1] : undefined, length);\n  var end = aLen > 2 ? arguments[2] : undefined;\n  var endPos = end === undefined ? length : toAbsoluteIndex(end, length);\n  while (endPos > index) O[index++] = value;\n  return O;\n};\n","// 22.1.3.6 Array.prototype.fill(value, start = 0, end = this.length)\nvar $export = require('./_export');\n\n$export($export.P, 'Array', { fill: require('./_array-fill') });\n\nrequire('./_add-to-unscopables')('fill');\n","'use strict';\n// 22.1.3.8 Array.prototype.find(predicate, thisArg = undefined)\nvar $export = require('./_export');\nvar $find = require('./_array-methods')(5);\nvar KEY = 'find';\nvar forced = true;\n// Shouldn't skip holes\nif (KEY in []) Array(1)[KEY](function () { forced = false; });\n$export($export.P + $export.F * forced, 'Array', {\n  find: function find(callbackfn /* , that = undefined */) {\n    return $find(this, callbackfn, arguments.length > 1 ? arguments[1] : undefined);\n  }\n});\nrequire('./_add-to-unscopables')(KEY);\n","'use strict';\n// 22.1.3.9 Array.prototype.findIndex(predicate, thisArg = undefined)\nvar $export = require('./_export');\nvar $find = require('./_array-methods')(6);\nvar KEY = 'findIndex';\nvar forced = true;\n// Shouldn't skip holes\nif (KEY in []) Array(1)[KEY](function () { forced = false; });\n$export($export.P + $export.F * forced, 'Array', {\n  findIndex: function findIndex(callbackfn /* , that = undefined */) {\n    return $find(this, callbackfn, arguments.length > 1 ? arguments[1] : undefined);\n  }\n});\nrequire('./_add-to-unscopables')(KEY);\n","'use strict';\nvar global = require('./_global');\nvar dP = require('./_object-dp');\nvar DESCRIPTORS = require('./_descriptors');\nvar SPECIES = require('./_wks')('species');\n\nmodule.exports = function (KEY) {\n  var C = global[KEY];\n  if (DESCRIPTORS && C && !C[SPECIES]) dP.f(C, SPECIES, {\n    configurable: true,\n    get: function () { return this; }\n  });\n};\n","require('./_set-species')('Array');\n","module.exports = function (done, value) {\n  return { value: value, done: !!done };\n};\n","'use strict';\nvar addToUnscopables = require('./_add-to-unscopables');\nvar step = require('./_iter-step');\nvar Iterators = require('./_iterators');\nvar toIObject = require('./_to-iobject');\n\n// 22.1.3.4 Array.prototype.entries()\n// 22.1.3.13 Array.prototype.keys()\n// 22.1.3.29 Array.prototype.values()\n// 22.1.3.30 Array.prototype[@@iterator]()\nmodule.exports = require('./_iter-define')(Array, 'Array', function (iterated, kind) {\n  this._t = toIObject(iterated); // target\n  this._i = 0;                   // next index\n  this._k = kind;                // kind\n// 22.1.5.2.1 %ArrayIteratorPrototype%.next()\n}, function () {\n  var O = this._t;\n  var kind = this._k;\n  var index = this._i++;\n  if (!O || index >= O.length) {\n    this._t = undefined;\n    return step(1);\n  }\n  if (kind == 'keys') return step(0, index);\n  if (kind == 'values') return step(0, O[index]);\n  return step(0, [index, O[index]]);\n}, 'values');\n\n// argumentsList[@@iterator] is %ArrayProto_values% (9.4.4.6, 9.4.4.7)\nIterators.Arguments = Iterators.Array;\n\naddToUnscopables('keys');\naddToUnscopables('values');\naddToUnscopables('entries');\n","'use strict';\n// 21.2.5.3 get RegExp.prototype.flags\nvar anObject = require('./_an-object');\nmodule.exports = function () {\n  var that = anObject(this);\n  var result = '';\n  if (that.global) result += 'g';\n  if (that.ignoreCase) result += 'i';\n  if (that.multiline) result += 'm';\n  if (that.unicode) result += 'u';\n  if (that.sticky) result += 'y';\n  return result;\n};\n","var global = require('./_global');\nvar inheritIfRequired = require('./_inherit-if-required');\nvar dP = require('./_object-dp').f;\nvar gOPN = require('./_object-gopn').f;\nvar isRegExp = require('./_is-regexp');\nvar $flags = require('./_flags');\nvar $RegExp = global.RegExp;\nvar Base = $RegExp;\nvar proto = $RegExp.prototype;\nvar re1 = /a/g;\nvar re2 = /a/g;\n// \"new\" creates a new object, old webkit buggy here\nvar CORRECT_NEW = new $RegExp(re1) !== re1;\n\nif (require('./_descriptors') && (!CORRECT_NEW || require('./_fails')(function () {\n  re2[require('./_wks')('match')] = false;\n  // RegExp constructor can alter flags and IsRegExp works correct with @@match\n  return $RegExp(re1) != re1 || $RegExp(re2) == re2 || $RegExp(re1, 'i') != '/a/i';\n}))) {\n  $RegExp = function RegExp(p, f) {\n    var tiRE = this instanceof $RegExp;\n    var piRE = isRegExp(p);\n    var fiU = f === undefined;\n    return !tiRE && piRE && p.constructor === $RegExp && fiU ? p\n      : inheritIfRequired(CORRECT_NEW\n        ? new Base(piRE && !fiU ? p.source : p, f)\n        : Base((piRE = p instanceof $RegExp) ? p.source : p, piRE && fiU ? $flags.call(p) : f)\n      , tiRE ? this : proto, $RegExp);\n  };\n  var proxy = function (key) {\n    key in $RegExp || dP($RegExp, key, {\n      configurable: true,\n      get: function () { return Base[key]; },\n      set: function (it) { Base[key] = it; }\n    });\n  };\n  for (var keys = gOPN(Base), i = 0; keys.length > i;) proxy(keys[i++]);\n  proto.constructor = $RegExp;\n  $RegExp.prototype = proto;\n  require('./_redefine')(global, 'RegExp', $RegExp);\n}\n\nrequire('./_set-species')('RegExp');\n","'use strict';\n\nvar regexpFlags = require('./_flags');\n\nvar nativeExec = RegExp.prototype.exec;\n// This always refers to the native implementation, because the\n// String#replace polyfill uses ./fix-regexp-well-known-symbol-logic.js,\n// which loads this file before patching the method.\nvar nativeReplace = String.prototype.replace;\n\nvar patchedExec = nativeExec;\n\nvar LAST_INDEX = 'lastIndex';\n\nvar UPDATES_LAST_INDEX_WRONG = (function () {\n  var re1 = /a/,\n      re2 = /b*/g;\n  nativeExec.call(re1, 'a');\n  nativeExec.call(re2, 'a');\n  return re1[LAST_INDEX] !== 0 || re2[LAST_INDEX] !== 0;\n})();\n\n// nonparticipating capturing group, copied from es5-shim's String#split patch.\nvar NPCG_INCLUDED = /()??/.exec('')[1] !== undefined;\n\nvar PATCH = UPDATES_LAST_INDEX_WRONG || NPCG_INCLUDED;\n\nif (PATCH) {\n  patchedExec = function exec(str) {\n    var re = this;\n    var lastIndex, reCopy, match, i;\n\n    if (NPCG_INCLUDED) {\n      reCopy = new RegExp('^' + re.source + '$(?!\\\\s)', regexpFlags.call(re));\n    }\n    if (UPDATES_LAST_INDEX_WRONG) lastIndex = re[LAST_INDEX];\n\n    match = nativeExec.call(re, str);\n\n    if (UPDATES_LAST_INDEX_WRONG && match) {\n      re[LAST_INDEX] = re.global ? match.index + match[0].length : lastIndex;\n    }\n    if (NPCG_INCLUDED && match && match.length > 1) {\n      // Fix browsers whose `exec` methods don't consistently return `undefined`\n      // for NPCG, like IE8. NOTE: This doesn' work for /(.?)?/\n      // eslint-disable-next-line no-loop-func\n      nativeReplace.call(match[0], reCopy, function () {\n        for (i = 1; i < arguments.length - 2; i++) {\n          if (arguments[i] === undefined) match[i] = undefined;\n        }\n      });\n    }\n\n    return match;\n  };\n}\n\nmodule.exports = patchedExec;\n","'use strict';\nvar regexpExec = require('./_regexp-exec');\nrequire('./_export')({\n  target: 'RegExp',\n  proto: true,\n  forced: regexpExec !== /./.exec\n}, {\n  exec: regexpExec\n});\n","// 21.2.5.3 get RegExp.prototype.flags()\nif (require('./_descriptors') && /./g.flags != 'g') require('./_object-dp').f(RegExp.prototype, 'flags', {\n  configurable: true,\n  get: require('./_flags')\n});\n","'use strict';\nrequire('./es6.regexp.flags');\nvar anObject = require('./_an-object');\nvar $flags = require('./_flags');\nvar DESCRIPTORS = require('./_descriptors');\nvar TO_STRING = 'toString';\nvar $toString = /./[TO_STRING];\n\nvar define = function (fn) {\n  require('./_redefine')(RegExp.prototype, TO_STRING, fn, true);\n};\n\n// 21.2.5.14 RegExp.prototype.toString()\nif (require('./_fails')(function () { return $toString.call({ source: 'a', flags: 'b' }) != '/a/b'; })) {\n  define(function toString() {\n    var R = anObject(this);\n    return '/'.concat(R.source, '/',\n      'flags' in R ? R.flags : !DESCRIPTORS && R instanceof RegExp ? $flags.call(R) : undefined);\n  });\n// FF44- RegExp#toString has a wrong name\n} else if ($toString.name != TO_STRING) {\n  define(function toString() {\n    return $toString.call(this);\n  });\n}\n","'use strict';\nvar at = require('./_string-at')(true);\n\n // `AdvanceStringIndex` abstract operation\n// https://tc39.github.io/ecma262/#sec-advancestringindex\nmodule.exports = function (S, index, unicode) {\n  return index + (unicode ? at(S, index).length : 1);\n};\n","'use strict';\n\nvar classof = require('./_classof');\nvar builtinExec = RegExp.prototype.exec;\n\n // `RegExpExec` abstract operation\n// https://tc39.github.io/ecma262/#sec-regexpexec\nmodule.exports = function (R, S) {\n  var exec = R.exec;\n  if (typeof exec === 'function') {\n    var result = exec.call(R, S);\n    if (typeof result !== 'object') {\n      throw new TypeError('RegExp exec method returned something other than an Object or null');\n    }\n    return result;\n  }\n  if (classof(R) !== 'RegExp') {\n    throw new TypeError('RegExp#exec called on incompatible receiver');\n  }\n  return builtinExec.call(R, S);\n};\n","'use strict';\nrequire('./es6.regexp.exec');\nvar redefine = require('./_redefine');\nvar hide = require('./_hide');\nvar fails = require('./_fails');\nvar defined = require('./_defined');\nvar wks = require('./_wks');\nvar regexpExec = require('./_regexp-exec');\n\nvar SPECIES = wks('species');\n\nvar REPLACE_SUPPORTS_NAMED_GROUPS = !fails(function () {\n  // #replace needs built-in support for named groups.\n  // #match works fine because it just return the exec results, even if it has\n  // a \"grops\" property.\n  var re = /./;\n  re.exec = function () {\n    var result = [];\n    result.groups = { a: '7' };\n    return result;\n  };\n  return ''.replace(re, '$<a>') !== '7';\n});\n\nvar SPLIT_WORKS_WITH_OVERWRITTEN_EXEC = (function () {\n  // Chrome 51 has a buggy \"split\" implementation when RegExp#exec !== nativeExec\n  var re = /(?:)/;\n  var originalExec = re.exec;\n  re.exec = function () { return originalExec.apply(this, arguments); };\n  var result = 'ab'.split(re);\n  return result.length === 2 && result[0] === 'a' && result[1] === 'b';\n})();\n\nmodule.exports = function (KEY, length, exec) {\n  var SYMBOL = wks(KEY);\n\n  var DELEGATES_TO_SYMBOL = !fails(function () {\n    // String methods call symbol-named RegEp methods\n    var O = {};\n    O[SYMBOL] = function () { return 7; };\n    return ''[KEY](O) != 7;\n  });\n\n  var DELEGATES_TO_EXEC = DELEGATES_TO_SYMBOL ? !fails(function () {\n    // Symbol-named RegExp methods call .exec\n    var execCalled = false;\n    var re = /a/;\n    re.exec = function () { execCalled = true; return null; };\n    if (KEY === 'split') {\n      // RegExp[@@split] doesn't call the regex's exec method, but first creates\n      // a new one. We need to return the patched regex when creating the new one.\n      re.constructor = {};\n      re.constructor[SPECIES] = function () { return re; };\n    }\n    re[SYMBOL]('');\n    return !execCalled;\n  }) : undefined;\n\n  if (\n    !DELEGATES_TO_SYMBOL ||\n    !DELEGATES_TO_EXEC ||\n    (KEY === 'replace' && !REPLACE_SUPPORTS_NAMED_GROUPS) ||\n    (KEY === 'split' && !SPLIT_WORKS_WITH_OVERWRITTEN_EXEC)\n  ) {\n    var nativeRegExpMethod = /./[SYMBOL];\n    var fns = exec(\n      defined,\n      SYMBOL,\n      ''[KEY],\n      function maybeCallNative(nativeMethod, regexp, str, arg2, forceStringMethod) {\n        if (regexp.exec === regexpExec) {\n          if (DELEGATES_TO_SYMBOL && !forceStringMethod) {\n            // The native String method already delegates to @@method (this\n            // polyfilled function), leasing to infinite recursion.\n            // We avoid it by directly calling the native @@method method.\n            return { done: true, value: nativeRegExpMethod.call(regexp, str, arg2) };\n          }\n          return { done: true, value: nativeMethod.call(str, regexp, arg2) };\n        }\n        return { done: false };\n      }\n    );\n    var strfn = fns[0];\n    var rxfn = fns[1];\n\n    redefine(String.prototype, KEY, strfn);\n    hide(RegExp.prototype, SYMBOL, length == 2\n      // 21.2.5.8 RegExp.prototype[@@replace](string, replaceValue)\n      // 21.2.5.11 RegExp.prototype[@@split](string, limit)\n      ? function (string, arg) { return rxfn.call(string, this, arg); }\n      // 21.2.5.6 RegExp.prototype[@@match](string)\n      // 21.2.5.9 RegExp.prototype[@@search](string)\n      : function (string) { return rxfn.call(string, this); }\n    );\n  }\n};\n","'use strict';\n\nvar anObject = require('./_an-object');\nvar toLength = require('./_to-length');\nvar advanceStringIndex = require('./_advance-string-index');\nvar regExpExec = require('./_regexp-exec-abstract');\n\n// @@match logic\nrequire('./_fix-re-wks')('match', 1, function (defined, MATCH, $match, maybeCallNative) {\n  return [\n    // `String.prototype.match` method\n    // https://tc39.github.io/ecma262/#sec-string.prototype.match\n    function match(regexp) {\n      var O = defined(this);\n      var fn = regexp == undefined ? undefined : regexp[MATCH];\n      return fn !== undefined ? fn.call(regexp, O) : new RegExp(regexp)[MATCH](String(O));\n    },\n    // `RegExp.prototype[@@match]` method\n    // https://tc39.github.io/ecma262/#sec-regexp.prototype-@@match\n    function (regexp) {\n      var res = maybeCallNative($match, regexp, this);\n      if (res.done) return res.value;\n      var rx = anObject(regexp);\n      var S = String(this);\n      if (!rx.global) return regExpExec(rx, S);\n      var fullUnicode = rx.unicode;\n      rx.lastIndex = 0;\n      var A = [];\n      var n = 0;\n      var result;\n      while ((result = regExpExec(rx, S)) !== null) {\n        var matchStr = String(result[0]);\n        A[n] = matchStr;\n        if (matchStr === '') rx.lastIndex = advanceStringIndex(S, toLength(rx.lastIndex), fullUnicode);\n        n++;\n      }\n      return n === 0 ? null : A;\n    }\n  ];\n});\n","'use strict';\n\nvar anObject = require('./_an-object');\nvar toObject = require('./_to-object');\nvar toLength = require('./_to-length');\nvar toInteger = require('./_to-integer');\nvar advanceStringIndex = require('./_advance-string-index');\nvar regExpExec = require('./_regexp-exec-abstract');\nvar max = Math.max;\nvar min = Math.min;\nvar floor = Math.floor;\nvar SUBSTITUTION_SYMBOLS = /\\$([$&`']|\\d\\d?|<[^>]*>)/g;\nvar SUBSTITUTION_SYMBOLS_NO_NAMED = /\\$([$&`']|\\d\\d?)/g;\n\nvar maybeToString = function (it) {\n  return it === undefined ? it : String(it);\n};\n\n// @@replace logic\nrequire('./_fix-re-wks')('replace', 2, function (defined, REPLACE, $replace, maybeCallNative) {\n  return [\n    // `String.prototype.replace` method\n    // https://tc39.github.io/ecma262/#sec-string.prototype.replace\n    function replace(searchValue, replaceValue) {\n      var O = defined(this);\n      var fn = searchValue == undefined ? undefined : searchValue[REPLACE];\n      return fn !== undefined\n        ? fn.call(searchValue, O, replaceValue)\n        : $replace.call(String(O), searchValue, replaceValue);\n    },\n    // `RegExp.prototype[@@replace]` method\n    // https://tc39.github.io/ecma262/#sec-regexp.prototype-@@replace\n    function (regexp, replaceValue) {\n      var res = maybeCallNative($replace, regexp, this, replaceValue);\n      if (res.done) return res.value;\n\n      var rx = anObject(regexp);\n      var S = String(this);\n      var functionalReplace = typeof replaceValue === 'function';\n      if (!functionalReplace) replaceValue = String(replaceValue);\n      var global = rx.global;\n      if (global) {\n        var fullUnicode = rx.unicode;\n        rx.lastIndex = 0;\n      }\n      var results = [];\n      while (true) {\n        var result = regExpExec(rx, S);\n        if (result === null) break;\n        results.push(result);\n        if (!global) break;\n        var matchStr = String(result[0]);\n        if (matchStr === '') rx.lastIndex = advanceStringIndex(S, toLength(rx.lastIndex), fullUnicode);\n      }\n      var accumulatedResult = '';\n      var nextSourcePosition = 0;\n      for (var i = 0; i < results.length; i++) {\n        result = results[i];\n        var matched = String(result[0]);\n        var position = max(min(toInteger(result.index), S.length), 0);\n        var captures = [];\n        // NOTE: This is equivalent to\n        //   captures = result.slice(1).map(maybeToString)\n        // but for some reason `nativeSlice.call(result, 1, result.length)` (called in\n        // the slice polyfill when slicing native arrays) \"doesn't work\" in safari 9 and\n        // causes a crash (https://pastebin.com/N21QzeQA) when trying to debug it.\n        for (var j = 1; j < result.length; j++) captures.push(maybeToString(result[j]));\n        var namedCaptures = result.groups;\n        if (functionalReplace) {\n          var replacerArgs = [matched].concat(captures, position, S);\n          if (namedCaptures !== undefined) replacerArgs.push(namedCaptures);\n          var replacement = String(replaceValue.apply(undefined, replacerArgs));\n        } else {\n          replacement = getSubstitution(matched, S, position, captures, namedCaptures, replaceValue);\n        }\n        if (position >= nextSourcePosition) {\n          accumulatedResult += S.slice(nextSourcePosition, position) + replacement;\n          nextSourcePosition = position + matched.length;\n        }\n      }\n      return accumulatedResult + S.slice(nextSourcePosition);\n    }\n  ];\n\n    // https://tc39.github.io/ecma262/#sec-getsubstitution\n  function getSubstitution(matched, str, position, captures, namedCaptures, replacement) {\n    var tailPos = position + matched.length;\n    var m = captures.length;\n    var symbols = SUBSTITUTION_SYMBOLS_NO_NAMED;\n    if (namedCaptures !== undefined) {\n      namedCaptures = toObject(namedCaptures);\n      symbols = SUBSTITUTION_SYMBOLS;\n    }\n    return $replace.call(replacement, symbols, function (match, ch) {\n      var capture;\n      switch (ch.charAt(0)) {\n        case '$': return '$';\n        case '&': return matched;\n        case '`': return str.slice(0, position);\n        case \"'\": return str.slice(tailPos);\n        case '<':\n          capture = namedCaptures[ch.slice(1, -1)];\n          break;\n        default: // \\d\\d?\n          var n = +ch;\n          if (n === 0) return match;\n          if (n > m) {\n            var f = floor(n / 10);\n            if (f === 0) return match;\n            if (f <= m) return captures[f - 1] === undefined ? ch.charAt(1) : captures[f - 1] + ch.charAt(1);\n            return match;\n          }\n          capture = captures[n - 1];\n      }\n      return capture === undefined ? '' : capture;\n    });\n  }\n});\n","'use strict';\n\nvar anObject = require('./_an-object');\nvar sameValue = require('./_same-value');\nvar regExpExec = require('./_regexp-exec-abstract');\n\n// @@search logic\nrequire('./_fix-re-wks')('search', 1, function (defined, SEARCH, $search, maybeCallNative) {\n  return [\n    // `String.prototype.search` method\n    // https://tc39.github.io/ecma262/#sec-string.prototype.search\n    function search(regexp) {\n      var O = defined(this);\n      var fn = regexp == undefined ? undefined : regexp[SEARCH];\n      return fn !== undefined ? fn.call(regexp, O) : new RegExp(regexp)[SEARCH](String(O));\n    },\n    // `RegExp.prototype[@@search]` method\n    // https://tc39.github.io/ecma262/#sec-regexp.prototype-@@search\n    function (regexp) {\n      var res = maybeCallNative($search, regexp, this);\n      if (res.done) return res.value;\n      var rx = anObject(regexp);\n      var S = String(this);\n      var previousLastIndex = rx.lastIndex;\n      if (!sameValue(previousLastIndex, 0)) rx.lastIndex = 0;\n      var result = regExpExec(rx, S);\n      if (!sameValue(rx.lastIndex, previousLastIndex)) rx.lastIndex = previousLastIndex;\n      return result === null ? -1 : result.index;\n    }\n  ];\n});\n","// 7.3.20 SpeciesConstructor(O, defaultConstructor)\nvar anObject = require('./_an-object');\nvar aFunction = require('./_a-function');\nvar SPECIES = require('./_wks')('species');\nmodule.exports = function (O, D) {\n  var C = anObject(O).constructor;\n  var S;\n  return C === undefined || (S = anObject(C)[SPECIES]) == undefined ? D : aFunction(S);\n};\n","'use strict';\n\nvar isRegExp = require('./_is-regexp');\nvar anObject = require('./_an-object');\nvar speciesConstructor = require('./_species-constructor');\nvar advanceStringIndex = require('./_advance-string-index');\nvar toLength = require('./_to-length');\nvar callRegExpExec = require('./_regexp-exec-abstract');\nvar regexpExec = require('./_regexp-exec');\nvar fails = require('./_fails');\nvar $min = Math.min;\nvar $push = [].push;\nvar $SPLIT = 'split';\nvar LENGTH = 'length';\nvar LAST_INDEX = 'lastIndex';\nvar MAX_UINT32 = 0xffffffff;\n\n// babel-minify transpiles RegExp('x', 'y') -> /x/y and it causes SyntaxError\nvar SUPPORTS_Y = !fails(function () { RegExp(MAX_UINT32, 'y'); });\n\n// @@split logic\nrequire('./_fix-re-wks')('split', 2, function (defined, SPLIT, $split, maybeCallNative) {\n  var internalSplit;\n  if (\n    'abbc'[$SPLIT](/(b)*/)[1] == 'c' ||\n    'test'[$SPLIT](/(?:)/, -1)[LENGTH] != 4 ||\n    'ab'[$SPLIT](/(?:ab)*/)[LENGTH] != 2 ||\n    '.'[$SPLIT](/(.?)(.?)/)[LENGTH] != 4 ||\n    '.'[$SPLIT](/()()/)[LENGTH] > 1 ||\n    ''[$SPLIT](/.?/)[LENGTH]\n  ) {\n    // based on es5-shim implementation, need to rework it\n    internalSplit = function (separator, limit) {\n      var string = String(this);\n      if (separator === undefined && limit === 0) return [];\n      // If `separator` is not a regex, use native split\n      if (!isRegExp(separator)) return $split.call(string, separator, limit);\n      var output = [];\n      var flags = (separator.ignoreCase ? 'i' : '') +\n                  (separator.multiline ? 'm' : '') +\n                  (separator.unicode ? 'u' : '') +\n                  (separator.sticky ? 'y' : '');\n      var lastLastIndex = 0;\n      var splitLimit = limit === undefined ? MAX_UINT32 : limit >>> 0;\n      // Make `global` and avoid `lastIndex` issues by working with a copy\n      var separatorCopy = new RegExp(separator.source, flags + 'g');\n      var match, lastIndex, lastLength;\n      while (match = regexpExec.call(separatorCopy, string)) {\n        lastIndex = separatorCopy[LAST_INDEX];\n        if (lastIndex > lastLastIndex) {\n          output.push(string.slice(lastLastIndex, match.index));\n          if (match[LENGTH] > 1 && match.index < string[LENGTH]) $push.apply(output, match.slice(1));\n          lastLength = match[0][LENGTH];\n          lastLastIndex = lastIndex;\n          if (output[LENGTH] >= splitLimit) break;\n        }\n        if (separatorCopy[LAST_INDEX] === match.index) separatorCopy[LAST_INDEX]++; // Avoid an infinite loop\n      }\n      if (lastLastIndex === string[LENGTH]) {\n        if (lastLength || !separatorCopy.test('')) output.push('');\n      } else output.push(string.slice(lastLastIndex));\n      return output[LENGTH] > splitLimit ? output.slice(0, splitLimit) : output;\n    };\n  // Chakra, V8\n  } else if ('0'[$SPLIT](undefined, 0)[LENGTH]) {\n    internalSplit = function (separator, limit) {\n      return separator === undefined && limit === 0 ? [] : $split.call(this, separator, limit);\n    };\n  } else {\n    internalSplit = $split;\n  }\n\n  return [\n    // `String.prototype.split` method\n    // https://tc39.github.io/ecma262/#sec-string.prototype.split\n    function split(separator, limit) {\n      var O = defined(this);\n      var splitter = separator == undefined ? undefined : separator[SPLIT];\n      return splitter !== undefined\n        ? splitter.call(separator, O, limit)\n        : internalSplit.call(String(O), separator, limit);\n    },\n    // `RegExp.prototype[@@split]` method\n    // https://tc39.github.io/ecma262/#sec-regexp.prototype-@@split\n    //\n    // NOTE: This cannot be properly polyfilled in engines that don't support\n    // the 'y' flag.\n    function (regexp, limit) {\n      var res = maybeCallNative(internalSplit, regexp, this, limit, internalSplit !== $split);\n      if (res.done) return res.value;\n\n      var rx = anObject(regexp);\n      var S = String(this);\n      var C = speciesConstructor(rx, RegExp);\n\n      var unicodeMatching = rx.unicode;\n      var flags = (rx.ignoreCase ? 'i' : '') +\n                  (rx.multiline ? 'm' : '') +\n                  (rx.unicode ? 'u' : '') +\n                  (SUPPORTS_Y ? 'y' : 'g');\n\n      // ^(? + rx + ) is needed, in combination with some S slicing, to\n      // simulate the 'y' flag.\n      var splitter = new C(SUPPORTS_Y ? rx : '^(?:' + rx.source + ')', flags);\n      var lim = limit === undefined ? MAX_UINT32 : limit >>> 0;\n      if (lim === 0) return [];\n      if (S.length === 0) return callRegExpExec(splitter, S) === null ? [S] : [];\n      var p = 0;\n      var q = 0;\n      var A = [];\n      while (q < S.length) {\n        splitter.lastIndex = SUPPORTS_Y ? q : 0;\n        var z = callRegExpExec(splitter, SUPPORTS_Y ? S : S.slice(q));\n        var e;\n        if (\n          z === null ||\n          (e = $min(toLength(splitter.lastIndex + (SUPPORTS_Y ? 0 : q)), S.length)) === p\n        ) {\n          q = advanceStringIndex(S, q, unicodeMatching);\n        } else {\n          A.push(S.slice(p, q));\n          if (A.length === lim) return A;\n          for (var i = 1; i <= z.length - 1; i++) {\n            A.push(z[i]);\n            if (A.length === lim) return A;\n          }\n          q = p = e;\n        }\n      }\n      A.push(S.slice(p));\n      return A;\n    }\n  ];\n});\n","module.exports = function (it, Constructor, name, forbiddenField) {\n  if (!(it instanceof Constructor) || (forbiddenField !== undefined && forbiddenField in it)) {\n    throw TypeError(name + ': incorrect invocation!');\n  } return it;\n};\n","var ctx = require('./_ctx');\nvar call = require('./_iter-call');\nvar isArrayIter = require('./_is-array-iter');\nvar anObject = require('./_an-object');\nvar toLength = require('./_to-length');\nvar getIterFn = require('./core.get-iterator-method');\nvar BREAK = {};\nvar RETURN = {};\nvar exports = module.exports = function (iterable, entries, fn, that, ITERATOR) {\n  var iterFn = ITERATOR ? function () { return iterable; } : getIterFn(iterable);\n  var f = ctx(fn, that, entries ? 2 : 1);\n  var index = 0;\n  var length, step, iterator, result;\n  if (typeof iterFn != 'function') throw TypeError(iterable + ' is not iterable!');\n  // fast case for arrays with default iterator\n  if (isArrayIter(iterFn)) for (length = toLength(iterable.length); length > index; index++) {\n    result = entries ? f(anObject(step = iterable[index])[0], step[1]) : f(iterable[index]);\n    if (result === BREAK || result === RETURN) return result;\n  } else for (iterator = iterFn.call(iterable); !(step = iterator.next()).done;) {\n    result = call(iterator, f, step.value, entries);\n    if (result === BREAK || result === RETURN) return result;\n  }\n};\nexports.BREAK = BREAK;\nexports.RETURN = RETURN;\n","var ctx = require('./_ctx');\nvar invoke = require('./_invoke');\nvar html = require('./_html');\nvar cel = require('./_dom-create');\nvar global = require('./_global');\nvar process = global.process;\nvar setTask = global.setImmediate;\nvar clearTask = global.clearImmediate;\nvar MessageChannel = global.MessageChannel;\nvar Dispatch = global.Dispatch;\nvar counter = 0;\nvar queue = {};\nvar ONREADYSTATECHANGE = 'onreadystatechange';\nvar defer, channel, port;\nvar run = function () {\n  var id = +this;\n  // eslint-disable-next-line no-prototype-builtins\n  if (queue.hasOwnProperty(id)) {\n    var fn = queue[id];\n    delete queue[id];\n    fn();\n  }\n};\nvar listener = function (event) {\n  run.call(event.data);\n};\n// Node.js 0.9+ & IE10+ has setImmediate, otherwise:\nif (!setTask || !clearTask) {\n  setTask = function setImmediate(fn) {\n    var args = [];\n    var i = 1;\n    while (arguments.length > i) args.push(arguments[i++]);\n    queue[++counter] = function () {\n      // eslint-disable-next-line no-new-func\n      invoke(typeof fn == 'function' ? fn : Function(fn), args);\n    };\n    defer(counter);\n    return counter;\n  };\n  clearTask = function clearImmediate(id) {\n    delete queue[id];\n  };\n  // Node.js 0.8-\n  if (require('./_cof')(process) == 'process') {\n    defer = function (id) {\n      process.nextTick(ctx(run, id, 1));\n    };\n  // Sphere (JS game engine) Dispatch API\n  } else if (Dispatch && Dispatch.now) {\n    defer = function (id) {\n      Dispatch.now(ctx(run, id, 1));\n    };\n  // Browsers with MessageChannel, includes WebWorkers\n  } else if (MessageChannel) {\n    channel = new MessageChannel();\n    port = channel.port2;\n    channel.port1.onmessage = listener;\n    defer = ctx(port.postMessage, port, 1);\n  // Browsers with postMessage, skip WebWorkers\n  // IE8 has postMessage, but it's sync & typeof its postMessage is 'object'\n  } else if (global.addEventListener && typeof postMessage == 'function' && !global.importScripts) {\n    defer = function (id) {\n      global.postMessage(id + '', '*');\n    };\n    global.addEventListener('message', listener, false);\n  // IE8-\n  } else if (ONREADYSTATECHANGE in cel('script')) {\n    defer = function (id) {\n      html.appendChild(cel('script'))[ONREADYSTATECHANGE] = function () {\n        html.removeChild(this);\n        run.call(id);\n      };\n    };\n  // Rest old browsers\n  } else {\n    defer = function (id) {\n      setTimeout(ctx(run, id, 1), 0);\n    };\n  }\n}\nmodule.exports = {\n  set: setTask,\n  clear: clearTask\n};\n","var global = require('./_global');\nvar macrotask = require('./_task').set;\nvar Observer = global.MutationObserver || global.WebKitMutationObserver;\nvar process = global.process;\nvar Promise = global.Promise;\nvar isNode = require('./_cof')(process) == 'process';\n\nmodule.exports = function () {\n  var head, last, notify;\n\n  var flush = function () {\n    var parent, fn;\n    if (isNode && (parent = process.domain)) parent.exit();\n    while (head) {\n      fn = head.fn;\n      head = head.next;\n      try {\n        fn();\n      } catch (e) {\n        if (head) notify();\n        else last = undefined;\n        throw e;\n      }\n    } last = undefined;\n    if (parent) parent.enter();\n  };\n\n  // Node.js\n  if (isNode) {\n    notify = function () {\n      process.nextTick(flush);\n    };\n  // browsers with MutationObserver, except iOS Safari - https://github.com/zloirock/core-js/issues/339\n  } else if (Observer && !(global.navigator && global.navigator.standalone)) {\n    var toggle = true;\n    var node = document.createTextNode('');\n    new Observer(flush).observe(node, { characterData: true }); // eslint-disable-line no-new\n    notify = function () {\n      node.data = toggle = !toggle;\n    };\n  // environments with maybe non-completely correct, but existent Promise\n  } else if (Promise && Promise.resolve) {\n    // Promise.resolve without an argument throws an error in LG WebOS 2\n    var promise = Promise.resolve(undefined);\n    notify = function () {\n      promise.then(flush);\n    };\n  // for other environments - macrotask based on:\n  // - setImmediate\n  // - MessageChannel\n  // - window.postMessag\n  // - onreadystatechange\n  // - setTimeout\n  } else {\n    notify = function () {\n      // strange IE + webpack dev server bug - use .call(global)\n      macrotask.call(global, flush);\n    };\n  }\n\n  return function (fn) {\n    var task = { fn: fn, next: undefined };\n    if (last) last.next = task;\n    if (!head) {\n      head = task;\n      notify();\n    } last = task;\n  };\n};\n","'use strict';\n// 25.4.1.5 NewPromiseCapability(C)\nvar aFunction = require('./_a-function');\n\nfunction PromiseCapability(C) {\n  var resolve, reject;\n  this.promise = new C(function ($$resolve, $$reject) {\n    if (resolve !== undefined || reject !== undefined) throw TypeError('Bad Promise constructor');\n    resolve = $$resolve;\n    reject = $$reject;\n  });\n  this.resolve = aFunction(resolve);\n  this.reject = aFunction(reject);\n}\n\nmodule.exports.f = function (C) {\n  return new PromiseCapability(C);\n};\n","module.exports = function (exec) {\n  try {\n    return { e: false, v: exec() };\n  } catch (e) {\n    return { e: true, v: e };\n  }\n};\n","var global = require('./_global');\nvar navigator = global.navigator;\n\nmodule.exports = navigator && navigator.userAgent || '';\n","var anObject = require('./_an-object');\nvar isObject = require('./_is-object');\nvar newPromiseCapability = require('./_new-promise-capability');\n\nmodule.exports = function (C, x) {\n  anObject(C);\n  if (isObject(x) && x.constructor === C) return x;\n  var promiseCapability = newPromiseCapability.f(C);\n  var resolve = promiseCapability.resolve;\n  resolve(x);\n  return promiseCapability.promise;\n};\n","var redefine = require('./_redefine');\nmodule.exports = function (target, src, safe) {\n  for (var key in src) redefine(target, key, src[key], safe);\n  return target;\n};\n","'use strict';\nvar LIBRARY = require('./_library');\nvar global = require('./_global');\nvar ctx = require('./_ctx');\nvar classof = require('./_classof');\nvar $export = require('./_export');\nvar isObject = require('./_is-object');\nvar aFunction = require('./_a-function');\nvar anInstance = require('./_an-instance');\nvar forOf = require('./_for-of');\nvar speciesConstructor = require('./_species-constructor');\nvar task = require('./_task').set;\nvar microtask = require('./_microtask')();\nvar newPromiseCapabilityModule = require('./_new-promise-capability');\nvar perform = require('./_perform');\nvar userAgent = require('./_user-agent');\nvar promiseResolve = require('./_promise-resolve');\nvar PROMISE = 'Promise';\nvar TypeError = global.TypeError;\nvar process = global.process;\nvar versions = process && process.versions;\nvar v8 = versions && versions.v8 || '';\nvar $Promise = global[PROMISE];\nvar isNode = classof(process) == 'process';\nvar empty = function () { /* empty */ };\nvar Internal, newGenericPromiseCapability, OwnPromiseCapability, Wrapper;\nvar newPromiseCapability = newGenericPromiseCapability = newPromiseCapabilityModule.f;\n\nvar USE_NATIVE = !!function () {\n  try {\n    // correct subclassing with @@species support\n    var promise = $Promise.resolve(1);\n    var FakePromise = (promise.constructor = {})[require('./_wks')('species')] = function (exec) {\n      exec(empty, empty);\n    };\n    // unhandled rejections tracking support, NodeJS Promise without it fails @@species test\n    return (isNode || typeof PromiseRejectionEvent == 'function')\n      && promise.then(empty) instanceof FakePromise\n      // v8 6.6 (Node 10 and Chrome 66) have a bug with resolving custom thenables\n      // https://bugs.chromium.org/p/chromium/issues/detail?id=830565\n      // we can't detect it synchronously, so just check versions\n      && v8.indexOf('6.6') !== 0\n      && userAgent.indexOf('Chrome/66') === -1;\n  } catch (e) { /* empty */ }\n}();\n\n// helpers\nvar isThenable = function (it) {\n  var then;\n  return isObject(it) && typeof (then = it.then) == 'function' ? then : false;\n};\nvar notify = function (promise, isReject) {\n  if (promise._n) return;\n  promise._n = true;\n  var chain = promise._c;\n  microtask(function () {\n    var value = promise._v;\n    var ok = promise._s == 1;\n    var i = 0;\n    var run = function (reaction) {\n      var handler = ok ? reaction.ok : reaction.fail;\n      var resolve = reaction.resolve;\n      var reject = reaction.reject;\n      var domain = reaction.domain;\n      var result, then, exited;\n      try {\n        if (handler) {\n          if (!ok) {\n            if (promise._h == 2) onHandleUnhandled(promise);\n            promise._h = 1;\n          }\n          if (handler === true) result = value;\n          else {\n            if (domain) domain.enter();\n            result = handler(value); // may throw\n            if (domain) {\n              domain.exit();\n              exited = true;\n            }\n          }\n          if (result === reaction.promise) {\n            reject(TypeError('Promise-chain cycle'));\n          } else if (then = isThenable(result)) {\n            then.call(result, resolve, reject);\n          } else resolve(result);\n        } else reject(value);\n      } catch (e) {\n        if (domain && !exited) domain.exit();\n        reject(e);\n      }\n    };\n    while (chain.length > i) run(chain[i++]); // variable length - can't use forEach\n    promise._c = [];\n    promise._n = false;\n    if (isReject && !promise._h) onUnhandled(promise);\n  });\n};\nvar onUnhandled = function (promise) {\n  task.call(global, function () {\n    var value = promise._v;\n    var unhandled = isUnhandled(promise);\n    var result, handler, console;\n    if (unhandled) {\n      result = perform(function () {\n        if (isNode) {\n          process.emit('unhandledRejection', value, promise);\n        } else if (handler = global.onunhandledrejection) {\n          handler({ promise: promise, reason: value });\n        } else if ((console = global.console) && console.error) {\n          console.error('Unhandled promise rejection', value);\n        }\n      });\n      // Browsers should not trigger `rejectionHandled` event if it was handled here, NodeJS - should\n      promise._h = isNode || isUnhandled(promise) ? 2 : 1;\n    } promise._a = undefined;\n    if (unhandled && result.e) throw result.v;\n  });\n};\nvar isUnhandled = function (promise) {\n  return promise._h !== 1 && (promise._a || promise._c).length === 0;\n};\nvar onHandleUnhandled = function (promise) {\n  task.call(global, function () {\n    var handler;\n    if (isNode) {\n      process.emit('rejectionHandled', promise);\n    } else if (handler = global.onrejectionhandled) {\n      handler({ promise: promise, reason: promise._v });\n    }\n  });\n};\nvar $reject = function (value) {\n  var promise = this;\n  if (promise._d) return;\n  promise._d = true;\n  promise = promise._w || promise; // unwrap\n  promise._v = value;\n  promise._s = 2;\n  if (!promise._a) promise._a = promise._c.slice();\n  notify(promise, true);\n};\nvar $resolve = function (value) {\n  var promise = this;\n  var then;\n  if (promise._d) return;\n  promise._d = true;\n  promise = promise._w || promise; // unwrap\n  try {\n    if (promise === value) throw TypeError(\"Promise can't be resolved itself\");\n    if (then = isThenable(value)) {\n      microtask(function () {\n        var wrapper = { _w: promise, _d: false }; // wrap\n        try {\n          then.call(value, ctx($resolve, wrapper, 1), ctx($reject, wrapper, 1));\n        } catch (e) {\n          $reject.call(wrapper, e);\n        }\n      });\n    } else {\n      promise._v = value;\n      promise._s = 1;\n      notify(promise, false);\n    }\n  } catch (e) {\n    $reject.call({ _w: promise, _d: false }, e); // wrap\n  }\n};\n\n// constructor polyfill\nif (!USE_NATIVE) {\n  // 25.4.3.1 Promise(executor)\n  $Promise = function Promise(executor) {\n    anInstance(this, $Promise, PROMISE, '_h');\n    aFunction(executor);\n    Internal.call(this);\n    try {\n      executor(ctx($resolve, this, 1), ctx($reject, this, 1));\n    } catch (err) {\n      $reject.call(this, err);\n    }\n  };\n  // eslint-disable-next-line no-unused-vars\n  Internal = function Promise(executor) {\n    this._c = [];             // <- awaiting reactions\n    this._a = undefined;      // <- checked in isUnhandled reactions\n    this._s = 0;              // <- state\n    this._d = false;          // <- done\n    this._v = undefined;      // <- value\n    this._h = 0;              // <- rejection state, 0 - default, 1 - handled, 2 - unhandled\n    this._n = false;          // <- notify\n  };\n  Internal.prototype = require('./_redefine-all')($Promise.prototype, {\n    // 25.4.5.3 Promise.prototype.then(onFulfilled, onRejected)\n    then: function then(onFulfilled, onRejected) {\n      var reaction = newPromiseCapability(speciesConstructor(this, $Promise));\n      reaction.ok = typeof onFulfilled == 'function' ? onFulfilled : true;\n      reaction.fail = typeof onRejected == 'function' && onRejected;\n      reaction.domain = isNode ? process.domain : undefined;\n      this._c.push(reaction);\n      if (this._a) this._a.push(reaction);\n      if (this._s) notify(this, false);\n      return reaction.promise;\n    },\n    // 25.4.5.1 Promise.prototype.catch(onRejected)\n    'catch': function (onRejected) {\n      return this.then(undefined, onRejected);\n    }\n  });\n  OwnPromiseCapability = function () {\n    var promise = new Internal();\n    this.promise = promise;\n    this.resolve = ctx($resolve, promise, 1);\n    this.reject = ctx($reject, promise, 1);\n  };\n  newPromiseCapabilityModule.f = newPromiseCapability = function (C) {\n    return C === $Promise || C === Wrapper\n      ? new OwnPromiseCapability(C)\n      : newGenericPromiseCapability(C);\n  };\n}\n\n$export($export.G + $export.W + $export.F * !USE_NATIVE, { Promise: $Promise });\nrequire('./_set-to-string-tag')($Promise, PROMISE);\nrequire('./_set-species')(PROMISE);\nWrapper = require('./_core')[PROMISE];\n\n// statics\n$export($export.S + $export.F * !USE_NATIVE, PROMISE, {\n  // 25.4.4.5 Promise.reject(r)\n  reject: function reject(r) {\n    var capability = newPromiseCapability(this);\n    var $$reject = capability.reject;\n    $$reject(r);\n    return capability.promise;\n  }\n});\n$export($export.S + $export.F * (LIBRARY || !USE_NATIVE), PROMISE, {\n  // 25.4.4.6 Promise.resolve(x)\n  resolve: function resolve(x) {\n    return promiseResolve(LIBRARY && this === Wrapper ? $Promise : this, x);\n  }\n});\n$export($export.S + $export.F * !(USE_NATIVE && require('./_iter-detect')(function (iter) {\n  $Promise.all(iter)['catch'](empty);\n})), PROMISE, {\n  // 25.4.4.1 Promise.all(iterable)\n  all: function all(iterable) {\n    var C = this;\n    var capability = newPromiseCapability(C);\n    var resolve = capability.resolve;\n    var reject = capability.reject;\n    var result = perform(function () {\n      var values = [];\n      var index = 0;\n      var remaining = 1;\n      forOf(iterable, false, function (promise) {\n        var $index = index++;\n        var alreadyCalled = false;\n        values.push(undefined);\n        remaining++;\n        C.resolve(promise).then(function (value) {\n          if (alreadyCalled) return;\n          alreadyCalled = true;\n          values[$index] = value;\n          --remaining || resolve(values);\n        }, reject);\n      });\n      --remaining || resolve(values);\n    });\n    if (result.e) reject(result.v);\n    return capability.promise;\n  },\n  // 25.4.4.4 Promise.race(iterable)\n  race: function race(iterable) {\n    var C = this;\n    var capability = newPromiseCapability(C);\n    var reject = capability.reject;\n    var result = perform(function () {\n      forOf(iterable, false, function (promise) {\n        C.resolve(promise).then(capability.resolve, reject);\n      });\n    });\n    if (result.e) reject(result.v);\n    return capability.promise;\n  }\n});\n","var isObject = require('./_is-object');\nmodule.exports = function (it, TYPE) {\n  if (!isObject(it) || it._t !== TYPE) throw TypeError('Incompatible receiver, ' + TYPE + ' required!');\n  return it;\n};\n","'use strict';\nvar dP = require('./_object-dp').f;\nvar create = require('./_object-create');\nvar redefineAll = require('./_redefine-all');\nvar ctx = require('./_ctx');\nvar anInstance = require('./_an-instance');\nvar forOf = require('./_for-of');\nvar $iterDefine = require('./_iter-define');\nvar step = require('./_iter-step');\nvar setSpecies = require('./_set-species');\nvar DESCRIPTORS = require('./_descriptors');\nvar fastKey = require('./_meta').fastKey;\nvar validate = require('./_validate-collection');\nvar SIZE = DESCRIPTORS ? '_s' : 'size';\n\nvar getEntry = function (that, key) {\n  // fast case\n  var index = fastKey(key);\n  var entry;\n  if (index !== 'F') return that._i[index];\n  // frozen object case\n  for (entry = that._f; entry; entry = entry.n) {\n    if (entry.k == key) return entry;\n  }\n};\n\nmodule.exports = {\n  getConstructor: function (wrapper, NAME, IS_MAP, ADDER) {\n    var C = wrapper(function (that, iterable) {\n      anInstance(that, C, NAME, '_i');\n      that._t = NAME;         // collection type\n      that._i = create(null); // index\n      that._f = undefined;    // first entry\n      that._l = undefined;    // last entry\n      that[SIZE] = 0;         // size\n      if (iterable != undefined) forOf(iterable, IS_MAP, that[ADDER], that);\n    });\n    redefineAll(C.prototype, {\n      // 23.1.3.1 Map.prototype.clear()\n      // 23.2.3.2 Set.prototype.clear()\n      clear: function clear() {\n        for (var that = validate(this, NAME), data = that._i, entry = that._f; entry; entry = entry.n) {\n          entry.r = true;\n          if (entry.p) entry.p = entry.p.n = undefined;\n          delete data[entry.i];\n        }\n        that._f = that._l = undefined;\n        that[SIZE] = 0;\n      },\n      // 23.1.3.3 Map.prototype.delete(key)\n      // 23.2.3.4 Set.prototype.delete(value)\n      'delete': function (key) {\n        var that = validate(this, NAME);\n        var entry = getEntry(that, key);\n        if (entry) {\n          var next = entry.n;\n          var prev = entry.p;\n          delete that._i[entry.i];\n          entry.r = true;\n          if (prev) prev.n = next;\n          if (next) next.p = prev;\n          if (that._f == entry) that._f = next;\n          if (that._l == entry) that._l = prev;\n          that[SIZE]--;\n        } return !!entry;\n      },\n      // 23.2.3.6 Set.prototype.forEach(callbackfn, thisArg = undefined)\n      // 23.1.3.5 Map.prototype.forEach(callbackfn, thisArg = undefined)\n      forEach: function forEach(callbackfn /* , that = undefined */) {\n        validate(this, NAME);\n        var f = ctx(callbackfn, arguments.length > 1 ? arguments[1] : undefined, 3);\n        var entry;\n        while (entry = entry ? entry.n : this._f) {\n          f(entry.v, entry.k, this);\n          // revert to the last existing entry\n          while (entry && entry.r) entry = entry.p;\n        }\n      },\n      // 23.1.3.7 Map.prototype.has(key)\n      // 23.2.3.7 Set.prototype.has(value)\n      has: function has(key) {\n        return !!getEntry(validate(this, NAME), key);\n      }\n    });\n    if (DESCRIPTORS) dP(C.prototype, 'size', {\n      get: function () {\n        return validate(this, NAME)[SIZE];\n      }\n    });\n    return C;\n  },\n  def: function (that, key, value) {\n    var entry = getEntry(that, key);\n    var prev, index;\n    // change existing entry\n    if (entry) {\n      entry.v = value;\n    // create new entry\n    } else {\n      that._l = entry = {\n        i: index = fastKey(key, true), // <- index\n        k: key,                        // <- key\n        v: value,                      // <- value\n        p: prev = that._l,             // <- previous entry\n        n: undefined,                  // <- next entry\n        r: false                       // <- removed\n      };\n      if (!that._f) that._f = entry;\n      if (prev) prev.n = entry;\n      that[SIZE]++;\n      // add to index\n      if (index !== 'F') that._i[index] = entry;\n    } return that;\n  },\n  getEntry: getEntry,\n  setStrong: function (C, NAME, IS_MAP) {\n    // add .keys, .values, .entries, [@@iterator]\n    // 23.1.3.4, 23.1.3.8, 23.1.3.11, 23.1.3.12, 23.2.3.5, 23.2.3.8, 23.2.3.10, 23.2.3.11\n    $iterDefine(C, NAME, function (iterated, kind) {\n      this._t = validate(iterated, NAME); // target\n      this._k = kind;                     // kind\n      this._l = undefined;                // previous\n    }, function () {\n      var that = this;\n      var kind = that._k;\n      var entry = that._l;\n      // revert to the last existing entry\n      while (entry && entry.r) entry = entry.p;\n      // get next entry\n      if (!that._t || !(that._l = entry = entry ? entry.n : that._t._f)) {\n        // or finish the iteration\n        that._t = undefined;\n        return step(1);\n      }\n      // return step by kind\n      if (kind == 'keys') return step(0, entry.k);\n      if (kind == 'values') return step(0, entry.v);\n      return step(0, [entry.k, entry.v]);\n    }, IS_MAP ? 'entries' : 'values', !IS_MAP, true);\n\n    // add [@@species], 23.1.2.2, 23.2.2.2\n    setSpecies(NAME);\n  }\n};\n","'use strict';\nvar global = require('./_global');\nvar $export = require('./_export');\nvar redefine = require('./_redefine');\nvar redefineAll = require('./_redefine-all');\nvar meta = require('./_meta');\nvar forOf = require('./_for-of');\nvar anInstance = require('./_an-instance');\nvar isObject = require('./_is-object');\nvar fails = require('./_fails');\nvar $iterDetect = require('./_iter-detect');\nvar setToStringTag = require('./_set-to-string-tag');\nvar inheritIfRequired = require('./_inherit-if-required');\n\nmodule.exports = function (NAME, wrapper, methods, common, IS_MAP, IS_WEAK) {\n  var Base = global[NAME];\n  var C = Base;\n  var ADDER = IS_MAP ? 'set' : 'add';\n  var proto = C && C.prototype;\n  var O = {};\n  var fixMethod = function (KEY) {\n    var fn = proto[KEY];\n    redefine(proto, KEY,\n      KEY == 'delete' ? function (a) {\n        return IS_WEAK && !isObject(a) ? false : fn.call(this, a === 0 ? 0 : a);\n      } : KEY == 'has' ? function has(a) {\n        return IS_WEAK && !isObject(a) ? false : fn.call(this, a === 0 ? 0 : a);\n      } : KEY == 'get' ? function get(a) {\n        return IS_WEAK && !isObject(a) ? undefined : fn.call(this, a === 0 ? 0 : a);\n      } : KEY == 'add' ? function add(a) { fn.call(this, a === 0 ? 0 : a); return this; }\n        : function set(a, b) { fn.call(this, a === 0 ? 0 : a, b); return this; }\n    );\n  };\n  if (typeof C != 'function' || !(IS_WEAK || proto.forEach && !fails(function () {\n    new C().entries().next();\n  }))) {\n    // create collection constructor\n    C = common.getConstructor(wrapper, NAME, IS_MAP, ADDER);\n    redefineAll(C.prototype, methods);\n    meta.NEED = true;\n  } else {\n    var instance = new C();\n    // early implementations not supports chaining\n    var HASNT_CHAINING = instance[ADDER](IS_WEAK ? {} : -0, 1) != instance;\n    // V8 ~  Chromium 40- weak-collections throws on primitives, but should return false\n    var THROWS_ON_PRIMITIVES = fails(function () { instance.has(1); });\n    // most early implementations doesn't supports iterables, most modern - not close it correctly\n    var ACCEPT_ITERABLES = $iterDetect(function (iter) { new C(iter); }); // eslint-disable-line no-new\n    // for early implementations -0 and +0 not the same\n    var BUGGY_ZERO = !IS_WEAK && fails(function () {\n      // V8 ~ Chromium 42- fails only with 5+ elements\n      var $instance = new C();\n      var index = 5;\n      while (index--) $instance[ADDER](index, index);\n      return !$instance.has(-0);\n    });\n    if (!ACCEPT_ITERABLES) {\n      C = wrapper(function (target, iterable) {\n        anInstance(target, C, NAME);\n        var that = inheritIfRequired(new Base(), target, C);\n        if (iterable != undefined) forOf(iterable, IS_MAP, that[ADDER], that);\n        return that;\n      });\n      C.prototype = proto;\n      proto.constructor = C;\n    }\n    if (THROWS_ON_PRIMITIVES || BUGGY_ZERO) {\n      fixMethod('delete');\n      fixMethod('has');\n      IS_MAP && fixMethod('get');\n    }\n    if (BUGGY_ZERO || HASNT_CHAINING) fixMethod(ADDER);\n    // weak collections should not contains .clear method\n    if (IS_WEAK && proto.clear) delete proto.clear;\n  }\n\n  setToStringTag(C, NAME);\n\n  O[NAME] = C;\n  $export($export.G + $export.W + $export.F * (C != Base), O);\n\n  if (!IS_WEAK) common.setStrong(C, NAME, IS_MAP);\n\n  return C;\n};\n","'use strict';\nvar strong = require('./_collection-strong');\nvar validate = require('./_validate-collection');\nvar MAP = 'Map';\n\n// 23.1 Map Objects\nmodule.exports = require('./_collection')(MAP, function (get) {\n  return function Map() { return get(this, arguments.length > 0 ? arguments[0] : undefined); };\n}, {\n  // 23.1.3.6 Map.prototype.get(key)\n  get: function get(key) {\n    var entry = strong.getEntry(validate(this, MAP), key);\n    return entry && entry.v;\n  },\n  // 23.1.3.9 Map.prototype.set(key, value)\n  set: function set(key, value) {\n    return strong.def(validate(this, MAP), key === 0 ? 0 : key, value);\n  }\n}, strong, true);\n","'use strict';\nvar strong = require('./_collection-strong');\nvar validate = require('./_validate-collection');\nvar SET = 'Set';\n\n// 23.2 Set Objects\nmodule.exports = require('./_collection')(SET, function (get) {\n  return function Set() { return get(this, arguments.length > 0 ? arguments[0] : undefined); };\n}, {\n  // 23.2.3.1 Set.prototype.add(value)\n  add: function add(value) {\n    return strong.def(validate(this, SET), value = value === 0 ? 0 : value, value);\n  }\n}, strong);\n","'use strict';\nvar redefineAll = require('./_redefine-all');\nvar getWeak = require('./_meta').getWeak;\nvar anObject = require('./_an-object');\nvar isObject = require('./_is-object');\nvar anInstance = require('./_an-instance');\nvar forOf = require('./_for-of');\nvar createArrayMethod = require('./_array-methods');\nvar $has = require('./_has');\nvar validate = require('./_validate-collection');\nvar arrayFind = createArrayMethod(5);\nvar arrayFindIndex = createArrayMethod(6);\nvar id = 0;\n\n// fallback for uncaught frozen keys\nvar uncaughtFrozenStore = function (that) {\n  return that._l || (that._l = new UncaughtFrozenStore());\n};\nvar UncaughtFrozenStore = function () {\n  this.a = [];\n};\nvar findUncaughtFrozen = function (store, key) {\n  return arrayFind(store.a, function (it) {\n    return it[0] === key;\n  });\n};\nUncaughtFrozenStore.prototype = {\n  get: function (key) {\n    var entry = findUncaughtFrozen(this, key);\n    if (entry) return entry[1];\n  },\n  has: function (key) {\n    return !!findUncaughtFrozen(this, key);\n  },\n  set: function (key, value) {\n    var entry = findUncaughtFrozen(this, key);\n    if (entry) entry[1] = value;\n    else this.a.push([key, value]);\n  },\n  'delete': function (key) {\n    var index = arrayFindIndex(this.a, function (it) {\n      return it[0] === key;\n    });\n    if (~index) this.a.splice(index, 1);\n    return !!~index;\n  }\n};\n\nmodule.exports = {\n  getConstructor: function (wrapper, NAME, IS_MAP, ADDER) {\n    var C = wrapper(function (that, iterable) {\n      anInstance(that, C, NAME, '_i');\n      that._t = NAME;      // collection type\n      that._i = id++;      // collection id\n      that._l = undefined; // leak store for uncaught frozen objects\n      if (iterable != undefined) forOf(iterable, IS_MAP, that[ADDER], that);\n    });\n    redefineAll(C.prototype, {\n      // 23.3.3.2 WeakMap.prototype.delete(key)\n      // 23.4.3.3 WeakSet.prototype.delete(value)\n      'delete': function (key) {\n        if (!isObject(key)) return false;\n        var data = getWeak(key);\n        if (data === true) return uncaughtFrozenStore(validate(this, NAME))['delete'](key);\n        return data && $has(data, this._i) && delete data[this._i];\n      },\n      // 23.3.3.4 WeakMap.prototype.has(key)\n      // 23.4.3.4 WeakSet.prototype.has(value)\n      has: function has(key) {\n        if (!isObject(key)) return false;\n        var data = getWeak(key);\n        if (data === true) return uncaughtFrozenStore(validate(this, NAME)).has(key);\n        return data && $has(data, this._i);\n      }\n    });\n    return C;\n  },\n  def: function (that, key, value) {\n    var data = getWeak(anObject(key), true);\n    if (data === true) uncaughtFrozenStore(that).set(key, value);\n    else data[that._i] = value;\n    return that;\n  },\n  ufstore: uncaughtFrozenStore\n};\n","'use strict';\nvar global = require('./_global');\nvar each = require('./_array-methods')(0);\nvar redefine = require('./_redefine');\nvar meta = require('./_meta');\nvar assign = require('./_object-assign');\nvar weak = require('./_collection-weak');\nvar isObject = require('./_is-object');\nvar validate = require('./_validate-collection');\nvar NATIVE_WEAK_MAP = require('./_validate-collection');\nvar IS_IE11 = !global.ActiveXObject && 'ActiveXObject' in global;\nvar WEAK_MAP = 'WeakMap';\nvar getWeak = meta.getWeak;\nvar isExtensible = Object.isExtensible;\nvar uncaughtFrozenStore = weak.ufstore;\nvar InternalMap;\n\nvar wrapper = function (get) {\n  return function WeakMap() {\n    return get(this, arguments.length > 0 ? arguments[0] : undefined);\n  };\n};\n\nvar methods = {\n  // 23.3.3.3 WeakMap.prototype.get(key)\n  get: function get(key) {\n    if (isObject(key)) {\n      var data = getWeak(key);\n      if (data === true) return uncaughtFrozenStore(validate(this, WEAK_MAP)).get(key);\n      return data ? data[this._i] : undefined;\n    }\n  },\n  // 23.3.3.5 WeakMap.prototype.set(key, value)\n  set: function set(key, value) {\n    return weak.def(validate(this, WEAK_MAP), key, value);\n  }\n};\n\n// 23.3 WeakMap Objects\nvar $WeakMap = module.exports = require('./_collection')(WEAK_MAP, wrapper, methods, weak, true, true);\n\n// IE11 WeakMap frozen keys fix\nif (NATIVE_WEAK_MAP && IS_IE11) {\n  InternalMap = weak.getConstructor(wrapper, WEAK_MAP);\n  assign(InternalMap.prototype, methods);\n  meta.NEED = true;\n  each(['delete', 'has', 'get', 'set'], function (key) {\n    var proto = $WeakMap.prototype;\n    var method = proto[key];\n    redefine(proto, key, function (a, b) {\n      // store frozen objects on internal weakmap shim\n      if (isObject(a) && !isExtensible(a)) {\n        if (!this._f) this._f = new InternalMap();\n        var result = this._f[key](a, b);\n        return key == 'set' ? this : result;\n      // store all the rest on native weakmap\n      } return method.call(this, a, b);\n    });\n  });\n}\n","'use strict';\nvar weak = require('./_collection-weak');\nvar validate = require('./_validate-collection');\nvar WEAK_SET = 'WeakSet';\n\n// 23.4 WeakSet Objects\nrequire('./_collection')(WEAK_SET, function (get) {\n  return function WeakSet() { return get(this, arguments.length > 0 ? arguments[0] : undefined); };\n}, {\n  // 23.4.3.1 WeakSet.prototype.add(value)\n  add: function add(value) {\n    return weak.def(validate(this, WEAK_SET), value, true);\n  }\n}, weak, false, true);\n","var global = require('./_global');\nvar hide = require('./_hide');\nvar uid = require('./_uid');\nvar TYPED = uid('typed_array');\nvar VIEW = uid('view');\nvar ABV = !!(global.ArrayBuffer && global.DataView);\nvar CONSTR = ABV;\nvar i = 0;\nvar l = 9;\nvar Typed;\n\nvar TypedArrayConstructors = (\n  'Int8Array,Uint8Array,Uint8ClampedArray,Int16Array,Uint16Array,Int32Array,Uint32Array,Float32Array,Float64Array'\n).split(',');\n\nwhile (i < l) {\n  if (Typed = global[TypedArrayConstructors[i++]]) {\n    hide(Typed.prototype, TYPED, true);\n    hide(Typed.prototype, VIEW, true);\n  } else CONSTR = false;\n}\n\nmodule.exports = {\n  ABV: ABV,\n  CONSTR: CONSTR,\n  TYPED: TYPED,\n  VIEW: VIEW\n};\n","// https://tc39.github.io/ecma262/#sec-toindex\nvar toInteger = require('./_to-integer');\nvar toLength = require('./_to-length');\nmodule.exports = function (it) {\n  if (it === undefined) return 0;\n  var number = toInteger(it);\n  var length = toLength(number);\n  if (number !== length) throw RangeError('Wrong length!');\n  return length;\n};\n","'use strict';\nvar global = require('./_global');\nvar DESCRIPTORS = require('./_descriptors');\nvar LIBRARY = require('./_library');\nvar $typed = require('./_typed');\nvar hide = require('./_hide');\nvar redefineAll = require('./_redefine-all');\nvar fails = require('./_fails');\nvar anInstance = require('./_an-instance');\nvar toInteger = require('./_to-integer');\nvar toLength = require('./_to-length');\nvar toIndex = require('./_to-index');\nvar gOPN = require('./_object-gopn').f;\nvar dP = require('./_object-dp').f;\nvar arrayFill = require('./_array-fill');\nvar setToStringTag = require('./_set-to-string-tag');\nvar ARRAY_BUFFER = 'ArrayBuffer';\nvar DATA_VIEW = 'DataView';\nvar PROTOTYPE = 'prototype';\nvar WRONG_LENGTH = 'Wrong length!';\nvar WRONG_INDEX = 'Wrong index!';\nvar $ArrayBuffer = global[ARRAY_BUFFER];\nvar $DataView = global[DATA_VIEW];\nvar Math = global.Math;\nvar RangeError = global.RangeError;\n// eslint-disable-next-line no-shadow-restricted-names\nvar Infinity = global.Infinity;\nvar BaseBuffer = $ArrayBuffer;\nvar abs = Math.abs;\nvar pow = Math.pow;\nvar floor = Math.floor;\nvar log = Math.log;\nvar LN2 = Math.LN2;\nvar BUFFER = 'buffer';\nvar BYTE_LENGTH = 'byteLength';\nvar BYTE_OFFSET = 'byteOffset';\nvar $BUFFER = DESCRIPTORS ? '_b' : BUFFER;\nvar $LENGTH = DESCRIPTORS ? '_l' : BYTE_LENGTH;\nvar $OFFSET = DESCRIPTORS ? '_o' : BYTE_OFFSET;\n\n// IEEE754 conversions based on https://github.com/feross/ieee754\nfunction packIEEE754(value, mLen, nBytes) {\n  var buffer = new Array(nBytes);\n  var eLen = nBytes * 8 - mLen - 1;\n  var eMax = (1 << eLen) - 1;\n  var eBias = eMax >> 1;\n  var rt = mLen === 23 ? pow(2, -24) - pow(2, -77) : 0;\n  var i = 0;\n  var s = value < 0 || value === 0 && 1 / value < 0 ? 1 : 0;\n  var e, m, c;\n  value = abs(value);\n  // eslint-disable-next-line no-self-compare\n  if (value != value || value === Infinity) {\n    // eslint-disable-next-line no-self-compare\n    m = value != value ? 1 : 0;\n    e = eMax;\n  } else {\n    e = floor(log(value) / LN2);\n    if (value * (c = pow(2, -e)) < 1) {\n      e--;\n      c *= 2;\n    }\n    if (e + eBias >= 1) {\n      value += rt / c;\n    } else {\n      value += rt * pow(2, 1 - eBias);\n    }\n    if (value * c >= 2) {\n      e++;\n      c /= 2;\n    }\n    if (e + eBias >= eMax) {\n      m = 0;\n      e = eMax;\n    } else if (e + eBias >= 1) {\n      m = (value * c - 1) * pow(2, mLen);\n      e = e + eBias;\n    } else {\n      m = value * pow(2, eBias - 1) * pow(2, mLen);\n      e = 0;\n    }\n  }\n  for (; mLen >= 8; buffer[i++] = m & 255, m /= 256, mLen -= 8);\n  e = e << mLen | m;\n  eLen += mLen;\n  for (; eLen > 0; buffer[i++] = e & 255, e /= 256, eLen -= 8);\n  buffer[--i] |= s * 128;\n  return buffer;\n}\nfunction unpackIEEE754(buffer, mLen, nBytes) {\n  var eLen = nBytes * 8 - mLen - 1;\n  var eMax = (1 << eLen) - 1;\n  var eBias = eMax >> 1;\n  var nBits = eLen - 7;\n  var i = nBytes - 1;\n  var s = buffer[i--];\n  var e = s & 127;\n  var m;\n  s >>= 7;\n  for (; nBits > 0; e = e * 256 + buffer[i], i--, nBits -= 8);\n  m = e & (1 << -nBits) - 1;\n  e >>= -nBits;\n  nBits += mLen;\n  for (; nBits > 0; m = m * 256 + buffer[i], i--, nBits -= 8);\n  if (e === 0) {\n    e = 1 - eBias;\n  } else if (e === eMax) {\n    return m ? NaN : s ? -Infinity : Infinity;\n  } else {\n    m = m + pow(2, mLen);\n    e = e - eBias;\n  } return (s ? -1 : 1) * m * pow(2, e - mLen);\n}\n\nfunction unpackI32(bytes) {\n  return bytes[3] << 24 | bytes[2] << 16 | bytes[1] << 8 | bytes[0];\n}\nfunction packI8(it) {\n  return [it & 0xff];\n}\nfunction packI16(it) {\n  return [it & 0xff, it >> 8 & 0xff];\n}\nfunction packI32(it) {\n  return [it & 0xff, it >> 8 & 0xff, it >> 16 & 0xff, it >> 24 & 0xff];\n}\nfunction packF64(it) {\n  return packIEEE754(it, 52, 8);\n}\nfunction packF32(it) {\n  return packIEEE754(it, 23, 4);\n}\n\nfunction addGetter(C, key, internal) {\n  dP(C[PROTOTYPE], key, { get: function () { return this[internal]; } });\n}\n\nfunction get(view, bytes, index, isLittleEndian) {\n  var numIndex = +index;\n  var intIndex = toIndex(numIndex);\n  if (intIndex + bytes > view[$LENGTH]) throw RangeError(WRONG_INDEX);\n  var store = view[$BUFFER]._b;\n  var start = intIndex + view[$OFFSET];\n  var pack = store.slice(start, start + bytes);\n  return isLittleEndian ? pack : pack.reverse();\n}\nfunction set(view, bytes, index, conversion, value, isLittleEndian) {\n  var numIndex = +index;\n  var intIndex = toIndex(numIndex);\n  if (intIndex + bytes > view[$LENGTH]) throw RangeError(WRONG_INDEX);\n  var store = view[$BUFFER]._b;\n  var start = intIndex + view[$OFFSET];\n  var pack = conversion(+value);\n  for (var i = 0; i < bytes; i++) store[start + i] = pack[isLittleEndian ? i : bytes - i - 1];\n}\n\nif (!$typed.ABV) {\n  $ArrayBuffer = function ArrayBuffer(length) {\n    anInstance(this, $ArrayBuffer, ARRAY_BUFFER);\n    var byteLength = toIndex(length);\n    this._b = arrayFill.call(new Array(byteLength), 0);\n    this[$LENGTH] = byteLength;\n  };\n\n  $DataView = function DataView(buffer, byteOffset, byteLength) {\n    anInstance(this, $DataView, DATA_VIEW);\n    anInstance(buffer, $ArrayBuffer, DATA_VIEW);\n    var bufferLength = buffer[$LENGTH];\n    var offset = toInteger(byteOffset);\n    if (offset < 0 || offset > bufferLength) throw RangeError('Wrong offset!');\n    byteLength = byteLength === undefined ? bufferLength - offset : toLength(byteLength);\n    if (offset + byteLength > bufferLength) throw RangeError(WRONG_LENGTH);\n    this[$BUFFER] = buffer;\n    this[$OFFSET] = offset;\n    this[$LENGTH] = byteLength;\n  };\n\n  if (DESCRIPTORS) {\n    addGetter($ArrayBuffer, BYTE_LENGTH, '_l');\n    addGetter($DataView, BUFFER, '_b');\n    addGetter($DataView, BYTE_LENGTH, '_l');\n    addGetter($DataView, BYTE_OFFSET, '_o');\n  }\n\n  redefineAll($DataView[PROTOTYPE], {\n    getInt8: function getInt8(byteOffset) {\n      return get(this, 1, byteOffset)[0] << 24 >> 24;\n    },\n    getUint8: function getUint8(byteOffset) {\n      return get(this, 1, byteOffset)[0];\n    },\n    getInt16: function getInt16(byteOffset /* , littleEndian */) {\n      var bytes = get(this, 2, byteOffset, arguments[1]);\n      return (bytes[1] << 8 | bytes[0]) << 16 >> 16;\n    },\n    getUint16: function getUint16(byteOffset /* , littleEndian */) {\n      var bytes = get(this, 2, byteOffset, arguments[1]);\n      return bytes[1] << 8 | bytes[0];\n    },\n    getInt32: function getInt32(byteOffset /* , littleEndian */) {\n      return unpackI32(get(this, 4, byteOffset, arguments[1]));\n    },\n    getUint32: function getUint32(byteOffset /* , littleEndian */) {\n      return unpackI32(get(this, 4, byteOffset, arguments[1])) >>> 0;\n    },\n    getFloat32: function getFloat32(byteOffset /* , littleEndian */) {\n      return unpackIEEE754(get(this, 4, byteOffset, arguments[1]), 23, 4);\n    },\n    getFloat64: function getFloat64(byteOffset /* , littleEndian */) {\n      return unpackIEEE754(get(this, 8, byteOffset, arguments[1]), 52, 8);\n    },\n    setInt8: function setInt8(byteOffset, value) {\n      set(this, 1, byteOffset, packI8, value);\n    },\n    setUint8: function setUint8(byteOffset, value) {\n      set(this, 1, byteOffset, packI8, value);\n    },\n    setInt16: function setInt16(byteOffset, value /* , littleEndian */) {\n      set(this, 2, byteOffset, packI16, value, arguments[2]);\n    },\n    setUint16: function setUint16(byteOffset, value /* , littleEndian */) {\n      set(this, 2, byteOffset, packI16, value, arguments[2]);\n    },\n    setInt32: function setInt32(byteOffset, value /* , littleEndian */) {\n      set(this, 4, byteOffset, packI32, value, arguments[2]);\n    },\n    setUint32: function setUint32(byteOffset, value /* , littleEndian */) {\n      set(this, 4, byteOffset, packI32, value, arguments[2]);\n    },\n    setFloat32: function setFloat32(byteOffset, value /* , littleEndian */) {\n      set(this, 4, byteOffset, packF32, value, arguments[2]);\n    },\n    setFloat64: function setFloat64(byteOffset, value /* , littleEndian */) {\n      set(this, 8, byteOffset, packF64, value, arguments[2]);\n    }\n  });\n} else {\n  if (!fails(function () {\n    $ArrayBuffer(1);\n  }) || !fails(function () {\n    new $ArrayBuffer(-1); // eslint-disable-line no-new\n  }) || fails(function () {\n    new $ArrayBuffer(); // eslint-disable-line no-new\n    new $ArrayBuffer(1.5); // eslint-disable-line no-new\n    new $ArrayBuffer(NaN); // eslint-disable-line no-new\n    return $ArrayBuffer.name != ARRAY_BUFFER;\n  })) {\n    $ArrayBuffer = function ArrayBuffer(length) {\n      anInstance(this, $ArrayBuffer);\n      return new BaseBuffer(toIndex(length));\n    };\n    var ArrayBufferProto = $ArrayBuffer[PROTOTYPE] = BaseBuffer[PROTOTYPE];\n    for (var keys = gOPN(BaseBuffer), j = 0, key; keys.length > j;) {\n      if (!((key = keys[j++]) in $ArrayBuffer)) hide($ArrayBuffer, key, BaseBuffer[key]);\n    }\n    if (!LIBRARY) ArrayBufferProto.constructor = $ArrayBuffer;\n  }\n  // iOS Safari 7.x bug\n  var view = new $DataView(new $ArrayBuffer(2));\n  var $setInt8 = $DataView[PROTOTYPE].setInt8;\n  view.setInt8(0, 2147483648);\n  view.setInt8(1, 2147483649);\n  if (view.getInt8(0) || !view.getInt8(1)) redefineAll($DataView[PROTOTYPE], {\n    setInt8: function setInt8(byteOffset, value) {\n      $setInt8.call(this, byteOffset, value << 24 >> 24);\n    },\n    setUint8: function setUint8(byteOffset, value) {\n      $setInt8.call(this, byteOffset, value << 24 >> 24);\n    }\n  }, true);\n}\nsetToStringTag($ArrayBuffer, ARRAY_BUFFER);\nsetToStringTag($DataView, DATA_VIEW);\nhide($DataView[PROTOTYPE], $typed.VIEW, true);\nexports[ARRAY_BUFFER] = $ArrayBuffer;\nexports[DATA_VIEW] = $DataView;\n","'use strict';\nvar $export = require('./_export');\nvar $typed = require('./_typed');\nvar buffer = require('./_typed-buffer');\nvar anObject = require('./_an-object');\nvar toAbsoluteIndex = require('./_to-absolute-index');\nvar toLength = require('./_to-length');\nvar isObject = require('./_is-object');\nvar ArrayBuffer = require('./_global').ArrayBuffer;\nvar speciesConstructor = require('./_species-constructor');\nvar $ArrayBuffer = buffer.ArrayBuffer;\nvar $DataView = buffer.DataView;\nvar $isView = $typed.ABV && ArrayBuffer.isView;\nvar $slice = $ArrayBuffer.prototype.slice;\nvar VIEW = $typed.VIEW;\nvar ARRAY_BUFFER = 'ArrayBuffer';\n\n$export($export.G + $export.W + $export.F * (ArrayBuffer !== $ArrayBuffer), { ArrayBuffer: $ArrayBuffer });\n\n$export($export.S + $export.F * !$typed.CONSTR, ARRAY_BUFFER, {\n  // 24.1.3.1 ArrayBuffer.isView(arg)\n  isView: function isView(it) {\n    return $isView && $isView(it) || isObject(it) && VIEW in it;\n  }\n});\n\n$export($export.P + $export.U + $export.F * require('./_fails')(function () {\n  return !new $ArrayBuffer(2).slice(1, undefined).byteLength;\n}), ARRAY_BUFFER, {\n  // 24.1.4.3 ArrayBuffer.prototype.slice(start, end)\n  slice: function slice(start, end) {\n    if ($slice !== undefined && end === undefined) return $slice.call(anObject(this), start); // FF fix\n    var len = anObject(this).byteLength;\n    var first = toAbsoluteIndex(start, len);\n    var fin = toAbsoluteIndex(end === undefined ? len : end, len);\n    var result = new (speciesConstructor(this, $ArrayBuffer))(toLength(fin - first));\n    var viewS = new $DataView(this);\n    var viewT = new $DataView(result);\n    var index = 0;\n    while (first < fin) {\n      viewT.setUint8(index++, viewS.getUint8(first++));\n    } return result;\n  }\n});\n\nrequire('./_set-species')(ARRAY_BUFFER);\n","var $export = require('./_export');\n$export($export.G + $export.W + $export.F * !require('./_typed').ABV, {\n  DataView: require('./_typed-buffer').DataView\n});\n","'use strict';\nif (require('./_descriptors')) {\n  var LIBRARY = require('./_library');\n  var global = require('./_global');\n  var fails = require('./_fails');\n  var $export = require('./_export');\n  var $typed = require('./_typed');\n  var $buffer = require('./_typed-buffer');\n  var ctx = require('./_ctx');\n  var anInstance = require('./_an-instance');\n  var propertyDesc = require('./_property-desc');\n  var hide = require('./_hide');\n  var redefineAll = require('./_redefine-all');\n  var toInteger = require('./_to-integer');\n  var toLength = require('./_to-length');\n  var toIndex = require('./_to-index');\n  var toAbsoluteIndex = require('./_to-absolute-index');\n  var toPrimitive = require('./_to-primitive');\n  var has = require('./_has');\n  var classof = require('./_classof');\n  var isObject = require('./_is-object');\n  var toObject = require('./_to-object');\n  var isArrayIter = require('./_is-array-iter');\n  var create = require('./_object-create');\n  var getPrototypeOf = require('./_object-gpo');\n  var gOPN = require('./_object-gopn').f;\n  var getIterFn = require('./core.get-iterator-method');\n  var uid = require('./_uid');\n  var wks = require('./_wks');\n  var createArrayMethod = require('./_array-methods');\n  var createArrayIncludes = require('./_array-includes');\n  var speciesConstructor = require('./_species-constructor');\n  var ArrayIterators = require('./es6.array.iterator');\n  var Iterators = require('./_iterators');\n  var $iterDetect = require('./_iter-detect');\n  var setSpecies = require('./_set-species');\n  var arrayFill = require('./_array-fill');\n  var arrayCopyWithin = require('./_array-copy-within');\n  var $DP = require('./_object-dp');\n  var $GOPD = require('./_object-gopd');\n  var dP = $DP.f;\n  var gOPD = $GOPD.f;\n  var RangeError = global.RangeError;\n  var TypeError = global.TypeError;\n  var Uint8Array = global.Uint8Array;\n  var ARRAY_BUFFER = 'ArrayBuffer';\n  var SHARED_BUFFER = 'Shared' + ARRAY_BUFFER;\n  var BYTES_PER_ELEMENT = 'BYTES_PER_ELEMENT';\n  var PROTOTYPE = 'prototype';\n  var ArrayProto = Array[PROTOTYPE];\n  var $ArrayBuffer = $buffer.ArrayBuffer;\n  var $DataView = $buffer.DataView;\n  var arrayForEach = createArrayMethod(0);\n  var arrayFilter = createArrayMethod(2);\n  var arraySome = createArrayMethod(3);\n  var arrayEvery = createArrayMethod(4);\n  var arrayFind = createArrayMethod(5);\n  var arrayFindIndex = createArrayMethod(6);\n  var arrayIncludes = createArrayIncludes(true);\n  var arrayIndexOf = createArrayIncludes(false);\n  var arrayValues = ArrayIterators.values;\n  var arrayKeys = ArrayIterators.keys;\n  var arrayEntries = ArrayIterators.entries;\n  var arrayLastIndexOf = ArrayProto.lastIndexOf;\n  var arrayReduce = ArrayProto.reduce;\n  var arrayReduceRight = ArrayProto.reduceRight;\n  var arrayJoin = ArrayProto.join;\n  var arraySort = ArrayProto.sort;\n  var arraySlice = ArrayProto.slice;\n  var arrayToString = ArrayProto.toString;\n  var arrayToLocaleString = ArrayProto.toLocaleString;\n  var ITERATOR = wks('iterator');\n  var TAG = wks('toStringTag');\n  var TYPED_CONSTRUCTOR = uid('typed_constructor');\n  var DEF_CONSTRUCTOR = uid('def_constructor');\n  var ALL_CONSTRUCTORS = $typed.CONSTR;\n  var TYPED_ARRAY = $typed.TYPED;\n  var VIEW = $typed.VIEW;\n  var WRONG_LENGTH = 'Wrong length!';\n\n  var $map = createArrayMethod(1, function (O, length) {\n    return allocate(speciesConstructor(O, O[DEF_CONSTRUCTOR]), length);\n  });\n\n  var LITTLE_ENDIAN = fails(function () {\n    // eslint-disable-next-line no-undef\n    return new Uint8Array(new Uint16Array([1]).buffer)[0] === 1;\n  });\n\n  var FORCED_SET = !!Uint8Array && !!Uint8Array[PROTOTYPE].set && fails(function () {\n    new Uint8Array(1).set({});\n  });\n\n  var toOffset = function (it, BYTES) {\n    var offset = toInteger(it);\n    if (offset < 0 || offset % BYTES) throw RangeError('Wrong offset!');\n    return offset;\n  };\n\n  var validate = function (it) {\n    if (isObject(it) && TYPED_ARRAY in it) return it;\n    throw TypeError(it + ' is not a typed array!');\n  };\n\n  var allocate = function (C, length) {\n    if (!(isObject(C) && TYPED_CONSTRUCTOR in C)) {\n      throw TypeError('It is not a typed array constructor!');\n    } return new C(length);\n  };\n\n  var speciesFromList = function (O, list) {\n    return fromList(speciesConstructor(O, O[DEF_CONSTRUCTOR]), list);\n  };\n\n  var fromList = function (C, list) {\n    var index = 0;\n    var length = list.length;\n    var result = allocate(C, length);\n    while (length > index) result[index] = list[index++];\n    return result;\n  };\n\n  var addGetter = function (it, key, internal) {\n    dP(it, key, { get: function () { return this._d[internal]; } });\n  };\n\n  var $from = function from(source /* , mapfn, thisArg */) {\n    var O = toObject(source);\n    var aLen = arguments.length;\n    var mapfn = aLen > 1 ? arguments[1] : undefined;\n    var mapping = mapfn !== undefined;\n    var iterFn = getIterFn(O);\n    var i, length, values, result, step, iterator;\n    if (iterFn != undefined && !isArrayIter(iterFn)) {\n      for (iterator = iterFn.call(O), values = [], i = 0; !(step = iterator.next()).done; i++) {\n        values.push(step.value);\n      } O = values;\n    }\n    if (mapping && aLen > 2) mapfn = ctx(mapfn, arguments[2], 2);\n    for (i = 0, length = toLength(O.length), result = allocate(this, length); length > i; i++) {\n      result[i] = mapping ? mapfn(O[i], i) : O[i];\n    }\n    return result;\n  };\n\n  var $of = function of(/* ...items */) {\n    var index = 0;\n    var length = arguments.length;\n    var result = allocate(this, length);\n    while (length > index) result[index] = arguments[index++];\n    return result;\n  };\n\n  // iOS Safari 6.x fails here\n  var TO_LOCALE_BUG = !!Uint8Array && fails(function () { arrayToLocaleString.call(new Uint8Array(1)); });\n\n  var $toLocaleString = function toLocaleString() {\n    return arrayToLocaleString.apply(TO_LOCALE_BUG ? arraySlice.call(validate(this)) : validate(this), arguments);\n  };\n\n  var proto = {\n    copyWithin: function copyWithin(target, start /* , end */) {\n      return arrayCopyWithin.call(validate(this), target, start, arguments.length > 2 ? arguments[2] : undefined);\n    },\n    every: function every(callbackfn /* , thisArg */) {\n      return arrayEvery(validate(this), callbackfn, arguments.length > 1 ? arguments[1] : undefined);\n    },\n    fill: function fill(value /* , start, end */) { // eslint-disable-line no-unused-vars\n      return arrayFill.apply(validate(this), arguments);\n    },\n    filter: function filter(callbackfn /* , thisArg */) {\n      return speciesFromList(this, arrayFilter(validate(this), callbackfn,\n        arguments.length > 1 ? arguments[1] : undefined));\n    },\n    find: function find(predicate /* , thisArg */) {\n      return arrayFind(validate(this), predicate, arguments.length > 1 ? arguments[1] : undefined);\n    },\n    findIndex: function findIndex(predicate /* , thisArg */) {\n      return arrayFindIndex(validate(this), predicate, arguments.length > 1 ? arguments[1] : undefined);\n    },\n    forEach: function forEach(callbackfn /* , thisArg */) {\n      arrayForEach(validate(this), callbackfn, arguments.length > 1 ? arguments[1] : undefined);\n    },\n    indexOf: function indexOf(searchElement /* , fromIndex */) {\n      return arrayIndexOf(validate(this), searchElement, arguments.length > 1 ? arguments[1] : undefined);\n    },\n    includes: function includes(searchElement /* , fromIndex */) {\n      return arrayIncludes(validate(this), searchElement, arguments.length > 1 ? arguments[1] : undefined);\n    },\n    join: function join(separator) { // eslint-disable-line no-unused-vars\n      return arrayJoin.apply(validate(this), arguments);\n    },\n    lastIndexOf: function lastIndexOf(searchElement /* , fromIndex */) { // eslint-disable-line no-unused-vars\n      return arrayLastIndexOf.apply(validate(this), arguments);\n    },\n    map: function map(mapfn /* , thisArg */) {\n      return $map(validate(this), mapfn, arguments.length > 1 ? arguments[1] : undefined);\n    },\n    reduce: function reduce(callbackfn /* , initialValue */) { // eslint-disable-line no-unused-vars\n      return arrayReduce.apply(validate(this), arguments);\n    },\n    reduceRight: function reduceRight(callbackfn /* , initialValue */) { // eslint-disable-line no-unused-vars\n      return arrayReduceRight.apply(validate(this), arguments);\n    },\n    reverse: function reverse() {\n      var that = this;\n      var length = validate(that).length;\n      var middle = Math.floor(length / 2);\n      var index = 0;\n      var value;\n      while (index < middle) {\n        value = that[index];\n        that[index++] = that[--length];\n        that[length] = value;\n      } return that;\n    },\n    some: function some(callbackfn /* , thisArg */) {\n      return arraySome(validate(this), callbackfn, arguments.length > 1 ? arguments[1] : undefined);\n    },\n    sort: function sort(comparefn) {\n      return arraySort.call(validate(this), comparefn);\n    },\n    subarray: function subarray(begin, end) {\n      var O = validate(this);\n      var length = O.length;\n      var $begin = toAbsoluteIndex(begin, length);\n      return new (speciesConstructor(O, O[DEF_CONSTRUCTOR]))(\n        O.buffer,\n        O.byteOffset + $begin * O.BYTES_PER_ELEMENT,\n        toLength((end === undefined ? length : toAbsoluteIndex(end, length)) - $begin)\n      );\n    }\n  };\n\n  var $slice = function slice(start, end) {\n    return speciesFromList(this, arraySlice.call(validate(this), start, end));\n  };\n\n  var $set = function set(arrayLike /* , offset */) {\n    validate(this);\n    var offset = toOffset(arguments[1], 1);\n    var length = this.length;\n    var src = toObject(arrayLike);\n    var len = toLength(src.length);\n    var index = 0;\n    if (len + offset > length) throw RangeError(WRONG_LENGTH);\n    while (index < len) this[offset + index] = src[index++];\n  };\n\n  var $iterators = {\n    entries: function entries() {\n      return arrayEntries.call(validate(this));\n    },\n    keys: function keys() {\n      return arrayKeys.call(validate(this));\n    },\n    values: function values() {\n      return arrayValues.call(validate(this));\n    }\n  };\n\n  var isTAIndex = function (target, key) {\n    return isObject(target)\n      && target[TYPED_ARRAY]\n      && typeof key != 'symbol'\n      && key in target\n      && String(+key) == String(key);\n  };\n  var $getDesc = function getOwnPropertyDescriptor(target, key) {\n    return isTAIndex(target, key = toPrimitive(key, true))\n      ? propertyDesc(2, target[key])\n      : gOPD(target, key);\n  };\n  var $setDesc = function defineProperty(target, key, desc) {\n    if (isTAIndex(target, key = toPrimitive(key, true))\n      && isObject(desc)\n      && has(desc, 'value')\n      && !has(desc, 'get')\n      && !has(desc, 'set')\n      // TODO: add validation descriptor w/o calling accessors\n      && !desc.configurable\n      && (!has(desc, 'writable') || desc.writable)\n      && (!has(desc, 'enumerable') || desc.enumerable)\n    ) {\n      target[key] = desc.value;\n      return target;\n    } return dP(target, key, desc);\n  };\n\n  if (!ALL_CONSTRUCTORS) {\n    $GOPD.f = $getDesc;\n    $DP.f = $setDesc;\n  }\n\n  $export($export.S + $export.F * !ALL_CONSTRUCTORS, 'Object', {\n    getOwnPropertyDescriptor: $getDesc,\n    defineProperty: $setDesc\n  });\n\n  if (fails(function () { arrayToString.call({}); })) {\n    arrayToString = arrayToLocaleString = function toString() {\n      return arrayJoin.call(this);\n    };\n  }\n\n  var $TypedArrayPrototype$ = redefineAll({}, proto);\n  redefineAll($TypedArrayPrototype$, $iterators);\n  hide($TypedArrayPrototype$, ITERATOR, $iterators.values);\n  redefineAll($TypedArrayPrototype$, {\n    slice: $slice,\n    set: $set,\n    constructor: function () { /* noop */ },\n    toString: arrayToString,\n    toLocaleString: $toLocaleString\n  });\n  addGetter($TypedArrayPrototype$, 'buffer', 'b');\n  addGetter($TypedArrayPrototype$, 'byteOffset', 'o');\n  addGetter($TypedArrayPrototype$, 'byteLength', 'l');\n  addGetter($TypedArrayPrototype$, 'length', 'e');\n  dP($TypedArrayPrototype$, TAG, {\n    get: function () { return this[TYPED_ARRAY]; }\n  });\n\n  // eslint-disable-next-line max-statements\n  module.exports = function (KEY, BYTES, wrapper, CLAMPED) {\n    CLAMPED = !!CLAMPED;\n    var NAME = KEY + (CLAMPED ? 'Clamped' : '') + 'Array';\n    var GETTER = 'get' + KEY;\n    var SETTER = 'set' + KEY;\n    var TypedArray = global[NAME];\n    var Base = TypedArray || {};\n    var TAC = TypedArray && getPrototypeOf(TypedArray);\n    var FORCED = !TypedArray || !$typed.ABV;\n    var O = {};\n    var TypedArrayPrototype = TypedArray && TypedArray[PROTOTYPE];\n    var getter = function (that, index) {\n      var data = that._d;\n      return data.v[GETTER](index * BYTES + data.o, LITTLE_ENDIAN);\n    };\n    var setter = function (that, index, value) {\n      var data = that._d;\n      if (CLAMPED) value = (value = Math.round(value)) < 0 ? 0 : value > 0xff ? 0xff : value & 0xff;\n      data.v[SETTER](index * BYTES + data.o, value, LITTLE_ENDIAN);\n    };\n    var addElement = function (that, index) {\n      dP(that, index, {\n        get: function () {\n          return getter(this, index);\n        },\n        set: function (value) {\n          return setter(this, index, value);\n        },\n        enumerable: true\n      });\n    };\n    if (FORCED) {\n      TypedArray = wrapper(function (that, data, $offset, $length) {\n        anInstance(that, TypedArray, NAME, '_d');\n        var index = 0;\n        var offset = 0;\n        var buffer, byteLength, length, klass;\n        if (!isObject(data)) {\n          length = toIndex(data);\n          byteLength = length * BYTES;\n          buffer = new $ArrayBuffer(byteLength);\n        } else if (data instanceof $ArrayBuffer || (klass = classof(data)) == ARRAY_BUFFER || klass == SHARED_BUFFER) {\n          buffer = data;\n          offset = toOffset($offset, BYTES);\n          var $len = data.byteLength;\n          if ($length === undefined) {\n            if ($len % BYTES) throw RangeError(WRONG_LENGTH);\n            byteLength = $len - offset;\n            if (byteLength < 0) throw RangeError(WRONG_LENGTH);\n          } else {\n            byteLength = toLength($length) * BYTES;\n            if (byteLength + offset > $len) throw RangeError(WRONG_LENGTH);\n          }\n          length = byteLength / BYTES;\n        } else if (TYPED_ARRAY in data) {\n          return fromList(TypedArray, data);\n        } else {\n          return $from.call(TypedArray, data);\n        }\n        hide(that, '_d', {\n          b: buffer,\n          o: offset,\n          l: byteLength,\n          e: length,\n          v: new $DataView(buffer)\n        });\n        while (index < length) addElement(that, index++);\n      });\n      TypedArrayPrototype = TypedArray[PROTOTYPE] = create($TypedArrayPrototype$);\n      hide(TypedArrayPrototype, 'constructor', TypedArray);\n    } else if (!fails(function () {\n      TypedArray(1);\n    }) || !fails(function () {\n      new TypedArray(-1); // eslint-disable-line no-new\n    }) || !$iterDetect(function (iter) {\n      new TypedArray(); // eslint-disable-line no-new\n      new TypedArray(null); // eslint-disable-line no-new\n      new TypedArray(1.5); // eslint-disable-line no-new\n      new TypedArray(iter); // eslint-disable-line no-new\n    }, true)) {\n      TypedArray = wrapper(function (that, data, $offset, $length) {\n        anInstance(that, TypedArray, NAME);\n        var klass;\n        // `ws` module bug, temporarily remove validation length for Uint8Array\n        // https://github.com/websockets/ws/pull/645\n        if (!isObject(data)) return new Base(toIndex(data));\n        if (data instanceof $ArrayBuffer || (klass = classof(data)) == ARRAY_BUFFER || klass == SHARED_BUFFER) {\n          return $length !== undefined\n            ? new Base(data, toOffset($offset, BYTES), $length)\n            : $offset !== undefined\n              ? new Base(data, toOffset($offset, BYTES))\n              : new Base(data);\n        }\n        if (TYPED_ARRAY in data) return fromList(TypedArray, data);\n        return $from.call(TypedArray, data);\n      });\n      arrayForEach(TAC !== Function.prototype ? gOPN(Base).concat(gOPN(TAC)) : gOPN(Base), function (key) {\n        if (!(key in TypedArray)) hide(TypedArray, key, Base[key]);\n      });\n      TypedArray[PROTOTYPE] = TypedArrayPrototype;\n      if (!LIBRARY) TypedArrayPrototype.constructor = TypedArray;\n    }\n    var $nativeIterator = TypedArrayPrototype[ITERATOR];\n    var CORRECT_ITER_NAME = !!$nativeIterator\n      && ($nativeIterator.name == 'values' || $nativeIterator.name == undefined);\n    var $iterator = $iterators.values;\n    hide(TypedArray, TYPED_CONSTRUCTOR, true);\n    hide(TypedArrayPrototype, TYPED_ARRAY, NAME);\n    hide(TypedArrayPrototype, VIEW, true);\n    hide(TypedArrayPrototype, DEF_CONSTRUCTOR, TypedArray);\n\n    if (CLAMPED ? new TypedArray(1)[TAG] != NAME : !(TAG in TypedArrayPrototype)) {\n      dP(TypedArrayPrototype, TAG, {\n        get: function () { return NAME; }\n      });\n    }\n\n    O[NAME] = TypedArray;\n\n    $export($export.G + $export.W + $export.F * (TypedArray != Base), O);\n\n    $export($export.S, NAME, {\n      BYTES_PER_ELEMENT: BYTES\n    });\n\n    $export($export.S + $export.F * fails(function () { Base.of.call(TypedArray, 1); }), NAME, {\n      from: $from,\n      of: $of\n    });\n\n    if (!(BYTES_PER_ELEMENT in TypedArrayPrototype)) hide(TypedArrayPrototype, BYTES_PER_ELEMENT, BYTES);\n\n    $export($export.P, NAME, proto);\n\n    setSpecies(NAME);\n\n    $export($export.P + $export.F * FORCED_SET, NAME, { set: $set });\n\n    $export($export.P + $export.F * !CORRECT_ITER_NAME, NAME, $iterators);\n\n    if (!LIBRARY && TypedArrayPrototype.toString != arrayToString) TypedArrayPrototype.toString = arrayToString;\n\n    $export($export.P + $export.F * fails(function () {\n      new TypedArray(1).slice();\n    }), NAME, { slice: $slice });\n\n    $export($export.P + $export.F * (fails(function () {\n      return [1, 2].toLocaleString() != new TypedArray([1, 2]).toLocaleString();\n    }) || !fails(function () {\n      TypedArrayPrototype.toLocaleString.call([1, 2]);\n    })), NAME, { toLocaleString: $toLocaleString });\n\n    Iterators[NAME] = CORRECT_ITER_NAME ? $nativeIterator : $iterator;\n    if (!LIBRARY && !CORRECT_ITER_NAME) hide(TypedArrayPrototype, ITERATOR, $iterator);\n  };\n} else module.exports = function () { /* empty */ };\n","require('./_typed-array')('Int8', 1, function (init) {\n  return function Int8Array(data, byteOffset, length) {\n    return init(this, data, byteOffset, length);\n  };\n});\n","require('./_typed-array')('Uint8', 1, function (init) {\n  return function Uint8Array(data, byteOffset, length) {\n    return init(this, data, byteOffset, length);\n  };\n});\n","require('./_typed-array')('Uint8', 1, function (init) {\n  return function Uint8ClampedArray(data, byteOffset, length) {\n    return init(this, data, byteOffset, length);\n  };\n}, true);\n","require('./_typed-array')('Int16', 2, function (init) {\n  return function Int16Array(data, byteOffset, length) {\n    return init(this, data, byteOffset, length);\n  };\n});\n","require('./_typed-array')('Uint16', 2, function (init) {\n  return function Uint16Array(data, byteOffset, length) {\n    return init(this, data, byteOffset, length);\n  };\n});\n","require('./_typed-array')('Int32', 4, function (init) {\n  return function Int32Array(data, byteOffset, length) {\n    return init(this, data, byteOffset, length);\n  };\n});\n","require('./_typed-array')('Uint32', 4, function (init) {\n  return function Uint32Array(data, byteOffset, length) {\n    return init(this, data, byteOffset, length);\n  };\n});\n","require('./_typed-array')('Float32', 4, function (init) {\n  return function Float32Array(data, byteOffset, length) {\n    return init(this, data, byteOffset, length);\n  };\n});\n","require('./_typed-array')('Float64', 8, function (init) {\n  return function Float64Array(data, byteOffset, length) {\n    return init(this, data, byteOffset, length);\n  };\n});\n","// 26.1.1 Reflect.apply(target, thisArgument, argumentsList)\nvar $export = require('./_export');\nvar aFunction = require('./_a-function');\nvar anObject = require('./_an-object');\nvar rApply = (require('./_global').Reflect || {}).apply;\nvar fApply = Function.apply;\n// MS Edge argumentsList argument is optional\n$export($export.S + $export.F * !require('./_fails')(function () {\n  rApply(function () { /* empty */ });\n}), 'Reflect', {\n  apply: function apply(target, thisArgument, argumentsList) {\n    var T = aFunction(target);\n    var L = anObject(argumentsList);\n    return rApply ? rApply(T, thisArgument, L) : fApply.call(T, thisArgument, L);\n  }\n});\n","// 26.1.2 Reflect.construct(target, argumentsList [, newTarget])\nvar $export = require('./_export');\nvar create = require('./_object-create');\nvar aFunction = require('./_a-function');\nvar anObject = require('./_an-object');\nvar isObject = require('./_is-object');\nvar fails = require('./_fails');\nvar bind = require('./_bind');\nvar rConstruct = (require('./_global').Reflect || {}).construct;\n\n// MS Edge supports only 2 arguments and argumentsList argument is optional\n// FF Nightly sets third argument as `new.target`, but does not create `this` from it\nvar NEW_TARGET_BUG = fails(function () {\n  function F() { /* empty */ }\n  return !(rConstruct(function () { /* empty */ }, [], F) instanceof F);\n});\nvar ARGS_BUG = !fails(function () {\n  rConstruct(function () { /* empty */ });\n});\n\n$export($export.S + $export.F * (NEW_TARGET_BUG || ARGS_BUG), 'Reflect', {\n  construct: function construct(Target, args /* , newTarget */) {\n    aFunction(Target);\n    anObject(args);\n    var newTarget = arguments.length < 3 ? Target : aFunction(arguments[2]);\n    if (ARGS_BUG && !NEW_TARGET_BUG) return rConstruct(Target, args, newTarget);\n    if (Target == newTarget) {\n      // w/o altered newTarget, optimization for 0-4 arguments\n      switch (args.length) {\n        case 0: return new Target();\n        case 1: return new Target(args[0]);\n        case 2: return new Target(args[0], args[1]);\n        case 3: return new Target(args[0], args[1], args[2]);\n        case 4: return new Target(args[0], args[1], args[2], args[3]);\n      }\n      // w/o altered newTarget, lot of arguments case\n      var $args = [null];\n      $args.push.apply($args, args);\n      return new (bind.apply(Target, $args))();\n    }\n    // with altered newTarget, not support built-in constructors\n    var proto = newTarget.prototype;\n    var instance = create(isObject(proto) ? proto : Object.prototype);\n    var result = Function.apply.call(Target, instance, args);\n    return isObject(result) ? result : instance;\n  }\n});\n","// 26.1.3 Reflect.defineProperty(target, propertyKey, attributes)\nvar dP = require('./_object-dp');\nvar $export = require('./_export');\nvar anObject = require('./_an-object');\nvar toPrimitive = require('./_to-primitive');\n\n// MS Edge has broken Reflect.defineProperty - throwing instead of returning false\n$export($export.S + $export.F * require('./_fails')(function () {\n  // eslint-disable-next-line no-undef\n  Reflect.defineProperty(dP.f({}, 1, { value: 1 }), 1, { value: 2 });\n}), 'Reflect', {\n  defineProperty: function defineProperty(target, propertyKey, attributes) {\n    anObject(target);\n    propertyKey = toPrimitive(propertyKey, true);\n    anObject(attributes);\n    try {\n      dP.f(target, propertyKey, attributes);\n      return true;\n    } catch (e) {\n      return false;\n    }\n  }\n});\n","// 26.1.4 Reflect.deleteProperty(target, propertyKey)\nvar $export = require('./_export');\nvar gOPD = require('./_object-gopd').f;\nvar anObject = require('./_an-object');\n\n$export($export.S, 'Reflect', {\n  deleteProperty: function deleteProperty(target, propertyKey) {\n    var desc = gOPD(anObject(target), propertyKey);\n    return desc && !desc.configurable ? false : delete target[propertyKey];\n  }\n});\n","'use strict';\n// 26.1.5 Reflect.enumerate(target)\nvar $export = require('./_export');\nvar anObject = require('./_an-object');\nvar Enumerate = function (iterated) {\n  this._t = anObject(iterated); // target\n  this._i = 0;                  // next index\n  var keys = this._k = [];      // keys\n  var key;\n  for (key in iterated) keys.push(key);\n};\nrequire('./_iter-create')(Enumerate, 'Object', function () {\n  var that = this;\n  var keys = that._k;\n  var key;\n  do {\n    if (that._i >= keys.length) return { value: undefined, done: true };\n  } while (!((key = keys[that._i++]) in that._t));\n  return { value: key, done: false };\n});\n\n$export($export.S, 'Reflect', {\n  enumerate: function enumerate(target) {\n    return new Enumerate(target);\n  }\n});\n","// 26.1.6 Reflect.get(target, propertyKey [, receiver])\nvar gOPD = require('./_object-gopd');\nvar getPrototypeOf = require('./_object-gpo');\nvar has = require('./_has');\nvar $export = require('./_export');\nvar isObject = require('./_is-object');\nvar anObject = require('./_an-object');\n\nfunction get(target, propertyKey /* , receiver */) {\n  var receiver = arguments.length < 3 ? target : arguments[2];\n  var desc, proto;\n  if (anObject(target) === receiver) return target[propertyKey];\n  if (desc = gOPD.f(target, propertyKey)) return has(desc, 'value')\n    ? desc.value\n    : desc.get !== undefined\n      ? desc.get.call(receiver)\n      : undefined;\n  if (isObject(proto = getPrototypeOf(target))) return get(proto, propertyKey, receiver);\n}\n\n$export($export.S, 'Reflect', { get: get });\n","// 26.1.7 Reflect.getOwnPropertyDescriptor(target, propertyKey)\nvar gOPD = require('./_object-gopd');\nvar $export = require('./_export');\nvar anObject = require('./_an-object');\n\n$export($export.S, 'Reflect', {\n  getOwnPropertyDescriptor: function getOwnPropertyDescriptor(target, propertyKey) {\n    return gOPD.f(anObject(target), propertyKey);\n  }\n});\n","// 26.1.8 Reflect.getPrototypeOf(target)\nvar $export = require('./_export');\nvar getProto = require('./_object-gpo');\nvar anObject = require('./_an-object');\n\n$export($export.S, 'Reflect', {\n  getPrototypeOf: function getPrototypeOf(target) {\n    return getProto(anObject(target));\n  }\n});\n","// 26.1.9 Reflect.has(target, propertyKey)\nvar $export = require('./_export');\n\n$export($export.S, 'Reflect', {\n  has: function has(target, propertyKey) {\n    return propertyKey in target;\n  }\n});\n","// 26.1.10 Reflect.isExtensible(target)\nvar $export = require('./_export');\nvar anObject = require('./_an-object');\nvar $isExtensible = Object.isExtensible;\n\n$export($export.S, 'Reflect', {\n  isExtensible: function isExtensible(target) {\n    anObject(target);\n    return $isExtensible ? $isExtensible(target) : true;\n  }\n});\n","// all object keys, includes non-enumerable and symbols\nvar gOPN = require('./_object-gopn');\nvar gOPS = require('./_object-gops');\nvar anObject = require('./_an-object');\nvar Reflect = require('./_global').Reflect;\nmodule.exports = Reflect && Reflect.ownKeys || function ownKeys(it) {\n  var keys = gOPN.f(anObject(it));\n  var getSymbols = gOPS.f;\n  return getSymbols ? keys.concat(getSymbols(it)) : keys;\n};\n","// 26.1.11 Reflect.ownKeys(target)\nvar $export = require('./_export');\n\n$export($export.S, 'Reflect', { ownKeys: require('./_own-keys') });\n","// 26.1.12 Reflect.preventExtensions(target)\nvar $export = require('./_export');\nvar anObject = require('./_an-object');\nvar $preventExtensions = Object.preventExtensions;\n\n$export($export.S, 'Reflect', {\n  preventExtensions: function preventExtensions(target) {\n    anObject(target);\n    try {\n      if ($preventExtensions) $preventExtensions(target);\n      return true;\n    } catch (e) {\n      return false;\n    }\n  }\n});\n","// 26.1.13 Reflect.set(target, propertyKey, V [, receiver])\nvar dP = require('./_object-dp');\nvar gOPD = require('./_object-gopd');\nvar getPrototypeOf = require('./_object-gpo');\nvar has = require('./_has');\nvar $export = require('./_export');\nvar createDesc = require('./_property-desc');\nvar anObject = require('./_an-object');\nvar isObject = require('./_is-object');\n\nfunction set(target, propertyKey, V /* , receiver */) {\n  var receiver = arguments.length < 4 ? target : arguments[3];\n  var ownDesc = gOPD.f(anObject(target), propertyKey);\n  var existingDescriptor, proto;\n  if (!ownDesc) {\n    if (isObject(proto = getPrototypeOf(target))) {\n      return set(proto, propertyKey, V, receiver);\n    }\n    ownDesc = createDesc(0);\n  }\n  if (has(ownDesc, 'value')) {\n    if (ownDesc.writable === false || !isObject(receiver)) return false;\n    if (existingDescriptor = gOPD.f(receiver, propertyKey)) {\n      if (existingDescriptor.get || existingDescriptor.set || existingDescriptor.writable === false) return false;\n      existingDescriptor.value = V;\n      dP.f(receiver, propertyKey, existingDescriptor);\n    } else dP.f(receiver, propertyKey, createDesc(0, V));\n    return true;\n  }\n  return ownDesc.set === undefined ? false : (ownDesc.set.call(receiver, V), true);\n}\n\n$export($export.S, 'Reflect', { set: set });\n","// 26.1.14 Reflect.setPrototypeOf(target, proto)\nvar $export = require('./_export');\nvar setProto = require('./_set-proto');\n\nif (setProto) $export($export.S, 'Reflect', {\n  setPrototypeOf: function setPrototypeOf(target, proto) {\n    setProto.check(target, proto);\n    try {\n      setProto.set(target, proto);\n      return true;\n    } catch (e) {\n      return false;\n    }\n  }\n});\n","'use strict';\n// https://github.com/tc39/Array.prototype.includes\nvar $export = require('./_export');\nvar $includes = require('./_array-includes')(true);\n\n$export($export.P, 'Array', {\n  includes: function includes(el /* , fromIndex = 0 */) {\n    return $includes(this, el, arguments.length > 1 ? arguments[1] : undefined);\n  }\n});\n\nrequire('./_add-to-unscopables')('includes');\n","'use strict';\n// https://tc39.github.io/proposal-flatMap/#sec-FlattenIntoArray\nvar isArray = require('./_is-array');\nvar isObject = require('./_is-object');\nvar toLength = require('./_to-length');\nvar ctx = require('./_ctx');\nvar IS_CONCAT_SPREADABLE = require('./_wks')('isConcatSpreadable');\n\nfunction flattenIntoArray(target, original, source, sourceLen, start, depth, mapper, thisArg) {\n  var targetIndex = start;\n  var sourceIndex = 0;\n  var mapFn = mapper ? ctx(mapper, thisArg, 3) : false;\n  var element, spreadable;\n\n  while (sourceIndex < sourceLen) {\n    if (sourceIndex in source) {\n      element = mapFn ? mapFn(source[sourceIndex], sourceIndex, original) : source[sourceIndex];\n\n      spreadable = false;\n      if (isObject(element)) {\n        spreadable = element[IS_CONCAT_SPREADABLE];\n        spreadable = spreadable !== undefined ? !!spreadable : isArray(element);\n      }\n\n      if (spreadable && depth > 0) {\n        targetIndex = flattenIntoArray(target, original, element, toLength(element.length), targetIndex, depth - 1) - 1;\n      } else {\n        if (targetIndex >= 0x1fffffffffffff) throw TypeError();\n        target[targetIndex] = element;\n      }\n\n      targetIndex++;\n    }\n    sourceIndex++;\n  }\n  return targetIndex;\n}\n\nmodule.exports = flattenIntoArray;\n","'use strict';\n// https://tc39.github.io/proposal-flatMap/#sec-Array.prototype.flatMap\nvar $export = require('./_export');\nvar flattenIntoArray = require('./_flatten-into-array');\nvar toObject = require('./_to-object');\nvar toLength = require('./_to-length');\nvar aFunction = require('./_a-function');\nvar arraySpeciesCreate = require('./_array-species-create');\n\n$export($export.P, 'Array', {\n  flatMap: function flatMap(callbackfn /* , thisArg */) {\n    var O = toObject(this);\n    var sourceLen, A;\n    aFunction(callbackfn);\n    sourceLen = toLength(O.length);\n    A = arraySpeciesCreate(O, 0);\n    flattenIntoArray(A, O, O, sourceLen, 0, 1, callbackfn, arguments[1]);\n    return A;\n  }\n});\n\nrequire('./_add-to-unscopables')('flatMap');\n","'use strict';\n// https://tc39.github.io/proposal-flatMap/#sec-Array.prototype.flatten\nvar $export = require('./_export');\nvar flattenIntoArray = require('./_flatten-into-array');\nvar toObject = require('./_to-object');\nvar toLength = require('./_to-length');\nvar toInteger = require('./_to-integer');\nvar arraySpeciesCreate = require('./_array-species-create');\n\n$export($export.P, 'Array', {\n  flatten: function flatten(/* depthArg = 1 */) {\n    var depthArg = arguments[0];\n    var O = toObject(this);\n    var sourceLen = toLength(O.length);\n    var A = arraySpeciesCreate(O, 0);\n    flattenIntoArray(A, O, O, sourceLen, 0, depthArg === undefined ? 1 : toInteger(depthArg));\n    return A;\n  }\n});\n\nrequire('./_add-to-unscopables')('flatten');\n","'use strict';\n// https://github.com/mathiasbynens/String.prototype.at\nvar $export = require('./_export');\nvar $at = require('./_string-at')(true);\n\n$export($export.P, 'String', {\n  at: function at(pos) {\n    return $at(this, pos);\n  }\n});\n","// https://github.com/tc39/proposal-string-pad-start-end\nvar toLength = require('./_to-length');\nvar repeat = require('./_string-repeat');\nvar defined = require('./_defined');\n\nmodule.exports = function (that, maxLength, fillString, left) {\n  var S = String(defined(that));\n  var stringLength = S.length;\n  var fillStr = fillString === undefined ? ' ' : String(fillString);\n  var intMaxLength = toLength(maxLength);\n  if (intMaxLength <= stringLength || fillStr == '') return S;\n  var fillLen = intMaxLength - stringLength;\n  var stringFiller = repeat.call(fillStr, Math.ceil(fillLen / fillStr.length));\n  if (stringFiller.length > fillLen) stringFiller = stringFiller.slice(0, fillLen);\n  return left ? stringFiller + S : S + stringFiller;\n};\n","'use strict';\n// https://github.com/tc39/proposal-string-pad-start-end\nvar $export = require('./_export');\nvar $pad = require('./_string-pad');\nvar userAgent = require('./_user-agent');\n\n// https://github.com/zloirock/core-js/issues/280\nvar WEBKIT_BUG = /Version\\/10\\.\\d+(\\.\\d+)?( Mobile\\/\\w+)? Safari\\//.test(userAgent);\n\n$export($export.P + $export.F * WEBKIT_BUG, 'String', {\n  padStart: function padStart(maxLength /* , fillString = ' ' */) {\n    return $pad(this, maxLength, arguments.length > 1 ? arguments[1] : undefined, true);\n  }\n});\n","'use strict';\n// https://github.com/tc39/proposal-string-pad-start-end\nvar $export = require('./_export');\nvar $pad = require('./_string-pad');\nvar userAgent = require('./_user-agent');\n\n// https://github.com/zloirock/core-js/issues/280\nvar WEBKIT_BUG = /Version\\/10\\.\\d+(\\.\\d+)?( Mobile\\/\\w+)? Safari\\//.test(userAgent);\n\n$export($export.P + $export.F * WEBKIT_BUG, 'String', {\n  padEnd: function padEnd(maxLength /* , fillString = ' ' */) {\n    return $pad(this, maxLength, arguments.length > 1 ? arguments[1] : undefined, false);\n  }\n});\n","'use strict';\n// https://github.com/sebmarkbage/ecmascript-string-left-right-trim\nrequire('./_string-trim')('trimLeft', function ($trim) {\n  return function trimLeft() {\n    return $trim(this, 1);\n  };\n}, 'trimStart');\n","'use strict';\n// https://github.com/sebmarkbage/ecmascript-string-left-right-trim\nrequire('./_string-trim')('trimRight', function ($trim) {\n  return function trimRight() {\n    return $trim(this, 2);\n  };\n}, 'trimEnd');\n","'use strict';\n// https://tc39.github.io/String.prototype.matchAll/\nvar $export = require('./_export');\nvar defined = require('./_defined');\nvar toLength = require('./_to-length');\nvar isRegExp = require('./_is-regexp');\nvar getFlags = require('./_flags');\nvar RegExpProto = RegExp.prototype;\n\nvar $RegExpStringIterator = function (regexp, string) {\n  this._r = regexp;\n  this._s = string;\n};\n\nrequire('./_iter-create')($RegExpStringIterator, 'RegExp String', function next() {\n  var match = this._r.exec(this._s);\n  return { value: match, done: match === null };\n});\n\n$export($export.P, 'String', {\n  matchAll: function matchAll(regexp) {\n    defined(this);\n    if (!isRegExp(regexp)) throw TypeError(regexp + ' is not a regexp!');\n    var S = String(this);\n    var flags = 'flags' in RegExpProto ? String(regexp.flags) : getFlags.call(regexp);\n    var rx = new RegExp(regexp.source, ~flags.indexOf('g') ? flags : 'g' + flags);\n    rx.lastIndex = toLength(regexp.lastIndex);\n    return new $RegExpStringIterator(rx, S);\n  }\n});\n","require('./_wks-define')('asyncIterator');\n","require('./_wks-define')('observable');\n","// https://github.com/tc39/proposal-object-getownpropertydescriptors\nvar $export = require('./_export');\nvar ownKeys = require('./_own-keys');\nvar toIObject = require('./_to-iobject');\nvar gOPD = require('./_object-gopd');\nvar createProperty = require('./_create-property');\n\n$export($export.S, 'Object', {\n  getOwnPropertyDescriptors: function getOwnPropertyDescriptors(object) {\n    var O = toIObject(object);\n    var getDesc = gOPD.f;\n    var keys = ownKeys(O);\n    var result = {};\n    var i = 0;\n    var key, desc;\n    while (keys.length > i) {\n      desc = getDesc(O, key = keys[i++]);\n      if (desc !== undefined) createProperty(result, key, desc);\n    }\n    return result;\n  }\n});\n","var DESCRIPTORS = require('./_descriptors');\nvar getKeys = require('./_object-keys');\nvar toIObject = require('./_to-iobject');\nvar isEnum = require('./_object-pie').f;\nmodule.exports = function (isEntries) {\n  return function (it) {\n    var O = toIObject(it);\n    var keys = getKeys(O);\n    var length = keys.length;\n    var i = 0;\n    var result = [];\n    var key;\n    while (length > i) {\n      key = keys[i++];\n      if (!DESCRIPTORS || isEnum.call(O, key)) {\n        result.push(isEntries ? [key, O[key]] : O[key]);\n      }\n    }\n    return result;\n  };\n};\n","// https://github.com/tc39/proposal-object-values-entries\nvar $export = require('./_export');\nvar $values = require('./_object-to-array')(false);\n\n$export($export.S, 'Object', {\n  values: function values(it) {\n    return $values(it);\n  }\n});\n","// https://github.com/tc39/proposal-object-values-entries\nvar $export = require('./_export');\nvar $entries = require('./_object-to-array')(true);\n\n$export($export.S, 'Object', {\n  entries: function entries(it) {\n    return $entries(it);\n  }\n});\n","'use strict';\n// Forced replacement prototype accessors methods\nmodule.exports = require('./_library') || !require('./_fails')(function () {\n  var K = Math.random();\n  // In FF throws only define methods\n  // eslint-disable-next-line no-undef, no-useless-call\n  __defineSetter__.call(null, K, function () { /* empty */ });\n  delete require('./_global')[K];\n});\n","'use strict';\nvar $export = require('./_export');\nvar toObject = require('./_to-object');\nvar aFunction = require('./_a-function');\nvar $defineProperty = require('./_object-dp');\n\n// B.2.2.2 Object.prototype.__defineGetter__(P, getter)\nrequire('./_descriptors') && $export($export.P + require('./_object-forced-pam'), 'Object', {\n  __defineGetter__: function __defineGetter__(P, getter) {\n    $defineProperty.f(toObject(this), P, { get: aFunction(getter), enumerable: true, configurable: true });\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar toObject = require('./_to-object');\nvar aFunction = require('./_a-function');\nvar $defineProperty = require('./_object-dp');\n\n// B.2.2.3 Object.prototype.__defineSetter__(P, setter)\nrequire('./_descriptors') && $export($export.P + require('./_object-forced-pam'), 'Object', {\n  __defineSetter__: function __defineSetter__(P, setter) {\n    $defineProperty.f(toObject(this), P, { set: aFunction(setter), enumerable: true, configurable: true });\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar toObject = require('./_to-object');\nvar toPrimitive = require('./_to-primitive');\nvar getPrototypeOf = require('./_object-gpo');\nvar getOwnPropertyDescriptor = require('./_object-gopd').f;\n\n// B.2.2.4 Object.prototype.__lookupGetter__(P)\nrequire('./_descriptors') && $export($export.P + require('./_object-forced-pam'), 'Object', {\n  __lookupGetter__: function __lookupGetter__(P) {\n    var O = toObject(this);\n    var K = toPrimitive(P, true);\n    var D;\n    do {\n      if (D = getOwnPropertyDescriptor(O, K)) return D.get;\n    } while (O = getPrototypeOf(O));\n  }\n});\n","'use strict';\nvar $export = require('./_export');\nvar toObject = require('./_to-object');\nvar toPrimitive = require('./_to-primitive');\nvar getPrototypeOf = require('./_object-gpo');\nvar getOwnPropertyDescriptor = require('./_object-gopd').f;\n\n// B.2.2.5 Object.prototype.__lookupSetter__(P)\nrequire('./_descriptors') && $export($export.P + require('./_object-forced-pam'), 'Object', {\n  __lookupSetter__: function __lookupSetter__(P) {\n    var O = toObject(this);\n    var K = toPrimitive(P, true);\n    var D;\n    do {\n      if (D = getOwnPropertyDescriptor(O, K)) return D.set;\n    } while (O = getPrototypeOf(O));\n  }\n});\n","var forOf = require('./_for-of');\n\nmodule.exports = function (iter, ITERATOR) {\n  var result = [];\n  forOf(iter, false, result.push, result, ITERATOR);\n  return result;\n};\n","// https://github.com/DavidBruant/Map-Set.prototype.toJSON\nvar classof = require('./_classof');\nvar from = require('./_array-from-iterable');\nmodule.exports = function (NAME) {\n  return function toJSON() {\n    if (classof(this) != NAME) throw TypeError(NAME + \"#toJSON isn't generic\");\n    return from(this);\n  };\n};\n","// https://github.com/DavidBruant/Map-Set.prototype.toJSON\nvar $export = require('./_export');\n\n$export($export.P + $export.R, 'Map', { toJSON: require('./_collection-to-json')('Map') });\n","// https://github.com/DavidBruant/Map-Set.prototype.toJSON\nvar $export = require('./_export');\n\n$export($export.P + $export.R, 'Set', { toJSON: require('./_collection-to-json')('Set') });\n","'use strict';\n// https://tc39.github.io/proposal-setmap-offrom/\nvar $export = require('./_export');\n\nmodule.exports = function (COLLECTION) {\n  $export($export.S, COLLECTION, { of: function of() {\n    var length = arguments.length;\n    var A = new Array(length);\n    while (length--) A[length] = arguments[length];\n    return new this(A);\n  } });\n};\n","// https://tc39.github.io/proposal-setmap-offrom/#sec-map.of\nrequire('./_set-collection-of')('Map');\n","// https://tc39.github.io/proposal-setmap-offrom/#sec-set.of\nrequire('./_set-collection-of')('Set');\n","// https://tc39.github.io/proposal-setmap-offrom/#sec-weakmap.of\nrequire('./_set-collection-of')('WeakMap');\n","// https://tc39.github.io/proposal-setmap-offrom/#sec-weakset.of\nrequire('./_set-collection-of')('WeakSet');\n","'use strict';\n// https://tc39.github.io/proposal-setmap-offrom/\nvar $export = require('./_export');\nvar aFunction = require('./_a-function');\nvar ctx = require('./_ctx');\nvar forOf = require('./_for-of');\n\nmodule.exports = function (COLLECTION) {\n  $export($export.S, COLLECTION, { from: function from(source /* , mapFn, thisArg */) {\n    var mapFn = arguments[1];\n    var mapping, A, n, cb;\n    aFunction(this);\n    mapping = mapFn !== undefined;\n    if (mapping) aFunction(mapFn);\n    if (source == undefined) return new this();\n    A = [];\n    if (mapping) {\n      n = 0;\n      cb = ctx(mapFn, arguments[2], 2);\n      forOf(source, false, function (nextItem) {\n        A.push(cb(nextItem, n++));\n      });\n    } else {\n      forOf(source, false, A.push, A);\n    }\n    return new this(A);\n  } });\n};\n","// https://tc39.github.io/proposal-setmap-offrom/#sec-map.from\nrequire('./_set-collection-from')('Map');\n","// https://tc39.github.io/proposal-setmap-offrom/#sec-set.from\nrequire('./_set-collection-from')('Set');\n","// https://tc39.github.io/proposal-setmap-offrom/#sec-weakmap.from\nrequire('./_set-collection-from')('WeakMap');\n","// https://tc39.github.io/proposal-setmap-offrom/#sec-weakset.from\nrequire('./_set-collection-from')('WeakSet');\n","// https://github.com/tc39/proposal-global\nvar $export = require('./_export');\n\n$export($export.G, { global: require('./_global') });\n","// https://github.com/tc39/proposal-global\nvar $export = require('./_export');\n\n$export($export.S, 'System', { global: require('./_global') });\n","// https://github.com/ljharb/proposal-is-error\nvar $export = require('./_export');\nvar cof = require('./_cof');\n\n$export($export.S, 'Error', {\n  isError: function isError(it) {\n    return cof(it) === 'Error';\n  }\n});\n","// https://rwaldron.github.io/proposal-math-extensions/\nvar $export = require('./_export');\n\n$export($export.S, 'Math', {\n  clamp: function clamp(x, lower, upper) {\n    return Math.min(upper, Math.max(lower, x));\n  }\n});\n","// https://rwaldron.github.io/proposal-math-extensions/\nvar $export = require('./_export');\n\n$export($export.S, 'Math', { DEG_PER_RAD: Math.PI / 180 });\n","// https://rwaldron.github.io/proposal-math-extensions/\nvar $export = require('./_export');\nvar RAD_PER_DEG = 180 / Math.PI;\n\n$export($export.S, 'Math', {\n  degrees: function degrees(radians) {\n    return radians * RAD_PER_DEG;\n  }\n});\n","// https://rwaldron.github.io/proposal-math-extensions/\nmodule.exports = Math.scale || function scale(x, inLow, inHigh, outLow, outHigh) {\n  if (\n    arguments.length === 0\n      // eslint-disable-next-line no-self-compare\n      || x != x\n      // eslint-disable-next-line no-self-compare\n      || inLow != inLow\n      // eslint-disable-next-line no-self-compare\n      || inHigh != inHigh\n      // eslint-disable-next-line no-self-compare\n      || outLow != outLow\n      // eslint-disable-next-line no-self-compare\n      || outHigh != outHigh\n  ) return NaN;\n  if (x === Infinity || x === -Infinity) return x;\n  return (x - inLow) * (outHigh - outLow) / (inHigh - inLow) + outLow;\n};\n","// https://rwaldron.github.io/proposal-math-extensions/\nvar $export = require('./_export');\nvar scale = require('./_math-scale');\nvar fround = require('./_math-fround');\n\n$export($export.S, 'Math', {\n  fscale: function fscale(x, inLow, inHigh, outLow, outHigh) {\n    return fround(scale(x, inLow, inHigh, outLow, outHigh));\n  }\n});\n","// https://gist.github.com/BrendanEich/4294d5c212a6d2254703\nvar $export = require('./_export');\n\n$export($export.S, 'Math', {\n  iaddh: function iaddh(x0, x1, y0, y1) {\n    var $x0 = x0 >>> 0;\n    var $x1 = x1 >>> 0;\n    var $y0 = y0 >>> 0;\n    return $x1 + (y1 >>> 0) + (($x0 & $y0 | ($x0 | $y0) & ~($x0 + $y0 >>> 0)) >>> 31) | 0;\n  }\n});\n","// https://gist.github.com/BrendanEich/4294d5c212a6d2254703\nvar $export = require('./_export');\n\n$export($export.S, 'Math', {\n  isubh: function isubh(x0, x1, y0, y1) {\n    var $x0 = x0 >>> 0;\n    var $x1 = x1 >>> 0;\n    var $y0 = y0 >>> 0;\n    return $x1 - (y1 >>> 0) - ((~$x0 & $y0 | ~($x0 ^ $y0) & $x0 - $y0 >>> 0) >>> 31) | 0;\n  }\n});\n","// https://gist.github.com/BrendanEich/4294d5c212a6d2254703\nvar $export = require('./_export');\n\n$export($export.S, 'Math', {\n  imulh: function imulh(u, v) {\n    var UINT16 = 0xffff;\n    var $u = +u;\n    var $v = +v;\n    var u0 = $u & UINT16;\n    var v0 = $v & UINT16;\n    var u1 = $u >> 16;\n    var v1 = $v >> 16;\n    var t = (u1 * v0 >>> 0) + (u0 * v0 >>> 16);\n    return u1 * v1 + (t >> 16) + ((u0 * v1 >>> 0) + (t & UINT16) >> 16);\n  }\n});\n","// https://rwaldron.github.io/proposal-math-extensions/\nvar $export = require('./_export');\n\n$export($export.S, 'Math', { RAD_PER_DEG: 180 / Math.PI });\n","// https://rwaldron.github.io/proposal-math-extensions/\nvar $export = require('./_export');\nvar DEG_PER_RAD = Math.PI / 180;\n\n$export($export.S, 'Math', {\n  radians: function radians(degrees) {\n    return degrees * DEG_PER_RAD;\n  }\n});\n","// https://rwaldron.github.io/proposal-math-extensions/\nvar $export = require('./_export');\n\n$export($export.S, 'Math', { scale: require('./_math-scale') });\n","// https://gist.github.com/BrendanEich/4294d5c212a6d2254703\nvar $export = require('./_export');\n\n$export($export.S, 'Math', {\n  umulh: function umulh(u, v) {\n    var UINT16 = 0xffff;\n    var $u = +u;\n    var $v = +v;\n    var u0 = $u & UINT16;\n    var v0 = $v & UINT16;\n    var u1 = $u >>> 16;\n    var v1 = $v >>> 16;\n    var t = (u1 * v0 >>> 0) + (u0 * v0 >>> 16);\n    return u1 * v1 + (t >>> 16) + ((u0 * v1 >>> 0) + (t & UINT16) >>> 16);\n  }\n});\n","// http://jfbastien.github.io/papers/Math.signbit.html\nvar $export = require('./_export');\n\n$export($export.S, 'Math', { signbit: function signbit(x) {\n  // eslint-disable-next-line no-self-compare\n  return (x = +x) != x ? x : x == 0 ? 1 / x == Infinity : x > 0;\n} });\n","// https://github.com/tc39/proposal-promise-finally\n'use strict';\nvar $export = require('./_export');\nvar core = require('./_core');\nvar global = require('./_global');\nvar speciesConstructor = require('./_species-constructor');\nvar promiseResolve = require('./_promise-resolve');\n\n$export($export.P + $export.R, 'Promise', { 'finally': function (onFinally) {\n  var C = speciesConstructor(this, core.Promise || global.Promise);\n  var isFunction = typeof onFinally == 'function';\n  return this.then(\n    isFunction ? function (x) {\n      return promiseResolve(C, onFinally()).then(function () { return x; });\n    } : onFinally,\n    isFunction ? function (e) {\n      return promiseResolve(C, onFinally()).then(function () { throw e; });\n    } : onFinally\n  );\n} });\n","'use strict';\n// https://github.com/tc39/proposal-promise-try\nvar $export = require('./_export');\nvar newPromiseCapability = require('./_new-promise-capability');\nvar perform = require('./_perform');\n\n$export($export.S, 'Promise', { 'try': function (callbackfn) {\n  var promiseCapability = newPromiseCapability.f(this);\n  var result = perform(callbackfn);\n  (result.e ? promiseCapability.reject : promiseCapability.resolve)(result.v);\n  return promiseCapability.promise;\n} });\n","var Map = require('./es6.map');\nvar $export = require('./_export');\nvar shared = require('./_shared')('metadata');\nvar store = shared.store || (shared.store = new (require('./es6.weak-map'))());\n\nvar getOrCreateMetadataMap = function (target, targetKey, create) {\n  var targetMetadata = store.get(target);\n  if (!targetMetadata) {\n    if (!create) return undefined;\n    store.set(target, targetMetadata = new Map());\n  }\n  var keyMetadata = targetMetadata.get(targetKey);\n  if (!keyMetadata) {\n    if (!create) return undefined;\n    targetMetadata.set(targetKey, keyMetadata = new Map());\n  } return keyMetadata;\n};\nvar ordinaryHasOwnMetadata = function (MetadataKey, O, P) {\n  var metadataMap = getOrCreateMetadataMap(O, P, false);\n  return metadataMap === undefined ? false : metadataMap.has(MetadataKey);\n};\nvar ordinaryGetOwnMetadata = function (MetadataKey, O, P) {\n  var metadataMap = getOrCreateMetadataMap(O, P, false);\n  return metadataMap === undefined ? undefined : metadataMap.get(MetadataKey);\n};\nvar ordinaryDefineOwnMetadata = function (MetadataKey, MetadataValue, O, P) {\n  getOrCreateMetadataMap(O, P, true).set(MetadataKey, MetadataValue);\n};\nvar ordinaryOwnMetadataKeys = function (target, targetKey) {\n  var metadataMap = getOrCreateMetadataMap(target, targetKey, false);\n  var keys = [];\n  if (metadataMap) metadataMap.forEach(function (_, key) { keys.push(key); });\n  return keys;\n};\nvar toMetaKey = function (it) {\n  return it === undefined || typeof it == 'symbol' ? it : String(it);\n};\nvar exp = function (O) {\n  $export($export.S, 'Reflect', O);\n};\n\nmodule.exports = {\n  store: store,\n  map: getOrCreateMetadataMap,\n  has: ordinaryHasOwnMetadata,\n  get: ordinaryGetOwnMetadata,\n  set: ordinaryDefineOwnMetadata,\n  keys: ordinaryOwnMetadataKeys,\n  key: toMetaKey,\n  exp: exp\n};\n","var metadata = require('./_metadata');\nvar anObject = require('./_an-object');\nvar toMetaKey = metadata.key;\nvar ordinaryDefineOwnMetadata = metadata.set;\n\nmetadata.exp({ defineMetadata: function defineMetadata(metadataKey, metadataValue, target, targetKey) {\n  ordinaryDefineOwnMetadata(metadataKey, metadataValue, anObject(target), toMetaKey(targetKey));\n} });\n","var metadata = require('./_metadata');\nvar anObject = require('./_an-object');\nvar toMetaKey = metadata.key;\nvar getOrCreateMetadataMap = metadata.map;\nvar store = metadata.store;\n\nmetadata.exp({ deleteMetadata: function deleteMetadata(metadataKey, target /* , targetKey */) {\n  var targetKey = arguments.length < 3 ? undefined : toMetaKey(arguments[2]);\n  var metadataMap = getOrCreateMetadataMap(anObject(target), targetKey, false);\n  if (metadataMap === undefined || !metadataMap['delete'](metadataKey)) return false;\n  if (metadataMap.size) return true;\n  var targetMetadata = store.get(target);\n  targetMetadata['delete'](targetKey);\n  return !!targetMetadata.size || store['delete'](target);\n} });\n","var metadata = require('./_metadata');\nvar anObject = require('./_an-object');\nvar getPrototypeOf = require('./_object-gpo');\nvar ordinaryHasOwnMetadata = metadata.has;\nvar ordinaryGetOwnMetadata = metadata.get;\nvar toMetaKey = metadata.key;\n\nvar ordinaryGetMetadata = function (MetadataKey, O, P) {\n  var hasOwn = ordinaryHasOwnMetadata(MetadataKey, O, P);\n  if (hasOwn) return ordinaryGetOwnMetadata(MetadataKey, O, P);\n  var parent = getPrototypeOf(O);\n  return parent !== null ? ordinaryGetMetadata(MetadataKey, parent, P) : undefined;\n};\n\nmetadata.exp({ getMetadata: function getMetadata(metadataKey, target /* , targetKey */) {\n  return ordinaryGetMetadata(metadataKey, anObject(target), arguments.length < 3 ? undefined : toMetaKey(arguments[2]));\n} });\n","var Set = require('./es6.set');\nvar from = require('./_array-from-iterable');\nvar metadata = require('./_metadata');\nvar anObject = require('./_an-object');\nvar getPrototypeOf = require('./_object-gpo');\nvar ordinaryOwnMetadataKeys = metadata.keys;\nvar toMetaKey = metadata.key;\n\nvar ordinaryMetadataKeys = function (O, P) {\n  var oKeys = ordinaryOwnMetadataKeys(O, P);\n  var parent = getPrototypeOf(O);\n  if (parent === null) return oKeys;\n  var pKeys = ordinaryMetadataKeys(parent, P);\n  return pKeys.length ? oKeys.length ? from(new Set(oKeys.concat(pKeys))) : pKeys : oKeys;\n};\n\nmetadata.exp({ getMetadataKeys: function getMetadataKeys(target /* , targetKey */) {\n  return ordinaryMetadataKeys(anObject(target), arguments.length < 2 ? undefined : toMetaKey(arguments[1]));\n} });\n","var metadata = require('./_metadata');\nvar anObject = require('./_an-object');\nvar ordinaryGetOwnMetadata = metadata.get;\nvar toMetaKey = metadata.key;\n\nmetadata.exp({ getOwnMetadata: function getOwnMetadata(metadataKey, target /* , targetKey */) {\n  return ordinaryGetOwnMetadata(metadataKey, anObject(target)\n    , arguments.length < 3 ? undefined : toMetaKey(arguments[2]));\n} });\n","var metadata = require('./_metadata');\nvar anObject = require('./_an-object');\nvar ordinaryOwnMetadataKeys = metadata.keys;\nvar toMetaKey = metadata.key;\n\nmetadata.exp({ getOwnMetadataKeys: function getOwnMetadataKeys(target /* , targetKey */) {\n  return ordinaryOwnMetadataKeys(anObject(target), arguments.length < 2 ? undefined : toMetaKey(arguments[1]));\n} });\n","var metadata = require('./_metadata');\nvar anObject = require('./_an-object');\nvar getPrototypeOf = require('./_object-gpo');\nvar ordinaryHasOwnMetadata = metadata.has;\nvar toMetaKey = metadata.key;\n\nvar ordinaryHasMetadata = function (MetadataKey, O, P) {\n  var hasOwn = ordinaryHasOwnMetadata(MetadataKey, O, P);\n  if (hasOwn) return true;\n  var parent = getPrototypeOf(O);\n  return parent !== null ? ordinaryHasMetadata(MetadataKey, parent, P) : false;\n};\n\nmetadata.exp({ hasMetadata: function hasMetadata(metadataKey, target /* , targetKey */) {\n  return ordinaryHasMetadata(metadataKey, anObject(target), arguments.length < 3 ? undefined : toMetaKey(arguments[2]));\n} });\n","var metadata = require('./_metadata');\nvar anObject = require('./_an-object');\nvar ordinaryHasOwnMetadata = metadata.has;\nvar toMetaKey = metadata.key;\n\nmetadata.exp({ hasOwnMetadata: function hasOwnMetadata(metadataKey, target /* , targetKey */) {\n  return ordinaryHasOwnMetadata(metadataKey, anObject(target)\n    , arguments.length < 3 ? undefined : toMetaKey(arguments[2]));\n} });\n","var $metadata = require('./_metadata');\nvar anObject = require('./_an-object');\nvar aFunction = require('./_a-function');\nvar toMetaKey = $metadata.key;\nvar ordinaryDefineOwnMetadata = $metadata.set;\n\n$metadata.exp({ metadata: function metadata(metadataKey, metadataValue) {\n  return function decorator(target, targetKey) {\n    ordinaryDefineOwnMetadata(\n      metadataKey, metadataValue,\n      (targetKey !== undefined ? anObject : aFunction)(target),\n      toMetaKey(targetKey)\n    );\n  };\n} });\n","// https://github.com/rwaldron/tc39-notes/blob/master/es6/2014-09/sept-25.md#510-globalasap-for-enqueuing-a-microtask\nvar $export = require('./_export');\nvar microtask = require('./_microtask')();\nvar process = require('./_global').process;\nvar isNode = require('./_cof')(process) == 'process';\n\n$export($export.G, {\n  asap: function asap(fn) {\n    var domain = isNode && process.domain;\n    microtask(domain ? domain.bind(fn) : fn);\n  }\n});\n","'use strict';\n// https://github.com/zenparsing/es-observable\nvar $export = require('./_export');\nvar global = require('./_global');\nvar core = require('./_core');\nvar microtask = require('./_microtask')();\nvar OBSERVABLE = require('./_wks')('observable');\nvar aFunction = require('./_a-function');\nvar anObject = require('./_an-object');\nvar anInstance = require('./_an-instance');\nvar redefineAll = require('./_redefine-all');\nvar hide = require('./_hide');\nvar forOf = require('./_for-of');\nvar RETURN = forOf.RETURN;\n\nvar getMethod = function (fn) {\n  return fn == null ? undefined : aFunction(fn);\n};\n\nvar cleanupSubscription = function (subscription) {\n  var cleanup = subscription._c;\n  if (cleanup) {\n    subscription._c = undefined;\n    cleanup();\n  }\n};\n\nvar subscriptionClosed = function (subscription) {\n  return subscription._o === undefined;\n};\n\nvar closeSubscription = function (subscription) {\n  if (!subscriptionClosed(subscription)) {\n    subscription._o = undefined;\n    cleanupSubscription(subscription);\n  }\n};\n\nvar Subscription = function (observer, subscriber) {\n  anObject(observer);\n  this._c = undefined;\n  this._o = observer;\n  observer = new SubscriptionObserver(this);\n  try {\n    var cleanup = subscriber(observer);\n    var subscription = cleanup;\n    if (cleanup != null) {\n      if (typeof cleanup.unsubscribe === 'function') cleanup = function () { subscription.unsubscribe(); };\n      else aFunction(cleanup);\n      this._c = cleanup;\n    }\n  } catch (e) {\n    observer.error(e);\n    return;\n  } if (subscriptionClosed(this)) cleanupSubscription(this);\n};\n\nSubscription.prototype = redefineAll({}, {\n  unsubscribe: function unsubscribe() { closeSubscription(this); }\n});\n\nvar SubscriptionObserver = function (subscription) {\n  this._s = subscription;\n};\n\nSubscriptionObserver.prototype = redefineAll({}, {\n  next: function next(value) {\n    var subscription = this._s;\n    if (!subscriptionClosed(subscription)) {\n      var observer = subscription._o;\n      try {\n        var m = getMethod(observer.next);\n        if (m) return m.call(observer, value);\n      } catch (e) {\n        try {\n          closeSubscription(subscription);\n        } finally {\n          throw e;\n        }\n      }\n    }\n  },\n  error: function error(value) {\n    var subscription = this._s;\n    if (subscriptionClosed(subscription)) throw value;\n    var observer = subscription._o;\n    subscription._o = undefined;\n    try {\n      var m = getMethod(observer.error);\n      if (!m) throw value;\n      value = m.call(observer, value);\n    } catch (e) {\n      try {\n        cleanupSubscription(subscription);\n      } finally {\n        throw e;\n      }\n    } cleanupSubscription(subscription);\n    return value;\n  },\n  complete: function complete(value) {\n    var subscription = this._s;\n    if (!subscriptionClosed(subscription)) {\n      var observer = subscription._o;\n      subscription._o = undefined;\n      try {\n        var m = getMethod(observer.complete);\n        value = m ? m.call(observer, value) : undefined;\n      } catch (e) {\n        try {\n          cleanupSubscription(subscription);\n        } finally {\n          throw e;\n        }\n      } cleanupSubscription(subscription);\n      return value;\n    }\n  }\n});\n\nvar $Observable = function Observable(subscriber) {\n  anInstance(this, $Observable, 'Observable', '_f')._f = aFunction(subscriber);\n};\n\nredefineAll($Observable.prototype, {\n  subscribe: function subscribe(observer) {\n    return new Subscription(observer, this._f);\n  },\n  forEach: function forEach(fn) {\n    var that = this;\n    return new (core.Promise || global.Promise)(function (resolve, reject) {\n      aFunction(fn);\n      var subscription = that.subscribe({\n        next: function (value) {\n          try {\n            return fn(value);\n          } catch (e) {\n            reject(e);\n            subscription.unsubscribe();\n          }\n        },\n        error: reject,\n        complete: resolve\n      });\n    });\n  }\n});\n\nredefineAll($Observable, {\n  from: function from(x) {\n    var C = typeof this === 'function' ? this : $Observable;\n    var method = getMethod(anObject(x)[OBSERVABLE]);\n    if (method) {\n      var observable = anObject(method.call(x));\n      return observable.constructor === C ? observable : new C(function (observer) {\n        return observable.subscribe(observer);\n      });\n    }\n    return new C(function (observer) {\n      var done = false;\n      microtask(function () {\n        if (!done) {\n          try {\n            if (forOf(x, false, function (it) {\n              observer.next(it);\n              if (done) return RETURN;\n            }) === RETURN) return;\n          } catch (e) {\n            if (done) throw e;\n            observer.error(e);\n            return;\n          } observer.complete();\n        }\n      });\n      return function () { done = true; };\n    });\n  },\n  of: function of() {\n    for (var i = 0, l = arguments.length, items = new Array(l); i < l;) items[i] = arguments[i++];\n    return new (typeof this === 'function' ? this : $Observable)(function (observer) {\n      var done = false;\n      microtask(function () {\n        if (!done) {\n          for (var j = 0; j < items.length; ++j) {\n            observer.next(items[j]);\n            if (done) return;\n          } observer.complete();\n        }\n      });\n      return function () { done = true; };\n    });\n  }\n});\n\nhide($Observable.prototype, OBSERVABLE, function () { return this; });\n\n$export($export.G, { Observable: $Observable });\n\nrequire('./_set-species')('Observable');\n","// ie9- setTimeout & setInterval additional parameters fix\nvar global = require('./_global');\nvar $export = require('./_export');\nvar userAgent = require('./_user-agent');\nvar slice = [].slice;\nvar MSIE = /MSIE .\\./.test(userAgent); // <- dirty ie9- check\nvar wrap = function (set) {\n  return function (fn, time /* , ...args */) {\n    var boundArgs = arguments.length > 2;\n    var args = boundArgs ? slice.call(arguments, 2) : false;\n    return set(boundArgs ? function () {\n      // eslint-disable-next-line no-new-func\n      (typeof fn == 'function' ? fn : Function(fn)).apply(this, args);\n    } : fn, time);\n  };\n};\n$export($export.G + $export.B + $export.F * MSIE, {\n  setTimeout: wrap(global.setTimeout),\n  setInterval: wrap(global.setInterval)\n});\n","var $export = require('./_export');\nvar $task = require('./_task');\n$export($export.G + $export.B, {\n  setImmediate: $task.set,\n  clearImmediate: $task.clear\n});\n","var $iterators = require('./es6.array.iterator');\nvar getKeys = require('./_object-keys');\nvar redefine = require('./_redefine');\nvar global = require('./_global');\nvar hide = require('./_hide');\nvar Iterators = require('./_iterators');\nvar wks = require('./_wks');\nvar ITERATOR = wks('iterator');\nvar TO_STRING_TAG = wks('toStringTag');\nvar ArrayValues = Iterators.Array;\n\nvar DOMIterables = {\n  CSSRuleList: true, // TODO: Not spec compliant, should be false.\n  CSSStyleDeclaration: false,\n  CSSValueList: false,\n  ClientRectList: false,\n  DOMRectList: false,\n  DOMStringList: false,\n  DOMTokenList: true,\n  DataTransferItemList: false,\n  FileList: false,\n  HTMLAllCollection: false,\n  HTMLCollection: false,\n  HTMLFormElement: false,\n  HTMLSelectElement: false,\n  MediaList: true, // TODO: Not spec compliant, should be false.\n  MimeTypeArray: false,\n  NamedNodeMap: false,\n  NodeList: true,\n  PaintRequestList: false,\n  Plugin: false,\n  PluginArray: false,\n  SVGLengthList: false,\n  SVGNumberList: false,\n  SVGPathSegList: false,\n  SVGPointList: false,\n  SVGStringList: false,\n  SVGTransformList: false,\n  SourceBufferList: false,\n  StyleSheetList: true, // TODO: Not spec compliant, should be false.\n  TextTrackCueList: false,\n  TextTrackList: false,\n  TouchList: false\n};\n\nfor (var collections = getKeys(DOMIterables), i = 0; i < collections.length; i++) {\n  var NAME = collections[i];\n  var explicit = DOMIterables[NAME];\n  var Collection = global[NAME];\n  var proto = Collection && Collection.prototype;\n  var key;\n  if (proto) {\n    if (!proto[ITERATOR]) hide(proto, ITERATOR, ArrayValues);\n    if (!proto[TO_STRING_TAG]) hide(proto, TO_STRING_TAG, NAME);\n    Iterators[NAME] = ArrayValues;\n    if (explicit) for (key in $iterators) if (!proto[key]) redefine(proto, key, $iterators[key], true);\n  }\n}\n","require('./modules/es6.symbol');\nrequire('./modules/es6.object.create');\nrequire('./modules/es6.object.define-property');\nrequire('./modules/es6.object.define-properties');\nrequire('./modules/es6.object.get-own-property-descriptor');\nrequire('./modules/es6.object.get-prototype-of');\nrequire('./modules/es6.object.keys');\nrequire('./modules/es6.object.get-own-property-names');\nrequire('./modules/es6.object.freeze');\nrequire('./modules/es6.object.seal');\nrequire('./modules/es6.object.prevent-extensions');\nrequire('./modules/es6.object.is-frozen');\nrequire('./modules/es6.object.is-sealed');\nrequire('./modules/es6.object.is-extensible');\nrequire('./modules/es6.object.assign');\nrequire('./modules/es6.object.is');\nrequire('./modules/es6.object.set-prototype-of');\nrequire('./modules/es6.object.to-string');\nrequire('./modules/es6.function.bind');\nrequire('./modules/es6.function.name');\nrequire('./modules/es6.function.has-instance');\nrequire('./modules/es6.parse-int');\nrequire('./modules/es6.parse-float');\nrequire('./modules/es6.number.constructor');\nrequire('./modules/es6.number.to-fixed');\nrequire('./modules/es6.number.to-precision');\nrequire('./modules/es6.number.epsilon');\nrequire('./modules/es6.number.is-finite');\nrequire('./modules/es6.number.is-integer');\nrequire('./modules/es6.number.is-nan');\nrequire('./modules/es6.number.is-safe-integer');\nrequire('./modules/es6.number.max-safe-integer');\nrequire('./modules/es6.number.min-safe-integer');\nrequire('./modules/es6.number.parse-float');\nrequire('./modules/es6.number.parse-int');\nrequire('./modules/es6.math.acosh');\nrequire('./modules/es6.math.asinh');\nrequire('./modules/es6.math.atanh');\nrequire('./modules/es6.math.cbrt');\nrequire('./modules/es6.math.clz32');\nrequire('./modules/es6.math.cosh');\nrequire('./modules/es6.math.expm1');\nrequire('./modules/es6.math.fround');\nrequire('./modules/es6.math.hypot');\nrequire('./modules/es6.math.imul');\nrequire('./modules/es6.math.log10');\nrequire('./modules/es6.math.log1p');\nrequire('./modules/es6.math.log2');\nrequire('./modules/es6.math.sign');\nrequire('./modules/es6.math.sinh');\nrequire('./modules/es6.math.tanh');\nrequire('./modules/es6.math.trunc');\nrequire('./modules/es6.string.from-code-point');\nrequire('./modules/es6.string.raw');\nrequire('./modules/es6.string.trim');\nrequire('./modules/es6.string.iterator');\nrequire('./modules/es6.string.code-point-at');\nrequire('./modules/es6.string.ends-with');\nrequire('./modules/es6.string.includes');\nrequire('./modules/es6.string.repeat');\nrequire('./modules/es6.string.starts-with');\nrequire('./modules/es6.string.anchor');\nrequire('./modules/es6.string.big');\nrequire('./modules/es6.string.blink');\nrequire('./modules/es6.string.bold');\nrequire('./modules/es6.string.fixed');\nrequire('./modules/es6.string.fontcolor');\nrequire('./modules/es6.string.fontsize');\nrequire('./modules/es6.string.italics');\nrequire('./modules/es6.string.link');\nrequire('./modules/es6.string.small');\nrequire('./modules/es6.string.strike');\nrequire('./modules/es6.string.sub');\nrequire('./modules/es6.string.sup');\nrequire('./modules/es6.date.now');\nrequire('./modules/es6.date.to-json');\nrequire('./modules/es6.date.to-iso-string');\nrequire('./modules/es6.date.to-string');\nrequire('./modules/es6.date.to-primitive');\nrequire('./modules/es6.array.is-array');\nrequire('./modules/es6.array.from');\nrequire('./modules/es6.array.of');\nrequire('./modules/es6.array.join');\nrequire('./modules/es6.array.slice');\nrequire('./modules/es6.array.sort');\nrequire('./modules/es6.array.for-each');\nrequire('./modules/es6.array.map');\nrequire('./modules/es6.array.filter');\nrequire('./modules/es6.array.some');\nrequire('./modules/es6.array.every');\nrequire('./modules/es6.array.reduce');\nrequire('./modules/es6.array.reduce-right');\nrequire('./modules/es6.array.index-of');\nrequire('./modules/es6.array.last-index-of');\nrequire('./modules/es6.array.copy-within');\nrequire('./modules/es6.array.fill');\nrequire('./modules/es6.array.find');\nrequire('./modules/es6.array.find-index');\nrequire('./modules/es6.array.species');\nrequire('./modules/es6.array.iterator');\nrequire('./modules/es6.regexp.constructor');\nrequire('./modules/es6.regexp.exec');\nrequire('./modules/es6.regexp.to-string');\nrequire('./modules/es6.regexp.flags');\nrequire('./modules/es6.regexp.match');\nrequire('./modules/es6.regexp.replace');\nrequire('./modules/es6.regexp.search');\nrequire('./modules/es6.regexp.split');\nrequire('./modules/es6.promise');\nrequire('./modules/es6.map');\nrequire('./modules/es6.set');\nrequire('./modules/es6.weak-map');\nrequire('./modules/es6.weak-set');\nrequire('./modules/es6.typed.array-buffer');\nrequire('./modules/es6.typed.data-view');\nrequire('./modules/es6.typed.int8-array');\nrequire('./modules/es6.typed.uint8-array');\nrequire('./modules/es6.typed.uint8-clamped-array');\nrequire('./modules/es6.typed.int16-array');\nrequire('./modules/es6.typed.uint16-array');\nrequire('./modules/es6.typed.int32-array');\nrequire('./modules/es6.typed.uint32-array');\nrequire('./modules/es6.typed.float32-array');\nrequire('./modules/es6.typed.float64-array');\nrequire('./modules/es6.reflect.apply');\nrequire('./modules/es6.reflect.construct');\nrequire('./modules/es6.reflect.define-property');\nrequire('./modules/es6.reflect.delete-property');\nrequire('./modules/es6.reflect.enumerate');\nrequire('./modules/es6.reflect.get');\nrequire('./modules/es6.reflect.get-own-property-descriptor');\nrequire('./modules/es6.reflect.get-prototype-of');\nrequire('./modules/es6.reflect.has');\nrequire('./modules/es6.reflect.is-extensible');\nrequire('./modules/es6.reflect.own-keys');\nrequire('./modules/es6.reflect.prevent-extensions');\nrequire('./modules/es6.reflect.set');\nrequire('./modules/es6.reflect.set-prototype-of');\nrequire('./modules/es7.array.includes');\nrequire('./modules/es7.array.flat-map');\nrequire('./modules/es7.array.flatten');\nrequire('./modules/es7.string.at');\nrequire('./modules/es7.string.pad-start');\nrequire('./modules/es7.string.pad-end');\nrequire('./modules/es7.string.trim-left');\nrequire('./modules/es7.string.trim-right');\nrequire('./modules/es7.string.match-all');\nrequire('./modules/es7.symbol.async-iterator');\nrequire('./modules/es7.symbol.observable');\nrequire('./modules/es7.object.get-own-property-descriptors');\nrequire('./modules/es7.object.values');\nrequire('./modules/es7.object.entries');\nrequire('./modules/es7.object.define-getter');\nrequire('./modules/es7.object.define-setter');\nrequire('./modules/es7.object.lookup-getter');\nrequire('./modules/es7.object.lookup-setter');\nrequire('./modules/es7.map.to-json');\nrequire('./modules/es7.set.to-json');\nrequire('./modules/es7.map.of');\nrequire('./modules/es7.set.of');\nrequire('./modules/es7.weak-map.of');\nrequire('./modules/es7.weak-set.of');\nrequire('./modules/es7.map.from');\nrequire('./modules/es7.set.from');\nrequire('./modules/es7.weak-map.from');\nrequire('./modules/es7.weak-set.from');\nrequire('./modules/es7.global');\nrequire('./modules/es7.system.global');\nrequire('./modules/es7.error.is-error');\nrequire('./modules/es7.math.clamp');\nrequire('./modules/es7.math.deg-per-rad');\nrequire('./modules/es7.math.degrees');\nrequire('./modules/es7.math.fscale');\nrequire('./modules/es7.math.iaddh');\nrequire('./modules/es7.math.isubh');\nrequire('./modules/es7.math.imulh');\nrequire('./modules/es7.math.rad-per-deg');\nrequire('./modules/es7.math.radians');\nrequire('./modules/es7.math.scale');\nrequire('./modules/es7.math.umulh');\nrequire('./modules/es7.math.signbit');\nrequire('./modules/es7.promise.finally');\nrequire('./modules/es7.promise.try');\nrequire('./modules/es7.reflect.define-metadata');\nrequire('./modules/es7.reflect.delete-metadata');\nrequire('./modules/es7.reflect.get-metadata');\nrequire('./modules/es7.reflect.get-metadata-keys');\nrequire('./modules/es7.reflect.get-own-metadata');\nrequire('./modules/es7.reflect.get-own-metadata-keys');\nrequire('./modules/es7.reflect.has-metadata');\nrequire('./modules/es7.reflect.has-own-metadata');\nrequire('./modules/es7.reflect.metadata');\nrequire('./modules/es7.asap');\nrequire('./modules/es7.observable');\nrequire('./modules/web.timers');\nrequire('./modules/web.immediate');\nrequire('./modules/web.dom.iterable');\nmodule.exports = require('./modules/_core');\n","/**\n * Copyright (c) 2014, Facebook, Inc.\n * All rights reserved.\n *\n * This source code is licensed under the BSD-style license found in the\n * https://raw.github.com/facebook/regenerator/master/LICENSE file. An\n * additional grant of patent rights can be found in the PATENTS file in\n * the same directory.\n */\n\n!(function(global) {\n  \"use strict\";\n\n  var Op = Object.prototype;\n  var hasOwn = Op.hasOwnProperty;\n  var undefined; // More compressible than void 0.\n  var $Symbol = typeof Symbol === \"function\" ? Symbol : {};\n  var iteratorSymbol = $Symbol.iterator || \"@@iterator\";\n  var asyncIteratorSymbol = $Symbol.asyncIterator || \"@@asyncIterator\";\n  var toStringTagSymbol = $Symbol.toStringTag || \"@@toStringTag\";\n\n  var inModule = typeof module === \"object\";\n  var runtime = global.regeneratorRuntime;\n  if (runtime) {\n    if (inModule) {\n      // If regeneratorRuntime is defined globally and we're in a module,\n      // make the exports object identical to regeneratorRuntime.\n      module.exports = runtime;\n    }\n    // Don't bother evaluating the rest of this file if the runtime was\n    // already defined globally.\n    return;\n  }\n\n  // Define the runtime globally (as expected by generated code) as either\n  // module.exports (if we're in a module) or a new, empty object.\n  runtime = global.regeneratorRuntime = inModule ? module.exports : {};\n\n  function wrap(innerFn, outerFn, self, tryLocsList) {\n    // If outerFn provided and outerFn.prototype is a Generator, then outerFn.prototype instanceof Generator.\n    var protoGenerator = outerFn && outerFn.prototype instanceof Generator ? outerFn : Generator;\n    var generator = Object.create(protoGenerator.prototype);\n    var context = new Context(tryLocsList || []);\n\n    // The ._invoke method unifies the implementations of the .next,\n    // .throw, and .return methods.\n    generator._invoke = makeInvokeMethod(innerFn, self, context);\n\n    return generator;\n  }\n  runtime.wrap = wrap;\n\n  // Try/catch helper to minimize deoptimizations. Returns a completion\n  // record like context.tryEntries[i].completion. This interface could\n  // have been (and was previously) designed to take a closure to be\n  // invoked without arguments, but in all the cases we care about we\n  // already have an existing method we want to call, so there's no need\n  // to create a new function object. We can even get away with assuming\n  // the method takes exactly one argument, since that happens to be true\n  // in every case, so we don't have to touch the arguments object. The\n  // only additional allocation required is the completion record, which\n  // has a stable shape and so hopefully should be cheap to allocate.\n  function tryCatch(fn, obj, arg) {\n    try {\n      return { type: \"normal\", arg: fn.call(obj, arg) };\n    } catch (err) {\n      return { type: \"throw\", arg: err };\n    }\n  }\n\n  var GenStateSuspendedStart = \"suspendedStart\";\n  var GenStateSuspendedYield = \"suspendedYield\";\n  var GenStateExecuting = \"executing\";\n  var GenStateCompleted = \"completed\";\n\n  // Returning this object from the innerFn has the same effect as\n  // breaking out of the dispatch switch statement.\n  var ContinueSentinel = {};\n\n  // Dummy constructor functions that we use as the .constructor and\n  // .constructor.prototype properties for functions that return Generator\n  // objects. For full spec compliance, you may wish to configure your\n  // minifier not to mangle the names of these two functions.\n  function Generator() {}\n  function GeneratorFunction() {}\n  function GeneratorFunctionPrototype() {}\n\n  // This is a polyfill for %IteratorPrototype% for environments that\n  // don't natively support it.\n  var IteratorPrototype = {};\n  IteratorPrototype[iteratorSymbol] = function () {\n    return this;\n  };\n\n  var getProto = Object.getPrototypeOf;\n  var NativeIteratorPrototype = getProto && getProto(getProto(values([])));\n  if (NativeIteratorPrototype &&\n      NativeIteratorPrototype !== Op &&\n      hasOwn.call(NativeIteratorPrototype, iteratorSymbol)) {\n    // This environment has a native %IteratorPrototype%; use it instead\n    // of the polyfill.\n    IteratorPrototype = NativeIteratorPrototype;\n  }\n\n  var Gp = GeneratorFunctionPrototype.prototype =\n    Generator.prototype = Object.create(IteratorPrototype);\n  GeneratorFunction.prototype = Gp.constructor = GeneratorFunctionPrototype;\n  GeneratorFunctionPrototype.constructor = GeneratorFunction;\n  GeneratorFunctionPrototype[toStringTagSymbol] =\n    GeneratorFunction.displayName = \"GeneratorFunction\";\n\n  // Helper for defining the .next, .throw, and .return methods of the\n  // Iterator interface in terms of a single ._invoke method.\n  function defineIteratorMethods(prototype) {\n    [\"next\", \"throw\", \"return\"].forEach(function(method) {\n      prototype[method] = function(arg) {\n        return this._invoke(method, arg);\n      };\n    });\n  }\n\n  runtime.isGeneratorFunction = function(genFun) {\n    var ctor = typeof genFun === \"function\" && genFun.constructor;\n    return ctor\n      ? ctor === GeneratorFunction ||\n        // For the native GeneratorFunction constructor, the best we can\n        // do is to check its .name property.\n        (ctor.displayName || ctor.name) === \"GeneratorFunction\"\n      : false;\n  };\n\n  runtime.mark = function(genFun) {\n    if (Object.setPrototypeOf) {\n      Object.setPrototypeOf(genFun, GeneratorFunctionPrototype);\n    } else {\n      genFun.__proto__ = GeneratorFunctionPrototype;\n      if (!(toStringTagSymbol in genFun)) {\n        genFun[toStringTagSymbol] = \"GeneratorFunction\";\n      }\n    }\n    genFun.prototype = Object.create(Gp);\n    return genFun;\n  };\n\n  // Within the body of any async function, `await x` is transformed to\n  // `yield regeneratorRuntime.awrap(x)`, so that the runtime can test\n  // `hasOwn.call(value, \"__await\")` to determine if the yielded value is\n  // meant to be awaited.\n  runtime.awrap = function(arg) {\n    return { __await: arg };\n  };\n\n  function AsyncIterator(generator) {\n    function invoke(method, arg, resolve, reject) {\n      var record = tryCatch(generator[method], generator, arg);\n      if (record.type === \"throw\") {\n        reject(record.arg);\n      } else {\n        var result = record.arg;\n        var value = result.value;\n        if (value &&\n            typeof value === \"object\" &&\n            hasOwn.call(value, \"__await\")) {\n          return Promise.resolve(value.__await).then(function(value) {\n            invoke(\"next\", value, resolve, reject);\n          }, function(err) {\n            invoke(\"throw\", err, resolve, reject);\n          });\n        }\n\n        return Promise.resolve(value).then(function(unwrapped) {\n          // When a yielded Promise is resolved, its final value becomes\n          // the .value of the Promise<{value,done}> result for the\n          // current iteration. If the Promise is rejected, however, the\n          // result for this iteration will be rejected with the same\n          // reason. Note that rejections of yielded Promises are not\n          // thrown back into the generator function, as is the case\n          // when an awaited Promise is rejected. This difference in\n          // behavior between yield and await is important, because it\n          // allows the consumer to decide what to do with the yielded\n          // rejection (swallow it and continue, manually .throw it back\n          // into the generator, abandon iteration, whatever). With\n          // await, by contrast, there is no opportunity to examine the\n          // rejection reason outside the generator function, so the\n          // only option is to throw it from the await expression, and\n          // let the generator function handle the exception.\n          result.value = unwrapped;\n          resolve(result);\n        }, reject);\n      }\n    }\n\n    if (typeof global.process === \"object\" && global.process.domain) {\n      invoke = global.process.domain.bind(invoke);\n    }\n\n    var previousPromise;\n\n    function enqueue(method, arg) {\n      function callInvokeWithMethodAndArg() {\n        return new Promise(function(resolve, reject) {\n          invoke(method, arg, resolve, reject);\n        });\n      }\n\n      return previousPromise =\n        // If enqueue has been called before, then we want to wait until\n        // all previous Promises have been resolved before calling invoke,\n        // so that results are always delivered in the correct order. If\n        // enqueue has not been called before, then it is important to\n        // call invoke immediately, without waiting on a callback to fire,\n        // so that the async generator function has the opportunity to do\n        // any necessary setup in a predictable way. This predictability\n        // is why the Promise constructor synchronously invokes its\n        // executor callback, and why async functions synchronously\n        // execute code before the first await. Since we implement simple\n        // async functions in terms of async generators, it is especially\n        // important to get this right, even though it requires care.\n        previousPromise ? previousPromise.then(\n          callInvokeWithMethodAndArg,\n          // Avoid propagating failures to Promises returned by later\n          // invocations of the iterator.\n          callInvokeWithMethodAndArg\n        ) : callInvokeWithMethodAndArg();\n    }\n\n    // Define the unified helper method that is used to implement .next,\n    // .throw, and .return (see defineIteratorMethods).\n    this._invoke = enqueue;\n  }\n\n  defineIteratorMethods(AsyncIterator.prototype);\n  AsyncIterator.prototype[asyncIteratorSymbol] = function () {\n    return this;\n  };\n  runtime.AsyncIterator = AsyncIterator;\n\n  // Note that simple async functions are implemented on top of\n  // AsyncIterator objects; they just return a Promise for the value of\n  // the final result produced by the iterator.\n  runtime.async = function(innerFn, outerFn, self, tryLocsList) {\n    var iter = new AsyncIterator(\n      wrap(innerFn, outerFn, self, tryLocsList)\n    );\n\n    return runtime.isGeneratorFunction(outerFn)\n      ? iter // If outerFn is a generator, return the full iterator.\n      : iter.next().then(function(result) {\n          return result.done ? result.value : iter.next();\n        });\n  };\n\n  function makeInvokeMethod(innerFn, self, context) {\n    var state = GenStateSuspendedStart;\n\n    return function invoke(method, arg) {\n      if (state === GenStateExecuting) {\n        throw new Error(\"Generator is already running\");\n      }\n\n      if (state === GenStateCompleted) {\n        if (method === \"throw\") {\n          throw arg;\n        }\n\n        // Be forgiving, per 25.3.3.3.3 of the spec:\n        // https://people.mozilla.org/~jorendorff/es6-draft.html#sec-generatorresume\n        return doneResult();\n      }\n\n      context.method = method;\n      context.arg = arg;\n\n      while (true) {\n        var delegate = context.delegate;\n        if (delegate) {\n          var delegateResult = maybeInvokeDelegate(delegate, context);\n          if (delegateResult) {\n            if (delegateResult === ContinueSentinel) continue;\n            return delegateResult;\n          }\n        }\n\n        if (context.method === \"next\") {\n          // Setting context._sent for legacy support of Babel's\n          // function.sent implementation.\n          context.sent = context._sent = context.arg;\n\n        } else if (context.method === \"throw\") {\n          if (state === GenStateSuspendedStart) {\n            state = GenStateCompleted;\n            throw context.arg;\n          }\n\n          context.dispatchException(context.arg);\n\n        } else if (context.method === \"return\") {\n          context.abrupt(\"return\", context.arg);\n        }\n\n        state = GenStateExecuting;\n\n        var record = tryCatch(innerFn, self, context);\n        if (record.type === \"normal\") {\n          // If an exception is thrown from innerFn, we leave state ===\n          // GenStateExecuting and loop back for another invocation.\n          state = context.done\n            ? GenStateCompleted\n            : GenStateSuspendedYield;\n\n          if (record.arg === ContinueSentinel) {\n            continue;\n          }\n\n          return {\n            value: record.arg,\n            done: context.done\n          };\n\n        } else if (record.type === \"throw\") {\n          state = GenStateCompleted;\n          // Dispatch the exception by looping back around to the\n          // context.dispatchException(context.arg) call above.\n          context.method = \"throw\";\n          context.arg = record.arg;\n        }\n      }\n    };\n  }\n\n  // Call delegate.iterator[context.method](context.arg) and handle the\n  // result, either by returning a { value, done } result from the\n  // delegate iterator, or by modifying context.method and context.arg,\n  // setting context.delegate to null, and returning the ContinueSentinel.\n  function maybeInvokeDelegate(delegate, context) {\n    var method = delegate.iterator[context.method];\n    if (method === undefined) {\n      // A .throw or .return when the delegate iterator has no .throw\n      // method always terminates the yield* loop.\n      context.delegate = null;\n\n      if (context.method === \"throw\") {\n        if (delegate.iterator.return) {\n          // If the delegate iterator has a return method, give it a\n          // chance to clean up.\n          context.method = \"return\";\n          context.arg = undefined;\n          maybeInvokeDelegate(delegate, context);\n\n          if (context.method === \"throw\") {\n            // If maybeInvokeDelegate(context) changed context.method from\n            // \"return\" to \"throw\", let that override the TypeError below.\n            return ContinueSentinel;\n          }\n        }\n\n        context.method = \"throw\";\n        context.arg = new TypeError(\n          \"The iterator does not provide a 'throw' method\");\n      }\n\n      return ContinueSentinel;\n    }\n\n    var record = tryCatch(method, delegate.iterator, context.arg);\n\n    if (record.type === \"throw\") {\n      context.method = \"throw\";\n      context.arg = record.arg;\n      context.delegate = null;\n      return ContinueSentinel;\n    }\n\n    var info = record.arg;\n\n    if (! info) {\n      context.method = \"throw\";\n      context.arg = new TypeError(\"iterator result is not an object\");\n      context.delegate = null;\n      return ContinueSentinel;\n    }\n\n    if (info.done) {\n      // Assign the result of the finished delegate to the temporary\n      // variable specified by delegate.resultName (see delegateYield).\n      context[delegate.resultName] = info.value;\n\n      // Resume execution at the desired location (see delegateYield).\n      context.next = delegate.nextLoc;\n\n      // If context.method was \"throw\" but the delegate handled the\n      // exception, let the outer generator proceed normally. If\n      // context.method was \"next\", forget context.arg since it has been\n      // \"consumed\" by the delegate iterator. If context.method was\n      // \"return\", allow the original .return call to continue in the\n      // outer generator.\n      if (context.method !== \"return\") {\n        context.method = \"next\";\n        context.arg = undefined;\n      }\n\n    } else {\n      // Re-yield the result returned by the delegate method.\n      return info;\n    }\n\n    // The delegate iterator is finished, so forget it and continue with\n    // the outer generator.\n    context.delegate = null;\n    return ContinueSentinel;\n  }\n\n  // Define Generator.prototype.{next,throw,return} in terms of the\n  // unified ._invoke helper method.\n  defineIteratorMethods(Gp);\n\n  Gp[toStringTagSymbol] = \"Generator\";\n\n  // A Generator should always return itself as the iterator object when the\n  // @@iterator function is called on it. Some browsers' implementations of the\n  // iterator prototype chain incorrectly implement this, causing the Generator\n  // object to not be returned from this call. This ensures that doesn't happen.\n  // See https://github.com/facebook/regenerator/issues/274 for more details.\n  Gp[iteratorSymbol] = function() {\n    return this;\n  };\n\n  Gp.toString = function() {\n    return \"[object Generator]\";\n  };\n\n  function pushTryEntry(locs) {\n    var entry = { tryLoc: locs[0] };\n\n    if (1 in locs) {\n      entry.catchLoc = locs[1];\n    }\n\n    if (2 in locs) {\n      entry.finallyLoc = locs[2];\n      entry.afterLoc = locs[3];\n    }\n\n    this.tryEntries.push(entry);\n  }\n\n  function resetTryEntry(entry) {\n    var record = entry.completion || {};\n    record.type = \"normal\";\n    delete record.arg;\n    entry.completion = record;\n  }\n\n  function Context(tryLocsList) {\n    // The root entry object (effectively a try statement without a catch\n    // or a finally block) gives us a place to store values thrown from\n    // locations where there is no enclosing try statement.\n    this.tryEntries = [{ tryLoc: \"root\" }];\n    tryLocsList.forEach(pushTryEntry, this);\n    this.reset(true);\n  }\n\n  runtime.keys = function(object) {\n    var keys = [];\n    for (var key in object) {\n      keys.push(key);\n    }\n    keys.reverse();\n\n    // Rather than returning an object with a next method, we keep\n    // things simple and return the next function itself.\n    return function next() {\n      while (keys.length) {\n        var key = keys.pop();\n        if (key in object) {\n          next.value = key;\n          next.done = false;\n          return next;\n        }\n      }\n\n      // To avoid creating an additional object, we just hang the .value\n      // and .done properties off the next function object itself. This\n      // also ensures that the minifier will not anonymize the function.\n      next.done = true;\n      return next;\n    };\n  };\n\n  function values(iterable) {\n    if (iterable) {\n      var iteratorMethod = iterable[iteratorSymbol];\n      if (iteratorMethod) {\n        return iteratorMethod.call(iterable);\n      }\n\n      if (typeof iterable.next === \"function\") {\n        return iterable;\n      }\n\n      if (!isNaN(iterable.length)) {\n        var i = -1, next = function next() {\n          while (++i < iterable.length) {\n            if (hasOwn.call(iterable, i)) {\n              next.value = iterable[i];\n              next.done = false;\n              return next;\n            }\n          }\n\n          next.value = undefined;\n          next.done = true;\n\n          return next;\n        };\n\n        return next.next = next;\n      }\n    }\n\n    // Return an iterator with no values.\n    return { next: doneResult };\n  }\n  runtime.values = values;\n\n  function doneResult() {\n    return { value: undefined, done: true };\n  }\n\n  Context.prototype = {\n    constructor: Context,\n\n    reset: function(skipTempReset) {\n      this.prev = 0;\n      this.next = 0;\n      // Resetting context._sent for legacy support of Babel's\n      // function.sent implementation.\n      this.sent = this._sent = undefined;\n      this.done = false;\n      this.delegate = null;\n\n      this.method = \"next\";\n      this.arg = undefined;\n\n      this.tryEntries.forEach(resetTryEntry);\n\n      if (!skipTempReset) {\n        for (var name in this) {\n          // Not sure about the optimal order of these conditions:\n          if (name.charAt(0) === \"t\" &&\n              hasOwn.call(this, name) &&\n              !isNaN(+name.slice(1))) {\n            this[name] = undefined;\n          }\n        }\n      }\n    },\n\n    stop: function() {\n      this.done = true;\n\n      var rootEntry = this.tryEntries[0];\n      var rootRecord = rootEntry.completion;\n      if (rootRecord.type === \"throw\") {\n        throw rootRecord.arg;\n      }\n\n      return this.rval;\n    },\n\n    dispatchException: function(exception) {\n      if (this.done) {\n        throw exception;\n      }\n\n      var context = this;\n      function handle(loc, caught) {\n        record.type = \"throw\";\n        record.arg = exception;\n        context.next = loc;\n\n        if (caught) {\n          // If the dispatched exception was caught by a catch block,\n          // then let that catch block handle the exception normally.\n          context.method = \"next\";\n          context.arg = undefined;\n        }\n\n        return !! caught;\n      }\n\n      for (var i = this.tryEntries.length - 1; i >= 0; --i) {\n        var entry = this.tryEntries[i];\n        var record = entry.completion;\n\n        if (entry.tryLoc === \"root\") {\n          // Exception thrown outside of any try block that could handle\n          // it, so set the completion value of the entire function to\n          // throw the exception.\n          return handle(\"end\");\n        }\n\n        if (entry.tryLoc <= this.prev) {\n          var hasCatch = hasOwn.call(entry, \"catchLoc\");\n          var hasFinally = hasOwn.call(entry, \"finallyLoc\");\n\n          if (hasCatch && hasFinally) {\n            if (this.prev < entry.catchLoc) {\n              return handle(entry.catchLoc, true);\n            } else if (this.prev < entry.finallyLoc) {\n              return handle(entry.finallyLoc);\n            }\n\n          } else if (hasCatch) {\n            if (this.prev < entry.catchLoc) {\n              return handle(entry.catchLoc, true);\n            }\n\n          } else if (hasFinally) {\n            if (this.prev < entry.finallyLoc) {\n              return handle(entry.finallyLoc);\n            }\n\n          } else {\n            throw new Error(\"try statement without catch or finally\");\n          }\n        }\n      }\n    },\n\n    abrupt: function(type, arg) {\n      for (var i = this.tryEntries.length - 1; i >= 0; --i) {\n        var entry = this.tryEntries[i];\n        if (entry.tryLoc <= this.prev &&\n            hasOwn.call(entry, \"finallyLoc\") &&\n            this.prev < entry.finallyLoc) {\n          var finallyEntry = entry;\n          break;\n        }\n      }\n\n      if (finallyEntry &&\n          (type === \"break\" ||\n           type === \"continue\") &&\n          finallyEntry.tryLoc <= arg &&\n          arg <= finallyEntry.finallyLoc) {\n        // Ignore the finally entry if control is not jumping to a\n        // location outside the try/catch block.\n        finallyEntry = null;\n      }\n\n      var record = finallyEntry ? finallyEntry.completion : {};\n      record.type = type;\n      record.arg = arg;\n\n      if (finallyEntry) {\n        this.method = \"next\";\n        this.next = finallyEntry.finallyLoc;\n        return ContinueSentinel;\n      }\n\n      return this.complete(record);\n    },\n\n    complete: function(record, afterLoc) {\n      if (record.type === \"throw\") {\n        throw record.arg;\n      }\n\n      if (record.type === \"break\" ||\n          record.type === \"continue\") {\n        this.next = record.arg;\n      } else if (record.type === \"return\") {\n        this.rval = this.arg = record.arg;\n        this.method = \"return\";\n        this.next = \"end\";\n      } else if (record.type === \"normal\" && afterLoc) {\n        this.next = afterLoc;\n      }\n\n      return ContinueSentinel;\n    },\n\n    finish: function(finallyLoc) {\n      for (var i = this.tryEntries.length - 1; i >= 0; --i) {\n        var entry = this.tryEntries[i];\n        if (entry.finallyLoc === finallyLoc) {\n          this.complete(entry.completion, entry.afterLoc);\n          resetTryEntry(entry);\n          return ContinueSentinel;\n        }\n      }\n    },\n\n    \"catch\": function(tryLoc) {\n      for (var i = this.tryEntries.length - 1; i >= 0; --i) {\n        var entry = this.tryEntries[i];\n        if (entry.tryLoc === tryLoc) {\n          var record = entry.completion;\n          if (record.type === \"throw\") {\n            var thrown = record.arg;\n            resetTryEntry(entry);\n          }\n          return thrown;\n        }\n      }\n\n      // The context.catch method must only be called with a location\n      // argument that corresponds to a known catch block.\n      throw new Error(\"illegal catch attempt\");\n    },\n\n    delegateYield: function(iterable, resultName, nextLoc) {\n      this.delegate = {\n        iterator: values(iterable),\n        resultName: resultName,\n        nextLoc: nextLoc\n      };\n\n      if (this.method === \"next\") {\n        // Deliberately forget the last sent value so that we don't\n        // accidentally pass it on to the delegate.\n        this.arg = undefined;\n      }\n\n      return ContinueSentinel;\n    }\n  };\n})(\n  // Among the various tricks for obtaining a reference to the global\n  // object, this seems to be the most reliable technique that does not\n  // use indirect eval (which violates Content Security Policy).\n  typeof global === \"object\" ? global :\n  typeof window === \"object\" ? window :\n  typeof self === \"object\" ? self : this\n);\n","module.exports = function (regExp, replace) {\n  var replacer = replace === Object(replace) ? function (part) {\n    return replace[part];\n  } : replace;\n  return function (it) {\n    return String(it).replace(regExp, replacer);\n  };\n};\n","// https://github.com/benjamingr/RexExp.escape\nvar $export = require('./_export');\nvar $re = require('./_replacer')(/[\\\\^$*+?.()|[\\]{}]/g, '\\\\$&');\n\n$export($export.S, 'RegExp', { escape: function escape(it) { return $re(it); } });\n","require('../../modules/core.regexp.escape');\nmodule.exports = require('../../modules/_core').RegExp.escape;\n","\"use strict\";\n\nrequire(\"core-js/shim\");\n\nrequire(\"regenerator-runtime/runtime\");\n\nrequire(\"core-js/fn/regexp/escape\");\n\nif (global._babelPolyfill) {\n  throw new Error(\"only one instance of babel-polyfill is allowed\");\n}\nglobal._babelPolyfill = true;\n\nvar DEFINE_PROPERTY = \"defineProperty\";\nfunction define(O, key, value) {\n  O[key] || Object[DEFINE_PROPERTY](O, key, {\n    writable: true,\n    configurable: true,\n    value: value\n  });\n}\n\ndefine(String.prototype, \"padLeft\", \"\".padStart);\ndefine(String.prototype, \"padRight\", \"\".padEnd);\n\n\"pop,reverse,shift,keys,values,entries,indexOf,every,some,forEach,map,filter,find,findIndex,includes,join,slice,concat,push,splice,unshift,sort,lastIndexOf,reduce,reduceRight,copyWithin,fill\".split(\",\").forEach(function (key) {\n  [][key] && define(Array, key, Function.call.bind([][key]));\n});","export default class ScrollSpy {\n    constructor(args) {\n\n        this.doc = document;\n        this.nav = this.doc.querySelectorAll(args.navSelector);\n\n        if(!this.nav.length === 0) { return }\n\n        this.win = window;\n        this.winHeight = this.win.innerHeight;\n\n        this.scrollElement = this.doc.querySelector(args.scrollSelector);\n        this.className = args.className;\n        this.offsetTop = args.offsetTop || 0;\n\n        this.contents = [];\n        this.contents = this.getContents(args.contentSelector);\n\n        this.attachEvent();\n    }\n\n    attachEvent() {\n        let scrollTimer;\n        this.scrollElement.addEventListener('scroll', () => {\n            if (scrollTimer) {\n                clearTimeout(scrollTimer);\n            }\n\n            scrollTimer = setTimeout(() => {\n                this.spy();\n            }, 1);\n        });\n\n        let resizeTimer;\n        this.scrollElement.addEventListener('resize', () => {\n            if (resizeTimer) {\n                clearTimeout(resizeTimer);\n            }\n\n            resizeTimer = setTimeout(() => {\n                this.spy();\n            }, 1);\n        });\n\n    }\n\n    getContents(contentSelector) {\n        const targets = [];\n        for (let i = 0, max = this.nav.length; i < max; i++) {\n            const href = this.nav[i].href;\n            targets.push(this.doc.getElementById(href.split('#')[1]));\n        }\n        return targets;\n    }\n\n    spy() {\n        let elements = this.getViewState();\n        this.toggleNavClass(elements);\n    }\n\n    getViewState() {\n        const elementListInView = [];\n        for (let i = 0, max = this.contents.length; i < max; i++) {\n            const current = this.contents[i];\n            if (current && this.isView(current)) {\n                elementListInView.push(current);\n            }\n        }\n\n        return elementListInView;\n    }\n\n    isView(element) {\n        const scrollTop = this.scrollElement.scrollTop;\n        const calcBotom = scrollTop + this.offsetTop;\n        const rect = element.getBoundingClientRect();\n        const elementTop = rect.top + scrollTop;\n        const elementBottom = elementTop + element.offsetHeight;\n\n        return elementTop <= calcBotom + this.offsetTop && elementBottom > scrollTop + this.offsetTop;\n    }\n\n    toggleNavClass(elements) {\n        let maxDepth = 0;\n        let maxDepthElement = $();\n\n        for (let i = 0, max = elements.length; i < max; i++) {\n            const el = elements[i];\n            const tempDepth = this.getTagDepth(el);\n            if (maxDepth < tempDepth) {\n                maxDepth = tempDepth;\n                maxDepthElement = el;\n            }\n        }\n\n        for (let i = 0, max = this.nav.length; i < max; i++) {\n            const navElement = this.nav[i];\n            if (navElement.href.split('#')[1] === maxDepthElement.id) {\n                navElement.classList.add(this.className);\n                navElement.classList.add('mdl-color-text--primary');\n            } else {\n                navElement.classList.remove(this.className);\n                navElement.classList.remove('mdl-color-text--primary');\n            }\n        }\n    }\n\n    getTagDepth(element) {\n        return parseInt($(element).find('h1,h2,h3,h4,h5,h6').get(0).tagName.split('H')[1]);\n    }\n}\n","export default class AdjustHeight {\n    \n    constructor() {\n        this.header = $('header.mdl-layout__header');\n        this.pagenation = $('div.pagenation');\n        this.footer = $('footer.mdl-mini-footer');\n        this.win = $(window);\n        this.scrollElement = $('main');\n        this.content = $('.page-content');\n        this.outline = $('.side-doc-outline--content');\n\n        this.attachEvent();\n        this.adjust();\n    }\n\n    adjust() {\n        this.setPageContentMinHeight();\n        this.setLocaltocHeight();\n    }\n\n    setPageContentMinHeight() {\n        const winH = this.win.innerHeight();\n        const headerHeight = this.header.outerHeight();\n        const footerHeight = this.footer.outerHeight(true) + this.pagenation.outerHeight(true);\n\n        this.content.css('min-height', this.win.innerHeight() - headerHeight - footerHeight);\n    }\n\n    setLocaltocHeight() {\n        const pagenationPotisionTop = this.pagenation.position().top + parseInt(this.pagenation.css('margin-top'), 10);\n        const outlineBottom = this.scrollElement.scrollTop() + this.content.outerHeight();\n        const winH = this.win.innerHeight() - this.header.outerHeight();\n\n        let min = 0;\n        min = outlineBottom > pagenationPotisionTop ? pagenationPotisionTop : outlineBottom;\n        min = min > winH ? winH : min;\n        this.outline.css('height', min);\n    }\n\n    attachEvent() {\n        let scrollTimer;\n        this.scrollElement.on('scroll', () => {\n            if (scrollTimer) {\n                clearTimeout(scrollTimer);\n            }\n\n            scrollTimer = setTimeout(() => {\n                this.adjust();\n            }, 1);\n        });\n\n        let resizeTimer;\n        this.win.on('resize', () => {\n            if (resizeTimer) {\n                clearTimeout(resizeTimer);\n            }\n\n            resizeTimer = setTimeout(() => {\n                this.adjust();\n            }, 1);\n        });\n    }\n}","import \"../scss/sphinx_materialdesign_theme.scss\";\r\nimport \"material-design-lite\";\r\nimport \"babel-polyfill\";\r\nimport ScrollSpy from \"./scrollspy\";\r\nimport AdjustHeight from \"./adjust-height\";\r\n\r\n$(function() {\r\n\r\n    function reconstructionDrawerGlobalToc() {\r\n        const $globaltoc = $('.mdl-layout__drawer nav');\r\n        const $lists = $globaltoc.find('li');\r\n        $.each($lists, function(index, li) {\r\n            const $li = $(li);\r\n            const $linkWrapper = $('<span class=\"link-wrapper\"></span>');\r\n            const $link = $li.children('a');\r\n            $li.append($linkWrapper.append($link));\r\n\r\n            const isCurrent = $li.hasClass('current') && !$link.hasClass('current');\r\n            const $ul = $li.children('ul');\r\n            if ($ul.length) {\r\n                const ulId = `globalnav-${index}`;\r\n                $ul.attr('id', ulId);\r\n                $ul.addClass('collapse');\r\n                const $toggleWrapper = $('<span class=\"nav-toggle\"></span>');\r\n                if (isCurrent) {\r\n                    $ul.addClass('show');\r\n                    $toggleWrapper.addClass('show');\r\n                } else {\r\n                    $ul.hide();\r\n                }\r\n\r\n                $li.append(\r\n                    $linkWrapper.append(\r\n                        $toggleWrapper.append(\r\n                            $(`<a class=\"mdl-button mdl-js-button mdl-button--icon\" data-toggle=\"#${ulId}\"><span style=\"color: #888\"><i class=\"material-icons\">keyboard_arrow_down</i></span></span>`)\r\n                        )\r\n                    )\r\n                ).append($ul);\r\n            }\r\n        });\r\n    }\r\n\r\n    function collapse() {\r\n        $('.mdl-layout__drawer nav .nav-toggle a').click(function() {\r\n            const $toggle = $(this);\r\n            const id = $toggle.attr('data-toggle');\r\n            $(`ul${id}`).toggleClass('show').animate({height: \"toggle\", opacity: \"toggle\"});\r\n            $toggle.parent().toggleClass('show');\r\n        });\r\n    }\r\n\r\n    function styleMdlCodeBlock() {\r\n        $('pre').hover(function() {\r\n            $(this).attr('click-to-copy', 'click to copy...');\r\n        });\r\n        $('pre').click(function(){\r\n            var result = copyClipboard(this);\r\n            if (result) {\r\n                $(this).attr('click-to-copy', 'copied!');\r\n            }\r\n        });\r\n    }\r\n\r\n    function copyClipboard(selector) {\r\n        var body = document.body;\r\n        if(!body) return false;\r\n\r\n        var $target = $(selector);\r\n        if ($target.length === 0) { return false; }\r\n\r\n        var text = $target.text();\r\n        var textarea = document.createElement('textarea');\r\n        textarea.value = text;\r\n        document.body.appendChild(textarea);\r\n        textarea.select();\r\n        var result = document.execCommand('copy');\r\n        document.body.removeChild(textarea);\r\n        return result;\r\n    }\r\n\r\n    function quickSearchClickEvent() {\r\n        const $breadcrumb = $('.breadcrumb');\r\n\r\n        $('#waterfall-exp').focus(() => {\r\n            if ($(window).width() <= 1024) {\r\n                $breadcrumb.hide();\r\n            }\r\n        }).blur(() => {\r\n            if ($(window).width() <= 1024) {\r\n                $breadcrumb.show();\r\n            }\r\n        });\r\n    }\r\n\r\n    // styleMdlCodeBlock();\r\n\r\n    reconstructionDrawerGlobalToc();\r\n    collapse();\r\n    quickSearchClickEvent();\r\n\r\n\r\n    const spy = new ScrollSpy({\r\n        contentSelector: '.page-content .section',\r\n        navSelector: '.localtoc a',\r\n        scrollSelector: 'main' ,\r\n        className: 'current',\r\n        offsetTop: 64});\r\n\r\n    $('.mdl-layout__content').focus();\r\n\r\n    $('.mx-card').each(function(){\r\n        $(this).addClass('mdl-card mdl-shadow--2dp');\r\n    });\r\n    $('.mx-card .mx-card-title').each(function(){\r\n        $(this).addClass('mdl-card__title');\r\n    });\r\n    $('.mx-card .mx-card-text').each(function(){\r\n        $(this).addClass('mdl-card__supporting-text');\r\n    });\r\n    $('.mx-card-link').each(function(){\r\n        $(this).hide();\r\n    });\r\n    $('.mdl-card').each(function(){\r\n        $(this).click(function() {\r\n            var url = $(this).find('.mx-card-link').text();\r\n            if (url) {\r\n                window.location = url;\r\n            }\r\n            return true;\r\n        });\r\n    });\r\n\r\n    $('a.download').each(function() {\r\n        // button\r\n        var button = document.createElement('button');\r\n        button.className = 'download mdl-button mdl-js-button mdl-button--fab mdl-js-ripple-effect';\r\n\r\n        // icon\r\n        var icon = document.createElement('i');\r\n        icon.className = 'material-icons';\r\n        var text = document.createTextNode('file_download');\r\n        icon.appendChild(text);\r\n        button.appendChild(icon);\r\n\r\n        // link\r\n        var link = $(this).attr('href');\r\n        button.onclick = function() {\r\n            window.location = link;\r\n        };\r\n        var fileName = link.split(\"/\").slice(-1).pop();\r\n        if (fileName) {\r\n            button.id = fileName.replace('.', '-');\r\n        } else {\r\n            button.id = 'download-button-' + $(this).index();\r\n        }\r\n\r\n        // hint\r\n        var hint = document.createElement('div');\r\n        hint.className = 'mdl-tooltip';\r\n        hint.setAttribute('data-mdl-for', button.id);\r\n        var hintText = $(this).find('span.pre').map(function() {\r\n            return $(this).text();\r\n        }).get().join(' ');\r\n        hint.innerHTML = hintText;\r\n\r\n        componentHandler.upgradeElement(button);\r\n        $(this).remove();\r\n        var header = $('.section h1').first();\r\n        header.append(button);\r\n        header.append(hint);\r\n    });\r\n\r\n    $('.mdl-layout').css('visibility', 'visible');\r\n\r\n});\r\n"]}
\ No newline at end of file
diff --git a/docs/python_docs/themes/mx-theme/mxtheme/static/webfonts/fa-solid-900.eot b/docs/python_docs/themes/mx-theme/mxtheme/static/webfonts/fa-solid-900.eot
new file mode 100644
index 0000000000000000000000000000000000000000..cb8d3f0747c96c1039cfa3c7065ddb8d7d893ed2
GIT binary patch
literal 197172
zcmeF4d3==B75C4%&+L;-mYHOd5JL8ZH6aNJ2*R)ksDL1gxCcZ<MMa>B7Bwm=b;XKG
z6)jq<)>=!|D(+FSqE(Aa-AXksRcp1KnaoVAV)FjZb0#66`1&q?y`R_dJLh@szCHIj
z_uQMI$yl_IEn+ex{~2;x_61zlvs}%Xt@nGrZD-sLdF;+@dt7_3s%a{nNoUY1Dy4~Z
z4xLCVaeq2wDUAYCX$7uNL~tL?h0iiNg_hthpBBJ%1wx-<$CuC}08zt_OtEyY^bk=B
z>ZdlcsD9eq`IAbotU3>lqv1O5u(@>&6SmF$C+@Gp{elxtU$lI?^Rrb%SzyeHlb4-)
z(lGaq$?#f3=KO_APh7Ml=k`+;BWwp~UJ3`tMCZ-8ejHRTJ$=>c-@f<li%9QdqRjQn
z&OBj}V`S|HqUoREe#7aDRxfv4!)RnKK={%#7M*_L?rVSb7c#HEfmZ)|`I#$MJ$A~N
zW68X^6D@nWl%3mq+TqvMljupBE9}}JYVUv6659pgw%z&IkmIw)CCRUS__hh1DWOCn
z?xW@kehIf8bgr}r5+*|I6qVX{63K@TQICCZ`{TZz#*>*cb;djjEL*hd3@Rc=KmY#M
zyzG>d7a@I($p|OD3>PL>uPb-)dUC?~Mm`B{)6z0Yg|xQ+xP#wb|II$>pd}QEQrAkm
zJw~7|QxQOGVuzFpa68h7NIJM~>)9>$d(-o+X|r3i+eCW0InvY9=P%cS#1-T^DpJzu
z*)3@rT-#Ev_k*UlCtX9DrbtEOro-9kqO2&F_`|PH_6rx)vZ%j=`7V;4#6?+qRO-FN
zvE3p)UG_ENvVi*c<|qDk{0QRM;nQJIf25`1BATvAhv|(gWkLDvy7i`Ew;SB;FeGUp
zO#A-n9}aDVzCqXyiEroGZPyp>(ihaOd?!8lnO-Cw^?Ymho0P3B?MISkn$mQ=aBr7e
zWJLWC?;EL8-+f!ZcnH^<hFu24(J=xk;rfTgwcXZf*&EN6b~uzRB1oEcneDpUX^6i7
zf4hBAp1!{5?GuuCc3kuk>LX!9MijdrWDK|Wh5H)*U-#X&N&3BhQdWtN`?fTs_jWzo
z)8QmcM6Mxm%^m0$`0Wq7OcIAA48q&xN#B>I$9+Wa_vCA*uj3B!?0k@iiKHRT+b%2I
zBwe|tc8RC-)=~ZT^rgn>`BuXAmf1w~I_;-@l1|62XZl*^1u0|yKK;IH6VWh|UZ20z
zeV@3!;rhFaTW=jSZ^Uay8zZjmhrYG1X-_!#@0NDe^7nT~{(bK0wwL?1-u4!mzK$Sl
zsgyT;Z@c3<vL~L7C){sN=Ye_4ZbOmMufkVpY3I8quHA>dVfxE<gpoYjktQU{BjdNn
zlxp54g7n(;S=O>i7`U}b9E=~*dt0I$ePgK2j%SC}H21Vizc}(d>_*wTO{tw`dajYM
zd-69CjoUk><i3BOY2TncZE#0Cy_fo=^As7e%d6#SQ+Im|ieD-6Y7>7Ax2GR55A|!)
zbo(GI!Xf<kwQ0KC(k}Y`Ls+!$w}Rbnt<qkY10+64In+IUz1u_(zFq1eQp;qI`-tQx
z;nUz=%5Rqm*M03#y2l@B+s`m}pdO_%ry@;&a;JLp?He~TUfMJd?oET#ja&OkaRYW4
z+mUYs*Pu6U|F)59J3oZC%Z+;tr}28-xD@UYNmJ`#yW`#tOHw8s=P0wZozT_mhd45Z
z?7q`7+41Z-vA3M?xBC&YZ%(uO+P)VzVCR88YeV`GJ5Nm$nxE}2{b!d+($_o?PTRMA
zPd)qEMcn#f*NHTo-CpTysYh?W^tF|g!7hiUVb`NAoi9oK)9#V<wfLcqy?NNu_DiP&
zf3%a<J%Vr=UUi>#+fSbvNgMIx8M51BGp-}>@0~9Y)-D(ImPPXYKGNkvc)Oh=5=Q&T
z4&Ns6C69joc9^s~X_+)_4X5!WjdVREzNFb(w%+lJxSFnn>-n$bgE*p#$TMkU*D390
zUrXMRbUq>_4Vjm0DSqj4_TCpDjJWN@yPyO4$aVXkFm|0OecuNErTw3ANn1gg((P{N
zr~NAR>A`(lFTLgI4Wsvc`JsL$B7VK$d&9{+{3IRmv;8$}Z~6MUe{0X1g!1%5+P621
zJvJkn4!3IBBBlLJq!;ZQ)XSdx?6_?b7U6_;q~FndE%$PrF2A%B!t9Ntm#%~OMO4@6
zEz=&UI#pl2Bu&!v?f&l_8{%#we(AJuZ^yHx`b#-AT<^V<OTx*0x{QST&A5iN%Mn4E
zrJw_Sjj%RwgY4%2^dx(qZN_A7Y<Jtwe!lGZZQ?Fz^?Qybo!;~b{{7%mozqQ3+%-SD
zjZs(p=L8lQ2CxpyzF*gBS@*(KUMIsKvD$>YBdZN53_;o+8WJ^8XEYEEM)RVD(c)-H
zbbNG5bXN4}=yB0AqHCgU(aWMgi(VbQE_y?BWAwJ@?a}+9_eWoez8ZZm+7bOI`mbmr
z+8y)90<oM}ajYa(9vcxG85<QlAT~ZWB{ns7Xlz#O$k=hQ6JwXe*2S)m-4MGe_KVo9
zvHN0MV-Lh0i@gwgCH8jg-Pn7vPhwxhQnBth$K7#%ydYi@A0BUxkBJ`~pA|ngzAS!D
z{Ji*;@vGxMkKYi#IeuGwOZ@KmL-9xAkHw#eKNEi?{#txj{2%d8<DbXBjK|}t_<!Tw
zozBjT&XUgB&e5GmcOKi>+PS#%;?6repXz+N^SRC!I$!R5we#<t9i9K^{Ic`w&Tl(=
z60SscqAAgwI3O`DF(ENAF*$KiVp`(R#H_^Z#N5Qf#IcFv6N?ikB~DMQNUTbno47D>
zapLmCm5FN-8xl7qZcF?w@j&9y#FL5k#50NQiB}S@C;parKk-r0o4hW0Yx0idUCBQr
zA53mbK9PJb`RC*-$=8x^CErc%O1_``Ao+3f)8xOB|4x3J%1#ASg{eWQvQ&MlB{eQJ
zF*PMMEp<rhu+*H?{M5qKajC_rlTxRpPEVbgT9rCGb#7`+>cZ68)X!2krfx~yp1Ldb
zo7C@852qeWJ(+4xZBP9r^=j(%)L&C?rQS)sm-;C6N$RuIm#KK_>(uTp>dNQ}bmeu0
zyGpvsy9RgFbsg9h>6+X%t!rl2+^z*(M|U0HbxPN1T`Qq`P?VyMs5_dCu@bh&N?CMb
zbb53)#!72+ImXH*>9KM>#>%GX=IF1YTchpKozXWjRz8e=8jVK3icu^xmK`g=SSgKF
z#_D5@v6k3@u?WV>A+edU*|DQzt+9(^m&UG3kCjca-(sxXAKMmdkG*V<m0hup*yk84
zU&Sfzi2LH<cu~A8-WVSpkHlxj=U}Xy6+hP=E9>pCa%+5Z{I2*P(_`i7`1A3d@xS$r
zl~_D!j}_DDwZ}?J=fd<@xd>zB$sZgmjTkFq_A^$F!B|;@v9dI=9Ao92#QBMf5^FJ5
zu1>5^+?XCK+Y(Qt$I8nXD{m&=OMIC4IvGx0pZsNVOY)xN1IdSyk0zf=KA(I!xidXh
z-s?A3cJ~`ABQRFR?lo5CVyqmCv9cs}O5a#no%$)p%0;QmF;+IFHmAqRL+P>d>|SH#
z@2L;%vGPSKDq|(p)8)rl38lwMZP$pdv3re`qq>f>$I1#BEC2t$|K9>Xq6HZ5p%JFu
zj5ky8nQnhBHtWm=a}Pd$GVkH@ar*O_`OJJ_{%tudk5y?^Tg~{4wj$PaYo2wgwahxh
zIvbz!tZS_G)&}clYm>DFpI=*dTaQ}p){E9l)@#<A>CfBNd-!x%pIBd6U)!H<htCnf
zr^r$4D0fsksvRR7QynuLvmC9C#g6mvxy-T7{#@md&oz!49UC1tr$3t<_c-ozZ0-Bp
z?|9JuJnCq-KhHRJq(3h@UUa<lgP&I%uQ+z@=kuE5HOCwHyyy53pHCd0JHBvW5_BXS
zyB*#7aT=#1{c$;6&TMC{GvsV=Rl5#wMO;%|^IQvDN4Z*E%Ur8mXS>czf7ZD6d@gY9
z`CRO}*tK@A&pOvS*A@6&<NA5uXM^iTd^Wi@<MV6R-RaLgF8$o=>i@aVwblOI?|R7o
zY;!$|&*SOOldg8x_PsuTa_x{0^sEIYU@qosOuKaek1}7H|CoQ%b!NA@$h4W6W+Oeu
zH_|=kG)nPr%n9ZleudxQ34E1xsCBS4!yG})<^%H~&7=8dE}g|wX$Jq^{L6d+EyzV)
za`UUEkngdk@?HEPIZQY8n5Q|98cf90m`8Xz=bLAsNd1yFn@POed_&imh5S2uotmhU
zs;HVO%+ENBc9<#LO+(qw^LRUt;SdKo!;Gg>&GGas@1oo3HnY@R#|QCbx{{XjgV@P<
z&OBlMYh_y%{G1tWnyqsBi&@S;qZ8;-Uc*1-^Z7g~r7|j~L435;YCh&>I+g}o<6xD4
z2<w@*=_NDBoM#@P7T(E!F@LrOm|grgE6+ISJi3T}N^9szv(?;de$OszAkU_m=4PwN
zRPmp!dh@K=Nt5YOzM1^|ZyrDs`7AS;vM8H!C_q69Q6A;eE`AcvBAKJjG3G(K)7;L7
za*Sv3ANfG@IXz-d<k#p`+GdrSBl$kgG_RSL*<%*)*Zf~PlSiA==@!~Vx6;j&X_e6u
zW;W(bGl9RQ^=7zPN6*vw)J7Mw*UX@Qn{%wi<}B-YZs1~bxA_g`g5R3onfuIFri+XC
zJ@cyZU<YFnT|fzIKEG`4XQwrYkK@NVOpECbQ%i5sTV@fRYyM$n(K|epM{)%Z<0>A=
zC0x&g_(j@cN_YcbVam*H=5lix&ElC>n5LPDCdXQU?XXE!fL^9yoNJ0`I$c3m(^VE(
zjD3)k>0ay%T|yU|tE?vDH!t$F)+GKHC#($1Z$<c1Ya)Nn|K!h1HXm-*T8-8iida7L
z3XP))bRc$k#+pq2l?j<Vvz>lRjr>c#jc?`6d?ByqHa>?h;tTj(%VU;U1=z$}hK;?`
z__zEKf6V`&r%bhZ&RlEO^JS*NDxpKE&a}``G=wtfU2_A!!)w`rC4yzu@)rKJiJPS9
zGzk+kKc@rCLDrGhQP$C%GIv@BTH~!^E7QD<nf6A^xr@!ge7C8?j68(@%5U;p{5A*Z
zH&jElG=Pd}AeC@7=dh1{M}Oowyn|oh*ZBkfihB&3QRYx{lDX3S-E^3*ttx9Fjixc!
zUi=09oUW%0bOYT;H_=A=mZ$MCe1kd4JZ%1EO~<Na6fLA9=@@K8uA;l>ZhDWtfU@}?
z9>Rn90N%*I;5$vw3^WIrIcBz*XRbC|%)91&ld>jTQ>;0d{}<5_^fWy~uTYGVd^WG*
zpYSDou_-kp%@}ivIm29HZZezAM^=s%v<|Y4v)>>!)?jOhHPjkr4Y%s7kye8>3h$H_
zyj8~1A#^w$N5|7Tx}1JRPtc#}4f-p6N*~aN^bvhb|DaFkbBa?ZySb1L=EL|1Zsp^7
zJ>ScZ^7H&Bj&ql>j4>|bG`XhOl$%B~-?W-#<|k%_S!q_8pPDu18nem#%Iq-Dn-|PW
zmSyEzhgq|+En7sxu@^X!rqFC^rIWBhdo4XoFVLUqMcPT9(Z8sRzM-$VfJ?cYE4i9$
zxrT@H2(IHMZsZmo%M<w!p3e*UNIr@e@fp0Bm+%REBA?7B@lrm8m+`558lTQ*@^ZeC
zujcFc7T&~v;79mj-p0@HQ{2u^^Sj)^@AD`8CI5$`#%;W2fT=J;%`m*T$C(4o;byft
z*IaLYVSZ`uGJi0CG!L0a&Ew`T=5_Oi`Kx);d~15})-JRrSaYpou~IF-7V}})Ha?nu
zLT6*MdNuuw{z?DkC-_<Z#^jqr%%$cj^Qrm7L@lqCYlX0a4bwrilzvU^w4Gj~zq7@Y
zcnZ(pV|gX7<E?x@Kfn+1WBhmSG(HnHm1eLRYo?j0W|le39BVEx7n)z2ZRRoaq<PD7
zS}tpvb%=EW9ZYku{k@hhqpkEHy-ywVC4EJ^shi7q9M9zyd?{bfKj-WDCcd5T;4e&u
z$ub4zRCBgD-`rwuH4j+Ea$6&;S=JHOF?1jOp8i1h(*yK39?wbB1fAwMbF#Su+RHz!
znbzTWlevC`X7vAhkPN9{ZM#;}S|Y1Ug%?2LMJt#9Bn%aTxfl|b4S}zLgvCQJNF(Z2
ztOFpiBQ7uwF+{zJ`4SS=55fEg(yu^+peRZr&~=cRirEb*c>{A1WVRx<plFU_W<usF
z&^{;{ROm6tJOvsFMZ*f+1Bp`z0<;v07AiOqK+$4_e*;;fm=hq&6!Q*bxx%kNRw#^9
z4ADx3CqPyya=Ic~tyqUb)+pA&khO|619Gqe?TDg76l#VXs+bQThbhpVC^}q$7Ddq!
z3eAVCQ=nN<v|gdJAV(_DwkX=5kmTE_@b4j;6!R}g=`#SWjG|Hw0R4=jEebR>ijG#G
zvr%-6Lel@z_kdr89IF^<<8ca0`yZ%SQl9Y&-vv2Qf#yfiNs3s}qLUS9gA|>j$a#(E
zbj3UkDQya%D^m0j1&3!SD(wzT1XAh@jP!$y8vt#MqO%m1^kyrZ4>?CM&p=|v5ExAn
zmG%YDLMbYJ0BAF$^Z_tZ_5}*d7?yqo&{iqBP@!uek5r7f9i>2*rRdQLv|5TDr$E1@
zXsZHEm!gXl=)4qNtU&vvsEk)YGB%ee=4X&6D$tE7dXhprAWv3|wChp@nlnXDQAoz%
zsS5icPg7Xxy-eZlkf$pwW8n;iLy*fA4noRU1e^i6LV-?B(Ul5GKC2Yy=@k8mLeD~;
ztw49D=s60sc#5u8=r+i66(jXHPchd){#4<EAlE3+`zhL{U<E+YOB9mwu2rxqpy;KF
zoOFs_rqFYc(x$*X0eQ7z{tGGj0V^Bwdc~5y*r2e)y+JYZ3~f{lp0VgA1uGAV-m1`F
zAf;}=NZDkZ0jx_XD)k3gp-}W!3Q2jlC@gioQ(<X?Un_h*q&#l`YZ{8)txzfCZxpO^
zC@Rk?z>0^Wzg1`u<h=^ZIF<1VtX9bT6!S6URt0M$ivB^tYKfxvD>N9gU9rYNKCQ@U
zt>`lfJp}oz!VyRrX8<cLib^{IlJI|0jFjzp#Ynwmd;|L#e*xEkWDa{#;hm6wR#@6u
z(gH@@URJCDkgq6a7o@}i{5Qx~6)O+&O$BQ`ioT^_)kje&E5Q1XqVFhJ2~zZ31#3cz
zN?L%Pgp@phk@ow$V(x`}PcbrAcPOI4L_bulfsh|5SXol^Q-x+iex{h4A^)jZMUbB>
zrV8?33QOIiid7F8Q;hUOT)|3}qMZuXtQ1Ws^eE(43f8d{mFEHA93n-(R`}nL-zZq+
zQgpY1<B}BpR^hWCyA`Z~v6xV>8m1U4SQk^wC|Ds=%u=vcrWpD_z^a*IP6g{{ilMy(
ztfVRCR<Nd~m`5SBd(5ja`Xc624B9@1Ita)TwsXZC4Jr8na}1=E31DSTF)0(EJ0S}c
zb30_A!iPel4+KU9V`vM3XF(P#{71+Vg%5-*Rj|^hSeZhPK$a`!M94}7b^wZ@Uj<kM
zC|0e|Hpm*qlJsg7b0p+oh3|tLqOjC;sA43|VT!@?5gV?swBHEDEP$+67=0caslfg~
zu|@?)Tq)M1$cfb0C<WFDisAVbV5gv%_yJffC^klc?Sf(lD6nEsOxgvIlykfS%Lc_F
ziX5+vO;ltLFg8h%)2J~STfj(prYL3t<WvP#5sDq6(0WMeGhn3NGZolOC?<UdU_GJO
zYz1}{ip^1|4f1e>E`*$`u#B~N3hXTuo3GHnA*CIGIS29x#aaxxP%&pgN}mDict{zG
zfEyrXECQBsbgW|ThCEI&zkzI3%sr5c6!Tk1$pe_*L7t$P`yizqf%ytj#u+eOkQXbA
zaU8ouVR>FIRg8?i%M@6PD0aC5yAj2t?EoxD6uVA=Es0`3S118_y<$n*+@SEwkT)sj
ze#l=a?1YrD0xY}>Vz(+R^_IE;ejF0>sQ@b##iX79_9}`=JptVTxmAG;i(--ofHjL^
z4=D5&<TeEsE{Z*((7BM0D&`-Mk14Q!QLJ5|cOaiucqrsE3Xg<*R^bZB?F!2?_ng92
zkbhEmAf${rz$K776qe`j1%;&@q`d&FZ4`S&p)HVaD@NkJtMCTMT?$_T`FF*XLB6M$
z+aNm>BmMb-0_z>cK2&HH<VOn6g#1{6ZI5EoE`X*%exjI(ke@3i2l8JEY=9K|LV-1q
zVkw0tLCRPJun<!0zY1)H6#H6{UHI5H3g<%ZR$xh_*tZI7iWKWsU|obMNr9b_;;hhB
zkPgKXf2RT~B*k5doXd{86<8)I?o;SqNWTJWCB;#H0d`A@7btWwB+4Wh$!maOVUCO!
zD@M{le+%YCNc5S&*Fu&l7TP3Ut}xm!jxi)Ko}+lBVr4*L3<;K`S*=(R$Qp$|g~YfQ
ztcj3=75*G@h{FGb9IEhVki!&{4LMxl!yy|Lvlde711xdFoG-v8OYvrfqzt1K%Lgfb
z02W({iywgPmf}(-04pxVrOp8MT#8RtVA-Yk6otk@9;BE|Na-iQzk-~mm=Gk!fnf3=
z4_06mrua++_F;-+ObaCCpQG?EA*G&xZ-bnx@U4*Z6y6LuU*QWOF{TAx4S9sZZIBBU
zJ_qthg)f3UO5qD2k5>3x$YT`C19_}smOw63V6~?BSqkje6knmhl1=fI3T)aGU!~A#
zkUvpa>U*}rA3;i)fc%9=T=E96f>ZoFg`R@EQZX{7u2#%*kn0sAWA!@4h}+K<z6|mP
z#WX<PtiY;HacM^Y`#Qy?eF4=$N}B@M+$ny$0_!`)?@(Zer?~VnfJL6-TNHX1@-D?l
zo_8z!4&)ydUJLn<0vkTXA5ld4i$A821^I+x)j~e4u;lZM!oP-mUNLdV9g0apzMz;+
z$QKopfc&#!VvsK>*fF5^%L*L;DbE8iGOl+j){&5}DwedvYl<b~<!=h7Aa^O|PDtrL
zU>yi4&pfckLw>GUGWUF`SecMgCScx%l==YkCrGIeFgHR<9>8pX{I6mbLv||$&sHbe
zQDC%vC)!ak=+jQLwO~d-Iu(O{?DQ)9S4f}2Z$kPNehU(PEHKK}S)yRqggVhT0`^a+
z6JtfdP6~C7R<Nf+oeLH0u23i1QNTV6b)p>w?6^=T+EE}$`&fmuAzKyBfkZt8?95On
zo?8KXG}L*qLVtury9+!A@=1kvK;rok7~`T7V@crGA)izD14!`${1qg|gCJ)XJ6}}{
zL;hVcqaZsJgJ-AnABs5%@=L{B3Hh~RB;L1*k$93WFz`>f6srmnbr!I%M2SWPJ4}>l
zQn1%ViDm`6PLz;)fc+;*9H3w)iW1`#>`75#f`Z*CN=#I+PeqBz3U;h0agc(&D@sgL
zu!}_r)J-6iJuypR@t>{mF_3c=mNcc@z@T3f;s?ybkjE+p&r0HW1-oLDSft3Qy#)GJ
zz)l$@(2fH3%qX!`!R{F)PEoLrMu}4u?5I)VGzEKWlvt)<myHsqE7)(N#Bv2YZ<IJo
z!5$nXRwxvOl=y)D19^_ZLm*cxJQ(s^g{8gES9l}j1qw@FT&VD!kQXT?2#K*On1PUM
z6(i|fsu+yh#AS++Hojaj^B^&v1amcH8iV;TagAc$g<P+g_aU!SObSxQ1hAyfWlR8T
z3giaGl6++x088?caR9LANQv7N>^@TBcMA3)De-`U9Z5=TQ?NHliANMMEF>OPC<*z5
z!e>L`*%TOUooHA1Cy<gB;7cI4D~$F^ysVf~NXY}3k&v$|W(?$;ijgt$mSWC;d|NS+
z<~xd!`bm2Mvl&vxB`_aBzNc6@kkXF83POIUSn{k$IA9$I`L$wILVlxI)sVXts|NC0
z#gg&UtynUydK7CYNfX&v!ys9)hC>>~!W^Eo6bp4vIuxq`(y3T@c9Jf|YKC+xRtuy@
zu|`9B73=|1GOS=Xn34qw_Jt`~s9=Yfl0^#kiYYlj!LBhSixuo2QxaoZz)mtHOBC!W
zQ*w}k-DOIm{{-waQ?g9Kjx#0873@7zvO>WwG$qld0`{XRS*2iSnv!T=0ejSxMEeTZ
zt)^tHf_-aBqOAq&U{expEyw~SDQyj?6H?k5up3g^8nD!Rgu(|yO1lF-46<I~BOs*@
z0JlO)9{`s2X;knxsg!I|_+Ciq8^Di3O5Xr}9#Z-Su(Xf#4PYrJ#<aj)kOwG6`d|7C
z7#SDi6yt(CP%$zV#w#Wla)M%{uOf<(_MWI1Deol3%!iz;7^&A3#Vmt7NHITwoT``=
zkkb^i5^}m?RzXVJ0rOKx8C$@tfjm?(*Fert%qGa0iuo1fEXC}Aly(P3+6r@`U|xWn
zqnMW<4_7Q{pSg;a4>?b<Bn_z>z{vwjE>Lg=fs#ikIE_Hbg$m9kQ1VCxCln}ol!CJg
zlssC&sRc?Nqu~4kC684|+D)DrfHMu0lraHtx`C472XM}Tl8Y3ac%bBB1!o^9d4hsd
z5R_b^;5-B+PgHO+f|8Ohz!?cjo~+=s1SOX$BxB|j1t%ydd8$HRL!PFvJnPc0fJ-4y
zS6Idd=1qYsA<tB}8gjY9wUB2iTmvcN0I;;1)D!RsNEru!>mX&^0B(XjTj55?a};iY
zT&*z1ck*0?Wt^U;u#6)aPk^OAWjq0vemh@b>9-3MmVUcXVQKF+g{6KMDJ*S&vBJ{c
zmnba#uvTGd`%4v;_P$JE>4(b|mbPD~u(bWp6qYu=LSgBLD;1W0xJqGZ`>Pd}_P$18
zX>S=TfF<5~g(d8D3X8m6;aea#C@jy^4GRAO@<xRpfxJoKhaopAybbaf3O@sRv%*h7
zO1gmCAthbFPeb0S@Vk(|RJa3jv%>E~-mdT`kasBjCFB-`{{wlaB7b+2{Iz1-kasD@
z3we)X20;E+F%^*aDrP9;?-Vl(@;=4XL;hYd;~=*xM&?Ut7hq)mct9~S1|L+6jKPN#
zBW*101&p+lv==Z^??)9Q<$X*slJ?_@`6J{LijnlBO@Wbk(x$*jSZPyW{sJj|0L<%<
z&no5($nA>xE97&Ec@y$^#e567Loq#&FDRA+@<qieg#5E&O@Mq^vF1X)qTm!0C3h+~
z&qT>r6`X9M<ZBAfI8pK~1*e@TDdPa(+!G~tDL4T|$@dhTg`(v93Qk2)@&g6uqbT{Y
zf|F8|{8Yi2DN6oJ!RaYV{#&8{Lhe>r+EwZd810nGR`?sppkneNQD?y%0y#)Ac&1Zj
zig^kW^$`s6Pt_>~<2HpcB$y~<i(+9+rp78(F61}`C%Gs!QIP`;sVNFhcTsAZf^%M!
zl5zl?_@WfrPQckOO3hISV<Lri6mTAlQu7p?45QS1h5in?P{B{FQ0iEPCqd%<Be3MZ
zSm7CvOB9x{Cn~%W@+5^Zhow$Ycq`<o3f~WTn!*o2o~|&)bLvcmG4@ld6qd4~j|J|8
zM868g2YIeyq)mRRm`cbsiWv-fp<<+sFH(%O-^GfV3VDfQW<jo1jI`zDijg`>eSpC`
zA@wuGNZL0lM%sL%Vzxp4LNQX8n-%jU<SmMM3v#n!;kiiNu2?R}yA(^x`5VQOe#X2c
z;4B`cq`d%6<x%Qk1?Tf9CHVoI)T5N-2XJPOQtb*(?@>y|H^4bQN^MtgqK{I4QE;}8
zQg}`U>VSM*!FfMQVZIP>@{dwd2Y@qxlzK;@Zb<1fz-5r{DJ<jqLxtx;ex$IJ^Am;T
zIr>atY5Ok}{yC)N4fuLUd42%j1Q}CU+Apr~9gtruENMvjfXRUDQA`$zdShb>ApMHL
zIPF3>!C=gF1r&2WB%VpZpbxw96mu(NSTPSkmME5iELSWyWUXRhUg*NO6fBIlF1ZKR
z5s-BX&PGz#SOupfsS9l<;JhSt9jM^sBy~j;oS~$y$qG(WQWwUJK!1arsqlD6w7bAb
z$OVdNf;>tw(;<&m%yE!u%*nVuUNLCfu2U3)zUsoWESP^nqAvwY@?5FNDb9ZXbs+aG
z2)zj)wzUC&f{9!myYXk44-&bS61iW(LAIUv$)D9Ylz@Bx7cdSl2MKH-!!5I&DBuCJ
z0n*88!{2IVgU5+-P@5dc+**LNgARbWLBtIqZV370A$;D)_&ulT;9jCIdOD0S;hjVU
zR}mE={i0<=16pBVgnJ3nDA`U_iuh%vU@Z>YAm55?umeA>gnX)s0n)6-b<I-n5>9N6
z1NRaQhTmYMJ0t^aBN__-p&j@$Q-mLmbVjtpSiX*EB*HgDuyNr4+liVGuIX_YvOdGl
zIU${vB}Ah)<F}t`!49GW+5p_g4g+sv)1@9jjt^ner4;NWicBY(xQ=KN{3app<Yh!t
zwh|qLv<})$H1!y4@S$GQaer_V(IE?A#7Ddtn}}x4Bbqgu=rF`Rtet2!$}(pLc!}t6
z+|NavdGMcKfZw2cgXoB>U}!^L3lVN%l;}v5;m9@MZK9))_EGD>cA}#<5FG>eV<SYz
zIl#R{$0Ob2A0%o;+KZ6pVx+kk>6|bQz<&wcmWcd{=)}!LCuf7Z@zQH0I>iGzh)%tV
zXjv&hKBp5{4&Z*qFt8dR?=unaO!zOK0&u?^>8&gV+lW@ZP4tsi@DkD4DDOE)>l~D6
zHPSc_;m=DD{d70c`6$x`yNE7)gQ#sOKspyKB)WJuHVl#eB}jiQ+Tn8et(yThgU^V5
z_65-u@Vl}eTt#%%4x+1%0Z8i_#J?8q*WOFCehCiP7K2ShKc5Z|_xd$N8yo;>-2i!G
z0oX=#6T;rKlV~H{e=!eyOmy>Z9L7L8w<3+(P{v=P+?$d2?aQ!fUJ16txPth<g4>o=
z0QI{Q+=cw_+Dddc{O<k=#(1Q4&w8TYE+o1)10b#YJYYFMn!k^LC|)94i2i``-QP;|
zz*R&KjwAY`18gFC2=3dE#v@a}-6V&P=uxEiSUo_!9^Xjx1pJ;r8c!n5lW!9}wFE%6
zBV7BvL{BdzdIott^D)u0tpI-8w-Y^waL?iTPqT5T1NC?w>Ft2q4!FI5d|#YG^ydPA
zvc80JzTzSJ%M1Yjof~lArWQO-^cv#7j{7&(6aBRb>>@chKyPirA+~iy@2n!(kfL{S
z|2N$K4gR}O&%b9Ay@#~kFUCu92hj)c`!EC^C;A9^e~ftlXeat~H5S+zppEFC^<V?o
z4TBQW_#E;6bqvuLTZq2&fL%oYUJk=C!rP~n;UC)wgVQ|Q&_tcfAh*HV*9uK_7(kgt
z<CVADS4jK6$m8qBiM~PFyAk$V$ZoiIKS<P*4R%A9yq6fKCfTedw(uKmj;+Mb)i`Ru
z1_rElVh`|c#|FtpV*hmT84iQ)BF=n+IM52dBF_4lID0*D4${gk23HXW@wW#-#0eox
zUM)xv=Oe8!@+cs%7Dlm3fV7H`|A1}wfe0??ARe@oxO6tab=fB3a$J{3i7Ot5@gN&)
zA+Bl#JBX{d64$IEuB`{ickm42A-Eoju*0Unc!02V$fq7<8HsX?+>Qe`^T12Qjmuy-
zScn7f@E?V=nwJx|Agz`tes&MxM}I{;2H_4U1t{a#>0lf2IHYqR;*MWJJOSm8d`3KR
zDe>ghP>gYX&`#oM4uHI-uO&XXlK2p$edsX&elyk+&zu1g#Ix4H2nhefkp67A&j|s9
zowI}ZaO8V<J2B=~o`>}3*W!=)5cUZ8E!;$Wq=5X7LRv>{1-prlo(?vE4&r0B6CaB(
z$BhQ?J3b1-Vk_~YVt{lPBfZ5a^J2K4uo0j<OOW@83rUO+e9{);lji}n!BV7u3j9t*
zTBjn-(@>_<J|<p<FlVeKJ`-`4R}!D~2JwowiC1oiA$}wAPo@x`T>w@AxSx{^t^z3E
zYNUHE%5z=>;Cf9d@%aw04#59{CBzpVL)?b^+P)yZXdJ-x#cRMW;!AcDuf_ePtBEf|
zI+r7lbxk;Ei?UvUJgz(jAf2m_&efFwWxJ*x!0lR;?OMcNzlQj_HYk#avtc3FMSR0J
z{On&bK$x3Q-kVS!Ja)WsJ%Il&5dP)}@h$M*Gy}8~--`0z2KQf<5^tUY9wfef3-KNB
zzXNf8wH&IB1E5@6J|?~s;qKf-{Od}9yzfFjcTWKc;@@l`zGoHjZ|4ETy|<P4cOHOn
z_l+a|J>vd;2k}<8{{ilQxR?0;HN+1fjR%{+gT#MaOZ*VRJ`Dedk>0il@gwki6nQ-6
z014tJ5dMkH#7`pJlRJr@S`OZZ5oR{=GsR#V7Qcw|>{rCwk>7KxU{Ib812f`155FC_
zegS29VLJ{gA<my`iC^*nl;`EO#IGRkD~}WZ1=oMsMZ9w%@vCcyUz-A=#INrlegkQ|
zfinCR;ockvb`rmZvc9z&8^cS9-$8nqbNSuTIKYcMc4dH##D9m|dqn*H7sMU7|6myL
zhcm#(#2+D_kL$tPIPkEQ_!Ibl3b#);5r1|!@jsUne~xm0jy(Uhf%uEn#9zYiKOl;{
zV-A3H;@h#vMEJy2pdE@b?o&I!ZWz#!UKiw7+larOPW(*<*h0KpK-h2BgO`ZA;n$6D
zJ#An+8JtWqTn{#Z1Q|1fj8zNpyPekCWE_x=ugEy>CgW-XUy#8(Y<%;`_?KYO5+ReB
z4G=CcolMq3fOy%f$>bm$bX*g}eP}Zd#;hfizYKguCfo`-$P^&Wf+(3n<Xwcki{2nJ
zU^baz#2<(}N_LVdEd}r|+d-y$9Em~9RG{pY%gI!MYNUht*3`m(FyalV1;}&gIx@p5
z$qYvshJQw8#8QBC>eiE~hg<yyY_Jso#BYFK!wxcy?PQwPkr~xQrWyG(Zy__<13n`&
z=3X)fOeZt;7&7BR0BwPJ*Nlh%c%(659hpf;Z&H-Z<XvQ@T!jM`@S8RpJWghME183b
z0r(w)c!#14Gmy`WkIBs3NoE$(o%Ir#!`70SO#or$tRi!GG1yFIF5=JIL1z9^G7I1a
zz1AE7_l1aeB*GqrI7e;5pZ4PZm^L!U&HxC1+!}x~9lxDSYdx7o9b^{c`h-fbi_DUC
zGAAP6li+qT%6jrPGD}yJY%-ctkk+XPcj^~pPHQEz4E0$yoy_T@L4wQ~>u~rLWjymU
z{BZ&5umXN75Pv1&tttR;`w9HcM!2()*Eu1ufz0Y`um-^GJfwf#7Hph&0LpOwLb1gm
zb4;`N^5ay+C^?1R(pVENcZKpBnwzRy%F11NIidW<vWDhSIZf48MR|1{SGWWC6XEje
z3HXz}vD}b9i1S@7As3u#TnNzA+}IEv)zmCrdAC*OZuJEMp+MlI?$%IoafmnL`=2>Z
zFMFnX*z3&kc=0#f8J;3fhShAmcY58~Zf9mz$&eu>85wfp-8!mD;)eq5#i8yFJB`v%
zad(HynV08uWqNLLd%f;kJedgUat#?b#8D{@QZ)<Oj{Hq#J9c53Wml3Lnh)fr>Y+TU
zsk)+H-45i&KA$~TH4XV8m((Y0SFRdSQK|ffmPS;JA9T9i&Jc>!ZWk$nOWQ@icNSGw
zH&s_(k?DQVn^{xr3=Ro8i)*;l?W}dW>m`OZN4@P3(eF};(p3GW$M5$93(7s7@&a<9
z4jm@Kn{leXfQHdHnnA~)CjCk;J>&eLvTEm~%a5{;>R+Glxi{I}XkqkK2fn+n`rfK^
zIPt>~&i#JzX4G)Aok(k_xb?dgE0@UG@>TymS28TA=Xw4w_82lKgi#qP;fC?tG=j?m
z9L{Md;ZT4*lUH1}VzT}9Rm1#|Bj>kIPTx*m@eMB@Ie*Fgk@h&~*^P0q7UQ6fMxzC(
zss_DW$ITc97~r9>+m%0-?XSFwa@RoC5uY9^2x|4VZZNN66#w+Nk$#_hg{!!>*tNp#
z^N&2vn-RETxr{n=Fus>x5y;q%i-6aAPRE6dg5C@KerXE7{{nAtkuR^*>u!~B_$tlg
zp%O0O?Ou)<7-kUE`)~M5;ycmbgHYZH=<B?_`^$-^QOYJGSjrmQ=e{ak2RwjwIBxa2
zJ0w4Rb#LX(a@mc%QQSS(JPp1YjyKfGrF=c5Lauvg&-IQym(spk=BN9YIR_H}W{rH5
z&xLYUhxaMtzGZIN$G?BsvilXYf06rN^Rm7&|7=g;`j@$FkMo`~f2W<Ltn!>o*`0ZN
z#(}i;zGbdTw?D#aJELEF$Zl7BcX#Zs4%{!4w_jsl-dBb0r}q^0f8N%?ecIZ+Ph0L=
z?tR;OGYV=KTMEB^KaG9Yo*>@;)yDSpeB1LB=HaJt8vIb2hrcg734dF%TK?9G?IA4h
z4rz8gjWS^Ne^)bt1Nb?L5_#`5Phh**Et#U_0j=r12=`um*QL*^r8f+3&J5Z!QLyMc
zvcOw5!09{Ro#+2k_5f>uGx#lf7f-8O#hCBAD^$m{;Mu{<=`z7>%nSxIH_FvEx!Ts}
z7Awni2Fn8X`h0oWXNB^&W(K==>Wt8;qHV9cQoW1p<%a={cX>~{xd?My4b|IiycqLb
z#i*t_-urzw7BdFk=8gKTBiGs681|c@Z@R(DT%LuocwO3W^MQCx=H~_b0#}S`44dWI
zfhB>^u-+G8_UPH8+dw^<p?ff9z1rg9HBM`^J<pGpTK8RURo`*zhb?Jc7D6ieUThB)
z*Nz@tD|p?dZtrqQqPF-e%ei-SJEiWo)pzIeC76eLx_kbFU%h!0e}#Jx{;KavjIR;w
zZpaVkyDA#;p|(`ixLwd-s+%X6@!Zl}!Y;SV-Q0JPA1=Z8a*trlpe3Rcw2bG*^6IL5
zSC)ahYXl=~3*w|NsxcnNGa_L$)l@els6+_2H0Oui<u1t&dD{t%V6Mn6&l)t;@6HYc
zvfcinMaNBc2k>JW4%Y#LorB9g`2phzWtP|abFF|soENIg^SOO_RgPR=RvzaCe7O!s
zhR2h^E58%N8|oj!yLUFl$4EAh^hS52(__nRoC69n3kx&D#ojW9({-df(@}QF&>6*U
zlauex35J~}ke_2rrqkuaOz58I^XHX>{9d=mJM6nbGsFFYIz7C1+SBZ4Zm*<(v1-sy
z?Pe0@1DOq=aY`ehv74b;;k8^{p2hM)mcAJU7nvBKvW>?u9Km>9=ZAaW(qlOf?XE9#
zjRl=kb+*h>e|6>3WlJlo{iPn4bBg7&rZ`=mionpC`X*7-nu;r1W{;^X)|>j8p@E7$
z;dlTZ(xPCnb!2r#HU1x&g%prLR!vdZ^`xz>IeiU-YDSwewSyXbPTS>4SGcIAHz1cv
z{Gy<|YfMj1&sX?;k#*2y2BW?;)lHbY8U|qm;wwLd>Xu{B$fJs?*Vr`)!K2~Yb#8aq
z9dfs};w$WSuXBe6sFQ2a9;bFVyYt-=3L=D1k9@~P^eH?IcjN4eT?WlxvJPwh$h*lM
z&ToJR%F$OCSE%DVX?-{KSHD}RbUFJMecv*)xLxIv?F3Xg-5~iPG>&vXs_&LBEzplC
z!|#7oTYaZ5`l{bonC1pd66vh_8l<}X4?n7oKdLWSe)EqbTx)B#eU<Mw8osCFTffr}
zu;a>qpV@s%-`k_5--+}+UA1>Q-`gwS?SSuTZrD1cUK6!m!S8F??`cH-ZW*OkqEqhR
zC~t%QUxE1?tG6Ij|Gs5_yQ#&Vd_@_M<!Cq`uPx}Am?30(#yl+3et=ut_JlC1sbyqE
zIi_ti6K4Id_AC@0@DFmk9Des~m&@`x4Kp6vy8}ap1YEgpSJu!GL$ciN+-ZY+a5l#2
zwOk09VYztQp2f#!0k6;aGhO(h8-Iq&I1DC_tFtpQvOSI*cWx+@>&|gJ>+=r4d*5=p
zGW-bV%JgGikasr5K|6jOqEX(~SdiEY94txtba>2ns>r8jLaa(L8@t??`0!fh+Tm5@
zW2cTSuNsbJwy!ndEA<6haryK7qU=C!ZXmm;ee9^AlPW4E4IMSs_T4A}H`<q5%S#Fd
z6@@}Yg9=K@WuDp%J#9PY<#IVY&uGAKKC;599#5r;nrckb_`nO_HR9Rp)HsMyFh0Jf
zFyG_$WK<5Fm>tY4%?xHw99o$Hm;Az-f+3ZKg_T1F1;W|EEMpu&`+}V=p5U$+R5x_!
zwE6>l9*^&U`e{Rl)(xs~JN!~we^sW-$${L;pvbIXcGzCqcffXWJAT=uP-ayqG@^))
z$|(!?YAdQqj@nmGFAZjL8`P}GX18;xybAEerQMOtAQa&aw>KyX8kf!xFOPh^5zG{I
z#$BHo)IKv4(Pw3HhO?|9r!1$huk3dfbWvHA^{&3GeO3vkKD(P>tMIxvyS-49Z0U7>
zi;TT)1AWKKt=&+`<;uNPw4kkWg<Eb<yW-j|#}53Km^>fox+*M(F}#~F>`S;x-jC_I
zQ<Xnx)Go%L?_Dl;@0#XuxmMz(xYFhFOmjOUPIte>^T<_M&Xtz6(wVgi_1`xyXJ1Ch
zGzb;5S|{v$ys6WJlxfccws&Hq_Y{IFr4TC|{R^>gUQ(UDs#%SA81w~IjfFzG+4srI
zwZgJixa@-We@4Hr#xl>yGuW>kTeJl=OQ&cbd-Rd?li1Jd@O?GdyBYhHX79X)?9*pi
zVv~|?8rWX}NLRMJf8VX29`I%R0@K}jZok`oDPH)Ox^b1)FXQSN56ffo@C@+?x!1b!
ze+ZZR)E8ygt1s(%yKiWJ1)%pO+`paomqEYY-Cr@kTZX;*cu;S*ib1AN0Z4a3=*N_y
zf3NSq5dHfd?XXwBPk_ZG{rvV*0MdiJp<n;+FZmzW74qKgc^|*Rwpspit*Y;RX)8mp
zs^;iCmY0K<8Q%&8?-_LBGpggQwCC@Y?$C##AHq@m!ftl2=0Vc+-5;!W+v)+{$vyvo
zJ?R!ZkA6xHy497%eI^=c;B0;%Eo(pd%QGbP_y^Yab{@fgY7Dv!FWf$p3SQVxiGrFg
zZoUV_?fyWD%Y#r{KL3A`M+e&KcB#i64cb;-v7*aK&-FR7=-N**zx;u`Rd>PLzYbP6
zSlnb*OY5OnEn{B8h)C<kFjDY&i9pD`z>WVyfqs{qKVpP0Ag&<b>wnYRPEX;N8lRRt
zTCjSCjlFkdW4y_%-8bT^-|QcIucW5-%dy`jijBU03h%_H<=0-S(tX{G<$HzbM#xFD
z>=OK_XX7Z*g>YjpKU_mOuMxUL?^<L8AIKGXjhK14^)OWBFjS@S#6Zw<nA?4rCm1-f
z(Vxk<*^f_tUobR<9m>O*{=E~}*9XVGQ1*7T|Fig|7qt>CmA`+6L84sr98r427LUen
zff^{i0cFX4+d&(WwRzr1SQ=&Hq7imtsC%}SBKj0o;u5;4y6>W@Y19Z)QyqYUQ^Htk
z!`E5k4!@mUkX<*a4&T^pD5#$!SEVhJT1x%yO1HlxKO;LkV_?HTw<9C$&8eFM6+)JH
zSF7OHx$f|0e;(|?kvWW*-M^S!KKpTrQ&v}32ALlS{JL&Vz3tLcTG~=p81z^8a|`oJ
z5E|)ve9nw;&cIq^{DmZ36l_!RxUoi`JR{fP%g=M<pE<>W6z)RW`rNhSmnNSUZLO*X
z(?lKY9a)&X?KjCFyh-qgyD*iYg+x)FfOfJcwmRs`q6$~@d)Hj!bvlF2kmpK^1O94%
zfUPS%A-Fib`}bNgYnC(UE%!QI^X~8m0{%PZxtzESI``wQ@4n}uuRKE2<Tvodj1OJD
zu_0Y(Q7%<|fy&vd-%0`Hm6(TBxOO_8m=*+R9NEx<?#Fxr<Dc|3>{2oc(yxcfhaBj2
zxP7i%&+z7kc?}KoPMlXzn3YqMf#04iFoBHWPEYQTsz_DSu+Wf9SANEz8MWo*rDq+T
z9SY`zb8@}j0qJ*0tt-!);kKLu8|E#U*HB$i)i9`_$YPHx>)ebSw==)ypkX7&<mZIl
zj^Q;ECO3^7-Mup}SehTqug%TMCHc)g==a!xf_2W1wO$dGLQ|cH-`rS${;F`fVS^gM
zHThvtquq_r=w&1~HixTgDqvHpKutx-4r6~u?9aVBaj=JA&DerniGI3K5VNe9t$BQn
z$;c~qIEwQ!OpDv?!HDp<>)`_T3{&GMo-Ypg&+}p+qx;dpEiHq2sz1wTjIX0&Y`MLd
zC?ET5cSj&swBYFAfxfDiaFuW1;KSU(a7jrxXna-S!PgEP9IhINJ2zZc^Nfr@%i!+z
z!7YJ|Y_Bh)$?N1%>7=kY>2Av{miDmsVPt(#hrd6bjNQf~=mZ?pJBQlP9<rQ}CIDD#
zxT}E+4WQroEhEty=ygbWv}>xJ)%H*byQ*Q36EhqPMlD$L$;xOv!$?^p{#XEtgDhI4
zH3JOS=9UsX8TiN)=jxjk_|{^VBhw!)ZOADo8ede9(@+}r2b`{Aan2oHFfi>ruwZyD
zoVzcxJU+kQa^!}~{eGXv70Pls&UNQ{vvYH^y}4L4rmseql$DhjYhYQ~K&#aX=Q=D2
zo-0vZSs|BalTIM*=<|Y5Mz*Uqzn~z$)|H(RDk!O_5!W)0!%|nv;VFab7*}DA&l?J{
z`);o<r_k>Tof67%x!tauP<LDJ)vbBi*?AQe_IE2*IH8b)y+Y!-{DoyYxAfZsc3>q@
zjCJTF%(~%<2}WK4vImS;tL%zkmr-{`YUG)eX+>@DjbZzurB{1Z&FNR(BCq3~Kp<RD
zczf?&r)>RpC&w6XrN<g$+^&HRH+z~qmUp1b{qU#}V`Ql{X2hshOG>h{7Z=tS9=~U|
zw>WXP$8$Ffk&coQuW!NdMz`~Br@IL+RR{JKp2V*gKZ9Qj8$y@L?=`lFSr%_Qm~rwO
zTf#EYVRmuWU@i<`cEoiJ=KTpAZfM3xkzMRw(yvJ96OzCl5J_I2=>#MZ1v-=%?=ZoZ
z*z+r-1@#A6*65Z*dG#VTmgV+lW{j|W-li&7j@6WtTf&|Z8JS+U{DN^wZcdYx<Em=%
z`hJQh$K%VgjKk@7c$`=tJ799~IQ&kBv9f&5Y%iSiAbr_RUmo1Okoyhp&h!O~GrR@f
zjN)5bX0_n|`6vs1V%gza?g^M`m*r{mO<e9n9w=g-ugzn*s!iZ0J}0KhY{@M{a&t*;
ze$CD0%eL6z+PiQr2P3#k3+I=@xukFzQn+j+<qTPFm&<M4hqdhN(WB94u!rKS4ZoR^
zW7`?ZdNnOwz~fEbYm@BLSHEi{Gm-AAi-I?Ky*CAmc;$eqs^v~+zBAJ~emuVNog(+X
z=eFRDz9Qd^!KwjF1FE|B6RbCyloRte{)HiwGk_-pPend7iyWvF=<`sxY_Hplc5h*B
zZlSk5!_Qc#r?2nY>@m5S9LdZzp3S}z-`=;#f}FPA(*}D&9`^U^@LQ;hF&B=6`Y8j@
z9&xZHz&t*lYr+-I`tS1?Rnro7SLEPrf+tq3<uD_;-021KSd7kauaTl}vyyxRM|jK2
zy~9g<_C(z_eZ85!kK2yv(?@U3EpS{c7Krs3Wz}UF>*exdM?tP_m9RGpm$Ns2%&|Ec
z897Id3wt=fGC#laq4n9{<C>qnetjF)cuHVtfe|AsCnw9k2$p!d-|1)EK)cA<jox*6
zp8T3eT4T^d0C;{~Zald8mh8mfX{=7a`fUTpgwmqSg6^J*1#>@^D{Q?inA?9<cy{UB
z1r^K%nMK{Z<g&X5m&jAr$Amq!R=l}`_$w=UTQ|rr6Xn?JL21IOikx&Ca~Uj6y}J#4
z7kkc(^m<hnwYC;jn}~c@cb|lRK#bBSVtiQn;9$!F73n)JZ7-_k2!gg(7vZuS2Ev7U
z{-yQ%gcLd3F1MWHu}}v;P7X-v2Vj*^f!)BN7*&nbf_hBAUl`;x{vUmy9OZf|y#sOw
zc;&yM+yS}rpEq}aIOK9$ciaC7+VA++(Rgk8`~&T~d%f>Tf_?9Q)M<fnd-m61YU|y1
z?{`TSo=deq*J2DDh-VTC1P-87Lsx?iVLxBk<*TlN9xGZJ^bS0v%`Me+M${P5HpI<^
zAW%5?tm2#jMFWeAin9j>OUsX3d}MiPa9}p>@qf<1kUEtGKQ6)S;Twdnmr8uh{*p|5
z%?p$dC_8MyVPyl#0@Z`cs;bILs<Ut-PQ_(`8rx}5O{uTEqI{6AvU>1JUujuoxwmF;
zbp_!>dAc4BjEroIi$PS0F;NeDy2~AI4&!YJr4zM69WaAb*JR;edXOtr3RRWmbXcxy
zTyGpSYSxhI+a}EAfyF-`=9}5_O!<O(N7HTPw+%Y9mP-cocbj_9sF_3gA>Xi>&6nTS
z<fvazK5NuLX6S_jN_g%B#3(8@X}2L46qmp$<r_Au1>;z32_0Am-VQC9s>-nR!)Y=X
z)VjWvF<!*wQ26k_J-_u$a(G5%EvI(Zvc9YFSk3l6vY&M?4QA$gGacC${_zQ)<9J(G
znHAaoicnddM@$i(oGjbE#$=1E9C2-5pBc;w*5$xpnqgVT!(<7O;c>vNY`q<>G_NWE
z(>vnZsgwq)@=9$F)CupA^t|GR)fm==#=MHWvVH8*eN#7PcpSzo?JnhLH_5RfgKsTb
zF_%Unk@iS&s6#xm1HSdXKz0c4CYdk$<x%xxa&edCmF+jDDc!BSc|X~;w*7DA=Y&SN
z-~76FcXa$fcK<u|`{DdHwYB|U&hH1?bBj#mKagSX7`X^f>~QEBqI!$rLJxC^Im%uM
z<1psF104FuR+tzj)z$Gu9d2*Op7*FB`?{p5U3@3iwc#bcQ{La*o8`N{UcTE)n(%hD
z_uD#P!@C{3Ddl)$;FKIou6r*$v04}X!VRq#)~ozHx1JD5pY!T}!^`Vog^bD*!LU}(
z$t$YM@wOmG(Hn4t=42JzS}Q@im&^B%N?%REMP+rb`W%JZyzZTP98Hd`?YTlx?Ya3W
zyr-YWzF9rqY~-wI5ks2nh*j4Bv7*bQrsj_@tF5C7)3Io7XhAouM-`!bbKcSHwZi_~
z>7%h6Drv~Q&Y9^5Iq*-A%+!nS66mwJmNz#LZz!s6tM*NGWcVBf;o1oYEb!;aK2e^l
zEW8{s3SF6w?lF4t+TfDxkmV~3wpI-ofJagG|6v6{d0luP6V@G)Ii}iuODu*DyM&^p
zHFZdiF<52O<r_Uc*B_R$a9bcZB1zxK)?Q`tFF0UAZMeXJtb>KBTXvwGZpXW{8Yf_9
z;^Y;2)jnjE9!BWP3VR|IyBW@3i2Xs{0;BdQkE&AI`XtJScS9p=7LE3)7W+i79NFqi
zjhDAdtH{^y=DSNn(8~veO5L-EjPW;ks)}6oecpUM+~>QBsyq$;F+*mj!|w6ktf@#+
zqjU4Cnyd123lFOw)D+AJ^z-8E{M_k*j9}BC`ojwCVBOI@5oN64eWEs)Ff<^si|)so
zQnl|vCq0SQ|1i|3J|j!}zNQnL*>cz0Pn-Qv^<941{gG`)8F<w9m`MKN_Vx?CT!#5_
zJe*N6mdLuG2Yq)DEF}lx%<tjy3ou-cF|C_K(&XXZX~7=SwvEiTsmVDSEN{h3DhmfQ
zf}Q!{3OuX!%hWaj!Y)z)Eu}F$!nD|nVX+<BW;B=K!u(n8%!m}HF>8g_eFQ9gzL6!N
zaQWB@{6Fj<e^aJ=K~^xo;(#e*D@?`M?u}WFp%r50^m>MS0)aW++AAwvl?83r4jN`c
z!>vqLrpvO1`+Q|}4NRW%aT+Mw?JWS%XzM-YV-Ffz(LBU|WkK%90mz{2fT`mu4$01M
z#gt_Z&M3?;YOOD=ecKEhUoZ{nc#+<8Q#ZL@Y!_)=!3$lz1(vmg&_=z6h(;%#cl*<)
z3y9s@<&*$;1@{hG`!TGk)`=E&@aiCfF?2z+0Cr;8;|%1uGT0tfr5KG^M`pHS4_PiV
zTsZ-!r~5w=)H^s5;q}sL^^t)^*(DX>>bha_Fl@$n%?xhdEZ4*8s>2l}*+sYN^xH$5
zF-Gk*Aave#Xy~WY8k}3Z8RHat?(z{^7(PFAhhfkRD}N)b_-SN9E*mB0GJIf<kk1d@
zHRAjw?5KG1n@7pQ1t-~l;FjAx9*4*8&K#8K_G5<}=N!B~Pey5m+Yh@d45RSNC>1}4
z#{)G5I!h!1HKjX}jUb57KZqS&5|ik*pPg8CH`>dA6XGske(s9zy^J_4m)GHPIlL|l
zmJ+vKyJ0oy=RP}Byx#t=IJC1h9kkbbpP-rqM}nC?mowAl&8n)-+ut?pPF>h*iNNku
zMiH7VzbVL#Em#w_^qrdg?v(|@RD~P1Tf73qxGgp%n0#d$LQGIj7<pwf?OmhWPpotY
zue&P;3#-zcQ{({Uirlp#uFAa);*%oda}LkU9Pad)A{dTp{DqzogUUv@3w<?C2T$nk
z7*GW}E(UAWZ!rL|ufT1Q?#&VV$ZP-a?$`wU1BewfEAc(SnPC;Uo!-VvE^dHTsKD|M
ztr}1&<F|A`RcqREs5*n}H?`yaQb3bvs%Y%{sX`Eq)BEBH|6n6w*FDUedtjnLxg#j|
zEn4mYXaoC%=*eED?`etdZ>IY3R@|qUycyau43Dk->#EmnZEaeIC-5)GwPVen0}DM?
zdQLeaiq%lQ3!6eE=$9I+R~rl0R9D~}QI>8%9mp+tZdZvMDjJ33pJD~XPVE}YWzP3F
zFEA!rR_QD(@H@}QEh?%kD*D}OJQW%412O4kH#;*53mlUM;a}D|$sNo#6JUdT#d7bo
zT+bf0FvIUF%-}T=r?Tib8@=Acj+ZHTcIc`bGGWfk_~Wt~jPZ*+CL?6O-`?kQ@s6*A
z)^-4TKz6_EnPbn3qSyEX)e+CQcw<pjf=ak>a0=ME9ERD*!s5!(+)z&8qcV+^mC5(^
z+5t5U)!V0p0~wjcg)`5}2+f&4usrxupm9*;Xl#xc1jK1~oLDb)dp-Wi!)iv~>~<C9
z6b)*3UQ|9XuN)7CocF}PKlU_MpU^+j+fz_<@D39jZ9_{CJ5jYOhggA(_1*xNVk7FD
z5x5Dsx8m_Az1Sbj@J9XF&t&9gaD7GrrcBs5<h(N>%_8VFjhHzP#csmMNFS#dp`KzB
zsrN(->T2&vPY~5dXMMDQ4n|dO;Gf>^&2OlYy+T=I%8FCJ#ihn6FHZzR8hCXyG~*8|
zi`?#<+|kFcIDYi#<HrmioP!<FqNbSzV#k)RiNNP}56rRLJ}bxJ@kSgzw*zZPw>7`-
zs{7LFB5CH@(a?%{Y105Ck~4Vt=vJhOgmZJ;NYgLr_}t?(T}iwp$Kl24GWkNnxWHFq
z-_@^+s*&1gv1>?M+2``0ODwYWA64N|4Ripu-OBK05mO@^WbQ%Zp~>x8$eyFHw^oK1
zh>Y#@3KgbUb3`zISV=*R$6&6{?*4Z+HUf>Orl4e4zU&9}^JDCV^z-|(uQJb5GOSn*
z8u&BZL3g-#ScxaE(uawD|6WoD(V^{sVGkB#*s+{)T(}8#&po^HqBvux6AOU8rF8Fv
zlLsRW#(N{4)5bi!3(K0hE~ms>IAEw3R$#x=*`&f-lbZ*vB)!_m9@bRkDaoxaXf3D;
zb@v1#xmm-OjzAQ|$V3!dcx!SpZuYq__U*5m3y(Z`SXS=0nGZdbnO{?beeFK|(*qr4
z6y6hy=u~t`-;A;!brgScgvZm}r+$vWW7`ayhrB1`;4peb&TpVrvKdi_=Sbcrn2Eya
zjli1T99U|E71zW2>a72RwJ!mXtSZl~bN8k0t$km*s;jH2x9NSGt*2%m1{j-Vm=P6P
zWEnt6D~KSdttN^Zb@CvJ&*e306yGz3nB`%NQuBBc%rnVEvk*~fvnY9ai6j~&i#6~2
z&$)H0s;6fdO`zx0x%*lF^Dp23pVQ@W(!YXt2}Q<_$P&|-E?;+@tRo}I2M(Nd=C|9-
z?5~P9G8(h^`}QWhE!$bmg>t4tt?8J^RzHWgyRytiMwpC@&Wgpe&O8Rz_XY7%>T|T+
zx1<JW8^<iuaD|;8A~5G`2<r#OH%{t=ZIrV)bXF>EpSgI89x}!bPRuzlihXO##WUO8
zc*L2TI5=j6*qUsIa*Ek{5Yr|V#IA`!cDWnfnhV)7i@L4P1|jnWQ<z=s)tEsfLwpH3
zsU~Fe0<?j|V+v3KMjQV3CJr%Kq<)9m4FA&<;^h?cjV+V$V`<^8!98O$!Dz^y7~VCc
zJO4B@wr3DdjDx#|C+tuZ;xIRo+?;kynT3PT+94(xc3d&^9~rZ<>7*6oN4lZJZ9`%q
z``I8@{VIHnk$jngd`W!p`K25ou48$q%_cTrCEKG%i?b%g^2Z2&$PQVq3BJ44RIo)Q
zIIW)KC#}CC#sDPaI9syZg~3*HZ~>Yx+ps~m{(<g~Q!?u3z6%DzsS#b`<z7X*T7f{q
zvo9thb$A|@;`wHSohB_;YhnfWQXSiPV6q2bYW3oQeXo?Z<;{2Xd7*<vd_u4i(}INo
zo|7{~k99DKkBKW_mnic#afHu9=g>OyZTOZv3@d4d(7G|KT$qEHaTow97&|^0X?lWf
z!+dl>QOAi-z)f^^C#LA^w4xbLs+Rone^pKCz%&5I|N3&-QlB)mr=6tUXy{2t*WT$`
zPlrQ)VY%<r*avRXUF>qKs1KEx`s$lhm6aZXHE=<4Z+5I(Z?zn${y94$&ne2B9I-!F
z=V~M_^YXP(2WJLF07LOe<{=p;Bu#^=cswR+O{<ykd%oZL$M=LY>Xh{T=Y@wZ(;o^&
zo6TtGA-(iGb;9$a4G3S<K!4GET*N4=B<ecJR5aLyUTUUR^~rD7#nfhZN)t<^(o&n=
zw(eP{tas{Q_ocPvL}D4LD!PuO!BhWS!_V>afk4o+FRo#}5>How-|Q^Pdyw<#*U|n7
z99Kjm#W(-UVn)x@eDCM~tV^p<Uf>U`EDa|IDiIw2f_Kz&^2?i@+;FG3r77O>Au&MN
zviL+(9IpKgXB*-fPIIY`PeHTxN}6Nbj0iwFX%lz?T|AASsd<kh0Ief8F;6u}783HS
z<;h4lUx6{Afm-rs=jQPOLhvlI)5(5K%2lOQ&WI*+Nj6*r+f+*xha(BdcF}Z|9Zg1!
zTuQ3uB&|Q0tEQt6`4W-gVyXs4t2oS(Io8Z2syTS&4Q8SdtD4Nfb4=$~hKqmas!UbF
z*jKn1I}Ee$>15T4MEM=LY9d$79gY=MNee5u&HcOLFfRhV%F`WT9<5;x6^N>Gji_L&
zG3eWP99o8|Q0b?Lo%R=OUkv2JIgwoMHhy}Eiz5(AJzx0NH&<8Jy1e4LvaG@7oe9uD
zuJ0$RSSmfesUsYppP-S?^76(&A4f1h1uBx@eC-)u0q(G^A>k7jCKl*JW~Uiu)v|6j
z3o*79D>P3o6=L{ZiWQ_l;p3~TD>U~ePR0s_*vYjC`k5%imT<WwuzO$P`q&tF@vYD^
z-b~oN7`hnDqQ~w7%?oCw&JkUd=k$VrIaPeVpO8LqHK3Ek*P&#@hzg7eoq)ZEMiuei
zDY;E}r}07}t?*Y$eV`wK1Pntmjed$PfY1U9ifLACC7V;MifJlvy_ZUgO<B0;m%NK-
zKX#7|l%f4rIBFE(=P~*INjZp|Dt~Wcf(0BSF`6*ogpi%fs@kWGkh%4~t!BvBxp#C1
zKee$v+f6zr_1<`WSSUinttYjh<VdP^*Ik;5Oi=siorvpzeiQ9iAfJM2N~nA&txwKl
zg_)~`Adrgxt9+^9s_hi!9O@34GjL2+4GWx3?XK-JP=;J9Ys!vHAyG^h5()O+xZ<kM
z^(#R+bJccDk^7%hrSIJqiO2Jae7LB}N+=#*0T?HFI2ExBMPi@NsFL!z5loA@+kz~p
zj(kpW_!>xhY|^`%ur3c^K5&tqMi8g}7-SqPOG(Ez4tIto*P68+sOB0(2r&y+n^5?}
z_|2LzXMG*c7$9gkPdDUB_X|U};Q2Y<)u0%cd^AG^jgdic9IZ2u`vLXA`oD{T5MkbN
zZ5T!#AzG&)`jI>cFjG<{5aBfJC9tSD^X^VFmbK;vfhB@KH9a_IWn<<}cixG|oP}|I
z?YsQk_<|EV11rgTapu1{(Qv7-gYKL!{g4E`AC~6n)*XdXIGT%XALEz!sj=-b7Q<u0
zzCw0bVFQR^J(}j|w^u;h_eRJv6<-GMXC!yKJs+RulNWIlse<P)d6Eh4!rOM-#qn&L
z26)#u9<$$!gVB4`axp7dQ*Zqq_hsCW%nVXpDLLrHs@QPDhQktjiko#75F&%#`(XQV
zb-~ZQy7SE1m2~#Ff2ng*Cx`Jz^P~yi8^XIn2CQP0!4d+AoJ63Gwu5K{_YjBXhSZqo
zdGKIZurMsaonih|W=Xb{8j+B0hHdj`>BtZ*sJVVAqlC<h!Tsx5B|mhebkwxNrXGqc
zmxr#KnYnJLd^%DzK4?L#*_XxAv7D=h!`o&JGm^IYN5bJ;b{~Wt>w`w|zHF(oTq$Ka
z-}-y-W1~108Bj;W_w3<Y<!)yh8lVE}gF)(R|GN0sy*$hX!dgroo$7!!D<VQiH^im2
z#G_yEge`QCm`xFh!)r^ninip~CSU>3Y#e4pO)7K!FIH*pR@c00W^VG>%CX70*`vdA
zZfg7Xlsm_omtJ<|w#DVSx#h)eS6<e7&+KqKKiOzZ=HtVFBa_@Kbc1uy3%&$$@2hA{
zpcNF@3DPj|u}*9wtxfnR_{XlCPfM`8ZV*#YKEdX9bgU^-ViAu~@fm;ioU!AkH{7&C
z7|-F>Mc!y2I7qdiOJ`f3Rpm!XnTo`d2JTn^hvlr4_)l1HTUdyb)kLD2giRAN&YBa$
zEASqXhU`!B)7(9shcszDCAg^-ZY3c7|FRQX@i?q*z3Cjr@k;{7f97_92<NqfnDNln
z{Pb<`CQ(V5s)QvD=}%Q{7~D8o9hF_Bhmmxu%55|hm*5n}f7!ET!M4#g&N<uEAy|S>
zs!t(=5b-6wyvkf1UK24+`vgma`OTiM@2S`K9HAtGWsfWZK}D-6T0=L%qqO-L_U^HJ
zR#)}c3m&HuTlb#9gYTzjE}>*jQ~o=fj1_zir?gz6C?dr9)P|vX)HxjoCHL@2)PL6^
znni%t!fuYR6URc(<&AWAL$%MM)0<o;7%n(%4#Q2blP&OP;MSIrw!USkHPzUVtZq2w
zhdn1--=K#W9%W$sTJ$Q;{<SXkUv{;h`=p=SaE@Q#=We81IOa&Kbz-Hv0(1Y4z}sjK
zDYVjAF(tYgWSYQ;Z<9nz`l#bndknN`MDOwJfAl*0%@&rPd|bq0TII_U7;n9G1$%_f
zQye4xp3Z93$%%VA$2_;`NmkO;fw&iJV1^dDnS6Wze5kIr{+-JLimHFrUjXr4@2#Ch
z@%-o7{C|_%5vkn|{cNqRatC~#bTC&J)I{v^JTO(a0O+j1|KM_fq~51oP`#J)ihRqs
zEa&mam9qR^1?nJSw<n#J7(+kP$bpua;3{p_VEV$UF-wjA7$p}}`evv9iAVPNJSz9G
zpRo_9%)O+~TPVFn60-A!)n&J?!fF7<Z3orI&t6c)esSYd{00AMb%a<)W|ppi7rw)%
zr`4}tSRJ(qc)MWGO-x|?VC3Pq=lFsuyLOI;+b^T}#raRnFQWaV0o*{RsvB0UjU7m4
z-C!3<VV^&n=BRWXp_aO+!supn1Iv)$vDUe~PqD(L10*FOJRx*8-3w7g^%x^e15SHk
zX7*_CJYSlK=Jv3mu#L4n=CHoP>;-`f+!|CHTpz(h_#uAXEu_)CQ&LR!>#a*D|1P+>
zAVQMz%`*A7!Ep{wZUZwjhh}E3)J<XLf{%S~Xt<K69=3p>P^1A#*&>_zVM%Gd;JpE_
zIZdxPG;_k#xm_y;qXM62qV677O)j8qKb7qy6q7Ur`41{S!Ep-iw$9xMArnf0wqMHe
zpq383DU{ZN$K(0?JD)9y&$5Y*_NDAbB5pbViL)x`$tJ|-43KWRT!AA9S!7_N;Q~83
z;30e>*ObygAi7jdRiu^7_h%JJBly_Rkm4XCDYM_F=ux@^b&T2i%AL=#!-oM!i{Z9~
z1ze6Pj(WKD+Z~+W+8W3V^wW<^625;KBqONbNL^5?a7_Yn^d69;Kq$F!Pv1iLWGG@t
z6@y%o9`X5b!XyxonZzc{RQBW2=wKEhE^J*+_3a!hh9gQ;iG+(|JNr_yZX=*Yc5t+0
zn&I^LSTv~0PTb9BILIKAcjJhAVMoTs(_!;?#2jBNrA?c`0$x=P9~p!dH9L6Zuu{zg
zRc4#%(&D%of&Zalgpv`{)ifv4AQrt5aWu^}BgqhWj2Ml7qP6SNUg+og;rqjVAh{h=
zr<5Bpxj?g%VH}XM5`zLTAB<@57qH}`UKkyHOd5Gxcq%`z&5;^gxV*k&rl{%sTp^oL
zTd!J1jHxH~1odbz3@!cc2fvWc4E1kW6e<Cd?|(UD;anm*zvI9PE#@D&CNdN>ZR;Ar
z>z`rkY!y40PC*BKF?xs0t+XbQ*ow)w8ERRKkM<xgd$J(m12M)a90#ujO!ogX7!Pjk
ze{*GPuoOj54U%nGfd7P3tuLsV(3{<i8b1>#As9%$-!bBBs|06Hr#~N!{=fWGsa7i;
z(aZ6PbaalGcOlZuMbi`U@=hlbai&&ak}Jb527rPcDsw+R!p5J0Ugj-)ZKgG@ql)Ru
z^^zfVgC2q(gQN(m<f}8fnZX2GFL9;hs)&`y1(YVz*4I^eg~kLD7Et9uKF|i~jGu+f
z##NRgjX7rm{BflB(|ue{L(=;Ye;#<O7W8pH4{XmUP<MA=ulskyVnA4a4M9(7S_?ZY
z8C-=4h-|W?i=0b_WS6Nl>&UuJhFQ9`?&a!u9P^KKmRzmJReJO+%{F>NwFWd0DJzng
zyx_!Wt`Z4RffIs86jv~W-xpM+ifT(ZtsK#0bvGQarW9SfRDl7P1(gxWkayy`ZHz#B
z{+y#Yit56I6f^rAM>bzD<doBA!n1(6a4%DXN*p?iDFmho=|-qOd~d`}<YX)Cw%+I_
zbzm@J2F$i;me81NChv9gW{FwukRp@)p6>Xov2YB&d}n=sWBIn4TlImDj7NfsJfowH
zBtuC##l2K0Lk^jm4wHH`GUg`XQMOmtg1REBl3G?wcQ|`RuzI~5%qIY_Wwtw%nbY+I
zT?&reqoiG1Q=qVP@(C!@I87kT>i}@nlicou@d+B-HRC|=Xn{Ld5WEU-k+X<q(-y9!
zQusDA8~U?Y8IH6$2!pWWdNJ-6T39nE1l`OXwM!>ndTGBnVuAZ@CmDSM3>HwFlM$8v
z!MHWJ@Vp3E_xv*c^YTQ?D0bZ$(C<}lzrB>!@vc&1;h^CGI(;5`L2=M%cme?=*1q$Q
zjUVoa1h-b+1hNs?0Z%Fn93`IYB_GK+RKmE>`mg7wA}l;iRAkuH(imffvnfd-rYdR8
zjGl{}kR2Gc-HIPua8;N)<Zp?|B{`P)7A>s3#k5!;gDi3|XySHC0;jM?A3cM$ANNyu
zgJiRP7u(%xb)KfX#WSB4&%kO!lb8HAYwbs%6YPG1_`P-TdlY*&hsV1(3{teHf=%V(
z1#1d95k{S503APaFZj`W5pV8ChQ?MpfpGI{_bNCa3UPj+>)phYQHkiiB>Tc;k{n+6
zGC-0(7ZLI1{Mvo8d>>v=5?>NuW!K*a&y@Sb_XMBX_3qdPJ{1u<i1X&G$OIMFD|-9Y
zy$o9G!{>W<cbSR%stUmo6KrbkoFxoz@=?EkFb+)WQy>Y6*iujmjg`B*a=G1I^bGaU
zmv}6}34uR^KUgw^xq_tys#Q4A!|DjYI#zp%UQOx~Z*tIW@CWZ%u<$X8@=;^|(t1Wi
z)CyP9LXq-VNs4|gQb{NJ7z;ZG4v=d5Cc}|T_RgRyk4w(&Bix2C(g`5AT8i2kJr_=u
zOXHF7M47R^L^yA-W85dmHnv3V)26j5q_TF*z>W1mY{Y8$KU5!33v`9g)OnWqpR#s0
z1I-@pK-InMh1KvO;akUF(3K^62U`-CU4{|eZ71~|(CrBL$Psp+&NL19FcK1chcksJ
zQk$=k9htajUrP?ivQUMx%0OxRz``Jw`)sV1eUt)vmB2xibTbkfJMdtm0rGu6cmrUU
zE)qX1U?=9T8YCwWViX`{=um+!pCkt250!1d*mygU@3#Y+$e-dVI*Y;g{EL!|m?iKL
zt#W=JiO2AB3mp<K_y+iE<TOA*u+$?V;0O|KzN@qUGiDjiuBZ}s7sGPS+OoyW%i%>g
zu0(e^1_ggP&(-D@E7x`N__(FJ-LL9gb#;1G_f^avKPE$&dpTmRU>+0q(xsFy`y*&-
z4yyQh4`?nz$w0co(n_4{H2adJgzeV*?XY5Laac~!<_y|X{DkAJ&m^N06BE(o1Tg*$
zST81SDOtgwRSM-+sG(Oxn){pNfc@pur@w3uBv)6tk)yI2;66W1_>7-|pLCIYe&#V+
zzz2JQL5SXvWub$5LnR7qDm9DnY>0mYCq@Dsz|eMDkrRUnS?QZDayPo-^h?mWz-F<h
z5j!DY?qkc1)k})zKZ7nVGaWFsnZ8&)AM2Ybmgu&n1=c7e#-dZL^+qFGD<s&CL_yFY
z(sMTh4)om64ZzK|?L9TEQW1E8+GhBf+A?c4Qju0Tl4?ldlg-xi)COGSg9GgvyE~G?
z)%#10Mn}#g`XI19yYZb6B)t=Xzz_rpn&xex649VxCWm>Y{VOg*$N&pT^sl2@N<_+@
z(L^BEJY*<XGa|j1X2hrxwG5FM&6ZkCDkEzk{R*Z6g(42?W?#m!ocPRuQO&L8s;p#0
zQuRXrRHhHEEPry1?C31D9s1=kLljo98o_M<SI~$MqoapQ$EobFahHu)vJodgFvE><
zt+ka@BvTxd<+1YJ#&8yoPP`j|`L58_TNV6HW3k}vPYL*^IJJM(vx9icDpev8sp@=E
zCdwG(I{P7M#9C=ep&w(jM~^Cwe2lGLE!j%L)9JAj{HCvdXX|SL5Ba4NzYAlz;4#nO
zTW>)hh#2iesXgLKY^ET)y~@Y(i(FUB7%%j2TX#cU#V&<vUTE=e@N=!d@w1(i%xEp~
z7KIJxG#VFb{<qVkJozr=cyfSpJb99GNE&Q{`l9tHg+D~winjFP9>n1_hBauDl94Wh
z;I=)AV$KjD5p2uzy(OyOg{V)Zi!MS8hJ7M5!x-h4ONgOmZY%{WA;4DY@L|*3FXA+e
zA&XmG>n>hIj>mgkDuW(a*n1lqmudwchJ+6aTt{>#>IAg7idg^zT~UIl1#_?z{`-$Y
zds3)r%t|RcU$tGg)+toGt#4nRRrcM!>*5_6Q`bJKD)~c0y1je*t9B|t16$kv-k=RD
zZn^K)U5t$=A(itTv_ApGU!?Koqs<XhMm<}#nVjkl;n(`R$3zRf|A4673B~P-8TaI4
zimRkl6<5_1vL_(a5T8T#)KeZ7mbZIX<If{*ui6cU_O7Mazf|>v@%)OB&<70TlZX%u
zakTX*)z$t)RfpA(`jP#}4+B{k$Mt`FjjmU5&!>Q(8Pt;}RP}3Wh^~Hkzqs3*Yd&8*
zfa$P-cdc(){ca}qJ7-tFho8OfG0`m402I>$4Zy^Q*_%=SuJ~^p*bk%-1cuLkii(~v
zR-Q*ytGeF$3q5I4eNio(GX{`teFHcQ(U$PWJq4W%;*rESwwjX&&`3D-x9uJx5EWv<
zK@KN;8<AF89NXmf-e4#dnFyyt!GY4<lH|f~^fiUBQ+TZ4gd^eEb&!{*iGDDmFRxV+
z4$*Wcp=7q+jawmnzh>7oT#Sru+l*M^lJU_at=l*`qJ*kjLZ3I$=S@)k^T&1A3vA=X
zutZ(~i^I!ck9-w8A#R0L@(#pSx;OA<j8D`e2LEfU+(3XO5Cmejg1>}{B!XiV`BeKD
zH|gHL6t343(>oCj2&V{0HcgMvt91O%;x-xos`y9muH!uY<B#EWxFtb(7HIJQRw;%5
zQIme*h}3AbC`bP_dAf>}ZpJTVR~u(=z_a|0RdHLB-_*o`_c+$lReoEOzqOsmqqr4+
zIMHB~Lq>cYCsw`xk!>`ny`?k%1|NejV_bxY!$8z?u2CkLf#fooN9^laLAy~5Tt+X0
z{2(Zb+Ion0JeCh*ZHjc`Q(U<%vTKR7eiaK}rKj_Lf3sZ^U3c*8M1SlDKZx}U-sf>(
zHAtsHyfS(dm@6=l0#yuN4S$g6P&6J(n<ShfgR`nXQu$ia*6Oq6Rx=&n>bOaSF@Q!v
zg<S})nDFIzaOU_-yi;_&^#ub_jDx}T_3>ogDrhNp%2w@hO$tg<PBuOYrF|asaprjI
zn;q8_wnUGFgY9`Xfpv0(IOH-sf9QVzhCMlqPdu#5n08<=Fs+~s<O=CNwz2TWX)|a#
zhFPL^9ZY5?4@_p0m{+=53fmD=&$nLSEp8w+gOPNxHpYu&4P`2mla<Vn#SgcSA*h#%
zEr!|+PyJ3j)#L2nI8T)(yJ_1(LcLC<lG{y}B0U_bU+a9WhFXsT|MOM5eSv`B*rFy@
zhEEe2u>66be?DC*jhD;6rC3q|)_s-f>X0GpnNmiVjUkW-Rg1d_041Bspdw2*v(n9y
zOsq(|tWuynR>t3lWLc@XHk!hSClYZ3ZD8X;L>80dvZ1!lFs2%)l&BYtn>uhuKup0i
zp$xBIzB~&)rAzt+ar1bWJ`8xOc!iiolIq(t;4)TXr4rksaP|oH@<Z6ni0762AUidP
z;`#Oc+k0=n{oY|ky_@=FbhMM+ttwZLF1GREcEv#LI9>>Sa0;51y`W)l!H(?@qr~lD
z0ZbQsm@#@lgS-OOQvmm&%pHFpzY3U(f(J_=M`T;nOn7Brc2Enzc_@ycd$WFXpu*x#
zKSM7;G-=Tm6j)%B*E~hD8=jZ;n{588NN)d$e%a8blXRtk5MQ!fplotlGagf<&q+#B
zR;0g{6nS4J_+T)@B$iiFk~Bj}o=F#Bq!;hT^6*)#DNK^SDoNj^o|OS_9_z1c$=2bG
zfy0}wy;ie|ij<O+JiR~9vqgaU#Gxz6!*qUFmOdI;oS9jSFzEoofA#fwiG;&h%`~;*
zF&ojiB`IVxsY00w*bOm%+cB6n@;LgYi#(ue?GV%7L%jTm5z}wHQIBg8!+3`t?_zO&
zivrWbw+&Z~Fz&ja7Cho{y1v&Fo478NZBShKABK-gpyS%%z0tVAf8#<|VcucrQK+kV
zVGx@SUqAbMJ#mO(AA)I`v$$<`09LRK-=linaD!n)@ap^ZZv0($?cpCSKDeQNJu!!2
zULb`Z`R#YOxUNvc-+PDn4Ap=d4mH@&oV|si{^L9oV&?Ae@Jt=&F;aZ?|B)9?_(LDU
zTa!GB@Lq}}3?@;>Ife|v^%Bp7cOk}a6)}F%azZahu4izPrT38XwX(^Be|+})H@>S1
zY16Zkko%g5Ldy!9yzJLDds$B%#hwElcQ{fAPyOTBkFlMbyk3mICe~T<^BMtPDD+9t
zHqlT(n0qeq_*$tpQgJqd5qUQ@p2Oc62sBsScol(wujMd0sMMiJy4GZOV@EsOQZUV9
z#|SWdP3xbD2Cl4dtCW3>l@;__=nzDFOvH3OFKxr}10jc70{L%^cC11m0GzLaY4gOe
z(<@xSgA$J<XOi_MRt$qYMwX2hY!WNC8MbuWZ7bNG<tzK9yYAxI>3vGb_y+)8fp+}|
zBlJtx13ot{hhyVSQ@`E{^Kn6P&~L&ch;}L?OP1$)i-Lh^Ce{NIm=7)`U984Rma**;
zj!wN~n_<Q5*`pJCU;B>N?439|8;r6KMxb2{U$={+U8i<k7Y3d_0#&Pf;QFomUb6?2
ze(UuI9NG28Qk!N3dSb+NPuA|K3I8Bk?>Q!M3%DtYib6Q-_XIu?_|?D{1Ah?s7HYED
z!q|1LBc_v1X`^j(wr$ot2j+ScY#&w@k0}B_wDI|$_Wsz5d|x_oKMx%R-BOslQz0F+
z+!!q2PRwn&p7w*YS7#@y=f4c@_v~jn=Qh2}0*{bV{m>E(&n1xpmCiTKVgTy?32_hn
ztqYsvp4;SH=f3;Td$sl0CYLd;xSoyE0@61E?ZYbE{RI3(TR}rT---9f$yhsdyC;^i
zr6u64m$>n!g!B`rB%B`J_C8pLE(b^nc%`vhIB1Ej0>ho{wjrg36U1}xm6a<vqK?U9
zH-nRqEe~HwbV_(aP5@VX5fIilpR1G@TY{F=&&q(o5{Q@=8#5cn<i@(S{#{hgi(}Xu
z-0)rJ+$fS?IJ@?(heY*zV;(le$<|5Vg1@v$ZbRRVp;fvpciTC03+kBkVNL>S+-4c!
z+P{j9Sf^g|y3cb~xk{0KlNc%_P=>DYZe5P&dC$#=qP>>;x>ABM)V<!9P>OEUvt0M5
zd!g;c-A%Eq<JeScLe(2E?gQM`O~6_oYbe?yG;H5RSj(D1%o=@5=n6ulkK+2y4j^he
zLe!;e?CxZqb1koCd0YUyO7T25Wb%{bZPr>IFQ0BV20{k%c|jk8o;3vf`o;JXB(?4t
zq4E_ROArJj3e64GGPkoKm}#6KMr6=C*Hw19wai;}4QcB~lE&h)65GBt8Y>sC%*awi
zQsvl|VqxU#(4WM>1bdgV&=)fep@xcu(N5=41=b}wr$F-(O=SkgvT2r)<)X}_Fw2e=
zr0DrBdTqLnbz%+CU*Yv#B@eU;#UA9=4iR1nA|j8d;c-C_y}J=CQdV0SETj{Mj~yNv
zo&!#ut-zwO?bmOCR__+wuy<oI*3I3v@vTU7a3MS}I&yf#2pt~BoiLJ&9eBwt3hs-q
z5LsLymeseLo#MW&J1OGDd|Nt2Pk6Y!xiN`~sp&OWg3~5eWrR>nrYQ0?0w<6!43wc1
z&60<07WJYi+NfCD%X2$<oJ}Km5QPr{FF_*&I0yEN;`iN(8i6DoPsdd|6;Tz-aV$kO
zgHa4yI%%kppvg~vWMNQ}#>-L2sI!YRMX69-7#z{eL@}`g|4eOUaDf0;{-53TzRoAG
z^Bt4uBT~>1-#t0Fpqu6Kkee?9f@Rs*k8N<lF%$UD3PP<X{1PAMw&4u)d<PMA{l>su
z=tqKwp|V^^qzZG}3Hr7^uW^k8sgSv0iK3kmFb55W#;ECDJUJYakr;dodL8Y$`C(<l
zWp=GLT8M_-!Svult~y?3f%15@@Quo1tuG6li{sSx)TKnx$k$X=Q_9;h^iOM!T$SO=
zr<iJ0R&F@&p>}QWW$oE~6)v@CeRZ@z!MO^f8q=~1mEvqjvY1reRm1kP)_8%jd`2;z
zz;_yWgJe*8fd_56Lwot9xfPUqX^O^82zOx;w=vK*G%c6s=Rk#lX`{fZXkK>Atj$E7
z(cItyE5LyJe0hBM@hw+O4OQTXh$z{|XCw<76XI$$GI%JX=+}lL*;+J}TNu==Np${r
zV{EHAaCmI!KwNg1j0b{_uImefsumuQs(mUfMPzptdh{aZ^Z|~unHb?)0b@M$P65nq
zlw5^ohn%AbFvNkB5CjN0c_HTzLX&MA`>X+bW9y_AF=Z8lUX_g~6lx!Rkz=yDZ5-^d
zMgAxOP^N7s3WbDi1B02+v<o=H=LX?L$)4cCw|NwOcbNEx`QAah;c&x<ulC-&;p+VP
zoAoh_vW7Rd@q2ae;gh|G><zugH})R(pQDwJ;QcGORWd0384mt=(?%!S0(}MAk8B7T
zc!pmLjnyrId!VyA4|P$w5g~;o0)yRyy`#+t7C@FX!Uoi_s~yD)2K50lcMh<>9tyLO
zq-?mn;gFr_5S;u#o8YwGgqXvJ3Z<%K0wH*DMF}gFiy;AK4#K%JgHr&RF>SAw4M(%J
zM3hlew`tITqHLrxFG7<xp+S}WQa-Ds5;>rT{f~Ca5!a(prtBQZTeZ1Z=BpP-h<a|L
z{^7b$ju#+(B4MIu=G)6R#|-dgk!Co~VntY#_yS3Eee<qCr_`~Il1Pi_&*&csSk5fy
z`CLDiA6A_~pp@|7!a!>_848VT+m>)mCsLFh^Z1Y&Rfmq7j$F9HFs>-z)Foyp5w91V
zpfZrRNkRPvCdt{FRUE*pOnL4I&?bG8<;1paBcV_d`x8qq?S8!T#!M_^Tw5H6wzoVV
zN#)=?PJG~n@+>;-3nT<jZtNFet8?%yU-Wqu-Q!-)!L!VG+zM_Z-UWR@x}$cP&d)`i
z<J;%>67@5W3&nkcbHt+oX#s5vx6)xwk@lCXM0hV#EDHoT#hL@g7%t;U5lfy8E__;d
z?bd&>UH#Jwk(4SwFRQ87j~x!pIHOqt{={Pgm6wovVfc-oM(Ep6T>tbNQxTFpA}Mxy
zb|jevB{l~V+@TO<;%f|K$5XH(kx$0uXaW(tfOa6El>`6Ja<-9|FO}e22rneKGR*&J
zup9V)kpaixiDzxeE!IB6RuLbwlx2x@XdsV$6f=D|?#u9>d`gYS><k-8RALF1F1AjC
zg9K~|wh{XI3-QAyh%Lws8|f_KYQm_XpVWtQ;++1Jm(2{Bh6N!+(X14}8N;{G3$R_i
zw=FfoMzrBjU2%CsuvBeDZ`6Uni;>Qehk9Z#Jpj;*YC&J<X;+g|08V+1KpY}PjuNnz
zr*^8UbiZV%-tX&2P==Si4BJnPK;}8A;Ll=*`LC&TU)vSIL+sCB&&Hk$Y73_8*N#EN
zURuIqV~YF^#ZulOdp|hgUqQc7jGuMTjE50FKS%m3xQ+uPEQU$j@C(U`CJn`cMeVp@
z5Z&2C(4&$7E4A)M6eM<nq{7k?wt94I3&DO4nW|+wjfFw(i|ty^fsH9aIe>uqmYIpi
zGp5Do?kV<t_BfAJgE$aqH31^$fxr+Ag0|+WA=m+UPaqmWfM^p`K2Z9=u9L!T4i6Q8
zC+C6}*Ul!n;)2*0_QhdEW|5j~D8spaRhgex)c)MCV#u`!I(S&qUgY{67-9JPNCwdD
z*&(<DWKzLNpoD`#;D;xJsSKP1hGrE*%2X;Ba1TZr-bZ{o?Fao<%$I|(VSga-D}lcc
z{4?eZ#6;AS0QA@p3HVsrU7N%bRGp>=0CjT|0t501siH|rNhYAMM(-f9eUeWz3^%Qw
zq(A_ec>hW5sn4NI^%?;A5D${-e{w0M0D_n(QCC6WFYKzK0tF94A@)>3#4p8-Du{9t
z4sW~#2D#olveByT;@^$^27;cJwTNEG6oW>sX4-w}f)P<m7D7>#HLZ;6R!NN*xNe)Z
znh`8!3VK8<S%@gouicxFq*}h1OGvDa*!`i6AO4S&4gLpIw;=4DZZM>&(HJ&Fa$Lof
zZ5@WskSvE_21O7STz9a!QY@-!p&(rk>KLxByy)81m>aBqCZa@)>>#4RMiO?vfu7cx
zYxLWR2qMAOa|SM#`XXxB$knl#S2$ty!}|gL2aefq$HS_u*YZYKjr7gtin+QZCFzGr
zA3Im+u*EOFjvaYktaQNvDxI)xwskXk@Y>|5cQ_E3^>pGp$@>a6W!mG#cU6Nb-*a9P
z7Xj+0`|8~~q?9-Z%*xF16US$Yvr~6X&9Z*TJ^UDE`5$^0rth4dWp5pwDeqeET>aG8
ze0FqjdSYZGQjFklBoVZ%ARCSp`{@|TXJ*8enT+c=u4~|)w0%4|FiKmzAXz|o#1ZUd
zC3W`WC9FK{J`&id7h>2sC5C^!I-Y-DTU%dS+kiM<rA<=6G!WnLMaU?m=k(vl;mo@T
z+eP|U)@*Uj>9V7}@gztt!>%c<^=$1*U01HDWmzd(BOalJxJV3ooGh33xRxU+WH<=#
zpe0Co+si&fB<RkZ2vanR1&XFTC$R(+A__}1NAryT;5CRAXe@Bxb^D7sBOS!<Uh3#@
zAv?W*Gy7jR#x?CUtCfgms9H%;HS9BnZNXCKyu#KHR<yb&-&cv+iWNm%8Xgj~^|dIe
zgQ61V0bT}=7{pQxshLQ+DxLERc#=*Vh<IoLq3zkU9nMBoci`3SDk3IePZgi!fa};D
zJwKf~#Lccccp)Q1;0TfWyd3#ZCTM?>c~V<p0Zi}^u|jQCMW{VuoHz%rw<S&{0{l17
z@+sT#i#1qleZiEr-H|Kqf8BwJWBoV7$+A1T|7kRCu^;<0$-H%~f6xE$cz(~o)@v8?
znakTT{kK6klT|7F5%;ldkE8Roo<9mUHqkj+gWgTN$FtB?tzqsQ<?$p0DS)1Y+JH9!
zAEi3bXtX5G!O}<S2^yKS@cU{<Z)Da8t3c5Qxhq6Fkf@3$>d?8kkYMWEClW6uKTn{R
z_~@z`AsvKN9UGb6freO*xH7{y3+j0&_LMDdG!x~{bLTqGuC5*0%68wr+l{7AG<Hmn
zjHw{PdWZ#}+Qi@VP%LI^*c>4<a(Fh(Y)O}GMK>&+Icho-%`*7p;RENmaL>>UOHs=r
zRnX{MCIgM54Np)pzxRM|OHzCtkBIV?gF782IB&Srrd98$Il`K^EB=PFs{x19?pgq;
zLzVdTq3bV;nzmmgMC+vfg#O=;SD6G#co6pFZrZI^uzTrr0;l|NHNHKfE8x#xmGELT
z+nT_r3#W!yw>97e+)x@h{BD6wUu{n&l7B_qAp^4si_pBM8}6t5Q^neX5eBd$s2Cql
z7*a*{9oVMfH{o<}72Efy_DKv1#ryqp7`JiH(;aZdgC}iukAi5c-Y>8FI`+F*<5<;2
zpb`TZZe8{cFlq#=5)SiZcY&NH@H$g-5y;~LX4B=mGT*U-ckX1rc^?qPWEQ0GQLXR!
zSrzA9>prrOT**&1=>|H>j~I6L>AINi%Db&^x9{SQh{wh47-!V6SdZQU=>YhEF5pqS
zPze~i%haq;#Z!eyzd#0XRtgi%UZx~GcYKO9r;h*E7VPn2$9a?O1o)cYf+VKzTArU@
z-t{AHfaeLxl>d6>ci{Z^Wgh#KEPl{5NwaWqszYHxod~f>{GlW9r?v&g`F6%1XzWa7
z8#emvXT!Dqwb(#R4{Ck$#o<eb>-%f*O04AtibudC8VnDJpC-c-{oDF=JW$(TAHHOy
zINt{+O@3HAAjX{+dD0(+?Q(1ENoW}<`V|!O@DYRjhrge?+^w#HZYVbnH!t_8o`P;D
zcm3eG$}O;Sr@(tec>K^n8LR<e;lOo3g9jAIbc`aOc1&Lw;gC$kUdpTi>-F>8q+L!u
z!fo5M3(g74y=M<LrWKpsuocT~J)J7E<0o+UNxGXE?$926Srf8g8($6#evj>tf_1gG
z%Z2bmK8##a5?VtdctSc#_5h@&tXOq)Xiv4a`-=XkDN9Krymm6#KNyQw(kX;;M|?o3
zo3ylkC1@vWdoJ5k1<l328x=)TG%Yx?JC$(_9jr{hY>FvC`y{>vHo*wl0OmP56?Rsx
zh9OI<*jodbRX(!`B;sjh5GT;jaec@dw!X`jL=a){ZfvcD*nq2W;L6s#Sk-LR3t3%T
zC;uDL&(t&o*jjer?1kuT_wYH!=h8i3Sc(55TD&ebK#xplYlq01Kii(0W#Ib+M<-Na
zv)H+_=a})@U?L~s&$u4X|D@T}RgNh@0w}~GA}vUyz)#=<N_T|Gg!$;J>HJ`C!uCy&
z8Li!BLMBM_n2!_#kj732UgS}ZHZ}A~B)<cY>gNU#I&salCM<UlW2g05KWp(T%M{CN
zx%C*0t3iH^0y($y<PN`#f)I}_%0U!`YtMvo2H<U-mx(CZ#*brDSk8XzBtD!IxaS79
z3#|V3uix=NS4p7h=sI5h!FGY{-@g0+%XLcAdcks+A87qe=M1$G-_M-Ko2=lV2Yn+}
z3xDjiOR>BRSScz7lI3)=tkHF1ovy9(+vpoYPfRk#IB@ePs0~0;{WS=(q+fS%`2Gi>
zrSrZ?9*bhV!gJoa5pwYN@5OWeJ1|e%0Z7;ni}no%_!v2GgH2Iy)r}|qf?ploWdR)w
zu&}jj^sEzteyyMD4V_p1*16x|z4YCJ(DQW;whh9U(EgWbdCyi#7JNuvkbFec)lQBC
zQJ9i_u>l_Q!E&rT-ulI0A#~3xM<o+}9Ew!i(#{R-ymWa1iZVh?ur<V=FDF{hn5Brm
z6X;-xv<xTOnU&}byO+Yb>LG!!K%bG0Ta+(EJ6@dS0Y^$ziq(K!JkUAfh(Lkj2s3{k
zJCI-t7HqT^Zt*o4kbTqK8qX_-f?4hID9m!v%e9<+NFl3q+&IKm4;h%aoduaS@^Q-=
z(RKTMp(M@sWaxdiu8&w24hHk=)G3A^8egzCewo|Zl0XrZzy(5IM5C9qoC3j5JbW#Y
zO6c|3*zA1b`={NA>zuZd)@cX1(~)MgxxU7J=Ln+Ie8){`@X0v=B>s00u;z%i?+`n3
zgth+qmv_Hi*rcApw?2*)tInez(5|1mX~ji5@CBE?-6;TpP1|Lt>$CHKM57cj<Ysk5
zo`q4$%ca=gzS6O-cbw}j=arUYo7acKCr*UJ*PFKUo})*<dL#b3^5{|ed0zZHIdGdD
zRba}lDN*~j0VQVNZbg+T!<d;dj435*-EPO~rTx_^WcHz<*0)DqQm+mV*Xt|oj5jx*
z<^K2HKCGlF=D1uQU7ExjtaaF76VuBx&(4z*7;f?rPjZ|4VaZP_O8P+h&Wuj1@}RDj
z%Nn-bz=58*6BiVv^$9Tpuv-&NtL7)69nNI#$PhLIx3O}W@kgT(oXkL<%rr6m&IH7-
zSLP<lO#YeV8%UT)cnOf=77Rb!GSK(lF;;Gr6*Q&im^FFK%1%;Q-1e?*ixqxC<k{=T
z;^jF0#LvAqiKCm+PB7@)-*d#*z9w`Z%|JA;2bPHINUnx|C&WeWc!B1@3?Z2S^olGH
zypS-;lc6FpNtP1c%(OZaTLJeC@V8)eeEmIm&6zHBJ{{Jd4yVJ=?IOjbO;21#IUZQa
z)(3REm<)!)!NOEQSAr=sGJe@NDSX#45diL(()zSwDi4YnUikT4x|xg;_)WU*C&w?F
z;AePvsZ7kS*wIX~h!5a}hNT8mk-{LpID<%F$MD*`EFV*>)^iG8$-{#^h_}Nu!Epej
zYnTB7^hWqdJwWq>e3_wOz%RD~Lhh!hD<mY5U7|?du$VOES$<m3!Kw~;Cf|Sq6R0+a
zO-5ixldu#0PkCPTmsEe{C`)gQCHB_--(?{sC|U9_BD;{*2l^hSu!|A$B!qCaeGV+8
zY}wMVA2@s)bE@3>6r!2S@wkk*=g`O}ZD6CUl+14bsZLXbsfKQ@@wBz}c(HHbve0k@
zW*C%1ar=X^0<*jHD#VAu_M~Rz)kolnf*4IQ0z)~ma0)&SmIWt=R9Mm!xGTgJP5LR$
zo*_O?KtWGpUJ07tz&NH|jROn<7O~#gKu8Qw4%I;4VFf0=pp2ckK-_@<%g38x&i4=l
z4v1;EiE{%jyop2Ia5Nzn7KB-64M5)3SP9>=`1oKLAIE?Xeig70KNol%A_4t;;9UWf
zr>c`5jc2z)*5hoUtMO+BDQkrgIN$A4h^=op>okiHT>|P)>l7_V#7EU>PvLQF%7jgZ
zb&=B3wG_SqqfVQcrRZU8o~KaW;1c_O#29c?C5ceq_V{*Y>P7h8Zy&c+7w1%G08R=P
z;4|mG2@cNQUEi!0N1V`v5^_e0dnZk~p=k}-oJ_otpNBn~sGZYwc^h5Tp8cslciu9+
z38V+#<zL-~vMAM;^3Pqv&+in}_KjVak5XBo<p}8Bye15P1m4TL;Lmj#?PO4;^`2;%
zA0hzgF3>ahC}J{`#0*tX9k4|LU%(}q)yedhQrqTMAL)y|L&b~WCDtqCKaU{`W9X+v
z%)50xV^1!u&rcx`NK{vxRNfsgsrz>vzA9S_MeQIQz9^DNX9w{2i^nDXC&Z2DJQhGx
zSXY-sD7xRZ5tTmnvX{jWklxN&Im305Qhz3ieKrxeMl*5<?tu5jT@N;Eh^GP7FVP#3
zU|b`%75hufBNfOW+T*+qUF%k?4TrGHBt^MOPN(W1WV~u%QZOtrjuA74#C7Por}+Q}
z(G$UGJU<{U6{B56cq{7jQ;2I(NzLC%5du%2zT{Gfjlm}r{j0Kc<r1K5ACRPP-*zof
z5!bR~mtMj&<=V?6=>w{%{I{>_$`i75^{Y<<W&8c>ufP6pKdczZtKWc#?efQD>6T-X
zs_Y>x`}<T=z78nd?T;A7BZaXy$ozVIk}1-4*i@zU`s5x&ggPc^U-?qfP(J?XqnywG
zBHx2m#NiG=Z{jUsn8ztmc;T%;UKC!%;i>S+PlozKU>%-(QnCJb*mwvVLbnb@LJt|?
z|E(D8@*7O^2Fa3M|9Z(9xpUpJ)K{2&ktbEvdNR^yzCyLE^*d!1<5C09DtH;%E3b@r
zb<=#0U5}1&APU(t+B4ioYWrFnKjU9Qpa>ozfi>Ws(fXSn=7l`$FiX83kE3C0)m-Nh
z#1F<DSwzI<Kg&Ho-bsv-v&-#M-wP=91r_!;dWwGDQhMcT7mu$CopwE|WF7XeyRE(;
zzSsMq4il$uQs*6fZ_yX+Ex)>F_m+R@eBI`CRBwZvxA!*fw%(H%ew+{Nv=fcLv)ann
z82V-RGn*E<cZl^i=338S@`^*{AbGpH92ZcEO+W6n(57E)ijM~jtOevxbqf35lip_v
z($GF|dsm_E6>?nj8LckSRcJrj&Ugr_(4LWgWB48EXZ%aj((5fVdcWA8{pSUBU--4q
zVCdIeSNycr#90KxAX44yE#aMHfz(9wonyqitjF_@36lN={}uG6QE_thJU;=dKNu(4
z_pcqNM8qmbJb*U;<j>$X5X|#fg233L2J4uY#Ema{yy1NtJ*1@NpENNExx-Pc^@J*~
zli3H!bDhwPVwX;Sym=PRYo}>lL&9LjZ@&T&(YvBOo(a&K4N4Y-ZHXJ0&X??^mrhXf
zsQ)aDUBQDAPZ*;413h++5QO!SoGg43GPNL@5lcy&{dgUof$OFzer`W1Tg$3kl2v#B
zTJllp&;3huhkOJExVZ*>d&!B-!GLx|A|FiBB?!9;@it|w)<W(jHzbcI*=TrJ$I5`+
zrm$By{XElJrAB9zk^><An&M}IhYaP3pcU=C)+mbN{tdP(RFp@xtO;*Y;Rgza$)5)E
zN#TRJ@vbhz*2e)dL2&gvL<YUkmEQ{^{!FcgaO=J2y?eUV(|>f<!{Vl2r5DW9p4j{Y
zh}9(Ud8g2iJD?po2#OGZjhW1;;0Mb^iE?BT1qTR5fSk_hf1$h9$b(*R`zi2^WSj*s
zbXuFoK3TPT1kgS3;+9_0OvpxTL1v~_)-7;OrJcEwYAA2+A6;4KH~1}<B(<KBB-%I<
zcjSk!ymC1Ije-1X{#rBm&7f%ppK**aY>EsQ?O+gFB#(~nn~7mJQKgVvc=@)Sbj7eN
zB(^>?y)-|+H2v*(It{f<INWMd>e1UK*9pa8f!RTnPYAih;L)BLT<p->M2@H(*O+yw
z=-ZC2U<BFo4P6PFvw1zfTFO;TY^FdgX6rBbf;s>NnJ+QEpDkmHjhtcF?;jh$ratgJ
z^>|sL&Q9N?^=CB0MAWZuz{G)~A)eaf@G(9GTs~sFw8OIa+<CVK7_2#YW_X*}0zCtA
z&V4a_nodB<G4{u=Ml^`v-dISr6<~I?n(%{_N;7bhAM3xV=c-y4R|mEQEobP@`{Xb%
zya;_OS5#Lma*8m4(9iudR*_$_6iM#a{7bA}E^Z$Mo$%J3ChVVkP!q_eB$GnD4<#GK
zRT5nNa8E*$%|%(?$zZyZ6a90Vtx{v1oR~O?U-qe1*fS=gyjRH3G|Er%^a@;seS`&c
zbG1Z4@6I6CU@P=6dZ2};4N_#rJHJ9K;I!p;xvxxA#7wP1+$5kT-`G&62(~GLbA|{A
zBa3LNqUbT|3Tct+g#E>m)pDtaHBH)qEv|N!ca|5CXGh??qy3pvOI|NWEMBIUzC0dR
z)RFBoee6#)t@SvH28HW?*CVr1Q2#AF6Td$GsyLoHUBtn`NQ|*zD=6yncj56|anw$Y
z=MT#E+vqt`xDtOs`YzgU<Zxhx<i0Lc6+%IH%UT;HfmsWga05z$K$gOllSqM&u}~|1
zoRW4_jO`qn*eeDGfSd4k1+G|HP_xo8*vi6M*s5!YLb4c7_h&5K*3C2mqxz?J#nSyO
zR4R__f{jgSJxJ-Ukz$DxY+5Tz-aD}?FxwU8!aQ?jTMlbBLrh$Y#k6uFJHUD_fxl>9
zXw^kLZoZ&q>U>Wcp73=|@JX3KKgDUmU?sR~qP=f4aS2>I(Iykp%8Kv`<h{rx)j#JX
zAjaO4C>9f~J9r=Q#A5}0TJ#pgOAO;Xu%PoVajTa{he$Ww^;J^jbUwM1kFW5UjIC2}
z$Xuc^&6YB|1S7>>5-&6WN~U4X9-oq<cIyFg@GL?Pe#_$s#TwqWs}iaViq8%h!Z*!y
z#d&Z9=Cym_dBFp&5@v^dXr4d}GH%&s9Ge5U4m1m-8^BblAxt6nb%GWL6NQ!xXb=f*
z28)SLc3ElEl45!(lHL3M^7xTA7L|mOO{MCQ<VnO7jTT21gVyd#fdeT~gM;F-OnzZM
zB>w7Tp?2+fdAo9azh%zvoSqu5h2iTI*7LEfY%GjiG`iMUHWAd;g{7A4pj2MM(~XEo
zLfE4ZPz)rXo;>6(XIa1)b6Yq19Mgqssc?%f)E-zR+nJ}AEcZBG{tzPcK4e<XahFZO
zlnsldYdtU-8V*f9V7d2+a6k?5L)*$elED*rAoB<e;P<c4-AE=wp-J~T5pc*5Ki-)6
z5-+oX)eD?RRntj0hp&u?ua8y*lnD`=feY@Kdk7=N<M5!_$zFxvD=4?G!(m^kkIv8G
zQJ_y1Esq%3eb;a8uk2N(Cn_^RDEWi?;;s@@^T5TY$_D`_)#naQ%^ZGf{Rd*0pD0DH
zP05C_?MuLv0ja8u&j;^3?hL#%3Gcpz!F2MjXhKz#u<WY+`TMpRh`D`l?9k%x_~Vdx
zxyL!?If7cjk9HTd%-})5L5j5uO9biw1FL`_l4b>>30WFRA5G2;0v<=T<i>1T?`m~8
z7>u##XJ3YO_u0_h(c#GJG+Uav-f<gw)9o{%Fb1&gVk`Xj;3^k)D9VoME%&|o;LSQX
z34{HhZ=oN-U+x-{+?d-e?bqW&!z0InlKVQ<waQa^%BiMI3+mpO{wlb8PGz9^$lNw}
zErzR|k`NW#DopoPc03Pg6pILZl#ul(&I0WMb{v|xThNCSmB~8H1Y|=cms;>n+{8##
zN^naA_0}}u({Np#MP8_RF&HcGS%v9>rYbjN(1GtIA-6Ae7NS*FH;g)?02V+8d<~F*
zTs0C3MY;~FtuKd+(=~n%tDQC;7L1DL;m~?iOgTR9utz%)9?Zby-Jw!wH@N2wFXM-E
zRr6~`sDZ1E&}+o~pXzzbhL^$~fw{RZHGp9Y^BrrqPy{lfPFQ3;ATaDMAT48<lTaL|
z`eCMkkwN2)u#?A4!J@Sb91(T}C>{BQOE3L}4^#56p4fh1-$FKYaj{S9AI9(@7O&JO
zjVCoDlwH_&kgdVjxG9_$=;x!EuvObqvqp0-k>i<6Tqa;`7XEV&hBJlSC|whDw28eJ
zo`Cicvyz-TXvU#4g?Tf@4NQcS^)eixNW?}c*>sr1U~7W@-bImlv0cV~|HdGk810Aa
zgQ!5c53^+(A&U-dL(*5?^&Q{}@uwdPYHr5<_||>9Z)XP=wJf;sWfhfpI5`wqrUmm2
zQTgZS_vhs38|ZgAGL(F{Z|+w(YI&AF@Jg!&1hc>xfM0krKyd=#q>d&A<wmJ64KdF^
zJm!h7<Q~)Hasi7YaKMW!1&7r?Djz#mE{39mM$C#-oM_Z8h7`k&PDQYdvm)z*SCtDz
zR?HqOu?%}}Ih86G`l?&58q`5_bSE+uwGAayw4+g{60u_XkX1fj%o>q?m~6SM2ig5$
zXfF6ZLfG{T<>Cn$5Ii=Q_)7RZdFF?WsEoRJ=IqN|PoDs_PLrsa=BN!sy!J8#ETId(
zf!|eejZS%4aQD^;Ri2R56GXqsxU$M~0`?>>5v>P}$C9u@sRx8QX(4b6#b6=1<P^tO
z(SDdm;mb}Qlplou$D@HSkOV+Iz^Nja%;qQ%4?2dJHR8*NB+7n+c!JD^;}~cte+R7@
zkmAs9peYdT{N(}ti+STo4<59kgV)&ntyQm~XCo+(-<Dbj6;pf56`Cu@WQcjM)C`1U
zljP6$HTr<8t->j3ST&@oto_+CNP@rw`ndxGvZG-OWLdic0*hg)@*lkt!XaMe;*1Oc
zATxFrhvAbsAjzs}D7JcqCfEGuJFjc~3upAK;9>q7zmxw2e=-dp&XF-4VxtSs;V)s!
zTnCT>O#`UFY5iX=!0`mERD;TggypZ+F+K(~SDA(b7I}=^W$9xh@JpYT;dSh4#>WRR
zRP?Jg@+yIaa9VM{*m=I|RsYcX28}gVlSY2RGr3@}^*5CJNz~)rcY{B1Agcn|0oEfP
zg3Pu{nw#2aa+VVavCRGO1*_Tybj^^Rf<17xEI;#d%VE|tvhgdE!IC`yA7iM22JB|5
z`CaVup=n0zFG&=lVDuq%P+_;x^=B;RSTBD@j!)Wn#K1Fnq}2L{8(BjhQbX+sEgv;B
zZ4j~sm-U2BCWJC0GGm8x0@s|ZQg{eYrC5X&KIU{(Fs&7QIw!f0y2JP<<r)nlRg_HU
z>MjKMrd!Y96Vxtfix-%|ZCVHvRc$k>z5)o`I+^cbKsY2KQJoO+rdn?mzfJMOV^Hx(
zRk(|{_fvsK5W`mNlOBlq#xC))ehmYi-rBD%Z}I|WZ2Bz^b3k*O^p0a#_qit>Nf2#h
zuV(~ppGu^_d}1OAL4&JQIME_43>auc-y-5igc$+76Hq=rx<+G@67Prd%h|*qqd(W;
z`9DtlN!ZQDk3{1^tQ^63^hi8kj4lHR_FM;N%O!{~z2}{w5Bw<&g+Kj3s1>wF<9Rov
z2ZI{DVl?HZtplI``~fTN$w^eN4EJvI0ioVzE{K!?GLuRsut;MNa;F~jJ*O@F0^lA5
zx2d=Ho{A3LXL=<x?;DElku>=Z@AwW`gM&7c?o5T!gXvJ}PUJbTkm+f+bhRGVsDS#_
zB{#iClBA>J_$aan<SAVlHQ=rjy%Je5e*Ad76)HK~V8<YnGnQ!@u++*V3)F#M^JD-~
zDPH!up1mmk=}68Lq~FXP(emm1Qv&sxyhsmQ@Eo$j`bE92J>OkO>KR%JGJ0}BO(Z7Y
zO9$_b%&I|(Ll{(NX{b|~k+4<ZtbrGXs{@!IUm)=sS(HZ?KUfNAC=n&Vt!QiB-0Vj(
zZQqFbt;^+mthCEp(z@$wPRx#5ZyHfean;mE@3rDKlndB8`>Qk%z6#f_%yBbnIA}%V
z=nd4=FY8zIV2rNDg1XwzyMWJsl<*Y0jE{B1%H}2Tf#3BF$84=L|GQ~H>|{p)3nEb>
zN`QpFAADf|d_W5LF?dgN%mLyZQ!U$aIdBgty;5iY<hpC_pnE3Zk_DJuPpH9@55H^Q
zYnMKY(`znW8N?+H`1^x*?Oj6AiSJ~)@qT!u!4IC0#kA`eUtLFF4fnI;Io(mk5zj8F
zig*E?^%A2W3=+UXY@V6eAZ2)W=(S=S8SU+o^njOrS9ozSl7OEm!q2L?%%Pu4mZ_*o
zi6uA;GB&^U(!L^`Q&of_7_TE5w`Qhdno`ir3JA5Tj$)5@b*TZwq^cwOvkK>w1H+>R
z!{(!6{G9nQc)E9@&49pyM*)U5v7u-(9`+DI7LeR3&UG~S7(kwu14H@*qK_}>_=~%(
zhb)&pNAalIXBhE0EDerl0v-jp2minyx$o^gv~YvaNBtgXH|_t-{V;kWh3jpyT~B$a
z5D!Vx9WDHYST0w~<#qvHgMd&-8>-$F`|)*jv6lOl`xW@S-LD~Zv<y9};LXXxUq`<}
zACI{K+!ma%lPURJUzN5QS~l7$MYEdmyjd=r!<&5C2oob4W@};F4!3^$tgqA51#cI4
z6m-&nty|#DNHCLQUL|8<A$U}%Rz3g{um?@*TGv=^Sm;*RUEm`T?FW1%Hp{_(G)|wz
zJbM_NB41^(l8}n7&bNc70}ue};d!`6cYICI-6?PmZLJ^HO(3=49F6S^z?`n2{|0Wo
zbpYLmEP8n0)vq2vpKo@BZi~Aoy!*t_x$o<~!q>xfj$@3&3d#LpXiS2K<%A6iFkvkN
z^U&e!+e_mHzF#n;6xP0LhfvY1Jzg!W)OLR1rym3o-FJc^SD7x)78dOGd3n!y?(BJs
z*UyN((8MSs9|cr{nwKk*I+3W8*vqKEfX5<ct*uyWFZGaVr3N+p4={~Vy8c7zi&e}0
zbXadS5%~{JF<4WcZu4m%YLDQmKJY^wcgOV~@ZM^jfzI<JBIJb8Lv0T2Q5&0Ci!zF~
z57A8Mjs!=`9ak%O3KcK-Dql%@_j5W<1EutYqVxe!_ylcO;P&k5G=Li5vL*K^beVU)
z_idqzwh<2j9#~jhQy{1qwN)ZK8|;TXwC!}PR9e`Qi@j-;O(Rgsr4DL2r0X9OKhCB3
zJ7T#l3#Afn_>DYW#??43^B-IWN$2*hFG&sdzkvB17r35k3Ur;gDFSh(ppitx5@^5R
zvWmsNR>xjj=p3w?b4268b=T1`fI+3^lBr2SEoLX-QJ<RA>spfal|tc{UVIqv=Ybtt
zjRENXQwN5JVP(bsf?Df~US@h|gx~Z~b+MYw#ACy;XeKj|aTSDf``8hAbYYy0j$CAC
z?Zu(XlPTP^GSo<HH{z;=7qH*+G8ftWCI{E8961gSWHQn0j!dpL^>g(_l9SQNyV*VP
zIxg`&AO*`vqB|GcA@#wA&y@{8-vZa=!C&Vm1#4TGM4gk;){(;(e~wqFooTJ(lhwt;
zl<eJk)y^RQaw<Er^XZF5Mn%cnnP2wq!_D`P?A$q$P4TJ*cXGOQ2J81*X!Hr+A)58`
zbA$w@6viMjmJ$k>QjJI%U!Ol?W}@^jnlV2^|M<xuJ3%YtLmx4usPpqYZ8aT4y7=(h
zc=`cc?foW=ctIX@jr56>q8wzpC9p50oDxX^{(=8tWuo*inz8<e|5bLU2<zSbS=QM2
zSr2<B?BiAR#eT^2Hw12n^%$&zKx+z17|55W%cQf!#gHCDZWmM(=%<c?I*;9KI0bFA
z*{2wLz#KCj(%CvFZ!Go)9kWCyCyWN2W1b7237kfTHNd4c+s~J-aPU5yp?5h9pAp;k
zlt_qJriAApig>xsxuui1<gH^(jE5#!T7<R<`UzT)XcG$z$z2RA?L39b=i&0Dz^?tT
zzvL#w7XJ;^F_qT81L3D{Z7g2A?S==!?q#>+LMCe}k^J06E0rz3p<0gG`U&WhzEw9S
zCzF}kJxddOJT##?_&xFig^r7J_`1fILI117x8a-E_h%mZ5%%@c6<a1ouNn$FTGxm7
z6o)2;rz_VzFn-<J`VqnYfl@3S89h4On3?@t=i7TsH{3rtJ~}ry<M9F%Ti<~HUq`|u
z#SG$f^w%*oK!Up0J#d_>0<Z92CJ^}7j@F<jbEFB`4KBD=3DH%8F7t2;t&cXLxW->I
z80G4x6H!!=cv|?ctdTbwc3Z6wKT8WH_P?mqE7-^a|LgF$sNjDDk;hqhsqs7J3BHYX
zAU}h8O)9NUq+d5^IF4~gcw%qAZA>&86NcTtcOrZTogI8#dSZ1;3U)kfeVAGghaZ$R
zIrxbsdw*Cqh7CEq-(LDe5a%8Yhhh37&lc?Sj$y#{y|kwT`4^Vq0k)g?@3TD$0WELy
zHpDKRH75{5qUVNr?DH{AeD3;FBw4W~%e~)n!6-jMWaAvk@+$0-*XeBG{<iUk9diGY
z9xy23p>%n!!Ba|Z(^m0BPdyTlipfHAgx3S*@Fu;O-dw6cG$PtmY8E?!c>j3QQ}gpK
zAhAs-w258p)TL44ee2Z*mhDoePsKv%|Chqnr%|QPh~;AVd)BW{snk`Y*Oppq_4F(K
zhmg^fW0~5l4ab6T_`B^Y_4G{zeM2#vHt8GCD~zo+uZegxi4+YRs2DMqL1pKQG{5mH
z&UUKP(={P=0MZLYm;p6Z)^!8}vg2_(V=hxwtgLD0a<-Yh!>>|Tg<36Wp$2#dRk8Qu
zaYUiFGw?s(p0JF}YW|{&a<-AN^GEVHd#hJF2uNp|@TsHum<4yRD{v(yOncP8APEGM
zAl9Pa)HXNQmY%3`DNNp3m|?it907NL8YbhR;Q26txS+*WMVT@F{0g`Jjj3$6g!LNR
z4KZnOP-3OLBrRg|0!lyHbw1ATWCwV;CtRy*Z2lu-J0(Rz4BG^#+Q0zRs|O80f4`2s
z_`Khh?u!Y2|Jify8j7BJ_MF@30h;J$)!{h<4Kfb_1v#0EkC2kV^}4hRo)9!h$3q)S
zgLz8E+AhI86LJm6E>e2-B&Wt{-57VU--wG4O%}7|h^?gDAe{~xa#V&1Rdz$eru^pX
z;6=RJJ7Evg3&WH^DevW;gQd!`H4Tdd!lSxj92#?mX&Ts`I)aVauG7&+J>z{6YxbRp
zhQ3L=@W@JSnZGT%w1+=v6SM$a2;-Ou8V+3&4tPiszBOo-Qp8~Umq<y*oul{}h-yTo
zXJ>j^3s5<O+JJ-jID7@U(IC|_sA^Ku%|yEo_JjcjL_@?PK<z|gJb%w74fb{@&}qTu
zH{|1`IAU?$%?}in#eyY7g2P+9{N0JZY_=~!-%MmDW2zdP%+k?^MH6yeYMY)DqJES1
zgG(aK5>(U|{xFg@ukt0!DEl&uxQM!kA|gWY{!MD|m1R|3*5h1}xfK69uP8lbp$K%F
zmIbUo2JAQw9Y7-&LJ1X(v;W`>XtmKdCs{jo?0~AFqRUm)iOI<5O8X3;Ic8Oyx_&c?
zwS3Jh5Nu$-l`R7obImI>M@xi*@%;!RaF0iy2*2|<mnjX%Of#TOf>ne!GH*4kD!n9%
z=oM`{3*m~E5$=O9!)SHF3~2(w*-d{Qg)!q^v{<7Nhs6;77r1TpG#Yrdi@@A(R(Q)d
z(CP;ILFlu6npvUVJs-tvE79R5#=~9nWK(MRkFi3bp3ndFnZO`z`g0Q`$iYo1URc1J
zo}U<$<-rs9C;FK4ktWA-5EfM6vEZ`FDL8Em;VHbR#XRl$EHFKtI0an$y+V_1l^TQw
z6I#MsXFtfBaE%YZ#zRDZU+`K)j7&5v?mgrVwZ|Q;En*JP+A<6a&fRFK_LTOaZyko9
zLk4IB&L;eMA()z&&)obhA#a$N^K1U%gYs@lcTe(pey)HvXP;-sc!kqlE0WtGz70--
zDQW$iu&Zto%jR4c1ZG0Cp$v^-5!RDMa8%cTkHzTrX~hM!<%YI}0-f5pO?nd>8An!X
zHeV__{6!m#1v)F2sjm}hM$&Bj`T5#qf=DNCn-&LZ^Xy~G^e`pF6Z2S}(3714&qdZh
z8poK`?SW0oJz_1q<(Kq);J-E<*Q|s}EYKI<qe}6ufT>gt(3h@Giu{H#&KFXFJXZ!^
ztEl_2PL94tRSrC|afo-(RvP1r=N;d0?M0*egV<jl{b7!pY&J?8fU5IVoY%n5ZZ_`T
zk7n(?i=IVWf;ac(qKa`8hwMdu56jR()n=xrz@I|qoUiF|5_Vv3L)-~&U=bP$v?)0p
z;u_Xw@UYONX!;~&I3$NeBw$j=VXiYG=?Ia$lk>l=NMJY|w=bG_wWXPslKlwHlt-y4
zpK_&w3&sw?MibZ`OP2Ihv=B}P;kW7(VxiG^xRQ057JdmIiI+(l0?J1Kl4#lk@JUN+
zK_&DmhPe45IekV%wqU2Zg!7r`087|$z#w&OMjOe^m0a-js#++kA=$LT2`%&tADu72
zI4m(;W)KoN9*)akI!7eZuEK(~BTrtzh@3ssuxprxJq~;4)BkeZkdyoR*%N4XFf|C3
zeIK#-mz4*9J(7#;{N&m5<U_L&OWH~|J*Db^5O<X=0u1qCilKBirz*%h8Ob?%ufv6J
zEFMbBwcgn6oX?)k7xw+Osuqi?Y9mGmejQ}g;`VlAqNQ9l8Y<)T%EScb(pNTH-#;D1
z#s>bQ?N@E5`}H-j2Dxa_C>ND_2@~x7-iqJGC6XSQ{16XPv}V5L5=C%07hj#?%iJcc
zK+NYCh+d)}p=bp^GxH73R432LQfsSNAAHic8jP<A7Gin-$M)S-!j_`>eR(~Nk!?w5
zv(g&7{LSNCEX1J0wsPL$nf)K%7cJ@hVZcp=J?O>w#7^&lWO(8foPE6!*~1#|_W48N
zRA^nkl#jt)(cll4r~{VTOZ<=d_!DL6$bwvh?`aP<8TA-KAh#QaJ*hB3!#qi^XQ_n&
zQlICVaGuOVDdn*?JqzdCNwq#-BjSLZYt`m;Sj~Fd=^Ue<<S;@%(z*t|8fyU$gybD^
z4mOcYaf>@~vmjzTgaP1lZE?4!DYvGvJ9Q0#Y-_MmBBmv*!ATrdaa5Ib4f_qmB^EPb
zu`EE_0C`mn>9=k+32ryFM-)|6#zumcX}^rJk7%Z<UYj#7snO9LA=!=;_bck##T1Zy
zS{=cfvdKK(yufRleLUU8M>oMo69!|5u!O+P;(t(fC|jYW7?*l15FAk|HlEYVXfgoa
z$kIHYLO5D8W=9C;Xvear&767pRU(AXRoCY8uY-x=+vb~E&@NuP-ONSJ?b|=T>I94F
zG6J?#bK!EjxO&>8NBBOzcw`CB;E~*-PESO0^aS-cZv*hNgsrHfw0z$B29k3YkNx;U
ziuAn;D}!J{2zM|KDs&9mf!7D#fq2B93j9y3^PO2)_Gf{oD(RBwU{mHJk3iKTLTT?p
zakd6;!}X1xa<dz*zo<rV!~N_zEI)%P%xV0`ph+gqz+grf=uGQ&o;_y*rvh4-HY6+O
zyq{lSt8scHE>gh)^qwI>qxDs~3FYt<jT>?=(1m464*5BLZo_?CNy*QltAMTkUG4)y
z%3vTcI9eKuB{~EvK@H~o2~eHSTT8+0=dr*vL8?Cghe2b(aIlbm9+Zk=Qj_l}*)qbA
z_YQz_x+CyQ7?;qEC5a(I?}9sm=N<5_bf!pZV`z?G(7_S$LEd#>7{wj!bJ*XZHcvQ4
zAP?uJi&E$5F)1|$dIkUyj3SfV(hDs#IT2tNADkn_3K;lB32Yet7Qw?<w6{6JBI}Oa
zC%JZUJj-0kjzy#pD^B!7@}Ny~H5O!xAB1Tx7H4q5;b-78QqZIulWEgdBW5^SZPXAY
z!~yym!9}Ky{=O86*%DCS06(zN>T7n*UUQ%dWE?Qwg@sFpXI@cM<6-6ZUF+7H;hbg}
zx4+hMFVBzVE;?X^5r^cGOA{qlN*y|AApnV~=@(x*yd$AWMtW=C)_HMG+p#!SF=G2w
zDH2DR5H_$luyp~KTw}+M$tx!Bv1}z`rRrt~yn51(-!`*@vD(3xP9?06Yo_amw)QbL
z2~`-vZBqPCNDb(#1?bTcH=Lvf_<o^l$Uo2P9O@9@W(7RiYswlnSWHmlL~iu$fW|-f
zh_y`(7OF){ucV`nVnk!I6LRBDDbzQ_x6T{t8)Wt)nqjn_#@b3A8K2Cf(W7Ok!J@zw
z^&^ZUb}M!rMamho<sZ|ud!amI<X8xMyc+7AsxeNFkzWScE1KX#Pa!rmcAXTcSF&Ex
zNDyEKGTIRJ=1}nVHZ=9x)8V9s7)ueWkTl%dY!w4J6~azAhz1>UGNDo>m|%|t9m$#-
z?$1Z^$zUvztJZQyVH-EnrP>f;hvILvRxCt{r6|R#BA$3%YQT140);tA7L*M93lQ-T
zv@QlKK985zMdW%R3{W&ckgp=nv!H(YI7Pf$Wex1QeGaK+iO0kf_H>xtTQ6tLr#2I6
z<QG~9x<zPB6qEWKNrNp1N?6|l3@AYzN+2@jCwCDDkDIg5<=l`{ulc^I0F3XPt{t=D
zQYItC&EvJ{or@EbL$y*O9#5c2CI`_~u~3LDV`qdtx^3NlyJhQpurER)Qq1Nv*=#Ob
zjK)2$Y_2;MJd_RF2Splr4NBu7t_!fIrv%mop_FL;av&RelK2+#ts_wiex~4AM1olk
z&kiV#0HLCwUATb3JUykNJ==&7q2}p5W&y6IF^orR*%j={9j`d5+?R}GH9!Edm9V7c
z61kvlm!hs4Eg42Enqz?hCsg8(l|rpkC1MdNxyjG5(n&h`fV>P>Pd$zx=`v=kJ7{7D
zH3NZr&B3Ud1rAX@k{OQzzB=Fx#6EWyJ+jg;5gQR1o~%(03H2KikFJu8znAZhL0%y@
zVGm9Z>F4dR4S5m>1aGOGjc{*JB78|52756(&@Ku170?_{f&T-ymrT_YbE(-Hfdt`<
zg}oB^bud4;5qwPoHz(%mi6ow4Uk?@C?Yq+1P(B@uV~1pyAX~Q6pT@&XEmzuCCR$$`
z&$+Id{6j=XMS!>P1B2xnMM~xQO1*!Ot<)>gSTHG<hO4*NDg*3|*u~}t5pxKD1?Z|%
z05*Y)z108&vCLR8;n+ch<;RtQ0}<sQQ)DBg*!n(MLxidWvcAuLKnZI5B;8c53@V4@
z&_faJ5d3~k_U;35?U3@`eYSc~g9V3lr3^7)UFZP!!Q1hkz*__F4m=1d(ra*!k#H{B
zK(ri)A^Bl=ppec*YF}KN6&KK+bb#mxXks#mp5sQKM}1eeIvGJoiuPxme+Kq)KEz^Y
zuz`K4HnIii$au99>njWl{(siq1VFN?ych3t_j~Ksty}wEUDegK^j<x^RL}G*1+&o1
z0Mi4*FpOwg){)IoTu>AnC5U3;s4)VG+W1TYK9fu|CP+}4Xp|)8|0ce~5H+EbJbe)J
z;{5Z}7$e2|edpX;Ro&AA=zGv}@40K8bM86c`PScOSUaZD*>JzOGFl%f#}DwE`02zz
zv{0-~7Q?mvY&w+dj}%JvEj>3az|<1T*fiQeQ@t!FTh|?N-)k<2veU5JpGJNIDDn3u
zbSQ~{ZVC%-7?I6`DOEl$F6}^8I}MKablVP`z6Yxm##w(fY{uaEE?WUy(i8WGRT)_c
z@d7Qfw;z@Yd0<8a+|6NU1rhelcis`r0Z#8ZuRBSUOLQ#8y2vX*JbDst5n_qz8S5fZ
zxvsHSg=2Oq7s{l{CBy^tCsG4JC_XTDk>cCCvTNQcg!ZymqCO_TcLKpUyw6J*en$IF
z8jG)jaRZs=wIMV7zatU72zvTc=!?FLH7GCmZ$C6#P#~&KoEB0jZ;_RTssdd*rpf2t
z$T`Z>^it!Ej#109E!mpU{%S*OX^pYRKf)`<Hy?RCIXzIGy73ajT3NA-OE@Nuz!@O_
z^8$XC>;1rEd9FErVI5?(E{4}YmF!Kk=p~qp{V~Umq8{+h`ZKXR(uJ}9o^VxvcA2~d
zXp;~Q?LQU>8e$8tN<V%1)8g_6DUANOc$Q=otT`kfaP$TE6?ptVX|#zt3kz9S$;wWz
zw&0B}eA07oF!fcUgxIhamNJoIbS;*9n9jPiq1O|Z?jHOG`OX7pjC*cE3jp`q62%kn
zMM3;9rm(c*LPWH+PdJWF1$Gj={9(3`i>=TP?+uRr`rAU`luesCEX4Gy|NN!|gv{Qy
z!0GxNZRrtoyjJzD{PDBex4_%6Kuzfs&uZ)X%luA0Cpvw*Np05u()w5OKBG9phk#W9
zixC`Wrn(-<{yHg(m~~yoH|t$Nqq9wr`||y%?0Yjws`$OM$-Wcr%M0)_erMJ{w-w<x
z+R!z{Lx;pVNsA?WBSp@%@9R0^hVkFqI3Go0`}3gEVa%!5pozrE*PvJh=T8e2{*n~5
z*{p%-)+#*)c2AwxRqS<3XBpa|7vgEo$@A4Ov42!z&eZHjnbRJZMJZ@Kz55N-dVp`)
z6e0}*l9YoMBY;4Id_olDTrw|7HduyS4o)lGJLKHicd)yIF~wmYotbXm?w20<TkC?k
z?fdz5QpO8(DWAi}mZW^re`Ufi_`iPcJN)_32md*T$JbCIA2^Ls$RJp|^7=#e!&UCO
zW<D9PUl2uWHDLek3VF*3Ke-jFU~M?$c86!6D|v`VsnU0XcRPI(lEcV3jrcGU97UWM
zvObYgjuuiKIw*0uhRH}b)H(|xdqNxCHmWWDB6JxUhLtB8JXp3<@kq@rYC%I%%ej47
zHxQQJe2lKmUj3lsx=yC{IzhYGFXSqfynf~0g-bKwzJk$4WCVN7PAgI%sAS@CJDe}y
zNmpKT#K;zNx)aI>GDc*I_;??~h5XLPi6`iZb?;J93b3J^gQ7)RXSMDDYxyjKXz=$(
zlhc#PoTC==+w(=01p)I&7kWeEpAx4&6$;6=nG3rvG(7f$;_(f2DsU@#Z=I69<+SV<
zAP?hHXz-!o!LD#)@2Qmsi}fM#lYbbVn4SphVetRk&)*JT)o^dU9u`#)+i%7aiI|~;
z^$b!W>S1&h^o?9+a2M9UB<#*tW46)<r+|`<@}MbL(B2yR>?r|8aAuyb+8w|Muugn|
z>ZFU|{Co#)g8kn0X2fY1orw8|?qX=r$n{@(D}ww-_8*}QkUYU~pKX6UR{{=}Ibo%v
z(X{nvzaF!<2TV7!P~Uxn<-HBBJi~2n=gO_IS<(&k3&xR;moP?X(CgzDCiPkq6fKDz
zEK1l-#*$44VThNlL5m1?VVvv@W@uz~g%P^!95~?Ytd-qenW}MlrBG-U3YE)^>W>ST
zTvF(XZP=aIc37T`_74yDM`z{3+Y;wb4+Jg-8hzyAz`(R})!f`wG}gHf@G^TfA}q*)
zfM|MF5}%3GG(3)+gMe_|+Doiy9k#X0RIIgE`Bxb!%_pu(%$N8<`-i$Z%@tSEq?qay
zAH{kPERyYF$Ch~ar;|yf3c)A%eN|t7JAd+KVcuU9J3bZ#PD7}#1-|}lgzJuClz2s7
zM}Wb_DHXB)aE0X_I-pU+nIJ@X3MtDu`vJ9|Y5wXV7;0=qspev!8PkJCHM0V^{h|?0
zR&vFO7y4F&-JdUP?D9hjZg!OF&UnB45V3m2!Rb|s3U9BRNQQIyQCS`-K9fCvne(<(
zcglU}YjH7?pr3~whC2bl!xiJC_fTk>dvv_)KghXXeH}U$`P}rk>)hF2tsl~$f4<Fx
z4=&$nhw2(#iW+az|098qfCr!e(e<|hpa46K9>D!sJivE&z`Nt^l$cV(*j|-dusg}H
zmj<v-CuxlV#z3tMgzz#z2jYNV&?Z<(hMN*%zd<S4caL4Sx3RbV&-<1S?!E51J7CSc
zfTiBU6t?@0>%N7f)fM`$_)|%Sj^d9c>HMF*AjyK?VnwzHegKA`s88Y6v9>2g@Jp#i
z$tEwM4I>z3{I$GeoD{T*bPj(EN;O827g3}gfy97S39Jm);*$p+I-2~uN$Ux3{?5A8
z$>j9d)RD=Uo>qg%pXew<Fdq$JKLT598g}7hCj?!+%_H)wQu7tl?H|J>CV~j8hN_P9
zzlO?`2>g_oJ9BL6AA6~+k7+=$YZ`PkGtkW~^z}`VC^1v6lxf|B(4*;CHHnXo`8=D?
zO_zwP_eA|Efx^X&monFdl#9{e$|_qOwx$OQsYEuLNEHUBtx=LO*&0^V_HT!i$uMxU
z95;RYk+kas3>#;zTnR>3gVB|hSZr`Q92y*izHBfKckV@E^nkaP97rlE6rz=gt138X
zBOY&=^9UqsuOcQ9e7PUVLW={hJSY5o`pq=;QjPt{iiX;ILQ(4*os^>CxnFOzpKY*v
z0@+wB8~DbmpF?zYtpS%Jyp&^th~Ie8U)cQZo4i<zm1m?D`vfc+mm$LeVO-0keInmY
zF<|H`lxc%SmWB(?l0D6VI+HkMKiHno{vmB~_y$#2-3WF9nMO5Xazk0#Ii@Cxm9Pu6
zCKa$vPNZL{4GqmV_c!N<hH6TG#L;YA(KI()DJIl0seh)wGJZaStY!K9@d}RG_pw5(
z5-$ucl-Xn<Ucv7x<%PkAKNrbIWQ|@L!pjxB;6Xc+u^+^5afbtXuO{QB&l$$P{^|Z5
zqqW-T4ixqomqxSscqJID#PiwcP%ub+O>&mVk7V-*h2B*i6ctTJ7l?kMPfJ=(oWmb?
zo`>XFBY}w4b1oR6&u|xdkLDadjmLW~it8Z7VEr*(dbpTZnju3+Mi&|G;X&0fHB*iv
z(<5>{27*S&fbQ7@UPaNivY`kI+K#48sG14qHWmMabJSHwlMU5KYv%lw4idoivORyJ
zTMBYo*$SeELmE)O^nj|QWd+F?f^N(<kWRgznZ=|N1OjdVm5^L!228x#)D=iSnCb97
z)#K)++n~4;ya35GHW0C2zk#C!4w&en@(gtE)Hz^@&<hjj5dfB;?X%cXFJjX0J^BYn
zIG_Q<yPIF~r#y9MJl3SKND|I2I?{W3E@S{%L6X9WI19#1y}uk{mN5{TDW{B(ZRZuj
z<xynWkl<S<S&AHj@4Y5Va8c|xbWjCgq(i~BI|!B0g{B%&6y%=_BGKieT`fTSLBw04
z2Pf35)er4I@N>e*7iZd212Z98F-@3>^B|fbNJ|R-89?(>)P6*^8qoZKebew&5xan&
z2u1<onAIGOz#WDs0*Gqt?a%XIU)sQ-YvoCQ&BW*veZ&LH;c!f$JLo5|W_nk#rs_%R
zU%xlS9$$j@o;N@-OvPZtQ`Esd9eN4AI<#R>YeCMil%!<HQB~h5&t5g#-#;b8u0t0k
zc+fnknyQYJozOca6bmr^vR0ENq%*WJw;3FJFmhzfOIz*j%`*nxu94OZ-@qKhEm1Wb
z!rQH?Yyu?Sx&&G@S-MZh+tnbnQ>rb=7Et4v6b3lC1P=}cFf_ITJ&6fgDdcOyv5vuR
zPKaWY;5v4IkdoLCVzJ~D$%q=lnn~D(L^n07mf^pS)9Q4yj1aM@DySNGL>*%kH)3`|
zJU?VNwlxBV{FsbCwl{IvOgXeVb6MhMhaG#pEWh4t-;0g;dbu8;8^iok`xL*A755xI
zyk`lq1ugtd9dNf?^~>&osjJ>r+F8P>{u1^B=hHp>^i}*0&?ES4!3Ok@Cx1-BvkY8p
zbBcd<qQdl1I}``_4}_S6F@TUZ;E`k9sX;cXq57Q?alX9=OKZ`f8;o*}m6up+X7umI
zW;m+c?>kZ3QUl4L;%@f~0Vmq(DMNW14GQxuN`KdUWiI#169ja3UDSaP8%$b?WlwZU
zv5UJ!qPH~GS2<sJ9gy_jf^|rs)`%sO5GG)=!@;I|ezLRS48#-yNGNCo99l=nqOcM$
zy6^(V|3dErbs3F&knb*{Ejt7BME>WPl)c*j2MwI~0~>(vS1c}a2otZ25!-%_v%_;-
z$+dJsH^DMsi$grOetQD%fEk1vKvtHQmX=p=FV2ZFKTT}7-db+Zt^xkyc`QorVs|6E
z0r?kK5Cn+ih5fYP#4yik5dx|@#xqgTCep!HRV-1uXA!JPx3-^4Cfm>1fKF?K859Z3
zghAV68ON2PUrr`j5|`TlvsC(k!|j6@E=P3zh!Tw|p?Jm|ux<n7g?^h=vXahr!?+w5
zK4w{?uM-(yVbl_9=4t5JK8vXCI;a&P+7TN;wc+%>BCswPq-G4+4m?5g)Tv-lwbiV{
z#we(}{q<lLhe39yzy)F_AyTlzd-f=)V8OQ4+W`*=00}tb+jhZDwg0AzKEyFXp9k;z
zE{ZH4W8w!PaIUWjyS4onb{Z?A8T{tAY!eWjX`4*}EcIXCk5hSEy8e1CZRc_6{m}Xk
zm@c&sj6p|d5(Pz}EIK2Ik(mNjA>`--l*xUa76r;gOiCJeG|XWB7i@hJqmV}8#(hV(
z!ksw8JqVoWA;JIE2uABj-vtXDSGg=S@L#L5!Bsjxg&XP!OIxRUY2S{sE>13qn?752
z>r39hmU!DTUhd-UXlt*2C<QqJ(%QO~>7)*Vrz5Nr7;@b*0=S^$s?HK3)+{_7(D6Oh
zmsL1ghG_GFPU*G%yAnxgi_D4pCrEolDTjKN*t<+KzB{pg)Bb)cp2(QC%+*MlL_Edm
z<5R#<AY9K9^bor_iZ_uyTK2fj1cv8nO5WgWxo}AX;vW~_xVTxJ*~y?<T7P1b2C=7N
zxnWp1hjX#ggt2qKZ$*d&-V-fLEGV17s%^=(RQ{z6$JcDq)>bZ>Y|3&o8O;^POHO3S
zV=s13HDqI`petH9k$@3NFAS|eYCn5cFRcG=d1&?E@38K>L5H@!eJ`iMJzp@mp+zsJ
zg*{*MB$@!7RZoL@jy7vh?>H=A{f4$<U3;)FL$kdTRK_=%LqW4~1j2)vxJ2kMK`^mF
z5A-zYAJmMaOG>H`R$@$(_qoo0Ir}qRzfaa!ObNSAN|AIYqk{VOv<n0b!JEObnhotk
z-kXS$dv4>$`O^sOxcfp`SpxbSQ|zEijtb;QL~`Sk&@Ut*1Mh(pc0K4j>WamV?5YV)
z;%Pd;F^DosqX0J`39gIHS*Vx^O?0PJ#aIS*N4%fxUEuMmO??6!0SRl|39<^{N6b|+
z>*5cOf!kpZUJ!Qj!;XstK<&RoxiG}bz?2L^TGA(OIerj9^{LYO!zbYVNct>=#0$2^
zFIrL(7oJYGw#UKVj^311BC5++E)FlQurpL}+y%ruta;@N5jWK*xuI||owX_lkKZz(
ztLqO-8bV>G)j)tH0fBvAr}PS(uOtD<6;YBm0n<fK6bm~OM4x-dN*3`Y;Plpy6^f^a
zw=Y;Bk{Kuz6C5sa#H5?uWZd)&lG37zl-;G0A_vq(?W1C+iIYob50~Dt@x=xtEVW+8
zMnRBr$=l1~^xDR8vc4S^_$(dT*baPDq>vJAp+z1DXfPekZbGV>1mV!VgMiZL!JdYF
zyBtFJL#PbaJ)<#0>4z&i(=xLBv9nwC4%`7>b^i{v^Js5#A5x2o0`<Qsr^_&qyJ2wd
z<+N<Jf3mTqKUseR^B<&dN%P_<;2VCP&kLgGhbbBpT4XZ*^0}a5o>S-nxuT-={cKzU
zZAaV!O$zw85#J6<@beJ7>${Ud(lKr|#)s`mDHls=dvCt;=Dk{qCWW1ni?`i-TTxEk
z{Ik==)$UY)od<ry{718~oTTo&Zrir&b|^F<E=|c>_w3m!r=D6*{nsCDMmqU?cmg)Y
zM}d7YjUEJdA~-Mz(<CTDIZRxzj9w!>F*ucVD>tNR%Id~N$}UoW0=OAs{$Lc+5M-4=
z91cW|#N>D&TA8V~upVByU=C}YHC-Dv7p~m>`s-d~4qb?eZs-Wz2&m?+Os$e_vD@*U
zgLgs~Hl7{y*ZRTiICNuo9>k@uzv#50F+?(xXukpdB5D4QfU^2d1Kcz7#hUFYp{t;m
zd<=m=2nJ5)T;=*s^gRo1JXhoIFzsr<!nl+x^@_|w9~6lUF5b|2$nXpQgqU;c#T)+*
z1Z<}nW+au0m_`YRt@yj)tUeU9MDzjq!EGVre4>dj`n}Iq1#i}3q9L1pX3yZ}?HU^!
z_~?tb?Acx&@w+!^(R%twwoCA)GeXY6U`6A=J&RA36lR3Z7bLTj$CP?ZLRXdZl=$6;
z@tMH&cRtg9bh*-<f=}%+KSc#6UG4c8hIwg+DHZf?V||H>S-&owZP(m{lJ}78^@hz?
zo=vBF$?-bN<_morX|P9YF)yBE>+`_CPeO=kBw3R9HuVz6B8#){nDo;3FSXEp9MAGY
zo0Qt0-Q;BV#uf1-e|(b-bn>0YHz|qrMc~@?=Dy)bG$fk(ZPqumdE>D_-+KA?ciX&4
zt)kr~Z+4aSw0ZL)q0#uw-uw=(*It8+z5@TDDSY*wxIXe967BN=gS$N;fOEt+SdV>m
zqKN3|u78VHTnB%(2Z{}@-hkPqr|8*C&I7jc0Qn!l4+8p34%<#qh0wBd<$<TfB(3Ch
z1^yEP1gBb)<`WcxmWTdytZ><-{l}t{!IGOtOd6d(rndi1RbPlzrX&BVEA1o<l=*PU
z9_x?C`^PZVCxeEPBb#MX#l57s`q#*GCH8^}CX#UbSCO4%6}Gbr`Kn32L7wudt7+r2
zqoa2d>L;#}A<l#J{3;l30`20i6XbcU!+XN>U1QJ7uGLDGQpkO&Xg&KDP%&lw`s_uA
zFUnr8%k39H;lsgTIFf4kg_IyWzhqjc$%J)}f>aQ{L#5xL(mhl<ZAGH`t@-}`{H=QQ
zT7>3=;jbU|_dWTxcuXyPTfPJ=usM7KZXG1X3SR<7K@d}w%f_T$<f<}~LU0iB<8Co(
zsIAB@;b^y{<ZN<#A`po0NM__Dd%ux3Ul~qvm)}e5m?T9*E5pT!CJh62T_W-kn|(#o
zUZ1`I2ssJcP5>k4g7m6k?02Ac3)xOGQ{~aW&Rfg^2$iU73EyQGaHC*{CHHsKrB($C
zPjFsX)x^Z*8Ct>P3+*0h6e#?(>oARG71+ycn6yPxm7=B^T3Auxib0-u$vm{#;hqaH
z$Mjrb6(eaLb>k-pv~+(UBFRzbBenjK89HW%;^FsOj5R_CGewS8yw-+#8<rVUk4TkN
zIIW)Z$`{VrH^Me}Uf*4W;l$D7_{e;*PbSU0^yj(#Tt7%Ys%zXQstQI6I#x>hLhdjg
z&F~}d{9`gDu;tazf6)?l`!kkPN`4n!wFxNK-j=N(8-!z@2o&FtOETHA;SCA^Nps2!
zRI>0M)h|HEALagmH5YO+X+Qa7JBKzH!*(48I4xc5fpE%z9P9rpnx(92y&X2Kueez`
zt0q}16~5wBIF)jL?{8@P^zYL4Uq*v!wB&r+vf}RR;>lliQ>X{|!9IyGIEH?PS^6xc
zwD<d%Iz+x`JYw|mO57h#8Za0eaN}_H8iN&AKhTdfpDisCIOD)KoF$)4hKn?Pi>Jve
z^z>&UZfG$2#k83Zo-hMp=Uzo!3@5q&2if<Zg!Ym2j(tgTaVPI1qBUSw@uLun#Q*pn
zS`Erv{BcK+Xh-8MC>e|Zf+k%RwB7z#KAMgN0rCE6sN&$atOsO$uxwPrp<yRe&cqyR
zmE1e9`74pHBl=-jddze<XB)Lbe=uYd?3EL<V=1>@(;VA^QS#bIHp@QRpa*F);3w}O
zpQ;z2`?$+*M|29alpC(7@;nzd8YccH0tCGW{XwZZJ&TA3FSJBhSb4s7YRTBoT_ZO5
zb9DjPE<l{99iv_j66#vGnDIYOphzQyfq`K_UkLwCk!6Ob3qm?kK2_-&eYj2<6{#;9
zxi-imm=Id6?xxVG4)v}b3icOjhMfziVUIIE9Lctqx*G7D_%1&VXPn_sxN4LKbvXb+
zBSRYdw3E_;v2-*a>v!#7CgOif=QI8%=^0yITi}U;Fcm19=nsR*`yRr;&zr@Kjrd*|
zSR!3aw>?RJxx4r0sSaEHzvuvb5{1(YF;^#{TLZE+AXG_Dh;M?`0s69j7ABbvAvCD{
zx;-7LgsI{E1MMlw3FmaoUbfV12}W_u<eq;o^cluc{&#vwRql1d?kT4fRfD4}Ml<@U
zI&JGI+S2CLOY|Q|8K%G*#?C`jJ5Z|4D2hkuX!#QK6~wg5nvwN(28iLcys_b5ICR2w
ztTUElGz_*Yme0j7zX4=4LE8o|->Wz1;QV+scugp-(-f7XkvBx({e<d(#y^9)eUfM$
z;Jje75KKU4k0I^pUy0TcsS)Po>eDZWkinVkCFr5(pC^FZ^(Ep~LD~?uLnHzp4vB`v
z9%o4pG3D)P#p!(Gai}Cq_KunoDdv>2)!_SS<gU&a!^&u&U&$3C%Al-d)U^AzQRf$J
z_AZ+Rwl4lxH)llL2mTx1{lg!-VI%MU&RuTA$hi*$0>e)5%1|_TXP`uOxfi|wyWE$6
zpaESwj{+uw1o96wSFr|AQjl2@FX5RTDR~`nQ0OiCki=JWa)u_bXYWTi!Ff)2F`#RU
zdM0qgdoooRW+{x~z5Muwh+7I_<UN`!;U3mEhfsvVTWe-MJTakV0{7v$?bQq<0ZtSu
z{Md?{kvnPZ9W5nu<64Hx0Z#w}@h;eQfGh~IN_FL`V@wTyq~VP4>Y)X!hUE@zA`f%!
z%L*plt3NGr6g|b4xxX+n3E8N^#2-X%@nhh%1IK}<VXAlQW`-RLMvox{c@W0^sv##b
z#@39f-wjt1S$~Zc{idb3nZOjB34^M3^RJM6_|0(e-KMR<rRBuP!2ga0f8}Pl72t-c
zK*m+9iw8zd=%%LG=DUO8ZvqgVeD<E_?MlE`ugd-3E~oFpT_w0n6IYNEy{c6O#~AJ?
zJVY2>xg(IP5I49xH$6_ieq>xlL-_{U_|F~*npX#71HnarZ!$IWK}BhrOXzyC+9J~E
z`9~m*EqMp*k3L*T&ex^HrH6xNGxCbqOgLiqn?)5_5KY9)EgP>0l%VV0uK=Yp^(I+)
zvT3%~V8rWNqjx!GF&bQ36~*XVb1`{jd+dPOT)Q+3lq^fJs|uoB1Az=8lF61%{DBG0
zAI0lXoaS5lei0GMzlLb_-$VA4ulD`-zGp}eflmtBR|Q4%O693dO7I)-?%;!fo+y`u
zY>dUL^E@|q`nM7@PR?&M!hN4T-0MvNN=b_4W0K^#Qcs!ku#>_%mY_uH{vCM8JG;Kr
zdwsL=v$9&`U_{uS=H&+8E^y!JVtaufu^&T-jRse_7`y64q0QA-PjNwIg|??w>vLw(
zaxfBY+j$-VM52UzgN}{=oUiz2_IoG%{ik{_@TchtFEG^V_S@m`a@H21e>k};E@0@x
zCy{EgihQuNKT)4+b!G0>TpeeNZY&wF;>noXDKt=u(-|ssuJJSemEH@TJ4AuYTWjET
zAA)5YIXWQ8Vh{-NPmAlof#qX@OM-~~!q+D40Z0OsghRp%5_5@?pn>8I_=I?RihW+k
zeFq@yXhVwbUJc#7R)?xRw5aF=)qfy)5ZD?o<DSo4le~smb=AC7H8tgk*lG$+Ec5Lq
zr|((D`sVwbtU?!3qA1}ZJ_)*`wE~~sep*^6aTeJWrlL(co?z@u8$(8TlA7&5ZMERc
z({fKAv5dLL2g9#D*>K%r5&wEV65UZO7XK`feB`Eh^(U;4-%!M4<?-pBz5yPv6(B3o
z@_;phx|2d!xB~;$Jl>grN+Bto>>e|<WP<X?VBM(dwPa0i>V#>NY$jp&(b<_>hh;rv
zylOx)ZBrU}l@ZeA;ad?`YFvH5ux#aKCE#j%_GoTEK^j!#eY+Z$esfwXm89vxiJ91#
z>yE`{CVuE1NBpU>{U%M<wVRL>E@T{cS<qExE;AFU*CD;^l~*F!?CVkzbQhP+C~oj(
zdv<tu)~=_GE%~Xb{1zik`~*}hXl#p7>>AN#<X+gA52v<;utQ0X3?VjJ_1Q;^D_SL|
zs}e=16?w=U0^=HP=+z`o{=iOXuMWBFlu(S^B6cU2=swzP*FInc0@epKPw^pCy@J1i
zePo441ayd)pj$qj5@e6gQG`3uGFt6Oo+Dc3)3@$9ifm_RMD7{A`n4*J5>7!Xl}>Tv
zX+}ykK2(-H{TT(1wFNyo>pW|DzLw9xU+N(q2Y5bK@C~)wsI8R=Qo>J0h3a$Pux@k4
zG0s*5C<}V;OJF$Q1O|+j`}WPx?S)UItDAF6Czgb7leBkkex;geWvXy}raQ6Whlm*;
zc+(z1qUM(7@F&W9-ZU@>0s>x_>b%0Uwm?x&Kkp04qDK+UL#~Sujwm~3x9^2Q4fl*Q
z*0=ACdo9C}&KpY+znV%8x!hyP9ZHH5W9I=S!rJ>rwzBt)`x>UJ=wrJa>y+h0TuC4r
zNN$9m*fmD!iW_(#4NadOh`dA>5zoshoZm|`kTLyg(z&T~^{F<(hX)N>F)*liLA(9_
zJ;lLqsN<G4bZ<I*Jx3bN#r~i&L*0BP7_DR**$UFVczdh`Y>B!Vp9J4Ubn|b9(-`OV
z+)U*+{l5MQ<O&VEJ51x~N+KkrRpkmw;rd@?|6g{``RQ0Jeg2+oe=3}N5;@49%!O0z
zuB4sF#A2C*oy=TxasI28^;GWSi$JG-igzmPH{c0H+7HeO>SYijLItPSiytBGI|1Bm
zidf{EDUc`Dnp9)T*21g)Kn-bsaLe6tde7c;E|=cBCoQKUPW<{oC9ZCpRAOrO&2cC4
zwQEfC9Se_X+GF34DQGF5r>&eiZ+GU1Wxn%j(`;sTgW(ps+Pk2SsX_Y2JU~V*IQ@Wz
z_!GFRl!_x9kcL#hoM@7tHJOJ1CIDi?zh;S>m8V&QVf1gmVaF-z-}Vx1Pn{l7jXJ#L
ziYrH~<%vDk+rO27?${2tAEV1`pBsq9wN^B^<A&|M3-dP3Hg0a+7JS^?GjU%6$Z32s
zD;feLJL9bcK21h!2~v5*lc3(5+Jk}<pIL)9K30Mx*3E0nySKt=I5@R5HDY1){cU#R
zE_cNJ>LZ^DNyTD$_owu@HG+#lSv7`#`_5e<DfFpFzUm^2F63b3q2)Ms7SNM80(X+<
z!lqe83Th}P@aZ(n#l)<z;-IIzwVWps<>Q{<o-m$0B`YEFchbrqlHvcNz@sxg9UGE#
zy|m59V6kfH0T#h9El-3~v7(xVvhxa}y%X_-6>M;#V!oS?+gvJ`goYw;-ORR+BO$3V
z(0?_ab(#KF+%<v0eUlN^j$B4l=CVw5;!x5&!#lo(^wnNe6qXh)uigdv!V$SS=7*+Z
zC=7t8@Gnn9g&e^A8NCBhFwKtExNGDP^SmP#NPB}JGmM;HjeID~W7RX~4}y|#|Iewn
zPVv2<+`;I;$51oy>kMiDLze+%>kyfmj3y8l#%p~+GI;?XRf6hAgI8sS7X@F>+d%Pj
zpT`W_h0zRDG%(c|1*Bvr)Dj-3hHpcT9T?2y^E=y*Yh0m(PsxQhK{zplxTnX+VhQ|I
zK#S^IT3arkoV<L?;CbmO*xEm!n=yUMz|l#VWIqQ-Y6I9cuH~F(#K_2*&**VH#nk%b
z<=0+5nMqCQaHIbK%=BAIgS%h%K^urc0(kx2DUEbs(9dH-dN7+tGbND#Ax8_4UIRT2
zvk1qOfEA>=_l@s+<KFJ3dBfFL-@uBQ(M)D^nl^K{@^a?ZddP7o!4K*F*Pw4+=_|pL
zp_AdKhaCI80Wp*!1DaiJKesD!@s^B8bdmOed2*~bP;4GYj(UUTD38OH|8eE;VGcEQ
zr85w0%S=uqr_1+n;d?;eP0Q@v?}tWOJweZ5<5=(4lm}(`L8ZC(K00^d{y^X_@kGBO
zKPlr61q;>p<n8l7r!MGQ1Vx541Cf~qC5-X(IcL@}7sTVi@Z~R#H1#^e6l)NjrvWij
z6Nc`XCbi5U1zst)BSY&-yy6UEbYkkk)i;#U)imVq^FhC|seAZfExJ)P6)2ejOdvj3
zZws2C00JD4B{dRa(}Qy~K<D5w8XI)>M59O*W*e_*C4ZGpf2K5cgI<NVKit8sZQj5|
z8wK7-3-m0_{Vgu+yAnv@zts0pw6|+gL}PoidURdaXR%hEox$Lx@XVT|ZS1H}g#d~-
zb+p{jtHh&SjpU}kAr+S){RN|7V@zGbtc<pA^DxIaWSGBM+3cjNNEt<nNlHdidWt7E
zyO(+PU!({*Nj=H1n72Po<A(pl(N5{%PN{qDog06G|62QxH@Q`ew^htV@`vRX6W{9<
zOJk=yx{!53^(@q&yo)%O<xNmz?t#?2#J4>~w%VP*h<Jd&dh{r5&vc6dXI9v4@}Out
zJlwqdU^4=W5&bIkuN%G=IOxPi6b2n!b<bQ+P>_^RTx{ffp*|PXPw-wdwJuffyrZ)k
z@VCT}>#Gf{I~X^Pa+FDcguovBKL4xhzuhU_f>KML?;O9S`;o2927xk4=ysG4Zh_W-
z1%TY$uOla!_}u(uo#HZ*ZXH$ucHB7w+mLtggY{#}A7<X1Tjg;Rl*?{6yhpD=q|m$i
z-iJ9StWQ`wpcxU&G?aO8jyuPBgtd0M&ezv$L>t^V8(->}v^m_q#6HGMBuPZ7+DtqP
z=Ri@;#<Dl~<qhXQyY|{}e1sTfhHNj<_J=%lXqS&fZ22(;<tD}U3Z9#-R2VYP2K2&I
zI8i@$;Z2tXE7>*rp_O3tQF`x*m)<-@ub+jfm1y27)+MqB?f{1JD}hr*{r@5KzZkMU
zf7vTQ_E0BpScg@A3JO{^6W?%*-~pPLSVuwcD9c2L)QOY8T?3`#)KD-5n^U?5k)7w)
zXe**pAgY4kalXWDuNi7pH8yOPyT?<VtH#{o9MZjXjyomRE5VBLBK_JT{~fkG)_MQ7
zPU)Ub>6Y#*m+NzL^<U~->E6xPQepojkG75di~wTzI>$RqJ@4Q$nAM$#afXp}3JeKP
z(dc1L@0pp|Lz9!hCLN289?ms#r*jPksC#fje6#zeNaUv78;&oo?XFgL*M!{2?Xg3c
zgM^mjixlUs%8VEsav3~QqFJt@TI{$0!9Ke}sKn)u1-ES95_~Lx0Go5yNYt>sJUBY~
z?ZUu-$9MRBMQBn#L(K&H_M)}gk8YZ4<X%RVTIi@67&o#h`Stl3zTAs{u$TN&SP#KJ
zu40}(35_^?A@2Q(kQ^w>I7rJ5Dt6Wd5)4%zjiAFZPP5{-5c>Gx%JJ>La=d;DnL%3f
zoSV81VMs6ZsY(K7i|@d_`#W$ziYx5mC*{xU=7p^{zo*qS_0P)-(;Pc!`j}<?BkoA4
z%AYCtLm}wtqL!i0xdPm+zu3)=OPK5=PYaHZlK@z^ymd=xT!k>B6(r1fVhV+GFQ*c)
z4R#j2CObj%a5x}kx8x31@-`s)V!%c~X5GW4t>?c+SMM3P_T}7knos7$mhnu=(qcvm
zuR?a-h@R!=i!Xka>w%tN#G71<FM&>nfTrmXLcL~9!GpU8*FPTf&y#=-&j7DUWyld^
zH}fdy3~=jbS)UH0Z8@DDh!4{0*&i?R{UBD>b6;V!)+0nZZD5BYGwi$ouRYP=C(d<E
z_;#*x**XN7XOL^QFvog1T<|VlKA`E@j%8`x!U-Xi*i;~JK&c5dup+j9wL~9+HcjCv
z{NAAX`)06~Q;{f9&DFRU9@lVELhGX7!k2|3@c)7vwGmAX7{&mi;J6?7sziKw6gh&&
zc^%P7u;Q+(r|M%)(9!~p@;nL0s22oH*=MSzB=_X%NnS~+YWt_6Zf}0fuq16DZlG=k
zNUvl;Vbuad&6byz{EV9f&AB2fxE9o-X~RfI^&rEl3q2UB*{6dxtf8KSEgD61-4ytl
zizv5+pjzM;tDY%oCR}={_42Is({CM|+Iw0?YpLXtN*Su<=f924N4uV1h43z)BsSn2
zn^U6Qm#u5FfnK0|)-b^2+9!A?HDF-!Sd_*lrTay9d`)zQVFzg~0kP`iI`S0xa=|81
zrK}6&orzJUF<%ufmtepIZQvVrx{coTfaKcmOO{IGrP2j#`6!~y@14U2kZ*igaa1|B
zK$pi$?H`{wD${itW(P4&*0_vK_;thNSMBTPJ9!_bD$x57OXH7l#2O136oD7MzAs*4
zYxwHRR3mqy#7_AKfPBuiSB1i6iK<n<7n`H)6&SCm_I<J3>C(g^?Q^jgX!}3#4{$l3
zL>+OjeDnl$kC)SP8AvI4)Dg@r)yOkOd#q~((Ur(~U6$r<^i2de8shV|um0)fcN9+)
z@A%rs$t>`3J9HQQ-d&;P<yH&tSFu)n3wE#H;JS6nfreOXktJI2TKKhU5_(3GNPPBA
z-f-O9a14JFu4aVh0g^deq1jE!#Zw?zD84}32fC-t-|9I(j=VcOA<@lsx|cTZ5j!fs
z(AO=4;qq^JEx`T*tCXUMl#R6Ru5mvTm|_^|!$L%PNmTN`Bx&^6x9&^Z_jy?RlshBM
za!2<dSNzd06DQ=w<GK#1FTJt448QR>Ja}QvfyT{`uLQiVudL#*$sDu+u3RN}Ya&D$
z{!gHZ>~9Y3+jnUE;&J?SO6^Y;Mn-_73(S>xj>R(B1Qjwk$(9Zt<S0qrZVj_1;bt;!
zLTqu!d9iLj?P;o0kL0WSL|`5H1tH{WG{IkUO56wimmh~2#%hvIU#P2G6i56qRuZJx
z#=h6j{#LqaAl;^`DUuRttE<=Qs^C}qo`j4*7?z0IKkEm5t{wvlQCyy_D;A5vEz3Az
z*nEFK4h{>{$<#?3E<L#RSKb~6>|qauQhe)Y*f4mBGTDXTlm$JnZ>%MAEJ@eGPAjhb
z#lX%XxM1iA3186=QHTA&PMA-5>EqagpcjD)r4@CXZ-5llDdYDzZ&k$lax-RQ6IRJG
z{7Lw7&Ruk2bb#PV<|mjd@$E_F4E-0>UkJ0{E>AKP&}7|&$2U(Rvy15*OpR32!mp8d
z1iT<w*5Xi8mm$ZZRZX@wud<1uKyz480at1javIapG0m8%NLdLOGfHe&*N0<@rS=Da
zT4EPe%RdDq3fwcY6}ls)<qCM(P?h01YY5g|Wxn43j*Ekb?kO$JXENcT$-@OXWTZ0#
z^Kmt#CU+JqGx~WEfTk!~$z;IqC~2k=X9#>#l=fO&G3m6!aGH=+BIlWV#xjiv9*u1u
z$fONN&L5r}3I`+c`Mp;tM~V0n&3S_RY>>ak9^`a7hK}VvRrp|n)SxSRbs*rGNRr1{
zM#HxwyQ8lKODLYw2Sy<D<30p%&|aKnk5lG5tVxATG|mg9zkkiKUBA5l%GU*o>~bJ^
z;9AJ(ALNDc=Op#e8)Nw^-xT}DN8c1Jgh32gz#fbjXi+Pawnl=L#7-)oU!41s@LX-^
zZrf%-Ex^z13>VDU`Bic6<Q4z=-UR*6i432wLO+!3y907_-^TR?EklTTbGE|c{77p`
zP*6Oe8)3a7FCExi9st;!-U1T}1-x<~889yJRZ3D2Ta5z2Nu$b5=in~A5_Ho*rz6uc
z>CK~{>$K8Bp0V4H4K<rXY+nEgT_mghCvgH7%7%(o%1uY506x8BL}WPvphm!$7%H$w
zm`Nw#$^=V>6ivG+s~E}>>QG#*$ciqd%pI5PFjJDQ$d%{Y|KvoFBRGGIU1U4Wq4w%f
z)3IICvZqb;I|+I!Qw)b9g=jbt>sLyM7?wgZpj8z9L$6ho6Bgo$9R;TdxrUi#r9YMc
zUQYxuwHc~xBH~Cz31PMI>f~e<sW741l$`cIMsg7m|MCT_^<Uv@+!*|WE+okV{YcVA
zo>gX(lteyA<{l`_Nv_yIpc51=OQQ`0g|rR5N6<jvP9kOGc@KR>&yh6zFNMOtST-|(
zG-2B9zi<Dm6Zp4y{NDl&N-NqTA{X%Qq_%_t{+-g4MZ_{J;*7F{!ji^rK4@77f&4JJ
zsOyW9Rycd<cVKXuRg_ue?D@{6%I(PX2>o|hW3)?ZQbrtQKn3cEHbxvZ9Qav6(&iKu
z7x_^}g2NcujfAeWObGjAu|P6(9TF7RD|eL3Xf*eAg;Ap{+!4U(_DvjWgx!=LfERn6
zk7@lA=ER|;LldV2pTpe7p$R!_)GY^pj&+Hpe<GX1xrsx9w;@~!RHDUszJ9EuyWnl}
z3qV16N8kJVKHm4ceNXiLw@vs!Z}s-4I>B($YX0*c)7Sog`F!Oi-Y1xD!H@yezpM4^
zQGmW=v3=@ic&_(mR@BvJX?@0~=U3J-$u=ax?(1Jp=@l>Yi5I^QHtqk!S`Yxga09Xh
z+<_5E2|g&;1w0!qpBze-3S=Pydm~Imac`*{2R;FA7a9XVDGGi9*!Dy;$v9q{!4g4=
z*)qXzlAW1MQ2?jb$<GNZHJsFwAfi}UR#bSH%IWIBMWaBk%Jdh~s-n-&8;X`L^k)o3
zAHBF#g;mT5sI@It*ox0le0+XF#YhKr8**Xpb%$6mih-|A<S77nrr&b6+>gBB+?9RV
z3F?lcIw5waEa5HniTb|8Y-|vgtI*VrsSqsYGc&pw;0Iw-AB@c=_TeVg0&Z7)|FO|@
z>O57Fhhrs%s99jj7R(3<lo7L#Gi{t=rP#16sXGR1yKY-_^M+lmhRv+&X3cQ5Y8#Lr
zxP0tm;pRXI!R@cd4d5b{M(`kzhJjp<o}!R@S~NKH80}}#r}#oxk`Z)H&ZeE)dm=Y)
z1Tsj_BxI0kS+cJh>epl~9FK>Qq61qT^bbW{H+o(NK!k4V<ubqB3!R~S0?P}KAPfUO
zv=YFSO#}qULB_H!?7CX)W9x1Pj{!RO6#FTU2OotN>~P<;SZ_U7GjLbFRgqc)`9G1|
zlH{%wf`bM}PlytvX5P>RG=MgA0q`eyXA$@SbZP8|TEA<^N-i9YA=t)HE{lZq-vf@A
zvt`mT0%4!ko}Nhk8onM}1a9i~4O6IdbOit?)HyC@frz1YFfM9=aRAClrW=*yV&pPK
zVF|SqRzv;4WGIr7N5_3eK7RP)^xjPL*I&HT9_d6?BUsB^H#XvkqCX2$V$&X><#9ug
z`1kdU1}{STj&4)e4+AgGulM_V(a|r_h7XFC(y~G^HqT&f`3=M}?&6knu2KZ45P=*Z
z30=!_Vyf#5!DX~5(1H1NcIG66H>o9+W(wBg?$W{GczQTJFg?m`&4`%d`(*f{iz1Q&
zKW0VMPk<62qJ3XI8UAjeksD8jU^h;bC(fI`GQFRq*;$QE(th3yOtSYFlBqsT55$}Y
zE;Pd;9tHXkichR_&6Urgblo&nGN9)f>4)Hx$2D8fZZW6?XfgCnS?ovO3k1HWz>%Eb
zKehHg7w9FcsBmkkwS;)pB2|Jene5tQigHXcRPM1?QH`rt0KwJJ%@z8O#BF{rHhfND
z?Y@gGL1$G&4Yv^PVFi4o(Ansm;l>=m(%O}{rfQ(h@SkmBsCsH0WM1^wUr*aP4{t4!
zeuy7~w>r6Q(F7GLP%hAMN!rtYGC+qCcfnF`kxkqzZI#WUDQ}e^Pr-}U++KixP8BIB
z*wgK28TrYLw{+Eoph}C}>&>`@d@XRy2N)upnH34}MwS{0ha;+mqNF%>5zq{}Uc^tp
zDDv;7r9EO8k7$;v6mJf@Af@rYW{5awpRt_(f!E^Q{Ppnk2`Cb>URX*nXqtdObbz@S
zaGU_Z7aavJx5{OHr;veG;k*AN@cM=jO~Jj6o7`Cglc^UaSn^MDO3S6jm|7MwNu<{7
z{qB%3g}$m(LvO!8F~h1lI4IIsZd26<DV-(u+d5ZR;@?=r*%6F=UpK26sSi=8O=z%|
zpd;*SwV~5|Ou!uv4w4U;qReAA;qB+Ump&B=$FY35P!(oqtZ|KC`&0|67%?wJTa(bW
zLC;rtS=E<LyhPQCFY==br~iZpVnYv1@m(9%xgL^D3m7bBT>DEToQ_q8tPv#Hb8Tb~
zB^%)jS_k<m`0)+Ltf<y^4!VO-DlkiD4(z~>cA*!(%8{*lfS`InqOK}$E0wmD^*~_#
zI!*XK$N{{5oqlnApL5p!#jt(;{lzN&i5G<+xwEJ0>(-GRzBg=8>+9a_YpieR`;MmS
zoYkK2kVKFKmm`Z@BUa8IyjYFDLVzIzB5N^(BAc^fq4;38roCe%&htyJuV=@A(ZS#c
z1VU#>fbr}Z73*;-u3Pu{QOVvRaKEUC*tXAzFRr6n{#^M2beCU(q*Ne1W0Xg9(*ldG
zicydb<b19@f}lD(t#3SGKNko*XNxWS!M0D>Df^*$@7U&tpV;PK9=%ajZ`5*{*u5%q
zp{ib((ef%CtNFKj$NqKbz9{~SY%Al$r<0NjbA$j^`r*J%ACVikz%WumK0&;(C7nc7
z8c^Vcb#P)4u7r%e@ex{pZacOB6}uWpr7b&w3}cgXm+aiW$7O$D8AB7e4=~1uVe1(#
z%?;J<v=vB)OW8_1t!ZPUCy!JPKsWwV<Tg~;m*MrZ2Q}jBJan#D?V&zHH6TY&Wh7Jr
zsC~RD*v!_dGb99Hit`pwMg`nbfOg@z@UR<91dC(o(dzh-SQv|e#1ODFu=(lsPx6R<
z!YYnrNX%^RAD|Fo_DI+?4bqt)o8OV~>S%hbh<Ccf1Dl_o(;KZk^c4YpV1E;8$RK7v
zmRZoR9(>8B%0(A~SP(`}Lc}D>uWnfRn{3SCfRaHUXfoLTI)EnG9X<Gx|IE=OgDGXt
z9yJAfZU<)a3*FdS)PeNq&$D+4d`U!VtpDYtS0#2!2UfEE7l4&C*pz^qJhl;6Qt<#M
zseLq-o?z!;EWvwHcvo}G1q2>osLzsmI!?|}F}Cfv5i^d%v9xs<S@Sf_fOfuxjOHf{
z<Ae^NWmlPl^3G~M>O?ob7u1`GxfjrY66a86C~RT+T<>KI-izC3#EaPiy%?&KPVR>Y
z>Rf{65`N+9H10|8FwI2AlU4?6Qk#`w#nr1SL6vH(N7a5)E1cVvc@T~ti*s`i@5+T+
zu^2zaGW?*IV%*Np{DkgYoMR5Zt^M7eXT%!&1ba35Gl*ytShJFtl+;`h2_zz{f%T>T
zfe?$K&!;3JZZQ0tu!)dWOMob23NNV<GrTiwM%0z1WZZGWi0BAAPCVH-^y;D7_^A@5
zYAj3_Fo8A8$T&{Mu&QTD^C>q4L<5%0p)g;9$|s#VG%!)3d5*jB494^eKY8IX`00NI
zyyK<lJIpv@_{JsT@Ob^`P_R2B-$U#qKVazB`F;Ejx|LFmiu67_h7-*a9->utK|U7C
z@5x_s$vpN|J<5R0rU&B;88>&w<EqW<gc48#A3<sj+=hEB94QH$SL3*2`!I_K!5aeM
zL$Bg8)BPLg@jw;nVY84vo%s9+-5iI%PQ-@qgKXUG|84;RM@)&^NCTkaj@|K*`8Y(k
zOcr@&tEd;ZAFRQDW`!sg`JV%M3^b}tSq@3DO<Ipm$>);sooKE@t<fzBmp?>pus1YR
zJ(a9g#@-E>a_ug$<!-%AIx#7>y1IDX9SRa|DUHEm*!?(EAIcEOR=&_FO%W@**Y++1
z3Z2rE5N)niVY2q^)_tgd2&l7UIo*bsTIlaVh&H#~>b{MqQ`o33i+98U(Ws5b>kYoY
zj?mr#ME9=SvCxKmyc-US&~|~R73O(%oL(@HTw1rgw@A&W%_}x3QQI}ptTo7I4}qiH
z4W#@#`+m9aV_2c5;nWHJJcu(XN-*c(tx4G}u{h4?gi}E)AT5o!fvka~-r^Fee-}L5
znzQ8VF^xyWTbeTnFvNi1Z-eTY6fPneWOReEb)#{1<FRaqpgcEa$og>|hBV;8?}ohs
z@VJzS+c3Z(IhhDf>u!+MY<rIixO>gg^^mI?z~0w@+inMKdz-C=osb<&#a!bgTcU~G
zUZL$d%52)MTQC$TNDLd(?87#G`=jv?a_mC8rWhIk@S<iQ<_2|$W40O4)DK#wCWql)
zW9Wv;keoLVz<n_#YDa7bzjIO7;0qzuxBd1M=^KGge;?oI13cGQ#v7k~a03=fiOwMG
z6gC>KH}>Dg<0YZ$ZU}{TOzbW?M<?`l4j+K9@82z6up)N<vS~}!HHzAF25maovC)&x
z9J$ejrvyeC1@@d}rQcX_kt@WfDOeCZaT;Te3y~X-mwP|0dyWRarA0&FTpM<g|CP)~
zHf<*paV7i>XtV#Go+7>};m|xTsmSe#=m<to;5;&Cwi!k2DEmTtDQRZXrz&HCCyekQ
zd;E#OSmjhYV<y>3WH3z6qyJVV;#=!D0K?jGxbpQS6xI!09}!thIP!JCiyt1|JG4eL
zLr{*!@camp{aDV>K4e<iH*`TRwn|sAxu7sITv#NP#NyDtAz@<yhQ=yl1HjZ1x;qxz
zbN_6dolFhwyL8`Bs`G1aI|&Q-+5+B&KlrowzqCO32WS#+(=&bF2OUnr?hS7S`igN1
z+oW}$R(+@f9_FjSID$Bi!2RtAA~>#R<3#1!Uq^Oif;{K>y<yYF3s>0$e^-aRfGTtD
ziQYFmbI;jN-hbBXdA+cCQoIYgAx}L)+IXrHh#RWOwza=b)nMb@YCL=P%jmnUp^Xnw
zy`o+kP{F;?dpKt|b@Z&~Smx}vbDzjB!Up)qzy|;Z1i;nE%I>M0II_Gx30y7M+KE$3
zG-Zq~!By~_9VBQ0XlqiCk*P_D8Z}UIqRP_Nk^Kj*9Ins6IWUM=_K~6TU`p5XS2o`s
zbXRKiFGgZN^)C(YOX-hABdU`gOs6A};aD`4sqCygec{NcwSZ?WHD+v!_m_qPnZT9{
zqo0X}zoRNF6g1nv*13A#P#|+dHoZL?N#iZZ43RVO3Lu7Xo%bsAbF18^>^%6BA^L`U
zVDPUXmfbjJeX`^Y7%|{^&vpzZQ{9p<pUd9z41OG%yQ#He5yCgS1yPzPG}_REE)flA
znEGWhXqx;LzP$|RKEgwiR~t)9%WKF`6pR+}Z!LP6@XjnE?DwQ@Vs*eQ!aC&bxyF$d
ziN40#XVwtVLAv}^(7z|)H5bBIASC*}D24OE_^o?;0EzV@P6(c@q=Of;pveW~W^zpg
zs27*BGc#HIV|RFk(aWuj+5Q#Nq&>UYd|=CwDYitx?D)6D@6BdE=k5Rfa-==@6*FUD
zk9#xMY`J1e_~39~Bf?g^0QFkuU=!0eGCNpz(Cd)mJ4thJjLcrdp7Ws0nQ7t!%b&bo
z)#IV#0IFeEh`ow4pH%g9;=E*9rvt|7gFCj}F~5DVhPebQ-(n~p4=uu7$1gxTpiYlv
zQ>pCOG)=fj#$1@6EfxyJ+4%*l2qh=c65)?QvRDd|^lr2Q*s)WnAZFPFM!2U|=GlI9
zt;r`f+1yZLky(5fN%-PCM7HE4jWKG3j^R=fF)yC)*wjiWV!7(nARs7MA3~4@r&QO9
zyzT{*BmL*YK{sGnaZ5SBKN9`l$Wj?yN)O7|MaNpLHo~wmjQCkgvz5V|b`P!u@6mFD
zND*zVBIwvjPv~Iz9i27OFv>-jFiK15dql)jtC*=S(G#mezFzGk8f`#c>Wg+%P-T3X
zj#e6N7wdu7ASC~$V2A^ONHr3O<=~tSvD9*)B!*wO2$MKz!1L~2%fX*x-HShwqn-Nr
zixRCtBt6!kJBLP6C{KF6JjjY~JMrXubS<1V$dN3bB;>QE9`@t|Rvg$iFqW?)S6Mwj
zhO+S8YX6fNaE$jl`iB#tQf4(%3jN@{@53p!id!msMjD#d7}-<7!NbCftAW6_pTMDL
z*K_H_YPeqcgVv8K_3&4Bwb$mv70M~tse1w1YS5ZZYW_1<xc&8)ThpgsvWk4o6>+}_
z%1MUQHNu+;szza}gb1k(H2n~rPiKa<xJfUTKIv}Rt!IKF;oNL>+d_J9vM7S*i<5)t
zg(d5Z$UKL+LTi%$JLme9O<um~TWRjW2Np2`MNYF5G!mO}&;fjPZD765v?Hfu*@2jT
zA^~lDB3uY{^W)WWb+FRVYw7yCK3Y=M(nn?1lPm9{dX7jybrv^yzYCg5Iov9+1Cuul
zaY3Aq=;e=S)$XuG<t99-VS%Eh7xUj|pV)tnn9c*7a6ilqGj}=W2=MRunbxxpw;rHl
z6*sZFaGl*WO4(&#E&#)1Ip(P1v|mHVCsv9@I#NbWeuer6WAE=J%GqW@vjILJ%?05w
z1mS2hTvPdUAa72Lc?|Q_)m3(*81MVrze%H-En^tBzt&>QEw-yo2NpYKh&WZmihmKb
zo8krs(eqFmPHG8EFDxS*R?<@+5d%bt8X=@jzp6H=G1qQhS39uGwr37x;}yFUc#WbY
zVN$swdiC;eWItBBu4%hWo5U@TXS4A^+dhu)QGl;r5u@t|I^P|?y~@!+4x;}-bl?%_
z`;;Pl7|sjeM7%ZFCkv!9pxbI3PmkCUWUki>5(P{5KEMtiG&D8bC=dGsBrzG5A=Uk<
z80KuZxSlV`A(@{ll$83;3hqu!0&Ggx*=k4)PbO+2`C@Ij+yJ<QdQjEdYhpGm)Afj4
zD$pUlW^jH0k9TjRc1kb&C)&2gE(hNQ8%71rj~HFuaYnP$kqQ!NrFx<lL3nB4)l^Sx
zi?MG*%a`JgDLFHr+rPgVi#7M}&*jY=KbZ=tbMQy}Un!m<@=u%46Q&T3h)BnJ0kG=0
z6%S+!b#X3bXd1a>pO@rK5%q*0bcH>E6%gojO#rBN#3L$s3@1q*-PM8g%moPvDabjG
zI}7}%_ZONl3U6MK1yRj!<W0mY!;vQKpgK@)I#}3onCWTZyE;`Q8i&5T3o+-sj^2pr
z4AM=;j$pYliuiIY`-6zRfq(okY?5*g$>1VAqa@-erjk>D@e0oZ{W)*6(6`1FT!7&;
z(laRdNf<nFJGz`k$7+XGcI!)4pmUD!P3yMUQ>8;2YjUWk0*lD6xZ!u@7k&h+&G(|8
z3RtV)aSBZ*mQGq{@g>nG@ZB#!MI&qhwJKp3bH7UtUd&Vd=o;}qXPso{z5UR>eA=-`
zMz<AaBWb&4XCgC&xzS<ANoDsPYX3(z<pLcd=%!}<!Vk|r#g^;k?X%mrR_pm}H5{&H
z^Y!XhoG90S-~?O*Rk>s|K4*c30mpj;R^XF;7WYU81&?AXkrdd^IIu+s{294A36B<#
z2MD2{VVkAMSlg>Q=4+vSdF=!{_*&CZ_wJf_t!Dm&JMO`|Z{pBc)nv8?%sqHDR*|%B
zI)wyuDI`W=J8m<Ku<`McUowo_tdWl!;T7%>$TuIYOoJOsBO!)9T|tg3V?x&_3`RbH
z#M7@~{aFE3Cp?e}d@}nWm-LVZ(1WsyD3B3Gh5(Fqu3wyI0z|aqso3GSA;N=MgAk9q
zmskrbzZC=vEiEszmKUee;*l3FD{^TC@<+oHTtu<Rmblc>C>9$OkinL6?Z+Yhqj>VH
zo9Pv32d&?}KNZ;!*IN)f+}xBu^?FQ(o=c3k>(%$K%L}mH7CZo3_U?_p0v|Trm)a({
zsu1*uBAa-JXiZY@pM6VDRsJc0>@_e*GUi4QS+KTj8HPohM5fw%skf3(M+yd0CE}-{
zn=uLz*#3Xj#eXNNnX@nPF}tp>`bitJp2z7j;eOR?=kB*JtWqUulR+Ke?$ld3FB8Bj
z)n{2)TDhEq1&Qcq9GaOtbTfNF8?Y9MxE8>7XgVVGkxUS&5k<x_%35|dUaF@o>f)%D
zq~&PvdB`bF1dhnd*tTzNwGd&0zjl$5>Mi&DYdycrc9f~(%4!;3hSyv}yW5xPzxHZY
zvn(xp_I3$e3dzBDVb1$lhf_RW=L*EZzZSlepGSZ4#fcyb1t!+HN??R%pp4<8ru_e?
z%;V>~D?5)f@z_Zcqx^N#XBr7oOfWZGbwx9*WnRg*pYJJO7=$AnT;VSJ8TZl{<UG!R
z;<NDmCLo98+ysa*{Ur1dXK#zqU?7)H=K}Ppe*O}E{}jFcR5U1lW+R{>KZ^-MHs4iN
z!uO?Ez`hF1T(m^-KBNVVRwNJG0MfTeY~uH72dk(zN)#F^;lemjWyNd?W=MQ-f*n-C
z$$iPN0!t6FhBLLQr?R^y2Z>!8<P3-?PVVwJy)&h)fWMglEW8p8#bO~~rx@x)QHyOY
zO<fYeDgd6P=jrLdB~yZL5VT8R+Lquo(Clkr#=$HKK^F}ob6>0=@im#ZQ~<@0-%STk
zr{<fFI{3=$dsiP*B}bob?5``ie12}>qJ^AX8!u#GhL0BWveW(=fMEva_t9pzAP!}G
z0Rk#|B43Q^nvyMy*W?@?oXh8PN_~H08txq0u~%Ec_D=6XJiRI?#}YjW119Lb=Q|%l
zv{4g2TfYGOgLeaG^moxJzEb&X6tp0Gf#MsCbFnIsrrvY3|C!HK2!9XEe(!QeE9B36
zj)FJWNXLfFk|ISEX?R#t;L7-N%5A8jU-r%PLUyvf#Fg7C*j{2ImPk?>3bJKg{W8if
zzf%%-F1@@1T5l*0Uc?#gAbSaH@T8RT%pK6*&Xj9C;b}9So~1A?BuN!GVM!)DFQCgp
ziY>%pQzGX$0G^WFsVB&o^wy67Gs=adZbls)y8Log(^PK@v;Aiw0>%^s&|EAE-=B-P
zTO?Z_+HOtP_Kk21q7@8M>#fz{29b+K29`FbSmYtKVoN9yWADXF(ppeyZxsb;K+<Xm
z<Jwvu(#H-?YXiIveAA&RuLez-23`l}!7&OQL{dpTov}%4K%RH<TPUY)r)xGViS7cj
z%p%%eYgLOg1^(;3t<vX)_tnkcs?YWIiYSv;1M5S3D?3VxT%9isQ|s#+nlhkG9vDTx
ztjyJWI;7hzEA_da?_DEq@~rPo4gSCWvYUQNH}Wz*C93!{e?D(bpzmkHOSBK`w%>{v
zpftEH`Dgi<r-Z-BQ!n+Umgefby4b9|+)pEPSzM=4;&DB&J#18<cG-Jgb)e3Z+5$1n
zae<!3y;p_88Jt9=he04y#fr_1h&HXq+*pVLye5(vDC;=IFSpOca>BDY*S`BS1?kad
z8O_Cz1f(>Wh;DINAltP#!OtNhys1NA=%Va!8p3(jAA5eJkR*E|Y-P^kaQ{%E?B!nf
zp0Cnk3SeK__PBqx*SNWgZ}_)egWZjMDWJ1tP^_<uPu}?YlWn4&!W`LtwC4vv%Db1^
zKkoSn@;AYopX@t>-ymOYGCp+mb}Q{=>Ptr3Gf<V&0XAs3&`Y+UpCUgGvYTSCQSC?{
zPWm=9Uho!852S~KTM@E@ixowzEqCL6V9c-SI%BLya%dOD0(&!W%N%X}K3`@2Z=d^@
z&;F&&Ur+DUu@=0*dH)hLQM1r^E&!1OUPxZ<ZKyOopYzF$be#+v>%PkjS_$<KB13u^
zm1LQ+27m#81<6%q2kec^;7_g#)TS23;qxRSKwN5J`}~)(l~$|r!!8csk6M*1&aD7g
z48GzzvJ6U6vpX8sgy{oc3y;IK8bD61Y-P1Dy?wgCH^ppar2=m{KCYfYTOWd*0p>I2
z#S_CjlgBAjTe#(hd^E`$1+$FD!~0)Ux@Bh9&~)K#h3TQm5g^b<Epy|E_Mdr*q4y>%
ztsi-$rHIr1lI1GhL$<?m+utPWzKCQ?<ip_CX$)GK?S1ES&CJWJn@TuG<v4miS%o+&
zMjn*>XaeLlM7zF>IzRtdc2F%RGZi-QZ=&|!PxdQ?ShC0p8TR50*{@59!dhc6BVQz1
zl+)yBR{o4!PRU%COgTKo_zJ<JKMjlk!f5R~Yfkr!9lsTwaU(5lGCsVTim|~UW|IAD
z!yq^%>ICRqJ^;wpw8p=G$V2IR`zAH%C{ReKfKrUl>}eSML^OIwn?l}o(D!q`NkACm
zNpI3)E#fK$;I4zQ74(X9F!34Tl0w%Z?<L~vn%3UUM|Ic%;-szEI_wgvGUi0mk#n5?
z`z}t|3P-s2HtjP+;hBp_j}>&xGVd5{+fU;Z-o+3nCI^794nKTg5QQQcx_8qHA%1@S
zX0dZ9#;@p86JM7yY+eZbxxPtnt$JyU7T7_lC-_1^u5xi-Tjh~Y8Yv>KM-T|;2q3;L
z(V~P+w*%VCXmNvT);>+!Q{F~uJP-C!S~kV#I6)5<X~S^}UApT8ZFp(1Q({N4yf-Mi
z$EW#sahjhc8~a9#c$qB1Wc<<PQf<Q*r!08%bKFnp2s18*8RudHHu=6qL)}NvX4rUD
zLcQ24Y~vzTy~wcJe{xF)_zc4obm9s2Eb0L44wX`EK)H?qN(wB(?)6fdv+VrwK|Ho&
zuDQE8q02@xwf9YX_r9sMWlRf6fmHI^9kZ{>sIH`N+(tnYSGm5_fKM25PUBsWKc|x3
zk~oiL5&pWuLl|;4*b<NVU_Y|euo*MH_K{bKxG8t{Y>YxX))3jD#%6aDN5S3|95jHk
z@Ia9_ar>*|S>Q+5dNCV+^>%&={i{&hFfYkJBL&^l6@7gke>4*=t5C?0#hv701pWJd
z-5f8Hi;tA?ijeRLFRG(?66_#82>+j7AhmT(AQDNt^GElN7{<unqj~nO_7V*ZT5?-9
zGS1l{({ACQh_lcCcU85KJBM_Bj%Cv+EHlfw{@rsUGnO?oGPk=w2XzSMR*SB+7UIx|
z0COc6jW38Z{32_~vH^Q|EKw{bAiY=yA6FgPcv0Ug5artkeSEFU+7YaeCzOE(CCjKk
z9dcF!uG0|ADWCxN1E6UZ@i<L_yzAsZ^$}P2r$YoRVo|4?o<lIy2@t(uq>+&Nx^yX-
zCdUGL6=xP{sGc8tFceSMnozb|b4AyV#Y=M~#s;>RNB0H}TS)ht&jsVDf%(1L2g=*p
zuLLPBY1$BCcn_I%cJsiN#1<KOUo=^s1o~7%Lc~y?-sC=F#xOFgk}Oz^S^>oO0jC2m
z-dZZ}D9;Xutikh>@yJwuU|V@$``&Hk(=eH^19mErks1TV<fV9{i8mjTwhZ)R8g}Ja
zT3h;53cY{<7JX78wLZE`uSs_x-S~jjUQ>;bDYeXy(O$j$@)rDwS3@Bnenvv38H%tw
zez8^MbL@+358@``#Q(q~K=hr49@nr#;?>yrjdQXFbEqTFTwx><2Fn}foA1>fIf2u6
zT?y<4_B*y|w7+E}`TJI(%l|SV6p`BpTmj-?OjQo<0w{&JUq<exjN4wd>~M$+BCO9Z
zthiC-7XWV1{!fMcD6;>2;Pax;GjE>cQF51Y)HtrS^`~fe^u#;Uc_SAGdA794ISi=^
zQX9z7^ODXN^iyv?yFg=nl{O3QXKADypQD2n+RU*=&%tr{FfA{yE-kU8rKLylz3bg9
z0d+nAdQbG0&G$n!yxMBD1Z`S_-fsop0~Q1_)IiJtY3N3)?(8~-up&3f?waIkx5-^6
zJx<K!hTa+!a&QfTe^B6(yl08iX%b2;=)Gh3LS5|6KE`{?W9&&U>#>2*A<`P(k^055
z1&uPHVekc?mNB@=tUEsII+i(~0igS(db`1KAxcU1G@NJJnv9^#=R|E$<v;I~{<>4@
zo>SZWBs-2f2?G&ys(mI|f;cp1Iqg%b{No?vbwIYG#;@A|^I+XEKg7v8-H8qN$)`Mr
zPeP~H#UsRWgVhdGqcf!7k<G;?p7I4w0U*a6D%KR(ZmDTcJ9BCKIT|g0ap0Y_Yfb^X
zBn`#SW&cixzkNUmTf04-(2ZQ`TH@Oc=jhYXy0OY}_h@{a&toS5@(HPA!>8*6(s9(|
z4ujYEP92^ORFPP;bu1fOT!iUokxQ6GY(EKdiP+>D5p=_Gaps6;=qa9}V{F(m1%38P
zpeYg4O#I2lhw%K#4(~y2BRNy#_Lze_fH;EAx+^Aisbf%}^<aHkl2S*zKw3RG+I5B|
zHLL^Y%fAt|7;tyqCvPu%pPwL$d69L_(6xu@EH=P_qTPb!9HxWf#;1S{C5BPwJ@31h
z9;MAIs4xv6WG>W_ZvPqhpFV^!hNx-8p7-?rOy>*HOhc1<8WQKqWB=~vdXk5HePV8k
zFG;RB-UubeV156z6lt2Hc5{4X6j^cOnN|PNaV+B8v+Z90O!wwx|J3SPkNYQDoi{J=
zw_vs|_}BcW>DzN`JHk|JKsyPmCeyhSQfE^6jft0m#=slKGc<rwL0$oAL0HvHQuTm7
zJl;uq(>Jy_p{*T#sy;XOUM(061@?iZf!gS=CGGj|HX7_t;5-{Jf`LHL2(V%+P5!II
z$<d~f#+|=lM<ja!!tXZ#bfl>7G<O;wg3E2hO1QCbIOZlg@%p4AxwY>e)W7TGEIg!$
zR~|vY5(SNdL*uU59v&PGZ*rl;2ZXG|oehZGfc4|TIo5TsNaU*^L<Zb|4xno!uZsww
zrl#}fuvpM(?$1EI!d`<I^n{E*zPoU!kg`G(Vbho_Ww%6CErXb+V3;O)(KHoTF-tUq
zhYRUQ0I81xp-8FLEN?4E5<~g4Rfxv}6J<GoC?_j|+|6Uzc~%|mFNQ5-_>g2PUcD|C
zR;37gSLX-mcOS^70)w^UxT=OCT1bf@4j#V2A){yjMx^Ql5_T5qajjUaCb9$bd;0N9
z@zAwqXhe~<s>ONHR6TX=(EQu;2%$D*z@<bZW!JK0dC-EiA|n=ORayqk-UYe=KWD_a
zfe4_(y_^@|`oIN}wu-C#A#78bx(9i^x}gp=Vk+ru7cLLnRC5YE<5Dpg&Bnr^2j-w&
ztUI=>HVQWr`949zWR0?@wLW}ck@mUfRmu8X^cGxzsi;=I-g)!I$U^{z!8nSCrSV#@
z8p~I}ZPRrG+C&e1fbVYIGFQjXxWdA;)XdF05$+rD(wK}JX31HYj2o^K938cj1}3C5
zE~^nH<7qU6x5oT@_oVsN&e<>jJa~*(uES##{42w^a!HejVT=C}=Y*k-O3u`x+&u>$
zi~@;qicEoUwX6QtKqBiXv5Hw3Xl>ia{yY&G*|j5*cFmS;71P<l;=EJ5Pj(_zE2r-s
z9&gQ=>>X~cITKD760zuv9E&%%+?pNA(K;jQK$_4H*NJidQP31(d^~!LDdy80F)F|k
ziUL<Ka0ZYq!H*rxV75YIp+y^L$t$dd^8k0n=Hg{aWF*8T&JGsm>QL3e&-&jH|H#;%
z_hn!|BvhK6u-L=vkX)01cCk~UG@o3-N)e8-ujlG0ygG%Xf9Y%cZw&=wk&*iGiC6}~
z@qtQeF<U*d_rk|f52#+@0!_Qy*6r9Ig>Koud)L;{;UQfv)T4><LN$BIJMzW$=ji54
zbXn|3I>ev110Mo8PROq@pp$GTTdp5G1*>LF$1=u=G{klqX~L`G9HxKY!NQ9qJJD*<
ztYZI@1|umlgjNDjKNzAYx7Suy@CS_u2Ig|H2sWJxU425UrwFDRRRc-j^_AJ5kl!eP
z9bq)D%qDahYy<goCT0;pRAt|8PdbuwBkNZT`TvKz{jRrLacMzHBe3WPdO;g~Ue=ZN
zHy7=odGWMr2iU0)k13*9R`zAcJWy^XdEQK<W|c^i@(wVR5x3{ZvwGnwoTB?zsw25r
zJpY%8`E8@2P~s00EhJKYKio*+DSA?7AbNio%GX0*&rB_(KP^swI)CtzNPh8P{%;bl
zJ32p~_@g8n+m-kblA$DU$A^@Ft~}-r7ee329+=6TQu*0ag;<>Tg}{A-6^W6G04qj(
zN-u*P&P+{Xg+g8yElvhyAjwWq)FPjzV7h=LNJayXd?ErJQ$p&~$A@?2+(_;X2y}0p
zh()$<-yVsbXn?JFV=m(6b`6i~(2<z$fnO1+4H$cm34KX;cC;SvADx)Ji!C?DhbJe8
z$D7OSuGxvv{&;;9uF3bqr3km<8NBy?i_4a4?DxPo4G@hN<`>_rgjk@CMZ%w__kVYE
z|0uE#aWNbSE(qAFgPCC~BNtw=f8;pvHSB2nVX^0zIS=aFVFtK%sI*?=FK<j9Lc$MN
zFVH9$w!21|-v<v-r182$roKi4?I1II_m1{jgDzv}LSws36N|>F$@zPdKh(Yem(d6h
zdKY{^V4sLi&5~m>EVPINAi`M2hpSQY@ESf)5(@A#56S4YPyq8(tao57YP~j!CisnU
z_RgAQ$Km3ci`!BYVVKbP8oEmx{AcXKrJ8gf&nuB7Vi$cS>dPx_zPamH0dR(zl4I^r
z-l{Z{6)Qh<P>&`Hg=F+N{^=;+ZB>#(|53@dmno$iTZLZM^Q2hnx>$T8Ka_-TDAsf2
z?ULS!KKT8<FZ6u{a#$6s#3Z7_!8#$To)!g4{6t}Gv|Kohvp#f<5aRH?V$c;quLLz3
zj!03>$1i#2;wGVh0Iww&C20B_2?<$o0dE57Mfmw7u6b+E<SaT0kAt2;ze_)j-{+V4
z&*Ob`1OK02o!kj(g1m3C@4(gp43S=!NZ2t%&uWTcX_4`W88l1+jI$EFXz55xX#>kL
zU545t5Y2AOMgu67(}2RXk*iX-Oe1JU67-15i(^s5-7Cn<8jRVAglXszOAQ7cGvwHT
z1o9!va;{_oZ<C%F?T^_(H)zNDN9iycam-Rqh6V?B+W&{UH-VGnD(?jI#hDSA8F}AX
zS$R|*b!Qz}M<3PMcem6>wa(UEYPHllQA=17$QVjok}P@L1`n3OKnaXTV6fZB1Hyon
zIvC3s;~vZ!?HZ&SY|nxhaAUJ$;X$eWfA2*^W>r@sU}t{w+wHE5<3(iTi+6nQJO7^*
zG^{{CH6yy0GP{8>`LWm?H@r#!vBADjpXCh)ji}#%M924DC5Y^0!ID&&hLR>4s(8JX
zP!v%y(-o-{Oa@Xx#cTMZs7%b_`>@yQ3ju`42;hPoiF##OHB2~?dt<(2Kuh|{sRHhu
zC<2Lgyf}fwLaOXbYJsFL=0zm33DvjkjYb&34!D1gOiXho=_w$ZhWuZN#pB{4CuX*6
zyqQIvUP+QxgCr%WIdChMfmvW@O;M7p<zf=CI!C<uQr>%nn(AiI4OGT@_GX?{oI-i-
zUP4s#4glsSi~CF~7!DKa#A(xFZ=xGx)+;a27a4RMpfXi$3HM@LV{X5|9Q!d@gw|M)
z9l1_OeCL=^dM(NqV;Q7*h|S6F;Tq%eQ*?c0?FMs;WY7NsJM-h<ePCxM$57D%kcr{7
zL@2Z*jSvMPBHzs=${->vk(_`{zY_;b)U44BrU!SW0>g0?KA;~%Pf(VU%^cf+Gj(WV
zho6I?k^W-}lcF2{DGfIVidn!Ur^8BB6jXIgCMrrWPZcXep;`(@eDSamOj|}I=8J?&
z)dCig&v}PNVj+AEq+*fLaeUZBVki_F!ROP-(MW6@pVe|<yiv{M6<Nnf!r0ejC7-D_
z#tY>@rq3Lm#%D~M-Cp(MGobcOkDB<Twd(d+KR!gC$yfQ$kURBr@WF^dbDyJVku{#H
z$(UYi<|Q4}XbOZwj_VvyqsPPcp5z0=yX!AbRj+7WQJrc%kgV_S?YdXS2T%N-ck0l8
zF-m*)mJD|5@0}PNA2~DyzSW6QF$m!r#1JA51)}f(6(`s?2`c8suCRaJV%4qr<$0^~
z-w~T~pWp&+N<QX>u6&8zyT#&n)JuN<y~JB6%1w#K+`yGD@iEgzp5A9+hr#d8g0Vru
zM)D&T@+!2&s0GDa(w+;rhlA>7^&0L+3=@_jU8DX{P%Yl@)z2RLvpi!Hh0@2=;2){i
z;0otP8M_Ae2QRtdr5BEUHe2qWV6gqzze}<L|6Ssa<>YMlcWZjG!UJ>!@+7sy!=a>l
zP4Imx#nZ=p=KF#y6-+3@%U^Svfi;yxu=AbZ`%J{_;V$)k!E4lH=(m;=PAiCc>hJ)h
zBRvPtLI!7-*d~b(W*hd>4Ygjw1$^cq4H@V*JqYW)q~4N8<aLrCN=d!YJBirXL-T|;
z{t%qPW+h)<$9;5!`(kdtw>>)i<2@`%+WBp)+yAV*-1*155oZ)&|KPsp4JZpBJn3#W
z*nKsg6Z21btQX}X9)kY^G%X~62@n-xM{6iZ;PK-1bQ@iTAdgG>RGb82_RnL876BIX
z7zWoC>+d`NmXIR;`<bRFcgp7PD$4s6zn3=kMyuc-V3T9~0&d6M^<3xQ?uR}FxWxP+
zKSJ=@`xP&376<%=7ReUQIHxS`6^g;Z;Bsrphdtu6E24rJj82V{JvSk`ff(%RJ02Wa
zfCB|!_ZCV<*0>Ua#dj@h6cr!4>!Hp!qEg6r8ymc!#BY+Mg^>r3^V_~c-y?8Q3E#ul
zfSZc%i~>LKt}8p=xXl-mqHOS!r3EN6=$?^x-^lOcGy?jFFG}x#ZO@DT>hs(}TE3_g
zZU_lea3#?is?K9iIGB<ICL<`hDo*qWr;0Sx`X$m{&|Oe$&5Cf_DNH#0fgz_mNQtpc
zT3{-f$7m`p^U<ASzbW?b85xwa2zNvJbzdzi$8z~VIp7QN6(Zyd4EXc87&Kd6bEk=(
zM9c~^<-l-Q*CSJ!Z^frgMKwJ<9FPHmXvye2+~<AuuSQf~I)(J62#&yfbN)JyzF{o7
z+wj3P@ukIJ$}f3iwUW6+OVN^9s>OB5p9(Gp^Zo-@2SQRTa7`>Yu@^SPq5{vwBCLpe
zCxWr7t(X+@Uvt2pzw`}+_vPOWT?nur+K{uz|BW&)4D;wL5zQi2(ph(T?_F8l$O~iy
zNWfY~%Ra)vPXdd`_L#|wR{GI7B?ATIfav^&?k`yukPP+Ju8WP0*>P?OYiInhZe;w(
zBO&yf(APh}$3hX~!0!D?m9wT84CvK0QR;U@$?7%3A`#kEfFzkQJ3lZ@+OvqsqN4o0
z;<kTFm~=IR%XBq^0Z9HQPfHYEY;Zs81Mp5*!dmahZHV0D`Z2!BlYGuKsa#M88oa>D
zhtk0rr?VJiv{cXL!4NdLhBaK)fgS~}s#11^uIVZy@&X+93sa*IX29oXqH<p(8`THn
zR&*2S{HN?zF7zvL%O93mgzFUAORWOsb}sO*M>%72dTA7ywJ8!j8n{s!=2o`(U|{PJ
zJSr2lhuhN29|$IcK|k>1={~knSZPP2oe+&Qc80sIx%Sd$5o-<XL6KKjcz1DTq^1!Z
z7YPdJ20oQYSg;Qre_!UGfH?_RU{Xp7crVzqYtv$sUH9Pzpqz}Gy&<FwP_!-KzRri?
zireegyK{@C;9|&ovtcF^Lkqr3etFBj3m3f>OG#clFQ`sd1{mC}g%j^4RZ=s^iA6k`
zv;L6J);b9kqZf6h=?}pMG9SnUfn!k|o4^uUvjTPgMD<ERro!dIQ2Gbv!xcqFjHRh=
zSM_>z$If*^DGVOl0eza6WTg_GALv&Md{JdVpu!&BxuXh|9nt3>!7t<87+(b*Q+GAS
z7MM6Ll~FB7(^0O0LSs5&L7`TyMiVjcCFD><n0vhP7*8O1eCff;ZKHVUJ@=uE-*|m1
zkw_%BAr`KM_AE%IHWv)eX{J;_ySG&TZx{y9w>7xPp@$?%Q+?OvW3fEEeN;uRAO(S{
z_=dAtoNMqhip8+e6yN67<(FJ?IV>ScUG{57;GsQo%`o)PM>M}&2h`t6s1#CxSR_Ht
zfg}U}bavUm(W3)UN+Viy^p{4l)#Qot(K6v0!APIkks)X$g;*EpSV)HTfM+V;g!`9}
z&Iv3Gy36q|6znj@9{avKjIvj4b_W}?yl=hzmW6F5`w#Ab*>rVwi!M8(YTLpsx4+ek
ztTdzpr#asSzkSeilx+E6KrutPR)=<WZYx_KhZ6E{>9s36nv|`8MaOjKif~7_Ser_s
znve^UGg70^N<|Ba3N&T<@}{G{VIQ0uK^@7UlqgC{DP1qY>$}Bua@a-!gme5LK9Jfz
zyBvbLgQZsUi33PWzkfFD_W~#vX=HQpbhWxOafD_sc_A745ulmt%M=R4#l1E#E}%-$
z;65m1zHdsqAjgEjg;(Ll$+>VSr$D8+Ndys&@Gu688c%}0LYknsVuVpsHhA0m0x`YT
zvkF9rt~@I1GV;IA?!T~K#yUwm>91v7O8S#Iwt@u$1Ru)=->semMT=b|$^94ZpT!OE
zF#E4r<dDZBz9v&)j>~)$r_}~6juN+3sgbgX8W*vP=z%7xRzU_nS_LpskfDRXfn28^
zw7PW19VZb;@aiK7ioXK}sxuAa0350g7!Bk&!UD16Ju(i`d)y3Vki@P%E{7iJd!uDs
zsHztl)*Jhjp!Ww$l;(5k2i~B~Q$K)l86@P+^<G`bHUcb1s8Qg4Byo}$Poic-8_~4V
zNuiGAir7~_a|Mtj3d7ZUAfFvRc3d{LdjpM|a<aDuErrm1VLABcMN>KC_xUFBeZFkl
zki)Xi`@6ZFUa(e%aU+F)a{jh0J<L9Oo;r~vI$J^Sfyj`9keEKO57I$$ZKeJXBlz|Z
z$j?YJof;b+zTt1Z=5}aEK6vv!FA~{9-{4m4HlO~RiGkV#OjVN?X=+i4=*Ol96cF$T
zMi|ZCHf*%daL-M9y##o*SU4$N57i1rfA@g=(@1y9R`0$=HIQ^#nm946;ztcuC?K;?
z-K!3tn2-$b4nw`=?#LJ^4aS(~DQ`gk<R;DUz5R9|o^1N09`HUDv4W)_j6c*iJvxIF
zs@2QaAb5#8RS?tOs~wm)?N6orr`G`Jch24jU!^marQfEL5d=i)wJ);gFb+UGMv-U%
zUOJdMvk<7N?1|2bRZ1l>9+yHukUe9lX}~~7U{p9`6bfQYBleu+kuv!Pl1CodCc6%F
z7}|s{NfXf??UZDaOajb<1-v2r14H{~TeJIVz5Z7evIeq3!=eVR6yv4<%5uXEzN4E2
zTM!c0*0cw$jceM2G$^hy9Rv@vY=%3{p{vMKj_m$Du$u>U75O*jRQ+l)&%1}cvUulv
z>#y<~MZY*}G-05uTceRw;`OW2t1d?1oLhmO!?mmPPd2U$*)~?;v%>RUIX>)YYEXZn
ztmlM=h>qPCY~>X9FF8f+-90?J?%cj4d=;THBEEYDkwZ5@BIc^pCh`sez6gUJ6oXK6
z>5Pol7)T%?W3XWf-zAt=Xm*gEL>RTf1Jr1;*r9!($e2fT4+Ve#7Kyk_EQ>zvf`E=C
zK)=TdF{xR}j%E8KxYHvsS2Bh4Pur^XUG)@F+kk4y+qa(BQZCKQ2x(;Uz))lHx+9H|
z3Qjb6t~9Xa#8v^=Gnj1DN0TEX$<g{BTGe=^DY5iuw%!MM+axdj_*|jVs^Z~0>-pK3
zge*fQ^jT$mf5&Vyi$yLSm8I07t0@%z+C!;W7Rz3?IlBYDzQP%IQepEpbH<0rZF5eB
zC-8A%fZAq#h$%nyoCh>O;U@G3$m)V6604)VS*0VsY0(id`shw>ZeU86>Md<}xM6+Q
zk7*vT*sbF^3d;3<-)q^>pKSQUJ=Iw|)L-ZII30MJkI4kba6?uBuDB#&nEPar@DG+0
zR0mEM*uqjknPj+fGX}3wT6-}_i88?Tmm-d6M}uy9Qv~FF7qSP05VW%y+LnNP6xm<Q
zY6eIlb<GHl7XfZ>_79I28zDIz+Lg$tIyCow&6_Wd51-tw85+_ud9TO*G0lHxD4eO1
zZ=)1X7ssoaa7Y?!jo%+il;UZB3@Hi5TVukn3;g0UTxYtMycj@=-P&*T4w&9VX88=`
z4tofaa^+vxM6g<d`b{R>xOw)R6&%W2iM8*Mu<}DeD^niIvy=Iua_8?r6d;#<%DZK5
zi;f&prxg`KITal?^WpqZv~SJZ^hJkIYAW?C!4YQb_t2f0=p*q$A+G59UUACT8{&uC
zWN~-jY=h2;K_z|8dpR|)z)=K2fAC-_hO(7)HMgrbS8?0AGPkaBLboUU=O~tGgQ$$I
z??<mb9yTAodh}+h{atv<OjoNvH9zf!iD_x=T}4dO<7D$E5FbLpu}L#5uvklzBF>al
z<HCidOUmsd>TqHr+4QKY|1vz%boG+bY+f<sK7bBK;{AnsYXHF%x|-wB*sPA@C0(6*
z8~0LcrHY_p@&Gh&0V7b@G_ZpgL;9op)SEdN9Pu8MQ?l);kvAllFEOrc2YfEtfmyZP
zuQ60X&q)xaB%3tN978cX)tK9P*MoQOoNE|yHKZtdVPN#&Rfk3g3c8|%)cAGW^vXBv
zS4+2TrPMWd?b>>4SyPRiH=?HY42~We9X&Kw+nZ7&-W)QeoF(%LZHU%VJ=^T<sHc~6
zBaY$%hEl4Jo#@A+$Q4u?4agNCq)wY<9<eAWzM}tVK#mvvn`*vNCGVYx#0sNV16MeF
zCY+E0Zz!{tAnu>=%jP~`F)f?pP-iN!;MK*wu`<wuQw9GUwsU<0`64|F?-tBq(F|BD
z(N`cy9K9M1!+Ct}@<J7)euVcLL;x~$fSW_hiLY>ui6^2XQIYp|2nYt2VI9PJnbz+O
zU1|Dt18w?bbv()rUKzR<n1Gso5O)}udE?b5(ZYuA2iFx?q;=t7Ci!+>2Dv~^r}~cM
zkydWWXZYE;xvjPwh<+r0q%U>a)B+jb+mo4tbpIl;2+Or?<~Rce?~+OW4rIq_^Stjt
zXy&&H?uUBY9h7b)407YGWx(jw)^bKoC(}AlMBn)XC~~0ofjjgX4^!9Klya`atj0GT
z=#6mu(i0mxm)hgv0nk)Feo6ah|NKTrZk^XhMi$;gUaD{E-8yw-`$Xz`UUlZ-5jCiH
zNJgSmz7_R@i?UN?XS}}5alaenF@nsYw)97lCTs0)VA-I3vuz8uM!QCgEVUvVJtXWo
zhb|dt%+*g(e-+?@w(Jxif;A_hs+}(_Q9YKH?DIpHs51lgIXn5TzKHwzeTVGtu|pU-
zs3`dw6U}}GTs}n)LJyDnK^7F=JkAWqA|yKACSgd>O#g*C!Szl;1k;!i=zJf3bQK8r
zXu%c`Lv^Ea)oWNBUh_bLZBQe@e9Am;4B%$?QdP1ibwgAh!f&?nnrCTw1z&nXSXpT-
z+I~pSzz(;AGMI_pb7G4fJLVb(*fEAA$=vOSFZja{DIw|z$&Q9`)FB{J=rs={>MrLZ
zdH8XDx^v}QbC47#6Zcc2-^7n&lKN{HDOs^BtO7&iEL{|$j?ie3{$P35Fm3>-ok4pf
zXryuSnuk^rr19-}pP<lci1ooy-6Gbum$)Y31~Gx~(`I@7ZESg&&s=Q&k~`g?|Mz?I
z;nwOZTX8Qf+m}$l3ikcW!SKmOtJS^2*X}=+S{Mg6qJ4#f9{eFl<A;Q`;M~a;2wsYF
zTevJ(d_p$I0yYsQOD|ob)y%dULQa4tjNb?sSSsexr*Lc`MKkV{^dLMEOsqvQ4eC3u
zw|}62IGtDJsH~M|qA^`Iff{jiWF%M2)si_?hW*GpFjGb(rmT)+wv_|G%7xoh+~?C%
zy5Gw5S)mVW$WJTF*+eZ{1dfXa*zug-r=^g7oGxcV^osx)ACZyw^hqG2Ft3@?yb4eW
ztx&d)puX@dpRZp5W_dIb%><E2me(f|<9<7W-<Lf)Ja>Z7_9XedfqYCI1c!t2EJR|$
zL4|sUpA(J;&)YQxXDb}n@M*W<vBo!@Wxj)Ae5rt3U4;VvR&f=@%SgvWN6Uq!c44_-
z%j8b}=PiU~Q~|Jq>+VzIoklA*L@e%fOp+Sb!!_<)bZNZ=2DA0M?Uc|z1VUL{mXtCS
zpKwx*g!>FFET!QyLNYWm!Df8X(o8v;%~rG7wv&Hu=iN7ig9Y#<8So`h-Hcj3uWB;O
z7tJWdYA^uFctiepAvd0Vg}zy(uw|Qe-;Xgz@xjpTaS5^e0s=RG<2T@gZH&o|82RN*
z!?gl{=7+seEfI`n)A3}$$V$~v1|In)dNrO2#Qjz_eWck}uWzc?Sv!>&#mz;^{b&X(
zD-lGL9E*o?b(5AJ%?Hfcpcw`~KU66Vb{-j=AH*Nko7P71i+l#&6@#=kRzSb;pN2bu
z{?nA9{Lj3p(cJyu5QMN>Cd@V>L+F<zW)tpRV2oWRBg1nbJU9lon!zGGX0C&Zc}u!4
zo=^25J>Wl;#>RGzjg@{qh9qX+DJgzAefcJ6$xGi+rT_DeFd*p@*@CIVdM^R3=Ol!h
z#0AMPoQB|g`k;sjV%yWIq<nf9g7VBAl>TD)(~7N^qu5~&_|!h~sUgh*2^5?U0fQ$J
zQ8)#~pL&&@S*#*C2-?mIf4p3b%#)q3oLkmzWvYC!#9e@I;5TkiB*w=o-eE=5tykB<
zWjvzesG4A|ie2_uNPuguvNKeh*qX|8zH)9E38*ODpgL=dge=Ep(<G$11IRadj_eOs
zjsptTfIlda1j>cy8fod9a449WnypPD<rKwL32qf;5s+N2Fgbs==J!I`;Y*`cU{YdI
zB0-6|BaBJu@pueux1!YM=4uM;A<=k$v%kOj@t>BA`R^nt0o6u0ElGaAB&EYpeO!ff
z4AR`8IV5$~`<n~R{?4!Qtjc2DTE!aiG1%+(k%kDgWz7J=fKTQnyWwdF5!Hw1T~7%-
z=hoc8Z{XQZ>_NtrjP6nhRjdEPg)MvelDubr%g)&=Ci(I3;O>1#8wZB@i336d#%RmL
z)Kp27g2CZe3r+T6b!hNi2i{Ff^{E}V14LBcaeV$EetKm0u1fWUHh0;?!~EPqrTJBU
zHg`=beX}-Sew3fl>UoME^)=vPOmyI7^u*(^RF^59r9vLSoU5m-Mu<A#avm5Fbhjfd
z6~Rhzrv)+wkW>h^#2vc0z<#Xji-76htSWn&P58>`of|G`Huos%ogk&`%_yKG(c+G8
za-r}}Rr%%>a64H9;D24YaMOGp830bqZ@Lg}cc9vIhWqWy-{WP<_dv&p_xmDv)aTHj
zO{^!EqDP@|=X?_1aN{v~PNQD-Mqq*Tip@~CHectQ-Bg_c*8L3MRK1aicis7zDxW0=
z{5%`>#nDk&79XT{>l6T8xu%4znnvf}(-XjG*<b<dj)-o<Z|#fF^Zh5#?FG-Z@NyF1
zm^^I678@}A;8`JTl1XkAr}aqCbI@*DPx%sylF1||=3JVi%xGZiah3RZ41J2(`J5Cn
z>QlNB@lVq#I6ctx$EF5h$9w}|HQzE==SMy%jlAmhp~$vvkqB1ak_xv;W8md1Kvql0
zE}5n8FQNBT6`i<*XZ(p7(9%cDB!V9y$dB*tKax@7a$3^${X3kaH_$yhuz2+K0b3-2
zyk>pD?~f>w6vtyn@ZAQYxffw!T0~HHV|+K~{{(L_j!^)=Szx0&O+wy5fb0=!c<^qs
zWR&JMT$@@k&&1Kv0EVPxz8iyWD7m=+Nq+R;SpR$yj6_{likFRZW`R95P#nN8#cdeK
zL-Ew;p{ov##^W&8)@7fZFL71`eMS0{7cjTlWYMPy5{J`$Zxy&W1@dE80pd~;&~ZJK
zrpkUa4u{h6u{r+BJI608V#x%aZE_2xt3qn?(SxZQHFV4iG_l&ccF`a*>OOea&JxVZ
zxKoB%jB=e1LAj#hQT#?!jnDf(V?PE)kPiPqSOzLc{R8DrBH7E2>9DAgtk<3Tg2F*8
zp##L4=aFygZ@awLuj#%F*p2MWUIUDA(zG%ox|Oo5ANI}pTU}P`_XDwpubv!ArY!S2
zwA${wIFRBxiI*hOH|4P&d7`ALN1g#E^^h>h*W#!k=*~18r)bsW_IT39)3|-;v^N^K
zt=UQ=ygX>gxqS8#gfpG?zKP23h(_<=XH0rbmjdR+-s<SpBO`-pRrY(Y9Ily{m;p(D
zOi%Rj%b{yS)A-nB)MjMAc!~2F@JQXLc3249>fu+TfrocqVpk=RX$)V~`|FERdE^%7
z*Y_R?huBf)rw9w>CFo`z2Y)^Q-t02Z+wtRgMVpx^RyKyjJr7du`HBU_=A@noy4|yD
zjzkl~v|9k5vx<*Y=$wj@r6QeDG$2V}vS)#j%K}+N`Zc<sq<)SIAH-q@0rOSJpi5kS
z#kMfmpBzjX%!6OUU+Xe%xCp>GQ_038ohfNqP6Gu}`Z>!VvorOaJWY$=;v#KN4i_e1
z)2}649884tb3c)Og}oC#TO^sdo_qwp3@;*#I-!W~1A9y!*fAALA{>$2wa})WI2X+b
z<C1jmjaavC)ODX8*Xv&}ydmJam_I^<2hM$Z=d9QGf;hxY-5V|35@7nx$94TUiha61
z{27FK5_x)Qm>w2O=${b>xWT>WB~FttR>=qVY4$AEMT0y;XB~=PQ)!Tb?rZRY<(_)9
zBG@ukGAcBY@CG(^c_Z4@i)`Ee3*I9Yt}H6khf8s|*6C(!`}TyX55uV>|6^!5cN>!8
zO=vk)(r-HY_9GX4REb2i&6ih`t=t7Z&GLbk`Ys41MoOLULrb625}fv+f(*W9ArIvw
z^w>Ol*CRsb2@rJd6VJVpb*gcRdG_72lY^t|yk{R`6n&*@Z@sqUn?8EUrr+5#{~@+q
z-BCYQ+g`11uN|xJsJ0RVi9jHhNW=mG96p>*Cu#k}Z|`Qy;C7}sza!JUvTZ_5?ujCk
zX=c`OZ^vQ*8i?6J3kLD*)mf<i!Tvfo03~4SqjJzVCaZEDpJPT)epHWypZ6L1E;vLy
zAC7d|Ora$Nd@sOE;kUH~vA(dnjNxO90?pM-Dypg^!2z^GeaDb}Ogq-ca<ZbxBLp<_
zD?YtJg$><@z6C&u9Y<=Q{VMQ}nB>{R_d|l`<4$<IVJ#QT*%~A;LXB`OEd&)zAGctZ
zhX|1Z7lv18TG*MBU_nokIfy}e82@3bRS-$+?rcHUcTWD0?v1vgo@qzl+aeaTHD2tT
zr4U^fDvo#Bg`*;G-J75Tun+gOx$1_GRmzn``nxf%zo!ujdjbSekSJnSDAhI%V3=zu
z>M<dd(Od@r-rVv1VLz74L?V#cH#=Ip*-B~v0tsAa!qZA_!KKPdCH=<&)|UPXV{d>Q
z1uszcDYBTdN`9;5zrmkOgcKicl_cGFUBFT&wWM`(ZER*=+DhCfoJLVsp=+cyoivRt
z!fb>}i$^XAr3Bg>dL>kxQ&g`1)N~j@pb>M7UMjx@?_+<X`vxmZ)OJhhTqF(eqtQ|F
zBjQE>HJ)V&I>U9?tvY{MpG!vw=NC8HWy`pbFXsnSbX%c?@1vl<NM20j%7DN2Ts<|2
zTkN~&3;0`WQ%#`{K>OG_KLLz-;YCk?8BG|>g?)7@FJb1aD<iWyO4piIzapCaSIden
z5AHyY1YMoEdQR4Fpn_d@@1%l@_U^tIxoFo$pDl}~hx_A>K}0pu?Q>VpsJf=^ynB~j
zFf{bYMX&^sj`d~kU*H7?0gd&nNPPvShHA_iD_&#1*3I%^<`nZd58Lx}xzemwo0Zeb
z;pGE}WzuG^nTKV%p&O@^&6}2Pcq5fvw|J=05TykS^Jb-T!GYz&qClCyW)>{I{>Ee1
z&u_7d8jVAX*P#$Yi0h12;cNRiMiAAK28R%>#FC8aA*9jf1H0?7qtGD6oR6!2Y8^S!
z`Y3?yIcF}mI^q-b$Eo1)L*;sX;NbF+<-@xASGo%OTY+=i&W&Ka3x8YRjofcsH%;*i
zLnJ?e209)o8y&fde~e$X8)UfQ5%?6x*0LgCHF~!^@|dv?0i>eW7Qv4PKa7|Qioc7l
zoO7)}lcdwa4IfVSK2IEfh@v+!X)uU6R>O9--LoAh8&<IEg|pF-J!EVXrc`L0Q<YvL
zN`tB;d9Y=zqa?x|J>S*_>cQJ_p7Lo%v=5d`#7cwo&cG72QNwr3+%Dh)c`JimX~SRZ
z{?54#Y}Z^gC2d=%%d4;bCE@{|<n6@;!!FL!&w6AdQew%J-m<7=e7iS;00%Iwx6I}k
zLk0#-%FS-Ek2cTc?5nw1_rBeJO`~tJANq8beh_E1oA52@2Bn+&8*rN;l0M$wQwFxs
zF0+qrtm$bVeUoLh45tfi_Bgk@COLwmV>sbz3&Hx5#9S%krNQN4(V?g=$j97)j&Hce
zpJZrKig0P0d@w>bSql<mci><UdU!L#q>+qKjO~pNMQT1dlrii6K(Jy4<Co@S<gHfC
zu+p#kt%U;6IIS5PFSeujx5}W$p+H;<zPTCAs&Pah=DpBmd-JBOUY-~WOkR!z*vxO<
z6wDg~hX(yYPQOXcd>MB771Dm%de3ek`y?$!LZ&1MpC5yhA$Fgq2?T#b>-{i%Oc^H)
zofnCGTsjzg5A8p!*7`4j4Eixyy%-s2erx`K+NAQ$2m^wh9z=wbmMlL6fE=#Mzbyx~
zN2EP-yete5SFy&tz<vmMGsjT`_6R>%_=VU$dGK7Yk9gJ(oqL&9!J>kCL%-<S=Vqat
zrVi4aYZS1yeHLQG*zqy4R&C#T_dESnXug9?uGA|s3u<$sm)?%-vA$Z*74o2a*ImUw
zCRyN1(dd_G|Hs9<-gWoR7Y(F}_8O5?Dq?uG&kC#8XH*by*1bUQrIL(~IJ>8MV?EZ!
z**VKTmU+U%<EeBJ;%qOdTqOWSD!rz~=w)nanqmd8Kz}rr8v-KhP)-r&9}JIYD2YUI
z3+(5SULQa^f5h2bG&w8Fv&m@gG(8{_eSZv*_qmKW1m=bGeZu||2BHJ`m4MY+yV%Q4
z)uJv<<g6|R4g2&ZR;=?QG$%*&T@_9VyLI(DGz|U$?5e^RF0tJ3aX+#l3U>rHaU<Pw
zGe(k(YViDh<Zn!|A9*0@Y()k?Pm>2}5MWY|cc3GgB3qw`s6tLhq2PyZXDU36W)-Z-
zY?+dz6C4=iyB2uYn^T-#b0jGB!4;%rU$K_CsJYx00A#PVAO}WQJ7+1SLbO~bI!d6o
z+$MgTs@`UXJCB^AVR4Gx6Mna_LjVE|aU1Y&p#JAR9pGKbZyM@U2B8kz&+ODJI21S3
zu3BR4<%@kG{o;kC&hkAzU(=WO-IEwzVC|)ai}jH2;^lALgQBJnr3)~`iGC98<3pwy
zgfm2)Nes3=+N5Nd<syZ`k`pu`fs_eX57){KLX%$1eSrbNqjNemnos6&#1>)FMYY?x
z7=+!$Sgv!!R_3YCB{H2;eKl37HVgGR+(LpdjS+#nkb`aL1UOvM|8SQ_M}LN2#Bktp
zZ^gCVam5T}A|YXkNG14q)JV`A?*2tAywI}}TMvV)%iZw0vmYPV&Ctl)b$2bz&0ROY
zZ;s2gIlXg+<HQ}>QLF9nk19}Q<#d3sLg8kGd0zpB)jChPdR(8|H-8<Thlh@YOp=y!
zdhD2;BMi^{ZOGY3bq?kq;dc($Y9<m)A^Xq97tlW>2XxzL6PF}^=2imJDQwRiHU>@P
zj^UrB1#11n*-#32MX6ABxSs@m%X<B{JUa}uiVOnja~Ysn3@_Q93DnoZcOnRCK%Nt8
zCw&6W+obqF#x`3^DMWJ~fgY0u?4O8bV2v&a*`K3{(b9Iy-+i6k@=sJ2E?B7W4fD7e
z%R4XSNQ1$99=EzZ&om^7wZGxa3CGW)h3FBxu5N{U7$iOfK^`W5&iZ4?LbYIUfQ7+1
z{2AY6?U(5LKkIuD4H54}^X=!DZSH94Z8ePU&Y3ReMR#Ply+Gst?k=2#kVinnR(Mt`
z<l{kqc7trt#T7*1B$5kKv(<uoF*itUIl*==+?*c{&0XDOOYkWcR1ycRZPVjhwS{Bz
z9s_a(*<}eUJ_St$Wz*Y<7{9B@KLZjbXLcbUa2-A7gl%7T4&bH#|N0KL+QCWyCm!(H
z(XC>xTp)Hg{Q_55!A4KQLyw9XB(|^ooaLOxJ`I0x!ePr1eXqE~SkTXkAhjUs)B)XO
zep8aiM)h2Q_t|o{quG{d)VR*9n%5c8=rS&f+orl5T_}WBLij6SGSQe5d_a)LRs&N3
zYyihtpdQgnKqVmv6EL})Sb-m-{+UgJatWVztR!8Km{W<+E=AcD8Z8#biW1+ChIUCf
zDH6!C!!YE&7aX*#!620<b)6ps2N6V0U*q-?@|A8w>rMVUyarg(-70`FfDc!1C6-E=
zn*allDM9J6I46mNItnG6&Mn7Vas1I?-DmQHc+2$hd+j!8^Vpd@dALF~M+1)R!NhT5
z^&)nSbER18Vo0+E{(@9Cd_|+B*O6_>vq+QXPU%tWKz$?~Q({JcJaKUATdk;m%G>U=
zr&J}L_o~L5orW;Aa9H!QAkb;eeas48u+tyVQ`N!28wLw(A9SV<>9PO3o3#J}phldA
zzCzO_L<U0rhstsr0oiR~ivSNtHf$)##IizRQK%;tX)eS}vsZp`V}#vKLa)O8E5H`o
za;Gp!xF8&q=A0+^K-*~CmPkuKH53>EEb!o2Ug3Hako70vt;AtLlEtSQx=~CDd=SgT
z5>YW&>Iq`H=ook<0IUV)vQ)eaPHHbEi>q|nf!PRGSse@ujpffnwjj)`JX!-T2=t$N
zko$3QIWEs+K;$4D4m(>d&W=q+BJ+)Az7(?Bkx1JLoxN~;S~|^ICobDJGm6aK$*I{3
zF1{8#0E~-^YO3>O<=~~aUm)g<@SE9+2uAXWgh0rd9GBDK;uZ!GNAs~|FAN+ZGtQFZ
z7|C1Ym&S^53JzmG+~fC#hd@Fs^OR|UwhV>6{yl+wfg|7?o%J>H%lU?H_9!TqIa28B
zJi|Qqn&!P1?m;#->(M~8P8WdG`>3Us1A9ubII=vO*5*vHnAyw^<1v;zT2&Ny?tyo~
z*b%XQm%@U4!gD*`170y$<$0$G(b1{6O`dJRk$TcGsXB&A`<&ASWS9VTaGb<B&NYcY
zpezoL4u!LHGGraMVk5a2Wh~@c8^LyQ)A<?Vzq?0|VfHTLgF#|+28AHUM#O&0sgdJL
zx5w}>Ful2$P|w&rf5>LzyQj;f7^M$iz)lLiqS&4}d7Rn><LHZ!NuKlQuuN3BKNpW4
zk0oRp!(6S-HPY~LLCRxt7ZeVI(JEO`;t=hK!zC&~Hq&;Y00HS^fYrmE9Z7GBtBGK`
zx@ojLJ$kTKY)%{*n<|fPs-}YpHNGjmf1+8evBEM0rq|%^y<E95V#z_(SPsGXWroZB
z)v-*9rA=Uwgq9682o%9oW~|y@=6#B~gK|Qiib7r(Ae#?{B@8G;7%2FiXjL-|M1Sf(
z_<`FU0-j0q3=;rXOXEdWfX03mBJc8wSSi0OcC1hk32O?Svji*7LZtc36xJ++PF)B$
z_9NhCn$RVIQxmHjnYu9)#$oX0D?5*Dp*U=~RxU8K9q5OOc_c&~80mYCowz(2UlJyY
zHzQ{wo!^XPW#O;2^EPiLp?DX+x9C+88Sia7fw26FSe#Fa-KyAq2Q%Zdkz_LRSw1t~
z>r+A*-yL`OGEo1&*ZPx_G?mV`L8DSTOFOVe!N-7Rf4K;qQFnF#{6#E~wJFZe;0=%v
zkM2VIOQCVu@^|ht5S@R5erL~~0Ih}LuZ3wF)FjK-`4^2<yn*+g@kK_xK3>Jn$Gw>#
zmdNo?XdLTiC=2CQq+nu<Vhsm>^*FZyQJ%W}KwrDcbKG->=U&g-JP&z(!}EU6M?IfJ
zwVeR=30ToVdq9=TVFb02+F=GbV#1sWCsr{W;0`ae7On=}z0`%SaBg-lZSbx0KL-{9
z@mucK-2?XS4?%kH>v~#BMH7Xw7VLT1iCA_%!HWaX^5Cx^b~ZfQiZ~-GfvcB`GrMr@
zoy%*!w<OM5VrSoJ@7UajbW|gz=MVS$#|oXVvPl>hV_~l^!pjefAJf<DYT}Gn!!knR
z%r4yUPP=gJE%uqc!=xgtj8)Jk@*^xlPfwOI@^$QLZzxEfNN*1T21-1@L3>DV)H#7K
zc2c_MK`YU@j2Oj&O=Js!=&S7|L0lhQS|Yv;cCugEvH1OugrRa@#Y$t-&}iV!aA9fb
zte~G~mpZ4Nqb0r`|0~AjGI$ecwTtd-cQlR8>=#Q3)*YJg_JYGP&bVTMtw8UHa3QLo
zIKq!#{se5`57FFt2+lW8;5syrT+hvqkT0`7_vcT5t6IXJY2gnyAprsRzlUw;S<Ktb
zo*RG!;po}d@5$DSDNR5Xd}V{C0@J%XJB32RumE?c)&K{L>--Gb;dOgHnaD@rs&HW8
zt^RP>_qO+A>wo{R_#*&1`glK5Pp6ExSV8|{D0G`Yw12>7Nr8Yj6@i+(rACrr#!h&X
zQQ3P?y-uh7waOvd>(ge^_~pn}Yrr4Y!l{rJPGX-7C-fBjyRS4OzvhohQ6=;PuUYnm
zO*xn}M;-&*QrKU8mmymz^O$l!-@jSD0asJTPEEhkp9nWh-e<2NhUx+EQ0O!AeE^Ae
zdyZ7I!~_#{rq&Q<3b#KUR$zwxa9#g&URd9Qb@b&Qp3_60IJX;hT1&bZxD?yrA)s-p
zK<{`F{3RAQP*~!ua8Hq)?VRGvJP`zf$u2;{cM`{)k2C1+*m6&K=M>$;z<}bWPJ5N>
zblcJ`pz(w~H*V*JbH}In6|znDdTPOH=CHb42l;yEFNzxAryq2We(WB-d4r=BLvK?}
zv036;Ovh^m3EJodP<xaE3Z71|r4y9lWL4zKp|$HZ%*zKz&qG#gj9b*2hA(k7u62NO
z_wbg4A`{Fi8QZZ+2=<k?@7b$$OZAQAQZ*~CX3AM*NC|ln-i7$B!lXBqXCa^L3(G|{
z5VREkSng_E0uxiHW|F@fO(vtC4hyH&@#;-45g3?xG7t<}1*mONUG%ww+4~II-}dRG
z*6eL<L?BEdatDfF;#qB$y1NPs^C_}pto;@Y)oY%zyhOBxHkEdx-AD$%Oty4_evJm|
zUk)Z4%<JCqcka=W_zHb*vC(L33MO0Kl2$Uf2}%O@X7`A{7x2Q^v*6Jv6D#)}Pzef@
z;^}OMdh&AMq5}zTc624?BJUBh)(DOcN?*sQ;G%Z2&;GlA2Y-4CIaogbc{P9oMLVaY
z^S>i_-1}CqPgV<6t^uun1TPCK!3|=k?%pj2v>YCE<&}6qP7BEAl>BYR3<k|ic++mm
zRl9rB?ZV0+wlrsj{+Mi<6pOV3vld(^xFU=pQv3D>+I1EAT*E<lsl>I)&N}7hAZMOE
z_w1e=g_EPf4E>PM(DUsf%xr_{`sn2Ddwx&EG}$}WhPG<@%7Pj)cIZA~9Tr=kzQYKq
zg)66_uj;ggbw_Ne{v=xq84so_6`ey<APL5v=7@Cht4|^fA*KW<Eh<0Q%Kl7&t13>A
z-;^JS^@ez}74-F9&uy4*8&ED+khUpT%?SrXLIp8Wr;6@9_8{-M#~sb<?*a8Y%_q|$
zP0B?<{d$f?&^i+rc=;KA&l$SCd)Nxmd5B-?Ib7*E?m0Y9G1`;lD#u2a1Lp!=%Nh%r
zZX5ZC5x@YcB2=7$!qMjN2!n)`XH8-GsRF2jn*)2yWPc%*t7VA=WFt`ZBTD!ui7h+G
zG#1vw<J05V>f(amzOUC}_Kp1DU|yHia3TRa!Y^fVxr`oGyyP+FRl;|QVqM&{?mgn*
zxv@?*h3Z>OOP8eukPp2F1~ad14JIM+9+=wV&AJ}5FNk^bVLy^D^_VQb=G0zu-lr#0
z$wo-;TVtfW#;&$*zsA~jrz|XpXkXLr2;2}%xJyLNZTX&-whfZ+6_nzPQ!lrjdt27F
zMT&R(#JYy1Io3<#X^kZr(H-+*g<Vg*&%pqi;?eG$D8gGztYx(2=}rN+gJln3?6d@S
z(#v#A8}{4I=%?~4ey4J6Wee<H_(PtIUG@A-EH;zp<|a%JF}(Rw>Kt|pPZtUtWzSz4
zO2y+TPEL8wNMRQ;JPB|QWq9Ttf@cTm4=6|`KHIC;m0>%Bl96c?QJ3uI7puV@OeiI=
z5)0SsIB6B2a3HIFbBtkWzjrBt%)%gw+#hoHJ$v8}Ilz3Ap^K*uk5lGw{T*+a+={}t
z0S1w2VI=4rNcnK6BMMt7pW-id*8cLN_w4SpaPQX1&i_5oE*u``&a!VAprS7ZeZZeh
z_1io2ord}B(8m#usedhUi4aGzW_6W`OSG9aPW*cpaD828Q@CMtjE9&z%Q$9#?>D+`
z3}SdlZ)%W=hc*`ha2flc)jz-w#y-+nCWR1jK}PNL>7&s&FE3`p2!+I*`Gou3B|E#%
zDP#pQG~WU}b0M5JVmqpE=7dM*#N|JL&6JF)c-qMplLD0lziUTfF6tkMi@S>R?e2rn
zMjTfSYkk}EDYQ(_Di2sp9*V)k=ETa+b%^k3keY6UwaeuS!Utn(T-?jW4(|W3hEqVu
z3y8-W?tyzu;<u!@Yl(K(3D;%0N%|^U-o_nngFRIQd<Y=%efpkOYme?@ilph85+YJV
zz@IAu(?$y-_9MD&Mgbx!kOlbqfUrKCDKp!m%#Z7E(kTI-E(Gl2Qby;G)AqWL)3WOx
z=acX_r1Qg~N{`b_T3&&`-E<x&A;ytE?nD$uTKs@6MJtu41htz6T+=AHo-AsJd=fsm
zh3VSffkRCMi{lYdXo!)4ISOcS6f_SF?8SE#JR^hrE?O3x((66Zj~4j3o+#+sk8S&K
z2=*P0`?d`V#5h&}jBCo0Q=N%UyD?Q$g25$zR_@+H@eSo~>|eP&AC3gZ(uGVsk+{n%
zvJI>VZ`)5@mItilD6YLRm2E#fyK;JaVt*l-j)n)XWlbQA!5OXq{*)YaJ7?`{mq%vu
zXW&Qd@;Omx>PBG)YJy+eO5R_Hl3bUV30?=Ft(dhKX7LGIpExmHLu6Q4FT>#hpD2)|
zqc{g8OS01qA!Mzh{oezy*3K9RQ7Im^qy;M)@BH?g%tyE19lcdO5nf9A-sej$g-?Wh
zw?+#EQ!D5O->{GQ0h{^!+X#F)AiZ^EY2{>N={3*Elgxv|lcJqph40hvLkAXz{d%0R
zEM|qSYX+&egeeUUBd|+w@dj|jC?`=?1DSy$5@J0fIeG~dq4M<7oniKQH^_(&b%Y(=
zi9m+|jH7v(z}n_9JO&KO7!D$V@=i%t-@@(3yMf;Qb~@VH6oF5yMMJUEj(ti|P3;M<
z_X*83a+Ar<mzQKEPIvaH`Y~Ba*oWtNKh)QL0d8g?qa9>9<S)b<{X*}jp7VaJ`SZOX
zyHLC#a*N>&Su1wc&-#Wcd=6jcI!xlI9BFUE9*o*tvanAe{=pR<giA?$-8njy`r0v9
zRL||kQRbogzD8T@dFA=0Ma6uxOh?$P??N`f#{%D+EYMA~`LTQSpnF8VP8+=A#=Yp|
zrujwR@!YQbm!JI|&*??1dCqIR-aC?R;T6{KJnji#24cSPC{l3r(=&rVfD-OLs8R3J
ztraNk?$hZEh&T$`<(upin7akgo!!t}lO>bzXGFK*DVT^fQEh!wQ>*HZ2OWhOLvg`H
zv&1ZzZ8E75s2i*=HR!fE;{h!<&sawK#42mXWMx=U`&At{Sh{)#ZPKb)ibS_1GO5f^
zBCIG34t{FN>KnNxY-qZy2mS2X25*L*XhU`=4nQKfqpLtE!kp!sO)C4pGO>6fGcuGf
z=|E%61KI&8B<lW9+_W?ku5m3ICz$_sAfz+D|EIJ;WlfqHTeM32qHWOm(Ep%D_<NT`
zbQ=L<2R+|l57F2my%Y@ru5Q#=vS;wXfa4AxviV}X@$Y2Uk2q+>NdOaO>gnF`#|(QQ
zns{!{V7$d1lonLQT<0kA9+oIF5}AL=p3JSu7)}mq2*xOx|H#x=Rq24Tz7T*bTxAl{
z-0<jyQ^U#R@YIE)!-`@hn$7LyQE9Ziz1d7yO6TvR=}gH#Rg?{-IOQ*9GRzZ*Pf*<Z
zL_DC%_2J#)(^1omPLJ;%hFjvDeThu8d1aq0_g&eHB94jPf0f4w&T~67t<6<Ru|u`M
z(%mFpyoBj96&G8q*ie{FLMBC3%QHl1aLirLh)`m#Y0{DOwqoboy7ATVT54asjFc3)
z0{-QYu%aB_m#U3_6~sMX+?GzH({Cf@7#W$*`U0kACao>L$ntU}+d>As5I!v!Fqb2~
zEmjg&k+CqHN+0CDmG!#t$A*3IS@;{>=Xn@XDXi_V%sYHA)1YC;{1q4!7`kAM0cc9C
z3+|)bWrV>3Q|P%9+Xjr&?!rAAM^8cSCWR6*F1oBQ%-mDpz=&?hI++|X)|@9Me=7qF
zX(^VHG%bW=K^eq2J~M5G5A8O!8AVnt<UXC;I$$dP_>Q<=F$cCzf={(nS((wy-G{>F
zG;Rt2T)Q#86$q{n88491BQqmu=>mSHZXItxL4^MOeE>G9Y9^~G8Oe{|(g5ygfbXvF
z!_Oiow|2<y|K4FMr5KBN98fT{1F`8yD3b|Areg=dPb1rnwrD6R>#%)&-}PPtsxU*}
zf1jMOGUK`2ICk=V`>E`Z;k|yJ_!hv4H7#Sz`2C0Qt15mS)^4(CdLXM%9)O-aZyer>
zOn9>6lQrucsAQ!jQ&Z9@0c%L-(7(F}4Pg@^<2=krUl6;_TSRF<oU({r{GaUeQ)_QK
zv*sCx#hKW$s@QY#0nGiEA+>yje3}V8o%$4`g~tkum;?+I?hb=aPb}~OK!c#lUfgNL
zl#o_$gvQLi;J$PfnQ#nA%bB6!)E>b9&%_#K#EFFh3yDDNC&=9y*cwl&;c=e{c+X5d
zI_=B)=VLW^?aP``4@$YjLLjO4`|9Lu0ev=PWs0pafb34W0La&QkLNI9Uq>DBc*<=&
zN9p0X7P*>G2>znUb*Fh<kGUzQ#O;e!?vAz$n!=U4oqvJvgTMltyV*{?+%GO&EOrQt
zCS8qvX7AVvN&`zmi31<vRXSa*&%Nfc0a;r1C9$(_6en%5v+d<HwxAVOi{YZ=>412(
zK)*N*dm4104J`>`&IHxANjJq@*e=GdunOrIS;DX?qkA1C0aCruia#?8)HKe<OPeXH
z+}N0a3d5e~lj}K8{Rmo+OE;-OgC|WIxgy*jvLf2C=U`O)5>UuC_=@`oG^nrh#aq1q
zU}~7w@8%3&oZZKd`4}Xc3?B^gcl3}3*YOb}&!;uvaBE}S=(|MENpJK#+Gz;ZN!YMx
zB?M`vOf?ee&yx-}#HpM!KnkqgX|d&<{%|~~YQvkF{YEnE-^u&C9f-~Wq$%t-2ZnNm
zzF}W#ye}S|tTTU@r|^VWSVsVw_-zSev>zUt9<>467DsTIN`icX?z)%-e8rgM%mk{$
z<6^*Cp@Tp|LA}`}vNnJHJpK?oA*t%f*zS9G<8N#jkrTgjpd|a{(g9soX5m<^$-8zT
zsTT{(DsqI)Uw`-Y^Gx@dQfT+Rzk2WPkmU6lKk@C>wB0`DS7r_X=4xPo>B@l_#n0P|
z`!J<Z=K{A2(sYMxidtdRK=nZqHjNBkF#@$n*24Xaz53qmNbVX`x4##-NVlftnB~>q
z@ebW<#pLv@X7JsS<XaXVfg$~!BoMqaj-|jj@g8L6*|iIvc<(u3q+<7nAKAgSzLod{
z;(5Qw&;jI$VgEX5WjL8(1p`0xo<NWpx2B~)%7;8F?jwIA03Wqo^d4Tq?`B`aGb-=`
z9fJ%$4ZZRv&<OG_p>dAEPC_^Jfp|8CJn54ukvHjW^cpTA*-Z^{_*9V1!Yqw7Ey>>3
z)YX`E+ngGXh1I#+teC1d&n?;!i}{-;o6VDR$+2Ip$MEmX$+2UxSo;cpCVVZm!nNUy
z|B5wde^e|U#lLH-bH!gT7HJ3HP5vI=#(VJW6&NUDQSDJ=;U(56sN5yl)`+neA9@Hn
zoB|9`4{_=1Ay}UZP||E%eD|S4SbXP@I^pmKX^s2fVE_irVTC?Vx!qsV2>iSHa6)T?
z+gk*5z%_D!lgGNj<TX9_4qqxDrEfJ@Fz}x3S~AK0z!@6;xTfverD<_L9~@Hroqn(I
z&&1exna6m7J5Ab(2=WPlS~tuCC2W7+maaBb{dprDiKLC^`ALjDK{CbZ|LBz>fzEOu
zB6<J#4^cwng~puwTxKf(wqnddkxULf04fktFLa;u;O9(wu=yN6VZYfq+I=$XeKOGi
z@<D$V_PPuCXa>RnxF!n&uL`_J(vWaYjQ1hxPPEDGWr3gsR3wbZtyomi>)nusr!xaA
z9E5^>bh|XW-{;+ChDZTt_Rk%j???I_SqhokyuSUjjG(Z`gW+lh{RuA~wphtT^4`00
zjohX||Izsm7y&83d`1Wl(C)8onyde^hVO>(48IgGJ}`fUZ{)%eK(^ck0Ksf!QTX%-
z`X=ZyaW@`FMie~bkY}_PpwGyP3@6DsWYp_$?=R-H9yz9)+VV2s_26zorSLnMJ*F>f
zh~lO8>gLG9?B_-Yygp1pJd?d6v#!H(2W-;{jaF=W?IoRV8a(mrr;$ID;&DNbZG^lg
zdUv)P80yHzaSn4sz8OJADiJRCI&3gIJ$D`4{>P3?aERSEQRnC8MzWAzAxh+nr@7M%
z-<;wxGV@bcY|_i!(vmR%-0&O#ck)xm9s|Ci@|YNiPVUP#WVSw{!0;gLx*3+SmZOC^
z$AyIothSKFps8Z5Hq?>CnY7sE%Zza6*06D@E=T-Q%O8;+;G%Y`wIWjLFwaHHmbL6l
zhC<2C$En>INUa3DL0h^Fv}S<XhbSa<C;v50^Jlre&E9}JK+ddEEnaLwWk3Kew%;~v
zxmRvS)``yT-k`EQgg8g68&%7yj?iYfW(AM~O3&BTqgJT191a*EKz&jmfe~W+Z`QqD
z{pQTpQH4c<!HDx=%IMZ#SB@VC=+T#mTl$iWfkO6hq2m(%B%bw}EETn<E_2rO^~RA9
z<70+WUnAWx-|4()Z_hDMB!tSUh4`-S;py%n(GOvpXJ|X}5^bU1G#$nz%yU}u;c;25
zxg)uoT1j_lwP3P_fe=R4EUGj^zM-kDe4K5a@_M%)<~F6ne<Wf9kwvu?hz6p$m_J+%
z`(wH2y+AM0v;CQ1Fw>valZxRVn;FSuMrOwTMuZBsmjv4xfFqjMi$snTX-D<`8_=i!
z0N!fMwia@&5Za9MMI?2DERkC^(JGri6wD#_Z5HnQLZ_5~@J`$!u{PXk9v1TgA6~22
zzLfL@2Frc%tf7zUMq^}Q&q%|3e>M<`MsoR*sg3EzXnlU?;Ba82s~`&@IcYTAXZjNo
zJt*49Wl96BzQmH>7fhu5z8J8DP@b<QV*SOf#T5F2>}bUE5pPUboGH&YJ*%X@PHtm0
z9k4yKweT9c!quWE7T8tbX&fz^(3e7Og0F2@Mw0{T?(Th#j%Btd72QuQ6ABspCTteA
ze#QpDIZv5e57d_{b9jAg`2H70M@M(zL;t!4%zOL@@y?AF!^l?TlcaDMS?zF$y*Mw8
zB37JAayfBFZnT8lTs-w%c>R}3V>vtxaDaV%fCc;o9?X9Vl~`AqA}ZlL_xDi!@8d(S
zauq%-*<($85wGw+yMn06Pp#n3_2L!00mdY)YpYxzKL$Ty4`s6v&g>kIMQB<$FsmJ8
z0kNd(8CI@2s}jZ<MqF2O3ze+9AofeMJ$iXP$sSP(10V*uT7NoaL4%UlU{{O;0zN%j
z4FNR3(0DWmO-+5lP^YyK)c}z*^vrw{0jq|-+feG-xMBcp$Ix@l%>{of8R{DtO2>nN
z1ei#m!Kx7}YHC&{S{#OL$;;=hn7#VN=00(L@j+hf-ELke3JzSt<CH~AQWmm1Wr;nA
z6^A&uGJ=G#?7-qeMttJEKveSQpvsdEdskSUoN>i>(0p58(jM4S+J<ypoioVux~;Tj
zpt8BpE^HZi1u1=r^}yzZ?xC$s>3q-Dm%t#F9?eY3KA$|98BH@!hN$UBHgI7d>s6kh
zugNb5u8`m}1QyZ}yqUccaW7m!B)gG-wuB8o?QX|zfSkyE^PHESVs<Km5O3RMzJ*>9
zX#X1^C7xbt@Wj0h2BqU_EGL{U#FiJlNLt4bZ5G|57Z(@tyx1at9~!$W(6t2M$66#g
z+OY%>t}#R*RGqAW#MPqDFb<cQCl^}*Tq+p77Gg-yyqzU#<5;7>PSHM8Xi%IAKpHw<
zfKLGq(RSodOtkwM=*%YIA?jgHcP2@XN$z}Le_)j-nN5hKTu`QskV$J$6^}^^NMQ#l
z*aO#QM5%rC+9Hx)9v_FoVH|m~l;X9f=@u!C<4F8MMIT7-*f9|fPw;IDf!_G);@sl7
zU%^v+!F5l$CGC6@-%sOJ_?#E~$uBbJk%$6p01b)%rE|XhKW@&`ym0*0$Zx8)&TmS0
zq8i*NnpwwRvBzENNudEdDO?f><^u}0UY`F2QS_W}JhZVIs6PZ>K>m{^M`ay>=hifF
z*g*!Zy)B7hbcJ<j#Ndif<b_mCa1C;$u37lgH1T0C-}Wau8^KS_UxnPT7qYJ#Kj9v(
z5We6XPa!dh7XB@NUggEVeWht$*m=eH-;C@jV#7Mn90E2<gw8yL8&N;-Vi+_Jup6MU
z;5lPw!FzFI2q*@HMG3<Q<^k8VI&vfA6KFSuLlegSMn}Dbat#x2^J_>PV{1YGR0hzB
z>6O!cp}w?zoXp-8(e&fdk~a}Oo*a)ZhJ3pW_PG#b$Y3}W4tnzg8yrP!@`8xK$vJn!
zNzbc47Ru6xDG9eMCzE0ymlgvGODKtINl7S%HSY@@QyZoiDOk<kEvI$iASF-}A6~E|
zl&92$7-uh|9UliDen0i^MorQ!d;P}Y1|C|mA=C0%>F~KCz+%HvbQjE3Cx7uq>fF)}
zpd+jh#y5XKkRsClI>$)F>op6#?IMmhZXLvwppgUWKhWS4JwpR<iOU4t^S{TcF4H&^
zv5d5aQQpEjH0$L5LBJp&z_23GG=W!*@GKR`Az+_5a7axjClJmOqvgRnQyL=9t73r&
zvOU}M<*MSB#<#(-sk+?xx{)xJ{Y!w;7+vxokYu@*hN384lVvFyU3>c9_j=85v(T`5
zV6UnUcg}v>WJnKolNs#1`<hf$QEPom?Eav6x1`o3sjf=>{q7N;TAcn6HY>-Wgl3bp
z-on2FvMg#xEVV;*tJAY8iXi@zUlAGgFj*2ntft|Fx8qV0dt+*IVbfI>)6{t^H@-b5
zwyc4>?R<lS-k$d+wmFg9JEy3II)8Pu7yg#8oQF2LW{+>OeYDXp#9P?tw~(@FI-{cW
zCa_{S+6qT*lTq=cBWQLnkMkEK`z5D%Xb|=}g2vr)Vvi5qI04Lf>$-(rKwLt+{TvEz
zBE828v>C_CpYt8|Bv5mkS0k~xs%$>61}azYT)y_40CwG5=^b4@phkMLXjlB|9tp**
zYt4Y>wq`@w5M$sPJRBHNcnFfe!t|Gc;zK#R=SH1_OaP+HP9f0jXhlka&ph;_!;Ua?
z0d6fx(kH}jzb&Y+@Ei(|ReWx2R@^z44U{6S9Vmr(v=iIFXCCZ3>_|!bB|JpOQ~2(J
zQ2>UZ4uao#S~A!Rz&WD$Ge`zxYld8zMC7Jk<C@*B#U@Fy^A1PE&Zbi1gLmFJIQR9$
zt_wc@&A8E5KA~$(Ev#KJ&^PEiC77SbHot9ru&)ny_2<<azgarcSH1$bG&TJMGDKzi
zB8LQ@!{c)VpC__cWMQ}9oa-8Ui@g9worUmBXNkBu23{}M`N<0Yt<q+#DW<c$)B;0|
z4f>XJ4sDJ{K=>8FXkg%udw&B1Rj>jGlO$1qI7-q-#Mc;mo)_!<M|HLH6pcZ)Pvtl~
z-THJ2f>*emU!J2aex9!n-QPd|uW$5gJPDus!p;vk^zxmsGg<IU<eACICmhONQa-`b
zM7c5JT#*Zp8nU5co?#iM=~}H+>5LN80%2gKzH3ZVWk!f>+Ssm>oinnE{UVjJM3xfL
zGTF{Z+vxyX2Z?8qt+s$nh0If4sLJpvt}c{M^DRGT+jAm0?O)@Y$V%%KJ3C{o2Ragx
zO}B%;kHP9Q2-MS^kj5@Uzi<x?l0@kxh$#Y>0lqW|85C4-dZyb^ZjVuxJ-v)!is`rj
z$^w!~R228STpcSMLMrK6O$i$8^Ux92YRE%<$OorZm_^X3Pl}E$i`_piB#k#1<@8oL
z5;c4wO+|4ayj@&mEA-vp*-2VM?C{i9z=!Y}^m}JjbpDFi?RzTPNBly%B0UADvB=JG
z2^Z{(_LzW-)8>|X4@f2!EnB_9fqL9omy3NRW_{hVzGBs}JBd*b8~pRY;lx=TrP$fF
zbsB3l)}MwyiFhs`sGz~M84mP5r$-g1?Er06s!ecTWdQ@k{`u<`e$9Du$Nn+)1N!Yh
z$Yj1WK{a{l>i`CG>$E6@(RZUFe~`T(`#jaku7~hzAq>wNao)C=td_~H0Am=}Pf|zI
zdf`@TRq)yD-hb&w4$b>dY(eY~I|YS6=N3U;n4P<5wg2KVg#GY)I1fTlsrVBywr9ES
zL+EMeV5PqaHL!n*bj-9=C12+pE1D4OqeAJnPF<$csD!MvzW59U@WDNdt2xed{BW$V
z552g5e^4=%aYYV6k)LcqJ0FI3*@lH%;QLIE#Rlq*4Gu36{lJFzVO%%xsGy=GTCyAl
z%KXYcoItOl;8J3nf?qKq8@Y1!<2>gL`7&^i3m5T&$MTYSjklJoI_`!t33HO_j3=6a
z#Iy8EP6_*JLLbihxJ2U14(o|TE*yll8lY|-z6*wPowtao<qgAQ?{5^7h2x^&tCwa=
zxD)r4vX?65WFwsS4)SweItb%XBU!#xOt+IefUB(TB$Q+Pc|0ax&mX`%2*R#G^l`7}
za^Mxc(er@k*FkCQG2je8wW|vO*+&2n<_u@Je)?HYe3ux;c#f+d<InjS&w$6?s?Sxj
z5&$FSI!C##A02uMQC|R687sO6P`p58hD#l~6PbhXt<G+o^YT-;m4C%OAlO}gi2*ej
z&Rp>9s?VLKkyM~nln>Q8U=kz&WNql(BYyQXATNk(FTe=r-kqYmQ*?LtsQWCULF6mb
z<}|3=_Lm}7`z~M^@Rsjgd%16tP5#zo?+`8D(|*nMz48lP6D=)T4lV68kJGGf!?`W*
zG^tbIwy5(JYS|9@0nbF+cDSwyeZ((gLk;(o+rs?9Q93=dLD`FR^SbZCk%PCV&{2iq
zUob}*XqiKuTvb6$5LMs@bsgq#5i6r3M^Y$~<^XWm02feevK2vDZ2;;|@o1<5kAo@z
zGCUkaBL&0bVc21<2#zCoV@_(9#cT_SxU1hAoZ#_RwaNM?#*5LCuT5{$_LZW=@n89C
zYLLHH(?Kc=rvHEOPEmTNX!o5+h9GQ*4}h;304+V&N+SaAodt-950jpSM>R0C-v53b
zyO{p0PM%1eGo&zP4b%F=XLX(MGCuGDY!W)I$NscH7ilB-h?k|E&^?S2hB?+aSDr-7
z38_eJr5>kd2&L%4Cr{6)P<<mgCT_BqGVmZ|nI^a0kXX79fi_#B_oO9QFU=aTD#LjB
zZJw*8$CcXGuumA4rzN_#LGYRdI>Lsv8gzsUDl*5cI-b+~fb+-S0;a>thVMYtTl)?O
zY3Y&z(#>L_!Z5}=kZcBaf>hlkdD-?>)cC%hceu}fhfkkVlWyHXW}T{w=oi$y^ZVz0
zX=g2E=m!UDz(ZnjZ^e7{KE(3K?usf@e~|t;E9BhA>TT+0+U#+UzAXx<wC6fovCI{^
zdCjKtihKHO_ma>{u0o#g*7dOGI*$i1WA3uYRS)ax7qtz=B=l4L*Yi{KKF)edLwx=9
ze{Y#LQXR4BIs6r;%-X}Ubh+~n*ka#3OLY&e?<~?iwE2X4#14rPD((PM$hp9taE`3b
z54y#CeLDC3@5Jf^&H$)rYg!?>&hCR&;%X0S0qUvst{U*UTcZf+n%u?xm36&L&<xJA
zh|eOw2B(GT`8`eb8Oywh-X5Dmw=Hq#lz!E1%LlvH**n})xBac>yI*1txvlhX8ysEf
zmW_3r@TrXoF$SIDo1FttD9C&8^Y}dQ$e#h#uYuRx3+%{iJ-1@SA&72fR<BJrCqQ{f
z;mMs8g``UQ%qc2BvQBblsF7T_z$#+|tbn-=FC}~f)4ZyZ+y~EoT$zS$YL+fo41WAN
zg>KWOzseR^q;R5e#c?%}PZvU&fq{EYL?TK-s}{#c%Vs7}Ee%}5<iJRTNx4FlePHMN
zE{yjn1wEU(X#4TXuwGCioo~@4$g;R=@8Fain%=fKZtgi67y-_Uxbzp^p;XML|1o0|
zaYT(I6>Z+E#h<K}^RXx^lzpkp<A}P{4P6aKwQRGJ@yq%6lY<i@yL<zM%HSWztC!P-
zu*z?GM&DkK_W6uJZREoiESYL3s^XfBgHHN5FJymM6ri>c9QQ1QVXj}~;mBN19G4+Q
zb7(snmlOeE53siI(kK*4o6D@UxkT2dkO{v#tnhHiGsg?8I8GenOr^4^(g0*wd2_ij
z@E64BbXMbG(^9`hi}|-y3z;bkjd8nPB-2L0al`Wwe$n)!oFBoM<}Uo09~_SnQItZt
zP5q2H$KPQMud<2?p>idLpDrx1!gz1x`cW;25}9Z)%$BH1e=&e6mhmo)%Euao_0=nm
zFB0zy3F5mXL44N>e8HY}B(4heJ+}@&Z-d%Zdi_SFs@y_Vxy75`^)t2Z|N8oJ;R|Bd
z|5xh!acbX>|LpZ8ypT3x51u201xA6`UES|SM--4vDwQcxqgC0ratE_xQ$hhdIXWqv
zTzU?!(4k|$;&>-wdA;nDNi8=vDU`QU8ywt-B#M@P#jBC&G?}S|4wKudNr!pJ;|Z@@
zCtT1r6`oTyxPRIIv9>@V21<zu2m%TS3;LVlqQEdEWuw`rYhBu17dAq~NtmjpDw-Ke
z^~I3AF}yogD##=8az^tbWxL{KUSBzVBOE{#EgNAEou#FTHebUB=E(n+@F!Wq44c97
zP+V63M%DW@pQ2SW)=)G&5;ZbbvTVj9W+c*x1<rjx5lG!#3tchCh6M$41*)N!0TCkp
zXw@bULM|1Ow+sK@#BK`4%<hUbDXPb2G{y8uH>S%zya}RkOuv>X$4BHsDYiQdc>7pi
zDr6!Vw5FPYY$UrXUXI?-h9hJTMo{~>Sx#CRBRUd}4q2J1rueje9d9eghssFVYbIFI
zk9ZU6L-P538aXbXAymhTgA(CL0~D`p6K5Bc9jq*j!64vl$Uj8w+u^M|<gX^vKu%*R
z$&^wI?yTwLKi<pZ7x?DhNMHYeY27SI4Mk~4(#@7x>CaIcLtxkrJU1D7SB&v!F<z{I
zX+uMz7LX9gDhpPcE58wTrokxfRF62j<}wG}I+cq7$`&#|k$-67SLvvM%AKYvQf_)y
zAoMx>!3zY-iG6ShT|Pq_A-6bww=~<<_{X^}XO4dDTvs*b>1J4fS)~r;*7ohtqSa-2
z$BuR9y~g^^pPlC>=S!lWP^0VFN%+%vNRHrv5}4xT2+MsyTpJKqsMh}FxM3vBjB)(9
zkuehn?bn>Q&r|N)N8JM2Ls%To!3Hu2k-tXWPBDOFoWa0=l!G5nU`BD4r-G0;n$;Ce
zy8jUt@ht>1K_#75qIF<K0n`WqVF$%QCyeZp#VyIsKSj;kd|JJdydpa`Y59CsYvDI=
zUm8;xa7X-Lxp7e=nfU7})rWKrXoKfKZBQRz22d?vVCYdUQ1t}z39OhPN0^c^?Jubs
z!E|WB7vUKzdEG{!A%ON^yhyU+C+No~N3&NZt2O)}{zRoZ58*D75{u)w7j=M+()HiS
z!d68bMejI)+XP`bQN9gQ!xGXE<Ua~nrg&nY;t+Au&~3VgeD5IAJzgEfMb(E?phZRY
zsf*eN#ZD82OPSe7jDRx+-#(}VHzO9A?HxjY+E7%#TCZcHG-Ru2?`2WEm`i5zW&r+<
za=^@IlAzwsyn6t$AxDM>k>bj4&J#gvqr&M7E@#4dm-OR^02SwKxee?V{E1!X(Jl%&
zYfTiA$-)H3a%IcYM{lMZv1#*?Jp}4?&A`=H5Ad&`IjK*v*$&*e5Okv4A00H4M+l)N
zc~<LfT&d;$D*y4Gr;57ppF7a45ApdOjNumOTQ~ObLdr%>y$0EaXD8$$A|@3FYnYjJ
zmB%hZ*trMmpmuCq-7@^+R3%`@rWatk@E{E+dLh?&9wP`b2lE^4KF3|^rpd1^1KxoA
zc<5a|nMww08E*ivQ<Z3;{K<tI`&Q{08~v?z+xa!vPKi6F{)OI$L4Pr9BOsB@#g!X*
zXlozqNp=DXiw1{s0A;`dn{opUx{3`P*}`<0{p*+!nBvZDQvqYl5V{F_+g@I7ab1Od
zE<D?pzdJ^`LEub>`vVt;XjX;9_ToURMR`)(0`8HqL_>oi_Oi{MXMavI1x$vpy%7(T
z7r~c&^7AQ0SThcupmR*CEz-w-!8=(RY#@1JpDODC?RdFRn)K@aa9;BUyizzGLN41x
zE}@j<P&_Q5Ov{J;fGnN@FM*u#z<<H4Y()+4R0;Wd>pL%=FZlui^N~O#5_rUfD`{!|
z;+^&6Ja6+h$4O3tE>P6rnqUwvQ)AVbovDyF2$5+ru2HKK!604%WP+?WID)?G{5j4$
ze}RmpO~y({-cw?%i4^78?72nX_-U!lzXLo_EK;*fmxqUYjytdLZ00n6+893+b49o<
z9?%D&1`zm%#00tAUf25KDh>p0qNc(#5_m6D>`iC)5mF4-xHW|O&BJ%11lE&Qr@gcU
z@RcRDQb7NU5yS_@S+V7};~`6hrB;i^2I-*5u1J28VQBL2CtW`BNeBy>%bdYVk2V+i
zkRka|9xgO6j`;G0WfL5mqaR5iT*qEWDME=l2bz!>$8BO9hx`?%$iW0=UDA@EvxKFd
z(qAT}(7^QczUgW3I>d4AX$1&$C8+k@G^eG4?~3L0kk+qh$TI`*xBvubUDd#yMKsk4
z28<B{xR$CG0bgq0awIg_mrYL@(auicFy^b?Hh7YLVc)b3KFtbsz)Sell&TGk)!z{a
z<znxJ-TbFK^qk+-5*hy#W^^Fx2H>J6(8hv3Kftc{q{+_)`Zil>>*x_})l?HPvKY+$
z5Il$(pg|qCNeBS;koy!v8?8Prm6KQug*vMlou0Y=Qu0XnF>NkG+D29X?sh{)NT&)Y
zC}l)8wyS!Imm7?+NWbizG~|2)$7ng^K}zeaPgjTO>)8B|_H5ewwm>`@jR)T5)l?)L
z!}eXlcx2{MulLfKNIdv1Rr9K>uEG^ktmkdc|B}t0e6TUno6ri7_Erp&G6k!-8y@+?
zuP-onh}E1S;2yB$R&D@;wT+QHkfXm!-q>^X9wT4Dg*NCQY^||1I~bA8iGIuk%#;2J
zvr3=Bq%kr-KVnQ4aP1lTPCZA)p6(9sAn=Dlc@TF)G2pN3L*C0j>&|>(=W`V&E}C=5
zr20P%3lSyWcs<YX8M+I3Hw4eNin<YYI8hPuS_b<A4^D@NwVkd=(+C#Hoq#+Y2wL8m
zo9=Y%5*e<&!Q|xgP<A?jef=={JiJ)Gq#ObeS060P(I{{byoXgisD+|Y5p@;|CALi<
zce$<(@SrqtII%5pBm`C0ckNjAc2Er;NhCrs5!V)rhSi|1@*lv1c&uA2R34f-<je7=
z@L)Lm(06ERsJv+Za;NP$XNoo<JFq}7wcCUkFudb0^v1trzwr>PUFUfzs`uKL!o5FF
zwS|2QHj!sQ3taU5FlQXF&eD{}^cQr2L^tw?r@iC3#9<w%lOeRh|3`G|x?{lO8?s-&
zTldSxNkIFk6jaLHs8&HaRd%zAtk!}FT3WMXx93bNs_D8GwM^=Rsm>RO*<ibH%yBAK
ziNDxr!^h~;?t6KD${WeTQL-iradhL_emKT*(f=0Bfw@!}+yl?4+H~hnqM1yT?Lqow
zqCp%JmeU|0!f+WJI3mzYl5;ay0!kN2f;bi<@H=!2B@bVjphD&MvHKVMgMQ<1pYB&B
z^$kD`c?0%-z3;H$5B4tt^TVe<oL3CMHXBO*Vcn-md#=p*uF?Ym^&S;}fq;J1|5MwS
z0LF1tX{)OGm|G*wNTXY$Tb4bxEL)auE5715vEwA*+_6TQmS&>4JUt^>0fI?@gg~<4
zK9<7)7Q%9czye_j7+4^?Tv?WT*<e`i)3Cs1fhhm?s(MBv*-8Af|L>#cRdscDb#=Y_
z)hlnyb%x((Hm!Hn*1Fa=nLZ@U0d0|T0LjlZS84Sw5k^Ho9Lyw*hBl`tV`|yo0&&wZ
zcsMZ`l!9?XW2C<iHWI@GC*^~%0+9Itt5BjH!#e}@KEHRkwGZPqsGEJQ!(P9yKCpAR
zBN|v-UspfQy%Ba-Lm(9K@^N-@khJ!}fM;{-#>Q@BDMzI6fsPg*A+)ch<ABTK4>g(H
zjT>7xdjc=;w|IiFRj!@hR<GZ+Dh8TKdI8}e<)<5gowb~+kn$x*Oj<z^fh^Hbhva7@
zP>%JHb_;@2SD`Z_+-rWzG#LFJYTns=$S^s3K62r`0}(TRY-G<~!Y_(JfplV)4&eM&
zw0#KIG~5`xA&dkmpFkM=$8SR0wYVl7+lAH$xf@p`9^(%t1v!lDGwa}`cxlb6YJOSs
z2h0-1o+_e@{1g>R3C*oB0uhCd%9Dd^{m5rS+eJ7;7v{J!e<MZSu_F8xl2FxZ?1BH^
z4?rk+$baxEcT4SYf9L^I?toy{HSW1=PvdTRL_%I<VZUO-dvpzd^#~G2!k{JvN0<ar
zieM*nLqPO``0$KP<`sMX;WM|q@SB|(_lJ^RtXiEw?1t=EM|XDz{g(wuwrBo|o}TCV
zP7K_U3%r{jgQd}kl!r#cSotfX=iE)n+~1SAp;We+JvEmR&4Q@pkOdh>11pfU1wobE
zK)xMmFjQ0VT;&?xN6gJiWF*gQ4Yguv?;?}{8L}aRHTc^(*e9<y-A_EK)MVSymPNmz
zufBmcNQUnGZ(BpS3>QB@F(L>M>2l!@SUG;(=Ft{sC5(y@Iis(xtc)SM4#i2_hxJzU
zPS~sZ>2a*e=mcuLyyV~<Wv?vDB;bQeNsx1RIHTt9DSs$P13to-3~*s=Zv`4rbNSGl
z9qPfbz?sOxDa@cdX$GCd@@F||xLXI=z(3v1t6_Qa^u!rOOe!Qr+OUYGoUWmL=C@GJ
z?3H^o(N{drrXT&4uUB~=S`1XR>k=~1f$Ik0COe7$!}JbMzp)ZsK)y2iFFTyC?ylTC
zN#_6Hx1qm%;6DR$c@N05N-s+bF3M(!jkG98+9LV<r-QpL+g1A+WSifD)UsP#2=SxD
z0JW|yNXxnf#CBq$p{1o^0vSOH2sLg4cU+V+2Q0D;(Z&Ts@;|?{fim_*X`LQN4$z63
ze9g(4duvYBJWzAG=Fyrn(28Jb01s42+$lE+GO%`IwKSPWiHUDI$6;R~zDmVJFqETg
zTv*xJDg!#~g|%OK|CQJGJ;U?$y*FNW-Hm%SjvR+R<7&t>#=5Ds1~c`mv7UCd#*Zzf
zb(r|S0Ff?R?eYwE|BTmFH)c_1U*MlEyO3S8w+Vmt!gaDw*LV;27~AXWwi_ORn!eBQ
zKY|A>J@P`J>o4$%r*Eh9AQ^v1dAn;^mx}g>EC;Tw<}XbFGMhkN?iZBJ>c|ridfUQ(
z0F}VxJxRw?zd%hdWx0|;_`sd0qGedqiqxoWi>FxB!SCA;hfnL4I!L++MX<J|A7U5e
zG$-wnF$Sp(lXTPQrhd^gHc&Q#l*KoDy7iKQi(eSi^_Ts+o^^J5{HrI<dNfz-v@5`O
zg?@9<_Dg<6w2M<v62zk&*G0s(%|>v;)!Q=%z1p$$Egc7M>=SO+f!h}E|6s!-61n98
zVhwx?$W6p|L#~NIuB_>k$ptYBNh=_WK6)x~O0D-JZy8WquKh!gbr>D6jz{j7Bl*k@
z;2SpX8tA)uJBDNX&3zyeesk5pP#?C34+on%d)km}6L8<^fkD&1_(``Psg5JfKHW9c
zjJ>bA-no0@@HV5KhPd9?HoS4S%VVx~nHQO6cQDdf7vUzsL#D@no9Q-mpU>UoZ)#uH
zbsGhfD1DCf?-6>JF_|XfJcbgmLBzkEYd|p#y=zHrBIoFti&w2_H#{EWE;_Ui9vEz+
zp?--htsYwZnat2($_yRb`v)(1@6zDDcVKXE@I6)K1FQNGs7)V<4m9@U^ZPveANCMr
z${JY0NdBvk63B;S+me=l^i}zI<njjyzwisNnq`Z-h9cXdvBPVT7zegdLB1}n>*zY^
z2V)<tUd%Pr&=fm-{h?Sx1EN(1g;#WR%Z&=0{yc*Ie2Vfr!N*!<VOri5nHvIRmgI1B
z1(;(x`sUXjA-qOTqMrDsy~i)wbA0c*qwu42!vz=F-Zgy1IyjtGA8ejPt254Wt-}MW
z_Z03pzNfnb@$TJ1G(@CtXrN2phsKHQi!@HO|9Ug>Y_F@C#CWNpQ7(#QUI4S&Re=>E
zDgY*8n8>b%$s3UR9Gqu8nB1h*5t{(xfLok)gd!b^)TItA{Hu}qa@5AVOD@Gd((58!
z92;vJ8af&pUNqWHTT62zfEA0Trepkd&BPMNfWd`VGuyGgNqEf>WZG;SF|h|VyI1B?
zk%z&heIhn5IS|&+eLET^ecG<x#x{7@V4XBWKHAgHhwYkSr8JGU#$K#j<X%4(cnR#w
zj)h}>N}NWz;u-8$e;<4;Lp9?VD`_08cyiU3#Y<x}1PEXi<L>WM+GCewM3f&~{%2=(
z0rTJD30MKKCjX~G{1p3T9zmFoU|13qSiBWR8{mT8JB~kWJSgD!BK;2w`E`B`$G^od
zV4pj=p5GHVdm3rWkgM!8{Xu`9{L_;k!#=s6gXd`*>9&33sH2thBtw)JzQ}NjO(T&3
zxl%<ghAf+iccLlr_;3UZ2R9C=g`g-jjxad6W9^3zZwrUpW6HZ5YZ%(z5W^=5;(x4R
z`w$911P|JU!`luYMmyyDLDcQy#VY!iJ!FQ5mY+xS%B9~G0G(S7E`QqMaf;f-^)h-F
z_gkV-pMuOsRtD&g%a`%W#V@dqFaIoZ-#ns7Fz2#+A0OJbS_b*9+JDu)RZYa4*aVJm
zjC4ov@AOQ^>Y<JxspY{{`&SL^Uo|5=MDp;ux=5sMm=2QufLa3I2QIuPPyO_#eJrM>
z<cE}h>@-%quS2BzMf6mnGB`LN+Ca!L;=L+Um7HN!_Qpn2<;R3xQwG=|rr|YykNb5<
zvHCi<$A3-W%P?jP)7#(K6*&^=LI}ijeL)}ojXOWsfwmd#(Y*P7Pr#L@wm7&b>fRNY
zbWvO6c#}WqciI0=_O@Q~zgMW<O3%CUWx_c(z;F3|*D4tHMh^MzfERt4*Ztz&oxS*C
zw?6K9IJEon-2*$;rOM9st*{rh=>B`q!V_)-Zb+Uc$IOS7!Zm%0;wlOk>5&<l)EXQ|
zTp-4P$I1Zxf!*&M>$$9F3_f2x?o_y~d)q1J#Npio(O?h}aY33qLA1boN*jk<3rY6t
z4Kjae9GqZ-w6#~Py_lgD`@~q;Tj_WbBD>&juZ2p0o~eFS?*ygvv5KWz&T-lfVLVWO
zWa3DDfOsP|+?qHN@&jDg7QPo-#|{n;Za(1a?e*dR+lTYR8}`9<DOU|03AgQ=MML;v
zXH4uBd1m$K=xP~AUIDJ5iQYsy&9J<I&-d!iPUEr-7+k1tP~6(Wo(-2RIPP3?7>W|n
zBei$#r@(KzX$PROXC8H^-mu+N3^-~v-I*nC`}6AXCF<0N2defW90971mLj6mrfFB;
z=N@r-$zzVMgT_Pjb%p?(L@KvrOhZ$TxKa~$3xvr?I=_^|0xRp5M?jQ0A2x)<j3v<x
zavS(`Bv2jfu*Y$etv=LRyD^GA3I-cA5#3nZdZ;?CVL~~&7Vqzkwsv77F4ol=1u6ii
z<yevLkJb!tg3b=lUnRTPS{7LzF%7piC>RpFL&jb<dErs69%OcQX%aUZ+I9_IC?5jE
zG~;Z&-&-}7*Y^xgERFJ+rRn_|y5urCAehS-?C0eHZMy7@OT+B+|2FiWd`?%?ETI3`
z6nzuqgZ^5?T2O!$<r*O2jHF@AY<NaUNZ5q{IZD_{&PQ@glF%?fb_T4K4jao6wR@mP
z=Ca34!O}({XkdN#3hW*0ALUa*YtfmfNz<EQwD0ON!gYvah<KRZtZB^#_e3<krF^DY
z*P3wVWtby_?d^--T^fd8BHyCv@znX+kieP<nFlc}Rf9HB>*JR;G$5C8v>DsU1<Vep
zkkBPN%s?&Inxlq*Tyws&*l>={`d}JhSfqIG(m?(IJIVPw2mH;Z?$XQ_znhiE8)CXM
zPn3_C=E*AJeNTr~{ku5k4wR2w+*wBtC-26mz^9PLTt`D2gUET{+Hu!`JNv<OhYk#x
z=R+s2=fKg1rkC$<A)UZo2U_}}OrT|0@}@J=o<=yonrtyo*FQ<g5KdlBRpO9hgikg=
z6h?g3T6S96g0_=EXx7!ezuVP0+6gio4YzfUeh3r^tE8^cB0~|ay*1RpJ`ir}*oaGH
zdmMoOXx~ow-wuERu<<YIa@96$Xp6OladV}+u}>ootDYhQ`pkg<uaf1~vc5<IWTJ|b
z0;16<IrBiymBUWCHb~TJWV?<X>y>;0)Tw*h;=2vxwBtIqebjla*S9+$KXm}sQq%p3
zaQGAWeg3gytZ#b{NGXVD&-T9Gv2A9vYp3pZ>pNY|=4i03T~5iK_O=KQ1o&D_Ly}`C
zY7m2*HTMRx-!wxr?nk~qP!5JveTKOWXjiMcxQ+pDsKaK%h5-oh$ZrqdTwf2F(>L~y
zNelu^QL2hPf&DeKv<{BfUin5}^KRm6jQs@x8rw1_CxbV&g&UEBe{6`-lJ^rnm_z%a
zb%Kw89)HNUrnkLe<N+{6WM0QVT)gIMk0FhF+v(6~he5%LCxgL>bzLouiYPCqe+`&7
zTcr-Iw)|N}-ug$EkvE;(1|;D*Vx9_bkcI~(;*Gm>q{Ule8O`o0D#zXyg$*`J{Ob%3
zH;zNsK1tgEl~Jy??#vQqMu*AlIt-fyU4R3bXe&M?<!|#rA|p;;O@cAnYIctycO<42
z1Z?;a2WTz5(|KrswAT?C+DJ4V8>KblNWLlKhbfXDRV##(Dhz(MiY5M5Td!$E1?+=b
zsC}R<qzM5pOsC=MZ8O@owRwH@jqqPXWF3d6ZaUP`y{!tZA-&OEp0{3K(>|nQhe)A;
zY%#5^v8AP{m2MP-g%MkH7;D<s=xe#hBlwkidx!+RP`iGG>(ccxaskFLean2Gcp1gw
z!CC|9w4#qwgo$1~KIfOdc>Xj=F?3i=Em7oi*#FrjQ^0W5w<>?Z$^?_Li1dlpOPTC^
zSngQ)D^e*ma=5Rt%7rkaXX7Te&$7BVcPS;IYxCkm;a<OQ&l-iCeLI8x!KO$!9JzPG
zj~oPVnVuGX+q#wH(Y>uN5e%#vl|3Bj3N<tj`Tau|hp<f`Ccl!CXuNu0zur&dCDq1d
z6T3=!m(c+iG=|EaK29UK1U~(Eu4wFB_q1ax_b0^Iej0eY6PP=RH;^$9n?~1TFu|<x
zm)wvBY{%gJ_zTGfJE?~wm>8WPn^n}d-yG=V1HcW{(4dQD$_fJ3Aq{EIDCDmOgPm+b
zRN}V!j_$rL)93Y?jh?nZpv}{0dc8igtFODGeyhu)HFxx>`QG!Zp^>KXXcJr$2KKHC
zcxz1_;I4o#;6WLC7Ob6KW4vsqE6~|x)P?GdNK<3Lt;0M3lY#CIG&V(KbysJ=wY`34
zuQT0uyy0|o@WhG1=qwivE?6lHcY{HajS#x45owpOAD|a@1SuaqEd8DlXy-$czL8|4
zlGT@uC9s~r{|M${t`K}Sj*Xrx=M41otnKyME>E~V+TQK;cC>bfBAypdXk9IfUs*z$
z+nHHK0w+A~H7)HQfOZ}e0oUl-q0V;r#eB^jtA@9_0v~_4f9sm=PcESnvMz}VLDYIS
zAc^pj9!D{#HF_U$L8)&;xFh?)QV2C>?S`&CWDmY&@oYaP(caBH;*~4(xF^03jJj#@
z`|HL%U-j=^g?@K#iWXLAa)Usm$J__2<YdZEJs|n@IoUS`%Fd}}%px+nQdq|BNBw;M
z`uf((1NHTk^6f@9)_f8r{Q1)8y{)0Hdi1boaobhbf%0G8ic4@sYlkAh*Ys?a@xc4H
z_T(-x$^<C1wXI@LCD|Ur{xa?W&-_>q4l5*%D*Ar=a5Op*jnd*UMR}G{?ygVXxq0)Q
zpCkc7ks3*X&qq@#uJVd<wG^48)U1K!!uATenyAiN#t*}QPJZaMYnA&swxYafEe+i{
z)cVn<xTt)Sj-`3GLUAJ*f##jEvq&9&`8b}pw^hS<PG73n-+7)zI{X><=uSC#OcOPC
z)VvyW?SG}ytc4X0_RvaVzQ_pe<pW)^6ZW>Hd@YeGH3#Q6Am9)R(E!C$5ut=!hZ+^j
zN2Q(Yuhn-*_h9XdH>h+le_~wtBfdJ1+a<gm8+UAg#C-=eARh3N24n_IFLPl*PfwRy
zir`Nge+jjWxAcenyY<1@^D8<RtOh|SyjLZ6jIwU{F|e?+xh(j9S}>Qdb08daA<z+9
zk2(^)$MaCAza{Dq^?)X+??wJ@ik<Dm&NjQ{{)T160xN5jr3)+v+C@2zgz~K`(gor8
z{F2D>p`LJ3=X^)~ZiO_3tmnJ^q5j1mm-~AjMdit*kvgMZ(|OrYaBC0ZBU}2PQf0xd
zotIR5ZENs@wUCStT1&hX%%&XSi^(Ea$(?Cgz3ONbn=Uqd6H(soi0kyg(3;-PC=9Zv
zkfJ`?*}G<F;Iu2^-r#b76Tw%#(W9%7#)xcWpJb=N$EDsD8eT7#f2mMzSQh1iS8#Ob
z>qIaBNFUZH0GQG-6P2%zbPs6uj|Tgqepc%l_tY|fv@giP6~OLXys?qH0Kh}A0RHDi
zG!tyF7l#_WKK6lQ-Th;}U(=rHzxIvwcOP@RT%NlHzsG~$d$_n8zl+~~$P>g8s7F14
z?XmU4PA(#~ZxF>4jR9kKArDOriPwZB5Zx-+f*rmplIZ9L<!mO2v}Y6vy%5{ohyRoX
z4T{AEARK8H@-8a;Tc!8qHFD4T)AC3n+=?QI_2Q{0{b3Vy^oP*2{KLi}9iHbR=N^a9
zUS2%DbO~;tY*c4pzr0UIcUgElHEqFQpDDl+s)jOQ=?xHMNdu9_VGNBj8I#Td>tg@F
zrZJ_dm;lwD$_7&@O3oXCc}B&5vLh<cR63s(*xAzHjqF*sZcoJ9FfalO)Gij3b|{L^
z+*s96f*TQ5s)AEz&pl^`TRNNYpr&YZCvLF;t1_J!zKdZ0?uVu6I*f%BBTZ<%W@ALX
zEVNv#l`6;6DubyG0(t@CMTD5_Px3~5ql`oy?im^xSn*Pg>yhIL?v1PI1<x0Fi~6dd
z+x3l|%U)%uXE=x@pNQcdmoIkyfx#(foE3haVO`)!GQ#h#m;o2^IWOo-ffqmV<fH8S
z>;s@hSm(WDv4v<aJ(^Ai=S>^2;cbLHc0Up-BIBa>cO6D+?W)Gr9e<?s^xgceZ+g|`
z%(x#Z6otWkzwNlHv%a3r;gq@eRhO%r;*Q)**7QBdY)}&-M5J$8v89)>x}k@yaKy<(
zB~Q`zfSg(6OoQ`HzZ4#~Gf$i%Wr<Ia-uc^ePM<tWHUK#BhUp?4CKk^uDWL4!(^ax^
zl(Hg{&jaxgqzpBvh{cr>vy7b>gTOrIs|z-HvIvCu{o$1o^L=ChtMf0u!ZXC`{S13l
z3G>pIG(8w@+fkOAX}KXhAUnftUIumn+K$6G)}_@Vs=kIssX9Yc#B88#ePq|lS;)!6
z*7o<=tfwg$Y+@&|9Q6#DsHnRC(l=jfiaPaV8GCwK#xs%jNBZl4+MOFmsBD-K2&R=>
ziUNHBm+7ver}E=e>cGM9$$HGut_Hqv?U>s=kIkyhk9@?$2GsLz_t>>A?m~u~sLat;
zIgYvb;<3}G;WAyfe!L!TqLJ~S5!8+z#b$NE@ex$ikFVEpf+D!2d!lm0Db_fCYJ9xL
zc=8YMkDp}^BhI7_qtuUV<q$1oW(*J(iZ~g-?1pa}`8@kcif9A_Mkwv3F~jT!2#N!6
zpg#mdTO-9NeUCNlJ60F&tGg`N*t75A+U8?_GB#}7FuZ!0ufKR**Une$@9+%`T=l+2
z&-Q(>E4vOI+;aQSfDwz`@TJY`qlNwJdbUs9KM?J|_sGtvJ1%|ch!Kd{yIO<ot5-LN
zSFQIjwyl3%Z)cmgv5ws}diy?BYaGFfA<4%V5r2IW(K0Z{giOiEXwc!hkl{Gpk$%ce
z84hsto>cU2UaRt((2Dp#xabPI^^n&a(%oTKLvu4bac#hJuP1=j^>XR4twR^~);HFB
zp(yLVRl}>~GoQrzsu$py@bsMbnHQjeEbqtJ6}m-PO(^RgY+-r7PCtWsfw7@jktZ3K
z{XB0kzf~RyS11q&t$c#CZ+{th^dR1Ipk_N|2{_-oj$wQ$fDV!26yh$&7(Um5bE>K4
zsXxRITw$-~UcY6_dbj2cySDG$+lHX;ut#&ZcXzkDHBT6^-}kKiS}R6njiCp_dRKBT
z*`<erx-oF;J-2>#_^M_sDG3>&9s76Sh-@g$S3U3C&nkJXvgh|Wkxe~iDT3BQII@E?
z!OEdlW@VkAaz|G-HBoEL`NVi5Ig}Q|bZi_afr!&FgFx5Q{?Jt|!Yx)EgXy?gh?AOI
z926(VovjQI`T)^r!V$WF?*uKL{xDjux=9GS^7%qsDkw0B>}pSfcf0@=7m9YNP(6}V
zNq(gT9<=^IA;r0|pnT#9bO*-XP}J3GL|P(7t1B8hg{s93DrpPrbAC^%t~b=}2-MaF
zcGNW>!VP^p+b5T=sw282$Ma#f2eOL`k*dfs7pf>GWdDT(Lk1N5>tN_2{z-}{`$@a5
zft<oc2C*A12ia0Zb5KI=b?i})X9U#RO>W<2tZnrnc`>cki8LFuf}mD&dAEDDdJ$>y
zi$={GtJB?)h_Bh_KC631+@TiFHn$!KM_}$D6FvUD+SlGaq+=l-QXx@07|k^A_F$)P
zx3Bh~-)}_x*9N^K`|6r~Uc=SYe233H;z2I+?QUOpV>8z!P52&Y!YNpRL5IL|AXL#D
z0cE5W7u5`?B5<)UE#6Kv8hf(2!E)}Ulrrq@hK>&CSQHwFt()MdHp6Xwt&68y`@oNC
z!flVLph1kUMmh|>`9C>1!ma{0S|Jgv+%alk#rd(meiMr%RLHQD9I4YXEVn#W2H4|^
z_mT*}@^oAVzZ`Mr8q1+Rx%iv<`o*(>pxl8Y6kxXoLJ|xHCaOe;CGhkT_|oGp*KwmM
zu=p-|z~eLvAD0ijI?x1N*O}YI&xa7(eG+d8;zEn?rB<kpAU)EsN^-sY=OhYuOsfBn
zoAQyaW>1@^{hD@cHh`nM)!Wo%AntHY&roI+{`IalJuc&d8g(~4^PR@EkjoWe5jR&y
zcRl$hAQ5PATC}uavjH<|xL>YXH9=~SZJ)N)Gu#Wq4teg+i1XBezs>Md)$|Ekd`lA=
z(^;CcA*aCPiKK&N4T3FRBI>-`4Y(bR?cO?ZiP_%7PBpbJ{#i|E&U$@;x>|~@2nGYb
z#qTz+TE(s|PiWYV@jQz4p@==i6SAI0X9F_I-cpT1qS}^0<c;FDov1x_x}`-1#bIRn
z`&-ya9KW;r;Of-}kJ4dvm+)b!RkxP{pV-OR45c}kiS@TEo|etWTl$}@aoXa7*Bv%=
z-Dh0xi$)^Yor~-Sr?Im9)6h$HVN5E4;)q9r#eNgAh#4li7nSZw5E;B02YLb^cycQ>
z$W`Q?B>9P*g?HSz$k$@*e3`GFM+aM*dw8@-N1&?PT+m<W4r*Ka4Bv;Fj9o*pxO6tV
zM|L0@a`ftkoo(3Zpv!RkUL5rPsI{N5-jP0mT&<WB*i+#-A+EKp8Y9D2?e5UEV+f+$
zs`6>kI*Iqo?+If9*;UA_0-K7I>?jak#^7(%%PUKf^-MJYT9GwGSx@<Mt}f>D#jAC_
z8w*Vi9J}F$V+WAqt6L{BMGWA9Ew6aR7CP(Ghf=8_{B-NA?}|43xk3x*-5S$(NAPF2
z&a`el@F~SKo;A|bGlCUx-I~_Z+}xwlDF^?*jaa|8V;1g{KF5lXhsYG{wj|?;$^0R;
zF_?uA8A8@Bxj`<N3XwAO9^V3FLal!bW6u#<VTCzEmrJ7wu0|``UG+_O(+9Z67v?;K
zq&osDGomgJH%5Ef)-`F`mIv99_TdP&<=!;XrE`xfifwHLG82V35BnA$#Kr86H@169
zq(k@{P0iEo@`%8J)t&21SIg$p@-x-?0ui4_*S%)<nvRk7-gVKgO&zU+-nxNi)2r(q
zUnJnGRUfp4HnjG8k=|fnHMW`S=p7%B_=&lA2KYkz3L`dRrJf@$B<)Rg_WPu!4I)-(
z&u#?OA}Vpu9;dtqfs9mMkrMy0r)XBbOyWwpf7noHW&cbBj7o0Pz^Z%EyLeJ97G3_h
zQ_rH{P$|>mo;4GA=X1E-vinRR8!?SP4m5`<w56`53%WI}vxaR*8N5_C%oS^1A;T)e
z^9EjnU7yx<J*pJKW!m9hQ~#lE{srs)&VV&{tsBN8U$vw>wjr$9)Um8v(G3RLrq&QR
zj){m_PGq30j050Z5QR2kCx{69^Iqk4xUBb3+nYn7>q4WUH@5|9k%$NTKrMdox?8Se
z^Ly1k)`w~XXfld6XmY6cGU>Nid<U91bR1q2_*~_)BeX^yv`W4mX{pkv<|w5Y5P`K%
z-x}OFt_R)58N(ga$2ShvMWo#z^6s~r4+cxVtH&OsjP(zWUF|DH>WEj@McAn$coqZC
zC_k&_nV$4iPkD+b(3~gR_xF%<<`g7>Yip)zk~M{zIXqU%E_fqnJvwcDtWrfRL0G6(
zHl2_!R=F2|^GZVAumn}^)wRLwi9kT@A&GOtE{=7WR2rqySkJ%)zLCD}a0r6yT5Loe
z$cC|x${NjJ;Y=O&q#nYiBK2|t>~!s?md{$n3~dYcBN-Y)D4Vtg*9^%V3j58)S6|`U
zJ)lE^glEFrf0&j4r{IrZLh}vy_n23(8fiehf;J<8s*kmhbad-i$m*y(euSz&qK>LM
zIu~lv0(HSn<L*dwE?5`PnnIh#qr}@OKa|cs>&UBQrICAelS~E0A54K}Vx!E$Qep3Z
z;eP$nz21YmFnbTSw%7Z=9_kwneSP5aj@IzvHswxaSD8XT=-qp%?&t1sYx|M@Z-hqs
zLSOgSx3^ug_>giSR%~6Q8xTg0*4z#2Lz(M9cvBt><WZ3ys;q5^p1Q6&=i`;@2BeX<
z!g5JvQ(4m~`wewnB_Xb;Z%c|?87c>)F1ts=S&(sv->+9cKdG{oAs2CtG_FY7*=u!i
zXOeIrIWCnFxd1pkuBz^&6V7R=)>W#dY#%@fs!R(+se-|~<pKM)Pb^lI9}#*~UK=G{
zB^(UO$2e8$ggjE1y=W>|04az+ung?zuv203L(?+lRF;@ujSl7I-^&80W#|Ph1g}^g
zfn4m^MJ_RhSU{J@JtkaOSR;;XrMZ1gSng7iHnG>XslYJS|C{>OBUrm|$~@lF7in!g
zZu*SZn0Otm5SKqUP?H-ZNNdM&;xz`XM<CsX>{AcHkKCX%+GUZdqC6+%t_mB}k`DRh
zWd+Tjs_a~Ql=$f<!Beip*hrA0WPNmLF~#DYN*1N{k5YuhualIVh!0}s9-~o}f2fxV
z#@++yrGhzDo^(2}fSlFqz#8Bk4y<EO*P{+>$S5jxtp|K*&Z+m}p7()kF^#nQrkW2q
zFcOW_e9?h9BH@1Lzyh&He{o=~W`wPEV7;c1O*pVoGsNZ`*i&;9G*sQ+Thq_}xIdRI
zMlYJT>|DmGhNGj={RPXqDwj?r&Ql+K`V&X3f}P4`qoZp_>0;;pqm?^eY-O!Nyl5q&
zlM7LMZf2xdoQ_Txa+xSKx6<icG+)TwY)uu{CX2=V)^+Qqo%*#?xeVrYt|nVktchY>
zz`WdzAy<>Z?^D%BL1&^kTR<BNS%Y&mY3x*&xWM+&|J4;o@x%i5i%7{nQ%~07f4Qg2
z`%m>N`*<;~&&qqm@x~T<9Yyrf0**Fj%?!SO5r3yqp2oE_4)msKd@Q_Y8gLZrU+LV<
zxPA()*Mfi2Re9Kc*WvH<iuP;KitZ%k7dh*7c|7MJ60kE0M9m@h4>OsIxtWJ~nGdNH
z0^lq`R>$gDh=o}L!bT&ki8Zqp*2>_Khvw1AqVV(}FE8tbl+w=z*dSZQhM;V&VKEqr
z*0Eu>o{g{#Y$F?Go7foJ%(k$tVBFi~uJXIsIJ=1LhNWpQ+sF2^i`fBo2|LIxWtXu-
z>@d5W9bs3nE7?`-S?t;DD7%_n!>(o5vFq6l>_&DIJH{qhoK3PRmS7f}W-~0wQtW0n
z%hI5pS(amYcAOQM&5EqV=GZ(t!4}vp>^bbY?0M`~_Izv>`T}+vyPds|y@<V--NEi;
zFJX7Fm$H|!yV*VLZ`i%;<?I#gmF!jQ)$BFwwd{55_3RDoZ`m8!o7kJ#Ti}PjkG++>
zjlG@y9eW3RC%d1$i~T)&H+v6zFMA(*KYIYXzCXx5$UemWfqj^Lgng7f#6HIUk$s#!
z%s#<rA6i7DvCkma+GpA4*yq_J?4Q}curIJLvM;eOv#+p6*}t-{vVUV=V_#?AVBcil
zV&7)pVgJs)%l?Bs#=ghSu<x@Uu>WK~WItj*W<Oy+Wj|v-XTM;-WWQp+X1@U){w@0*
z`!Du;_6PPy_TTJrtgQPJ`!m91pJ0pZNnXPlN9+{WkSEeW5?U8`Ba^5XJBj%bcvj1U
zypGrN5D)VP-pC`oi8u2W-pbo}JN7y4<Wb(myRpu<m-q30KEMb0Dn7(l^EEui*Yb57
zE(AWpA#d?fzKM_V&3p^8actw;`3~%qx{HtVi}-H7hwtV4_<nvdKfo{H2l=J^GJc33
z=9lv${0e?0zluMLKbs%rS7T|}wfs7MJ->n9$Zz7u_ymvhNj}9B+~U)Gh9`N7-^^!u
znrC>H=XjnU=LK%_A}{edKF?3^1%3;E4u39x9>0}8pP%F};J5MH`3w1r_>1`+{7(K7
zeiwf!e;L1<-^2fg-^*XlU%_9=U&UX|U&CL^U&mk1-@yNtzmdO*znQ;<pW^rNxAM0k
zQNrKxckp-e`}w=D)!MuHd-!|#`}q6$1N;O0LH<GhA^s2i!~7%sqx>QMG5(MI<NRU%
z3I0ibntzIantz7>6aOs#9RECjg#R=D7ybqQMgArJW&RcZDF0XfRsL`MYy9i{8~mI6
zTm0MnJN)1Iclm$t$N2a78UB6#1OA`<hx|wU$NVS!r~GI9=lmD^m;6`!*Zep9EdMS4
z9se)>d;SOhNB-aZasEI2PyEmPFZ>C<$e$E7SQp9>0f<eS;psGm3#Mrg)`|MCY&Rfk
z5m8$w>P1L|vE_23h=?Z94D)rXXhVXh4$&#1qDyp(9?>iMM86oo(z;b*NURoXL=4GQ
z*1`L@UW|whVxt%po5Yydj4S|K#Wt~B><~M}E-@}H61&A7u~+O9`^ClLfVc#P^h?EM
z;*dBjF2|<8SBNXcRpMFV+2W|UT3jQp71xRD#SP*{ag#VECPZ9JiYbv0mY5bZA}Lbh
zW-%+$A|tXQC-UOBC<t2=MM=zwd2vE4h+D*S#B;^-#H}#GpA;_;w~5=u3&o4Xi^Uz{
zPVo|Pmw2go8CKogBmPF*D_$;MAzmq7C0;FFBVH?BCtfe!ApTaoQM^gKS-eG@68DL>
zinocki@y`^5bqTCi+72?7w;DD5$_f66Ym!fh!2Pd#RtWQ#6O4+i;swpiigC<#6OCU
zi-*N0#3#jR@hS0X@fq<?;<Ms&;`8DW@z3I4#23UD#h1jF#aH0+{a5i-Y`XR}@pbVH
z@lEk9@on)P@$cfh;y=V=;(OwZ_`djo_)qae@gwnL@e}b=@iXyr@eA=w@hkCb@f+;O
z{9Exm@n7Qi;t%4F;=je?;(x@S#Gl1q#1mpsJgL<nV45Quo3fA?nh9?P{56^v%WwQp
zVQaOZR;Sf#A-E<Rurq2zYtovv7Ohol)7rHTty7C?U0S!+qxEWiTE8{`Yr`t6B3P}h
z(PG+K>@+p3t=C4h4cbO+RNJJDX`7)%ZPm76jn58kr?yKQ*Dlg_YkRc4+CFW+cCmIq
zyF@#vU5d3lhqS}m<=PP}mby~AN_&>}Z0)FawRVkmt#+Muy>^3kqjr;aOq<Z++N3t6
zB{Zvs#}mGIB2lnxJ2sii%~lFi@j}9gCvGm;MSnb1h)ttOlH?a(Je``!#-=PtECs(R
zP^;ARf=89o)^yRUz(Q&!S#-zKX{#`kvTS!eGg(T<vr|@2JYyA7Q}Jxfrn{kTrWBvC
zVkx?7zF-wo*%<>3let12&ZVtbB3GJ}Kl9n;HT1apWo_hBYnN4(@}6>eKI?$0pYrDN
z3YTh8%Ig3M3tBuon{>zXc|0|qE}HSeR5CSZ$=~_-LPNYz$j#H4*i@=8<#g@bT6#+3
zIc?<wL+4bN@^z}N>XyrE)Z5BOJ8hh|t*)ZCmE}Z^-d2L@Z6)Zut%OT?pFD5NH%V3(
z=G2w)Ol#b>Q~1bpRxFKQD~s<FvljqmY*!pVg_J$Z<3&AQOvi04Ud(0c;-y3?7fV?7
zR3ViwLNM0j^O#lc$@tW)osUmhE`q4aKN**^B4$s;)0UWwXK^Z<h{aP0a}wj8OIR9y
zZM3s3v`xq8P>a&6RjicJq?kmLB6_nBvoiVOg0EbfE~V3zLNcD7#>J}L@lw2i3mVJi
zViF2K%&E1-q?OPnEvw*}gnS;e@p4I&>1Ng~DXS2hDi!7|eKLi)X-=l57BCS!lc{WO
z3iF&goGKKPiTFZnDn4uZ(I}h1Mfqedi{`4NPo`5@JS3Nvha#ZNBo0};b1pmMCbYyT
z#xZaN<?>e6E1?4FtO6S4W?gj1#0#^&$=p0{HzNlKwI^hI0YJxp%Qhzq@f6Ter{8Sd
zWFciuPhn7F87l#FOuH*JwUdQhJb`n?STQ$m6>vo^m%*`QC&grG#x+?=r4x7*fOHyo
zif;zMwieLuQXy+jmgLMqTMR?VPGyP46jK??gXhrj6mtb1eeY}{UZB|*s1&A>*3|4$
zF_X&T9+k8Cbje;iNq4ke<&(G}2unN$3R1`!s(|NZ@-u~8DVspMY&Jfbq6_kNDUAUE
zp`(X;>DQS9Zi1%@_|#}t0EJ3e0s!uV+ky}QrxYl0N`Vrm>J)6#>>-K;Qnjo`BJZ*q
zg}lqEN_l!J9#qQL0w~6oP?<VbjM4j}Kj^Ym0NKT7D_Z(g5>&b#$Mo!E4k#5XT5${`
zI$fy>0t6W*#4W}NAL*RDzZG{Y5J=`!ViH+SWj<y3<QH)Wol{T**(s=3MlbN;sEXK0
z%1-9;UZ+5P!ma7VEV|&dnavV|aO(5%`K;6Yc*(M;d(<4$ZkBFr+aVn13OO*JC4#%M
zj)pkAqMh??mR}6QS8hfhs#2gE_{(SHmDCUUSa1fgfONu`N|wM3d`>sAx#CpPPz7x&
zRa~f@N~iMbE8tR^$(1q@Pj{uLPo;BHc)2u?y$(m)N`O#ET8S~LM@=aqr%@WHs8B>!
zWvsX)dw3AF$mB34gwLL(vIA0WNY&zN0FCWf(u&V5pi6Y6T~ZgKOHMJUpi;j4Na|e)
zh=Yz6K{~udjANi4;LRGr##C;4+5&>)W>B87^{HGc3$)6mOBpO;f#D(pQ9*UyffKnj
zh@-E3Dv>J!yPb2A!s2RqRju=DV-V;P^g`C#bS*dtkTGRXQR_VT6P<<VV1qd1vQr?=
zkSB;#qYt^+6yQ{mdP^{RnqP3!L4INMxippZP8C1|!Ok&NT~mczo;s}&pyOcM9(g1@
zv#EBTYUi`sRB00EhXatZRCFcc#W=Ak4J3Qom9R27P?DkvHaJ_%<$VdDTn3mKpDo1Q
ziBx<hi?43GKzu<kMutrVY{Ds2OoQJ#P_9%^jWAUdV5d%aWGl(BR9$gCr%_#fA~lmL
z#?!GP9+jIZ#PdnCEx@t_w6~2!Y7R6_Po!?SWkDpeagoT)c-0U~dCX7nLTnl|8vIX}
zB_ol`g3zE@4#ZK;RkwtRA$jl(9jw$6xjFO@BSSMNm?*@jNtlZ{AIU()OfKug&wMPO
zOD~|{PZUZS5GBh>=i^z8N&z}R-k6$|pUsshWhUucnu$_&#>!<i`ck?@wlWW@Q+Y@y
zvE&jsS8rwKtTg&286c?PiK2g5Rc)oDXqT&8HY||uv~7Tm*jB-9l?pjIC_d{1J{z7+
z)1n@QbUY);sPp3C>Z)QPC8bGkRb`!Z0<Q-=Cc#VcMnl!$1SF6wJ{PF&EZx3od5yzR
zh)sdL#uAv4dFmQqi7x_>f0|fzS)%gE(sZss;+2?=pYTqDW`G~i-AxBJrmYP4uXmb=
zFizcU6<yOQCGPqZpk!W;Dv&(qmLMU%-+{8l0x(fsIAzbN>hkwjS7H#XG#-On<BfBL
zqN+}1Ao8o~REDZUvPmjCmy_tA$V7ErP!=3ng<eH<)2UE>$S0<2WwSidyF92-u}+mi
z8_J?+K-Q4%5igbsPA3%6Q+*=@ab9*lS4e2op58r;(NE>6n5IL<zyV{2x4_JR=Axbk
z?vF2ssuGwWWVC7E9<DCn3qhtu(N5wDlt7<B^aH>&U<mOF3pB_jizPd*5uoE>&zjTV
z`bjI{n}(c-ZzW|SuTzjRp#zh2U?^B4^Ra(Ams^sNO=n7IFgwq9aG1|Z1kokvpDtyy
z7G6<~yfIxWLMk<9AX4Sy3D->Aj^#_4ygm~LYKa*u<C&52EW{{VpGoHIqBfJ7F4oPU
z!*&efC`KSfU&+6$#*`(?_RXYolfb5FQUn7|L8*nd$0^#WxGq8WOfEewrCM_)moF9K
znE=RIqU2y;JYPQpd6;H#5l9-7IP9ygGG_{yZ%{h$o25?GN?_88gY?YfQg@~7l@R!3
zp=j4h7zl_DOEOi#R%ca>N1ZLAT7j|!x`Ia(A-tD>pz_jSb=5quT-`;rQBS5?jha!%
z@-ax%sax><;%UDu&ylbPG1rx(TS7oIl5sG@f{q_MUXPbf;2J5B6N@htgUhN)cEVR(
z;gyAS>K2mAB%F_frZ^yEN2^xV68=aTq`FK(lDfadubAYb{;CTgT`iSBs$`)68A3I(
zr&6|^!=TA}J6}FQ(n&fGyun*#6Slm}2CuYSD3@ka@uZoASYC|J;L2ibUUnY?u`nA?
zi)12U;>}4X3DB?9vLv1LEEQ!7+bZgEjNI~wIZ#BCbP56i;c^o65JnE7iAFL-;;NoZ
z<?}f+36#wh7Q9LH*IEEFOwDRZxR7*wEZgz{1M-sV((I}f?1c=l$W<<zNwCUdX%g4K
z<>OC+u`BEVDg~BGQb%1XLSioz-IX#PRf5u%00)|}%w%C<E}M$GQlyPySY4?EFeL%n
z3V@^;n(n3p>yqjk@@VU+EYv~+`X^*G7k*&KDJ%d0IfEeU1&}NAb%G6gz0{O;GuW?w
zGfj=a&3FM~UGtEP)3!bvhx8*bIxhT|GO#gA-!kr=#V3b67SGg5h^D)ms2Y`VdOihS
z577&zfCTBX1W2opHw~E>;+PaS73@jJOEXD%JD&m_gj5B(xh_Fmy8xm<>R(<<LnPDF
z5XrK&M6;?$g}YJ?q%BZ4IpZaRQN@xJ2Hok@w1vqItz6CzD7=#z@oW^KbP_vpk%TYD
zTLK`T^0$;mJG&Twyss#h6aw5N{!99+5fp+RFyp9u_+qjTE9<0wv!b2SZ&p;o29$<<
zYA!`CNng4&Geh<@QXq9qn$(nmW7aCVGZ5-yc#&Bx16d%DiRX!O&q5PH7d%VFr8827
zSUOWMsS%JCx5~>gumvF8A9pEu@k8P}8SnfO#th5NTCwD^(wu6i&Y>p|u`}R$Iyieg
zZQwYYLRT$(aZHNYAVCE%DSU|-WFI`xN}^Q;$Z0~|$Q3{+KpTM$3#cwyxq>^Bg18R#
zEgzPUBo6e7N`eY3uMAcdrG({Ks!*T}T4DJl7j_WDDJbHs%QK!#Zqkvb3<U--_dq#N
zo2G|{s*3aAs#I6Iq7FAHU*epSf&w-5s#CJ;0$d_hOCxpAkD&m`h~+K2n6~h6bj!k>
zK)$4n3av}vEh%b9lthwoFp~@nHB)}f3kW^4ps$cdjI31xC3I)4d06d8?u5dt7_1+V
z1x^oY0M6vgO_DsWa4p~zAg&YYnK{fd7+-iUqvy!*X68yoB0AnYIsv(r%t}55(}zWa
zJf<UB8u$f(c6c;wA92_vBurYgKOcwinMD6!y*`0@C>Nyc;Nk+&Z5Qc<Bv}Rk6ywuz
z63bI|MyH~M=h3?>%^g)J2pA#sJh49$Y7LFCn|`OE?k>ohY--X&HR>}9(tYrM3U7zD
zJ_jtovQ$7JN48_29A+gtPbPCTmcPJ7oCmSalG(kO^s4zN=bF!fC6{*!bEHmNx;*2_
zr)Fj_hkyuro``M`qXKO)Hyy)QvoXc}a+qRxaB41|u2rQ3gt!6;ySglUafDe?T2eHE
zaVX887G^tDt6=q_bka8SIcX3yptq%CNsWHo`5e?EOsk9lgsNQLMFS4O7B5RSH%SuE
z48l0NIIZOi@dZB}2ze!&kC4`0mdr68MNC;5&d_=4ebsfkl(#(ynwYW@cr&}!iJyR7
zfF?r5moMFN3vLDz)|6!&mzLyOU;;GDiW$jOF1sPAC7@PLB|XPWnAkC*wO$1(Gr@7l
zomrD?DloGefD^fl39Z_KeVcyC`s)J9<f!)=0iJ?21G-OAy}MxLp;Tiw>k`7LJg6ct
z2e$;mSys^~q$XkW)riArWPa5PIShwEf)hj*AX3rt6u`Kg*$2>5kZuuTJpnoGNYRD?
z+DZgfaY>GJ&X)yAU7j}!5c%;q+$RsaH{HjyVQiX$7(l-y&}ueFlVQV>kV|{<p{#l6
zK)4O62v_|!+Ds)u<jF=;>y*lz&gYbs!r~O9lA>UjZCME-<OZ~DdDA2?-3~(RgBFEx
zbY9k7sqiUCje#x;UZ_U1;M&xE4=}(=OZKHnWS|4dl)z%Uh?Y6b2aIsh#*Lsz)0xTm
zOl?(x+zS3mnRupw5+*RNF3H(B7tea>VKJpk_$6GHSA4RBk0qx86%0DSx-NeR2TdEs
zC~(Q3Jqx2bMjQ4eGWpxOtk5MPaN7t&k|sNkogqF#(vt&92}jmyb_UiP9Eji%RWKdc
zZD(`yP#$M3-Npp6y><@YCk7jYm9B-DNZrdP<5JA<SCvcXl{_nXgghOnt|;Z*vMBA_
z5+cR}B~CU~cP`g}^2-(wzUWtggZQQIC+3W#0g)N+XyON|C;g_$oa(lTLJ-IBs_z^l
z4^FKE!z3)n)+o<`A}suMBqPUwlNe2q6A)X!tRYF7K7_kcQuQF0i8$1JpR7lTggcE2
zIB(@KlFB)S^C&rbvloRTM0w)=fMK6nh!GP6OaiK;rg%^;KOdKG0m3LJu}&lsI8=m^
znn7dun2M0ita&`Ekiv9J0=Z|BbOsc3K0a+>vXmwjRWty>;~2C{AWr10aYP?ixkA8>
z>$QQv1$6;VkSW5pJhtoyd527UK1KSXU;ffm0j<VE7P7F%K~Z%9no<)MfN8@r3X6sh
z#!O5+IQ+miby@JpqZ)q&n(D_6NNXd>TT(<6P}4O0w{gf@xY(X0@I=Z3OJ6am><$tP
z0vFJEX^M%F_ElS50WsEvTY`YZ(_S*<L2V%I47UM1pGp*yF5C`t8@dnyAh;Z!K$yFv
zL4X8FyN--s8R%-%Iq4e=R@cz%T2?``CRE*mxG}D6sjge@EA&>K0t|~K3BL1Lw*v!j
zB!p3#_0ZJ86u`XnNe@<83Xuhzg-j%AfJUHRBtgtm%*~J|98A<*2D82*BrDuVibQ4+
zpOk!k_$?-JgdB{k3aM0V218*=OC4lk{K7B|5y=l|8XhE!83t4*uAQyLF>5Q!4)xDl
zRi-QLB-K(|xI$?n8<?~C<O1#meK<x|U(6h*1fi6;pHH33<mPb7DdaH%r9AF~tBfLu
zG{{4-xWJ2Z0SsLMR^2!eys4zCTy#U4unOQ6mJ5(#vIGQ6Sz<>vw9xp}6cjs;Qvns$
zC}l}OLygq6&~vH*IW&~DF94xW&Q>+tu5!_-Cw$DtOGS^Ymx8ueffCi7YPz4g5*`q@
zJWuv%pM+rEf@*L3Wx=soS?W|Odoy@JLR~>OF=eAnLfu#C5V8s^`sTQ5N4!(@!L~r@
ziViH9p1Mv>9MvD7p97>Uby7|+jczJ$2VpNaqf-$*1e)8BWFax<vM#4&`{y9k!{)DO
zt#8hPGZyoj#14-tkh-QzP@7A|(>fh&ue6}5QAG``sm5FiJUw0uSvC#ZK-rD&RkE!_
zl`@<mLC2}5>F$7Vjn0u^wqV75bMQjIv>{Cl<~-b<m{|sXfedaKy)9`W(Lw&KLSSCf
z3lcWT{b{J8XFdss15Em<f;JB~Dh}C%j}B0qrGul^DP%yiGO1gvN*TrxDhHhNU^hhF
zy(p3%LIMJvvSP}xgE{9}D!S$&c}kgJz5vA`sh@xmc)_(mIt7FT7sTS&0y>JQ)Bgji
CY7TJ#

literal 0
HcmV?d00001

diff --git a/docs/python_docs/themes/mx-theme/mxtheme/static/webfonts/fa-solid-900.svg b/docs/python_docs/themes/mx-theme/mxtheme/static/webfonts/fa-solid-900.svg
new file mode 100644
index 000000000000..bd7565a1b64b
--- /dev/null
+++ b/docs/python_docs/themes/mx-theme/mxtheme/static/webfonts/fa-solid-900.svg
@@ -0,0 +1,2618 @@
+<?xml version="1.0" standalone="no"?> 
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd" >
+<svg xmlns="http://www.w3.org/2000/svg">
+<defs>
+  <font id="fontawesome-free" horiz-adv-x="640">
+    <font-face font-family="Font Awesome 5 Free"
+      units-per-em="512" ascent="448"
+      descent="64"
+      font-weight="900"
+      font-style="Solid" />
+    <missing-glyph horiz-adv-x="0" />
+    <glyph glyph-name="ad"
+      unicode="&#xF641;"
+      horiz-adv-x="512" d=" M157.52 176H194.48L176 229.22L157.52 176zM352 192C338.77 192 328 181.23 328 168S338.77 144 352 144S376 154.77 376 168S365.23 192 352 192zM464 384H48C21.5 384 0 362.5 0 336V48C0 21.5 21.5 0 48 0H464C490.5 0 512 21.5 512 48V336C512 362.5 490.5 384 464 384zM250.58 96H233.64C226.83 96 220.76 100.32 218.52 106.75L211.15 128H140.86L133.48 106.75A16 16 0 0 0 118.36 96H101.42C90.41 96 82.69 106.85 86.3 117.25L140 271.88A23.995000000000005 23.995000000000005 0 0 0 162.67 288H189.33A23.99 23.99 0 0 0 212 271.87L265.69 117.25C269.3 106.85 261.58 96 250.58 96zM424 112C424 103.16 416.84 96 408 96H392C387.15 96 382.96 98.27 380.02 101.68C371.4 98.02 361.93 96 352 96C312.3 96 280 128.3 280 168S312.3 240 352 240C360.46 240 368.46 238.27 376 235.58V272C376 280.8400000000001 383.1600000000001 288 392 288H408C416.84 288 424 280.8400000000001 424 272V112z" />
+    <glyph glyph-name="address-book"
+      unicode="&#xF2B9;"
+      horiz-adv-x="448" d=" M436 288C442.6 288 448 293.4 448 300V340C448 346.6 442.6 352 436 352H416V400C416 426.5 394.5 448 368 448H48C21.5 448 0 426.5 0 400V-16C0 -42.5 21.5 -64 48 -64H368C394.5 -64 416 -42.5 416 -16V32H436C442.6 32 448 37.4 448 44V84C448 90.6 442.6 96 436 96H416V160H436C442.6 160 448 165.4 448 172V212C448 218.6 442.6 224 436 224H416V288H436zM208 320C243.3 320 272 291.3 272 256S243.3 192 208 192S144 220.7 144 256S172.7 320 208 320zM320 83.2C320 72.6 310 64 297.6 64H118.4C106 64 96 72.6 96 83.2V102.4C96 134.2 126.1 160 163.2 160H168.2C180.5 154.9 193.9 152 208 152S235.6 154.9 247.8 160H252.8C289.9000000000001 160 320 134.2 320 102.4V83.2z" />
+    <glyph glyph-name="address-card"
+      unicode="&#xF2BB;"
+      horiz-adv-x="576" d=" M528 416H48C21.5 416 0 394.5 0 368V16C0 -10.5 21.5 -32 48 -32H528C554.5 -32 576 -10.5 576 16V368C576 394.5 554.5 416 528 416zM176 320C211.3 320 240 291.3 240 256S211.3 192 176 192S112 220.7 112 256S140.7 320 176 320zM288 83.2C288 72.6 278 64 265.6 64H86.4C74 64 64 72.6 64 83.2V102.4C64 134.2 94.1 160 131.2 160H136.2C148.5 154.9 161.9 152 176 152S203.6 154.9 215.8 160H220.8C257.9000000000001 160 288 134.2 288 102.4V83.2zM512 136C512 131.6 508.4 128 504 128H360C355.6 128 352 131.6 352 136V152C352 156.4 355.6 160 360 160H504C508.4 160 512 156.4 512 152V136zM512 200C512 195.6 508.4 192 504 192H360C355.6 192 352 195.6 352 200V216C352 220.4 355.6 224 360 224H504C508.4 224 512 220.4 512 216V200zM512 264C512 259.6 508.4 256 504 256H360C355.6 256 352 259.6 352 264V280C352 284.4 355.6 288 360 288H504C508.4 288 512 284.4 512 280V264z" />
+    <glyph glyph-name="adjust"
+      unicode="&#xF042;"
+      horiz-adv-x="512" d=" M8 192C8 55.034 119.033 -56 256 -56S504 55.034 504 192S392.966 440 256 440S8 328.967 8 192zM256 8V376C357.705 376 440 293.689 440 192C440 90.295 357.689 8 256 8z" />
+    <glyph glyph-name="air-freshener"
+      unicode="&#xF5D0;"
+      horiz-adv-x="384" d=" M378.94 126.59L284.7 224H333.92C349.22 224 357.58 240.6 347.78 251.53L234.45 378.04C237.88 384.65 240 392.04 240 400C240 426.51 218.51 448 192 448S144 426.51 144 400C144 392.04 146.12 384.65 149.55 378.04L36.22 251.53C26.42 240.6 34.78 224 50.08 224H99.3L5.06 126.59C-6.69 114.44 3.34 96 21.7 96H160V64H48C39.16 64 32 56.84 32 48V-48C32 -56.84 39.16 -64 48 -64H336C344.84 -64 352 -56.84 352 -48V48C352 56.84 344.84 64 336 64H224V96H362.3C380.6600000000001 96 390.69 114.44 378.94 126.59zM192 416.02C200.85 416.02 208.02 408.85 208.02 400C208.02 391.16 200.85 383.98 192 383.98S175.98 391.16 175.98 400C175.98 408.85 183.15 416.02 192 416.02zM304 16V-16H80V16H304z" />
+    <glyph glyph-name="align-center"
+      unicode="&#xF037;"
+      horiz-adv-x="448" d=" M352 404V364C352 355.163 344.837 348 336 348H112C103.163 348 96 355.163 96 364V404C96 412.837 103.163 420 112 420H336C344.837 420 352 412.837 352 404zM16 220H432C440.837 220 448 227.163 448 236V276C448 284.837 440.837 292 432 292H16C7.163 292 0 284.837 0 276V236C0 227.163 7.163 220 16 220zM16 -36H432C440.837 -36 448 -28.837 448 -20V20C448 28.837 440.837 36 432 36H16C7.163 36 0 28.837 0 20V-20C0 -28.837 7.163 -36 16 -36zM336 164H112C103.163 164 96 156.837 96 148V108C96 99.163 103.163 92 112 92H336C344.837 92 352 99.163 352 108V148C352 156.837 344.837 164 336 164z" />
+    <glyph glyph-name="align-justify"
+      unicode="&#xF039;"
+      horiz-adv-x="448" d=" M0 364V404C0 412.837 7.163 420 16 420H432C440.837 420 448 412.837 448 404V364C448 355.163 440.837 348 432 348H16C7.163 348 0 355.163 0 364zM16 220H432C440.837 220 448 227.163 448 236V276C448 284.837 440.837 292 432 292H16C7.163 292 0 284.837 0 276V236C0 227.163 7.163 220 16 220zM16 -36H432C440.837 -36 448 -28.837 448 -20V20C448 28.837 440.837 36 432 36H16C7.163 36 0 28.837 0 20V-20C0 -28.837 7.163 -36 16 -36zM16 92H432C440.837 92 448 99.163 448 108V148C448 156.837 440.837 164 432 164H16C7.163 164 0 156.837 0 148V108C0 99.163 7.163 92 16 92z" />
+    <glyph glyph-name="align-left"
+      unicode="&#xF036;"
+      horiz-adv-x="448" d=" M288 404V364C288 355.163 280.837 348 272 348H16C7.163 348 0 355.163 0 364V404C0 412.837 7.163 420 16 420H272C280.837 420 288 412.837 288 404zM0 276V236C0 227.163 7.163 220 16 220H432C440.837 220 448 227.163 448 236V276C448 284.837 440.837 292 432 292H16C7.163 292 0 284.837 0 276zM16 -36H432C440.837 -36 448 -28.837 448 -20V20C448 28.837 440.837 36 432 36H16C7.163 36 0 28.837 0 20V-20C0 -28.837 7.163 -36 16 -36zM272 164H16C7.163 164 0 156.837 0 148V108C0 99.163 7.163 92 16 92H272C280.837 92 288 99.163 288 108V148C288 156.837 280.837 164 272 164z" />
+    <glyph glyph-name="align-right"
+      unicode="&#xF038;"
+      horiz-adv-x="448" d=" M160 364V404C160 412.837 167.163 420 176 420H432C440.837 420 448 412.837 448 404V364C448 355.163 440.837 348 432 348H176C167.163 348 160 355.163 160 364zM16 220H432C440.837 220 448 227.163 448 236V276C448 284.837 440.837 292 432 292H16C7.163 292 0 284.837 0 276V236C0 227.163 7.163 220 16 220zM16 -36H432C440.837 -36 448 -28.837 448 -20V20C448 28.837 440.837 36 432 36H16C7.163 36 0 28.837 0 20V-20C0 -28.837 7.163 -36 16 -36zM176 92H432C440.837 92 448 99.163 448 108V148C448 156.837 440.837 164 432 164H176C167.163 164 160 156.837 160 148V108C160 99.163 167.163 92 176 92z" />
+    <glyph glyph-name="allergies"
+      unicode="&#xF461;"
+      horiz-adv-x="448" d=" M416 336C398.4 336 384 321.6 384 304V232C384 227.6 380.4 224 376 224H360C355.6 224 352 227.6 352 232V384C352 401.6 337.6 416 320 416S288 401.6 288 384V232C288 227.6 284.4 224 280 224H264C259.6 224 256 227.6 256 232V416C256 433.6 241.6 448 224 448S192 433.6 192 416V232C192 227.6 188.4 224 184 224H168C163.6 224 160 227.6 160 232V384C160 401.6 145.6 416 128 416S96 401.6 96 384V143L72.4 175.5C59.4 193.4 34.4 197.3 16.5 184.3S-5.3 146.3 7.7 128.4L133.3 -44.3C142.3 -56.6999999999999 156.8 -64.0999999999999 172.1 -64.0999999999999H369.7000000000001C392.0000000000001 -64.0999999999999 411.3000000000001 -48.7999999999999 416.4000000000001 -27.0999999999999L442.9000000000001 85.6000000000001C446.1 99.3000000000001 447.8 113.9000000000001 448.0000000000001 127.9000000000001V304C448.0000000000001 321.6 433.6000000000001 336 416.0000000000001 336zM176 32C167.2 32 160 39.2 160 48S167.2 64 176 64S192 56.8 192 48S184.8 32 176 32zM176 128C167.2 128 160 135.2 160 144S167.2 160 176 160S192 152.8 192 144S184.8 128 176 128zM240 0C231.2 0 224 7.2 224 16S231.2 32 240 32S256 24.8 256 16S248.8 0 240 0zM240 96C231.2 96 224 103.2 224 112S231.2 128 240 128S256 120.8 256 112S248.8 96 240 96zM304 64C295.2 64 288 71.2 288 80S295.2 96 304 96S320 88.8 320 80S312.8 64 304 64zM336 0C327.2 0 320 7.2 320 16S327.2 32 336 32S352 24.8 352 16S344.8 0 336 0zM368 128C359.2 128 352 135.2 352 144S359.2 160 368 160S384 152.8 384 144S376.8 128 368 128z" />
+    <glyph glyph-name="ambulance"
+      unicode="&#xF0F9;"
+      horiz-adv-x="640" d=" M624 96H608V204.1C608 216.8 602.9 229 593.9 238L494 337.9C485 346.9 472.8 352 460.1 352H416V400C416 426.5 394.5 448 368 448H48C21.5 448 0 426.5 0 400V80C0 53.5 21.5 32 48 32H64C64 -21 107 -64 160 -64S256 -21 256 32H384C384 -21 427 -64 480 -64S576 -21 576 32H624C632.8 32 640 39.2 640 48V80C640 88.8 632.8 96 624 96zM160 -16C133.5 -16 112 5.5 112 32S133.5 80 160 80S208 58.5 208 32S186.5 -16 160 -16zM304 232C304 227.6 300.4 224 296 224H240V168C240 163.6 236.4 160 232 160H184C179.6 160 176 163.6 176 168V224H120C115.6 224 112 227.6 112 232V280C112 284.4 115.6 288 120 288H176V344C176 348.4 179.6 352 184 352H232C236.4 352 240 348.4 240 344V288H296C300.4 288 304 284.4 304 280V232zM480 -16C453.5 -16 432 5.5 432 32S453.5 80 480 80S528 58.5 528 32S506.5 -16 480 -16zM560 192H416V304H460.1L560 204.1V192z" />
+    <glyph glyph-name="american-sign-language-interpreting"
+      unicode="&#xF2A3;"
+      horiz-adv-x="640" d=" M290.5470000000001 258.961C270.252 269.11 246.4000000000001 270.1600000000001 225.808 262.851C268.4140000000001 262.851 297.016 242.376 311.386 212.275C319.9620000000001 194.376 306.238 174.204 287.769 174.204C306.198 174.204 319.98 154.068 311.386 136.1330000000001C296.661 105.287 265.2630000000001 85.2790000000001 231.088 85.2790000000001C230.5310000000001 85.2790000000001 136.617 93.8940000000001 136.617 93.8940000000001L70.211 60.5470000000001C60.827 55.8540000000001 50.396 60.1680000000001 46.316 68.3280000000001L1.86 157.253C-2.307 165.868 0.749 176.15 8.806 180.874L66.878 213.943L108 288.139C114.39 345.384 142.731 397.906 187.743 434.865C199.134 444.313 216.084 442.646 225.253 431.252C234.699 419.858 233.033 403.185 221.641 393.736C209.138 383.177 198.023 371.227 189.132 358.166C210.804 372.895 235.811 382.898 263.318 386.233C278.043 388.178 291.381 377.897 293.048 363.168C294.993 348.44 284.712 335.101 269.986 333.434C253.87 331.4890000000001 238.866 325.931 225.808 318.15C251.922 323.863 284.52 321.288 313.887 307.035C327.223 300.366 332.78 284.526 326.111 271.187C319.722 258.127 303.607 252.57 290.547 258.961zM263.3180000000001 189.489C257.206 201.994 244.9800000000001 209.775 231.0870000000001 209.775A35.46 35.46 0 0 1 195.5220000000001 174.2050000000001C195.5220000000001 152.7770000000001 213.3300000000001 138.6350000000001 231.0870000000001 138.6350000000001C244.9800000000001 138.6350000000001 257.206 146.4160000000001 263.3180000000001 158.9210000000001C267.7640000000001 168.3700000000001 276.932 173.927 286.6570000000001 174.2050000000001C276.932 174.482 267.764 180.04 263.3180000000001 189.489zM638.1390000000001 226.7260000000001C642.3070000000001 218.111 639.2500000000001 207.829 631.1930000000001 203.105L573.1220000000001 170.036L532 95.84C525.61 38.595 497.269 -13.927 452.257 -50.886C441.325 -59.998 424.4580000000001 -59.03 414.747 -47.273C405.301 -35.879 406.9670000000001 -19.206 418.36 -9.757C430.863 0.802 441.9770000000001 12.752 450.868 25.813C429.196 11.084 404.189 1.081 376.682 -2.254C366.661 -4.76 349.13 3.389 346.952 20.811C345.007 35.539 355.288 48.878 370.014 50.545C386.13 52.491 401.134 58.048 414.192 65.829C388.0780000000001 60.1159999999999 355.48 62.691 326.113 76.944C312.777 83.6129999999999 307.2200000000001 99.453 313.889 112.792C320.278 125.853 336.394 131.411 349.454 125.019C369.749 114.87 393.601 113.8199999999999 414.193 121.129C371.587 121.129 342.985 141.604 328.615 171.705C320.039 189.604 333.7630000000001 209.776 352.2320000000001 209.776C333.8030000000001 209.776 320.021 229.912 328.615 247.847C342.648 277.243 372.654 298.734 410.581 298.701L503.384 290.086L569.79 323.433C579.198 328.137 589.6179999999999 323.787 593.684 315.652L638.139 226.726zM408.9120000000002 245.344C395.0190000000001 245.344 382.7930000000001 237.563 376.6810000000002 225.058C372.2350000000002 215.609 363.0670000000002 210.052 353.3420000000002 209.774C363.0670000000002 209.4960000000001 372.2350000000002 203.938 376.6810000000002 194.4900000000001C382.7930000000002 181.985 395.0190000000002 174.2040000000001 408.9120000000002 174.2040000000001A35.46 35.46 0 0 1 444.4770000000002 209.7740000000001C444.4770000000002 231.2030000000001 426.6690000000002 245.3440000000001 408.9120000000002 245.3440000000001z" />
+    <glyph glyph-name="anchor"
+      unicode="&#xF13D;"
+      horiz-adv-x="576" d=" M12.971 96H45.365C67.172 -6.735 181.944 -64 288 -64C394.229 -64 508.853 -6.62 530.635 96H563.029C573.72 96 579.074 108.926 571.514 116.485L504.485 183.514C499.799 188.2 492.201 188.2 487.514 183.514L420.485 116.485C412.925 108.925 418.279 96 428.9700000000001 96H464.116C443.826 41.683 379.153 9.412 319.999 1.985V192H371.999C378.6260000000001 192 383.999 197.373 383.999 204V244C383.999 250.627 378.6260000000001 256 371.999 256H319.999V261.4700000000001C357.2800000000001 274.648 383.9940000000001 310.195 383.999 351.988C384.005 404.228 341.605 447.262 289.37 447.99C235.723 448.739 192 405.475 192 352C192 310.202 218.716 274.65 256 261.4700000000001V256H204C197.373 256 192 250.627 192 244V204C192 197.373 197.373 192 204 192H256V1.985C197.064 9.384 132.18 41.664 111.883 96H147.029C157.72 96 163.074 108.926 155.514 116.485L88.485 183.514C83.799 188.2 76.201 188.2 71.514 183.514L4.485 116.485C-3.074 108.926 2.28 96 12.971 96zM288 384C305.645 384 320 369.645 320 352S305.645 320 288 320S256 334.355 256 352S270.355 384 288 384z" />
+    <glyph glyph-name="angle-double-down"
+      unicode="&#xF103;"
+      horiz-adv-x="320" d=" M143 191.7L7 327.7C-2.4 337.1 -2.4 352.3 7 361.6L29.6 384.2C39 393.6 54.2 393.6 63.5 384.2L159.9 287.8L256.3 384.2C265.7 393.6 280.9000000000001 393.6 290.2 384.2L313 361.7C322.4 352.3 322.4 337.1 313 327.8L177 191.8C167.6 182.3 152.4 182.3 143 191.7zM177 -0.3L313 135.7C322.4 145.1 322.4 160.3 313 169.6L290.4 192.2C281 201.6 265.8 201.6 256.5 192.2L160 95.9L63.6 192.3C54.2 201.7 39 201.7 29.7 192.3L7 169.7C-2.4 160.3 -2.4 145.1 7 135.8L143 -0.2C152.4 -9.7 167.6 -9.7 177 -0.3z" />
+    <glyph glyph-name="angle-double-left"
+      unicode="&#xF100;"
+      horiz-adv-x="448" d=" M223.7 209L359.7 345C369.1 354.4 384.3 354.4 393.6 345L416.2 322.4C425.6 313 425.6 297.8 416.2 288.5L319.9 192L416.3 95.6C425.7 86.2000000000001 425.7 71 416.3 61.7000000000001L393.7 39C384.3 29.6 369.1 29.6 359.8 39L223.8 175C214.3 184.4 214.3 199.6 223.7 209zM31.7 175L167.7 39C177.1 29.6 192.3 29.6 201.6 39L224.2 61.6C233.6 71 233.6 86.2000000000001 224.2 95.5L127.9 192L224.3 288.4C233.7 297.8 233.7 313 224.3 322.3L201.7 345C192.3 354.4 177.1 354.4 167.8 345L31.8 209C22.3 199.6 22.3 184.4 31.7 175z" />
+    <glyph glyph-name="angle-double-right"
+      unicode="&#xF101;"
+      horiz-adv-x="448" d=" M224.3 175L88.3 39C78.9 29.6 63.7 29.6 54.4 39L31.8 61.6C22.4 71 22.4 86.2000000000001 31.8 95.5L128.2 191.9L31.8 288.3C22.4 297.7 22.4 312.9 31.8 322.2L54.3 345C63.7 354.4 78.9 354.4 88.2 345L224.2 209C233.7 199.6 233.7 184.4 224.3 175zM416.3 209L280.3 345C270.9000000000001 354.4 255.7 354.4 246.4 345L223.8 322.4C214.4 313 214.4 297.8 223.8 288.5L320.2000000000001 192.1L223.8000000000001 95.7C214.4 86.3 214.4 71.1 223.8000000000001 61.8L246.4000000000001 39.2C255.8000000000001 29.8 271.0000000000001 29.8 280.3 39.2L416.3 175.2C425.7 184.4 425.7 199.6 416.3 209z" />
+    <glyph glyph-name="angle-double-up"
+      unicode="&#xF102;"
+      horiz-adv-x="320" d=" M177 192.3L313 56.3C322.4 46.9 322.4 31.7 313 22.4L290.4 -0.2C281 -9.6 265.8 -9.6 256.5 -0.2L160 96.1L63.6 -0.3C54.2 -9.6999999999999 39 -9.6999999999999 29.7 -0.3L7 22.3C-2.4 31.7 -2.4 46.9 7 56.2L143 192.2C152.4 201.7 167.6 201.7 177 192.3zM143 384.3L7 248.3C-2.4 238.9 -2.4 223.7 7 214.4L29.6 191.8C39 182.4 54.2 182.4 63.5 191.8L159.9 288.2000000000001L256.3 191.8C265.7 182.4 280.9000000000001 182.4 290.2 191.8L312.8 214.4C322.2 223.8 322.2 239 312.8 248.3L176.8 384.3C167.6 393.7 152.4 393.7 143 384.3z" />
+    <glyph glyph-name="angle-down"
+      unicode="&#xF107;"
+      horiz-adv-x="320" d=" M143 95.7L7 231.7C-2.4 241.1 -2.4 256.3 7 265.6L29.6 288.2C39 297.6 54.2 297.6 63.5 288.2L159.9 191.8L256.3 288.2C265.7 297.6 280.9000000000001 297.6 290.2 288.2L312.8 265.6C322.2 256.2 322.2 241 312.8 231.7L176.8 95.6999999999999C167.6 86.3 152.4 86.3 143 95.6999999999999z" />
+    <glyph glyph-name="angle-left"
+      unicode="&#xF104;"
+      horiz-adv-x="256" d=" M31.7 209L167.7 345C177.1 354.4 192.3 354.4 201.6 345L224.2 322.4C233.6 313 233.6 297.8 224.2 288.5L127.9 192L224.3 95.6C233.7 86.2000000000001 233.7 71 224.3 61.7000000000001L201.7 39C192.3 29.6 177.1 29.6 167.8 39L31.8 175C22.3 184.4 22.3 199.6 31.7 209z" />
+    <glyph glyph-name="angle-right"
+      unicode="&#xF105;"
+      horiz-adv-x="256" d=" M224.3 175L88.3 39C78.9 29.6 63.7 29.6 54.4 39L31.8 61.6C22.4 71 22.4 86.2000000000001 31.8 95.5L128.2 191.9L31.8 288.3C22.4 297.7 22.4 312.9 31.8 322.2L54.3 345C63.7 354.4 78.9 354.4 88.2 345L224.2 209C233.7 199.6 233.7 184.4 224.3 175z" />
+    <glyph glyph-name="angle-up"
+      unicode="&#xF106;"
+      horiz-adv-x="320" d=" M177 288.3L313 152.3C322.4 142.9 322.4 127.7 313 118.4L290.4 95.8C281 86.4 265.8 86.4 256.5 95.8L160 192.1L63.6 95.7C54.2 86.3 39 86.3 29.7 95.7L7 118.3C-2.4 127.7 -2.4 142.9 7 152.2L143 288.2C152.4 297.7 167.6 297.7 177 288.3z" />
+    <glyph glyph-name="angry"
+      unicode="&#xF556;"
+      horiz-adv-x="496" d=" M248 440C111 440 0 329 0 192S111 -56 248 -56S496 55 496 192S385 440 248 440zM136 208C136 217.3 140.1 225.5 146.5 231.4L115.5 240.7C107 243.2 102.2 252.2 104.8 260.6C107.3 269.1 116.2 273.8 124.7 271.3L204.7 247.3C213.2 244.8 218 235.8 215.4 227.4C213.3 220.5 207 216 200.1 216C199.6 216 199 216.2 198.4 216.2C199.1 213.5 200.1 210.9 200.1 208C200.1 190.3 185.8 176 168.1 176S136 190.3 136 208zM304 53.8C276.2 87.2 219.8 87.2 191.9 53.8C178.4 37.5 153.7 58 167.3 74.3C187.3 98.3 216.7 112.1 247.9 112.1S308.5 98.3 328.5 74.3C342.3 57.8 317.4 37.7 304 53.8zM380.6 240.7L349.6 231.4C355.9000000000001 225.6 360.1 217.3 360.1 208C360.1 190.3 345.8 176 328.1 176S296.1 190.3 296.1 208C296.1 210.9 297 213.6 297.8 216.2C297.2 216.1 296.7 216 296.1 216C289.2000000000001 216 282.9000000000001 220.5 280.8 227.4C278.3 235.9 283.1 244.8 291.5 247.3L371.5 271.3C379.9 273.8 388.9 269 391.4 260.6C393.9 252.1 389.1 243.2 380.6 240.7z" />
+    <glyph glyph-name="ankh"
+      unicode="&#xF644;"
+      horiz-adv-x="320" d=" M296 192H251.38C272.46 225.99 288 266.35 288 304C288 392.37 230.69 448 160 448S32 392.37 32 304C32 266.35 47.54 225.99 68.62 192H24C10.75 192 0 181.26 0 168V136C0 122.75 10.75 112 24 112H120V-40C120 -53.25 130.75 -64 144 -64H176C189.25 -64 200 -53.25 200 -40V112H296C309.25 112 320 122.75 320 136V168C320 181.26 309.25 192 296 192zM160 368C189.61 368 208 343.48 208 304C208 269.3400000000001 180.86 225.86 160 203.13C139.14 225.85 112 269.34 112 304C112 343.48 130.39 368 160 368z" />
+    <glyph glyph-name="apple-alt"
+      unicode="&#xF5D1;"
+      horiz-adv-x="448" d=" M350.85 319C376.8200000000001 314.3300000000001 398.12 300.33 414.7700000000001 277C429.42 256.33 439.4100000000001 230.33 444.73 199C449.4000000000001 170.33 449.05 141.67 443.73 113C435.74 65.67 419.76 26 395.79 -6C367.1500000000001 -44.67 331.2000000000001 -64 287.92 -64C277.26 -64 265.62 -60.67 252.96 -54C244.3 -48.67 234.65 -46 223.99 -46S203.69 -48.67 195.02 -54C182.36 -60.67 170.72 -64 160.06 -64C116.78 -64 80.83 -44.67 52.19 -6C28.22 26 12.24 65.67 4.25 113C-1.07 141.67 -1.42 170.33 3.25 199C8.57 230.33 18.56 256.33 33.21 277C49.86 300.33 71.16 314.33 97.13 319C113.11 321.67 135.08 319.3300000000001 163.05 312C187.02 305.3300000000001 207.33 297.3300000000001 223.98 288C240.63 297.3300000000001 260.94 305.33 284.9100000000001 312C312.8900000000001 319.3300000000001 334.87 321.67 350.85 319zM295.9100000000001 360C286.5900000000001 351.33 274.2600000000001 345 258.9500000000001 341C248.2900000000001 337.67 236.6500000000001 336 223.9900000000001 336L209.0100000000001 337C207.68 346.33 207.68 357 209.0100000000001 369C211.68 393 219.3300000000001 411.33 231.9800000000001 424C241.3000000000001 432.67 253.6300000000001 439 268.9400000000001 443C279.6000000000001 446.33 291.2400000000001 448 303.9000000000001 448L318.8800000000001 447L319.8800000000001 432C319.8800000000001 419.33 318.2100000000001 407.67 314.8900000000001 397C310.9000000000001 381.67 304.5800000000001 369.33 295.9100000000001 360z" />
+    <glyph glyph-name="archive"
+      unicode="&#xF187;"
+      horiz-adv-x="512" d=" M32 0C32 -17.7 46.3 -32 64 -32H448C465.7 -32 480 -17.7 480 0V288H32V0zM192 212C192 218.6 197.4 224 204 224H308C314.6 224 320 218.6 320 212V204C320 197.4 314.6 192 308 192H204C197.4 192 192 197.4 192 204V212zM480 416H32C14.3 416 0 401.7 0 384V336C0 327.2 7.2 320 16 320H496C504.8 320 512 327.2 512 336V384C512 401.7 497.7 416 480 416z" />
+    <glyph glyph-name="archway"
+      unicode="&#xF557;"
+      horiz-adv-x="576" d=" M560 0H544V352H32V0H16.02C7.18 0 0.02 -7.16 0.02 -16V-48C0.02 -56.84 7.18 -64 16.02 -64H176C184.84 -64 192 -56.84 192 -48V128C192 181.02 234.98 224 288 224S384 181.02 384 128L384.02 -32V-48C384.02 -56.84 391.18 -64 400.02 -64H560C568.84 -64 576 -56.84 576 -48V-16C576 -7.16 568.84 0 560 0zM560 448H16C7.16 448 0 440.84 0 432V400C0 391.16 7.16 384 16 384H560C568.84 384 576 391.16 576 400V432C576 440.84 568.84 448 560 448z" />
+    <glyph glyph-name="arrow-alt-circle-down"
+      unicode="&#xF358;"
+      horiz-adv-x="512" d=" M504 192C504 55 393 -56 256 -56S8 55 8 192S119 440 256 440S504 329 504 192zM212 308V192H141.1C130.4 192 125 179 132.6 171.5L247.5 57.2C252.2 52.5 259.7 52.5 264.4 57.2L379.3 171.5C386.9 179.1 381.5 192 370.8 192H300V308C300 314.6 294.6 320 288 320H224C217.4 320 212 314.6 212 308z" />
+    <glyph glyph-name="arrow-alt-circle-left"
+      unicode="&#xF359;"
+      horiz-adv-x="512" d=" M256 -56C119 -56 8 55 8 192S119 440 256 440S504 329 504 192S393 -56 256 -56zM372 236H256V306.9C256 317.6 243 323 235.5 315.4L121.2 200.5C116.5 195.8 116.5 188.3 121.2 183.6L235.5 68.7000000000001C243.1 61.1 256 66.5000000000001 256 77.2000000000001V148H372C378.6 148 384 153.4 384 160V224C384 230.6 378.6 236 372 236z" />
+    <glyph glyph-name="arrow-alt-circle-right"
+      unicode="&#xF35A;"
+      horiz-adv-x="512" d=" M256 440C393 440 504 329 504 192S393 -56 256 -56S8 55 8 192S119 440 256 440zM140 148H256V77.1C256 66.4 269 61 276.5 68.6L390.8 183.5C395.5 188.2 395.5 195.7 390.8 200.4L276.5 315.4C268.9 323 256 317.6 256 306.9V236H140C133.4 236 128 230.6 128 224V160C128 153.4 133.4 148 140 148z" />
+    <glyph glyph-name="arrow-alt-circle-up"
+      unicode="&#xF35B;"
+      horiz-adv-x="512" d=" M8 192C8 329 119 440 256 440S504 329 504 192S393 -56 256 -56S8 55 8 192zM300 76V192H370.9C381.6 192 387 205 379.4 212.5L264.5 326.8C259.8 331.5 252.3 331.5 247.6 326.8L132.6 212.5C125 204.9 130.4 192 141.1 192H212V76C212 69.4 217.4 64 224 64H288C294.6 64 300 69.4 300 76z" />
+    <glyph glyph-name="arrow-circle-down"
+      unicode="&#xF0AB;"
+      horiz-adv-x="512" d=" M504 192C504 55 393 -56 256 -56S8 55 8 192S119 440 256 440S504 329 504 192zM360.4 220.9L288 145.4V328C288 341.3 277.3 352 264 352H248C234.7 352 224 341.3 224 328V145.4L151.6 220.9C142.3 230.6 126.8 230.8 117.3 221.3L106.4 210.3C97 200.9 97 185.7 106.4 176.4L239 43.7C248.4 34.3 263.6 34.3 272.9 43.7L405.6 176.4C415 185.8 415 201 405.6 210.3L394.7 221.3C385.2 230.8 369.7 230.6 360.4 220.9z" />
+    <glyph glyph-name="arrow-circle-left"
+      unicode="&#xF0A8;"
+      horiz-adv-x="512" d=" M256 -56C119 -56 8 55 8 192S119 440 256 440S504 329 504 192S393 -56 256 -56zM284.9 87.6L209.4 160H392C405.3 160 416 170.7 416 184V200C416 213.3 405.3 224 392 224H209.4L284.9 296.4C294.6 305.7000000000001 294.8 321.2 285.3 330.7L274.3 341.6C264.9 351 249.7 351 240.4 341.6L107.7 209C98.3 199.6 98.3 184.4 107.7 175.1L240.4 42.4C249.8 33.0000000000001 265 33.0000000000001 274.3 42.4L285.3 53.3C294.8 62.8 294.6 78.3 284.9 87.6z" />
+    <glyph glyph-name="arrow-circle-right"
+      unicode="&#xF0A9;"
+      horiz-adv-x="512" d=" M256 440C393 440 504 329 504 192S393 -56 256 -56S8 55 8 192S119 440 256 440zM227.1 296.4L302.6 224H120C106.7 224 96 213.3 96 200V184C96 170.7 106.7 160 120 160H302.6L227.1 87.6C217.4 78.3 217.2 62.8 226.7 53.3L237.7 42.4C247.1 33.0000000000001 262.3 33.0000000000001 271.6 42.4L404.3 175C413.7 184.4 413.7 199.6 404.3 208.9L271.6 341.7C262.2000000000001 351.1 247.0000000000001 351.1 237.7 341.7L226.7 330.8C217.2 321.2 217.4 305.7 227.1 296.4z" />
+    <glyph glyph-name="arrow-circle-up"
+      unicode="&#xF0AA;"
+      horiz-adv-x="512" d=" M8 192C8 329 119 440 256 440S504 329 504 192S393 -56 256 -56S8 55 8 192zM151.6 163.1L224 238.6V56C224 42.7 234.7 32 248 32H264C277.3 32 288 42.7 288 56V238.6L360.4 163.1C369.7 153.4 385.2 153.2000000000001 394.7 162.7000000000001L405.6 173.7000000000001C415 183.1 415 198.3000000000001 405.6 207.6000000000001L273 340.3C263.6 349.7 248.4 349.7 239.1 340.3L106.3 207.6C96.9 198.2 96.9 183 106.3 173.7L117.2 162.7C126.8 153.2 142.3 153.4 151.6 163.1z" />
+    <glyph glyph-name="arrow-down"
+      unicode="&#xF063;"
+      horiz-adv-x="448" d=" M413.1 225.5L435.3 203.3C444.7 193.9 444.7 178.7 435.3 169.4L241 -25C231.6 -34.4 216.4 -34.4 207.1 -25L12.7 169.4C3.3 178.8 3.3 194 12.7 203.3L34.9 225.5C44.4 235 59.9 234.8 69.2 225.1L184 104.6V392C184 405.3 194.7 416 208 416H240C253.3 416 264 405.3 264 392V104.6L378.8 225.1C388.1 234.9000000000001 403.6 235.1 413.1 225.5000000000001z" />
+    <glyph glyph-name="arrow-left"
+      unicode="&#xF060;"
+      horiz-adv-x="448" d=" M257.5 2.9L235.3 -19.3C225.9 -28.7 210.7 -28.7 201.4 -19.3L7 175C-2.4 184.4 -2.4 199.6 7 208.9L201.4 403.3C210.8 412.7 226 412.7 235.3 403.3L257.5 381.1C267 371.6 266.8 356.1 257.1 346.8L136.6 232H424C437.3 232 448 221.3 448 208V176C448 162.7 437.3 152 424 152H136.6L257.1 37.2C266.9000000000001 27.9 267.1 12.4 257.5 2.9z" />
+    <glyph glyph-name="arrow-right"
+      unicode="&#xF061;"
+      horiz-adv-x="448" d=" M190.5 381.1L212.7 403.3C222.1 412.7 237.3 412.7 246.6 403.3L441 209C450.4 199.6 450.4 184.4 441 175.1L246.6 -19.3C237.2 -28.7 222 -28.7 212.7 -19.3L190.5 2.9C181 12.4 181.2 27.9 190.9 37.2L311.4 152H24C10.7 152 0 162.7 0 176V208C0 221.3 10.7 232 24 232H311.4L190.9 346.8C181.1 356.1 180.9 371.6 190.5 381.1z" />
+    <glyph glyph-name="arrow-up"
+      unicode="&#xF062;"
+      horiz-adv-x="448" d=" M34.9 158.5L12.7 180.7C3.3 190.1 3.3 205.3 12.7 214.6L207 409C216.4 418.4 231.6 418.4 240.9 409L435.2000000000001 214.7C444.6 205.3 444.6 190.1 435.2000000000001 180.8L413 158.6C403.5 149.1 388 149.3 378.7 159L264 279.4V-8C264 -21.3 253.3 -32 240 -32H208C194.7 -32 184 -21.3 184 -8V279.4L69.2 158.9C59.9 149.1 44.4 148.9 34.9 158.5z" />
+    <glyph glyph-name="arrows-alt-h"
+      unicode="&#xF337;"
+      horiz-adv-x="512" d=" M377.941 278.059V232H134.059V278.059C134.059 299.4410000000001 108.208 310.149 93.088 295.03L7.029 208.971C-2.344 199.598 -2.344 184.403 7.029 175.03L93.088 88.971C108.207 73.852 134.059 84.56 134.059 105.942V152H377.9410000000001V105.941C377.9410000000001 84.559 403.7920000000001 73.851 418.9120000000001 88.97L504.971 175.029C514.344 184.402 514.344 199.597 504.971 208.97L418.9120000000001 295.029C403.793 310.149 377.9410000000001 299.4410000000001 377.9410000000001 278.059z" />
+    <glyph glyph-name="arrows-alt-v"
+      unicode="&#xF338;"
+      horiz-adv-x="256" d=" M214.059 70.059H168V313.9410000000001H214.059C235.441 313.9410000000001 246.149 339.7920000000001 231.03 354.9120000000001L144.971 440.971C135.598 450.344 120.403 450.344 111.03 440.971L24.971 354.9120000000001C9.852 339.793 20.56 313.9410000000001 41.942 313.9410000000001H88V70.059H41.941C20.559 70.059 9.851 44.208 24.97 29.088L111.029 -56.971C120.402 -66.344 135.597 -66.344 144.97 -56.971L231.029 29.088C246.149 44.207 235.441 70.059 214.059 70.059z" />
+    <glyph glyph-name="arrows-alt"
+      unicode="&#xF0B2;"
+      horiz-adv-x="512" d=" M352.201 22.225L273.005 -56.971C263.632 -66.344 248.437 -66.344 239.064 -56.971L159.868 22.225C144.749 37.3440000000001 155.457 63.196 176.839 63.1950000000001H228.001L228 164H127.196V112.838C127.196 91.456 101.345 80.7480000000001 86.225 95.867L7.029 175.063C-2.344 184.436 -2.344 199.632 7.029 209.004L86.225 288.2C101.344 303.319 127.196 292.611 127.196 271.229V220H228V320.804H176.77C155.388 320.804 144.68 346.655 159.799 361.775L238.995 440.971C248.368 450.344 263.563 450.344 272.9360000000001 440.971L352.1320000000001 361.775C367.2510000000001 346.656 356.5430000000001 320.804 335.1610000000001 320.804H283.9990000000001V220H384.8030000000001V271.1620000000001C384.8030000000001 292.544 410.6540000000001 303.252 425.7730000000002 288.1330000000001L504.9690000000002 208.937C514.3420000000002 199.564 514.3420000000002 184.368 504.9690000000002 174.996L425.773 95.8C410.654 80.681 384.802 91.389 384.803 112.771V164H284V63.196H335.23C356.612 63.196 367.3200000000001 37.345 352.201 22.225z" />
+    <glyph glyph-name="assistive-listening-systems"
+      unicode="&#xF2A2;"
+      horiz-adv-x="512" d=" M216 188C216 172.536 203.464 160 188 160S160 172.536 160 188C160 232.112 195.888 268 240 268S320 232.112 320 188C320 172.536 307.464 160 292 160S264 172.536 264 188C264 201.234 253.233 212 240 212S216 201.234 216 188zM240 364C142.953 364 64 285.0470000000001 64 188C64 172.536 76.536 160 92 160S120 172.536 120 188C120 254.168 173.832 308 240 308S360 254.168 360 188C360 112.836 288.991 117.689 288.003 44.378L288 44C288 15.327 264.673 -8 236 -8C220.536 -8 208 -20.536 208 -36S220.536 -64 236 -64C295.475 -64 343.876 -15.672 344 43.774C344.5950000000001 78.202 416 92.014 416 188C416 285.0470000000001 337.0470000000001 364 240 364zM160 128C142.327 128 128 113.673 128 96S142.327 64 160 64S192 78.327 192 96S177.673 128 160 128zM32 0C14.327 0 0 -14.327 0 -32S14.327 -64 32 -64S64 -49.673 64 -32S49.673 0 32 0zM512 187.993C512 189.511 511.988 191.018 511.955 192.524C510.076 307.475 436.157 409.53 327.9940000000001 446.489C313.3610000000001 451.487 297.4450000000001 443.68 292.444 429.0470000000001C287.444 414.414 295.254 398.498 309.886 393.497C395.7920000000001 364.1430000000001 454.496 282.984 455.963 191.544L455.966 191.356C455.992 190.238 455.999 189.12 455.999 187.993C455.999 172.529 468.535 159.993 483.999 159.993S512 172.529 512 187.993zM152.971 8.971L72.971 88.971L39.03 55.03L119.03 -24.97L152.971 8.971z" />
+    <glyph glyph-name="asterisk"
+      unicode="&#xF069;"
+      horiz-adv-x="512" d=" M478.21 113.907L336 192L478.21 270.093C490.0050000000001 276.5700000000001 494.171 291.4770000000001 487.442 303.13L467.962 336.8710000000001C461.234 348.524 446.242 352.37 434.735 345.394L296 261.2820000000001L299.475 423.486C299.763 436.939 288.937 448 275.48 448H236.52C223.064 448 212.237 436.939 212.526 423.486L216 261.2820000000001L77.265 345.3930000000001C65.759 352.3690000000001 50.766 348.523 44.038 336.87L24.558 303.129C17.83 291.476 21.996 276.569 33.791 270.092L176 192L33.79 113.907C21.995 107.43 17.829 92.523 24.558 80.87L44.038 47.129C50.766 35.476 65.759 31.63 77.265 38.606L216 122.718L212.525 -39.486C212.237 -52.939 223.064 -64 236.52 -64H275.481C288.937 -64 299.764 -52.939 299.476 -39.486L296 122.718L434.735 38.607C446.241 31.631 461.234 35.477 467.962 47.1300000000001L487.442 80.871C494.17 92.5240000000001 490.005 107.4300000000001 478.21 113.9070000000001z" />
+    <glyph glyph-name="at"
+      unicode="&#xF1FA;"
+      horiz-adv-x="512" d=" M256 440C118.941 440 8 329.081 8 192C8 54.941 118.919 -56 256 -56C304.154 -56 351.342 -41.86 391.408 -15.777C403.413 -7.962 406.033 8.511 396.9600000000001 19.595L386.783 32.028C379.112 41.399 365.6040000000001 43.695 355.4100000000001 37.157C325.92 18.243 291.314 8 256 8C154.542 8 72 90.542 72 192S154.542 376 256 376C356.139 376 440 318.381 440 216C440 177.214 418.907 136.258 381.83 132.307C364.481 132.761 364.92 145.164 368.354 162.331L391.787 283.4410000000001C394.653 298.25 383.308 312 368.225 312H323.2440000000001A13.518 13.518 0 0 1 309.812 300.007L309.802 299.915C295.105 317.8160000000001 269.3540000000001 321.69 249.831 321.69C175.251 321.69 112 259.456 112 170.23C112 104.927 148.785 64.36 208 64.36C234.9840000000001 64.36 265.3690000000001 79.997 282.9910000000001 102.693C292.5130000000001 68.589 323.6040000000001 68.59 353.701 68.59C462.609 68.59 504 140.202 504 216C504 352.347 394.023 440 256 440zM234.32 135.57C212.071 135.57 198.25 151.193 198.25 176.341C198.25 221.334 229.029 249.07 256.88 249.07C279.1720000000001 249.07 292.481 233.829 292.481 208.3C292.481 163.239 258.606 135.57 234.32 135.57z" />
+    <glyph glyph-name="atlas"
+      unicode="&#xF558;"
+      horiz-adv-x="448" d=" M318.38 240H279.29C277.8 212.97 272.75 188.65 265.08 169.59C292.79 182.8300000000001 313.1 208.78 318.38 240.0000000000001zM318.38 272C313.09 303.2200000000001 292.79 329.17 265.08 342.41C272.76 323.35 277.8 299.03 279.29 272H318.38zM224 350.69C216.31 343.24 203.23 316.27 200.57 272H247.44C244.77 316.26 231.69 343.24 224 350.69zM182.92 342.41C155.21 329.17 134.9 303.2200000000001 129.62 272H168.71C170.2 299.03 175.24 323.35 182.92 342.41zM182.92 169.59C175.24 188.65 170.2 212.97 168.71 240.0000000000001H129.62C134.9 208.78 155.21 182.8300000000001 182.92 169.59zM247.43 240H200.56C203.22 195.74 216.3 168.76 223.99 161.31C231.69 168.76 244.77 195.74 247.43 240zM448 89.6V422.4C448 438.4 438.4 448 422.4 448H96C41.6 448 0 406.4 0 352V32C0 -22.4 41.6 -64 96 -64H422.4C435.2 -64 448 -54.4 448 -38.4V-22.4C448 -16 444.8 -9.6 438.4 -3.2C435.2 12.8 435.2 57.6 438.4 70.4C444.8 73.6 448 80 448 89.6zM224 384C294.69 384 352 326.69 352 256S294.69 128 224 128S96 185.31 96 256S153.31 384 224 384zM384 0H96C76.8 0 64 12.8 64 32S80 64 96 64H384V0z" />
+    <glyph glyph-name="atom"
+      unicode="&#xF5D2;"
+      horiz-adv-x="448" d=" M413.03 192C453.16 246.89 454.54 290.62 438.17 320C427.26 339.52 397.63 370.73 321.84 361.88C300.36 413.11 267.64 448 224 448S147.64 413.11 126.16 361.88C50.43 370.66 20.73 339.52 9.83 320C-6.55 290.6 -5.17 246.91 34.97 192C-5.16 137.11 -6.54 93.38 9.83 64C39.04 11.66 111.51 20.42 126.16 22.12C147.63 -29.1 180.36 -64 224 -64S300.37 -29.1 321.8400000000001 22.12C336.48 20.42 408.9500000000001 11.66 438.17 64C454.55 93.4 453.17 137.09 413.0300000000001 192zM63.38 96C59.35 103.21 63.19 120.8 78.33 144.29C85.29 137.7600000000001 92.53 131.4 100.2 125.11C101.91 111.4 104.2 98.03 106.96 85.03C82.4 84.14 67.07 89.4 63.38 96.0000000000001zM100.2 258.88C92.54 252.59 85.3 246.23 78.33 239.7C63.2 263.2 59.36 280.79 63.38 288C66.79 294.14 79.77 299.4700000000001 101.3 299.4700000000001C103.01 299.4700000000001 105.17 299.17 106.99 299.1A472.19100000000003 472.19100000000003 0 0 1 100.2 258.88zM224 384C233.47 384 246.2 370.48 257.86 346.74C246.67 343.04 235.42 338.74 224 333.88C212.58 338.74 201.33 343.04 190.14 346.74C201.8 370.48 214.53 384 224 384zM224 0C214.53 0 201.8 13.52 190.14 37.26C201.33 40.96 212.58 45.26 224 50.12C235.42 45.26 246.67 40.96 257.86 37.26C246.2 13.52 233.47 0 224 0zM286.5 157.3300000000001C259.8 138.2500000000001 240.36 128.0000000000001 224 119.85C207.65 127.99 188.2 138.2600000000001 161.5 157.3300000000001C159.51 185.1200000000001 159.51 198.87 161.5 226.6600000000001C188.17 245.7100000000001 207.63 255.98 224 264.14C240.39 255.9700000000001 259.86 245.7 286.5 226.6600000000001C288.48 198.88 288.49 185.13 286.5 157.3300000000001zM384.62 96C380.95 89.38 365.62 84.18 341.04 85.05C343.8 98.05 346.0900000000001 111.42 347.8 125.11C355.4600000000001 131.4 362.7 137.76 369.67 144.29C384.8 120.8 388.64 103.21 384.62 96zM369.67 239.71C362.7100000000001 246.24 355.4700000000001 252.6 347.8 258.89A473.535 473.535 0 0 1 341.01 299.11C342.83 299.18 344.98 299.48 346.7 299.48C368.22 299.48 381.21 294.14 384.62 288.01C388.64 280.79 384.8 263.2000000000001 369.67 239.7100000000001zM224 224C206.33 224 192 209.67 192 192S206.33 160 224 160S256 174.33 256 192S241.67 224 224 224z" />
+    <glyph glyph-name="audio-description"
+      unicode="&#xF29E;"
+      horiz-adv-x="512" d=" M162.925 209.291L171.747 178.636H146.141L155.182 209.288C156.459 213.709 157.833 219.282 159.054 224.533C160.274 219.282 161.648 213.71 162.9250000000001 209.291zM329.399 241.39H314.876V142.609H329.399C359.175 142.609 375.574 160.287 375.574 192.385C375.574 224.624 358.084 241.39 329.399 241.39zM512 336V48C512 21.49 490.51 0 464 0H48C21.49 0 0 21.49 0 48V336C0 362.51 21.49 384 48 384H464C490.51 384 512 362.51 512 336zM245.459 111.861L188.362 279.861A12.001000000000001 12.001000000000001 0 0 1 177 288H141.106A12.001000000000001 12.001000000000001 0 0 1 129.744 279.861L72.647 111.861C70.003 104.078 75.789 96 84.009 96H113.142A12 12 0 0 1 124.677 104.693L133.251 134.599H184.618L193.411 104.622A12 12 0 0 1 204.926 96H234.098C242.318 96 248.104 104.078 245.459 111.861zM430.16 192.386C430.16 251.363 392.241 288 331.2 288H273.834C267.207 288 261.834 282.627 261.834 276V108C261.834 101.373 267.207 96 273.834 96H331.2C392.241 96 430.16 132.933 430.16 192.386z" />
+    <glyph glyph-name="award"
+      unicode="&#xF559;"
+      horiz-adv-x="384" d=" M97.12 85.37C88.43 94.06 92.96 91.61 72 97.22C62.49 99.77 54.13 104.67 46.57 110.54L1.2 -0.7C-3.19 -11.47 5.01 -23.17 16.63 -22.73L69.32 -20.72L105.56 -59C113.56 -67.44 127.6 -64.8099999999999 131.99 -54.04L184.04 73.58C173.2 67.54 161.17 64 148.73 64C129.23 64 110.91 71.59 97.12 85.37zM382.8 -0.7L337.43 110.54C329.87 104.66 321.51 99.77 312 97.22C290.93 91.58 295.55 94.04 286.88 85.37C273.09 71.59 254.76 64 235.26 64C222.82 64 210.79 67.55 199.95 73.58L252 -54.04C256.39 -64.8100000000001 270.44 -67.44 278.43 -59L314.68 -20.72L367.37 -22.73C378.99 -23.17 387.19 -11.46 382.8 -0.7zM263 108C278.28 123.55 280.03 122.21 301.79 128.14C315.68 131.93 326.54 142.98 330.26 157.12C337.74 185.52 335.8 182.09 356.21 202.87C366.38 213.22 370.35 228.31 366.63 242.45C359.16 270.83 359.15 266.87 366.63 295.28C370.35 309.42 366.38 324.51 356.21 334.86C335.8 355.64 337.74 352.22 330.26 380.61C326.54 394.75 315.68 405.8 301.79 409.59C273.91 417.2 277.27 415.21 256.84 436C246.67 446.35 231.84 450.4 217.95 446.61C190.08 439.01 193.97 439 166.05 446.61C152.16 450.4 137.33 446.36 127.16 436C106.75 415.22 110.11 417.2 82.22 409.59C68.33 405.8 57.47 394.75 53.75 380.61C46.28 352.22 48.21 355.64 27.8 334.86C17.63 324.51 13.65 309.42 17.38 295.28C24.85 266.92 24.86 270.88 17.38 242.46C13.66 228.32 17.63 213.23 27.8 202.87C48.21 182.09 46.27 185.52 53.75 157.12C57.47 142.98 68.33 131.93 82.22 128.14C104.6 122.04 106.27 123 121 108C134.23 94.53 154.84 92.12 170.74 102.18A39.676 39.676 0 0 0 213.27 102.18C229.1600000000001 92.12 249.77 94.53 263 108zM97.66 272.04C97.66 325.07 139.9 368.06 192 368.06S286.3400000000001 325.07 286.3400000000001 272.04S244.1 176.02 192 176.02S97.66 219.01 97.66 272.04z" />
+    <glyph glyph-name="backspace"
+      unicode="&#xF55A;"
+      horiz-adv-x="640" d=" M576 384H205.26A63.97 63.97 0 0 1 160 365.25L9.37 214.63C-3.13 202.13 -3.13 181.87 9.37 169.38L160 18.75C172 6.75 188.28 0 205.25 0H576C611.35 0 640 28.65 640 64V320C640 355.35 611.35 384 576 384zM491.31 129.94C497.56 123.69 497.56 113.56 491.31 107.31L468.69 84.69C462.44 78.44 452.31 78.44 446.06 84.69L384 146.75L321.94 84.69C315.69 78.44 305.56 78.44 299.31 84.69L276.69 107.31C270.44 113.56 270.44 123.69 276.69 129.94L338.75 192L276.69 254.06C270.44 260.31 270.44 270.44 276.69 276.69L299.31 299.31C305.56 305.56 315.69 305.56 321.94 299.31L384 237.25L446.06 299.31C452.31 305.56 462.44 305.56 468.69 299.31L491.31 276.69C497.56 270.44 497.56 260.31 491.31 254.06L429.25 192L491.31 129.94z" />
+    <glyph glyph-name="backward"
+      unicode="&#xF04A;"
+      horiz-adv-x="512" d=" M11.5 167.4L203.5 7.4C224.1 -9.8 256 4.6 256 32V352C256 379.4 224.1 393.8 203.5 376.6L11.5 216.6C-3.8 203.8 -3.8 180.2 11.5 167.4zM267.5 167.4L459.5 7.4C480.1 -9.8 512 4.6 512 32V352C512 379.4 480.1 393.8 459.5 376.6L267.5 216.6C252.2 203.8 252.2 180.2 267.5 167.4z" />
+    <glyph glyph-name="balance-scale"
+      unicode="&#xF24E;"
+      horiz-adv-x="640" d=" M256 112H255.98C255.98 128.18 257.32 120.73 170.93 293.51C153.28 328.8 102.74 328.87 85.06 293.51C-2.06 119.25 0.02 127.67 0.02 112H0C0 67.82 57.31 32 128 32S256 67.82 256 112zM128 272L200 128H56L128 272zM639.98 112C639.98 128.18 641.32 120.73 554.9300000000001 293.51C537.2800000000001 328.8 486.7400000000001 328.87 469.0600000000001 293.51C381.9400000000001 119.25 384.0200000000001 127.67 384.0200000000001 112H384C384 67.82 441.31 32 512 32S640 67.82 640 112H639.98zM440 128L512 272L584 128H440zM528 0H352V294.75C375.51 305.04 393.16 326.23 398.39 352H528C536.84 352 544 359.16 544 368V400C544 408.8400000000001 536.84 416 528 416H383.64C369.04 435.32 346.09 448 320 448S270.96 435.32 256.36 416H112C103.16 416 96 408.8400000000001 96 400V368C96 359.16 103.16 352 112 352H241.61C246.84 326.24 264.48 305.04 288 294.75V0H112C103.16 0 96 -7.16 96 -16V-48C96 -56.84 103.16 -64 112 -64H528C536.84 -64 544 -56.84 544 -48V-16C544 -7.16 536.84 0 528 0z" />
+    <glyph glyph-name="ban"
+      unicode="&#xF05E;"
+      horiz-adv-x="512" d=" M256 440C119.034 440 8 328.967 8 192S119.034 -56 256 -56S504 55.034 504 192S392.967 440 256 440zM386.108 322.108C451.556 256.6600000000001 456.108 156.627 406.7850000000001 86.471L150.47 342.784C220.674 392.14 320.6960000000001 387.519 386.108 322.108zM125.892 61.892C60.444 127.34 55.892 227.373 105.215 297.529L361.53 41.216C291.327 -8.14 191.304 -3.52 125.892 61.892z" />
+    <glyph glyph-name="band-aid"
+      unicode="&#xF462;"
+      horiz-adv-x="640" d=" M0 288V96C0 60.7 28.7 32 64 32H160V352H64C28.7 352 0 323.3 0 288zM576 352H480V32H576C611.3 32 640 60.7 640 96V288C640 323.3 611.3 352 576 352zM192 32H448V352H192V32zM368 264C381.3 264 392 253.3 392 240S381.3 216 368 216S344 226.7 344 240S354.7 264 368 264zM368 168C381.3 168 392 157.3 392 144S381.3 120 368 120S344 130.7 344 144S354.7 168 368 168zM272 264C285.3 264 296 253.3 296 240S285.3 216 272 216S248 226.7 248 240S258.7 264 272 264zM272 168C285.3 168 296 157.3 296 144S285.3 120 272 120S248 130.7 248 144S258.7 168 272 168z" />
+    <glyph glyph-name="barcode"
+      unicode="&#xF02A;"
+      horiz-adv-x="512" d=" M0 0V384H18V0H0zM26.857 0.273V384H36V0.273H26.857zM54 0.273V384H62.857V0.273H54zM98.857 0.273V384H107.714V0.273H98.857zM134.857 0.273V384H152.571V0.273H134.857zM179.714 0.273V384H188.571V0.273H179.714zM197.714 0.273V384H206.571V0.273H197.714zM215.714 0.273V384H224.571V0.273H215.714zM251.429 0.273V384H269.429V0.273H251.429zM296.286 0.273V384H314.286V0.273H296.286zM332.2850000000001 0.273V384H350.286V0.273H332.2850000000001zM368.286 0.273V384H386.287V0.273H368.286zM395.1430000000001 0.273V384H413.1430000000001V0.273H395.1430000000001zM440.2860000000001 0.273V384H467.143V0.273H440.2860000000001zM476.0000000000001 0.273V384H485.143V0.273H476zM494.0000000000001 0V384H512V0H494z" />
+    <glyph glyph-name="bars"
+      unicode="&#xF0C9;"
+      horiz-adv-x="448" d=" M16 316H432C440.837 316 448 323.163 448 332V372C448 380.837 440.837 388 432 388H16C7.163 388 0 380.837 0 372V332C0 323.163 7.163 316 16 316zM16 156H432C440.837 156 448 163.163 448 172V212C448 220.837 440.837 228 432 228H16C7.163 228 0 220.837 0 212V172C0 163.163 7.163 156 16 156zM16 -4H432C440.837 -4 448 3.163 448 12V52C448 60.837 440.837 68 432 68H16C7.163 68 0 60.837 0 52V12C0 3.163 7.163 -4 16 -4z" />
+    <glyph glyph-name="baseball-ball"
+      unicode="&#xF433;"
+      horiz-adv-x="496" d=" M368.5 84.1L397.3 98C408.4000000000001 75.1 423.3 54.8 441.4000000000001 37.1C475.4 79.6 495.9 133.4 495.9 192C495.9 250.5000000000001 475.5000000000001 304.2000000000001 441.7000000000001 346.6C423.9000000000001 329.3 409.1 309.5 398.1 287.1L369.4000000000001 301.2000000000001C382.2000000000001 327.2000000000001 399.4000000000001 350.2000000000001 420.2000000000001 370.2000000000001C375.6 413.3 315 440 248 440C181.1 440 120.5 413.4 75.9 370.3C96.6 350.4 113.8 327.4 126.6 301.5L97.9 287.4C86.9 309.7000000000001 72.2 329.5 54.4 346.8C20.4 304.3 0 250.6 0 192C0 133.4 20.4 79.7 54.4 37.3C72.6 55 87.6 75.3 98.7 98.3L127.5 84.4C114.6 57.7000000000001 97.2 34.1 76 13.7C120.5 -29.4 181.1 -55.9999999999999 248 -55.9999999999999C314.8 -55.9999999999999 375.3 -29.4999999999999 419.9 13.5000000000001C398.8 33.9 381.4 57.4 368.5 84.1zM140.2 116.1L109.7 125.9C124.6 172.3 122.4 219.7000000000001 109.1 259.9000000000001L139.5 269.9000000000001C154.5 224.3000000000001 157.5 170 140.2 116.1zM356.5 269.5L386.9 259.5C373.7 219.4 371.4 172 386.3 125.5L355.8 115.7C338.5 169.7 341.5 224 356.5 269.5z" />
+    <glyph glyph-name="basketball-ball"
+      unicode="&#xF434;"
+      horiz-adv-x="496" d=" M212.3 437.7C168.5 431.4 126.1 413.6 90.1 383.9L167.5 306.5C195.3 342.3 210.8 387.7 212.3 437.7zM248 226L405.9 383.9C363.5 418.9 312.3 437.4 260.4 440C259.2 376.1 238.9 317.7000000000001 201.7 272.3L248 226zM56.1 349.9C26.4 313.9 8.6 271.5 2.3 227.7C52.3 229.2 97.8 244.7 133.5 272.5L56.1 349.9zM328.3 145.7000000000001C373.6 182.8000000000001 432 203.1000000000001 496 204.4C493.4 256.3000000000001 474.9 307.5 439.9 349.9000000000001L282 192L328.3 145.7zM248 158L90.1 0.1C132.5 -34.8 183.7 -53.4 235.6 -56C236.9 8 257.2 66.4 294.3 111.7L248 158zM439.9 34.1C469.6 70.1 487.4 112.5 493.7 156.3C443.6 154.7 398.2 139.2 362.5 111.5L439.9 34.1zM167.7 238.3C122.3 201.1 63.9 180.7 0 179.6C2.6 127.7000000000001 21.1 76.5 56.1 34.1L214 192L167.7 238.3zM283.7 -53.7C327.5 -47.4 369.9 -29.6 405.9 0.1L328.5 77.5C300.8 41.8 285.3 -3.7 283.7 -53.7z" />
+    <glyph glyph-name="bath"
+      unicode="&#xF2CD;"
+      horiz-adv-x="512" d=" M488 192H80V336C80 353.645 94.355 368 112 368C123.351 368 133.332 362.055 139.015 353.12C122.523 327.913 124.328 293.544 145.853 270.0850000000001C141.677 265.372 141.832 258.169 146.344 253.657L157.658 242.343C162.344 237.657 169.942 237.657 174.629 242.343L269.659 337.372C274.345 342.058 274.345 349.656 269.659 354.343L258.345 365.6570000000001C253.833 370.169 246.63 370.323 241.917 366.148C223.968 382.617 199.623 387.577 177.739 381.5130000000001C163.281 402.333 139.212 416 112 416C67.888 416 32 380.112 32 336V192H24C10.745 192 0 181.255 0 168V152C0 138.745 10.745 128 24 128H32V96C32 67.57 44.362 42.031 64 24.453V-8C64 -21.255 74.745 -32 88 -32H104C117.255 -32 128 -21.255 128 -8V0H384V-8C384 -21.255 394.745 -32 408 -32H424C437.255 -32 448 -21.255 448 -8V24.453C467.638 42.031 480 67.57 480 96V128H488C501.255 128 512 138.745 512 152V168C512 181.255 501.255 192 488 192z" />
+    <glyph glyph-name="battery-empty"
+      unicode="&#xF244;"
+      horiz-adv-x="640" d=" M544 288V224H576V160H544V96H64V288H544M560 352H48C21.49 352 0 330.51 0 304V80C0 53.49 21.49 32 48 32H560C586.51 32 608 53.49 608 80V96H616C629.255 96 640 106.745 640 120V264C640 277.255 629.255 288 616 288H608V304C608 330.51 586.51 352 560 352z" />
+    <glyph glyph-name="battery-full"
+      unicode="&#xF240;"
+      horiz-adv-x="640" d=" M544 288V224H576V160H544V96H64V288H544M560 352H48C21.49 352 0 330.51 0 304V80C0 53.49 21.49 32 48 32H560C586.51 32 608 53.49 608 80V96H616C629.255 96 640 106.745 640 120V264C640 277.255 629.255 288 616 288H608V304C608 330.51 586.51 352 560 352zM512 256H96V128H512V256z" />
+    <glyph glyph-name="battery-half"
+      unicode="&#xF242;"
+      horiz-adv-x="640" d=" M544 288V224H576V160H544V96H64V288H544M560 352H48C21.49 352 0 330.51 0 304V80C0 53.49 21.49 32 48 32H560C586.51 32 608 53.49 608 80V96H616C629.255 96 640 106.745 640 120V264C640 277.255 629.255 288 616 288H608V304C608 330.51 586.51 352 560 352zM320 256H96V128H320V256z" />
+    <glyph glyph-name="battery-quarter"
+      unicode="&#xF243;"
+      horiz-adv-x="640" d=" M544 288V224H576V160H544V96H64V288H544M560 352H48C21.49 352 0 330.51 0 304V80C0 53.49 21.49 32 48 32H560C586.51 32 608 53.49 608 80V96H616C629.255 96 640 106.745 640 120V264C640 277.255 629.255 288 616 288H608V304C608 330.51 586.51 352 560 352zM224 256H96V128H224V256z" />
+    <glyph glyph-name="battery-three-quarters"
+      unicode="&#xF241;"
+      horiz-adv-x="640" d=" M544 288V224H576V160H544V96H64V288H544M560 352H48C21.49 352 0 330.51 0 304V80C0 53.49 21.49 32 48 32H560C586.51 32 608 53.49 608 80V96H616C629.255 96 640 106.745 640 120V264C640 277.255 629.255 288 616 288H608V304C608 330.51 586.51 352 560 352zM416 256H96V128H416V256z" />
+    <glyph glyph-name="bed"
+      unicode="&#xF236;"
+      horiz-adv-x="640" d=" M176 192C220.11 192 256 227.89 256 272S220.11 352 176 352S96 316.11 96 272S131.89 192 176 192zM528 320H304C295.1600000000001 320 288 312.8400000000001 288 304V160H64V368C64 376.8400000000001 56.84 384 48 384H16C7.16 384 0 376.8400000000001 0 368V16C0 7.16 7.16 0 16 0H48C56.84 0 64 7.16 64 16V64H576V16C576 7.16 583.16 0 592 0H624C632.84 0 640 7.16 640 16V208C640 269.86 589.86 320 528 320z" />
+    <glyph glyph-name="beer"
+      unicode="&#xF0FC;"
+      horiz-adv-x="448" d=" M368 352H320V392C320 405.255 309.255 416 296 416H24C10.745 416 0 405.255 0 392V-8C0 -21.255 10.745 -32 24 -32H296C309.255 -32 320 -21.255 320 -8V34.11L400.606 70.087C429.396 82.937 448 111.612 448 143.14V272C448 316.112 412.112 352 368 352zM384 143.14A16.018 16.018 0 0 0 374.521 128.529L320 104.195V288H368C376.822 288 384 280.822 384 272V143.14zM208 64C199.164 64 192 71.164 192 80V304C192 312.836 199.164 320 208 320S224 312.836 224 304V80C224 71.164 216.836 64 208 64zM112 64C103.164 64 96 71.164 96 80V304C96 312.836 103.164 320 112 320S128 312.836 128 304V80C128 71.164 120.836 64 112 64z" />
+    <glyph glyph-name="bell-slash"
+      unicode="&#xF1F6;"
+      horiz-adv-x="640" d=" M633.82 -10.1L543.2 59.95C543.3900000000001 61.33 544 62.61 544 64.01C544.05 71.56 541.39 79.28 535.39 85.72C516.0699999999999 106.48 479.92 137.71 479.92 240.01C479.92 317.71 425.44 379.91 351.98 395.17V416C351.98 433.67 337.66 448 320 448S288.0199999999999 433.67 288.0199999999999 416V395.16C247.69 386.78 213.3599999999999 364.0900000000001 190.4299999999999 332.5900000000001L45.47 444.63C38.49 450.05 28.43 448.8 23.01 441.82L3.37 416.55C-2.05 409.58 -0.8 399.53 6.18 394.1L594.53 -60.63C601.51 -66.0599999999999 611.56 -64.7999999999999 616.99 -57.82L636.63 -32.55C642.05 -25.58 640.8 -15.53 633.82 -10.1zM157.23 196.46C148.62 128.5 120.82 103.13 104.61 85.71C98.61 79.2600000000001 95.95 71.55 96 64.0000000000001C96.11 47.6000000000001 108.98 32.0000000000001 128.1 32.0000000000001H370.02L157.23 196.46zM320 -64C355.32 -64 383.9700000000001 -35.35 383.9700000000001 0H256.03C256.03 -35.35 284.68 -64 320 -64z" />
+    <glyph glyph-name="bell"
+      unicode="&#xF0F3;"
+      horiz-adv-x="448" d=" M224 -64C259.32 -64 287.9700000000001 -35.35 287.9700000000001 0H160.03C160.03 -35.35 188.68 -64 224 -64zM439.39 85.71C420.07 106.47 383.92 137.7000000000001 383.92 240.0000000000001C383.92 317.7000000000001 329.44 379.9000000000001 255.98 395.1600000000001V416C255.98 433.67 241.66 448 224 448S192.02 433.67 192.02 416V395.16C118.56 379.9 64.08 317.7 64.08 240C64.08 137.7 27.93 106.47 8.61 85.71C2.61 79.2600000000001 -0.05 71.55 0 64.0000000000001C0.11 47.6000000000001 12.98 32.0000000000001 32.1 32.0000000000001H415.9000000000001C435.0200000000001 32.0000000000001 447.9000000000001 47.6000000000001 448.0000000000001 64.0000000000001C448.0500000000001 71.5500000000001 445.3900000000001 79.27 439.3900000000001 85.71z" />
+    <glyph glyph-name="bezier-curve"
+      unicode="&#xF55B;"
+      horiz-adv-x="640" d=" M368 416H272C254.33 416 240 401.67 240 384V288C240 270.33 254.33 256 272 256H368C385.67 256 400 270.3300000000001 400 288V384C400 401.67 385.67 416 368 416zM208 360H123.25C113.75 383.44 90.84 400 64 400C28.66 400 0 371.35 0 336S28.66 272 64 272C90.84 272 113.75 288.56 123.25 312H202.98C147.61 279.48 107.12 224.68 93.44 160H142.84C154.14 201.61 179.61 237.21 213.88 261.56C210.18 269.64 208 278.55 208 288V360zM160 128H64C46.33 128 32 113.67 32 96V0C32 -17.67 46.33 -32 64 -32H160C177.67 -32 192 -17.67 192 0V96C192 113.67 177.67 128 160 128zM576 400C549.16 400 526.25 383.44 516.75 360H432V288C432 278.55 429.81 269.64 426.12 261.56C460.39 237.21 485.86 201.61 497.16 160H546.5600000000001C532.8800000000001 224.68 492.39 279.48 437.0200000000001 312H516.75C526.25 288.56 549.16 272 576 272C611.34 272 640 300.65 640 336S611.34 400 576 400zM576 128H480C462.33 128 448 113.67 448 96V0C448 -17.67 462.33 -32 480 -32H576C593.67 -32 608 -17.67 608 0V96C608 113.67 593.67 128 576 128z" />
+    <glyph glyph-name="bible"
+      unicode="&#xF647;"
+      horiz-adv-x="448" d=" M448 89.6V422.4C448 438.4 438.4 448 422.4 448H96C41.6 448 0 406.4 0 352V32C0 -22.4 41.6 -64 96 -64H422.4C435.2 -64 448 -54.4 448 -38.4V-22.4C448 -16 444.8 -9.6 438.4 -3.2C435.2 12.8 435.2 57.6 438.4 70.4C444.8 73.6 448 80 448 89.6zM144 304C144 312.8400000000001 151.16 320 160 320H208V368C208 376.8400000000001 215.16 384 224 384H256C264.84 384 272 376.8400000000001 272 368V320H320C328.84 320 336 312.8400000000001 336 304V272C336 263.16 328.84 256 320 256H272V144C272 135.16 264.84 128 256 128H224C215.16 128 208 135.16 208 144V256H160C151.16 256 144 263.16 144 272V304zM380.8 0H96C76.8 0 64 12.8 64 32S80 64 96 64H380.8V0z" />
+    <glyph glyph-name="bicycle"
+      unicode="&#xF206;"
+      horiz-adv-x="640" d=" M512.509 255.999C496.136 256.063 480.479 253.044 466.073 247.504L388.3930000000001 372.657A24 24 0 0 1 368.001 384H304.001C295.164 384 288.001 376.837 288.001 368V352C288.001 343.163 295.164 336 304.001 336H354.65L369.546 312H256.002V328C256.002 336.837 248.839 344 240.002 344H152.543C139.102 344 127.766 333.001 128.007 319.563C128.239 306.519 138.883 296 152.002 296H200.728L171.311 248.48C157.878 253.31 143.407 255.963 128.319 256C58.094 256.17 0.412 198.988 0.002 128.764C-0.413 57.721 57.055 0 128.002 0C187.644 0 237.76 40.793 251.969 96H304.002A24 24 0 0 1 324.408 107.367L410.37 246.23L425.308 222.163C399.853 198.715 383.923 165.082 384.001 127.726C384.146 58.893 441.9 0.675 510.73 0.007C581.336 -0.678 638.911 55.81 639.9849999999999 126.003C641.0709999999999 196.944 583.459 255.723 512.5089999999999 255.999zM186.75 182.228C196.477 171.699 203.423 158.567 206.392 144H163.086L186.75 182.228zM128.002 48C83.89 48 48.002 83.888 48.002 128S83.89 208 128.002 208C133.871 208 139.588 207.347 145.101 206.141L99.596 132.632C89.715 116.673 101.213 96 120.002 96H201.302C188.932 67.775 160.74 48 128.002 48zM290.632 144H255.008C251.048 175.756 235.452 203.894 212.625 224.026L237.371 264H364.918L290.632 144zM507.689 48.114C466.653 50.2790000000001 433.64 83.806 432.062 124.869C431.25 145.99 438.695 165.3870000000001 451.3969999999999 180.132L495.8299999999999 108.546C500.49 101.038 510.3539999999999 98.73 517.862 103.39L531.456 111.827C538.964 116.487 541.273 126.351 536.612 133.859L492.1439999999999 205.502A79.901 79.901 0 0 0 512.002 207.999C556.1139999999999 207.999 592.002 172.111 592.002 127.999C592.001 82.459 553.75 45.683 507.689 48.114z" />
+    <glyph glyph-name="binoculars"
+      unicode="&#xF1E5;"
+      horiz-adv-x="512" d=" M416 400C416 408.8400000000001 408.84 416 400 416H336C327.1600000000001 416 320 408.8400000000001 320 400V352H416V400zM63.91 288.01C61.4 194.16 3.46 173.78 0 44V0C0 -17.67 14.33 -32 32 -32H128C145.67 -32 160 -17.67 160 0V160H192V320H95.84C78.21 320 64.39 305.63 63.91 288.01zM448.0900000000001 288.01C447.61 305.63 433.79 320 416.1600000000001 320H320V160H352V0C352 -17.67 366.33 -32 384 -32H480C497.67 -32 512 -17.67 512 0V44C508.54 173.78 450.6 194.16 448.0900000000001 288.01zM176 416H112C103.16 416 96 408.8400000000001 96 400V352H192V400C192 408.8400000000001 184.84 416 176 416zM224 160H288V320H224V160z" />
+    <glyph glyph-name="birthday-cake"
+      unicode="&#xF1FD;"
+      horiz-adv-x="448" d=" M448 64C419.98 64 416.74 96 373.5 96C330.07 96 326.675 64 298.75 64C271.055 64 267.296 96 224 96C181.158 96 176.782 64 149.5 64C121.352 64 118.298 96 74.75 96C31.203 96 28.097 64 0 64V144C0 170.5 21.5 192 48 192H64V336H128V192H192V336H256V192H320V336H384V192H400C426.5 192 448 170.5 448 144V64zM448 -64H0V32C43.356 32 46.767 64 74.75 64C102.701 64 106.003 32 149.5 32C192.343 32 196.717 64 224 64C252.148 64 255.201 32 298.75 32C342.107 32 345.517 64 373.5 64C400.988 64 404.752 32 448 32V-64zM96 352C78.25 352 64 366.25 64 384C64 415 96 407 96 448C108 448 128 418.5 128 392S113.75 352 96 352zM224 352C206.25 352 192 366.25 192 384C192 415 224 407 224 448C236 448 256 418.5 256 392S241.75 352 224 352zM352 352C334.25 352 320 366.25 320 384C320 415 352 407 352 448C364 448 384 418.5 384 392S369.75 352 352 352z" />
+    <glyph glyph-name="blender-phone"
+      unicode="&#xF6B6;"
+      horiz-adv-x="576" d=" M392 384H558.54L576 448H192V96H480L497.46 160H392C387.58 160 384 163.58 384 168V184C384 188.42 387.58 192 392 192H506.18L523.64 256H392C387.58 256 384 259.5800000000001 384 264V280C384 284.42 387.58 288 392 288H532.36L549.82 352H392C387.58 352 384 355.58 384 360V376C384 380.42 387.58 384 392 384zM158.8 112.99L133.02 176.25C130.24 183.06 123.22 187.24 115.78 186.51L70.75 182.09C53.47 229.03 53.1 281.87 70.75 329.81L115.78 325.39C123.21 324.66 130.24 328.85 133.02 335.65L158.8 398.91C161.82 406.3 159 414.76 152.12 418.98L112.84 443.08C98.51 451.87 80.09 448.5 68.95 436.03C-23.62 332.43 -23.05 176.48 71.05 73.54C80.92 62.74 100.17 61.06 112.7 68.74L152.11 92.92C159 97.14 161.81 105.59 158.8 112.99zM480 64H192C156.65 64 128 35.35 128 0V-32C128 -49.67 142.33 -64 160 -64H512C529.67 -64 544 -49.67 544 -32V0C544 35.35 515.35 64 480 64zM336 -32C318.33 -32 304 -17.67 304 0S318.33 32 336 32S368 17.67 368 0S353.67 -32 336 -32z" />
+    <glyph glyph-name="blender"
+      unicode="&#xF517;"
+      horiz-adv-x="512" d=" M416 64H160C124.65 64 96 35.35 96 0V-32C96 -49.67 110.33 -64 128 -64H448C465.67 -64 480 -49.67 480 -32V0C480 35.35 451.35 64 416 64zM288 -32C270.33 -32 256 -17.67 256 0S270.33 32 288 32S320 17.67 320 0S305.67 -32 288 -32zM328 384H494.54L512 448H48C21.49 448 0 426.51 0 400V240C0 213.49 21.49 192 48 192H151.27L160 96H416L433.46 160H328C323.58 160 320 163.58 320 168V184C320 188.42 323.58 192 328 192H442.18L459.64 256H328C323.58 256 320 259.5800000000001 320 264V280C320 284.42 323.58 288 328 288H468.36L485.82 352H328C323.58 352 320 355.58 320 360V376C320 380.42 323.58 384 328 384zM64 256V384H133.82L145.46 256H64z" />
+    <glyph glyph-name="blind"
+      unicode="&#xF29D;"
+      horiz-adv-x="384" d=" M380.15 -62.837A8 8 0 0 0 369.161 -60.15L243.831 146.2770000000001A31.923 31.923 0 0 1 256.7890000000001 155.7620000000001L382.8370000000001 -51.8459999999999A8 8 0 0 0 380.1500000000001 -62.8369999999999zM142.803 133.662L110.263 44.177L146.383 -44.1080000000001C153.076 -60.4680000000001 171.76 -68.3000000000001 188.116 -61.609C204.473 -54.917 212.309 -36.2330000000001 205.617 -19.8750000000001L142.803 133.6619999999999zM96 360C120.301 360 140 379.699 140 404S120.301 448 96 448S52 428.301 52 404S71.699 360 96 360zM250.837 190.872L130.837 342.872C126.104 348.867 119.087 351.98 112 351.9840000000001V352H80V351.974C72.854 351.971 65.783 348.813 61.056 342.7340000000001L0 264.2340000000001V168.54C0 155.085 11.011 143.749 24.464 144.004C37.505 144.252 48 154.9 48 168V247.766L64 268.337V127.639L9.927 -21.055C3.887 -37.664 12.455 -56.024 29.065 -62.064C45.667 -68.1030000000001 64.033 -59.54 70.074 -42.926L136 138.362V245.559L104.594 285.375A4 4 0 1 0 110.863 290.346L213.163 161.129C222.308 149.545 237.531 149.79 246.871 157.1640000000001C257.281 165.3800000000001 259.0300000000001 180.4980000000001 250.837 190.8720000000001z" />
+    <glyph glyph-name="bold"
+      unicode="&#xF032;"
+      horiz-adv-x="384" d=" M304.793 204.109C338.432 222.646 358.45 258.269 358.45 299.802C358.45 348.038 332.2 387.428 289.824 403.981C265.138 413.99 240.849 416 209.661 416H24C15.163 416 8 408.837 8 400V366.951C8 358.114 15.163 350.951 24 350.951H57.113V32.4210000000001H24C15.163 32.4210000000001 8 25.258 8 16.4210000000001V-16C8 -24.837 15.163 -32 24 -32H219.69C243.893 -32 264.524 -30.711 286.556 -24.416C337.52 -9.193 376 37.353 376 97.986C376 150.154 349.427 189.67 304.793 204.109zM142.217 347.1910000000001H209.661C225.955 347.1910000000001 237.197 345.1720000000001 247.186 340.474C263.014 331.995 272.092 313.972 272.092 291.028C272.092 255.999 251.772 234.2380000000001 219.063 234.2380000000001H142.217V347.1910000000001zM254.859 41.716C244.719 37.66 232.182 36.809 223.45 36.809H142.217V166.057H226.584C266.229 166.057 289.641 140.677 289.641 103C289.642 74.575 275.981 50.517 254.8590000000001 41.716z" />
+    <glyph glyph-name="bolt"
+      unicode="&#xF0E7;"
+      horiz-adv-x="320" d=" M296 288H180.6L223.2 417.8C227.2 433 215.7 448 200 448H56C44 448 33.8 439.1 32.2 427.2L0.2 187.2C-1.7 172.8 9.5 160 24 160H142.7L96.6 -34.5C93 -49.7 104.6 -64 119.9 -64C128.3 -64 136.3 -59.6 140.7 -52L316.7 252C326 267.9 314.5 288 296 288z" />
+    <glyph glyph-name="bomb"
+      unicode="&#xF1E2;"
+      horiz-adv-x="512" d=" M440.5 359.5L388.5 307.5L415 281C424.4 271.6 424.4 256.4 415 247.1L397.6 229.7C409.4000000000001 203.6 416 174.6 416 144.1C416 29.2 322.9 -63.9 208 -63.9S0 29.1 0 144S93.1 352 208 352C238.5 352 267.5 345.4 293.6 333.6L311 351C320.4 360.4 335.6 360.4 344.9 351L371.4 324.5L423.4 376.5L440.5 359.5zM500 388H476C469.4 388 464 382.6 464 376S469.4 364 476 364H500C506.6 364 512 369.4 512 376S506.6 388 500 388zM440 448C433.4 448 428 442.6 428 436V412C428 405.4 433.4 400 440 400S452 405.4 452 412V436C452 442.6 446.6 448 440 448zM473.9 393L490.9 410C495.6 414.7 495.6 422.3 490.9 427C486.2 431.7 478.6 431.7 473.9 427L456.9 410C452.2 405.3 452.2 397.7 456.9 393C461.7 388.3 469.3 388.3 473.9 393zM406.1 393C410.8 388.3 418.4 388.3 423.1 393C427.8 397.7 427.8 405.3 423.1 410L406.1 427C401.4 431.7 393.8 431.7 389.1 427C384.4 422.3 384.4 414.7 389.1 410L406.1 393zM473.9 359C469.2 363.7 461.6 363.7 456.9 359C452.2 354.3 452.2 346.7 456.9 342L473.9 325C478.6 320.3 486.2 320.3 490.9 325C495.6 329.7 495.6 337.3 490.9 342L473.9 359zM112 176C112 211.3 140.7 240 176 240C184.8 240 192 247.2 192 256S184.8 272 176 272C123.1 272 80 228.9 80 176C80 167.2 87.2 160 96 160S112 167.2 112 176z" />
+    <glyph glyph-name="bone"
+      unicode="&#xF5D7;"
+      horiz-adv-x="640" d=" M598.88 203.44C624.08 216.04 640 241.8 640 269.9700000000001V277.61C640 318.7 606.7 352 565.61 352C533.59 352 505.17 331.51 495.04 301.14C487.36 278.11 483.44 256 456.93 256H183.06C155.68 256 151.48 281.54 144.95 301.14C134.83 331.51 106.4 352 74.39 352C33.3 352 0 318.7 0 277.61V269.9700000000001C0 241.8 15.92 216.04 41.12 203.44C50.55 198.73 50.55 185.2700000000001 41.12 180.56C15.92 167.96 0 142.2 0 114.03V106.39C0 65.3 33.3 32 74.38 32C106.4 32 134.82 52.49 144.95 82.86C152.63 105.89 156.55 128 183.06 128H456.93C484.31 128 488.51 102.46 495.04 82.86C505.17 52.49 533.6 32 565.61 32C606.69 32 639.99 65.3 639.99 106.39V114.03C639.99 142.21 624.07 167.96 598.87 180.56C589.45 185.2699999999999 589.45 198.73 598.88 203.44z" />
+    <glyph glyph-name="bong"
+      unicode="&#xF55C;"
+      horiz-adv-x="448" d=" M302.5 -64C325.68 -64 346.93 -51.42 358.5 -31.34C374.69 -3.26 384 29.25 384 64C384 100.12 373.92 133.81 356.56 162.62L400 206.06L409.38 196.68C415.63 190.43 425.76 190.43 432.01 196.68L443.31 208C449.56 214.25 449.56 224.38 443.31 230.63L390.62 283.32C384.37 289.57 374.24 289.57 367.99 283.32L356.68 272.01C350.43 265.76 350.43 255.63 356.68 249.38L366.06 240L326.65 200.59C315.09 211.96 302.12 221.92 288 230.1V384.26L303.9700000000001 384.28C312.79 384.29 319.9400000000001 391.44 319.9500000000001 400.26L319.9900000000001 431.98C320 440.83 312.82 448.01 303.9700000000001 448L80.03 447.74C71.21 447.73 64.06 440.58 64.05 431.76L64.01 400.03C64 391.18 71.18 384.01 80.03 384.02L96 384.04V230.11C38.67 196.9 0 135.03 0 64C0 29.25 9.31 -3.27 25.5 -31.34C37.08 -51.42 58.33 -64 81.5 -64H302.5zM120.06 188.57L144 202.44V384.0900000000001L240 384.2V202.44L263.94 188.57C288.75 174.2 308.06 152.84 320.5 128H63.5C75.95 152.84 95.25 174.2 120.06 188.57z" />
+    <glyph glyph-name="book-dead"
+      unicode="&#xF6B7;"
+      horiz-adv-x="448" d=" M272 312C280.84 312 288 319.16 288 328S280.84 344 272 344S256 336.8400000000001 256 328S263.1600000000001 312 272 312zM448 89.6V422.4C448 438.4 438.4 448 422.4 448H96C41.6 448 0 406.4 0 352V32C0 -22.4 41.6 -64 96 -64H422.4C435.2 -64 448 -54.4 448 -38.4V-22.4C448 -16 444.8 -9.6 438.4 -3.2C435.2 12.8 435.2 57.6 438.4 70.4C444.8 73.6 448 80 448 89.6zM240 392C284.18 392 320 363.35 320 328C320 307.13 307.32 288.77 288 277.0900000000001V264C288 255.16 280.84 248 272 248H208C199.16 248 192 255.16 192 264V277.0900000000001C172.68 288.77 160 307.13 160 328C160 363.35 195.82 392 240 392zM129.05 214.15A7.996000000000001 7.996000000000001 0 0 0 124.85 224.65L131.14 239.35A7.995 7.995 0 0 0 141.65 243.55L240 201.4L338.35 243.55C342.4100000000001 245.29 347.12 243.4100000000001 348.86 239.35L355.1500000000001 224.65A7.996000000000001 7.996000000000001 0 0 0 350.9500000000001 214.15L280.6 184L350.9400000000001 153.85A7.996000000000001 7.996000000000001 0 0 0 355.1400000000001 143.35L348.85 128.65A8.008 8.008 0 0 0 338.3400000000001 124.4400000000001L240 166.6L141.65 124.4500000000001A8.003 8.003 0 0 0 131.14 128.66L124.85 143.36A7.996000000000001 7.996000000000001 0 0 0 129.05 153.86L199.4 184L129.05 214.15zM380.8 0H96C76.8 0 64 12.8 64 32S80 64 96 64H380.8V0zM208 312C216.84 312 224 319.16 224 328S216.84 344 208 344S192 336.8400000000001 192 328S199.16 312 208 312z" />
+    <glyph glyph-name="book-open"
+      unicode="&#xF518;"
+      horiz-adv-x="576" d=" M542.22 415.95C487.42 412.8400000000001 378.5 401.52 311.26 360.36C306.62 357.52 303.99 352.4700000000001 303.99 347.19V-16.68C303.99 -28.23 316.62 -35.53 327.27 -30.17C396.45 4.65 496.5 14.15 545.97 16.75C562.86 17.64 575.99 31.18 575.99 47.41V385.25C576 402.96 560.64 416.99 542.22 415.95zM264.73 360.36C197.5 401.52 88.58 412.83 33.78 415.95C15.36 416.99 0 402.96 0 385.25V47.4C0 31.16 13.13 17.62 30.02 16.74C79.51 14.1399999999999 179.61 4.6299999999999 248.79 -30.21C259.4100000000001 -35.5600000000001 272 -28.27 272 -16.7500000000001V347.37C272 352.6600000000001 269.38 357.51 264.73 360.36z" />
+    <glyph glyph-name="book-reader"
+      unicode="&#xF5DA;"
+      horiz-adv-x="512" d=" M352 352C352 405.02 309.02 448 256 448S160 405.02 160 352S202.98 256 256 256S352 298.98 352 352zM233.59 206.9C174.26 243.22 78.16 253.2 29.8 255.95C13.55 256.87 0 244.49 0 228.86V6.06C0 -8.27 11.59 -20.22 26.49 -20.99C70.15 -23.28 158.48 -31.67 219.53 -62.42C228.9 -67.14 240.01 -60.71 240.01 -50.55V195.44C240 200.11 237.69 204.39 233.59 206.9zM482.2 255.95C433.85 253.21 337.74 243.22 278.4200000000001 206.9C274.3200000000001 204.39 272.0100000000001 199.94 272.0100000000001 195.27V-50.52C272.0100000000001 -60.71 283.1500000000001 -67.15 292.5500000000001 -62.42C353.5900000000001 -31.6999999999999 441.8700000000001 -23.3099999999999 485.5200000000001 -21.02C500.4200000000001 -20.24 512.0100000000001 -8.29 512.0100000000001 6.04V228.86C512.0000000000001 244.49 498.4500000000001 256.87 482.2000000000001 255.95z" />
+    <glyph glyph-name="book"
+      unicode="&#xF02D;"
+      horiz-adv-x="448" d=" M448 88V424C448 437.3 437.3 448 424 448H96C43 448 0 405 0 352V32C0 -21 43 -64 96 -64H424C437.3 -64 448 -53.3 448 -40V-24C448 -16.5 444.5 -9.7 439.1 -5.3C434.9000000000001 10.1 434.9000000000001 54 439.1 69.4C444.5 73.7 448 80.5 448 88zM128 314C128 317.3 130.7 320 134 320H346C349.3 320 352 317.3 352 314V294C352 290.7 349.3 288 346 288H134C130.7 288 128 290.7 128 294V314zM128 250C128 253.3 130.7 256 134 256H346C349.3 256 352 253.3 352 250V230C352 226.7 349.3 224 346 224H134C130.7 224 128 226.7 128 230V250zM381.4 0H96C78.3 0 64 14.3 64 32C64 49.6 78.4 64 96 64H381.4C379.5 46.9 379.5 17.1 381.4 0z" />
+    <glyph glyph-name="bookmark"
+      unicode="&#xF02E;"
+      horiz-adv-x="384" d=" M0 -64V400C0 426.51 21.49 448 48 448H336C362.51 448 384 426.51 384 400V-64L192 48L0 -64z" />
+    <glyph glyph-name="bowling-ball"
+      unicode="&#xF436;"
+      horiz-adv-x="496" d=" M248 440C111 440 0 329 0 192S111 -56 248 -56S496 55 496 192S385 440 248 440zM120 256C102.3 256 88 270.3 88 288S102.3 320 120 320S152 305.7 152 288S137.7 256 120 256zM184 352C184 369.7 198.3 384 216 384S248 369.7 248 352S233.7 320 216 320S184 334.3 184 352zM232 208C214.3 208 200 222.3 200 240S214.3 272 232 272S264 257.7 264 240S249.7 208 232 208z" />
+    <glyph glyph-name="box-open"
+      unicode="&#xF49E;"
+      horiz-adv-x="640" d=" M53.2 407L1.7 304.2C-2.9 295 2 284 11.8 281.2L209.7 224.7C216.8 222.7 224.4 225.7 228.2 232L320 384L69.8 415.9C62.9 416.7 56.3 413.2 53.2 407zM638.3000000000001 304.2L586.8 407C583.6999999999999 413.2 577 416.8 570.0999999999999 415.9L320 384L411.7 231.9C415.5 225.6 423.1 222.6 430.2 224.6L628.1 281.1C638 284 642.8000000000001 295 638.3000000000001 304.2zM425.7 192C408.8 192 392.9 201 384.3 215.4L320 322L255.8 215.4C247.1 200.9 231.2 191.9 214.3 191.9C209.8 191.9 205.3 192.5 201 193.8L64 233V55C64 40.3 74 27.5 88.2 24L304.4 -30.1C314.6 -32.6 325.3 -32.6 335.4 -30.1L551.8 24C566 27.6 576 40.4 576 55V233L439 193.9C434.7 192.6 430.2 192 425.7 192z" />
+    <glyph glyph-name="box"
+      unicode="&#xF466;"
+      horiz-adv-x="512" d=" M509.5 263.4L458.9 415.2C452.4 434.8 434.1 448 413.4 448H272V256H510.7C510.3 258.5 510.3 261 509.5 263.4zM240 448H98.6C77.9 448 59.6 434.8 53.1 415.2L2.5 263.4C1.7 261 1.7 258.5 1.3 256H240V448zM0 224V-16C0 -42.5 21.5 -64 48 -64H464C490.5 -64 512 -42.5 512 -16V224H0z" />
+    <glyph glyph-name="boxes"
+      unicode="&#xF468;"
+      horiz-adv-x="576" d=" M560 160H480V64L448 85.3L416 64V160H336C327.2 160 320 152.8 320 144V-48C320 -56.8 327.2 -64 336 -64H560C568.8 -64 576 -56.8 576 -48V144C576 152.8 568.8 160 560 160zM176 224H400C408.8 224 416 231.2 416 240V432C416 440.8 408.8 448 400 448H320V352L288 373.3L256 352V448H176C167.2 448 160 440.8 160 432V240C160 231.2 167.2 224 176 224zM240 160H160V64L128 85.3L96 64V160H16C7.2 160 0 152.8 0 144V-48C0 -56.8 7.2 -64 16 -64H240C248.8 -64 256 -56.8 256 -48V144C256 152.8 248.8 160 240 160z" />
+    <glyph glyph-name="braille"
+      unicode="&#xF2A1;"
+      horiz-adv-x="640" d=" M128 192C128 156.654 99.346 128 64 128S0 156.654 0 192S28.654 256 64 256S128 227.346 128 192zM64 64C46.327 64 32 49.673 32 32S46.327 0 64 0S96 14.327 96 32S81.673 64 64 64zM64 416C28.654 416 0 387.346 0 352S28.654 288 64 288S128 316.654 128 352S99.346 416 64 416zM224 224C206.327 224 192 209.673 192 192S206.327 160 224 160S256 174.327 256 192S241.673 224 224 224zM224 64C206.327 64 192 49.673 192 32S206.327 0 224 0S256 14.327 256 32S241.673 64 224 64zM224 416C188.654 416 160 387.346 160 352S188.654 288 224 288S288 316.654 288 352S259.346 416 224 416zM448 224C430.327 224 416 209.673 416 192S430.327 160 448 160S480 174.327 480 192S465.673 224 448 224zM448 64C430.327 64 416 49.673 416 32S430.327 0 448 0S480 14.327 480 32S465.673 64 448 64zM448 416C412.654 416 384 387.346 384 352S412.654 288 448 288S512 316.654 512 352S483.346 416 448 416zM608 224C590.327 224 576 209.673 576 192S590.327 160 608 160S640 174.327 640 192S625.673 224 608 224zM608 64C590.327 64 576 49.673 576 32S590.327 0 608 0S640 14.327 640 32S625.673 64 608 64zM608 384C590.327 384 576 369.673 576 352S590.327 320 608 320S640 334.327 640 352S625.673 384 608 384z" />
+    <glyph glyph-name="brain"
+      unicode="&#xF5DC;"
+      horiz-adv-x="576" d=" M208 448C178.13 448 153.26 427.45 146.2 399.78C145.45 399.8 144.75 400 144 400C108.66 400 80 371.35 80 336C80 331.16 80.64 326.49 81.66 321.9600000000001C52.54 310 32 281.43 32 248C32 235.42 35.16 223.68 40.34 213.09C16.34 199.28 0 173.67 0 144C0 110.66 20.42 82.12 49.42 70.11C48.52 65.54 48 60.83 48 56C48 16.24 80.23 -16 120 -16C124.12 -16 128.1 -15.45 132.03 -14.79C141.61 -43.31 168.25 -64 200 -64C239.77 -64 272 -31.76 272 8V242.55C261.09 233.57 248.02 227.1 233.64 224.16C228.67 223.14 224 226.98 224 232.05V248.23C224 251.8 226.35 255.01 229.8 255.89C254 262.05 272 283.8400000000001 272 309.93V384C272 419.35 243.34 448 208 448zM576 144C576 173.67 559.66 199.28 535.66 213.09C540.8299999999999 223.68 544 235.42 544 248C544 281.43 523.46 310 494.34 321.96C495.36 326.49 496.0000000000001 331.16 496.0000000000001 336C496.0000000000001 371.35 467.34 400 432.0000000000001 400C431.2500000000001 400 430.5500000000001 399.8 429.8000000000001 399.78C422.74 427.45 397.87 448 368 448C332.66 448 304 419.35 304 384V309.93C304 283.8400000000001 321.99 262.05 346.2 255.89C349.66 255.01 352 251.8 352 248.23V232.05C352 226.98 347.32 223.14 342.36 224.16C327.98 227.1 314.92 233.57 304 242.55V8C304 -31.76 336.23 -64 376 -64C407.75 -64 434.39 -43.31 443.9700000000001 -14.79C447.9000000000001 -15.46 451.8800000000001 -16 456 -16C495.77 -16 528 16.24 528 56C528 60.83 527.48 65.54 526.58 70.11C555.58 82.12 576 110.66 576 144z" />
+    <glyph glyph-name="briefcase-medical"
+      unicode="&#xF469;"
+      horiz-adv-x="512" d=" M464 320H384V368C384 394.5 362.5 416 336 416H176C149.5 416 128 394.5 128 368V320H48C21.5 320 0 298.5 0 272V-16C0 -42.5 21.5 -64 48 -64H464C490.5 -64 512 -42.5 512 -16V272C512 298.5 490.5 320 464 320zM192 352H320V320H192V352zM352 104C352 99.6 348.4 96 344 96H288V40C288 35.6 284.4 32 280 32H232C227.6 32 224 35.6 224 40V96H168C163.6 96 160 99.6 160 104V152C160 156.4 163.6 160 168 160H224V216C224 220.4 227.6 224 232 224H280C284.4 224 288 220.4 288 216V160H344C348.4 160 352 156.4 352 152V104z" />
+    <glyph glyph-name="briefcase"
+      unicode="&#xF0B1;"
+      horiz-adv-x="512" d=" M320 112C320 103.16 312.84 96 304 96H208C199.16 96 192 103.16 192 112V160H0V16C0 -9.6 22.4 -32 48 -32H464C489.6 -32 512 -9.6 512 16V160H320V112zM464 320H384V368C384 393.6 361.6 416 336 416H176C150.4 416 128 393.6 128 368V320H48C22.4 320 0 297.6 0 272V192H512V272C512 297.6 489.6 320 464 320zM320 320H192V352H320V320z" />
+    <glyph glyph-name="broadcast-tower"
+      unicode="&#xF519;"
+      horiz-adv-x="640" d=" M150.94 256H184.67C195.68 256 203.28 266.8300000000001 199.53 277.18C194.6 290.76 191.98 305.16 191.98 320S194.6 349.24 199.53 362.82C203.29 373.17 195.68 384 184.67 384H150.94C143.93 384 137.48 379.51 135.53 372.77C130.64 355.79 128 338.12 128 320C128 301.88 130.64 284.2100000000001 135.54 267.24C137.48 260.5 143.93 256 150.94 256zM89.92 424.6600000000001C95.56 435.28 87.97 448 75.96 448H40.63C34.36 448 28.49 444.4100000000001 25.89 438.69C9.4 402.46 0 362.35 0 320C0 295.25 3.12 251.67 26.69 201.14C29.31 195.51 35.11 192 41.3 192H76.14C88.16 192 95.75 204.74 90.09 215.37C40.31 308.69 73.38 393.52 89.92 424.66zM614.06 438.71C611.46 444.42 605.6 448 599.33 448H563.9100000000001C551.9300000000001 448 544.2500000000001 435.34 549.8900000000001 424.75C568.1600000000001 390.46 598.3100000000001 305.33 550.1700000000001 215.52C544.45 204.84 551.97 192 564.08 192H599.3100000000001C605.58 192 611.44 195.58 614.0400000000001 201.29C630.57 237.52 640 277.64 640 320S630.58 402.48 614.06 438.71zM489.06 384H455.33C444.32 384 436.72 373.17 440.47 362.82C445.4 349.24 448.02 334.84 448.02 320S445.4 290.76 440.47 277.18C436.71 266.8300000000001 444.32 256 455.33 256H489.06C496.08 256 502.52 260.49 504.47 267.24C509.37 284.2100000000001 512 301.88 512 320C512 338.12 509.36 355.79 504.46 372.76C502.52 379.51 496.07 384 489.06 384zM372.76 283.88C379.81 294.17 383.96 306.5900000000001 383.96 320C383.96 355.35 355.33 384 320 384C284.68 384 256.04 355.35 256.04 320C256.04 306.5900000000001 260.19 294.17 267.24 283.88L136.74 -29.53C133.34 -37.68 137.2 -47.05 145.35 -50.45L174.86 -62.76C183.01 -66.1600000000001 192.38 -62.3000000000001 195.77 -54.15L244.96 64H395.03L444.23 -54.15C447.63 -62.31 456.99 -66.16 465.14 -62.76L494.65 -50.45C502.8 -47.05 506.65 -37.68 503.26 -29.53L372.76 283.8800000000001zM271.62 128L320 244.19L368.38 128H271.62z" />
+    <glyph glyph-name="broom"
+      unicode="&#xF51A;"
+      horiz-adv-x="640" d=" M256.4700000000001 231.23L343.2000000000001 122.05S326.6 19.6899999999999 266.6300000000001 -28.07C206.66 -75.85 0 -62.19 0 -62.19S3.8 -39.05 11 -6.76L105.62 105.41C109.59 110.11 104.75 117.03 98.97 114.91L38.57 92.8200000000001C53.01 134.48 71.29 172.8600000000001 93.17 190.2900000000001C153.14 238.0500000000001 256.4700000000001 231.2300000000001 256.4700000000001 231.2300000000001zM636.53 416.9700000000001L616.67 441.9700000000001C611.18 448.87 601.15 450.02 594.26 444.53L361.78 266.73L327.64 309.7C322.55 316.11 312.5 314.91 309.05 307.49L283.7200000000001 252.94L370.4500000000001 143.76L429.2500000000001 156.21C437.2500000000001 157.9 440.6700000000001 167.41 435.5900000000001 173.81L401.5 216.73L633.98 394.5300000000001C640.87 400.0100000000001 642.02 410.0600000000001 636.53 416.9700000000001z" />
+    <glyph glyph-name="brush"
+      unicode="&#xF55D;"
+      horiz-adv-x="384" d=" M352 448H32C14.33 448 0 433.67 0 416V192H384V416C384 433.67 369.67 448 352 448zM0 128C0 92.65 28.66 64 64 64H128V0C128 -35.35 156.66 -64 192 -64S256 -35.35 256 0V64H320C355.3400000000001 64 384 92.65 384 128V160H0V128zM192 24C205.25 24 216 13.26 216 0C216 -13.25 205.25 -24 192 -24S168 -13.25 168 0C168 13.26 178.75 24 192 24z" />
+    <glyph glyph-name="bug"
+      unicode="&#xF188;"
+      horiz-adv-x="512" d=" M511.988 159.1C511.51 141.67 496.771 128 479.335 128H424V112C424 90.136 419.118 69.416 410.4 50.855L470.628 -9.373C483.124 -21.87 483.124 -42.131 470.628 -54.628C458.13 -67.125 437.869 -67.124 425.372 -54.628L370.636 0.108C345.886 -19.965 314.351 -32 280 -32V212C280 218.627 274.627 224 268 224H244C237.373 224 232 218.627 232 212V-32C197.649 -32 166.114 -19.965 141.364 0.108L86.628 -54.628C74.13 -67.125 53.869 -67.124 41.372 -54.628C28.876 -42.131 28.876 -21.87 41.372 -9.373L101.6 50.855C92.882 69.416 88 90.136 88 112V128H32.666C15.23 128 0.491 141.67 0.013 159.1C-0.484 177.184 14.028 192 32 192H88V250.745L41.372 297.373C28.876 309.87 28.876 330.131 41.372 342.628C53.87 355.125 74.13 355.125 86.628 342.628L141.255 288H370.7440000000001L425.3710000000001 342.627C437.8690000000001 355.124 458.129 355.124 470.6270000000001 342.627C483.123 330.13 483.123 309.8690000000001 470.6270000000001 297.372L424 250.745V192H480C497.972 192 512.484 177.184 511.988 159.1zM257 448C195.144 448 145 397.856 145 336H369C369 397.856 318.856 448 257 448z" />
+    <glyph glyph-name="building"
+      unicode="&#xF1AD;"
+      horiz-adv-x="448" d=" M436 -32H416V424C416 437.255 405.255 448 392 448H56C42.745 448 32 437.255 32 424V-32H12C5.373 -32 0 -37.373 0 -44V-64H448V-44C448 -37.373 442.627 -32 436 -32zM128 372C128 378.627 133.373 384 140 384H180C186.627 384 192 378.627 192 372V332C192 325.373 186.627 320 180 320H140C133.373 320 128 325.373 128 332V372zM128 276C128 282.627 133.373 288 140 288H180C186.627 288 192 282.627 192 276V236C192 229.373 186.627 224 180 224H140C133.373 224 128 229.373 128 236V276zM180 128H140C133.373 128 128 133.373 128 140V180C128 186.627 133.373 192 140 192H180C186.627 192 192 186.627 192 180V140C192 133.373 186.627 128 180 128zM256 -32H192V52C192 58.627 197.373 64 204 64H244C250.627 64 256 58.627 256 52V-32zM320 140C320 133.373 314.627 128 308 128H268C261.373 128 256 133.373 256 140V180C256 186.627 261.373 192 268 192H308C314.627 192 320 186.627 320 180V140zM320 236C320 229.373 314.627 224 308 224H268C261.373 224 256 229.373 256 236V276C256 282.627 261.373 288 268 288H308C314.627 288 320 282.627 320 276V236zM320 332C320 325.373 314.627 320 308 320H268C261.373 320 256 325.373 256 332V372C256 378.627 261.373 384 268 384H308C314.627 384 320 378.627 320 372V332z" />
+    <glyph glyph-name="bullhorn"
+      unicode="&#xF0A1;"
+      horiz-adv-x="576" d=" M576 208C576 231.63 563.05 252.04 544 263.12V415.99C544 424.74 537.02 448 512 448C504.88 448 497.81 445.62 492.02 440.98L406.99 372.95C364.28 338.81 310.6600000000001 320 256 320H64C28.65 320 0 291.35 0 256V160C0 124.65 28.65 96 64 96H97.7C96.31 85.52 95.52 74.86 95.52 64C95.52 24.23 104.78 -13.35 121.08 -46.94C126.27 -57.63 137.6 -64 149.48 -64H223.76C249.81 -64 265.45 -34.16 249.66 -13.44C233.26 8.08 223.51 34.92 223.51 64C223.51 75.11 225.13 85.79 227.92 96H256C310.66 96 364.28 77.19 406.98 43.05L492.01 -24.98A32.023 32.023 0 0 1 511.99 -32C536.91 -32 543.99 -9.22 543.99 0V152.87C563.05 163.96 576 184.37 576 208zM480 66.58L446.95 93.02C392.95 136.22 325.12 160 256 160V256C325.12 256 392.95 279.78 446.95 322.98L480 349.42V66.58z" />
+    <glyph glyph-name="bullseye"
+      unicode="&#xF140;"
+      horiz-adv-x="496" d=" M248 440C111.03 440 0 328.9700000000001 0 192S111.03 -56 248 -56S496 55.03 496 192S384.9700000000001 440 248 440zM248 8C146.31 8 64 90.29 64 192C64 293.69 146.29 376 248 376C349.69 376 432 293.71 432 192C432 90.31 349.71 8 248 8zM248 320C177.31 320 120 262.69 120 192S177.31 64 248 64S376 121.31 376 192S318.69 320 248 320zM248 128C212.71 128 184 156.71 184 192S212.71 256 248 256S312 227.29 312 192S283.29 128 248 128z" />
+    <glyph glyph-name="burn"
+      unicode="&#xF46A;"
+      horiz-adv-x="384" d=" M192 448C79.7 346.7 0 227.1 0 147.5C0 23 79 -64 192 -64S384 23 384 147.5C384 227.4 303.8 347.1 192 448zM192 0C135.5 0 96 39 96 94.8C96 108.3 100.6 156.3 192 256C283.4 156.3 288 108.3 288 94.8C288 39 248.5 0 192 0z" />
+    <glyph glyph-name="bus-alt"
+      unicode="&#xF55E;"
+      horiz-adv-x="512" d=" M488 320H480V368C480 412.8 380.8 448 256 448S32 412.8 32 368V320H24C10.75 320 0 309.26 0 296V216C0 202.75 10.75 192 24 192H32V32C32 14.33 46.33 0 64 0V-32C64 -49.67 78.33 -64 96 -64H128C145.67 -64 160 -49.67 160 -32V0H352V-32C352 -49.67 366.33 -64 384 -64H416C433.67 -64 448 -49.67 448 -32V0H454.4C470.4 0 480 12.8 480 25.6V192H488C501.25 192 512 202.75 512 216V296C512 309.26 501.25 320 488 320zM160 376C160 380.42 163.58 384 168 384H344C348.42 384 352 380.42 352 376V360C352 355.58 348.42 352 344 352H168C163.58 352 160 355.58 160 360V376zM112 48C94.33 48 80 62.33 80 80S94.33 112 112 112S144 97.67 144 80S129.67 48 112 48zM240 160H128C110.33 160 96 174.33 96 192V288C96 305.67 110.33 320 128 320H240V160zM272 160V320H384C401.67 320 416 305.67 416 288V192C416 174.33 401.67 160 384 160H272zM400 48C382.33 48 368 62.33 368 80S382.33 112 400 112S432 97.67 432 80S417.67 48 400 48z" />
+    <glyph glyph-name="bus"
+      unicode="&#xF207;"
+      horiz-adv-x="512" d=" M488 320H480V368C480 412.8 380.8 448 256 448S32 412.8 32 368V320H24C10.75 320 0 309.26 0 296V216C0 202.75 10.75 192 24 192H32V32C32 14.33 46.33 0 64 0V-32C64 -49.67 78.33 -64 96 -64H128C145.67 -64 160 -49.67 160 -32V0H352V-32C352 -49.67 366.33 -64 384 -64H416C433.67 -64 448 -49.67 448 -32V0H454.4C470.4 0 480 12.8 480 25.6V192H488C501.25 192 512 202.75 512 216V296C512 309.26 501.25 320 488 320zM112 48C94.33 48 80 62.33 80 80S94.33 112 112 112S144 97.67 144 80S129.67 48 112 48zM128 160C110.33 160 96 174.33 96 192V320C96 337.67 110.33 352 128 352H384C401.67 352 416 337.67 416 320V192C416 174.33 401.67 160 384 160H128zM400 48C382.33 48 368 62.33 368 80S382.33 112 400 112S432 97.67 432 80S417.67 48 400 48z" />
+    <glyph glyph-name="business-time"
+      unicode="&#xF64A;"
+      horiz-adv-x="640" d=" M496 224C416.41 224 352 159.59 352 80S416.41 -64 496 -64S640 0.41 640 80S575.59 224 496 224zM560 73.71C560 68.3700000000001 555.63 64.0000000000001 550.29 64.0000000000001H489.72C484.38 64.0000000000001 480.01 68.3700000000001 480.01 73.71V150.28C480.01 155.62 484.38 159.99 489.72 159.99H502.29C507.6299999999999 159.99 511.9999999999999 155.62 511.9999999999999 150.28V96H550.29C555.63 96 560 91.63 560 86.29V73.71zM496 256C501.4 256 506.72 255.67 512 255.19V304C512 329.6 489.6 352 464 352H384V400C384 425.6 361.6 448 336 448H176C150.4 448 128 425.6 128 400V352H48C22.4 352 0 329.6 0 304V224H395.12C423.7200000000001 244.09 458.47 256 496 256zM320 352H192V384H320V352zM326.82 128H208C199.16 128 192 135.16 192 144V192H0V48C0 22.4 22.4 0 48 0H339.43C327.1 24.04 320 51.18 320 80C320 96.66 322.48 112.72 326.82 128z" />
+    <glyph glyph-name="calculator"
+      unicode="&#xF1EC;"
+      horiz-adv-x="448" d=" M400 448H48C22.4 448 0 425.6 0 400V-16C0 -41.6 22.4 -64 48 -64H400C425.6 -64 448 -41.6 448 -16V400C448 425.6 425.6 448 400 448zM128 12.8C128 6.4 121.6 0 115.2 0H76.8C70.4 0 64 6.4 64 12.8V51.2C64 57.6 70.4 64 76.8 64H115.2C121.6 64 128 57.6 128 51.2V12.8zM128 140.8C128 134.4 121.6 128 115.2 128H76.8C70.4 128 64 134.4 64 140.8V179.2C64 185.6 70.4 192 76.8 192H115.2C121.6 192 128 185.6 128 179.2V140.8zM256 12.8C256 6.4 249.6 0 243.2 0H204.8C198.4 0 192 6.4 192 12.8V51.2C192 57.6 198.4 64 204.8 64H243.2C249.6 64 256 57.6 256 51.2V12.8zM256 140.8C256 134.4 249.6 128 243.2 128H204.8C198.4 128 192 134.4 192 140.8V179.2C192 185.6 198.4 192 204.8 192H243.2C249.6 192 256 185.6 256 179.2V140.8zM384 12.8C384 6.4 377.6 0 371.2 0H332.8C326.4000000000001 0 320 6.4 320 12.8V179.2C320 185.6 326.4 192 332.8 192H371.2C377.6 192 384 185.6 384 179.2V12.8zM384 268.8C384 262.4 377.6 256 371.2 256H76.8C70.4 256 64 262.4 64 268.8V371.2C64 377.6 70.4 384 76.8 384H371.2C377.6 384 384 377.6 384 371.2V268.8z" />
+    <glyph glyph-name="calendar-alt"
+      unicode="&#xF073;"
+      horiz-adv-x="448" d=" M436 288H12C5.4 288 0 293.4 0 300V336C0 362.5 21.5 384 48 384H96V436C96 442.6 101.4 448 108 448H148C154.6 448 160 442.6 160 436V384H288V436C288 442.6 293.4 448 300 448H340C346.6 448 352 442.6 352 436V384H400C426.5 384 448 362.5 448 336V300C448 293.4 442.6 288 436 288zM12 256H436C442.6 256 448 250.6 448 244V-16C448 -42.5 426.5 -64 400 -64H48C21.5 -64 0 -42.5 0 -16V244C0 250.6 5.4 256 12 256zM128 52C128 58.6 122.6 64 116 64H76C69.4 64 64 58.6 64 52V12C64 5.4 69.4 0 76 0H116C122.6 0 128 5.4 128 12V52zM128 180C128 186.6 122.6 192 116 192H76C69.4 192 64 186.6 64 180V140C64 133.4 69.4 128 76 128H116C122.6 128 128 133.4 128 140V180zM256 52C256 58.6 250.6 64 244 64H204C197.4 64 192 58.6 192 52V12C192 5.4 197.4 0 204 0H244C250.6 0 256 5.4 256 12V52zM256 180C256 186.6 250.6 192 244 192H204C197.4 192 192 186.6 192 180V140C192 133.4 197.4 128 204 128H244C250.6 128 256 133.4 256 140V180zM384 52C384 58.6 378.6 64 372 64H332C325.4 64 320 58.6 320 52V12C320 5.4 325.4 0 332 0H372C378.6 0 384 5.4 384 12V52zM384 180C384 186.6 378.6 192 372 192H332C325.4 192 320 186.6 320 180V140C320 133.4 325.4 128 332 128H372C378.6 128 384 133.4 384 140V180z" />
+    <glyph glyph-name="calendar-check"
+      unicode="&#xF274;"
+      horiz-adv-x="448" d=" M436 288H12C5.373 288 0 293.373 0 300V336C0 362.51 21.49 384 48 384H96V436C96 442.627 101.373 448 108 448H148C154.627 448 160 442.627 160 436V384H288V436C288 442.627 293.373 448 300 448H340C346.627 448 352 442.627 352 436V384H400C426.51 384 448 362.51 448 336V300C448 293.373 442.627 288 436 288zM12 256H436C442.627 256 448 250.627 448 244V-16C448 -42.51 426.51 -64 400 -64H48C21.49 -64 0 -42.51 0 -16V244C0 250.627 5.373 256 12 256zM345.296 160.053L317.127 188.451C312.4600000000001 193.156 304.862 193.187 300.1570000000001 188.519L194.12 83.335L148.14 129.687C143.473 134.392 135.874 134.423 131.169 129.755L102.772 101.5849999999999C98.067 96.918 98.036 89.3199999999999 102.704 84.6149999999999L185.305 1.3459999999999C189.972 -3.3590000000001 197.57 -3.3900000000001 202.275 1.2779999999999L345.228 143.0829999999999C349.933 147.7499999999999 349.964 155.3479999999999 345.296 160.0529999999999z" />
+    <glyph glyph-name="calendar-minus"
+      unicode="&#xF272;"
+      horiz-adv-x="448" d=" M436 288H12C5.4 288 0 293.4 0 300V336C0 362.5 21.5 384 48 384H96V436C96 442.6 101.4 448 108 448H148C154.6 448 160 442.6 160 436V384H288V436C288 442.6 293.4 448 300 448H340C346.6 448 352 442.6 352 436V384H400C426.5 384 448 362.5 448 336V300C448 293.4 442.6 288 436 288zM12 256H436C442.6 256 448 250.6 448 244V-16C448 -42.5 426.5 -64 400 -64H48C21.5 -64 0 -42.5 0 -16V244C0 250.6 5.4 256 12 256zM316 64C322.6 64 328 69.4 328 76V116C328 122.6 322.6 128 316 128H132C125.4 128 120 122.6 120 116V76C120 69.4 125.4 64 132 64H316z" />
+    <glyph glyph-name="calendar-plus"
+      unicode="&#xF271;"
+      horiz-adv-x="448" d=" M436 288H12C5.4 288 0 293.4 0 300V336C0 362.5 21.5 384 48 384H96V436C96 442.6 101.4 448 108 448H148C154.6 448 160 442.6 160 436V384H288V436C288 442.6 293.4 448 300 448H340C346.6 448 352 442.6 352 436V384H400C426.5 384 448 362.5 448 336V300C448 293.4 442.6 288 436 288zM12 256H436C442.6 256 448 250.6 448 244V-16C448 -42.5 426.5 -64 400 -64H48C21.5 -64 0 -42.5 0 -16V244C0 250.6 5.4 256 12 256zM328 116C328 122.6 322.6 128 316 128H256V188C256 194.6 250.6 200 244 200H204C197.4 200 192 194.6 192 188V128H132C125.4 128 120 122.6 120 116V76C120 69.4 125.4 64 132 64H192V4C192 -2.6 197.4 -8 204 -8H244C250.6 -8 256 -2.6 256 4V64H316C322.6 64 328 69.4 328 76V116z" />
+    <glyph glyph-name="calendar-times"
+      unicode="&#xF273;"
+      horiz-adv-x="448" d=" M436 288H12C5.4 288 0 293.4 0 300V336C0 362.5 21.5 384 48 384H96V436C96 442.6 101.4 448 108 448H148C154.6 448 160 442.6 160 436V384H288V436C288 442.6 293.4 448 300 448H340C346.6 448 352 442.6 352 436V384H400C426.5 384 448 362.5 448 336V300C448 293.4 442.6 288 436 288zM12 256H436C442.6 256 448 250.6 448 244V-16C448 -42.5 426.5 -64 400 -64H48C21.5 -64 0 -42.5 0 -16V244C0 250.6 5.4 256 12 256zM269.3 96L317.4000000000001 144.1C322.1 148.8 322.1 156.4 317.4000000000001 161.1L289.1 189.4C284.4000000000001 194.1 276.8 194.1 272.1 189.4L224 141.3L175.9 189.4C171.2 194.1 163.6 194.1 158.9 189.4L130.6 161.1C125.9 156.4 125.9 148.8 130.6 144.1L178.7 96L130.6 47.9C125.9 43.2 125.9 35.6 130.6 30.9L158.9 2.6C163.6 -2.1 171.2 -2.1 175.9 2.6L224 50.7L272.1 2.6C276.8 -2.1 284.4000000000001 -2.1 289.1 2.6L317.4000000000001 30.9C322.1 35.6 322.1 43.2 317.4000000000001 47.9L269.3 96z" />
+    <glyph glyph-name="calendar"
+      unicode="&#xF133;"
+      horiz-adv-x="448" d=" M12 256H436C442.6 256 448 250.6 448 244V-16C448 -42.5 426.5 -64 400 -64H48C21.5 -64 0 -42.5 0 -16V244C0 250.6 5.4 256 12 256zM448 300V336C448 362.5 426.5 384 400 384H352V436C352 442.6 346.6 448 340 448H300C293.4 448 288 442.6 288 436V384H160V436C160 442.6 154.6 448 148 448H108C101.4 448 96 442.6 96 436V384H48C21.5 384 0 362.5 0 336V300C0 293.4 5.4 288 12 288H436C442.6 288 448 293.4 448 300z" />
+    <glyph glyph-name="camera-retro"
+      unicode="&#xF083;"
+      horiz-adv-x="512" d=" M48 416C21.5 416 0 394.5 0 368V16C0 -10.5 21.5 -32 48 -32H464C490.5 -32 512 -10.5 512 16V368C512 394.5 490.5 416 464 416H48zM48 384H154C157.3 384 160 381.3 160 378V358C160 354.7 157.3 352 154 352H38C34.7 352 32 354.7 32 358V368C32 376.8 39.2 384 48 384zM474 288H38C34.7 288 32 290.7 32 294V330C32 333.3 34.7 336 38 336H176L206.2 381.3C207.3 383 209.2 384 211.2 384H464C472.8 384 480 376.8 480 368V294C480 290.7 477.3 288 474 288zM256 24C189.8 24 136 77.8 136 144S189.8 264 256 264S376 210.2 376 144S322.2 24 256 24zM256 232C207.5 232 168 192.5 168 144S207.5 56 256 56S344 95.5 344 144S304.5 232 256 232zM208 128C199.2 128 192 135.2 192 144C192 179.3 220.7 208 256 208C264.8 208 272 200.8 272 192S264.8 176 256 176C238.4 176 224 161.6 224 144C224 135.2 216.8 128 208 128z" />
+    <glyph glyph-name="camera"
+      unicode="&#xF030;"
+      horiz-adv-x="512" d=" M512 304V16C512 -10.5 490.5 -32 464 -32H48C21.5 -32 0 -10.5 0 16V304C0 330.5 21.5 352 48 352H136L148.3 384.9C155.3 403.6 173.2 416 193.2 416H318.7000000000001C338.7000000000001 416 356.6 403.6 363.6 384.9L376 352H464C490.5 352 512 330.5 512 304zM376 160C376 226.2 322.2 280 256 280S136 226.2 136 160S189.8 40 256 40S376 93.8 376 160zM344 160C344 111.5 304.5 72 256 72S168 111.5 168 160S207.5 248 256 248S344 208.5 344 160z" />
+    <glyph glyph-name="campground"
+      unicode="&#xF6BB;"
+      horiz-adv-x="640" d=" M624 0H599.32L359.54 330.25L412.9500000000001 403.8C418.1400000000001 410.95 416.5600000000001 420.96 409.4100000000001 426.15L383.5100000000001 444.94C376.3600000000001 450.13 366.3600000000001 448.55 361.1600000000001 441.39L320 384.7L278.83 441.4C273.64 448.55 263.63 450.14 256.48 444.95L230.6 426.15C223.45 420.96 221.86 410.95 227.06 403.8L280.47 330.25L40.68 0H16C7.16 0 0 -7.16 0 -16V-48C0 -56.84 7.16 -64 16 -64H624C632.84 -64 640 -56.84 640 -48V-16C640 -7.16 632.84 0 624 0zM320 160L436.36 0H203.64L320 160z" />
+    <glyph glyph-name="cannabis"
+      unicode="&#xF55F;"
+      horiz-adv-x="512" d=" M503.47 87.75C501.91 88.57 471.08 104.64 426.6900000000001 113.56C490.9400000000001 188.68 510.7400000000001 275.23 511.6200000000001 279.2C512.8000000000001 284.53 511.1800000000001 290.1 507.3200000000001 293.9700000000001C504.2900000000001 297.01 500.2 298.67 496.0000000000001 298.67C494.8600000000001 298.67 493.71 298.55 492.5600000000001 298.29C488.6800000000001 297.44 406.0200000000001 278.7 331.98 218.53C331.99 219.99 331.99 221.46 331.99 222.93C331.99 341.72 272.01 436.65 269.4600000000001 440.63A15.973000000000003 15.973000000000003 0 0 1 256 448C250.55 448 245.47 445.2200000000001 242.53 440.63C239.98 436.65 180 341.7200000000001 180 222.93C180 221.46 180.01 219.99 180.01 218.53C105.98 278.69 23.32 297.43 19.43 298.29C18.29 298.54 17.14 298.67 15.99 298.67C11.79 298.67 7.7 297.01 4.67 293.9700000000001A15.986 15.986 0 0 1 0.38 279.2C1.26 275.23 21.06 188.68 85.31 113.56C40.92 104.64 10.1 88.57 8.53 87.75A16.003 16.003 0 0 1 8.51 59.46C10.96 58.17 69.27 27.74 142 27.74C148.14 27.74 153.96 27.84 159.5 28.05C148.13 5.82 142.98 -10.26 142.69 -11.17C140.89 -16.85 142.4 -23.0599999999999 146.6 -27.28A16.019 16.019 0 0 1 162.7 -31.27C164.53 -30.7 200.42 -19.28 240 8.02V-56C240 -60.42 243.58 -64 248 -64H264C268.42 -64 272 -60.42 272 -56V8.01C311.58 -19.29 347.4700000000001 -30.7 349.3 -31.28A16.019 16.019 0 0 1 365.4000000000001 -27.29C369.6 -23.07 371.11 -16.86 369.3100000000001 -11.18C369.0200000000001 -10.27 363.8600000000001 5.81 352.5000000000001 28.04C358.0400000000001 27.83 363.8700000000001 27.73 370.0000000000001 27.73C442.7200000000001 27.73 501.0400000000001 58.16 503.4900000000001 59.4499999999999C508.7300000000001 62.2299999999999 512.0100000000001 67.67 512.0000000000001 73.5999999999999C511.9900000000001 79.5399999999999 508.7100000000001 84.9899999999999 503.4700000000001 87.7499999999999z" />
+    <glyph glyph-name="capsules"
+      unicode="&#xF46B;"
+      horiz-adv-x="576" d=" M555.3 147.9L424.2 335.2C401.9 367 366.4 384 330.4 384C307.8 384 284.9 377.3 264.9 363.3C245.2 349.5 231.2 330.5 223.4 309.5C220.5 368.8 172 416 112 416C50.1 416 0 365.9 0 304V80C0 18.1 50.1 -32 112 -32S224 18.1 224 80V229.1C227.3 220.5 231.3 212 236.8 204.1L368 16.8C390.2 -15 425.7 -32 461.8 -32C484.5 -32 507.3 -25.3 527.3 -11.3C579 24.9 591.5 96.2 555.3 147.9zM160 192H64V304C64 330.5 85.5 352 112 352S160 330.5 160 304V192zM354.8 147.1L289.2000000000001 240.8C281.5000000000001 251.8 278.5000000000001 265.2000000000001 280.9000000000001 278.4C283.2000000000001 291.6 290.6 303.2000000000001 301.6 310.9C310.1 316.9 320.1 320 330.4000000000001 320C346.9000000000001 320 362.3 312 371.7000000000001 298.5L437.3000000000001 204.8L354.8000000000001 147.1z" />
+    <glyph glyph-name="car-alt"
+      unicode="&#xF5DE;"
+      horiz-adv-x="480" d=" M438.6600000000001 235.67L427.42 263.77L407.49 313.6C390.38 356.37 349.57 384 303.5 384H176.5C130.44 384 89.62 356.37 72.51 313.6L52.58 263.77L41.34 235.67C17.22 226.5 0 203.34 0 176V128C0 111.88 6.16 97.33 16 86.07V32C16 14.33 30.33 0 48 0H80C97.67 0 112 14.33 112 32V64H368V32C368 14.33 382.33 0 400 0H432C449.67 0 464 14.33 464 32V86.07C473.84 97.32 480 111.87 480 128V176C480 203.34 462.78 226.5 438.66 235.67zM131.93 289.83C139.22 308.05 156.87 320 176.5 320H303.5C323.13 320 340.78 308.05 348.07 289.83L368 240H112L131.93 289.83zM80 128.2C60.8 128.2 48 140.96 48 160.1S60.8 192 80 192S128 163.29 128 144.15S99.2 128.2 80 128.2zM400 128.2C380.8 128.2 352 125.01 352 144.15S380.8 192 400 192S432 179.24 432 160.1S419.2 128.2000000000001 400 128.2000000000001z" />
+    <glyph glyph-name="car-battery"
+      unicode="&#xF5DF;"
+      horiz-adv-x="512" d=" M480 320H448V368C448 376.8400000000001 440.84 384 432 384H336C327.1600000000001 384 320 376.8400000000001 320 368V320H192V368C192 376.8400000000001 184.84 384 176 384H80C71.16 384 64 376.8400000000001 64 368V320H32C14.33 320 0 305.67 0 288V32C0 14.33 14.33 0 32 0H480C497.67 0 512 14.33 512 32V288C512 305.67 497.67 320 480 320zM192 184C192 179.58 188.42 176 184 176H72C67.58 176 64 179.58 64 184V200C64 204.42 67.58 208 72 208H184C188.42 208 192 204.42 192 200V184zM448 184C448 179.58 444.42 176 440 176H400V136C400 131.58 396.42 128 392 128H376C371.58 128 368 131.58 368 136V176H328C323.58 176 320 179.58 320 184V200C320 204.42 323.58 208 328 208H368V248C368 252.42 371.58 256 376 256H392C396.42 256 400 252.42 400 248V208H440C444.42 208 448 204.42 448 200V184z" />
+    <glyph glyph-name="car-crash"
+      unicode="&#xF5E1;"
+      horiz-adv-x="640" d=" M143.25 227.19L130.83 180.82C127.82 169.57 127.2 157.93 128.42 146.43L93.22 117.45C86.65 112.04 76.91 117.88 78.6 126.22L94.04 202.9C95.1 208.16 91.38 213.18 86.04 213.69L8.18 221.24C-0.29 222.06 -3.05 233.07 4.04 237.78L69.19 281.08C73.65 284.05 74.57 290.23 71.17 294.37L21.46 354.78C16.05 361.35 21.89 371.08 30.24 369.4L106.92 353.9600000000001C112.18 352.9 117.2 356.62 117.72 361.9600000000001L125.27 439.82C126.09 448.3 137.1 451.05 141.82 443.96L185.12 378.82C188.09 374.36 194.27 373.44 198.41 376.8400000000001L258.81 426.55C265.38 431.96 275.11 426.12 273.43 417.7800000000001L262.1 361.62C259.3900000000001 358.57 256.67 355.53 254.1900000000001 352.2200000000001L222.04 309.25L211.33 294.93C178.6 286.17 152.15 260.4 143.25 227.19zM637.8199999999999 94.68L625.4 48.32C622.27 36.64 616.02 26.71 607.85 18.96A66.876 66.876 0 0 0 599.09 11.96L585.1 -40.27C583.96 -44.54 582 -48.3700000000001 579.45 -51.65C571.7800000000001 -61.49 558.71 -66.33 545.9100000000001 -62.9L515 -54.62C497.93 -50.05 487.8 -32.5 492.37 -15.43L500.65 15.48L253.37 81.74L245.09 50.83C240.52 33.76 222.97 23.63 205.9 28.2L174.99 36.48C162.19 39.91 153.29 50.64 151.57 62.99C151 67.11 151.22 71.41 152.36 75.67L166.35 127.9A66.62 66.62 0 0 0 162.26 138.35C159.06 149.14 158.61 160.87 161.74 172.55L174.16 218.92C179.47 238.72 193.52 253.75 211.05 261.13A64.336 64.336 0 0 0 229.54 265.85L247.67 290.08L279.82 333.05C283.2699999999999 337.66 287.01 341.95 291.0199999999999 345.89C299.0199999999999 353.78 308.05 360.33 317.76 365.4C322.62 367.94 327.6499999999999 370.11 332.81 371.89C343.14 375.47 354 377.52 365.05 377.93C376.1 378.34 387.36 377.11 398.48 374.13L521.16 341.26C532.28 338.28 542.64 333.72 552.01 327.83A111.10999999999999 111.10999999999999 0 0 0 586.7 293.33C595.5200000000001 279.45 601.34 263.49 603.38 246.34L609.74 193.05L613.33 163A64.49 64.49 0 0 0 636.07 133.0699999999999C640.46 121.19 641.36 107.8799999999999 637.82 94.68zM255.58 213.66C237.03 218.63 221.37 209.62 216.41 191.13C211.45 172.64 220.5200000000001 157.01 239.0600000000001 152.04C257.61 147.0699999999999 284.6 136.53 289.55 155.02C294.51 173.51 274.12 208.69 255.58 213.66zM546.19 185.49L539.83 238.78C539.25 243.65 537.94 248.31 536.01 252.64C530.21 265.63 518.81 275.65 504.59 279.46L381.91 312.33A48.008 48.008 0 0 1 331.05 294.72L298.9 251.75L470.9 205.67L546.1899999999999 185.49zM564.6800000000001 130.84C546.1300000000001 135.8100000000001 510.8800000000001 115.53 505.9300000000001 97.05C500.9800000000001 78.56 529.6200000000001 74.19 548.1700000000001 69.22C566.72 64.25 582.3800000000001 73.2600000000001 587.34 91.75C592.2900000000001 110.23 583.23 125.87 564.6800000000001 130.84z" />
+    <glyph glyph-name="car-side"
+      unicode="&#xF5E4;"
+      horiz-adv-x="640" d=" M544 256H528L419.2200000000001 391.98A64.025 64.025 0 0 1 369.24 416H155.33C129.16 416 105.63 400.07 95.91 375.77L48 253.74C20.44 246.6 0 221.79 0 192V80C0 71.16 7.16 64 16 64H64C64 10.98 106.98 -32 160 -32S256 10.98 256 64H384C384 10.98 426.98 -32 480 -32S576 10.98 576 64H624C632.84 64 640 71.16 640 80V160C640 213.02 597.02 256 544 256zM160 16C133.53 16 112 37.53 112 64S133.53 112 160 112S208 90.47 208 64S186.47 16 160 16zM232 256H116.93L155.33 352H232V256zM280 256V352H369.24L446.04 256H280zM480 16C453.53 16 432 37.53 432 64S453.53 112 480 112S528 90.47 528 64S506.47 16 480 16z" />
+    <glyph glyph-name="car"
+      unicode="&#xF1B9;"
+      horiz-adv-x="512" d=" M499.99 272H440.12L423.48 313.6C406.38 356.37 365.57 384 319.5 384H192.5C146.44 384 105.62 356.37 88.51 313.6L71.87 272H12.01C4.2 272 -1.53 264.66 0.37 257.0900000000001L6.37 233.09C7.7 227.75 12.5 224 18.01 224H38.08C24.65 212.27 16 195.22 16 176V128C16 111.88 22.16 97.33 32 86.07V32C32 14.33 46.33 0 64 0H96C113.67 0 128 14.33 128 32V64H384V32C384 14.33 398.33 0 416 0H448C465.67 0 480 14.33 480 32V86.07C489.84 97.32 496 111.87 496 128V176C496 195.22 487.35 212.27 473.93 224H494C499.51 224 504.31 227.75 505.64 233.09L511.64 257.0900000000001C513.53 264.66 507.8 272 499.99 272zM147.93 289.83C155.22 308.05 172.87 320 192.5 320H319.5C339.13 320 356.78 308.05 364.07 289.83L384 240H128L147.93 289.83zM96 128.2C76.8 128.2 64 140.96 64 160.1S76.8 192 96 192S144 163.29 144 144.15S115.2 128.2 96 128.2zM416 128.2C396.8 128.2 368 125.01 368 144.15S396.8 192 416 192S448 179.24 448 160.1S435.2 128.2000000000001 416 128.2000000000001z" />
+    <glyph glyph-name="caret-down"
+      unicode="&#xF0D7;"
+      horiz-adv-x="320" d=" M31.3 256H288.6C306.4000000000001 256 315.3 234.5 302.7000000000001 221.9L174.1 93.2C166.3 85.4 153.6 85.4 145.8 93.2L17.2 221.9C4.6 234.5 13.5 256 31.3 256z" />
+    <glyph glyph-name="caret-left"
+      unicode="&#xF0D9;"
+      horiz-adv-x="192" d=" M192 320.6620000000001V63.338C192 45.52 170.457 36.597 157.858 49.196L29.196 177.858C21.386 185.668 21.386 198.332 29.196 206.142L157.858 334.804C170.457 347.404 192 338.48 192 320.6620000000001z" />
+    <glyph glyph-name="caret-right"
+      unicode="&#xF0DA;"
+      horiz-adv-x="192" d=" M0 63.338V320.6620000000001C0 338.48 21.543 347.403 34.142 334.804L162.804 206.142C170.614 198.332 170.614 185.668 162.804 177.858L34.142 49.196C21.543 36.596 0 45.52 0 63.338z" />
+    <glyph glyph-name="caret-square-down"
+      unicode="&#xF150;"
+      horiz-adv-x="448" d=" M448 368V16C448 -10.5 426.5 -32 400 -32H48C21.5 -32 0 -10.5 0 16V368C0 394.5 21.5 416 48 416H400C426.5 416 448 394.5 448 368zM92.5 227.5L215.5 104.5C220.2 99.8 227.8 99.8 232.5 104.5L355.5 227.5C363.1 235.1 357.7 248 347 248H101C90.3 248 84.9 235.1 92.5 227.5z" />
+    <glyph glyph-name="caret-square-left"
+      unicode="&#xF191;"
+      horiz-adv-x="448" d=" M400 -32H48C21.49 -32 0 -10.51 0 16V368C0 394.51 21.49 416 48 416H400C426.51 416 448 394.51 448 368V16C448 -10.51 426.51 -32 400 -32zM259.515 323.515L136.485 200.485C131.799 195.799 131.799 188.201 136.485 183.514L259.514 60.485C267.074 52.925 279.999 58.279 279.999 68.97V315.029C280 325.7200000000001 267.074 331.074 259.5150000000001 323.515z" />
+    <glyph glyph-name="caret-square-right"
+      unicode="&#xF152;"
+      horiz-adv-x="448" d=" M48 416H400C426.51 416 448 394.51 448 368V16C448 -10.51 426.51 -32 400 -32H48C21.49 -32 0 -10.51 0 16V368C0 394.51 21.49 416 48 416zM188.485 60.485L311.514 183.514C316.2 188.2 316.2 195.798 311.514 200.485L188.485 323.515C180.925 331.0750000000001 168 325.721 168 315.0300000000001V68.971C168 58.28 180.926 52.926 188.485 60.485z" />
+    <glyph glyph-name="caret-square-up"
+      unicode="&#xF151;"
+      horiz-adv-x="448" d=" M0 16V368C0 394.51 21.49 416 48 416H400C426.51 416 448 394.51 448 368V16C448 -10.51 426.51 -32 400 -32H48C21.49 -32 0 -10.51 0 16zM355.515 156.485L232.485 279.515C227.799 284.201 220.201 284.201 215.514 279.515L92.485 156.485C84.925 148.925 90.279 136 100.97 136H347.029C357.72 136 363.074 148.926 355.515 156.485z" />
+    <glyph glyph-name="caret-up"
+      unicode="&#xF0D8;"
+      horiz-adv-x="320" d=" M288.662 96H31.338C13.52 96 4.597 117.543 17.196 130.142L145.858 258.804C153.668 266.6140000000001 166.332 266.6140000000001 174.142 258.804L302.804 130.142C315.404 117.543 306.48 96 288.662 96z" />
+    <glyph glyph-name="cart-arrow-down"
+      unicode="&#xF218;"
+      horiz-adv-x="576" d=" M504.717 128H211.572L218.117 96H486.535C501.936 96 513.351 81.699 509.938 66.681L504.421 42.405C523.112 33.332 536 14.172 536 -8C536 -39.202 510.481 -64.444 479.176 -63.994C449.353 -63.565 424.826 -39.3630000000001 424.021 -9.547C423.581 6.74 430.106 21.502 440.824 32.001H231.176C241.553 21.835 248 7.674 248 -8C248 -39.813 221.472 -65.431 189.33 -63.938C160.79 -62.613 137.579 -39.553 136.079 -11.021C134.921 11.013 146.515 30.434 164.13 40.5650000000001L93.883 384H24C10.745 384 0 394.745 0 408V424C0 437.255 10.745 448 24 448H126.529C137.93 448 147.757 439.979 150.042 428.81L159.208 384H551.99C567.391 384 578.806 369.699 575.393 354.681L528.12 146.681C525.637 135.754 515.923 128 504.717 128zM403.029 256H360V316C360 322.627 354.627 328 348 328H324C317.373 328 312 322.627 312 316V256H268.971C258.2800000000001 256 252.926 243.074 260.486 235.515L327.515 168.486C332.201 163.8 339.799 163.8 344.486 168.486L411.515 235.515C419.074 243.074 413.72 256 403.029 256z" />
+    <glyph glyph-name="cart-plus"
+      unicode="&#xF217;"
+      horiz-adv-x="576" d=" M504.717 128H211.572L218.117 96H486.535C501.936 96 513.351 81.699 509.938 66.681L504.421 42.405C523.112 33.332 536 14.172 536 -8C536 -39.202 510.481 -64.444 479.176 -63.994C449.353 -63.565 424.826 -39.3630000000001 424.021 -9.547C423.581 6.74 430.106 21.502 440.824 32.001H231.176C241.553 21.835 248 7.674 248 -8C248 -39.813 221.472 -65.431 189.33 -63.938C160.79 -62.613 137.579 -39.553 136.079 -11.021C134.921 11.013 146.515 30.434 164.13 40.5650000000001L93.883 384H24C10.745 384 0 394.745 0 408V424C0 437.255 10.745 448 24 448H126.529C137.93 448 147.757 439.979 150.042 428.81L159.208 384H551.99C567.391 384 578.806 369.699 575.393 354.681L528.12 146.681C525.637 135.754 515.923 128 504.717 128zM408 280H360V320C360 328.837 352.837 336 344 336H328C319.163 336 312 328.837 312 320V280H264C255.163 280 248 272.837 248 264V248C248 239.163 255.163 232 264 232H312V192C312 183.163 319.163 176 328 176H344C352.837 176 360 183.163 360 192V232H408C416.837 232 424 239.163 424 248V264C424 272.837 416.837 280 408 280z" />
+    <glyph glyph-name="cat"
+      unicode="&#xF6BE;"
+      horiz-adv-x="512" d=" M290.59 256C270.41 256 183.77 254.02 128 170.05V256C128 308.94 84.94 352 32 352C14.33 352 0 337.67 0 320S14.33 288 32 288C49.64 288 64 273.64 64 256V0C64 -35.3 92.7 -64 128 -64H304C312.84 -64 320 -56.84 320 -48V-32C320 -14.33 305.67 0 288 0H256L384 96V-48C384 -56.84 391.1600000000001 -64 400 -64H432C440.84 -64 448 -56.84 448 -48V158.14C437.71 155.47 427.11 153.6 416 153.6C354.19 153.6 302.48 197.65 290.5900000000001 256zM448 352H384L320 416V281.6C320 228.58 362.98 185.6 416 185.6S512 228.58 512 281.6V416L448 352zM376 272C367.1600000000001 272 360 279.16 360 288S367.1600000000001 304 376 304S392 296.8400000000001 392 288S384.84 272 376 272zM456 272C447.1600000000001 272 440 279.16 440 288S447.1600000000001 304 456 304S472 296.8400000000001 472 288S464.84 272 456 272z" />
+    <glyph glyph-name="certificate"
+      unicode="&#xF0A3;"
+      horiz-adv-x="512" d=" M458.622 192.08L504.607 237.085C518.315 250.062 511.923 273.124 493.943 277.424L431.2930000000001 293.414L448.9540000000001 355.4290000000001C453.9450000000001 373.2670000000001 437.1250000000001 390.092 419.2930000000001 385.1L357.2990000000001 367.433L341.3150000000001 430.1040000000001C337.085 447.803 313.765 454.276 300.99 440.772L256 394.43L211.011 440.771C198.381 454.122 174.964 448.005 170.686 430.103L154.702 367.432L92.707 385.099C74.87 390.093 58.056 373.262 63.046 355.428L80.707 293.413L18.057 277.423C0.069 273.122 -6.31 250.056 7.392 237.085L53.377 192.08L7.392 147.076C-6.316 134.0990000000001 0.076 111.037 18.056 106.737L80.706 90.747L63.045 28.732C58.054 10.894 74.874 -5.931 92.706 -0.939L154.7 16.7280000000001L170.684 -45.9429999999999C175.123 -64.5179999999999 198.38 -69.9609999999999 211.009 -56.6109999999999L256 -10.61L300.989 -56.611C313.489 -70.0989999999999 336.976 -64.097 341.314 -45.943L357.298 16.728L419.2919999999999 -0.939C437.128 -5.933 453.9429999999999 10.898 448.9529999999999 28.732L431.2919999999999 90.747L493.9419999999999 106.737C511.9289999999999 111.0390000000001 518.3079999999999 134.1040000000001 504.6059999999999 147.076L458.6219999999999 192.08z" />
+    <glyph glyph-name="chair"
+      unicode="&#xF6C0;"
+      horiz-adv-x="448" d=" M446.33 106.12L435.66 138.12A31.996 31.996 0 0 1 405.3 160H42.69C28.92 160 16.69 151.19 12.33 138.12L1.66 106.12C-5.24 85.4 10.18 64 32.03 64H32V-48C32 -56.84 39.16 -64 48 -64H80C88.84 -64 96 -56.84 96 -48V64H352V-48C352 -56.84 359.1600000000001 -64 368 -64H400C408.84 -64 416 -56.84 416 -48V64H415.9700000000001C437.8200000000001 64 453.24 85.4 446.3300000000001 106.12zM112 320C112 349.48 128.2 374.99 152 388.87V192H200V400H248V192H296V388.87C319.8 374.99 336 349.48 336 320V192H384V320C384 390.69 326.69 448 256 448H192C121.31 448 64 390.69 64 320V192H112V320z" />
+    <glyph glyph-name="chalkboard-teacher"
+      unicode="&#xF51C;"
+      horiz-adv-x="640" d=" M208 96C205.61 96 203.22 95.65 200.94 94.91C187.98 90.7 174.35 88 160 88C145.65 88 132.02 90.7 119.05 94.91C116.77 95.65 114.39 96 112 96C49.94 96 -0.33 45.52 0 -16.62C0.14 -42.88 21.73 -64 48 -64H272C298.27 -64 319.86 -42.88 320 -16.62C320.33 45.52 270.06 96 208 96zM160 128C213.02 128 256 170.98 256 224S213.02 320 160 320S64 277.02 64 224S106.98 128 160 128zM592 448H208C181.53 448 160 425.75 160 398.41V352C183.42 352 205.1 345.2200000000001 224 334.2V384H576V96H512V160H384V96H307.76C326.86 79.31 340.88 57.27 347.45 32H592C618.47 32 640 54.25 640 81.59V398.41C640 425.75 618.47 448 592 448z" />
+    <glyph glyph-name="chalkboard"
+      unicode="&#xF51B;"
+      horiz-adv-x="640" d=" M96 384H544V32H608V408C608 430.06 590.06 448 568 448H72C49.94 448 32 430.06 32 408V32H96V384zM624 0H480V64H288V0H16C7.16 0 0 -7.16 0 -16V-48C0 -56.84 7.16 -64 16 -64H624C632.84 -64 640 -56.84 640 -48V-16C640 -7.16 632.84 0 624 0z" />
+    <glyph glyph-name="charging-station"
+      unicode="&#xF5E7;"
+      horiz-adv-x="576" d=" M336 0H16C7.16 0 0 -7.16 0 -16V-48C0 -56.84 7.16 -64 16 -64H336C344.84 -64 352 -56.84 352 -48V-16C352 -7.16 344.84 0 336 0zM544 320V368C544 376.8400000000001 536.84 384 528 384S512 376.8400000000001 512 368V320H480V368C480 376.8400000000001 472.84 384 464 384S448 376.8400000000001 448 368V320H432C423.1600000000001 320 416 312.8400000000001 416 304V272C416 236.24 439.62 206.31 472 196.07V77.58C472 63.63 462.5 50.66 448.74 48.39C431.2200000000001 45.5 416 59.01 416 76V104C416 152.6 376.6 192 328 192H320V384C320 419.35 291.35 448 256 448H96C60.65 448 32 419.35 32 384V32H320V144H328C350.09 144 368 126.09 368 104V79.39C368 39.72 396.92 4.23 436.41 0.38C481.71 -4.05 520 31.59 520 76V196.07C552.38 206.31 576 236.24 576 272V304C576 312.8400000000001 568.84 320 560 320H544zM260.09 272.24L166.39 133.24C164.19 129.91 160.18 128 156 128C148.33 128 142.53 134.28 144.33 140.92L167.35 224H108C100.75 224 95.15 229.59 96.11 235.89L112.11 342.89C112.9 348.1 117.98 352 124 352H192C199.88 352 205.62 345.46 203.6 338.79L192 288H249.7C258.94 288 264.71 279.2200000000001 260.09 272.24z" />
+    <glyph glyph-name="chart-area"
+      unicode="&#xF1FE;"
+      horiz-adv-x="512" d=" M500 64C506.6 64 512 58.6 512 52V12C512 5.4 506.6 0 500 0H12C5.4 0 0 5.4 0 12V372C0 378.6 5.4 384 12 384H52C58.6 384 64 378.6 64 372V64H500zM372.7 288.5L288 232L202.7 345.7C197.6 352.5 187.2 352 182.8 344.7L96 200V96H480L390.1 283.8C386.9000000000001 290.3 378.7000000000001 292.5 372.7000000000001 288.5z" />
+    <glyph glyph-name="chart-bar"
+      unicode="&#xF080;"
+      horiz-adv-x="512" d=" M332.8 128H371.2C377.6 128 384 134.4 384 140.8V275.2C384 281.6 377.6 288 371.2 288H332.8C326.4000000000001 288 320 281.6 320 275.2V140.8C320 134.4 326.4 128 332.8 128zM428.8 128H467.2C473.6 128 480 134.4 480 140.8V371.2C480 377.6 473.6 384 467.2 384H428.8C422.4000000000001 384 416 377.6 416 371.2V140.8C416 134.4 422.4 128 428.8 128zM140.8 128H179.2C185.6 128 192 134.4 192 140.8V211.2C192 217.6 185.6 224 179.2 224H140.8C134.4 224 128 217.6 128 211.2V140.8C128 134.4 134.4 128 140.8 128zM236.8 128H275.2C281.6 128 288 134.4 288 140.8V339.2C288 345.6 281.6 352 275.2 352H236.8C230.4 352 224 345.6 224 339.2V140.8C224 134.4 230.4 128 236.8 128zM496 64H64V368C64 376.8400000000001 56.84 384 48 384H16C7.16 384 0 376.8400000000001 0 368V32C0 14.33 14.33 0 32 0H496C504.84 0 512 7.16 512 16V48C512 56.84 504.84 64 496 64z" />
+    <glyph glyph-name="chart-line"
+      unicode="&#xF201;"
+      horiz-adv-x="512" d=" M496 64H64V368C64 376.8400000000001 56.84 384 48 384H16C7.16 384 0 376.8400000000001 0 368V32C0 14.33 14.33 0 32 0H496C504.84 0 512 7.16 512 16V48C512 56.84 504.84 64 496 64zM464 352H345.94C324.56 352 313.85 326.15 328.9700000000001 311.03L361.37 278.63L288 205.25L214.63 278.62C202.13 291.12 181.87 291.12 169.38 278.62L100.69 209.93C94.44 203.68 94.44 193.55 100.69 187.3L123.31 164.68C129.56 158.43 139.69 158.43 145.94 164.68L192 210.75L265.37 137.38C277.87 124.88 298.13 124.88 310.62 137.38L406.62 233.38L439.02 200.98C454.14 185.86 479.99 196.57 479.99 217.95V336C480 344.8400000000001 472.84 352 464 352z" />
+    <glyph glyph-name="chart-pie"
+      unicode="&#xF200;"
+      horiz-adv-x="544" d=" M527.79 160H290.5L448.53 1.97C454.57 -4.07 464.51 -4.5599999999999 470.72 1.29C509.42 37.75 536.04 86.9 543.8499999999999 142.15C545.1899999999999 151.61 537.3399999999999 160.0000000000001 527.79 160.0000000000001zM511.96 224.8C503.72 344.26 408.26 439.7200000000001 288.8 447.96C279.68 448.59 272 440.9 272 431.76V208H495.77C504.91 208 512.59 215.68 511.96 224.8zM224 160V397.29C224 406.8400000000001 215.61 414.69 206.16 413.35C86.99 396.51 -4.1 292.4 0.14 167.63C4.5 39.49 114.83 -65.59 243.03 -63.98C293.43 -63.35 340 -47.11 378.29 -19.95C386.19 -14.35 386.71 -2.72 379.86 4.1299999999999L224 160z" />
+    <glyph glyph-name="check-circle"
+      unicode="&#xF058;"
+      horiz-adv-x="512" d=" M504 192C504 55.033 392.967 -56 256 -56S8 55.033 8 192S119.033 440 256 440S504 328.967 504 192zM227.314 60.686L411.314 244.686C417.562 250.934 417.562 261.065 411.314 267.313L388.687 289.94C382.439 296.189 372.308 296.189 366.059 289.94L216 139.882L145.941 209.941C139.693 216.189 129.562 216.189 123.313 209.941L100.686 187.314C94.438 181.066 94.438 170.935 100.686 164.687L204.686 60.687C210.935 54.438 221.065 54.438 227.314 60.686z" />
+    <glyph glyph-name="check-double"
+      unicode="&#xF560;"
+      horiz-adv-x="512" d=" M504.5 276.05L468.3 312.4600000000001C458.3 322.51 442.0900000000001 322.51 432.1 312.4600000000001L192 70.98L79.9 183.72C69.9 193.78 53.69 193.78 43.7 183.72L7.5 147.31C-2.5 137.26 -2.5 120.95 7.5 110.9L173.9 -56.46C183.9 -66.52 200.11 -66.52 210.1 -56.46L504.5 239.63C514.5 249.69 514.5 265.99 504.5 276.05zM166.57 165.29C173.41 158.2700000000001 184.75 158.2700000000001 191.78 165.11L403.85 375.38C410.87 382.2200000000001 410.87 393.56 404.0300000000001 400.59L362.08 442.71C355.24 449.73 343.9 449.73 336.87 442.89L179.71 286.81L111.48 355.58C104.64 362.6 93.3 362.6 86.28 355.76L44.15 313.99C37.13 307.15 37.13 295.81 43.97 288.79L166.57 165.29z" />
+    <glyph glyph-name="check-square"
+      unicode="&#xF14A;"
+      horiz-adv-x="448" d=" M400 -32H48C21.49 -32 0 -10.51 0 16V368C0 394.51 21.49 416 48 416H400C426.51 416 448 394.51 448 368V16C448 -10.51 426.51 -32 400 -32zM195.314 66.059L379.314 250.059C385.562 256.307 385.562 266.438 379.314 272.686L356.687 295.313C350.439 301.561 340.308 301.562 334.059 295.313L184 145.255L113.941 215.314C107.693 221.562 97.562 221.562 91.313 215.314L68.686 192.687C62.438 186.439 62.438 176.308 68.686 170.06L172.686 66.06C178.935 59.81 189.065 59.81 195.314 66.059z" />
+    <glyph glyph-name="check"
+      unicode="&#xF00C;"
+      horiz-adv-x="512" d=" M173.898 8.596L7.498 174.996C-2.499 184.993 -2.499 201.202 7.498 211.2L43.701 247.404C53.698 257.402 69.908 257.402 79.905 247.404L192 135.31L432.0950000000001 375.404C442.0920000000001 385.401 458.302 385.401 468.299 375.404L504.5020000000001 339.2C514.499 329.203 514.499 312.9940000000001 504.5020000000001 302.996L210.1020000000001 8.595C200.1040000000001 -1.402 183.8950000000001 -1.402 173.8980000000001 8.5959999999999z" />
+    <glyph glyph-name="chess-bishop"
+      unicode="&#xF43A;"
+      horiz-adv-x="320" d=" M123.158 370.1190000000001C107.369 375.4700000000001 96 390.403 96 408C96 430.091 113.909 448 136 448H183.796C205.887 448 223.796 430.091 223.796 408C223.796 390.459 212.501 375.5660000000001 196.791 370.171C220.784 353.514 245.368 323.332 265.4940000000001 288.121L144.929 167.557A6 6 0 0 1 144.929 159.072L159.071 144.93A6 6 0 0 1 167.556 144.93L280.9 258.274C298.658 219.977 310.271 178.831 310.271 144.001C310.271 90.215 287.374 68.213 251.825 57.968V0H68.174V57.97C32.631 68.216 9.739 90.219 9.739 144C9.739 222.029 68.02 331.766 123.158 370.1190000000001zM320 -52V-28C320 -21.373 314.627 -16 308 -16H12C5.373 -16 0 -21.373 0 -28V-52C0 -58.627 5.373 -64 12 -64H308C314.627 -64 320 -58.627 320 -52z" />
+    <glyph glyph-name="chess-board"
+      unicode="&#xF43C;"
+      horiz-adv-x="512" d=" M256 192V128H192V192H256zM256 448H192V384H256V448zM256 192H320V256H256V192zM384 448H320V384H384V448zM384 -64H448V0H384V-64zM512 0V64H448V0H512zM128 -64H192V0H128V-64zM128 448H64V384H128V448zM512 256V320H448V256H512zM512 128V192H448V128H512zM0 -64H64V0H0V-64zM0 384V320H64V384H0zM0 256V192H64V256H0zM0 128V64H64V128H0zM256 -64H320V0H256V-64zM192 64V0H256V64H192zM256 256V320H192V256H256zM64 64V0H128V64H64zM128 192H64V128H128V192zM384 64H448V128H384V64zM512 448H448V384H512V448zM384 192H448V256H384V192zM384 384V320H448V384H384zM320 64V0H384V64H320zM128 128V64H192V128H128zM256 128V64H320V128H256zM192 256H128V192H192V256zM128 320H64V256H128V320zM320 128H384V192H320V128zM192 320V384H128V320H192zM320 320V384H256V320H320zM320 256H384V320H320V256z" />
+    <glyph glyph-name="chess-king"
+      unicode="&#xF43F;"
+      horiz-adv-x="448" d=" M416 -28V-52C416 -58.627 410.627 -64 404 -64H44C37.373 -64 32 -58.627 32 -52V-28C32 -21.373 37.373 -16 44 -16H404C410.627 -16 416 -21.373 416 -28zM407.967 296H248V344H298A6 6 0 0 1 304 350V386A6 6 0 0 1 298 392H248V442A6 6 0 0 1 242 448H206A6 6 0 0 1 200 442V392H150A6 6 0 0 1 144 386V350A6 6 0 0 1 150 344H200V296H40.033C12.459 296 -6.846 268.756 2.295 242.741L87.582 0H360.418L445.7050000000001 242.741C454.846 268.756 435.541 296 407.967 296z" />
+    <glyph glyph-name="chess-knight"
+      unicode="&#xF441;"
+      horiz-adv-x="384" d=" M352 224V0H32V46.557C32 76.859 49.12 104.56 76.223 118.111L133.466 146.733A48 48 0 0 1 160 189.666V240L137.873 228.937A23.996 23.996 0 0 1 125.323 213.292L113.488 165.954A12 12 0 0 0 106.303 157.723L76.702 145.8830000000001A11.998 11.998 0 0 0 67.372 146.059L7.126 172.833A12 12 0 0 0 0 183.799V342.059C0 348.424 2.529 354.529 7.03 359.03L16 368L1.789 396.4220000000001A16.937 16.937 0 0 0 0 404C0 410.627 5.373 416 12 416H160C266.039 416 352 330.039 352 224zM372 -16H12C5.373 -16 0 -21.373 0 -28V-52C0 -58.627 5.373 -64 12 -64H372C378.627 -64 384 -58.627 384 -52V-28C384 -21.373 378.627 -16 372 -16zM52 320C40.954 320 32 311.046 32 300S40.954 280 52 280S72 288.954 72 300S63.046 320 52 320z" />
+    <glyph glyph-name="chess-pawn"
+      unicode="&#xF443;"
+      horiz-adv-x="320" d=" M264 0H56S116 42.743 116 176H84C77.373 176 72 181.373 72 188V212C72 218.627 77.373 224 84 224H124.209C95.721 237.44 76 266.4120000000001 76 300C76 346.392 113.608 384 160 384S244 346.392 244 300C244 266.4120000000001 224.279 237.44 195.791 224H236C242.627 224 248 218.627 248 212V188C248 181.373 242.627 176 236 176H204C204 42.743 264 0 264 0zM292 -16H28C21.373 -16 16 -21.373 16 -28V-52C16 -58.627 21.373 -64 28 -64H292C298.627 -64 304 -58.627 304 -52V-28C304 -21.373 298.627 -16 292 -16z" />
+    <glyph glyph-name="chess-queen"
+      unicode="&#xF445;"
+      horiz-adv-x="512" d=" M436 -64H76C69.373 -64 64 -58.627 64 -52V-28C64 -21.373 69.373 -16 76 -16H436C442.627 -16 448 -21.373 448 -28V-52C448 -58.627 442.627 -64 436 -64zM255.579 448C224.651 448 199.579 422.928 199.579 392S224.651 336 255.579 336S311.579 361.072 311.579 392S286.507 448 255.579 448zM460.147 293.366C454.3790000000001 296.411 447.2310000000001 294.298 444.0650000000001 288.596C435.4490000000001 273.0800000000001 421.3180000000001 250.795 400.0000000000001 250.795C371.2860000000001 250.795 369.3750000000001 270.5990000000001 368.3140000000001 308.337C368.1310000000001 314.829 362.8130000000001 320.0010000000001 356.3190000000001 320.0010000000001H315.3130000000001C310.1380000000001 320.0010000000001 305.5590000000001 316.673 303.9250000000002 311.7630000000001C295.0350000000002 285.0540000000001 277.8520000000002 270.7710000000001 256.0000000000001 270.7710000000001S216.9660000000001 285.0540000000001 208.0750000000001 311.7630000000001C206.4410000000001 316.673 201.8620000000001 320.0010000000001 196.6870000000001 320.0010000000001H155.6820000000001C149.1870000000001 320.0010000000001 143.8690000000001 314.827 143.6870000000001 308.3340000000001C142.6350000000001 270.692 140.7530000000001 250.795 111.9990000000001 250.795C91.3080000000001 250.795 78.1820000000001 271.019 67.5740000000001 288.8200000000001C64.3080000000001 294.3 57.3160000000001 296.2510000000001 51.6750000000001 293.273L12.4960000000001 272.5940000000001A12 12 0 0 1 6.9860000000001 257.449L112 0H400L505.014 257.448A12 12 0 0 1 499.504 272.593L460.147 293.366z" />
+    <glyph glyph-name="chess-rook"
+      unicode="&#xF447;"
+      horiz-adv-x="384" d=" M81.241 232.973C80.957 189.08 77.411 99.924 48 0H335.9820000000001C306.5820000000001 99.604 303.0460000000001 188.912 302.761 232.975L348.1790000000001 275.287A11.998 11.998 0 0 1 351.999 284.067V404C351.999 410.627 346.6260000000001 416 339.999 416H299.999C293.372 416 287.999 410.627 287.999 404V360H239.999V404C239.999 410.627 234.6260000000001 416 227.999 416H155.999C149.372 416 143.999 410.627 143.999 404V360H96V404C96 410.627 90.627 416 84 416H44C37.373 416 32 410.627 32 404V284.068C32 280.738 33.384 277.558 35.82 275.288L81.241 232.973zM160 192C160 209.673 174.327 224 192 224C209.673 224 224 209.673 224 192V127.996H160V192zM384 -28V-52C384 -58.627 378.627 -64 372 -64H12C5.373 -64 0 -58.627 0 -52V-28C0 -21.373 5.373 -16 12 -16H372C378.627 -16 384 -21.373 384 -28z" />
+    <glyph glyph-name="chess"
+      unicode="&#xF439;"
+      horiz-adv-x="512" d=" M199.821 230.367A6 6 0 0 0 205.821 224.367L205.82 203.601A6 6 0 0 0 199.82 197.601H174.015C174.015 165.886 171.862 101.4 191.413 44.372H64.597C84.54 102.548 82.02 168.755 82.02 197.601H56.183A6 6 0 0 0 50.183 203.601L50.185 224.367A6 6 0 0 0 56.185 230.367H199.821zM41.554 332.9120000000001L76.134 237.841H179.868L214.44 332.913C217.286 340.7390000000001 211.49 349.014 203.162 349.014H152V379.2340000000001H173.57A6 6 0 0 1 179.57 385.2340000000001V407.617A6 6 0 0 1 173.57 413.617H145.203V442A6 6 0 0 1 139.203 448H116.82A6 6 0 0 1 110.82 442V413.617H82.421A6 6 0 0 1 76.421 407.617V385.2340000000001A6 6 0 0 1 82.421 379.2340000000001H104V349.015H52.831C44.503 349.014 38.707 340.7390000000001 41.554 332.9120000000001zM222.678 2.83V30.897A6 6 0 0 1 216.678 36.897H39.322A6 6 0 0 1 33.322 30.897V2.83L11.174 -11.334A6 6 0 0 1 8.407 -16.389V-58A6 6 0 0 1 14.407 -64H241.594A6 6 0 0 1 247.594 -58V-16.388A6 6 0 0 1 244.8270000000001 -11.333L222.678 2.83zM313.256 147.055L338.136 130.0919999999999C338.226 111.9679999999999 337.969 66.1879999999999 326.231 15.5699999999999H473.757C462.044 66.045 461.788 111.894 461.875 130.107L486.734 147.056A3.856 3.856 0 0 1 488.418 150.243V220.144A3.8569999999999998 3.8569999999999998 0 0 1 484.561 224.001H456.9059999999999A3.8569999999999998 3.8569999999999998 0 0 1 453.0489999999999 220.144V188.63H430.3119999999999V220.144A3.8569999999999998 3.8569999999999998 0 0 1 426.4539999999999 224.001H373.5359999999999A3.8569999999999998 3.8569999999999998 0 0 1 369.6789999999999 220.144V188.63H346.9419999999999V220.144A3.8569999999999998 3.8569999999999998 0 0 1 343.0849999999998 224.001H315.43A3.8569999999999998 3.8569999999999998 0 0 1 311.573 220.144V150.243A3.8549999999999995 3.8549999999999995 0 0 1 313.256 147.055zM384.841 95.149C384.841 103.521 391.628 110.307 400 110.307S415.158 103.52 415.158 95.149V64.831H384.841V95.149zM504.62 -22.059L490.956 -11.42V4.132A3.8569999999999998 3.8569999999999998 0 0 1 487.099 7.989H312.915A3.8569999999999998 3.8569999999999998 0 0 1 309.058 4.132V-11.42L295.381 -22.059A3.8569999999999998 3.8569999999999998 0 0 1 293.893 -25.103V-60.142A3.8569999999999998 3.8569999999999998 0 0 1 297.75 -63.999H502.25A3.8569999999999998 3.8569999999999998 0 0 1 506.107 -60.142V-25.102A3.859 3.859 0 0 1 504.62 -22.059z" />
+    <glyph glyph-name="chevron-circle-down"
+      unicode="&#xF13A;"
+      horiz-adv-x="512" d=" M504 192C504 55 393 -56 256 -56S8 55 8 192S119 440 256 440S504 329 504 192zM273 78.1L408.5 213.6C417.9 223 417.9 238.2 408.5 247.5000000000001L391.5 264.5C382.1 273.9000000000001 366.9 273.9000000000001 357.6 264.5L256 162.9L154.4 264.5C145 273.9 129.8 273.9 120.5 264.5L103.5 247.5C94.1 238.1 94.1 222.9 103.5 213.6L239 78.1C248.4 68.7000000000001 263.6 68.7000000000001 273 78.1z" />
+    <glyph glyph-name="chevron-circle-left"
+      unicode="&#xF137;"
+      horiz-adv-x="512" d=" M256 -56C119 -56 8 55 8 192S119 440 256 440S504 329 504 192S393 -56 256 -56zM142.1 175L277.6 39.5C287 30.1 302.2000000000001 30.1 311.5 39.5L328.5 56.5C337.9 65.9 337.9 81.1 328.5 90.4L226.9 192L328.5 293.6C337.9 303 337.9 318.2 328.5 327.5L311.5 344.5C302.1 353.9 286.9 353.9 277.6 344.5L142.1 209C132.7 199.6 132.7 184.4 142.1 175z" />
+    <glyph glyph-name="chevron-circle-right"
+      unicode="&#xF138;"
+      horiz-adv-x="512" d=" M256 440C393 440 504 329 504 192S393 -56 256 -56S8 55 8 192S119 440 256 440zM369.9 209L234.4 344.5C225 353.9 209.8 353.9 200.5 344.5L183.5 327.5C174.1 318.1 174.1 302.9 183.5 293.6L285.1 192L183.5 90.4C174.1 81 174.1 65.8 183.5 56.5L200.5 39.5C209.9 30.1 225.1 30.1 234.4 39.5L369.9 175C379.3 184.4 379.3 199.6 369.9 209z" />
+    <glyph glyph-name="chevron-circle-up"
+      unicode="&#xF139;"
+      horiz-adv-x="512" d=" M8 192C8 329 119 440 256 440S504 329 504 192S393 -56 256 -56S8 55 8 192zM239 305.9L103.5 170.4C94.1 161 94.1 145.8 103.5 136.5L120.5 119.5C129.9 110.1 145.1 110.1 154.4 119.5L256 221.1L357.6 119.5C367 110.1 382.2000000000001 110.1 391.5 119.5L408.5 136.5C417.9 145.9 417.9 161.1 408.5 170.4L273 305.9C263.6 315.3 248.4 315.3 239 305.9z" />
+    <glyph glyph-name="chevron-down"
+      unicode="&#xF078;"
+      horiz-adv-x="448" d=" M207.029 66.524L12.686 260.868C3.313 270.241 3.313 285.437 12.686 294.809L35.353 317.476C44.71 326.833 59.875 326.851 69.254 317.516L224 163.495L378.745 317.516C388.124 326.851 403.289 326.833 412.646 317.476L435.313 294.809C444.686 285.4360000000001 444.686 270.24 435.313 260.868L240.971 66.524C231.598 57.152 216.402 57.152 207.029 66.524z" />
+    <glyph glyph-name="chevron-left"
+      unicode="&#xF053;"
+      horiz-adv-x="320" d=" M34.52 208.97L228.87 403.31C238.24 412.68 253.44 412.68 262.81 403.31L285.48 380.64C294.8400000000001 371.28 294.85 356.12 285.5200000000001 346.74L131.49 192L285.51 37.25C294.85 27.87 294.83 12.71 285.47 3.35L262.8 -19.32C253.43 -28.69 238.23 -28.69 228.86 -19.32L34.52 175.03C25.15 184.4 25.15 199.6 34.52 208.97z" />
+    <glyph glyph-name="chevron-right"
+      unicode="&#xF054;"
+      horiz-adv-x="320" d=" M285.476 175.029L91.132 -19.314C81.759 -28.687 66.563 -28.687 57.191 -19.314L34.524 3.353C25.167 12.7099999999999 25.149 27.8749999999999 34.484 37.254L188.505 192L34.484 346.745C25.149 356.124 25.167 371.289 34.524 380.646L57.191 403.313C66.564 412.6860000000001 81.76 412.6860000000001 91.132 403.313L285.475 208.97C294.848 199.598 294.848 184.402 285.476 175.029z" />
+    <glyph glyph-name="chevron-up"
+      unicode="&#xF077;"
+      horiz-adv-x="448" d=" M240.971 317.476L435.314 123.1330000000001C444.687 113.7600000000001 444.687 98.564 435.314 89.192L412.647 66.525C403.29 57.168 388.125 57.15 378.7459999999999 66.485L224 220.505L69.255 66.484C59.876 57.149 44.711 57.167 35.354 66.524L12.687 89.191C3.314 98.564 3.314 113.7600000000001 12.687 123.1320000000001L207.03 317.475C216.402 326.848 231.598 326.848 240.971 317.476z" />
+    <glyph glyph-name="child"
+      unicode="&#xF1AE;"
+      horiz-adv-x="384" d=" M120 376C120 415.765 152.235 448 192 448S264 415.765 264 376C264 336.236 231.765 304 192 304S120 336.236 120 376zM374.627 374.627C362.1310000000001 387.124 341.8690000000001 387.124 329.373 374.627L242.745 288H141.254L54.627 374.627C42.131 387.124 21.869 387.124 9.373 374.627C-3.124 362.13 -3.124 341.8690000000001 9.373 329.372L104 234.746V-32C104 -49.673 118.327 -64 136 -64H152C169.673 -64 184 -49.673 184 -32V80H200V-32C200 -49.673 214.327 -64 232 -64H248C265.673 -64 280 -49.673 280 -32V234.746L374.627 329.373C387.124 341.87 387.124 362.13 374.627 374.627z" />
+    <glyph glyph-name="church"
+      unicode="&#xF51D;"
+      horiz-adv-x="640" d=" M464.46 201.32L352 268.8V320H400C408.84 320 416 327.16 416 336V368C416 376.8400000000001 408.84 384 400 384H352V432C352 440.84 344.84 448 336 448H304C295.1600000000001 448 288 440.84 288 432V384H240C231.16 384 224 376.8400000000001 224 368V336C224 327.16 231.16 320 240 320H288V268.8L175.54 201.32A31.997 31.997 0 0 1 160 173.88V-64H256V32C256 67.35 284.65 96 320 96S384 67.35 384 32V-64H480V173.88C480 185.12 474.1 195.54 464.46 201.32zM0 52.04V-48C0 -56.84 7.16 -64 16 -64H128V128L19.39 81.46A32.024 32.024 0 0 1 0 52.04zM620.61 81.46L512 128V-64H624C632.84 -64 640 -56.84 640 -48V52.04C640 64.84 632.37 76.41 620.61 81.46z" />
+    <glyph glyph-name="circle-notch"
+      unicode="&#xF1CE;"
+      horiz-adv-x="512" d=" M288 408.944V392.2850000000001C288 381.481 295.281 372.126 305.686 369.219C383.204 347.5660000000001 440 276.482 440 192C440 90.311 357.705 8 256 8C154.311 8 72 90.295 72 192C72 276.4700000000001 128.786 347.564 206.312 369.219C216.719 372.126 224 381.483 224 392.288V408.936C224 424.645 209.166 436.089 193.954 432.17C86.603 404.5180000000001 7.394 306.794 8.003 190.668C8.723 53.616 119.48 -56.288 256.534 -55.999C393.255 -55.711 504 55.212 504 192C504 307.633 424.86 404.779 317.789 432.236C302.678 436.111 288 424.544 288 408.944z" />
+    <glyph glyph-name="circle"
+      unicode="&#xF111;"
+      horiz-adv-x="512" d=" M256 440C119 440 8 329 8 192S119 -56 256 -56S504 55 504 192S393 440 256 440z" />
+    <glyph glyph-name="city"
+      unicode="&#xF64F;"
+      horiz-adv-x="640" d=" M616 256H480V424C480 437.26 469.26 448 456 448H312C298.74 448 288 437.26 288 424V352H224V432C224 440.84 216.84 448 208 448H192C183.16 448 176 440.84 176 432V352H112V432C112 440.84 104.84 448 96 448H80C71.16 448 64 440.84 64 432V352H24C10.74 352 0 341.26 0 328V-32C0 -49.67 14.33 -64 32 -64H608C625.67 -64 640 -49.67 640 -32V232C640 245.26 629.25 256 616 256zM128 44C128 37.37 122.63 32 116 32H76C69.37 32 64 37.37 64 44V84C64 90.63 69.37 96 76 96H116C122.63 96 128 90.63 128 84V44zM128 140C128 133.37 122.63 128 116 128H76C69.37 128 64 133.37 64 140V180C64 186.63 69.37 192 76 192H116C122.63 192 128 186.63 128 180V140zM128 236C128 229.37 122.63 224 116 224H76C69.37 224 64 229.37 64 236V276C64 282.63 69.37 288 76 288H116C122.63 288 128 282.63 128 276V236zM256 44C256 37.37 250.63 32 244 32H204C197.37 32 192 37.37 192 44V84C192 90.63 197.37 96 204 96H244C250.63 96 256 90.63 256 84V44zM256 140C256 133.37 250.63 128 244 128H204C197.37 128 192 133.37 192 140V180C192 186.63 197.37 192 204 192H244C250.63 192 256 186.63 256 180V140zM256 236C256 229.37 250.63 224 244 224H204C197.37 224 192 229.37 192 236V276C192 282.63 197.37 288 204 288H244C250.63 288 256 282.63 256 276V236zM416 140C416 133.37 410.63 128 404 128H364C357.37 128 352 133.37 352 140V180C352 186.63 357.37 192 364 192H404C410.63 192 416 186.63 416 180V140zM416 236C416 229.37 410.63 224 404 224H364C357.37 224 352 229.37 352 236V276C352 282.63 357.37 288 364 288H404C410.63 288 416 282.63 416 276V236zM416 332C416 325.37 410.63 320 404 320H364C357.37 320 352 325.37 352 332V372C352 378.63 357.37 384 364 384H404C410.63 384 416 378.63 416 372V332zM576 44C576 37.37 570.63 32 564 32H524C517.37 32 512 37.37 512 44V84C512 90.63 517.37 96 524 96H564C570.63 96 576 90.63 576 84V44zM576 140C576 133.37 570.63 128 564 128H524C517.37 128 512 133.37 512 140V180C512 186.63 517.37 192 524 192H564C570.63 192 576 186.63 576 180V140z" />
+    <glyph glyph-name="clipboard-check"
+      unicode="&#xF46C;"
+      horiz-adv-x="384" d=" M336 384H256C256 419.3 227.3 448 192 448S128 419.3 128 384H48C21.5 384 0 362.5 0 336V-16C0 -42.5 21.5 -64 48 -64H336C362.5 -64 384 -42.5 384 -16V336C384 362.5 362.5 384 336 384zM192 408C205.3 408 216 397.3 216 384S205.3 360 192 360S168 370.7 168 384S178.7 408 192 408zM313.2 176.2L170.2 34.4C165.5 29.7 157.9 29.8 153.2 34.5L70.6 117.8C65.9 122.5 66 130.1 70.7 134.8L99.1 163C103.8 167.7 111.4 167.6 116.1 162.9L162.1 116.5L268.1 221.7C272.8 226.4 280.4000000000001 226.3 285.1 221.6L313.3 193.2C318 188.4 317.9000000000001 180.9 313.2 176.2z" />
+    <glyph glyph-name="clipboard-list"
+      unicode="&#xF46D;"
+      horiz-adv-x="384" d=" M336 384H256C256 419.3 227.3 448 192 448S128 419.3 128 384H48C21.5 384 0 362.5 0 336V-16C0 -42.5 21.5 -64 48 -64H336C362.5 -64 384 -42.5 384 -16V336C384 362.5 362.5 384 336 384zM96 24C82.7 24 72 34.7 72 48S82.7 72 96 72S120 61.3 120 48S109.3 24 96 24zM96 120C82.7 120 72 130.7 72 144S82.7 168 96 168S120 157.3 120 144S109.3 120 96 120zM96 216C82.7 216 72 226.7 72 240S82.7 264 96 264S120 253.3 120 240S109.3 216 96 216zM192 408C205.3 408 216 397.3 216 384S205.3 360 192 360S168 370.7 168 384S178.7 408 192 408zM320 40C320 35.6 316.4 32 312 32H168C163.6 32 160 35.6 160 40V56C160 60.4 163.6 64 168 64H312C316.4 64 320 60.4 320 56V40zM320 136C320 131.6 316.4 128 312 128H168C163.6 128 160 131.6 160 136V152C160 156.4 163.6 160 168 160H312C316.4 160 320 156.4 320 152V136zM320 232C320 227.6 316.4 224 312 224H168C163.6 224 160 227.6 160 232V248C160 252.4 163.6 256 168 256H312C316.4 256 320 252.4 320 248V232z" />
+    <glyph glyph-name="clipboard"
+      unicode="&#xF328;"
+      horiz-adv-x="384" d=" M384 336V-16C384 -42.51 362.51 -64 336 -64H48C21.49 -64 0 -42.51 0 -16V336C0 362.51 21.49 384 48 384H128C128 419.29 156.71 448 192 448S256 419.29 256 384H336C362.51 384 384 362.51 384 336zM192 408C178.745 408 168 397.255 168 384S178.745 360 192 360S216 370.745 216 384S205.255 408 192 408M288 294V314A6 6 0 0 1 282 320H102A6 6 0 0 1 96 314V294A6 6 0 0 1 102 288H282A6 6 0 0 1 288 294z" />
+    <glyph glyph-name="clock"
+      unicode="&#xF017;"
+      horiz-adv-x="512" d=" M256 440C119 440 8 329 8 192S119 -56 256 -56S504 55 504 192S393 440 256 440zM313.1 89.9L224.9 154C221.8 156.3 220 159.9 220 163.7V332C220 338.6 225.4 344 232 344H280C286.6 344 292 338.6 292 332V194.3L355.5 148.1C360.9 144.2000000000001 362 136.7000000000001 358.1 131.3L329.9000000000001 92.5C326.0000000000001 87.2 318.5000000000001 86 313.1 89.9z" />
+    <glyph glyph-name="clone"
+      unicode="&#xF24D;"
+      horiz-adv-x="512" d=" M464 448C490.51 448 512 426.51 512 400V112C512 85.49 490.51 64 464 64H176C149.49 64 128 85.49 128 112V400C128 426.51 149.49 448 176 448H464M176 32C131.888 32 96 67.888 96 112V320H48C21.49 320 0 298.51 0 272V-16C0 -42.51 21.49 -64 48 -64H336C362.51 -64 384 -42.51 384 -16V32H176z" />
+    <glyph glyph-name="closed-captioning"
+      unicode="&#xF20A;"
+      horiz-adv-x="512" d=" M464 384H48C21.5 384 0 362.5 0 336V48C0 21.5 21.5 0 48 0H464C490.5 0 512 21.5 512 48V336C512 362.5 490.5 384 464 384zM218.1 160.3C220.9 162.8 225.2 162.4 227.3 159.4L246.8 131.7000000000001C248.5 129.3000000000001 248.3 126.1 246.3 124.0000000000001C192.7 67.2000000000001 73.5 91.9 73.5 191.9000000000001C73.5 289.2000000000001 195.2 311.4000000000001 246 262.0000000000001C248.1 260.0000000000001 248.5 258.8000000000001 247 256.3000000000001L229.5 225.8000000000001C227.6 222.7000000000001 223.3 221.8000000000001 220.4 224.1000000000001C179.6 256.1000000000002 125.8 239.0000000000001 125.8 192.9000000000001C125.9 144.9000000000001 176.9 122.4000000000001 218.1 160.3000000000001zM408.5 160.3C411.3 162.8 415.6 162.4 417.7 159.4L437.2 131.7000000000001C438.9 129.3000000000001 438.7 126.1 436.7 124.0000000000001C383.2 67.1000000000001 264 91.9 264 191.9000000000001C264 289.2000000000001 385.7 311.4000000000001 436.5 262.0000000000001C438.6 260.0000000000001 439 258.8000000000001 437.5 256.3000000000001L420 225.8C418.1 222.7 413.8 221.8 410.9 224.1C370.1 256.1 316.3 239.0000000000001 316.3 192.9C316.3 144.9 367.3 122.4 408.5 160.3z" />
+    <glyph glyph-name="cloud-download-alt"
+      unicode="&#xF381;"
+      horiz-adv-x="640" d=" M537.6 221.4C541.7 232.1 544 243.8 544 256C544 309 501 352 448 352C428.3 352 409.9 346 394.7 335.8C367 383.8 315.3 416 256 416C167.6 416 96 344.4 96 256C96 253.3 96.1 250.6 96.2 247.9C40.2 228.2 0 174.8 0 112C0 32.5 64.5 -32 144 -32H512C582.7 -32 640 25.3 640 96C640 157.9 596 209.6 537.6 221.4zM404.7000000000001 132.7L299.3 27.3C293.1 21.1 282.9000000000001 21.1 276.7 27.3L171.3 132.7C161.2 142.8 168.4 160 182.6 160H248V272C248 280.8 255.2 288 264 288H312C320.8 288 328 280.8 328 272V160H393.4C407.6 160 414.8 142.8 404.7 132.7z" />
+    <glyph glyph-name="cloud-meatball"
+      unicode="&#xF73B;"
+      horiz-adv-x="512" d=" M48 96C21.5 96 0 74.5 0 48S21.5 0 48 0S96 21.5 96 48S74.5 96 48 96zM464 96C437.5 96 416 74.5 416 48S437.5 0 464 0S512 21.5 512 48S490.5 96 464 96zM345 84.9C349.6 99.4 346.6 115.7 335.2 127.2C323.7 138.7 307.4 141.6 292.9 137.1C285.9 150.6 272.2 160.1 256 160.1S226.1 150.6 219.1 137.1C204.6 141.7 188.3 138.7 176.8 127.2C165.3 115.7 162.4 99.4 166.9 84.9C153.4 77.9 143.9 64.2 143.9 48S153.4 18.1 166.9 11.1C162.3 -3.4 165.3 -19.7 176.8 -31.2C185 -39.4 195.7 -43.5 206.5 -43.5C210.8 -43.5 215 -42.4 219.0999999999999 -41C226.1 -54.5 239.8 -64 256 -64S285.8999999999999 -54.5 292.8999999999999 -41C297 -42.3 301.2 -43.5 305.5 -43.5C316.3 -43.5 327 -39.4 335.2 -31.2C346.7 -19.7 349.5999999999999 -3.4 345 11.1C358.5 18.1 368 31.8 368 48S358.5 77.9 345 84.9zM512 224C512 277 469 320 416 320C415.4 320 414.9 319.8 414.4 319.8C415.5 325 416 330.4 416 336C416 380.2000000000001 380.2 416 336 416C311.4 416 289.7 404.7000000000001 275 387.2000000000001C256.4 423.2 219.3 448 176 448C114.1 448 64 397.9 64 336C64 328.7 64.8 321.7 66.1 314.8C27.8 302.2 0 266.5 0 224C0 171 43 128 96 128H139.4C143 136 147.8 143.4 154.2 149.8C167.7 163.3 185.7 170.9 205 171.1C218.5 184.3 236.7 192 256 192S293.5 184.3 307 171.1C326.3 170.9 344.3 163.3 357.8 149.8C364.2 143.4 369.1 136 372.6 128H416C469 128 512 171 512 224z" />
+    <glyph glyph-name="cloud-moon-rain"
+      unicode="&#xF73C;"
+      horiz-adv-x="576" d=" M350.5 222.5C343.6 259.7 311.2 288 272 288C259.7 288 248.1 285 237.7 280C220.3 304.1 192.1 320 160 320C107 320 64 277 64 224C64 223.5 64.2 222.9 64.2 222.4C27.6 215.1 0 182.8 0 144C0 99.8 35.8 64 80 64H336C380.2 64 416 99.8 416 144C416 183.2 387.8 215.7 350.5 222.5zM567.9 224.2C497.5 210.9 432.9 264.5 432.9 335C432.9 375.6 454.8 413 490.4 433.1C495.9 436.2 494.5 444.5 488.3 445.6C479.6 447.2 470.7 448 461.8 448C383.9 448 320.7000000000001 386.8 317.4 310.1C344.1 298.2 365.6 276.3 376.3 248.4C413.4 234.1 440.3 201 446.5 161.6C451.6 161.1 456.4999999999999 160.1 461.6999999999999 160.1C506.3999999999999 160.1 547.3 180.3 574.3 213.4C578.5 218.2 574.0999999999999 225.4 567.9 224.2zM364.5 29.9C356.9 34.2 347.1 31.7 342.7 23.9L306.1 -40.1C301.7 -47.8 304.4 -57.5 312.1 -61.9C314.6 -63.3 317.3 -64 320 -64C325.5 -64 330.8999999999999 -61.1 333.8999999999999 -55.9L370.5 8.1C374.8 15.8 372.2 25.5 364.5 29.9zM268.5 29.9C260.9 34.2 251.1 31.7 246.7 23.9L210.1 -40.1C205.7 -47.8 208.4 -57.5 216.1 -61.9C218.6 -63.3 221.3 -64 224 -64C229.5 -64 234.9 -61.1 237.9 -55.9L274.5 8.1C278.8 15.8 276.2 25.5 268.5 29.9zM172.5 29.9C164.9 34.2 155.1 31.7 150.7 23.9L114.1 -40.1C109.7 -47.8 112.4 -57.5 120.1 -61.9C122.6 -63.3 125.3 -64 128 -64C133.5 -64 138.9 -61.1 141.9 -55.9L178.5 8.1C182.8 15.8 180.2 25.5 172.5 29.9zM76.5 29.9C68.9 34.2 59.1 31.7 54.7 23.9L18.1 -40.1C13.7 -47.8 16.4 -57.5 24.1 -61.9C26.6 -63.3 29.3 -64 32 -64C37.5 -64 42.9 -61.1 45.9 -55.9L82.5 8.1C86.8 15.8 84.2 25.5 76.5 29.9z" />
+    <glyph glyph-name="cloud-moon"
+      unicode="&#xF6C3;"
+      horiz-adv-x="576" d=" M342.8 95.3C348.5 104.9 352 116 352 128C352 163.3 323.3 192 288 192C270.8 192 255.2 185.1 243.7 174.1C227.4 203.7 196.2 224 160 224C107 224 64 181 64 128C64 126 64.5 124.2 64.6 122.3C27.1 109.2 0 73.9 0 32C0 -21 43 -64 96 -64H336C380.2 -64 416 -28.2 416 16C416 57.9 383.7 91.8 342.8 95.3zM565.3 149.6C472.1999999999999 131.9 386.8 203.3 386.8 297.3C386.8 351.5 415.8 401.3 462.9 428.1C470.2 432.2000000000001 468.3 443.2000000000001 460.1 444.8C448.4 446.9 436.7 448 425 448C319.1 448 233.1 362.1 233.1 256C233.1 247.5 233.8 239.2 234.9 231C240.8 226.7 246.5 222.1 251.6 216.8C263 221.5 275.3 224 288 224C340.9 224 384 180.9 384 128C384 124.4 383.8 120.8 383.4 117.3C407 106.5 425.8 87.8 436.9 64.7C491.3 68.1 540.6 94 574 135.1C579.3 141.6 573.5 151.2000000000001 565.3 149.6z" />
+    <glyph glyph-name="cloud-rain"
+      unicode="&#xF73D;"
+      horiz-adv-x="512" d=" M416 320C415.4 320 414.9 319.8 414.4 319.8C415.5 325 416 330.4 416 336C416 380.2000000000001 380.2 416 336 416C311.4 416 289.7 404.7000000000001 275 387.2000000000001C256.4 423.2 219.3 448 176 448C114.1 448 64 397.9 64 336C64 328.7 64.8 321.7 66.1 314.8C27.8 302.2 0 266.5 0 224C0 171 43 128 96 128H416C469 128 512 171 512 224S469 320 416 320zM88 73.8C75.2 29.4 48 17.4 48 -13.9C48 -41.6 69.5 -64 96 -64S144 -41.6 144 -13.9C144 17.5 116.8 29.2 104 73.8C101.8 81.9 90.5 82.3 88 73.8zM248 73.8C235.2 29.4 208 17.4 208 -13.9C208 -41.6 229.5 -64 256 -64S304 -41.6 304 -13.9C304 17.5 276.8 29.2 264 73.8C261.8 81.9 250.5 82.3 248 73.8zM408 73.8C395.2 29.4 368 17.4 368 -13.9C368 -41.6 389.5 -64 416 -64S464 -41.6 464 -13.9C464 17.5 436.8 29.2 424 73.8C421.8 81.9 410.5 82.3 408 73.8z" />
+    <glyph glyph-name="cloud-showers-heavy"
+      unicode="&#xF740;"
+      horiz-adv-x="512" d=" M183.9 77.9C176.3 82.3 166.5 79.7 162.1 71.9L98.1 -40.1C93.7 -47.8 96.4 -57.6 104.1 -61.9C106.6 -63.3 109.3 -64 112 -64C117.5 -64 122.9 -61.1 125.9 -55.9L189.9 56.1C194.3 63.7000000000001 191.6 73.5 183.9 77.9zM279.9 77.9C272.3 82.3 262.5 79.7 258.1 71.9L194.1 -40.1C189.7 -47.8 192.4 -57.6 200.1 -61.9C202.6 -63.3 205.3 -64 208 -64C213.5 -64 218.9 -61.1 221.9 -55.9L285.9 56.1C290.3 63.7000000000001 287.6 73.5 279.9 77.9zM87.9 77.9C80.3 82.3 70.5 79.7 66.1 71.9L2.1 -40.1C-2.3 -47.8 0.4 -57.6 8.1 -61.9C10.6 -63.3 13.3 -64 16 -64C21.5 -64 26.9 -61.1 29.9 -55.9L93.9 56.1C98.3 63.7000000000001 95.6 73.5 87.9 77.9zM471.9 77.9C464.3 82.3 454.5 79.7 450.1 71.9L386.1 -40.1C381.7 -47.8 384.4 -57.6 392.1 -61.9C394.6 -63.3 397.3 -64 400 -64C405.5 -64 410.8999999999999 -61.1 413.8999999999999 -55.9L477.8999999999999 56.1C482.2999999999999 63.7000000000001 479.5999999999999 73.5 471.8999999999999 77.9zM375.9 77.9C368.3 82.3 358.5 79.7 354.1 71.9L290.1 -40.1C285.7 -47.8 288.4 -57.6 296.1 -61.9C298.6 -63.3 301.3 -64 304 -64C309.5 -64 314.8999999999999 -61.1 317.8999999999999 -55.9L381.8999999999999 56.1C386.2999999999999 63.7000000000001 383.5999999999999 73.5 375.8999999999999 77.9zM416 320C415.4 320 414.9 319.8 414.4 319.8C415.5 325 416 330.4 416 336C416 380.2000000000001 380.2 416 336 416C311.4 416 289.7 404.7000000000001 275 387.2000000000001C256.4 423.2 219.3 448 176 448C114.2 448 64 397.9 64 336C64 328.7 64.8 321.7 66.1 314.8C27.8 302.2 0 266.5 0 224C0 171 43 128 96 128H416C469 128 512 171 512 224S469 320 416 320z" />
+    <glyph glyph-name="cloud-sun-rain"
+      unicode="&#xF743;"
+      horiz-adv-x="576" d=" M510.5 222.5C503.6 259.7 471.2 288 432 288C419.7 288 408.1 285 397.7 280C380.3 304.1 352.1 320 320 320C267 320 224 277 224 224C224 223.5 224.2 222.9 224.2 222.4C187.6 215 160 182.8 160 144C160 99.8 195.8 64 240 64H496C540.2 64 576 99.8 576 144C576 183.2 547.8 215.7 510.5 222.5zM124.1 188.1C86.7 225.5000000000001 86.7 286.4000000000001 124.1 323.9000000000001C158.7 358.5 213.2 360.7000000000001 250.8 331.3000000000001C270.8 344.2000000000001 294.4000000000001 352.0000000000001 320 352.0000000000001C320.7 352.0000000000001 321.3 351.8000000000001 322 351.8000000000001L330.9 378.5000000000001C334.3 388.7000000000001 324.6 398.3000000000001 314.4 394.9000000000001L239.1 369.8000000000001L203.6 440.8000000000001C198.8 450.4 185.1 450.4 180.3 440.8000000000001L144.8 369.8000000000001L69.5 394.9000000000001C59.3 398.3000000000001 49.7 388.6 53.1 378.4000000000001L78.2 303.1L7.2 267.6C-2.4 262.8 -2.4 249.1 7.2 244.3L78.2 208.8L53.1 133.5C49.7 123.3 59.4 113.7 69.6 117L128.8 136.7C128.6 139.1 128.1 141.4 128.1 143.9C128.1 156.4 130.4 168.4 134.3 179.8C130.7 182.5 127.2 185 124.1 188.1zM193.9 246.1C198.2000000000001 270.6 209.7000000000001 292.5 225.8000000000001 310.1C216 316.3 204.4 320 192.0000000000001 320C156.7000000000001 320 128.0000000000001 291.3000000000001 128.0000000000001 256C128.0000000000001 237.3000000000001 136.2000000000001 220.6 149.1000000000001 208.9C160.4000000000001 224.8000000000001 175.7000000000001 237.8000000000001 193.9 246.1zM524.5 29.9C516.9 34.2 507.1 31.7 502.7 23.9L466.1 -40.1C461.7 -47.8 464.4 -57.4999999999999 472.1 -61.9C474.6 -63.2999999999999 477.3 -64 479.9999999999999 -64C485.4999999999999 -64 490.8999999999999 -61.1 493.8999999999999 -55.9L530.4999999999999 8.1C534.7999999999998 15.8 532.1999999999999 25.5 524.4999999999999 29.9zM428.5 29.9C420.9 34.2 411.1 31.7 406.7 23.9L370.1 -40.1C365.7 -47.8 368.4 -57.4999999999999 376.1 -61.9C378.6 -63.2999999999999 381.3 -64 384 -64C389.5 -64 394.8999999999999 -61.1 397.8999999999999 -55.9L434.5 8.1C438.8 15.8 436.2 25.5 428.5 29.9zM332.5 29.9C324.9 34.2 315.1 31.7 310.7 23.9L274.1 -40.1C269.7 -47.8 272.4 -57.4999999999999 280.1 -61.9C282.6 -63.2999999999999 285.3 -64 288 -64C293.5 -64 298.8999999999999 -61.1 301.8999999999999 -55.9L338.5 8.1C342.8 15.8 340.2 25.5 332.5 29.9zM236.5 29.9C228.9 34.2 219.1 31.7 214.7 23.9L178.1 -40.1C173.7 -47.8 176.4 -57.4999999999999 184.1 -61.9C186.6 -63.2999999999999 189.3 -64 192 -64C197.5 -64 202.9 -61.1 205.9 -55.9L242.5 8.1C246.8 15.8 244.2 25.5 236.5 29.9z" />
+    <glyph glyph-name="cloud-sun"
+      unicode="&#xF6C4;"
+      horiz-adv-x="640" d=" M575.2 122.3C575.4000000000001 124.2 576 126 576 127.9C576 163.2000000000001 547.3 191.9 512 191.9C499.4 191.9 487.8 188.1 477.9 181.9C460.3 220.7000000000001 421.4 247.9000000000001 376 247.9000000000001C314.2 247.9000000000001 264 197.8000000000001 264 135.9C264 132.9 264.7 130.1 264.9 127.2000000000001C215.3 123.5000000000001 176 82.5000000000001 176 31.9C176 -21.1 219 -64.0999999999999 272 -64.0999999999999H544C597 -64.0999999999999 640 -21.0999999999999 640 31.9000000000001C640 74.0000000000001 612.8 109.3000000000001 575.2 122.3000000000001zM144.8000000000001 144.9C101.1000000000001 188.6 101.1000000000001 259.6 144.8000000000001 303.2000000000001C188.5000000000001 346.9000000000001 259.5000000000001 346.9000000000001 303.2000000000001 303.2000000000001C312.9000000000001 293.5000000000001 320.1 282.3000000000001 325.5000000000001 270.5000000000001C335.3000000000001 274.2000000000001 345.6000000000001 276.5000000000001 356.2000000000001 278.0000000000001L386 366.9C390 378.8 378.7 390 366.8 386.1L279 356.8L237.5 439.6C232 450.8 216 450.8 210.4 439.6L169 356.8L81.1 386.1C69.3 390 58 378.7 61.9 366.9L91.2 279.1L8.4 237.6C-2.8 232.0000000000001 -2.8 216.1 8.4 210.5L91.2 169.1L61.9 81.3C57.9 69.4 69.2 58.2 81.1 62.1L157.2 87.4C163.3 99.8 171.2 111.1 180.8 120.9C167.7 126.3 155.4 134.3 144.8 144.9zM140.0000000000001 224.1C140.0000000000001 183.3 169.3000000000001 149.3 207.9000000000001 141.8C215.9000000000001 146.5 224.2000000000001 150.6 233.1000000000001 153.5C238.5000000000001 197.8 264.1 236 300.5000000000001 258.5C287.3 287.6 258 308 224 308C177.7 308 140 270.4 140 224.1z" />
+    <glyph glyph-name="cloud-upload-alt"
+      unicode="&#xF382;"
+      horiz-adv-x="640" d=" M537.6 221.4C541.7 232.1 544 243.8 544 256C544 309 501 352 448 352C428.3 352 409.9 346 394.7 335.8C367 383.8 315.3 416 256 416C167.6 416 96 344.4 96 256C96 253.3 96.1 250.6 96.2 247.9C40.2 228.2 0 174.8 0 112C0 32.5 64.5 -32 144 -32H512C582.7 -32 640 25.3 640 96C640 157.9 596 209.6 537.6 221.4zM393.4 160H328V48C328 39.2 320.8 32 312 32H264C255.2 32 248 39.2 248 48V160H182.6C168.3 160 161.2 177.2 171.3 187.3L276.7 292.7000000000001C282.9 298.9 293.1 298.9 299.3 292.7000000000001L404.7000000000001 187.3C414.8000000000001 177.2 407.6 160 393.4000000000001 160z" />
+    <glyph glyph-name="cloud"
+      unicode="&#xF0C2;"
+      horiz-adv-x="640" d=" M537.6 221.4C541.7 232.1 544 243.8 544 256C544 309 501 352 448 352C428.3 352 409.9 346 394.7 335.8C367 383.8 315.3 416 256 416C167.6 416 96 344.4 96 256C96 253.3 96.1 250.6 96.2 247.9C40.2 228.2 0 174.8 0 112C0 32.5 64.5 -32 144 -32H512C582.7 -32 640 25.3 640 96C640 157.9 596 209.6 537.6 221.4z" />
+    <glyph glyph-name="cocktail"
+      unicode="&#xF561;"
+      horiz-adv-x="576" d=" M296 -16H240V109.22L408.74 277.9500000000001C424.26 293.4700000000001 413.27 320 391.32 320H24.68C2.73 320 -8.26 293.4700000000001 7.26 277.9500000000001L176 109.22V-16H120C97.91 -16 80 -33.91 80 -56C80 -60.42 83.58 -64 88 -64H328C332.42 -64 336 -60.42 336 -56C336 -33.91 318.09 -16 296 -16zM432 448C369.39 448 316.65 407.8 296.82 352H349.36C366.01 380.55 396.63 400 432 400C484.93 400 528 356.94 528 304S484.93 208 432 208C417.96 208 404.71 211.2 392.68 216.64L357.42 181.38C379.23 168.08 404.59 160 432 160C511.53 160 576 224.47 576 304S511.53 448 432 448z" />
+    <glyph glyph-name="code-branch"
+      unicode="&#xF126;"
+      horiz-adv-x="384" d=" M384 304C384 348.2 348.2 384 304 384S224 348.2 224 304C224 267.6 248.3 236.9 281.5 227.2C280.9 211.1 277.3 198.7 270.5 190.3C255.1 171.1 221.2 167.9 185.3 164.6C157.1 162 127.9 159.2000000000001 104 147.7000000000001V291.7000000000001C136.5 301.9000000000001 160 332.2000000000001 160 368.0000000000001C160 412.2000000000001 124.2 448.0000000000001 80 448.0000000000001S0 412.2 0 368C0 332.2 23.5 301.9 56 291.7V92.4C23.5 82.1 0 51.8 0 16C0 -28.2 35.8 -64 80 -64S160 -28.2 160 16C160 50 138.8 79.1 108.8 90.6C111.9 95.8 116.6 100.4 123.7 104C139.9 112.2 164.1 114.4 189.8 116.8C232 120.7 279.8 125.2 308 160.2C322 177.6 329.1 200 329.6 228.1C361.2000000000001 238.9 384 268.8 384 304zM80 384C88.8 384 96 376.8 96 368S88.8 352 80 352S64 359.2 64 368S71.2 384 80 384zM80 0C71.2 0 64 7.2 64 16S71.2 32 80 32S96 24.8 96 16S88.8 0 80 0zM304 320C312.8 320 320 312.8 320 304S312.8 288 304 288S288 295.2 288 304S295.2 320 304 320z" />
+    <glyph glyph-name="code"
+      unicode="&#xF121;"
+      horiz-adv-x="640" d=" M278.9 -63.5L217.9 -45.8C211.5 -44 207.9 -37.3 209.7 -30.9L346.2 439.3C348 445.7 354.7 449.3 361.1 447.5L422.1 429.8C428.5 428 432.1 421.3 430.3 414.9L293.8 -55.3C291.9000000000001 -61.7 285.3 -65.4 278.9000000000001 -63.5zM164.9 48.7L208.4 95.1C213 99.9999999999999 212.7 107.8 207.6 112.3L117 192L207.6 271.7C212.7 276.2 213.1 284 208.4 288.9L164.9 335.3C160.4 340.1 152.8 340.4 147.9 335.8L3.8 200.8C-1.3 196.1 -1.3 188 3.8 183.3L147.9 48.2C152.8 43.6 160.4 43.8000000000001 164.9 48.7zM492.1 48.1L636.1999999999999 183.1999999999999C641.3 187.8999999999999 641.3 196 636.1999999999999 200.6999999999999L492.1 335.9C487.3 340.4 479.7 340.2 475.1 335.4L431.6 289C427 284.1 427.3 276.3 432.4000000000001 271.8L523 192L432.4 112.3C427.3 107.8 426.9 100 431.6 95.1L475.1 48.7C479.6 43.8000000000001 487.2 43.6 492.1 48.1z" />
+    <glyph glyph-name="coffee"
+      unicode="&#xF0F4;"
+      horiz-adv-x="640" d=" M192 64H384C437 64 480 107 480 160H512C582.6 160 640 217.4 640 288S582.6 416 512 416H120C106.7 416 96 405.3 96 392V160C96 107 139 64 192 64zM512 352C547.3 352 576 323.3 576 288S547.3 224 512 224H480V352H512zM559.7 -32H48.3C0.7 -32 -12.7 32 12.3 32H595.5999999999999C620.5999999999999 32 607.3999999999999 -32 559.6999999999999 -32z" />
+    <glyph glyph-name="cog"
+      unicode="&#xF013;"
+      horiz-adv-x="512" d=" M444.788 156.9L487.404 132.301C492.271 129.492 494.53 123.683 492.863 118.316C481.793 82.674 462.893 50.474 438.174 23.73A12.015999999999998 12.015999999999998 0 0 0 423.342 21.4759999999999L380.758 46.0709999999999A191.577 191.577 0 0 0 319.999 10.9409999999999V-38.2410000000001A12.01 12.01 0 0 0 310.622 -49.9590000000001C275.666 -57.8090000000001 238.123 -58.2150000000001 201.403 -49.9660000000001C195.913 -48.7330000000001 192 -43.8700000000001 192 -38.2430000000001V10.9409999999999A191.555 191.555 0 0 0 131.241 46.0709999999999L88.657 21.4759999999999A12.015999999999998 12.015999999999998 0 0 0 73.825 23.7299999999999C49.107 50.4739999999999 30.206 82.6739999999999 19.136 118.3159999999999C17.469 123.6819999999999 19.728 129.4909999999999 24.595 132.3009999999999L67.212 156.9A193.48000000000002 193.48000000000002 0 0 0 67.212 227.099L24.596 251.698C19.729 254.507 17.47 260.316 19.137 265.683C30.207 301.325 49.107 333.525 73.826 360.269A12.015999999999998 12.015999999999998 0 0 0 88.658 362.523L131.242 337.928A191.577 191.577 0 0 0 192.001 373.058V422.241A12.01 12.01 0 0 0 201.3780000000001 433.959C236.3340000000001 441.8090000000001 273.8770000000001 442.215 310.5970000000001 433.966C316.0870000000001 432.733 320.0000000000001 427.87 320.0000000000001 422.243V373.059A191.555 191.555 0 0 0 380.7590000000001 337.929L423.3430000000001 362.524A12.015999999999998 12.015999999999998 0 0 0 438.1750000000001 360.27C462.8930000000001 333.526 481.7940000000001 301.326 492.8640000000001 265.684C494.5310000000001 260.318 492.2720000000001 254.509 487.4050000000001 251.699L444.788 227.1A193.485 193.485 0 0 0 444.788 156.9zM336 192C336 236.112 300.112 272 256 272S176 236.112 176 192S211.888 112 256 112S336 147.888 336 192z" />
+    <glyph glyph-name="cogs"
+      unicode="&#xF085;"
+      horiz-adv-x="640" d=" M512.1 257L503.9 242.7C500.9 237.4 494.5000000000001 235.2 488.8 237.3C477 241.7 466.2 248 456.7 255.9C452.1 259.7 450.9 266.4 453.9 271.6L462.1 285.9C455.2 293.9 449.8 303.2 446.2 313.3H429.7C423.7 313.3 418.5 317.6 417.5 323.6C415.5 335.6 415.4 348.2 417.5 360.7C418.5 366.7 423.7 371.1 429.7 371.1H446.2C449.8 381.2 455.2 390.5 462.1 398.5L453.9 412.8C450.9 418 452 424.7 456.7 428.5C466.2 436.4 477.1 442.7 488.8 447.1C494.5 449.2 500.9 447 503.9 441.7L512.1 427.4C522.6 429.3 533.3000000000001 429.3 543.8000000000001 427.4L552 441.7C555 447 561.4 449.2 567.1 447.1C578.9 442.7 589.7 436.4 599.2 428.5C603.8000000000001 424.7 605 418 602 412.8L593.8 398.5C600.6999999999999 390.5 606.0999999999999 381.2 609.6999999999999 371.1H626.1999999999999C632.1999999999999 371.1 637.4 366.8 638.4 360.8C640.4 348.8 640.5 336.2 638.4 323.7C637.4 317.7 632.1999999999999 313.3 626.1999999999999 313.3H609.6999999999999C606.0999999999999 303.2 600.6999999999999 293.9 593.8 285.9L602 271.6C605 266.4 603.9 259.7 599.2 255.9C589.7 248 578.8000000000001 241.7 567.1 237.3C561.4 235.2 555 237.4 552 242.7L543.8 257C533.4 255.1 522.5999999999999 255.1 512.0999999999999 257zM501.6 315.8C540.1 286.2000000000001 584 330.1 554.4 368.6C515.9 398.3 472 354.3 501.6 315.8zM386.3 161.9L420 145.1C430.1 139.3 434.5 127 430.5 116C421.6 91.8 404.1 69.6 387.9 50.1999999999999C380.5 41.3 367.7 39.0999999999999 357.6 44.8999999999999L328.5 61.6999999999999C312.5 47.9999999999999 293.8999999999999 37.0999999999999 273.6 29.9999999999999V-3.6000000000001C273.6 -15.2000000000001 265.3 -25.2000000000001 253.9 -27.2000000000001C229.3 -31.4000000000001 203.5 -31.6000000000001 178 -27.2000000000001C166.5 -25.2000000000001 158 -15.3000000000001 158 -3.6000000000001V30C137.7 37.2 119.1 48 103.1 61.7L74 45C64 39.2 51.1 41.4 43.7 50.3C27.5 69.7 10.4 91.9 1.5 116C-2.5 126.9 1.9 139.2 12 145.1L45.3 161.9C41.4 182.8 41.4 204.3000000000001 45.3 225.3000000000001L12 242.2C1.9 248 -2.6 260.3 1.5 271.2C10.4 295.4 27.5 317.6 43.7 337C51.1 345.9 63.9 348.1 74 342.3L103.1 325.5C119.1 339.2 137.7 350.1 158 357.2V390.9C158 402.4 166.2 412.4 177.6 414.4C202.2 418.6 228.1 418.8 253.6 414.5C265.1 412.5 273.6 402.6 273.6 390.9V357.3C293.9000000000001 350.1 312.5 339.3 328.5 325.6L357.6 342.4C367.6 348.2 380.5 346 387.9000000000001 337.1C404.1 317.7 421.1 295.5 430.0000000000001 271.3C434.0000000000001 260.4 430.1000000000001 248.1 420.0000000000001 242.2L386.3000000000001 225.4C390.2000000000001 204.4 390.2000000000001 182.9 386.3000000000001 161.9zM268.7000000000001 140.8C327.9000000000001 217.8 240.0000000000001 305.7 163.0000000000001 246.5C103.8000000000001 169.5 191.7000000000001 81.5999999999999 268.7000000000001 140.8zM512.1 -41.9L503.9 -56.2C500.9 -61.5000000000001 494.5000000000001 -63.7 488.8 -61.6C477 -57.2 466.2 -50.9 456.7 -43C452.1 -39.2 450.9 -32.5 453.9 -27.3L462.1 -13C455.2 -5 449.8 4.3 446.2 14.4H429.7C423.7 14.4 418.5 18.7 417.5 24.7C415.5 36.7 415.4 49.3 417.5 61.8C418.5 67.8 423.7 72.2 429.7 72.2H446.2C449.8 82.3 455.2 91.6 462.1 99.6L453.9 113.9C450.9 119.1 452 125.8 456.7 129.6C466.2 137.5 477.1 143.8 488.8 148.2C494.5 150.3 500.9 148.1 503.9 142.8L512.1 128.5C522.6 130.4 533.3000000000001 130.4 543.8000000000001 128.5L552.0000000000001 142.8C555.0000000000001 148.1 561.4000000000001 150.3 567.1000000000001 148.2C578.9000000000001 143.8 589.7000000000002 137.5 599.2000000000002 129.6C603.8000000000002 125.8 605.0000000000001 119.1 602.0000000000001 113.9L593.8000000000001 99.6C600.7 91.6 606.1 82.3 609.7 72.2H626.2C632.2 72.2 637.4000000000001 67.9 638.4000000000001 61.9C640.4000000000001 49.9 640.5000000000001 37.3 638.4000000000001 24.8C637.4000000000001 18.8 632.2 14.4 626.2 14.4H609.7C606.1 4.3 600.7 -5 593.8000000000001 -13L602.0000000000001 -27.3C605.0000000000001 -32.5 603.9000000000001 -39.2 599.2000000000002 -43C589.7000000000002 -50.9 578.8000000000002 -57.2 567.1000000000001 -61.6C561.4000000000001 -63.7 555.0000000000001 -61.5 552.0000000000001 -56.2L543.8000000000001 -41.9C533.4000000000001 -43.8 522.6 -43.8 512.1 -41.9zM501.6 17C540.1 -12.6 584 31.3 554.4 69.8C515.9 99.4 472 55.5 501.6 17z" />
+    <glyph glyph-name="coins"
+      unicode="&#xF51E;"
+      horiz-adv-x="512" d=" M0 42.7V0C0 -35.3 86 -64 192 -64S384 -35.3 384 0V42.7C342.7 13.6 267.2 0 192 0S41.3 13.6 0 42.7zM320 320C426 320 512 348.7 512 384S426 448 320 448S128 419.3 128 384S214 320 320 320zM0 147.6V96C0 60.7 86 32 192 32S384 60.7 384 96V147.6C342.7 113.6 267.1 96 192 96S41.3 113.6 0 147.6zM416 136.6C473.3 147.7000000000001 512 168.3 512 192V234.7000000000001C488.8 218.3000000000001 454.7 207.1000000000001 416 200.2000000000001V136.6zM192 288C86 288 0 252.2 0 208S86 128 192 128S384 163.8 384 208S298 288 192 288zM411.3 231.7C471.3 242.5 512 263.7 512 288V330.7C476.5 305.6 415.5 292.1 351.3 288.9C380.8 274.6 402.5 255.4 411.3 231.7z" />
+    <glyph glyph-name="columns"
+      unicode="&#xF0DB;"
+      horiz-adv-x="512" d=" M464 416H48C21.49 416 0 394.51 0 368V16C0 -10.51 21.49 -32 48 -32H464C490.51 -32 512 -10.51 512 16V368C512 394.51 490.51 416 464 416zM224 32H64V288H224V32zM448 32H288V288H448V32z" />
+    <glyph glyph-name="comment-alt"
+      unicode="&#xF27A;"
+      horiz-adv-x="512" d=" M448 448H64C28.7 448 0 419.3 0 384V96C0 60.7 28.7 32 64 32H160V-52C160 -61.8 171.2 -67.5 179.1 -61.7L304 32H448C483.3 32 512 60.7 512 96V384C512 419.3 483.3 448 448 448z" />
+    <glyph glyph-name="comment-dollar"
+      unicode="&#xF651;"
+      horiz-adv-x="512" d=" M256 416C114.62 416 0 322.88 0 208C0 158.44 21.41 112.99 57.02 77.26C44.46 26.95 2.7 -17.97 2.2 -18.5A7.995 7.995 0 0 1 8 -32C74.26 -32 123.99 -0.25 148.6 19.38C181.29 7.07 217.59 0 256 0C397.38 0 512 93.12 512 208S397.38 416 256 416zM280 113.56V96C280 87.16 272.84 80 264 80H248C239.16 80 232 87.16 232 96V113.73C220.58 115.0800000000001 209.72 118.92 200.22 125.19C194 129.3 193.4 138.3 198.67 143.57L216.19 161.09C219.93 164.83 225.5 165.33 230.3 163.12C233.48 161.66 236.96 160.8999999999999 240.56 160.8999999999999H273.3400000000001C278.0000000000001 160.8999999999999 281.7800000000001 164.6799999999999 281.7800000000001 169.3199999999999C281.7800000000001 173.0699999999999 279.2600000000001 176.3999999999999 275.6600000000001 177.43L225.5900000000001 191.73C203.34 198.08 185.5800000000001 216.44 182.68 239.4C178.63 271.47 201.71 298.83 232.0000000000001 302.45V320C232.0000000000001 328.8400000000001 239.1600000000001 336 248.0000000000001 336H264C272.84 336 280 328.8400000000001 280 320V302.27C291.42 300.92 302.28 297.0800000000001 311.78 290.81C318 286.7 318.6 277.7 313.33 272.43L295.81 254.91C292.07 251.17 286.5 250.67 281.7 252.88A24.516 24.516 0 0 1 271.44 255.1H238.66C234 255.1 230.22 251.32 230.22 246.68C230.22 242.93 232.74 239.6 236.34 238.57L286.4100000000001 224.27C308.6600000000001 217.91 326.42 199.56 329.3200000000001 176.6C333.3700000000001 144.54 310.2900000000001 117.18 280.0000000000001 113.56z" />
+    <glyph glyph-name="comment-dots"
+      unicode="&#xF4AD;"
+      horiz-adv-x="512" d=" M256 416C114.6 416 0 322.9 0 208C0 158.4 21.4 113 57 77.3C44.5 26.9 2.7 -18 2.2 -18.5C0 -20.8 -0.6 -24.2 0.7 -27.2S4.8 -32 8 -32C74.3 -32 124 -0.2 148.6 19.4C181.3 7.1 217.6 0 256 0C397.4 0 512 93.1 512 208S397.4 416 256 416zM128 176C110.3 176 96 190.3 96 208S110.3 240 128 240S160 225.7 160 208S145.7 176 128 176zM256 176C238.3 176 224 190.3 224 208S238.3 240 256 240S288 225.7 288 208S273.7 176 256 176zM384 176C366.3 176 352 190.3 352 208S366.3 240 384 240S416 225.7 416 208S401.7 176 384 176z" />
+    <glyph glyph-name="comment-slash"
+      unicode="&#xF4B3;"
+      horiz-adv-x="640" d=" M64 208C64 158.4 85.4 113 121 77.3C108.4 27 66.7 -17.9 66.2 -18.5C64 -20.8 63.4 -24.2 64.7 -27.2C66 -30.1 68.8 -32 72 -32C138.3 -32 188 -0.2 212.6 19.4C245.3 7.1 281.6 0 320 0C347.4 0 373.7 3.6 398.4 10L72.9 261.6C67.3 244.5 64 226.6 64 208zM633.8 -10.1L519.4 78.3C554.6 113.9 576 158.8 576 208C576 322.9 461.4 416 320 416C254.9 416 195.8 395.9 150.6 363.3L45.5 444.6C38.5 450 28.5 448.8 23 441.8L3.4 416.6C-2 409.6 -0.8 399.6 6.2 394.2L594.6 -60.5C601.6 -65.9 611.6 -64.7 617.1 -57.7L636.7 -32.4C642.1 -25.6 640.8000000000001 -15.5 633.8000000000001 -10.1z" />
+    <glyph glyph-name="comment"
+      unicode="&#xF075;"
+      horiz-adv-x="512" d=" M256 416C114.6 416 0 322.9 0 208C0 158.4 21.4 113 57 77.3C44.5 26.9 2.7 -18 2.2 -18.5C0 -20.8 -0.6 -24.2 0.7 -27.2S4.8 -32 8 -32C74.3 -32 124 -0.2 148.6 19.4C181.3 7.1 217.6 0 256 0C397.4 0 512 93.1 512 208S397.4 416 256 416z" />
+    <glyph glyph-name="comments-dollar"
+      unicode="&#xF653;"
+      horiz-adv-x="576" d=" M416 256C416 344.37 322.88 416 208 416S0 344.37 0 256C0 221.73 14.13 190.05 37.97 164.02C24.61 133.78 2.52 109.84 2.2 109.5A7.995 7.995 0 0 1 8 96C44.58 96 74.93 108.25 96.73 120.98C128.93 105.24 167.02 96 208 96C322.88 96 416 167.63 416 256zM192 160V176.29C180.71 176.87 169.73 180.81 160.63 187.6400000000001C156.73 190.5700000000001 156.53 196.4100000000001 160.06 199.78L171.81 210.9900000000001C174.58 213.63 178.7 213.75 181.94 211.72C185.81 209.3000000000001 190.2 208 194.76 208H222.87C229.37 208 234.67 213.92 234.67 221.19C234.67 227.14 231.06 232.38 225.9 233.92L180.9 247.42C162.31 253.0000000000001 149.32 270.8400000000001 149.32 290.81C149.32 315.3300000000001 168.37 335.25 191.99 335.88V352C191.99 356.42 195.57 360 199.99 360H215.99C220.41 360 223.99 356.42 223.99 352V335.7100000000001C235.28 335.13 246.26 331.2 255.36 324.36C259.26 321.43 259.4600000000001 315.5900000000001 255.93 312.2200000000001L244.18 301.01C241.41 298.37 237.29 298.25 234.05 300.2800000000001C230.18 302.7100000000001 225.79 304 221.23 304H193.12C186.62 304 181.32 298.0800000000001 181.32 290.8100000000001C181.32 284.86 184.93 279.62 190.09 278.0800000000001L235.09 264.5800000000001C253.68 259 266.67 241.1600000000001 266.67 221.1900000000001C266.67 196.6600000000001 247.62 176.7500000000001 224 176.1200000000001V160C224 155.58 220.42 152 216 152H200C195.58 152 192 155.58 192 160zM538.01 36.01C561.87 62.04 576 93.73 576 128C576 194.94 522.51 252.2 446.67 276.07C447.53 269.4700000000001 448 262.78 448 256C448 150.13 340.3399999999999 64 208 64C197.22 64 186.68 64.77 176.27 65.88C207.8 8.37 281.77 -32 368 -32C408.98 -32 447.07 -22.76 479.27 -7.02C501.07 -19.75 531.42 -32 568 -32C571.2 -32 574.09 -30.09 575.34 -27.16C576.61 -24.22 576 -20.82 573.7900000000001 -18.49C573.4800000000001 -18.16 551.3700000000001 5.75 538.0100000000001 36.01z" />
+    <glyph glyph-name="comments"
+      unicode="&#xF086;"
+      horiz-adv-x="576" d=" M416 256C416 344.4 322.9 416 208 416S0 344.4 0 256C0 221.7 14.1 190.1 38 164C24.6 133.8 2.5 109.8 2.2 109.5C0 107.2 -0.6 103.8 0.7 100.8S4.8 96 8 96C44.6 96 74.9 108.3 96.7 121C128.9 105.3 167 96 208 96C322.9 96 416 167.6 416 256zM538 36C561.9 62 576 93.7 576 128C576 194.9 522.5 252.2 446.7 276.1C447.6 269.5 448 262.8 448 256C448 150.1 340.3 64 208 64C197.2 64 186.7 64.8 176.3 65.9C207.8 8.4 281.8 -32 368 -32C409 -32 447.1 -22.8 479.3 -7C501.1 -19.7 531.4 -32 568 -32C571.2 -32 574.1 -30.1 575.3 -27.2C576.5999999999999 -24.3 576 -20.9 573.8 -18.5C573.5 -18.2 551.4 5.7 538 36z" />
+    <glyph glyph-name="compact-disc"
+      unicode="&#xF51F;"
+      horiz-adv-x="496" d=" M248 440C111 440 0 329 0 192S111 -56 248 -56S496 55 496 192S385 440 248 440zM88 192H56C56 297.9 142.1 384 248 384V352C159.8 352 88 280.2 88 192zM248 96C195 96 152 139 152 192S195 288 248 288S344 245 344 192S301 96 248 96zM248 224C230.3 224 216 209.7 216 192S230.3 160 248 160S280 174.3 280 192S265.7 224 248 224z" />
+    <glyph glyph-name="compass"
+      unicode="&#xF14E;"
+      horiz-adv-x="496" d=" M225.38 214.63C212.88 202.13 212.88 181.87 225.38 169.38C237.87 156.88 258.14 156.88 270.63 169.38C283.13 181.88 283.13 202.14 270.63 214.63C258.13 227.12 237.87 227.12 225.38 214.63zM248 440C111.03 440 0 328.9700000000001 0 192S111.03 -56 248 -56S496 55.03 496 192S384.9700000000001 440 248 440zM374.14 291.95L308.17 147.6A31.938 31.938 0 0 0 292.4000000000001 131.8300000000001L148.06 65.86C131.41 58.25 114.25 75.41 121.86 92.06L187.84 236.41A31.938 31.938 0 0 0 203.6100000000001 252.18L347.9500000000001 318.15C364.6 325.75 381.7500000000001 308.6 374.1400000000001 291.9500000000001z" />
+    <glyph glyph-name="compress"
+      unicode="&#xF066;"
+      horiz-adv-x="448" d=" M436 256H312C298.7 256 288 266.7 288 280V404C288 410.6 293.4 416 300 416H340C346.6 416 352 410.6 352 404V320H436C442.6 320 448 314.6 448 308V268C448 261.4 442.6 256 436 256zM160 280V404C160 410.6 154.6 416 148 416H108C101.4 416 96 410.6 96 404V320H12C5.4 320 0 314.6 0 308V268C0 261.4 5.4 256 12 256H136C149.3 256 160 266.7 160 280zM160 -20V104C160 117.3 149.3 128 136 128H12C5.4 128 0 122.6 0 116V76C0 69.4 5.4 64 12 64H96V-20C96 -26.6 101.4 -32 108 -32H148C154.6 -32 160 -26.6 160 -20zM352 -20V64H436C442.6 64 448 69.4 448 76V116C448 122.6 442.6 128 436 128H312C298.7 128 288 117.3 288 104V-20C288 -26.6 293.4 -32 300 -32H340C346.6 -32 352 -26.6 352 -20z" />
+    <glyph glyph-name="concierge-bell"
+      unicode="&#xF562;"
+      horiz-adv-x="512" d=" M288 317.4600000000001V336H304C312.84 336 320 343.16 320 352V368C320 376.8400000000001 312.84 384 304 384H208C199.16 384 192 376.8400000000001 192 368V352C192 343.16 199.16 336 208 336H224V317.4600000000001C115.49 301.89 32 208.82 32 96H480C480 208.82 396.51 301.89 288 317.4600000000001zM496 64H16C7.16 64 0 56.84 0 48V16C0 7.16 7.16 0 16 0H496C504.84 0 512 7.16 512 16V48C512 56.84 504.84 64 496 64z" />
+    <glyph glyph-name="cookie-bite"
+      unicode="&#xF564;"
+      horiz-adv-x="512" d=" M510.52 192.18C440.55 193.03 384.05 249.87 384.05 320.04C313.88 320.04 257.05 376.53 256.19 446.49C228.93 450.63 201.06 446.19 176.47 433.67L107.34 398.45A132.221 132.221 0 0 1 49.55 340.64L14.4499999999999 271.76A132.645 132.645 0 0 1 1.6299999999999 190.81L13.7099999999999 114.54A132.521 132.521 0 0 1 50.8699999999999 41.58L105.6399999999999 -13.1799999999999A132.036 132.036 0 0 1 178.3499999999999 -50.2399999999999L255.06 -62.3899999999999C282.57 -66.7499999999999 310.76 -62.2799999999999 335.5899999999999 -49.6299999999999L404.7199999999999 -14.42A132.273 132.273 0 0 1 462.5099999999999 43.39L497.61 112.27C510.17 136.91 514.62 164.85 510.52 192.18zM176 80C158.33 80 144 94.33 144 112S158.33 144 176 144S208 129.67 208 112S193.67 80 176 80zM208 240C190.33 240 176 254.33 176 272S190.33 304 208 304S240 289.67 240 272S225.67 240 208 240zM368 112C350.33 112 336 126.33 336 144S350.33 176 368 176S400 161.67 400 144S385.67 112 368 112z" />
+    <glyph glyph-name="cookie"
+      unicode="&#xF563;"
+      horiz-adv-x="512" d=" M510.37 193.21L498.29 269.4700000000001A132.493 132.493 0 0 1 461.13 342.42L406.37 397.17C386.64 416.8900000000001 361.19 429.87 333.6600000000001 434.2200000000001L256.9600000000001 446.37C229.4500000000001 450.73 201.2700000000001 446.2600000000001 176.4400000000001 433.61L107.32 398.4A132.25 132.25 0 0 1 49.53 340.6L14.43 271.7200000000001A132.602 132.602 0 0 1 1.61 190.78L13.69 114.51A132.493 132.493 0 0 1 50.85 41.56L105.61 -13.19A132.087 132.087 0 0 1 178.32 -50.24L255.02 -62.38C282.53 -66.74 310.71 -62.27 335.54 -49.63L404.66 -14.42A132.302 132.302 0 0 1 462.45 43.38L497.55 112.25C510.26 137.21 514.75 165.55 510.37 193.21zM176 80C158.33 80 144 94.33 144 112S158.33 144 176 144S208 129.67 208 112S193.67 80 176 80zM208 240C190.33 240 176 254.33 176 272S190.33 304 208 304S240 289.67 240 272S225.67 240 208 240zM368 112C350.33 112 336 126.33 336 144S350.33 176 368 176S400 161.67 400 144S385.67 112 368 112z" />
+    <glyph glyph-name="copy"
+      unicode="&#xF0C5;"
+      horiz-adv-x="448" d=" M320 0V-40C320 -53.255 309.255 -64 296 -64H24C10.745 -64 0 -53.255 0 -40V328C0 341.255 10.745 352 24 352H96V56C96 25.121 121.121 0 152 0H320zM320 344V448H152C138.745 448 128 437.255 128 424V56C128 42.745 138.745 32 152 32H424C437.255 32 448 42.745 448 56V320H344C330.8 320 320 330.8 320 344zM440.971 375.029L375.029 440.971A24 24 0 0 1 358.0590000000001 448H352V352H448V358.059A24 24 0 0 1 440.971 375.029z" />
+    <glyph glyph-name="copyright"
+      unicode="&#xF1F9;"
+      horiz-adv-x="512" d=" M256 440C119.033 440 8 328.967 8 192S119.033 -56 256 -56S504 55.033 504 192S392.967 440 256 440zM373.134 93.247C371.5420000000001 91.38 333.358 47.516 263.283 47.516C178.591 47.516 118.799 110.776 118.799 193.083C118.799 274.386 180.803 336.4840000000001 262.5610000000001 336.4840000000001C329.5180000000001 336.4840000000001 364.5260000000001 299.1690000000001 365.9830000000001 297.5800000000001A12 12 0 0 0 367.2210000000001 282.9570000000001L344.8410000000001 248.3020000000001C340.7920000000001 242.0350000000001 332.0670000000001 240.9510000000001 326.6070000000001 246.0070000000001C326.3740000000001 246.2210000000001 300.0780000000001 269.8870000000001 264.7270000000001 269.8870000000001C218.6110000000001 269.8870000000001 190.8110000000001 236.3120000000001 190.8110000000001 193.8050000000001C190.8110000000001 154.2030000000001 216.3250000000001 114.1130000000001 265.0880000000001 114.1130000000001C303.7850000000001 114.1130000000001 330.3680000000001 142.4510000000001 330.6320000000001 142.7380000000001C335.7640000000001 148.3030000000001 344.6910000000001 147.7710000000001 349.1400000000001 141.6850000000001L373.6870000000001 108.1130000000001A12.001000000000001 12.001000000000001 0 0 0 373.1340000000001 93.2470000000001z" />
+    <glyph glyph-name="couch"
+      unicode="&#xF4B8;"
+      horiz-adv-x="640" d=" M160 224V160H480V224C480 259.3 508.7 288 544 288H576C576 341 533 384 480 384H160C107 384 64 341 64 288H96C131.3 288 160 259.3 160 224zM576 256H544C526.3 256 512 241.7 512 224V128H128V224C128 241.7 113.7 256 96 256H64C28.7 256 0 227.3 0 192C0 168.4 13 148 32 136.9V16C32 7.2 39.2 0 48 0H112C120.8 0 128 7.2 128 16V32H512V16C512 7.2 519.2 0 528 0H592C600.8 0 608 7.2 608 16V136.9C627 148 640 168.4 640 192C640 227.3 611.3 256 576 256z" />
+    <glyph glyph-name="credit-card"
+      unicode="&#xF09D;"
+      horiz-adv-x="576" d=" M0 16C0 -10.5 21.5 -32 48 -32H528C554.5 -32 576 -10.5 576 16V192H0V16zM192 84C192 90.6 197.4 96 204 96H340C346.6 96 352 90.6 352 84V44C352 37.4 346.6 32 340 32H204C197.4 32 192 37.4 192 44V84zM64 84C64 90.6 69.4 96 76 96H148C154.6 96 160 90.6 160 84V44C160 37.4 154.6 32 148 32H76C69.4 32 64 37.4 64 44V84zM576 368V320H0V368C0 394.5 21.5 416 48 416H528C554.5 416 576 394.5 576 368z" />
+    <glyph glyph-name="crop-alt"
+      unicode="&#xF565;"
+      horiz-adv-x="512" d=" M488 96H448V352C448 369.67 433.67 384 416 384H192V288H352V-40C352 -53.25 362.75 -64 376 -64H424C437.25 -64 448 -53.25 448 -40V0H488C501.25 0 512 10.75 512 24V72C512 85.26 501.25 96 488 96zM160 424C160 437.26 149.25 448 136 448H88C74.75 448 64 437.26 64 424V384H24C10.75 384 0 373.26 0 360V312C0 298.75 10.75 288 24 288H64V32C64 14.33 78.33 0 96 0H320V96H160V424z" />
+    <glyph glyph-name="crop"
+      unicode="&#xF125;"
+      horiz-adv-x="512" d=" M488 96H448V338.75L507.31 398.06C513.56 404.31 513.56 414.44 507.31 420.69L484.69 443.31C478.44 449.56 468.31 449.56 462.06 443.31L402.75 384H192V288H306.75L160 141.25V424C160 437.26 149.25 448 136 448H88C74.75 448 64 437.26 64 424V384H24C10.75 384 0 373.26 0 360V312C0 298.75 10.75 288 24 288H64V24C64 10.75 74.75 0 88 0H320V96H205.25L352 242.75V-40C352 -53.25 362.75 -64 376 -64H424C437.25 -64 448 -53.25 448 -40V0H488C501.25 0 512 10.75 512 24V72C512 85.26 501.25 96 488 96z" />
+    <glyph glyph-name="cross"
+      unicode="&#xF654;"
+      horiz-adv-x="384" d=" M352 320H256V416C256 433.67 241.67 448 224 448H160C142.33 448 128 433.67 128 416V320H32C14.33 320 0 305.67 0 288V224C0 206.33 14.33 192 32 192H128V-32C128 -49.67 142.33 -64 160 -64H224C241.67 -64 256 -49.67 256 -32V192H352C369.67 192 384 206.33 384 224V288C384 305.67 369.67 320 352 320z" />
+    <glyph glyph-name="crosshairs"
+      unicode="&#xF05B;"
+      horiz-adv-x="512" d=" M500 224H469.636C455.724 317.675 381.675 391.724 288 405.636V436C288 442.627 282.627 448 276 448H236C229.373 448 224 442.627 224 436V405.636C130.325 391.724 56.276 317.675 42.364 224H12C5.373 224 0 218.627 0 212V172C0 165.373 5.373 160 12 160H42.364C56.276 66.325 130.325 -7.724 224 -21.636V-52C224 -58.627 229.373 -64 236 -64H276C282.627 -64 288 -58.627 288 -52V-21.636C381.675 -7.724 455.724 66.325 469.636 160H500C506.627 160 512 165.373 512 172V212C512 218.627 506.627 224 500 224zM288 43.366V84C288 90.627 282.627 96 276 96H236C229.373 96 224 90.627 224 84V43.366C165.826 55.768 119.783 101.757 107.366 160H148C154.627 160 160 165.373 160 172V212C160 218.627 154.627 224 148 224H107.366C119.768 282.174 165.757 328.217 224 340.634V300C224 293.373 229.373 288 236 288H276C282.627 288 288 293.373 288 300V340.634C346.174 328.232 392.217 282.243 404.634 224H364C357.373 224 352 218.627 352 212V172C352 165.373 357.373 160 364 160H404.634C392.2320000000001 101.826 346.243 55.783 288 43.366zM288 192C288 174.327 273.673 160 256 160S224 174.327 224 192C224 209.673 238.327 224 256 224S288 209.673 288 192z" />
+    <glyph glyph-name="crow"
+      unicode="&#xF520;"
+      horiz-adv-x="640" d=" M544 416H527.64C513.04 435.32 490.09 448 464 448C419.82 448 384 412.18 384 368V347.02L12.09 54.43A30.216 30.216 0 0 1 0 30.26C0 7.8 23.64 -6.81 43.73 3.23L165.27 64H261.76L306.17 -56.1C308.44 -62.33 315.32 -65.5400000000001 321.55 -63.27L344.1 -55.0600000000001C350.33 -52.7900000000001 353.54 -45.9100000000001 351.27 -39.6800000000001L312.94 64H352C353.9100000000001 64 355.76 64.23 357.6600000000001 64.29L402.17 -56.09C404.44 -62.32 411.32 -65.53 417.55 -63.26L440.1 -55.05C446.3300000000001 -52.78 449.54 -45.9 447.2700000000001 -39.67L406.0300000000001 71.86C485.74 95.2 544 168.74 544 256V336L640 352C640 387.35 597.02 416 544 416zM464 344C450.75 344 440 354.75 440 368C440 381.26 450.75 392 464 392S488 381.26 488 368C488 354.75 477.25 344 464 344z" />
+    <glyph glyph-name="crown"
+      unicode="&#xF521;"
+      horiz-adv-x="640" d=" M528 0H112C103.2 0 96 -7.2 96 -16V-48C96 -56.8 103.2 -64 112 -64H528C536.8 -64 544 -56.8 544 -48V-16C544 -7.2 536.8 0 528 0zM592 320C565.5 320 544 298.5 544 272C544 264.9 545.6 258.3 548.4 252.2L476 208.8C460.6 199.6 440.7 204.8 431.8 220.4L350.3 363C361 371.8 368 385 368 400C368 426.5 346.5 448 320 448S272 426.5 272 400C272 385 279 371.8 289.7 363L208.2 220.4C199.3 204.8 179.3 199.6 164 208.8L91.7 252.2C94.4 258.2000000000001 96.1 264.9 96.1 272C96.1 298.5 74.6 320 48.1 320S0 298.5 0 272S21.5 224 48 224C50.6 224 53.2 224.4 55.7 224.8L128 32H512L584.3 224.8C586.8 224.4 589.4 224 592 224C618.5 224 640 245.5 640 272S618.5 320 592 320z" />
+    <glyph glyph-name="cube"
+      unicode="&#xF1B2;"
+      horiz-adv-x="512" d=" M239.1 441.7L31.1 363.7C12.4 356.7 0 338.7 0 318.7V93.6C0 75.4 10.3 58.8 26.5 50.7L234.5 -53.3C248 -60.1 263.9 -60.1 277.4 -53.3L485.4 50.7C501.7 58.8000000000001 511.9 75.5000000000001 511.9 93.6V318.7C511.9 338.7 499.5 356.6 480.8 363.6L272.8 441.6C262 445.8 250 445.8 239.1 441.7zM256 379.6L448 307.6V306.5L256 228.5L64 306.5V307.6L256 379.6zM288 23.6V172.5L448 237.5V103.6L288 23.6z" />
+    <glyph glyph-name="cubes"
+      unicode="&#xF1B3;"
+      horiz-adv-x="512" d=" M488.6 197.8L392 234V342.5C392 357.5 382.7 370.9 368.6 376.2L268.6 413.7C260.5 416.8 251.5000000000001 416.8 243.3 413.7L143.3 376.2C129.2 370.9 119.9 357.5 119.9 342.5V234L23.3 197.8C9.3 192.5 0 179.1 0 164.1V54C0 40.4 7.7 27.9 19.9 21.8L119.9 -28.2C130 -33.3 142 -33.3 152.1 -28.2L256 23.8L359.9 -28.2C370 -33.3 382 -33.3 392.1 -28.2L492.1 21.8C504.3 27.9 511.9999999999999 40.4 511.9999999999999 54V164.1C511.9999999999999 179.1 502.6999999999999 192.5 488.6 197.8zM358 233.2L273 201.3V269.5L358 306.5V233.2zM154 343.9L256 382.1L358 343.9V343.3L256 301.9L154 343.3V343.9zM238 52.8L153 10.3V89.4L238 128.2V52.8zM238 164.8L136 123.4L34 164.8V165.4L136 203.6L238 165.4V164.8zM478 52.8L393 10.3V89.4L478 128.2V52.8zM478 164.8L376 123.4L274 164.8V165.4L376 203.6L478 165.4V164.8z" />
+    <glyph glyph-name="cut"
+      unicode="&#xF0C4;"
+      horiz-adv-x="448" d=" M278.06 192L444.48 358.43C449.17 363.12 449.17 370.7200000000001 444.48 375.4C411.68 408.2 358.49 408.2 325.69 375.4L210.18 259.88L185.32 284.74C189.63 295.66 192 307.55 192 320C192 373.02 149.02 416 96 416S0 373.02 0 320S42.98 224 96 224C100.54 224 104.99 224.32 109.36 224.93L142.29 192L109.36 159.07C104.99 159.68 100.53 160 96 160C42.98 160 0 117.02 0 64S42.98 -32 96 -32S192 10.98 192 64C192 76.45 189.63 88.34 185.32 99.26L210.18 124.12L325.69 8.6C358.49 -24.2 411.68 -24.2 444.48 8.6C449.17 13.28 449.17 20.88 444.48 25.5700000000001L278.06 192zM96 288C78.36 288 64 302.36 64 320S78.36 352 96 352S128 337.64 128 320S113.64 288 96 288zM96 32C78.36 32 64 46.36 64 64S78.36 96 96 96S128 81.64 128 64S113.64 32 96 32z" />
+    <glyph glyph-name="database"
+      unicode="&#xF1C0;"
+      horiz-adv-x="448" d=" M448 374.857V329.1430000000001C448 288.857 347.667 256 224 256S0 288.857 0 329.1430000000001V374.857C0 415.1430000000001 100.333 448 224 448S448 415.1430000000001 448 374.857zM448 272V169.143C448 128.857 347.667 96 224 96S0 128.857 0 169.143V272C48.125 238.857 136.208 223.428 224 223.428S399.874 238.857 448 272zM448 112V9.143C448 -31.143 347.667 -64 224 -64S0 -31.143 0 9.143V112C48.125 78.857 136.208 63.428 224 63.428S399.874 78.857 448 112z" />
+    <glyph glyph-name="deaf"
+      unicode="&#xF2A4;"
+      horiz-adv-x="512" d=" M216 188C216 172.536 203.464 160 188 160S160 172.536 160 188C160 232.112 195.888 268 240 268S320 232.112 320 188C320 172.536 307.464 160 292 160S264 172.536 264 188C264 201.234 253.233 212 240 212S216 201.234 216 188zM240 364C142.953 364 64 285.0470000000001 64 188C64 172.536 76.536 160 92 160S120 172.536 120 188C120 254.168 173.832 308 240 308S360 254.168 360 188C360 112.836 288.991 117.689 288.003 44.378L288 44C288 15.327 264.673 -8 236 -8C220.536 -8 208 -20.536 208 -36S220.536 -64 236 -64C295.475 -64 343.876 -15.672 344 43.774C344.5950000000001 78.202 416 92.014 416 188C416 285.0470000000001 337.0470000000001 364 240 364zM508.485 416.201L480.2 444.485C475.513 449.171 467.916 449.171 463.229 444.485L376.2 357.456C371.514 352.77 371.514 345.1720000000001 376.2 340.485L404.485 312.2C409.171 307.514 416.769 307.514 421.4550000000001 312.2L508.485 399.229C513.172 403.917 513.172 411.515 508.485 416.201zM168.97 133.255C164.284 137.941 156.686 137.941 152 133.255L3.515 -15.23C-1.171 -19.916 -1.171 -27.514 3.515 -32.201L31.8 -60.485C36.487 -65.171 44.084 -65.171 48.771 -60.485L197.256 88C201.942 92.686 201.942 100.284 197.256 104.971L168.97 133.255z" />
+    <glyph glyph-name="democrat"
+      unicode="&#xF747;"
+      horiz-adv-x="640" d=" M637.3 191.1L617.6999999999999 220.5C589.4999999999999 262.8 542.4 288 491.5999999999999 288H256L174.8 369.2000000000001C194.9 389.3 197.4 420.3 182.3 443.1C178.9 448.3 171.5 449.0000000000001 167.1 444.6L125.3 402.8000000000001L82.4 445.6C78.8 449.2 72.8 448.6 70 444.4C57.7 425.8 59.7 400.4 76.1 384C79.4 380.7 83.4 378.7 87.4 376.5C85.2 374.8 82.7 373.4 81 371.1L6.4 271.8C-0.9 262.1 -2 249.1 3.4 238.3L17.7 209.7C23.1 198.9 34.2 192 46.3 192H77.3C85.8 192 93.9 195.4 99.9 201.4L138 236L192 128H544V205.8C560.2 193.6 562.3 188.2 584.1 155.5C589 148.1 598.9 146.2 606.3000000000001 151.1L632.9000000000001 168.8C640.2 173.8 642.2 183.7 637.3000000000001 191.1zM296.2 204.7L279.7 188.6L283.5999999999999 165.9C284.2999999999999 161.8 279.9999999999999 158.7000000000001 276.3999999999999 160.6L256 171.3L235.6 160.6C232 158.7000000000001 227.7 161.8 228.4 165.9L232.3 188.6L215.8 204.7C212.8 207.6 214.5 212.6 218.6 213.2L241.4000000000001 216.5L251.6 237.2C253.4000000000001 240.9 258.7000000000001 240.9 260.6 237.2L270.8 216.5L293.6 213.2C297.6 212.6 299.2000000000001 207.6 296.2000000000001 204.7zM408.2 204.7L391.7 188.6L395.5999999999999 165.9C396.2999999999999 161.8 391.9999999999999 158.7000000000001 388.3999999999999 160.6L368 171.3L347.6 160.6C344 158.7000000000001 339.7000000000001 161.8 340.4000000000001 165.9L344.3 188.6L327.8 204.7C324.8 207.6 326.5 212.6 330.6 213.2L353.4000000000001 216.5L363.6 237.2C365.4000000000001 240.9 370.7000000000001 240.9 372.6 237.2L382.8 216.5L405.6 213.2C409.6 212.6 411.2000000000001 207.6 408.2000000000001 204.7zM520.1999999999999 204.7L503.6999999999999 188.6L507.5999999999999 165.9C508.2999999999999 161.8 503.9999999999999 158.7000000000001 500.3999999999999 160.6L480 171.3L459.6 160.6C456 158.7000000000001 451.7 161.8 452.4 165.9L456.3 188.6L439.8 204.7C436.8 207.6 438.5 212.6 442.6 213.2L465.4 216.5L475.6 237.2C477.4 240.9 482.7 240.9 484.6 237.2L494.8 216.5L517.6 213.2C521.6 212.6 523.2 207.6 520.2 204.7zM192 -48C192 -56.8 199.2 -64 208 -64H272C280.8 -64 288 -56.8 288 -48V32H448V-48C448 -56.8 455.2 -64 464 -64H528C536.8 -64 544 -56.8 544 -48V96H192V-48z" />
+    <glyph glyph-name="desktop"
+      unicode="&#xF108;"
+      horiz-adv-x="576" d=" M528 448H48C21.5 448 0 426.5 0 400V80C0 53.5 21.5 32 48 32H240L224 -16H152C138.7 -16 128 -26.7 128 -40S138.7 -64 152 -64H424C437.3 -64 448 -53.3 448 -40S437.3 -16 424 -16H352L336 32H528C554.5 32 576 53.5 576 80V400C576 426.5 554.5 448 528 448zM512 96H64V384H512V96z" />
+    <glyph glyph-name="dharmachakra"
+      unicode="&#xF655;"
+      horiz-adv-x="512" d=" M495 222.94L477.78 221.86C472.51 261.35 456.99 297.5 433.92 327.7L446.87 339.13C453.79 345.24 454.1199999999999 355.92 447.6 362.44L426.44 383.6C419.9100000000001 390.13 409.23 389.79 403.13 382.87L391.7 369.93C361.5 392.99 325.35 408.51 285.87 413.79L286.94 431C287.52 440.21 280.2 448 270.97 448H241.03C231.8 448 224.49 440.21 225.06 431L226.14 413.78C186.65 408.51 150.5 392.99 120.31 369.92L108.88 382.87C102.77 389.79 92.09 390.12 85.57 383.6L64.4 362.44C57.87 355.91 58.21 345.23 65.13 339.13L78.08 327.7C55.02 297.5 39.5 261.35 34.22 221.86L17 222.94C7.79 223.52 0 216.2 0 206.97V177.03C0 167.8 7.79 160.49 17 161.06L34.22 162.1399999999999C39.49 122.6499999999999 55.01 86.4999999999999 78.08 56.31L65.13 44.8799999999999C58.21 38.7699999999999 57.88 28.0899999999999 64.4 21.5699999999999L85.57 0.3999999999999C92.1 -6.1300000000001 102.78 -5.7900000000001 108.88 1.1299999999999L120.31 14.0799999999999C150.51 -8.9800000000001 186.66 -24.5000000000001 226.15 -29.7800000000001L225.06 -47C224.48 -56.21 231.8 -64 241.03 -64H270.9700000000001C280.2000000000001 -64 287.5100000000001 -56.21 286.9400000000001 -47L285.8600000000001 -29.78C325.3500000000001 -24.51 361.5000000000001 -8.99 391.7000000000001 14.08L403.1300000000001 1.1300000000001C409.2400000000001 -5.79 419.9200000000001 -6.1199999999999 426.4400000000001 0.4L447.6100000000001 21.5700000000001C454.14 28.1 453.8000000000001 38.78 446.8800000000001 44.8800000000001L433.9300000000001 56.3100000000001C456.9900000000001 86.5100000000001 472.51 122.6600000000001 477.7900000000001 162.1400000000001L495.0100000000001 161.0600000000001C504.2200000000001 160.4800000000001 512.0100000000001 167.8000000000001 512.0100000000001 177.0300000000001V206.9700000000001C512.0000000000001 216.2000000000001 504.2100000000001 223.5100000000001 495.0000000000001 222.9400000000001zM281.84 349.39C306.65 345.32 329.47 335.73 349.07 321.61L306.45 273.32C297.72 278.76 288.13 282.86 277.83 285.27L281.84 349.39zM230.16 349.39L234.17 285.27C223.88 282.86 214.28 278.75 205.55 273.32L162.93 321.61C182.53 335.73 205.35 345.32 230.16 349.39zM126.39 285.06L174.69 242.45C169.25 233.72 165.15 224.12 162.73 213.83L98.61 217.84C102.68 242.65 112.27 265.46 126.39 285.06zM98.61 166.16L162.73 170.17C165.14 159.88 169.25 150.28 174.68 141.55L126.39 98.93C112.27 118.53 102.68 141.35 98.61 166.16zM230.16 34.61C205.35 38.68 182.53 48.27 162.93 62.39L205.54 110.69C214.27 105.25 223.87 101.15 234.16 98.73L230.16 34.61zM256 160C238.33 160 224 174.33 224 192S238.33 224 256 224S288 209.67 288 192S273.67 160 256 160zM281.84 34.61L277.83 98.73C288.12 101.14 297.72 105.25 306.45 110.69L349.06 62.39C329.46 48.27 306.65 38.68 281.8400000000001 34.61zM385.61 98.94L337.32 141.56C342.76 150.29 346.86 159.88 349.2699999999999 170.18L413.39 166.17C409.32 141.35 399.7299999999999 118.53 385.6099999999999 98.94zM349.27 213.83C346.86 224.12 342.75 233.72 337.31 242.45L385.61 285.06C399.73 265.4600000000001 409.32 242.64 413.39 217.83L349.27 213.83z" />
+    <glyph glyph-name="diagnoses"
+      unicode="&#xF470;"
+      horiz-adv-x="640" d=" M496 192C504.8 192 512 199.2 512 208S504.8 224 496 224S480 216.8 480 208S487.2 192 496 192zM320 272C368.5 272 408 311.5 408 360S368.5 448 320 448S232 408.5 232 360S271.5 272 320 272zM59.8 84C70 68.7 89.1 66.2 102.7 74.2C118.9 83.8 158.9 105.9 208 122.8V32H432V122.7C481.1 105.9 521.1 83.7 537.3 74.1C550.9 66.1 570 68.8 580.1999999999999 83.9L597.9999999999999 110.6C606.7999999999998 123.8 605.5999999999999 145.2 587.9999999999999 155.7C576.0999999999999 162.8 558.2999999999998 172.7 536.8999999999999 183.1C508.7999999999998 137 437.4999999999999 165.3 449.1999999999999 218.2C409.3 230.8 365.1 240 320 240C263 240 207.1 225.5 160 207.8C159.8 167.6 112.4 144.5 80.8 171.8C69.6 165.8 59.5 160.2 52.1 155.8C34.5 145.3 33.3 124 42.1 110.7L59.8 84zM368 104C381.3 104 392 93.3 392 80S381.3 56 368 56S344 66.7 344 80S354.7 104 368 104zM272 200C285.3 200 296 189.3 296 176S285.3 152 272 152S248 162.7 248 176S258.7 200 272 200zM112 192C120.8 192 128 199.2 128 208S120.8 224 112 224S96 216.8 96 208S103.2 192 112 192zM624 0H16C7.2 0 0 -7.2 0 -16V-48C0 -56.8 7.2 -64 16 -64H624C632.8 -64 640 -56.8 640 -48V-16C640 -7.2 632.8 0 624 0z" />
+    <glyph glyph-name="dice-d20"
+      unicode="&#xF6CF;"
+      horiz-adv-x="480" d=" M106.75 232.94L1.2 77.05C-1.88 72.05 1.3 65.55 7.13 64.91L215.39 42.84L106.75 232.9400000000001zM7.41 132.57L82.7 254.92L6.06 300.9C3.39 302.5 0 300.5800000000001 0 297.4700000000001V134.66C0 130.63 5.29 129.13 7.41 132.57zM18.25 24.4L212.65 -63.26C217.95 -65.71 224 -61.83 224 -56V9.67L20.45 31.97C16 32.47 14.22 26.3800000000001 18.25 24.4zM99.47 282.18L179.4 425.12C183.74 432.18 175.81 440.37 168.62 436.26L17.81 337.65C15.34 336.03 15.42 332.39 17.94 330.87L99.47 282.18zM240 272H349.21L253.63 440.38C250.5 445.46 245.25 448 240 448S229.5 445.46 226.37 440.38L130.79 272H240zM473.94 300.9L397.3 254.91L472.59 132.56C474.7 129.12 480.0000000000001 130.62 480.0000000000001 134.66V297.4700000000001C480.0000000000001 300.5800000000001 476.6100000000001 302.5 473.9400000000001 300.9zM380.53 282.18L462.0599999999999 330.88C464.5899999999999 332.4 464.66 336.04 462.1899999999999 337.6600000000001L311.38 436.26C304.19 440.37 296.26 432.18 300.6 425.12L380.53 282.18zM459.55 31.97L256 9.68V-55.99C256 -61.83 262.05 -65.7 267.35 -63.25L461.75 24.41C465.78 26.38 464 32.47 459.55 31.97zM373.25 232.9400000000001L264.62 42.84L472.8799999999999 64.91C478.7099999999999 65.56 481.8899999999999 72.05 478.8099999999999 77.05L373.25 232.94zM240 240H139.57L240 64.25L340.43 240H240z" />
+    <glyph glyph-name="dice-d6"
+      unicode="&#xF6D1;"
+      horiz-adv-x="448" d=" M422.19 338.05L256.21 438.93C236.3 451.03 211.69 451.03 191.78 438.93L25.81 338.05C20.49 334.82 20.52 326.78 25.87 323.5900000000001L224 205.45L422.14 323.59C427.49 326.78 427.52 334.81 422.19 338.05zM436.03 293.42L240 176.54V-47.28C240 -60.16 253.39 -68.1899999999999 264.05 -61.71L416.2100000000001 30.77C435.8900000000001 42.73 448.0000000000001 64.71 448.0000000000001 88.47V286.17C448.0000000000001 292.5800000000001 441.3600000000001 296.6 436.0300000000001 293.42zM0 286.17V88.47C0 64.7000000000001 12.11 42.73 31.79 30.77L183.95 -61.6999999999999C194.62 -68.1799999999999 208 -60.1599999999999 208 -47.2699999999999V176.54L11.97 293.42C6.64 296.6 0 292.5800000000001 0 286.17z" />
+    <glyph glyph-name="dice-five"
+      unicode="&#xF523;"
+      horiz-adv-x="448" d=" M384 416H64C28.65 416 0 387.35 0 352V32C0 -3.35 28.65 -32 64 -32H384C419.35 -32 448 -3.35 448 32V352C448 387.35 419.35 416 384 416zM128 64C110.33 64 96 78.33 96 96S110.33 128 128 128S160 113.67 160 96S145.67 64 128 64zM128 256C110.33 256 96 270.3300000000001 96 288S110.33 320 128 320S160 305.67 160 288S145.67 256 128 256zM224 160C206.33 160 192 174.33 192 192S206.33 224 224 224S256 209.67 256 192S241.67 160 224 160zM320 64C302.33 64 288 78.33 288 96S302.33 128 320 128S352 113.67 352 96S337.67 64 320 64zM320 256C302.33 256 288 270.3300000000001 288 288S302.33 320 320 320S352 305.67 352 288S337.67 256 320 256z" />
+    <glyph glyph-name="dice-four"
+      unicode="&#xF524;"
+      horiz-adv-x="448" d=" M384 416H64C28.65 416 0 387.35 0 352V32C0 -3.35 28.65 -32 64 -32H384C419.35 -32 448 -3.35 448 32V352C448 387.35 419.35 416 384 416zM128 64C110.33 64 96 78.33 96 96S110.33 128 128 128S160 113.67 160 96S145.67 64 128 64zM128 256C110.33 256 96 270.3300000000001 96 288S110.33 320 128 320S160 305.67 160 288S145.67 256 128 256zM320 64C302.33 64 288 78.33 288 96S302.33 128 320 128S352 113.67 352 96S337.67 64 320 64zM320 256C302.33 256 288 270.3300000000001 288 288S302.33 320 320 320S352 305.67 352 288S337.67 256 320 256z" />
+    <glyph glyph-name="dice-one"
+      unicode="&#xF525;"
+      horiz-adv-x="448" d=" M384 416H64C28.65 416 0 387.35 0 352V32C0 -3.35 28.65 -32 64 -32H384C419.35 -32 448 -3.35 448 32V352C448 387.35 419.35 416 384 416zM224 160C206.33 160 192 174.33 192 192S206.33 224 224 224S256 209.67 256 192S241.67 160 224 160z" />
+    <glyph glyph-name="dice-six"
+      unicode="&#xF526;"
+      horiz-adv-x="448" d=" M384 416H64C28.65 416 0 387.35 0 352V32C0 -3.35 28.65 -32 64 -32H384C419.35 -32 448 -3.35 448 32V352C448 387.35 419.35 416 384 416zM128 64C110.33 64 96 78.33 96 96S110.33 128 128 128S160 113.67 160 96S145.67 64 128 64zM128 160C110.33 160 96 174.33 96 192S110.33 224 128 224S160 209.67 160 192S145.67 160 128 160zM128 256C110.33 256 96 270.3300000000001 96 288S110.33 320 128 320S160 305.67 160 288S145.67 256 128 256zM320 64C302.33 64 288 78.33 288 96S302.33 128 320 128S352 113.67 352 96S337.67 64 320 64zM320 160C302.33 160 288 174.33 288 192S302.33 224 320 224S352 209.67 352 192S337.67 160 320 160zM320 256C302.33 256 288 270.3300000000001 288 288S302.33 320 320 320S352 305.67 352 288S337.67 256 320 256z" />
+    <glyph glyph-name="dice-three"
+      unicode="&#xF527;"
+      horiz-adv-x="448" d=" M384 416H64C28.65 416 0 387.35 0 352V32C0 -3.35 28.65 -32 64 -32H384C419.35 -32 448 -3.35 448 32V352C448 387.35 419.35 416 384 416zM128 256C110.33 256 96 270.3300000000001 96 288S110.33 320 128 320S160 305.67 160 288S145.67 256 128 256zM224 160C206.33 160 192 174.33 192 192S206.33 224 224 224S256 209.67 256 192S241.67 160 224 160zM320 64C302.33 64 288 78.33 288 96S302.33 128 320 128S352 113.67 352 96S337.67 64 320 64z" />
+    <glyph glyph-name="dice-two"
+      unicode="&#xF528;"
+      horiz-adv-x="448" d=" M384 416H64C28.65 416 0 387.35 0 352V32C0 -3.35 28.65 -32 64 -32H384C419.35 -32 448 -3.35 448 32V352C448 387.35 419.35 416 384 416zM128 256C110.33 256 96 270.3300000000001 96 288S110.33 320 128 320S160 305.67 160 288S145.67 256 128 256zM320 64C302.33 64 288 78.33 288 96S302.33 128 320 128S352 113.67 352 96S337.67 64 320 64z" />
+    <glyph glyph-name="dice"
+      unicode="&#xF522;"
+      horiz-adv-x="640" d=" M592 256H473.26C485.95 226.41 480.38 190.8 456.26 166.68L320 30.42V-16C320 -42.51 341.49 -64 368 -64H592C618.51 -64 640 -42.51 640 -16V208C640 234.51 618.51 256 592 256zM480 72C466.75 72 456 82.75 456 96C456 109.26 466.75 120 480 120S504 109.26 504 96C504 82.75 493.25 72 480 72zM433.63 258.7L258.7 433.63C239.54 452.79 208.47 452.79 189.31 433.63L14.37 258.7C-4.79 239.54 -4.79 208.47 14.37 189.31L189.3 14.37C208.46 -4.79 239.53 -4.79 258.69 14.37L433.63 189.3C452.79 208.47 452.79 239.54 433.63 258.7000000000001zM96 200C82.75 200 72 210.75 72 224C72 237.26 82.75 248 96 248S120 237.26 120 224C120 210.75 109.25 200 96 200zM224 72C210.75 72 200 82.75 200 96C200 109.26 210.75 120 224 120S248 109.26 248 96C248 82.75 237.25 72 224 72zM224 200C210.75 200 200 210.75 200 224C200 237.26 210.75 248 224 248S248 237.26 248 224C248 210.75 237.25 200 224 200zM224 328C210.75 328 200 338.75 200 352C200 365.26 210.75 376 224 376S248 365.26 248 352C248 338.75 237.25 328 224 328zM352 200C338.75 200 328 210.75 328 224C328 237.26 338.75 248 352 248S376 237.26 376 224C376 210.75 365.25 200 352 200z" />
+    <glyph glyph-name="digital-tachograph"
+      unicode="&#xF566;"
+      horiz-adv-x="640" d=" M608 352H32C14.33 352 0 337.67 0 320V64C0 46.33 14.33 32 32 32H608C625.67 32 640 46.33 640 64V320C640 337.67 625.67 352 608 352zM304 96C304 91.58 300.42 88 296 88H72C67.58 88 64 91.58 64 96V104C64 108.42 67.58 112 72 112H296C300.42 112 304 108.42 304 104V96zM72 160V176C72 180.42 75.58 184 80 184H96C100.42 184 104 180.42 104 176V160C104 155.58 100.42 152 96 152H80C75.58 152 72 155.58 72 160zM136 160V176C136 180.42 139.58 184 144 184H160C164.42 184 168 180.42 168 176V160C168 155.58 164.42 152 160 152H144C139.58 152 136 155.58 136 160zM200 160V176C200 180.42 203.58 184 208 184H224C228.42 184 232 180.42 232 176V160C232 155.58 228.42 152 224 152H208C203.58 152 200 155.58 200 160zM264 160V176C264 180.42 267.58 184 272 184H288C292.42 184 296 180.42 296 176V160C296 155.58 292.42 152 288 152H272C267.58 152 264 155.58 264 160zM304 224C304 215.16 296.84 208 288 208H80C71.16 208 64 215.16 64 224V272C64 280.8400000000001 71.16 288 80 288H288C296.84 288 304 280.8400000000001 304 272V224zM576 96C576 91.58 572.42 88 568 88H344C339.58 88 336 91.58 336 96V104C336 108.42 339.58 112 344 112H568C572.42 112 576 108.42 576 104V96z" />
+    <glyph glyph-name="directions"
+      unicode="&#xF5EB;"
+      horiz-adv-x="512" d=" M502.61 214.68L278.68 438.61C266.1600000000001 451.13 245.85 451.13 233.32 438.61L9.39 214.68C-3.13 202.15 -3.13 181.85 9.39 169.32L233.32 -54.61C245.84 -67.14 266.15 -67.14 278.68 -54.61L502.61 169.32C515.13 181.85 515.13 202.15 502.61 214.68zM401.63 202.12L317.42 124.39C312.3 119.66 303.99 123.29 303.99 130.27V184H207.99V120C207.99 115.58 204.41 112 199.99 112H167.99C163.57 112 159.99 115.58 159.99 120V200C159.99 217.67 174.32 232 191.99 232H303.99V285.73C303.99 292.7 312.29 296.34 317.42 291.61L401.63 213.88C405.06 210.71 405.06 205.29 401.63 202.12z" />
+    <glyph glyph-name="divide"
+      unicode="&#xF529;"
+      horiz-adv-x="448" d=" M224 96C188.65 96 160 67.35 160 32S188.65 -32 224 -32S288 -3.35 288 32S259.35 96 224 96zM224 288C259.35 288 288 316.65 288 352S259.35 416 224 416S160 387.35 160 352S188.65 288 224 288zM416 240H32C14.33 240 0 225.67 0 208V176C0 158.33 14.33 144 32 144H416C433.67 144 448 158.33 448 176V208C448 225.67 433.67 240 416 240z" />
+    <glyph glyph-name="dizzy"
+      unicode="&#xF567;"
+      horiz-adv-x="496" d=" M248 440C111 440 0 329 0 192S111 -56 248 -56S496 55 496 192S385 440 248 440zM152 233.4L123.3 204.7C108.5 189.9 85.5 212.2 100.7 227.3L129.4 256L100.7 284.7C85.7 299.7 108.4 322.3 123.3 307.3L152 278.6L180.7 307.3C195.7 322.3 218.3 299.6 203.3 284.7L174.6 256L203.3 227.3C218.5 212.1 195.4 189.9 180.7 204.7L152 233.4zM248 32C212.7 32 184 60.7 184 96S212.7 160 248 160S312 131.3 312 96S283.3 32 248 32zM395.3 227.3C410.5 212.1 387.4000000000001 189.9 372.7 204.7L344 233.4L315.3 204.7C300.5 189.9 277.5 212.2 292.7 227.3L321.4 256L292.7 284.7C277.7 299.7 300.4 322.3 315.3 307.3L344 278.6L372.7 307.3C387.7 322.3 410.3 299.6 395.3 284.7L366.6 256L395.3 227.3z" />
+    <glyph glyph-name="dna"
+      unicode="&#xF471;"
+      horiz-adv-x="448" d=" M0.1 -46.1C-1 -55.6 6.4 -63.9 16 -63.9L48.3 -64C56.4 -64 63.2 -58.1 64.3 -50.1C65 -45.2 66.1 -39 67.7 -32H380C381.6 -38.9 382.9 -45.2 383.5 -50.1C384.6 -58.1 391.4 -64.1 399.5 -64L431.8 -63.9C441.4000000000001 -63.9 448.9000000000001 -55.6 447.7 -46.1C443.1 -8.2 422.1 82.9 328.8 161.6C311.2 149.2000000000001 291.7 137.4 270.3 126.2000000000001C276.5 121.6 281.7 116.8000000000001 287.3 112.0000000000001H159.7C181 130.1000000000001 206.7 147.6000000000001 238.4 163.4C410.5 248.9 442.1 382.2 447.9 430.1C449 439.6 441.6 447.9 432 447.9L399.6 448C391.5 448 384.7000000000001 442.1 383.6 434.1C382.9000000000001 429.2 381.8 423 380.2000000000001 416H67.8C66.2 423 65.1 429.1 64.4 434.1C63.3 442.1 56.5 448.1 48.4 448L16.1 447.9C6.5 447.9 -1 439.6 0.1 430.1C5.3 387.2 31.4 276.2 160 192C31.5 107.8 5.3 -3.2 0.1 -46.1zM224 228.4C198.9 242.1 177.6 256.8 159.7 272H288.2C270.4 256.8 249.1 242 224 228.4zM355.1 352C349.3 341.6 342.3 330.9 334.1 320H114C105.7 330.9 98.7 341.6 93 352H355.1zM92.9 32C98.7 42.4 105.7 53.1 113.9 64H333.3C341.6 53.1 348.7 42.4 354.5 32H92.9z" />
+    <glyph glyph-name="dog"
+      unicode="&#xF6D3;"
+      horiz-adv-x="512" d=" M496 352H432L424.84 366.31A32 32 0 0 1 396.2200000000001 384H342.6L315.3200000000001 411.28C305.23 421.36 288 414.2200000000001 288 399.9700000000001V250.13L416 204.42V240H448C483.35 240 512 268.65 512 304V336C512 344.8400000000001 504.84 352 496 352zM384 304C375.1600000000001 304 368 311.16 368 320S375.1600000000001 336 384 336S400 328.8400000000001 400 320S392.84 304 384 304zM96 224C78.36 224 64 238.36 64 256C64 273.67 49.67 288 32 288S0 273.67 0 256C0 214.34 26.83 179.15 64 165.9V-48C64 -56.84 71.16 -64 80 -64H144C152.84 -64 160 -56.84 160 -48V64H320V-48C320 -56.84 327.1600000000001 -64 336 -64H400C408.84 -64 416 -56.84 416 -48V170.45L266.05 224H96z" />
+    <glyph glyph-name="dollar-sign"
+      unicode="&#xF155;"
+      horiz-adv-x="288" d=" M209.2 214.6L101.2 246.2C88.7 249.8 80 261.5 80 274.5C80 290.8 93.2 304 109.5 304H175.8C188 304 200 300.3 210 293.5C216.1 289.4 224.3 290.4 229.5 295.5L264.3 329.5C271.4000000000001 336.4 270.4000000000001 347.9 262.5 354C238 373.2 207.4 383.9 176 384V432C176 440.8 168.8 448 160 448H128C119.2 448 112 440.8 112 432V384H109.5C45.8 384 -5.4 329.3 0.5 264.4C4.7 218.3 39.9 180.8 84.3 167.8L186.8 137.8C199.3 134.1 208 122.5 208 109.5C208 93.2 194.8 80 178.5 80H112.2C100 80 88 83.7 78 90.5C71.9 94.6 63.7 93.6 58.5 88.5L23.7 54.5C16.6 47.6 17.6 36.1 25.5 30C50 10.8 80.6 0.1 112 0V-48C112 -56.8 119.2 -64 128 -64H160C168.8 -64 176 -56.8 176 -48V0.2C222.6 1.1 266.3 28.8 281.7 72.9C303.2 134.5 267.1 197.7 209.2 214.6z" />
+    <glyph glyph-name="dolly-flatbed"
+      unicode="&#xF474;"
+      horiz-adv-x="640" d=" M208 128H592C600.8 128 608 135.2 608 144V400C608 408.8 600.8 416 592 416H448V288L400 320L352 288V416H208C199.2 416 192 408.8 192 400V144C192 135.2 199.2 128 208 128zM624 64H128V432C128 440.8 120.8 448 112 448H16C7.2 448 0 440.8 0 432V400C0 391.2 7.2 384 16 384H64V16C64 7.2 71.2 0 80 0H162.9C161.1 -5 160 -10.4 160 -16C160 -42.5 181.5 -64 208 -64S256 -42.5 256 -16C256 -10.4 254.8 -5 253.1 0H451C449.2 -5 448.1 -10.4 448.1 -16C448.1 -42.5 469.6 -64 496.1 -64S544.1 -42.5 544.1 -16C544.1 -10.4 542.9 -5 541.2 0H624C632.8 0 640 7.2 640 16V48C640 56.8 632.8 64 624 64z" />
+    <glyph glyph-name="dolly"
+      unicode="&#xF472;"
+      horiz-adv-x="576" d=" M294.2 170.3C312.2 165.3 328.9 156.9 343.7 145.6L505.2 199.4C513.6 202.2000000000001 518.1 211.3000000000001 515.3 219.6L454.9 400.8C452.1 409.2 443 413.7 434.7 410.9L373.6 390.5L406.7 291.1L346 271L312.9 370.4L251.3 349.9C242.9 347.1 238.4 338 241.2 329.7L294.2 170.3zM575.2 121.6L565 152C562.2 160.4 553.1 164.9 544.8 162.1L331.3 90.9C314.1 112.9 287.7 127.3 257.8 127.9L158.4 426.1C154 439.2 141.8 448 128 448H16C7.2 448 0 440.8 0 432V400C0 391.2 7.2 384 16 384H104.9L197.1 107.3C171 86.9 155.4 53.7 161.1 16.8C167.2 -22.6 199 -55.5 238.4000000000001 -62.4C298.6 -73.1 350.7000000000001 -27.6 351.8000000000001 30.2L565.1000000000001 101.4C573.4000000000001 104.2000000000001 578.0000000000001 113.2000000000001 575.2000000000002 121.6zM256 -16C229.5 -16 208 5.5 208 32S229.5 80 256 80S304 58.5 304 32S282.5 -16 256 -16z" />
+    <glyph glyph-name="donate"
+      unicode="&#xF4B9;"
+      horiz-adv-x="512" d=" M256 32C370.9 32 464 125.1 464 240S370.9 448 256 448S48 354.9 48 240S141.1 32 256 32zM233.8 350.6V367.4C233.8 376.6 241.2 384 250.4 384H261.5C270.7 384 278.1 376.6 278.1 367.4V350.4C293.6 349.6 308.6 344.3 321.1 335C326.7000000000001 330.9 327.3 322.7 322.3 317.9L306 302.4C302.2 298.7000000000001 296.5 298.6 292 301.4C286.6 304.8 280.6 306.5 274.2 306.5H235.3C226.3 306.5 219 298.3 219 288.2C219 280 224 272.7 231.1 270.6L293.4 251.9C319.1 244.2 337.1 219.5 337.1 191.8C337.1 157.8 310.7 130.3 278 129.4V112.6C278 103.4 270.6 96 261.3999999999999 96H250.3C241.1 96 233.7 103.4 233.7 112.6V129.6C218.1999999999999 130.4 203.1999999999999 135.7000000000001 190.6999999999999 145C185.0999999999999 149.1 184.5 157.3 189.5 162.1L205.8 177.6C209.6 181.3 215.3 181.4 219.8 178.6C225.2 175.2000000000001 231.2 173.5 237.6 173.5H276.5C285.5 173.5 292.8 181.7 292.8 191.8C292.8 200 287.8 207.3 280.7 209.4L218.3999999999999 228.1C192.6999999999999 235.8 174.6999999999999 260.5 174.6999999999999 288.2C174.7999999999999 322.2 201.0999999999999 349.7 233.8 350.6zM480 96H447.5C427.9 70 402.9 48.3 374.5 32H438.3C443.6 32 447.9000000000001 28.4 447.9000000000001 24V8C447.9000000000001 3.6 443.6 0 438.3 0H73.6C68.3 0 64 3.6 64 8V24C64 28.4 68.3 32 73.6 32H137.4C109 48.3 84.1 70 64.4 96H32C14.3 96 0 81.7 0 64V-32C0 -49.7 14.3 -64 32 -64H480C497.7 -64 512 -49.7 512 -32V64C512 81.7 497.7 96 480 96z" />
+    <glyph glyph-name="door-closed"
+      unicode="&#xF52A;"
+      horiz-adv-x="640" d=" M624 0H512V397.2C512 425.2200000000001 490.47 448 464 448H175.99C149.52 448 127.99 425.2200000000001 127.99 397.2V0H16C7.16 0 0 -7.16 0 -16V-48C0 -56.84 7.16 -64 16 -64H624C632.84 -64 640 -56.84 640 -48V-16C640 -7.16 632.84 0 624 0zM415.99 160C398.32 160 383.99 174.33 383.99 192S398.32 224 415.99 224S447.99 209.67 447.99 192C448 174.33 433.67 160 415.99 160z" />
+    <glyph glyph-name="door-open"
+      unicode="&#xF52B;"
+      horiz-adv-x="640" d=" M624 0H544V334.55C544 361.81 522.47 384 496 384H384V320H480V-64H624C632.84 -64 640 -56.84 640 -48V-16C640 -7.16 632.84 0 624 0zM312.24 446.99L120.24 397.25C105.99 393.56 96 380.3 96 365.08V0H16C7.16 0 0 -7.16 0 -16V-48C0 -56.84 7.16 -64 16 -64H352V414.82C352 436.4 332.44 452.23 312.24 446.99zM264 160C250.75 160 240 174.33 240 192S250.75 224 264 224S288 209.67 288 192S277.25 160 264 160z" />
+    <glyph glyph-name="dot-circle"
+      unicode="&#xF192;"
+      horiz-adv-x="512" d=" M256 440C119.033 440 8 328.967 8 192S119.033 -56 256 -56S504 55.033 504 192S392.967 440 256 440zM336 192C336 147.888 300.112 112 256 112S176 147.888 176 192S211.888 272 256 272S336 236.112 336 192z" />
+    <glyph glyph-name="dove"
+      unicode="&#xF4BA;"
+      horiz-adv-x="512" d=" M288 280.8V308.9C259.8 345.2 240.9 388.2 233.9 434.1C231.8 447.6 214.9 452.9 206.1 442.4000000000001C185 417.5 168.4 388.3 157.2 355.9C191.4 317.6 237.2 291.3 288 280.8zM400 384C355.8 384 320 348.1 320 303.9V244.5C215.6 250.7 127 315 87 406.2C81.5 418.7 63.8 419.4 58 407.1C41.4 372 32 332.8 32 291.4C32 220.6 66.1 154.5 117.1 105.5C130.3 92.8 143.2 82.3 156 72.7L12.1 36.7C1.4 34 -3.4 21.6 2.6 12.3C20 -14.6 63 -60.2 155.8 -64C163.8 -64.2999999999999 171.8 -61.4 177.9 -56.1L243.1 0H320C408.4 0 480 71.5 480 159.9V320L512 384H400zM400 287.9C391.2 287.9 384 295.1 384 303.9S391.2 319.9 400 319.9S416 312.7000000000001 416 303.9S408.8 287.9 400 287.9z" />
+    <glyph glyph-name="download"
+      unicode="&#xF019;"
+      horiz-adv-x="512" d=" M216 448H296C309.3 448 320 437.3 320 424V256H407.7C425.5 256 434.4 234.5 421.8 221.9L269.7 69.7C262.2 62.2 249.9 62.2 242.4 69.7L90.1 221.9C77.5 234.5 86.4 256 104.2 256H192V424C192 437.3 202.7 448 216 448zM512 72V-40C512 -53.3 501.3 -64 488 -64H24C10.7 -64 0 -53.3 0 -40V72C0 85.3 10.7 96 24 96H170.7L219.7 47C239.8 26.9 272.2 26.9 292.3 47L341.3 96H488C501.3 96 512 85.3 512 72zM388 -16C388 -5 379 4 368 4S348 -5 348 -16S357 -36 368 -36S388 -27 388 -16zM452 -16C452 -5 443 4 432 4S412 -5 412 -16S421 -36 432 -36S452 -27 452 -16z" />
+    <glyph glyph-name="drafting-compass"
+      unicode="&#xF568;"
+      horiz-adv-x="512" d=" M457.01 103.58C431.96 83.25 404.38 66.4 374.47 54.53L428.85 -39.66L482.8 -62.7C492.61 -66.8900000000001 503.6899999999999 -60.4900000000001 504.97 -49.9L511.99 8.35L457.0099999999999 103.58zM499.5 198.14C504.36 205.81 501.39 216.13 493.45 220.53L465.38 236.1C457.9 240.25 448.77 237.56 444.12 230.38C403.01 166.85 332.25 128 256 128C232.07 128 208.77 132.25 186.59 139.53L253.95 256.21C254.65 256.19 255.29 256 255.99 256S257.34 256.19 258.03 256.21L309.12 167.71C340.35 176.67 368.68 193.46 391.73 216.63L339.94 306.34C347.39 319.9700000000001 352 335.37 352 352C352 405.02 309.02 448 256 448S160 405.02 160 352C160 335.37 164.61 319.9700000000001 172.05 306.3400000000001L103.75 188.03C91.2 199.64 79.79 212.62 70.07 227.03C65.28 234.13 56.1 236.65 48.69 232.36L20.94 216.29C13.09 211.75 10.31 201.39 15.3 193.82C30.87 170.18 49.99 149.61 71.28 131.8L0 8.34L7.02 -49.91C8.3 -60.5 19.38 -66.9 29.19 -62.71L83.14 -39.67L153.94 82.96C186.13 70.72 220.62 64 256 64C355.05 64 446.88 115.01 499.5 198.14zM256 384C273.67 384 288 369.67 288 352S273.67 320 256 320S224 334.33 224 352S238.33 384 256 384z" />
+    <glyph glyph-name="dragon"
+      unicode="&#xF6D5;"
+      horiz-adv-x="640" d=" M18.32 192.22L192 224.04L100.72 155.35C90.64 145.2700000000001 97.78 128.04 112.03 128.04H334.73C325.29 154.44 320 182.51 320 211.42V253.6900000000001L200.27 341.29C176.45 357.17 144.98 355.3 123.21 336.7000000000001L5.81 220.36C-6.57 210.03 2.36 189.94 18.32 192.22zM575.19 158.12L474.5300000000001 208.43A47.992 47.992 0 0 0 448 251.35V288.04H512L540.09 265.41C546.09 259.41 554.23 256.04 562.72 256.04H593.69A32 32 0 0 1 622.3100000000001 273.73L636.62 302.35A32.005 32.005 0 0 1 633.6 335.86L559.07 435.24C553.02 443.3 543.54 448 533.47 448H296.02C288.89 448 285.32 439.43 290.36 434.39L352 384.04L292.42 359.2C286.5200000000001 356.25 286.5200000000001 347.8400000000001 292.42 344.89L352 320.04V211.42C352 139.34 388.03 72.03 448 32.04C252.41 25.23 103.44 -8.97 13.9 -28.87C5.78 -30.67 0 -37.88 0 -46.2C0 -56 7.95 -64 17.76 -64H516.84C580.13 -64.01 636.45 -16.44 639.83 46.76C642.35 94.04 617.1 137.16 575.19 158.12zM489.18 381.75L534.83 370.3400000000001C532.08 359.43 522.36 351.45 510.7 352.08C497.7400000000001 352.79 484.85 364.61 489.1800000000001 381.75z" />
+    <glyph glyph-name="draw-polygon"
+      unicode="&#xF5EE;"
+      horiz-adv-x="448" d=" M384 96C383.65 96 383.33 95.9 382.98 95.9L343.7800000000001 161.22C348.85 170.39 352.0000000000001 180.78 352.0000000000001 192S348.8600000000001 213.61 343.7800000000001 222.78L382.98 288.1C383.3300000000001 288.0900000000001 383.6500000000001 288 384 288C419.35 288 448 316.65 448 352S419.35 416 384 416C360.37 416 339.96 403.05 328.88 384H119.12C108.04 403.05 87.63 416 64 416C28.65 416 0 387.35 0 352C0 328.37 12.95 307.9600000000001 32 296.88V87.13C12.95 76.04 0 55.63 0 32C0 -3.35 28.65 -32 64 -32C87.63 -32 108.04 -19.05 119.12 0H328.87C339.96 -19.05 360.36 -32 383.99 -32C419.3400000000001 -32 447.99 -3.35 447.99 32C448 67.35 419.35 96 384 96zM96 87.12V296.88A63.824999999999996 63.824999999999996 0 0 1 119.12 320H327.48L289.0200000000001 255.9C288.67 255.91 288.35 256 288.0000000000001 256C252.6500000000001 256 224.0000000000001 227.35 224.0000000000001 192S252.6500000000001 128 288.0000000000001 128C288.3500000000001 128 288.6700000000001 128.1 289.0200000000001 128.1L327.48 64H119.12A63.748000000000005 63.748000000000005 0 0 1 96 87.12zM272 192C272 200.82 279.18 208 288 208S304 200.82 304 192S296.82 176 288 176S272 183.18 272 192zM400 352C400 343.18 392.82 336 384 336S368 343.18 368 352S375.18 368 384 368S400 360.82 400 352zM64 368C72.82 368 80 360.82 80 352S72.82 336 64 336S48 343.18 48 352S55.18 368 64 368zM48 32C48 40.82 55.18 48 64 48S80 40.82 80 32S72.82 16 64 16S48 23.18 48 32zM384 16C375.18 16 368 23.18 368 32S375.18 48 384 48S400 40.82 400 32S392.82 16 384 16z" />
+    <glyph glyph-name="drum-steelpan"
+      unicode="&#xF56A;"
+      horiz-adv-x="576" d=" M288 416C128.94 416 0 358.69 0 288V96C0 25.31 128.94 -32 288 -32S576 25.31 576 96V288C576 358.69 447.06 416 288 416zM205.01 257.64C200.56 241.03 190.47 227.07 176.7 217.16C100.23 230.54 48 257.2200000000001 48 288C48 318.16 98.11 344.39 172.04 358.03L197.64 313.69C207.5 296.6 210.12 276.7 205.01 257.64zM288 208C266.92 208 246.59 209 227.11 210.7C235.17 236.83 259.26 256 288 256S340.83 236.83 348.89 210.7C329.4100000000001 209 309.08 208 288 208zM352 352C352 316.7100000000001 323.29 288 288 288S224 316.7100000000001 224 352V365.04C244.4 366.92 265.8 368 288 368S331.6 366.92 352 365.04V352zM398.93 217.1C385.12 227.01 374.99 241 370.5300000000001 257.64C365.42 276.7 368.04 296.6 377.9100000000001 313.68L403.56 358.1C477.72 344.5 528 318.2100000000001 528 288C528 257.17 475.6 230.46 398.93 217.1z" />
+    <glyph glyph-name="drum"
+      unicode="&#xF569;"
+      horiz-adv-x="576" d=" M458.08 327.12L560.47 388.55C575.63 397.64 580.53 417.3 571.44 432.46C562.34 447.61 542.7 452.53 527.53 443.43L366.84 347.02A629.3200000000002 629.3200000000002 0 0 1 288 352C128.94 352 0 294.69 0 224V63.17C0 32.71 24.03 4.77 64 -17.2V79.17C64 96.77 78.4 111.17 96 111.17S128 96.77 128 79.17V-43.24C165.4 -54.37 209 -61.68 256 -63.99V47.16C256 64.7600000000001 270.4 79.16 288 79.16S320 64.7600000000001 320 47.16V-64C367 -61.69 410.6 -54.38 448 -43.25V79.16C448 96.7600000000001 462.4 111.16 480 111.16S512 96.7600000000001 512 79.16V-17.21C551.97 4.76 576 32.7 576 63.16V223.99C575.99 266.37 529.46 303.8300000000001 458.08 327.12zM288 144C155.45 144 48 179.82 48 224S155.45 304 288 304C290.34 304 292.62 303.9 294.94 303.88L207.53 251.44C192.37 242.35 187.47 222.69 196.56 207.53C206.12 191.6 226.07 187.92 240.47 196.56L403.18 294.18C477.55 280.5900000000001 528 254.26 528 223.99C528 179.81 420.54 144 288 144z" />
+    <glyph glyph-name="drumstick-bite"
+      unicode="&#xF6D7;"
+      horiz-adv-x="512" d=" M462.79 398.43C396.6500000000001 464.52 289.43 464.52 223.29 398.43C187.81 362.98 160.12 320 160.12 256V170.17L119.5 129.5800000000001C109.8 119.8900000000001 95.46 118.5100000000001 82.72 123.6C61 132.28 35.3 127.8900000000001 17.7 110.31C-5.91 86.72 -5.91 48.47 17.7 24.88C32.98 9.61 54.23 5.3 73.84 9.79C69.34 -9.81 73.66 -31.04 88.94 -46.31C112.55 -69.9 150.82 -69.9 174.43 -46.31C192.03 -28.73 196.42 -3.05 187.74 18.66C182.65 31.39 184.02 45.71 193.73 55.41L234.35 96H320.24C343.44 96 363.81 99.72 382.13 106.03C342.49 149.92 342.3 216.26 383.18 257.1C417.56 291.46 469.94 296.56 511.92 273.9C513.22 318.83 497.11 364.15 462.79 398.43z" />
+    <glyph glyph-name="dumbbell"
+      unicode="&#xF44B;"
+      horiz-adv-x="640" d=" M104 352H56C42.7 352 32 341.3 32 328V224H8C3.6 224 0 220.4 0 216V168C0 163.6 3.6 160 8 160H32V56C32 42.7 42.7 32 56 32H104C117.3 32 128 42.7 128 56V328C128 341.3 117.3 352 104 352zM632 224H608V328C608 341.3 597.3 352 584 352H536C522.7 352 512 341.3 512 328V56C512 42.7 522.7 32 536 32H584C597.3 32 608 42.7 608 56V160H632C636.4 160 640 163.6 640 168V216C640 220.4 636.4 224 632 224zM456 416H408C394.7 416 384 405.3 384 392V224H256V392C256 405.3 245.3 416 232 416H184C170.7 416 160 405.3 160 392V-8C160 -21.3 170.7 -32 184 -32H232C245.3 -32 256 -21.3 256 -8V160H384V-8C384 -21.3 394.7 -32 408 -32H456C469.3 -32 480 -21.3 480 -8V392C480 405.3 469.3 416 456 416z" />
+    <glyph glyph-name="dungeon"
+      unicode="&#xF6D9;"
+      horiz-adv-x="512" d=" M128.73 252.68L45.92 304.44C37.88 309.4600000000001 26.93 306.61 22.99 297.99A254.18999999999997 254.18999999999997 0 0 1 0.54 208.72C-0.05 199.63 7.59 192 16.69 192H113.82C121.78 192 127.9 198.25 128.83 206.16C129.92 215.49 132.07 224.49 135.07 233.1C137.63 240.44 135.32 248.56 128.73 252.68zM319.03 440C298.86 445.18 277.77 448 256 448S213.14 445.18 192.97 440C183.8 437.65 179.06 427.4 182.58 418.61L220.05 314.58A16.003 16.003 0 0 1 235.1 304H276.9C283.65 304 289.67 308.23 291.95 314.5800000000001L329.42 418.61C332.94 427.4000000000001 328.2 437.64 319.03 440zM112 160H16C7.16 160 0 152.84 0 144V80C0 71.16 7.16 64 16 64H112C120.84 64 128 71.16 128 80V144C128 152.84 120.84 160 112 160zM112 32H16C7.16 32 0 24.84 0 16V-48C0 -56.84 7.16 -64 16 -64H112C120.84 -64 128 -56.84 128 -48V16C128 24.84 120.84 32 112 32zM189.31 315.67L152.99 406.4700000000001C149.46 415.3 138.86 419.4600000000001 130.57 414.7800000000001A257.308 257.308 0 0 1 58.96 354.89C52.9 347.5700000000001 55.11 336.4100000000001 63.18 331.37L146.11 279.54C152.62 275.4700000000001 160.77 276.92 166.22 282.3300000000001C171.4 287.48 177.01 292.18 183.01 296.38C189.29 300.79 192.16 308.55 189.31 315.67zM398.18 192H495.31C504.41 192 512.05 199.63 511.46 208.72A254.13500000000002 254.13500000000002 0 0 1 489.01 297.99C485.07 306.61 474.12 309.4600000000001 466.08 304.44L383.27 252.68C376.68 248.56 374.37 240.44 376.93 233.1C379.94 224.49 382.08 215.48 383.17 206.16C384.1 198.25 390.2200000000001 192 398.18 192zM453.03 354.89A257.308 257.308 0 0 1 381.42 414.78C373.1400000000001 419.46 362.54 415.3 359 406.47L322.68 315.67C319.83 308.55 322.7 300.79 328.98 296.39C334.98 292.19 340.5900000000001 287.49 345.7700000000001 282.34C351.2100000000001 276.93 359.3700000000001 275.48 365.8800000000001 279.55L448.8100000000001 331.38C456.8800000000001 336.41 459.1000000000001 347.57 453.0300000000001 354.89zM496 160H400C391.1600000000001 160 384 152.84 384 144V80C384 71.16 391.1600000000001 64 400 64H496C504.84 64 512 71.16 512 80V144C512 152.84 504.84 160 496 160zM496 32H400C391.1600000000001 32 384 24.84 384 16V-48C384 -56.84 391.1600000000001 -64 400 -64H496C504.84 -64 512 -56.84 512 -48V16C512 24.84 504.84 32 496 32zM240 270.38V-24C240 -28.42 243.58 -32 248 -32H264C268.42 -32 272 -28.42 272 -24V270.38C266.77 271.27 261.48 272 256 272S245.23 271.27 240 270.38zM176 228.87V-24C176 -28.42 179.58 -32 184 -32H200C204.42 -32 208 -28.42 208 -24V258.64C195.22 251.19 184.16 241.17 176 228.87zM304 258.64V-24C304 -28.42 307.58 -32 312 -32H328C332.42 -32 336 -28.42 336 -24V228.87C327.84 241.17 316.78 251.19 304 258.64z" />
+    <glyph glyph-name="edit"
+      unicode="&#xF044;"
+      horiz-adv-x="576" d=" M402.6 364.8L492.8 274.6C496.6 270.8 496.6 264.6 492.8 260.8L274.4 42.4L181.6 32.1C169.2 30.7 158.7 41.2 160.1 53.6L170.4 146.4L388.8 364.8C392.6 368.6 398.8 368.6 402.6 364.8zM564.6 387.7L515.8000000000001 436.5C500.6000000000001 451.7 475.9000000000001 451.7 460.6000000000001 436.5L425.2000000000001 401.1C421.4000000000001 397.3 421.4000000000001 391.1 425.2000000000001 387.3L515.4000000000001 297.1C519.2 293.3 525.4000000000001 293.3 529.2 297.1L564.6 332.5C579.8000000000001 347.8 579.8000000000001 372.5 564.6 387.7zM384 101.8V0H64V320H293.8C297 320 300 321.3 302.3 323.5L342.3 363.5C349.9000000000001 371.1 344.5 384 333.8 384H48C21.5 384 0 362.5 0 336V-16C0 -42.5 21.5 -64 48 -64H400C426.5 -64 448 -42.5 448 -16V141.8C448 152.5 435.1 157.8 427.5 150.3L387.5 110.3C385.3 108 384 105 384 101.8z" />
+    <glyph glyph-name="eject"
+      unicode="&#xF052;"
+      horiz-adv-x="448" d=" M448 64V0C448 -17.673 433.673 -32 416 -32H32C14.327 -32 0 -17.673 0 0V64C0 81.673 14.327 96 32 96H416C433.673 96 448 81.673 448 64zM48.053 128H399.939C441.5900000000001 128 463.52 177.674 435.322 208.435L259.383 400.442C240.369 421.185 207.632 421.186 188.616 400.442L12.67 208.435C-15.475 177.732 6.324 128 48.053 128z" />
+    <glyph glyph-name="ellipsis-h"
+      unicode="&#xF141;"
+      horiz-adv-x="512" d=" M328 192C328 152.2 295.8 120 256 120S184 152.2 184 192S216.2 264 256 264S328 231.8 328 192zM432 264C392.2 264 360 231.8 360 192S392.2 120 432 120S504 152.2 504 192S471.8 264 432 264zM80 264C40.2 264 8 231.8 8 192S40.2 120 80 120S152 152.2 152 192S119.8 264 80 264z" />
+    <glyph glyph-name="ellipsis-v"
+      unicode="&#xF142;"
+      horiz-adv-x="192" d=" M96 264C135.8 264 168 231.8 168 192S135.8 120 96 120S24 152.2 24 192S56.2 264 96 264zM24 368C24 328.2 56.2 296 96 296S168 328.2 168 368S135.8 440 96 440S24 407.8 24 368zM24 16C24 -23.8 56.2 -56 96 -56S168 -23.8 168 16S135.8 88 96 88S24 55.8 24 16z" />
+    <glyph glyph-name="envelope-open-text"
+      unicode="&#xF658;"
+      horiz-adv-x="512" d=" M176 232H336C344.84 232 352 239.16 352 248V264C352 272.8400000000001 344.84 280 336 280H176C167.16 280 160 272.8400000000001 160 264V248C160 239.16 167.16 232 176 232zM160 152C160 143.16 167.16 136 176 136H336C344.84 136 352 143.16 352 152V168C352 176.84 344.84 184 336 184H176C167.16 184 160 176.84 160 168V152zM256 30.87C239.58 30.87 223.16 35.93 209.14 46.06L0 197.14V-16C0 -42.51 21.49 -64 48 -64H464C490.51 -64 512 -42.51 512 -16V197.14L302.86 46.06C288.8400000000001 35.94 272.42 30.87 256 30.87zM493.61 285.05C484.76 291.99 476.37 298.52 464 307.86V352C464 378.51 442.51 400 416 400H338.45C335.41 402.2 332.58 404.26 329.41 406.56C312.6 418.83 279.2 448.35 256 448C232.8 448.35 199.41 418.83 182.59 406.56C179.42 404.26 176.59 402.2 173.55 400H96C69.49 400 48 378.51 48 352V307.86C35.63 298.53 27.24 291.99 18.39 285.05A47.995 47.995 0 0 1 0 247.28V236.63L96 167.28V352H416V167.28L512 236.63V247.28C512 262.02 505.22 275.95 493.61 285.05z" />
+    <glyph glyph-name="envelope-open"
+      unicode="&#xF2B6;"
+      horiz-adv-x="512" d=" M512 -16C512 -42.51 490.51 -64 464 -64H48C21.49 -64 0 -42.51 0 -16V247.276A48 48 0 0 0 18.387 285.052C43.3 304.581 63.888 320.4170000000001 182.587 406.563C199.412 418.83 232.797 448.347 256 447.997C279.198 448.351 312.596 418.825 329.413 406.564C448.1 320.427 468.716 304.569 493.613 285.052A48 48 0 0 0 512 247.276V-16zM446.334 180.605C443.771 184.333 438.634 185.2000000000001 434.995 182.512C412.15 165.639 379.533 141.807 329.413 105.433C312.588 93.167 279.2030000000001 63.652 256 64.003C232.789 63.659 199.441 93.146 182.587 105.433C132.473 141.803 99.853 165.637 77.005 182.512C73.366 185.2 68.229 184.333 65.666 180.605L56.594 167.409A7.997999999999999 7.997999999999999 0 0 1 58.433 156.442C81.32 139.543 113.887 115.752 163.736 79.574C184.01 64.793 220.26 31.761 256 32.001C291.724 31.759 327.961 64.7720000000001 348.2630000000001 79.574C398.1130000000001 115.7530000000001 430.6810000000001 139.544 453.566 156.442A7.997999999999999 7.997999999999999 0 0 1 455.405 167.409L446.334 180.605z" />
+    <glyph glyph-name="envelope-square"
+      unicode="&#xF199;"
+      horiz-adv-x="448" d=" M400 416H48C21.49 416 0 394.51 0 368V16C0 -10.51 21.49 -32 48 -32H400C426.51 -32 448 -10.51 448 16V368C448 394.51 426.51 416 400 416zM178.117 185.896C87.429 251.713 88.353 251.879 64 270.833V296C64 309.255 74.745 320 88 320H360C373.255 320 384 309.255 384 296V270.833C359.629 251.864 360.566 251.709 269.883 185.895C259.383 178.24 238.491 159.775 224 160.001C209.497 159.783 188.633 178.228 178.117 185.896zM384 230.225V88C384 74.745 373.255 64 360 64H88C74.745 64 64 74.745 64 88V230.225C77.958 219.431 97.329 204.989 159.303 160.011C173.465 149.67 197.278 127.866 223.997 128.001C250.884 127.867 275.034 150.042 288.717 160.026C350.675 204.991 370.042 219.432 384 230.225z" />
+    <glyph glyph-name="envelope"
+      unicode="&#xF0E0;"
+      horiz-adv-x="512" d=" M502.3 257.2C506.2 260.3 512 257.4 512 252.5V48C512 21.5 490.5 0 464 0H48C21.5 0 0 21.5 0 48V252.4C0 257.4 5.7 260.2000000000001 9.7 257.1C32.1 239.7 61.8 217.6 163.8 143.5C184.9 128.1 220.5 95.7 256 95.9C291.7 95.6 328 128.7 348.3 143.5C450.3 217.6 479.9 239.8 502.3 257.2zM256 128C279.2 127.6 312.6 157.2 329.4 169.4C462.1 265.7 472.2 274.1 502.8 298.1C508.6 302.6 511.9999999999999 309.6 511.9999999999999 317V336C511.9999999999999 362.5 490.4999999999999 384 463.9999999999999 384H48C21.5 384 0 362.5 0 336V317C0 309.6 3.4 302.7 9.2 298.1C39.8 274.2 49.9 265.7 182.6 169.4C199.4 157.2 232.8 127.6 256 128z" />
+    <glyph glyph-name="equals"
+      unicode="&#xF52C;"
+      horiz-adv-x="448" d=" M416 144H32C14.33 144 0 129.67 0 112V80C0 62.33 14.33 48 32 48H416C433.67 48 448 62.33 448 80V112C448 129.67 433.67 144 416 144zM416 336H32C14.33 336 0 321.67 0 304V272C0 254.33 14.33 240 32 240H416C433.67 240 448 254.33 448 272V304C448 321.67 433.67 336 416 336z" />
+    <glyph glyph-name="eraser"
+      unicode="&#xF12D;"
+      horiz-adv-x="512" d=" M497.941 174.059C516.6859999999999 192.804 516.6859999999999 223.196 497.941 241.9410000000001L337.941 401.9410000000001C319.196 420.6860000000001 288.805 420.687 270.058 401.9410000000001L14.058 145.941C-4.687 127.196 -4.687 96.804 14.058 78.059L110.058 -17.941A48.004000000000005 48.004000000000005 0 0 1 144 -32H500C506.627 -32 512 -26.627 512 -20V20C512 26.627 506.627 32 500 32H355.883L497.941 174.059zM195.314 236.6860000000001L332.687 99.3130000000001L265.373 32H150.628L70.628 112L195.314 236.686z" />
+    <glyph glyph-name="euro-sign"
+      unicode="&#xF153;"
+      horiz-adv-x="320" d=" M310.706 34.235C309.392 40.865 302.8710000000001 45.107 296.2820000000001 43.604C285.5900000000001 41.165 268.86 38.191 250.8560000000001 38.191C194.0930000000001 38.191 148.9270000000001 72.9810000000001 129.3950000000001 123.6400000000001H243.0840000000001A12 12 0 0 1 254.7920000000001 133.009L261.165 161.369C262.851 168.8710000000001 257.146 176 249.457 176H115.22C114.01 190.328 113.806 204.287 115.357 218.245H261.95A12 12 0 0 1 273.673 227.679L280.185 257.434C281.823 264.918 276.124 272 268.462 272H130.184C150.817 316.991 192.874 347.03 247.803 347.03C262.289 347.03 276.367 344.78 285.654 342.885C291.87 341.617 298.001 345.3830000000001 299.656 351.5080000000001L311.647 395.8760000000001C313.469 402.617 309.182 409.492 302.321 410.793C290.217 413.088 270.71 416 249.635 416C152.451 416 74.03 355.748 45.075 272H12C5.373 272 0 266.627 0 260V230.245C0 223.618 5.373 218.245 12 218.245H33.569C32.56 204.638 32.388 188.958 33.388 176H12C5.373 176 0 170.627 0 164V135.64C0 129.013 5.373 123.64 12 123.64H42.114C67.139 33.308 145.264 -32 249.635 -32C275.936 -32 298.197 -27.456 310.736 -24.212C316.903 -22.617 320.763 -16.504 319.524 -10.255L310.706 34.235z" />
+    <glyph glyph-name="exchange-alt"
+      unicode="&#xF362;"
+      horiz-adv-x="512" d=" M0 280V296C0 309.255 10.745 320 24 320H384V368C384 389.367 409.899 400.0420000000001 424.971 384.971L504.971 304.971C514.343 295.598 514.343 280.4020000000001 504.971 271.03L424.971 191.03C409.956 176.018 384 186.544 384 208V256H24C10.745 256 0 266.745 0 280zM488 128H128V176C128 197.314 102.138 208.08 87.029 192.971L7.029 112.971C-2.343 103.598 -2.343 88.402 7.029 79.03L87.029 -0.97C102.057 -15.997 128 -5.437 128 16V64H488C501.255 64 512 74.745 512 88V104C512 117.255 501.255 128 488 128z" />
+    <glyph glyph-name="exclamation-circle"
+      unicode="&#xF06A;"
+      horiz-adv-x="512" d=" M504 192C504 55.003 392.957 -56 256 -56S8 55.003 8 192C8 328.9170000000001 119.043 440 256 440S504 328.9170000000001 504 192zM256 142C230.595 142 210 121.405 210 96S230.595 50 256 50S302 70.595 302 96S281.405 142 256 142zM212.327 307.346L219.745 171.346C220.092 164.982 225.354 160 231.727 160H280.273C286.646 160 291.908 164.982 292.255 171.346L299.673 307.346C300.048 314.2200000000001 294.575 320 287.6910000000001 320H224.308C217.424 320 211.952 314.2200000000001 212.327 307.346z" />
+    <glyph glyph-name="exclamation-triangle"
+      unicode="&#xF071;"
+      horiz-adv-x="576" d=" M569.517 7.987C587.975 -24.007 564.806 -64 527.94 -64H48.054C11.117 -64 -11.945 -23.945 6.477 7.987L246.423 424.015C264.89 456.024 311.1430000000001 455.966 329.577 424.015L569.517 7.987zM288 94C262.5950000000001 94 242 73.405 242 48S262.5950000000001 2 288 2S334 22.595 334 48S313.405 94 288 94zM244.327 259.346L251.745 123.346C252.092 116.982 257.354 112 263.727 112H312.273C318.646 112 323.908 116.982 324.255 123.346L331.673 259.346C332.048 266.2200000000001 326.575 272 319.6910000000001 272H256.3080000000001C249.4240000000001 272 243.9520000000001 266.2200000000001 244.3270000000001 259.346z" />
+    <glyph glyph-name="exclamation"
+      unicode="&#xF12A;"
+      horiz-adv-x="192" d=" M176 16C176 -28.112 140.112 -64 96 -64S16 -28.112 16 16S51.888 96 96 96S176 60.112 176 16zM25.26 422.801L38.86 150.801C39.499 138.028 50.041 128 62.83 128H129.17C141.959 128 152.501 138.028 153.14 150.801L166.74 422.801C167.425 436.51 156.496 448 142.77 448H49.23C35.504 448 24.575 436.51 25.26 422.801z" />
+    <glyph glyph-name="expand-arrows-alt"
+      unicode="&#xF31E;"
+      horiz-adv-x="448.1" d=" M448.1 104V-8C448.1 -21.3 437.4000000000001 -32 424.1 -32H312.1C290.7000000000001 -32 280 -6.1 295.1 9L331.3 45.2L224 152.4L116.8 45.1L153 9C168.1 -6.1 157.4 -32 136 -32H24C10.7 -32 0 -21.3 0 -8V104C0 125.4 25.9 136.1 41 121L77.2 84.8L184.5 192L77.2 299.3L41 263C25.9 247.9 0 258.6 0 280V392C0 405.3 10.7 416 24 416H136C157.4 416 168.1 390.1 153 375L116.8 338.8L224 231.6L331.3 338.9L295.1 375C280 390.1 290.7000000000001 416 312.1 416H424.1C437.4000000000001 416 448.1 405.3 448.1 392V280C448.1 258.6 422.2000000000001 247.9 407.1 263L370.9000000000001 299.2L263.6 192L370.9000000000001 84.7L407.1 120.9C422.2000000000001 136.1 448.1 125.4 448.1 104z" />
+    <glyph glyph-name="expand"
+      unicode="&#xF065;"
+      horiz-adv-x="448" d=" M0 268V392C0 405.3 10.7 416 24 416H148C154.6 416 160 410.6 160 404V364C160 357.4 154.6 352 148 352H64V268C64 261.4 58.6 256 52 256H12C5.4 256 0 261.4 0 268zM288 404V364C288 357.4 293.4 352 300 352H384V268C384 261.4 389.4 256 396 256H436C442.6 256 448 261.4 448 268V392C448 405.3 437.3 416 424 416H300C293.4 416 288 410.6 288 404zM436 128H396C389.4 128 384 122.6 384 116V32H300C293.4 32 288 26.6 288 20V-20C288 -26.6 293.4 -32 300 -32H424C437.3 -32 448 -21.3 448 -8V116C448 122.6 442.6 128 436 128zM160 -20V20C160 26.6 154.6 32 148 32H64V116C64 122.6 58.6 128 52 128H12C5.4 128 0 122.6 0 116V-8C0 -21.3 10.7 -32 24 -32H148C154.6 -32 160 -26.6 160 -20z" />
+    <glyph glyph-name="external-link-alt"
+      unicode="&#xF35D;"
+      horiz-adv-x="576" d=" M576 424V296.016C576 274.555 550.04 264.0360000000001 535.029 279.045L499.322 314.754L255.799 71.231C246.426 61.858 231.231 61.858 221.858 71.231L199.231 93.858C189.858 103.231 189.858 118.427 199.231 127.799L442.756 371.324L407.053 407.029C391.9820000000001 422.1 402.656 448 424.024 448H552C565.255 448 576 437.255 576 424zM407.029 177.206L391.029 161.206A23.999000000000002 23.999000000000002 0 0 1 384 144.235V0H64V320H328A24.003 24.003 0 0 1 344.9700000000001 327.029L360.9700000000001 343.029C376.089 358.149 365.381 384 344 384H48C21.49 384 0 362.51 0 336V-16C0 -42.51 21.49 -64 48 -64H400C426.51 -64 448 -42.51 448 -16V160.236C448 181.618 422.148 192.326 407.029 177.206z" />
+    <glyph glyph-name="external-link-square-alt"
+      unicode="&#xF360;"
+      horiz-adv-x="448" d=" M448 368V16C448 -10.51 426.51 -32 400 -32H48C21.49 -32 0 -10.51 0 16V368C0 394.51 21.49 416 48 416H400C426.51 416 448 394.51 448 368zM360 352H248.029C226.716 352 215.949 326.139 231.058 311.029L263.042 279.0420000000001L67.515 83.515C62.829 78.829 62.829 71.231 67.515 66.544L98.544 35.515C103.231 30.829 110.829 30.829 115.515 35.515L311.041 231.041L343.029 199.05C358.058 184.023 384 194.575 384 216.021V328C384 341.255 373.255 352 360 352z" />
+    <glyph glyph-name="eye-dropper"
+      unicode="&#xF1FB;"
+      horiz-adv-x="512" d=" M50.75 114.75C38.75 102.75 32 86.47 32 69.49V24L0 -32L32 -64L88 -32H133.49C150.46 -32 166.74 -25.26 178.74 -13.26L305.38 113.36L177.38 241.36L50.75 114.75zM483.88 419.88C446.41 457.38 385.6 457.38 348.13 419.88L271.04 342.79L257.94 355.89C248.5 365.33 233.29 365.2 224 355.89L183.03 314.92C173.66 305.55 173.66 290.35 183.03 280.98L344.9699999999999 119.04C354.4099999999999 109.6 369.6199999999999 109.73 378.9099999999999 119.04L419.88 160C429.25 169.37 429.25 184.57 419.88 193.94L406.78 207.04L483.87 284.13C521.38 321.61 521.38 382.39 483.88 419.88z" />
+    <glyph glyph-name="eye-slash"
+      unicode="&#xF070;"
+      horiz-adv-x="576" d=" M286.693 56.016L319.272 9.474A333.958 333.958 0 0 0 288 8C168.19 8 63.031 71.949 6.646 167.631A47.999 47.999 0 0 0 6.646 216.37C30.669 257.136 63.559 292.145 102.67 318.907L159.747 237.368C154.736 223.18 152 207.913 152 192C152 117.264 212.135 56.718 286.693 56.016zM569.354 167.631C537.687 113.894 490.607 70.1710000000001 434.1790000000001 42.1560000000001L434.1900000000001 42.1410000000001L475.6600000000001 -17.0589999999999C483.2600000000001 -27.9189999999999 480.6200000000001 -42.8789999999999 469.7600000000001 -50.4789999999999L456.6500000000001 -59.6589999999999C445.7900000000001 -67.2589999999999 430.8300000000001 -64.6189999999999 423.2300000000001 -53.759L100.34 401.06C92.74 411.92 95.38 426.88 106.24 434.48L119.35 443.6600000000001C130.21 451.26 145.17 448.62 152.77 437.76L203.808 365.1430000000001C230.68 372.224 258.905 376 288 376C407.81 376 512.969 312.051 569.354 216.369A48.00200000000001 48.00200000000001 0 0 0 569.354 167.631zM424 192C424 267.174 363.162 328 288 328C270.061 328 252.944 324.527 237.271 318.228L256.57 291.17C282.439 299.341 311.614 297.3330000000001 336.9700000000001 283.76H336.9400000000001C313.2900000000001 283.76 294.1200000000001 264.5900000000001 294.1200000000001 240.9400000000001C294.1200000000001 217.314 313.2670000000001 198.12 336.9400000000001 198.12C360.5900000000001 198.12 379.7600000000001 217.29 379.7600000000001 240.9400000000001V240.9700000000001C398.2220000000001 206.48 396.0720000000001 163.0560000000001 371.5100000000001 130.0200000000001V130.0100000000001L390.8240000000001 102.9490000000001C411.496 126.8 424 157.926 424 192zM262.014 91.273L184.484 202.03C179.47 149.643 213.798 103.6760000000001 262.014 91.273z" />
+    <glyph glyph-name="eye"
+      unicode="&#xF06E;"
+      horiz-adv-x="576" d=" M569.354 216.369C512.969 312.051 407.81 376 288 376C168.14 376 63.004 312.006 6.646 216.369A47.999 47.999 0 0 1 6.646 167.63C63.031 71.949 168.19 8 288 8C407.86 8 512.996 71.994 569.354 167.631A47.997 47.997 0 0 1 569.354 216.369zM288 56C212.838 56 152 116.827 152 192C152 267.1620000000001 212.826 328 288 328C363.1620000000001 328 424 267.174 424 192C424 116.838 363.174 56 288 56zM392 192C392 134.562 345.438 88 288 88S184 134.562 184 192C184 209.708 188.431 226.379 196.236 240.973L196.235 240.941C196.235 217.29 215.408 198.118 239.059 198.118S281.883 217.291 281.883 240.941C281.883 264.592 262.71 283.765 239.059 283.765L239.027 283.764C253.621 291.569 270.292 296 288 296C345.438 296 392 249.438 392 192z" />
+    <glyph glyph-name="fast-backward"
+      unicode="&#xF049;"
+      horiz-adv-x="512" d=" M0 12V372C0 378.6 5.4 384 12 384H52C58.6 384 64 378.6 64 372V220.1L235.5 376.6C256.1 393.7 288 379.4 288 352V220.1L459.5 376.6C480.1 393.7 512 379.4 512 352V32C512 4.6 480.1 -9.7 459.5 7.4L288 162.7V32C288 4.6 256.1 -9.7 235.5 7.4L64 162.7V12C64 5.4 58.6 0 52 0H12C5.4 0 0 5.4 0 12z" />
+    <glyph glyph-name="fast-forward"
+      unicode="&#xF050;"
+      horiz-adv-x="512" d=" M512 372V12C512 5.4 506.6 0 500 0H460C453.4 0 448 5.4 448 12V163.9L276.5 7.4C255.9 -9.8 224 4.6 224 32V163.9L52.5 7.4C31.9 -9.8 0 4.6 0 32V352C0 379.4 31.9 393.7 52.5 376.6L224 221.2V352C224 379.4 255.9 393.7 276.5 376.6L448 221.2V372C448 378.6 453.4 384 460 384H500C506.6 384 512 378.6 512 372z" />
+    <glyph glyph-name="fax"
+      unicode="&#xF1AC;"
+      horiz-adv-x="512" d=" M64 320H32C14.33 320 0 305.67 0 288V-32C0 -49.67 14.33 -64 32 -64H64C81.67 -64 96 -49.67 96 -32V288C96 305.67 81.67 320 64 320zM480 288V370.75C480 379.24 476.63 387.37 470.63 393.38L425.37 438.63C419.37 444.63 411.23 448 402.74 448H160C142.33 448 128 433.67 128 416V-32C128 -49.67 142.33 -64 160 -64H480C497.67 -64 512 -49.67 512 -32V256C512 273.67 497.67 288 480 288zM288 16C288 7.16 280.84 0 272 0H240C231.16 0 224 7.16 224 16V48C224 56.84 231.16 64 240 64H272C280.84 64 288 56.84 288 48V16zM288 144C288 135.16 280.84 128 272 128H240C231.16 128 224 135.16 224 144V176C224 184.84 231.16 192 240 192H272C280.84 192 288 184.84 288 176V144zM416 16C416 7.16 408.84 0 400 0H368C359.1600000000001 0 352 7.16 352 16V48C352 56.84 359.1600000000001 64 368 64H400C408.84 64 416 56.84 416 48V16zM416 144C416 135.16 408.84 128 400 128H368C359.1600000000001 128 352 135.16 352 144V176C352 184.84 359.1600000000001 192 368 192H400C408.84 192 416 184.84 416 176V144zM432 256H176V400H384V368C384 359.16 391.1600000000001 352 400 352H432V256z" />
+    <glyph glyph-name="feather-alt"
+      unicode="&#xF56B;"
+      horiz-adv-x="512" d=" M512 448C460.22 444.44 96.44 409.8 71.01 160.39C67.92 133.73 66.17 106.95 65.02 80.15L243.89 258.84C250.14 265.09 260.29 265.09 266.54 258.84S272.79 242.46 266.54 236.21L7.04 -23.03C-2.34 -32.4 -2.34 -47.6 7.04 -56.97C16.42 -66.3399999999999 31.63 -66.3399999999999 41.02 -56.97L98.15 0.1C140.24 0.24 182.3 2.63 224.11 7.46C277.5900000000001 12.9 321.13 33.9300000000001 356.6900000000001 64.0000000000001H255.74L402.53 112.8800000000001C413.78 127.7700000000001 423.9 143.59 432.98 160.0000000000001H351.84L458.38 213.2100000000001C500.29 315.14 510.19 421.74 512 448z" />
+    <glyph glyph-name="feather"
+      unicode="&#xF52D;"
+      horiz-adv-x="512" d=" M467.14 403.16C404.59 465.64 305.4700000000001 467.94 214.86 377.43C136.25 298.91 153.88 316.51 129.11 291.77C68.65 231.38 58.72 140.94 65.47 80.6L243.91 258.85C250.17 265.1 260.31 265.1 266.56 258.85S272.81 242.4700000000001 266.56 236.2200000000001L7.04 -23.03C-2.34 -32.4 -2.34 -47.6 7.04 -56.97C16.42 -66.3399999999999 31.64 -66.3399999999999 41.02 -56.97L107.12 9.0600000000001C159.42 -6.65 279 -9.11 353.95 64H255.76L403.33 113.14C453.32 163.07 439.71 149.32 449.64 160H351.86L483.4 203.8C528.8399999999999 278.26 517.71 352.64 467.14 403.1600000000001z" />
+    <glyph glyph-name="female"
+      unicode="&#xF182;"
+      horiz-adv-x="256" d=" M128 448C163.346 448 192 419.346 192 384S163.346 320 128 320C92.654 320 64 348.654 64 384S92.654 448 128 448M247.283 93.821L199.283 285.8210000000001A24 24 0 0 1 176 304H164.64C141.929 293.557 115.05 293.106 91.36 304H80A24 24 0 0 1 56.717 285.8210000000001L8.717 93.821C4.935 78.695 16.383 64 32 64H88V-40C88 -53.255 98.745 -64 112 -64H144C157.255 -64 168 -53.255 168 -40V64H224C239.591 64 251.071 78.671 247.283 93.821z" />
+    <glyph glyph-name="fighter-jet"
+      unicode="&#xF0FB;"
+      horiz-adv-x="640" d=" M544 224L416 240L368 256H344L227.158 404H266.667C278.3330000000001 404 288 406.625 288 410S278.3330000000001 416 266.6670000000001 416H152V404H168V240H120L53.333 320H18.667L8 309.333V240H16V224H64V221.334L0 213.334V170.667L64 162.667V160H16V144H8V74.667L18.667 64H53.334L120 144H168V-20H152V-32H266.6670000000001C278.334 -32 288 -29.375 288 -26S278.3330000000001 -20 266.6670000000001 -20H227.158L344 128H368L416 144L544 160C640 181.333 640 186.583 640 192C640 197.417 640 202.667 544 224z" />
+    <glyph glyph-name="file-alt"
+      unicode="&#xF15C;"
+      horiz-adv-x="384" d=" M224 312V448H24C10.7 448 0 437.3 0 424V-40C0 -53.3 10.7 -64 24 -64H360C373.3 -64 384 -53.3 384 -40V288H248C234.8 288 224 298.8 224 312zM288 76C288 69.4 282.6 64 276 64H108C101.4 64 96 69.4 96 76V84C96 90.6 101.4 96 108 96H276C282.6 96 288 90.6 288 84V76zM288 140C288 133.4 282.6 128 276 128H108C101.4 128 96 133.4 96 140V148C96 154.6 101.4 160 108 160H276C282.6 160 288 154.6 288 148V140zM288 212V204C288 197.4 282.6 192 276 192H108C101.4 192 96 197.4 96 204V212C96 218.6 101.4 224 108 224H276C282.6 224 288 218.6 288 212zM384 326.1V320H256V448H262.1C268.5 448 274.6 445.5 279.1 441L377 343C381.5 338.5 384 332.4 384 326.1z" />
+    <glyph glyph-name="file-archive"
+      unicode="&#xF1C6;"
+      horiz-adv-x="384" d=" M224 312V448H160.4V416H128.4V448H24C10.7 448 0 437.3 0 424V-40C0 -53.3 10.7 -64 24 -64H360C373.3 -64 384 -53.3 384 -40V288H248C234.8 288 224 298.8 224 312zM95.9 416H127.9V384H95.9V416zM128.2 32C95 32 70.2 62.4 76.8 94.9L96.4 192V224H128.4V256H96.4V288H128.4V320H96.4V352H128.4V384H160.4V352H128.4V320H160.4V288H128.4V256H160.4V224H128.4V192H150.5C156.2 192 161.2 187.9 162.3 182.3L179.6 94.6C186 62.2000000000001 161.2 32 128.2 32zM160.9 85C160.9 70.1 146.4 58 128.5 58S96 70 96 85C96 99.9 110.5 112 128.4 112S160.9 99.9 160.9 85zM384 326.1V320H256V448H262.1C268.5 448 274.6 445.5 279.1 441L377 343C381.5 338.5 384 332.4 384 326.1z" />
+    <glyph glyph-name="file-audio"
+      unicode="&#xF1C7;"
+      horiz-adv-x="384" d=" M224 312V448H24C10.7 448 0 437.3 0 424V-40C0 -53.3 10.7 -64 24 -64H360C373.3 -64 384 -53.3 384 -40V288H248C234.8 288 224 298.8 224 312zM160 44C160 33.3 147.1 28 139.5 35.5L104 72H76C69.4 72 64 77.4 64 84V140C64 146.6 69.4 152 76 152H104L139.5 188.5C147.1 196.1 160 190.7 160 180V44zM193.2 91.6C202.3 100.9 202.3 115.7000000000001 193.2 125C171.1 147.8 205.4 181.2 227.6 158.5C254.8 130.6 254.8 86.1 227.6 58.1C205.8 35.8 170.7 68.5 193.2 91.6zM279.2 208.7C333.6 152.8 333.6 63.9 279.2 7.9C257.4 -14.5 222.2 18.2 244.8 41.4C281 78.6 281.1 137.9 244.8 175.2C222.7 198 257.1 231.5 279.2 208.7zM384 326.1V320H256V448H262.1C268.5 448 274.6 445.5 279.1 441L377 343C381.5 338.5 384 332.4 384 326.1z" />
+    <glyph glyph-name="file-code"
+      unicode="&#xF1C9;"
+      horiz-adv-x="384" d=" M384 326.059V320H256V448H262.0590000000001C268.4240000000001 448 274.5290000000001 445.471 279.0300000000001 440.971L376.971 343.03A24.005 24.005 0 0 0 384 326.059zM248 288C234.8 288 224 298.8 224 312V448H24C10.745 448 0 437.255 0 424V-40C0 -53.255 10.745 -64 24 -64H360C373.255 -64 384 -53.255 384 -40V288H248zM123.206 47.495A5.4 5.4 0 0 0 115.573 47.249L50.707 108.061A5.4 5.4 0 0 0 50.707 115.9400000000001L115.573 176.7520000000001A5.4 5.4 0 0 0 123.206 176.5060000000001L142.785 155.6210000000001A5.4 5.4 0 0 0 142.413 147.8740000000001L101.65 112L142.413 76.126A5.4 5.4 0 0 0 142.785 68.379L123.206 47.495zM174.501 -2.984L147.048 4.986A5.401999999999999 5.401999999999999 0 0 0 143.367 11.6780000000001L204.807 223.3040000000001A5.401999999999999 5.401999999999999 0 0 0 211.499 226.9850000000001L238.951 219.0150000000001A5.4 5.4 0 0 0 242.631 212.3230000000001L181.191 0.6970000000001A5.396999999999999 5.396999999999999 0 0 0 174.501 -2.9839999999999zM335.293 108.061L270.427 47.249A5.4 5.4 0 0 0 262.7940000000001 47.495L243.2140000000001 68.38A5.4 5.4 0 0 0 243.5860000000001 76.127L284.35 112L243.587 147.874A5.4 5.4 0 0 0 243.215 155.6210000000001L262.795 176.506A5.4 5.4 0 0 0 270.428 176.752L335.294 115.94A5.4 5.4 0 0 0 335.293 108.061z" />
+    <glyph glyph-name="file-contract"
+      unicode="&#xF56C;"
+      horiz-adv-x="384" d=" M224 312V448H24C10.7 448 0 437.3 0 424V-40C0 -53.3 10.7 -64 24 -64H360C373.3 -64 384 -53.3 384 -40V288H248C234.8 288 224 298.8 224 312zM64 376C64 380.42 67.58 384 72 384H152C156.42 384 160 380.42 160 376V360C160 355.58 156.42 352 152 352H72C67.58 352 64 355.58 64 360V376zM64 312C64 316.42 67.58 320 72 320H152C156.42 320 160 316.42 160 312V296C160 291.5800000000001 156.42 288 152 288H72C67.58 288 64 291.5800000000001 64 296V312zM256.81 64H304C312.84 64 320 56.84 320 48S312.84 32 304 32H256.81C240.36 32 225.54 41.14 218.17 55.86C215.22 61.78 210.08 62.38 208 62.38S200.78 61.79 197.98 56.19L190.31 40.85A15.986 15.986 0 0 0 176 32.0100000000001C175.62 32.0100000000001 175.25 32.03 174.8600000000001 32.0600000000001C168.4100000000001 32.5100000000001 162.8600000000001 36.8100000000001 160.8300000000001 42.95L144 93.41L133.39 61.53C127.5 43.87 111.01 32 92.39 32H80C71.16 32 64 39.16 64 48S71.16 64 80 64H92.39C97.22 64 101.5 67.08 103.03 71.66L121.22 126.3C124.52 136.11 133.66 142.71 144 142.71S163.48 136.1200000000001 166.77 126.3L180.65 84.66C200.42 100.85 234.7 94.36 246.65 70.5C248.67 66.44 252.61 64 256.81 64zM377 343L279.1 441C274.6 445.5 268.5 448 262.1 448H256V320H384V326.1C384 332.4 381.5 338.5 377 343z" />
+    <glyph glyph-name="file-csv"
+      unicode="&#xF6DD;"
+      horiz-adv-x="384" d=" M224 312V448H24C10.7 448 0 437.3 0 424V-40C0 -53.3 10.7 -64 24 -64H360C373.3 -64 384 -53.3 384 -40V288H248C234.8 288 224 298.8 224 312zM128 168C128 163.58 124.42 160 120 160H112C103.16 160 96 152.84 96 144V112C96 103.16 103.16 96 112 96H120C124.42 96 128 92.42 128 88V72C128 67.58 124.42 64 120 64H112C85.49 64 64 85.49 64 112V144C64 170.51 85.49 192 112 192H120C124.42 192 128 188.42 128 184V168zM172.27 64H160C155.58 64 152 67.58 152 72V88C152 92.42 155.58 96 160 96H172.27C178.22 96 182.68 99.5 182.68 102.62C182.68 103.92 181.93 105.28 180.56 106.46L158.67 125.23C150.2 132.45 145.34 142.71 145.34 153.37C145.34 174.67 164.36 191.99 187.75 191.99H200C204.42 191.99 208 188.41 208 183.99V167.99C208 163.5699999999999 204.42 159.99 200 159.99H187.73C181.78 159.99 177.32 156.49 177.32 153.37C177.32 152.0699999999999 178.07 150.7099999999999 179.44 149.53L201.33 130.76C209.8 123.54 214.66 113.28 214.66 102.62C214.67 81.33 195.66 64 172.27 64zM256 184V163.2C256 142.93 261.7 123.03 272 106.32C282.3 123.02 288 142.93 288 163.2V184C288 188.42 291.58 192 296 192H312C316.42 192 320 188.42 320 184V163.2C320 127.72 307.12 94.31 283.7200000000001 69.11C280.7000000000001 65.86 276.4500000000001 64 272 64S263.3 65.86 260.28 69.11C236.88 94.31 224 127.72 224 163.2000000000001V184C224 188.42 227.58 192 232 192H248C252.42 192 256 188.42 256 184zM377 343L279.1 441C274.6 445.5 268.5 448 262.1 448H256V320H384V326.1C384 332.4 381.5 338.5 377 343z" />
+    <glyph glyph-name="file-download"
+      unicode="&#xF56D;"
+      horiz-adv-x="384" d=" M224 312V448H24C10.7 448 0 437.3 0 424V-40C0 -53.3 10.7 -64 24 -64H360C373.3 -64 384 -53.3 384 -40V288H248C234.8 288 224 298.8 224 312zM300.45 100.64L204.03 4.94C197.38 -1.67 186.64 -1.67 179.99 4.94L83.57 100.64C73.42 110.71 80.54 128 94.82 128H160V208C160 216.84 167.16 224 176 224H208C216.84 224 224 216.84 224 208V128H289.18C303.46 128 310.58 110.71 300.45 100.64zM377 343L279.1 441C274.6 445.5 268.5 448 262.1 448H256V320H384V326.1C384 332.4 381.5 338.5 377 343z" />
+    <glyph glyph-name="file-excel"
+      unicode="&#xF1C3;"
+      horiz-adv-x="384" d=" M224 312V448H24C10.7 448 0 437.3 0 424V-40C0 -53.3 10.7 -64 24 -64H360C373.3 -64 384 -53.3 384 -40V288H248C234.8 288 224 298.8 224 312zM284.1 205.5L224 112L284.1 18.5C289.2000000000001 10.5 283.5 0 274 0H239.1C234.7 0 230.6 2.4 228.5 6.3C208.9 42.5 192 75 192 75C185.6 60.2 182 55 155.4 6.2C153.3 2.3 149.3 -0.1 144.9 -0.1H110C100.5 -0.1 94.8 10.4 99.9 18.4L160.2 111.9L99.9 205.4C94.7 213.4 100.5 223.9 110 223.9H144.8C149.2 223.9 153.3 221.5 155.4 217.6C181.5 168.8 175.4 184 192 149.1C192 149.1 198.1 160.8 228.6 217.6C230.7 221.5 234.8 223.9 239.2 223.9H274C283.5 224 289.2 213.5 284.1 205.5zM384 326.1V320H256V448H262.1C268.5 448 274.6 445.5 279.1 441L377 343C381.5 338.5 384 332.4 384 326.1z" />
+    <glyph glyph-name="file-export"
+      unicode="&#xF56E;"
+      horiz-adv-x="576" d=" M384 326.1C384 332.4 381.5 338.5 377 343L279.1 441C274.6 445.5 268.5 448 262.1 448H256V320H384V326.1zM192 112V144C192 152.84 199.16 160 208 160H384V288H248C234.8 288 224 298.8 224 312V448H24C10.7 448 0 437.3 0 424V-40C0 -53.3 10.7 -64 24 -64H360C373.3 -64 384 -53.3 384 -40V96H208C199.16 96 192 103.16 192 112zM571.05 140.02L475.35 236.45C465.29 246.59 447.99 239.46 447.99 225.18V160H384V96H447.99V30.82C447.99 16.54 465.28 9.41 475.35 19.55L571.0500000000001 115.97C577.6500000000001 122.6300000000001 577.6500000000001 133.37 571.0500000000001 140.0200000000001z" />
+    <glyph glyph-name="file-image"
+      unicode="&#xF1C5;"
+      horiz-adv-x="384" d=" M384 326.059V320H256V448H262.0590000000001A24 24 0 0 0 279.029 440.971L376.9700000000001 343.03A24.002000000000002 24.002000000000002 0 0 0 384 326.059zM248 288C234.8 288 224 298.8 224 312V448H24C10.745 448 0 437.255 0 424V-40C0 -53.255 10.745 -64 24 -64H360C373.255 -64 384 -53.255 384 -40V288H248zM112.545 272C139.055 272 160.545 250.51 160.545 224S139.055 176 112.545 176S64.545 197.49 64.545 224S86.036 272 112.545 272zM320.545 32H64.545L65.03 80.485L104.545 120C109.231 124.686 116.344 124.201 121.03 119.515L160.545 80L264.06 183.515C268.746 188.201 276.344 188.201 281.031 183.515L320.545 144V32z" />
+    <glyph glyph-name="file-import"
+      unicode="&#xF56F;"
+      horiz-adv-x="512" d=" M16 160C7.16 160 0 152.84 0 144V112C0 103.16 7.16 96 16 96H128V160H16zM352 312V448H152C138.7 448 128 437.3 128 424V160H255.99V225.18C255.99 239.46 273.2800000000001 246.59 283.35 236.45L379.05 140.02C385.6500000000001 133.37 385.6500000000001 122.63 379.05 115.98L283.35 19.5599999999999C273.29 9.42 255.99 16.55 255.99 30.8299999999999V96H128V-40C128 -53.3 138.7 -64 152 -64H488C501.3 -64 512 -53.3 512 -40V288H376C362.8 288 352 298.8 352 312zM505 343L407.1 441C402.6 445.5 396.5 448 390.1 448H384V320H512V326.1C512 332.4 509.5 338.5 505 343z" />
+    <glyph glyph-name="file-invoice-dollar"
+      unicode="&#xF571;"
+      horiz-adv-x="384" d=" M377 343L279.1 441C274.6 445.5 268.5 448 262.1 448H256V320H384V326.1C384 332.4 381.5 338.5 377 343zM224 312V448H24C10.7 448 0 437.3 0 424V-40C0 -53.3 10.7 -64 24 -64H360C373.3 -64 384 -53.3 384 -40V288H248C234.8 288 224 298.8 224 312zM64 376C64 380.42 67.58 384 72 384H152C156.42 384 160 380.42 160 376V360C160 355.58 156.42 352 152 352H72C67.58 352 64 355.58 64 360V376zM64 296V312C64 316.42 67.58 320 72 320H152C156.42 320 160 316.42 160 312V296C160 291.5800000000001 156.42 288 152 288H72C67.58 288 64 291.5800000000001 64 296zM208 32.12V8C208 3.58 204.42 0 200 0H184C179.58 0 176 3.58 176 8V32.29C164.71 32.87 153.73 36.81 144.63 43.64C140.73 46.5700000000001 140.53 52.41 144.06 55.78L155.81 66.99C158.58 69.63 162.7 69.75 165.94 67.72C169.81 65.3 174.2 64 178.76 64H206.87C213.37 64 218.67 69.92 218.67 77.19C218.67 83.14 215.06 88.38 209.9 89.92L164.9 103.42C146.31 109 133.32 126.84 133.32 146.81C133.32 171.33 152.37 191.25 175.99 191.88V216C175.99 220.42 179.57 224 183.99 224H199.99C204.41 224 207.99 220.42 207.99 216V191.71C219.28 191.13 230.26 187.2 239.36 180.36C243.26 177.43 243.46 171.59 239.93 168.22L228.18 157.01C225.41 154.37 221.29 154.25 218.05 156.28C214.18 158.71 209.79 160 205.23 160H177.12C170.62 160 165.32 154.08 165.32 146.81C165.32 140.86 168.93 135.62 174.09 134.08L219.09 120.58C237.68 115 250.67 97.16 250.67 77.19C250.67 52.66 231.62 32.75 208 32.12z" />
+    <glyph glyph-name="file-invoice"
+      unicode="&#xF570;"
+      horiz-adv-x="384" d=" M288 192H96V128H288V192zM377 343L279.1 441C274.6 445.5 268.5 448 262.1 448H256V320H384V326.1C384 332.4 381.5 338.5 377 343zM224 312V448H24C10.7 448 0 437.3 0 424V-40C0 -53.3 10.7 -64 24 -64H360C373.3 -64 384 -53.3 384 -40V288H248C234.8 288 224 298.8 224 312zM64 376C64 380.42 67.58 384 72 384H152C156.42 384 160 380.42 160 376V360C160 355.58 156.42 352 152 352H72C67.58 352 64 355.58 64 360V376zM64 312C64 316.42 67.58 320 72 320H152C156.42 320 160 316.42 160 312V296C160 291.5800000000001 156.42 288 152 288H72C67.58 288 64 291.5800000000001 64 296V312zM320 8C320 3.58 316.42 0 312 0H232C227.58 0 224 3.58 224 8V24C224 28.42 227.58 32 232 32H312C316.42 32 320 28.42 320 24V8zM320 208V112C320 103.16 312.84 96 304 96H80C71.16 96 64 103.16 64 112V208C64 216.84 71.16 224 80 224H304C312.84 224 320 216.84 320 208z" />
+    <glyph glyph-name="file-medical-alt"
+      unicode="&#xF478;"
+      horiz-adv-x="448" d=" M288 312V448H88C74.7 448 64 437.3 64 424V192H8C3.6 192 0 188.4 0 184V168C0 163.6 3.6 160 8 160H148.9C151.9 160 154.7 158.3 156.1 155.6L176 115.8L232.8 229.5C235.7 235.4 244.2 235.4 247.1 229.5L281.8 160H352C360.8 160 368 152.8 368 144S360.8 128 352 128H262.1L240 172.2L183.2 58.5C180.3 52.6 171.8 52.6 168.9 58.5L134.1 128H64V-40C64 -53.3 74.7 -64 88 -64H424C437.3 -64 448 -53.3 448 -40V288H312C298.8 288 288 298.8 288 312zM441 343L343.1 441C338.6 445.5 332.5 448 326.1 448H320V320H448V326.1C448 332.4 445.5 338.5 441 343z" />
+    <glyph glyph-name="file-medical"
+      unicode="&#xF477;"
+      horiz-adv-x="384" d=" M377 343L279.1 441C274.6 445.5 268.5 448 262.1 448H256V320H384V326.1C384 332.4 381.5 338.5 377 343zM224 312V448H24C10.7 448 0 437.3 0 424V-40C0 -53.3 10.7 -64 24 -64H360C373.3 -64 384 -53.3 384 -40V288H248C234.8 288 224 298.8 224 312zM288 152V104C288 99.6 284.4 96 280 96H224V40C224 35.6 220.4 32 216 32H168C163.6 32 160 35.6 160 40V96H104C99.6 96 96 99.6 96 104V152C96 156.4 99.6 160 104 160H160V216C160 220.4 163.6 224 168 224H216C220.4 224 224 220.4 224 216V160H280C284.4 160 288 156.4 288 152z" />
+    <glyph glyph-name="file-pdf"
+      unicode="&#xF1C1;"
+      horiz-adv-x="384" d=" M181.9 191.9C176.9 207.9 177 238.8 179.9 238.8C188.3 238.8 187.5 201.9 181.9 191.9zM180.2 144.7C172.5 124.5 162.9 101.4 151.8 82C170.1 89 190.8 99.2 214.7 103.9C202 113.5 189.8 127.3 180.2 144.7zM86.1 19.9C86.1 19.1 99.3 25.3 121 60.1C114.3 53.8 91.9 35.6 86.1 19.9zM248 288H384V-40C384 -53.3 373.3 -64 360 -64H24C10.7 -64 0 -53.3 0 -40V424C0 437.3 10.7 448 24 448H224V312C224 298.8 234.8 288 248 288zM240 116.2C220 128.4 206.7 145.2 197.3 170C201.8 188.5 208.9 216.6 203.5 234.2C198.8 263.6 161.1 260.7 155.7 241C150.7 222.7 155.3 196.9 163.8 164C152.2 136.4 135.1 99.4 123 78.2C122.9 78.2 122.9 78.1 122.8 78.1C95.7 64.2 49.2 33.6 68.3 10.1C73.9 3.2 84.3 0.1 89.8 0.1C107.7 0.1 125.5 18.1 150.9 61.9C176.7 70.4 205 81 229.9 85.1C251.6 73.3 277 65.6 293.9 65.6C323.1 65.6 325.1 97.6 313.6 108.9999999999999C299.7 122.6 259.3 118.6999999999999 240 116.1999999999999zM377 343L279 441C274.5 445.5 268.4 448 262 448H256V320H384V326.1C384 332.4 381.5 338.5 377 343zM302.9 87.7C307 90.4 300.4 99.6 260.1 96.7C297.2 80.9 302.9 87.7 302.9 87.7z" />
+    <glyph glyph-name="file-powerpoint"
+      unicode="&#xF1C4;"
+      horiz-adv-x="384" d=" M193.7 176.8C202.5 176.8 209.2 174.1 214 168.7C223.6 157.8 223.8 136 213.8 124.6C208.9 119 201.9 116.1 192.7 116.1H165.8V176.8H193.7zM377 343L279 441C274.5 445.5 268.4 448 262 448H256V320H384V326.1C384 332.4 381.5 338.5 377 343zM224 312V448H24C10.7 448 0 437.3 0 424V-40C0 -53.3 10.7 -64 24 -64H360C373.3 -64 384 -53.3 384 -40V288H248C234.8 288 224 298.8 224 312zM277 146.8C277 56.5 188.2 69.2000000000001 165.9 69.2000000000001V12C165.9 5.4 160.5 0 153.9 0H123.1C116.5 0 111.1 5.4 111.1 12V211.8C111.1 218.4 116.5 223.8 123.1 223.8H204.1C248.6 223.8 277 191 277 146.8z" />
+    <glyph glyph-name="file-prescription"
+      unicode="&#xF572;"
+      horiz-adv-x="384" d=" M224 312V448H24C10.7 448 0 437.3 0 424V-40C0 -53.3 10.7 -64 24 -64H360C373.3 -64 384 -53.3 384 -40V288H248C234.8 288 224 298.8 224 312zM292.53 132.52L303.84 121.21C310.09 114.96 310.09 104.83 303.84 98.58L273.94 68.68L304 38.62C310.25 32.37 310.25 22.24 304 15.99L292.69 4.68C286.44 -1.57 276.31 -1.57 270.06 4.68L240 34.75L209.94 4.69C203.69 -1.56 193.56 -1.56 187.31 4.69L176 16C169.75 22.25 169.75 32.38 176 38.63L206.06 68.69L146.74 128H128V80C128 71.16 120.84 64 112 64H96C87.16 64 80 71.16 80 80V240C80 248.84 87.16 256 96 256H176C211.35 256 240 227.35 240 192C240 167.78 226.38 146.95 206.54 136.08L240 102.62L269.9 132.52C276.15 138.77 286.28 138.77 292.53 132.52zM176 176H128V208H176C184.82 208 192 200.82 192 192S184.82 176 176 176zM384 326.1V320H256V448H262.1C268.5 448 274.6 445.5 279.1 441L377 343C381.5 338.5 384 332.4 384 326.1z" />
+    <glyph glyph-name="file-signature"
+      unicode="&#xF573;"
+      horiz-adv-x="576" d=" M218.17 23.86C215.22 29.78 210.08 30.38 208 30.38S200.78 29.79 197.98 24.19L190.31 8.85C183.94 -3.9299999999999 165.28 -2.52 160.83 10.94L144 61.41L133.39 29.53C127.5 11.87 111.01 0 92.39 0H80C71.16 0 64 7.16 64 16S71.16 32 80 32H92.39C97.22 32 101.5 35.08 103.03 39.66L121.22 94.3C124.52 104.11 133.66 110.71 144 110.71S163.48 104.1200000000001 166.77 94.3L180.65 52.66C200.4 68.85 234.71 62.36 246.65 38.5C248.54 34.72 252.14 32.55 256.01 32.24V114.36L384.01 241.45V288H248C234.8 288 224 298.8 224 312V448H24C10.7 448 0 437.3 0 424V-40C0 -53.3 10.7 -64 24 -64H360C373.3 -64 384 -53.3 384 -40V0L256 0.11C239.88 0.42 225.42 9.39 218.17 23.86zM384 326.1C384 332.4 381.5 338.5 377 343L279.1 441C274.6 445.5 268.5 448 262.1 448H256V320H384V326.1zM288 101.04V32H356.99L518.6700000000001 194.78L450.7900000000001 262.66L288 101.04zM568.54 280.67L536.67 312.54C526.7299999999999 322.48 510.6 322.48 500.66 312.54L473.41 285.29L541.29 217.41L568.54 244.66C578.49 254.6 578.49 270.73 568.54 280.67z" />
+    <glyph glyph-name="file-upload"
+      unicode="&#xF574;"
+      horiz-adv-x="384" d=" M224 312V448H24C10.7 448 0 437.3 0 424V-40C0 -53.3 10.7 -64 24 -64H360C373.3 -64 384 -53.3 384 -40V288H248C234.8 288 224 298.8 224 312zM289.18 95.99H224V15.99C224 7.15 216.84 -0.01 208 -0.01H176C167.16 -0.01 160 7.15 160 15.99V95.99H94.82C80.54 95.99 73.41 113.28 83.55 123.35L179.97 219.05C186.62 225.6600000000001 197.36 225.6600000000001 204.01 219.05L300.43 123.35C310.58 113.28 303.46 95.99 289.18 95.99zM377 343L279.1 441C274.6 445.5 268.5 448 262.1 448H256V320H384V326.1C384 332.4 381.5 338.5 377 343z" />
+    <glyph glyph-name="file-video"
+      unicode="&#xF1C8;"
+      horiz-adv-x="384" d=" M384 326.059V320H256V448H262.0590000000001C268.4240000000001 448 274.5290000000001 445.471 279.0300000000001 440.971L376.971 343.03A24.005 24.005 0 0 0 384 326.059zM224 312V448H24C10.745 448 0 437.255 0 424V-40C0 -53.255 10.745 -64 24 -64H360C373.255 -64 384 -53.255 384 -40V288H248C234.8 288 224 298.8 224 312zM320 167.984V56.0210000000001C320 34.5760000000001 294.057 24.0230000000001 279.029 39.0500000000001L224 94.059V56C224 42.745 213.255 32 200 32H88C74.745 32 64 42.745 64 56V168C64 181.255 74.745 192 88 192H200C213.255 192 224 181.255 224 168V129.941L279.029 184.954C294.04 199.964 320 189.445 320 167.984z" />
+    <glyph glyph-name="file-word"
+      unicode="&#xF1C2;"
+      horiz-adv-x="384" d=" M224 312V448H24C10.7 448 0 437.3 0 424V-40C0 -53.3 10.7 -64 24 -64H360C373.3 -64 384 -53.3 384 -40V288H248C234.8 288 224 298.8 224 312zM281.1 192H305C312.7 192 318.4 184.9 316.7 177.3L278.7 9.3C277.5 3.8 272.6 0 267 0H229C223.5 0 218.7 3.8 217.4 9.1C191.6 112.6 196.6 90.3 191.8 119.6H191.3C190.2 105.3 188.9 102.2000000000001 165.7 9.1C164.4 3.8 159.6 0 154.1 0H117C111.4 0 106.5 3.9 105.3 9.4L67.5 177.4C65.8 184.9 71.5 192 79.2 192H103.7C109.4 192 114.4 188 115.5 182.3C131.1 104.3 135.6 72.8 136.5 60.1C138.1 70.3 143.8 92.8 165.9 182.8C167.2 188.2 172 191.9 177.6 191.9H206.7C212.3 191.9 217.1 188.1 218.4 182.7000000000001C242.4 82.3000000000001 247.2 58.7000000000001 248 53.3000000000001C247.8 64.5000000000001 245.4 71.1000000000001 269.6 182.5000000000001C270.6 188.1000000000001 275.5 192.0000000000001 281.1 192.0000000000001zM384 326.1V320H256V448H262.1C268.5 448 274.6 445.5 279.1 441L377 343C381.5 338.5 384 332.4 384 326.1z" />
+    <glyph glyph-name="file"
+      unicode="&#xF15B;"
+      horiz-adv-x="384" d=" M224 312V448H24C10.7 448 0 437.3 0 424V-40C0 -53.3 10.7 -64 24 -64H360C373.3 -64 384 -53.3 384 -40V288H248C234.8 288 224 298.8 224 312zM384 326.1V320H256V448H262.1C268.5 448 274.6 445.5 279.1 441L377 343C381.5 338.5 384 332.4 384 326.1z" />
+    <glyph glyph-name="fill-drip"
+      unicode="&#xF576;"
+      horiz-adv-x="576" d=" M512 128S448 35.35 448 0C448 -35.35 476.66 -64 512 -64S576 -35.35 576 0S512 128 512 128zM502.63 230.94L294.94 438.63C288.69 444.88 280.5 448 272.31 448S255.93 444.88 249.69 438.63L168.11 357.05L81.93 443.24C75.68 449.49 65.55 449.49 59.31 443.24L36.69 420.62C30.45 414.37 30.45 404.24 36.69 398L122.88 311.82L28.12 217.06C-9.37 179.58 -9.37 118.8 28.12 81.31L145.31 -35.88C164.05 -54.62 188.62 -64 213.18 -64C237.75 -64 262.31 -54.63 281.05 -35.88L502.62 185.69C515.12 198.19 515.12 218.44 502.63 230.94zM386.41 159.97H65.93C67.29 163.81 69.5 167.9500000000001 73.36 171.8L86.51 184.95L168.12 266.56L226.72 207.96C239.21 195.47 259.4700000000001 195.47 271.96 207.96S284.45 240.71 271.96 253.2L213.36 311.8L272.31 370.75L434.75 208.31L386.41 159.97z" />
+    <glyph glyph-name="fill"
+      unicode="&#xF575;"
+      horiz-adv-x="512" d=" M502.63 230.94L294.94 438.63C288.69 444.88 280.5 448 272.31 448S255.93 444.88 249.69 438.63L168.11 357.05L81.93 443.23C75.69 449.48 65.55 449.48 59.31 443.23L36.69 420.62C30.45 414.37 30.45 404.24 36.69 397.99L122.88 311.81L28.12 217.05C-9.37 179.56 -9.37 118.79 28.12 81.3L145.31 -35.89C164.06 -54.63 188.62 -64.01 213.18 -64.01C237.75 -64.01 262.31 -54.64 281.06 -35.89L502.63 185.68C515.12 198.18 515.12 218.44 502.63 230.94zM386.41 159.97H65.93C67.29 163.81 69.5 167.9500000000001 73.36 171.8L86.51 184.95L168.12 266.56L226.73 207.96C239.2200000000001 195.47 259.48 195.47 271.9700000000001 207.96C284.4600000000001 220.45 284.4600000000001 240.71 271.9700000000001 253.2L213.36 311.8L272.31 370.75L434.76 208.31L386.41 159.97z" />
+    <glyph glyph-name="film"
+      unicode="&#xF008;"
+      horiz-adv-x="512" d=" M488 384H480V364C480 357.4 474.6 352 468 352H428C421.4 352 416 357.4 416 364V384H96V364C96 357.4 90.6 352 84 352H44C37.4 352 32 357.4 32 364V384H24C10.7 384 0 373.3 0 360V24C0 10.7 10.7 0 24 0H32V20C32 26.6 37.4 32 44 32H84C90.6 32 96 26.6 96 20V0H416V20C416 26.6 421.4 32 428 32H468C474.6 32 480 26.6 480 20V0H488C501.3 0 512 10.7 512 24V360C512 373.3 501.3 384 488 384zM96 76C96 69.4 90.6 64 84 64H44C37.4 64 32 69.4 32 76V116C32 122.6 37.4 128 44 128H84C90.6 128 96 122.6 96 116V76zM96 172C96 165.4 90.6 160 84 160H44C37.4 160 32 165.4 32 172V212C32 218.6 37.4 224 44 224H84C90.6 224 96 218.6 96 212V172zM96 268C96 261.4 90.6 256 84 256H44C37.4 256 32 261.4 32 268V308C32 314.6 37.4 320 44 320H84C90.6 320 96 314.6 96 308V268zM368 60C368 53.4 362.6 48 356 48H156C149.4 48 144 53.4 144 60V156C144 162.6 149.4 168 156 168H356C362.6 168 368 162.6 368 156V60zM368 228C368 221.4 362.6 216 356 216H156C149.4 216 144 221.4 144 228V324C144 330.6 149.4 336 156 336H356C362.6 336 368 330.6 368 324V228zM480 76C480 69.4 474.6 64 468 64H428C421.4 64 416 69.4 416 76V116C416 122.6 421.4 128 428 128H468C474.6 128 480 122.6 480 116V76zM480 172C480 165.4 474.6 160 468 160H428C421.4 160 416 165.4 416 172V212C416 218.6 421.4 224 428 224H468C474.6 224 480 218.6 480 212V172zM480 268C480 261.4 474.6 256 468 256H428C421.4 256 416 261.4 416 268V308C416 314.6 421.4 320 428 320H468C474.6 320 480 314.6 480 308V268z" />
+    <glyph glyph-name="filter"
+      unicode="&#xF0B0;"
+      horiz-adv-x="512" d=" M487.976 448H24.028C2.71 448 -8.047 422.134 7.058 407.029L192 222.059V16C192 8.169 195.821 0.83 202.237 -3.662L282.237 -59.642C298.02 -70.69 320 -59.493 320 -39.98V222.059L504.947 407.029C520.021 422.104 509.338 448 487.976 448z" />
+    <glyph glyph-name="fingerprint"
+      unicode="&#xF577;"
+      horiz-adv-x="512" d=" M256.12 202.04C242.87 202.04 232.12 191.3 232.12 178.04C233.26 105.79 223.98 36.14 204.42 -33.51C201.69 -43.2300000000001 206.57 -64 227.54 -64C238.02 -64 247.6500000000001 -57.08 250.63 -46.48C264.1600000000001 1.43 281.67 78.93 280.11 178.04C280.12 191.29 269.38 202.04 256.12 202.04zM255.26 283.77C194 283.8400000000001 151.25 236.7 152.1 182.68C152.85 134.74 148.35 86.77 138.73 40.13C136.04 27.15 144.4 14.44 157.37 11.77C170.42 9.1 183.04 17.43 185.73 30.41C196.07 80.5 200.9 131.99 200.1 183.43C199.69 209.38 220.0200000000001 235.92 254.55 235.77C285.86 235.3 311.7 210.43 312.17 180.3C312.94 132.25 309.36 83.97 301.56 36.7499999999999C299.39 23.6899999999999 308.25 11.3299999999999 321.32 9.17C341.29 5.84 348.13 24.27 348.9 28.9399999999999C357.18 78.9699999999999 360.96 130.1499999999999 360.17 181.05C359.29 236.85 312.23 282.93 255.26 283.77zM144.57 303.55C134.27 311.89 119.2 310.35 110.81 300.07C85.19 268.57 71.42 228.79 72.06 188.07C72.65 150.49 69.59 112.8 62.95 76.02C60.61 62.97 69.26 50.49 82.31 48.13C102.42 44.63 109.38 62.94 110.2 67.49C117.39 107.33 120.7 148.15 120.06 188.82C119.59 218.7 129.26 246.7 148.06 269.79C156.41 280.07 154.85 295.18 144.57 303.55zM254.04 365.88C238.63 366.29 223.17 364.44 208.26 360.91C195.37 357.85 187.39 344.93 190.43 332.02C193.49 319.13 206.43 311.19 219.32 314.19C230.37 316.8 241.79 317.9600000000001 253.32 317.88C328.75 316.75 391.05 256.38 392.2 183.3C392.79 145.42 390.92 107.1899999999999 386.62 69.67C385.12 56.4999999999999 394.57 44.59 407.73 43.09C424.4500000000001 41.14 433.24 54.97 434.31 64.2A929.06 929.06 0 0 1 440.2 184.05C438.64 282.8 355.13 364.38 254.04 365.88zM506.11 244.43C503.25 257.35 490.6 265.63 477.5 262.7C464.56 259.84 456.38 247.04 459.24 234.09C463.95 212.68 464.15 196.68 463.94 172.49C463.83 159.22 474.49 148.4 487.74 148.29H487.94C501.11 148.29 511.83 158.9 511.94 172.09C512.12 194.27 512.34 216.2 506.11 244.43zM465.99 335.15C417.29 404.54 337.6 446.71 252.81 447.98C183.02 448.82 118.47 423.09 70.46 375.06C24.09 328.63 -0.9 266.9600000000001 0.14 201.35L0.02 179.88C-0.37 166.63 10.05 155.57 23.3 155.19C23.53 155.17 23.78 155.17 24.02 155.17C36.94 155.17 47.61 165.47 47.99 178.47L48.15 202.11C47.32 254.61 67.31 303.9700000000001 104.43 341.11C143.19 379.9100000000001 195.77 400.7800000000001 252.11 399.9700000000001C321.56 398.94 386.8400000000001 364.4100000000001 426.73 307.5800000000001C434.3400000000001 296.7200000000001 449.29 294.13 460.15 301.7200000000001C470.99 309.3400000000001 473.61 324.31 465.99 335.15z" />
+    <glyph glyph-name="fire-extinguisher"
+      unicode="&#xF134;"
+      horiz-adv-x="448" d=" M434.027 421.671L266.027 393.671C254.693 391.782 256 380.2 256 376H197.668C208.353 411.892 181.446 448 144 448C104.565 448 77.632 408.324 91.772 371.7970000000001C39.733 358.746 16.391 317.584 1.723 280.913C-3.2 268.606 2.786 254.639 15.093 249.716C27.41 244.79 41.372 250.791 46.289 263.086C75.058 335.01 106.964 328 168 328V300.924C126.457 290.062 96 251.689 96 206.795V-40C96 -53.255 106.745 -64 120 -64H264C277.255 -64 288 -53.255 288 -40V208C288 252.731 257.404 290.312 216 300.9700000000001V328H256C256 325.026 254.297 312.284 266.027 310.329L434.027 282.329C441.342 281.11 448 286.75 448 294.166V409.834C448 417.25 441.342 422.89 434.027 421.671zM144 376C135.178 376 128 383.178 128 392S135.178 408 144 408S160 400.822 160 392S152.822 376 144 376z" />
+    <glyph glyph-name="fire"
+      unicode="&#xF06D;"
+      horiz-adv-x="384" d=" M216 424.142C216 447.944 185.347 456.907 171.851 437.18C48 256.149 224 248 224 160C224 124.371 194.886 95.542 159.15 96.006C123.98 96.462 96 125.78 96 160.954V246.464C96 268.1670000000001 69.529 278.689 54.568 262.968C27.801 234.842 0 186.668 0 128C0 22.131 86.131 -64 192 -64S384 22.131 384 128C384 298.29 216 321.003 216 424.142z" />
+    <glyph glyph-name="first-aid"
+      unicode="&#xF479;"
+      horiz-adv-x="576" d=" M0 368V16C0 -10.5 21.5 -32 48 -32H96V416H48C21.5 416 0 394.5 0 368zM128 -32H448V416H128V-32zM192 216C192 220.4 195.6 224 200 224H256V280C256 284.4 259.6 288 264 288H312C316.4 288 320 284.4 320 280V224H376C380.4 224 384 220.4 384 216V168C384 163.6 380.4 160 376 160H320V104C320 99.6 316.4 96 312 96H264C259.6 96 256 99.6 256 104V160H200C195.6 160 192 163.6 192 168V216zM528 416H480V-32H528C554.5 -32 576 -10.5 576 16V368C576 394.5 554.5 416 528 416z" />
+    <glyph glyph-name="fish"
+      unicode="&#xF578;"
+      horiz-adv-x="576" d=" M327.1 352C237.13 352 158.56 297.23 114.83 250.37L27.5 316.42C15.37 325.6 -2.74 315.82 0.36 301.76L24.54 192L0.35 82.23C-2.75 68.17 15.36 58.4 27.49 67.57L114.82 133.62C158.55 86.77 237.13 32 327.1 32C464.56 32 576 160 576 192S464.56 352 327.1 352zM414.5300000000001 168C401.2800000000001 168 390.5300000000001 178.75 390.5300000000001 192C390.5300000000001 205.26 401.2800000000001 216 414.5300000000001 216C427.79 216 438.5300000000001 205.26 438.5300000000001 192C438.5300000000001 178.75 427.7800000000001 168 414.5300000000001 168z" />
+    <glyph glyph-name="fist-raised"
+      unicode="&#xF6DE;"
+      horiz-adv-x="384" d=" M255.98 288V432C255.98 440.84 248.82 448 239.98 448H207.98C199.14 448 191.98 440.84 191.98 432V285.07C197 286.85 202.32 288 207.95 288H255.98zM383.98 192.01C383.9700000000001 227.35 355.32 256 319.99 256H207.85C199.07 256 191.95 248.93 191.95 240.15V239.59C191.95 213.32 213.25 192 239.52 192H274.78C284.46 192 287.98 188.42 287.98 184V167.8C287.98 163.51 284.39 160.0200000000001 280.1 159.8C235.58 157.5200000000001 215.94 135.09 184.05 87.25L177.74 77.78A7.993999999999999 7.993999999999999 0 0 0 166.65 75.56L153.34 84.4399999999999A7.993999999999999 7.993999999999999 0 0 0 151.12 95.5299999999999L157.43 104.9999999999999C173.16 128.6 187.63 148.2599999999999 204.74 163.0799999999999C187.47 168.5899999999999 173.34 181.1999999999999 165.87 197.5299999999999C159.28 194.1199999999999 151.91 192.0099999999999 144 192.0099999999999H111.9999999999999C99.6599999999999 192.0099999999999 88.51 196.8199999999999 79.9999999999999 204.4899999999999C71.48 196.81 60.33 192 48 192H16C10.36 192 5.03 193.15 0 194.95V117.02C0 83.07 13.48 50.52 37.49 26.51L63.99 0V-64H319.97V-0.04L355.88 35.88A96.035 96.035 0 0 1 384 103.79L383.98 192.01zM351.9700000000001 282.1V400C351.9700000000001 408.8400000000001 344.81 416 335.9700000000001 416H303.9700000000001C295.1300000000001 416 287.9700000000001 408.8400000000001 287.9700000000001 400V288H319.9700000000001C331.25 288 341.9100000000001 285.69 351.9700000000001 282.1zM16 224H48C56.84 224 64 231.16 64 240V368C64 376.8400000000001 56.84 384 48 384H16C7.16 384 0 376.8400000000001 0 368V240C0 231.16 7.16 224 16 224zM111.99 224H143.99C152.83 224 159.99 231.16 159.99 240V400C159.99 408.8400000000001 152.83 416 143.99 416H111.99C103.15 416 95.99 408.8400000000001 95.99 400V240C95.99 231.16 103.15 224 111.99 224z" />
+    <glyph glyph-name="flag-checkered"
+      unicode="&#xF11E;"
+      horiz-adv-x="512" d=" M466.515 381.072C487.731 390.926 512 375.449 512 352.056V108.956C512 98.43 506.839 88.549 498.157 82.598C462.32 58.034 423.822 41.74 375.652 41.74C308.279 41.74 264.022 76.523 210.435 76.523C159.582 76.523 124.311 66.465 96 54.401V-40C96 -53.255 85.255 -64 72 -64H56C42.745 -64 32 -53.255 32 -40V346.055C17.497 356.175 8 372.974 8 392C8 423.704 34.345 449.254 66.338 447.952C94.806 446.794 118.117 423.984 119.889 395.548C120.409 387.206 119.079 379.238 116.303 371.986C137.039 379.616 159.393 384 184.348 384C251.721 384 295.978 349.217 349.5650000000001 349.217C390.0610000000001 349.217 432.177 365.123 466.515 381.072zM96 313.37V242.88C125 253.55 147.18 260.71 169.6 263.79V335.36C146.1 333.19 129.16 325.57 96 313.37zM316.8 304.18C290.3830000000001 308.852 266.914 318.159 243.2 325.52V258.1C267.375 251.3940000000001 290.766 241.6560000000001 316.8 235.79V304.18zM169.6 263.79V193.75C202.396 196.728 223.51 194.385 243.2 189.95V258.1C217.953 265.135 196.619 267.523 169.6 263.79zM243.2 121.5600000000001C269.538 116.9080000000001 292.932 107.633 316.8 100.2200000000001V167.6300000000001C292.523 174.3760000000001 269.26 184.0800000000001 243.2 189.9500000000001V121.5600000000001zM96 105.9C119.62 114.29 143.79 119.74 169.6 122.46V193.75C143.49 191.4 122.24 185.71 96 176.39V105.9zM464 327.5C442.7 318.65 417.41 309.86 390.4 305.03V233.12C417.71 237.48 440.43 247.22 464 257.01V327.5zM464 117.54V188.03C441.81 173.83 415.2200000000001 165.42 390.4 162.01V90.43C415.47 92.81 438.89 101.47 464 117.54zM316.8 235.79V167.63C342.464 160.496 363.416 158.288 390.4 162.01V233.12C364.401 228.933 340.457 230.444 316.8 235.79z" />
+    <glyph glyph-name="flag-usa"
+      unicode="&#xF74D;"
+      horiz-adv-x="512" d=" M32 448C14.3 448 0 433.7 0 416V-48C0 -56.8 7.2 -64 16 -64H48C56.8 -64 64 -56.8 64 -48V416C64 433.7 49.7 448 32 448zM299.9 144.4C242.7 159.5 188.2 173.2 96 133.3V64C281.7 156.2 317.7 10.7 493.5 87.1C504.9 92.1 512 103.6 512 115.9V151.9C468.4 134.6 431.8 127.8 399.9 127.8C362.5 127.9 331 136.2 299.9 144.4zM299.9 240.4C242.7 255.5 188.2 269.2 96 229.3V167.8C190.8 205.4 250.6 190.5 308.1 175.4C365.3 160.3 419.8 146.6 512 186.5V248C468.4 230.7 431.8 223.9 399.9 223.9C362.5 223.9 331 232.2 299.9 240.4zM309.4 366.3C361.2 350.7 406.8 337.3 512 386.4V417.2C512 442.3 485.2 455.3 462.6 443.8C291.3 356.5 305.4 510.2 96 415.6V263.7C190.8 301.2 250.6 286.4 308.1 271.3C365.3 256.3 419.8 242.6 512 282.4V351.3C458.4 327.8 418.7 319.9 385.9 319.9S326.9 327.7 300.2 335.8C296.2 337 292.1 338.2000000000001 288.1 339.3V372.5C295.3 370.5 302.4 368.4 309.4 366.3zM160 319.9C151.2 319.9 144 327 144 335.9C144 344.7 151.2 351.9 160 351.9S176 344.8 176 335.9S168.8 319.9 160 319.9zM160 375.7C151.2 375.7 144 382.8 144 391.7C144 400.5 151.2 407.7 160 407.7S176 400.6 176 391.7C176 382.9 168.8 375.7 160 375.7zM224 327.8C215.2 327.8 208 334.9 208 343.8C208 352.6 215.2 359.8 224 359.8S240 352.7000000000001 240 343.8C240 335 232.8 327.8 224 327.8zM224 383.7000000000001C215.2 383.7000000000001 208 390.8 208 399.7000000000001C208 408.5 215.2 415.7000000000001 224 415.7000000000001S240 408.6 240 399.7000000000001C240 390.9000000000001 232.8 383.7000000000001 224 383.7000000000001z" />
+    <glyph glyph-name="flag"
+      unicode="&#xF024;"
+      horiz-adv-x="512" d=" M349.565 349.217C295.978 349.217 251.721 384 184.348 384C159.393 384 137.039 379.616 116.303 371.987A55.947 55.947 0 0 1 119.889 395.549C118.117 423.985 94.806 446.794 66.338 447.952C34.345 449.254 8 423.704 8 392C8 372.974 17.497 356.175 32 346.055V-40C32 -53.255 42.745 -64 56 -64H72C85.255 -64 96 -53.255 96 -40V54.4C124.311 66.464 159.582 76.522 210.435 76.522C264.023 76.522 308.279 41.739 375.6520000000001 41.739C423.8210000000001 41.739 462.3190000000001 58.033 498.157 82.597C506.84 88.548 512 98.429 512 108.955V352.055C512 375.448 487.731 390.925 466.515 381.071C432.177 365.123 390.061 349.217 349.565 349.217z" />
+    <glyph glyph-name="flask"
+      unicode="&#xF0C3;"
+      horiz-adv-x="448" d=" M437.2 44.5L320 233V384H328C341.3 384 352 394.7 352 408V424C352 437.3 341.3 448 328 448H120C106.7 448 96 437.3 96 424V408C96 394.7 106.7 384 120 384H128V233L10.8 44.5C-18.5 -2.6 15.3 -64 70.9 -64H377.1C432.8 -64 466.5 -2.5 437.2000000000001 44.5zM137.9 128L186.1 205.6C189.8 210.8 191.9 217.2 191.9 224V384H255.9000000000001V224C255.9000000000001 217.1 258.1 210.8 261.7000000000001 205.6L309.9000000000001 128H137.9z" />
+    <glyph glyph-name="flushed"
+      unicode="&#xF579;"
+      horiz-adv-x="496" d=" M344 248C330.7 248 320 237.3 320 224S330.7 200 344 200S368 210.7 368 224S357.3 248 344 248zM152 248C138.7 248 128 237.3 128 224S138.7 200 152 200S176 210.7 176 224S165.3 248 152 248zM248 440C111 440 0 329 0 192S111 -56 248 -56S496 55 496 192S385 440 248 440zM80 224C80 263.8 112.2 296 152 296S224 263.8 224 224S191.8 152 152 152S80 184.2 80 224zM312 48H184C162.8 48 162.8 80 184 80H312C333.2 80 333.2 48 312 48zM344 152C304.2 152 272 184.2 272 224S304.2 296 344 296S416 263.8 416 224S383.8 152 344 152z" />
+    <glyph glyph-name="folder-minus"
+      unicode="&#xF65D;"
+      horiz-adv-x="512" d=" M464 320H272L208 384H48C21.49 384 0 362.51 0 336V48C0 21.49 21.49 0 48 0H464C490.51 0 512 21.49 512 48V272C512 298.51 490.51 320 464 320zM368 152C368 143.16 360.84 136 352 136H160C151.16 136 144 143.16 144 152V168C144 176.84 151.16 184 160 184H352C360.84 184 368 176.84 368 168V152z" />
+    <glyph glyph-name="folder-open"
+      unicode="&#xF07C;"
+      horiz-adv-x="576" d=" M572.694 155.907L500.27 31.752A63.99699999999999 63.99699999999999 0 0 0 444.989 0H45.025C26.502 0 14.961 20.093 24.294 36.093L96.718 160.2480000000001A64 64 0 0 0 152 192H551.9639999999999C570.487 192 582.0279999999999 171.907 572.694 155.907zM152 224H480V272C480 298.51 458.51 320 432 320H272L208 384H48C21.49 384 0 362.51 0 336V57.954L69.077 176.372C86.214 205.75 117.989 224 152 224z" />
+    <glyph glyph-name="folder-plus"
+      unicode="&#xF65E;"
+      horiz-adv-x="512" d=" M464 320H272L208 384H48C21.49 384 0 362.51 0 336V48C0 21.49 21.49 0 48 0H464C490.51 0 512 21.49 512 48V272C512 298.51 490.51 320 464 320zM368 152C368 143.16 360.84 136 352 136H280V64C280 55.16 272.84 48 264 48H248C239.16 48 232 55.16 232 64V136H160C151.16 136 144 143.16 144 152V168C144 176.84 151.16 184 160 184H232V256C232 264.8400000000001 239.16 272 248 272H264C272.84 272 280 264.8400000000001 280 256V184H352C360.84 184 368 176.84 368 168V152z" />
+    <glyph glyph-name="folder"
+      unicode="&#xF07B;"
+      horiz-adv-x="512" d=" M464 320H272L208 384H48C21.49 384 0 362.51 0 336V48C0 21.49 21.49 0 48 0H464C490.51 0 512 21.49 512 48V272C512 298.51 490.51 320 464 320z" />
+    <glyph glyph-name="font"
+      unicode="&#xF031;"
+      horiz-adv-x="448" d=" M152 32H127.987L154.573 112.782H292.8L319.386 32H296C287.163 32 280 24.837 280 16V-16C280 -24.837 287.163 -32 296 -32H432C440.837 -32 448 -24.837 448 -16V16C448 24.837 440.837 32 432 32H405.261L275.495 405.254A16 16 0 0 1 260.382 416H187.616A16 16 0 0 1 172.503 405.254L42.739 32H16C7.163 32 0 24.837 0 16V-16C0 -24.837 7.163 -32 16 -32H152C160.837 -32 168 -24.837 168 -16V16C168 24.837 160.837 32 152 32zM216.353 303.778C220.701 318.9940000000001 222.963 331.9340000000001 223.939 338.4220000000001C224.778 331.901 226.878 318.9460000000001 231.6660000000001 303.716L273.0010000000001 179.71H174.382L216.353 303.778z" />
+    <glyph glyph-name="football-ball"
+      unicode="&#xF44E;"
+      horiz-adv-x="496" d=" M481.5 387.7C476.7 405.9 462.4 420.2 444.2 425.1C420.3 431.5 383 439.1 339.4 440L496 283.2C495.2 326.7 487.8 363.8 481.5 387.7zM14.5 -3.7C19.3 -21.9 33.6 -36.2 51.8 -41.1C75.7 -47.4999999999999 113 -55.1 156.6 -55.9999999999999L0 100.8C0.8 57.3 8.2 20.2 14.5 -3.7zM4.2 164.6L220.4 -52C352.9 -32.6 469.2 66.7 491.9 219.4L275.6 436C143.1 416.6 26.8 317.3 4.2 164.6zM321.5 288.2000000000001C324.6 291.3 329.7 291.3 332.8 288.2000000000001L344.1 276.9C347.2000000000001 273.8 347.2000000000001 268.7000000000001 344.1 265.6L315.8 237.3L344.1 209C347.2000000000001 205.9 347.2000000000001 200.8 344.1 197.7L332.8 186.4C329.7 183.3 324.6 183.3 321.5 186.4L293.2 214.7L270.6 192L298.9 163.7C302 160.6 302 155.5 298.9 152.4L287.6 141.1C284.5 138 279.4 138 276.3 141.1L248 169.4L225.4 146.8L253.7 118.5C256.8 115.3999999999999 256.8 110.3 253.7 107.1999999999999L242.4 95.8999999999999C239.3 92.7999999999999 234.2 92.7999999999999 231.1 95.8999999999999L202.8 124.1999999999999L174.5 95.8999999999999C171.4 92.7999999999999 166.3 92.7999999999999 163.2 95.8999999999999L151.9 107.1999999999999C148.8 110.3 148.8 115.3999999999999 151.9 118.5L180.2 146.8L151.9 175C148.8 178.1 148.8 183.1999999999999 151.9 186.3L163.2 197.6C166.3 200.7 171.4 200.7 174.5 197.6L202.8 169.3L225.4 191.9L197.1 220.2C194 223.3 194 228.4 197.1 231.5L208.4 242.8C211.5 245.9 216.6 245.9 219.7 242.8L248 214.5L270.6 237.1L242.3 265.4C239.2 268.5 239.2 273.6 242.3 276.7000000000001L253.6 288C256.7000000000001 291.1 261.8 291.1 264.9000000000001 288L293.2000000000001 259.7000000000001L321.5000000000001 288.2000000000001z" />
+    <glyph glyph-name="forward"
+      unicode="&#xF04E;"
+      horiz-adv-x="512" d=" M500.5 216.6L308.5 376.6C287.9 393.7 256 379.4 256 352V32C256 4.6 287.9 -9.8 308.5 7.4L500.5 167.4C515.8 180.2 515.8 203.8 500.5 216.6zM244.5 216.6L52.5 376.6C31.9 393.7 0 379.4 0 352V32C0 4.6 31.9 -9.8 52.5 7.4L244.5 167.4C259.8 180.2 259.8 203.8 244.5 216.6z" />
+    <glyph glyph-name="frog"
+      unicode="&#xF52E;"
+      horiz-adv-x="576" d=" M446.53 350.57C439.67 387.77 407.19 416 368 416C328.77 416 296.28 387.71 289.46 350.46C126.75 335.04 -0.5 197.88 0 31.02C0.11 -3.9 29.08 -32 64 -32H368C376.84 -32 384 -24.84 384 -16C384 1.67 369.67 16 352 16H272.51L308.31 64.33C332.45 100.56 318.6600000000001 152.61 274.6 170.93C250.7100000000001 180.86 223.05 175.5799999999999 202.36 160.05L169.6 135.46C162.54 130.15 152.51 131.55 147.19 138.65C141.89 145.73 143.31 155.76 150.38 161.06L185.16 187.15C222 214.81 273.4400000000001 214.77 310.29 187.15C321.1600000000001 179 356.1600000000001 148.09 351.0900000000001 93.94L469.62 -32H560C568.84 -32 576 -24.84 576 -16C576 1.67 561.67 16 544 16H490.37L391.85 120.68L546.29 207.33A58.15999999999999 58.15999999999999 0 0 1 576 258.06C576 279.4600000000001 564.28 299.01 545.52 309.29C504.96 331.51 446.53 350.57 446.53 350.57zM368 312C354.74 312 344 322.75 344 336C344 349.26 354.74 360 368 360C381.25 360 392 349.26 392 336C392 322.75 381.25 312 368 312z" />
+    <glyph glyph-name="frown-open"
+      unicode="&#xF57A;"
+      horiz-adv-x="496" d=" M248 440C111 440 0 329 0 192S111 -56 248 -56S496 55 496 192S385 440 248 440zM136 240C136 257.7 150.3 272 168 272S200 257.7 200 240S185.7 208 168 208S136 222.3 136 240zM323.3 56.7C292.1 66.3 263.9000000000001 72 248 72S203.9 66.3 172.7 56.7C161.2 53.2 150.2 63 152.2 74.8C159.2 114.8 212.3 136 248 136S336.8 114.7 343.8 74.8C345.8 62.9 334.7 53.2 323.3 56.7zM328 208C310.3 208 296 222.3 296 240S310.3 272 328 272S360 257.7 360 240S345.7 208 328 208z" />
+    <glyph glyph-name="frown"
+      unicode="&#xF119;"
+      horiz-adv-x="496" d=" M248 440C111 440 0 329 0 192S111 -56 248 -56S496 55 496 192S385 440 248 440zM328 272C345.7 272 360 257.7 360 240S345.7 208 328 208S296 222.3 296 240S310.3 272 328 272zM168 272C185.7 272 200 257.7 200 240S185.7 208 168 208S136 222.3 136 240S150.3 272 168 272zM338.2 53.8C315.8 80.6 282.9 96 248 96S180.2 80.6 157.8 53.8C144.3 37.5 119.7 58 133.2 74.3C161.7 108.4 203.6 128 248 128S334.3 108.4 362.7 74.2C376.3 58 351.7 37.5 338.2 53.8z" />
+    <glyph glyph-name="funnel-dollar"
+      unicode="&#xF662;"
+      horiz-adv-x="640" d=" M433.46 282.06L534.66 393.93C554.61 413.88 540.48 448 512.26 448H31.74C3.52 448 -10.61 413.88 9.34 393.93L192 192V36.08C192 23.4900000000001 197.93 11.64 208 4.08L287.99 -55.92C308.85 -71.5599999999999 336.4600000000001 -62.89 347.2100000000001 -42.35C310.8 -7.38 288 41.65 288 96C288 185.79 350.05 261.17 433.4600000000001 282.06zM480 256C391.63 256 320 184.37 320 96S391.63 -64 480 -64S640 7.63 640 96S568.37 256 480 256zM496 16.12V0C496 -4.42 492.42 -8 488 -8H472C467.58 -8 464 -4.42 464 0V16.29C452.71 16.87 441.73 20.81 432.63 27.64C428.73 30.5700000000001 428.53 36.41 432.06 39.78L443.81 50.99C446.58 53.63 450.7 53.75 453.94 51.72C457.81 49.3 462.2 48 466.76 48H494.87C501.37 48 506.67 53.92 506.67 61.19C506.67 67.14 503.06 72.38 497.9 73.92L452.9 87.42C434.3100000000001 93 421.3200000000001 110.84 421.3200000000001 130.81C421.3200000000001 155.33 440.3700000000001 175.25 463.9900000000001 175.88V192C463.9900000000001 196.42 467.5700000000001 200 471.9900000000001 200H487.9900000000001C492.4100000000001 200 495.9900000000001 196.42 495.9900000000001 192V175.71C507.2800000000001 175.13 518.2600000000001 171.2 527.36 164.36C531.26 161.43 531.46 155.59 527.9300000000001 152.22L516.1800000000001 141.01C513.4100000000001 138.37 509.2900000000001 138.25 506.0500000000001 140.28C502.1800000000001 142.71 497.7900000000001 144 493.2300000000001 144H465.1200000000001C458.6200000000001 144 453.3200000000001 138.08 453.3200000000001 130.81C453.3200000000001 124.86 456.9300000000001 119.62 462.09 118.08L507.09 104.58C525.6800000000001 99 538.6700000000001 81.16 538.6700000000001 61.19C538.6700000000001 36.66 519.6300000000001 16.75 496.0000000000001 16.12z" />
+    <glyph glyph-name="futbol"
+      unicode="&#xF1E3;"
+      horiz-adv-x="512" d=" M504 192C504 55.033 392.967 -56 256 -56S8 55.033 8 192S119.033 440 256 440S504 328.967 504 192zM456 192L455.997 192.282L429.933 169.541L367.254 228.041L383.7080000000001 312.396L418.011 309.324C393.122 343.54 358.007 369.413 317.302 382.4650000000001L330.9530000000001 350.526L256 309L181.047 350.525L194.698 382.464C154.067 369.436 118.918 343.594 93.989 309.323L128.554 312.396L144.746 228.041L82.068 169.541L56.004 192.282L56.001 192C56.001 148.985 69.498 108.048 94.473 74.009L102.177 107.906L187.315 97.459L223.616 19.633L193.714 1.847C233.916 -11.275 278.004 -11.301 318.286 1.847L288.384 19.633L324.685 97.4590000000001L409.823 107.9060000000001L417.527 74.0090000000001C442.503 108.048 456 148.985 456 192zM207.898 122.429L178.004 213.741L256 270.2680000000001L333.996 213.741L304.374 122.429H207.898z" />
+    <glyph glyph-name="gamepad"
+      unicode="&#xF11B;"
+      horiz-adv-x="640" d=" M480 352H160C71.6 352 0 280.4 0 192S71.6 32 160 32C204.8 32 245.2 50.4 274.2 80H365.7C394.7 50.4 435.2 32 479.9 32C568.3 32 639.9 103.6 639.9 192S568.4 352 480 352zM256 172C256 165.4 250.6 160 244 160H192V108C192 101.4 186.6 96 180 96H140C133.4 96 128 101.4 128 108V160H76C69.4 160 64 165.4 64 172V212C64 218.6 69.4 224 76 224H128V276C128 282.6 133.4 288 140 288H180C186.6 288 192 282.6 192 276V224H244C250.6 224 256 218.6 256 212V172zM440 104C413.5 104 392 125.5 392 152S413.5 200 440 200S488 178.5 488 152S466.5 104 440 104zM520 184C493.5 184 472 205.5 472 232S493.5 280 520 280S568 258.5 568 232S546.5 184 520 184z" />
+    <glyph glyph-name="gas-pump"
+      unicode="&#xF52F;"
+      horiz-adv-x="512" d=" M336 0H16C7.2 0 0 -7.2 0 -16V-48C0 -56.8 7.2 -64 16 -64H336C344.8 -64 352 -56.8 352 -48V-16C352 -7.2 344.8 0 336 0zM493.2 340.7L412.2 421.7C406 427.9 395.8 427.9 389.6 421.7L378.3 410.4C372.1 404.2 372.1 394 378.3 387.8L416 350.1V288C416 259.9 436.9 236.7 464 232.8V72C464 58.8 453.2 48 440 48S416 58.8 416 72V104C416 152.6 376.6 192 328 192H320V384C320 419.3 291.3 448 256 448H96C60.7 448 32 419.3 32 384V32H320V144H328C350.1 144 368 126.1 368 104V76.2C368 38.5 395 4.2 432.5 0.3C475.5 -4.0000000000001 512 29.8 512 71.9999999999999V295.4C512 312.4 505.2 328.7 493.2 340.7zM256 256H96V384H256V256z" />
+    <glyph glyph-name="gavel"
+      unicode="&#xF0E3;"
+      horiz-adv-x="512" d=" M504.971 248.638L482.344 271.265C472.971 280.6380000000001 457.775 280.6380000000001 448.403 271.265L442.7460000000001 265.608L329.608 378.745L335.265 384.402C344.638 393.775 344.638 408.971 335.265 418.343L312.638 440.971C303.265 450.344 288.069 450.344 278.697 440.971L154.246 316.52C144.873 307.1470000000001 144.873 291.951 154.246 282.579L176.873 259.952C186.246 250.579 201.442 250.579 210.814 259.952L216.471 265.6090000000001L256.069 226.011L175.029 144.971L169.372 150.628C156.875 163.125 136.614 163.125 124.117 150.628L9.373 35.882C-3.124 23.385 -3.124 3.124 9.373 -9.373L54.628 -54.628C67.125 -67.125 87.386 -67.125 99.883 -54.628L214.628 60.117C227.125 72.614 227.125 92.875 214.628 105.372L208.971 111.029L290.011 192.069L329.6090000000001 152.471L323.9520000000001 146.814C314.5790000000001 137.441 314.5790000000001 122.245 323.9520000000001 112.8730000000001L346.5790000000001 90.246C355.9520000000001 80.8730000000001 371.1480000000001 80.8730000000001 380.5200000000001 90.246L504.9710000000001 214.697C514.3430000000001 224.069 514.3430000000001 239.2650000000001 504.9710000000001 248.6380000000001z" />
+    <glyph glyph-name="gem"
+      unicode="&#xF3A5;"
+      horiz-adv-x="576" d=" M485.5 448L576 288H474.9L405.7 448H485.5zM357.5 448L426.7 288H149.3L218.5 448H357.5zM90.5 448H170.3L101.1 288H0L90.5 448zM0 256H100.7L223.7 4.3C225.2 1.2 221 -1.6 218.7 1L0 256zM148.2 256H427.8L290.8 -62.2C289.8 -64.6 286.3 -64.6 285.3 -62.2L148.2 256zM352.3 4.3L475.3 256H576L357.3 1.1C355 -1.6 350.8 1.2 352.3 4.3z" />
+    <glyph glyph-name="genderless"
+      unicode="&#xF22D;"
+      horiz-adv-x="288" d=" M144 272C188.1 272 224 236.1 224 192S188.1 112 144 112S64 147.9 64 192S99.9 272 144 272M144 336C64.5 336 0 271.5 0 192S64.5 48 144 48S288 112.5 288 192S223.5 336 144 336z" />
+    <glyph glyph-name="ghost"
+      unicode="&#xF6E2;"
+      horiz-adv-x="384" d=" M186.1 447.9100000000001C81.01 444.76 0 353.08 0 247.95V-15.97C0 -30.23 17.23 -37.36 27.31 -27.28L52.23 -8.75C58.89 -3.8 68.23 -4.76 73.74 -10.96L116.69 -59.31C122.94 -65.5599999999999 133.07 -65.5599999999999 139.32 -59.31L180.04 -13.46C186.41 -6.29 197.6 -6.29 203.96 -13.46L244.6800000000001 -59.31C250.9300000000001 -65.5599999999999 261.0600000000001 -65.5599999999999 267.3100000000001 -59.31L310.2600000000001 -10.96C315.7700000000001 -4.76 325.1100000000001 -3.79 331.7700000000001 -8.75L356.6900000000001 -27.28C366.7700000000001 -37.36 384.0000000000001 -30.22 384.0000000000001 -15.97V256C384 364 294.83 451.17 186.1 447.9100000000001zM128 224C110.33 224 96 238.33 96 256S110.33 288 128 288S160 273.67 160 256S145.67 224 128 224zM256 224C238.33 224 224 238.33 224 256S238.33 288 256 288S288 273.67 288 256S273.67 224 256 224z" />
+    <glyph glyph-name="gift"
+      unicode="&#xF06B;"
+      horiz-adv-x="512" d=" M32 0C32 -17.7 46.3 -32 64 -32H224V128H32V0zM480 288H437.9C444.1 300.1 448 313.5 448 328C448 376.5 408.5 416 360 416C318.4 416 291.5 394.7 257 347.7C222.5 394.7 195.6 416 154 416C105.5 416 66 376.5 66 328C66 313.5 69.8 300.1 76.1 288H32C14.3 288 0 273.7 0 256V176C0 167.2 7.2 160 16 160H496C504.8 160 512 167.2 512 176V256C512 273.7 497.7 288 480 288zM153.9 288C131.8 288 113.9 305.9 113.9 328S131.8 368 153.9 368C173.8 368 188.5 364.7 240 288H153.9zM360 288H273.9C325.3 364.5 339.6 368 360 368C382.1 368 400 350.1 400 328S382.1 288 360 288zM288 -32H448C465.7 -32 480 -17.7 480 0V128H288V-32z" />
+    <glyph glyph-name="glass-martini-alt"
+      unicode="&#xF57B;"
+      horiz-adv-x="512" d=" M502.05 390.4C523.3 411.66 508.25 448 478.2 448H33.8C3.75 448 -11.3 411.66 9.95 390.4L224 176.36V-16H168C145.91 -16 128 -33.91 128 -56C128 -60.42 131.58 -64 136 -64H376C380.42 -64 384 -60.42 384 -56C384 -33.91 366.09 -16 344 -16H288V176.36L502.05 390.4zM443.77 400L395.77 352H116.24L68.24 400H443.77z" />
+    <glyph glyph-name="glass-martini"
+      unicode="&#xF000;"
+      horiz-adv-x="512" d=" M502.05 390.4C523.3 411.66 508.25 448 478.2 448H33.8C3.75 448 -11.3 411.66 9.95 390.4L224 176.36V-16H168C145.91 -16 128 -33.91 128 -56C128 -60.42 131.58 -64 136 -64H376C380.42 -64 384 -60.42 384 -56C384 -33.91 366.09 -16 344 -16H288V176.36L502.05 390.4z" />
+    <glyph glyph-name="glasses"
+      unicode="&#xF530;"
+      horiz-adv-x="576" d=" M574.1 167.63L528.75 349.3400000000001C522.84 373.04 507.16 393.39 485.75 405.15C464.31 416.88 438.78 419.26 415.56 411.48L400.31 406.4C391.92 403.61 387.39 394.54 390.19 386.1600000000001L395.25 370.98C398.04 362.6 407.1 358.0700000000001 415.48 360.86L428.6600000000001 365.25C439.5300000000001 368.87 451.66 368.82 461.8200000000001 363.52C472.1100000000001 358.15 479.39 348.9600000000001 482.1900000000001 337.7000000000001L520.6500000000001 183.88C498.4600000000001 190.69 470.8600000000001 196.34 439.4500000000001 196.34C404.6800000000001 196.34 365.4700000000001 189.32 324.6000000000002 169.6H251.4200000000002C210.5500000000001 189.34 171.3400000000002 196.35 136.5600000000001 196.35C105.1400000000001 196.35 77.5400000000001 190.7000000000001 55.3500000000001 183.8900000000001L93.8100000000001 337.7200000000001C96.6000000000001 348.9700000000001 103.9000000000001 358.1700000000001 114.1900000000001 363.5300000000001C124.3500000000001 368.8300000000001 136.4700000000001 368.8800000000001 147.3400000000001 365.2600000000001L160.5100000000001 360.8700000000001C168.8900000000001 358.0800000000001 177.9500000000001 362.6100000000001 180.7400000000001 370.9900000000001L185.8000000000001 386.1700000000001C188.6000000000001 394.5500000000001 184.0700000000001 403.6200000000001 175.6800000000001 406.4100000000001L160.4300000000001 411.4900000000001C137.2100000000001 419.2700000000001 111.6800000000001 416.9000000000001 90.2400000000001 405.1600000000001C68.8300000000001 393.3900000000001 53.1500000000001 373.0500000000001 47.2400000000001 349.3600000000001L1.9 167.63A64.218 64.218 0 0 1 0 152.14V81.89C0 18.99 51.58 -32 115.2 -32H152.32C212.6 -32 262.69 13.94 267.2 73.37L270.13 112H305.88L308.81 73.37C313.31 13.94 363.4 -32 423.68 -32H460.8C524.42 -32 576 18.99 576 81.88V152.13C576 157.36 575.36 162.56 574.1 167.63zM203.38 78.21C201.41 52.3 178.98 32 152.32 32H115.2C86.97 32 64 54.38 64 81.89V119.43C82.12 125.92 107.42 132.35 136.58 132.35C160.44 132.35 183.84 128.0200000000001 206.51 119.43L203.38 78.21zM512 81.88C512 54.37 489.03 32 460.8 32H423.68C397.01 32 374.58 52.3 372.62 78.21L369.49 119.43C392.1600000000001 128.0199999999999 415.57 132.35 439.44 132.35C468.56 132.35 493.87 125.91 511.99 119.42V81.8799999999999z" />
+    <glyph glyph-name="globe-africa"
+      unicode="&#xF57C;"
+      horiz-adv-x="496" d=" M248 440C111.03 440 0 328.9700000000001 0 192S111.03 -56 248 -56S496 55.03 496 192S384.9700000000001 440 248 440zM408 224.5V217.57C408 211.7 404.68 206.33 399.43 203.71L384.04 196.01A15.485 15.485 0 0 0 368.5100000000001 196.98L350.3000000000001 209.12A15.519999999999998 15.519999999999998 0 0 1 336.8000000000001 210.93L334.1500000000001 210.05C324.4500000000001 206.82 320.4900000000001 195.26 326.1600000000001 186.75L339.4000000000001 166.89C342.2700000000001 162.58 347.1100000000001 159.99 352.2900000000001 159.99H360.5000000000001C369.0600000000001 159.99 376.0000000000001 153.05 376.0000000000001 144.49V133.15C376.0000000000001 129.8 374.9100000000001 126.53 372.9000000000001 123.85L354.1600000000001 98.87C352.74 96.97 351.7700000000001 94.77 351.3300000000001 92.44L347.0300000000001 69.61C346.4100000000001 66.32 344.74 63.32 342.2700000000001 61.05A159.60800000000003 159.60800000000003 0 0 1 317.2700000000001 31.89L304.2400000000001 12.34A27.756 27.756 0 0 0 281.1500000000001 -0.02C270.6400000000001 -0.02 261.0300000000001 5.92 256.3300000000001 15.3199999999999A78.902 78.902 0 0 0 248.0000000000001 50.61V80.5C248.0000000000001 89.06 241.0600000000001 96 232.5000000000001 96H206.6200000000001C192.1300000000001 96 178.2400000000001 101.76 167.9900000000001 112A54.65899999999999 54.65899999999999 0 0 0 151.9900000000001 150.63V164.69C151.9900000000001 181.88 160.0900000000001 198.07 173.8400000000001 208.39L201.4200000000001 229.08A54.663 54.663 0 0 0 234.2000000000001 240.01H235.0900000000001C243.5700000000001 240.01 251.9400000000001 238.04 259.5200000000001 234.24L274.2400000000001 226.88C277.9200000000001 225.04 282.1700000000001 224.74 286.0700000000001 226.04L333.3800000000001 241.81C339.7100000000001 243.92 343.9800000000001 249.84 343.9800000000001 256.51C343.9800000000001 265.07 337.0400000000001 272.01 328.4800000000001 272.01H318.3900000000001C314.2800000000001 272.01 310.3400000000001 273.64 307.4300000000001 276.55L300.5100000000001 283.4699999999999A15.493 15.493 0 0 1 289.5500000000002 288.01H199.5C190.94 288.01 184 294.95 184 303.51V307.91C184 315.02 188.84 321.2199999999999 195.74 322.95L210.19 326.56C213.93 327.5 217.19 329.79 219.33 333L227.41 345.1099999999999C230.28 349.42 235.12 352.01 240.3 352.01H264.51C273.07 352.01 280.01 358.95 280.01 367.51V389.2099999999999C359.23 376.37 422.86 316.98 441.93 240H423.5C414.94 240 408 233.06 408 224.5z" />
+    <glyph glyph-name="globe-americas"
+      unicode="&#xF57D;"
+      horiz-adv-x="496" d=" M248 440C111.03 440 0 328.9700000000001 0 192S111.03 -56 248 -56S496 55.03 496 192S384.9700000000001 440 248 440zM330.29 82.4C326.3900000000001 78.52 322.3 74.45 318.98 71.12C315.99 68.12 313.88 64.42 312.81 60.41C311.3 54.75 310.08 49.03 308.04 43.54L290.6500000000001 -3.31C276.8900000000001 -6.31 262.6500000000001 -8 248.0000000000001 -8V19.38C249.6900000000001 32 240.3600000000001 55.64 225.3700000000001 70.63C219.37 76.63 216 84.77 216 93.26V125.27C216 136.91 209.73 147.61 199.54 153.24C185.17 161.19 164.73 172.3 150.73 179.35C139.25 185.13 128.63 192.49 119.08 201.1L118.28 201.82A114.792 114.792 0 0 0 100.22 222.56C90.84 236.3300000000001 75.56 258.98 65.63 273.7000000000001C86.1 319.2000000000001 122.99 355.7400000000001 168.83 375.5900000000001L192.84 363.5800000000001C203.48 358.26 216 365.99 216 377.89V389.19C223.99 390.48 232.12 391.3 240.39 391.61L268.69 363.31C274.94 357.06 274.94 346.93 268.69 340.68L264 336L253.66 325.66C250.54 322.54 250.54 317.4700000000001 253.66 314.35L258.35 309.66C261.4700000000001 306.54 261.4700000000001 301.4700000000001 258.35 298.35L250.35 290.35A8.008 8.008 0 0 0 244.6900000000001 288.01H235.7C233.62 288.01 231.62 287.2 230.12 285.74L220.2 276.09A8.008 8.008 0 0 1 218.62 266.78L234.21 235.59C236.87 230.27 233 224.01 227.06 224.01H221.42C219.49 224.01 217.63 224.71 216.18 225.97L206.9 234.03A16.017 16.017 0 0 1 191.35 237.13L160.18 226.74A11.95 11.95 0 0 1 152.01 215.4C152.01 210.87 154.57 206.74 158.62 204.71L169.7000000000001 199.17C179.1100000000001 194.46 189.4900000000001 192.01 200.0100000000001 192.01S222.6000000000001 164.72 232.0100000000001 160.01H298.7600000000001C307.2500000000001 160.01 315.3800000000001 156.64 321.3900000000001 150.64L335.0800000000001 136.95A30.503000000000004 30.503000000000004 0 0 0 344.0100000000001 115.38A46.536 46.536 0 0 0 330.29 82.4zM417 173.75C411.21 175.2 406.1600000000001 178.75 402.85 183.72L384.87 210.69A23.97 23.97 0 0 0 384.87 237.3100000000001L404.46 266.6900000000001C406.78 270.1600000000001 409.96 272.98 413.7 274.8400000000001L426.68 281.3300000000001C440.2 254.41 448 224.13 448 192C448 183.33 447.26 174.84 446.18 166.46L417 173.75z" />
+    <glyph glyph-name="globe-asia"
+      unicode="&#xF57E;"
+      horiz-adv-x="496" d=" M248 440C111.03 440 0 328.9700000000001 0 192S111.03 -56 248 -56S496 55.03 496 192S384.9700000000001 440 248 440zM236.66 199.77C233.77 194.95 228.56 192 222.94 192H222.63C218.39 192 214.32 190.31 211.32 187.31L205.66 181.65C202.54 178.53 202.54 173.46 205.66 170.34L211.32 164.68C214.32 161.68 216.01 157.61 216.01 153.37V144C216.01 135.16 208.85 128 200.01 128H193.9C187.84 128 182.3 131.42 179.59 136.85L156.97 182.0800000000001C154.53 186.96 148.02 188.0200000000001 144.16 184.16L124.69 164.7000000000001C121.69 161.7000000000001 117.62 160.0100000000001 113.38 160.0100000000001H50.81C49.12 170.45 48 181.08 48 192C48 302.28 137.72 392 248 392C269.51 392 290.2 388.49 309.63 382.18L259.4700000000001 343.65C254.36 340.24 254.8400000000001 332.5900000000001 260.3300000000001 329.8400000000001L271.1600000000001 324.43C276.5800000000001 321.7200000000001 280 316.18 280 310.12V232C280 227.58 276.42 224 272 224H268.94C265.9100000000001 224 263.14 225.71 261.79 228.42C260.23 231.54 255.83 231.71 254.0300000000001 228.72L236.6600000000001 199.77zM408 89.57C408 85.33 406.31 81.26 403.31 78.26L393.74 68.69C390.74 65.69 386.67 64 382.43 64H367.27C363.03 64 358.96 65.69 355.96 68.69L342.95 81.7A26.767 26.767 0 0 1 317.53 88.74L296.26 83.42C294.99 83.1 293.69 82.94 292.38 82.94H282.04C277.8 82.94 273.73 84.63 270.73 87.63L258.82 99.54A8.008 8.008 0 0 0 256.48 105.2000000000001V115.4C256.48 118.67 258.4700000000001 121.61 261.51 122.8300000000001L300.85 138.5700000000001C302.8300000000001 139.3600000000001 304.7100000000001 140.3900000000001 306.44 141.6200000000001L330.15 158.5100000000001A7.978 7.978 0 0 0 334.79 159.9900000000001H346.88C350.11 159.9900000000001 353.0299999999999 158.0500000000001 354.2699999999999 155.0600000000001L359.62 142.21A4 4 0 0 1 363.31 139.7500000000001H367.11C368.89 139.7500000000001 370.46 140.9300000000001 370.95 142.6300000000001L375.1499999999999 157.1000000000001C375.6499999999999 158.8100000000001 377.2099999999999 159.9800000000001 378.9899999999999 159.9800000000001H385.0499999999999C387.2599999999999 159.9800000000001 389.0499999999999 158.1900000000001 389.0499999999999 155.9800000000001V143.0500000000001C389.0499999999999 140.9300000000001 389.8899999999999 138.8900000000001 391.3899999999999 137.3900000000001L403.2999999999999 125.48C406.2999999999999 122.48 407.9899999999999 118.41 407.9899999999999 114.17V89.57z" />
+    <glyph glyph-name="globe"
+      unicode="&#xF0AC;"
+      horiz-adv-x="496" d=" M336.5 288C322 377.3 287.8 440 248 440S174 377.3 159.5 288H336.5zM152 192C152 169.8 153.2 148.5 155.3 128H340.6C342.7000000000001 148.5 343.9000000000001 169.8 343.9000000000001 192S342.7000000000001 235.5 340.6 256H155.3C153.2 235.5 152 214.2 152 192zM476.7 288C448.1 355.9 390.2 408.4 318.7 429.6C343.1 395.8 359.9 344.9 368.7 288H476.7zM177.2 429.6C105.8 408.4 47.8 355.9 19.3 288H127.3C136 344.9 152.8 395.8 177.2 429.6zM487.4 256H372.7C374.8 235 376 213.5 376 192S374.8 149 372.7 128H487.3C492.8 148.5 495.9 169.8 495.9 192S492.8 235.5 487.4 256zM120 192C120 213.5 121.2 235 123.3 256H8.6C3.2 235.5 0 214.2 0 192S3.2 148.5 8.6 128H123.2C121.2 149 120 170.5 120 192zM159.5 96C174 6.7 208.2 -56 248 -56S322 6.7 336.5 96H159.5zM318.8 -45.6C390.2000000000001 -24.4 448.2000000000001 28.1 476.8 96H368.8C360 39.1 343.2 -11.8 318.8 -45.6zM19.3 96C47.9 28.1 105.8 -24.4 177.3 -45.6C152.9 -11.8 136.1 39.1 127.3 96H19.3z" />
+    <glyph glyph-name="golf-ball"
+      unicode="&#xF450;"
+      horiz-adv-x="416" d=" M96 32H320C320 14.3 305.7 0 288 0H272C254.3 0 240 -14.3 240 -32V-52C240 -58.6 234.6 -64 228 -64H188C181.4 -64 176 -58.6 176 -52V-32C176 -14.3 161.7 0 144 0H128C110.3 0 96 14.3 96 32zM416 240C416 165.8 377 100.8 318.5 64H97.5C39 100.8 0 165.8 0 240C0 354.9 93.1 448 208 448S416 354.9 416 240zM235.9 196.1C254.2 196.1 269 210.9 269 229.2C269 243.6 259.7 255.5 246.9 260.1C256.5 233.3 231.3 208.8 205 218.2C209.6 205.4 221.5 196.1 235.9 196.1zM285 149.2C285 163.6 275.7 175.5 262.9 180.1C272.5 153.3 247.3 128.8 221 138.2C225.6 125.4 237.5 116.1 251.9 116.1C270.2 116.1 285 131 285 149.2zM349 213.2C349 227.6 339.7 239.5 326.9 244.1C336.5 217.3 311.3 192.8 285 202.2C289.6 189.4 301.5 180.1 315.9 180.1C334.2 180.1 349 195 349 213.2z" />
+    <glyph glyph-name="gopuram"
+      unicode="&#xF664;"
+      horiz-adv-x="512" d=" M496 96H480V208C480 216.84 472.84 224 464 224H448V304C448 312.8400000000001 440.84 320 432 320H416V432C416 440.84 408.84 448 400 448S384 440.84 384 432V416H320V432C320 440.84 312.84 448 304 448S288 440.84 288 432V416H224V432C224 440.84 216.84 448 208 448S192 440.84 192 432V416H128V432C128 440.84 120.84 448 112 448S96 440.84 96 432V320H80C71.16 320 64 312.8400000000001 64 304V224H48C39.16 224 32 216.84 32 208V96H16C7.16 96 0 88.84 0 80V-48C0 -56.84 7.16 -64 16 -64H96V96H128V224H160V320H192V224H160V96H128V-64H208V16C208 24.84 215.16 32 224 32H288C296.84 32 304 24.84 304 16V-64H384V96H352V224H320V320H352V224H384V96H416V-64H496C504.84 -64 512 -56.84 512 -48V80C512 88.84 504.84 96 496 96zM224 96V160C224 168.84 231.16 176 240 176H272C280.84 176 288 168.84 288 160V96H224zM232 224V272C232 280.8400000000001 239.16 288 248 288H264C272.84 288 280 280.8400000000001 280 272V224H232z" />
+    <glyph glyph-name="graduation-cap"
+      unicode="&#xF19D;"
+      horiz-adv-x="640" d=" M622.34 294.8L343.4 380.5C328.2 385.17 311.8 385.17 296.61 380.5L17.66 294.8C-5.88 287.5700000000001 -5.88 256.44 17.66 249.21L66.29 234.27C55.62 221.08 49.06 204.99 48.41 187.37C38.78 181.85 32 171.89 32 160C32 149.22 37.68 140.15 45.86 134.35L20.33 19.47C18.11 9.48 25.71 0 35.94 0H92.05C102.29 0 109.89 9.48 107.67 19.47L82.14 134.35C90.32 140.15 96 149.22 96 160C96 171.57 89.53 181.25 80.34 186.87C81.1 201.89 88.78 215.17 101.03 223.59L296.6 163.5C305.6600000000001 160.72 323.04 157.25 343.3900000000001 163.5L622.34 249.2C645.89 256.44 645.89 287.56 622.34 294.8zM352.79 132.91C324.26 124.15 299.9500000000001 128.99 287.2000000000001 132.91L142.18 177.46L128 64C128 28.65 213.96 0 320 0S512 28.65 512 64L497.82 177.47L352.79 132.91z" />
+    <glyph glyph-name="greater-than-equal"
+      unicode="&#xF532;"
+      horiz-adv-x="448" d=" M55.22 340.31L230.78 272.2200000000001L55.34 204.17C36.95 198.14 27.46 179.78 34.14 163.17L46.23 133.09C52.91 116.48 73.22 107.9 91.61 113.9400000000001L393.02 233.8C406.79 238.32 416 250.4100000000001 416 263.9700000000001V279.9300000000001C416 293.49 406.79 305.5800000000001 393.02 310.1L91.3 430.08C73.01 436.08 52.79 427.55 46.15 411.02L34.12 381.1C27.48 364.57 36.93 346.31 55.22 340.31zM424 48H24C10.75 48 0 37.26 0 24V-24C0 -37.25 10.75 -48 24 -48H424C437.25 -48 448 -37.25 448 -24V24C448 37.26 437.25 48 424 48z" />
+    <glyph glyph-name="greater-than"
+      unicode="&#xF531;"
+      horiz-adv-x="384" d=" M365.52 238.15L59.22 380.99C43.16 388.48 24.07 381.53 16.58 365.4700000000001L3.01 336.39C-4.48 320.33 2.47 301.24 18.53 293.75L236.96 191.9L18.49 90.01C2.47 82.54 -4.46 63.5 3.01 47.48L16.53 18.48C24 2.46 43.04 -4.47 59.06 3L365.5300000000001 145.91A32.003 32.003 0 0 1 384.0100000000001 174.91V209.14C384.0000000000001 221.59 376.8000000000001 232.9 365.5200000000001 238.15z" />
+    <glyph glyph-name="grimace"
+      unicode="&#xF57F;"
+      horiz-adv-x="496" d=" M248 440C111 440 0 329 0 192S111 -56 248 -56S496 55 496 192S385 440 248 440zM144 48H136C118.3 48 104 62.3 104 80V88H144V48zM144 104H104V112C104 129.7 118.3 144 136 144H144V104zM136 240C136 257.7 150.3 272 168 272S200 257.7 200 240S185.7 208 168 208S136 222.3 136 240zM208 48H160V88H208V48zM208 104H160V144H208V104zM272 48H224V88H272V48zM272 104H224V144H272V104zM336 48H288V88H336V48zM336 104H288V144H336V104zM328 208C310.3 208 296 222.3 296 240S310.3 272 328 272S360 257.7 360 240S345.7 208 328 208zM392 80C392 62.3 377.7 48 360 48H352V88H392V80zM392 104H352V144H360C377.7 144 392 129.7 392 112V104z" />
+    <glyph glyph-name="grin-alt"
+      unicode="&#xF581;"
+      horiz-adv-x="496" d=" M248 440C111 440 0 329 0 192S111 -56 248 -56S496 55 496 192S385 440 248 440zM311.7 311.3C319.3 322.7000000000001 336.4 323 344.4 311.3C356.8 292.9 359.5 274.4 360.1 256C359.6 237.6 356.8 219.1 344.4 200.7C336.8 189.3 319.7 189 311.7 200.7C299.3 219.1 296.6 237.6 296 256C296.5 274.4 299.3 292.9 311.7 311.3zM151.7 311.3C159.3 322.7000000000001 176.4 323 184.4 311.3C196.8 292.9 199.5 274.4 200.1 256C199.6 237.6 196.8 219.1 184.4 200.7C176.8 189.3 159.7 189 151.7 200.7C139.3 219.1 136.6 237.6 136 256C136.5 274.4 139.3 292.9 151.7 311.3zM248 16C187.4 16 113.5 54.3 104.2 109.3C102.2 121.1 113.5 130.9 124.9 127.2C155.1 117.5 200 112 248 112S340.9 117.5 371.1 127.2C382.5 130.9 393.7000000000001 121.1 391.8 109.3C382.5 54.3 308.6 16 248 16z" />
+    <glyph glyph-name="grin-beam-sweat"
+      unicode="&#xF583;"
+      horiz-adv-x="504" d=" M456 320C482.5 320 504 341 504 367C504 387 475.5 427.4 462.4 444.8C459.2 449.1 452.8 449.1 449.6 444.8C436.5 427.4 408 387 408 367C408 341 429.5 320 456 320zM456 288C411.9 288 376 323.4 376 367C376 371.4 376.3 381.2 384.1 399.2C345 424.9 298.3 440 248 440C111 440 0 329 0 192S111 -56 248 -56S496 55 496 192C496 227.1 488.6 260.4 475.5 290.6C469.2 289.1 462.8 288 456 288zM328 296C351.8 296 380.7 266.7 384 224.6C384.7 216 373.2 212.6 369.1 220.1L359.6 237.1C351.9000000000001 250.8 340.4000000000001 258.7 328.1 258.7S304.3 250.8 296.6 237.1L287.1 220.1C283 212.7 271.5 216.1 272.2000000000001 224.6C275.3000000000001 266.7 304.2000000000001 296 328.0000000000001 296zM168 296C191.8 296 220.7 266.7 224 224.6C224.7 216 213.2 212.6 209.1 220.1L199.6 237.1C191.9 250.8 180.4 258.7 168.1 258.7S144.3 250.8 136.6 237.1L127.1 220.1C122.9 212.7 111.5 216.1 112.2 224.6C115.3 266.7 144.2 296 168 296zM248 16C187.4 16 113.5 54.3 104.2 109.3C102.2 121.1 113.5 130.9 124.9 127.2C155.1 117.5 200 112 248 112S340.9 117.5 371.1 127.2C382.6 130.9 393.7000000000001 121 391.8 109.3C382.5 54.3 308.6 16 248 16z" />
+    <glyph glyph-name="grin-beam"
+      unicode="&#xF582;"
+      horiz-adv-x="496" d=" M248 440C111 440 0 329 0 192S111 -56 248 -56S496 55 496 192S385 440 248 440zM328 296C351.8 296 380.7 266.7 384 224.6C384.7 216 373.2 212.7 369.1 220.1L359.6 237.1C351.9000000000001 250.8 340.4000000000001 258.7 328.1 258.7S304.3 250.8 296.6 237.1L287.1 220.1C283 212.8 271.5 216.1 272.2000000000001 224.6C275.3000000000001 266.7 304.2000000000001 296 328.0000000000001 296zM168 296C191.8 296 220.7 266.7 224 224.6C224.7 216 213.2 212.7 209.1 220.1L199.6 237.1C191.9 250.8 180.4 258.7 168.1 258.7S144.3 250.8 136.6 237.1L127.1 220.1C122.9 212.7 111.5 216.1 112.2 224.6C115.3 266.7 144.2 296 168 296zM248 16C187.4 16 113.5 54.3 104.2 109.3C102.2 121.2 113.6 130.9 124.9 127.2C155.1 117.5 200 112 248 112S340.9 117.5 371.1 127.2C382.5 130.9 393.7000000000001 121.1 391.8 109.3C382.5 54.3 308.6 16 248 16z" />
+    <glyph glyph-name="grin-hearts"
+      unicode="&#xF584;"
+      horiz-adv-x="496" d=" M248 440C111 440 0 329 0 192S111 -56 248 -56S496 55 496 192S385 440 248 440zM90.4 264.4C97.1 282 117.1 291.1 135.3 286.3L142.4 284.4L144.4 291.5C149.4 309.6 167.2 322.4 185.9 319.4C207.3 316 220.3 295.2000000000001 214.7 274.9L195.3 205C194.1 200.5 189.4 197.8 184.8 199L114.6 217.2C94.2 222.6 82.7 244.2 90.4 264.4zM248 16C187.4 16 113.5 54.3 104.2 109.3C102.2 121.1 113.4 130.8 124.9 127.2C155.1 117.5 200 112 248 112S340.9 117.5 371.1 127.2C382.5 130.8 393.7000000000001 121.1 391.8 109.3C382.5 54.3 308.6 16 248 16zM381.4 217.3L311.2 199.1C306.7 197.9 302 200.6 300.7 205.1L281.3 275C275.7 295.3 288.7 316.1 310.1 319.5C328.7000000000001 322.5 346.5 309.7 351.6 291.6L353.6 284.5L360.7000000000001 286.4C378.9000000000001 291.1 398.9000000000001 282.1 405.6 264.5C413.3 244.2 401.8 222.6 381.4000000000001 217.3z" />
+    <glyph glyph-name="grin-squint-tears"
+      unicode="&#xF586;"
+      horiz-adv-x="512" d=" M409.6 336.1C432.2000000000001 339.3 483.1 348.1 497.9 362.9C517.1 382.1 516.8000000000001 413.5 497.2 433.1S446 453 426.9 433.8C412.1 419 403.4 368.1 400.1 345.5C399.3 340 404 335.3 409.6 336.1zM102.4 47.9C79.8 44.7 28.9 35.9 14.1 21.1C-5 1.9999999999999 -4.7 -29.5000000000001 14.9 -49.1S65.9 -69 85.1 -49.8C99.9 -35 108.6 15.9 111.9 38.5C112.7 44 108 48.7 102.4 47.9zM414.1 304.4C381.1 300.5 365.5 329.5 368.4000000000001 350.1C371.8 374.1 375.8 392.2 379.9000000000001 406.6C285.1 461.4 161.8 448.5 80.6 367.4C-0.5 286.3 -13.4 163 41.4 68.1C55.8 72.2000000000001 73.8 76.1 97.9 79.6C131.1 83.5 146.5 54.4 143.6 33.9C140.2 9.9 136.2 -8.2 132.1 -22.6C226.9000000000001 -77.4 350.2000000000001 -64.5 431.4000000000001 16.6S525.4000000000001 221 470.6 315.9000000000001C456.2 311.8000000000001 438.1 307.9000000000001 414.1 304.4000000000001zM255.7 342C259 355.2 278.1 353.5 279.3 340.2L284.1 287.9L336.4000000000001 283.1C349.8 281.9 351.3 262.8 338.2000000000001 259.5L247.7000000000001 236.9C238.8000000000001 234.7 231.0000000000001 242.8 233.2000000000001 251.4L255.7000000000001 342zM164.8 111.7L160 164L107.7 168.8C94.3 170 92.8 189.1 105.9 192.4L196.4 215C205.2 217.2 213.1 209.2 210.9 200.5L188.3 110C185.2 96.8 166.1 98.3 164.8 111.7zM380.5 67.5C351.2 38.2 304.8 17.1 263.8 17.1C244.9 17.1 227.2 21.6 212.8 31.8C203 38.7 204.1 53.6 214.8 59C243.1 73.6 278.7 101.4 312.6 135.3S374.3 204.9 388.9000000000001 233.1C394.3 243.6 409.1 245.0000000000001 416.2000000000001 235.1C448.5000000000001 189.8 423.3000000000001 110.4 380.5000000000001 67.5z" />
+    <glyph glyph-name="grin-squint"
+      unicode="&#xF585;"
+      horiz-adv-x="496" d=" M248 440C111 440 0 329 0 192S111 -56 248 -56S496 55 496 192S385 440 248 440zM281.8 250.3L361.8 298.3C373.4000000000001 305.2000000000001 385.8 290.6 377.2 280.3L343.6 240L377.2000000000001 199.7C385.9000000000001 189.3 373.3000000000001 174.9 361.8000000000001 181.7L281.8000000000001 229.7C274.1000000000001 234.4 274.1000000000001 245.6 281.8000000000001 250.3zM118.8 280.3C110.2 290.6 122.6 305.2000000000001 134.2 298.3L214.2 250.3C222 245.6 222 234.4 214.2 229.7L134.2 181.7000000000001C122.7 174.9 110.2 189.3000000000001 118.8 199.7000000000001L152.4 240.0000000000001L118.8 280.3000000000001zM248 16C187.4 16 113.5 54.3 104.2 109.3C102.2 121.2 113.6 130.9 124.9 127.2C155.1 117.5 200 112 248 112S340.9 117.5 371.1 127.2C382.6 130.9 393.7000000000001 121 391.8 109.3C382.5 54.3 308.6 16 248 16z" />
+    <glyph glyph-name="grin-stars"
+      unicode="&#xF587;"
+      horiz-adv-x="496" d=" M248 440C111 440 0 329 0 192S111 -56 248 -56S496 55 496 192S385 440 248 440zM94.6 279.1L129.5 284.1L145 315.7C147.9 321.5 156 321.5 158.9 315.7L174.4 284.1L209.3 279.1C215.5 278.1 218.2 270.5 213.6 265.9L188.2 241.3L194.2 206.4C195.2 200.2 188.9 195.4 183.2 198.5L152 214.7L120.7 198.4C115 195.3 108.7 200.1 109.7 206.3L115.7 241.2L90.3 265.8C85.7 270.5 88.4 278.1 94.6 279.1zM248 16C187.4 16 113.5 54.3 104.2 109.3C102.2 121.1 113.5 130.8 124.9 127.2C155.1 117.5 200 112 248 112S340.9 117.5 371.1 127.2C382.6 130.9 393.7000000000001 121.1 391.8 109.3C382.5 54.3 308.6 16 248 16zM405.7 265.9L380.3 241.3L386.3 206.4C387.3 200.2 381 195.4 375.3 198.5L344 214.7L312.7 198.4C307 195.3 300.7 200.1 301.7 206.3L307.7 241.2L282.3 265.8C277.8 270.4 280.4000000000001 278 286.6 279L321.5 284L337 315.6C339.9 321.4 348 321.4 350.9 315.6L366.4 284L401.3 279C407.6 278.1 410.3 270.5 405.7 265.9z" />
+    <glyph glyph-name="grin-tears"
+      unicode="&#xF588;"
+      horiz-adv-x="640" d=" M102.4 191.9C79.8 188.7 28.9 179.9 14.1 165.1C-5 146 -4.7 114.5 14.9 94.9S65.9 75 85.1 94.2C99.9 109 108.6 159.9 111.9 182.5C112.7 188 108 192.7 102.4 191.9zM625.8 165.1C611 179.9 560.0999999999999 188.6 537.5 191.9C532 192.7 527.2 188 528 182.4C531.2 159.8 540 108.9 554.8 94.1C574 74.9 605.4 75.2 625 94.8S645 146 625.8 165.1zM496.4 177.9C492.6 204.5 515.5 227.4 542.1 223.6C551 222.3 558.9 220.9 566.4 219.5C552.7 343.5 447.7 440 320 440S87.3 343.5 73.6 219.5C81.1 220.9 89 222.3 97.9 223.6C131.1 227.5 146.5 198.3 143.6 177.9C131.8 95.6 113.7 77.5 107.8 71.5C106.9 70.6 105.8 69.9 104.8 69C147.5 -5.6 227.8 -56 320 -56S492.5 -5.6 535.2 69.1C534.2 70 533.1 70.6 532.2 71.6C526.3000000000001 77.5 508.2 95.6 496.4 177.9zM400 296C423.8 296 452.7 266.7 456 224.6C456.7 216 445.2 212.6 441.1 220.1L431.6 237.1C423.9000000000001 250.8 412.4000000000001 258.7 400.1 258.7S376.3 250.8 368.6 237.1L359.1 220.1C354.9000000000001 212.7 343.5 216.1 344.2000000000001 224.6C347.3000000000001 266.7 376.2000000000001 296 400.0000000000001 296zM240 296C263.8 296 292.7 266.7 296 224.6C296.7 216 285.2 212.6 281.1 220.1L271.6 237.1C263.9000000000001 250.8 252.4000000000001 258.7 240.1 258.7S216.3 250.8 208.6 237.1L199.1 220.1C194.9 212.7 183.5 216.1 184.2 224.6C187.3 266.7 216.2 296 240 296zM320 16C259.4 16 185.5 54.3 176.2 109.3C174.2 121 185.4 130.9 196.9 127.2C227.1 117.5 272 112 320 112S412.9 117.5 443.1 127.2C454.5 130.9 465.7 121.1 463.8 109.3C454.5 54.3 380.6 16 320 16z" />
+    <glyph glyph-name="grin-tongue-squint"
+      unicode="&#xF58A;"
+      horiz-adv-x="496" d=" M293.1 73.4C278.7000000000001 79.9 262 71.2 258.5 55.8L256.7 47.9999999999999C254.6 38.8 241.5 38.8 239.4 47.9999999999999L237.6 55.8C234.1 71.1999999999999 217.4 79.9 203 73.4C202.1 73 203.3 73.6 184.1 64V1C184.1 -34.2 212.1 -63.5 247.2 -63.9C282.9 -64.4 312.1 -35.5 312.1 0.1V64.1C292.6 73.7000000000001 293.9 73 293.1 73.4zM248 440C111 440 0 329 0 192C0 85.7 67 -4.7 161 -40C155.4 -27.8 152 -14.3 152 0V45.5C127.3 61.7 108.5 83.6 104.2 109.3C102.2 121.1 113.4 130.8 124.9 127.2C155.1 117.5 200 112 248 112S340.9 117.5 371.1 127.2C382.5 130.9 393.7000000000001 121.1 391.8 109.3C387.5 83.6 368.7 61.7 344 45.5V0C344 -14.3 340.6 -27.8 335 -40C429 -4.7 496 85.7 496 192C496 329 385 440 248 440zM214.2 229.7L134.2 181.7C122.7 174.9 110.2 189.3 118.8 199.7L152.4 240L118.8 280.3C110.2 290.6 122.6 305.2000000000001 134.2 298.3L214.2 250.3C221.9 245.6 221.9 234.4 214.2 229.7zM377.2 199.7C385.9 189.3 373.3 174.9 361.8 181.7L281.8 229.7C274 234.4 274 245.6 281.8 250.3L361.8 298.3C373.5 305.2 385.7 290.6 377.2 280.3L343.6 240L377.2000000000001 199.7z" />
+    <glyph glyph-name="grin-tongue-wink"
+      unicode="&#xF58B;"
+      horiz-adv-x="496" d=" M344 264C330.7 264 320 253.3 320 240S330.7 216 344 216S368 226.7 368 240S357.3 264 344 264zM248 440C111 440 0 329 0 192C0 85.7 67 -4.7 161 -40C155.4 -27.8 152 -14.3 152 0V45.5C127.3 61.7 108.5 83.6 104.2 109.3C102.2 121.1 113.5 130.8 124.9 127.2C155.1 117.5 200 112 248 112S340.9 117.5 371.1 127.2C382.6 130.9 393.7000000000001 121.1 391.8 109.3C387.5 83.6 368.7 61.7 344 45.5V0C344 -14.3 340.6 -27.8 335 -40C429 -4.7 496 85.7 496 192C496 329 385 440 248 440zM192 215L182.5 223.5C167.7 236.7 136.3 236.7 121.5 223.5L112 215C103.5 207.6 90.4 214.7 92.2 225.8C96.2 251 126.4 267.9 152.1 267.9S208 251 212 225.8C213.6 214.7 200.4 207.6 192 215zM344 176C308.7 176 280 204.7 280 240S308.7 304 344 304S408 275.3 408 240S379.3 176 344 176zM293.1 73.4C278.7000000000001 79.9 262 71.2 258.5 55.8L256.7 47.9999999999999C254.6 38.8 241.5 38.8 239.4 47.9999999999999L237.6 55.8C234.1 71.1999999999999 217.4 79.9 203 73.4C202.1 73 203.3 73.6 184.1 64V1C184.1 -34.2 212.1 -63.5 247.2 -63.9C282.9 -64.4 312.1 -35.5 312.1 0.1V64.1C292.6 73.7000000000001 293.9 73 293.1 73.4z" />
+    <glyph glyph-name="grin-tongue"
+      unicode="&#xF589;"
+      horiz-adv-x="496" d=" M248 440C111 440 0 329 0 192C0 85.7 67 -4.7 161 -40C155.4 -27.8 152 -14.3 152 0V45.5C127.3 61.7 108.5 83.6 104.2 109.3C102.2 121.1 113.5 130.8 124.9 127.2C155.1 117.5 200 112 248 112S340.9 117.5 371.1 127.2C382.5 130.8 393.7000000000001 121.1 391.8 109.3C387.5 83.6 368.7 61.7 344 45.5V0C344 -14.3 340.6 -27.8 335 -40C429 -4.7 496 85.7 496 192C496 329 385 440 248 440zM168 208C150.3 208 136 222.3 136 240S150.3 272 168 272S200 257.7 200 240S185.7 208 168 208zM328 208C310.3 208 296 222.3 296 240S310.3 272 328 272S360 257.7 360 240S345.7 208 328 208zM293.1 73.4C278.7000000000001 79.9 262 71.2 258.5 55.8L256.7 47.9999999999999C254.6 38.8 241.5 38.8 239.4 47.9999999999999L237.6 55.8C234.1 71.1999999999999 217.4 79.9 203 73.4C202.1 73 203.3 73.6 184.1 64V1C184.1 -34.2 212.1 -63.5 247.2 -63.9C282.9 -64.4 312.1 -35.5 312.1 0.1V64.1C292.6 73.7000000000001 293.9 73 293.1 73.4z" />
+    <glyph glyph-name="grin-wink"
+      unicode="&#xF58C;"
+      horiz-adv-x="496" d=" M0 192C0 55 111 -56 248 -56S496 55 496 192S385 440 248 440S0 329 0 192zM200 240C200 222.3 185.7 208 168 208S136 222.3 136 240S150.3 272 168 272S200 257.7 200 240zM368 215L358.5 223.5C343.7 236.7 312.3 236.7 297.5 223.5L288 215C279.7 207.6 266.4 214.6 268.2 225.8C272.2 251 302.4 267.9 328.1 267.9S384 251 388 225.8C389.6 214.8 376.5 207.6 368 215zM124.9 127.2C155.1 117.5 200 112 248 112S340.9 117.5 371.1 127.2C382.4000000000001 130.9 393.7000000000001 121.2 391.8 109.3C382.6 54.3 308.6 16 248 16S113.5 54.3 104.2 109.3C102.2 121.2 113.5 130.9 124.9 127.2z" />
+    <glyph glyph-name="grin"
+      unicode="&#xF580;"
+      horiz-adv-x="496" d=" M248 440C111 440 0 329 0 192S111 -56 248 -56S496 55 496 192S385 440 248 440zM328 272C345.7 272 360 257.7 360 240S345.7 208 328 208S296 222.3 296 240S310.3 272 328 272zM168 272C185.7 272 200 257.7 200 240S185.7 208 168 208S136 222.3 136 240S150.3 272 168 272zM248 16C187.4 16 113.5 54.3 104.2 109.3C102.2 121.1 113.5 130.9 124.9 127.2C155.1 117.5 200 112 248 112S340.9 117.5 371.1 127.2C382.4000000000001 130.9 393.7000000000001 121.1 391.8 109.3C382.5 54.3 308.6 16 248 16z" />
+    <glyph glyph-name="grip-horizontal"
+      unicode="&#xF58D;"
+      horiz-adv-x="448" d=" M96 160H32C14.33 160 0 145.67 0 128V64C0 46.33 14.33 32 32 32H96C113.67 32 128 46.33 128 64V128C128 145.67 113.67 160 96 160zM256 160H192C174.33 160 160 145.67 160 128V64C160 46.33 174.33 32 192 32H256C273.67 32 288 46.33 288 64V128C288 145.67 273.67 160 256 160zM416 160H352C334.33 160 320 145.67 320 128V64C320 46.33 334.33 32 352 32H416C433.67 32 448 46.33 448 64V128C448 145.67 433.67 160 416 160zM96 352H32C14.33 352 0 337.67 0 320V256C0 238.33 14.33 224 32 224H96C113.67 224 128 238.33 128 256V320C128 337.67 113.67 352 96 352zM256 352H192C174.33 352 160 337.67 160 320V256C160 238.33 174.33 224 192 224H256C273.67 224 288 238.33 288 256V320C288 337.67 273.67 352 256 352zM416 352H352C334.33 352 320 337.67 320 320V256C320 238.33 334.33 224 352 224H416C433.67 224 448 238.33 448 256V320C448 337.67 433.67 352 416 352z" />
+    <glyph glyph-name="grip-vertical"
+      unicode="&#xF58E;"
+      horiz-adv-x="320" d=" M96 416H32C14.33 416 0 401.67 0 384V320C0 302.33 14.33 288 32 288H96C113.67 288 128 302.3300000000001 128 320V384C128 401.67 113.67 416 96 416zM96 256H32C14.33 256 0 241.67 0 224V160C0 142.33 14.33 128 32 128H96C113.67 128 128 142.33 128 160V224C128 241.67 113.67 256 96 256zM96 96H32C14.33 96 0 81.67 0 64V0C0 -17.67 14.33 -32 32 -32H96C113.67 -32 128 -17.67 128 0V64C128 81.67 113.67 96 96 96zM288 416H224C206.33 416 192 401.67 192 384V320C192 302.33 206.33 288 224 288H288C305.67 288 320 302.3300000000001 320 320V384C320 401.67 305.67 416 288 416zM288 256H224C206.33 256 192 241.67 192 224V160C192 142.33 206.33 128 224 128H288C305.67 128 320 142.33 320 160V224C320 241.67 305.67 256 288 256zM288 96H224C206.33 96 192 81.67 192 64V0C192 -17.67 206.33 -32 224 -32H288C305.67 -32 320 -17.67 320 0V64C320 81.67 305.67 96 288 96z" />
+    <glyph glyph-name="h-square"
+      unicode="&#xF0FD;"
+      horiz-adv-x="448" d=" M448 368V16C448 -10.51 426.51 -32 400 -32H48C21.49 -32 0 -10.51 0 16V368C0 394.51 21.49 416 48 416H400C426.51 416 448 394.51 448 368zM336 320H304C295.163 320 288 312.837 288 304V224H160V304C160 312.837 152.837 320 144 320H112C103.163 320 96 312.837 96 304V80C96 71.163 103.163 64 112 64H144C152.837 64 160 71.163 160 80V160H288V80C288 71.163 295.163 64 304 64H336C344.837 64 352 71.163 352 80V304C352 312.837 344.837 320 336 320z" />
+    <glyph glyph-name="hammer"
+      unicode="&#xF6E3;"
+      horiz-adv-x="576" d=" M571.31 254.06L548.68 276.69C542.43 282.94 532.3 282.94 526.05 276.69L514.74 265.38L485.84 294.28C491.47 315.5900000000001 486.2 339.18 469.49 355.89L424.24 401.14C361.76 463.62 260.4500000000001 463.62 197.96 401.14L288.4700000000001 355.89V337.14C288.4700000000001 320.17 295.2100000000001 303.89 307.2200000000001 291.89L356.36 242.75C373.07 226.04 396.6600000000001 220.77 417.9700000000001 226.4L446.87 197.5L435.56 186.19C429.31 179.94 429.31 169.81 435.56 163.56L458.19 140.93C464.44 134.68 474.57 134.68 480.82 140.93L571.33 231.44C577.5600000000001 237.68 577.5600000000001 247.81 571.3100000000001 254.06zM284.5899999999999 269.26C280.89 272.96 277.75 277.05 274.7399999999999 281.21L19.64 43.04C-5.93 19.16 -6.62 -21.15 18.11 -45.89S83.16 -69.9399999999999 107.04 -44.36L345.17 210.71C341.2100000000001 213.62 337.2700000000001 216.58 333.73 220.12L284.5900000000001 269.26z" />
+    <glyph glyph-name="hamsa"
+      unicode="&#xF665;"
+      horiz-adv-x="512" d=" M509.34 140.75C504.28 152.44 492.75 160 480 160H416V368C416 390 398 408 376 408S336 390 336 368V234C336 228.48 331.52 224 326 224H306C300.48 224 296 228.48 296 234V408C296 430 278 448 256 448S216 430 216 408V234C216 228.48 211.52 224 206 224H186C180.48 224 176 228.48 176 234V368C176 390 158 408 136 408S96 390 96 368V160H32C19.25 160 7.72 152.44 2.66 140.75A31.966 31.966 0 0 1 8.6 106.17L111.29 -3.86C146.97 -42.08 199.69 -64 256 -64S365.03 -42.08 400.7200000000001 -3.86L503.4 106.17A31.966 31.966 0 0 1 509.34 140.75zM256 32C202.98 32 160 96 160 96S202.98 160 256 160S352 96 352 96S309.02 32 256 32zM256 128C238.33 128 224 113.67 224 96S238.33 64 256 64S288 78.33 288 96S273.67 128 256 128z" />
+    <glyph glyph-name="hand-holding-heart"
+      unicode="&#xF4BE;"
+      horiz-adv-x="576" d=" M275.3 197.5C282.3 190.1 293.7 190.1 300.8 197.5L409.7000000000001 311.7C441.3000000000001 344.9 439.5000000000001 399.9 404.1 430.5C373.3 457.2 327.4000000000001 452.4 299.2000000000001 422.8L288 411.1L276.9 422.7C248.7 452.4 202.8 457.2 172 430.5C136.7 399.9 134.8 344.9 166.4 311.7L275.3 197.5zM565.3 119.9C553.5 130.6 535.0999999999999 129.9 522.6999999999999 119.9L430.3 46C419 36.9 404.9000000000001 32 390.3 32H272C263.2 32 256 39.2 256 48S263.2 64 272 64H350.3C366.2 64 381 74.9 383.6 90.6C386.9000000000001 110.6 371.5 128 352 128H192C165 128 138.9 118.7 117.9 101.7L71.4 64H16C7.2 64 0 56.8 0 48V-48C0 -56.8 7.2 -64 16 -64H372.8C387.3 -64 401.4000000000001 -59.1 412.8 -50L564 71C579.2 83.1 580.4 106.3 565.3 119.9z" />
+    <glyph glyph-name="hand-holding-usd"
+      unicode="&#xF4C0;"
+      horiz-adv-x="544" d=" M257.6 303.7L307.6 289.4C311.2000000000001 288.4 313.7000000000001 285 313.7000000000001 281.3C313.7000000000001 276.7 309.9000000000001 272.9 305.3000000000001 272.9H272.5000000000001C268.9000000000001 272.9 265.4000000000001 273.7 262.2000000000001 275.1C257.4000000000001 277.3 251.8000000000001 276.8 248.1000000000001 273.1L230.6000000000001 255.6C225.3000000000001 250.3 225.9000000000001 241.3 232.1000000000001 237.2C241.6000000000001 230.9 252.4000000000001 227.1 263.9000000000001 225.7V208C263.9000000000001 199.2 271.1 192 279.9000000000001 192H295.9000000000001C304.7000000000001 192 311.9000000000001 199.2 311.9000000000001 208V225.6C342.2000000000001 229.2 365.2000000000001 256.6 361.2000000000001 288.6C358.3000000000001 311.6 340.5000000000001 329.9 318.3000000000001 336.3L268.3000000000001 350.6C264.7000000000001 351.6 262.2000000000001 355 262.2000000000001 358.7C262.2000000000001 363.3 266.0000000000001 367.1 270.6 367.1H303.4000000000001C307.0000000000001 367.1 310.5000000000001 366.3 313.7000000000001 364.9C318.5000000000001 362.7 324.1 363.2 327.8000000000001 366.9L345.3000000000001 384.4C350.6000000000001 389.7 350.0000000000001 398.7 343.8000000000001 402.8C334.3000000000001 409.1 323.5000000000001 412.9 312.0000000000001 414.3V432C312.0000000000001 440.8 304.8000000000001 448 296.0000000000001 448H280.0000000000001C271.2000000000001 448 264.0000000000001 440.8 264.0000000000001 432V414.4C233.7000000000001 410.8 210.7000000000001 383.4 214.7000000000001 351.4C217.6000000000001 328.4 235.4000000000001 310.1 257.6 303.7zM533.9000000000001 119.9C522.7 130.6 505.4000000000001 129.9 493.6000000000001 119.9L406.4 46C395.7 36.9 382.4 32 368.6 32H256.9C248.6 32 241.8 39.2 241.8 48S248.6 64 256.9 64H330.8C345.9 64 359.8 74.9 362.2 90.6C365.3 110.6 350.7 128 332.3999999999999 128H181.3C155.8 128 131.1 118.7 111.4 101.7L67.5 64H15.1C6.8 64 0 56.8 0 48V-48C0 -56.8 6.8 -64 15.1 -64H352C365.7 -64 379 -59.1 389.8 -50L532.6 71C547 83.1 548.1 106.3 533.9 119.9z" />
+    <glyph glyph-name="hand-holding"
+      unicode="&#xF4BD;"
+      horiz-adv-x="576" d=" M565.3 119.9C553.5 130.6 535.0999999999999 129.9 522.6999999999999 119.9L430.3 46C419 36.9 404.9000000000001 32 390.3 32H272C263.2 32 256 39.2 256 48S263.2 64 272 64H350.3C366.2 64 381 74.9 383.6 90.6C386.9000000000001 110.6 371.5 128 352 128H192C165 128 138.9 118.7 117.9 101.7L71.4 64H16C7.2 64 0 56.8 0 48V-48C0 -56.8 7.2 -64 16 -64H372.8C387.3 -64 401.4000000000001 -59.1 412.8 -50L564 71C579.2 83.1 580.4 106.3 565.3 119.9z" />
+    <glyph glyph-name="hand-lizard"
+      unicode="&#xF258;"
+      horiz-adv-x="576" d=" M384 -32H576V84.222A95.998 95.998 0 0 1 561.167 135.485L398.127 393.632A48 48 0 0 1 357.544 416H24C10.745 416 0 405.255 0 392V376C0 345.072 25.072 320 56 320H285.981C298.825 320 307.537 306.933 302.596 295.077L281.186 243.692A32 32 0 0 0 251.648 224H128C92.654 224 64 195.346 64 160V152C64 138.745 74.745 128 88 128H235.406A47.995 47.995 0 0 0 261.098 120.545L372.846 49.734A24.001000000000005 24.001000000000005 0 0 0 384 29.461V-32z" />
+    <glyph glyph-name="hand-paper"
+      unicode="&#xF256;"
+      horiz-adv-x="448" d=" M408.781 319.993C386.356 320.4220000000001 368 301.64 368 279.2100000000001V192H360V368.21C360 390.64 341.644 409.4220000000001 319.219 408.993C297.488 408.577 280 390.831 280 369V192H272V407.21C272 429.64 253.644 448.4220000000001 231.219 447.993C209.488 447.577 192 429.831 192 408V192H184V367.21C184 389.64 165.644 408.4220000000001 143.219 407.993C121.488 407.577 104 389.831 104 368V132.0080000000001L72.352 175.5270000000001C59.359 193.393 34.343 197.3440000000001 16.475 184.35C-1.39 171.356 -5.34 146.34 7.653 128.473L133.254 -44.232A48 48 0 0 1 172.073 -64H369.663C391.937 -64 411.2850000000001 -48.676 416.387 -26.994L442.895 85.6659999999999A192.011 192.011 0 0 1 447.999 129.641V280C448 301.831 430.512 319.577 408.781 319.993z" />
+    <glyph glyph-name="hand-peace"
+      unicode="&#xF25B;"
+      horiz-adv-x="448" d=" M408 232C385.908 232 368 214.091 368 192H360V224C360 246.091 342.092 264 320 264S280 246.091 280 224V192H272V400C272 426.51 250.51 448 224 448S176 426.51 176 400V192H162.428L92.688 369.551C82.994 394.226 55.134 406.37 30.461 396.676C5.787 386.983 -6.356 359.123 3.337 334.449L78.102 144.107L47.012 119.235C31.631 106.929 27.497 85.257 37.271 68.1539999999999L101.271 -43.8460000000001A39.998 39.998 0 0 1 136 -64H376C394.562 -64 410.686 -51.23 414.937 -33.162L446.937 102.838A39.97 39.97 0 0 1 448 112V192C448 214.091 430.092 232 408 232z" />
+    <glyph glyph-name="hand-point-down"
+      unicode="&#xF0A7;"
+      horiz-adv-x="384" d=" M91.826 -19.2V130.034C83.578 124.193 75.268 119.464 66.908 115.881C35.098 102.248 -0.014 125.778 0 160C0.008 178.616 10.897 192.203 29.092 200C57.378 212.122 93.421 278.648 106.415 307.534C114.371 325.391 131.894 335.987 150.26 335.998L150.261 336H321.7870000000001C333.5990000000001 336 343.6840000000001 327.404 345.49 315.731C352.74 268.894 383.973 253.971 383.805 192C383.798 189.276 384 178.746 384 176C384 125.346 361.878 94.426 312.737 103.4C303.44 84.803 273.251 72.662 250.422 86.95C229.245 62.305 196.526 64.311 179.478 80.651V-19.2C179.478 -43.35 159.277 -64 135.652 -64C112.369 -64 91.826 -42.65 91.826 -19.2zM112 376V424C112 437.255 122.745 448 136 448H328C341.255 448 352 437.255 352 424V376C352 362.745 341.255 352 328 352H136C122.745 352 112 362.745 112 376zM324 400C324 411.046 315.046 420 304 420S284 411.046 284 400S292.954 380 304 380S324 388.954 324 400z" />
+    <glyph glyph-name="hand-point-left"
+      unicode="&#xF0A5;"
+      horiz-adv-x="512" d=" M44.8 292.174H194.034C188.193 300.4220000000001 183.464 308.732 179.881 317.092C166.248 348.902 189.778 384.014 224 384C242.616 383.992 256.203 373.103 264 354.908C276.122 326.622 342.648 290.579 371.534 277.5850000000001C389.391 269.629 399.987 252.106 399.998 233.74L400 233.739V62.213C400 50.401 391.404 40.316 379.731 38.51C332.894 31.26 317.971 0.027 256 0.195C253.276 0.202 242.746 0 240 0C189.346 0 158.426 22.122 167.4 71.263C148.803 80.5600000000001 136.662 110.749 150.95 133.578C126.305 154.7550000000001 128.311 187.4740000000001 144.651 204.5220000000001H44.8C20.65 204.5220000000001 0 224.7230000000001 0 248.3480000000001C0 271.6310000000001 21.35 292.1740000000001 44.8 292.1740000000001zM440 272H488C501.255 272 512 261.255 512 248V56C512 42.745 501.255 32 488 32H440C426.745 32 416 42.745 416 56V248C416 261.255 426.745 272 440 272zM464 60C475.046 60 484 68.954 484 80S475.046 100 464 100S444 91.046 444 80S452.954 60 464 60z" />
+    <glyph glyph-name="hand-point-right"
+      unicode="&#xF0A4;"
+      horiz-adv-x="512" d=" M512 248.348C512 224.723 491.35 204.522 467.2 204.522H367.349C383.689 187.4740000000001 385.695 154.756 361.05 133.578C375.338 110.749 363.197 80.561 344.6 71.263C353.574 22.122 322.654 0 272 0C269.254 0 258.724 0.203 256 0.195C194.029 0.027 179.106 31.26 132.269 38.51C120.596 40.317 112 50.401 112 62.214V233.739L112.002 233.74C112.013 252.106 122.609 269.629 140.466 277.5850000000001C169.352 290.579 235.879 326.623 248 354.908C255.797 373.102 269.384 383.992 288 384C322.222 384.014 345.752 348.902 332.1190000000001 317.092C328.536 308.733 323.807 300.422 317.966 292.174H467.2C490.65 292.174 512 271.631 512 248.348zM96 248V56C96 42.745 85.255 32 72 32H24C10.745 32 0 42.745 0 56V248C0 261.255 10.745 272 24 272H72C85.255 272 96 261.255 96 248zM68 80C68 91.046 59.046 100 48 100S28 91.046 28 80S36.954 60 48 60S68 68.954 68 80z" />
+    <glyph glyph-name="hand-point-up"
+      unicode="&#xF0A6;"
+      horiz-adv-x="384" d=" M135.652 448C159.277 448 179.478 427.35 179.478 403.2V303.349C196.526 319.689 229.244 321.695 250.422 297.05C273.251 311.338 303.439 299.197 312.737 280.6C361.878 289.574 384 258.654 384 208C384 205.254 383.7970000000001 194.724 383.805 192C383.973 130.029 352.74 115.106 345.49 68.269C343.683 56.596 333.599 48 321.786 48H150.261L150.26 48.002C131.894 48.013 114.371 58.609 106.415 76.466C93.421 105.352 57.377 171.878 29.092 184C10.897 191.797 0.008 205.384 0 224C-0.014 258.222 35.098 281.752 66.908 268.1190000000001C75.267 264.536 83.578 259.807 91.826 253.966V403.2C91.826 426.65 112.369 448 135.652 448zM136 32H328C341.255 32 352 21.255 352 8V-40C352 -53.255 341.255 -64 328 -64H136C122.745 -64 112 -53.255 112 -40V8C112 21.255 122.745 32 136 32zM304 4C292.954 4 284 -4.954 284 -16S292.954 -36 304 -36S324 -27.046 324 -16S315.046 4 304 4z" />
+    <glyph glyph-name="hand-pointer"
+      unicode="&#xF25A;"
+      horiz-adv-x="448" d=" M448 208V112C448 108.916 447.644 105.841 446.937 102.838L414.937 -33.162C410.686 -51.23 394.562 -64 376 -64H168A40.004 40.004 0 0 0 135.65 -47.527L7.653 128.473C-5.34 146.339 -1.39 171.356 16.475 184.349C34.342 197.343 59.359 193.392 72.352 175.526L104 132.008V408C104 430.091 121.908 448 144 448S184 430.091 184 408V208H192V248C192 270.091 209.908 288 232 288S272 270.091 272 248V208H280V232C280 254.091 297.908 272 320 272S360 254.091 360 232V208H368C368 230.091 385.908 248 408 248S448 230.091 448 208zM192 128H184V32H192V128zM280 128H272V32H280V128zM368 128H360V32H368V128z" />
+    <glyph glyph-name="hand-rock"
+      unicode="&#xF255;"
+      horiz-adv-x="512" d=" M512 319.2100000000001C512 345.5320000000001 491.139 367.554 464.82 367.993C437.935 368.442 416 346.783 416 320H408V351.21C408 377.532 387.139 399.554 360.82 399.993C333.935 400.442 312 378.783 312 352V320H304V367.21C304 393.532 283.139 415.554 256.82 415.993C229.935 416.442 208 394.783 208 368V320H200V351.21C200 377.532 179.139 399.554 152.82 399.993C125.935 400.442 104 378.783 104 352V216L96 223.111V271.2100000000001C96 297.5320000000001 75.139 319.554 48.82 319.993C21.935 320.442 0 298.783 0 272V205.555A95.998 95.998 0 0 1 32.221 133.804L143.889 34.543A47.999 47.999 0 0 0 160 -1.333V-8C160 -21.255 170.745 -32 184 -32H424C437.255 -32 448 -21.255 448 -8V-5.079A96.01 96.01 0 0 0 455.523 32.175L504.477 148.44A96.002 96.002 0 0 1 512 185.694V319.2100000000001z" />
+    <glyph glyph-name="hand-scissors"
+      unicode="&#xF257;"
+      horiz-adv-x="512" d=" M216 8C216 30.092 233.909 48 256 48V56H224C201.909 56 184 73.908 184 96S201.909 136 224 136H256V144H48C21.49 144 0 165.49 0 192S21.49 240 48 240H256V253.572L78.449 323.312C53.775 333.006 41.631 360.867 51.324 385.54C61.017 410.214 88.878 422.357 113.552 412.664L303.894 337.899L328.766 368.9890000000001C341.072 384.37 362.7440000000001 388.504 379.8470000000001 378.73L491.847 314.73A40.002 40.002 0 0 0 512 280V40C512 21.438 499.23 5.314 481.162 1.063L345.162 -30.937A39.982 39.982 0 0 0 336 -32H256C233.909 -32 216 -14.092 216 8z" />
+    <glyph glyph-name="hand-spock"
+      unicode="&#xF259;"
+      horiz-adv-x="512" d=" M10.872 131.415C26.011 147.501 51.326 148.269 67.415 133.1280000000001L128 76.107V155.5120000000001L88.995 327.135C84.099 348.677 97.593 370.1090000000001 119.135 375.005C140.684 379.899 162.11 366.406 167.005 344.8640000000001L201.747 192H211.58L164.016 399.034C159.07 420.565 172.514 442.028 194.044 446.974C215.576 451.924 237.038 438.476 241.984 416.9460000000001L293.664 192H308.769L357.194 385.702C362.5510000000001 407.134 384.269 420.164 405.701 414.806C427.1330000000001 409.448 440.1640000000001 387.731 434.805 366.299L391.231 192H402.311L433.079 321.265C438.196 342.756 459.764 356.033 481.256 350.912C502.747 345.795 516.021 324.226 510.903 302.735L474.611 150.268A96.02400000000002 96.02400000000002 0 0 1 472 128.033V85.931A96.002 96.002 0 0 0 468.04 58.6440000000001L441.8660000000001 -29.6429999999999C435.825 -50.022 417.101 -64 395.846 -64H179.172A48.00200000000001 48.00200000000001 0 0 0 146.274 -50.954L12.585 74.872C-3.502 90.013 -4.268 115.328 10.872 131.415z" />
+    <glyph glyph-name="hands-helping"
+      unicode="&#xF4C4;"
+      horiz-adv-x="640" d=" M488 256H336V200C336 160.3 303.7 128 264 128S192 160.3 192 200V321.6L127.1 282.6C107.8 271.1 96 250.2 96 227.8V180.5L16 134.3C0.7 125.5 -4.6 105.9 4.3 90.6L84.3 -48C93.1 -63.3 112.7 -68.5 128 -59.7L231.4 0H368C403.3 0 432 28.7 432 64H448C465.7 64 480 78.3 480 96V160H488C501.3 160 512 170.7 512 184V232C512 245.3 501.3 256 488 256zM635.7 293.4L555.7 432C546.9 447.3 527.3 452.5 512 443.7L408.6 384H306.4C294.4 384 282.7 380.6 272.5 374.3L239 353.4C229.6 347.6 224 337.3 224 326.3V200C224 177.9 241.9 160 264 160S304 177.9 304 200V288H488C518.9 288 544 262.9 544 232V203.5L624 249.7C639.3 258.6 644.5 278.1 635.7 293.4z" />
+    <glyph glyph-name="hands"
+      unicode="&#xF4C2;"
+      horiz-adv-x="640" d=" M204.8 217.6C194.2 231.7 174.1 234.6 160 224C145.9 213.4 143 193.3 153.6 179.2L191.7 128.4C196.5 122 195.8 113.1 190.2 107.5L177.4 94.7C170.7 88 159.8 88.5 153.8 95.8L64 203.6V352C64 369.7 49.7 384 32 384S0 369.7 0 352V133.6C0 122.7000000000001 3.7 112.1 10.5 103.6L114.6 -30.7C119.6 -37.2 123 -44.6 125 -52.4C126.8 -59.2999999999999 133.1 -64 140.3 -64H272C280.8 -64 288 -56.8 288 -48V64C288 91.7 279 118.6 262.4 140.8L204.8 217.6zM608 384C590.3 384 576 369.7 576 352V203.6L486.2 95.8C480.2 88.6 469.2 88.1 462.6 94.7L449.8 107.5C444.2 113.1 443.5 122 448.3 128.4L486.4 179.2C497 193.3 494.1 213.4 480 224C465.9 234.6 445.8 231.7 435.2 217.6L377.6 140.8C361 118.6 352 91.7 352 64V-48C352 -56.8 359.2 -64 368 -64H499.7C506.8 -64 513.2 -59.3 515 -52.4C517 -44.6 520.4 -37.2 525.4 -30.7L629.5 103.6C636.3 112.1 640 122.7000000000001 640 133.6V352C640 369.7 625.7 384 608 384z" />
+    <glyph glyph-name="handshake"
+      unicode="&#xF2B5;"
+      horiz-adv-x="640" d=" M434.7 384H348.8C340.8 384 333.1 381 327.2 375.6L228.8999999999999 285.6C228.8 285.5 228.7 285.3 228.5999999999999 285.2C211.9999999999999 269.6 212.2999999999999 244.7 226.4999999999999 229.2C239.1999999999999 215.3 265.8999999999999 211.6 282.5999999999999 226.5C282.7 226.6 282.8999999999999 226.6 282.9999999999999 226.7L362.8999999999999 299.9C369.3999999999999 305.8 379.5999999999999 305.4 385.4999999999999 298.9C391.4999999999999 292.4 390.9999999999999 282.3 384.4999999999999 276.3L358.3999999999999 252.4L504 134.2C506.9 131.8 509.5 129.2 511.9 126.5V320L457.3 374.6C451.4 380.6 443.2 384 434.7 384zM544 319.8V95.9C544 78.2 558.3 63.9 576 63.9H640V319.8H544zM592 95.9C583.2 95.9 576 103.1 576 111.9S583.2 127.9 592 127.9S608 120.7 608 111.9S600.8 95.9 592 95.9zM0 64H64C81.7 64 96 78.3 96 96V319.8H0V64zM48 127.9C56.8 127.9 64 120.7 64 111.9S56.8 95.9 48 95.9S32 103.1 32 111.9C32 120.8 39.2 127.9 48 127.9zM483.9 109.3L334.6 230.5L304.6 203C274.9000000000001 175.9 229.4000000000001 178.5 202.9 207.4C176 236.8 178.1 282.3 207.3000000000001 309.1L289.1 384H205.3C196.8 384 188.7 380.6 182.7 374.6L128 320V96.1H146.3L236.8 14.2C264.2 -8.1 304.5 -3.9 326.8 23.5000000000001L327 23.7L344.9 8.2C360.8 -4.8 384.3 -2.3 397.2 13.6L428.6 52.2L434 47.8000000000001C447.7 36.7 467.8999999999999 38.7 478.9999999999999 52.5000000000001L488.4999999999999 64.2000000000001C499.6999999999999 78.0000000000001 497.6 98.1 483.8999999999999 109.3000000000001z" />
+    <glyph glyph-name="hanukiah"
+      unicode="&#xF6E6;"
+      horiz-adv-x="640" d=" M232 288C227.58 288 224 284.42 224 280V160H256V280C256 284.42 252.42 288 248 288H232zM168 288C163.58 288 160 284.42 160 280V160H192V280C192 284.42 188.42 288 184 288H168zM392 288C387.58 288 384 284.42 384 280V160H416V280C416 284.42 412.42 288 408 288H392zM456 288C451.58 288 448 284.42 448 280V160H480V280C480 284.42 476.42 288 472 288H456zM544 280C544 284.42 540.42 288 536 288H520C515.58 288 512 284.42 512 280V160H544V280zM104 288C99.58 288 96 284.42 96 280V160H128V280C128 284.42 124.42 288 120 288H104zM624 288H592C583.16 288 576 280.8400000000001 576 272V160C576 142.33 561.67 128 544 128H352V320C352 328.8400000000001 344.84 336 336 336H304C295.1600000000001 336 288 328.8400000000001 288 320V128H96C78.33 128 64 142.33 64 160V272C64 280.8400000000001 56.84 288 48 288H16C7.16 288 0 280.8400000000001 0 272V160C0 106.98 42.98 64 96 64H288V0H112C103.16 0 96 -7.16 96 -16V-48C96 -56.84 103.16 -64 112 -64H528C536.84 -64 544 -56.84 544 -48V-16C544 -7.16 536.84 0 528 0H352V64H544C597.02 64 640 106.98 640 160V272C640 280.8400000000001 632.84 288 624 288zM608 320C621.25 320 632 331.94 632 346.67S608 400 608 400S584 361.39 584 346.67S594.75 320 608 320zM32 320C45.25 320 56 331.94 56 346.67S32 400 32 400S8 361.39 8 346.67S18.75 320 32 320zM320 368C333.25 368 344 379.94 344 394.67S320 448 320 448S296 409.39 296 394.67S306.75 368 320 368zM112 320C125.25 320 136 331.94 136 346.67S112 400 112 400S88 361.39 88 346.67S98.75 320 112 320zM176 320C189.25 320 200 331.94 200 346.67S176 400 176 400S152 361.39 152 346.67S162.75 320 176 320zM240 320C253.25 320 264 331.94 264 346.67S240 400 240 400S216 361.39 216 346.67S226.75 320 240 320zM400 320C413.25 320 424 331.94 424 346.67S400 400 400 400S376 361.39 376 346.67S386.75 320 400 320zM464 320C477.25 320 488 331.94 488 346.67S464 400 464 400S440 361.39 440 346.67S450.75 320 464 320zM528 320C541.25 320 552 331.94 552 346.67S528 400 528 400S504 361.39 504 346.67S514.75 320 528 320z" />
+    <glyph glyph-name="hashtag"
+      unicode="&#xF292;"
+      horiz-adv-x="448" d=" M440.667 265.891L447.81 305.891C449.123 313.246 443.468 320 435.997 320H361.187L375.81 401.891C377.123 409.246 371.468 416 363.997 416H323.365A12 12 0 0 1 311.552 406.109L296.175 320H197.54L212.163 401.891C213.477 409.246 207.822 416 200.35 416H159.718A12 12 0 0 1 147.905 406.109L132.528 320H53.432A12 12 0 0 1 41.619 310.1090000000001L34.476 270.1090000000001C33.163 262.754 38.818 256 46.289 256H121.099L98.242 128H19.146A12 12 0 0 1 7.333 118.109L0.19 78.109C-1.123 70.754 4.532 64 12.003 64H86.813L72.19 -17.891C70.877 -25.246 76.532 -32 84.003 -32H124.635A12 12 0 0 1 136.448 -22.109L151.826 64H250.46L235.837 -17.891C234.523 -25.246 240.178 -32 247.65 -32H288.282A12 12 0 0 1 300.095 -22.109L315.472 64H394.568A12 12 0 0 1 406.381 73.891L413.524 113.891C414.837 121.2460000000001 409.182 128 401.711 128H326.901L349.7579999999999 256H428.854A12 12 0 0 1 440.6669999999999 265.891zM261.889 128H163.255L186.112 256H284.746L261.889 128z" />
+    <glyph glyph-name="hat-wizard"
+      unicode="&#xF6E8;"
+      horiz-adv-x="512" d=" M496 0H16C7.16 0 0 -7.16 0 -16V-48C0 -56.84 7.16 -64 16 -64H496C504.84 -64 512 -56.84 512 -48V-16C512 -7.16 504.84 0 496 0zM192 64L128 96L192 128L224 192L256 128L320 96L256 64L240 32H448L361.5900000000001 233.63A63.95499999999999 63.95499999999999 0 0 0 359.7000000000001 279.08L416 448L228.42 340.81A127.98900000000002 127.98900000000002 0 0 1 174.96 281.66L64 32H208L192 64zM256 288L272 320L288 288L320 272L288 256L272 224L256 256L224 272L256 288z" />
+    <glyph glyph-name="haykal"
+      unicode="&#xF666;"
+      horiz-adv-x="512" d=" M496.25 245.48L386.25 260.92L428.07 365.26C434.74 381.9 416.47 397.44 401.48 387.89L307.44 328L273.35 435.18C270.64 443.73 263.32 448 256 448C248.68 448 241.36 443.73 238.65 435.18L204.56 327.99L110.52 387.88C95.53 397.43 77.27 381.89 83.93 365.25L125.75 260.9100000000001L15.75 245.48C-1.79 243.02 -5.93 219.21 9.72 210.81L107.88 158.15L33.4 74.61C22.48 62.36 31.68 43.68 46.69 43.68C48 43.68 49.36 43.8199999999999 50.76 44.1299999999999L159.33 67.7799999999999L155.22 -44.7700000000001C154.79 -56.4200000000001 164.09 -63.9900000000001 173.63 -63.9900000000001C178.78 -63.9900000000001 184.02 -61.7800000000001 187.83 -56.8100000000001L256.01 32.0899999999999L324.19 -56.8100000000001C328 -61.7800000000001 333.23 -63.9900000000001 338.39 -63.9900000000001C347.93 -63.9900000000001 357.23 -56.4200000000001 356.8 -44.7700000000001L352.69 67.7799999999999L461.26 44.1299999999999C478.62 40.37 490.47 61.3299999999999 478.61 74.62L404.13 158.16L502.29 210.82C517.93 219.21 513.79 243.02 496.2499999999999 245.48zM338.51 136.32L286.62 147.62L288.5900000000001 93.83L256 136.32L223.41 93.83L225.37 147.62L173.48 136.32L209.08 176.25L162.16 201.42L214.73 208.8L194.74 258.67L239.69 230.05L256 281.28L272.29 230.05L317.24 258.67L297.25 208.8L349.82 201.42L302.9 176.25L338.51 136.32z" />
+    <glyph glyph-name="hdd"
+      unicode="&#xF0A0;"
+      horiz-adv-x="576" d=" M576 144V48C576 21.49 554.51 0 528 0H48C21.49 0 0 21.49 0 48V144C0 170.51 21.49 192 48 192H528C554.51 192 576 170.51 576 144zM528 224A79.557 79.557 0 0 0 558.777 217.835L462.25 362.626A48.003 48.003 0 0 1 422.311 384H153.689A48 48 0 0 1 113.751 362.626L17.223 217.835A79.557 79.557 0 0 0 48 224H528zM480 128C462.327 128 448 113.673 448 96S462.327 64 480 64S512 78.327 512 96S497.673 128 480 128zM384 128C366.327 128 352 113.673 352 96S366.327 64 384 64S416 78.327 416 96S401.673 128 384 128z" />
+    <glyph glyph-name="heading"
+      unicode="&#xF1DC;"
+      horiz-adv-x="512" d=" M496 368V400C496 408.837 488.837 416 480 416H320C311.163 416 304 408.837 304 400V368C304 359.163 311.163 352 320 352H357.621V224H154.379V352H192C200.837 352 208 359.163 208 368V400C208 408.837 200.837 416 192 416H32C23.163 416 16 408.837 16 400V368C16 359.163 23.163 352 32 352H69.275V32H32C23.163 32 16 24.837 16 16V-16C16 -24.837 23.163 -32 32 -32H192C200.837 -32 208 -24.837 208 -16V16C208 24.837 200.837 32 192 32H154.379V160H357.62V32H320C311.163 32 304 24.837 304 16V-16C304 -24.837 311.163 -32 320 -32H480C488.837 -32 496 -24.837 496 -16V16C496 24.837 488.837 32 480 32H442.725V352H480C488.837 352 496 359.163 496 368z" />
+    <glyph glyph-name="headphones-alt"
+      unicode="&#xF58F;"
+      horiz-adv-x="512" d=" M160 160H144C108.65 160 80 131.3 80 95.88V32.12C80 -3.29 108.65 -32 144 -32H160C177.67 -32 192 -17.64 192 0.06V127.94C192 145.65 177.67 160 160 160zM368 160H352C334.33 160 320 145.65 320 127.94V0.06C320 -17.64 334.33 -32 352 -32H368C403.35 -32 432 -3.29 432 32.12V95.88C432 131.29 403.35 160 368 160zM256 416C112.91 416 4.57 296.87 0 160V48C0 39.16 7.16 32 16 32H32C40.84 32 48 39.16 48 48V160C48 274.67 141.33 367.8 256 367.82C370.67 367.8 464 274.67 464 160V48C464 39.16 471.16 32 480 32H496C504.84 32 512 39.16 512 48V160C507.43 296.87 399.09 416 256 416z" />
+    <glyph glyph-name="headphones"
+      unicode="&#xF025;"
+      horiz-adv-x="512" d=" M256 416C114.52 416 0 301.504 0 160V112A32 32 0 0 1 17.689 83.378L32.072 76.187C34.083 16.097 83.421 -32 144 -32H168C181.255 -32 192 -21.255 192 -8V168C192 181.255 181.255 192 168 192H144C112.658 192 84.329 179.121 64 158.373V160C64 265.8690000000001 150.131 352 256 352S448 265.8690000000001 448 160V158.373C427.671 179.121 399.342 192 368 192H344C330.745 192 320 181.255 320 168V-8C320 -21.255 330.745 -32 344 -32H368C428.579 -32 477.917 16.098 479.928 76.187L494.31 83.378A32 32 0 0 1 512 112V160C512 301.4790000000001 397.504 416 256 416z" />
+    <glyph glyph-name="headset"
+      unicode="&#xF590;"
+      horiz-adv-x="512" d=" M192 240C192 257.67 177.67 272 160 272H144C108.65 272 80 243.35 80 208V160C80 124.65 108.65 96 144 96H160C177.67 96 192 110.33 192 128V240zM368 96C403.35 96 432 124.65 432 160V208C432 243.35 403.35 272 368 272H352C334.33 272 320 257.67 320 240V128C320 110.33 334.33 96 352 96H368zM256 448C113.18 448 4.58 329.17 0 192V176C0 167.16 7.16 160 16 160H32C40.84 160 48 167.16 48 176V192C48 306.69 141.31 400 256 400S464 306.69 464 192H463.88C463.96 189.57 464 26.28 464 26.28C464 2.93 445.07 -16 421.7200000000001 -16H320C320 10.51 298.51 32 272 32H240C213.49 32 192 10.51 192 -16S213.49 -64 240 -64H421.7200000000001C471.58 -64 512 -23.58 512 26.28V192C507.42 329.17 398.82 448 256 448z" />
+    <glyph glyph-name="heart"
+      unicode="&#xF004;"
+      horiz-adv-x="512" d=" M462.3 385.4C407.5 432.1 326 423.7 275.7 371.8L256 351.5L236.3 371.8C186.1 423.7 104.5 432.1 49.7 385.4C-13.1 331.8 -16.4 235.6 39.8 177.5L233.3 -22.3C245.8 -35.2 266.1 -35.2 278.6 -22.3L472.1 177.5C528.4 235.6 525.1 331.8 462.3 385.4z" />
+    <glyph glyph-name="heartbeat"
+      unicode="&#xF21E;"
+      horiz-adv-x="512" d=" M320.2 204.2L270.5 104.8C264.5 92.6999999999999 247.1 93.1 241.6 105.4L184.7 231.7L154.7 160H60.6L243.1 -26.5C250.2 -33.8 261.7 -33.8 268.8 -26.5L451.4 160H342.3L320.2 204.2zM473.7 374.1L471.3 376.6C419.8 429.2 335.5 429.2 283.9 376.6L256 348L228.1 376.5C176.6 429.2 92.2 429.2 40.7 376.5L38.3 374.1C-10.4 324.3 -12.5 245 31 192H133.4L169.3 278.2C174.7 291.1 192.9 291.4 198.7 278.6L256.9000000000001 149.3L305.9000000000001 247.2C311.8 259 328.6 259 334.5000000000001 247.2L362.1000000000001 192H481C524.5 245 522.4 324.3 473.7 374.0999999999999z" />
+    <glyph glyph-name="helicopter"
+      unicode="&#xF533;"
+      horiz-adv-x="640" d=" M304 64H576C593.67 64 608 78.33 608 96C608 219.71 507.71 320 384 320V384H560C568.84 384 576 391.16 576 400V432C576 440.84 568.84 448 560 448H144C135.16 448 128 440.84 128 432V400C128 391.16 135.16 384 144 384H320V320H112L68.8 377.6C65.78 381.63 61.03 384 56 384H16.01C5.6 384 -2.04 374.2200000000001 0.49 364.12L32 256L192 192L278.4 76.8A31.991999999999997 31.991999999999997 0 0 1 304 64zM416 252.49C478.55 239.7 528.03 190.56 540.79 128H416V252.49zM635.37 -10.81L613.22 11.39C606.97 17.65 596.98 17.49 590.58 11.38C583.49 4.61 576.74 0.13 565.94 0.13H240C231.16 0.13 224 -7.05 224 -15.9V-47.96C224 -56.81 231.16 -63.99 240 -63.99H565.94C580.82 -63.99 601.24 -63.52 634.3900000000001 -34.47C641.4100000000001 -28.33 641.9600000000002 -17.42 635.3700000000001 -10.81z" />
+    <glyph glyph-name="highlighter"
+      unicode="&#xF591;"
+      horiz-adv-x="544" d=" M0 -31.98L99.92 -64L135.37 -28.55L68.33 38.49L0 -31.98zM124.61 208.03A36.592 36.592 0 0 1 113.82 169.93L126.87 127.1L75.94 76.16L172.17 -20.07L223.03 30.79L265.7700000000001 17.71C279.5000000000001 13.51 294.42 17.72 303.92 28.49L339.4700000000001 70.1299999999999L166.13 243.47L124.61 208.03zM527.92 368.73L464.72 431.93C444.23 452.42 411.34 453.45 389.6 434.28L190.55 264.32L360.3200000000001 94.54L530.27 293.6C549.4499999999999 315.3400000000001 548.42 348.23 527.92 368.73z" />
+    <glyph glyph-name="hiking"
+      unicode="&#xF6EC;"
+      horiz-adv-x="384" d=" M80.95 -24.23C76.67 -41.39 87.09 -58.76 104.23 -63.04C106.84 -63.7 109.45 -63.99 112.03 -63.99C126.36 -63.99 139.4 -54.29 143.05 -39.76L168.29 61.21L115.51 113.99L80.95 -24.23zM95.84 171.89L137 331C139.19 339.42 133.86 347.95 125.08 350.06C81.2 360.58 36.73 334.99 25.76 292.89L0.49 194.76C-1.7 186.34 3.63 177.81 12.41 175.7L75.97 160.45C84.76 158.35 93.65 163.47 95.84 171.89zM368 288H352C343.1600000000001 288 336 280.8400000000001 336 272V256H301.25L254.47 302.78C243.38 313.89 228.61 320 212.91 320C185.89 320 162.44 301.7 155.88 275.48L128.96 167.76A32.012 32.012 0 0 1 137.38 137.37L224 50.75V-32C224 -49.67 238.33 -64 256 -64S288 -49.67 288 -32V50.75C288 67.84 281.34 83.91 269.25 96L222.43 142.82C222.58 143.32 222.92 143.71 223.05 144.23L242.94 223.8L265.37 201.37C271.37 195.37 279.51 191.99 287.99 191.99H335.99V-48.01C335.99 -56.85 343.1500000000001 -64.01 351.99 -64.01H367.99C376.83 -64.01 383.99 -56.85 383.99 -48.01V272C384 280.8400000000001 376.8400000000001 288 368 288zM240 352C266.51 352 288 373.49 288 400S266.51 448 240 448S192 426.51 192 400S213.49 352 240 352z" />
+    <glyph glyph-name="hippo"
+      unicode="&#xF6ED;"
+      horiz-adv-x="640" d=" M581.12 351.8C553.45 351.95 528.62 334.2200000000001 504.52 325.18C489.98 359.73 455.83 384 416 384C404.7200000000001 384 394.05 381.7 384 378.12V392C384 405.26 373.25 416 360 416H344C330.75 416 320 405.26 320 392V343.02C286.01 368.42 241.24 384 192 384C85.96 384 0 312.36 0 224V-16C0 -24.84 7.16 -32 16 -32H80C88.84 -32 96 -24.84 96 -16V54.79C128.35 40.43 166.72 32 208 32S287.65 40.43 320 54.79V-16C320 -24.84 327.1600000000001 -32 336 -32H400C408.84 -32 416 -24.84 416 -16V160H544V128C544 119.16 551.16 112 560 112H592C600.84 112 608 119.16 608 128V160C625.67 160 640 174.33 640 192V284.02C640 318.11 615.21 351.61 581.12 351.8zM448 272C439.1600000000001 272 432 279.16 432 288S439.1600000000001 304 448 304S464 296.8400000000001 464 288S456.84 272 448 272z" />
+    <glyph glyph-name="history"
+      unicode="&#xF1DA;"
+      horiz-adv-x="512" d=" M504 192.469C504.253 55.829 392.82 -55.903 256.18 -55.999C197.165 -56.041 142.957 -35.469 100.358 -1.088C89.281 7.852 88.453 24.453 98.519 34.519L109.786 45.786C118.395 54.395 132.139 55.337 141.677 47.77C173.062 22.865 212.781 8 256 8C357.705 8 440 90.311 440 192C440 293.705 357.689 376 256 376C207.186 376 162.851 357.031 129.932 326.068L180.686 275.314C190.766 265.234 183.627 248 169.373 248H24C15.163 248 8 255.163 8 264V409.373C8 423.627 25.234 430.766 35.314 420.687L84.686 371.315C129.209 413.864 189.552 440 256 440C392.81 440 503.747 329.2200000000001 504 192.469zM323.088 113.685L332.911 126.315C341.0489999999999 136.778 339.164 151.857 328.701 159.994L288 191.651V296C288 309.255 277.255 320 264 320H248C234.745 320 224 309.255 224 296V160.349L289.409 109.475C299.872 101.338 314.95 103.222 323.088 113.685z" />
+    <glyph glyph-name="hockey-puck"
+      unicode="&#xF453;"
+      horiz-adv-x="512" d=" M0 288C0 341 114.6 384 256 384S512 341 512 288S397.4 192 256 192S0 235 0 288zM0 205.8V96C0 43 114.6 0 256 0S512 43 512 96V205.8C398.6 123.5 113.5 123.4 0 205.8z" />
+    <glyph glyph-name="home"
+      unicode="&#xF015;"
+      horiz-adv-x="576" d=" M488 135.3V-8C488 -21.3 477.3 -32 464 -32H348C341.4 -32 336 -26.6 336 -20V92C336 98.6 330.6 104 324 104H252C245.4 104 240 98.6 240 92V-20C240 -26.6 234.6 -32 228 -32H112C98.7 -32 88 -21.3 88 -8V135.3C88 138.9 89.6 142.3 92.4 144.6L280.4 299.4000000000001C284.8 303 291.2 303 295.7 299.4000000000001L483.7 144.6C486.4 142.3 488 138.9 488 135.3zM571.6 196.2L488 265.1V403.6C488 410.2 482.6 415.6 476 415.6H420C413.4 415.6 408 410.2 408 403.6V331L318.5 404.7C300.8 419.3 275.2 419.3 257.5 404.7L4.4 196.2C-0.7 192 -1.4 184.4 2.8 179.3L28.3 148.3C32.5 143.2 40.1 142.5 45.2 146.7L280.4 340.4C284.8 344 291.2 344 295.7 340.4L530.9 146.7C536 142.5 543.6 143.2 547.8 148.3L573.3 179.3C577.5 184.5 576.6999999999999 192 571.5999999999999 196.2z" />
+    <glyph glyph-name="horse"
+      unicode="&#xF6F0;"
+      horiz-adv-x="576" d=" M575.92 371.4C575.91 379.53 572.9 387.27 567.3399999999999 393.2C563.56 397.23 558.7599999999999 402.32 553.6499999999999 407.7C564.7099999999998 414.54 573.1499999999999 425.19 575.8299999999998 438.36C576.85 443.32 572.96 448 567.9 448H447.92C377.23 448 319.92 390.69 319.92 320H160C131.16 320 105.6 307.02 88 286.89V288C39.47 288 0 248.53 0 200V144C0 135.16 7.16 128 16 128H32C40.84 128 48 135.16 48 144V200C48 213.22 54.87 224.39 64.78 231.68C64.57 229.1 64 226.63 64 224C64 196.36 75.84 171.64 94.54 154.12L68.82 85.52A63.94500000000001 63.94500000000001 0 0 1 66.66 47.53L91.51 -51.88A15.982 15.982 0 0 1 107.02 -64H172.98C183.39 -64 191.03 -54.22 188.5 -44.12L162.19 61.14L186.03 124.73L320 102.4V-48C320 -56.84 327.1600000000001 -64 336 -64H400C408.84 -64 416 -56.84 416 -48V129.78C435.74 149.97 448 177.53 448 208C448 208.22 447.93 208.42 447.92 208.64V311.11L463.92 304L482.82 266.3C490.27 251.43 507.87 244.75 523.31 250.93L555.8599999999999 263.9500000000001A31.997 31.997 0 0 1 575.9799999999999 293.6900000000001L575.92 371.4000000000001zM511.92 352C503.08 352 495.92 359.16 495.92 368S503.08 384 511.92 384S527.92 376.8400000000001 527.92 368S520.76 352 511.92 352z" />
+    <glyph glyph-name="hospital-alt"
+      unicode="&#xF47D;"
+      horiz-adv-x="576" d=" M544 352H416V416C416 433.7 401.7 448 384 448H192C174.3 448 160 433.7 160 416V352H32C14.3 352 0 337.7 0 320V-48C0 -56.8 7.2 -64 16 -64H560C568.8 -64 576 -56.8 576 -48V320C576 337.7 561.7 352 544 352zM160 12C160 5.4 154.6 0 148 0H108C101.4 0 96 5.4 96 12V52C96 58.6 101.4 64 108 64H148C154.6 64 160 58.6 160 52V12zM160 140C160 133.4 154.6 128 148 128H108C101.4 128 96 133.4 96 140V180C96 186.6 101.4 192 108 192H148C154.6 192 160 186.6 160 180V140zM320 12C320 5.4 314.6 0 308 0H268C261.4 0 256 5.4 256 12V52C256 58.6 261.4 64 268 64H308C314.6 64 320 58.6 320 52V12zM320 140C320 133.4 314.6 128 308 128H268C261.4 128 256 133.4 256 140V180C256 186.6 261.4 192 268 192H308C314.6 192 320 186.6 320 180V140zM336 310C336 306.7 333.3 304 330 304H304V278C304 274.7 301.3 272 298 272H278C274.7 272 272 274.7 272 278V304H246C242.7 304 240 306.7 240 310V330C240 333.3 242.7 336 246 336H272V362C272 365.3 274.7 368 278 368H298C301.3 368 304 365.3 304 362V336H330C333.3 336 336 333.3 336 330V310zM480 12C480 5.4 474.6 0 468 0H428C421.4 0 416 5.4 416 12V52C416 58.6 421.4 64 428 64H468C474.6 64 480 58.6 480 52V12zM480 140C480 133.4 474.6 128 468 128H428C421.4 128 416 133.4 416 140V180C416 186.6 421.4 192 428 192H468C474.6 192 480 186.6 480 180V140z" />
+    <glyph glyph-name="hospital-symbol"
+      unicode="&#xF47E;"
+      horiz-adv-x="512" d=" M256 448C114.6 448 0 333.4 0 192S114.6 -64 256 -64S512 50.6 512 192S397.4 448 256 448zM368 72C368 67.6 364.4 64 360 64H312C307.6 64 304 67.6 304 72V160H208V72C208 67.6 204.4 64 200 64H152C147.6 64 144 67.6 144 72V312C144 316.4 147.6 320 152 320H200C204.4 320 208 316.4 208 312V224H304V312C304 316.4 307.6 320 312 320H360C364.4 320 368 316.4 368 312V72z" />
+    <glyph glyph-name="hospital"
+      unicode="&#xF0F8;"
+      horiz-adv-x="448" d=" M448 -44V-64H0V-44C0 -37.373 5.373 -32 12 -32H32V328C32 341.255 42.745 352 56 352H144V424C144 437.255 154.745 448 168 448H280C293.255 448 304 437.255 304 424V352H392C405.255 352 416 341.255 416 328V-32H436C442.627 -32 448 -37.373 448 -44zM308 256H268C261.373 256 256 250.627 256 244V204C256 197.373 261.373 192 268 192H308C314.627 192 320 197.373 320 204V244C320 250.627 314.627 256 308 256zM140 192H180C186.627 192 192 197.373 192 204V244C192 250.627 186.627 256 180 256H140C133.373 256 128 250.627 128 244V204C128 197.373 133.373 192 140 192zM244 64H204C197.373 64 192 58.627 192 52V-32H256V52C256 58.627 250.627 64 244 64zM308 160H268C261.373 160 256 154.627 256 148V108C256 101.373 261.373 96 268 96H308C314.627 96 320 101.373 320 108V148C320 154.627 314.627 160 308 160zM192 148C192 154.627 186.627 160 180 160H140C133.373 160 128 154.627 128 148V108C128 101.373 133.373 96 140 96H180C186.627 96 192 101.373 192 108V148zM182 352H208V326A6 6 0 0 1 214 320H234A6 6 0 0 1 240 326V352H266A6 6 0 0 1 272 358V378A6 6 0 0 1 266 384H240V410A6 6 0 0 1 234 416H214A6 6 0 0 1 208 410V384H182A6 6 0 0 1 176 378V358A6 6 0 0 1 182 352z" />
+    <glyph glyph-name="hot-tub"
+      unicode="&#xF593;"
+      horiz-adv-x="512" d=" M414.21 270.35C415.23 262.14 421.96 256 429.96 256H446.08C455.59 256 463.16 264.57 462.08 274.35C457.74 313.46 439.68 348.88 411.95 371.51C394.58 385.68 383.13 408.26 379.97 433.66C378.96 441.86 372.2200000000001 448 364.23 448H348.11C338.6 448 331.0200000000001 439.43 332.11 429.65C336.45 390.54 354.51 355.12 382.24 332.49C399.6 318.3200000000001 411.06 295.74 414.2100000000001 270.35zM306.21 270.35C307.23 262.14 313.96 256 321.96 256H338.08C347.59 256 355.16 264.57 354.08 274.35C349.74 313.46 331.68 348.88 303.95 371.51C286.58 385.68 275.13 408.26 271.97 433.66C270.96 441.86 264.2200000000001 448 256.23 448H240.11C230.6 448 223.02 439.43 224.11 429.65C228.45 390.54 246.51 355.12 274.24 332.49C291.6 318.3200000000001 303.06 295.74 306.2100000000001 270.35zM480 192H256L145.07 275.2A63.99 63.99 0 0 1 106.67 288H64C28.65 288 0 259.35 0 224V0C0 -35.35 28.65 -64 64 -64H448C483.35 -64 512 -35.35 512 0V160C512 177.67 497.67 192 480 192zM128 8C128 3.58 124.42 0 120 0H104C99.58 0 96 3.58 96 8V120C96 124.42 99.58 128 104 128H120C124.42 128 128 124.42 128 120V8zM224 8C224 3.58 220.42 0 216 0H200C195.58 0 192 3.58 192 8V120C192 124.42 195.58 128 200 128H216C220.42 128 224 124.42 224 120V8zM320 8C320 3.58 316.42 0 312 0H296C291.58 0 288 3.58 288 8V120C288 124.42 291.58 128 296 128H312C316.42 128 320 124.42 320 120V8zM416 8C416 3.58 412.42 0 408 0H392C387.58 0 384 3.58 384 8V120C384 124.42 387.58 128 392 128H408C412.42 128 416 124.42 416 120V8zM64 320C99.35 320 128 348.65 128 384S99.35 448 64 448S0 419.35 0 384S28.65 320 64 320z" />
+    <glyph glyph-name="hotel"
+      unicode="&#xF594;"
+      horiz-adv-x="576" d=" M560 384C568.84 384 576 391.16 576 400V432C576 440.84 568.84 448 560 448H16C7.16 448 0 440.84 0 432V400C0 391.16 7.16 384 16 384H31.98V0H16C7.16 0 0 -7.16 0 -16V-48C0 -56.84 7.16 -64 16 -64H256V16C256 24.8 263.2 32 272 32H304C312.8 32 320 24.8 320 16V-64H560C568.84 -64 576 -56.84 576 -48V-16C576 -7.16 568.84 0 560 0H544V384H560zM256 339.2C256 345.6 262.4 352 268.8 352H307.2C313.6 352 320 345.6 320 339.2V300.8C320 294.4 313.6 288 307.2 288H268.8C262.4000000000001 288 256 294.4 256 300.8V339.2000000000001zM256 243.2C256 249.6 262.4 256 268.8 256H307.2C313.6 256 320 249.6 320 243.2V204.8C320 198.4 313.6 192 307.2 192H268.8C262.4000000000001 192 256 198.4 256 204.8V243.2zM128 339.2C128 345.6 134.4 352 140.8 352H179.2C185.6 352 192 345.6 192 339.2V300.8C192 294.4 185.6 288 179.2 288H140.8C134.4 288 128 294.4 128 300.8V339.2zM179.2 192H140.8C134.4 192 128 198.4 128 204.8V243.2C128 249.6 134.4 256 140.8 256H179.2C185.6 256 192 249.6 192 243.2V204.8C192 198.4 185.6 192 179.2 192zM192 64C192 117.02 234.98 160 288 160S384 117.02 384 64H192zM448 204.8C448 198.4 441.6 192 435.2 192H396.8C390.4000000000001 192 384 198.4 384 204.8V243.2C384 249.6 390.4 256 396.8 256H435.2C441.6 256 448 249.6 448 243.2V204.8zM448 300.8C448 294.4 441.6 288 435.2 288H396.8C390.4000000000001 288 384 294.4 384 300.8V339.2000000000001C384 345.6 390.4 352 396.8 352H435.2C441.6 352 448 345.6 448 339.2000000000001V300.8z" />
+    <glyph glyph-name="hourglass-end"
+      unicode="&#xF253;"
+      horiz-adv-x="384" d=" M360 384C373.255 384 384 394.745 384 408V424C384 437.255 373.255 448 360 448H24C10.745 448 0 437.255 0 424V408C0 394.745 10.745 384 24 384C24 293.035 75.016 216.266 144.842 192C75.016 167.734 24 90.965 24 0C10.745 0 0 -10.745 0 -24V-40C0 -53.255 10.745 -64 24 -64H360C373.255 -64 384 -53.255 384 -40V-24C384 -10.745 373.255 0 360 0C360 90.965 308.984 167.734 239.158 192C308.984 216.266 360 293.035 360 384zM192 240C134.213 240 88 306.5180000000001 88 384H296C296 306.055 249.49 240 192 240z" />
+    <glyph glyph-name="hourglass-half"
+      unicode="&#xF252;"
+      horiz-adv-x="384" d=" M360 448H24C10.745 448 0 437.255 0 424V408C0 394.745 10.745 384 24 384C24 293.035 75.016 216.266 144.842 192C75.016 167.734 24 90.965 24 0C10.745 0 0 -10.745 0 -24V-40C0 -53.255 10.745 -64 24 -64H360C373.255 -64 384 -53.255 384 -40V-24C384 -10.745 373.255 0 360 0C360 90.965 308.984 167.734 239.158 192C308.984 216.266 360 293.035 360 384C373.255 384 384 394.745 384 408V424C384 437.255 373.255 448 360 448zM284.9220000000001 64H99.08C116.139 110.797 151.176 144 192 144C232.821 144 267.862 110.804 284.9220000000001 64zM284.9410000000001 320H99.078C91.988 339.452 88 361.252 88 384H296C296 361.195 292.013 339.413 284.941 320z" />
+    <glyph glyph-name="hourglass-start"
+      unicode="&#xF251;"
+      horiz-adv-x="384" d=" M360 448H24C10.745 448 0 437.255 0 424V408C0 394.745 10.745 384 24 384C24 293.035 75.016 216.266 144.842 192C75.016 167.734 24 90.965 24 0C10.745 0 0 -10.745 0 -24V-40C0 -53.255 10.745 -64 24 -64H360C373.255 -64 384 -53.255 384 -40V-24C384 -10.745 373.255 0 360 0C360 90.965 308.984 167.734 239.158 192C308.984 216.266 360 293.035 360 384C373.255 384 384 394.745 384 408V424C384 437.255 373.255 448 360 448zM296 0H88C88 77.458 134.204 144 192 144C249.786 144 296 77.483 296 0z" />
+    <glyph glyph-name="hourglass"
+      unicode="&#xF254;"
+      horiz-adv-x="384" d=" M360 384C373.255 384 384 394.745 384 408V424C384 437.255 373.255 448 360 448H24C10.745 448 0 437.255 0 424V408C0 394.745 10.745 384 24 384C24 293.035 75.016 216.266 144.842 192C75.016 167.734 24 90.965 24 0C10.745 0 0 -10.745 0 -24V-40C0 -53.255 10.745 -64 24 -64H360C373.255 -64 384 -53.255 384 -40V-24C384 -10.745 373.255 0 360 0C360 90.965 308.984 167.734 239.158 192C308.984 216.266 360 293.035 360 384z" />
+    <glyph glyph-name="house-damage"
+      unicode="&#xF6F1;"
+      horiz-adv-x="576" d=" M288 333.04L69.47 140.29C67.85 138.8300000000001 65.78 138.15 64 136.94V-48C64 -56.84 71.16 -64 80 -64H229.23L192 8.81L296.11 72.81L235.95 192.03L384 55.25L279.89 -8.75L319.81 -64H496C504.84 -64 512 -56.84 512 -48V136.9C510.3 138.06 508.28 138.72 506.74 140.1L288 333.04zM570.69 211.72L512 263.55V400C512 408.8400000000001 504.84 416 496 416H432C423.1600000000001 416 416 408.8400000000001 416 400V348.31L314.75 437.69C307.12 444.55 297.56 447.99 288 448S268.9 444.59 261.3 437.73L5.31 211.72C-1.26 205.81 -1.81 195.7 4.1 189.12L25.5 165.3C31.4 158.73 41.52 158.18 48.1 164.09L277.42 366.37C283.4700000000001 371.7 292.54 371.7 298.5900000000001 366.37L527.91 164.1C534.48 158.2000000000001 544.6 158.74 550.51 165.31L571.91 189.13C577.81 195.7 577.27 205.82 570.6899999999999 211.72z" />
+    <glyph glyph-name="hryvnia"
+      unicode="&#xF6F2;"
+      horiz-adv-x="384" d=" M368 208C376.84 208 384 215.16 384 224V256C384 264.8400000000001 376.84 272 368 272H326.14C339.55 300.63 339.88 335.33 322.01 366.05C303.34 398.16 267.1 416 229.96 416H151.14C126.82 416 103.28 407.4700000000001 84.6 391.91L72.83 382.1C62.65 373.61 61.27 358.48 69.76 348.3L90.25 323.71C98.74 313.52 113.87 312.15 124.06 320.64L135.79 330.42C140.11 334.02 145.56 335.99 151.18 335.99H234.8C246.49 335.99 256 326.4700000000001 256 314.79C256 308.88 253.52 303.2100000000001 249.19 299.2100000000001L219.7 272H16C7.16 272 0 264.8400000000001 0 256V224C0 215.16 7.16 208 16 208H150.37L115.7 176H16C7.16 176 0 168.84 0 160V128C0 119.16 7.16 112 16 112H57.86C44.45 83.37 44.12 48.67 61.99 17.95C80.66 -14.15 116.9 -32 154.04 -32H232.86C257.18 -32 280.72 -23.47 299.4 -7.91L311.17 1.9C321.35 10.39 322.73 25.52 314.24 35.7L293.75 60.29C285.26 70.48 270.13 71.85 259.94 63.36L248.19 53.5599999999999A23.992 23.992 0 0 0 232.83 47.9999999999999H149.2C137.51 47.9999999999999 128 57.5199999999999 128 69.1999999999999C128 75.11 130.48 80.7799999999999 134.81 84.7799999999999L164.3 112H368C376.84 112 384 119.16 384 128V160C384 168.84 376.84 176 368 176H233.63L268.3 208H368z" />
+    <glyph glyph-name="i-cursor"
+      unicode="&#xF246;"
+      horiz-adv-x="256" d=" M256 395.952V435.935C256 442.504 250.726 447.852 244.158 447.9340000000001C211.621 448.344 166.469 447.989 128 410.041C90.266 447.264 46.979 448.114 11.913 447.886C5.318 447.843 0 442.481 0 435.886V396.241C0 389.554 5.458 384.163 12.145 384.243C38.111 384.553 96 380.757 96 335.818V224H60C53.373 224 48 218.627 48 212V172C48 165.373 53.373 160 60 160H96V48C96 3.068 39.925 -0.031 12.05 0.041C5.404 0.058 0 -5.306 0 -11.952V-51.935C0 -58.504 5.274 -63.852 11.842 -63.934C44.379 -64.3430000000001 89.531 -63.988 128 -26.04C165.734 -63.263 209.021 -64.1130000000001 244.087 -63.885C250.682 -63.842 256 -58.48 256 -51.885V-12.24C256 -5.553 250.542 -0.162 243.855 -0.242C217.889 -0.553 160 3.061 160 48V160H196C202.627 160 208 165.373 208 172V212C208 218.627 202.627 224 196 224H160V335.818C160 380.75 216.075 384.031 243.95 383.9600000000001C250.596 383.942 256 389.3060000000001 256 395.952z" />
+    <glyph glyph-name="id-badge"
+      unicode="&#xF2C1;"
+      horiz-adv-x="384" d=" M336 448H48C21.5 448 0 426.5 0 400V-16C0 -42.5 21.5 -64 48 -64H336C362.5 -64 384 -42.5 384 -16V400C384 426.5 362.5 448 336 448zM144 416H240C248.8 416 256 408.8 256 400S248.8 384 240 384H144C135.2 384 128 391.2 128 400S135.2 416 144 416zM192 288C227.3 288 256 259.3 256 224S227.3 160 192 160S128 188.7 128 224S156.7 288 192 288zM304 51.2C304 40.6 294 32 281.6 32H102.4C90 32 80 40.6 80 51.2V70.4C80 102.2 110.1 128 147.2 128H152.2C164.5 122.9 177.9 120 192 120S219.6 122.9 231.8 128H236.8C273.9000000000001 128 304 102.2 304 70.4V51.2z" />
+    <glyph glyph-name="id-card-alt"
+      unicode="&#xF47F;"
+      horiz-adv-x="576" d=" M528 384H384V288H192V384H48C21.5 384 0 362.5 0 336V-16C0 -42.5 21.5 -64 48 -64H528C554.5 -64 576 -42.5 576 -16V336C576 362.5 554.5 384 528 384zM288 224C323.3 224 352 195.3 352 160S323.3 96 288 96S224 124.7 224 160S252.7 224 288 224zM381.3 0H194.7C184.3 0 175.9 10 179.1 19.8C187.4 45.4 211.5 64 240 64H248.2C260.5 58.9 273.9 56 288 56S315.6 58.9 327.8 64H336C364.4 64 388.5 45.5 396.9 19.8C400.1 10 391.7 0 381.3 0zM352 416C352 433.7 337.7 448 320 448H256C238.3 448 224 433.7 224 416V320H352V416z" />
+    <glyph glyph-name="id-card"
+      unicode="&#xF2C2;"
+      horiz-adv-x="576" d=" M528 416H48C21.5 416 0 394.5 0 368V352H576V368C576 394.5 554.5 416 528 416zM0 16C0 -10.5 21.5 -32 48 -32H528C554.5 -32 576 -10.5 576 16V320H0V16zM352 248C352 252.4 355.6 256 360 256H504C508.4 256 512 252.4 512 248V232C512 227.6 508.4 224 504 224H360C355.6 224 352 227.6 352 232V248zM352 184C352 188.4 355.6 192 360 192H504C508.4 192 512 188.4 512 184V168C512 163.6 508.4 160 504 160H360C355.6 160 352 163.6 352 168V184zM352 120C352 124.4 355.6 128 360 128H504C508.4 128 512 124.4 512 120V104C512 99.6 508.4 96 504 96H360C355.6 96 352 99.6 352 104V120zM176 256C211.3 256 240 227.3 240 192S211.3 128 176 128S112 156.7 112 192S140.7 256 176 256zM67.1 51.8C75.5 77.5 99.6 96 128 96H136.2C148.5 90.9 161.9 88 176 88S203.6 90.9 215.8 96H224C252.4 96 276.5 77.5 284.9 51.8C288.1 41.9 279.7 32 269.3 32H82.7C72.3 32 63.9 42 67.1 51.8z" />
+    <glyph glyph-name="image"
+      unicode="&#xF03E;"
+      horiz-adv-x="512" d=" M464 0H48C21.49 0 0 21.49 0 48V336C0 362.51 21.49 384 48 384H464C490.51 384 512 362.51 512 336V48C512 21.49 490.51 0 464 0zM112 328C81.072 328 56 302.928 56 272S81.072 216 112 216S168 241.072 168 272S142.928 328 112 328zM64 64H448V176L360.485 263.515C355.7990000000001 268.201 348.201 268.201 343.514 263.515L208 128L152.485 183.515C147.799 188.201 140.201 188.201 135.514 183.515L64 112V64z" />
+    <glyph glyph-name="images"
+      unicode="&#xF302;"
+      horiz-adv-x="576" d=" M480 32V16C480 -10.51 458.51 -32 432 -32H48C21.49 -32 0 -10.51 0 16V272C0 298.51 21.49 320 48 320H64V112C64 67.888 99.888 32 144 32H480zM576 112V368C576 394.51 554.51 416 528 416H144C117.49 416 96 394.51 96 368V112C96 85.49 117.49 64 144 64H528C554.51 64 576 85.49 576 112zM256 320C256 293.49 234.51 272 208 272S160 293.49 160 320S181.49 368 208 368S256 346.51 256 320zM160 176L215.515 231.515C220.201 236.201 227.799 236.201 232.486 231.515L272 192L407.515 327.515C412.201 332.201 419.799 332.201 424.486 327.515L512 240V128H160V176z" />
+    <glyph glyph-name="inbox"
+      unicode="&#xF01C;"
+      horiz-adv-x="576" d=" M567.938 204.092L462.25 362.626A48.003 48.003 0 0 1 422.311 384H153.689A48 48 0 0 1 113.751 362.626L8.062 204.092A47.994 47.994 0 0 1 0 177.467V48C0 21.49 21.49 0 48 0H528C554.51 0 576 21.49 576 48V177.467A47.994 47.994 0 0 1 567.938 204.092zM162.252 320H413.749L499.082 192H376L344 128H232L200 192H76.918L162.252 320z" />
+    <glyph glyph-name="indent"
+      unicode="&#xF03C;"
+      horiz-adv-x="448" d=" M0 364V404C0 412.837 7.163 420 16 420H432C440.837 420 448 412.837 448 404V364C448 355.163 440.837 348 432 348H16C7.163 348 0 355.163 0 364zM176 220H432C440.837 220 448 227.163 448 236V276C448 284.837 440.837 292 432 292H176C167.163 292 160 284.837 160 276V236C160 227.163 167.163 220 176 220zM16 -36H432C440.837 -36 448 -28.837 448 -20V20C448 28.837 440.837 36 432 36H16C7.163 36 0 28.837 0 20V-20C0 -28.837 7.163 -36 16 -36zM176 92H432C440.837 92 448 99.163 448 108V148C448 156.837 440.837 164 432 164H176C167.163 164 160 156.837 160 148V108C160 99.163 167.163 92 176 92zM123.313 203.313L27.313 299.297C17.266 309.348 0 302.224 0 287.9840000000001V96.009C0 81.68 17.325 74.705 27.313 84.696L123.313 180.6880000000001C129.562 186.9350000000001 129.562 197.0650000000001 123.313 203.3130000000001z" />
+    <glyph glyph-name="industry"
+      unicode="&#xF275;"
+      horiz-adv-x="512" d=" M475.115 284.219L336 195.691V263.971C336 282.887 315.069 294.37 299.115 284.219L160 195.691V392C160 405.255 149.255 416 136 416H24C10.745 416 0 405.255 0 392V-8C0 -21.255 10.745 -32 24 -32H488C501.255 -32 512 -21.255 512 -8V263.971C512 282.8880000000001 491.069 294.37 475.115 284.219z" />
+    <glyph glyph-name="infinity"
+      unicode="&#xF534;"
+      horiz-adv-x="640" d=" M471.1 352C405 352 353.3 310.7 320 273.4C286.7 310.7 235 352 168.9 352C75.8 352 0 280.2 0 192S75.8 32 168.9 32C235 32 286.7 73.3 320 110.6C353.3 73.3 405 32 471.1 32C564.2 32 640 103.8 640 192S564.2 352 471.1 352zM168.9 128C128.7 128 96 156.7 96 192S128.7 256 168.9 256C207.1 256 242.3 219.9 262.9 192C242.5 164.4 207 128 168.9 128zM471.1 128C432.9000000000001 128 397.7000000000001 164.1 377.1 192C397.5 219.6 433 256 471.1 256C511.3 256 544 227.3 544 192S511.3 128 471.1 128z" />
+    <glyph glyph-name="info-circle"
+      unicode="&#xF05A;"
+      horiz-adv-x="512" d=" M256 440C119.043 440 8 328.9170000000001 8 192C8 55.003 119.043 -56 256 -56S504 55.003 504 192C504 328.9170000000001 392.957 440 256 440zM256 330C279.1960000000001 330 298 311.1960000000001 298 288S279.1960000000001 246 256 246S214 264.804 214 288S232.804 330 256 330zM312 76C312 69.373 306.627 64 300 64H212C205.373 64 200 69.373 200 76V100C200 106.627 205.373 112 212 112H224V176H212C205.373 176 200 181.373 200 188V212C200 218.627 205.373 224 212 224H276C282.627 224 288 218.627 288 212V112H300C306.627 112 312 106.627 312 100V76z" />
+    <glyph glyph-name="info"
+      unicode="&#xF129;"
+      horiz-adv-x="192" d=" M20 23.771H40V168.229H20C8.954 168.229 0 177.183 0 188.229V236C0 247.046 8.954 256 20 256H132C143.046 256 152 247.046 152 236V23.771H172C183.046 23.771 192 14.817 192 3.771V-44C192 -55.046 183.046 -64 172 -64H20C8.954 -64 0 -55.046 0 -44V3.771C0 14.817 8.954 23.771 20 23.771zM96 448C56.235 448 24 415.765 24 376S56.235 304 96 304S168 336.235 168 376S135.764 448 96 448z" />
+    <glyph glyph-name="italic"
+      unicode="&#xF033;"
+      horiz-adv-x="320" d=" M204.758 32H170.909L233.0010000000001 352H273.7260000000001A16 16 0 0 1 289.4300000000001 364.937L295.6720000000001 396.937C297.599 406.8160000000001 290.034 416 279.968 416H120.235A16 16 0 0 1 104.531 403.063L98.289 371.063C96.362 361.184 103.927 352 113.993 352H147.839L85.749 32H46.278A16 16 0 0 1 30.574 19.065L24.329 -12.935C22.402 -22.815 29.967 -32 40.034 -32H198.513A16 16 0 0 1 214.217 -19.065L220.462 12.935C222.389 22.815 214.824 32 204.758 32z" />
+    <glyph glyph-name="jedi"
+      unicode="&#xF669;"
+      horiz-adv-x="544" d=" M479.99 96L538.87 154.87C542.16 171.67 544 188.99 544 206.73C544 212.54 543.32 218.24 542.95 224H496L537.25 265.24C522.75 330.0300000000001 484.82 388.29 429.3400000000001 427.5100000000001C426.5700000000001 429.4700000000001 423.37 430.5000000000001 420.0900000000001 430.5000000000001C414.7200000000001 430.5000000000001 409.68 427.7900000000001 406.6 423.2600000000001C403.55 418.7700000000001 402.9600000000001 413.2700000000001 404.99 408.1700000000001C411.54 391.7100000000001 414.85 374.4400000000001 414.85 356.86C414.85 311.74 393.8200000000001 270.2900000000001 357.1600000000001 243.1300000000001C353.1400000000001 240.1500000000001 350.7000000000001 235.6300000000001 350.4600000000001 230.7300000000001C350.2200000000001 225.8100000000001 352.2200000000001 221.0700000000001 355.9500000000001 217.7000000000001C388.8800000000001 187.9500000000001 403.3000000000001 144.1900000000001 394.5200000000001 100.6300000000001C384.7800000000001 52.28 345.6800000000001 13.5300000000001 297.2100000000001 4.1300000000001L294.7100000000001 69.47L321.88 51C324.86 48.94 329.27 49.31 331.9 51.8A8.002 8.002 0 0 1 333.24 61.72L313.13 95.4500000000001L355.2 104.1700000000001C358.8999999999999 104.9200000000001 361.58 108.2200000000001 361.58 112.0000000000001C361.58 115.7700000000001 358.89 119.0600000000001 355.2 119.8300000000001L313.13 128.5600000000001L333.26 162.3300000000001C335.18 165.5600000000001 334.5999999999999 169.6400000000001 331.88 172.2400000000001C329.18 174.7900000000001 324.9099999999999 175.1300000000001 321.88 173.0400000000001L291.49 152.3700000000001L279.96 440.3A7.9639999999999995 7.9639999999999995 0 0 1 271.96 448C267.63 448 264.12 444.62 263.96 440.33L252.44 152.36L222.05 173.02C218.91 175.14 214.78 174.85 212.05 172.24C209.33 169.65 208.75 165.57 210.69 162.3L230.8 128.57L188.73 119.84C185.03 119.09 182.35 115.79 182.35 112.01S185.02 104.93 188.73 104.18L230.8 95.46L210.67 61.69C208.75 58.46 209.33 54.36 212.06 51.75C214.65 49.3 219.09 49 222.06 51L249.22 69.48L246.72 4.22C189.78 15.86 146.8299999999999 66.11 146.8299999999999 126.1400000000001C146.8299999999999 161.22 161.4499999999999 193.7400000000001 188 217.72C191.72 221.0800000000001 193.72 225.8300000000001 193.4799999999999 230.73C193.2399999999999 235.63 190.7999999999999 240.14 186.7899999999999 243.11C150.12 270.27 129.0799999999999 311.73 129.0799999999999 356.85C129.0799999999999 374.4100000000001 132.3899999999999 391.6600000000001 138.9199999999999 408.11C140.94 413.2 140.3499999999999 418.7 137.2999999999999 423.2C134.2199999999999 427.74 129.1699999999999 430.45 123.7899999999999 430.45C120.4899999999999 430.45 117.2899999999999 429.4100000000001 114.5199999999999 427.45C58.6499999999999 387.93 20.9199999999999 330.08 6.5499999999999 265.38L47.93 224H0.72C0.09 214.08 -0.25 204.09 0.22 194.01C0.84 180.58 2.76 167.48 5.33 154.6L63.93 96H24.02C65.27 5.77 155.15 -58.94 259.12 -63.71C263.42 -63.91 267.71 -64 271.9700000000001 -64C382.3100000000001 -64 477.3200000000001 1.83 519.95 96H479.9900000000001z" />
+    <glyph glyph-name="joint"
+      unicode="&#xF595;"
+      horiz-adv-x="640" d=" M444.34 266.9C466.72 251.22 480 225.74 480 198.31V168C480 163.58 483.58 160 488 160H536C540.42 160 544 163.58 544 168V198.31C544 241.55 522.99 281.7200000000001 487.66 306.37C463.85 322.98 448 348.66 448 377.69V440C448 444.42 444.42 448 440 448H392C387.58 448 384 444.42 384 440V373.6C384 329.91 408.56 291.9700000000001 444.3400000000001 266.9zM194.97 89.02C126.03 77.93 59.69 53.31 0 16C83.65 -36.28 180.3 -64 278.94 -64H367.51L254.79 67.51C240.05 84.71 217.34 92.62 194.97 89.02zM553.28 360.91C547.61 364.71 544 370.87 544 377.69V440C544 444.42 540.42 448 536 448H488C483.58 448 480 444.42 480 440V377.69C480 355.67 490.17 334.28 508.64 322.3C550.79 294.9600000000001 576 248.46 576 198.31V168C576 163.58 579.58 160 584 160H632C636.42 160 640 163.58 640 168V198.31C640 263.75 607.59 324.5 553.28 360.91zM360.89 95.95C326.49 95.89 274.08 95.8 272.68 95.78L390.48 -41.65A63.98700000000001 63.98700000000001 0 0 1 439.07 -64H527.52L409.57 73.6A63.95499999999999 63.95499999999999 0 0 1 360.89 95.9500000000001zM616 96H432L549.99 -41.65A63.98700000000001 63.98700000000001 0 0 1 598.58 -64H616C629.25 -64 640 -53.25 640 -40V72C640 85.26 629.25 96 616 96z" />
+    <glyph glyph-name="journal-whills"
+      unicode="&#xF66A;"
+      horiz-adv-x="448" d=" M448 89.6V422.4C448 438.4 438.4 448 422.4 448H96C41.6 448 0 406.4 0 352V32C0 -22.4 41.6 -64 96 -64H422.4C435.2 -64 448 -54.4 448 -38.4V-22.4C448 -16 444.8 -9.6 438.4 -3.2C435.2 12.8 435.2 57.6 438.4 70.4C444.8 73.6 448 80 448 89.6zM133.08 303.61L154.34 282.35C155.9 280.79 157.95 280.01 160 280.01S164.09 280.79 165.66 282.35C168.78 285.4700000000001 168.78 290.54 165.66 293.6600000000001L139.24 320.0800000000001C149.24 340.98 165.48 358.05 185.61 369.3400000000001C179.62 359.6 176 348.26 176 336C176 316.04 185.33 298.43 199.66 286.69C190.01 276.63 184 263.04 184 248C184 221.06 203.04 198.6 228.38 193.24L229.74 225.95L219.37 218.91C218.68 218.46 217.9 218.22 217.12 218.22C216.12 218.22 215.14 218.6 214.37 219.31A4.006 4.006 0 0 0 213.68 224.26L222.22 238.57L204.31 242.29C202.45 242.68 201.12 244.32 201.12 246.21S202.45 249.74 204.31 250.13L222.22 253.85L213.68 268.16C212.73 269.77 213.01 271.83 214.37 273.11C215.73 274.41 217.81 274.55 219.37 273.52L231.38 265.36L236 376.17C236.09 378.31 237.86 380 240 380S243.91 378.31 244 376.17L248.68 263.88L262.88 273.53A4.067 4.067 0 0 0 267.88 273.12A4.006 4.006 0 0 0 268.57 268.17L260.03 253.86L277.94 250.14C279.8 249.7500000000001 281.13 248.11 281.13 246.2200000000001S279.8 242.6900000000001 277.94 242.3000000000001L260.03 238.5800000000001L268.57 224.2700000000001C269.52 222.66 269.24 220.6000000000001 267.88 219.3200000000001C267.11 218.6000000000001 266.11 218.2300000000001 265.13 218.2300000000001C264.35 218.2300000000001 263.57 218.46 262.88 218.9200000000001L250.2 227.5400000000001L251.63 193.2600000000001C276.96 198.6 296 221.06 296 248C296 263.04 289.99 276.63 280.34 286.69C294.67 298.43 304 316.04 304 336C304 348.26 300.38 359.6 294.4 369.33C314.53 358.05 330.77 340.9700000000001 340.77 320.07L314.35 293.65C311.23 290.53 311.23 285.46 314.35 282.34C315.91 280.78 317.96 280 320.01 280S324.1 280.78 325.67 282.34L346.93 303.6C349.9000000000001 293.52 352 283.05 352 272C352 271.48 351.86 271.01 351.85 270.49L314.74 238.02A7.974999999999999 7.974999999999999 0 0 1 313.99 226.74A7.97 7.97 0 0 1 320.01 224.01C321.89 224.01 323.76 224.67 325.28 225.99L348.87 246.63C337.32 197.04 293.09 160 240 160S142.68 197.04 131.14 246.62L154.73 225.98A7.957 7.957 0 0 1 160 224C162.22 224 164.44 224.92 166.02 226.73C168.94 230.06 168.6 235.11 165.27 238.01L128.16 270.48C128.15 271 128.01 271.4700000000001 128.01 271.99C128 283.05 130.1 293.52 133.08 303.61zM380.8 0H96C76.8 0 64 12.8 64 32S80 64 96 64H380.8V0z" />
+    <glyph glyph-name="kaaba"
+      unicode="&#xF66B;"
+      horiz-adv-x="576" d=" M554.12 364.49L318.36 443.07A95.962 95.962 0 0 1 257.6500000000001 443.07L21.88 364.49A32.006 32.006 0 0 1 0 334.13V285.12L265.02 364.63C280.05 369.13 295.94 369.13 311 364.63L576 285.12V334.13C576 347.9 567.19 360.13 554.12 364.49zM274.2200000000001 333.9700000000001L0 251.7V23.32C0 8.32 10.42 -4.66 25.06 -7.92L267.18 -61.72A95.937 95.937 0 0 1 308.83 -61.72L550.95 -7.92C565.59 -4.67 576.01 8.32 576.01 23.32V251.71L301.81 333.9700000000001C292.77 336.69 283.2200000000001 336.69 274.2200000000001 333.9700000000001zM128 217.89C128 214.28 125.59 211.12 122.11 210.17L42.11 188.35C37.02 186.97 32 190.8 32 196.07V212.65C32 216.26 34.41 219.42 37.89 220.37L117.89 242.19C122.98 243.58 128 239.75 128 234.47V217.89zM272 257.17C272 253.56 269.59 250.4 266.11 249.45L170.11 223.27C165.02 221.88 160 225.71 160 230.99V247.57C160 251.18 162.41 254.34 165.89 255.29L261.89 281.4700000000001C266.98 282.86 272 279.03 272 273.75V257.17zM448 234.47C448 239.75 453.02 243.58 458.11 242.19L538.11 220.37C541.59 219.42 544 216.26 544 212.65V196.07C544 190.79 538.98 186.96 533.89 188.35L453.89 210.17A7.997 7.997 0 0 0 448 217.89V234.47zM304 273.74C304 279.02 309.02 282.85 314.11 281.4600000000001L410.11 255.28C413.5900000000001 254.33 416 251.17 416 247.56V230.98C416 225.7 410.98 221.87 405.89 223.26L309.89 249.4400000000001A7.997 7.997 0 0 0 304 257.1600000000001V273.74z" />
+    <glyph glyph-name="key"
+      unicode="&#xF084;"
+      horiz-adv-x="512" d=" M512 271.999C512 174.797 433.202 96 336 96C324.78 96 313.81 97.062 303.173 99.069L279.161 72.055A23.999000000000002 23.999000000000002 0 0 0 261.223 64H224V24C224 10.745 213.255 0 200 0H160V-40C160 -53.255 149.255 -64 136 -64H24C10.745 -64 0 -53.255 0 -40V38.059C0 44.424 2.529 50.529 7.029 55.03L168.831 216.832C163.108 234.186 160 252.729 160 272C160 369.202 238.797 447.999 335.999 448C433.488 448.001 512 369.4890000000001 512 271.999zM336 320C336 293.49 357.49 272 384 272S432 293.49 432 320S410.51 368 384 368S336 346.51 336 320z" />
+    <glyph glyph-name="keyboard"
+      unicode="&#xF11C;"
+      horiz-adv-x="576" d=" M528 0H48C21.49 0 0 21.49 0 48V336C0 362.51 21.49 384 48 384H528C554.51 384 576 362.51 576 336V48C576 21.49 554.51 0 528 0zM128 268V308C128 314.627 122.627 320 116 320H76C69.373 320 64 314.627 64 308V268C64 261.373 69.373 256 76 256H116C122.627 256 128 261.373 128 268zM224 268V308C224 314.627 218.627 320 212 320H172C165.373 320 160 314.627 160 308V268C160 261.373 165.373 256 172 256H212C218.627 256 224 261.373 224 268zM320 268V308C320 314.627 314.627 320 308 320H268C261.373 320 256 314.627 256 308V268C256 261.373 261.373 256 268 256H308C314.627 256 320 261.373 320 268zM416 268V308C416 314.627 410.627 320 404 320H364C357.373 320 352 314.627 352 308V268C352 261.373 357.373 256 364 256H404C410.627 256 416 261.373 416 268zM512 268V308C512 314.627 506.627 320 500 320H460C453.373 320 448 314.627 448 308V268C448 261.373 453.373 256 460 256H500C506.627 256 512 261.373 512 268zM176 172V212C176 218.627 170.627 224 164 224H124C117.373 224 112 218.627 112 212V172C112 165.373 117.373 160 124 160H164C170.627 160 176 165.373 176 172zM272 172V212C272 218.627 266.627 224 260 224H220C213.373 224 208 218.627 208 212V172C208 165.373 213.373 160 220 160H260C266.627 160 272 165.373 272 172zM368 172V212C368 218.627 362.627 224 356 224H316C309.373 224 304 218.627 304 212V172C304 165.373 309.373 160 316 160H356C362.627 160 368 165.373 368 172zM464 172V212C464 218.627 458.627 224 452 224H412C405.373 224 400 218.627 400 212V172C400 165.373 405.373 160 412 160H452C458.627 160 464 165.373 464 172zM128 76V116C128 122.627 122.627 128 116 128H76C69.373 128 64 122.627 64 116V76C64 69.373 69.373 64 76 64H116C122.627 64 128 69.373 128 76zM416 76V116C416 122.627 410.627 128 404 128H172C165.373 128 160 122.627 160 116V76C160 69.373 165.373 64 172 64H404C410.627 64 416 69.373 416 76zM512 76V116C512 122.627 506.627 128 500 128H460C453.373 128 448 122.627 448 116V76C448 69.373 453.373 64 460 64H500C506.627 64 512 69.373 512 76z" />
+    <glyph glyph-name="khanda"
+      unicode="&#xF66D;"
+      horiz-adv-x="512" d=" M415.81 382C409.44 385.5 401.44 384.33 396.45 378.98A15.973999999999998 15.973999999999998 0 0 1 394.54 359.4600000000001C411.03 333.3 419.74 303.07 419.74 272.05C419.55 218.8 392.97 169.36 348.47 139.64L271.84 86.29V106.39L315.89 142.48C319.81 146.68 320.89 152.57 318.7 157.76L310.85 175C344.6900000000001 194.26 367.79 230.25 367.79 271.99C367.79 312.78 345.7700000000001 348.12 313.2000000000001 367.7L318.4200000000001 379.14C320.7600000000001 384.67 319.3500000000001 390.9700000000001 314.8500000000001 395.18L255.86 448L196.87 395.19C192.37 390.98 190.97 384.68 193.3 379.15L198.52 367.7100000000001C165.95 348.13 143.93 312.78 143.93 271.99C143.93 230.24 167.02 194.26 200.87 175L193.02 157.76C190.83 152.58 191.92 146.69 195.83 142.48L239.88 106.39V86.49L163.29 139.82C119.02 169.38 92.44 218.81 92.26 272C92.26 303.08 100.97 333.31 117.46 359.4700000000001C121.33 365.63 119.86 373.24 114.87 378.55C109.87 383.89 101.19 384.75 94.85 381.25C16.32 338.4 -22.3 242.7 13.36 152.01C20.43 134.02 31.25 117.63 43.82 102.95L99.79 37.59C104.66 31.9 112.83 30.35 119.44 33.87L198.79 76.1L228 55.77L180.92 22.99C179.25 23.36 177.69 24 175.91 24C162.66 24 151.92 13.26 151.92 0C151.92 -13.25 162.66 -24 175.91 -24C188.01 -24 197.6 -14.89 199.24 -3.24L239.87 25.04V-4.91C230.48 -10.48 223.88 -20.29 223.88 -32.01C223.88 -49.6800000000001 238.2 -64.01 255.86 -64.01S287.84 -49.68 287.84 -32.01C287.84 -20.3 281.23 -10.49 271.85 -4.91V25.24L312.76 -3.24C314.4100000000001 -14.89 324 -24 336.09 -24C349.34 -24 360.08 -13.25 360.08 0C360.08 13.26 349.34 24 336.09 24C334.31 24 332.75 23.36 331.08 22.99L284 55.77L313.21 76.11L392.56 33.8799999999999C399.17 30.36 407.3399999999999 31.9099999999999 412.2099999999999 37.5899999999999L464.7199999999999 98.8999999999999C483.5899999999999 120.9199999999999 498.7199999999999 146.3999999999999 505.9699999999999 174.4899999999999C527.5899999999999 258.1499999999999 489.5199999999999 341.7599999999999 415.81 381.9999999999999zM319.82 272C319.82 249.7 308.33 230.08 290.99 218.62L285.3400000000001 231.03C276.5900000000001 255.55 276.5900000000001 282.07 285.3400000000001 306.5900000000001L293.17 323.77C309.24 312.12 319.82 293.3200000000001 319.82 272zM191.89 272C191.89 293.32 202.47 312.12 218.55 323.76L226.38 306.58C235.13 282.06 235.13 255.55 226.38 231.02L220.73 218.61C203.39 230.07 191.89 249.7 191.89 272z" />
+    <glyph glyph-name="kiss-beam"
+      unicode="&#xF597;"
+      horiz-adv-x="496" d=" M248 440C111 440 0 329 0 192S111 -56 248 -56S496 55 496 192S385 440 248 440zM209 220.1L199.5 237.1C191.8 250.8 180.3 258.7 168 258.7S144.2 250.8 136.5 237.1L127 220.1C122.8 212.7 111.4 216.1 112.1 224.6C115.4 266.7 144.3 296 168.1 296S220.8 266.7 224.1 224.6C224.6 216.1 213.2 212.6 209 220.1zM304 52C304 32.8 275.3 10.5 232.5 8C224 7.2 220.4 19.8 228.9 23.4L245.9 30.6C258.9 36.1 266.7 44.1 266.7 52.1S258.9 68.1 245.9 73.6L228.9 80.8C222.9 83.3 222.8 92.9999999999999 228.9 95.6L245.9 102.8C258.9 108.3 266.7 116.3 266.7 124.3S258.9 140.3 245.9 145.8L228.9 153C220.3 156.6 224.1 169.5 232.5 168.3999999999999C275.3 165.8999999999999 304 143.5999999999999 304 124.3999999999999C304 111.3999999999999 290.6 97.0999999999999 268.8 88.3999999999999C290.6 79.3 304 65 304 52zM369 220.1L359.5 237.1C351.8 250.8 340.3 258.7 328 258.7S304.2 250.8 296.5 237.1L287 220.1C282.9 212.8 271.4 216.1 272.1 224.6C275.4000000000001 266.7 304.3 296 328.1 296S380.8 266.7 384.1 224.6C384.6 216.1 373.2000000000001 212.6 369 220.1z" />
+    <glyph glyph-name="kiss-wink-heart"
+      unicode="&#xF598;"
+      horiz-adv-x="504" d=" M501.1 45.5C493.1 66.3 469.6 77 448 71.4L439.6 69.2L437.3 77.6C431.4000000000001 98.9999999999999 410.3 114.1 388.3 110.6C363.1 106.6 347.7 81.9999999999999 354.3 57.9999999999999L377.2 -24.6C378.7 -29.9 384.2 -33.1 389.6 -31.7L472.6 -10.2C496.7 -3.9 510.3 21.6 501.1 45.4999999999999zM323.5 49.5C317.9 69.8 321.2 91.5 332.5 109.2C362.2 155.5 431.2 154.7 460.3 104.9C466.7 104.8 472.9 103.5 478.9 102C489.8 129.9 496.0000000000001 160.2 496.0000000000001 192C496 329 385 440 248 440S0 329 0 192S111 -56 248 -56C283.4 -56 316.9 -48.5 347.4 -35.1C347.1 -34.4 323.5 49.5 323.5 49.5zM168 208C150.3 208 136 222.3 136 240S150.3 272 168 272S200 257.7 200 240S185.7 208 168 208zM288 52C288 32.8 259.3 10.5 216.5 8C208 7.2 204.4 19.8 212.9 23.4L229.9 30.6C242.9 36.1 250.7 44.1 250.7 52.1S242.9 68.1 229.9 73.6L212.9 80.8C206.9 83.3 207.2 93.1 212.9 95.6L229.9 102.8C242.9 108.3 250.7 116.3 250.7 124.3S242.9 140.3 229.9 145.8L212.9 153C204.1 156.6999999999999 208.3 169.6 216.5 168.3999999999999C259.3 165.8999999999999 288 143.5999999999999 288 124.3999999999999C288 111.3999999999999 274.6 97.0999999999999 252.8 88.3999999999999C274.6 79.3 288 65 288 52zM304 231C295.7 223.6 282.4 230.6 284.2 241.8C288.2 267 318.4 283.9 344.1 283.9S400 267 404 241.8C405.7 230.7 392.7 223.5 384.2 231L374.7 239.5C359.9 252.7 328.5 252.7 313.7 239.5L304 231z" />
+    <glyph glyph-name="kiss"
+      unicode="&#xF596;"
+      horiz-adv-x="496" d=" M248 440C111 440 0 329 0 192S111 -56 248 -56S496 55 496 192S385 440 248 440zM168 208C150.3 208 136 222.3 136 240S150.3 272 168 272S200 257.7 200 240S185.7 208 168 208zM304 52C304 32.8 275.3 10.5 232.5 8C224 7.2 220.4 19.8 228.9 23.4L245.9 30.6C258.9 36.1 266.7 44.1 266.7 52.1S258.9 68.1 245.9 73.6L228.9 80.8C222.9 83.3 222.8 92.9999999999999 228.9 95.6L245.9 102.8C258.9 108.3 266.7 116.3 266.7 124.3S258.9 140.3 245.9 145.8L228.9 153C220.3 156.6 224.1 169.5 232.5 168.3999999999999C275.3 165.8999999999999 304 143.5999999999999 304 124.3999999999999C304 111.3999999999999 290.6 97.0999999999999 268.8 88.3999999999999C290.6 79.3 304 65 304 52zM328 208C310.3 208 296 222.3 296 240S310.3 272 328 272S360 257.7 360 240S345.7 208 328 208z" />
+    <glyph glyph-name="kiwi-bird"
+      unicode="&#xF535;"
+      horiz-adv-x="576" d=" M575.81 230.02C572.64 290.5900000000001 518.28 336 457.63 336H448.26C395.44 336 344.01 352.25 300.52 382.24C258.53 411.2 204.48 423.86 147.31 410.94C129.3 406.88 -0.08 369.76 0 224C0.04 153.05 38.68 91.2 95.99 57.99V-16C95.99 -24.84 103.15 -32 111.99 -32H127.99C136.83 -32 143.99 -24.84 143.99 -16V38.26C159.35 34.3 175.39 32 191.99 32C197.43 32 202.67 32.73 207.99 33.18V-16C207.99 -24.84 215.15 -32 223.99 -32H239.99C248.83 -32 255.99 -24.84 255.99 -16V43.43C270.23 48.49 283.87 54.82 296.3300000000001 62.94C342.07 92.75 393.86 112 448.46 112C473.94 112 464.47 112.31 471.51 112.78L545.92 -23.66C548.78 -28.89 554.2199999999999 -32 559.9699999999999 -32C561.2799999999999 -32 562.6099999999999 -31.84 563.92 -31.5C571.01 -29.7 575.9699999999999 -23.31 575.9699999999999 -16C575.9699999999999 -16 576.1099999999999 224.24 575.81 230.02zM463.97 200C450.72 200 439.9700000000001 210.75 439.9700000000001 224C439.9700000000001 237.26 450.72 248 463.97 248S487.97 237.26 487.97 224C487.97 210.75 477.22 200 463.97 200zM543.97 46.75L504.11 119.83C519.23 125.66 532.84 134.43 543.97 145.81V46.75z" />
+    <glyph glyph-name="landmark"
+      unicode="&#xF66F;"
+      horiz-adv-x="512" d=" M501.62 355.89L267.24 445.96A31.958 31.958 0 0 1 244.77 445.96L10.38 355.89A16.001 16.001 0 0 1 0 340.91V304C0 295.16 7.16 288 16 288H496C504.84 288 512 295.16 512 304V340.91C512 347.58 507.86 353.55 501.62 355.89zM64 256V96H48C39.16 96 32 88.84 32 80V32H480V80C480 88.84 472.84 96 464 96H448V256H384V96H288V256H224V96H128V256H64zM496 0H16C7.16 0 0 -7.16 0 -16V-48C0 -56.84 7.16 -64 16 -64H496C504.84 -64 512 -56.84 512 -48V-16C512 -7.16 504.84 0 496 0z" />
+    <glyph glyph-name="language"
+      unicode="&#xF1AB;"
+      horiz-adv-x="640" d=" M304 32H24C10.745 32 0 42.745 0 56V328C0 341.255 10.745 352 24 352H304V32zM183.324 104.622A12 12 0 0 1 194.839 96H217.702C225.922 96 231.709 104.078 229.064 111.861L171.61 280.915A12 12 0 0 1 160.248 289.054H127.759A12.001000000000001 12.001000000000001 0 0 1 116.397 280.915L58.942 111.861C56.297 104.078 62.084 96 70.304 96H93.109A12 12 0 0 1 104.644 104.693L113.762 136.5H173.973L183.324 104.622zM144.273 245.042S148.593 223.981 152.103 211.832L162.9030000000001 174.301H124.8330000000001L135.9030000000001 211.832C139.4130000000001 223.982 143.7330000000001 245.042 143.7330000000001 245.042H144.2730000000001zM616 32H336V352H616C629.255 352 640 341.255 640 328V56C640 42.745 629.255 32 616 32zM580 260H516V276C516 282.627 510.627 288 504 288H488C481.373 288 476 282.627 476 276V260H412C405.373 260 400 254.627 400 248V232C400 225.373 405.373 220 412 220H526.106C519.843 205.701 509.588 191.028 496.083 176.794C489.523 183.692 483.686 190.704 478.718 197.727C475.079 202.871 468.133 204.402 462.723 201.173L455.443 196.827L448.945 192.948C442.989 189.392 441.252 181.527 445.21 175.831C451.275 167.102 458.308 158.495 466.194 150.105C458.0719999999999 143.879 449.353 137.861 440.091 132.141C434.57 128.731 432.7099999999999 121.585 435.929 115.951L443.87 102.055C447.232 96.172 454.8049999999999 94.2289999999999 460.576 97.7789999999999C473.308 105.61 485.147 113.954 496.0189999999999 122.67C506.9359999999999 113.9089999999999 518.785 105.568 531.415 97.789C537.189 94.233 544.7679999999999 96.171 548.132 102.059L556.0759999999999 115.962C559.2889999999999 121.585 557.4459999999999 128.722 551.9409999999999 132.133A312.737 312.737 0 0 0 525.881 150.152C546.905 172.577 561.649 196.441 568.5939999999999 220.002H580C586.627 220.002 592 225.375 592 232.002V248.002C592 254.627 586.627 260 580 260z" />
+    <glyph glyph-name="laptop-code"
+      unicode="&#xF5FC;"
+      horiz-adv-x="640" d=" M255.03 186.35C261.28 180.1 271.4100000000001 180.1 277.6600000000001 186.35L288.9700000000001 197.66C295.2200000000001 203.91 295.2200000000001 214.04 288.9700000000001 220.29L253.25 256L288.96 291.7200000000001C295.21 297.9700000000001 295.21 308.1 288.96 314.35L277.65 325.66C271.4 331.91 261.27 331.91 255.02 325.66L196.68 267.32C190.43 261.07 190.43 250.94 196.68 244.69L255.03 186.35zM351.04 197.65L362.35 186.34C368.6 180.09 378.73 180.09 384.98 186.34L443.3200000000001 244.6800000000001C449.5700000000001 250.9300000000001 449.5700000000001 261.0600000000001 443.3200000000001 267.3100000000001L384.98 325.6500000000001C378.73 331.9000000000001 368.6 331.9000000000001 362.35 325.6500000000001L351.04 314.3400000000001C344.79 308.0900000000001 344.79 297.9600000000001 351.04 291.7100000000001L386.75 256L351.04 220.28C344.79 214.03 344.79 203.9 351.04 197.65zM624 32H381.54C380.8 12.19 366.8300000000001 0 348.8 0H288C269.31 0 254.98 17.47 255.23 32H16C7.2 32 0 24.8 0 16V0C0 -35.2 28.8 -64 64 -64H576C611.2 -64 640 -35.2 640 0V16C640 24.8 632.8 32 624 32zM576 400C576 426.4 554.4 448 528 448H112C85.6 448 64 426.4 64 400V64H576V400zM512 128H128V384H512V128z" />
+    <glyph glyph-name="laptop"
+      unicode="&#xF109;"
+      horiz-adv-x="640" d=" M624 32H381.54C380.8 12.19 366.8300000000001 0 348.8 0H288C269.31 0 254.98 17.47 255.23 32H16C7.2 32 0 24.8 0 16V0C0 -35.2 28.8 -64 64 -64H576C611.2 -64 640 -35.2 640 0V16C640 24.8 632.8 32 624 32zM576 400C576 426.4 554.4 448 528 448H112C85.6 448 64 426.4 64 400V64H576V400zM512 128H128V384H512V128z" />
+    <glyph glyph-name="laugh-beam"
+      unicode="&#xF59A;"
+      horiz-adv-x="496" d=" M248 440C111 440 0 329 0 192S111 -56 248 -56S496 55 496 192S385 440 248 440zM272 240.6C275.3 282.7 304.2 312 328 312S380.7 282.7 384 240.6C384.7 232 373.2 228.7 369.1 236.1L359.6 253.1C351.9000000000001 266.8 340.4000000000001 274.7 328.1 274.7S304.3 266.8 296.6 253.1L287.1 236.1C282.9000000000001 228.7 271.3 232 272 240.6zM112 240.6C115.3 282.7 144.2 312 168 312S220.7 282.7 224 240.6C224.7 232 213.2 228.7 209.1 236.1L199.6 253.1C191.9 266.8 180.4 274.7 168.1 274.7S144.3 266.8 136.6 253.1L127.1 236.1C122.8 228.7 111.3 232.1 112 240.6zM398.9 142C390 71 329.4 16 256 16H240C166.6 16 106 71 97.1 142C95.9 151.5 103.4 160 113 160H383C392.6 160 400.1 151.6 398.9 142z" />
+    <glyph glyph-name="laugh-squint"
+      unicode="&#xF59B;"
+      horiz-adv-x="496" d=" M248 440C111 440 0 329 0 192S111 -56 248 -56S496 55 496 192S385 440 248 440zM281.8 278.3L361.8 326.3C373.4000000000001 333.2000000000001 385.8 318.6 377.2 308.3L343.6 268L377.2000000000001 227.7C385.9000000000001 217.3 373.3000000000001 202.9 361.8000000000001 209.7L281.8000000000001 257.7C274.1000000000001 262.4 274.1000000000001 273.6 281.8000000000001 278.3zM118.8 308.3C110.2 318.6 122.6 333.2000000000001 134.2 326.3L214.2 278.3C222 273.6 222 262.4 214.2 257.7000000000001L134.2 209.7C122.7 202.9 110.2 217.3 118.8 227.7L152.4 268L118.8 308.3zM398.9 142C390 71 329.4 16 256 16H240C166.6 16 106 71 97.1 142C95.9 151.5 103.4 160 113 160H383C392.6 160 400.1 151.6 398.9 142z" />
+    <glyph glyph-name="laugh-wink"
+      unicode="&#xF59C;"
+      horiz-adv-x="496" d=" M248 440C111 440 0 329 0 192S111 -56 248 -56S496 55 496 192S385 440 248 440zM268.1 241.9C272.1 267.1 302.3 284 328 284S383.9 267.1 387.9 241.9C389.6 230.8 376.5 223.6 368.1 231.1L358.6 239.6C343.8 252.8 312.4 252.8 297.6 239.6L288 231C279.6 223.6 266.4 230.7 268.1 241.9zM168 288C185.7 288 200 273.7 200 256S185.7 224 168 224S136 238.3 136 256S150.3 288 168 288zM398.9 142C390 71 329.4 16 256 16H240C166.6 16 106 71 97.1 142C95.9 151.5 103.4 160 113 160H383C392.6 160 400.1 151.6 398.9 142z" />
+    <glyph glyph-name="laugh"
+      unicode="&#xF599;"
+      horiz-adv-x="496" d=" M248 440C111 440 0 329 0 192S111 -56 248 -56S496 55 496 192S385 440 248 440zM328 288C345.7 288 360 273.7 360 256S345.7 224 328 224S296 238.3 296 256S310.3 288 328 288zM168 288C185.7 288 200 273.7 200 256S185.7 224 168 224S136 238.3 136 256S150.3 288 168 288zM256 16H240C166.6 16 106 71 97.1 142C95.9 151.5 103.4 160 113 160H383C392.6 160 400.1 151.6 398.9 142C390 71 329.4 16 256 16z" />
+    <glyph glyph-name="layer-group"
+      unicode="&#xF5FD;"
+      horiz-adv-x="512" d=" M12.41 299.98L245.35 194.31C252.15 191.22 259.84 191.22 266.64 194.31L499.58 299.98C516.13 307.49 516.13 332.5 499.58 340.01L266.65 445.69A25.606999999999996 25.606999999999996 0 0 1 245.36 445.69L12.41 340.02C-4.14 332.51 -4.14 307.49 12.41 299.98zM499.59 211.7L441.5 238.03L279.86 164.76C272.3 161.33 264.2700000000001 159.59 256 159.59S239.71 161.33 232.14 164.76L70.51 238.03L12.41 211.7C-4.14 204.2 -4.14 179.2 12.41 171.7L245.35 66.11C252.15 63.03 259.84 63.03 266.64 66.11L499.59 171.7C516.14 179.2 516.14 204.2 499.59 211.7zM499.59 83.9L441.7200000000001 110.13L279.86 36.76C272.3 33.33 264.2700000000001 31.59 256 31.59S239.71 33.33 232.14 36.76L70.29 110.13L12.41 83.9C-4.14 76.4 -4.14 51.4 12.41 43.9L245.35 -61.69C252.15 -64.7700000000001 259.84 -64.7700000000001 266.64 -61.69L499.59 43.9C516.14 51.4 516.14 76.4 499.59 83.9z" />
+    <glyph glyph-name="leaf"
+      unicode="&#xF06C;"
+      horiz-adv-x="576" d=" M546.2 438.3C540.6 450.8 524.6 451.3 517.9000000000001 439.5C486.9 385.6 431.4 352 368 352H288C182 352 96 266 96 160C96 153 96.8 146.3 97.5 139.5C161.3 185.2 253.4 224 384 224C392.8 224 400 216.8 400 208S392.8 192 384 192C132.6 192 26 37.9 2.4 -20C-4.2 -36.3 3.6 -54.9 19.9 -61.6C36.3 -68.4 54.9 -60.5 61.7 -44.3C63.2 -40.7 82.6 3.6 133.6 46.3C166 2.4 227.6 -39.5000000000001 308.5 -30.9C465.5 -19.5 576 121.3 576 293.7C576 343.9 565.2 395.9 546.2 438.3z" />
+    <glyph glyph-name="lemon"
+      unicode="&#xF094;"
+      horiz-adv-x="512" d=" M489.038 425.037C465.944 448.13 434.648 453.93 413.947 441.871C355.041 407.5590000000001 232.697 494.948 92.874 355.125S40.441 92.959 6.129 34.055C-5.93 13.353 -0.131 -17.944 22.962 -41.038C46.057 -64.133 77.354 -69.929 98.057 -57.87C156.958 -23.56 279.303 -110.9489999999999 419.125 28.873S471.56 291.04 505.871 349.944C517.93 370.646 512.132 401.943 489.038 425.0370000000001zM243.881 352.478C185.692 337.931 110.073 262.323 95.523 204.12C93.706 196.85 87.181 191.996 80.012 191.996C78.728 191.996 77.422 192.152 76.119 192.477C67.547 194.621 62.335 203.307 64.477 211.88C81.901 281.573 166.316 366.07 236.119 383.522C244.694 385.665 253.38 380.453 255.522 371.88S252.453 354.621 243.881 352.478z" />
+    <glyph glyph-name="less-than-equal"
+      unicode="&#xF537;"
+      horiz-adv-x="448" d=" M54.98 233.8L356.3900000000001 113.93C374.7800000000001 107.9 395.1 116.47 401.7700000000001 133.08L413.86 163.16C420.54 179.77 411.04 198.13 392.6500000000001 204.16L217.21 272.21L392.7700000000001 340.3C411.0600000000001 346.3 420.5100000000001 364.57 413.8700000000001 381.09L401.8400000000001 411.01C395.2000000000001 427.54 374.9800000000001 436.07 356.6900000000001 430.07L54.98 310.11C41.21 305.5900000000001 32 293.5 32 279.93V263.9700000000001C32 250.41 41.21 238.32 54.98 233.8zM424 48H24C10.75 48 0 37.26 0 24V-24C0 -37.25 10.75 -48 24 -48H424C437.25 -48 448 -37.25 448 -24V24C448 37.26 437.25 48 424 48z" />
+    <glyph glyph-name="less-than"
+      unicode="&#xF536;"
+      horiz-adv-x="384" d=" M365.46 90.26L147.04 192.11L365.51 293.99C381.53 301.4600000000001 388.46 320.5 380.99 336.52L367.4700000000001 365.52C360 381.54 340.96 388.4700000000001 324.94 381L18.48 238.09A32.014 32.014 0 0 1 0 209.09V174.85C0 162.41 7.21 151.1 18.48 145.85L324.79 3.02C340.85 -4.47 359.94 2.48 367.43 18.54L380.99 47.62C388.48 63.68 381.5300000000001 82.7699999999999 365.4600000000001 90.2599999999999z" />
+    <glyph glyph-name="level-down-alt"
+      unicode="&#xF3BE;"
+      horiz-adv-x="320" d=" M313.553 55.669L209.587 -56.334C200.102 -66.548 183.911 -66.563 174.413 -56.334L70.438 55.669C56.232 70.969 67.062 96 88.025 96H152V368H68.024A11.996 11.996 0 0 0 59.539 371.515L3.539 427.515C-4.021 435.074 1.333 448 12.024 448H208C221.255 448 232 437.255 232 424V96H295.966C316.844 96 327.817 71.031 313.553 55.669z" />
+    <glyph glyph-name="level-up-alt"
+      unicode="&#xF3BF;"
+      horiz-adv-x="320" d=" M313.553 328.331L209.587 440.334C200.102 450.548 183.911 450.563 174.413 440.334L70.438 328.331C56.232 313.031 67.062 288 88.025 288H152V16H68.024A11.996 11.996 0 0 1 59.539 12.485L3.539 -43.515C-4.021 -51.074 1.333 -64 12.024 -64H208C221.255 -64 232 -53.255 232 -40V288H295.966C316.844 288 327.817 312.969 313.553 328.331z" />
+    <glyph glyph-name="life-ring"
+      unicode="&#xF1CD;"
+      horiz-adv-x="512" d=" M256 440C119.033 440 8 328.967 8 192S119.033 -56 256 -56S504 55.033 504 192S392.967 440 256 440zM429.6960000000001 320.4410000000001L366.2970000000001 257.0420000000001C355.31 275.601 339.627 291.294 321.0420000000001 302.2970000000001L384.4410000000001 365.6960000000001A218.396 218.396 0 0 0 429.6960000000001 320.4410000000001zM256 96C202.981 96 160 138.981 160 192S202.981 288 256 288S352 245.019 352 192S309.019 96 256 96zM127.559 365.6960000000001L190.958 302.2970000000001C172.399 291.31 156.706 275.627 145.703 257.0420000000001L82.304 320.4410000000001A218.372 218.372 0 0 0 127.559 365.6960000000001zM82.304 63.559L145.703 126.958C156.69 108.399 172.373 92.706 190.958 81.703L127.559 18.304A218.396 218.396 0 0 0 82.304 63.559zM384.4410000000001 18.304L321.0420000000001 81.703C339.6010000000001 92.6900000000001 355.2940000000001 108.3730000000001 366.2970000000001 126.958L429.6960000000001 63.559A218.40299999999996 218.40299999999996 0 0 0 384.4410000000001 18.304z" />
+    <glyph glyph-name="lightbulb"
+      unicode="&#xF0EB;"
+      horiz-adv-x="352" d=" M96.06 -6.35C96.07 -12.64 97.93 -18.8 101.42 -24.04L118.51 -49.73A31.989999999999995 31.989999999999995 0 0 1 145.15 -64.01H206.86A31.989999999999995 31.989999999999995 0 0 1 233.5 -49.73L250.59 -24.04A31.989000000000004 31.989000000000004 0 0 1 255.95 -6.35L255.99 32H96.01L96.06 -6.35zM0 272C0 227.63 16.45 187.15 43.56 156.22C60.08 137.37 85.92 97.99 95.77 64.77C95.81 64.5100000000001 95.84 64.2500000000001 95.88 63.9900000000001H256.12C256.1600000000001 64.2500000000001 256.19 64.5000000000001 256.23 64.77C266.0800000000001 97.99 291.92 137.37 308.44 156.22C335.55 187.15 352 227.63 352 272C352 369.39 272.9100000000001 448.3 175.45 448C73.44 447.69 0 365.03 0 272zM176 352C131.89 352 96 316.11 96 272C96 263.16 88.84 256 80 256S64 263.16 64 272C64 333.76 114.24 384 176 384C184.84 384 192 376.8400000000001 192 368S184.84 352 176 352z" />
+    <glyph glyph-name="link"
+      unicode="&#xF0C1;"
+      horiz-adv-x="512" d=" M326.612 262.6090000000001C386.3590000000001 202.8 385.5390000000001 106.911 326.9720000000001 48.019C326.862 47.899 326.7320000000001 47.769 326.612 47.649L259.4120000000001 -19.551C200.142 -78.821 103.713 -78.813 44.452 -19.551C-14.818 39.709 -14.818 136.149 44.452 195.409L81.558 232.515C91.398 242.355 108.344 235.8150000000001 108.852 221.909C109.5 204.187 112.678 186.382 118.542 169.1880000000001C120.528 163.3660000000001 119.109 156.9260000000001 114.759 152.576L101.672 139.489C73.646 111.463 72.767 65.8290000000001 100.517 37.5290000000001C128.541 8.95 174.603 8.78 202.842 37.0190000000001L270.0420000000001 104.2090000000001C298.233 132.4 298.115 177.9660000000001 270.0420000000001 206.0390000000001C266.341 209.733 262.6130000000001 212.603 259.701 214.608A16.037 16.037 0 0 0 252.754 227.2140000000001C252.3580000000001 237.7810000000001 256.1020000000001 248.67 264.452 257.0200000000001L285.506 278.0750000000001C291.027 283.596 299.688 284.2740000000001 306.09 279.8060000000001A152.482 152.482 0 0 0 326.612 262.6090000000001zM467.547 403.551C408.286 462.813 311.8570000000001 462.821 252.587 403.551L185.387 336.351C185.267 336.231 185.137 336.101 185.027 335.981C126.461 277.089 125.64 181.2 185.387 121.391A152.454 152.454 0 0 1 205.908 104.1949999999999C212.31 99.7269999999999 220.972 100.406 226.492 105.9259999999999L247.546 126.981C255.896 135.331 259.6400000000001 146.2199999999999 259.2440000000001 156.7869999999999A16.037 16.037 0 0 1 252.2970000000001 169.3929999999999C249.385 171.3979999999999 245.6570000000001 174.2679999999999 241.956 177.9619999999999C213.883 206.035 213.765 251.601 241.956 279.7919999999999L309.156 346.9819999999999C337.395 375.2209999999999 383.456 375.051 411.481 346.4719999999999C439.231 318.1719999999999 438.353 272.5379999999999 410.326 244.5119999999999L397.2390000000001 231.425C392.889 227.075 391.4700000000001 220.6349999999999 393.456 214.8129999999999C399.32 197.619 402.498 179.8139999999999 403.146 162.0919999999999C403.6550000000001 148.1859999999999 420.6 141.6459999999999 430.44 151.4859999999999L467.546 188.5919999999999C526.817 247.851 526.817 344.291 467.547 403.551z" />
+    <glyph glyph-name="lira-sign"
+      unicode="&#xF195;"
+      horiz-adv-x="384" d=" M371.9940000000001 192H323.975C317.64 192 312 187.088 312 180.754C312 80 230.179 32 144 32V191.219L278.603 221.131A12 12 0 0 1 288 232.845V273.8210000000001C288 281.498 280.891 287.201 273.397 285.535L144 256.781V287.2200000000001L278.603 317.132A12 12 0 0 1 288 328.846V369.8210000000001C288 377.498 280.891 383.201 273.397 381.535L144 352.781V404C144 410.627 138.627 416 132 416H76C69.373 416 64 410.627 64 404V335.003L9.397 322.8690000000001A12 12 0 0 1 0 311.155V270.179C0 262.502 7.109 256.799 14.603 258.4650000000001L64 269.442V239.003L9.397 226.869A12 12 0 0 1 0 215.155V174.179C0 166.502 7.109 160.799 14.603 162.465L64 173.442V-20C64 -26.627 69.373 -32 76 -32H155.583C289.674 -32 378.838 45.834 383.991 179.592C384.252 186.374 378.78 192 371.994 192z" />
+    <glyph glyph-name="list-alt"
+      unicode="&#xF022;"
+      horiz-adv-x="512" d=" M464 -32H48C21.49 -32 0 -10.51 0 16V368C0 394.51 21.49 416 48 416H464C490.51 416 512 394.51 512 368V16C512 -10.51 490.51 -32 464 -32zM128 328C105.909 328 88 310.091 88 288S105.909 248 128 248S168 265.909 168 288S150.091 328 128 328zM128 232C105.909 232 88 214.091 88 192S105.909 152 128 152S168 169.909 168 192S150.091 232 128 232zM128 136C105.909 136 88 118.091 88 96S105.909 56 128 56S168 73.909 168 96S150.091 136 128 136zM416 272V304C416 310.627 410.627 316 404 316H204C197.373 316 192 310.627 192 304V272C192 265.373 197.373 260 204 260H404C410.627 260 416 265.373 416 272zM416 176V208C416 214.627 410.627 220 404 220H204C197.373 220 192 214.627 192 208V176C192 169.373 197.373 164 204 164H404C410.627 164 416 169.373 416 176zM416 80V112C416 118.627 410.627 124 404 124H204C197.373 124 192 118.627 192 112V80C192 73.373 197.373 68 204 68H404C410.627 68 416 73.373 416 80z" />
+    <glyph glyph-name="list-ol"
+      unicode="&#xF0CB;"
+      horiz-adv-x="512" d=" M3.263 308.473C3.263 315.9500000000001 7.18 320.045 14.836 320.045H29.967V359.9220000000001C29.967 365.085 30.501 370.425 30.501 370.425H30.145S28.366 367.755 27.297 366.687C22.846 362.414 16.793 362.236 11.631 367.755L6.113 373.986C0.771 379.327 1.129 385.202 6.647 390.365L28.367 410.303C32.815 414.398 36.732 416 42.785 416H54.89C62.546 416 66.639 412.084 66.639 404.428V320.044H82.127C89.782 320.044 93.699 315.95 93.699 308.472V299.5710000000001C93.699 292.094 89.782 287.999 82.127 287.999H14.836C7.18 287.999 3.263 292.094 3.263 299.5710000000001V308.473zM2.211 143.409C2.211 190.687 53.166 199.792 53.166 212.574C53.166 219.754 47.212 221.329 43.886 221.329C40.733 221.329 37.407 220.278 34.431 217.477C29.352 212.574 23.924 210.473 18.32 215.026L9.741 221.855C3.962 226.408 2.561 231.6600000000001 6.938 237.264C13.592 246.019 26.025 256 47.387 256C66.824 256 91.863 245.494 91.863 216.427C91.863 178.08 45.11 170.025 43.184 159.518H82.233C89.762 159.518 93.79 155.491 93.79 148.136V139.381C93.79 132.027 89.762 127.999 82.233 127.999H14.293C7.288 127.999 2.21 132.027 2.21 139.381V143.409zM5.654 -6.61L11.257 2.67C15.11 9.324 20.362 9.674 26.841 5.8219999999999C31.744 3.7209999999999 36.471 2.67 41.2 2.67C51.355 2.67 55.558 6.172 55.558 10.9C55.558 17.554 49.954 20.006 39.624 20.006H34.896C28.942 20.006 25.616 22.107 22.638 27.886L21.588 29.812C19.137 34.54 20.362 39.6179999999999 24.389 44.696L29.993 51.7C36.822 60.105 42.25 65.183 42.25 65.183V65.533S38.047 64.482 29.642 64.482H16.685C9.155 64.482 5.302 68.5100000000001 5.302 75.864V84.619C5.302 92.149 9.155 96.001 16.685 96.001H75.169C82.698 96.001 86.551 91.9740000000001 86.551 84.619V81.292C86.551 75.514 85.15 71.4860000000001 81.472 67.109L63.963 46.972C83.574 41.8940000000001 92.679 26.485 92.679 12.1270000000001C92.679 -9.2359999999999 78.321 -31.9989999999999 44.176 -31.9989999999999C27.54 -31.9989999999999 15.984 -27.2709999999999 8.28 -22.5439999999999C2.501 -18.3419999999999 1.976 -12.7389999999999 5.654 -6.6099999999999zM144 316H496C504.837 316 512 323.163 512 332V372C512 380.837 504.837 388 496 388H144C135.163 388 128 380.837 128 372V332C128 323.163 135.163 316 144 316zM144 156H496C504.837 156 512 163.163 512 172V212C512 220.837 504.837 228 496 228H144C135.163 228 128 220.837 128 212V172C128 163.163 135.163 156 144 156zM144 -4H496C504.837 -4 512 3.163 512 12V52C512 60.837 504.837 68 496 68H144C135.163 68 128 60.837 128 52V12C128 3.163 135.163 -4 144 -4z" />
+    <glyph glyph-name="list-ul"
+      unicode="&#xF0CA;"
+      horiz-adv-x="512" d=" M96 352C96 325.49 74.51 304 48 304S0 325.49 0 352S21.49 400 48 400S96 378.51 96 352zM48 240C21.49 240 0 218.51 0 192S21.49 144 48 144S96 165.49 96 192S74.51 240 48 240zM48 80C21.49 80 0 58.51 0 32S21.49 -16 48 -16S96 5.49 96 32S74.51 80 48 80zM144 316H496C504.837 316 512 323.163 512 332V372C512 380.837 504.837 388 496 388H144C135.163 388 128 380.837 128 372V332C128 323.163 135.163 316 144 316zM144 156H496C504.837 156 512 163.163 512 172V212C512 220.837 504.837 228 496 228H144C135.163 228 128 220.837 128 212V172C128 163.163 135.163 156 144 156zM144 -4H496C504.837 -4 512 3.163 512 12V52C512 60.837 504.837 68 496 68H144C135.163 68 128 60.837 128 52V12C128 3.163 135.163 -4 144 -4z" />
+    <glyph glyph-name="list"
+      unicode="&#xF03A;"
+      horiz-adv-x="512" d=" M128 332V372C128 380.837 135.163 388 144 388H496C504.837 388 512 380.837 512 372V332C512 323.163 504.837 316 496 316H144C135.163 316 128 323.163 128 332zM144 156H496C504.837 156 512 163.163 512 172V212C512 220.837 504.837 228 496 228H144C135.163 228 128 220.837 128 212V172C128 163.163 135.163 156 144 156zM144 -4H496C504.837 -4 512 3.163 512 12V52C512 60.837 504.837 68 496 68H144C135.163 68 128 60.837 128 52V12C128 3.163 135.163 -4 144 -4zM16 304H80C88.837 304 96 311.163 96 320V384C96 392.837 88.837 400 80 400H16C7.163 400 0 392.837 0 384V320C0 311.163 7.163 304 16 304zM16 144H80C88.837 144 96 151.163 96 160V224C96 232.837 88.837 240 80 240H16C7.163 240 0 232.837 0 224V160C0 151.163 7.163 144 16 144zM16 -16H80C88.837 -16 96 -8.837 96 0V64C96 72.837 88.837 80 80 80H16C7.163 80 0 72.837 0 64V0C0 -8.837 7.163 -16 16 -16z" />
+    <glyph glyph-name="location-arrow"
+      unicode="&#xF124;"
+      horiz-adv-x="512" d=" M444.52 444.48L28.74 252.58C-19.23 230.19 -3.24 159.8300000000001 47.93 159.8300000000001H223.84V-16.0799999999999C223.84 -67.2499999999999 294.2 -83.2499999999999 316.5900000000001 -35.2699999999999L508.49 380.5100000000001C524.48 418.9000000000001 482.9 460.48 444.52 444.48z" />
+    <glyph glyph-name="lock-open"
+      unicode="&#xF3C1;"
+      horiz-adv-x="576" d=" M423.5 448C339.5 447.7 272 378.5 272 294.5V224H48C21.5 224 0 202.5 0 176V-16C0 -42.5 21.5 -64 48 -64H400C426.5 -64 448 -42.5 448 -16V176C448 202.5 426.5 224 400 224H352V295.1C352 334.7 383.7 367.6 423.3 368C463.3 368.4 496 335.9 496 296V216C496 202.7 506.7 192 520 192H552C565.3 192 576 202.7 576 216V296C576 380 507.5 448.3 423.5 448z" />
+    <glyph glyph-name="lock"
+      unicode="&#xF023;"
+      horiz-adv-x="448" d=" M400 224H376V296C376 379.8 307.8 448 224 448S72 379.8 72 296V224H48C21.5 224 0 202.5 0 176V-16C0 -42.5 21.5 -64 48 -64H400C426.5 -64 448 -42.5 448 -16V176C448 202.5 426.5 224 400 224zM296 224H152V296C152 335.7 184.3 368 224 368S296 335.7 296 296V224z" />
+    <glyph glyph-name="long-arrow-alt-down"
+      unicode="&#xF309;"
+      horiz-adv-x="256" d=" M168 102.059V404C168 410.627 162.627 416 156 416H100C93.373 416 88 410.627 88 404V102.059H41.941C20.559 102.059 9.851 76.208 24.97 61.088L111.029 -24.971C120.402 -34.344 135.598 -34.344 144.97 -24.971L231.029 61.088C246.148 76.207 235.44 102.059 214.058 102.059H168z" />
+    <glyph glyph-name="long-arrow-alt-left"
+      unicode="&#xF30A;"
+      horiz-adv-x="448" d=" M134.059 152H436C442.627 152 448 157.373 448 164V220C448 226.627 442.627 232 436 232H134.059V278.059C134.059 299.4410000000001 108.208 310.149 93.088 295.03L7.029 208.971C-2.344 199.598 -2.344 184.402 7.029 175.03L93.088 88.971C108.207 73.852 134.059 84.56 134.059 105.942V152z" />
+    <glyph glyph-name="long-arrow-alt-right"
+      unicode="&#xF30B;"
+      horiz-adv-x="448" d=" M313.941 232H12C5.373 232 0 226.627 0 220V164C0 157.373 5.373 152 12 152H313.941V105.941C313.941 84.559 339.792 73.851 354.912 88.97L440.971 175.029C450.344 184.402 450.344 199.598 440.971 208.97L354.9120000000001 295.029C339.793 310.148 313.9410000000001 299.44 313.9410000000001 278.058V232z" />
+    <glyph glyph-name="long-arrow-alt-up"
+      unicode="&#xF30C;"
+      horiz-adv-x="256" d=" M88 281.9410000000001V-20C88 -26.627 93.373 -32 100 -32H156C162.627 -32 168 -26.627 168 -20V281.9410000000001H214.059C235.441 281.9410000000001 246.149 307.7920000000001 231.03 322.9120000000001L144.971 408.971C135.598 418.344 120.402 418.344 111.03 408.971L24.971 322.9120000000001C9.852 307.793 20.56 281.9410000000001 41.942 281.9410000000001H88z" />
+    <glyph glyph-name="low-vision"
+      unicode="&#xF2A8;"
+      horiz-adv-x="576" d=" M569.344 216.369C512.96 312.051 407.81 376 288 376C259.532 376 231.898 372.381 205.549 365.591L152.778 437.76C145.177 448.618 130.214 451.26 119.355 443.6600000000001L106.241 434.482C95.381 426.881 92.739 411.916 100.341 401.056L143.472 342.661C89.449 316.27 40.228 273.317 6.682 216.419C6.672 216.402 6.659 216.386 6.648 216.369C-2.117 201.494 -2.316 182.841 6.648 167.63C45.148 102.298 106.39 51.768 179.507 26.281L55.316 203.698A272.194 272.194 0 0 0 83.61 239.61L203.01 69.03H203.02L243.65 10.99A330.055 330.055 0 0 1 322.59 9.8199999999999L132.61 281.2199999999999A277.628 277.628 0 0 0 171.387 302.7829999999999L423.223 -53.7610000000001C430.824 -64.619 445.7870000000001 -67.2600000000001 456.646 -59.6610000000001L469.76 -50.4830000000001C480.62 -42.8820000000001 483.262 -27.9160000000001 475.66 -17.0570000000001L432.54 41.3199999999999L432.533 41.329C489.694 69.307 537.3679999999999 113.369 569.343 167.63A47.937999999999995 47.937999999999995 0 0 1 569.3439999999999 216.369zM390.026 102.06L370.9600000000001 129.29C395.6420000000001 161.857 398.6710000000001 205.643 379.7600000000001 240.9700000000001V240.9400000000001C379.7600000000001 217.29 360.5900000000001 198.12 336.9400000000001 198.12C313.1120000000001 198.12 294.1200000000001 217.469 294.1200000000001 240.9400000000001C294.1200000000001 264.5900000000001 313.2900000000001 283.76 336.9400000000001 283.76H336.9700000000001C312.2200000000001 297.009 283.4480000000001 299.403 257.4600000000001 291.4400000000001L238.392 318.677C253.758 324.694 270.488 328 288 328C363.1620000000001 328 424 267.174 424 192C424 157.496 411.167 126.025 390.026 102.06z" />
+    <glyph glyph-name="luggage-cart"
+      unicode="&#xF59D;"
+      horiz-adv-x="640" d=" M224 128H256V352H224C206.33 352 192 337.67 192 320V160C192 142.33 206.33 128 224 128zM576 160V320C576 337.67 561.67 352 544 352H512V128H544C561.67 128 576 142.33 576 160zM624 64H128V432C128 440.84 120.84 448 112 448H16C7.16 448 0 440.84 0 432V400C0 391.16 7.16 384 16 384H64V16C64 7.16 71.16 0 80 0H162.94C161.15 -5.03 160 -10.36 160 -16C160 -42.51 181.49 -64 208 -64S256 -42.51 256 -16C256 -10.36 254.85 -5.03 253.06 0H450.94C449.15 -5.03 448 -10.36 448 -16C448 -42.51 469.49 -64 496 -64S544 -42.51 544 -16C544 -10.36 542.85 -5.03 541.06 0H624C632.84 0 640 7.16 640 16V48C640 56.84 632.84 64 624 64zM480 352V400C480 426.51 458.51 448 432 448H336C309.49 448 288 426.51 288 400V128H480V352zM432 352H336V400H432V352z" />
+    <glyph glyph-name="magic"
+      unicode="&#xF0D0;"
+      horiz-adv-x="512" d=" M224 352L240 384L272 400L240 416L224 448L208 416L176 400L208 384L224 352zM80 288L106.66 341.33L160 368L106.66 394.67L80 448L53.34 394.67L0 368L53.34 341.33L80 288zM432 160L405.34 106.67L352 80L405.3400000000001 53.33L432 0L458.66 53.33L512 80L458.66 106.67L432 160zM502.62 353.77L417.77 438.62C411.53 444.88 403.34 448 395.15 448C386.96 448 378.77 444.88 372.52 438.62L9.38 75.48C-3.12 62.98 -3.12 42.72 9.38 30.23L94.23 -54.62C100.48 -60.87 108.67 -63.99 116.85 -63.99C125.04 -63.99 133.23 -60.87 139.48 -54.62L502.62 308.53C515.12 321.01 515.12 341.28 502.62 353.77zM359.45 244.54L308.54 295.45L395.14 382.05L446.05 331.14L359.45 244.54z" />
+    <glyph glyph-name="magnet"
+      unicode="&#xF076;"
+      horiz-adv-x="512.1" d=" M164.1 288H12C5.4 288 0 293.4 0 300V380C0 399.9 16.1 416 36 416H140C159.9 416 176 399.9 176 380V300C176.1 293.4 170.7 288 164.1 288zM512.1 300V380.1C512.1 400 496 416.1 476.1 416.1H372.1C352.2000000000001 416.1 336.1 400 336.1 380.1V300.1C336.1 293.5 341.5 288.1 348.1 288.1H500.1C506.7 288 512.1 293.4 512.1 300zM348.1 256C341.5 256 336.1 250.6 336.1 244V192C336.1 63.9 176.1 64.1 176.1 192V244C176.1 250.6 170.7 256 164.1 256H12.1C5.4 256 0.1 250.6 0.1 243.9C0.2 222.5 0.7 203.6 0.1 190.6C0.1 40 136.3 -56 256.9 -56C377.5 -56 512 40 512 190.7C511.4 203.5 511.8 223.7 512 243.9C512 250.6 506.7 256 500 256H348.1z" />
+    <glyph glyph-name="mail-bulk"
+      unicode="&#xF674;"
+      horiz-adv-x="576" d=" M160 0C134.4 0 108.8 22.4 96 32C32 76.8 12.8 92.8 0 102.4V-32C0 -49.67 14.33 -64 32 -64H288C305.67 -64 320 -49.67 320 -32V102.4C307.2 92.8 288 76.8 224 32C211.2 22.4 185.6 0 160 0zM288 192H32C14.33 192 0 177.67 0 160V144C25.6 124.8 22.4 124.8 115.2 57.6C124.8 51.2 144 32 160 32S195.2 51.2 204.8 54.4C297.6 121.6 294.4 121.6 320 140.8V160C320 177.67 305.67 192 288 192zM544 288H224C206.33 288 192 273.67 192 256V224H288C321.21 224 348.5900000000001 198.58 351.71 166.18L352 166.4V32H544C561.67 32 576 46.33 576 64V256C576 273.67 561.67 288 544 288zM512 160H448V224H512V160zM160 256C160 291.29 188.71 320 224 320H448V416C448 433.67 433.67 448 416 448H96C78.33 448 64 433.67 64 416V224H160V256z" />
+    <glyph glyph-name="male"
+      unicode="&#xF183;"
+      horiz-adv-x="192" d=" M96 448C131.346 448 160 419.346 160 384S131.346 320 96 320S32 348.654 32 384S60.654 448 96 448M144 304H132.64C109.929 293.557 83.05 293.106 59.36 304H48C21.49 304 0 282.51 0 256V120C0 106.745 10.745 96 24 96H40V-40C40 -53.255 50.745 -64 64 -64H128C141.255 -64 152 -53.255 152 -40V96H168C181.255 96 192 106.745 192 120V256C192 282.51 170.51 304 144 304z" />
+    <glyph glyph-name="map-marked-alt"
+      unicode="&#xF5A0;"
+      horiz-adv-x="576" d=" M288 448C218.41 448 162 391.5900000000001 162 322C162 265.74 244.35 163.2 275.9 125.98C282.29 118.44 293.72 118.44 300.1 125.98C331.65 163.2 414 265.74 414 322C414 391.5900000000001 357.59 448 288 448zM288 280C264.8 280 246 298.8 246 322S264.8 364 288 364S330 345.2 330 322S311.2 280 288 280zM20.12 232.05A32.006 32.006 0 0 1 0 202.34V-47.98C0 -59.3 11.43 -67.04 21.94 -62.84L160 0V233.08C151.16 249.06 143.93 264.62 138.75 279.5L20.12 232.05zM288 88.33C273.93 88.33 260.62 94.51 251.49 105.29C231.83 128.49 210.92 154.91 192 182.01V0.01L384 -63.99V182C365.08 154.91 344.18 128.48 324.51 105.28C315.38 94.51 302.07 88.33 288 88.33zM554.06 286.84L416 224V-64L555.88 -8.05A31.996 31.996 0 0 1 576 21.66V271.98C576 283.3 564.57 291.04 554.06 286.84z" />
+    <glyph glyph-name="map-marked"
+      unicode="&#xF59F;"
+      horiz-adv-x="576" d=" M288 448C218.41 448 162 391.5900000000001 162 322C162 265.74 244.35 163.2 275.9 125.98C282.29 118.44 293.72 118.44 300.1 125.98C331.65 163.2 414 265.74 414 322C414 391.5900000000001 357.59 448 288 448zM20.12 232.05A32.006 32.006 0 0 1 0 202.34V-47.98C0 -59.3 11.43 -67.04 21.94 -62.84L160 0V233.08C151.16 249.06 143.93 264.62 138.75 279.5L20.12 232.05zM288 88.33C273.93 88.33 260.62 94.51 251.49 105.29C231.83 128.49 210.92 154.91 192 182.01V0.01L384 -63.99V182C365.08 154.91 344.18 128.48 324.51 105.28C315.38 94.51 302.07 88.33 288 88.33zM554.06 286.84L416 224V-64L555.88 -8.05A31.996 31.996 0 0 1 576 21.66V271.98C576 283.3 564.57 291.04 554.06 286.84z" />
+    <glyph glyph-name="map-marker-alt"
+      unicode="&#xF3C5;"
+      horiz-adv-x="384" d=" M172.268 -53.67C26.97 156.969 0 178.587 0 256C0 362.039 85.961 448 192 448S384 362.039 384 256C384 178.587 357.03 156.969 211.732 -53.67C202.197 -67.444 181.802 -67.443 172.268 -53.67zM192 176C236.183 176 272 211.817 272 256S236.183 336 192 336S112 300.183 112 256S147.817 176 192 176z" />
+    <glyph glyph-name="map-marker"
+      unicode="&#xF041;"
+      horiz-adv-x="384" d=" M172.268 -53.67C26.97 156.969 0 178.587 0 256C0 362.039 85.961 448 192 448S384 362.039 384 256C384 178.587 357.03 156.969 211.732 -53.67C202.197 -67.444 181.802 -67.443 172.268 -53.67z" />
+    <glyph glyph-name="map-pin"
+      unicode="&#xF276;"
+      horiz-adv-x="288" d=" M112 131.06V-25.63L134.02 -58.65C138.77 -65.77 149.24 -65.77 153.99 -58.65L176 -25.63V131.06C165.61 129.14 154.94 128 144 128S122.39 129.14 112 131.06zM144 448C64.47 448 0 383.53 0 304S64.47 160 144 160S288 224.47 288 304S223.53 448 144 448zM144 372C106.5 372 76 341.5 76 304C76 297.38 70.62 292 64 292S52 297.38 52 304C52 354.73 93.28 396 144 396C150.62 396 156 390.62 156 384S150.62 372 144 372z" />
+    <glyph glyph-name="map-signs"
+      unicode="&#xF277;"
+      horiz-adv-x="512" d=" M507.31 363.31L464 406.63C458 412.63 449.86 416 441.37 416H288V432C288 440.84 280.84 448 272 448H240C231.16 448 224 440.84 224 432V416H56C42.75 416 32 405.25 32 392V312C32 298.75 42.75 288 56 288H441.37C449.86 288 457.99 291.37 464 297.37L507.31 340.68C513.56 346.94 513.56 357.06 507.31 363.31zM224 -48C224 -56.84 231.16 -64 240 -64H272C280.84 -64 288 -56.84 288 -48V64H224V-48zM456 224H288V256H224V224H70.63C62.14 224 54.01 220.63 48 214.63L4.69 171.31C-1.56 165.06 -1.56 154.93 4.69 148.68L48 105.37C54 99.37 62.14 96 70.63 96H456C469.25 96 480 106.75 480 120V200C480 213.25 469.25 224 456 224z" />
+    <glyph glyph-name="map"
+      unicode="&#xF279;"
+      horiz-adv-x="576" d=" M0 330.3400000000001V-15.98C0 -27.3 11.43 -35.04 21.94 -30.84L160 32V416L20.12 360.05A32.006 32.006 0 0 1 0 330.3400000000001zM192 32L384 -32V352L192 416V32zM554.06 414.8400000000001L416 352V-32L555.88 23.95A31.996 31.996 0 0 1 576 53.66V399.98C576 411.3 564.57 419.04 554.06 414.84z" />
+    <glyph glyph-name="marker"
+      unicode="&#xF5A1;"
+      horiz-adv-x="512" d=" M93.95 157.97A327.038 327.038 0 0 1 0.17 -37.11L0.14 -37.34C-1.56 -52.62 11.35 -65.5400000000001 26.63 -63.85A327.02000000000004 327.02000000000004 0 0 1 221.97 29.95L297.37 105.35L169.35 233.3700000000001L93.95 157.97zM485.49 421.49C450.14 456.84 392.82 456.84 357.4700000000001 421.49L335.7100000000001 399.73L299.1500000000001 436.28C283.5300000000001 451.9 258.2000000000001 451.9 242.5900000000001 436.28L138.47 332.16C132.22 325.91 132.22 315.78 138.47 309.53L161.09 286.91C167.34 280.66 177.47 280.66 183.72 286.91L270.87 374.06L290.46 354.4700000000001L191.98 256L320 127.98L485.49 293.4700000000001C520.84 328.8200000000001 520.84 386.13 485.49 421.49z" />
+    <glyph glyph-name="mars-double"
+      unicode="&#xF227;"
+      horiz-adv-x="512" d=" M340 448H261C250.3 448 245 435.1 252.5 427.5L269.4 410.6L220.7 361.9C198.5 375.9 172.2 384 144 384C64.5 384 0 319.5 0 240S64.5 96 144 96S288 160.5 288 240C288 268.2 279.9 294.5 265.9 316.7L314.6 365.4L331.5 348.5C333.8999999999999 346.1 337 345 339.8999999999999 345C346.0999999999999 345 352 349.8 352 357V436C352 442.6 346.6 448 340 448zM144 160C99.9 160 64 195.9 64 240S99.9 320 144 320S224 284.1 224 240S188.1 160 144 160zM500 288.1H421C410.3 288.1 405 275.2 412.5 267.6L429.4 250.7L380.7 202C362.5 213.4 341.7 220.9 319.2 223.3C317.1 201.5 311 180 300.8 160C301.9000000000001 160 303 160.1 304 160.1C348.1 160.1 384 124.2000000000001 384 80.1S348.1 0.1 304 0.1S224 36 224 80.1C224 81.2000000000001 224 82.3 224.1 83.3C204.1 73.1 182.6 66.9 160.8 64.9C168.4 -7.6 229.6 -64 304 -64C383.5 -64 448 0.5 448 80C448 108.2 439.9 134.5 425.9 156.7L474.6 205.4L491.4999999999999 188.5C493.8999999999999 186.1 496.8999999999999 185 499.8999999999999 185C506.0999999999999 185 511.9999999999999 189.8 511.9999999999999 197V276C511.9999999999999 282.7 506.6 288.1 499.9999999999999 288.1z" />
+    <glyph glyph-name="mars-stroke-h"
+      unicode="&#xF22B;"
+      horiz-adv-x="480" d=" M476.2 200.5L420.3 256.4C412.7 264 399.8 258.6 399.8 247.9V224H376V244C376 250.6 370.6 256 364 256H324C317.4 256 312 250.6 312 244V224H284.4C278.6 249.6 265.7 273.9 245.8 293.8C189.6 350 98.4 350 42.2 293.8C-14 237.6 -14 146.4 42.2 90.2000000000001C98.4 34.0000000000001 189.6 34.0000000000001 245.8 90.2000000000001C265.7 110.1 278.6 134.4 284.4000000000001 160.0000000000001H312V140.0000000000001C312 133.4 317.4 128.0000000000001 324 128.0000000000001H364C370.6 128.0000000000001 376 133.4 376 140.0000000000001V160.0000000000001H399.9V136.1000000000001C399.9 125.4000000000001 412.8 120.1000000000001 420.4 127.6000000000001L476.3 183.5000000000001C480.9 188.2000000000001 480.9 195.8000000000001 476.1999999999999 200.5000000000001zM200.6 135.4C169.4 104.2 118.7 104.2 87.5 135.4C56.3 166.6 56.3 217.3 87.5 248.5C118.7 279.7 169.4 279.7 200.6 248.5C231.8 217.4 231.8 166.6 200.6 135.4z" />
+    <glyph glyph-name="mars-stroke-v"
+      unicode="&#xF22A;"
+      horiz-adv-x="288" d=" M245.8 213.8C225.9 233.7 201.6 246.6 176 252.4V277.8H196C202.6 277.8 208 283.2000000000001 208 289.8V329.8C208 336.4 202.6 341.8 196 341.8H176V366.6H199.9C210.6 366.6 215.9 379.5 208.4 387.1L152.5 442.9C147.8 447.6 140.2 447.6 135.5 442.9L79.6 387C72 379.4 77.4 366.5 88.1 366.5H112V341.8H92C85.4 341.8 80 336.4 80 329.8V289.8C80 283.2000000000001 85.4 277.8 92 277.8H112V252.4C86.4 246.6 62.1 233.7 42.2 213.8C-14 157.6 -14 66.4 42.2 10.2C98.4 -45.9999999999999 189.6 -45.9999999999999 245.8 10.2C302.1 66.4 302.1 157.6 245.8 213.8000000000001zM200.6 55.4C169.4 24.2 118.7 24.2 87.5 55.4C56.3 86.6 56.3 137.3 87.5 168.5C118.7 199.7 169.4 199.7 200.6 168.5C231.8 137.4 231.8 86.6 200.6 55.4z" />
+    <glyph glyph-name="mars-stroke"
+      unicode="&#xF229;"
+      horiz-adv-x="384" d=" M372 384H293C282.3 384 277 371.1 284.5 363.5L301.4 346.6L283.9 329.1L269.8 343.2C265.1 347.9 257.5 347.9 252.8 343.2L224.5 315C219.8 310.3 219.8 302.7 224.5 298L238.6 283.9L220.6 265.9C198.4 279.9 172.1 288 143.9 288C64.5 288 0 223.5 0 144S64.5 0 144 0S288 64.5 288 144C288 172.2 279.9 198.5 265.9 220.7L283.9 238.7L298 224.6C302.7 219.9 310.3 219.9 315 224.6L343.3 252.9C348 257.6 348 265.2000000000001 343.3 269.9L329.2 284L346.7 301.5L363.6 284.6C371.2 277 384.1 282.4 384.1 293.1V372C384 378.6 378.6 384 372 384zM144 64C99.9 64 64 99.9 64 144S99.9 224 144 224S224 188.1 224 144S188.1 64 144 64z" />
+    <glyph glyph-name="mars"
+      unicode="&#xF222;"
+      horiz-adv-x="384" d=" M372 384H293C282.3 384 277 371.1 284.5 363.5L301.4 346.6L220.7 265.9C198.5 279.9 172.2 288 144 288C64.5 288 0 223.5 0 144S64.5 0 144 0S288 64.5 288 144C288 172.2 279.9 198.5 265.9 220.7L346.6 301.4L363.5 284.5C371.1 276.9 384 282.3 384 293V372C384 378.6 378.6 384 372 384zM144 64C99.9 64 64 99.9 64 144S99.9 224 144 224S224 188.1 224 144S188.1 64 144 64z" />
+    <glyph glyph-name="mask"
+      unicode="&#xF6FA;"
+      horiz-adv-x="640" d=" M320.67 384C-121.93 384 -36.9 0 162.21 0C202.11 0 239.68 20.69 263.63 55.86L289.36 93.65C305.0200000000001 116.6400000000001 336.3300000000001 116.6400000000001 351.99 93.65L377.7200000000001 55.86C401.6600000000001 20.69 439.23 0 479.13 0C668.99 0 769.76 384 320.67 384zM184 139.64C142.94 139.64 116.24 165.3 103.92 180.69C98.69 187.22 98.69 196.78 103.92 203.32C116.24 218.72 142.93 244.37 184 244.37S251.76 218.71 264.08 203.32C269.31 196.79 269.31 187.23 264.08 180.69C251.76 165.29 225.06 139.64 184 139.64zM456 139.64C414.94 139.64 388.24 165.3 375.92 180.69C370.69 187.22 370.69 196.78 375.92 203.32C388.24 218.72 414.93 244.37 456 244.37S523.76 218.71 536.08 203.32C541.3100000000001 196.79 541.3100000000001 187.23 536.08 180.69C523.76 165.29 497.0600000000001 139.64 456.0000000000001 139.64z" />
+    <glyph glyph-name="medal"
+      unicode="&#xF5A2;"
+      horiz-adv-x="512" d=" M223.75 317.25L154.62 432.46A31.997 31.997 0 0 1 127.18 448H16.03C3.08 448 -4.5 433.43 2.92 422.82L114.19 263.86C143.91 291.63 181.71 310.69 223.75 317.25zM495.97 448H384.82C373.58 448 363.16 442.1 357.38 432.46L288.25 317.25C330.29 310.69 368.0900000000001 291.63 397.81 263.87L509.08 422.82C516.5 433.43 508.92 448 495.97 448zM256 288C158.8 288 80 209.2 80 112S158.8 -64 256 -64S432 14.8 432 112S353.2 288 256 288zM348.52 130.74L310.59 93.78L319.56 41.5600000000001C321.1600000000001 32.2 311.3 25.0500000000001 302.9100000000001 29.4700000000001L256 54.12L209.1 29.47C200.7 25.02 190.85 32.21 192.45 41.56L201.42 93.78L163.49 130.74C156.67 137.38 160.44 148.97 169.84 150.3299999999999L222.27 157.9699999999999L245.7 205.4899999999999C247.81 209.7699999999999 251.89 211.8799999999999 255.98 211.8799999999999C260.09 211.8799999999999 264.2 209.7399999999999 266.31 205.4899999999999L289.74 157.9699999999999L342.17 150.3299999999999C351.57 148.9699999999999 355.3400000000001 137.38 348.5200000000001 130.74z" />
+    <glyph glyph-name="medkit"
+      unicode="&#xF0FA;"
+      horiz-adv-x="512" d=" M96 -32H416V320H384V368C384 394.51 362.51 416 336 416H176C149.49 416 128 394.51 128 368V320H96V-32zM192 352H320V320H192V352zM512 272V16C512 -10.51 490.51 -32 464 -32H448V320H464C490.51 320 512 298.51 512 272zM64 -32H48C21.49 -32 0 -10.51 0 16V272C0 298.51 21.49 320 48 320H64V-32zM352 176V144C352 135.163 344.837 128 336 128H288V80C288 71.163 280.837 64 272 64H240C231.163 64 224 71.163 224 80V128H176C167.163 128 160 135.163 160 144V176C160 184.837 167.163 192 176 192H224V240C224 248.837 231.163 256 240 256H272C280.837 256 288 248.837 288 240V192H336C344.837 192 352 184.837 352 176z" />
+    <glyph glyph-name="meh-blank"
+      unicode="&#xF5A4;"
+      horiz-adv-x="496" d=" M248 440C111 440 0 329 0 192S111 -56 248 -56S496 55 496 192S385 440 248 440zM168 208C150.3 208 136 222.3 136 240S150.3 272 168 272S200 257.7 200 240S185.7 208 168 208zM328 208C310.3 208 296 222.3 296 240S310.3 272 328 272S360 257.7 360 240S345.7 208 328 208z" />
+    <glyph glyph-name="meh-rolling-eyes"
+      unicode="&#xF5A5;"
+      horiz-adv-x="496" d=" M248 440C111 440 0 329 0 192S111 -56 248 -56S496 55 496 192S385 440 248 440zM88 224C88 248.3 101.7 269.2 121.6 280C120.9 277.4 120 274.8 120 272C120 254.3 134.3 240 152 240S184 254.3 184 272C184 274.8 183.1 277.4 182.4 280C202.3 269.2 216 248.3 216 224C216 188.7 187.3 160 152 160S88 188.7 88 224zM312 48H184C162.8 48 162.8 80 184 80H312C333.2 80 333.2 48 312 48zM344 160C308.7 160 280 188.7 280 224C280 248.3 293.7 269.2 313.6 280C312.9000000000001 277.4 312 274.8 312 272C312 254.3 326.3 240 344 240S376 254.3 376 272C376 274.8 375.1 277.4 374.4 280C394.3 269.2 408 248.3 408 224C408 188.7 379.3 160 344 160z" />
+    <glyph glyph-name="meh"
+      unicode="&#xF11A;"
+      horiz-adv-x="496" d=" M248 440C111 440 0 329 0 192S111 -56 248 -56S496 55 496 192S385 440 248 440zM168 272C185.7 272 200 257.7 200 240S185.7 208 168 208S136 222.3 136 240S150.3 272 168 272zM344 80H152C130.8 80 130.8 112 152 112H344C365.2 112 365.2 80 344 80zM328 208C310.3 208 296 222.3 296 240S310.3 272 328 272S360 257.7 360 240S345.7 208 328 208z" />
+    <glyph glyph-name="memory"
+      unicode="&#xF538;"
+      horiz-adv-x="640" d=" M640 317.06V352C640 369.67 625.67 384 608 384H32C14.33 384 0 369.67 0 352V317.06C18.6 310.45 32 292.87 32 272S18.6 233.55 0 226.94V128H640V226.94C621.4 233.55 608 251.13 608 272S621.4 310.45 640 317.06zM224 192H160V320H224V192zM352 192H288V320H352V192zM480 192H416V320H480V192zM0 0H64V26.67C64 35.51 71.16 42.67 80 42.67S96 35.51 96 26.67V0H224V26.67C224 35.51 231.16 42.67 240 42.67S256 35.51 256 26.67V0H384V26.67C384 35.51 391.1600000000001 42.67 400 42.67S416 35.51 416 26.67V0H544V26.67C544 35.51 551.16 42.67 560 42.67S576 35.51 576 26.67V0H640V96H0V0z" />
+    <glyph glyph-name="menorah"
+      unicode="&#xF676;"
+      horiz-adv-x="640" d=" M144 320H112C103.16 320 96 312.8400000000001 96 304V160H160V304C160 312.8400000000001 152.84 320 144 320zM240 320H208C199.16 320 192 312.8400000000001 192 304V160H256V304C256 312.8400000000001 248.84 320 240 320zM432 320H400C391.1600000000001 320 384 312.8400000000001 384 304V160H448V304C448 312.8400000000001 440.84 320 432 320zM528 320H496C487.16 320 480 312.8400000000001 480 304V160H544V304C544 312.8400000000001 536.84 320 528 320zM608 352C625.67 352 640 366.33 640 384S608 448 608 448S576 401.67 576 384S590.33 352 608 352zM512 352C529.67 352 544 366.33 544 384S512 448 512 448S480 401.67 480 384S494.33 352 512 352zM416 352C433.67 352 448 366.33 448 384S416 448 416 448S384 401.67 384 384S398.33 352 416 352zM320 352C337.67 352 352 366.33 352 384S320 448 320 448S288 401.67 288 384S302.33 352 320 352zM224 352C241.67 352 256 366.33 256 384S224 448 224 448S192 401.67 192 384S206.33 352 224 352zM128 352C145.67 352 160 366.33 160 384S128 448 128 448S96 401.67 96 384S110.33 352 128 352zM32 352C49.67 352 64 366.33 64 384S32 448 32 448S0 401.67 0 384S14.33 352 32 352zM576 160C576 142.33 561.67 128 544 128H352V304C352 312.8400000000001 344.84 320 336 320H304C295.1600000000001 320 288 312.8400000000001 288 304V128H96C78.33 128 64 142.33 64 160V304C64 312.8400000000001 56.84 320 48 320H16C7.16 320 0 312.8400000000001 0 304V160C0 106.98 42.98 64 96 64H288V0H112C103.16 0 96 -7.16 96 -16V-48C96 -56.84 103.16 -64 112 -64H528C536.84 -64 544 -56.84 544 -48V-16C544 -7.16 536.84 0 528 0H352V64H544C597.02 64 640 106.98 640 160V304C640 312.8400000000001 632.84 320 624 320H592C583.16 320 576 312.8400000000001 576 304V160z" />
+    <glyph glyph-name="mercury"
+      unicode="&#xF223;"
+      horiz-adv-x="288" d=" M288 240C288 284.2 268.1 323.7 236.8 350.1C239.3 351.9 241.7 353.9 244 355.9C268.7 377.1 283.8 404.7 287.2 434.7C288.1 441.8 282.5 448 275.3 448H234.8C229 448 224.1 443.9 223 438.2C220.6 425.7 213.4 413.9 202.3 404.4C187 391.2 166.3 384 144 384S101 391.2 85.6 404.4C74.5 413.9 67.4 425.7 64.9 438.2C63.8 443.9 58.9 448 53.2 448H12.7C5.5 448 -0.1 441.8 0.8 434.7C4.2 404.6 19.2 377 44 355.8C46.3 353.8 48.7 351.9 51.2 350C19.9 323.7 0 284.2 0 240C0 171.5 47.9 114.1 112 99.6V48H76C69.4 48 64 42.6 64 36V-4C64 -10.6 69.4 -16 76 -16H112V-52C112 -58.6 117.4 -64 124 -64H164C170.6 -64 176 -58.6 176 -52V-16H212C218.6 -16 224 -10.6 224 -4V36C224 42.6 218.6 48 212 48H176V99.6C240.1 114.1 288 171.5 288 240.0000000000001zM64 240C64 284.1 99.9 320 144 320S224 284.1 224 240S188.1 160 144 160S64 195.9 64 240z" />
+    <glyph glyph-name="meteor"
+      unicode="&#xF753;"
+      horiz-adv-x="512" d=" M491.2 447.3C452.5 435.7 379.4 413 303.5 386C301.4 393 299.5 399.5 297.9 404.6C294.9 414.3 284 418.8 275 414.1C232.6 392 122.2 331.5 60.6 271.6C59.5 270.6 58.1 269.6 57.1 268.6C19 230.6 0 180.7 0 130.8C0 81 19 31.1 57 -7C95 -45 144.9 -64.1 194.8 -64C244.7 -64 294.6 -45 332.7000000000001 -6.9C333.7000000000001 -5.9 334.7000000000001 -4.5 335.7000000000001 -3.4C395.5000000000001 58.2000000000001 456.1 168.7000000000001 478.2 211C482.9 220 478.4 230.9000000000001 468.7 233.9000000000001C463.5000000000001 235.5000000000001 457.1 237.4000000000001 450.1 239.5000000000001C477.1 315.5 499.8 388.5 511.4 427.2000000000001C515 439.6 503.6 451 491.2 447.3zM192 0C121.3 0 64 57.3 64 128S121.3 256 192 256S320 198.7 320 128S262.7 0 192 0zM160 192C142.3 192 128 177.7 128 160S142.3 128 160 128S192 142.3 192 160S177.7 192 160 192zM208 96C199.2 96 192 88.8 192 80S199.2 64 208 64S224 71.2 224 80S216.8 96 208 96z" />
+    <glyph glyph-name="microchip"
+      unicode="&#xF2DB;"
+      horiz-adv-x="512" d=" M416 400V-16C416 -42.51 394.51 -64 368 -64H144C117.49 -64 96 -42.51 96 -16V400C96 426.51 117.49 448 144 448H368C394.51 448 416 426.51 416 400zM512 342V330A6 6 0 0 0 506 324H488V318A6 6 0 0 0 482 312H440V360H482A6 6 0 0 0 488 354V348H506A6 6 0 0 0 512 342zM512 246V234A6 6 0 0 0 506 228H488V222A6 6 0 0 0 482 216H440V264H482A6 6 0 0 0 488 258V252H506A6 6 0 0 0 512 246zM512 150V138A6 6 0 0 0 506 132H488V126A6 6 0 0 0 482 120H440V168H482A6 6 0 0 0 488 162V156H506A6 6 0 0 0 512 150zM512 54V42A6 6 0 0 0 506 36H488V30A6 6 0 0 0 482 24H440V72H482A6 6 0 0 0 488 66V60H506A6 6 0 0 0 512 54zM30 72H72V24H30A6 6 0 0 0 24 30V36H6A6 6 0 0 0 0 42V54A6 6 0 0 0 6 60H24V66A6 6 0 0 0 30 72zM30 168H72V120H30A6 6 0 0 0 24 126V132H6A6 6 0 0 0 0 138V150A6 6 0 0 0 6 156H24V162A6 6 0 0 0 30 168zM30 264H72V216H30A6 6 0 0 0 24 222V228H6A6 6 0 0 0 0 234V246A6 6 0 0 0 6 252H24V258A6 6 0 0 0 30 264zM30 360H72V312H30A6 6 0 0 0 24 318V324H6A6 6 0 0 0 0 330V342A6 6 0 0 0 6 348H24V354A6 6 0 0 0 30 360z" />
+    <glyph glyph-name="microphone-alt-slash"
+      unicode="&#xF539;"
+      horiz-adv-x="640" d=" M633.82 -10.1L476.26 111.67C488.74 135.79 496 163.02 496 192V240C496 248.84 488.84 256 480 256H464C455.16 256 448 248.84 448 240V192C448 174.08 444.04 157.2 437.28 141.8L410.73 162.32C413.83 171.72 416.01 181.54 416.01 191.99H372.3399999999999L330.94 223.99H416V255.99H330.67C324.7800000000001 255.99 320 259.5700000000001 320 263.99V279.99C320 284.41 324.78 287.99 330.67 287.99H416V319.99H330.67C324.7800000000001 319.99 320 323.57 320 327.99V343.99C320 348.4100000000001 324.78 351.99 330.67 351.99H416C416 405.01 373.02 447.99 320 447.99S224 405.01 224 351.99V306.63L45.47 444.63C38.49 450.05 28.43 448.8 23.01 441.82L3.37 416.55C-2.05 409.58 -0.8 399.53 6.18 394.1L594.54 -60.63C601.52 -66.0599999999999 611.5699999999999 -64.7999999999999 617 -57.82L636.64 -32.55C642.05 -25.58 640.8 -15.53 633.8199999999999 -10.1zM400 -16H344V17.78C355.71 19.4 367.1 22.0599999999999 377.96 25.86L327.56 64.8199999999999C320.85 64.42 314.15 63.9499999999999 307.21 64.62C251.36 70.0699999999999 208.47 113.25 196.03 166.4699999999999L144 206.69V199.84C144 110.2 207.97 30.29 296 18.15V-16H240C231.16 -16 224 -23.16 224 -32V-48C224 -56.84 231.16 -64 240 -64H400C408.84 -64 416 -56.84 416 -48V-32C416 -23.16 408.84 -16 400 -16z" />
+    <glyph glyph-name="microphone-alt"
+      unicode="&#xF3C9;"
+      horiz-adv-x="352" d=" M336 256H320C311.1600000000001 256 304 248.84 304 240V192C304 117.2 239.51 57.18 163.21 64.62C96.71 71.11 48 130.89 48 197.7V240C48 248.84 40.84 256 32 256H16C7.16 256 0 248.84 0 240V199.84C0 110.2 63.97 30.29 152 18.15V-16H96C87.16 -16 80 -23.16 80 -32V-48C80 -56.84 87.16 -64 96 -64H256C264.84 -64 272 -56.84 272 -48V-32C272 -23.16 264.84 -16 256 -16H200V17.77C285.71 29.53 352 103.1 352 192V240C352 248.84 344.84 256 336 256zM176 96C229.02 96 272 138.98 272 192H186.67C180.78 192 176 195.58 176 200V216C176 220.42 180.78 224 186.67 224H272V256H186.67C180.78 256 176 259.5800000000001 176 264V280C176 284.42 180.78 288 186.67 288H272V320H186.67C180.78 320 176 323.58 176 328V344C176 348.42 180.78 352 186.67 352H272C272 405.02 229.02 448 176 448S80 405.02 80 352V192C80 138.98 122.98 96 176 96z" />
+    <glyph glyph-name="microphone-slash"
+      unicode="&#xF131;"
+      horiz-adv-x="640" d=" M633.82 -10.1L476.02 111.86C488.61 135.87 496 162.99 496 192V240C496 248.84 488.84 256 480 256H464C455.16 256 448 248.84 448 240V192C448 174.08 444.04 157.2 437.28 141.8L410.73 162.32C413.83 171.72 416.01 181.54 416.01 191.99V352C416.01 405.02 373.0299999999999 448 320.01 448S224.0099999999999 405.02 224.0099999999999 352V306.64L45.47 444.63C38.49 450.05 28.43 448.8 23.01 441.82L3.37 416.55C-2.05 409.58 -0.8 399.53 6.18 394.1L594.54 -60.63C601.52 -66.0599999999999 611.5699999999999 -64.7999999999999 617 -57.82L636.64 -32.55C642.05 -25.58 640.8 -15.53 633.8199999999999 -10.1zM400 -16H344V17.77C355.6600000000001 19.37 366.85 22.31 377.67 26.08L327.56 64.81C320.85 64.41 314.15 63.94 307.21 64.61C251.36 70.06 208.47 113.24 196.03 166.46L144 206.69V199.84C144 110.2 207.97 30.29 296 18.15V-16H240C231.16 -16 224 -23.16 224 -32V-48C224 -56.84 231.16 -64 240 -64H400C408.84 -64 416 -56.84 416 -48V-32C416 -23.16 408.84 -16 400 -16z" />
+    <glyph glyph-name="microphone"
+      unicode="&#xF130;"
+      horiz-adv-x="352" d=" M176 96C229.02 96 272 138.98 272 192V352C272 405.02 229.02 448 176 448S80 405.02 80 352V192C80 138.98 122.98 96 176 96zM336 256H320C311.1600000000001 256 304 248.84 304 240V192C304 117.2 239.51 57.18 163.21 64.62C96.71 71.11 48 130.89 48 197.7V240C48 248.84 40.84 256 32 256H16C7.16 256 0 248.84 0 240V199.84C0 110.2 63.97 30.29 152 18.15V-16H96C87.16 -16 80 -23.16 80 -32V-48C80 -56.84 87.16 -64 96 -64H256C264.84 -64 272 -56.84 272 -48V-32C272 -23.16 264.84 -16 256 -16H200V17.77C285.71 29.53 352 103.1 352 192V240C352 248.84 344.84 256 336 256z" />
+    <glyph glyph-name="microscope"
+      unicode="&#xF610;"
+      horiz-adv-x="512" d=" M160 128H172V112C172 103.16 179.16 96 188 96H228C236.84 96 244 103.16 244 112V128H256C273.67 128 288 142.33 288 160V384C288 401.67 273.67 416 256 416V432C256 440.84 248.84 448 240 448H176C167.16 448 160 440.84 160 432V416C142.33 416 128 401.67 128 384V160C128 142.33 142.33 128 160 128zM464 0H462.71C493.24 34.01 512 78.8 512 128C512 233.88 425.88 320 320 320V256C390.58 256 448 198.58 448 128S390.58 0 320 0H48C21.49 0 0 -21.49 0 -48C0 -56.84 7.16 -64 16 -64H496C504.84 -64 512 -56.84 512 -48C512 -21.49 490.51 0 464 0zM104 32H312C316.42 32 320 35.58 320 40V56C320 60.42 316.42 64 312 64H104C99.58 64 96 60.42 96 56V40C96 35.58 99.58 32 104 32z" />
+    <glyph glyph-name="minus-circle"
+      unicode="&#xF056;"
+      horiz-adv-x="512" d=" M256 440C119 440 8 329 8 192S119 -56 256 -56S504 55 504 192S393 440 256 440zM124 152C117.4 152 112 157.4 112 164V220C112 226.6 117.4 232 124 232H388C394.6 232 400 226.6 400 220V164C400 157.4 394.6 152 388 152H124z" />
+    <glyph glyph-name="minus-square"
+      unicode="&#xF146;"
+      horiz-adv-x="448" d=" M400 416H48C21.5 416 0 394.5 0 368V16C0 -10.5 21.5 -32 48 -32H400C426.5 -32 448 -10.5 448 16V368C448 394.5 426.5 416 400 416zM92 152C85.4 152 80 157.4 80 164V220C80 226.6 85.4 232 92 232H356C362.6 232 368 226.6 368 220V164C368 157.4 362.6 152 356 152H92z" />
+    <glyph glyph-name="minus"
+      unicode="&#xF068;"
+      horiz-adv-x="448" d=" M416 240H32C14.33 240 0 225.67 0 208V176C0 158.33 14.33 144 32 144H416C433.67 144 448 158.33 448 176V208C448 225.67 433.67 240 416 240z" />
+    <glyph glyph-name="mobile-alt"
+      unicode="&#xF3CD;"
+      horiz-adv-x="320" d=" M272 448H48C21.5 448 0 426.5 0 400V-16C0 -42.5 21.5 -64 48 -64H272C298.5 -64 320 -42.5 320 -16V400C320 426.5 298.5 448 272 448zM160 -32C142.3 -32 128 -17.7 128 0S142.3 32 160 32S192 17.7 192 0S177.7 -32 160 -32zM272 76C272 69.4 266.6 64 260 64H60C53.4 64 48 69.4 48 76V388C48 394.6 53.4 400 60 400H260C266.6 400 272 394.6 272 388V76z" />
+    <glyph glyph-name="mobile"
+      unicode="&#xF10B;"
+      horiz-adv-x="320" d=" M272 448H48C21.5 448 0 426.5 0 400V-16C0 -42.5 21.5 -64 48 -64H272C298.5 -64 320 -42.5 320 -16V400C320 426.5 298.5 448 272 448zM160 -32C142.3 -32 128 -17.7 128 0S142.3 32 160 32S192 17.7 192 0S177.7 -32 160 -32z" />
+    <glyph glyph-name="money-bill-alt"
+      unicode="&#xF3D1;"
+      horiz-adv-x="640" d=" M352 160H336V248C336 252.42 332.42 256 328 256H314.42C309.68 256 305.05 254.6 301.11 251.97L285.7800000000001 241.75A7.993999999999999 7.993999999999999 0 0 1 283.56 230.66L292.44 217.35A7.993999999999999 7.993999999999999 0 0 1 303.53 215.13L304 215.44V160H288C283.58 160 280 156.42 280 152V136C280 131.58 283.58 128 288 128H352C356.42 128 360 131.58 360 136V152C360 156.42 356.42 160 352 160zM608 384H32C14.33 384 0 369.67 0 352V32C0 14.33 14.33 0 32 0H608C625.67 0 640 14.33 640 32V352C640 369.67 625.67 384 608 384zM48 48V112C83.35 112 112 83.35 112 48H48zM48 272V336H112C112 300.65 83.35 272 48 272zM320 80C266.98 80 224 130.15 224 192C224 253.86 266.98 304 320 304S416 253.86 416 192C416 130.13 373 80 320 80zM592 48H528C528 83.35 556.65 112 592 112V48zM592 272C556.65 272 528 300.65 528 336H592V272z" />
+    <glyph glyph-name="money-bill-wave-alt"
+      unicode="&#xF53B;"
+      horiz-adv-x="640" d=" M621.16 393.54C582.37 409.81 543.55 416 504.75 416C381.58 416.01 258.42 353.66 135.25 353.66C104.36 353.66 73.49 357.58 42.6 367.38C39.13 368.48 35.65 369 32.25 369C15.04 369 0 355.68 0 337.19V19.93C0 7.3 7.23 -4.67 18.84 -9.53C57.63 -25.81 96.45 -32 135.25 -32C258.42 -32 381.5900000000001 30.35 504.76 30.35C535.65 30.35 566.52 26.43 597.41 16.63C600.88 15.53 604.36 15.01 607.76 15.01C624.97 15.01 640.01 28.33 640.01 46.82V364.07C640 376.71 632.77 388.67 621.16 393.54zM320 96C275.81 96 240 138.99 240 192C240 245.02 275.82 288 320 288S400 245.02 400 192C400 138.97 364.17 96 320 96z" />
+    <glyph glyph-name="money-bill-wave"
+      unicode="&#xF53A;"
+      horiz-adv-x="640" d=" M621.16 393.54C582.37 409.81 543.55 416 504.75 416C381.58 416.01 258.42 353.66 135.25 353.66C104.36 353.66 73.49 357.58 42.6 367.38C39.13 368.48 35.65 369 32.25 369C15.04 369 0 355.68 0 337.19V19.93C0 7.3 7.23 -4.67 18.84 -9.53C57.63 -25.81 96.45 -32 135.25 -32C258.42 -32 381.5900000000001 30.35 504.76 30.35C535.65 30.35 566.52 26.43 597.41 16.63C600.88 15.53 604.36 15.01 607.76 15.01C624.97 15.01 640.01 28.33 640.01 46.82V364.07C640 376.71 632.77 388.67 621.16 393.54zM48 315.78C68.12 310.74 89.12 308.2100000000001 110.72 306.85C104.84 277.4600000000001 79 255.31 48 255.31V315.78zM48 30.78V78.56C82.37 78.56 110.18 51.29 111.71 17.16C89.18 18.97 68.12 23.47 48 30.78zM320 96C275.81 96 240 138.99 240 192C240 245.02 275.82 288 320 288S400 245.02 400 192C400 138.97 364.17 96 320 96zM592 68.22C574.48 72.61 556.29 75.0700000000001 537.68 76.66C543.55 102.74 565.18 122.54 592 125.9400000000001V68.22zM592 304.3300000000001C561.11 308.24 537.14 334.0300000000001 536.19 365.8800000000001C555.73 363.7100000000001 574.2800000000001 359.6500000000001 592 353.2200000000001V304.3300000000001z" />
+    <glyph glyph-name="money-bill"
+      unicode="&#xF0D6;"
+      horiz-adv-x="640" d=" M608 384H32C14.33 384 0 369.67 0 352V32C0 14.33 14.33 0 32 0H608C625.67 0 640 14.33 640 32V352C640 369.67 625.67 384 608 384zM48 48V112C83.35 112 112 83.35 112 48H48zM48 272V336H112C112 300.65 83.35 272 48 272zM320 96C275.81 96 240 138.99 240 192C240 245.02 275.82 288 320 288S400 245.02 400 192C400 138.97 364.17 96 320 96zM592 48H528C528 83.35 556.65 112 592 112V48zM592 272C556.65 272 528 300.65 528 336H592V272z" />
+    <glyph glyph-name="money-check-alt"
+      unicode="&#xF53D;"
+      horiz-adv-x="640" d=" M608 416H32C14.33 416 0 401.67 0 384V0C0 -17.67 14.33 -32 32 -32H608C625.67 -32 640 -17.67 640 0V384C640 401.67 625.67 416 608 416zM176 120.12V104C176 99.58 172.42 96 168 96H152C147.58 96 144 99.58 144 104V120.29C132.71 120.87 121.73 124.81 112.63 131.6400000000001C108.73 134.5700000000001 108.53 140.41 112.06 143.78L123.81 154.99C126.58 157.63 130.7 157.75 133.94 155.72C137.81 153.3 142.2 152 146.76 152H174.87C181.37 152 186.67 157.92 186.67 165.19C186.67 171.14 183.06 176.38 177.9 177.92L132.9 191.42C114.31 197 101.32 214.84 101.32 234.81C101.32 259.3300000000001 120.37 279.25 143.99 279.88V296C143.99 300.42 147.57 304 151.99 304H167.99C172.41 304 175.99 300.42 175.99 296V279.7100000000001C187.28 279.13 198.26 275.2000000000001 207.36 268.36C211.26 265.43 211.46 259.5900000000001 207.93 256.2200000000001L196.18 245.01C193.41 242.3700000000001 189.29 242.2500000000001 186.05 244.2800000000001C182.18 246.7100000000001 177.79 248.0000000000001 173.23 248.0000000000001H145.12C138.62 248.0000000000001 133.32 242.0800000000001 133.32 234.8100000000001C133.32 228.8600000000001 136.93 223.62 142.09 222.0800000000001L187.09 208.5800000000001C205.68 203 218.67 185.16 218.67 165.1900000000001C218.67 140.6600000000001 199.62 120.7500000000001 176 120.1200000000001zM416 136C416 131.58 412.42 128 408 128H296C291.58 128 288 131.58 288 136V152C288 156.42 291.58 160 296 160H408C412.42 160 416 156.42 416 152V136zM576 136C576 131.58 572.42 128 568 128H488C483.58 128 480 131.58 480 136V152C480 156.42 483.58 160 488 160H568C572.42 160 576 156.42 576 152V136zM576 232C576 227.58 572.42 224 568 224H296C291.58 224 288 227.58 288 232V248C288 252.42 291.58 256 296 256H568C572.42 256 576 252.42 576 248V232z" />
+    <glyph glyph-name="money-check"
+      unicode="&#xF53C;"
+      horiz-adv-x="640" d=" M0 0C0 -17.67 14.33 -32 32 -32H608C625.67 -32 640 -17.67 640 0V320H0V0zM448 208C448 216.84 455.16 224 464 224H560C568.84 224 576 216.84 576 208V176C576 167.16 568.84 160 560 160H464C455.16 160 448 167.16 448 176V208zM448 88C448 92.42 451.58 96 456 96H568C572.42 96 576 92.42 576 88V72C576 67.58 572.42 64 568 64H456C451.58 64 448 67.58 448 72V88zM64 184C64 188.42 67.58 192 72 192H376C380.42 192 384 188.42 384 184V168C384 163.58 380.42 160 376 160H72C67.58 160 64 163.58 64 168V184zM64 88C64 92.42 67.58 96 72 96H248C252.42 96 256 92.42 256 88V72C256 67.58 252.42 64 248 64H72C67.58 64 64 67.58 64 72V88zM624 416H16C7.16 416 0 408.8400000000001 0 400V352H640V400C640 408.8400000000001 632.84 416 624 416z" />
+    <glyph glyph-name="monument"
+      unicode="&#xF5A6;"
+      horiz-adv-x="384" d=" M368 0H16C7.16 0 0 -7.16 0 -16V-48C0 -56.84 7.16 -64 16 -64H368C376.84 -64 384 -56.84 384 -48V-16C384 -7.16 376.84 0 368 0zM289.14 347.26A31.97 31.97 0 0 1 279.93 366.7L203.31 443.31C197.06 449.56 186.93 449.56 180.68 443.31L104.08 366.7A31.97 31.97 0 0 1 94.87 347.26L64 32H320L289.14 347.26zM240 140.8C240 134.4 233.6 128 227.2 128H156.8C150.4 128 144 134.4 144 140.8V179.2C144 185.6 150.4 192 156.8 192H227.2C233.6 192 240 185.6 240 179.2V140.8z" />
+    <glyph glyph-name="moon"
+      unicode="&#xF186;"
+      horiz-adv-x="512" d=" M283.211 -64C362.173 -64 434.29 -28.075 482.068 30.792C489.136 39.5000000000001 481.429 52.222 470.506 50.1420000000001C346.303 26.4880000000001 232.244 121.7180000000001 232.244 247.0960000000001C232.244 319.3180000000001 270.906 385.7310000000001 333.742 421.4900000000001C343.428 427.0020000000001 340.992 441.6870000000001 329.986 443.7200000000001A258.156 258.156 0 0 1 283.211 448C141.902 448 27.211 333.4890000000001 27.211 192C27.211 50.691 141.722 -64 283.211 -64z" />
+    <glyph glyph-name="mortar-pestle"
+      unicode="&#xF5A7;"
+      horiz-adv-x="512" d=" M501.54 387.0900000000001C518.76 404.31 514.0500000000001 433.3400000000001 492.27 444.23A35.696 35.696 0 0 1 454.9 440.86L251.09 288H402.4600000000001L501.54 387.0900000000001zM496 256H16C7.16 256 0 248.84 0 240V208C0 199.16 7.16 192 16 192H32C32 111.02 82.2 41.89 153.13 13.68C140.37 -3.19 131.41 -23.12 128.18 -45.01C126.72 -54.93 134.22 -63.99 144.25 -63.99H367.75C377.78 -63.99 385.28 -54.93 383.82 -45.01C380.6 -23.12 371.64 -3.19 358.87 13.68C429.8 41.89 480 111.02 480 192H496C504.84 192 512 199.16 512 208V240C512 248.84 504.84 256 496 256z" />
+    <glyph glyph-name="mosque"
+      unicode="&#xF678;"
+      horiz-adv-x="640" d=" M0 -32C0 -49.67 14.33 -64 32 -64H96C113.67 -64 128 -49.67 128 -32V288H0V-32zM579.16 160C597.02 177.39 608 197.34 608 218.91C608 271.77 566.21 312.7 520.08 341.81C478.14 368.28 439.4500000000001 399.58 408.1200000000001 438.03L400 448L391.88 438.03C360.55 399.58 321.87 368.27 279.92 341.81C233.79 312.7 192 271.77 192 218.91C192 197.34 202.98 177.39 220.84 160H579.16zM608 128H192C174.33 128 160 113.67 160 96V-32C160 -49.67 174.33 -64 192 -64H224V0C224 17.67 238.33 32 256 32S288 17.67 288 0V-64H352V8C352 56 400 80 400 80S448 56 448 8V-64H512V0C512 17.67 526.33 32 544 32S576 17.67 576 0V-64H608C625.67 -64 640 -49.67 640 -32V96C640 113.67 625.67 128 608 128zM64 448S0 416 0 352V320H128V352C128 416 64 448 64 448z" />
+    <glyph glyph-name="motorcycle"
+      unicode="&#xF21C;"
+      horiz-adv-x="640" d=" M512.949 255.997C498.0869999999999 256.105 483.809 253.675 470.515 249.123L437.589 304H520C533.255 304 544 314.745 544 328V360C544 373.255 533.255 384 520 384H474.689A24 24 0 0 1 456.85 376.055L419.3540000000001 334.392L396.5800000000001 372.348A24 24 0 0 1 376 384H296C287.163 384 280 376.837 280 368V352C280 343.163 287.163 336 296 336H362.411L381.611 304H227.904C210.177 327.073 182.98 344 128 344H72.54C59.085 344 47.749 332.9890000000001 48.004 319.536C48.252 306.495 58.9 296 72 296H128C152.504 296 166.686 285.081 175.787 271.231L164.496 250.702C151.49 254.567 137.625 256.438 123.245 255.912C55.857 253.451 1.565 198.395 0.034 130.979C-1.603 58.924 56.317 0 128 0C187.642 0 237.744 40.794 251.953 96H336.189C349.862 96 360.778 107.421 360.165 121.077C358.0470000000001 168.197 377.687 214.742 416.35 246.103L428.8350000000001 225.295C401.189 201.641 383.7380000000001 166.415 384.004 127.116C384.4740000000001 57.56 441.207 0.664 510.762 0.006C582.391 -0.672 640.601 57.493 639.996 129.105C639.408 198.696 582.5409999999999 255.491 512.949 255.997zM128 48C83.888 48 48 83.888 48 128S83.888 208 128 208C132.242 208 136.405 207.659 140.469 207.018L98.97 131.566C90.187 115.593 101.762 96 120 96H201.297C188.927 67.775 160.737 48 128 48zM516.351 48.116C470.272 45.663 432 82.446 432 128C432 149.363 440.4340000000001 168.781 454.125 183.144L503.537 100.792C508.083 93.215 517.912 90.758 525.489 95.304L539.2090000000001 103.5360000000001C546.7860000000001 108.0820000000001 549.243 117.9110000000001 544.6970000000001 125.4880000000001L496.1410000000001 206.4150000000001A80.005 80.005 0 0 0 512 208C557.554 208 594.338 169.727 591.884 123.648C589.724 83.09 556.91 50.276 516.351 48.116z" />
+    <glyph glyph-name="mountain"
+      unicode="&#xF6FC;"
+      horiz-adv-x="640" d=" M634.92 -14.7L346.92 433.3C341.03 442.46 330.89 448 320 448S298.9700000000001 442.46 293.08 433.3L5.08 -14.7A32.001 32.001 0 0 1 3.91 -47.34A32.004 32.004 0 0 1 32 -64H608C619.71 -64 630.48 -57.61 636.09 -47.33A31.982999999999997 31.982999999999997 0 0 1 634.9200000000001 -14.7zM320 356.82L405.39 224H320L256 160L217.94 198.06L320 356.82z" />
+    <glyph glyph-name="mouse-pointer"
+      unicode="&#xF245;"
+      horiz-adv-x="320" d=" M302.189 118.874H196.105L251.936 -17.119C255.825 -26.547 251.381 -37.118 242.492 -41.118L193.327 -62.545C184.162 -66.5450000000001 173.884 -61.974 169.995 -52.831L116.942 76.305L30.278 -12.8330000000001C18.729 -24.71 0 -15.554 0 0.023V429.701C0 446.101 19.921 454.096 30.277 442.557L314.689 150.0150000000001C326.161 138.8360000000001 317.696 118.874 302.189 118.874z" />
+    <glyph glyph-name="music"
+      unicode="&#xF001;"
+      horiz-adv-x="512" d=" M511.99 415.99C511.99 437.7 490.89 453 470.39 446.5L150.4 352C137.1 347.8 128 335.5 128 321.5V60.08C117.95 62.46 107.28 64 96 64C42.98 64 0 35.35 0 0S42.98 -64 96 -64S192 -35.35 192 0V233.69L448 308.71V124.08C437.95 126.46 427.28 128 416 128C362.98 128 320 99.35 320 64S362.98 0 416 0S512 28.65 512 64L511.99 415.99z" />
+    <glyph glyph-name="network-wired"
+      unicode="&#xF6FF;"
+      horiz-adv-x="640" d=" M640 184V200C640 208.84 632.84 216 624 216H344V256H416C433.67 256 448 270.3300000000001 448 288V416C448 433.67 433.67 448 416 448H224C206.33 448 192 433.67 192 416V288C192 270.33 206.33 256 224 256H296V216H16C7.16 216 0 208.84 0 200V184C0 175.16 7.16 168 16 168H120V128H64C46.33 128 32 113.67 32 96V-32C32 -49.67 46.33 -64 64 -64H224C241.67 -64 256 -49.67 256 -32V96C256 113.67 241.67 128 224 128H168V168H472V128H416C398.33 128 384 113.67 384 96V-32C384 -49.67 398.33 -64 416 -64H576C593.67 -64 608 -49.67 608 -32V96C608 113.67 593.67 128 576 128H520V168H624C632.84 168 640 175.16 640 184zM256 320V384H384V320H256zM192 0H96V64H192V0zM544 0H448V64H544V0z" />
+    <glyph glyph-name="neuter"
+      unicode="&#xF22C;"
+      horiz-adv-x="288" d=" M288 272C288 351.5 223.5 416 144 416S0 351.5 0 272C0 203.5 47.9 146.1 112 131.6V-20C112 -26.6 117.4 -32 124 -32H164C170.6 -32 176 -26.6 176 -20V131.6C240.1 146.1 288 203.5 288 272zM144 192C99.9 192 64 227.9 64 272S99.9 352 144 352S224 316.1 224 272S188.1 192 144 192z" />
+    <glyph glyph-name="newspaper"
+      unicode="&#xF1EA;"
+      horiz-adv-x="576" d=" M552 384H88C74.745 384 64 373.255 64 360V352H24C10.745 352 0 341.255 0 328V56C0 25.072 25.072 0 56 0H528C554.51 0 576 21.49 576 48V360C576 373.255 565.255 384 552 384zM56 48A8 8 0 0 0 48 56V304H64V56A8 8 0 0 0 56 48zM292 64H140C133.373 64 128 69.373 128 76V84C128 90.627 133.373 96 140 96H292C298.627 96 304 90.627 304 84V76C304 69.373 298.627 64 292 64zM500 64H348C341.373 64 336 69.373 336 76V84C336 90.627 341.373 96 348 96H500C506.627 96 512 90.627 512 84V76C512 69.373 506.627 64 500 64zM292 160H140C133.373 160 128 165.373 128 172V180C128 186.627 133.373 192 140 192H292C298.627 192 304 186.627 304 180V172C304 165.373 298.627 160 292 160zM500 160H348C341.373 160 336 165.373 336 172V180C336 186.627 341.373 192 348 192H500C506.627 192 512 186.627 512 180V172C512 165.373 506.627 160 500 160zM500 256H140C133.373 256 128 261.373 128 268V308C128 314.627 133.373 320 140 320H500C506.627 320 512 314.627 512 308V268C512 261.373 506.627 256 500 256z" />
+    <glyph glyph-name="not-equal"
+      unicode="&#xF53E;"
+      horiz-adv-x="448" d=" M416 240C433.67 240 448 254.33 448 272V304C448 321.67 433.67 336 416 336H392.12L443.99 402.81C449.36 409.83 448.0300000000001 419.87 441.02 425.24L415.61 444.7C408.5900000000001 450.08 398.55 448.74 393.17 441.73L311.09 336H32C14.33 336 0 321.67 0 304V272C0 254.33 14.33 240 32 240H236.56L162.03 144H32C14.33 144 0 129.67 0 112V80C0 62.33 14.33 48 32 48H87.49L35.62 -18.81C30.25 -25.82 31.58 -35.86 38.59 -41.24L64 -60.7C71.02 -66.08 81.06 -64.74 86.43 -57.73L168.52 48H416C433.67 48 448 62.33 448 80V112C448 129.67 433.67 144 416 144H243.05L317.5800000000001 240H416z" />
+    <glyph glyph-name="notes-medical"
+      unicode="&#xF481;"
+      horiz-adv-x="384" d=" M336 384H256C256 419.3 227.3 448 192 448S128 419.3 128 384H48C21.5 384 0 362.5 0 336V-16C0 -42.5 21.5 -64 48 -64H336C362.5 -64 384 -42.5 384 -16V336C384 362.5 362.5 384 336 384zM192 408C205.3 408 216 397.3 216 384S205.3 360 192 360S168 370.7 168 384S178.7 408 192 408zM288 104C288 99.6 284.4 96 280 96H224V40C224 35.6 220.4 32 216 32H168C163.6 32 160 35.6 160 40V96H104C99.6 96 96 99.6 96 104V152C96 156.4 99.6 160 104 160H160V216C160 220.4 163.6 224 168 224H216C220.4 224 224 220.4 224 216V160H280C284.4 160 288 156.4 288 152V104zM288 296C288 291.6 284.4 288 280 288H104C99.6 288 96 291.6 96 296V312C96 316.4 99.6 320 104 320H280C284.4 320 288 316.4 288 312V296z" />
+    <glyph glyph-name="object-group"
+      unicode="&#xF247;"
+      horiz-adv-x="512" d=" M480 320V352H500C506.627 352 512 357.373 512 364V404C512 410.627 506.627 416 500 416H460C453.373 416 448 410.627 448 404V384H64V404C64 410.627 58.627 416 52 416H12C5.373 416 0 410.627 0 404V364C0 357.373 5.373 352 12 352H32V32H12C5.373 32 0 26.627 0 20V-20C0 -26.627 5.373 -32 12 -32H52C58.627 -32 64 -26.627 64 -20V0H448V-20C448 -26.627 453.373 -32 460 -32H500C506.627 -32 512 -26.627 512 -20V20C512 26.627 506.627 32 500 32H480V320zM96 172V308C96 314.627 101.373 320 108 320H276C282.627 320 288 314.627 288 308V172C288 165.373 282.627 160 276 160H108C101.373 160 96 165.373 96 172zM416 76C416 69.373 410.627 64 404 64H236C229.373 64 224 69.373 224 76V128H296C309.255 128 320 138.745 320 152V224H404C410.627 224 416 218.627 416 212V76z" />
+    <glyph glyph-name="object-ungroup"
+      unicode="&#xF248;"
+      horiz-adv-x="576" d=" M64 128V102A6 6 0 0 0 58 96H6A6 6 0 0 0 0 102V154A6 6 0 0 0 6 160H32V352H6A6 6 0 0 0 0 358V410A6 6 0 0 0 6 416H58A6 6 0 0 0 64 410V384H352V410A6 6 0 0 0 358 416H410A6 6 0 0 0 416 410V358A6 6 0 0 0 410 352H384V160H410A6 6 0 0 0 416 154V102A6 6 0 0 0 410 96H358A6 6 0 0 0 352 102V128H64zM544 192V224H570A6 6 0 0 1 576 230V282A6 6 0 0 1 570 288H518A6 6 0 0 1 512 282V256H408V184H416C429.255 184 440 173.255 440 160V96C440 82.745 429.255 72 416 72H352C338.745 72 328 82.745 328 96V104H192V32H166A6 6 0 0 1 160 26V-26A6 6 0 0 1 166 -32H218A6 6 0 0 1 224 -26V0H512V-26A6 6 0 0 1 518 -32H570A6 6 0 0 1 576 -26V26A6 6 0 0 1 570 32H544V192z" />
+    <glyph glyph-name="oil-can"
+      unicode="&#xF613;"
+      horiz-adv-x="640" d=" M629.8 287.69L416 224L365.51 249.24A64.07 64.07 0 0 1 336.89 256H280V304H336C344.84 304 352 311.16 352 320V336C352 344.8400000000001 344.84 352 336 352H176C167.16 352 160 344.8400000000001 160 336V320C160 311.16 167.16 304 176 304H232V256H176L37.72 281.14A31.899999999999995 31.899999999999995 0 0 1 31.93 281.67C14.67 281.67 0 267.64 0 249.66V154.71C0 139.25 11.06 125.99 26.28 123.23L96 110.54V64C96 46.33 110.33 32 128 32H402.63C411.18 32 419.38 35.42 425.39 41.51L637.65 256.26C639.15 257.76 639.99 259.8 639.99 261.92V280C640 285.31 634.91 289.15 629.8 287.69zM96 159.33L48 168.06V230.49L96 221.76V159.3300000000001zM549.3299999999999 74.67C549.3299999999999 51.11 568.43 31.9999999999999 591.9999999999999 31.9999999999999S634.6699999999998 51.1 634.6699999999998 74.67S592 160 592 160S549.33 98.23 549.33 74.67z" />
+    <glyph glyph-name="om"
+      unicode="&#xF679;"
+      horiz-adv-x="512" d=" M360.6 387.06A10.43 10.43 0 0 1 375.36 387.06L396.93 408.62A10.43 10.43 0 0 1 396.93 423.38L375.35 444.94C371.2700000000001 449.01 364.67 449.01 360.5900000000001 444.94L339.0200000000001 423.38A10.43 10.43 0 0 1 339.0200000000001 408.62L360.6 387.06zM412.11 256C385.42 256 360.3400000000001 245.61 341.4700000000001 226.75L317.2200000000001 202.5C310.4400000000001 195.73 301.4400000000001 192 291.8400000000001 192H245C255.54 214.1 259.17 240.11 252.73 267.23C242.63 309.7800000000001 206.37 343.3400000000001 163.21 350.42C127.06 356.35 92.31 345.38 67.2 321.64C59.84 314.68 60.23 302.79 68.32 296.7100000000001L94.47 277.0800000000001C100.19 272.78 108.13 272.76 113.67 277.29C122.12 284.1900000000001 132.69 288 143.94 288C170.41 288 191.95 266.4700000000001 191.95 240.0000000000001S170.41 192 143.94 192H112.04C100.08 192 92.3 179.42 97.65 168.72L113.74 136.55C116.27 131.49 121.34 128.45 126.91 128H159.94C195.24 128 223.95 99.3 223.95 64S195.24 0 159.94 0C63.92 0 37.59 54.02 14.79 92.03C10.26 99.58 0.02 95.61 0 86.81C-0.09 32 41.13 -64 159.94 -64C230.53 -64 287.9600000000001 -6.58 287.9600000000001 64C287.9600000000001 87.42 281.1800000000001 109.1 270.1500000000001 128H291.8400000000001C318.5300000000001 128 343.61 138.39 362.48 157.25L386.73 181.5C393.51 188.27 402.51 192 412.11 192C431.89 192 447.99 175.91 447.99 156.12V56C447.99 42.77 429.2200000000001 32 415.98 32C376.5800000000001 32 349.31 56.24 334.1600000000001 74.89C329.3900000000001 80.76 319.9600000000001 77.43 319.9600000000001 69.87V32S319.9600000000001 -32 415.98 -32C464.52 -32 512 7.47 512 56V156.12C512 211.2 467.2 256 412.11 256zM454.29 380.73C368.74 315.61 285.24 377.98 281.7100000000001 380.68C275.6900000000001 385.3 267.2700000000001 385.06 261.5700000000001 380.13C255.8300000000001 375.2100000000001 254.3000000000001 366.9600000000001 257.9100000000001 360.3300000000001C259.5200000000001 357.38 298.2800000000001 287.99 376.7100000000001 287.99C456.6300000000001 287.99 475.49 319.35 478.46 325.65C479.48 327.77 479.99 330.12 479.99 332.48V368C479.99 381.2200000000001 464.85 388.69 454.29 380.73z" />
+    <glyph glyph-name="otter"
+      unicode="&#xF700;"
+      horiz-adv-x="640" d=" M608 416H576L562.75 429.25A63.97 63.97 0 0 1 517.49 448H497C485.86 448 474.92 445.09 465.25 439.57L312 352H256C149.96 352 64 266.04 64 160V158.39C64 125.64 48 96.25 24.44 73.5C6.25 55.92 -3.66 29.82 1.25 1.7C8.01 -37.1 44.15 -64 83.53 -64H192C209.67 -64 224 -49.67 224 -32S209.67 0 192 0H80C71.17 0 64 7.17 64 16S71.17 32 80 32H304C312.84 32 320 39.16 320 48V64C320 81.67 305.67 96 288 96H224L373.49 176.5L448 32H528C536.84 32 544 39.16 544 48V64C544 81.67 529.67 96 512 96H483.78L428.67 206.21L521.14 256H544C597.02 256 640 298.98 640 352V384C640 401.67 625.67 416 608 416zM512 400C520.84 400 528 392.8400000000001 528 384S520.84 368 512 368S496 375.16 496 384S503.16 400 512 400zM544 304H509.04L407.2 249.16L393.43 276.7100000000001L512 336H589.05C582.43 317.42 564.8299999999999 304 544 304z" />
+    <glyph glyph-name="outdent"
+      unicode="&#xF03B;"
+      horiz-adv-x="448" d=" M0 364V404C0 412.837 7.163 420 16 420H432C440.837 420 448 412.837 448 404V364C448 355.163 440.837 348 432 348H16C7.163 348 0 355.163 0 364zM208 220H432C440.837 220 448 227.163 448 236V276C448 284.837 440.837 292 432 292H208C199.163 292 192 284.837 192 276V236C192 227.163 199.163 220 208 220zM16 -36H432C440.837 -36 448 -28.837 448 -20V20C448 28.837 440.837 36 432 36H16C7.163 36 0 28.837 0 20V-20C0 -28.837 7.163 -36 16 -36zM208 92H432C440.837 92 448 99.163 448 108V148C448 156.837 440.837 164 432 164H208C199.163 164 192 156.837 192 148V108C192 99.163 199.163 92 208 92zM4.687 180.687L100.687 84.703C110.734 74.652 128 81.776 128 96.016V287.992C128 302.3210000000001 110.675 309.296 100.687 299.305L4.687 203.313C-1.562 197.065 -1.562 186.935 4.687 180.687z" />
+    <glyph glyph-name="paint-brush"
+      unicode="&#xF1FC;"
+      horiz-adv-x="512" d=" M167.02 138.66C126.9 136.0800000000001 90.49 120.8 69.83 66.36C67.48 60.15 61.83 56.38 55.24 56.38C44.13 56.38 9.78 84.05 -0.01 90.73C0 8.38 37.93 -64 128 -64C203.86 -64 256 -20.23 256 56.19C256 59.3 255.35 62.27 255.03 65.32L167.02 138.66zM457.89 448C442.73 448 428.52 441.29 417.68 431.55C213.27 248.95 192 244.66 192 190.91C192 177.21 195.25 164.15 200.73 152.21L264.55 99.03C271.76 97.23 279.19 96.0000000000001 286.94 96.0000000000001C349.05 96.0000000000001 385.05 141.47 498.1 352.4600000000001C505.48 366.8100000000001 512 382.3100000000001 512 398.4500000000001C512 427.36 486 448 457.89 448z" />
+    <glyph glyph-name="paint-roller"
+      unicode="&#xF5AA;"
+      horiz-adv-x="512" d=" M416 320V416C416 433.67 401.67 448 384 448H32C14.33 448 0 433.67 0 416V320C0 302.33 14.33 288 32 288H384C401.67 288 416 302.3300000000001 416 320zM448 384V256C448 238.33 433.67 224 416 224H256C220.65 224 192 195.35 192 160V128C174.33 128 160 113.67 160 96V-32C160 -49.67 174.33 -64 192 -64H256C273.67 -64 288 -49.67 288 -32V96C288 113.67 273.67 128 256 128V160H416C469.02 160 512 202.98 512 256V320C512 355.35 483.35 384 448 384z" />
+    <glyph glyph-name="palette"
+      unicode="&#xF53F;"
+      horiz-adv-x="512" d=" M204.3 443C104.9 423.6 24.8 343.7 5.2 244.6C-31.8 57.6 136.9 -81.7999999999999 264 -62.1C305.2 -55.7 325.4 -7.5 306.5 29.6C283.4 74.9999999999999 316.4 128 367.4 128H447.1C482.9 128 511.9 157.6 512 193.3C511.5 350.9 368.1 474.9 204.3 443zM96 128C78.3 128 64 142.3 64 160S78.3 192 96 192S128 177.7 128 160S113.7 128 96 128zM128 256C110.3 256 96 270.3 96 288S110.3 320 128 320S160 305.7 160 288S145.7 256 128 256zM256 320C238.3 320 224 334.3 224 352S238.3 384 256 384S288 369.7 288 352S273.7 320 256 320zM384 256C366.3 256 352 270.3 352 288S366.3 320 384 320S416 305.7 416 288S401.7 256 384 256z" />
+    <glyph glyph-name="pallet"
+      unicode="&#xF482;"
+      horiz-adv-x="640" d=" M144 192H496C504.8 192 512 199.2 512 208V432C512 440.8 504.8 448 496 448H384V320L320 352L256 320V448H144C135.2 448 128 440.8 128 432V208C128 199.2 135.2 192 144 192zM624 64C632.8 64 640 71.2 640 80V112C640 120.8 632.8 128 624 128H16C7.2 128 0 120.8 0 112V80C0 71.2 7.2 64 16 64H64V0H16C7.2 0 0 -7.2 0 -16V-48C0 -56.8 7.2 -64 16 -64H624C632.8 -64 640 -56.8 640 -48V-16C640 -7.2 632.8 0 624 0H576V64H624zM288 0H128V64H288V0zM512 0H352V64H512V0z" />
+    <glyph glyph-name="paper-plane"
+      unicode="&#xF1D8;"
+      horiz-adv-x="512" d=" M476 444.8L12.5 177.4C-5.6 167 -3.3 141.8 14.7 134.2L121 89.6L408.3 342.8C413.8 347.7000000000001 421.6 340.2000000000001 416.9000000000001 334.5L176 41V-39.5C176 -63.1 204.5 -72.4 218.5 -55.3L282 22L406.6 -30.2C420.8 -36.2 437 -27.3 439.6 -12L511.6 420C515 440.2 493.3 454.8 476 444.8z" />
+    <glyph glyph-name="paperclip"
+      unicode="&#xF0C6;"
+      horiz-adv-x="448" d=" M43.246 -18.142C-15.184 42.147 -14.095 139.369 44.632 199.439L254.392 414C298.708 459.332 370.743 459.336 415.063 414C458.953 369.106 459.006 296.671 415.063 251.724L232.214 64.872C202.359 34.335 153.581 34.761 124.232 65.87C95.957 95.84 96.864 143.343 125.684 172.823L269.427 319.658C275.6090000000001 325.972 285.7390000000001 326.08 292.053 319.899L314.914 297.52C321.229 291.338 321.336 281.208 315.155 274.894L171.427 128.073C166.495 123.028 166.191 114.645 170.779 109.781C175.151 105.1469999999999 182.024 105.0699999999999 186.467 109.6159999999999L369.316 296.467C388.929 316.529 388.929 349.1919999999999 369.305 369.265C350.116 388.892 319.348 388.902 300.151 369.265L90.39 154.705C55.627 119.145 55.091 61.585 89.199 26.392C123.209 -8.701 178.184 -8.745 212.257 26.106L384.317 202.105C390.4940000000001 208.424 400.624 208.538 406.943 202.361L429.82 179.997C436.139 173.82 436.254 163.69 430.076 157.3710000000001L258.016 -18.627C198.44 -79.5649999999999 102.073 -78.843 43.246 -18.1419999999999z" />
+    <glyph glyph-name="parachute-box"
+      unicode="&#xF4CD;"
+      horiz-adv-x="512" d=" M511.9 273C502.8 348.6 433.5 405.4 353.6 431.7C390 392.3 416 331.1 416 256H444.1L327.5 126.5C325 127.1 322.7 128 320 128H272V256H384C384 371.2 315.1 448 256 448S128 371.2 128 256H240V128H192C189.3 128 187 127.1 184.5 126.5L67.9 256H96C96 331.1 122 392.3 158.4 431.7C78.5 405.3 9.2 348.5 0.1 273C-1 263.9 6.9 256 16.1 256H24.8L161.5 104.1C160.8 101.5 159.9 98.9 159.9 96V-32C159.9 -49.7 174.2 -64 191.9 -64H319.9C337.6 -64 351.9 -49.7 351.9 -32V96C351.9 98.9 351 101.4 350.3 104.1L487.1 256H495.8C505.1 256 513 263.8 511.9 273z" />
+    <glyph glyph-name="paragraph"
+      unicode="&#xF1DD;"
+      horiz-adv-x="448" d=" M408 416H177.531C88.948 416 16.045 344.665 16 256.082C15.956 167.679 87.607 96 176 96V-8C176 -21.255 186.745 -32 200 -32H232C245.255 -32 256 -21.255 256 -8V336H288V-8C288 -21.255 298.745 -32 312 -32H344C357.255 -32 368 -21.255 368 -8V336H408C421.255 336 432 346.745 432 360V392C432 405.255 421.255 416 408 416z" />
+    <glyph glyph-name="parking"
+      unicode="&#xF540;"
+      horiz-adv-x="448" d=" M400 416H48C21.5 416 0 394.5 0 368V16C0 -10.5 21.5 -32 48 -32H400C426.5 -32 448 -10.5 448 16V368C448 394.5 426.5 416 400 416zM240 128H192V80C192 71.2 184.8 64 176 64H144C135.2 64 128 71.2 128 80V304C128 312.8 135.2 320 144 320H240C292.9 320 336 276.9 336 224S292.9 128 240 128zM240 256H192V192H240C257.6 192 272 206.4 272 224S257.6 256 240 256z" />
+    <glyph glyph-name="passport"
+      unicode="&#xF5AB;"
+      horiz-adv-x="448" d=" M129.62 272H168.71C170.2 299.03 175.25 323.35 182.92 342.41C155.21 329.17 134.9 303.2200000000001 129.62 272zM129.62 240C134.91 208.78 155.21 182.83 182.92 169.59C175.24 188.65 170.2 212.97 168.71 240.0000000000001H129.62zM224 161.31C231.69 168.76 244.77 195.73 247.43 240H200.56C203.23 195.74 216.31 168.76 224 161.31zM200.57 272H247.44C244.78 316.26 231.7 343.24 224.01 350.69C216.31 343.24 203.23 316.26 200.57 272zM265.08 169.59C292.79 182.8300000000001 313.1 208.78 318.38 240.0000000000001H279.29C277.8 212.97 272.76 188.65 265.08 169.59zM416 448H64C28.65 448 0 419.35 0 384V0C0 -35.35 28.65 -64 64 -64H416C433.67 -64 448 -49.67 448 -32V416C448 433.67 433.67 448 416 448zM336 32H112C103.2 32 96 39.2 96 48S103.2 64 112 64H336C344.8 64 352 56.8 352 48S344.8 32 336 32zM224 128C153.31 128 96 185.31 96 256S153.31 384 224 384S352 326.69 352 256S294.69 128 224 128zM265.08 342.41C272.76 323.35 277.8 299.03 279.29 272H318.38C313.1 303.2200000000001 292.79 329.17 265.08 342.41z" />
+    <glyph glyph-name="pastafarianism"
+      unicode="&#xF67B;"
+      horiz-adv-x="640" d=" M624.54 100.33C591.8399999999999 112.85 567.18 96.08 549.17 83.88C532.11 72.35 525.92 69.46 517.76 72.52C509.64 75.61 506.93 81.9 501.87 101.9C498.54 115.05 494.43 131.22 483.92 144.55C486.16 147.46 488.35 150.34 490.3 153.12C500.47 143.55 513.71 136 532 136C565.95 136 582.87 161.78 594.06 178.83C604.65 194.97 609.06 200 616 200C629.25 200 640 210.75 640 224S629.25 248 616 248C582.05 248 565.13 222.22 553.94 205.17C543.34 189.03 538.94 184 532 184C514.69 184 494.52 245.43 434.74 285.91L451.99 320.41C485.43 322.5 512 350.02 512 384C512 419.35 483.35 448 448 448S384 419.35 384 384C384 370.98 387.94 358.9 394.62 348.79L376.4700000000001 312.49C359.49 317.0900000000001 340.87 320 320.0100000000001 320S280.5200000000001 317.0900000000001 263.5500000000001 312.49L245.4000000000001 348.79C252.06 358.9 256 370.98 256 384C256 419.35 227.35 448 192 448S128 419.35 128 384C128 350.02 154.56 322.5 188.02 320.4L205.27 285.9C145.68 245.56 125.15 184 108 184C101.06 184 96.66 189.03 86.06 205.17C74.88 222.22 57.96 248 24 248C10.75 248 0 237.25 0 224S10.75 200 24 200C30.94 200 35.34 194.97 45.94 178.83C57.13 161.78 74.05 136 108 136C126.29 136 139.53 143.55 149.7 153.11C151.65 150.32 153.84 147.45 156.08 144.54C145.57 131.21 141.46 115.04 138.13 101.89C133.07 81.89 130.36 75.61 122.24 72.5100000000001C114.13 69.4500000000001 107.91 72.34 90.83 83.8700000000001C72.8 96.0700000000001 48.11 112.7900000000001 15.46 100.3200000000001C3.07 95.6 -3.13 81.7400000000001 1.59 69.35C6.31 56.94 20.2 50.74 32.56 55.47C40.72 58.56 46.9 55.66 63.95 44.11C77.5 34.95 94.78 23.25 116.37 23.27C123.54 23.27 131.2 24.55 139.34 27.66C172 40.1 179.32 68.99 184.67 90.1C186.88 98.8200000000001 188.66 104.59 190.62 108.97C207.24 95.36 227.57 83.09 252.26 74.8C242.3 37.8 220.08 -16 192 -16C178.75 -16 168 -26.75 168 -40S178.75 -64 192 -64C258.74 -64 289.05 24.63 299.42 65.14C306.11 64.54 312.8400000000001 64 320 64S333.89 64.54 340.58 65.14C350.95 24.63 381.26 -64 448 -64C461.25 -64 472 -53.25 472 -40S461.25 -16 448 -16C420.06 -16 397.79 37.81 387.78 74.81C412.47 83.1 432.78 95.37 449.4 108.97C451.36 104.59 453.14 98.8200000000001 455.35 90.1C460.6899999999999 68.99 468.02 40.1 500.6799999999999 27.66C508.8199999999999 24.55 516.4799999999999 23.27 523.65 23.27C545.24 23.27 562.52 34.96 576.0699999999999 44.11C593.1199999999999 55.66 599.3499999999999 58.56 607.4599999999999 55.47C619.8499999999999 50.72 633.7299999999999 56.9400000000001 638.43 69.35C643.14 81.75 636.9399999999999 95.61 624.54 100.33zM448 400C456.82 400 464 392.82 464 384S456.82 368 448 368S432 375.18 432 384S439.18 400 448 400zM192 400C200.82 400 208 392.82 208 384S200.82 368 192 368S176 375.18 176 384S183.18 400 192 400z" />
+    <glyph glyph-name="paste"
+      unicode="&#xF0EA;"
+      horiz-adv-x="448" d=" M128 264C128 294.879 153.122 320 184 320H320V392C320 405.255 309.255 416 296 416H215.39C204.306 435.11 183.637 448 160 448S115.694 435.11 104.61 416H24C10.745 416 0 405.255 0 392V56C0 42.745 10.745 32 24 32H128V264zM160 408C173.255 408 184 397.255 184 384S173.255 360 160 360S136 370.745 136 384S146.745 408 160 408zM344 160H448V-40C448 -53.255 437.255 -64 424 -64H184C170.745 -64 160 -53.255 160 -40V264C160 277.255 170.745 288 184 288H320V184C320 170.8 330.8 160 344 160zM448 198.059V192H352V288H358.0590000000001A24 24 0 0 0 375.029 280.971L440.9700000000001 215.03A24.002000000000002 24.002000000000002 0 0 0 448 198.059z" />
+    <glyph glyph-name="pause-circle"
+      unicode="&#xF28B;"
+      horiz-adv-x="512" d=" M256 440C119 440 8 329 8 192S119 -56 256 -56S504 55 504 192S393 440 256 440zM240 112C240 103.2 232.8 96 224 96H176C167.2 96 160 103.2 160 112V272C160 280.8 167.2 288 176 288H224C232.8 288 240 280.8 240 272V112zM352 112C352 103.2 344.8 96 336 96H288C279.2 96 272 103.2 272 112V272C272 280.8 279.2 288 288 288H336C344.8 288 352 280.8 352 272V112z" />
+    <glyph glyph-name="pause"
+      unicode="&#xF04C;"
+      horiz-adv-x="448" d=" M144 -31H48C21.5 -31 0 -9.5 0 17V369C0 395.5 21.5 417 48 417H144C170.5 417 192 395.5 192 369V17C192 -9.5 170.5 -31 144 -31zM448 17V369C448 395.5 426.5 417 400 417H304C277.5 417 256 395.5 256 369V17C256 -9.5 277.5 -31 304 -31H400C426.5 -31 448 -9.5 448 17z" />
+    <glyph glyph-name="paw"
+      unicode="&#xF1B0;"
+      horiz-adv-x="512" d=" M256 224C176.59 224 64 101.24 64 23.75C64 -11.15 90.81 -32 135.74 -32C184.58 -32 216.83 -6.92 256 -6.92C295.51 -6.92 327.85 -32 376.26 -32C421.19 -32 448 -11.15 448 23.75C448 101.24 335.4100000000001 224 256 224zM108.72 236.61C98.32 271.26 66.28 293.7000000000001 37.16 286.74C8.04 279.78 -7.13 246.05 3.27 211.4C13.67 176.75 45.71 154.31 74.83 161.27C103.95 168.23 119.12 201.96 108.72 236.61zM193.44 257.39C224.38 265.5300000000001 239.86 307.3300000000001 228.02 350.75S181.5 422.76 150.56 414.62S104.14 364.68 115.98 321.26C127.82 277.8400000000001 162.51 249.24 193.44 257.39zM474.83 286.73C445.71 293.6900000000001 413.68 271.25 403.27 236.6C392.87 201.95 408.04 168.22 437.16 161.26C466.28 154.3 498.3099999999999 176.74 508.72 211.39C519.12 246.04 503.95 279.77 474.83 286.73zM318.56 257.39C349.5 249.25 384.18 277.8400000000001 396.0199999999999 321.26C407.8599999999999 364.68 392.38 406.4700000000001 361.44 414.62S295.82 394.17 283.98 350.75C272.14 307.3300000000001 287.62 265.5300000000001 318.56 257.3900000000001z" />
+    <glyph glyph-name="peace"
+      unicode="&#xF67C;"
+      horiz-adv-x="496" d=" M248 440C111.03 440 0 328.9700000000001 0 192S111.03 -56 248 -56S496 55.03 496 192S384.9700000000001 440 248 440zM432 192C432 160.07 423.8 130.03 409.43 103.83L280 207.37V373.03C366.23 357.82 432 282.53 432 192zM216 10.97C182.14 16.9400000000001 151.51 32.17 126.71 53.99L216 125.43V10.97zM280 125.43L369.29 54C344.49 32.18 313.86 16.95 280 10.98V125.43zM216 373.03V207.37L86.57 103.83C72.2 130.03 64 160.07 64 192C64 282.53 129.77 357.82 216 373.03z" />
+    <glyph glyph-name="pen-alt"
+      unicode="&#xF305;"
+      horiz-adv-x="512" d=" M497.94 373.83L437.83 433.94C419.08 452.69 388.67 452.69 369.92 433.94L313.37 377.39L441.39 249.37L497.94 305.92C516.69 324.67 516.69 355.0700000000001 497.94 373.8300000000001zM251.14 394.36C235.52 409.98 210.2 409.98 194.58 394.36L75.8 275.57C69.55 269.32 69.55 259.19 75.8 252.95L98.43 230.32C104.68 224.07 114.81 224.07 121.06 230.32L222.88 332.14L245.51 309.52L93.95 157.97A327.038 327.038 0 0 1 0.17 -37.11L0.14 -37.34C-1.56 -52.62 11.35 -65.5400000000001 26.63 -63.85A327.02000000000004 327.02000000000004 0 0 1 221.97 29.95L418.76 226.74L335.99 309.51L251.14 394.36z" />
+    <glyph glyph-name="pen-fancy"
+      unicode="&#xF5AC;"
+      horiz-adv-x="512" d=" M79.18 165.06A32.005 32.005 0 0 1 58.94 144.82L0 -32L4.69 -36.69L97.58 56.2C96.92 58.76 96.01 61.23 96.01 64C96.01 81.67 110.34 96 128.01 96S160.01 81.67 160.01 64S145.68 32 128.01 32C125.24 32 122.77 32.91 120.21 33.57L27.32 -59.32L32 -64L208.82 -5.06A31.982999999999997 31.982999999999997 0 0 1 229.06 15.18L262.13 99.25L163.25 198.13L79.18 165.06zM369.25 419.68L186.14 220.19L283.99 122.34L483.48 305.4500000000001C568.4 380.52 443.73 503.94 369.25 419.68z" />
+    <glyph glyph-name="pen-nib"
+      unicode="&#xF5AD;"
+      horiz-adv-x="512" d=" M136.6 309.2100000000001A64.003 64.003 0 0 1 93.29 267.86L0 -12L14.69 -26.69L164.8 123.42C161.81 129.68 160 136.6 160 144C160 170.51 181.49 192 208 192S256 170.51 256 144S234.51 96 208 96C200.6 96 193.68 97.81 187.42 100.8L37.31 -49.31L52 -64L331.86 29.29A64.003 64.003 0 0 1 373.2100000000001 72.6L416 224L288 352L136.6 309.2100000000001zM497.9399999999999 373.8300000000001L437.83 433.94C419.08 452.69 388.67 452.69 369.92 433.94L313.37 377.39L441.39 249.37L497.94 305.92C516.69 324.67 516.69 355.0700000000001 497.94 373.8300000000001z" />
+    <glyph glyph-name="pen-square"
+      unicode="&#xF14B;"
+      horiz-adv-x="448" d=" M400 -32H48C21.5 -32 0 -10.5 0 16V368C0 394.5 21.5 416 48 416H400C426.5 416 448 394.5 448 368V16C448 -10.5 426.5 -32 400 -32zM238.1 270.1L102.4 134.4L96.1 77.3C95.3 69.6999999999999 101.7 63.1999999999999 109.4 63.9999999999999L166.5 70.3L302.2 206C304.5 208.3 304.5 212.1 302.2 214.5L246.7 270C244.2 272.4 240.4 272.4 238.1 270.1zM345 282.9L314.9 313C305.5 322.4 290.3 322.4 281 313L257.9 289.9C255.6 287.6 255.6 283.8 257.9 281.4L313.4 225.9C315.7 223.6 319.5 223.6 321.9 225.9L345 249C354.3 258.3 354.3 273.5 345 282.9z" />
+    <glyph glyph-name="pen"
+      unicode="&#xF304;"
+      horiz-adv-x="512" d=" M290.74 354.76L418.76 226.74L140.77 -51.25L26.63 -63.85C11.35 -65.54 -1.56 -52.62 0.14 -37.34L12.84 76.88L290.74 354.76zM497.94 373.82L437.83 433.93C419.08 452.68 388.67 452.68 369.92 433.93L313.37 377.38L441.39 249.36L497.94 305.9100000000001C516.69 324.67 516.69 355.0700000000001 497.94 373.8200000000001z" />
+    <glyph glyph-name="pencil-alt"
+      unicode="&#xF303;"
+      horiz-adv-x="512" d=" M497.9 305.9L451.8 259.8C447.1 255.1 439.5 255.1 434.8 259.8L323.8 370.8C319.1 375.5 319.1 383.1 323.8 387.8L369.9 433.9000000000001C388.6 452.6 419 452.6 437.8 433.9000000000001L497.9 373.8C516.6999999999999 355.1 516.6999999999999 324.7000000000001 497.9 305.9zM284.2 348.2L21.6 85.6L0.4 -35.9C-2.5 -52.3 11.8 -66.5 28.2 -63.7L149.7 -42.4L412.3 220.2000000000001C417 224.9 417 232.5000000000001 412.3 237.2000000000001L301.3 348.2000000000001C296.5 352.9000000000001 288.9000000000001 352.9000000000001 284.2 348.2000000000001zM124.1 108.1C118.6 113.6 118.6 122.4 124.1 127.9L278.1 281.9000000000001C283.6 287.4000000000001 292.4000000000001 287.4000000000001 297.9000000000001 281.9000000000001S303.4000000000001 267.6 297.9000000000001 262.1L143.9 108.1C138.4 102.6 129.6 102.6 124.1 108.1zM88 24H136V-12.3L71.5 -23.6L40.4 7.5L51.7 72H88V24z" />
+    <glyph glyph-name="pencil-ruler"
+      unicode="&#xF5AE;"
+      horiz-adv-x="512" d=" M109.46 203.96L244.04 338.52L199.92 382.64L138.24 320.9600000000001A7.919 7.919 0 0 0 127.03 320.9600000000001L115.82 332.17C112.72 335.27 112.72 340.29 115.82 343.38L177.5 405.06L143.86 438.7100000000001C131.47 451.1 111.39 451.1 99 438.71L9.29 349C-3.09 336.61 -3.1 316.53 9.29 304.14L109.46 203.96zM497.93 320.76C516.69 339.52 516.6800000000001 369.93 497.93 388.69L452.68 433.94C433.92 452.7 403.5 452.7 384.73 433.94L338.7100000000001 387.93L451.91 274.73L497.93 320.76zM316.08 365.29L19.08 68.33L0.32 -39.11C-2.21 -53.6 10.41 -66.22 24.91 -63.67L132.36 -44.83L429.28 252.1L316.08 365.29zM502.71 79.86L469.07 113.5L407.39 51.82C404.29 48.72 399.27 48.72 396.18 51.82L384.9700000000001 63.03C381.8800000000001 66.13 381.8800000000001 71.15 384.9700000000001 74.24L446.6500000000001 135.92L402.5100000000001 180.06L267.93 45.5L368.14 -54.7C380.53 -67.09 400.61 -67.09 413 -54.7L502.71 35C515.1 47.39 515.1 67.47 502.71 79.86z" />
+    <glyph glyph-name="people-carry"
+      unicode="&#xF4CE;"
+      horiz-adv-x="640" d=" M128 352C154.5 352 176 373.5 176 400S154.5 448 128 448S80 426.5 80 400S101.5 352 128 352zM512 352C538.5 352 560 373.5 560 400S538.5 448 512 448S464 426.5 464 400S485.5 352 512 352zM637.7 -20.1L593.7 89.9L552.6 43.5L550.6 25.3L578.3000000000001 -43.9C583.3000000000001 -56.4 595.3000000000001 -64 608.0000000000001 -64C612.0000000000001 -64 616.0000000000001 -63.3 619.9000000000001 -61.7C636.3000000000001 -55.1 644.3000000000001 -36.5 637.7 -20.1zM603.5 189.7L585 269.9C580.4 289.9 566.4 306.7 547.5 314.8C529 322.8 508.5 321.5 491.4 311.5C468.7 298.1 451.7 277 443.3 252.1L432 218.2L416 208V304C416 312.8 408.8 320 400 320H240C231.2 320 224 312.8 224 304V208L207.9 218.2L196.6 252.1C188.3 277.1 171.2 298.1 148.5 311.5C131.3 321.5 110.9 322.8 92.4 314.8C73.5 306.7000000000001 59.5 289.9 54.9 269.9L36.5 189.7C31.9 169.7 37.2 148.5 50.9 133L118.1 57.1L128.2 -35.5C130 -51.8 143.8 -64 160 -64C161.2 -64 162.3 -63.9 163.5 -63.8C181.1 -61.9 193.7 -46.1 191.8 -28.5L181.7 64.3C180.2 77.3 174.8 89.4 166.1 99.3L122.8 148.3L140.4 218.6L147.2000000000001 198.2C151.3000000000001 185.7000000000001 159.1000000000001 174.8 171.7000000000001 165.6L222.8000000000001 133.1C227.4000000000001 130.2000000000001 234.9000000000001 128.5 240.0000000000001 128.1H400C405.1 128.5 412.6 130.2000000000001 417.2 133.1L468.3 165.6C480.9 174.8 488.7 185.6 492.8 198.2L499.6 218.6L517.2 148.3L473.9 99.3C465.2 89.4 459.8 77.3 458.3 64.3L448.2 -28.5C446.3 -46.1 459 -61.9 476.5 -63.8C477.7 -63.9 478.8 -64 480 -64C496.1 -64 510 -51.9 511.8 -35.5L521.9 57.1L589.1 133C602.7 148.5 608.1 169.7 603.5 189.7zM46.3 89.9L2.3 -20.1C-4.3 -36.5 3.7 -55.1 20.1 -61.7C36.9 -68.3000000000001 55.2 -60.0000000000001 61.7 -43.9L89.4 25.3L87.4 43.4999999999999L46.3 89.8999999999999z" />
+    <glyph glyph-name="percent"
+      unicode="&#xF295;"
+      horiz-adv-x="448" d=" M112 224C173.9 224 224 274.1 224 336S173.9 448 112 448S0 397.9 0 336S50.1 224 112 224zM112 384C138.5 384 160 362.5 160 336S138.5 288 112 288S64 309.5 64 336S85.5 384 112 384zM336 160C274.1 160 224 109.9 224 48S274.1 -64 336 -64S448 -13.9 448 48S397.9 160 336 160zM336 0C309.5 0 288 21.5 288 48S309.5 96 336 96S384 74.5 384 48S362.5 0 336 0zM392.3 447.8L423.9000000000001 447.9C443.3 448 454.8 426.1 443.6 410.1L77.4 -53.6A23.95 23.95 0 0 0 57.8 -63.8L24.4 -63.9C4.9 -63.9 -6.5 -42.0000000000001 4.7 -26.1L372.7 437.6C377.2 444 384.5 447.8 392.3 447.8z" />
+    <glyph glyph-name="percentage"
+      unicode="&#xF541;"
+      horiz-adv-x="384" d=" M109.25 274.75C134.24 299.74 134.24 340.27 109.25 365.26C84.26 390.25 43.73 390.25 18.74 365.26C-6.25 340.27 -6.25 299.74 18.74 274.75C43.74 249.75 84.26 249.75 109.25 274.75zM365.25 109.26C340.26 134.25 299.73 134.25 274.74 109.26C249.75 84.27 249.75 43.74 274.74 18.75C299.73 -6.24 340.26 -6.24 365.25 18.75C390.25 43.74 390.25 84.26 365.25 109.26zM363.31 340.69L340.69 363.31C328.19 375.81 307.93 375.81 295.44 363.31L20.69 88.56C8.19 76.06 8.19 55.8 20.69 43.31L43.31 20.69C55.81 8.19 76.07 8.19 88.56 20.69L363.31 295.44C375.81 307.93 375.81 328.19 363.31 340.69z" />
+    <glyph glyph-name="person-booth"
+      unicode="&#xF756;"
+      horiz-adv-x="576" d=" M192 -48C192 -56.8 199.2 -64 208 -64H240C248.8 -64 256 -56.8 256 -48V128H192V-48zM224 224H173.1L127.9 269.3C115.8 281.4 99.7 288 82.7 288H64C46.9 288 30.8 281.3 18.7 269.2C6.7 257.1 0 241 0 223.9L0.2 128L0 -32C0 -49.7 14.3 -64 31.9 -64C49.5 -64 63.9 -49.7 63.9 -32L64 68.7C64.9 68.2 65.6 67.4 66.5 67L95.6 24V-32C95.6 -49.7 109.9 -64 127.6 -64S159.6 -49.7 159.6 -32V24.5C159.6 34.4 157.3 44.3 152.9 53.1L111.7 114.4V195L132.6 174.1C141.7 165 153.7 160 166.5 160H224C241.7 160 256 174.3 256 192S241.7 224 224 224zM64 320C90.5 320 112 341.5 112 368S90.5 416 64 416S16 394.5 16 368S37.5 320 64 320zM288 416L319.5 192.9L288.6 38.3C284.3 16.7 301.6 0 320 0C335.2 0 348 9.1 352.3 30.4C353.2 13.5 366.9000000000001 0 384 0C401.7 0 416 14.3 416 32C416 14.3 430.3 0 448 0S480 14.3 480 32V448H288V416zM192 416V256H256V448H224C206.3 448 192 433.7 192 416zM544 448H512V-48C512 -56.8 519.2 -64 528 -64H560C568.8 -64 576 -56.8 576 -48V416C576 433.7 561.7 448 544 448z" />
+    <glyph glyph-name="phone-slash"
+      unicode="&#xF3DD;"
+      horiz-adv-x="640" d=" M268.2 66.6L218.6 127.2000000000001C211.8 135.5000000000001 200.4 138.3000000000001 190.6 134.1L78.6 86.1C67.9 81.5 62.1 70 64.7 58.6L88.7 -45.4C91.2 -56.2 100.8 -64 112.1 -64C212.8 -64 305.8 -31.6 381.8 22.9L301.8 84.7C290.9 78.2 279.7 72 268.2 66.6zM633.8 -10.1L475.1 112.5C537.9 191.6 576 291.1 576 400C576 411.2 568.3 420.9 557.4 423.4L453.4 447.4C442.1 450 430.5 444.1 425.9 433.5L377.9 321.5C373.7 311.7 376.5 300.2 384.8 293.5L445.4 243.9C433.2 217.8 417.5 193.6 399.4 171.1L45.5 444.6C38.5 450 28.5 448.8 23 441.8L3.4 416.6C-2 409.6 -0.8 399.6 6.2 394.2L594.6 -60.5C601.6 -65.9 611.6 -64.7 617.1 -57.7L636.7 -32.4C642.1 -25.6 640.8000000000001 -15.5 633.8000000000001 -10.1z" />
+    <glyph glyph-name="phone-square"
+      unicode="&#xF098;"
+      horiz-adv-x="448" d=" M400 416H48C21.49 416 0 394.51 0 368V16C0 -10.51 21.49 -32 48 -32H400C426.51 -32 448 -10.51 448 16V368C448 394.51 426.51 416 400 416zM94 32C86.967 32 80.943 36.873 79.384 43.627L64.386 108.627A15 15 0 0 0 73.093 125.787L143.091 155.7860000000001A15 15 0 0 0 160.609 151.4970000000001L191.606 113.6120000000001C240.55 136.5750000000001 279.903 176.4700000000001 302.387 224.3920000000001L264.501 255.3890000000001A15.000999999999998 15.000999999999998 0 0 0 260.212 272.9070000000001L290.212 342.9050000000001A15 15 0 0 0 307.372 351.6120000000001L372.372 336.6140000000001A14.997000000000002 14.997000000000002 0 0 0 384 322C384 161.708 254.055 32 94 32z" />
+    <glyph glyph-name="phone-volume"
+      unicode="&#xF2A0;"
+      horiz-adv-x="384" d=" M97.333 -58.966C-32.541 70.908 -32.348 281.286 97.333 410.967C103.031 416.665 111.86 417.599 118.596 413.389L183.413 372.876A17.187 17.187 0 0 0 190.262 351.918L157.854 270.897A17.188 17.188 0 0 0 140.185 260.178L84.375 265.7580000000001C63.324 207.497 63.763 143.287 84.375 86.2430000000001L140.186 91.8240000000001A17.188 17.188 0 0 0 157.855 81.1050000000001L190.263 0.0830000000001A17.188 17.188 0 0 0 183.414 -20.8749999999999L118.597 -61.3879999999999A17.19 17.19 0 0 0 97.333 -58.9659999999998zM247.126 352.527C258.9580000000001 332.48 258.9580000000001 307.519 247.126 287.472C243.176 280.779 234.018 279.513 228.408 284.891L222.433 290.617C218.522 294.365 217.64 300.239 220.172 305.027A32.063 32.063 0 0 1 220.172 334.972C217.6390000000001 339.76 218.522 345.634 222.433 349.382L228.408 355.108C234.0180000000001 360.486 243.176 359.22 247.126 352.527zM338.913 443.714C399.053 372.11 399.005 267.832 338.913 196.286C334.439 190.959 326.3830000000001 190.54 321.361 195.353L315.563 200.91C311.003 205.281 310.586 212.439 314.633 217.289C364.32 276.827 364.279 363.222 314.633 422.711C310.586 427.561 311.002 434.719 315.563 439.09L321.361 444.647C326.383 449.46 334.439 449.041 338.913 443.714zM292.9410000000001 398.773C328.9910000000001 352.451 329.0490000000001 287.624 292.9410000000001 241.227C288.5510000000001 235.586 280.2440000000001 234.976 275.0850000000001 239.923L269.2670000000001 245.502C264.8670000000001 249.721 264.2690000000001 256.597 267.9820000000001 261.433C294.5180000000001 295.997 294.516 344.005 267.9820000000001 378.567C264.269 383.403 264.867 390.278 269.2670000000001 394.4980000000001L275.0850000000001 400.077C280.2440000000001 405.024 288.5510000000001 404.414 292.9410000000001 398.773z" />
+    <glyph glyph-name="phone"
+      unicode="&#xF095;"
+      horiz-adv-x="512" d=" M493.4 423.4L389.4 447.4C378.1 450 366.5 444.1 361.9 433.5L313.9 321.5C309.7 311.7 312.5 300.2 320.8 293.5L381.4 243.9C345.4 167.2 282.5 103.4 204.2 66.7000000000001L154.6 127.3000000000001C147.8 135.6000000000001 136.4 138.4000000000001 126.6 134.2000000000001L14.6 86.2000000000001C3.9 81.5 -2 69.9 0.6 58.6L24.6 -45.4C27.1 -56.2 36.7 -64 48 -64C304.1 -64 512 143.5 512 400C512 411.2 504.3 420.9 493.4 423.4z" />
+    <glyph glyph-name="piggy-bank"
+      unicode="&#xF4D3;"
+      horiz-adv-x="576" d=" M560 224H530.5C521.7 244 508.9 261.7 493.1 276.5L512 352H480C450.6 352 424.6 338.5 407 317.7C399.4 318.8 391.9 320 384 320H256C178.6 320 114.1 265 99.2 192H56C41.2 192 29.5 205.5 32.5 220.8C34.7 232.2 45.4 240 57 240H58C61.3 240 64 242.7 64 246V266C64 269.3 61.3 272 58 272C29.5 272 4.1 251.6 0.5 223.4C-3.9 189.2 22.7 160 56 160H96C96 107.8 121.4 61.9 160 32.7V-48C160 -56.8 167.2 -64 176 -64H240C248.8 -64 256 -56.8 256 -48V0H384V-48C384 -56.8 391.2 -64 400 -64H464C472.8 -64 480 -56.8 480 -48V32.7C491.8 41.6 502.3 52.1 511.3 64H560C568.8 64 576 71.2 576 80V208C576 216.8 568.8 224 560 224zM432 160C423.2 160 416 167.2 416 176S423.2 192 432 192S448 184.8 448 176S440.8 160 432 160zM256 352H384C389.4 352 394.7 351.6 399.9 351.2C399.9 351.5 400 351.7 400 352C400 405 357 448 304 448S208 405 208 352C208 349.9 208.5 347.9 208.6 345.8C223.8 349.7 239.6 352 256 352z" />
+    <glyph glyph-name="pills"
+      unicode="&#xF484;"
+      horiz-adv-x="576" d=" M112 416C50.1 416 0 365.9 0 304V80C0 18.1 50.1 -32 112 -32S224 18.1 224 80V304C224 365.9 173.9 416 112 416zM160 192H64V304C64 330.5 85.5 352 112 352S160 330.5 160 304V192zM299.7 221.7C296.2 225.2 290.3 224.8 287.4 220.9C242.1 158.4 247 70.8 303.3 14.5C359.6 -41.8 447.2 -46.7 509.6999999999999 -1.4C513.6999999999999 1.5 513.9999999999999 7.4 510.4999999999999 10.9L299.7 221.7zM529.5 240.7C473.2 297 385.6 301.9 323.1 256.6C319.1 253.7 318.8 247.8 322.3 244.3L533.1 33.5C536.6 30 542.5 30.4 545.4 34.3C590.6999999999999 96.9 585.9 184.4 529.5 240.7z" />
+    <glyph glyph-name="place-of-worship"
+      unicode="&#xF67F;"
+      horiz-adv-x="640" d=" M620.61 81.45L512 128V-64H624C632.84 -64 640 -56.84 640 -48V52.04A32 32 0 0 1 620.61 81.4500000000001zM0 52.04V-48C0 -56.84 7.16 -64 16 -64H128V128L19.39 81.45A32 32 0 0 1 0 52.04zM464.46 201.32L416 230.4V345.37C416 353.86 412.63 361.99 406.62 368L331.31 443.31C325.06 449.56 314.93 449.56 308.69 443.31L233.38 368C227.38 362 224 353.86 224 345.37V230.4L175.54 201.32A31.997 31.997 0 0 1 160 173.88V-64H256V32C256 67.35 284.6600000000001 96 320 96S384 67.35 384 32V-64H480V173.88C480 185.12 474.1 195.54 464.46 201.32z" />
+    <glyph glyph-name="plane-arrival"
+      unicode="&#xF5AF;"
+      horiz-adv-x="640" d=" M624 0H16C7.16 0 0 -7.16 0 -16V-48C0 -56.84 7.16 -64 16 -64H624C632.84 -64 640 -56.84 640 -48V-16C640 -7.16 632.84 0 624 0zM44.81 242.34L133.55 162.34A62.60699999999999 62.60699999999999 0 0 1 159.02 148.41L446.62 70.0600000000001C473.1 62.8500000000001 501.18 61.34 527.62 68.7000000000001C557.29 76.97 571.06 89.91 574.87 104.41C578.7 118.91 573.14 137.12 551.5 159.37C532.22 179.19 507.15 192.16 480.67 199.37L383.1600000000001 225.93L282.8 417.78C281.29 423.59 276.85 428.13 271.14 429.69L206.05 447.42C195.49 450.3 185.15 442.1 185.34 430.98L233.26 266.77L131.06 294.61L103.47 362.49C101.54 367.38 97.46 371.06 92.45 372.42L52.72 383.25C42.38 386.07 32.19 378.25 32 367.37L32.23 265.5900000000001C32.42 256.68 38.26 248.25 44.81 242.34z" />
+    <glyph glyph-name="plane-departure"
+      unicode="&#xF5B0;"
+      horiz-adv-x="640" d=" M624 0H16C7.16 0 0 -7.16 0 -16V-48C0 -56.84 7.16 -64 16 -64H624C632.84 -64 640 -56.84 640 -48V-16C640 -7.16 632.84 0 624 0zM80.55 106.73C86.83 99.89 95.65 96.01 104.88 96.02L235.42 96.2000000000001A65.62 65.62 0 0 1 265.06 103.3200000000001L556.02 250.9700000000001C582.76 264.5400000000001 606.73 283.9100000000001 623.04 309.2800000000001C641.3499999999999 337.7600000000001 643.3399999999999 358.3700000000001 636.11 372.9300000000001C628.9 387.5000000000001 611.37 398.2000000000001 577.86 400.3800000000001C548.01 402.3200000000001 518.32 394.4600000000001 491.58 380.9000000000001L393.0700000000001 330.9100000000001L174.3700000000001 412.9700000000001A17.799 17.799 0 0 1 156.3700000000001 414.0800000000001L90.62 380.71C79.95 375.3 77.37 361.06 85.45 352.18L241.67 254.08L138.46 201.7L66.11 238.17A17.804 17.804 0 0 1 50.04 238.15L9.91 217.78C-0.53 212.48 -3.28 198.66 4.34 189.7L80.55 106.73z" />
+    <glyph glyph-name="plane"
+      unicode="&#xF072;"
+      horiz-adv-x="576" d=" M480 256H365.71L260.61 439.94A16.014 16.014 0 0 1 246.71 448H181.21C170.58 448 162.91 437.83 165.83 427.61L214.86 256H112L68.8 313.6C65.78 317.63 61.03 320 56 320H16.01C5.6 320 -2.04 310.2200000000001 0.49 300.12L32 192L0.49 83.88C-2.04 73.78 5.6 64 16.01 64H56C61.04 64 65.78 66.37 68.8 70.4L112 128H214.86L165.83 -43.6C162.91 -53.82 170.58 -64 181.21 -64H246.71C252.45 -64 257.75 -60.92 260.6 -55.94L365.71 128H480C515.35 128 576 156.65 576 192S515.35 256 480 256z" />
+    <glyph glyph-name="play-circle"
+      unicode="&#xF144;"
+      horiz-adv-x="512" d=" M256 440C119 440 8 329 8 192S119 -56 256 -56S504 55 504 192S393 440 256 440zM371.7 168L195.7 67C179.9 58.2 160 69.5 160 88V296C160 314.4 179.8 325.8 195.7 317L371.7 210C388.1 200.8 388.1 177.1 371.7 168z" />
+    <glyph glyph-name="play"
+      unicode="&#xF04B;"
+      horiz-adv-x="448" d=" M424.4 233.3L72.4 441.4C43.8 458.3 0 441.9 0 400.1V-16C0 -53.5 40.7 -76.1 72.4 -57.3L424.4 150.7C455.8 169.2 455.9 214.8 424.4 233.3z" />
+    <glyph glyph-name="plug"
+      unicode="&#xF1E6;"
+      horiz-adv-x="384" d=" M256 304V416C256 433.673 270.327 448 288 448S320 433.673 320 416V304H256zM368 288H16C7.163 288 0 280.837 0 272V240C0 231.163 7.163 224 16 224H32V192C32 114.594 86.969 50.029 160 35.204V-64H224V35.204C297.031 50.029 352 114.594 352 192V224H368C376.837 224 384 231.163 384 240V272C384 280.837 376.837 288 368 288zM128 304V416C128 433.673 113.673 448 96 448S64 433.673 64 416V304H128z" />
+    <glyph glyph-name="plus-circle"
+      unicode="&#xF055;"
+      horiz-adv-x="512" d=" M256 440C119 440 8 329 8 192S119 -56 256 -56S504 55 504 192S393 440 256 440zM400 164C400 157.4 394.6 152 388 152H296V60C296 53.4 290.6 48 284 48H228C221.4 48 216 53.4 216 60V152H124C117.4 152 112 157.4 112 164V220C112 226.6 117.4 232 124 232H216V324C216 330.6 221.4 336 228 336H284C290.6 336 296 330.6 296 324V232H388C394.6 232 400 226.6 400 220V164z" />
+    <glyph glyph-name="plus-square"
+      unicode="&#xF0FE;"
+      horiz-adv-x="448" d=" M400 416H48C21.5 416 0 394.5 0 368V16C0 -10.5 21.5 -32 48 -32H400C426.5 -32 448 -10.5 448 16V368C448 394.5 426.5 416 400 416zM368 164C368 157.4 362.6 152 356 152H264V60C264 53.4 258.6 48 252 48H196C189.4 48 184 53.4 184 60V152H92C85.4 152 80 157.4 80 164V220C80 226.6 85.4 232 92 232H184V324C184 330.6 189.4 336 196 336H252C258.6 336 264 330.6 264 324V232H356C362.6 232 368 226.6 368 220V164z" />
+    <glyph glyph-name="plus"
+      unicode="&#xF067;"
+      horiz-adv-x="448" d=" M416 240H272V384C272 401.67 257.67 416 240 416H208C190.33 416 176 401.67 176 384V240H32C14.33 240 0 225.67 0 208V176C0 158.33 14.33 144 32 144H176V0C176 -17.67 190.33 -32 208 -32H240C257.67 -32 272 -17.67 272 0V144H416C433.67 144 448 158.33 448 176V208C448 225.67 433.67 240 416 240z" />
+    <glyph glyph-name="podcast"
+      unicode="&#xF2CE;"
+      horiz-adv-x="448" d=" M267.429 -40.563C262.286 -59.573 242.858 -64 224 -64C205.143 -64 185.714 -59.573 180.572 -40.563C172.927 -12.134 160 59.102 160 92.25C160 127.406 191.142 136 224 136S288 127.406 288 92.25C288 59.301 275.129 -11.929 267.429 -40.563zM156.867 159.446C138.174 177.754 126.909 203.619 128.083 232.045C130.137 281.769 170.478 322.0010000000001 220.207 323.9260000000001C274.862 326.0420000000001 320 282.193 320 228C320 201.173 308.936 176.884 291.134 159.448C288.459 156.828 288.733 152.462 291.762 150.261C301.074 143.496 308.222 134.918 312.996 124.898C314.737 121.244 319.493 120.238 322.445 123.007C351.271 150.05 368.998 188.79 367.956 231.572C366.101 307.778 304.361 369.78 228.163 371.9409999999999C146.869 374.247 80 308.785 80 228C80 186.639 97.532 149.3 125.55 123.011C128.503 120.24 133.261 121.241 135.003 124.898C139.777 134.919 146.926 143.496 156.238 150.261C159.267 152.461 159.542 156.827 156.867 159.446zM224 448C100.204 448 0 347.815 0 224C0 134.008 52.602 58.353 125.739 22.592C130.072 20.474 135.006 24.136 134.274 28.902C131.892 44.414 129.932 59.848 128.868 73.241C128.722 75.077 127.719 76.727 126.19 77.753C78.79 109.559 47.626 163.769 48.003 225.1C48.595 321.337 127.293 399.748 223.532 399.999C320.793 400.253 400 321.203 400 224C400 162.068 367.842 107.51 319.35 76.133C318.351 62.0960000000001 316.281 45.545 313.726 28.903C312.994 24.136 317.929 20.474 322.261 22.593C395.227 58.273 448 133.813 448 224C448 347.795 347.815 448 224 448zM224 288C188.654 288 160 259.346 160 224S188.654 160 224 160S288 188.654 288 224S259.346 288 224 288z" />
+    <glyph glyph-name="poll-h"
+      unicode="&#xF682;"
+      horiz-adv-x="448" d=" M448 16V368C448 394.5 426.5 416 400 416H48C21.5 416 0 394.5 0 368V16C0 -10.5 21.5 -32 48 -32H400C426.5 -32 448 -10.5 448 16zM112 256C103.16 256 96 263.16 96 272V304C96 312.8400000000001 103.16 320 112 320H240C248.84 320 256 312.8400000000001 256 304V272C256 263.16 248.84 256 240 256H112zM112 160C103.16 160 96 167.16 96 176V208C96 216.84 103.16 224 112 224H336C344.84 224 352 216.84 352 208V176C352 167.16 344.84 160 336 160H112zM112 64C103.16 64 96 71.16 96 80V112C96 120.84 103.16 128 112 128H176C184.84 128 192 120.84 192 112V80C192 71.16 184.84 64 176 64H112z" />
+    <glyph glyph-name="poll"
+      unicode="&#xF681;"
+      horiz-adv-x="448" d=" M400 416H48C21.5 416 0 394.5 0 368V16C0 -10.5 21.5 -32 48 -32H400C426.5 -32 448 -10.5 448 16V368C448 394.5 426.5 416 400 416zM160 80C160 71.16 152.84 64 144 64H112C103.16 64 96 71.16 96 80V208C96 216.84 103.16 224 112 224H144C152.84 224 160 216.84 160 208V80zM256 80C256 71.16 248.84 64 240 64H208C199.16 64 192 71.16 192 80V304C192 312.8400000000001 199.16 320 208 320H240C248.84 320 256 312.8400000000001 256 304V80zM352 80C352 71.16 344.84 64 336 64H304C295.1600000000001 64 288 71.16 288 80V144C288 152.84 295.1600000000001 160 304 160H336C344.84 160 352 152.84 352 144V80z" />
+    <glyph glyph-name="poo-storm"
+      unicode="&#xF75A;"
+      horiz-adv-x="448" d=" M308 112H250.3L267.6 176.9C269.6 184.5 263.9000000000001 192 256 192H188C182 192 176.9 187.5 176.1 181.6L160.1 61.6C159.1 54.4 164.7 48 172 48H231.3L208.3 -49.2C206.5 -56.8 212.3 -64 220 -64C224.2 -64 228.2 -61.8 230.4 -58L318.4 94C323 102 317.2 112 308 112zM374.4 223.3C380.3 232.9 384 243.9 384 256C384 291.3 355.3 320 320 320H314.1C317.7000000000001 330.1 320 340.7 320 352C320 405 277 448 224 448C218.8 448 213.8 447.3 208.9 446.5C218.3 433.4 224 417.4 224 400C224 355.8 188.2 320 144 320H128C92.7 320 64 291.3 64 256C64 243.9 67.7 232.9 73.6 223.3C32.6 220 0 185.8 0 144C0 100 36 64 80 64H128.3C128.4 64.6 128.3 65.2 128.3 65.8L144.3 185.8C147.3 207.6 166 224 188 224H256C269.8 224 282.5 217.7 290.9 206.8S302.1 182 298.5 168.7L291.9 144H307.9C323.6 144 338.2 135.6 346 122C353.8 108.4 353.8 91.5 346 78L337.9 64H367.9C411.9 64 447.9 100 447.9 144C448 185.8 415.4 220 374.4 223.3z" />
+    <glyph glyph-name="poo"
+      unicode="&#xF2FE;"
+      horiz-adv-x="512" d=" M451.4 78.9C468.7 92 480 112.6 480 136C480 175.8 447.8 208 408 208H393.9C407.3 219.7 416 236.8 416 256C416 291.3 387.3 320 352 320H346.1C349.7000000000001 330.1 352 340.7 352 352C352 405 309 448 256 448C250.8 448 245.8 447.3 240.9 446.5C250.3 433.4 256 417.4 256 400C256 355.8 220.2 320 176 320H160C124.7 320 96 291.3 96 256C96 236.8 104.7 219.7 118.1 208H104C64.2 208 32 175.8 32 136C32 112.6 43.3 92 60.6 78.9C26.3 73.4 0 43.9 0 8C0 -31.8 32.2 -64 72 -64H440C479.8 -64 512 -31.8 512 8C512 43.9 485.7 73.4 451.4 78.9zM192 192C209.7 192 224 177.7 224 160S209.7 128 192 128S160 142.3 160 160S174.3 192 192 192zM351.5 53C341 25.1 293 0 256 0S171 25.1 160.5 53C158.5 58.3 162.5 64 168.3 64H343.7000000000001C349.5000000000001 64 353.5000000000001 58.3 351.5000000000001 53zM320 128C302.3 128 288 142.3 288 160S302.3 192 320 192S352 177.7 352 160S337.7 128 320 128z" />
+    <glyph glyph-name="poop"
+      unicode="&#xF619;"
+      horiz-adv-x="512" d=" M451.36 78.86C468.66 92.01 480 112.59 480 136C480 175.77 447.76 208 408 208H393.93C407.35 219.73 416 236.78 416 256C416 291.35 387.35 320 352 320H346.12C349.69 330.05 352 340.7200000000001 352 352C352 405.02 309.02 448 256 448C250.83 448 245.85 447.26 240.89 446.48C250.31 433.36 256 417.38 256 400C256 355.82 220.18 320 176 320H160C124.65 320 96 291.35 96 256C96 236.78 104.65 219.73 118.07 208H104C64.24 208 32 175.77 32 136C32 112.59 43.34 92.01 60.64 78.86C26.31 73.38 0 43.88 0 8C0 -31.76 32.24 -64 72 -64H440C479.76 -64 512 -31.76 512 8C512 43.88 485.69 73.38 451.36 78.86z" />
+    <glyph glyph-name="portrait"
+      unicode="&#xF3E0;"
+      horiz-adv-x="384" d=" M336 448H48C21.5 448 0 426.5 0 400V-16C0 -42.5 21.5 -64 48 -64H336C362.5 -64 384 -42.5 384 -16V400C384 426.5 362.5 448 336 448zM192 320C227.3 320 256 291.3 256 256S227.3 192 192 192S128 220.7 128 256S156.7 320 192 320zM304 83.2C304 72.6 294 64 281.6 64H102.4C90 64 80 72.6 80 83.2V102.4C80 134.2 110.1 160 147.2 160H152.2C164.5 154.9 177.9 152 192 152S219.6 154.9 231.8 160H236.8C273.9000000000001 160 304 134.2 304 102.4V83.2z" />
+    <glyph glyph-name="pound-sign"
+      unicode="&#xF154;"
+      horiz-adv-x="320" d=" M308 96H262.505C255.878 96 250.505 90.627 250.505 84V33.152H128V160H212C218.627 160 224 165.373 224 172V212C224 218.627 218.627 224 212 224H128V287.556C128 319.822 152.562 344.642 189.792 344.642C213.45 344.642 235.67 333.137 247.444 325.793C252.5950000000001 322.58 259.332 323.742 263.132 328.478L291.625 363.991C295.858 369.267 294.904 376.996 289.506 381.072C273.124 393.44 236.576 416 187.931 416C106.026 416 48 363.258 48 290.039V224H20C13.373 224 8 218.627 8 212V172C8 165.373 13.373 160 20 160H48V32H12C5.373 32 0 26.627 0 20V-20C0 -26.627 5.373 -32 12 -32H308C314.627 -32 320 -26.627 320 -20V84C320 90.627 314.627 96 308 96z" />
+    <glyph glyph-name="power-off"
+      unicode="&#xF011;"
+      horiz-adv-x="512" d=" M400 393.9C463 348.9 504 275.3 504 192C504 55.2 393.2 -55.7 256.5 -56C120 -56.3 8.2 55 8 191.6C7.9 274.9 48.9 348.7 111.8 393.8C123.5 402.1 139.8 398.6 146.8 386.1L162.6 358C168.5 347.5 165.7 334.2 156 327C114.5 296.2 88 247.4 88 192.1C87.9 99.8 162.5 24 256 24C347.6 24 424.6 98.2 424 193.1C423.7 244.9 399.3 294.9 355.9 327.1C346.2 334.3 343.5 347.6 349.4 358L365.2 386.1C372.2 398.5 388.4 402.2 400 393.9zM296 184V424C296 437.3 285.3 448 272 448H240C226.7 448 216 437.3 216 424V184C216 170.7 226.7 160 240 160H272C285.3 160 296 170.7 296 184z" />
+    <glyph glyph-name="pray"
+      unicode="&#xF683;"
+      horiz-adv-x="384" d=" M256 320C291.35 320 320 348.65 320 384S291.35 448 256 448S192 419.35 192 384S220.65 320 256 320zM225.37 150.25C239.43 133.53 264.37 131.16 281.3400000000001 145.03L369.3400000000001 217.05C386.43 231.03 388.93 256.24 374.9600000000001 273.33C360.99 290.44 335.7700000000001 292.92 318.6500000000001 278.95L261.2100000000001 231.95L222.3000000000001 278.26C206.8600000000001 296.65 183.0800000000001 306.18 158.3000000000001 303.59C134.1100000000001 301.11 113.0500000000001 287.32 101.93 266.67L52.56 174.64C29.16 131 43.87 78.27 86.75 50.89L131.56 16H40C17.91 16 0 -1.91 0 -24S17.91 -64 40 -64H248C282.08 -64 301.77 -21.21 276.28 4.28L166.42 114.14L201.22 179.01L225.37 150.25z" />
+    <glyph glyph-name="praying-hands"
+      unicode="&#xF684;"
+      horiz-adv-x="640" d=" M272 256.0900000000001C254.4 256.0900000000001 240 241.69 240 224.09V144.09C240 135.2500000000001 232.84 128.09 224 128.09S208 135.2500000000001 208 144.09V220.6400000000001C208 238.0300000000001 212.72 255.1100000000001 221.69 270.0300000000001L299.44 399.62C308.53 414.7800000000001 303.63 434.4300000000001 288.47 443.5300000000001C274.02 452.2 255.75 447.8300000000001 246.17 434.3200000000001C245.97 434.0900000000001 245.55 434.11 245.38 433.8400000000001L128.12 257.9400000000001C117.56 242.1 112 223.69 112 204.71V124.48L21.88 94.44A31.974000000000004 31.974000000000004 0 0 1 0 64.09V-31.91C0 -42.73 8.52 -63.91 32 -63.91C34.69 -63.91 37.41 -63.57 40.06 -62.88L219.25 -16.26C269.1600000000001 -1.99 304 44.2 304 96.09V224.09C304 241.69 289.6 256.09 272 256.09zM618.12 94.36L528 124.4V204.63C528 223.61 522.44 242.02 511.88 257.86L394.62 433.75C394.44 434.02 394.0300000000001 433.99 393.83 434.23C384.25 447.74 365.98 452.11 351.53 443.44C336.37 434.35 331.47 414.69 340.56 399.53L418.31 269.94C427.28 255.02 432 237.94 432 220.55V144C432 135.16 424.8399999999999 128 416 128S400 135.16 400 144V224C400 241.6 385.6 256 368 256S336 241.6 336 224V96C336 44.11 370.8399999999999 -2.08 420.75 -16.34L599.9399999999999 -62.96C602.5999999999999 -63.65 605.3199999999999 -63.99 607.9999999999999 -63.99C631.4799999999999 -63.99 639.9999999999999 -42.81 639.9999999999999 -31.99V64.01C639.9999999999999 77.78 631.1899999999999 90 618.1199999999999 94.36z" />
+    <glyph glyph-name="prescription-bottle-alt"
+      unicode="&#xF486;"
+      horiz-adv-x="384" d=" M360 448H24C10.8 448 0 437.2 0 424V376C0 362.8 10.8 352 24 352H360C373.2 352 384 362.8 384 376V424C384 437.2 373.2 448 360 448zM32 -32C32 -49.6 46.4 -64 64 -64H320C337.6 -64 352 -49.6 352 -32V320H32V-32zM96 152C96 156.4 99.6 160 104 160H160V216C160 220.4 163.6 224 168 224H216C220.4 224 224 220.4 224 216V160H280C284.4 160 288 156.4 288 152V104C288 99.6 284.4 96 280 96H224V40C224 35.6 220.4 32 216 32H168C163.6 32 160 35.6 160 40V96H104C99.6 96 96 99.6 96 104V152z" />
+    <glyph glyph-name="prescription-bottle"
+      unicode="&#xF485;"
+      horiz-adv-x="384" d=" M32 256H152C156.4 256 160 252.4 160 248V232C160 227.6 156.4 224 152 224H32V160H152C156.4 160 160 156.4 160 152V136C160 131.6 156.4 128 152 128H32V64H152C156.4 64 160 60.4 160 56V40C160 35.6 156.4 32 152 32H32V-32C32 -49.6 46.4 -64 64 -64H320C337.6 -64 352 -49.6 352 -32V320H32V256zM360 448H24C10.8 448 0 437.2 0 424V376C0 362.8 10.8 352 24 352H360C373.2 352 384 362.8 384 376V424C384 437.2 373.2 448 360 448z" />
+    <glyph glyph-name="prescription"
+      unicode="&#xF5B1;"
+      horiz-adv-x="384" d=" M301.26 96L379.32 174.06C385.57 180.31 385.57 190.44 379.32 196.69L356.69 219.32C350.44 225.57 340.31 225.57 334.06 219.32L256 141.26L172.04 225.22C219.31 231.2 256 271.11 256 320C256 373.02 213.02 416 160 416H16C7.16 416 0 408.8400000000001 0 400V144C0 135.16 7.16 128 16 128H48C56.84 128 64 135.16 64 144V224H82.75L210.75 96L132.69 17.94C126.44 11.69 126.44 1.56 132.69 -4.69L155.32 -27.32C161.57 -33.57 171.7 -33.57 177.95 -27.32L256 50.75L334.06 -27.31C340.31 -33.56 350.44 -33.56 356.69 -27.31L379.32 -4.68C385.57 1.57 385.57 11.7 379.32 17.95L301.26 96zM64 352H160C177.64 352 192 337.64 192 320S177.64 288 160 288H64V352z" />
+    <glyph glyph-name="print"
+      unicode="&#xF02F;"
+      horiz-adv-x="512" d=" M448 256V370.75C448 379.24 444.63 387.37 438.63 393.38L393.37 438.63C387.37 444.63 379.23 448 370.74 448H96C78.33 448 64 433.67 64 416V256C28.65 256 0 227.35 0 192V80C0 71.16 7.16 64 16 64H64V-32C64 -49.67 78.33 -64 96 -64H416C433.67 -64 448 -49.67 448 -32V64H496C504.84 64 512 71.16 512 80V192C512 227.35 483.35 256 448 256zM384 0H128V96H384V0zM384 224H128V384H320V336C320 327.16 327.1600000000001 320 336 320H384V224zM432 152C418.75 152 408 162.75 408 176C408 189.26 418.75 200 432 200S456 189.26 456 176C456 162.75 445.25 152 432 152z" />
+    <glyph glyph-name="procedures"
+      unicode="&#xF487;"
+      horiz-adv-x="640" d=" M528 224H272C263.2 224 256 216.8 256 208V64H64V304C64 312.8 56.8 320 48 320H16C7.2 320 0 312.8 0 304V-48C0 -56.8 7.2 -64 16 -64H48C56.8 -64 64 -56.8 64 -48V0H576V-48C576 -56.8 583.2 -64 592 -64H624C632.8 -64 640 -56.8 640 -48V112C640 173.9 589.9 224 528 224zM136 352H262.1L289.7000000000001 296.8C295.6 285 312.4000000000001 285 318.3000000000001 296.8L368 396.2L390.1 352H512C520.8 352 528 359.2 528 368S520.8 384 512 384H409.9L382.3 439.2C376.4 451 359.6 451 353.7 439.2L304 339.8L284.1 379.6C282.7000000000001 382.3 280 384 276.9000000000001 384H136C131.6 384 128 380.4 128 376V360C128 355.6 131.6 352 136 352zM160 96C195.3 96 224 124.7 224 160S195.3 224 160 224S96 195.3 96 160S124.7 96 160 96z" />
+    <glyph glyph-name="project-diagram"
+      unicode="&#xF542;"
+      horiz-adv-x="640" d=" M384 128H256C238.33 128 224 113.67 224 96V-32C224 -49.67 238.33 -64 256 -64H384C401.67 -64 416 -49.67 416 -32V96C416 113.67 401.67 128 384 128zM192 416C192 433.67 177.67 448 160 448H32C14.33 448 0 433.67 0 416V288C0 270.33 14.33 256 32 256H127.72L200.88 127.96C211.98 147.02 232.4 160 256 160H256.28L192 272.49V320H416V384H192V416zM608 448H480C462.33 448 448 433.67 448 416V288C448 270.33 462.33 256 480 256H608C625.67 256 640 270.3300000000001 640 288V416C640 433.67 625.67 448 608 448z" />
+    <glyph glyph-name="puzzle-piece"
+      unicode="&#xF12E;"
+      horiz-adv-x="576" d=" M519.442 159.349C477.923 159.349 459.942 127.756 437.384 127.756C377.409 127.756 432 304 432 304S235.712 224 235.712 307.2970000000001C235.712 343.124 272 353.5470000000001 272 393.282C272 428.784 243.885 448 210.539 448C175.885 448 144.173 429.109 144.173 391.654C144.173 350.29 175.884 332.377 175.884 309.904C175.885 240.281 0 281.242 0 281.242V-51.995S178.635 -93.042 178.635 -23.333C178.635 -0.86 138.635 16.774 138.635 58.138C138.635 95.594 167.885 114.484 202.212 114.484C235.885 114.484 264 95.268 264 59.767C264 20.032 227.712 9.609 227.712 -26.218C227.712 -87.021 357.387 -51.948 408.942 -51.948C408.942 -51.948 374.217 68.153 434.769 68.153C470.731 68.153 481.192 32.001 521.077 32.001C556.712 32 576 60.01 576 93.557C576 127.756 557.038 159.3490000000001 519.442 159.3490000000001z" />
+    <glyph glyph-name="qrcode"
+      unicode="&#xF029;"
+      horiz-adv-x="448" d=" M0 224H192V416H0V224zM64 352H128V288H64V352zM256 416V224H448V416H256zM384 288H320V352H384V288zM0 -32H192V160H0V-32zM64 96H128V32H64V96zM416 160H448V32H352V64H320V-32H256V160H352V128H416V160zM416 0H448V-32H416V0zM352 0H384V-32H352V0z" />
+    <glyph glyph-name="question-circle"
+      unicode="&#xF059;"
+      horiz-adv-x="512" d=" M504 192C504 55.003 392.957 -56 256 -56S8 55.003 8 192C8 328.9170000000001 119.043 440 256 440S504 328.9170000000001 504 192zM262.655 358C208.158 358 173.4 335.043 146.106 294.242C142.57 288.956 143.753 281.827 148.821 277.984L183.52 251.674C188.725 247.727 196.141 248.666 200.185 253.796C218.049 276.454 230.298 289.593 257.488 289.593C277.9169999999999 289.593 303.1859999999999 276.445 303.1859999999999 256.635C303.1859999999999 241.659 290.8229999999999 233.968 270.652 222.659C247.128 209.472 216 193.059 216 152V148C216 141.373 221.373 136 228 136H284C290.627 136 296 141.373 296 148V149.333C296 177.795 379.1860000000001 178.98 379.1860000000001 256C379.1860000000001 314.0020000000001 319.021 358 262.6550000000001 358zM256 110C230.635 110 210 89.365 210 64C210 38.636 230.635 18 256 18S302 38.636 302 64C302 89.365 281.365 110 256 110z" />
+    <glyph glyph-name="question"
+      unicode="&#xF128;"
+      horiz-adv-x="384" d=" M202.021 448C122.202 448 70.503 415.2970000000001 29.914 356.974C22.551 346.394 24.821 331.8880000000001 35.092 324.1L78.23 291.391C88.603 283.526 103.362 285.365 111.483 295.539C136.532 326.92 155.113 344.988 194.24 344.988C225.004 344.988 263.0560000000001 325.189 263.0560000000001 295.357C263.0560000000001 272.805 244.4390000000001 261.223 214.0630000000001 244.193C178.6400000000001 224.333 131.7640000000001 199.617 131.7640000000001 137.788V128C131.7640000000001 114.745 142.5090000000001 104 155.7640000000001 104H228.2350000000001C241.4900000000001 104 252.2350000000001 114.745 252.2350000000001 128V133.773C252.2350000000001 176.6330000000001 377.5030000000001 178.418 377.5030000000001 294.4000000000001C377.504 381.7440000000001 286.902 448 202.021 448zM192 74.541C153.804 74.541 122.729 43.466 122.729 5.27C122.729 -32.925 153.804 -64 192 -64S261.271 -32.925 261.271 5.271S230.1960000000001 74.541 192 74.541z" />
+    <glyph glyph-name="quidditch"
+      unicode="&#xF458;"
+      horiz-adv-x="640" d=" M256.5 231.2L343.2 122S326.6 19.6 266.6 -28.1C206.7 -75.7999999999999 0 -62.2 0 -62.2S3.8 -39.1 11 -6.8L105.6 105.4C109.6 110.1 104.7 117 99 114.9L38.6 92.8C53 134.5 71.3 172.8 93.2 190.3C153.1 238.1 256.5 231.2 256.5 231.2zM494.5 96.2C450.5 96.2 414.7 60.4 414.7 16.3C414.7 -27.8000000000001 450.4 -63.6 494.5 -63.6C538.6 -63.6 574.3 -27.8 574.3 16.3C574.3 60.4999999999999 538.5 96.1999999999999 494.4999999999999 96.1999999999999zM636.5 417L616.7 442C611.2 448.9 601.2 450 594.3000000000001 444.6L361.8 266.7L327.7 309.7C322.6 316.1 312.6 314.9 309.1 307.5L283.8 252.9L370.5 143.7L429.3 156.1C437.3 157.8 440.7 167.3 435.6 173.7L401.5 216.6L634 394.5C640.9 400 642 410.1 636.5 417z" />
+    <glyph glyph-name="quote-left"
+      unicode="&#xF10D;"
+      horiz-adv-x="512" d=" M464 192H384V256C384 291.3 412.7 320 448 320H456C469.3 320 480 330.7 480 344V392C480 405.3 469.3 416 456 416H448C359.6 416 288 344.4 288 256V16C288 -10.5 309.5 -32 336 -32H464C490.5 -32 512 -10.5 512 16V144C512 170.5 490.5 192 464 192zM176 192H96V256C96 291.3 124.7 320 160 320H168C181.3 320 192 330.7 192 344V392C192 405.3 181.3 416 168 416H160C71.6 416 0 344.4 0 256V16C0 -10.5 21.5 -32 48 -32H176C202.5 -32 224 -10.5 224 16V144C224 170.5 202.5 192 176 192z" />
+    <glyph glyph-name="quote-right"
+      unicode="&#xF10E;"
+      horiz-adv-x="512" d=" M464 416H336C309.5 416 288 394.5 288 368V240C288 213.5 309.5 192 336 192H416V128C416 92.7 387.3 64 352 64H344C330.7 64 320 53.3 320 40V-8C320 -21.3 330.7 -32 344 -32H352C440.4 -32 512 39.6 512 128V368C512 394.5 490.5 416 464 416zM176 416H48C21.5 416 0 394.5 0 368V240C0 213.5 21.5 192 48 192H128V128C128 92.7 99.3 64 64 64H56C42.7 64 32 53.3 32 40V-8C32 -21.3 42.7 -32 56 -32H64C152.4 -32 224 39.6 224 128V368C224 394.5 202.5 416 176 416z" />
+    <glyph glyph-name="quran"
+      unicode="&#xF687;"
+      horiz-adv-x="448" d=" M448 89.6V422.4C448 438.4 438.4 448 422.4 448H96C41.6 448 0 406.4 0 352V32C0 -22.4 41.6 -64 96 -64H422.4C435.2 -64 448 -54.4 448 -38.4V-22.4C448 -16 444.8 -9.6 438.4 -3.2C435.2 12.8 435.2 57.6 438.4 70.4C444.8 73.6 448 80 448 89.6zM301.08 302.18C301.68 303.39 302.84 304 304 304S306.32 303.39 306.92 302.18L318.1 279.53L343.1 275.9C345.7700000000001 275.51 346.8400000000001 272.23 344.9100000000001 270.3400000000001L326.8200000000001 252.71L331.0900000000001 227.82C331.4500000000001 225.71 329.7800000000001 224 327.8800000000001 224C327.3800000000001 224 326.8600000000001 224.12 326.3600000000001 224.38L304 236.13L281.64 224.38C281.14 224.12 280.62 224 280.12 224C278.2200000000001 224 276.55 225.71 276.9100000000001 227.82L281.18 252.71L263.0900000000001 270.34C261.1500000000001 272.23 262.2200000000001 275.51 264.9000000000001 275.9L289.8900000000001 279.53L301.0800000000001 302.18zM243.19 371.19C256.86 371.19 270.45 368.7000000000001 283.57 363.7800000000001A6.775 6.775 0 1 0 281.19 350.6600000000001C280.52 350.6600000000001 278.1 350.87 277.06 350.87C224.75 350.87 182.2 308.32 182.2 256.01C182.2 203.71 224.75 161.15 277.06 161.15C278.09 161.15 280.54 161.36 281.19 161.36C285.12 161.36 287.99 158.22 287.99 154.58C287.99 151.6 286.05 149.07 283.37 148.16C270.3 143.29 256.7800000000001 140.82 243.18 140.82C179.67 140.81 128 192.49 128 256C128 319.52 179.67 371.19 243.19 371.19zM380.8 0H96C76.8 0 64 12.8 64 32S80 64 96 64H380.8V0z" />
+    <glyph glyph-name="rainbow"
+      unicode="&#xF75B;"
+      horiz-adv-x="576" d=" M268.3 415.3C115.4 405.1 0 271.1 0 117.8V-16C0 -24.8 7.2 -32 16 -32H48C56.8 -32 64 -24.8 64 -16V128C64 261.2 180.9 367.7 317.5 350.1C430.4 335.6 512 234 512 120.2V-16C512 -24.8 519.2 -32 528 -32H560C568.8 -32 576 -24.8 576 -16V128C576 293.3 436 426.6 268.3 415.3zM262.7 318.4C166 306 96 218.9 96 121.3V-16C96 -24.8 103.2 -32 112 -32H144C152.8 -32 160 -24.8 160 -16V128C160 202.8 224.5 262.8 300.8 255.4C367.3 248.9 416 189.2 416 122.3V-16C416 -24.8 423.2 -32 432 -32H464C472.8 -32 480 -24.8 480 -16V128C480 242.2 379.8 333.4 262.7 318.4zM268.9 222.1C223.3 213.2 192 170.6 192 124.1999999999999V-16C192 -24.8 199.2 -32 208 -32H240C248.8 -32 256 -24.8 256 -16V128C256 145.6 270.3 160 288 160S320 145.6 320 128V-16C320 -24.8 327.2 -32 336 -32H368C376.8 -32 384 -24.8 384 -16V128C384 187.2 330.2 234 268.9 222.1z" />
+    <glyph glyph-name="random"
+      unicode="&#xF074;"
+      horiz-adv-x="512" d=" M504.971 88.971C514.344 79.598 514.344 64.402 504.971 55.03L424.971 -24.954C409.961 -39.964 384 -29.444 384 -7.983V32H325.2150000000001A12.004 12.004 0 0 0 316.442 35.812L245.886 111.408L299.2190000000001 168.5510000000001L352 112H384V151.981C384 173.419 409.943 183.979 424.971 168.952L504.971 88.971zM12 272H96L148.781 215.449L202.114 272.592L131.558 348.188A11.999000000000002 11.999000000000002 0 0 1 122.785 352H12C5.373 352 0 346.627 0 340V284C0 277.373 5.373 272 12 272zM384 272V232.016C384 210.556 409.961 200.036 424.971 215.045L504.971 295.029C514.344 304.402 514.344 319.598 504.971 328.9700000000001L424.971 408.951C409.943 423.979 384 413.418 384 391.981V352H325.2150000000001A12.004 12.004 0 0 1 316.442 348.188L96 112H12C5.373 112 0 106.627 0 100V44C0 37.373 5.373 32 12 32H122.785C126.111 32 129.288 33.381 131.558 35.812L352 272H384z" />
+    <glyph glyph-name="receipt"
+      unicode="&#xF543;"
+      horiz-adv-x="384" d=" M358.4 444.8L320 400L265.6 444.8A15.9 15.9 0 0 1 246.4000000000001 444.8L192 400L137.6 444.8A15.9 15.9 0 0 1 118.4 444.8L64 400L25.6 444.8C15 452.7 0 445.2 0 432V-48C0 -61.2 15 -68.7 25.6 -60.8L64 -16L118.4 -60.8A15.9 15.9 0 0 1 137.6 -60.8L192 -16L246.4 -60.8A15.9 15.9 0 0 1 265.6 -60.8L320 -16L358.4 -60.8C368.9 -68.7 384 -61.2 384 -48V432C384 445.2 369 452.7 358.4 444.8zM320 88C320 83.6 316.4 80 312 80H72C67.6 80 64 83.6 64 88V104C64 108.4 67.6 112 72 112H312C316.4 112 320 108.4 320 104V88zM320 184C320 179.6 316.4 176 312 176H72C67.6 176 64 179.6 64 184V200C64 204.4 67.6 208 72 208H312C316.4 208 320 204.4 320 200V184zM320 280C320 275.6 316.4 272 312 272H72C67.6 272 64 275.6 64 280V296C64 300.4 67.6 304 72 304H312C316.4 304 320 300.4 320 296V280z" />
+    <glyph glyph-name="recycle"
+      unicode="&#xF1B8;"
+      horiz-adv-x="512" d=" M184.561 186.097C187.793 172.1 172.438 161.462 160.493 168.929L119.757 194.384L68.89 112.982C55.606 91.727 70.96 64 96.012 64H148C154.627 64 160 58.627 160 52V12C160 5.373 154.627 0 148 0H96.115C20.781 0 -25.187 83.048 14.707 146.88L65.529 228.268L24.804 253.716C12.723 261.2630000000001 15.838 279.677 29.683 282.874L139.92 308.324C148.531 310.312 157.121 304.943 159.109 296.334L184.561 186.097zM283.122 369.012L324.411 302.936L283.671 277.479C271.62 269.951 274.671 251.526 288.55 248.321L398.7870000000001 222.871C407.4590000000001 220.872 416.002 226.309 417.9760000000001 234.861L443.4260000000001 345.098C446.6230000000001 358.942 431.4360000000001 369.817 419.3580000000001 362.266L378.6710000000001 336.842L337.4080000000001 402.924C299.8870000000001 462.957 212.1990000000001 463.095 174.5920000000001 402.924L156.6290000000001 374.158C153.1190000000001 368.538 154.8290000000001 361.137 160.4490000000001 357.625L194.3680000000001 336.43C199.9880000000001 332.918 207.3920000000001 334.627 210.9040000000001 340.247L228.8650000000001 368.99C241.5770000000001 389.331 270.8380000000001 388.666 283.1220000000001 369.012zM497.288 146.88L469.773 190.945C466.262 196.568 458.857 198.279 453.235 194.766L419.374 173.607C413.754 170.095 412.0440000000001 162.692 415.5560000000001 157.071L443.1200000000001 112.959C456.3770000000001 91.7479999999999 441.0630000000001 63.999 415.9840000000001 63.999H320V111.98C320 126.1930000000001 302.758 133.363 292.687 123.293L212.687 43.312C206.438 37.064 206.438 26.933 212.687 20.685L292.687 -59.304C302.689 -69.308 320 -62.3 320 -47.989V0H415.88C491.154 0 537.215 82.997 497.288 146.88z" />
+    <glyph glyph-name="redo-alt"
+      unicode="&#xF2F9;"
+      horiz-adv-x="512" d=" M256.455 440C322.724 439.881 382.892 413.767 427.314 371.315L463.029 407.03C478.149 422.149 504 411.4410000000001 504 390.059V256C504 242.745 493.255 232 480 232H345.941C324.559 232 313.851 257.851 328.97 272.971L370.72 314.721C339.856 343.62 299.919 359.628 257.49 359.9940000000001C165.092 360.7920000000001 87.207 286.017 88.0059999999999 190.552C88.764 99.991 162.184 24 256 24C297.127 24 335.997 38.678 366.629 65.556C371.372 69.717 378.5350000000001 69.464 382.997 65.003L422.659 25.341C427.531 20.469 427.29 12.526 422.177 7.908C378.202 -31.813 319.926 -56 256 -56C119.034 -56 8.001 55.033 8 191.998C7.999 328.807 119.646 440.245 256.455 440z" />
+    <glyph glyph-name="redo"
+      unicode="&#xF01E;"
+      horiz-adv-x="512.333" d=" M500.333 448H452.922C446.069 448 440.608 442.271 440.9360000000001 435.426L444.9020000000001 352.6670000000001C399.416 406.101 331.6720000000001 440 256.001 440C119.34 440 7.899 328.474 8 191.813C8.101 54.932 119.096 -56 256 -56C319.926 -56 378.202 -31.813 422.178 7.908C427.291 12.526 427.532 20.469 422.6600000000001 25.341L388.689 59.312C384.223 63.778 377.0490000000001 64.029 372.3090000000001 59.855C341.308 32.552 300.606 16 256 16C158.733 16 80 94.716 80 192C80 289.267 158.716 368 256 368C316.892 368 370.506 337.142 402.099 290.2L300.574 295.065C293.729 295.3930000000001 288 289.932 288 283.079V235.668C288 229.041 293.373 223.668 300 223.668H500.333C506.96 223.668 512.333 229.041 512.333 235.668V436C512.333 442.627 506.96 448 500.333 448z" />
+    <glyph glyph-name="registered"
+      unicode="&#xF25D;"
+      horiz-adv-x="512" d=" M285.363 240.525C285.363 221.925 275.532 212.094 256.932 212.094H227.056V268.234H250.4340000000001C279.1020000000001 268.234 285.3630000000001 259.461 285.3630000000001 240.525zM504 192C504 55.033 392.967 -56 256 -56S8 55.033 8 192S119.033 440 256 440S504 328.967 504 192zM363.411 87.586C316.682 172.411 320.112 166.222 318.709 168.566C342.141 183.7380000000001 356.654 211.545 356.654 243.052C356.654 297.2960000000001 325.154 332.3040000000001 251.156 332.3040000000001H180.489C167.234 332.3040000000001 156.489 321.5590000000001 156.489 308.3040000000001V76C156.489 62.745 167.234 52 180.489 52H203.056C216.311 52 227.056 62.745 227.056 76V147.663H252.612L296.7410000000001 64.726A24.001000000000005 24.001000000000005 0 0 1 317.9290000000001 51.999H342.3930000000001C360.6540000000001 52 372.2220000000001 71.59 363.4110000000001 87.586z" />
+    <glyph glyph-name="reply-all"
+      unicode="&#xF122;"
+      horiz-adv-x="576" d=" M136.309 258.164L312.313 410.149C327.7200000000001 423.454 352 412.652 352 391.985V309.222C481.182 298.991 576 257.01 576 125.674C576 64.233 536.418 3.365 492.667 -28.458C479.014 -38.389 459.556 -25.925 464.59 -9.827C503.102 113.335 460.668 159.655 352 172.188V88.013C352 67.312 327.7 56.56 312.313 69.849L136.309 221.836C125.238 231.397 125.223 248.589 136.309 258.164zM8.309 221.836L184.313 69.85C199.7 56.561 224 67.313 224 88.014V103.832L115.394 197.617A55.96 55.96 0 0 0 96 240.002A55.95300000000001 55.95300000000001 0 0 0 115.393 282.382L224 376.168V391.985C224 412.652 199.72 423.454 184.313 410.149L8.309 258.164C-2.777 248.589 -2.762 231.397 8.309 221.836z" />
+    <glyph glyph-name="reply"
+      unicode="&#xF3E5;"
+      horiz-adv-x="512" d=" M8.309 258.164L184.313 410.149C199.719 423.454 224 412.653 224 391.985V311.932C384.629 310.093 512 277.9000000000001 512 125.674C512 64.233 472.419 3.365 428.6670000000001 -28.458C415.014 -38.389 395.5560000000001 -25.925 400.5900000000001 -9.827C445.9340000000001 135.185 379.0830000000001 173.683 224 175.915V88C224 67.3 199.7 56.547 184.3130000000001 69.836L8.3090000000001 221.836C-2.7619999999999 231.398 -2.7769999999999 248.589 8.3090000000001 258.164z" />
+    <glyph glyph-name="republican"
+      unicode="&#xF75E;"
+      horiz-adv-x="640" d=" M544 256C544 344.4 472.4 416 384 416H160C71.6 416 0 344.4 0 256V192H544V256zM176.3 277.6L156.5 258.3L161.2 231C162 226.1 156.9 222.4 152.5 224.7L128 237.6L103.5 224.7C99.2 222.4 94 226.1 94.8 231L99.5 258.3L79.7 277.6C76.1 281.1 78.1 287.1 83 287.8L110.4 291.8L122.6 316.6C124.8 321.1 131.2 321 133.3 316.6L145.5 291.8L172.9 287.8C177.9 287.1 179.8 281.1 176.3 277.6zM320.3 277.6L300.5 258.3L305.2 231C306 226.1 300.9 222.4 296.5 224.7L272 237.6L247.5 224.7C243.2 222.4 238 226.1 238.8 231L243.5 258.3L223.7 277.6C220.1 281.1 222.1 287.1 227 287.8L254.4 291.8L266.6 316.6C268.8 321.1 275.2000000000001 321 277.3 316.6L289.5 291.8L316.9 287.8C321.9 287.1 323.8 281.1 320.3 277.6zM464.3 277.6L444.5 258.3L449.2 231C450 226.1 444.9 222.4 440.5 224.7L416 237.6L391.5 224.7C387.2 222.4 382 226.1 382.8 231L387.5 258.3L367.7 277.6C364.1 281.1 366.1 287.1 371 287.8L398.4 291.8L410.6 316.6C412.8 321.1 419.2 321 421.3 316.6L433.5 291.8L460.8999999999999 287.8C465.8999999999999 287.1 467.7999999999999 281.1 464.2999999999999 277.6zM624 128H592C583.2 128 576 120.8 576 112V48C576 39.2 568.8 32 560 32S544 39.2 544 48V160H0V-16C0 -24.8 7.2 -32 16 -32H112C120.8 -32 128 -24.8 128 -16V64H320V-16C320 -24.8 327.2 -32 336 -32H432C440.8 -32 448 -24.8 448 -16V96H480V52.7C480 10.9 510 -27.4 551.6 -31.6C599.4 -36.5 640 1.1 640 48V112C640 120.8 632.8 128 624 128z" />
+    <glyph glyph-name="retweet"
+      unicode="&#xF079;"
+      horiz-adv-x="640" d=" M629.657 104.402L528.971 3.716C519.598 -5.656 504.403 -5.656 495.03 3.716L394.343 104.402C384.9700000000001 113.775 384.9700000000001 128.971 394.343 138.343L405.166 149.166C414.728 158.728 430.299 158.5059999999999 439.585 148.6739999999999L480 105.882V288H292.451A24.005 24.005 0 0 0 275.48 295.029L259.48 311.029C244.361 326.149 255.069 352 276.451 352H520C533.255 352 544 341.255 544 328V105.882L584.4159999999999 148.674C593.7009999999999 158.505 609.2719999999999 158.728 618.8349999999999 149.166L629.6579999999999 138.343C639.0299999999999 128.971 639.0299999999999 113.774 629.6569999999999 104.402zM364.5190000000001 88.971A23.999000000000002 23.999000000000002 0 0 1 347.548 96H160V278.1190000000001L200.416 235.327C209.702 225.496 225.272 225.273 234.835 234.836L245.657 245.658C255.03 255.031 255.03 270.227 245.657 279.599L144.971 380.284C135.598 389.657 120.402 389.657 111.03 380.284L10.343 279.598C0.97 270.225 0.97 255.029 10.343 245.657L21.165 234.835C30.727 225.273 46.298 225.495 55.584 235.326L96 278.1190000000001V56C96 42.745 106.745 32 120 32H363.549C384.931 32 395.639 57.851 380.52 72.971L364.519 88.971z" />
+    <glyph glyph-name="ribbon"
+      unicode="&#xF4D6;"
+      horiz-adv-x="448" d=" M6.1 3.7C-3.5 -7.1 -1.4 -23.9 10.6 -32L79.4 -59.9C89.3 -66.6 102.7 -64.9 110.7 -56.1L202.5 45.8000000000001L123.3 133.7000000000001L6.1 3.7zM441.9000000000001 3.7S149.9 328.3 146.5000000000001 333.8C161.9000000000001 342.2000000000001 186.7000000000001 351.7000000000001 224.0000000000001 351.7000000000001S286.1000000000001 342.2000000000001 301.5000000000001 333.8C298.2000000000001 328.2000000000001 245.5000000000001 269.2000000000001 245.5000000000001 269.2000000000001L324.6 181.5L358.8 219.5C387.5 251.4 392.1 298.1 370.2 335L326.5 408.5C322.2 415.7 316.6 421.8 309.7 426.5C269 454.1 182.3 456.2 138.3 426.5C131.4 421.8 125.8 415.7 121.5 408.5L77.9 335.3C76.4 332.8 40.8 273.1 89.4 219.3L337.5 -56C345.5 -64.9 358.9 -66.5 368.8 -59.8L437.6 -31.9C449.5 -23.9 451.6 -7.1 441.9000000000001 3.7z" />
+    <glyph glyph-name="ring"
+      unicode="&#xF70B;"
+      horiz-adv-x="512" d=" M256 384C110.06 384 0 322.0900000000001 0 240V141.87C0 63.52 114.62 0 256 0S512 63.52 512 141.87V240C512 322.0900000000001 401.94 384 256 384zM256 320C362.04 320 448 284.18 448 240C448 230.74 444.03 221.88 437.09 213.61C392.15 239.79 328.23 256 256 256S119.85 239.79 74.91 213.61C67.97 221.88 64 230.74 64 240C64 284.18 149.96 320 256 320zM120.43 183.36C155.04 198.07 201.64 208 256 208S356.96 198.07 391.57 183.36C356.84 168.93 308.93 160 256 160S155.16 168.93 120.43 183.36z" />
+    <glyph glyph-name="road"
+      unicode="&#xF018;"
+      horiz-adv-x="576" d=" M573.19 45.33L433.4000000000001 365.33C428.43 376.71 417.6 384 405.68 384H308.0900000000001L310.54 360.8400000000001C311.04 356.12 307.3300000000001 352 302.5800000000001 352H273.42C268.67 352 264.9600000000001 356.12 265.4600000000001 360.8400000000001L267.9100000000001 384H170.32C158.39 384 147.56 376.71 142.59 365.33L2.8 45.33C-6.45 24.14 8.31 0 30.54 0H227.38L237.69 97.68C238.55 105.82 245.41 112 253.6 112H322.4C330.59 112 337.45 105.82 338.31 97.68L348.62 0H545.46C567.69 0 582.45 24.14 573.19 45.33zM260.4 312.8400000000001A8 8 0 0 0 268.36 320H307.65C311.74 320 315.18 316.91 315.61 312.8400000000001L320.21 269.26C320.96 262.17 315.4 256 308.28 256H267.74C260.61 256 255.06 262.17 255.81 269.26L260.3999999999999 312.84zM315.64 144H260.35C250.85 144 243.44 152.23 244.44 161.68L249.51 209.68C250.37 217.82 257.23 224 265.42 224H310.57C318.76 224 325.62 217.82 326.48 209.68L331.55 161.68C332.55 152.23 325.14 144 315.64 144z" />
+    <glyph glyph-name="robot"
+      unicode="&#xF544;"
+      horiz-adv-x="640" d=" M0 192V64C0 46.3 14.3 32 32 32H64V224H32C14.3 224 0 209.7 0 192zM464 352H352V416C352 433.7 337.7 448 320 448S288 433.7 288 416V352H176C131.8 352 96 316.2 96 272V0C96 -35.3 124.7 -64 160 -64H480C515.3 -64 544 -35.3 544 0V272C544 316.2 508.2 352 464 352zM256 32H192V64H256V32zM224 152C201.9 152 184 169.9 184 192S201.9 232 224 232S264 214.1 264 192S246.1 152 224 152zM352 32H288V64H352V32zM448 32H384V64H448V32zM416 152C393.9 152 376 169.9 376 192S393.9 232 416 232S456 214.1 456 192S438.1 152 416 152zM608 224H576V32H608C625.7 32 640 46.3 640 64V192C640 209.7 625.7 224 608 224z" />
+    <glyph glyph-name="rocket"
+      unicode="&#xF135;"
+      horiz-adv-x="512" d=" M505.1 428.9C503.8 435 499 439.8 492.9 441.1C460.7 448 435.5 448 410.4 448C307.2 448 245.3 392.8 199.1 320H94.9C76.7 320 60.1 309.7 52 293.5L2.6 194.7C-5.4 178.7 6.2 160 24.1 160H119.2C113.3 147.2 107.3 134.5 101.2 122.3C98.1 116.1 99.3 108.7 104.2 103.8L167.8 40.2C172.7 35.3 180.1 34.1 186.3 37.2C198.5 43.3 211.2 49.2 224 55.1V-40C224 -57.8 242.8 -69.4 258.7 -61.5L357.4 -12.1C373.7 -4 383.9 12.7 383.9 30.8V135.2C456.5 181.5 511.9 243.6 511.9 346.3C512 371.5 512 396.7 505.1 428.9zM400 288C373.5 288 352 309.5 352 336S373.5 384 400 384S448 362.5 448 336S426.5 288 400 288z" />
+    <glyph glyph-name="route"
+      unicode="&#xF4D7;"
+      horiz-adv-x="512" d=" M416 128H320C302.4 128 288 142.4 288 160S302.4 192 320 192H416S512 299 512 352S469 448 416 448S320 405 320 352C320 326.5 342.2 288.6 365.3 256H320C267.1 256 224 212.9 224 160S267.1 64 320 64H416C433.6 64 448 49.6 448 32S433.6 0 416 0H185.5C169.5 -24.8 151.7 -47.7 138.2 -64H416C468.9 -64 512 -20.9 512 32S468.9 128 416 128zM416 384C433.7 384 448 369.7 448 352S433.7 320 416 320S384 334.3 384 352S398.3 384 416 384zM96 192C43 192 0 149 0 96S96 -64 96 -64S192 43 192 96S149 192 96 192zM96 64C78.3 64 64 78.3 64 96S78.3 128 96 128S128 113.7 128 96S113.7 64 96 64z" />
+    <glyph glyph-name="rss-square"
+      unicode="&#xF143;"
+      horiz-adv-x="448" d=" M400 416H48C21.49 416 0 394.51 0 368V16C0 -10.51 21.49 -32 48 -32H400C426.51 -32 448 -10.51 448 16V368C448 394.51 426.51 416 400 416zM112 32C85.49 32 64 53.49 64 80S85.49 128 112 128S160 106.51 160 80S138.51 32 112 32zM269.533 32H235.198C229.187 32 224.147 36.636 223.756 42.634C218.542 122.684 154.513 186.554 74.633 191.757C68.636 192.147 64 197.188 64 203.198V237.533C64 244.068 69.468 249.31 75.994 248.9580000000001C186.54 242.9840000000001 274.9910000000001 154.422 280.9580000000001 43.994C281.31 37.468 276.0680000000001 32 269.533 32zM372.56 32H338.226C332.065 32 327.051 36.882 326.799 43.038C321.201 179.573 211.595 289.199 75.039 294.798C68.882 295.051 64 300.065 64 306.226V340.56C64 347.014 69.338 352.224 75.787 351.992C243.617 345.967 377.997 210.801 383.992 43.787C384.224 37.338 379.014 32.0000000000001 372.56 32.0000000000001z" />
+    <glyph glyph-name="rss"
+      unicode="&#xF09E;"
+      horiz-adv-x="448" d=" M128.081 32.041C128.081 -3.328 99.409 -32 64.04 -32S0 -3.328 0 32.041S28.672 96.082 64.041 96.082S128.081 67.409 128.081 32.041zM303.741 -15.209C295.387 139.391 171.556 263.378 16.791 271.741C7.656 272.235 0 264.895 0 255.747V207.678C0 199.263 6.49 192.206 14.887 191.66C126.719 184.376 216.36 94.958 223.659 -17.112C224.206 -25.509 231.263 -31.999 239.677 -31.999H287.746C296.895 -32 304.235 -24.344 303.741 -15.209zM447.99 -15.497C439.596 218.323 251.465 407.555 16.503 415.99C7.473 416.314 0 409.019 0 399.984V351.916C0 343.291 6.835 336.271 15.453 335.9170000000001C206.632 328.078 360.08 174.601 367.918 -16.548C368.271 -25.166 375.291 -32.001 383.917 -32.001H431.985C441.019 -32 448.314 -24.527 447.99 -15.497z" />
+    <glyph glyph-name="ruble-sign"
+      unicode="&#xF158;"
+      horiz-adv-x="384" d=" M239.36 128C324.48 128 384 187.458 384 272.929S324.48 416 239.36 416H76C69.373 416 64 410.627 64 404V197.368H12C5.373 197.368 0 191.995 0 185.368V140C0 133.373 5.373 128 12 128H64V96H12C5.373 96 0 90.627 0 84V44C0 37.373 5.373 32 12 32H64V-20C64 -26.627 69.373 -32 76 -32H134.56C141.187 -32 146.56 -26.627 146.56 -20V32H308C314.627 32 320 37.373 320 44V84C320 90.627 314.627 96 308 96H146.56V128H239.36zM146.56 347.252H225.28C272 347.252 300.16 318.142 300.16 272.9290000000001C300.16 227.0970000000001 272 197.368 224 197.368H146.56V347.252z" />
+    <glyph glyph-name="ruler-combined"
+      unicode="&#xF546;"
+      horiz-adv-x="512" d=" M160 160H104C99.58 160 96 163.58 96 168V184C96 188.42 99.58 192 104 192H160V256H104C99.58 256 96 259.5800000000001 96 264V280C96 284.42 99.58 288 104 288H160V352H104C99.58 352 96 355.58 96 360V376C96 380.42 99.58 384 104 384H160V416C160 433.67 145.67 448 128 448H32C14.33 448 0 433.67 0 416V-32C0 -34.77 0.91 -37.24 1.57 -39.8L160 118.62V160zM480 96H448V40C448 35.58 444.42 32 440 32H424C419.58 32 416 35.58 416 40V96H352V40C352 35.58 348.42 32 344 32H328C323.58 32 320 35.58 320 40V96H256V40C256 35.58 252.42 32 248 32H232C227.58 32 224 35.58 224 40V96H182.63L24.2 -62.43C26.76 -63.09 29.24 -64 32 -64H480C497.67 -64 512 -49.67 512 -32V64C512 81.67 497.67 96 480 96z" />
+    <glyph glyph-name="ruler-horizontal"
+      unicode="&#xF547;"
+      horiz-adv-x="576" d=" M544 320H496V232C496 227.58 492.42 224 488 224H472C467.58 224 464 227.58 464 232V320H400V232C400 227.58 396.42 224 392 224H376C371.58 224 368 227.58 368 232V320H304V232C304 227.58 300.42 224 296 224H280C275.58 224 272 227.58 272 232V320H208V232C208 227.58 204.42 224 200 224H184C179.58 224 176 227.58 176 232V320H112V232C112 227.58 108.42 224 104 224H88C83.58 224 80 227.58 80 232V320H32C14.33 320 0 305.67 0 288V96C0 78.33 14.33 64 32 64H544C561.67 64 576 78.33 576 96V288C576 305.67 561.67 320 544 320z" />
+    <glyph glyph-name="ruler-vertical"
+      unicode="&#xF548;"
+      horiz-adv-x="256" d=" M168 32C163.58 32 160 35.58 160 40V56C160 60.42 163.58 64 168 64H256V128H168C163.58 128 160 131.58 160 136V152C160 156.42 163.58 160 168 160H256V224H168C163.58 224 160 227.58 160 232V248C160 252.42 163.58 256 168 256H256V320H168C163.58 320 160 323.58 160 328V344C160 348.42 163.58 352 168 352H256V416C256 433.67 241.67 448 224 448H32C14.33 448 0 433.67 0 416V-32C0 -49.67 14.33 -64 32 -64H224C241.67 -64 256 -49.67 256 -32V32H168z" />
+    <glyph glyph-name="ruler"
+      unicode="&#xF545;"
+      horiz-adv-x="640" d=" M635.7 280.8L556.1 416.3C547.3000000000001 431.3 527.8000000000001 436.4 512.6 427.8L443.6 388.7L503.3 287C505.5 283.2 504.2 278.5 500.4 276.3L486.6 268.5C482.8 266.3 477.9 267.6 475.7 271.4L416 373L360.8 341.7L388.7 294.3C390.9 290.5 389.6 285.8 385.8 283.6L372 275.8C368.2 273.6 363.3 274.9 361.1 278.7000000000001L333.2 326L278 294.7L337.8 193C340 189.3 338.7 184.5 334.9000000000001 182.3L321.1 174.5C317.3 172.3 312.4000000000001 173.6 310.2000000000001 177.4L250.5000000000001 279.1L195.3000000000001 247.8L223.2000000000001 200.4C225.4000000000001 196.5999999999999 224.1000000000001 191.9 220.3000000000001 189.6999999999999L206.5000000000001 181.8999999999999C202.7000000000001 179.6999999999999 197.8000000000001 181 195.6000000000001 184.7999999999999L167.7000000000001 232.2999999999999L112.5 200.9999999999999L172.2000000000001 99.2999999999999C174.4 95.5999999999999 173.1000000000001 90.7999999999999 169.3000000000001 88.5999999999999L155.5 80.7999999999999C151.7 78.5999999999999 146.8000000000001 79.8999999999999 144.6 83.6999999999999L84.9 185.1L15.9 146C0.7 137.3 -4.6 118.2 4.2 103.2L83.8 -32.4C92.6 -47.4 112.1 -52.5 127.3 -43.9L624.1 238C639.3000000000001 246.6 644.5 265.8 635.7 280.8z" />
+    <glyph glyph-name="running"
+      unicode="&#xF70C;"
+      horiz-adv-x="416" d=" M272 352C298.51 352 320 373.49 320 400S298.51 448 272 448S224 426.51 224 400S245.49 352 272 352zM113.69 130.53L98.89 96.01H32C14.33 96.01 0 81.68 0 64.01S14.33 32.01 32 32.01H109.45C128.7 32.01 146.03 43.45 153.56 61.1L162.35 81.62L151.68 87.92C134.36 98.15 121.62 113.29 113.69 130.53zM384 224.01H339.9700000000001L313.9100000000001 277.26C301.4100000000001 302.81 278.4600000000001 321.49 252.13 328.2L181.05 349.34C152.75 356.14 123.28 349.89 100.21 332.2L60.54 301.79C46.51 291.04 43.85 270.96 54.62 256.93S85.46 240.27 99.48 251.01L139.17 281.42C146.84 287.31 156.61 289.42 164.44 287.56L179.14 283.19L141.68 195.8C129.06 166.32 140.37 131.79 167.98 115.49L252.9600000000001 65.32L225.4900000000001 -22.41C220.21 -39.27 229.6000000000001 -57.22 246.4600000000001 -62.5C249.6500000000001 -63.5 252.8700000000001 -63.98 256.04 -63.98C269.6500000000001 -63.98 282.2700000000001 -55.21 286.56 -41.53L318.2 59.53C324.11 80.3 315.31 102.61 296.56 113.92L235.32 150.06L266.63 228.34L286.9 186.91C294.9 170.57 311.82 160.02 330.01 160.02H384C401.67 160.02 416 174.35 416 192.02S401.67 224.01 384 224.01z" />
+    <glyph glyph-name="rupee-sign"
+      unicode="&#xF156;"
+      horiz-adv-x="320" d=" M308 352C314.627 352 320 357.373 320 364V404C320 410.627 314.627 416 308 416H12C5.373 416 0 410.627 0 404V359.252C0 352.625 5.373 347.252 12 347.252H97.28C124.588 347.252 145.541 337.294 158.25 320H12C5.373 320 0 314.627 0 308V268C0 261.373 5.373 256 12 256H170.757C164.54 219.914 137.796 197.368 96 197.368H12C5.373 197.368 0 191.995 0 185.368V132.356C0 129.007 1.4 125.81 3.861 123.538L168.913 -28.818A12.001000000000001 12.001000000000001 0 0 1 177.052 -32H259.614C270.538 -32 275.78 -18.592 267.753 -11.182L116.871 128.094C193.37 130.434 248.015 181.489 255.189 256H308C314.627 256 320 261.373 320 268V308C320 314.627 314.627 320 308 320H249.31C245.824 331.541 241.03 342.246 235.058 352H308z" />
+    <glyph glyph-name="sad-cry"
+      unicode="&#xF5B3;"
+      horiz-adv-x="496" d=" M248 440C111 440 0 329 0 192C0 101.9 48.2 23.3 120 -20.1V160C120 168.8 127.2 176 136 176S152 168.8 152 160V-36.7C181.5 -49.1 214 -56 248 -56S314.5 -49.1 344 -36.7V160C344 168.8 351.2 176 360 176S376 168.8 376 160V-20.1C447.8 23.3 496 102 496 192C496 329 385 440 248 440zM182.5 223.5C167.7 236.7 136.3 236.7 121.5 223.5L112 215C108.2 211.7 102.7 211 98.3 213.4C93.9 215.8 91.4 220.8 92.2 225.8C96.2 251 126.4 267.9 152.1 267.9S208 251 212 225.8C212.8 220.8 210.3 215.8 205.9 213.4C200.1 210.3 194.7 212.7 192.2 215L182.5 223.5zM248 32C221.5 32 200 60.7 200 96S221.5 160 248 160S296 131.3 296 96S274.5 32 248 32zM397.8 213.5C392 210.4 386.6 212.8 384.1 215.1L374.6 223.6C359.8 236.8 328.4000000000001 236.8 313.6 223.6L304 215C300.2 211.7 294.7 211 290.3 213.4C285.9000000000001 215.8 283.4000000000001 220.8 284.2 225.8C288.2 251 318.4 267.9 344.1 267.9S400 251 404 225.8C404.6 220.9 402.2 215.9 397.8 213.5z" />
+    <glyph glyph-name="sad-tear"
+      unicode="&#xF5B4;"
+      horiz-adv-x="496" d=" M248 440C111 440 0 329 0 192S111 -56 248 -56S496 55 496 192S385 440 248 440zM328 272C345.7 272 360 257.7 360 240S345.7 208 328 208S296 222.3 296 240S310.3 272 328 272zM152 32C125.5 32 104 53 104 79C104 99 132.5 139.4 145.6 156.8C148.8 161.1 155.2 161.1 158.4 156.8C171.5 139.4 200 99 200 79C200 53 178.5 32 152 32zM168 208C150.3 208 136 222.3 136 240S150.3 272 168 272S200 257.7 200 240S185.7 208 168 208zM338.2 53.8C315.8 80.6 282.9 96 248 96C226.8 96 226.8 128 248 128C292.4 128 334.3 108.4 362.7 74.2C376.5 57.8 351.5 37.7 338.2 53.8z" />
+    <glyph glyph-name="save"
+      unicode="&#xF0C7;"
+      horiz-adv-x="448" d=" M433.941 318.059L350.059 401.9410000000001A48 48 0 0 1 316.118 416H48C21.49 416 0 394.51 0 368V16C0 -10.51 21.49 -32 48 -32H400C426.51 -32 448 -10.51 448 16V284.118A48 48 0 0 1 433.941 318.059zM224 32C188.654 32 160 60.654 160 96C160 131.346 188.654 160 224 160S288 131.346 288 96C288 60.654 259.346 32 224 32zM320 336.52V236C320 229.373 314.627 224 308 224H76C69.373 224 64 229.373 64 236V340C64 346.627 69.373 352 76 352H304.52C307.703 352 310.755 350.736 313.005 348.485L316.485 345.005A11.996 11.996 0 0 0 320 336.52z" />
+    <glyph glyph-name="school"
+      unicode="&#xF549;"
+      horiz-adv-x="640" d=" M0 224V-48C0 -56.84 7.16 -64 16 -64H96V256H32C14.33 256 0 241.67 0 224zM360 272H336V312C336 316.42 332.42 320 328 320H312C307.58 320 304 316.42 304 312V248C304 243.58 307.58 240 312 240H360C364.42 240 368 243.58 368 248V264C368 268.42 364.42 272 360 272zM497.75 335.9600000000001L337.75 442.63A32.02 32.02 0 0 1 302.25 442.63L142.25 335.9600000000001A32.002 32.002 0 0 1 128 309.3400000000001V-64H256V80C256 88.84 263.1600000000001 96 272 96H368C376.84 96 384 88.84 384 80V-64H512V309.3300000000001C512 320.0300000000001 506.65 330.0300000000001 497.75 335.9600000000001zM320 192C275.82 192 240 227.82 240 272S275.82 352 320 352S400 316.18 400 272S364.18 192 320 192zM608 256H544V-64H624C632.84 -64 640 -56.84 640 -48V224C640 241.67 625.67 256 608 256z" />
+    <glyph glyph-name="screwdriver"
+      unicode="&#xF54A;"
+      horiz-adv-x="512" d=" M448 448L320 352V289.94L236.97 206.91C243.76 202.66 250.24 197.85 256.04 192.04C261.8400000000001 186.24 266.6600000000001 179.76 270.9100000000001 172.97L353.94 256H416L512 384L448 448zM128 169.41L10.92 52.33C-3.63 37.78 -3.63 14.18 10.92 -0.38L63.62 -53.08C78.18 -67.64 101.77 -67.64 116.33 -53.08L233.41 64C262.52 93.11 262.52 140.3 233.41 169.41S157.11 198.52 128 169.41z" />
+    <glyph glyph-name="scroll"
+      unicode="&#xF70E;"
+      horiz-adv-x="640" d=" M48 448C21.53 448 0 426.4700000000001 0 400V336C0 327.16 7.16 320 16 320H96V400C96 426.4700000000001 74.47 448 48 448zM256 35.43V96H544V352C544 404.94 500.94 448 448 448H111.59C121.74 434.59 128 418.08 128 400V32C128 -6.87 162.65 -37.65 202.75 -31.12C234.22 -26 256 3.54 256 35.43zM288 64V32C288 -20.93 244.94 -64 192 -64H528C589.86 -64 640 -13.86 640 48C640 56.84 632.84 64 624 64H288z" />
+    <glyph glyph-name="search-dollar"
+      unicode="&#xF688;"
+      horiz-adv-x="512" d=" M505.04 5.34L405.3300000000001 105.03C400.8300000000001 109.53 394.73 112.03 388.3300000000001 112.03H372.0300000000001C399.6300000000001 147.33 416.0300000000001 191.72 416.0300000000001 240.02C416.03 354.91 322.92 448 208.02 448S0 354.91 0 240.02S93.11 32.04 208.02 32.04C256.32 32.04 300.73 48.44 336.03 76.04V59.74C336.03 53.34 338.53 47.24 343.03 42.74L442.74 -56.95C452.1399999999999 -66.35 467.34 -66.35 476.6399999999999 -56.95L504.9399999999999 -28.65C514.3399999999999 -19.25 514.3399999999999 -4.06 505.04 5.34zM208.0200000000001 96.04C128.48 96.04 64.02 160.38 64.02 240.02C64.02 319.55 128.37 384 208.0200000000001 384C287.5600000000001 384 352.0200000000001 319.66 352.0200000000001 240.02C352.0200000000001 160.49 287.6700000000001 96.04 208.0200000000001 96.04zM235.1300000000001 248.58L190.1200000000001 262.08C184.9600000000001 263.63 181.3500000000001 268.86 181.3500000000001 274.81C181.3500000000001 282.08 186.6500000000001 288 193.1500000000001 288H221.2600000000001C225.8200000000001 288 230.2200000000001 286.7099999999999 234.0800000000001 284.28C237.3200000000001 282.25 241.4400000000001 282.37 244.2100000000001 285.01L255.9600000000001 296.2199999999999C259.49 299.5899999999999 259.29 305.43 255.3900000000001 308.36C246.2900000000001 315.19 235.3100000000001 319.13 224.0200000000001 319.7099999999999V336C224.0200000000001 340.42 220.44 344 216.0200000000001 344H200.0200000000001C195.6000000000001 344 192.0200000000001 340.42 192.0200000000001 336V319.88C168.3900000000001 319.25 149.34 299.33 149.34 274.81C149.34 254.84 162.3300000000001 237 180.92 231.42L225.93 217.92C231.09 216.37 234.7 211.14 234.7 205.19C234.7 197.92 229.4 192 222.9 192H194.8C190.24 192 185.84 193.29 181.98 195.72C178.74 197.75 174.62 197.63 171.85 194.9900000000001L160.1 183.78C156.57 180.41 156.77 174.5700000000001 160.67 171.6400000000001C169.77 164.8100000000001 180.75 160.8700000000001 192.04 160.29V144C192.04 139.58 195.62 136 200.04 136H216.04C220.46 136 224.04 139.58 224.04 144V160.12C247.67 160.75 266.7200000000001 180.66 266.7200000000001 205.19C266.7200000000001 225.16 253.73 243 235.13 248.58z" />
+    <glyph glyph-name="search-location"
+      unicode="&#xF689;"
+      horiz-adv-x="512" d=" M505.04 5.34L405.3300000000001 105.03C400.8300000000001 109.53 394.73 112.03 388.3300000000001 112.03H372.0300000000001C399.6300000000001 147.33 416.0300000000001 191.72 416.0300000000001 240.02C416.03 354.91 322.92 448 208.02 448S0 354.91 0 240.02S93.11 32.04 208.02 32.04C256.32 32.04 300.73 48.44 336.03 76.04V59.74C336.03 53.34 338.53 47.24 343.03 42.74L442.74 -56.95C452.1399999999999 -66.35 467.34 -66.35 476.6399999999999 -56.95L504.9399999999999 -28.65C514.3399999999999 -19.25 514.3399999999999 -4.06 505.04 5.34zM208.0200000000001 96.04C128.48 96.04 64.02 160.38 64.02 240.02C64.02 319.55 128.37 384 208.0200000000001 384C287.5600000000001 384 352.0200000000001 319.66 352.0200000000001 240.02C352.0200000000001 160.49 287.6700000000001 96.04 208.0200000000001 96.04zM208.0400000000001 336C167.2600000000001 336 134.2000000000001 302.95 134.2000000000001 262.17C134.2000000000001 229.21 182.46 169.12 200.9500000000001 147.31A9.24 9.24 0 0 1 215.1300000000001 147.31C233.6200000000001 169.12 281.8800000000001 229.2 281.8800000000001 262.17C281.8800000000001 302.95 248.8200000000001 336 208.0400000000001 336zM208.0400000000001 240C194.7800000000001 240 184.0400000000001 250.75 184.0400000000001 264C184.0400000000001 277.26 194.7900000000001 288 208.0400000000001 288S232.0400000000001 277.26 232.0400000000001 264C232.0400000000001 250.75 221.2900000000001 240 208.0400000000001 240z" />
+    <glyph glyph-name="search-minus"
+      unicode="&#xF010;"
+      horiz-adv-x="512" d=" M304 256V224C304 217.4 298.6 212 292 212H124C117.4 212 112 217.4 112 224V256C112 262.6 117.4 268 124 268H292C298.6 268 304 262.6 304 256zM505 -28.7L476.7 -57C467.3 -66.4 452.1 -66.4 442.8 -57L343 42.7C338.5 47.2 336 53.3 336 59.7V76C300.7 48.4 256.3 32 208 32C93.1 32 0 125.1 0 240S93.1 448 208 448S416 354.9 416 240C416 191.7 399.6 147.3 372 112H388.3C394.7 112 400.8 109.5 405.3 105L505 5.3C514.3 -4.1 514.3 -19.3 505 -28.7zM344 240C344 315.2 283.2 376 208 376S72 315.2 72 240S132.8 104 208 104S344 164.8 344 240z" />
+    <glyph glyph-name="search-plus"
+      unicode="&#xF00E;"
+      horiz-adv-x="512" d=" M304 256V224C304 217.4 298.6 212 292 212H236V156C236 149.4 230.6 144 224 144H192C185.4 144 180 149.4 180 156V212H124C117.4 212 112 217.4 112 224V256C112 262.6 117.4 268 124 268H180V324C180 330.6 185.4 336 192 336H224C230.6 336 236 330.6 236 324V268H292C298.6 268 304 262.6 304 256zM505 -28.7L476.7 -57C467.3 -66.4 452.1 -66.4 442.8 -57L343 42.7C338.5 47.2 336 53.3 336 59.7V76C300.7 48.4 256.3 32 208 32C93.1 32 0 125.1 0 240S93.1 448 208 448S416 354.9 416 240C416 191.7 399.6 147.3 372 112H388.3C394.7 112 400.8 109.5 405.3 105L505 5.3C514.3 -4.1 514.3 -19.3 505 -28.7zM344 240C344 315.2 283.2 376 208 376S72 315.2 72 240S132.8 104 208 104S344 164.8 344 240z" />
+    <glyph glyph-name="search"
+      unicode="&#xF002;"
+      horiz-adv-x="512" d=" M505 5.3L405.3 105C400.8 109.5 394.7 112 388.3 112H372C399.6 147.3 416 191.7 416 240C416 354.9 322.9 448 208 448S0 354.9 0 240S93.1 32 208 32C256.3 32 300.7 48.4 336 76V59.7C336 53.3 338.5 47.2 343 42.7L442.7 -57C452.1 -66.4 467.3 -66.4 476.6 -57L504.9 -28.7C514.3 -19.3 514.3 -4.1 505 5.3zM208 112C137.3 112 80 169.2 80 240C80 310.7 137.2 368 208 368C278.7 368 336 310.8 336 240C336 169.3 278.8 112 208 112z" />
+    <glyph glyph-name="seedling"
+      unicode="&#xF4D8;"
+      horiz-adv-x="512" d=" M64 352H0C0 228.3 100.3 128 224 128V-16C224 -24.8 231.2 -32 240 -32H272C280.8 -32 288 -24.8 288 -16V128C288 251.7 187.7 352 64 352zM448 416C363.8 416 290.6 369.5 252.3 300.8C280 270.6 300.5 233.9 311.3 193.2C424 204.9 512 300.1 512 416H448z" />
+    <glyph glyph-name="server"
+      unicode="&#xF233;"
+      horiz-adv-x="512" d=" M480 288H32C14.327 288 0 302.327 0 320V384C0 401.673 14.327 416 32 416H480C497.673 416 512 401.673 512 384V320C512 302.327 497.673 288 480 288zM432 376C418.745 376 408 365.255 408 352S418.745 328 432 328S456 338.745 456 352S445.255 376 432 376zM368 376C354.745 376 344 365.255 344 352S354.745 328 368 328S392 338.745 392 352S381.255 376 368 376zM480 128H32C14.327 128 0 142.327 0 160V224C0 241.673 14.327 256 32 256H480C497.673 256 512 241.673 512 224V160C512 142.327 497.673 128 480 128zM432 216C418.745 216 408 205.255 408 192S418.745 168 432 168S456 178.745 456 192S445.255 216 432 216zM368 216C354.745 216 344 205.255 344 192S354.745 168 368 168S392 178.745 392 192S381.255 216 368 216zM480 -32H32C14.327 -32 0 -17.673 0 0V64C0 81.673 14.327 96 32 96H480C497.673 96 512 81.673 512 64V0C512 -17.673 497.673 -32 480 -32zM432 56C418.745 56 408 45.255 408 32S418.745 8 432 8S456 18.745 456 32S445.255 56 432 56zM368 56C354.745 56 344 45.255 344 32S354.745 8 368 8S392 18.745 392 32S381.255 56 368 56z" />
+    <glyph glyph-name="shapes"
+      unicode="&#xF61F;"
+      horiz-adv-x="512" d=" M512 128V-32C512 -49.67 497.67 -64 480 -64H320C302.33 -64 288 -49.67 288 -32V128C288 145.67 302.33 160 320 160H480C497.67 160 512 145.67 512 128zM128 192C57.31 192 0 134.69 0 64S57.31 -64 128 -64S256 -6.69 256 64S198.69 192 128 192zM479.03 224C504.3699999999999 224 520.2099999999999 250.67 507.54 272L412.51 432C399.84 453.33 368.16 453.33 355.49 432L260.4600000000001 272C247.7900000000001 250.67 263.6300000000001 224 288.9700000000001 224H479.03z" />
+    <glyph glyph-name="share-alt-square"
+      unicode="&#xF1E1;"
+      horiz-adv-x="448" d=" M448 368V16C448 -10.51 426.51 -32 400 -32H48C21.49 -32 0 -10.51 0 16V368C0 394.51 21.49 416 48 416H400C426.51 416 448 394.51 448 368zM304 152C289.438 152 276.177 146.439 266.217 137.329L198.259 178.104A56.33899999999999 56.33899999999999 0 0 1 198.259 205.897L266.217 246.672C276.177 237.561 289.438 232 304 232C334.928 232 360 257.072 360 288S334.928 344 304 344S248 318.928 248 288C248 283.203 248.605 278.5470000000001 249.74 274.103L181.782 233.328C171.823 242.439 158.562 248 144 248C113.072 248 88 222.928 88 192S113.072 136 144 136C158.562 136 171.823 141.561 181.783 150.671L249.741 109.896A56.08800000000001 56.08800000000001 0 0 1 248.001 95.999C248.001 65.071 273.073 39.999 304.001 39.999S360.001 65.071 360.001 95.999C360 126.928 334.928 152 304 152z" />
+    <glyph glyph-name="share-alt"
+      unicode="&#xF1E0;"
+      horiz-adv-x="448" d=" M352 128C329.392 128 308.613 120.181 292.21 107.105L189.724 171.159A96.551 96.551 0 0 1 189.724 212.842L292.21 276.896C308.613 263.819 329.392 256 352 256C405.019 256 448 298.981 448 352S405.019 448 352 448S256 405.019 256 352C256 344.842 256.79 337.87 258.276 331.159L155.79 267.105C139.387 280.1810000000001 118.608 288 96 288C42.981 288 0 245.019 0 192S42.981 96 96 96C118.608 96 139.387 103.819 155.79 116.895L258.276 52.841A96.301 96.301 0 0 1 256 32C256 -21.019 298.981 -64 352 -64S448 -21.019 448 32S405.019 128 352 128z" />
+    <glyph glyph-name="share-square"
+      unicode="&#xF14D;"
+      horiz-adv-x="576" d=" M568.482 270.552L424.479 134.567C409.3 120.232 384 130.86 384 152.015V223.978C239.425 223.008 178.434 188.865 219.225 52.625C223.708 37.652 206.379 26.058 194.219 35.295C155.252 64.895 120 121.512 120 178.661C120 322.598 237.599 351.161 384 351.973V423.988C384 445.162 409.317 455.756 424.479 441.436L568.482 305.448C578.502 295.985 578.51 280.023 568.482 270.552zM384 68.872V0H64V320H114.916A11.989999999999998 11.989999999999998 0 0 1 123.564 323.693C138.517 339.261 155.801 351.583 174.578 361.3690000000001C185.708 367.17 181.584 384 169.033 384H48C21.49 384 0 362.51 0 336V-16C0 -42.51 21.49 -64 48 -64H400C426.51 -64 448 -42.51 448 -16V72.806C448 81.094 439.803 86.872 431.989 84.108A71.83 71.83 0 0 0 397.8 80.731C390.53 81.777 384 76.217 384 68.872z" />
+    <glyph glyph-name="share"
+      unicode="&#xF064;"
+      horiz-adv-x="512" d=" M503.691 258.164L327.687 410.149C312.281 423.454 288 412.653 288 391.985V311.932C127.371 310.093 0 277.9 0 125.674C0 64.233 39.581 3.365 83.333 -28.458C96.986 -38.389 116.444 -25.925 111.41 -9.827C66.066 135.186 132.917 173.684 288 175.915V88C288 67.3 312.3 56.547 327.687 69.836L503.691 221.836C514.7620000000001 231.398 514.777 248.589 503.691 258.164z" />
+    <glyph glyph-name="shekel-sign"
+      unicode="&#xF20B;"
+      horiz-adv-x="448" d=" M248 280V112C248 103.16 255.16 96 264 96H312C320.84 96 328 103.16 328 112V280C328 355.11 267.11 416 192 416H24C10.75 416 0 405.26 0 392V-16C0 -24.84 7.16 -32 16 -32H64C72.84 -32 80 -24.84 80 -16V336H192C222.93 336 248 310.93 248 280zM432 416H384C375.1600000000001 416 368 408.8400000000001 368 400V104C368 73.07 342.93 48 312 48H200V272C200 280.8400000000001 192.84 288 184 288H136C127.16 288 120 280.8400000000001 120 272V-8C120 -21.25 130.75 -32 144 -32H312C387.11 -32 448 28.89 448 104V400C448 408.8400000000001 440.84 416 432 416z" />
+    <glyph glyph-name="shield-alt"
+      unicode="&#xF3ED;"
+      horiz-adv-x="512" d=" M496 320C496 98.718 360.0660000000001 -24.645 274.461 -60.308A48 48 0 0 0 237.538 -60.308C130.495 -15.713 16 121.513 16 320A48 48 0 0 0 45.539 364.308L237.539 444.308A48 48 0 0 0 274.462 444.308L466.462 364.308A48 48 0 0 0 496 320zM256 1.687L256.066 1.653C349.801 48.342 428.563 157.961 431.883 309.382L256 382.6670000000001V1.687z" />
+    <glyph glyph-name="ship"
+      unicode="&#xF21A;"
+      horiz-adv-x="640" d=" M496.616 75.361L566.6279999999999 145.373C583.5269999999999 162.273 576.5699999999999 191.144 553.7919999999999 198.465L512 211.898V352C512 369.673 497.673 384 480 384H416V424C416 437.255 405.255 448 392 448H248C234.745 448 224 437.255 224 424V384H160C142.327 384 128 369.673 128 352V211.898L86.208 198.465C63.455 191.152 56.454 162.292 73.372 145.373L143.384 75.361C125.828 31.713 85.587 0 24 0C10.745 0 0 -10.745 0 -24V-40C0 -53.255 10.745 -64 24 -64C85.023 -64 131.499 -43.39 167.258 -4.604C181.677 -39.432 216.021 -64 256 -64H384C423.979 -64 458.323 -39.432 472.742 -4.604C508.495 -43.384 554.968 -64 616 -64C629.255 -64 640 -53.255 640 -40V-24C640 -10.745 629.255 0 616 0C555.183 0 514.458 31.001 496.616 75.361zM192 320H448V232.469L329.7920000000001 270.464A31.995 31.995 0 0 1 310.2080000000001 270.464L192 232.469V320z" />
+    <glyph glyph-name="shipping-fast"
+      unicode="&#xF48B;"
+      horiz-adv-x="640" d=" M624 96H608V204.1C608 216.8 602.9 229 593.9 238L494 337.9C485 346.9 472.8 352 460.1 352H416V400C416 426.5 394.5 448 368 448H112C85.5 448 64 426.5 64 400V352H8C3.6 352 0 348.4 0 344V328C0 323.6 3.6 320 8 320H280C284.4 320 288 316.4 288 312V296C288 291.6 284.4 288 280 288H40C35.6 288 32 284.4 32 280V264C32 259.6 35.6 256 40 256H248C252.4 256 256 252.4 256 248V232C256 227.6 252.4 224 248 224H8C3.6 224 0 220.4 0 216V200C0 195.6 3.6 192 8 192H216C220.4 192 224 188.4 224 184V168C224 163.6 220.4 160 216 160H64V32C64 -21 107 -64 160 -64S256 -21 256 32H384C384 -21 427 -64 480 -64S576 -21 576 32H624C632.8 32 640 39.2 640 48V80C640 88.8 632.8 96 624 96zM160 -16C133.5 -16 112 5.5 112 32S133.5 80 160 80S208 58.5 208 32S186.5 -16 160 -16zM480 -16C453.5 -16 432 5.5 432 32S453.5 80 480 80S528 58.5 528 32S506.5 -16 480 -16zM560 192H416V304H460.1L560 204.1V192z" />
+    <glyph glyph-name="shoe-prints"
+      unicode="&#xF54B;"
+      horiz-adv-x="640" d=" M192 288H224V416H192C156.65 416 128 387.35 128 352S156.65 288 192 288zM0 32C0 -3.35 28.65 -32 64 -32H96V96H64C28.65 96 0 67.35 0 32zM337.46 160C302.55 160 261.3 146.88 232.73 128C207.94 111.62 188.21 96 128 96V-32L185.53 -47.97C211.74 -55.25 238.54 -61.09 265.84 -63.02C298.53 -65.33 331.44 -63.69 363.42 -56.82C472.9 -33.3 512 18.78 512 64C512 128 427.82 160 337.4600000000001 160zM491.42 440.81C459.44 447.68 426.53 449.33 393.84 447.01C366.54 445.08 339.74 439.24 313.53 431.9700000000001L256 416V288C316.2 288 335.94 272.38 360.73 256C389.3 237.12 430.55 224 465.46 224C555.82 224 640 256 640 320C640 365.2200000000001 600.9 417.3 491.42 440.81z" />
+    <glyph glyph-name="shopping-bag"
+      unicode="&#xF290;"
+      horiz-adv-x="448" d=" M352 288V320C352 390.58 294.579 448 224 448C153.42 448 96 390.58 96 320V288H0V16C0 -28.183 35.817 -64 80 -64H368C412.183 -64 448 -28.183 448 16V288H352zM160 320C160 355.29 188.71 384 224 384S288 355.29 288 320V288H160V320zM320 200C306.745 200 296 210.745 296 224S306.745 248 320 248S344 237.255 344 224S333.255 200 320 200zM128 200C114.745 200 104 210.745 104 224S114.745 248 128 248S152 237.255 152 224S141.255 200 128 200z" />
+    <glyph glyph-name="shopping-basket"
+      unicode="&#xF291;"
+      horiz-adv-x="576" d=" M576 232V216C576 202.745 565.255 192 552 192H544L517.887 9.212C514.509 -14.435 494.257 -32 470.37 -32H105.63C81.743 -32 61.491 -14.435 58.112 9.212L32 192H24C10.745 192 0 202.745 0 216V232C0 245.255 10.745 256 24 256H91.341L198.121 402.8210000000001C208.516 417.113 228.528 420.274 242.822 409.879C257.115 399.484 260.275 379.471 249.88 365.178L170.477 256H405.523L326.12 365.179C315.725 379.471 318.886 399.485 333.1790000000001 409.88C347.4700000000001 420.275 367.485 417.115 377.8800000000001 402.822L484.659 256H552C565.255 256 576 245.255 576 232zM312 56V168C312 181.255 301.255 192 288 192S264 181.255 264 168V56C264 42.745 274.745 32 288 32S312 42.745 312 56zM424 56V168C424 181.255 413.255 192 400 192S376 181.255 376 168V56C376 42.745 386.745 32 400 32S424 42.745 424 56zM200 56V168C200 181.255 189.255 192 176 192S152 181.255 152 168V56C152 42.745 162.745 32 176 32S200 42.745 200 56z" />
+    <glyph glyph-name="shopping-cart"
+      unicode="&#xF07A;"
+      horiz-adv-x="576" d=" M528.12 146.681L575.393 354.681C578.806 369.699 567.391 384 551.99 384H159.208L150.042 428.81C147.758 439.979 137.93 448 126.529 448H24C10.745 448 0 437.255 0 424V408C0 394.745 10.745 384 24 384H93.883L164.131 40.565C147.325 30.9 136 12.778 136 -8C136 -38.928 161.072 -64 192 -64S248 -38.928 248 -8C248 7.674 241.553 21.835 231.176 32H440.823C430.447 21.835 424 7.674 424 -8C424 -38.928 449.072 -64 480 -64S536 -38.928 536 -8C536 14.172 523.112 33.332 504.421 42.405L509.938 66.681C513.351 81.699 501.936 96 486.535 96H218.117L211.572 128H504.717C515.923 128 525.637 135.754 528.12 146.681z" />
+    <glyph glyph-name="shower"
+      unicode="&#xF2CC;"
+      horiz-adv-x="512" d=" M389.6600000000001 312.4L231.6 154.34C222.23 144.97 207.03 144.97 197.66 154.34L186.34 165.66C176.97 175.03 176.97 190.23 186.34 199.6L186.45 199.71C152.42 239.92 151.29 298.65 183.06 340.09C171.09 347.64 156.92 352 141.7600000000001 352C98.88 352 64 317.12 64 274.24V-32H0V274.24C0 352.41 63.59 416 141.76 416C178.69 416 212.37 401.8 237.62 378.58C273.52 390.09 314.12 383.08 344.29 357.55L344.4000000000001 357.66C353.7700000000001 367.03 368.9700000000001 367.03 378.3400000000001 357.66L389.6600000000001 346.3400000000001C399.0300000000001 336.9700000000001 399.0300000000001 321.77 389.6600000000001 312.4zM384 240C384 231.163 376.837 224 368 224S352 231.163 352 240S359.163 256 368 256S384 248.837 384 240zM416 240C416 248.837 423.163 256 432 256S448 248.837 448 240S440.837 224 432 224S416 231.163 416 240zM512 240C512 231.163 504.837 224 496 224S480 231.163 480 240S487.163 256 496 256S512 248.837 512 240zM352 208C352 199.163 344.837 192 336 192S320 199.163 320 208S327.163 224 336 224S352 216.837 352 208zM400 224C408.837 224 416 216.837 416 208S408.837 192 400 192S384 199.163 384 208S391.163 224 400 224zM480 208C480 199.163 472.837 192 464 192S448 199.163 448 208S455.163 224 464 224S480 216.837 480 208zM320 176C320 167.163 312.837 160 304 160S288 167.163 288 176S295.163 192 304 192S320 184.837 320 176zM352 176C352 184.837 359.163 192 368 192S384 184.837 384 176S376.837 160 368 160S352 167.163 352 176zM448 176C448 167.163 440.837 160 432 160S416 167.163 416 176S423.163 192 432 192S448 184.837 448 176zM320 144C320 152.837 327.163 160 336 160S352 152.837 352 144S344.837 128 336 128S320 135.163 320 144zM416 144C416 135.163 408.837 128 400 128S384 135.163 384 144S391.163 160 400 160S416 152.837 416 144zM320 112C320 103.163 312.837 96 304 96S288 103.163 288 112S295.163 128 304 128S320 120.837 320 112zM384 112C384 103.163 376.837 96 368 96S352 103.163 352 112S359.163 128 368 128S384 120.837 384 112zM352 80C352 71.163 344.837 64 336 64S320 71.163 320 80S327.163 96 336 96S352 88.837 352 80zM320 48C320 39.163 312.837 32 304 32S288 39.163 288 48S295.163 64 304 64S320 56.837 320 48z" />
+    <glyph glyph-name="shuttle-van"
+      unicode="&#xF5B6;"
+      horiz-adv-x="640" d=" M628.88 237.35L494.39 398.73A48.01 48.01 0 0 1 457.52 416H32C14.33 416 0 401.67 0 384V96C0 78.33 14.33 64 32 64H64C64 10.98 106.98 -32 160 -32S256 10.98 256 64H384C384 10.98 426.98 -32 480 -32S576 10.98 576 64H608C625.67 64 640 78.33 640 96V206.62C640 217.85 636.06 228.72 628.88 237.35zM64 256V352H160V256H64zM160 16C133.49 16 112 37.49 112 64S133.49 112 160 112S208 90.51 208 64S186.51 16 160 16zM320 256H224V352H320V256zM480 16C453.49 16 432 37.49 432 64S453.49 112 480 112S528 90.51 528 64S506.51 16 480 16zM384 256V352H450.02L530.02 256H384z" />
+    <glyph glyph-name="sign-in-alt"
+      unicode="&#xF2F6;"
+      horiz-adv-x="512" d=" M416 0H332C325.4 0 320 5.4 320 12V52C320 58.6 325.4 64 332 64H416C433.7 64 448 78.3 448 96V288C448 305.7 433.7 320 416 320H332C325.4 320 320 325.4 320 332V372C320 378.6 325.4 384 332 384H416C469 384 512 341 512 288V96C512 43 469 0 416 0zM369 201L201 369C186 384 160 373.5 160 352V256H24C10.7 256 0 245.3 0 232V136C0 122.7 10.7 112 24 112H160V16C160 -5.5 186 -16 201 -1L369 167C378.3 176.4 378.3 191.6 369 201z" />
+    <glyph glyph-name="sign-language"
+      unicode="&#xF2A7;"
+      horiz-adv-x="448" d=" M91.434 -35.987C91.127 -19.969 104.543 -6.858 120.564 -6.858H182.857V-1.144H56.993C40.972 -1.144 27.556 11.967 27.863 27.985C28.16 43.509 40.835 56 56.428 56H182.857V61.714H29.136C13.115 61.714 -0.301 74.825 0.006 90.843C0.303 106.365 12.979 118.856 28.572 118.856H182.858V124.57H57.707C41.686 124.57 28.27 137.681 28.577 153.699C28.874 169.221 41.55 181.712 57.143 181.712H225.709L194.624 204.318C181.862 213.599 179.041 231.467 188.322 244.23C197.603 256.991 215.472 259.812 228.234 250.532L351.5950000000001 160.817A34.287 34.287 0 0 0 365.7150000000001 133.089V-8.047C365.7150000000001 -23.957 354.769 -37.777 339.2820000000001 -41.421L258.8110000000001 -60.3550000000001A137.16 137.16 0 0 0 227.4000000000001 -64.0010000000001H120C104.407 -64.0000000000001 91.731 -51.5090000000001 91.434 -35.9870000000001zM164.683 189.714H201.106L189.919 197.8499999999999C171.34 211.3609999999999 169.606 238.737 186.749 254.386L173.745 271.0859999999999C163.902 283.7269999999999 145.315 286.257 132.865 276.1739999999999C120.8 266.4029999999999 118.732 248.7269999999999 128.312 236.4239999999999L164.683 189.714zM447.981 191.817L442.978 344.269C442.4600000000001 360.04 429.2560000000001 372.405 413.485 371.8879999999999C397.712 371.37 385.348 358.166 385.866 342.395L387.128 303.98L283.565 436.981C273.985 449.284 256.342 451.611 243.912 442.3090000000001C231.085 432.71 228.983 414.069 238.826 401.428L315.715 302.683L311.206 299.1720000000001L216.416 420.906C206.8359999999999 433.209 189.1929999999999 435.536 176.763 426.234C163.936 416.635 161.834 397.994 171.6769999999999 385.353L266.12 264.065L261.611 260.554L183.9359999999999 360.308C174.3559999999999 372.611 156.7129999999999 374.938 144.2829999999999 365.636C131.4559999999999 356.0370000000001 129.3539999999999 337.396 139.1969999999999 324.755L191.2499999999999 257.906C203.7469999999999 266.163 220.3049999999999 266.1910000000001 232.9399999999999 257.002L356.2999999999999 167.288C367.2039999999999 159.358 373.7149999999999 146.573 373.7149999999999 133.09V116.091L434.779 163.64A34.285 34.285 0 0 1 447.981 191.817z" />
+    <glyph glyph-name="sign-out-alt"
+      unicode="&#xF2F5;"
+      horiz-adv-x="512" d=" M497 175L329 7C314 -8 288 2.5 288 24V120H152C138.7 120 128 130.7 128 144V240C128 253.3 138.7 264 152 264H288V360C288 381.4 313.9 392 329 377L497 209C506.3 199.6 506.3 184.4 497 175zM192 12V52C192 58.6 186.6 64 180 64H96C78.3 64 64 78.3 64 96V288C64 305.7 78.3 320 96 320H180C186.6 320 192 325.4 192 332V372C192 378.6 186.6 384 180 384H96C43 384 0 341 0 288V96C0 43 43 0 96 0H180C186.6 0 192 5.4 192 12z" />
+    <glyph glyph-name="sign"
+      unicode="&#xF4D9;"
+      horiz-adv-x="512" d=" M496 384H128V432C128 440.8 120.8 448 112 448H80C71.2 448 64 440.8 64 432V384H16C7.2 384 0 376.8 0 368V336C0 327.2 7.2 320 16 320H64V-48C64 -56.8 71.2 -64 80 -64H112C120.8 -64 128 -56.8 128 -48V320H496C504.8 320 512 327.2 512 336V368C512 376.8 504.8 384 496 384zM160 64H480V288H160V64z" />
+    <glyph glyph-name="signal"
+      unicode="&#xF012;"
+      horiz-adv-x="640" d=" M216 160H168C159.16 160 152 152.84 152 144V-48C152 -56.84 159.16 -64 168 -64H216C224.84 -64 232 -56.84 232 -48V144C232 152.84 224.84 160 216 160zM88 64H40C31.16 64 24 56.84 24 48V-48C24 -56.84 31.16 -64 40 -64H88C96.84 -64 104 -56.84 104 -48V48C104 56.84 96.84 64 88 64zM344 256H296C287.1600000000001 256 280 248.84 280 240V-48C280 -56.84 287.1600000000001 -64 296 -64H344C352.84 -64 360 -56.84 360 -48V240C360 248.84 352.84 256 344 256zM472 352H424C415.1600000000001 352 408 344.8400000000001 408 336V-48C408 -56.84 415.1600000000001 -64 424 -64H472C480.84 -64 488 -56.84 488 -48V336C488 344.8400000000001 480.84 352 472 352zM600 448H552C543.16 448 536 440.84 536 432V-48C536 -56.84 543.16 -64 552 -64H600C608.84 -64 616 -56.84 616 -48V432C616 440.84 608.84 448 600 448z" />
+    <glyph glyph-name="signature"
+      unicode="&#xF5B7;"
+      horiz-adv-x="512" d=" M496 128H404.14C394.87 128 384.42 139.31 378.36 156.52C363.13 199.88 330.25 226.82 292.56 226.82C261.7200000000001 226.82 234.09 208.77 216.45 177.59L194.8 341.5C188.84 366.92 169.34 384 146.28 384C123.23 384 103.73 366.92 97.78 341.5L56.16 163.8C50.7 140.55 37.75 128 28.33 128H16C7.16 128 0 120.84 0 112V80C0 71.16 7.16 64 16 64H28.33C70.3 64 106.52 98.23 118.47 149.2L142.31 250.98L171.56 28.87C173.54 13.82 185.38 1.42 200.5 0.12C201.49 0.03 202.46 -0.01 203.43 -0.01C217.51 -0.01 230.07 9.26 234.12 22.94L267.15 135.28C273.03 152 282.99 162.8 292.56 162.8C302.14 162.8 312.11 152 318.3400000000001 134.28C333.5700000000001 90.92 366.4500000000001 63.98 404.1400000000001 63.98H496C504.84 63.98 512 71.14 512 79.98V111.98C512 120.84 504.84 128 496 128z" />
+    <glyph glyph-name="sitemap"
+      unicode="&#xF0E8;"
+      horiz-adv-x="640" d=" M128 96H32C14.33 96 0 81.67 0 64V-32C0 -49.67 14.33 -64 32 -64H128C145.67 -64 160 -49.67 160 -32V64C160 81.67 145.67 96 128 96zM104 176H296V128H344V176H536V128H584V185.59C584 206.7600000000001 566.77 224 545.59 224H344V288H384C401.67 288 416 302.3300000000001 416 320V416C416 433.67 401.67 448 384 448H256C238.33 448 224 433.67 224 416V320C224 302.33 238.33 288 256 288H296V224H94.41C73.23 224 56 206.77 56 185.59V128H104V176zM368 96H272C254.33 96 240 81.67 240 64V-32C240 -49.67 254.33 -64 272 -64H368C385.67 -64 400 -49.67 400 -32V64C400 81.67 385.67 96 368 96zM608 96H512C494.33 96 480 81.67 480 64V-32C480 -49.67 494.33 -64 512 -64H608C625.67 -64 640 -49.67 640 -32V64C640 81.67 625.67 96 608 96z" />
+    <glyph glyph-name="skull-crossbones"
+      unicode="&#xF714;"
+      horiz-adv-x="448" d=" M439.15 -5.06L297.17 64L439.1600000000001 133.06C447.06 137.01 450.2700000000001 146.62 446.31 154.52L432 183.15C428.05 191.05 418.44 194.26 410.53 190.31L224 99.59L37.47 190.31C29.57 194.26 19.96 191.06 16 183.15L1.69 154.52C-2.26 146.62 0.94 137.01 8.84 133.06L150.83 64L8.85 -5.06C0.95 -9.01 -2.26 -18.62 1.7 -26.53L16.01 -55.16C19.96 -63.0599999999999 29.57 -66.27 37.48 -62.3099999999999L224 28.41L410.53 -62.3099999999999C418.43 -66.26 428.04 -63.0599999999999 432 -55.16L446.31 -26.53C450.26 -18.6199999999999 447.05 -9.01 439.15 -5.0599999999999zM150 210.72L144.52 184.85C141.85 172.23 149.94 160 160.97 160H287.05C298.08 160 306.17 172.23 303.5 184.85L298 210.72C339.78 233.13 368 273.4700000000001 368 320C368 390.69 303.53 448 224 448S80 390.69 80 320C80 273.4700000000001 108.22 233.13 150 210.72zM280 336C297.65 336 312 321.65 312 304S297.65 272 280 272S248 286.35 248 304S262.35 336 280 336zM168 336C185.65 336 200 321.65 200 304S185.65 272 168 272S136 286.35 136 304S150.35 336 168 336z" />
+    <glyph glyph-name="skull"
+      unicode="&#xF54C;"
+      horiz-adv-x="512" d=" M256 448C114.6 448 0 347.7 0 224C0 153.9 36.9 91.4 94.5 50.3C104.1 43.4 109.7 32.2 108 20.4L98.6 -45.8C97.2 -55.4 104.6 -63.9999999999999 114.3 -63.9999999999999H192V-7.9999999999999C192 -3.6 195.6 1e-13 200 1e-13H216C220.4 1e-13 224 -3.6 224 -7.9999999999999V-63.9999999999999H288V-7.9999999999999C288 -3.6 291.6 1e-13 296 1e-13H312C316.4 1e-13 320 -3.6 320 -7.9999999999999V-63.9999999999999H397.7C407.4 -63.9999999999999 414.8 -55.3999999999999 413.4 -45.8L404 20.4C402.3 32.1 407.8 43.4 417.5 50.3C475.1 91.4 512 153.9 512 224C512 347.7 397.4 448 256 448zM160 128C124.7 128 96 156.7 96 192S124.7 256 160 256S224 227.3 224 192S195.3 128 160 128zM352 128C316.7 128 288 156.7 288 192S316.7 256 352 256S416 227.3 416 192S387.3 128 352 128z" />
+    <glyph glyph-name="slash"
+      unicode="&#xF715;"
+      horiz-adv-x="640" d=" M594.53 -60.63L6.18 394.1C-0.79 399.52 -2.05 409.57 3.37 416.55L23.01 441.82C28.43 448.8 38.49 450.06 45.47 444.63L633.82 -10.1C640.7900000000001 -15.52 642.0500000000001 -25.57 636.63 -32.55L616.99 -57.82C611.57 -64.7999999999999 601.51 -66.0499999999999 594.53 -60.63z" />
+    <glyph glyph-name="sliders-h"
+      unicode="&#xF1DE;"
+      horiz-adv-x="512" d=" M496 64H160V80C160 88.8 152.8 96 144 96H112C103.2 96 96 88.8 96 80V64H16C7.2 64 0 56.8 0 48V16C0 7.2 7.2 0 16 0H96V-16C96 -24.8 103.2 -32 112 -32H144C152.8 -32 160 -24.8 160 -16V0H496C504.8 0 512 7.2 512 16V48C512 56.8 504.8 64 496 64zM496 224H416V240C416 248.8 408.8 256 400 256H368C359.2 256 352 248.8 352 240V224H16C7.2 224 0 216.8 0 208V176C0 167.2 7.2 160 16 160H352V144C352 135.2 359.2 128 368 128H400C408.8 128 416 135.2 416 144V160H496C504.8 160 512 167.2 512 176V208C512 216.8 504.8 224 496 224zM496 384H288V400C288 408.8 280.8 416 272 416H240C231.2 416 224 408.8 224 400V384H16C7.2 384 0 376.8 0 368V336C0 327.2 7.2 320 16 320H224V304C224 295.2 231.2 288 240 288H272C280.8 288 288 295.2 288 304V320H496C504.8 320 512 327.2 512 336V368C512 376.8 504.8 384 496 384z" />
+    <glyph glyph-name="smile-beam"
+      unicode="&#xF5B8;"
+      horiz-adv-x="496" d=" M248 440C111 440 0 329 0 192S111 -56 248 -56S496 55 496 192S385 440 248 440zM112 224.6C115.3 266.7 144.2 296 168 296S220.7 266.7 224 224.6C224.7 216 213.2 212.7 209.1 220.1L199.6 237.1C191.9 250.8 180.4 258.7 168.1 258.7S144.3 250.8 136.6 237.1L127.1 220.1C122.8 212.7 111.3 216.1 112 224.6zM362.8 101.8C334.3 67.6 292.5 48 248 48S161.7 67.6 133.2 101.8C119.7 118.1 144.2 138.5 157.8 122.3C180.2 95.4 213 80.1 248 80.1S315.8 95.5 338.2 122.3C351.8 138.5 376.3 118 362.8 101.8zM369 220.1L359.5 237.1C351.8 250.8 340.3 258.7000000000001 328 258.7000000000001S304.2 250.8 296.5 237.1L287 220.1C282.9 212.8 271.4 216.1 272.1 224.6C275.4000000000001 266.7000000000001 304.3 296 328.1 296S380.8 266.7000000000001 384.1 224.6C384.7000000000001 216 373.1 212.7 369 220.1z" />
+    <glyph glyph-name="smile-wink"
+      unicode="&#xF4DA;"
+      horiz-adv-x="496" d=" M0 192C0 55 111 -56 248 -56S496 55 496 192S385 440 248 440S0 329 0 192zM200 240C200 222.3 185.7 208 168 208S136 222.3 136 240S150.3 272 168 272S200 257.7 200 240zM358.5 223.5C343.7 236.7 312.3 236.7 297.5 223.5L288 215C279.7 207.6 266.4 214.6 268.2 225.8C272.2 251 302.4 267.9 328.1 267.9S384 251 388 225.8C389.7 214.7 376.6 207.5 368.2 215L358.5 223.5zM157.8 122.2C180.2 95.3 213 80 248 80S315.8 95.4 338.2 122.2C351.8 138.4 376.3 118 362.8 101.7C334.3 67.6 292.5 48 248 48S161.7 67.6 133.2 101.8C119.7 118.1 144.4 138.5 157.8 122.2z" />
+    <glyph glyph-name="smile"
+      unicode="&#xF118;"
+      horiz-adv-x="496" d=" M248 440C111 440 0 329 0 192S111 -56 248 -56S496 55 496 192S385 440 248 440zM328 272C345.7 272 360 257.7 360 240S345.7 208 328 208S296 222.3 296 240S310.3 272 328 272zM168 272C185.7 272 200 257.7 200 240S185.7 208 168 208S136 222.3 136 240S150.3 272 168 272zM362.8 101.8C334.3 67.6 292.5 48 248 48S161.7 67.6 133.2 101.8C119.6 118.1 144.2 138.5 157.8 122.3C180.2 95.4 213 80.1 248 80.1S315.8 95.5 338.2 122.3C351.6 138.5 376.3 118.1 362.8 101.8z" />
+    <glyph glyph-name="smog"
+      unicode="&#xF75F;"
+      horiz-adv-x="640" d=" M624 80H80C71.2 80 64 72.8 64 64V48C64 39.2 71.2 32 80 32H624C632.8 32 640 39.2 640 48V64C640 72.8 632.8 80 624 80zM144 -16H16C7.2 -16 0 -23.2 0 -32V-48C0 -56.8 7.2 -64 16 -64H144C152.8 -64 160 -56.8 160 -48V-32C160 -23.2 152.8 -16 144 -16zM560 -16H224C215.2 -16 208 -23.2 208 -32V-48C208 -56.8 215.2 -64 224 -64H560C568.8 -64 576 -56.8 576 -48V-32C576 -23.2 568.8 -16 560 -16zM144 160H300.1C322.6 140.3 351.7000000000001 128 384 128S445.3 140.3 467.9 160H528C589.9 160 640 210.1 640 272S589.9 384 528 384C510 384 493.3 379.4 478.3 371.9C454 417 406.8 448 352 448C311 448 274.2 430.7 248 403.2C221.8 430.7 185 448 144 448C64.5 448 0 383.5 0 304S64.5 160 144 160z" />
+    <glyph glyph-name="smoking-ban"
+      unicode="&#xF54D;"
+      horiz-adv-x="512" d=" M96 144C96 135.2 103.2 128 112 128H229.5L133.5 224H112C103.2 224 96 216.8 96 208V144zM256 448C114.6 448 0 333.4 0 192S114.6 -64 256 -64S512 50.6 512 192S397.4 448 256 448zM256 0C150.1 0 64 86.1 64 192C64 233.4 77.3 271.7 99.7 303.1L367.1 35.7C335.7 13.3 297.4 0 256 0zM301.2 192H384V160H333.2L301.2 192zM412.3 80.9L365.2 128H400C408.8 128 416 135.2 416 144V208C416 216.8 408.8 224 400 224H269.2L144.9 348.3C176.3 370.7 214.6 384 256 384C361.9 384 448 297.9 448 192C448 150.6 434.7 112.3 412.3 80.9zM320.6 320C305 320 292 331.2 289.2000000000001 345.9C288.5000000000001 349.5 285.2000000000001 352 281.5000000000001 352H265.3000000000001C260.3000000000001 352 256.6000000000001 347.5 257.3000000000001 342.6C261.9000000000001 311.7 288.5000000000001 288 320.6000000000001 288C336.2000000000001 288 349.2000000000001 276.8 352.0000000000001 262.1C352.7000000000001 258.5 356.0000000000001 256 359.7000000000001 256H375.9000000000001C380.9000000000001 256 384.6 260.5 383.9000000000001 265.4C379.3 296.3 352.7000000000001 320 320.6 320z" />
+    <glyph glyph-name="smoking"
+      unicode="&#xF48D;"
+      horiz-adv-x="640" d=" M632 96H584C579.6 96 576 92.4 576 88V-56C576 -60.4 579.6 -64 584 -64H632C636.4 -64 640 -60.4 640 -56V88C640 92.4 636.4 96 632 96zM553.3 360.9C547.5999999999999 364.7 544 370.9 544 377.7V440C544 444.4 540.4 448 536 448H488C483.6 448 480 444.4 480 440V377.7C480 355.7 490.2 334.3 508.6 322.3C550.8000000000001 295 576 248.5 576 198.3V168C576 163.6 579.6 160 584 160H632C636.4 160 640 163.6 640 168V198.3C640 263.8 607.6 324.5 553.3 360.9zM432 96H48C21.5 96 0 74.5 0 48V-16C0 -42.5 21.5 -64 48 -64H432C440.8 -64 448 -56.8 448 -48V80C448 88.8 440.8 96 432 96zM400 -16H224V48H400V-16zM487.7 306.4C463.8 323 448 348.7 448 377.7V440C448 444.4 444.4 448 440 448H392C387.6 448 384 444.4 384 440V373.6C384 329.9 408.6 292 444.3 266.9C466.7 251.2 480 225.7 480 198.3V168C480 163.6 483.6 160 488 160H536C540.4 160 544 163.6 544 168V198.3C544 241.6 523 281.7000000000001 487.7 306.4zM536 96H488C483.6 96 480 92.4 480 88V-56C480 -60.4 483.6 -64 488 -64H536C540.4 -64 544 -60.4 544 -56V88C544 92.4 540.4 96 536 96z" />
+    <glyph glyph-name="snowflake"
+      unicode="&#xF2DC;"
+      horiz-adv-x="448" d=" M440.3 102.8L406.5 122.3L432.5 129.3C440.7 131.5 445.6 140 443.4 148.2L439.4 163.1C437.2 171.3 428.7 176.2 420.5 174L349.7 155L285.8 192L349.6 228.9L420.4000000000001 209.9C428.6 207.7 437.1 212.5999999999999 439.3 220.8L443.3 235.7C445.5 243.9 440.6 252.4 432.4000000000001 254.6L406.4000000000001 261.6L440.2000000000001 281.1C447.6 285.4 450.1 294.8 445.9000000000001 302.2L430.4 329C426.1 336.4 416.7 338.9 409.3 334.7L375.5 315.2L382.5 341.2C384.7 349.4 379.8 357.9 371.6 360.1L356.7 364.1C348.5 366.3 340 361.4 337.8 353.2L318.8 282.4L256 246.2V323.7L309.7 377.4C315.9 383.6 315.9 393.8 309.7 400L298.4 411.3C292.2 417.5 282 417.5 275.8 411.3L256 391.6V432C256 440.8 248.8 448 240 448H208C199.2 448 192 440.8 192 432V391.6L172.3 411.3C166.1 417.5 155.9 417.5 149.7 411.3L138.3 400C132 393.8 132 383.6 138.3 377.4L192 323.7V246.2L129.2 282.4L110.2 353.2C108 361.4 99.5 366.3 91.3 364.1L76.4 360.1C68.2 357.9 63.3 349.4 65.5 341.2L72.5 315.2L38.7 334.7C31.3 339 21.9 336.4 17.6 329L2.1 302.3C-2.2 294.9 0.4 285.5 7.8 281.2000000000001L41.6 261.7000000000001L15.6 254.7C7.3 252.5000000000001 2.4 244.0000000000001 4.6 235.7L8.6 220.8C10.8 212.6 19.3 207.7 27.5 209.9L98.3 228.9L162.1 192L98.3 155.1L27.5 174.1C19.3 176.3 10.8 171.4 8.6 163.2000000000001L4.6 148.3000000000001C2.4 140.1000000000001 7.3 131.6000000000001 15.5 129.4000000000001L41.5 122.4000000000001L7.7 102.8000000000001C0.3 98.5000000000001 -2.2 89.1000000000001 2 81.7000000000001L17.5 54.9C21.8 47.5000000000001 31.2 45.0000000000001 38.6 49.2L72.4 68.7000000000001L65.4 42.7C63.2 34.5000000000001 68.1 26.0000000000001 76.3 23.8000000000001L91.2 19.8000000000001C99.4 17.6000000000001 107.9 22.5000000000001 110.1 30.7L129.1 101.5000000000001L191.9 137.7000000000001V60.2000000000001L138.2 6.5000000000001C131.9 0.3000000000001 131.9 -9.8999999999999 138.2 -16.1L149.5 -27.4C155.7 -33.6 165.9 -33.6 172.1 -27.4L191.8 -7.7V-48C191.8 -56.8 199 -64 207.8 -64H239.8C248.6 -64 255.8 -56.8 255.8 -48V-7.6L275.5 -27.3C281.7 -33.5 291.9 -33.5 298.1 -27.3L309.4000000000001 -16C315.6 -9.8 315.6 0.4 309.4000000000001 6.6L256 60.3V137.8L318.8 101.6L337.8 30.8C340 22.6 348.5 17.7 356.7 19.9L371.6 23.9C379.8 26.1 384.7 34.6 382.5 42.8L375.5 68.8L409.3 49.3C416.7 45 426.1 47.6 430.4 55L445.9 81.8C450.2 89.1 447.7 98.6 440.3 102.8z" />
+    <glyph glyph-name="socks"
+      unicode="&#xF696;"
+      horiz-adv-x="512" d=" M214.66 136.99L288 192V352H128V176L41.35 111.39C1.95 81.83 -12.51 26.97 12.14 -15.67C30.39 -47.25 63.27 -64 96.08 -64C116.11 -64 136.33 -57.75 153.6 -44.8L175.46 -28.41C145.61 26.97 161.92 97.43 214.66 136.99zM288 416C288 427.05 291.07 437.3 296.02 446.38C293.4 447.08 290.85 448 288 448H160C142.33 448 128 433.67 128 416V384H288V416zM480 448H352C334.33 448 320 433.67 320 416V384H512V416C512 433.67 497.67 448 480 448zM320 176L233.87 111.39C194.47 81.83 180.01 26.97 204.66 -15.67C222.91 -47.25 255.27 -64 288.08 -64C308.11 -64 328.33 -57.75 345.6 -44.8L460.8 41.6A127.99699999999999 127.99699999999999 0 0 1 512 144V352H320V176z" />
+    <glyph glyph-name="solar-panel"
+      unicode="&#xF5BA;"
+      horiz-adv-x="640" d=" M431.98 -0.01L384.01 -0.06V32H256.01V-0.21L208.03 -0.26C199.21 -0.27 192.06 -7.42 192.05 -16.25L192 -47.98C191.99 -56.83 199.17 -64.01 208.02 -64L431.98 -63.74C440.8 -63.73 447.9500000000001 -56.58 447.9600000000001 -47.76L448.0000000000001 -16.03C448.0100000000001 -7.1799999999999 440.8300000000001 0 431.9800000000001 -0.01zM585.2 421.26C582.58 436.69 568.99 448 553.06 448H86.93C71 448 57.41 436.69 54.79 421.26C-3.32 78.84 0.04 99.92 0.03 96C0 78.68 14.32 64 32.63 64H607.37C625.6 64 639.88 78.56 639.96 95.79C639.98 99.87 643.3100000000001 78.84 585.2 421.2600000000001zM259.83 384H380.16L389.93 288H250.06L259.83 384zM184.66 128H71.09L90.1 240H196.07L184.66 128zM200.95 288H98.24L114.53 384H210.72L200.95 288zM233.77 128L245.17 240H394.82L406.2199999999999 128H233.77zM429.27 384H525.46L541.75 288H439.04L429.2700000000001 384zM455.33 128L443.93 240H549.9L568.91 128H455.33z" />
+    <glyph glyph-name="sort-alpha-down"
+      unicode="&#xF15D;"
+      horiz-adv-x="448" d=" M187.298 52.686L107.314 -27.316C101.066 -33.563 90.931 -33.561 84.687 -27.316L4.705 52.686C-5.365 62.756 1.807 80 16.019 80H64V400C64 408.837 71.163 416 80 416H112C120.837 416 128 408.837 128 400V80H175.984C190.225 80 197.347 62.736 187.298 52.686zM306.373 232.693A12 12 0 0 0 294.838 224H259.1210000000001C250.9010000000001 224 245.1140000000001 232.078 247.7590000000001 239.861L304.855 407.861A12 12 0 0 0 316.217 416H355.783C360.9220000000001 416 365.4910000000001 412.727 367.1450000000001 407.861L424.2410000000001 239.861C426.886 232.078 421.1 224 412.879 224H377.144A12 12 0 0 0 365.629 232.622L357.3280000000001 260.9210000000001H314.4650000000001L306.3730000000001 232.693zM329.23 311.39H342.5970000000001L335.997 334.327L329.23 311.39zM341.805 24.067L409.256 119.765A12 12 0 0 1 411.448 126.678V148C411.448 154.627 406.075 160 399.448 160H274.522C267.895 160 262.522 154.627 262.522 148V119.07C262.522 112.443 267.895 107.07 274.522 107.07H330.991C330.252 106.079 329.494 105.034 328.721 103.937L261.5180000000001 8.732A12.001000000000001 12.001000000000001 0 0 1 259.322 1.812V-20C259.322 -26.627 264.695 -32 271.322 -32H400.677C407.3040000000001 -32 412.677 -26.627 412.677 -20V8.93C412.677 15.557 407.3040000000001 20.93 400.677 20.93H339.531C340.271 21.923 341.031 22.969 341.805 24.067z" />
+    <glyph glyph-name="sort-alpha-up"
+      unicode="&#xF15E;"
+      horiz-adv-x="448" d=" M4.702 331.314L84.686 411.316C90.934 417.563 101.069 417.561 107.313 411.316L187.294 331.314C197.364 321.2440000000001 190.193 304 175.98 304H128V-16C128 -24.837 120.837 -32 112 -32H80C71.163 -32 64 -24.837 64 -16V304H16.016C1.775 304 -5.347 321.264 4.702 331.314zM306.373 232.693A12 12 0 0 0 294.838 224H259.1210000000001C250.9010000000001 224 245.1140000000001 232.078 247.7590000000001 239.861L304.855 407.861A12 12 0 0 0 316.217 416H355.783C360.9220000000001 416 365.4910000000001 412.727 367.1450000000001 407.861L424.2410000000001 239.861C426.886 232.078 421.1 224 412.879 224H377.144A12 12 0 0 0 365.629 232.622L357.3280000000001 260.9210000000001H314.4650000000001L306.3730000000001 232.693zM329.23 311.39H342.5970000000001L335.997 334.327L329.23 311.39zM341.805 24.067L409.256 119.765A12 12 0 0 1 411.448 126.678V148C411.448 154.627 406.075 160 399.448 160H274.522C267.895 160 262.522 154.627 262.522 148V119.07C262.522 112.443 267.895 107.07 274.522 107.07H330.991C330.252 106.079 329.494 105.034 328.721 103.937L261.5180000000001 8.732A12.001000000000001 12.001000000000001 0 0 1 259.322 1.812V-20C259.322 -26.627 264.695 -32 271.322 -32H400.677C407.3040000000001 -32 412.677 -26.627 412.677 -20V8.93C412.677 15.557 407.3040000000001 20.93 400.677 20.93H339.531C340.271 21.923 341.031 22.969 341.805 24.067z" />
+    <glyph glyph-name="sort-amount-down"
+      unicode="&#xF160;"
+      horiz-adv-x="512" d=" M187.298 52.686L107.314 -27.316C101.066 -33.563 90.931 -33.561 84.687 -27.316L4.705 52.686C-5.365 62.756 1.807 80 16.019 80H64V400C64 408.837 71.163 416 80 416H112C120.837 416 128 408.837 128 400V80H175.984C190.225 80 197.347 62.736 187.298 52.686zM240 352H496C504.837 352 512 359.163 512 368V400C512 408.837 504.837 416 496 416H240C231.163 416 224 408.837 224 400V368C224 359.163 231.163 352 240 352zM224 240V272C224 280.837 231.163 288 240 288H432C440.837 288 448 280.837 448 272V240C448 231.163 440.837 224 432 224H240C231.163 224 224 231.163 224 240zM224 -16V16C224 24.837 231.163 32 240 32H304C312.837 32 320 24.837 320 16V-16C320 -24.837 312.837 -32 304 -32H240C231.163 -32 224 -24.837 224 -16zM224 112V144C224 152.837 231.163 160 240 160H368C376.837 160 384 152.837 384 144V112C384 103.163 376.837 96 368 96H240C231.163 96 224 103.163 224 112z" />
+    <glyph glyph-name="sort-amount-up"
+      unicode="&#xF161;"
+      horiz-adv-x="512" d=" M4.702 331.314L84.686 411.316C90.934 417.563 101.069 417.561 107.313 411.316L187.294 331.314C197.364 321.2440000000001 190.193 304 175.98 304H128V-16C128 -24.837 120.837 -32 112 -32H80C71.163 -32 64 -24.837 64 -16V304H16.016C1.775 304 -5.347 321.264 4.702 331.314zM240 352H496C504.837 352 512 359.163 512 368V400C512 408.837 504.837 416 496 416H240C231.163 416 224 408.837 224 400V368C224 359.163 231.163 352 240 352zM224 240V272C224 280.837 231.163 288 240 288H432C440.837 288 448 280.837 448 272V240C448 231.163 440.837 224 432 224H240C231.163 224 224 231.163 224 240zM224 -16V16C224 24.837 231.163 32 240 32H304C312.837 32 320 24.837 320 16V-16C320 -24.837 312.837 -32 304 -32H240C231.163 -32 224 -24.837 224 -16zM224 112V144C224 152.837 231.163 160 240 160H368C376.837 160 384 152.837 384 144V112C384 103.163 376.837 96 368 96H240C231.163 96 224 103.163 224 112z" />
+    <glyph glyph-name="sort-down"
+      unicode="&#xF0DD;"
+      horiz-adv-x="320" d=" M41 160H279C300.4 160 311.1 134.1 296 119L177 0C167.6 -9.4 152.4 -9.4 143.1 0L24 119C8.9 134.1 19.6 160 41 160z" />
+    <glyph glyph-name="sort-numeric-down"
+      unicode="&#xF162;"
+      horiz-adv-x="448" d=" M308.811 334.213L289.363 355.008C284.841 359.844 285.089 367.429 289.919 371.958L333.362 412.699A11.999000000000002 11.999000000000002 0 0 0 341.571 415.9460000000001H373.162C379.789 415.9460000000001 385.162 410.573 385.162 403.9460000000001V276.876H410.822C417.449 276.876 422.822 271.5030000000001 422.822 264.876V235.946C422.822 229.319 417.449 223.946 410.822 223.946H301.649C295.022 223.946 289.649 229.319 289.649 235.946V264.876C289.649 271.5030000000001 295.022 276.876 301.649 276.876H327.063V334.814C319.809 328.2340000000001 312.852 329.8930000000001 308.811 334.213zM278.241 95.644C278.241 128.297 302.106 163 346.335 163C384.588 163 425.759 134.139 425.759 70.772C425.759 19.496 393.522 -35 333.776 -35C315.94 -35 303.23 -31.443 295.228 -28.219C289.438 -25.886 286.439 -19.473 288.306 -13.516L297.543 15.964C299.5780000000001 22.46 306.592 25.947 313.01 23.68C326.039 19.078 340.888 18.405 351.113 27.818C312.371 22.746 278.241 53.178 278.241 95.644zM370.514 76.306C370.514 98.591 355.212 112.811 344.6790000000001 112.811C336.0370000000001 112.811 331.5150000000001 104.846 331.5150000000001 96.979C331.5150000000001 91.31 333.3300000000001 72.811 356.6830000000001 72.811C366.6560000000001 72.811 370.0600000000001 74.965 370.427 75.542C370.4480000000001 75.588 370.514 75.833 370.514 76.306zM175.984 80H128V400C128 408.837 120.837 416 112 416H80C71.163 416 64 408.837 64 400V80H16.019C1.807 80 -5.365 62.756 4.705 52.686L84.686 -27.316C90.931 -33.561 101.066 -33.563 107.313 -27.316L187.297 52.686C197.347 62.736 190.225 80 175.984 80z" />
+    <glyph glyph-name="sort-numeric-up"
+      unicode="&#xF163;"
+      horiz-adv-x="448" d=" M308.811 334.213L289.363 355.008C284.841 359.844 285.089 367.429 289.919 371.958L333.362 412.699A11.999000000000002 11.999000000000002 0 0 0 341.571 415.9460000000001H373.162C379.789 415.9460000000001 385.162 410.573 385.162 403.9460000000001V276.876H410.822C417.449 276.876 422.822 271.5030000000001 422.822 264.876V235.946C422.822 229.319 417.449 223.946 410.822 223.946H301.649C295.022 223.946 289.649 229.319 289.649 235.946V264.876C289.649 271.5030000000001 295.022 276.876 301.649 276.876H327.063V334.814C319.809 328.2340000000001 312.852 329.8930000000001 308.811 334.213zM278.241 95.644C278.241 128.297 302.106 163 346.335 163C384.588 163 425.759 134.139 425.759 70.772C425.759 19.496 393.522 -35 333.776 -35C315.94 -35 303.23 -31.443 295.228 -28.219C289.438 -25.886 286.439 -19.473 288.306 -13.516L297.543 15.964C299.5780000000001 22.46 306.592 25.947 313.01 23.68C326.039 19.078 340.888 18.405 351.113 27.818C312.371 22.746 278.241 53.178 278.241 95.644zM370.514 76.306C370.514 98.591 355.212 112.811 344.6790000000001 112.811C336.0370000000001 112.811 331.5150000000001 104.846 331.5150000000001 96.979C331.5150000000001 91.31 333.3300000000001 72.811 356.6830000000001 72.811C366.6560000000001 72.811 370.0600000000001 74.965 370.427 75.542C370.4480000000001 75.588 370.514 75.833 370.514 76.306zM16.016 304H64V-16C64 -24.837 71.163 -32 80 -32H112C120.837 -32 128 -24.837 128 -16V304H175.981C190.193 304 197.365 321.2440000000001 187.295 331.314L107.314 411.316C101.069 417.561 90.934 417.563 84.687 411.316L4.702 331.314C-5.347 321.264 1.775 304 16.016 304z" />
+    <glyph glyph-name="sort-up"
+      unicode="&#xF0DE;"
+      horiz-adv-x="320" d=" M279 224H41C19.6 224 8.9 249.9 24 265L143 384C152.4 393.4 167.6 393.4 176.9 384L295.9 265C311.1 249.9 300.4 224 279 224z" />
+    <glyph glyph-name="sort"
+      unicode="&#xF0DC;"
+      horiz-adv-x="320" d=" M41 160H279C300.4 160 311.1 134.1 296 119L177 0C167.6 -9.4 152.4 -9.4 143.1 0L24 119C8.9 134.1 19.6 160 41 160zM296 265L177 384C167.6 393.4 152.4 393.4 143.1 384L24 265C8.9 249.9 19.6 224 41 224H279C300.4 224 311.1 249.9 296 265z" />
+    <glyph glyph-name="spa"
+      unicode="&#xF5BB;"
+      horiz-adv-x="576" d=" M568.25 256C539.21 255.87 433.24 249.84 354.41 173C321.29 143.37 301.05 109.7 288 78.14C274.95 109.7 254.71 143.37 221.59 173C142.76 249.84 36.79 255.87 7.75 256C3.34 256.02 -0.04 252.6 0 248.18C0.23 220.26 7.14 122.04 88.77 48.88C172.79 -32.94 256 -32 288 -32S403.19 -32.95 487.23 48.88C568.87 122.05 575.77 220.26 576 248.18C576.04 252.6 572.66 256.02 568.25 256zM287.98 145.4C300.8 164.25 315.5800000000001 181.18 332.0700000000001 195.92C351.1600000000001 214.53 371.6500000000001 229.22 392.3300000000001 241.1C375.8900000000001 311.6 340.61 374.15 295.6 413.32C291.49 416.9 284.5800000000001 416.9 280.4600000000001 413.32C235.4700000000001 374.18 200.1900000000001 311.69 183.72 241.25C204.09 229.55 224.22 215.11 242.9400000000001 196.86A282.768 282.768 0 0 0 287.98 145.4z" />
+    <glyph glyph-name="space-shuttle"
+      unicode="&#xF197;"
+      horiz-adv-x="640" d=" M592.604 239.756C559.735 255.164 515.777 264 472 264H186.327C181.375 270.555 175.742 275.978 169.607 280H376C229.157 310.253 219.403 416 96.003 416H96V288H80V416C53.49 416 32 387.346 32 352V288C8.803 288 0 277.968 0 264V224C0 210.017 8.819 200 32 200V184C8.803 184 0 173.968 0 160V120C0 106.017 8.819 96 32 96V32C32 -3.346 53.49 -32 80 -32V96H96V-32H96.003C219.403 -32 229.157 73.747 376 104H169.606C175.741 108.022 181.374 113.445 186.326 120H472C515.777 120 559.735 128.836 592.604 144.244C622.282 158.155 640 176.008 640 192S622.282 225.845 592.604 239.756zM488 152A8 8 0 0 0 480 160V224A8 8 0 0 0 488 232C519.909 232 519.942 152 488 152z" />
+    <glyph glyph-name="spider"
+      unicode="&#xF717;"
+      horiz-adv-x="576" d=" M151.17 280.65L177.1 272H181.77L186.99 298.12C187.71 301.7000000000001 188.79 305.7000000000001 190.2 309.91L169.91 350.49L193.71 421.88C196.5 430.26 191.98 439.32 183.59 442.12L168.42 447.18C160.04 449.98 150.97 445.45 148.18 437.06L122.29 359.38A32.04 32.04 0 0 1 124.02 334.95L151.17 280.65zM573.31 98.62L520.56 177.74A32.002 32.002 0 0 1 493.9399999999999 191.99H416L484.99 216.35A32.03 32.03 0 0 1 501.5 228.9600000000001L555.1 309.37C560 316.7200000000001 558.01 326.6600000000001 550.66 331.5600000000001L537.35 340.4400000000001C530 345.3400000000001 520.0600000000001 343.35 515.16 336L464.6 260.17L404.1 240H368L357.63 291.85C355.44 302.82 340.26 352 288 352C235.74 352 220.56 302.82 218.37 291.85L208 240H171.9L111.41 260.17L60.84 336C55.94 343.35 46.01 345.3400000000001 38.65 340.44L25.34 331.56C17.99 326.66 16 316.73 20.9 309.37L74.5 228.96A32.03 32.03 0 0 1 91.01 216.35L160 192H82.06A32.02 32.02 0 0 1 55.43 177.75L2.69 98.62C-2.21 91.27 -0.23 81.33 7.13 76.43L20.44 67.55C27.79 62.65 37.73 64.64 42.63 71.99L90.63 143.99H137.69L76.86 46.66A31.988 31.988 0 0 1 72 29.7V-48C72 -56.84 79.16 -64 88 -64H104C112.84 -64 120 -56.84 120 -48V25.11L194.08 143.64C193.07 129.59 192 115.53 192 101.43C192 48.36 232.76 0 288 0S384 48.36 384 101.43C384 115.53 382.92 129.59 381.92 143.64L456 25.11V-48C456 -56.84 463.16 -64 472 -64H488C496.84 -64 504 -56.84 504 -48V29.71C504 35.71 502.31 41.59 499.14 46.67L438.31 144H485.37L533.37 72C538.27 64.65 548.21 62.66 555.5600000000001 67.56L568.87 76.44C576.23 81.34 578.21 91.27 573.3100000000001 98.62zM406.09 350.49L385.8 309.91C387.21 305.7 388.29 301.7 389.01 298.12L394.23 272H398.9L424.83 280.65L451.98 334.95A31.995 31.995 0 0 1 453.71 359.38L427.82 437.06C425.03 445.44 415.96 449.98 407.58 447.18L392.41 442.12C384.03 439.32 379.5 430.26 382.29 421.88L406.09 350.49z" />
+    <glyph glyph-name="spinner"
+      unicode="&#xF110;"
+      horiz-adv-x="512" d=" M304 400C304 373.49 282.51 352 256 352S208 373.49 208 400S229.49 448 256 448S304 426.51 304 400zM256 32C229.49 32 208 10.51 208 -16S229.49 -64 256 -64S304 -42.51 304 -16S282.51 32 256 32zM464 240C437.49 240 416 218.51 416 192S437.49 144 464 144S512 165.49 512 192S490.51 240 464 240zM96 192C96 218.51 74.51 240 48 240S0 218.51 0 192S21.49 144 48 144S96 165.49 96 192zM108.922 92.922C82.412 92.922 60.922 71.432 60.922 44.922S82.412 -3.078 108.922 -3.078S156.922 18.412 156.922 44.922C156.922 71.431 135.431 92.922 108.922 92.922zM403.078 92.922C376.568 92.922 355.078 71.432 355.078 44.922S376.568 -3.078 403.078 -3.078S451.078 18.412 451.078 44.922C451.078 71.431 429.588 92.922 403.078 92.922zM108.922 387.078C82.412 387.078 60.922 365.588 60.922 339.078S82.412 291.078 108.922 291.078S156.922 312.568 156.922 339.078S135.431 387.078 108.922 387.078z" />
+    <glyph glyph-name="splotch"
+      unicode="&#xF5BC;"
+      horiz-adv-x="512" d=" M472.29 252.11L405.23 275.06C385.9500000000001 281.66 371.69 295.98 367.0900000000001 313.36L351.1 373.81C339.5200000000001 417.58 274.5300000000001 430.94 241.12 396.43L194.98 348.76C181.72 335.05 161.44 327.8300000000001 140.78 329.45L68.9 335.07C16.85 339.14 -18.03 290.19 9.87 252.24L48.41 199.82C59.49 184.75 61.23 165.96 53.05 149.58L24.62 92.6C4.03 51.35 47.46 7.73 98.11 18.79L168.07 34.07C188.18 38.46 209.52 34.07 225.14 22.34L279.46 -18.49C318.78 -48.05 380.5 -26.06 383.91 18.73L388.61 80.59C389.96 98.3800000000001 401.41 114.4500000000001 419.24 123.5800000000001L481.24 155.3200000000001C526.12 178.28 520.8299999999999 235.4900000000001 472.29 252.1100000000001z" />
+    <glyph glyph-name="spray-can"
+      unicode="&#xF5BD;"
+      horiz-adv-x="512" d=" M224 416C224 433.67 209.67 448 192 448H128C110.33 448 96 433.67 96 416V320H224V416zM480 320C462.33 320 448 305.67 448 288S462.33 256 480 256S512 270.3300000000001 512 288S497.67 320 480 320zM224 288H96C42.98 288 0 245.02 0 192V-32C0 -49.67 14.33 -64 32 -64H288C305.67 -64 320 -49.67 320 -32V192C320 245.02 277.02 288 224 288zM160 32C115.82 32 80 67.82 80 112S115.82 192 160 192S240 156.18 240 112S204.18 32 160 32zM480 352C497.67 352 512 366.33 512 384S497.67 416 480 416S448 401.67 448 384S462.33 352 480 352zM384 320C366.33 320 352 305.67 352 288S366.33 256 384 256S416 270.3300000000001 416 288S401.67 320 384 320zM288 416C270.33 416 256 401.67 256 384S270.33 352 288 352S320 366.33 320 384S305.67 416 288 416zM384 416C366.33 416 352 401.67 352 384S366.33 352 384 352S416 366.33 416 384S401.67 416 384 416zM480 224C462.33 224 448 209.67 448 192S462.33 160 480 160S512 174.33 512 192S497.67 224 480 224z" />
+    <glyph glyph-name="square-full"
+      unicode="&#xF45C;"
+      horiz-adv-x="512" d=" M512 -64H0V448H512V-64z" />
+    <glyph glyph-name="square-root-alt"
+      unicode="&#xF698;"
+      horiz-adv-x="576" d=" M571.31 196.69L548.6899999999999 219.31C542.4399999999999 225.56 532.31 225.56 526.06 219.31L480 173.25L433.94 219.31C427.69 225.56 417.56 225.56 411.31 219.31L388.69 196.69C382.44 190.44 382.44 180.31 388.69 174.06L434.75 128L388.69 81.94C382.44 75.69 382.44 65.56 388.69 59.31L411.31 36.69C417.56 30.44 427.69 30.44 433.94 36.69L480 82.75L526.06 36.69C532.31 30.44 542.4399999999999 30.44 548.6899999999999 36.69L571.31 59.31C577.56 65.56 577.56 75.69 571.31 81.94L525.25 128L571.31 174.06C577.56 180.31 577.56 190.44 571.31 196.69zM552 448H307.65C293.11 448 280.39 438.2 276.7 424.13L191.91 101.33L133.5 207.43A32.008 32.008 0 0 1 105.47 224H24C10.75 224 0 213.26 0 200V152C0 138.75 10.75 128 24 128H67.62L156.5 -35.73C168.99 -55.5 186.3 -64 204.94 -64C222.21 -64 249.38 -55 259.2200000000001 -22.52L357.03 352H552C565.25 352 576 362.75 576 376V424C576 437.26 565.25 448 552 448z" />
+    <glyph glyph-name="square"
+      unicode="&#xF0C8;"
+      horiz-adv-x="448" d=" M400 416H48C21.5 416 0 394.5 0 368V16C0 -10.5 21.5 -32 48 -32H400C426.5 -32 448 -10.5 448 16V368C448 394.5 426.5 416 400 416z" />
+    <glyph glyph-name="stamp"
+      unicode="&#xF5BF;"
+      horiz-adv-x="512" d=" M32 -64H480V0H32V-64zM416 192H349.44C333.18 192 320 205.18 320 221.44V230.9C320 258.27 328.88 284.31 341.46 308.62C350.57 326.23 354.36 347.01 350.51 369.04C343.74 407.82 312.04 439.74 273.25 446.49C212.62 457.04 160 410.67 160 352C160 337.8400000000001 163.12 324.4600000000001 168.69 312.42C182.02 283.57 192 253.3 192 221.51V221.44C192 205.18 178.82 192 162.56 192H96C42.98 192 0 149.02 0 96V64C0 46.33 14.33 32 32 32H480C497.67 32 512 46.33 512 64V96C512 149.02 469.02 192 416 192z" />
+    <glyph glyph-name="star-and-crescent"
+      unicode="&#xF699;"
+      horiz-adv-x="512" d=" M340.4700000000001 -18.36C339.0200000000001 -18.36 333.5800000000001 -18.82 331.29 -18.82C215.04 -18.82 120.47 75.75 120.47 192S215.04 402.82 331.29 402.82C333.61 402.82 338.99 402.36 340.4700000000001 402.36C347.6 402.36 353.8 407.39 355.2200000000001 414.43C356.68 421.68 352.67 428.92 345.75 431.52C316.58 442.46 286.39 448 256 448C114.84 448 0 333.16 0 192S114.84 -64 256 -64C286.23 -64 316.28 -58.51 345.32 -47.68C351.28 -45.66 355.6 -40.04 355.6 -33.42C355.6 -25.33 349.21 -18.36 340.47 -18.36zM503.46 234.14L427.0800000000001 245.24L392.9200000000001 314.45C391.0900000000001 318.15 387.5400000000001 320 383.9900000000001 320S376.8900000000001 318.15 375.0600000000001 314.45L340.9000000000001 245.24L264.5200000000001 234.14C256.3500000000001 232.96 253.0900000000001 222.92 259.0000000000001 217.15L314.2700000000001 163.28L301.2200000000001 87.21C300.1100000000001 80.77 305.2300000000001 75.55 311.0300000000001 75.55C312.5600000000001 75.55 314.1400000000001 75.91 315.6700000000001 76.72L384 112.63L452.31 76.72C453.84 75.92 455.42 75.55 456.95 75.55C462.75 75.55 467.87 80.78 466.76 87.21L453.71 163.28L508.98 217.15C514.89 222.9200000000001 511.6299999999999 232.9600000000001 503.46 234.1400000000001z" />
+    <glyph glyph-name="star-half-alt"
+      unicode="&#xF5C0;"
+      horiz-adv-x="536" d=" M508.55 276.49L362.18 297.8L296.77 430.19C290.89 442.02 279.42 448 267.95 448C256.55 448 245.16 442.1 239.26 430.19L173.83 297.81L27.45 276.52C1.2 272.7200000000001 -9.32 240.43 9.71 221.93L115.6 118.93L90.54 -26.55C86.98 -47.33 103.57 -64 122.15 -64C127.08 -64 132.15 -62.83 137.02 -60.25L267.9700000000001 8.43L398.9100000000001 -60.27C403.7700000000001 -62.82 408.8300000000001 -63.98 413.74 -63.98C432.3400000000001 -63.98 448.9600000000001 -47.3699999999999 445.4000000000001 -26.58L420.37 118.91L526.28 221.8900000000001C545.3199999999999 240.3900000000001 534.8 272.6900000000001 508.55 276.49zM386.81 153.29L368.69 135.67L372.97 110.79L392.49 -2.66L290.36 50.9300000000001L267.98 62.6700000000001L268.01 379.8600000000001L319.04 276.5700000000001L330.22 253.9400000000001L355.23 250.3000000000001L469.46 233.6700000000001L386.81 153.2900000000001z" />
+    <glyph glyph-name="star-half"
+      unicode="&#xF089;"
+      horiz-adv-x="576" d=" M288 448C276.6 448 265.2 442.1 259.3 430.2L194 297.8L47.9 276.6C21.7 272.8 11.2 240.5 30.2 222L135.9 119L110.9 -26.5C106.4 -52.6 133.9 -72.5 157.3 -60.2L288 8.4V448z" />
+    <glyph glyph-name="star-of-david"
+      unicode="&#xF69A;"
+      horiz-adv-x="464" d=" M405.68 192L458.89 281.39C473.3 305.6 455.48 336 426.88 336H319.96L264.01 429.98C256.86 441.99 244.43 448 232 448S207.14 441.99 199.99 429.98L144.04 336H37.11C8.51 336 -9.31 305.6 5.1 281.39L58.32 192L5.1 102.61C-9.31 78.4 8.51 48 37.11 48H144.04L199.99 -45.98C207.14 -57.99 219.57 -64 232 -64S256.86 -57.99 264.01 -45.98L319.96 48H426.89C455.49 48 473.31 78.4 458.9 102.61L405.68 192zM392.9000000000001 280L373.1 246.74L353.3 280H392.9000000000001zM340.5100000000001 192L288.1200000000001 104H175.88L123.49 192L175.87 280H288.12L340.51 192zM232 374.28L254.79 336H209.22L232 374.28zM71.1 280H110.7L90.9 246.74L71.1 280zM71.1 104L90.9 137.26L110.7 104H71.1zM232 9.72L209.21 48H254.78L232 9.72zM353.29 104L373.0900000000001 137.26L392.9 104H353.29z" />
+    <glyph glyph-name="star-of-life"
+      unicode="&#xF621;"
+      horiz-adv-x="480" d=" M471.99 113.57L336.06 192L471.99 270.43C479.65 274.85 482.27 284.63 477.85 292.29L445.8300000000001 347.7200000000001C441.4100000000001 355.37 431.6200000000001 358 423.9600000000001 353.5800000000001L288.0300000000001 275.1500000000001V432C288.0300000000001 440.84 280.86 448 272.0200000000001 448H207.98C199.14 448 191.97 440.84 191.97 432V275.14L56.04 353.57C48.38 357.99 38.59 355.36 34.17 347.71L2.15 292.29C-2.27 284.64 0.35 274.85 8.01 270.43L143.94 192L8.01 113.57C0.35 109.15 -2.27 99.36 2.15 91.71L34.17 36.28C38.59 28.63 48.38 26.01 56.04 30.42L191.97 108.85V-48C191.97 -56.84 199.14 -64 207.98 -64H272.0200000000001C280.86 -64 288.0300000000001 -56.84 288.0300000000001 -48V108.86L423.9600000000001 30.43C431.6200000000001 26.01 441.4100000000001 28.63 445.8300000000001 36.29L477.85 91.72C482.27 99.37 479.65 109.15 471.99 113.5700000000001z" />
+    <glyph glyph-name="star"
+      unicode="&#xF005;"
+      horiz-adv-x="576" d=" M259.3 430.2L194 297.8L47.9 276.5C21.7 272.7 11.2 240.4 30.2 221.9L135.9 118.9L110.9 -26.6C106.4 -52.9 134.1 -72.6 157.3 -60.3L288 8.4L418.7 -60.3C441.9 -72.5 469.6 -52.9 465.1 -26.6L440.1 118.9L545.8 221.9C564.8 240.4 554.3 272.7 528.0999999999999 276.5L382 297.8L316.7 430.2C305 453.8 271.1 454.1 259.3 430.2z" />
+    <glyph glyph-name="step-backward"
+      unicode="&#xF048;"
+      horiz-adv-x="448" d=" M64 -20V404C64 410.6 69.4 416 76 416H124C130.6 416 136 410.6 136 404V227.6L331.5 408.6C352.1 425.7 384 411.4 384 384V0C384 -27.4 352.1 -41.7 331.5 -24.6L136 155.3V-20C136 -26.6 130.6 -32 124 -32H76C69.4 -32 64 -26.6 64 -20z" />
+    <glyph glyph-name="step-forward"
+      unicode="&#xF051;"
+      horiz-adv-x="448" d=" M384 404V-20C384 -26.6 378.6 -32 372 -32H324C317.4 -32 312 -26.6 312 -20V156.4L116.5 -24.6C95.9 -41.7 64 -27.4 64 0V384C64 411.4 95.9 425.7 116.5 408.6L312 228.7V404C312 410.6 317.4 416 324 416H372C378.6 416 384 410.6 384 404z" />
+    <glyph glyph-name="stethoscope"
+      unicode="&#xF0F1;"
+      horiz-adv-x="512" d=" M447.1 336C412.9000000000001 335.5 384.8 307.6 384.1 273.4C383.6 249.1 396.6 227.8 416.1 216.6V104C416.1 46.7 365.9000000000001 0 304.1 0C244.1 0 194.9 44.1 192.2 99.2C265 114.2 320 178.8 320 256V411.4C320 422.8 311.9 432.7 300.7 434.9L237.8 447.5C224.8 450.1 212.2 441.7 209.6 428.7L206.4 413C203.8 400 212.2 387.4 225.2 384.8L255.9 378.7V257.3C255.9 204.4 213.7 160.6 160.8 160.1C107.4 159.6 63.9 202.8 63.9 256.1V378.6L94.6 384.7C107.6 387.3 116 399.9 113.4 412.9L110.3 428.6C107.7 441.6 95.1 450 82.1 447.4L19.3 435C8.1 432.7 0 422.9 0 411.4V256C0 178.7 55.1 114 128.1 99.2C130.7 8.8 208.6 -64 304 -64C401 -64 480 11.4 480 104V216.6C499.1 227.7 512 248.3 512 272C512 307.7 482.8 336.5 447.1 336zM448 256C439.2 256 432 263.2 432 272S439.2 288 448 288S464 280.8 464 272S456.8 256 448 256z" />
+    <glyph glyph-name="sticky-note"
+      unicode="&#xF249;"
+      horiz-adv-x="448" d=" M312 128H448V392C448 405.3 437.3 416 424 416H24C10.7 416 0 405.3 0 392V-8C0 -21.3 10.7 -32 24 -32H288V104C288 117.2 298.8 128 312 128zM441 73L343 -25C338.5 -29.5 332.4 -32 326 -32H320V96H448V89.9C448 83.6 445.5 77.5 441 73z" />
+    <glyph glyph-name="stop-circle"
+      unicode="&#xF28D;"
+      horiz-adv-x="512" d=" M256 440C119 440 8 329 8 192S119 -56 256 -56S504 55 504 192S393 440 256 440zM352 112C352 103.2 344.8 96 336 96H176C167.2 96 160 103.2 160 112V272C160 280.8 167.2 288 176 288H336C344.8 288 352 280.8 352 272V112z" />
+    <glyph glyph-name="stop"
+      unicode="&#xF04D;"
+      horiz-adv-x="448" d=" M400 416H48C21.5 416 0 394.5 0 368V16C0 -10.5 21.5 -32 48 -32H400C426.5 -32 448 -10.5 448 16V368C448 394.5 426.5 416 400 416z" />
+    <glyph glyph-name="stopwatch"
+      unicode="&#xF2F2;"
+      horiz-adv-x="448" d=" M432 144C432 29.1 338.9 -64 224 -64S16 29.1 16 144C16 248 92.3 334.2 192 349.5V384H164C157.4 384 152 389.4 152 396V436C152 442.6 157.4 448 164 448H284C290.6 448 296 442.6 296 436V396C296 389.4 290.6 384 284 384H256V349.5C293.5 343.7 327.7 327.9 355.7 304.9L383.2 332.4C387.9 337.1 395.5 337.1 400.2 332.4L428.5 304.1C433.2 299.4 433.2 291.8 428.5 287.1L399.1 257.7L398.5 257.1C419.7 224.7 432 185.8 432 144zM256 108V259.5C256 266.1 250.6 271.5 244 271.5H204C197.4 271.5 192 266.1 192 259.5V108C192 101.4 197.4 96 204 96H244C250.6 96 256 101.4 256 108z" />
+    <glyph glyph-name="store-alt"
+      unicode="&#xF54F;"
+      horiz-adv-x="640" d=" M320 64H128V224H64V-32C64 -49.7 78.3 -64 96 -64H352C369.7 -64 384 -49.7 384 -32V224H320V64zM634.6 305.8L549.3000000000001 433.8C543.3000000000001 442.7 533.3000000000001 448 522.6 448H117.4C106.7 448 96.7 442.7 90.8 433.8L5.5 305.8C-8.7 284.5 6.5 256 32.1 256H608C633.5 256 648.7 284.5 634.6 305.8zM512 -48C512 -56.8 519.2 -64 528 -64H560C568.8 -64 576 -56.8 576 -48V224H512V-48z" />
+    <glyph glyph-name="store"
+      unicode="&#xF54E;"
+      horiz-adv-x="616" d=" M602 329.4L537.1 433C531.3 442.3 521 448 510 448H106C95 448 84.7 442.3 78.9 433L14 329.4C-19.5 275.9 10.2 201.5 72.8 193C77.3 192.4 81.9 192.1 86.5 192.1C116.1 192.1 142.3 205.1 160.3 225.2C178.3 205.1 204.6 192.1 234.1 192.1C263.7000000000001 192.1 289.9000000000001 205.1 307.9000000000001 225.2C325.9000000000001 205.1 352.2000000000001 192.1 381.7000000000001 192.1C411.3000000000001 192.1 437.5000000000001 205.1 455.5000000000001 225.2C473.6000000000001 205.1 499.8000000000001 192.1 529.3000000000001 192.1C534.0000000000001 192.1 538.5000000000001 192.4 543.0000000000001 193C605.8000000000001 201.4 635.6000000000001 275.8 602.0000000000001 329.4zM529.5 160C519.5 160 509.6 161.5 500 163.8V64H116V163.8C106.4 161.6 96.5 160 86.5 160C80.5 160 74.4 160.4 68.5 161.2C62.9 162 57.4 163.3 52.1 164.8V-32C52.1 -49.7 66.4 -64 84.1 -64H532.1C549.8000000000001 -64 564.1 -49.7 564.1 -32V164.8C558.7 163.2 553.3000000000001 161.9 547.7 161.2C541.6 160.4 535.6 160 529.5 160z" />
+    <glyph glyph-name="stream"
+      unicode="&#xF550;"
+      horiz-adv-x="512" d=" M16 320H432C440.84 320 448 327.16 448 336V400C448 408.8400000000001 440.84 416 432 416H16C7.16 416 0 408.8400000000001 0 400V336C0 327.16 7.16 320 16 320zM496 240H80C71.16 240 64 232.84 64 224V160C64 151.16 71.16 144 80 144H496C504.84 144 512 151.16 512 160V224C512 232.84 504.84 240 496 240zM432 64H16C7.16 64 0 56.84 0 48V-16C0 -24.84 7.16 -32 16 -32H432C440.84 -32 448 -24.84 448 -16V48C448 56.84 440.84 64 432 64z" />
+    <glyph glyph-name="street-view"
+      unicode="&#xF21D;"
+      horiz-adv-x="512" d=" M367.9 118.24C363.28 112.94 358.12 108.14 352 104.59V81.65C418.52 72.3100000000001 464 53.6 464 32.0000000000001C464 1.0700000000001 370.88 -23.9999999999999 256 -23.9999999999999S48 1.07 48 32C48 53.6 93.48 72.3 160 81.65V104.59C153.88 108.14 148.72 112.94 144.1 118.24C58.87 102.66 0 69.95 0 32C0 -21.02 114.62 -64 256 -64S512 -21.02 512 32C512 69.95 453.13 102.66 367.9 118.24zM256 320C291.35 320 320 348.65 320 384S291.35 448 256 448S192 419.35 192 384S220.65 320 256 320zM192 128V32C192 14.33 206.33 0 224 0H288C305.67 0 320 14.33 320 32V128C337.67 128 352 142.33 352 160V256C352 282.51 330.51 304 304 304H292.2C281.13 298.9700000000001 268.94 296 256 296S230.87 298.9700000000001 219.8 304H208C181.49 304 160 282.51 160 256V160C160 142.33 174.33 128 192 128z" />
+    <glyph glyph-name="strikethrough"
+      unicode="&#xF0CC;"
+      horiz-adv-x="512" d=" M496 160H16C7.163 160 0 167.163 0 176V208C0 216.837 7.163 224 16 224H496C504.837 224 512 216.837 512 208V176C512 167.163 504.837 160 496 160zM281.334 144C308.592 131.063 327.858 115.317 327.858 87.757C327.858 54.649 298.8810000000001 34.081 252.237 34.081C219.912 34.081 175.363 46.161 175.363 78.352V80C175.363 88.837 168.1990000000001 96 159.363 96H113.75C104.914 96 97.75 88.837 97.75 80V60.796C97.75 -6.049 175.467 -41.024 252.237 -41.024C340.815 -41.024 414.25 4.414 414.25 93.4C414.25 113.215 410.632 129.817 404.107 144H281.334zM250.382 240C217.96 253.505 193.546 268.9460000000001 193.546 299.683C193.546 333.603 224.447 347.089 258.508 347.089C301.155 347.089 323.47 330.496 323.47 314.104V312C323.47 303.163 330.634 296 339.47 296H385.083C393.919 296 401.083 303.163 401.083 312V342.318C401.083 394.756 329.358 422.193 258.508 422.193C173.305 422.193 107.782 381.221 107.782 296.5470000000001C107.782 273.837 112.447 255.371 120.559 240H250.382z" />
+    <glyph glyph-name="stroopwafel"
+      unicode="&#xF551;"
+      horiz-adv-x="512" d=" M188.12 237.26L142.86 192L188.11 146.75L233.37 192L188.12 237.26zM301.25 259.88L256 305.14L210.75 259.89L256 214.63L301.25 259.88zM210.75 124.12L256 78.86L301.26 124.12L256 169.37L210.75 124.12zM256 448C114.62 448 0 333.38 0 192S114.62 -64 256 -64S512 50.62 512 192S397.38 448 256 448zM442.68 152.4L431.37 141.09C428.25 137.97 423.18 137.97 420.06 141.09L391.77 169.38L346.52 124.13L380.46 90.19L397.43 107.16C400.55 110.28 405.62 110.28 408.74 107.16L420.05 95.85C423.17 92.73 423.17 87.66 420.05 84.54L403.08 67.5699999999999L420.05 50.5999999999999C423.17 47.4799999999999 423.17 42.4099999999999 420.05 39.2899999999999L408.74 27.9799999999999C405.62 24.8599999999999 400.55 24.8599999999999 397.43 27.9799999999999L380.4599999999999 44.9499999999999L363.4899999999999 27.9799999999999C360.3699999999999 24.8599999999999 355.2999999999999 24.8599999999999 352.1799999999999 27.9799999999999L340.8699999999999 39.2899999999999C337.7499999999999 42.4099999999999 337.7499999999999 47.4799999999999 340.8699999999999 50.5999999999999L357.8399999999999 67.5699999999999L323.8999999999999 101.5099999999999L278.64 56.2499999999999L306.93 27.9599999999999C310.05 24.8399999999999 310.05 19.7699999999999 306.93 16.6499999999999L295.62 5.3399999999999C292.5 2.2199999999999 287.43 2.2199999999999 284.31 5.3399999999999L256 33.61L227.71 5.32C224.59 2.2 219.52 2.2 216.4 5.32L205.09 16.63C201.97 19.75 201.97 24.82 205.09 27.94L233.38 56.23L188.13 101.49L154.19 67.55L171.16 50.58C174.28 47.46 174.28 42.39 171.16 39.27L159.85 27.96C156.73 24.84 151.66 24.84 148.54 27.96L131.57 44.9300000000001L114.6 27.96C111.48 24.84 106.41 24.84 103.29 27.96L91.98 39.27C88.86 42.39 88.86 47.46 91.98 50.58L108.95 67.5500000000001L91.98 84.5200000000001C88.86 87.6400000000001 88.86 92.7100000000001 91.98 95.8300000000001L103.29 107.1400000000001C106.41 110.2600000000001 111.48 110.2600000000001 114.6 107.1400000000001L131.57 90.1700000000001L165.51 124.1100000000001L120.26 169.3600000000001L91.97 141.0700000000001C88.85 137.9500000000001 83.78 137.9500000000001 80.66 141.0700000000001L69.32 152.4C66.2 155.52 66.2 160.59 69.32 163.71L97.61 192L69.32 220.29C66.2 223.41 66.2 228.48 69.32 231.6L80.63 242.91C83.75 246.03 88.82 246.03 91.94 242.91L120.23 214.62L165.48 259.88L131.54 293.82L114.57 276.85C111.45 273.73 106.38 273.73 103.26 276.85L91.95 288.16C88.83 291.28 88.83 296.35 91.95 299.4700000000001L108.92 316.44L91.95 333.41C88.83 336.53 88.83 341.6 91.95 344.7200000000001L103.26 356.03C106.38 359.15 111.45 359.15 114.57 356.03L131.54 339.06L148.51 356.03C151.63 359.15 156.7 359.15 159.82 356.03L171.13 344.7200000000001C174.25 341.6 174.25 336.53 171.13 333.41L154.16 316.44L188.1 282.5L233.36 327.75L205.07 356.04C201.95 359.16 201.95 364.23 205.07 367.35L216.38 378.66C219.5 381.78 224.57 381.78 227.69 378.66L256 350.39L284.29 378.68C287.4100000000001 381.8 292.48 381.8 295.6 378.68L306.9100000000001 367.37C310.0300000000001 364.25 310.0300000000001 359.18 306.9100000000001 356.06L278.62 327.77L323.88 282.52L357.82 316.4600000000001L340.85 333.43C337.73 336.55 337.73 341.62 340.85 344.74L352.1600000000001 356.05C355.2800000000001 359.17 360.35 359.17 363.4700000000001 356.05L380.4400000000001 339.0800000000001L397.4100000000001 356.05C400.5300000000001 359.17 405.6000000000001 359.17 408.7200000000001 356.05L420.0300000000001 344.74C423.1500000000001 341.62 423.1500000000001 336.55 420.0300000000001 333.43L403.0600000000001 316.4600000000001L420.0300000000001 299.49C423.1500000000001 296.37 423.1500000000001 291.3 420.0300000000001 288.18L408.7200000000001 276.87C405.6000000000001 273.75 400.5300000000001 273.75 397.4100000000001 276.87L380.4400000000001 293.8400000000001L346.5000000000001 259.9L391.7500000000001 214.64L420.0400000000001 242.93C423.1600000000001 246.05 428.2300000000001 246.05 431.3500000000001 242.93L442.6600000000001 231.62C445.7800000000001 228.5 445.7800000000001 223.43 442.6600000000001 220.31L414.39 192L442.68 163.72A8.015 8.015 0 0 0 442.68 152.4zM278.63 192L323.89 146.75L369.14 192L323.89 237.26L278.63 192z" />
+    <glyph glyph-name="subscript"
+      unicode="&#xF12C;"
+      horiz-adv-x="512" d=" M395.198 32C398.659 42.526 413.994 53.28 431.463 64.425C448.088 75.03 466.9299999999999 87.051 481.804 103.287C499.2619999999999 122.341 507.748 143.4620000000001 507.748 167.8540000000001C507.748 228.4160000000001 457.046 256.0000000000001 409.938 256.0000000000001C367.447 256.0000000000001 333.56 233.9840000000001 315.506 205.5530000000001C310.852 198.2240000000001 312.914 188.5170000000001 320.129 183.6880000000001L350.457 163.3920000000001C357.4889999999999 158.686 366.9169999999999 160.3080000000001 372.087 167.006C380.1089999999999 177.4 390.9049999999999 185.2310000000001 403.7539999999999 185.2310000000001C423.1409999999999 185.2310000000001 430.0199999999999 172.3300000000001 430.0199999999999 161.2830000000001C430.0199999999999 125.1240000000001 310.5829999999999 104.2600000000001 310.5829999999999 1.2590000000001C310.5829999999999 -5.3949999999999 311.1439999999999 -11.7549999999999 311.998 -18.0719999999999C313.074 -26.0359999999999 319.832 -31.9999999999999 327.868 -31.9999999999999H496C504.837 -31.9999999999999 512 -24.8369999999999 512 -15.9999999999999V16.0000000000001C512 24.837 504.837 32.0000000000001 496 32.0000000000001H395.198zM272 192C280.837 192 288 184.837 288 176V144C288 135.163 280.837 128 272 128H209.601A16 16 0 0 0 196.06 135.478L150.359 208.093C148.062 211.445 145.937 215.062 144.164 218.302C142.514 215.058 140.517 211.365 138.29 207.72L93.578 135.573A15.999 15.999 0 0 0 79.978 128.001H16C7.163 128.001 0 135.1640000000001 0 144.001V176.001C0 184.838 7.163 192.001 16 192.001H42.325L98.877 274.7100000000001L46.111 352H16C7.163 352 0 359.163 0 368V400C0 408.837 7.163 416 16 416H84.806A16 16 0 0 0 98.451 408.356L138.333 343.23C140.405 339.707 142.386 336.059 144.06 332.86C145.837 336.104 147.98 339.814 150.297 343.397L190.629 408.432A15.999 15.999 0 0 0 204.226 416H272C280.837 416 288 408.837 288 400V368C288 359.163 280.837 352 272 352H244.021L191.331 276.329L249.974 192H272z" />
+    <glyph glyph-name="subway"
+      unicode="&#xF239;"
+      horiz-adv-x="448" d=" M448 352V96C448 44.185 386.376 0 317.978 0L380.9580000000001 -49.721C386.905 -54.417 383.562 -64 376 -64H72C64.422 -64 61.108 -54.406 67.043 -49.721L130.022 0C61.82 0 0 44.046 0 96V352C0 405.019 64 448 128 448H320C385 448 448 405.019 448 352zM200 216V328C200 341.255 189.255 352 176 352H72C58.745 352 48 341.255 48 328V216C48 202.745 58.745 192 72 192H176C189.255 192 200 202.745 200 216zM400 216V328C400 341.255 389.255 352 376 352H272C258.745 352 248 341.255 248 328V216C248 202.745 258.745 192 272 192H376C389.255 192 400 202.745 400 216zM352 160C325.49 160 304 138.51 304 112S325.49 64 352 64S400 85.49 400 112S378.51 160 352 160zM96 160C69.49 160 48 138.51 48 112S69.49 64 96 64S144 85.49 144 112S122.51 160 96 160z" />
+    <glyph glyph-name="suitcase-rolling"
+      unicode="&#xF5C1;"
+      horiz-adv-x="384" d=" M336 288H48C21.49 288 0 266.51 0 240V16C0 -10.51 21.49 -32 48 -32H64V-48C64 -56.84 71.16 -64 80 -64H112C120.84 -64 128 -56.84 128 -48V-32H256V-48C256 -56.84 263.1600000000001 -64 272 -64H304C312.84 -64 320 -56.84 320 -48V-32H336C362.51 -32 384 -10.51 384 16V240C384 266.51 362.51 288 336 288zM320 72C320 67.58 316.42 64 312 64H72C67.58 64 64 67.58 64 72V88C64 92.42 67.58 96 72 96H312C316.42 96 320 92.42 320 88V72zM320 168C320 163.58 316.42 160 312 160H72C67.58 160 64 163.58 64 168V184C64 188.42 67.58 192 72 192H312C316.42 192 320 188.42 320 184V168zM144 400H240V320H288V400C288 426.51 266.51 448 240 448H144C117.49 448 96 426.51 96 400V320H144V400z" />
+    <glyph glyph-name="suitcase"
+      unicode="&#xF0F2;"
+      horiz-adv-x="512" d=" M128 -32H384V368C384 394.5 362.5 416 336 416H176C149.5 416 128 394.5 128 368V-32zM192 352H320V320H192V352zM512 272V16C512 -10.5 490.5 -32 464 -32H416V320H464C490.5 320 512 298.5 512 272zM96 -32H48C21.5 -32 0 -10.5 0 16V272C0 298.5 21.5 320 48 320H96V-32z" />
+    <glyph glyph-name="sun"
+      unicode="&#xF185;"
+      horiz-adv-x="512" d=" M256 288C203.1 288 160 244.9 160 192S203.1 96 256 96S352 139.1 352 192S308.9 288 256 288zM502.4 207.5L407.7 254.8L441.2 355.2000000000001C445.7 368.8 432.8 381.7000000000001 419.3 377.1L318.9 343.6L271.5 438.4000000000001C265.1 451.2 246.9 451.2 240.5 438.4000000000001L193.2 343.7000000000001L92.7 377.2C79.1 381.7 66.2 368.8 70.8 355.3L104.3 254.9L9.6 207.5C-3.2 201.1 -3.2 182.9 9.6 176.5L104.3 129.2L70.8 28.7C66.3 15.1 79.2 2.2 92.7 6.8L193.1 40.3L240.4000000000001 -54.4C246.8000000000001 -67.1999999999999 265.0000000000001 -67.1999999999999 271.4000000000001 -54.4L318.7000000000001 40.3L419.1 6.8C432.7000000000001 2.3 445.6 15.2 441 28.7L407.5 129.1L502.2 176.4C515.2 182.9 515.2 201.1 502.4 207.5zM346.5 101.5C296.6 51.6 215.4 51.6 165.5 101.5C115.6 151.4 115.6 232.6 165.5 282.5C215.4 332.4 296.6 332.4 346.5 282.5C396.4 232.6 396.4 151.4 346.5 101.5z" />
+    <glyph glyph-name="superscript"
+      unicode="&#xF12B;"
+      horiz-adv-x="512" d=" M395.198 192C398.659 202.526 413.994 213.28 431.463 224.425C448.088 235.03 466.9299999999999 247.051 481.804 263.2870000000001C499.2619999999999 282.341 507.748 303.462 507.748 327.854C507.748 388.416 457.046 416 409.938 416C367.447 416 333.56 393.984 315.506 365.553C310.852 358.224 312.914 348.517 320.129 343.688L350.457 323.392C357.4889999999999 318.6860000000001 366.9169999999999 320.308 372.087 327.006C380.1089999999999 337.4 390.9049999999999 345.231 403.7539999999999 345.231C423.1409999999999 345.231 430.0199999999999 332.33 430.0199999999999 321.283C430.0199999999999 285.124 310.5829999999999 264.26 310.5829999999999 161.259C310.5829999999999 154.605 311.1439999999999 148.245 311.998 141.928C313.074 133.964 319.832 128 327.868 128H496C504.837 128 512 135.163 512 144V176C512 184.837 504.837 192 496 192H395.198zM272 32C280.837 32 288 24.837 288 16V-16C288 -24.837 280.837 -32 272 -32H209.601A16 16 0 0 0 196.06 -24.522L150.359 48.093C148.062 51.445 145.937 55.062 144.164 58.302C142.514 55.058 140.517 51.365 138.29 47.72L93.578 -24.427A15.999 15.999 0 0 0 79.978 -31.999H16C7.163 -31.999 0 -24.836 0 -15.999V16.001C0 24.838 7.163 32.001 16 32.001H42.325L98.877 114.71L46.111 192H16C7.163 192 0 199.163 0 208V240C0 248.837 7.163 256 16 256H84.806A16 16 0 0 0 98.451 248.356L138.333 183.23C140.405 179.707 142.386 176.059 144.06 172.86C145.837 176.1040000000001 147.98 179.814 150.297 183.397L190.629 248.432A16 16 0 0 0 204.227 255.999H272C280.837 255.999 288 248.836 288 239.999V207.999C288 199.162 280.837 191.999 272 191.999H244.021L191.331 116.328L249.974 32H272z" />
+    <glyph glyph-name="surprise"
+      unicode="&#xF5C2;"
+      horiz-adv-x="496" d=" M248 440C111 440 0 329 0 192S111 -56 248 -56S496 55 496 192S385 440 248 440zM136 240C136 257.7 150.3 272 168 272S200 257.7 200 240S185.7 208 168 208S136 222.3 136 240zM248 32C212.7 32 184 60.7 184 96S212.7 160 248 160S312 131.3 312 96S283.3 32 248 32zM328 208C310.3 208 296 222.3 296 240S310.3 272 328 272S360 257.7 360 240S345.7 208 328 208z" />
+    <glyph glyph-name="swatchbook"
+      unicode="&#xF5C3;"
+      horiz-adv-x="511" d=" M479.06 128H372.29L186.15 -58.51C184.09 -60.58 181.66 -62.09 179.48 -64H479.06C496.7 -64 511 -49.67 511 -32V96C511 113.67 496.7 128 479.06 128zM434.56 280.9L344.23 371.4100000000001C331.76 383.9100000000001 311.54 383.9100000000001 299.06 371.4100000000001L223.56 295.76V32C223.56 29.04 222.89 26.27 222.69 23.36L434.56 235.64C447.0300000000001 248.14 447.0300000000001 268.4100000000001 434.56 280.9zM191.62 416C191.62 433.67 177.32 448 159.68 448H31.94C14.3 448 0 433.67 0 416V32C0 -21.02 42.9 -64 95.81 -64S191.62 -21.02 191.62 32V416zM95.81 8C82.58 8 71.86 18.75 71.86 32C71.86 45.26 82.59 56 95.81 56S119.76 45.26 119.76 32C119.77 18.75 109.04 8 95.81 8zM127.75 192H63.88V256H127.76V192zM127.75 320H63.88V384H127.76V320z" />
+    <glyph glyph-name="swimmer"
+      unicode="&#xF5C4;"
+      horiz-adv-x="640" d=" M189.61 137.42C193.15 134.16 204.88 128 224 128S254.86 134.16 258.39 137.42C274.41 152.19 292.89 160 311.85 160H328.15C347.11 160 365.6 152.19 381.61 137.42C385.15 134.16 396.88 128 416 128S446.86 134.16 450.3899999999999 137.42C465.2499999999999 151.13 482.2699999999999 158.54 499.7799999999999 159.5800000000001L386.94 240.1800000000001L404.94 253.04C408.58 255.6200000000001 413.2199999999999 256.5600000000001 417.56 255.6500000000001L517.91 234.1200000000001C543.8199999999999 228.5900000000001 569.3499999999999 245.0900000000001 574.91 271C580.4599999999999 296.92 563.9599999999999 322.4400000000001 538.03 328L437.68 349.53C406.95 356.11 374.6600000000001 349.41 349.12 331.15L269.1 273.98C258.7200000000001 266.5900000000001 249.74 257.54 242.38 247.04L173.75 149C179.22 145.77 184.57 142.07 189.61 137.42zM624 96H608C581.96 96 562.2 104.42 551.91 113.9C543.01 122.11 532.25 128 520.14 128H503.84C491.73 128 480.97 122.11 472.07 113.9C461.8 104.42 442.04 96 416 96S370.2 104.42 359.91 113.9C351.01 122.11 340.25 128 328.14 128H311.84C299.73 128 288.97 122.11 280.07 113.9C269.8 104.42 250.04 96 224 96S178.2 104.42 167.91 113.9C159.01 122.11 148.25 128 136.14 128H119.84C107.73 128 96.97 122.11 88.07 113.9C77.8 104.42 58.04 96 32 96H16C7.16 96 0 88.84 0 80V48C0 39.16 7.16 32 16 32H32C70.62 32 104.72 44.19 128 63.84C151.28 44.18 185.38 32 224 32S296.7200000000001 44.19 320 63.84C343.28 44.18 377.38 32 416 32S488.72 44.19 512 63.84C535.28 44.18 569.38 32 608 32H624C632.84 32 640 39.16 640 48V80C640 88.84 632.84 96 624 96zM112 192C156.18 192 192 227.82 192 272S156.18 352 112 352S32 316.18 32 272S67.82 192 112 192z" />
+    <glyph glyph-name="swimming-pool"
+      unicode="&#xF5C5;"
+      horiz-adv-x="640" d=" M624 32H608C581.96 32 562.2 40.42 551.91 49.9C543.01 58.11 532.25 64 520.14 64H503.84C491.73 64 480.97 58.11 472.07 49.9C461.8 40.42 442.04 32 416 32S370.2 40.42 359.91 49.9C351.01 58.11 340.25 64 328.14 64H311.84C299.73 64 288.97 58.11 280.07 49.9C269.8 40.42 250.04 32 224 32S178.2 40.42 167.91 49.9C159.01 58.11 148.25 64 136.14 64H119.84C107.73 64 96.97 58.11 88.07 49.9C77.8 40.42 58.04 32 32 32H16C7.16 32 0 24.84 0 16V-16C0 -24.84 7.16 -32 16 -32H32C70.62 -32 104.72 -19.81 128 -0.16C151.28 -19.82 185.38 -32 224 -32S296.7200000000001 -19.81 320 -0.16C343.28 -19.82 377.38 -32 416 -32S488.72 -19.81 512 -0.16C535.28 -19.82 569.38 -32 608 -32H624C632.84 -32 640 -24.84 640 -16V16C640 24.84 632.84 32 624 32zM224 64V160H416V64C435.12 64 446.86 70.16 450.39 73.42C459.56 81.88 469.59 87.76 480 91.49V320C480 337.64 494.36 352 512 352S544 337.64 544 320V304C544 295.16 551.16 288 560 288H592C600.84 288 608 295.16 608 304V320C608 372.94 564.94 416 512 416S416 372.94 416 320V224H224V320C224 337.64 238.36 352 256 352S288 337.64 288 320V304C288 295.16 295.1600000000001 288 304 288H336C344.84 288 352 295.16 352 304V320C352 372.94 308.94 416 256 416S160 372.94 160 320V91.5C170.41 87.77 180.44 81.88 189.61 73.43C193.14 70.16 204.88 64 224 64z" />
+    <glyph glyph-name="synagogue"
+      unicode="&#xF69B;"
+      horiz-adv-x="640" d=" M70 251.49L6.67 179.71A26.643000000000004 26.643000000000004 0 0 1 0 162.07V-64H128V208.42L90 251.49C84.69 257.5 75.31 257.5 70 251.49zM633.33 179.71L570 251.49C564.69 257.51 555.31 257.51 550 251.49L512 208.42V-64H640V162.07C640 168.57 637.63 174.84 633.33 179.71zM339.99 440.99C328.3 450.34 311.7 450.34 300.01 440.99L172.01 338.59A32.005 32.005 0 0 1 160 313.6V-64H256V28.57C256 60.45 277.78 90 309.25 95.12C349.34 101.65 384 70.87 384 32V-64H480V313.6C480 323.32 475.58 332.52 467.99 338.59L339.99 440.99zM392.06 225.44C394.04 222.29 391.77 218.2 388.06 218.2H349.12L324 178.21C322.15 175.26 317.85 175.26 316 178.21L290.88 218.19H251.94C248.22 218.19 245.96 222.28 247.94 225.43L267.14 255.99L247.94 286.55C245.96 289.7 248.23 293.79 251.94 293.79H290.88L316 333.79C317.85 336.74 322.15 336.74 324 333.79L349.12 293.81H388.07C391.78 293.81 394.05 289.7200000000001 392.07 286.57L372.87 256L392.06 225.44z" />
+    <glyph glyph-name="sync-alt"
+      unicode="&#xF2F1;"
+      horiz-adv-x="512" d=" M370.7200000000001 314.7200000000001C339.4580000000001 343.992 298.888 360.038 255.848 360C178.39 359.932 111.52 306.822 93.057 233.15C91.713 227.787 86.935 224 81.406 224H24.103C16.605 224 10.909 230.807 12.296 238.176C33.933 353.076 134.813 440 256 440C322.448 440 382.791 413.8640000000001 427.315 371.315L463.03 407.03C478.149 422.149 504 411.4410000000001 504 390.059V256C504 242.745 493.255 232 480 232H345.941C324.559 232 313.851 257.851 328.97 272.971L370.72 314.7200000000001zM32 152H166.059C187.441 152 198.149 126.149 183.03 111.029L141.28 69.279C172.542 40.006 213.115 23.96 256.156 23.999C333.574 24.069 400.471 77.143 418.943 150.848C420.287 156.211 425.065 159.998 430.594 159.998H487.898C495.396 159.998 501.092 153.191 499.705 145.822C478.067 30.924 377.187 -56 256 -56C189.552 -56 129.209 -29.864 84.685 12.685L48.97 -23.03C33.851 -38.149 8 -27.441 8 -6.059V128C8 141.255 18.745 152 32 152z" />
+    <glyph glyph-name="sync"
+      unicode="&#xF021;"
+      horiz-adv-x="512.333" d=" M440.935 435.426L444.901 352.66C399.416 406.096 331.674 440 256 440C134.813 440 33.933 353.076 12.296 238.176C10.908 230.807 16.604 224 24.103 224H73.187C78.757 224 83.564 227.842 84.863 233.259C103.407 310.592 172.931 368 256 368C316.8930000000001 368 370.512 337.144 402.1040000000001 290.199L300.5740000000001 295.064C293.7290000000001 295.3920000000001 288.0000000000001 289.9310000000001 288.0000000000001 283.0780000000001V235.6670000000001C288.0000000000001 229.04 293.3730000000001 223.667 300.0000000000001 223.667H500.3330000000001C506.9600000000001 223.667 512.3330000000001 229.04 512.3330000000001 235.6670000000001V436C512.3330000000001 442.627 506.9600000000001 448 500.3330000000001 448H452.9220000000001C446.0690000000001 448 440.6070000000001 442.271 440.9350000000001 435.426zM256 16C195.105 16 141.483 46.858 109.891 93.805L211.759 88.934C218.604 88.607 224.332 94.068 224.332 100.92V148.332C224.332 154.959 218.959 160.332 212.332 160.332H12C5.373 160.332 0 154.959 0 148.332V-52C0 -58.627 5.373 -64 12 -64H59.385C66.248 -64 71.713 -58.255 71.37 -51.401L67.241 31.174C112.725 -22.166 180.405 -56 256 -56C377.187 -56 478.067 30.924 499.704 145.824C501.092 153.193 495.396 160 487.897 160H438.813C433.243 160 428.436 156.158 427.137 150.741C408.593 73.408 339.069 16 256 16z" />
+    <glyph glyph-name="syringe"
+      unicode="&#xF48E;"
+      horiz-adv-x="512" d=" M201.5 273.2L257.2 217.4C260.3 214.3 260.3 209.2 257.2 206.1L245.9 194.8C242.8 191.6999999999999 237.7 191.6999999999999 234.6 194.8L178.9 250.6L133.6 205.3L189.4 149.5C192.5 146.3999999999999 192.5 141.3 189.4 138.1999999999999L178.1 126.8999999999999C175 123.7999999999999 169.9 123.7999999999999 166.8 126.8999999999999L111 182.8L84.6 156.4C67.3 139.1 59 115.3 61.6 91L68.7 27.4L2.3 -39C-0.8 -42.1 -0.8 -47.2 2.3 -50.3L13.6 -61.6C16.7 -64.7 21.8 -64.7 24.9 -61.6L91.2 4.7L154.8 -2.4C178.7 -5.0000000000001 202.7 2.9999999999999 220.2 20.6L402.1 202.5L266.4000000000001 338.2L201.5 273.2zM509.7 366.5L430.5 445.7C427.4 448.8 422.3 448.8 419.2 445.7L407.9 434.4C404.8 431.3 404.8 426.2 407.9 423.1L436.2 394.8L390.9 349.5L334.3 406.1L317.3 423.1C314.2 426.2 309.1 426.2 306 423.1L272.1 389.2C269 386.1 269 381 272.1 377.9L289.1 360.9L424.8 225L441.8 208C444.9000000000001 204.9 450 204.9 453.1 208L487 242C490.1 245.1 490.1 250.2 487 253.3L413.5 326.8L458.8 372.1L487.1 343.8C490.2 340.7000000000001 495.3 340.7000000000001 498.4 343.8L509.7 355.1C512.8000000000001 358.3 512.8000000000001 363.3 509.7 366.5z" />
+    <glyph glyph-name="table-tennis"
+      unicode="&#xF45D;"
+      horiz-adv-x="512" d=" M496.2 151.5C527.7 229.3 512 321.8 449 384.9C365.1 469 229 469 145.1 384.9L89.1 328.8L300.6 117.3C346.7000000000001 179.4 432.1 194.7 496.2 151.5zM278.3 71.8L57.9 292.1C30.6 246.8 36.2 187.1 75.2 148L109.7 113.4L6.7 24C-1.9 16.5 -2.4 3.3 5.7 -4.8L59.1 -58.3C67.1 -66.4 80.3 -65.9 87.8 -57.3L177.1 46L212.8 10.3C232.5 -9.4 257.4000000000001 -20.2 283.1 -23C276 -6 272.1 12.6 272.1 32.1C272 45.9 274.6 59.1 278.3 71.8zM416 128C363 128 320 85 320 32S363 -64 416 -64S512 -21 512 32S469 128 416 128z" />
+    <glyph glyph-name="table"
+      unicode="&#xF0CE;"
+      horiz-adv-x="512" d=" M464 416H48C21.49 416 0 394.51 0 368V16C0 -10.51 21.49 -32 48 -32H464C490.51 -32 512 -10.51 512 16V368C512 394.51 490.51 416 464 416zM224 32H64V128H224V32zM224 192H64V288H224V192zM448 32H288V128H448V32zM448 192H288V288H448V192z" />
+    <glyph glyph-name="tablet-alt"
+      unicode="&#xF3FA;"
+      horiz-adv-x="448" d=" M400 448H48C21.5 448 0 426.5 0 400V-16C0 -42.5 21.5 -64 48 -64H400C426.5 -64 448 -42.5 448 -16V400C448 426.5 426.5 448 400 448zM224 -32C206.3 -32 192 -17.7 192 0S206.3 32 224 32S256 17.7 256 0S241.7 -32 224 -32zM400 76C400 69.4 394.6 64 388 64H60C53.4 64 48 69.4 48 76V388C48 394.6 53.4 400 60 400H388C394.6 400 400 394.6 400 388V76z" />
+    <glyph glyph-name="tablet"
+      unicode="&#xF10A;"
+      horiz-adv-x="448" d=" M400 448H48C21.5 448 0 426.5 0 400V-16C0 -42.5 21.5 -64 48 -64H400C426.5 -64 448 -42.5 448 -16V400C448 426.5 426.5 448 400 448zM224 -32C206.3 -32 192 -17.7 192 0S206.3 32 224 32S256 17.7 256 0S241.7 -32 224 -32z" />
+    <glyph glyph-name="tablets"
+      unicode="&#xF490;"
+      horiz-adv-x="640" d=" M160 256C78.9 256 12.5 197.5 0.1 121.3C-0.7 116.5 3.4 112 8.4 112H311.7C316.7 112 320.8 116.5 320 121.3C307.5 197.5 241.1 256 160 256zM311.6 80H8.4C3.4 80 -0.7 75.5 0.1 70.7C12.5 -5.5 78.9 -64 160 -64S307.5 -5.5 319.9 70.7C320.7 75.5 316.6 80 311.6 80zM593.4 401.4C536.9 457.9 449.2 462.8 386.5 417.4C382.5 414.5 382.2 408.5 385.7 405.1L597 193.7C600.5 190.2 606.5 190.5 609.3 194.5C654.8 257.2 649.9 344.9 593.4 401.4zM363 382.3C359.5 385.8 353.5 385.5 350.7 381.5C305.3 318.8 310.2 231.1 366.6 174.6C423.1 118.1 510.8 113.2000000000001 573.5 158.7000000000001C577.5 161.6 577.8 167.6 574.3 171.0000000000001L363 382.3z" />
+    <glyph glyph-name="tachometer-alt"
+      unicode="&#xF3FD;"
+      horiz-adv-x="576" d=" M288 416C128.94 416 0 287.06 0 128C0 75.2 14.25 25.74 39.06 -16.8C44.67 -26.42 55.36 -32 66.5 -32H509.5C520.64 -32 531.33 -26.42 536.94 -16.8C561.75 25.74 576 75.2 576 128C576 287.06 447.06 416 288 416zM288 352C302.71 352 314.58 341.87 318.32 328.35C317.21 326.09 315.68 324.12 314.87 321.68L305.65 294.01C300.52 290.52 294.68 288 288.01 288C270.34 288 256.01 302.3300000000001 256.01 320S270.33 352 288 352zM96 64C78.33 64 64 78.33 64 96S78.33 128 96 128S128 113.67 128 96S113.67 64 96 64zM144 224C126.33 224 112 238.33 112 256S126.33 288 144 288S176 273.67 176 256S161.67 224 144 224zM390.77 296.41L329.44 112.41C343.13 100.67 352 83.46 352 64C352 52.28 348.62 41.45 343.12 32H232.88C227.38 41.45 224 52.28 224 64C224 97.94 250.5 125.43 283.9 127.59L345.24 311.6C349.4100000000001 324.1600000000001 362.9700000000001 331.05 375.6 326.7700000000001C388.17 322.5800000000001 394.9500000000001 308.98 390.7700000000001 296.4100000000001zM405.43 239.21L420.95 285.76C424.42 287.05 428.08 287.99 432 287.99C449.67 287.99 464 273.66 464 255.99S449.67 223.99 432 223.99C420.62 224 411.11 230.27 405.43 239.21zM480 64C462.33 64 448 78.33 448 96S462.33 128 480 128S512 113.67 512 96S497.67 64 480 64z" />
+    <glyph glyph-name="tag"
+      unicode="&#xF02B;"
+      horiz-adv-x="512" d=" M0 195.882V400C0 426.51 21.49 448 48 448H252.118A48 48 0 0 0 286.059 433.941L497.941 222.059C516.6859999999999 203.314 516.6859999999999 172.922 497.941 154.177L293.823 -49.941C275.078 -68.6859999999999 244.686 -68.6859999999999 225.941 -49.941L14.059 161.941A48 48 0 0 0 0 195.882zM112 384C85.49 384 64 362.51 64 336S85.49 288 112 288S160 309.49 160 336S138.51 384 112 384z" />
+    <glyph glyph-name="tags"
+      unicode="&#xF02C;"
+      horiz-adv-x="640" d=" M497.941 222.059L286.0590000000001 433.941A48 48 0 0 1 252.118 448H48C21.49 448 0 426.51 0 400V195.882A48 48 0 0 1 14.059 161.941L225.941 -49.941C244.685 -68.6859999999999 275.077 -68.687 293.823 -49.941L497.941 154.177C516.6859999999999 172.922 516.6859999999999 203.314 497.941 222.059zM112 288C85.49 288 64 309.49 64 336S85.49 384 112 384S160 362.51 160 336S138.51 288 112 288zM625.941 154.177L421.823 -49.941C403.078 -68.6859999999999 372.686 -68.6859999999999 353.941 -49.941L353.581 -49.581L527.64 124.478C544.639 141.477 554 164.078 554 188.118S544.638 234.759 527.64 251.758L331.397 448H380.118A48 48 0 0 0 414.059 433.941L625.941 222.059C644.686 203.314 644.686 172.922 625.941 154.177z" />
+    <glyph glyph-name="tape"
+      unicode="&#xF4DB;"
+      horiz-adv-x="640" d=" M224 256C188.7 256 160 227.3 160 192S188.7 128 224 128S288 156.7 288 192S259.3 256 224 256zM624 32H380.6C422.1 72.7 448 129.3 448 192C448 315.7 347.7 416 224 416S0 315.7 0 192S100.3 -32 224 -32H624C632.8 -32 640 -24.8 640 -16V16C640 24.8 632.8 32 624 32zM224 96C171 96 128 139 128 192S171 288 224 288S320 245 320 192S277 96 224 96z" />
+    <glyph glyph-name="tasks"
+      unicode="&#xF0AE;"
+      horiz-adv-x="512" d=" M208 316H496C504.8 316 512 323.2 512 332V372C512 380.8 504.8 388 496 388H208C199.2 388 192 380.8 192 372V332C192 323.2 199.2 316 208 316zM208 156H496C504.8 156 512 163.2 512 172V212C512 220.8 504.8 228 496 228H208C199.2 228 192 220.8 192 212V172C192 163.2 199.2 156 208 156zM208 -4H496C504.8 -4 512 3.2 512 12V52C512 60.8 504.8 68 496 68H208C199.2 68 192 60.8 192 52V12C192 3.2 199.2 -4 208 -4zM64 80C37.5 80 15.4 58.5 15.4 32S37.5 -16 64 -16S112 5.5 112 32S90.5 80 64 80zM156.5 379L84.3 306.8L68.7 291.2000000000001C64 286.5 55.8 286.5 51.1 291.2000000000001L3.5 338.6C-1.2 343.3 -1.2 350.9 3.5 355.6L19.2 371.3C23.9 376 31.5 376 36.2 371.3L58.9 349.2L122.6 412.5C127.3 417.2 134.9 417.2 139.6 412.5L156.6 396C161.2 391.3 161.2 383.7 156.5 379zM156.5 219.4L84.3 147.2L68.6 131.5C63.9 126.8 55.7 126.8 51 131.5L3.5 179C-1.2 183.7 -1.2 191.3 3.5 196L19.2 211.7C23.9 216.4 31.5 216.4 36.2 211.7L58.9 189.6L122.6 253.3C127.3 258 134.9 258 139.6 253.3L156.6 236.3C161.2 231.7 161.2 224.1 156.5 219.4z" />
+    <glyph glyph-name="taxi"
+      unicode="&#xF1BA;"
+      horiz-adv-x="512" d=" M462 206.36L440 291.2000000000001C430.4 326.4000000000001 398.4 352 363.2 352H352V384C352 401.67 337.67 416 320 416H192C174.33 416 160 401.67 160 384V352H148.8C113.6 352 81.6 326.4 72 291.2L50 206.36C21.41 199.96 0 174.53 0 144V96C0 72.37 12.95 51.96 32 40.88V0C32 -17.67 46.33 -32 64 -32H96C113.67 -32 128 -17.67 128 0V32H384V0C384 -17.67 398.33 -32 416 -32H448C465.67 -32 480 -17.67 480 0V40.88C499.05 51.97 512 72.38 512 96V144C512 174.53 490.59 199.96 462 206.36zM96 96C78.33 96 64 110.33 64 128S78.33 160 96 160S128 145.67 128 128S113.67 96 96 96zM116.55 208L133.75 274.36C135.98 282.52 143.34 288 148.81 288H363.2100000000001C368.6800000000001 288 376.04 282.52 378.0600000000001 275.14L395.45 208H116.55zM416 96C398.33 96 384 110.33 384 128S398.33 160 416 160S448 145.67 448 128S433.67 96 416 96z" />
+    <glyph glyph-name="teeth-open"
+      unicode="&#xF62F;"
+      horiz-adv-x="640" d=" M544 448H96C42.98 448 0 405.02 0 352V288C0 252.65 28.66 224 64 224H576C611.34 224 640 252.65 640 288V352C640 405.02 597.02 448 544 448zM160 272C160 263.16 152.84 256 144 256H80C71.16 256 64 263.16 64 272V304C64 330.51 85.49 352 112 352S160 330.51 160 304V272zM304 272C304 263.16 296.84 256 288 256H208C199.16 256 192 263.16 192 272V328C192 358.93 217.07 384 248 384S304 358.93 304 328V272zM448 272C448 263.16 440.84 256 432 256H352C343.1600000000001 256 336 263.16 336 272V328C336 358.93 361.07 384 392 384S448 358.93 448 328V272zM576 272C576 263.16 568.84 256 560 256H496C487.16 256 480 263.16 480 272V304C480 330.51 501.49 352 528 352S576 330.51 576 304V272zM576 128H64C28.66 128 0 99.35 0 64V32C0 -21.02 42.98 -64 96 -64H544C597.02 -64 640 -21.02 640 32V64C640 99.35 611.34 128 576 128zM160 48C160 21.49 138.51 0 112 0S64 21.49 64 48V80C64 88.84 71.16 96 80 96H144C152.84 96 160 88.84 160 80V48zM304 56C304 25.07 278.93 0 248 0S192 25.07 192 56V80C192 88.84 199.16 96 208 96H288C296.84 96 304 88.84 304 80V56zM448 56C448 25.07 422.93 0 392 0S336 25.07 336 56V80C336 88.84 343.1600000000001 96 352 96H432C440.84 96 448 88.84 448 80V56zM576 48C576 21.49 554.51 0 528 0S480 21.49 480 48V80C480 88.84 487.16 96 496 96H560C568.84 96 576 88.84 576 80V48z" />
+    <glyph glyph-name="teeth"
+      unicode="&#xF62E;"
+      horiz-adv-x="640" d=" M544 448H96C42.98 448 0 405.02 0 352V32C0 -21.02 42.98 -64 96 -64H544C597.02 -64 640 -21.02 640 32V352C640 405.02 597.02 448 544 448zM160 80C160 53.49 138.51 32 112 32S64 53.49 64 80V144C64 152.84 71.16 160 80 160H144C152.84 160 160 152.84 160 144V80zM160 208C160 199.16 152.84 192 144 192H80C71.16 192 64 199.16 64 208V272C64 298.51 85.49 320 112 320S160 298.51 160 272V208zM304 88C304 57.07 278.93 32 248 32S192 57.07 192 88V144C192 152.84 199.16 160 208 160H288C296.84 160 304 152.84 304 144V88zM304 208C304 199.16 296.84 192 288 192H208C199.16 192 192 199.16 192 208V296C192 326.93 217.07 352 248 352S304 326.93 304 296V208zM448 88C448 57.07 422.93 32 392 32S336 57.07 336 88V144C336 152.84 343.1600000000001 160 352 160H432C440.84 160 448 152.84 448 144V88zM448 208C448 199.16 440.84 192 432 192H352C343.1600000000001 192 336 199.16 336 208V296C336 326.93 361.07 352 392 352S448 326.93 448 296V208zM576 80C576 53.49 554.51 32 528 32S480 53.49 480 80V144C480 152.84 487.16 160 496 160H560C568.84 160 576 152.84 576 144V80zM576 208C576 199.16 568.84 192 560 192H496C487.16 192 480 199.16 480 208V272C480 298.51 501.49 320 528 320S576 298.51 576 272V208z" />
+    <glyph glyph-name="temperature-high"
+      unicode="&#xF769;"
+      horiz-adv-x="512" d=" M416 448C363.1 448 320 404.9 320 352S363.1 256 416 256S512 299.1 512 352S468.9 448 416 448zM416 320C398.3 320 384 334.3 384 352S398.3 384 416 384S448 369.7 448 352S433.7 320 416 320zM256 336C256 397.9 205.9 448 144 448S32 397.9 32 336V169.5C12.3 144.8 0 114 0 80C0 0.5 64.5 -64 144 -64S288 0.5 288 80C288 114 275.7 144.9 256 169.5V336zM144 0C99.9 0 64 35.9 64 80C64 105.5 76.2 128.9 96 143.8V336C96 362.5 117.5 384 144 384S192 362.5 192 336V143.8C211.8 129 224 105.5 224 80C224 35.9 188.1 0 144 0zM160 125.1V336C160 344.8 152.8 352 144 352S128 344.8 128 336V125.1C109.4 118.5 96 100.9 96 80C96 53.5 117.5 32 144 32S192 53.5 192 80C192 100.9 178.6 118.5 160 125.1z" />
+    <glyph glyph-name="temperature-low"
+      unicode="&#xF76B;"
+      horiz-adv-x="512" d=" M416 448C363.1 448 320 404.9 320 352S363.1 256 416 256S512 299.1 512 352S468.9 448 416 448zM416 320C398.3 320 384 334.3 384 352S398.3 384 416 384S448 369.7 448 352S433.7 320 416 320zM256 336C256 397.9 205.9 448 144 448S32 397.9 32 336V169.5C12.3 144.8 0 114 0 80C0 0.5 64.5 -64 144 -64S288 0.5 288 80C288 114 275.7 144.9 256 169.5V336zM144 0C99.9 0 64 35.9 64 80C64 105.5 76.2 128.9 96 143.8V336C96 362.5 117.5 384 144 384S192 362.5 192 336V143.8C211.8 129 224 105.5 224 80C224 35.9 188.1 0 144 0zM160 125.1V144C160 152.8 152.8 160 144 160S128 152.8 128 144V125.1C109.4 118.5 96 100.9 96 80C96 53.5 117.5 32 144 32S192 53.5 192 80C192 100.9 178.6 118.5 160 125.1z" />
+    <glyph glyph-name="terminal"
+      unicode="&#xF120;"
+      horiz-adv-x="640" d=" M257.981 175.029L63.638 -19.314C54.265 -28.687 39.069 -28.687 29.697 -19.314L7.029 3.353C-2.328 12.71 -2.346 27.875 6.989 37.254L161.011 192L6.99 346.745C-2.345 356.124 -2.327 371.289 7.03 380.646L29.697 403.313C39.07 412.6860000000001 54.266 412.6860000000001 63.638 403.313L257.981 208.97C267.354 199.598 267.354 184.402 257.981 175.029zM640 -8V24C640 37.255 629.255 48 616 48H312C298.745 48 288 37.255 288 24V-8C288 -21.255 298.745 -32 312 -32H616C629.255 -32 640 -21.255 640 -8z" />
+    <glyph glyph-name="text-height"
+      unicode="&#xF034;"
+      horiz-adv-x="576" d=" M16 416H304C312.837 416 320 408.837 320 400V304C320 295.163 312.837 288 304 288H268.504C259.6670000000001 288 252.504 295.163 252.504 304V352H197.743V32H232C240.837 32 248 24.837 248 16V-16C248 -24.837 240.837 -32 232 -32H88C79.163 -32 72 -24.837 72 -16V16C72 24.837 79.163 32 88 32H122.257V352H67.496V304C67.496 295.163 60.333 288 51.496 288H16C7.163 288 0 295.163 0 304V400C0 408.837 7.163 416 16 416zM491.308 411.315L571.303 331.314C581.309 321.307 574.297 304 559.99 304H512V80H560C575.639 80 580.635 62.009 571.313 52.686L491.318 -27.3150000000001C485.071 -33.5620000000001 474.937 -33.5600000000001 468.692 -27.3150000000001L388.697 52.6859999999999C378.691 62.693 385.703 80 400.01 80H448V304H400C384.361 304 379.365 321.991 388.687 331.314L468.682 411.315C474.929 417.563 485.063 417.56 491.308 411.315z" />
+    <glyph glyph-name="text-width"
+      unicode="&#xF035;"
+      horiz-adv-x="448" d=" M16 416H432C440.837 416 448 408.837 448 400V304C448 295.163 440.837 288 432 288H396.504C387.6670000000001 288 380.504 295.163 380.504 304V352H261.743V224H296C304.837 224 312 216.837 312 208V176C312 167.163 304.837 160 296 160H152C143.163 160 136 167.163 136 176V208C136 216.837 143.163 224 152 224H186.257V352H67.496V304C67.496 295.163 60.333 288 51.496 288H16C7.163 288 0 295.163 0 304V400C0 408.837 7.163 416 16 416zM443.315 75.318L363.314 155.313C353.991 164.635 336 159.638 336 144V96H112V143.99C112 158.297 94.693 165.309 84.686 155.303L4.685 75.308C-1.56 69.063 -1.562 58.929 4.685 52.682L84.686 -27.313C94.009 -36.635 112 -31.638 112 -16V32H336V-15.99C336 -30.297 353.307 -37.309 363.314 -27.303L443.3150000000001 52.692C449.5600000000001 58.937 449.5630000000001 69.071 443.3150000000001 75.318z" />
+    <glyph glyph-name="th-large"
+      unicode="&#xF009;"
+      horiz-adv-x="512" d=" M296 416H488C501.255 416 512 405.255 512 392V232C512 218.745 501.255 208 488 208H296C282.745 208 272 218.745 272 232V392C272 405.255 282.745 416 296 416zM216 416H24C10.745 416 0 405.255 0 392V232C0 218.745 10.745 208 24 208H216C229.255 208 240 218.745 240 232V392C240 405.255 229.255 416 216 416zM0 152V-8C0 -21.255 10.745 -32 24 -32H216C229.255 -32 240 -21.255 240 -8V152C240 165.255 229.255 176 216 176H24C10.745 176 0 165.255 0 152zM296 -32H488C501.255 -32 512 -21.255 512 -8V152C512 165.255 501.255 176 488 176H296C282.745 176 272 165.255 272 152V-8C272 -21.255 282.745 -32 296 -32z" />
+    <glyph glyph-name="th-list"
+      unicode="&#xF00B;"
+      horiz-adv-x="512" d=" M149.333 232V152C149.333 138.745 138.588 128 125.333 128H24C10.745 128 0 138.745 0 152V232C0 245.255 10.745 256 24 256H125.333C138.588 256 149.333 245.255 149.333 232zM0 72V-8C0 -21.255 10.745 -32 24 -32H125.333C138.588 -32 149.333 -21.255 149.333 -8V72C149.333 85.255 138.588 96 125.333 96H24C10.745 96 0 85.255 0 72zM125.333 416H24C10.745 416 0 405.255 0 392V312C0 298.745 10.745 288 24 288H125.333C138.588 288 149.333 298.745 149.333 312V392C149.333 405.255 138.588 416 125.333 416zM205.333 -32H488C501.255 -32 512 -21.255 512 -8V72C512 85.255 501.255 96 488 96H205.333C192.078 96 181.333 85.255 181.333 72V-8C181.333 -21.255 192.078 -32 205.333 -32zM181.333 392V312C181.333 298.745 192.078 288 205.333 288H488C501.255 288 512 298.745 512 312V392C512 405.255 501.255 416 488 416H205.333C192.078 416 181.333 405.255 181.333 392zM205.333 128H488C501.255 128 512 138.745 512 152V232C512 245.255 501.255 256 488 256H205.333C192.078 256 181.333 245.255 181.333 232V152C181.333 138.745 192.078 128 205.333 128z" />
+    <glyph glyph-name="th"
+      unicode="&#xF00A;"
+      horiz-adv-x="512" d=" M149.333 392V312C149.333 298.745 138.588 288 125.333 288H24C10.745 288 0 298.745 0 312V392C0 405.255 10.745 416 24 416H125.333C138.588 416 149.333 405.255 149.333 392zM330.6670000000001 152V232C330.6670000000001 245.255 319.9220000000001 256 306.6670000000001 256H205.333C192.078 256 181.333 245.255 181.333 232V152C181.333 138.745 192.078 128 205.333 128H306.666C319.9220000000001 128 330.667 138.745 330.667 152zM362.6670000000001 392V312C362.6670000000001 298.745 373.4120000000001 288 386.6670000000001 288H488C501.255 288 512 298.745 512 312V392C512 405.255 501.255 416 488 416H386.667C373.412 416 362.667 405.255 362.667 392zM330.6670000000001 312V392C330.6670000000001 405.255 319.9220000000001 416 306.6670000000001 416H205.333C192.078 416 181.333 405.255 181.333 392V312C181.333 298.745 192.078 288 205.333 288H306.666C319.9220000000001 288 330.667 298.745 330.667 312zM125.333 256H24C10.745 256 0 245.255 0 232V152C0 138.745 10.745 128 24 128H125.333C138.588 128 149.333 138.745 149.333 152V232C149.333 245.255 138.588 256 125.333 256zM0 72V-8C0 -21.255 10.745 -32 24 -32H125.333C138.588 -32 149.333 -21.255 149.333 -8V72C149.333 85.255 138.588 96 125.333 96H24C10.745 96 0 85.255 0 72zM386.667 128H488C501.255 128 512 138.745 512 152V232C512 245.255 501.255 256 488 256H386.667C373.412 256 362.667 245.255 362.667 232V152C362.667 138.745 373.412 128 386.667 128zM386.667 -32H488C501.255 -32 512 -21.255 512 -8V72C512 85.255 501.255 96 488 96H386.667C373.412 96 362.667 85.255 362.667 72V-8C362.667 -21.255 373.412 -32 386.667 -32zM181.333 72V-8C181.333 -21.255 192.078 -32 205.333 -32H306.666C319.921 -32 330.666 -21.255 330.666 -8V72C330.666 85.255 319.921 96 306.666 96H205.333C192.078 96 181.333 85.255 181.333 72z" />
+    <glyph glyph-name="theater-masks"
+      unicode="&#xF630;"
+      horiz-adv-x="640" d=" M206.86 202.85C170.98 192.4 146.91 161.65 149.33 128.75C160.73 141.47 178.14 152.45 199.23 159.67L206.86 202.85zM95.81 153L64.08 332.51C63.79 334.13 64.36 335.13 64.32 335.1600000000001C122.08 367.2200000000001 187.44 384.17 253.33 384.17C254.94 384.17 256.56 384 258.18 383.98C272.13 397.45 289.9100000000001 406.81 309.77 409.98C328.66 413 347.82 414.53 366.95 415.3C356.96 429.25 342.47 439.5300000000001 325.18 442.3C301.27 446.11 277.24 448 253.32 448C176.66 448 101.02 428.58 33.2 390.94C9.03 377.52 -3.92 349.52 1.05 321.42L32.78 141.91C47.01 61.39 169.11 -0.17 237.23 -0.17C240.82 -0.17 243.98 0.29 247.24 0.6300000000001C233.72 17.71 218.3 41.1100000000001 207.74 68.21C160.13 81.1900000000001 101.68 119.8300000000001 95.81 153.0000000000001zM193.36 290.4600000000001C192.63 294.5800000000001 191.13 298.3300000000001 189.29 301.86C181.04 292.9500000000001 168.62 286.11 153.97 283.54C139.32 280.9600000000001 125.3 283.14 114.4900000000001 288.7100000000001C113.9700000000001 284.77 113.85 280.73 114.5800000000001 276.61C118.4200000000001 254.9100000000001 139.16 240.42 160.9200000000001 244.24C182.6700000000001 248.06 197.2000000000001 268.76 193.3600000000001 290.4600000000001zM606.8 327.1C517.8199999999999 376.48 415.37 394.51 314.82 378.45C287.51 374.09 265.74 352.19 260.7799999999999 324.09L229.05 144.58C213.6599999999999 57.53 324.33 -51.6900000000001 387.3599999999999 -62.77C450.3899999999999 -73.86 591.8299999999999 -8.98 607.2199999999999 78.0700000000001L638.9499999999999 257.5800000000001C643.92 285.6900000000001 630.9699999999999 313.6900000000001 606.8 327.1zM333.56 230.3C337.3999999999999 252 358.14 266.49 379.9 262.6600000000001C401.66 258.8300000000001 416.18 238.14 412.35 216.44C411.62 212.32 410.12 208.57 408.28 205.04C400.03 213.95 387.61 220.79 372.96 223.36C358.31 225.9400000000001 344.29 223.76 333.48 218.19C332.95 222.14 332.83 226.1800000000001 333.56 230.3zM404.03 31.54C348.35 41.33 310.51 90.81 314.99 144.4400000000001C335.59 118.9 371.2 98.27 414.48 90.6600000000001C457.76 83.0500000000001 498.3 90.2900000000001 526.41 107.2600000000001C512.23 55.3200000000001 459.7 21.7500000000001 404.03 31.5400000000001zM534.3299999999999 182.88C526.0799999999999 191.79 513.65 198.63 498.9999999999999 201.2C484.35 203.78 470.3299999999999 201.6 459.5199999999999 196.03C458.9999999999999 199.97 458.8799999999999 204.01 459.6099999999999 208.13C463.4499999999999 229.83 484.1899999999999 244.32 505.9499999999999 240.5C527.6999999999999 236.67 542.2299999999999 215.98 538.4 194.28C537.67 190.15 536.17 186.4 534.3299999999999 182.88z" />
+    <glyph glyph-name="thermometer-empty"
+      unicode="&#xF2CB;"
+      horiz-adv-x="256" d=" M192 64C192 28.654 163.346 0 128 0S64 28.654 64 64C64 99.346 92.654 128 128 128S192 99.346 192 64zM224 148.653C243.912 126.09 256 96.459 256 64C256 -6.696 198.697 -64 128 -64C127.701 -64 127.391 -63.999 127.091 -63.997C56.789 -63.509 -0.357 -5.636 0.002 64.667C0.166 96.865 12.225 126.245 32 148.653V352C32 405.019 74.981 448 128 448S224 405.019 224 352V148.653zM208 64C208 98.339 188.63 116.19 176 130.502V352C176 378.467 154.467 400 128 400S80 378.467 80 352V130.502C67.268 116.074 48.175 98.402 48.001 64.422C47.777 20.5460000000001 83.564 -15.694 127.424 -15.998L128 -16C172.112 -16 208 19.888 208 64z" />
+    <glyph glyph-name="thermometer-full"
+      unicode="&#xF2C7;"
+      horiz-adv-x="256" d=" M224 352C224 405.019 181.019 448 128 448S32 405.019 32 352V148.653C12.225 126.244 0.166 96.864 0.002 64.667C-0.357 -5.636 56.789 -63.509 127.091 -63.997C127.39 -63.999 127.701 -64 128 -64C198.698 -64 256 -6.696 256 64C256 96.459 243.912 126.09 224 148.653V352zM128 -16L127.424 -15.998C83.564 -15.694 47.777 20.546 48.001 64.422C48.174 98.402 67.267 116.074 80 130.502V352C80 378.467 101.533 400 128 400S176 378.467 176 352V130.502C188.63 116.19 208 98.338 208 64C208 19.888 172.112 -16 128 -16zM192 64C192 28.654 163.346 0 128 0S64 28.654 64 64C64 87.685 76.876 108.349 96 119.417V352C96 369.673 110.327 384 128 384S160 369.673 160 352V119.417C179.124 108.3490000000001 192 87.6850000000001 192 64z" />
+    <glyph glyph-name="thermometer-half"
+      unicode="&#xF2C9;"
+      horiz-adv-x="256" d=" M192 64C192 28.654 163.346 0 128 0S64 28.654 64 64C64 87.685 76.876 108.349 96 119.417V224C96 241.673 110.327 256 128 256S160 241.673 160 224V119.417C179.124 108.3490000000001 192 87.6850000000001 192 64zM224 148.653C243.912 126.09 256 96.459 256 64C256 -6.696 198.697 -64 128 -64C127.701 -64 127.391 -63.999 127.091 -63.997C56.789 -63.509 -0.357 -5.636 0.002 64.667C0.166 96.865 12.225 126.245 32 148.653V352C32 405.019 74.981 448 128 448S224 405.019 224 352V148.653zM208 64C208 98.339 188.63 116.19 176 130.502V352C176 378.467 154.467 400 128 400S80 378.467 80 352V130.502C67.268 116.074 48.175 98.402 48.001 64.422C47.777 20.5460000000001 83.564 -15.694 127.424 -15.998L128 -16C172.112 -16 208 19.888 208 64z" />
+    <glyph glyph-name="thermometer-quarter"
+      unicode="&#xF2CA;"
+      horiz-adv-x="256" d=" M192 64C192 28.654 163.346 0 128 0S64 28.654 64 64C64 87.685 76.876 108.349 96 119.417V160C96 177.673 110.327 192 128 192S160 177.673 160 160V119.417C179.124 108.3490000000001 192 87.6850000000001 192 64zM224 148.653C243.912 126.09 256 96.459 256 64C256 -6.696 198.697 -64 128 -64C127.701 -64 127.391 -63.999 127.091 -63.997C56.789 -63.509 -0.357 -5.636 0.002 64.667C0.166 96.865 12.225 126.245 32 148.653V352C32 405.019 74.981 448 128 448S224 405.019 224 352V148.653zM208 64C208 98.339 188.63 116.19 176 130.502V352C176 378.467 154.467 400 128 400S80 378.467 80 352V130.502C67.268 116.074 48.175 98.402 48.001 64.422C47.777 20.5460000000001 83.564 -15.694 127.424 -15.998L128 -16C172.112 -16 208 19.888 208 64z" />
+    <glyph glyph-name="thermometer-three-quarters"
+      unicode="&#xF2C8;"
+      horiz-adv-x="256" d=" M192 64C192 28.654 163.346 0 128 0C92.654 0 64 28.654 64 64C64 87.685 76.876 108.349 96 119.417V288C96 305.673 110.327 320 128 320S160 305.673 160 288V119.417C179.124 108.3490000000001 192 87.6850000000001 192 64zM224 148.653C243.912 126.09 256 96.459 256 64C256 -6.696 198.697 -64 128 -64C127.701 -64 127.391 -63.999 127.091 -63.997C56.789 -63.509 -0.357 -5.636 0.002 64.667C0.166 96.865 12.225 126.245 32 148.653V352C32 405.019 74.981 448 128 448S224 405.019 224 352V148.653zM208 64C208 98.339 188.63 116.19 176 130.502V352C176 378.467 154.467 400 128 400S80 378.467 80 352V130.502C67.268 116.074 48.175 98.402 48.001 64.422C47.777 20.5460000000001 83.564 -15.694 127.424 -15.998L128 -16C172.112 -16 208 19.888 208 64z" />
+    <glyph glyph-name="thermometer"
+      unicode="&#xF491;"
+      horiz-adv-x="512" d=" M476.8 427.6C439.3 458.3 381.3 453.9 344.9 417.4L299.2 371.4L349.7 320.9C352.8 317.8 352.8 312.7000000000001 349.7 309.6L338.4 298.3C335.3 295.2 330.2 295.2 327.1 298.3L276.7 348.8L231.6 303.4L281.9 253C285 249.9 285 244.8 281.9 241.7L270.6 230.4C267.5 227.3 262.4 227.3 259.3 230.4L209 280.6L163.9 235.2L214 185C217.1 181.9 217.1 176.8 214 173.7L202.7 162.4C199.6 159.3 194.5 159.3 191.4 162.4L141.3 212.6L96 166.9V66L7 -23C-2.4 -32.4 -2.4 -47.6 7 -56.9C16.4 -66.2999999999999 31.6 -66.2999999999999 40.9 -56.9L129.9 32.1H229.8L484 285.4C518.9 320.3 526.2 386.9 476.8 427.6z" />
+    <glyph glyph-name="thumbs-down"
+      unicode="&#xF165;"
+      horiz-adv-x="512" d=" M0 392V152C0 138.745 10.745 128 24 128H104C117.255 128 128 138.745 128 152V392C128 405.255 117.255 416 104 416H24C10.745 416 0 405.255 0 392zM40 192C40 205.255 50.745 216 64 216S88 205.255 88 192S77.255 168 64 168S40 178.745 40 192zM312 -64C291.817 -64 282.515 -24.707 278.069 -6.205C272.863 15.461 267.48 37.865 252.676 52.697C220.207 85.221 203.173 126.664 163.559 165.808A11.98 11.98 0 0 0 160.001 174.329V388.099C160.001 394.64 165.244 399.977 171.784 400.097C187.615 400.387 208.478 409.176 224.435 416.275C256.189 430.402 295.709 447.983 343.995 448H346.839C389.616 448 440.202 447.587 460.613 418.263C469.005 406.206 471.059 391.229 466.761 373.631C483.073 356.578 491.824 324.7680000000001 483.143 298.874C500.687 275.442 502.2860000000001 242.742 492.451 219.405L492.561 219.295C504.4540000000001 207.346 512.0840000000001 188.036 512 170.098C511.844 139.746 485.843 111.9999999999999 452.447 111.9999999999999H350.723C358.03 83.66 384 59.868 384 17.452C384 -56 336 -64 312 -64z" />
+    <glyph glyph-name="thumbs-up"
+      unicode="&#xF164;"
+      horiz-adv-x="512" d=" M104 224H24C10.745 224 0 213.255 0 200V-40C0 -53.255 10.745 -64 24 -64H104C117.255 -64 128 -53.255 128 -40V200C128 213.255 117.255 224 104 224zM64 -24C50.745 -24 40 -13.255 40 0S50.745 24 64 24S88 13.255 88 0S77.255 -24 64 -24zM384 366.548C384 324.132 358.03 300.3400000000001 350.723 272H452.446C485.843 272 511.843 244.254 511.999 213.902C512.083 195.964 504.453 176.653 492.56 164.705L492.45 164.595C502.286 141.258 500.687 108.558 483.142 85.126C491.823 59.231 483.073 27.422 466.76 10.369C471.058 -7.229 469.004 -22.206 460.612 -34.263C440.202 -63.587 389.616 -64 346.839 -64L343.994 -63.999C295.707 -63.982 256.188 -46.401 224.434 -32.274C208.477 -25.175 187.613 -16.387 171.783 -16.096C165.243 -15.976 160 -10.639 160 -4.098V209.672C160 212.872 161.282 215.943 163.558 218.193C203.172 257.337 220.206 298.78 252.675 331.304C267.479 346.136 272.863 368.54 278.068 390.206C282.515 408.707 291.817 448 312 448C336 448 384 440 384 366.548z" />
+    <glyph glyph-name="thumbtack"
+      unicode="&#xF08D;"
+      horiz-adv-x="384" d=" M298.028 233.733L285.793 352H328C341.255 352 352 362.745 352 376V424C352 437.255 341.255 448 328 448H56C42.745 448 32 437.255 32 424V376C32 362.745 42.745 352 56 352H98.207L85.972 233.733C37.465 211.18 0 170.739 0 120C0 106.745 10.745 96 24 96H160V-8.007C160 -9.249 160.289 -10.474 160.845 -11.585L184.845 -59.585C187.786 -65.467 196.209 -65.4779999999999 199.156 -59.585L223.156 -11.585A8.008 8.008 0 0 1 224.001 -8.007V96H360.001C373.256 96 384.001 106.745 384.001 120C384 171.183 346.018 211.42 298.028 233.733z" />
+    <glyph glyph-name="ticket-alt"
+      unicode="&#xF3FF;"
+      horiz-adv-x="576" d=" M128 288H448V96H128V288zM528 192C528 165.49 549.49 144 576 144V48C576 21.49 554.51 0 528 0H48C21.49 0 0 21.49 0 48V144C26.51 144 48 165.49 48 192S26.51 240 0 240V336C0 362.51 21.49 384 48 384H528C554.51 384 576 362.51 576 336V240C549.49 240 528 218.51 528 192zM480 296C480 309.255 469.255 320 456 320H120C106.745 320 96 309.255 96 296V88C96 74.745 106.745 64 120 64H456C469.255 64 480 74.745 480 88V296z" />
+    <glyph glyph-name="times-circle"
+      unicode="&#xF057;"
+      horiz-adv-x="512" d=" M256 440C119 440 8 329 8 192S119 -56 256 -56S504 55 504 192S393 440 256 440zM377.6 126.9C382.3 122.2 382.3 114.6 377.6 109.9L338 70.4C333.3 65.7 325.7 65.7 321 70.4L256 136L190.9 70.4C186.2 65.7 178.6 65.7 173.9 70.4L134.4 110C129.7 114.7 129.7 122.3 134.4 127L200 192L134.4 257.1C129.7 261.8 129.7 269.4 134.4 274.1L174 313.7C178.7 318.4 186.3 318.4 191 313.7L256 248L321.1 313.6C325.8 318.3 333.4000000000001 318.3 338.1 313.6L377.7000000000001 274C382.4000000000001 269.3 382.4000000000001 261.7 377.7000000000001 257L312 192L377.6 126.9z" />
+    <glyph glyph-name="times"
+      unicode="&#xF00D;"
+      horiz-adv-x="352" d=" M242.72 192L342.79 292.07C355.07 304.35 355.07 324.26 342.79 336.55L320.55 358.79C308.27 371.07 288.36 371.07 276.07 358.79L176 258.7200000000001L75.93 358.79C63.65 371.07 43.74 371.07 31.45 358.79L9.21 336.55C-3.07 324.27 -3.07 304.36 9.21 292.07L109.28 192L9.21 91.93C-3.07 79.65 -3.07 59.74 9.21 47.45L31.45 25.21C43.73 12.93 63.65 12.93 75.93 25.21L176 125.28L276.07 25.21C288.35 12.93 308.27 12.93 320.55 25.21L342.79 47.45C355.07 59.73 355.07 79.64 342.79 91.93L242.72 192z" />
+    <glyph glyph-name="tint-slash"
+      unicode="&#xF5C7;"
+      horiz-adv-x="640" d=" M633.82 -10.1L494.97 97.22C495.49 102.79 496 108.3800000000001 496 114.09C496 225.85 396.21 267.4300000000001 349.2200000000001 425.9100000000001C341.2800000000001 454.6900000000001 299.7800000000001 456.03 290.7800000000001 425.9100000000001C275.2600000000001 373.5700000000001 253.9100000000001 333.9500000000001 232.29 300.23L45.47 444.63C38.49 450.05 28.43 448.8 23.01 441.82L3.37 416.55C-2.05 409.58 -0.8 399.53 6.18 394.1L594.54 -60.63C601.52 -66.0599999999999 611.5699999999999 -64.7999999999999 617 -57.82L636.64 -32.55C642.05 -25.58 640.8 -15.53 633.8199999999999 -10.1zM144 114.09C144 15.65 222.72 -64 320 -64C364.71 -64 405.37 -47.04 436.4 -19.3L162.72 192.22C151.31 168.72 144 143.87 144 114.09z" />
+    <glyph glyph-name="tint"
+      unicode="&#xF043;"
+      horiz-adv-x="352" d=" M205.22 425.9100000000001C197.28 454.69 155.78 456.03 146.78 425.9100000000001C100.01 268.15 0 225.28 0 114.09C0 15.65 78.72 -64 176 -64S352 15.65 352 114.09C352 225.8400000000001 252.21 267.4300000000001 205.22 425.9100000000001zM176 0C114.25 0 64 50.25 64 112C64 120.84 71.16 128 80 128S96 120.84 96 112C96 67.89 131.89 32 176 32C184.84 32 192 24.84 192 16S184.84 0 176 0z" />
+    <glyph glyph-name="tired"
+      unicode="&#xF5C8;"
+      horiz-adv-x="496" d=" M248 440C111 440 0 329 0 192S111 -56 248 -56S496 55 496 192S385 440 248 440zM281.8 250.3L361.8 298.3C373.4000000000001 305.2000000000001 385.8 290.6 377.2 280.3L343.6 240L377.2000000000001 199.7C385.9000000000001 189.3 373.3000000000001 174.9 361.8000000000001 181.7L281.8000000000001 229.7C274.1000000000001 234.4 274.1000000000001 245.6 281.8000000000001 250.3zM118.8 280.3C110.2 290.6 122.6 305.2000000000001 134.2 298.3L214.2 250.3C222 245.6 222 234.4 214.2 229.7L134.2 181.7000000000001C122.7 174.9 110.2 189.3000000000001 118.8 199.7000000000001L152.4 240.0000000000001L118.8 280.3000000000001zM248 160C299.9 160 363.3 116.2 371.2 53.3C372.9 39.7 363.2 28.7 353.5 32.9C327.6 44.0000000000001 289.1 50.3 248 50.3S168.4 44 142.5 32.9C132.7 28.7 123.1 39.9 124.8 53.3C132.7 116.2 196.1 160 248 160z" />
+    <glyph glyph-name="toggle-off"
+      unicode="&#xF204;"
+      horiz-adv-x="576" d=" M384 384H192C85.961 384 0 298.039 0 192S85.961 0 192 0H384C490.039 0 576 85.961 576 192S490.039 384 384 384zM64 192C64 262.741 121.249 320 192 320C262.741 320 320 262.751 320 192C320 121.259 262.751 64 192 64C121.259 64 64 121.249 64 192zM384 64H335.0950000000001C400.312 136.858 400.331 247.12 335.0950000000001 320H384C454.741 320 512 262.751 512 192C512 121.26 454.751 64 384 64z" />
+    <glyph glyph-name="toggle-on"
+      unicode="&#xF205;"
+      horiz-adv-x="576" d=" M576 192C576 85.961 490.039 0 384 0H192C85.961 0 0 85.961 0 192S85.961 384 192 384H384C490.039 384 576 298.039 576 192zM384 320C313.259 320 256 262.751 256 192C256 121.259 313.249 64 384 64C454.741 64 512 121.249 512 192C512 262.741 454.751 320 384 320" />
+    <glyph glyph-name="toilet-paper"
+      unicode="&#xF71E;"
+      horiz-adv-x="576" d=" M128 448C74.98 448 32 362.04 32 256V83.93C32 42.81 22.2 21.16 0.83 -42.94C-2.62 -53.3 5.09 -64 16.01 -64H296.93C310.7 -64 322.93 -55.19 327.29 -42.12C340.12 -3.64 352 30.28 352 83.93V256C352 339.6 375.67 409.52 412.44 448H128zM96 224C87.16 224 80 231.16 80 240S87.16 256 96 256S112 248.84 112 240S104.84 224 96 224zM160 224C151.16 224 144 231.16 144 240S151.16 256 160 256S176 248.84 176 240S168.84 224 160 224zM224 224C215.16 224 208 231.16 208 240S215.16 256 224 256S240 248.84 240 240S232.84 224 224 224zM288 224C279.1600000000001 224 272 231.16 272 240S279.1600000000001 256 288 256S304 248.84 304 240S296.84 224 288 224zM480 448C426.98 448 384 362.04 384 256S426.98 64 480 64S576 149.96 576 256S533.02 448 480 448zM480 192C462.33 192 448 220.65 448 256S462.33 320 480 320S512 291.35 512 256S497.67 192 480 192z" />
+    <glyph glyph-name="toolbox"
+      unicode="&#xF552;"
+      horiz-adv-x="512" d=" M502.63 233.37L457.38 278.62C451.38 284.62 443.24 287.99 434.75 287.99H384V368C384 394.51 362.51 416 336 416H176C149.49 416 128 394.51 128 368V288H77.25C68.76 288 60.63 284.63 54.62 278.63L9.37 233.37C3.37 227.37 0 219.23 0 210.74V128H128V144C128 152.84 135.16 160 144 160H176C184.84 160 192 152.84 192 144V128H320V144C320 152.84 327.1600000000001 160 336 160H368C376.84 160 384 152.84 384 144V128H512V210.75C512 219.23 508.63 227.37 502.63 233.37zM320 288H192V352H320V288zM384 80C384 71.16 376.84 64 368 64H336C327.1600000000001 64 320 71.16 320 80V96H192V80C192 71.16 184.84 64 176 64H144C135.16 64 128 71.16 128 80V96H0V0C0 -17.67 14.33 -32 32 -32H480C497.67 -32 512 -17.67 512 0V96H384V80z" />
+    <glyph glyph-name="tooth"
+      unicode="&#xF5C9;"
+      horiz-adv-x="448" d=" M443.98 351.75C432.9700000000001 396.9700000000001 396.87 433.81 351.9700000000001 445.4700000000001C319.7800000000001 453.83 288.9700000000001 440.37 262.8300000000001 421.14C259.5800000000001 418.75 255.8700000000001 417.4100000000001 252.3300000000001 415.66L280.6500000000001 397.45C288.0700000000001 392.68 290.23 382.78 285.4500000000001 375.3400000000001C280.9900000000001 368.39 271.1800000000001 365.48 263.3400000000001 370.54L162.83 435.1600000000001C142.13 446.01 119.45 451.56 96.02 445.4700000000001C51.12 433.8 15.02 396.9700000000001 4.01 351.75C-6.12 310.13 3.59 270.94 25.51 241.32C48.87 209.75 58.19 172.66 61.8 133.97C66.2 86.8100000000001 72.13 39.8100000000001 82.74 -6.35L90.54 -40.3C93.73 -54.17 106.03 -63.9999999999999 120.21 -63.9999999999999C134.18 -63.9999999999999 146.36 -54.4499999999999 149.75 -40.8399999999999L184.22 97.58C188.78 115.9 205.18 128.7400000000001 223.98 128.7400000000001S259.18 115.8900000000001 263.74 97.58L298.2100000000001 -40.8399999999999C301.6 -54.4499999999999 313.7800000000001 -63.9999999999999 327.7500000000001 -63.9999999999999C341.9300000000001 -63.9999999999999 354.2300000000001 -54.17 357.4200000000001 -40.3L365.2200000000001 -6.35C375.8300000000001 39.8 381.7500000000001 86.8100000000001 386.1600000000001 133.97C389.7700000000001 172.67 399.0900000000001 209.75 422.4500000000001 241.32C444.4000000000001 270.93 454.1100000000001 310.12 443.9800000000002 351.75z" />
+    <glyph glyph-name="torah"
+      unicode="&#xF6A0;"
+      horiz-adv-x="448" d=" M448 89.6V422.4C448 438.4 438.4 448 422.4 448H96C41.6 448 0 406.4 0 352V32C0 -22.4 41.6 -64 96 -64H422.4C435.2 -64 448 -54.4 448 -38.4V-22.4C448 -16 444.8 -9.6 438.4 -3.2C435.2 12.8 435.2 57.6 438.4 70.4C444.8 73.6 448 80 448 89.6zM139.11 306.79C136.34 311.2 139.51 316.93 144.71 316.93H197.79C198.69 316.93 199.52 317.39 200 318.15L234.4000000000001 372.91C237.0000000000001 377.04 243.0100000000001 377.04 245.6100000000001 372.91L280.0100000000001 318.15A2.61 2.61 0 0 1 282.2200000000001 316.93H335.3C340.5 316.93 343.67 311.2000000000001 340.9000000000001 306.79L314.01 264L340.89 221.21C343.66 216.81 340.49 211.08 335.29 211.08H282.21C281.31 211.08 280.48 210.62 280 209.86L245.6 155.1C243 150.97 236.99 150.97 234.39 155.1L200 209.85A2.61 2.61 0 0 1 197.79 211.07H144.71C139.51 211.07 136.34 216.8 139.11 221.2L165.99 264L139.11 306.79zM380.8 0H96C76.8 0 64 12.8 64 32S80 64 96 64H380.8V0z" />
+    <glyph glyph-name="torii-gate"
+      unicode="&#xF6A1;"
+      horiz-adv-x="512" d=" M376.45 416H135.55A303.17 303.17 0 0 0 0 448V352C0 334.33 14.33 320 32 320H64V256H16C7.16 256 0 248.84 0 240V208C0 199.16 7.16 192 16 192H64V-48C64 -56.84 71.16 -64 80 -64H112C120.84 -64 128 -56.84 128 -48V192H384V-48C384 -56.84 391.1600000000001 -64 400 -64H432C440.84 -64 448 -56.84 448 -48V192H496C504.84 192 512 199.16 512 208V240C512 248.84 504.84 256 496 256H448V320H480C497.67 320 512 334.33 512 352V448A303.17 303.17 0 0 0 376.45 416zM128 320H224V256H128V320zM384 256H288V320H384V256z" />
+    <glyph glyph-name="tractor"
+      unicode="&#xF722;"
+      horiz-adv-x="640" d=" M528 112C479.4 112 440 72.6 440 24S479.4 -64 528 -64S616 -24.6 616 24S576.6 112 528 112zM528 0C514.77 0 504 10.77 504 24S514.77 48 528 48S552 37.23 552 24S541.23 0 528 0zM608 288H544V328.2C544 342.32 548.7 355.9600000000001 557.15 367.04C561.5699999999999 372.8400000000001 560.6999999999999 381.1 555.8299999999999 386.53L534.2 410.7C527.5400000000001 418.15 515.88 417.62 509.5000000000001 409.92C490.58 387.1 480 358.19 480 328.2V288H377.67L321.58 418.86A47.914 47.914 0 0 1 277.45 448H144C117.53 448 96 426.4700000000001 96 400V253.48C87.37 260.21 75.04 259.94 67.11 252.01L36 220.9C27.41 212.31 27.41 198.38 36 189.79L41.06 184.73C36.07 175.47 32.1 165.91 29.15 156.01H22C9.85 156.01 0 146.16 0 134.01V90.01C0 77.86 9.85 68.01 22 68.01H29.14C32.1 58.1 36.06 48.55 41.05 39.28L35.99 34.22C27.4 25.63 27.4 11.7 35.99 3.11L67.1 -28C75.69 -36.59 89.62 -36.59 98.21 -28L103.27 -22.94C112.53 -27.93 122.09 -31.9 131.99 -34.85V-42C131.99 -54.15 141.84 -64 153.99 -64H197.99C210.14 -64 219.99 -54.15 219.99 -42V-34.86C229.89 -31.91 239.45 -27.94 248.71 -22.95L253.77 -28.01C262.36 -36.6 276.29 -36.6 284.88 -28.01L315.99 3.1C324.58 11.69 324.58 25.62 315.99 34.21L310.93 39.27C315.92 48.53 319.89 58.09 322.8400000000001 67.99H330C342.15 67.99 352 77.84 352 89.99V95.99H432.54C454.45 124.98 488.86 143.99 528 143.99C546.64 143.99 564.07 139.38 579.8 131.79L630.62 182.61C636.62 188.61 639.99 196.75 639.99 205.24V256C640 273.67 625.67 288 608 288zM176 32C131.82 32 96 67.82 96 112S131.82 192 176 192S256 156.18 256 112S220.18 32 176 32zM198 288H160V384H266.89L308.04 288H198z" />
+    <glyph glyph-name="trademark"
+      unicode="&#xF25C;"
+      horiz-adv-x="640" d=" M97.119 284.867H12C5.373 284.867 0 290.24 0 296.867V340C0 346.627 5.373 352 12 352H260.559C267.186 352 272.559 346.627 272.559 340V296.867C272.559 290.24 267.186 284.867 260.559 284.867H175.44V44C175.44 37.373 170.067 32 163.44 32H109.118C102.491 32 97.118 37.373 97.118 44V284.867zM329.825 352H395.25A12 12 0 0 0 406.596 343.907L450.355 216.839C457.516 196.251 466.466 164.027 466.466 164.027H467.362S476.312 196.251 483.473 216.839L527.231 343.907A12 12 0 0 0 538.577 352H603.987A12 12 0 0 0 615.948 340.9700000000001L639.96 44.97C640.527 37.983 635.009 32 627.999 32H573.898A12 12 0 0 0 561.926 43.182L552.844 176.112C551.0540000000001 200.28 552.844 229.8180000000001 552.844 229.8180000000001H551.9480000000001S541.2070000000001 196.2520000000001 534.046 176.112L503.3460000000001 91.381A12 12 0 0 0 492.0640000000001 83.4690000000001H441.7620000000001A12 12 0 0 0 430.4800000000001 91.381L399.7800000000001 176.112C392.6190000000001 196.252 381.8770000000001 229.8180000000001 381.8770000000001 229.8180000000001H380.9820000000001S382.7720000000001 200.28 380.9820000000001 176.112L371.9000000000001 43.182C371.4720000000001 36.887 366.2400000000001 32 359.9300000000001 32H305.4C298.383 32 292.864 37.994 293.441 44.987L317.866 340.987A11.999000000000002 11.999000000000002 0 0 0 329.825 352z" />
+    <glyph glyph-name="traffic-light"
+      unicode="&#xF637;"
+      horiz-adv-x="384" d=" M384 256H320V293.88C357.2 307.1 384 342.26 384 384H320V416C320 433.67 305.67 448 288 448H96C78.33 448 64 433.67 64 416V384H0C0 342.26 26.8 307.1 64 293.88V256H0C0 214.26 26.8 179.1 64 165.88V128H0C0 85.16 28.25 49.31 66.99 36.95C79.42 -20.72 130.6 -64 192 -64S304.58 -20.72 317.01 36.95C355.75 49.31 384 85.16 384 128H320V165.88C357.2 179.1 384 214.26 384 256zM192 32C165.49 32 144 53.49 144 80S165.49 128 192 128S240 106.51 240 80S218.51 32 192 32zM192 160C165.49 160 144 181.49 144 208S165.49 256 192 256S240 234.51 240 208S218.51 160 192 160zM192 288C165.49 288 144 309.49 144 336S165.49 384 192 384S240 362.51 240 336S218.51 288 192 288z" />
+    <glyph glyph-name="train"
+      unicode="&#xF238;"
+      horiz-adv-x="448" d=" M448 352V96C448 44.185 386.376 0 317.978 0L380.9580000000001 -49.721C386.905 -54.417 383.562 -64 376 -64H72C64.422 -64 61.108 -54.406 67.043 -49.721L130.022 0C61.82 0 0 44.046 0 96V352C0 405.019 64 448 128 448H320C385 448 448 405.019 448 352zM400 216V328C400 341.255 389.255 352 376 352H72C58.745 352 48 341.255 48 328V216C48 202.745 58.745 192 72 192H376C389.255 192 400 202.745 400 216zM224 152C193.072 152 168 126.928 168 96S193.072 40 224 40S280 65.072 280 96S254.928 152 224 152z" />
+    <glyph glyph-name="transgender-alt"
+      unicode="&#xF225;"
+      horiz-adv-x="480" d=" M468 448H389C378.3 448 373 435.1 380.5 427.5L397.4 410.6L316.7 329.9C294.5 343.9 268.2 352 240 352C211.8 352 185.5 343.9 163.3 329.9L146.8 346.4L166.6 366.2C171.3 370.9 171.3 378.5 166.6 383.2L138.3 411.5C133.6 416.2 126 416.2 121.3 411.5L101.5 391.7L82.5 410.7L99.4 427.6C107.1 435.1 101.7 448 91 448H12C5.4 448 0 442.6 0 436V357C0 346.3 12.9 341 20.5 348.5L37.4 365.4L56.4 346.4L36.6 326.6C31.9 321.9 31.9 314.3 36.6 309.6L64.9 281.3C69.6 276.6 77.2 276.6 81.9 281.3L101.7 301.1L118.2 284.6C104.1 262.5 96 236.2 96 208C96 139.5 143.9 82.1 208 67.6V40H172C165.4 40 160 34.6 160 28V-12C160 -18.6 165.4 -24 172 -24H208V-52C208 -58.6 213.4 -64 220 -64H260C266.6 -64 272 -58.6 272 -52V-24H308C314.6 -24 320 -18.6 320 -12V28C320 34.6 314.6 40 308 40H272V67.6C336.1 82.2000000000001 384 139.5 384 208C384 236.2 375.9 262.5 361.9 284.7000000000001L442.6 365.4000000000001L459.4999999999999 348.5000000000001C467.1 340.9000000000001 479.9999999999999 346.3000000000001 479.9999999999999 357.0000000000001V436C479.9999999999999 442.6 474.6 448 467.9999999999999 448zM240 128C195.9 128 160 163.9 160 208S195.9 288 240 288S320 252.1 320 208S284.1 128 240 128z" />
+    <glyph glyph-name="transgender"
+      unicode="&#xF224;"
+      horiz-adv-x="384" d=" M372 448H293C282.3 448 277 435.1 284.5 427.5L301.4 410.6L220.7 329.9C198.5 343.9 172.2 352 144 352C64.5 352 0 287.5 0 208C0 139.5 47.9 82.1 112 67.6V40H76C69.4 40 64 34.6 64 28V-12C64 -18.6 69.4 -24 76 -24H112V-52C112 -58.6 117.4 -64 124 -64H164C170.6 -64 176 -58.6 176 -52V-24H212C218.6 -24 224 -18.6 224 -12V28C224 34.6 218.6 40 212 40H176V67.6C240.1 82.2000000000001 288 139.5 288 208C288 236.2 279.9 262.5 265.9 284.7000000000001L346.6 365.4000000000001L363.5 348.5000000000001C371.1 340.9000000000001 384 346.3000000000001 384 357.0000000000001V436C384 442.6 378.6 448 372 448zM144 128C99.9 128 64 163.9 64 208S99.9 288 144 288S224 252.1 224 208S188.1 128 144 128z" />
+    <glyph glyph-name="trash-alt"
+      unicode="&#xF2ED;"
+      horiz-adv-x="448" d=" M0 364V392C0 405.3 10.7 416 24 416H136L145.4 434.7C149.4 442.9 157.7 448 166.8 448H281.1C290.2000000000001 448 298.5 442.9 302.6 434.7L312 416H424C437.3 416 448 405.3 448 392V364C448 357.4 442.6 352 436 352H12C5.4 352 0 357.4 0 364zM416 308V-16C416 -42.5 394.5 -64 368 -64H80C53.5 -64 32 -42.5 32 -16V308C32 314.6 37.4 320 44 320H404C410.6 320 416 314.6 416 308zM144 240C144 248.8 136.8 256 128 256S112 248.8 112 240V16C112 7.2 119.2 0 128 0S144 7.2 144 16V240zM240 240C240 248.8 232.8 256 224 256S208 248.8 208 240V16C208 7.2 215.2 0 224 0S240 7.2 240 16V240zM336 240C336 248.8 328.8 256 320 256S304 248.8 304 240V16C304 7.2 311.2 0 320 0S336 7.2 336 16V240z" />
+    <glyph glyph-name="trash"
+      unicode="&#xF1F8;"
+      horiz-adv-x="448" d=" M0 364V392C0 405.3 10.7 416 24 416H136L145.4 434.7C149.4 442.9 157.7 448 166.8 448H281.1C290.2000000000001 448 298.5 442.9 302.6 434.7L312 416H424C437.3 416 448 405.3 448 392V364C448 357.4 442.6 352 436 352H12C5.4 352 0 357.4 0 364zM415.2 307.3L394.8 -19C393.2 -44.3 372.2 -64 346.9000000000001 -64H101.1C75.8 -64 54.8 -44.3 53.2 -19L32.8 307.3C32.4 314.2000000000001 37.9 320 44.8 320H403.3C410.1 320 415.6 314.2000000000001 415.2 307.3z" />
+    <glyph glyph-name="tree"
+      unicode="&#xF1BB;"
+      horiz-adv-x="384" d=" M378.31 69.51L298.42 160H329.05C338.06 160 346.0300000000001 165 349.8300000000001 173.06C353.6300000000001 181.1 352.3800000000001 190.32 346.5500000000001 197.11L268.42 288H297.31C306.4100000000001 288 314.61 293.35 318.17 301.61C321.69 309.74 320.0300000000001 319.2000000000001 313.93 325.69L203.66 443.17C197.63 449.62 186.38 449.62 180.34 443.17L70.06 325.69C63.96 319.2 62.31 309.74 65.82 301.61C69.38 293.35 77.59 288 86.69 288H115.58L37.44 197.09C31.63 190.31 30.38 181.1 34.17 173.05C37.97 165 45.93 160 54.95 160H85.58L5.69 69.51C-0.31 62.72 -1.67 53.42 2.13 45.25C5.88 37.2 14.13 32 23.14 32H160V7.55L129.71 -40.85C124.39 -51.4899999999999 132.13 -64.01 144.02 -64.01H239.98C251.87 -64.01 259.61 -51.49 254.29 -40.85L224 7.55V32H360.86C369.87 32 378.12 37.2 381.87 45.25C385.67 53.42 384.31 62.72 378.31 69.51z" />
+    <glyph glyph-name="trophy"
+      unicode="&#xF091;"
+      horiz-adv-x="576" d=" M552 384H448V424C448 437.3 437.3 448 424 448H152C138.7 448 128 437.3 128 424V384H24C10.7 384 0 373.3 0 360V304C0 268.3 22.5 231.6 61.9 203.3C93.4 180.6 131.7 166.2 171.9 161.6C203.3 109.5 240 88 240 88V16H192C156.7 16 128 -4.7 128 -40V-52C128 -58.6 133.4 -64 140 -64H436C442.6 -64 448 -58.6 448 -52V-40C448 -4.7 419.3 16 384 16H336V88S372.7 109.5 404.1 161.6C444.4000000000001 166.2000000000001 482.7 180.6 514.1 203.3C553.4 231.6 576 268.3 576 304V360C576 373.3 565.3 384 552 384zM99.3 255.2C74.9 272.8 64 292.4 64 304V320H128.2C129.2 287.4 134 258.8 141 233.8C125.9 239 111.8 246.2 99.3 255.2zM512 304C512 287.9 494.3 267.9 476.7 255.2C464.2 246.2 450 239 434.9 233.8C441.9 258.8 446.7 287.4 447.7 320H512V304z" />
+    <glyph glyph-name="truck-loading"
+      unicode="&#xF4DE;"
+      horiz-adv-x="640" d=" M50.2 72.4C52.5 63.9 61.3 58.8 69.8 61.1L286.2000000000001 119.1C294.7000000000001 121.4 299.8000000000001 130.2 297.5000000000001 138.7L247.8000000000001 324.2C245.5000000000001 332.7 236.7000000000001 337.8 228.2000000000001 335.5L151 314.7L175.8 222L114 205.5L89.2 298.2L11.9 277.5C3.4 275.2 -1.7 266.4 0.6 257.9L50.2 72.4zM384 448C366.3 448 352 433.7 352 416V92.4L5.9 -2C1.6 -3.2 -0.9 -7.6 0.3 -11.8L12.9 -58.1C14.1 -62.4 18.5 -64.9 22.7 -63.7L416.4 43.6999999999999C418.8 -16.1 467.6 -64 528 -64C589.9 -64 640 -13.9 640 48V448H384zM528 0C501.5 0 480 21.5 480 48S501.5 96 528 96S576 74.5 576 48S554.5 0 528 0z" />
+    <glyph glyph-name="truck-monster"
+      unicode="&#xF63B;"
+      horiz-adv-x="640" d=" M624 224H608V288C608 305.67 593.67 320 576 320H502.4L419.2200000000001 423.98A64.025 64.025 0 0 1 369.24 448H256C238.33 448 224 433.67 224 416V320H48C39.16 320 32 312.8400000000001 32 304V224H16C7.16 224 0 216.84 0 208V176C0 167.16 7.16 160 16 160H32.72C61.93 198.65 107.82 224 160 224S258.07 198.65 287.28 160H352.73C381.94 198.65 427.83 224 480.01 224S578.0799999999999 198.65 607.29 160H624C632.84 160 640 167.16 640 176V208C640 216.84 632.84 224 624 224zM288 320V384H369.24L420.44 320H288zM592 96H586.8C584.5999999999999 103.33 581.7299999999999 110.28 578.15 116.89L581.8199999999999 120.56C588.0699999999999 126.81 588.0699999999999 136.94 581.8199999999999 143.19L559.1899999999999 165.82C552.9399999999999 172.07 542.81 172.07 536.56 165.82L532.89 162.15A110.84999999999998 110.84999999999998 0 0 1 512 170.8V176C512 184.84 504.84 192 496 192H464C455.16 192 448 184.84 448 176V170.8C440.67 168.6 433.7200000000001 165.73 427.11 162.15L423.44 165.8200000000001C417.19 172.0700000000001 407.06 172.0700000000001 400.81 165.8200000000001L378.18 143.1900000000001C371.93 136.9400000000001 371.93 126.8100000000001 378.18 120.5600000000001L381.85 116.8900000000001A110.84999999999998 110.84999999999998 0 0 1 373.2 96H368C359.1600000000001 96 352 88.84 352 80V48C352 39.16 359.1600000000001 32 368 32H373.2C375.4 24.67 378.27 17.72 381.85 11.11L378.18 7.44C371.93 1.19 371.93 -8.94 378.18 -15.19L400.81 -37.82C407.06 -44.07 417.19 -44.07 423.44 -37.82L427.11 -34.15C433.72 -37.72 440.68 -40.6 448.01 -42.8V-47.9999999999999C448.01 -56.8399999999999 455.17 -63.9999999999999 464.0099999999999 -63.9999999999999H496.0099999999999C504.8499999999999 -63.9999999999999 512.01 -56.8399999999999 512.01 -47.9999999999999V-42.8C519.34 -40.6 526.29 -37.73 532.91 -34.15L536.5799999999999 -37.82C542.8299999999999 -44.07 552.9599999999999 -44.07 559.2099999999999 -37.82L581.8399999999999 -15.19C588.0899999999999 -8.94 588.0899999999999 1.19 581.8399999999999 7.44L578.17 11.11A110.84999999999998 110.84999999999998 0 0 1 586.8199999999999 32H592.02C600.86 32 608.02 39.16 608.02 48V80C608 88.84 600.84 96 592 96zM480 16C453.49 16 432 37.49 432 64S453.49 112 480 112S528 90.51 528 64S506.51 16 480 16zM272 96H266.8C264.6 103.33 261.73 110.28 258.1500000000001 116.89L261.8200000000001 120.56C268.0700000000001 126.81 268.0700000000001 136.94 261.8200000000001 143.19L239.1900000000001 165.82C232.9400000000001 172.07 222.8100000000001 172.07 216.5600000000001 165.82L212.8900000000001 162.15A110.84999999999998 110.84999999999998 0 0 1 192 170.8V176C192 184.84 184.84 192 176 192H144C135.16 192 128 184.84 128 176V170.8C120.67 168.6 113.72 165.73 107.11 162.15L103.44 165.8200000000001C97.19 172.0700000000001 87.06 172.0700000000001 80.81 165.8200000000001L58.18 143.2C51.93 136.95 51.93 126.82 58.18 120.57L61.85 116.9A110.84999999999998 110.84999999999998 0 0 1 53.2 96.01H48C39.16 96.01 32 88.85 32 80.01V48.01C32 39.17 39.16 32.01 48 32.01H53.2C55.4 24.68 58.27 17.73 61.85 11.12L58.18 7.45C51.93 1.2 51.93 -8.93 58.18 -15.18L80.81 -37.81C87.06 -44.06 97.19 -44.06 103.44 -37.81L107.11 -34.14C113.72 -37.71 120.68 -40.59 128.01 -42.79V-47.9899999999999C128.01 -56.8299999999999 135.17 -63.9899999999999 144.01 -63.9899999999999H176.01C184.85 -63.9899999999999 192.01 -56.8299999999999 192.01 -47.9899999999999V-42.79C199.34 -40.59 206.29 -37.72 212.91 -34.14L216.58 -37.81C222.83 -44.06 232.96 -44.06 239.21 -37.81L261.84 -15.18C268.09 -8.93 268.09 1.2 261.84 7.45L258.17 11.12A110.84999999999998 110.84999999999998 0 0 1 266.82 32.01H272.0199999999999C280.8599999999999 32.01 288.0199999999999 39.17 288.0199999999999 48.01V80.01C288 88.84 280.84 96 272 96zM160 16C133.49 16 112 37.49 112 64S133.49 112 160 112S208 90.51 208 64S186.51 16 160 16z" />
+    <glyph glyph-name="truck-moving"
+      unicode="&#xF4DF;"
+      horiz-adv-x="640" d=" M621.3 210.7L562.8 269.2C550.8 281.2 534.5 287.9 517.5 287.9H480V384C480 401.7 465.7 416 448 416H32C14.3 416 0 401.7 0 384V48C0 3.8 35.8 -32 80 -32C106.3 -32 129.4 -19.1 144 0.4C158.6 -19.2 181.7 -32 208 -32C252.2 -32 288 3.8 288 48C288 53.5 287.4 58.8 286.4 64H449.6C448.5 58.8 448 53.5 448 48C448 3.8 483.8 -32 528 -32S608 3.8 608 48C608 53.5 607.4 58.8 606.4 64H624C632.8 64 640 71.2 640 80V165.5C640 182.5 633.3 198.7 621.3 210.7zM80 16C62.4 16 48 30.4 48 48S62.4 80 80 80S112 65.6 112 48S97.6 16 80 16zM208 16C190.4 16 176 30.4 176 48S190.4 80 208 80S240 65.6 240 48S225.6 16 208 16zM480 240H517.5C521.8 240 525.8 238.3 528.8 235.3L572.0999999999999 192H480V240zM528 16C510.4 16 496 30.4 496 48S510.4 80 528 80S560 65.6 560 48S545.6 16 528 16z" />
+    <glyph glyph-name="truck-pickup"
+      unicode="&#xF63C;"
+      horiz-adv-x="640" d=" M624 160H608V224C608 241.67 593.67 256 576 256H528L419.2200000000001 391.98A64.025 64.025 0 0 1 369.24 416H256C238.33 416 224 401.67 224 384V256H64C46.33 256 32 241.67 32 224V160H16C7.16 160 0 152.84 0 144V112C0 103.16 7.16 96 16 96H65.61C64.85 90.73 64 85.48 64 80C64 18.14 114.14 -32 176 -32S288 18.14 288 80C288 85.48 287.15 90.73 286.39 96H353.62C352.86 90.73 352.01 85.48 352.01 80C352.01 18.14 402.15 -32 464.01 -32S576.01 18.14 576.01 80C576.01 85.48 575.16 90.73 574.4 96H624C632.84 96 640 103.16 640 112V144C640 152.84 632.84 160 624 160zM288 352H369.24L446.04 256H288V352zM176 32C149.53 32 128 53.53 128 80S149.53 128 176 128S224 106.47 224 80S202.47 32 176 32zM464 32C437.53 32 416 53.53 416 80S437.53 128 464 128S512 106.47 512 80S490.47 32 464 32z" />
+    <glyph glyph-name="truck"
+      unicode="&#xF0D1;"
+      horiz-adv-x="640" d=" M624 96H608V204.1C608 216.8 602.9 229 593.9 238L494 337.9C485 346.9 472.8 352 460.1 352H416V400C416 426.5 394.5 448 368 448H48C21.5 448 0 426.5 0 400V80C0 53.5 21.5 32 48 32H64C64 -21 107 -64 160 -64S256 -21 256 32H384C384 -21 427 -64 480 -64S576 -21 576 32H624C632.8 32 640 39.2 640 48V80C640 88.8 632.8 96 624 96zM160 -16C133.5 -16 112 5.5 112 32S133.5 80 160 80S208 58.5 208 32S186.5 -16 160 -16zM480 -16C453.5 -16 432 5.5 432 32S453.5 80 480 80S528 58.5 528 32S506.5 -16 480 -16zM560 192H416V304H460.1L560 204.1V192z" />
+    <glyph glyph-name="tshirt"
+      unicode="&#xF553;"
+      horiz-adv-x="640" d=" M631.2 351.5L436.5 448C416.4 420.2 371.9 400.8 320 400.8S223.6 420.2 203.5 448L8.8 351.5C0.9 347.5 -2.3 337.9 1.6 330L58.8 215.5C62.8 207.6 72.4 204.4 80.3 208.3L136.9 236C147.5 241.2 159.9 233.5 159.9 221.6V-32C159.9 -49.7 174.2 -64 191.9 -64H447.9C465.6 -64 479.9 -49.7 479.9 -32V221.7C479.9 233.5 492.3 241.3 502.9 236.1L559.5 208.4C567.4 204.4 577 207.6 581 215.6L638.3 330C642.3 337.9 639.0999999999999 347.6 631.1999999999999 351.5z" />
+    <glyph glyph-name="tty"
+      unicode="&#xF1E4;"
+      horiz-adv-x="512" d=" M5.37 344.178C143.902 482.71 368.306 482.504 506.632 344.178C512.71 338.1 513.706 328.682 509.215 321.497L466.001 252.359A18.332 18.332 0 0 0 443.6450000000001 245.054L357.2230000000001 279.623A18.335 18.335 0 0 0 345.7890000000001 298.469L351.741 358C289.596 380.454 221.105 379.986 160.258 358L166.211 298.468A18.331 18.331 0 0 0 154.777 279.622L68.354 245.0540000000001A18.334 18.334 0 0 0 45.998 252.3590000000001L2.787 321.498A18.333 18.333 0 0 0 5.37 344.178zM96 140V180C96 186.627 90.627 192 84 192H44C37.373 192 32 186.627 32 180V140C32 133.373 37.373 128 44 128H84C90.627 128 96 133.373 96 140zM192 140V180C192 186.627 186.627 192 180 192H140C133.373 192 128 186.627 128 180V140C128 133.373 133.373 128 140 128H180C186.627 128 192 133.373 192 140zM288 140V180C288 186.627 282.627 192 276 192H236C229.373 192 224 186.627 224 180V140C224 133.373 229.373 128 236 128H276C282.627 128 288 133.373 288 140zM384 140V180C384 186.627 378.627 192 372 192H332C325.373 192 320 186.627 320 180V140C320 133.373 325.373 128 332 128H372C378.627 128 384 133.373 384 140zM480 140V180C480 186.627 474.627 192 468 192H428C421.373 192 416 186.627 416 180V140C416 133.373 421.373 128 428 128H468C474.627 128 480 133.373 480 140zM144 44V84C144 90.627 138.627 96 132 96H92C85.373 96 80 90.627 80 84V44C80 37.373 85.373 32 92 32H132C138.627 32 144 37.373 144 44zM240 44V84C240 90.627 234.627 96 228 96H188C181.373 96 176 90.627 176 84V44C176 37.373 181.373 32 188 32H228C234.627 32 240 37.373 240 44zM336 44V84C336 90.627 330.627 96 324 96H284C277.373 96 272 90.627 272 84V44C272 37.373 277.373 32 284 32H324C330.627 32 336 37.373 336 44zM432 44V84C432 90.627 426.627 96 420 96H380C373.373 96 368 90.627 368 84V44C368 37.373 373.373 32 380 32H420C426.627 32 432 37.373 432 44zM96 -52V-12C96 -5.373 90.627 0 84 0H44C37.373 0 32 -5.373 32 -12V-52C32 -58.627 37.373 -64 44 -64H84C90.627 -64 96 -58.627 96 -52zM384 -52V-12C384 -5.373 378.627 0 372 0H140C133.373 0 128 -5.373 128 -12V-52C128 -58.627 133.373 -64 140 -64H372C378.627 -64 384 -58.627 384 -52zM480 -52V-12C480 -5.373 474.627 0 468 0H428C421.373 0 416 -5.373 416 -12V-52C416 -58.627 421.373 -64 428 -64H468C474.627 -64 480 -58.627 480 -52z" />
+    <glyph glyph-name="tv"
+      unicode="&#xF26C;"
+      horiz-adv-x="640" d=" M592 448H48C21.5 448 0 426.5 0 400V80C0 53.5 21.5 32 48 32H293.1V0H133.1C115.4 0 101.1 -14.3 101.1 -32S115.4 -64 133.1 -64H517.1C534.8000000000001 -64 549.1 -49.7 549.1 -32S534.8000000000001 0 517.1 0H357.1V32H592C618.5 32 640 53.5 640 80V400C640 426.5 618.5 448 592 448zM576 96H64V384H576V96z" />
+    <glyph glyph-name="umbrella-beach"
+      unicode="&#xF5CA;"
+      horiz-adv-x="640" d=" M115.38 311.1L217.49 273.92C252.68 355.46 303.7 418.21 356.49 447.62C260.61 452.51 167.71 410.66 107.96 335.82C101.27 327.42 105.3 314.77 115.38 311.1zM247.63 262.94L486.11 176.11C521.87 297.49 504.81 407.77 443.48 430.0900000000001C436.0800000000001 432.79 428.35 434.0900000000001 420.3900000000001 434.0900000000001C362.3700000000001 434.08 292.12 364.92 247.6300000000001 262.94zM521.48 387.5C527.7 371.2 532.3100000000001 352.9 534.6800000000001 332.31C540.4200000000001 282.42 533.2600000000001 224.08 515.73 165.3300000000001L618.35 127.97C628.44 124.3 639.66 131.4 639.9200000000001 142.1400000000001C642.2400000000001 237.8300000000001 598.0100000000001 329.5800000000001 521.48 387.5000000000001zM560 0.02H321.06L386 178.5L325.86 200.4L252.96 0.03H16C7.16 0.03 0 -7.1300000000001 0 -15.98V-47.99C0 -56.83 7.16 -64 16 -64H560C568.84 -64 576 -56.83 576 -47.99V-15.98C576 -7.14 568.84 0.02 560 0.02z" />
+    <glyph glyph-name="umbrella"
+      unicode="&#xF0E9;"
+      horiz-adv-x="576" d=" M575.7 167.2C547.1 303.5 437.3 385.4 320 398.1V416C320 433.7 305.7 448 288 448S256 433.7 256 416V398.1C138.3 385.4 29.5 303.5 0.3 167.2C-1.9 157.1 8.8 145.9 19 155.8C71 210.8 126.7 208.2 177.6 118.8C182.9 109.3 192.5 110.2 197.3 118.8C217.5 154.2 242.2 192 288 192C346.5 192 376.2 123.2 378.7 118.8C383.5 110.2 393.1 109.3 398.4 118.8C449.4 208.3 505.5 210.2 557 155.8C567.3 145.8 577.9 157.1 575.7 167.2zM256 146.3V16C256 7.2 248.8 0 240 0C232.2 0 226.8 5.3 224.9 10.7C219 27.4 200.8 36.1 184.1 30.2C167.4 24.3 158.7 6 164.6 -10.6C175.8 -42.5 206.2 -63.9 240.0000000000001 -63.9C284.1 -63.9 320 -28.0000000000001 320 16.1V146.4C310.9 154.3 300.2 160 288 160C275.7 159.9 265.6 155.2 256 146.3z" />
+    <glyph glyph-name="underline"
+      unicode="&#xF0CD;"
+      horiz-adv-x="448" d=" M224.264 59.76C132.595 59.76 67.661 110.925 67.661 211.152V384H39.37C30.533 384 23.37 391.163 23.37 400V432C23.37 440.837 30.533 448 39.37 448H176.76C185.597 448 192.76 440.837 192.76 432V400C192.76 391.163 185.597 384 176.76 384H147.947V211.152C147.947 157.453 176.261 131.708 224.264 131.708C271.23 131.708 300.06 157.142 300.06 211.673V384H271.769C262.932 384 255.769 391.163 255.769 400V432C255.769 440.837 262.932 448 271.769 448H408.637C417.474 448 424.637 440.837 424.637 432V400C424.637 391.163 417.474 384 408.637 384H380.346V211.152C380.346 111.747 315.4650000000001 59.76 224.264 59.76zM16 0H432C440.837 0 448 -7.163 448 -16V-48C448 -56.837 440.837 -64 432 -64H16C7.163 -64 0 -56.837 0 -48V-16C0 -7.163 7.163 0 16 0z" />
+    <glyph glyph-name="undo-alt"
+      unicode="&#xF2EA;"
+      horiz-adv-x="512" d=" M255.545 440C189.276 439.881 129.107 413.767 84.685 371.315L48.971 407.029C33.851 422.149 8 411.4410000000001 8 390.059V256C8 242.745 18.745 232 32 232H166.059C187.441 232 198.149 257.851 183.03 272.971L141.28 314.721C172.144 343.62 212.081 359.628 254.51 359.9940000000001C346.908 360.7920000000001 424.793 286.017 423.9940000000001 190.552C423.236 99.991 349.816 24 256 24C214.873 24 176.003 38.678 145.37 65.556C140.627 69.717 133.464 69.464 129.002 65.003L89.34 25.341C84.468 20.469 84.709 12.526 89.822 7.908C133.798 -31.813 192.074 -56 256 -56C392.966 -56 503.999 55.033 504 191.998C504.001 328.807 392.354 440.245 255.545 440z" />
+    <glyph glyph-name="undo"
+      unicode="&#xF0E2;"
+      horiz-adv-x="512" d=" M212.333 223.667H12C5.373 223.667 0 229.04 0 235.667V436C0 442.627 5.373 448 12 448H60C66.627 448 72 442.627 72 436V357.8880000000001C117.773 408.721 184.26 440.53 258.175 439.993C395.081 438.999 504.623 328.37 504.332 191.461C504.041 54.742 393.12 -56 256.3330000000001 -56C192.244 -56 133.8370000000001 -31.687 89.823 8.215C84.724 12.837 84.489 20.769 89.356 25.635L123.323 59.602C127.7970000000001 64.076 134.9850000000001 64.319 139.7240000000001 60.127C170.76 32.664 211.58 16 256.3330000000001 16C353.601 16 432.3330000000001 94.716 432.3330000000001 192C432.3330000000001 289.267 353.617 368 256.3330000000001 368C197.837 368 146.053 339.524 114.059 295.6670000000001H212.333C218.96 295.6670000000001 224.333 290.294 224.333 283.6670000000001V235.667C224.333 229.04 218.96 223.667 212.333 223.667z" />
+    <glyph glyph-name="universal-access"
+      unicode="&#xF29A;"
+      horiz-adv-x="512" d=" M256 400C370.953 400 464 306.971 464 192C464 77.047 370.971 -16 256 -16C141.047 -16 48 77.029 48 192C48 306.953 141.029 400 256 400M256 440C119.033 440 8 328.967 8 192S119.033 -56 256 -56S504 55.033 504 192S392.967 440 256 440zM256 384C149.961 384 64 298.039 64 192S149.961 0 256 0S448 85.961 448 192S362.039 384 256 384zM256 340C275.882 340 292 323.882 292 304S275.882 268 256 268S220 284.118 220 304S236.118 340 256 340zM373.741 241.977C345.029 235.198 318.23 229.229 291.601 226.17C292.452 125.1470000000001 303.907 103.118 316.638 70.549C320.255 61.289 315.681 50.851 306.421 47.234C297.16 43.617 286.722 48.191 283.105 57.451C274.4000000000001 79.759 266.019 98.087 260.844 136H251.158C245.991 98.149 237.624 79.792 228.896 57.451C225.281 48.196 214.846 43.615 205.581 47.234C196.321 50.8510000000001 191.747 61.29 195.364 70.549C208.077 103.09 219.549 125.09 220.401 226.1700000000001C193.772 229.2280000000001 166.973 235.1970000000001 138.26 241.9770000000001C129.66 244.0080000000001 124.334 252.6250000000001 126.365 261.226S137.012 275.1520000000001 145.614 273.1210000000001C242.3 250.2920000000001 269.897 250.338 366.389 273.1210000000001C374.988 275.1510000000001 383.6070000000001 269.827 385.6380000000001 261.226C387.6670000000001 252.6250000000001 382.341 244.0070000000001 373.7410000000001 241.9770000000001z" />
+    <glyph glyph-name="university"
+      unicode="&#xF19C;"
+      horiz-adv-x="512" d=" M496 320V304A8 8 0 0 0 488 296H464V284C464 277.373 458.627 272 452 272H60C53.373 272 48 277.373 48 284V296H24A8 8 0 0 0 16 304V320A8 8 0 0 0 20.941 327.392L252.941 415.392A7.996000000000001 7.996000000000001 0 0 0 259.0590000000001 415.392L491.059 327.392A8 8 0 0 0 496 320zM472 16H40C26.745 16 16 5.255 16 -8V-24A8 8 0 0 1 24 -32H488A8 8 0 0 1 496 -24V-8C496 5.255 485.255 16 472 16zM96 256V64H60C53.373 64 48 58.627 48 52V32H464V52C464 58.627 458.627 64 452 64H416V256H352V64H288V256H224V64H160V256H96z" />
+    <glyph glyph-name="unlink"
+      unicode="&#xF127;"
+      horiz-adv-x="512" d=" M304.0830000000001 42.093C308.769 37.407 308.769 29.809 304.0830000000001 25.122L259.4090000000001 -19.552C200.1460000000001 -78.814 103.716 -78.818 44.448 -19.552C-14.816 39.713 -14.816 136.144 44.448 195.4080000000001L89.123 240.0830000000001C93.809 244.7690000000001 101.407 244.7690000000001 106.094 240.0830000000001L145.692 200.485C150.3780000000001 195.799 150.3780000000001 188.201 145.692 183.514L101.017 138.84C72.945 110.7670000000001 72.945 65.09 101.017 37.0170000000001C129.089 8.9450000000001 174.7670000000001 8.9440000000001 202.841 37.0170000000001L247.5150000000001 81.691C252.2010000000001 86.377 259.7990000000001 86.377 264.4860000000001 81.691L304.0830000000001 42.093zM247.5150000000001 302.3090000000001C252.2010000000001 297.6230000000001 259.7990000000001 297.6230000000001 264.4860000000001 302.3090000000001L309.1600000000001 346.9830000000001C337.2320000000001 375.0580000000001 382.9100000000001 375.0560000000001 410.9840000000001 346.9830000000001C439.0560000000001 318.9100000000001 439.0560000000001 273.2330000000001 410.9840000000001 245.1600000000001L366.3090000000001 200.486C361.6230000000001 195.8 361.6230000000001 188.202 366.3090000000001 183.5150000000001L405.9070000000001 143.917C410.593 139.2310000000001 418.1910000000001 139.2310000000001 422.8780000000001 143.917L467.5530000000001 188.5920000000001C526.8180000000001 247.8570000000001 526.8180000000001 344.2870000000001 467.5530000000001 403.552C408.2870000000001 462.816 311.8580000000001 462.816 252.5920000000001 403.552L207.918 358.8780000000001C203.232 354.192 203.232 346.5940000000001 207.918 341.9070000000001L247.5150000000001 302.3090000000001zM482.3430000000001 -56.9709999999999L504.9700000000001 -34.3439999999999C514.3430000000001 -24.9709999999999 514.3430000000001 -9.7749999999999 504.9700000000001 -0.4029999999999L63.598 440.971C54.225 450.344 39.029 450.344 29.657 440.971L7.029 418.343C-2.344 408.9700000000001 -2.344 393.774 7.029 384.402L448.402 -56.971C457.775 -66.343 472.971 -66.343 482.343 -56.971z" />
+    <glyph glyph-name="unlock-alt"
+      unicode="&#xF13E;"
+      horiz-adv-x="448" d=" M400 192H152V295.1C152 334.7 183.7 367.6 223.3 368C263.3 368.4 296 335.9 296 296V280C296 266.7 306.7 256 320 256H352C365.3 256 376 266.7 376 280V296C376 380 307.5 448.3 223.5 448C139.5 447.7 72 378.5 72 294.5V192H48C21.5 192 0 170.5 0 144V-16C0 -42.5 21.5 -64 48 -64H400C426.5 -64 448 -42.5 448 -16V144C448 170.5 426.5 192 400 192zM264 40C264 17.9 246.1 0 224 0S184 17.9 184 40V88C184 110.1 201.9 128 224 128S264 110.1 264 88V40z" />
+    <glyph glyph-name="unlock"
+      unicode="&#xF09C;"
+      horiz-adv-x="448" d=" M400 192H152V295.1C152 334.7 183.7 367.6 223.3 368C263.3 368.4 296 335.9 296 296V280C296 266.7 306.7 256 320 256H352C365.3 256 376 266.7 376 280V296C376 380 307.5 448.3 223.5 448C139.5 447.7 72 378.5 72 294.5V192H48C21.5 192 0 170.5 0 144V-16C0 -42.5 21.5 -64 48 -64H400C426.5 -64 448 -42.5 448 -16V144C448 170.5 426.5 192 400 192z" />
+    <glyph glyph-name="upload"
+      unicode="&#xF093;"
+      horiz-adv-x="512" d=" M296 64H216C202.7 64 192 74.7 192 88V256H104.3C86.5 256 77.6 277.5 90.2 290.1L242.3 442.3C249.8 449.8 262.1 449.8 269.6 442.3L421.8 290.1C434.4000000000001 277.5 425.5 256 407.7 256H320V88C320 74.7 309.3 64 296 64zM512 72V-40C512 -53.3 501.3 -64 488 -64H24C10.7 -64 0 -53.3 0 -40V72C0 85.3 10.7 96 24 96H160V88C160 57.1 185.1 32 216 32H296C326.9 32 352 57.1 352 88V96H488C501.3 96 512 85.3 512 72zM388 -16C388 -5 379 4 368 4S348 -5 348 -16S357 -36 368 -36S388 -27 388 -16zM452 -16C452 -5 443 4 432 4S412 -5 412 -16S421 -36 432 -36S452 -27 452 -16z" />
+    <glyph glyph-name="user-alt-slash"
+      unicode="&#xF4FA;"
+      horiz-adv-x="640" d=" M633.8 -10.1L389.6 178.7C433.8 203.3 464 249.9 464 304C464 383.5 399.5 448 320 448C252.9 448 197 401.9 181 339.8L45.5 444.6C38.5 450 28.5 448.8 23 441.8L3.4 416.6C-2 409.6 -0.8 399.6 6.2 394.2L594.6 -60.5C601.6 -65.9 611.6 -64.7 617.1 -57.7L636.7 -32.4C642.1 -25.6 640.8000000000001 -15.5 633.8000000000001 -10.1zM198.4 128C124.2 128 64 67.8 64 -6.4V-16C64 -42.5 85.5 -64 112 -64H494.2L245.8 128H198.4z" />
+    <glyph glyph-name="user-alt"
+      unicode="&#xF406;"
+      horiz-adv-x="512" d=" M256 160C335.5 160 400 224.5 400 304S335.5 448 256 448S112 383.5 112 304S176.5 160 256 160zM384 128H328.9C306.7 117.8 282 112 256 112S205.4 117.8 183.1 128H128C57.3 128 0 70.7 0 0V-16C0 -42.5 21.5 -64 48 -64H464C490.5 -64 512 -42.5 512 -16V0C512 70.7 454.7 128 384 128z" />
+    <glyph glyph-name="user-astronaut"
+      unicode="&#xF4FB;"
+      horiz-adv-x="448" d=" M64 224H77.5C102.2 167.5 158.4 128 224 128S345.8 167.5 370.5 224H384C392.8 224 400 231.2 400 240V336C400 344.8 392.8 352 384 352H370.5C345.8 408.5 289.6 448 224 448S102.2 408.5 77.5 352H64C55.2 352 48 344.8 48 336V240C48 231.2 55.2 224 64 224zM104 312C104 334.1 125.5 352 152 352H296C322.5 352 344 334.1 344 312V288C344 235 301 192 248 192H200C147 192 104 235 104 288V312zM176 240L188 276L224 288L188 300L176 336L164 300L128 288L164 276L176 240zM327.6 126.6C297.7 107.3 262.2 96 224 96S150.3 107.3 120.4 126.6C52.9 119.5 0 63 0 -6.4V-16C0 -42.5 21.5 -64 48 -64H128V0C128 17.7 142.3 32 160 32H288C305.7 32 320 17.7 320 0V-64H400C426.5 -64 448 -42.5 448 -16V-6.4C448 63 395.1 119.5 327.6 126.6zM272 0C263.2 0 256 -7.2 256 -16S263.2 -32 272 -32S288 -24.8 288 -16S280.8 0 272 0zM176 0C167.2 0 160 -7.2 160 -16V-64H192V-16C192 -7.2 184.8 0 176 0z" />
+    <glyph glyph-name="user-check"
+      unicode="&#xF4FC;"
+      horiz-adv-x="640" d=" M224 192C294.7 192 352 249.3 352 320S294.7 448 224 448S96 390.7 96 320S153.3 192 224 192zM313.6 160H296.9000000000001C274.7000000000001 149.8 250.0000000000001 144 224 144S173.4 149.8 151.1 160H134.4C60.2 160 0 99.8 0 25.6V-16C0 -42.5 21.5 -64 48 -64H400C426.5 -64 448 -42.5 448 -16V25.6C448 99.8 387.8 160 313.6 160zM636.6 288.4L608.8000000000001 316.5C604.2 321.2 596.7 321.2 592.0000000000001 316.6L487.2000000000001 212.6L441.7000000000001 258.4C437.1000000000001 263.1 429.6000000000001 263.1 424.9000000000001 258.5L396.8000000000001 230.6C392.1000000000001 226 392.1000000000001 218.5 396.7000000000001 213.8L478.4 131.5C483.0000000000001 126.8 490.5000000000001 126.8 495.2 131.3999999999999L636.5 271.5999999999999C641.1 276.2999999999999 641.2 283.7999999999999 636.6 288.3999999999999z" />
+    <glyph glyph-name="user-circle"
+      unicode="&#xF2BD;"
+      horiz-adv-x="496" d=" M248 440C111 440 0 329 0 192S111 -56 248 -56S496 55 496 192S385 440 248 440zM248 344C296.6 344 336 304.6 336 256S296.6 168 248 168S160 207.4 160 256S199.4 344 248 344zM248 0C189.3 0 136.7 26.6 101.5 68.2C120.3 103.6 157.1 128 200 128C202.4 128 204.8 127.6 207.1 126.9C220.1 122.7 233.7 120 248 120C262.3 120 276 122.7 288.9 126.9C291.2 127.6 293.6 128 296 128C338.9 128 375.7 103.6 394.5 68.2C359.3 26.6 306.7 0 248 0z" />
+    <glyph glyph-name="user-clock"
+      unicode="&#xF4FD;"
+      horiz-adv-x="640" d=" M496 224C416.4 224 352 159.6 352 80S416.4 -64 496 -64S640 0.4 640 80S575.6 224 496 224zM560 73.7C560 68.4 555.6 64 550.3 64H489.6999999999999C484.3999999999999 64 479.9999999999999 68.4 479.9999999999999 73.7V150.3C479.9999999999999 155.6 484.3999999999999 160 489.6999999999999 160H502.3C507.6 160 511.9999999999999 155.6 511.9999999999999 150.3V96H550.3C555.5999999999999 96 560 91.6 560 86.3V73.7zM320 80C320 107.8 326.7 134.1 338.2 157.5C330.2 159 322 160 313.6 160H296.9C274.7 149.8 250 144 224 144S173.4 149.8 151.1 160H134.4C60.2 160 0 99.8 0 25.6V-16C0 -42.5 21.5 -64 48 -64H395.1C349.8 -32.1 320 20.5 320 80zM224 192C294.7 192 352 249.3 352 320S294.7 448 224 448S96 390.7 96 320S153.3 192 224 192z" />
+    <glyph glyph-name="user-cog"
+      unicode="&#xF4FE;"
+      horiz-adv-x="640" d=" M610.5 74.7C613.1 88.8 613.1 103.2 610.5 117.3L636.3 132.2C639.3 133.9 640.5999999999999 137.4 639.5999999999999 140.7C632.8999999999999 162.3 621.3999999999999 181.9 606.3999999999999 198.1C604.0999999999999 200.6 600.3999999999999 201.2 597.3999999999999 199.5L571.5999999999999 184.6C560.6999999999999 193.9 548.1999999999999 201.1 534.6999999999999 205.9V235.7000000000001C534.6999999999999 239.1000000000001 532.3 242.1000000000001 528.9999999999999 242.8000000000001C506.6999999999999 247.8000000000001 483.9999999999999 247.6000000000001 462.7999999999999 242.8000000000001C459.4999999999999 242.1000000000001 457.0999999999999 239.1000000000001 457.0999999999999 235.7000000000001V205.9C443.5999999999999 201.1 431.0999999999999 193.9 420.2 184.6L394.3999999999999 199.5C391.5 201.2 387.7 200.6 385.3999999999999 198.1C370.3999999999999 181.9 358.8999999999999 162.3 352.2 140.7000000000001C351.2 137.4 352.5999999999999 133.9 355.5 132.2000000000001L381.3 117.3000000000001C378.7 103.2000000000001 378.7 88.8000000000001 381.3 74.7000000000001L355.5 59.8000000000001C352.5 58.1000000000001 351.2 54.6000000000001 352.2 51.3000000000001C358.8999999999999 29.7 370.3999999999999 10.2 385.3999999999999 -6.0999999999999C387.7 -8.5999999999999 391.3999999999999 -9.1999999999999 394.3999999999999 -7.4999999999999L420.2 7.4000000000001C431.0999999999999 -1.8999999999999 443.5999999999999 -9.0999999999999 457.0999999999999 -13.8999999999999V-43.6999999999999C457.0999999999999 -47.0999999999999 459.4999999999999 -50.0999999999999 462.7999999999999 -50.8C485.0999999999999 -55.8 507.7999999999999 -55.6 528.9999999999999 -50.8C532.2999999999998 -50.1 534.6999999999999 -47.1 534.6999999999999 -43.6999999999999V-13.8999999999999C548.1999999999999 -9.0999999999999 560.6999999999999 -1.8999999999999 571.5999999999999 7.4000000000001L597.3999999999999 -7.4999999999999C600.2999999999998 -9.1999999999999 604.0999999999999 -8.5999999999999 606.3999999999999 -6.0999999999999C621.3999999999999 10.1000000000001 632.8999999999999 29.7000000000001 639.5999999999999 51.3000000000001C640.5999999999999 54.6000000000001 639.1999999999999 58.1000000000001 636.3 59.8000000000001L610.5 74.7000000000001zM496 47.5C469.2 47.5 447.5 69.3 447.5 96S469.3 144.5 496 144.5S544.5 122.7 544.5 96S522.8 47.5 496 47.5zM224 192C294.7 192 352 249.3 352 320S294.7 448 224 448S96 390.7 96 320S153.3 192 224 192zM425.2 -34.5C422.9 -33.3 420.6 -31.9 418.4 -30.6L410.5 -35.2C404.5 -38.6 397.7 -40.5000000000001 390.9 -40.5000000000001C380 -40.5000000000001 369.5 -35.9 362 -27.9C343.7 -8.1 329.7 15.9999999999999 321.8 41.6999999999999C316.3 59.3999999999999 323.7 78.0999999999999 339.7 87.3999999999999L347.6 91.9999999999999C347.5 94.6 347.5 97.1999999999999 347.6 99.8L339.7 104.4C323.7 113.6 316.3 132.4 321.8 150.1C322.7 153 324 155.9 325 158.8C321.2 159.1 317.5 160 313.6 160H296.9000000000001C274.7000000000001 149.8 250.0000000000001 144 224 144S173.4 149.8 151.1 160H134.4C60.2 160 0 99.8 0 25.6V-16C0 -42.5 21.5 -64 48 -64H400C410.1 -64 419.5 -60.8 427.2 -55.5C426 -51.7 425.2 -47.8 425.2 -43.7V-34.5z" />
+    <glyph glyph-name="user-edit"
+      unicode="&#xF4FF;"
+      horiz-adv-x="640" d=" M224 192C294.7 192 352 249.3 352 320S294.7 448 224 448S96 390.7 96 320S153.3 192 224 192zM313.6 160H296.9000000000001C274.7000000000001 149.8 250.0000000000001 144 224 144S173.4 149.8 151.1 160H134.4C60.2 160 0 99.8 0 25.6V-16C0 -42.5 21.5 -64 48 -64H322.9C320.5 -57.2 319.5 -50 320.3 -42.7L327.1 18.2L328.3 29.3L336.2 37.2L413.5 114.5C389 142.2 353.5 160 313.5999999999999 160zM358.9000000000001 14.7L352.1 -46.3C351 -56.5 359.6 -65.1 369.7000000000001 -63.9L430.6 -57.1L568.5 80.8L496.8 152.5L358.9 14.6999999999999zM633 179.1L595.1 217C585.8000000000001 226.3 570.6 226.3 561.3000000000001 217L523.5000000000001 179.2L519.4000000000001 175.1L591.2 103.4L633 145.2C642.3 154.6 642.3 169.7 633 179.1z" />
+    <glyph glyph-name="user-friends"
+      unicode="&#xF500;"
+      horiz-adv-x="640" d=" M192 192C253.9 192 304 242.1 304 304S253.9 416 192 416S80 365.9 80 304S130.1 192 192 192zM268.8 160H260.5C239.7 150 216.6 144 192 144S144.4 150 123.5 160H115.2C51.6 160 0 108.4 0 44.8V16C0 -10.5 21.5 -32 48 -32H336C362.5 -32 384 -10.5 384 16V44.8C384 108.4 332.4 160 268.8 160zM480 192C533 192 576 235 576 288S533 384 480 384S384 341 384 288S427 192 480 192zM528 160H524.2C510.3000000000001 155.2 495.6 152 480.0000000000001 152S449.7000000000001 155.2 435.8000000000001 160H432C411.6 160 392.8 154.1 376.3 144.6C400.7 118.3 416 83.4 416 44.8V6.4C416 4.2 415.5 2.1 415.4 1e-13H592C618.5 1e-13 640 21.5000000000001 640 48.0000000000001C640 109.9 589.9 160.0000000000001 528 160.0000000000001z" />
+    <glyph glyph-name="user-graduate"
+      unicode="&#xF501;"
+      horiz-adv-x="448" d=" M319.4 127.4L224 32L128.6 127.4C57.1 124.3 0 65.8 0 -6.4V-16C0 -42.5 21.5 -64 48 -64H400C426.5 -64 448 -42.5 448 -16V-6.4C448 65.8 390.9 124.3 319.4 127.4zM13.6 368.2L20 366.7V308.3C13 304.1 8 296.8 8 288C8 279.6 12.6 272.6 19.1 268.3L3.5 206C1.8 199.1 5.6 192 11.1 192H52.9C58.4 192 62.2 199.1 60.5 206L44.9 268.3C51.4 272.6 56 279.6 56 288C56 296.8 51 304.1 44 308.3V360.9L110 345C101.4 327.8 96 308.6 96 288C96 217.3 153.3 160 224 160S352 217.3 352 288C352 308.6 346.7 327.8 338 345L434.3 368.2C452.5 372.6 452.5 395.3 434.3 399.7L243.9 445.7C230.9 448.8 217.2 448.8 204.2 445.7L13.6 399.8C-4.5 395.4 -4.5 372.6 13.6 368.2z" />
+    <glyph glyph-name="user-injured"
+      unicode="&#xF728;"
+      horiz-adv-x="448" d=" M277.37 436.02C261.08 443.53 243.11 448 224 448C170.31 448 124.5 414.87 105.49 368H186.68L277.37 436.02zM342.51 368C334.61 387.4700000000001 321.84 404.2 306.02 417.52L239.99 368H342.51zM224 192C294.69 192 352 249.31 352 320C352 325.48 351.05 330.7 350.39 336H97.61C96.94 330.7 96 325.48 96 320C96 249.31 153.31 192 224 192zM80 148.3V-64H208.26L109.81 157.52A132.835 132.835 0 0 1 80 148.3zM0 -16C0 -42.51 21.49 -64 48 -64V127.76C18.88 103.11 0 66.74 0 25.6V-16zM256 32H200.62L243.29 -64H256C282.4700000000001 -64 304 -42.47 304 -16S282.4700000000001 32 256 32zM313.6 160H296.8900000000001C274.6500000000001 149.82 250.0100000000001 144 224.0000000000001 144S173.3500000000001 149.82 151.1100000000001 160H143.7400000000001L186.4100000000001 64H256C300.11 64 336 28.11 336 -16C336 -34.08 329.74 -50.59 319.59 -64H400C426.51 -64 448 -42.51 448 -16V25.6C448 99.83 387.83 160 313.6 160z" />
+    <glyph glyph-name="user-lock"
+      unicode="&#xF502;"
+      horiz-adv-x="640" d=" M320 128C320 139.1 323.1 149.4 328.1 158.5C323.3 159 318.6 160 313.6 160H296.9000000000001C274.7000000000001 149.8 250.0000000000001 144 224 144S173.4 149.8 151.1 160H134.4C60.2 160 0 99.8 0 25.6V-16C0 -42.5 21.5 -64 48 -64H328.9C323.4 -54.5 320 -43.7 320 -32V128zM224 192C294.7 192 352 249.3 352 320S294.7 448 224 448S96 390.7 96 320S153.3 192 224 192zM608 160H576V208C576 252.2 540.2 288 496 288S416 252.2 416 208V160H384C366.3 160 352 145.7 352 128V-32C352 -49.7 366.3 -64 384 -64H608C625.7 -64 640 -49.7 640 -32V128C640 145.7 625.7 160 608 160zM528 160H464V208C464 225.6 478.4 240 496 240S528 225.6 528 208V160z" />
+    <glyph glyph-name="user-md"
+      unicode="&#xF0F0;"
+      horiz-adv-x="448" d=" M224 192C294.7 192 352 249.3 352 320S294.7 448 224 448S96 390.7 96 320S153.3 192 224 192zM104 24C104 10.7 114.7 0 128 0S152 10.7 152 24S141.3 48 128 48S104 37.3 104 24zM320 159.4V110.4C356.5 103 384 70.6 384 32V-9.7C384 -17.3 378.6 -23.9 371.1 -25.4L338.9000000000001 -31.8C334.6 -32.6999999999999 330.4000000000001 -29.9 329.5000000000001 -25.4999999999999L326.4000000000001 -9.8C325.5000000000001 -5.4999999999999 328.3 -1.1999999999999 332.7000000000001 -0.4L352.0000000000001 3.5V32C352.0000000000001 94.8 256.0000000000001 97.1 256.0000000000001 30.1V3.4L275.3000000000001 -0.4999999999999C279.6000000000001 -1.3999999999999 282.4000000000001 -5.6 281.6000000000001 -9.8999999999999L278.5000000000001 -25.5999999999999C277.6000000000001 -29.8999999999999 273.4000000000001 -32.6999999999999 269.1000000000001 -31.8999999999999L237.9000000000001 -27.6999999999999C230.0000000000001 -26.5999999999999 224.1000000000001 -19.8999999999999 224.1000000000001 -11.8V32C224.1000000000001 70.6 251.6000000000001 102.9 288.1000000000001 110.4V155.6C285.9000000000001 154.9 283.7000000000001 154.5 281.5000000000001 153.7C263.5000000000001 147.4 244.2000000000001 143.9 224.1000000000001 143.9S184.7000000000001 147.4 166.7000000000001 153.7C159.3000000000001 156.3 151.8000000000001 157.9 144.1000000000001 158.9V77.3C167.2000000000001 70.4 184.1000000000001 49.1999999999999 184.1000000000001 23.9C184.1000000000001 -7 159.0000000000001 -32.1 128.1000000000001 -32.1S72.1000000000001 -7 72.1000000000001 23.9C72.1000000000001 49.2 89.0000000000001 70.4 112.1000000000001 77.3V157.6999999999999C48.5 147 0 92.2 0 25.6V-19.2C0 -43.9 20.1 -64 44.8 -64H403.2C427.9 -64 448 -43.9 448 -19.2V25.6C448 97.6 391.2 155.9 320 159.4z" />
+    <glyph glyph-name="user-minus"
+      unicode="&#xF503;"
+      horiz-adv-x="640" d=" M624 240H432C423.2 240 416 232.8 416 224V192C416 183.2 423.2 176 432 176H624C632.8 176 640 183.2 640 192V224C640 232.8 632.8 240 624 240zM224 192C294.7 192 352 249.3 352 320S294.7 448 224 448S96 390.7 96 320S153.3 192 224 192zM313.6 160H296.9000000000001C274.7000000000001 149.8 250.0000000000001 144 224 144S173.4 149.8 151.1 160H134.4C60.2 160 0 99.8 0 25.6V-16C0 -42.5 21.5 -64 48 -64H400C426.5 -64 448 -42.5 448 -16V25.6C448 99.8 387.8 160 313.6 160z" />
+    <glyph glyph-name="user-ninja"
+      unicode="&#xF504;"
+      horiz-adv-x="448" d=" M325.4 158.8L224 57.4L122.6 158.8C54 152.7 0 95.8 0 25.6V-16C0 -42.5 21.5 -64 48 -64H400C426.5 -64 448 -42.5 448 -16V25.6C448 95.8 394 152.7000000000001 325.4 158.8zM32 256C59.3 256 83.8 267.5 101.2 285.7C116.3 231.8 165.2 192 224 192C294.7 192 352 249.3 352 320S294.7 448 224 448C173.6 448 130.4 418.6 109.5 376.2C92.1 400.2 64 416 32 416C32 382.6 49.1 353.2 75.1 336C49.1 318.8 32 289.4 32 256zM176 352H272C289.7 352 304 337.7 304 320H144C144 337.7 158.3 352 176 352z" />
+    <glyph glyph-name="user-plus"
+      unicode="&#xF234;"
+      horiz-adv-x="640" d=" M624 240H560V304C560 312.8 552.8 320 544 320H512C503.2 320 496 312.8 496 304V240H432C423.2 240 416 232.8 416 224V192C416 183.2 423.2 176 432 176H496V112C496 103.2 503.2 96 512 96H544C552.8 96 560 103.2 560 112V176H624C632.8 176 640 183.2 640 192V224C640 232.8 632.8 240 624 240zM224 192C294.7 192 352 249.3 352 320S294.7 448 224 448S96 390.7 96 320S153.3 192 224 192zM313.6 160H296.9000000000001C274.7000000000001 149.8 250.0000000000001 144 224 144S173.4 149.8 151.1 160H134.4C60.2 160 0 99.8 0 25.6V-16C0 -42.5 21.5 -64 48 -64H400C426.5 -64 448 -42.5 448 -16V25.6C448 99.8 387.8 160 313.6 160z" />
+    <glyph glyph-name="user-secret"
+      unicode="&#xF21B;"
+      horiz-adv-x="448" d=" M383.9 139.7L407.8 202.3C411.8 212.8 404.1 224 392.8 224H334.3C345.3 242.9 352.1 264.6 352.1 288V288.3C391.3 296.1 416.1 307.4 416.1 320C416.1 333.3 388.8 345.1 346 353C336.8 385.8 319 418.8 305.4 435.8C295.9 447.7 279.5 451.4 265.9 444.6L238.3 430.8C229.3 426.3 218.7 426.3 209.7 430.8L182.1 444.6C168.5 451.4 152.1 447.7 142.6 435.8C129.1 418.8 111.2 385.8 102 353C59.3 345.1 32 333.3 32 320C32 307.4 56.8 296.1 96 288.3V288C96 264.6 102.8 242.9 113.8 224H56.3C44.8 224 37.1 212.3 41.6 201.7L67.4 141.5C27.3 118.2 0 75.3 0 25.6V-19.2C0 -43.9 20.1 -64 44.8 -64H403.2C427.9 -64 448 -43.9 448 -19.2V25.6C448 74 422.2 116 383.9 139.7000000000001zM176 -32L134.4 160L184 128L208 88L176 -32zM272 -32L240 88L264 128L313.6 160L272 -32zM313.7 266.5C309.8 254.6 306.7 241.9 297.2 233.1C287.1 223.8 249.2 210.7 233.2 258.1C230.4 266.5 217.8 266.5 214.9 258.1C197.9 207.9 158.9 225.7 150.9 233.1C141.4 241.9 138.2 254.6 134.4 266.5C133.6 269 128.1 272.2 128.1 272.3V283.1C156.4 279.5 189.1 277.3 224.1 277.3S291.8 279.4 320.1 283.1V272.3C320 272.2000000000001 314.5 269.1 313.7 266.5z" />
+    <glyph glyph-name="user-shield"
+      unicode="&#xF505;"
+      horiz-adv-x="640" d=" M622.3 176.9L507.1 221.9C502.9999999999999 223.5 494.4999999999999 225.6 484.9 221.9L369.7 176.9C359 172.7 352 162.9 352 152C352 40.4 420.7 -36.8 484.9 -61.9C494.5 -65.6 502.9 -63.5 507.1 -61.9C558.4 -41.9 640 27.5 640 152C640 162.9 633 172.7 622.3 176.9zM496 -14.4V174.7L591.5 137.4C585.9 50.3 530.6 2 496 -14.4zM224 192C294.7 192 352 249.3 352 320S294.7 448 224 448S96 390.7 96 320S153.3 192 224 192zM320 152C320 154.5 320.8 156.8 321.1 159.2C318.6 159.3 316.2000000000001 160 313.6 160H296.9000000000001C274.7000000000001 149.8 250.0000000000001 144 224 144S173.4 149.8 151.1 160H134.4C60.2 160 0 99.8 0 25.6V-16C0 -42.5 21.5 -64 48 -64H400C406.8 -64 413.3 -62.5 419.2 -60C365.2 -17.1 320 56.7 320 152z" />
+    <glyph glyph-name="user-slash"
+      unicode="&#xF506;"
+      horiz-adv-x="640" d=" M633.8 -10.1L362.3 199.7C412.1 217.3 448 264.2 448 320C448 390.7 390.7 448 320 448C252.9 448 198.5 396.2 193.1 330.6L45.5 444.6C38.5 450 28.5 448.8 23 441.8L3.4 416.6C-2 409.6 -0.8 399.6 6.2 394.2L594.6 -60.5C601.6 -65.9 611.6 -64.7 617.1 -57.7L636.7 -32.4C642.1 -25.6 640.8000000000001 -15.5 633.8000000000001 -10.1zM96 25.6V-16C96 -42.5 117.5 -64 144 -64H494.2L207.4 157.7C144.2 146.7 96 92 96 25.6z" />
+    <glyph glyph-name="user-tag"
+      unicode="&#xF507;"
+      horiz-adv-x="640" d=" M630.6 83.1L540.3000000000001 173.3C528.3000000000001 185.3 512.0000000000001 192 495.0000000000001 192H415.7000000000001C398.0000000000001 192 383.7000000000001 177.7 383.7000000000001 160V80.8C383.7000000000001 63.8 390.4000000000001 47.6 402.4000000000001 35.6L492.7 -54.6C505.2 -67.0999999999999 525.5 -67.0999999999999 538 -54.6L630.5 37.9C643.1 50.4 643.1 70.6 630.6 83.1zM447.8 104.1C434.5 104.1 423.8 114.8 423.8 128.1S434.5 152.1 447.8 152.1S471.8 141.4 471.8 128.1C471.8 114.9 461.1 104.1 447.8 104.1zM224 192.1C294.7 192.1 352 249.4000000000001 352 320.1C352 390.7 294.7 448 224 448S96 390.7 96 320C96 249.4 153.3 192.1 224 192.1zM351.8 80.9V154C339.6 157.6 326.9000000000001 160.2 313.6 160.2H296.9000000000001C274.7000000000001 150 250.0000000000001 144.2 224 144.2S173.4 150 151.1 160.2H134.4C60.2 160.1 0 99.9 0 25.7V-15.9C0 -42.4 21.5 -63.9 48 -63.9H400C415.5 -63.9 429.1 -56.4 437.9 -45.0000000000001L379.9 12.9999999999999C361.8 31.1 351.8 55.1999999999999 351.8 80.9z" />
+    <glyph glyph-name="user-tie"
+      unicode="&#xF508;"
+      horiz-adv-x="448" d=" M224 192C294.7 192 352 249.3 352 320S294.7 448 224 448S96 390.7 96 320S153.3 192 224 192zM319.8 159.4L272 -32L240 104L272 160H176L208 104L176 -32L128.2 159.4C56.9 156 0 97.7 0 25.6V-16C0 -42.5 21.5 -64 48 -64H400C426.5 -64 448 -42.5 448 -16V25.6C448 97.7000000000001 391.1 156 319.8 159.4z" />
+    <glyph glyph-name="user-times"
+      unicode="&#xF235;"
+      horiz-adv-x="640" d=" M589.6 208L635.2 253.6C641.5 259.9 641.5 270.1 635.2 276.4L612.4000000000001 299.2000000000001C606.1000000000001 305.5 595.9000000000001 305.5 589.6000000000001 299.2000000000001L544 253.6L498.4 299.2C492.1 305.5 481.9 305.5 475.6 299.2L452.8 276.4C446.5 270.1 446.5 259.9 452.8 253.6L498.4 208L452.8 162.4C446.5 156.1 446.5 145.9 452.8 139.6L475.6 116.8C481.9 110.4999999999999 492.1 110.4999999999999 498.4 116.8L544 162.4L589.6 116.8C595.9 110.4999999999999 606.1 110.4999999999999 612.4 116.8L635.1999999999999 139.6C641.4999999999999 145.9 641.4999999999999 156.1 635.1999999999999 162.4L589.6 208zM224 192C294.7 192 352 249.3 352 320S294.7 448 224 448S96 390.7 96 320S153.3 192 224 192zM313.6 160H296.9000000000001C274.7000000000001 149.8 250.0000000000001 144 224 144S173.4 149.8 151.1 160H134.4C60.2 160 0 99.8 0 25.6V-16C0 -42.5 21.5 -64 48 -64H400C426.5 -64 448 -42.5 448 -16V25.6C448 99.8 387.8 160 313.6 160z" />
+    <glyph glyph-name="user"
+      unicode="&#xF007;"
+      horiz-adv-x="448" d=" M224 192C294.7 192 352 249.3 352 320S294.7 448 224 448S96 390.7 96 320S153.3 192 224 192zM313.6 160H296.9000000000001C274.7000000000001 149.8 250.0000000000001 144 224 144S173.4 149.8 151.1 160H134.4C60.2 160 0 99.8 0 25.6V-16C0 -42.5 21.5 -64 48 -64H400C426.5 -64 448 -42.5 448 -16V25.6C448 99.8 387.8 160 313.6 160z" />
+    <glyph glyph-name="users-cog"
+      unicode="&#xF509;"
+      horiz-adv-x="640" d=" M610.5 106.7C613.1 120.8 613.1 135.2 610.5 149.3L636.3 164.2C639.3 165.9 640.5999999999999 169.4 639.5999999999999 172.7C632.8999999999999 194.3 621.3999999999999 213.9 606.3999999999999 230.1C604.0999999999999 232.6 600.3999999999999 233.2 597.3999999999999 231.5L571.5999999999999 216.6C560.6999999999999 225.9 548.1999999999999 233.1 534.6999999999999 237.9V267.7000000000001C534.6999999999999 271.1 532.3 274.1 528.9999999999999 274.8C506.6999999999999 279.8 483.9999999999999 279.6 462.7999999999999 274.8C459.4999999999999 274.1 457.0999999999999 271.1 457.0999999999999 267.7000000000001V237.9C443.5999999999999 233.1 431.0999999999999 225.9 420.2 216.6L394.3999999999999 231.5C391.5 233.2 387.7 232.6 385.3999999999999 230.1C370.3999999999999 213.9 358.8999999999999 194.3 352.2 172.7C351.2 169.4 352.5999999999999 165.9 355.5 164.2L381.3 149.3C378.7 135.2 378.7 120.8 381.3 106.7L355.5 91.8C352.5 90.1 351.2 86.6 352.2 83.3C358.8999999999999 61.7 370.3999999999999 42.2 385.3999999999999 25.9C387.7 23.4 391.3999999999999 22.8 394.3999999999999 24.5000000000001L420.2 39.4C431.0999999999999 30.1 443.5999999999999 22.9 457.0999999999999 18.1V-11.7C457.0999999999999 -15.1 459.4999999999999 -18.1 462.7999999999999 -18.8C485.0999999999999 -23.8 507.7999999999999 -23.6 528.9999999999999 -18.8C532.2999999999998 -18.1 534.6999999999999 -15.1 534.6999999999999 -11.7V18.1C548.1999999999999 22.9 560.6999999999999 30.1 571.5999999999999 39.4L597.3999999999999 24.5000000000001C600.2999999999998 22.8000000000001 604.0999999999999 23.4 606.3999999999999 25.9C621.3999999999999 42.1 632.8999999999999 61.7000000000001 639.5999999999999 83.3C640.5999999999999 86.6 639.1999999999999 90.1 636.3 91.8L610.5 106.7zM496 79.5C469.2 79.5 447.5 101.3 447.5 128S469.3 176.5 496 176.5S544.5 154.7 544.5 128S522.8 79.5 496 79.5zM96 224C131.3 224 160 252.7 160 288S131.3 352 96 352S32 323.3 32 288S60.7 224 96 224zM320 192C321.9 192 323.7 192.5 325.6 192.6C333.9000000000001 214.3 346.1 234.7 361.9000000000001 251.8C369.3 259.8 379.8 264.4 390.8 264.4C397.7 264.4 404.5 262.6 410.4000000000001 259.1L418.3 254.5C419.1 255 419.9000000000001 255.4 420.7 255.9C427.7 270.5 431.9 286.7000000000001 431.9 303.9C431.9 365.8 381.8 415.9 319.9 415.9S208 365.9 208 304C208 242.1 258.1 192 320 192zM425.2 -2.5C422.9 -1.3 420.6 0.1 418.4 1.4C410.2 -3.4 403.1 -8.4 390.9 -8.4C380 -8.4 369.5 -3.8 362 4.2C343.7 24 329.7 48.1 321.8 73.8C311.1 108.3 346.7 123.5 347.6 124.1C347.5 126.7 347.5 129.3 347.6 131.9L339.7000000000001 136.5C335.9000000000001 138.7 332.7000000000001 141.5 329.9000000000001 144.6C326.6 144.4 323.4000000000001 144 320.1 144C295.5 144 272.5 150 251.6 160H243.3C179.6 160 128 108.4 128 44.8V16C128 -10.5 149.5 -32 176 -32H431.4C427.7 -26 425.2 -19.2 425.2 -11.7V-2.5zM173.1 173.4C161.5 184.9 145.6 192 128 192H64C28.7 192 0 163.3 0 128V96C0 78.3 14.3 64 32 64H97.9C104.2 111.4 132.8 151.3 173.1 173.4z" />
+    <glyph glyph-name="users"
+      unicode="&#xF0C0;"
+      horiz-adv-x="640" d=" M96 224C131.3 224 160 252.7 160 288S131.3 352 96 352S32 323.3 32 288S60.7 224 96 224zM544 224C579.3 224 608 252.7 608 288S579.3 352 544 352S480 323.3 480 288S508.7 224 544 224zM576 192H512C494.4 192 478.5 184.9 466.9 173.4C507.2 151.3 535.8 111.4 542 64H608C625.7 64 640 78.3 640 96V128C640 163.3 611.3 192 576 192zM320 192C381.9 192 432 242.1 432 304S381.9 416 320 416S208 365.9 208 304S258.1 192 320 192zM396.8 160H388.5C367.7 150 344.6 144 320 144S272.4 150 251.5 160H243.2C179.6 160 128 108.4 128 44.8V16C128 -10.5 149.5 -32 176 -32H464C490.5 -32 512 -10.5 512 16V44.8C512 108.4 460.4 160 396.8 160zM173.1 173.4C161.5 184.9 145.6 192 128 192H64C28.7 192 0 163.3 0 128V96C0 78.3 14.3 64 32 64H97.9C104.2 111.4 132.8 151.3 173.1 173.4z" />
+    <glyph glyph-name="utensil-spoon"
+      unicode="&#xF2E5;"
+      horiz-adv-x="512" d=" M480.1 416.1C425.1 471.2 315.2000000000001 450.6 252.3 387.6C203 338.3 197.2 277.6 223.5 227.2L9 34.8C-2.6 24.3 -3.1 6.3 8 -4.7L59.3 -56C70.3 -67 88.4 -66.5 98.8 -54.9L291.2 159.5C341.6 133.2 402.3 139 451.6 188.3C514.6 251.2 535.2 361.1 480.1 416.1z" />
+    <glyph glyph-name="utensils"
+      unicode="&#xF2E7;"
+      horiz-adv-x="416" d=" M207.9 432.8C208.7 428.1 224 338.3 224 304C224 251.7 196.2 214.4 155.1 199.4L168 -38.7C168.7 -52.4 157.8 -64 144 -64H80C66.3 -64 55.3 -52.5 56 -38.7L68.9 199.4C27.7 214.4 0 251.8 0 304C0 338.4 15.3 428.1 16.1 432.8C19.3 453.1 61.4 453.4 64 431.7V290.5C65.3 287.1 79.1 287.3 80 290.5C81.4 315.8 87.9 429.7 88 432.3C91.3 453.1 132.7 453.1 135.9 432.3C136.1 429.6 142.5 315.8 143.9 290.5C144.8 287.3 158.7 287.1 159.9 290.5V431.7C162.5 453.3 204.7 453.1 207.9 432.8zM327.1 147.1L312.1 -38C310.9000000000001 -52 322 -64 336 -64H392C405.3 -64 416 -53.3 416 -40V424C416 437.2 405.3 448 392 448C309.5 448 170.6 269.5 327.1 147.1z" />
+    <glyph glyph-name="vector-square"
+      unicode="&#xF5CB;"
+      horiz-adv-x="512" d=" M512 320V416C512 433.67 497.67 448 480 448H384C366.33 448 352 433.67 352 416H160C160 433.67 145.67 448 128 448H32C14.33 448 0 433.67 0 416V320C0 302.33 14.33 288 32 288V96C14.33 96 0 81.67 0 64V-32C0 -49.67 14.33 -64 32 -64H128C145.67 -64 160 -49.67 160 -32H352C352 -49.67 366.33 -64 384 -64H480C497.67 -64 512 -49.67 512 -32V64C512 81.67 497.67 96 480 96V288C497.67 288 512 302.3300000000001 512 320zM416 384H448V352H416V384zM64 384H96V352H64V384zM96 0H64V32H96V0zM448 0H416V32H448V0zM416 96H384C366.33 96 352 81.67 352 64V32H160V64C160 81.67 145.67 96 128 96H96V288H128C145.67 288 160 302.3300000000001 160 320V352H352V320C352 302.33 366.33 288 384 288H416V96z" />
+    <glyph glyph-name="venus-double"
+      unicode="&#xF226;"
+      horiz-adv-x="512" d=" M288 272C288 351.5 223.5 416 144 416S0 351.5 0 272C0 203.5 47.9 146.1 112 131.6V80H76C69.4 80 64 74.6 64 68V28C64 21.4 69.4 16 76 16H112V-20C112 -26.6 117.4 -32 124 -32H164C170.6 -32 176 -26.6 176 -20V16H212C218.6 16 224 21.4 224 28V68C224 74.6 218.6 80 212 80H176V131.6C240.1 146.1 288 203.5 288 272zM64 272C64 316.1 99.9 352 144 352S224 316.1 224 272S188.1 192 144 192S64 227.9 64 272zM400 131.6V80H436C442.6 80 448 74.6 448 68V28C448 21.4 442.6 16 436 16H400V-20C400 -26.6 394.6 -32 388 -32H348C341.4 -32 336 -26.6 336 -20V16H300C293.4 16 288 21.4 288 28V68C288 74.6 293.4 80 300 80H336V131.6C314.8 136.4 295.4 145.9 278.8 158.9C292.8 175.6 303.8 194.9 310.9000000000001 216C325.4000000000001 201.2 345.6 192 368.0000000000001 192C412.1000000000001 192 448.0000000000001 227.9000000000001 448.0000000000001 272S412.1000000000001 352 368.0000000000001 352C345.7000000000001 352 325.4000000000001 342.8 310.9000000000001 328C303.8 349.1 292.9000000000001 368.4000000000001 278.8 385.1C303.4 404.4 334.3 416 368 416C447.5 416 512 351.5 512 272C512 203.5 464.1 146.1 400 131.6z" />
+    <glyph glyph-name="venus-mars"
+      unicode="&#xF228;"
+      horiz-adv-x="576" d=" M564 448H485C474.3 448 469 435.1 476.5 427.5L493.4 410.6L444.7 361.9C422.5 375.9 396.2 384 368 384C334.3 384 303.4 372.4 278.8 353.1C292.8 336.4 303.8 317.1 310.9000000000001 296C325.4000000000001 310.8 345.6 320 368.0000000000001 320C412.1000000000001 320 448.0000000000001 284.1 448.0000000000001 240S412.1000000000001 160 368.0000000000001 160C345.7000000000001 160 325.4000000000001 169.2 310.9000000000001 184C303.8 162.9 292.9000000000001 143.6 278.8 126.9C303.3 107.5 334.3 96 368 96C447.5 96 512 160.5 512 240C512 268.2 503.9 294.5 489.9 316.7L538.6 365.4L555.5 348.5C557.9 346.1 560.9 345 563.9 345C570.1 345 576 349.8 576 357V436C576 442.6 570.6 448 564 448zM144 384C64.5 384 0 319.5 0 240C0 171.5 47.9 114.1 112 99.6V48H76C69.4 48 64 42.6 64 36V-4C64 -10.6 69.4 -16 76 -16H112V-52C112 -58.6 117.4 -64 124 -64H164C170.6 -64 176 -58.6 176 -52V-16H212C218.6 -16 224 -10.6 224 -4V36C224 42.6 218.6 48 212 48H176V99.6C240.1 114.2000000000001 288 171.5 288 240.0000000000001C288 319.5 223.5 384 144 384zM144 160C99.9 160 64 195.9 64 240S99.9 320 144 320S224 284.1 224 240S188.1 160 144 160z" />
+    <glyph glyph-name="venus"
+      unicode="&#xF221;"
+      horiz-adv-x="288" d=" M288 272C288 351.5 223.5 416 144 416S0 351.5 0 272C0 203.5 47.9 146.1 112 131.6V80H76C69.4 80 64 74.6 64 68V28C64 21.4 69.4 16 76 16H112V-20C112 -26.6 117.4 -32 124 -32H164C170.6 -32 176 -26.6 176 -20V16H212C218.6 16 224 21.4 224 28V68C224 74.6 218.6 80 212 80H176V131.6C240.1 146.1 288 203.5 288 272zM64 272C64 316.1 99.9 352 144 352S224 316.1 224 272S188.1 192 144 192S64 227.9 64 272z" />
+    <glyph glyph-name="vial"
+      unicode="&#xF492;"
+      horiz-adv-x="480" d=" M477.7 261.9L309.5 429.7C306.4 432.8 301.3 432.8 298.2 429.7L264.2 395.8C261.1 392.7 261.1 387.6 264.2 384.5L275.4 373.4L33 131.5C-5.8 92.8 -12.1 29.5 23.6 -12C44.2 -36 73.1 -48 102 -47.9C128.4 -47.9 154.8 -37.9 174.9 -17.8L421.2000000000001 227.9000000000001L432.4000000000001 216.8000000000001C435.5000000000001 213.7000000000001 440.6 213.7000000000001 443.7000000000001 216.8000000000001L477.7 250.7000000000001C480.8000000000001 253.7000000000001 480.8000000000001 258.8000000000001 477.7 261.9000000000001zM318 192H161L309 339.7L387.5 261.4L318 192z" />
+    <glyph glyph-name="vials"
+      unicode="&#xF493;"
+      horiz-adv-x="640" d=" M72 384H96V144C96 99.9 131.9 64 176 64S256 99.9 256 144V384H280C284.4 384 288 387.6 288 392V440C288 444.4 284.4 448 280 448H72C67.6 448 64 444.4 64 440V392C64 387.6 67.6 384 72 384zM144 384H208V288H144V384zM624 0H16C7.2 0 0 -7.2 0 -16V-48C0 -56.8 7.2 -64 16 -64H624C632.8 -64 640 -56.8 640 -48V-16C640 -7.2 632.8 0 624 0zM360 384H384V144C384 99.9 419.9 64 464 64S544 99.9 544 144V384H568C572.4 384 576 387.6 576 392V440C576 444.4 572.4 448 568 448H360C355.6 448 352 444.4 352 440V392C352 387.6 355.6 384 360 384zM432 384H496V288H432V384z" />
+    <glyph glyph-name="video-slash"
+      unicode="&#xF4E2;"
+      horiz-adv-x="640" d=" M633.8 -10.1L578.8 32.4C594.1999999999999 33.8 608 46.1 608 63.5V320.5C608 346 578.9 360.9 557.6 346.3L448 270.7V133.5L416 158.2V336.2C416 362.6 394.6 384 368.2 384H123.9L45.5 444.6C38.5 450 28.5 448.8 23 441.8L3.4 416.6C-2 409.6 -0.8 399.6 6.2 394.2L42.7 366L416 77.4L594.5 -60.6C601.5 -66 611.5 -64.8000000000001 617 -57.8L636.6 -32.5C642.1 -25.6 640.8000000000001 -15.5 633.8000000000001 -10.1zM32 47.8C32 21.4 53.4 0 79.8 0H368.2C379.4 0 389.6 4 397.8 10.5L32 293.3V47.8z" />
+    <glyph glyph-name="video"
+      unicode="&#xF03D;"
+      horiz-adv-x="576" d=" M336.2 384H47.8C21.4 384 0 362.6 0 336.2V47.8C0 21.4 21.4 0 47.8 0H336.2C362.6 0 384 21.4 384 47.8V336.2C384 362.6 362.6 384 336.2 384zM525.6 346.3L416 270.7V113.3L525.6 37.8C546.8000000000001 23.1999999999999 576 38.1 576 63.6V320.5C576 345.9 546.9 360.9 525.6 346.3z" />
+    <glyph glyph-name="vihara"
+      unicode="&#xF6A7;"
+      horiz-adv-x="640" d=" M632.88 47.29L544 96V160L599.16 177.69C610.9499999999999 183.59 610.9499999999999 200.41 599.16 206.31L480 256V320L507.31 336.3C515.03 344.02 512.92 357.04 503.15 361.92L320 448L136.85 361.93C127.08 357.05 124.97 344.03 132.69 336.31L160 320V256L40.84 206.31C29.05 200.41 29.05 183.59 40.84 177.69L96 160V96L7.12 47.29C1.7 43.67 -0.58 37.66 0.12 32C0.74 26.99 3.69 22.25 8.84 19.67L64 0V-48C64 -56.84 71.16 -64 80 -64H112C120.84 -64 128 -56.84 128 -48V0H288V-48C288 -56.84 295.1600000000001 -64 304 -64H336C344.84 -64 352 -56.84 352 -48V0H512V-48C512 -56.84 519.16 -64 528 -64H560C568.84 -64 576 -56.84 576 -48V0L631.15 19.67C636.31 22.25 639.25 26.99 639.87 32C640.58 37.67 638.3 43.68 632.88 47.29zM224 320H416V256H224V320zM160 96V160H480V96H160z" />
+    <glyph glyph-name="volleyball-ball"
+      unicode="&#xF45F;"
+      horiz-adv-x="495.9" d=" M223.3 204.6C222.4 241.6 214.7 277.4 200.6 310.3C109.8 267.9 43.1 187.9 20.3 93.5C33.7 62.7 53.2 35.2 77.2 12.4C99.9 91.6 151.4 160.2 223.3 204.6zM186.4 339C171.4 365.4 151.9 389.1 129 409.7C38 359.9 -15.8 256.8 4 147.5C37.4 230.6 102.4 299.5 186.4 339zM374 173.9C382.6 273.7 346.7 371.4 276.5 438.3C261.8 440 224.9 443.8 177.6 429.8C234.9 370.5 268.6 291.6 271.1 207.1C303.6 189.4 338.4000000000001 178.1 374 173.9zM249.3 164.4C217.7 145.1 190.6 120.5 169.1 91.8C251.1 34.4999999999999 353.6 16.6999999999999 446.6 43.9999999999999C466.3 70.3999999999999 480.8 100.8 488.8 133.9C462.2 127.3 435.1 123.5 407.9 123.5C353.3 123.6 299 137.6 249.3 164.4zM151 64.7C135.8 38.7 125.3 10.3 118.9 -19.5C156.5 -42.5 200.6 -56 248 -56C309 -56 364.7 -33.9 407.9 2.6C295 -13.5 204.6 27.4 151 64.7zM331.3 425.3C386.6 354.9 413.8 264.1 405.9 171.7C436.2 171.5 466.4 176.5 495.6 185.9C495.6 187.9 495.9 189.9 495.9 191.9C495.9 299.7 427.2 391 331.3 425.3z" />
+    <glyph glyph-name="volume-down"
+      unicode="&#xF027;"
+      horiz-adv-x="384" d=" M215.03 375.96L126.06 287H24C10.74 287 0 276.26 0 263V119C0 105.75 10.74 95 24 95H126.06L215.03 6.05C230.06 -8.98 256 1.58 256 23.02V358.98C256 380.45 230.04 390.9600000000001 215.03 375.9600000000001zM338.23 267.88C326.6500000000001 274.2100000000001 312.04 270.04 305.62 258.43C299.23 246.82 303.46 232.23 315.07 225.82C327.98 218.72 336 205.38 336 191C336 176.62 327.98 163.28 315.08 156.19C303.47 149.78 299.24 135.19 305.63 123.58C312.06 111.92 326.68 107.78 338.24 114.13C366.4700000000001 129.68 384.01 159.13 384.01 191.01S366.47 252.33 338.23 267.88z" />
+    <glyph glyph-name="volume-mute"
+      unicode="&#xF6A9;"
+      horiz-adv-x="512" d=" M215.03 376.95L126.06 288H24C10.74 288 0 277.26 0 264V120C0 106.75 10.74 96 24 96H126.06L215.03 7.05C230.06 -7.98 256 2.58 256 24.02V359.98C256 381.44 230.04 391.9600000000001 215.03 376.95zM461.64 192L507.28 237.64C513.5799999999999 243.94 513.5799999999999 254.16 507.28 260.46L484.46 283.28C478.16 289.58 467.94 289.58 461.64 283.28L416 237.64L370.36 283.28C364.06 289.58 353.8400000000001 289.58 347.54 283.28L324.7200000000001 260.46C318.42 254.16 318.42 243.94 324.7200000000001 237.64L370.36 192L324.73 146.37C318.43 140.07 318.43 129.85 324.73 123.55L347.55 100.73C353.85 94.43 364.07 94.43 370.37 100.73L416 146.36L461.64 100.72C467.94 94.42 478.16 94.42 484.46 100.72L507.28 123.54C513.5799999999999 129.84 513.5799999999999 140.06 507.28 146.36L461.64 192z" />
+    <glyph glyph-name="volume-off"
+      unicode="&#xF026;"
+      horiz-adv-x="257.33" d=" M216.36 376.96L127.39 288H25.33C12.07 288 1.33 277.26 1.33 264V120C1.33 106.75 12.07 96 25.33 96H127.39L216.36 7.05C231.39 -7.98 257.3300000000001 2.58 257.3300000000001 24.02V359.98C257.3300000000001 381.45 231.3700000000001 391.9600000000001 216.3600000000001 376.9600000000001z" />
+    <glyph glyph-name="volume-up"
+      unicode="&#xF028;"
+      horiz-adv-x="576" d=" M215.03 376.95L126.06 288H24C10.74 288 0 277.26 0 264V120C0 106.75 10.74 96 24 96H126.06L215.03 7.05C230.06 -7.98 256 2.58 256 24.02V359.98C256 381.44 230.04 391.9600000000001 215.03 376.95zM448.35 428.03C437.18 435.36 422.17 432.27 414.8400000000001 421.08C407.5000000000001 409.9100000000001 410.62 394.9 421.79 387.57C488.06 344.0800000000001 527.61 270.9700000000001 527.61 191.99C527.61 113.01 488.06 39.9 421.79 -3.59C410.62 -10.91 407.5 -25.93 414.8400000000001 -37.09C421.8800000000001 -47.8 436.7700000000001 -51.65 448.35 -44.04C528.27 8.42 576 96.67 576 192S528.27 375.57 448.35 428.03zM480 192C480 255.53 447.94 313.94 394.23 348.24C383.04 355.38 368.2000000000001 352.06 361.11 340.7800000000001S357.3300000000001 314.5700000000001 368.5200000000001 307.42C408.27 282.03 432 238.89 432 192S408.27 101.97 368.52 76.58C357.33 69.44 354.02 54.51 361.11 43.22C367.62 32.86 382.23 28.08 394.23 35.76C447.94 70.06 480 128.46 480 192zM338.23 268.87C326.6500000000001 275.2000000000001 312.04 271.03 305.62 259.42C299.23 247.81 303.46 233.2200000000001 315.07 226.81C327.98 219.72 336 206.37 336 192C336 177.62 327.98 164.28 315.08 157.19C303.47 150.78 299.24 136.19 305.63 124.58C312.06 112.92 326.68 108.78 338.24 115.13C366.4700000000001 130.68 384.01 160.13 384.01 192.01S366.47 253.33 338.23 268.87z" />
+    <glyph glyph-name="vote-yea"
+      unicode="&#xF772;"
+      horiz-adv-x="640" d=" M608 128H544V64H566.4C571.6999999999999 64 576 60.4 576 56V40C576 35.6 571.7 32 566.4 32H73.6C68.3 32 64 35.6 64 40V56C64 60.4 68.3 64 73.6 64H96V128H32C14.3 128 0 113.7 0 96V0C0 -17.7 14.3 -32 32 -32H608C625.7 -32 640 -17.7 640 0V96C640 113.7 625.7 128 608 128zM512 64V383.7C512 401.6 497.5 416 479.7 416H160.4C142.5 416 128 401.5 128 383.7V64H512zM211.2 246L236.7 271.3C240.9 275.5 247.7 275.5 251.9 271.2000000000001L293.2 229.6L388.4 324C392.6 328.2000000000001 399.4 328.2000000000001 403.6 323.9000000000001L428.9 298.4000000000001C433.1 294.2000000000001 433.1 287.4000000000001 428.8 283.2000000000001L300.5 156C296.3 151.8 289.5 151.8 285.3 156.1L211.2 230.8C206.9 235 207 241.8 211.2 246z" />
+    <glyph glyph-name="vr-cardboard"
+      unicode="&#xF729;"
+      horiz-adv-x="640" d=" M608 384H32C14.33 384 0 369.67 0 352V32C0 14.33 14.33 0 32 0H192.22C217.41 0 240.25 14.77 250.58 37.74L278.32 99.38C286.21 116.92 302.35 128 320 128S353.79 116.92 361.68 99.38L389.42 37.74C399.75 14.77 422.6 0 447.78 0H608C625.67 0 640 14.33 640 32V352C640 369.67 625.67 384 608 384zM160 144C124.65 144 96 172.65 96 208S124.65 272 160 272S224 243.35 224 208S195.35 144 160 144zM480 144C444.65 144 416 172.65 416 208S444.65 272 480 272S544 243.35 544 208S515.35 144 480 144z" />
+    <glyph glyph-name="walking"
+      unicode="&#xF554;"
+      horiz-adv-x="320" d=" M208 352C234.5 352 256 373.5 256 400S234.5 448 208 448S160 426.5 160 400S181.5 352 208 352zM302.5 202.9L279.2 214.7L269.5 244.1C254.8 288.7000000000001 213.8 319.9000000000001 167.3 320C131.3 320.1 111.4 309.9000000000001 74 294.8000000000001C52.4 286.1 34.7 269.6 24.3 248.6L17.6 235C9.8 219.2 16.1 200 31.8 192.1C47.4 184.2 66.4 190.6 74.3 206.4L81 220C84.5 227 90.3 232.5 97.5 235.4L124.3 246.2L109.1 185.5C103.9 164.7 109.5 142.6 124 126.7L183.9 61.3C191.1 53.4 196.2 43.9 198.8 33.6L217.1 -39.7C221.4 -56.8000000000001 238.8 -67.3000000000001 255.9000000000001 -63.0000000000001C273.0000000000001 -58.7 283.5000000000001 -41.3000000000001 279.2000000000001 -24.2L257.0000000000001 64.8C254.4000000000001 75.1 249.3000000000001 84.6999999999999 242.1000000000001 92.4999999999999L196.6000000000001 142.1999999999999L213.8000000000001 210.8999999999999L219.3000000000001 194.3999999999999C224.6000000000001 178.2999999999999 236.0000000000001 165 251.0000000000001 157.3999999999999L274.3 145.5999999999999C289.9000000000001 137.6999999999999 308.9000000000001 144.0999999999999 316.8 159.8999999999999C324.5 175.5999999999999 318.2 194.9999999999999 302.5 202.8999999999999zM73.6 62.2C70.4 54.1 65.6 46.8 59.4 40.7L9.4 -9.4C-3.1 -21.9 -3.1 -42.2 9.4 -54.7S42.1 -67.2 54.6 -54.7L114 4.6999999999999C120.1 10.8 124.9 18.0999999999999 128.2 26.1999999999999L141.7 59.9999999999999C86.4 120.3 103 101.8 94.3 113.6999999999999L73.6 62.1999999999999z" />
+    <glyph glyph-name="wallet"
+      unicode="&#xF555;"
+      horiz-adv-x="512" d=" M461.2 320H80C71.16 320 64 327.16 64 336S71.16 352 80 352H464C472.84 352 480 359.16 480 368C480 394.51 458.51 416 432 416H64C28.65 416 0 387.35 0 352V32C0 -3.35 28.65 -32 64 -32H461.2C489.22 -32 512 -10.47 512 16V272C512 298.4700000000001 489.22 320 461.2 320zM416 112C398.33 112 384 126.33 384 144S398.33 176 416 176S448 161.67 448 144S433.67 112 416 112z" />
+    <glyph glyph-name="warehouse"
+      unicode="&#xF494;"
+      horiz-adv-x="640" d=" M504 96H136.4C132 96 128.4 92.4 128.4 88L128.3 40C128.3 35.6 131.9 32 136.3 32H504C508.4 32 512 35.6 512 40V88C512 92.4 508.4 96 504 96zM504 0H136.1C131.7 0 128.1 -3.6 128.1 -8L128 -56C128 -60.4 131.6 -64 136 -64H504C508.4 -64 512 -60.4 512 -56V-8C512 -3.6 508.4 0 504 0zM504 192H136.6C132.2 192 128.6 188.4 128.6 184L128.5 136C128.5 131.6 132.1 128 136.5 128H504C508.4 128 512 131.6 512 136V184C512 188.4 508.4 192 504 192zM610.5 331L338.4 444.3A48.15 48.15 0 0 1 301.5 444.3L29.5 331C11.7 323.5 0 306.1 0 286.7V-56C0 -60.4 3.6 -64 8 -64H88C92.4 -64 96 -60.4 96 -56V192C96 209.6 110.6 224 128.6 224H511.4C529.4 224 544 209.6 544 192V-56C544 -60.4 547.6 -64 552 -64H632C636.4 -64 640 -60.4 640 -56V286.7C640 306.1 628.3 323.5 610.5 331z" />
+    <glyph glyph-name="water"
+      unicode="&#xF773;"
+      horiz-adv-x="576" d=" M562.1 64.1C540.6 66.5 520 74.6 504.2 87C490.1 98.1 470.0000000000001 98.3 456.0000000000001 87C418.1000000000001 56.6 348.8000000000001 56.6 310.3000000000001 88.5C296.8000000000001 99.7 277.3000000000001 97.6 263.6000000000001 86.7C225.6000000000001 56.6 156.7000000000001 56.7 118.4000000000001 88.4C104.9000000000001 99.6 85.1000000000001 97.3 71.3000000000001 86.4C55.8000000000001 74.2 35.3000000000001 66.3 13.6000000000001 64C5.7000000000001 63.2 1e-13 56.2 1e-13 48.3V16.1C1e-13 7 7.6000000000001 -0.7 16.7000000000001 0.1C45.5000000000001 2.6 72.8000000000001 11.5 96.1000000000001 26C152.6000000000001 -8.6 233.1000000000001 -8.1 288.1000000000002 26C344.6000000000002 -8.6 425.1000000000002 -8.1 480.1000000000001 26C503.4000000000001 11.8 531.0000000000001 2.7 559.2000000000002 0.2C568.3000000000002 -0.6 575.9000000000002 7.1 575.9000000000002 16.2V47.8C576.0000000000002 55.8 570.2000000000002 63.2 562.1000000000003 64.1zM562.1 208.1C540.6 210.5 520 218.6 504.2 231.0000000000001C490.1 242.1 470.0000000000001 242.3000000000001 456.0000000000001 231.0000000000001C418.1000000000001 200.6 348.8000000000001 200.6 310.3000000000001 232.5000000000001C296.8000000000001 243.7 277.3000000000001 241.6 263.6000000000001 230.7C225.6000000000001 200.6 156.7000000000001 200.7 118.4000000000001 232.4C104.9000000000001 243.6 85.1000000000001 241.3 71.3000000000001 230.4C55.8000000000001 218.2 35.3000000000001 210.3 13.6000000000001 208C5.7000000000001 207.2 1e-13 200.2 1e-13 192.3V160.1C1e-13 151 7.6000000000001 143.3 16.7000000000001 144.1C45.5000000000001 146.6 72.8000000000001 155.5 96.1000000000001 170C152.6000000000001 135.4 233.1000000000001 135.9 288.1000000000002 170C344.6000000000002 135.4 425.1000000000002 135.9 480.1000000000001 170C503.4000000000001 155.8 531.0000000000001 146.7 559.2000000000002 144.2C568.3000000000002 143.4 575.9000000000002 151.1 575.9000000000002 160.2V191.8C576.0000000000002 199.8 570.2000000000002 207.2 562.1000000000003 208.1zM562.1 352.1C540.6 354.6 520 362.6 504.2 375C490.1 386.1 470 386.3 456 375C418.1 344.6 348.8 344.6 310.3 376.5C296.8 387.7 277.3 385.6 263.6 374.7C225.6 344.6 156.7 344.7 118.4 376.4C104.9 387.6 85.1 385.3 71.3 374.4C55.8 362.2 35.3 354.3 13.6 352C5.7 351.2 0 344.2 0 336.3V304.1C0 295 7.6 287.3 16.7 288.1C45.5 290.6 72.8 299.5 96.1000000000001 314C152.6000000000001 279.4 233.1000000000001 279.9 288.1 314C344.6 279.4 425.1 279.9 480.1 314C503.4 299.8 531 290.7 559.2 288.2C568.3000000000001 287.4 575.9000000000001 295.1 575.9000000000001 304.2V335.8C576.0000000000001 343.8 570.2 351.2 562.1000000000001 352.1z" />
+    <glyph glyph-name="weight-hanging"
+      unicode="&#xF5CD;"
+      horiz-adv-x="512" d=" M510.28 2.14L437.25 294.27C433.45 309.46 420.81 319.99 406.38 319.99H346.13C349.7 330.04 352.01 340.71 352.01 351.99C352.01 405.01 309.03 447.99 256.01 447.99S160.01 405.01 160.01 351.99C160.01 340.71 162.31 330.04 165.89 319.99H105.64C91.21 319.99 78.56 309.45 74.77 294.27L1.72 2.14C-6.61 -31.17 16.38 -64 48.03 -64H463.98C495.62 -64 518.61 -31.17 510.28 2.14zM256 320C238.36 320 224 334.36 224 352S238.36 384 256 384S288 369.64 288 352S273.64 320 256 320z" />
+    <glyph glyph-name="weight"
+      unicode="&#xF496;"
+      horiz-adv-x="512" d=" M448 384H422.02C438.44 355.7200000000001 448 322.99 448 288C448 182.13 361.87 96 256 96S64 182.13 64 288C64 322.99 73.56 355.7200000000001 89.98 384H64C28.71 384 0 355.29 0 320V0C0 -35.29 28.71 -64 64 -64H448C483.29 -64 512 -35.29 512 0V320C512 355.29 483.29 384 448 384zM256 128C344.37 128 416 199.63 416 288S344.37 448 256 448S96 376.37 96 288S167.63 128 256 128zM255.7 279.94L289.28 358.3C292.78 366.4700000000001 302.22 370.2200000000001 310.31 366.71C318.43 363.23 322.19 353.82 318.72 345.71L285.05 267.16C291.73 260 296 250.55 296 240C296 217.91 278.09 200 256 200S216 217.91 216 240C216 261.98 233.76 279.77 255.7 279.94z" />
+    <glyph glyph-name="wheelchair"
+      unicode="&#xF193;"
+      horiz-adv-x="512" d=" M496.101 62.331L510.328 33.668C514.257 25.753 511.025 16.152 503.11 12.223L437.645 -20.663C421.596 -28.63 402.089 -21.857 394.456 -5.608L331.679 128H192C176.075 128 162.574 139.71 160.321 155.475C126.433 392.692 128.38 377.956 128 384C128 420.358 158.318 449.635 195.052 447.929C228.3230000000001 446.384 255.1 419.024 255.9770000000001 385.728C256.8450000000001 352.795 232.8250000000001 325.305 201.369 320.689L206.039 287.999H336C344.837 287.999 352 280.836 352 271.999V239.999C352 231.162 344.837 223.999 336 223.999H215.182L219.754 191.999H352A32 32 0 0 0 380.962 173.607L438.477 51.2L474.655 69.549C482.57 73.478 492.172 70.246 496.101 62.331zM311.358 96H286.852C279.064 41.796 232.324 0 176 0C114.243 0 64 50.243 64 112C64 153.505 86.694 189.809 120.324 209.156C116.612 235.121 113.48 257.016 110.836 275.4890000000001C45.956 249.536 0 186.037 0 112C0 14.953 78.953 -64 176 -64C247.87 -64 309.8060000000001 -20.692 337.11 41.192L311.358 96z" />
+    <glyph glyph-name="wifi"
+      unicode="&#xF1EB;"
+      horiz-adv-x="640" d=" M634.91 293.12C457.74 456.99 182.19 456.93 5.09 293.12C-1.57 286.9600000000001 -1.7 276.53 4.74 270.14L38.98 236.17C45.12 230.07 55 229.9400000000001 61.38 235.79C207.3 369.4700000000001 432.68 369.5 578.63 235.79C585.01 229.9400000000001 594.89 230.08 601.03 236.17L635.27 270.14C641.6999999999999 276.53 641.5699999999999 286.9600000000001 634.91 293.12zM320 96C284.65 96 256 67.35 256 32S284.65 -32 320 -32S384 -3.35 384 32S355.35 96 320 96zM522.67 179.59C407.41 281.5200000000001 232.46 281.4100000000001 117.33 179.59C110.43 173.49 110.21 162.9 116.76 156.4400000000001L151.2 122.4500000000001C157.2 116.53 166.86 116.1300000000001 173.25 121.65C257.2 194.22 382.99 194.06 466.74 121.65C473.13 116.1300000000001 482.79 116.5200000000001 488.79 122.4500000000001L523.23 156.4400000000001C529.79 162.9 529.5600000000001 173.5000000000001 522.6700000000001 179.59z" />
+    <glyph glyph-name="wind"
+      unicode="&#xF72E;"
+      horiz-adv-x="512" d=" M156.7 192H16C7.2 192 0 184.8 0 176V144C0 135.2 7.2 128 16 128H158.2C174.1 128 189 117.1 191.6 101.4C194.9 81.4 179.5 64 160 64C145.9 64 133.9 73.2 129.6 85.9C127.5 92.2 121 96 114.4 96H81.6C71.8 96 63.9 87.2 65.7 77.6C74.3 33.5 113.3 0 159.9 0C217 0 262.6 50.1 255.1 108.6C249 157 205.4 192 156.7 192zM16 224H352C411.7 224 458.8 278.8 445.8 340.7C438.2 376.9 408.9000000000001 406.2 372.7000000000001 413.8C317.3000000000001 425.4 267.6 388.9 257.8000000000001 338.3C255.9000000000001 328.7 263.9000000000001 320 273.6000000000001 320H306.4000000000001C313.1000000000001 320 319.5000000000001 323.8 321.6000000000001 330.1C325.9 342.8 337.9 352 352 352C371.4 352 386.9 334.6 383.6 314.6C381 298.9 366.2000000000001 288 350.2000000000001 288H16C7.2 288 0 280.8 0 272V240C0 231.2 7.2 224 16 224zM400 192H243.7C263 175.4 276.9 153.2 283.5 128H400C426.5 128 448 106.5 448 80S426.5 32 400 32C382.1 32 366.7 41.9 358.4 56.4C355.5 61.4 349.7 64 343.9 64H310.1C299.2 64 291.1 53.2 294.8 42.9C312.6 -7.7 365.3 -41.9 424.2 -29.4C465.3999999999999 -20.7 499.3 12.2 508.8999999999999 53.3C526 126.5 470.5 192 400 192z" />
+    <glyph glyph-name="window-close"
+      unicode="&#xF410;"
+      horiz-adv-x="512" d=" M464 416H48C21.5 416 0 394.5 0 368V16C0 -10.5 21.5 -32 48 -32H464C490.5 -32 512 -10.5 512 16V368C512 394.5 490.5 416 464 416zM380.4 125.5C385.2 120.7 385.2 112.9 380.4 108.1L339.9 67.6C335.1 62.8 327.3 62.8 322.5 67.6L256 134.7L189.5 67.6C184.7 62.8 176.9 62.8 172.1 67.6L131.6 108.1C126.8 112.9 126.8 120.7000000000001 131.6 125.5L198.7 192L131.6 258.5C126.8 263.3 126.8 271.1 131.6 275.9L172.1 316.4C176.9 321.2 184.7 321.2 189.5 316.4L256 249.3L322.5 316.4C327.3 321.2 335.1 321.2 339.9 316.4L380.4 275.9C385.2 271.1 385.2 263.3 380.4 258.5L313.3 192L380.4 125.5z" />
+    <glyph glyph-name="window-maximize"
+      unicode="&#xF2D0;"
+      horiz-adv-x="512" d=" M464 416H48C21.5 416 0 394.5 0 368V16C0 -10.5 21.5 -32 48 -32H464C490.5 -32 512 -10.5 512 16V368C512 394.5 490.5 416 464 416zM448 256H64V340C64 346.6 69.4 352 76 352H436C442.6 352 448 346.6 448 340V256z" />
+    <glyph glyph-name="window-minimize"
+      unicode="&#xF2D1;"
+      horiz-adv-x="512" d=" M464 96H48C21.5 96 0 74.5 0 48V16C0 -10.5 21.5 -32 48 -32H464C490.5 -32 512 -10.5 512 16V48C512 74.5 490.5 96 464 96z" />
+    <glyph glyph-name="window-restore"
+      unicode="&#xF2D2;"
+      horiz-adv-x="512" d=" M512 400V112C512 85.5 490.5 64 464 64H416V272C416 316.1 380.1 352 336 352H128V400C128 426.5 149.5 448 176 448H464C490.5 448 512 426.5 512 400zM384 272V-16C384 -42.5 362.5 -64 336 -64H48C21.5 -64 0 -42.5 0 -16V272C0 298.5 21.5 320 48 320H336C362.5 320 384 298.5 384 272zM316 244C316 250.6 310.6 256 304 256H76C69.4 256 64 250.6 64 244V192H316V244z" />
+    <glyph glyph-name="wine-bottle"
+      unicode="&#xF72F;"
+      horiz-adv-x="512" d=" M507.31 375.43L439.43 443.31C433.18 449.56 423.05 449.56 416.8 443.31L394.17 420.68C387.92 414.43 387.92 404.3 394.17 398.05L317.5 321.38C270.92 341.08 215.1 332.11 177.13 294.15L18.75 135.77C-6.24 110.78 -6.24 70.25 18.75 45.26L109.26 -45.25C134.25 -70.24 174.78 -70.24 199.77 -45.25L358.16 113.14C396.12 151.1 405.09 206.93 385.39 253.51L462.06 330.18C468.31 323.93 478.44 323.93 484.69 330.18L507.32 352.81C513.56 359.05 513.56 369.18 507.31 375.43zM179.22 24.71L88.71 115.22L210.75 237.26L301.26 146.75L179.22 24.71z" />
+    <glyph glyph-name="wine-glass-alt"
+      unicode="&#xF5CE;"
+      horiz-adv-x="288" d=" M216 -16H176V101.19C244.47 117.08 294.05 181.1 287.4 255.35L271.45 433.45C270.71 441.69 263.9 448 255.74 448H32.26C24.11 448 17.29 441.69 16.56 433.45L0.6 255.34C-6.05 181.09 43.53 117.07 112 101.18V-16H72C49.91 -16 32 -33.91 32 -56C32 -60.42 35.58 -64 40 -64H248C252.42 -64 256 -60.42 256 -56C256 -33.91 238.09 -16 216 -16zM61.75 400H226.25L233.42 320H54.58L61.75 400z" />
+    <glyph glyph-name="wine-glass"
+      unicode="&#xF4E3;"
+      horiz-adv-x="288" d=" M216 -16H176V101.19C244.47 117.08 294.05 181.1 287.4 255.35L271.45 433.45C270.71 441.69 263.9 448 255.74 448H32.26C24.11 448 17.29 441.69 16.56 433.45L0.6 255.34C-6.05 181.09 43.53 117.07 112 101.18V-16H72C49.91 -16 32 -33.91 32 -56C32 -60.42 35.58 -64 40 -64H248C252.42 -64 256 -60.42 256 -56C256 -33.91 238.09 -16 216 -16z" />
+    <glyph glyph-name="won-sign"
+      unicode="&#xF159;"
+      horiz-adv-x="576" d=" M564 256C570.627 256 576 261.373 576 268V308C576 314.627 570.627 320 564 320H515.972L534.544 400.61C536.276 408.128 530.566 415.304 522.851 415.304H476.744A11.998 11.998 0 0 1 465.008 405.804L450.73 320H340.839L321.114 405.987A12 12 0 0 1 309.418 415.304H265.43A12 12 0 0 1 253.743 406.027L233.696 320H124.975L107.5 405.701A12 12 0 0 1 95.742 415.303H53.628C45.942 415.303 40.238 408.179 41.919 400.679L60 320H12C5.373 320 0 314.627 0 308V268C0 261.373 5.373 256 12 256H74.342L81.513 224H12C5.373 224 0 218.627 0 212V172C0 165.373 5.373 160 12 160H95.856L136.783 -22.624A12 12 0 0 1 148.492 -32H205.259C210.842 -32 215.687 -28.15 216.948 -22.712L259.335 160H314.421L356.807 -22.712A12 12 0 0 1 368.496 -32H425.322A12 12 0 0 1 437.016 -22.694L479.108 160H564C570.627 160 576 165.373 576 172V212C576 218.627 570.627 224 564 224H493.854L501.227 256H564zM138.024 256H218.781L211.324 224H144.548L138.024 256zM183.82 105.971C177.626 80.14 177.062 58.721 176.499 58.721H175.373S173.684 80.771 168.615 105.971L157.599 160H196.411L183.82 105.971zM274.182 224L276.178 232.602C278.034 240.564 279.635 248.57 280.981 256H292.775C294.122 248.57 295.722 240.564 297.578 232.602L299.574 224H274.182zM405.141 105.971C400.0730000000001 80.771 398.3830000000001 58.721 398.3830000000001 58.721H397.2570000000001C396.6940000000001 58.721 396.1310000000001 80.141 389.9360000000001 105.971L377.542 160H416.649L405.141 105.971zM430.281 224H362.861L355.521 256H437.098L430.281 224z" />
+    <glyph glyph-name="wrench"
+      unicode="&#xF0AD;"
+      horiz-adv-x="512" d=" M507.73 338.9C505.49 347.93 494.19 350.99 487.61 344.4100000000001L413.25 270.05L345.37 281.36L334.06 349.24L408.42 423.6C415.04 430.2200000000001 411.85 441.5 402.76 443.76C355.38 455.5 303.21 442.85 266.18 405.8300000000001C226.54 366.19 215.63 308.73 232.13 258.63L18.74 45.24C-6.25 20.25 -6.25 -20.27 18.74 -45.26C43.73 -70.25 84.25 -70.25 109.24 -45.26L322.45 167.9500000000001C372.57 151.2400000000001 429.92 162.2700000000001 469.82 202.1700000000001C506.89 239.2400000000001 519.52 291.49 507.73 338.9000000000001zM64 -24C50.75 -24 40 -13.25 40 0C40 13.26 50.75 24 64 24S88 13.26 88 0C88 -13.25 77.25 -24 64 -24z" />
+    <glyph glyph-name="x-ray"
+      unicode="&#xF497;"
+      horiz-adv-x="640" d=" M240 64C231.2 64 224 56.8 224 48S231.2 32 240 32S256 39.2 256 48S248.8 64 240 64zM400 32C408.8 32 416 39.2 416 48S408.8 64 400 64S384 56.8 384 48S391.2 32 400 32zM624 448H16C7.2 448 0 440.8 0 432V400C0 391.2 7.2 384 16 384H624C632.8 384 640 391.2 640 400V432C640 440.8 632.8 448 624 448zM624 0H576V352H64V0H16C7.2 0 0 -7.2 0 -16V-48C0 -56.8 7.2 -64 16 -64H624C632.8 -64 640 -56.8 640 -48V-16C640 -7.2 632.8 0 624 0zM480 200C480 195.6 476.4 192 472 192H336V160H440C444.4 160 448 156.4 448 152V136C448 131.6 444.4 128 440 128H336V96H400C426.5 96 448 74.5 448 48S426.5 0 400 0S352 21.5 352 48V64H288V48C288 21.5 266.5 0 240 0S192 21.5 192 48S213.5 96 240 96H304V128H200C195.6 128 192 131.6 192 136V152C192 156.4 195.6 160 200 160H304V192H168C163.6 192 160 195.6 160 200V216C160 220.4 163.6 224 168 224H304V256H200C195.6 256 192 259.6 192 264V280C192 284.4 195.6 288 200 288H304V312C304 316.4 307.6 320 312 320H328C332.4 320 336 316.4 336 312V288H440C444.4 288 448 284.4 448 280V264C448 259.6 444.4 256 440 256H336V224H472C476.4 224 480 220.4 480 216V200z" />
+    <glyph glyph-name="yen-sign"
+      unicode="&#xF157;"
+      horiz-adv-x="384" d=" M351.2080000000001 416H285.9310000000001A12 12 0 0 1 275.153 409.276L219.763 296.113C205.25 261.409 192.63 224.181 192.63 224.181H191.368S178.748 261.409 164.235 296.113L108.845 409.276A11.997 11.997 0 0 1 98.068 416H32.792C23.735 416 17.942 406.35 22.202 398.357L102.322 248H44C37.373 248 32 242.627 32 236V204C32 197.373 37.373 192 44 192H132.162L152 154.772V128H44C37.373 128 32 122.627 32 116V84C32 77.373 37.373 72 44 72H152V-20C152 -26.627 157.373 -32 164 -32H220C226.627 -32 232 -26.627 232 -20V72H340C346.627 72 352 77.373 352 84V116C352 122.627 346.627 128 340 128H232V154.772L251.838 192H340C346.627 192 352 197.373 352 204V236C352 242.627 346.627 248 340 248H281.678L361.798 398.357C366.058 406.35 360.266 416 351.2080000000001 416z" />
+    <glyph glyph-name="yin-yang"
+      unicode="&#xF6AD;"
+      horiz-adv-x="496" d=" M248 440C111.03 440 0 328.9700000000001 0 192S111.03 -56 248 -56S496 55.03 496 192S384.9700000000001 440 248 440zM248 64C230.33 64 216 78.33 216 96S230.33 128 248 128S280 113.67 280 96S265.67 64 248 64zM248 192C194.98 192 152 149.02 152 96S194.98 0 248 0C141.96 0 56 85.96 56 192S141.96 384 248 384C301.02 384 344 341.02 344 288S301.02 192 248 192zM248 320C230.33 320 216 305.67 216 288S230.33 256 248 256S280 270.3300000000001 280 288S265.67 320 248 320z" />
+  </font>
+</defs>
+</svg>
diff --git a/docs/python_docs/themes/mx-theme/mxtheme/static/webfonts/fa-solid-900.ttf b/docs/python_docs/themes/mx-theme/mxtheme/static/webfonts/fa-solid-900.ttf
new file mode 100644
index 0000000000000000000000000000000000000000..7c92e986f213487d285fedf4744c4959c4ecaef5
GIT binary patch
literal 196952
zcmeF4d3==B75C4%&+L;-mYHOd5JL8ZH6aNJ2*R)ksDL1gxCcZ<MMa>B7Bwm=b;XKG
z6)jq<)~cmy75AuE(W=FzZlxNRs<qnAOlBrlF?oOIIg=1jeETkcy`PuyJLh@szCHIj
z_uQM2n27w;Mi$jin>&9}>6KOI5%Fla&O2;wUBiTJbN_++t8l;Igwq!--|qZ;6;T!#
zv*P4s=bkjoy<;-G){r@W;nEWqEy=n4l*I_!0h*V>!7<T!Gp?Til}k@wwfeX3fA<p7
z`-CWS{jxJpSmYR4yMbu>=eXZ+`l8j#9oMjde*wamp0Vik6L(+xt3Q)@<4q#-&GIu>
zu6q2GF~^d5YbVi=%L!%Y_MUe5we=)=lI99=>Hp7KV!Oa~+s5UG9G^8VNq!`cfA5&k
znY|KmA2V0*%eeQDNvDB0h@GNR2?bXoA3j7qa*KbGlaK0YJees|XUwC(vPG-TpdxbE
zzUtrqnwOn&@*<?KK8$eU%Wz?G^}2EwuO}y*Z{(BUHZ3iaR7h*35KarOKk#q%Ne3;V
zNR+x(+U+p{b(xA_QpWT@sQ|Yljfhr;+j@4({oeF^XWHQ3*3)ewJ>4AX>FM*AYk@Rb
z`#Oqiq=!6rOZo=awv_Arpy};N*N~<uQqj2SaCW*VE6OGQ@avQP!bOn=%A)=f=KDx`
z5*KCdQK|P5$99YKblKO4%L3}(o1ggG@gs<1hfjx*Jf*GRhW4`4H4zQd8&}GL^4oRm
zO+(5mdDvk{(!hQD{^=hMZG^r-*ba$r=h<!77w*y*)UJFlJ@}bkBp&sAXZM?wtu5_G
zl4hFHbiHtIms?~+{Sfb4sZ-y5TfcY+*PDi22E@@Z0x9A8hsCws)@j)r&z5#Llr17i
zns%A(y4z`pzW{%`eNmpizUb`}l6ZDp^bzVKVMIn0yC0-4+WW$N1OIRO?%O2&UOy?T
z#K(PG8q#~ap6%&y5+)+okhta!^b7p<hg~L#LlOqz?ee7WOVi^%qW639wbR#ehj?~A
z$iqa^kmhZd6>gHQTvNNmQ+n&DetY^-<MezdVSCGLB6^+n(>_V3W7ji%EpvmEv45Zb
z(6xzZ7)h_sU+TV3+}?2g-Nmi94w^UOwWEy@*Y-o-+Sjxv9Q=3p%@2|O?#N%BVF`;i
z?i~+ypNLzVv|GCE5syoyyy<(}9oLaP@iac}H>cCYyk)ncNa<JM>$J4<-4oaD!`?9c
zWjn%19_?C2lt;#Ik15r>O$6z+>$9w7)AGr+xc8Nx;Er<ijiEL>o{ncb&2*Vbq^1EW
z&%<uS>o%nbFB0*z{rBW=BI?#VrsTeVpK0HqJZ*4C+Io*T2&Zi?*LHceJZ<W3k3k7j
zYL~rV8%THwgL$Z5o7(LnWp9IDTRKkvw#2nvw}{;?`u;;$wC{I<l!aRnPv!uLk9%C(
z?&<5@CW0{SQV)?*X1%uaZj<n7B0W7)e%l?_eeF@Y#~*3i&oFnO9;Gs;A}xTjr+V}4
z8#hvRDU*aLO{YQX#;yINxB<IdQoabTL2umtZDXg^rs3^!%e9?{#_M(CQn*JXO|6IR
zj(a;S+3jqPbCg-yPU!0OLmU}H+D`3SF8JAVVsAO&Z}%f)-<+mnN$%6M^&8_6J5Nm$
znx7p;`p+(tl*=xg&hwOR3vuhcZr{5+npfH%;q3O3uo9<D>)h8jaJTCQcN0n12W}cx
z(vbS6-6QGiHrxGMyWQYtOWRNDVfRCOy6%ylZ|(BSbC*tMPrFN=D39bJ?Xelx5#-f7
z#@o|nlRVne`N;M6NSCX<x1172+r$nlaoZ%1e*TekI^s^^uF|frgom5mR=xGG!y(Py
zvh|K%JDeR~`u0ERIHHTlTxDa|3HRb=Uq_I3M9X7KNh94pcE0fM--o6EVa07H##sm2
zHSOOUW-r+W|E2w(@pN5C>uC2YuI+k?o7ATV_ierOwnJ|iy_Y=fe54&B;@2C#H=NwV
zuPyCo`)k<V^4W3J{X2W!B<=g&>)tX+IJ=w?O^aJKZIM!56X`|A7~0jI`|R}FB&^1l
zKIM+yYshxFPM2TWDIIo?l=RYd5WmQt=h{x2;AW#bRbRa%O}O{Ak6lJ_>qGp~X~C^8
zT)T#ma_$McufNtu%BAmN&C5iPW+~`EUn5N$xIuRFzk8Cs{mhuyjqPsx+0R#BJ4GZd
zJf8$VLDE4!efPcYkSy~#FcAr>`AHs9SNwAV3k(BT2WCH@YqhL<;VQ3|VUSpD!rhV8
zh7^V%4-XBAny51xhz6s1(ZXnPv?Mw{Iwd+QdUW)-=o!&9(YEMi(Vs=Hj$RkNA-XYo
zTlDtm{m}=auSQ>sz8~#~ejNR0G!gBN`D1}tPOLaq5-X35h>eVmiX9LeADa@J8ap&L
zD|Te;xY&uYOJeI{*T-&%-4y#p?AF-*v8}NOV~@vPjJ+CrC-z?K{n)3mFJq}#cbwzy
zxIbPHFNqJ2H^;}s501}@9~)m5KPP@({L1*%@t?<Uh~FH)Exsjwcl_b_qw&Y%PsX2(
zzZ!o%zAOIs_-FAi;$Ow%@l^aj@$OD%XGUj9XKm-`&Z9ez?QHE_+<9^5ot;m2KGXSp
z=Zl@MbiUU4x6Y2vzjuDs`Az3{ojnOxB0JHPXiglE7?+rkn3$NHI4ChKacE*zVs>I~
zVqxOg#PNy6iIWniCsrg@CC*J;n7BA`dE&~%HHi(0n-aGr?n^wFcr5W$qCN3!VteA%
z#2bmfCO$}fob)EIOWvBiBY9Wy_sNHn+mcTvpHKcN`D*g@<lD*jlDm>0BtJ}klKd?B
z&*Z<7-=(rs!Bk;tP^v6dpK3{sOHE8oNli-~k~%CkCpAB{Fm+sNaq6VhX{pmwXQo!A
z&Q6`1T9djkwKny$)QzcIQn#n>O8q8vU+R(6<Ef`o?WyglKc`+xy^;D$>h09KsrOSK
zr#?-6p86^kPkocx-9=p)U4gE=u5edLS9#aquDY%RyCPkayQX!`?3&xPpzG+a<GW7j
zI<0FZbPtMB)Dd+@voTh}_E;&4PK-{E&c;}2jV{Mnxg<SSuE$u}6x|&CRdj2#J-Rdc
z7RJg)(a)mM=+`ldWyZ2&1sE%(vC3F|tTEOSJ1`c(SUDs%Gd4SRbgVUYaqQCAb?LFP
zDfU~8l?P(mV(qb4?6I;d))D&xW992O#T{{9JRC2Im&F_7qvMhI%=jFPm9ygK+GAzC
zJyvdwZ;sy;|3iALJQIH*zBB&UzOfREC+)FfI=%K-Y3W>;9xE4NtUUFjW2F&eWz2rY
z$}t!#i!fG}CYEEYoRc^|aZzF|#>&-+^@$tPV`W?7$@Exx1!Lu{#QTYl65k}l$?KE9
zOm0culYB7waPqO_)5#Z-uOxS-$IAQt#>(z~V`T)!%GkZe%3O?<V=-2iq)zD@E2~pK
z#aOv0bvee$#?<EYSa~=-R-W5yto$wYkv&$vOhsj^q<Xsi7%QRlSgGwA(KU9jv2s+`
zarRhQA!FtL|NH+f@L#k5<2^LO)SK~U3O>{A&&6h)*<kL$=a1%nd_GBkJ~yA6FU`L!
zhvl&<t!k?opV3ytnr_XrPPLX<XIN+BbDnjLwcgrb-E3{Lw&3$?>u&2YtKE9Zdf9s2
zdMo{T$9f;14(n6vYwH{P)9vs%0{9d;iXG*SN=LP0gk!2>hGUka)v?%d9zK^j*4dw{
z9P+uwaie3S<L2~dlj9!8{f@1Dp9dTd*`LQ8?e^zc$By*pCC5vSmw)v0s^eA1&i#B|
zcf9U+6QB1TAK~+<;|s@^4orfMgk!g(TR%?YbfiBnr^}h`%yovG4X$d}0j`K^s%xHW
zf$Jz&t81BSmFsNRdFjs@*PhP>u05ZNT^GC7?)6#cTIad~pKDw{@B3_U-H6X7*JgZv
z?YcYtxyPlSdtLoM_q(>*p9fqI+n;T&$MAU~{dvmO?%KZB=Z~%(@`0YSzy!?2e2r<h
z4&YJdEAwyjFS^d`HW!&TGt+FO$N5IO$DBqf{*5`oyvwihn>>N9vJSNlwq}?ksM&mI
zKB9Rv-^``6cq+}{-<f}!FQEmw$V+a1%@p!I)>OWWKO%?erXKSQ=TU=+m>TmaPv?B|
zEEK6<(q=P>cbjkN8nck^qc^CDDyfR9slxn>vuKBz!re5K{XCDi^B4|skTcA9I@KIc
z&+#t0oo+Kr&2@YbPo^trIX{G*jOWdh=08@pRl(1j(Wcodr$3wJ{4+X%F6A}+Q$C;1
zqf#oPavH=(Tdn33Zl+^tur&@=`G>Kdd52y$gUos6QEK6x{AcqgYk=9sf3@<AgU+Li
z=%=)Xo-$j_z2<l9vIg>OnrUvficA&%$*MQcnVmG59^;$I&;Q~9G?C9TlPQa`DTe|S
zq!8s%F74u{@GO!!+8kpZqC3s)d??3w7XN_{G+)r8=0tv-UZZVRsX3DG=S=gud4)Y@
z0e{2)p)+~3Ih}5yO>`^WOqo_0En#M3&NLJFJ6dmsn|1U8olk9aA$!dX`j<J!T5Qg;
zj^_q0Hg}ufU@rKrxzF5hzBXN4#P6Hej0Zayi|7JMSo8T6^8h=oL3|uP!C_iVcbHmw
zi{3Vi=v?!6D~sOcp*)f+co<jlKrZ2W9>g!v7E{6-_zF{IZZnsg%V-wQw8AvaOf)&x
z0y>2zSpj;5hH<VbqUm%6T}@Y6WHI(ZPNsXYGjs`EY_77JjNiP(*IJYKpPaBVEWZ`u
z&#a031^<ITH`#o+S!*>~V<=+z%&RnxCeVS{;TdZ(`Bx@n^2~PnEj98l`8K|lH}i$O
zn%npszKAd2b1jcqVinNow2aQ6)A+aiF@M5;r>9M|dEQ)W*7Iei!78Ccsm`>}Q8a`y
z=sj}-zsqacfhB@v)$$hpwTYXg=`;xwGe4&T%t6+X)=}2coHBP>2U_E;Vk^_UgPHb5
z%(;ur!F;!=!;CzH|H5za+x!j(=r>eDwKRZ=X&{wwHs`R9?xR2O9Nxh%@*Dgif6YCH
z%_wuIImujU{$@JNH&&IkkVexOT0p;`pVRfUfo`B1=_cAp-|;j)hHo%OnMcfDt?5{m
zjG~2fBppL1(ki-(?xy$YODLQF<{><o58#db3%=6?%|LU2nPX;~dFE=f#k^-eFez)Y
zHN~2P`F{}|LC?^$^eV+D$!GH_{s~{g7n@Qu(u^^um@~{J<|eb*d~D@dLF*vvIQtD!
zV-2>3SVOI0)^MxN8fi6Hqwr2?!CPf49YTlGadbSbqs!@6^d$X}-lV_KXY?U`L?6>9
z^mqD{zMwdDvYQL}U_OkG;8s4K*Ymyn7{9=O<T!U3%NXM_PLpejO}S|_^G&N+W`1H;
zn3ZOg`KeiBt}&a;ugnhff_c%rY*|*mb(l4q^0DtX9D9KyX$sAzRyv7R(zWymy-0te
zmuM$_PXDAX`j)=o0xsopuH<U2<r*H&Be;&6xRG0UEKlS^cs?)WBl##^#AongUcx8v
ziF`7j#7p@UUdE^LX?!}L$;<glzM8M&TX+-yo*(5$cpE><Pjfpz!|!nif54yeSNv~|
z8n^M90j9zXHN)`U9%l|Thnv;rTywqoh54nq%lzK_!8~jpGf$X5n>Wmx<}c<g^PTCz
zTf5MjV9m9T#Y(k+M$ln2myV{N(Ajhjt)`#RKj=UFBtOUBntXGJxzs#uJ~N-1sO7bC
ztq@kQVLFJG(yytVw$tnMH@0{ZPvIGSEU)Bsyp<o|2l-)sod3q1#%IE&(hN3Z%``LB
z%rb|WW6cHTLi200%{*?NGH+W>%VkZo4zW(4gJ}+}z+UKOw3QyB52%B_qOWN;b#oby
z<GH+oFXhYm=X^ci#JBSu{H4h-S*F08YR)$2n_JAS=0VF?Zfk@!%R0h3hVG}|(eLR2
zdXWCg<2h-Xpwk>@PBwQyd-;bo(>fe)GS`2h8U4Q=Btt4#+pg8LmdNT-;RTR*(F!I2
z2}6ZoE{23<L*Q#5Vet?Q(uleh>i|gXhzmRl(yN%SAYuIw%)cT13N#3cq9g)c2brmu
z-H?(uFc(2)D`KaL<|t++WUd13gQ7u&9*4|Rppj5Ctk6A>IE5fUOQC3?f)fE0Emrt9
zkR^&a0kTXn??RR<{3>LH!Z^hctyFjdWR)VPE27nkbtq(wVjT=wt5`E22P@EyC^|%;
zX2_w6`4DoL0^NzC!xd;z6dj?^e8@TlniWOs6*>!YqylY=q74d3zKsh14zfux|Adr2
z1JKGSD&+vs&nViWKvSdWXazbOMaL*4{V#nF_%+C}ijg)Rr?9mDfr=&N8L#kNkP{VX
zeiWUgh!rh5S%Ef4(J6|Y*N9G6%rlVErU1GkMGsL(>MHFHOaxNu42<-Hj2i%LjiR#@
zmh@&ToDVriG0#F`#t;}y5ta4@&_XFHeE?`Pr1SwWQuYN3%NUk^1<+P0x=^8OAdggx
zxE-ZHm!;^@3ba~^9;ZORrD&@HO_!pJ6zIGZU93R+rKpToKr%L$DCTF7Co0g5DSDDZ
zJ0MS1jI`@g1)4KOPf<w5;He7xAx~3S>b*?i?U1J{EMwseg+q|b6%InmSOlB_xk7<X
zPSKSLNj|F-=;;*wi9*joo~=N4r|3Bfw0MfHR_Hd!a}^`?I8QOxLH<<XgCN%^(EBOc
zreFm?(MuGP@~%~|Dxm14ikx(cUZ&9VkkY2WJPCQVV*Udu`2i~%@_NORzSy9!#Jxc=
z@(gWM44$#*CIu@Gir%WwpCP4gz)0C-oB^y$C@S>_SfNn#R|-jaw<s)iy;EUngI_Cr
zKBPQv0BahG-mOq6<Zl$Lb0{j$D!__|qQ6yW5ahiI%Q%(s3anPh`xWyE<W>c1B#QoC
z!D@-34=6MkvR$#pK|Z6%X|3q93Ox+@oWc=E8D{`1Es9Dz0+R55RE(7E1;t3cWPAhr
z8GjMifMgDPN#UK4e^OZ5S<(VV++I<v0g$gMW*4Ny0sL3U*Ay!c@+}2xJ&L}qVAV%a
zDJ#JGkD~7?SP4?}Jq2q*ib`65o`RG-fRXn5n_}*Td|xp#S9d6)!9+h&tbvdpD_B`l
z^fQHKLVm88n<4+9SVfRuD5eVXp9)Leql#4z8B>h(LtMd1m7<*r)~pmwDD)WQ*9z9L
z6qV-z;2a`Fzft&Kkl!j;<x+IFLK7jsQ}`^%ZUt*#EG87JhAGAh*2NSv3RcJzvlOhA
zDTY1}uxh55Q^ER~VrVY`D`|?k6|AW#=1~ak9`h=UzKHo0gSL;M4g#`-?OZWOLrQ+Y
z90Mt30$7<-Ov(i4PRIhq+zwf&@S%|C1A$S&7}`SMS&+pF{{gZ@;R7K{6|D3rR;JLS
zkmZUw5wcQ&9e`r!R{<6Qid8GL4YEeDB)wY290@sC;rk(nC@ggysu)Rgm}2mJ#D*&@
z?KeU(3n1$iMxVz<DzHCLtWlveA)6FAks2GNz&b%OJf8yW6ciIb0E-31#wf5|Q0xE&
zRt$<sy8x1Mj#psWpjbqqC6E&p*#nGCQsgvhOvV;4Ql2S_nE*LefmMWJhbXijQu+)S
zsrO6;HWP|Tp8;4;C^lPx9fe|Z6l#M!T%ijg=PE2?ZJq*q3&rLu^e;$hM_|r@JVLP+
zLoQUzS&-6az&ajM#v<SbNEwTOWgH!=n7bj5Q_OE5TNQH;<RZoV7E<y6=03<16mvhM
zv?DNIL&`V<rVH|7g)xp}mnbaH%cY8uv3HpQYZ1jRS70}yn6w>$1&LzUDX=9`?B@z4
zAg@;}X`34qeg*O-#XJD{3x%DKGFE_vcR}n{g{9t7H^5IoVm=jMrJ|VB6Tn_YF{vk@
zJ0Q0zuwhY5@&K@AQS3p5-iF+!z`{kbM-@63@-fBy9rAGn_AiRHEA%epGYSudd{*I+
zkk2Vx0l8gadFGy1xC-)*3J-*oF$cH=a)-k5{Jp5Kw1c!4fVGWcuPU?!@*TxU-1ijT
z0J%%yD<J=-m@>%s6>}S8hhn5ZKU847qu56Z&4T<`;hB)1D6s8OOxgv|G{{dCGZFF&
z#pFQ#Q-KYTVqYq-22w1g&?HD1ivSiviv34{t&n2hD6$J5`&Qvx$lVGoi4^-zflZNO
z-3qLWFeNFlGg6!tx(d>vSmN(gV1=Z(OObQgakm1?B*lFS-3#egV6CJ$>My`<N$~=O
zE`~&z1S5G3P%O-m@nXeD8t8Aqyab6p6Zl%lGQ~oh#LE>%`^7Pa1jcg|uT-oINQ@!D
zk~FIoD*{=g@Mn-1_kuMMa<IZ*Kn_v(ACN;8{v2|cVzMEJD||R)qhi)VN_~JOZkY20
z*kmc*tdNvpv|{-n#Sg$@OL6f7u-#Hz$^>A=rMT1?z@AI-$qFpH6rZBdSjdAElL;yP
z1o&5w(-aee#5fR49^}CatilwZslYx=ag1q!r2KOf{w1W;6Yy=2a}~Z7a-PDQA?GW6
zAtc7Mz^fsTP`C|pp~B}t9;xs}kVh$e0p!sNp9^`6VtF8sRm>8|WeTj;6hBLW{hHz{
z6j-t;zEXiro8qe!It}tC3QK*@R`_E`DHD*t@Q6#^09J5{pQq5%kXI^3#?;k{c^-1T
zVq~mdrx<bjxx$x0-k_KU$eR^d)hRCR2w-2QxU?^zI!I|#0Gm6-Z&zS_r}!NT?C=zq
zJ_fMJQ+$g;??K+B7|HW)h2MqzgTiYeA68()r}(3aD1Y(C6|x|oRIFOaXB3uvo>lnQ
zkS{1E4!J`yNyrx!(+T;KViJ&lQcMi;Wd%D16n{mb10dyj07l03PQ^MB@-@Yhc6eQ}
zWW4-U;S}U9#oP%g{RgZAA?29|)_BM-6ieowuM{g2QpyC(JCITzVEzax^#SHaNXY}3
z4Uqp)%wouH#o*cML^}$Mw(mqc3I=`JiMAHZ2uP=5(2t#7h5rKSQ}``Nzrt@rqK^ee
z**Z%U?3z#~`bNP133Xzu2-r!X&e00?RH$>Ig54GBL^}%DXQ58CqktV3>O?yVBxxV3
za5iMC!a0zrr+}Rq>cn#^V2_46FIMOekZ5;-=RiKC@D4~kKLTT1bYd(C{08Ln3V#SG
zet^G*#CQ<o%wp$jiebpVDP|O8hhp&TbpBm2CqaItm@6T_QH;d<PB9Ws(gg<o372A3
zL88tA_LV5ns9=YQ5={#Bnkdn%VAqKfau2ZoM2Q0w>_kywoPs?mN=#6&J4K0!3ihce
zF<HTm6(tT*uy;j?X$p3+D1o{Ogt8}QDJ=f86+Q-XuELV0lp7fIYeM{hc?9xU#o$><
z9Is$kj1r3!IklHS-wN0%qXgPfz@8Z;mMYjiqr@o+_R%PDs)8LgN}Q%(Z;cYm6zsB5
z;&cW3ZIoE9VCRhzXDQf&qr?h@qL30F(7z$iQFsXCYJ~?wo~y95_xTEMguFmu>5B^$
zz7z5y#RMTSRs}N<a;;({ol6yiahteIG1A7DD`p-f#*<*KhD>8HA11C*%zKdQ74re)
zb&5$r%9sF_^tp@)U`>JCpjeWxj00dvUNQ~<_8cj3n}Xd(O5CSlACeLeD%g>v#5M(c
zlazQ=5yL{_F@=(lPbz#iB%V!y(bkD}g?|DmX#u_ja=XH4uf!{gDTS0gfEfw-hGND*
zzNHu$BX29_49IsBBWb>?7^$DM7ciS4Wn2RDG35J-l>;g52&^FFM~WrSii88!agg6A
zRwd-Oid7A{Td`^&zf&w3Ki!HY<ElrohLSXqjWrCC6>B)8Q7p{iNlUR%_oPFy8X%pD
zg=Z(}Qmkf3w_>$GdK7Ckq*uWnFeSqZc7rKdpkQB^l7$L(h$&g5V6T{x0~G8UQ?gjW
z{xKynrUmRIQ?f+Co-!o|DcD`6B>GRlJ~JiD6zn)tvRuL5GbJk&>_Sr#Z7N_tnvzut
zcBUzb_7$*4O-Zz`fZb|J)+*SyrX<>0zz#Mg(bj@2K$6ndfI1<itpU3srL6%=y+<f~
zFr>6Q;KLy66+Qw|`T%e%r1Sw`X`e;~f0IhdCWY^Xl)eG{7^L(K;1?jJZvadCNZ$aK
za$-yi+y!}nVx<42&w!C}F-|cq$O9E4V`02vav>)uM*1qE7-{c`ijne8Qp|kF$%>JB
zO;OA;$b%H~6UeEGSphjsF)JabD`pj>v>h-%g_N-c%o@l;6>|;b48?4MoT->!LC#Xl
z4oGQtV5F@uCkp08$T^C68S-$&lJ=RaSox6i6id>Ox&fR#pyUDtXAmfPgo4uulw7Fb
zTmmJJRB%Fpl1C{xt3b)46`We2<S`1)FHrJWg{0l&nE^P{KuH-B0H+%$DSiOw94NU+
z!HEY-E>>{%fs!XEI0Zq;B?`_%Q1V0tCnG2+=>nXQpybI4PD@a7sX{VlPEl}zf|92y
z^bO=`3d^%D{R+4g@^pn|Y+&9LxDxVAg{vW#D_jeCmcliVG7bPsyGcC(kAReM0JsiP
z#tq;m$g>r0ggi&#7Rc2KV|*viRanO9c?!!olJNvs`cuXeVClE>6_$RxKw;^(3l*03
zZc|w5cag%<_7^KG?R|;D(hqADmbSlCVQKHn6qbItTw!VZbqY({|4d<N<0}-Fez;O$
z>4&QnmbSlIVQKGc6qfduu>x4)tyfsWUZ=3g>lMBQa)ZM1Ox>XH?;&qg_)*B46n+G9
zqr%%Df1&WRkT)y*G^C^pxE)f`1^f);tqQ*f`Ada6AU7-g0p#rpe+qer!e2pdQTX4G
zcPjFC9?4%T#tnIwV!V*|C}sfUZxvGkd9PxILf)sCVUYJLrXKQliWvvFRWUMOO1l6f
z^T&gVkums?Vq^?HtQcuyX)j=;ous{hk$OL-7%A`LijlORP|P17pHz&bCv6Ih#FI7!
zM#4&)0`q4`=>uTifP79dZ$fTY%wHg%SIk?GFDT|a$Q_F5fqYT19FQ+5Rw3k{6l((H
zD~dH2@>K<=m?*hZ!FeW1zNX-06D40)aK?#}Z!0+ML`fM30Oy`4xl6$bC`!Js;4Bm+
zKTvQgijp5HI3GpHPZXS#qU2`^&P-AAp9)S-QSx63{ReWl!qTo%XTWHuRJOw3LIxF+
z4~aSp<`Bq1ior9TDpSnUkf@JfkbkO9F&MWgj3L29AzKs+V=^^Xv2r2DDLBbRsfmgl
zXh=;_aJq|9(-fTZqLh>a;KUcD&~^gOeo<<ULKqV%w4;FYV3eAt;A9x3<}36!$b|}9
zkjE-K2@>xgfhGUN3eSLCqOgQLQQ?)4Cn=0MEOm;)TOm(X_yNe%6n+r$bcHdVQ)eoS
zv7cI{u#^pbEN~|z`c*JK$a57VZSqsaR6?#%%wWh16(enYkz%C%E>_G`$V(J63v#Vu
zq%AL3jMP!;0}S2?sh=rE(!NnK(&if#vkmeWijlh9teB@DZ&A$Kked|?&qeBX#d1O3
zrC3tV-zb*!Gv*}$XYnW{?FDcuk5Z2)IG;x;$q(S99;GBdfHQlPYFBW2k5V$e0nYJJ
zYP*6HeU$pMg0p>;!gDH62jm+H&ihdc^M!zuf0U9s0Gt7&)Vm6GLrR|kE`xkuVHwvS
zDLfbQV}+%hpDHZR(dP<F+kdI>&mkpmz}G{{^8@%M$e6;?esP8Gfc!>bNkhs9Oa^3+
zVzNlo8yiyq=~oQKX&1r?24k)(pqTR^@k|N^eb|+!m|G#kig^&SM6nEHxnj8?YZVLg
zLKnuRU}3y<$vv=+fUHw+Hj=u=DmW!cU1&Q2=OwA@Km{i!sVk!33?+3<R&bh<x-f18
z`YYs2g~vmp-33lUE>KJp<WY*54tca<j)P2NPR8}|ib30UouU}@RTrLR!TbXfeJNOy
z=SoFRarXPyf!wzs^d^AV)&~3uCUSM`#-C+AMC4jZ<bD|k*>>Uwepcg90`C1^!Z^Gf
zB(Q-Dx6F2;fCtP5NGGcef2)}do*>FWZE_%UYXQ;@IsoDZ5jTXmA>@;X@OhsQ<xdCq
z5{1#zVT1|qBr3Rys1WHFEh8Gx3IikDOOQs%cA`?mFDnIWao7g=R%C-6_-Q5NQ&kL*
zW;L#BmV%dYVsjk0muN8j1|!`e8DJaHQ1}n+z@M2S{BWc*q8-Nabwnc(z9E8*3kTRv
z)P!(NPr#7%IeyLw>9i~%8oe36{ZtEf5FOA4;68R3cn6y<^#F2w2%9dYU?)*zI?=>+
zM3dk*32`ScBbu_6=pdwZ&~Bos$6$jG^_q_RgPVvBSqLLO;?3AZG;<!&tkFb=A?{)A
zM6*$rIWxe^M2F*kF5=9C|NH{{2GyHHM_dI%8}eF+a0{bEN1_Zzt^w~59fh=yS`W4p
z9le3*7`PuBAv(?h?j<@N=^p<OQ7h73gftf;&BaLPgmD1=OW?Ld<kv(eZYDZ88{CbT
zUMtZl9?(H_>QzL`N&)gYoxpMc_cMlp)c|>)iFjwifB6)E`{hV)Wii-BwCWwApR|IP
ziOxoO&p}$}piHZg#(4;TUV`YSyNS+6nJ(Bxbm5ysZA$^txo9EL#j~+ti1aT(`fJe+
zm&0$}46qq|PV}=ciLQX(mG$5%qN{cgU40BdTGt@{wQ#@oUZV9&aKN@0Y$E#kbbz?m
zuOZst07&Zw$Quj5Hlmvl_NJXg8{z(odEgVGn|I?d2GY3|Y21b~{u1ThjJ$7OhE4NI
zupPz~#QzoCwyXlE-<{wt<bT&zqPyXD_t!ASBdvSZ6a97}(Y+Y}Y2EJu%K_5-T?9n&
z64^rZdz9~iR-y;5B6?^X(H|UO6VbzP--a|EodWJAIebKqA-%`z0qXU{MxrO-_axGI
z3UQu#hv?}g0J0t7+V3TLW+~CL$m`iph@NW&@Y}wf=y`;D9@l@IjYA!%#|ubr2i$hR
z?M3AK(iEaU6#$g=Wt8((57D1z0Qm3RfCD$R;0dDF5&sR`zqy|1FHK+<$-x17dlL?^
zts{DO70HGay@&h1;{LDj--UYqEt}|lr1e2DUXnYAK7`*#A@Bsz$H@B=#QS?Y(Pyi%
zz|H_|ME|G<8^CTDl#s?3i1*K9h`!uH^pywfBKp^I7>*I%KCKM@*hUzf=GlfO>Rbl7
z4c5L^XsW{i$}AeMyxqP=+W$cw-#kI|Ez;hNu-`#;!@c_<qMmH98@lAZ#5gs{W-YOW
z-)M7eC3ddHQTsJ8V6_u_fOk7KNH!Atr-RRN7<3nL=9|QUR`4}()+fZ->xpxaR&Fu4
zia3bBJqRLB2x0PSL4r6RX@!wT0fDtJid6!nRfPNpY_ktUa7hR8pryp6vjMKlHW8QO
zx;#o;@dS(q*<cHCRV&y*T)maJW)*R5JwU#LXAlp;^-zQzHU-85gsnq9^(f0olw;&}
z9JrYWUM6l_2E)NZ9C(NSD5TZAoVW#PwM6l=dk8=JYvM5ocR(pX8OKfs+la>@odXef
z{1V~`D1YR0;)zR%C$ENLjO&AT5>Img<TZUQ@xhhEhal}kj{)$Tv7UJ543Hq6wGKu=
z_#cM!XTyC?2q5g79mIzt-^1I9F}LzOq(8qFf6RxlN5F64CgLLn<bM>>I%+G}O?>op
zumN-sAG4kKScExlG=SgnQ5Y6mi5C?Eq`MgDEk>Ca!~KMf0OeVNyiZ(6Vuaw6wh*5@
z51<W}BK=d~cPi346=|M^GM)Ab@iK%tV=eKSh_k$s_^daHSG+^Kaytz18;O51h4}0O
zunNHaoNRCvK>1c9-E&c%^CAG(Yf6dFcYt*O{ueAEzVI00Hssg#CGkb$0In}y19lN#
zvYU7<?k`<Ud>PWY9C@s3!a-Y<^$O&1<uL&1T!nP5t^_FCHT3{)*P?9KBL4a{#MiY!
zkwlyg3&Aep8^+;h|B3;^+=TMpg!16A<BjV9{C|P)H%EwXf&Zo%pq=<ul>auk|FV>L
z^Azw9@$FlP?|}aui1VxEP<0#t<=XNI@tp{F=O*G`R|4dH7xKA#3P=$DW()B>tB8L)
z4<PQnt;F|v0K(lrj`(+o`@0>)TjBnDxc~lM;s@3cKZrCQY61@t|6wih!wCBb{2xJj
z+akn|!tXKU@wfvdh@V9GCpQy6g>X;pBz}51cn3z9*~HHlgKb#+BF=MP6K_X;&#!_(
zc{&Wti1z~gcHsI&l;y?kIH-g;f2t*Z*#l6XSJo20iny;nLHuW2|9Kbj&V|IUts#DW
z3WyTFv4i+cr12)o@E3%8YaG}~{5Hz^_HJwpFC~5#>0!?0_eSFYFY?%x0X7o<4Q}rf
z@dsZLci{fRVZ<NJ0G|+ljC?++2k+p(!&c%?;r|)jKHEh6`Q60-SWf%}%KZiM{O1PZ
zFIN+P1;2lTDDsXu0MdzX$08Hq6IX$DD9X4`?Et%BKu3CAkY8^j{$@Jyw;5my@ooWO
zzgrJpChmq`H^TL_f$d~)GRbf~*aQ+}%nUMCEx_+~TJMl?KsvrA<Gh=Us|kEb2J^7-
z%_HMqf<;S&OlCGfxWIHWSqlN;Wv?cagK*GsO%V5?%{UmdmQ4OK@HLrmE9f9ofHVuD
zWD1dY5%Mm2lgxnGWQq}gAo3{LNv5<Ez`tw<neuTY1~F5CvR5uAQw6G#4(3}^3;)50
zH>4II&!Owc467tF9Ay~(Ihhel0n({kPo^Gj^&7CkRsayc0e%fT$TYT-X<A2SR1=wI
z<kP%`%xDkzoXnVe$s919%-Cbdj0*v@1?F8d9{%Hz#)NfbCLz5^Q8JTvk(qK84p_i%
z+HCLyndz-$4ju;JcL?GgiZaYVJ~KWcGjk`ISx9%*%VZ8)OJ+6!gqgF7%;CjgGnu)F
zKW_(_`Af+xfE)B$a|GNMBHob*dlcdvwF!UPi~D2R$Q(NZApCJ_0Lpayb~3H?WEORh
zS&ZuwD#0!?OWMhth<s0i+sP>F$=k>*T}`sdXih;|ry|^`Uy?bkmCQ2KXW4Wzr;i2+
zGH0yA;a8OL%+K-11*pRc_^m+vm58^h0Kn}h@H-pf&PHD6gun(etFyrx0Jrmy{&`!l
zapD0e!}$xv7Kh9+&Em_CQx&7+6naZzO}N|@%5P|Hs%|MOcje`T@*B$<nn&d{Ra+J1
z)pcCq4&YCO%d02gPxi)gL;fJnceR9EaH?@3KvQ#LLwHnEvv}p*R++oi7YKv`fs?vh
zL&e1*-i+^m<T$<Tnd)J$Gsok_-*9JmiaZ%sv+>^Pb!WSsnOP-6hLmJv$c=aFs49sc
z3bYr8x;yMNN<+on9WG~Hp3{}-xy9}Ex^MAhBB;wXWZV!(r8r2{ENDCOH<|6&g=v;u
zNp5I9kejN9@~EcjihgxFkQ@7a_FUC8<cC~RpRirIYD7h)@*7$jQ89kV>2^CqC{nv!
zqzEo;7yaH@R9)RvU42ES_aSd)O|dgLB<L)z;ZnD=*6FU77}^~5wnId}OC?HE^;aIh
z-xDk-_jt++$b~v|m<VsisrmvMM&oD(9fO+mE4}oL^T*1nos%v<%08-peSYBHWOt*5
z(N`V#?!M{=tJ2}b4@Wrn`^B44!_9Ugt)b%9?^mo`B4^82{SREpu%w<B_&?ZV$e<8L
zWvGN3#&gpME)Q@xr=f&H0rpH@aoLK=_Se@9^GA-H-#$5gJ9)*oynN*RCG$tx<Dh3Z
z#=%;QgE|_G7Nn{g^l}|HV;Eq7hr(`G{#drZ@+!(*16fCWdZ-|%)!Vwkyoyo$)8j_^
zeeM;m;@V=@3b)Tc@;Gls;ELrk>d?XXUVcR&V>>PaUhg>_7cL5VFYx=NDg6Emyun4j
zyi%{bRl?z`G>?Z$xPZ5NIc8v(K~V3%;xCEsM1K!Lc_*N+^Y-p9C!R(rn~Y#7YjB_Y
zs&pOj0NUZW)$8t%{P5Mil{d>}H}Xbt_gwQd_-Z)bP%D@6^|T7P?x8)`JN8^k`)Zk=
z>0jm?OaPcQ@=-n)%2^%Wr;PiSxn&>!{$<PVSIquJ?tjh8`pW#XJ%#IE=C(b~d&>O1
zc9ycrb1r3f=It2=($@QyxhmcM2&?Uke(fQ<UGd%BvA;TSzfj(OjeU7v6}q3<Q`rA`
zTL<@PYxh2Fxo^4mZR^b_s9kI+{QCVg_Fa2|c>h-$+tc%1&(oNPpTTMHLunrVzU(CY
zZOLl+TPwDQu)I5@+3_^WfZ6|D%?J+Q=O{|#z0*8_?Pj-Rik1horuQP;d+~jjKChPE
zFuXZ4XwO8!qVLH9Z`lB+?|gTj|Bu-NtO3s8cj#R_t!@=#e(0`H9n*qm2Q#P31h+9W
z7|h%#SKH)jTc2C3EYle*3*77T<z=50%HNt9?B1y}LaU0lz3xi&F0z*&1~lH~J?-Wq
z%yBhTZ@2Mc%ySi^n(BD(_uW{`7<ijE>bH(uXK!QJZ;HO@1}}4Y7RKUrX}`?};x(C{
z7wijMF{&|amS+c+1VY1lUxeACXOC_J^=O9f!I<@Gi;LGdt<m;8KU!+tcez!4$E_c>
zq;**cspxyLJycvfdUUPeb(gxm%O#20;;${|-qG!py5CXXoy(VC9_s1t`6GVy<}v&g
z?m_sgzAG`lMzFgfKb-HXXvl}!Qc>e}L4&Doo?ymvOLGak+%9)>-$j171mnv+f-!@Z
zh)&Qlo*T=ntMXl02JWsAjIb?;lfJ0NcpT4&gwa$}-Hf0TA>7iOA9k0!BtPVBCp3b&
zBD*|m&``fSI}pfr`-c`CH`yJ)k7+nu2Ml%&F8AaIj3<;?UhB`b0{(Dbs4mav_T^PM
za(!8OoEPxrIvg1uPX@32UJP%je+=*5*%Tim**w}C-H}d@Ew^zFD99`<%nTQM%N$PE
zk?u@K*&#z`6uV7MzB?xvcA7wbjxm`|mk%?cd!o;uR}%7j-5&3-?+48c_Y3Ou@ZM=p
zv!l7ak^;u6K|i&dNth30Hh{({jfBQ-hGvD=a&>tY%L`fhW)xgxVt~pv9>Z`1<8_@M
z?tM#-<vg^zzRWcibWYXTGE4o{l}nc`t*rK!dR)#amd~2vba^TQLu=}rL{V!hu56h-
zrm|RX>T8AuD)xlq0eDD@g2C32)fLtFZ)6ryKmu7cMPb)dwzlT<H4Lg5ZN}6NYVbL2
zm#19eqMF`-Tq^O4g7U60Jv}{N<M&0@L6aGb`qor8VeV=egb|3Z{1B>JjzJ@jDym*%
z*CYgwhHKZk-C=jg-P($;u-m=P9U7ocu0?yC+TrZZcS|UU5JElrJr~iZ@HE_wvnzHP
zG=IrDtobAFCU-c$0UjtvUtwIKj_;-Q{nTIkexcIk>|gYK%h2L>l}ol0P~~)k<cH8W
z(*3BuTfVeF|4kWw_g}Tu_xhr*`hA6IZonjw&bqHbs>^@>U)Axy>I;_N{No7M+L~=&
z<@=3>AL#hj@AU)hxbp9FyHDu{d$ja>k$#}7_D<&qd*%Bb@B_^aTZhzZqSh<;LoNFQ
zjmY0Gqtr@t${if#ZP5QKFrQ=f7KG~Gw+wJMwb+xdC<C$_4d>&v1w9ipgiOzvhh^Fi
zaEsfX5Jok%jI1cfw2fxMtpD|%g~EgWL2j4B@1E^)Szf1M#zT8|V91bwE7$GH8aiS~
zmfM{>ZIBPn#yGu}3n4Qs7jN6M`1m~F^%;Mr3qN$@&u|%s!Q^puc1A|F$C2aC4TW;u
zIgaOi-T`>;TTWMoAK_e?e#{H<&c--s$FD;)%G(+X5_^GzB}tzSkNHj&`SeVPRVij;
zmm3owUdvoNysCWc)UoAN!?DcvwFZ2pzCbH3f1Y2I9mvfMWEZuM9W``PMa86{qsH34
z8ztaI`*Le}Nx`6^P^f57K}osHQ@f$3ZO6P^E@$T%4H(WxR#?^JsZ>!@jcFPmc;UN7
zJbRrQ2Qdo9$JZ3*d)%Ik%ApgpgPEn7!R(1cD>LAdUszKxq_VKEa>$@SI6IhSj3a1Y
zu+zm8+!cfBh7O%pe}K>9@f}b<ZRpUtK^1O?UrOt*%5*t7kXsoPnH9_q+iUv{*e-6z
zFPjv~tO|uj6!B3xW#L|JMK#G$`|9bX!Ax$0nibjXb}p4y0lv7jJCYfMBHZEj21P;R
z(i!6Ak*_y{nWE0P>obGeXJ#V$tW3^umR01G<@EKH{jP#8Dyy>I)t9x;D#6recN1(C
zUiW6V7mAWCz3%UjvDa;&?|8Yj8!EY6xwncIv{kNf%k61bT-)W?f!`97=L20=h2=1Y
zcN2zv30KMcF+F#x@&}FD#TfLx%jND}(>yNMO1u<Tx?G-VZfC^l?zeazxhl)K(y~@M
zvsR)0`{w2B%LthUp<-6+gq@E!b$XC8?RmiVPHgm^LU5%NVuhoBA@<Ens?%3Bs}T=_
zzM!hHP)IlXK6$xTSk?-cUGV<T=nvIc<{5bg`?X_>wxDL|6zyY=K9YVC`&k`+s0MpC
zW53euo!5|k`YcOqQqoNW`zrwH%9i)<yY<rpzHDD$x;xM9ce^je3;$9#uJZb2Ts`9v
zd2Ak;As!+3S~va+;c}n)q6~ZWWnFLg4ehT0^uC1qxAXoo=-0dZE9UphuvZ@s>g`rB
z$n+@y=}rj!H)ZJG>-#T6|9(e1?A7lRU~x%5zx@<|^dN8O*Z=!V{=e%AdGGdofL~$T
zEPuIH)%U)%l_6MFb95fd%fZWxZ-s*QEIRR7)$vx^^Y==3=p)e&;V6D#H@jE!AnE$<
z4_CWw^#JeWp1;GMbc>xwKP3m<>dN9i6Ad(QHb0V<wV(Xu8IpSZ9qW5Lk6=GF2Hl1i
zZl6g7FYKp9K}{Do--F_Ie<;P}K`1Vt|3As218sG?)MJkZZ7Z)>(dDG)`W#tw?I)RE
z{z%@cyWs6#2df(_ZZfN-^-!#qF|T1nq;+E$DfqlZAmm=)#($weze~;^F~S!RR}k>^
zzv*qKr}0aT&qy9ESUtnW-aE1}-elJ98}Zd|^^d(*Qd9fo*zXd>MqfXTcj7bhYcEyl
zzHY|yy+U*&<Rn^l34YYGag^vnxUrWXuA!XQ2wkFgEi!@+<chpT%)H!s7^-p@s?vC3
zAm};F?LN#C44l~L&t%-}$0xrp7@EQk<>5^K-U;mMgJWMPdpp|yIsDR#T8Wm*-#^13
zQ7(FpC_Q3}N8`6Z4V2!1vSh#Qpbg2|JZ~f{jk0mk2s<&<JzGl=eF`ga3EfoPcTv?e
zYJ{n&4nV;vVJx-b>#T8y-^nh>uA5YcZ)`Rc)X$Nt(w0drrG9s%+h3BOk)53}uwkIv
zkrDRh)XjkkAxpfgRqz{JcX+cu5BA{597fFUU(7C_{e;9RtE(%6%nt;9T{oxRc4;Xs
zZ7C}Z`YZgoh5022jdVReXGS<@U@bEKQW7o-wyAi+Sffv#k?Zi~=Q;AvoZ>(VcOh+k
z?%MH7lh25@R#k&(q7L?sEKJ_^n`98)BzVMKm`cz>q9{*5JJ}Ok9rR^Ug{%4fYp(G+
zok3^FbEU-rf3-ir)|H+RT%6whd##u?%Ng{Rd!4R%clZMV{~hyOPTU5a`*GKI-wV)J
z9;IpW8+c;Ihc4gPkS??+m#V%%<?PjOrGWBE%)=^NI~`9<3j#EbY-mCEV?KfLPx=~m
zDH#Rn*TdvP4)i+QK3A@1cyq(NhK6}3&MPR)$|=ggZ%-DOK*n&VCwE9yq^fCHXh^0j
zKV#60+Vb+!vyRRV1#`kVxnA#p^gE>1mFLZHTh4(E^OnqOsII7L7*tSXvB#BlZbpvV
znO}3zu#sc(bHZ-N@R|ven?{cA-kBFH%@5|+=4R!R{N^6?d+b2LI_Jk)uZT*asZPXi
zZY)55Rk+-+L5<*={IICe?nY?zGLjpc!__qvuqjobrlMqrvA-ks=iZ$-*h8>pY{9NX
zKiw#ZSys%}Jif+c<P|#{#d#T~#qIWBM0niwaDjV<sc{s~7YF?FycEdjer#|{%V3`B
z&+-}L>!=u8ZZ9Uv$3EBH5y%xSI68Qsuc{?n<r_HoFn2IqQW6dtUsZVUwF3u-tH$BZ
z4cFB?BO}l<xVwFDOCTfL>&s~JI(bw&DJ)L9+j5JgJ?woLSzpxQ?~f;AxA6!%0SEQY
zp*FOKEGMK10M;7rYT!Zx=y!h0NVEoe9a0|cnrdgYJru&OY8d3i3<rZz3)XzHG8)e?
zQr3t+7J%X)ixz3k0K>Jpr36m~J~G9*`ep^bwb<pz^oL6uateyZ7Zv0*l!pBQr>j_;
zbB7lUOgj%O7@iB~?#nEX&+oS!x#4oZ-{*0KvRsaH-MQZE+}vz$E*6dHtI;K8WhKTM
zSXMUBYPG_-4oiaPN>o=?$mQ9j6G%Jyydad3?W)Z$D9EpMWoLv6N-Ao^wanwN)YWo$
z%HTT2RhZ-RhC=MV+w03I^t(c*gmPSNw<{;q-PU_`YhHGCUPXod-HH`XC?sL8ka#YC
zVVTY?{q}$zSV<IP9XbiKZn$ECkyn810prywyCT?S)LoGpc_w9AQ5$??*uH4#)m~L|
z`jxlH>$oQn2p1II-n-W+Tfg1OF~(czvBns;YoNo;o+gjw9q4jDGHS#aS!#_LG3vFF
zlI-loh4qEU@7e7wPTcMB+zmsdqol;^TQI!Q?Y!IRZo*5|fxU&N@ax6T;@854(53Qw
zjV)r9#oG>MoczX?uuOEAU7R(T3j>%Pab1IXe*%XanlVyj7rU4AD-!yIB(MiWlGkTC
z0ZBxG4kgArOt2;P{0eD7{Xv#Bx+PIwy@-uvxxJYgBP^e{smhgOHRa@%uxCU@rq?aM
zU|f=$(`4nis+zpMpW?~!__8eHaQYn{C)URfm>fI~ztdr?ET1#m3+FsYU$)bi2X`;z
zeuKL+eZk@kZ-F<X_?DJgE%<Lf%7UL*cKDWi0;bw!dD?svm-~<hikRnX^H{EG6ZnbG
ziD@!ha?6n1T#}n#b94E!Eq1u}E}YB32=3Ct`K53!DO`pWE*nWXLzdg+a$EOfEjxSk
zX!IHEq4;XUZ>Hqfc80QEO-mQ>cvJV<B>VK$?;FWXr2Fck;7wldO~E2wIiRX)xzm~N
z%yf<)kFR{E$i45mEqJ4^$aiC~Y5>!Ks_y*+>y0Mm#Qcr_yAaA5z>|TeA|IMX4pa*C
zc_>`A*KJ0-w=g%i(A%EjXDrmy*LQ99nA}W`Wab*rW?zYK?^|R+PFwG3gFPV+`}=kH
zE!4%B3r9lzlmTduI9L;49v{y&;R<K{4|$BLX$iY4a_~066RXy8n2}uW^a6P-MrXL!
zNYS@hNxp$2yyfNI;UzwMqHdeM-b~-eZO8QKqqpW3I4%|o#QKb~>avXWa(S_%AlJ4^
z*qepR*_%J+*qn@voTJ8tJ)B>epI`a#`s^QY&Cg!HzKv@<C9t%>h>?|(lVx87OFZ50
z_A_pvUF7UW@47rse$6ATG3X%xJijhC9^8COc4F`}R;OS6wt-_pX;EfDcTdHFxu3`t
zwq6#@?Y}BKyL9e?3g&{$qV8RC+1-On<SFZ8!X8>H-rPa_m6g1$8|0UXa_se>G+|Xm
zPP&b`43?(e-G;u4J!eLGy{d~^TZ^hqM82!LPs0B^jM67!d|3J5V9Np(={qiMFRJDU
zg0@!|;j$YB!i9SNrS*q|6gk^2x18g#PzOIw4oK+-V3kpU-N2z3RgKhwdQ8Ay800km
zAAO)4<$5c<19As=<zG?mfL!_K%^e^Px!l&>_J4x*JO1xzyf%IQf%e_K-uEQIzW4vt
zX@PNj_Sa!*>)m(ncS#nWOSM1OVhkLJXA%ko4xm&+SAz~=KVR78tFD0_D_R=#4m_mI
zE!A~K)ELn=#La~uP&oLk;+z3R1B;7_vj+xC%a2@qWO-?DU^edYU(UdgI+X=KDZ%XF
z8-%ZyOMJ}!l1zNf3zQEiJ8Z#WWdq6r)q~2as>({Lvv4C$#btpS+i6fusjs}Ee2}lQ
zdhkkLX<22tw`Op41>r<_x*iUUjBJdHK~#w`Q4f2%%N=eG<82A06SYDeFoRUrWa0nx
zAXlgqsw&Iruw2);-aKg3tRdC6O_<9Ai+?`MH?!s0@&)ydrrXMI8+2$bmkj9dHua!U
zGl%lSzF{+)FTbtHQNN&k)~JKb&<h8Y@Z1TAQB-WwZbL38E`d|ZH*8i5#<AEEI<OAB
z9a=I~m0{_J(_}8Fb$u&iyok%8@Zmpue(Rg$@Qlh@PVKH`eOKeLn(cjLKkr@|%*^#>
zI<hVN&nJA2<85JOR%H7tLS=OxF-3TCvTXYrlP$7x#I=2WW-u#Qmji=ohGiWOlO;rk
z#{svp^>(<@ys7|9?}%@wQW~hrE44jPC%i|}^NJf*V^|j&^D6Sn_OVO%P2HH`aTv3-
zyOg8dB*%sfzO`t@TpEc)+9SoG4)Mqi_}2RZ*&)1}WWMZ|N7a9mi@PkZY`-~8>2BrC
z`^m1g?SCsjCp60a=GVQuqvJ=i``@YGkLS0kt?mDEem~ltTVx{tkqmps$VGT!heOv8
z)msb~dYDVhQT9q0hcWjZ;Lu05!o)DCu8uG2aC<xUyhjb$*CkEu;ybCX4KMMX^8W7L
zEZ_C@^4(t2gtx1`-_`*e-tE{;DaRWFr{q|2-FxAQ)w<{xZfL!*UghVx^@K?JoLB!F
zUS1C?WK^CAhP8T5UQu0+w*@(h-hd-CC#&GrS_#s<T)u}?`f3U;Dyw_V=P2Cfb??;U
zXmV_A&lQSl&&^NcJ^c*!&Fb-HBWF#E7}8`%thxq>6<sDZHGhOzZ5>sZjzx1r3%X%F
zstDzq^Nwb(753*&AC2WuNki^+&P+$hf&T@`OugtXfj*mSd2<8thN9}WYTr~xhR;zD
zuAOke0)L+D6Xm(e!pjk((3R=v9-|kp4=%|LS-!$xYt?`Ocob#-A65XA*M;{nVcj8_
zW2)V^#A5icODI}eQ-|ajgH<+NzR}Zj{b4B!w*_(|lJt#i?Nt{4f&(Vhh6^0XI#{T>
zWe3{ncDzfgaRPQGPF|r`?L$`SVT8V{uqRTno8kP0*dOFAFlvwTs4AtcPojKyH#EX#
z(P*D)u}=ick*&VeczLU|ihSKS-(4DlUOpgH>YhDhjK9HCRphGg^XBW}KHpVT<!SJb
z88SN^c8~XFO+}I#ott0PT$P_&cv$_QreH>(pBHE6=S~k~1e*rcA694w>yGY;C}Rch
z6ScvFp#h0qbU)UVs(lYS=}ENyhoMIG8Clx*HJ#whmb>15+U$p_@A9kek8L~3z@vV^
zMDlmHw_oVxGR&9b;f#v0MAij8=(~$xDLD{leh-&lfZ=kCY274}CJ*;c3-*w<ZDh7h
zP0rC^c`Ig8SvZ&x?92~W;90d_rnU(Xc99BbDUIO~ro~<ii|x=hqqz(h=Ff6xMx;26
zSu4EmBVgh4jVuX;%g0vWzhMXYn=;)CvV!>)2TU1TVJgOUZ_IKGtq?P(*E8G`2+Z-;
zURmj?ENHuS&@dAkZe_YMU6wW6=PRpgVDg-g(?Hp7ZvlWtTkk0!d(ha5<{|zo3vx#e
zKn7(8OdVHoNOpcJrYv)CMqz$YYkg_$J7(DUf@w&{i}a?Oy2<royGZK_Ug+vAu&f<~
zHtID*G&=FT+n+vNK<wTwrv$(&xOdRnk6}%<PPDLtR|gS{p$nn~uoKH3XCTLw!S<*s
z#c0GjGP4zX$a0zC$_Y3<-T#)L-ocRwua{P<j|?ozE~yAt*A0`0VKc^SW^nUnxgJ(m
z9j++JF1l5x-yYhGF>0>?q4TywLqDC?;N05H7^m2Cmyg)O@cFSj41;D^`5R%yPa_j@
z*(foW;RAbwe17b%5$7*qN5zxhJW3WWILY=Sx7_aWI6QuL=AcZsA3NkY=iv2uGD<Vt
ze%M`M7=>R(srWfO9;hkMSt1drDczB51VM!ULG0*~m_)bz?8LIW(OwRm5O?|Vb65P}
zWyE2*ybhPk;dNQCl(_ZU4Xa5%_t~N1_4dEw(9YI$&|dF-f@%^R31<3S&P<m#tExJ0
zf7h@(bz!e10=rWgMQFDCrXV-AU`^Q4cWUzcR~8IY6>ivW@d^;*w%C+l@|A4}F+n+D
z<dw;^ca3g8vC<v9?yejxtV(lEkpq+~a@UHuD)%;sPm7GtIXp9SxYK8fU^uGr7kWkv
zDjVT0^wl^WJfXW|Ko#t`7_3#l#Q?;<0=GrFH%IIvul>KfV-xT{K&+TqiSG%{46DHH
z^fq2{aRaPE1(ttk)qqkNzoi4JTGN(8)fr^JsU7c^0-8iqMPuJj6@qA--WN~!M;i&d
z?qU3|)nTGRxg#j|En4mYXaoC%=*eEDA83j0Z>Re4R@|qUycyau43Dk->#EmnZEaeI
zC-HxfYsZ>D2Nrs)^qg`;6sw_p7dC}T&@VMsuQnF0sjk2|qAcBjI*?oP+^!NiR5S|5
zKg9}&o!T{)%bf3VUSLeLtkPLn;CG&pTU1n8RCM2JJQW%412O4kH#;*53mlUM;s304
zk~^4fCcp;ws^#8kxt=>}VTRvVn89l#PG!+=HhR5>9WPVx?9f#=WWt=6@rPwK7~>at
zOh(9lzkR^x;vHWJt?dBxfb4$RGsm76MX&J(sw19p@y4R61eI{%;1sZRISjLrg~gSn
zxuKlG$7C8SE0gc-wF7Dzs<%%G2Qo5?3um5{5t=i9V0rN6K;xjw(bya@2#C|}II&*p
z_Imu2ht-U}+3hOIDH_!7yr_I&UO65NIq!-8|JXBFeM0|8Z%;wd!8=TBv<)pm>_pYB
z9AX7B)_Vh7ijAmqM&Ksk-ipVg^kRQ7!yENyKbw)8!Sxvhm@;AKkn_%jG>f3yG-BpF
z6uSv0BYm7=gnEiiq}~%TsH?pvJwa3-o%PWIIv7>Cfq#0tH@~4q_6lW<DJxF>7MB{Q
zygU&MY2ek-(2PH<EONVZaz`J(;`q^{j~_F9a1M4vi<)K@h#gzLCIX+^Jut^|`>Y&?
z#~X3@+zzZE-PZiRtL{syi=>%rM?)*-rA-5nNY3Elqg#<C63)$WBTc`g<8zPGbS3eY
z9ETUD%j63Q;{soeeOJFOszz#~#jYW3WuMD~F0shge^iA>HP8Xrb}Pf1MNExwkhuqq
zhbFgYA$yL(-dY)6ATqYoD^!?b%@M);VI>7M9)r0)yZc|+*a$S9nu3yH`LZ9>&yTSe
z($DWtzREmL$*^KMXyDIq2i@V~VI`itN*^Zr{d-9rM2EKj4|}i}!;a;Y<HAj_d+ynl
z7sVMnomc?$Ev0)WoIDt5Fy0&SoHpj+U0Bx4bvY&8!U03Qumby?&L$P!n%q2SCF#{h
z_OPZRPf2cdL2E%(sJkZ^$;}$JbOfRxMkb=z!dsJ*akI~bv2TCfTzKTk!?JR}%Y69Z
z%>0@f>}&VwpC0Haqwt<sM5m%l`eu~<sH6CkBRrn&KJ{}19@}QvJmftg2Zzxka()A~
zlFf)ZJV)|2!AulRZv@u#=D<=TthgT8S7+T>RwwpX=q_0~b+atPE_Pds7h7&z7;Ea(
z@3}nOtIXb~DYA@N{P+G3=(c=d)qt!4-b`uDOz;0-?M(nAtIB)vI(J{{-rD!2tGc?D
zUZ(eL_L_YdU~HCQMpS5#WdI?qAcCN_nkZ`2$%809m)B^b_@2)YvpkGZY93F5`I}^-
zS%|o_S(N<q5=k^l7Hi(`JLlG|s-B(!O`zx0x%*ka^DV#cJIGc)i?_S7%tl9<jEv5T
z#dFR)2G;j^@lxt@wB0wQCTJVSEYom>ogX4F=W7V-2gf%~>V$2SvpIBDDsG>>c#9q~
z#t%)-J1~lUbIZlE+ueA?nV&o~ZiLvHY=?4+*?thyCKbf4i9vR`8{L`<*)of|?au@u
z^956wUF_AEK_o+b5jv?BWb*>Ffy849Pyt38{`V#hF<GR3huRGP(-h+66!VQOlksC|
z@vg?+@!4QBWKWLl9@d?InjPQUfD>b5_sFCjib5RbMv|M;jw!Qn@Y{BXNroL)4E;yO
zoNPL2#rTnKC~@16Sjhf%kgI+bKE_DCOhLXRKKQ~?ju6+eJk;hA8?cg{(WAv#3u5_W
zgg<15E!PC!-EJw^q7s}|U-FaoUlC&fl5v79S?*$^-D)gC^JN=0=+-~b{c%dh{M>iJ
zKsYs`OT65xXjdx`NO<<eM5GSS!%{roYO*t=<!Vo^;9ja@2M<j408FiZJh1PT(y_ex
zu0GFqv4~FyR$@l5Fu-$ihUl>_Ch;+G1?&=K-X@OldFUM4XTJsCl80d>%@A5QhLsC*
z5Hk(~Km}vRCnHTyux*%+E-2~*@d>zz&hEq%otsfK<4M($U;eMEDIJ^v;P_u(E?er8
zhW50R)SFE`>FC-!UF+#^=r1hyof`YVO}dL+jurKx5>sD&ld7`PL$C%eO76{$b?dE`
zBh^1^N91`$nU^E>XX{*z#ARN-HtOKapa@_n9?3i;<AkIcP!*5IWbGL>6MoP4+yD5U
za7LY$zW<!?&}I5Vp=hfW4Lzino}*59PP75xi<;;!nvc6M$|{MvPBIk@cA=M=tyO*U
z+jB9s*`3nlQmM4mp||aO)+y_qI@Eh<Z8?!xhN_CLBWd!~KiBkg{Cpq~^z4gkn6Jds
zRp2)}hw>iceEM~?e*(u9(Ma*l|FW3Tvo+uQ`9JH@DwG%a11n3z$$?4)$G_km^_~3E
zW+yk?DQ;<rw|qzpP_`^S(GrJiKf~Fkc!twl>f_VUti6)v7&jvVkWShJo<J8*<7axo
z;|M_O$W6>s4U&a~{AzhJ+RIm9jA)>i{Mq>hynqlqyV#lJpeE(2QYvReler`tDS~aP
zrHUhw1Z2Bty2_3vqed<zRdbRyn9NnvQHXqr$Vf3&1EW<OVaXh8<r38#yz&~EXvC@}
zGw>YK`IV94-?=JNl`!@dF2;_)?0Y6zwIWe|N3NR4RdYvTMOD(m3T|`%t~kPrK(F$2
zN0>)zm_r4ks$3%~*lG;=4jzY=p(<4RDPpJn1=|+`xo}P-m%ELhS>oad#8S@}zWt5W
z)wLe4xSlL)a(QPGG?452i7J*#PjBi7$LA+#<hOZwW1x?tn4ba_NpQZ-jIRK9*wK*i
zi3<}8^dWOI46|xkH(P}mTZ<K1r<Mva{4T``QlRkh)zuZ6dy}VPg+lDq+9dr<7Gg`d
zToTy5FLHfs47~VO=oxP&>|P9A3}(?|_krdGvr^}XF3NLyLBO0UK0ioEAGjLON#g5J
zGGasp#)MA5-b15`c<+?lA-pqqA(2-2E2Ta#h(H2{A(_S?#TGzlfknkME4Gr&DOSZa
z6}a9@CB>#JT=Yxc#d9CK#|FyKek&X`itzK8djFIhL{62zH#x}yj*%Ek7;r+!&SzEa
zQ%1<#df!$vWbE8GHjAIy_}=X%os;@+ygn=xq2czET2OK%RlDmhO+_ZCee_PmbwIy~
z_A8K2K{X{*K9ts{7O=w1*Fq3T#s5{l)Ns{y3Udy1hs+r`E~|zGPN#O)_E{)Hu9Y=q
zN2ZV{rVEJ#dv9EE)n^Blpq#mCyQav4&#Kb*Zi~d@`9wZkRAnU;kFNlXlRT1&ScW38
z&t+6e`RpjB#r$nS7F0(+t2lfOBt16i-A!1RhcF+wNKYe((|-&yj+LdPYa53<LyK$8
z+7DE7O(KMtg{v(n{9*iNt(ddE4rdGyG@Pd!a;5u)p<D3$obPH<j7vV6p@PQ9AUKZJ
z9ms=#`e6Ov%|M7S@3=M$BaaZRGZ6hq9t4;vDHDir2KEwI)SLx(ry0vy^9^8$AW+RT
z=B;eZ-03bj@tCtX!LNOnpPN{8VrO9`Suf81Hzyh{6?V{_3#A{Dp!dVl0^Pc!PzpzL
zvF+ph5<fM*J;q{qOxRb*4l8T`F|0>39R2nRX#3s>S*GI40RD{RZnqcW(|qzGZX#9i
zJSI;v!CiPej=MOX>(Bu2`o?4Sn{hCDk6JEf1#9W;-{ZcF8<N=u)s>P%UaX1@H*7d8
zv8T9MXAvPX=)Dhi9#<Fr+^f6KtX)ZGPxzO*H+6Fue>6{8@Vz0tD`dbbRv9cIkjO~{
z>S{ZPMsN>tXl_W2iJk`!h6M}565JW)Pi2l|Td5fd>1NnAkCl!N(}J2Clrl=lycpcS
zo>lV0M@z>{J8bHq$Z~o3y4l(5hRbInMdO1O#G3tCEFH_aYB;=Y&M+fsYj89i&Sm#Q
z*s(rn6z|KHD$A8pmh-K@2R}B7W03)MM10RazE$pZrlA2Uus#^1zV@$&f8EE!Tp+B)
z)UoL<ShFG`bo4@8+Dkn81y9&w7m3*tkvP1zWUFXPj%@-K0L{i>M%1J-*Z*Rb=5BSZ
zt7hk?j;|b_nx8v1GVi9gZ%?`Ntaa&SS8m(2JU_p@YulBVwcj&063<UHn^XDt2;j&h
z_X^$MJoJJuf!zBlS`%mm1$Kfo41BB;+em8@{t5oEC+E`=EUz2H6qHY}`CT1rij-Ky
zV^n;`pFL~rxakcy?GVOuxOI^?8VC+jE$GtO_GeW2QBtNN@uYz}R={C7D<%FD7TgvV
z;$$_Es3u|4gp9N1#PABdN2DS96Z|xHPv;>`+D{2?YK2<~NdLd=#8x~Gt5<J2k8%8x
zz=@x^T_D1F?I316bTvPH8@x$WQl=_li9`BRRT~C3j#fuySLtCS-KugM4aFrmjqzXh
zY+0~v^o(=PHgyS>;FIdp2q8p#Nk6YLUx(L3jMF~B(qMkG=jwaw^}R<a$za(di$GA(
zYKqp>P4FlkK8C$}{GQcS{q=&!sl?X3XYk<r>6uF?nb(y6&Zc4opTj9Fmnez|aXz&X
zXdZP=$3e+Gd=mBFwTNaBptZ1<Bkabp5OjGX-Q7^_bLjLY*9nFTPMgDU6YNwQ{292l
zWu)zI8fr~7HYBSXj`?BV$@bUjA%;g87{4~XinD+1OZ}H!?dLw>=Qf<<7x=jw=@yPT
z5^J4U>8`-sza#KA+CvJhbWTi(UIv*aFyh-J(ULyuIMo>g9U9SpJog{H&VI9vr6(U3
zv6xo*vINFkZ(qS4q4N~SNWZ7MT6J^c-tIBaZF-WGbag211sj;9g>E(<9|9k$tL=a1
zvVfxMU-1_}eAjzxcTv3Xxeou|;&w!8_rpJ1YpdJ=pC?_+)r)E(c6lC{s#^ebR^Wed
zIY3hHQ(jcPm-C8z%Y-cF@yM03{9Xm>AYr#Bot79wKhwy8mYCow9oAsx#Z_aD8vij$
zUR3E@-To&Y+2`}9+{b^$KA<x9lRj^u^cG3T&M&Smdvq0612Aqos6Kx7MOEw<H$KH*
z@Sj#kh;?LU=?Qq@JA8Ut{rZcmqc#a|7Yw?|NsJ$iJpA?@zo^Qto#)}s%V>Ua{uA?y
zXg_HHH_)l-h81gL2a;Jg*h5m-=gy@$DqTmYr5>s<wi(^PG9-AcbuRBytgx8?Nl6G#
z2%SyuLR3*b#t74Z(_WaFJsP~gmnNdQeQYRfV;zq<tgkS8LEr+n2Gs`FNAM7Sh+lUL
zX>{+D6qEgW`x45(3vMomkfeO0O#W?foP(3w(CqBt+1V>~Q<%BnW8WVduH>nQEg&cq
zX-HDG$fkZoQrgdZZ@_EL&?^qlo-}oC*NVZYz~`B$yT?_N3uwnrWjhJQBn?6SgNjdZ
zoPxWpb2mcBgi@g6mvSPgr9*EDrM2LRc>ezGXG`L<Y_h9;DZ7z~Th9N=ITiF|6XJ7*
zNH<-sz!8KjGO*Ebft?)i5I&J>N@*YvU8<%k(n{w0bBd%9d~A4FagdRex$jf-C|!a&
z#%zE2&S%+?BY>mDaNFV{F2@u{J<|T|F3xXl4P*xT>Bl7r-@go!5!7#_E~r(wCV@D5
z4@goVl-z`;Zy|g#6fvZVK`u#;_<T5F5(vmlViRU6`*CTkkwu6LTbEM<JI9ORh!RyI
z;o|ttft0M<2xyUQjFn6?oSqnu236UKyZH<U8D#Qq9C0t~$oND$Y@Udi6T3=j(`K-M
zSCu128?d5g8%K{Q)l5)jwwW&NnlK~qKQxR`GGe-#=0uvrqBkRsrnzP$83K<Hqw!C)
zc3s*B{oEjYf4C1Mw`1y*aw8@eXm&D;15#FEPyptG5e@zVmVDF;qho+cqi+jO=ZCgA
zQgaKJ*LTboHJzU;WHV~}Rm+Gm^~Bzw9u0<}rQh@5=hK<t!7aOlN`U11Uk+I~mxwOx
zIJiQK`A4pa3<piyx<>H&XW2Si#SW&^&_Q2}-XU`<tw|)dV)AW<S{CD@Gl<KcEJ*l3
zjByIb!D|7N{r?KagIfpRT-h2dMG;hkWE&RXKjBpS^J*saW;dh8&jv~e29h6ij5ymW
z!P(Op%txdDH$Ppf)k;V8a(pr!ohRm9h&1!j^klre(}_f!=@ppd%CL(8pkRl}+>ej2
z@n@lzc?(~gX^rcuVtR7@WJukhhv3H`DS|5b>Wp4yFu~SKTq(IKVkL3`rG>QpHC0}r
zF@b~yRJp+iT7%B`S;%Z$Whv5_b2h*qM|wZq$K^C6{SWcyfyZh?ANTXX_KX2_cL(;m
ze>W@!gyq)|^pvKxu)~tURhWRtCQG`=`D93TnM$*ctm|Z$rCaM>u8zks|43)a)p}f|
zN6*r1qc>D*Km(DoB8kZhPK@R%kq{L)A!tN#1w;6KK~<`#wuIBlQB79&zyWJo(X~qz
z7;sro8I=rqC$8JZD75F#I*OyHE=))<bHH(A^Lax~IRhp<3z!S{GBv2gp|hAqV49F_
zga*U+M%+YBw!&`vjc!s01|w#`Y@22Yjmc*6UN>)+nB@*DGTHCxj=vfY$KcC%&i6N#
zZ>zagANt5dB&f)<I@(Awl$2B4M};!vkg4e~sYfH@ZW114`*ba+E3zu7WyN$yvR4GF
z*UQ0t0svcPyTh4zT~E-Z;OIR{+O;(W3QH%SfHIBK1j4)y0Y^Q>?LHWvpus&e4g`-D
zxbp?Us{j`{hiEn(;aVz%Z!@!@KZ}*&NSlK&2s^G9<8HByHG@LX&E8SFbn>N_4vHfd
zxZie?(Ko<g0mV5PQRyFyTaCr%M8LY|mhqpLCt60a>&}9HuX6kCrL>Oslo|^M4G+-i
z3(yOSgGR#>2q3ZcU4U%-a7QG#welvAjmQpoQeogI@nk>wNXDTO#)bBOy)YGF;Sr)D
zBc_(d7%QAhNeVGlNo!{GeB^}ez^Lt3{Mdr4!rUQ$OH3}wvCOw<VeKuZ#R3^*k%K`K
zw^I^0jXnD48La)dpTZj?Tb;Yuo_4$YG~F$p`ILADRvVhU<iA<#JOZ6y?-RuDt%KjA
z*t<DA-os&#qD2*KDi<$UOUQ{Z>MR53_>p_TkKT)Tb3ZaPw$crRn_s(E!TC^#^9w!i
zCZ3E+MDHcp7cP_J@WPh?lJvQVh&Sig?vv&F@Pd-~lK3jS{yuo7+$X*#_|%?v$2Rb(
zh|octH)lmAskmOzJFo6%(Apoq(7SufOx#yh2#%Ow)AQ#oVR(~|`u&4(U{ap~Nl3(&
zf>LO#+}oAQo$jJ%sE@wLV+l?Q{2~0ok|E3$EG<y2!igSMM*!Bb+EesuQlEH}gKmRA
zc;AABk5QD58vB>_Ga8~+xRMr%l*da_^sA9dIx)al*g1HRRNFThj%2cT23>hVa&8~x
zHiXe`0KwH#)XwO+aH?FIh=eE0j145hd4nD2K0&szC2F5BtyLkFbz%l?tPf%%R?GjP
z`hZ%XD}1KTv&{dLwYwQ;_HY-f?qy$G4IdJ|b^HZAS)zBaC2`qh1kv4gQr`jHj)0FG
zVF&6=(|`{nA;EV!Q-~t9g$mh`iHr8N<bW&-RVb?rm9`HpHn7}hW3}v~6ws>#4x*%+
zk<j?T2b)ch@B6_U0K0UN_+bG%F@IHqoIr?CfRv#_1-g8S7>GYqw*6xB?L@xc4s0TS
zil^u-2H*29NHSuUz(=&o`F$iF!_O^rNW9=1;IEO>00qHPkA#3DNVxf~&i=n~%W!r_
zmAJbrEa$8(Tg<#1-sQ%X=x)cL;4c@r+S+2}dTySWuynWgRo$zuPOs{{iuvQmWGHhl
zN6Zz>W8z+Vl=5YN1WnIF6~EvC%|$2~NLN@|iIbgSU$m64-G09vRxB+J%L&??L3@gy
zaJ>ELWOQ<JGMbzO#=i;c#pEp|D;Ts&q1*~J^omGxf0G=tzjWrzm+YbB>MA#KR8|Ar
z=cfgq@zd~=E|SmB0!9n?U@tHT(HpWXbWv}pM1f7G<`A9@@o(V7NPq(v+Ri9)qLGl5
zftezAqbtt51f2_P7JC}86Y}K&w%lC3q-g##=;AWd0aKeDh~@LKf!Sh-Zd+Pp%|c>4
zI^AAxHnX)tg6&8Y1RWwhcPrpP&kfxG+-y7EQ!^?RffuM_hM%o1vsN<|X@?`JrW8Ka
zYClJ9z(qbd(5|t&BPm?Hztn7Y<vgMf0^73(-w8p|I~fQJLy(|p-VrJh4H{;0m{&T#
z;xdE`u#iOmx~io_r0f|@1ai$ohJrOC(u-+Ej4Dyf5Q))jsokP7vL@0mV=7Q6;;?QG
zWE{(h&kh;Y+*+>6N=77AFAPp+2H?u_C)dc1&QjZ<Uk)=wVFjxZ+y-z3jR-M1`nYtQ
z%8nR!*@z_@aq>g6+$h&xTS-MS#c^34FW+s9Wbx?ayAhc03SGTb!S4(f3*P>efPacp
z`&T_Xh_|d#B@&UU&L?G}j6trmACg9_ou(A}F*bMXnBvIC+3MAjtu#HI9y`fz`pS2<
zz83J1Upo1_FqR7*^DMsg7W9FL(N2`wBd)||3bNa;d@R4fb+wG~LJxOzH`G<^QmE#I
z7XJo6*Zvzn+davQ_7ZPV*l^CEaiQjaJ3Y#i?^2E@2PwysCn<-d$tI~U+K*EBL$s}E
zOE2z09A0BsgElD{=`sjz+ovez3=tB+wmjEgqWWEk`c%5;BE(?WFG4enQ+~OG7+U7W
zQm_&NY?Y22G0g)aPQy5|xYf1p;zi_myvL<7=z)d3x1n*VR`6j+_@KabM0cW2K#QxG
z1whahC5T!u2TS3<|0uL4g__2!l(O?x+jVQ5Lbcoa_T@Qc|Lwak-k~vd?W3xaKRm44
zd$zx7rvfyvwe9Z>+OXo5`)=LM*r*axIp0D16Hxp`8gD+@95H3ob5)zk>D~~2?Z10W
zw7~lhirSr2+@6?mPd=u&N=j96RZSs#5<(5}Ib=^g?O|bgyZ1Ey0^;_ny<lkXT8jNk
zRZkettr!V?$S^*E2*D6X+n-cj?N3y7L=CAQIe`2KkcDwv|Hs$pdKLG45(t`xo;<0l
zUsXeN^}`3m-QHaD`Qjl=hYh@IebegqGO^z|xB7kj>~)WcW}yb4m>y^VCO*vGjQaP)
zf8)S@AcY_>eCCr>^n|hU9I9H?_4Z%rNt5b}YT=wQglzlkz+s5Cgg5SK=wuL&B*wAT
zoJ4>|!l}Pw_Yi@o5DN}+IN{rfw9?|(A-DGiL#fDQI2{TOmF|`#7k;C!DTJNEV+AK1
z3D2#AygW_xgAsjst&(tvraK8Gv-NJ=3gP=TyJz5HWNh1J#1fZGj2&&?#>o*SRNWH#
zyoEk*f$Cp4uERcH8!v_>@(NfSUIu&QtKbQ7E3A@tAhy!Ifj47(q82guUt{G40xW?b
z5OWp$B~&C49IMEuI>)$4_x`1Dy`GraiD*DLML@C{dW2r3<980X$@o{rKYDi^=jk7R
z46nm23CgoTlmE9$Dg2LG^b1F%X0uH>`me>)Ritz?ekr@!Jc|RK<#()#+gkjl77o0}
zv6im#+gkjsoje}Jt@y)<CZilO;^R26>iv&wvq|kOo&7iX7<>uiB19YpqMmb&GRX`i
zm&rV0U*8Jai(=q1dKu&gK~dE9L%id$d>Cufq#K{+%59NdOQiLySokVEo%j2j?Vjwp
zgJ&lPV?X#oY*6q%j{~bgIt}8L(VM_rfq@jLV(@DCgG7g-@mSg<;S?F1RsE65*OHD_
zpDnjr>G)R1O(Ki|Gzu#0LU6@|FUNzkCuZZ_qU-I?8;D{Y46d(FB<of|OS#jwYENiV
zP?B=8@lh!4^PrEjC)(fWx~8xtdL$g|%(F?XlcU5Tm*M$C{{t}W$zgorVP(d&1A~ES
z1#KW#NDr`$g*VQaLDMnJ618h1nVmW~l}%z^>1ru#M@&86exA3ufz%8}(#6_1FOoH!
zsZ32(GQ$=>+&+$=UMjX2>NGs{JMmPHvw!0}RhsOk9SaHdI+aT9G+m1HaHM{%^R*gk
zJqrBKSMAOP0)k_Unphb=O=Q6G2ZH{&bg48^F8`KdNeNi@Ri>-MhOB2w8C^DpK_XNw
z?jit`Y$^>!mTqRHn<bf8kxp5qKzY23zYoc>QgdxIg%M9A;s)Bl#)F6~CdXw%ZJ%XK
zHBc#0FB&&>;EsTpf@eY*UcY>K7JN#N^b6wV@h$@x@Ko^%F^weEcV@t4ti(zswngFW
z5$xrMv6&IiEB8TmY7)is>-)F&-hTVNBZ_)A^~=~;H@#a`t{`1(^TVBrf!cAr5c=RW
zG%fo;!`^}&+aE@WJHrB)F8DBG^neC=1*)e2?n9Zo{yu&cFc$?6mOzfkwy2r#%E0WP
z7J&0m96|Ty{N_M~#hreJUV>=SqAe(}z$UMGie@)FFYPzk{8y3O!IOisq0J=eN&z9h
zWVt}u<cwxKrbwTal%%Xke=RBU{!H+}V1`L7ucRbtmXbV^F2YDJ-i_tqGgwoYBz;Aa
zzDqqT1KvD7SlN=T!y5yKH(h(JW)&4FB`JA&f1YQH0P~4MSCU8Q{D>@lG_q@Uc2|T+
z2NC{jV8BZx9L{N`sTGggh{i2RA)84R%2dE^i22)z!L*Ub(KlV>0afdSnEoE(<wuN|
ze&daLT#FdSJM?%Di}PC)m>#}uxN3xP*Zs8M5s%aJy}sDQb)js7;>!Opd{hEm*ADNE
z#tr@(7kdix4nvPZUCj%F*nIf<x!>!HLk#;6Ow*jj?XUx|f^GO7)$4{E3?qV9->>)L
z?|N$w|7h{S4fX4bISlgxDg4N9zstq-gc|<dJH%(G2Gnq<!G`ASFAVh`=a~>Q_du6t
z>N<~+;&bqiyl}!F`Viil<WYq8Q6yn7i8{_PWDu^GcqY6HF@CFv@r#xddO31EgOe=1
zhm^0CO&<K?bKk%5T~$b%o|S~$*F+RrR@mfazqZ-S`syh59O$~kkwSR-AJ2V^?cC(`
zV*ItR&XS+kDELC5PlC3Ih62LebBV{-N_CKma}kWlyRq>c{?0<6x$4HN2n2jBhtWZ$
z4o%XvCc7Iu+ToUhX&ygLfZ=P}|4cM+WrbU%>}#y7pw~i&AmU>prt1Z18<rmkIouM+
ze`~a36#@a^d=*TaCx)F_;Q}6%cqBQKtT(x0800asY_wsMSh>xxrQ2>>!S*a)-apfG
z7thY@S3<@=0O$&|>pvKwU%DRfxd}NOn`oK(^;VdV3zCC=6COddQyE#ZJl9(k3`{ez
z9+1F%a4G3xHCD2WZI^I#>LuF@D`w9fo80%>cf4lb<gvM6lzlJ)?P~bC-5l*Yz5BW_
z@bnR=THS-!Z{7czy_ob{uRrL>t~ZuCG$YU#Bd&L{_D)Us2hn=ZF^OBiO;J=7!ePHB
z@R7j(416K*2Z3**CYvpcJ?FY&I_Z=)+BWApX3g_pt~bH<VP)}{BJe{SpZ{s^kG;V6
zrIYvb&{5DWg}FNw(uU>6U;%exZrk;=ADq29H(9;#WpKaeKGQw7>17spgp}%smuPq{
zi4>@GzG)T%Q1?%Wd*E+f+$8txCg-~M-G9NW?Z-B`jB&;FY@8O5z7c32R^jd^;3ql?
z8tVCOygyFHI-%P=v6L+>0dKv;jW;Evzk*7_>EUhfgLUX~fRun&8oz~ume?vV+}U0m
zQd&4cJo{c*xq>6=m^^+nI0@PE@RdZTgeT-AaJ3f!VSV%YN{O*0Xj%Q73>Ykdh<Twg
zvvEvrtXuovMdiFWhW)_}-*w)NBKd`LYu|oIRKGvwVN0BBpYkpEOPl02^xZgGrN?r&
zoin$fj!7TpB%sC}mJzP~tLTVz>NT(XJZF`w6zMmKp+W*>=o;_V<#?X=+^i_tYq_s0
zB^X1!>wO8O=tVutb$_}SI$qqp6w5k+O{FGPy$Rz!z-_$*to^a3qCG;x_FaUvtSQ8-
z(YJ)IAVm5ouJ7yuqGlpQU3$juPUbn+@@keR1hA_V&vQd2KS|zZ?d6H`nNDLMWFVgx
z^fBmJL$I%3j4we_>zxrQU%{~iK`^4w+)yoZI~#(T#tC9X2EFrLWoO#Uyj9nbwtpmP
zEG{dt?OUU<a`DQHEJY+$j%_IxM!yFANeoP|cPR^fG1Cxgs8|^5b{<t=U6OMOG%wLq
zW@tQ{W*J#7%1jEg?07+nUg)CNrt4TI))4&_Uf)&nK&w#fL2m63;gujF@`xH97X;C}
z7r`QBwZ%puoj7v*$mqyCaO!LY7L9GcehajEx9Enw2aB<8?y-$;Mxu?y@X*-kkx?Ub
zWCC}>NHTu#CATQJFTO%#afMh`-)?rA`?l_+h!+bT=@dQT;r8aoB`T(-*IWrsn^=_*
zLNS@5$kzy*K)x_ghEg<39=2K3i=t?wVjVBf?c{MbgWy3FJ_x)7jS%1**e{CTcPnZH
zl5{*BSM5|pRV>G`6x9qyF>L9ip+<rxKmC!#h9pgtqmWVOcFh*0LUpk*s+oymVh8@2
z+Gt~u09O8=-SfWgC$RG!ljtK-&=B7})mYTc@<hnZmjS`DZ0yI@Sai$;{<DHm>j}Ta
z$GL4d13lj%L|wlza2NWK;9;mN7ZRz$+;)P#tuJU?BS9);ZdjsdX9UbagP}2Mx))Cl
zhh!uMAA?>;yKa71*>IU%tBn<+VYiWPEas{cWfmw;R106P?5Yi9fpc-3+TOa9C>r^i
zs%lDkJBI!l&5^4zeEAept;)&`7d+Ii?Z2#jJ70xMEm~h4D^PH*!kEUi>|&)j7m_R{
zRd?60{j4=nU@V_ej3@A&Cf*<!l)lJ=4&9->{L<VC%Dps2<0gc=Fp1k3Xd9ZA%k%S~
z!oajqU{y3PJ8sryqs~~avB(NA;67KL7<qil71P5NI3glS_K8`^!p4NST8%UgXB7R~
za3oucrgDo7&6+~zPc+B3nnOp%hY!YOhsk&#=;*q>*ig0bkW?K|VJRZJv(Td#F{cl5
zoXzAY-wGJxp??ZsZlmNXG&|%RMSvj=q=X<q$jKLT4k0wz#<9<uus60(X%SOaG3Zs<
zh(e+E(HA%-tJ}uG4qN1p5&&h|cA`*7*fubj2~B$uXZYM8yeQcdT=+JRq3@0m|FF<M
zXg3^g81dEqn>SotxNx&RhEdk^#x{Ph?mv98|B$_*|M<rK!@={k@=?5h1-D8Dr9Z>L
zKX2OTL|dS*K>LvmAp_6wi=nZ)C2$XPRu`ZyDmNmeutZ?6d$4!38NmX`l1A8oI(D_A
zc)_4PK<3T^_SZ*YHj<PLmp2@;vt5FdAL<aC_L~rM_;8_Al}sQ6FRmzIrE)POz|0{y
zcV=)3ATy@z)3V`cww8!8YU&OR8d8*vROSU}(k3*hl3&Val~f`J)Uf~2ZaLz5G|H5n
zLwTz<AIp5@MG~UE+o*rI?vvvMNS{cU=-GwN^35>=d|9L!j<Z-1c1e7JB)YzN*Pv7C
zSVu{uMf7L%j|41d7W9065X%p%&LU7sxUo3Yo=b*8quaJ6T+@jZWyd@*tVY%06Q&~<
zt}u)%3OIF%8A`<K1t+Ks<!w?>zkx||wq_NF@G4WDKMJ(Tz*IT0ZQE!ll*InT(o1_E
z@4hh;3mMlIC!p;uFGNx~IFAz__+oh$o%RJ1f+sij3$WFBc$P2tyo&B|FX!P|W<72N
zw-N7xz98LEr%V^-qR#W}^L&Z=na73VKEXNS(SWpoHild2FsDfS%T*%0mnoJ7f}3K^
z0b>l8@uY|)&l-!L(p|g#Uu;+Z)M6y1%FoGa>h<GCLbJ|TmViI;*ihvq<X#wl<EIe%
zHWb%C^~O|$B#%gnotYa=W<iO~p#*m*M49*+1KII3tVraOaXFem#4ey6NNDB2|8tye
z<mF2xI2XbT39bzDe+KLZ{@=xbWAMbYj^q~WoMEeok6FsHL^?E-$3BXg0UQrx_)k8i
z#$$GdjV3Cw1WOm&XTU)MwglS<{rrXaVUxrb<VK8i7I8IURM1c9BRO$Sf6B{dhE2nQ
z5Ta;S3gC>9Tj&MYuHM_08et<kaHyWRydhYswxT!cK;Xql=g31nF_<0zXhyZ5FZ8sl
z$!P$mJVzi7ks?P4Sj$s8RaLrQGF0#P^`j`m%U*`<Cq^OjoKo;-u_OG~RC=%Nj^H8o
zXRv2u&jqy&)Aeh|A!08r;jwW=eurWy?~uJ8oba!p-zdh<I%vkjh@YP$eHL8D0TLF&
zq+|GnWJQyPV!@(z+%SmlY$51TNr08wcOwcCJ4sSuX$f0BI<|#iKZi}#vYqB)gZpB;
z*0W$^N>C0UV7_H$;_-}W@wt1NeV;wfBh?@d1X@ji$ax?zM1!EMxoQY@0NxXbMi3y{
z1eFhz0kG?&aGS?N1>niK;KjAGNv^mc_Jw_6M3GsfCL78~ZctSg78G?bH=-DFErJdn
z(X<!1eg{Sv{yvfcbbD?XE&-WTa0)2lU=aA>sbDGtCxPKP#gH<U%8R%MBMt8(KArZ1
zek<n7A=t1#5ct)=-v|B~a|U7}>PY~4Y={JWEbXpMVhO5F(*uCIc?y96`Gi!_B&8%1
zP*|gP5ZOM-rx}KuR!>qO08G69r1sS3QKotgfP9DtN%cRulu`geOq8grAn+G<RZ)S0
zhoKOAsvzQ*;zkujISGe1-U5SM{~g(A)pqgk#z6x?Ps>_FFJy{AqgFHRfpo!$s3i-b
zsLGmF#&xTtMhslH&05U}7BdAsqLnN}6dBa+O-NELU(6*WR!8jq(8dq{N6H5O1FBmP
z_D(k#($r`S8zMQbV#>A-!)HjALokCP2n((|*jy<VRkcu%t_O7tS6ANU+SQmFtbRJ8
zM2u_$QD7qpd(c2n>&!I<?L-8TVCy*pmrDZ?HEiVS*vu=Oum<7%0RIEW9JJ$MRn}{H
zBdkUS=5obcU6PXY!=#U$uXNbr7hlJYJTFwb;1HEg*fu-5nLK!H^3*#L2+Vmp@tx#-
z1)DPM@#4FxL6z@0FNuo)_0xUzUL8_OoC9WM_Qc5(v&FgTyQb&ZAmko?472<Xy$dsU
z&djm5j?I>LuXnG0a(p2>)|i<b9gP$t_!~_GEi1@IBE>;EM)K)dab-5+I*#iaxF>C&
zNDhtB7B5H^5gu_AJ6TEH{dfs0PrHu<HtK~Kc20@mU%!s$-`CdG*VZ;5&R1!Z6fh0M
zH+%sy3h6oh_i;G$Zo+nv{*|@bTywhYXm30PlFP7bN_#z9yHeMcYie0m%GQWSXd^BX
zgB~Z#<vp(DND3Ja!aHaQQr`Bm&kzZ^GcUpv&0&F}DbGnP0fmUd63x*(<3D%}q6HcY
z9DLn@V$MhhvAdT#Hd4sWEaJ?8*Nt;cJI!h(q8X}IQdAB5jA2`_)OoM4HG~zd?#&NW
zqPAj15toLC1Z{scO6s7fgn59M!6OE-6hmqzlCDbUy#k)3+Xf;YT0m%fHf@J<QPo{|
zb*GAm3D{S~Cpq9cc1Pb&w+?Z$rw(4o2oX3!q&_c4K9mXCpJaj5R#*TNJVdNeTU8Ni
zj~FM;f$JTKlZgQTO|*Q<cKl*BYVFUP(zZKt#RIQ9IC*^VW;j{)M)yC1#w`wFe<qo?
zuJ!NvAD+nX9ol;BVm@<uC#L^4=w`Aig+JmxmYs2Qq1N+9!Nw*!PixS-iTC(6bX9AZ
zJI8oD2|)^=C!sdrO~6N~4m27qiSw}Zk$QqgCN2EF+R+=CHNq-T^g-?l(Fr7~;)yzR
zE-oaPI`@ghOUcg@s3ktSYDP#0AyvmmXLg_=)+4UWFwTN{9*R9>iyO^Ex%1rl&a<m)
zhqtmlx9@SI>66VJGo#}wh_D`F0jM_dH!~cI*%~%S$c!GD3o~2NWn0k=OJ|On4n?yJ
zK6&`Sc`n>De8W=IvPcy)HlN8r<7mSZRLt+a;M<ZEU&kY&yyf6dhY8LbF12aZ`)ZD`
z=ADYa;oNG#A+@&_K<ZE>etr1*%c7?37YWfisXw9r_v2M2ff8=Op4>~j^$K<`ok`%7
zAFjr?NAv{z`KuCMjAmOC7<J*)5bL!DJdYbnqetE?u<5Iv$wcz6h&yCpHenH3@N~n2
zw128tTQI@^b_5mU;|W8m$i4&HH2fx<F0NwxUe!K@L7{lRe-7g|?s>Wku6Xd2t?pG2
zZPokbbzjGR7i%1=x(lep0ESzSy#tII!K#GAJlS0!rwP2y^n3*JxPaO8xUMX8?cm)z
z*>BzlL@}8KDSTA>dwy2MdDps+EF@R*lP$V|&hjIMoqc*PrhD>k`&*s6_#@(RaXZEt
zbu8ASw?H}oKA;D9)G1T~hVC*oD^&4RA<{390i2b>M6;JE3D2LHX07QH|FsQ!yx4Kx
zWIF-A=C>e;>ARN~7M6Gah#TN}LNevQp8Xv-KYoeFJ|&AEG)>YRT%77qSWqWIY!ZLy
zNc^cCfpMXe@dp|^TiJ$<J_p%I?LaLy6w`y+z(R54(vkXsTD%f#dx7E+Fo_1k1LCK}
z@WkM@K^+g&4%A0387(dhz)6!I)((ns=S80MM`5Sj8ha92Mv8s~#XNk(Apha-r!IH9
zr=T0kjl<2$eY&rp8_HcjbiQ&6?A&Sa9uXctG*AX>Kv+0%9njzb1u_$($fsS?7e+WF
z6S0>vYr=Z{95-o~Q;%@lHtm9Q(sJ+Fi;Zc;rZ;TGa@$X*%Iw5R+<l7fW`;YwS6|kI
zEZD)91B2gZJEUM;o$YcV{E!bLmz0FokO-cTj*@)<sc9=#9UI<Tt?jvDFlx$Dk_fMz
zOb#|;@k%;{Q0|BiDD{$-_OAx*WNq(dd#j+i*mt9%NQ$NfNB5*MuAzgK8I(;iC1{_-
zx4<SCAsfH~N2kKh%GEGrX%%~G0JF+xHi1MutqkG>`Z=x-S;N+M*^&q%4Bm~cl@J?n
z6%Jh4nis2@t$HD=YwP5HL;9JTh5%d34xGIZo$Vez$M{^j2MjCme?*Jdr6%Z+32p5#
zS@Y*QbF&P5pWx_(Dr^orclI4KUK>p0B>WlI<N2SoTDr<H1xNseI83Aki4^z=d_d`r
zFqtqPeKnnL^e1fpB$?4VZ6;)bG>`d6F#u`obm2uF<>*jDpG5LI0I7bqfzXL-t~F`7
z4UC=kXZ)<iuPjq6ujTe*G_D%_8U=Fh<jEa=83iF8Ta<$+3fG<q<qW{vIxiDZvW*|d
zrm&p-*hzdiCveX;whOHO_OIRXKu<}a>F7CL{=s&E?BBlp0Lyht(|+D^mmg^VP4^77
z5#P_8$D6F+pbvc`RttaZbV{+j3|J{D29o7;v#i;3Vx6w7^V{efLQhOG#sqNlC#elU
zQvEduvZP;kaQOZQp{4V_Ngj)0y~1<exlwZP_wU7X{yQ*FI{`@84~zB<2l*H|c!Nz*
zZ`F+_{(@f}+id|I46v}Zd+eMOf_|-^?+x8o{?_^5;l1?Tg3$AI4z>-#m(c!~XnD_8
zN)~)bUXXl5)YWc|1W}lheX#)^^TBegJkkEeU?FtRE5{@gejJKa+tSGm@4R$*5sETG
zO|Uh@pD!oc&zPl%z7yzRiL?wSJDHW}4SSZtx$0qout1-Yj$4#3L_1!b;{iuXRf^Ss
zT|Ce^;)p<j;s`T;0XvXj3l?m&7jE-48IXO`+#1g-hl5$|@+i!5(aW`*eOMu@blf=1
zRu3DPxZMSrHS=-H8r60CeW4`H_hjgOwyuv_77iMDcKS5K4~;L_8^6TuY)PO9O5g&a
zFQU;)T26uBCmz0*NG0_8Tx@P3@%=My#C6VCN$ZS*+?hzL)mmR;zjG8(YQE#9H2CD4
z1QP!{2v~De+kcoHJ<8gD{mXmaE^JcI;9DQZidE;)4`|oVJ+$JY9r%Jv-|iNGz^0ut
z)b+UqK%!BK7;<wuBG1Ao<>gZBZ(r$H*E`Pjmh(!>vCZql;gcuB;p<J?dC#$9U%3(g
zU3u&n{X8dro*cT(jw&!^*OaJz+mI5oZ?~e#v|-H78pgB|wQjd#_0oZA6*BwqaQj=M
zFR52YM(XvIPR5&?-{$`J-af3PD(1Lc9$lKm8?1HMVH4BKGtVxN6Bust5l?c5`(epX
zC`$TZ`p%3_ta3xw%4H4PZs0)A+=&Z{(*6}O1F%~YO{>-?pdHR+?#K`}1Glkqnej)X
z5uD6GpUgBd{mur&uUF<4%1r*5<QqtsNO%d5;uZ`)+%nMj-Z55glod3k@0hiC%*t+3
z*|qIm+jdp>36W>7ACH&g_!B?(-XxB0N;|=zbAR6vU;A3neY676z+PA)t|Pe`{+$pP
zx#I<z2Q!3Z0?;e6K=4AsC{Kop#1vUdcr(-LOl$?*H^AS5(ed^7;5BD^)cJH+e>$8F
zL$`|*lQunh8Rd9jC0ifT?P4+*4hIX<1zicI%*e!L-=Oea$3+0R<4XHeim5y(VtC=_
zcj;y_O5iu?x}TW1Y?7bh;iWP$yJAN($s#_07aEotOhpO}d~p_$z>ed!d09TLSnX#O
zypo3pdk}AjX@cVbNY^k61n7<Mk$QmU3HdTZ!GK?G1%%v9QCCPvBD+M9ykRkE%5(g*
zpo3K%@Jzk|2PRN$9-EB7jwWFz`k(T=>MyDO%2Afy7)$J{{ol(%N>H-o5kz(&tq=4)
zOko!z<VgtOY6l!xO4+idVLx#AHs(~h{YgYKm*a66anGTVPujpnSt*&_{!^W%2vben
zT;pkb?eXHk&}E^K2+S}jhvE(fWd&w;=~ajigY8Mp%BzpU5d|@tWCVtCV&N2g94re?
z4ymxDDR5VaE1L9EoIOi?oPdI!!n_hRzlm{7yBY@=1T12`v4M~npd6}!zQYPkdO;aG
zae=r40hW)q!kq6R1{@I6a0};#+ISO(y5VS2EG!7K&YFO{t+5imXYujDFg}h0AN(p{
zBYrOMIz$5c`M|pZC{I-<KN`<&g{;TfLRaI@3{ut#A#kDBrx07;aMo!KA-V+ApVlc_
zj);${)1JZ;*pvyI4C^ALr)w#E14f+=F-y_I+5%6Zyul^*{fIH-s7ex{zU_(a%+!nU
zz281zt1ix|&JdgwEWl^Ze-j*>y}Q0qEsi>&NhRcr7WYk=a#PcqvN@G_BR>y&G*LUJ
z=khkXs(t&X`rHM}^d^uVe3yT98_J?oU&=ps4L`qAP}?{5Ts}r+g_a|rd-Iww{1JFB
z?}k6uWwet)mDYQrWqybNpu0fN;G>AiOcFCxL3O|u348&UWL78BTT1PiTYaQ2_6`*<
zf|ppokpCQpER3O_5;5=A^^85WxV|urKp;_FaZ-7AqNE<!apbCOEflqbaQLD~BHbOp
z-!C4Q^q&wnqVre)En!_<5~1jR*G5$O*vnoPLqK{vXXOmnNlJs6B=*@v;2O=yA-DtH
z7k53_tRbETRKG-TM1paR*jDT>F^^Ore`t^MI&`gDu{IpWE|V1HDmjy?gOKs6fl0xz
z#5hLG7!uc^=bqsM97In9r}6xNv{a0C72&O@FH9q@MJ2UxD@6!AbLNsuAvOk|Q1q|J
z(v?eqvVB03zIEHRKt){3j$e8S)0As3lcW!*rt;suqAO3x($%j%1C;IeufP8Kzx}Xc
zB(Hu0BDTvPlcigZORBP$wCwLwP5C;YaJN5V7>^Xj-yrkr@hPTA*I`qY_Un^-5fSRR
zq<#5|NkjSgqmObv{|kH%RuP9g0KJK~gkc`1MB#<E0(nt*6^Ez7Cq5Ax41slc@=3+|
zKVjn`YzW;x90@&Sg#V9Xu*+{S%^M_3dj0DqYxK@_%Tixq4n&?*RqM&ffcXm5vexgE
zRg6mwJgeYkXs^67;?>RYJ$5}h#(^kg&*;o>AF1tYZTyUX34tPbgap=vdq(?j`j{8;
zu){3%emstbtyOc~M-V?4cVrO}oBu5L{dgxaO3p2}PkmoRsb5rKf1|JH7c8Y$u1@jz
zy3lRcb4u1_|9ac%^WuB`AL=r3`X+VW!S@z@!QS$#dv0&}moC(8UPtve$OU_E({AfO
ziQ&ijz-~Ly_&cYqe2t-Bc0aRek^6^Oe`Buo4JNNRR1T82d&}`6O0nt3y%yT^t1a>I
zfPuAu{Hac3-+R*gOhX#l4{q-&)V)HEYd)jZBf1LhN5>ftK@~bP(r*mEBmInjNm_cn
zWk&B8`?LSNpze#m7HWik&2`03drh20FbpEq{oWGZNft;=M&CJ3yvuq#|Ck`@pZ8xu
zZyFOPS1<4rp!$PxqJ955aY{t2a>N7Z@K62>?f}6&k0l6<eQL0-c}d*(g2x-)x6wmN
zTK-83laM<c#oAA(@;aG)kUZNB%_w&1<j0$5;k<T+)-@yyX8g`85D~p6+T+;(z1g5-
zQP`Haf$2iYK6>dSC6D^g(%2O|DDi|Lnm^EE_X$B*AIZtWHz89Cq8YK2#MzJ6;TgDY
zn&Rj7W3siZ$|YHa2cRV%lm6VlM0dzXVSt-&(zlnK*gOnqM<w#XBwd2As}OHf#%e9(
zUUEb7c#@5VhjpwB*lh}Xh11V7?Nw@YMkzS};;$usCV9wEo(Nje*=voWC?42gyFx{I
zRLh$1CKZ05V3_=Au#gl!m>civF>HMtAQJ>v&qHL;3qARLFyhbFY6!R9f8M*NS3Uhl
z=R7QK`k(ZI+1eAEe*m$X1U~OH`f&%eBZoi{0<bZYITie1xhPSNOrqcb!3dDkIsGqm
z*BW`y3vNFJzLAWx0EW(J3)m;CR*wL>2VUILOPUGUh%L&@)XKUA&Z)FBS5gh-&4Xhr
zi-QKg#ge4<Q<6j*N8*nB$dy-)<i9?YU(H`@2EP$B&EPYRF^)}<;i4T3VvFRlvHi0#
z>?W!dl8Z0jwv(<HmW9ODXJ?ic7M5nd6;G$3mI;U3ElNFl+u}N*I4m$bi1G;`ml!<S
zGlPpAdWXmnwc{GI9u<AZ(G`p!d#<S~VRJ68$5%_as)@}Mh{bIG1z%8ypdj-l#`m*j
zY_X9u4Ez1#L)g>@zNa29OVrsJn6mziW|)Zj^>vsyP&C9-dmKK-r-92yjF(PW7N0xs
z)c}JvC(jIT6I-BXK+d@@hELN8NIA~__|=F85!@FGskQ>lu2vI%uu^FjPV(b}H}zdr
z>*DIrwxHz<|M`F%28I`*Z{>>Wszpu_CK391aMmjFOO_(ZgPMPd)yu{0W1th>y3>OF
zb1!NF*_32bsP~~{gSbk9s~_%3XtKE|>pL0DbaSGA&ahQ#tW%Sdr|`=@*$#WgM3nam
z8Jb4<NuFMTtFVu-fNrjqDCpf;<eF@S9!3we@w7pT%y{=#hy|Rs{4V#EiHex1RfwAe
z)Z`l*>J-5?MR3j#0byhjO;r><MqMH8;yPh}v1GMe>S0Zjc3_LEo#mb7UC6VeaNg1W
z%&8@>mm?N0(@S3-k1Oiv_SpgUr<&G&97Th|^}p+pIVq_B7M_V;AAeOGPn{{^;7}yS
z*oYMrb@{vSM6NhyrzY};WczLO94TChzaV`V?Kg5HutIWQ52^~GAiQO*gOb3kg-o~s
zB|#ud;mS#*z{gmq6+cc%Co0Bv4o&PA0|UTKc)J2uEG?*6=@@KfVJ&RcHAErV6;BUl
zEZx@4Gy<dgr+3HFgDg}kj_!tyO=&+!>F&{Di4$yED@)!xu`4j!9p=J3b7fl&Yc@kn
zT#LoDaw0p#`YwUL=v-*mMLcf4pl0fPPa2-^bxrU|nZO{$X~AG6xND-bZ!~cUTszSr
z6Vl3x@CxL;$R*W3=OiG;-jgU66YV>AAMwOv1$|od7Q{;o<2$gR^Dl9$mq&+4H{J79
zQsi_$xs;Eu@R*G4({RXKqA|^uGP?yM#a<FGGyzJcVa^_(mZNt20deqcgdY5+#}SG(
zykl1-R2dYXT`+`in(2x2;0VlX@51xE2V5o04*AeLff!`mvduU)2XGx|7DzXMsZv9j
zLhkDXEe<9MEg8@t65I?H6QAs|(wHU1^im|d@BQV8qi-xK2_>6K)g#GMh$$K^j_wLt
zdoBeIq(lu4ipw(j#RHJ|t5b#AwG-v-%83J(xv+C)dZHGFuTxmh$Fj1qIC|08T65V%
zP+J$4TC#&uc?nN9Dk2GCk3K*#kbrvfkh`2^0b|T<-RN^n7p|qkExJ&9V3q7-o?f!t
z6L|SUh|v3xX*nlcHVsoYERwGEz*J}?H1&Yx-Y3EVHN_8YEBi<WPvC*fBQSv9ze0B-
znF@ub-0MWZAw&FlW9Eyz%m!93a3WPrC*d5vG9tb{S`|<xL~I5wxNGhqj1-T<gJvgt
z6@ssz+`bNneYrliFpo!pK2@|lVqgzkzjd&(Pnnsl%m$(45AKh<N>I%M7oRF00-RKz
zKQuji<gN7|h+%%R6uCAf8^*RT0#gR0sy49@yz_)J^wK1}`xYDN<XzE(swiRERR{C;
zZ8H#a``*~$UBBaxL*nHg=a}aRY6U;q-Ow_F2LT5u)-o&+r~?eF0)j}I6^JHeX(W9#
zIX4J+9MzH=vuVAn)!|?;&Z3`r8P?rzhwhG!L|&)a((Lt)+svEpfC+^$fNd9B;lBr0
zxod}_?3med-<uEJtb>y<*bfF42NC?`u5rnYxvkOxJw7}#dORq(uTx#CJgujkYRa^r
z?v3fMg1hH*2AYq|ZS&V+xY{WRQNgXk%s^$wbAU#%h_FWqS&!l@&@NynpozN$eK=W}
zs>4h`HdJz{1@FX7j8vrrw?t5H%@95f*VQ@Xg_;+Gu>zk}m@a6lazh3k_+Aon=Tdhe
zT4i;^s51&+0d&Au0SU-eBcV{F=djxTQph+{<M*)I8RKEWsCXU@?MKCw<MR%Cv=iaM
z3|!t5Duwodd*1LeemGw>ziNb<xY`W8M%@3&zPD_6DeMuLo9j{&7`8Cqv33hZAS3F8
zMb-lX!|npoGLAV3#c^s7W(pV?G~Nh1dE68%TD!p!VON0C(O<aq(qH&6B@gR~?FaWS
zW<wVj2eiQv3?E|gO3l(lQZqu?#r=oa8hnjg!g+yyKAH(zwJkMkEcX&Qp2@^z0@h~X
zKlflbQ^<|cH9<#P*n8m#Xb&+f$*F^896D2&H&fieL^xS5!x4%^Y=n|che-^!Cg|^7
z6qy&>W$gEF48n=gez@L11<C`ME!zlLbZ{Gzf%5L}09S}V{diDwGxo=~?%#7eJG4v7
zf(u_(QHh6>!;xiLFy9cBe~x~CPL95TewQP|$%hB#f0d(_=lBD!v}!;w3ycByg(m|P
zCjd_BXkt)qlnT=j^9;mef%r=9F-<NPus8w-yo;sau=+>k<HyUzP_$vhtVqR)M(tuq
zG3@Ac1lu?(vfj9=Tqv?)_E3ps*n7*VRJkxv-Evh!2hq`;$aK^;lu*%*Mx9E;is{2v
z`9v{mL<V89<+2`R_lKdm;QI(+*E5uhCuBhI*j(Z(;q&B~A2y;g>f)JmFZVos64W|P
zqGp<-HW2aJ%Mh@HF8l_5SH(3t<z>O$TPIa{QdUnA{U+neD$fbnlek2*9yA_H!V0Ax
z5bBhLz%3Mmh2)ac9A8EIVIG4oJ9$uk5dI&J20l*`0QCT;id-_Ar$9XD7-H6lFC&sD
z`wij=G8>L#prQO7v}QnxL%)HhK(zCh2lOxIjVC>L(1tEvWAnFGy@sBPpg?|GYF$)J
z?JZYmt{jsg=Dkug5ROffKR3`E0J63Ur=$_pkgBrwXUiZ70u$)x4i3qVhAog~?FtAi
zhN;Sb^hyYac$JGYG5~<g*jXHbPv(#$tEQpY>J^$?^Plg&uKh2Z(X)a__;379{uBJk
z41738$90H}E<A_7h%Iv+KngSspaQ4;|8oJ3Ct#(TR6Zmuf3=SBF{HW53>>h?W8^MN
zA0LHZ`iu;(V^=diK7^s7U#*c>2`q#&iu;A`^F6QnhxXTLtg)Ij`V*eX1%vIsq1;cR
z9`C*f{D}iu703>-9`O)lw%yYF^hT4joIr?W?uRc})efL*mh2SlfvaWtnU`A*v!0QS
zU!4k;>>>CVLk%=!x7w}mVxJF9GunSiq7Vh652+1>-A31+v6y4M{24huW#bV8&)|_#
z`yXy(O?g-ibt1HU)X=mBWDPFs37t#`WkzJiF6RWUIa#If5S~i02rGQd>8fDbEBJIy
zavya^@K4G$n?$N8naR~%2=Fbpp2H`oUC<WKGlSc-5GbnJVN`tu5V&<R-@|}#SVW>a
zDdJ7F-zt7v;)lne;*qLw7jN&U0*@eut=K0$5c7>);$?#x20FcUP+Q*Q1<cs=TOQ_s
z<~Hda$Fc5nPdbtyI>=tn2--Q7NP+ppL=u7qSE+EKMOqjz(1^Z8#E%Fw0(vK)e0+3`
z#wI1+59OD$i9bevuEq0zocNQln~xuj#)DWng7N6lc)l221`zDoF3y%q5Mg@HJ3}A%
zQyL0?`hie8XphD7Zb%OXHG0KZ%1v7bKlizVR@#%3s9qWF-RJ{Cz0F(@DFb9Cl}uof
z#vtTQJ?MK*TlfXQJqT`7@9aGl9lp=>N@(6U9NjBv@*Ups9kK=oZ6@8B3Z)zAQ0h+P
zIk1rFX}5H>9@eOU`qd>jy;qW?W8(N2vIyiUT^Tjtt`ofySuuY6c)b-WIon{zAd@qe
zX&SK9$|MWafnW1v08uGk_W8cODE{ea&J?8I%pKM8>HJdy^_sj$4_ojYvcmdB{jNRV
zT}<j3S_(3Ha#2krrrt{j?~Tl<L5f2dROe`@Q<;&lRp6|F7lx|?m>^#u@ful_M;AX>
z3TP-1CBUs{Yu?=KM>1{Si21F{<$J8O%UjaA>#I)8j$3aURZVf#)W`0%;x?2E*gE?w
zG!VW5*RIS7Gio?!MdR2F)YLB<RP<nsuEv78I>@_#&wrHgG`ozCb;Qc%CGdgY^$o{t
ztvmmFX+i8%R{;wmQ6fr!gufqrVE}wU3ivU2Pjk!x;vG{hJ90U24=KG;XaD57Yi`3m
z8*s@2%&sTY;Hih-wg0tCpTX%hm##E$i39%r;9dKcP;}xu*&e(f9%=A{CuA}0`o&k*
zQCP$MEO}0MRdK|#i>e}CKzF^w=m&!Yun=2dCN@YJ*%NxL*hWTsyCgl}W#1Lv)rchE
zCyMa1YA$p5=aOYAYEohe4ugy>Y`t`#2<KE4p$I1Gh{mm%shFk|G_wLit*T?#<6T{9
z0x_xTi2kg?IpyHUn8C35s2D$Ie+-`PooF*4u;5XEp-pTknv91%gpdU!w~BLJ4L$~t
zr{%zqK8fh#OFI7IZu=q2WzSMPs?Hfkd=5*4<C%m<0q(&+@JH@@`wuPLAoNkc2ii^h
zKXX5fzDVJEhiunV9xB8`lJrIke?FGW)pEJrfY%@(6w;=u_r!jD9bK&De)WC@K5zGH
z2pug$k1BX`vhdf@uh7S1ZUDChXY6E3KHpcRZHAVOwoB2hW;|z>%jU=?pEkn8$cEWk
z*tWy%-#+K-G<Ct-1s(;RG+^r%cry~r<d|2<m{<rN6{?jFfCTJCle*S5mKzqj6?PZ+
zNJRSqUy03f@E?uSZ)2W43{H`+vRFw-MOWwB!P5Z<0QK+!+@rg`Cg|=IIERkb59=n7
zT5yiWb_QThSI~b$x86F0?n4$mJoM^U524RDyF$0c-ILyZ;^_SM^<LrY;X21L#$koz
z{xCEq!NYRG1_hX~mVtTba`v62aRc8k7*Yyr-?KxgXx2Wj7FOywzwpx!0*UTB!H}y=
zk7o-D_WHcM=K^>3y~XQi#9nA&l#!1DszJ@m6-nJl)Jg1RRA9hk5wq4&EOwT9$h1<E
z8vX~EMk!tYA@#+o<$fxxw_Aw(2d5aUDNlF!G!V5%a8)1rp^m%b`VV+-wa-H5c?uD7
z!sww6hxVwA&8$TkMcapHCiF&vqvejP6+DHC7krhkB>nq2ouGkIdO}eK04RKdHY{-a
zc6Ay;4RG0#2Nb%@JKy`Z&_&ybhX4;OEUqaKRE*jx5uOe9Lmt|8CRQpfZpp>ow8~}>
zDCJTIwH(&<kBJ}W()=B<+?K^s2{-&ko-X5R9GCeIE`y|V`_>nwCi~yOd`<{lPc;R)
zPTUlMI8)F_B4P=&UvOE)VqdFcuPt;AR?T^$@!-1a=orADQuE2wl%N)KQ}C!y&Fgh7
z$p%WH@JlZ~0{HXLj;+QJbpNS?BO|b~Vt+xc{RJ;GGd#*~dZ@aqn$5&xBe7^EGn8=^
zgme4YQF&}}f{l${WM}PN!<Q#hxM^j$nb>Z`RSPd*zvpExviVI8u3I^B930AIqS+mp
zTy6U2>bpozMknuK_rUA8#P@&{EF+2TTx^Hb2OB<DHUNDKT$cxbU6>NAZDk5|PD)!x
zk6ip&UZqZ^y^c>-cO9W*-_EOc2Kkp$+0mU(Uo<)<O5Vx*vUeYDzJGM*&e3d&S2ehk
z)2*{uzu!WmPxubetY4TXBrv5g29dFpP{5RGM9TR3{An{2rGL?k`DyycPX^gZS|K0$
zh#^ItpXX`2<sj0<hu_B258!J5H)+HR@~CU1Poxy(Ak!^@eJSOXNDA-|{0}P=rGL?k
z^*{WtvO7gs@7~X{=El!@*gIh#uc9vwK&HPTa67EWU=;*fQ&_@4zC2wfoh2@Y^cZrd
zprSxObrjSE>}JC$XtTpU#n=PpxapA2)<Jn=u|MdVCAv9bH0U1lT=;C@3^J?<F0Ivh
zzI26y_u&k^%VGG8*tVxcLc}s9JO@$4%XQB!ox&w=9cy7cw8+vTv`x@Y(1JvpSYSx*
zW?*R-C{#WVmoEi&A9(#GHzBt8Z>Wx`wErClKYeR+*Tvgzcp&Uvc1tc~vX&Cb&tJ4s
z+438z<*4nSfIjJ4bz^EOnVH+WG|9(93#x<PBR^2+xHyNeYkV2>ze;=?zKMN*7N8$t
zUn^a)WpeDQ;jp9ie0XnhcyeT>a@_+H*S&2J5$qo*#ln%XV<XMkxzBdLz1MWZgHsb@
z^YgPFFF>*N4fy|cB}`JxAWp|%9YX^osCV51$GIx-3jbvifq(624SF(1nv~t(qHC29
zT_xx;54X_%XbXyK{I!Bnu6{ZhMHPvsh5yPLd81*s)e7;mv|wWYi%PwMjV$oL4v&ip
z{znjboQ0Pfzhi;m+h_;!GpN_3(&}XTbq&LDj61@U`vz@eve}$8?7@AL;XCMT<8|rD
z)h#L5@v!w_YCRl&P}bz&uPoUI!m=@9$l(L_(ys(@?!j;vra$s*!9MR823+4udpeMR
zVHqA^dx-x&*P{^7@(yo9?7}&70x=}|ZdkxRA2Y<~u0KVR6<f00`z;rY@*_kx&XX*!
z!Y+B8&KB?Q7;o5N_b=%IgAyJ}m*<*1rQ|kk6;JflBLS(HEHp=XJx~sB(u?WMr3yqN
zqD`gdup@}~k2gIvKkot(+k`@!*u`#LnkC-1UTt96E@cK(ER_C#DQtZjRR)Y$E{4Bv
z`}HZ6dTR9AQhTkQex?5qGMaKcQ=7BlSP%|>w^OCQzNw&ZD2CG}eFJ)hvDM)<5sxO3
zqG1CSBjz%w>_U;|H-5$0Zgu*)CZrBQdVvTtq=w46j$lA`JZ@*qWvYsmHSJu^HnVs5
zRqClwyA3VW5bvNW_I^BpDD-v){^#2hmXTS_UvyE<HZpepXdY*8^=bzJ>1+!=bu=Hd
z;0|^NuEd1tj2ajufnXBETJ)RR;pRHh6ICvS$vX!#3^$u2;0{p3WIPl+A0`kNwAiXB
zGsd4^;r72VmF<zRUSqo<CN&xoE9E6=7d9`T^rJoJ<NQu`ke7SXwR*<pKQgvcQY6H%
zO@OKm4MDwn$N=>BYuJm=`(5e1nBe!HJLj&U=&9$<xs4v6iC$J6o-@!O^AJ#wlezc^
zDH&X^OS|9+L4$NWw6Qdpr(~?-65KZ-*MRIIrDtDqdV<!C2?zU)xCqf?F<Xw<O3Dq=
z>7XG;WtdQ9H#B0(Z@vy*#H;-i_6WT&ObL|oUhY{~svKL>ut*?0svE|kF>jcrf$gay
z*qH4)9eva{-lwo;--&4Go3sm$tkjYDJEBWx_=7e<3&4dij+vn0&?Di1ha}-!gH|a;
z47PuXlw{mFil2d~MpSxkwy(7Sl{2UfIEatKSCAVEQZ0k3CMDfWwD(|d7+^p&L@WZ-
zP9(<j_iWN&Z-)Y%7Hob)K3<9=7U$jkKv7vNSVAN?yv57kofybw2NLwnM0P5ss<Ejo
z9er3dA=jmL=s6+kH)%h(B+@KFMSbB9BWd$0U$Ts{FT;q7sCy_PA_O1Uqy}GJR@G%a
z&J~$U@xSwm(pMIWK(}dG!1`mrPVmqHG;$%7P|-O156*yA8+~(<wPVK)s2VD|TveTz
zjEt^y&H$QYR>i67H=|g~*SrG31`b%+GH@~1yh3xdL^v2ffG`60c=U<zJCAdj(tyk~
z3)&=DMR+6gR>P{&Pojuk(YCV?u4ozIJ_xgnRwvAm79gCx^yg6+Gwwx;HJfo*4B>x)
z+g8t@fmeG7%>8DCw|o<=ZlE88KHI0673$p!QOu4K9d2Sg+%-=&rH20)D-`Pa{9m69
zG-%VGn;=0pHl=uB0dIP4vLVZjllUk4nDdb)$8r!BRN%4TvdJkpZ4BWlyr{)I?fM)r
zJ>573T>HI3lWmn6ga#8@!dquQ$eVDD55VR_M1P<6T11RYG%W5t><)Lv9jz^54$#^%
z0t?RFXsOPW_MvZGhM-FZXavqB{COdmnwZbr{461Fn3(fx{^Eo3Zc2Ag@p*o}fHvoz
zXUBPk(>*Ja+a<nDPJ$_E|C_L@ZW7Dpd=~^}LbRa_jbRbilU?Abt^ps5(e2ZU7txj*
z+7=3QYU2*+O>ATwS*g`}sp#+*Y%mt+u3V<RPNW%0tNG^_YL`hOoxE+@9H`B+k1f-~
zln_tMV|hYPb{aevS^sDpV^((tHYxXrweXf-(({4;+H_pA5-PDsUwn@$#kT^cQaMOp
zx;iQH8^$<aNCom-X}nfZ4`7`fdyT3bd}QMg@1d<U#&=zCe8aUDjqVR(e|hwWIcl=m
zC~X3&&R20>13$ajxO+dEwf`=97HtXM+?$Ik#!(!y7x_IbLkm@#otXxI3Yl}ErpHOx
zfxQiJC%Az{XeiL8<Zy^<SewDaLX)EDQ<UM591@X$Ng;>1?ueu#MDk89{I(*2;c(o6
zXyVnDW?D-2BQ#SUrKWt!l?pBxI|Lg|V0$cC(o@kwI2nZBs#A!C#^T{h)@54wC43}a
zCTR#L9|1_BWe>q8Ev*HW(5o2Y=7;3;SrOTSo#7JBr=vqGVaEZ3)Ug?DBsX7j!PBd1
zp{#~v(+VfF&@+5=J`dxt#B`ZKNaT1pE`#YDkx07=3)YT2c?BbK?o7k3VH);0?4M8n
z+i^oq?rY~xpt(k>0hRp#vG|vj8^0dOMRtDT+<Ee$xrilgrJJ5obwG%F$`%2J_z1;N
zI+s%w<eiM<9KF}&!Z#KVrR7?0>~_v)&*lsJ0b5mzMOC#CqXWMVGHP*qCo<7et{M%M
zae8HP5_9Rxo2~Dk3Swge|IyB?w$uIk8d!r|v}lxzO1*>$_I_{0@8A+ik4%1uhbUSz
z-*Sl}IGm5KPVr@K6ILMR^9w{T(T`BHf}ffFI%ler=Vht2Rjdy_>01rP*8~f(eBfjI
z?<!$S(ft0rp2o<wq;pwmja~lci5?cB;jpcow|M5j$M;7|I)4~&Q(+H!AwIF&dmtH}
zI0a{4Z$$R7#(RAJkT?}umoMdGuwOLz!zJo~rOp!nV?O>wS-P?y*Wi2FgH1*~h7ic@
zhG9=COwce-((7AlVSv;ZxF(z@3s6dVtWDp-`F2vRFVu)QAm>`O1szti{&qUg=qEXh
z(2umPfv?6|zyl$9hn#~=BvahtPTVYr7!P3p_*_Ta?Q6=dY3xp2Lm=B4tdxjp32Sf?
zM^zkEC0)aQ196GPOjs-n&^AC`RYUr%n@xh-P3;jyRh9A4pk>-GqwFJ^sjAoJ%u8x?
zbVo?GBgF%X`gSn|B%fACu%>J>&o?jd+GZb5_wdmz@X>_97$z(saI^RylpV@eXelP7
zJ_`g#l!}e#^fH<ZfH$%<FQgET){NN^!a3Tp>=`p>UVfDb;d9ls`TXl(qWG5irZ%*T
z*KRj+QFHtDkFPqxV!DigE!AAOoGz}OG3gP$k1rls!ZUazw@ardqB(kk`kS`__*ueM
z)KOYKZ+!#Fd5gz>{9=mqy%$#o!GsX*U;$L<IJ5(=54;2Mh(8(lUs&h6v$E{Z0#8-a
zBhkU8%ts!9szrp-{)OUP4c><98$IRbHe7!}jo^m+*|S)F8Y;|b{Kue4CeFZMMi=Nz
z`*xl^Zvv+RT9`H@E9bqRUtp_odL%AV!2<N3Awi@46}k!K@Dz<3a?jI+Wl9eFIeu=#
zeOyV&&!MY;t^Qr^147DRATT&u8jB@51S>%e=KM)eozGcI!R+U-z_dWBKKF+~W6^N1
zkbVx7iegfe?<m<a!jSh4f^)hf@Jkq%(2XUDAwuthJA&sO@UC>GNNQtfj$qKi5%EFZ
zbzm699i4O7-=VfZI7T23=Vyvi_vvvdH4b_P01=ELQ{2)EEi^e1U>6^pBgG0B_+1j%
zF#PQT4`b2Z<_L?dJMw_!+Qo@1b0s?#kwUCEIS9#vHqF&oknQ>)Omnd~g9{En1D}zC
zCf%4!o3<J;!_jK9hA1Hp(ANkqGJWj#rAW+{fcggbfsIvPvwQBEgH<5ofblLYUOF=S
zilQ11E5Gkrx84lrG|RaCwU&E%emr;4K`V?nB$r&8D6vxN@F5EUNK8$?_|lOb2~9H6
zTL-o-h;!PGUE>uac0iRPafAtBL%W8yF5;4F?AS4N#ne5PtwgL;-3)<OPulU@W_K`F
zJM_}&gcWklbp7zw0mi1F3PZR}ivJ0z0e!UyJv!oslhgp;FLVw0=Xsq&9Rl2}fG2xR
zS;Gd435uM^jlNyb_-7xnwyD8FwP@*;bktFdXiRoOZrmw_28Q|8dBXz@W<R1CM*C^3
zt>lsMi98xTT80`d3S7}3!Z>2LV%JfmoH1AaF-^PY%d<v~g|Nq~q28$)6Z9DQWstq1
z1wQmNVnbusNr8GL>m`i@0cIeh4N-3n1@CM_Q?ET8PHKp;6tN0P!>!F#F_2Rs?39CO
z&><%iDpi6B_DIl?tof0_d?cR?#uB+|Eq4sIaU)%-4I_3a{>Ey>LZn!VQoJhSiPxnj
zY$qmAm{Vjy$-ut=5f4G@VzA=#czHcUt{=hxMFRx+D&jl~>X(mG#Jg42#ID=tk!qHB
zOgv#vhuOXTa@KlkGoeO)zKx(;gw{kcsn3x#*>a$S^)0}F64aptB2#{HH-YfDISXCR
z4LS9i@0$w1_|BQyaVsumGE&?;QJdMhYjSG1R!YR<2{g%616>shh1fE7M%b&{*6p`j
zw!RnpA|xWkY(A6C=CZ|T-1Exjx>LbJ*|2?3q><O4G#=u*0DF2$U|kSOiRLc{vY{u5
zZz1105~bj03Z6wInC0;7fZ_-cDhk?#3mD8Z(<<7tg9s67p8jJN;A$Gfc(j&X!LHo#
zilfQ{$w*cM1Q1&ZOIj|G3)*%m>blXAVZ@?278r6uCH`0`)IMDz7Lk&h{2VKtqLUBE
z%W(D7;|P*2W45{t6FaCG2;6HnqGlF2MEPiDA`1BGkTVqf>|ON8O4CGaL}YlfMmZ$Z
zZ%914N;3XFzB>kah1`TaI6b7Fx5GB%NgxosrFJgDy+MiaC3P6=#q2=4B;Z#-b36t9
z58PfdRZq;P=4u2IgfkZQO5oSQ{NP6LH3{6Dn6D?2c#3^3RCKrRPG>{;bTEz`l3jvq
z+0I}Z4>Pq~>0Fs?e{~|~x@PhZ5gio)-og(w$~B6V%JY@_V1upHE74dmDVIj7x7R8|
z?2XvP<_8gT7=Q)ns#5?qfsDP?00gnjSTW()L4@VUm7#+X<q%V3Bc#~+epy3=s)Mq=
z-+n*|YWpSKRIUsvhvm>i5$!Pieogl7gK_P!^4|TndPsu>hjgV3F=1Wk0QbY&@t(k2
z1Mdzz2rANVaF3C2F4{n}9Ec(LVR)dB?nP=}T$>XY(4KUF=m=<HGKik%MxaN1SGGDC
zK}d@BXPkcq_HsVNVrQ{|eW^CO1?b3lwGtaB3^fdE=S(IS84y>-8iSR@#r!6IIyo3C
zmg-ZbNPQre3FQZ(#d2f1=cdCjwS=-k8f~Dd|BtmV0g&S=&zx6xRaaM6-*-><^z<=z
zkLKu+Mn~H^EK9aEvSnEoh73N1F9gP5Fqkno#$XZzgkU=X#w<x}mLv*<#5e|z0|6%?
zy9%2S2S_|g7S;~gpiO`P!L9xN_o}*kMkCv>Ymcg4o&Dac_x|^v-|uBP*}Ue6`(ATF
zl%0az{uJ^XK#9LQp+iXobW>Px!-#AiOsVoQacLW}+G%jSr`xvS^u1W6FwXj;VKWBL
zci9TylAd@Vtjfqrh!<#)-Tkms$OAJX;BF2(D~PaXzVnV~4sd$UdEH5(T%uz!)<s?k
z;?a|Eix5jx&sZ0U%5{ysDjc&@xlkrmE+HPEKam;;Lh*sIixl79l^yd=A+(#l67?|w
zz7q(>;eB4h@H5(X(O7&Hj2p-_uML^ue~(1;BIxN)p)dLp)}XxLzx~i~L4l|`aau^B
zyhT<TstR=Nm?odSopY3@=%vQ(j#109P1%~!{z^k@X^pWbKFlk|Hy?f?IXzIGy73aj
zT3)t{OE@Nuz!@O_^8$XC>;1rEd9FErVGU%pE{4}YmF!Kk=p~qp{V~UmpdRqf`ZKXR
z+=a3Ju5eX<Zi&1FXp;~Q?LQI-8e$8tN<V%1Gve|GD2)D?c$Q=otT`kfaP$TE6?ptV
zX|#zt3kz9S$;wWxwBU^{eA07oF!dFpgxIhamNJoIbTyWHgwDFOq1O|Z?k@ZW`OX7p
zjC*cE3jp`q62%knMM3;9rm(c_LPWH+k2{V|1$F|w{2{hC7h9$u-W?qM^|yw?DVsKP
zScvIY|M`sx2${WYfz$Ol+R`KFc&+MP`QvA`Z!d4hUTR9Gcvf52Ugmf5Inn9c4QjLY
zm)5?L_Zh_*J`Ai1Sd8E}Gu8D-_SZ>S#H{NwzFF@I8l7!|+?O9nW#5xYQpNA1P4*pd
zU)~EZ<9B5JbDI%<qYYhCJa|y7leAc}+bMFUeSgmxH;n(@%K0c7+n)oK4r5Ne22CVR
zz6QlAIDcBG@Ry{Z&1MZuw^r#fuzTvfu41oSI?K=wy%0}xPM)uRiT$GzbEalL!kqTF
zEJ{J^nVoN_)&qRYrVwcmkfa>67y$$t<P)MG=aP9zvcWRsa&TJd-XZ7CzJuKpj42NL
z$jo&64!`v1-&z;UZQaMWlQLeQOZgl&wj||~{wouH!T<Gh-{H@PKKRc$Jidk!`M_z6
zLI%OwmDe7!AFOcKHS?)}eMS_mm4N-X%j7L5{N$Fcg0=3D+Z~>TuH<1JrAprk-tF{F
zNDd?CG~&ZZa1?Q3$ofP|Ia)|{=%B>q8YUy%Q0pv&>`85O%c!>Si_m3c7*?KW@Icv4
z#UnMhs09s4E$8-R-9T7=(^0xMd-X$(>pGd*>jdp$zmTg`^7@s#_g<O-_Z5sbA|u#q
zc3P1FK_wH9+u?lqF1qrX!$!84)16R8kTD`t#K-#>F64JUPCP+Rtb3P=Qh*KR9270m
zI;(XLSj%S+M1#LSnw*|Q<{Y({-<mI~EC`rKy3iXM|Fk&u$xukP&0N@Zq2aL~5|6K|
zQ-NE_d+U_+EvIF_0C^anLW2(t4|atcdrz%ASga3<pZvq{#Pmd14}<^Tdj3}Us)l>(
z^@yl~*nT6HNW=^!tY?r4Q4gc5pl{?lgS)Z*C1H2I8ncx?I0ck+lm|`0g7((fXHN+*
zf;01c)$RaBfOX;vR3}{w=jS_c6YTe{HzQ8F=tRsvbQeN{My~(TTM^_xvhOf$faD2=
z`)vE;xe{=&%yBCnji#+X`}LT;HDJ1#z4e_pSl-+4%G2EDcCOqS8ztR9zhE5scnM>K
z2E8_ZVN$O(LD7=f!J>rSWGvZ)5Qcc!8nlRT7skonV1`C!M;M{Y&i?(*_FCE9k*OM&
zR|<tzp-{QpsQ##M$t8uJ*oK{nEr;aUX#enVe{@zpv?X!=^g!TZpwUMz4h&2ySIx~`
zMPr@&057puBf^3#2#BU<CGnX^O~d2JIS2^Xt-ZvW)?r(_OvP$@g@2Wi(tP5o#C(Y#
zw11$h(_C>iO^T^b@iDCTz#`cyc5IP%e>#~&st|mF-&gguxAP}&7Uum0vEyS=;53B#
zTHx!?M!4=MMu}JSbp#k(oKg|%4_8?3p#vI4oC!jNr;xIovma3VndYw^grUY(lxi*p
znlU|SR5Qzf+b<g7WF=ReIMcT*?EZXVW0xOPaI>RSx5xYChl$lI4o<I7RCs&&cru*J
zkIM2$@!9P8OPsf*x>N2$UyF;G1pPehFx&|U9<CTCy@x{6+@s@VKPKmX^>yf2<a5*C
zrgLY1wSG{8{`oc&KDd0R9jt3~DQdh`|8W8z0S`a{qU&!3Kmm3dJ%IbOc!2NlfOp5+
zC^4mmvArU-V0V&XFAZRwPSP3!jDcDi2;pUb4#WY!piQun3^ygjeuGl7?;O2ucVl<^
zpZ6>s*nQn~cfy)^0ZYA`DQxGR*L@R5E6em>@u!ju9mO9@()m9<Bgul_Vnwz9egKA`
zs88Y6u(l^e@Jp#i$tEwM4I>z3{I$GeoD{T*bPj(EN;O827g3}gfy97S39Jm);*$p+
zI-2~uN$Ux3{?5A8$>j9-)ZxjPo>qg%pXew<Fdq$JKLT598g}8M#|2%z%_H(FQu7tl
z?H|D<CV~j8hN_P9zlO?`2>g_oJ9BjEAA6~+k7+=$YZ`PkGtkZL?dzK&QDUZCDbu<M
zp-0oPY7!qE^LaL(n=TPo?}_?T0)>kkFJ-O^DHo%`<rTIvY)uaqQi*Ifktz&MTcadn
zvQ@08?cWY3lVRXyId1xn!)ezE7&gvaxg3nH1f$E#vDn~rI5ap2ec50f?%WH+=mBpn
zIgnITC`2m}S5<J(Mm*jU=MhNOUO`ME_;Npzg%$^1c~1EG^qXnwr5gL86%Dm_g`(Ei
zJ1IrObHCnbKi6RQ2C}hOHt_WoKZoebY6C7scqzvO5x?=Gzp(M!H+ZoaE6++T_HkG=
zE<=U`!nl@6`$WE*V!+T>DANXuEDaZ&C3~6!btZAjey}~E{X^Q~@C~Z4x)JOIGL34&
z<c6}eeN0UhD`6LCO)6lUoJhY?8ycE#?rY8u4b_zXh@;uKqG@ipQcS30QvXbUW&C^u
zS<CYI;}smW?`MTrC0-cZTV|7mcm=<&l=luk^4Ul}B5U;05MHj}1rOPojQtRPi#r_9
zdo>w1ebzAc^iTJ18?Dtwx1q4dxHOv0$1A~LC7#blhk`-sYm&1>ek7YmDD<xCpr~j%
zx<K?3eOl6T;vD|C^E@Qa8VN+So^!zneTKWxdo<_xX*}L@QCtTp2J4UU(!<5P(hM0o
zGP=lc4-cw_shM&VnI4hrF%UFD26WFR@G6S7l?_E$&~`L!Le)$-x2gCaoTIKfnrx^>
zS~KUbbdUh9m+ko*-BOU#%2p6P9MXXLr3X|cEh|XI5Oia<fpqEx%`7IJAP{f^sD$J)
zGhpJ?rmjHx!AytusU9~k-2%m(-~~vgv4M#F`gI&7aKJ<lm1m%Pr_KROgkG3Hj{vX)
zZJ))CdJ&U`@6kUv!T}8+-rf9?Kjo=A<FO`<MUrrK(2?HLb0GuB3X&8~#91(A>iy*q
zvy6ezOgUwQY&)+IE{`J1h6LX_$x`GHeD5_`f{S9mp@S*_BOMC1ok6IKE;QAMq9Ffd
z5Q#1y>uLeo4<X(XJvgCet$t|#fu9pbzBtpK8kh;$ifO`3oCnbiL0VGq&j6aAqV^-Q
z)qv&??3;$Kir5AGL@){v$E@aP1nw|A5kOR9Z-1T#`_eiNT`N!eYbHjY=p!Cj4u@k3
z-9bNzHPgF_HC0bi|N6Zt_V^OK_q+j$VJZeAo}v!!>Cj8?)u9c8S_^W9r6eUoj;i{0
zdG@N={{AT$b{)DX!Gq=@)l_w)?1bJap;&<Nm$jNKA)TR(xy|6%gOMX+UfODJZ=N>r
zc8#=V_y*<}Zi%Yl5Z-Q8WfLIr)+NxQ$<qBg-mV6rol<Q{wtyPXq%gqAC3tWsfT6J!
z=t)e_N+Dkpj&%%nb3zoG1lO?*gp|aF5Q`<JNJi8U)=a`SB)X|twG97toK~lsWrT=L
zRYBFjBkCBVxDm4x;`u?lv853(<i}<FvE7NwX3C+JnadJ4JM8G|W%>1H`#x;U*UR+)
z-5BPV+9&yathnpYp<RoJEok9yYQMYbs$X{ZPhIua()JQg^_Q?8IG^s}r?297fF8kT
z3pSvKJ^5o2o@L--n^XL=6BVY9+MzhWe;~vpi~)qS0goK(P7ShA4b|_Ii1Y0|SX_+;
z-C&e+th~fpGoybuHp5Zne&31OmKsO~6?dy&2sqJJPZ`SFXi%7MQTn^)D|5M59w(r?
z>!J>X*kIC9EPJ9;ie20-61}CdzQXy!>wu*HW~@U3wMHzNgfIb{9S%0!^OKzoXCS5!
zKte$y;Ltik7KN37(S;W<{ug>5sLN>FgM4=pZP{s{C-OhXr0muHKWN~@AJ_nVzhZHL
zLzsAFjM(<`oE@IyO0LD@x(Sv6TO8uKwcF!(2h1Sc0J6NaxVW^8dvQ*b`DtRq_102@
zb`9_sFJMu6C%XsP4amQ^f*?R7FYKoUCx&@Wix5!NF`kKvHjxgts$z-KJ&RyXy0!g$
zGTDCK26S2@%%DhMCJfpp%Q&tS{ZcZ?lDO3VU!~If9c~}Qa5=2&hm~kl3B@z!fOQ)n
zFZA22l9hC}8ph?g@KMVeeVxbv3!|1;GfzR!_8CNV*Fmia(T>;%stu?26@hiZAT?vi
zcHjw`Cr<{0s;y=nHbz0+?XLy1I1IA81TGLe0g-|o+O<nb1q-&V-T`<>07$?Y-?j^O
zs{J=z^dXKB`T}_0cT!~e7!yATfpcw5*sblqu+vx>&EPk_X`6uPOxtV<V5$H5KAg(q
z()HJCX*-Wg?}OHVz;vm7U<^7slPD+(WziWyjLa0M3L!`Dr%di^v?x#}Vp7t$qhSW~
zzhLW=7=<(v*Y7*J74F0#?m^&04+;LSMlf1O`Yu@LxXNX31OK%;8(gLHQ@Ejyu(UO*
zm-Zbv>*D01xal)>x4!88Yl*il;pHygj<)vdhf<IuAg!%wnNI2;csjy5fg#r|BY+D^
zuIelyV$H(S0Uh5{eOZO0Wr#NK@04EKzax=^w#b}#V1l$qlyazNiM`u2<2w^;H|_7G
z;)#rD%Uq3=NyJl}K0XZ`1;X_#K@YK$qj(ePqh*iVOkjAPrsNI2mJ63OApUUyj*FYs
zne7a!rL`wEXb^ikmK%nJb2t|(O&Htv`BsEj;N8)(#DcOJtlE}rOXXi$cYMtTZEfYE
z$)+qflhIspyyQfNJoaMeR6{m~3c8|&6A2iR^uo~EqxN%W^}^ckmWNgk{tj!t8+2%E
z+xK!B-17y4>ss`3TG;b7PoN3VS@kri=V+q_^^U^=)^BJ#*0cu;Gc?=VL1lcCITSSO
zM<6_yiA#hI69f|*^gvIO{z1(+x}>BEVI{^id5`P-m$N_9^?PKE#gwq?q!dYaGAgKV
zPrE?C5WE=-tJ%;V<h_X~x#!n^oIj1gj=Lw6l_j9RF~ttL<fuS?L?kyp1^q%2GVm@)
zVb_Dcqpn!&$gY~;B%Y=d9D^vMGzxG7lHj`7oP~;+&_uUORg7g|cf|Y2-US}7+SDh&
z5s<LPogk|Ke#BfQvo8Md7`Pqw&;?;9KkT?j0M!0VlnX<=3{1%&q$PdgmSYDHRG%uX
zJ$w@0kEG91NW5UX{Guf#ap9R{Yik_r?dVNOC8D~F<>K(-3Ohpu$K8vVhgGk9A>yX`
zBsUZ;rn6S%z_D8<bam}vNkb^?v>FJoBp|Tw@04DF^OYnZxgtvPCSbbgiDF@Ug6MPa
zSji&31f1U5u|o0m@b(2OL^1<~VuHgZj+k__8;qNtK~h>!k+Qo~QsjWTpnXK_G;wnA
z?BUWoHonk+gr(N&*eD26E_r)doL*f&PS&=g0-vRW>)U~kiWE|!Ewso30S%_3*$qfl
zlOP<rcMwn-J=oK*Z<Rv`e+ZSqx@R<IDE)9nXIe&<KYDhn-i|xqtM1>Sb{_3*?!#(P
zQK0@e<#ZVaayJaly_}ZK_D|Ng^e1a?VE%*jEoojn4Sd7T@p(b?{18QBLW@ksUp^O9
z%ySAoAXikhzMqXtpzVlTph*G$Hsaes34R`ecYSv<NIJ%?#`v&3A?0EzZTHQ0-Mm{%
z(WJ0ba`CqNZY#>En}2q?xYC^pu=BugnEz-tmXp-&*KOHy-8O|L#HA^D^R8W+<<!$_
zssGxejYub-4^P6z_!zJ+rqP4oP6P)AVVVR*D2Is)meFgZCkCgoX61%7O<CQzNZCc|
zPXISV%pZ(G8iK46h{J)%k(eA0L@P7Z7S_Wn_nO06XHD0J&AnIdeEoGVGKVh2L^pJV
zZUj_wN2XTEw%8qb&w;z33meZ4`fL4Qb{x8~yAI&e*Ism5(HJ6`NwnX9evvf)he26=
zrvdJn`C`rXl+absOFo7`AOr)abFOl2C;FZRH=e8UcbaxJU}0R!m3l>Hp$~{e1{bgE
zJY@KVe?rVT`QnZL2LiU!3^S5SMNFdv#8&*>a8@4*S|a*@{NOebaz4?-7yaI6tAaOb
zG0~6>KeK0W^LCB(4SejyTlQS9j`&?0v}i4TB-<r;(-|S>V6dWb;GV^&N(wVV=L?eA
z$zw`ACZVg!d0PDLBlt|<`a7TL$6c;;yWmrM%ui9lNmqM5hGAaXVM+zPTVG$|V%Dxp
zXWKP5pyWLyd#z#fm1ooGUUIy~vU#R&Jq`9~E#}2jY;7JG_(=#cjU-DF-=<#TSY&bb
z9g|-A{>2u$kK<W>aD!6&GaH=j-ncBD<d1K#flj{j_y#4hz6e~q-rP4FiH1Z|zs>r(
zHm^Sx=vy!U{%)H$s8zJv<c+Sfo;Gh>Bs3bo*&E-%_1deD(U;*rG=;C;6W2%nL!x~?
zU~so51aOWR2Wzpfju#Of-SuzrifiDnc0sYh)f+Io^b|dt$$7w59w7e%_(4FQ$zj_m
zst{Ust~~IRn531QuE2jnfZ$YX(tLtK(DKlqjukGuwEt*yGFWo+h)JXKN7eS<sp^?n
zWjgY&y3$U<K$#Dh?6LlMynhT+eKKe$IkH(MRoqL8tACA5S7K*WFp-4Yzk=*6E3lni
z$X8AB4f2#vT}>OG9UZ-!P(N{%3~?T$=U2gS6KEHAogmL+9o`e3?;3kScCA*jltS)H
zMeEtOfQl*W*Jm#}bW!$tU2dNNg%1aV;Yh0C7gB=k{E}&%A`{lV3Q|G*4wZg~O7~Lf
zlog5Ux90o%^SA2JYZ00ghQEH;-}mI(;xV=GZTTXwz~=A`xOI>iD|`tU1wl+zE*q12
zk*mr`3c*3hkGsXFp|&EsgrnV(lC#OJi9jH}Et!#%?0rVsd}TPvU4AdIW0DjNEe{tb
znlud9b&1G_ZT4kNdwu!>Amk)$I{}QG3(_lwvCo0pEo3{%OqECfI&U@$AXK8RC4841
zz>R_(mfYV_ms%ApJi&QkRTC4JXJ`eFFSL84QK0bCuER8%RbVf(VbUf|Rf?KwXkkT#
zD+YPuCG*f`hkGu-9Mf}!Rg9#0#El;((9(T@h$Kgy57+ufX6Tq5jECQ6G1dqn%oI6V
z@md?|ZCGYZJt9?7;k0_rE1y4S-w4~_d3|>ih7(7R<0JFMKAAN0(x2y!bNwLssIGFK
zs45sO=vXP~3%SF1G{cX)^N-1tz?N1*|3ypK?N3`yDfu0E)h3`^duz6WY!HrpJWzZ?
zF3DuehBqhxB+V%^P|3o3RKEZrf0X+N)?CQRr2XWR?Ht-*4BIsr;IwqH2g4}?a;*Qa
zXqK|7^)}eFzU*e@teRx4RQQUM;Z(}~y}zODGrvpQe;Ey`(US8i%Zj_Nizk28O`#s-
z2m2Jp;28QDX6dt((%$c5>Ja&&@rcpKEAc=$X~1A?z>UM%Z46dieSbgFe73Ym;IsqZ
zaF%>787|WFEuJE;&{Lm|xS_%57t&@vc-#zxo%<AZA)Ms?A7tNu3ff1~JN6~X#htv5
zh}M8z#g9TP6940SXf-Hv@y8uOq8*L5pky!t2%2<N(02P{`Di*81jPHNpo)XvvL2B2
z!Lm^ehlZU@ITLfN6>{&u=C4G)j_8MA=`qvcoNd$!{lSn;uvbpZj-}jsO>=AuM#*a<
z*)01=gC3;KfS<gBe5%es_i?x1j_4F-DK}hE<#{e_G)(+Y1PFQ$`h!w+dKM86UTBH1
zu=0HE)RM8EyGCsA=jsBoU4S@IJ4U@6B-FKVG2?%nK#@iY0|Uc=z7YPOBFhX<7ld@8
ze5%ql`f!~zDpFrIa&3@BFd?*B-A$oW9qL^>6znh53_BN2!yadTD3Wb2b~WHR@m+ot
z&N#!NaMdUe>T&>tMus%@DJP``W9eu<*6-TEOvL||&ZqrP(lfTaw!jkwVJc8I(H{nr
z_dSGxpErve8}Yp`utd6;ZhMmca(Ca)QysSQ|Ih*WBnqb)Vy;d?w+3WuK&X<Q5Z?r=
z1N3G6EKD*TLTFI?HG4W#2~)%S2ijAV6V9oay=1A`5{%-Q$vywh^cltx{&#9oRqk`b
z?n$Q<RfD4}Ml<^9I&JGI+S2CLi}W8z8K%H0#?Hf3J5Z{PD2hkuX!#QK6~wg5nvwN(
z28iLcys_?IICR`~tkafbGz_*Qme0j7zX4=4LE8o|-={a|;QV+scugp-(-f7XkvBx(
z{e<d(#y^X?eS&Bm;Jje75KKU4k0I^pUy0TcsS)Po>N78gkinVkCFr5(pC^Ia^+n=V
zLD~?uLnHzp4vB`v9%o4pG3D)P#p!(Gai}Cq_O_Z5Ddv>2)!_SS<gU&a!^&u&U&$3C
z%Al-d)U^AzQRf$J_D-7xHZS~FH)llL2md?Y{lg!-VI%MU&fRXr$hi*&0>e)5%1|_T
zSD-|8xig=KUG9rO(15O;M*$N-0{I7;t5^dlDafpdm+;Jvl)R2ODD)P6NaCwGIYSfJ
za}OY#;5;Y15YV*+JrlU$-I*#3vlK@09)5g7#4Uv|@*Yc;a1ZO7LnuPwEj2SAo|w=w
zf&1~?)@lZl04E9+er(0f$Xzt{j+BzQaV^8;fG2^0csFc2Ko$g9rMhy}F{Xw;(r`w2
z_0R%V!*Yi<k%u|=Wd)P&)t?qQik{-j++P@(gltq{;twLX_)+lMfn&haFx7i>GsBJs
zqeqc~JP6}{)sPbzV{^vT?}00ctiQ&Je#27SOkfJmgh5ri`Bz9j{6;wVF4NZF(sF!c
z;NPRcU%44>1-M}<kZ~33;(?Llx~XZl`L1C28vsNnpS>4&yAtr#t8)Lh%jvstR|)RY
z#1-U3uWD7nF@`$|4-rOJ?g->6#0~DwO^=hW9~oECP`-gS{xgSz=GDR2KyU%zn@r7o
zKvA0JBD$Wewum%(;ZcZVi{1hIqYo95^K~h4>7k(6jJzT?6OP#ZW>G~JL=!P{OU5e#
zCFr{MDM0B=y-`-4YMQN881ed6>0ORlj0RU%L^1l7TudI>8ryF+S1%0%CCgIms)A_O
zKp=yNWU{3be_%rMNAWror}^f-UqpoRuOV9f_mDm1D}Dc??^)7A;FE&(RY4KGQh92F
z68r|dJNO`=C(0!u8)Nb6JkQOY{;kA}lk*#maNlPS_j*%+Qj%i%m?U|w)KjKB?4+=c
zB`A@)e+M4&&aN%>Uf-zvoU9f(7!kIoc)7v1d%5p)vAvfcu^&N*jRse^7`x&{q0QA-
zPI5tInYJfa>T_n&axfBY+j#*2M52UzgN}{=oG<%l_IW4#{U>`b@TchtFEG^VjyvG+
za@H21e>k}$E@0@xCy{Egf_$*FKVF|}b!G0>TpeeNZY&wF;>noXDKt=u(-|ssuJJSe
zmEH@TJ4AuYTdUx8ABJTcIXWQ8Vh{-NPmAlof#qX@OM-~~!q+D40Z0OsghRp%5_5@?
zpn>8I_=I?RihWMUefuHoXhVwbUJc#7R)?xRw4mq&)xSS^0N5HY<DO4nle~smb=AC7
zH8th1*lG$+Eb;9Ir|(_D`sVwbtUwo1qA1}ZJ_)*`wE~~sep*^6aTeJWrlL(co?z^B
z8$(8TlA7&5ZMERc({fK8wv4$a2E(sC(Qw^j5&wEV65UZO7XK`feDtPx^(U;4-%!M4
z<?-pBz5yPv6(B3o@_;phx|2d!xB~;$Jl>grN+Bto>>e|<WP<X?VBM(dwPa0i>V#>N
zY$jp&(b?%+hh;rvylOx)ZBrU}l@ZeA;ad?`YFxeFux#aKCE#kic4=-vK^j!#eY+Z$
zesfwXm89vxiJ91#>yE`{CVt=^L;R_-^(IZ%wVRL>E@T{YS<qExE;AFU*CD;^l~*F!
z?CVkzbQhP+C~oj(dv<tu)~=_GP5G&*{3at!`~*}hXl#p7>>AN#<X%{x52v<?utQ0X
z3?VjJ_1Q;^D_SL|s}e=16?w=U0^=HP=+z`oe&0@LuMWBFq)?39B6cSh=|0+Q)!uIf
z0@nLAPw^pCy@J1iePo$O1ayd)pj$qj5@e6gQG`3uGFt6Oo+Dc3)3@$9ifm`6MeZ5B
z`qe6p5>7!Xl}>T}X+}ykK2(-G{TT(1wFNyo>pW|DzLw9zU+Q5V2Y5bK@O8CYudS5{
zQo>J0h3a$Pux@k4G0v6+C<}V;i(ok51O|+j`}fSx?S@aJtDAF+#}|cfleBwoez}@y
zWvXy}raQ6Whlm*;c;hZXqUILo@F&W<-Z(G_0s>x_>b%Uewm?x&Kkp04qDK+UL#~Su
zjwm~3x9^2Q4fl*Q*0<;O-In1<=Z&R^Uri;4T<)>t4kg8jvGafuVeP)1E$_a4Ps4N-
zeQbwgowS^YD+xpc$&K(6JH{woaRV=;q3P2Dk(cNq;(0lR^LuFqGNxZmIyZH$KGjC}
z@Ss5}1_t#mXt&?LCpq{Hb==~*?oEfU=SZWu*dJ78sGCm*qm^tUTS2-PZ;!QrEmAk*
zli<6EZvM@18sog4o2mS!-`78ZT%mz?hiM#LNrZ&7s$4-ST>q==zh!rwpN_@S=kLn)
zr^30Xkc0fGTsXz<PTGk~ES5>w$;?F;=f7fEPv<Va2z2VFc&EaC1D;T%{ot&iUIrl|
zRB(E|_z~j16Tr=;h(*4c0(oMsNj0WyF1+dw)R6WEx7;JAckNE+a_QZ>(sC-|#IGMz
z;_8-3C8lQI6n7$Dy~Z@(zV~rWd;IG%1uf<Cw3Som?aUmu%y(REn$65kFx)~{dpGnk
zHAvr>2gs-erytM|e*$-vQgMU>(va$x6HW57Ci4)$1VC)~*DP_f@-%BOjQ*`RY&%K)
z+g_yY$x|b$QHQr&arv;dG_lKi+cy)?9oxb7<8+zraRafq)`|wV-LSQHVcw$I#?7tU
zf=`&cChkuFIgL+dMMGd@XS|ibr^$#dK`O6!64aYhdr)xVGi&h1$4ZdIx_NDR=Vmw!
z2d5UNMl7tpzs+vk;f}apdGwPZsaP!U{G=YYMsP7GtH$te-?bwog+BS{S6pP#g&d4L
zv>eCI0(ufh;7;;f*fgt1K@H^uKAncSn3xq-9Q2g8mh(iSeB2Y<6UMWrWF<uYPFnec
zGW=f@cyy+xV?&azm$n!gELJT&z#<r?<%w`AR#dZ4c3wfWcOss!f(=eo%y;o|n@a_g
z&`<=fo7wU)BqTKk`me^bF4NzFyCyKWZ!*H#;mc^sT$YJW988+0dB?YqzS@h5!qUR!
z)w@AoI3hR4{Lpj^g#i#1{^g0NkOP=MqqiXnrrFUNca0oko_E9oX?HMWhLQ8Dkq?D=
zta|4BK~NIz|2g%RDZUq!I~X1KC~5|Nok0y?=rW*e9U@bc(FEecc&#r;CNJQlN>Kf1
z@T$!4g5c|U8z`RcbC_W}Fq(mi2BsRLfRyZnTEYX>@NLMk1B01-erNk}jVqM!DY@__
z2q%UR_w*Q9D1n~}Xi<GrYt!YElb3HAJTE;3Tl@QUGp27EI5G*7>}TOfZ2-H*wVd;e
z7#TV989k1tm|CB_{MySWGpQ*ZZuIYmnSN7gaOdkjU;{Bo0I%OSrI8K{`gv?f4`$P7
zrX&&|<Y)oXYoNzr7U7r@u!2-~-~Qg)cXv0<8?L_k23E|BW-_DGw3)k=movB4LykiU
zen|Jf3Vri(UkRQJoeV!c<k;^Gh@li2(Cljaxm}5iw`4@3i?j#KlViPsV)Hn1)Eg{E
zc>=EdPbh~Daj2;)oq=FmW^x)iUA~J8-v#<^T4wKhA2ibHae59L$9kWpJS58xDb3yY
z)42=x1p<GGC;AonDH(q#Sg5`yZ=VM`bwS?(C^DoOh|Dx7VT`BGIkSejARZ5fFMn~Q
zsn;2%ScB+14TzbVFm%T>sbvN!@JhJ@8CsX)6=x8m6H^bazM+h+rXhcy5Bil&-OC4S
z(T%F9K*<bX0`b9mThI&z5a56;sgV$y9-N~AItP!@*r2m38bzuw+jvbY`KxsL)1|Q+
z^eVjl;SO$X@dhs1DDXyFpl50BZ*gJYl|TyrrM{1#y<L+c8rz%IqwBgpi?#CX3<fWS
zXVxTbV@HK51W>%GqveKPB_8c+BscsGskj8`FBk<IW9kxSWwd>phdIU}!~D(iMkifG
z$|zDyQZka#Q#`TJz09-!B1Omv>Pd#hy!|N}H~c4#bV`qOO5JnsSpO6J*V=!)!L4Gv
ztza&aKP<PH_+GDA8av(5g{%{*XQ2k=UBtO8Z-OFo52WTrzU?WpmF@&a!~+D@BS&a^
zx?2=Dv%+qZ2SwZF;pW{Bn-Nfq=vSeCUH7%XK_}LuFzDc_d**tAf~17vVm;pr^|_#a
zg7=!Kb*Xyi9i7#Hza@rTUu|IB!MJgRqf7!M1oq(f`d?lD?M~?ylv?_H=lIRtk8Eu;
z2$WGmx1)q`dua{W3y`}9bmSxxpPRp|Q(QvQtwSonjyq>y8}bgmzjkc-!_1p=D?Dz3
za@p;K_vkf<6nbahdokyP^$BYSG$VqUhB6P%apyRXu+~o3`TCmmXoKr#<4YZrHiz4n
z*hiU(B#B5>n~7)P94N}!SoQ|LyzcyGR$m*Aj}W8GknJVf{-B2r?edX`EkDMf+@RQA
z#&ffk3Pa}EfL@piC+g=eyz#PNCA&&Lv>c2+M(;iT(wnF0^|LUw63tt|x<vNCZNN}|
zC2*>!|38TS7em(PFM9>Z9_r)`>#*uiK|!l#;_HqPJU|l@>nP|QWtr%ZI&l)XYoK(T
z8VaUhV@lT`vh%_!ZADZHL{$(x&X>6DRYR?+#=6Z?_jsyv)tFnDL%NsFai_$3C0J2j
zpkG_yzr&WsI`7}oDc##C-O_#KQhjc&{!5)J-Mjf(D(s)+(YC&y5kL%I=Xi&y=N()I
zv$`EI&M=Zrfg#~38a>SET{APgXmS$Rq+`+1Be_QIRIb4Qbq{WcZ+6}kiQKev-SNe>
zoz?2jnvfg0J$49lkkE2`k>cD{nGu6SE`vu(G|N>~iyadn*k@M=mAL%z;HIscf{zCf
zU~}#oi5j+-21iG~RTvoX_zu6X2u<o|sF`5jUbI&G&`ooV+{>s^3msJh<3=_mzdk?1
zmwWLK_L5%;>mm5Z70lD8pb@7p#Jyh;k^^NK2Wi<x#m>4wf}!f85p+1lX;%CuLLWa=
zIkxpzj@3^hGe~Qmb5qwL4Czdtsw7~x_%__TzYPbZxWX=eO8%T~Uf6omyIW0D|D3#c
znqvn|AGNH1#2qPB`7;H7C<HxS)DrYLSAe_q7rWVU36q`VX~FSv5&-L#w{8iIs}N?i
zf`l1QOrdb@<x~Q;!Oo)BWXEY94hN*{rrd!_-UdWp4A=<Btb53`_54@q>b(QkzMPv*
z@yVRnG@eOWTFgk{Rmjd8(X;$~@x`xlJ<yYkc$16qCD7>*&@>%FsMoA1cyRaN`p0Ab
zc@og!8Q?Xk3^{`AW*!Bd0dD;)>(gPhEvM51@j+TW`{PBvAH>Rf?kkMedW1-)4eU^4
zhMgDSwZ|L$#JR2s-_8{-TZbU?406pD=2$O>3*N=c2Q)p~u`I1wI3a`*n+gODC^dlw
zR>bzN7U@IKrYSsy-xD-{-wf7rDiS5Cxf=Jv;~Gv%Xk8Rs_>yo0{@-w;HlnEk!x%sm
z9QOlXk%%vkB1iBzuOm7MR@^o9RDH||T3Vn{o+sfL^@5-&`%Kl8<epeL!7E8sZU0o%
z?M;sxmZS~D4b;s5>6I)ftXg2G+0x>opK+6*IhRERSA%*qZ5ZjO9%NW`p$9`X`*hHT
zRn(KPMWcwWn*u*`5#_cJR15rK)iWi{gi9~AUY@mn`mKXgdr!+~EtOnSDMPjV!ne@*
zXx9rX5Z>hz#0H#Wb5gYXvNdfs&<m8$8U~nL`#A5U1`JFdi_+Mj^nmD&uZqqv>>#Zr
zAXa@`N1h^IF4!chly!l;Gcl?(=BvWx5)7E24Sd5+wb8pCkX-w{$x>;&RJwpI9YK`&
zeRJ3V@{JEGjw;9Y(&h0|`$xx*$aGzX*+GnxRW4%_e%&znRr~t+PTq&93iLk2()c4B
zvBp9MMc{?6?~9k%D!%#>)ySPFv6KD*AfI#X6``<Mq-xdg!{$hP8OAHBeP1khsx+}c
z`&{e{ZT~0!0WRkgs3Y!`j~u7&@p5`D0VySqI)b^S8hOTOk9Dmex)M3B%hKG9zKP&Q
zLww%$)jz%b&f@XnonQSJnFT&(hwi4|yF0YB)N0}VD%Of`!tV7OT(?d+&=6}avP27B
z3%^!PLeEGNiO=52>yDf2j^S^@)r`<QKr&~`G`mT;coHNF#WS>huzTA4t)BDa$h*T6
z65U*<duj7-v7_>tzHS)|mw(G^0rnqQp%g`=Y@~H}mHU~%6vId#79z?^qLTk5Nu$TU
zd4Jlz-^1Fc+!<+>JF*M8;*WfZI3X_{*L6sJ>5bJT_>ITm!3%2+G;V%;CE#^^Wfg}_
z=AaF5<to8j6Cuj*e*#Tpe{*opo`d5TkK?aXYJZ|IG6E!BV6Mb-ESAY8sF1-)ws_zG
zM@jN_tC&3rH<NJ_Vv9r0i*@s9Pg9+GBwyVp0&B=G2q9Oa3I3W>;y&oV{5Z@oR+Du4
zLS5yeIO317k|4!4_Pu`gx6(}m={8+Wk(5YVUA<OU1;5(&6l4U#ute1USwHA=^%ziy
z;__@=u~-alS;le0=KBY5a7ds|rcT&!>A|(X^7c4jk9a7Q;#)t<hQUje$u0z^Ea-WC
zV=b9uNxBwxT5;ts2DT5u1w%(j__BtGI_w9w!+gq1AHyC5y$D<=t*G041Ei=<8NbJQ
zt0LByn=u=kuu7KUPr{dT?xG8$0|ZYpKfzpyZ%-;`=)a);LYM`2d6J=kChI0VzIhUv
z9Zct7YNVPLevQN<;04LD7KfU;3^^99YO>XNl}!u<n!}0;xKgW-)0mcyX~s-N%1XeP
zQDVcoJ{(glwLbvV61$*U{wW|);GU7q&>b-?SHRPTstnIrL$K~D^Y#9>UmQGmZ)tHp
zlL-$^9xBKoBb^zTkE<ayxxHAK(a(zjG)2)$CIfy)Ni&r=L*SdDv{&PbNv9o#(}b)N
zInUfRmT5%rXl(01CT%!!{?O!5I2eh~@4iAgLd2J7&Xe3{gZwpiA*a((bS(F&!Uq$i
z23^ss0|C!Ol042b8onLb9epiWLh+P7Fan_;_aT6T_Tnsif->J>O)6xfab76>{cDcy
z_~m_9zAjK?mjlTI*FsMJATNwRFR2G_kL9m?W9%Ovdt<Z^1~Fg(doW(0MXgZU90^tu
z+o^nhaqdsTbG4y+Y?}qO06(`qTrgwjSH-=PSN!XH67)YOGJL)Y{ZO*+PRPxD>(>{w
z3?b&t*$R*IBdsYxLGgfYg!PKNbYOFN0AO=^6HF)+@XCQ?z_`FyDM>+WH3|eLjVd>t
zgS+%f&`krKj!es>H;;m@(@G0@#%@18)NBs1Jpm+ik*xNg#0gv|8!B2UHyxD%`1Fzy
zk>v=08UbTssK6d!CY^vQ6D%20H0`FWVkk?fLvghtE4q|2w_UQ$Oi8*TS6*oUlM_LX
z;QUc`k?k~x+ABj%$97H2o;KBQC+Mk6F&v5%qTxiWUnwDCSPIF2R#Es5y;f0<TZkui
z6r3XD8fKQ2{#XKdJrTszW~j1>h$9sxgw@8Ylap1X!h~v5a@zkG$wfr`%jdDyf0?gw
zWAG2UkR%WEBS{;1R+&vw68RvRd!R5UxndiEPEfQgjW!Sz(l+!SK?8w1iIkD&J@gel
zN7C@W6bk=h*~|peglV_`zWuLG;NRl$e+xJ$Eo%#iT)@8*+9C@0cT!Up5X-QDGs+?g
ziyFK6fMp#3^26kUt}jel;q0Z~hQVo8QD%{|=i8SmcOcUv^xt8P(JrY;8F7>W6{sWH
z7;)5a;AaU*n^RO=<VP6^4r63D61vhdA?%aI0?E*INKjm_+)*y0(cIS+Mvby?M*yeW
zH*v5Lc2jx)UhH)~ruC1T69*R$PMj2c4s#m^C*-VAw;cRA)+Lty@oWy~CJqYThHxcN
zi5BPi`mv7gfVa&r00rgkeedi0Sl{pVJ=yo)H{b)k)!U!y1j9|M`OkYyU;F>(^Ocu)
zpJ2KLLk3X)uGX_h0s4}~_Q{{&x!#*uQCFX%^%<L<Us=N>+mHmiuYWnESG>$8Ui?1T
zwEq)pK>+;14agR7Cq^VC_@H1H@NBevawu6Ukc9~BjW89(y`^#-_yo9JXbb?QDEJ9r
z+Y`|w<9KZbO9Uxq%LKnkc4jg~0i0SVKPRlza8gf#h+<(`R^eePr>g@OjRL(Y(_cud
ziatMYC|bJEpD`4D^x{$#Rxu->);3vTD?Uf@@%afABOTOj$c4Gr9b&;K2EICxrvTuY
ze#_nT0P==&SN0_*s5_48gxFoOgtycu>U$Eiu|Zg_LQ~tOLa><6%;;u-AB0VPFgBam
zgPT+fxLxsmM@Q4C^HfP5j+GdqW`QYNFe4;TM$AIav~h}+V#BheZX2xaxNX7B8+NrC
zHnXmqHN(}aZ9snD^0AMFn*${Tx4#xQfQwif!Gk~=268=mibC#b(csWyw4X(v;tOF(
zM$kDqn|5yRiQK#q$RI(JkU^?t$-Zi+Uz4?PJRU}h4s3DIKNNM{=y@Fg5xT9H%luX^
zbcXVAEH6NUFbw$6N&r(f5fC5;8Oyq`>uRx&uDKaJ2I$<=?58{)d=y%+Lw(m`z4ctp
zz+L%PMQRP?|3q?AlDkp}4jLRiAxe;%d0iLK09w}tz@OlqMc@O_rLiAq{jMP^xo|Xw
zU>ifZEE3j#4>)4Zrb)*Lgnd?fdLs2}_<C>=xT)JWOrg%v6#$@6=eU>!B8J+*xTpoj
z0VpGxZd8(sk;@c?CDc+_4fO|;p-4&|9rqde_@R%{do$5rfALOxv=dd0U@dda*oY&F
z{wz$14SR%^$8|m8-`6u5ya?$#yG>m?47@nM-tX^4N54cHJ|J33%L>KVJd3sEHxSFX
zgImtIN)e<&1ag2RbS=w?sjf2wm(iv`2j<t<nUfISq?S~gDOd|TO9zJI>EZOi^eDGA
zBVvm0li`alibx9lm=#q&4oZNC_C57v_&bG0Zaf)+-8fO6IB)vO^gfbiXEioS`*|}k
z$=+>9ruqy$5OW^9&<u-s6zD@JKC#X<S3ZZ*b<<SIfSzZhAA(OF*K9$%#h?<P#n3lp
zu^)an5csYFM{<Jy)Y^MopqH$m!mY*DBH~qxR0+CdvTKhj%2CNsxyN2bHLhL;1Xn{h
zm+3zexB0!;@HvIG`);-fomCMv+(fvC74VTlXQOk58*>0lYggi$s)0Jgf3}IC>Zx^*
zdC^~gJ#FVaytPRBA$|<r>g2jb6I7@`xj@GyX;1&j03Ax)1xvkIHgU7GSvHHNyjg-g
z1ut52YXSZ_Riva~&$ORo<R>@Y(p49NDlKxaH{%xawZJhSV2E&LRwTe1S!yI4j;I!j
zlH%A!Kr`ri5kCQ=$bXoY_NZYzs#&U1ygBTGl*a#>A>yEY+H(FUUW<40*Td5%ph(Di
zVJX3&X#)Px0p?=BaRLBebQHYYDwp}4LIzra@BUN3>l;Ee1@}5`a%T-pre2g_$v?>{
zEteW&YFWf2ky^9&yF<bh`l?b5y?usahE;WNP^7WkqN)#3I!o-gbgr<(zp;q3Eg1Wr
zZdNl=AEHp3&|ob>N7&bDL#O$;fIA)>Bp)zEna6Iz+s}6|eKHh|WBGESD$LMW;~K&C
z$re;GVqS{2CZTJCp0Dz<sxKXXiK-P}<VO`w{|OJoh8~#WyVk99JtUhJFj&mE_LoRF
z9jgvmBS^C6+Q=SCHo_OQ4)Rs-W9yDtQLS$ubO)hSV3y7t*nuDGLN9!UBU|+VLG^${
zT~*#vDs3t2fxy~zn(%#)19<H^{o?pO=dAsUVf*~Yi&gyNFA70&S5MW~tRp#mZ`h#L
z*1g-;Sl`h19Zl6at3BZ%i699sM;5t8teoF}u^NAw07D2w)?x@nHfO~`@qunld&fwe
z=a*hv&yE43gTW67gwBuv<JmJR*5XuLx90PslD$LV0Z|dLZJ!ojTt~J1x$=4FF24v#
zsX%(hD39o-1r}WuqaYo~`CNMhL3MUoUw^`WJ`i}`7F+iHEg!d2_QUhuvCR)ZzQw;h
zdZVh|sO2=VdsXH_RlP8y<yAUX^KbEv{p-+uQT!L#R>p}>CnXi;2m!40!-1VXA~$Y<
zVWfn7f_P&~I*F<@puh|3z{CPv2^qWnQCfg*JGvJtb~TVnTXq5&#wO=3*}ip`%l^PJ
zh9+<yV2lsJ)-zn18>-uBE07MCvXyvR)5b<m9Iot#Zv3anZK$v>!Ru!iYQ)!h=v=Yd
zLw$y7K#riwNT>u*`*>BbnXOf4NC?0b=PjU&3b>^J?ZWfnVK<lv7RS<~)$zlzFct%e
zAz*1><J0Y*<PrUZRUFBXnAzMnKq18J(XeS6q%%P_zr*9z(ezjm?{tR;Ha<P4H(Gh<
zD+2n!z9!U=LCk(Ev!Gu+_>xVPi!KDQAdH@bh)I-RUAOW#*_gutC4)ZDWU&1;08O$x
zd+;UynWIStQ_8MgY6|w;4$S1|yRo&X1L@IUVDA+8l8Dq;`^!nMO6-;ntYrHy04r&*
zDFHWmbUm)5;sH)l`)DjZ$<D)Ag7>8GuI88v2t2@0pC$EloSdU#Y|AktW*mcKY3mTO
z=4qM%?R*Ov&5s+#aUDR*t}+MZoz;HKiLQSys5cRFFQ5S>&Y{du*uwO=-plrSFK(X}
zFJ^n`#ZaAeaz8{+=MprR@C#p~aZiGWX(l?Jv@%$e+N=yKu3l9Ms#Ie=s`i^&;oOeQ
zLvZ|9n45cKM=soo#rP?f;Rn4G;|_l2Cv@k+9CP?>?eFwFBi7g_*{ji?K}4Itnw7+)
zq~?N1AQ52=tS|i!gjfuHJ|z)xgW=zVO@ypk0z?^8cu|d*;q75FqAo8c<Bk(XL`T?h
z;>pIrR}a<3PnIZEV_~|039MO0#&I%+RXtsrPq`@|8n9#zh4~UxKIzoKfr%2$bKH$*
zFs9G^<jmvn)BiGf$4k+7m~q7LjZ4Jg@%quBV0TEqhuBGez|gPr`}iGnE2SD0>3w(%
zCz>TZM62wAd@PpVmA~YYdF-otlmVGd55^fXZtjf7Rh!ufC7=dAjMN&q4fj|$QW7|?
z#&O5iVHOX9Hw40mUd3gm`!~+xfhy9&W+8n#@%a(DISzlFhz;Kd*|^95-2wuRm=d><
z20+CfJL4nsafoi2Eb`1&Q7>*kScU)0GEpq@KL_#{XjGZ99Fk(2v>u(3&n4qK(OidG
zqgxU#e~8*(Z)m7`Dp{+Hy$dkq+TCKy-FlmJVp42%b>X@@6(rnJ8iU2K`!T3Klp&C<
ze4$gCB35>{?Og~II;E!|+FYx`WbNCn`%wQ7P-n?<x&<+{(BFd)ZEU;MeH%}wuwGpj
z?}!7UQR|P_8hn2pp}hl$?p?EEp>_Fq*But2?E+6L%=7FRy<i}@xMp`>k(y7NS8PzC
zwrikStB}tg21mIQNcng5{c_(&u|iM7sT2Bn5NA@9V9vo?ld@Z4ah%Z!r-D{MS{iW!
zSp!ME#U)bzE_k>#XUW%N8jpy#G-nWChylUh2GuhuTtqU+=sIKTdgJW+W7!Ttd2Y&(
z_2W7WX~2Wu33~<LaVZhEVSqz&G7+5C-5{&k_AV80_nM{aAy+kky{`ec-45FJ7F!ED
zAv>6gxyA{$NE5rgOxtsm*|c4^U?@<K7&fNahiv@zN8%ym*oAgYF*E?+Ma@9W4eAib
zY%`##AFxbK4#U63&<&L#Id33<`(jGej@S-<=c2B`7ecCU`|T;xHv*mh9=_50d9JaH
zH$MB|1}u~kok7?stT$e-@4xlOi$c}i5DM*>*j;pvPU!6%J^*3gzel`aS?vC0!<MXR
z6t(Fz+H|60qbHp?a-$1R35+xf>^aLyzrNxkSBOtjupoHiG{zhkBG(@;^?q9S91VPP
zi-y3tHtZt*E18dO*iI(mO86VlX8%1sMSN4jp?N}5k=ql|5saX~d1TIPGm6+z_WAZ=
z(#)h!R>lHP8sS0q#FK%s%E@%bOtR(3V3?jq|E);Gx7Kk0hPC5x<?Bl*tQ)#MBC?op
z<m-SJKQg>~Xq9M&pd5|i`4J@hv7Dhj$h5L&=z?5qg|1?AL1ARLus|w_g`qt|!o~s&
zjTOWOfT<^RcPzH&{@FM?ks8`_>7Jof=hxnP0v7Joy?7h`;Lqa!(q6(pK$Cczp6&Y{
z=x`EtZ+J7%SBz8GCawFl>O&Rq2ww%p5yWvA?r(<?!ErqsCo0$e8nPo3<T=mp4VyMz
zxWXp*yE^0rRGD*6^uF1dd(VFIfwNxE>xIoz;$6@UdFlz$##5a@+)z!nrTsOk1{?2I
z<GHh6M&E4}ZG4#O74_193hs^G!#TUDBWFFwGH1V?`$T>LHo!jyJ^(Nv0Io(>c2DKR
zk>&MC;A+9vPMlhzDPwdAu7c<6AVCX2Ta$u}Oie=6sDY9bRhBl7?Aw3kaD4{OfkDKw
zj|`OuQ@WnNviY{4yIiY(Arkwke`$D6N`E{WQJwT)Ivt4&$D*lBWqakB3r9w+y?EAA
zW5$+ve`z?732eGB`srx++p5AsL9_j9ovY^!1u{2e(_6EVG~R;D5IGaC0AdK&d9Oe}
zx59nO&VxT0qHnkd2LB3T*^Oh?CrjRd5d)t0Y{y_S)h!A0x$G^^;K!l4n_4XvA$+r2
z5T%JiqYXXiBGG_`sb408rpaIC+skn7!#pH;rLnlUw2BNx!DtcxR-=~*@5~~?eoyEo
zRtL-?tV7<OYaCgT=xeNfdKCd3q|09c{d)>tb0LfcLZa`BQaB%s-@3O4kXS$Bgy7jq
zI(RV)np{9`CRas(dT}W`Gn2(XcBfYuz1+%}?O!oX+OwO@2R9v_Vv7{aj(>~%-fZ@>
z-u^!>N7{p5F*6qSxHognrYoj|4-WS=B5cJAP_H!(HZg5Ivx9X9y$&h9lQaj%$m~Vz
zIS<O5nI=B4{D}utJsvs+pc;0C*sD162~|%g&P%3sI$*3mxNXaw^IHdNm`kwoErjCn
z&;s0b`~tKC>hxGPmCBAy(}as;%)Rro#X_MtJHOW|Ldi+AMEGNnES7>Ky%VhfcI*@?
zh*>s)5$>s#dA1*2Yw}4=HaFB*WES5+623SOku5n%V~iT1W4KgA%!}tcHnkjzSgtxX
z2nY(+hY+N}Db=+iuRDWsr2l+4=mrccZYk&YN233ZES1s4^q`Dgbgb2CBMckEh@Z7I
zTN%u0_u@+MUM)9>6w%fSf{vZ^gbtS9(ODx6qg-?mqqLO1S42Fuika#nJ+UI>>y<vD
z(FWwDzGz1URmPX;Xr<A1u^xC0Lh^44hS(p7R3m{{4$kQiODzXVV)%uNFo_ceJn!za
z9Q--fefSeO+NqDfAki8`(qj#}b7&-m@}%d>gRJ<r6HmTJ*TQLo9LeHILOyHiVNX6_
z#eppYWBEFAmDTfOC=1`M_CJ{c$9RvUe<%?uWmYn!(D&c-UYuepxTUgdq@ihzkzExW
zJR-cf8VGFr2^@-cJfBXigzJ?*X#KEK4}WDxdv#7+p`3!9x)-3W2Cdnk=09_V+h2RR
zHGSqKtH{?}5%-&*oMcE{BfP1gY819gh>+Sq(+|=4RAy+CoAhGolkTRSdL}3m&dpZ0
z>`f0&7De!UadI%dchNd6GS6YI(3<4`&bfYNgO_jkR+@Y8fkli!k<;u1jl^agbO2vn
z8(6C|?a1j^b|9u7Pe2==2p2-#{CKro9jr9;TDtzukCark^bwi$<jT9Ko+A=aoyASw
z?}DaM4z~*Iz~l`>ToC6Ydif(-wL5H4xd~5dSfFU>#r*f#C-$Esrt=^t+>da>%-xPT
z0{nY^ruEz-tq18?#ZByPTxU0pQg#`b3&1d0jybA0?bp!p@#SKXj+9Z8U!nfN*!z2l
za<-Y!Y=93)b3r%^K{%QW*Hk_o$eR;m9>aWPWrf`+#{0hZZ_=n{OBlxOueR7yi|uIB
zfyIs*B2E>t;$HymrntdD^gNV?lUf4P3(E+HmGsm{!~jvEMhI!suc}RI%(a`>)%Gv3
zt(pDVc*QOSUZW^Um{hKaUcK}i*^kz)YuYZ;CUMIX*=&5!wvQov6yU2@#OV6|&UeRe
zuW)pb1L%Jc9e4!#KBWjBhVueA5pNCl$pWbi=(ZZi(<62Snd|j}M8VR%53oZA3{4F;
z%ESHuNlb=iNOgZIhB@0QuIEc~Nam*sC8fT-g1Zxw0GrZvwh~gqlZl#0zE~SBHvlf7
z9#Hl6s+bK+bUh-M3Uo-X8Jr)$<J}vnozj{AMB7%`<>0$u!>GXd5u>X+&S;i8Qb8iE
zR8RCG2rmu1n(B#dG4?HJ`BL05C1>Vy`}Q?svF5&gxxAU<CsQGH4*rP$JH=B({%I3>
z!W6<05$RYj09GBh;(=_TF3zP4O(U1=^OC$NqMq=BuCOPu0s@_`2>{iOctj<S;UvkU
zyE>4bxga4S1v%$&XMrE~{z4N*;f*V@S5))sc@y!<aHL5)s1B5y4i<JCW_nuq&Q2AH
z#-T6oM$9>{qc>tYgLH$jBUo;XBEB5U{vcv+;2%E>o1~mWGPp?3D2X_VspJ%3yu!0U
zf8HA{^sR9P7hpJz^b87q5(ZD)jxMLsvC^TH-TIOh=$s>b!@4c>RO#UQnjGw@zyk6s
zuKQj2%nyOJ`5yFB0c#aJPNC_<(n;$qz9jktzWW8JXoM}GRwe9W?sv(-i+QRaT_gVI
ztds1#w;kM*PdoO==$684ByHF1Ok}1oH#+P%sqCJE?H^}TF3=%@Zfe#q{NU_UY^h$}
zI=gjqwVuyb!{KT+U$1V)iE{n>PQXP_l}kqBa~5bAaJ)xg1wPehagTIR@F=DdNrC;0
z16zc^pOLGR@Mr;ffDj5Awo!_V)!nLNz82b-*G{kluQeTY_l}9zYUWS4;~u>GCJv5O
zO=fGr+=EwR6-n!+Q%FFULShuQ?KZ;*8y_3_CBwMQ8u^$JUgi#geDjgYG`O)e5@P7n
z738=wCUkwmVB`ZxJpC%xpJh;W!UL(mC$kT7Ne^iNJt(V)0vTas2*7CP`o(!BKtwy9
ziXDC%B0QKi2=Takk+q=mTSl<Z;?fdpd2uQ&9(m!iB9~Sme>6P7MHGu{kxLzoVzEI1
z8Ei4vegfh@iYLyxnO=c*(E9EBQ;`jEtp%~e%}x1Jug7HQxx{$8UVZ<XyZ~!$!2_^m
z?_B>Y@L|(^scnL*3PF!3vWa(y)+F`**|+pm<)0$RUIT+9V{QbI1*=PzVOX?DWU9TF
zdMo)%q+l>rB7PdW8KV$^?f+L@{CB*XIr|bHvupaQpR_S+d7Lg2?pM8b?tc6H3RRLe
z8PoypPQ8`$G6AemeU^l!mCHF;kcfW9p_$1;H?teG0c(+nYXN+RrXx}x$pn!aQDiKm
ztYv58rFzPuE{<wRT8ai=fSlq);E240ZTr?%3lTQ>YZoc0-crxM)(cB)TbVkptft{*
zc+EAmyJLy|Yp-NA%hIxEZx_L(kQ{tB=Dd$}IK|_2u0S08YvDWjIrJxAoCvZ|U}BxC
z1V(rU${0Rs%Kx9rJbu2rvhz3-kDVkj%3n8qrja1U1arexS2V*~=9PT=xt{WcK{&#}
z74EX1aW9QQ&f^RyJ`3M(0CGsqO@J8FPe2cG_O=)e26E|iE<m5^=P%*+Ptxm8MuXyK
z)&m;yvzQ=c^Ic&jd|!$M?5n`cMN1U#Lt4OSMe?u>AbpF(CVsDWu!?%4M4_<~E{p?J
zR?MbghQt>q*g++n+>;C|u=F5nI8&>7D!XHHkl3X`&VY#G<PMM1J6+lg_?rp9!Yk2G
zEEWQGilI&vwb<s;)FlC|0^nJCo}La|G9~y1LAwN|Z3$ij&At|99L%B+bkQI(_r(el
zUz2%D1yBt6-E{DDYQFiXgRjiKd-YLOa`frOzPggj=jZlbv^OW$#tT`P;iJX8?6kiM
zV3>jVJ+#>=h(j4)fPjjg%on4&req7_H93a|=kocSQs38@hC7FL^wn0dz1@2dPp=5d
zu}DwCfC+l<`Ob$BZPbL%)-M46;9bBO{av(*uT=gT1uY0)p!f#kT&zl@srMZ1f97))
z!rue4-@Dw=3i<P%qu|X|(y?K)s7Mh-8XlGuxH7(+avN&smwhw6kez5Ra^?0iwwKt5
zC6bhef^1n=zl^fW@07%yi!bkh)*Fh07jZ`0$X)^)JSn9-a|iUdGv!)Oc-l;-XDLhz
zNm2z)Sdt0P3+S?tVheHDl*l;_fTv`4>IpI?z2&38jB??qn^8xHF27vWG}YU}Z2uXE
zfH4IDG#87)_vRw*Cdt-^wp!D*JtG{0Xc>dldP{Y<LFA&5fu+qU7I{dm*b+*_*n9Aj
zv=&s_n?*qykhB`YxHi{^^sxie+5oQu-*jlot3gwyf!D!#aEw9+kyKJoXKc_Kkmv3E
z7RssH>6(p7qPu`Bvw*hOTGirAf&Y4MtMs|yJ$3W9>T|ulBFf~|!1~bM%C?dsSLaK^
z)cX3mrVMD4`$y3)%X9Uf4(WEwa(%And)J7YJnMT?ga4nu?1rDxjl7Ici7NigpU+zp
z==<65679pf?YANZC=ISl{#iceN#Squ^h<rI#ko4KE;h?A_tOYn7T0N%cw7%`59<}E
zUG|<=9jNo9wm?jCT%f0M?^U621}9PJVGzhvv21fAqD|{DHx{A*uZd&^$~sQ*%k9&#
zobYVUweLAaL3*@VLUS=B0Vxe8qFY=N$aXDG@N>urZ|V>jx+puGhH#$sN1h)kB*~r#
zTbZ*s+&`2kd%4%W=PR_B0@#<fJ?@|FRc@~08~$xqVRs{63g|2u6zgl^lh?ogM4PCm
zFh{l@>-hnY^6sVfk9vNB{7vxYr}|FgH^^6;j1OJC-Ew=0`jXN1G*snufDIZh^pY*;
zr^wHP?4}rOR6Ej#lfDg&7raH&1L@)5R)p-}Vnq>a%RP7>81rko&KPTv9NI;(z~0Q;
zGDlm#&sUiL+von}vwvyh*V8+7tOaK{?_Yu@Y8D#Ly+GuE7m}BI8!AoD=X`QKT_?lF
zn(s1$Rzf|5$dFz}C0VAd0bl@NL2^~u0ed4e_>=1bwW)=1_yUOt5SLomzVO9trPZqZ
zpo;_e!&W7WbISl0gRi)bEQ6BN?2g7&Vfw(=!sBqQ29Q%LTUjYgZ=EjiO)*<puE3j)
zkE>_V)`wwdfccDh@x<`X<Z;T>7H+vAA5HQ`!7StP@ctK-ZkgFJG+lUWVR~qC1PJs|
z%Upk={b!zH=sgKb>qj1GDdM!hXt_%FkZrTv_BV*SFCf_x`7ro(8iQ75Yv1`?GxIX*
zrV`FkIgXxBRw2%ckq0F|ngDqX(XKC}&M!Qk9aPK7Ooa{no2dQwlKn~{mMpSDhP`-0
z_UTfhu-X{R$QOwg<uo~(l|L<)Q!>{jQw~otzC!TmPXi->Fk1W0n$taF$8SYv+(?TX
zj1RA-Vr(#onPmT3HwaFOIsrPD4*;??t@7_5@=&_own0rg3KSA5pcLaXdm08m5slu_
zrjU0X^!=P~5)j6C(wp>Hi@1sbxa(kS1-&92OngSTq|kN9dx`j(rnPtSQ5|-GIB6@k
z4!eY^j5(2X<Q(V!zKc_~%n|OrP5U%ac;+I~V;LQ@#5)Gt_A@wzcQM3?$pIj&!w(-A
zM4?EA?%nW0h@W4(S?C;!@hke&#Mh+^n->Cqu5Z#?t6mzT1$I#C3BFK}t6bdIR(a%;
zMv92*5d;D{0*J4Rv?yWI?SS?YTHK(TwNKIZq_>e8FMxfNmP|1^j?;rh+Hjmgm+m@F
z8(v!Il-Ln0?+uFX@oE0uoaSfA#=agSUM7n$8Gm%SR9pAODGMI`9QPAC%#4d+#<|#l
zO}=lzQ1=kD88%*(P%rih+qg(oFEZ@*pWM;`KEp5top_QxhdKbeL#0$3P_AQuk^+mc
zd%cwAEIWT}5RYw}Ywm1L=(3Sa?SA9#-EVAd8q-2jAeFp!+w7|{sw*iRw^7i<6|OHe
z;1h<N(|8x;&#9!hB+g@5gukxv5Qdx$w!~vT*bi+rY{rbQe)v@)Zpz&`8>7&URYZ2E
zvDux(QLuLg2MwSsJXqvS-1_Qx7WfgiUd+Z{y_KIr|0>ir%uDjmNI~~>MPHxCAI*fz
zDiktgaVNPLLI1vAH^+<Q;v;3eA|!mmi|S~e1UrZi!vE(NNNrsch(yxP{E^)whB31H
zNS?j3y+}iYmfV(&jB|F#v|BhR;_M6mLsf0$&LN$jW7%{H%gj=)f9KrDjAhM?%<b&Y
zK^=m*)uL;yy>VznfVmQk#`lUd{32_~vH^Q|EKw{bAiY=yA6FgPcv0Ug5artkeSEFU
z+7YaeCzOE(CCjKk9dcF!uG0|ADWCxN1E6UZ@i<L_yzAsZ^$}P2r$YoRVo|4?o<lIy
z2@t(uq>+&Nx^yX-CdUGL6=xP`sGc8tC=^fEnozb|b4AyV#Y=M~#s;>QM|TGfSxEPr
z&jsVDf%)BA2g+O8uLLPBY1$BCcn_L&cJsid#3mVeUo=^s1o~7%Lc~y?-sC=F#xOFg
zk}Oz^S^>oO0jC2m-drkgE6)yxtikh>@yJwuU`u&m>+UV(Q!tsZ{dOvmks1TV<fV9{
zi8mjVHVyP+8g}JaT3h;53cY{<7JX78wLZE`uSs_z-S~jjUR8~dDYeXy(O$Xy@)rDw
zS3)5menvv38H%twf3a2NbL<Oj7vd)3#Q(q~K=hr49@nr#;?>yrjdQXFbEqTFTwx><
z2Fn}fo9@#cIf2u6UkU66_B*y|w7+R2`TJI&%l{G~6p`BpTmj-?OjQo<0w{&JUq<ex
zjN4wZ>~M$+BCO9ZEW1(V7XWV1{!fMcD6;>&|8t_yGjE>aQF51Y)HtrS^`~fe^u#;U
zc|8{gdA78{ISi=^QX9z7^ODXN^iyv?x0lBF3T^hbpQDj-Y>p0=X*0(fJqO3&!?d)t
zvbe|=7Z)GH_pW!d1l0Ke=snR}Hs1%;@Jg%I60~U*dcS3S4_FY$Py;aoq@f$Fy1nZd
z!iwA^yJM28-6nUO@HjD-8+vO{$iX!T{y~9D@}4D5r%5Qap!bg93w5zO`xx&nkFh7c
ztj7jIhe&IDN9q^L7BtF)hQSwnTE^fev*!4$>saP|27vCD>g@){g(xN2GjN`1Ychf|
zpBJ@7mH)g``s+@qdrochlk6DoBn(8*srKn)3F6S4<+M+#@{fLm*8$m%8oy=(%!4(@
z{17MWbSF04C!g{hJ_Vg#7mpCj4OTl$jn0sQM>ZFqc*++z1%MoPs8~~AyQQW*?aZa^
z=V`S3#r}8Dt~mwlk~9=Qm;DDF{`P($Z0+=PLN{`$Ymsl)ouf}j>&6Pl-J|hwK98LM
z$S0(db)T*iNXJo+I}BduJ9T(EP(@<V*05}BVF9L}1ukJ0vHb+dC1R6rM9>Y##hD|X
zp{IC~j<I1&6!h6Efu=-EGw~<uAHwq|I=lz9jpR&`+hY#$0OANb>#ms8rH(;?)`PWa
zNlG2<0%`T&XxAB<)UXbmFaJi=V!+*bpS-=~eSU&0=0(;yL)RXmv)BL!igtS~=MWtf
z*FObpC^3vW?|JWi^eAm!L4|1mA#<UYbo<Z3|MX#uF+@!x_PnR}XF6YqW*VB@(~vk<
z9{YDU*OENs>l1TRd`WW6@kS^y25bALrAX5pwVUHBqsWTu&#d^Dj$sk!o^AK}XSz2p
z`KMOSdfY$J>b!X`e+y>oUjLf^G<|!HZAX}D4QMA})nqz%Lh4K^zcKML&=`2bc!ma0
zD#$A!EeNZcNva;uhsQffZ~XcuC$zbvPu1t<-lGMhp}-!nG*BD;wWPi9okoNG37lsG
zMlcWv8Ua>prOAJlI62xh(zx>%?1*GfK=}PSfQ}UP9p-l9gK)WxSP3^44#(U?Ctja)
zB)9h6i~4t+oP~!J@ya6zSfZd&aA@2$+rxvS;Y}`-_<)d=xU&I~8?b&{ILEpU7KwZn
zgvfv!&;fLf<aH4t)YNqT92N^Y&HWjuSJ-P1gPxG_$9ESF6;f7cB5WFyrR=7ts$~%K
z6b#cuFPf&}DrSjh@Ngj=2_W@RAQUOpn&mC!NMb0TwhHlhV4^Gs5anb=kh^&-JI|`4
z{l&0_3?Gtg#jDrl!m1Qu@9g{_{qBSLRA8`H99Pv)L<=b~#KFTiIAjzJz=%|xK*G*K
zJ+2jt)kJn+epf$!DIU7k42>wVR<$@UnyRO+9h!ez9wF4G47ikNr0iO@EDu_cR%FED
ztVm0s**ict;OC4OHxL1IxR>(+TpzeV(pGVmKZI>6Q}-aRS2xt5MocB0?ZV}On`%yh
zXIv}>quE$E^xz!Si*?7A)kfiFBHzbpn5<G3wbqCBFVH^Myee6ri{63@FcsCx*E?^z
z7<mZ5Fc?Shuryu^R%7`JxNW+QK%3~n_w(JYo962H8CO`C7Mr<wC&GOrUK*2e-7Gl^
zlX2a3f}^8$(!hk2#$`3aWITn2@Ya~0>z*{f(mDI3p9hce$~Ab5f`4WBRxW80F>LWa
z;+!zlQOTKFl)LBPgHa$cPLU}Pu6EVm8c1XvB~~#D1FbE4*q<jNBRjT5(yrOEtztSm
zSe$o?_sdSCYUT8u!{e<vlfB)oHD|)<LLwHOkz?`ZrdzW^Ia+5#9Y_-z;yN+TKMI;c
zjE_f;F~xkEBSr;SLQ&uf2F?JoMfkCU8O&B_EVO6?EqR5ta30{U*j&6siHwA}#M!~(
zTpg-9_*wr4;vX6N^S%u1hlEPA9Tt0d9g=Gj&@Q%1l;)EwSSi9$_VrvHg;%GL^e=sN
z->sowEHY9*HWAApI6hEGEo7^QcVGAf>H*a&T%c)p+nOEwqtGq;cJA0bIy|Jyg?cnG
zUZ`d-d3(Ot{w&>`i7ts9Nr(9JcHl!G#|im026U3GWXtt~r(o5r=~%`%k%rh#BTaZ!
zoWu0*J6L#;WXD@AnpNz7(O@J+hR{j?>IXv<<@W0GGX9_u!N6Q97Qv=dp{q}5^%TKW
zqiP@tyuLE~6Y?7cup^A-mDz+YgKZ#x&crMNh^p*c?MX*+Ze;z6A^#t^+wXXr6_@r(
zX#^I1UoU8*FUY#m{>FkGG%uc3?EpI&;xR=O%gVk4nFq?vB+r|P)T|OoQr-cEGUE39
zcvdf5g;R9@N_8X`i|79`F~4Or6iWPIqJ>1t?}HmDJVj5)3`Fk_L-~5>YniFN=}(E%
zpUNM&B$8h^kpG*6>yFOPC;lkO#&#q=h-4@U-0?vrpev8N!-de-v-@W<Csls-WFZ#k
zeIan)U`1l2BEX6fpVG@9hci>tSfP-YMT?U`8A!5I6t&2wDVQ!G36jykBcF&s$CQxz
z^zq>xIX9Af0|MO}$77MLTen7H#~WZPZqG&B+>YUK9Xb;8-S8_SwE<)AHlZ&G&yLpP
z{i74JceADD`0(W9@OX2H-90-o+8?iv!ZrB;xD?@bJcIW>U~$=UmHi(0rU9by!u;Z!
zl@JTmu}Jvy^nve;?i)q+Aufgk!36<Zbucq*dE~+?_Kh4PzJ?uXKO*-0GUq{kJInyr
z4wcqw{N?q@LrC}m>jfGG!*<sw^ZVc-iZosq$<)_qpdDmp@7~c~ZO~;5U1)5VXkyVg
zH93Dz@`u_N;4&KFLGOSM2<#KlsabMNhJ_Y!07Mwe_;58!9$v!-N<sl%<{=rq77AdV
ziuDexMXlFH(FDIS&fZaz>^NLJb8%a0A`BB6Uqg3kga3>jxKxww=XoWvMC_ukM16Us
z%{O)ZDge$<Q*z85%3GCYvSQ_j4(QQjp^%Io!#^G6d#p-w=*N|8dx=uIu@&fLJx_|o
zu8YOj^FvAahGIQO-Y)4K=!4(y`+VP*A%|75N=za;9IO+f>S<A+#7`8~M$3i6IO{{#
z2q6yND+XN=^h!{p;fNH~eEgDUE^ZPE2=H2hQG%w=k&uuT7w{&KUWA`d;+nViOwOXC
z@Hprh^t<%a_<eqv|2*DDH}L=Y)yeIkCdm6H`wna!z!2$miG&?P^sJ^BmKGV0m_frN
zz&I<xi<XYGls2#|(`BeV0@3W2Y&3vUISnXG8@VcV%QS*!Bteg;yf_v`+`WR#tihO_
zNSKBmvD9GDF++|WNFX1wEayrl@HXj*(f*hnbc1%Rf0PcR5yvd$WN2`3r|pDHCm2+%
zh;C43H!vnY7Q1UrLkS`_IN;_TBOEfLwh4)j?+qn{>}8<=sWJ^EP1OCr+`S2$Bv*MS
zm@m$Z$oq`EDl3o5qwcID>*%98`|g(dsMguKORbh#Cu#{x0vSVzOOhpz+u*@67$|}9
z2n==`c|aJjQU_xhW88yTqg{hkgY8-H0&Z+}EIcT+|L?ts$gJvW1nkUje!EgvWW<Y?
zkuToyz3=>g6|c7vjv*>$x+0ZARv;BpyoNu9#>9jCGvW>Qg#kij1aL!+#=Nqu8YUdc
zy>Xuv&@5j$RY2W|B9LguixW64q{==^3s}Cm7m>&&RNt~U7G(rG;Qn)DVp=nnr+{c0
z@_!{3kBf_(nAx)NW)^cs#UiZ+NlH+2penY3Szu>PQIf3XqJ>zUBi?)|?>$0Ybu;J&
z8WVi>W}a1?LV509LR9n)0Oni8eP%Eei4f|<X*0;)L>1$~S6-kmGUzx!Wvbc|>SA7F
zZNI=A`!QLB)>x1oxlTxY=a^A?Ey@>T8>DrJ!^!UH8sqX)bbn>-0c(q7&;J5D^W)%s
zU}q-BP|*XBiQ%<GD6}Mv5CtJ3-_0e;AR;W0oPa~W69-GwtkDgo2Y00c!*LZppdZ6X
zP?nO-9NU01b?9S<pM#;1{>Ks~MK}JZG~660W&w+wPAgSWP}Q-Rs3^%iRqPCfYAF)+
zB_c*B9W<hGUo=vx7O;tY&O0;`599AZDjppj#~+(W4u#_*`1`aq8jX+RZ?#+)Z&WjR
zMb<HsF!wcC$!DsK@j^L}=`%;C@i(T;Zm;_BH=y=SkDB;PYt`+ue*6)ACST=0L+;ei
z!3QG_&3%rdMb>z(T5-MB%u70`(G&=WoX|O-MvsT>J=O!myX!AbRj+7WQJrc%VAXf`
z4&5u`gC~B^J9X&47^S^?O9s33_f8Csj~to;-|EDu7=&;QVh9n30#SH?iWBT>fr`1Y
zE9{@Q1na^1<@sRezauu~KEVatWIg7Fu6&8zyCukL)JuN<y~JB6%1z0~+`yGD@j25*
zp5A9+hr#d8g0VruM)D&T@+!2&Xa&Vv(kB;i4~Nvv>NVVt7$z)5x<>t@kXpRqtDimg
zXL-gZ3Z;*!p+8cu!5z+xGIkB>hc3C{r5BEUHe2qWV6gqzze}<L|6SsaWox$kyEWaa
z@BkfwJV`zAaM)6>3B6CHc>1`{d|!yALP=$K`D;!$u%?m>b-ojNpNW_~)KcFUx<<9a
zzqOondO@sHhX)`X={a~7GB~^BHc5mq+pw2zsP!5y;4=?t$UwL0L0Io4^_Dy$uPuHm
zS$d&&5wWp{<_T~7AvlH2O1`{~dUS?*ad+I?9v%Mi9%hktejDqKKPxYH{_$?a83ous
zxG#DG$^r;ax|<DlUybL){8Jw5MY)KF;Qs(k3khHXM1|PV8VZtlym&p`Mpq%o<B~p=
zAc2_u^BAH<fW<t9$+ac;_nm)BN>Ts)OjDFQW%G9x<^77^ONV-+Rqzk6$uWKd)ls{i
z>-^jO(5C>Gm_Ou42wr=?;-$mlfWOcp*}|FUl*PS5F*ul9ZY%k;M|^fgG!T=~X|dH)
z3DFJ2U{Bxi;K%|TC;+>+P%^T{l@Khx8w`%3;bV6_)cHnC3j1zjgBO%|B}rNsdGI)|
z_7(abfs0D`9=-<LRD5R?_<?s_+4;t8zOWQygP$xdK$$^xM&5lRuf=Hu^buc_-T~X5
z7vt6ExrMZRF(=#*5~kovVl_0K$DVL7B?(MMNOD!27!ghtX{z;0q`jb8P;AYLaN8*?
zIQ#=cPBlo0u}xZFDw)S<DK7KbonyZ#KHoDkC}k1uhV<*cT1<}T@_}-|7v?)e*cTY^
z=W}srw!G#}6C;V36=uqT;fStBr!?P+Pn(KqdSo~t0|YTBWAISV`|4kfsJ?Ux=}i$F
zf%WG6>pc2~vDj|I2iL@x7DFk&<c-%#<`Qj1OJ=E-&?SE=v>3|!4_qAxOYy)p@zBIx
z*bs{fJQs_wBJQ0C#jg&=rLg~+1OEJ_Zy>xc|8D3)fc?;hoJIa`lzCy8M`wv>7O|7g
zy4!p2&gw>9AR|B$)-u}m5e|M5SVXqROkVWTkIpF>C?E$!=QnhJDHsHjp}yL6v9U2b
z&Mjf>j33sGj30R<gkBT+`Um)2C}JMiqd%!~))bQgqq-(a{f-z}y=GW6N*@&<NoLH>
z4@{FjS=3}PQT|?0?cb6n-Ob=O-OXSElK;um62%uA+|T*|yc3qN*E@0>A~(5yjPLR!
zpL0zr7u10UFR=2Vba3YBEan(()w6jp1Wm4Cjg)nuM}e!VlwF}~x(bQB0LT5p)F^}*
z@cEgT+!xKp^ua_hwh46pQ+6*G`jte`ACXy<>lE5ctpeqCF7U6%IAe2qX%v~YDH1#u
zxKSGBR<`+2VCxY)DigD(+tSM)2w9<!A9(UqkF69|+Ob$COf!w0;jU}0z4TecS_6Af
z<P{d)U7Q)IX#~ebf&zwtFC`Kd>{G|zm-#1PP68H~l#&A83-;{Vv>0R8eYgQAC*x*s
z7%2l3ZA+xD^I^E+_WJei+@dMC8TQ_6m{xLV!FS0oZ`pU@qBqD=k{8blsgsof26t=W
z#JfqA)C_WB5s&8VKjgEuP6Ea3MO$h4L$HC&2Qoq6SQN)5u!Pp^K%GBPy;6v&aJev)
z{(<>OMUfF>X{y^*y<Xk1bDdBMgU5D2pXMc5sYK=n`V|9TR9Oh9u!nc<s6u5&^!Z2d
z%Xl~DSAoaWU5&W~CXP#G)C$sclxv{SSdQ3Gs8_48WE^}6ITR7*9&bG66G$H4da!fb
zC|-I`J(Tf^*SC_%q_qvPa4qy_K{B<uP-spwr2_iBrTTxvFo3?T!9@-uBuSd;yDlG(
z=i%+6DslxW2vo&4oXz4|gO^b}j)SK7Hn%Rn<dVx_2~p~@UpoR1?U8GSp@%-A`Q<vG
z{#L@JunNQ?333i38ThBO%La}f9e`3A(W0ZjG>W4pPn3_A3D*cl`pk|DK`SZ5x<JQ5
zGOPzYQvny;zl3y7U|}#^j(?$GhcWlq_uXlfy=t>N*;vr~*4uAc*k-c-;7*uLS7*2A
zvNNl;E!=YZTfN9iLppF;^KJ0k2R%p0mJbFLE0k+>=)=x!W$WWmLjEnic4bGCvK6rC
zSngaA?&ub4Qx=*Dxxku{8hyc3tdOigQ>HI(I_ewt!MPFCkqk=7qNJ44^%A_kTU;lH
zV>CcG#}DEUQrl;j!%%my)M`F?0BPy>&qn-S0OcZ$Y%Y<mR(B?k(CQ^GBtt&}G;@8K
zLV>ur*9OJ~R4E$V2ZhY{O=%b8m@v5TD!e#37Y^kVs1!GeAi@zI#$Zts7U(Oa35qL5
z7&T>sx2-P_(`!AuK$Pgpqp~g||NHFz3;Sj4lk_3|wX92)-^#HSY!D#$*f#jb>Pb+v
z_(hW3f8qXFRDg%sf6XF?JRb2inF@1U=A$^RHfV8-xUEW!ltt9Jh+V`8G|{vQGVswZ
zfQ5n#9Rv>KI`yE}r9198i9mu^A3;$39WYRxX&49KP<6m)Ajc6lh$Zilagg5QW;lZ+
zcI|OF{7By$gT{rbdZ7_~W1kZ8{$PpHd@lXK8?t%o2QV)~gxtB_s|(pifaM4^3fzw*
zP7)IqT1K=HT`Qdw>R7Ibef2X}07;@ST&)N4+2LcyWn;TH(6}ilduz~A2;CR9gMVH$
zl|z1?ZzA94%eD<UBKy3*o7?FHYh@TWQurt5Z`;zt?4##t6G@`86^tH;3^@pi<pcX5
zomgud^?w+lw}(M~MyzydY<T#FzxA5ip&|L;&HKDaWDk9V+pybw`fnx&Y7;P3O<ts_
zMJ1{qn;uX=z@wO9w0_&L(LTdHH|_lr;MHQ|q;x&hDj5CU6Y@_Z-6>nW`xeze(rIbp
z#ITAVHCUm5%tCdqI(%Y6GQ2ws^_IJ%W27_~W1gqH0sWJkG{5)u+ktqp>63cE`&2X-
zDurPDp}y(S8KlsxUcLswOWdh~nD$=nz{F{PD&;@D20*`a_D1+Boe2i@+jKI5fJnXe
zMfM!#0f@&a5-q?>2TNxb0#%hg(OC(WQWoapQWyxbXACtB80aXB3TKQ$LCk5yo|8OM
zCf`8v$Rpci*MSaGoA4!RA=<N@l1!3GfO)WiH-vv+c>io`c0cXc|B6D^Kvrm2)WDTu
z-V{JtZn(jBbdz8U!s6bV{-C{aO@ELE#Wkjb;9-@`aHly86?w{$-M<HR^PsLG|HhoE
zUrpwD_q10Q?|g6lU0zX)i?c@)2FkiU8c8KyzbU=yVg$~)4d^*tyF346<HnF}V--Ft
zJnxm`!;Y>7^%u%|PH2ee*ki#~PI3Q|Q`Fzx)3fWY?OVcE5jrE{yJrwNbQ2_Eu1akp
z?-1aNFzG=t2sM|k$Y_m;1QId^8<y~0f_a5j2kA+KQ5!r!jTVa?+6Rh^bwqV200gi|
z#ARYz^l29abZi0oJ;5-Onw9KWwoigPJrZ+SDWreeR;}-<r;yqPR8!u*^~9EPX<kN1
zBa;V)8jIH*X^d2Gp~-Wlfh{Mt3c#L0t5F}dMn<gB`X2_XiAqyq>CtSx5AwE2Uiyi-
zLZwy3!*|y6vvCPohD_+Q%J}|{*=81-TskI8sY6#&DEzgDQt>Rdy=-%K2Y!8pGw!6q
z=6&YO50TsEoD5Ik<HQ8D&H4~ie(E(3Xn?{^=nat71zRL`NBgi!XZ+BjGhp;lO>S;r
zN|)*_ZFst2d)Lot9<bPL<2ee-^?u)L+0dVC_`^NTSv%EV=k+)pc$&}2B*$<=RspWK
zBw?8QWRdU>mK0P6P8T@BQb3txxN<WNuTk22F-eIs!1b3Rj_89X-Snm?$onp24+tY@
zXEU@d0r@DhznIkwkV5L35gIQ7+}`XT9xpb+ayq;#nNf9U?){oKUmPDkxm`0fq-FA6
zkI%<6|DEATrb@nzQX*X(uVx})X{<GVe>ho6r2TQEBp7dv3BNAzi_dVK>0a_;04a7`
zztKBjc@vrCD~vnrAxO%Ve_<2BZVBo)nRMgk*>l0rP(GMk`yR<)ekc^ol!x-{WPYgJ
z`8yB=$Yr1MZkgMnBZt&!MTJmKMTgCNBtI1ETk|%3u_2V2N<B+(gxUH%R5KHMBvB|N
z6kXpdF8O{#{BWBr?(UmyFgS6jq|bRTr{xtmiXi9@9xTOhwz96}cJt;cZeLgC)-_J(
z_Jsc&#WHOWmC^P6=+(!==EHZ7-b}T>3s0HpYW1h)r`<3yE$zLlh-rG9Z2kn|Lnt^l
zX@&(Bd#NSjOi48^Tv)oK+&-fLCnl0jkE;4F!y`>sFDcFD6+`X==x{XAU#PbR5KN(~
zIUbG8>NsE0)v32}FSS;x2r4EIKm!*r0)<TjJ9sgqKdPtR%)#J@_n@4TZBLE7A-Q~s
zd1X7`bI}gWs_lM_sS0{df+!`~q-Ew9irJ~g+|IimynE+d!$_!MMbQfbqX(}#G&)ew
z6(y`DuH&Xxeqg^^x@{|^uDNU1)?3S(YUI38HMM7O^w8+&p|RTDlp6KskSXOXnOEpQ
zw3gc0W^YG3y__3y6c;d*QhV$~KS7FIL8H-uToFR*v{~j6n}Xsi`i}<WMA5&g<||e5
z-ic_uFnTp`g(GJoNh$D#GHVIq{t3Tq?(-GXvN;ZQrV<ZbUECWl13frZ@V{X@*Ef(a
z(zEbx!5S9bfXxzP1%kxUtI;u>$LB6D)Ib_Xc&|YOAVUYZIrN<P3g?)3B03Tk`FMwc
zU=TEd2eDtK^?Sotntt6tpFUX~kFkSShVKO?pr#*04FfB0y!s@1*wFppx&n)|FC5HR
zZ}(-83*>aF??@hL<(7PgpN*T_YRiGxNAgGdQm0KVknz3U${eKni^L)<*S4AC3>dsi
zCiy#%9jndrzK5Wh-zvBt8f|w{x{)x*jklHoqgPwY88w|u>pT&C=MSLBf!YV|&}%$Q
zU1w9uxel`$KXjlsLiMF5Hgqnv=fwk{seJyD_Rs$Ljn3RQuaAr@yotP2-_(0_+Q{~a
z)b+gS%)=vU(C)B|M5+8J+6Nb9r^(KIeVOBaH^^fInL};qk0MRh+TXykLHlOg7Ho}n
zjTl+#MK*d!*mDkDGSHZ-pQ7<9zy)pDDLw>iPEu7nUs|GeEG^mBhb~cP2I_Nm@?Cuq
z^?AKR_V?H!OdT|oe2s}_KLakGq6eXeNBbZP3U3}~g<}&E18<WsBxt7pg*w6YPC^9J
zm=WlFAAWQd2>9s177#;qqjJ@2*c@K-K!R;hC&7HmJZ}u3GJL5j*^{~<8V}(&TY1g1
zw7h~ZJt3^DG#70@q-S7<+d&!3#OOJ(#f}|wjRWi$Ly~0f_QN;)VThCvb%bO`(>Uf3
z5Gnkc2NHFcbCEp!JU`vJ@~t^Yij&Fvsnc)b=W$8>HO!Q(*cMiSA##>(3Q<RBG)RB2
zJZl&?0MyQ)Pb6rhdGeZvb`qrV?RlS&&}xYN!BO2J*0z_pT5yAyK=^61yuKP+Ugj$o
zhri@bHyHo@-h8CBy2@7ETg&z>6tIF_e>oI6*=V)8clh4@r&0^^;70VXaL|K41Zn(`
zuoj#<*#f~!ac&E@1&dF}=Ged{B4p{MTeO?mRzt`M(1h`daDk;_9(@YO7E&~$rlg19
zkziskifd5cdA<Du{ln?JD#v84JQIuSvI*3Pqa!1^Vy<T8R2lXo@4!qMk(jbNlG#=c
z04o=6Qwg6>PwD<(rY{)&u!j7!vYbuUvPIyyXn-Bh`F&an>Bs4ICQQEwkns^2c~74N
zG79sWDb1?@l`t62_7T(<p5^oPE5Iy|C1aToGRgAxMC06VNAUZyM~CN52-+TtuN%n6
zG(d1TD9=J9CLC0#clb5oc<{VkQ*gGzc@2N<Hayn&p|i|Cpcr2&AXitRfPbsFi{fRZ
zW1_R=!cx1iT(D(wC;#&n!ZNA=*ui!8sqsOh9UCGRcRD6X4g295cP_fMUIK&J`rUR)
z=pO>1EFnuu8H!IhsYWAxh8B_1@EIW)8kt}-zF29d9Lr{_*=*a%KezMl8zP|s_>v6x
zl9+DBf<CWmvY;=PQHa%G0Fv>B{fR<uJo^fLvr1vhHtoJ2bB^MJq1)pUV)q3EZvMt^
zz$e=nlN~Yg%bSL41pv(tdt+KM6w9U)R=~(g)o=zL`6fm+kqIRH!EE|Sv#(y?RIjsk
zDmjYEMaunX27<w42vKq@5zf_3+Iln}FlR$%1pNF^r8L-iWN>~E|ES%xH<DlEGw`k$
zq`k2M`i=i-x)bO>Eg8!H%)1)h-46~y2)kv%Y$Gy+aj~$PP<MebcA1O}&w<F`7~E<G
zi}0Aa4l3p?>Aplh)ra(e|5O?q+c`E?`t>-Hn0=?D_~rEFo1i5xeMgo4&pRT3q)%oG
zrVi`91hk%$5NeVaSP?i4!T0n*5fj9=r&US$^e_bFnL8-`#qg&UTQ5hk!yfRdedJR^
zngtRlI3EHAPb8vn3W`7VE<3YWMRE}IofrOixj308J6}1stli2~`DTe)fN$UxHz*SG
zV-@ePBHGrg>)<jT(Q!0QFjvJc`z$2DHFwz=YE67iV>(|sw~Pc-ly1<RwM9af<Fcs*
zY3=~>O`aqBgO%fef;Hd|N+f}D;kiay`X(F-W~OFqlSny5aaDp_g;@k7mn%%ppRM`5
zP<Hsz=oOfhn3POXqV6bTQhGcQ2ivVEwYj;P0((d-(ckRvZ+`rzC1d_ONlHSs5lKsu
z-!Dn&2vi?eAsvG>cW4eto%R0aLbJc~Ydov6*tb@(M|=$S`hBD!0&Q6{K``O1ykvJg
zO(CNC@Vx6Of#=+s8oUC}c4AL5u4Ht#La18(7cOks%eUk`^ILY#UNOnfhX;4>JK8uf
z%r6`e8Zbs%CZ?uJq7)1czguXs5356i?>g{q+Nw|OxE&y(`i|rC5An+*yLVNpC$zcC
zCLZS34l2#B@~gRPQt6wu`SPRuigwRa{H(757h|FWFJmMgho!nq@hlbc0OnjhWi>+7
z0hjZ@h+w!KX{iWSiaRZkF@U5(uqE!)#SQjjU0(!D|7KO$(`>?5PVd}sNwc{}QSSsP
zWp73SC5aX_zR88cJ5}YISHSIL5rF@7<-$$#bz}fIF~8|TxZQzj(-rF5x4*~Bl<$F#
z5%2d!@TkvWJe$~0F2#sK<Iedce&EJq@|;Gy?1R7p=@pxyaBaTMHM^-g1FZWQeyDn*
zQSZ9zF;zZG4ET8t?2DtLvMm0P-mOyrbmf{7wrU!ke@{;Uqh*5ytUDvR4ZpQ7LeKY~
zK(`k>*TTz5fMfEo5nF7)@`GoEut{3nDo*>6py#07w4d@V7A2DwC+1w5qs(Yv>v5O(
zcuakY+4-ClHR@Bk67^5hE;v2V^v9<LVaI#}U^U+|Sm$RxDUH19^`YptZP6%p-jWKp
zNn_yUEI?LE$S#?s?=NBWR274`glGJT8PL*4Obfw}5ah>q_aDiq2{|q4`u-ix*&C?N
z4s0HMeZUq;BClCr==&pzBqi|J5q!6SXzoQ=m=+P#-5B4^`9Hy1jAItSZx-07PM45(
z5FmSm8XmmcEE%P_4cDet%rkMcG=M1?G~bQMHe_usK$0IlIMzRJfsv@oO7XIB&MdH}
z28siirl^LAJd{X{9=ht_Xd(e~ZC&=s`4VSEFjk~Lc>!yyO%{DxAPG3#_cnoxQy@Qf
z6Cf^GfR5{-G*$MaaX6HgkInI4-Z_3*5nCqkY^^Pnt_rEmM-QfM)G#nF(86l#+C_uN
zsQchuJ4-MtqoxeA809)2f^tR0qj*I$jj#JZV?PE)kPiPqSOzLc{R8Dr((2{MbXe3#
z*6S{PLE#{l&<SGA^T@aLw_V=r*K}V7>_&EGuK~u`GJ}~BJ(vmxf7mzYZ*^I%-w(ta
zzPdGJrGn;nXt&*WaUjKY5-&-lZ^~mo^2A6}k30iT>LFp0uO-kx(4A>GPSLK(?eV0I
zr+NF(X>TlWTeFo$czMW>bNTEg2xmI&eG`@65sTfyubA|hE(Oesz17jHM@9zIs_ge(
zIb1U@F$0qRn4aw8x5L+lr}1Z(QJ<0h;w8>!z$0~|)?p)Xn}=VG1s>jciQSZBrZId`
z@2@XP<&j&QU*CHq5@tu8pCT-jm!O+@9Q^qJc(cnqZ^w`06>VmwSlJj7_dH0s=PMQz
zo0EDX==R92ITKwB({2HL&MN+-Lg!SBEEVaLq5(+)i#-dBTo%YG(y!4CCG~UM_#ier
z2$-)z23_LvD~^T1erqsgFb{qW|5}%E!$knjnMyV;=}bw>avCU*($87`n4PKT<Z0Uc
z78mJwa=0)7n|{q|aWE0m&;3OD74}Z}Y>{N*dh!wYGQ5Z|>x3e{59~2{V8>N#iEu=6
z_d=U?;#zbgj7!qJH)7wqQP+KXLa%?p@P>iwV*Usb9=P`DowHu!3*r=&x)p<{5@7nx
z$94TUiha61{27FK5_x)Qm>w2S>YouOsNmLliPI#^Rr0}onmvns(IC&zS%>1+RGOrq
z`x<;;xu+iO2)2x6#e^mj-oVB#Z&bT_k!{<5!F!~_l|^Oxa47-TI^B$K-<~w}VK|lK
ze+(_>ZbMSMNiC;J`b|gQe&nK$D$%I6`SPmO%3a{of<Dkv-v#01NU8IEXz6oWlG7eE
zkipk1?4g{59-Bw+dPL|v0fNqb;<;C{PAx7m&%S$ha&VNL_v}NAqOWxAt=E=((?>7a
z^gEm8Kg5=+JL<=3+pE>>wPW=i)mCyK83@Fa$#@`u(}&Y(i}p|a_HMQeZfA<~J2I^+
z+a}cHo+vVzW@a7tc5D`)fmj{1VGz$=orUTj?5|S+C;?j^l|#lcS(WqnJ7$FBNA+mr
zd7q*0f<wgfk!YvQ6xu?-_X5lmep_1*>kF&P82*e=pt+isqN<7o4xk<GJBI9I+Oa;C
zlNCiCA)uLG@#zgJZ0J6WEdWaFI8p=sSAl=TB+nkc9}+yDcf#Whd%0lF)*yiqYJ_WP
zA*f*ba|>p9h!81oVR(h6g`GJG7W5>UgBYZT@gKHY1(C$=&K6{S=i(2kZmbRUOgr}8
z7O|ME@nYvJh3K+yalF$m92I%%-UJ<heY&sBRX2RDQm!o0-;HtoJ<U+q6Cj9!L=m$>
zskUhX!(2;Ik4d47<~jiI=8o?V`>|vulYz{>+0oj~K}!P=NZ>jXo>p=TE>*#l<v$h(
zZt1Tu_6EpN@B(F@B8w@j<hKU>H~6h&Sn;8%B<a5E0zq|Bvw}C*#%A`VgUS1Z(<s_1
zbd9v9lcupnn2k_r@yI2ilt7<DuY`tkipuq$nhqlfG-8g?OXXGYKK3`dZ?LjNeYcd(
zMbq#;8XYA+B3|@g6Iqs|E8K_Os`Hoixpb6peo@gbTgHuiIX{@9YK0cQkAnUpc`=bI
z1OBz=>Zw6gv1`#6@Ncb6HH9$%?PKfw1TgD`7d-)HG+{6o_SLDpgq5?djLhmNU29hT
zis<rREi1Y_xC1#7bam$HIa$Af3U=MSlL{`{yZd6~qFoz(wk*0H?vFbL5!Fc5=dPYn
zbxqxQ_b$6&Xy}oPU<o1}>&x7~zzYrn8tYk+`U*@9wU{$jyv2O4o8{BYDduq=w&&?`
zrCF^uE2ovi%Lfq4q|IJ456g5zH%=*=H!a=pMk>2*@lc~7N(-3g%}V8h1Ivd+fii#1
zELeQ~jmNH^-(nXv8iy9ILm{RR*BPzC*Y<JDAZjH|4k22JEg8*2NTbaMcK2gPp+Sr}
zpI86XI&!4-Q2^U>&RiUI#3$&FQ^Dhh%Jurd!Q~^%hjsO@bQSiu0_V1!ieS78e_P*;
z+;3brP4NpuBtL-$Ivyz-ow<sC%wM$|WVqoO_!P(1vLawLdXGHvn6VE5q@vdr!OsUj
zjF=0Gzl*J$bFV;)q|-u$4_m#@6XzeI=uIpdOk$4Ju$^u9Y{$ih4eWa1Y;<N%8QX*@
z6&mL>rPqkkpk_%P99ioqiEu~HceH_e@OGT1e3}vMgXI#j(jdJvutaUt@!c|a2>3+a
z%3xR8@YlM(b8ZLQEf-x$#}?Z1>T7?Acz`E)e{sREi?j5zp4o_$*fOQJENU6w?#&>;
z0W9k+vpL3)fkBgUvs>)5&2u^XZf@4Cx7)92^iB3dpU%<`;;MENz6IT&bW?u=ZZkyE
z$NPKAz!utN_Sua!J?*1!vW%ADbfL{2=XO_%BRD#S6Rx%ptS?E-l`>u$Tpl(Zit2)V
z%pK_XfouFph9;#5m$u0VBW#nk5J7ea4u+tIH#1Bc$r#1>-o#L}=99x2v+fUsDrP8g
zX--DoYSoM={i;8>PyiZdaE9iK?I`}OGU#zQkkCSJZpN}|0uhLLFLc@7yeX@fC&vPl
zmm>i-^P4w?^2WfSL4SzTZ;~@#hMj(ew4b)#vm3}hNt=<7DM`ZT=ip?B-REfn!Qaq+
zKMWsJ#z{lxMIs-U4#wX@pC49h{g*%n{g|v?j0`ltHGe>DQu$_t0l`iWBEm^amY)JZ
z4tM3>mP6Vj(w;e976FK>*kfK`KZLxQ<0t}qgdZ&YLTsNrcrMsyJnM(fy-cfMQ9-?-
zUv%wrvrtac0BO!W3Rv4d3o&Bs_!wEMw(q?Ao&G8`-ytSf>J^!Vv^g<KZ%6i6U#;g3
zdC<M<uHqk)EbygR>`V0d$Hlweb@$E}4Wx?p8qriLYIwEJ3ai&=R1k31y+QA#l8lcy
zyQg|%J=VtAIom$AdBVfvsdN$IY;UMsB>+V#y=IWn%h=L1#R_19{%AZm1Vq-MoFcA2
z7#Yt{5{cp#*w3TAK7e-qh_ktvH7m=rRxEd#9*~K>KaR-zT*ey)^FsPQVgHE$(SiI*
zz-q1C>}98EF_$KCRu_|oefpAMyz?V;Cr9*M6;27eb@e+m4gLY_s=^j73A*9qeq=)w
z?g(t+Myhf%W|E9%@cez`Z%nZtc_8U*MFu}llLu)KU{a5Fpd*<gTc3!iLQY4a;D>H!
zDl(036|Bi@nUbUv92n%g7I@d2Q=DFNBq;U46{KWe2`+O{bGa=5$X;tf4vejK&QeN+
zSh-Gglt6E}P5d@hz0Hht9yvwR;uO0l@@`>=00bK1HsIet<IjCMz`K&)G}NgKLLIoD
z*{NA@C~l}-wZz)X7yH8c#S2TF<$HX-rZ4ZiCpo;p+Di);>tWx;%ip*MMNJ<{7hs4J
z<0SgWhfFgFXNWnA7;Js4Ny#wFMGA!_Cul+fDHE<9?v)#aCcT*Z0t13a=X7{9Z{=~u
z7Gcswv)j2igx$q>u5-gy=BdvmGo4d?HC3rL3-vivAwihth`?RQ!8UXP94_g9xXYuX
zKf^C#I&itS;#%*xVumu2kg!Cg5_~*bBxnwI{~{J%=vj%ahr!k5Zg|_-kB{qScx3Ln
zyO!qWuAARC$K~3b-Z{f@;tuVo)pqzt6{xauIzU*Va0^EGSOJDLc%E|gxIVXU{yIDl
z4;=}cBrWIk_%S<27@qmtkh78M9Lzt$?;NnzOf-~2_MeS!V0=gp==RYjE=m5(y#%IH
z*q%9T47$jj!#_(8)cS|B;S}(SQsL}yKMDN7;OoET*<qknWDrQ7%K*({c**umps^Od
z6CqFo@|;+^=#y~XCdCIbw%J-rA)50D^tdcw|3oYUYji=#{v2J5p0<1b?(6KHf1<K*
z!9s-}n8(dn-gzlU8Vo-2sOpY9(~u<A{)V$A96yg1qDSnux()7OkoXh?d6@h;+m9^^
z&4R%J76#|^XMC5nU!w2-tnWoMM7$TRx1VFRxwECW*D$*~XS$dd-I?Kz0?q%syKojl
z9svzo;aRPaj|bz~4YI)yR}h7hv=*dhs|ELBZjjn?g6&+mIX@hpySmAi;8QH9Bu-k}
zrpLEx3&-X?2ILB|%Mw<63YrSarneI@epi!!1|&?*>_R@^I(n=L+rI3az)S!Cbq%)K
z!A<}t9`M?+tzxZQAU<yT1@5qdjh=*u9u+G{9AEi4%Q=mG8vfvf!<HlZUU8?fpq~{%
zYC+U#0J_QirX-Jz>bU~%v*qqUvn{chah+K;uQOt?W!x0irn&=ND1=wS_*cMUqB$q{
zfDn(Z2Breo0FJRhBchjpN<t7OU~)OJ0zXFMGi!lz37>cDBwdi0Q_1iyMcEY|Ef&X$
z68{_x?~-s)B#>o?VaR<iG#CsHhN#@qb$${WL=ZWBjoVAeSGo<YH~H`I7GO(vn*hcD
zK3u(xSSn?10t`T=1f|E~oFq=_D3owHx14At@Q+UGK9ip$TBeWJwfmsWV`uW@;R@9p
zO*pa#6UT|&i`X^Jm13`pBh4203sTwe9gVhLN46!;B2Ah*rN@E?>Lcm65;yu2$%9+p
z8jR_uyzNeVN>vhhuWG#6=?GJcL^Ll80iD*|$AX~?cKQQ)syaA$!(f5!gU<9JJ^r6}
zvlc)A)Tq<ZS7^F~$Uvz7&{%FGAiFJW5#Zs-h7Bc|*j6Yk3hl%u&4rk0_R24AjIjGj
z=vBCX1=vDc?i3~o7leb-obv=9=o^jO5^3wFjsin~4IVtpD_oBPvi=0Tl{hSjReY+U
z8%0atg9J@%5fy`_o*<@+&Vg3~z*=xFOU1k3r1qj!T&2qn%tpA&>R?!CE`J`f1z~38
z(Hn3<p#NzExgQso<MK=fL=Mv7u(Q?T?AT;9I^SsKOW|NU8f^!|XD=L|mQJ(QiOcrQ
zj3TqQH8p#|#n*xdfN^nAO?95E9K7`Q3&gq+eluGU!AL%l5C}Pw<8nG&+`=T{Xg;>=
zg^43%##wS4BYBJb(pWJ;!C~x&d;H$W5J*VSJY@z!TZSTD|DHg;z!7kc&iWep<$S|8
zdlZz*94Yj5o?)JQP4nIh_aGZv@X<i5PB(zm`)E)t2lkZW31oRTgPSwOVrDZxO~jdX
zw5llZ+yn1|xg%ozE`<g8gy(j=2fSji%kx1KqNCGrn>^csBaNhEQgsZK_BE#q$S?uw
z;5dnMoNE$)Kv^6f9SUdZWH@*{7$3>SDPtkm+6cCbo6gS=|J^-%46An;f0!gjXHW=o
zY(#u+IW=;8>Gn7t2BtR`7wQ?C=MUR#eD`vh6r=RVH?WgJuPBaZP9CRz!94mRWRmAR
zIxG`a?$5=e$Ky$v<}g>QbB#27T#)jZ+y#ZhV5~|OlmtXO;&6#dkj=DRC_q5^7-04A
z$&REqCDddnUEMTVo*q3|D>f&Nj7^nCH&xT2q?*{2-apYS)>vT~0@G`7_g=2t7!ArH
z)mRS0_+>`Q{nfEdilt3pk%X5GH3SsFRA#K&U*=<qwu5p)o{B+U7$BPurX>t0L>MUe
zooH1v0z`irKlp*$0|K5&j0_V1S4-nXR)EHS6(aBQir6W?EIwGFAQILTI%f%1oP|mA
znJKK<2%WYNZtO?E%`~A)0;eW+H!^i&DvZP6&3ASl*+OyHaIIWm=sVC473)ZdIxy1r
zoI7!OG`}P)6mLe(MmxV5&C0@GYv*m=Oj7YKes9sMBs1RIb^>Af6|p&=6d$YN<2zUx
zpN(2p^s{_ryw|6MGrl|S@MWO>e{b+lPSR33-vOOU{VeUk9t9r*TK(lBbVl9P0q_^G
zLDr@?KZ7?wLOiO4{+Ghza?szo&p>qk3HqHqdjhl<M!puIV@Q*NzRtgBtl|y4_lz$(
z>h<v^c0TUSgs?@9hr{F8H^W&dx1t3Ta};|x_^Zdc4T$p8?FahWRi5LXJ3RM#-sXA8
z^BbP`dp_#<B%196uus5>4%!2%T#g{9jnob^z!4MXOgOQM=>T_lp|x-|=<cm9bcIvd
zy|ux&&i@=(2*hu>Uw2Q~yFUc!!LRFSDHTf=##*rFWhdg<`6Mq6K+A)F1@U3Sv#p3L
zq7t}!xwx_m*VbHK^Svc;)e;|eP5Xn*eMm<$;(GpYzkjUI`6`=)aWNk8`l7u2u=p{3
z%|}gK@n!^#u(+}dH>_zFuB~EU*&kR`gq5)h+C+YYMd<0tQbxXxUF{78$rI`AA;3V1
z2RLXC>5VxT@Wr;IdmaoXJC_loSg?s~ArO1Dy(Eb1qf1M~x4};KOFI_7|B(n(?yJ~o
zY#JI1+!-k>Eu9tg^XyXRv~#w^_v3%Xyj%uv0<Ct@UG0vh(OLasE5W`)3*O#vIK~-Q
zEU*>m9T6@>4HRei@ynlp4g4WmI}gG6<_X+~29oQ!`5E$M*605G32;?Q_-6+350#LB
zfcxLWHuNmk?PkvnK!R}e?CU?t){7-gKoxvvgQfz@yE;3CLc*{Bcc|6?2aNmt3fkdy
zpL{WqkHA%tz`|Sok%;eY@5j;q{$KG&0d(~7ex#mG8E*-O{EOl6ZT|570bfuG1iYyz
z)Z{HSYDE}3;k9D2_n>;6PM_B*hv-wEHZ9|qqg#Um{)iSyg|&!<Pb-qtQ}FM;(v1F^
zKPJVL@DIFZ*%vY8kY$cM2Dqh&zxpmi4yMdw%KiNF&GHSnn=*E4`j!4<q+#+gdkryE
z4}gcln33-TNVGe0q?#orn5Z-LhA>mO<LR&hGwg@!#;5bb`Vp*SEdTJF5&Fcr!>H3<
z(#61~*bWZ?jZ*`9=ZoMkvAKc55@&@vMRvAxif{8o5C|r_01e+soOeFXpuc0wJ>{KK
zREL29MWs%AmFsld(k-CzggrNI=Y@0Ur}!1JP4{|g!D{BPx?BhOde<+C7T~8JbkBb5
zp1pa4vlT;cQ%iAJ;#y3{YX%A0=mpSvlmiN$E^wp^l;L7k<jSGF>ou&)2T0FDR%^^#
zw3?<baW$@WfOGfomV_b`%qkh%u}cW{mG|%2tM#Dj8_T6?!GxMAXO$r(>_vDN;<pNu
z-c+82eX=hi7u7&0sQAZnSK}6#m_jvU{cg;%VxNu(r`GZ6O)n7`n0Yb~3Iz*L+oHJ`
za|g5c8T7yH(@DMA+uMjhm_p<Z6v4!^+AMW<7Z&DIWXD+hEf}iTJY{)_XbT-G?MAy{
zg}zL-bb@}31?pc8Sq<iOYy6#iwj{nn-&<@n8k<5^t6S2tLYtr@a4WlK{Jnq|#-0U_
zMwwW-?|@2BpcGGMJJgex0~Z}gaI>Q;u@?D=khMl|bWr*_Mg<qOlYREz{X6*62a$v2
z^Pg7(I8n57N;>~Lg2%mY_4;JBP~{rX>PPUhuoB!LKGfa2<$#vMgRZ<156Ec&`J9r!
z&6uH(nTc%LO}T1!Z@OJr8N`v+tk55mO_O4=c3{<l3k6q%IYesT-axyqBA;tG2rrem
zR@vF7+#KZ0v*(`OlcR8QG?<|u5*m7bJcN~PFkK&=+<njQiI^t)gSDZpn!d82hK(J%
zPgsY=(Wmb)!fN5lY3QpuZDHLJM`}OG)<VXE=}N`m&=Rn~*wY%3E`Ie%gdxP00HsCc
zCtKN{DR5QA1@fEn6S3bAZ?=N5-s`yy>um$d<qFa^<*GU1U`VJSX6jVYtz%E}o;vPq
zUS9{)?=)Xbi?k>g1@-GW7eVVx+~DPBc%3s;y?fdU({-5N>N#EMIqx|=PcizF<SNHT
zmILPkUCSB^nQj~Th!Ma9sUlRIg2K_~@Cbv1m1j+1`Kbb^gPQ|;%w&Hdm8)fm1!N;o
z^&?97C&?{4$TSwwBjeNKIO^ht->%ndG5bb-a4@gSY9yJ29pRTUxm-q%C|>dy^D2=$
zMX@d_t$U9Icy8>IO`-Z0%hF|O0pvsPfyvBUTZ2hRya$%Hc(bm@><ePueAtiVOFbsb
zuQ{#PocHO8RI(A$`_>pKud$o0+pn><-zf_VBHGvVI|4TZ6Ydg`b9=t0r)`7edj+L9
z<Fw1|=iZ*RZIR;LKC!N2X^r*Lc-mu0Ms(-A*kRXG?{hGKrg*eFCyMYE6MGpQdAd`;
z?O@vj7&~o&o%AxD(}DfAGyAFhir=YRTiF7;7ycnn#;$sPCLW*3b8{1xhnU`cD|Jr0
zg{KPzj<V-34W$x^6ep)VXQZ$T8J;A#hcZ0#4#Bg7^am6qlbG$*>&md5LCMH8im0=?
z`Ne9m2NOyOti;0gIzd_mC>+RY-yCDu+V5RTBC{}vBKL>fea{~FLk=+CWccE#!{d}W
zTz|(~Cby#SZGb^!S_BC?2U0$q>WIQt%BT2CowdLG=smkTE!5pQ+4;W*+J(d8+*$T5
z161_IkPrB?sebzdW2a#~JM?jcW9navTq49#tl3><;u39UjT8Ug4P0M0*c5IU8{;A7
z&NhzK-}{ZO8-ti0(wiEj;-SMu09?jrDA+&1PsTpdStf-LaY07y^y#Cq1TQaUBM5~=
z&3w}R?vkC|=M=I68Jcf_p1Bao8}S`gICH|IbK>$Jz-CHDRXpuvi%Ee>lGoZ%n2Y%b
z5~5adzTJHg`iSGIVXtp{K82p?+2sL?$wM)C*qqq;xegIN4N}vMuy(mzLHJ;7O^CW&
z?BM<nYd8gjynuMD;U2ihBz{XuxRz*lpKx84o20Lz?QPuQHrP`|z=r@5->2_swf5*f
zrbwEeDIp>?4E(twFm1FDVn1TrW)vWz0$G6X4+!hSl`^v}#{9SsC!G@T>B7J+E@gE7
zIBl={I4!&GaXtx;Lpncfs`NO`r0o?5+)d|k5@H<r<4(k2q{R>DQmj&mNl?3Kz%`A5
z>&c>p$S2{0TbQox9XQlPus9wOgN7Ixm}7tjM?v$@z+QY;!80<*@1kYFDZSnk{pf+8
z>xqKC{n)k-hhg90xNqB_Kull<z`Uj`In|liv>Q`3B^X@dXXS1cif<@?WB<zK`A9S{
zmM&xx$>d#Lk!@f_c-wyJvOHiVM{)0sscie<*_G4Vllu!+Iu;qcmNkJe24}be_)~Jw
z?VPpmT^^mupMf8-%jd+PsT+kIs0n^;D|vq*N^)IdCU_lywqn&{nk6P|ed5G)4Uu7G
zy$pv3{KbGI9m6#!nZ-^wgpjq0{(ld^T03JPM5RP5C@lnIiOz4o$$WJC-LYHM6Okp$
z_dcJs6gd(0-5M(tOs$|B{J=iu2W;l^ZzJ&Kfb`atrInM7rPn+sPcjcqPl|qi6~0fu
z4;@$n_Um!NvX~XRt{J4>5~egbjKD6z#T&p8qnt!p4P*w2NQm`_<me?-gv!%PcZS*L
z-5?`A)e&}dCjuP?FpuVC0&APc^cXNCV>pBa$~z@peG9iA?*@AJ+v#j;Qv^P-7EQ%Y
zJN_v}HMJ+a-X}EE$W2<EFE7bTf@=1u`Y~Ba+NbAvKeX3=0d8g?qa9>9<S)b<{X*}j
zp7VaJ`SZOXyHLC#a*N>&Su1|k&-#Wcd=6jcI!xlI9BFUE5scYfvanAe{=pR<giA?c
z-8BZ4#@aDgRL>p8QRboczD7rU^2YN+i;DSSna*%nKZI<6&jo%sS)fXE__2HTpnFEX
zP8+=A#-r%urujwR@!X;Om!JI|&lyGRdCqIR-aC?R;T6{KJnji#1!BGNC{l3r(=&rV
zfD-OLs8R3JgDX(l-KWzP5OEar%Qx94uyzZeJG-H|CQBya&xm2eQ?L+eq1yVUrdHJ*
z4>}4nhT?*YX31GF+hkHBP&Zg#YS3+S#sgYzp0SMdiB;B&%gV5#_NzK@uypkfI;2&z
z6pd|5W>T4<WJFOI9Q@Q&uy5p=h@t7S9`dti8@wBOq7B)h1OSQPj;;cw2y2!fHmU6Y
z%ES}N%*ar_qyvpL4`>IZkf{5^2{WjfaE)uxJi+?610kLD{XeA-Dr?fr*rHYP7wv=2
zhyDk3!r!|jqT30WI~e%}dx+)^>7{52aCM``te(jO1CBd+$mWar#=nzYKjWYkCjm^D
zsi%AAA2aNUXyUm&lkpaNQU;+a<~m1__pn5fk;wc@_GE5N#&mK}Loi3l{70s~s!Au6
z^@RXj;VP4q=7vWvoEo;Q;i(HphZQB5Y&N%-N2Ss7_GU8~R62hjOJ_>{siJHs#VLO=
zlVP4fVuIq{ClUcwt`F}XpN^SkY<hh6Fx(RF>`P{1%`5w4x$nwm3~@~K{;NDjaGu+t
zX>YDliXCbNw(chJ;w3Dfsf0LU$A-de5;7^8TAm?7gLCeBMuZY;O_PqKw-r0z){U=@
z*HZfuWu&CY74TmU2`kEpeW}{`S3%tK#ck<iI{h|cj**f1tS?|{rWM@ci!Lu$vMpr5
z3*)Z^1Lktnw<T!dE;1ITQ|W`;x3XRr{@Ab&J_~=N`#cXrDuuNjmU)K{W*Ri?Sib^;
z0#g^PF#t`ecfoy>yNobcU<y5VV%vam+TFNk6BsGT-K0=L#zmL)g_(N_92n6JStpYt
z=9=?F>$ft%ke1>pNz=ke7L-AZ<1^D{<j`(Yn^9ynh}@@>TL(<VpV*P`E9St~N${yb
zRaRy+bN8W$IgLsIfNM9#w*tWxBI5;8dSqrKEnUE`)UD$UD2On=zYoAhRn25IB_sLq
zTN=O}4e;IdefU}A<kk-P{ogwrOew}99tRW*?Ld4w8qQ?G(dqaB@YBe4qb(XrDtOqw
zzwdgl0acix@4rvZ1T*8g+&Dhu`}R}WA;Ww9KJhJp6Kh(=nDP4$;a64sI;`Df)AT@A
zp*#RRdEPj@7n$&6$0uvnIZ??<OQt5vDFJIp*U-PaCk<f}BI7*FNM8^iowtb6fVgB)
zyZArZ*QeH2JG15)hsBjRva0yx<O7)dFGFhi2>CP<dOD3MW($uM7BLB!DBK+egPvUA
z6M!Z`mA$w#7+1nty%8QW`$GHDRb;|3BrRu#hf{k1|34FNlo2Nu4lE=C@t+`fXJBi>
zQX}I&6Y!pydTiR4_0Pv^@Y<I(qaKoS$%TNW_xtMPYyo{XWMzu2F@Wq&xd6!5d5`BX
zVPD4_@p#JZJV)u_xE8sZPze5_%XO!FU5~jbr^FqLRql?q44T4~yPbc5?}NYso4eUg
zz1%NuT`WEj7)`nw`^x@cD<};t2_+7Eh*#-ywLbTn#|C6+*|)@pT~S=L#fNP#r?~~K
zuv&~1B~J&$s|EVSY1q@C18rzY5OXG|woSSz=E8O{c7;_)$H)?fT^YmcC<&12jduK*
zS)itIHeT9HS>?vY1XLJ4c|N(G>(q~+1-W#S8Zvm&q>(Ek{o!C#JN6umieCZ>*#=*6
zAAt__b-sA37XVBRGx)nX!<S(9@pC>0i6+AbgZv#mq``H3#K`k$O*q`z7&rPZ(R0!p
zJ&%4Gf^`x$EZPY{nkiF_MEY~f;f6Ssb0$cEwL2}gywe{^gj8*KQ?uW&BL1CxyxW1;
z96*{Pesf?bSLhq|rN;XbvB^5~M|cWPh=p|opo!m>Fh~30q3KZ@ux)V!mnjS66Li<b
zEZ{rFEN3RrBpw$7)(Qgz5(?_gE|InQ>*w(g!4s0Ij*RWTcQ^ix4I^^mcMg<fzg#+?
z%gQVqt2KGoE+qA0fmub4via-pzJ8wRK2r+szV}z}-5r*^KI138-I})B$Nb970l-`h
z3@}|eFr)Z+e{mnCG}>I?c0pS1kWJAl%o?aZNW!L>!5c=P7FI3N&)BQ)-Hzn0A$9wE
zfs1r&T8;<3`a9mCdxLQ~eXALIchq{z!Xq%Gzmo)lcP6kE7$@F?%sjhx!4vO2CyZ45
z{>URc*w(iapFlkC7a0bCJTdHFC#?)8Gpu0XXWkPCG2_;>6iE4yXT^QwZv^0@wu|1w
zOZeUFYj{QlUZ7);!Ka~D-UJ#!-X%28G1*DzrZEuD#*`<0G9~gRy^UVOO(eUiK@Oh^
zv00d<v8P$=eNA1B2XC8GBk_njcUv&7>dkYDcEscU=E-LB<eWA3tMxekd$TomEFN!P
z;m<^_rCzu;lJQ@$=IW1%#iRJ|+Ui{K*Na8^fbS-Mk8k5Wc=ifR6tSuHD6;SpYZO%O
zmTYUp*ozN61RYKR2B?R)^z{&|PX#DxHZH#V&>?KTb4Z<V_=B{^eef^<gXXY9pQqd&
zFKGn+U46Kqy}=zV0y^LtIl#$d-(d2Zk$Z<P6_C=m8Y~oe&vwnS*dI7k!=KQ!UAr_b
z;pdY>O1#tW75<r+8!z)1PjIJ}t%x9>0H}2%JW#^+_igEFL)D)*($Q$zc%EOx*%KsF
zoc@nqDH`Z32cnYqkN*%QG+$`WxzA;`0$?lV92Cjq&;y_XA@xG{Ne_O`qz9YN@eB5w
zoul0+v)(5Y4Im%%XJN0qkk4iy41jA2V&YYS_edHN&WZ6cMB9lrxuYx)lz@ta8Mzgk
zDn`8<((rU<fJH)3u#aw+X7~HN+srU2;LQHH!}I+}zavXwbDP(<f0hvx_IM~#&0sv?
z#lsdWnP}d7SFVxUH0VD%{{bT)1(?qW;{n?JwM}#NU)J#5FrML;0>%gCukejrI0DF)
zy8s}Vtt<+k9zov(T_*0v1IdVjXB_g3{sQzFS&`u+IfsmT9q#?byun9~>87^440t`b
zn@}nIPG*nk%NnA1slU28^04~3(E+ayQxMN&@5rp{u-pOLv_i8LhhBS0r<(>(Jo{<n
z52biq&|@1RuZhu}?FNQAvT=gL+>mcZh>=Q!%e@X8%udf;2e<#RBNH5A_f0hTxw(-n
zq*sU%`QmBrG{X<4c#O>a)D@fba<{Z(3;;Jg2f&^D)Un5aZ>T&bCZd!3vJIK7k0>xa
zNV{%^W$fkXVa{=3qXMffWHD%}SgQ?nBylD!w)rw6(z!KaT&l}aztr+a<p;Q^-D<6f
zlse3F(Q+`j?6bmQtMhT{HwIEGL2uBOZUe0up#C8WNyEv1jnn*DZf~;>;0};8t5i!A
zn@|}LK#T3S4O{M&+mUsmbGtXBY!4&O5&K3p7_5%aVYn6yAP1D5ud7Fc;m&d-V1xnn
zNr41LnC-t=_j>i4Gh0U$77c}>&L2}oxBj|v{5U|5zC_&8mt;&7vWE*Dm+&X?tk-0z
zXgv*?v!|~&j)WPXGnD!o>4y1-&WrZ;oC8HdsH|Fu@9LhO?w%6;5Vm=Sz9TQu7RF7}
zVO+vGr!5~Im(`j(le?*vbeC2OCTkc7VPwssMl<9an%c_e+14qqcl%*(Q#$-dA~q0N
zR9k^qAeM{!Bh`pMo{QZJ^dddmp9zIB{aM{o4FA~7NG3BfGxj$kRIt4y*v<eP(Y#(H
za->K*YWLrOKK%#qR%5ockZXm|XPhr0sT*X8+^UII+5Dkk4!Li$aOW2~r6h!R;ueXu
z;ZF0gm>2l;TE+1t%NH0d_a(B1KB^myk%c`Y4fFlkKsXl7<x8eErW>R6`JIEqft9X;
zER5u&v2>s5PfGNlSR<Dy4Yc}_OMYJ{nezMMz!pMzzM72p7q=Eu7z?ta5zj}wF=270
zJm2)JlK$G-#%elXduD5qHFSlmMNurUtH9GZS~j6Ch1vvP+pvrl2h`o&dXA1|wkH+c
zPc0J)8T=+}7PfxI2EjQ`ncEJumn(C4du#aq7e+@%cj1rz>l!fc@gu}LH(HD!Taiza
zA`xV@!zn(+b!il_;#88$i5j`l5^{6#)OV5fUn-5|@HD^y_Voc4@E3S6|0y(LU1N%9
zg!A0rL-W6nKYEp`@W-qkYwC-5h5y+NL`!~Z1And;uiy<ZCuv_><@)$B_z`<3n~iX0
z=Xfka)53vS?H~(?C0)<3a?RP5FxN2Sx|&<4WZez1Uz+XF%j*_<L@5k_802dG=~NIJ
zl)MJJVl)u&>9J}UpaF))qd{nC>I;TCt&OM#h@7Ek=9>suHT2zvQrE^6186&jo@;I{
z_~TZ%Z(t~$2nCX0B7p|0MuRa^3ua=)Vc3?ueBFxGt6yyH6W13X<i+0Y=7pl*z$H9R
zS;Qn|A-hwS*n`+{h=VI5NC?{wEG}fkC*BK0C4Ua8Jo&J9h1JO!S9}M}xAi6Mfi0zN
zNY~XlgFLU>N?Qghn+xs2mVsB0(wA5d9A4<2+S-)P_iTL$3}Wfg%%tq|$&;DUH1lML
znto&h7xuAU<q5``{Bqz52|hz$AsxY+**g*U!WBfa8wqGj*znWt4(tZViQG5OdFd%;
zry>aRzFp===oNwXzX4L>>7@ow+}mJKI<CfZ!s$XBdBKaMbqv#C(LH-{aS_jpFY@=H
zxw`^gO8|bXMUtZ(O90^-Lli>O$r?ypEyfJ<aG7~>@fE<Og3)Ush6LT)S)x9UHwx?&
zeTEASic<kdL+1<dDZnZEj{J#<c0U81*#ta9J<J(Si}aY}&Ik4fR(X=ygh<K-WjY9%
zv<6l2n6!Wtc94QSaBW7E+E=eFBKhU<aVQ+dkta(jUTd0ek<vJh#4l9zf%J|Y6OqUS
zKc*1qjjt}wEuQ-oJjEAU_mo@G&NuP>G+u?TdBLCjB5NLrD8L5Ll=xq|=G*_{);z5X
z$6t;7rfTc_rgSH&!HuGsb^H~3+@+or8nBbXC6Qo0pkV9e`Ckx4&k4sv8>@lFL+}OU
zKWTDQ))9DaO%sP5V$j;#k{D)JM2AKUuINNwNYw<_AXn;|g+EObf9&Pk{zPXZ_^J7;
zkQ??w_I2YY+`|>Z7o6iMBqrIyf6Jd&dGT*wX_^;yUNQbRBYTS2unshbfQ=HNGf$x+
z+6P_?ljZ?-12h&qXY4F^FK!G0#h|b#VHm+W;F?xPZiIXS?WS;O!rb5Jtd~%(Vd8Cm
z4T)oHE$E-h09rA<a=I_vm$uKX>|Ie!KOQT2ld<F0cx*B3+hwrNg&{+RBH>8Ln;+QV
zENYV%L<COGxd%>qUj4C9mOe~LxMkV0#AiZU3@9wAB&#JQsTjd|U-+2XFuh2@YW5yE
zt&4;xftvW^1xrGCN?nL~_A>hMaq!{y)A(-GCEc>u?;LL6p&c7CEnk%mpDO|^HY~+(
z!CZCn7w@FbE$sjXA{fT}<}V0RMA~2H9Eo_nW}&xV#QDa(gP0O@azOnDntWnpXaX*A
znV@_9_t@2Cnuj8mk@hgkTUdu?o%}xt7z6|uRwP;`@Tw7>r2;tw>@x=rsp;ed!dYVU
zJa}hHL&SMiY!D%~XPdrURs7QUHaIp_mpflKlE$)s32+*tOa23rEZ5Rd6s2pjEX87L
zFaP^qula2j9##+RRn_6n*>9T+>A`L?L!EbDld39et#66lA2RQj)Vd_qRjI$<J>yG@
z(;vcS<v5hkY?9Vn_;)~-MeB&AcBpQ3dR9dd#DDTDBBLG_OA?6HG@bBvTuNeZOl>Y~
zx+=&tbspP|Z_kM>Yv67>-{7FP=dHvxC#}76ifX9yS2ug%ZwbqJXrp`f{3hE+8{<N}
zg^hj-DVvrv8cJ^hD~6-3aO5@_4YwRYvwM4-zaZH!ImJVVu+I@R?v@jKd>F<FV8#cp
zTj&MECB)m$q2MOcd%Qr0alHIF-(gPzHMe;+5}T{a<^yY>a`n#TYp)4l*R4wL=<)$I
z(woJ);#c=fC~jSA1~j)d8_I?l1J~f;z=*;_ko*;<zYG)~%Go_P>YQW(5M_1>fo4Z5
zS_*vTp&uP~grN&iwIoTO5Fh((L4`%;P=KuBb7Qlj=3F*Viney36yniN90Q+uu<x)V
zCGD5+5FJn99~X=QFa&K7{La&o!CnB)5yhWDG9X(s<Vp*Xn|h6FcDoiEi(=;;j)<L2
zrN#&EymN5w>&aageEypWqpy5I*P2>HyJDbk(058OKaXvG+xTE#A8PgI)f>NAI?`9Z
z0#%xtegYYyGJVlQg3sacIfBm<*(<WJTX4>GjlIR*0AtQZc&4*N+#Cb1m+Smw1^=zm
zVXY~qv%J&-LyZH*mUIqnjz>WF6~Jg<;EsEL0~1xS0tk~NQGhr~(nloL7<--<`}{|B
zweu9sLAFojI6U3<bP9r3xSijgqa%Kv?+@MIKmV_9^lLl`pZmhj4><Jlov<?n;g`rW
zlao(4l)a>Uf~ARaV<xyF7alcaL&ZA7Hcrd6TB*_%C8!0$z)F4Bn5N2%5ZSb`T_-zd
zWEG!_RLYWBN=VCOJ0oqU18f~6o<+9W0x}gcPkEs#!>hQuP(IC%{F-ggiR83@jc+0=
ztyg^58EZYzk&tY<9sGS9R-Zwjp6-M+b{WQnduWg(N-sf75x5NSr50pRP{HY$?m)RC
zMp^dsGNvh(;{qrPNGefL)OWc$Ryc%I(zTirGT7&#Bdpbshx(8YPOUJDU{IeF16vj!
z|F~coZ!pT~t#UMG_`;fs;y`4(xXD)NyT7xOw1)V=Q(FNa!fP<@omDaTE8=6{Q?Wkc
z7t$5!DL{=ycaBTAVc)dp1Z12xx72$;GO=md>J<*u<F2|~{44R`*Mq^Y1nc-XiCGUD
z{PV!!#8n-o_^@s3G}maZKMj8p@mxSqL4#{E92k90k19^z0otlmo8Z360tSfv^Vfs;
zHRs74`^VT1=(qnMlljsFwdAF*0~pM0)1nYY-;IX+LH2^|^VBN49m21LFg$C-dD~*L
zS|+;!jA2|qNdry$h1;lA!Dq93|D_)}H19vL1+hQu6chrTTLgJwcJ89p{)@*D_QUIN
z9)zG$@lV9qp5?j^p{JdLmHsBQ!2T)HG1FFMz0NguG$Gi>gwk!Dx=g2830Y}<@fix>
zgL@cPbDZb=;doykMsffCkYXz1iX4U_-)ccSAAxt-hJ{<;`%I6;0qTwoPA?JTz=ro>
zUN`WlkfJ18vK#@*{K`IDK(C_UR&twyUojyYxpMa7Jm(GhGH{Oz7x9C~@{)Ot_m-<V
z?uIf6bCT-JCt85Sv-C?&3HxeNAI|!?MB>X1>&av;5`wiFpl%-i5sKtGZxKt&8-d5(
z-zX-FBt*eiFU^)v6ZJ~jOO>+Kh~&M4{F;{z!Z_5h%D0N;c5(-BmDQbua*Tf-kIDD*
z2e1x8uxk)~-0Qg<ctvmYJmC3tP#Sv<IMYw<>Ow&F5defW!x^rhe$^A-C8jZ+<Lbxw
zbAHAv;IX&rbCs+Fz=*ldQLgL9fSy9s7eH0UitY&%FHo7`R)=aLa}d7O*^O&nehO9j
zSKJeV-Q~9!P=n#j1<$Vf+-aIg1=>aVRGkARfdwFIL+_sPyQcwpL0o$QMmV>2ifX5*
zcK59NETTc=E7RsQsN46KB3An@U>We9@7;U3Z<0;^)@1JxJ>S!R&Gn=53*8evEqV?;
z?R1aRt!~G;J@0g>Q{eWf^A+mZ4#ok`MBjF}t_fqrZ{t86_mtbi{KiqbJhMUBi&S~t
zcj3sv`%~zsBJeMmqYSjnp-!%<peBea@PoPzbGV6}(UBu56iIUcIBb9ms5RM&psY3k
z^{03=RDs7q6#y9?389lhk?{!Zu)!$Kqj+OZYL~@o3yZj`-y59Z@m96T`X|PVv68P%
zZ`1abV#V=a`D^NszgE*BDhs9mfALN+dZ$?Tok)ftY=;kkuNeR>J=aPj0`Hv-h=>o9
zo`pv>FtpzPejOii{aKwnkveBcVayt4@DHEWb;8T|zz1+h>bM{O(*oV3gWw}xmUcq-
zFiIHa*yCJz5-}&FBDIxzoSGq&q6?oqJ)=VPjpUf9WN&5QLC7*qZo6Tzbs++6wnXnq
zTd-c5HDFbS@$%a|S4)p8wXb1cFl<jtRJTF!ngu$;fwdZRh8rp}$E-S@)BJ?<$KL{`
z!^(#5K+RkG4hU)Kk^<7rVxz(|#ygN~26lo}-6VP0_Exm`zMglu&whtbpVN|V+d*cX
zri<tow7m2C=Y45sFJ%}92W!AXVsUT7d-XoV^2qLvDpY@v{y8h;)MNEFjWZqgxM$xM
z1ytH|pREMV6{@`E(0RqZe71W_=p|Pn&v)B;*mIr71DG*)+vBQ-b?uAUhGG)>sr~Eu
zDS97gKcy+Y{{FuYnm1A#ap*by6{jq?hh^z@=O1vyXZI@AJq><mk?PRl6Yd#1Buc2b
z14tp~27AIe3wD0cE#~{vx$l1`b|-KKKt)^A3)VWj4|<8KJ*Wq0q}ID?z~^p_BBX0_
z7xgRaMwy@)oM#cAMScxV57YB|y6Q7Q^Co(G917jO#HmyIRktr6?A~YZa7*3(4?f@h
z5_`z)rGMMt>`J$6tlNc8ZB&Rk=oH`VoPa_>-h-dV=YdE545)q$yyjkDM_%i>6*CS&
zbThMhZMr!D%0miI?xZLrRnlipQ2~;5k~2e%<iZ7386#i?taW%P;Tu@yRh8sEc=qGY
zG;~w5bR)>%$FEc9Hr@KGY=K1!Ckj^_SCjd4A)FZ)xaUMPsuZ+paeTCFW&+jHz%@(`
zj6|7~E5z6bcE0bzM4wX7v#E==AFmAS1tr?~7Ttm@i&}dJr{wVTw#^B1&(XjLa9+f%
zzwi#F;y(S48JkETYQ$2sd9#*yvRcl^W2{j2r818r>QXm!H4@XZ%}T~E=Mzs3PK@mG
z4HPPaf1IdZPB$Vduk?(*y&miH8G+i!hl8+Ws^OT5do~U_>EpbR{b5mn+Cp&Lvk->4
zevyYGb3JiFh7`@A?Py+71cW`o+QLhtP$+FKv(n}gS)alt{O+*B!y(TcFR<b`af~yS
z%BD&KkYVM`<;K8Y5TnysO+?I~`YqbbzoiC|nX=Frx7$TBZ44YYJRji~O+U){5zJ}s
z!jJXA@fZ<BDU{nZ&RBE&9oFzFt7s4!S7P|-!V)Wt_cpE{&4MVAiG?C;iJJ5m188Cy
z@6xDztWj9syyEyG@xG8CzDp9scfG(D?CD41s?gqZ+wk*tsNJO3?^J5aE!31-y!l-}
zQ}6z-Z!Z_VAa?zKrM(}g{{8sR-d@5BX(RUFIYL-q6o}o`{cdzd0okNdnIbh>m3=FB
zFgrFS6tI({lfucR=i~|<I`%7$ccMYBmwnRGa$}Q1c{{bi$&E;&7}T$LH9DOpGquoR
zayvEYFb{b=k#+lo3)-f_bE*dSFZ+M&El`MoQepvufC9pT{!MXFV3?A!(dyH+F72)h
z8zJH(OjT1A%?zjd;>g|@*&Qzx<dH-<qxq4tUGXxnubjRS4xoybjk1T%($++Wui+2o
z$p4n~TP$ft%usnKA*+9*>iwEe(W;r?P%JVMGcrM|Y$l>+G}?#<&V4@-NZs8FT`|Xo
z1qE{js-c$w5hDK4u1y|<Tq-1Q7yf?}A5$=9c6X#n(L6SzDW*@lF<tiIO%R1+`n60s
zF(MaA@!b)?+sFG-VH3%qHPsAcquEvQa`c8a93gu!iq<F0vK7o2v5`n@D43~gicjm;
z@wReesEm}oW|CQc#GB9<lF#qc$Z`1$p*mI^ln6%}pm=4QIJ=<iU}a$p1_5V7{vqn$
z4)5h5f7MC@IgO<xQ%W(ov!<<oyqCu>@WZ{)zWxC-c(Wum6r~|aHwVp1e~$VX0>gIT
zxyjJGVvfg(iDCsz8#)rLfP_F+S+LSv`HiqM4Mu6FdBoW@mpSOxsazaTwvhRW{6iDJ
zN@oo;?ld)#a?`T{q0iwTyg;y>*e93J?K5-`a*N}4ORH^-f1K-b=IGbXbyZ`YZiWSz
zRq9Y~ZQl+pT3wcR>{xf*Ypn15*?B5CUlQYl7G2Ly!k@-Nas&^Qz!E1%SndPj+JLx1
zwe~N^4I^o0jN`|RjF~j(bIo=8JmpS38WzwV!s2)iHjqJx{52YOiUB0!3?>Go9Q=3^
zD~hu`6@<jms;+3#{g1GyZy}TkDe1Hls{=C%phgG?J19;%5oDJvZm~N56f<x0Y4wVA
zMRsg5=<@|z3%`MSX)I;H9r2Up#zhS)`PWry59u7x2hV}ppgq70pjN=Z(4$<S<_Y8z
zSTRA4uq5N!Us5wd>F|Ot$}?8-wv9kT0PVqekz~hD(2q}!X0NoWHT)p{iAHrELM@UK
zi{q$^Hb6(|`fp@mt0In~cb>p)g0P$@-v+5+326xO9|bH^JTXvlh`4DOHeEx$cM$0w
zua4rP>O(5fq9Xg$MeT#)LlcEdnb~NZfHMZ)KBxmXBOaaYokD-wP*lHKuj8OJ<Y3YM
zltuAk&dTJ?0Q?>0fSJ!&px(~9djPT_M}`NH;>vH%6G3aE!s!eyXTo`x^y7#C71wOJ
z4eS>D6Ca&NyC~qSHBqpv!UV^1Wy{k?Z>EYkw0X%M0`<CP;OeUf_*c-KG^RLg2X0&#
zI#KSA4w}g$gwT>atMzuS)N+57|9H<+MO*mK9q6`)`1}s$a0~RU8+&*mWuvB1gKWdI
z6LJv|i;9Ca%*?vVV;3Rp+>>=sJGQNE8UAsq5-?=b3ou=HkOmaJkn22;8HAXF`Hen4
z$6e~C$*(R0-hljg=v_XUvO=|tHvrhFN~}=+<id@8tMrVG{;hWV`8C*1i94q8h2Doj
ze=%$$Ad$_*l^c0zYai=Lb^;2E4u^68WxxTOasv&ziVd9E!gQJa>zEOk;?8YT0b|S%
zx(WN(US4i-U4?xuJlmJQJ4U%d;7o`5fs4bms>0%UaiG<rJSlDg_sCeHslgC?*=EnP
zKPQ<2CPUcXhzH7x;7h*v`H~{683#|$Ij7wg>EplPoh%JDkUX(ZmGyvjyj&<vdUby!
zuXzJrDUt{ymu)haR7!F<5s^@)<s*JT7EghfK+bsJzhG6iVup9BgnYgAofprSe1U-Z
zNFW*wJYvF?v^0P5&bl?v`@GF@k}c2$iaJ~q48mn<>>9H(74illGEK%c>UAO*#4CVI
zko5*<(083b$9d;3kg>GMSP98{N{ls;qCA^Dx9A%`Ew%Y~fCq|AYL@Bp@Nmz0=M|pK
zoaRp(^M_)t2)D%p`XJN*0{@VhAeY<gT3=knfxu1FRCq=L?`4X;>Fhp2is2f!hETtG
z_=hNg^`zBlFD(IlWr?j6F#ci&@kw!39C>v-WT~*!YSG*v9W>b$$xku@P5%9)%SS#5
zVIgywGg#@-=OQ06BtOc-g$~9U-@dSIf@5>^BT0no*c&NDDAC|R6EfqtO^oA|zv2`*
zn82({S`rMFu+&rfOG^q5Oi%Bdo(8W&9Os@^fIwG5YTr$BS}OFecuo&%{hEe6GXReZ
zK!Da&4cu8&Q-h&^F=7DMQq`j1OYJI0!jpa3^pp|n>=X`TzUpm*C+QdVP21qptWXEM
zgilSW+Q3--9f5Ez{$AM4f67D8`CToU@lRnz2V!mjE=B@<Ea>wC?0Qd{{A{3avz4}v
z9?@1!H4!6=$=naYgO~xD)Crq}0ALTfPcgO8>*G?{!e%JcS<Tq=%=MR&N5YTka2e7z
zs`_`g8!|#VRX{;0BeJnw)l<COV2nlkW$&aR=c71B&mj*|T4#N_I!s^3;fM6eroC?q
zBx12d;B8(_MZz&0-xW$kXD;=6FP(`dLhn*FugdBwTp`7N-sb!-+5E`|8#BELtpI6n
z#WX2Xu$p_|kw5(Y0&}NW&6xu330rRE1~6IMn8^b<`d7&td#>JN<SV$*1|5X0HNNHp
zMr3oMA1eXtq<_M!(qCcH7@40RF(wPR_Y8ffo+D#V_XF=B@P|Qp5O+f{;IHdL-pfDh
z&U#_za}_5pnsdmc`acZ|5hdPuJ<sqNs)f88f@fPr+Xy?Hs0ev2gZ+UAr^CbAPFJL9
z1dHS@K%Nc+J@2edcR6;64A<UZaq@L2JA=T!ei&mOSu9^t4g-j*50>Rv3^)kh!>S(A
z!m*f$I*W&s+a{2^TvrEpP?|WM*p@sJhN|nkb}V~4q(+V;li|3CYm3JsYDic458y#O
z)-4_`4^18N<@i&0Fr0nrJ2W*^UbKI?({`LQMW2uzSRk0%eL@Tv-tiZD<6pAhcnH?6
z^Sl(bd+kf1?$6U~VIPA{<QdQc7kxj%83*jMwB)h;1zjM~jXdJ%)A3y5unsiH5Zd7X
zBZhU|IpFaP*{|QN`(@)KpnX&dD&=letDu}JyIDn6YrzC9t@&WL=geSC({(KtG-(W`
zI$t1WgYCjO$EjE){$is8pQBH^@8$U^ZzKyx$(k(0(T!XC;W*31{#z^u=2B&F4?L%7
z)15zwWim0g2kDoI260SSPJ@65!)0*bh(I$*&dp#6C|x88;@FJ9@6b7vJbY(@3YFJm
z_b>K`{Knxv-LFdO8-N<}27LPUzQcw;)V~PK51;;UUNHdMY$*AMb)P2fxiaIsN)H6o
zdsO@j1oW$XnJcw`Ur$YVLm}@(O7{aW2egIr0mMJkT&3t;B8-B7*ejDH8cI&#tW?L|
z0(R3@c-TG}1i?6!w5k>8NHiTgsZ@s+;Qy)ZO910Is<c&Aeax+qW~9-r(Jjj!Tb3=$
zw-sM;oY-*^aPC+mO-nP;T%MkhtN_6zKtdqda39Oz01IKcLSTWg1Pm;YU9K$4y=*Wn
z_i0#Qvp|&pdsRK7k?bV?+5h*^^QyYKySlpG{puB&53mX)+A+K{Q1A15hg<tFZiBkn
z*E;O=`|1NbhdZKy#r1Xd<J=ozcQph;5icKSCkIJu9}IXlw{C3gMwW6!3Lofb@exA%
zS~?E6JpNFV+1<FYb+ae%0)LAq7+dAq>23A;U8`cCnWPsG4pM%)5!hMFxe6&?a>S$+
z6cNZ04RuI<MgrwnA8EHBICT{|Gs3;*w@ic4@1f?M&4&z=v*#lh-a8O6<Htt!>?Qo7
z7!*h+X6XRVZ$;aOa81LF!5hLzkn#zH!GHWFv|Wp9(y?7=jgY%>MdC63U{a97$Ud_U
zUW%92ysGAxHGjY?QS7NA%E(Vqp_I_v8Y2)<=%_q7$kva1Hnd%YLv&$|EAux}<Q*%*
zUm*!qt;Qbs|NQ`jl85{UuX4B49`}bHFy#&iW?kc+%l0(xmPaJyMHcoeHoQmI@K=u@
zaU=|CQgDPx5TyuqLN^3NFNhD%*koR@=N~?E%L~8RnQ?z8>BXwm3B+#5j&*c*chG-X
zfMk2-ujuJ{p6|rK9l5}}`7u};jYxTDG>nzMGJ4M4l+67-nHx%Fo7q!y8PP0=N)B0&
zaWt?3Nm~$9$qnS&kp@FG70*?!;eEv1tVBlg%+^pVmi8_}36LQhGFXGZor8Vydei;H
zqe@M-9c@|k8~W-SXoF<v&i}SGgv)U80~8~I0Ff>i{(zO^*KHncaaO{p7?Csj>dML(
zvg=Ts#C=$AMel^Ys-GUms*Fyc*2_x{&QbQtvP=R#sFVabhlew24xjRef;8YGjL85O
z#`adA5jB?&z1g823=5ozES$m&x|3$mNi2VslZLx>kPZCP-Mku>Cr?kDQN*M|Qlt%w
zXv*mt+Gl<X<;-5WM-zR;^KAOjU-^2K_o2l=Rl6=B10A?-5N@)Q_&-eV@bnui(FNoy
zqyMtQ`ReY<&68yQ4}Kf^+XwzLAeZ-mJgfAwwBVv_me@#(f}|~y&wo0&>#|+7pFy_y
zEl4f9#f1<*N(@lz+JdyKTR?0lCK_5=8YYktq<~Q4HgLy9Idi}w+YoJBFeLx;OB*O-
zUzFDAapVA<sL9uythu-5RLui5r)wUqIRmW-mIm-ZmBgKLlOO|YH&#oNd6by=rgI$j
z72>N@OawzY%EpD2ovkvU!(LeXmG@tHecv-YU*CJ<b=TdvSL4WW=rgW{Ok=E@T5B*<
zzZ&alS8M#(Qd)<J{|gZ5vehonVE4~>U3Fs?b@m1R>9PyiHG7-zXD?hQ`*e->aF4OQ
zu5P>G0jTNw4F4l|(9$C>1iJnLuXy@)N)M9phm^OwhIOfEf5>v++G_sN6d<z+<mG-r
z*{qH{0im}o{0C48Ox}}pJoO9I^iq~98H5kqi7Hx#HLXaE+O~L#MIHRU4RQFiZmEN$
zn@|L6Tlyh(K~8hhE*WEx+Av8sjc)1}J!1o9BS=|%v!`1x8Myd`F<pPzuj^T7r^ml~
z;;ct=wNASNd{^i<7j3`fS46uw1tmc|+HqY(Y};%EH(b3vbI_|DTi?=g@Wwvjb{)8F
z@%|4sJR*@>9w64hw}9M4d^hBp805;DKABt)vyij`vgo6y5~tL9Kk}9VwdLAB^jL?{
z0qc0=emRoQ>;S%D<F0|eo3~>)w%^<bBH=ez4Gi^Rd-!m$sk5gI$u<G^tsWRO{fnP;
z`;qE6((Kb+L(SOxs_UJ*Hx6$z>S>7Ujcvmlce_01YL|JDX?6!Aopljz5<Fyj{I{8I
zL-+aIP5!3#bzQenFp1LVNdF$8cNvpuBF<wd0UJd8+qni5)6lz?)FyI{p1F9{ns&qE
zG47&6`{04WHX7=e$kOVe#h=Lx9j46CvAuurlJ_nR?t2FY2M6C%RX(t)AA#ERk?25U
zPd>lTv;ScaL8h#MC5+_13MqknNVY9$`A1)sk4G+lfba{y0IONHxN9h~EgCz#7Kw3S
z8x`d1(z=eWlYTJv(dxxqLk&%_!`B~*H8dbvWl(rUN4MOl!0FE;=+CDpzY~0{RTie@
zU6HvVKxRn}M^}J3rlW6u?GeIj<Rt2eZ`ynOqCLm=t~&}pN;h0^k?mc>SFD4>Y4yS8
zNwhlSEY~_buzFA7j^lf}I}q>QEkr{^`i2I&<b7zI$i7J9MEkEdBhU7_nn{e88XD!I
zSmp&Vt6ddXA)*3cB8G|VYM8tMsn5ZA)`Q7SS{<<oFb=rIX-6p1p-5foz{0;8nJ-6e
zyu0L5+#|g%(#5f{wxOY;q2WcN?X<NtHv(9(XlgpfU)M}5aSRw-cr~*f>zjnv96_ed
zwh<G1P_uhwE){tgT-qmM^O6H$4c)h+VbZ7V>TPU;cMaA_GvuQ^?R?m-8CFWuXlv}n
zx<&5wV}X~zzU){y=BLDIq${4me)adk*D_Qyj<J%)!HOqWeObITMnixARx$4WKBYZ&
zNk&BZ!R3E;Ru?e;EuMfC5Nq;(D#TB*U*-{n`3Qz3L4n0vVYC4*=)L3k!^VRGjxW;x
zu#jKp*Kqt>`~vp5lk53CfwQNPwhXz-PSYRs_sKs!`7!L1`#E@?wvlezM~*sLIZrY~
zdEtu;r`R+S8IUVg<YLIOiFhZP5|0l@uyAnWfLaKOLgNU7lRMUa`0%!HxIL!4yRn9$
z?F}(}q9Fdq8nzFi07UShT{yh$@L{w=z8^&0E?%snZ`ng;cxd@~G_PFxT>;R!<>2zC
zEgq++U0g4tcX7WZ8ucm2d}L*S{<wS@uUz~B>-h4|BKOTBiUe~myZ7;-ZL4LF@2dS*
z?OWAE%!y6l_{K<g1piLYbgUlg2$EVJT(y7I(Ee33(nBN<ud9nh>W1kc=?|zS@O|LI
zd-Bvzf7-`lT1tLM`NvLU#rrx$s$WD;B`Sl1^Pvrd93$SVGF8bLR%LH&G*x~~=rv`4
z4PqKz<M+5<hZL)?b9?;P1ilPo#xTA8on4V5kuHQlJl7ZW;orFPgB@s_(H_m4@Am{;
zd1{M;i=ysbfk_v&MUFT5gMOF&?__W5CI5Sc>aFy=D_<s@a|8UA-*>HoVQ=J+?+$p;
zmwDYU?%mmoFLvwWo`*xbFW)_|V_mB3Y~KodQH$=s2Q56|HsFTjX>!bbSSei7rzozX
zaFHIFp-HX5al{2;40x;z&>z_S&as}$ddA@M#p6ze+q$=%a!wrHJrE5B5fK-p$rD5i
zyr;Br$hDAUzuqA8m&U;fHb`50#oCJ*TCq=zmA#dYCn2&6?)F-!1n8OSSM^R%N*}9O
zy5$_F?GVNT^+zU-)CY(+V#BS8BOyP)b#38$v32a=@ZjbHzTRFR{=a=VKfGZdT$ggy
z(2;Q4zF9PcFLuVnUXf>3kB+XEf#en78k*=$q|*$`8~A*$?(8%!+knA^`Ub_VE$rEF
z*@ENFHHV=n5j|3S=Y9(Orki#E8hhqZhw2U6O~rttR@0qX^0q&(4qu{9eR!a1FTxR^
z>S!q<N^P2U1%B=kr<XkD_&R7jL|<nJz)7TXOU5)b^@uAqakoI2jHL5RIV`ZUZg~Vm
zne$;oNX%Fg-5|GtPe%gP!47*IH`(e#t+gAY*rQ;uK@-u9wXKJ$;~FNEqiga0-e_wV
zHsWGktx=!?a9WNP`Tl6l@FwW&@cdP>i>+mm<q^|xYlDIz!8>H^Ws?^k<?2CZXO|{%
zv!QL*;Dz!bKuj~v*89CxV|jhg;Kb4>pIMsTuc1pWqXUAujKO|h9?+)C-ncZ(PXBL1
z|H<ccMa=^Gk4@1xK|biOMXUt{SW&J463$2(#>|FigoK1$2#}+Mt>k<p$0P|217v5w
zTIsN{98tRmdSotp+!QQr6oLlUhp)iivHnp$CA1cud73o68AkiAE+brrIEILa>CKwf
zY;aFR(_6}Cnsu!SXI_RmGT7d}_}!&p_$Bf!njTM`uMG*TiI8~^!%{V96SY2mX+r~Y
z8AqG3om{}|fC>p+vcnA2a;-UP2*@?(JBtnH=&TQ>0ft42_bv_O53rM*zjMIfZ0at}
zZ1KBUdAuQ}JM%>Oh-sdzBHs6OSk=FaW9~rt*u|Z7^l<WSd<uLDY0Pyrv@wXB2d*7=
z9k{a}On2zOka<3I@_G&&ZD@M=4j0l1+;yO(AIbz;h9z%0BkgH~^Q*}g^K|`_lnmkI
z<y0jODMt8Y14LoOXRT$Yr7dVX8H8qC-TS*;oui!~!_jbC=jew(k+4eY8Z9yu(b`)>
z4eSHqwvLUsM7GBP_>cDOg#YaTC;%J(qApi$!-lq4TNpQ2x*Pj6;;`x|GN8{K2=FRd
zZY}GJG(aY*I4K|+jgm7D<Xk!Ilxu@Ttwy%%*s)&8CqSLLw=KTgKu$ZZW7|ia*Lr=s
z1M*V`U@bM>p9qIPf#2sJJI4C9_kfgwi1uvn`yJb6HoJD}ZnwVE)ohLi+uG%n>}hX{
z@IZjC)ifkIhN1>B$XRo5Ap1=-G~<5c`vc`*NY!VU+kkeps*CFw@P;~UHf$Jx0FV6k
z@Xhu0kU4#0|Cq!ez!asb*b~@aLrd%6c<q&M^fm7$zQ)*J5TLOwb8<3xQ(L$ZIrzth
zC@pzE;e$D}A6h5)2<Y*Ld~15!8%7=gQ$*%<{KLg-zV;Z>xVN1SjdmCmtavgQoLJY@
z(x`~?a{AYRd9zjO&}z${W#p}YbQyWm$!$Oqo+IX|@CIpkP$J&AOGjF~C6>|buA*}6
zZBf`@qr|_?;BezObnTP04Nw{7YU|D{VP<rg%&x<*S<nSIkcqb9Q&RpmA0#s3^wlI7
zqpfE57;;BqT0y{u4{?Cj(mS1p21t7yk)e%5)3H%nGmhk&GJcpM`BAk(IH|(mXRBD^
zZ?*NBMpVE)sD;`G+CrKT@WON&uHH7IZCjhySKkQ#HAL2Nc<QD@E#2Fy&>GSk-Q{`f
z^)>B7I(CQ@8psyY+8SG0np){bL0A~EMTfDbeT}}Bdpv?)sketn&<nNeSGX=+A0roF
z4AZyF_lcKLEFP>ikWMT5I7OJ~)#G!1>5J!2lN3XT#nci-K8O9ET`~m>SADDU7pzP$
zDT_#-XuXul&WGiWmA@jDLL-O!8mn9gGkP{|V*4zsdvljk61p}oJ{0ct`}V9+$l13u
z=pSr~gu{`0C;Z4k@RsRm(YLKzNgmzX`Vzsws!`d)fv!+P^N`;^ba4pV1Y+_lIf=%r
z2lngzG+t6|TsE<*q<0w|a6x0J?CIk)l1t#zkLQZU&UH^awsL<$jP0j^w>yEklXwFe
z1F>mzJq8oZ8h^<RX~1?2-jBbKY_OAhID(1M39?y5ZTroEPCfwKPz?>bSf;EXU>(ws
z_KZUQYB1QzCPXD}tMBOU>oR>_ui5Bn3k2Fcji%S@GrRh_JL<Q(JX&){pPKJIzZx28
z8jm)?HDO@yx`4OV<N@vq_yQi3v1h^B={3g7cDe$cT}EA~&WJQM2HZN#127rr?m%Nx
zL{@io23*_gclJ8dea9P4M+Z-w7>v$x(cprW!f-bjB-sd|yBd*p3Ht$hVMmbi(Zka3
z8G&{_B<UMTMk-l-*;oSW3H*;>F6IisXXDuDxpK}xKhN4;ukG@L>!a=6UT;ThcPQd{
z@r2gZviOxHq`94$RU~l2<6hI!{sCy`F%fW$t{v)ZhhNOs+_7qSt1IyFhx@m#>Hg#r
zDk1BVs1QW0X9JQ5FX?d<gIc5a5f_yDHiSE}A1sAXW7cly>O=P6TNcmuV-oG%+#_DO
zLXUgm`@pE17Qer4-1Alc?p5e_=cZ_3g(f!$M0(79uu4v*{L}-IU!RkGW1#GuTE;9Q
zqbr4F?0(eG=dZ7Cy*yA~PbuGSbYsmYQNo`ujo#ZD>Z(T%dlt7{bsZ@G<*m2`SG0C0
z0(?!+Rv8bxe``<f5~ECjLR;G^_EeJXA?z>X4)Dy6_295V;;5qUw+~076VWIw4pWq8
z8RhQ!<ei&0-}y-rAQY*Q6!?5JrQ#~DC|667NlMKcST1a@kgJL6tY!Q#4Cv&CUb|Mg
zpJOY^i`LT6okOi3eTs|9H|bcKcPkV(k`ZX$DLaeQ;g^r&d3#$mjOX;Fiv6AES){|C
zfsgK#lgBhsb4SgqLD&9QI?Y;G;b0G~B<72Z&|W^!B|BknTgulGsZw)reggsyp%4vF
zJQWd2$aSbuv3yk8$^Ke>hjb6tzIcO52lFS!g+Jn}^SE8Y+p%%S21wj@Km+0dFKIw#
z!1OW~7WDLVxuppHr16(f%XmwF$iG`3oISsybHQp5gu;7Oa>pp^h93h9JDbab|EC3W
z`8o%}K^FoYvGu4U(R(}(h5B2f{!kBSlKNic@21$<PV8*6TkdaIRxGfxMp?SRa-dz5
z<47pqx*}Z=j?XWNEFbC#Cw0zu)bCbEQ^<P0+aKy*{BgOz=TTIiTpFn}>NTC04F$LM
zAU?9C|0z`#+}e3bwb!-=KUfRN_@K4KOTlc)5x$r#a+Tbfmes3{MzQH)!#5G-?T)xk
z4-Bp8?To@8dkQJ)qn*8Lh6YZ%BJK??_csxI)f+v!3TceUM)pZ|3VdAZZK2`ya`~4E
z<%VTZE_ek;hrUh(1Az2ljRJrv9Wzn+`bhVHX8&lgFY0Hto^ekt^GExF99#kH&cz!W
zxeEY11PkDQUPLp&277U+!RuomIM&@i=KD46nf`0vSbz61x69?ZTkv~4_`QdVyYajD
z?T0);EP;B|6WAVGKkVcpQu_u`OwkxHb{F!{)R1^hSOU?lf-TtLt0IYxZcxr<l1O_-
zk<bgV-F^5^S<s+ZYyiTMW+CsQ!oO8|UtS~ktUoP}B*LvIf><w}iqao8K}UZGP0K%Q
z9Ma)=E^_X12<_#?<4c#|2FgZt2KLMQWOSE>w^P#=4EC7<ETL*B6PDfpL6$TSX&lDT
zD3dYi9I!6-4{RD!nu-Zf?Wt@qm7?UlA(&@W{3ko20!^j!S%IA`4c^F}b?f#-ybS{*
zut4o%L1~Ag_{@z}4JEh{VWlcKb@tqIX1JxZ2@h(DHh1C{8?Y+ViQ&5l_V0dJny$lG
zNHNlc)@wFK)XPH4#agLyJgqXA>L8#OFkVE6$^Im7#5c-F)Zw0?k%1L2)wmuxp5WfN
znqKgHfw!oy3c6k2*tzUghI)pBSn`P&-f{V2=N}lHa>iNV=NZ-oo+Kmu{)!oJA)oVt
zz7%-z6Hh+MzRx}YT7-4pOBP#*_R^#2WN_ZJ5gXn{*kku2p&~LadVkkpwAQX_T;1_U
zN>AU--}<IkUCxaAkwQ@z-1pm#t2*oJ=^Rd(dtY_A$|>&1&16mAgUkjsAwop@rWIRy
z8LJz5*a}CSOjPm|Z4bzqMb0!h-}Fo2aXa(GDN>gB1nHf>J?Hevvt$E+6K|L<!eL_Z
z%#s4i&OKcvD@Q3SBKbTJ4?)UMgNj&ODKX2~i7^PwW4^jzlP8Nnh~FPxDKXzi2CzE+
z;wwButlrPCN0l%yeM!@U;kF%RxtW$5!UM81+~#Fq7ohDpjALC|Eu!jcXq2imL`BR7
z+SW&Qt(=9NOl)m`pUrxjg25(s63bD~kco<_`!9X-rKYG;PnNN#r)4}7X@8`@4yfI^
zafHf-8G&G0$)zaJ7jT*GDtanEPNfbU44<sW9PMi03)hah-SgP2+Wg2zOl&|s?{<$}
z>*6kC$cf4vZI$Dgi!UBKeHt#)b?e9L;U*dx4;n%3=uvD|7aSi!Mg91C9VaM)OS&g2
zN1S4f<EO^QYm6uV0RQ+|_Aug1>M%<E$W{)~LT1JQVWEhV0nBdrwvo@XpQMOJFkpnz
zZW=Spet@7j00;U*FtjyNjMDd5!@gs6;l8@df{i`<F0O4p_9tV*#tp-(hxz)8*LCfD
z#r_W8(7;viYxHd27rV0S(7`RY4-FWx*bQIWygpjkzpiKd)cpg|{(Fz?oVw%EmyQ^L
zn7ykt*uHvob9mKy4`bW<*Y$R`c^m84U8A?}W3|Q+tQeAfd=c^2ClM_Jb4<vTjEn{y
zt_vBC(;exj+?3$}NAF2R|K_zSzX`2~4}^=Zuv-s#y&>Hlb~Q9NvlG__JokD6SY0od
z9@{!}VQ+n7y%&nI?prmyN<Q;Rtgm_jo(WISd7pU!8p!f~oL!+?l+}c??!gw8_v`dC
zs23O;iWPa1aoNxF_VQchk#L0qfzZk)Nc;AefkzMGJqK#GQ<i}9z3UjpmjdVz8BQVY
za*W|~9XO|&YM%N-{J<6VYVP%0wybw+-mq)?-o0%I`VM<EcYAktyIb>w5&M15%CEIz
zRMr@JFsyea=aOA|IH(%~x88H>XNRw9#*&hd5!$hT2ad>w(tOqP&i$;C*D8B{j}zI{
zQ<frVErcUGI1{WKYGqc|2`YDVWm6Ni)|^j_H<CkXF-*tCaT16)9Ww}YJ?#%&)gs(t
z)iIckn}s;3xy3<oa@^U<0HF^MjV2tS3;0gZ;^_~g<*J*6pevs*#HE4)gUGJ-BzVUQ
zU~!>nmkQM*NtNVRTHrzJ4-`_I8w<)Oo<MhC><vX-twy9JVzj!Vp;M?@+@O-Sus-Ma
zr0RM@-Ht$QZD2=T10vkex3hh6`KmgiOL9CPW_uvJxDcs|9CM+HVnX&`NHAnT!M_fM
zF5;i0n6jU=>l(-@Tx1Zt;c}2IRWt`B<X*=f1$jn5t=;7IZN}PGACec-TAfI<K`RJq
zHJ5k0SF0D17QblJys<jn9f|mweeScmXT%+9@oaPJfp7%o9x~D6->ZG?-9tJS;vp3h
zwS&=2^KK7z`gZ$j5BmK^#D8tjJF>5?+2=J}P0e@s+#??3GT-j@bvHJ1UDAZ_fhL@S
z6&Q2~JO@G*%@I&WT5(a$fGPqP`_kg=M5D1Mn;R_WUP>v$?r!MlfR06>f!MkUerhw^
z*4Mgty0s7ds3zR@s0tdy_-drX;G6%GlOyaZaHACx!O9(@23DLO>+3hMNJ51SOUaQs
zEyHrlQ)PfXzIZQ*04z_(W$?=pcdoG<>XVDVsjpu=8wkoBI6?t-Yak@SU|^z3gjfPk
zFM%&T?s6SBngWaOq6a)qv+!~Gz^emI&~=@;P5gWavE3)}mLM*)2w!T2>Il*!4XY&A
z%YROyaL1(j|F|h1>1y`0dD^dO$7TaKx?8<XT?XO~*Ypf!R^eaoYSZH~E~rs=(=*>`
zTno8e5f*WCb#&L0e*zML2B$?!3pN`tqlWwCs#O!D2HEy$TRp?QFzk@${){+J9r)V}
zKUGbipvAW|p)sAMIU8~cOrA(OSk@re;w7TayWN1>(b(>-6PK9nP3%-t`{JL~gyyW*
z7pSYH*ot5<;9LA|^Qu+s>hgq!?HJFaSRabmLp&ktX>>LqqwFo!C?u+F8ARSFe%p!K
zW2akMWKbMNroX?1oy74ws}HVTeeft9R(A;>mRfatDe#G%jLlG*gPB-=%i?L-e7vRq
z$r`6EE_mHxL)U%A<-TYng59~uZg3ha%RddhWEaMy5-5&%Bv|Y>A&Zz{l6z6<o&=G>
zt8t(w0D>pCQiEJY?n#oL*jaeTjf;FO#?F`d>UngqwYi5!n{))Ky3Gash3=rXrO)tv
zxXIWx1dB^&vwLI*q9I4GZrIs|oesJTx9`P4?~hvh8S5SC6UfzyIe|SDo)h9)+o~}#
zY}M`#T|0&#%B?D&7Oj(bzx<vsCXii)%qp;{NXd=@;bjc|M!meU6j{$y1E3XILzMND
zKj-RVK3}|A*SoRM<iN2TZa8)TIlj7eB2&Zw9@z4VS8SoPK7A;a8p2Pv&ibxs!=Ed(
zfZnY!eRl+ZcI!;*)&rkXOygN2Jv}2>0oSc*J<ZKM8l7_R|J#W5dpl;~KIwC;2ziK1
z!EQ@3o|w!ZQX7L=2$3OV?UEbhf~gQGL+|k|Kql1sw=nh`p%qq`GjzE$n&4`*qTN;B
zbT@r~dwgNeLrA(Kured+@^E9cr)^!6rfqqU9cdqqU|a4@BV9W8xT4tBRv<G`i1V;-
z@j+b7?s#Lnr$jo0ztPk@-7b#^99Z4C&UCeGJ}o~}tuGMqd34=tcCYCeY42SZ?b_7Q
zI_Rw%Xg0mN?(szezFPG`TWCXTzZdBZ23BL6xsKlP0g0cOi)Vl@w68E?BUb7;;zH8i
zRA;|WYT6)Th4$=5P%WYo_v~@Xdl1M-<rOLMAA5>s<;x_ll>3Jbg;w^@M8K%zHVv%0
z7rl!o)nd`*k301&3J#SrE$&$}fp<QK+bz4#1hNs+_~Srxs6t!nYPz6X(>iO|hLpie
zb;De-<`pumGCXhKHQ4oOUDu;ZAzY>%?ltuv>gHdt?(Ym(bJx0IJn~gb%3~YCnoS+c
zx)t4Eplxamf#aBnnB_zU%E~wZ-UU%;BX)v_us`orZimZy54F8H6uK@n8hUeEpcaXE
zun*MY2d}&3IyS#o?PGnYHh?ChXoDt)dM}fHi^X@Ki9^TXHG$7nK088d<Uy<C+mV(k
zjcSfkiUAQ=`}D2BjpKUIZJaUOL4ADVU|mGo4I=M;yZK<S<hy$8LCRSF;Mmo^QlyS}
zbzOv=I)Z00@Qm`aYM$vyPxX|icmmCNvVDIKIcH8m61cWzswP=esF}lKrR;(?a@M2M
z*2gMU#1e#sYGu<2`C^rO0XVND<PA$u<z8JI%$^7Y)E<&JH|*kAhe@STDvk9FY~UN|
z>kfw?xUR)U)PZam`>3qZ3>MDRVNdEIY${SOC%{hEeroxwRm{+~U_X+fF@&;dTX4;g
z%%QN~TzvHvuH6GVBuIEBy#0r132+Mj2qrY&fPas91*?$;#4Bhs5~%uE3rR<}eub=#
z%Hv0<`XlP7s-ttECM{4G+%)cvMCXEa0j(*tX*^22o$^EJ?6Z!%N>&=VS2xL2Q2fCZ
zXeKtwEG!lF{ul1oFWu`ss0*|AU~7B5|LdW?(a_fiF7Id!FK$!rM0S-a^n>2Lm+F4*
z4!5=+>HkJ(v@i5^e|>w~C5sO!2V%w6MY;iD<Y>*^us)Qz4um)5!9X4r`Ju|%mguSL
zs&hVGxo$ujc`GcJR5q10owDCh*HseYiu$&s$d#dTK<cu4G@Jz)hxq+^_4AV|YZ-D8
z*GS`vw4J?H7k4HJ2a@AbDUl0+!{e&zPCDV7mTFz4TFUkTgrLf_K$I#NyjvcyZ~Mez
zRrwL2N9DCq(pAF2pnQx|rB28rh1rXyas`lr_yfzpjt)B&CO<SSQ%+@x`PJx9UjDr-
za9W05&_eKv<q^omj$PyuV~7QGdE8^dg@rZZ$X1%$*M#LRC213TZJP=VWBtFWZ#{yw
z3#ZKEJ$;eZ#^a{Xc#VnI!3uHta|1QGL4ve)94B65(0T;YZOA_L5d6puN~2vCsVd5I
zQtqm-K`rT!UtU(w{He;$wMU7ceiA(8N{o#JIZD<?mljhj-l=3!TK_0TNc=iU$%*(N
zX6`W>Rr!Z{sbK6qfL<z?W93Pw0}IGmy$-Aa-r>MH_H;e!z=n*XQrCLGm*$*$FYb9C
zxE9k$yKk!bkOL#pNX-`=m?IMIcMdENd-N9v)@nxBS_jr^8rg&c8#P00&VfBOM?pi?
z{k=8)?2r3%*<$pfdCSgatZFzq8r@&8tgCYARN_4K(WgIg)GFAiTsAtoc9br5?mt?&
z<Hc6iD#VLcB09McwdZC=ipA;ZbRn0CQgbVv&PDTu+|AZhac#0#%x_({ZrZ6|JC(~|
zUgv7EHN~1J)&<PV-57E;8T>v~eH3&iin9f@v5+-5SCht0b%_gXAN^llaTHH1V84iz
z>@)RbE&i8#y1f5Xzp{@P<NB<;M;vc#q1RDFA1&Z$W7f>z`xo(d8s%wROXEOqs>a8{
zd!_+LvHq3L-Hhv}(0VQSCta0??ROpiPOoUc7Om(`Qht%MUYEyn4k7_Nqd?Rga{n-s
zxtN=In3wsGN+AHw5@dC(o`qPLH6Uy>!kSn!YhkSn9(iaUoh%AZ5AyP|UPvkZY=8~2
zRcr{#<{B1*p=cc&X6xAq+rT!mQMQSVvCV7?+X}|LUG6Hsi;c63*lt*w_Og9!Kf9P6
zV3)9i>{50aJH!sN%h?fj1-p`6#h%5U&5p9G*){B1b{)H(-N0^SH?d=Eg2mY+n_>xO
zv1vBLk}Sn;X0t2}>X~IZmS@LVf!VCcN^FkJvlDEA-NK&3p39!cZe`EMW}z=&x3SyV
z3)zd<i`gCQPWBRZ7kept8M~X^!~TZd%U;f2!CuK;#a_)`!(Pi?$6n9g!2XuKk-dq%
znY{&m==<1P+1uFL+265uuy?Zi*}K@^vv;%iu=ld}vG=nFu<QGS?1St>>>t>N*+<w%
z*+cAO>>t_3*~9D;jP{{LL>l`Ha;<%qeU5#eJ;MH({R{g7`y%@i`!f3qdzAev`zrf4
z_BHl(_6_z;_AT~p_8s=`?7Qqg*kkN_><s%p`vLn;_Cxj~_G9)F_EYvV_H*_N_Dl9F
z_G|VV(Ba>*-?9H<zh{47e`Np79>>bMKe0a}O!f)3$e!dioN>fXaSeGQ4J4s;aW^uF
zda;w3AAx7JJjm;KJrD6PZ{UqQ!kc(AZ{e-HjkjZ;(@q}cUA!CXe0zBx@8<)2kgwuH
zd^KOgV|*=N$KgWYBOLM;ALX0)7~jmdAREUvzMb#DPN}>2IKPPR=6m>FzK`$c7xM%B
z5`K_h$}i)G_+frIKf<rzSMsa)v-q?5QGPX+mR-xQ<Ja>W_>KG~evD7>IG^NGJi#qK
z&1ZO$r})i$mZy1!XL*k2`Eg$0HZSrLpX2lV1Yh8{@aOR7^5^ke`SbZn{sMj*zn#C3
zzlgt>-@)(XFX4Cbm-3hKyZJr*Z}`3Z<@^==mHbuw)%-R5wfuGb_52O|Z}}VfoA{gg
zTlgt{AAc)<8xke_9e)RZC%>P+3tO$do4<#@m%op{pFhAqz#rrv<R9Yyz(34C!avF%
z;veJx$Un{>=AYo7<fr+k_^0`2_&@Q_^3U<l^GEnU^MB!A;9ull;$P-p;g9lv<zMCh
z#=pkD&cDIG$-l+F&A-F{oqw1A2Y-xzkDuY+=Re^8$$!Xy#DC0x!hgzt#(&O#!GFnr
z#edCz!_V^H^560Q;=kvA;D6-*%^&Cg!~ewp%>Tll;EVi8QG<1%91(!nq#2%0Q@CK7
z_F$c;56gA~q81Ugb)sH`L>OBxH;Ra863sAQw~96-c<K<HA}YE>x9AbQqEGaT0W7Us
zC5FUmu|~v@TxA`+kL$&V*dR8FQL#ykiOt9YuvKgm+r<vCQ|uDs;v%tI>=Ap#KCxe1
zEDnfEU`W4ITqX{Q!{Tym3VemQQd}jTC7vygimSyn;#zT?xL({KZWK3(V`4(Y#iW=L
z31Nw8F(Z;9C2kh8A}ul^D{>+)j*Eh@MNyQ*oR}9U#Dcg*JV!iNJWt#TBm7D60&$zT
zUA$1dNW56wA?_3}5qF7~ikD&4%{}68#J%F>;uYeR;#K0+;x*#6;&tNn;tk?&#T&(&
z#GA!i#3^x~c&m7uc)R#J@ec7$ald$%_<Qkg@gDJB@jmf>@qqY%cu;&$d`SF*_^|kh
z_^5bDd`$eK__%mjd_sIuoED!FpBA4H|0F&uJ|{je9ufa6{zZI2d{KN!d|7-2F5iC@
zU&W?tUlU&!-w@vv-xA*z-x2>VzAOGiJSM&;&WP`eABg`HKNLR_KNdd`KNUX{KNr6c
zzZAa`zZSp2j?BLmzZ3r@elPwY{wV%iJTCr6{7L*-{6#z=7R8fV4FaY)qOmCpiJ_VB
zX24&gd9nP)4;8jn3u<**y%vINvH?4zMzkiaS!>Z+wKlC?>(Dy2sMe)*Ydu=8)~EGr
z1F$x%!YYE*+8QmUt;J4L!`ganMBAWk)JC;U+L*Q(TGUo;8`k*j&~|FOv~lesZMU{Z
z+pF!<_G=ew2eeDHgW9E7%X3IOtX-}h!D6W^wX3veY0uV<YFBI5XxD1jY1eBvXg6v%
zX~(n)Ev`*!Q(8i^YIr>1izgBV%eG^ax!i1}FcmK(jCkVal3n!2Q-#<xnj}en@x{}r
znQUyzg2Ynrs{*x3O)q#<DQ!&`y$UR(W|BpBJe{@*Gbzh<$1{_qbUZs{^~5t)AvG1x
z#%#J9>Sjvu87r2eyXFg4F_oP$&@h=R)Ztv(iY0QTN%=FMU0y?vt6$beKDBmPRVnW&
zm*=w%sQM{yF0XK@7Nxunps=9Dv$IKeJfFu?<LROqFH9v<bC&#_k1sUD3x(V~orz7Q
z3R6zk&aI`VG@jE|J}`7nbtzw`>Z)$JyhgpPe6-WXdE4qLdRtje<mhcBsNPnB&f7}3
zl=sQ=wtSOhbzx3jDbKXVZ99dJJZHty__eb5J~4X%P{ww}@l#0IvpinZ<HdB`*5bun
zrY>Geq;j!@Wlt4S`62{kJwA_F<(`aB&D#0+l;t9bs{E62IV)oJR6K2o$#@p0vWZwc
zl`tnU?zx1e;nzkx+d|uPj1ILZ&057u8BK~wG%2Dt3o$E`FE04XrRh>ST`45v>1kZ7
z+8r;&3%H=MTrMV|0K}YHTTEIBZPKy|o=M2(F&i(JM44`8-IB5jv8hsF&eA7Sn49Kg
zYH9%!!84i4=B6;usl%y4F`0-j#HQl2mLH9>30#y<=CWw6O8R6vmBmAHX?Z9D%1q*r
z#XIM+Gj2jljA9%EM^G+rWxWzApw23wQEt{nhfKUM>zmBY<90K0fKYovwif_&{I_g#
zvJg)J9d-K6)=d^t*7OtxHI}gwK*zMZQd2uw$i)*lSBw>N^Hu>@<Z>AtOLkIBmS$X&
zrBphBM*&Eufv5Op0Bmak{Vo-<=446E9JIwSl<ZWNXiPDcu{?MV4Noyw@X`0qCgKH}
zeSu10Drrs4E)_GWEbdV`n@^YQrIU0=+f_b^8-lRJW1t{~oS_PMUM4?N$d$4Qw997W
zlPS6&Z<o>-5D+?gxR-vNIp8LEs(??8W(82Fge3srKDaFi5pYU@5~ma>ajH(iHq9QQ
zSRhr)Y9#V5t5L|itg4i!r{Y1Sd@X=tYzdXAW5pP~Kl+0%TLq9^e72&cPbER6>v2ra
zPUe78v7!~nFrw3ysvtm+VM5$uobZv($@^Pzw*rA=P9-Lh<y7WVmQQ{WhtN3%MUb6>
zdS&ziAC9Vsouuq!F7I^;)F<4UPRybUPMg^*F$kwVAD_=U&5xHXi@Hb6G3{pQ#<m^8
zajuX9^I0OeE9+>8!z<c3-)8y6AbjO!^r0#Rx`DrZMqWw%kdFmt01HSbjHzS^%)sY#
zBbzHuB@I>3rc%X)+NpFZuf75<rI}nQ6Y+FciuzPKH-(o=1KI0vw5<dPg`|}jqk7bo
z5^@@)fr<)6WL3tBOR|RtQHx9tV?y}sSt>gq)rM3pz6Q|PjwP-5+yc5pSK1|YA-d!g
zg9<9;%a5eqm4G<tXc45tOT;(^>H*%Y5o}E5rl&0+NNxt@8C##qrLsV)T)LFOA{H1f
zG7uG1=N&kaOM^K2%BK>!BCy*zCn+qhmRHp}zcvPeE<rD3y-nAGg8&&*_7t_wgFn$(
zhz>T0LoPc7;tY9$NHzM9n@s^u6{)ubqo?@=Hyz{`MxRSlN$*qvL=fy8Q`I$9$mOZi
z8UZ>Ew(XHe!ZVv{=c#r+t4)<AfqpmuDN99HB3_IWo6<nCr(FpvlLI9wnqY&o#a!N(
z0Lo>6neo{|+?`0pXR`R}whP1;1Y=~_RKO;jLd7)rtpnvs1=R>sMFDo|gh#fL981*|
z=W`m>)hALjsbV}GE8<bPnL<3DMB4%^OF(<uNTlXK)AU5@mRlA?A{!Tp+>BQZv6RRB
z1TVy<L8HO{bXhVIxhx0`n&m(o<y>`3m>7}=-_XHIEs>i;4>2+{gMx`de42#0nDdbg
zRLtbEKK#tb^11W^3jRc)lmSt)taLt})u<Gp1LTdVS^3#qiBe{gzNMKcWoN8hR--Sa
zTVyNqpgNU@bP`K0fphg%cFszpZ;}Cm8lEWnmsQnPN{V*5%4Nd>`A*vg*obWv+*YZO
zlY`>3PT;fQ=`=0sQAo!#l8ibp9<Ht`7E)50^j1~YStsy%z+)1;ByTiS4NgD;$>MW?
z>dw;bo0iu&429Sf*lR3-DVe9P0hah80QskhRhK0ypDa!13M5{M>G%onG-w9+0o~nn
zU}M_Kfd6`@i3sD=%~sJhol@eiPXS8i^{4{Lb8ZO|;`<#aTPy$*)rC{`oT@H=e|04W
z!Aj#XxHaB5S179LR0blys!nC7IwYH<vU53!4vI`v*9B$4kyYqbR5zUp)rWjyx>h#J
z6TQoW8WrnQ8ML7+iUwp2=^pW7so-=%5k1v6LJ;R==W~UGM(yd{(-{3!o{DKYWDFcI
zc6bZS3}`OuY2g0&f~YEi2|`Aj2JYeN0=^JrS`_Uhu0RR&8ALw-Oaq1xudqOaT(VfQ
z;~D`v4)&}$4X&TG62583iTGAhCh|H3DHA#{Ne704H8LOjr*pX_8QFBEga)(oj0cDL
ztV9r9g8u1JHf!M(<;WY;r6QzKa|R++KAv#R#O+wVl*#KeaiEr%u`-?+DbGTTvh|r{
z&Ms;*sp(?f3_5JbAdX@LQuLMl%W6znvTWZ>IyVVynkGds;1rZvXnUNZor>!cbkF3{
z(^9H6XL9*cA)X0<tR+eg2FCOCGmwXA78ik}F^R*z>MC=lfcXZc1HW17RILOitvE=}
zJT7%t%3cY9PZo-HorHma_^>2X6>N1@)p*p|BB~WATc9g=L=nP!2?#1L4OUmp1IyK2
zR2%hVs@13&bu1r)M4h??-!Go_%kmrvdk}M7NxCHjL?amoGc4%%vE%i4=>)Ek5;?K>
zLNU0ks$?g8)fHY@NT+TgxlF?OIB1FkLUy!jRW0F<ltHS?BqXW(OZ<vSF6ytk0MgY`
z38YFE3XmaGBYP@k+c^xHthe*!6C|Ca^S~RtRW@PE%WUvU%Y|}jHWg2rNr>ge_zbQr
z#^z=BF%S#0@w7-L5+>f9bdmu5N-ay$S<g~Ywy>?DF2~3%kC+2RG)bo*5D+dWK@VZ%
zAev|-QzWkH$y7d{Gm}8sTw%eRM1QRXAj8zGmV^sQ$H%fQA21*<xh~DFO2J;p0E=AZ
zvY7;{ES4s54O~9{BpAEG4xmzCsU&sOr6MHuLeX6*<549jZ3%Fo8Oux-7Ur_4xGP24
zD2COQN&r(5psfH%nxW}#I<PLOt|5=Mp2|WkG@yS%Mswi@hMd9z0FW~XvR(kWGG8ay
zpw~-HX*YxY>NnHW2;7VpAl5Yx$vAE6vvEj20;A)?e<=eSv-B<F?pb_t$Yb$Lt%PX0
ztBI;n8K>t{;Pnu_U<yc(E=z#43VG9zi6M?jaZ|ybbi6c^l(+LK&_PI5pquLw#I*|`
z3Z(w!wKPOBJq?j8TT3*nid48O<v`j3b(1q*G8k1XNny~PPEA{w?9j^P{D8tcsS(dc
z5lSbq6BkMNa=aw~@+p5yX|%J80m%D`Vo4#uP2#_#zZyXy=m9g1x`!_&`>?W3>NhLe
zDg9<eC2T-x*r(=F<dXEIOEWWMUn2!l$D~P388~LGqB{ejK86>W)iRI;0-1Q8DEBNh
z5p=<`R9reEWr(FS1(O;9X>qH(90OYb!u@fVf)_s|zLW9JFJa8E+^iK#E-THccIq5@
z0ueg{uBU^u$I}Lmvnh1d!WYM+m<<wC0F%O(h(Y$j1Fa-lWq_O})QwyLgaWh?=&*q5
zqLnMSGbxDcP~Y-l2}$BWuc#!b!1BsqRZ&V<o}~%}+MpGdPjX=gL7ajj&bmC~$>b&-
zdCE{=0CNwN1GQ;-c&Ms453WjewJYjylkz3bDJdvWQ?EKD%PznrQnfTv2mKfdkc?Q~
zvWsa84@b8w+zI4M+NjXF1m2ROhD1pu83!}Tz)&;g$Gm{hGYk3(X~f7{B~U_l)|!Xa
zj^s`#yo$m40a@Vmpa$SfzT70q;|kXTP66UNp`Mw;EQ9fd=Q4VZ3~y$xR3xI~&7%{L
zTgj~CQ!ss4G{|E*qNRaf0BDCt!}bw}T|&a7Mf>w{2%kyx57z4wsE2Yv$__3r5Z!i>
zUPzK<06;N59Vf9oWoL9MT6iA4yVBfIg@S+)LeCTXGojYd7`y3r8tU$XtjVS(JyfGU
zvmo6E|EKVFXzO#p0xU}f6mn!c2FhVpqVr@jM`QU5T*P@0>nxewi%GAVk8-a099VLB
zr!YtAw57{4o_uO%26G6Apy!F`1~Dqo7IV`vd^H<W+%JbIh6ktS;^|sdN<fG!kg%)E
zvKL2~C8Z@rBN&I$3~FJvQ?&|KFG?qEGoO<NK?8bQI+oPv$DPkXJ;Jog2tcUH<y|!3
z5Nz?XWOI`w0nH$cql?p8z7SvV(}9pzviS&U?PbXv<59$vrQr;nr`}gxr%QR;gP@5i
zD}gt&Yn}KB*ac`JWPJJ3Ew|uiFkwwu#&Ky$t_3DQv#gkrT;;MGl3D_4<y6vhyo8Az
zBU<ZKpfVF2huoPp$)*A`s{uHX%b3urE!el|m#n`opiGW>uMyxWSTmseB-OhMRvt<<
zX0t9KoXUeL0&{RnAe?0tjY4V?HeZc6j7H{Hy^zCj7$i7BWC0=-El&ZA%b9%uJq76&
zA=VR+(~cBv7@)00P!*TtNauW6kksXQvjCAFkHdZPuzS;eOdH0gDTo2|O9HKCgESd7
zED5=^7az)+hYp0>po(zSZ==mr5=5SCG__8t%;|hiSt%?|K`JQ<cG;GdAVO|H+m<&?
z0@Ljv#6D<I7)R%2-IWTTg47u3vfzbkGz+dx-S+?kth8ibnnVUVkW2|Iwu@+)!+gL9
z7j4`KiZq>>jL+0o709jNuat>r8Yp1`<LZ)}opbT5mmU^Vx`bcCWqHLXOZZrF8c@NY
z1FY-vhj7reVT=No4BE3Wnq#zKUm}ygt;-5s5(2l4FeGWR^Vk{UBP2aJpp<ZAt!8In
zy}^M99#I9;f!%gCHxK1;*3xZEAlqx_@O@&iL0IWph>6s_d@?S@41ZO*gkH(Bl1IqX
zf$EA<-YtvLzAYhQJW%3fQ+4NZ4Jf~C0pW{&1vrRb`hH^0NE#5C@s1{bpnB48n#`$g
zn<xZv46pjmG4kNlIxtMaa%_$A94NxVUq>=>3^<9=1UUh*^~)NPr0GMrD<xGAa+!!j
z&G*TAlt{SKsDSfU9wVuoQ#g;3qc?j|C_<Dc?hhFDsf8FZLBJ%SN@|J+<?{1!`4%9I
zauVxAB7s9iD5)7VhL5QT+02^9vkECpw<M5zCP`;NLFePs7A8w+Qc*<%5Il}Sy9DAy
z&KgJbag{3s?6_VV2wYGX-~^c>Y|CTIevo&_wC7W#FZ$&#O%>2;JY*pYdmI#17oaIM
zVF8#nETgb!_+ZS$#Dl{RY*Uv7k36dJSD>kW?0~d3lDs8FL;*ET!+#rxyoHPHSprX_
zJh1c?lgjQO!60w}otLJV7-?U%)fEt9UAQF(NIdN&Lmt!y(#~)j!1Jj@G3mnXFt?!#
z5debA;R%GfOBw`7khJT__?3aKMxB$s!C-X_&8}q?G;2cDEr=WA+Lr3N<-S61)hWQR
zSd!p7pLIJh@J2!yrCAS69ZUhtOP};$m8B3_z*)#dk_Ko5>O~U7JjL7$dBVX&-DNQA
zD?+lujig9q7V$~R*N5L?5=Y3vxT=s!#bz)RrnJ;S7RE0O(-4vTfTrO=!kA$|b>iCD
zS{$>svg}a*yj5ko(oRw>wS_B`CbEG!n@=v_UeJeQWc9_&aY_(MiTnA~xlC>jr<_6_
zBT&lYKDf##f=Gir6pIVII2XXs6=2nk6TzEGy2?d2qzS74USYWaDJDxmu#_csWJ3#$
zPfbCw^EeeyVU1Fj6g1RGT?;*@8jwRnS^EMI`s8d?!|f^;oqEE@Y`j$T$a*Pgdle{A
z-KnPgsVm_Dam({$pY}-z<}Il9wqF(;o0X+brLs4J7bMgbbQ4oH$|Tf%l@1}Rz@l%C
zt9HaYRUd2%l&<K&lIf}I<it_^0s1*W%2Frg1k>oI@^%pRax*#=(L<oQ4M`Rfb1v(0
zO16IvLOpE$iq`t(EI4B^uSx9ir~;{Lx&*bkR6MQI!S+fEsv1?)z?y2zrNGnUwUA}g
zunm;m_+BO3N>nMs84`4ydYbMI2-oNw31$mc+&2d=1WX&!#9+?D?TMLX;1|f?hSA%S
z77`ug&ng7wCA}bFliZ(%DthLVa5%uEpDJkcaHHaoP59^lwOKkiYMnv`G%J(3#j2EH
z9HDZ+IS+P2)ZL3B=^-Q_&?zgX3_F-}o~5E|9+Ib&3FZq>9FqD87=agD3#3y(NN_<c
LjxC_0h&ug0dkOUI

literal 0
HcmV?d00001

diff --git a/docs/python_docs/themes/mx-theme/mxtheme/static/webfonts/fa-solid-900.woff b/docs/python_docs/themes/mx-theme/mxtheme/static/webfonts/fa-solid-900.woff
new file mode 100644
index 0000000000000000000000000000000000000000..b7d52cf24205fcc022587ae859a8269b9a59b8aa
GIT binary patch
literal 95436
zcmZ5_b95!o^Y*<-Zfx7w*mgFyZQIty+KsibZEkGmv$35GZfx_-_xI;J=TyzqbGoLh
z@l^NpcqvIr0)PMj0O<Z70O9Kn3Do{S`|I)lzogVO#J&o`e&PIIqE7mg%2!E^ne7V`
z|FXY-iKcLy+Oe5~iSrj01pwer0sy?OL!cvfD|;VH0Dw>(0Ay<cKm=$rz>IyjwlFaV
z0F@BG{8+z)3$4J$)cOnj!bHAovM-^85(Q;iJGgs)Vdh`K=>q^r2~}I~6ZTGKU*%N$
z007vdFDwO$^;?RAiMKNVs8{~wNAe}4P;o#AM-vB&FKp~99&G>s;^i&AXxiDy%^d(V
zAO!$$ssI4G$W9wx1Mh4zV{>EU9a9$LdNX5Jms>cKok<ynakB(dW0N32f&g0<D-F9~
zVld<g<U~V|4Kk@^XcPp1nja($_&?vbDW2fq)Zkzjd^3DV$WZ>2j|tN8nSs8(p}vX#
zg0P^9+e=w^{EeRs|MTAY1VRgrup>H(m`ElN0+4%==)T@@zV6edXt4}))w1Nl&qkTb
zMk#?lp?t{7hLK2it4>=$j%LGRULJ)g6GdGjO&2|eODEPui#DO`XY{!B%7nMoGIi;n
zJ3UV_{p$1Sw=HbA3|{xmb<2M3d2Cs3Sy}@hlNIWV?=K+HNvJ#XHhj=@91*?KS^d~l
z**dE$WAaUso<NudsS72LYAKVpakMdf!^cFCg}M(N5zWfin_+Jg#>EoA`-MUcN)EN@
z|IgTyMOYh`0VxfJKg6Ivc*oe8hX+l(zn#TMo3x9EfjA9TE*xu+V9?g&ygqP+#~!~G
zem%@!Aa{3jcg5M1M=%?&2ek)x3L!mWa^P`raktf3>gjtMjQ}b!l2U|>_f*sL`Ry}#
zPgvbf)vIn#xYM5BQ*dA#$#LFw^80OIY2F_Ab@?qTF#L0+?!oR2x_9vM;q?ve$A2V`
zWnGWapOblVU|FzIx^%k2vb>kvqx7TVqe8q)Z|(<2w@^24H)FS8H+{Eo080R609^oI
z09Sy}L=3#7M$w7NdAXyz+X9O%CTDb}L`@-=%6fUg0`CdheawsGd!guTg0(dVxi2|B
zhC)<sbg4LdAx`1*Y|_!YwMH|SE{;PKmjr)dT~YU}((Epek^o~aPA@uh%=S;=D6J8@
ze}AuWuH(GD-U7O4ws1Z%KM6n4Nq#B~^^RI4hm4oV%<fkfo2&9xBrYi6Q^zJ-%1rLJ
z78}g59%tXlzfiv?0mt+AgUzkk81NE*$>vIrP$ehnjLVG=n5*1qHc~!+)5)n;Sfa{G
zs!K8$UmpK6{x}{#9=z}D%JIy}oBFix?n?bV<1GUsW0EbLor<l5?SL(n?Vb&ZEtc&s
zTMhd;dk)(sTQ}P~J0u%Qj<6D@ass?742V+;-U=)`o^LWEU|rQd$M3D|W?`>bE5EFt
zLBIfOh?E984aw>E%A}7BF%w07!&CJ((Pm*NY<Kn0=Bp;ugtH5~AAruJXB=EIS+Cby
z;U~lp!cv?ZPi@~_&!nFmOx^8p_VcVgLqLfb<<$DP+gB5+V?O)&oYWIjcTDiK)RS{K
zVJu9}uT^RZo)GBmW4<9d+@J969lpHt`%I-Th;MR}nDc3LE0OGQQ>^{9q_6Y3eJG3d
zH&yqOOsm7L_HI_co!j>-X74*~4nF*{b~fHj{J#_9sj0J>+4`%s?KcMQq<ok;aR{UH
zd+B@2|Awz$uVcJ5-fX(4wumqP8&8G&Ory8@VAP_stzCGQs%>7@KQ3r@)tdV>YQK@b
zDL<SzG7mYNI5QVjtgRj!PmTQ?OMM?NIMe6NfJ&nuf0h^en<~cJuF9pxss<)$pW~co
z=huqRu6_H=`~18Wei~ehq?5I%V2<_RoHjHwFZ?fIxI-uzvLkR@r}u=9wzo5-voF=t
zLf^xH(*SaL!^8QpJ@_J!TYg!ZdsxAPeS?sDxx4ff>9JJrV5xn2Gu`peE0fjx4)PJ-
zQCx@c=JA;aQ!2NHh*pF8wR(m6v3iF3u=@N$O$ArQRz*)mV#i1swPl@E({jo}^}^Xg
zb~Yz>3r`D2%bAg#&q?T63tbD7pM$i+U!A<umz!P#e)sb?6uUpQPVPj~L~|Y-9s(Y0
zB@!%HB~!&yCI?CS71^mJRmQb)u2>TuYML3<YdT1cnU)@7Cn<}ZRbESd)df0(jgp7e
zDxa;ypMN+dm)m|+M|b2daZ;T=|ER&!X>$MjS2zDOwwl{*>0kE!l<?AAwZ*EP&r-};
z3!7bPt&;1~TD6Bx)<=WT<eIIVT1hfy0;_<;Qu~^|g5o&I4ZlJ%JDYB9$`V_a<}HIR
z1LU{eKl_T$iC$OS{_Mi1y6xB(n(d*LlhdwIe9BTbN}=tvn-iB4aJ&_2%Gd`58(nfS
zH>S%eIM-yd<Gg&#r_XF1GWcU2H>}*?NsCZqRpxn!_VDSkZ4L>(TlAOAN>!VVw22u`
zwrJ+Unz`T8v-jEY{8!Yq!{JY=Q_T0rLdOyWJ|v9A&#rayd(<0IxdkxKkS}3ellae*
zSnkVLmKC&%2DC4-#QQStG=jrLPbz`Gq~;dYlVoQ#HvZnH8MX1<=eyQ2{g9z=1HOwG
zO=z&G3YFq_CHE#VZ(udXKXyz~N>JEv2+9^&*Dxe|?Hf$FYPss!GP>!rwx{(vr&<)9
z6-ze9fR%+la_aIU1hnyICDap`N+H~7u?i=T(AP%o?irHYlP_+7-AoAWSdkkM!u!6i
z*+OZZKHTSpsTa1#19Hz|tBmW<5XVDh&&<9-b`zw&(T8&ancs0blH76Fe+rOt-|A%2
zVVRRV6pS9p_=en$)!cO=dIeb2pAK`oxcI1T&FNmXG9;GVl$p2Tk|Y?o0<lVj?sz0>
zDV)*|vL_9O1)njP<O7beXyvwNALk+^$I!gc-p7`2WBrk~`<8D~^+V`yHQso;sj0l7
zg&C<VdgK$X7b5gJ2NvY<Cx&Y=?#uM_foqD~S5&IMGUw+xGIBlke`n4K-o*(hnA!Xi
zIIWZwdd|*s`*Q^sryrO;&ADS%OGX0Te$t{H`!mU-HN02|%~W@m-{H=_SoESU#k8+g
zlAuqWb>vVvz<leUJBsiG7Z{eezblYQ0!F)lXrII?5amd?C<DH6ksKO5!3NM-m^TT~
zsQGkl?8_?CS5k78)2EibHA`9E#0$i!QSR&O1jcToGB*@cB`N2Yt<4R2xEL64EAD$}
zO)q*4Z-(E5YOd+%y1mLG1gg>5NH9%r-*~+Ve)fTm7(3!7=CIVWYm3qK6PORXn!|+l
zJ-br>Tra<|e~yF0OER020&jkurH8t%8JzN|DO5`297*`*md+z)$}}B;G8L@a@w~tp
za~eWP-L>sPsp>^j3*jB}n;a?FvsvyziYN8YFzH}d{Vc27pKl%dbXu|wHZ%?;q4H_V
z=J9Wo(RTazou0a%uQNo=xm+{I@U({}0x2Yi4FZz2X#^!{cj>Pd)m@|r`}LhG1}B+q
zxvNC`d|FefUdx>X`XH_G%_B$G#pbewnOfCI-MRCHWZzlwOp&@d{Fh%n1y0Af@A8pH
zEbn4cN8gBOlx(K{O0<KvN4%V!E{4^fi42GJp4cC<(P+qZlH`~Y|EbDWN=qFxLLQjl
zk)w<&`>0EfV`k9TO=zx?ukXu&Hx4eimeZKZOx1(w=3^Iruc<nnNTJNS-Jv-YF(0|D
z$@I>_P0Vb&Xa2le)Q^#K!(*CUb^+CnrMA&&?c(c461d7;jODZ=cpV7&3qKxoJ}LHO
zUG4_@Ge3fZ{Yg-EMBf0yW7AjWxg*Ti`hoGnhw<K2PCH6IM0H?6|AZg^iu_|fcSKy}
zh}*?#6HgDI&(}RlayOXb$6zy<m?tJ1rMHq%EW)dkl=kVLb$b$>4)}TL!7X+;9^9P|
zwUI&<*ab1}1TAHbcPvOnyLYe(h1*BWhJyspPVM@-6KXdVgvmwdZ2}kQTGDyO2x=G|
z>09|8&C#Cofu8;d+=qJYFv3F^K6uRFM;}n#;d^#km#Vg`9B&21-{Okq#P#Ax0Vbs}
zf<{%%N!{nI16Q5e47=ltV(f*uh#4MyrO6}6muTcU+Lz?18D6Gwk9I+n-7;?o;pzEj
zkl|R}mCQIZjtpA6QpC8~P11kKk2!4{wS{(j7tWhDwB2%-v#!2*53T`mC6$UCf(OJt
z5@qSz`gUHOo{iO?=MATyeR*#W@r|cfMjHkX?r<A26rq?i-$>0kN`uO*{{t1)q3uI!
z>Ft5~7C8=qyd|v6V<h;u!HTGu`!JcM*J26m_y|G2mb6j#Wnytco0q)dj_ySJOGK(T
z4WaoGm6dU=8%D_U)!;!A!RWEW!P|4%5c`U;RHA_k+VI|yBX&W(v)cGima)qa!c`53
zZJPLXkjr9bI?jJ=8DRK`RFsL!J4Q}L1DCuZkIuwJK{K>5Au%FEoSG0h)!)hyIkfj;
z^XIWa+`!dWO<<swnMMKzepRrd1*SSo<Esv&yJ}HBb;9Plu88NS9pvSlHq6jN8&m!H
z4?*V#pA$&txfRrv+H36k?LttjNSP=+KIE#CSY~MT@(RStg^wI*=aMEV62}(2>0<%s
zQM05dfw5nAYhSvZJxrtM-nnt_5W-rhVgaT*KsUdEA}ZiPu`g}VD@d^l(;d38X&yAx
zvxMcIR+3L}q%J3_MA|9b9n7%&0??_w2Q}(<VvM=oG9}31Uqa`^(jLJS-}*bFM4g{@
z_q9&H2Q$E2Lbl%W0aj}LNUx}4LxUJ`E?}67uSJdd(?f&Lt5JXrub-$1-P6i5yg4C}
z!Cbb4(0rY0EQIbcWjkp3&-hY7D;~d~8hmfQIb*~Y1u>T8LIptN8#5#XY`NqsfiML?
zFa9F^-uZv=X@aX~dGPj`W9R#HYBG_hE`||}Usp3oJtHhbS9r4!rfmNc$gks=f@zp;
z1*~lSAWPW1)e1hl?SZyme&MS>?<eSsx6y~Pf4!qVnnwaT0d@ZBA)4ob9rCL<dLZq6
zI-s%-4pkG3IG7+?PkmADY@$OBL^N-gf}7UriSKk`LXbr0IUJ!{Z#&`cWPXPH6?u3R
zhWx4vG*oLxZ=QGUAfh=a1hZ9fjbOYEW)?lOTkkh0aDj<B*izi-#`g+t%Dg3tTCBPZ
zK4X6an7^M&k#^$v1)UL}gLJE0NTSZS15wv6AxSl>P(l<~`pKNNYy}YQInQtt7Tb+P
z53S#j^jTv^MXMvS0j+i@eL5&`qGv+hsOzB|2{}cu)~O@H!ROr{5RDx!_$$8e+kLHn
ztb(c^@<6S8F35Ly+9cE>c@ebcGhkDA&>dWc7|ctxyb1hWMaF=B{`54aWw4Np+HZop
zjLZRqAouM-fj(d6fiBXHOB12`KM2fFvu`M0__8+Y-FWuE&)~bWa0!vS>IjM87Mk$k
zp!<IV^g%5s;lshg3)&FcuNK~%N6A9rRKXm7231LQw+H9?Iynsyih1$VfVvgee9jNP
z7NX54;RiwYh>-`u=el(;4a;3X@o7nI;L1ZCdDKH!JzM?O<&~uI-_Uya8A$vp2s<Ri
zs$lC+SUW&>aRYV2(ojA2{}flkp|&>4n2=R~b1OGeQ1fRvQZS>JBd~SD2Ff(Aj5TV?
z$wbZg?{GcUjCyR1$XSP8sQkhTwDhUs3bwSJ{R*<QpS(B!3<mx>q+YEDV78s|47S*c
z@(ijlfT;^I`{AjcJt{Y`9)agP)r8wP$7kT8FHK5Nq`4<jQ1nZ6|4#+{&kPW4E{*gG
z(H7bO6<YCk5l|MnAl4t;XdCl0D+Oy`#KJTPWB|;;#^edzpB(w4oI)tYoK)G!QxBHZ
z^#@xBq-$~mIA4UB29<S_X2aMozjM_;Twf6x2N1)i16DnlLB0}uY^eMslfhq=T|(Lf
zplF4xfIo4i!ST*mV`#Be{W|BmFvWtT`Y85Rc98Wssj+>0>|t3!=l?lCx3a(E)i1p{
zBh)WO*YtUQv;x<K>|vI2aV`LJ&!lg_p+HZR`l)ZbLOW%g7cdR%9sqHx#4FOM&FQ?a
zSw;fU$nJu>GBv@xV?P}a;GFly9GW_EL9NQ6>09OHfh)eX{tZ~={lLgx-02NE(^U$6
zue;ja?$ZQM2Wi*lg61-W1z?JotIin-js?JY(s_IS@YTal`a5T@IwHEv{*I(MWdVky
zf7$a;h75aI5^^z&prohcnW0TDJwQA)@jX0I-6N@_z9a!~J7A79s4~BQa=_EAXS<+#
z!sodi_lJk&kfvn@NJYDnUSw@Bv=Mgp7LF0J_GK(HN;P{}FR`{2!Vk!(2Vo<i_GwNX
zMB#079sE>}CQ|m2yAjct(02vr)*me*97X|<q&Zjt-$`p0Kd?#9fjwvm4}fi4={(^8
z4AgV99@_e;i5|SDDd-;F|0z&ZuHZJlv|XeVq<LO1QN0l4%X;v={?kLBu9rWIan5pp
zlyS{x4@SMvpC|sH7OAJHKHb{@9OE2<04?L1*dBuVCA@8->?MtDNO8Mn64<F5JC=k8
zO<}09p0AWCRQ6mT@}dtWfo?q|g08k&L4jFfKa&ES;yq9V3oSk&Mjb4eqt@sI(nTGd
zaG<`f-S+8nzsaMX8wDcsOkVqlta-ir=gZ>b1RYiH;6SVjbwil@ywI&UwZGYk=<dCd
zqxwyA2e(kaq69N)X#v*TKXL7!Zmas?gN-1krY3xi{qm6dUaAlN1X-(G>xr7iu8X)w
zJmh_)+II+XE3r?isPlPG(j31xWYo#ioE-)u&)}wj6{M*f3+DPIg<ceKu<8$}?3^64
zowGAS7^@aACJ#S9S=0mK<rnjxam~|4pu}IJJ`u$Is)S(*!E}s$9hWvRg^wIL5VO2&
zISAsHW_&1DfADW1?3fk7kV}TyQz+GWj0RunMXZA%VM^c_|En2uoWW<WIl;|`rJaD|
zma<L=o4*QPkd4b1!0HyJO#*E{%?{@JfRR}dKE2{mfQ@5`5zNwBq9H;xZ{i20wi|gj
z@snL_R&Yyi=^voow44)+P@M}-)I-r-AZks`aVV*;=bV(ti;ch+r#S=ghXOAcqTV-r
zP~QzxK#G2n>ZsWlTIwL_N7hRC;(NYI<YG>yN>K5wjyi$#EpH`4ag%{Nd%~go0;=@1
z<pQkqEuAeyF}u+MqIK(trpQZs5m(>CduyLANzp^!LuQWXU616ZsL<RBr1-SV0z&pf
zXgSJCZwt;nXs$c<1-N>;iBRl=aSzkD+=C)*bzluhSX#9vO<2meMoq{KKN2O)X(;R$
z;d>PS-WSM9f&_Z`zUOQ#NHI&%w^mk!*4Mfn4_r4CM7DqY2ltf`tvRBEn5E1_IKV|N
zqz`;b!PZ-nd!qL7G(pyRKXZYz9V7>+HIcKJs5RZQ>Ir!qijcFuXo4tH7tij_YrREy
zJ7)n_!A+08!0gSuXOT6jA}SHzz{1DiW^5_?;ATfjlF)ZmGF?#dJ-seuwIH)Dg8geZ
zvi7TKGf*4+#~P#leAP3dit_?6>m&4nKQz_vMVdyj9cuj<8whAvenT6obwY`HXt!pL
zdiZw);HlxARoXe*B7(58I$+@mc!*XEoquM2A;^9>Izr_6ic`lqO&Hv#W0(Wu74U#<
z;l>9ZMpOiMY4_P6#S`&#Kk$4Fm~S>hc5V(2LiSP=4>wQE<TYrgId{mX@AwTH2YL>C
z)drg|rRSnT+C9>Sn~=Ny1Wy{!`s*j8P27kU#IZbTI$-XV$Ou&2&Cr8?L=a0A+<ZAy
z64Wd;vJ9|Mh?9dn?ipQ%_)-=@?uIdSkaI5t>7Y5Q+&035hqX3=l|d<g!GtO3Yoxwb
zC9I(H4tfYX;aGa$c4-4!eQx-Zr181ZwL)L3u*O$f8_@{9=cTHJt_G9UB3C!b=|iqO
zlu_3QOzHOF#kPo^eJleTy2=quAG?U74#Wab1DJj~K{xm<0)`$s$)W<b^uEX%M-aT?
z!3jO<i_Z<5b;IWd&352%L(ckeJ0cf?`5b{a<s)gNMI5aVvzb`i@N8OUcBqqiT%>EA
zd+Q?hb!#Ekd2#E(M~FDvUyD?kVA~|8C%~$17A?D5l7{q^-wU7<=LOxUe*|Iv*hT-f
z*K!nT<=zZF<Macp_S{3w5o13>G`=4r6*u{Az(~KEZQzX6o+^^&sO@zIxd+BxLeKed
zT)-4hM_dbvyvXBkBbf8flSrSc_>&|oRd@sHbMAxtJegpCdX5_?_TWpPF~1uM#5ykl
zqCMCNw*Gwmd!J`S1jIU?@7KaK#9}<zz1!!BObl!#Bmvs{@S(5V9H|(0<`o5<_XtAU
zKc2f7cRDEn)&qs%?3cOmR~`Zp>(3vxjXP2Hf*daMAX?vvQS2X0AnMQG#f{frW%^o2
zl_1vxyrEYv2Ald?ACrR}ddvRj4r4FOjkWS|!D0M!KG<lE2XbX<L4N0vV+vwvi?A19
z-_A{-{p$5bUjMM;ja)zV_ge0Ye0%MEe2_!8K`rqk%#f%41k8Y?Ej(tZ*338f`l-=t
zyFNa<;X24uMC@LmDW?xw)P2=3>C%ra=mx`U<fz4aDpJkm^pKV30Gy$d12)vE^vhuF
zcW;s**DFZW$$l)-)=`q+6`>lahEqe3X`UBnRQCf7>5Dyj9k5~f6>})iktphY^Pbem
zW&==otI&lu^m0gzdYY9b>V6UZC+H{}%?UKSO1=d>br?R~uM__Yk$oR|zEkaa0kFEa
zr&;;DrQQOWdzf)2<Xm3{SC3o(8<$sM%<pdi35!8_LDkT2h~}I-IHc>JW+I*uih%dh
zYcJA(i`Z$vdQUEBNl+FZ!fsQJ2{g->hY6a+Ptpe!f_Z7-3LhydU<)sE)FEcO6zD>&
z1F1RtY!s<I`fTzPPD1F58^~6AXA(G|=kVxlpo;@IY~YGLDB6*vZ-s1Nig^>8Vdh?Q
z+yQd|N_-H<ZWeQR+I~iJklKDea~LaIz0CCwYx_*bubp0ycHN3EK)ZL@7syjynF~n6
zS{FclZn8OQ{h`|pp#Cs`4Rb|koy9n(B}q!;PKOx}XzF+KE$SiD95ZUlVI1SDcZ)>r
zKSzR&-V%R-W_8H$U`+jZfkU+}U)$7BbM&vyW<XuPT_yF+O~w&D(>)A2YLdM{k2>^i
z1Z3y_G$-T9=}(f_Idkg)O<mlgJFlrFVTte&P>}#v1&xrt+NFbFzTAu~{i@K2Hg+*d
zO<JQit|j7In`9uO8<|`e+`L7-4e?%a?MEsoJRbi=m83J6R(2P_%FPRE{lT7z@oTnJ
z@Hyc-yuIHKbnQIeWVJqB;befepAtI6`iGD4Inua|v0p4Z=`&|8O#3&#u5oJJpKQ+}
z98&Dhq8wm0kcd0=7$(H*YkE7P_^W0qOVpuTDHKmm6?;ja4a!6csLX>*4LSQzuaul8
zhloA3@0j6t1!QqIjT(-$pHeAt!b7+`B&ncUhNQ>?UMBFk$-0!Ye(E~oq3<Q=_c2tV
zn|djI)YL|XBdPBpJFmD^sys95=DPe3)NZ8=AgQhv`}e+8KOv}#hfj#*)WYkpe_7St
zaM^@XKHs($weDFr*#!mJ1s|sx*qhQlwfL81tIQ}@8xtIKgv=3OtyzW4L!;~AS{sn;
zNR1t-P@Q?QsbGFTPVb_M$-{cb_LF^%ehO0Urc0qiOv*`T8;Lm^!nzn<oqfJm`ufIg
zrkabaRn*p2)^_&1oOH@6Lp{;Jn}Z0|#$Xk8Wks!*?F?mNsjzipZ9BD9_Y@gan3<Y0
z6^rOn<raBWOSv+rOHKkC;~5gehj3-e&ws;<a$=!#=-z|sR;^9c-x9Bn%px2{?O)R_
zTp6-r6E&PaXI0z_iI$>jc`}(=ixbk8#8l-Leta)OtJL0oTTmCm9Kd;RumttSuiC9@
zd*vLcL|Js6R-(t#SC#~Xo$$R!S&FJ^<gBq%#hU@R6wt$BId3R+Fg#%ycgsl=!j}x^
z$`z9>i*$*t*9}!sOkaA|#K=oc{REcU=%BtAp(C90as2X^EKrXYO<mGX<=e3!+Yn5z
z)g)Lv;b(kj@6ec}yGW@rw<{J&g$tkB!|CEb)Dem71icljyyseGW@a(i=+e@b&qR1M
zeAf)GPr$Sc6CA)Am==+fl$7K(GFT5ax!UUL>%=|A)-J05(|NhoNP8ONuBqDUF-F(?
zb(RcquD`WWk52t7pS?F6rL3g>dURiS92}N_8mfagnH(Na%ql0#b)6k95WOnS?+#wS
z5E#k^V=JC*_?Vt2eEJ?ueUe*w=C&OVDRQB31w!5K)Uco&hDjWW7N~}*<WeyHhMtLK
zjjxQR8p!r;FFurZ(!8S%TR1h~HgrsSHSC$^ILfj0B(K_%L2?66-Y2|IvGbf72lWfy
zD)Qf1T&;8jKE7lb?w&YtD4fC1`xjmG3`F6$cl<i;qa7OOC~dmG$Pfdb(0%Xx%&bn_
z4!R}|eYuEih(n2w|F?`}#j><W<Wu+LpnX%uud{T_mj<Kw$l4gQ#Z=W7l&8NWSD1_<
zpO2G6^8}fLRB-ueyG;+}5$IY!g$w{34MHt^5BJsRMp$SUyb;p=GsdMsT=`KI%GYal
z%2#Kv0akeb<uM#M(m{Xy@%3fya032!rBB9EO8`-Y+qQXsz9s^#isbpQ4r%l_jx)D>
zA#F5|v$9+jsG;35<v8D;cShsWpHt%?(0dKF@GP^N@7(U3bH%ocy=xWj7Y0)>t%Kz|
z-|1WMXN{p@9Cv)LXMRmdo1IescbkZvVEmr|=^dL#0rN#yyaku}tX!Gy-zvXgtT+7m
zk`mcsUImO6Q-nPo?$hvn6ZUF7pvcw+(@u3!iY{I51c}4m?PgUs3g5FL(Biwe(!Bq3
z?F93PYahm^7W{$u;U4iXKOSr2bO;7GvKZ#GG|CN;P0vl>X71gqN8a{_`*h=gT@=n2
z)X5LvJ|O<@+Uu6Ee{%>4Jw3}qNdTk4snLwRJC>!<+usEgc*_$@LuO8%)oqlBjpuGO
zqzSV%>V1~m#Lmsw%Lm<l6@b>5p{<|Ym!}BK&FvS*SOrT8TWwu%d!MzlbN8>U961Uw
z_GG*j%%C!1ZIN~%94<2yAiyyfq~V|kwWipqgAhyC)|f9S&NXw()k|vnlCN%9dXE^I
z_5V^R&TL7Yl-N=gL`cADS-}-MR0Ws8-@1?l#~X7kRx(P*ka#<cMWv}TI02i&ep$lN
zO3Cgv*d$BZ^NamGzMDi*(aTIU_joL3)0snWEg!C)3&TsbpZ_^}_z#-%9~Q4|*dC=L
zfr<{Yjc(LN%Q1o=*HgHVzWjq#{qN~}q4f8+C~*m@<Yrac$;lH59Tg*a#&&P_mly`D
z94HTcCA9M}ikP%Qd;r`raPSw{tW=RRz|u{1l0drB+MMTda>Yu0lo0hG%taRi1wuf?
z8h}gVIS29ZZ9T23gr&b5H_{wHv<@@xoYv==bM>zu87DHZ4#{7_m%l}WrE0ItD8RSN
z7Mnw(U7W304pW1EeZJ)xy3jiI%=`7J_L|{GMxDa?K_FjJa7l*e=0VP)E7pf+1Q|5>
zKudF3V4{g#OaqIi9;TPVFGgH=gy+JO61(oO;-Kv^g9uY6dwT&X<b+5-az@z0QYBK5
z0fHF|WWUT%{y(bU97tPH_;xag<k(w}s3pX)vj-N=0<t894TvbJMzbiop(F~*@}PKJ
zr~pSb*nevE_{3CoJ03d+X0yc1BomIx%nblE<@kIyTl;r%*U$OCsT20KFV;kBDL9>Z
zP((>lEun{MTQJ%MKPv_=_0^3W@PFn%i(2R+wB#S<f3Gar?k?vtT+mL4MLLQ^&M^Lc
zuGwVrGuUX0oa9Vt7jG#~FW!3BG{TlAk5S6v7GOWS8Q6(@<=L)xe}<hS&9&SR3HDDF
zkRyh9>qWdGRCHp>|LA88;#xy|B4-O4RbfUO4Zr-04xI}`rdatSlf@P^W8dtRCI@IO
zzy_S<oswV%P7@YY+RyD((jCW0!!oQ%ombH6#j0`&2=Zf<a`>+P2KE=0A0@%leovAm
zz9bRKZSeTc!Kfi0V=0Ygffh9CM_!;To=k)vJ6yPsn8$X9c=nJ~7$GlRrb{inSKTk5
zjX@_y<aMf<OyayFJs0ULD83aL=1d1dtx^T}*E1$Z6Q$10?Utxozz{aYTC9J=ePHwc
z!I~JiGFwh0Q^&|zhLSc3Q<f#i@L(eU#BwX7IYBcz*P!jokikrr{ere*V+7r%kDker
zG!twH5fh>&SS|ZeuCWeVu7?V8M56E$y=R5MqA0Vi2$O7?dAbXZvcqEFb~W}DfFg@l
zXXNF~m5o)3T{Rp+`dN6!h5`)g%u5?REG=Ws+IFzcZ1DSNJU(u3U`th~uF+{w4_}#e
z!O1h>{@2j--tjFV9-$*)AXWd8DB`Cfir%ibyc?zxa~rk}kfW+eHgz8My`L=Hanu#K
z7Y>>5(;AOfKUf8y&&Z2W-0G<7;D!|grSCTuh({lLQgIef$f-qpw*OcTpVlBd-aQ=F
z5@w8(RQ9Z59=v)jH*`y(b5YCopHx{q8TK9S%T8+bbHPr%@7K_eF2NugfZ_&Ox(Qg$
zZ;L}-i%|G^Z(!c@lfW<Rt*?J4IldMHuFg(%jm}?mMB&JUxn4a?)f-eYUbMT*qOVeu
zsv#ly$~emZRrVU))6k-|PTDsZ!Lh}qwy6<U4_WI3(sqegAph)-9Gxi({1cy2vPCQ7
zRasr#&)K2zllR%KyvuWj<(sMv@-D@P?84M`NXxk`yZ+L`*za(^!4v{<fN$3H&PtA;
zDFND`kl5Zr@f{dw`XK-^^3UHn!TJ$2d5BWm7lj)@AKr`W9j@Ski$B>egl7)`9xa;H
z=l!8Kz8+ZI(2p!&C8p@NEgTF8@@W6fhZy(rkcwL|IMa4{32ETe=3gEEFpm$v;l<sf
zAf%)jPWWF$umeGi{4caono-{RjKuhU<Y%g<8#({vuAlvvoz`bR`~<G#?7vgp5wk@c
zc=WoVSgiA%jT3BB+3izQ{|c_bt!OntPe1}d(){zeNou9-U5>jPc+O4TMb2kEE0%yr
zg128$qI{8{<oH(jO2iQIOB8KC7$KzrIpE%fp-3<&Q-s&K(9K`_a;Ly9%RcZxz`|_<
z<oU^Kf=DY0yBLBd)dtN@{u_?YXAtEKw>x*h=s)7Cq@lBFlOmkJR@SFEyFrYBN0T4n
zY*%+(GkH2f=d8c47kgK@ss{CNrbb`jRxt7~k?%+G<1a0SV&8(NF?dF}C+#zDiH9hD
z5IIL1dQGA8yo{dK6OP?zDDP`12fkabFV+^`(fw_4s?EZlZH<+IVpg_Dlch-G&9>)|
zL{%?;#lQPh6I&=l&%ri~bvKIWYQg65ja1L)`ks*B+rP|U{N0-?{xZzw*RP*15V@GV
zvLR2H=Pv9JN+q+|Sauq?Ouz4mC*>+sE(sFN%`bKc+dThMoHB~-5qmZ6G@f&@J;W%G
zrcBWrQ{mk|p4wQXe~Wq|{viFKcvx$b;bQwcrwY!0_5EP4Wba4VHYWHm=1P9$f#$F_
z*NW_1tiV9N-E#F;DY}m|(z9&@`c3u*6z1FX+gaNH$q!1|pMMO|Qe(yS72nRN*!Hhv
z#<Hf%IBx`TFRAC6f%5+)>a57DF}5cE>Q^lMw*<1HRpeZsj^1di@PQK#uiDqU45+L9
zR*OErMTNFroynEBRWeK}KKm!W<93C0cu}sS(D1RU)FG?aH>a|%s>&W?s|YMCB}Ai7
zoh{NqGI(%`5LklNzR#~RfN-T3*OOvwU1cG3B%Krtb)SR4(+W_cj9B|~;(Qj#^|C%#
zP~_*4fp4q6KC9d2XtLXpRVP<EpvXrVt*YAmogb5tPvck}pS^)rP9bch_V>3`___ca
z%0s?fjK%w3y^-?eE`s7zRDYZ|6_y#*CsV^4%yr2#@q4)Um8n!Z-7UejVS6e*QVyR5
zBU8f{?QMF}ZPC%zJahodn7t<Gy&_oZ?C3c$Aj=};0PC*r(1S)te6y7d<1QJ#!s(1@
z_V|68R!!<{v4Ns9<+__>y9QFwkMlH+Hm44W5g+=b08UoS=?PaZV)XSC+nIL!oJdqy
zlxR*~KM2U3yob-Vf(sJG*7fB5dOR-iS#4(K)D@G$<~wz-%{X%Gw=?sp^5*WC#H_BE
z4nv{K_Cl3w>h1KbJ37CeSg|9){g{N!+_PGR)^lZXZZRYSG6i9J=MMT=h6k9v!JTJQ
z#T5|CiR>=)Zt4Khz~%t_9Sku|JwoMKwP=<1RS@!yV_`Q2A+CpatB!2RL}^Hw<~m)D
zMYVKGchGSa=TV$$OT@J^z+A23FUERtgfp3vZMI{WdoQs%DamAIqlIrch$dqzU6Bs~
zG~JBF`LlnPXgr%FTkaxC%=ytF$?|t8rBrv44Sps*Le)(-N2OHZY!q=xuoJ5h!=VJN
z7Q<n&#rV$&bn5tjWwtz}wj>7tbEFXgJOqh_XmPfT-^vsP+H6L(Ce|v+RK#@Sx@clv
z6||!>W~^c)i%2vUl@05rak3RBLml*j+oS&yrMvVkFW9o3O<Cn%72gy&)g<x+7Qw*D
zAUKO%LSNJh@`HDBN8lKwCy!Ra@soZceyvO#8U<3%1*=kLWVh>o$IExkOrPBA4@njA
zkKQ?BFXtbxOe!SH#RlXWZfsP#-;?jMWk2(II}ze_J1w(*WKK(wbC({fmA!0iouLpf
zHcM%IPrUWnYzvgip^K+$4t5WT1#|aEZ;AC#4gup|eRjK*{U|2~9JL^gwVc>RU}hvL
zfDFw*zJIJs-?fhzn~h6JaXKGw|BTUz75C@GPd?$@{rHH;5Lr3-q!js)KZZ|}m|j^&
zT1cs~3IA6%VynD7Zo#?Gr|@qCLyl}x%9vD6#C~~dgyRMby%hUE(l`3jYFcNZGzpbi
zbG1YXTKjKkL?L@u9ON-*k<LW9>(pgwyO!U5RZ%R2=Y*CbdNGu7(30@1IP|J3lUhv)
zdCdBxl!x0I3T13Hntx0w1LP&^n^DtBO>L-|6*$S!6Bl>q)H6g}Q?;gsCMvYsQ8k$$
zbZA+lE9p`L38orHad3DAF|)C5@^@JHzBfeL+_$Z|V;ozvC(56>Zzp>S7oU>2&hOXC
zm$*u*pvRP)t-~ma^@FeYbzFq4i(^qbQW4SEQZKm1eJ?+FEg+XhLPL<~A#K8gba;@s
zNK6e~jyRi3%L->xda&8x;NJ4|7l+Cv_%ziQG-JD1-(uvHOuVPzbwZ!BpxWB{p1Amd
z??cg}mikj=zKdOrY`q%dO-m2ftHGO;JiUJhbc_3C;Ohyk!WtByD7dTAWnbdD`i<`<
z5}W=dCf3&x^EN(dbI|tURUlh^AGT$6$L@AYOP(EtfZoL`r19+j4=y}A{p^|}30H}7
zMA(n3QNo(s_2CS@!p0{ZjddUn!;+Z0&g0WHsBL%B@ovh>g^a`)G5ZBfwi5x$N=U$f
zG+-5XSuv8<8^4fzezE&R4hRc!x>A378E9Efhm+AgzTlo+-vltHjKe{oBw4Jq8}yrX
z-iOpv!XopdnBr(oD8>(|xW^chdd%J~aD}VSPfi|Ju896w>kfw{2w_GuyBfX!?o?c#
zR-1%OX;4+#zivR0ldS(?;Zp^p$&23S47X+sZ=(5%n9VjJrtmvLM+0YVChk3?e!k*U
z{1eC<bF$}4ojH4L#FrWUPgpRsKj@pzaqSzWu+W7tMD}rw&<{P_1H*V(^j|$jDb0Pk
zLO*;x<myQKJNECuaK9n_OcWp>8c>ew=s<)aHFaSy<>9d&)-AwGV}a_E>?mtQQv!#G
zPURuNF}o&3>YT~aro?Q$Z{1HLIO*_?Oc0k>;C)kp@}my};`~Tf=&9u!Xfn$!2ryq~
z3az2XyT-8mY;7DCeX3~>KGRLU{cLSReuHlRw^TW^5pRzhrXpoR=Hr%eTb&sQz79lo
z3W!@%dE7`|RusW1)w+Y02Ds)3du3~qu5bwe&dzPZyw2cWbL2<n%e`FIXlZ5qS#R2Y
zV=s@H{_2P7@rzIUwe)df!z7Kg5Ie5t)7)X|{C?BOf2lQpgy#~#b<9L|hIER(w8=Ef
z7m>_!I<qKQzGhq~qQV@OaV>see;$}X!g})pReLrYHmsQrGF?<s|7^J|iqJ4u=KOU`
z$ZesyGFhQ{!E;@e%_NE>%p}BTOh0K&Ueh$QVP2$hU?zs8I1zch)Jp$MOH&$wCzw#0
zFsWFL%`c1tZy?4>r$emE6ldj;Aw9$(8b=#yTgtypESy{w=j7V8i$^T|f}M{*qe1zb
zJHq~IZ4hh%EK{9s8YW-Plr9Z{*>9C0t|@#^%Cv(k*Zf}-!zp4vum81D8O#|_SFL0I
z>AEpXEdxllJNRsp)YRpbwdDo+kQnGZ)24fEeY|-<?SpCSxuuB1usR`~k;J>N%?HBU
zCxl#nBRC@}Ij<m-&`<p(%Fd!dsZ|T;?v0Ou@Y^S_!}W7^1aV!vA;PjL^GyH7w|kb{
z0{^K=rZL$p2}AGO6rA~YL(MwQRZDG6yJMt~0xHOE24kSX7>#B4tRD$Y!rIrv*T$8B
z3A>S~ghoJSj`s&<X*9Ue*N7czdy<UL#x561NWAxLiqrSQOE1Xj;O~6Pq0VvD07A&}
zf`v6$>;wwU8qW4|R0vMRk1N<R4|M?HYcUtRw<T{H>h_gtp<FJWU?Z$m!JF6}4yIh(
zcp<srxZtM1PLqmE{Mx4#nSntCe&j&{8BKTeduJW@-WW|xmBb~Yf1)a5d*@HT4|_I}
zXc@@~gIaSotf@~Uvl}J4v(b=I1gAYT_qGw~Cu7c`EyrrJ=ug=p!*}d>aRro9G0ih;
zn5GZX#E279!(PA7u=JDyYwlr}Nsptl_(4NmEM4cVK)kq<dUsMrc997idXHk^9j2M$
zBC)V2C9!v7r}F6}-5i+~mgt>bbE~ul>Z7+MINd0qVQw_m(RGEvVTh(o)vYLpk8oI?
z@;sJ9{|2YWhSGYx;enDnc2Tr!RZ>)GZ6=9b8<`(zj5zu%h3A;VufImArsEyc4`z^f
z#>M^KK|nGCcLG~k3-y`kTNIe^f=pndg+q$_T=+QOv?c)<^Py||^n<aeXy33A6usa1
zy&?l}jHjJFP2K=C>KNJ|Y7i8lPo__?5yHb#gGAWWO{{IKmN`3Ss&8Kr*NDKAhiZat
z6`gc7v4Hc}l2RnAc`~SEp<AcnFY=)GlZ82l24%$%tF1>p-~Ck%Uw404OFKKst*o5I
zQ&#(ez4qNpeb%?j9`g!@wDb;?$b)0HBwi1PNXvDtQ;bV|t<hxqOM)ykGk!|B7Im35
z6Wrl;5V<wVZ~0n&IAIZEaLe3WiQK_b%5$x`yA?G2w+o@JGJlRFZRiB5o^L6##pz%K
zjBQk_2P)=JfA_J=C>}5C-0?0KeNh<wZbm{>rf{JXH1ZR8)y1nHdEr}O$Cbt*-h!~^
zll7VhL9NJwCt_?6ZKQX7u3o9uG3VuE1JvrkvJH4|1pkL{^-;~{i$~T9q19tlPl3$K
z{kcT{)vBq#JL3A`&$jJVRY!RlRlW!Ql<%y_f!tw>TX?94NBG7^Ua*e{m*_%>x6MPR
z8vkN>w)HX;)-44MFW;RXu(J@dVS9F1B|o0GPQqqYRH$pC-SPvsv>Hoo8ZA}r{wpjk
zor$78jliZ0E3cSbD1aXdhsUL{!#{H)dV_h@|APV7fC8OdKh4@C#vEsXeJu84Tvc2(
zR{U;?v71Oo)k8<a!XVXLr+7n2!iVL}o53&beR5=q@m0oemO{n{yLeAGqEfq(xK@2{
z{Tbx5Cv1#zlE4h~oI#62V2;SpavIF2Cz4ihe30P`YQfBp9WB;UK}Rcr!uWt!mlOwC
zlNP<q&=B#EVr8!@pIuKK>0Mu698p|7y*68a<7cpoG>x)R(X_Rc%_5?onoChpGjHte
z%&h>t1&`}K|L|8FmM!aCGa;`Hb$m2>q_0%%aV6Y%mz9=Pm8k1I_=}KPOikt1kwm-m
z#%NAA8Ej1O4{^*%*Q^5$CVVie!D$}q&lVH3^J!O3F~=#f&L*mMH^1^_{FSd&KFaT~
zqZ&T6)}!c)A4FXIS(P%Hz)f8cp`F`LElo{tf*uiNM@<|;vRE2H5MGQkzHB)sJVjId
zLNPJd;bP@M&VlN0_Iv<Ij2dUWn8aV*mT=6zfUphbihq!qH<Bv;tvt2GNu@WL)_j7w
zil0k@n9E~^0N=zkbXlr{P_rbuO|i|WHY3cEPqJCJq?ZLb#Rz!{Eb_-a?!99r!Rk}3
z95GaXfrksD#w6cwcdvD|{X+rD7~ff2djqmI5LlcE>svQjAjaNJX9{{%5T(nTeK^h_
zeR}@*oN$aD7-yc#TZ|&i{!V`R4rMk>-eHtGwt!t})HBrdU6ae>!0vlPmD0ZK#q!z9
z%9Cl7EIDPdyeZKG%U=NwHN7_$jd?|1dj+5Rl%GYYFcW@Bax27$Pm$BP|0vCiXPKHe
zHB;p`IH^_(nmQEVhqx|t!*Q~NpeKv2Ea#Dqr!y0o!wbpSNMzx&HJchnymM^&98Lhd
z%s7syj^S^#X2swAe5=u{>5K3$z~0DW0Y|_s3jegitCHymgK{eWT83PnDj})X#d6v<
zAF)O@+g~EE&-xS!6&MBr9^i`MVYN<Ac|H!w`B?g7=}}2A;0;mMElwP1Jdv}Tf<Qfj
zoibGss^?-_YyQ_^J?s$+@8*~Y_0)dc@PR~{%R)gyS@bimJIXXpPAkP}=6{4^+9MkW
zzB{kt9q{dp<gA0~Kx3<)S}bpoMOPMF08E)zPDz!!vZzs98+$l*+(pU4-qCLxg1~0m
z?_K>0|LrGpDnN-4AAiV+YpJVLQQfk`?ZY4qB_1hn{NNth(+#Bl83Jt?)v+)+wcqm8
z20V3nEzd<f$3Tp?BPWs*b=j4y>6Q@+h2SG1oPd}EkV<}w>?YxsS-Fg1WCQQtt6S=7
zF$}AxK>s*uu@*qj<|1d0ZBC2VOr9K<t?u0QEZohQZ@Pk`Y}vthn6Xnl8jOH<%i|?c
z?0#CxaK7_7ZOUsw=M;HZ1bi+4q-+L+uHkn*OQR3Jr#NKzwUOHd1x}GW%37@o1*=~{
zrEg!|{6Y&(ab;RY?n~V(Z!v_U9EjKb!4lJyOFu>S-Vz$N(r%U{tu9tV;FR*sTm1NH
zSAi_=oUEz$fMg51{iXj;tLf)?jrjoH=0IGt`<12;lFw?AcxhQQ6tV2hvjSN><jJdk
z{(R8gV{fqF!=AooI$T&&oN3$7x~CBlqD!HO3FGqLMz;Drp%E>slZSd~7^fe9<0c8@
zJpXIZy-ojXYtl%UvHPpqOBnET7~vSbFV*hss8)U@MS9`l!xMJOpA1D4$ERg#8r29&
zzzu1Hg@%EJFxd@>mN|!+P?PVMQPyIz&6=^`voFQu*c(sJ(@S({Q>-$F{gl#qUh&eW
z&40w%%8U-Cp9@`haRV;DS!EpMbUVb!3!Jh{$Zaj<myS?cN{jVmbY968_kUMJQ`!ge
zV;+xB+}xJHfaQilr+-Y~CB*)U?|QV1#}8$Xn++g5Kp96HTmoKR)Q<mKPCRGd7ubH+
zqh`*apc!`=G|WD#RbW_?99obM()$dlFg^MYs;4*7VYL!o@ENopfE$AP`25qQY{Vzf
zj>t8RHtfGVyhIquKoJLZVxAe<W0x%mjXa>qb{P+y3b?JmD;^c89-0`;Iw^3-7=RZD
z_fYQ??G(|Di>~^u=2Uh6mCOQ29i;l5DItDDMLkJ^w|I{5A6$a9aFLj5i%z=6xy^7w
z;Nd4-hj$y(@x<pl-neq^(t?vV+}iO>n8c#j$_O|4ZZF4~SNZ<;KdO5AP+b?h5GsZN
zRfP!ygE5F?4-RS9m0Ead+ON&W(u_KeGI7txnYc&Yjopb5mT4hz5huGDFw;CH4T_M%
zTyr(_6=Qxq+waSd;;IuNPw!T|S!pwogR?Cy)=wt7-q<j^t3sQggH#v6i|=?5-mzR#
z(tuRJ8?v~sUI`{R9tVEZ@2b_0IkY2i#v)XuYV!K2QE?f!JLZJw5ZWYXS6dYJ`E6K3
zyDn-zH5@Of>osf=H#e~8iq5DOwoIz;Bi)zSDY<J#3AaVnMq{UL5S)_QpFT1D>hwL>
zXqC>HvV@tjZ%^nSMf+q|6z50Bi&>cGTLlwab`PYR6|pCsw%M;(+;~=72siZH2}+kW
zAxS_z^7R98HY61A-Zq0>FiMN`7tne&`Cmm5K+A!IH~m|MX(_oc9%JSh<(N9La&~2U
zp6fqnmY>x0`{Lx476J`PXp5@kYBG|$UD#fwH-=cw1#g%#=3~0lhU3bWO2vh7Dl)~?
z?y@@nsek0zhktVw$4c$;<4`-!l~YHgnNT&SyOyVNTlh;~h@VK7@vihts|or#+K$sS
zK{`OKgm%{+@x)!|tzFo&xFk;jkG1ce@wf`igkJ`_Sy*`3fq3Ukz&X#N-H&3s1bTD>
z_e<}8bOH=U9x>=dEkdx298p?;<52pO8ScNuZgi1loZ4HOM%`GrT}L;wzwHM)ScI#T
z9Zq+&XZv}25R=^UKJtj;w4H6?;0Z2)^!?MFOPmQNF4|d!?B`8}Rj^d*@%==FLm|HX
zRx_E^Uku7o2Gr4;iTsD7IS2q;1-G^?Kz7{+xh_5ksn5Bq%!?b)7dQZ8kA61RrYrE~
zB*r$@PJ$|<K&#?T@&zDa*ITQ{%tgsX|EA({A5O~iw;e@VC?ScL$Y&pNZC!urTAj)y
z#}cf2HC^p(k54AS>T^Mk`5-Behn=K2lYh<wS>)NM_{Tyu5#n;{3(LKR8hse5BtDy`
zE17%m>EClgVx;_1=v!Kw{(b|QlsZR!(%V;=6g$MjO&7S=JBJoqLmTrgE;Dmd>IhL)
z+~le=<tiQibGh02ZBIX)vY(LB5h#;dGRGeCgyO~1pv{#VyN@M<JZfrXE{iTDI67mV
z=+9IVbyHSmqSJf<o0!1%ZnJu``-$K0dm;usmGvJyFi%tvS-%hC_;zI6)hkxzZ^vbw
zIUg*n307_O^VtRGvaZpe4JJS{@&j7#4#|zqIwIK0TT9A2Q8;UlRbab7J8CU&tOS3!
z@ByReVx_W5BSYM-JC+APxMFfI^G#eE##|KBRC9f+s?(;3Q^g9V@<n`7y7R@b(_vWO
z&LwX6T@WEluX!WZpQ1O{=`98;7Sq*_rP<&Km^&P(Y;86t*e*ty>5^`#gNcx7;2MpP
z=kgJQy<^RECgnl>{=l}2ybI)+Oo23K)K1(N^VV&5LIFT_yH5WbLEwB45yF5JzhoVZ
z&=6g+evHpx+KZZp-C{aL)UIAxqD`n{Yn>wJjuI6@6=#C((Rxg-GmOkl)}uUmeY*vm
zY&}`^!JFkQR7%I2V9WULLzqC#qi#zj^oCHgD7V4_5L`oP=XNr^Te0{Rf1jYK39ImM
zMHASTQkL%52l5(!axhxa#A7KQS&EmLShe%`m=(F~nO|ooF9)s$nf;}*S>a1#Pn9<J
zQk%~yR>_PLXFHbLsW*FTOzRak_{N-$U=#7@gLS7S&&=B_RfYrcU(H-TL_{-{B!5sZ
zv8g4KqnK3zbG6PPHebYoLEP(jAKA|Ung2K)ql?P?S|I|)&9n3cbe`wLXG(gs9kSr|
zD(#lJb@`om+y<vKZ->+;AJWnUIwh8ByIwjrU(@tz4gxysIy+u-57ikuo90(DVih=_
z+tcTv^#)9K>H0biqx~Q*^5usee!Fa!jVwprZ~E)I-mQ?|d(8N$t=hc^WVr@CC0jKM
z{^{9W63hmj>dF&1#J1axjm%s)(a)K+g@)|qL0|BNj%Kif9-^LT{vQBEK)Sz#&U~U0
zk5>}&wgG3HRm<g6M(&2qPq5&{z<f#fs~np;!?*;ioL_Zj3%Y3w=J8d+$sfC4$bdP6
zp4o~Q+y`&ujg;6iPI$r1?mind7DeLG={Gg7OYkJj|Dw;!5-LNoT*Cl~<4H9%A=KAh
z>)|S`TDe(w%YBpvrYU*0cCc1EcnqBsY<oz~56Nm(Ry$M&9HocHkax}AyRt&><~UB-
zUH88M=Y#mo73j27`M;5!ZZ5!Z3d;pFY=M|hbt>u89T`2RXMf`=;gs;Rn=SH?3&36P
zg~tyXCyA4@-Bqk4Sq8WJH=0sav=L|Jf`Q$?LYIJ_K&Nz<S%#2p_sSs2O9I~~whQ}2
z5OxbX1A86c>8^#ff0ytU;r*<aHlf9%Ojm_b0s2zM-mqx#Fli4u`zMVjpXtKZ)3e32
z(lgOSyVsIOF&ym}(R;Vr=2k8`5G_BA$9!W0zWssNIBb+u>Hdh}_Oe317JTt&;5&Jd
zVSRg7$VbF}Kl0=Cwo)50LcnzYE~bmfHCf4!Z=VGxE6dl&hV(x9qT0Qjm8IL~Bq<A@
zTqjBIm5sjbuHGE_@!B#x+VzWTMiZ;~I64<q`?dZk3#0YE;m54^X3#gP3Vw03mK>nU
z9u~C+s~C7Ms$uUQ{fc>iYHi$X@M6^LLha((sLsnW&b;|~m_IP{=11Nys<ErveB68Q
z=?eE?f1OFD4z;K<vSW=7AnpvI!HR!wbITNrC&FOOX11z36IE7y9r#tY7uDG*=_QP2
zGq@3zm3kj@Vhq+`wAukyHH3!N`n0Sn6TnO`)jGfg=L51uth;<W5_4ZC$~yVo?iJ{N
zXI_+^r@H)2rr&muDY=a|8b=z9>!{AzT$Za3n}#d+L}7#K%d$8w$~z>T9u?*8bAArs
zoWm@RG*0W3@wHqF<#U6!`&cy@7>Ol36m!#rP<hHRD_@AdNB9$ko?gnykeUp?F`QIG
zCu7-%`o$J_v1GnaeJR?GV;8f(zNvx!*#z;NaimQb%Tk;3_rru8*il(T8kh(StM$s_
znbh~2vZ$i?*yN;afk%{^-%l}P04S>5{gc-{LyjI*EZK!*i;EEL%9e7p``Nyn-|8yo
zu|6S+Q2y0`Usai!Q>$8LR0PE6ePNLtio4AD^cIg1WfC5@wUfY40uX~T2`#20g&8%-
ze-vgW(v~WkRPx65&gLVI9F?6&es=GeCs9*Wt@Ol9LDwV6x!Gt)k*t`VO)+#hm9=A*
zq?pd^Tr#4cbo9A>g`{p0C6ubjM~_XUDNRotJ1SRFA%&QFvaoMXcN7;eiEzTvZB@0L
z4kFbZ$5K^WcM@U1G2GsMw=N!nIZzU&xDF(vH!{)SzIp^ShO;kWQdH3-7HX4vwQ7s$
z4UQ3U`Yn-#?D%d=?CfCY^*xQeN?BMgol?55T+U8ppE?+#(NM%n&;uX%d@?mz+Odz5
z1n_+SOJM`T;?ee=!)I`+eC*}UWJouSmvdZygRGMkCP%(cxEyMU>{iYJXO-MaYMIZE
z-Xt#iXF-$?ggK6OIGR3s{|BL1Xjkb?<z1md)Z_Lxh``Nmc-_w{sqmZZloHzz3U)M_
zEm>NO>=KojW|gwh=s#x{3e{@i7%j%;lhIZmw{Jz0^RePy%W<rQGtq3c7&awQl+19E
z>G4TSHjR)61NdLw8tM&Ms2ss(m5ZU4u5(j~E8JEhJD{wA+x>GzI)ifp93m*v1e<6R
z7!r8l@X{A@op-Gbp?AJ{C^)BW`iOZ21=i9R#kZ0#2{XbifVsXAEs3>JmS3&*)Wdvr
zu@<L9?(@}aTZyn_6NPd~Q)&~QwC4LzrIqQmqA1mVpQMN1;%dVjDyWo<#EJwgUZuv?
zDuNKD%G4ks2Bz@)LW)>cOc8?RW2&SakcqS)Q}s$&C6t8ZX;G8*LcFO>CoAL`OSWXi
z7G=fN$1F?IpVK7I8q+O8h%JhW5|U$<ZY(HjC``3*DRQ4<$1{=<vAYl02^zAgtJ#v2
zByp8Udg4Ais~3o2Ps$Q9=`qFM&PLqa7UhrDZ<oG{j(=><3CU7}LLZ5m>{<CkSSd{k
z>nd$1O4OOP6RJ!OQ8h$mNfDKztlLxRYeSWrq);|4ivT0+$yAHdI7Wr0@0F9bsmh8d
zTiLka%LG2i<Y9IQI~m^x=4Wka#a+M%Ub$WOn)R{Za4i-hcj)QxU${j&AJ4#S>f5+m
z?4IOS>>77fubhAJl_ma0gojKk5q*PNN}Qp3-<;j?iN$BR!MbOc;h*nkJY1M_8vwso
z82|Q4+{XuM&Fr}#O-nrwyr^-Bjf(;sKiK1hc9q`<V8hv?B72|8vBWU=NW^3o(f#oG
zp$LggArzU?)g;WZ+-69Ua}_zM>e21M3AaB*cEQ8sSXIw<1i)s?&>vJI>VvvLgcNwe
zmlAs42v3q1vu|MU@7c{*>!l(Gy50WQ{ck?Szd<~qXFsC%$^OCeIKH<I_#Wa{nPCir
zX~KFv3p*v1tPLVH-H+V|DC2#Ks{K$?$(epccJ)3P!o%Uwyp@4`5vP=B&!SGFiYNVk
z2KP(S{g6Sye`lqVo9>qk2xjGNs`n_K>b83pbYhA5ekMWI!w6dUqvy+eu+Er&rB)4e
zgSV|=WQ#(Dfq9_g_*9B$VVc`1A;rO=TppYUoOg1N@hh49p~vPA6K(^Xwp~Uupa^+e
zZE1jJK1NtFz;%Z<te(*%@}J$mR*7ZWq8fIJvjs8wXHGd8A0s4U9X^btv|BYx)X6(S
zwlpVNubE~%gz3Hk!IeVPOwmllD;DOQ$b6BIv3Mk_ksFv!kg4s6n&)(5g&(SVHUmf7
zbKOteo~=!Yy};JpHqYd>d!p*T_u^XkFjuH!Uk!K>-fj52)hVGN?8UJ|z94Nf(KLX|
ziN3B2Ge{IP0`*MV<(kiI3aI4ufZ|ymFD#8OPB0o~HT@VG^eU_{MA-??9{xb51EBjM
zF2VF7Uyw;K<E`r_78!$mLx58Tjacq76XyOO^6qzc-iF}&ZD+Y;QXrp1HlE-3f+&%v
zP)D@F@clNj>iQfyaD3w%Oj-n-{ai|fX{Ce1*mpJZ-)9ZY+8>o;_P&UeF?Q_Gvr=TA
z9h0N`Ee(yooF{f|hmje0otrbLJ(##3Yg5c>FqYeV<2D(JOv@25=pq-+tZ%W;ET9BY
z+ZQzFreqpylfr$qPQGZ!5wrV2Ga?&mEW)jpaV4ZktoxgZ==}VAG%;UVDtF3DrTN<n
zMkr(y!Wpjmz}?Ju6XWJr&Yk;;Ii6TqVN8zliqCr#kzaw*KW)IrN=yviHw?lEc~FzS
z)Ei?>E_ltNQ*M^y<%Cz~c<4D<iciEPd90pidRY1TRb1wZe51PpC%iGT+*!FIum3v$
zr)5GZ$v4K_Y}OrX<O^Jci*$1F+2}%dz0*lob8)gKo)d!e-<n{dj-Oj}>H$};u3!~L
zB%_zfTE}y`5y$I@k+W;vXM-ajS?R8ldmIm9A1ZV@oAsi({kbrl^iB)HBpJ@v^|gu0
zy{9jKfzpIbP@_+yh&x4}X~HGfe92G+nW-o&8(8=cBQ{QscbSL{LCugq@l6rKS{_ST
zh81g!Yn9Aura}su<JEGdh18f%yI+5~WKrTRDH@3o0FHoagmzQfZ_+T94ngTD?e(T3
zijI{XZ!o4@clC_tr1G<pG+Vq!n@Xc%&ASv$xt1!glHpl5g+Rv=b)aL||CPQu>}Fs`
ztHL$H;%JD962`vmCVK?h=7|%sCEY+)ULu-ukG)BleZcP5zO(C2g($yp`i~Sz<~Sy|
z3((^MAf(<TbvpcByg<BCbox4eftLQ20j>V#z}Nlm7J)?T9_PCB7C{}q6-{{?#xdvb
z1LttyJncIj!ur1ZnC12+S>NtKOlaXAv_;J%Zmfz95sE$E;dOs@xJI?t8#XPx=tV}_
zxP;qSm_`3`fyQiov=*db$7tc`QC+{3+hv#qFC?{%*Y|V)k={`Rcwh(#q_|YdsMLZ7
zt7>glWa8b>;|l6!+G1X02d$bI`Q9V4rR1tAF+6$iE0?H&M)B88y0qMsFL}-W%lD{6
zS$#y2vqvVWd0^?4du5%j8@s=!;e{2q-~X!pgiOm}CAgp9amLpgw&k}4X*01fWPb6V
z9_Kyq!Na_Dr)7J<X57<{%eL$(7^`^TJ*^17To%goaaq_=Dt()@8p@zBv>)e@|E$os
z_UsugPRBLv6Es2LINJTBVyj<Qlqn^weC$&2PbmtCLHxhImeLBO`6SG!37R;qD1WAe
zG4`XE^3**XrH2EKZ^m6~TQ+|XiT%#z<`3bsH$Kk8u>&w2bO7<!_nWq~nF|M%(r<qf
zs~*?RJPS>&P}==Vn$WSm&@2ek#=+bDE6uO}69i@4CWJ+R)w4aS1H#CJ7m%U3A*y(}
zi8AjnecWc>%mA)~`$A#QnU5sHq4B~!qG*eX_}W~=$~~U5B2J{a4v^y;42mFpxl@i?
z2-B^&oZ58{B!$EGYxmcyaaY^DTXW-A%*`C@-VrDdU~>z;{Mp(C?K&J1E)y;nt`e>l
zZV+B7oDyCs+$y|E_*vmD;cnqR;Y~0<p%rd9j=Ofb4NnK2@E2FWao}A6zt?+*MAZLz
z@Lr48_lo5>yfV9Rm;|$;@N7a734RqHBd9?*{@7=b9TtIpA}}4W&I)(BYxpGi*6sNJ
z)`A3>c!hgcIvYaoGfc6<ldZ8tYw+Ly9QIQF#Tv`C=jTb+{A|$&9$q-hmH%h|xQ@Yp
z!Mykiqwk|u@yLA;Qn@*(?mdcRv*SY%zz<o4fq%HSeQ2u-NZ(#y#BJ_fjVI~#Byv4X
zW`pr2`{xHzFz<ZH{kMN}OTOGKSz$Dd2E)?gXc(a<RUAfZFnIojsxeDBIG6Ki5p3Av
zOJL|~xx1E(?Xv8IA%%#VQ<OchCEFrZKF~PXi1n*ZyPwxWp-3pSzCM?z89CLn7fi*R
zQ^k-dW+d$qgX&p;kByVvZ}wGF$Z`B666&q9dDus$5koGPeWQHAxrHq}zU%@X1tUn0
zk<rR)=k$<nX?lU}*NODb!#mRnwnrBtrlZqr_c>N=G}x<$oMgT_%W7jxrph~amQ#}k
zdtI6>c%~BR*S3dKJwg7<*JC@vA=;ogMPJId>RUsuvGE7YzT_)f#d{H)*zdVT7d9gD
znStK?T(VG@D;9rWHbms^D}*YOnnY8D6qU3|=r~1<*&)r4ba_IS#M?;WHc>)Uq*qr_
zD9#q)?<0~VS8WrzLW{-YF%9~_gb$;#B*i36>246BXi~3TIB#nFrjWKui`80t0Pom}
za0}3nYw?%8kri+?DUd>e?2tLXiYiB#3x4^2I6HOL2PghN-uIf<+&3jF_h7)xOy9jn
zk*`JCQ0Joq&`$No;7NdQhX95>D7-`XINCu7^sr%Glv`MLfBMvGUY*I7UT9UiK`<S}
zylQPPM(fpP&>cY^40VRr>x<gf*?bijY+97fH=67zi+1UBNz&AM0$*~Hl#rwxdJ}b3
zdt4SjEy@W=7XMC^rAtzw4}?-gBw5)L#RfWABEAS_dj1~R9)2746e5aW6UD#Bk(Km`
z)NHA|BVE(k>#e6wsYYHFJyFhL{#oYD8{NnFt1U`X=$VqlN1T0)#y*FLhec5y8}l6z
zUYn|}tN9zuFusOOB9_rp7JK`A&j!;djzcem<NU)zlscMAZ@GoWR7ca^PGbWo&hMkq
zcZXuE7J*a`joKzIj@TxyZFM)npwTBkNA2by#{px+|L}H=MyX$!D7RL>sk|XO#NJl-
z5R8_G?YjA9O>-!gHz<EM*m@e#c!4A34cQ!SZ*}K@Hq-)E49n2rcMY7cGJSg5=gFmg
zoT;xo#w@J;Pv1uFM?S)B@PCQM4l(n%lwcTPkzdk;o{irM`AGk+0aQKnPzjA2X)AB>
z;h%2Ke>5*{r<QR^P<@T#b0oJ#X1}vlW<zb{cMkN`;TV$mubV$3d$(+4PbL!eyrv<y
zo+eQnr$V{Ll|h|RiM*x*GxDCPJyY<v0SB7vZ@C_YfKM?ox(IVm>Exa~#!uyR{e~M*
zI{4+?e`j)rOw5p&FE`S?v;BkYp0Zs`SF?6r*v1q!ndM$Ca8XrQm6{M<sl|K5zH?`W
zO;UjOjHaO$$g<X*(CL{wG*i6ejx$s@|KyVTs11wyC30B%mt|&~<6pG!FWf}+wK*x`
z&aLV6CL_Ye63;=OVW$$*4na~So6=CRp1l}b41`OOcBztrq3yncNvB@1TQgj<d1C(1
zoo|2bq4^WdP?Y?#qgi6)#{Ep%bz%RF5z*3|kS*DVZ`yUqYY)P_-*wYrOS1jB)LT=-
zHsS^s>)_H9Fr{xlW(QzXJvr?62p<!EOZbBD{|SGy9Y^zGd7BrKxxSF>-%py}z0d`J
zrypXrhdPWH%iRgXb}g}Expvn+zi{`K0&WAs+&@M%5gtXd^AV=;b)37l(PbB85zVl_
zeqPb=XU-e@(0OCKk8dq&E(Ndv>5X-q5lsDLWaRty{^+3TcK=vPjvuc!s>cH>roYaW
z9^Udk$&jy+Wt4ZDy`9M!k(G3{njYl4B)JmAGw+q;YnenHk!EiLOhPhz`O041@V>8I
z!i$6#3%6}o%Yc(v+vI5nR~x=iy@8Rob$>Ku$8gxV;X5yITjZwJcOTx$*x?#~bk7DH
z{Nr2rM#gS-R5pkmIpDkzh&g%9VX_=q>1kZ~P4tyju9wBnAVLKWIo_`;t9yf2?ao+F
z&2<F7D2zZSsWu?->}zokLM-E?LX~-ic3|AIk})LP|EnEYWs;xoL|MzKEC#v}+!hDJ
zWrkROb%zUC43O}A<yzLv;@o_19Lex4tIKo6bG?9hA+U>vbk;CH&&#*CiIh!!*n*gG
zE4&Jg^QC6dx<adc-dJ+3Zz(w2{h_Fmm?XPPyP|F}e_cuv9Z`|o9r@h!pNop<0ut;;
zrA5&psu+W)nf~Bm1J*?;Bg=6s>ZQhK(@Bz&q`XAL2uaW8#OV1Vdvq??Csu_lYJFE)
zj5$SpIel(k37X9;YV=sv!xAiFT3wvTCF4hLI66JmsvWG6Gv%ey?%%y#w&mNYW*&fT
zmg)yg?Qfju#A0N8X8P!~7Ct%$DN9S`*~3@eE<?H)=ZwU-oWO>!+l#ubd(q-WyEl)E
zf!tneR;0{CWz`lvW}#B$#>AP8$^;XIs-ZPYX11BPOQpTLhkT9-%15;pJTwsmxWv3K
z+3;@ue3z^^aFUKCV~XiHifmYxAuD<)3ey$?HN^?(Ecj!K6QVd*jKUeUxv!BIbCt!3
zX;qKs<9pzztJ4#U%UlkE|Cs~%^^4F#goqd@hBO{{c4Cq0#ksJZE#@0}!!#DgCKfF{
z4*!hMB9o6~`eVpTO$m#_Wy1BsEyC+z98o;1XFigfmB+aYz`U(;tOZW)jIo5)&QLIi
zX{@%Y)VC*>s52&U?fkFhk#}-RoykQb_C#`GF;kf<5}`O($^BJ%Uv(_4h>B%Z57xwZ
zUdvV$MU{(7F!j%=mQ;}>RhD(7BFQ(O_f<VL^8W2?MT;h&_m!C(8t2N*s6<UKmh;W9
zXb`cozp5AF#$1k&Y)aOif-gIegQ%f<kuN>CgMFN2Tu`Q^$vZdh1*|!kXd_+A&hss1
zcZQ;2RivBUpjR7FYbG<XNOI$g6VDdsrk>bw?ZRYP5!I;EIN1;l>bMYFaVCzWWO^#%
zq^nUcvpAs|J7MtWI<vd<@uRbohhvgOB={g?QA!sl6g4s~R>l;QuOCiF&%>HN%;aq5
zw^;(r3SX6*5%X`H@a=AS7a2xYVb+e!ebSj&T<o4z9bHmjx+{_vRmIRF7kEdm+vsFp
zJihqDc-m4;GoH)EO;fefaaFyD5VkhDem~ekPMCqQJBs*2dw9~e&pX8{=Z$Tjzgr)N
z>C^FND?DE^{QC6pD|y54`z^z-rEPlo7$iBvChHkKb6@Dozi-*;J^MpkPd+6afPL_#
z!tKJn!aL3fT@=Pd@R;0nHQeLis6K^($p+LKZFW}ylY#JB$^}5m`CrLKAK+xidjO~V
z#%wlyBv+`2x@c;bm*t3Dz8p?~smmz6ECoSJ1i-{0H64kjtMMqgWdN#-%bTFg1z^$^
zFsPh8o=wYMJVOTx?bjnlqfsL79nTuomYe$8j|Y`}+9#72Kzbs|L^s;K?VG_Lwk`TY
z17o&NWE&*cwccuIU{Gqz(lfiX1I9<(PpWC?-c|`F2dOk*&P65`$Ggo$I6S?3cih%3
zCoft0$w?)uOrF#&DR-@=U7LfTEA((YR?Ar-c|2<(f%*+Zl+slrKMq;xQtQ~_;>6g_
zVtn`R>2Nq<xr+GW!O#0SrrfZ0DnF-aQnBrLnVl~TXF-FWefis-u+=s=%NGJ%h5Fdb
zHaJVe$5trEU4))ZXI`iC*Jzuvw<#&G3&nJTGuU3G+(cVm$fbjUi*sXLTsWkPwk+ys
z4veWY)M&z|6HF|AirQxP7fqXfYSHl&=~+qf?w&mwZdfyE6#R*~<K?SRRWS0vr)Vr3
zj?qs&;5m5Ia6EFZIh{!3J$~bHrc3DKXiqTR+BVb4xW=~_gwzqds)!5avj+3PcyOZ;
z-d%$C;Pym=E5Y$BFr*jg)vwGQoGYYBJQ*I(+Fm3z2Jd4j_Q-ll%r#SFI$n0;B$@A?
z8@j6cFMYA6d0dj2DJ_{+VFgJtJxixDJdA$T_ogOw&45FQtQuZ=N}IafzpmcDdYW_s
zai{^iyvP^cg?4#oN!#VZ(NqF7pUX%w$?1S-T<H^Zdp8v&7Kpc3QN)KtP4S<1PeUDE
z`cjF=({ScFE5n~bj<TmN5610x;3M)EibA5`=}sxiowIPnK7JfNo0X-v%ZB`R$$!WL
zF}_T}x`_M|7iJUQ4qIp9!EBoJ7{Ba>;c0@lV4-!4Eds;2wl=X?;KEAXdklM#oW@gO
z;dobwSf-)q;t^djOslgv!PKyA;~79>3UIny>8==hDi%xW23xyNlJAozn57z_0=OsT
zLgdU47)8Oqz3g5ShFt)#s^TT6z6^P*&G0J^rLq$24(bG>&L+w|YgOb6Q?f*ys-($N
znUW&6+p<#1Ov##5bzp#}RP_S!cX2z?A4`dv+?<p|G3AAJ%9MmcgvvWZUP=_D$)>D{
zsdD)u(r~NQ92Z#=-wgBSGT|!WL&9$e-xK~FE&17mG1O3K9?kDonO0A$%~UrS6}yEd
zFuVjiKjSGS5l>JY9hG_sw#;C<skH<e0)U10Q|eFJVgi^{#oNOY%1^nLqM(Vo9JOU4
zYK9t7C=3ZzBC4T@M7E={E^3j;XcBoeMLJqBZTQ_%(o|V5st(Pi@*%BS)y=VFPIHuk
zA*)(ZRf`aB6ck5;cvG)dwNO5lqmEiIBt<K!_r*oAn$2h8BB^Dd{y)vS5n|)_AxW3*
zP*_!>uB?VFTh=9$ijo|bq_8ZBDvP(ms_aG;H5|hD5QXVzOZ#lI;@Y9gZ#uH0r6*KL
zcH(A9vr%f#)=FmFktMa3(IC1o<|q*@Q=^g+i5n%|f|(#%ddZAM6p2=|T10Winwfm2
zCW;As5b;;Ht6fNi+S%fTYZn^F+W9sBecDVGaBbAoJ1PjxcuzlxTlDoQL!3&aj6_zr
zw>G3X??=-_<K*d+jeK+A^$Sf>f^!dhZ=CG@z>lcEuHGbXo@o^KulHj=IonRpOw{M6
zr=7e5f79`hVT8z(lP}>rIKSE8F^!aMS+=b~nxuI$F+PKrxq!2%<)hP4(-ge{eLOCu
zvRNO#?9^TO4bymjw7R;!x;i3pzJiye5RNx|f%RLwpC1!v-cNjKPO{czw8Ldfec&v3
z$XYesT~Al9qf~x*HBAcXD!*_3+aiIwrP+C}<eRa=rN``ov=cs%mjZ-{ujrQNBM)4A
zhk*o6+~KQP^_l-M+~Hs`6XEdBU7F8m$&e|@%FI+QU0;NdOMh;b(b#cW$d0NhYC%@i
z1t%#=Id9t-vbuO`SLI-KtQ<9EBWlH5W)igfXHi73q9V*R_(T?A0+X<ka*`Es+Zd)_
z((40m5AA&Yuvx0QFJ9eiqOA`%@oC?F#?YhRhL}Fk2H#}F;9r2tJ;-Ey=g~$RuUoud
zxpqZWYMhIN$I1}{{~f57XIeqECaT@f>*DUaGWkn??(qB#rQ6hD>;C7UV);_kA`+{a
z@uTbyPG%2|?>e=ZO<mKo>AzbMm9%1UJF5LYJYVl^+eO>-Mo*sc+u}jU+Y?SOdlJD_
z;6Vv2f7<k?rcYRz<5D!6W~Of#evGRQ^bCnAX!@4f#sv(R=|m=&l`eojtE?_nEldHB
zDznq|J<t*Bjx7<GO(B|9s3PyMqp2t(vu_tpUQ#`>iyU~(0Xv#J-Pu#0o>c${(=ZVL
zd56FHr0bfhWTaBlN1G91ic~UXsu`46N-`Wx6Rw!IO~k#EHy@8029kniTB(#FE2i69
z-v<G2OE68~fu6HT&~e_DO&@BxrUeau!{%ma+E6P(8f(PwPTq8NR5ybvX=bcvle1^9
zG9io!6QlMj%V?Q64nba@C*zZE2MqrFZ2>Zd0#~_=`ow07mCr$f!t~L1aoO~h-eQsh
zyF(hRrV7gb@XSl?p)G`okFoj0?uPx2&N8t2HCQ^Rm}g;9$o}(x8LWhF^lR{ev!-%T
zreV#026u_9`gS6(Ww%ZYTX;dKp2}vP_Cm`UV#fHVxAtb6zJSht=6-{-D$#sY_a6eU
zaz8Q???>NtEO4zC>N6XMB9jAtyZf!dJbR%md0)o-K{y>uLz}iTG%Uu8*sY`DYXxRj
znB7AaX`NgkYYQho++{-Wc;mcI_PT5vV2(@oFSpyv`+qnn`7dw$-;F;M74nsU<0n*R
z#byb&B`yFs#xzHnwNV^PWxzBL_w;Q7ZIpMzNu@-ls+U&XahHbFv37py%Bk9=)mYi>
z`iA0N{&>G{agx9nrQIb8A5<@`O<ggaZ;z=V{#rdeWKD9VZ~uqaTP05mIi{z-h*AwG
zn*o0>xZd4?ijLG9Ubn9IlS369sdw$N?dr{C=bi-I!(scSP>c`q5D~F)n?r0?R7<K=
z`CZDS)4TlUNYQ)r(cUdMrw#kwgVa#@r8ne^VRygb70JodkoqhO((K8DbXhfq<jY$l
zyhB&*UzdxA5+?IfV}y0o=jST>OvY!BqqtzWm6^$dmFj_OOHo}C69`|;M4~j|#>z=A
zM5U0F5{DR#zY#JM)q__btN_fFbg3+hvZ{ur4|pkCqkyoKB)ummhIf^$Kwm8Zq+!yj
zOa7f~$Z4$%Seo=H9$Q`11@Wt-1IK1=L>O>4vRbg*?uu)H-?vpwR(zAyRi=M~^qH#a
zTFc9pTTMH8?OwKu*jl<55LU$h7(5a?0FQJb1rVtYxOZ=D7QGSEVzrTX!dipJe85iE
zW7+4|)_j?QhnOhD5%eBG;mqgPc!~%jvX8xb3-JeX$vm>r0$U;sw@osek7b8nOX7^`
z>TLp%EJd?So|$m1^;O%LH|z;!$}I32EM^&X-Iu$Mvj7%{hMaqTREJ;oX>+_D2vHdI
zj1yx}!rR%d@b;!^J~OezW%ZZ-{I2&8)WkNP8F*j*w<RvwzqI^*lIho``<!7fzrXw4
zehA06kdF2svoj-Xwx_a%V&n3%YjrG_-~SVMNoOEn9lh(!+oux?Oy=qo>Qhz908)dZ
z;4C?Ki{`*&?cKFWgS2=pQg1`pH=wWx-vxQteFG%q2YK`6!)#_8zS%^px5{Y1U$U2(
z{l+B5P8$1XHU)5qwZ2^r{ea)!J`X?OT^wNcbWKoxVv<=qFZ<jK<k1gWUG4kuAle?d
z?L#a?LPfVY*L`;=7ryruGolWIE{oM2J>TTsE0-7V@oj*vhMl=$y!+RB!J&KafmkFV
zobGwfL~lNDJd&v#2`;?s*Ds7WUTo5UV^(4rRVA<)%cws5Eryo@7x4;~MpcXq+zq)a
zmX(i$(&{zQ>_j$tjhZo!$jFk8X-CM)5e*jZpx#<GW*F0yn(qrIaJ?tO?=vZ#HVnod
zCr>^}xMDxjM8CrLYza8~7tn4vH$lLKJ-K`wthl%rr?sZrY{$QU&US3;oRKijS>QY8
ztgWrBuaZAJrY6JRu|4&)qMTMe`#a&JdQ83K2sw6)bpP&`4!n)?r2ZPp`ZcB>g4PdE
zJLCY9BW&;u7t8Kf0EJC^b*Ry1yVk~9xZrEj+RpYE>G?eJkFT(dn=I=l!+M2bnfgtU
z$m!FO$W6Lwz30S<uiXOwt~+r8AJ6i~)8luTQMsxpRXJ+jF)q92YmBJ8plOYUrY*=(
z<29yRD_mNsz?pq=viq&+t7?_0saow!&*QJnZ!<k3{~lH(6?5K9Fq8{zkNO(J$aZbB
zn~fT^K;kK|6iE`Fkmcmz<m*zDVbWAB7FEj7YMOc-M96aY*N1Ive?m5-RO+r2avxCd
zvsfhT)2IW1DanwD%9r1U5FieH8*Kjlr9ElLy}m4Sj`ZI7Elgf~_?@gVdyRdkyl?kA
zcke3?_{rU~v0@DV_~X7?`P;2YD-^OG8hYb*127x9QQ@F)4c;!yY@hcOFQ9v%Bip5b
z1Hmd8$4fw{8sEtbKgHPji5Ub%^$oy4+N1X2hF@#nh$JI(2j{?r6`P#D8hy-Ql1ay@
znNNfwkx*_SN9B;GJ9Afm6D@4rzzx9NAa_3{>+%P<*)4ed5hg}X1UwVA{fW7&=UE7|
zzLav!vKdV!@=yR|s2NJgb8-_<Vk0X<mImYkSsLAEWXOwI^!fng&hOmfw1$RorErVz
zCbUjiVFov9`+6GOC8{!I6smFqPRyz-Ua`?k@GDVp@l>dX)+YeoG?C_8XM8kqA>qBi
zaAmWp&!YH%8$VS2=c~hVNHnCWX#|LXrcpv<a=9YPVL7aiSw^)*mJL-lb=gufiq!pN
zL{_9&Oj6{CC`t*FsG{LX<TWoS7%fb7*rmAOc2}RskBwg)o^oV@^^omSNRkN=#aB{S
z%W8&Re)TbpDvqT}a#Tj^Ly{~ShA2y3L{#ONB*kP^d;y^weNoV}0nEp{2gVpS5Ksdt
zpP)>pV4Cf~NEeifP7#1R3MR(ZB1d`%IN;-vH3%E;c5sbmV_kTg=NrpBU~ipRw!2CI
zN(+W>d-_S?2H|DGD}~#HpA~*ic%$%E;hn-+lv=O$)f~a=W44~!SBOPoE#H^ggF1yC
z^%;`2mVbK<zirCDTnFzIo+Fq~i*gsgu6pcB;r@{bKUUk!<+lx!?>pMKrN{}{R?N92
zqSHL=QcH8DVndi>jcX1y40@hK3hAZ#W+gvuh3Dn4HJv}SQ<plb+L82~@dsFV!UvKA
z(YrBL{q_qg?!1ZK2%rZtW~J_iGUdvPS#d9C;d?o>ePAH^1jbfSF2i5fYfL^4?du*9
zuEv`TDm}{pfr$W`M^+CU*AgD5AJ*_H<SGhZP}Dd<>XD~xy4jodz8}4=jXcZQs-NN}
z;nr!&+_|{kUNBQMN@dH-+H(cv(mhA7PglcHGo(;*ct}Y0gZy)mKEE2+0$AhR)#Kb0
z-5;5Xns;CNQa7(EX2!^9wv`Y|sYD@`5iLX2G6o%zm(}>!YXgJ?18rN8{|)`42sXRp
zoKq7HLO)%>tXw7PUajq4YQs&?z&v)D$O=y4I-B4C^tf>vUk^z2T*~P3(2KOa(3&G<
zul*{t5P0s~6<5NsG4zy7zb1*-9hY_GL!$VtJ5DK4<rKN$$}5N}pSoHUKcwjLuY8Tl
zPf6lSUVToM#qZyA(@p>QQCUm8<PEb#lzvqbZ@)oQ<bz0Mf1jdDH;OV@dQ{UM&CR|+
zV)3z^L>6z9bftTD;-IXEH;C$=d@-TPzxLQ;Y+wEYvsuaQa0`9CC1`=iE6*_@B_1@q
zs*+E9B3uds>hSc_vhn4J_HfwIx<{Pw!&>CavPQ1CS=VnC4e{=~MPvGP>xQAcOdoTe
zRutoDXH0*YVi@bMlN8@p<F|dC>7-B;rr;F1XA3ULCIM}4lP8EGD_v$GfuQC5?stcf
zmurU}R*xEA$@D+z`cx19CXnT!habpn9gNodh1B|@D*JmEU(4YK-xvDz+f*}Y%i=%3
zRY83uj&3<tmtIV}&(i@H)e~EG$a(u^%V8T1?9ro#+q7A7d;2N3WtE4g*l_K(teh}}
z)AM><L@l-~{P)gNYHxCAOja6kya=QF;2Gi)!1k`kGcB_6`*&#fkFIkgxa{WooM*7b
z<98c+^dWx1_ggtwU;Lf$MEG}Xn?Jg%JoNQjX-oKlMDXUL@0dm01@d{E!}8AuS>R)a
z2d<pwAz=Icd;f-QmA~o2*2-(mVKP|zg}!fP?Qu{8Y593H<j53`-0o9~w2o{aaGvR#
zX6#(ZJE2o{ojEup($9h{4_bTN5HMI{*W^S7EkADW9AfHk=sXsDD+D+wVe(B=jE+4t
zQ;_M#?9vZ3waJalk^fjX(#E>3^T%sWNXD`v6(q&A&KS}O@qYzT_>hiCveIHkPmfz}
zOOd5xqUO^QI9~;Eo8o}ve}L1BG9K3XJy99{`0FmS&`;3qT?54*^UUTSbB1|WXhreT
z5#E*0F{)L$HYwKwMHStEbf4fln4_r%7+b%VqmDweSvWG#+<^ZOzsus@(Qw~CNS^+m
zn?C0L-{N;%JhgQJxt@F;;sSew1Hxqh5rlZ}s-bo<FY=p-;J-=%5h(jN@ATCCi@x%I
zU_!TD{r_2e6F51J`d+-Nuj+fcr+cR7+BtT1X7<uvv$M0(N~_T=%a*;iWLp--EItK3
zSf7~7EJuvN0SqQ!2*(;iVhG7oAO|K8){ub1ha?I~z`;pS_$NFQ@_2rMJe&lN{`ITs
z?m2cP+2lz()z#HkRdsdM?^pHveZRH3+>~*-T65-_bs~#vxG1YcH$w9QQsolXNGmDs
z$QC6<eB<Ew%KV^0Ptkb3{Upx=aU?k-H+uBwXzsg1xz*guRO9aqRW+VAl?kjXGKm_B
zj>pIM&bp3(#ewAf)!TN!5k=ETpyS#4QnR^K{|7IXI$@K4?R5wvyeOCx^0)yiaWGU#
zD&YCy3RcwnNo+o#XNta~kJiw$EiBq<BZs}!Vz#2bnu)CKzootLP_9d3d_P*Y6e6N3
z`uit_Dp~L<4P;q5q!p&MZ+B3?zB|VScxqop74ly8gTHbJ%MwZF-7~-z3LsP2=hm@(
z?w+EZn?Qegi)uK=9yccG;-JjSRp!>p7iV+4I5Bur&rzwyjt*@zG;`!T1;JKLM<>E%
zS+b-&^NKAsi-WUTo*vRfUKo_4L#S5DFN`xg2Ij}>5xJUc27PQqhzA{S1ozZ6uO2xM
z)H^ZueomoPst8X_0VW;#jkX;uCX)Of5pu&h;cGNJ0?1D)0EnxrMF71!Lt-to5=j4T
za<u{znO<ibtS>^}@<r;$5oegGl_aB=qQr){Ms*=NEi!>>!bBI{jtj4XUt|o{(U@J$
z`j5&oznwSa9i<(mMG{9BYh>@+W;J=e;Ak{YS6%ISqBOQJTR?v!%k3{S-=a@rr@<Te
z_sBKg_1<+Jx$0D&>>O}hghn-kmE}kFWHvvp`;)l?g8r5eNmU5uyFkuA#I1nd*JV}Z
zZm^OdU+qRK2}%k-yTQgnDn~daAu7fKHTvsS1G%j)n-5rl%bIM+TFMoXXv?-%lQk?Y
zda1#*hIOo_T&a`1(@hPcSTR4g6L?74?}KpXSiS>#Tnz3>0<_bniZikVU9e>xA>GoD
zE0+?PA=Gn-2o~Xib}ii*<I*+CNkfA!?D8}>2skahd{mvHMFJ1MvckOVRw7OHJApv<
z&O|<+Xy4g|PTo5=N}lsoB99Hi3VJD8^$HhoNHgntYPSH-uPo)f6^hB&eqvp-=*$pW
zO79G0rc1nmR-3^*qA$+~alQR8+xedDwg0|jW7M^-n&?|s^`oVku4Pk`*}V3cTm*)z
zLf}L5RZe3o+lbocU>Rl>f)yc{L!`b=NmzrR+ZnK4qbnvIg7@OM#=E%aWcIwjG<oqG
z@?t{F_<qevKBUM}JU_N*XuA%N9U3dH1g6{R-26VG@mHn?sxO-?Er`eVX=-yveP*(1
z+e5sKb8bdZ=Ep7=Uu!L^rbaEaJG?^)Qu`E1$gKzV7(M=rDAC=*=`0n+(GTp1n_=Xs
zCCxfUZvKdD%a5p<dCWpHme!tviMEHQW23R@hc)Z<3=XKpHb5);SejfxE=WJ7S?vc_
z;A|31$70jgwG41bVH+;w-sR|g4%NXbG7e~08NNOxTCFwf{#>)xS?-B&cwo9Uk9P9Z
zg)f@JL4R5sZ_dFSw;_mfPBO&8^;-wad&K%wdDfU;nm6`(mS{*hSrGiv0gNm>cVK4r
z(3@*NVa@zR(RrCKD9W}!l1;Ewl_#6VUB}F!tCA^LkxnJ=i6<mcv;|8V%)Ne_q6q%$
z+=Gi>=zI%wo(gQ99a5?b0aJt09aeWR<WQJjqUOnUW`fA(3F(Ltc%=l!XHZJ|g)-<n
z-y*Fp{@X7h<L>uj_r^z^yJel9z23B1In^qtF)=3c(uJD+zlc=1xLp*t*SEa>jR#(V
zi6o(*pA_Z?dA@wl1aG<4dT}52Mn=c3G<fT7$<j(Q*f%S_swp|e#n+jdIFsfzK7H%l
z%Sdz8eIAj%W}sduZ-3TMG~{&a(OW3OD==MA_Up=2dAbJl-65koW=o76UkG?xbxJTw
zG{Odn<AA%}FIOXm--yID@{$n3L!E(W71b1_hCoGh^2)cAypydsv6$1fTWSAEOgUAh
z=b-8-<xwW61Rf6UlkAfVa9`09+g;ujE5>#a(Yfztba$?zeoKi_vcuTz?EK&8xl7+o
zL)>!Uy0)o><`zI8Lp>}8v$SiOAm3z@H0#0IA{oehnO4FM>bpas<?{%-lZ$`p@Zn$j
zFa(d{#KQi)^O@L%`GPz+N}3Odc*Rz6GAS#u%>3R1Xl-zzyw02#VDr(mtyQ;FwejpF
zf|pKv0vOh2OhI^`ogT=J!?DnQVGrs1rs&KFkrm*Js|LgiNk4!ki6YcO5h4L32(w>V
zfF2*#Q78@$`o@ihY-aRFYr~`fr2_d>wt;2Q{%s^Ely?5(uG@BzKYXPjTWS3hTlem|
z4INmNGvqs1mPqa&O^!HpRC{e)col44CB$D#M>%IC`DkJ8lhkUtK`(f@RwZ#}V?gAE
zuTh+U2^4U5Q=|TXv$QEE8MGvI5vb2Jt8>(kuZsSnbmf($d@Me!xSCTo<8eJ76BRu^
z<Cs_$1w4F3X&{gCnFB?XM(-;5ercdk*>c4&CY=s8ote0<h_SpLkDFyjbMc5)I+o8U
z&fu;<*P}T0X~wbOmNzqfpfe_+T%72)%3z|&CWpJOK0$ix6i5XrYIkArauCsi1D_+?
z)lO0nCDOfif-=ya2p3mYX^fdYu|v?;Q{$&L?c@VPsE5G1!-zPloq*x#a_%PTLHR-M
zliW$}4=G|1Gkpy-GjK;8#L+q&?GlA9%R$<#`u1Hm9y7*R>e%5kd)H`qAn&*1IT;u<
z_~*H6#R=<=WGJdjxnmVmebW)y5?q0(c`uh0gd|>gywECOS*Y-eG%6{4MUcO}9N7d$
z!Tuq^lt=TDARi$riK0rvU%Z?shvZfkNfCGnA!SE?R5pbnUXWBp)TJY`P~GHC?f;;H
zo@N}PTkkHqF`loBqC7T%iP~tHvi?Vk!kh95NtUG(vf2I@s=Co&wG&0T<CwIMA=wh^
z0usSv<Q@%AjL9O|*Jaa^l}`+jrh<>l;8h|bp)Oiq+VoaGZhsfr8mjVR&(lcOFxvkN
zvFGDm*v2N&RkxAv5p3It&bE`Eo9SP277`_gIlA`o+`~Erdib~?JbkrhBJF8G`Q)@w
z)Q1G&xS$9_`g(i)2a+%>%S!wEq02!`8Wz#5aQtZvncCG)3*NL&E>Xxe<dS0h$2X#u
zFe1f5KZTDfvOFvVHtTFoCPwniRr(O~DljewQ;K;6Y`2{$nDz>Jbe6YHTBGF0XIm|b
zibsNawq_9pe%-2NBg_r-98v;4b6~<{<J(5)2Xv4jQJr9TQ|&ji?K<0xvV-i8*7uX#
zV?-7X?i=|Q)&g5$*$E@do7?~?n?5VBIp6}0#0fI)bIm?eS=<YNobZY88F5t8DHYmj
z1-wNu*6(3DWl>6yk>n&@B7-2<K(L%i{3UZN`OCy#+g8rIIPMu_<S@MW#a=ESUq-Fv
zXF7JaOTkvfZ;yT8Z{V%|%?Dy_Lm&5YRty`447V8ft(3O^@yGXTsmNvz^d#_Zj0{2p
zGM5?3(BdZ(n9h+=za7TkBC`WCO$yps3%RG_Bd=G39ICG$iSOoR;m%<HPC@2X9`Sei
zvD9!X=HEr))UXiYlvO;AZ5c9%j~A`fZl32aXZx4)07HxUqjANul=x8+4P@vLueS_2
z@1OmJCFe#6cS$Y6i9{W><gUEW^}zW4+r?Rxb^U7gVmX(}J;}QN<OSH)oV=si_<~;7
zo*&F7aT*4JG)~S-iNy4~VCP*<Loxt|(2yFvSl(HxRn7IN+<td{Fx%{8gh_#qW+BKj
z`o98Z6^(_`YqXR_Yf`)CTc)df+I?e^%8sgd{B@eAy9f!Q_)W;`H${O@A5-IsNorBK
z{06A%mkf&7aN($HU}=!P0{Z=v96o_ArEMKpD|$8N{x#yYt|{2AzQBE`GXcBaJr_&+
z7g86Gaz68(rZxwdb%fzv$%o?Oat-~PWvyAm))~%%6<~Bd82CN(=sWh_vGm&{dCh_&
z!{iV(`1`YW>{$wYv(YYc|3ehJ04$5EA(PA)sy3r2cusew;y`AP2$wtKB}E?~qih2;
zkt+Gg(Ot1S7>$g4KhHlLMBiaA4m$}^;SD59+4R9zB}<UiB##o*X3=eKJzU6((x4<G
zd9o&nIa&2xSsaknGU=!#X<Sl~wA3P}N;v6ZDJS9mqvHxvB7Ewf5t;7oq|PQe&Y?}a
zG0QNypmCFSL>iL>2FcNw0_HZXHc68{VR?87lRwXDKcZRa8Nj0oQxM2GC`Fn*5riOx
z{D@Fwy{mT@XfQ%QjOQr-GxfvhMbV5V@clrF2rNmuku;xkv)O7kyHlZnP$X<gxC{I7
zZa7%Ye)0hkWAOnQV<I~~AIRpg!VlQ!Neld!nKO3M7tZ!new!j^;_YHQBP-9UrII?j
z$)gn;1X&xc*}87Ge}BW%1Kof-Nim92fxQ`wR)QNm&Sj4~$);95K)Qh4q)ILA8qKoV
z+zPsfbR?z;olC?GLv+a}NITubMp>Ar1Gr;K2OloD03tZ$f)5v($m{l-0VFc*MaVdV
zec*(Li3lQ<;-Q;w9>O$A-tf>Zw+vy=+UyAIc-D!Yy|bU!eI)RQS?~6TaeCIvg5p#X
zJx0x}lKSNSfUByPYLXr3@oE9m%P#Mg_jS3(P*<6*W60?Cd3onK&g{8MhmU=ow6ZyB
z=}4<L1i}leU4U`B<dmq`)-SD1Aif3F_7n2m_!$2bYI0SxK4s(fI#M(;K9e!!sc`%t
zo!VooP_aM7<ZKWBgw}QYjE%L2P>Y=GS>bSVQa4c_R()+FhZ7EH47$KqSwG#w=X4BS
zX7Q_{SP(Svt3X&_^{_e(0a?LUMNKG(aG1XQ$i^S$qIJN@Z-NNpH$iBvRW>2nrO>u@
zw^*Ftl6CJ}MfKcL?yyNpIfC)W*~UDayVK2XnJ*T}37^ZsVUp~T!*oLqF9rA$f5f-Y
zSGYLo|EOJ0#b@Z$RWRc8Db;%gJmM*QYqf?b^eVWj*O^C2je1yOe=g}y2gbG2Ntx{9
znw&(1V$8nk!b4*i4{hJ73@xIa{{GQXNl;A1liObkBK46mdeS46#Y!gaxub48ogPYC
zV%#l#{9<8zeiDt3U7%<5#gWUBJ~?S+q?K4uJV_%rKwl3c7wGgPlN{Hw;5ayxPRBFb
z)7k3Gt7?m&CzFcag<iwCT#<4>hE$hy(v`7S_8<ud=C93Z)(4iS0fma+I(F#7&(b1=
zk@jiw$jag&2=?r_VuwMW>}SSyJaxg?ILmn$`IX>25<D=rW5-y=r$se((0+??Y19~7
z7W?X9J(PvyTY$yVL=e)BfPQhGQPXkw#nb9%;71b;#z^wWM-)D8zLkdUbu-Q@AASoB
zKLCzuJ<o&|&$>rgk7Xgr2Q11qf{^Bk=ja1|Q%lF;7f)-y89f!<#bCX=AB$T3j}2_^
z*!p;dyo-HAr@w)_jm=#ooUR!QaH#H~6rr{{|4kIsW~sp@Hd>+h<f4bw3Dx`wm9jvw
z`m@fmL?>9?EYUv0ogxv`a-o8Q>x)NBazBznH8ljjNzX<XEl07nP12SgV%#bnf15$F
zBL`qGFdzYmg$F$WwApg-o8!5i`(Ay~O}fZ`PBK-o{c}wEnyszH3%A|yux(xX%4|$U
z>!OpJyI`ff<#Uo|O6})K|Ae<<WqLZ9ZtPx~3Z5yUuY>%7CYXeeWxwaC%x$c^6OqT(
zs_3tZN489jUom2va@T`*=SQYS>*Z@7p1k%ggR+DkF1ofee)(u?w(;4{vv;eOJvco%
zJ~tQe;&T5*=kKds8m3EEp^2VRW@J;n&OEEkUg5n{ys7B%YFt-LeoC;6c}pv5*1Tb%
zz;2=a(RD>r$ltmVr_)a-;vwG9D(UkVDBEheR%h11iF%@3D`TL+NZ^E@C?|lPqDfK{
z!5PhZK+8!H-1y?^RO;?w#Wa;W?WsM3x-!*jO)2`|o+<lINFBaAHMP3McO_AjT>rFf
zzfX__<JXq-eYT*CDuTUFU-~tJq`l9!HB22^M8-KF1=#%S)RJSG+XeFXMvubtBL60O
z6PgJisURcD=_l!2+mdEIpjoYeDgG?M@~W)iYcZOCAY8mb2d(eJK_$Y)rLmS0MsJl@
z$rU~2ND!?UR%kA!<skX!FH4QovkN#=4PXvoKU($S4`}R?awINx%F-&H=qk->u~<wO
zBr;G&|9nwKaj~GdS(p5MFDg&5*j1uCitV*p>g8RR6=m_tbhV+|cG9+g5EiND-IU3@
z0Su>2-i-jpVv%2SbB^>-&};ze#e-h9nTM|o@MLJ}Szb*{8p=Vf=7ywL31dx7>z=2l
z)nzD(mQnR=R#!83ZuW}WZAF%c=nJYyvT_W&SWhduw~)}3^lI*c3$nVB)^iu<Nb1c&
z=_Kl(!tA3nA$JuDvxfU%VjGSW!5RnJ6BNz|+l(fdHj(iQdvdc|=8G`gS6QA(A)^Af
z{|iZI7f;4(33-Sg9_CRo$McIKPs5LPrF-;Dw4dgiXj5GU|33)r;6;81LJ88V4GkfQ
zKcFCy|0`ZV!FHwlV1k~%@teB_&{H?Q*M1MsMBlz&p9xeBCcuq>Gc&dj)kx;3hsYwO
zWCX|{H*^W^dFyKdUU5(G#3T$9lcpyNmY|3l(gjBseanDkLlNSFVH<)K8&!ojUW+w(
zwYN1Kifq`XqS!O4rJA}dlR-k8DOfhyRpt~`RYnZe!N|WBlXuipFVkeqzKeUqX7xfb
ziz0QShf<!6&mA9sI!n=0ZB)!9EhXXka~&W6k4}<N=P1^FZCYE)L=1#FpoVzVY=y+e
z4cIdznNM2jc=yh3+or{L9m$d$m&RYSNrl}nNOW+fC*-`Mmy?8h>5eEN*C_hk@p}`6
zOs0^4$0Rb-t|YnB8QA+UtHOwD1?mti>No4l@n#Ugn-}?_Ws>)0g(3tODeB(7O-k^m
z%aXK=J%*R={V2#vPhJ3l08>D$zi!jK5YeB!mPzV@53iB(FDx*Jav~W({_y5q*7oh&
ziLzhDLPauNfkdzvrWh(#D=g{y&3dfmYhH>Cd7qXk$%cH*OJ!3|*oL=HHt=hL{)zeH
z?xOt@upM!TiHeCjrnSb%gL;sx%anE&l;lL6`5??9xEZOgbrqlN?te~V6}^tsSgYmX
zWeM$v9Il=M8L+E^c|a}Gns1R>SIB!{^Vw0KS%$ZJt{yXjgfBIk5l7jkz2X0H2L@`n
z+&`V+hJp0wCZa(OZ`$M80crHqupkVdAiplZWtG};aLw?`Y&6;1t~WXa$J8u!6PyA%
z`@I5hcNHoG8q;d@o<B&daE&&=)+3Oc=YkqxE%SlkrH+K{uG5b|J&khPxO=Ie1$!1g
ze@EMAMC|sXF}*0r8^pf(we$A751kXxW@9?Kk`_3%dFR`T%!u3n#nx3f!Lm8qfdE&Q
z)}g=|6uo|tNL1H=UwWe6d=Yio4`Xm1!4VbL*ROhk6^6J@T*ZK20>OIgJLjsG&Y;vj
z9YsGMEmuZZo@kxukJEJZuhBR46?{s;@LQwZ>E`Vk6-goH;R)}QcyBAO@#X#Sq~l2z
z?`z|<lVX<V;_w}!w2zFF<F`xV{>S>8cvs&FZG7>Z?b}auP44aJh>G3a|7NYUB?<Gi
z!e|Mi=w|Ki{Tt4LDA(PNB+`yNqI-eg!!jT|&(>!=s&h8UkPgZYmYek+h6J#~0sqD(
zARU6H2$n|EGDSRO>j4x<p2$C)772Tpj2+G27kL?w@(S_9Et;%qV&)@|<CBo%CoO)!
zB7&VD2#F*w3Ox4X19s97P01W^W8<D(&R9scFQJukDKD!=+|d<rT_377Ybn_fW7i?9
z=rJL6h9O&^Q?$xH6CXke-P0`%Yo_O9=ZY4Q=_P5PB*g?(vlDXcX<B>FAsO)q3kU`F
zaI}svwMcAIzA=jKd5$m5CzV$#_kBKJF|C-H{i}`dvN7xrD_AOkh<|Bm_;;MFv*Xt{
zzQ<o}I2ykdPI^+p<6L*%9Bwz!4UcR#iboPTv)=1S;r-bI+g4EbYcOh2F>JGeeV;B#
z`Mf0Qk_lT=g8hXMnP@3niN{JLd4;m|{^@4p`=<=i5~EAQTP;ilvm%vjJg=1UVhyN+
z-yi8)Vl?K1AL0Q(Yo?S-@;T&cpAK`IhyrdTFF=3EtOiKYXTM8@>f~8PYI(JLXkInE
zH6}tV@B8@Pdx}Z|=l14sinMHv-zZAU=(0CXc8%D>CfZ76i>LQ}VsE^N>BR|-dtq|o
zrm)PL%Za-p`7j)p4{stT{&^rF21ESMx@6XlTr<}#)oj|A4NjuIb_G+Iqz?;Zo1TWh
zY<alJRz)Ca!onF7ZF(C1{V=uOtOjRUTD6I-{(3q~>-X!7@*Xlo0j0`b*Dh=K6w;fK
zSVNnE*>B)4->mg-P01Kl5f-Z=M!L=;BqYgRh3r*$ET={6@yJz?EDeY<BKoQn!#8jC
z5!|NAkBO2bPK+6vs=oxHkIAYey)3I<RE51eV}kDF_leT|>=WR-%9`1%q?>1WZL^1`
zy5#6}ii;iNMxbY_^wJYd@;xgMT_kt7G{@FKU_rv?IYo7KuxNC2H*-qOs+V2CAbhTP
zSuS^XZYlQ<>V55XUV7PrnvJUq3!hjujeM$PgE6IDO66Bisc;F!1|pX%k!#2$*+mRj
z#ItZk$G3{vR@6e@s`F<L{p^Kg>3_bkJgAY~L6h`CS8_LTujbyyeSrH7?hn9kCTjbo
zuJ1FL*N%qtQrF<Vsb}5!c$Ln_CH7*uz|b=aox^Xh(~Sb{gr%TXl+wQK+@xK|rUO%7
zuyUS@Ry~M$EMy`;Zwgg{zIjf5F3Re<9AIn(vFFZt1;dT{B3*}wGg}vGEe#fcl&i0b
zo`KH=Kdvnqna9Zhvrf9I$N$_=<`t7Px5qK~PRi7W2T|YADQ-WJobKe_L)s;I;YkpP
zC?iY_Tp}z4DS1TqVL>NJQA3)+acZI7omK@y#zAWK+MKKB`Oei7ygxyW5<#Y&W+bIm
ziZyNV9e1qRMc${pG>b%r(SX}rl4IBu3cRJ~Co{<6b=TozC_gnw6bvBEm63rKKd5-J
z>me;j(GePu`5TidRhJyqj#pY$B=cs%LKvZ$%fG=puFgx~n2OMN<@TM8YxY-=sUgi8
zm_Ix^`_jDR+2S`W?dDggs-Y>j-Jw~R<tDNh?AL5jS1&r8D59c&@PKCXyei`h507q7
z$h?x;TG-lTY4Y~PiL&DElX%C|co7XP4sD$$hb(3L_UR+juhDeT(fpbkBh4hKd$-PR
zN2q$>s+oiqv(!}W;8vnjPm^(5_bC47I`!w92ecz<Csv-LK0gdRHID)xK0s(h-3#fp
zMm-@jXa6qOWAxd_v~7|xP|0h!oQj*G5_bhNW_e~YRv4kwc_W2kq(3GrO8cq6BjeX|
zq|)Q1gkO%UId4#Pv7#0&Q{=Nsqx3WQ?4B#lDp?fcb4Zcyl9b8N_8SF!WlsRkpgYT~
zUg<mxL+A-jzvYNTk<rW4s!!QTxhhGHHjq@TYNJ9LvLACOzH`h>$BJbmfgUqVUYi>o
z%sIKF;U=<`YW8y7L`tey9id>*v*XqLfRiuAb6hW0o=Nnu1&&KI4>e%YEjbJ?uUmuZ
zF=3!0OQTYDg5EFZ0pi^%0@m%bXf;a|6OXN@qxD?-YPA03W=f6lT)V~A+nKJ|CP51=
zb4AKL2}`~KV@GmlV20FL$f;F3-sA@schsv_Y960X^PYOFTHmoaH9b--COj`es$?2)
z>fBfiEtj&RyRoj_cAKW--J{u3!pUcH=}ab@$;Z9GE4ynxUFY^uq><Y}a}6ys=IVZ6
z;W$kj9W`VNyfzr?De#I=4@MeL=!rzB0VY%(6uSD1v<d!c_depN9H=NKtC<x{+F#j}
zghJBE$UFw7d0x&YvWBh~<CYaKDvBG=B5ufx73pQgSo?`+_4B4EhKk^Ag&!7{vlSJ4
zIfdxQykiZk0^$`O3+ixO&B%r!T%4YaXIrfybIASdJ#<Q`rDE!_xEig8uF>UB7-NHe
z6?FVPlpO=SLT=)24K4iwB5i7A7JB4096IGg5%+OmqyX<&O3XDI@V)p%=1V|gEivae
zs$eA8fO}wioCrVUL?SpO8W5``k|Y)VRV;5U>`Y~1xs>5Ki?h^ZOE(8o<YFY1%Hffz
z_O~XpmZc{D+$+19m$M%pE>){xyj&X`Mk}>)+%=LyakO$<wLFC0faCF>IO>QjXo?{D
z*HG!~%}UBjl@Q7&Ox=)0l^hw`?}!JGC@3*e$9n}?6LR|nyjOo%G~~TJR>h-+cu<Hv
z;>ZUDEUM_i{hoYKeAixGIv`UXNLX+G6VBqsxV_v8_Zsfa+=JZvz+by(!QBProYeuV
zxhA7#jcPhP2=&{9Dx(M3iNF*CWC>?KaS}D~*+CJal%na<hUK2ABud5V*p^yp(5sZ)
z!obk5qV1@svrd5>86O@jCoZH>nw%VT^Tpb9(Ww=(=~%81&zFX0x=uPsRKi%sSnXjs
z+1j&5ZLg^!icXns`;<m{70KO~#H4f4$c`vFqOA_4B;f=*w8N1^S#VjLB;QX)Dbmgg
zuA_RgC<>ZR4&me@jwDEMnkdKj6#y+@u)!Se=E+7g)hV_K_Ci}WW=A{?Jy+D$8LtHO
zf{Hu|y4pG;D#tRq#_^0)E|y7^OA<nbWNOgVY1^G$43F$wu=25e=%pk-swf+}naCql
zBF#@}-v@2+8lN1|1bIYteiD!4BI!?mPIQnzCV3iQ^7kg-#xRglN3%+kIgyah<oE8T
z66Gl>R^DwXwJe&+)|B?wr{tDAwdIQ+pc$jV2fpaf4wmb$yjaoJ)->f}iiyK;2EhIt
zaE<A_UnMk8EQgUE$Z8z=c)@xT9fmqk!RQ$C2K`0=tkkiR{1G!%e`Xb|1t3T=WA*1)
zKOXRlc8o5cVu#-gZuBPtd-;xCGDTlN<yyJd^8okD-lx`EYZ2MOT`FFO_O}l3k*$!W
zk_3-MhUg)d?q15T3HHR8T5eWBiDmu-%r2fjn0&(gjti8$(_XPG3=#AYk>!t~gE?;v
z9=y*S|M2T$PRf8_o(!|_)PK1rnc;fM(l~2b&OLXzv3v(<ISxWmqUeUQ_8z9ufX6$?
z&nCI)eNyi&BMhKNxh-@hq};6cd+>~*z-JxzdkndAO#pd#B$a(f#)s@Y073T6NF^GF
zir$<J)3!<iFOOVTy!6s87|^@HbEf@ZSBmYBf3J@)G#;mIdw_ED!q;8%t2N3M;gjCA
zMu`ubHNaY|bS<z4(|Mc4HY~I^H;JL|i=I`V`v|hy69S8w+EaV(st)TkM0H*kbe_-U
zcwQG|ejv#rLe3xHd4q@yxt!2_hLGEM270UMi5B`uW43*782i-UY6s@GAEM#3KyHBf
zfgA}8o*(eTTPAz&eXcXY?*VMtEb3na6gi|}fS(o-?aIBo=-=0=>6-e4Zk%CJZCyA1
zcCBM4x2EN_zFoTR@I4-E;K553-qWDW@Gm2VxG}Ke2>iq*sJ{X2mN9sfu`jX-bgCe5
zM19eh<?(If^3pGnJZBIYd0g|7vXM%}Yj#mK6<#Xm_GfL~5ng>9j?G>BA<MR{Ozrl-
zq5WmKN@W0FweR3%86tg|?o@n?Y-J-Y^13Nz5(&c@DBlN1UU7wzE#|Nl%R~^lJs2)v
zcRtb2KM?hzU@ZxpgRDfI(Ygz)<#Wt!`46XOr&SY4#ewYuMG2YWMmB;cS<<ItF~LxC
zj%~-Ys_~(~>$x{ic{=;q<9{EnoR*^{RQf8whK4%3CarD=8M6oH{STbU*+~aGME>8t
zdwWvGPItZ@>!up{u9r-Dis;}>E|<ZM7;=|=kzyeD+z_CoqHZ2JI(g7!B4|%7{`XcW
zf9FOuZncY6T>S%kDK@0!3YXnR2_~+9Ap2t1B{M5p+I7>~H$Lnc+jZ5>930+rlNQ{K
z1kaw6TVt~Wq5lHIOV~mg1$+D#rg5!FdNiMGWZWd1Y0n4T;N(oRwiAK5FiGroW|+$C
zbOvjS)`b^Zi?y=7GgDPAujKQse7<tIQhhFe@x}Qr--bQOZAXPUw=g<daOZ@h+mgFy
z2lc~oO^YAa2WQ2r=jX3x_RgfESVb=<tqN8U7%Me>O_3iteO=NE7}Gi+M$w0Sy1mY5
zKS~S9tCI^Qy3_u5EX~p>r&*YFilU!q7$(~Ty5wbA^J(8t)5!1kpH8E<u;u-w0K`NH
zIBEeyxJBgpjR@D##3%#mVRHl>kAQ?zYB0?_$zU3#a-)ux+En}iCRz3Umx`8Xh$2zY
zNry2zq*OC&nJOwOj$g?YC(m$eYh7d7<(G;gtn5@569wT>5a)_Rv+Lj$-d<bro!r2<
zAdD5im)*TeWm`6n+l*#$I|P5Bl|9~Q`tEnl@uL3#<9_LOQawo$c>^={mxeEu)vS7_
zs!p(teCe<Zhg{|L_&<`EUO>GFUaz>Yk>LVzVwPM$H{^ogj5h!=CFA94(Y1C=^mm7R
z`vc&0O2iMp)GXI=IZ-EF;v^~w@*ru?b<$I!9~{47-_*YLzwBSVWZw-pybej|07|_b
ziD=L3Zg`sPt*^nq;?H@3KhypS&+q>E8D3!W7Am48%E!{NgY6M*A7aigr5cb;UJL=U
znU72>*rx=O&^}<!;VTrKYmVH_;z3wqCj7JtXF_;!HtV@5rPCMcS4?|2Et%pNdAB2y
zC5@2nm}p9A?9Clt=_PVn=bJB?Z9k_gN?b(Jlq5~izbOfcaYfOQ-8f$Ves?b$Vj6(&
zn#_$5DRUQhkmKr%L7-9&ctB;$+{qGND6@sFyaX8y)So&-;i8L|0@X~)Mb}(gN9&{7
z>`*?H%x06R{Lrj64mu`qs<`dna(v%06y37Z_g;~<EnP84%2jKoyKcH`Yo0eW>%@kJ
zV$SSP!gWoyMrOMHpf5^xyi$qVl1O&iL~&dVxTyd~I}36-nmY^0D6AdhQVsoI&5gD9
z#$4^&5v8az|KX|jGgIh(J?nW{{oCsyhv@q0si}zXAb7?(pRoDeH@PuuE8in+{NvmK
z?g;lXig7K3Xx6O3z*{Io&}7XefEuxQ@`Y0L3Fd4MX#WcV5{1N;*#LIJ4;CyYA<i#u
zk&?xVW6Lrx@rG>03u0|#WTAPexiB(P6AN)mHpmfKww+2bDQ)2kjY4H&w;&7&!tRL*
z*=s+D@?Is8A39h@)A>Y&JYOsy9D3~c;sbF(hMUI7%|&v94;h(^@gedoYH$Gel?8Ir
z?<vau!fav3c&#?RgGBZ#m$})2M8z~KiGi#;Vw&N46XQoRsE0z29FhQOJHX2)H4+%o
zFuEFQJP!spL6^%05mIN=K!t+H(q!h~5FTX5BQl6CDdweWOu;VEOUNj0N{TA0g3FWM
zM6`5Mi7BQ?k}`hL(6X^OG7U?XCnZ_Mnk<q3L~_KEB@2qAq-AyCDl1NU>n^e9J6IDH
zQ8Gjx@iEyWXX}!f7Q`fZmhBk|cHF$I7JbX~aY84V;BBPpD!H|aMWX$XFWn#)CDhBd
zsok7X26r8IM_;YE7GgD2Zp=+EN`VPP*JX*4Lx&;2E%AbG-(zNnjQ@vOA_f4%$?%hh
z>P&deYG8l76ZUXd&BYW20LUGphnk*>3*{Knl)+e|oKj+jF(3jik0=NVFQ_7~i9(DQ
zNekgstQHhZdIeDuuqy5`RZG1<mExl4in5vLWx)9mRxqi-NhzxpJhQ0AL?><{)u<0P
zVuq-yI*$fOrx_#qib#G6PaY%{M1@ymK_g<COx~(u@?eJRUd$_5*^&X=VRTZD_15Pu
z?#_M*T`o_DgJfXF#n9Nm;22zYNIl{1qu}@C2lYy8lWobHh#DYk%q-3vr4wBhdNFzH
zq~9QY2hlS$(dQMxmGGi4clBJMP!~kfFvvk3$>>9pD&d$cnYxt}H7pu}Tod>dFBs(8
zM!YDHmLrhw(vZ4q^I3)5U4~gR@($E3<P=wOV&v{xRZw|Z)GihzQQ#lM<nEHGk+0q0
z1x+Sxl6R0~@`5FZra**V%yBB|*O-0{IMyDQp@?F@c(EhU;9|)0>!2gb6{2_(Wt(Vp
z^*O1W^vGsbnr)V4azfPyOQt3!ZIqlyzD}Z^UusNko6;5G3j+B=`;tc*<=A@TNb(j7
z9lt{m?oitgkU+gd7}nuLhaPI5r01dH-lIqNE*Fc%7Wu1RXwO{zF8jjz)vqrtmPk^e
zM7D#w;T)QLH9dn1?eZTD^p9z75v15=Ju<ltCzfcZQt>EaQXqpf-e>^eB-xK*b&Au~
z*;zjAnzrduxs%3Ft1<rfTN)Hl?sqNM&?MbAMSFV~(Ji;t6{kK`Y~hx~{=WIrT<)bS
zfJb+OYY`>Z^fgg4CZm{lxDyTSIoO=%4cx1^*HEsZNMfb1T@0vZ(`|1+1&Dg!JW>Xe
zm{5Fgl)<Tly7vULm!0MK4x%l3)>bk7v(#4C&&I(Y5d)}q$P$Gt3E}{$@<S>P&(o=^
z<rRt`vC^TN>JC@PJ!rU!)wR{-<<&JNUa&Y#W{@bW3_6m?7tfPH=`HB3fqikMN;-DH
zF`%A|0SL|{OmaXk+N><1sst<&!HQswzped4-*5jApwp%tWLp`eD)WkpGM3G|f9(6n
zCx_bqvs8MIMb`&OySM`5D@4~7V~LD9sNIPT1K+8YG~e2;D3_B1AJw$++ZhAw?ctbt
z3az8haVg4Y=m%gt%}PKaQ(;&as1Nj@dGe%bN`{oR&=zp&Zhy<nl3f$s$8doF5y(PE
z_wE%_X5KKQdj&_cHAlEtGK{>DYJaDLK14A>pC_{KTfnn?O9ZabmlJeb`_GIt85vdc
zyH6XcY)NSY)fG+q@w-XV06BEyjdI!;Acx*9+UlSRP^p~RH9z4}OYa)#Qzp4O<<Z$o
zzR1h^`e=dbkkIa+an(9A)6Bi8f7@9bp+<%@ZKkIS{!PVnhlgELQJ4^YaEknEMS;SE
z@j5wSc$n<;j8*rNR9j(`SAdW`Y!5F7zgEC+M{aJDyZ6lxrAUv{>>qt#I>@6CCZx%A
z;_e{h%BHThNlK^Pm3=`<$gvm%?}=isD(p=95;E1vM<!wR2q=fTvi&AiP3%eb2JQcy
zN+dI?A<${0Ofr$8{o|A9Ig0C9;%2x#6vdm_GEs=dq))wiDF9itOfJ`8)+ogEt~M4?
zxj$i(0--0p+$gW`qdBiMsVp7}%jfBDcgs971=Xw?nqctdclPaHw@F!BIoEFrLeqD1
z#fg#?9|`9+>QjO;lE<R#B$E!2Me-xPd+ld7)I#rj8{j6u-l2aE?Zp?kds6KeC}G#z
ze25f5kh%-hwYOP;y33O%<KEu>c1Y2q7f~LCOXmK@{5L7tSCz3IQl|E93Hu+VjD>kI
zm3KrB$-;iy`ozXU+_+zmktaH~l@fVuWhB!3c9n~CU?Q)Wj+Bk<7eo{nb3g1a%b&)P
zDD01A1>P|+5)IP^Lj^hViPp);-XP^W!rchk9ZWA$KlLQ-;+dy<6&R(PFw<!9NuRFP
zQZ&&;zDnBiA`#aLu-*l4vWX{&BtYHvp=PSS1ri`E>r%3Ln7>9JI^b9Xqn52Bx&4<U
zo*$ucqRN>>Tf&pKp17o<WmBcz-7h0uhgm5R)C;sXjB3134m{<zwkL>qJASh-#w8n}
zTmmg=jy00E?1PfjJ{`m_5GBO%cFZZJvs&en6Sq!csdu*|i!cMG>&WLd`N1gm5|Uo=
zb1@>3`!|m(2q%mA#bp1Sc9t8Z_@zB<rGrHdIs-!<r<w-L&2G|ex*ADoNzw(eR1yVU
zT9QA)HZn_GK6jh{qbZ_cNyEb=5T^vI6l?{SeEQsNzQWCey>o9l7$p=d3!{pn-dUS`
zxlP=21^}hOg<S>vyK;=z_*j{U?im>=Vxb^mBxeNSqZ>>0Msfz0qBErE((d9uDiuYs
zC?Qoymql5y9U{4x(}LRm&;FABXYUCpypRjNizoRv5&0dy3+T^}f;VQf>syduy@i4}
zfDzAJ0Zs6vmN5G$Vu;G^q&|L;4)4a!$G~IKZe>K9?L&OdOUe6gx$l;Jatb~QBPA5?
zeBjQakh<l?_lxVDPaz9WlITOTUXGU*Z`ii&h8-e&5SOKdt$X)w6;e<BLSw;~X*+Vc
zSyF?S0s4i^779U8q{hBGkPtAHGg#|RH4tr@QK|Rf`6s>b`=Nx;F@!35g2IX+A)&jK
zMzuv6*Hs7AQ8_BDJgOeNYR?@voZ}n1fPCHx#tP|L)Sa1HCEG&xlKWh8U)J*`vO{6X
zhO!f$m%Z;2a_C#<{H!{D+RT1ngwFhfOb~$^ov+xzBy=@cTk06B;7uwqv@Ce#*$V$U
z)u`$kxlpJKR|FJ$FC#KI+*f%>VQX|J&t3R`1YkQ^QRAspTvbYp(cq>Hb=choij?7G
zKkG}*UGNQ4!JC!%xwDn@;huH3sr~|f{@f*drhAU~ZJU&6<Jx5t_<F<65sW%RgaE(q
z4dTuh9(?m%E0gSrkCE3zr2gnN{YU3}U1VQy*YZ=qBwg)#8H##Y^a-+0vOm8d#hh<l
z^E@(GPX)j9+@nv=%H|nvlWqd~T8dHE%LBtT`z#^q=h?5k$n%#YJj;hSiM2m>&ckbL
zbGBgp+=tot!f@?+%Iuy8*>wYc?29~q`CPejKX=ZfsH@DIM*)rI+-LOQUDY?oyBg@C
zQr^h%(T{z#(u<H2M0?<`_J%}_r#qaDBpR%l>_T7T2EqOySPh(`tK%~EVO2ffyYN@3
z)-Z#ksn+-faM#cWM8({aKXO^&xI1l@>;av*aU7T0e=kXAyvl6+$5?FpqG${_C1Xn=
zktl2-U&6Ghh&foZ^d)jGOkDjqK3nn5NFqmZ``39Mt#fnK{uFQzqe-aFOxwmi&@JiW
z{mdL=y8zS2W{CVrjU*!+*5?IVYx$*=EPA5cwQXrO7VwSPi;iBDy%7uTGo({-Ow);{
zrou>yefaNGwNtQ!bw5x+{5HgX8)ElE?35OF@ofWz!oY3Vz24DX$8mKh+;;J8h40|2
zTpX}8D~xfH8nOU1GO&6CAQpj>UBGk`L!H$)C@XShOTINFWc}?)T~F-rGlGxat)$hL
zIzBb|z1Z-0-i@t|7AKo@nfZof`~wF1nk?UuKH$4j(lC;e>mNw3E6O2DwbYnl`I##9
z{<U7C=A*op$a~qz-9XWQLvk0G2KSTQ^xJH0AtZ<i*c-cOyZt<Rfd%7dWJxT_k|H~z
zWX0*^o<AV)DW~fI61$I}isGxs?8FLymLAgMyx>|Ns1?Q<u#YZHIPcaFnu;N!{PIMI
z6AeM-<4`GazLIj%(pisu>8y3b)&_TRZ=x8IFQ{aE<NUAZ-m^WBVJJcWStWv2y)g@<
zF9E(PT@VXg?K-~M$pl(mk9|T;8tu<&R>}W?rU^;WR$rg3SlYvuv7#66%K1pp3`?{9
zlC9QNy^>Wl3m?F2UT>ZHeos*u?(DO{wF%UN0ZzND4>~D@=&|9isFt#l_6D5Lzh-BJ
ztmLCs%DM8SlS<jY^BoAE`X3Pf>$oYoCF?VqmauP6`0uw<Bp*Mg+)(S>7Uu1<AsfN-
z@y1L%;`oX`=qtJ5oV8CGs@V9#f+69S9M_+<l0p&r&-hLeKHuUg=sZt-Hg3m;+&@aI
z1LlgVJJtiDwB-2F`u-E#7@c?I{023G3K*CpcSiaj`ly<P;Ex)DxP2fB5n0F8rePPn
z0XOZLa?1J)5hVnoTId2EDl1hdHfm+c8PC$zVWyP?f6e$h++R^8tfrluq15sPGiCtn
zmE{>;${w!CmZ8Owd3`*aMIQ;LXsyAJyaUQ{hDYd4VL9A-6_ku%fsb-whX`mauM-IV
z%FwQ*dN0I`A5xrc`Gs?YhJK+cVEEg#9OD5831kbzP52uNEw}?5n`U6CkcR<*6hi9r
znT=>vcrF{i-b8WoLCCdXJ4K3ML0OELS;*HEBj==5EUCW|&$gF4GvGOvspp)GH5zlO
zN_hwiI_XXXA|ZapO39{|b_cwIZJ3#OSP<*89X^$)Z3Sg<DLF&Zr+?J@g8pX<{H$QG
z*hsx+=iL665)O9{{36*w>;H`kpmtx+Q@^iu%5~oWWa~;I^Z_QL!y*jU4r86DH~O})
zkpNR8_>sP+C?u^@p0TP)*^*j>FS+Y?hEtSd^zYQNBtBp{_DQScO6EB7;ER587{cKc
zgb=)Z8UC?;`!s1gk5b%iN>6jvjxCfxl59;k6v#;T(%`vVZ9wV!Ik6Sn(w@~UWlBLi
zy@8xpfS@=D;S`NOfTv)yJ5e>SizP5rq2R{vihB{JF3H=kL0{J-0%D`gMwDnvnshM#
z3Vj{C`C#06>V<S<%6a?4a9k6Jd_tsE`kY3My|{X~4Q7ACtxQcZf_FTDbiHKksEP4n
zPAqFvbUPcH5(kQoIIb7OTrn;V31UV{+kfj?zht1d7)al`^qY20iQ6Ci3%dEouh@<<
zVE^`;?6{J%Kd9@YmU&goHSg0)ur7DzOZ;0%{;JfsupC(1Veliwp&1QW0oS{TH=2}R
zqCCiQn`B48Tb{@p&pa}^eRP-QEa_NY!WsRhw`Z!h>4F=@JLvvRak~^F_4awcM9x9n
ze13E^|Js^5;7m@+8T~<W-S%pRi2L+&sL*{ap~mln7jmrR=O*M#;7`3u`m7;97HqOX
zD;cQyVM{Ae!_=Ui)hY~Re=u-No1oSlVMA<&!`z=KnWUgp1tgl1B_1borhWnoNR{4-
z)eJgry2nLJGDRd*6(N~Xwq{iPR!Kzyeud_KR}<}wUKauIL%!vGptO9~G2f;dvaI5j
zvB96X=KF4u$<>Bf*E6=L9UdH8!Ky49>f21`yF|YMo4x00xsu!fc_(21b~(HkYAQiZ
zn&<>M^jEbilNg;2y&*FlixhIHhM74xTn@H=u((S7%c&I<`rH+!dadaVnoE+YA}RA;
zQEaNqr0S=bQ_=IEB0AS{u!H{aSMvVCFrU2asHryNFYy{q+$g9;Ny{p#Dxg*6C3?x{
zwL_w2N~wDU@rkC|I!&}X?lkiRExP9EfbiRWZO#+MwtE+<&C{1Ta>CX`qbjO`uIm}j
zj`-SC&ZYi3;HUW-?w7dtaKFm^2KPJMA97#k{)+n^^#h}QUg$^&Hh1++Vt#Z+B}yxm
zLk*~lcQLd<2z`S3Z1!MPGw`gXv$nNq(C12B@e1|ahZycktw*W7vF_uW#h($RBE=yh
z;VBwU(eNO(kuJ6m(mnJXc%7My2Yo&8LYp66KS?EF>YjLVeRy8=HH)-F!?2$JMi@h*
zBu4&OUkg(X1qtEy)7=MpBZxfv-h06ouRo+vcK5Eb9n$nom$?SHCN(Hn8J=%-bne#t
zFi9=ip08^O-?O906p1CmNOUaGx9z(qibUKck8&3RM+c&6L`Zas%R!8)6$xsAfFrQh
zz#yT~tOqgH2eLbm7`eKR9>?Uo3yHELkBHd57Cb73B^kw*L<~^<7y6fo<c#y5^V#eC
z>yS1qsh3HrEMCDvStJRoG~6Wl{?#s<6R@K$ftPT<9d)I)lXQwP7;1J>RNT7Tg#Af`
zo^8|G2TM}5{iiK3=4siduF#bEFAh1cdT7eFi$(J5dP#6bu~_^@(*M-WiRynMj-F7o
zIp&`p%r_i_c@Q8brTRBiC%CXsb%IVIkjiXgFN7j*)chpy$B=QOifeuiH!)z^_)Wiw
zeT<&HZB)Q9<(ffWHB^4^8YPB>(c4r-P_DgD(G2kxQMcv2du3Y}Z&3|iy6{?Z=+|fY
zQi-1(nrwJmY<r8>nEZG9gc1|Q?KjI9%QtTq#h7x!My4$`j;P7h?TU`Cx(e&c?I~Wf
z@sWmTo3|KqqoZ@ia9WuesMiN(lyop}OMS9eDHl!^xEK2QaB4Gv9SVA649(T04W52H
zICd53%+hHuH)Y3O4*KLzjimhYn2k=diIH2`<{_AS@*vnQzem$`?LG2xj{!2{40sl;
zQICK|Z@;zCztP#?GX@q${b}IYj-CyinycTeMh?!EN)+u+Mk~?uS=Urrd%bH~tt%b1
z0$a=PQQK*lzubNP*;)Z0C2$u?1lWdkP5|GtH3rH;YVWHMn6Ufc{)PE{Je}H@UtU>e
zwoUxL`GvJ=rj@C>CZ#AL0o_I1(BM6LSr;|GJWu{ueD6JjLrj)~Jg-sKmVgK@RFcEG
z#*Hv*-6XPXLX83)Aa4KN`!vPkcWp_5yqfYyY-+J&kN7NM%P!thw0(D@wS9N*pHgiR
zZ`o;SCp9Z>^9<2|x8pQn=N6zVo+3BwwLybAy$EF9o@vmA-`gbWXRA{(B+dq|Je}HI
zXB7(ddorwNbGfg2)6R_)X*B2kex(7`{H*C#vQycLYZiko84J)dR5LRtW7YiYPMSKD
zG%CLy*7bi9y+S6kdy?_i(f&kc-3T-NucANC?%kdCy!7t9*+R<6J)vc^Cvr{-y~#I{
z8PCfk4L@_y;eoGf+LO7%7f~PJ)4Yg2$(eNagNlN<O!+^{Akqd80qHyG*E`^rb$D27
zTJprL`D=b(ipjr!>sy8N-hJs@F1>GWT1dsM#EnB@LfSShdQ$e)2`m21>s0lP2frZ8
zU--5FPD=v=5DKYXdoovO>YJ}s)n;bTu5hjv@D*xAW8Do5J7Dxf`jGGg?o3K0$~F2{
z3PQ38cGf;M4Jvo6(I_4SnnL8&Y`<v-9sS!Bf$-$1F-aL#R8(BMLR+2OtG(grBsj>J
z?Jv+#V85+<3AyE(J8s(EJuq*R4ds^Bo#q$Sy^{|nB}u2rqU?!Q)ZR*@y$PHHHvI{o
z^xm|$_nDkot58M3C*$Vz<vm*!SupF%^)Zc%zQ2WDxzirAzy7ID$M|Bgyyw$6p^cG)
zCJ`P+f9t-TF+TR`Pkr4Uqk3>Vz>m_nG(+I_DHk?;Rg;t!G$NszW2-o2>T|);g7rS?
zgp=%H7K~`+FBMEv6m>^P&w3*~#-(jah749MtfM$-rsYW|<rSqYFUnU!-#eK|YUUL4
zX?`1Xgi4vdfK2^{#<q{iI<E{Ct|iyn2)~w`HAx!x&59#maRfe@BN=z{QeS;G^i^tw
z)XvP$m$Zd7hR97ZKY$RkJcy&teFB9Hl<dn)&NMe6GVfzO=l7X0)rnzaY9QuNU-iuH
zA<~Ud`_KAo>miFBdAFuJ_|ZVzUzS7#3z?E^L9aCJCdUqq(Oe(!{R6aBv3BB`S7%0-
z0<f2mfB13oh3#bCbl^-d!FUIgfukDGD=~mxI&u~$`BENiA1G5-P*&1}=(6G1Qi;e)
zy6oba*39M8)0fW-?Mm0NqQ3{L9-bLIHcd|YJx%775n8xiiU*6F0~wqkS0QP5`ts{9
zpU$M}*i-R)bOq0phW6b4UPJL@2E6`2T@JYJf_X5ozu}ySQe$wUi>8!9MeMu#m+#)!
z37R)ud+klAm>JJx#%Cdzzm3K-w++WED;DzT@w>>oEp;>ebalt!e&F5I?9BGlbtS6a
zcDX(g?d=&$yDLJDx=XXfFNzBJ9X(3nqOP(A&9;Dqw4jMUA_smXY9x-{_HH1<S%FJd
zfcECyviKoE_>kD#_aLNQa7fqxl3Y;`g(n2^M<QAWzYH)84{%EiwK$lTx2TeY8$H7{
z$ap@M!@&_gy{K`LKQ^lXcD+#(ZK)<WUWxaX7Y5f76>DggeqRo<V;L-^iT=Ht^k3Q3
z{j{+bZC6U^B~@<)jrC4bjp?o^4=$ABF*G|g4^4C4H5G5j+UvU4OW06e(emF9$)7E4
zxd~TwmFVHNwk<>{;sU&p7R6`-dy5OWtGG9C@8muL`gfO(*u&~^KK!%uvU<D#pH25X
z_uz}GMEf(r{#C_1=z{G#H$yC2+bq!*`HaYWyqMv|uINLXLY@WdzpmM)&^d?<=Izf!
zXB>-Sk8P6n=Ke>}X!|RhoZ8JTWUv*JZcm)3wl=^uhQ{&<DrE&9=qS~imjjI6D2moQ
zA8`8tICSh7gwJ-O3}=?D+YC^aXNH^iAje4wW_#~!cw0n*PHy0B?UTltFCa`q^vk`t
zPiCvtyNN@3++ZVr0kqz3QNX!l6lL-l#i9G<=*|6B6uXrKExZuzzozqQwaprVI1X5L
zKnQmb`pJWq)_w$|TIZ@(C%THIqemsY+(|K__xaOJUc3jC1@xO^GAnS|?ct7a*Kv1p
zZ{dEKymQufP|%nf6Q&tH(pl2|Ks@T!fcwz~`@hEX?N>qx0euvyvJF(VnM76!<5@3z
z(^=9#cluRMVvJI5uM)Ah{cr#YZPVs=?r3|B@xvfXCdz5P?j(oL9=Yd;S;?M;2d$az
z=iv@3=MSD_^fF=n@1koAJGjH#OSxNV{Xa~t&wJ(1jsn>Y0vV`{(s=;7`vJVW*c51@
z590vq4bRonQFoH(Pj@^~o~2?7l}qgQX+^3^N?)+riPob7%KXxNYqPUaw+74N%(aE-
zuB%(3lea~&`=i*cU5_1}pC5kbCTFw0jjmCFMO%M8*Gca{#?;<+TqeTmA_7wJMjeR6
z85K3foZj1L><v*<+1_KhsobgD6k6?I6YaS<9=~}{-~Qp+o@#YZEyBr#{7h1`obzX;
z5oqOQFimTgt0Y_K#Mv@LwwHgwoY_8OenCG4#Dr(d5!-LC4vmlhU4C#d<d_TcHvQsq
z#>BUC=IRisrunJd3&>K7R8(zGh-Jv~^CI5dxnJ0M-xQM{8Q!f+-%yO{F)&$1L0Wb|
zv|D1tQe<{C6X6aNpO#eoE0q)5-*;k|sE1N(fl5<17<uCiCy7bXl>S*V$j_7#B0Btp
z@Hkd4XubOFt)_|}7Y@!+?4a4>n)ZEiMoJRDA(B55F@w3NRrDxuxmUtoVQzw_h8vMP
z_5$Oqj5?AolGQvPKww8+pp*bJ>S3CQ&c%%Ce0C;xNoBxLc<f<UF=X|qYT$uy!qNK&
zuYVyYouVIea%Liv(qvCbk+W3Uh~q4S3q1Exwx)+bHaX1Z-MZZT>8qauvxP1y`!F38
zbf&k`mLY?Wb=RN{GjAN0%87D1J(w7RfwPb(((MqXr99ga1Y^CVq|=JT#5BbiVBobY
zQ*?8-V_d-fu}bHVv)mA!+3IIcj`)DUN+Cq9>F$_`5=!B+mKbIgf(f9%j;Ifs%u@Ir
zruw_8S<6Z8s4L}a)CzAoguE&;DSVX~0{<^X*A+K4s3?P>9r!wx%Uv$VP0&1UAmAcn
zeUvwVlm;sjmM@}c3eoZpZ$GsDP&ogZYyX_(?bTmUG+rJ|C?szR`G9$?Ld^|V(dzPY
zx3%CJ%iw7fyJ<y9yVyi)bRLWgI0-^*QI{)obt2DP1l$&^*8*rW6lS+{zVULy_!*WC
z_mm8d@it5;^R3+Tf5*!8{5nzIg@<6EKTB|umHS9fnH5qCz-MjSUaq}Di#&C36M~CQ
z>=9NS-(;1c7-nEg!qGYM6xec&ppJDWd0TR1zb=#-9T04Es?!#7?O*mwrHNAM09rjJ
zXu<>YBsdr}D+-H-4#MGyQv11;V*(r(4u*EsOveWNx>1VIlIY`os8^g`j@F249(U&Z
z5+!t+JZTl4XHS;U$#4geu)WSEY?dKg!w-<)SbMF=xXU>&cd9hG1lyc<2Eu=0(?h>T
z@<`4Vj;%!V47*4jB^c&cxMI*fa8&Ak8JBBie*Tr=62U7K_Ilgb{`~US6<3O{`{u`B
zS>R(v>`m~zH^o+0TVP-=k^bpv^aT23WE*^8$PzshQH}eh-oZ=g+i&*m1JOHdVH^n;
z6=8DmB<Zq9^bCX_>?Etd(Um^2j6F(7bjvWLLGX6Af#3|+iLZ8|uL2UDbpZZ{$wp?c
z2X-cDKtLv+Qmlgp)TNC-{b1U7Fu>Xe?u<0b9ouVJ%CSGD129Xstq_Ah!c`iC)f_a4
zL;p(BSBH~TQiRhYW4Q#zP$5za`p%{M_g^}3c!K;zvG%X!$HwxRT*7q|Ipk%sNr+@f
zB3i!W5{i-(Y)+F;CuwIgc9Ml;w~tSIwomiLn|8mA9#NY1#9SA~ltadhbozoozc#2=
z)}q>3x1IfMaMED94M$T{u}eYpdK}2B9M~ZNh9%dmZ+Kw0&6FE{;GX9xhM}w|2HieF
zf};#|GWC$5OhMXT2U`-L#{v|}kPCa1yz4To3%L<Rd$>9)MC-Kd$e#@^jz}uPNLAKk
zg>DBIhl7-lkuB-mil~L<A~tByUhl-&y3RUZB;(61<ZEmaox9uw<&r#wn&@I`ZVO8#
zHL#$VqeRAZ-Z_Q@YVsrm(99Id-v>ztGk~-?&``my5o81)BreMdfMX>3DgYRu(+d)s
z9MPMjqNHeome0vZPJ6P_sPI`{<|WY^#dy>cHL0LWvSj2XEliTg@hm^K6@b>{T%KI5
zNaE<cHZqBjxG-FJ<6-mC`%B9UnT#_seKaq`lyqirAtA*if3a9;;9YSZ@uFN(k;$ti
zSrrqgD3X8e(+N?9WDAjGKvs$GQul7jOvTBi-uA&vTCs$Iqthdf8BZ+iyHY%MFz9o>
zOl>y6USltJF?XD5@I7GU3^pay(W&R>0<5^89;*w#_bNO%hG5>lR;`!)juHPCf%%S%
zMEQ)Hppnu)yyE!IcOAOwcD;x$S7}nrDh$!c#1DDt(!0HZtM2i>|M`2|yaNvTx-pc<
z!zh+7ZH=3i<RZj(7w7-PnXiq!)i97L>ojfA$*bP(Dm!=j${)WY3I7=jwh+w^`P}P>
z-puunFJO2Ctkji&(_yX&P*BvN8)zk~0n{aR0Bp|AB>W~gy!s3&6Bej^>gf%R;4q`g
zxNHYd5TlMj!JQkkE`yXR5q-vJe_^EA96|f_n8x#3`%e-8E|iTGwUnK9d7Zp^UWp4r
zTmc{>Ns%l;L@FexNDxSqaMN~5E5@>bI+Tzqf{6K)y5r&<YKq6AP<g)n|5<U@L<7gs
zMTXTJX|InoEyGqdV^)>^ISE%~icTz^cb%kH5K9<|d`v(x66u3pC5kH=5(P^nNq}5~
z)UsIcl8&2?$DCpYGMkKBe38czpQuhxR};L1$*$G@emoav{x4smz85gYZQ&NU3qbQg
zH417pfGTr7kjR^eB9157>T9Kn1G+evWrx{@?meIK$w5~JmiM)uJ%03G^Z9?(45a!#
zQjPXMw0~^r|CUJnn{JWVn!F^+@OwyJCK2*GDT_-o{2r3UWf^|xmP<7462Z}?moQ$M
z)|~8R|ExR0oG8u-j{eV=iTBEU5=o*XBiQ&dpJ96$(hCfW5=jLEew5_pdGhUN*j|R0
zyXQ*FfUu9~noA9b^U8^?rG;v+I$dU6G?Dta$xEjkJB9U%r47?IjX$nVUb=kg<Vhyy
zAbaZ4Ng=BYYZgGqYZq(y<JlZZo4l0CHk{dI7t!%WdKLFZ?%mwSxc|X@nfr@P<iI{3
zo#uaYU;2N1edRpoGod@wBd+eudM>|+X#3<Zcrq$t=^2PW({oCXkZhcbykyhhCI3&(
z<AgK+MaBZ1$O|`dx07Cx1F$tZk`kE>hEED53lUf$vQV&8ln92(2_D1gY{tOHfy>cO
zl!(}U8Whxs^s@lJNlh8a0E<0fNZ3iP!n0uJ7(--WS(9{(gmiWAqH$SJGKG9v67j-<
zBFgD}A)|<R{BWt77LcM#wHeLP67%5HJ1{8$NrH(+Y&YZDBgk|~!<Qxpz}L4?(CnE<
zY*VI|+^d#}ElaXu=stlbcNv}>-k+TFhOp|y>O1N&UB!(CR&~1LsCdYmOYSEpNt(h7
zi9^T7)2Uq&FN}I6WO<gM=JIMB6w0`o&#4AULM3lh;H4cywVih^*#nAEtvYJfwzH~J
zts2TepFe4i^n@4o``gzdUvZ){QgjyTP|sLwgDpVbgRGNB&A!vSJU3sdnpIN;fADIV
zH?AHj$b#%75{@K6NOlS%u5G)!BDZlkty{|cyB`1VkCWjAS-hfTRLtu<R?{jLOoXzw
z>X8BNqdg~c0Y=KtsXzEQ*WiwF*HhoN(A6tkRiscEizw;O_|%jllISN<7eGy?3g$Le
zLC5D79PKgsce!9If|zq$Px6r>9*H~ncO*%*W~MDgcS2Enb~5#=%y{eOPTe38HdrL(
zfXcBW=Uj`li<&+Wi%Gm{ySz}09}z{AluC{iE0}&Po)X3<!pct^{TMU*`tZ3c?NgCg
zwXCXYpYQJMMofuKYh;r~u?cK);K2L3vo(X%M*QSwUQij{+wt!U@f|<O&5>~hR?g{^
zVvP~TFkqZ$fh<|?7(-AUtxhdIy4RU~qP+1fzSPKTOM6O}j3&~f>A~4?y4IXZ*~|yd
zx#*%eFCG##QNk<CFMj{9@BAP?m7DNms$?e1le=cGN*@AMbxuapuw78~Y4mo5SEZ+z
z3;YK!P#xxv!gJI=ahT3rg%V2FA{Dd3W6<}ETAuv+QKr!U{gJN!NR*k;rrh37a*EbT
zhFi<6Wwg>|&xx)-E{ex_MWPma6-l{vO~pWJNReC5aGeDX`c09s`%P$>Q@J9^;SAs&
zRw9pD9N=;>|GUT(wgx&(bFN96YA~GF%KeLy3kc>!Cn&BB)tv(fD>?-_7kD(zHG}z2
zG}V5F>gK7}2r4<5-zum@RoKcCeM%&1-1fXE>Q!Bk&{OSa5ZK905Ea!XiYg2{cc=*s
z3#w?5u2eM)OXPWl*QB`P#3hYHdC@Y8x=v0lg8HvnKT|dNQ;PB_S(C)#EsjmP(!}4O
zmZi0y)vSNA;hr|VeIlvpA}=U{CYq+I@|tLgf}mTLE(jz_ZmyNf^h~A$t@FTh<Z&b1
zE@pMyj1YjkP=fg|v5NpsDl?y?MxV*K5b6`98ma9wjC*ouh;dJDlcWy?+ilSil>9do
zw|1D`|Hf)H!*iLc+GLZhJlATkqSY@jxZ|NA29mr$Hj}}2p>ycdF(*NWFB@=>p{>yw
z!S=}(3cpKMR-Y?TpUoGPeSYOUSu397p}pfA_;+o<QtH7*srBtk7IYn;*;6UBt0x<y
zBUOZVVt%Y|9~GtH#UXoWaackcCLOod-Kl-O9|)>z@9OflQfXTm>-t7~pKUJ=-{-8k
zKNq&o&(4+ckDn8Q<i4(~_tkv`)}YQ-_Y}S<nyx;3wkHFK3OPU)xsDANKRs8Dzs4{s
zWK}~d3Fp{A@!n2O`>`tec>y-Tswj7IM+DEa;#AzwlP8kN?tDGcZJ6DDmSqN$Y=z&-
zmxzA$RiY{7VV=>Y9^JD6B&@`ko*7I4rGV^DFn*}(KQvf~e!A`BM#^|}A=o$Q?#H)<
zhsR$jNw1W1GTU5}xj>RG$jAc{>`Mc$4fez1)Gzr<bZun<<n%%27->;T05-{4W@d;o
z5c09koBHUI?w%hMT`6_R<kIZ1S%mKX6bveN9zR&GIFg?F|5<wzI7yD`PB<g4$h)#C
zt19d2>N>iPzN@>sx@x9-dTQ>`T<Xy|x*MG%bd3ZOH<ZL7aoJ;IV}T87Y-|fJtjFNx
z#xxiV#&}oD{?=%XKivECXRq+bG1%A^7}b37A~Lg%?$N<|rYkctGBPtVGcw}6c<=rH
zNzI7o(-U*86D!O6ZS+Y^8J{KPWP`s#wbY4xYrJYCH9Z;5r%SP<EKg6Jy{dGS`sjY1
z7tvR^8bs70I?fj;FO!8BZ(Iy2!;?zPdsShygAIzQWbyP4fKh>m&q+q#3s2Z)+{{fU
zr^+)|MZ;t;;E|%Lh3&JuKO1S+Fd4-~nG8G4BL#?`MIQ;PsseK+d^md5OnE9fog)X^
z6NT-wTe#L9k%(x63rCtN!lu3rzGPGKv8bEO=n06J&~-|~_>ySaqJWa74>W0ZzonTb
zdR-sB<Udk0Ni!kt-!CPMKA6cb^r1_#xz*>;n;E`jsWNnB*Q*k`CxDgg{-vU63aT@>
z$!mvkB}ET#lHq$lcj1c&Y}9GryPAeshKq%Ouoj%7qG;!7C90g3Ev<cpB(!8%`HE(>
zHLG<-QO;n;LblkFLS!)hwB-z+*C$P7{wTgo(txmqg^QgR9rjM#J<m>z4#SD%KJJGI
zId`6`fM58)jJr!@m}WeXk_94~R2BvD72vW2P^EJJRQp}+aAtSv{nb`$wbgoZcP8A9
zMrjsG(T)Bd<JC0hXHasrg)CaE`wxA`uzur<=w;--O|A^qEH@ee9PdKKN|c4j$Lftm
z?~^3D4uGZsoVr}_K$J<X*QAIVUJ0uaX?@L&SymXY<FIAL+}g>PjaO#Q<$<a(yO1Rx
zu&gO5%StI)`FwsUVJ9*!LT-j6mh!cev&qEC!fYPCb5fdmP4X9hcHy(UjQ)(Y=*#Gg
zFLj@v;VE(-)Q@`8Q*j6M`!e$bZU%1lo-jzML>K(%Mwy1sGbjH;Bhl!{{*e<WmdLt{
z9fVW?n=v%89NQO*Nd_|FqAuybkv(yO6eDFcvL(hzz7!)RmM2imB(fnw4la}32>S(>
z$hwRbElrdVE4DNVg=3PUM2wh-1m!LMb+f7>ATee{6h(@`MJJbHM0HE0HHDn<#0eGy
zjlgevKgdDw^r5FJHku5r6=pqxlrJS?_QmZ+v0loXX(H0_D{B%?xRuiMqoSnB53?n8
z>uo}EjxDjYdfV$nyn)5qST1ZokwsV>C*9UaHb~V$ls#a0n|d}#eTk?xw@SEYx8^9O
zPJ&uacXCHy{$9#o#CD6fB!;KUVlS{gXz=~5u<I6FH$1RHq3uX{dyAY0&qK}g=rkNs
zaMuP_08c=$zmMghEG(|uCdII$p7S^yGDN$)mwO}kOWX&6rF0314k+xV4<}v+NO5ux
z4ZPreZZI^G0!?ZiWG@~2rOFK#ko@ZN3oKY4CaD^@SRl=Sfs=<QNe&}}bcT$r!^YVy
z##$qUL_rmZt{=mSVT250pMd}#7l^nOg-A$NDnhccZK8@{?3b`8$r{EXTT(<M$}*M=
zlgJK+9JWG+nTXoTSwuZHy3~Ywku+K`s+s^NP?V@_Tw#!Fe;^i8bx9T!QIRoL9956n
zCMN2bq3W{qtC}hcVTH#ERwTreJe`z_ijEO6EOPE+J%jK1wn>il(?25b=vOG$Sjuak
zoD*2?x3tD#<Mj(Y&owsHU1Jm4QMNkvVjjpEEBh99f_1j~{tMp}+|zk-PiKelyI#Ob
zzoj7z`xU>ycBjAg_C;>$H4M7@A`5gZFyen?+nYlRz0&L3Ch3?Ca-M{~O75RS00mCn
zGZvf9;tWDx=&reHDtWFnt$$GokD<?hQJ*fIOQuv8tw+Yf0mmfBtyMo3Z)>PFgTe?*
ziR8pjP8=BD41m3&wTY!kOUr52_(8NWad7-lCb|LnWN|1vIgzCvdaL6H$2~tp@}Elr
zHzJ_B&ph8{W9V#R{NQB=#}mQTUVnCCd1CW0IUD&q+`H0Yz&{A^D!3n!e%K|_Vc7R>
zC>DAEV&&qKbQGA;EPNG?lAopBj%J`o>we3EZOFE$O`9w>&@4Sy&}sk}!6znKY`ksj
zk&kaVy~kaMzQoQ|3WU>Gv^R?($I#C1x8P)GrgySuww{doagclb5S+^iC|p@oyeHY!
zPi;94rM8}(`b7Q-zXyGpn}hzK#8^hXDJP07kLTnl_=Yo3d4aSpe})*e2BK)KpvoNU
zM(T4gQBHAfVo`p_<dLJ-OjH|kR52Annj9~VB`_YjruhcbUawUDBoh6(ziZ-P0)N(t
zNLF$znT$jxqD~@JS}8q!$>fxFm=w^YsIoIQnxD{9`uru%$DQ!sOCk!HYWFvTy?e&>
z)E(*MaypVEXVC;Xqmon9ecl8b9|%5Wd$?l|eM3Dk=v!dJF0cW(`S3unFP(t%4JP$o
z%A1u5d}r#FVxzv91NsAWk7Gij*0q`E9<HhQa)ESc!k^LQ<s|PnXh`x#ZEdZy>6q)L
zlOw-P=W^zq2~N{zfu|YTHW`P!HS?X$)#zw_ezWG^7x?{S(gU~@iJo&HoG(OVZf`Ax
z$ZOmUo~<wk&suGhD#%TDlLe^fI_X9uO@8QgUSjG>Ev0sUO;ury?pEJ7e^ni=u~>C`
zjTT9#|BJW&w<}dBo6^Xdlug|-e|4St;80&9=$j8wRK)>b^<iNL(<B9u?{&6u9HrpC
zpq(}r=;!z0$0ZyKoesq?x|*#elJj8+C*ynEB;7!$I<{-)>z0<sDx|gNh=do4#X_qR
zHv9x8I~JzXi9~vu@~cHs>fxouTsE6qTso}fbSBeD9xmROh(uQ-lFcE&j;&KbKVXvf
z`zy>Sf0}s~Z#UXw7T*n`D$55?!#SUE0+Z%aVlgk?C{$k$MKoKgkExD{IMY$b>XNNR
z?!Q3Nk<q=1A?u13)5N`_5$B(~8>X|C923aOnQphc>*W+_Ml@HG4RI_ZzYWY^Z<8})
zq9JP#bZjMOF%kJKjA}{CAq7b*%_rZ+BA(j0R2c{X%ni>D&Oslo0IHkQ^9Lnwrq3OB
zFq~$M#abNIBjt!5%~+aE)JM&-H7kQQSeV3FMae`T)2zp6ups$k9PJYAe_#v(?Xkkm
z!;B=GJn4J$eKr=!m-HLuiVKcpF^YWF#9>cAK)J%s!t_X0vZU(BG)Xhxt?u8cx~06s
z!oMC5<x?A}eCQ|dco)e+8>C2S|71;;Ym@s+WaCNZ#Z?n+?PrQ6?EYRdz7ejLKH2`)
zQZ@Xw-QCR=+XI|}LEW?5IK|T3TJx7&;qJFyY)zkj!78$tL+YCx2Tn4VVUT$q3}BX`
zXg^IqMCYTa@p;?zV(DFbejiSmjBswTyz_8!Y%a%w=W}yo$-`^fdB!~Fjl2GJE_Rh|
zPQL9~gE)MYqkVQ9T4FOsW6T?JVaWF8>*;K!?Mz%W#CSLx>ha@MGF2i<FP6!N{^k$l
zB`N;_0rhd^9jj)b3n(v=Lj8CkX|BXA(YcbX!D19Wad9CKi;7KZZ}P>0z})ojldAPT
z+HpTgCz&3$)Jdtk$pXWld9wXJ*p^5k^e}i+-8IFiWyse<q$YJrw7Pdv?1c4P4z|Q8
zmG1H8L%&No+f>d3IUq^fSePinjvKB>v~__uCuvEf@oa2tpqH~&bfo)x&}2}Dw2|&N
z+o;n<ySuQVp=%Wur+T4F+BJ(C93xNOLPQ3Llk-6^df;x{BWff{+QN;MIT_hT^R~*-
z4q8qfO~*<`UcXZmUCETLc5d$cZu*0j+nR=r<T+C0^XYVK%rH(%CV;PA9fkczd-w~$
zZwKfgmr~Rwa0?vd>}a}iAJM|=7D`^Rip*T?VX%>Lmah*TfUdYyk)?30IN>*t_*_^Z
zTHT+sW{&o;{UdoHB+#sEUaYQ^Dp1fCNkOy`lEQOwzi~|zYqp4`OC{XhWM4xE_D6(#
zmhv=`(~K<@1`4ug{O{!6Hqn(HpLwM$_harB;rp@pEDy6%^?~SgItwIuYFiZjO-l+T
znB$5%k~wmu8I3lN9LbER8Jd|0Ni9+A{vbiC_UBDmRuYV%shUmGqU3;dw#xDniY$Xm
z_8yns3ajUZ@1PR;A{ha9khW0nBYlUICvf(`JdqU4GiUh~zKs29{%ijgnxZCdUy;MC
zntwH-rfBVx7O9TVlC31&O!eL2n}aHPyqOQ9hgluH785kmZQ72{CmJ7YMCg_D<%ca%
z6EdO@iuARTh$X6GMii|`SPJ3qd94NBHQ7UUsANxHqoA3B2F1ypLhINF`pRB?!3uQI
z7QSuWR{N@Sa=0cZ`zo+%Z@I2;;a@LY;NL;6n<Zn_VILjA^<f~uZJ+vhP+LHyEK$(P
zg@E^&PR`V~%S3zLaPr_t(lREec4ikNNuy$<B8_ZoYQnM->4PV`KTRiWQxr`*vFIoM
zWos7dREx`t%R9=|k#sp6E~iJT<sBrWSpA8m+ma#KU^KpHg7fhnd4cp@8udsgJ-Fiq
zxQ-$UY!M>z8MrzP&QNblk+FF|vef%DRlaW)U3#BtNe6b%-Y2U+qmFxPu$V5Z$dG4e
zWdo^YNx};W!4eV+MC{!43PlMkPfWgBQC^`<KB0uyy+g?21EmEbjU|(UiWf?fiIiE4
zXBA{}JwDWR(yNE*7<364PxIlvu7UIiMJWspiC_qTaCa7&A;n%e41Nu@bD1av3$1lJ
zsO`n6v}xppjdE05A!Ad`Q(Rawht{apQOo6O5Rie^GTqO!NR6{w3PaNC$NO!F7x9>>
zU)&|;Js0(S@jYAm?UV&>3Orke`OBNw*V<+opEeM06__0b*qa;M6zQwV-$iVM$h8o%
zBM`FG(G*34MLbpR-_>8qrz2SfNj%8Yyo8i&BvmfI*CzkYlv7)GZRIi;rMzj|M=S(Y
z-;ROZdthJKfJ(w5we<;lOaL2DpH9HROJixEKZ<clGVbO9^R=g&WC4LUmT%0E)+BjM
zW$@dIm%3%0=R1!19MLhXNW|hhWZAv9-R{u8ZVm|bI(@(Pa~-s+2v4UdC9_HCmRn%;
z>JI$tZlq;Rlha$5Yvid7V;$CMyw26!E!=(F>$uPOnErqjP=DT3leGidB#Qq*Y3Ub*
zN2HSr@P)P2Yv}Sbed$X~2VSCc`Ab~$(=P(#(1RGmXDLF(@G|F^dM267=<uq3@f5i}
zz3w^3WTzemG^Dvf{4!u9K`bCwqK>Z~@*s>pg&u}M9DHG^KifrhRq}jSdNh0}mrmfa
z<UtCGVfUaL77-S!GDcDvC(^s;ScoVUKv;5aw<qbH&+mxJxmih(MJE)Eh8$5=q}iMt
z-I1@K&=>j+VN~w=i8>ptJx&&iX`AO}xF**oU!06Rmpqtzi)Q(G=9NnUeJJr)!RPag
zS}0%<2=JBB|9A7X5^v#!+L5Z585wCEK6W@GRA#biQN~VgM6kNwEG`uaO9x@Gk97`3
z5BuedBRL1lVmdoh5i;b!*2qXktRAT?V2hWpeVJx<SG)tsVuSS@YaZ;28u-qKxFWfi
zqufp0E4W9w-{k&~u}e%Me~g-^RP^2`#rWrg(5GK4uLSseL&IJ3{~`~9H|gv)S*(eC
zMC2zXcrgNi8!w#hW?w>KNTaiTCcF~{FUq`F6M0#@`9-8{y5w2T+Kb!h@jXC)xeM$i
zQl;O#fh&uRVx=!Ut(Wp3s2NU}>#>02Vq>vcDaw>=Ax6e5cNh<mf9(gO)F)I=NmJuj
zUMb14<SkpPtxpgYFe>sAf1D+L)QZ^iyn)A;wS~&T$(9e2sJ*s4!NiMHB5GT?97AuV
zOF)U3eg`>8QZ~iz4wm2xyj(#@+)*9J)0ZyD1%@>Si=a;dzkmhq1}1}#(9ddy?|T^A
z+zoUev<PtOZu5|aA6GPvR>>tR?Q*V>rC0C2E8LnmSXF<&+UkEQ!Lw;0j@)l)S6&p#
zOZf?E8ydQmf;@M03fxj#)xHPPdo1hKR^R)sfHb+~eee2@c(dESN;~o*Ugi2P@cR1R
zAMj#x&H3OiqVeWSf0^f)zsc8M=uNHBCrTIVFZR{+&1vMRjgo6_!6WL4_pE|ak-4H2
zW30EQ?w?+W?n1dVWRYAqsL{}X(Ulqp!4R<vxv7Oy3$({CcF#vM%(FSueall2ga?Zb
ztEyve65*hi>@Z}zHtp*(QP@Egh90zz|NMb#mH9Fb`uhkyELUoS?RW3_8nr+vblFc#
zyGhMebTPnBG`bZ_0S%x2Y#+~1_tSkBSnr1&=sw$b3D36oCGI@fPJ8w^Z<1)eOCe1W
zEYCY8<+aWMWt2BB*``<z)C0}OGV}2e1BJagIPS%e?GG!Nx3IDImYyHRAd*8j#|GHD
z>0PuKnwQ_^{cB(R%3H6r{pqRS3gzDb*+0+Caf?J>KFl4bd`DiB^npj3bHg7`bNIb9
z$X!+%^{DLj3o33ASOds|sG~Jo5-cL#2#8mB%-U0%ES>`uf#^g4jP~50rc3R1>0f#{
zfdASqrAglUDM1o79`hE@^UYps^h_V*ZIQ#f=SbnrcDl5YU07bo(nT&^S}&#hupvK0
z`a!@$^WurA_lWl_spSUjn&6F6589iDuXj&l_xM8g_1T5-xk*_v9Zen1=zhbSj(JC1
zlSgIM<Re!1Pc>WYZK7S8-TiCkv&MWF{5nk&y=9r(OJ`<YWZfh{%PGdllXuHOc!>*M
z+sy`Lw0{wGe(tmBF{$XLN~rKptoA>0N5yQ^&7o}S1#aXB=HuDT+E_|B#_lL13r<@2
zxKK<88uJbE+gFH-678};F<Q5N(|v8nzl)&V@N3(&53i=IZJ^G!jo=)sQ_X7t62*^v
znA#*6O4l2<sYx(_!VotXX?tIwgk&Hx2D2&PT?by;4C8l{%^eJ5Z*7BI*bj9b8@RRZ
zKH8wemVg99G;l`3k~nQel97w#f42umwoVc5y+xP8?;&yw@Wu7|vhLIDqd_|P`g9R1
z8b+p+2t`upVmm?sPDyXE8l+hJWzSR%5Fan+xVaaN(NY?8*4eVZhx@{w(sLaM*ztjJ
zdJmx6A$LqsRl0P1JLfI<+H++1<~u5D9lnKK#ixS1IJ3>2p;Ii}r)P_L*u(dk+=>w|
zl)Y(iEaWRgZyZ?g0^gvkkaAqK#N!I-Qn^({ItW;1WYGugeVw5klcZyc(f#T@J-9Q^
zzV;0Do#7-(=Y{1;0FdBM9;5Wq=`nJ|u2yqja~2DVn>g^$fddb<=cnZmuP5AFcP-wS
zl5Ae2kd1!aGt8GN+yrn=GaTT$8z9z2DP{-#3Mv#E%#SwuS3?S`QRSPzaU+YHviB`U
z!MA@CT>GPo`#_>VZ#KsiiI?BU@Sv7o7E9-_Vc=Xk_OfL!e9F^>nSVxtI{-200l;uk
zqV(;vhy|*-Qc$nnWK)x}ak%|6PhJ&0-YB|*@)R*dB7Wb<sRNUWGI`+C2zqmu+P+~-
zY#VCGFhZ))CL1v5KleW*$<Qo1=Vxg~j<umqW^`X`vY}~>$=1HnOpaooGf>aNF~`&u
zMK_(;VIStR1Eh?CplFsIjpuT4BJygAAJf44#$()d-2HSuzS0xzLa2wuxI~80a-|&L
ziMzN=6mtkD00@jayqd)(0H*~)n2)$ZU!8^Wa+y3R*LU3kV<{P5codK3F9Xx$w2sRp
zhr;yjjlMq=OIDgX$#3OsBO1%M@(2}{i&F>mE3^<oBN;Q6C@dXVE);imZy;T9UY5tP
zWt~*3=<dRNd|ogmOBRGVMJd#HeiCuGNqxkWiLyb3<D8@ER@6!oX$2K_<cqtCixVMj
zY_A)O)JF<Ci-qL_JBv?|E*>2<67dvYE9BhE$Qe~~=9Bz<VRW)zkA<;?lMwT8A<rZa
z&wxBKT;X5W-P8(NcT-YAD&JN^N_XSRE8FD|+6aZZZ5#=yYAAwU_ttiqeq(=v_M@+G
zF_3>;Mxsv50wBc^y+}qgFK-Fp5@iXEtCe_MK_iO#s>iS;#7XwU*QlC-{<EPf-R~%_
z=U>N>^NmnmCx{S;g?%D}U8ofE(+!?W+1(Az2#2U5f;d01Zad;lLRjzqy9j<1>3=->
z8J6h#ZoWjL<SwVEadg&pD{nz8KRn&orvl}-$*V*3emy1>8+DlTCH*JvK698liNNA;
z_Zes@r(3W=(Ysox*0*sQfFC;>Yinq2ZSB+KeOG%d0ae-n`ZET0{&KtB-Dm>{6Vn6o
zL=T`A1drKwAX;^$XG%ahYW&(gN9Wq+cAxbmF`L@UD)kfk;~+>suAdn3EMnNhTzHtY
zo{>K6P7iBr7^E0F^*W64L!M0aq3y~G?avP?FnqOJqqq=x7d;)#SY26=Z0UQfwq)@a
zLF#{QlP7iQ5}hVXz(7<W{?g48Nx!98-E)%g?6U&IVN#7~V6k?QOdo{6!|3}GX<yAg
zJVH7h31dXiq<R2^nBm6{AP&+7ky!B5`^&oDgYCaO`UtF=brH!)$rLaDH`x5xQKoF|
z^Cl8rPPf*!%k%9f40jLO$6gvcQDpRKgRd?Kq;p;bQ1mcdDq3AFFO*jUP`(d)B;S(I
zqPRFM5EuHAzTkm65P#XPfhJf?Gsq{y2emv{IZvwU=k{n3eE@KP20!ZjfcxAaElEDs
z|0$P-$Q}bYUyqE8+~H=_hsWr6uRrSNpdbhKJ?Y_6qc&X~)+|6yZ0ClL>TV3@yz4PY
zz~VYcELhrMs?;)_3^+ua5H;P5T^y7vKX4<l0m!W8;L|A%?k&ci=lQfneu2U%Q&44+
zM7iOd4L@-j7}&tG?Xj(eJ3;9!hx-}rpzvXO7V@<o9xlw}0uNKI#w~zV6Py_)(Kfw$
zan?(ZdhUoG|C&^3xRU3s>d8+0Lw_}Ig?9AKXSLoTn@&hSNJJXPRR1S0Klcx{8v1H-
zEU7D|uA7REa_uCp^=7ncNmBAABf=ZAL~r~nL5PUbBkGFso=7qo(c*SA9FE%YAYLEj
z<$Jlek@^pZYBOqD20tx(+|C{z9JADe(>;xw1gQ<W8|G9;(d3ATL}Q$&%21$=GmPf6
zKF=KLnl#$6QZCS-dFF{n#lbrTg^bhWkFG8m&nC3cY*<z1^67a;l2ei{nql}_bE+!Z
zqMC;<cp{sO=pj+mLy>%?S=?ES#K%XHS~eEbXN!W4h+r3S^NH#75-LxP=E9n!tGuAa
z%C}|05+6Zt4lW3*_l+d<u}W@6l0p$VBt`{6&r~xZC8r?L%UF8cNXxP$=W^wEy0El=
zlw2tmx>XHLih^9$s4SYOCT<;Hdcz1{UkHjWIgz5R8G<mT0Vfdke}I_s-K5_DmH~$7
z9r8IZ7!VuNX(t1AV6U_8g*sGxP=P+<GbWitW?UovZ#o(dy|1-!YN2Wwf>g`i4c#}@
ztu}#0t^MmqS7DuLUhh_0&OOY?R4LwWz3RA>gdssW1r$6p6|)>2DG_NK_G8<0PW}pA
z-8<i^lFQi4!nD@RELjoi8}Y)Jj6+|^7EH#W{T0vA1{!8RCgW3^-rcC5=|g1vT9Esd
zUj&cwngKjUCclE+%%-ud%$PZgqHKDn;uJiZ)fjauoM)`D3+3N0#M71-EveZ;d*?y)
z7xBpC?p=|jt+ov<mrRf4maN=61uIh4GI-y_OuMC`H`$eDBb>~}qfSGJ#+vi@rpGfq
zTM*+rs`Xp_+34x@$HBa`-&O)<9Xi24hj>KPD546jr%M52{d%=kLT##E!Qyxah>UVQ
zVK3LJw$mj}y6|roK1-g%UvMdkzSZDXIL?9DFB-zwT;U;%pX!4U;n<sD-I53<kG^^2
z-jErMOjb|NMpLqC=%vJJx_s4vOFqvgL>;zz#lQ-EE_Bb4eY<x|O^jn9TXo_y*>d{C
zn?`co|H39rJ&%d@z>1@Oz)XLQlF@n@Y`K242pOj;*bg+2wvrKTG5z}v7G5OTnKqrV
zK>r6eo}=xsMolL?i!JDGuCJ574caC<xm<@O`poEk$u^a$dwi;21;0_1^81t)<5-A!
zoN^FAR7QW(owIoB<!Dq?gdc}uc=sE$7=M^gqAzrRg0u3}a{?B-f4yp$>hT51(9yXN
zjVXdyR`eC3^Rzh5rM#IdD9g(ffPro>I0iqS)eBegA-3tB@?<6&8~N|?rJYluQ2bNz
z_9Tk`vSdoGB%Kuy>GVDo8mWf9m8u_3ew1Z@bmY<#k&)F)N4_1m?Wv`u_~%?Sy*vIM
z5lL}U;yt2{#n0Lk+0b95k2X^0B$|6J8;yCmmGBJ$b1O^{kP#zR$3;5eE>QkGEXTl5
zjEj{z_`T836f|<&|4GbJPD7r<GZVWrb|mvkUgT?MqLJn0<w*2QjYtX)W+HZG_rwfF
z8dl$q5Stl5q}R|;cyX#48=ab6d>D0_GZS-j6En>YdU$bmYBW}zTEyDpNaERNL(coS
z7XyU;h{#O^Y6)QQOu@8Cy+105ey5NB!_<+f$s;UOD@T_AMTgcWFS+{2B!yN-r@G8d
z`4rtvWl-M^Q=qd$Lvy9VFnI_lKM>?VuGepr`Qw_`3fI7PR;z(|YS1>bS@Zf;qVsmz
zo^68q6;S;{;&iH`6{%;>F_83~`XV?cXOUa3xX>{c$&0H1d3XhU8dp%WC=XjG1dp?r
zR<z$n(IoQ540@!(8?lTiW?}~4#8^QyNWnHN$UkK_*;V1+NqHsG(AhaR1cSk=dVUqM
z!B@l#vd2fXQqwJIBjcB1$IWJ4=QR0Yl75R;a>svKN_T<N!bC)$?s}dSYdsf>zZw~L
zOB~}i`y&4cc?N&XeS!Nkh|q$NEQ`{~Rhm?|fe7_Q4?*1un8uqc)F4U(CtNOoRiAnI
zkNT5hjJcamK-3vk{CpD-hWK)2j_d#zUZU&8-MJu~r8x)C6%dup4gw^Lc@K;oE>E$D
z;=U{f<snRc1(k&@fFb<-@wgE+4MP?cO^(b&R8vs_Fiwlpq=rS^Hlp!xvRGOy=}vlQ
z+R;g>m<-3GhOLWOQx#K<#NiN$CZ`=)Q$*P?%%~BMs|t>2l4)9M$TIY}j%7i}<W(!;
zz!6iUQNy%NBRV<-n@+@1^BF;qF)3+SA=NTXNsD0RvrKULDO=sDDx#@Ml5US!N;srC
zhDy{qx>m$c!gNCgzO+DEhhvu%rDQv#r7V>ALdZ1}AyH8chg2qN(REm{Mr>V@RFmuw
zB90;mlB$VB2aoEmDZ6?xkt1d2^0Nrd=I2N<mniD4Y`S`ssOh4nN`jy`5j4x1j?cg(
z2H%X!<+yq3|LR2=0ip-#%mS-nFSe&1>$g>sPNYe>FG+vg$-9qP+DRo_$SNn{QJu~)
z_4muCDXTby@;tC6Yj`h4ikl}2VJI99F_s4OYNqc#`*qr>)HI9qGG%#<l=b~XpAB$`
zhAhZ0U`FY;C|^#7K{^&<^-M20U|jxmp!D<>p6>a-N&m)T)Ht`;XRQrK<x&tm%KFkC
zmnibquo&%+ps)3k8L&6~`-M$jBJygEvXm@QY=eRax#r7r^Zp03=*>|B4L1dfSwSpW
zDzl6-j<c*d;>u)X$dwD>h#m{8p`@ioqIx7;DCfu^@)>1fDr%G8u|za7JxhLQE<RyL
zr^xT)?sOzNOMaWh+-$9!%8CLeEs3;!SrD_Sa&0zOG*ctm^aA<K$jv=vgZ##%k%eiE
z{E~8cPtzbj&hu6N8ahvLwxe8vyvMO&q@XCSG_pM4f7D6GA;d5R)aY|seb9Y)@<8?J
zdik36HRXEyVYhmq|J6M^JATLSDfJWoTP++qR8Y~&fA5a**{Kutz8Do15UyEB3Iw8*
z24hqDVpq^VEn8J<M`wrC{dXg;zluo#x4VxAp(|fS4=r1?M73ZT4}okUinqrf4+2-d
z>e)+p^kptdk+p$FyjdwXnAiy*Ef5X^@s_Zr3ht#LX-T?)Itx1{8NF|i{wO5nZ~3pE
zy7_BagywRE-<3jtB;7#vP%#vt8%X)k6}No%>o<QYT^yZ5$hZB|`=!?f=~!@^z3W!6
zTUznsJlT_YyX{Ihgx)LBaC}{RZwMtqadEQqE&nl~h8PcZp9#HJ(`f|0^xn`7l577)
zC+<H5Za@ZrInph776P~o?dF+w-yDbxT3i^A0zUbej8tjoqdec_r8~1Yid~xI-60r?
zna6ej-uPo=SCiMXn3RJQDHjdi`v!+OB5xv>Iqmn}KPq;*|M&oTT_*bb;6`sDc_w+s
z51{ehtx-<QKk?=%Mu>a^+&CWMjxUZ@U`msmp4}ey&{Y6Vtbpq=P>9h#&4`+CpD50d
zi!EFKZ}(?$K4QEU$)b3lpnX^r-!B>pEULA3&KN`UGjs<jPD)oZ-JiYIi12Yz98U5R
z(I7<?Sgej2xwc<NM0q<#xDv#-ke3|P(tF-+$)8<u{TjP{g|HX$a?hhvD?H|PL0BoM
zY4uv7SbWd2rb)$T?tiTNZHKq@yU_TN0xiVzD^ri&N{f9HjsuKIo*YNE(`uL;Gi{N>
zuI+yNF5TuGH2%@TiXhN3Q*XPCmSXXpfQ|KL8c!M~cW@`n@;QFEVct(Tl@LrzPzQ%u
zG{b`vEFVm9&LY+;QZ7;+W-ll}DUINvb4B2<246e#UxQ#4eg#Qj9)nM@!|)~0ud(&7
zPL1<v(bi<y&?}A*&1B7@soQj1uyu3H$Y!EKgvcfPH1Z}z(?*(LPKGg#)Mb5Lm+Ov<
z!;_`}90&q=d8C~3+<%*r^khOx^T@-&V1M0r-8<tPP<38Pes?vLFnA?eDQL`ktraRU
z%o~Z&YA9<QyWX_<sCh#)G<PWGn0XN)F>gAtLvx|%^;VR(jT?>`*(>h_yf5QGggn=u
zlX3nl)c+0QTqbFB7VC9c58Mof_iZCD9IyZ~hM*=|Jd^dDWyqR6rJroc8OvFg()|@|
z6f8?cXYgjv#m2+z*qVbjQbxC?r400x#^yB<QEO;h$dh*9z5RKq>^U*dqQNNjdmI`)
zj3N;lL>!B2DXsfMO@lRxXvkst?_tG$7S~{JitL8HDbN3e@lmSO&-xg5IkyHVP`${_
zN-)aLQ(Da)*KNb~oIe7R+a}mk!Q=|y$qhQg8ZP1(#S$nL`<$^hJi_<-H0jdQFvBH`
zMA8l(k6F%6($9a)d&;>{F=iQI0Yz9~##%cE+|JBg?oesw+}gBLPC+ENW8TJ3QY+hz
zkh$w5IaSK>n%mmn{$|Jxg^Y+}(#e?h-1>&&bm`13dWyQP1@_V}a}mHEECnbJUeH%X
z1~@KI6g&h%sw|*EuTW&nNj5&CD13r9l_LlD?_71zO;6NPX-z$?*g{PZ<>l~5_X&B>
z8Fb&WERvnJa$42g_{55S#p{+2A6-=}l;9O|zK}Fu8bcA^3v{Q%ODV}5@CB@G>kH_C
z566K3x9O4Oj(WI2SRQl^{H&z#AtXr$B~=_9+Yv5_0-xnIX^(`f<-Plcl+q3+J2X;2
z5KG}5W22%<4wO)cSENh#?JZYXyxC7Kd=b5kw67eEsk@#<KU1v?Y5@<pSRp+$`5Z~3
zrWR!<4zsY}Py}&ywDP1q$zFd=#xM`X3-^^<@Cv@H9goM|-I5`-$@9$dn%oM7TC&FH
z$a8N?#;>TVpi45(^W+Wjye#QAWuwuopo@|ylu80w>67U+*_H(&W=F|F7WJj})mL0`
zbz9`cs$j?`C0U-jVN#Z*ld>UHMINo&1zSR*%o9C_XfnJkqN~PEof;DbS<r;(S5A|q
zEX);86#>^sP=(aq6hJG94EBWTupDW3IV!1nO$)d{hFT!BJ@kX18KtjV3K|=-lsDde
z=gMvk{eIA3c3z)e#)99fcCXxd_Zt<9&4I%=zd_{f<J>8*<r4uE2zHAsX2j>V^5)|T
zY(A&ouH19b4bXLip0iSSsV*IJ7x~(Vm2h(LlEquNv-6ZbsfPqadPgwk#PhsZNLCA3
zpBkDhBPQS+Kg#sUPjzfC@h3L3@naF2zoZ#96rGZKW@5>5d0+fwZ@%xO8BuWu=JTkc
zw}B8$rO2{x2mKp<Kha}sB88W^MJ9!h1a!+Fi5D59j!WSwX<YF_1rX$X>kCmYDt)6s
z1p1YC30R05Zu63(qXHQxVa5Ls0rRfmX3#n*yh#tBt4)eBdl@f`9=)VV3h-F?hqP<N
z$tk`iKxW4CgZ6P)=AZ}i5iunCyc?uyrAos$6nOw|ePLx~{+@fzU{!zaohm+akD{Ze
zYU(l35|61hMOh<bz?yP$mUO+hYIf>YtZvK-_B%&jVW~$Y>8NVGVnhrnKLo6b^x6-V
zkS9|^e;5Mf&S9^vk_%Xl<iS+Rvph3Gxm4<)Bu@MpHXTbRVxRluHM)gzljW+JO;6r@
ztDx>t%-Zc4L8&NaZwB_CRyFa2q3d(m5k0-33SmK4KAhR75Yb9iZ!`Va`wjl>=yxts
zCuo(;67%^Ac{tcK*qK5^4_4~0)zBk0>3*i%WMXD=@|N!?+8#xeKXCf6qEr<@>Q(G6
z9sgQ<tTHDE@%hVSDKAFw%?o29>9->?`+ga3;#6Qyl1F<9<{h>h)q})5ZC*17Z9Z_P
zr0!Kze(sJ*iCk1gxg1hSLYll|j#rhvs&wZAkr|jYm_gj96%&7Sr)(&9->n$(&W~bK
z`E<kz6+%{I*ylVk%x8>cj|M#+?VlZ(J7**k#<>9i{qFhOuw^}ES@<rT@vtktfc})U
z1JZd+qcV8ufB;nj=)@<x>sBG*lJ>Y}>-zVeQl(_HyB0~w>Qibi*XJ9EiC#8IEhx0L
z_8jP_!%DpYAEMXVD-9N^fH*RM*O-5xeMz(3yadMUe*-13c7|HiY(|$zdX`&m(NFDU
zVu8)}4Lpx+`JG?D)1m)49mmr3)HVKkFwY0cfyeg!;k`7;4UEzCZMY9#OjY`a>4l5b
zWngsv`Sz7zIFestyjP`d0M&;hFVOl~>QQnQo@p<6erVfEGM_8TRb-HU_6F{DqT<l0
z)W*U*NE{VF%|(Ek3tLgofCZ5#l``{PiZd&o6ClNaTP1uf-fT28FZ6J4{OCSiK4M}r
z1mFiP8}W@&dL})>3l@=<c{hPjd3U+GznX~iGU+viJ-hB$E*5qOG7^z6Hc?x>>11uH
zL^fohRTx{oV;2ML8Fy>dX?JSMov!|&RgRS!JW5Wdt0P2g)4)qV*2<OIWpeU;)oe4$
zBO#>m5-O78_cj}8GRP$zflr*c9^%?>Jduc|$*`AhH20FLulwxC9;aXs&jEJZEeua+
z&_$|<UtpUCH1s4j8b{g*7QwtcL(8-KX62CB>-E;QO>SG?Epq2e<iH*jLx284br=#v
zpa1qej2~!|af;zqZ1xkqk;Vkdf0+tblW37~t;RjWE@^@m{Vte56b~UlZ;OVQNdYbF
zjF1!T7csJtA}zTHo5Cp(FKOkJEED-d4bA55R9PFHoXyv4A!+ZArzC6}QA1X;`Ps=c
zdt_A>Uo9&yBkRXy<32l_DuZt$A4}$E%c-!<&$MS>YsU+*q!CSM)@*ym<0=K4G|ZVE
z0xyQr3JPk!-91piSjrsS3zF)grvt$k3!D$)=Fy*8p^2;&AH0vam7NG#sp3QyoykrV
zyMM<F!yi+YTg#Z2#B-vQXIYnO*>HBk85y{n5odzrYGO4FaD?gVgUmww&R8xN6EQyI
z8H}kfH=TE?_1)PvH@5A4`86-4q2`hXHBXm@YVK99RSIfXY7JG+_iOKX@kHB1Wo%EC
zxE)jNhhGFg?Y6|U%#PV6{q+jN{RI@9K0aTV9`}hZ%Ef-L$gSpcv@k8<*HM<=D+<l5
zs0t$j5+kwET(vz$L7p-+8XJLY+8UPXZ=$Yg?L?mRV#1iH38rf1c8=|(*}mi3Y00-0
z0H@>zR{?KGs$W_VkWC%%scMG<_v^+&*;7REFl6u9T5I3^k3O)kRa0Y<Es8idHhuiM
z6VqckEQ+=iyNQ}!=>ol@aMvyvq3_?n>*YmRQZq_KN*o-YJ~2IgVy1E^Aw`soDm_ij
zFtF&wh&@8*+a@WyHk-J>%=6nL`thdHP|<TIhv~E?;@F@d>UraoDa7)|&Wc_rWtF)|
zG&g;H*mc8Ch2y+=ZxOXw=l%zVpdHrpNkN;Pu`Dqfx;}p>T67C;B4^yY$K%`X5V@*K
zJ(1{RkUbW)MT%Y>fH;Ezgb~b9@3n<lYUuFT;}~Ru@99P@C-i;WCio3m$4M8L#1Glm
zY6cOGMDY{lSqB}z)_w>l$y+&2N~q+sn=OBWJYf|ZMCvlBpZoEY`-q-0CH-7t<YZO|
zYiqh{pjmBqrDHn3ojo~{IH$>GN`J&n9f$I()c#)Ct<54s)z`FeUu+2Y&m@5vXba^`
z5DDWBOFezz?4D%Q@R`;ruX*=BGcdsJMtgf_Q{Y_pap}7(IAQI{ZJkT|IN)jfb?RIF
zDEm@DorfbMbFYRIzPf+u*AX4=t2*MSFKfGh$?De`fYN9FpA`4I1|B1&fZ?UG($7un
ziEJ(oEIS??I`W(b(ew#s&pC0$Sj~fufEl!opM8QX;*!+;Dsx9#^R`c1AuWyp;sqty
z#j71Z-#ZSSV9p4l*JlGiV8z3kqKYc>=IN6z*`x9?Lh}vy&!!Us?<BOL&ZuVh2d0xQ
z5e1(`tr0c9blnBgAzt7%K<j6AR>Zx+B!wMwy_6=JFRMIJe$%B3+*)Uy9P}i!vU>3(
zEFNHt+Y7#N?Clk|JjXr<*cnu+(`V_{nMa_<C+&Ay!BjnO!xP%*$tXPIK*}z3uYF^N
zXo~ak*TSCLX*$YF?<TDy&4z_d6`wti1DIqsqXF{=ou;bZGDW^mSXV{bPA+h4oan!w
z5S!H~4aF%4{hh13g6eaXM!iuSE`~b3!<qA!p7Yt-B}z8j-rPj%!LClvFAe3qL?;wJ
zQ){<-dsvJJolm~ax8<@RO)&aF8Z-f_0f3H&J-<~zHYWqvT-c@5AD*F?S?^*|8t4fH
zn~U`dojk111L7|AtEiND915wHM#iG3EJ~uHjE;>?CbN>@2y$`JiDE&MdGXZLR3@LP
zxEV=M3{e?dEXt&SG?m(2G$j!stj2U5C$M3qMlAaYSyW{~NXIMbd`35AMaCIJmlKL%
z!tRs}R}>X_O5nBbCnOmmMN7zvsFBXpPLBZ87de-%kBWjKI&miza=iLPqSSAP$<G4O
zigoh#9L}ZR4H=Q(1-bQDu}M^7%us~rHsE+r&aQe4{ue9cw*he=bkXh56<W)!<=SiQ
zT#o#0k{puVft@7P$*pbVIyp}#_xYcf0n4bwhH9o@Rp?8jaSBaFLJgW4g>da_G|V<X
zsDVA((}jOr9ZAOoUMvclV5?RnJfg~BKB+~hHmXQgN_Ps2MJJsur_&ofHT#xuC`aUy
z6p>3DtT~phNE)(qCnd75by-wwBbLj|rk{l)%jw>j{#w%J>Wr^x=my;9xdYnRASB<r
ziEr$jtmI@tnN%D(9&*yjm}{zOzHFx~?XX7PYAj{O3@e>H*%+x-cUG%tBN3k_h4aAu
zsF{`(4;g9*#q3N~gMM7rHQ5MhVIpHrlnUeBcaHBECx3nn1N(kQxp5d9ORne83M*jq
z0>LhXyoxqQ18#((LuZERRIy7<f@FcvRf42wW_Wz2v``w)3xaf$AdAb%kyth{5{rd@
zRG69BH#1ZC<)~;$&lE&MNM5~D7KOqy690eh2}h%ucsi$Ho=1Er8a+dln)nentSKUo
z(4#CS2<=Hqy!i1+q9`xk1N0Y@9~VvEcEUIgM!=^AW)}J!r`c?4(Bb5tcQ1NswVa}X
z1j^)*$xIZ?lihD_&D&_F%5<D0Ip;x$CYjr0dGjyi9@bgi`sX_2KH-xISNZ4Ni=Kij
zM+Yj?{pQv@P*At8&R~}3kHom0=ec9t6&GiBt^nvBXrNTvsK6|JqY0!^&C0wBm}kDU
z8mEj8^n1QITl0I6i1Z|RDiNUHj>mzhJAx3OoQ*|^*e!~cR;wZkqU6Lz8>6F*k31h4
z^WX7&Ton0mlIIPB=aXSx6tBYy=36IPBIfaEW2G_LeK%!QX5-c-<y$Oqhhc^Y$#|d%
zkjCz2`M!(_b-3%#SwVv(m`7g!Ifh~RjiW2ehv<-eaL4k#<~8#)Jvn~h@TuCdNxI=f
zn?NM)T)kdkx!`?IXBivl329>dt;gO*(YW^Bof%Q_-dlG(MmJ9$*k3B&A-ArYdpq6s
zp-<jHw^}zOlBeYz#dpyyI$uUpBQ@8LWdbbl7#RhCf0B7nP?;Bu_;iNfC4xw^CwL4(
z<Ac^0>NRY#1N{`^RUEaJB=KOQk+Y(>d&?D##z9fK4<g}SLo!x_5`ROL!uv?#8VvcC
zRuzx#+)>pme8-NRM@8Ce!`5oB`};Ic{JtmqeSyfRe@Z#q4schph#VgXm-JH5ls%c$
zTQGmqt<nF_yNzxyFG`xUNEc-#5>bY>XC#4!_FrUyzA!y42<#`TS~sVu@<0yS)YR^;
zZxim`AJR{`hrhr}=+8*My~5oH{EHMElZK7>VO5~tdNxUyTE#~_12I)FJrf_znRf#-
zUmSSF8Z6iX_JW+ef<D7X)M_1z5rcxUFN`&eXnkB2^m}Cky?(q(Q#zj%NU@2??%k0H
z8F>qmO18)THZ5pkLF9!r9KS~1o+Oc%xJJ(LS<RG_CpFimiu~+>(UU1DCM0=`FWKv-
z?u9aY$>1?EB5Hh`=f#oG52i$(kCD?(kz;GRqO6LRxT+|6ZT0|_|Cnqs3)(e6iS{DR
zh*~=n_+rm{^8z5=dA$|l=1vvHNRza*w~@v+;V$KfCO>_AW^{*31c|C3=C7Ki!UFpA
zSbhxCVx%EY#1hjdt~)**iwTI7(uHh+$`hXL;_K8Os^KBRxPw(-E)_u)z}2@hBp&}{
zmNfL@%`JN6eY01Q54#|$qPv`@K&Q3o<B8j3k2DBvVEMA~nSBpDdjGycmQ<x63Ne?W
znof*KSeo?!ZT#_{af4wl3YLLVlmWN7{qh(t1Cs=Z#QhT`pzN_3laWl;jalF6$zJcq
zdWwh}>BU1T5sY2UN=;!aVOc*OX&LRFsP&)BXicxW6K=xNC@t#Y%gqEG#{oC{LtK`O
zN1OvQ^?>E!Q4cYbd<Euqz!s0eeR&t6_IQv5q1`@qPI1h;8ttT8#35D4WYbrej&e?U
zHRRvpIQP&k#6OOCQ@dO#PhUSZHJ+3NL%DXcqFtexJboO<N9b<*Mtgz$ygWZP<GWPO
z7~Q^h<-Y1EaLKptyTYqVJXM>#tpDn(d~xbd|LTWMhHZ4pzm!C;(;?T<iMF_lyNY{+
zMOb^{O1`u`B<@9!axa!$?9KV)w=u5QsX>H^9(<0744dS~=elPduv8=yqKu>{`PkDY
zQjnPz`FFz(G4T?1d>}`S?ayseybuN*k{cg&#}g{z$koVS`zmU<FikS5r=vVZVp0&2
zBEtMj%zxaQkv~Hx>{sa|)0xTKoXAHjZacu8`#Jvx^cJo_HIo5<!v-b!Tk*QtCUZfQ
zU>?*Rj-Z6?m?yYZ_=j#I<JN5$>o|t1pH~$dBUAf{W>v|yj=Sd-_46!=`wLp6h>7s&
zTQR<sWRty<pTsupp1wN?WukHXNtVEUWxh-MB+pj;Mf7DdE~?-e+Vp$;ibQFN=Uo{y
z;T>i}WluwKJ^C0K3+jF)B459Xc3*PjSNYtvd2!*=LJWr|u@>F4C$8a1O^~xcl_JUk
zl^2z`oRN5Z`>97xUiLd;BqA?eU3S}<Bf4zqq%YNv*zu`C_Xn7lGIE^uJ*0vNJ<QpZ
zlhETg>hp-el$rojpLpt(jG@K_#2tQc9|cF*_u%1$Qww_G#+Tn%&=*c!vGccf?)V_;
zl=oI|uIwq7_f&4K?k%_DV{y}r#^X`bB+0iYlP-*(<k|;NhsLXbyd%JO<xQeDs3(d5
zrkSR1Eh0TNVy_)0lBrJ~^+s>@%t`#NcL^c&W<e6N<ae_g65fR);jic_-j7xNE8$3Y
z1Boz%5V;r20AIS?!m6ywGvudEqqeHKq9lnfkpMRAk(*^jk#8PB89@|<DZrR{gN|#E
zS;IiIsZbkChx*L{H}MSVErC7!5~9H~Uk&Q<<{7e!!*Mo^dXFvKi)afnnnmC1gHh$6
zr_K-&^b8nK5YZlHf81{87<=7)ZKCV@8-Gaobv8Ptv*EmlVXMsMyXPT97uosQ?ndqu
zBdmLMF6So?Z?IV#&f`SoC`W74i}Yd<GezG}#*}Kxu1#U~p`9@Ox##<Xe#&d{xS2ZK
zoUWX<TzO8m%$qcY=Y_VQi&nxlZZ@su(Go)UdQPVw1B)ptWp`P|EruJnMV%Dod92@L
zTGG7iTBj>Bi-(g|{8h}6FPLjY)Yeer+VIRvUgVPJBjww?Jave)7#UMv+91+KqD9F4
zct`Z^V4ck&CNq&_Yqd2!4Sqy4>mOoi6o)Nl;p+ZnwUvwjRxc^&<v{>swwN7HK(Sn#
z98WSv^AxefLzZq;6XT?aS1Q1m?zgEX$U8W&tuLPk3Jy(whOc-q8Cl-lk_TpWk~?Tt
z{U&?l?<k5`7~flwWGpRS-xBaGkg@-PeUNe4p#zr-c-Ya8J$hNH>>U?~C`5|4u3wa}
zEbV(>zn3vF@y^SLSOdD<-%|eqg-8%B((CyQdM?zCO_oEA=?K?kp;%rv3yorXI;BRr
z+$fzBFYO$|L`-kqutTJQD`&-}oolze0`hKJJyEN%+*}GumyR6kT*@-U9XB+IK#yN>
z^UHQDds(&GiPf7(rax!2iC}!E^Xg46+*{tpe6}4#@-BZ~{YUMSC)>XxVJ4ZgC6<;X
z{2@qw=tQwv9XsAR*|`);{|!si5@uKsenFDVeW!Y0yf4;y0`#Zm^CGAACag*Om3l#j
z+op)*;aexxnYp8XNh6OD`XFlOwpb*_p8&pMz_VH3vX9OHciW`E6Ykcteh{KJ*Pvse
zIFkGxw1>uzzCcyCOAXpr_*`wP;iPX_qhN913XIwm!0gn;mL5>g2FzJg92bt^xHO;l
z!Ef^lyr%Kw3tngI1MV~YbXkv0d5Hg#e>3_7eO^>C?BOiE*p!DzNqT7h^{aA9Kd?kP
zV3T~-%gqczf+fg&rn&5;mRcEaZ>AZPJ7CB%9EpCcW0ZW9Y*lV2Wiik5w~y8Y89|pm
zJKC2=#5^z0OWjt%Nga+vDLKV3FVKgm-PNTCj=td}6j)h7G%A;h6i>HILB2s0S{!At
zIb`AUaL~vGAEW`<Ubhwk$Zqp^C?P9a3h`4ZH6J|`n}}3&!A@ya!wi+QQ0&T#U>K65
zg~d_HuvT(p0JavPy?Bn||4)(h$F>=hL$7H#X(=YDW>!hZ1SP8p($(=9bN*^GAtFP&
zJ(N|)PK+BN+J94CMbsw!_Y%Fg!9ezTH|R?!_c^2)&GI6p2@Lv`as|<@q5Cu}uN;s5
zDy*N7Dx+5r9rSkv>2gJue`CinsR8**l8SslBGR>_X);ZEn1&?0PYB8H<PWxJUO3d_
zQTQ>@H!~DP;Gh?c7I!_Ly!d?6OR-o8DolebFvP#gmB2pNlsyM{e;)*_?P(G<V&>Kv
zuvYEa_rP0>vaXRbLaAC3P)KgExAcf2t9qqx4|veM_5S=HgD&t@$N4I(|2TjDTOZi>
zw5qDIqDB&lh^ok+VpgwDN-#~++u_IA_4pyYSdYOtJAc^6Kxsi^3S-e8Nd%n|C@<15
zU#ma*&Sb;-??f{byuwdpM7I5b@N5c5B=XB~8ArC^k2s%k+@>HjT_<yn<-9){x9v<y
zvBmzFF<_dVqCVfn!NESjaBv1A$VsC?AHTwic7Ngz&YR3B;Q*GNfoAacL|hfo$`w{H
zfE&^7dFGCQ=5F&GHb@zc;eGnN5Ls@4Xt{@o^cwa7llr^^`#^13L{%(Y?^)`@3%Dn{
zhCSp}qTi7&4V_MS^JV}&`RKY5Ro1NzRWdsp41nxH8=z1&yXS#Y!6{aK&|BIy-X%$Q
zY2ofW*=#9#F#I-VhiG?+rV9QIf&GX23#owEfQar!q@QNJDd9d=(*oM)T&~;r@|Cr2
z=RsX>=vn>2_~Z)OSX;Rq+xq35Z$C(~8al~cnGA3(>GRRKG-DkJuocTrBTwh)A;=G$
zpaBV_m}kx{#TuYVuVy}va;<9joIRa&vm}L9fzg~|853o9l?Yb1>_S|%6;E}~j#MPE
z+{jg1lp?*y^Ytb2t&VedPzEgO|Kx`+)#sbB;O0vG!TR7D!rqPN7Zkik>LxWx2}__&
z(Xf>PusCp^`suA$v!_}&-M`jq-L&Iyi|Vx*+<mIY7-vk25|%R95DkG0B4PTjBFTt#
zk$mY^+&aADrWQTvl&yiboWaqXdn9x3BYL)`U?TiuUiZOPQ;|?Yy4VgM-z<1WJE<o5
zqMuTwAP*1`_UkY&`~)halW994C_=(cPmVHIgcrWc^OK{oYAPcLnN&44I=SX~CJ2G=
zM2PeT;5jh_8{x27kX^_(>oC=vMxaN9;ATnVL$w|V*`H_Vyo6V$2X6A}^kiw}$V!PW
z5Et;2cfXr~84P;g58TwdZzPwz!ljj-&64?fw7KJcU7z25`K8srMQg;M$o=GtyqCd)
zAo*hNix2J#+R{sCM?q`pKRazLJ<JPfYt5l$JkW!)@NASE9TU>P$I}N_!rV{_GLE||
ze52i7zGD}&QQd*|t(?wI+O6vwXl>UW%dD3qiT3V=*<JF=%{w^Nv&%BQGEdp`_HmbU
z*Mols8bVqOI8%xNdCGr43AD+CTdGOyV&BRUwz~Zn*n@H&dJ<WjVqcJlqwo^*y|ye<
z|IQ5b@1>#duGB9>lzL=-b)NHpJjtTl>Ah&Q>^SO8T3NeEb)1f0!m0OObS`JF+vG3D
z4aNn`F(#N|U-UQzR1$zNS?uAIz$0zCjF5{w*#R%_vvGUBDDJnX^ZA)PPuJ7-ex7XP
z0c1It2l#$y+_J_)kndtl6QOa8T{z6cQ3ih7G3viVYf$g2f{$&|tHfF%b-O6uo+?m8
zR-PrQB$Eet@lLEABY%*r>KaYN+L~Tv@b2E&4Y4y>@Nk9ra>`5z&~!5M0cuiEaoShl
zSU$`G;R5Z;*9^h4NakB-v11*pP9>vaR2_}QkMDY;<>0f*Mt7qw07pQ$zlpJ|BB`(O
zAA}}{!?J=xk6}wYjI7X+eTIn><?-=b#&hVfX5q(h^uHfKZ808_BL0KEM#j7>Af;!E
zK+G0_Abw#4$-YYyghegVsWPg>ETCt9abrZ_2lyr5zryM78u}Gc05qZmAF*&Bt(Lc!
zF`hn4XG2K$_oQYqSP(b=X|l?@3?IbONVi&2QQ}GHP9SY*-O~)CJAc25yHC4$>RsP`
zMzhFXR3!on)bDddw*bs6z@m*Z&#%Cbi|TP%B+S7xsi5=ad~;?#64_B}WDB;n5s7SA
z_W7f;3;a3MzT>LHi_=j>aqG<^m)}TafK^3Vl@i@gl#X9{_YuFZX8b(+sZS(P2ph~F
zx_=S}dkpRV9Kw8)>5Q~Nv&hCu8RcUT9EN^;&``n?q?fR?vzkTviixmd95k~zihy&f
zsn@cdY)x;TqWCzuk?xle_mHMNbo8KEl&p7|4xmM8RmXgnB^Awsg=oyuY|UCq<@2c}
znv6w}d#WsojML7Oy*Ickxf_XmcQ^ByAwA4w4`Z%3`Qe*TeOde>+l;8M&X!)}pfA?s
zdop^X?(%1B>sBi|m5BmlVavzE_iW`)4W=Fk)F~heAaA`p?oHfIvGwxVsax0Xj*^ox
z4#X{J_yda$bPe#M1K62<D1fKV-0J%h@Zd{;Ih!j}e=Zt59*qmZw0eP=N{TV&F39Gf
zoibQZV)Jg?50{19ja-f>NWWW3!y27T?u<$CP_n#py0|cXypnIsot&u`r+1c<p|}*=
znY?7Kk*}a!hiFR|xOAygx-DV}AxZ7nCNFB?;%Ip$l|V^N<W0MyN+H3DBvLcw(P9s8
zcY|V4q)GodO?_Zzm}R9HD0d~MViyB-x)?~F4B%>QHji@o+3qIMcRTBB)cb}%_~jU3
zO|E+$V8xLQGoPv4;K1h91#n}3n@BSaZVyOmY;?nvEQfqBfuu&17l^~I1Bf7bcBKE1
z8ooFAJoLRmy3&{C$%mq(gmk3)Ymv0TT%Y#crKIAbvikj1MU1DEyY^{T_gPk_Gi<fV
zR?m>n;ZqUUjeLrJ8Sl|WJEh-qkDjtc{XN!a&%ntpw%|P6@V(^QnPp#pG4Ju)v9H0g
z*oUxCsWbF&N{9!g$n)pzS-~>8uTptaxdYC5@Sq7pVE9{MScYWY(!2jGqmp92=P5lh
zt?0Ch-H#}#5E&w8ZF`oCn|2x}^+--5ZItu;-bx-%-6h;*+;!Zo+&$bw+?%+^xL@Jk
z&;1Vf(H?e6b8ZGT8EHu|?718Oj+i&Z1@q-T1z#|O9_(uN#hnc8+UBT>pJ(ggK?1$)
zgG7uqFi!FbC!U*Wm-kfCbJ6sUIL$U$n03y>Wn-&#wyLq{?W+ee2TOJaw!Bq)prp4#
ztq)>yy(rFJI%>@1y8ji;SN4>nVMUM7{7c!z@W86VRvze=J&?I=$>-bhRvuOcaCxj#
z4$ukuzL%)3;LqMHp)DP7sZk$6{avpU#K}W9{NSTjyn7WTSnxX8oasEbvBo;rcdf0h
zv(zj1uKwV+!;abABqNR2hdSnc;oREVdDefPU+bRpQ)_gL{5R4rJ47~Va$LTL6zw}*
z4g?GaPGmic2LmWtXW>FnL6#yHzWPZVwH{;OW>Nek1F)u3+%(myZd6-;{v?sA*2te`
zkv~!>>iMe^8RpC6yItaL;W*cyQy*S~t(Sb#D}I1W$OB@jL-;6-&vB*7LK(*Q%8VD`
zJ-DVH6Zi;R7dBVkXoSQ1o8C{B#{1u3M38NKWK<Kag!+0bWUSiuU50(hm~Qc=sU#u-
zMr|qLh7r0$aUDT9F5QG-eWQ2+)_6g4)z?LKSz|_64kv6m?1teN#|cM}uhk;&HXPm&
z?H?*yQ4ecE$knDEH*6F(%5PN#E1}&izLu_EBiusvCe(d0zSf9`YZ`rL7tpilVIo5{
zneW4pM$H@^8Uycd2=jV=5&d}R{dB)R{06He^@m&D&>w7ljd&0Pw@5t&@{KLFvOxOu
zqk8g<JUZV!JIt1h0IjwA5!CK(piW<Ym#VJl4BOb)q;t9({GC)cV=<<eQmfd&WF#Ki
zZVoYiz0mg4J{q3-Xps78kb2EFsdW`^Kzf6Qqxvj68$=eS5;0<7s$Cj}iaKnA27~~$
z9OwC?T_Df$VVLIut91qDo+-0wftoz}-{fw<e4=noh|aTT156Hd{(5Xl`b;KKv0_rH
zm=-5QTQT5DMs8k7WRa~4dRWLyX2=qanauTM7ZGA|<&^tj$90{LhnZ9BZ29)@0vMQf
z#ten5Tsh#J>5DD#d^(4pex)9(Lo`BTn3K2Pd|H==j;arBcLMn1(nf8g=7zpe^&?Kq
z>Ni4e4Jkp1zY9`pYz2$eTCKJ-<hFY`Z8x+t*W2tZd`vU+W!je6Xw69#2CC&d)Kg;<
zGoevWRhUIBc?>_ZC_s?vjluzHC;HSc{gNySmS9Ss`<!HwgeY&x>HaT?*OWIZx*+As
zbXu_d+aBK?TS*5F2&SANhh2LuIYCA?g)KSXNok>wmJ07Y0Mg)rop%p1fBW;t@XbOj
z)?Vh{1Kwnmlq=ppKi4-?2QwUi%+p{jg+m<Vh&%Y;f%$2Sk&0A&qR)hRf_&L3!gzZA
zz=OZXVw$`a+E~70*TS_q$yWDbz28Qx<Grda<*r@Wb;ojdgIRa{$R;0ynYBMoLM&R;
z^#FtT%b)v%#EVhU1o$MH*oD3(nlc0@ba$|X?`IBkUq$W^cNfHaJrCZ2*K<{RR*xXI
zZ`l4{c`>*N=ey_V$3(r{R$1@Ymu5lhXth`7DVlt0Fxh6@0u0xpmt22I+1wz9dWJ|Y
zE5jV>o{`U+;wpPV;oxQu8uMZnHJIh6-0({FnaTcwFP4iuG6mHv8=7GLJifdaOk-gj
zo?VzFOU!l{Ub%jY*|)Rf<5?_7;dneO39n3LGASGu74R5S#PEGA8?!>(Fc*9Y#>qx-
z6F_JX$hJeqOYGkV_V27$hp)5m<_V*0sC_2OZ~3*C{rl;QRPqqgM+S_PH+a>?-faxm
zHwzK%+gm1sPGswo-)fNbsO+EG@|?G^MY6v?7<#bWP#+It?D4HkpToWP7QCH0jdrKE
zQY>ILmIV&h{F&$Iu*ky@*i8lq4d{J->xcYneyen2X_+A<C6kf;)$C$4x|pTLmen4)
z##%4gTb#?~C?Jzjm`KE83EG`f&b<u}`!r7OtjH~LC%_Yl+y(^6#F~R_tPrH$L^4Fw
zxjlZd%3__GDJlhzSS*&Msv46XW)P{*Lu+yJ&5=%#`a>Rg@ZiF!g)yYh+n3ibodxD_
z{N~rs?;@FRG9o;Z!-6D^C3KR+wkV3lv-GCUdk2$6w@u3Kn(zM0*hcQsS?VnNdSIH}
z^@Wfw%R*w*TXBq6(0_MR6jT33?%r)8K+FAuvYQ<Uw8BF#8^R5p8T33yxI?GsUtRNU
zyO*hS9kNEpXkzBKyEH5cq#(6&?%XLSM)ULOutO8scyR2R$L>QGNR{;!NUwymYIJWo
z3+e3K)j!Nc+h9~BXFJnIyjhCVQhReP#~6#TQu!Sly%YJkYGkb6;64UgT5ptZdZRiG
zn`3!CYZKoHBCLZxJ^*cvv9eU{*eWUDt$BXf2Oq<OPRZoy1un}CRu*dF)eC((pOR%>
zL^?j$ZXd)t5_uV?3bH^3TSLxcB+DV$K+f((Q8YwLjM8pCN>YX>F79@aLH5f?DaZyn
zLXZn7Oiw2t3Qi}dhfb&S<aCl`kU<qrCu=ag>XOv(Pshu7cDg$p%<Civ^G>Pc@R%Nq
z9U|kT$tj~LvL%w-Lt`fz5<P{(_&pqnY?xBx#MmKntVqt0ijvBuiCpwTXEe}DJR|Gh
ze(LoDHW~A1g!FErUd9;33*ae~7oCM5rmD^aT##plUJ>weEq?ow^{cbth&hwYrDE~;
z{R(3nSZCh0AG<0Wam;D5?-hyk#@n0g=k~-e$+<}<Jboi;*cLd$<=V`dw|m~(cXebj
z`xM71K_BdJ<J>eiN2Ki?+%E9`%F}q?!4Hmno-o${9vD7_=-fi3RCkNG*d#a5tUJxR
z>yT~Iley?zt>Y61sK3fbjP4BShzc>s;#Vvu)_vcrwRi1#z<IfJM|jQE->bW8;X7>o
z<xVcA$)r8e1^Qhh6f(Z@CZ2lMy>WeQ{Y-7`0(a&N;z;rgd(O{Y_*?X!X#Cm~8An!0
zU(4pYh+r82hJ#&#syAiQ#F;A#DD|_5RJ!6chqQ-*P+gM$xgf}hsg$~MACko}^450<
z0M@pHG><WrS0_UjmiO^kdOfxO9uTnf2&8s3Sl|<)0SMiV=*L7!lb=+SCuL2|%)8xh
ztO;TaN{&eQW<iX5$&1{d!-5aU4TODYc%sWmzxe%0Tka1vUfK<!qwEg5R6#}U=yfml
z4grIhajC_65q#UfL1&092oI5`#fG-~=a^u7>ua1w6j*}7OQ=2V+KUuv_ai$Ezd|(D
zLt8lGC)A$?sYip<MecF?x7hr@yvMDt@}FP)J#Kl6nHPVLFt_k5s&OCUNcRUAL^O&N
zrkoeYf2djbRX9q1*182N*s@sL{u}fM<hkTXzjJ`Qj#@GoSQGAtVF_lT<TCa>=qR#-
zr<%o^MA&9lf68uADF*ZaMao!4LAFpMDu|P!G%Dc~w7+{`k(9Lmzq>bqljJDxL@V-&
zyz9uS%DT_4qtB}D>gwvA?&+>MHTU!|NSe_#EisZt5`vLrB#;eiA&dqDc)-B2=J2eS
zXN$wOI37mkP)mq4?_sm^GcsuR$I<48--6f1<8x(ODs6lbnOR5o9G3BWyRWAvGcxju
zjEs!<zAygY|0{&7rD!6S7>Nc20jWq4V`h5n#-Ji$9{Y9lj8~Vgw;?(d5mbJdH2;Ta
zGp36zBKq%%a3q=-8_5(fCkq)-K#Cz$^*}^5C6yPv#^X3by5IM|Ngb5>!pyknfmd1w
zosa#TD&fao<LXudBciRNGthRZH-$^_E+M<8@vQeW9*(s)dQNh%#g!bXi9Y{yZ~H@v
zlTb-!Ph-5pS({<U=E&k67AR*TV-j&gvbd}vK=$d7IFv<5Ks*5&7@q9pW=F^OH%4uH
zw6TADR1nN)v$?G}&W#thHJedW==|7<CklEa&nrT{q306`<TE0Z;P*ZmF+{#Px~n#A
zsj4+y+cjF{#n-2!39ETsn&;EkHLbYqV0OTVV2;j1!`NJg5Iax`pz5H^H>Bw_B5X-U
z>}Dgvg4DN)Gtkk{C^QaxAE`fyI}%^YcYcJGZ`CTXJ(1!_Ae_pPzf?FdQjF}0RchZN
zZ!VKxibvz|hd|c#>-q)FP$kti7qrmoYAM-{Rbv72znv_tu7<P)(<XZjO^e6k2Pw9)
zT6JO&ZbQ%bg1!fQ?;w4l-GG^SFM6qDiy#=a96=dG<01_j2(zrkt{_ZVvbx^PjoyK+
z6_Gr~uS&BEG$cZA!wI#_W0-Oz_i(Rh|GR`JA}$;wB5FX=xr890r>E86p<SvpBk-ar
z8p71pAyv>L+atQ54sD$xa;hov!i=QuIuul=$w7u>2=&@lk?M&1xcJ!2Se)BOx5TZr
zx<K}e{HMYk62(MP5)zzF?j><3t`NDqx`*6WB)31L>wj_Bj0wszxtvU7<^$pBP#}>A
zgr>s>h&(Nm>ntlm%slMu-*dC99D+}4?*n|oOw>}T8d>oV?1j8TihT1Pb{0ZaNlGX)
zx_*e<RU~(ZSi25}Ri>z5FU}is4H_OrNy|-$4m<pe?dIe?%$1j@vpJzUn@0LFTXlYk
z<r>ahe#puGPtNw){=?4qUE^@?tUW4wZdpX%dXapy0{RWWG?yrhpL)7eXI#^v`<5si
z23pG^Z2<7UjGo_NhJ}Dstp_I5w0}>$teRzolTvD6G`8EH(PzT-A}6ANu^2VNFWO?*
z*c!3LU`<nz#3icMw3gK8!j*{R=Ov};=Tgx{!^T;yT6W@OJ>}Q~LKGhkSm)io!@zxQ
zxl&oft30vTuNQlk&@Nk?d#HzYYj}7#H?huQsRQ{Jbr<{F)#@zc0-HVMgkH|FT}RpK
z?!GN&#li!xFe6TGwz>|R>($wpeGVau6J;;QRx7<WpS2!7Mb@@>h))vCbG{DgyxN?H
zd)!9$6{){%m$m^E)<HTk;G~)6sefkQjJp%h%5sb++&oZ&O-wMVFtQd0wqt*%g}AG?
zh<=3zO&U8M%m&Pmbj#Ps*4H;b#oHz|sCB+_r>syKm5-+sErK5K_|i}hrf#DLU9US_
zzmHoNM~=|5q@G56K0Dvcm)PtZM5^H!A8jx2#$$a$Yn?V)-Ju5~eo-3T(#$G$P~Sn{
z_ZoTM?e<zwSBFMYx%8+OtED5>R2At#8p4yPh1FU2-W=ru+Ctt^9lL}C_7qT$mp#q`
zI%2e_Fu~CoPsw>fGw^oG=+(S5ck>+iDU!^I;@HHl`*)Gw#Hc7KA30Fqb-r)_^FqrY
zXYxCD@)AZyOW;Fj?&f=Mo<mquxxlXb-+BM804Hn8i`p(p+NB{~m^mOyXlMvw;lPZb
zyYqr;T%2<I!@OXP2GD|1(WXNg3#rQvZ8l{Hz7otL^wO_x6a68-xb0VUzj|k!51TT6
z<Pj{JVLpDR>VJF4{-wpoNbSB2gypwI$WWl%{yu+H*tt`P`rmiE5(_^Vd~7?~`j)WA
zO-)_^ATgY~Q)eOQW>8Kcw|Sr8N6MXX&WLG`pf&H3?=^_*wiBL12L5jJ$K)C%U)483
zbnt0ki@b%c6kP)C+?kz&caU|6KB#qeiSl#Q5U!A&6{6=6J-p#Ztt_gLG0jFFY+^BN
z-Zd)*!$EQOE;B6R=Ekh;;jrF3-E5wowI|+L4U@k&*%P;f!)wR&MDQl4g`0v2{dnKj
zXY%=D<nN~PZ2njBd06Ec4>|e~<GSdvcl8*u1`Sj_)+lCqdj8BAiRW{?A)TR`$r;HY
zTfDS+_PvJ=k->LX6!^m*g)#1<hk1c)8p1{Lo%h9!p|APUWP`@Jbl(<WCaFcPT;s^N
zf%v{R_lOoVxcHq4@*D5lCfPRn9k*%d5lPy)Q<5S&Z5&+WZMrPGeZdc@k0+5%?Vzir
zQS#{^b(C=MzHKblMf|K14~62&vvebjD2)Dz|5N5dMrYLsaq{PXm*i}?uB$q3MZiDN
z1VB&Lom_+9;N)6gQ{lqu*XRa%f9F{D%Bc6sE(ZM>#!d*1_cZ1Lkdu@vj0K__c_hMU
zBfPiF*pKWCM;-X7FGts<;i<$B3i>nCnelC0Yp*6RsR1yTsoB}Xb6G{>crKtW$=co)
z0xIm9KUhvM*b7=NB|;hbo>V=xWmrEp_hH5045TRmaslbV%9h#cFG}Rx0J(<F8On#}
zj%#E4$Hos%+#_d#$<neLcdv_e2I<B}G^1XUD^g!J2DmDbk<siKQ|(Z=7n04#Zo#Uw
zx{4^i406w?X6qKbDyjW^_o>bJ-7gXmFWrrEcabo7&bvULq5imAJcc}=L1^_yU8k^1
zN^`@)vqm>i+CD*H{}V?hDaGy^DbMcgSdu7iM3u<opKu)b$l@&Z5twTn--3(X+=4Qc
z6oeG73Yo?&9};9)_z-J|ZtTl7qJO2K2QC$xU6^5!;gYDt)d0sN-ql5msJ+%^qDULf
zU!w#&cLtTKF(1;owjSaicB0{)V<B~r?^Ua&xvJTLfZh27RU8A*_d^`*yC_b43-eTU
z43iEqBaHy$E@0H+aG?^(Hv#Qf@n}Fz#9g;d@XMXo%6?&6K&)iRxKTFE@)#^eE2dG2
zV4SIn$IL)yHE1XSg^j670PQ`6Wf`AJY#kR+$nOui|B*1h^;d+GCk2lGIy}JFdCuk7
z@;R{#YeeoFu=0q5lOY4!fu1PW=)chwi-vtwPa3tmHl5{e@`-M;J2wyf1%MK5llLa!
z2z4fbA%CXTELXgi+zqXSy}sJD8A^qUBdMa(vfS88+u7EJEN?qZr;`qUhWQ4Ts3dAw
zsjwa_2la5ux?d4&oXjTt{zNv3Z9&l|X2ueUv6+eQFjv7f2ALqga7va_VG5pra({=^
z;cwB{8*H|)&-VjUCLT)n*<<Zw!Xd>rGdO>vkD86qOc2ouUFbq(lpoldF<eYXk_sNj
zN_}i`_gG#1wWJZSLa9tal_sz<UY*-9JZhZl8pr~gZd&oQsz*7<vFfQrVW^#suIQRS
z8q>9~LGv@^XgHhSnvXg2XzX1uF3y<md%ktBe%drH9JX7P;6NM=Cx&5^eebqw^XLmF
zw^^PvnCIG9T9qrzbNd+JfxDtSuN-!7UWFX{<?-?Ho#X7UJAwHIox@yeJRjsGUgNl6
zkmEFxBx|-^7*DaBREiyu8ZQKSL_^;N2Tv+Yq)4{Tap^S2y^;&loQ>Dbk9V*8o22-E
z={(By<Ug`|W>cU4f1!YzUAMP@|Dx-b$P>7JLA0-$@SP-InM+g3_*h(K(+1^vWWiE<
zkYUA&XRWLu2RG^9yer*-E6cTd#xHE6$AsJv=?qepY&>QPyqJ-6!3-IOhOKfSg^(gW
z?oK4(MMa#J#zckoX^FXJ2G+Y2p(@n`WhZQ;n)5k5YzNXqBk_peh!SZr!gFHCv{cDV
zSou*!m1Krkp-X&J-NUwz9;7RIo4TK69C$VLQ)WI%Nus;Y)2LM-cM-XWd{uyEU!sFE
z$X@f5GHqZ8ZVhbXjrNkYp@qVdz)PL;5-%(j7KTdmxwYKF&<mzYV}FyyH@Zp364Ln#
z$6m7XvM)ZKnBp~!pGu6!kuL$g>2GX;UZ3bSo_Ib3s}zq0Tu4W{4C%d?qCs>Kz6?`h
z#l_`O%Z3|@fT4j_Szk#qhBhNfd}5_e1NYWZE)^!HF{lMw(v0UfVMTm?d6{ycG9DX>
zxl)K!VnY-|JMYc}0FSXq<z<*lLS8)KRpd*Bi7MVEf?hinu9NERtT1<y9JNJ-1Gzf*
zsi2iq=gUN4_fdtN_hQq_L}y!~60>830Xt@we$p{y(7>NA|0LB(D?|~R;1;<IN3>uc
z^+}uk)=haKpq{LWQL%PX4G8(0RMpE8>L*n{4_P0MZ{I!{3{KKz3;}wZ{FXPKLaw6u
z2d;8Q+&z;l<DP%lEB@y3{~F)?+RyKsAH+>n2JvdHS3{SS!cFwRr9Q_jV5b=tamF#J
z^~n6+U|C;dz{4lH_({pbPpXuab<9`yO@oB(cY%UzIwy#s8Fjtvh<uHE(f&=K@c!Xl
zzmL%vxz*hDN>bU6o>N|=2v?$ftuViyXq)`4eo3U+|Nc5v-QRgZ`4c5csbV|SYbwTE
zXTC@d^tt?P52M%ksw`$~%XMW3wh=cC_Izq-b@fJ~pNK^V(BuH&22*_<XRi?NwziTh
zk$#nz)r2bZ@pK>^chYw9o{)r3S_L_3owRG#azNXupeF-Fhx7*nLBE_C+9VZnx|gX?
zXlmA5(5~mDe`LneckuMjw%IzuEgJ%g3ej>whzg21rv+{i>#A&;vf8_(?J4Mo0BY<%
z3nj#ir`~t>F!}D<z6Tvd_P?rf=mgJ<<Fnc;=a!vV1DoX1xh(GVE3c%^9cu=ud^6y3
z6_c)LttTBY$?M5oY3<}zR!9vS;-5j|V>hKFXe<5OKPF!|&wIX%j0?{q?Qu6n4*)R;
z>3~Jr2?JdS>R}Ec;xi=<sbH9Wr=o7Lz_>Bznf%%Srt95Hc(p9(Ty06vh4O0WIVGyB
z>MI#LGrpo9;CQ|gM-qxxc%HMY{>^{BN>+b_0;A%At3+|MbKyrSGBCbP^>^NWW2`KQ
zmGlaF(68RhiB*oPid;79rQH7b8<d-s@=zj%Y-IQjqRTc0nQdDloBd?0vW%+`n;bMJ
z=EgBSgYJvX=eAsLB1xPh!;QB4_JuyW+s=0>>Fq@iLi17ks#!r)#JL-qz4W)h<vg<4
zKF_`n`c0L}77qKZ17~k-N_Y#6oF@ghH~N;;=mWz1CCw%kf}R9w+=bhBYviJn0!HSE
z#a=>OV7z^jWZWit8RWS(oP$31s^N*|4RhoPg!uz~RJpiwZU45^d#GoJMl^QIewfMZ
zsy$wc8E(DVj2SNyJQWNE!m`uA*);Wkw^+>X*>N|KFo;CIgUR*V+o6K-g)@J5*i{Mk
zk<0?eeUh#AI(h&K&a$ZDvlA_L<ZRL?gxcGgmau~?jW0Z!KI|$BdpUA8CRd@Wed8&5
zYd7A9g1$k<MDRZ&QbL(=N!yDkiYtsI%X>x}lx<JIy%3AlhF|~s;o0Y+JNJF*dl4mF
zyd6tTDJUHuN)Kyineg+W`G;!5=`=Yin-OpQUg1c(c$^&4l<@6CDKU`_9b$40_0Ms6
zB_Mi5lKQ|8&bK%NpydvP=R01st5oO3bL4Lw7K3zyD{UfBlZ9(5p|U^lS5!CN!<(FI
z^|L4Rhm?EPA#%cE=ZiFw#2yj%N8jo=Gia}+yD57}H@1X(|BK&!v%7761Gm3?8+z7f
z`chrCD$FihncyHcW`r7YTci>vC>;V70LegJDoaj)+C@}3SKT=wi97;EHfdt#>CSmx
zB<p3!MbRV#q(!_Fk+#!e>(<`=IS%p2S6nRflEjx6i%-xc-A3C|EI94A$(h;xdu6tA
zBG&roJoP%ajlB1;FXtQfO%a{tpzj(Np#f4UJOmBMT!8>-wjVJEXMTgA)`CE8xX9=M
zzzNxA5-vHu8wx0QXvg+)r6Twh^d;FZR4V1|I}T|9o>vrwRP*OpRTtUn&lhdwbxJY5
zl@D2p7LY_3v4Y#&sZ==kBRjxqNLJ*kZ9|h%64}>TXYb}5TkZLxm4?wUUgEx}C{k!g
zjqC30<(s0m>@GEzi+=IGx>WcN!{&3Q`G;netWGm+@!3BNr^r^7<dW6-?)*99BUyE>
zJL<5|xMXn-^^8+5A{kXGlN77WXmrruJZFZxSME4BLqCIi|4t(D^+_nn51uo7%CyYx
z{dQ82-%0LEJ`3kM<zToL*_FJ#nej!`LyF@%0l0-)cYJ>C|J&@)!tD><zOZonR&<ui
zC#-uxPVA!f?9qv>u$vsitj$ihLVav6^w?=<eTN}J#Gtj-zRb2aFK@Ykq(0Lawl~i{
zkEkiJ>&&-F`W@kPnpE4~y?#LzY62e+RNij$su1J_v}q=JL<U!;s?R3LS36jW(LDi(
z)~yPDL5Q|_J}4-{xjkfq=vV^T6<rd@1y#YZ8!#IY#JmA414Uf)Q($<LLR}}P9<r}K
zc8DdHUzzj1yl+MEXi}pZiIyD2(P%2@r$JsJKpa{5gQ?Chu}@15B1Qa;prT-eWqj-E
zWPwd?7m`;CMY|r%$isA-<_^o5VBId>>8NMhC0Q1CoOZu2)#JwS`_i<3yvld2?^fS^
zzK4CkvN3|3*tLE1M;5*GR(g}`^hYSY!YwY>X;)`UNzRun&32AaGe3EoXBE7RmHb5B
zORT!PI&fqat&y`jyT~@pKTD3FM|z1>cNbclt+pX7H@OdWoPm&%VcH~T?R?&z?N8CY
zPn425%0iqhEqh1D;Fwr{s(US;gOyq1R{QGyYTs*o_xK*#G|KDdHmD7C^z+%rYMRv?
zscE-*yc%~a&a3&sjMc$Ypk_O3^qS<#9cpXpf^yri`9-f@Y2Go|Jikfa^P8OObNrZ@
zjVkCXk^XF7kDbah6;xcBkRHGs<*M}bNN`1xTmlY<-~#CS&as}=A)x+k>J62neo(p7
z`C8CV9?~DI1tAV(h@?a0iD_5sFi!`V-_>spPf~xY%2f4}wY*i()}Z)nTEWWK-u!KN
zo^OkBKjiu2|G#)9i%t-ApNVt09X?FtnxWpf1C=fkR%~_$7#ftBdj^xC1mj=(HB44v
z{0s&pQs+FKyRNI|?>>Vu@MV1X!(<V~WPkXpZuC;qwLR=0efT)|AzS%goG}n-4#19s
z#U6e#(FV;qLe*p!so$hJ^vqO%4Od01v<i@*&h>>B%~8;gC^o$>aEr5n&N)8q^-fP9
zL)4}|BxDC*h1XrsFip^B_&$1?$b*|bLu4a#y7w8tHp}8K_5{44-10Ni&U`B=@dG{2
z@PP9SpWRTBZrNA6MPGGG-ucu;AKD#DktcJ2C3&FWgEXjoq+eYY2i)ILbq(G#EOvXT
zAF&lIdiJAprV1oK{fo{E-sUsiTxOT-FY96737@Yuz+8t1%4ZGw5e>*oe55-LLf+v1
z518t$PzJK-Nxs=ln7dJu0yO@NETQpxTd|%UKY_@nDl9(frO+X^hDE2{O{2%XLpnd}
zX0zCd8=n95WOVu!%8J%k3-;hV&_JyW&JzvtNetFvY2Yo}1KX8><9n*=3#NJ-B*`Mz
z&CPXlzvb2Cquu@J5ihs9c(!{II^)&SKW&n_&Pz>ntMH4PWuEIMPkH-69>6cZ^zxUu
zM@U~U5>22%q=BQpn|ybY7H1-RriClh%}G+%VDMxCBdDed_RIz&_iq81p#pm0A}URQ
zAqKGJpubYUnFx9x2A^R(ZBL*U>@ZOa$fsLu*MCmtP$+kM?)XVDnu+HEiJ_r4+#U)E
zIjNkljThB~Q7#PKh<IZxgt%1BLLc7o!Tpi6ki*H?tG1mijpCdT>U<w|@${&xh8uif
zdTBnQ?mlLW1rcJqzA2By!W#ZOLX#0uK(-*wsg=m*%Ee6BLb;+AOZ*2(@?!;yK}$+D
zO9`FNL_Rk>Ikr<9%9VycA1Pl8JAxuT=xMyIYNa*BsEqx(DU*`|mPqzFJm`qxyi+iv
z;IT5bqJZ=oWiTT)LPmI^MN?`!N@qu>eP{z)V<?SWt}tIjh4})^J_S_K5XfjK7@}Is
zp?s~)&>ve$_2PV?IA5#}eG>$o&Uz%Mn&S6i;QPL4s=-LEUUSL?I;};dzO57&4L7A@
z_q;oL7a4u_5^@G`7P(cT&(JrnuLF@0DHtNR&aI$at+#MlQmi1*ru&0v1&Z{|AyUL5
zd6s(V|ET8%i<hr0gX~K*h@XP)@KZVC&i&DP1PNrTQ;&UR_?K3wQ>4pRDiq}oD9Rmj
zX6F^v?*I4Y9d^{N|6R)a2`KL;uDra6>Wl*V8Za!-xvtHuz}*x$WP?ei0cNx^`hMzQ
za-zWuuv6nx43g24IF8LQK7O(jGG!Tk+LlriQ_OhV*d%f5aU3@B@s~o=anG*9<YllS
z9`gBu1LK58+9no&uA2o#DOVh)AZJdG^o8u#$S~Zh6E_)4-M+iZ<_J;1P(?`;BsCCA
zhXvAO?Ftui{8*%zkaQKCRgkO|<F|5}DoDu?I&%Tehs7VW>BxVG>Nbk1LDgRziSXig
zM4Xj0K`JNA5i2-mDGAdqs*#Wy3f05L#^>9>)8yF{*z7fpDJ<b)RKn~Bqc$Z%Vqkmc
zU(<=++Z~an92UeGNl-QJ)_74PPa+tSs!NGtWQ@-h!n=YZ7Q*RRK$Qei2da?_CD+-*
z!NQ><M306@=_6{<HWP|97PLmpL|GCvDT~R|@{y6EpFDjO*?QCnyO`gnXngo_u(UM#
z#iRLdTlZ+9DOLk!@X?4qLS-OYh7rAN$4&JNig79zLuXVoZvXlH-JtLHhtk<0G758C
zT@dOVcgj>t*_1QiyA7?9RF1NU<RkeK5!xcm?7fxxroi+f1LGBl`qihbG*>C<*4b1z
z748t7A3mT|7_CF9P73G6rbjtopQC1L2#Mh&UJbjcV$+Yitq<aEH$vYwz|wlLxGkQq
z3W~68n*i}0`R&^Ww)+6Oe{hk5+-=W~@n&?|R|Ic=m&@Uz9xau>J*g;BHKCk5sU*~>
z0_(o*wTtAt+a5m}aF#+GXP;PMF{LX`ST~m!!gd0HmYmR&C5e0RF%;4k{RzJij|*1y
zOQbUl3mlLd(L^U$J5tXt*qy(!)VnmPTC$HPC#FnIGuw;rCCA0dr`(xiiPie6>UQ+o
zWpB>nW>TIQl*eMFU_sPfMI4I9m``Yk^(eKw{}B|!9}g^QAsVrgmTe4z7MMyc4|>-p
z;pS80$?NQLh1`(*SfM&+Xxst?=W7)1-G|>u`bMZk(TCpv6h3s)EK(!z-8cqx@TIk<
zXy+zTcf)cE<~n*%9r<NZ<Ar=);5Bhs`Y2mTEORB%3Wb3<WB9GZKy(ofwR)S-SL=eP
zi`6Pw2zB1fJ8Pb0FQ@E8Mm6|gkT=v!!cO_T@9tsB5k5>~h|<THcOFFanBYlmf_=PP
zX*kUAt83H8rpb@3I`8VTfTQ+g&bD)tl*<*ZP9Hl3N07x@mrMM{p&M=(V&w4bojT<R
zfagTX9eG{ITcx*hxwdy#y6U+q%cZwF&_h2@<b1%Aw@Lrj^*!7V(WosJ7m!9Lq#el;
z2>UQHwZsh!VH*<zR69=YYGN!Q3Wma~GK#A~9vK48r8>_tpM$y0R!>q$-83u`?(`aY
zmQTlQe<dLsy2_WVT=CP3x2D&b&%wsudbgfm_Av?Cnd^Yq;G+%$Ql+0qnml$NM_O;H
z0&|<2Mz3;|=_2|c6N=HG;I@XLOgPv(XSuez+V0(E$oAErPACjaM~*j+_QLOu8tpd3
zN%1lmO563lTtm;I{|j^q1YbA2F(Ne3?~8u?^pgU&AD2(iP0!E|41HOiDh$_okxz>}
zHl&lqTwzMadN3o&hRg*c0lpwaQ&FM72O>d^<Vl&Jj#df{B1>=_vUr1hfNe{W8-?tg
zUEOhXuAmu)`j`<48IP%kp%vzi?x@;x-kdJPJl{&a4lfe%yaYzADaZT<wN8U!kxF(<
zUW{zx;xW!U-{b_LiBN%)xdK8>LEw_fjalElRA}>`@EAcTAB6eQ(VleY1sct~i{)p$
zEzmzOGr%U{<2De<xx9&z*oYMKFhl;v3wwYmrk66)*F&P2Gb}QSudE;+`F}14WQTMw
zZ4?*Sl1-?Qc9C15P%&z+eTw39z+QWg??JHTmp0gIts>}lEFLm6eb2*_4qsuQQuiJy
zYI_4|&+O7n%kvYRVKJ>505Gu``H9DZ%TogKz|i#cp6Tf!CerP08$e0o7t^=RN-_U0
zhf_EpWhF_(B32B8D9~7xh;$Z`MAL65V~XK7MJa^6LyiQd(#d#3u{t{#U`#9DHGCTG
zuxHvKpGLWAG$?3dOq7Nus*f0fRQOjVbg7GOxUUpV=ne8k8<yvQOWuUnKR=9a_Qe5b
zV{P;iau`M|I$fJ3ipPvYFcEROkY)v9pECGnjVsz@Fm%tEp1JvIKqUMlEUv-gjEH}_
zP2oq0fQuwa7~_>~B975~g(z*IEH6(fd?rNF<Oy0Lj$@R@;wYR>7Jp4K4%6~OM#Qor
z#zV3M0msPlm;I5@%+<1d^-L(@|7B5<MN}1~F1I<zzcA+y7;Mtgn?#i8p+ba-MsJZ}
z@S-Lh)SVL8kU*>L)KHtU&bEhA@GE7M-8bx3G9|KOjZI>Q`&P(TRwuJ;b}>7tmf?SH
zN*SA*8&jroWZ%<lI(4;g<#YLmT^#xJ<uT;F@-x0HRJ`76`hS9k)5u>I89oC?P2?S!
zZJm@2xWj3r@+<^k!xfK@tm1&KphQ47=1H=kc-WVUR@tN?AF!VFyR$!cn7s4ga`Dy0
zK$z##yee2$K$7IcBKAuG%VJ(<;Xrh0atNBt5cNl6$;qYYk$~jKKXrWB*ZgAeNHiJ<
zGrzX56%_qgq&FZJVtIk$NaK)}qF15La86P?)EFr)J9qA^x!_FBX>)51gQ-&|MTg1s
zl|1n;I8QuM*<QKGLqWOwABr6NFDW*gA45-*ZzbMk-w!q&u6;Vo?#?ej!xIPMzKC6t
z{buVTY0`KUUdQ)homWUR!Xh}8QWzCc=*YKyiWKO=W+dzAwJFuKB#b4?RN-M7oi9^_
z(oT}z>%qM@TR1+E?sHx0iFgK-tnloKHZSe3hf&J<2P;K{OA*~hG6st_-T8ehk+9Hi
zQTF1kFde+%=-@R9Ms%Rq5H50_AsaDEM?BWvCOr;4xSaLt%HcHDMNWL3Xp;Y&sN?iu
zMfYcyMNZQ2J2HYoDoYVE@4%YG?Y=IdU5^bze1k}ShJmlw64yzFj$@Ou-!D(bux@m1
zJRm>MIo`X)!7v`SBY;H)=p{&NbqpR3CIcuK8&R7milDcVk(8^RV&;QWs+r5w7tN4v
zX!T?XL-&kJ$+~9fA#<^w$(fzWV5rS$HhO2ov}}!(Xq5#fG1b}RbhLnDmZ8dfG6@|T
zvYyE7kyXQr;X-seIjfov8woWqJ}fV4NzIUl#~mNjhpAl{dVXKa<tn71Nild(*d-9%
zL?`NKi6KOrLGP=Z;xzm`mJqBD#uwuUCCs6R46E~-ynqZO@#r1gwsE3BI+&$BWd9a&
z?33i2$c=#;!kjLB)iOF?zKI+=LC$&Y_!2o9o$a)}zFqQS_0>r0c+B^H-(UFtp2but
zQ~&)Qx?Zo}pxm$xw)EiJSyU`{JKdh>c~Tc9`U)hW-ih&l>l<*V1D3JxsFLvCV^~i(
zRKUULjsrWQ+vze0n5<76X?$7~$Uo5*OhHgJDmZGYK(=gA6eM1R^DckL1MIc^_~&`C
z^|oQ1(hMTSx`fG@LLmeH8GygtUVjA_-{cSn1GjvIS2IYC0DIg+1agiscr`W#{E%Yk
z7~9Ma-+``f>FGL-b2QkC8j=HlaBPbkmFp5nj@_s++}&HOWQlwhL<ykq1`2H8KF*Or
zhznumrSnWpzR%H?`LZOA-cV;--`-+T-Ldlo;3@pUT3#l95K%^!6?{$NiC!L=b#c`5
zjA3)-;4iU6pXut$ePcaFry8(B^=`Qw!gkSXy7|5~T)&&O;d6#X+wc@Z6bypUPA@gZ
z+Va7Vxrj%dt<RAbbOu@w(W{w~#3>>}hKLH+o2EP0R@Mhx^PKpS4?A(@=YWlAFh<iw
zf__{+>QaY-Q36*USP4$JOz<Si|3UcxO5l-C{Z>4tkC|xCm@|`gY?0gp&jgkZEcw4C
z3H%(dOLMZH5d-+;Inf|l0$N##BodJo-jME<L_S4E#|~{dWQa`hBFUuVpW(zW|3hk>
zZu^#fE56gdRo_|PIo}h$HQ#ygs<y}=XEO?V7N&FwbWJaAb$MpPg#EvATl_hnU%K4!
zJ8!)1x*K;2oG>cr(zQgUk!r7*kPsHHm2{HFo#@g!to#d-ohM%5!6@g%1^!9jA?Vo7
z82Q^N7}73L;EJ3oEd+xLl1h?-xJxp=;U0O#i|c<b$2r()z#jr{w}&`n<`mX`U9$<r
z-vgV~@=Mga_3tTF7NU_O&i!|AZv~bs3c@Gfj)T6>5mC1tPIb<qob!FZ6+KJ+mNFpS
zSoR`nm6^k(L%Sq_XB}yzGtHSYUu;$IfcSRbZOOfrtKK#)itqYa2xYUXF}iX=73Ab?
zvdJx3f3<C4?_WaSMM=<!c(nC8o1dSR0*z}I?%t;fCngh_eK(ePMc(t)&f}kpe4~qA
zVjN2y8tV$?orn%pqU3@eVvf3o(r=3S`!#6vGnOVL7AZ#zi<gsO-EFzRwarTD&IM8z
z3wM@CCt={>%1DVfh<X;w7E^v5k#uRaQpHB+DaDYHXvcL?9*O5`f&6c6pRUhKA*kYz
zG+&?IE~|J{#@jG11ng|k<}jp3uxh*&E0U<|O3a9*Yx%djzDUfMVhWz6MQI{7F_c89
z>2za0mc)El$$-7<R}GJ)B~_K)35#@fPc;Q~_YS%>I@0-R4{66jxw`kayOsOfm1?#6
z>7MO9!{vqkx`#QxKS2M8o&>%SfD2X1-A{cQLV-Aze(E$xjbt_xey1l8^7T1)jo9<K
z@k0~i2Pd*~HV@NQLjIZ>ozp;H>~-cEiNwYaU4L*q65$a!O5-yHI&<Na2Yk|=27aej
zUj_9Sg-*D2ivpf&j&}AO^}wo&xOnr-&U?1)xMyeWn!Lal1VykH^7SLN+L8L`zW6QV
zsCCC(>riEM$Gwl<v!jp^RH4B05u45pRr2&Yu6^+jsIQz(G{Z8fty@T8y-5+^%;}r@
z>D%za9!NM$8?Q<Axk|ZGlrTlG!!QBP&}AAC$_@FKK@#%0F?DB8%yp-Sc66sWru~sf
zCKCDOW*Q<$bCPNBJQhS!|Du3--Y_MO*94qyN|@L1R4SKCO<|44=&W;DvZ!`vk7m0Q
z2xD;FOym|_SQ?6^By5V3ge@In+UXXSB+<lDDmt_z=$vL?PUi)I*EwwHu73r(KxC+d
zZv^;Xyco)?U9&e!BW6bV0g!r$X^$+B3Gjo{|LB6q$B0_}MiIw&QTz$d|3r*+mQ_>a
z(Rp4pJ8u~R^8glyz|R=91H8!eKamfJ+%b;)I^Q%^7Nt=&FRWoP92W5!{DIx<pI-ib
zPD1~Q#vUcQZ3!moz;k}ag~n2*S8A!ZD{Rg3nBKBT4rQMQb-HJcpkyODh3bMZo<4MF
zJ{(Suv)SD7$jCxuoV=m{`9B_67$F-ZUnR$d!}Es@k>dmuvN7&l#n89tNgN*O-(T%H
z^?N;+toGlna}S_)$@vt$ORm@Lqn@T{H!AP-JM6mQ90%BfT+}*sJbPheew2cIhj$;{
zH5~K&!aiabZ1P*XJu^Cz34mH27~VZRvU~V;IzvPk9}3!bunr5x+vt?#ic50wyI=CQ
zuDtsc^=pby&zo(`A9^ctV8x6o8}MF{ZWr8VeT!n3lC<R}P?a>pV}`1HND4?FQdHxZ
z`7KlJP`JaHyuEDa&#2}v=>eVm+Rn;UACHsc$>H(GRa172r82<mW%Cvp4$@_|>Y0BB
zyVMZOzk9vj;LJOl%OrJG;U4_E{4ij{59*If1GDZ|4J{6lI(y3t>Q}7ohqqU@*6wg8
z+qa0Em=KNkkR#M^YOU87O!pN?eR+XsNDpThDMOQcPv@l}UpI{WZ?%dCiml>&fw>44
z=FfG5#peQn!hA76)r2CQ1eUz<6YSRw%3pdj@GetZyJ!0Zu0mHxY4uZ+H|-?^3=vu2
zjxwFl9=-9Om>HoP!JCCcXq%zsmF18LW20e_q-Bs~Efqc+PR&;9)!9Az(2!34f4qLW
z-q@82FH^6f<#1}(U2OLDcC6%-_0eW?l)|yS#4%{-G0<u1^a8s6foxVf&>)q|Vs@v(
zYU9A0oGDyfufxd9JN`jW(>iaF8n}F|4-lI&+sS%&Ui{5L@+N}p#gmU^Xc1+Jsw3P1
z-9$<dj*!)R__dxm-hlu)cm&?g2uZA_dH&S6Kn_*;R|)*PdDZFXyBrqPK?H<5@>!JJ
z4@9>q>i=k)y<mqO&NSKR!K8mW#|x2aMBsDN{^Y@4T*C^R=;}N^luPCj=@awG+>o!&
zHy?b4XNb=3>x*h`_amk$*?9y{w_JKLw;GAm*;~ozLbnmeC1F93BtaLXvLIau8Cp+W
zUSF)PbZdFN+q^#rryPLAj3h|aGUrxk?7&BQfC;SpZzbi2Ij1AOH-Xo4r8NWaIA%P9
znQmq~18JDFY#U1;BrtOs2n)<w20{b)pAc~bj46FhhCC_daVHlAa4Y3=uMD)KR=LT&
zh8GedQe%P`4-(xzFNK2%QLt4c#swiRajGqd33pFi6k=r0yFi^!r#s*4R>NO#Jcm^^
zsHb#6g^o-W_})re@$20FNW{-exwycaCeBFkPNfVs{hScbNxXmay(Ulh>fqUwuBOg*
zEAmIYAQ{<;5yzq|;Dn(dx4wy(?sH>7x$R^a#=c8^Rplja0;Zs%OPu3JZ?`V`Jy`;h
zud${B7-{m>ckVeuMoT=hX9REdIce&iYa+4tZk08J-?=AI#`MUrV|N03nob#<7q956
zZ@E+@&TgDR5lroE6KIXve->bImt5Fgkh9G!DQqsB$~HgG9FUF)_J}Q{lU4+MCY;Jl
zuQ|tqJ5nG(j!(n)pC$>1{wF8P{zxM=o(hwTGu;h6gTCeqQ7&0rF}eOu$&L^)$eW~?
z0h06{j8=<HZBUX_v~=Rc5FIC0V4UcDO42nyCki8i$oVyWyGg6jT!G-VUk!)9O4bjZ
zIDtwFMHR}Z78go?gXVEuUKABYT$JOu8AzpBEa`O0=1h~D5QGU1Q2*E)XC$9W#?%av
z=_jD)KrkfWdGgu$T^nAJS@dwEQ31BnM$<%*QW4DQ5N;DH-KJ<y+;E4#NFrHn`(O1D
zJ-!{rHH2P7s4#!`>MenrQsHO<`C21DOJ0TsghzH0t#g_7$Dh~7hSHI#CrE(>Ss*`h
z^4N1f;6FviVXK*u0Iawg2&~ldi74xo`}@BLX*XM_4$VUT^tb0`U-u>!9Tj^3$U5ra
z<xpD|CBCPL$CaL54*kbmZF`MQPy=1}jX=@83SofUM(z);_cZ-Fm|g3ZLCqIr0VY~+
zPKnL8!T53WK#Rw0kS-8uj0Wi<k`7=9rYSm~7T$)|&J1l7uhj&f@p2`SVcfA4%sueI
z^nAfrGtk8IsUa-ocuC*|D_u!h0?#9f&r0%8N=nVAG(8l}PvnWn+D=&LHY-t>@A<4B
zXW))EPl{vd5m7;y7f82-gj6(<h$Z1d0bW1?Qlzo;m^i_yD$l)2Oj{u6S!wZzynk{W
zrU1uD^L5AlRf;oCfwg8Z+L?Iy9)UYeV_&RMr}Rq<{};NZfciy8eGexO?<hoNvdzA7
z$I9<Xr4tk?VIl~a5h+g3pk24#TA0nVTjysxPlksKea9I4I7^EGqZ+frVf*ZgVV$#n
z{kGfq(tLrHp)g-M88C;Nv~ZQY6^V}+#>iEcL1UeE>#OLi11nGI3gT8?Q5`aSI`eb&
zl9#T}frFjoCF@r3;ze&E{rz^ZDwHS&Vx~DssZq-mVoM}CV7I~y<VtFGGHg?*Kwq)T
zxRT{6<lA5eN=_^bmZ%}X@)7wfBB9k7ugdeGOrey=x~Ac%nljCl8pWEX<9w-*32l*8
zA)YC*_FnwUk*Qca7n?#zQY6jP{FpO2+0;#yY;z)-xNS^&*P?7@^HR_XN_H%2Dxzp&
zY>J{{Mq@V3&Sy<|A+$K;H21B)ww9~D@r~8oT^t{gC61Srhy;=i&x>-D=Xp(%G?#nn
zD^wP?iFQ6h`?m@mGTay`UVs!#1{XXa_)NE&8|9oznS0%Rns%S8hC{h@LDMqHf@Q03
zUlH<&&VTN{<VO(hea)&emPmhwXy4<!DK{rZvS~vQ^mt~tzC|{_{FU;SvBFc`*Inf}
z-s=#BVq;6M9tRjypIGw9CN?N?6k%C15%*x{LYXw8owG&$R|oXCm;RKL>ZZ=0*4pYH
z8{3CTxwA95dj~YRD!Ka)lzlzp1aNOu=y<(>eWMFs>Z`=&;Ak>M1{m&v>qE)IW+()d
zZ#ODytS9{QZteZYNU*o|Z2y+j(d$Uhe>h1_A!j5rtOGt)+`{T?OY!x4CAY?`EvX(`
zg${aezdVF1t)ji5FY4*vAF1bZE4f^~*p(`JdhYyFXJ%*5JOu&-`y^=(yjh>px;)1I
z3bbPG&^ViHotR+Lu;T;Uom(%_yPUtsN$z&$Qr!&Wk8<j<a7A+Er4+}DD~T?z;5yM~
z&N)1$Cw-6lKJc&AX(rIjR0c^NF`qrvV6y?>bkW>Y%^~|6O_IRfrz3J*v(hu2wTND+
zz5}`kN_TExbTI#fINq@JpsL8cmYLq#AQJSWL<8bX4KyHZVhzcXpceBA6~SMYUbGVJ
zMA<U7i`BdSm1+>sLNLym94f?haO{k`g8wr-lJ#sQ9FQeh<VEC2^s4%#RZbA`t+>T~
zUk_6*oS-l38zVg|#5Z)SI^WeWK)Y~pB>Bs?q5i5~aj0|tD_d_N(p07@Za1uQ=l{8c
zqVjULrq+8yXy>fJmZD|k0R=Pq%o62`sw{^0kSx}=fSv_5BQU+w0kQ{nLZL~OGFnfJ
z4quZSl5pg^iri3ad95-sHk8c?0y>uzgj{xLY^1U#+e$-Lz8k^PQ0|)HQI|{q92v){
z-e%P&srwg$a+jA7^&cc7I%(U?TFmCKnj~R4$`Q=<f#^iOg7bk=&Om;(t@@FXD+M?*
zDv;q$=AuzfCJEB(yoro|+AJknxXOxXI{M6sLb;{?3}UALOmCG7Clpy$-_3LHQOWu}
z9RF^z?);Z0)qts@^QyTpK3R9Th*;bpz!bH}7rW%Umei0VL=n-=dxeciR}uvzfXB?*
zoSK2S!X@$_SkQ=K(O{(QOcY~y0Y>l3`Dl>(H78!Z6SKa%NjmG!Ifw<8Lo2YP``i_3
zZ+?=qc`)ak-0q%|b2;4^j}V}{ZY590n(Y+;EGwSZVwdKj>jU8tK9nmnEvBj9vKv!b
zLX6I1QwZj|1FT-&urHg4X!ed;ZHKKzDpLUAL;*(e(Kw_{_Z&!*)7qg1!*SlVaUTvR
zvN5tRmWyZU`5uN3Vj-1%M|{`yB2IDCW)DlW+-PIUMVGo@k1GVEPIQsb+%!d^){7%k
zm4SzfP6l}uSPw6TQ6>33_|(a0w(n6!iuHhi#ls?QU!r2juJvA=?K;YnF^MAlUO@}E
zlGk}fZ;Hm1IFJ4meTMW!85)Pt6)L=lypB|Xi!B@d;Bj8!3mm8YO-4%khohsJf6!>o
zZ+!IqhmrI+#}#;q)BieiG#d)RKC%n%eE(s_DelP4U`^lQyNdKo4QKLsfY$-g2ZL-;
z=n}{7p|1lb)6SE{{&pL!zeFdZIhcs<{Pl+2s~5lqaDhd`hQ&%}y@!Bh<L(|=IR}<y
znDZcgBq&1>1}NO%bV%h=A~3h~U?8TxE|&`b-JwB=`9Hw85;QvRQ%6w9Ku8brc>g<M
zpADzB_PQGqxp^_1(on+nduUVtxa3BLC>uv0tPd@k%R&w#SL&0mdz~5!1Y&5FVU}5T
zdH=`uV?N04OtGg7&ji{Z$eI-vox4e+6jEpt3#=W%EF#WEx^yoE9!{ha#?5?$d(-ij
zqCAk4c>H_6hj}UafTFaH%beWmp{HicmdtfdtgW?CyEfSlkvf`c2c&>-%{2m9wWml%
zs68o?9n3wacS&}kX!~5dO~#SGfB6s41@sl4LS%>}>0KrvW(sA-ARP-JPDqNZ?HS-q
zga9gJ0M0;=NC|@kNP&VxQMEGUc)%R}5Jh&K2!>0+1A%C9*H!-biGPwB(~bIQotwO>
zmS23|?u<TCIr=+Mbz#@|tMUi;%{?+wk;cbw_@mj$+`YSN#f8^AUdffuE-$|3(fyB2
zN#^){OUXcbbTl3wo>UQ<FV}{$DJ>dA?`%G@3;CtxhaCJy2R)2deHI|htS&|sdO~>J
z1-lH~lwp(0AR4FayT=(-$#ELj^B3f>B3hbeiAq?G#N+6V$4&LDY97bT_4q!K=)zhk
z8q$bHEb7DcVS3M($r$`Fxo7Jl_q+lPq`w>&E~9IpH}#im?K0|xgUxsuakQM9mhySG
z1hQqC*5DneefwMFJNhg+zv5c}FZ#`{(jxT>0J<<G0cCZJ!4fi<UAg3o_>mme1Z8q=
zZc-7nu)MHyXKH*{4y%HaE)>#=poZn)@%IeAt${L+Nn#)@=1<*!DldiuqEvayd*1T(
z`q4Oed`Q;T-CM~rC`$38Z{GM?dpIVG4zkH3i|WoSHwS0gJwL$#k8<W~&aJegFrm~5
z!!k-I5II=hZ5f?4!#bMa6@K`HAPaGxUlkO7AHUjm0vM1*+Yy?;sSeSTJ8NGcM-Jb_
z^Kj-vJinh;93A*&()T{>OH;H<4@#}qwbGSaH)27#m6z72vDeDUNy$#wQc}(V$kpmJ
z7-<VCIqOwc^hj{4>GzvkgAvPsvRz>AtBw<^BfESB?V!9ebHK-Dd5TGml2>C!|1M<^
zj0a1I#T0;Y0+_!{DUTUdJUx=3Ilz0rhR!oU>U}XqpDjkhdeSI>(~BLKz=Oaq$l8J?
zgm^n)@KHe<4~mLy>v3JVAgWV}l~Csu(G1%hk5-sM_5nRz7!d_cG;<~#r<t0dEd;Xq
zcHO_vFeKYJ9?+(C1>?FV$+7sOx-z9EEM-B_3(+_yI=pG%vzwwwSI7U8a>o@12B0qW
z#2<AYp-i5uSRq5sS>PE#?~Y_L+Z}v#o6azYQ>A2QEm>kgCeC{S0T?#oVWJ~3-l?NB
z`q3pLMpXv3Tct7<P2@I|t=+&$o{CdJFLcgAb3~_M*_VE=K>pC{N%~ahuR@{D1v5Y+
za9AdKi)qnhz+CB(5xU8>Zt}5vWceN`W_CUS881L9e1YENS~DhjZ5zh(5#QCmRf;&I
zPWV(SbaX>UlLfh+{v4w4%D>5>+*CfUrquMYw3>jWlGI{(NmlV#apdk{@*5h(sw`d6
zL50}m4{8gREZfLdIJQ(mFh4<L0U??oe^G%529ok#c2rEhlA|tjEoOuIXCmu}<ok_N
z90?C-`WZTr6QSm8BBwCYL9an~_VPh(L7H8jl@_!hzZa)t=v*w_`DfOkIW(l3K|f$C
z0s&L+d@nvcjIMPXH1FY70I}BuT_@_m^?<#lkK?-6mcfXc{m8*CptVGT;^D~Cl*<XU
zN|yh1bl>RczH4AHn&)+0<O&)9pU~>~?SP)UeY~9LtkJ{UiSo-n=UB4%&>=|_b?LC4
zv+aD$i;4OS(My&{op?ZTqFmO<*JDH27TeQ3Aq8VXv{D2dBn_oj0X>G%ZJ-M#FG$<;
zgtYiBJ;ddz$#{{=#l*Kluka@EZAw6xD@pnnV$#w`W;&CND^pu*N9xUMBa11?;PaBA
zzdfM+S+b1K&{T=fvrq}D{EHSRq=s8y*z#|;j<@PZYFike7TAq`m%h(gW>=vM+0;pj
z0pY1=J}r8IoheZVm0Vh24Pmki_lwv5!s*k$aIGj7h?KtP#0@u`*drKZ1G*^~0G^n8
z-}~lZuP%<<amNT*6+~1zk|KXc1XC;sNZf9dzwIIt3ZnTm8#G=$RV+@4uv;j`<3#~>
zIp6<(;Ux5N8u#GlJ}w_%<S0@+F(oZxb6S8$&eJtF5LSSE&|Yx8B6wI)W^Wcs1J0&l
z&?nGG-wiL|J$jhqEWaA$IYrFLDkn9Isai}B=01y-({)>tsxwo0kyGWIBy)UF^;;Yl
z);pgCQ1zo9Nvm%F9m0rWK`qECZ|)h**07wIWjvOC-L!R86g6BJ%S@$*YPtMOCRx>j
zl{nT!QPpiz_p=w8fPmJ9CL3~PRFx(&L+y&o1Ml+PH#XsczY^M;UiSM_f>0Gye#dq}
zB?)224rhA@&$b6B@qciU!`$t@^2Iln59gaMEq-q)hB~XvS+xIl=Uz?e?-9GEZv|p0
z4~}+y*I7x08I3TD%hbb<o(CRt79+Lk%%4Ls@PN-N_F`3WzErqg%b#ZoVV`!m6N^6<
z@r&pzWS!5~z%S<gJvwC_N<H0;<LvqnIBJ27T_OW!WvsN=2SyS$`sbZYq(3lpF!eFZ
zy3T4^A4{43gEanh=X2LRcpZ9RCksM-&~K7Mn&cRA$ibllR0rt%COO3E$Dcwa8poO3
z+XnadnqwXSEtQ&79Ho?02;Z=a&sV40VnC7BB_$xXr>jAm8V2l7eH?!_@CN<b)@OmS
z{<E!X^*7i-7^{OeI=4*yVY~Ob?42%lmy6uNovZjE<@&$ccii_H-znd{zWV{$?HD{9
ze{{b`712#)yA#ZIH^pS)CDiDqyldqfxM<TWkiNaK#5J-Yt`?iH)hbpR+%!e5TM%Yf
zl#uzlFp|VEK|<lXg9r;F5=J4~02bxluzK|{l7u+{qmkAqq{ulVltW=R?tI{gyuBil
z7J@ilD<1+E;5&FS(C~s@F?Qf1$VbgmM<BuoQT0W(RKz1RJ4ctl2id>JmMlAWzZDbA
zU|^=L*tz=yK~soXGwqx^uPLIhJMt=6X=tqO3XSDpJ5+QuzC(9-e~~l9{X4aNB9Djm
zCDS3}PpneY`jg7xOfuY=cZkrAVqV;*?c6UKoDxo^m&@O=nkDN`j8HnYxAP>MK=f=~
zYjh4HPe%K9gZG6SE6r6Dtbs3_F9!3$0S&AYc1VXEb8my${}}rX6rGU}d+OW9R1eOu
zdyU8i1{3}FZ1vxItYx^~@zuSpe<$*1PC*Mj)uu`bbpQ^xS=Jd?I=iVx=ViAUfHdd$
z!1sg<?X=NKr)T>cyqKfs=IE6J0eTxJt7l2gcQH4^jXQrg(Pwv4yRjc!c%dnm2S0~P
z+ElwJUI^uWVw=pj{5mJAN{g4bVazXYfhK$`Orw;%7K^hYX2~!OD!pZy2bljo#gd(j
z-h*}NgP0T#my&dK0~~S#O@i+@S$$B_9o+`(Q%_Qy5!Gn>kg9x_wF6wRoVfH}2DG1E
zz$z0z4G=sT1y$9f^6q1EJHupA@PDKtWS79(=lh>=jMs+%004NLV_;-pU;yGBGYY)o
z`E9;3a5FK0zzrsmd>H-z%l}<WU5sadTn+{%kZJ%g(F=nB004NLV_;-pU_9{u00RS4
z!~ZY;zcO_(07Z}ia|!^uwFln-004NLeOAep;~)@4j$)1ug;4r9VGv4(q7WtoXD~kW
zY}Ue1`!DS715W}$Ex&$!R7xqAF_v{atG>6s(>#xUT-x~F`gy7STx#$BjoxX{*`o71
zJ^9>DHqG`|L;bkjOA(v&l#%xB8taHlzxfmP-6a-Ze=*osBfsN(6aQw5*J}SGzI2TI
z&7Z~V*sZ$fu2^yIVmDzxbG9eBnGMFR;l8;JdEmZb=VTi<#f<#xI@O}AGM?;IjlVk9
z2ji<TKGp83d6kG^@%;NnIr+Sm&ud+cBf4+S8%8?cG|p<6a?G52sx9%bDfZ24cIH@U
zPB@tj<@a4{;TZP^4WCcAEXX~JuS|7n5HDeL8-Bj3t;JrnFYd}mnqQUob@ppHg-_=v
z__m%jdj+`UHC>gPSpV7&<j|!;eih;=fdSikQPWq0?X}<z56b<Mee+uO&HZ`$x2o?2
za~)b|`cN|lv98$PQDZ=pD3&X5!1M~v$r$r@ugf94A5Z)KH}-Pu;4|AXZmRCCioNi+
zkN))DAl>F1A>KuF>xlX=CyuO%{_T9m)%#)Rev;fz;9vA+&768&H?41xC!W)~b&p4V
zFb6AZQD}CJdW;x0#8mO=dWZE_m}=aF%kDZp<_oWl_cyljfI1(5%Px599ccHM@+WEA
zm=}w;0=KWwN7}<fBev1&n`71bZ}XdCxBKJ@ekjK`cp|q~>1)Q&bRMv=UQL>7xYbx=
ze>Hm4@i)FH&*Kgscd6fb%%MJM-WmE6bwo|m^%Jx^I9`JfWB#E%aY#&_hv>Xv(I#9V
z9=v0P5An`^kjDk^tDvLi7w!b(s+{Xvx|5u%T8GvDBQ~|Or+Cdaamg@$*ZSf;HvJ#H
z9@D*5Rf`M<=K*}Lkb6nm5xf`8Ib&b$Ka5TG^?!QiP_5#j|CGGu{I&k_yGQ>yi*Xb3
z`=+(|F?e><|0Sq%dJ<2*V~=AG0pATX%B3fsO?Tmht^W_e8PnI+vt~@3g7t%Sa~kRH
zZBZlC1JOkr)(@h67?a<@H+#3f(fCdK)8HD0uGHx3iEodmf26>_hqmQ@h<|qHR>!An
zRr($n%RM~Z-#7iIg1nZW{g>;*V%nxQv3An4TdG&V`U7{aqUll-jk8HT{sWOHnd*3)
zJ=S}imva^e@bf%Et0J0eq;1oMu5_6)UAB_i!gM1{_uJ6LbXiT!Y+DT4HB<_z*boMT
zwnB(5Qc-9Xq7ag-N>)^~r1srE+v{~+zu)sc=X}oR^Euz=*F^mP->!(HG$LIn_C}mC
zH=<m3aV+B0<q@Ziia339MER`|XB0(L@Lb_QM8yf>a6~0%mDWa_Szh!OaLy`@s9agB
zia0w<_&le9fL*1W&|5`shTaTws;aNLC!(6U)fPuouPD@1H#2i)#JTWm<VT!0G@_>S
zS}<yDh^Vdq{MurAL>+VMnpt;KL_Krr*Ay@>@clxZFQlPCdjY?pTtm2x%xSbDqOp8q
zJ~UC^6wXEFU0f2;Tz!j95iQMYX<nAPELvLesWqHS@*=VqMYN%<jk#^<XzRRP6R{(r
zeKX;+Lq<eLJUi0UiN;G|cb4m{_cFS=EQ{z0t1B=5G%%tYUEMuj&bupeBd*k!gIABf
z5k0dbdg<+jV{csg<OtfX@_e<PzVg>(MqIls;yN00&B|4mdoZFO4*jMIochD=KS$6t
zfJXzJ52AH2t%GS80(Xf04~0Dp=5Uz9;f&zP2>Co`c_%-QMqE$FNZyYuix_1`quqtk
z{2XI`zM1*vjCIw<+TB>^<M=a<#&Kqk*Ee2%LKoqA0=$X#GtpiO@SFs15)PB`EreBw
zQxS|Q>ZiCvf92UUoNnG3aSP4Gc2o@KR=(ZlKHd)hcKFlzaEDw8T_yOI*x{Y-@m+E=
z_&EdDnRYmf|FdA-t$()j*)-1K>peK#V+Z%z-@SV8<JVkX&4qQp&w2crhx2^3^X+Sa
z^9Stf0e9*_92dfQ2*$%WK7#imvmdql$La}uAJ_l5v(f@V-xJ~~^Ph4z%H+!U@-(bx
z@O>7y#qRE6SW9@lgs)3rF15d<_Posd{2YGI^X>(@mh<OD9ACu$CFd((yu!a%?CVu~
zc@^GDvsTizYC*(mxz#Y%=viY2uhI0H&$a5-((yVU*O|2rpY>*}m)k(Y8|vQh{Y}1a
zq+=tzP0ltsd&}In;l4x5JGgGf^IhDx*vA(6t?uA^-lO;2vG?_V;CUOKAKLqe_OTt$
z?fCDo$B$uu;`tNLpL+gOekYxu+4bkJzTopNyZlmaw|8$h%&*Meqwj0)$v5uaUjFU%
zzV4%YpIQ6tXg`eo`o47s4$%J{e&50V4}ZT`f6%TD%KyNlAMN%K4u|aUki8$~!x8uT
z2+l|N@)NwHu>WhuFXsOW`<U9_d>(gx+&w;l?}=lP#NtSj<&mV*BRM56l5)!;IkhN~
z)22pp`r1g!i{EXC<ctN8ROl}DMpAKSB$c*Ba^_HRG?KISL{fQ9Bxl1qr>2-0NtFzt
zCqqru2I6oe)nHXO!y1xI^Ukd&VAU}HycLnua$aj!B!3tcN$uXk_wyG<Qpfi?2P3Jw
zDw2AYMQJ4U;nv?2$p!dcSP)5rX2RTtIgvE-y|LORT_S0!_F{9I;nEzB=CrivD>g^c
zGCz{6+(=r<w}RDrLL`^Ky2Pw(_1UzwQP;M%z_A_u?eJ_5=Z|_j(9#kAPCU63=gauk
z#rLjTBk5L7nAM%m%i&)E`^terE{9h=@a(~po+Xj=lJ5n-xAQ)H>!Y?0K3AFRw>9zG
zn)HQ#jk(vVy-qHdFa1u2`Tb$_=R<${9Dt+W)MNnf2AV%e&ERYyH-xUCd>dv*!{82w
zHyr2T&PUKY0-rpyujk1Pc5wsmM)GPT4@SWsEjI?%7??NWbfdlJs~Jb*cs&zv`?Gs-
z(~d|cnq2^Mk{wLW6nvfRywLs%?WqvIBDjCSXG$mG`&4?T@ogGUrpe!I#w|Q6Hm~?V
zB)7H~zTf6<-bVNBG)?#XHyZv9=MFPUvLd+?*SpM^p?3zHnQ&&|HVe1A-Hp4Q&BkrE
z{yDJjDURemJ#%T83-^9==J9Brn)&$7$H!}zEU=#i@(-wg5a)l$FXa6~UOt5X!_FU}
zVG+%X?DJ9HJSP8`p2vAtN?R#jrF?rr-4nP!iPw{6KV?ptzA`&_THiCYJj?rM^)9CE
zpXG(RCAclsyVM<6=KMM5&w1~jr{e_}%jsWk_KSA%lG>N$Uxv2=-&f>bHDe_nE6rK0
ze)XbA)~H+KUcQE--`eDL@7p^1*SYKK^{n?EY%uQ)oZfW5HtO3bx5@XnaCytF-`4Yv
z`?T4-&G`Jw{CD-f>pk4!`xZL3^7lPF-^by7ckTn2+hA<N;X`#F;qeh3+i~4)H#^+<
zkKy^vO+In&J~d-!MZx#aoPBO5U$_^$Jb&3FlHI)C&D*cc*<;UpX!)9F|Cay8**E;&
zTNcSaI``wf-^_2lD+k=4@8rH02hIM0KR>|vk*-7P58K&cJ3iw1C!GDhCO^ab+2>I{
z{6fR8c>T)fV<$uHZ|=%(?!<Aq<LXYh8z(kI8Z#qJnu&$taHMHYq^C3xQ^k%*%lRyK
zG}2ScB0Vij9EjBKaC%1HNGs$;TCr24l`0E$XLgVDtX!e5a#5sbtMfN*TE%n5qDZSw
zkF;98fLDE}*cE9e%*=z4o@;guvukXN^t|4Y*3|!dGiq&(w03PFfBvRO>$H!wu0PM~
z;$Ck;r1ixGaQxn;4dff@YlMFzGaHvg+N55jO>t<tH`0qn2{;$eiL{xsW^*HLo+<RV
zkZYm8CC*v;TH)2&oYtk0W|tTG+boW>EsgC4M%q3@@WStI+CjbpjE?#`!|P1XWjiD7
z!rQKL-RSAID$?#WU*1H>U7_zv9CFOb*%N6GI(x$HxjE8aeDdFZ+FMPZf=I8bDfoA_
z{=PU~gX^`<{9T(~=R8+mKehe!^tZ<Wo(C33I*6Zx)DO0s!8i@!*-$<W!*#gtBX~0c
zmpq!UH}?i-BXJ$MBGOTO91U+YtTF1w(0Zf!`Sj%TU~IPF<v5zh<2ar_6KJ?e?k4>c
z^%d}<z}Y0Z$?7JXRmitOz8ASOMR*lCn*x6dpQpl}io>+r{{bT!LECtoV_;-pU`}E9
z#-P9e0!%>61%wO?|G|6)04@*$lmK{~rIO7`!$1^;&-4#$T`7vLybI|<A%#LV1sC0<
z4^YV3+D_V(Boih>OW#J{!N>3!d<Yk=TzHdSi%J#243m84%(;g^0^kVGP}p9Fytk}y
z%xcRuG;twZ$2l&A8_YMtO>__nw{U`qa2wZH33s^jfg4ogfYliug%!GZ60Y&Yi*Ox%
zya_kZSDtVar|MR?g)6lZZX;Cp!X0$gYhW@*!?i9<sy{RN6j-gtCW&WzeER($)V7FC
zM!x5l8HxD2#*xmnoj5(CyA2hqdEdE+B5P7A<2p$U<<>0q)Oic%@<Fc`iSJEKiU0-~
z90)_Kp;53-`T3Ji@OcNY%o<~M6U3PPF;D-R2w7Xs#noh)^LT&sd~NafyyJ*fR&`7`
zuh}8oVZ--=|C%{}f5$6g9dV}Yp<TP?j)a+zS7%FhrtEuIR8u(y=<$la<vmVu(>t-`
zj)HicZCVAGBgfIbu4=KgyLV@H5;MfM5>p%p&CGVpcASx>CCzCxGdCceF3il#%*@Qp
z%*@Qp|EuoNuGabQ`?lx38tUops(RJBEwsh`t8Dpy|FN$~vC^uds;Z{ys-c>yrP`{a
zR@ACmQ(M(GwO#F@_EdYRz12QyU$vjwU)@68Qr$`&pbk_Ase{!a>QHr<I$Ry0j#Rf+
zN2#OLF{-O>qi(B?RmZ90)d}iEbvt#Ey1hDC-9g<^-AUb9-9_D1ouW=vr>WD`8R|@R
zmO5LVqs~?5sq@tZ>Oysqx>#MJE>)MQ%heU?Zt6;PmAYD8qpnrgsq57Z>PB^Ub(6Y>
z@>Eatm9LB%sG%CEK<!Xt6{?AfRIC!UQ>7|Xxhm9D&D1V6SG(0c)xFfc)qT``)&11{
z)dSQ6)q~W7)kD-n)x*@o)g#m+)uYs-)nn9S)#KFT)f3bc)sxhd)l<|{)zj3|)icyH
z)w9&I)pOKy)$`Qz)eF=M)r-`N)l1Y%)yve&)hpC1)vMI2)oavi)$7#j)f?0s)tl6t
z)mzkC)!WqD)jQNX)w|TY)qB)?)%(=@)d$oE)rZuF)koAv)yLGw)hE;^)u+^_)o0Xa
z)#udb)fdzk)tA(l)mPM4)z{S5)i=~P)wk5Q)pyi))%Vo*)eqDU)sNJV)lbw<)z8$=
z)i2a9)vwgA)o;{q)$i2r)gRO!)t}U#)nC+K)!)?L)j!lf)xXrg)qm7~X$vVLV$xKh
zD%Ge?4Qf)0+SH*HTBSAGO512V?Lm9eUbHvuL;KQxv_IW~Zb`SI1L#0Hhz_Ph=ukS0
z4yPmNNV+v0MMu*y)TP_dZRuD#j*h1j=tR05okX{%lj#n0N4gW;neIY&rBmosI*m@J
zGw4h@i_WHV=v+FF&Zi6LLb`}9rc3Bjx{NNTE9h=?C0#{V(=~K0T}Ri`4Rj;joo=Fg
zkVif0lTU^QG^7y)w1dVJ(u5+4DWRQ|QbsuyG^H8sqB-rRd(yq=-gF<jFWrysPY<96
z(u3&1^bmR|J&YbskDy1=qv+A}7<w!{jvh}>peNFk=*jdHdMZ7Qo=(r8XVSCi+4LNG
zE<KN)PcNVs(u?TD^b&e0y^LN?ub@}btLWAA8hS0gj$Ti1pf}Q+=*{#NdMmw+-cIkJ
zchbA)-Si%MFTIc6PamKU(ue57^bz_feT+U%pP*0Dr|8r48Tu@Jjy_LcpfA#w=*#pK
z`YL^mzE0nuZ_>Bu+w>j!E`5)_Pd}g^(vRrJ^b`6i{fvH2zo1{zujtqG8~QE%j($&n
zpg+=|=+E>Q`YZj7{!ag(f6~9`-}E2)FK=PR#LSv2T;&?qxxr0tahp55!mGT-TX`FA
z=RJ5&-i!C<eRyBqkN4+W@GbdPd;lNF2l2st2p`Ia@!@;~AIZ1oqxfh(hP!+lzAYci
z$MNxe0-wmY<CFOId@|pG@5p!JJM&%mu6zog%BS(^d<LJ%XYtv54xh{C@%elKU&t5n
z#e4~0%9ruwd<EZ)ujH%vYQBcA<?Hx*zJYJ#yYo$a5B9jnefHV#fQLNdfOqhiL!NNN
zF(<r}Q_eW&f~P#=T|DRAd{4d?-<$8l_vQQX{rLg>Kz<NEm><Fq<%jXZ`4Rj`eiT2N
zAH$F3$MNI&3H(HU5<i)r!cXO=@zePk{7ilpKbxP!&*kUw^Z5n*LVgjym|wy#<(Kix
z`4#+1eigr(U&F8E*YWH54g5xa6Tg|?!f)lb@!R<w{7!xsznkB~@8$RL`}qU>LH-bb
zm_NcF<&W{l`4jv}{uF<jKf|Bp&++H^3;ad?5`US$!e8aD@z?nq{7wEAf1AI<-{tS|
z_xT6>L;ex}n18}Q<)87-`4{|4{uTe4f5X4!-|_GH5Bx{|6aSh2!hhwz@!$C${7?QD
z|C|5A|J7Tx(xh2yUC~ut({<g@P2JLM-O($0Rj=u-dYj&^_t1Olz4YFCAHA>MPw%g9
zp>L^gr4P^t>Vx#b`Vf7nK1?63kI+ZzTkE6r(fSzO)wj{N)yL}N^zr%xeWJddK1ttR
zpRDho@2Kyj@2u~l@2XGHr|Q%6>G}+Pranubt<TZt>htvZ`T~8SzDQrJFVUCk%k<^?
z3Vk<yrM^mEt*_D7>g)9N`UZWYzPr9j-$Q%4r~BI1W(#?K+w=X@WLdWt$K!R<_fo&=
z`8$d%?|4Dl9l$y=kxAQL7!0Fs-$c1dJC4Xz!C>BWEHs0><zN~NM|r~wLz4~zlQq0a
zuL!-UZw~P$CJp*t)XnTIUNkJcq3H(l)Fd@|5Dlxia1^IoaV|7nKQ4OqFpD-@<aOIN
zS?pU^H<^m0x!|*?gsz_!+B@nF7fA&moog=|j~ZT*z~+T{%}e{EU~24f=FRu?(lnmQ
znQlKw`{kqm*D7z>>wm2Fh1>tfTqIkaZRwGlE$(ZVE#=oPo8)UPcVBCv`&tXjueG>H
z+7@KC8>3}1ZEleLy(|kb$y4+IM-yRwy4f6PlGQyN(jXg?msh+z^fK+`@nox4_(9zD
zP1a9?BoE@K;?1zC8a=N+&JwS0>Vm}7>3MclbhEw}8t!=!PDOs#3;bFS;~o1(<Cx*P
z%;4IvE6pm{*yQURm*gW{lA|~Cu9+nHyuDz9A`I8b$O{Ln3$nBDQh>b{JRhaTEUX##
zjIVpfq|KheYchP?NDFND%<jOX-F}fyO{EuLk=1%Ze~#gA_JSzxW1UNfgESxc-W>ik
zHXU3P`M4<=#St!dtkMgE2rr34+vI2_Jv5P#X4nw7#3*{cLvdoFmPILSCe?!L(oDQ`
z-0sD*Q07Jk1lC>ldIqTA&t$b;>IHDeiX5Y@y)-a`J_fZrF+SWeY^+;Wdui<XKzTQh
zXVT3yjwfh~Y{b1{Snm}<=;KuYVF*8sEXd3p{Vvj|)+_AF!L=BMA`2o3D|s+6O}s~j
zCy&#%%y;B_sa(0TPWmI$A8*h}5aAi?Gzp7r<D@(>t1l=Xh`{1uZ1Oa&I)e92l3^Mb
zk&o-5$m<1iLy{FC2Bhy^-jZXv1{#9<DW<wEC<}bU0C)}_>wL;V>r)O|pW5nhCab4(
z-c1(kyqhe}c{iDgr0#f;$cenW;mQrm<@?beyxF7(yO_4zRQe-C>1}AkaW95Tb#vok
z7}4o<(;6VcusCk+i9gCY`+Vay9PD{%X~ZHjr7;Oi+s;TCUP|I3Ye`(+E0%~!(n*kw
z;-pm)=|I!rF}hG*Gmhl@OMBwYqVn>cg)vcS#<Ck%EZmNEn#R#`biBDuhIo&g*~+=u
zY((H&SmZhDL>|ytkaW>K%a9q60z$vq9~DRf?ea-cocBjnM|3~P=d1lNNZc%xD|wwu
z@NBHpN<WPIV`(F^6>Vm4hi79Y#%LQXg>QQo!WEs1SkuIKHtemIG?uQypPL)Jgks(9
zAZ7z@oOMUWo6gGzWre%3u};?<Dw4%3E7nl&qoX-OM@xcn7tsT`SqrZA<H5kdLE<6!
zFst<AAc9-PVKIp^Tt1mVD!9&<*pI_dTy>r1@a~e>2wN-JYI)4cTj~og_acMf-XQDO
zEXguCn}~S9i=sY)GxUUnYV;u<2f#rty%mg}CUA2!c81aCqCaZ&Qv|`>N}qZ^jT7m#
z7EnQxWlh_PpJig5iFFp~e$m6iM+2uU^1AQko}?*_kUgmTW)dTk<Ta$haULgaA1*b4
zXL{q*YxsdTj4<n29l;mt`S@dN4xH4m#{u$fiQ*!47h$P5kOjM%7TApCZ22tK0=D_V
zFvz{Io8wjSF!hoVu1!r}mR45vgDGNK#SeDxp0gi$?8n2F8)B=+I)afj5u=g+Dwb9K
zI6|PoWif)IU8@a?CBZZ?hoxg_Kc1pD<)U5lQ*R(*u3Ju06^3CPwQ-nrlQ^7%bo{iK
zASf9VCSIh4tw;mCfzTq;R`-iZPv)}b7tzqfk(QaNm>u|{Cd@$sb<!Pe!09#<O-+ct
z+5RCK?#erxOsnh6#jg5-R}FZWRgpO|lQv9|#&%HJW*4RnZ<l4U4J7m?HjI`ZzQ?jD
zPXntaTT8~RW>;cW&AO_pItxx9?L?R^MBTBR*k`k)%nXvIkY2k!mSiGb1GfIcK%If4
z>V=|eTQ-POAw2MQwFZb8$Ol%+mbf}F6Xea-KzsnFc9^_A2%Ow)JK!{L(-F~g4GYEb
zJ0*(p6rSjA>}OMFUd+F{6N6yFRtb|h&7C=zK=QXrDCX@BN7LAPfeRDMwlzyit+LV0
z(%|}#_=8mkB;F;6M)_99(+w%QVl6VQUS6bSCtT2TeG>;MKXw+Uz7`}j#OMc!&`_F5
z6%ED?55USm<>KC!%7@CbFOUSG(ZqtgQ_Kl8Eoe5v9bgE)aHJJ@+R)_+gBVn|w8%Uy
zpn@hF*9OS?BZG;BPBf(^YL&!l!V-%*s5-0%n4HcajyE)Nty~g%kjBI2KmwS(yJBHy
zP(+al%aN}RiX4KpHiU{wJik8lkfV!9QW<*i6dsyMb7&PTWK>oej^ZrW!(fna9iqco
z7jhIM5XdZbHd$(xnXEkw;~tjPKnlT?lDM)kYnC(%yo!a5VH^(JCk*4HNWIAl!kS-{
z9_S_8hLAC`igP$=*ZOdK*;E^*SZ^pgaEzo=s}}T(htM;_b&Yl2vIu!H&9ki*!vQg|
zqN&oXwPa~JoWty(y<(e#5buR4FI-zT&EV6mC4+mjShXPOSki@}4t8Vyys%^WRK%Vv
z@=+NMsv{4{Fs<N_dD})3Sw^u{<gV*$n@mOKx0ekqOTu8c=rW5F4>6^L(2i!+LGd5!
z&dXdx(%KTYS+uicfV$dX2vwG(&=BsTtRG}qj6riOS)34c5+>+g#WH&vGVa*afr@e9
zjcOyryWAV%&KyG~-N!)8$6m-I->>1z<?B}DXsaw6XPX<;UXhu+V#laqTg<s!5(W?i
z;>#m^P3U4`brg(kcY-8|Ya_U9oX%S#^w-Sc4E?bl#S>G(WM!re4@hj*mDROQviSsF
zRA2Dg2&pnJdblSyVP}NI?z{u8w6ei$jk-Z)iJ`X(j#m{Z+I&wUXl<0vr%~Y51F1$a
zto6W$r}&7ifp67}4f-Xn*r;2zZB_{)l!aB)pHNkG9EvPY=Ku(25V9?Rc#+oySLF+W
zzTSa=TiGFVyRrj)l0-FwG7huK*n|3Ej7}YYR^(R4GK*eg97r7i_gb|`mb+W1S~yP6
z0_1u~uMn3@En8pg3bjILV#qNoHyv(<UNIcm$F&_S9nukbc*R28J4XbN@-NXLBv~bd
zB#Tx<TaB)VipDx$3C+}oz?qE_ZAS|$1{+~8ka7Z5xm_P9czaqhG$@KrNhftt_;$Q4
zK=`!BBGhtRf!=o!%Zh-8$bTDuwV>^&1BTs=O(9#~+{|X{o158OeRDHofiVJ{1{q%0
z4vXPX_Sd9<tYGSceidzG^2P+BzKahTYtaHL6EBfCJvMgHG&ktRnRN4+v?dTv+mNG-
zLB>E;cd~^;R@RCgi?PDuvFVOBv1x0GX~Af+aRr&(3#(|y0lI21<5(2qH9;wolteaY
zAH2|vaMc9PSwn#krw9~?jc|uK%(;ov#w37Thnh+Du*gOOJ5W|Zt!y@~Em3>ExnXcH
zL#!}un}ru;u_I+AY)Nwx_ewogb#Mi150RrMLIT$9OLT^;Y9q(ZY|RZ`o^mC(sKF&(
z2QH*I(o+6)Fci7$m6$9K4PK6J$&(Pi>>d@aE|6OSxlkfW8K*0gA`AK*tP6;qG294h
zq#Bt*LU?3mnavRm6ui!BIzWi9rTl8gJ<;RN*H%gbxh}L8W0m#2h~h~lmQ9vgT;vjT
zT8St5!fq?I9hQm31qqfTuB;}qeJs}{R^51!X;>^(bSLp3K1b+Z7jdj1%oaPiIhD9w
zmy$!2<qAOV4Lp(MK{lxfHF%$VyPLj>C7gJ(icNntDI3J+<v2jOJGYi7=r!f&rkSPo
z!CyacYP5uDfe!$QW$zd+m&(1VY?(L9BYY9(5v(KGcF#wxrM1>Bv3)~HrczF$k9Gkj
z!ElHL0Y|7L5_H!vDpDB^M5<;Z85S8P2?oK`3s)WUA>vXIyA{h@Xy=RAE*MQQY>9j<
ztq!|x$jPjh#CAilin=ZAj<g&aNsRIc%WA?vFvUq-1{}f`A1ixqHY7Bx7)KWeI!V2G
zM;fbCB*E{!tjsYUdG35>`~PEaFWV|sZ#NS-w(;?0*(#u2c2ZeGBes)b_imB?f$5v-
zPP>!53Qx$E>bClVH;_Sml$HHab7z5t?Y8$EblU_wp*y3RY?$=oSryoiCpA>n2HlaP
z^(?qdY<CU>G*dH_{c_+&Y7!LHSj`oSa4JDmlpjHGZ$2|7uckq-hvHT;jF#=!N*d#{
zt0J5rS>Q;yX{Om?QW&tA+TS;1j92WklR~=}M8;opbVEm$=NCe<Vreaf<j3pqoW!{2
z)G`lJ0Wv_2B4~AnFjUR@qc{#*7<@BB4TQ(QB);0ojMRx`)v#K!MW$<)+!ZV(u|<l*
z^~@Mw0y%hUA3B0}XKOg5Wq`IH+gLZW9V!E@B`uUjW29;6eiI&GLYuy{bp~lfQ+A~$
z#+9*@f`RNJ$p&TOcFTvWOGJKF=&W<enN_!YhPw;9I;URLvcFupTB5V*SFE;Wm@L-^
zkgBaT!^V2SS<8YP#dornjSKja-C2??|EyvS6&nZ|S%@lBw(OeOMDmEpof2AwW1-F_
z@vwzv>^hJ6bGVU3@eIY|7=wfbgu#zxgC~LWhpUi@(!FHlS((vU;sttTnavUQ^vbfK
zNE(*f{cVdR@t|yTWx^&4BERJjUvgw*kdFQBr(2F>6{;BDSi=FOryN7sPHkioAtYn8
z%FPe?1!r}!YmSg<lj>p(<lZFNDjK;f^+aDn1cGeGT0}|PK)%kL9pTdVQ0BK6OvIfQ
zg7YTX*h;aiteaaP`N(DemffG4%l@xm5vVqxGz8B)`xOX`cFk5SszTpLeJKZ%Lz|fy
z-jxPeZX-B1)T#&|xp`o)Sc;yDbXB04H($#N1Sk2irj$PQg@LmI<5WmOu{Yp^Y(-?I
znc05GuS2zL7D&C=u}4`dh}C$>Jo1JyiqJYx-z`{xS~iy;WZJnW;UzZ+5?RHPrfuE$
zJ6LmvY{l+2ir%skk&)#z{xUR)!OhuN;I5#V<LgIm-@(E)_yRg_J)<kt*XptXGPaIK
zqR(E~l8ro+4R&AN;#uJ5qdFdkwT)Ux00J)MqfS0@)xQYIY^!W|P0G&M-v-x~EwZ{c
z8DyPoUpAD#2=2FR+x%N$Yv~klG3jOQSDO;UZ!E$njhnJ`+#t5?FIWo|VhPSJw!*bQ
zSdl=Qc|4RKsF6e)3mCO?C@Vb3N}^hhDV5*)a?uoMp@VT(8U)=ThN5P7>Y#;j^o{8A
z4$xo#KegpdMY49ZiZ;sJ&W;tCOI+^EMs`74q-7yjb5X`~JPY-3SGLfwa!LkKD%r2S
zU{m?+$B_ghP$U&gsyTu*!b6_VDW9%jkWzzWA^{H>VnG{)-EBg0s{^?tD`2gNBpqc^
zdH4G$<eH@cC2X~bq(Fnk)>^V?9jv<Dz7>eRbfXTaFKB5Oe~i2$Z(3@FcFRFKU8Px`
z=kDys!-fU2Ki#%ykOmmLtYb;JXJy=}AlgxgGO$D*QnP5{yXU$Z!a}6z9iF>Ra;NJ<
zW)SJ}65FKbE?)W)xL*l^va@!9X@T}}nRK~gQ3vWG${=5%nB%BkvaB;TvS=21bZVr^
zaRpt|5h>Ry7V2pLVIfV{vOB1*FBOZc(*U{JTb+u&pRaF8ww$)LuCl-r5jrK7<zB)f
zsZT|X&5hTdrgl5SZZXtmUMLx_I<vJx1CiR;9g@mSBEZVb#tRXfLwl7AAG47$q5a)d
z&w@dKCi2_T$bY1eA2?P^GC|Cm1iLqQ5Lv!ba-<uH?k!ML%G4E$-QJB<%Nw*li`{>E
hsm@YV3!}=eE;3zxZk)9aS=^nYqg%H8A9OCXBLEw+*$)5!

literal 0
HcmV?d00001

diff --git a/docs/python_docs/themes/mx-theme/mxtheme/static/webfonts/fa-solid-900.woff2 b/docs/python_docs/themes/mx-theme/mxtheme/static/webfonts/fa-solid-900.woff2
new file mode 100644
index 0000000000000000000000000000000000000000..59d92b2b0e9f7639ce4be5dac843ee591e554400
GIT binary patch
literal 73852
zcmV(`K-0f>Pew8T0RR910U&$;3jhEB0|8h70U#*=0RR9100000000000000000000
z0000SR0dW6@^T7^`dEkfA^|o6Bm<ie3!4-G1Rw>4dI!}9TgY*egW&E14pq2q0Ce9u
zV-@da76Q(L2qO7-SP9#vAv5TXD4_<u56}Mp|NsAYC5w>DnLU}k<ZcNOK>@<IzHJLx
zp1p+&T`s1CN`abFRUK|Ec}c5;e!UsC1*I6G(GbZH$q<ITi-<@W(D7u9w8x=LhwfPE
z$Emhx*K|hPw$2w_bAMf9n)=d`uY?-6t6981TsL>r^59GlJju~5sUTCPB^;}6o?at|
z`IcE9wI1si>QBSrZaCZxhr8i8zFrZ5FSDXw&p1PyzRM%saYAN?!`V^NyF3iEM@KK#
zTUd&x^`Yj8x?(Cp$=Q?NJ7f~t@Jy=h&6G^ZluXIIr9Nplj;Dcdjr%p4gBl|x2Ts=2
z-lnvR`YiB6CD_Rg*^>EM*f)19`(sNaKJo9%LeJkkdWy?%xh!`#CBw5^td%=St&)z`
z`ob#w*(DxHc1iq$J??LJM=z_lXKa>+(~D-fC1`8(Pi$E<n~!ds=RNkQ&6~}LhzJB~
z^B!hI1Z^rx6DQ+Af6b8f5C0qTfQejo04$ANM_tX+Cw%l{(02EqxB>~UuF*9yCPTs-
zqdMJ0Lgnr)rab)r{^<8RcOW*oj=x>m;0Twj_`Nz4n7|4(C;*1wTo<%pEO`s!0^|kj
z0X96pHXmI7tKb5-=8it@j>!90MBM>*q;f~y0S1v4oCSx84HnplZeY}K1MN+ml}tC?
zbZ#n}ZqB($5YYd_J#FrJ>}i%P1q)H&l=iRI^d{ru;y0l0gIHk4@@em%_bB9Kg|Cqn
zx<-~GAO;FPUR8s2ww<KuwRZ(1gqRq9TK4y!n(Mzsmssag7Y+p!LBs&sXos(zpa1=y
zl=6BzyIJgJWGzjq-4XK>bwJLuwi}pJwVc*Lz*4po7<(f>G#3**G*Y7xa-EppMC{*-
zz+hA_+3^*gAc*f58xRUK3L$i|CEJ?iQZ(Avw|nZyefa)=?0;wG{Ev4+7jNo`N+@O_
znw6^3JtQI?ite|Vi|2m;fQq_mvaDmTj&uYM6QKPxAA+WGTK`hLn^f<n?iK_VWXlj(
zFeDjRCfj;Ivs3l*KOe5rRk}9VR$##(2n+@dgOFro>F3L~{;PC%ue?=NHQ`~Tp##A%
zfzb|{(G1++$VO~OR`18f2DY%h=bj3L3EfrIRRnbi4VWfol)q@YzZ6ORQj_0J1<{mA
z#W$EHm@Xb9%mod;m>=eg2lt0b;fe-7JjMPM)6M(0f3VnAa<v8!<$gCX;9}bW{d2{E
zMxJ5BK@f%#=wiMD1ONtxz@IwnrMXV}P#rIU9S0W^Ud$3bN!fFgtZ&BUQPDW9-Bf@6
z^mkk>00{j4b^Y#q(H8Bpn78=6FQ-SW+hE?}GucHuYRyAT=Ov3tQ7%+sp%dfze~wu1
zzi*kZB-`=8B3d?}60PF_22``&!3p{T7~uKu$7uJyKO6eahAt2~Nj!QY6c0@jB~Sv1
zsy5xE!5kDKT+7MI2lrnBU85^p_EJz+iYy5;MwHNsKfZMOZ?wP8?82qF{qK}YYB#CP
z_!VPa043q%)WhY?b>_O-1wkN16yugcacM<J|L0HrW`4Svc=3W@mj#yn7m&IENw0xX
zJWKxXpH<Q5wNgulgzZ>T5S>(u3<}(~CkbJRl~{m@?J=#Yx9hHKc~mmNNO<(3zG`os
zezq)o>D!LjxF;mEz|i8A&6fQ)^@3qfdYRdebJq1BY{mFvWS1n8rmg;7d4Czde<`hi
z{-1;-gsqJ?pi&8A_%N+mVieXmL_@%#B2SJ-G`0X%`#Axt-)AdLcMl9PU=RTYsh`u}
zw(D2*yQ`^b0ne?7Rt(jBl3&l5=wx#yWeydaGJd(E*z@13>bE6TT&kR8W@JxG^FQ`f
z=eM<^G^hN!SWW4xvU>O4S9o9Ldk^nHXrurT5&@7BA3#t9NJ<Y6AO$><GWaOU5+&!$
za+0x?<bO(bnv?m1B1!~lS|Djz&5A86rA=`%eWmC(=U1mpsdb&%W%<u6Rx_L3Y?iZK
z&SpRG_~WOa8@4YR;C15EI4M>2o%vO5XMX?Cn58HhlMTjegb+fAhY%sBIh<BysG?-=
zAR)UhB|Y1ZZ2KID{~LI6Z!ks(14f0BVB|#}GM7ET$!mviA(#*lWGGWsLI^|JLFS6u
zpw7&eb~+of(gNL50Z}Od5|U76@BSB=t1*`T>&Xcopn@P23UnlGTJ&CY`p+5vr)xt~
zni4Q0*kChlJS15s=}srLM3ckU=9#ptrT2^sXc`zYA(TJv9$1;zw=G$d9AN7PUgz!g
z6M3xQdk5qUAcKN>USR~~$GYX-&z8&u9q{<}UqPc20&>yVLq~2u582fBAAw`fD+AfM
z`F?##`ZlEjXs6lf&bd|`&hC<)OFKPQib`Kl4&f2P6EFqza8Dz6_(BLe)9J^uxLPMS
zC;~&E(76Jw(O(K6l4@=I*Xe!wQEzsao5S__kEXJX?&LFGs?-{t?f&%kN3q=v7Q06+
zJvf5_4;x>Ci6EL3SR86L4t#|{M8U)*CTHc~#)A(;RD5zu8hY%v5x*St>yH_$tgP$!
z;p5nYKrn(5M0tN(pX;01ihf-8>wyrAVtGk-yfAIXd4GLi6f0|PGo8;1#YN2v8&c|@
zA8T|B%^aNFefULAQB_M<-_Y31($>Lex9D-=CrnXfacP6YBI6QL(sK(-t7__+TibW)
z+AAV8tDvl+rmb7guJELkw2Z8rKxsu)eM^5$^*_JsYpJSXOs+)baD!kN5|zbMs7$Md
z$e}p9iOk|14;)XvUWFiuZN5nFA~U6zD6#rSwX`QkZ1>l@)8j3N$V|S~|A-WX>{ar3
ze--@xD2(RPA5H+_1s5SIqJUu#ga`IApYGt3v1GqPA>;9ke3{_Pk>B~a7yg8*kZ9Nl
zD4#xloAcZU4l~1#YToAo;Z<|MBy;obIz3}cCs$8jep65uJ<vZ8VvL0Z#~N4At|Q&U
z%zD1M8Ziz^w64kC`hiy&#qvF5k7aXrrSTJvC<UlJB}-RsD#{1OUsqa1b<lD^%}|NE
z#`NKL4gK49L^frQ#mmd}_A27TANg{#J(ff1{Q1h}t4$$0NRAdSslxo_|IgF>T>9(I
zTaVuR-d{yy`9cTb4{(`IXQ^~%wyo6ac{KCsGNWj)h4T;#k)c8-Ac-$DI!ovm|KtDM
zkudVr6F!P~$bDY&^U-p(*<YWp528QmFL)SX;2^DRk>o1beRDcjOJmLLp2sWgPxW5>
ze8n{~{h!pf%7g*_D}p#Cr`byj?{yz~zx@8Tu()(8+vG~tk>|c|e~z8mb_iz0wXSjP
z%qHHC-OrdwM)RNQwAb64-VPfY4rWiIHh5L7?uXw0O}&bI%HTLVMIA+F0kEkqMSuIW
zRhjGF0Z|fCe#CVlbU9`J7dU*j)*>kkbcC2#b%-tOa`kF*E1wFbW%Aq5rYtqxuTw5|
zA;}^x+O_<o&r$NCM2Z$MCPE_o72U2!;+u~9;*4)(@SWO>vAK;;V%P1oaoQfcIC)5O
z#fubWroOuBg_&BJr&GmglDcxi&b2!w6RQ~RzM`FW{wa+R1r-S$0~5{T&2$~`d}ZCx
znyaVflT5?pdbRPK?QhT6F;$=kemrXncC7c?$JI=-S*ui~S_S;;T=`J4h+V<_zwY>R
zZ$-2?vBQa_dtc}s@;`zw?*?kGqt3eP>1Q8s{m@?6I7-C$dRAKvz)0<iO`%+=hPZau
zN%jlx*$F)uuP|akq<r{N;QMY(F<37n=C+qw%5<l5$|a=`@pI=+@d#YSH(J~!%aASW
z0Adok>MCQ263V)0__!zcir)BoLMl#EUR=Rzue^*%c1ag`^x%WUqsh{UmmoG`b}q7*
zonI%B^{G*<POLC-LdA;_e(czKs?FlXhvzyyc=T+@IJ!`7c~TO-@6tevTp=C=4=Z*i
z2|7=R>#|!*pZlwKz3-3KPCDZ)Zh0%;+jF@m-223M-f|@s<&cK9poJ9Rj$<cI5!jED
z9FI32zN4Umd|`M7R;@X3=cg&hcxkzm*;TjwzV!K%6fIY|0)>hcD^aS<za_C&oMoe3
zTzwGX@>#IvEJ&+1*WUW*?RfZ$(W6{*r1=z6R{9y2T~p`X^!@rP-{8i#pP4!Hwte!~
zngt1#C?Tf8imoc35-Y8)hZCQE<89A&zsv6bfCoP4!4G}dl@EW!BOmqHr7~m-Ax)}A
z)0Q1N^1_{a4<0@9+-sk`8zyU%LXR0c2zk0pSh4K04<RBVFLI+Q??kDxW|vpN1r%6N
zkwukLRgJB-(psBGOLaG0e-yZaDTFc5XU9dLT%*=(Shwq?H-7lU{?lLE^}{$S%Bt_C
z?I9K2FoQOXqa;i7qAshZ>%TIBWe>-;e?S-FMq$y9rDUHB6NS;7xWIx^6jyxNwKk`9
zl~-G9{S7zLL{sf_tJ~e_ZvQux8c%aMH(f4hFkpo>wm9IRLk>Ip!7r4rx3EvKN^NP#
zUi3?ohf7t*kb!#h>~!&_yPkP7L^d<S(l>n#bFXg$8{EhyceQsxQJ*CLVoNHq%uG$M
zt@idd)?_oC94h6d1($kd7FlJl6Q{1+c;~$@euBT?KZ<a1%US)FE_DSfT<Pl9u(uJS
zJQ}L5xdm;hy1_$D`Dx%XFYl(gbouJF>#vruzfzZ6an5%3vR^ObvN0x-B$=K8NLy~c
znHo+2az0^GKp_NxN)iAa=Kuzo1uTX%U}G`BA@2Y}J^(Ix4|wQy;FG^VK;nUj!~rqU
z0TNOMr098&;}W1mPlAeg0yX*#fDX3<191dqat17T8(8rnuwfXm<1FA{XaOff1GpI4
zz)j462kj2L<PP}AJ@BJrK>!^GP+|mv=wcAUsvtbIOa$BuqT~&Tk+&d@rT|EwNdS^)
zDu5K43?Pm7K!#9*Ebag~hAPP8R#3qApok^_D3Sl5jQ&TR%z*|b2TimyXc0JQ6Hd?}
z<Dffxr4Ob612PYW7!XG2J{Y6>VL}wa6#oS?@(0Y(EwETVX9?~GEA%<oqFuobZ2)_;
z5ggDaa763DiKK%wCIlDs4!9C!aKrz>9qkAn7#BRTF?iuc@WxW$gUP@TlY&3Sg8(8A
zf#@6vA_xdZuR{pFgi!PV!qB}4CvFfy@*xtdKonMjICLWt(M6DihaefffE4s5q?2jL
zCchw`K?H>iRw$b5Duzfx84-sn1`AZ9kD-S6K^;zqdUPr@q8reJqo5f_LJN+DRvZCs
zXbrTZwa|gqK_^-bUDyh`8FtV^9-$A5Lq9qm2GCZRWyr!D-h_Gd6c#WX7ST7bG>zIa
zgb`Lq1gw6CcWa<MU>)zm2Ht_q<#SswXknXN!Y;mmJq9@(6LdI1m%%Bz7H8;kI7e5&
z1$qgWqz|r07hIEGxW$-o&!C3~tPd~HVepbvz$*p~ye91M25-Peh9G<*3-Foj!WXm;
zd_~XU=iH`Wpy>d=@q74#Y2h#03;tn$_)nB5K(m3Opg9y3&828)7Ep9FpJJc|6ca6^
zSZE%_Mn6y-JVPPmn&J{b#V1yjfLK!^GDL~dBT7Z;DK*xjH24#x#k!OZ4^sv#N14cL
z%8Z^>7UE3V@IGZnpHdF=jB;*x#RajT+_;DGV0_BE_{9g=qWl<}3J_B&h<{Td{ELd9
z3#llUq+-OIN}#(`65Xv*=pL0ucd8tk2~?iJOyTG>s(?|bB1WZ3%dDgffl*cbk7}aZ
zREzLa9g;?M(c4rXT}chlHEM{irbZZt8WU}5g0@jp5=zY&4%D3ZQws)yT47;ojp?Wj
zX`{BJo!XHOYEL?;1L>xYq=!0@pVXQBpb40UCgN+FO!jCB!KSHbDNREs(|mLlEkM5j
zEo7L`V!TC5$p9_K7_^GPNUO0e?Zi{G3scf=bPDZ34{9Ibq66qFI*7ieW5k}0qf6-o
zc}6E0gme~tOXn~zoyS6S0khIY%tx0nJ6*=~bOkfeRV+x?FeBZ<>vS7Etfxy|=inlG
zi8JUGVW!ulhTf7?dPh>|Jvy8|U>*9%prp^x0rWXhpsxureZw%NZ_(NGJKCH6WDw9_
z=tKG&?L_}z68e|W(0}ManxXrjx&)xEf<j#ZodyL4jRH(+8?dN-fKYe9oxP)kIt>+d
z3Yxi1w9ulU!?>Ww>tH~+V5Swpg5H1?eT9w213Qfi4jK+l^a5P80=Us@@Ss=VMK8gJ
ziNKExA%c@2y8fIPv=qdt4UnWEkiy%5Q)^J5)}u(RMTuI6GPN2Nj1N`R2sKOwbxaNo
zya*lYHoDYG3{Wi$Q4NexHH=XmOi(OLQ5ein9L!NDEKocwQ4Fk5IIL-G*kC7k(NyrK
zjv!z)s6eQr2%-)l7<D29Js=c4A{;$K1of0m%z<pYggl&!{5ezrN<lFeMg`4)YHBxX
zsHN0l3Di@2(LgPrk(vi+qTZkt7o!cGVHhQ01fgS`GGLNwz%<Q;xv53-pcbs)&#dBa
ztl=-L<F9PsPi*1>Y*DM&#wysQEwD#hVxP9c1^Uh<ZpJfO4lihVyr${!j>g9)e2UL_
z8Q*XTzT+bN!VdV4HB3-Zn+Y0*>hK|12%%U2;qXon-VKlUBH;aekwkL%6bbjD#-Q?n
zO9gPP5N;I>-=>2UvLlsB=j^o*__hds?ZGU4gO?k{Z##pxx`03SE&ijf;#4=72TibW
zddkxDGjr%QTdt7*d#&joD?LH~1(TIR#O5PHPKc6I;^d4Jc_vM6$dX$yaz}yOQz8#k
z$RjoKM2q|fbT@<2hna@a#xHYdA=dN+HuMn)HW_<z0636CF64q6x#Yh1<^j3lNv?U3
z7vAKRANk}@z66k8KoI#UlnEe=@h6;#A)4tShUq1i*+V>~lR$k)T0KgJW|2b8q*4o+
zuPdRu$)OL)qff}Ee^bEfDP#>4ZCh5twoRq5CDf2}-Q>+6c{fHrjFaEM1o;O{GtJB}
zjm$Dl%rlEwWEQbS1#HtW4rnyTw2Bito72^=Gw3GHX+IbA8P_b88<x&JYvh3~<~J+h
z59`NYR>nWppZ{zCPwdIfya0<23X2~q3kr=T1f3-egCz!&B@T-v0h=WWhb4oO>_<h8
zqb6t2kh5sXb#&w!dU6W`xr341#YA3XCU3Bi&sfP@Y~&qw@*W5IfRlX0MLyvs-|&zh
z05ACo@KG%Ml$rq50Ht~asTm<^N0=gr&^)5Fix{0CPFG0KWs>wWDf)vngGPoyCCgxv
zV=%}wBw!33IKx7L5uwPKP-5&TGhS4fV5&?2H71Zc6GVfFq{&3lVsdCRRdkp}y38s)
zW`#bp#(>#m$U<YpqF~I@W5UvBx(v<?vc;Tivmkp|lD(|RKGsXf26B!qIS)ZDup>9w
zliM7~1CHboC-RUpdCrA=;Yz-9BfkN6@*nUZ{{T-)#fviWrYw9Y8(+%7k8<&+oB}An
zKq?@JLIqP%Ayh;tl@LZ{gi{3(R8u515k<{KQwuTFQY^I>M}5Ro2MN?kB6XBRoh4IO
zDbz(Ob(2QjrBg2%)I%oqltsN|Q(rmMPwrY%9_wi3b9xohN=39vG3{4E$Cc7iWpqqA
zomWATD(S2$I;WbRtEIQ<=#6^%TmyZliGI*bKWbsHv@$q47+ReSt1gCDH^Zlg5!B1b
z>SN^eGlmA4FhfkZ5vI^6Q*Ml@HNmu+Wcp1p!={-L$W3ULO=u2g$2@ajfjP9u99d$H
ztuX(rGEdf6=+;@(HdruQEF;@28@pr!*dv?v$&M3p#5p<PlALr+F1aC>-H|IE$yG1N
z4R6R-@5x`_FP-*}E@ZTZ2>l5urVwhZDU3K-m@!fW@C$+B=OTzdMuNr>8Ja|7X);lu
z1fopMj~caAG^zcfM_r2n^-PSYS7J=P78B~dm?Jc?L5gCFl*IvQi_=8Tpg?g!Y2uFh
z!~+e8CmIxQG$OueQi7)0Dg^W;VHhIem{KA!n?zx5iN!J!kJTgrt4bo)l_abusn|x+
zaEzqSzLkJmqzsQqIi8dXJR_C(MCxb^si!fel~$8>T2s1dM;W1Z$|$u*CaIG$L!HSi
zbuJ6kgDg@{vO|58J?fJjP@m<9`U!GE{RKIr{)1d0X1PYPa*vGUVVcFBgOcSXN|m>$
zK;EH3`G`v86RMZbs6l>D_5B6o$rC0t0H!i<v4??&Lkt2OZIIzggB({Ilz7l!pivDL
z8q46K1q^Ol*ASr13@B}F$WZr&EOmd#Q4a^4dNh<)?Nft#Yv>?(!w7vFrdZUl#Oj9C
zv#o($4Fqm5-0@4p6TdP7*6j-9PaCRgr-2!(=5l!Z>E}fsZOfD2C;e8CMB@1RAs82d
z&$Wuhfe0^7KrJ4>9HbnPF~Fo9xi<?E%zGwKBgt$j2*Fk@iX9Dy|4Ye28P4;ms5S}v
z4uX!haukHH*TL~L&|e=53yebz-*qv=2v}LZS`h$KPzoRM2Gr13SI8k1AC>Zj4{B<&
zsZ1*2N$b208Ir^US)q-!T4xm!nm!JhD+WYu<0PmNJ{okwZ_~I+cu*7t-D+)9$i!)A
z6V$m9t7HfZp|H>eE$s~hx}^x;Qof3TEGPG+RT5cef<5Udrda8u%Y2|iW|0_#;S04g
z;FTa=g12m|h7FR{Ho7AxcGaSuQf?uNqVbOy$4QLeScB;rZdq@AkSL{<5$iCc&<@qg
zb>deGH`kWZS}D+S^RZaii>%43WjzcLemm(il8{TWqVQHs39?uVz+@g0iUvVasA@W_
zTS>7ye(!+;8R#l%Iqjg(9vf_N!ZFYyp3TPJ+ZQn81h)K;3=uRrWAU5{NS#eji1dyB
zUMw0NWbx{N2lb_&;%%ba02eSxd%c)CLxiQAqGBrdqk}O4U&3iwlxPu$d7&br^l6ti
z)i^)BvJ-6UdlUqjo)D?_1Vg*&OXxXa#OyhX`+gfGi)odGeKO=iZmEGaJ*3`x{pI`w
z6z1jYNfaUBx&;<(ae5AzSypK%e+56LNw})6P6~(^up!gL&V$6WdBnEc_&wVqnN%6O
z7OMMrKHson&U&;LlSYM9m6*@pr`FJRMG!|L7Q-%%>xEM%75U{CL0yg(WvO%4q{Iq9
zwY64C?u`|l&#f7Y!W4O4bXR>L9$#2#Sn|dfQGGf{lUG6nuOPInO-hl*M_m4F8375z
zC9qIrtzvie!@<S}aauU;-9=;F!5F;B6jHiWcMj+AMu3moE}%&Yez7W*c8{{$#xBzm
zygZl#4Ns5Lj<_Xpnk>7i-rHKs0Fg1QokNa#PhJyH2B4RFQVV+a{jxl6kL@Z=$!1s;
ztV*M+b`pMWw40J;Sa#JL0G4H8ldt3J_Upa5EtPW>8WR?uUjVqTzNp{Zs!;);Y;Wwi
z0?_C4@*aBhM<JzYF5~HhBY0CqW;9Xcjx*FFKatOky|!4JpGPc$0XqPElrd*xWHj7B
z@a)Jp=pOtKvcDqjQ@a4DHR{i1QB-2}wemh6pzr$@nTI0YAp`(V@K4cw-MpDV<XnEz
zwxjo??}oTIPPk+;q4Qw6Y1b@5w<PP?x9VEK6a>FK*Gs5ZtX-2Y8&haGO$@X|414q+
z{j)ciVRgt!+It~NmY%$WUym#J0KW%1!b4>Xc=FwdhkSUKK1OrvuOYT3&7UnUn91k_
zp3Z;xwUKQSwKv*I@@yU2y#IhZF{5nYF3y3C)DWf1<`z2Op%*|Q6R7J)e?JG@x6||;
z3g)@tYkk+vEQ|op=FMnRv~1`Om=Dg)JJWsLIsK#lEY;@+S8UZ2)vB}oRb<qLjn3!C
zf$Esg)wx{DEX!4^_&j&6Pt#n*(BzGCi&D8_lcv}Z#RjZb(C>$wR(~AaYmR*X`ThR-
ze1A)%)pxD;hBBA?r|ZRTU(FHKm9XjEIiXb?pU!U^di&~`0N*Bu59D2ytfyHpiNxtG
zzJPnL>2~{dw1>Pw$DWiQ!MZM}91f{QBl3u26dmoc*#7*xbSMgVA%i45q1h8GS?}7Q
z^|4xE{7)}SarK`qnaGbA!HRzuQLb1tSaH<)c!kLWrLyw*vaH1<tgKkmluA$mCRz5$
zxoFsQo$fEalO{VHzrq9Hw&nFr*1-_vp|!Q$<Dn6`kBwad4LLP+Zc_9)Tr7EBQiV#C
zodhwWb&taX&av7OtFnw^Z4$F#D*+m$QV7DmJc_Mx-}n8?*ebxH`uq0zFm3gD+fykR
z>D61#X6l<T9q8a)6Ewb`ib2<aV~ee%cm6wR;4JjtHs%i~7vyQY1&sVa|MuzGr2Jj|
zc`Ru|R&%p}Rw_qh>JMKWm|O2ARC5~rue*kaio5~+)EZ-@ZBta3&~^zrFT@sy0QhnC
ziUgqSP|h_UxY^`ovH1$(%qA4+xr!Ng>QdWTy&Sab@p?I5mg?yd4rikH+(ZnAi84++
z+tC2K9<2CRV-=k-dcqUdQuJ~*^`f==p%2M=PjbClpI)r4R>j&ivgF%gkTJ`mk(AoN
z7|17>z_6lFA+%?EbMwWwFCsmEToT#T&*|Z%3=l~ib*b;^E*ZGgk<e~01V~fI?QXSR
zPk!$C`~0WC%p!!s0!L>*%4rWzZx9Bp>yw-6^?1v%?!tZlu>j5hM~x^w_ugi|vR)J>
zueqe@`QK~_i(m+c>S~iQL0v4_G%jdMqTg%CF0ucshkhr={Q1ndrMX@&HuG$M6<ra3
z>AUVr8#m#G31#QgvqQ(#%4lQy|J|341V%CuBZ5Qn39GI+juRMj_yWFg5h?p&I2@)?
zOPq(%j@p_gQ7zrs0ecq65I_=D^#s9zZ0exKfg<;8DtX^A3aw1Eb%l!^iW=GbjMD9A
z;-%WXSyIPwElqZ8j8iq!UD}|yHU0DDzUGTD!0$0@-;TIs^!zn0Xz8qc%cl!a1f0yz
z5nu8x5aII%n1I=SL&~p>NL<{Pk37Aoyom6)VCZ{JZVD~X#u_@>4$x34axF{!2%WUF
zq5*k+GdJnX+A?Tu72l?Yys@K(Pt8sdinLy=2haz6Bx3!KKs6*lq9Iw_<|kuw)JHsC
z-{6RasSQ&D8%y^&W5h}vqwvL8;3@sMKFR_(XG@SAZ!olIH9bZtUY#%u9Y+d1Wo?wt
z-%pLq#4G`@`1#iq89Zwl=(Btlk7koaIrpjfB4VI3jby@M7ll$M*`TnZ^RPswNeObm
zKF-&KN{~_cS}QQw`D+kenFOW^fkPFO8|Y|4F+VpPy^PG+s4#WAT4POfZky^*JSJq!
zsZXiVWE#d($T?e_sK}V6)}fRv(U(erl%A*Rk-%buL9&3UmZtA3G$>dQiOzEH^WycD
zjHcsxx#$^$aU*&$^~a~d>?|pU=++Q>CQ4HVoEZC0h1#%oo)lsjLB>&(CKdSG_Fiq1
zX2&jpx2oAke3AWeg+%(3dT)6iIdrF?h-JWHyUhrjQ*@@irLOtWHJ`f1A5($AP&B&~
zil@;NzLMuEsf6W?$!fIppG=yVYNMM63zklOE*XK`l-`56!zwhDw!l45o++B%f{gFV
zrL=(E{XK}~-Ku$)x*pwZ!eu!!MNf&pztY-gWLjvbW4OiU<E-i(9CE6)N<Z!r8TGxE
zKIapJn>>3|)1H<#2glk;B*<nvps-Sc&6Tdv!P?WW7hbNwhRW+Vsj>oGsRK+AP18%Q
zQujL9!d=W~`W|_^w*k*cW_4v}CIlNR2P>KGy|%fCE@l^XF%Omxc1*8L1uhtLr<}5o
zgX`~Njy*Z$B!g`eyJ1^wF#bKt^5KC{VspgpVEJf(FPO?Mb4y-j^mcUJT_)nmwUfr&
z$;tiVQIn@^NhFpmAq^PyYPwi(W{~MrVJ~ov6Sc+OLs(K^k)S3JI`R(y%P1L{C{&gu
z29^Uh_#Y*?4X-8g1G=MA74x}wx-FF5T%!6s@5Yt+=JdkUUhxf)tahL-$cHByGqX#@
zwQ5w}$VAnx1*I#YPisp>G*Ildo9>FtQBPf+rM}eKp$<p4?qdkwiAh(5;q3b=dLRO{
z-{rw~pX!Ae%TVj4F_BVRCK_DmO^mu1!0m#q7vN)E23*=8h$)ABa)<QA)@lGQ=B*?&
z7PV3G!2!p_2GnI9r!7y%R<O_BB;aYR#z;}#>u4uE+qrKCe2)|NpvxJw)3u$sk_p~s
z$HuYe>Dw+sk#Q`XSPEjHDK^yDMqN8tEf+<=et5(c4RzFv9QTleR?;{dsFfox^7E5k
zxBWmEG~H3p4pj8R6017VAaLgPEhNw+=3w*Gi`|&aW+u{Nxow??^nQQeexV7_#3Tm0
zGl8Q`=)r3}Q-w(4WCnY431}WA`#$3FT=IBwDkxi~>6KU(VL1!Q*Z&+{d`HS^04`Ue
zZ)(%&Gp$d5Vn(<ZR8l_O?IiwaHMvo))rA7{za@IU=<My|Pl|=KQ0hy(CM0?^sC&R-
zB#fPjLU*8FweBgeZsGuRQ5=HtDV0N?hp>H%d#HXFvf0sFLSkVPsc~SeYp9XLcH;7t
zB_R#p^_1#y-OE$~eeX5TAO4lp(tNa-iW-#W^7;X%<!JY#{!YuA7D{$y+ED#ceXBxe
z>x~&Qx$SXM@c?Q;J$Yh!PEf>1rp#qQZ8QEwpC1JK*WvA9^XP6f&5F-TXSKE2K=rfX
z#|H|Dvl-8Q-&2=eWnq4KD$1{v8-}?*-*mUrMRQ(?t;IlH{8!=C0eDKi=X1$#FHST~
zm>N`-9>O3xlAUD@jRtUizgXHVEY7CYjr>AA@3aC9k=Jr<dtas6o$a0Xfqo;|oRP#3
z+p%CJ`l?WhOg=&yq`tugsdU5KMK_v<cs?Usrt_63pjMLudE^huOQ3?R3v$UT$x;xF
z1<CS-v~%^!9CIb(48TQ$ZQ^QEU|Pk!P}mXqj>@RfCfZ;L%`K5Y=jxczh0=Gsx&gs`
z`1mE2T>F4~&8BgHM@A6bJa$hT9}^SW=y-n;zb85LWl-*V*D7wq|4#G(-2Sxi_a0W*
z)21K}{}^5EaYuH~IGzN1$r(u&qB>*RRve9=LU|o&GH4z^*(^&iErsow$oP}pRMnOe
zllr6;>u2#+(3GGI3an6>nqcc7OHwDFGN!$wexhVT6}pV4I!v@Aqv7F|{8}06C*!H!
zwc@KJl*Q(J&thS@bV-SHF}s|TX_m6HAbOW(7S+Cm=>_vacWQpR=ScWG&ra+ux^Gzc
zn@`7a@di38ZZ{o$w{A<UIT>C`Q~VpzTJ)`$tgi+B&z1W(UJ4v(1@3y4r8hgRdQbSM
zz6ym@)E>8yuS*$nm%E~@#?X*KlS1uZqIH@;caLqR$f4(ZX;PCt@V`WghJL`z28X$E
z`D*-fbUN*z#kVdZhXvgOfz8+c<m3AiqXBwDw7X6n$3$twBqgHuOH|*Lh0VU}Gc(Xh
z)K#97>TuuCd}axaH?k<f{{^;bJuR9~%PMCnYo*D~jU~pR{p;k5aJ|k+<ed9hN~Td6
zF_WtZlr4mfP?ZbVuUefRid*Q^T}SiQPiGXrLXasnZ@;E9NuIXIL37x3)v*NbZ2$Av
zz@G5~&9oo!-K;J&2NXA!G#uRX@lcRw`@z9Yg|wY2k{$6CVVy0yJ2Hp2^}0grlp0Mu
z=bbq4II}WDT{ybB&4KJ0cO-$L4o83S2SF#H5m#37vOGVPiIWnb_!)-^4LJ;&nei8V
z=>Hs`8Zfm4Seu1fd4&TiZ(?TwIMM`UE}0q#Qc#4#D_n%U6;=F3R>tUf+<Dy|E%ko;
zNY5RrWY0~^a-j3?KC7vEe$oEm`*V!XAf>aq18<}nuXp(zvMk-W={blt9+}D9n1Ult
z(xYp`*xhquDUh2y{hJ372kb0|?#UrNI84H0XUuvnz_F1W`gmZou`KcNp)X`Uso9Bd
znZ;pPxGwolUr19kF-zzkV=MJ)gp&ej34Mi52?(Ua7TAg?pOB&##G9cNO<+5Id9Z5F
zt#DGUm+JC?ERY|UwXaKAId@MW`Gfqaq63Y_JhRF7Wi)bw$47MK@)Y}UZA@b2T**lo
zzTgB;s^p+zxsno6fm(HZW||han_xkGYs3yaO~=m!2*peRD7JSfnr7c=X*<)1*X)m1
zZ3$W!8_FMImy#^+Vi<D+hvFasfew8pnFqJ$4;@@>Y?;3?&IVN5hC(0OXGRf99-D%0
zKOBy#WgBYG!+~U}Vj9giJDTKOSoE=bhUccv*1;r&@UFS`*}W)$RM(Nlg=zsa7|svB
z%>iS?R{1QvM;j91NMrTWqO;{8+$et+5(75R4W+S)&}VB^TpW9^voR}lc4nT=U?8`I
zI5v$4uf#Io1^LxBUfTt4Vik}FN-7Jzw^lVRgG83djP!0dc9f*Yh^+vCF~E~x)vDKB
z$<AX$d|(!}jpvxYBW56HB3e>jedZ;j+Ld?@?71hWQYYK*${}yIx5~gS38p4ESzRH6
z%7T?sQTgy~Hx-i#h%pB?bwwpGiueLPx1!1ck1<+wh3wrF05yUs%s#RZFU~O9jQ~_L
zI)D)72b+^Y2RccY%b3!2z>uO!_v~?i0#!!7UDtn-!L_PD+(U=-)cTa>6Mk;a{{ONB
zT$z5GRskXOrR{$BJgah%!8ovMnwlJ<l8N0{1hcHcQkH1y+eAWgf)g78ON#YY?OYZ5
z15F}@k$opMqMlMK)FER<V>rpL1&&uFve=PU$$-N_9BMLE<CQ=kCfT%Nh`vG-<x#Mf
zop5(cX!W&a<(_iZC6D_&U|ly5Y}59Qjya}Moz)0R25HcjN8ix2)$(f_n4q{HR0d3U
z+E5lS3|vYqqQoPJDu-@4q=pN5zCVshEm=R*lzZL=BK)n64r!HVM0H|w5EUU0Wn^C6
zNeF0KICoN1gE9Db@&affG&Ol>D7{B7lNPj4WDn+sRP_wA_T^8vGalzj9enf)9m;Fz
zwjhuM%u_QcW!d2r>;yo463X6Rb>@)Y9(~12cEy^cLBiC9ZFW-})E=v{X8G_B3YtN7
z)9_?R8-qgEf)00p=T(!!cm`FuyEDuT!MjACDT<o5X^Qfx;53p+t)9Sak7wBf{+KG0
zVI!U$E<<t)D&Q~Fphi9stF0ON_yr6<W;2=@Tpjr|g+a(#Eag%wmyqY1t7SX6x)7L-
z-V*aRG1#k3!fq4PNXx>ICCw~udaBiRbmhqpu&g7v`v)#e_?nM^tgX{ZVA}hMBOK$Q
zt%z^e0K2i-4}$%nC%E^=i)(BW^?>My;a}V(k(Tgo7|vhR^avRFeS-RC=8n{o%IkN7
z{si2`Ncgd6a$}}Ys>D-!h!bDVXPsUnq&&N@UP4jGY>0I3v5lN?RIGsbo(yysU{c*`
zgwQU$|Aff1mc=uYCl}&+UJP}>y~k9G>Gi8QLUUh&Nbhg9QPeK-TIXs11&N}*Vv?k!
za1be*vQuY5o^RKwL*h=YQ=z1UOay^oB-J_P-bYy`#++??IKy;C+oF_VMY&KFp}#Tf
z#PUq8BJBrr@i-DVYMrkSvMsKpCZQUtL%q7qU7~%OPD!H^8v5GuV>u3oUTf|nZ-B;j
zmBV@teiDmOtRvjX?&IUKNN03XAAo9pLYe);g95$oDS@0UtI>%0>t1IwW1!9?$H9ds
ziyHT%+pq)1x>)46Fn({j3u(zme5Nf(e5;pKVh35?EEDcy$hHw&WQ+~_3eJ9mjZf-Z
z83Ig`MLM3;GcQbtWOW!O1-rslWg!(nJJby6#>~CU?GOkD<MqXDGH1PPg<(y5{GdL#
zusatMvEx6qU1V1#k%yURU=aPsG2k;IzQTO)!>~g*RA`83X(FBo95Sj|2Wu4-^<<tf
zh;6!h216J*u6rF^wGJG;p?y4cSv9-4%obibsRF)v42y**ZS51+A)?dQ(4$(XmZ;tz
zw5veFV<{TtyxWEZ)Vb}Bmb+7r@V=ZJW~I8*(yg9i9k6R+IX{DZjT8@$uX!`8AfUlL
z)3hmWO|l9lwp@jFFR@9xsZlnJ*dlG+rt%DKH;uT>u_5ED1SNRzsYNEjBn_JzFEJ>y
zADyZZrkpuTx`PZyaX~PQOVXJ03s_@9V_-(+HHBJFYn~s^*|0#CCO0HELZ_pK0JV=g
zxyUR|XmCQc4^R}U6gX>0({`lp#tB<Z{nex_g--ZZO4W}#(^)AQ8P!IuTY2@k?Z=u@
zH&3CRLa5olTei+oT~f#fD^|_6&E=-YzNBdP(;y01MDFdkjc?FWbmgvYAk3@2{fVV?
z6GMgtQPmrSKnxiI{BEv*p=?G_G(kh7W6{k=41s;e4mA(m0e}W*EG^<HX|@;;-U))Q
z&E1|D^5L>KB_QJM*lv_^QPg*B$<d@Mqij`qTL^Z7NnukdcN0Ac4=Cy1N(u$uNFt0f
zotm+xo2botE$)zXKFS96qZVjOLy>EK$CTUdRzt2hwm9wO0b~;>$|TbPMBJCH(Jf^;
zR$y2b034L%UXtsN*R{<1-Xs<HvT6#$3ggFZRiq^MAZznM<{b^Ez6f}KJ{{<asla2W
zGbszxWOg<fTK%3=6Z0?%(v5pTMGv&59ihxmzea!KAKNnKBKv}`L)Go*oW-J^BrFWh
z*)@4hkA<B%+woydr-gYr+u>ntHruM4ucO1N{)W1%TGuq!gjMYgd8<0t_DI#yJAUP}
z7$sgi%RJd1(9<6GB%mF96}W5;BZJt`w@TCBnjuhlBNmR%0Dh+c$r$EzraWhRoQTm~
z!9HqY(b0bQ`~xx;%@euqfnsmcR8jv<&Q;%%Q?(<?VkLf>taBy;fuCTxs>fihZa;96
zVkV+bYIpWBZlPA7eGA5-<3M(H5VJrn3Va$*gwN^(`W24y*->;wMiO?x78G+X&2J?s
zzIPc*cs`d-EI-|I3RAeZ9Ic-C5Ve?+l|1@tQY0T@nZZD;^2;Mx02wq}h}T;l927Su
z4VtpOz7D!KJ_6lY?xAXq_EBjdWDr4ja>D3Mt4C1F1B*8T2LY$*zmRhJf%+}vDJ68K
z;$F8yBLJ$eyoNt3A&5;U@#a7{v_iiSJZj1HCx@7?w~>+-B;Idn1l7i{YJ;dx9i&EM
zQxuuK4qbZlofDBuwzg%i@#ZG!Cj#N}m0xa+xG-rB!6XI%L}FhOl{V(L(oFX#@@}!2
z{FYds$SFy21?H6uEqCt@?bt=t7uE~3jZM;Ob^~tgA!HmrgL#(u@gFAkcADC$NSl#%
z?f~<+iw5Yg1%z)pGn%8#*`gL?FzTe23VJyE#rNMcXd6MFC8z%7`pMNV8q9aLmVUeT
z`u8U`C}_xxL}h@&Mu#<yXz8w#n_<-TEbWz1cJhzj)j$X+XMyYxuc3xSh@C^@s&cGd
zDY}ScOWW<8_aWo{+fCPRs8U@bl&0wb)_~X{>I)(CQ}o+t&^2SLn9fGKId`J@)ip`o
z>|;%N0vTQh0{0beYp}F$<(be)_P}DGXn9MD6iqV5GPs^pT?CX;!lH8O4|XjIf4&a?
zOQ3Ea>TYfAfx7$(5O{iCVLBU^jELdeYC$Ee7KRn2SMO-X=N4zrYRX@}hYUYTt0pu4
zVY*HPEBSLs@a5ZpVR%6>tcNDAV-E+yklUNlu)xzB8Zu2Ka*~nJ5P~*>3@XzAK)?b(
zwJGm%<#=+*M(3q;Mafa`H1;pm!W-w!E6@V>yy{RP*0=74^@=iIaQ<RfbxQ9!UpxD5
z!6NoCn;JK4mA~6uz7M;&qWnLTSKF)lqgI%U6>r#VT*_d5Y2srQnJWy7AD;`W7ktyu
zywssQXW7-4$ljWfcIab28(#-EuCcr3t!9fyUM#JDd>Ee%VnPl8iyx@H$bpcjjyrJ3
zSsXdB-8olMMC-Yz_!Av~m^zPg87LyCzcRdfGT!d*wmW{5Lw`=e!OSDLveVsDZ!1~G
zre?1vf?v77xJ;@(a{t$-(#_$B#90@JeAmHy^z>fH3$Cn|per7BM$O>GA744A4|FIw
zQZ02cU^K(`ArLYSn7b>@PTK#QuYVn5NhG^6q59QfQ5k9IZ_0ErI*SbY7IsD!4b9lh
z^9*Qg5AL}O8J~Yo82gnKX1i_#mjN_AnFUz?VTd@FH>XkdhE<phn9PHAJxV-fjljvy
zH*KXD^$4pk4zY}vt8H_MwdUCJG+)S%Hp_hDoZMZMYnT~5VeWCbHu}CDODf_|#T$46
zWbA#bCOX_05)|sa@K8g*Mcuxc>$F9jTh^>4U~x2juV66hG(|NKA;AH#yvasNsC;H%
zR;Eh-Y4Z8hSn}`#Vc_tzHIh7%f|+fXbFrPD4~!dKwi|!IgmPk6!LaOiDiLWLDS$Ns
zJ9MkZbdMOa2P3T_wjntyGb_uJ*~mnadA49%^U7~;Nl1}fr;?J11v*CDN@lh8)w4Fn
zX`g*HdfL8n+8W<KeZn2*Ba_t-$z%`tDz{cPQ`2<&UkrvY^8%Q&eJ6HBjXR0-NJOJh
zlnhhcIGh_WTIqh?7Hk`~VpVR17VL;TC@zFV$fdE~95#bd^Cq`RXo;z&;DfT{)7Jb9
zZaZrRA*17uDK;au2hLB?uo_F8O;!*TAWYYrDk_(>V2nt_tQL9G)ltPKwVCM@PaREs
zCPRITfnS<P-JtnDvcsR8k7h2iUF|kpDh9x?!$`CTvsLK73rRxI<0g!O3_KZX9Rb*u
zDjpK*hA5i14eB(NzwZUYMPzwT$26wi9!2G5M>G9!7rNMyog+3-N^35|L{jSI<f1LR
z!(|RO0sKE5fi)yevRcpQ7c*@mV!irw^HpT?+oFReMC=!@_12=u!=GKSb~loF`N@X0
zsGELpuo+TBO3v#yv;GIqaV39V!;8n1bTO@crlhM&wGRN?O6seHO90Mrs%LN?q6@M4
zIif4`jRM|nPS+?g418M|>RtY2!4O=YtM}8GzfED*y>Yw#jz}%~52@~&Yd`6LDw;&>
zc1Io{m+gQP62a#z${+T#*vrFlWg(|Ru~uXFzhLlwKYq+S430~km2->)adPSuga84A
z_lLP6iBq@&OxLZka6yrR8N0N-<JtB$c%=TA-|D-`B$RK8`6WHZG!Mg!c9a4q*ESj9
zl7ShRk|mqi7}K#^_AXc(cGdkcbQ|9s2IymU=MUyB$r~FQ?Jl?6Z>(|^prer1W8kG*
z-mOS<PPC#W3G!KSScpRGBcp^o#e#mF>%{pYcK0EdhvpG+w++_MjZ#=aFsL{LeQkB}
z?`hED!n^gw8~~dfJ-08;c4OmDdXf$maOVEp@UyqyvKIVd9x2d8GnYsK50r@3!4&D!
zzP$!?N8l(oZl=`+s~Gh#;N7qsoC!8Po}%(00E{)Sq8;RI;InV97j6%8AjHJ5K#di!
zg6-JJYcJb_-(Gns2F7VKuMUegCR2y0cV}<`^OO?HymNUubXL_AM)-&rQ5YvX@EMtZ
z)Ow()lb+%F*2zuqw^v`^@|uJ$#@7=pn*vuvUXytRkOE)7X{9?j4NI8MdLPgNHhTkN
zpK+Ywz9ni1S_RV_Ss?Ym{Z6!5Sy|SZrYXHsM509_*grE-*^Ki)0cn?qll{@2>pxAD
zTSkO4yy(I5Tg{1(fuOA!c}Y(>-(I=U$S{Smncx0Ik))y8F+8`=#RTsv0u-$HTz&>`
z2bagG62qO_CW{|T<_U+E_;p}>LLjAHo568nA{9y9)_X!mh#?i0?3?O63ggN$I|hVr
zN<G3o={*g(KyA*L;lYPD8m#+I^|~#A9`z+*QEq|f<#BXGS$N%uliVKA)uZ~zw5LaJ
zm+tTv)W$O~umB!yl3-R}B|eZMlxo%7cg>4OOzS~duM|o|-z1B7(OaZ23j}crvUC_c
zLhJ^MS+Q{uDWTwMcv%}J^zU`k%sk=MCF>Q))g2iP5*FMhziIWP+R%3@kbXe#;5x8P
z;m@vNhv*RCGMn7oSz!0JPwfmGcQLIIq5Wf)2~?B*i9{A-d6@$Ys2bjY*y=!+RSQKh
zcJnErO4V3Rv8;nwN?e=r!z4_1UH|DZiTU^jWW|Jiayu+faj6(mKkrnti^I>uNu*s9
zr=59XlXk4N<_M@jaWTv1fE%p0EwFDU{=f>n2W((G6=qWu^Q>-33ddX*G<62Gu}U<!
zLZ&E7iLM{AYBp74U6Up>wV~eYGE)xivcaw?B?ss3!X<CIw)ItLl1wilo}Gxv$aUV$
zw~b%Eac1XLQ#)f61}l^#x)g)0n*}tb5Iz*b(pL4JE{A)Bvu~G~0DK8vHyFESe}q=R
z49M7Ta}BN*Dpxnc%ULmLw$gYLG~6~8hqi>dY%%SaV<p_!6W7%4Y)SqQ-c#>|cr&5Q
zzF!hiEZq#Z>%D1uGW(!48Lmo(sz45wJ%IhP-rJzv<=)fI%@+p0%0fQ&ei6?Z@ik4M
zE=bZwKq266X#cL8eF=%H!VyGUO0$T2LOBnvFI}g3b1~_AZ}w+wc8-(^%~F5L(Xw%|
zjB~N(T(zwUzM{TX9-p9#Jc0X<@ct?c@mcE+2d=3KVGx?B<f>q05L;%NU|EWil3`{V
zL5a(XmV3U+F;y6^qB}by3{i>M1d0i{A=@6n0t0PQlOK613=^kEPSb0Rc6$`Zxe9XR
zw{ASv)E>c57-;Q`CN5O>iyTkqZ&EEvQPVtaoc6gaa%5_UCzK$KHZN@cX2rSQGxfZE
zarOc<!BRbXqP-kc?IPb2wZ6{kmP3I(qUT@9=gqya3<;CJZ029r{QT_?ZQc%{m5HRc
zY&{Yk5f=Gjl0KQNAkTGR*38ON6xc=>7m#j3*<F2t>P3!77^kSD^;oI2h*iDy8K}N;
zf_xvHp)|zA5=N<gN(Rmqc7mPn{_I*PwU+X6@A$XSm&T{3B)TpbtTouD&wbX}zz}Gp
z2EMA<)um*pb)LO=?bFvIq|1h=)gLM(d~N@x>+_Dy(DRxq+_{iGDa9=(rruuRKOmlJ
zwnjq+J?YVmWZpIJ8B~J3G*Co^CI<SF+%h7S_W(K@0%F6;qeC%2V%{K7(+2SX!!oJa
z9T^;+-Kk5H0N+tY=u=inieJUnN2fdE>*dMhI4P@Sz`}7;<uW#iL}U&?>t1PEl0NpY
zZo+Rbr?$w?q#~|P_?nf*oMQ2a$2i!~aT%YI_i8G`48trCQ6oOi!F{Wh96LEIB0Nbg
z^`i8e9JXje0Z+H9&Jq+Ygw7x%uWiGdRro6`X}t-~BtUl;blMbo9N5i-djxAxL1Yxf
z4`#NO-b5vE%HRIVrgA8`R-JincnsFOn|EY|!$!NIP;&p#ZWN&}lYj?6kqH#Qo+1x4
z;xlB^b_A?0xX==yc0}z_W0x$QZ{^Y=aoDCX*tMe%y6jvKqLvUiN<ORvGzhV{IvHoJ
z+HK&{re`e~N!!AYS3pBy%~tc{8E(B!5qc*T445$~R^^_o?2#>wDOyACozEAxR_jCa
zawamuT9i~DWyK~{VEcY161y~$3HUb)>fRA3#Z#W<%%&6rAX=kOWVQ^rIawS~h77fm
zlGX(UN&W6%6++1!gN;k|3Mr1!hIQE63l_-0SswCip{=k^M_@blw^~9U%0+Y?Lg6t?
z&6zEJjJ+!k)WjTh-Q7#t17raN#6E0}7qOw`wnm%w71Ks?pH4TXv3l{C1UpK1wh1tX
ziUeG3dpf)b9TI2BhCP6Y)oV1j;`L8ceEZyFEijq!b|%<+IhV9B(xUz71-~GKn`4N@
zRtFdWBS750|LX{ewn<AQN5Q>O(%3k!tx_;mK!s^hQR*18(}fw6BE@WX4oE)VNh9S+
z=!5|PD)gPlmO1x=#R_r&qB7p+Or0FuT=0_PlPh2J=-HJI0fn(=PVL?C74iSt;=nK7
zSX{)m7hMmIqqy??U<rGC1s-n2Y<nBDhaM-<;QrUsZ=BK93$hn<O0vfE$qav|5)nLy
z(W06-wXq&A&sW!T@swXK8<@Rk5Xm%eq4Pd>`b;vpn>mAQDhYcTC$!Axy?k?{NZt1{
zy<Q_II@`t>zrgU0n*2eeOG!}tUUs0K;fx{=)JR1Hj7ItTQn5Ck19GOiEMI<co$6Od
z&!xrG-bxBPD?hXCVPte)DLR6)7mKo1P7@!{%+=>Cg^906){3Ee1UG=M$cWpWyph2m
zZb~!RgCYHwyRs*if##KdB?=`QYFXmvJ1|dDv?Y(5ji3tBmZ*Vx8laI_$Q(6H>OhQ#
zm0lyDnlRSN=4ySeoHgTDVOv#mxm0VF42npFi~)-!W36FU7^D{sjX=~1d#l+^eSOI|
zTHPu)zCY5}&@i^nA(L>4NH%ON@l1L*`qT}Il<^gC*l6kXZ6>&XY5nsvuC*%#j{E#I
zAF==0f)w-JSwiTClO0&t=xr>4tLNNz>hA>DBU73vl<LH<$h-eq9)OlMz-w-HqMBq|
z&P8zF3|ka>u1`I#{LqL)MDubC%_v31Dn$%(UU=$j<r$_x{^ejoa2$I$<6v^cK;uiS
z8AsivciR>KqJMlPQ<|r6GOk1VzUn@I<1qyI-L(Jgb;0+|<XPeO<k%^%bg_@iIi>qO
zT)=EbXwG>K#WR#u^x!~bR-W>&&Zw0=4@+1v94nt{gMod!72bfHqST<FsoMdiImPKR
z#Y<PfeJHFztrq)?9)(@$LO7LE4vLZ6+(0A@3TH><*;QM4LruEu($V;-@&Im#LVJQg
z282~;p8GNPIY}_5Y?HfCuFFO{E)6^~Cg1;hej^iIKaRb`?-k7W!B)<54Yys8vilqG
zlCxs~b(p2iPaV34&NALQ1KFpmgtd<cebg_uJO&#KP#SrR>$t8lPU0E@)oiCFB6DCm
z(n1<2GiO#T9~wrI_ER7{$LNSB?oc3Pi6IA=MB7f~kd)Las`(aur=(fey~H1}OWZ}6
zh2VZ;mLxbP=HWm@yF!q}Jg~D4uVxbgOt&Eib0hu!=TQK_o2PhCLxf^%u4|er#c^iH
zcHDcs6+$Q-+7<?0)4R;!`f<Zj7;PY&iMmz3Dahw|g-A&T{iY3!Gf2q^Z!DJ;rbhDB
zqNHj25&FM{3AM?00sm{l*Y><??&ly$U|wH4Y{A;BX{#RCtJOA)9eQG?lv7f>;77M0
zl>CkohXgvcD<U=jF!~T#P#bhKX9KwcL$WJ5rt_+k2zHpkIF&@Hzb-)8PANz!ISMPo
zi_dm+W`Rin$v*_^P*NTNMFoZ8@mdh^U$vpw38E~7In6@r#>H$8v%)Dg_DC*(Bt|JZ
zECjf5PEZ^C!8s;oZiPg&1AM(`ZRCT82QDp>bD)yBL|Cd9xNh1y7qHU`ow93W;__cf
z^)&O}$~5gX@Mr6NZdFpACDYdX=}bLa3kmy!86>T(KPS^?c%XYH`zW1&^bJFdv*bvd
zPtZtmn{1ZtKz=rTh}Ov8mIMS0Eq$?a`es?EbR4`QG$?D_E=J)-GGz0Y&MuMs^i)5M
zh5AF)+{gE0Sy_18x5d$>z~Bv|hTcKCToKF_(eEcX=|k?aJ1cYc1Rv=Rw5!%RS{U_P
zAx6$|R4%T@oN!CGaiqz2@)Vk6`7PaNVTT1zE>5F?U>vn{56|#OhkV=pUsg|^#1d6A
z+;G2u1l(Nw$lo`o{IGD~4KnD=jwEZplXSj8F)yfw4y%Bv(|0QEipr_6`jIP1tG;NL
z#{Oo2vX6EKqvI7Yg-PhPU6~h$RSiXqLBw<JA~a*iL3=$0Rm;hsWhrYlVKhq`noz$9
zwQ(nry}U|9)d+y>%G(puYU*UYQL>phyfR0e43n50zdB2A;hL+>?c$@bPkX$Zy3HQJ
zlie$u*#v>u8PbOIb~5R*9Udre0T!oJ<vYo}1p4_VdLIbnx&c#fPEkHm(5!fD#7v17
zEA-o0f46897~k69-Y8uoYGx}vU}H*0-|hzYEbG}u-~EG5a)3FHN{@4>^@JXT4t|$W
z>UONb!;zVkUGqyulwEarC<LKWCW0*^o{LPJYO{{dt%H;^3KUgU!sZd9nH;@kRqJbN
z&Q%lx0yGWZv$MG<Fr`D4*RVQfxelupy4ZO1<nGMVZYUX#Nk%lusk6y;qO%5#_?Q;m
zte3G6Y)~ahq{WSxABCHoZ&N)<UO*OGr;}}|VNIjd4GT3|L8;lF6K_r;<4E*UDTIa2
zuGC1~P9xvZ9_umOm<=0vInpGS+_Nkvd0@q{J*gdF60KmLG1QdgN=S>?2(c}`2Td?9
zVH+^J<XPCQRqk9@_0>iNU`prK*;0UJCtR?Bw@8h@$VV(AVAZr`b=ovcQ359wCY6C%
ziUST@FBiIetbXJ~+Qz}sIxR%0oZ)<x=T!6UDhyf3C4E%}8_{B_VLt~feroan2o654
zZczFuWSsoA3HXXO7@04j0&4GuR>fOxW?&(NC)X;~Syez8eNb=I!wn{9kX=TOAzFwa
zq6ivTUu$@4urN>pI>M%dDJpp!6?e5j)L=hC@W(Dy5K1N+6*Ul)H>=T2+#0_5WF;r0
z#zXcC6+1L2Cj<Cn+sg-Cqbb2$1rX?IkkFeq9^t@2(+wwrAMmc#AcXLR8!zm<4wcY%
ztHka7YPy`TcG<RsnMh+G(La)XxN+%8liP=)GUfeKuV(1WeH=Btj!ssllH?CQG`n;%
zL2<hf*a#@c0!9QFhNl7PXi-R*lB@?Eg&7eE$&3%CCJzOn^t>9*cL8v9D~cTfa~;4P
zyKwZW*y{KMlrLfX`Vp|@Ek)=TsMg_7B$HQ<UZU=73c28Mb4b^XB8?HP`Cp4Wl5-hK
z^SSuJ?*H4;Z^k`?T=SumCH}?sip}8vQpksHh2u9xlA9@wJ>{|o)K)W@z!ipSoUxp8
z1+cHiSH3mJ%9@7e2G^Rs<NsBG?Mw>IlxwwBk~J&WyOg*UsY1#OUDR8+uT(4mJMd-=
zknLk#h6L_s7n4f^1q8IEJ@4xI$2ZTMx<1lQy{}1EhfEr}I<h_OoxOO>9CcS?>88MU
zbWd7krL;5!Hf`A)Ur5gZPwX2E+1$K;tWr90xq9~6>5onvJ9qNR@P8ZGgukso)PNw4
zw4pd!zl&;Q6D4V*pmfc?qJEkl$UMafl00FT^|cnE>dxTg>uVAgLH#1rD#C5Oe4~8=
zOe)%fFr5we0i-M~j+)I-+MdDLfkX>u4Em*c_j00o`LXQUtj|}t6z6A=f0YB)Z*-Mh
z;hPf2RAcaJ=t?!px5~)mJ|s9PSnFP=ftW!#MN_Jon$??%vmh;E;cd)qq9=m6GHE5Q
z!7ow5sRFX&dT5Ir6jC*@`yL;EKm8F0Lv2j&0o#DGVQp#QwQq4bc3L8(&1bKeB6tND
zMlHK-4+ArJ;!}0Z5x7v1c|$hQ(QPV2u}WhlCr?S=_kpE^918O7fi6c2-4z-GltxNH
zj!v^AdEMp=_VG8_6^Nw68inTTMDFA0>y!$`5TjZ;yRnD3djE_|zX>cj!!>B}ws$R|
zPfmK0(t}MjJ-vN^DcoU#&mahY*N~wT(ftAOD(JK-m6IcRh+$2yBXbpFye%Zo@MnAn
zc|b<r`XMqow6(TF#6r?obyp*#4WDIWILli|aLmMrmQ--`*f*N=-adI59ExopLTFW2
zNVZY1^{K2ny!M``CkH`-5X(QwzqXak=;@COdRIHm+O#co^kEgiW#@k$q*Nm{DP929
zg<%dy>8cg8wTNV0IG&LK`*C?3MMOz5t~Oj~RjnLToXlO4r~&<@$jSUw12Xy_rWkMg
z>3IAL?otB&B#UkYHh79ld>C=M7oGk$TRz`fC79%;;OxS~bbWH{65f!d+o70)Q@`E*
zlfve#WS}{(kOB2cxqTzOJkFT-_0}ILX%TsVE1=<%5J9Q9*Ob>LzV{V<J(pC%W%ggl
zGz@|1pIEzLw>8U+1@~`e!y_5Ytj$7cfDj^)&zr-E1X|_8;{@KIe;e{O>}!kt>W3qj
zC`4~6zP;2`x<-#g%Snb^s@S{$oNl*)R3z@ebiLgS-YIPCD+Bh0saU!Y9cl;tT8X*T
zKsP%KcD8UM{jQ}_sGzKWl71#fo+i%{Pfu@&;T)uhf)gFZn%o0ssEywD<zAFpn`mEj
zbNzwD6$ce%@WTGF?+jj8v5YL_R*Wno>ms!W*WA&Lo|@}pF)7h@k5yPMLJ-?RH0>Od
zQ`5qZfZs$0KY!k$N)SQ4E(BxqYGwXpNaTSr!od&GrVoaYLhga`xc13_#yIevvhe+W
z<*b*$HWSRdz4GglYj9)B6R8z<l*t75u8fj(#Yj^zPETf{y5Dyz+^PE0ZbJ*MD@s?e
zG#1kiZDDh=gfRgT&^5iNiTHz(6X!+bOrmIK6oR7S%)5hD*B`Mwoawb&Zl9wW+H%Gf
z*^dOQELhNEgK%W%jv~qG(g&LgJ$ElIdK@p0@jO*3-?rAavnEr|hkp{GZta;_TgXhB
z+)hVwuJDFBAueG|*VLC}S7U!7+60%jPDvaQlc?OyH{5uKNQb$rc~+}M@Y|vSvGaCZ
zzVq_3QWo8xe)2mhUG^;r-dJE;miam3EHeiYc1G9B`>hPJhIoKom$O-^*{(p@@JABN
z$&=_H)`exl%TsFm^Cd@V(9Y)!$0on|3TXHLcI&AWIWQhyoBbkz*aT;BwhLN@J?>bT
zvb2-ENr}7Q<g_}CZ9Hfw%6*aDI*!nftHrQ!DD(jWKQ9{^i)2TKjk;gW79eSzRA`Tb
zhZnEsz#4BQbqLjS!pbn~1>OWRsY?;FMjjaC!^$O{##=N-S3+<W6Nc(~Ywm^H4|CSF
z&x!qyX$!_aRk5FcZYoW#g<&5Rr<|U%uhQ<+&_TNtngD~P?WmrI2J~r}?KWF+++=zW
zbd#;9O`5qsJP{EI9J?lR8tNg<HULVK8i|ZXX6TflzBVgMp~&yGO4T!o;hwUP+nB;}
zN_>b#>(4OPsFFASq3*Wl6Z=h)+&#2M<Nq#&#o<9GhG~ev&P^s{)3>e(5+iWB;To|)
zlKdAloiO;u5xS|n?&Q5h4yk)#Z$1qEbl3SA1AE}JvX2E`XhlX&vo*hYhlOLsMv)(B
z_CCRGtkzmRHU|td9@f{+tt|f}GqL7PgE{e+_CV>g03%N;jt|CEd)njPB(;9}h|!oc
zAp%WGp!cR)Gd@3sa3gI#+vm}2Dhx}h8AbBuABDb^TU4S)owp?a&_Un&cr-tY{wUy5
zX2vd0f|5umQcAEY8AU%|N(GN6N>m0jo3bGLe3=x+n>E6Y^6$3Cxv}(5JDT}>$)##3
zu57){s)*EDP2d6j^i^Y*8j3+MRSZyiy|w^nz%OI~!vJ0NQPHsx=Dr*j$NEtlr;1(l
z6p*a=7l}VKeD_e<2rtACOxeRKrUD>DF{m??65B(9dwO}(QVV&zTvbKXM!~|lcxhdL
zfd7{Q8`J0Djqm2Ad@Ug*Eu0`gwCea-G6gUr%PYco#AI^9B0<09MS|4RD;!^vqkzKO
z87sF0$%07g*^PmyP&rlLbc%IUx-I~s+B$!XyeFGIM0nse#iEphRstM$t7cx3lCG-t
zPVQE}nvvuywe5@7!<p=iBlWdg7;NI<llnxk^W5pu%EeYwNIGchkb(F?UBB{6DK$jZ
zZ2xx!YWpo*zD&MSv;FD0#knD(g<n1BN)#qi68$nz4<2nTy_hpYs{L#-=L3yzR3Ajf
z$KsvWql*L2y=^m){$4ZpOqz!Iy+$DtyB&2SO@b{TamIiA3~6NTrp3NWXQ@G+09CvC
zdOO>Dvh2*w8>df>T%TOxQ)|S>S@4+Nb(<NIqZ97$Bw4x`RznpvXI}o$T<mPgzYUYp
z^arQs2yF4IlV>r%-I*k-q4Rs*ISk&O*4M*6h;LZsK}$j?g0>*KoIaDoX_y{9ZqsNa
zw+9-V8Edj3ETL*I5?LHg>I>{-C5*~3ul4=VtMKc^ee6AfYGqJW>FKaE8!DewUo998
z_MLZ4d!RbWwczm5_q7K;Y`<CkO*Xrezr5+zta8}5@|q=^CpK`nA+zbUoah>re|0zN
zHWW@Svx^f4+pS{g){ri9Qu(!VrjqBu2d%<AchFDfwkbKDKNfeGIpH5n#ocZ~t|F`O
z<Qf0+F(V4b{P`Vo693&StXAym>-jKGS5ldm>E4M;XqFbZu2AZ=_B3HgV|lVO3h1x$
zUaALWoOG5-Xi+Ojdl<jKG=8bVCylyCsBtq(H`ryBHwbC-lhLpgxdl=VHN_^kEn3k=
zT|JCb%)>5`OWClrjxw9VAhlNd>FWdYLX*eXP~cVv%pSoIy$N5u^SsfnUj(ea20*J^
zF97QT7*+fObd63_C{5(AOY>s_`>rIhZJO+a*N*|Fv&i!={X|7GzBYs|4qO3ZvqX_Q
zAW(y&2ckc;(H0C)l`-B?yO%uL@S|&(>LI`pV>|K={<(!=@@Vq;e54M{B8>3C4!37j
z7q07~V01%)6;d!2{GxqD`RDTd;qI!+b;qv9tj``Ca3k=mAyAL^%kH2dnvE~(DB3c+
zvm8GudhBe4s?7Kkxv*@<SA>&k0{K1);iBPyaZhYPFeCh!-78x0xyKKSn3H*J9^_9u
zO7FfX>A?DxfmAR<-^d_@IgJPu*mt9WOT)-MojxaXOx|BU*>~5kmGs9Y7m&wSDX6s&
zdW9Vbedz{a0o@@v6bE*UcZ@hu!vlwWcs=8->yhxqkB#J-FRT#Oh$CcbE^V%tx6H^S
z@igaSf;_(KrNQ}=iCX1u7a1<{#}(P7VwNJ?DO!d^hrBs)P*Y8v($jI6xALq!9%WOL
z+bbeSt67%#0d~=Xy12{rZbt6UZ!QYDs*BWLw>OK=3bCm2+n_@+0L6ioqdr?eX%>K|
z{nR3lHONTPPd^j+xEc3+OQfPV`6L8?qWalF+W!O9GHl*M2XeFF=w(FaPW$y-**#DO
za!LB8x!_M<HlGcmwmH93Yh?aEoAkV<ELy$2{|5E~yVLU|iRDFyr{cF6>vdSq0>{8%
zy@9oF2(4eV&<`iwZ}WNiDo0H#Na5T0D_F=NQAYi8qooP?I(4gpLqNi!+S)^Yp~Z-&
zdj>UqBkoRd!bW?d&w2wbWPb7#H5&$nG(;H#Dl<%oJc9Y(7)5dJA6ZpNbjW|t>KD(M
zeWlW1*WdprX?Tx>{~1+j5B2qDoziIUqK{y-m^!bTn-;Ghy|MfPvtQ9(w_Du7?l+<U
zH`4(VSYXGIFDo_T0K<r)MDmEYCFv7rMh>MGhL{)RS+IdE7=rQ0f7hF$s|gcEqZveC
z0XBR%g^)c3v-BLu>_ni^r8tgM3Lbd$W$C_X{Gyc%Q+#+~SaYnVM<Nrk*HPN($#J<^
zRvZdq$RX`pNn(fE1f>jFMNo&~mNeyX=-<cN8#doiu_8MiYYKrW&gyo%G1wks6-rg}
z?;#P*3<VWuOIm1wtVmg{X6z}N4DJ6DyTJlsI$c!n{7-^Hpy<QN=evIWqWQvP>rzp5
zmn$H-tKE6A;BE`5HnS-K56<{6M#6iRfJ3^@u<Et113<q8x(e1TXw}hJsH`k+lSwDV
zs1}RZ8xD4qT*R{ouCYu*)Z;63@Jt*k1*rnHGo7NBf9Vof4B&x#L+neW$P^SMeqOCr
zJrf^vPLLo_n$bt%KV4q70QJzE5Kq{m?UXe42NA7G$U3o_1^MtfsXII;W%*@2o>I=2
zo$2|PqNccSVpf0g{VgkQ9=1FR#~V*S`rDj>S<x6i=u}m`DKK%+>@#O8)ooQqiR+x4
zc{Wyw0i#iLspfCKFrrd8;VQJW^2$Mu%cx_z(R}=&nQPk&z+h(x*4h+8uRs}p3>-?S
zA%Xh_^vu}j2@JoK5F`i7^x?_Qf0s8ISP)reA@^`n#<TN!3@o<(899MPEELDu|Lx+d
zFZWEZa8pZ8OAjxs7NxxLWiAUZr$jvE#iinp#TTzBqRCmRNEGPo0Wl8BGUiJ~q(p+5
zx9sxRb<JELu`<QUUp=8&t-kdkZ@cNwa8?)?X8_xXKHy5AlHK~kZk<e<=#ETaC*-BN
z|7hw+ZZ2YSnl^Tdd0pvdj^}goVW#P}QA5W48`hPqd_t%CFv$)@V_%_N{h`ws^c<Qr
zI&BWrbAVdbnMVkTS|M0C)Nr@$g;S?<GUs7v&Fv>>V&l&mX{ebcYL@5ac!{C!uFZnW
z3-ix59V_@VTgBBVUM6)Mky$d&XG}CEq8jmus%R&wun5OXu$-5w7tsVVZ9;f?Fi!v3
z9nxVL+L(JmAbie;2$)-!K`-Z9NyF=*#=?>#G@~qn<zty}{xP-ks?j)lc=O7$CwKh9
z_5bbuy8GW9Z~ox&tw%4e-8OUM|Nr>&@;~1Z0GBZ*%$13P3uQ^&i;s61_JA~r^Q9n1
z;NxiL${VwCXRuTfv!$c?iz}!EI{FwQDWsI=if8<@zRkbpKHRtE?AJ28h7q&&9DHo}
zC~#eq!&RcMYWdNs*B9Mm!ZlOwwD>xu4YZ7G83lH>%>gM#<qg^mIu~bk44HQaPS7AL
z_gs3#apjay^FpPR__EEUkk277$*!2d1I3L|QqPy#IUGMH^_<t?^&@Ca{oAS=fv||^
zitZgP7rqv9WB3)99tj^_lpS8?HUL_pyOGw#&nweJWWX4a=>6Kqx7;baKr#%B1;RRn
zW@H3-GvoM^XbT=I0j14T_}9JXkZ+PLeUH2@-GE|tO}@}d`S@+-qI^Ij_1*y!$ydOO
zCVbC;BV*n%737kI*=VkCg4U2Qmj<+~*TN75A1a=zW@p9MFZpjo2Xp(GMC3cn=<?i?
zzGp5$4R}P>R=sdw_f_gFHzHi2BE+8B#P0Am@P-lkhymM@)%)Cu{IwW!`ob+mle$G$
zAR~;VDbwxuPh@=W`d`~$x8sg{bHDXw8mLu5Z={&X>7K^3yMlHiR$!wb%_doLxRto0
z<lV?x9zR8LP;rNv;>0!;)fTjQm=AtOy}j!Fm(gGOEx|<=8Ly?ka6lr7m_*@_7bR~$
zBu#b9Taxr^4@zy}Ra2`lEyv5Amh9e1nY_ST4#4C_b?f)7q$0$AQbdlLKX4|_3%A6G
zYP*YT*FG<|o}>v^kdK;&wHhJclsL?n)PM)jC3<@mB-LY%?3O-)XCibW5~(O0#`KL7
zf_>y*sU0L@j`+fRj@s{-a_%MsO3Gm%2jp{D=mVA@wc(~8?<IYF{cOUy_<=>9UE$Px
zqu8i-6^=!Rw}kr+Aya0g*K3P+ZbvqS5zsb@@GQ{~D~pCg*t#E7eFi$wdIOpDmdf$)
z2$JuUCQ_ErGMgQNV=Br{4vX9PEy;pQO99F;fMnG{50piEisW$)6ni?NPvwH|&sy7+
zcB<X|5m<CP)Y4mE40{*j^4})3&<l^0*N<HjkHX70s$b@gSy`^T+;`TeLEa!mIwA_Q
z$)hr!>_c`)3V^e(PLK9|&t*v!O5<_QW>CxR>40V`H}RT5pKOQi!oC?Qru9LJsav=_
z<>vY^+^Xokhf83~6T588kE4eUC*5TzoCB9869(0v^nj#p-91v{-egtS;JCRrKbb(P
zW|DJ!I&MeBh|M6BoZ*nuj23!&S@Y5Bv8!#MAo$S(yGjL$V#1iUcl>w4lU^BLqe4Vt
z2W|UiW1@@dGcqY;KyJvO;_N|@(%EK*yW!oUXErDdpbxUo=FTKzDYT(;WQ#aOeRW2%
z!$^ZS@f$1>v520czo`CfFx%C$PH1Dc5n&_(@uP90&eb9W@5$L%t(cVuH&i3?k_CXw
zbB$uBP*T3qILnRC*2GPPd(wD-4Gw(kZn?sZ4&F`;27|y3hM-AYTV4-H+RrQa@zX3k
zj}Vt!y$Nsrhzt6`a5In8t-2H|J)w4rMQ3x#pCFO~WvLZy4fGY;J4KPGnZ{BZYBd{*
z7IbvoNv*1*A_>3;L;g&=T>0RQp5=2*rm9f|r~%JjMw~@GYNoKqes)G-tmTUab~wCQ
zF7MmCz@0+F$HUSf*D;M1KGZ{m%FPAM^uuBfr%s78gM@Uu>H@nrZLBGe?{W3q8$D#<
zDm(8|w2>Qa?gz{995dd%8*V)Rem7$>e+8Cr{f8P^=6(ClpFC{aUMNtEkH+wG$RoOa
zJXR4afCG=bg5LdsNKTdRXa{b6^;C4{XRyoF+wQr$Hy{x})8pOR4ah50@6g$HvswO>
z{6ahOtoGWDUde~M;K-x)4m}ta$&+*IWbH1ZYoGK@nlb>*BZUeLgg<sNAbn|Eos_D$
zR@md%CwW51X%I$B#DZ4Lr^EU?+ny(q=e4vTVrcQB<VaK}2{8b~N9|RSy{xVh6zX8K
zE4h%kO0(|H<t6bI0YSuzBj=Wv6rJ;aa<yS1K8mO+y}yv)L%S`%qHUt*zYkk)*ZIx+
zNWqCd-o4#pIba=RB4PP76gYV|1F?T3vyP6P(Yo5hEO7{M4GEY|3+75F=Bj1Wh)M98
z64=*kvL8cQ2ncUP&!lm6ZN5Xw?C+-ja>e93-hPp~UsB(@YWn+@&@fD7PZz~2&(E9D
zG)en}mR;7#30;A-lY}y>d46f&YBYB#{D3_rY&`n7%_vk`RiG(!h%gnpC_F&58&W9D
z=M%O*+p#;1r5YdgnHbCTZ2pWxx(H-AOG%=b-^hT9P&EX*7EL<*^`ZCCUI0quSxI04
zKU`>l{I9^1a5MD{X6Q$+Lb?97KDI`Ra9{kOazRa{x;V#q$bczv`nB8n=6pVNmah>j
zfsIO#!aFjP=#fhlWJW@#8oZ_51wIADK|%!)!<w_x$)g;lhHpd6xA`Y(8t@JjYU=yZ
zw5f@2-~5KK6`+TMR~Uv(8OYNkBe3OUw<YJe&Dx+n9M5pRoHtoP_Z&QjK1;bWM_rS+
zpDe0jueZsHkQf;l@A=@!Q`$m%^Avhf#MjN1c4JzfyPWVcKHEzM^s>MWOWQKeA+VXd
z-n7Z3TrYcJo9@T~mw(4{ZS&Mlnm-u+i39Zrwn&;rxBp<e4~(k;du+lI+#+z;{mPiT
z3GT3<);JmQ*`4yv<ksk>H~yuj=V9vm)ffpSe^k2o6=J|iTsl%h7n%yK3EOz&ad*Pd
z9GgUe8U{A-9YG;Ei>*lx1>f&3jJB{lVGUEjV?|mrYqW-~6O=SOWD+k{%5cO4l9mV@
z_8?0b*x1QBVqiPk{frS$NXB6GF{r8uE#O(hd+xj9MJ?ds+|RW3qL$o}Cgf)HiSOwP
z1AHII2+{jzQCF~0+hv3{5tM<DcL7-MpK5E4u@*(>B0cu$Ej(N!3x|>KQ-b@=jZEYs
z<MjYXBekYs?1ovTJ;6m)J4}65Jc-jUU;n`vJbozf5POHZcz@fO$db%Nemx<`qd5t9
z8eRXK15LHEgGd6Q1Xs?kGTkMPFjrg`dXVHqpvtD`iwi!_C)WW*i|^Re_xzVG-&3}R
z)?-%hP_nSfvw2l89`J_sLQRd3f-K$y>sgIcRJb!0&>$`_zp@N$LGMgq%QW%ITVf~W
zih@2c^|3&huA%?UX`p&wn_eE_1WJq$`vyO6W`CZz!`qlWmQjX7s|y`LkFUwbce@Xw
z>^Q0Tg6HdN$mesh6(jePF0E}`|La476q0{uMx|vP9H6=X&R~w)2%>VU$+4PH0*g1o
z1e7P}Pbkn6;`C2%kVieKU|lfpO#^8C=ze8);Fnk2)sL4;mks|Mp2lNJFWU?A%Bxj4
zzS|Mlmf*+aeG>L(MK$7zY^l~gUJ7bD>v946$NRvj*WQi@vV|0J%@v7T6a9E^mr?|5
ztJD&i+;RV~Y93?W7H$3jGljZRq-<(a7OfOEZZ0KG*!fX0=@$2TY)AKPvy&$puR8Wk
zKRe8yr7!4Xtdm?z<hc&<dx`y<qk}2D9~L4!#vk>6K*k(Earl=<I3U+n)6pgj^dvt0
zcGwf{HO~$bpCpUZGVw&?wp@33$PEO0h-~YGCS6dnr#`*Gosyf4lVT<VOYD~8*jYc|
zCEKUL@kM!Am^0O*=zK0n5fcj|As88b!O4S&DR|k0XDqD_dT%mFgU6j>f1wX^vMKl|
z`Ef&eg&$%&l)B9et=G$T$${un6l5C1!r?};6)K{OLW=^2Si(!tcq%lCp2cN+(#V&X
zDzrN)=|x)Y0G>=Tyb;>aNA67T)gB$n`?kHXK%!{LAfGgnp5_ibhGWAa+^%sP(<%}5
zNZtLBrx>&56y(i*w6KSma;Flr?OB$SLUxyV*g=NmZw<y-{4>O1;XRzZ;HfK?R)u>4
z7*EaOPA9gg2rC=bt@VrA6+;+y>CtFSR)eVk%G2RC<nMe7kw;eFpA`$SesS9tdeO-+
zfAsaQoD90krQ`E|(5^^FLwfYK*ZZN?E^c_b#!-=xvG?tVO-2;dc{L4C)e_mpwo|sV
zt4<8B_8}wZ$PL`N%k2ua-g8H@Jlv&Aax?~Z%fBu%d1qEE3|JL*j~btQO};9eJ^8hd
zdZ3SO^7buXQCfERf?&_h#P)_F@~-ivz-|irMtn4uFWvmipv=|z(K(dqgNep2#&&rn
z7mKT@%cumqd;x{;TY$sv^G{Hw*Ck+v210F26Zd*)4>m}EZQMsK33Tg{cb`4P3CMT@
zS(~g1Ca_g7hj;Md*gDDmbkoNYY-DE*VTCGVfm>k&j(>ts+S$tT$epEimz@D<*@?*w
z$vm=nfBH4<<4Gf&R2PwPCJ+1nrD=kk8ZM@8r^%UH79^`_B1mW0K=qO5@FzKNQ7VW-
zme9QW4rs5p=#goS;B~UuRujZTQ)x5Zoq}LFX;4-cQp%ksy!HIz-+VQ9^E&-@={8#t
zNvr!CH^prI6WUu$&mk8etbvQ%&Nu&m{sBn~NLN?Fq;E$s2(l#*jwpUxAGbBgdXA#E
z#%`6As7)XCNA`H+ww*}V&nFMk&4>o&1)QwFq|gF-OQ(K6@d&xkhl#VP<fG){bGVg*
zNOxuO9L+w_Ji(aQF8d6swuM#GO%s@<x+ycL|2TjS`tTWln{aL7R9((+n2CPGr|O33
zCpT>P6pT|PIG3J~jx)Hh71qM%EW(5H)eF#qlkWPnw!|*pZtTCPpFodqMw_=23=n1|
zeA;E9#S!UD_%_Kq==nr9x<)g3`e3rIe|CQt-V=1cKAJ=6z<tpMR$UXCG?od|tgcVB
zL0ao(fk{|~{L$~u151Nyc_&|GyRE^J^4Qxu#}Ka=2{S^o<~@r@7gHEVwXd^ra2F)T
zkw%ZJb_Y|<5zG*<Bfv#5rwlzXk8p9&@u}IX_91g5uBD^!TA>eQ!^4Oy;MDL+lZ#@5
zm-@xV`Ue>!UQ$0mEPb2}4e&u4f~&QTCY;vRwXiu5Pj-@4onlTQ*Z?)1DUKWWaOj5)
z888(Rtyh~LF{h0+*wTs;hO}^$LB(4=yQP9>+W-TbnbOt9i8EN*L}(64%iq9c`e%tS
z@@Q|8VN9ST!bJ`OE_oaT1TqnAips8ZIm|n~nSrV?V$PR}P(jDsSA)G<0s<a0NZrGw
z^-$kq-L3t@ZViR89uVb<1+Nevr3s=QGaP<}r{NWiuNi?x3Qa+%Q>9~$uNRh<<aTB<
zE##7M{vtR(oS810S2Iw+?Phq6O@5!rq(IqQIt2j;ooMC&a2^=Wny+{8jq=`Wt+Y3U
znnrYAQ4brxxmI=rS$e^#$Wpl~D{f&qv2ySLAWjEdI<b2~(8tjpR7y|Bg!v%$LbZ~J
zMZSr>P_8CS9$;QgH`pCeQVol{DAfgF8O<pL2<L)pk5}m}-!UeV%yz2$6;Ferhe!$c
zW`O#kfZOu;lPym_@xdR+b^rxe;PKffyEx(TpOpvmcn45U#IIJB;@MG2Ob0`A4Ef~2
zE@D?E#X&>^N;l-%l6PY{woMeP5s&Q|^FfgCyk-RRh(w!620lEWjCxSgD&n`k&Bw0?
zBO5ji)acgoaEi8Ky8}HEZKwPP2WeuO5j+jN5F)4Cnu?_P#~Oy{L%<uymu{=_Q<;(&
zTtYGAfRgHr&3pEcr~Qq`U+Ene-#W+d*J3RlIgp9=(sJM#*_(}yQxN2#nrRgWCEK*K
zTRJn84iNCaCGX+gb~L<8fDEug@Ly<<2)#T})NCFX2VQ-Un-b}SjxIR&#SpEP)|Y#D
z__-1viO?P2mGWtPE0#T~3h@nsZ_CP}$ffE1coYimN|NLrl6)jy;%!lW+CkWwyS+AW
zkCnza$NVoHi*(9+rLhmKrTv;rmoEv<Q%zKbs!;elDYHN)-s5Siz#9oM`i8#cX4&W2
zJackHQeweBU^y$>Rc>uMT4q#dnhQp$LpCISTD%ib$H4E~Dl$jyGn9T5bwycb+}OTV
zVCbX5$W5A{FJScrUAzx(@F_vY4-j@P*MM5K$tjV7=W$!PsoAYA8@kkIQ?f25h~j;P
zHann{Vu(h35c4J2Y`$J*NbN1L9OJ{8c4VYlXtX+k#!4Ac{0*jUswDOZ5G&UWI?&uP
z@IO7YCj3!+gTf%bx5)yG%ng^z65ipyXCgOeN=O~19a0(>sNf7;64;sFLX~q~6X-nV
zy3DZ1*mXj{qqOofg9}Mcv^*xVQWZQJ(;W2`PWbHo47-xEtaaFxB=6~YuCJg6*hkBJ
zN#J$~g3C=_VA1NlTd!@cZwm*qb1nB>+P_o6)r=Z~*LKJt1hR=qW=myX=_8jW_$!ww
z0<4(-DIbhhYuT`QBjC?*n_8IJ(5Qq~i&^Sa?YecSLq-p_9=Y8rtzEJ%%jnjN`Y!aj
zqF=X5)@^IMLspxz>@HcVzb*4No<MA4=h9+7Y(SONU}A=c4kgn!gP+|Lq-GW53m3L?
z&Yu2cX4K{$vw)KJKM&Ku3r=o?wuYu(Ve@aVOq40So|VB)Iu#wSDoWCOjhXst((XMz
zIqRQ~g`U%lD+LvhhVq#eyKh9VMSx!nAB{b6HKp<Ak^3p<a2lU9((FD5Qc`CJ5-&^c
zp#v-R4q39Q4pt6~^q(fH2d_tJv;V^``TpWkY?ZFt=CRALjMy>_@_U^8KkU^48$Lo0
zRjXzLGNKn&3(B2n+P@`|q_7^_6<Du$a?v`R*;`k<Vsy^c^)3wOAoN+Fx{@DPA~%uA
zwTeREZGE<V=C6dwah-tD#WjTPRP0oz_wbqKWsl(<v8`OJ3E%+w4E640TuA<!nY1<?
zXd-sf^Es{IZW4=1$VLjxu!Tj3*e+J;uk66@CSfu_JZsZm?|%e(R|Vp7*f;#@IzD|r
zh-3=B=Y6#N*I7W4PHFtAj)e=sP%23Slmj@_{`iJLTo;;qY&Qojd^^q;x%%AcQ`bG3
zP?dogfcVtAfd^Baq?21e#8?tV4@V$dv&O&Em0J>FqpYR`3(wH!%#s!;)2GhE&<%5p
zu=g7c)IoH#ef<zXH2a5bx{CnLf41%^YyAEj2sCWeL`NK3Ykcu+Wb?d|tUMIWLOc5U
zNsIeyXQjU0*$v$|A3V`yb*?bk^~dkX7iK4w{*HgD7R+)agf3pVj}hn}fX&6F^U5Ln
zl6J~NaM(5?Uk>3}4|?T-grQcC;ZmIpW}1TUuE11LDG63o<gpf5<_MG0=L?RdZ5G-}
zdJ%UW1W@A!@WEXoY#<k+4YO0;J@Ghd<1iKYV2MuRCR9RH(Q?(U<*_IobaQ}Ym~e?+
z^P9=j>|qBePLqw(5?jDG!$K_o=KYEy`lr9f7bcNupG9D-5D?6;R08I6nXyTsagW-a
zFFG#NLIKGa&e_IYc?aqTTeO!kXg*NP3(}@m8i~a>lqCaQ5H@#n3p+wIDa_(E1wTqi
z3U}BO86NXp6Tj21p{w>Fq5f?Mjw*5~w0><i``aO?N6kDvc&wf_)3}FN(&Sc#u6AC2
z60YGRZ3E@tK76DQ;u}(asOZ6-9@BI_lt4dEhnJ%db$w};hQK&@ir`qjEH<ZLt*?eh
zRNQT&)D~*T_MbQrrugC%VoraHLE-MbX{79Lj7Yz2J0}H<L8w57Oz6&~WTej@0;Xg2
z+j`~u6+_8C2cZqMBvlxr=JCTZO6>oK=<4wdq@YY(@!i|kdz9-R*bA!(S|dHZBi3|i
zWdDi@3!@|T|ApHW<>YuM-$XL(0HbI_TBF`cM|9Xav$urDjLaueHh+vE59t4A&MZwc
zgJa_&1A;|1ztj~1DlY1DCEZp2Wm<SA$8us(4c(^&iN&PyHk_nm>*yMW8w^w6(4swY
zA|&<9;v2{bzb<dIQ!>_2Dr(+Sj=kgWk8WK6mYRsibK)gOC)mdah2!PoD;};4@U+W0
z?J(zG{40w!$D2s3yJEJqS4oxhDK*z*2~2{+G9z&t@!bOHgk&qBeVdT2F~&D<gh(9U
zY<Ap|HY*fx=3k^mh(_pK&-C=RNAYw{P7zm8NkHn@(t78diB&Ug{?mdk7WiH7+uKK<
zm1H)JO=3X`UQ&?kc-j65zL6WZnXR=i8ERRwJIR$i%E1X%sLZ-j_jI&LfCz7J>W!1{
zxKG^5s5<50J^?r2h~`8P6^sPoeG*MpBE4;ha{%pdu@AO~>TOIeA>f|CuHQX0wk>U6
z1QUdtDfS@_ZO3rg`8;_BWg>d#R?z;7CZ!rt82X<W7q=Ob&-4?5a)#ZJ4?i%@PVc?X
zTh5oimBdBlY*OK0nt+KbjhctLwPY7Jj|`0Jg_3MV0mz@mE8JsKl34H(D3+KHp{UPa
z$8j{!N2t80g^liaB;k1qeV9`FNSn?>OFd!MWVl&U98JAfOR=m#)>V3FTVz0zy5mL!
z8&#ZfISWr<6=8kd{G>SFNuNMM$D5JNXpb{yA8U}Ev+W0sG+#8m3nXRogb^qxLcV)+
ziC57k5unl|>vFm{35X&JFNy}WTW?+!J3-bS&a+~@^i#Hajod=Tb}u@PHkkpmAfaAW
zWh(~q2P}YRabA#TEtDDs#%8c^D`C1P1w&rdVxVF1ifHKmK?syJux=Sbm+9Un4SX!N
zw3t5_t_B8*m<#m@ooCr)#>43r8&AENyYObi&@)SA-wflviaFsyAY!4sN+Zdi+BSpU
z@k3*dkP=~+O<a!4!xFSL-t;KWRjOXy=Z+Ry4wl+4UH?eM?Ph(;lLlhKQ31jm_RB_}
zL#l8cTR1MCe;(-tdNzeyt90O>&-fbG4@S?LX3P`8cq(3Q4)E6pxEQ)==8`KZ#-LWU
z^YePBXLDvCffe@)M*jcg`GD0sly8=STk&cvuJKF_xKGb7pZL==GLQ85t9PFN?^yi$
zN4gEq_g~dn+*>KOj;+TZJX}-AR|BSC!Lrvs9n1@N(`aMKrSyKRI76P<Bjf!L*7Z0a
zmG^L~wOv`MY<Ka-WOZTbsp%zH75kTpC@Qcs0e}9R>Y1pI5Ya^`v?_$nRPf%59MHAR
z-W4koH%7gGgpzCAZau@MI67OoSlph(U#e`z){Ck6`9UYoc6IB~I$PV~UzuM=-s<5_
zb2Vjn2eS!f{}_#Z3)E%~pNKu~L2tse-Ld)U^k=K0ZQ;a-H;Y!PfL^C7(8!U&v86DK
z38z0xJZaMM0FIW*!ipOSdepIGPVb}=JU(AYwPsOvZ2E;=b5M8ug&sM))Kt<{9BIqV
zZHq|ByCoHGVM~h^jYl~1V$>cM*rt@cJ&^bagMm*(VsA~XX7@F`cC0(;yu0sUiyDTu
zr5OX_ki~gU)&;gt^oL$+4M-4Lu2$jAPtYb|K8)^cAf>RzX@#%9H@9-zno?uWC+O{4
zJ+x+K!f)^FN!Wkk%XH*e@}A7qFQ0Yv`h}sFlg^*u(D~n#IQZsz=^gBw`pup9%J%pb
z_q2kQs>z9~&PRmrv4rW^M%*1b-XHCKfBziqmbX(3w`$3`OwOksUv!SO%5N%=i+OW)
z%noxha3LLRJqeq)gMG_+Z~EPoXRDwXthrZe;g%>FauGcg*{aO{td2=!I+;fGGuWRT
z<0+3Gl~Hbq8zTl&DZxI}x%h&FqZPK+WsCd>XP(5|QR`hE{9P0-p=p!YEK!$Yat|l$
z>LG@X6cQMF7Dr_%JQI4esmikcWsCfQOm=RP_u&AT=pGpREV|MOwdub%4_j-T#5EmP
zNv$Egc5d7KER<LM?KdA6!k44tX5#?xa3*(KXS%x+mbtLMCuWy&F)lJKemKYr1$rLD
zF*R&_-_(O99ju7OWZUq{%|>^f#%&xB?tKo4!|4+|0)Jcl7zZpUBe4HJ7|*xDqR<FS
z3|LloNU2{hP?Zu^`&Qo9srnA=%tIETE`q;dnxs1Voa*s|n1cDfa}SIp#cc+#ov^Pp
z0M#*CLt%g;%|2yjFXMs)!6T3BOV|EpiZEPFgJpW%qy}r&7{UmQgs5hylf~9J<T1>^
z;`p!yW{*QAlpF9E<mGuE8+gfiovd|<^lJU3{x*kP@m}@Gk=asVB+hvArHoTe;{vn|
zDBM`DkVRW4Z|U_^*nEgK5a-GkDX)8v)9^R^VE(fffrfgV-cL5sahDiQFtb7yS)rA&
zo!tm9uHd~Mg0_OBdAjYA#-phF_eW7r?HzUu9|+dqtK_>$<?lXiO;1B1J7Z~5TSp|D
zd{vam*q>-PO+v9#z`!0%YINvSNz*@Mg}A!u!kvcI)yhZwzXX5@#&2WAXg_0wAppu~
z{3V}?T8eB;Y(y?aQL!t$!Z-V!#B#AG{hn9kwPH6J<Hk@h#tUTM-nd?0!Vw9X?#6(9
z2<1S@MVX;ULV)9fL{~6e3t$w##^+kqzn9+iJN7%=QQUV}I=jRhB@2+Dyi3?X?0Xsx
z+krCnyus$%@I;6(Ai4#CqKrpD_6Pb=y{@iK*P(V@T?gv_;dialqEJ}ML1aT<<k;!<
zF#Ouex&?)J#)y1`fiIk!D%aw}?p5eIN?InU#ketc5ClezTEQ~X(Q^S9H|ckJ^J>9U
z0U!(H?)a%lsR7eB{TKyK=qX!i;w8Tjl>!GqG9|J*yc4=zRRK!GS^z{xen*TOOennM
z7pYFHe+1)Otvl~+hi)rTL`<%T)E=UUD<bv~t4|i6bbsc5)UH78gtyH<P?dteNZB3A
zt<|%JiK73Yg{-4(Xp*RQ+J3buu^%`gkO*ZaW1_5Vf)P|}AtY1eE#?-v>&h!CSGilW
z_@N@t`;ad!Dyl){W9maPzyGBcd#$@RsH_Ym`TR|N<PYwf`40i<pP73PaHnH+g0kDv
zz<j^BoNjqasyvO?oOPYqtT@YR+~Fg=zM~{v{d3Ct@^y(4IGPKQ4MaT&;zmWBz+z9<
z2L|}z3*-FuTk?VESvPqX=_eDzyCR|@ECT{<f_|&gDV?dZN^8FWTWrwlMUcp|7>cpt
zyZaq~&8R!=snn=bVSJg><l&a{!%m@`j`Gc4F8l9_11Vhoa)U}bm;;@i4u+!wux&<J
z(5Z7zy1s*4yAP;Eo2J}XR@m%obM=#4prfMUpn$qlH3hI#4+`vt1elU$ndpN9bBOcU
zke+Ya4i4-_#W2nHQ9Xx3XnBWUkOqD&DU91wV)Du=3|xxx^gwtv_j32Gc(P^7lRUaT
z_s3l!>wa|D>4|55?cr@j83+dOK`jj|3_jPfph8dqf>Xl-h1#8oJDnC7o*E2IKmyS0
zJ?2e4_AmOHA=VmS_UAKF`h(9@3~NA_0>`4_w(7a~M9qFJ#s8Avl0QYeUxQDaTisT?
z$kDJldgaRK&CefKyeGZ&p{hhb3?#ZK7Z^8t>=NEUFxHoba-b>&tl{C`8gmsCEN>PC
zQ3O&AcF@(pT>(-t!<|tW)|&;|sNf!TnH+p)>Se`>#w2X9rM_MSi5#^QW0M*)dyFv3
z0k}lT$_g>>YkxM@*NN|I{Q1osRjhSM6E3tWW7VgHfLy0U_pP($k@ob}(xkK#&;&$*
zmJ@SF2h%a=U^td6{yvyN2QwF2q^r}CLJ>O!`r;+u2;L^6_m4iM%F1W!y?*A5nwslS
zIO<#YwXTIfkLpkWoo9vj?^NMHI9lon9MypUCr4Z1omiXSQPS#BoUbvChzNNv0GaTB
zd=NWNK-7Z<M|B?vA|f&~EyO;nlRg2dDk@aa1ijgQM~mPw{=LUv^=whA1J5zS6nlyw
ze4~3Kq{n4VFebeAD9j-tW`*m*fBWic{${4$S*@(z!2ywvS^H<=<9)^lTcsXxxCnAZ
zh}j&{{u@8mSG23kyE)>Cz=Sxm(z14?RIK0im;P)}yL8x2M~lfsRVR2naAgJ?SfWc7
zCaas4<#_IZCh&gv38;ugZc;O*q+4<~yJ<C`PrbBMUAZNf);J!a%n1w2DHoA1o{Ai4
z{Sqbr1#a!Ij2Yt9%DYYoMm3{AL}lsUY|24lQ9+CiW4XCU_@K#XmPSvX^ouMErKsrw
zsizl}DPOp12guiC?9(aVp0D1~l+)~pOh;)mF@)G$`p5hx85_UqxkdI+vBV14Ay@jg
zC7m!qNc4C3!?2=6{A&UQ*UMA0*FRB<cW@KAI})!i#MyQMiRvvR3cyM@o*jQ_*NiD9
zrfE~crX~z?YDVL|jO^oX-IPlWXTc^4&J4apkv<!9qNJ_e`v=4-ibJ?>V1IXOVK^DB
zYOTc3U4Kzf{1rz>w_E)qx85d`Z_`BQBlEn-W|KH5Dsgay+7)_xG&#Jmb&H@ked+6p
zR&rNr_LE7aB(GM5yT4d=honFBE~?cFC&9{|md>m~`yk5^3)BnbI3IM?aci(HBpT&F
z4Zzt(hv-4qRv;uvShDckso)yfHe8s|>M2vT<7mDZ`0Dd_>fFK-p-5!$rQuZVGEb{9
z47Y7=W7qn_b!*@HGc?HuR4oP$Qv+!jTtka$Fj>R&f4dfbmpsV&E!Sw@LEdTCc$U6{
z^ENRdlDQ0YJ(JUPpqa6RlC$f@hmS91FFk&E^A~bwrqc}N0HzIG(Z&s3+%ESNaT|CB
z9>Cpd{#Pa%ta~^yVa@??_m1{;E~FA+B6VTs>)}rM?)R|$-3M@Apu=sr96Zg%<GF$g
z9&N;Wu;_ad_t4vqC9UpGB71bQE|HT~b4X<Su*_?La#4?y+WieEFPxl9%(zIoz)LpV
zUjdR2Z~fX6nx;6}1ZzDIWUUF=oM&RZKQNx+TDf-E9;!yV!Q8pNy&7N<cC{V5={Osj
z#A>4r0JuV3I0dR$A~y3l9!07g_e-JyhXdM^z_dV&mF&mWr#A?kEx+~{?Do#g<z;q%
zHMzz9naEsHssWV$*|#Srx_NHDX#`x$_#Bh`p79^-@buXJ>n;@bO8tXAP-@5hi&)f7
zaO;2pmzI&fLE5ox+p}y0+1q+(O~z52A>E_;OdHqgGl`a+@Y+J%hWwh!t0VhZcX+I?
zI<tVcS|J<%ihcp<8GC^jdb)`sB5l>#gh>{NXhZ}li;KnPtfDKjcReD8@Qtt=E3o=t
z1x(~?0VV>cZoz9LQvgxGh+u)z{dDZD224^RVJUuWcbErHVT}0r;PtkCSN}u5{|MVt
z*VBz<Hdh}$1b5kB*ZRc5u>~rMtG(^DZ}-#dlUg_qC_pL-j``k-KWowy@)8lcjrju1
z;*^c-g~B(K>(JuLlR;fW*M@Q&>00E(Rscr$6&ewQF0pg3mnJ63I+r=odotz#Pe8E0
z9SW<vMt?>f74EH1zi8I|@ka9hwRBMzherA{48gUmpdA*VVmRDnF$fLcGIu7#XuS41
z#oSlwBGEWNVs@g4c=_A#V5GI=FEUy~gn`HS%ii1*l{utr)2}Teg+3&&C40_pO(qqs
z)wd~!qB48Tft<Kx<zN7`7br^T^Twk!lexB*t#$Z*C;Rj?v-tpFiaIZ2el-zZTJfr0
z)N7YJ4?n?}fWn?cdWm)f7`Z#eL5A@CYGSo@*$bhWCkj|===b+92JUpGybTW&*`$RR
z7*`jRJnud!Yw4V2Hi=Es;#zg@#}S{%phW4}?916f(x9X@knoM3515Q`V^W)4VYg`r
zb4(MSxyMLB8T0fD`bDLkU<IF)%W;B8$8tR<Omiq(!+;W;t$Yfug8^V{gR*I$>;nHn
z*+7%7jM5O+(bI5DaO_~{2rHwO>DUAy3Mm_~=C#q?4gUsm665-qopLOg*{9sBjRi$1
z3fvKV3#ZsLL=h1Rm;A?WPKGRgjCy<)FqQ5jyX*pT)f_{hp1%aWY6XA$r|)OdcE$$O
z`Lh}4&Z9OkwliXN&b>=oZ~870EWF`dw85#1NeB?|3_L+80NFig>zxA(1NSxXwVS)S
zef!AQ`|9`DiM4?QToz=zD-mi3qY8vswE0)-6Bk^gG2fmQ6o~WEmdzPRn>CNtEBRgf
z;6Tf!N&+wjR>o!8n#T)gewP)#KK+DyUaa=$%wNPgq6(K&_=yyxUR`;ZFNyp_A?(5V
z3ggBILDL8u;Gc8n<@>6`bV0JbbG-(yyT`tza=+nZ)Q%oyWG%G%P<@y}PysdgFATUz
z=*B(5^4~&F@GZe-0&fsVGloKE+yi5F)n*670ivr#McRrZ>$NVjxSq&hbnrspTBBE$
z4zW=V$bq^B;8+xbXh|0VtDh4R1G8TX?wtza#Hm1XZ(p%R`7=Iu57)}&9$tBioSX}2
zbHzW0iS-@vWzT44l_k!l&?w6jvsukE*v=0fmYL&UN-6cM2dZQ0jaADlzZi5Ih=xHB
zRB8>kgOawHg6TpF_!bGzn6mk#D&M)K?FLQyU(|8?@%hFG4?iGA&MEE5@d*|%^je(o
zIgbpzB(}4;i7z2C?|H@{0n2l`XQ`khfU=;}*u)YTeUQ^dJ9dIU=m$T%=nDe=V>@F~
zN#B*>W@2A9+)c#)lTGv>WRt=~QQxJKVs>5+BV`jjh}r+B;_o)R#LmP4Le8O~@_A~i
zY|#?#D~I`nC-^u0f0VvjTy_cbH}(&;@O>?zB?tRA=2An$t4)8@y}>^r@By?nLlaPP
zvtt-=vRRpe?+j1}bZB0CsyuHafnnGMk-q<arf173J_J8tDUwqU=K5&O!iIkKL~D_h
z?M4q>gw6t_Emk@rrq^AF)UJ~hZTiRvUt*l0bV!!K&%`>1WS#axWZm?8mBeXPnreR9
ztELe#S#H0>`0H!$g}-|^4PSd5f7tDp1^!|xH)SrQtQ>nw8eK_Qm>IT-_4X})vSU`6
z1fk4qfB)5PKHO0Y5`btOnoSYLW70&;V_rbvAORI!vpMBD!A9S@WOS51k2n8(?r@?;
zId`A{i)-=Z8F(00<vtFrmSbUY_T{57zP-LEdqtraIhWsv289JlL1!@*&{KH^ODN02
zsikrDRkAQqf}R#GORJnpU%<#hQ;-&gf%w}r>Qz`_M~Oo*==^;^!DdBCIVj9)2p?K8
z-z9MM-CHCL04zxuM#We+eVpD8DcxhM`^*YVR+zDD1`^(tTo5egEcYZ{u*RT9uN7Dk
zYAJF~#2n<($braJI59q|XZ}DGB*v|Z{0~!?##cq*&6MyAwDw;~ugNxg1Z}jnY>2_L
zs4YCk(86eGTGr#Nqf$lFM#<8nVvjWGFWW*J_&EdoIiD%eH*7hN*Gz9h?o@&5W>s;G
zI9G9uopKR=&~i=-uRIy0zM+3{h^sy%U22_qz#J>#e#i9QfWUD8wB3S4(M(OPsxT~f
zKy4dO;2P~5us@xr8e$kRk|KC~rHd=#_T;v!-m^#p>b)v9x5xP;T~{6pD3Qc4o`@I~
z`}zU?7uTw<W21<o&m;`-w|t*=&~D#{7?<G|YrM=rIZ%u-Rf}4;WJ_P{qR44fGvvG9
znAitfrlQFUXsIj-V1-J2(F;|spx`?!`j0wX|FP;~J3NmfJ|$*J<V#!{fg-C~6G+i`
z`)6=J8tXgologdw?+c=B9&UAsNNrbpPgdjDM2T+bbB$DvCWJ+f;dRmIr=vLmfHhrZ
z4$p~6P}p+AYei&hLd-gs+H9#0ummt3*@uSgj~H`N;Kf)4>!^b<*TFc-QeaDv#ddo{
z!9^NF-DtuO_`N<-vLX=%#IPWTL=TPZl7ae~T4`Z(Ex)h-<|fy4*<JKPeIYpl&9i2P
zRRD%L?tYpo)B5>oGexA7i45!wNGmc9n$w#Ja)cj_Y4&4WVcVTWCk!rs(rW9sqo-QB
z>kzRtfDL4@Zz*F&(P2~z%I{)VNbZ&PJ$E;>cdY}g+<3skO7=d?N`&M>aL*K$pd1H%
z2*}TgM?#_;tygNO-2nN3w&&g56{#wdWg8Jg%Brd#MA15;8h>hJ<tQQ1s+B4zE=@h^
zc|V>^lMF9MT3no;e9T=DuLspPRY_1~Sv4iTN<5;ppr!)>Q$eP3GVm!F)dvS~N)h^v
zKp($aCTVUoKp6M1?Wm3=u<)|SG{pZHHzw6n^#~)Z`CD;T6{vB+HHj+uH=A?*O3R<?
zh$7;CfJ2wwo-%)UdM&w)TswU@pE1(8OBfue;>S}D;EZ{bqGko}oJBcj!aF;|&*+=H
z)#{W7Hk=9f*+=v>^t}Y%%_hg%wq46(4`Kx6zg@nKO!dk5fD=KBz<t;*J{5WU@^9q=
z%pg1O+P1cnlS%X?A41=R(2l|KtKoM}yI>~tNy4tvcfzmA;XTxlH!UBM=%Dg~MPGfO
zqLcKT@&2D*Sl~F~-%woTI^g`8j<l^td+mqs&&1iS+UdiUypt4SVHG?b9DFwtHo8>Q
zE?zpy&tTaZ3ico3EJ*~+X(<tZPzf^ZhPZSWxt-)n4+BW*xDu>KA>+^1F`_+DjJmUU
zGR1+Zr}DZ$F$j=cND#0ve$0@AIx8X6<^~G#6j{>baw;FVv|!HjuT8Wd{az+19br^t
zpv{4LI3Nro@>W-8rIQpE1UdtCSRDY<Yei=`{;D5n9cb3S*!sQVib{8jrB0MCd~xn#
z(8>>rZ9y~7zF71<7KOgWU})j5<HIj3?hex>T1S2<tA9>?A0a^8&)Dye%l>qjP<FFu
z#?f0P672PW;8q6PiuPM#nJc(;a{0eFt+zVk1CEtRN^VV?aTFj<nc2B)EBP)RRT4VK
zxPw(&YOB@f?=s6<_Pss1>TQG6mJw>R7o^*SF6rPiVH|FBskm)%{ItWxp({~&h;k$&
z+ow`~Ci`SFkmZOxRAmMA?UYO4p2Qxv%Vnily_35KfW?#8MNZQNuoeKx-r1;2u(WUP
z+^w1L6EhTJ|Hc*r8QtyKS2k~+2ogay{EYC=o9%GGj!3(utdES{!AMej(LZ9PHX{B8
zH~$xi;svU6U&>9gLQo68L6SdRQprtR!dl#k{myGe7vV-g$Tjx2v)TjWWUmQvfveji
zA5)}qlS00ngWf5Wt*PUIBrf^4DLhx+A{W^*5$tW)VGn@X1nst&=33t)$RWn)b<5$Y
zj#}dG==}c%DMkC6egz))&OQ6qCth{y#%I3P-tWKfIv)Ia??4sr^Y-lA-p3xlJAX^>
z)vKohC;Ik9f5NTCe~PXxj-kv|NdQ%##N<3@<!~=3t}7KUaC2BLg-gucA%?qND-gRB
zKp{v$qC8Q6<Okp4)0ZFn(D}E5Gx_7sCEe4w7^j&b!XK4(0Svp~^ao4-nyVAXJPimP
zv%{@a2XzvjNGnX{cf(^ag*yg-&7x3-@3Kb0m^Fm}knj1pzjij`$JU%t5WOWT3AXdu
z+#j?HYO%>y<M_@xOY;%7cK*ukUu0nD^kovF6UoYsMKvSI)d&m9fOaAe$6%_#XPAHy
zUVgHMW_`8#sU^x%ryi5!c2~LzMi}P>N)y^T5jqAbfUK$zejx!`#S;=iNc38J5eRJ<
z&F~T-HZODj`hIetIhFC_H$xK)$5gTgxAt&*^oF$%gs0hTQx38Ts5$I8h<&fOZBrBV
zSENb#=*=uRCa$zJZWpEp^YqHbw;X=-YwUo1pUtSQLd?@w&q{cj3XZlU8CImz7#B{Q
z(3AAJSp+w1Fd8+SYkeM_PBjVqAXRcQTi#Ck);2R8>Sk8+p1n?%(@*AL<vR@|%dHy*
zvaxqW4O_Pu3}a~IU<Q$=B_~egfr=Z72oiVr?%m^HIZ{Zgsd@Xhh3V8=Tm7UkfRO`_
zJSQ&DdJ0N2mH7Nf)IXsuR1|drPP4Z5dzIh&`IVTHF_+H1*Cu^Z>pWS@u0G1x1UM4z
zvSte7G0@saP1eP@M#Hw-Al64m-7W0dVOiIN)qECdR0PP)298f`$Az(2B7nuQ>l$Tr
zV-orm$|1`rO0%D$pIKLG{gX)la68liqCSN~S<TTaF5D$43)csHgUY(1)qYiq-3~e2
zkLWlDYP~v7rOKm=BZnkzFkwe1zfS~wue(QFmliAy5fsyj3VJwVP)|sX=_x3>p@P`|
zd(q3vN3kSjOb7PiNSAhUifU5eBNS8@_Aghir5n8%(^JX4O8!_`*%HB$G8xw2)&Mzm
zI)gOQvPQf}*`Sm$DuGeu!Ye3&tr$e0S%OAn<gyCYT6LZvMbL+ojG!~#ic-zXUUE7d
z3_saYe5VU8zVcKe7zm{&7bj=O?`Yc@$C2T<N+;1FEUuJ6ItQ)tWz{G4g4uk$J;ewX
zLhR}7L#45NRp>b<Ox~Z(hoj!pg@}O9=I5VO079tW=1MrS{z!rg_#}E#J-m@fz8Bw<
z>Zvw8-X2tD<zzM5O>m=qeOq!Lk{S3*6eT|dsA7W0fj~Z8PyOYn;zThN1;fG|6*B_A
zMIO0@-rSJ~B}mPqzjSsu84eEzy}8`A-1J8@t`odDqrk(P3tWI;cQd1z>;z-c?@oG0
zXEUP-*u$KolyWD-33@B8>$NB^njCoZ3}_@nMz@mzl8UU?Al4$9n%}kfoqJXy8=B&B
zF{i@c@@KkzUxzi<VI_{NE1r&LiO<kLEu|K<;O~4KU0k4Cs?n=jXP6%YRW<sCHtZX_
zz0QWngg4l>d)?)azn)_=L+XcS(t;g(BA%)o8Uko36}z>rFRetQBuTU?HS5y)>b7F3
zgy5YS7Gr2xv_V%}r|qn5uvUFEJ`jsB{jkp^=mvJ~9%y9?B*W&SLV+2<W;mg;t)T^r
zz4^xhfo5;Ezi|D7+SmD(B!z&Ln!jpXbLP(rpuWqCELLXGi&ZN{kSZ%%>2f2gZqY<l
zr4kaI9(nnj1mSEDH=$dsqfT8lw87&s>-PoJx(SJ~VSQ8u6l-1OQaD=41fzVeY65P0
zo<@(Z)M5q9w(Ngl9Pu&n8lXVfG?=kp_(phLVE~;fJIc2G4-f7M73~=#R!I&nWXUBV
zxO+KZS`v*&gN31E4?@Ow+@w-bv;eVK?R4GJ&^tWMR80eiK+GdEP2M8+Y1gL&=A{G#
zL;`kucuJ^@Jfz5Kb8}@vqK(6uRaGjI%3((XURzE9(6?5LZvZAz4iQ(V7q$$FNd!ID
zr^K=2Qg)StdO7RQvCplKN$eb<z$KWjIuv!J^HtuuI%SV13s+79?Q{1dIN>}r3UR;H
z^4*Mq)7%DMLmbT8RTL<10bQOH9rdAo*b8QR_v}vdkuVVc9l<B<!UMp5KTquYe7lwd
zpCS5bC9|2yPa01Zr^9BX@IaI}^4i(#vr>~ZC_4;^OEO90%02b(DBBhVZMv+cF7F`q
z;#a)1y{K;$yVo*_mB8D+iMpIE4oeB~mm9@8Z!?YhR83S9yf-?~bZ?m`6QV9jvPxau
zLbwMyk0WK_obg|;)cj?PTOm!wGf&G;Gx4d?`5X(AunlG50f(CAS<}=kpz2uQY1;6G
z4bRzd#T10^_j)4ucV+H-G`8YG(TI7ZHwkg^Jv-`)q5bMo*`mY<N`#3jQ#a_vPE8b*
zQOZEzW%e4Z+l8iOftI!6a<+UVp|M8j*cDf<g0m+4stoTc&I1ZYcz7VVRxa)1m63?x
zVZ`dfGh5`8>DloW-w7<g{yMmQyYca&^Bz9=^6^D^-tz1uT9Q1wDlHn9d*@}#8)=Q%
zJ;46!f;9G`H%atj-`VFc9acmP?_I_bgY+};v?|p|j?^>)e&+-g#Ha-W0}jRy_}Ta$
z7>=2~Ad2dsA`FO0s7ZA+Ukgc5zfxZ~#jOx-W5`t!6PecssEH-uNqHYjF)2+yDG%Q)
z`#Z1WSDr=NuN{}yXTmvInG`MVU*xgN=<k<;Yk3e_MOlAMyMAA@m>onGjA@e?op%_G
z@ENsVbKMszoT$*mvLc-73o)cz-Jyb1-mHXDJpTgMZ0A7$@R+<sE+E}k5xb!Y=%u{H
z$$$NKSFXTiY6phP13h|NGEUz!@GY2H*%Ky1AoE3aVLf|!VaP<qj2VhVWEgL6PgtEO
zABm8K^<-x&Vgu{T^9G*4g0Mvj)*~gBzF<o+1yz9x6Co;4l;W)m=vd`LR@fqX+n=A?
zaAO#A)z&+Z$}rTbE$be!6n{=H__Nw4b@xr5wA~d4C=KSwE{xT)Y;4Ix5%%la++5ce
zSkbG-u?A1$-xZPO20*%iAtZA1->ROC*OojG;SRcTt#zkyqKAvGm3!v?RVgzyQi|uQ
zQ+>F(hi>f2#~E_;-yrSS$?G36e-IY@aYNd!exOPfYJ480QWfK=fw$8*&73r$zuG2<
zFc7xm52#&KP~Xo8Y7EL)NIVg^4Cd97JaryZ6{NCIIs5t88+mUQEpW|ObA^9{YpkCP
zYecm7gEi`u5ETGi+(ipukllj0m@|KRxAp=6uT5->G!#cmeY6MK>JJTyqftR?#@C6W
ztXk?tWFR=0mIwN44vl(WiC2NvgSBV@(tyC2eUYsc+4&MDs|~!fTW<Zr+vXGd2Gt9&
z5~$CDk{#n7qQyM&0>h`w=yw5m2Yj@WX#YbJ6}dciWmXPd({&dN4?0@yz2W|Vao)(3
z;(&S)X$(~5*Z)*)O(hmGAdibSUc?hM1w2SZi@s7F$9SWhP@-b|wilV!MWGZ|hGF-2
zN<nLWMUrVD4pPH;vs79Vb)GXEj*}0BprXZ=>Yuh;I<IB^@yr>{FHbTr#Nq=UOv~J7
z4R+Oj`oJWr%F9!!1%Y=sy>D4fkXzP%M*UCkcA*1u3HO4ZG-jQ4b?WZb+J;|dM}i?>
zACH1eK=X5yE{jEVWP6z~D#)lJcN0dD_Ox9$KzVdZ91`rX427PXmk?%)9E&tjf!@Dv
z%L&x%oWVb&U=3F!+QISIm~9YeOT`AiDtubuYsU{8((xNh=lg_<3{s2nXmwg2Dp-34
z9RAL_&lLeQ?ztrqh!!@z=GvjjaxW0&&Z`{IL)<SGY$Y_&D%WZq5ZZ1>Lj~2(MOfjM
z0ArB9Rw~hENwu&fhsx;^;b__79jb~wMJ&-=tzVRwJ?n%+JS<*F!vUtk9p1zgvk2Il
z)jkxv6z6&c?%=+#Fo$p#ptvY#$eb5MD%T1O?21E%;n5i=2g-d(Ak^ArC4E5-x~BAk
zu)=E$c!?Y|v@7<0`V_1Hmt3woWhEIz$P@ITz3U(^9sA%dd&AnbJa4d>b<~&E-2j2@
z_NtdjUHepcs>}vH-!MSN@^T~Ot8DZ&LhG%NF>i;!{iFW4E&iw8Y*`=7mN%z>J8;>x
zHy}p&284CK&XCz4IVl+Xr8TZ3u!XT$o2QMUOP#^9lgn$)q?c>WHZrfVC5axvfoKgi
zvmB+V<yug5Qo!+zg;}e7S#3kDEZ^C?aPQuvfM}@T-^r*=8gmJ?9L){>KXNVO4u<?A
z>F$MV&h@PI1{b2r|47E%xt4)4j3#-EX0RcTC%g@Pa0e(pZx_$PpIKKXJ%4^QvhECi
zmR-E=EPgh$V{uv7haXNAh#<~$FWv#o#-Cl`XW<(yk}Hx5>9)?JLf$h+XbCV`{MHtK
ztOLEuJH4&>{-huR_UutX%GSizhI+T1mhq&@F=il`^_nQn?rx~}+^U3BMW}PA6Q)pq
z0K-BU!w&jfxrZ6RJ<n-=;!fG)Ez7n%7In_QO*(TZk5tX0uguLAwTNn{3d;3T;Z*C^
zwVVR<-!J1-VD!o0Or6;8ceD9T^f0|!)H_JU(pL&cL}fq-)1{Us(jtG0{1wM1_GE|8
zuxD<{Rrjw5KdaxN56Y)aMIiLT=xCuH5ivz&B64<lqG{{O5U5!mSS{sq+76ie%_ybY
z4#d%?9GCLg)TYWY?nOZLCr^#x&P8k+6O4iPPe4S$cLtKmsv;Q<KHCTMT^L`80QZZ7
z-`0JXFt@pfz%absS5LgKM+=O%`!D}ArTOV{|7~3~v?Ao4F)II^Ko*oSf4a3a?a~*`
zccDaE=9?`*gW|6~U~1j{C&-hL>Lq)K^+ivMN`3Y$speM2T$*+I&X0en8<=saRz72M
z*6*A>CsM17XzG1^1175J(4+Q-j?Y8C;;Jq5NaQu<wSu%c>A{pR#7;yOzUpjlSUFO6
zg^6J1Oqp`jZr#QbDv~l0Gpl&tnG4~xT<`SYv^kzxoX=NSG2N`7AgLQmbZ+%__Z6lN
zSsr#aSI8{5Jmc$YOk^be&Cc?&A@&0so9`)qDSEkehCyK$nn7e^o6j{og+4ZJn)Z@J
zy@+Z&fxAd0y*zAc{0Mpa@S`gm0c9}Hz+1Yxw$mn#qIZ&QGdYYv@F^c24K($yrXtLR
z%u(lZ$Q99;`;IJcucocc0<65HWHq(Z9rDBjpf5fVBYH-D_2rkZ<*(iTgZu}i+XC(M
zrgreAaHcO2fFN*dItAspDb)N_zykM+$O0n?cITh^6qyd=#$2!3LdA(Z6n+$ZJHB~x
zNtSSX7q<&lnB{575UoQ+*kNxh0M=YXdy)v$I*}!ViUlg&qs|>gfrp{4wrmRKlh=HS
zJHNl;K*EmGryVfTab>OR4pN0Vww}dP%H;yp%WlR9(48nYJ1P+0i56luql;qs`g1!c
z6`NJ<-p6VpBa1lmT^Ki^y*N=EFM<W*9yy2oO*s)HVy?pvCR8U}CyJe`M<0)_hTDoB
zU26v#foDa2#0me(8HwN69xP!cZ^yWsdD>lmrtjTEzT(u~%ra2hUyEM8ha18{(cKxo
zi$-xo5U8iW((O>vEIyX$<mwZ63KYlrVYfIA9~amCs{dW6Rv*KxSri+Gw|%!9DAd(G
zzgM?w>dDou*>A6AQo;eN0KOa<_F><SkzYi7j%@>=+?Kueesxw6EA}Y!Sk^f~nOaM4
ztwCnSwQbS~?j(+7o6G$D@lez8859&TGzl>o2L>bF;BO~p7s87e8QM8xH0!ujkIr3`
zK*98)3Fux7wKiA@DW|Nz^JM1Dq$>DkcrBAlcX{1`!DG6v*Wq9D3nD}J-td!j5z_yH
z;{grQf}kKQ$eFNBGjNxMpC1EP2RNN8nO`#mjIWt1YqMYJZn~WfIBh1zX+|w<cQwWj
zP55RsZ0!yFEVKJc<|mZJ2_onw_a+1KDNefyA8H-yGDdr!z!^AF=X<cVmUKgLSM9uG
zGZ6Fhd<@adsEzTelGznx+iG#0Es=L~|4qUsI5MN`R3ka4f65nuaTB|UFmrmq3{5}-
z`NSUgrcG9g)tT-dV*UU$5!O=B7p@ohPqYsFU-=Cv^q-hl(!wWxP%jg>WUm>HD9$at
zhpEDUEse|hMt3;;O<6>=C{WjcRObE1Dw2PUU9<F@*~j!vVYk&s^rP9qk`jF4yeiDS
z(%i(kG<E92q*;HCpQ%d}0$=b84b2VsCocm6TXK18h>eXF(teeoEbQW4q7**vCh>8<
zc0!D_#MIKQl-DPo@{N;+o#*%DtO#ZC)p>|EMj}+nDlyL?^<v-gqiDXgz^1p4ydDUy
zgIEG|puD9`&}Pn~X(o+&z3pb(bt;d(j`-r{3nFb^A7O~W5}&-yTgbz9z3qk~Q=Hzu
z7u{mQPJ%U=L3_Gi<`Q9gjCf3Jq6)WuUU~nvO4EL$G*~a4v*lH4rteQV>;QJoPr}vp
zSI?ICC;X>Cp)G&@t1WZ%(%?iXxLmIy{#e1q4SV}-<?;}{J6PBfLjqSsoV&)z$_Z#o
z&6B!jE||;y6wBJd|M}@|?JOoN4ib?IXfe6!V+D7Y?%iqc(o2m!!1dXnO(b7<@aTU=
zaBfSpXdTLLoT8)--g}m?V34Zx7%w$PiyW=F!f&qwv&X%Om`M*d4D6&-qgArmvo&^Q
zIp<LnXad!)D}uZzhettRz%_sqx|M8G1rYbgP?>W}8W6ngHeP~mK@roL0VltH0lz4j
zCY3lDO{f4=6QjDN)9x_sSsVqO(CvovHS)v-e&@3{-7*H}WGi+3s+OgC|L>bhFumyD
z0O_sIOATPA6HzU3J8x~uKJO<kVEXHqwy64b=|8Ao>FKBg=+}OavlPEIgp`2)HQl_}
z20U-x$p44Hd`Cfn2rGK{un5;Zj>8qzJw@2vba2!-sNzKUm#i8Tj8Yvi{0mCdYAMG0
z(l^Yq9s9GfMF{}IPcI6VU9Ge;23ZqBqfY{!xX%P@5Y&gZOx@{^#I|`GAo|xy{GASg
zUEY3T1qESTF2a~oZEkMn^^JFG<y!7cL*}jj|2*WADrNd491r9M9@F?_ev}Ei>%)d@
z##8VqcSCHz2|W+Quzgm~E9lT1bJx-_XIbsT)@rF)!g`CDs5ymM)Rj~;wIQeE#RB<)
z7bOkY9Dz*VmHNjsoya{MoW!+})3IkwwH#$1y`)%WEWgT}tS<jljP=EC_~bNG!B-&a
zfgIn7YU9XKA+|NIr|#bT=ojDAWVumJ4BVsHxS(){zJow_^YcU}bd(>OTd+~HClD|>
z|KLOzHGuylX>>w9(btEspPK5n*(Mm}73@pY-9O&drPtM)Odul>xfHHo1;8P{)FaCg
zMW{-22aC22S%H{lENj`HCtOs$qLDFz%!hY#l4qN2!pc2>Ad87$BGy}5m1eA3p25GT
zNJ4?=BOP3+fF>9YlgY9AxF;Y*B{cD3r4>9i`A`YOKLpVS{eH(2c4In`4UHX0Oeb30
z#JA#XeY<%h?I~@ex&3Y5Y6x2GpW;^EB|fauHZ~E-&|^pkK;QTSRE!RQwAVgdf((bM
zdd8}D&1C#hXH?I~<$(s10=(KeNB#;T9<-ly9UJpb+3UgpyImVG{Q1Sfa5zAH_j&U`
ztU3U=3#1!{v5;fxtxz62A7&W;SV7gO=85;X%+%RK6{LJ4hEyd?9m|q|@E{Q{_`?(B
z=ZO|9Xm3{@9x;!kr;lfb-JtE*F{?eEo<3p*37Ma7i3(Dm=NDDYt&Tq#mDS`WjlZ2x
zQ)$$!-<%|F{I*e<r@IKYD3g~3daS#NAkDZf<#xqL*ZPCdX%gj3Y2bb?I{>vKEMvCc
z*C5Likem{b8hAKrEB!$Jdg<+q)QsDy?>p-{1IKg61G&dT>eD|qa2(*#yLo#KZ0%6b
zFZqM`>iq|{EC%8xIw~p9<0}TYZXy*3-r=S}H^$tOBpkYypF?y{xQXv_Wt6qUI9c2a
zQ!u5MQ)%zBeI3~Z<`(@Y0C40p1Eral7Vb!fYLwoG4;zSGL^Z<G!I)q;-0<C0On1dv
z#b2ieRcfx5WLgbw&Yv<Qxi&dzwRBA_z~Mt)Uq-F=zQ1BysIYXc;!WwIK&DmS`D?B<
zC848U-(vAzeqMO&w_Xc)XHtu*lt1!CsEi`q0E}uod5Yb*PL4f`K8tN2P<AVHCefe~
zOHm*`1pGi#P_V{9k<O&pO(8TmpQgNV9eb*s@LPvOABGLnOB_JWbYITlHJekC-Oz;Q
z$F2zi7TQJW9boLDhHWO=<w8S_l9H%eVL`X6j81N^bNK}DZJb|{Xm){8#|vpiP;;Yx
zZp4T3LRm2Jho8f=-bL(jB-B*i`_{O(+VC@0kmsN~HoH3qRk7;*oo-wE0a`MzY;f}?
z_uzrd1B32O_0{3IY2CHl89KHIy$o^^pnxThiliFQXOyNxQ5qU3C=Y((2@2ga{-&pr
zGI!WkV=t%!<=kPdFNHBgpg=o?ULAW;maB*fCB_v>Nz%3`$m9L&_?PiDBm%B30V`w5
zPEt<F?*G=~@@r!ioP&-b=a#OwEfugaSio*Uz*2xfeIT%ffVs*9R1S)g{YcP3`#wb5
z!sLJa#V7wh3zIAGG>{CQ0xsC|)-5{_OuKBe@e&5@cu8By0k^RC-0&*M5@Kz!(_^Pa
z%)p2-Qi&nuTY;1+Gr}!y2%njBboGqI62oG@h_cp;iXe<s8NIflX@_0zYn$dC#<=H-
zY_`+rRxR8^T70B)4~G*YJ{c03enoyoHX<K+_7(`bL^x%O!>NQsEkMjZ!nN>|O+Iw2
zD@5L~3A6+BftB>gkJ~-G&3cM`UWvs4rYr)hk!;Nv*e!O$u=_~|5RAcN`FFQ(nP8o!
z_y+Byy-kaPE`Tns_A<&v&6Oy6WK<m;Vu%chvHEBG!lI~J21FMb69>-GgVbzLbR1m*
zH~=vcPqZxemrHkkqi4=2_4pa8soRyCwth}ajs`~eS-$LsCOi(DxdoK{#R0)7xtusL
zrMiL-u5l|;eI7*|7d-z)`42bE6QuRC?!!I$R&D-3Wa2tRLGP?pfYZB)*LhEgy!e}Y
z_Bc_!IlQ*24l7=#nU62S0keej(n6A9;DDZ_msukY3a>%xzJqi{zI=8w&%mSkyZ~3V
zi8X%o>?M~sqzvVL)E#BdqO)F(S!&L1ZwpN=+sR#oxW1@VHJ9z7(YRx$Uf)EWY9@D#
z)B#5v%srk6Urp~)b2d<t{`W;Sr?^}|wJj|uwLAQv!X8>GevU7>wpTG~78_lOlI}y4
zBmG*{>0b=;a2~h-W}&@V+C#FBS&8P<-_=p{VlI}JwiIDO`5-L_UcD~@Pstz|kmc1&
zA37y{UUma`Y}E4LeZW8zSd0SP)i~|fuDN;ctSqawU63eaaq)<Gq_GkIb@Rl8o3|hy
zW*Lf$2i-g>&pkM}b0_%r-1~vN6e}c^;r+}Lzc<&Ys4zA1hgd1tp<S|mZ`OQ>ECjr8
z{c@H&dGdS0CR5~ztz`6s7gBy&8`Sft_Nini7m+dF%6w;e)H%KcS-!t*<ADZ2M$V8J
zVw2K9lZYpijFh0he<Eje;h9z|8lMW9RDRUMXbQ`dMe(s4t<q5kh{Usbv<kzwxqM;z
z^|*!aNZ{{mB-lL8HSGju1|HOCVNrbB%7MiFoJ1M!2MBEWFEAE_2%M>}jeQDFml_i#
z`5sK@d&mQtz_DfCNv-L=-HK_72g%BRMZlho`*>@}9x>YC^U}L(OXTzDXQJv^GWoUF
zk5y*`WZsJ<!Vk3?XY!9Q@dZ!rLE5p8XKq0?qFQ7jbwW$@xC#Gd6<AZW`0gv#c{iCn
zOSq9|J!yzLoo`Y83=do-J7Be-pRcZX`aiTk4LC~?KVubR3dTtLE_e*qT<S8fnm)LO
zRgXD9J(4gMP>`$L7{g)D;)*#j9eLV<fUyJx(lz~~Runfn^`p%~gsdT<Fa0&+HF#p|
zK9s($9y?^?S-7<(261`CL~K_68T`>q+>}qVQ}0n(wETVAfm3_%0@|ZTGy%3glM_N6
zgn`98{*2B))clML3CIZ=Rd5*qDRed$LvWhl=ICE;(B2WB4rtgLemd21SWQicD6Y~k
zW6{1#BsQhi{2EC}#f#Y+o}F}h{uROE#HYKGuHRaeca@|H@H4CpKlNt;FR>nND3q>M
z1xgKTB2K<5$O$Y<rnS{<vP-kR-;sR5^2B$=$@*C<4TX}V@S~xLN9I$4xlR;)hHB`L
z?=3rx7aT~2GR0r?{G6(`p${jyq~h%M^mRH6iXU#IOLeI<4f=6m%%vRRt-oB*eEKAI
z+0VNc|8(jV?KZNR*)!Zolel}Tch()oB;N>D`A?e;i`v!ov7A#?x`^^;Nx+y+A9U_S
zRUd9S`D_DQj87#XUz=+wY|^EDi_VF+I3mI6x>G+bt{J#beMhqQi-8aJS8W{Gg#9dB
zhmB%4HICeEDB&r2rS(x&p&5BlKim)HJ-30j-y46#@4%$iQ9`u->K@4`@x>Y!)ZPxQ
zBql-WQ9!|CQw+yVPql`9@-74ljDoxs$@4C<Ztf=Co1umdL?$cTBcC4_%g}V;R5)64
z@**EKr>|MS62#`3mvPKqS6FaEg||?M^`>OhQe@fi@TVO`h6NO}d|nxNHZe|hE7-hs
zEArNj&Y-_1K8G8)cVCBVNX(lDrOYcS6*RPHkcy$69>L`f2+`E>+z}d@Hi+AWBNbQh
zS1fsC#M(YcLwk6q!qZ#9ms4b4S$L+-vA+Gjr=|=yY#Lo5IN7D>Xw1=RCiIegiNP-^
zn`_}JFkHva!W4QaurZlc@#$yInYrG2|D%b!Kh+UL%?k>hP7zU3*=xP`FVmmn>e;oN
z#=m?d$%>ci=%2|~`$=ljqAS?6$sO5ie`sQ_AbPFy33dx%pSVt4_VEXZpVdDjoCtev
z!H908AlHrnyJIS9L~%7gT~XKZZ?`f&>BYW^YCq+hB-dv-X5py7T5?6Cl#BKP_bu~U
zK6d%kgq07~fkkg9IkbwKKcxDzu^(<$h{!p=ZUD7f)>$A)`i%$hgBw}K0@dYwK=Uce
zhy)Sf)?)#@cONBCIFLIncR*-Tp(9-Y8D7BLs{6zYfzYA}S@SNYZLpZlDw1jh608Z#
zZ<ISOn+gdy7K2<Xw_l(@HE*A^!+*YJdcvF13XfBM{FIa$G1S%?gbIh}&vViVTNR8g
zX0z4JbJy2naYBt-&E^&h>1z|cdER{V2d|)@ufou$P$$dS=Q3pj6@rSnpHOrjiJz&`
z;ysWaI_<i>^TW?5Iw05ojY^g!Q2`tQp*2ELsK%ZzRbHqcQxB<0>XinPVI?6ho-zHx
zv!{=P5*}Vqpse|kl@iwdr1kfYjrIksV^lfKx3+lKuHsr>Lt(C!Mkbg`BFi-sMLAT5
z$U!AE?0)h`Qfuy#KW>^D2u3AeBQE1>ca4E>$(wm%@@cY|xB1SUF&614HEL|jlZR@C
z<6H^Ixr2^sr~RRbdVd!aXAb7N#eA#q>s{_2Ta{WB>%V*&6mOZgvW~IOqA2S<hVBn*
zZO~CKY$E?zCSMw&jSKXX_yy*2L!|u7XZ%ELW0vPbhLri3DUC}GQb^Rthw;!eDkwQF
zb-hTY-NitQUo?~l=9CCZGQ-a$VsU6U29FZUX^3#7>haLId1UORh+4WY+KuLVO?i=s
z$2abNMe`sYyPKW$7S6}JF%Y^H%A&J{N=Q^Ce9J7Emx$#g@CjiK<YzsgVcb715viiN
z1&#2clPpXg?t2wcfLm4eRQNk68%6uk5HhdfB@TaRcziXDz7e5CD!A|#DguY3_NpXr
z=#eNig4$WJ=+}Bm>7Kh3q(SUl5vH%r`^7^~&RCel4JU;MtFtepgyMZSotE)72?z8B
zL#%`^0S1S0W6)64OnR+IWWXc{$PwX1Md1<rU!@{k*q+5u=F+;P2o&8D?a{joV{jWV
zWVB&?|4l0r!l*}t2nd~3BcRq&5CjxsnujM6@&A=?0D(p#i3r+=9s_I$uMf?MQ97i@
z*aV?pg~CwQpvlfoH3>;Yqbp4{XZB2DQNg@=L4u%uHQd7I=MJs}SG`cCg#w8bH=3#F
zO9T+~l*f$8x0CvQn#YmqX(JO?EY-GepXx|CNkqaAuT9@ubuRkT40;qUNvuiN`G7_^
zu@#DW1rR1_lf#q+u0;yQyh4a|>7xAtm%y$Nq4%39v5L(Y(v#$MXL3ijcUJES29#%6
z9gfk+k^1^0pV4vuh|W0m=;hcb>~eHfdLaBr!GA8+>10{_c4BYC;kpBfwd~?OiM1f3
z<&d6@ox7C&b=ra3K`m>?APO|LHn`>dfwY^$Tv{OSZa{1lcz;l?I_6G)T;x9gpyFZ0
zF?Z49H21M@&|Nzi5w0)U)EMl<5=BI;oFa!3h8D+=bXXx*P{2`RQyFSmtfZZSXh1@n
ze4-!V=}KniKiB_!E$wDvm1k4<FDw-)*ei?P^t?He{_T`fVR->oFk^)7wULIUZS<m#
zXoRnYq`Ld5k$$7<Rhpe{3|;PnJ|}fu@H^HmIgIg<ue428>J@oDqM2PC(&?-urYze?
z!8gm(q4ce{O1$D~Z$k-mqi2r5NPTzF@G|UWtGH~W3UWN@#suik8-8&t`>ieMrbnRM
znur6BD_sqkn1Yg!mJmJ;6awFT$q0*bs$}OIimCF|Oq^4anJIDNW`5a>q|f9V0czK7
zj9!gNjWHLnO1e<`WnaNwCacirTgyF(<CCmzBemgpb@hx$O5$IH#IKDxdPah;O`-bT
zfocp!9k^R>3e6^M6(AwodGFYs6wu!hkNPEV=@9*`1eS|>vJp+nw@<Ydi0mGbd>bkP
zx$UF>tra>64!A$)1w~T>&7Deuz*ZzK(V^^WE`21dak`;=Ms%BqRHijFQa^lFi|C&K
zy?gr!_=hEQ#-)0+Qnm)WQ@LI&5(^WMx?8T{CWc#hu%;!WC)1nq7bI~PGeS1!1gMd-
zIfSt|iJQN`ByCNx{6<-}fwp>ybgxtmMfyzUK6Rh;8|bmVzzUP(NkZ(4HG==%nk?Os
zT>9+{S9f0fLWZ3*v?IDvPYo#bOSAy;K571LIq5zG^YR~qLgf>Rd4z`1lTDkdm;i#o
zYhl!7JnP84nLg?}NA%+(*!J#WjsF}v2Mwuejh?XR;4*ilurOUD$oXrkC+ZK3D{8Nd
z|B%hdV!SB`qH{%{Q2*{qr#OmBQ&3<ais**K&+mlP1~vWlXD1c-lO_KMfv-~?!e(j*
z(u!mN_oZ&_m#aI<Ux%TOB_c<~tb=L37PTC#r*McskLX&i=h&Dhw^s3~z>C7Oq0sYI
zl67WXt7qcg(xcV`2aM-SH~!YDTs7Ll>715A^lE+%&zea{;Q9EG`vX2clOUy+uVDFX
zY&}T?x=>#;rkxZiKx-SUz#OQ%PwljH)ROZuN-NUo9R?^XNlv$Tb>^k~Pwt0KX9097
z-j^5bN7^L&bx%0@(_PV7Oya86p1qRh+M%ixjWv4zveUDmh>%HQn9mDDy@vYa*-`)E
z%;y<+<cB%ek&f3S?j?bxnA$?k!wyH1&`CLjy$4@;A3Nmu`==g!Y8$*ZQk-Ckm+GKm
z1qFDY0DGO5@XPHw$#AGnlljge_3M`>(`RaG^11j)k~x^)i9ngD4$v`l!{wuYzm%kU
zUy*ghe?P5_bP#t16O=BSwJWfUO_4`P7Zl-s?0#HM+brHp-&%-RGX|mimXN`%e%0g+
zE7EFKEv9;(uCKiH`-K;=yy5BEv*+vY#vG2gM_Km0W8J>EtG}oH;{99cw+e6lL(hL?
z?X>c%IN$DL_p;066n4bnn7j4o&(@yC9VETD@cXUG`qSRj#j9%4R&1c{@1wrKnVWYg
zlJ!%8y7>(rcmIehS|E*h!?M6#vt;Q(m@9W}oA!NthwRF$qh9_!boocyOzpe%5O0N`
z*ii_6XRw)coiW-zv<=>gT=9wQB>OI7<;pANShm8(&Noikv8m_TRy6>ZXPlN@VPN@+
zPk;?w2nzix-OY-09TW9?P3dQHtkm%&{1hVSb)gM)4B*jU)cS`EUhf`ZeqX1qT7Ldw
zjb`bN-s?~|JNb&0_%AvDj$ShR4QhBF`Zb~+DD-=DSyfNTp=UH>AMiARU*LURnnNFG
zVd9<ud;lqqlb^YW@__z$$%3zd@PhY54NCUx=E8pW3Di(i$eyY~)3WM~dmOP{Tq~DC
z6cJ(US<<>BmdQ@qNA#Dpww;voPp;c5jyDDGsRD)f=d82&&&?L&RpSOyc)^VIba$8X
zA@!iMqcRY+6tx87#?%I`Dfkm#L|k7mBb;OySK-CkbNy}W=0v5h39R+QOL#6<U}a^;
zR_a5d2<)Y~#Lkk!?}QQ5Z%rwBVi&=%X-$(-b;jYgY50WNfJv=GcIEQ9hNMu=g0uXJ
zwtmi|vYm5zIa#R|qTBJ5R+yzQBKfDDE&ofY=TU6gGJoK0v<tKfT3<+X?e%LFS`tRt
z(keL7j^X|x<8~88#IdoARvsI(F@m0sByW+A!2=rvF~jf}3;17o4fKR@ys!Z5Kgf6z
zu~z&;7wplO(hH$8Db>n^YzLikeXjt<{G7;2bQ&p3&G1K7Aj#HJA~4EPRs_jvcJymO
zwxZHl?WT|2gmAa<(9*KgbY-A~htD6OnfkTpb@uIs=E#AOYBg&1$Gp&~PYr>$U%biw
zPTA~d4bx+V)yxh;2Q!J;qv6?XnZNUXgRc3Z4JGQ-?A8qg<P0xFEq0KStIubTT*@F8
zQG2s#;VFLNKYuez)D5BY_soc8;&9B^$d^fB<9~i07fN~wVDb=n5cc~-^vvE}P)O*l
zg$33AGiB)cBhCW*0ZqC(NAh!xyZo-$bW&Jq%Fn}b0b>-NkqdK;JPJ*ma1IJY+$_tc
zR2%CIO&<E%hf&Yks7RYFvQ|V&QRXBMy`iaYS7+BH7z~Yx3S3{qU2ixcm<>%p!|+J9
z*TN~(#*r<aQwzQD(O@{i`|io!Kt-aVvC#lP+p@>NQvlqP(jmSE#$U~5+n=?RdsDjh
zQr%QX{qYd_2;;caAl<uX<*tp}tH*1ZT`E;qG@{4v{naatl74#ZVlTIM?9vD&eQf~X
ztBTTOMpRUrPg3!*Q%17Y<!3FvBXa9$2AccyY4>(+dYW6*LWPOC6o=Rv%}kD7s3?hI
zB=2oWA?m1GIE#1iF~!F^9I^3f>0+<%H)7@3<rw~qK8>}0Yx~Gcy?*<Ghs&&LlKLxR
zqL;6*M9QvkeGUm2d+<d%UDW<&i~n~bn@x0Zt-<Mk@L<(~H=pZ2FSid2?7#Sy3v5wA
z7tbn?ctQZXTpcan&a5uMtZCzvpsUSO_7>`#le3Wc4WJ7DM~PeQrLg8&b27Wxa=4;5
z40CP|Q89TASy=uxL%Lj~4k12YjQOSX)FeG2iqr~m+_1G;rbL|=ph^jx^qhi8@|)|I
zv~~}>7_mSTY?;R8Y@@EG$+a&*A87K%H6vRFt%7ndSi0{rl@gfKDRMO6*I?Y3HTXye
zm69h8D@G5kJ-TK~9=&OG<@U?FVP%sN7-J2Ajhq!CQc|OmsM(mF+}TbCsf0*_g9N-&
zR)^|tL^)8kZK4q3MrXg(Xw^us600;;z0IK+mbZY`PsvYtDEjXIT^5{pl^9aK8uJDs
zhHTYw01qO%zvn6uMr5ndhG;)i`-8{jOCjnM){mkSFpPKasP&~fb{Vn*rCZu5+{*T)
ztc_>r6o)7IUH@j+Y~4**JOq6_Ihzr2!PIa?u8jF`MA3?zR$04z{Z#t)*`|^gda69R
zT#+^Ts8d-TAAt{WWcp@Tq*G}H()?Q<KY@IEKCquO`2bWAP_3XC7g5ho=2yn~7+12U
zS?(v_lG8zr;f9&*$7X>oC`XJ9@6d|>@}N+-cSj23rvj^jP@QNBW-~f?Exadb{ekRM
z>~Y8}D;{ooIy;-d4CEPjPodfM$bO<|<_WQ-5bkALjX}J`OgJ4dQL09gdm4U~CTTRJ
zNxTz3NVKwDjNYkz>0%n?OuvPSY47kc70rrB0>DnwpkRibdVRH4JI0{5>c21D`)^uB
zzw3T}AN&oR;Db_r8MU;3b&eOuBRu-*3(<8^Ks9A_gIF@{5#8ZN0BZ7ZOm_<7#%z!z
zJ2kP*>)gRM-^)`@=`=~Z+_!L0U0!hNwqDh{DWW=0P1HaCR!>+BAsUj@L?9`b=3)Z&
zP`}`Ko1~XcHPM}sPGR#&E6#!&p*g-AcYJqs@XNg|26(0%<v@uIGPm2yAc^Z+pC)~x
z_tQiqwF=A2Fx^ZCtkB1p?-t))aYXM^iD)0p_ZZr{?PK=t0ex>l^i^nn@)dR~E1S*4
zq8+eIb~bCPq^}FT;2H`zl>$>3BjVLT-<Jy5mNek6KD23DrP)`u<MG;p&DaSPiPDNU
zBzb}Azbn?$yK3|DM3AVnvqg<2B>@%XLua^#Qo`5Y(%N^(1dM>_nr9D4#kR|jr=_z~
z9h#R{YX_NEtF{;3hziUjFFc&9zL31F{XgUOc~iAd^M?C%8gdVzJHDPlcGck7bc);<
z)nYk42|S2fmS-TXJQ2eAELH5L9q30|3GR|CV<x>>A?fUKgov~aF)X9#iSHiu9$)S;
zlfS>|7=ydV23f8}KAdfF-7KnG^jk~*|0&VT4iI1h!Z2aWf2M=X@NNcn1gn7NTJbjE
zduHyOeGCW-79vr+#vj#zszEQu_M@8tm|rW$eaK^DZMk3j1szP`0@Jkqh@5Ro6n49;
zhbww`)Izd!#M>GTi5~C{s+p<}SM=D4B|VDp9{qEOkJO{}NId_xz6W<b$&U3L6?S;Q
z15x^5t|=_+r|-xk5w}OS4U1B4jm+tGhk_sv^`P9>(3YEyAZ9Ji6C{CS{&+FdH~9_m
z72aPHWMCy|pP^2GnK)lbDTw@Kr98EucJYBv#)PGKhn*6rukSw-YyEuA5f+^L^0g-_
ztFbHGr=ZE#{dCeV^`&0DJo}saJj6UV3eg5MZU5WJ8k%qYBn8<jF=a3CQBm7y0<?g>
zfTpLMM9xNHk;s|{0+r`VI%8?HRUTplxD+a0#b8Ya_|mpderTs9uoR(DOY$@cF=`C8
zap_p**wRL-*+*#dB>m^pV^xnz{eR1R@L=3y?JyLEH8~t}g+aqYE;Z|C{PmrGh$-pY
zN*b=?f2L;L>T5w~nV1R-l8QFNUaMK?EE~r_ped}zkPK;2n3!fX_0y&-^y)~wD$K?q
zK)?q$q5Oj#6x2T>oOr2R5C=@q$(fL2$Bt#*PAzs3DGldUHN(>sPC=(#_`<;hY(pA;
zwV4Wouja3P`KDfeB>^@@x5EiZIz#1BBQq_WaU@q>yZxv5o2ycz9J_ZnM-{HR8UNGv
z+9<~nMtV3(YLtg|N^mVZbmd_wxB`4nQY0+dp)>iPdkQm&#jjwO`~E}HP9xI(VaUW@
zp)>yJ1cINQGx_UwNYbBe{45l($wc{Y#p=R^+LN!^$&`ne?RV~^-TR%iEmu9d<8b2z
z*FP+Nav#i*q8;RAKbnKHFiG5Gv=*;LCv%d~*+J%~%P2VU-`~XWZA}!&^*^4#7k=9}
z@BQVP!yY1ymX(cYP-HV1pzCgAX6A~!z8?3mJMkf;`QK5me(sgCdHaOwE!(>h3j2+v
zarWzO!hwc`$K_sydq6gDAf~2*Da_q+1SOPfq(q(eD<5*2FJ#sQqccf8h7}tY`D)dw
z{FA9Ag6@bW_N37InZ#B?X^M23ry|822_dzWsG56iB_U&vEcw6tdZ**7f?c~tBrFdl
zt*c0oqVi6a^!rs6V{NtJez@O0bF7(?O%yO8c5Y@)cI)3<I9})~X4HTTC&0<_G?`Us
zFJw7lb)3%(g=UZ6o<PIB+icita_}>lf^wji0<4}}{SsquwRoZOo{l44ar)%_Fb;hI
zeXWj5Byx3YKYDY*?w$Ix0q@ZZ{*9CFqk58FrNa1trFpa78-fZq+f`%k|C4}1iYKb^
zZx1HOwQ>P;Q!ws`ng?~K8Sps3y&)l2d0b32;9?8_JwU?0V;V)LJG&ii_{GC7(#^Rp
zp-8UJ(V;hENSMu$Oi{3-<!dkez2LhRa-jt&Xmy`5-}GvGSsI>@_QJb-|IUO>ElQ>3
zWIq@kM^Ato__AvE?$LJ4`<NPiXVbeQ`Nhj#{mJ`}`5eXkhNC|CIfM&)y&VrE9*CwV
zwPuEdnx$PPq46G#Is0>m|2EZ7;GZOrC1cVC<}B+OHbcADW1BKik4N;YbEV`|P`nu|
z{js7?{Nr5wA@eP(H6F!d$MK1%`K!CAmgk0wyf`&AldJ=U%&JB&j7Uj|VB7ZwRfCK-
zK`pnRAn>MNI!9k{+xbgrH=hr7oO_z7U^1`v)C6J!qd`BAo$0J;VYIkHE`0)mDHfEt
z<@H^dbNZ8z3_}xikX_#?kK)jd)h(ZKhu^O2{V4Ek;6CEpVf^Q4QVve#&~=vvkTOsb
zXC*a(x{{Lw`Ssi03agWz+`~IO`aK-@dry+8g^JLFo3X_03^or5JD=!8gP-5Q`l>xZ
zy|P#784Ql=_2ZgKAJoikb;xi~m)vpH0Bw#l=yV2GH=MwFj966tx_uiLKlTJY!=J~$
z+OtpBud<AIU{4qfpK5M~3|AfUE(j-cSe^eSg2A9bIgpBm8oMdDufs=?i1bHym|fn!
zYe7|0q8Vox-H<QBXINLuTrsx>QHF8=1xW`~%A6#5_<ItH4FyP@0(x}{j;BZuT0V0U
z2xK`<DRzmiEuH!bECH<+@&KeouP-uK8S#DVzd=?67KhTeG&OhR-Q?0vzG6^LJ3cO>
z$_NA;o?t*#pb%?P@;8)X$W#gn8|e{|m+Lfm93tACRty1;#99%zej?CDuCf{bgse`p
z7ssjDl+b+!Hy|=}_0Yu&o!*D`a@uc^mD0vK2&Y6I@E&MY!BtmEnG(9saGv1!vA%Dr
zSFGm@4|7s3@BOB)4xs>U2r&4|js8iwEn)1^!r2f-lJDl&{k~@IM{Ev|Y?K38WhbMS
zY=H>xAerr(-<{mJ69MjCAqmPhWGME71e2HG-^Fb2yVy2{=A1Pa1k6z<<tg92{p7?+
z`<H|$=74^&)o1dPOXnXtJ6Zd@yy0t-chn~*$CIu%!mZWZQ;q<3+QUw?hM4)`fvwyZ
zZIh)cpi;q8TE`$!G?R}xqIM5Mvqi4lf|cEjl?68P6{p8Y84G=4SJ=;b_^DrpcEs+~
z)ZL)XJ*u8G@NZ*F57#|ohMU{9sM1qPwdRVzden0(2FV6;rT9oQ%8`rLV3dcv%a06J
z2QSOhI+q2j%}YShaM<Lz;n%j!`(iY_IvEn+d$WNxB4@)VC2{Q14q-!8I+n(Y^`DA4
zwWGQfQhZ)&Lz+)!@5MqH8x`8nmp*i&=ugQ+g9LVtLJp>!s^MgVdO_G~<8!TC5qjH*
zV|GAzYSq-$-DyF|S$Dw%t}qOWn*!kA%X5Kf4M!vm(tp=(BpKVkt3FZ@XN_=Ql~nc)
zy|nu5{>471?@IXfe#QDx)>F*-<D;6ti!e{$EvF|4mPb0}6UyEQzUTJ}JJUCGg(dyt
zILXw!u}LuL_|Ki~bhauk;jIXm;duH51k!|UW;H==-sb5`($L<<{nET!yoXfv`dR&8
zU+~N0fSc4y7!)bp@jMGj?hcllzp<lZ@{7I!0W9dX!usI+LRUVZ+2QC|*O7FNszx;~
zXjMRuA@&OaY*slG<+&hWYPh0Y;YdwwZ;u_>oHl6Cj|dM`I@Ls#V_=YPtXy$PNqhV5
z-GgbMC77_q22jGzv-f3W0oi1-7;>OYO~1@PAue6J+palO!E1!YjzYu_o~pSwq_O}v
zO{sPU4=*(vf5x)e)RsNGJFk9~LML{BlHx#HzPj4!ZKtx?LO8l1B@J%g)OO(O(Pb;w
zv59lrbgOear*%4(&B1Nqwmv=(9g5hUw8%d{cz+e$ME_;)SA`S5rXFaFI-$a;1~>Ce
zP(gt~(cLdK@qqF_snr%}db5e_`VzwF0<+90y0Y}q$vr1xe5Y}j4$Fo&aMOLGPHedU
zsOf-FWR{`d@uL6TS$noz`!_lQjrx~G<3P<b)F<7;)j+Pi+1p=buBgG?OET2|`sz2F
z=lWPHuTZj=u@QRCL0c`0(I>NCzRW&}UTiU>+aufjNPcaR_H;lHY!?g`st9bugQ}X)
z+OxiP3S(qcPa&r534iqf3|Z>%N@e3&$F-30(><Ec2Y~c^OC)+C!FPHR;$X>>4c%VY
z8ad%PT;#RcYxa3hkB$Y0s{)P))&R{DXJ47(+iYL<?J`#x46SK_UA?_ftDi1UG%yd{
zMe0KHTuL0ayMWx)YP)W@8{;lOqn$F#$^5MT&w&5*&p)5=L{ZLK4jPrjv35&#_&zOk
zD`V|u5TKoF0i+2B8Y*m=NM|$rnJY<UiDe`uq5>&LJCT{TiiQE9Dj@?>6QFab+o_?1
zj3>%O-_YxmCEf*m9qUe6%>Np}&B_t)q;#!sN%BswJ0f-lr##seF}*~}FYvbezs#4_
zH>ji&z$b~u4ATLa+z$i>OtWPWJXM}FH1-520;E<n4fj{6x#UOkTJ&^!;5=*=+k+8)
z+vbD^&}rA=?ptRf1+BIUtSCWeg(!rYai$AC+||?m*Zqx0l!*El1?sSU@ySh5m|(w5
zqiT;R3L`(r2dq5mq0*U?Mt`b26b9IhLGCsk6D8VURLD|;awYHWTM2UQij~sRAGr@?
zto^(E1Nwv9w~NTbB}0EeF7+Ki7%)n32d%>dqKHW0PZfiCioSPTyGIpAD-m+d!?BGO
zXKz&fOE+U=9{+mN1O@Y{ENU0|xl#eE<Pe(Vn1r!+WSS5oQp~D3PV0-G#ColCMmb4{
zf_b|P0m3M4wI?NEa()$}sSBtjLLf164ea#a4V|YFXA*qr(L8~N1K%ULU$2W>_qF;x
z@E0SzFSc)97&xXIHU80e=K0=<@((+hg(3<`@lfAl3uCxc_`>``2O}2Xsf;Om!;HaR
zPZeA}j`oL^|6kN^cojb>A1V)*{)u(77ZAGRQm>DK(48h(Wz7P-9=OvozBQ)_(jb>k
zePnqIHfE8VZ3rOj>tS4@HJEWJ+2x0%wb!&P6jCHVWcODr<@Lhp1e_9R`&$d~E$L}$
zlG?}8g7=L9R!1*(&k6-%ajY{e&e&G>U5i6k79SBY{nzr)x-z!0i4B0rrvP6QPU|sC
z!kVu1Y?{umm3(@XIGx=RV?uAiJjHB5n_?_FdmIJt2F4>RuWP@;*XKnV0(Q@tvpa}$
zSkJ$*sN{rVxd8X}HG4Ai@QPO_PP|%in3)V)P&dLw1Zi}-6TK7d>sSJpfVHlDqW1(4
zrYIuN*%i^g!FXqZbp~l@r2#t4=ATlQ$s(s!P&-QS&tNzV2KoXY@5AG7Sg8eigF|?0
zkDu4p`-nI@jfZ$f|Ft`>z5NzwvpjXiy|bQ4<%<NlVOi3%y8GEd5{0=AEBb?&VGxWA
z=8tX{ZTDXyiL|ztI2iY`ji#AvSz?{Cf@;I6BRl=3m!h)o_^DmF`K<+nFA4cA-jbFM
zz-uXM-@$Cx4&zY>vb-^((cH*;HI+K9@dA2i#{+D6@@WyqU?5q}KBz#YJjzU;x&uYY
z6=0r9jJtgm?1D!%cGzxP9RiRj%ZbA0w9qrhJ;&r*S9>d*v~f|x${B|<?=eOgQP9Lk
zJzB$5%1<=IOLEU0i-LAn^i#QQv(=F{5mVF*?DnX+k*(6Hz4eQK-a;U&%F7(Nnrd70
z*BGhPsR=oY4<3Q+st?M8{36xX57ktwLc8bNb>B{~f7?PvbInACOE*>+A823hmd`{%
zO;f06)yy$+vSs20n6jz}>g7c4m@-xY-eFYp8mLUhR-obXV6pDxNUky%itcFGcgfL;
zCuKcFR2SP!6cKHNA9on>p>%#avFr978iIzja2Ea}|6{R$9P-j>(4O{l{r%_K_kcbV
zL|Bn^x$@*5|Hmzx$)Sjjb|P}Ouh2-&S*OD*zoQaAz?+Ei$f-z|@H)N70w$I*54;Kw
z-;EZ$WumGh#cKi#zAXQ7cdxI3T#n1chX#muqWy4zbGCU^3E7wSvCI7j!RSuI{Qog~
zbofrLWUV&4VicD~Lr5U@zWg|XaS@L}ebJK_RhwPIGo<<PF>(5P2Cw^wSUZXl^r%mP
z*PBzj&6))7h*?EpUPB!aaL*)*^1aG7aYe+V=9r9(g$?^o*WBa%vONi$F19;5oXZFQ
z*xNLNHhssPXhvFx{(%o1W7I@pziVUh_x^I8;{Kn4Y|;AxNU(T!#2?b$5B)!ZK0Iv^
zm7jjIQ&ZM3Kf^pkL4PGai!jdst{zPx&6-I{A?Om=2j)2CzbLomEjso^2HVWD@-nS$
z2JdA4Na_$2*R)SkuR(C2c<MOVT3N$<t;XS(_Q%ZOF$!2=O5khy;r=J97cY2t{CoUe
zpF357zW)h_h$q?g3BNNd6Vm6a)~9ikhJ0OM?Qyz^kvV?-u63wy<kc(d@vm|xyGYhm
zYxQG>3IoxwdaW38;KavIy$!f3e{<u`<}^ui*MT7>bRep+tC{o+2H->$k2XF3<`aXs
zcZ17Q!OE`M$av0h+%P9TvesokO^-&GcZBb!ndh4xRiJw@IQJ${+S7IN>bN`+9_~#z
zOfOi%ehGSWTusjFKu!yOYs(kpBVInwk+*Xn;g@^i`BLrjVNyWDz@G;O<HczY>15|v
zht!0{V;~AF`^SzFjS_V1p4K-Nmxqwt&9)Mein=piq7?&YkNmtNap2PaqlKFJ+wv$O
zzV5U0+$fH8j<v1|IaObNn2IR&SSV4;%&9;hlI)AwCmEvi<Q1qT3q6V>PJif8IU)DF
zvu<BjAPzNaFj$bcZN8@P=+VnSc3)opqPkMAwYu}qD><9^3yH8hZ&!6Ls9NqgwCEn6
z9-vmxQ^hx9l(BCVPlJ9w7drpmBIvj@D7Si7-fjZvm&Fgj>N(w7uhP0x!dOg9lQPvG
zdyr75)U3AaqU&v0HDGO({FZxq<je=R%bggo0<e4WsCCl8P!7tW)sFnipPhsebT0Hm
zr<y*)bU;qHmQK7-a0vmw>HBH)&6FQ^x`;~OnTTBU6X)h4X82AzRWzOJH}z)kr;~&^
zTTjKkgN(G`rU)v0;kT7hA+~INCp(TZ`q>p1sBpw@ARppHBaI)RZZ5~(eFmj0<K9zw
zR(>L^%aA44RPA*+Dno;083rZz7(6l|pK!OgyC>vG;fU}+bJL){8;SagK4H_QI=)%Y
zp9i`do@d4k8>USgdl<p~L{9}}+BCy(%-#6}Ii;mnCyrk#DY<z3p(pA8=d)R-rM+{`
zoaXgr!-f?65Fr@<i$_P_cujkr`}b)a(%yc9x#=AJ0oOkk+}W9rbi*4uI;kkCgNj-@
z9S(-Wp&>@p!2n6!ui0E7vyL!4g-fV=E{{n0C`5)+X3r+H0R~{s`HY7`G3W58hvqO*
zC>5l9?JtIQy%WJx_4b-?`EzbElS1<82_(RAwWDKXf{LLI3NgY7s+;F7C_wT{N-}sH
zmOD{f+gTu&)2g{sJF01NdCCFm1Pw!z6_PSB(y_Fx+_M|d#vO6}{NmO$1kPGX;~Jv=
zg`l&J4){&bOd*jYH!g`U?)Yezh@LO|$NG!v<2ObkL#U>N1zU7!xFT;p0eR*f9&VcW
z`+iDdd8xM`r@wdR2!gW9*UQuYy%<+Se0Kwlz~|wn>C(0=P*?(Ir44k=P2(D<KvI3<
zZ^iZZ4^+vgacL~?aOK3K0@8zdv#(VVM5pVP*K`0!sIdM$5<y){LurhUCcWHVDizae
zI{*^_s21O*8W(ZtJR=u8L6=b>{4R<}<B5pT<e4k73={MiE9v0Lsq+rO|G{^2LL!Mk
z3c*tYqW~xFAG7iM;V}VGWTOeFTmSWWohSkUI=u0nLrrLXyO)=f{Vg`t$kH==P4NN6
zIos!cqE_Rknz7qlPOrKT9MBqBg5?nMoSixtmvzSbIWV@Pni(V*nW8oNXcwuTE8K`o
zm7L*YWUPl_lGceb$#hb!R;rVMt~pc8TxJd%W#zurAJTs?jFs}uj1B0f-=@E6LT_L!
zo20SXA09T|-M3n_TGFq#QoZPHZFDcHRo`E1y+zO4D&G?0l}05bN^eXTK{Ny3zcYWQ
zxL;Ki@<i*R)fug-e(-+N5%k?(STu-F{PUnco!AH1wiyYP@$r=jGup7*fJyb4XriSF
zSlTC^H!!Z4yyU%s_@{xGhd2)==UG{JMii4H_Y>;wWDzjP5g0%e%<?`HCN;UFj=uil
z$46b0C?u3ep_+ii#*P4?Mtw42gu2VKgcwmVK9!a1NM@ztf3bP#KY#UR_*sOZ-MoR>
z*$#({0Eeyj+>%C4uexd1mi-q<a;K%9YZ5-Jo7C{%0KC>!(|>HI{9$g|e~X`Spm6Tk
z#KFN5ZrP}O5lpH74ja0wg2&Uz>Z<E5<0^>0E`hXq8F>-qu4vt{vX0uDJNJ}^J<Mua
z4+|Q?a<`T^RY~H8*{Ov_pz?aR6G=E7b_Gjuz{#mWP{u4bzQq{#I~505E3i6TQss$y
z+KtyF5$W}f=f*iH$V27F7m%)#791a{KJE?N#kOnr8Lf<dS``!Z{-<{caA<9AQ=Hn>
zG892fa$a>ATvnZ0jG%O^{Y~g5pRJ^((@_r8v}xp>eAFDte#)v(ej-fk?yfrJIeYN}
ze|c`|HRnvPAXkvrOlRYl6MvyI4BGzt2YK>wDoXfZwx^j|-a<y&em2+Yr81q|w>gJu
z_MH7dh=N7&j@Vd7yv~oYOqhd+@<a$D{WGS8noS`#^8+Wfivu%~5HW}x;ao<9j;qEU
z*T*971mXc{Cd3!PHFs#Yu|?^aCx*3fj<e!~Fop1rZ0+yR_GB)z6hnnkm1qO90zqy;
z@tk8>c2p%%nzt!@M$GI!D%Z+Io5}JPIeOu9j_{3;B6d>ye*6NS|0Gn9=Y%e$7+7lz
zl@vnk5q7}>U*Q}03vi~{gV$8wUSCqSPe6}PHp#<4ef?majHlyT*vyl8Hx|L4>fo}*
z(WudKdLnWdnJ7rKGDql-GVta%q1GIbXNa2Tpbj6;E_U!8%_JD*K&j@nGoYWY1!hws
zZz>mx&WUkK@8sVF@~LQv&ii#=Z<nMz40~^{Do_7I!XsX<tE<&@a{n}Jh>xuLGue>*
zNkXXn1FMREa8O2iP*FxuE)pUWCw_1Wi~OW+D84sY(57G;c!DL8GKl>LJWu5%eJ4oX
z7ur7S3m^ThHboJIp7?Iwnm6>g*Qt5@E3;;aU?5J(AX*a1jmhBNO<!1DFl|Y7)6fTc
zg94w2UoV3lU%8xuR2&W|cq4NWMd>>Ve6oB#1P@g$omNntzVI$L!yjk9Sr>E3-V_`T
zQ6MSHEAe({n0rA`6iflpvYs@bzt?tHvu`{-ZHTva?Hm<GOju4y?VITad?X`7VkJ@C
zIXUXT*qUra(3xakdV9M)^Vb!V(OX5A9RLrM@I#d*(J;4y{VQi5@U|MyP&ftb!Z=~g
zDSf*lbf0iCh49H%-lOiZNvx@dHTT!r)82MGiV+o7S~T}8@(x``BptQ#2zBmM%xedf
zR>H_-={R5Su_w2Gf4V0Iu%vhjKkd`F`^yTH-A?{_@%;_DQVgRoJTazr-mAEu_bt8a
zO-Pl>WJN@7P(-kXOPMX-RzQzL2jzJlIt7~UM-AJZr`YHu@tm)t7EpL`5kVYMk)O;j
z70*Ai_Jx?J3-`p$Uc|)jF0C&;M&h20xbY4=J=M9Xi5~!+)c~!%-8h%6UEs0{1a^4L
zE`VKvj;A0W!DbgZ=^!8b(j;ap{YBm4KT{sisAoy|P798*ax;yK%&?!r1KXs(*LOf7
z@b)bfeGv42-rzFC?5p_COw`ohB+4uJHHgQ_n8hMraeqm%Ol^@vL?!@BMdMFZq;3Be
zK6+mmuzR6du5R>cjc$$Ug@N!(>(LQiwF}Vd2Z>=?mq&<L|0xapKSlI|?gu>zh*rTP
zCAv;E{OfXOZt*H$?lY3=rmaF)8EabLByR<P@u-~wKw)Ss3{T@xD|u4Y|Cw9cMStJd
z&*RZt^L(A#^Dy>`j(A8RGXfdu2t&$RGFxOwgbJheWAZbfCp^y_sHc)YLBRG@<b#um
zOVT@9OP@k#oU#104(rI*hLuITG0z|?z#!KfBuUad$6s2J0oJfCEZtZoE;F1cDs~!1
zu5>a7#4BD=N@Ut&xAU1w1V7A&ifBY0i&7a=Q<4R8XoBI`>aZlItbP@$CTTd$k@qbD
z-TEK>W^n)<z%m3lAD=c&2kBOirHKtah1g9-Ex5XipFn}*G3!qb4j<uoFKtztt0MRW
z(Roi#l8HAYD9v3<Bs*uZvyP+ZCCjyQenj`O*t7h(&&yxy{rtJNx0n;g)Giczuaf-m
z`vLKqFou#NJ`jy;S6n8$&SYxQ8(cI~6!z#qODP<dC#LlLpmspZ{tQQN`948#Mvz{X
zqM-Ejpb-9&a#6RaMTA!pt-{b^Rc-JY8H#eC5n1w*;3%tgox0kWq7StSiAubPs=_JD
zj|<r&W>W;G`!|#$(GFB-0~>&!QV?H4Pv;xu#eDQob1N@z#o)|ceNd8DK*zp_B;0H0
zCG;BSbrW7fZeaFI5}R}nr0Rw?H4Dd~oNA2mKkK07mGbhRmdkhr5BKKaEYP^H*~`yr
z@pjRJGIM&UI@BV?QUiLwJDA-bI#Etw)$7j8M2>l~Hw(1$<U>5L&WeAULSnF>#wyNl
zEpNsAupC6HO2|^|y6lPdyu3@nTH%5HK?|F&ZQb+(iYwCu!`XhoJZ46jwSqu_>siU-
zW_e@-z)*X6nS<e2J*`0p(=qq>X<YM^1MMpG9-4rDm^<Rk6f(0Jclx--&fyvV5a@I9
z9=eKffR=XYQa!Jcnp;$1%1c<7Y);8qvy=XZrAK&7W-c?a1|N`8y&)ckVBGYL)t9sA
zlNR0hheVayXy7en0v@a0DAj4@r7{CtEh}(W11u|*>$FOeW+?2QEaE}>bY``_4Cn`m
zEL9#e$KA8Fr(-+FtGZkx{}os2hcqC35eB4RDehPKa!rjl^*mI9?!*`{ooJ}!Jk?vn
z^Hd?^2$g3nqXc+1qEhVqD9kd94c&<jfj&0OGH`0#sGtJZ*4DOj3AKek42vz4OW#Qt
zTmbX)C}-u_Eng<u_-6IXB>(7{cd34Kan@MOW8CEOt1~M}vx*d@MR$p|4efNUXiBsr
z?Cx9}an{k2u%F<1lFH++jmaD$YF0CcjmBs(pkw7>ANLSpKEbtMNLq$|$?RvnNV5JJ
zXHkyITjXrF&*7Ec&4WPaD3OTmn@L+HMpmHw<l4I5h{{DS)f}koqR7Z1DauiX9pb6e
z*VXBDp06)NmS>{B!9RPMXBNr<DB>E!t>Kn%!%zw$EwjBnGYx_1sjEvzmraX`nihd}
zi%-$D8jYxrDT%h*qDo`yZ4Jc;Q)ou$SHB5S>}EdjKZVU^mpK#Og>s;-Wm>C(iv`j@
z*;rNBVLI^+!J|xZi?LOb0T>gaKUoxvn7-*g;hqSOZQC_0fyX}n+qY2^p8eQG5LV=a
zy^#;20>xyDAbfWKHwsJzp_gY5gHh3@j-m~F41O^cG^X&Pu*4&_V)m#fN^nn@Mf+y<
z(tsyQcvqN3jj3oGrLqK<ge9I22v9G{vE>H8<N1aD<w@%A$RdeEp>WsZpY-wQ^N~Sr
zpVqixyGLu#`bCgBkzwfZp0zLFeZi&$r_Mf?<ZgJKZVBV%2Awzpqab{=Z$;+U_pb}Y
zqKamdB7*J<%%nmAFIU9sAG3B5>PxNRKk+Jrqm;XQM5r8W@n4DAy)ey~6mHuV3$LnW
zjW303?ME}N_=TJ0H!q;rQ9E3_G#htVd{1Ionl<t`<CyxG9A;j4tkLS;Z+94;V6X{0
z-wF}b5kt7x)?UQVT$;0&C>$xstD%EE`8hcg48m=*nZS5LF6kU%P*kACu+#fzq)Ma$
zPp1?zGfWgYMQk}U2d`N7?9lX8H!>RSdq8a(@DOIoeY#yu)X}Aly^+r_INS(U4gj><
z60)=RPI<Uef~~#%Ze!!1@1OyQGFlS`6MzYFM9Q699%E!=YrFtdbji(s(fIjXyj^zZ
zJ(CXBD*@kX;0JnHnFFGnNRAejd3~LYts7&YAv$>;1#++-E5kA3V1$-1!=3@YXIzm8
zIZ@u2<EpPjXyHS^H1Q#zbkWFOx9xV_cIWha%OB)3_kD~MVMX&kwlWU|zT@)@K)Ik1
zmxA(TVJw_uOHT8W2frk+_C(u0l(d&7pi&WNQBjLL)RB+W;-JlwIkW&pb*)An025fS
z36u9k+NaiO5}XOQzWgt^C|+F>4OG;l69uKXDUFJ$^IruOADqDfTBxIDpq3NbmAhLe
zmQN2|UEJ5_*XIMQ4yB8v>$mzh)@LPoeEhC{=H~|wMCn;;T+P+T!g#&)7i?6tzCxo`
z4{g0@5OE#P(Xev7i-X5q8ju;hy3;|QfRfX|n3L=jx-K@Z<ApKbzbsvTZ2@TDKS6h_
z{yl08qt}fvtZJ)2@`pw(Zq6PV3eX#~P@b{up6`XXz<0{F)+lyhpfE@SF7y}@?6qFt
zkQ2d)0cg&rR*>6|9q6XO$;X}4s8ebcBvm}7dwF&Isp^v=%q}K>xQ#Te#903A4<EEa
zqm+<zmBnMmPzor~G#XFWBe$)%AcCCVij`lY*;Z^r)}J;S&)j;?$9p(ie)axIhB)8J
z_Ue37xuEw8@+{D9sC3=BVL3+YX5gfVqJ6TNWTLay?{SZ7MAx=KW3`R}61W7}q`G@*
za(=-4qL|A$Ych1j&)t5mzHy=I+jL4P$uA@YV`e`P3&;Che(vYRx{NiP6iiBpAE^`&
zBoN20pjcq1w8XFgbP9hG8_s=s^7y>~<~{B*Sk7_bUfN9BUSZMl{mbdA*UnvMi=Jzm
z`MWyjM%&iSy-LtS6PQ!AJ~`;Uv<9bnqbG|1!nGmS0&WnxR(O4fb%vLaXw1FzD>=!=
zOyW&)XmWmX1N2Ki_XeJ5lVZOG$(h@>rR`_q0JC+OZGkMogg^-^Wl|WH%6JIHzGYd!
zRwrb(35b8Ix(&U!rO!|N2TYPnw#gM}l6F7yC^pwQ)E&T(_Z>T~vj)dJX1?lB-&sS|
z@a<ikyx#Kiw>G`cv}kJBrZd^V8j>WHtuT5>-As&7?&W>^%8atzEn&NW-B@8bEc$~E
z$pjFTfNqQDH(JEEqc;;kln}TXy?{VLPZIY3yfN}-{nB{_4`?e1KOHjB-Vu2crjp-D
z_FbLlcVlzvef_F5fyhk+z8wUh97|eDgJpBlOZ3HaD$%#T>8;9)(yG!BS*1$1!K-6#
z0gpw!_5ghB*^@nu+<ydEZhudKMr)^~N~syS8u%#;Jj_-Y%NhW2ci2YiMgsRgNdF|@
zS{>KAM3;fiUVsQvKiz4VC+S0XyxCQE8M|~F#e0?hhZKKe6Fxr+g21=QtHNgjKh=-G
z6Z{1=>EDxoh#$S<J<WDHKnW|$CW&cG(E+5!kR9ajU^!NBL602%ZgRL71%@BQ?wP(5
z94x)kz<XxLNgqki>AotEb?1!9<FYXg_&rSFY{#}?zw}R;v@lj{g_+t~Z7kR^BY!5A
zsYpK3FI=p_5w8v_xK>_+#cI*ZFqsAUXOxLqhQ?~KHRadB3e@oiM`P?B4Ovs%JM(}R
zdCx%i@eYqtpV0%btCq~%K8z?JOViZReJ-xO&h(;UspOMqb^7iu2etkwa5xCjIY2~l
zO&9+uq0frfov__|4%*U_+?dwe#K_E?ukGI%ACRnvMIT-_#{G-UWuTzeYyLuk-g}3(
znu<+v?dDhJ%UMp<`^$av)apFn9AO1OWQ-frY_qYnN&DUk$Z{D+8O;`n4}%~&c{E3$
z%FKKtZa?Z?JZ~O7-l<;OX@V~og{C5mh{yys-dvBNAj6Lk0$fBP{ALPSSkWSZ9j{&E
zZ{S_Wb}W81!|-sPhrAM-ozhI4n4?>m6CCR8xiPDdnSk0`Vx+zQIzC+`dr=9@!jg@&
z>>$a%>;u_|DpX<Cv#jGNTH>=zi<+qZN=;HL<tJE{D{c5IhS8GwjFyNx&b2@xY7Ra7
zu=HOxzHHjYl5p>vm3ZH*z1J0)Z`{jVXCNAGA{uT$_mBgt6&S#)m<5Pu3GPlyj_B_n
z&V}z#lKB_!a|CPQeCG68HDv*?_%Oe@M>WKi3J1wn_zpEW^y1|}KM(b}b$fiTkAc2a
zflHtmBkc3OW;f55aFgK66P{zVd4eDjDS;#+0w2Z#M_((>wgT>}r`1%OoD`d#H3ijd
z6p=mcL^db654vZh)qtaECR1Y&-qPHFSBT)xRNxzCA4FJ?5=jk_eKM?kjMw!fIQH_u
zA4v{WxXwmHGC!N=W1q1h%1|OgvUFjQ;XR25rBlQGAypUVx5t4XG0;&kfUOZcMJQ18
zv6&PgyUwAc^4#SI_D|Yqy%pJ;XQjWn*|DlI0Ysas#gKA9If$Ru&OJI|7W_w>6JzKW
zzT(m)8f$M*z=AaaEOb~TVjFTX-1_+3iD`HW5{IrLNvp^N&ohV}m6{LW+a&yVspOes
zq<T!&M_GccT(c0qJjmpM<Eae6UZ-=f;PgC>2O`x7`9r25QxDVpgFS=&VVXXZX?Q3{
zsCl!4miE$)DhmiAoRaW#IgWbvYy>uK+3!N%s-@rm>R6Bzn<@-&QCy6pJXpY%vjvoS
z5au0aFitN(n3@#3!13$%ORIc^zb`9vv)yg!<c4|3Cr*IH(LJaX6f@E*sb3ZF<5iw7
zPDX6e*l()LZPZMI*Sh?0Q+Sa6p8iaDSlT(sy0Fm{UCL<KI?1_J4nM=H926dI%3o*j
z%A{^nWpZ72DQ8G%QfQn#372{2KAn`5W(0@CT{RLhvWiRMAX1uBuuI6qCB@l8LzBYK
z<XqAR4T8*wd>DH66vc8fe*Oy_2Ic_3By;KCGIC8{9ExrsWQw%89xVm`QUcF!Mb>Rq
z+<(T+??3)f@$J(j)WU*IH*=P8i%zclyQ#@Y`svQeyo5h^$ukIlU%W276b@9mX!H|I
zCh%Lgn*3>vUCYyp?wlgWNWL_;dNv>3GSepI*jzITZiizqn^MfViIxL>5wEHDu{YPE
zi>^v~iOXt3J8n?^E<B%dpKB>;K4;89z&#hhXUE3cNA&~{2uV_YGff-Zob<+rxu`_$
zfp|UbG|I2eC6Kp(mLb4cHrgH)Wv>^Jnb9tEGdD#$8q%GlwQ1Q#paEmZkTwtR-ttKc
z5*nlgsnAem?SZNaLK?CHEzpa(Nq}}B4s%tg0>K~B0w%mT`6caTa?ZV$ap)Plr^>-z
z6B16smMzk6=T(L!g@>@$R5^Os&z8=iu$Il!#>X#MMh6IR?(epSapoDu-Eg<}x#m5K
z^k5OF^Jk<eNHZd*Cu14d<n6J~ASqI2sPnC=XonRFe4aY5R=uhmIlUsJD^jkYuTm&l
z&Hg2k3I*^C?9AXSIYE*SYzx_)ncK3imTgncKIfbQUyj|rz~_rSynPMtd=tUYo~HA>
zLpre=^X$wT2E4e)=>(rAp5`4scXSx<RWARzBSV;WKo>aunzKhY-MZ=Qm&r*hSB;|)
zU(|U&iS-SJquDc(^P;!l4%YqN{Vx@aGqmRCxw1K?PWil_7Y=bk+5hJMR(BA$B|163
z8XuZ!3bdJnLefL(#^+FC;fR23%cSlRio<0q*6osI5&etiLEof#CoxIV%YDEuxz1{b
zXjO2&IT|fyB@tR*%6N-fj|{3H@fFc-obVhf=|MVKniI*(k_s>$>xaQD^-O^!C(GKt
zlDd()($Ze-$0xu9zMm_%h<H9QXx{*KM!dkT$G(VwogcMlKC`1)SwH-c6|_3-z>*)n
zFJWTrx8|qkts=cEca<ZExIY$MNaCp%7X1Opn*QrZL8o6#hIf=WR31n}TN}LmzT?j+
zCe`btbNJu+c{Q}c_{`u45w|T7i#~j(*4Hf=WyF9>SSRem_+jdZk3<v!jR}dWGeNFC
zaFxzkA@)f*!18qBV0gjlicjMSiHF|7eKz#k4t)2Sf+~>f?Mo+mVtUXa7&pe(??OYg
zepP?G@DiAOKRzx|hUjuoAs*GM$g4KWdc-r1dw?XHaL~|z&|jVp2-Lpxw$MPxwO4$4
z${ldsM7gO9O%Qqkhzz8JJOcPDS-R5a9!MN|$pgG|Lm>%rg16I+%X=hc`!s?K6#Gv6
z!&XVa%cC;>s`+=qgLJpE52<g^h*#<RD}oFD><h%<P@U+3Q>;DdQ%{h9OH^eS1STcn
z9^@(9sBCq*uVywQE>Z>;grr8|45k;xTb&k@1Au^-yi=kdl>ul4IzaiO=u{jB#5Ue9
z7!#a|{ZKCrPpFiN2Z+-poI(8mAtW-?Hef~K@gz$5M{*&af}e``UMv3Ap?h|-qWe^;
zSpg^lbD6`WDX#x@3cJGHrzh#T0iP7;oL5cLLj+~fQ|rnEA=6V>DNZjFtecV&luZx$
z?U5k<=9tenM2Ft=*1yNP&6RE~g&2Ta3()^YtzZURm0|U?sMnk=y-VQ@ID^0wI^)z=
z<w1msr;h3=Ea>8OrKV!Bw{E+6pK|GvWO>W}RI+tO^D|GtzZL^0ritp;a@W?2I(#TT
z9qT#Dchs7^bL?eIF|#R4oZob1FM~}{|Mu!jmYfWw&>pnBrc$V{TOLS|M7!qOc~g9S
zDN!rI+tQ7;@%cTVz9=NaM!tQ_PAshN@%v{@dyH$c<#@6&JI3?;ugfaV1?v8iS9L-^
zxhREdLP*+$h=0te3n{Ljpg=+gtE0W*6nU<FI{Qx9?TbEk`&{y=v^uH;A?x%&dvwYW
z5^6}u&u<nmD5q(!Af_<!9>Ri(Mpq&Sj71K09HWRTZ~Zf_UXOk-wwF$1W1K_%$4}`1
zvJxGJEJr*nPD@}3fotXD0QN_#K%xLw9+4QpWrI_Q***2b&|0oKM8lM(wQL?gM6J<j
z4S1hAg;w`BG41%VW1SuE=yWqPI5{O*#8gq+zh4a|r;Lxp0>m;vTQb+H?Tvf3H$OA(
zmRq-q^*$-cugV`i(qhfyB1e7&)oQR(bMqalD@*qK&{k*Myt$|QMv5ZTo=mBb3(VvM
zp>(d3IlGf1eQh#S@^+a980XT9Caf1jDk%_FPwRwUR5PToLByM}ekOc?2E7+rbn~Xj
z&UUVQU}mht=DNSBaAGh0b$(i@RO_ePs$j$m+{nQktr3-f6~7D({=WeB<x6o}mJS!F
zZxQva{MV%T6Qw@XRkK%7Ig*8OC**ADLP+=70F`vzRM=HraITOs!Wd^v+`2U(pSW@b
z11i;2AQbRXZ_98YttM5gub28547zu0PThR|T$thpD)nP>)021aS+hgh-`Kef)J1a}
z=1Mkdt`wJB2SeAzz&gKS84}z%j4SdhyO5R~kep<2`_8k4b>9Cj<=(o|5_hNX?xt<K
z%np^?ds=bd^`s-}?Q~U$hA7igy@Q9BPp}Ek+fjY}L~{;%DX7F3{=^Xr@96)6NL6?p
zUR-tFUIM0uqD6J0?jkoKDLTdkxBUpl_!Z$t#^}R?<t_V$%Kq?-1sJROb#`o2)5rc%
za-?=5jdaNl2Pc@IrhF#g8NM<BAtr2I?FB4)UebQNo$pfiiX#d@Yd}ei+7=AFk9s1c
z#Vmur%;!JyJ(aK7>?yq$<^ceMsn%%+$-Eo;?)^uIt2?Ot;Jg2xRhaJaYAo2mu~!~+
z;Ty{QThtBVTesO{s|TFS4skX8-gI6!_ETH|k>1U{oBu*{p!sV-Nt1$YfUu8Mc$GL1
z35ym&w-OP-kqQUE;rbCtL@IttEHUdrY;V;6j$2&!b+xz-S*Qx_o>CbyGZj=As%dRM
zAa;pL>5rH<s5r^aI0iwH*DMJY^x8mlTEE{=IzK}Lm4I=Sr%<4jxNio5lNZ%-I#4t<
zLgm=UAA-XGh!g*#<2&LPI9(0i38#(|?rj$-PPjhcbj}&#iv(<6+4m8P{;vy~f5gKB
zv`Z~VS#B?${Jt(+(&UKZu+;p+)@<Ulv*PZ)vxQ#K)A~`;?~k)FNXp^Acq-I7$p%g;
zUU!va8I}F>vh*-iCN}M^<R4KpMY(S(6VmOH6URD~u<TFS*8}Mrje&XFub#l7k^IB&
zr)~he0^W~b71LZ^=ONnZQ@q>Ai3}j~ncXQ@)f0(#p$YZXly0Wrt-wS02HX%^`O3Qx
z-kl<k9zmjt_lVsdUe@kAgl~LJlUVW*U;O&G2>kkjxK(IgUDZ^z_xSqln*y))e_Q#l
z-v8F!Kjd=T@pROI31My|K)Xt<Ze<}~MSlKtT8Nyb{QtkygVuRqT1gC`pf0<S^Zl2Z
z{1<UUTCRsN+T8m&;9Ip4br<D8Q4PM~Tmd3TFWpe^b#L}qY`sV8ev+4nQ^DE2COV6z
z*yRoEYE)2>!g|R!hIi1iqSx%^T(ZcEw7=D(K6Wg-_v?ZUQhg9YzzsL}Qj`DN;4Vt3
z{<i*exjEYCk(;)G$n)L>D!sus1wa#IY6-XwX3bnB5h}YYJw4j#5WU^Qv(i1i8q!rW
z07dW^yxKM_Lb3eYKA}0#ST-Qcr}m><M3fj3wJ32)SWB!lJYt-<KPrr=3_c;t%G}&8
zYY*35Hrwjv_E@`SmdZCO7~!|98bZq%d*kz8ZTm5P*|PZ11z&Z`Uz)dF5t3yNRS1t)
zP;N>=;TA6geq8adoW)+I%(}%cA_YYluD}+yt^mo65na2>`et2%i93T89gjO$>RRL~
zJ&22sX4R`o$C}ExO8HCXbz8%znC%CsJv7t8-ceXF@J@)8dC3vfo~iZ3(KI@z=SqQl
zgp;Q}$WBbS*d^PF<v@6U&-2O}@xcUs`RUlc+{PHKDkq=#Z>f}aL=BCcDWW7o!i62c
zx0hFLqRK40np1VEnJmXuA@{g)h<B(S4t8nCcxGB-&$kO4BXu;dWq&d&2+rz)s?X+>
zX+-}}lGM#UiN1%@VX`mRaS6=-xS5y5WqD`C<u5}2|H$#Ub(b;ODBV5uNsfqb4)qIK
z1Tx$MM22E%ET9WVM$iTajrv^-2Sm)pCe^3*NfL;6&=ivk_as0J9?9(q*{3u%!835F
zi!~GN)5SG6>?JhvVL59|j92Fw$i{Oaa!2H%Y3<~tj1k7Vib<0<xiqU`Rwf2Fjtdgf
zF`2UrR$A_x3dXH>pI`s{ZefMDQ=qf{qNe997rBWlqUdyudOc{$&ptzL16|{-C<iJ~
z6h1;Z_YbK#;v8P8v^ZHnTe?N(k!^_!Y?I-%s0x&q4CVA9FfVoqiRt0N^tH&aN}SBc
zqG=)B0Zbg}dvdl6RRJi6IZi3~Fd4iH<||J5t$GTy_m>-^rYW@izE<GIJvt^Tc$9WD
zj5?y#p=%X@=tJRspR@_LKmuhQi*!mQhD75cNGTRgexK`lCX@qZ!v1WTWMNT3%G0cn
zYATyY6p-dESxA!3dnQu|O2|+NuY8A7R);{4cDZ|G(c}<>sd&X7O6DTzaZEktxOCA$
z!Jth8fj*^yN0B$MzLt=aWndtl%{(;uhF5O8if;5gMgN<0+_PFyP7q^xU_@P00iS$o
zS!SUR=6JfdVOFDowCeOuKaTOavxH@*$b3_Q(Yt6bri*<&auv@0&1_C|o!y1mTjUKY
z(8>{e%JNEa@hINAqff^twA0`2OIGkQxP*iMvLtXbc#7nG-=3!3yZgt{7g|YZ;(Hf?
z15se_0cTe{KJ{UcbHE!0HR?0>mfVXjDMW{zWbLu1GuYFfu#3{^Su>bzUlT<{L&0=G
zxaU>R9D#erfP64pZtt!cO*mdjrPLOCQnQ`Er#`Nu6YRruBAwLfI$^qqpde4hRmru%
zS)m7>pkh*l{10)cNov)Goha`?)UNd^2kTJW1p$~5@+LKxfcNe*Q}}-@qQRjesZ{HC
z0mp$I1|k>&95;JKJgJ?1lr@)LqLeScq|o&k)@!#HHpu~sX`v3E%MBfx(!xxhPyEn^
z!D(3V(mXn!oMP(#5U{RDMHobs9#~Y>>7gjBhe0PO&u!n)taRJ<nkPG0K)6;T{jv6}
z86YbSBWG;yUupmP{H*Ocr&*%vq(`%tH@w7sZV5^JjC<LzTzB3wJr0!4)9a^s6(~gf
ze<<knD(~<BgZfBTu1H&jDtcOWy$DsM%@t)GQ3n{py^AmPxk7e&h9}nl7gHTKFM9X)
z3x31>>qcw?MnS00sE`~?E^s7zE{?fmG%Z)Y&gB6|LdvUk#6Vl|TSad?CWSk#LQ;Pp
z>yebOke2rrK72I!(re6zn;VKZ5w&Hes+ePgva}qVOE#4ewU}=m%bXh}upiMcY^s@2
z{LRzj9?~);@LtCp$G0$H5ynkyLZ;1dpGa@n%EMh}(f`aq%}rW*BIOGobWgm29`rIe
z)aMIXOLB|+p!X)FF5Hol2R5)`CxW``y3gsi)EZ5!fXqxkdq9MtRtIy@5y=|B?@<Mb
zsmiXv6hBv~j{^dpJ4(dSI|rgFh%CxUl_(rbHu(PjVl632JEtiNKOkjIzkNaKR~oDJ
zgZ#9yrGC;2x2LnD2g1uV=hCvOgZ7s24!`?ym{+nlXa>;mNSp+MTsDp{Hm4pa?Tb92
zaCo&|>VZoc7UUQ-vGDEKW6R6vXS{;D-{kL^0ko15QjPc1fX7P>`wTOV8{&H74C8bn
z`H@y>5xa)o*uel$i7Bb89F9@=P!n2%-@xTk^&eL;`)etyDO`?7eg`L;l&Wso)XAMw
zypN#2g8>!rs1x7rD#qZ(dVwe#zy{r%BTpPK!tdQ_Pr24K;^|{jrpJBLQ7N9qcyguW
zR=fo2KgPBXNWg2mTqN%0M~0?{Mpm!|%1$a2alXEXO5&Q-c4=G^VMiqf=x+)jW`^WZ
zk2AU%$EhRN#tOJwQs|M`*dv*{)_?Z<%&wfK+Nxxh^$82+eIS(~t-3WouQ@UQdcuA{
zg|dQYKA&0~mb&9Yz)6z>B>Qr?{q))`S2~xTEE-vYvN=nCk5;Y_K_7QUvK4y&`Ytap
zT|D$kAwVb+<%tS}K=EoQUHlKe9Q7?q&OC$Pr=u3hWNEf7Rh%VsZzNG~*P;4aRl1eL
z2w$`r{q^hP8$sNzRaN$(bgR_rRoTqGN|kP#!|f||s#b2fl&7QAj?1rzS4a4b^4pJ|
zBG0l75cTvo$K!gAH)0!(_r}Tl&(C{7KFLI#!sXIqaF`^kRT4?=o{1T(z1Y>2l+?+w
zk_(Ay>BW8kK$=msnvdltLL~D!-zNvS&4>Ry^GKUxojg9$8Q?_vd@0d+6ApQSBW@5B
zj4hsZD|Yk5&{exANX*-9`9nYUIA`B$Mlv!0nap^-k8?a+OMc|T@?X~Hk&U%*)~tnJ
z2dh*A(0M`lzAZfYrw7BWr<Z8M$U_O!h{PcNLtv1D^qGOTT_qEL+6*RKWh{HW?-VN1
z-$p!dinRS`TQCQlxYrJF|I&{&9}~bRWdUqPoQg+rR#9zFPE~j(U!W+KXbM5Gg?|e9
z>7wK8h^JPv2x9Qun;-Uzn=d^8RcZbkEgPADkSfI9tZ!wf*gTO!iU{o{uH>1y6&>Ug
zdo(2LRP3pphq4*d=ylJP!)7A0KEO1?g6X|<2jJkE+80O~GaiIf-57dfc!Bcje>-3c
ztq~Gg9ajW3CYCWYS5NBHLH-vJOYct32eCO-adB0v6LV-FGYy6pd8#9@2cFoO3x3?+
zKTNT{LU6}Ghf$6bd%mc&tpED%-LKbMD!<rs!hwS6lskYqAqKlGa`}x6$zk^o`n}hG
zl;5Ih8HAQH!^6|kY29&i&&zTqiP?Pj*v+-}xoHTU2mX%Z7(N>e*j*pAYuDDlej7GC
zp7VK=?kD<az(-z_<X5qD<ZGKW8ox&I&E6ivBAKTL!oJ5*Xz}5--|j_!zQ{MGg=+o9
z+#QBCY3SOz7sXfR@@$HICthk&z_1pD1vH+CL?fDb)WSkH&pmCL^--Ug(EV$kcqeCr
z$HLjUb9Rc6IzOD(PrW1YsQ)5C=r{b%4|X3+6c%ha%;^Lu0-7pU3+vkB$2oNStEMS$
z_PH{{zsLSq0*8KgFpRm)MGSq{>Snci9~n_R1!pQpsL+xI!CuZzLWvK)zsMbD_G+)D
z+{05xPF(iHxyJ+Bn62&>lW}*Pq^SbPr;{SSY?vbZ=yY=cQ6m%|s)PGruIR}eosKw7
z>fzqO96sZPxCE7L-nH1u|14rvm3Viw)2mekXFGbgeB&|ITfQ|a1}M9MKrR;fC4Rzt
zje7&Z&>LJe_DR)GL@ox=ZJoH4o+J;<N={xLCl>P{d9K7c^IH8#Cvi{kQP1<81&Ogi
z@X!gs#fcV<b)U$0z0sB~=(3HEb-oQ1PW^LBS?@VS4UbU20DEq#_I9^l)}?&u)NiME
z4kgc4);PF<xqp($l(X&4LzKLG1~jsL;#9{_;`e02ue<1|URZEdX5T&`T1cg#sR#b}
zIDwJ<<&O(lffIjy8x)~MR4QhNe%rcrnVFzIWl#Fhg_Bb{<*&nIN`vk26TS?zF_S!k
z9g0#l{6DYlrB2pwoR!-3VSq8hc&`(eQwt3CDspjr|6>TK-_7CxEBz1)06O<eXQghJ
zZ)5#r1dV{<)$<i_$zsOh<}i538d`ST%FeP->kxbdE<2UL`HDtrLm3fpw0;yVYoPWs
z+t7-pkN)K^<R^s~*uXEYqZku(__R{;zT8lQG;uY_w6eJLV+tx&xhzcje^+R|FO*3B
z9eTKiZKU5H?zLxw9XZzRM~uN)zW8(~h3Q(<wcc+L$}sjl+FaBI?z-~x9ju7hVQ`FE
z_-}SbLw#eoOBb+%wIe|13U5qvE8O7jaCcLEJ9@8k03M$=Hq*yM53$^jflcZm)e?|p
zZ&n#F$vmWPI<8-^s{I|nJFf`Jb~mZJl}pchCwiY<s_a%bxr4G-&fnQ?LSdLS7GB|Z
zsIy?^KnfJhoZ3m{>|;G5oXA}~U5Qn8(T(kGO1Gw@!Ynrb$@=N<{SHo_&7D2{VC{R-
zI*dO4w%tsWs&1fk=>KBYub;Q#&>S&SePr;w&(w6Q)*0@?S*b@h|E6g>5UV|qKurmn
zx#40EGhZ`!hGKx-Gpdhuo^ZeINb8|~1hSe3+-Wz$E2$Dth!@n=LFWDlV}wyV?&goS
zlKF@M!XIj{^$MdQYU4Kr?L7^*kJcd7uTmpxW_2#3)OvZ5Ywvs@$YXdL`VUb=Y9on=
z|9n|N9_hm!AcPT%HhFKlm<KK_UGpb8ZO1?zqP3JR!1Q2_NPA~>eU-y^S+)w$t*CB~
zKj4c?_7c55+Dz~lC0QIu#K*~S1J`_dP;7B{V&L^om6zH5?90g9GqDXQcdN5x9B^*s
ziVopjaMA};BlKEcz$QV4^ytwvrZDq>z#(!7oj}Xyj(di%;8;&l{L&Q!K9~QT*VOM4
zQ{M-`|1xL&Ii@AJkCBpdby-J_@!0vl1RU80$N#hntEI)|NA~MmF+4Xq6O2sBH8=2T
z6e5Uo6+Ov7)sLx@w8d>TEv${$c2JM$sRoi}EGcyk<t$tyqO#E7u9Ki&Tge7qtAhX$
zuo6~onqM{D6>zKO1!XKj39y@4S!rVL*L88KRfqUypGoKGg)M~?bTVr*R)B6~_~Rk6
zpARpglPmO_)WP5S?S`fnx^D*JwCYGmgxxB;n-sAB=hw3u`kHB;3{Ig6Qnj4TcXfSb
zP#i$BE$+H#a9P}gTX1*S;3PPK;O-vW7I$}dcL^3OIE%Zx>&tiR)~kE#)%)M4`b^LH
z)73Ml51n<_zU!1_gv+BrtD!l<MTNcS<71;6O4J<}tfIPCSA!hha$p^-zNf6!doI-N
zA4uQ%ZEj$7`HH}r>1oL+sL0aASVL%0a*A1vwpV`!r@k`_pXEGSDzP0_IqgRw_{${n
z4p}+kjE1sj9hMv_D>ebE-<N2gfNh%JKiCORA<;af_xMoe@9a19KlWu78AEfKk@8)5
z)*@<i16_qB|GluP40AN?m2ieG*V$r;o6Zgs!hgET!in+SWI6Q{`fP|!g@aY8?1J;t
z+_dTBHQo6e5+)Um^HhOuN=TM$X_+-j(W5(s*_cWF&*yaJOGuo{LR@-&D20!FIb|vE
z*e$zCP?_7H)kg-`7Kvc3TbMK9xuI(%VT#VtLV>(6=UCW#_f}z};YNG907dXH-D;-&
z3P6R_k{d6;qsrhfMPBgb44W~S6pokO%Us&Wa?1!~`j=&$FLsj%>7Dg@=PlP9X2lSf
zmd<ZDen)aDHk$0k?+htRqp2|IEBa6V#WJF8mCa)9g^|eoPq0oIrRa+?Jnl0VWEv_y
zolo}{+T@0z9yv#m8jImO8^0kR7nh4uC5hb+)R!vWuzo%bZ#_zH%I>d%SI-Y0A3;{{
za8yxl<V4krprhCi3BRtYy_2_|i&wj@8UnSngT)UK^6qLj2Lcv$3Ebx4MYOlqg_HH=
z;RS@77YZ0Y)wH8xbop*{v7u^Mm+3Ah$mys_5nlWHoj1Hl|BxRXQ@IlBQCcpKAemTC
zpwpahzO=7*gVb7<M%2ftkC98Sn$@-2zlBSkm@mKkLRC4E#%Z>@RjIV!3evI-VN&Oz
zOG`5+E=L8_fr%SX#VKv=OGMaD>#|&$yjb4Z=^ik`?ny{$(9$^p4QkLF6UbMezQD<I
zv>4T-70W0L;#GwW;hN8I2IWoZwr=Iu!f58hqp2%->{K72?5%jjdAGsE*UyA-3}iM&
z4gp+p8Et;eYeW>dHPbX4^C_wYH8o~z>&V~z%1R0c&6G<5k9Oa5#?XPwsLCXV-%wZj
zg&7!>0cYuQ5c7pZU0BVq?5pJS#5_Fid~+J)+Y+gfK#*&n4Wwo>2o95GRgvq+us`bS
zBg@y*qYFpgb$61+MI4?+)kxty68d>98Fbwdr?@}r=hKAo%z$jGaR%K%3iRmbPYS?c
zY_o4IlB?!68@~QzN>&!oa)QFf6Zb!~&Z=BL2Wy1aXtlJPdun&!djj($OzE@gb@wYo
zSp?!GKsk_8VfVqGR+$3QGE|?u%mDxgVm+<S3^Q}1PhBs~NtzaHvnYRPWAXq3$^f*c
zV#Z%{KfZ!3TATRY7OE%sarS)0XJGK?bRPIy)~P{tT&Dp90V1|1(t-?Cg2q5nb|DjI
zG6VawIbX>Ra3_34f3pig*{wt{7M(blqrBuC9lpYqdv-^CLgHt$ow6rxWMyK5F}Tuw
zuA92yJR&d`MD>Sga?>_CCWm!roKx~<HtaC(mNj05@qwABf~^PS9eKU#D^+y3O`l7D
zg_L8DO*qjB-4rncXP3(&cxM#5pHzUWrsh@gie2f5XE4w7qdaK%cLBR`*mh^#{QfpV
z2}Ro%@hEOpPK%@F+g04QEo&-bXRHXxb?ss9X=65PzJWA<)gl>-^;KE^dd6=Z+6&G$
zrw)^FiqVahD7dwSc}1Xfla|sULGFQYvp+KmI&qv}Mj9vZ<bsK*jO1Y0gfP0T+g$T(
zeuXm7)&r}-lE%w9mOY{Uv@ozab+nn)eo?Jqp^pBux3n$?3pGlz#BpOfX=Js)lr`>J
zw-U==Nu3=X4V-yZD~vMXg8<k-ZN~_}-{Zz?7_$Zs`-`YinqeouzYk|hD?5F=4&icg
z!u`^$Q@9bv1E+K|pHvATx`#SX*Mw%|;P(xzG{ZOkza4qRELP2uUSQrm;gWD}X{GO+
zE8gMEnR;dvwwdq@61`sJRU`HLU@;}nFY<)~xGP=^v<-uwikh+O`bYB*(oJ1h#uX(P
zP`SJ{sZyJa=cF1Zz(CQEzOM|Cw`PYGDlU>!6P-$*OoDWAxn`|828YBokLl7h>+!Kt
zEzQw2Rf&+h7Gm`Ag+XdS+0*E8PNpUBMD_@K^O1}&c6;g8=~MLCyX=Xed<AXEB}X{}
zmeh*u99|qHHV1m<#3`b9I$RzkA<gB7NzH7jg!C;@!`|Bb1kblDv;|9~=%^5yMC^vm
zp41tqQ7h??Keb@9p`AX7V+a+fdD%>_nrh*5;VTIk<-~w7Y10SnUl3bl1sh!p$h-8G
zfmU^TH%O{shY9#%#=17*Ca)91WZDt&S-h@}D{f9#=&MdMh3yUS-}t3!C6sJCiH^l|
zS&fRyz2pnYQ~XoO3#G=-Y4`HmfqVeNd<2umYI1AKNOis>zl85@ScAzahabMAo%Fe)
z^RV<>Yi0(9@}>9gz|lB5G~FMc#$Z#*Z5}phNjg<~9o)k@gfeHtBgNh2WG(-RXmAYG
zU*78<u6U4h0HZ&Z9^R2-%4d}a5qK4$;ZvFw-(b@rZ%}pq$(<BE{%+As()>sTmCd5E
zN_NxOycu%B{7pRmH?BX-rs`?)Ql3(0o#zb`2lFiEC7nuyEn$&)jd;y1VmYcBAcw6A
zvih)uS1NX2pF|zsIV)-w;5e`y=TKxqF}ym4%7)YeSR(iRLec)0la}fjT4Q;Q8_hnF
zqZXU#mm(>DT%Q?8mVU>0MLP~!y15(RA=KE9n{3+vtdER?O`%zDno!X_N}6b{qst7;
z+?GA!C$UWKd$o?W<uz`mqH#mH@pY__{yO_<dvrUC+RQ&RSH`X<7%&M(m35Bv-sN>v
z%sToQs&3U9SpjT_YpH@HNRz2XzvE3LB3VR|_lS0xyQGXhj!j0Zq0Idfj4ZrA8Aun5
z`G|*xTO_?Uq*8t&&O(QJJidXv50=|bvBA)}J~K{mDk4NRU-`MAe>@MEII#QU|Bl5m
zZfvm?@gGejCFT6Qj2WwXwXx+<^n<J3Hl)q1^Cp7NX`KCHGp_j+%_aj^el>O?#}b98
zu4RF&d6=%^_x}Dg<g4q#Wp{>4l4^8)8mS)TtdsvtWz+|>5$h(+4p2vB5|^9)R7aTI
znYg80--!F+x9!&QhPugdo~U`3{P^uyyUt))|1<Dm*i<lQqvFuIvq3*-=k(_nlrM~O
zqz2nrC!X<XlCCpix~l^>!J5C-WP`p@UKA^He@OuzSqkRyz(rf-wM&dxXc_$C(Zd2g
zE>n$TUdwOoEJ*!)ZrYYfyF(UgmxO3dgKb_pa<w>H(#s^Lt}~l%^_@y|whfMasCWK(
zY4qz}n_F!>g7xe>#0IeRIPbOZzXcG)7QFCNzoGkx1im2^ff5kLRmk>ApB>I|LR@}*
z>dc^ARCLvL%i_Nt!k-v!0u4~#X7XeJl3*}QJ`8N*rP&UL6gCYOJttGh!qtlF%`0t!
zlA|w^3;S>M;=anXR%=I+qi5}zS}I=MRcjUtnT35Z^!@eL3C$e4NEcl;9DBbV6dQpQ
zc{ugfFnL_8m5=7KOL>2xvoJ&-sn{{p@wtRW?K9TUxQs>{9(D2zlSvx0y3=ZeDPB*}
zSG*Tac{WXx$d1SLg>I3&uDSyUEj(f60W^!%3cDrO`|2f>P~34$*g+WTb(y>L<pRQ*
z+CZ=A2xFn}e}4EE<|JWC9QBf9GIg~Gqo0G#q(8*xWsEX*n?Vrfe);7?jC*xc2mM_z
z0Yj2^`d(33B6Fa=`Pj6kXu18`%2j6o!wQo_h|I)#MBRXg6Vi+`pccCQ)U#lQ_)XwP
z-7x+U%Yz%)r^#cg#UQEomZe;^#PRgKzIfq$a>O`dM=}5<I@i>_kgE@~IXx7>AX+W;
z<aDQ{O2nPuIPk4A-nYqd$==c_IPnIY&<*$r&o)@#Xgf8DSCK$~tsh}qalkNj6t-c_
z)fmj3opMyH|6)<q`MRu3PmVe=8PEyBV>4a5DbFD={J@`T{iOfl*W~xy+JAMz<vDcg
zwi_yl{L8PzzA?Sa)xn~7yo-~=!%g|$pMj0TZyvk1@YUpYY;K;HH!;FNmlka}Vn;p5
z9ygC!^p$OH`S8|mbU?SG#gK!RsYrR$3}GV1%e@jb1p7Sf3kH?`b-qoXCb~J=JOF~X
zy<%4a!tCS@Kw&O>2<w$P)nKkk(|;P&`!YKqL>HH?j>a5Ym83C@-PKcZ<&WYTkjPwH
z7mB9dOMpqZdkBxdqY~o<OhKAj_q&{VgtOeo!|3BoweSoX3Y|f?we1ML%osN5qa@qF
zE~vvfdcV`&u2GrZprME3`@p9Yy>}?*Qza+Blq_PPTM2LY!kW5W{%F;8V;`{6j_e&|
z2nYBT@Ov=&S2SWx+T<8TfADA4!oLA-tiS6?7jJ^tBNDjA;T_+_D>9l-LLJgmTp+PG
z?a)|(*ti48@%f*Uci`3H@xD@zJzdvX58u5~L~R2nuJbF8i_e~oGWX5aTweyRoV)c2
zC^1_zzC#5X^NFc*4LEj+h9)pGl}p!ZKpOwSyrnb8@rLBm_oQ^@l^}Nmd=ulxnVb-Z
z_48OdR}4aE5uE>qQOMWlTM58SMUX7sz+y=|z>MDD86<vPYcvpvn(eHF_-3Y@oV~ku
zf624^M@|tRF(ZMOfS0^m2#N}v%|>RXFIz-hY$jNJfM|qEXPHc3_LcYSC1K@13ETUQ
z^$$JPppg2IBDUb<X3(N0$K1sdO{M0Y2X-HxZu$Qw$Yv39SFkDB1#*UYDd=aJ&9+Dk
zCC>hTPxoIm+l_RacRYRnv5Yqdis)zQ{)gjf>y3r=zY@Co34JH{U-2*(-ZXmu0YLTB
zEB|F?8!NY6sV4tz`E9{QWODrOKLESii1^CZn;{nTrea$b8(~iuD9KdS-@icroyvbz
zgAg%g>$uUa%UR{R-d3<{ieT^my{uUq-}j54v({|#V71Ibop1l6eztym*^?<J=unh|
zVjP(i4CzMv->_(O)VSB!Ss)I=B1i>%at)pI744QCzJw(nIxTN}b`QfhE|l~kI6x^*
z=3&%)AX9nqf7kf`5ySmXr~ZH9_q2=m-nB&e49IL%HomR<zCJ^RvFAmhmN^gnM-Kl>
zMO(+uQ*Zy3sfPIfPfPoyNtpY*K^YJXf;PCnw+aky?*Cr00s}Z41(-}mr<#Vyf&)fE
zxKVcxWv715btI}}Y}U7Ko5M=dhKD=*n+S8(mwXI}#S487{!p|R;sEF)7k{NxAc2VX
zRgKUI^5fJ_-PH_aFr;c;5#$o!i`+JkIn;D5Q_Y4!L2sO7%a>@yR|K@#!J4`(1lD4V
z*&Rv!e&+r5aVhV?*%1KzqWet5N*T1}2P16rsQ10<b@k;C<@<;w0$LgwRu;4=G!mig
z_rS?wD}ZMp{G!jj*U(2_nH)<OQeq)CT?`f`kx(Pz87j5hgGen&Wz<S9<1n4GEPU!Q
z%~yqAq^qj^{+At^w{z5d8;!XLYS6|5io9?g8%ePK;ucunVYuO#Vk-wrOnV=Ver}Aq
z_P%&Rlz&J&iV2n*G}ITlO|G~hU*BMR6H8%zJURWupmfwpUysWZi;o(~q=a#iU@vxg
zRU&|!yQT`&<#eI<ds3v1h!k-$W>T<5b%!=8p%E281k|eW(aTAaN^j$W4lc|)-nH!v
z+gUNX#xWGp4qceS=}wi|<Zk-zjrRlUrw-k|vDWhVkGu&**a^7CDX$nAjkVSKXJY#q
z(|CG<0!QP7_5(f()uJE}amx1G5LT*VG+mcL&;{?87($)>l_@&$jNF_4N-62L4%l)%
zN?*d<0BOI<l5znY*Wju~tOvtjQBs^mgaZ+td=ApK579`B9gay;Q>%sjXRa^+a3yk<
z970yViHb5w+>QUV7WW2~XQwq5&^6%XmFJNDfn%JjDw$Eu)guRP5!9bc>LSUC_WB)L
zSMr0#CyKF5VUd>wpu0twlB#JC&QghSweO0FN9IK9ZWX18_=!??6@278zN5<f1D~T!
z8+S0pl9sG+@O2sgNISeBBXav=g>IA@qh$Q3MvG`tB^>?AKruVP?|;)!3M1d5PUl~C
zvxB`+&rZIq<1)r#?B1{;xIw$U*fxHC+43Bhg1aA;%$0cdjsa@YKD0@?A*1axWWVK(
zJd~3N*4GKXV+d`2!R?vQtr^~L!T)JF4XH+yD#sJ$HV#=WRwr(RlTmxEUjKcYR*380
zONU4s^Bs2&jp1YjUgAac{t`&^P;eQ$-@hp<WJ{0wdBK|vP|zud8~oUQuGbAK2RrFU
zayNER<vM;k>dUL>L4^pzY}&)UFRXigBj-FY)6H65k*F=`_yuzz*I-!88o6XHZHd3&
zPbkl<S!8R)^ilr+)?Ck00{e;$o{#o%M??NGPL+6h8OS6@mJ0gw!J@|fZQck9%0x0k
ztL-*a!C2MKNXZ5*Bg&{~DZXd9I&?n~so+eBfUPkxt=jK$x8E7W$4QyfaqFd37KGKM
zbL>~kEh=|vow+WGYgKb;h+_f10|xenM!Wfi!s9rX4}NClJs*v-i;-~~C6AXia>q58
zZ&9+^;9F7;rU~_KZH`aovX51geeG9k8Y6m)m1iV1xFdDGel^4@B)-FC?qKHMtme_E
zp1D=HtCLIhpP~aFBSks)5SlLJx4|qux0Xw6$3IV*$@L!_2eH>xb;p0zT~>ZtH=Il<
zvMlo?!-}NW+LboG;Hyr4eV-&cX^V9!$`rJX0kv9sS+SM9(b$<pW|q^!*IAv$+H}*W
z$Xg0B0{yJ(6fgsK^^E=6yKPWSZS?Zu(W;7YNBrB?-a<%d#9qy=%!IV*laBIxAU-;B
z@ywaZCnAupWVpD}$9pvWiciQU2zVF58Gf)dxc;hUR+uo=>OZ^v!`o51XTB(e63J?;
zD!O+RZ8#HOa=PT(0QZB>msBAhhQ(z}w$d;nx*H>TtRfkO|2!A9J0%zg79=prz@-^V
z(}>rzPmcK&*=R|B&$F*hUFN8YyWa)a0nlL!-Y@$V&QS4ErZcOE(xVK;Ky{w$JJMzm
za@qhfU=y%er9L+#Z}kn1z!L5(5iSlbR*_(puQaOrURxZtsz(Z;8nhf$BcdA%4n7|O
z7mh`0Gte}945T?zZyD2UuSubciY-JFFU1_l%5Sa17NmwHs2<*_W-T2qg&(!M2`jJt
z$dk^59hK-0ru2vtFy7hHYgL$?XvW2T(mp&6$~*eH9X8J!x|^BQersN=f1FZuX7h7&
ztyc_BLL}GF2gG}s?@cW9o3iVjI#q5yT5T>puf!)t^lK`XLMBu{e#A+bl-vQf8I6A=
zFxD-a1jkAsyWc|G!UcWcDeZMR^n}5ERmO-<a3iqvdjV23oFj$A155hZ(ORi<Zb>$f
zLv+c+=`)ZFi&vaj)>d2zfImC6mNfjqStkaNqKhEUGs!Cr5qe@f<QY!z#`9Uy!;pVC
ziu7hMt+X<%w$j2<cxJIqCHUOQpg90<1aDcj$tRnsDRHIH3qAOkFYR<2^F`tq?It;+
z)^wgtp0LSQf@bEM%I&S8fmJJm1sHmd(-sg1#iD=f!+6jYYdS5k>ifXK*IcM8DI=vg
zOoY;}k0XOfKeFaLko8P<kZpg0H9x`bd-W-^khke;4e5>2{WNl&@o82rGX8cQJavqa
zoz~|A9OCNrBzuza(Uf*lxA5)cC!<!pGr2bF@+N1hCaOi?z2HCVs0V59IGo78%nOY6
zTj@iFK06MTG!LPT%GPmR*EPIjktc~U4zd|r%DG2na#|)ui3?%^Df=Lp-vMBOvXdd?
zmlK?(AK>>DXlyDa&s2(lEe#R=VTZPRPfV9Z{c#my^uYy%84V@YxAUpN(P}wnDpKFH
ziZ1;9-3)RGN0Ea0^S-Pu&>N@v<gx8$L`#Mi<`4gh#s`36|5maUWc%N`>SI$DGh)HZ
z_VX-dX5_BGfq?o4RPoV-eBCmbP&+%hg6$|h(5rW$F11G>hm$h=jHWxdx72Y#I$Whh
z2t~x1YOEI7l48JZFY>L-?%Y6Dzl;P#2vUI|knxOdUH05>0(%gE`nn5?Ld!K5Mx3jr
zzp2eQug+)d<v~M{RqmQ*+Z>{8SjDhY2N&~ba=|cB^vTmv#9CzzBp3HLxJD5oBN^LL
z(QcHFo7Edpne|S3ae|M&sO9bRmvg{V+d8iKuTo?U4_?`Gf7GwjZ>;wv&>nJu^=!#G
zlM}N<T_4uSmk^*ct2;8#QuXB4HNJ4RK_gthi}{>)j%-<C%AX0?CVAktwac*v5{E~K
z>^bU<l#a0P6+N=d_fQWat5Q-PjqabTJ_b<NctsZ`6?TKt;n{_0X*sL_Q52t*YwB?~
zblrMXI1T*yB)53a1>J>BA97Kp;~i;-A)(?+g`4a8*B<|RYy<Z+d<p0R-Qq;CY(uIq
zO!Y$Wd?s|n#iBMPfLPW8^Pkt_5?&@Wf+Wbl?zb#~0|PA)p)p%^E`*(`S<Z)hS&KQ<
ze9IHTg2fA~2KFt90lj_;TMM7#ZJXeYGtk1(_Y)tVNSqH&JuoF`IjsVaJX#r-gTa=q
z6I)K@kz&(!CD^7PwV*yQ29gKCGS2XNscz(wL5<a;q}+%cro~VqUM0`dIbZ0IT}aEx
zOMe!)LlAg{aZlhfT;KxS;OLIdo8L<S%Im0IC|++dakubl<`;$0HP6Ixn0pW5vY;Ir
zL+1VjKZO{V+-`UZ<{-A+kSTF|^WR)cb6mhgy@fl08P3<sA`yv0&INX{i#Vt8heH^4
zmEF~cB5NdYHX)OIGfior5$ao{pBCBZe|}8x2X$H|AvgtSOT`ew-<-&w{ft1SC7z%|
zbcG58`6cwW3lTGQ;zt!G$^-<xD==QbH0rp0I)haGDCVd8IVGTrr?Q3nci2z45?7lL
zK6d%}ffpxrMB{~(O62;A=O|I>baW`BMFVN{m8aX_bO*|kvh-jbe|Hk>&T`?Z&yrZ+
z<S9RobPP(HRE6p%T4NV5Q3qx8Z&p6Rgq>p9tCSw3C&XT`Hu8d5<W6*39UQDz2{XM|
zY}H<}{``I5B6^z(K}2)8Z9-q_T<w}9MI0GUPwhvvji+Q_(>)+5bXorsZUW%_I(7;J
zU0R*ZEFAt9?%Uk&=A`(NrQmh3zes<kXlV{;!nRwGgDxq4P}2ROb8HmPnnC&{RQw=@
z!tc=N1jsWoq<Nc$Gn%9?_-nXhAt_MhI;w%T!ayhN5e23X-Iig2-o8!e6AvXyslnp<
zR9Sa9bf+rmZ3uCu)59=;t8?8#liZyQ`LwdhKtooHJv5Gj&x`XufA7g|tfweSB%Oik
zLRkQNBJ(Qf6e3kr*u-Ae;1Gk%%qLkWnon`zP^c(DIwt-X9Yz92CLCw~A^7L&+CGVK
z@&o6NGV7_E6u$*7JNq7@X<b>3E#Dx$zN|jH_;*>MTrVlNj2g!!@x}R^<5?3P$*!vb
zt!108B=qogDu)1nI(jKPVjbaCOU9dh*0$6Q1Z7P{u59x!?Oq43txu~(`a{jn1*oyl
zWvoCbPAI%Gy5?Uf$k&{?Tv&yWm=l;NEfmf{kc!oFZ%y#A5Q;iO@9c<K#wKFHMe?g}
zpUJGe8SSb69PfT_lf2U&$v>OU;7cTJu`sme!>*BVOFBrQGbrFsa@<LFqCuA<|LR|y
z?yKi8<$X2FCF=X;oz;Agh-zSWz=>yB{2a$$RWBB^i|yo?AX5&}!FEGdk1#|N=BvW&
z>+WV4;iVyoSAu;0I`crFlX|GbP7<@%-|dc`@^u2DH^k*jc29*tgj(Y^by+DllW7&A
z1XD;nY~XI}u9}>{keVoNOQ?<1v0F|eHOD@~lL>`>yiCheOrVVqhN^6#GC6aT2!ApO
zj-P9BgosT#39`gVpcFZ-enWk%Jt@O8f)#h|VUGyQ0x~L{g`sQaa5)nXZY!hOpXrMo
zD+zoG-kNVS0Hl6@OkvDP9t6Wm92Ab>)FUWHjImt)5)Imn*5P*N(T67}u#JXrpk8o5
zJOGW*CDJ*YD(DBAbM7hXzrjHQBA!hMv*N3&L1m=C4}VKqBcK#7!ol=dI&(fx#oU>0
zy>d^G=d}{{hF$u+tof^BNlZ`E=!Jz#j%mh0@}O;7wTMU8Vjap}l)q%pouFCiX#qdS
zUE--xo-YwO_iUsj2ds5ANJj6Msg@om-8v1|#Ib+v!ZzgBmyfPV=6ETVd%nGUVU>h_
z_mwb&<yX_Ze}d~P98vAbCJA+iBupGYcL5xxrHYa(+#Sz-CVG_fF4?1uTn^H`GIwvG
zp?w3dTK%$y$~5GsQJ2y0+zVBlSYg6{pvcpg8(0Uaf-3h@oPN|5;nx+Tfs5=_PIJeL
zTd3;kp|hrDNJPkXaPn<mj?Gayd&`w0OH2j$+ytdMn({VH1TEV4sh{MkrOtvig&SDR
z0h{H@qy(j>Ezz6QlO6k?r(eZO1xI8ASL316tog8XNj`02MD6;zOOe2lkc+zibw^P9
z#u?2cGZp;3&&W0DVHkZYV1nx^A?DVrRju>BT7X|LTN(ykEw3Zea9KKJB&6@{?MkFm
c$kHof$?p7vZg}3$nd3Hxusi&k7YgeC0Nr<wmH+?%

literal 0
HcmV?d00001

diff --git a/docs/python_docs/themes/mx-theme/src/js/sphinx_materialdesign_theme.js b/docs/python_docs/themes/mx-theme/src/js/sphinx_materialdesign_theme.js
index 15e62e905267..5f14255205f4 100644
--- a/docs/python_docs/themes/mx-theme/src/js/sphinx_materialdesign_theme.js
+++ b/docs/python_docs/themes/mx-theme/src/js/sphinx_materialdesign_theme.js
@@ -170,5 +170,6 @@ $(function() {
         header.append(hint);
     });
 
-    $('.mdl-layout__container').css('visibility', 'visible');
+    $('.mdl-layout').css('visibility', 'visible');
+
 });
diff --git a/docs/python_docs/themes/mx-theme/src/scss/layout/_layout.scss b/docs/python_docs/themes/mx-theme/src/scss/layout/_layout.scss
index fbc02229d3e1..2654a2c1e72c 100644
--- a/docs/python_docs/themes/mx-theme/src/scss/layout/_layout.scss
+++ b/docs/python_docs/themes/mx-theme/src/scss/layout/_layout.scss
@@ -25,9 +25,8 @@
     )
 );
 
-.mdl-layout__container {
+.mdl-layout {
     margin-top: 76px;
-    height: calc(100% - 76px);   
 }
 
 
diff --git a/docs/settings.ini b/docs/settings.ini
deleted file mode 100644
index 9a57ac1292e8..000000000000
--- a/docs/settings.ini
+++ /dev/null
@@ -1,172 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-[mxnet]
-build_mxnet = 0
-
-[document_sets_tutorial]
-artifacts = 0
-clojure_docs = 0
-doxygen_docs = 1
-java_docs = 0
-r_docs = 0
-julia_docs = 0
-scala_docs = 0
-
-[document_sets_default]
-artifacts = 1
-clojure_docs = 0
-doxygen_docs = 0
-java_docs = 0
-r_docs = 0
-julia_docs = 0
-scala_docs = 0
-
-[document_sets_1.4.0]
-artifacts = 0
-clojure_docs = 1
-doxygen_docs = 1
-java_docs = 1
-r_docs = 0
-julia_docs = 0
-scala_docs = 1
-
-[document_sets_v1.4.x]
-artifacts = 0
-clojure_docs = 1
-doxygen_docs = 1
-java_docs = 1
-r_docs = 0
-julia_docs = 0
-scala_docs = 1
-
-[document_sets_1.3.1]
-artifacts = 0
-clojure_docs = 1
-doxygen_docs = 1
-java_docs = 0
-r_docs = 0
-julia_docs = 0
-scala_docs = 1
-
-[document_sets_1.3.0]
-artifacts = 0
-clojure_docs = 1
-doxygen_docs = 1
-java_docs = 0
-r_docs = 0
-julia_docs = 0
-scala_docs = 1
-
-[document_sets_v1.3.x]
-artifacts = 0
-clojure_docs = 0
-doxygen_docs = 1
-java_docs = 0
-r_docs = 0
-julia_docs = 0
-scala_docs = 0
-
-[document_sets_1.2.0]
-artifacts = 0
-clojure_docs = 0
-doxygen_docs = 1
-java_docs = 0
-r_docs = 0
-julia_docs = 0
-scala_docs = 1
-
-[document_sets_v1.2.0]
-artifacts = 0
-clojure_docs = 0
-doxygen_docs = 1
-java_docs = 0
-r_docs = 0
-julia_docs = 0
-scala_docs = 1
-
-[document_sets_1.1.0]
-artifacts = 0
-clojure_docs = 0
-doxygen_docs = 1
-java_docs = 0
-r_docs = 0
-julia_docs = 0
-scala_docs = 0
-
-[document_sets_v1.1.0]
-artifacts = 0
-clojure_docs = 0
-doxygen_docs = 1
-java_docs = 0
-r_docs = 0
-julia_docs = 0
-scala_docs = 0
-
-[document_sets_1.0.0]
-artifacts = 0
-clojure_docs = 0
-doxygen_docs = 1
-java_docs = 0
-r_docs = 0
-julia_docs = 0
-scala_docs = 0
-
-[document_sets_v1.0.0]
-artifacts = 0
-clojure_docs = 0
-doxygen_docs = 1
-java_docs = 0
-r_docs = 0
-julia_docs = 0
-scala_docs = 0
-
-[document_sets_0.12.0]
-artifacts = 0
-clojure_docs = 0
-doxygen_docs = 1
-java_docs = 0
-r_docs = 0
-julia_docs = 0
-scala_docs = 0
-
-[document_sets_v0.12.0]
-artifacts = 0
-clojure_docs = 0
-doxygen_docs = 1
-java_docs = 0
-r_docs = 0
-julia_docs = 0
-scala_docs = 0
-
-[document_sets_0.11.0]
-artifacts = 0
-clojure_docs = 0
-doxygen_docs = 1
-java_docs = 0
-r_docs = 0
-julia_docs = 0
-scala_docs = 0
-
-[document_sets_v0.11.0]
-artifacts = 0
-clojure_docs = 0
-doxygen_docs = 1
-java_docs = 0
-r_docs = 0
-julia_docs = 0
-scala_docs = 0
diff --git a/docs/static_site/src/pages/api/architecture/note_engine.md b/docs/static_site/src/pages/api/architecture/note_engine.md
index d339f45c9160..55be7187b9e8 100644
--- a/docs/static_site/src/pages/api/architecture/note_engine.md
+++ b/docs/static_site/src/pages/api/architecture/note_engine.md
@@ -379,13 +379,12 @@ Allowing mutation mitigates these issues.
 
 
 ## Source Code of the Generic Dependency Engine
-[MXNet](https://github.com/dmlc/mxnet) provides an implementation
+[MXNet](https://github.com/apache/incubator-mxnet) provides an implementation
 of the generic dependency engine described in this page.
-You can find more details in [this topic](http://mxnet.io/architecture/note_engine.html).
 We welcome your contributions.
 
 ## Next Steps
 
-* [Squeeze the Memory Consumption of Deep Learning](http://mxnet.io/architecture/note_memory.html)
-* [Efficient Data Loading Module for Deep Learning](http://mxnet.io/architecture/note_data_loading.html)
+* [Squeeze the Memory Consumption of Deep Learning](note_memory)
+* [Efficient Data Loading Module for Deep Learning](note_data_loading)
 * [Survey of RNN Interface](http://mxnet.io/architecture/rnn_interface.html)
diff --git a/docs/static_site/src/pages/api/architecture/note_memory.md b/docs/static_site/src/pages/api/architecture/note_memory.md
index 58c76027aef9..d2e575b6d111 100644
--- a/docs/static_site/src/pages/api/architecture/note_memory.md
+++ b/docs/static_site/src/pages/api/architecture/note_memory.md
@@ -347,5 +347,4 @@ So here are two takeaways:
 
 ## Next Steps
 
-* [Efficient Data Loading Module for Deep Learning](http://mxnet.io/architecture/note_data_loading.html)
-* [Survey of RNN Interface](http://mxnet.io/architecture/rnn_interface.html)
+* [Efficient Data Loading Module for Deep Learning](note_data_loading)
diff --git a/docs/static_site/src/pages/api/architecture/overview.md b/docs/static_site/src/pages/api/architecture/overview.md
index c0ef07c0d64e..d595ed031d7b 100644
--- a/docs/static_site/src/pages/api/architecture/overview.md
+++ b/docs/static_site/src/pages/api/architecture/overview.md
@@ -57,7 +57,7 @@ execute a bunch of functions following their dependencies.
 Execution of any two functions with dependencies should be serialized.
 To boost performance, functions with no dependencies *can* be executed in parallel.
 For a general discussion of this topic,
-see our [notes on the dependency engine](note_engine.md).
+see our [notes on the dependency engine](note_engine).
 
 ### Interface
 
@@ -871,4 +871,4 @@ The gradient, which can be found in `src/operator/smooth_l1_unary-inl.h`, is sim
 
 ### Beyond Two Operands
 The new unified API is designed to fulfill the fundamentals of an operation. For operators with more than two inputs,
-more than one output, or that need more features, see the original [Operator API](http://mxnet.io/architecture/overview.html#operators-in-mxnet).
+more than one output, or that need more features, see the original [Operator API](overview#operators-in-mxnet).
diff --git a/docs/static_site/src/pages/api/architecture/program_model.md b/docs/static_site/src/pages/api/architecture/program_model.md
index 14cfba70f281..46ec7c7b5172 100644
--- a/docs/static_site/src/pages/api/architecture/program_model.md
+++ b/docs/static_site/src/pages/api/architecture/program_model.md
@@ -617,13 +617,12 @@ to create more interesting and intelligent deep learning libraries.
 
 ## Contribute to MXNet
 
-This document is part of our effort to provide [open-source system design notes](index.md)
+This document is part of our effort to provide [open-source system design notes](overview)
 for deep learning libraries. If you're interested in contributing to _MXNet_ or its
-documentation, [fork us on GitHub](http://github.com/dmlc/mxnet).
+documentation, [fork us on GitHub](http://github.com/apache/incubator-mxnet).
 
 ## Next Steps
 
-* [Dependency Engine for Deep Learning](http://mxnet.io/architecture/note_engine.html)
-* [Squeeze the Memory Consumption of Deep Learning](http://mxnet.io/architecture/note_memory.html)
-* [Efficient Data Loading Module for Deep Learning](http://mxnet.io/architecture/note_data_loading.html)
-* [Survey of RNN Interface](http://mxnet.io/architecture/rnn_interface.html)
+* [Dependency Engine for Deep Learning](note_engine)
+* [Squeeze the Memory Consumption of Deep Learning](note_memory)
+* [Efficient Data Loading Module for Deep Learning](note_data_loading)
diff --git a/docs/static_site/src/pages/api/clojure/docs/tutorials/kvstore.md b/docs/static_site/src/pages/api/clojure/docs/tutorials/kvstore.md
index 243cb8a9e7db..b294f5b7080d 100644
--- a/docs/static_site/src/pages/api/clojure/docs/tutorials/kvstore.md
+++ b/docs/static_site/src/pages/api/clojure/docs/tutorials/kvstore.md
@@ -27,7 +27,7 @@ Topics:
 
 * [Basic Push and Pull](#basic-push-and-pull)
 * [List Key-Value Pairs](#list-key-value-pairs)
-* [API Reference](http://mxnet.incubator.apache.org/api/clojure/docs/org.apache.clojure-mxnet.kvstore.html)
+* [Clojure API Reference]({{'/api/clojure/docs/api'|relative_url}})
 
 To follow along with this documentation, you can use this namespace to with the needed requires:
 
diff --git a/docs/static_site/src/pages/api/clojure/docs/tutorials/module.md b/docs/static_site/src/pages/api/clojure/docs/tutorials/module.md
index fdd739a7a333..f6a9fdd53bf3 100644
--- a/docs/static_site/src/pages/api/clojure/docs/tutorials/module.md
+++ b/docs/static_site/src/pages/api/clojure/docs/tutorials/module.md
@@ -33,7 +33,7 @@ Topics:
 * [Preparing a Module for Computation](#preparing-a-module-for-computation)
 * [Training and Predicting](#training-and-predicting)
 * [Saving and Loading](#saving-and-loading)
-* [API Reference](http://mxnet.incubator.apache.org/api/clojure/docs/org.apache.clojure-mxnet.module.html)
+* [Clojure API Reference]({{'/api/clojure/docs/api'|relative_url}})
 
 
 To follow along with this documentation, you can use this namespace to with the needed requires:
@@ -146,9 +146,10 @@ You can pass in batch-end callbacks using batch-end-callback and epoch-end callb
 (first (ndarray/->vec (first results))) ;=>0.08261358
 ```
 
-The module collects and returns all of the prediction results. For more details about the format of the return values, see the documentation for the [`predict`](docs/org.apache.clojure-mxnet.module.html#var-fit-params) function.
+The module collects and returns all of the prediction results. For more details about the format of the return values, see the documentation for the [`predict`]({{'/api/clojure/docs/api/org.apache.clojure-mxnet.module.html#var-predict'|relative_url}}) function.
+
+When prediction results might be too large to fit in memory, use the [`predict-every-batch`]({{'/api/clojure/docs/api/org.apache.clojure-mxnet.module.html#var-predict-every-batch'|relative_url}}) API.
 
-When prediction results might be too large to fit in memory, use the [`predict-every-batch`](docs/org.apache.clojure-mxnet.module.html#predict-every-batch) API.
 
 ```clojure
 (let [preds (m/predict-every-batch mod {:eval-data test-data})]
@@ -254,6 +255,6 @@ Create fit-params and then use it to set `begin-epoch` so that `fit` knows to re
 
 
 ## Next Steps
-* See [Symbolic API](symbol.md) for operations on NDArrays that assemble neural networks from layers.
-* See [NDArray API](ndarray.md) for vector/matrix/tensor operations.
-* See [KVStore API](kvstore.md) for multi-GPU and multi-host distributed training.
+* See [Symbolic API](symbol) for operations on NDArrays that assemble neural networks from layers.
+* See [NDArray API](ndarray) for vector/matrix/tensor operations.
+* See [KVStore API](kvstore) for multi-GPU and multi-host distributed training.
diff --git a/docs/static_site/src/pages/api/clojure/docs/tutorials/ndarray.md b/docs/static_site/src/pages/api/clojure/docs/tutorials/ndarray.md
index 42d06c0d8802..f179ed627bc2 100644
--- a/docs/static_site/src/pages/api/clojure/docs/tutorials/ndarray.md
+++ b/docs/static_site/src/pages/api/clojure/docs/tutorials/ndarray.md
@@ -31,7 +31,8 @@ Topics:
 
 * [Create NDArray](#create-ndarray)
 * [NDArray Operations](#ndarray-operations)
-* [NDArray API Reference](http://mxnet.incubator.apache.org/api/clojure/docs/org.apache.clojure-mxnet.ndarray.html)
+* [NDArray API Reference]({{'/api/clojure/docs/api/org.apache.clojure-mxnet.ndarray-api.html'|relative_url}})
+
 
 To follow along with this documentation, you can use this namespace with the needed requires:
 
@@ -140,4 +141,5 @@ Device information is stored in the `mxnet.Context` structure. When creating NDA
 ```
 
 ## Next Steps
-* See [KVStore API](kvstore.md) for multi-GPU and multi-host distributed training.
+* See [KVStore API](kvstore) for multi-GPU and multi-host distributed training.
+
diff --git a/docs/static_site/src/pages/api/clojure/docs/tutorials/symbol.md b/docs/static_site/src/pages/api/clojure/docs/tutorials/symbol.md
index 5d902629919f..b9deaf4689a9 100644
--- a/docs/static_site/src/pages/api/clojure/docs/tutorials/symbol.md
+++ b/docs/static_site/src/pages/api/clojure/docs/tutorials/symbol.md
@@ -31,10 +31,11 @@ Topics:
 * [Group Multiple Symbols](#group-multiple-symbols)
 * [Serialization](#serialization)
 * [Executing Symbols](#executing-symbols)
-* [Symbol API Reference](http://mxnet.incubator.apache.org/api/clojure/docs/org.apache.clojure-mxnet.symbol.html)
+* [Symbol API Reference]({{'/api/clojure/docs/api/org.apache.clojure-mxnet.symbol'|relative_url}})
 
 
-We also highly encourage you to read [Symbolic Configuration and Execution in Pictures](symbol_in_pictures.md).
+We also highly encourage you to read [Symbolic Configuration and Execution in Pictures](symbol_in_pictures).
+
 
 To follow along with this documentation, you can use this namespace to with the following requirements:
 
@@ -111,7 +112,7 @@ To construct neural networks with multiple loss layers, we can use `group` to gr
 ```
 
 ## Serialization
-You can use the [`save`](docs/org.apache.clojure-mxnet.symbol.html#var-save) and [`load`](docs/org.apache.clojure-mxnet.symbol.html#var-load) functions to serialize the Symbol objects. The advantage of using save and load functions is that it is language agnostic and cloud friendly. The symbol is saved in JSON format. You can also get a JSON string directly using mxnet.Symbol.toJson. Refer to API documentation for more details.
+You can use the [`save`]({{'/api/clojure/docs/api/org.apache.clojure-mxnet.symbol.html#var-save'|relative_url}}) and [`load`]({{'/api/clojure/docs/api/org.apache.clojure-mxnet.symbol.html#var-load'|relative_url}}) functions to serialize the Symbol objects. The advantage of using save and load functions is that it is language agnostic and cloud friendly. The symbol is saved in JSON format. You can also get a JSON string directly using mxnet.Symbol.toJson. Refer to API documentation for more details.
 
  The following example shows how to save a symbol to a file, load it back, and compare two symbols using a JSON string. You can also save to S3 as well
 
@@ -152,5 +153,5 @@ _To do this you must have the correct native library jar defined as a dependency
 ```
 
 ## Next Steps
-* See [NDArray API](ndarray.md) for vector/matrix/tensor operations.
-* See [KVStore API](kvstore.md) for multi-GPU and multi-host distributed training.
+* See [NDArray API](ndarray) for vector/matrix/tensor operations.
+* See [KVStore API](kvstore) for multi-GPU and multi-host distributed training.
\ No newline at end of file
diff --git a/docs/static_site/src/pages/api/clojure/docs/tutorials/symbol_in_pictures.md b/docs/static_site/src/pages/api/clojure/docs/tutorials/symbol_in_pictures.md
index 705872191962..e288ecde593d 100644
--- a/docs/static_site/src/pages/api/clojure/docs/tutorials/symbol_in_pictures.md
+++ b/docs/static_site/src/pages/api/clojure/docs/tutorials/symbol_in_pictures.md
@@ -26,7 +26,7 @@ permalink: /api/clojure/docs/tutorials/symbol_in_pictures
 
 This topic explains symbolic construction and execution in pictures.
 
-We recommend that you read the [Symbolic API](symbol.md) as another useful reference.
+We recommend that you read the [Symbolic API](symbol) as another useful reference.
 
 ## Compose Symbols
 
@@ -98,4 +98,4 @@ Auxiliary states are just like arguments, except that you can't take the gradien
 
 ## Next Steps
 
-See [Symbolic API](symbol.md) and [Python Documentation](index.md).
+See [Symbolic API](symbol) and [Python Documentation]({{'/api/python'|relative_url}}).
diff --git a/docs/static_site/src/pages/api/cpp/docs/tutorials/basics.md b/docs/static_site/src/pages/api/cpp/docs/tutorials/basics.md
index 00e07e5e96ac..40773d93b325 100644
--- a/docs/static_site/src/pages/api/cpp/docs/tutorials/basics.md
+++ b/docs/static_site/src/pages/api/cpp/docs/tutorials/basics.md
@@ -41,7 +41,7 @@ and decompress them into `data/mnist_data` folder.
 Except linking the MXNet shared library, the C++ package itself is a header-only package,
 which means all you need to do is to include the header files. Among the header files,
 `op.h` is special since it is generated dynamically. The generation should be done when
-[building the C++ package](http://mxnet.incubator.apache.org/versions/master/api/c++/index.html).
+[building the C++ package]({{'/api/cpp/'|relative_url}}).
 It is important to note that you need to **copy the shared library** (`libmxnet.so` in Linux and MacOS,
 `libmxnet.dll` in Windows) from `/path/to/mxnet/lib` to the working directory.
 We do not recommend you to use pre-built binaries because MXNet is under heavy development,
diff --git a/docs/static_site/src/pages/api/cpp/docs/tutorials/mxnet_cpp_inference_tutorial.md b/docs/static_site/src/pages/api/cpp/docs/tutorials/mxnet_cpp_inference_tutorial.md
index c706e08a617d..9392eca2977f 100644
--- a/docs/static_site/src/pages/api/cpp/docs/tutorials/mxnet_cpp_inference_tutorial.md
+++ b/docs/static_site/src/pages/api/cpp/docs/tutorials/mxnet_cpp_inference_tutorial.md
@@ -28,25 +28,23 @@ tag: cpp
 
 ## Overview
 MXNet provides various useful tools and interfaces for deploying your model for inference. For example, you can use [MXNet Model Server](https://github.com/awslabs/mxnet-model-server) to start a service and host your trained model easily.
-Besides that, you can also use MXNet's different language APIs to integrate your model with your existing service. We provide [Python](https://mxnet.incubator.apache.org/api/python/module/module.html),    [Java](https://mxnet.incubator.apache.org/api/java/index.html), [Scala](https://mxnet.incubator.apache.org/api/scala/index.html), and [C++](https://mxnet.incubator.apache.org/api/c++/index.html) APIs.
-
-This tutorial is a continuation of the [Gluon end to end tutorial](https://mxnet.apache.org/versions/master/tutorials/gluon/gluon_from_experiment_to_deployment.html), we will focus on the MXNet C++ API. We have slightly modified the code in [C++ Inference Example](https://github.com/apache/incubator-mxnet/tree/master/cpp-package/example/inference) for our use case.
+Besides that, you can also use MXNet's different language APIs to integrate your model with your existing service. We provide [Python]({{'/api/python/docs/api/symbol-related/mxnet.module'|relative_url}}),    [Java]({{'/api/java/docs/api'|relative_url}}), [Scala]({{'/api/scala/docs/api'|relative_url}}), and [C++]({{'/api/cpp/docs/api'|relative_url}}) APIs.
+We will focus on the MXNet C++ API. We have slightly modified the code in [C++ Inference Example](https://github.com/apache/incubator-mxnet/tree/master/example/inference) for our use case.
 
 ## Prerequisites
 
 To complete this tutorial, you need:
-- Complete the training part of [Gluon end to end tutorial](https://mxnet.apache.org/versions/master/tutorials/gluon/gluon_from_experiment_to_deployment.html)
-- Learn the basics about [MXNet C++ API](https://github.com/apache/incubator-mxnet/tree/master/cpp-package)
+- Complete the training part of [Gluon end to end tutorial]({{'api/python/docs/tutorials/packages/gluon/image-augmentation.html'|relative_url}})
+- Learn the basics about [MXNet C++ API]({{'/api/cpp'|relative_url}})
 
 
 ## Setup the MXNet C++ API
-To use the C++ API in MXNet, you need to build MXNet from source with C++ package. Please follow the [built from source guide](https://mxnet.incubator.apache.org/install/ubuntu_setup.html), and [C++ Package documentation](https://github.com/apache/incubator-mxnet/tree/master/cpp-package)
-to enable the C++ API.
+To use the C++ API in MXNet, you need to build MXNet from source with C++ package. Please follow the [built from source guide]({{'/get_started/ubuntu_setup.html'|relative_url}}), and [C++ Package documentation]({{'/api/cpp'|relative_url}})
 The summary of those two documents is that you need to build MXNet from source with `USE_CPP_PACKAGE` flag set to 1. For example: `make -j USE_CPP_PACKAGE=1`.
 
 ## Load the model and run inference
 
-After you complete [the previous tutorial](https://mxnet.apache.org/versions/master/tutorials/gluon/gluon_from_experiment_to_deployment.html), you will get the following output files:
+After you complete [the previous tutorial]({{'/api/python/docs/tutorials/packages/gluon/gluon_from_experiment_to_deployment.html'|relative_url}}), you will get the following output files:
 1. Model Architecture stored in `flower-recognition-symbol.json`
 2. Model parameter values stored in `flower-recognition-0040.params` (`0040` is for 40 epochs we ran)
 3. Label names stored in `synset.txt`
@@ -282,12 +280,12 @@ Then it will predict your image:
 
 Now you can explore more ways to run inference and deploy your models:
 1. [Java Inference examples](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer)
-2. [Scala Inference examples](https://mxnet.incubator.apache.org/tutorials/scala/)
-3. [ONNX model inference examples](https://mxnet.incubator.apache.org/tutorials/onnx/inference_on_onnx_model.html)
+2. [Scala Inference examples]({{'/api/scala/docs/tutorials'|relative_url}})
+3. [ONNX model inference examples]({{'/api/python/docs/tutorials/deploy/index.html'|relative_url}})
 4. [MXNet Model Server Examples](https://github.com/awslabs/mxnet-model-server/tree/master/examples)
 
 ## References
 
-1. [Gluon end to end tutorial](https://mxnet.apache.org/versions/master/tutorials/gluon/gluon_from_experiment_to_deployment.html)
+1. [Gluon end to end tutorial]({{'/api/python/docs/tutorials/packages/gluon/gluon_from_experiment_to_deployment.html'|relative_url}})
 2. [Gluon C++ inference example](https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/inference/)
 3. [Gluon C++ package](https://github.com/apache/incubator-mxnet/tree/master/cpp-package)
diff --git a/docs/static_site/src/pages/api/faq/add_op_in_backend.md b/docs/static_site/src/pages/api/faq/add_op_in_backend.md
index 65f1c635187d..71296d72f45c 100644
--- a/docs/static_site/src/pages/api/faq/add_op_in_backend.md
+++ b/docs/static_site/src/pages/api/faq/add_op_in_backend.md
@@ -119,7 +119,8 @@ skip passing 0-value parameters through the quadratic operator interface. You
 can choose not to define the default value for a parameter if it is required
 at runtime. Meanwhile, adding brief descriptions to the parameters enables
 the documentation engine to display them on
-[MXNet documentation web page](https://mxnet.incubator.apache.org/api/python/index.html).
+[MXNet documentation web page]({{'/api/python/docs/api'|relative_url}}).
+
 
 ### Attribute Inference
 Attribute inference is the process of deducing the properties of `NDArray`s
@@ -373,8 +374,8 @@ with respect to the outputs of the last layer throughout the network to the firs
 layer. The whole process is often known as backward propagation. We are not
 going to delineate the principle of backward propagation here since users can find
 great details covered in other resources, such as
-[CS231n](http://cs231n.github.io/optimization-2/) and
-[How the backgropagation algorithm works](http://neuralnetworksanddeeplearning.com/chap2.html).
+[CS231n](https://cs231n.github.io/optimization-2/) and
+[How the backgropagation algorithm works](https://neuralnetworksanddeeplearning.com/chap2.html).
 The problem we are going to solve here for the `quadratic` operator is that
 given a tensor representing the gradient of the loss function with respect
 to the output of the operator, calculate the gradient with respect to
@@ -688,5 +689,3 @@ We welcome your contributions to MXNet.
 and
 [test_operator.py](https://github.com/apache/incubator-mxnet/blob/master/tests/python/unittest/test_operator.py#L6514).
 
-## Additional Resources
-- [Use TensorInspector to Help Debug Operators](tensor_inspector_tutorial.md)
diff --git a/docs/static_site/src/pages/api/faq/caffe.md b/docs/static_site/src/pages/api/faq/caffe.md
index 6728fa81d0b8..f05b9087aca4 100644
--- a/docs/static_site/src/pages/api/faq/caffe.md
+++ b/docs/static_site/src/pages/api/faq/caffe.md
@@ -104,7 +104,7 @@ are the steps of how to rebuild Caffe:
    cd caffe && wget https://github.com/BVLC/caffe/pull/4527.patch && git apply 4527.patch
    ```
 3. Build and install Caffe by following the
-   [official guide](http://caffe.berkeleyvision.org/installation.html).
+   [official guide](https://caffe.berkeleyvision.org/installation.html).
 
 Next we need to compile MXNet with Caffe supports
 
diff --git a/docs/static_site/src/pages/api/faq/cloud.md b/docs/static_site/src/pages/api/faq/cloud.md
index 25c5841fef47..480773515c49 100644
--- a/docs/static_site/src/pages/api/faq/cloud.md
+++ b/docs/static_site/src/pages/api/faq/cloud.md
@@ -45,7 +45,7 @@ how to set up an AWS cluster with _MXNet_. We show how to:
 ### Use Amazon S3 to Host Data
 
 Amazon S3 provides distributed data storage which proves especially convenient for hosting large datasets.
-To use S3, you need [AWS credentials](http://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSGettingStartedGuide/AWSCredentials.html),
+To use S3, you need [AWS credentials](https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSGettingStartedGuide/AWSCredentials.html),
 including an `ACCESS_KEY_ID` and a `SECRET_ACCESS_KEY`.
 
 To use _MXNet_ with S3, set the environment variables `AWS_ACCESS_KEY_ID` and
@@ -58,7 +58,7 @@ export AWS_SECRET_ACCESS_KEY=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
 ```
 
 There are several ways to upload data to S3. One simple way is to use
-[s3cmd](http://s3tools.org/s3cmd). For example:
+[s3cmd](https://s3tools.org/s3cmd). For example:
 
 ```bash
 wget http://data.mxnet.io/mxnet/data/mnist.zip
@@ -68,16 +68,16 @@ unzip mnist.zip && s3cmd put t*-ubyte s3://dmlc/mnist/
 ### Use Pre-installed EC2 GPU Instance
 The [Deep Learning AMI](https://aws.amazon.com/marketplace/pp/B01M0AXXQB?qid=1475211685369&sr=0-1&ref_=srh_res_product_title) is an Amazon Linux image
 supported and maintained by Amazon Web Services for use on Amazon Elastic Compute Cloud (Amazon EC2).
-It contains [MXNet-v0.9.3 tag](https://github.com/dmlc/mxnet) and the necessary components to get going with deep learning,
-including Nvidia drivers, CUDA, cuDNN, Anaconda, Python2 and Python3.
+It contains [MXNet-v0.9.3 tag](https://github.com/apache/incubator-mxnet) and the necessary components to get going with deep learning,
+including Nvidia drivers, CUDA, cuDNN, Anaconda, Python2 and Python3.   
 The AMI IDs are the following:
 
 * us-east-1: ami-e7c96af1
 * us-west-2: ami-dfb13ebf
 * eu-west-1: ami-6e5d6808
 
-Now you can launch _MXNet_ directly on an EC2 GPU instance.
-You can also use [Jupyter](http://jupyter.org) notebook on EC2 machine.
+Now you can launch _MXNet_ directly on an EC2 GPU instance.  
+You can also use [Jupyter](https://jupyter.org) notebook on EC2 machine.
 Here is a [good tutorial](https://github.com/dmlc/mxnet-notebooks)
 on how to connect to a Jupyter notebook running on an EC2 instance.
 
@@ -202,7 +202,7 @@ Put all of the record files into a folder, and point the data path to the folder
 
 #### Use YARN and SGE
 Although using SSH can be simple when you don't have a cluster scheduling framework,
-_MXNet_ is designed to be portable to various platforms.
+_MXNet_ is designed to be portable to various platforms.  
 We provide scripts available in [tracker](https://github.com/dmlc/dmlc-core/tree/master/tracker)
 to allow running on other cluster frameworks, including Hadoop (YARN) and SGE.
 We welcome contributions from the community of examples of running _MXNet_ on your favorite distributed platform.
diff --git a/docs/static_site/src/pages/api/faq/distributed_training.md b/docs/static_site/src/pages/api/faq/distributed_training.md
index b1629794d308..169b52183d75 100644
--- a/docs/static_site/src/pages/api/faq/distributed_training.md
+++ b/docs/static_site/src/pages/api/faq/distributed_training.md
@@ -37,7 +37,7 @@ In this document, we describe how to train a model with devices distributed acro
 
 When models are so large that they don't fit into device memory, then a second way called *model parallelism* is useful.
 Here, different devices are assigned the task of learning different parts of the model.
-Currently, MXNet supports Model parallelism in a single machine only. Refer [Training with multiple GPUs using model parallelism](https://mxnet.incubator.apache.org/versions/master/faq/model_parallel_lstm.html) for more on this.
+Currently, MXNet supports Model parallelism in a single machine only. Refer [Training with multiple GPUs using model parallelism](model_parallel_lstm) for more on this.
 
 ## How Does Distributed Training Work?
 The following concepts are key to understanding distributed training in MXNet:
@@ -69,7 +69,7 @@ The distributed mode of KVStore is enabled by calling `mxnet.kvstore.create` fun
 with a string argument which contains the word `dist` as follows:
 > kv = mxnet.kvstore.create('dist_sync')
 
-Refer [KVStore API](https://mxnet.incubator.apache.org/versions/master/api/python/kvstore/kvstore.html) for more information about KVStore.
+Refer [KVStore API]({{'/api/python/docs/api/gluon-related/mxnet.kvstore.KVStore.html#mxnet.kvstore.KVStore'|relative_url}}) for more information about KVStore.
 
 ### Distribution of Keys
 Each server doesn't necessarily store all the keys or parameter arrays.
@@ -91,7 +91,7 @@ In the case of distributed training though, we would need to divide the dataset
 
 Typically, this split of data for each worker happens through the data iterator,
 on passing the number of parts and the index of parts to iterate over.
-Some iterators in MXNet that support this feature are [mxnet.io.MNISTIterator](https://mxnet.incubator.apache.org/versions/master/api/python/io/io.html#mxnet.io.MNISTIter) and [mxnet.io.ImageRecordIter](https://mxnet.incubator.apache.org/versions/master/api/python/io/io.html#mxnet.io.ImageRecordIter).
+Some iterators in MXNet that support this feature are [mxnet.io.MNISTIterator]({{'/api/python/docs/api/gluon-related/_autogen/mxnet.io.MNISTIter.html#mxnet.io.MNISTIter'|relative_url}}) and [mxnet.io.ImageRecordIter]({{'/api/python/docs/api/gluon-related/_autogen/mxnet.io.ImageRecordIter.html#mxnet.io.ImageRecordIter'|relative_url}}).
 If you are using a different iterator, you can look at how the above iterators implement this.
 We can use the kvstore object to get the number of workers (`kv.num_workers`) and rank of the current worker (`kv.rank`).
 These can be passed as arguments to the iterator.
@@ -101,7 +101,7 @@ to see an example usage.
 ### Updating weights
 KVStore server supports two modes, one which aggregates the gradients and updates the weights using those gradients, and second where the server only aggregates gradients. In the latter case, when a worker process pulls from kvstore, it gets the aggregated gradients. The worker then uses these gradients and applies the weights locally.
 
-When using Gluon there is an option to choose between these modes by passing `update_on_kvstore` variable when you create the [Trainer](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/gluon.html#mxnet.gluon.Trainer) object like this:
+When using Gluon there is an option to choose between these modes by passing `update_on_kvstore` variable when you create the [Trainer]({{'/api/python/docs/api/gluon/mxnet.gluon.Trainer.html'|relative_url}}) object like this:
 
 ```
 trainer = gluon.Trainer(net.collect_params(), optimizer='sgd',
@@ -142,7 +142,7 @@ This is faster than `dist_sync` because it reduces expensive communication betwe
 ### Gradient Compression
 When communication is expensive, and the ratio of computation time to communication time is low, communication can become a bottleneck.
 In such cases, gradient compression can be used to reduce the cost of communication, thereby speeding up training.
-Refer [Gradient compression](https://mxnet.incubator.apache.org/versions/master/faq/gradient_compression.html) for more details.
+Refer [Gradient compression]({{'/api/faq/gradient_compression'|relative_url}}) for more details.
 
 Note: For small models when the cost of computation is much lower than cost of communication,
 distributed training might actually be slower than training on a single machine because of the overhead of communication and synchronization.
@@ -179,7 +179,7 @@ The options in the following command are the recommended options when mounting a
 sudo mkdir efs && sudo mount -t nfs4 -o nfsvers=4.1,rsize=1048576,wsize=1048576,hard,timeo=600,retrans=2 NETWORK_FILE_SYSTEM_IP:/ efs
 ```
 
-Tip: You might find it helpful to store large datasets on S3 for easy access from all machines in the cluster. Refer [Using data from S3 for training](https://mxnet.incubator.apache.org/versions/master/faq/s3_integration.html) for more information.
+Tip: You might find it helpful to store large datasets on S3 for easy access from all machines in the cluster. Refer [Using data from S3 for training]({{'/api/faq/s3_integration'|relative_url}}) for more information.
 
 ### Using Launch.py
 MXNet provides a script [tools/launch.py](https://github.com/apache/incubator-mxnet/blob/master/tools/launch.py) to make it easy to launch distributed training on a cluster with `ssh`, `mpi`, `sge` or `yarn`.
diff --git a/docs/static_site/src/pages/api/faq/env_var.md b/docs/static_site/src/pages/api/faq/env_var.md
index a5e3613229a9..e4c4ac20fc02 100644
--- a/docs/static_site/src/pages/api/faq/env_var.md
+++ b/docs/static_site/src/pages/api/faq/env_var.md
@@ -61,7 +61,7 @@ $env:MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
   - The number of threads given to prioritized CPU jobs.
 * MXNET_CPU_NNPACK_NTHREADS
   - Values: Int ```(default=4)```
-  - The number of threads used for NNPACK. NNPACK package aims to provide high-performance implementations of some layers for multi-core CPUs. Checkout [NNPACK](http://mxnet.io/faq/nnpack.html) to know more about it.
+  - The number of threads used for NNPACK. NNPACK package aims to provide high-performance implementations of some layers for multi-core CPUs. Checkout [NNPACK]({{'/api/faq/nnpack'|relative_url}}) to know more about it.
 * MXNET_MP_WORKER_NTHREADS
   - Values: Int ```(default=1)```
   - The number of scheduling threads on CPU given to multiprocess workers. Enlarge this number allows more operators to run in parallel in individual workers but please consider reducing the overall `num_workers` to avoid thread contention (not available on Windows).
@@ -73,7 +73,7 @@ $env:MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
 
 * MXNET_EXEC_ENABLE_INPLACE
   - Values: true or false ```(default=true)```
-  - Whether to enable in-place optimization in symbolic execution. Checkout [in-place optimization](http://mxnet.io/architecture/note_memory.html#in-place-operations) to know more about it.
+    - Whether to enable in-place optimization in symbolic execution. Checkout [in-place optimization]({{'/api/architecture/note_memory#in-place-operations'|relative_url}}) to know more about it.
 * NNVM_EXEC_MATCH_RANGE
   - Values: Int ```(default=16)```
   - The approximate matching scale in the symbolic execution memory allocator.
@@ -83,7 +83,7 @@ $env:MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
   - Values: Int ```(default=1)```
   - The maximum number of temporary workspaces to allocate to each device. This controls space replicas and in turn reduces the memory usage.
   - Setting this to a small number can save GPU memory. It will also likely decrease the level of parallelism, which is usually acceptable.
-  - MXNet internally uses graph coloring algorithm to [optimize memory consumption](http://mxnet.io/architecture/note_memory.html).
+    - MXNet internally uses graph coloring algorithm to [optimize memory consumption]({{'/api/architecture/note_memory'|relative_url}}).
   - This parameter is also used to get number of matching colors in graph and in turn how much parallelism one can get in each GPU. Color based match usually costs more memory but also enables more parallelism.
 * MXNET_GPU_MEM_POOL_RESERVE
   - Values: Int ```(default=5)```
diff --git a/docs/static_site/src/pages/api/faq/float16.md b/docs/static_site/src/pages/api/faq/float16.md
index 3938a8f1ca2d..4cc115f894d4 100644
--- a/docs/static_site/src/pages/api/faq/float16.md
+++ b/docs/static_site/src/pages/api/faq/float16.md
@@ -3,8 +3,8 @@ layout: page_category
 title:  Float16
 category: faq
 faq_c: Speed
-question: How do I use mixed precision (float16) with MXNet or Gluon?
-permalink: /api/faq/floa16
+question: How do I use mixed precision (float16) with MXNet or Gluon? 
+permalink: /api/faq/float16
 ---
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
@@ -47,19 +47,19 @@ This tutorial also assumes understanding of how to train a network with float32
 
 With Gluon API, you need to take care of three things to convert a model to support computation with float16.
 
-1. Cast Gluon `Block`'s parameters and expected input type to float16 by calling the [cast](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.Block.cast) method of the `Block` representing the network.
+1. Cast Gluon `Block`'s parameters and expected input type to float16 by calling the [cast]({{'/api/python/docs/api/gluon/mxnet.gluon.nn.Block.html#mxnet.gluon.nn.Block.cast'|relative_url}}) method of the `Block` representing the network.
 
 ```python
 net.cast('float16')
 ```
 
-2. Ensure the data input to the network is of float16 type. If your `DataLoader` or `Iterator` produces output in another datatype, then you would have to cast your data. There are different ways you can do this. The easiest would be to use the [astype](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.astype) method of NDArrays.
+2. Ensure the data input to the network is of float16 type. If your `DataLoader` or `Iterator` produces output in another datatype, then you would have to cast your data. There are different ways you can do this. The easiest would be to use the [astype]({{'/api/python/docs/api/ndarray/_autogen/mxnet.ndarray.NDArray.astype.html#mxnet.ndarray.NDArray.astype'|relative_url}}) method of NDArrays.
 
 ```python
 data = data.astype('float16', copy=False)
 ```
 
-If you are using images and DataLoader, you can also use a [Cast transform](https://mxnet.incubator.apache.org/api/python/gluon/data.html#mxnet.gluon.data.vision.transforms.Cast).
+If you are using images and DataLoader, you can also use a [Cast transform]({{'/api/python/docs/api/gluon/_autogen/mxnet.gluon.data.vision.transforms.Cast.html#mxnet.gluon.data.vision.transforms.Cast'|relative_url}}).
 
 3. It is preferable to use **multi_precision mode of optimizer** when training in float16. This mode of optimizer maintains a master copy of the weights in float32 even when the training (i.e. forward and backward pass) is in float16. This helps increase precision of the weight updates and can lead to faster convergence in some scenarios.
 
@@ -98,7 +98,7 @@ net.features = pretrained_net.features
 net.cast('float16')
 ```
 
-You can check the parameters of the model by calling [summary](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.Block.summary) with some fake data. Notice the provided `dtype=np.float16` in the line below. As it was mentioned earlier, we have to provide data as float16 as well.
+You can check the parameters of the model by calling [summary]({{'/api/python/docs/api/gluon/mxnet.gluon.nn.Block.html#mxnet.gluon.nn.Block.summary'|relative_url}}) with some fake data. Notice the provided `dtype=np.float16` in the line below. As it was mentioned earlier, we have to provide data as float16 as well.
 
 ```python
 net.summary(mx.nd.uniform(shape=(1, 3, 224, 224), dtype=np.float16))
@@ -253,4 +253,4 @@ You can choose a constant scaling factor while ensuring that the absolute value
 
 ## Recommended Next Steps
 
-* Check out our video tutorial on [Using Mixed Precision with MXNet](https://www.youtube.com/watch?v=pR4KMh1lGC0)
+* Check out our video tutorial on [Using Mixed Precision with MXNet](https://www.youtube.com/watch?v=pR4KMh1lGC0)
\ No newline at end of file
diff --git a/docs/static_site/src/pages/api/faq/model_parallel_lstm.md b/docs/static_site/src/pages/api/faq/model_parallel_lstm.md
index 3263bfb9e9ca..60df280b38fe 100644
--- a/docs/static_site/src/pages/api/faq/model_parallel_lstm.md
+++ b/docs/static_site/src/pages/api/faq/model_parallel_lstm.md
@@ -37,7 +37,7 @@ One key strength of _MXNet_ is its ability to leverage
 powerful heterogeneous hardware environments to achieve significant speedups.
 
 There are two primary ways that we can spread a workload across multiple devices.
-In a previous document, [we addressed data parallelism](./multi_devices.md),
+In a previous document, [we addressed data parallelism](multi_devices),
 an approach in which samples within a batch are divided among the available devices.
 With data parallelism, each device stores a complete copy of the model.
 Here, we explore _model parallelism_, a different approach.
@@ -50,7 +50,7 @@ LSTMS are powerful sequence models, that have proven especially useful
 for [natural language translation](https://arxiv.org/pdf/1409.0473.pdf), [speech recognition](https://arxiv.org/abs/1512.02595),
 and working with [time series data](https://arxiv.org/abs/1511.03677).
 For a general high-level introduction to LSTMs,
-see the excellent [tutorial](http://colah.github.io/posts/2015-08-Understanding-LSTMs/) by Christopher Olah.
+see the excellent [tutorial](https://colah.github.io/posts/2015-08-Understanding-LSTMs/) by Christopher Olah.
 
 
 ## Model Parallelism: Using Multiple GPUs As a Pipeline
diff --git a/docs/static_site/src/pages/api/faq/new_op.md b/docs/static_site/src/pages/api/faq/new_op.md
index 8763cccc03a6..0e559e56b74b 100644
--- a/docs/static_site/src/pages/api/faq/new_op.md
+++ b/docs/static_site/src/pages/api/faq/new_op.md
@@ -396,5 +396,5 @@ NNVM_REGISTER_OP(_backward_abs)
 ### Legacy Operators
 
 For the legacy (pre 0.9) way of defining operators with C++, please see:
-- [Developer Guide - Operators](http://mxnet.io/architecture/overview.html#operators-in-mxnet)
-- [Developer Guide - SimpleOp](http://mxnet.io/architecture/overview.html#simpleop-the-unified-operator-api)
+- [Developer Guide - Operators]({{'/api/architecture/overview.html#operators-in-mxnet'|relative_url}})
+- [Developer Guide - SimpleOp]({{'/api/architecture/overview.html#simpleop-the-unified-operator-api'|relative_url}})
\ No newline at end of file
diff --git a/docs/static_site/src/pages/api/faq/nnpack.md b/docs/static_site/src/pages/api/faq/nnpack.md
index 72950f86a410..84bedee6052c 100644
--- a/docs/static_site/src/pages/api/faq/nnpack.md
+++ b/docs/static_site/src/pages/api/faq/nnpack.md
@@ -93,7 +93,7 @@ $ cd ~
 * Set lib path of NNPACK as the environment variable, e.g. `export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$YOUR_NNPACK_INSTALL_PATH/lib`
 * Add the include file of NNPACK and its third-party to  `ADD_CFLAGS` in config.mk, e.g. `ADD_CFLAGS = -I$(YOUR_NNPACK_INSTALL_PATH)/include/ -I$(YOUR_NNPACK_INSTALL_PATH)/third-party/pthreadpool/include/`
 * Set `USE_NNPACK = 1` in config.mk.
-* Build MXNet from source following the [install guide](http://mxnet.io/install/index.html).
+* Build MXNet from source following the [install guide]({{'/get_started'|relative_url}}).
 
 ### NNPACK Performance
 
diff --git a/docs/static_site/src/pages/api/faq/perf.md b/docs/static_site/src/pages/api/faq/perf.md
index 1cf512d93da4..4ea605262634 100644
--- a/docs/static_site/src/pages/api/faq/perf.md
+++ b/docs/static_site/src/pages/api/faq/perf.md
@@ -134,7 +134,7 @@ AWS EC2 C5.xlarge:
 
 ## Other CPU
 
-If using CPUs (not just Intel CPUs -- ARMs also), NNPACK can improve the running performance with 2x~7x, please check [nnpack.md](./nnpack.md) for details.
+If using CPUs (not just Intel CPUs -- ARMs also), NNPACK can improve the running performance with 2x~7x, please check [nnpack.md](nnpack) for details.
 
 ## Nvidia GPU
 
@@ -289,7 +289,7 @@ that gives detailed information about execution time at the symbol level.
 This feature complements general profiling tools like _nvprof_ and _gprof_
 by summarizing at the operator level, instead of a function, kernel, or instruction level.
 
-The profiler can be turned on with an [environment variable](http://mxnet.io/faq/env_var.html#control-the-profiler)
+The profiler can be turned on with an [environment variable]({{'/api/faq/env_var#control-the-profiler' | relative_url}})
 for an entire program run, or programmatically for just part of a run.
 See [example/profiler](https://github.com/dmlc/mxnet/tree/master/example/profiler)
 for complete examples of how to use the profiler in code, but briefly, the Python code looks like:
diff --git a/docs/static_site/src/pages/api/faq/s3_integration.md b/docs/static_site/src/pages/api/faq/s3_integration.md
index d45942a31f0c..de617e452211 100644
--- a/docs/static_site/src/pages/api/faq/s3_integration.md
+++ b/docs/static_site/src/pages/api/faq/s3_integration.md
@@ -41,7 +41,7 @@ Following are detailed instructions on how to use data from S3 for training.
 
 ## Step 1: Build MXNet with S3 integration enabled
 
-Follow instructions [here](http://mxnet.io/install/index.html) to install MXNet from source with the following additional steps to enable S3 integration.
+Follow instructions [here]({{'/get_started'|relative_url}}) to install MXNet from source with the following additional steps to enable S3 integration.
 
 1. Install `libcurl4-openssl-dev` and `libssl-dev` before building MXNet. These packages are required to read/write from AWS S3.
 2. Append `USE_S3=1` to `config.mk` before building MXNet.
diff --git a/docs/static_site/src/pages/api/faq/visualize_graph.md b/docs/static_site/src/pages/api/faq/visualize_graph.md
index c9167a9d81a3..8d477779b54f 100644
--- a/docs/static_site/src/pages/api/faq/visualize_graph.md
+++ b/docs/static_site/src/pages/api/faq/visualize_graph.md
@@ -34,13 +34,13 @@ from which the result can be read.
 
 ## Prerequisites
 You need the [Jupyter Notebook](http://jupyter.readthedocs.io/en/latest/)
-and [Graphviz](http://www.graphviz.org/) libraries to visualize the network.
-Please make sure you have followed [installation instructions](http://mxnet.io/install/index.html)
+and [Graphviz](https://www.graphviz.org/) libraries to visualize the network.
+Please make sure you have followed [installation instructions]({{'get_started'|relative_url}})
 in setting up above dependencies along with setting up MXNet.
 
 ## Visualize the sample Neural Network
 
-```mx.viz.plot_network``` takes [Symbol](http://mxnet.io/api/python/symbol/symbol.html), with your Network definition, and optional node_attrs, parameters for the shape of the node in the graph,  as input and generates a computation graph.
+```mx.viz.plot_network``` takes [Symbol]({{'/api/python/docs/api/symbol/index'|relative}}), with your Network definition, and optional node_attrs, parameters for the shape of the node in the graph,  as input and generates a computation graph.
 
 We will now try to visualize a sample Neural Network for linear matrix factorization:
 - Start Jupyter notebook server
diff --git a/docs/static_site/src/pages/api/faq/why_mxnet.md b/docs/static_site/src/pages/api/faq/why_mxnet.md
index 66ec6dc7e3a2..79a54f651b1b 100644
--- a/docs/static_site/src/pages/api/faq/why_mxnet.md
+++ b/docs/static_site/src/pages/api/faq/why_mxnet.md
@@ -201,7 +201,7 @@ Symbolic computation is useful for several reasons. First, because we define a f
 
 ### Gluon for briding the gap between the two
 
-[MXNet Gluon]({{'/api/python'|relative_url}}) aims to bridge the gap between the imperative nature of MXNet and its symbolic capabilities and keep the advantages of both through [hybridization](http://d2l.ai/chapter_computational-performance/hybridize.html).
+[MXNet Gluon]({{'/api/python'|relative_url}}) aims to bridge the gap between the imperative nature of MXNet and its symbolic capabilities and keep the advantages of both through [hybridization](https://d2l.ai/chapter_computational-performance/hybridize.html).
 
 ## Conclusions
 Given its combination of high performance, clean code, access to a high-level API, and low-level control, _MXNet_ stands out as a unique choice among deep learning frameworks.
diff --git a/docs/static_site/src/pages/api/java/docs/tutorials/mxnet_java_on_intellij.md b/docs/static_site/src/pages/api/java/docs/tutorials/mxnet_java_on_intellij.md
index dd35ce033e07..866b696a9c5c 100644
--- a/docs/static_site/src/pages/api/java/docs/tutorials/mxnet_java_on_intellij.md
+++ b/docs/static_site/src/pages/api/java/docs/tutorials/mxnet_java_on_intellij.md
@@ -131,8 +131,8 @@ Click "Import Changes" in this prompt.
 **Step 5.** Build the project:
 - To build the project, from the menu choose Build, and then choose Build Project.
 
-**Step 6.** Navigate to the App.java class in the project and paste the code in `main` method from HelloWorld.java from [Java Demo project](https://github.com/apache/incubator-mxnet/blob/java-api/scala-package/mxnet-demo/java-demo/src/main/java/mxnet/HelloWorld.java) on MXNet repository, overwriting the original hello world code.
-You can also grab the entire [Java Demo project](https://github.com/apache/incubator-mxnet/tree/java-api/scala-package/mxnet-demo/java-demo) and run it by following the instructions on the [README](https://github.com/apache/incubator-mxnet/blob/java-api/scala-package/mxnet-demo/java-demo/README.md).
+**Step 6.** Navigate to the App.java class in the project and paste the code in `main` method from HelloWorld.java from [Java Demo project](https://github.com/apache/incubator-mxnet/tree/master/scala-package/mxnet-demo/java-demo/src/main/java/mxnet/HelloWorld.java) on MXNet repository, overwriting the original hello world code.
+You can also grab the entire [Java Demo project](https://github.com/apache/incubator-mxnet/tree/master/scala-package/mxnet-demo/java-demo) and run it by following the instructions on the [README](https://github.com/apache/incubator-mxnet/blob/master/scala-package/mxnet-demo/java-demo/README.md).
 
 **Step 7.** Now run the App.java.
 
@@ -183,6 +183,6 @@ java -cp "target/javaMXNet-1.0-SNAPSHOT.jar:target/dependency/*" mxnet.App
 ## Next Steps
 For more information about MXNet Java resources, see the following:
 
-* [Java Inference API](/api/java/index.html)
+* [Java Inference API]({{'/api/java'|relative_url}})
 * [Java Inference Examples](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer)
-* [MXNet Tutorials Index](http://mxnet.io/tutorials/index.html)
+* [MXNet Tutorials Index]({{'/api'|relative_url}})
diff --git a/docs/static_site/src/pages/api/java/docs/tutorials/ssd_inference.md b/docs/static_site/src/pages/api/java/docs/tutorials/ssd_inference.md
index 6f8ee59b89b5..0767e50eda04 100644
--- a/docs/static_site/src/pages/api/java/docs/tutorials/ssd_inference.md
+++ b/docs/static_site/src/pages/api/java/docs/tutorials/ssd_inference.md
@@ -31,7 +31,7 @@ The SSD model is trained on the Pascal VOC 2012 dataset. The network is a SSD mo
 ## Prerequisites
 
 To complete this tutorial, you need the following:
-* [MXNet Java Setup on IntelliJ IDEA](mxnet_java_on_intellij.md) (Optional)
+* [MXNet Java Setup on IntelliJ IDEA](mxnet_java_on_intellij) (Optional)
 * [wget](https://www.gnu.org/software/wget/) To download model artifacts
 * SSD Model artifacts
     * Use the following script to get the SSD Model files :
@@ -55,7 +55,7 @@ wget https://cloud.githubusercontent.com/assets/3307514/20012563/cbb41382-a27d-1
 Alternately, you can get the entire SSD Model artifacts + images in one single script from the MXNet Repository by running [get_ssd_data.sh script](https://github.com/apache/incubator-mxnet/blob/master/scala-package/examples/scripts/infer/objectdetector/get_ssd_data.sh)
 
 ## Time to code!
-1\. Following the [MXNet Java Setup on IntelliJ IDEA](mxnet_java_on_intellij.md) tutorial, in the same project `JavaMXNet`, create a new empty class called : `ObjectDetectionTutorial.java`.
+1\. Following the [MXNet Java Setup on IntelliJ IDEA](mxnet_java_on_intellij) tutorial, in the same project `JavaMXNet`, create a new empty class called : `ObjectDetectionTutorial.java`.
 
 2\. In the `main` function of `ObjectDetectionTutorial.java` define the downloaded model path and the image data paths. This is the same path where we downloaded the model artifacts and images in a previous step.
 
@@ -205,6 +205,6 @@ The results returned by the inference call translate into the regions in the ima
 ## Next Steps
 For more information about MXNet Java resources, see the following:
 
-* [Java Inference API](/api/java/index.md)
+* [Java Inference API]({{'/api/java'|relative_url}})
 * [Java Inference Examples](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer)
-* [MXNet Tutorials Index](/tutorials/index.md)
+* [MXNet Tutorials Index]({{'/api'|relative_url}})
diff --git a/docs/static_site/src/pages/api/java/index.md b/docs/static_site/src/pages/api/java/index.md
index c3e3d2a88b49..55db4ff4f954 100644
--- a/docs/static_site/src/pages/api/java/index.md
+++ b/docs/static_site/src/pages/api/java/index.md
@@ -26,16 +26,15 @@ tag: java
 
 # MXNet - Java Inference API
 
-MXNet supports Java for performing inference on a trained model. The MXNet Java Inference API is an extension of the [Scala Infer API](../../api/scala/infer.html) which provides model loading and inference functionality.
-
+MXNet supports Java for performing inference on a trained model. The MXNet Java Inference API is an extension of the [Scala Infer API]({{'/api/scala/docs/api/#org.apache.mxnet.infer.package'|relative_url}}) which provides model loading and inference functionality.
 The goal of the MXNet Java package is to provide an efficient and easy to use inference API.
 The MXNet Java package makes it easy to quickly deploy an existing model into a production level Java ecosystem.
 
 ## Installation
-* [MXNet Java Inference API setup instructions](../../install/java_setup.md)
+* [MXNet Java Inference API setup instructions]({{'/get_started/java_setup'|relative_url}})
 
 ## Tutorials
-See the [tutorial page](../../tutorials/index.html#java-tutorials) for detailed tutorials and examples using the Java Inference API.
+See the [Java tutorial page]({{'/api/java/docs/tutorials'|relative_url}}) for detailed tutorials and examples using the Java Inference API.
 
 ## Java Inference API Reference
-The [Java Infer API javadocs](docs/index.html#org.apache.mxnet.infer.package) provides detailed API information.
+The [Java Infer API javadocs]({{'/api/java/docs/api/#org.apache.mxnet.infer.javaapi.package'|relative_url}}) provides detailed API information.
\ No newline at end of file
diff --git a/docs/static_site/src/pages/api/julia/index.md b/docs/static_site/src/pages/api/julia/index.md
index 28de72089aa0..52c1e2561ba0 100644
--- a/docs/static_site/src/pages/api/julia/index.md
+++ b/docs/static_site/src/pages/api/julia/index.md
@@ -35,5 +35,5 @@ computing and the state-of-art deep learning to Julia.
   and apply them to tasks such as image classification and data science challenges.
 
 ## Installation
-* [Ubuntu installation guide]({{'/get_started' | relative_url}})
+* [Ubuntu installation guide]({{'/get_started/ubuntu_setup' | relative_url}})
 * Mac / Windows guides are not available (contributions welcome!)
diff --git a/docs/static_site/src/pages/api/perl/docs/tutorials/symbol.md b/docs/static_site/src/pages/api/perl/docs/tutorials/symbol.md
index 8f0ecccdb8f7..0a70c7d280ce 100644
--- a/docs/static_site/src/pages/api/perl/docs/tutorials/symbol.md
+++ b/docs/static_site/src/pages/api/perl/docs/tutorials/symbol.md
@@ -118,7 +118,7 @@ ok 1
 
 After you have assembled a set of symbols into a computation graph, the MXNet engine can evaluate them.
 If you are training a neural network, this is typically
-handled by the high-level [AI::MXNet::Module package](module.md) and the [`fit()`] function.
+handled by the high-level [AI::MXNet::Module package](module) and the [`fit()`] function.
 
 For neural networks used in "feed-forward", "prediction", or "inference" mode (all terms for the same
 thing: running a trained network), the input arguments are the
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/char_lstm.md b/docs/static_site/src/pages/api/scala/docs/tutorials/char_lstm.md
index 9b1b65fceedd..e5805145dca3 100644
--- a/docs/static_site/src/pages/api/scala/docs/tutorials/char_lstm.md
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/char_lstm.md
@@ -27,7 +27,7 @@ permalink: /api/scala/docs/tutorials/char_lstm
 This tutorial shows how to train a character-level language model with a multilayer recurrent neural network (RNN) using Scala. This model takes one text file as input and trains an RNN that learns to predict the next character in the sequence. In this tutorial, you train a multilayer LSTM (Long Short-Term Memory) network that generates relevant text using Barack Obama's speech patterns.
 
 There are many documents that explain LSTM concepts. If you aren't familiar with LSTM, refer to the following before you proceed:
-- Christopher Olah's [Understanding LSTM blog post](http://colah.github.io/posts/2015-08-Understanding-LSTMs/)
+- Christopher Olah's [Understanding LSTM blog post](https://colah.github.io/posts/2015-08-Understanding-LSTMs/)
 - [Training a LSTM char-rnn in Julia to Generate Random Sentences](http://dmlc.ml/mxnet/2015/11/15/char-lstm-in-julia.html)
 - [Bucketing in MXNet in Python](https://github.com/dmlc/mxnet-notebooks/blob/master/python/tutorials/char_lstm.ipynb)
 - [Bucketing in MXNet](http://mxnet.io/faq/bucketing.html)
@@ -78,7 +78,7 @@ In this tutorial, you will accomplish the following:
 
 ## Prerequisites
 
-To complete this tutorial, setup and run the scala interpreter by following the [instructions](https://mxnet.incubator.apache.org/install/scala_setup.html#interpreter).
+To complete this tutorial, setup and run the scala interpreter by following the [instructions]({{'/get_started/scala_setup#interpreter'|relative_url}}).
 
 ## Download the Data
 
@@ -132,64 +132,63 @@ To prepare the data:
 
 1) Read the dataset with the following function:
 
-    ```scala
-        scala> import scala.io.Source
+```scala
+scala> import scala.io.Source
 
-        import scala.io.Source
+import scala.io.Source
 
-        scala> // Read file
-        scala> def readContent(path: String): String = Source.fromFile(path).mkString
+scala> // Read file
+scala> def readContent(path: String): String = Source.fromFile(path).mkString
 
-        readContent: (path: String)String
-
-    ```
+readContent: (path: String)String
+```
 
 2) Build a vocabulary with the following function:
 
-    ```scala
-        scala> // Build  a vocabulary of what char we have in the content
-        scala> def buildVocab(path: String): Map[String, Int] = {
-                val content = readContent(path).split("\n")
-                var idx = 1 // 0 is left for zero padding
-                var theVocab = Map[String, Int]()
-                for (line <- content) {
-                 for (char <- line) {
-                   val key = s"$char"
-                   if (!theVocab.contains(key)) {
-                     theVocab = theVocab + (key -> idx)
-                     idx += 1
-                   }
-                 }
-                }
-                theVocab
-               }
-
-               buildVocab: (path: String)Map[String,Int]
-    ```
+```scala
+scala> // Build  a vocabulary of what char we have in the content
+scala> def buildVocab(path: String): Map[String, Int] = {
+        val content = readContent(path).split("\n")
+        var idx = 1 // 0 is left for zero padding
+        var theVocab = Map[String, Int]()
+        for (line <- content) {
+         for (char <- line) {
+           val key = s"$char"
+           if (!theVocab.contains(key)) {
+             theVocab = theVocab + (key -> idx)
+             idx += 1
+           }
+         }
+        }
+        theVocab
+       }
+
+       buildVocab: (path: String)Map[String,Int]
+```
 
 3) To assign each character a unique numerical ID, use the following function:
 
-    ```scala
-        scala> def text2Id(sentence: String, theVocab: Map[String, Int]): Array[Int] = {
-                val words = for (char <- sentence) yield theVocab(s"$char")
-                words.toArray
-              }
+```scala
+scala> def text2Id(sentence: String, theVocab: Map[String, Int]): Array[Int] = {
+        val words = for (char <- sentence) yield theVocab(s"$char")
+        words.toArray
+      }
 
-              text2Id: (sentence: String, theVocab: Map[String,Int])Array[Int]
-    ```
+      text2Id: (sentence: String, theVocab: Map[String,Int])Array[Int]
+```
 
-4) Now, build a character vocabulary from the dataset (obama.txt). Change the input filepath (dataPath) to reflect your settings.
+4) Now, build a character vocabulary from the dataset (obama.txt). Change the input filepath (dataPath) to reflect your settings.   
 
-    ```scala
-        scala> // Give your system path to the "obama.txt" we have downloaded using previous steps.
-        scala> val dataPath = "obama.txt"
-        dataPath: String = obama.txt
+```scala
+scala> // Give your system path to the "obama.txt" we have downloaded using previous steps.
+scala> val dataPath = "obama.txt"
+dataPath: String = obama.txt
 
-        scala> val vocab = buildVocab(dataPath)
+scala> val vocab = buildVocab(dataPath)
 
-        scala> vocab.size
-        res23: Int = 82
-    ```
+scala> vocab.size
+res23: Int = 82
+```
 
 
 ## Build a Multi-layer LSTM model
@@ -202,172 +201,170 @@ To create the model:
 `Lstm.scala` contains the definition of the LSTM cell. `BucketIo.scala` creates a sentence iterator. `RnnModel.scala` is used for model inference. The helper files are available on the [MXNet site](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/scala/org/apache/mxnetexamples/rnn).
 To load them, at the Scala command prompt type:
 
-    ```scala
-        scala> :load ../../../scala-package/examples/src/main/scala/org/apache/mxnet/examples/rnn/Lstm.scala
-        scala> :load ../../../scala-package/examples/src/main/scala/org/apache/mxnet/examples/rnn/BucketIo.scala
-        scala> :load ../../../scala-package/examples/src/main/scala/org/apache/mxnet/examples/rnn/RnnModel.scala
-    ```
+```scala
+scala> :load ../../../scala-package/examples/src/main/scala/org/apache/mxnet/examples/rnn/Lstm.scala
+scala> :load ../../../scala-package/examples/src/main/scala/org/apache/mxnet/examples/rnn/BucketIo.scala
+scala> :load ../../../scala-package/examples/src/main/scala/org/apache/mxnet/examples/rnn/RnnModel.scala
+```
 
 2) Set the LSTM hyperparameters as follows:
 
-    ```scala
-        scala> // We can support various input lengths.
-        scala> // For this problem, we cut each input sentence to a length of 129 characters.
-        scala> // So we only need a fixed length bucket length.
-        scala> val buckets = Array(129)
-        buckets: Array[Int] = Array(129)
+```scala
+scala> // We can support various input lengths.
+scala> // For this problem, we cut each input sentence to a length of 129 characters.
+scala> // So we only need a fixed length bucket length.
+scala> val buckets = Array(129)
+buckets: Array[Int] = Array(129)
 
-        scala> // hidden unit in LSTM cell
-        scala> val numHidden = 512
-        numHidden: Int = 512
+scala> // hidden unit in LSTM cell
+scala> val numHidden = 512
+numHidden: Int = 512
 
-        scala> // The embedding dimension, which maps a char to a 256 dim vector
-        scala> val numEmbed = 256
-        numEmbed: Int = 256
+scala> // The embedding dimension, which maps a char to a 256 dim vector
+scala> val numEmbed = 256
+numEmbed: Int = 256
 
-        scala> // The number of lstm layers
-        scala> val numLstmLayer = 3
-        numLstmLayer: Int = 3
+scala> // The number of lstm layers
+scala> val numLstmLayer = 3
+numLstmLayer: Int = 3
 
-        scala> // The batch size for training
-        scala> val batchSize = 32
-        batchSize: Int = 32
-    ```
+scala> // The batch size for training
+scala> val batchSize = 32
+batchSize: Int = 32
+```
 
 3) Now, construct the LSTM network as a symbolic computation graph. Type the following to create a graph in which the model is unrolled for a fixed length explicitly in time.
 
-    ```scala
-        scala> // generate symbol for a length
-        scala> def symGen(seqLen: Int): Symbol = {
-            Lstm.lstmUnroll(numLstmLayer, seqLen, vocab.size + 1,
-                        numHidden = numHidden, numEmbed = numEmbed,
-                        numLabel = vocab.size + 1, dropout = 0.2f)
-          }
-        symGen: (seqLen: Int)org.apache.mxnet.Symbol
+```scala
+scala> // generate symbol for a length
+scala> def symGen(seqLen: Int): Symbol = {
+    Lstm.lstmUnroll(numLstmLayer, seqLen, vocab.size + 1,
+                numHidden = numHidden, numEmbed = numEmbed,
+                numLabel = vocab.size + 1, dropout = 0.2f)
+  }
+symGen: (seqLen: Int)org.apache.mxnet.Symbol
 
-        scala> // create the network symbol
-        scala> val symbol = symGen(buckets(0))
-        symbol: org.apache.mxnet.Symbol = org.apache.mxnet.Symbol@3a589eed
+scala> // create the network symbol
+scala> val symbol = symGen(buckets(0))
+symbol: org.apache.mxnet.Symbol = org.apache.mxnet.Symbol@3a589eed
 
-    ```
+```      
 
 4) To train the model, initialize states for the LSTM and create a data iterator, which groups the data into buckets.
 Note: The BucketSentenceIter data iterator supports various length examples; however, we use only the fixed length version in this tutorial.
 
-    ```scala
+```scala
 
-        scala> // initialize states for LSTM
-        scala> val initC = for (l <- 0 until numLstmLayer) yield (s"l${l}_init_c", (batchSize, numHidden))
+scala> // initialize states for LSTM
+scala> val initC = for (l <- 0 until numLstmLayer) yield (s"l${l}_init_c", (batchSize, numHidden))
 
-        initC: scala.collection.immutable.IndexedSeq[(String, (Int, Int))] = Vector((l0_init_c,(32,512)),
-        (l1_init_c,(32,512)), (l2_init_c,(32,512)))
+initC: scala.collection.immutable.IndexedSeq[(String, (Int, Int))] = Vector((l0_init_c,(32,512)),
+(l1_init_c,(32,512)), (l2_init_c,(32,512)))
 
-        scala> val initH = for (l <- 0 until numLstmLayer) yield (s"l${l}_init_h", (batchSize, numHidden))
+scala> val initH = for (l <- 0 until numLstmLayer) yield (s"l${l}_init_h", (batchSize, numHidden))
 
-        initH: scala.collection.immutable.IndexedSeq[(String, (Int, Int))] = Vector((l0_init_h,(32,512)),
-        (l1_init_h,(32,512)), (l2_init_h,(32,512)))
+initH: scala.collection.immutable.IndexedSeq[(String, (Int, Int))] = Vector((l0_init_h,(32,512)),
+(l1_init_h,(32,512)), (l2_init_h,(32,512)))
 
-        scala> val initStates = initC ++ initH
+scala> val initStates = initC ++ initH
 
-        initStates: scala.collection.immutable.IndexedSeq[(String, (Int, Int))] =
-        Vector((l0_init_c,(32,512)), (l1_init_c,(32,512)), (l2_init_c,(32,512)), (l0_init_h,(32,512)),
-        (l1_init_h,(32,512)), (l2_init_h,(32,512)))
+initStates: scala.collection.immutable.IndexedSeq[(String, (Int, Int))] =
+Vector((l0_init_c,(32,512)), (l1_init_c,(32,512)), (l2_init_c,(32,512)), (l0_init_h,(32,512)),
+(l1_init_h,(32,512)), (l2_init_h,(32,512)))
 
-        scala> val dataTrain = new BucketIo.BucketSentenceIter(dataPath, vocab, buckets,
-                                              batchSize, initStates, seperateChar = "\n",
-                                              text2Id = text2Id, readContent = readContent)
+scala> val dataTrain = new BucketIo.BucketSentenceIter(dataPath, vocab, buckets,
+                                      batchSize, initStates, seperateChar = "\n",
+                                      text2Id = text2Id, readContent = readContent)
 
-        dataTrain: BucketIo.BucketSentenceIter = non-empty iterator
+dataTrain: BucketIo.BucketSentenceIter = non-empty iterator
 
-    ```
-
-5) You can set more than 100 epochs, but for this tutorial, specify 75 epochs. Each epoch can take as long as 4 minutes on a GPU. In this tutorial, you will use the [ADAM optimizer](http://mxnet.io/api/scala/docs/index.html#org.apache.mxnet.optimizer.Adam):
+```
 
-    ```scala
-        scala> import org.apache.mxnet._
-        import org.apache.mxnet._
+5) You can set more than 100 epochs, but for this tutorial, specify 75 epochs. Each epoch can take as long as 4 minutes on a GPU. In this tutorial, you will use the [ADAM optimizer]({{'/api/scala/docs/api/#org.apache.mxnet.optimizer.Adam'|relative_url}}):
+```scala
+scala> import org.apache.mxnet._
+import org.apache.mxnet._
 
-        scala> import org.apache.mxnet.Callback.Speedometer
-        import org.apache.mxnet.Callback.Speedometer
+scala> import org.apache.mxnet.Callback.Speedometer
+import org.apache.mxnet.Callback.Speedometer
 
-        scala> import org.apache.mxnet.optimizer.Adam
-        import org.apache.mxnet.optimizer.Adam
+scala> import org.apache.mxnet.optimizer.Adam
+import org.apache.mxnet.optimizer.Adam
 
-        scala> // and we will see result by training 75 epochs
-        scala> val numEpoch = 75
-        numEpoch: Int = 75
+scala> // and we will see result by training 75 epochs
+scala> val numEpoch = 75
+numEpoch: Int = 75
 
-        scala> // learning rate
-        scala> val learningRate = 0.001f
-        learningRate: Float = 0.001
+scala> // learning rate
+scala> val learningRate = 0.001f
+learningRate: Float = 0.001
 
-    ```
+```
 
 6) Define the perplexity utility function for the evaluation metric which is used to calculate the negative log-likelihood during training.
 
-    ```scala
-        scala> def perplexity(label: NDArray, pred: NDArray): Float = {
-                val shape = label.shape
-                val size = shape(0) * shape(1)
-                val labelT = {
-                  val tmp = label.toArray.grouped(shape(1)).toArray
-                  val result = Array.fill[Float](size)(0f)
-                  var idx = 0
-                  for (i <- 0 until shape(1)) {
-                    for (j <- 0 until shape(0)) {
-                      result(idx) = tmp(j)(i)
-                      idx += 1
-                    }
-                  }
-                  result
-                }
-                var loss = 0f
-                val predArray = pred.toArray.grouped(pred.shape(1)).toArray
-                for (i <- 0 until pred.shape(0)) {
-                  loss += -Math.log(Math.max(1e-10, predArray(i)(labelT(i).toInt)).toFloat).toFloat
-                }
-                loss / size
-                }
-
-        perplexity: (label: org.apache.mxnet.NDArray, pred: org.apache.mxnet.NDArray)Float
-
-        scala> def doCheckpoint(prefix: String): EpochEndCallback = new EpochEndCallback {
-                    override def invoke(epoch: Int, symbol: Symbol,
-                                        argParams: Map[String, NDArray],
-                                        auxStates: Map[String, NDArray]): Unit = {
-                      Model.saveCheckpoint(prefix, epoch + 1, symbol, argParams, auxStates)
-                    }
-                }
-
-        doCheckpoint: (prefix: String)org.apache.mxnet.EpochEndCallback
+```scala
+scala> def perplexity(label: NDArray, pred: NDArray): Float = {
+        val shape = label.shape
+        val size = shape(0) * shape(1)
+        val labelT = {
+          val tmp = label.toArray.grouped(shape(1)).toArray
+          val result = Array.fill[Float](size)(0f)
+          var idx = 0
+          for (i <- 0 until shape(1)) {
+            for (j <- 0 until shape(0)) {
+              result(idx) = tmp(j)(i)
+              idx += 1
+            }
+          }
+          result
+        }
+        var loss = 0f
+        val predArray = pred.toArray.grouped(pred.shape(1)).toArray
+        for (i <- 0 until pred.shape(0)) {
+          loss += -Math.log(Math.max(1e-10, predArray(i)(labelT(i).toInt)).toFloat).toFloat
+        }
+        loss / size
+        }
+
+perplexity: (label: org.apache.mxnet.NDArray, pred: org.apache.mxnet.NDArray)Float
+
+scala> def doCheckpoint(prefix: String): EpochEndCallback = new EpochEndCallback {
+            override def invoke(epoch: Int, symbol: Symbol,
+                                argParams: Map[String, NDArray],
+                                auxStates: Map[String, NDArray]): Unit = {
+              Model.saveCheckpoint(prefix, epoch + 1, symbol, argParams, auxStates)
+            }
+        }
 
-    ```
+doCheckpoint: (prefix: String)org.apache.mxnet.EpochEndCallback
+
+```
 
 7) Define the initializer that is required for creating a model, as follows:
 
-    ```scala
-        scala> val initializer = new Xavier(factorType = "in", magnitude = 2.34f)
+```scala
+scala> val initializer = new Xavier(factorType = "in", magnitude = 2.34f)
 
-        initializer: org.apache.mxnet.Xavier = org.apache.mxnet.Xavier@54e8f10a
+initializer: org.apache.mxnet.Xavier = org.apache.mxnet.Xavier@54e8f10a
 
-    ```
+```
 
-8) Now, you have implemented all the supporting infrastructures for the char-lstm model. To train the model, use the standard [MXNet high-level API](http://mxnet.io/api/scala/docs/index.html#org.apache.mxnet.FeedForward). You can train the model on a single GPU or CPU from multiple GPUs or CPUs by changing ```scala .setContext(Array(Context.gpu(0),Context.gpu(1),Context.gpu(2),Context.gpu(3)))``` to ```scala .setContext(Array(Context.gpu(0)))```:
-
-    ```scala
-        scala> val model = FeedForward.newBuilder(symbol)
-                .setContext(Array(Context.gpu(0),Context.gpu(1),Context.gpu(2),Context.gpu(3)))
-                .setNumEpoch(numEpoch)
-                .setOptimizer(new Adam(learningRate = learningRate, wd = 0.00001f))
-                .setInitializer(initializer)
-                .setTrainData(dataTrain)
-                .setEvalMetric(new CustomMetric(perplexity, name = "perplexity"))
-                .setBatchEndCallback(new Speedometer(batchSize, 20))
-                .setEpochEndCallback(doCheckpoint("obama"))
-                .build()
-
-        model: org.apache.mxnet.FeedForward = org.apache.mxnet.FeedForward@4926f6c7
-    ```
+8) Now, you have implemented all the supporting infrastructures for the char-lstm model. To train the model, use the standard [MXNet high-level API]({{'/api/scala/docs/api/#org.apache.mxnet.FeedForward'|relative_url}}). You can train the model on a single GPU or CPU from multiple GPUs or CPUs by changing ```scala .setContext(Array(Context.gpu(0),Context.gpu(1),Context.gpu(2),Context.gpu(3)))``` to ```scala .setContext(Array(Context.gpu(0)))```:
+```scala
+scala> val model = FeedForward.newBuilder(symbol)
+        .setContext(Array(Context.gpu(0),Context.gpu(1),Context.gpu(2),Context.gpu(3)))
+        .setNumEpoch(numEpoch)
+        .setOptimizer(new Adam(learningRate = learningRate, wd = 0.00001f))
+        .setInitializer(initializer)
+        .setTrainData(dataTrain)
+        .setEvalMetric(new CustomMetric(perplexity, name = "perplexity"))
+        .setBatchEndCallback(new Speedometer(batchSize, 20))
+        .setEpochEndCallback(doCheckpoint("obama"))
+        .build()
+
+model: org.apache.mxnet.FeedForward = org.apache.mxnet.FeedForward@4926f6c7
+```
 
 Now, you have an LSTM model and you've trained it. Use this model to create the inference.
 
@@ -386,148 +383,148 @@ To build the inference model, define the following utility functions that help M
 * `cdf`, `choice` - `cdf` is a helper function for the `choice` function, which is used to create random samples
 * `makeOutput` - Directs the model to use either random output or fixed output by choosing the option with the greatest probability.
 
-    ```scala
-        scala> import scala.util.Random
-
-        scala> // helper structure for prediction
-        scala> def makeRevertVocab(vocab: Map[String, Int]): Map[Int, String] = {
-                  var dic = Map[Int, String]()
-                  vocab.foreach { case (k, v) =>
-                    dic = dic + (v -> k)
-                  }
-                  dic
-                }
-
-      makeRevertVocab: (vocab: Map[String,Int])Map[Int,String]
-
-      scala> // make input from char
-      scala> def makeInput(char: Char, vocab: Map[String, Int], arr: NDArray): Unit = {
-              val idx = vocab(s"$char")
-              val tmp = NDArray.zeros(1)
-              tmp.set(idx)
-              arr.set(tmp)
-            }
-
-      makeInput: (char: Char, vocab: Map[String,Int], arr: org.apache.mxnet.NDArray)Unit
-
-      scala> // helper function for random sample
-      scala> def cdf(weights: Array[Float]): Array[Float] = {
-                val total = weights.sum
-                var result = Array[Float]()
-                var cumsum = 0f
-                for (w <- weights) {
-                  cumsum += w
-                  result = result :+ (cumsum / total)
-                }
-                result
-              }
-
-      cdf: (weights: Array[Float])Array[Float]
-
-      scala> def choice(population: Array[String], weights: Array[Float]): String = {
-              assert(population.length == weights.length)
-              val cdfVals = cdf(weights)
-              val x = Random.nextFloat()
-              var idx = 0
-              var found = false
-              for (i <- 0 until cdfVals.length) {
-                if (cdfVals(i) >= x && !found) {
-                  idx = i
-                  found = true
-                }
-              }
-              population(idx)
-            }
+```scala
+scala> import scala.util.Random
 
-      choice: (population: Array[String], weights: Array[Float])String
-
-      scala> // we can use random output or fixed output by choosing largest probability
-      scala> def makeOutput(prob: Array[Float], vocab: Map[Int, String],
-                              sample: Boolean = false, temperature: Float = 1f): String = {
-                 var idx = -1
-                 val char = if (sample == false) {
-                   idx = ((-1f, -1) /: prob.zipWithIndex) { (max, elem) =>
-                     if (max._1 < elem._1) elem else max
-                   }._2
-                   if (vocab.contains(idx)) vocab(idx)
-                   else ""
-                 } else {
-                   val fixDict = Array("") ++ (1 until vocab.size + 1).map(i => vocab(i))
-                   var scaleProb = prob.map(x => if (x < 1e-6) 1e-6 else if (x > 1 - 1e-6) 1 - 1e-6 else x)
-                   var rescale = scaleProb.map(x => Math.exp(Math.log(x) / temperature).toFloat)
-                   val sum = rescale.sum.toFloat
-                   rescale = rescale.map(_ / sum)
-                   choice(fixDict, rescale)
-                 }
-                 char
-               }
-
-      makeOutput: (prob: Array[Float], vocab: Map[Int,String], sample: Boolean, temperature: Float)String
+scala> // helper structure for prediction
+scala> def makeRevertVocab(vocab: Map[String, Int]): Map[Int, String] = {
+          var dic = Map[Int, String]()
+          vocab.foreach { case (k, v) =>
+            dic = dic + (v -> k)
+          }
+          dic
+        }
+
+makeRevertVocab: (vocab: Map[String,Int])Map[Int,String]
+
+scala> // make input from char
+scala> def makeInput(char: Char, vocab: Map[String, Int], arr: NDArray): Unit = {
+      val idx = vocab(s"$char")
+      val tmp = NDArray.zeros(1)
+      tmp.set(idx)
+      arr.set(tmp)
+    }
+
+makeInput: (char: Char, vocab: Map[String,Int], arr: org.apache.mxnet.NDArray)Unit
+
+scala> // helper function for random sample
+scala> def cdf(weights: Array[Float]): Array[Float] = {
+        val total = weights.sum
+        var result = Array[Float]()
+        var cumsum = 0f
+        for (w <- weights) {
+          cumsum += w
+          result = result :+ (cumsum / total)
+        }
+        result
+      }
+
+cdf: (weights: Array[Float])Array[Float]
+
+scala> def choice(population: Array[String], weights: Array[Float]): String = {
+      assert(population.length == weights.length)
+      val cdfVals = cdf(weights)
+      val x = Random.nextFloat()
+      var idx = 0
+      var found = false
+      for (i <- 0 until cdfVals.length) {
+        if (cdfVals(i) >= x && !found) {
+          idx = i
+          found = true
+        }
+      }
+      population(idx)
+    }
+
+choice: (population: Array[String], weights: Array[Float])String
+
+scala> // we can use random output or fixed output by choosing largest probability
+scala> def makeOutput(prob: Array[Float], vocab: Map[Int, String],
+                      sample: Boolean = false, temperature: Float = 1f): String = {
+         var idx = -1
+         val char = if (sample == false) {
+           idx = ((-1f, -1) /: prob.zipWithIndex) { (max, elem) =>
+             if (max._1 < elem._1) elem else max
+           }._2
+           if (vocab.contains(idx)) vocab(idx)
+           else ""
+         } else {
+           val fixDict = Array("") ++ (1 until vocab.size + 1).map(i => vocab(i))
+           var scaleProb = prob.map(x => if (x < 1e-6) 1e-6 else if (x > 1 - 1e-6) 1 - 1e-6 else x)
+           var rescale = scaleProb.map(x => Math.exp(Math.log(x) / temperature).toFloat)
+           val sum = rescale.sum.toFloat
+           rescale = rescale.map(_ / sum)
+           choice(fixDict, rescale)
+         }
+         char
+       }
+
+makeOutput: (prob: Array[Float], vocab: Map[Int,String], sample: Boolean, temperature: Float)String
 
-    ```
+```
 
 1) Build the inference model:
 
-    ```scala
-        scala> // load from check-point
-        scala> val (_, argParams, _) = Model.loadCheckpoint("obama", 75)
+```scala
+scala> // load from check-point
+scala> val (_, argParams, _) = Model.loadCheckpoint("obama", 75)
 
-        scala> // build an inference model
-        scala> val model = new RnnModel.LSTMInferenceModel(numLstmLayer, vocab.size + 1, \
-                                   numHidden = numHidden, numEmbed = numEmbed, \
-                                   numLabel = vocab.size + 1, argParams = argParams, \
-                                   ctx = Context.cpu(), dropout = 0.2f)
+scala> // build an inference model
+scala> val model = new RnnModel.LSTMInferenceModel(numLstmLayer, vocab.size + 1, \
+                           numHidden = numHidden, numEmbed = numEmbed, \
+                           numLabel = vocab.size + 1, argParams = argParams, \
+                           ctx = Context.cpu(), dropout = 0.2f)
 
-        model: RnnModel.LSTMInferenceModel = RnnModel$LSTMInferenceModel@2f0c0319
-    ```
+model: RnnModel.LSTMInferenceModel = RnnModel$LSTMInferenceModel@2f0c0319
+```
 
 2) Now you can generate a sequence of 1200 characters (you can select any number of characters you want) starting with "The United States" as follows:
 
-    ```scala
+```scala
 
-        scala> val seqLength = 1200
-        seqLength: Int = 1200
+scala> val seqLength = 1200
+seqLength: Int = 1200
 
-        scala> val inputNdarray = NDArray.zeros(1)
-        inputNdarray: org.apache.mxnet.NDArray = org.apache.mxnet.NDArray@9c231a24
+scala> val inputNdarray = NDArray.zeros(1)
+inputNdarray: org.apache.mxnet.NDArray = org.apache.mxnet.NDArray@9c231a24
 
-        scala> val revertVocab = makeRevertVocab(vocab)
+scala> val revertVocab = makeRevertVocab(vocab)
 
-        scala> // Feel free to change the starter sentence
+scala> // Feel free to change the starter sentence
 
-        scala> var output = "The United States"
-        output: String = The United States
+scala> var output = "The United States"
+output: String = The United States
 
-        scala> val randomSample = true
-        randomSample: Boolean = true
+scala> val randomSample = true
+randomSample: Boolean = true
 
-        scala> var newSentence = true
-        newSentence: Boolean = true
+scala> var newSentence = true
+newSentence: Boolean = true
 
-        scala> val ignoreLength = output.length()
-        ignoreLength: Int = 17
+scala> val ignoreLength = output.length()
+ignoreLength: Int = 17
 
-        scala> for (i <- 0 until seqLength) {
-                if (i <= ignoreLength - 1) makeInput(output(i), vocab, inputNdarray)
-                else makeInput(output.takeRight(1)(0), vocab, inputNdarray)
-                val prob = model.forward(inputNdarray, newSentence)
-                newSentence = false
-                val nextChar = makeOutput(prob, revertVocab, randomSample)
-                if (nextChar == "") newSentence = true
-                if (i >= ignoreLength) output = output ++ nextChar
-              }
+scala> for (i <- 0 until seqLength) {
+        if (i <= ignoreLength - 1) makeInput(output(i), vocab, inputNdarray)
+        else makeInput(output.takeRight(1)(0), vocab, inputNdarray)
+        val prob = model.forward(inputNdarray, newSentence)
+        newSentence = false
+        val nextChar = makeOutput(prob, revertVocab, randomSample)
+        if (nextChar == "") newSentence = true
+        if (i >= ignoreLength) output = output ++ nextChar
+      }
 
-        scala> output
+scala> output
 
-        res7: String = The United States who have been blessed no companies would be proud that the challenges we face, it's not as directly untelle are in my daughters - you can afford -- life-saving march care and poor information and receiving battle against other speeces and lead its people. After champions of 2006, and because Africa in America, separate has been conferenced by children ation of discrimination, we remember all of this, succeeded in any other feelings of a palently better political process - at lliims being disability payment. All across all different mights of a more just a few global personal morality and industrialized ready to succeed.One can afford when the earliest days of a pension you can add to the system be confructive despair. They have starting in the demand for...
+res7: String = The United States who have been blessed no companies would be proud that the challenges we face, it's not as directly untelle are in my daughters - you can afford -- life-saving march care and poor information and receiving battle against other speeces and lead its people. After champions of 2006, and because Africa in America, separate has been conferenced by children ation of discrimination, we remember all of this, succeeded in any other feelings of a palently better political process - at lliims being disability payment. All across all different mights of a more just a few global personal morality and industrialized ready to succeed.One can afford when the earliest days of a pension you can add to the system be confructive despair. They have starting in the demand for...
 
-    ```
+```
 
 
 You can see the output generated from Obama's speeches. All of the line breaks, punctuation, and uppercase and lowercase letters were produced by the sampler (no post-processing was performed).
 
 
 ## Next Steps
-* [Scala API](http://mxnet.io/api/scala/)
+* [Scala API]({{'/api/scala'|relative_url}})
 * [More Scala Examples](https://github.com/dmlc/mxnet/tree/master/scala-package/examples/)
-* [MXNet tutorials index](http://mxnet.io/tutorials/index.html)
+* [MXNet tutorials index]({{'/api'|relative_url}})
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/infer.md b/docs/static_site/src/pages/api/scala/docs/tutorials/infer.md
index 9baeb7806dac..6e1e11935b20 100644
--- a/docs/static_site/src/pages/api/scala/docs/tutorials/infer.md
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/infer.md
@@ -28,10 +28,9 @@ The MXNet Scala Infer API provides you with model loading and inference function
 
 ## Prerequisites
 To use the Infer API you must first install the MXNet Scala package. Instructions for this are provided in the following variations:
-* [Tutorial for setting up a project in the IntelliJ IDE](../../tutorials/scala/mxnet_scala_on_intellij.html)
-* [Installing the MXNet Scala Package for macOS](../../install/ubuntu_setup.html#install-the-mxnet-package-for-scala)
-* [Installing the MXNet Scala for Linux](../../install/ubuntu_setup.html#install-the-mxnet-package-for-scala)
-
+* [Tutorial for setting up a project in the IntelliJ IDE](mxnet_scala_on_intellij)
+* [Installing the MXNet Scala Package for macOS]({{'get_started/ubuntu_setup.html#install-the-mxnet-package-for-scala'|relative_url}})
+* [Installing the MXNet Scala for Linux]({{'get_started/ubuntu_setup.html#install-the-mxnet-package-for-scala'|relative_url}})
 
 ## Inference
 The Scala Infer API includes both single image and batch modes. Here is an example of running inference on a single image by using the `ImageClassifier` class. A complete [image classification example](https://github.com/apache/incubator-mxnet/blob/master/scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/imageclassifier/ImageClassifierExample.scala) using ResNet-152 is provided in the [Scala package's example folder](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/scala/org/apache/mxnetexamples). This example also demonstrates inference with batches of images.
@@ -61,6 +60,6 @@ IndexedSeq[IndexedSeq[(String, Float)]] = {
 
 
 ## Related Resources
-* [Infer API Scaladocs](docs/index.html#org.apache.mxnet.infer.package)
+* [Infer API Scaladocs]({{'/api/scala/docs/api/#org.apache.mxnet.infer.package'|relative_url}})
 * [Single Shot Detector Inference Example](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/objectdetector)
 * [Image Classification Example](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/imageclassifier)
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/io.md b/docs/static_site/src/pages/api/scala/docs/tutorials/io.md
index c07526ef07c9..6f661fdcc2ef 100644
--- a/docs/static_site/src/pages/api/scala/docs/tutorials/io.md
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/io.md
@@ -32,7 +32,7 @@ Topics:
 * [Data Iterator Parameters](#parameters-for-data-iterator) clarifies the different usages for dataiter parameters.
 * [Create a Data Iterator](#create-a-data-iterator) introduces how to create a data iterator in MXNet for Scala.
 * [How to Get Data](#how-to-get-data) introduces the data resource and data preparation tools.
-* [IO API Reference](http://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.IO$) explains the IO API.
+* [IO API Reference]({{'/api/scala/docs/api/#org.apache.mxnet.io.package'|relative_url}}) explains the IO API.
 
 
 ## Data Iterator Parameters
@@ -53,53 +53,53 @@ The IO API provides a simple way to create a data iterator in Scala.
 The following example code shows how to create a CIFAR data iterator.
 
 ```scala
-     val dataiter = IO.ImageRecordIter(Map(
-            // Utility Parameter
-            // Optional
-            // Name of the data, should match the name of the data input of the network
-            // data_name='data',
-            // Utility Parameter
-            // Optional
-            // Name of the label, should match the name of the label parameter of the network
-            // Usually, if the loss layer is named 'foo', then the label input has the name
-            // 'foo_label', unless overwritten
-            // label_name='softmax_label',
-            // Dataset Parameter
-            // Impulsary
-            // indicating the data file, please check the data is already there
-            "path_imgrec" -> "data/cifar/train.rec",
-            // Dataset Parameter
-            // Impulsary
-            // indicating the image size after preprocessing
-            "data_shape" -> "(3,28,28)",
-            // Batch Parameter
-            // Impulsary
-            // tells how many images in a batch
-            "batch_size" -> "100",
-            // Augmentation Parameter
-            // Optional
-            // when offers mean_img, each image will subtract the mean value at each pixel
-            "mean_img" -> "data/cifar/cifar10_mean.bin",
-            // Augmentation Parameter
-            // Optional
-            // randomly crop a patch of the data_shape from the original image
-           "rand_crop" -> "True",
-            // Augmentation Parameter
-            // Optional
-            // randomly mirror the image horizontally
-            "rand_mirror" -> "True",
-            // Augmentation Parameter
-            // Optional
-            // randomly shuffle the data
-            "shuffle" -> "False",
-            // Backend Parameter
-            // Optional
-            // Preprocessing thread number
-            "preprocess_threads" -> "4",
-            // Backend Parameter
-            // Optional
-            // Prefetch buffer size
-            "prefetch_buffer" = "1"))
+val dataiter = IO.ImageRecordIter(Map(
+    // Utility Parameter
+    // Optional
+    // Name of the data, should match the name of the data input of the network
+    // data_name='data',
+    // Utility Parameter
+    // Optional
+    // Name of the label, should match the name of the label parameter of the network
+    // Usually, if the loss layer is named 'foo', then the label input has the name
+    // 'foo_label', unless overwritten
+    // label_name='softmax_label',
+    // Dataset Parameter
+    // Impulsary
+    // indicating the data file, please check the data is already there
+    "path_imgrec" -> "data/cifar/train.rec",
+    // Dataset Parameter
+    // Impulsary
+    // indicating the image size after preprocessing
+    "data_shape" -> "(3,28,28)",
+    // Batch Parameter
+    // Impulsary
+    // tells how many images in a batch
+    "batch_size" -> "100",
+    // Augmentation Parameter
+    // Optional
+    // when offers mean_img, each image will subtract the mean value at each pixel
+    "mean_img" -> "data/cifar/cifar10_mean.bin",
+    // Augmentation Parameter
+    // Optional
+    // randomly crop a patch of the data_shape from the original image
+   "rand_crop" -> "True",
+    // Augmentation Parameter
+    // Optional
+    // randomly mirror the image horizontally
+    "rand_mirror" -> "True",
+    // Augmentation Parameter
+    // Optional
+    // randomly shuffle the data
+    "shuffle" -> "False",
+    // Backend Parameter
+    // Optional
+    // Preprocessing thread number
+    "preprocess_threads" -> "4",
+    // Backend Parameter
+    // Optional
+    // Prefetch buffer size
+    "prefetch_buffer" = "1"))
 ```
 
 First, explicitly specify the kind of data (MNIST, ImageRecord, etc.) to fetch. Then, provide the options for the dataset, batching, image augmentation, multi-tread processing,  and prefetching operations. The code automatically validates the parameters. If a required parameter is missing, MXNet returns an error.
@@ -126,24 +126,23 @@ Download the data. You don't need to resize the images manually. You can use `im
 After you download the data, you need to make an image list file.  The format is:
 
 ```
-    integer_image_index \t label_index \t path_to_image
+integer_image_index \t label_index \t path_to_image
 ```
 Typically, the program takes the list of names of all of the images, shuffles them, then separates them into two lists: a training filename list and a testing filename list. Write the list in the right format.
 
 This is an example file:
 
 ```bash
-    95099  464     n04467665_17283.JPEG
-    10025081        412     ILSVRC2010_val_00025082.JPEG
-    74181   789     n01915811_2739.JPEG
-    10035553        859     ILSVRC2010_val_00035554.JPEG
-    10048727        929     ILSVRC2010_val_00048728.JPEG
-    94028   924     n01980166_4956.JPEG
-    1080682 650     n11807979_571.JPEG
-    972457  633     n07723039_1627.JPEG
-    7534    11      n01630670_4486.JPEG
-    1191261 249     n12407079_5106.JPEG
-
+95099  464     n04467665_17283.JPEG
+10025081        412     ILSVRC2010_val_00025082.JPEG
+74181   789     n01915811_2739.JPEG
+10035553        859     ILSVRC2010_val_00035554.JPEG
+10048727        929     ILSVRC2010_val_00048728.JPEG
+94028   924     n01980166_4956.JPEG
+1080682 650     n11807979_571.JPEG
+972457  633     n07723039_1627.JPEG
+7534    11      n01630670_4486.JPEG
+1191261 249     n12407079_5106.JPEG
 ```
 
 ### Step 2. Create the Binary File
@@ -152,7 +151,7 @@ To generate a binary image, use `im2rec` in the tool folder. `im2rec` takes the
 A sample command:
 
 ```bash
-    ./bin/im2rec image.lst image_root_dir output.bin resize=256
+./bin/im2rec image.lst image_root_dir output.bin resize=256
 ```
 For more details, run ```./bin/im2rec```.
 
@@ -163,27 +162,27 @@ For example, if you have four labels for a single image, you can use the followi
 
 1. Write the image list files as follows:
 
-     ```
-         integer_image_index \t label_1 \t label_2 \t   label_3 \t label_4 \t path_to_image
-     ```
+ ```
+ integer_image_index \t label_1 \t label_2 \t   label_3 \t label_4 \t path_to_image
+ ```
 
 2. Run `im2rec`, adding a 'label_width=4' to the command argument, for example:
 
-     ```bash
-         ./bin/im2rec image.lst image_root_dir output.bin resize=256 label_width=4
-     ```
+ ```bash
+ ./bin/im2rec image.lst image_root_dir output.bin resize=256 label_width=4
+ ```
 
 3. In the iterator generation code, set `label_width=4` and `path_imglist=<<The PATH TO YOUR image.lst>>`, for example:
 
-     ```scala
-         val dataiter = IO.ImageRecordIter(Map(
-           "path_imgrec" -> "data/cifar/train.rec",
-           "data_shape" -> "(3,28,28)",
-           "path_imglist" -> "data/cifar/image.lst",
-           "label_width" -> "4"
-         ))
-     ```
+```scala
+val dataiter = IO.ImageRecordIter(Map(
+    "path_imgrec" -> "data/cifar/train.rec",
+    "data_shape" -> "(3,28,28)",
+    "path_imglist" -> "data/cifar/image.lst",
+    "label_width" -> "4"
+))
+```
 
 ## Next Steps
-* [NDArray API](ndarray.md) for vector/matrix/tensor operations
-* [KVStore API](kvstore.md) for multi-GPU and multi-host distributed training
+* [NDArray API](ndarray) for vector/matrix/tensor operations
+* [KVStore API](kvstore) for multi-GPU and multi-host distributed training
\ No newline at end of file
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/kvstore.md b/docs/static_site/src/pages/api/scala/docs/tutorials/kvstore.md
index aa71705acd8d..6242ba399eb9 100644
--- a/docs/static_site/src/pages/api/scala/docs/tutorials/kvstore.md
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/kvstore.md
@@ -27,7 +27,7 @@ tag: scala
 Topics:
 * [Basic Push and Pull](#basic-push-and-pull)
 * [List Key-Value Pairs](#list-key-value-pairs)
-* [API Reference](http://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.KVStore)
+* [API Reference]({{'/api/scala/docs/api/#org.apache.mxnet.KVStore'|relative_url}})
 
 
 ## Basic Push and Pull
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/mnist.md b/docs/static_site/src/pages/api/scala/docs/tutorials/mnist.md
index 30c72e0339c4..d121b41a6ef4 100644
--- a/docs/static_site/src/pages/api/scala/docs/tutorials/mnist.md
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/mnist.md
@@ -31,7 +31,7 @@ Let's train a 3-layer network (i.e multilayer perceptron network) on the MNIST d
 ## Prerequisites
 To complete this tutorial, we need:
 
-- to compile the latest MXNet version. See the MXNet installation instructions for your operating system in [Setup and Installation](http://mxnet.io/install/index.html).
+- to compile the latest MXNet version. See the MXNet installation instructions for your operating system in [Setup and Installation]({{'/get_started'|relative_url}}).
 - to compile the Scala API. See Scala API build instructions in [Build](https://github.com/dmlc/mxnet/tree/master/scala-package).
 
 ## Define the Network
@@ -136,6 +136,6 @@ println(s"Final accuracy = $acc")
 Check out more MXNet Scala examples below.
 
 ## Next Steps
-* [Scala API](http://mxnet.io/api/scala/)
+* [Scala API]({{'/api/scala'|relative_url}})
 * [More Scala Examples](https://github.com/dmlc/mxnet/tree/master/scala-package/examples/)
-* [MXNet tutorials index](http://mxnet.io/tutorials/index.html)
+* [MXNet tutorials index]({{'/api'|relative_url}})
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/model.md b/docs/static_site/src/pages/api/scala/docs/tutorials/model.md
index 6423200e584f..06d2ee0c0eef 100644
--- a/docs/static_site/src/pages/api/scala/docs/tutorials/model.md
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/model.md
@@ -25,16 +25,16 @@ tag: scala
 # MXNet Scala Model API
 
 The model API provides a simplified way to train neural networks using common best practices.
-It's a thin wrapper built on top of the [ndarray](ndarray.md) and [symbolic](symbol.md)
+It's a thin wrapper built on top of the [ndarray](ndarray) and [symbolic](symbol)
 modules that make neural network training easy.
 
 Topics:
 
-* [Train a Model](#train-a-model)
+* [Train the Model](#train-the-model)
 * [Save the Model](#save-the-model)
 * [Periodic Checkpoint](#periodic-checkpointing)
 * [Multiple Devices](#use-multiple-devices)
-* [Model API Reference](#http://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.Model)
+* [Model API Reference]({{'/api/scala/docs/api/#org.apache.mxnet.Model'|relative_url}})
 
 ## Train the Model
 
@@ -72,7 +72,7 @@ You can also use the `scikit-learn-style` construct and `fit` function to create
 
   model.fit(trainData = train)
 ```
-For more information, see [API Reference](http://mxnet.incubator.apache.org/api/scala/docs/index.html).
+For more information, see [API Reference]({{'/api/scala/docs/api/#package'|relative_url}}).
 
 ## Save the Model
 
@@ -136,7 +136,7 @@ Set ```ctx``` to the list of devices that you want to train on. You can create a
 Training occurs in parallel on the GPUs that you specify.
 
 ## Next Steps
-* See [Symbolic API](symbol.md) for operations on NDArrays that assemble neural networks from layers.
-* See [IO Data Loading API](io.md) for parsing and loading data.
-* See [NDArray API](ndarray.md) for vector/matrix/tensor operations.
-* See [KVStore API](kvstore.md) for multi-GPU and multi-host distributed training.
+* See [Symbolic API](symbol) for operations on NDArrays that assemble neural networks from layers.
+* See [IO Data Loading API](io) for parsing and loading data.
+* See [NDArray API](ndarray) for vector/matrix/tensor operations.
+* See [KVStore API](kvstore) for multi-GPU and multi-host distributed training.
\ No newline at end of file
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/module.md b/docs/static_site/src/pages/api/scala/docs/tutorials/module.md
index fb58461fe751..936e066cea43 100644
--- a/docs/static_site/src/pages/api/scala/docs/tutorials/module.md
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/module.md
@@ -72,13 +72,13 @@ Modules provide high-level APIs for training, predicting, and evaluating. To fit
     .setOptimizer(new SGD(learningRate = 0.1f, momentum = 0.9f, wd = 0.0001f)))
 ```
 
-The interface is very similar to the old `FeedForward` class. You can pass in batch-end callbacks using `setBatchEndCallback` and epoch-end callbacks using `setEpochEndCallback`. You can also set parameters using methods like `setOptimizer` and `setEvalMetric`. To learn more about the `FitParams()`, see the [API page](http://mxnet.io/api/scala/docs/index.html#org.apache.mxnet.module.FitParams). To predict with a module, call `predict()` with a `DataIter`:
+The interface is very similar to the old `FeedForward` class. You can pass in batch-end callbacks using `setBatchEndCallback` and epoch-end callbacks using `setEpochEndCallback`. You can also set parameters using methods like `setOptimizer` and `setEvalMetric`. To learn more about the `FitParams()`, see the [API page]({{'/api/scala/docs/api/#org.apache.mxnet.module.FitParams'|relative_url}}). To predict with a module, call `predict()` with a `DataIter`:
 
 ```scala
     mod.predict(val_dataiter)
 ```
 
-The module collects and returns all of the prediction results. For more details about the format of the return values, see the documentation for the [`predict()` function](http://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.module.BaseModule).
+The module collects and returns all of the prediction results. For more details about the format of the return values, see the documentation for the [`predict()` function]({{'/api/scala/docs/api/#org.apache.mxnet.module.BaseModule'|relative_url}}).
 
 When prediction results might be too large to fit in memory, use the `predictEveryBatch` API:
 
@@ -155,8 +155,8 @@ To resume training from a saved checkpoint, instead of calling `setParams()`, di
 Create an object of the `FitParams()` class, and then use it to call the `setBeginEpoch()` method to pass `beginEpoch` so that `fit()` knows to resume from a saved epoch.
 
 ## Next Steps
-* See [Model API](model.md) for an alternative simple high-level interface for training neural networks.
-* See [Symbolic API](symbol.md) for operations on NDArrays that assemble neural networks from layers.
-* See [IO Data Loading API](io.md) for parsing and loading data.
-* See [NDArray API](ndarray.md) for vector/matrix/tensor operations.
-* See [KVStore API](kvstore.md) for multi-GPU and multi-host distributed training.
+* See [Model API](model) for an alternative simple high-level interface for training neural networks.
+* See [Symbolic API](symbol) for operations on NDArrays that assemble neural networks from layers.
+* See [IO Data Loading API](io) for parsing and loading data.
+* See [NDArray API](ndarray) for vector/matrix/tensor operations.
+* See [KVStore API](kvstore) for multi-GPU and multi-host distributed training.
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/mxnet_scala_on_intellij.md b/docs/static_site/src/pages/api/scala/docs/tutorials/mxnet_scala_on_intellij.md
index c934c3eaf4cc..115d7aeda095 100644
--- a/docs/static_site/src/pages/api/scala/docs/tutorials/mxnet_scala_on_intellij.md
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/mxnet_scala_on_intellij.md
@@ -404,7 +404,7 @@ This can be resolved be installing OpenCV.
 
 ### Using MXNet from source
 
-If you chose to "Build from Source" when following the [install instructions](https://mxnet.incubator.apache.org/install/index.html) (or the detailed [build from source instructions](https://mxnet.incubator.apache.org/install/build_from_source.html#installing-mxnet-language-bindings)), you can use your custom build instead of the build from maven.  Use your build by editing the `pom.xml` file and replacing the `org.apache.mxnet` dependency with the following:
+If you chose to "Build from Source" when following the [install instructions]({{'/get_started'|relative_url}}) (or the detailed [build from source instructions]({{'/get_started/build_from_source.html#installing-mxnet-language-bindings'|relative_url}})), you can use your custom build instead of the build from maven.  Use your build by editing the `pom.xml` file and replacing the `org.apache.mxnet` dependency with the following:
 
 ```
       <groupId>org.apache.mxnet</groupId>
@@ -450,7 +450,6 @@ The build generates a new jar file in the `target` folder called `scalaInference
 ## Next Steps
 For more information about MXNet Scala resources, see the following:
 
-* [Scala API](http://mxnet.io/api/scala/)
-* [Scala Inference API](https://mxnet.incubator.apache.org/api/scala/infer.html)
+* [Scala API]({{'/api/scala'|relative_url}})
 * [Scala Examples](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/)
-* [MXNet Tutorials Index](http://mxnet.io/tutorials/index.html)
+* [MXNet Tutorials Index]({{'/api'|relative_url}})
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/ndarray.md b/docs/static_site/src/pages/api/scala/docs/tutorials/ndarray.md
index 9a811c44ea8d..5534dbbaca68 100644
--- a/docs/static_site/src/pages/api/scala/docs/tutorials/ndarray.md
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/ndarray.md
@@ -31,7 +31,7 @@ Topics:
 
 * [Create NDArray](#create-ndarray)
 * [NDArray Operations](#ndarray-operations)
-* [NDArray API Reference](http://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.NDArray)
+* [NDArray API Reference]({{'/api/scala/docs/api/#org.apache.mxnet.NDArray'|relative_url}})
 
 ## Create NDArray
 
@@ -178,4 +178,4 @@ val z = x + cpu_y
 ```
 
 ## Next Steps
-* See [KVStore API](kvstore.md) for multi-GPU and multi-host distributed training.
+* See [KVStore API](kvstore) for multi-GPU and multi-host distributed training.
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/symbol.md b/docs/static_site/src/pages/api/scala/docs/tutorials/symbol.md
index 5136aed5b847..6bb8a471cef7 100644
--- a/docs/static_site/src/pages/api/scala/docs/tutorials/symbol.md
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/symbol.md
@@ -26,15 +26,15 @@ tag: scala
 
 Topics:
 
-* [How to Compose Symbols](#overloaded-operators) introduces operator overloading of symbols.
+* [How to Compose Symbols](#how-to-compose-symbols) introduces operator overloading of symbols.
 * [Symbol Attributes](#symbol-attributes) describes how to attach attributes to symbols.
 * [Serialization](#serialization) explains how to save and load symbols.
 * [Executing Symbols](#executing-symbols) explains how to evaluate the symbols with data.
-* [Execution API Reference](http://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.Executor) documents the execution APIs.
+* [Execution API Reference]({{'/api/scala/docs/api/#org.apache.mxnet.Executor'|relative_url}}) documents the execution APIs.
 * [Multiple Outputs](#multiple-outputs) explains how to configure multiple outputs.
-* [Symbol Creation API Reference](http://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.Symbol) documents functions.
+* [Symbol Creation API Reference]({{'/api/scala/docs/api/#org.apache.mxnet.Symbol'|relative_url}}) documents functions.
 
-We also highly encourage you to read [Symbolic Configuration and Execution in Pictures](symbol_in_pictures.md).
+We also highly encourage you to read [Symbolic Configuration and Execution in Pictures](symbol_in_pictures).
 
 ## How to Compose Symbols
 
@@ -102,7 +102,7 @@ To attach attributes, you can use ```AttrScope```. ```AttrScope``` automatically
 There are two ways to save and load the symbols. You can use the `mxnet.Symbol.save` and `mxnet.Symbol.load` functions to serialize the ```Symbol``` objects.
 The advantage of using `save` and `load` functions is that it is language agnostic and cloud friendly.
 The symbol is saved in JSON format. You can also get a JSON string directly using `mxnet.Symbol.toJson`.
-Refer to [API documentation](http://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.Symbol) for more details.
+Refer to [API documentation]({{'/api/scala/docs/api/#org.apache.mxnet.Symbol'|relative_url}}) for more details.
 
 The following example shows how to save a symbol to an S3 bucket, load it back, and compare two symbols using a JSON string.
 
@@ -121,7 +121,7 @@ The following example shows how to save a symbol to an S3 bucket, load it back,
 
 After you have assembled a set of symbols into a computation graph, the MXNet engine can evaluate them.
 If you are training a neural network, this is typically
-handled by the high-level [Model class](model.md) and the [`fit()`] function.
+handled by the high-level [Model class](model) and the [`fit()`] function.
 
 For neural networks used in "feed-forward", "prediction", or "inference" mode (all terms for the same
 thing: running a trained network), the input arguments are the
@@ -150,6 +150,6 @@ After you get the ```group```, you can bind on ```group``` instead.
 The resulting executor will have two outputs, one for fc1_output and one for softmax_output.
 
 ## Next Steps
-* See [IO Data Loading API](io.md) for parsing and loading data.
-* See [NDArray API](ndarray.md) for vector/matrix/tensor operations.
-* See [KVStore API](kvstore.md) for multi-GPU and multi-host distributed training.
+* See [IO Data Loading API](io) for parsing and loading data.
+* See [NDArray API](ndarray) for vector/matrix/tensor operations.
+* See [KVStore API](kvstore) for multi-GPU and multi-host distributed training.
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/symbol_in_pictures.md b/docs/static_site/src/pages/api/scala/docs/tutorials/symbol_in_pictures.md
index acbcbaf4ac73..67bd695a1e45 100644
--- a/docs/static_site/src/pages/api/scala/docs/tutorials/symbol_in_pictures.md
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/symbol_in_pictures.md
@@ -25,7 +25,7 @@ tag: scala
 # Symbolic Configuration and Execution in Pictures
 
 This topic explains symbolic construction and execution in pictures.
-We recommend that you also read [Symbolic API](symbol.md).
+We recommend that you also read [Symbolic API](symbol).
 
 ## Compose Symbols
 
@@ -97,4 +97,4 @@ Auxiliary states are just like arguments, except that you can't take the gradien
 
 ## Next Steps
 
-See [Symbolic API](symbol.md) and [Python Documentation](index.md).
+See [Symbolic API](symbol) and [Python Documentation]({{'/api/python'|relative_url}}).
diff --git a/docs/static_site/src/pages/get_started/build_from_source.md b/docs/static_site/src/pages/get_started/build_from_source.md
index 6256e25f28d4..5f4c2731afb6 100644
--- a/docs/static_site/src/pages/get_started/build_from_source.md
+++ b/docs/static_site/src/pages/get_started/build_from_source.md
@@ -27,7 +27,7 @@ permalink: /get_started/build_from_source
 
 This document explains how to build MXNet from source code.
 
-**For Java/Scala/Clojure, please follow [this guide instead](./scala_setup.md)**
+**For Java/Scala/Clojure, please follow [this guide instead](scala_setup)**
 
 ## Overview
 
@@ -50,14 +50,14 @@ Building from source follows this general two-step flow of building the shared l
             * [non-Intel CPUs](#recommended-for-Systems-with-non-Intel-CPUs)
 2. [Install the language API binding(s)](#installing-mxnet-language-bindings) you would like to use for MXNet.
 MXNet's newest and most popular API is Gluon. Gluon is built into the Python binding. If Python isn't your preference, you still have more options. MXNet supports several other language APIs:
-    - [Python (includes Gluon)](../api/python/index.md)
-    - [C++](../api/cpp/index.md)
-    - [Clojure](../api/clojure/index.md)
-    - [Java](../api/java/index.md)
-    - [Julia](../api/julia/index.md)
-    - [Perl](../api/perl/index.md)
-    - [R](../api/r/index.md)
-    - [Scala](../api/scala/index.md)
+    - [Python (includes Gluon)]({{'/api/python/index'|relative_url}})
+    - [C++]({{'/api/cpp'|relative_url}})
+    - [Clojure]({{'/api/clojure'|relative_url}})
+    - [Java]({{'/api/java'|relative_url}})
+    - [Julia]({{'/api/julia'|relative_url}})
+    - [Perl]({{'/api/perl'|relative_url}})
+    - [R]({{'/api/r'|relative_url}})
+    - [Scala]({{'/api/scala'|relative_url}})
 
 <hr>
 
@@ -66,11 +66,11 @@ MXNet's newest and most popular API is Gluon. Gluon is built into the Python bin
 Detailed instructions are provided per operating system. Each of these guides also covers how to install the specific [Language Bindings](#installing-mxnet-language-bindings) you require.
 You may jump to those, but it is recommended that you continue reading to understand more general "build from source" options.
 
-* [Amazon Linux / CentOS / RHEL](centos_setup.md)
-* [macOS](osx_setup.md)
-* [Devices](https://mxnet.incubator.apache.org/versions/master/install/index.html?platform=Devices&language=Python&processor=CPU)
-* [Ubuntu](ubuntu_setup.md)
-* [Windows](windows_setup.md)
+* [Amazon Linux / CentOS / RHEL](centos_setup)
+* [macOS](osx_setup)
+* [Devices](index.html?&platform=devices&language=python&environ=pip&processor=cpu)
+* [Ubuntu](ubuntu_setup)
+* [Windows](windows_setup)
 
 
 <hr>
@@ -98,10 +98,10 @@ Those can be extended with [LAPACK (Linear Algebra Package)](https://github.com/
 
 MXNet supports multiple mathematical backends for computations on the CPU:
 * [Apple Accelerate](https://developer.apple.com/documentation/accelerate)
-* [ATLAS](http://math-atlas.sourceforge.net/)
+* [ATLAS](https://math-atlas.sourceforge.net/)
 * [MKL](https://software.intel.com/en-us/intel-mkl) (MKL, MKLML)
 * [MKL-DNN](https://github.com/intel/mkl-dnn)
-* [OpenBLAS](http://www.openblas.net/)
+* [OpenBLAS](https://www.openblas.net/)
 
 The default order of choice for the libraries if found follows the path from the most
 (recommended) to less performant backends.
@@ -314,13 +314,13 @@ ninja -v
 ## Installing MXNet Language Bindings
 After building MXNet's shared library, you can install other language bindings.
 
-**NOTE:** The C++ API binding must be built when you build MXNet from source. See [Build MXNet with C++](#build-mxnet-with-c++).
+**NOTE:** The C++ API binding must be built when you build MXNet from source. See [Build MXNet with C++]({{'/api/cpp'|relative_url}}).
 
 The following table provides links to each language binding by operating system:
-| | [Ubuntu](ubuntu_setup.html) | [macOS](osx_setup.html) | [Windows](windows_setup.html) |
+| | [Ubuntu](ubuntu_setup) | [macOS](osx_setup) | [Windows](windows_setup) |
 | --- | ----  | --- | ------- |
-| Python | [Ubuntu guide](ubuntu_setup.html#install-mxnet-for-python) | [OSX guide](osx_setup.html) | [Windows guide](windows_setup.html#install-mxnet-for-python) |
-| C++ | [C++ guide](c_plus_plus.html) | [C++ guide](c_plus_plus.html) | [C++ guide](c_plus_plus.html) |
+| Python | [Ubuntu guide](ubuntu_setup.html#install-mxnet-for-python) | [OSX guide](osx_setup) | [Windows guide](windows_setup.html#install-mxnet-for-python) |
+| C++ | [C++ guide](cpp_setup) | [C++ guide](cpp_setup) | [C++ guide](cpp_setup) |
 | Clojure | [Clojure guide](https://github.com/apache/incubator-mxnet/tree/master/contrib/clojure-package) | [Clojure guide](https://github.com/apache/incubator-mxnet/tree/master/contrib/clojure-package) | n/a |
 | Julia | [Ubuntu guide](ubuntu_setup.html#install-the-mxnet-package-for-julia) | [OSX guide](osx_setup.html#install-the-mxnet-package-for-julia) | [Windows guide](windows_setup.html#install-the-mxnet-package-for-julia) |
 | Perl | [Ubuntu guide](ubuntu_setup.html#install-the-mxnet-package-for-perl) | [OSX guide](osx_setup.html#install-the-mxnet-package-for-perl) | n/a |
diff --git a/docs/static_site/src/pages/get_started/c_plus_plus.md b/docs/static_site/src/pages/get_started/c_plus_plus.md
index 0559c7022710..a32496dd68a7 100644
--- a/docs/static_site/src/pages/get_started/c_plus_plus.md
+++ b/docs/static_site/src/pages/get_started/c_plus_plus.md
@@ -26,7 +26,7 @@ permalink: /get_started/cpp_setup
 ## Build the C++ package
 The C++ package has the same prerequisites as the MXNet library.
 
-To enable C++ package, just add `USE_CPP_PACKAGE=1` in the [build from source](build_from_source.md) options when building the MXNet shared library.
+To enable C++ package, just add `USE_CPP_PACKAGE=1` in the [build from source](build_from_source) options when building the MXNet shared library.
 
 For example to build MXNet with GPU support and the C++ package, OpenCV, and OpenBLAS, from the project root you would run:
 
@@ -48,7 +48,7 @@ You can find C++ code examples in the `cpp-package/example` folder of the MXNet
 
 ## Tutorials
 
-* [MXNet C++ API Basics](../api/cpp/docs/tutorials/basics)
+* [MXNet C++ API Basics]({{'/api/cpp/docs/tutorials/basics'|relative_url}})
 
 ## Related Topics
 
diff --git a/docs/static_site/src/pages/get_started/java_setup.md b/docs/static_site/src/pages/get_started/java_setup.md
index 8e789d79351c..0e3e8cfc0354 100644
--- a/docs/static_site/src/pages/get_started/java_setup.md
+++ b/docs/static_site/src/pages/get_started/java_setup.md
@@ -26,7 +26,7 @@ permalink: /get_started/java_setup
 
 The following instructions are provided for macOS and Ubuntu. Windows is not yet available.
 
-**Note:** If you use IntelliJ or a similar IDE, you may want to follow the [MXNet-Java on IntelliJ tutorial](../tutorials/java/mxnet_java_on_intellij.md) instead of these instructions.
+**Note:** If you use IntelliJ or a similar IDE, you may want to follow the [MXNet-Java on IntelliJ tutorial]({{'/api/java/docs/tutorials/mxnet_java_on_intellij'|relative_url}}) instead of these instructions.
 
 <hr>
 
@@ -122,7 +122,7 @@ This will install both the Java Inference API and the required MXNet-Scala packa
 
 ## Documentation
 
-Javadocs are generated as part of the docs build pipeline. You can find them published in the [Java API](../api/java/index.md) section of the website or by going to the [scaladocs output](https://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.package) directly.
+Javadocs are generated as part of the docs build pipeline. You can find them published in the [Java API]({{'/api/java'|relative_url}}) section of the website or by going to the [scaladocs output]({{'/api/scala/docs/api/#org.apache.mxnet.package'|relative_url}}) directly.
 
 To build the docs yourself, follow the [developer build docs instructions](https://github.com/apache/incubator-mxnet/tree/master/docs/build_version_doc#developer-instructions).
 
@@ -130,6 +130,6 @@ To build the docs yourself, follow the [developer build docs instructions](https
 
 ## Resources
 
-* [Java API](../api/java/)
-* [javadocs](https://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.package)
-* [MXNet-Java Tutorials](../tutorials/java/)
+* [Java API]({{'/api/java'|relative_url}})
+* [javadocs]({{'/api/scala/docs/api/#org.apache.mxnet.package'|relative_url}})
+* [MXNet-Java Tutorials]({{'/api/java/docs/tutorials'|relative_url}})
diff --git a/docs/static_site/src/pages/get_started/osx_setup.md b/docs/static_site/src/pages/get_started/osx_setup.md
index abd278001146..b5234126e334 100644
--- a/docs/static_site/src/pages/get_started/osx_setup.md
+++ b/docs/static_site/src/pages/get_started/osx_setup.md
@@ -102,7 +102,8 @@ Install the dependencies, required for MXNet, with the following commands:
 ```
 
 ### Build MXNet Shared Library
-After you have installed the dependencies, pull the MXNet source code from Git and build MXNet to produce an MXNet library called ```libmxnet.so```. You can clone the repository as described in the following code block, or you may try the [download links](download.md) for your desired MXNet version.
+
+After you have installed the dependencies, pull the MXNet source code from Git and build MXNet to produce an MXNet library called ```libmxnet.so```. You can clone the repository as described in the following code block, or you may try the [download links](download) for your desired MXNet version.
 
 The file called ```osx.mk``` has the configuration required for building MXNet on OS X. First copy ```make/osx.mk``` into ```config.mk```, which is used by the ```make``` command:
 
@@ -223,19 +224,19 @@ You might want to add this command to your ```~/.bashrc``` file. If you do, you
 	Pkg.add("MXNet")
 ```
 
-For more details about installing and using MXNet with Julia, see the [MXNet Julia documentation](http://dmlc.ml/MXNet.jl/latest/user-guide/install/).
+For more details about installing and using MXNet with Julia, see the [MXNet Julia documentation]({{'/api/julia'|relative_url}}).
 
 
 ## Install the MXNet Package for Scala
 
 To use the MXNet-Scala package, you can acquire the Maven package as a dependency.
 
-Further information is in the [MXNet-Scala Setup Instructions](./scala_setup.md).
-
-If you use IntelliJ or a similar IDE, you may want to follow the [MXNet-Scala on IntelliJ tutorial](../tutorials/scala/mxnet_scala_on_intellij.md) instead.
+Further information is in the [MXNet-Scala Setup Instructions](scala_setup).
 
+If you use IntelliJ or a similar IDE, you may want to follow the [MXNet-Scala on IntelliJ tutorial]({{'/api/scala/docs/tutorials/mxnet_scala_on_intellij'|relative_url}}) instead.
 
 ## Install the MXNet Package for Perl
+
 Before you build MXNet for Perl from source code, you must complete [building the shared library](#build-the-shared-library).
 After you build the shared library, run the following command from the MXNet source root directory to build the MXNet Perl package:
 
@@ -270,6 +271,6 @@ After you build the shared library, run the following command from the MXNet sou
 
 ## Next Steps
 
-* [Tutorials](../tutorials/index.md)
-* [How To](../faq/index.md)
-* [Architecture](../architecture/index.md)
+* [Tutorials]({{'/api'|relative_url}})
+* [How To]({{'/api/faq/add_op_in_backend'|relative_url}})
+* [Architecture]({{'/api/architecture/overview'|relative_url}})
diff --git a/docs/static_site/src/pages/get_started/scala_setup.md b/docs/static_site/src/pages/get_started/scala_setup.md
index 5cf71ba451ce..827340c44a13 100644
--- a/docs/static_site/src/pages/get_started/scala_setup.md
+++ b/docs/static_site/src/pages/get_started/scala_setup.md
@@ -27,7 +27,7 @@ permalink: /get_started/scala_setup
 
 The following instructions are provided for macOS and Ubuntu. Windows is not yet available.
 
-**Note:** If you use IntelliJ or a similar IDE, you may want to follow the [MXNet-Scala on IntelliJ tutorial](../tutorials/scala/mxnet_scala_on_intellij.md) instead of these instructions.
+**Note:** If you use IntelliJ or a similar IDE, you may want to follow the [MXNet-Scala on IntelliJ tutorial]({{'/api/scala/docs/tutorials/mxnet_scala_on_intellij'|relative_url}}) instead of these instructions.
 **Note:** Currently, we only support scala 2.11
 
 <hr>
@@ -152,7 +152,7 @@ If you receive a "NumberFormatException" when running the interpreter, run `expo
 
 ## Documentation
 
-Scaladocs are generated as part of the docs build pipeline. You can find them published in the [Scala API](../api/scala/index.md) section of the website or by going to the [scaladocs output](https://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.package) directly.
+Scaladocs are generated as part of the docs build pipeline. You can find them published in the [Scala API]({{'/api/scala'|relative_url}}) section of the website or by going to the [scaladocs output]({{'/api/scala/docs/api/#org.apache.mxnet.package'|relative_url}}) directly.
 
 To build the docs yourself, follow the [developer build docs instructions](https://github.com/apache/incubator-mxnet/tree/master/docs/build_version_doc#developer-instructions).
 
@@ -160,6 +160,6 @@ To build the docs yourself, follow the [developer build docs instructions](https
 
 ## Resources
 
-* [Scala API](../api/scala/)
-* [scaladocs](https://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.package)
-* [MXNet-Scala Tutorials](../tutorials/scala/)
+* [Scala API]({{'/api/scala'|relative_url}})
+* [scaladocs]({{'/api/scala/docs/api/#org.apache.mxnet.package'|relative_url}})
+* [MXNet-Scala Tutorials]({{'/api/scala/docs/tutorials'|relative_url}})
diff --git a/docs/static_site/src/pages/get_started/ubuntu_setup.md b/docs/static_site/src/pages/get_started/ubuntu_setup.md
index 602f93d11608..7b309c27cbd6 100644
--- a/docs/static_site/src/pages/get_started/ubuntu_setup.md
+++ b/docs/static_site/src/pages/get_started/ubuntu_setup.md
@@ -403,7 +403,7 @@ julia --color=yes --project=./ -e \
 	   Pkg.develop(PackageSpec(name="MXNet", path = joinpath(ENV["MXNET_HOME"], "julia")))'
 ```
 
-For more details about installing and using MXNet with Julia, see the [MXNet Julia documentation](../api/julia/site/).
+For more details about installing and using MXNet with Julia, see the [MXNet Julia documentation]({{'/api/julia'|relative_url}}).
 <hr>
 
 
@@ -470,14 +470,14 @@ $ sudo apt-get install -y build-essential git
 
 **Step 2** Install OpenBLAS.
 
-*MXNet* uses [BLAS](https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) and [LAPACK](https://en.wikipedia.org/wiki/LAPACK) libraries for accelerated numerical computations on CPU machine. There are several flavors of BLAS/LAPACK libraries - [OpenBLAS](http://www.openblas.net/), [ATLAS](http://math-atlas.sourceforge.net/) and [MKL](https://software.intel.com/en-us/intel-mkl). In this step we install OpenBLAS. You can choose to install ATLAS or MKL.
+*MXNet* uses [BLAS](https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) and [LAPACK](https://en.wikipedia.org/wiki/LAPACK) libraries for accelerated numerical computations on CPU machine. There are several flavors of BLAS/LAPACK libraries - [OpenBLAS](https://www.openblas.net/), [ATLAS](http://math-atlas.sourceforge.net/) and [MKL](https://software.intel.com/en-us/intel-mkl). In this step we install OpenBLAS. You can choose to install ATLAS or MKL.
 ```bash
 $ sudo apt-get install -y libopenblas-dev liblapack-dev
 ```
 
 **Step 3** Install OpenCV.
 
-*MXNet* uses [OpenCV](http://opencv.org/) for efficient image loading and augmentation operations.
+*MXNet* uses [OpenCV](https://opencv.org/) for efficient image loading and augmentation operations.
 ```bash
 $ sudo apt-get install -y libopencv-dev
 ```
@@ -535,7 +535,7 @@ To use the MXNet-Scala package, you can acquire the Maven package as a dependenc
 
 Further information is in the [MXNet-Scala Setup Instructions](scala_setup).
 
-If you use IntelliJ or a similar IDE, you may want to follow the [MXNet-Scala on IntelliJ tutorial](../api/scala/docs/tutorials/mxnet_scala_on_intellij) instead.
+If you use IntelliJ or a similar IDE, you may want to follow the [MXNet-Scala on IntelliJ tutorial]({{'/api/scala/docs/tutorials/mxnet_scala_on_intellij'|relative_url}}) instead.
 <hr>
 
 ### Install the MXNet Package for Java
@@ -544,18 +544,18 @@ To use the MXNet-Java package, you can acquire the Maven package as a dependency
 
 Further information is in the [MXNet-Java Setup Instructions](java_setup).
 
-If you use IntelliJ or a similar IDE, you may want to follow the [MXNet-Java on IntelliJ tutorial](../api/java/docs/tutorials/mxnet_java_on_intellij) instead.
+If you use IntelliJ or a similar IDE, you may want to follow the [MXNet-Java on IntelliJ tutorial]({{'/api/java/docs/tutorials/mxnet_java_on_intellij'|relative_url}}) instead.
 <hr>
 
 ## Contributions
 
-You are more than welcome to contribute easy installation scripts for other operating systems and programming languages. See the [community contributions page](../community/contribute) for further information.
+You are more than welcome to contribute easy installation scripts for other operating systems and programming languages. See the [community contributions page]({{'/community/contribute'|relative_url}}) for further information.
 
 ## Next Steps
 
-* [Tutorials](../api/python/docs/tutorials)
-* [How To](../api)
-* [Architecture](../api)
+* [Tutorials]({{'/api'|relative_url}})
+* [How To]({{'/api/faq/add_op_in_backend'|relative_url}})
+* [Architecture]({{'/api/architecture/overview'|relative_url}})
 
 
 <link rel="stylesheet" href="https://use.fontawesome.com/releases/v5.1.0/css/all.css" integrity="sha384-lKuwvrZot6UHsBSfcMvOkWwlCMgc0TaWr+30HWe3a4ltaBwTZhyTEggF5tJv8tbt" crossorigin="anonymous">
diff --git a/docs/static_site/src/pages/get_started/windows_setup.md b/docs/static_site/src/pages/get_started/windows_setup.md
index d0ae2a8abbe7..9bf413826a17 100644
--- a/docs/static_site/src/pages/get_started/windows_setup.md
+++ b/docs/static_site/src/pages/get_started/windows_setup.md
@@ -30,7 +30,7 @@ The following describes how to install with pip for computers with CPUs, Intel C
 - [Install MXNet with Python](#install-mxnet-with-python)
     - [Install with CPUs](#install-with-cpus)
     - [Install with Intel CPUs](#install-with-intel-cpus)
-    - [Install with GPUs](#install-with-gpus)
+    - [Install with NVIDIA GPUs](#install-with-nvidia-gpus)
     - [Notes on the Python Packages](#notes-on-the-python-packages)
 - [Build from Source](#build-from-source)
 - Install MXNet with a Programming Language API
@@ -74,7 +74,7 @@ Install MXNet with CPU support with Python:
 pip install mxnet
 ```
 
-Now [validate your MXNet installation with Python](validate_mxnet.md).
+Now [validate your MXNet installation with Python](validate_mxnet).
 
 ### Install with Intel CPUs
 
@@ -88,7 +88,7 @@ The following steps will setup MXNet with MKL. MKL-DNN can be enabled only when
 pip install mxnet-mkl
 ```
 
-Now [validate your MXNet installation with Python](validate_mxnet.md).
+Now [validate your MXNet installation with Python](validate_mxnet).
 
 ### Install with NVIDIA GPUs
 
@@ -108,7 +108,7 @@ The following steps will setup MXNet with CUDA. cuDNN can be enabled only when b
 pip install mxnet-cu92
 ```
 
-Once you have installed a version of MXNet, [validate your MXNet installation with Python](validate_mxnet.md).
+Once you have installed a version of MXNet, [validate your MXNet installation with Python](validate_mxnet).
 
 #### Install with CUDA and MKL Support
 
@@ -125,7 +125,7 @@ The following steps will setup MXNet with CUDA and MKL.
 pip install mxnet-cu92mkl
 ```
 
-Once you have installed a version of MXNet, [validate your MXNet installation with Python](validate_mxnet.md).
+Once you have installed a version of MXNet, [validate your MXNet installation with Python](validate_mxnet).
 
 ### Notes on the Python Packages
 To get further enhancements for deep neural networks, you may want to enable MKL-DNN and/or cuDNN. Each of these require you to [build from source](#build-from-source) and to enable the build flags for each.
@@ -137,7 +137,7 @@ Check the chart below for other options or refer to [PyPI for other MXNet pip pa
 
 ## Build from Source
 
-**IMPORTANT: It is recommended that you review the [build from source guide](build_from_source.md) first.** It describes many of the build options that come with MXNet in more detail. You may decide to install additional dependencies and modify your build flags after reviewing this material.
+**IMPORTANT: It is recommended that you review the [build from source guide](build_from_source) first.** It describes many of the build options that come with MXNet in more detail. You may decide to install additional dependencies and modify your build flags after reviewing this material.
 
 We provide two primary options to build and install MXNet yourself using [Microsoft Visual Studio 2017](https://www.visualstudio.com/downloads/) or [Microsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/).
 
@@ -227,7 +227,7 @@ Next, we install ```graphviz``` library that we use for visualizing network grap
 
 We have installed MXNet core library. Next, we will install MXNet interface package for programming language of your choice:
 - [Python](#install-the-mxnet-package-for-python)
-- [R](#install-mxnet-package-for-r)
+- [R](#install-the-mxnet-package-for-r)
 - [Julia](#install-the-mxnet-package-for-julia)
 - **Scala** is not yet available for Windows
 
@@ -261,7 +261,7 @@ opencv_world341.dll (in OpenCV folder you download)
 
 Done! We have installed MXNet with Python interface.
 
-You can continue with using MXNet-Python, or if you want to try a different language API for MXNet, keep reading. Otherwise, jump ahead to [next steps](#next-steps).
+You can continue with using MXNet-Python, or if you want to try a different language API for MXNet, keep reading.
 
 ## Install the MXNet Package for R
 MXNet for R is available for both CPUs and GPUs.
@@ -482,7 +482,7 @@ You might want to add this command to your ```~/.bashrc``` file. If you do, you
 Pkg.add("MXNet")
 ```
 
-For more details about installing and using MXNet with Julia, see the [MXNet Julia documentation](/api/julia/site/).
+For more details about installing and using MXNet with Julia, see the [MXNet Julia documentation]({{'/api/julia'|relative_url}}).
 
 
 ## Installing the MXNet Package for Scala
diff --git a/docs/tutorials/python/profiler.md b/docs/tutorials/python/profiler.md
deleted file mode 100644
index 49d50f494042..000000000000
--- a/docs/tutorials/python/profiler.md
+++ /dev/null
@@ -1,336 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Profiling MXNet Models
-
-It is often helpful to check the execution time of each operation in a neural network. You can then determine where to focus your effort to speed up model training or inference. In this tutorial, we will learn how to profile MXNet models to measure their running time and memory consumption using the MXNet profiler.
-
-## The incorrect way to profile
-
-If you have just started to use MXNet, you might be tempted to measure the execution time of your model using Python's `time` module like shown below:
-
-```python
-from time import time
-from mxnet import autograd, nd
-import mxnet as mx
-
-start = time()
-x = nd.random_uniform(shape=(2000,2000))
-y = nd.dot(x, x)
-print('Time for matrix multiplication: %f sec\n' % (time() - start))
-
-start = time()                                
-y_np = y.asnumpy()                             
-print('Time for converting to numpy: %f sec' % (time() - start))
-```
-
-**Time for matrix multiplication: 0.005051 sec**<!--notebook-skip-line-->
-
-**Time for converting to numpy: 0.167693 sec**<!--notebook-skip-line-->
-
-From the timings above, it seems as if converting to numpy takes lot more time than multiplying two large matrices. That doesn't seem right.
-
-This is because, in MXNet, all operations are executed asynchronously. So, when `nd.dot(x, x)` returns, the matrix multiplication is not complete, it has only been queued for execution. However, [`asnumpy`](http://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html?highlight=asnumpy#mxnet.ndarray.NDArray.asnumpy) has to wait for the result to be calculated in order to convert it to numpy array on CPU, hence taking a longer time. Other examples of 'blocking' operations include [`asscalar`](http://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html?highlight=asscalar#mxnet.ndarray.NDArray.asscalar) and [`wait_to_read`](http://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html?highlight=wait_to_read#mxnet.ndarray.NDArray.wait_to_read).
-
-While it is possible to use [`NDArray.waitall()`](http://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html?highlight=waitall#mxnet.ndarray.waitall) before and after operations to get running time of operations, it is not a scalable method to measure running time of multiple sets of operations, especially in a [`Sequential`](http://mxnet.incubator.apache.org/api/python/gluon/gluon.html?highlight=sequential#mxnet.gluon.nn.Sequential) or hybridized network.
-
-## The correct way to profile
-
-The correct way to measure running time of MXNet models is to use MXNet profiler. In the rest of this tutorial, we will learn how to use the MXNet profiler to measure the running time and memory consumption of MXNet models. You can import the profiler and configure it from Python code.
-
-```python
-from mxnet import profiler
-
-profiler.set_config(profile_all=True,
-                    aggregate_stats=True,
-                    continuous_dump=True,
-                    filename='profile_output.json')
-```
-
-`profile_all` enables all types of profiling. You can also individually enable the following types of profiling:
-
-- `profile_symbolic` (boolean): whether to profile symbolic operators
-- `profile_imperative` (boolean): whether to profile imperative operators
-- `profile_memory` (boolean): whether to profile memory usage
-- `profile_api` (boolean): whether to profile the C API
-
-`aggregate_stats` aggregates statistics in memory which can then be printed to console by calling `profiler.dumps()`.
-
-### Setup: Build a model
-
-Let's build a small convolutional neural network that we can use to demonstrate profiling.
-
-```python
-from mxnet import gluon
-
-net = gluon.nn.HybridSequential()
-with net.name_scope():
-    net.add(gluon.nn.Conv2D(channels=20, kernel_size=5, activation='relu'))
-    net.add(gluon.nn.MaxPool2D(pool_size=2, strides=2))
-    net.add(gluon.nn.Conv2D(channels=50, kernel_size=5, activation='relu'))
-    net.add(gluon.nn.MaxPool2D(pool_size=2, strides=2))
-    net.add(gluon.nn.Flatten())
-    net.add(gluon.nn.Dense(512, activation="relu"))
-    net.add(gluon.nn.Dense(10))
-```
-
-We need data that we can run through the network for profiling. We'll use the MNIST dataset.
-
-```python
-from mxnet.gluon.data.vision import transforms
-
-dataset = gluon.data.vision.MNIST(train=True)
-dataset = dataset.transform_first(transforms.ToTensor())
-dataloader = gluon.data.DataLoader(dataset, batch_size=64, shuffle=True)
-```
-
-Let's define a function that will run a single training iteration given `data` and `label`.
-
-```python
-# Use GPU if available
-if mx.context.num_gpus():
-    ctx=mx.gpu()
-else:
-    ctx=mx.cpu()
-
-# Initialize the parameters with random weights
-net.collect_params().initialize(mx.init.Xavier(), ctx=ctx)
-
-# Use SGD optimizer
-trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.1})
-
-# Softmax Cross Entropy is a frequently used loss function for multi-class classification
-softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()
-
-# A helper function to run one training iteration
-def run_training_iteration(data, label):
-    # Load data and label is the right context
-    data = data.as_in_context(ctx)
-    label = label.as_in_context(ctx)
-    # Run the forward pass
-    with autograd.record():
-        output = net(data)
-        loss = softmax_cross_entropy(output, label)
-    # Run the backward pass
-    loss.backward()
-    # Apply changes to parameters
-    trainer.step(data.shape[0])
-```
-
-### Starting and stopping the profiler from Python
-
-When the first forward pass is run on a network, MXNet does a number of housekeeping tasks including inferring the shapes of various parameters, allocating memory for intermediate and final outputs, etc. For these reasons, profiling the first iteration doesn't provide representative results for the rest of training. We will, therefore, skip the first iteration.
-
-```python
-# Run the first iteration without profiling
-itr = iter(dataloader)
-run_training_iteration(*next(itr))
-```
-
-We'll run the next iteration with the profiler turned on.
-
-```python
-data, label = next(itr)
-
-# Ask the profiler to start recording
-profiler.set_state('run')
-
-run_training_iteration(*next(itr))
-
-# Make sure all operations have completed
-mx.nd.waitall()
-# Ask the profiler to stop recording
-profiler.set_state('stop')
-# Dump all results to log file before download
-profiler.dump()
-```
-
-Between running and stopping the profiler, you can also pause and resume the profiler using `profiler.pause()` and `profiler.resume()` respectively to profile only parts of the code you want to profile.
-
-### Starting the profiler automatically using an environment variable
-
-The method described above requires code changes to start and stop the profiler. You can also start the profiler automatically and profile the entire code without any code changes using the `MXNET_PROFILER_AUTOSTART` environment variable.
-
-`$ MXNET_PROFILER_AUTOSTART=1 python my_script.py`
-
-MXNet will start the profiler automatically if you run your code with the environment variable `MXNET_PROFILER_AUTOSTART` set to `1`. The profiler output is stored in `profile.json` inside the current directory.
-
-Note that the profiler output could be large depending on your code. It might be helpful to profile only sections of your code using the `set_state` API described in the previous section.
-
-### Increasing granularity of the profiler output
-
-MXNet executes computation graphs in 'bulk mode' which reduces kernel launch gaps in between symbolic operators for faster execution. This could reduce the granularity of the profiler output. If you need profiling result of every operator, please set the environment variables `MXNET_EXEC_BULK_EXEC_INFERENCE` and `MXNET_EXEC_BULK_EXEC_TRAIN` to `0` to disable the bulk execution mode.
-
-When working with networks created using the Gluon API, you will get a more granular profiling outputs if you profile networks that haven't been hybridized. Operations can appear fused together in the profiling outputs after hybridization, which can make debugging tricky.
-
-### Viewing profiler output
-
-There are a few ways to view the information collected by the profiler. You can view it in the console, you can view a more graphical version in a browser, or you can use a vendor tool such as Intel VTune or Nvidia NVProf to view output. For most scenarios the information you need can be obtained with MXNet's built in profiler support, but if you want to investigate the performance of operators alongside extra context about your hardware (e.g. cache hit rates, or CUDA kernel timings) then profiling jointly with vendor tools is recommended.
-
-#### 1. View in console
-
-You can use the `profiler.dumps()` method to view the information collected by the profiler in the console. The collected information contains time taken by each operator, time taken by each C API and memory consumed in both CPU and GPU.
-
-```python
-print(profiler.dumps())
-```
-
-![Profile Statistics](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tutorials/python/profiler/profile_stats.png)<!--notebook-skip-line-->
-
-#### 2. View in browser
-
-You can also dump the information collected by the profiler into a `json` file using the `profiler.dump()` function and view it in a browser.
-
-```python
-profiler.set_state('run')
-profiler.set_state('stop')
-profiler.dump(finished=False)
-```
-
-`dump()` creates a `json` file which can be viewed using a trace consumer like `chrome://tracing` in the Chrome browser. Here is a snapshot that shows the output of the profiling we did above. Note that setting the `finished` parameter to `False` will prevent the profiler from finishing dumping to file. If you just use `profiler.dump()`, you will no longer be able to profile the remaining sections of your model. 
-
-![Tracing Screenshot](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tutorials/python/profiler/profiler_output_chrome.png)
-
-Let's zoom in to check the time taken by operators
-
-![Operator profiling](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tutorials/python/profiler/profile_operators.png)
-
-The above picture visualizes the sequence in which the operators were executed and the time taken by each operator.
-
-### Profiling Custom Operators
-Should the existing NDArray operators fail to meet all your model's needs, MXNet supports [Custom Operators](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/customop.html) that you can define in Python. In `forward()` and `backward()` of a custom operator, there are two kinds of code: "pure Python" code (NumPy operators included) and "sub-operators" (NDArray operators called within `forward()` and `backward()`). With that said, MXNet can profile the execution time of both kinds without additional setup. Specifically, the MXNet profiler will break a single custom operator call into a pure Python event and several sub-operator events if there are any. Furthermore, all of those events will have a prefix in their names, which is, conveniently, the name of the custom operator you called.
-
-Let's try profiling custom operators with the following code example:
-
-```python
-class MyAddOne(mx.operator.CustomOp):
-    def forward(self, is_train, req, in_data, out_data, aux):  
-        self.assign(out_data[0], req[0], in_data[0]+1)
-
-    def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
-        self.assign(in_grad[0], req[0], out_grad[0])
-
-@mx.operator.register('MyAddOne')
-class CustomAddOneProp(mx.operator.CustomOpProp):
-    def __init__(self):
-        super(CustomAddOneProp, self).__init__(need_top_grad=True)
-
-    def list_arguments(self):
-        return ['data']
-
-    def list_outputs(self):
-        return ['output']
-
-    def infer_shape(self, in_shape):
-        return [in_shape[0]], [in_shape[0]], []
-
-    def create_operator(self, ctx, shapes, dtypes):
-        return MyAddOne()
-
-
-inp = mx.nd.zeros(shape=(500, 500))
-
-profiler.set_config(profile_all=True, continuous_dump=True, \
-                    aggregate_stats=True)
-profiler.set_state('run')
-
-w = nd.Custom(inp, op_type="MyAddOne")
-
-mx.nd.waitall()
-
-profiler.set_state('stop')
-print(profiler.dumps())
-profiler.dump(finished=False)
-```
-
-Here, we have created a custom operator called `MyAddOne`, and within its `forward()` function, we simply add one to the input. We can visualize the dump file in `chrome://tracing/`:
-
-![Custom Operator Profiling Screenshot](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tutorials/python/profiler/profiler_output_custom_operator_chrome.png)
-
-As shown by the screenshot, in the **Custom Operator** domain where all the custom operator-related events fall into, we can easily visualize the execution time of each segment of `MyAddOne`. We can tell that `MyAddOne::pure_python` is executed first. We also know that `CopyCPU2CPU` and `_plus_scalr` are two "sub-operators" of `MyAddOne` and the sequence in which they are executed.
-
-Please note that: to be able to see the previously described information, you need to set `profile_imperative` to `True` even when you are using custom operators in [symbolic mode](https://mxnet.incubator.apache.org/versions/master/tutorials/basic/symbol.html) (refer to the code snippet below, which is the symbolic-mode equivelent of the code example above). The reason is that within custom operators, pure python code and sub-operators are still called imperatively. 
-
-```python 
-# Set profile_all to True
-profiler.set_config(profile_all=True, aggregate_stats=True, continuous_dump=True)
-# OR, Explicitly Set profile_symbolic and profile_imperative to True
-profiler.set_config(profile_symbolic=True, profile_imperative=True, \
-                    aggregate_stats=True, continuous_dump=True)
-
-profiler.set_state('run')
-# Use Symbolic Mode
-a = mx.symbol.Variable('a')
-b = mx.symbol.Custom(data=a, op_type='MyAddOne')
-c = b.bind(mx.cpu(), {'a': inp})
-y = c.forward()
-mx.nd.waitall()
-profiler.set_state('stop')
-print(profiler.dumps())
-profiler.dump()
-```
-
-### Some Rules to Pay Attention to
-1. Always use `profiler.dump(finished=False)` if you do not intend to finish dumping to file. Otherwise, calling `profiler.dump()` in the middle of your model may lead to unexpected behaviors; and if you subsequently call `profiler.set_config()`, the program will error out.
-
-2. You can only dump to one file. Do not change the target file by calling `profiler.set_config(filename='new_name.json')` in the middle of your model. This will lead to incomplete dump outputs.
-
-## Advanced: Using NVIDIA Profiling Tools
-
-MXNet's Profiler is the recommended starting point for profiling MXNet code, but NVIDIA also provides a couple of tools for low-level profiling of CUDA code: [NVProf](https://devblogs.nvidia.com/cuda-pro-tip-nvprof-your-handy-universal-gpu-profiler/), [Visual Profiler](https://developer.nvidia.com/nvidia-visual-profiler) and [Nsight Compute](https://developer.nvidia.com/nsight-compute). You can use these tools to profile all kinds of executables, so they can be used for profiling Python scripts running MXNet. And you can use these in conjunction with the MXNet Profiler to see high-level information from MXNet alongside the low-level CUDA kernel information.
-
-#### NVProf and Visual Profiler
-
-NVProf and Visual Profiler are available in CUDA 9 and CUDA 10 toolkits. You can get a timeline view of CUDA kernel executions, and also analyse the profiling results to get automated recommendations. It is useful for profiling end-to-end training but the interface can sometimes become slow and unresponsive.
-
-You can initiate the profiling directly from inside Visual Profiler or from the command line with `nvprof` which wraps the execution of your Python script. If it's not on your path already, you can find `nvprof` inside your CUDA directory. See [this discussion post](https://discuss.mxnet.io/t/using-nvidia-profiling-tools-visual-profiler-and-nsight-compute/) for more details on setup.
-
-`$ nvprof -o my_profile.nvvp python my_profiler_script.py`
-
-`==11588== NVPROF is profiling process 11588, command: python my_profiler_script.py`
-
-`==11588== Generated result file: /home/user/Development/incubator-mxnet/ci/my_profile.nvvp`
-
-We specified an output file called `my_profile.nvvp` and this will be annotated with NVTX ranges (for MXNet operations) that will be displayed alongside the standard NVProf timeline. This can be very useful when you're trying to find patterns between operators run by MXNet, and their associated CUDA kernel calls.
-
-You can open this file in Visual Profiler to visualize the results.
-
-![Operator profiling](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tutorials/python/profiler/profiler_nvprof.png)
-
-At the top of the plot we have CPU tasks such as driver operations, memory copy calls, MXNet engine operator invocations, and imperative MXNet API calls.  Below we see the kernels active on the GPU during the same time period.
-
-![Operator profiling](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tutorials/python/profiler/profiler_nvprof_zoomed.png)
-
-Zooming in on a backwards convolution operator we can see that it is in fact made up of a number of different GPU kernel calls, including a cuDNN winograd convolution call, and a fast-fourier transform call.
-
-![Operator profiling](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tutorials/python/profiler/profiler_winograd.png)
-
-Selecting any of these kernel calls (the winograd convolution call shown here) will get you some interesting GPU performance information such as occupancy rates (vs theoretical), shared memory usage and execution duration.
-
-#### Nsight Compute
-
-Nsight Compute is available in CUDA 10 toolkit, but can be used to profile code running CUDA 9. You don't get a timeline view, but you get many low level statistics about each individual kernel executed and can compare multiple runs (i.e. create a baseline).
-
-![Nsight Compute](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tutorials/python/profiler/profile_nsight_compute.png)
-
-### Further reading
-
-- [Examples using MXNet profiler.](https://github.com/apache/incubator-mxnet/tree/master/example/profiler)
-- [Some tips for improving MXNet performance.](https://mxnet.incubator.apache.org/faq/perf.html)
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
-
diff --git a/docs/tutorials/sparse/train_gluon.md b/docs/tutorials/sparse/train_gluon.md
deleted file mode 100644
index f9f1a50f0925..000000000000
--- a/docs/tutorials/sparse/train_gluon.md
+++ /dev/null
@@ -1,470 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-
-# Sparse NDArrays with Gluon
-
-When working on machine learning problems, you may encounter situations where the input data is sparse (i.e. the majority of values are zero). One example of this is in recommendation systems. You could have millions of user and product features, but only a few of these features are present for each sample. Without special treatment, the sheer magnitude of the feature space can lead to out-of-memory situations and cause significant slowdowns when training and making predictions.
-
-MXNet supports a number of sparse storage types (often called 'stype' for short) for these situations. In this tutorial, we'll start by generating some sparse data, write it to disk in the LibSVM format and then read back using the [`LibSVMIter`](https://mxnet.incubator.apache.org/api/python/io/io.html) for training. We use the Gluon API to train the model and leverage sparse storage types such as [`CSRNDArray`](https://mxnet.incubator.apache.org/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray) and [`RowSparseNDArray`](https://mxnet.incubator.apache.org/api/python/ndarray/sparse.html?highlight=rowsparsendarray#mxnet.ndarray.sparse.RowSparseNDArray) to maximise performance and memory efficiency.
-
-
-```python
-import mxnet as mx
-import numpy as np
-import time
-```
-
-### Generating Sparse Data
-
-You will most likely have a sparse dataset in mind already if you're reading this tutorial, but let's create a dummy dataset to use in the examples that follow. Using `rand_ndarray` we will generate 1000 samples, each with 1,000,000 features of which 99.999% of values will be zero (i.e. 10 non-zero features for each sample). We take this as our input data for training and calculate a label based on an arbitrary rule: whether the feature sum is higher than average.
-
-
-```python
-num_samples = 1000
-num_features = 1000000
-data = mx.test_utils.rand_ndarray((num_samples, num_features), stype='csr', density=0.00001)
-# generate label: 1 if row sum above average, 0 otherwise.
-label = data.sum(axis=1) > data.sum(axis=1).mean()
-```
-
-
-```python
-print(type(data))
-print(data[:10].asnumpy())
-print('{:,.0f} elements'.format(np.product(data.shape)))
-print('{:,.0f} non-zero elements'.format(data.data.size))
-```
-
-    <class 'mxnet.ndarray.sparse.CSRNDArray'>
-    [[0. 0. 0. ... 0. 0. 0.]
-     [0. 0. 0. ... 0. 0. 0.]
-     [0. 0. 0. ... 0. 0. 0.]
-     ...
-     [0. 0. 0. ... 0. 0. 0.]
-     [0. 0. 0. ... 0. 0. 0.]
-     [0. 0. 0. ... 0. 0. 0.]]
-    1,000,000,000 elements
-    10,000 non-zero elements
-
-
-Our storage type is CSR (Compressed Sparse Row) which is the ideal type for sparse data along multiple axes. See [this in-depth tutorial](https://mxnet.incubator.apache.org/versions/master/tutorials/sparse/csr.html) for more information. Just to confirm the generation process ran correctly, we can see that the vast majority of values are indeed zero. One of the first questions to ask would be how much memory is saved by storing this data in a [`CSRNDArray`](https://mxnet.incubator.apache.org/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray) versus a standard [`NDArray`](https://mxnet.incubator.apache.org/versions/master/api/python/ndarray/sparse.html?highlight=ndarray#module-mxnet.ndarray). Since sparse arrays are constructed from many components (e.g. `data`, `indices` and `indptr`) we define a function called `get_nbytes` to calculate the number of bytes taken in memory to store an array. We compare the same data stored in a standard [`NDArray`](https://mxnet.incubator.apache.org/versions/master/api/python/ndarray/sparse.html?highlight=ndarray#module-mxnet.ndarray) (with `data.tostype('default')`) to the [`CSRNDArray`](https://mxnet.incubator.apache.org/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray).
-
-
-```python
-def get_nbytes(array):
-    fn = lambda a: a.size * np.dtype(a).itemsize
-    if isinstance(array, mx.ndarray.sparse.CSRNDArray):
-        return fn(array.data) + fn(array.indices) + fn(array.indptr)
-    elif isinstance(array, mx.ndarray.sparse.RowSparseNDArray):
-        return fn(array.data) + fn(array.indices)
-    elif isinstance(array, mx.ndarray.NDArray):
-        return fn(array)
-    else:
-        TypeError('{} not supported'.format(type(array)))
-```
-
-
-```python
-print('NDarray:', get_nbytes(data.tostype('default'))/1000000, 'MBs')
-print('CSRNDArray', get_nbytes(data)/1000000, 'MBs')
-```
-
-    NDarray: 4000.0 MBs
-    CSRNDArray 0.128008 MBs
-
-
-Given the extremely high sparsity of the data, we observe a huge memory saving here! 0.13 MBs versus 4 GBs: ~30,000 times smaller. You can experiment with the amount of sparsity and see how these two storage types compare. When the number of non-zero values increases, this difference will reduce. And when the number of non-zero values exceeds ~1/3 you will find that this sparse storage type take more memory than dense! So use wisely.
-
-### Writing Sparse Data
-
-Since there is such a large size difference between dense and sparse storage formats here, we ideally want to store the data on disk in a sparse storage format too. MXNet supports a format called LibSVM and has a data iterator called [`LibSVMIter`](https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=libsvmiter) specifically for data formatted this way.
-
-A LibSVM file has a row for each sample, and each row starts with the label: in this case `0.0` or `1.0` since we have a classification task. After this we have a variable number of `key:value` pairs separated by spaces, where the key is column/feature index and the value is the value of that feature. When working with your own sparse data in a custom format you should try to convert your data into this format. We define a `save_as_libsvm` function to save the `data` ([`CSRNDArray`](https://mxnet.incubator.apache.org/versions/master/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray)) and `label` (`NDArray`) to disk in LibSVM format.
-
-
-```python
-def save_as_libsvm(filepath, data, label):
-    with open(filepath, 'w') as openfile:
-        for row_idx in range(data.shape[0]):
-            data_sample = data[row_idx]
-            label_sample = label[row_idx]
-            col_idxs = data_sample.indices.asnumpy().tolist()
-            values = data_sample.data.asnumpy().tolist()
-            label_str = str(label_sample.asscalar())
-            value_strs = ['{}:{}'.format(idx, value) for idx, value in zip(col_idxs, values)]
-            value_str = " ".join(value_strs)
-            sample_str = '{} {}\n'.format(label_str, value_str)
-            openfile.write(sample_str)
-```
-
-
-```python
-filepath = 'dataset.libsvm'
-save_as_libsvm(filepath, data, label)
-```
-
-We have now written the `data` and `label` to disk, and can inspect the first 10 lines of the file:
-
-
-```python
-with open(filepath, 'r') as openfile:
-    lines = [openfile.readline() for _ in range(10)]
-for line in lines:
-    print(line[:80] + '...' if len(line) > 80 else line)
-```
-
-    0.0 35454:0.22486156225204468 80954:0.39130592346191406 81941:0.1988530308008194...
-    1.0 37029:0.5980494618415833 52916:0.15797750651836395 71623:0.32251599431037903...
-    1.0 89962:0.47770974040031433 216426:0.21326342225074768 271027:0.18589609861373...
-    1.0 7071:0.9432336688041687 81664:0.7788773775100708 117459:0.8166475296020508 4...
-    0.0 380966:0.16906292736530304 394363:0.7987179756164551 458442:0.56873309612274...
-    0.0 89361:0.9099966287612915 141813:0.5927085280418396 282489:0.293381005525589 ...
-    0.0 150427:0.4747847020626068 169376:0.2603490948677063 179377:0.237988427281379...
-    0.0 49774:0.2822582423686981 91245:0.5794865489006042 102970:0.7004560232162476 ...
-    1.0 97133:0.0024336236529052258 109855:0.9895315766334534 116765:0.2465638816356...
-    0.0 803440:0.4020800292491913
-    
-
-
-Some storage overhead is introduced by serializing the data as characters (with spaces and colons). `dataset.libsvm` is 250 KBs but the original `data` and `label` were 132 KBs combined. Compared with the 4GB dense `NDArray` though, this isn't a huge issue.
-
-### Reading Sparse Data
-
-Using [`LibSVMIter`](https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=libsvmiter), we can quickly and easily load data into batches ready for training. Although Gluon [`Dataset`](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset)s can be written to return sparse arrays, Gluon [`DataLoader`](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader)s currently convert each sample to dense before stacking up to create the batch. As a result, [`LibSVMIter`](https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=libsvmiter) is the recommended method of loading sparse data in batches.
-
-Similar to using a [`DataLoader`](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader), you must specify the required `batch_size`. Since we're dealing with sparse data and the column shape isn't explicitly stored in the LibSVM file, we additionally need to provide the shape of the data and label. Our [`LibSVMIter`](https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=libsvmiter) returns batches in a slightly different form to a [`DataLoader`](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader). We get `DataBatch` objects instead of `tuple`. See the [appendix of this tutorial](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/datasets.html) for more information.
-
-
-```python
-data_iter = mx.io.LibSVMIter(data_libsvm=filepath, data_shape=(num_features,), label_shape=(1,), batch_size=10)
-for batch in data_iter:
-    data = batch.data[0]
-    print('data.stype: {}'.format(data.stype))
-    label = batch.label[0]
-    print('label.stype: {}'.format(label.stype))
-    break
-```
-
-    data.stype: csr
-    label.stype: default
-
-
-We can see that `data` and `label` are in the appropriate storage formats, given their sparse and dense values respectively. We can avoid out-of-memory issues that might have occurred if `data` was in dense storage format. Another benefit of storing the data efficiently is the reduced data transfer time when using GPUs. Although the transfer time for a single batch is small, we transfer `data` and `label` to the GPU every iteration so this time can become significant. We will time the transfer of the sparse `data` to GPU (if available) and compare to the time for its dense counterpart.
-
-
-```python
-ctx = mx.gpu() if mx.test_utils.list_gpus() else mx.cpu()
-```
-
-
-```python
-%%timeit
-data_on_ctx = data.as_in_context(ctx)
-data_on_ctx.wait_to_read()
-```
-
-    192 microseconds +- 51.1 microseconds per loop (mean +- std. dev. of 7 runs, 1 loop each)
-
-
-
-```python
-print('sparse batch: {} MBs'.format(get_nbytes(data)/1000000))
-data = data.tostype('default')  # avoid timing this sparse to dense conversion
-print('dense batch: {} MBs'.format(get_nbytes(data)/1000000))
-```
-
-    sparse batch: 0.001348 MBs
-    dense batch: 40.0 MBs
-
-
-
-```python
-%%timeit
-data_on_ctx = data.as_in_context(ctx)
-data_on_ctx.wait_to_read()
-```
-
-    4 ms +- 36.8 microseconds per loop (mean +- std. dev. of 7 runs, 100 loops each)
-
-
-Although results will change depending on system specifications and degree of sparsity, the sparse array can be transferred from CPU to GPU significantly faster than the dense array. We see a ~25x speed up for sparse vs dense for this specific batch of data.
-
-## Gluon Models for Sparse Data
-
-Our next step is to define a network. We have an input of 1,000,000 features and we want to make a binary prediction. We don't have any spatial or temporal relationships between features, so we'll use a 3 layer fully-connected network where the last layer has 1 output unit (with sigmoid activation). Since we're working with sparse data, we'd ideally like to use network operators that can exploit this sparsity for improved performance and memory efficiency.
-
-Gluon's [`nn.Dense`](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/nn.html?highlight=dense#mxnet.gluon.nn.Dense) block can used with [`CSRNDArray`](https://mxnet.incubator.apache.org/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray) input arrays but it doesn't exploit the sparsity. Under the hood, [`Dense`](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/nn.html?highlight=dense#mxnet.gluon.nn.Dense) uses the [`FullyConnected`](https://mxnet.incubator.apache.org/versions/master/api/python/ndarray/ndarray.html?highlight=fullyconnected#mxnet.ndarray.FullyConnected) operator which isn't optimized for [`CSRNDArray`](https://mxnet.incubator.apache.org/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray) arrays. We'll implement a `Block` that does exploit this sparsity, *but first*, let's just remind ourselves of the [`Dense`](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/nn.html?highlight=dense#mxnet.gluon.nn.Dense) implementation by creating an equivalent `Block` called `FullyConnected`.
-
-
-```python
-class FullyConnected(mx.gluon.HybridBlock):
-    def __init__(self, in_units, units):
-        super(FullyConnected, self).__init__()
-        with self.name_scope():
-            self._units = units
-            self.weight = self.params.get('weight', shape=(units, in_units),
-                                          init=None, allow_deferred_init=True,
-                                          dtype='float32', stype='default', grad_stype='default')
-            self.bias = self.params.get('bias', shape=(units),
-                                          init='zeros', allow_deferred_init=True,
-                                          dtype='float32', stype='default', grad_stype='default')
-
-    def hybrid_forward(self, F, x, weight, bias):
-        return F.FullyConnected(x, weight, bias, num_hidden=self._units)
-```
-
-Our `weight` and `bias` parameters are dense (see `stype='default'`) and so are their gradients (see `grad_stype='default'`). Our `weight` parameter has shape `(units, in_units)` because the [`FullyConnected`](https://mxnet.incubator.apache.org/versions/master/api/python/ndarray/ndarray.html?highlight=fullyconnected#mxnet.ndarray.FullyConnected) operator performs the following calculation:
-
-$$Y = XW^T + b$$
-
-We could instead have created our parameter with shape `(in_units, units)` and avoid the transpose of the weight matrix. We'll see why this is so important later on. And instead of [`FullyConnected`](https://mxnet.incubator.apache.org/versions/master/api/python/ndarray/ndarray.html?highlight=fullyconnected#mxnet.ndarray.FullyConnected) we could have used [`mx.sparse.dot`](https://mxnet.incubator.apache.org/versions/master/api/python/ndarray/sparse.html?highlight=sparse.dot#mxnet.ndarray.sparse.dot) to fully exploit the sparsity of the [`CSRNDArray`](https://mxnet.incubator.apache.org/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray) input arrays. We'll now implement an alternative `Block` called `FullyConnectedSparse` using these ideas. We take `grad_stype` of the `weight` as an argument (called `weight_grad_stype`), since we're going to change this later on.
-
-
-```python
-class FullyConnectedSparse(mx.gluon.HybridBlock):
-    def __init__(self, in_units, units, weight_grad_stype='default'):
-        super(FullyConnectedSparse, self).__init__()
-        with self.name_scope():
-            self._units = units
-            self.weight = self.params.get('weight', shape=(in_units, units),
-                                          init=None, allow_deferred_init=True,
-                                          dtype='float32', stype='default', grad_stype=weight_grad_stype)
-            self.bias = self.params.get('bias', shape=(units),
-                                          init='zeros', allow_deferred_init=True,
-                                          dtype='float32', stype='default', grad_stype='default')
-
-    def hybrid_forward(self, F, x, weight, bias):
-        return F.sparse.dot(x, weight) + bias
-```
-
-Once again, we're using a dense `weight`, so both `FullyConnected` and `FullyConnectedSparse` will return dense array outputs. When constructing a multi-layer network therefore, only the first layer needs to be optimized for sparse inputs. Our first layer is often responsible for reducing the feature dimension dramatically (e.g. 1,000,000 features down to 128 features). We'll set the number of units in our 3 layers to be 128, 8 and 1.
-
-We will use [`timeit`](https://docs.python.org/2/library/timeit.html) to check the performance of these two variants, and analyse some [MXNet Profiler](https://mxnet.incubator.apache.org/versions/master/tutorials/python/profiler.html) traces that have been created from these benchmarks. Additionally, we will inspect the memory usage of the weights (and gradients) using the `print_memory_allocation` function defined below:
-
-
-```python
-def print_memory_allocation(net, block_idxs):
-    blocks = [net[block_idx] for block_idx in block_idxs]
-    weight_nbytes = [get_nbytes(b.weight.data()) for b in blocks]
-    weight_nbytes_pct = [b/sum(weight_nbytes) for b in weight_nbytes]
-    weight_grad_nbytes = [get_nbytes(b.weight.grad()) for b in blocks]
-    weight_grad_nbytes_pct = [b/sum(weight_grad_nbytes) for b in weight_grad_nbytes]
-    print("Memory Allocation for Weight:")
-    for i in range(len(block_idxs)):
-        print('{:7.3f} MBs ({:7.3f}%) for {:<40}'.format(weight_nbytes[i]/1000000,
-                                                              weight_nbytes_pct[i]*100,
-                                                              blocks[i].name))
-    print("Memory Allocation for Weight Gradient:")
-    for i in range(len(block_idxs)):
-        print('{:7.3f} MBs ({:7.3f}%) for {:<40}'.format(weight_grad_nbytes[i]/1000000,
-                                                              weight_grad_nbytes_pct[i]*100,
-                                                              blocks[i].name))
-```
-
-### Benchmark: `FullyConnected`
-
-We'll create a network using `nn.Dense` and benchmark the training.
-
-```python
-net = mx.gluon.nn.Sequential()
-net.add(
-    mx.gluon.nn.Dense(in_units=num_features, units=128),
-    mx.gluon.nn.Activation('sigmoid'),
-    mx.gluon.nn.Dense(in_units=128, units=8),
-    mx.gluon.nn.Activation('sigmoid'),
-    mx.gluon.nn.Dense(in_units=8, units=1),
-    mx.gluon.nn.Activation('sigmoid'),
-)
-net.initialize(ctx=ctx)
-trainer = mx.gluon.Trainer(net.collect_params(), 'sgd')
-loss_fn = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
-```
-
-
-```python
-%%timeit
-data_iter.reset()
-for batch in data_iter:
-    data = batch.data[0]
-    data = data.as_in_context(ctx)
-    label = batch.label[0].as_in_context(ctx)
-    with mx.autograd.record():
-        pred = net(data)
-        loss = loss_fn(pred, label)
-    loss.backward()
-    trainer.step(data.shape[0])
-    loss.wait_to_read()
-```
-
-    532 ms +- 3.47 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
-
-
-![fully connected](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/ndarray/sparse/fully_connected.png)
-
-We can see the first [`FullyConnected`](https://mxnet.incubator.apache.org/versions/master/api/python/ndarray/ndarray.html?highlight=fullyconnected#mxnet.ndarray.FullyConnected) operator takes a significant proportion of time to execute (~25% of the iteration) because there are 1,000,000 input features (to 128). After this, the other [`FullyConnected`](https://mxnet.incubator.apache.org/versions/master/api/python/ndarray/ndarray.html?highlight=fullyconnected#mxnet.ndarray.FullyConnected) operators are much faster because they have input features of 128 (to 8) and 8 (to 1). On the backward pass, we see the same pattern (but in reverse). And finally, the parameter update step takes a large amount of time on the weight matrix of the first `FullyConnected` `Block`. When checking the memory allocations below, we can see the weight matrix of the first `FullyConnected` `Block` is responsible for 99.999% of the memory compared to other [`FullyConnected`](https://mxnet.incubator.apache.org/versions/master/api/python/ndarray/ndarray.html?highlight=fullyconnected#mxnet.ndarray.FullyConnected) weight matrices.
-
-
-```python
-print_memory_allocation(net, block_idxs=[0, 2, 4])
-```
-
-    Memory Allocation for Weight:
-    512.000 MBs ( 99.999%) for dense0                                  
-      0.004 MBs (  0.001%) for dense1                                  
-      0.000 MBs (  0.000%) for dense2                                  
-    Memory Allocation for Weight Gradient:
-    512.000 MBs ( 99.999%) for dense0                                  
-      0.004 MBs (  0.001%) for dense1                                  
-      0.000 MBs (  0.000%) for dense2                                  
-
-
-### Benchmark: `FullyConnectedSparse`
-
-We will now switch the first layer from `FullyConnected` to `FullyConnectedSparse`.
-
-
-```python
-net = mx.gluon.nn.Sequential()
-net.add(
-    FullyConnectedSparse(in_units=num_features, units=128),
-    mx.gluon.nn.Activation('sigmoid'),
-    FullyConnected(in_units=128, units=8),
-    mx.gluon.nn.Activation('sigmoid'),
-    FullyConnected(in_units=8, units=1),
-    mx.gluon.nn.Activation('sigmoid'),
-)
-net.initialize(ctx=ctx)
-trainer = mx.gluon.Trainer(net.collect_params(), 'sgd')
-loss_fn = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
-```
-
-
-```python
-%%timeit
-data_iter.reset()
-for batch in data_iter:
-    data = batch.data[0]
-    data = data.as_in_context(ctx)
-    label = batch.label[0].as_in_context(ctx)
-    with mx.autograd.record():
-        pred = net(data)
-        loss = loss_fn(pred, label)
-    loss.backward()
-    trainer.step(data.shape[0])
-    loss.wait_to_read()
-```
-
-    528 ms +- 22.7 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
-
-
-![fully connected sparse](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/ndarray/sparse/fully_connected_sparse.png)
-
-We see the forward pass of `dot` and `add` (equivalent to [`FullyConnected`](https://mxnet.incubator.apache.org/versions/master/api/python/ndarray/ndarray.html?highlight=fullyconnected#mxnet.ndarray.FullyConnected) operator) is much faster now: 1.54ms vs 0.26ms. And this explains the reduction in overall time for the epoch. We didn't gain any benefit on the backward pass or parameter updates though.
-
-![fully connected sparse backward](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/ndarray/sparse/fully_connected_sparse_backward.png)
-
-Our first weight matrix and its gradients still take up the same amount of memory as before.
-
-
-```python
-print_memory_allocation(net, block_idxs=[0, 2, 4])
-```
-
-    Memory Allocation for Weight:
-    512.000 MBs ( 99.999%) for fullyconnectedsparse0                   
-      0.004 MBs (  0.001%) for fullyconnected0                         
-      0.000 MBs (  0.000%) for fullyconnected1                         
-    Memory Allocation for Weight Gradient:
-    512.000 MBs ( 99.999%) for fullyconnectedsparse0                   
-      0.004 MBs (  0.001%) for fullyconnected0                         
-      0.000 MBs (  0.000%) for fullyconnected1                         
-
-
-### Benchmark: `FullyConnectedSparse` with `grad_stype=row_sparse` 
-
-One useful outcome of sparsity in our [`CSRNDArray`](https://mxnet.incubator.apache.org/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray) input is that our gradients will be row sparse. We can exploit this fact to give us potentially huge memory savings and speed improvements. Creating our `weight` parameter with shape `(units, in_units)` and not transposing in the forward pass are important pre-requisite for obtaining row sparse gradients. Using [`nn.Dense`](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/nn.html?highlight=dense#mxnet.gluon.nn.Dense) would have led to column sparse gradients which are not supported in MXNet. We previously had `grad_stype` of the `weight` parameter in the first layer set to `'default'` so we were handling the gradient as a dense array. Switching this to `'row_sparse'` can give us these potential improvements.
-
-
-```python
-net = mx.gluon.nn.Sequential()
-net.add(
-    FullyConnectedSparse(in_units=num_features, units=128, weight_grad_stype='row_sparse'),
-    mx.gluon.nn.Activation('sigmoid'),
-    FullyConnected(in_units=128, units=8),
-    mx.gluon.nn.Activation('sigmoid'),
-    FullyConnected(in_units=8, units=1),
-    mx.gluon.nn.Activation('sigmoid'),
-)
-net.initialize(ctx=ctx)
-trainer = mx.gluon.Trainer(net.collect_params(), 'sgd')
-loss_fn = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
-```
-
-
-```python
-%%timeit
-data_iter.reset()
-for batch in data_iter:
-    data = batch.data[0]
-    data = data.as_in_context(ctx)
-    label = batch.label[0].as_in_context(ctx)
-    with mx.autograd.record():
-        pred = net(data)
-        loss = loss_fn(pred, label)
-    loss.backward()
-    trainer.step(data.shape[0])
-    loss.wait_to_read()
-```
-
-    334 ms +- 16.9 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
-
-
-![fully connected sparse grad backward](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/ndarray/sparse/fully_connected_sparse_grad_backward.png)
-
-We can see a huge reduction in the time taken for the backward pass and parameter update step: 3.99ms vs 0.18ms. And this reduces the overall time of the epoch significantly. Our gradient consumes a much smaller amount of memory and means only a subset of parameters need updating as part of the `sgd_update` step. Some optimizers don't support sparse gradients however, so reference the specific optimizer's documentation for more details.
-
-
-```python
-print_memory_allocation(net, block_idxs=[0, 2, 4])
-```
-
-    Memory Allocation for Weight:
-    512.000 MBs ( 99.999%) for fullyconnectedsparse1                   
-      0.004 MBs (  0.001%) for fullyconnected2                         
-      0.000 MBs (  0.000%) for fullyconnected3                         
-    Memory Allocation for Weight Gradient:
-      0.059 MBs ( 93.490%) for fullyconnectedsparse1                   
-      0.004 MBs (  6.460%) for fullyconnected2                         
-      0.000 MBs (  0.050%) for fullyconnected3                         
-
-
-### Advanced: Sparse `weight`
-
-You can optimize this example further by setting the weight's `stype` to `'row_sparse'`, but whether `'row_sparse'` weights make sense or not will depends on your specific task. See [`contrib.SparseEmbedding `](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/contrib/nn/basic_layers.py#L118) for an example of this.
-
-# Conclusion
-
-As part of this tutorial, we learned how to write sparse data to disk in LibSVM format and load it back in sparse batches with the [`LibSVMIter`](https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=libsvmiter). We learned how to improve the performance of Gluon's [`nn.Dense`](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/nn.html?highlight=dense#mxnet.gluon.nn.Dense) on sparse arrays using `mx.nd.sparse`. And lastly, we set `grad_stype` to `'row_sparse'` to reduce the size of the gradient and speed up the parameter update step.
-
-## Recommended Next Steps
-
-* More detail on the [`CSRNDArray`](https://mxnet.incubator.apache.org/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray) sparse array format can be found in [this tutorial](https://mxnet.incubator.apache.org/versions/master/tutorials/sparse/csr.html).
-* More detail on the [`RowSparseNDArray`](https://mxnet.incubator.apache.org/api/python/ndarray/sparse.html?highlight=rowsparsendarray#mxnet.ndarray.sparse.RowSparseNDArray) sparse array format can be found in [this tutorial](https://mxnet.incubator.apache.org/versions/master/tutorials/sparse/row_sparse.html).
-* Users of the Module API can see a symbolic only example in [this tutorial](https://mxnet.incubator.apache.org/versions/master/tutorials/sparse/train.html).
-
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/tests/nightly/JenkinsfileForBinaries b/tests/nightly/JenkinsfileForBinaries
index 8be95adb9194..5158274010b3 100755
--- a/tests/nightly/JenkinsfileForBinaries
+++ b/tests/nightly/JenkinsfileForBinaries
@@ -127,22 +127,6 @@ core_logic: {
         }
       }
     },
-    'Tutorial: Python2': {
-      node(NODE_LINUX_GPU_P3) {
-        ws('workspace/tutorial-test-python2') {
-          utils.unpack_and_init('gpu', mx_lib)
-          utils.docker_run('ubuntu_nightly_gpu', 'nightly_tutorial_test_ubuntu_python2_gpu', true, '1500m')
-        }
-      }
-    },
-    'Tutorial: Python3': {
-      node(NODE_LINUX_GPU_P3) {
-        ws('workspace/tutorial-test-python3') {
-          utils.unpack_and_init('gpu', mx_lib)
-          utils.docker_run('ubuntu_nightly_gpu', 'nightly_tutorial_test_ubuntu_python3_gpu', true, '1500m')
-        }
-      }
-    },
     'Gluon estimator: GPU': {
       node(NODE_LINUX_GPU) {
         ws('workspace/estimator-test-gpu') {
diff --git a/tests/tutorials/test_tutorials.py b/tests/tutorials/test_tutorials.py
index 1d2da0c1eedd..2ebd2f8e92ca 100644
--- a/tests/tutorials/test_tutorials.py
+++ b/tests/tutorials/test_tutorials.py
@@ -38,6 +38,7 @@
 sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'utils'))
 from notebook_test import run_notebook
 
+# This is outdated and need to be completely redone.
 
 TUTORIAL_DIR = os.path.join(os.path.dirname(__file__), '..', '..', 'docs', '_build', 'html', 'tutorials')
 KERNEL = os.getenv('MXNET_TUTORIAL_TEST_KERNEL', None)
diff --git a/tools/dependencies/README.md b/tools/dependencies/README.md
index 0f10d368c3bf..024fedf16f73 100644
--- a/tools/dependencies/README.md
+++ b/tools/dependencies/README.md
@@ -227,7 +227,7 @@ Please run performance test aginast the MXNet you build before raising the PR.
 #### Checklist
 - [ ] Python/setup.py
 - [ ] tools/pip/setup.py
-- [ ] ci/docker/install/docs_requirements
+- [ ] ci/docker/install/requirements
 - [ ] ci/docker/install/ubuntu_publish.sh
 - [ ] ci/docker/install/ubuntu_python.sh
 - [ ] ci/qemu/mxnet_requirements.txt

From 986cecd1a2e10222573f815cc7aaaae2c7afdd2d Mon Sep 17 00:00:00 2001
From: Tao Lv <tao.a.lv@intel.com>
Date: Fri, 20 Sep 2019 15:31:01 +0800
Subject: [PATCH 347/813] Update MKL-DNN dependency (#16073)

* update mkldnn to v0.21-rc

* update mkldnn to v0.21

* update mklml downloading url

* retrigger
---
 3rdparty/mkldnn                   | 2 +-
 ci/docker/install/ubuntu_mklml.sh | 2 +-
 cmake/DownloadMKLML.cmake         | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/3rdparty/mkldnn b/3rdparty/mkldnn
index d89bf4babd7c..9008d8ab096a 160000
--- a/3rdparty/mkldnn
+++ b/3rdparty/mkldnn
@@ -1 +1 @@
-Subproject commit d89bf4babd7cce7efa6613387dca79c123164084
+Subproject commit 9008d8ab096ae29f158840231ff431aea8bf3467
diff --git a/ci/docker/install/ubuntu_mklml.sh b/ci/docker/install/ubuntu_mklml.sh
index f97ce10e8e85..99fd0b9e01f2 100755
--- a/ci/docker/install/ubuntu_mklml.sh
+++ b/ci/docker/install/ubuntu_mklml.sh
@@ -21,5 +21,5 @@
 # the whole docker cache for the image
 
 set -ex
-wget -q --no-check-certificate -O /tmp/mklml.tgz https://github.com/intel/mkl-dnn/releases/download/v0.19/mklml_lnx_2019.0.5.20190502.tgz
+wget -q --no-check-certificate -O /tmp/mklml.tgz https://github.com/intel/mkl-dnn/releases/download/v0.21/mklml_lnx_2019.0.5.20190502.tgz
 tar -zxf /tmp/mklml.tgz && cp -rf mklml_*/* /usr/local/ && rm -rf mklml_*
diff --git a/cmake/DownloadMKLML.cmake b/cmake/DownloadMKLML.cmake
index 73a588fa8afe..bcb2fb408fd4 100644
--- a/cmake/DownloadMKLML.cmake
+++ b/cmake/DownloadMKLML.cmake
@@ -19,7 +19,7 @@
 
 message(STATUS "Downloading MKLML...")
 
-set(MKLDNN_RELEASE v0.19)
+set(MKLDNN_RELEASE v0.21)
 set(MKLML_RELEASE_FILE_SUFFIX 2019.0.5.20190502)
 
 set(MKLML_LNX_MD5 dfcea335652dbf3518e1d02cab2cea97)

From d61ed3f822eb9c8a77574f4745c30dc1582b0b04 Mon Sep 17 00:00:00 2001
From: Ye Zhihao <vigilans@foxmail.com>
Date: Sat, 21 Sep 2019 05:31:49 +0800
Subject: [PATCH 348/813] Solve #14116, #15143 (#15144)

Relax Visual Studio version constraint in the specialization of `dmlc::type_name_helper<DT>` for `DT=mxnet::Tuple<T>`
---
 include/mxnet/tuple.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/mxnet/tuple.h b/include/mxnet/tuple.h
index 51f860995e74..e4ca75ffa6d3 100644
--- a/include/mxnet/tuple.h
+++ b/include/mxnet/tuple.h
@@ -766,7 +766,7 @@ namespace dmlc {
 DMLC_DECLARE_TYPE_NAME(optional<mxnet::TShape>, "Shape or None");
 DMLC_DECLARE_TYPE_NAME(optional<mxnet::Tuple<int>>, "Shape or None");
 // avoid low version of MSVC
-#if !defined(_MSC_VER)
+#if !(defined(_MSC_VER) && _MSC_VER < 1900)
 template<typename T>
 struct type_name_helper<mxnet::Tuple<T> > {
   static inline std::string value() {

From 618c4811e417fb86cbb3fc0f7f38d55972eeb2af Mon Sep 17 00:00:00 2001
From: JackieWu <wkcn@live.cn>
Date: Sat, 21 Sep 2019 06:31:56 +0800
Subject: [PATCH 349/813] [MXNET-1422] Fix wrong results of min([inf, inf]) and
 max([-inf,-inf]) (#16226)

* fix min max infinity value

* add test maximum

* use HUGE_VAL macro for nvcc
---
 3rdparty/mshadow/mshadow/base.h        | 43 ++++++++++++++++++++++++--
 tests/python/unittest/test_operator.py | 17 ++++++++++
 2 files changed, 58 insertions(+), 2 deletions(-)

diff --git a/3rdparty/mshadow/mshadow/base.h b/3rdparty/mshadow/mshadow/base.h
index d08efd387c7e..1076f122c3ae 100755
--- a/3rdparty/mshadow/mshadow/base.h
+++ b/3rdparty/mshadow/mshadow/base.h
@@ -645,6 +645,25 @@ MSHADOW_XINLINE int64_t MinValue<int64_t>(void) {
   return LLONG_MIN;
 }
 
+/*!
+ * \brief negative infinity of certain types
+ * \tparam DType data type
+ */
+template<typename DType>
+MSHADOW_XINLINE DType NegInfValue(void) {
+  return MinValue<DType>();
+}
+/*! \brief negative infinity value of float */
+template<>
+MSHADOW_XINLINE float NegInfValue<float>(void) {
+  return -HUGE_VALF;
+}
+/*! \brief negative infinity value of double */
+template<>
+MSHADOW_XINLINE double NegInfValue<double>(void) {
+  return -HUGE_VAL;
+}
+
 /*!
  * \brief maximum value of certain types
  * \tparam DType data type
@@ -686,6 +705,26 @@ template<>
 MSHADOW_XINLINE int64_t MaxValue<int64_t>(void) {
   return LLONG_MAX;
 }
+
+/*!
+ * \brief positive infinity of certain types
+ * \tparam DType data type
+ */
+template<typename DType>
+MSHADOW_XINLINE DType PosInfValue(void) {
+  return MaxValue<DType>();
+}
+/*! \brief positive infinity value of float */
+template<>
+MSHADOW_XINLINE float PosInfValue<float>(void) {
+  return HUGE_VALF;
+}
+/*! \brief positive infinity value of double */
+template<>
+MSHADOW_XINLINE double PosInfValue<double>(void) {
+  return HUGE_VAL;
+}
+
 }  // namespace limits
 
 /*! \brief sum reducer */
@@ -793,7 +832,7 @@ struct maximum {
    */
   template<typename DType>
   MSHADOW_XINLINE static void SetInitValue(DType &initv) { // NOLINT(*)
-    initv = limits::MinValue<DType>();
+    initv = limits::NegInfValue<DType>();
   }
   /*!
    *\brief set the initial value during reduction
@@ -849,7 +888,7 @@ struct minimum {
    */
   template<typename DType>
   MSHADOW_XINLINE static void SetInitValue(DType &initv) { // NOLINT(*)
-    initv = limits::MaxValue<DType>();
+    initv = limits::PosInfValue<DType>();
   }
   /*!
    *\brief set the initial value during reduction
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 77b8080422d9..962e8687885b 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -9286,6 +9286,23 @@ def test_sample_normal_default_shape():
     assert s.shape == (1, 1)
 
 
+def test_min_max_inf():
+    dtypes = [np.float32, np.double]
+    elem_list = [-1, 1, 0, np.inf, -np.inf]
+
+    for dtype in dtypes:
+        for a in elem_list:
+            for b in elem_list:
+                data_np = np.array([a, b], dtype=dtype)
+                data_mx = mx.nd.array(data_np, dtype=dtype)
+
+                min_data_np, max_data_np = data_np.min(), data_np.max()
+                min_data_mx, max_data_mx = data_mx.min(), data_mx.max()
+
+                assert_array_equal(min_data_np, min_data_mx.asnumpy())
+                assert_array_equal(max_data_np, max_data_mx.asnumpy())
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From be7296bcaa104e333ac68e27d78576ceedc78d1f Mon Sep 17 00:00:00 2001
From: Rohit Kumar Srivastava <srivastava.141@osu.edu>
Date: Fri, 20 Sep 2019 16:29:54 -0700
Subject: [PATCH 350/813] removing MXNDArrayLoadFromBuffer64 and
 MXNDArrayLoad64 (#16203)

---
 include/mxnet/c_api.h | 13 -------------
 1 file changed, 13 deletions(-)

diff --git a/include/mxnet/c_api.h b/include/mxnet/c_api.h
index 46953b11714b..242d5a4abd36 100644
--- a/include/mxnet/c_api.h
+++ b/include/mxnet/c_api.h
@@ -675,12 +675,6 @@ MXNET_DLL int MXNDArrayLoad(const char* fname,
                             uint32_t *out_name_size,
                             const char*** out_names);
 
-MXNET_DLL int MXNDArrayLoad64(const char* fname,
-                              int64_t *out_size,
-                              NDArrayHandle** out_arr,
-                              int64_t *out_name_size,
-                              const char*** out_names);
-
 /*!
  * \brief Load list / dictionary of narrays from file content loaded into memory.
  * This will load a list of ndarrays in a similar
@@ -702,13 +696,6 @@ MXNET_DLL int MXNDArrayLoadFromBuffer(const void *ndarray_buffer,
                                       uint32_t *out_name_size,
                                       const char*** out_names);
 
-MXNET_DLL int MXNDArrayLoadFromBuffer64(const void *ndarray_buffer,
-                                        size_t size,
-                                        int64_t *out_size,
-                                        NDArrayHandle** out_arr,
-                                        int64_t *out_name_size,
-                                        const char*** out_names);
-
 /*!
  * \brief Perform a synchronize copy from a continugous CPU memory region.
  *

From 11f73ed59eb481b76a8ffca683293eec2cf41889 Mon Sep 17 00:00:00 2001
From: Haozheng Fan <fhztc1997618@gmail.com>
Date: Sun, 22 Sep 2019 00:11:28 +0800
Subject: [PATCH 351/813] np compatible vstack (#15850)

---
 python/mxnet/ndarray/numpy/_op.py      |  53 ++++++++-
 python/mxnet/numpy/multiarray.py       |  49 ++++++++-
 python/mxnet/symbol/numpy/_symbol.py   |  35 +++++-
 src/operator/numpy/np_matrix_op-inl.h  |  80 ++++++++++++++
 src/operator/numpy/np_matrix_op.cc     | 143 +++++++++++++++++++++++++
 src/operator/numpy/np_matrix_op.cu     |   7 ++
 tests/python/unittest/test_numpy_op.py |  56 ++++++++++
 7 files changed, 419 insertions(+), 4 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index 163d90835e2a..bb63e94691f1 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -32,7 +32,7 @@
            'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2', 'log1p',
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
-           'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'mean',
+           'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'mean',
            'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
            'ravel']
 
@@ -1990,6 +1990,57 @@ def get_list(arrays):
     return _npi.stack(*arrays, axis=axis, out=out)
 
 
+@set_module('mxnet.ndarray.numpy')
+def vstack(arrays, out=None):
+    r"""Stack arrays in sequence vertically (row wise).
+
+    This is equivalent to concatenation along the first axis after 1-D arrays
+    of shape `(N,)` have been reshaped to `(1,N)`. Rebuilds arrays divided by
+    `vsplit`.
+
+    This function makes most sense for arrays with up to 3 dimensions. For
+    instance, for pixel-data with a height (first axis), width (second axis),
+    and r/g/b channels (third axis). The functions `concatenate` and `stack`
+    provide more general stacking and concatenation operations.
+
+    Parameters
+    ----------
+    tup : sequence of ndarrays
+        The arrays must have the same shape along all but the first axis.
+        1-D arrays must have the same length.
+
+    Returns
+    -------
+    stacked : ndarray
+        The array formed by stacking the given arrays, will be at least 2-D.
+
+    Examples
+    --------
+    >>> a = np.array([1, 2, 3])
+    >>> b = np.array([2, 3, 4])
+    >>> np.vstack((a, b))
+    array([[1., 2., 3.],
+            [2., 3., 4.]])
+
+    >>> a = np.array([[1], [2], [3]])
+    >>> b = np.array([[2], [3], [4]])
+    >>> np.vstack((a, b))
+    array([[1.],
+            [2.],
+            [3.],
+            [2.],
+            [3.],
+            [4.]])
+    """
+    def get_list(arrays):
+        if not hasattr(arrays, '__getitem__') and hasattr(arrays, '__iter__'):
+            raise ValueError("expected iterable for arrays but got {}".format(type(arrays)))
+        return [arr for arr in arrays]
+
+    arrays = get_list(arrays)
+    return _npi.vstack(*arrays)
+
+
 @set_module('mxnet.ndarray.numpy')
 def maximum(x1, x2, out=None):
     """Returns element-wise maximum of the input arrays with broadcasting.
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 52dc9fb7a685..f738d630fe8a 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -52,8 +52,8 @@
            'degrees', 'log2', 'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative',
            'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh',
            'tensordot', 'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate',
-           'stack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
-           'ravel']
+           'stack', 'vstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices',
+           'copysign', 'ravel']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -3560,6 +3560,51 @@ def stack(arrays, axis=0, out=None):
     return _mx_nd_np.stack(arrays, axis=axis, out=out)
 
 
+@set_module('mxnet.numpy')
+def vstack(arrays, out=None):
+    r"""Stack arrays in sequence vertically (row wise).
+
+    This is equivalent to concatenation along the first axis after 1-D arrays
+    of shape `(N,)` have been reshaped to `(1,N)`. Rebuilds arrays divided by
+    `vsplit`.
+
+    This function makes most sense for arrays with up to 3 dimensions. For
+    instance, for pixel-data with a height (first axis), width (second axis),
+    and r/g/b channels (third axis). The functions `concatenate` and `stack`
+    provide more general stacking and concatenation operations.
+
+    Parameters
+    ----------
+    tup : sequence of ndarrays
+        The arrays must have the same shape along all but the first axis.
+        1-D arrays must have the same length.
+
+    Returns
+    -------
+    stacked : ndarray
+        The array formed by stacking the given arrays, will be at least 2-D.
+
+    Examples
+    --------
+    >>> a = np.array([1, 2, 3])
+    >>> b = np.array([2, 3, 4])
+    >>> np.vstack((a, b))
+    array([[1., 2., 3.],
+           [2., 3., 4.]])
+
+    >>> a = np.array([[1], [2], [3]])
+    >>> b = np.array([[2], [3], [4]])
+    >>> np.vstack((a, b))
+    array([[1.],
+           [2.],
+           [3.],
+           [2.],
+           [3.],
+           [4.]])
+    """
+    return _mx_nd_np.vstack(arrays)
+
+
 @set_module('mxnet.numpy')
 def maximum(x1, x2, out=None):
     """Returns element-wise maximum of the input arrays with broadcasting.
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 962fee2d9375..03d3d0df15eb 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -34,7 +34,7 @@
            'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2', 'log1p',
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
-           'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'mean',
+           'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'mean',
            'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
            'ravel']
 
@@ -2396,6 +2396,39 @@ def get_list(arrays):
     return _npi.stack(*arrays, axis=axis, out=out)
 
 
+@set_module('mxnet.symbol.numpy')
+def vstack(arrays, out=None):
+    r"""Stack arrays in sequence vertically (row wise).
+
+    This is equivalent to concatenation along the first axis after 1-D arrays
+    of shape `(N,)` have been reshaped to `(1,N)`. Rebuilds arrays divided by
+    `vsplit`.
+
+    This function makes most sense for arrays with up to 3 dimensions. For
+    instance, for pixel-data with a height (first axis), width (second axis),
+    and r/g/b channels (third axis). The functions `concatenate` and `stack`
+    provide more general stacking and concatenation operations.
+
+    Parameters
+    ----------
+    tup : sequence of _Symbol
+        The arrays must have the same shape along all but the first axis.
+        1-D arrays must have the same length.
+
+    Returns
+    -------
+    stacked : _Symbol
+        The array formed by stacking the given arrays, will be at least 2-D.
+    """
+    def get_list(arrays):
+        if not hasattr(arrays, '__getitem__') and hasattr(arrays, '__iter__'):
+            raise ValueError("expected iterable for arrays but got {}".format(type(arrays)))
+        return [arr for arr in arrays]
+
+    arrays = get_list(arrays)
+    return _npi.vstack(*arrays)
+
+
 @set_module('mxnet.symbol.numpy')
 def maximum(x1, x2, out=None):
     return _ufunc_helper(x1, x2, _npi.maximum, _np.maximum, _npi.maximum_scalar, None, out)
diff --git a/src/operator/numpy/np_matrix_op-inl.h b/src/operator/numpy/np_matrix_op-inl.h
index 6d3d9ea5ec85..fe2faddbe9ae 100644
--- a/src/operator/numpy/np_matrix_op-inl.h
+++ b/src/operator/numpy/np_matrix_op-inl.h
@@ -41,6 +41,14 @@ struct NumpyTransposeParam : public dmlc::Parameter<NumpyTransposeParam> {
   }
 };
 
+struct NumpyVstackParam : public dmlc::Parameter<NumpyVstackParam> {
+  int num_args;
+  DMLC_DECLARE_PARAMETER(NumpyVstackParam) {
+    DMLC_DECLARE_FIELD(num_args).set_lower_bound(1)
+    .describe("Number of inputs to be vstacked.");
+  }
+};
+
 template<typename xpu>
 void NumpyTranspose(const nnvm::NodeAttrs& attrs,
                     const OpContext& ctx,
@@ -60,6 +68,78 @@ void NumpyTranspose(const nnvm::NodeAttrs& attrs,
   }
 }
 
+template<typename xpu>
+void NumpyVstackForward(const nnvm::NodeAttrs& attrs,
+                        const OpContext& ctx,
+                        const std::vector<TBlob>& inputs,
+                        const std::vector<OpReqType>& req,
+                        const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mshadow_op;
+
+  const NumpyVstackParam& param = nnvm::get<NumpyVstackParam>(attrs.parsed);
+  CHECK_EQ(inputs.size(), param.num_args);
+  CHECK_EQ(outputs.size(), 1);
+  CHECK_EQ(req.size(), 1);
+
+  // reshape if necessary
+  std::vector<TBlob> data(param.num_args);
+  for (int i = 0; i < param.num_args; i++) {
+    if (inputs[i].shape_.ndim() == 0 || inputs[i].shape_.ndim() == 1) {
+      TShape shape = Shape2(1, inputs[i].shape_.Size());
+      data[i] = inputs[i].reshape(shape);
+    } else {
+      data[i] = inputs[i];
+    }
+  }
+
+  // initialize ConcatOp
+  ConcatParam cparam;
+  cparam.num_args = param.num_args;
+  cparam.dim = 0;
+  MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    ConcatOp<xpu, DType> op;
+    op.Init(cparam);
+    op.Forward(ctx, data, req, outputs);
+  });
+}
+
+template<typename xpu>
+void NumpyVstackBackward(const nnvm::NodeAttrs& attrs,
+                         const OpContext& ctx,
+                         const std::vector<TBlob>& inputs,
+                         const std::vector<OpReqType>& req,
+                         const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mshadow_op;
+
+  const NumpyVstackParam& param = nnvm::get<NumpyVstackParam>(attrs.parsed);
+  CHECK_EQ(inputs.size(), 1);
+  CHECK_EQ(outputs.size(), param.num_args);
+  CHECK_EQ(req.size(), param.num_args);
+
+  // reshape if necessary
+  std::vector<TBlob> data(param.num_args);
+  for (int i = 0; i < param.num_args; i++) {
+    if (outputs[i].shape_.ndim() == 0 || outputs[i].shape_.ndim() == 1) {
+      TShape shape = Shape2(1, outputs[i].shape_.Size());
+      data[i] = outputs[i].reshape(shape);
+    } else {
+      data[i] = outputs[i];
+    }
+  }
+
+  // initialize ConcatOp
+  ConcatParam cparam;
+  cparam.num_args = param.num_args;
+  cparam.dim = 0;
+  MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    ConcatOp<xpu, DType> op;
+    op.Init(cparam);
+    op.Backward(ctx, inputs[0], req, data);
+  });
+}
+
 }  // namespace op
 }  // namespace mxnet
 
diff --git a/src/operator/numpy/np_matrix_op.cc b/src/operator/numpy/np_matrix_op.cc
index f88dd56065f4..5509f343d6d7 100644
--- a/src/operator/numpy/np_matrix_op.cc
+++ b/src/operator/numpy/np_matrix_op.cc
@@ -25,6 +25,7 @@
 
 #include <vector>
 #include "./np_matrix_op-inl.h"
+#include "../nn/concat-inl.h"
 
 namespace mxnet {
 namespace op {
@@ -346,5 +347,147 @@ Examples::
 .add_argument("data", "NDArray-or-Symbol[]", "List of arrays to stack")
 .add_arguments(StackParam::__FIELDS__());
 
+bool NumpyVstackType(const nnvm::NodeAttrs& attrs,
+                     std::vector<int> *in_type,
+                     std::vector<int> *out_type) {
+  const NumpyVstackParam& param = nnvm::get<NumpyVstackParam>(attrs.parsed);
+  CHECK_EQ(in_type->size(), param.num_args);
+  CHECK_EQ(out_type->size(), 1);
+  int dtype = -1;
+  for (int i = 0; i < param.num_args; i++) {
+    if (dtype == -1) {
+      dtype = in_type->at(i);
+    }
+  }
+  if (dtype == -1) {
+    dtype = out_type->at(0);
+  }
+  for (int i = 0; i < param.num_args; i++) {
+    TYPE_ASSIGN_CHECK(*in_type, i, dtype);
+  }
+  TYPE_ASSIGN_CHECK(*out_type, 0, dtype);
+  return dtype != -1;
+}
+
+bool NumpyVstackShape(const nnvm::NodeAttrs& attrs,
+                      mxnet::ShapeVector* in_attrs,
+                      mxnet::ShapeVector* out_attrs) {
+  CHECK_EQ(out_attrs->size(), 1U);
+  const NumpyVstackParam& param = nnvm::get<NumpyVstackParam>(attrs.parsed);
+  CHECK_EQ(in_attrs->size(), param.num_args);
+  std::vector<mxnet::TShape> in_attrs_tmp(param.num_args);
+  TShape dshape;
+  for (int i = 0; i < param.num_args; i++) {
+    if ((*in_attrs)[i].ndim() == 0) {
+      in_attrs_tmp[i] = TShape(2, 1);
+    } else if ((*in_attrs)[i].ndim() == 1) {
+      in_attrs_tmp[i] = TShape(2, 1);
+      in_attrs_tmp[i][1] = (*in_attrs)[i][0];
+    } else {
+      in_attrs_tmp[i] = (*in_attrs)[i];
+    }
+    TShape tmp(in_attrs_tmp[i].ndim(), -1);
+    shape_assign(&dshape, tmp);
+  }
+  TShape tmp((*out_attrs)[0].ndim(), -1);
+  shape_assign(&dshape, tmp);
+  for (int i = 0; i < param.num_args; i++) {
+    SHAPE_ASSIGN_CHECK(in_attrs_tmp, i, dshape)
+  }
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, dshape)
+  if (dshape.ndim() == -1) {
+    return false;
+  }
+  int cnt = 0, sum = 0, pos = -1;
+  for (int i = 0; i < param.num_args; i++) {
+    TShape tmp = in_attrs_tmp[i];
+    if (!dim_size_is_known(tmp, 0)) {
+      cnt++;
+      pos = i;
+    } else {
+      sum += tmp[0];
+    }
+    tmp[0] = -1;
+    shape_assign(&dshape, tmp);
+  }
+  tmp = out_attrs->at(0);
+  if (!dim_size_is_known(tmp, 0)) {
+    cnt++;
+    pos = -1;
+  } else {
+    sum += tmp[0];
+  }
+  tmp[0] = -1;
+  shape_assign(&dshape, tmp);
+  for (int i = 0; i < param.num_args; i++) {
+    SHAPE_ASSIGN_CHECK(in_attrs_tmp, i, dshape)
+  }
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, dshape)\
+  dshape[0] = 0;
+  if (!shape_is_known(dshape)) {
+    return false;
+  }
+
+  dshape[0] = sum;
+  if (cnt == 0) {
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, dshape);
+  } else if (cnt == 1) {
+    if (pos >= 0) {
+      in_attrs_tmp[pos][0] = out_attrs->at(0)[0] - sum;
+    } else {
+      out_attrs->at(0)[0] = sum;
+    }
+  } else {
+    return false;
+  }
+
+  for (int i = 0; i < param.num_args; i++) {
+    if (in_attrs->at(i).ndim() == 1) {
+      in_attrs->at(i)[0] = in_attrs_tmp[i][1];
+    } else if (in_attrs->at(i).ndim() >= 2) {
+      in_attrs->at(i) = in_attrs_tmp[i];
+    }
+  }
+
+  return true;
+}
+
+DMLC_REGISTER_PARAMETER(NumpyVstackParam);
+
+NNVM_REGISTER_OP(_npi_vstack)
+.describe(R"code()code" ADD_FILELINE)
+.set_attr_parser(ParamParser<NumpyVstackParam>)
+.set_num_inputs([](const nnvm::NodeAttrs& attrs) {
+  const NumpyVstackParam& param = dmlc::get<NumpyVstackParam>(attrs.parsed);
+  return static_cast<uint32_t>(param.num_args);
+})
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const nnvm::NodeAttrs& attrs) {
+    int num_args = dmlc::get<NumpyVstackParam>(attrs.parsed).num_args;
+    std::vector<std::string> ret;
+    for (int i = 0; i < num_args; i++) {
+      ret.push_back(std::string("arg") + std::to_string(i));
+    }
+    return ret;
+  })
+.set_attr<std::string>("key_var_num_args", "num_args")
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyVstackShape)
+.set_attr<nnvm::FInferType>("FInferType", NumpyVstackType)
+.set_attr<FCompute>("FCompute<cpu>", NumpyVstackForward<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_np_vstack"})
+.add_argument("data", "NDArray-or-Symbol[]", "List of arrays to vstack")
+.add_arguments(NumpyVstackParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_backward_np_vstack)
+.set_attr_parser(ParamParser<NumpyVstackParam>)
+.set_num_inputs(1)
+.set_num_outputs([](const nnvm::NodeAttrs& attrs) {
+  const NumpyVstackParam& param = dmlc::get<NumpyVstackParam>(attrs.parsed);
+  return static_cast<uint32_t>(param.num_args);
+})
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FCompute>("FCompute<cpu>", NumpyVstackBackward<cpu>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/np_matrix_op.cu b/src/operator/numpy/np_matrix_op.cu
index 4ba527deca09..b017ad619dfa 100644
--- a/src/operator/numpy/np_matrix_op.cu
+++ b/src/operator/numpy/np_matrix_op.cu
@@ -24,6 +24,7 @@
  */
 
 #include "./np_matrix_op-inl.h"
+#include "../nn/concat-inl.h"
 
 namespace mxnet {
 namespace op {
@@ -46,5 +47,11 @@ NNVM_REGISTER_OP(_backward_np_concat)
 NNVM_REGISTER_OP(_npi_stack)
 .set_attr<FCompute>("FCompute<gpu>", StackOpForward<gpu>);
 
+NNVM_REGISTER_OP(_npi_vstack)
+.set_attr<FCompute>("FCompute<gpu>", NumpyVstackForward<gpu>);
+
+NNVM_REGISTER_OP(_backward_np_vstack)
+.set_attr<FCompute>("FCompute<gpu>", NumpyVstackBackward<gpu>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 2ce0fa4fd9cb..87c7ab2e9679 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -2075,6 +2075,62 @@ def get_grad(UT, L, V):
                     assert_almost_equal(data.grad.asnumpy(), backward_expected, rtol=rtol, atol=atol)
 
 
+@with_seed()
+@use_np
+def test_np_vstack():
+    class TestVstack(HybridBlock):
+        def __init__(self):
+            super(TestVstack, self).__init__()
+        
+        def hybrid_forward(self, F, a, *args):
+            return F.np.vstack([a] + list(args))
+    
+    def g(data):
+        return _np.ones_like(data)
+
+    configs = [
+        ((), (), ()),
+        ((2), (2), (2)),
+        ((0), (0), (0)),
+        ((2, 2), (3, 2), (0, 2)),
+        ((2, 3), (1, 3), (4, 3)),
+        ((2, 2, 2), (3, 2, 2), (1, 2, 2)),
+        ((0, 1, 1), (4, 1, 1), (5, 1, 1)),
+        ((2), (0, 2), (2, 2))
+    ]
+    types = ['float16', 'float32', 'float64', 'int8', 'int32', 'int64']
+    for config in configs:
+        for hybridize in [True, False]:
+            for dtype in types:
+                test_vstack = TestVstack()
+                if hybridize:
+                    test_vstack.hybridize()
+                rtol = 1e-3
+                atol = 1e-5
+                v = []
+                v_np = []
+                for i in range(3):
+                    v_np.append(_np.array(_np.random.uniform(-10.0, 10.0, config[i]), dtype=dtype))
+                    v.append(mx.nd.array(v_np[i]).as_np_ndarray())
+                    v[i].attach_grad()
+                expected_np = _np.vstack(v_np)
+                with mx.autograd.record():
+                    mx_out = test_vstack(*v)
+                assert mx_out.shape == expected_np.shape
+                assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol)
+
+                # Test gradient
+                mx_out.backward()
+                for i in range(3):
+                    expected_grad = g(v_np[i])
+                    assert_almost_equal(v[i].grad.asnumpy(), expected_grad, rtol=rtol, atol=atol)
+
+                # Test imperative once again
+                mx_out = np.vstack(v)
+                expected_np = _np.vstack(v_np)
+                assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 37c641fa5f917f4c704048d8c41a7fd5f47b015f Mon Sep 17 00:00:00 2001
From: Zach Kimberg <kimbergz@amazon.com>
Date: Sat, 21 Sep 2019 19:00:06 -0700
Subject: [PATCH 352/813] Faster Scala NDArray to BufferedImage function
 (#16219)

---
 .../core/src/main/scala/org/apache/mxnet/Image.scala   | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/scala-package/core/src/main/scala/org/apache/mxnet/Image.scala b/scala-package/core/src/main/scala/org/apache/mxnet/Image.scala
index b54ecc05818e..a6665f82e29b 100644
--- a/scala-package/core/src/main/scala/org/apache/mxnet/Image.scala
+++ b/scala-package/core/src/main/scala/org/apache/mxnet/Image.scala
@@ -174,16 +174,18 @@ object Image {
   def toImage(src: NDArray): BufferedImage = {
     require(src.dtype == DType.UInt8, "The input NDArray must be bytes")
     require(src.shape.length == 3, "The input should contains height, width and channel")
+    require(src.shape(2) == 3, "There should be three channels: RGB")
     val height = src.shape.get(0)
     val width = src.shape.get(1)
     val img = new BufferedImage(width, height, BufferedImage.TYPE_INT_RGB)
+    val arr = src.toArray
     (0 until height).par.foreach(r => {
       (0 until width).par.foreach(c => {
-        val arr = src.at(r).at(c).toArray
         // NDArray in RGB
-        val red = arr(0).toByte & 0xFF
-        val green = arr(1).toByte & 0xFF
-        val blue = arr(2).toByte & 0xFF
+        val cellIndex = r * width * 3 + c * 3
+        val red = arr(cellIndex).toByte & 0xFF
+        val green = arr(cellIndex + 1).toByte & 0xFF
+        val blue = arr(cellIndex + 2).toByte & 0xFF
         val rgb = (red << 16) | (green << 8) | blue
         img.setRGB(c, r, rgb)
       })

From 93b794e68e0ad5414a866eefdb87de48d73af357 Mon Sep 17 00:00:00 2001
From: Ruizhi Yang <yang.rayzhi@gmail.com>
Date: Sun, 22 Sep 2019 10:01:56 +0800
Subject: [PATCH 353/813] Fix inconsistent interpolation method values (#16212)

In cv2, bicubic interpolation should be represented by 2, and area-based interpolation should be 3. This is correct in the docstring of `imresize` method, but wrong in the docstring of several other methods, which can be quite confusing when following the documentation.
---
 python/mxnet/image/image.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/python/mxnet/image/image.py b/python/mxnet/image/image.py
index eb32dd5c8533..5236027bfa3b 100644
--- a/python/mxnet/image/image.py
+++ b/python/mxnet/image/image.py
@@ -309,11 +309,11 @@ def _get_interp_method(interp, sizes=()):
         Possible values:
         0: Nearest Neighbors Interpolation.
         1: Bilinear interpolation.
-        2: Area-based (resampling using pixel area relation). It may be a
+        2: Bicubic interpolation over 4x4 pixel neighborhood.
+        3: Area-based (resampling using pixel area relation). It may be a
         preferred method for image decimation, as it gives moire-free
         results. But when the image is zoomed, it is similar to the Nearest
         Neighbors method. (used by default).
-        3: Bicubic interpolation over 4x4 pixel neighborhood.
         4: Lanczos interpolation over 8x8 pixel neighborhood.
         9: Cubic for enlarge, area for shrink, bilinear for others
         10: Random select from interpolation method metioned above.
@@ -372,11 +372,11 @@ def resize_short(src, size, interp=2):
         Possible values:
         0: Nearest Neighbors Interpolation.
         1: Bilinear interpolation.
-        2: Area-based (resampling using pixel area relation). It may be a
+        2: Bicubic interpolation over 4x4 pixel neighborhood.
+        3: Area-based (resampling using pixel area relation). It may be a
         preferred method for image decimation, as it gives moire-free
         results. But when the image is zoomed, it is similar to the Nearest
         Neighbors method. (used by default).
-        3: Bicubic interpolation over 4x4 pixel neighborhood.
         4: Lanczos interpolation over 8x8 pixel neighborhood.
         9: Cubic for enlarge, area for shrink, bilinear for others
         10: Random select from interpolation method metioned above.
@@ -1062,11 +1062,11 @@ def CreateAugmenter(data_shape, resize=0, rand_crop=False, rand_resize=False, ra
         Possible values:
         0: Nearest Neighbors Interpolation.
         1: Bilinear interpolation.
-        2: Area-based (resampling using pixel area relation). It may be a
+        2: Bicubic interpolation over 4x4 pixel neighborhood.
+        3: Area-based (resampling using pixel area relation). It may be a
         preferred method for image decimation, as it gives moire-free
         results. But when the image is zoomed, it is similar to the Nearest
         Neighbors method. (used by default).
-        3: Bicubic interpolation over 4x4 pixel neighborhood.
         4: Lanczos interpolation over 8x8 pixel neighborhood.
         9: Cubic for enlarge, area for shrink, bilinear for others
         10: Random select from interpolation method metioned above.

From 89cb4ad30459b16b89b0e2ec4a9d8f76ee497dbd Mon Sep 17 00:00:00 2001
From: "Shi, guangyong" <35016185+gyshi@users.noreply.github.com>
Date: Sun, 22 Sep 2019 11:37:40 +0800
Subject: [PATCH 354/813] Numpy add numpy op roll (#15902)

* add numpy op roll

updata roll

address comments

fix backward and docs

* fix docs and test fils
---
 python/mxnet/_numpy_op_doc.py          |  67 +++++++++++
 src/operator/numpy/np_matrix_op-inl.h  | 157 +++++++++++++++++++++++++
 src/operator/numpy/np_matrix_op.cc     |  76 ++++++++++++
 src/operator/numpy/np_matrix_op.cu     |   3 +
 tests/python/unittest/test_numpy_op.py |  66 ++++++++++-
 5 files changed, 365 insertions(+), 4 deletions(-)

diff --git a/python/mxnet/_numpy_op_doc.py b/python/mxnet/_numpy_op_doc.py
index ae215803a524..a83447f64952 100644
--- a/python/mxnet/_numpy_op_doc.py
+++ b/python/mxnet/_numpy_op_doc.py
@@ -516,3 +516,70 @@ def _np__linalg_svd(a):
     array(0.)
     """
     pass
+
+
+def _np_roll(a, shift, axis=None):
+    """
+    roll(a, shift, axis=None):
+
+    Roll array elements along a given axis.
+    
+    Elements that roll beyond the last position are re-introduced at
+    the first.
+
+    Parameters
+    ----------
+    a : ndarray
+    Input array.
+    shift : int or tuple of ints
+    The number of places by which elements are shifted.  If a tuple,
+    then `axis` must be a tuple of the same size, and each of the
+    given axes is shifted by the corresponding number.  If an int
+    while `axis` is a tuple of ints, then the same value is used for
+    all given axes.
+    axis : int or tuple of ints, optional
+    Axis or axes along which elements are shifted.  By default, the
+    array is flattened before shifting, after which the original
+    shape is restored.
+
+    Returns
+    -------
+    res : ndarray
+    Output array, with the same shape as `a`.
+
+    Notes
+    -----
+    Supports rolling over multiple dimensions simultaneously.
+
+    Examples
+    --------
+    >>> x = np.arange(10)
+    >>> np.roll(x, 2)
+    array([8., 9., 0., 1., 2., 3., 4., 5., 6., 7.])
+    >>> np.roll(x, -2)
+    array([2., 3., 4., 5., 6., 7., 8., 9., 0., 1.])
+
+    >>> x2 = np.reshape(x, (2,5))
+    >>> x2
+    array([[0., 1., 2., 3., 4.],
+           [5., 6., 7., 8., 9.]])
+    >>> np.roll(x2, 1)
+    array([[9., 0., 1., 2., 3.],
+           [4., 5., 6., 7., 8.]])
+    >>> np.roll(x2, -1)
+    array([[1., 2., 3., 4., 5.],
+           [6., 7., 8., 9., 0.]])
+    >>> np.roll(x2, 1, axis=0)
+    array([[5., 6., 7., 8., 9.],
+           [0., 1., 2., 3., 4.]])
+    >>> np.roll(x2, -1, axis=0)
+    array([[5., 6., 7., 8., 9.],
+           [0., 1., 2., 3., 4.]])
+    >>> np.roll(x2, 1, axis=1)
+    array([[4., 0., 1., 2., 3.],
+           [9., 5., 6., 7., 8.]])
+    >>> np.roll(x2, -1, axis=1)
+    array([[1., 2., 3., 4., 0.],
+           [6., 7., 8., 9., 5.]])
+    """
+    pass
diff --git a/src/operator/numpy/np_matrix_op-inl.h b/src/operator/numpy/np_matrix_op-inl.h
index fe2faddbe9ae..631196e3cd1a 100644
--- a/src/operator/numpy/np_matrix_op-inl.h
+++ b/src/operator/numpy/np_matrix_op-inl.h
@@ -26,6 +26,7 @@
 #define MXNET_OPERATOR_NUMPY_NP_MATRIX_OP_INL_H_
 
 #include <vector>
+#include <algorithm>
 #include "../tensor/matrix_op-inl.h"
 #include "../nn/concat-inl.h"
 
@@ -140,6 +141,162 @@ void NumpyVstackBackward(const nnvm::NodeAttrs& attrs,
   });
 }
 
+struct NumpyRollParam : public dmlc::Parameter<NumpyRollParam> {
+  dmlc::optional<mxnet::TShape> shift;
+  dmlc::optional<mxnet::TShape> axis;
+  DMLC_DECLARE_PARAMETER(NumpyRollParam) {
+    DMLC_DECLARE_FIELD(shift)
+    .set_default(dmlc::optional<mxnet::TShape>())
+    .describe("The number of places by which elements are shifted. If a tuple,"
+              "then axis must be a tuple of the same size, and each of the given axes is shifted"
+              "by the corresponding number. If an int while axis is a tuple of ints, "
+              "then the same value is used for all given axes.");
+    DMLC_DECLARE_FIELD(axis)
+    .set_default(dmlc::optional<mxnet::TShape>())
+    .describe("Axis or axes along which elements are shifted. By default, the array is flattened"
+              "before shifting, after which the original shape is restored.");
+  }
+};
+
+template<int req>
+struct RollAxisNone_forward {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int i, DType* out_data, const DType* in_data,
+                                  const int size, const int shift) {
+    int new_index = i - shift < 0 ? i - shift + size : i - shift;
+    KERNEL_ASSIGN(out_data[i], req, in_data[new_index]);
+  }
+};
+
+template<int req>
+struct RollAxis_forward {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int i, DType* out_data, const DType* in_data,
+                                  const size_t* new_index) {
+    KERNEL_ASSIGN(out_data[i], req, in_data[new_index[i]]);
+  }
+};
+
+inline void RollDfs(const std::vector<std::vector<size_t>>& new_axes,
+                    const std::vector<size_t>& value,
+                    std::vector<size_t>* new_index,
+                    int index, int ndim, int mid) {
+  for (int a : new_axes[index]) {
+    if (index == ndim - 1) {
+      std::vector<size_t>& out = (*new_index);
+      out.push_back(mid + a);
+    } else {
+      mid += a * value[ndim - 1 - index];
+      RollDfs(new_axes, value, new_index, index + 1, ndim, mid);
+      mid -= a * value[ndim - 1 - index];
+    }
+  }
+}
+
+template<typename xpu>
+void NumpyRollCompute(const nnvm::NodeAttrs& attrs,
+                      const OpContext& ctx,
+                      const std::vector<TBlob>& inputs,
+                      const std::vector<OpReqType>& req,
+                      const std::vector<TBlob>& outputs) {
+  using namespace mxnet_op;
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 1U);
+  CHECK_EQ(req.size(), 1U);
+  if (inputs[0].Size() == 0U) return;
+  const NumpyRollParam& param = nnvm::get<NumpyRollParam>(attrs.parsed);
+  const index_t ndim(inputs[0].shape_.ndim());
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  std::vector<int> shifts(ndim, 0);
+  index_t input_size = inputs[0].Size();
+  if (!param.axis.has_value()) {
+    int shift = param.shift.value()[0];
+    shift = shift % input_size;
+    if (shift < 0) {
+      shift += inputs[0].shape_.Size();
+    }
+    MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+      MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
+        Kernel<RollAxisNone_forward<req_type>, xpu>::Launch(
+            s, outputs[0].Size(), outputs[0].dptr<DType>(), inputs[0].dptr<DType>(),
+            inputs[0].Size(), shift);
+      });
+    });
+  } else {
+    mxnet::TShape axes(param.axis.value());
+    for (int i = 0; i < axes.ndim(); ++i) {
+      if (axes[i] < 0) {
+        axes[i] += ndim;
+      }
+    }
+    for (int i = 0; i < axes.ndim(); ++i) {
+      CHECK_LT(axes[i], ndim)
+        << "axis " << axes[i]
+        << " Exceeds input dimensions " << inputs[0].shape_;
+      CHECK_GE(axes[0], 0)
+        << "Reduction axis " << param.axis.value()
+        << " Exceeds input dimensions " << inputs[0].shape_;
+    }
+    if (param.shift.value().ndim() == 1) {
+      for (int i = 0; i < axes.ndim(); ++i) {
+        shifts[axes[i]] = param.shift.value()[0];
+      }
+    } else {
+      if (param.shift.value().ndim() != axes.ndim()) {
+        LOG(FATAL) << "shift and `axis` must be a tuple of the same size,";
+      }
+      for (int i = 0; i < axes.ndim(); ++i) {
+        shifts[axes[i]] = param.shift.value()[i];
+      }
+    }
+    // keep shift in a legal range
+    for (int i = 0; i < ndim; ++i) {
+      int trans_shift = shifts[i] % inputs[0].shape_[i];
+      if (trans_shift < 0) {
+        trans_shift = shifts[i] + inputs[0].shape_[i];
+      }
+      shifts[i] = trans_shift;
+    }
+    // the result of new axis after shift.
+    std::vector<std::vector<size_t>> new_axes;
+    std::vector<size_t> new_index;
+    std::vector<size_t> temp;
+    std::vector<size_t> value(ndim, 0);
+    int mid_val = 1;
+    for (int i = 0; i < ndim; ++i) {
+      if (shifts[i] != 0) {
+        for (int j = 0; j < inputs[0].shape_[i]; ++j) {
+          int new_axis = (j + inputs[0].shape_[i] - shifts[i]) % inputs[0].shape_[i];
+          temp.push_back(new_axis);
+        }
+      } else {
+        for (int j = 0; j < inputs[0].shape_[i]; ++j) {
+          temp.push_back(j);
+        }
+      }
+      new_axes.push_back(temp);
+      temp.clear();
+      value[i] = mid_val;
+      mid_val *= inputs[0].shape_[ndim - 1 - i];
+    }
+    RollDfs(new_axes, value, &new_index, 0, ndim, 0);
+    size_t workspace_size = new_index.size() * sizeof(size_t);
+    Tensor<xpu, 1, char> workspace =
+        ctx.requested[0].get_space_typed<xpu, 1, char>(Shape1(workspace_size), s);
+    Tensor<cpu, 1, size_t> index_cpu_tensor(new_index.data(), Shape1(new_index.size()));
+    Tensor<xpu, 1, size_t> index_xpu_tensor(
+        reinterpret_cast<size_t*>(workspace.dptr_), Shape1(new_index.size()));
+    mshadow::Copy(index_xpu_tensor, index_cpu_tensor, s);
+    MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+      MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
+        Kernel<RollAxis_forward<req_type>, xpu>::Launch(
+            s, outputs[0].Size(), outputs[0].dptr<DType>(), inputs[0].dptr<DType>(),
+            index_xpu_tensor.dptr_);
+      });
+    });
+  }
+}
+
 }  // namespace op
 }  // namespace mxnet
 
diff --git a/src/operator/numpy/np_matrix_op.cc b/src/operator/numpy/np_matrix_op.cc
index 5509f343d6d7..45e4c3f2d5b4 100644
--- a/src/operator/numpy/np_matrix_op.cc
+++ b/src/operator/numpy/np_matrix_op.cc
@@ -31,6 +31,7 @@ namespace mxnet {
 namespace op {
 
 DMLC_REGISTER_PARAMETER(NumpyTransposeParam);
+DMLC_REGISTER_PARAMETER(NumpyRollParam);
 
 bool NumpyTransposeShape(const nnvm::NodeAttrs& attrs,
                          mxnet::ShapeVector *in_attrs,
@@ -489,5 +490,80 @@ NNVM_REGISTER_OP(_backward_np_vstack)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
 .set_attr<FCompute>("FCompute<cpu>", NumpyVstackBackward<cpu>);
 
+inline bool NumpyRollShape(const nnvm::NodeAttrs& attrs,
+                           mxnet::ShapeVector *in_attrs,
+                           mxnet::ShapeVector *out_attrs) {
+  using namespace mshadow;
+  const NumpyRollParam& param = nnvm::get<NumpyRollParam>(attrs.parsed);
+
+  if (!param.shift.has_value()) {
+    LOG(FATAL) << "roll missing 1 required positional argument: 'shift'.";
+  }
+  if (param.shift.value().ndim() > 1 &&
+      param.axis.has_value() &&
+      param.axis.value().ndim() != param.shift.value().ndim()) {
+    LOG(FATAL) << "shift and `axis` must be a tuple of the same size.";
+  }
+  if (!param.axis.has_value() && param.shift.has_value() && param.shift.value().ndim() > 1) {
+    LOG(FATAL) << "shift must be an int.";
+  }
+  if (param.axis.has_value()) {
+    mxnet::TShape axes(param.axis.value());
+    const index_t ndim = (*in_attrs)[0].ndim();
+    for (index_t i = 0; i < axes.ndim(); i++) {
+      if (axes[i] < 0) {
+        axes[i] += ndim;
+      }
+    }
+    std::sort(axes.begin(), axes.end());
+    for (index_t i = 1; i < axes.ndim(); i++) {
+      CHECK_LT(axes[i - 1], axes[i])
+        << "axes have duplicates " << axes;
+    }
+    CHECK_LT(axes[axes.ndim() - 1], ndim)
+      << "axis " << axes[axes.ndim() - 1]
+      << " Exceeds input dimensions " << (*in_attrs)[0];
+    CHECK_GE(axes[0], 0)
+      << "Reduction axis " << param.axis.value()
+      << " Exceeds input dimensions " << (*in_attrs)[0];
+  }
+  return ElemwiseShape<1, 1>(attrs, in_attrs, out_attrs);
+}
+
+NNVM_REGISTER_OP(_np_roll)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<NumpyRollParam>)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+     return std::vector<std::string>{"data"};
+})
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyRollShape)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
+.set_attr<mxnet::FCompute>("FCompute<cpu>", NumpyRollCompute<cpu>)
+.set_attr<nnvm::FGradient>("FGradient",
+  [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
+     const NumpyRollParam& param = nnvm::get<NumpyRollParam>(n->attrs.parsed);
+     if (!param.shift.has_value()) {
+       LOG(FATAL) << "roll missing 1 required positional argument: 'shift'.";
+     }
+     mxnet::TShape shifts(param.shift.value());
+     for (int i = 0; i < shifts.ndim(); ++i) {
+       shifts[i] = -shifts[i];
+     }
+     std::ostringstream os1;
+     os1 << dmlc::optional<mxnet::TShape>(shifts);
+     std::ostringstream os2;
+     os2 << param.axis;
+     return MakeNonlossGradNode("_np_roll", n, ograds, {},
+                                {{"shift", os1.str()}, {"axis", os2.str()}});
+})
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& n) {
+     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+})
+.add_argument("data", "NDArray-or-Symbol", "Input ndarray")
+.add_arguments(NumpyRollParam::__FIELDS__());
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/np_matrix_op.cu b/src/operator/numpy/np_matrix_op.cu
index b017ad619dfa..ba49d320575c 100644
--- a/src/operator/numpy/np_matrix_op.cu
+++ b/src/operator/numpy/np_matrix_op.cu
@@ -53,5 +53,8 @@ NNVM_REGISTER_OP(_npi_vstack)
 NNVM_REGISTER_OP(_backward_np_vstack)
 .set_attr<FCompute>("FCompute<gpu>", NumpyVstackBackward<gpu>);
 
+NNVM_REGISTER_OP(_np_roll)
+.set_attr<FCompute>("FCompute<gpu>", NumpyRollCompute<gpu>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 87c7ab2e9679..42d90434516e 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -1883,7 +1883,7 @@ def __init__(self):
             super(TestCopysign, self).__init__()
 
         def hybrid_forward(self, F, a1, a2):
-	            return F.np.copysign(a1, a2)
+            return F.np.copysign(a1, a2)
 
     def get_grad(a1, a2):
         sign = _np.logical_or(_np.logical_and(a1 < 0, a2 < 0),
@@ -1892,7 +1892,7 @@ def get_grad(a1, a2):
         sign = sign.reshape(-1, *a1.shape)
         sign = _np.sum(sign, axis=0)
         return sign, _np.zeros_like(a2)
-    
+
     def get_grad_left(a1, a2):
         sign = _np.logical_or(_np.logical_and(a1 < 0, a2 < 0),
                               _np.logical_and(a1 >= 0, a2 >= 0))
@@ -2081,10 +2081,10 @@ def test_np_vstack():
     class TestVstack(HybridBlock):
         def __init__(self):
             super(TestVstack, self).__init__()
-        
+
         def hybrid_forward(self, F, a, *args):
             return F.np.vstack([a] + list(args))
-    
+
     def g(data):
         return _np.ones_like(data)
 
@@ -2131,6 +2131,64 @@ def g(data):
                 assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol)
 
 
+@with_seed()
+@use_np
+def test_np_roll():
+    class TestRoll(HybridBlock):
+        def __init__(self, shift=None, axis=None):
+            super(TestRoll, self).__init__()
+            self._shift = shift
+            self._axis = axis
+
+        def hybrid_forward(self, F, x):
+            return F.np.roll(x, shift=self._shift, axis=self._axis)
+
+    dtypes = ['int32', 'int64', 'float16', 'float32', 'float64']
+    configs = [
+        ((), (3,), None),
+        ((1,), (-3,), None),
+        ((20,), (-3,), None),
+        ((3,), (2,), 0),
+        ((2, 3, 4), (12,), (1,)),
+        ((2, 3, 4), (10, -10), (0, 1)),
+        ((2, 3, 4, 5), (0, 1), (-1, 2)),
+        ((2, 3, 0, 1), (0, 1), (-1, 2)),
+        ((2, 3, 4, 5), 10, (0, 2)),
+    ]
+    i_dtype = {"float32" : _np.float32,
+               "float64" : _np.float64
+               }
+    for dtype in dtypes:
+        for config in configs:
+            for hybridize in [False, True]:
+                shape, shift, axis = config[0], config[1], config[2]
+                x = rand_ndarray(shape=shape, dtype=dtype).as_np_ndarray()
+                net = TestRoll(shift=shift, axis=axis)
+                np_out = _np.roll(x.asnumpy(), shift=shift, axis=axis)
+                if hybridize:
+                    net.hybridize()
+                x.attach_grad()
+                with mx.autograd.record():
+                    mx_out = net(x)
+                assert mx_out.shape == np_out.shape
+                mx_out.backward()
+                assert same(mx_out.asnumpy(), np_out)
+                assert same(x.grad.shape, x.shape)
+                assert same(x.grad.asnumpy(), _np.ones(shape))
+
+                # test imperativen
+                np_out = _np.roll(x.asnumpy(), shift=shift, axis=axis)
+                mx_out = np.roll(x, shift=shift, axis=axis)
+                assert same(mx_out.asnumpy(), np_out)
+
+                # test numeric
+                if dtype in ['float32', 'float64'] and len(shape)> 0 and  _np.prod(shape) > 0:
+                    x_sym = mx.sym.Variable("x").as_np_ndarray()
+                    mx_sym = mx.sym.np.roll(x_sym, shift=shift, axis=axis).as_nd_ndarray()
+                    check_numeric_gradient(mx_sym, [x.as_nd_ndarray()],
+                                           numeric_eps=1e-3, rtol=1e-3, atol=1e-5, dtype=i_dtype[dtype])
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From da6e7442e6a4e00246c063298aadfb231a5ac63a Mon Sep 17 00:00:00 2001
From: Haozheng Fan <fhztc1997618@gmail.com>
Date: Sun, 22 Sep 2019 15:19:24 +0800
Subject: [PATCH 355/813] add numpy compatible trace (#16008)

add doc for trace
---
 python/mxnet/_numpy_op_doc.py          |  66 ++++++-
 src/operator/numpy/np_trace_op-inl.h   | 255 +++++++++++++++++++++++++
 src/operator/numpy/np_trace_op.cc      |  98 ++++++++++
 src/operator/numpy/np_trace_op.cu      |  36 ++++
 tests/python/unittest/test_numpy_op.py |  82 ++++++++
 5 files changed, 527 insertions(+), 10 deletions(-)
 create mode 100644 src/operator/numpy/np_trace_op-inl.h
 create mode 100644 src/operator/numpy/np_trace_op.cc
 create mode 100644 src/operator/numpy/np_trace_op.cu

diff --git a/python/mxnet/_numpy_op_doc.py b/python/mxnet/_numpy_op_doc.py
index a83447f64952..f4787be559a1 100644
--- a/python/mxnet/_numpy_op_doc.py
+++ b/python/mxnet/_numpy_op_doc.py
@@ -530,22 +530,22 @@ def _np_roll(a, shift, axis=None):
     Parameters
     ----------
     a : ndarray
-    Input array.
+        Input array.
     shift : int or tuple of ints
-    The number of places by which elements are shifted.  If a tuple,
-    then `axis` must be a tuple of the same size, and each of the
-    given axes is shifted by the corresponding number.  If an int
-    while `axis` is a tuple of ints, then the same value is used for
-    all given axes.
+        The number of places by which elements are shifted.  If a tuple,
+        then `axis` must be a tuple of the same size, and each of the
+        given axes is shifted by the corresponding number.  If an int
+        while `axis` is a tuple of ints, then the same value is used for
+        all given axes.
     axis : int or tuple of ints, optional
-    Axis or axes along which elements are shifted.  By default, the
-    array is flattened before shifting, after which the original
-    shape is restored.
+        Axis or axes along which elements are shifted.  By default, the
+        array is flattened before shifting, after which the original
+        shape is restored.
 
     Returns
     -------
     res : ndarray
-    Output array, with the same shape as `a`.
+        Output array, with the same shape as `a`.
 
     Notes
     -----
@@ -581,5 +581,51 @@ def _np_roll(a, shift, axis=None):
     >>> np.roll(x2, -1, axis=1)
     array([[1., 2., 3., 4., 0.],
            [6., 7., 8., 9., 5.]])
+   """
+
+
+def _np_trace(a, offset=0, axis1=0, axis2=1, out=None):
+    """trace(a, offset=0, axis1=0, axis2=1, out=None)
+
+    Return the sum along diagonals of the array.
+    If `a` is 2-D, the sum along its diagonal with the given offset
+    is returned, i.e., the sum of elements ``a[i,i+offset]`` for all i.
+    If `a` has more than two dimensions, then the axes specified by axis1 and
+    axis2 are used to determine the 2-D sub-arrays whose traces are returned.
+    The shape of the resulting array is the same as that of `a` with `axis1`
+    and `axis2` removed.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input array, from which the diagonals are taken.
+    offset : int, optional
+        Offset of the diagonal from the main diagonal. Can be both positive
+        and negative. Defaults to 0.
+    axis1, axis2 : int, optional
+        Axes to be used as the first and second axis of the 2-D sub-arrays
+        from which the diagonals should be taken. Defaults are the first two
+        axes of `a`.
+    out : ndarray, optional
+        Array into which the output is placed. It must be of the right shape
+        and right type to hold the output.
+
+    Returns
+    -------
+    sum_along_diagonals : ndarray
+        If `a` is 2-D, the sum along the diagonal is returned.  If `a` has
+        larger dimensions, then an array of sums along diagonals is returned.
+
+    Examples
+    --------
+    >>> a = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
+    >>> np.trace(a)
+    array(3.)
+    >>> a = np.arange(8).reshape((2, 2, 2))
+    >>> np.trace(a)
+    array([6., 8.])
+    >>> a = np.arange(24).reshape((2, 2, 2, 3))
+    >>> np.trace(a).shape
+    (2, 3)
     """
     pass
diff --git a/src/operator/numpy/np_trace_op-inl.h b/src/operator/numpy/np_trace_op-inl.h
new file mode 100644
index 000000000000..741c20b61d80
--- /dev/null
+++ b/src/operator/numpy/np_trace_op-inl.h
@@ -0,0 +1,255 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file np_trace_op-inl.h
+ * \brief Function definition of matrix numpy-compatible trace operator
+ */
+
+#ifndef MXNET_OPERATOR_NUMPY_NP_TRACE_OP_INL_H_
+#define MXNET_OPERATOR_NUMPY_NP_TRACE_OP_INL_H_
+
+#include <dmlc/parameter.h>
+#include <mxnet/operator_util.h>
+#include <vector>
+#include <utility>
+#include <algorithm>
+#include "../mxnet_op.h"
+#include "../operator_common.h"
+#include "../elemwise_op_common.h"
+#include "../tensor/broadcast_reduce_op.h"
+
+namespace mxnet {
+namespace op {
+
+struct NumpyTraceParam: public dmlc::Parameter<NumpyTraceParam> {
+  int offset, axis1, axis2;
+  DMLC_DECLARE_PARAMETER(NumpyTraceParam) {
+    DMLC_DECLARE_FIELD(offset)
+    .set_default(0)
+    .describe("Offset of the diagonal from the main diagonal. "
+              "Can be both positive and negative. Defaults to 0.");
+    DMLC_DECLARE_FIELD(axis1)
+    .set_default(0)
+    .describe("Axes to be used as the first axis of the 2-D sub-arrays "
+              "from which the diagonals should be taken. Defaults to 0.");
+    DMLC_DECLARE_FIELD(axis2)
+    .set_default(1)
+    .describe("Axes to be used as the second axis of the 2-D sub-arrays "
+              "from which the diagonals should be taken. Defaults to 1.");
+  }
+};
+
+template<int ndim, int req, bool back>
+struct numpy_trace {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(index_t i, DType* out, const DType* a,
+                                  mshadow::Shape<ndim> oshape,
+                                  mshadow::Shape<ndim> ishape,
+                                  index_t stride, index_t offset, int dlength) {
+    using namespace mxnet_op;
+    using namespace mshadow;
+    index_t j = ravel(unravel(i, oshape), ishape) + offset;
+    if (back) {
+      for (index_t k = 0; k < dlength; ++k) {
+        KERNEL_ASSIGN(out[j], req, a[i]);
+        j += stride;
+      }
+    } else {
+      if (req == kWriteTo) {
+        out[i] = 0;
+        for (index_t k = 0; k < dlength; ++k) {
+          out[i] += a[j];
+          j += stride;
+        }
+      } else if (req == kAddTo) {
+        for (index_t k = 0; k < dlength; ++k) {
+          out[i] += a[j];
+          j += stride;
+        }
+      }
+    }
+  }
+};
+
+template<typename xpu, bool back>
+void NumpyTraceOpProcess(const TBlob& in_data,
+                         const TBlob& out_data,
+                         const mxnet::TShape& ishape,
+                         const mxnet::TShape& oshape,
+                         index_t dsize,
+                         const NumpyTraceParam& param,
+                         mxnet_op::Stream<xpu> *s,
+                         const std::vector<OpReqType>& req) {
+  using namespace mxnet_op;
+  using namespace mshadow;
+  if (dsize == 0) {
+    if (back) {
+      if (out_data.Size() != 0) {
+        MSHADOW_TYPE_SWITCH(out_data.type_flag_, DType, {
+          MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
+            if (req_type == kWriteTo) {
+              out_data.FlatTo1D<xpu, DType>(s) = 0;
+            }
+          });
+        });
+      }
+    }
+    return;
+  } else if (ishape.Size() == 0) {
+    if (!back) {
+      MSHADOW_TYPE_SWITCH(out_data.type_flag_, DType, {
+        MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
+          if (req_type == kWriteTo) {
+            out_data.FlatTo1D<xpu, DType>(s) = 0;
+          }
+        });
+      });
+    }
+    return;
+  }
+  uint32_t x1 = CheckAxis(param.axis1, ishape.ndim());
+  uint32_t x2 = CheckAxis(param.axis2, ishape.ndim());
+
+  uint32_t idim = ishape.ndim();
+
+  uint32_t minx = x1, maxx = x2;
+  if (minx > maxx) {
+    std::swap(minx, maxx);
+  }
+
+  // merges contiguous axes that are not separated
+  // by axis1 or axis2 since they can be directly
+  // mapped to the output and there is no need
+  // to distinguish them
+  // (After this the input will have no more than
+  // three axes, hence improving the rave and
+  // unravel efficiency)
+
+  index_t oleading = 1,
+          obody = 1,
+          otrailing = 1;
+
+  for (uint32_t i = 0; i < minx; ++i) {
+    oleading *= ishape[i];
+  }
+  for (uint32_t i = minx + 1; i < maxx; ++i) {
+    obody *= ishape[i];
+  }
+  for (uint32_t i = maxx + 1; i < idim; ++i) {
+    otrailing *= ishape[i];
+  }
+
+  index_t ileading = oleading,
+          ibody = obody * ishape[minx],
+          itrailing = otrailing * ishape[maxx];
+
+  index_t stride1 = itrailing * obody,
+          stride2 = otrailing;
+  // stride1 + stride2 is the stride for
+  // iterating over the diagonal in question
+
+  if (x1 == maxx) {
+    std::swap(stride1, stride2);
+  }
+
+  // the extra index offset introduced by offset
+  index_t offset;
+  if (param.offset > 0) {
+    offset = stride2 * param.offset;
+  } else if (param.offset < 0) {
+    offset = stride1 * -param.offset;
+  } else {
+    offset = 0;
+  }
+
+  // number of elements in the offset diagonal
+  // may be negative
+  int dlength;
+  if (param.offset > 0) {
+    dlength = std::min(ishape[x1], ishape[x2] - param.offset);
+  } else if (param.offset < 0) {
+    dlength = std::min(ishape[x1] - (-param.offset), ishape[x2]);
+  } else {
+    dlength = std::min(ishape[x1], ishape[x2]);
+  }
+
+  MSHADOW_TYPE_SWITCH(out_data.type_flag_, DType, {
+    MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
+      if (back) {
+        out_data.FlatTo1D<xpu, DType>(s) = 0;
+      }
+      Kernel<numpy_trace<3, req_type, back>, xpu>::Launch(s, dsize, out_data.dptr<DType>(),
+                                                          in_data.dptr<DType>(),
+                                                          Shape3(oleading, obody, otrailing),
+                                                          Shape3(ileading, ibody, itrailing),
+                                                          stride1 + stride2, offset, dlength);
+    });
+  });
+}
+
+template<typename xpu>
+void NumpyTraceOpForward(const nnvm::NodeAttrs& attrs,
+                         const OpContext& ctx,
+                         const std::vector<TBlob>& inputs,
+                         const std::vector<OpReqType>& req,
+                         const std::vector<TBlob>& outputs) {
+  using namespace mxnet_op;
+  using namespace mshadow;
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 1U);
+  CHECK_EQ(req.size(), 1U);
+  mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+  const TBlob& in_data = inputs[0];
+  const TBlob& out_data = outputs[0];
+  const mxnet::TShape& ishape = inputs[0].shape_;
+  const mxnet::TShape& oshape = outputs[0].shape_;
+  const NumpyTraceParam& param = nnvm::get<NumpyTraceParam>(attrs.parsed);
+
+  NumpyTraceOpProcess<xpu, false>(in_data, out_data, ishape, oshape,
+                                  out_data.Size(), param, s, req);
+}
+
+template<typename xpu>
+void NumpyTraceOpBackward(const nnvm::NodeAttrs& attrs,
+                          const OpContext& ctx,
+                          const std::vector<TBlob>& inputs,
+                          const std::vector<OpReqType>& req,
+                          const std::vector<TBlob>& outputs) {
+  using namespace mxnet_op;
+  using namespace mshadow;
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 1U);
+  CHECK_EQ(req.size(), 1U);
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+
+  const TBlob& in_data = inputs[0];
+  const TBlob& out_data = outputs[0];
+  const mxnet::TShape& ishape = inputs[0].shape_;
+  const mxnet::TShape& oshape = outputs[0].shape_;
+  const NumpyTraceParam& param = nnvm::get<NumpyTraceParam>(attrs.parsed);
+
+  NumpyTraceOpProcess<xpu, true>(in_data, out_data, oshape, ishape,
+                                 in_data.Size(), param, s, req);
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_NUMPY_NP_TRACE_OP_INL_H_
diff --git a/src/operator/numpy/np_trace_op.cc b/src/operator/numpy/np_trace_op.cc
new file mode 100644
index 000000000000..d97ac3040384
--- /dev/null
+++ b/src/operator/numpy/np_trace_op.cc
@@ -0,0 +1,98 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file np_trace_op.cc
+ * \brief CPU Implementation of numpy-compatible trace operator
+ */
+
+#include "./np_trace_op-inl.h"
+
+namespace mxnet {
+namespace op {
+
+inline bool NumpyTraceOpShape(const nnvm::NodeAttrs& attrs,
+                              mxnet::ShapeVector* in_attrs,
+                              mxnet::ShapeVector* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1);
+  CHECK_EQ(out_attrs->size(), 1);
+  const int ndim((*in_attrs)[0].ndim());
+  if (ndim < 2) {
+    return false;
+  }
+  std::vector<int> oshape(ndim - 2);
+  const NumpyTraceParam& param = nnvm::get<NumpyTraceParam>(attrs.parsed);
+  int x1 = CheckAxis(param.axis1, (*in_attrs)[0].ndim());
+  int x2 = CheckAxis(param.axis2, (*in_attrs)[0].ndim());
+  CHECK_NE(x1, x2) << "axis1 and axis2 cannot refer to the the same axis " << x1;
+  for ( int i = 0, j = 0; i < ndim; ++i ) {
+    if (i != x1 && i != x2) {
+      oshape[j++] = (*in_attrs)[0][i];
+    }
+  }
+  mxnet::TShape tshape(oshape.begin(), oshape.end());
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, tshape);
+  return true;
+}
+
+DMLC_REGISTER_PARAMETER(NumpyTraceParam);
+
+NNVM_REGISTER_OP(_np_trace)
+.describe(R"code(Computes the sum of the diagonal elements of a matrix.
+Input is a tensor *A* of dimension *n >= 2*.
+
+If *n=2*, we sum the diagonal elements. The result has shape ().
+
+If *n>2*, *trace* is performed separately on the matrix defined by *axis1* and *axis2* for all
+inputs (batch mode).
+
+Examples::
+
+   // Single matrix reduction
+   A = [[1.0, 1.0], [1.0, 7.0]]
+   trace(A) = 8.0
+
+   // Batch matrix reduction
+   A = [[[1.0, 1.0], [1.0, 7.0]], [[3.0, 0], [0, 17.0]]]
+   trace(A) = [1.0, 18.0]
+)code" ADD_FILELINE)
+.set_attr_parser(ParamParser<NumpyTraceParam>)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"data"};
+  })
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyTraceOpShape)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
+.set_attr<FCompute>("FCompute<cpu>", NumpyTraceOpForward<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_np_trace"})
+.add_argument("data", "NDArray-or-Symbol", "Input ndarray")
+.add_arguments(NumpyTraceParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_backward_np_trace)
+.set_attr_parser(ParamParser<NumpyTraceParam>)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FCompute>("FCompute<cpu>", NumpyTraceOpBackward<cpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_trace_op.cu b/src/operator/numpy/np_trace_op.cu
new file mode 100644
index 000000000000..220e4ae62a59
--- /dev/null
+++ b/src/operator/numpy/np_trace_op.cu
@@ -0,0 +1,36 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file np_trace_op.cu
+ * \brief GPU Implementation of numpy-compatible trace operator
+ */
+#include "./np_trace_op-inl.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(_np_trace)
+.set_attr<FCompute>("FCompute<gpu>", NumpyTraceOpForward<gpu>);
+
+NNVM_REGISTER_OP(_backward_np_trace)
+.set_attr<FCompute>("FCompute<gpu>", NumpyTraceOpBackward<gpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 42d90434516e..8846d57dbe32 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -2189,6 +2189,88 @@ def hybrid_forward(self, F, x):
                                            numeric_eps=1e-3, rtol=1e-3, atol=1e-5, dtype=i_dtype[dtype])
 
 
+@with_seed()
+@use_np
+def test_np_trace():
+    class TestTrace(HybridBlock):
+        def __init__(self, axis1, axis2, offset):
+            super(TestTrace, self).__init__()
+            self._axis1 = axis1
+            self._axis2 = axis2
+            self._offset = offset
+
+        def hybrid_forward(self, F, data):
+            return F.np.trace(data, axis1=self._axis1, axis2=self._axis2, offset=self._offset)
+
+    def g(data, axis1, axis2, offset):
+        idx = _np.indices(data.shape)
+        ret = _np.zeros_like(data)
+        ret[idx[axis1] + offset == idx[axis2]] = 1.0
+        return ret
+
+    shapes = [
+        (3, 3),
+        (3, 4),
+        (0, 0),
+        (3, 3, 3),
+        (0, 0, 0),
+        (2, 2, 4, 3),
+        (2, 2, 4, 3),
+        (2, 0, 3, 0),
+        (2, 0, 2, 3)
+    ]
+    offsets = range(-5, 5)
+    dtypes = ['int32', 'float16', 'float32', 'float64']
+    for hybridize in [True, False]:
+        for shape in shapes:
+            ndim = len(shape)
+            for axis1 in range(-ndim, ndim):
+                for axis2 in range(-ndim, ndim):
+                    if (axis1 + ndim) % ndim != (axis2 + ndim) % ndim:
+                        for offset in offsets:
+                            for dtype in dtypes:
+                                if dtype == 'float16':
+                                    rtol = atol = 1e-2
+                                else:
+                                    rtol = atol = 1e-5
+                                test_trace = TestTrace(axis1, axis2, offset)
+                                if hybridize:
+                                    test_trace.hybridize()
+                                data_np = _np.random.uniform(-10.0, 10.0, shape)
+                                data = mx.nd.array(data_np, dtype=dtype)
+                                data_np = data.asnumpy()
+                                data.attach_grad()
+                                expected_np = _np.trace(data_np, axis1=axis1, axis2=axis2, offset=offset)
+                                with mx.autograd.record():
+                                    out_mx = test_trace(data.as_np_ndarray())
+                                assert out_mx.shape == expected_np.shape
+                                assert_almost_equal(out_mx.asnumpy(), expected_np, rtol=rtol, atol=atol)
+                                out_mx.backward()
+                                backward_expected = g(data_np, axis1=axis1, axis2=axis2, offset=offset)
+                                assert_almost_equal(data.grad.asnumpy(), backward_expected, rtol=rtol, atol=atol)
+
+                                # Test imperative once again
+                                data = mx.nd.array(data_np, dtype=dtype)
+                                out_mx = np.trace(data.as_np_ndarray(), axis1=axis1, axis2=axis2, offset=offset)
+                                assert_almost_equal(out_mx.asnumpy(), expected_np, rtol=rtol, atol=atol)
+
+    # bad params
+    params = [
+        ([], 0, 1, 0),
+        ([2], 0, 1, 0),
+        ([3, 2, 2], 1, 1, 1),
+        ([3, 2, 2], 0, -4, 1)
+    ]
+    for shape, axis1, axis2, offset in params:
+        data_np = _np.random.uniform(-1.0, 1.0, shape)
+        data_mx = mx.nd.array(data_np)
+        try:
+            output = np.trace(data_mx.as_np_ndarray(), axis1=axis1, axis2=axis2, offset=offset)
+        except mx.base.MXNetError:
+            continue
+        assert False
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 53ebe1290860bf6d45b46eeb3313f6b54efc2a8f Mon Sep 17 00:00:00 2001
From: "Shi, guangyong" <35016185+gyshi@users.noreply.github.com>
Date: Mon, 23 Sep 2019 00:39:20 +0800
Subject: [PATCH 356/813] add numpy op hanning, hamming, blackman (#15815)

address test file

fix test file

make default dtype is float32
---
 python/mxnet/ndarray/numpy/_op.py      | 242 ++++++++++++++++++++++++-
 python/mxnet/numpy/multiarray.py       | 236 +++++++++++++++++++++++-
 python/mxnet/symbol/numpy/_symbol.py   | 242 ++++++++++++++++++++++++-
 src/operator/numpy/np_window_op.cc     |  79 ++++++++
 src/operator/numpy/np_window_op.cu     |  41 +++++
 src/operator/numpy/np_window_op.h      | 127 +++++++++++++
 tests/python/unittest/test_numpy_op.py |  48 ++++-
 7 files changed, 1006 insertions(+), 9 deletions(-)
 create mode 100644 src/operator/numpy/np_window_op.cc
 create mode 100644 src/operator/numpy/np_window_op.cu
 create mode 100644 src/operator/numpy/np_window_op.h

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index bb63e94691f1..99e35f37844d 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -34,7 +34,7 @@
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'mean',
            'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
-           'ravel']
+           'ravel', 'hanning', 'hamming', 'blackman']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -2588,3 +2588,243 @@ def ravel(x, order='C'):
         return _npi.reshape(x, -1)
     else:
         raise TypeError('type {} not supported'.format(str(type(x))))
+
+
+@set_module('mxnet.ndarray.numpy')
+def hanning(M, dtype=_np.float32, ctx=None):
+    r"""Return the Hanning window.
+
+    The Hanning window is a taper formed by using a weighted cosine.
+
+    Parameters
+    ----------
+    M : int
+        Number of points in the output window. If zero or less, an
+        empty array is returned.
+    dtype : str or numpy.dtype, optional
+        An optional value type. Default is `float32`. Note that you need
+        select numpy.float32 or float64 in this operator.
+    ctx : Context, optional
+        An optional device context (default is the current default context).
+
+    Returns
+    -------
+    out : ndarray, shape(M,)
+        The window, with the maximum value normalized to one (the value
+        one appears only if `M` is odd).
+
+    See Also
+    --------
+    blackman, hamming
+
+    Notes
+    -----
+    The Hanning window is defined as
+
+    .. math::  w(n) = 0.5 - 0.5cos\left(\frac{2\pi{n}}{M-1}\right)
+               \qquad 0 \leq n \leq M-1
+
+    The Hanning was named for Julius von Hann, an Austrian meteorologist.
+    It is also known as the Cosine Bell. Some authors prefer that it be
+    called a Hann window, to help avoid confusion with the very similar
+    Hamming window.
+
+    Most references to the Hanning window come from the signal processing
+    literature, where it is used as one of many windowing functions for
+    smoothing values.  It is also known as an apodization (which means
+    "removing the foot", i.e. smoothing discontinuities at the beginning
+    and end of the sampled signal) or tapering function.
+
+    References
+    ----------
+    .. [1] Blackman, R.B. and Tukey, J.W., (1958) The measurement of power
+           spectra, Dover Publications, New York.
+    .. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics",
+           The University of Alberta Press, 1975, pp. 106-108.
+    .. [3] Wikipedia, "Window function",
+           http://en.wikipedia.org/wiki/Window_function
+    .. [4] W.H. Press,  B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling,
+           "Numerical Recipes", Cambridge University Press, 1986, page 425.
+
+    Examples
+    --------
+    >>> np.hanning(12)
+    array([0.        , 0.07937324, 0.29229254, 0.5711574 , 0.8274304 ,
+           0.9797465 , 0.97974646, 0.82743025, 0.5711573 , 0.29229245,
+           0.07937312, 0.        ])
+
+    Plot the window and its frequency response:
+
+    >>> import matplotlib.pyplot as plt
+    >>> window = np.hanning(51)
+    >>> plt.plot(window.asnumpy())
+    [<matplotlib.lines.Line2D object at 0x...>]
+    >>> plt.title("Hann window")
+    Text(0.5, 1.0, 'Hann window')
+    >>> plt.ylabel("Amplitude")
+    Text(0, 0.5, 'Amplitude')
+    >>> plt.xlabel("Sample")
+    Text(0.5, 0, 'Sample')
+    >>> plt.show()
+    """
+    if ctx is None:
+        ctx = current_context()
+    return _npi.hanning(M, dtype=dtype, ctx=ctx)
+
+
+@set_module('mxnet.ndarray.numpy')
+def hamming(M, dtype=_np.float32, ctx=None):
+    r"""Return the hamming window.
+
+    The hamming window is a taper formed by using a weighted cosine.
+
+    Parameters
+    ----------
+    M : int
+        Number of points in the output window. If zero or less, an
+        empty array is returned.
+    dtype : str or numpy.dtype, optional
+        An optional value type. Default is `float32`. Note that you need
+        select numpy.float32 or float64 in this operator.
+    ctx : Context, optional
+        An optional device context (default is the current default context).
+
+    Returns
+    -------
+    out : ndarray, shape(M,)
+        The window, with the maximum value normalized to one (the value
+        one appears only if `M` is odd).
+
+    See Also
+    --------
+    blackman, hanning
+
+    Notes
+    -----
+    The Hamming window is defined as
+
+    .. math::  w(n) = 0.54 - 0.46cos\left(\frac{2\pi{n}}{M-1}\right)
+               \qquad 0 \leq n \leq M-1
+
+    The Hamming was named for R. W. Hamming, an associate of J. W. Tukey
+    and is described in Blackman and Tukey. It was recommended for
+    smoothing the truncated autocovariance function in the time domain.
+    Most references to the Hamming window come from the signal processing
+    literature, where it is used as one of many windowing functions for
+    smoothing values.  It is also known as an apodization (which means
+    "removing the foot", i.e. smoothing discontinuities at the beginning
+    and end of the sampled signal) or tapering function.
+
+    References
+    ----------
+    .. [1] Blackman, R.B. and Tukey, J.W., (1958) The measurement of power
+           spectra, Dover Publications, New York.
+    .. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics", The
+           University of Alberta Press, 1975, pp. 109-110.
+    .. [3] Wikipedia, "Window function",
+           https://en.wikipedia.org/wiki/Window_function
+    .. [4] W.H. Press,  B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling,
+           "Numerical Recipes", Cambridge University Press, 1986, page 425.
+
+    Examples
+    --------
+    >>> np.hamming(12)
+    array([0.08000001, 0.15302339, 0.34890914, 0.6054648 , 0.841236  ,
+           0.9813669 , 0.9813668 , 0.8412359 , 0.6054647 , 0.34890908,
+           0.15302327, 0.08000001])
+
+    Plot the window and its frequency response:
+
+    >>> import matplotlib.pyplot as plt
+    >>> window = np.hamming(51)
+    >>> plt.plot(window.asnumpy())
+    [<matplotlib.lines.Line2D object at 0x...>]
+    >>> plt.title("hamming window")
+    Text(0.5, 1.0, 'hamming window')
+    >>> plt.ylabel("Amplitude")
+    Text(0, 0.5, 'Amplitude')
+    >>> plt.xlabel("Sample")
+    Text(0.5, 0, 'Sample')
+    >>> plt.show()
+    """
+    if ctx is None:
+        ctx = current_context()
+    return _npi.hamming(M, dtype=dtype, ctx=ctx)
+
+
+@set_module('mxnet.ndarray.numpy')
+def blackman(M, dtype=_np.float32, ctx=None):
+    r"""Return the Blackman window.
+
+    The Blackman window is a taper formed by using the first three
+    terms of a summation of cosines. It was designed to have close to the
+    minimal leakage possible.  It is close to optimal, only slightly worse
+    than a Kaiser window.
+
+    Parameters
+    ----------
+    M : int
+        Number of points in the output window. If zero or less, an
+        empty array is returned.
+    dtype : str or numpy.dtype, optional
+        An optional value type. Default is `float32`. Note that you need
+        select numpy.float32 or float64 in this operator.
+    ctx : Context, optional
+        An optional device context (default is the current default context).
+
+    Returns
+    -------
+    out : ndarray
+        The window, with the maximum value normalized to one (the value one
+        appears only if the number of samples is odd).
+
+    See Also
+    --------
+    hamming, hanning
+
+    Notes
+    -----
+    The Blackman window is defined as
+
+    .. math::  w(n) = 0.42 - 0.5 \cos(2\pi n/{M-1}) + 0.08 \cos(4\pi n/{M-1})
+
+    Most references to the Blackman window come from the signal processing
+    literature, where it is used as one of many windowing functions for
+    smoothing values.  It is also known as an apodization (which means
+    "removing the foot", i.e. smoothing discontinuities at the beginning
+    and end of the sampled signal) or tapering function. It is known as a
+    "near optimal" tapering function, almost as good (by some measures)
+    as the kaiser window.
+
+    References
+    ----------
+    Blackman, R.B. and Tukey, J.W., (1958) The measurement of power spectra,
+    Dover Publications, New York.
+
+    Oppenheim, A.V., and R.W. Schafer. Discrete-Time Signal Processing.
+    Upper Saddle River, NJ: Prentice-Hall, 1999, pp. 468-471.
+
+    Examples
+    --------
+    >>> np.blackman(12)
+    array([-1.4901161e-08,  3.2606423e-02,  1.5990365e-01,  4.1439798e-01,
+            7.3604530e-01,  9.6704686e-01,  9.6704674e-01,  7.3604506e-01,
+            4.1439781e-01,  1.5990359e-01,  3.2606363e-02, -1.4901161e-08])
+
+    Plot the window and its frequency response:
+
+    >>> import matplotlib.pyplot as plt
+    >>> window = np.blackman(51)
+    >>> plt.plot(window.asnumpy())
+    [<matplotlib.lines.Line2D object at 0x...>]
+    >>> plt.title("blackman window")
+    Text(0.5, 1.0, 'blackman window')
+    >>> plt.ylabel("Amplitude")
+    Text(0, 0.5, 'Amplitude')
+    >>> plt.xlabel("Sample")
+    Text(0.5, 0, 'Sample')
+    >>> plt.show()
+    """
+    if ctx is None:
+        ctx = current_context()
+    return _npi.blackman(M, dtype=dtype, ctx=ctx)
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index f738d630fe8a..801538557c5d 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -53,7 +53,7 @@
            'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh',
            'tensordot', 'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate',
            'stack', 'vstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices',
-           'copysign', 'ravel']
+           'copysign', 'ravel', 'hanning', 'hamming', 'blackman']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -4133,3 +4133,237 @@ def ravel(x, order='C'):
     [1. 4. 2. 5. 3. 6.]
     """
     return _mx_nd_np.ravel(x, order)
+
+
+@set_module('mxnet.numpy')
+def hanning(M, dtype=_np.float32, ctx=None):
+    r"""Return the Hanning window.
+
+    The Hanning window is a taper formed by using a weighted cosine.
+
+    Parameters
+    ----------
+    M : int
+        Number of points in the output window. If zero or less, an
+        empty array is returned.
+    dtype : str or numpy.dtype, optional
+        An optional value type. Default is `float32`. Note that you need
+        select numpy.float32 or float64 in this operator.
+    ctx : Context, optional
+        An optional device context (default is the current default context).
+
+    Returns
+    -------
+    out : ndarray, shape(M,)
+        The window, with the maximum value normalized to one (the value
+        one appears only if `M` is odd).
+
+    See Also
+    --------
+    blackman, hamming
+
+    Notes
+    -----
+    The Hanning window is defined as
+
+    .. math::  w(n) = 0.5 - 0.5cos\left(\frac{2\pi{n}}{M-1}\right)
+               \qquad 0 \leq n \leq M-1
+
+    The Hanning was named for Julius von Hann, an Austrian meteorologist.
+    It is also known as the Cosine Bell. Some authors prefer that it be
+    called a Hann window, to help avoid confusion with the very similar
+    Hamming window.
+
+    Most references to the Hanning window come from the signal processing
+    literature, where it is used as one of many windowing functions for
+    smoothing values.  It is also known as an apodization (which means
+    "removing the foot", i.e. smoothing discontinuities at the beginning
+    and end of the sampled signal) or tapering function.
+
+    References
+    ----------
+    .. [1] Blackman, R.B. and Tukey, J.W., (1958) The measurement of power
+           spectra, Dover Publications, New York.
+    .. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics",
+           The University of Alberta Press, 1975, pp. 106-108.
+    .. [3] Wikipedia, "Window function",
+           http://en.wikipedia.org/wiki/Window_function
+    .. [4] W.H. Press,  B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling,
+           "Numerical Recipes", Cambridge University Press, 1986, page 425.
+
+    Examples
+    --------
+    >>> np.hanning(12)
+    array([0.        , 0.07937324, 0.29229254, 0.5711574 , 0.8274304 ,
+           0.9797465 , 0.97974646, 0.82743025, 0.5711573 , 0.29229245,
+           0.07937312, 0.        ])
+
+    Plot the window and its frequency response:
+
+    >>> import matplotlib.pyplot as plt
+    >>> window = np.hanning(51)
+    >>> plt.plot(window.asnumpy())
+    [<matplotlib.lines.Line2D object at 0x...>]
+    >>> plt.title("Hann window")
+    Text(0.5, 1.0, 'Hann window')
+    >>> plt.ylabel("Amplitude")
+    Text(0, 0.5, 'Amplitude')
+    >>> plt.xlabel("Sample")
+    Text(0.5, 0, 'Sample')
+    >>> plt.show()
+    """
+    return _mx_nd_np.hanning(M, dtype=dtype, ctx=ctx)
+
+
+@set_module('mxnet.numpy')
+def hamming(M, dtype=_np.float32, ctx=None):
+    r"""Return the hamming window.
+
+    The hamming window is a taper formed by using a weighted cosine.
+
+    Parameters
+    ----------
+    M : int
+        Number of points in the output window. If zero or less, an
+        empty array is returned.
+    dtype : str or numpy.dtype, optional
+        An optional value type. Default is `float32`. Note that you need
+        select numpy.float32 or float64 in this operator.
+    ctx : Context, optional
+        An optional device context (default is the current default context).
+
+    Returns
+    -------
+    out : ndarray, shape(M,)
+        The window, with the maximum value normalized to one (the value
+        one appears only if `M` is odd).
+
+    See Also
+    --------
+    blackman, hanning
+
+    Notes
+    -----
+    The Hamming window is defined as
+
+    .. math::  w(n) = 0.54 - 0.46cos\left(\frac{2\pi{n}}{M-1}\right)
+               \qquad 0 \leq n \leq M-1
+
+    The Hamming was named for R. W. Hamming, an associate of J. W. Tukey
+    and is described in Blackman and Tukey. It was recommended for
+    smoothing the truncated autocovariance function in the time domain.
+    Most references to the Hamming window come from the signal processing
+    literature, where it is used as one of many windowing functions for
+    smoothing values.  It is also known as an apodization (which means
+    "removing the foot", i.e. smoothing discontinuities at the beginning
+    and end of the sampled signal) or tapering function.
+
+    References
+    ----------
+    .. [1] Blackman, R.B. and Tukey, J.W., (1958) The measurement of power
+           spectra, Dover Publications, New York.
+    .. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics", The
+           University of Alberta Press, 1975, pp. 109-110.
+    .. [3] Wikipedia, "Window function",
+           https://en.wikipedia.org/wiki/Window_function
+    .. [4] W.H. Press,  B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling,
+           "Numerical Recipes", Cambridge University Press, 1986, page 425.
+
+    Examples
+    --------
+    >>> np.hamming(12)
+    array([0.08000001, 0.15302339, 0.34890914, 0.6054648 , 0.841236  ,
+           0.9813669 , 0.9813668 , 0.8412359 , 0.6054647 , 0.34890908,
+           0.15302327, 0.08000001])
+
+    Plot the window and its frequency response:
+
+    >>> import matplotlib.pyplot as plt
+    >>> window = np.hamming(51)
+    >>> plt.plot(window.asnumpy())
+    [<matplotlib.lines.Line2D object at 0x...>]
+    >>> plt.title("hamming window")
+    Text(0.5, 1.0, 'hamming window')
+    >>> plt.ylabel("Amplitude")
+    Text(0, 0.5, 'Amplitude')
+    >>> plt.xlabel("Sample")
+    Text(0.5, 0, 'Sample')
+    >>> plt.show()
+    """
+    return _mx_nd_np.hamming(M, dtype=dtype, ctx=ctx)
+
+
+@set_module('mxnet.numpy')
+def blackman(M, dtype=_np.float32, ctx=None):
+    r"""Return the Blackman window.
+
+    The Blackman window is a taper formed by using the first three
+    terms of a summation of cosines. It was designed to have close to the
+    minimal leakage possible.  It is close to optimal, only slightly worse
+    than a Kaiser window.
+
+    Parameters
+    ----------
+    M : int
+        Number of points in the output window. If zero or less, an
+        empty array is returned.
+    dtype : str or numpy.dtype, optional
+        An optional value type. Default is `float32`. Note that you need
+        select numpy.float32 or float64 in this operator.
+    ctx : Context, optional
+        An optional device context (default is the current default context).
+
+    Returns
+    -------
+    out : ndarray
+        The window, with the maximum value normalized to one (the value one
+        appears only if the number of samples is odd).
+
+    See Also
+    --------
+    hamming, hanning
+
+    Notes
+    -----
+    The Blackman window is defined as
+
+    .. math::  w(n) = 0.42 - 0.5 \cos(2\pi n/{M-1}) + 0.08 \cos(4\pi n/{M-1})
+
+    Most references to the Blackman window come from the signal processing
+    literature, where it is used as one of many windowing functions for
+    smoothing values.  It is also known as an apodization (which means
+    "removing the foot", i.e. smoothing discontinuities at the beginning
+    and end of the sampled signal) or tapering function. It is known as a
+    "near optimal" tapering function, almost as good (by some measures)
+    as the kaiser window.
+
+    References
+    ----------
+    Blackman, R.B. and Tukey, J.W., (1958) The measurement of power spectra,
+    Dover Publications, New York.
+
+    Oppenheim, A.V., and R.W. Schafer. Discrete-Time Signal Processing.
+    Upper Saddle River, NJ: Prentice-Hall, 1999, pp. 468-471.
+
+    Examples
+    --------
+    >>> np.blackman(12)
+    array([-1.4901161e-08,  3.2606423e-02,  1.5990365e-01,  4.1439798e-01,
+            7.3604530e-01,  9.6704686e-01,  9.6704674e-01,  7.3604506e-01,
+            4.1439781e-01,  1.5990359e-01,  3.2606363e-02, -1.4901161e-08])
+
+    Plot the window and its frequency response:
+
+    >>> import matplotlib.pyplot as plt
+    >>> window = np.blackman(51)
+    >>> plt.plot(window.asnumpy())
+    [<matplotlib.lines.Line2D object at 0x...>]
+    >>> plt.title("blackman window")
+    Text(0.5, 1.0, 'blackman window')
+    >>> plt.ylabel("Amplitude")
+    Text(0, 0.5, 'Amplitude')
+    >>> plt.xlabel("Sample")
+    Text(0.5, 0, 'Sample')
+    >>> plt.show()
+    """
+    return _mx_nd_np.blackman(M, dtype=dtype, ctx=ctx)
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 03d3d0df15eb..f39d201638cb 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -36,7 +36,7 @@
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'mean',
            'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
-           'ravel']
+           'ravel', 'hanning', 'hamming', 'blackman']
 
 
 def _num_outputs(sym):
@@ -2851,4 +2851,244 @@ def ravel(x, order='C'):
         raise TypeError('type {} not supported'.format(str(type(x))))
 
 
+@set_module('mxnet.symbol.numpy')
+def hanning(M, dtype=_np.float32, ctx=None):
+    r"""Return the Hanning window.
+
+    The Hanning window is a taper formed by using a weighted cosine.
+
+    Parameters
+    ----------
+    M : int
+        Number of points in the output window. If zero or less, an
+        empty array is returned.
+    dtype : str or numpy.dtype, optional
+        An optional value type. Default is `float32`. Note that you need
+        select numpy.float32 or float64 in this operator.
+    ctx : Context, optional
+        An optional device context (default is the current default context).
+
+    Returns
+    -------
+    out : _Symbol, shape(M,)
+        The window, with the maximum value normalized to one (the value
+        one appears only if `M` is odd).
+
+    See Also
+    --------
+    blackman, hamming
+
+    Notes
+    -----
+    The Hanning window is defined as
+
+    .. math::  w(n) = 0.5 - 0.5cos\left(\frac{2\pi{n}}{M-1}\right)
+               \qquad 0 \leq n \leq M-1
+
+    The Hanning was named for Julius von Hann, an Austrian meteorologist.
+    It is also known as the Cosine Bell. Some authors prefer that it be
+    called a Hann window, to help avoid confusion with the very similar
+    Hamming window.
+
+    Most references to the Hanning window come from the signal processing
+    literature, where it is used as one of many windowing functions for
+    smoothing values.  It is also known as an apodization (which means
+    "removing the foot", i.e. smoothing discontinuities at the beginning
+    and end of the sampled signal) or tapering function.
+
+    References
+    ----------
+    .. [1] Blackman, R.B. and Tukey, J.W., (1958) The measurement of power
+           spectra, Dover Publications, New York.
+    .. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics",
+           The University of Alberta Press, 1975, pp. 106-108.
+    .. [3] Wikipedia, "Window function",
+           http://en.wikipedia.org/wiki/Window_function
+    .. [4] W.H. Press,  B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling,
+           "Numerical Recipes", Cambridge University Press, 1986, page 425.
+
+    Examples
+    --------
+    >>> np.hanning(12)
+    array([0.        , 0.07937324, 0.29229254, 0.5711574 , 0.8274304 ,
+           0.9797465 , 0.97974646, 0.82743025, 0.5711573 , 0.29229245,
+           0.07937312, 0.        ])
+
+    Plot the window and its frequency response:
+
+    >>> import matplotlib.pyplot as plt
+    >>> window = np.hanning(51)
+    >>> plt.plot(window.asnumpy())
+    [<matplotlib.lines.Line2D object at 0x...>]
+    >>> plt.title("Hann window")
+    Text(0.5, 1.0, 'Hann window')
+    >>> plt.ylabel("Amplitude")
+    Text(0, 0.5, 'Amplitude')
+    >>> plt.xlabel("Sample")
+    Text(0.5, 0, 'Sample')
+    >>> plt.show()
+    """
+    if ctx is None:
+        ctx = current_context()
+    return _npi.hanning(M, dtype=dtype, ctx=ctx)
+
+
+@set_module('mxnet.symbol.numpy')
+def hamming(M, dtype=_np.float32, ctx=None):
+    r"""Return the hamming window.
+
+    The hamming window is a taper formed by using a weighted cosine.
+
+    Parameters
+    ----------
+    M : int
+        Number of points in the output window. If zero or less, an
+        empty array is returned.
+    dtype : str or numpy.dtype, optional
+        An optional value type. Default is `float32`. Note that you need
+        select numpy.float32 or float64 in this operator.
+    ctx : Context, optional
+        An optional device context (default is the current default context).
+
+    Returns
+    -------
+    out : _Symbol, shape(M,)
+        The window, with the maximum value normalized to one (the value
+        one appears only if `M` is odd).
+
+    See Also
+    --------
+    blackman, hanning
+
+    Notes
+    -----
+    The Hamming window is defined as
+
+    .. math::  w(n) = 0.54 - 0.46cos\left(\frac{2\pi{n}}{M-1}\right)
+               \qquad 0 \leq n \leq M-1
+
+    The Hamming was named for R. W. Hamming, an associate of J. W. Tukey
+    and is described in Blackman and Tukey. It was recommended for
+    smoothing the truncated autocovariance function in the time domain.
+    Most references to the Hamming window come from the signal processing
+    literature, where it is used as one of many windowing functions for
+    smoothing values.  It is also known as an apodization (which means
+    "removing the foot", i.e. smoothing discontinuities at the beginning
+    and end of the sampled signal) or tapering function.
+
+    References
+    ----------
+    .. [1] Blackman, R.B. and Tukey, J.W., (1958) The measurement of power
+           spectra, Dover Publications, New York.
+    .. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics", The
+           University of Alberta Press, 1975, pp. 109-110.
+    .. [3] Wikipedia, "Window function",
+           https://en.wikipedia.org/wiki/Window_function
+    .. [4] W.H. Press,  B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling,
+           "Numerical Recipes", Cambridge University Press, 1986, page 425.
+
+    Examples
+    --------
+    >>> np.hamming(12)
+    array([0.08000001, 0.15302339, 0.34890914, 0.6054648 , 0.841236  ,
+           0.9813669 , 0.9813668 , 0.8412359 , 0.6054647 , 0.34890908,
+           0.15302327, 0.08000001])
+
+    Plot the window and its frequency response:
+
+    >>> import matplotlib.pyplot as plt
+    >>> window = np.hamming(51)
+    >>> plt.plot(window.asnumpy())
+    [<matplotlib.lines.Line2D object at 0x...>]
+    >>> plt.title("hamming window")
+    Text(0.5, 1.0, 'hamming window')
+    >>> plt.ylabel("Amplitude")
+    Text(0, 0.5, 'Amplitude')
+    >>> plt.xlabel("Sample")
+    Text(0.5, 0, 'Sample')
+    >>> plt.show()
+    """
+    if ctx is None:
+        ctx = current_context()
+    return _npi.hamming(M, dtype=dtype, ctx=ctx)
+
+
+@set_module('mxnet.symbol.numpy')
+def blackman(M, dtype=_np.float32, ctx=None):
+    r"""Return the Blackman window.
+
+    The Blackman window is a taper formed by using the first three
+    terms of a summation of cosines. It was designed to have close to the
+    minimal leakage possible.  It is close to optimal, only slightly worse
+    than a Kaiser window.
+
+    Parameters
+    ----------
+    M : int
+        Number of points in the output window. If zero or less, an
+        empty array is returned.
+    dtype : str or numpy.dtype, optional
+        An optional value type. Default is `float32`. Note that you need
+        select numpy.float32 or float64 in this operator.
+    ctx : Context, optional
+        An optional device context (default is the current default context).
+
+    Returns
+    -------
+    out : _Symbol
+        The window, with the maximum value normalized to one (the value one
+        appears only if the number of samples is odd).
+
+    See Also
+    --------
+    hamming, hanning
+
+    Notes
+    -----
+    The Blackman window is defined as
+
+    .. math::  w(n) = 0.42 - 0.5 \cos(2\pi n/{M-1}) + 0.08 \cos(4\pi n/{M-1})
+
+    Most references to the Blackman window come from the signal processing
+    literature, where it is used as one of many windowing functions for
+    smoothing values.  It is also known as an apodization (which means
+    "removing the foot", i.e. smoothing discontinuities at the beginning
+    and end of the sampled signal) or tapering function. It is known as a
+    "near optimal" tapering function, almost as good (by some measures)
+    as the kaiser window.
+
+    References
+    ----------
+    Blackman, R.B. and Tukey, J.W., (1958) The measurement of power spectra,
+    Dover Publications, New York.
+
+    Oppenheim, A.V., and R.W. Schafer. Discrete-Time Signal Processing.
+    Upper Saddle River, NJ: Prentice-Hall, 1999, pp. 468-471.
+
+    Examples
+    --------
+    >>> np.blackman(12)
+    array([-1.4901161e-08,  3.2606423e-02,  1.5990365e-01,  4.1439798e-01,
+            7.3604530e-01,  9.6704686e-01,  9.6704674e-01,  7.3604506e-01,
+            4.1439781e-01,  1.5990359e-01,  3.2606363e-02, -1.4901161e-08])
+
+    Plot the window and its frequency response:
+
+    >>> import matplotlib.pyplot as plt
+    >>> window = np.blackman(51)
+    >>> plt.plot(window.asnumpy())
+    [<matplotlib.lines.Line2D object at 0x...>]
+    >>> plt.title("blackman window")
+    Text(0.5, 1.0, 'blackman window')
+    >>> plt.ylabel("Amplitude")
+    Text(0, 0.5, 'Amplitude')
+    >>> plt.xlabel("Sample")
+    Text(0.5, 0, 'Sample')
+    >>> plt.show()
+    """
+    if ctx is None:
+        ctx = current_context()
+    return _npi.blackman(M, dtype=dtype, ctx=ctx)
+
+
 _set_np_symbol_class(_Symbol)
diff --git a/src/operator/numpy/np_window_op.cc b/src/operator/numpy/np_window_op.cc
new file mode 100644
index 000000000000..91338a134edc
--- /dev/null
+++ b/src/operator/numpy/np_window_op.cc
@@ -0,0 +1,79 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file np_window_op.cc
+ * \brief CPU Implementation of unary op hanning, hamming, blackman window.
+ */
+
+#include "np_window_op.h"
+
+namespace mxnet {
+namespace op {
+
+DMLC_REGISTER_PARAMETER(NumpyWindowsParam);
+
+inline bool NumpyWindowsShape(const nnvm::NodeAttrs& attrs,
+                              mxnet::ShapeVector* in_shapes,
+                              mxnet::ShapeVector* out_shapes) {
+  const NumpyWindowsParam& param = nnvm::get<NumpyWindowsParam>(attrs.parsed);
+  CHECK_EQ(in_shapes->size(), 0U);
+  CHECK_EQ(out_shapes->size(), 1U);
+  CHECK(param.M.has_value()) << "missing 1 required positional argument: 'M'";
+  int64_t out_size = param.M.value() <= 0 ? 0 : param.M.value();
+  SHAPE_ASSIGN_CHECK(*out_shapes, 0, mxnet::TShape({static_cast<nnvm::dim_t>(out_size)}));
+  return true;
+}
+
+NNVM_REGISTER_OP(_npi_hanning)
+.describe("Return the Hanning window."
+          "The Hanning window is a taper formed by using a weighted cosine.")
+.set_num_inputs(0)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<NumpyWindowsParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyWindowsShape)
+.set_attr<nnvm::FInferType>("FInferType", InitType<NumpyWindowsParam>)
+.set_attr<FCompute>("FCompute<cpu>", NumpyWindowCompute<cpu, 0>)
+.add_arguments(NumpyWindowsParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_npi_hamming)
+.describe("Return the Hamming window."
+          "The Hamming window is a taper formed by using a weighted cosine.")
+.set_num_inputs(0)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<NumpyWindowsParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyWindowsShape)
+.set_attr<nnvm::FInferType>("FInferType", InitType<NumpyWindowsParam>)
+.set_attr<FCompute>("FCompute<cpu>", NumpyWindowCompute<cpu, 1>)
+.add_arguments(NumpyWindowsParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_npi_blackman)
+.describe("Return the Blackman window."
+          "The Blackman window is a taper formed by using a weighted cosine.")
+.set_num_inputs(0)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<NumpyWindowsParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyWindowsShape)
+.set_attr<nnvm::FInferType>("FInferType", InitType<NumpyWindowsParam>)
+.set_attr<FCompute>("FCompute<cpu>", NumpyWindowCompute<cpu, 2>)
+.add_arguments(NumpyWindowsParam::__FIELDS__());
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_window_op.cu b/src/operator/numpy/np_window_op.cu
new file mode 100644
index 000000000000..d56a96e9282f
--- /dev/null
+++ b/src/operator/numpy/np_window_op.cu
@@ -0,0 +1,41 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file np_window_op.cu
+ * \brief CPU Implementation of unary op hanning, hamming, blackman window.
+ */
+
+#include "np_window_op.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(_npi_hanning)
+.set_attr<FCompute>("FCompute<gpu>", NumpyWindowCompute<gpu, 0>);
+
+NNVM_REGISTER_OP(_npi_hamming)
+.set_attr<FCompute>("FCompute<gpu>", NumpyWindowCompute<gpu, 1>);
+
+NNVM_REGISTER_OP(_npi_blackman)
+.set_attr<FCompute>("FCompute<gpu>", NumpyWindowCompute<gpu, 2>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_window_op.h b/src/operator/numpy/np_window_op.h
new file mode 100644
index 000000000000..7501c7643b8e
--- /dev/null
+++ b/src/operator/numpy/np_window_op.h
@@ -0,0 +1,127 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file np_window_op.h
+ * \brief CPU Implementation of unary op hanning, hamming, blackman window.
+ */
+
+#ifndef MXNET_OPERATOR_NUMPY_NP_WINDOW_OP_H_
+#define MXNET_OPERATOR_NUMPY_NP_WINDOW_OP_H_
+
+#include <string>
+#include <vector>
+#include "../tensor/init_op.h"
+
+namespace mxnet {
+namespace op {
+
+#ifdef __CUDA_ARCH__
+__constant__ const float PI = 3.14159265358979323846;
+#else
+const float PI = 3.14159265358979323846;
+using std::isnan;
+#endif
+
+struct NumpyWindowsParam : public dmlc::Parameter<NumpyWindowsParam> {
+  dmlc::optional<nnvm::dim_t> M;
+  std::string ctx;
+  int dtype;
+  DMLC_DECLARE_PARAMETER(NumpyWindowsParam) {
+    DMLC_DECLARE_FIELD(M)
+    .set_default(dmlc::optional<nnvm::dim_t>())
+    .describe("Number of points in the output window. "
+              "If zero or less, an empty array is returned.");
+    DMLC_DECLARE_FIELD(ctx)
+    .set_default("")
+    .describe("Context of output, in format [cpu|gpu|cpu_pinned](n)."
+    "Only used for imperative calls.");
+    DMLC_DECLARE_FIELD(dtype)
+    .set_default(mshadow::kFloat32)
+    MXNET_ADD_ALL_TYPES
+    .describe("Data-type of the returned array.");
+  }
+};
+
+struct hanning_fwd {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(index_t i, index_t M, int req, DType* out) {
+    if (M == 1) {
+      KERNEL_ASSIGN(out[i], req, static_cast<int64_t>(1));
+    } else {
+      KERNEL_ASSIGN(out[i], req, DType(0.5) - DType(0.5) * math::cos(DType(2 * PI * i / (M - 1))));
+    }
+  }
+};
+
+struct hamming_fwd {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(index_t i, index_t M, int req, DType* out) {
+    if (M == 1) {
+      KERNEL_ASSIGN(out[i], req, static_cast<int64_t>(1));
+    } else {
+      KERNEL_ASSIGN(out[i], req,
+                    DType(0.54) - DType(0.46) * math::cos(DType(2 * PI * i / (M - 1))));
+    }
+  }
+};
+
+struct blackman_fwd {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(index_t i, index_t M, int req, DType* out) {
+    if (M == 1) {
+      KERNEL_ASSIGN(out[i], req, static_cast<int64_t>(1));
+    } else {
+      KERNEL_ASSIGN(out[i], req, DType(0.42) - DType(0.5) * math::cos(DType(2 * PI * i /(M - 1))) +
+          DType(0.08) * math::cos(DType(4 * PI * i /(M - 1))));
+    }
+  }
+};
+
+template<typename xpu, int window_select>
+void NumpyWindowCompute(const nnvm::NodeAttrs& attrs,
+                        const OpContext& ctx,
+                        const std::vector<mxnet::TBlob>& inputs,
+                        const std::vector<OpReqType>& req,
+                        const std::vector<TBlob>& outputs) {
+  using namespace mxnet_op;
+  mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+  const NumpyWindowsParam& param = nnvm::get<NumpyWindowsParam>(attrs.parsed);
+  if (param.M.has_value() && param.M.value() <= 0) return;
+  MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+    if (window_select == 0) {
+      Kernel<hanning_fwd, xpu>::Launch(s, outputs[0].Size(), static_cast<int>(param.M.value()),
+                                       req[0], outputs[0].dptr<DType>());
+    } else if (window_select == 1) {
+      Kernel<hamming_fwd, xpu>::Launch(s, outputs[0].Size(), static_cast<int>(param.M.value()),
+                                       req[0], outputs[0].dptr<DType>());
+    } else if (window_select == 2) {
+      Kernel<blackman_fwd, xpu>::Launch(s, outputs[0].Size(), static_cast<int>(param.M.value()),
+                                        req[0], outputs[0].dptr<DType>());
+    } else {
+      LOG(FATAL) << "window_select must be (0, 1, 2)";
+    }
+  });
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_NUMPY_NP_WINDOW_OP_H_
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 8846d57dbe32..ad877bab5b7d 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -1899,10 +1899,10 @@ def get_grad_left(a1, a2):
         sign = 2 * sign.astype(int) - 1
         sign = sign.reshape(a1.shape)
         return sign
-    
+
     def get_grad_right(a1, a2):
         return _np.zeros_like(a2)
-        
+
     shapes = [
         (),
         (1),
@@ -1943,7 +1943,7 @@ def get_grad_right(a1, a2):
                     mx_out = np.copysign(a1, a2)
                     expected_np = _np.copysign(a1_np, a2_np)
                     assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol)
-    
+
     types = ['float16', 'float32', 'float64']
     for x_shape in shapes:
         for dtype in types:
@@ -1957,12 +1957,12 @@ def get_grad_right(a1, a2):
                 mx_out = np.copysign(x, scalar)
             assert mx_out.shape == expected_np.shape
             assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol)
-            
+
             # Test gradient
             mx_out.backward()
             x_grad = get_grad_left(x_np, scalar)
             assert_almost_equal(x.grad.asnumpy(), x_grad, rtol=rtol, atol=atol)
-            
+
             # Test right
             x_np = _np.array(_np.random.uniform(-2.0, 2.0, x_shape), dtype=dtype)
             scalar = _np.random.uniform(-2.0, 2.0)
@@ -1973,7 +1973,7 @@ def get_grad_right(a1, a2):
                 mx_out = np.copysign(scalar, x)
             assert mx_out.shape == expected_np.shape
             assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol)
-            
+
             # Test gradient
             mx_out.backward()
             x_grad = get_grad_right(scalar, x_np)
@@ -2271,6 +2271,42 @@ def g(data, axis1, axis2, offset):
         assert False
 
 
+@with_seed()
+@use_np
+def test_np_windows():
+    class TestWindows(HybridBlock):
+        def __init__(self, func, M, dtype):
+            super(TestWindows, self).__init__()
+            self._func = func
+            self._M = M
+            self._dtype = dtype
+
+        def hybrid_forward(self, F, x, *args, **kwargs):
+            op = getattr(F.np, self._func)
+            assert op is not None
+            return x + op(M=self._M, dtype=self._dtype)
+
+    configs = [-10, -3, -1, 0, 1, 6, 10, 20]
+    dtypes = ['float32', 'float64']
+    funcs = ['hanning', 'hamming', 'blackman']
+    for config in configs:
+        for dtype in dtypes:
+            for func in funcs:
+                x = np.zeros(shape=(), dtype=dtype)
+                for hybridize in [False, True]:
+                    np_func = getattr(_np, func)
+                    mx_func = TestWindows(func, M=config, dtype=dtype)
+                    np_out = np_func(M=config).astype(dtype)
+                    if hybridize:
+                        mx_func.hybridize()
+                    mx_out = mx_func(x)
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+                    # test imperative
+                    mx_out = getattr(np, func)(M=config, dtype=dtype)
+                    np_out = np_func(M=config).astype(dtype)
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 0994ea0e976425866f919f108578b3d221bcfe7c Mon Sep 17 00:00:00 2001
From: Iblis Lin <iblis@hs.ntnu.edu.tw>
Date: Mon, 23 Sep 2019 05:20:56 +0800
Subject: [PATCH 357/813] julia: implement `context.num_gpus` (#16236)

MXNET-1427 #resolve
---
 julia/src/MXNet.jl             |  3 ++-
 julia/src/context.jl           | 11 +++++++++++
 julia/test/unittest/context.jl | 34 ++++++++++++++++++++++++++++++++++
 python/mxnet/context.py        |  2 ++
 4 files changed, 49 insertions(+), 1 deletion(-)
 create mode 100644 julia/test/unittest/context.jl

diff --git a/julia/src/MXNet.jl b/julia/src/MXNet.jl
index 89ec88b52cdc..5a146396642b 100644
--- a/julia/src/MXNet.jl
+++ b/julia/src/MXNet.jl
@@ -74,7 +74,8 @@ export Executor,
 # context.jl
 export Context,
        cpu,
-       gpu
+       gpu,
+       num_gpus
 
 # model.jl
 export AbstractModel,
diff --git a/julia/src/context.jl b/julia/src/context.jl
index 71aee3020daa..bce67a593414 100644
--- a/julia/src/context.jl
+++ b/julia/src/context.jl
@@ -57,3 +57,14 @@ Get a GPU context with a specific id. The K GPUs on a node is typically numbered
 * `dev_id::Integer = 0` the GPU device id.
 """
 gpu(dev_id::Integer = 0) = Context(GPU, dev_id)
+
+"""
+    num_gpus()
+
+Query CUDA for the number of GPUs present.
+"""
+function num_gpus()
+  n = Ref{Cint}()
+  @mxcall :MXGetGPUCount (Ref{Cint},) n
+  n[]
+end
diff --git a/julia/test/unittest/context.jl b/julia/test/unittest/context.jl
new file mode 100644
index 000000000000..0a8f086a194a
--- /dev/null
+++ b/julia/test/unittest/context.jl
@@ -0,0 +1,34 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+module TestContext
+
+using MXNet
+using Test
+
+function test_num_gpus()
+  @info "Context::num_gpus"
+
+  @test num_gpus() >= 0
+end
+
+@testset "Context Test" begin
+  test_num_gpus()
+end
+
+
+end  # module TestContext
diff --git a/python/mxnet/context.py b/python/mxnet/context.py
index f284e00127b4..f2b01373b280 100644
--- a/python/mxnet/context.py
+++ b/python/mxnet/context.py
@@ -276,6 +276,7 @@ def num_gpus():
     check_call(_LIB.MXGetGPUCount(ctypes.byref(count)))
     return count.value
 
+
 def gpu_memory_info(device_id=0):
     """Query CUDA for the free and total bytes of GPU global memory.
 
@@ -300,6 +301,7 @@ def gpu_memory_info(device_id=0):
     check_call(_LIB.MXGetGPUMemoryInformation64(dev_id, ctypes.byref(free), ctypes.byref(total)))
     return (free.value, total.value)
 
+
 def current_context():
     """Returns the current context.
 

From 8e48cecd23222bfc58f3acb5114dd9f4e883eb3b Mon Sep 17 00:00:00 2001
From: Iblis Lin <iblis@hs.ntnu.edu.tw>
Date: Mon, 23 Sep 2019 05:21:25 +0800
Subject: [PATCH 358/813] julia: add `AbstractMXError` as parent type (#16235)

---
 julia/NEWS.md                     |  3 +++
 julia/src/MXNet.jl                |  5 ++++
 julia/src/base.jl                 |  7 ------
 julia/src/exceptions.jl           | 31 +++++++++++++++++++++++
 julia/test/unittest/exceptions.jl | 42 +++++++++++++++++++++++++++++++
 5 files changed, 81 insertions(+), 7 deletions(-)
 create mode 100644 julia/src/exceptions.jl
 create mode 100644 julia/test/unittest/exceptions.jl

diff --git a/julia/NEWS.md b/julia/NEWS.md
index e678b97176d1..7e3076a303d4 100644
--- a/julia/NEWS.md
+++ b/julia/NEWS.md
@@ -17,6 +17,9 @@
 
 # v1.6.0
 
+* Add an abstract type `AbstractMXError` as the parent type for all MXNet-related
+  API errors. (#16235)
+
 
 # v1.5.0
 
diff --git a/julia/src/MXNet.jl b/julia/src/MXNet.jl
index 5a146396642b..69bbcf85180d 100644
--- a/julia/src/MXNet.jl
+++ b/julia/src/MXNet.jl
@@ -43,6 +43,10 @@ import Base.Iterators: filter
 #  exports
 ###############################################################################
 
+# exceptions.jl
+export AbstractMXError,
+       MXError
+
 # symbolic-node.jl
 export SymbolicNode,
        Variable,
@@ -136,6 +140,7 @@ export to_graphviz
 #  includes
 ###############################################################################
 
+include("exceptions.jl")
 include("base.jl")
 
 include("runtime.jl")
diff --git a/julia/src/base.jl b/julia/src/base.jl
index 683146402620..2b60dca4f783 100644
--- a/julia/src/base.jl
+++ b/julia/src/base.jl
@@ -15,13 +15,6 @@
 # specific language governing permissions and limitations
 # under the License.
 
-"Exception thrown when an error occurred calling MXNet API."
-struct MXError <: Exception
-  msg :: AbstractString
-end
-
-Base.show(io::IO, e::MXError) = print(io, e.msg)
-
 ################################################################################
 # Common types used in MXNet API
 ################################################################################
diff --git a/julia/src/exceptions.jl b/julia/src/exceptions.jl
new file mode 100644
index 000000000000..9faa2f8c189e
--- /dev/null
+++ b/julia/src/exceptions.jl
@@ -0,0 +1,31 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+
+"""
+Exception thrown when an error occurred calling MXNet API.
+"""
+abstract type AbstractMXError <: Exception end
+
+"General MXNet API error"
+struct MXError <: AbstractMXError
+  msg::String
+
+  MXError(s::AbstractString) = new(string(s))
+end
+
+Base.show(io::IO, e::AbstractMXError) = print(io, e.msg)
diff --git a/julia/test/unittest/exceptions.jl b/julia/test/unittest/exceptions.jl
new file mode 100644
index 000000000000..ad952c6483f5
--- /dev/null
+++ b/julia/test/unittest/exceptions.jl
@@ -0,0 +1,42 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+module TestExceptions
+
+using MXNet
+using Test
+
+struct MXError′ <: AbstractMXError
+  msg::String
+end
+
+function test_show()
+  @info "AbstractMXError::Base.show"
+
+  io = IOBuffer()
+  e = MXError′("magic")
+  print(io, e)
+  str = String(take!(io))
+  @test str == "magic"
+end
+
+@testset "Exception Test" begin
+  test_show()
+end
+
+
+end  # module TestExceptions

From dccfc88f66d23366bd478ec42fd9b106ae74d2ad Mon Sep 17 00:00:00 2001
From: tingying <tingying2019@u.northwestern.edu>
Date: Mon, 23 Sep 2019 07:13:23 +0800
Subject: [PATCH 359/813] [Numpy]flip (#15819)

* Numpy flip operator

* Implement flip

* fix some bug and add gpu test

* register param and edit test

* add testcase for backward

* remove print

* optimize 0-dim and 0-shape

* adjust format and add doc in _symbol.py

* fix bug in symbol

* add flip in __all__

* fix format error

* import ndarray

* move flip implementation to np_matrix_op and remove test in gpu

* delate redundant blank line

* fix error in review

* remove **kwargs and change copy

* fix error in review

* Fix import

* Fix lint
---
 python/mxnet/ndarray/numpy/_op.py      | 70 +++++++++++++++++++++++++-
 python/mxnet/numpy/multiarray.py       | 64 ++++++++++++++++++++++-
 python/mxnet/symbol/numpy/_symbol.py   | 40 ++++++++++++++-
 src/operator/numpy/np_matrix_op-inl.h  | 66 ++++++++++++++++++++++++
 src/operator/numpy/np_matrix_op.cc     | 47 +++++++++++++++++
 src/operator/numpy/np_matrix_op.cu     | 34 +++++++++++++
 tests/python/unittest/test_numpy_op.py | 41 +++++++++++++++
 7 files changed, 359 insertions(+), 3 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index 99e35f37844d..7a5cf37403b4 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -34,7 +34,7 @@
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'mean',
            'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
-           'ravel', 'hanning', 'hamming', 'blackman']
+           'ravel', 'hanning', 'hamming', 'blackman', 'flip']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -2828,3 +2828,71 @@ def blackman(M, dtype=_np.float32, ctx=None):
     if ctx is None:
         ctx = current_context()
     return _npi.blackman(M, dtype=dtype, ctx=ctx)
+
+
+@set_module('mxnet.ndarray.numpy')
+def flip(m, axis=None, out=None):
+    r"""
+    flip(m, axis=None, out=None)
+
+    Reverse the order of elements in an array along the given axis.
+
+    The shape of the array is preserved, but the elements are reordered.
+
+    Parameters
+    ----------
+    m : ndarray or scalar
+        Input array.
+    axis : None or int or tuple of ints, optional
+        Axis or axes along which to flip over. The default,
+        axis=None, will flip over all of the axes of the input array.
+        If axis is negative it counts from the last to the first axis.
+
+        If axis is a tuple of ints, flipping is performed on all of the axes
+        specified in the tuple.
+    out : ndarray or scalar, optional
+        Alternative output array in which to place the result. It must have
+        the same shape and type as the expected output.
+
+    Returns
+    -------
+    out : ndarray or scalar
+        A view of `m` with the entries of axis reversed.  Since a view is
+        returned, this operation is done in constant time.
+
+    Examples
+    --------
+    >>> A = np.arange(8).reshape((2,2,2))
+    >>> A
+    array([[[0, 1],
+            [2, 3]],
+           [[4, 5],
+            [6, 7]]])
+    >>> np.flip(A, 0)
+    array([[[4, 5],
+            [6, 7]],
+           [[0, 1],
+            [2, 3]]])
+    >>> np.flip(A, 1)
+    array([[[2, 3],
+            [0, 1]],
+           [[6, 7],
+            [4, 5]]])
+    >>> np.flip(A)
+    array([[[7, 6],
+            [5, 4]],
+           [[3, 2],
+            [1, 0]]])
+    >>> np.flip(A, (0, 2))
+    array([[[5, 4],
+            [7, 6]],
+           [[1, 0],
+            [3, 2]]])
+    """
+    from ...numpy import ndarray
+    if isinstance(m, numeric_types):
+        return _np.flip(m, axis)
+    elif isinstance(m, ndarray):
+        return _npi.flip(m, axis, out=out)
+    else:
+        raise TypeError('type {} not supported'.format(str(type(m))))
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 801538557c5d..5b387601f59d 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -53,7 +53,7 @@
            'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh',
            'tensordot', 'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate',
            'stack', 'vstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices',
-           'copysign', 'ravel', 'hanning', 'hamming', 'blackman']
+           'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -4367,3 +4367,65 @@ def blackman(M, dtype=_np.float32, ctx=None):
     >>> plt.show()
     """
     return _mx_nd_np.blackman(M, dtype=dtype, ctx=ctx)
+
+
+@set_module('mxnet.numpy')
+def flip(m, axis=None, out=None):
+    r"""
+    flip(m, axis=None, out=None)
+
+    Reverse the order of elements in an array along the given axis.
+
+    The shape of the array is preserved, but the elements are reordered.
+
+    Parameters
+    ----------
+    m : ndarray or scalar
+        Input array.
+    axis : None or int or tuple of ints, optional
+        Axis or axes along which to flip over. The default,
+        axis=None, will flip over all of the axes of the input array.
+        If axis is negative it counts from the last to the first axis.
+
+        If axis is a tuple of ints, flipping is performed on all of the axes
+        specified in the tuple.
+    out : ndarray or scalar, optional
+        Alternative output array in which to place the result. It must have
+        the same shape and type as the expected output.
+
+    Returns
+    -------
+    out : ndarray or scalar
+        A view of `m` with the entries of axis reversed.  Since a view is
+        returned, this operation is done in constant time.
+
+    Examples
+    --------
+    >>> A = np.arange(8).reshape((2,2,2))
+    >>> A
+    array([[[0, 1],
+            [2, 3]],
+           [[4, 5],
+            [6, 7]]])
+    >>> np.flip(A, 0)
+    array([[[4, 5],
+            [6, 7]],
+           [[0, 1],
+            [2, 3]]])
+    >>> np.flip(A, 1)
+    array([[[2, 3],
+            [0, 1]],
+           [[6, 7],
+            [4, 5]]])
+    >>> np.flip(A)
+    array([[[7, 6],
+            [5, 4]],
+           [[3, 2],
+            [1, 0]]])
+    >>> np.flip(A, (0, 2))
+    array([[[5, 4],
+            [7, 6]],
+           [[1, 0],
+            [3, 2]]])
+    """
+    return _mx_nd_np.flip(m, axis, out=out)
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index f39d201638cb..dc2c84da167d 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -36,7 +36,7 @@
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'mean',
            'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
-           'ravel', 'hanning', 'hamming', 'blackman']
+           'ravel', 'hanning', 'hamming', 'blackman', 'flip']
 
 
 def _num_outputs(sym):
@@ -3091,4 +3091,42 @@ def blackman(M, dtype=_np.float32, ctx=None):
     return _npi.blackman(M, dtype=dtype, ctx=ctx)
 
 
+@set_module('mxnet.symbol.numpy')
+def flip(m, axis=None, out=None):
+    r"""
+    flip(m, axis=None, out=None)
+
+    Reverse the order of elements in an array along the given axis.
+
+    The shape of the array is preserved, but the elements are reordered.
+
+    Parameters
+    ----------
+    m : _Symbol or scalar
+        Input array.
+    axis : None or int or tuple of ints, optional
+        Axis or axes along which to flip over. The default,
+        axis=None, will flip over all of the axes of the input array.
+        If axis is negative it counts from the last to the first axis.
+
+        If axis is a tuple of ints, flipping is performed on all of the axes
+        specified in the tuple.
+    out : _Symbol or scalar, optional
+        Alternative output array in which to place the result. It must have
+        the same shape and type as the expected output.
+
+    Returns
+    -------
+    out : _Symbol or scalar
+        A view of `m` with the entries of axis reversed.  Since a view is
+        returned, this operation is done in constant time.
+    """
+    if isinstance(m, numeric_types):
+        return _np.flip(m, axis)
+    elif isinstance(m, _Symbol):
+        return _npi.flip(m, axis, out=out)
+    else:
+        raise TypeError('type {} not supported'.format(str(type(m))))
+
+
 _set_np_symbol_class(_Symbol)
diff --git a/src/operator/numpy/np_matrix_op-inl.h b/src/operator/numpy/np_matrix_op-inl.h
index 631196e3cd1a..5e25192d9298 100644
--- a/src/operator/numpy/np_matrix_op-inl.h
+++ b/src/operator/numpy/np_matrix_op-inl.h
@@ -297,6 +297,72 @@ void NumpyRollCompute(const nnvm::NodeAttrs& attrs,
   }
 }
 
+struct FlipParam : public dmlc::Parameter<FlipParam> {
+  mxnet::Tuple<int> axis;
+  DMLC_DECLARE_PARAMETER(FlipParam) {
+      DMLC_DECLARE_FIELD(axis)
+          .describe("The axis which to flip elements.");
+  }
+};
+
+#define FLIP_MAX_DIM 10
+#define FLIP_MIN_DIM -1
+
+template<typename xpu>
+void NumpyFlipForwardImpl(const OpContext& ctx,
+                          const std::vector<TBlob>& inputs,
+                          const std::vector<TBlob>& outputs,
+                          const std::vector<index_t>& stride_,
+                          const std::vector<index_t>& trailing_,
+                          const index_t& flip_index);
+
+template<typename xpu>
+void NumpyFlipForward(const nnvm::NodeAttrs& attrs,
+                      const OpContext& ctx,
+                      const std::vector<TBlob>& inputs,
+                      const std::vector<OpReqType>& req,
+                      const std::vector<TBlob>& outputs) {
+  const FlipParam& param = nnvm::get<FlipParam>(attrs.parsed);
+  mxnet::Tuple<int> axistemp;
+  CHECK_EQ(inputs[0].type_flag_, outputs[0].type_flag_);
+  CHECK_LT(param.axis.ndim(), FLIP_MAX_DIM);
+  CHECK_GE(param.axis.ndim(), FLIP_MIN_DIM);
+  if (param.axis.ndim() == FLIP_MIN_DIM) {
+    if (inputs[0].shape_.ndim() == 0) {
+      mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+      MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+        mshadow::Copy(outputs[0].FlatTo1D<xpu, DType>(s), inputs[0].FlatTo1D<xpu, DType>(s), s);
+    });
+      return;
+    }
+    std::vector<int> temp;
+    for (int i = 0; i < inputs[0].shape_.ndim(); i++) {
+      temp.push_back(i);
+    }
+    axistemp.assign(temp.begin(), temp.end());
+  } else {
+    axistemp = param.axis;
+  }
+
+  const mxnet::TShape& ishape = inputs[0].shape_;
+  if (ishape.ProdShape(0, ishape.ndim()) == 0) {
+    return;  // zero shape
+  }
+  std::vector<index_t> stride_(axistemp.ndim());
+  std::vector<index_t>  trailing_(axistemp.ndim());
+  index_t flip_index = 0;
+  for (int axis : axistemp) {
+    CHECK_LT(axis, ishape.ndim());
+    stride_[flip_index] = ishape[axis];
+    trailing_[flip_index] = 1;
+    for (int i2 = axis + 1; i2 < ishape.ndim(); ++i2) {
+      trailing_[flip_index] *= ishape[i2];
+    }
+    flip_index++;
+  }
+  NumpyFlipForwardImpl<xpu>(ctx, inputs, outputs, stride_, trailing_, flip_index);
+}
+
 }  // namespace op
 }  // namespace mxnet
 
diff --git a/src/operator/numpy/np_matrix_op.cc b/src/operator/numpy/np_matrix_op.cc
index 45e4c3f2d5b4..96a10561be28 100644
--- a/src/operator/numpy/np_matrix_op.cc
+++ b/src/operator/numpy/np_matrix_op.cc
@@ -565,5 +565,52 @@ NNVM_REGISTER_OP(_np_roll)
 .add_argument("data", "NDArray-or-Symbol", "Input ndarray")
 .add_arguments(NumpyRollParam::__FIELDS__());
 
+template<>
+void NumpyFlipForwardImpl<cpu>(const OpContext& ctx,
+                               const std::vector<TBlob>& inputs,
+                               const std::vector<TBlob>& outputs,
+                               const std::vector<index_t>& stride_,
+                               const std::vector<index_t>& trailing_,
+                               const index_t& flip_index) {
+  mshadow::Stream<cpu> *s = ctx.get_stream<cpu>();
+  MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+    mxnet_op::Kernel<reverse, cpu>::Launch(s, inputs[0].Size(), flip_index,
+      inputs[0].dptr<DType>(), outputs[0].dptr<DType>(),
+      stride_.data(), trailing_.data());
+  });
+}
+
+DMLC_REGISTER_PARAMETER(FlipParam);
+
+NNVM_REGISTER_OP(_npi_flip)
+.set_num_outputs(1)
+.set_num_inputs(1)
+.set_attr_parser(ParamParser<FlipParam>)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+[](const NodeAttrs& attrs) {
+  return std::vector<std::string> {"data"};
+})
+.set_attr<FResourceRequest>("FResourceRequest",
+[](const NodeAttrs& attrs) {
+  return std::vector<ResourceRequest> {ResourceRequest::kTempSpace};
+})
+.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
+.set_attr<FCompute>("FCompute<cpu>", NumpyFlipForward<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_npi_flip"})
+.add_argument("data", "NDArray-or-Symbol", "Input data array")
+.add_arguments(FlipParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_backward_npi_flip)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<FlipParam>)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FResourceRequest>("FResourceRequest",
+[](const NodeAttrs& attrs) {
+  return std::vector<ResourceRequest> {ResourceRequest::kTempSpace};
+})
+.set_attr<FCompute>("FCompute<cpu>", NumpyFlipForward<cpu>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/np_matrix_op.cu b/src/operator/numpy/np_matrix_op.cu
index ba49d320575c..caab4108b40e 100644
--- a/src/operator/numpy/np_matrix_op.cu
+++ b/src/operator/numpy/np_matrix_op.cu
@@ -56,5 +56,39 @@ NNVM_REGISTER_OP(_backward_np_vstack)
 NNVM_REGISTER_OP(_np_roll)
 .set_attr<FCompute>("FCompute<gpu>", NumpyRollCompute<gpu>);
 
+template<>
+void NumpyFlipForwardImpl<gpu>(const OpContext& ctx,
+                               const std::vector<TBlob>& inputs,
+                               const std::vector<TBlob>& outputs,
+                               const std::vector<index_t>& stride_,
+                               const std::vector<index_t>& trailing_,
+                               const index_t& flip_index) {
+  mshadow::Stream<gpu> *s = ctx.get_stream<gpu>();
+  mshadow::Tensor<gpu, 1, uint8_t> workspace =
+    ctx.requested[0].get_space_typed<gpu, 1, uint8_t>(
+      mshadow::Shape1(flip_index * sizeof(index_t) * 2), s);
+
+  auto stride_workspace = workspace.dptr_;
+  auto trailing_workspace = workspace.dptr_ + flip_index * sizeof(index_t);
+
+  cudaMemcpyAsync(stride_workspace, thrust::raw_pointer_cast(stride_.data()),
+                  stride_.size() * sizeof(index_t),
+                  cudaMemcpyHostToDevice, mshadow::Stream<gpu>::GetStream(s));
+  cudaMemcpyAsync(trailing_workspace, thrust::raw_pointer_cast(trailing_.data()),
+                  trailing_.size() * sizeof(index_t),
+                  cudaMemcpyHostToDevice, mshadow::Stream<gpu>::GetStream(s));
+
+  MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+    mxnet_op::Kernel<reverse, gpu>::Launch(s, inputs[0].Size(), flip_index,
+      inputs[0].dptr<DType>(), outputs[0].dptr<DType>(),
+      reinterpret_cast<index_t*>(stride_workspace), reinterpret_cast<index_t*>(trailing_workspace));
+  });
+}
+
+NNVM_REGISTER_OP(_npi_flip)
+.set_attr<FCompute>("FCompute<gpu>", NumpyFlipForward<gpu>);
+
+NNVM_REGISTER_OP(_backward_npi_flip)
+.set_attr<FCompute>("FCompute<gpu>", NumpyFlipForward<gpu>);
 }  // namespace op
 }  // namespace mxnet
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index ad877bab5b7d..95140a69ab45 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -30,6 +30,7 @@
 from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf
 import platform
 
+
 @with_seed()
 @use_np
 def test_np_tensordot():
@@ -2307,6 +2308,46 @@ def hybrid_forward(self, F, x, *args, **kwargs):
                     assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
 
 
+@with_seed()
+@use_np
+def test_np_flip():
+    class TestFlip(HybridBlock):
+        def __init__(self, axis):
+            super(TestFlip, self).__init__()
+            self.axis = axis
+
+        def hybrid_forward(self, F, x):
+            return F.np.flip(x, self.axis)
+
+    shapes = [(1, 2, 3), (1, 0), ()]
+    types = ['int32', 'int64', 'float16', 'float32', 'float64']
+    for hybridize in [True, False]:
+        for oneType in types:
+            rtol, atol=1e-3, 1e-5
+            for shape in shapes:
+                axis = random.randint(-1, len(shape) - 1)
+                if axis is -1:
+                    axis = None
+                test_flip = TestFlip(axis)
+                if hybridize:
+                    test_flip.hybridize()
+                x = rand_ndarray(shape, dtype=oneType).as_np_ndarray()
+                x.attach_grad()
+                np_out = _np.flip(x.asnumpy(), axis)
+                with mx.autograd.record():
+                    mx_out = test_flip(x)
+                assert mx_out.shape == np_out.shape
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
+                mx_out.backward()
+                np_backward = _np.ones(np_out.shape)
+                assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=rtol, atol=atol)
+
+                # Test imperative once again
+                mx_out = np.flip(x, axis)
+                np_out = _np.flip(x.asnumpy(), axis)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 7344c91b962368f54490a952acd33b729081cbcc Mon Sep 17 00:00:00 2001
From: tingying <tingying2019@u.northwestern.edu>
Date: Mon, 23 Sep 2019 10:13:42 +0800
Subject: [PATCH 360/813] numpy operator around (#16126)

* change the name of argument

* add doc in three files and fix some bug

* change the data type in .h and add test function

    cancel optimization when abs(temp) < 0.5
    modify test on cpu and add test on gpu
    do not support float16
    edit testcase on gpu and add 'Do not support float16 on doc'

* edit doc: support scalar

* adjust the format

* add license

* fix format error

* delete gpu test

* move around to np_elemwise_unary_op_basic

* edit AroundOpType

* replace int kernel with identity_with_cast and fix format error

* delete unused req_type
---
 python/mxnet/ndarray/numpy/_op.py             | 59 ++++++++++++-
 python/mxnet/numpy/multiarray.py              | 54 +++++++++++-
 python/mxnet/symbol/numpy/_symbol.py          | 45 +++++++++-
 .../numpy/np_elemwise_unary_op_basic.cc       | 34 ++++++++
 .../numpy/np_elemwise_unary_op_basic.cu       |  3 +
 src/operator/tensor/elemwise_unary_op.h       | 83 +++++++++++++++++++
 tests/python/gpu/test_operator_gpu.py         |  2 +-
 tests/python/unittest/test_numpy_op.py        | 32 +++++++
 8 files changed, 308 insertions(+), 4 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index 7a5cf37403b4..2cdfff173b8e 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -34,7 +34,7 @@
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'mean',
            'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
-           'ravel', 'hanning', 'hamming', 'blackman', 'flip']
+           'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -2896,3 +2896,60 @@ def flip(m, axis=None, out=None):
         return _npi.flip(m, axis, out=out)
     else:
         raise TypeError('type {} not supported'.format(str(type(m))))
+
+
+@set_module('mxnet.ndarray.numpy')
+def around(x, decimals=0, out=None, **kwargs):
+    r"""
+    around(x, decimals=0, out=None)
+
+    Evenly round to the given number of decimals.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input data.
+    decimals : int, optional
+        Number of decimal places to round to (default: 0).  If
+        decimals is negative, it specifies the number of positions to
+        the left of the decimal point.
+    out : ndarray, optional
+        Alternative output array in which to place the result. It must have
+        the same shape and type as the expected output.
+
+    Returns
+    -------
+    rounded_array : ndarray or scalar
+        An array of the same type as `x`, containing the rounded values.
+        A reference to the result is returned.
+
+    Notes
+    -----
+    For values exactly halfway between rounded decimal values, NumPy
+    rounds to the nearest even value. Thus 1.5 and 2.5 round to 2.0,
+    -0.5 and 0.5 round to 0.0, etc.
+
+    This function differs from the original numpy.prod in the following aspects:
+
+        - Cannot cast type automatically. Dtype of `out` must be same as the expected one.
+        - Cannot support complex-valued number.
+
+    Examples
+    --------
+    >>> np.around([0.37, 1.64])
+    array([ 0.,  2.])
+    >>> np.around([0.37, 1.64], decimals=1)
+    array([ 0.4,  1.6])
+    >>> np.around([.5, 1.5, 2.5, 3.5, 4.5]) # rounds to nearest even value
+    array([ 0.,  2.,  2.,  4.,  4.])
+    >>> np.around([1, 2, 3, 11], decimals=1) # ndarray of ints is returned
+    array([ 1,  2,  3, 11])
+    >>> np.around([1, 2, 3, 11], decimals=-1)
+    array([ 0,  0,  0, 10])
+    """
+    from ...numpy import ndarray
+    if isinstance(x, numeric_types):
+        return _np.around(x, decimals, **kwargs)
+    elif isinstance(x, ndarray):
+        return _npi.around(x, decimals, out=out, **kwargs)
+    else:
+        raise TypeError('type {} not supported'.format(str(type(x))))
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 5b387601f59d..e0c7a67d4715 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -53,7 +53,7 @@
            'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh',
            'tensordot', 'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate',
            'stack', 'vstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices',
-           'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip']
+           'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -4429,3 +4429,55 @@ def flip(m, axis=None, out=None):
             [3, 2]]])
     """
     return _mx_nd_np.flip(m, axis, out=out)
+
+
+@set_module('mxnet.numpy')
+def around(x, decimals=0, out=None, **kwargs):
+    r"""
+    around(x, decimals=0, out=None)
+
+    Evenly round to the given number of decimals.
+
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input data.
+    decimals : int, optional
+        Number of decimal places to round to (default: 0).  If
+        decimals is negative, it specifies the number of positions to
+        the left of the decimal point.
+    out : ndarray, optional
+        Alternative output array in which to place the result. It must have
+        the same shape and type as the expected output.
+
+    Returns
+    -------
+    rounded_array : ndarray or scalar
+        An array of the same type as `x`, containing the rounded values.
+        A reference to the result is returned.
+
+    Notes
+    -----
+    For values exactly halfway between rounded decimal values, NumPy
+    rounds to the nearest even value. Thus 1.5 and 2.5 round to 2.0,
+    -0.5 and 0.5 round to 0.0, etc.
+
+    This function differs from the original numpy.prod in the following aspects:
+
+        - Cannot cast type automatically. Dtype of `out` must be same as the expected one.
+        - Cannot support complex-valued number.
+
+    Examples
+    --------
+    >>> np.around([0.37, 1.64])
+    array([ 0.,  2.])
+    >>> np.around([0.37, 1.64], decimals=1)
+    array([ 0.4,  1.6])
+    >>> np.around([.5, 1.5, 2.5, 3.5, 4.5]) # rounds to nearest even value
+    array([ 0.,  2.,  2.,  4.,  4.])
+    >>> np.around([1, 2, 3, 11], decimals=1) # ndarray of ints is returned
+    array([ 1,  2,  3, 11])
+    >>> np.around([1, 2, 3, 11], decimals=-1)
+    array([ 0,  0,  0, 10])
+    """
+    return _mx_nd_np.around(x, decimals, out=out, **kwargs)
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index dc2c84da167d..1bfbba209989 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -36,7 +36,7 @@
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'mean',
            'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
-           'ravel', 'hanning', 'hamming', 'blackman', 'flip']
+           'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around']
 
 
 def _num_outputs(sym):
@@ -3129,4 +3129,47 @@ def flip(m, axis=None, out=None):
         raise TypeError('type {} not supported'.format(str(type(m))))
 
 
+@set_module('mxnet.symbol.numpy')
+def around(x, decimals=0, out=None, **kwargs):
+    r"""
+    around(x, decimals=0, out=None)
+
+    Evenly round to the given number of decimals.
+    Parameters
+    ----------
+    x : _Symbol or scalar
+        Input data.
+    decimals : int, optional
+        Number of decimal places to round to (default: 0).  If
+        decimals is negative, it specifies the number of positions to
+        the left of the decimal point.
+    out : _Symbol, optional
+        Alternative output array in which to place the result. It must have
+        the same shape and type as the expected output.
+
+    Returns
+    -------
+    rounded_array : _Symbol or scalar
+        An array of the same type as `x`, containing the rounded values.
+        A reference to the result is returned.
+
+    Notes
+    -----
+    For values exactly halfway between rounded decimal values, NumPy
+    rounds to the nearest even value. Thus 1.5 and 2.5 round to 2.0,
+    -0.5 and 0.5 round to 0.0, etc.
+
+    This function differs from the original numpy.prod in the following aspects:
+
+        - Cannot cast type automatically. Dtype of `out` must be same as the expected one.
+        - Cannot support complex-valued number.
+    """
+    if isinstance(x, numeric_types):
+        return _np.around(x, decimals, **kwargs)
+    elif isinstance(x, _Symbol):
+        return _npi.around(x, decimals, out=out, **kwargs)
+    else:
+        raise TypeError('type {} not supported'.format(str(type(x))))
+
+
 _set_np_symbol_class(_Symbol)
diff --git a/src/operator/numpy/np_elemwise_unary_op_basic.cc b/src/operator/numpy/np_elemwise_unary_op_basic.cc
index b5764a7fbb4b..b454d89212e2 100644
--- a/src/operator/numpy/np_elemwise_unary_op_basic.cc
+++ b/src/operator/numpy/np_elemwise_unary_op_basic.cc
@@ -362,5 +362,39 @@ computed element-wise.
 )code" ADD_FILELINE)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{ "_backward_arctanh" });
 
+inline bool AroundOpType(const nnvm::NodeAttrs& attrs,
+                         std::vector<int>* in_attrs,
+                         std::vector<int>* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
+  TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
+
+  CHECK(in_attrs->at(0) != mshadow::kFloat16) << "Do not support `float16` as input.\n";
+  return out_attrs->at(0) != -1;
+}
+
+DMLC_REGISTER_PARAMETER(AroundParam);
+
+NNVM_REGISTER_OP(_npi_around)
+.set_attr_parser(ParamParser<AroundParam>)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"x"};
+  })
+.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
+.set_attr<nnvm::FInferType>("FInferType", AroundOpType)
+.set_attr<FCompute>("FCompute<cpu>", AroundOpForward<cpu>)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs){
+    return std::vector<std::pair<int, int> >{{0, 0}};
+  })
+.add_argument("x", "NDArray-or-Symbol", "Input ndarray")
+.add_arguments(AroundParam::__FIELDS__())
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/np_elemwise_unary_op_basic.cu b/src/operator/numpy/np_elemwise_unary_op_basic.cu
index cb8bb771b27d..7f68386560f3 100644
--- a/src/operator/numpy/np_elemwise_unary_op_basic.cu
+++ b/src/operator/numpy/np_elemwise_unary_op_basic.cu
@@ -106,5 +106,8 @@ MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_arccosh, mshadow_op::arccosh);
 
 MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_arctanh, mshadow_op::arctanh);
 
+NNVM_REGISTER_OP(_npi_around)
+.set_attr<FCompute>("FCompute<gpu>", AroundOpForward<gpu>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/tensor/elemwise_unary_op.h b/src/operator/tensor/elemwise_unary_op.h
index 87964ac246f0..b50f89b8c7fa 100644
--- a/src/operator/tensor/elemwise_unary_op.h
+++ b/src/operator/tensor/elemwise_unary_op.h
@@ -560,6 +560,89 @@ struct ReshapeLikeParam : public dmlc::Parameter<ReshapeLikeParam> {
   }
 };
 
+struct AroundParam : public dmlc::Parameter<AroundParam> {
+  int decimals;
+  DMLC_DECLARE_PARAMETER(AroundParam) {
+    DMLC_DECLARE_FIELD(decimals)
+      .set_default(0)
+      .describe("Number of decimal places to round to.");
+  }
+};
+
+template<int req>
+struct around_forward {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int i, DType* out_data, const DType* in_data,
+                                  const int decimals) {
+    int d = 0;
+    DType temp = in_data[i];
+    DType roundtemp;
+    while (d != decimals) {
+      if (decimals > 0) {
+        d++;
+        temp *= 10;
+      } else {
+        d--;
+        temp /= 10;
+      }
+    }
+    roundtemp = (DType)round(static_cast<double>(temp));
+    // If temp is x.5 and roundtemp is odd number, decrease or increase roundtemp by 1.
+    // For example, in numpy, around(0.5) should be 0 but in c, round(0.5) is 1.
+    if (roundtemp - temp == 0.5 && (static_cast<int>(roundtemp)) % 2 != 0) {
+      roundtemp -= 1;
+    } else if (temp - roundtemp == 0.5 && (static_cast<int>(roundtemp)) % 2 != 0) {
+      roundtemp += 1;
+    }
+    while (d != 0) {
+      if (roundtemp == 0) {
+        break;
+      }
+      if (decimals > 0) {
+        d--;
+        roundtemp /= 10;
+      } else {
+        d++;
+        roundtemp *= 10;
+      }
+    }
+    KERNEL_ASSIGN(out_data[i], req, roundtemp);
+  }
+};
+
+template<typename xpu>
+void AroundOpForward(const nnvm::NodeAttrs& attrs,
+                     const OpContext& ctx,
+                     const std::vector<TBlob>& inputs,
+                     const std::vector<OpReqType>& req,
+                     const std::vector<TBlob>& outputs) {
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 1U);
+  CHECK_EQ(req.size(), 1U);
+  mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+  const TBlob& in_data = inputs[0];
+  const TBlob& out_data = outputs[0];
+  const AroundParam& param = nnvm::get<AroundParam>(attrs.parsed);
+  using namespace mxnet_op;
+  // if the type is uint8, int8, int32 or int64 and decimals is greater than 0
+  // we simply return the number back.
+  if (in_data.type_flag_ >= mshadow::kUint8 && in_data.type_flag_ <= mshadow::kInt64 \
+     && param.decimals > 0) {
+    MSHADOW_TYPE_SWITCH(out_data.type_flag_, DType, {
+      Kernel<mshadow_op::identity_with_cast, xpu>::Launch(
+        s, out_data.Size(), out_data.dptr<DType>(), in_data.dptr<DType>());
+    });
+  } else {
+    MSHADOW_TYPE_SWITCH(out_data.type_flag_, DType, {
+      MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
+        Kernel<around_forward<req_type>, xpu>::Launch(
+          s, out_data.Size(), out_data.dptr<DType>(), in_data.dptr<DType>(),
+          param.decimals);
+      });
+    });
+  }
+}
+
 /*! \brief Unary compute */
 #define MXNET_OPERATOR_REGISTER_UNARY(__name$)                      \
   NNVM_REGISTER_OP(__name$)                                         \
diff --git a/tests/python/gpu/test_operator_gpu.py b/tests/python/gpu/test_operator_gpu.py
index c4d96fb550c0..e3862abbebd1 100644
--- a/tests/python/gpu/test_operator_gpu.py
+++ b/tests/python/gpu/test_operator_gpu.py
@@ -2346,7 +2346,7 @@ def test_arange_like_dtype():
         out = mod.forward(is_train=False)
         for v in out:
             assert v.dtype == t
-    
+
 
 if __name__ == '__main__':
     import nose
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 95140a69ab45..1324af03d1b5 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -2348,6 +2348,38 @@ def hybrid_forward(self, F, x):
                 assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
 
 
+@with_seed()
+@use_np
+def test_np_around():
+    class TestAround(HybridBlock):
+        def __init__(self, decimals):
+            super(TestAround, self).__init__()
+            self.decimals = decimals
+
+        def hybrid_forward(self, F, x):
+            return F.np.around(x, self.decimals)
+
+    shapes = [(), (1, 2, 3), (1, 0)]
+    types = ['int32', 'int64', 'float32', 'float64']
+    for hybridize in [True, False]:
+        for oneType in types:
+            rtol, atol = 1e-3, 1e-5
+            for shape in shapes:
+                for d in range(-5, 6):
+                    test_around = TestAround(d)
+                    if hybridize:
+                        test_around.hybridize()
+                    x = rand_ndarray(shape, dtype=oneType).as_np_ndarray()
+                    np_out = _np.around(x.asnumpy(), d)
+                    mx_out = test_around(x)
+                    assert mx_out.shape == np_out.shape
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
+
+                    mx_out = np.around(x, d)
+                    np_out = _np.around(x.asnumpy(), d)
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)    
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 8af1b5756d965128e19b3c6535a9e3fc2f457013 Mon Sep 17 00:00:00 2001
From: tingying <tingying2019@u.northwestern.edu>
Date: Mon, 23 Sep 2019 13:38:37 +0800
Subject: [PATCH 361/813] numpy operator arctan2 (#15890)

* change the test code

* add @use_np in test code

* only support float16, float32 and float64.

* fix format error

* remove redundant backslash

* change wrapper in symbol

* delete gpu test

* edit test

* change infer type

* remove redundant **kwargs

* change atol and rtol in test

* edit test shape
---
 python/mxnet/ndarray/numpy/_op.py             | 92 ++++++++++++++++++-
 python/mxnet/numpy/multiarray.py              | 91 +++++++++++++++++-
 python/mxnet/symbol/numpy/_symbol.py          | 72 ++++++++++++++-
 src/operator/math_functions-inl.h             |  2 +
 src/operator/mshadow_op.h                     | 10 ++
 .../numpy/np_elemwise_broadcast_op.cc         | 70 ++++++++++++++
 .../numpy/np_elemwise_broadcast_op.cu         | 19 ++++
 src/operator/operator_tune.cc                 |  5 +
 tests/python/unittest/test_numpy_op.py        | 67 ++++++++++++++
 9 files changed, 420 insertions(+), 8 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index 2cdfff173b8e..197bae614745 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -28,9 +28,9 @@
 from ..ndarray import NDArray
 
 __all__ = ['zeros', 'ones', 'full', 'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power',
-           'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs', 'absolute',
-           'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2', 'log1p',
-           'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
+           'arctan2', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs',
+           'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2',
+           'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'mean',
            'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
@@ -2953,3 +2953,89 @@ def around(x, decimals=0, out=None, **kwargs):
         return _npi.around(x, decimals, out=out, **kwargs)
     else:
         raise TypeError('type {} not supported'.format(str(type(x))))
+
+
+@set_module('mxnet.ndarray.numpy')
+def arctan2(x1, x2, out=None):
+    r"""
+    arctan2(x1, x2, out=None)
+
+    Element-wise arc tangent of ``x1/x2`` choosing the quadrant correctly.
+
+    The quadrant (i.e., branch) is chosen so that ``arctan2(x1, x2)`` is
+    the signed angle in radians between the ray ending at the origin and
+    passing through the point (1,0), and the ray ending at the origin and
+    passing through the point (`x2`, `x1`).  (Note the role reversal: the
+    "`y`-coordinate" is the first function parameter, the "`x`-coordinate"
+    is the second.)  By IEEE convention, this function is defined for
+    `x2` = +/-0 and for either or both of `x1` and `x2` = +/-inf (see
+    Notes for specific values).
+
+    This function is not defined for complex-valued arguments; for the
+    so-called argument of complex values, use `angle`.
+
+    Parameters
+    ----------
+    x1 : ndarray or scalar
+        `y`-coordinates.
+    x2 : ndarray or scalar
+        `x`-coordinates. `x2` must be broadcastable to match the shape of
+        `x1` or vice versa.
+    out : ndarray or None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned.
+
+    Returns
+    -------
+    out : ndarray or scalar
+        Array of angles in radians, in the range ``[-pi, pi]``. This is a scalar if
+        `x1` and `x2` are scalars.
+
+    Notes
+    -----
+    *arctan2* is identical to the `atan2` function of the underlying
+    C library.  The following special values are defined in the C
+    standard: [1]_
+
+    ====== ====== ================
+    `x1`   `x2`   `arctan2(x1,x2)`
+    ====== ====== ================
+    +/- 0  +0     +/- 0
+    +/- 0  -0     +/- pi
+        > 0   +/-inf +0 / +pi
+        < 0   +/-inf -0 / -pi
+    +/-inf +inf   +/- (pi/4)
+    +/-inf -inf   +/- (3*pi/4)
+    ====== ====== ================
+
+    Note that +0 and -0 are distinct floating point numbers, as are +inf
+    and -inf.
+
+    This function differs from the original numpy.arange in the following aspects:
+        - Only support float16, float32 and float64.
+
+    References
+    ----------
+    .. [1] ISO/IEC standard 9899:1999, "Programming language C."
+
+    Examples
+    --------
+    Consider four points in different quadrants:
+
+    >>> x = np.array([-1, +1, +1, -1])
+    >>> y = np.array([-1, -1, +1, +1])
+    >>> np.arctan2(y, x) * 180 / np.pi
+    array([-135.,  -45.,   45.,  135.])
+
+    Note the order of the parameters. `arctan2` is defined also when `x2` = 0
+    and at several other special points, obtaining values in
+    the range ``[-pi, pi]``:
+
+    >>> x = np.array([1, -1])
+    >>> y = np.array([0, 0])
+    >>> np.arctan2(x, y)
+    array([ 1.5707964, -1.5707964])
+    """
+    return _ufunc_helper(x1, x2, _npi.arctan2, _np.arctan2,
+                         _npi.arctan2_scalar, _npi.rarctan2_scalar, out=out)
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index e0c7a67d4715..0cd90365be02 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -47,13 +47,13 @@
 from ..ndarray.numpy import _internal as _npi
 
 __all__ = ['ndarray', 'empty', 'array', 'zeros', 'ones', 'full', 'add', 'subtract', 'multiply', 'divide',
-           'mod', 'remainder', 'power', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt',
-           'abs', 'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log',
+           'mod', 'remainder', 'power', 'arctan2', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10',
+           'sqrt', 'cbrt', 'abs', 'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log',
            'degrees', 'log2', 'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative',
            'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh',
            'tensordot', 'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate',
            'stack', 'vstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices',
-           'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around']
+           'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'arctan2']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -4481,3 +4481,88 @@ def around(x, decimals=0, out=None, **kwargs):
     array([ 0,  0,  0, 10])
     """
     return _mx_nd_np.around(x, decimals, out=out, **kwargs)
+
+
+@set_module('mxnet.numpy')
+def arctan2(x1, x2, out=None):
+    r"""
+    arctan2(x1, x2, out=None)
+
+    Element-wise arc tangent of ``x1/x2`` choosing the quadrant correctly.
+
+    The quadrant (i.e., branch) is chosen so that ``arctan2(x1, x2)`` is
+    the signed angle in radians between the ray ending at the origin and
+    passing through the point (1,0), and the ray ending at the origin and
+    passing through the point (`x2`, `x1`).  (Note the role reversal: the
+    "`y`-coordinate" is the first function parameter, the "`x`-coordinate"
+    is the second.)  By IEEE convention, this function is defined for
+    `x2` = +/-0 and for either or both of `x1` and `x2` = +/-inf (see
+    Notes for specific values).
+
+    This function is not defined for complex-valued arguments; for the
+    so-called argument of complex values, use `angle`.
+
+    Parameters
+    ----------
+    x1 : ndarray or scalar
+        `y`-coordinates.
+    x2 : ndarray or scalar
+        `x`-coordinates. `x2` must be broadcastable to match the shape of
+        `x1` or vice versa.
+    out : ndarray or None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned.
+
+    Returns
+    -------
+    out : ndarray or scalar
+        Array of angles in radians, in the range ``[-pi, pi]``. This is a scalar if
+        `x1` and `x2` are scalars.
+
+    Notes
+    -----
+    *arctan2* is identical to the `atan2` function of the underlying
+    C library.  The following special values are defined in the C
+    standard: [1]_
+
+    ====== ====== ================
+    `x1`   `x2`   `arctan2(x1,x2)`
+    ====== ====== ================
+    +/- 0  +0     +/- 0
+    +/- 0  -0     +/- pi
+        > 0   +/-inf +0 / +pi
+        < 0   +/-inf -0 / -pi
+    +/-inf +inf   +/- (pi/4)
+    +/-inf -inf   +/- (3*pi/4)
+    ====== ====== ================
+
+    Note that +0 and -0 are distinct floating point numbers, as are +inf
+    and -inf.
+
+    This function differs from the original numpy.arange in the following aspects:
+        - Only support float16, float32 and float64.
+
+    References
+    ----------
+    .. [1] ISO/IEC standard 9899:1999, "Programming language C."
+
+    Examples
+    --------
+    Consider four points in different quadrants:
+
+    >>> x = np.array([-1, +1, +1, -1])
+    >>> y = np.array([-1, -1, +1, +1])
+    >>> np.arctan2(y, x) * 180 / np.pi
+    array([-135.,  -45.,   45.,  135.])
+
+    Note the order of the parameters. `arctan2` is defined also when `x2` = 0
+    and at several other special points, obtaining values in
+    the range ``[-pi, pi]``:
+
+    >>> x = np.array([1, -1])
+    >>> y = np.array([0, 0])
+    >>> np.arctan2(x, y)
+    array([ 1.5707964, -1.5707964])
+    """
+    return _mx_nd_np.arctan2(x1, x2, out=out)
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 1bfbba209989..94a4a37d273e 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -29,8 +29,8 @@
 from .._internal import _set_np_symbol_class
 from . import _internal as _npi
 
-__all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power', 'sin',
-           'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs', 'absolute', 'exp',
+__all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power', 'arctan2',
+           'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs', 'absolute', 'exp',
            'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2', 'log1p',
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
@@ -3172,4 +3172,72 @@ def around(x, decimals=0, out=None, **kwargs):
         raise TypeError('type {} not supported'.format(str(type(x))))
 
 
+@set_module('mxnet.symbol.numpy')
+def arctan2(x1, x2, out=None):
+    r"""
+    arctan2(x1, x2, out=None)
+
+    Element-wise arc tangent of ``x1/x2`` choosing the quadrant correctly.
+
+    The quadrant (i.e., branch) is chosen so that ``arctan2(x1, x2)`` is
+    the signed angle in radians between the ray ending at the origin and
+    passing through the point (1,0), and the ray ending at the origin and
+    passing through the point (`x2`, `x1`).  (Note the role reversal: the
+    "`y`-coordinate" is the first function parameter, the "`x`-coordinate"
+    is the second.)  By IEEE convention, this function is defined for
+    `x2` = +/-0 and for either or both of `x1` and `x2` = +/-inf (see
+    Notes for specific values).
+
+    This function is not defined for complex-valued arguments; for the
+    so-called argument of complex values, use `angle`.
+
+    Parameters
+    ----------
+    x1 : _Symbol or scalar
+        `y`-coordinates.
+    x2 : _Symbol or scalar
+        `x`-coordinates. `x2` must be broadcastable to match the shape of
+        `x1` or vice versa.
+    out : _Symbol or None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned.
+
+    Returns
+    -------
+    out : _Symbol or scalar
+        Array of angles in radians, in the range ``[-pi, pi]``. This is a scalar if
+        `x1` and `x2` are scalars.
+
+    Notes
+    -----
+    *arctan2* is identical to the `atan2` function of the underlying
+    C library.  The following special values are defined in the C
+    standard: [1]_
+
+    ====== ====== ================
+    `x1`   `x2`   `arctan2(x1,x2)`
+    ====== ====== ================
+    +/- 0  +0     +/- 0
+    +/- 0  -0     +/- pi
+        > 0   +/-inf +0 / +pi
+        < 0   +/-inf -0 / -pi
+    +/-inf +inf   +/- (pi/4)
+    +/-inf -inf   +/- (3*pi/4)
+    ====== ====== ================
+
+    Note that +0 and -0 are distinct floating point numbers, as are +inf
+    and -inf.
+
+    This function differs from the original numpy.arange in the following aspects:
+        - Only support float16, float32 and float64.
+
+    References
+    ----------
+    .. [1] ISO/IEC standard 9899:1999, "Programming language C."
+    """
+    return _ufunc_helper(x1, x2, _npi.arctan2, _np.arctan2,
+                         _npi.arctan2_scalar, _npi.rarctan2_scalar, out=out)
+
+
 _set_np_symbol_class(_Symbol)
diff --git a/src/operator/math_functions-inl.h b/src/operator/math_functions-inl.h
index 45d74a62d8bd..5f95654cad37 100644
--- a/src/operator/math_functions-inl.h
+++ b/src/operator/math_functions-inl.h
@@ -125,6 +125,8 @@ MXNET_BINARY_MATH_FUNC(hypot)
 
 MXNET_BINARY_MATH_FUNC(pow)
 
+MXNET_BINARY_MATH_FUNC(atan2)
+
 template<typename DType> MSHADOW_XINLINE
 float id(DType a) {
   return static_cast<float>(a);
diff --git a/src/operator/mshadow_op.h b/src/operator/mshadow_op.h
index f3d24b2c119e..6261638c03ec 100644
--- a/src/operator/mshadow_op.h
+++ b/src/operator/mshadow_op.h
@@ -322,6 +322,16 @@ MXNET_BINARY_MATH_OP(rpower, math::pow(b, a));
 
 MXNET_BINARY_MATH_OP(rpower_grad, math::id(a) * math::log(b));
 
+MXNET_BINARY_MATH_OP(arctan2, math::atan2(a, b));
+
+MXNET_BINARY_MATH_OP(arctan2_grad, math::id(b) / (math::id(a * a + b * b)));
+
+MXNET_BINARY_MATH_OP(arctan2_rgrad, -math::id(a) / (math::id(a * a + b * b)));
+
+MXNET_BINARY_MATH_OP(rarctan2, math::atan2(b, a));
+
+MXNET_BINARY_MATH_OP(rarctan2_grad, math::id(a) / (math::id(a * a + b * b)));
+
 MXNET_UNARY_MATH_OP_NC(nt, a != DType(0) ? DType(0) : DType(1));
 
 MXNET_BINARY_MATH_OP_NC(ge, a >= b ? DType(1) : DType(0));
diff --git a/src/operator/numpy/np_elemwise_broadcast_op.cc b/src/operator/numpy/np_elemwise_broadcast_op.cc
index a9254e8a7d02..f9293ee35a60 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op.cc
+++ b/src/operator/numpy/np_elemwise_broadcast_op.cc
@@ -144,5 +144,75 @@ MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_backward_npi_rcopysign_scalar)
 .set_attr<FCompute>("FCompute<cpu>",
                     BinaryScalarOp::Backward<cpu, mshadow_op::rcopysign_grad>);
 
+inline bool IsFloatType(const int dtype) {
+  return (dtype == mshadow::kFloat16 ||
+          dtype == mshadow::kFloat32 ||
+          dtype == mshadow::kFloat64);
+}
+
+inline bool Arctan2OpType(const nnvm::NodeAttrs& attrs,
+                          std::vector<int>* in_attrs,
+                          std::vector<int>* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 2U);
+  CHECK_EQ(out_attrs->size(), 1U);
+
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(1));
+  TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
+  TYPE_ASSIGN_CHECK(*in_attrs, 1, out_attrs->at(0));
+  // check if it is float16, float32 or float64. If not, raise error.
+  CHECK(IsFloatType(in_attrs->at(0))) << "Do not support `int` as input.\n";
+  return out_attrs->at(0) != -1;
+}
+
+NNVM_REGISTER_OP(_npi_arctan2)
+.set_num_inputs(2)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"x1", "x2"};
+  })
+.set_attr<mxnet::FInferShape>("FInferShape", BinaryBroadcastShape)
+.set_attr<nnvm::FInferType>("FInferType", Arctan2OpType)
+.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, mshadow_op::arctan2>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_arctan2"})
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::pair<int, int> >{{0, 0}};
+  })
+.add_argument("x1", "NDArray-or-Symbol", "The input array")
+.add_argument("x2", "NDArray-or-Symbol", "The input array");
+
+NNVM_REGISTER_OP(_backward_npi_arctan2)
+.set_num_inputs(3)
+.set_num_outputs(2)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastBackwardUseIn<cpu, mshadow_op::arctan2_grad,
+                                                                  mshadow_op::arctan2_rgrad>);
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_arctan2_scalar)
+.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::arctan2>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_arctan2_scalar"});
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_rarctan2_scalar)
+.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::rarctan2>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_rarctan2_scalar"});
+
+MXNET_OPERATOR_REGISTER_BINARY(_backward_npi_arctan2_scalar)
+.add_argument("scalar", "float", "scalar value")
+.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = std::stod(attrs->dict["scalar"]); })
+.set_attr<FCompute>("FCompute<cpu>",
+                    BinaryScalarOp::Backward<cpu, mshadow_op::arctan2_grad>);
+
+MXNET_OPERATOR_REGISTER_BINARY(_backward_npi_rarctan2_scalar)
+.add_argument("scalar", "float", "scalar value")
+.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = std::stod(attrs->dict["scalar"]); })
+.set_attr<FCompute>("FCompute<cpu>",
+                    BinaryScalarOp::Backward<cpu, mshadow_op::arctan2_rgrad>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/np_elemwise_broadcast_op.cu b/src/operator/numpy/np_elemwise_broadcast_op.cu
index ecf8e8531334..ab76e5c6fd7d 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op.cu
+++ b/src/operator/numpy/np_elemwise_broadcast_op.cu
@@ -49,6 +49,13 @@ NNVM_REGISTER_OP(_backward_npi_copysign)
 .set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastBackwardUseIn<gpu, mshadow_op::copysign_grad,
                                                                   mshadow_op::copysign_rgrad>);
 
+NNVM_REGISTER_OP(_npi_arctan2)
+.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, mshadow_op::arctan2>);
+
+NNVM_REGISTER_OP(_backward_npi_arctan2)
+.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastBackwardUseIn<gpu, mshadow_op::arctan2_grad,
+                                                                  mshadow_op::arctan2_rgrad>);
+
 NNVM_REGISTER_OP(_npi_add_scalar)
 .set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, op::mshadow_op::plus>);
 
@@ -87,5 +94,17 @@ NNVM_REGISTER_OP(_backward_npi_rcopysign_scalar)
 .set_attr<FCompute>("FCompute<gpu>",
                     BinaryScalarOp::Backward<gpu, mshadow_op::rcopysign_grad>);
 
+NNVM_REGISTER_OP(_npi_arctan2_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::arctan2>);
+
+NNVM_REGISTER_OP(_backward_npi_arctan2_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::arctan2_grad>);
+
+NNVM_REGISTER_OP(_npi_rarctan2_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::rarctan2>);
+
+NNVM_REGISTER_OP(_backward_npi_rarctan2_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::rarctan2_grad>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/operator_tune.cc b/src/operator/operator_tune.cc
index 51595254cafc..1d644386cdbb 100644
--- a/src/operator/operator_tune.cc
+++ b/src/operator/operator_tune.cc
@@ -333,6 +333,11 @@ IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::rcopysign);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::copysign_grad);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::copysign_rgrad);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::rcopysign_grad);  // NOLINT()
+IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::arctan2);  // NOLINT()
+IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::rarctan2);  // NOLINT()
+IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::arctan2_grad);  // NOLINT()
+IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::rarctan2_grad);  // NOLINT()
+IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::arctan2_rgrad);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::xelu_grad); // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::gelu_grad); // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::prelu_grad); // NOLINT()
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 1324af03d1b5..3d30012750e9 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -2380,6 +2380,73 @@ def hybrid_forward(self, F, x):
                     assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)    
 
 
+@with_seed()
+@use_np
+def test_np_arctan2():
+    class TestArctan2(HybridBlock):
+        def __init__(self):
+            super(TestArctan2, self).__init__()
+
+        def hybrid_forward(self, F, x1, x2):
+            return F.np.arctan2(x1, x2)
+
+    # Reduce dimension of src to dimension of des.
+    def dimReduce(src, des):
+        srcShape = src.shape
+        desShape = des.shape
+        if len(desShape) == 0:
+            return src.sum()
+        redu = []
+        for i, j in zip(range(len(srcShape)-1, -1, -1), range(len(desShape)-1, -1, -1)):
+            if srcShape[i] != desShape[j] and desShape[j] == 1:
+                redu.append(i)
+            if j == 0:
+                for k in range(0, i):
+                    redu.append(k)
+                break
+        if len(redu) > 0:
+            src = _np.reshape(src.sum(axis=tuple(redu)), desShape)
+        return src
+
+    types = ['float64', 'float32', 'float16']
+    for hybridize in [True, False]:
+        for shape1, shape2 in [[(3, 2), (3, 2)],  # tall matrices
+                               [(), ()],  # scalar only
+                               [(3, 0, 2), (3, 0, 2)],  # zero-dim
+                               [(3, 4, 5), (4, 1)],  # trailing dim broadcasting
+                               [(3, 4, 5), ()],  # scalar broadcasting
+                               [(), (1, 2, 3)],  # scalar broadcasting
+                               ]:
+            for oneType in types:
+                rtol = 1e-2 if oneType == 'float16' else 1e-3
+                atol = 1e-2 if oneType == 'float16' else 1e-5
+                test_arctan2 = TestArctan2()
+                if hybridize:
+                    test_arctan2.hybridize()
+                x1 = rand_ndarray(shape1, dtype=oneType).as_np_ndarray()
+                x2 = rand_ndarray(shape2, dtype=oneType).as_np_ndarray()
+                x11 = x1.asnumpy()
+                x21 = x2.asnumpy()
+                x1.attach_grad()
+                x2.attach_grad()
+                np_out = _np.arctan2(x1.asnumpy(), x2.asnumpy())
+                with mx.autograd.record():
+                    mx_out = test_arctan2(x1, x2)
+                assert mx_out.shape == np_out.shape
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
+                mx_out.backward()
+                np_backward_1 = x21 / (x11 * x11 + x21 * x21)
+                np_backward_2 = -1 * x11 / (x11 * x11 + x21 * x21)
+                np_backward_1 = dimReduce(np_backward_1, x11)
+                np_backward_2 = dimReduce(np_backward_2, x21)
+                assert_almost_equal(x1.grad.asnumpy(), np_backward_1, rtol=rtol, atol=atol)
+                assert_almost_equal(x2.grad.asnumpy(), np_backward_2, rtol=rtol, atol=atol)
+
+                mx_out = np.arctan2(x1, x2)
+                np_out = _np.arctan2(x1.asnumpy(), x2.asnumpy())
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From ae2f1bb7525ada28ae6002bdeeffbbde7fdfda37 Mon Sep 17 00:00:00 2001
From: tingying <tingying2019@u.northwestern.edu>
Date: Mon, 23 Sep 2019 13:43:14 +0800
Subject: [PATCH 362/813] numpy operator nonzero (#15838)

* add cpu test and handle 0-dim

* add FGradient with MakeZeroGradNodes

* handle 0-dim and 0-shape and add test on gpu

* add doc

* fix bug in review

* do not use thrust::inclusive_scan on cpu

* fix format error

* edit test and remove gpu test

The output is same as numpy.transpose(numpy.nonzero(x))

* fix error of review

* edit test
---
 python/mxnet/_numpy_op_doc.py          |  48 +++++++++
 src/operator/numpy/np_nonzero_op-inl.h |  65 +++++++++++++
 src/operator/numpy/np_nonzero_op.cc    | 129 ++++++++++++++++++++++++
 src/operator/numpy/np_nonzero_op.cu    | 130 +++++++++++++++++++++++++
 tests/python/unittest/test_numpy_op.py |  32 ++++++
 5 files changed, 404 insertions(+)
 create mode 100644 src/operator/numpy/np_nonzero_op-inl.h
 create mode 100644 src/operator/numpy/np_nonzero_op.cc
 create mode 100644 src/operator/numpy/np_nonzero_op.cu

diff --git a/python/mxnet/_numpy_op_doc.py b/python/mxnet/_numpy_op_doc.py
index f4787be559a1..6d2776e98bc9 100644
--- a/python/mxnet/_numpy_op_doc.py
+++ b/python/mxnet/_numpy_op_doc.py
@@ -109,7 +109,55 @@ def _np_cumsum(a, axis=None, dtype=None, out=None):
     >>> np.cumsum(a,axis=1)      # sum over columns for each of the 2 rows
     array([[ 1,  3,  6],
            [ 4,  9, 15]])
+    """
+    pass
+
+
+def _npx_nonzero(a):
+    """
+    nonzero(a)
+
+    Return the indices of the elements that are non-zero.
+
+    Returns a ndarray with ndim is 2. Each row contains the indices 
+    of the non-zero elements. The values in `a` are always tested and returned in
+    row-major, C-style order.
+
+    The result of this is always a 2-D array, with a row for
+    each non-zero element.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array.
+
+    Returns
+    -------
+    array : ndarray
+        Indices of elements that are non-zero.
 
+    Notes
+    -----
+    This function differs from the original numpy.prod in the following aspects:
+        - Do not support python numeric.
+        - The return value is same as numpy.transpose(numpy.nonzero(a)).
+
+    Examples
+    --------
+    >>> x = np.array([[3, 0, 0], [0, 4, 0], [5, 6, 0]])
+    >>> x
+    array([[3, 0, 0],
+           [0, 4, 0],
+           [5, 6, 0]])
+    >>> npx.nonzero(x)
+    array([[0, 0],
+           [1, 1],
+           [2, 0],
+           [2, 1]], dtype=int64)
+
+    >>> np.transpose(npx.nonzero(x))
+    array([[0, 1, 2, 2],
+           [0, 1, 0, 1]], dtype=int64)
     """
     pass
 
diff --git a/src/operator/numpy/np_nonzero_op-inl.h b/src/operator/numpy/np_nonzero_op-inl.h
new file mode 100644
index 000000000000..88929c43e5b5
--- /dev/null
+++ b/src/operator/numpy/np_nonzero_op-inl.h
@@ -0,0 +1,65 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+/*!
+ * Copyright (c) 2018 by Contributors
+ * \file np_nonzero_op-inl.h
+*/
+
+#ifndef MXNET_OPERATOR_NUMPY_NP_NONZERO_OP_INL_H_
+#define MXNET_OPERATOR_NUMPY_NP_NONZERO_OP_INL_H_
+
+#include <dmlc/logging.h>
+#include <dmlc/parameter.h>
+#include <mxnet/operator.h>
+#include <mxnet/ndarray.h>
+#include <map>
+#include <vector>
+#include <string>
+#include <utility>
+#include <algorithm>
+#include "../operator_common.h"
+#include "../mxnet_op.h"
+#include "../tensor/init_op.h"
+#include "../mshadow_op.h"
+#include "../elemwise_op_common.h"
+
+namespace mxnet {
+namespace op {
+
+struct NonzeroForwardKernel {
+  template<int ndim>
+  MSHADOW_XINLINE static void Map(int i,
+                                  int64_t* out,
+                                  const int32_t* idx,
+                                  const mshadow::Shape<ndim> shape) {
+    int32_t prev = (i == 0) ? 0 : idx[i - 1];
+    int32_t curr = idx[i];
+    if (prev != curr) {
+      mshadow::Shape<ndim> coord = mxnet_op::unravel<ndim>(i, shape);
+      for (int j = 0; j < ndim; j++) {
+        out[prev * ndim + j] = coord[j];
+      }
+    }
+  }
+};
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_NUMPY_NP_NONZERO_OP_INL_H_
diff --git a/src/operator/numpy/np_nonzero_op.cc b/src/operator/numpy/np_nonzero_op.cc
new file mode 100644
index 000000000000..00f9081ba984
--- /dev/null
+++ b/src/operator/numpy/np_nonzero_op.cc
@@ -0,0 +1,129 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+/*!
+ * Copyright (c) 2018 by Contributors
+ * \file np_nonzero_op.cc
+*/
+#include "np_nonzero_op-inl.h"
+
+namespace mxnet {
+namespace op {
+
+bool NonzeroType(const nnvm::NodeAttrs& attrs,
+                 std::vector<int> *in_attrs,
+                 std::vector<int> *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1);
+  CHECK_EQ(out_attrs->size(), 1);
+  // Output must be int64.
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::kInt64);
+  return out_attrs->at(0) != -1;
+}
+
+#define MAXDIM 5
+
+bool NonzeroStorageType(const nnvm::NodeAttrs& attrs,
+                        const int dev_mask,
+                        DispatchMode* dispatch_mode,
+                        std::vector<int> *in_attrs,
+                        std::vector<int> *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1);
+  CHECK_EQ(out_attrs->size(), 1);
+  for (int &attr : *in_attrs) {
+    CHECK_EQ(attr, kDefaultStorage) << "Only default storage is supported";
+  }
+  for (int &attr : *out_attrs) {
+    attr = kDefaultStorage;
+  }
+  *dispatch_mode = DispatchMode::kFComputeEx;
+  return true;
+}
+
+void NonzeroForwardCPU(const nnvm::NodeAttrs& attrs,
+                       const OpContext &ctx,
+                       const std::vector<NDArray> &inputs,
+                       const std::vector<OpReqType> &req,
+                       const std::vector<NDArray> &outputs) {
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 1U);
+  const NDArray &in = inputs[0];
+  const NDArray &out = outputs[0];
+  CHECK_LE(in.shape().ndim(), MAXDIM) << "ndim of input cannot larger than " << MAXDIM;
+  // 0-dim
+  if (0 == in.shape().ndim()) {
+    MSHADOW_TYPE_SWITCH(in.dtype(), DType, {
+      DType* in_dptr = in.data().dptr<DType>();
+      if (*in_dptr) {
+        mxnet::TShape s(2, 1);
+        const_cast<NDArray &>(out).Init(s);
+        *(out.data().dptr<int64_t>()) = 0;
+      } else {
+        mxnet::TShape s(2, 1);
+        s[0] = 0;
+        const_cast<NDArray &>(out).Init(s);
+      }
+    });
+    return;
+  }
+  size_t in_size = in.shape().Size();
+  // 0-shape
+  if (0 == in_size) {
+    mxnet::TShape s(2, in.shape().ndim());
+    s[0] = 0;
+    const_cast<NDArray &>(out).Init(s);
+    return;
+  }
+  std::vector<int32_t> prefix_sum(in_size, 0);
+  size_t valid_num = 0;
+  // Calculate prefix sum
+  MSHADOW_TYPE_SWITCH(in.dtype(), DType, {
+    DType* in_dptr = in.data().dptr<DType>();
+    for (size_t i = 0; i < in_size; i++) {
+      prefix_sum[i] = (i == 0) ? 0 : prefix_sum[i - 1];
+      prefix_sum[i] += (in_dptr[i]) ? 1 : 0;
+    }
+  });
+  valid_num = prefix_sum[in_size - 1];
+  // set the output shape forcefully
+  mxnet::TShape s(2, in.shape().ndim());
+  s[0] = valid_num;
+  const_cast<NDArray &>(out).Init(s);
+  // get the shape from the input
+  MXNET_NDIM_SWITCH(in.shape().ndim(), ndim, {
+    mshadow::Shape<ndim> shape = in.shape().get<ndim>();
+    mshadow::Stream<cpu> *stream = ctx.get_stream<cpu>();
+    mxnet_op::Kernel<NonzeroForwardKernel, cpu>::Launch(
+      stream, in_size, out.data().dptr<int64_t>(), prefix_sum.data(), shape);
+  })
+}
+
+NNVM_REGISTER_OP(_npx_nonzero)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"x"};
+  })
+.set_attr<nnvm::FInferType>("FInferType", NonzeroType)
+.set_attr<FComputeEx>("FComputeEx<cpu>", NonzeroForwardCPU)
+.set_attr<FInferStorageType>("FInferStorageType", NonzeroStorageType)
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.add_argument("x", "NDArray-or-Symbol", "The input array.");
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_nonzero_op.cu b/src/operator/numpy/np_nonzero_op.cu
new file mode 100644
index 000000000000..33925ea2e156
--- /dev/null
+++ b/src/operator/numpy/np_nonzero_op.cu
@@ -0,0 +1,130 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+/*!
+ * Copyright (c) 2018 by Contributors
+ * \file np_nonzero_op.cu
+*/
+
+#include "np_nonzero_op-inl.h"
+#include <cub/cub.cuh>
+
+namespace mxnet {
+namespace op {
+
+struct PrefixSumInit {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int i,
+                                  int32_t* out,
+                                  DType* in) {
+    if (in[i]) {
+      out[i] = 1;
+    } else {
+      out[i] = 0;
+    }
+  }
+};
+
+#define MAXDIM 5
+
+void NonzeroForwardGPU(const nnvm::NodeAttrs& attrs,
+                       const OpContext &ctx,
+                       const std::vector<NDArray> &inputs,
+                       const std::vector<OpReqType> &req,
+                       const std::vector<NDArray> &outputs) {
+  using namespace mshadow;
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 1U);
+  const NDArray &in = inputs[0];
+  const NDArray &out = outputs[0];
+  CHECK_LE(in.shape().ndim(), MAXDIM) << "ndim of input cannot larger than " << MAXDIM;
+  size_t in_size = in.shape().Size();
+  // 0-shape
+  if (0 == in_size) {
+    mxnet::TShape s(2, in.shape().ndim());
+    s[0] = 0;
+    const_cast<NDArray &>(out).Init(s);
+    return;
+  }
+  int32_t valid_num = 0;
+  Stream<gpu>* stream = ctx.get_stream<gpu>();
+  int32_t* prefix_sum = nullptr;
+  void* d_temp_storage = nullptr;
+  size_t temp_storage_bytes = 0;
+  // Calculate total temporary memory size
+  cub::DeviceScan::InclusiveSum(d_temp_storage,
+                                temp_storage_bytes,
+                                prefix_sum,
+                                prefix_sum,
+                                in_size,
+                                Stream<gpu>::GetStream(stream));
+  size_t buffer_size = in_size * sizeof(int32_t);
+  temp_storage_bytes += buffer_size;
+  // Allocate memory on GPU and allocate pointer
+  Tensor<gpu, 1, char> workspace =
+    ctx.requested[0].get_space_typed<gpu, 1, char>(Shape1(temp_storage_bytes), stream);
+  prefix_sum = reinterpret_cast<int32_t*>(workspace.dptr_);
+  d_temp_storage = workspace.dptr_ + buffer_size;
+  MSHADOW_TYPE_SWITCH(in.dtype(), DType, {
+    mxnet_op::Kernel<PrefixSumInit, gpu>::Launch(
+      stream, in_size, prefix_sum, in.data().dptr<DType>());
+  });
+  // Calculate prefix sum
+  cub::DeviceScan::InclusiveSum(d_temp_storage,
+                                temp_storage_bytes,
+                                prefix_sum,
+                                prefix_sum,
+                                in_size,
+                                Stream<gpu>::GetStream(stream));
+  CUDA_CALL(cudaMemcpy(&valid_num, &prefix_sum[in_size - 1], sizeof(int32_t),
+                       cudaMemcpyDeviceToHost));
+  // 0-dim
+  if (0 == in.shape().ndim()) {
+    mxnet::TShape s(2, 1);
+    if (valid_num) {
+      const_cast<NDArray &>(out).Init(s);
+      int64_t temp = 0;
+      CUDA_CALL(cudaMemcpy(out.data().dptr<int64_t>(), &temp, sizeof(int64_t),
+                           cudaMemcpyHostToDevice));
+    } else {
+      s[0] = 0;
+      const_cast<NDArray &>(out).Init(s);
+    }
+    return;
+  }
+  // Set the output shape forcefully
+  mxnet::TShape s(2, in.shape().ndim());
+  s[0] = valid_num;
+  const_cast<NDArray &>(out).Init(s);
+  // get the shape from the input
+  MXNET_NDIM_SWITCH(in.shape().ndim(), ndim, {
+    mshadow::Shape<ndim> shape = in.shape().get<ndim>();
+    mxnet_op::Kernel<NonzeroForwardKernel, gpu>::Launch(
+      stream, in_size, out.data().dptr<int64_t>(), prefix_sum, shape);
+  })
+}
+
+NNVM_REGISTER_OP(_npx_nonzero)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<FComputeEx>("FComputeEx<gpu>", NonzeroForwardGPU);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 3d30012750e9..8d1241921d80 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -2447,6 +2447,38 @@ def dimReduce(src, des):
                 assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
 
 
+@with_seed()
+@use_np
+def test_np_nonzero():
+    class TestNonzero(HybridBlock):
+        def __init__(self):
+            super(TestNonzero, self).__init__()
+            
+        def hybrid_forward(self, F, x):
+            return F.npx.nonzero(x)
+
+    types = ['int32', 'int64', 'float64', 'float32', 'float16']
+    for hybridize in [True, False]:
+        for shape in [(), (1, 2, 3), (1, 0)]:
+            for oneType in types:
+                rtol, atol = 1e-3, 1e-5
+                test_nonzero = TestNonzero()
+                if hybridize:
+                    test_nonzero.hybridize()
+                x = rand_ndarray(shape, dtype=oneType).as_np_ndarray()
+                np_out = _np.nonzero(x.asnumpy())
+                np_out = _np.transpose(np_out)
+                mx_out = test_nonzero(x)
+                assert mx_out.shape == np_out.shape
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol, atol)
+
+                # Test imperative once again
+                mx_out = npx.nonzero(x)
+                np_out = _np.nonzero(x.asnumpy())
+                np_out = _np.transpose(np_out)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol, atol)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From bea565394fdd2efd120027f5aeff2afb2bc32312 Mon Sep 17 00:00:00 2001
From: tingying <tingying2019@u.northwestern.edu>
Date: Mon, 23 Sep 2019 14:02:38 +0800
Subject: [PATCH 363/813] numpy operator hypot (#15901)

* rebase master

* edit test

* add IsIntType to check the input type

* fix error in test

* remove hypot in doc
---
 python/mxnet/ndarray/numpy/_op.py             | 49 +++++++++++++-
 python/mxnet/numpy/multiarray.py              | 51 +++++++++++++-
 python/mxnet/symbol/numpy/_symbol.py          | 35 +++++++++-
 .../numpy/np_elemwise_broadcast_op.cc         | 49 ++++++++++++++
 .../numpy/np_elemwise_broadcast_op.cu         |  6 ++
 .../elemwise_binary_scalar_op_extended.cc     |  1 +
 tests/python/unittest/test_numpy_op.py        | 66 +++++++++++++++++++
 7 files changed, 254 insertions(+), 3 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index 197bae614745..385e2633ac00 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -34,7 +34,7 @@
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'mean',
            'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
-           'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around']
+           'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'hypot']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -3039,3 +3039,50 @@ def arctan2(x1, x2, out=None):
     """
     return _ufunc_helper(x1, x2, _npi.arctan2, _np.arctan2,
                          _npi.arctan2_scalar, _npi.rarctan2_scalar, out=out)
+
+
+@set_module('mxnet.ndarray.numpy')
+def hypot(x1, x2, out=None):
+    r"""
+    Given the "legs" of a right triangle, return its hypotenuse.
+
+    Equivalent to ``sqrt(x1**2 + x2**2)``, element-wise.  If `x1` or
+    `x2` is scalar_like (i.e., unambiguously cast-able to a scalar type),
+    it is broadcast for use with each element of the other argument.
+
+    Parameters
+    ----------
+    x1, x2 : array_like
+        Leg of the triangle(s).
+    out : ndarray, None, or tuple of ndarray and None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned. A tuple (possible only as a
+        keyword argument) must have length equal to the number of outputs.
+
+    Returns
+    -------
+    z : ndarray
+        The hypotenuse of the triangle(s).
+        This is a scalar if both `x1` and `x2` are scalars.
+
+    Notes
+    -----
+    This function differs from the original numpy.arange in the following aspects:
+	    - Only support float16, float32 and float64.
+
+    Examples
+    --------
+    >>> np.hypot(3*np.ones((3, 3)), 4*np.ones((3, 3)))
+    array([[ 5.,  5.,  5.],
+           [ 5.,  5.,  5.],
+           [ 5.,  5.,  5.]])
+
+    Example showing broadcast of scalar_like argument:
+
+    >>> np.hypot(3*np.ones((3, 3)), [4])
+    array([[ 5.,  5.,  5.],
+           [ 5.,  5.,  5.],
+           [ 5.,  5.,  5.]])
+    """
+    return _ufunc_helper(x1, x2, _npi.hypot, _np.hypot, _npi.hypot_scalar, None, out)
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 0cd90365be02..b1cb96158bf3 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -53,7 +53,7 @@
            'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh',
            'tensordot', 'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate',
            'stack', 'vstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices',
-           'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'arctan2']
+           'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'arctan2', 'hypot']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -4566,3 +4566,52 @@ def arctan2(x1, x2, out=None):
     array([ 1.5707964, -1.5707964])
     """
     return _mx_nd_np.arctan2(x1, x2, out=out)
+
+
+@set_module('mxnet.numpy')
+def hypot(x1, x2, out=None):
+    r"""
+    hypot(x1, x2, out=None)
+
+    Given the "legs" of a right triangle, return its hypotenuse.
+
+    Equivalent to ``sqrt(x1**2 + x2**2)``, element-wise.  If `x1` or
+    `x2` is scalar_like (i.e., unambiguously cast-able to a scalar type),
+    it is broadcast for use with each element of the other argument.
+
+    Parameters
+    ----------
+    x1, x2 : array_like
+        Leg of the triangle(s).
+    out : ndarray, None, or tuple of ndarray and None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned. A tuple (possible only as a
+        keyword argument) must have length equal to the number of outputs.
+
+    Returns
+    -------
+    z : ndarray
+        The hypotenuse of the triangle(s).
+        This is a scalar if both `x1` and `x2` are scalars.
+
+    Notes
+    -----
+    This function differs from the original numpy.arange in the following aspects:
+	    - Only support float16, float32 and float64.
+
+    Examples
+    --------
+    >>> np.hypot(3*np.ones((3, 3)), 4*np.ones((3, 3)))
+    array([[ 5.,  5.,  5.],
+           [ 5.,  5.,  5.],
+           [ 5.,  5.,  5.]])
+
+    Example showing broadcast of scalar_like argument:
+
+    >>> np.hypot(3*np.ones((3, 3)), [4])
+    array([[ 5.,  5.,  5.],
+           [ 5.,  5.,  5.],
+           [ 5.,  5.,  5.]])
+    """
+    return _mx_nd_np.hypot(x1, x2, out=out)
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 94a4a37d273e..b13a89c73b84 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -36,7 +36,7 @@
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'mean',
            'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
-           'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around']
+           'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'hypot']
 
 
 def _num_outputs(sym):
@@ -3240,4 +3240,37 @@ def arctan2(x1, x2, out=None):
                          _npi.arctan2_scalar, _npi.rarctan2_scalar, out=out)
 
 
+@set_module('mxnet.symbol.numpy')
+def hypot(x1, x2, out=None):
+    r"""
+    Given the "legs" of a right triangle, return its hypotenuse.
+
+    Equivalent to ``sqrt(x1**2 + x2**2)``, element-wise.  If `x1` or
+    `x2` is scalar_like (i.e., unambiguously cast-able to a scalar type),
+    it is broadcast for use with each element of the other argument.
+
+    Parameters
+    ----------
+    x1, x2 : array_like
+        Leg of the triangle(s).
+    out : ndarray, None, or tuple of ndarray and None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned. A tuple (possible only as a
+        keyword argument) must have length equal to the number of outputs.
+
+    Returns
+    -------
+    z : ndarray
+        The hypotenuse of the triangle(s).
+        This is a scalar if both `x1` and `x2` are scalars.
+
+    Notes
+    -----
+    This function differs from the original numpy.arange in the following aspects:
+        - Only support float16, float32 and float64.
+    """
+    return _ufunc_helper(x1, x2, _npi.hypot, _np.hypot, _npi.hypot_scalar, None, out)
+
+
 _set_np_symbol_class(_Symbol)
diff --git a/src/operator/numpy/np_elemwise_broadcast_op.cc b/src/operator/numpy/np_elemwise_broadcast_op.cc
index f9293ee35a60..16d4ef88f5c5 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op.cc
+++ b/src/operator/numpy/np_elemwise_broadcast_op.cc
@@ -214,5 +214,54 @@ MXNET_OPERATOR_REGISTER_BINARY(_backward_npi_rarctan2_scalar)
 .set_attr<FCompute>("FCompute<cpu>",
                     BinaryScalarOp::Backward<cpu, mshadow_op::arctan2_rgrad>);
 
+bool HypotOpType(const nnvm::NodeAttrs& attrs,
+                 std::vector<int>* in_attrs,
+                 std::vector<int>* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 2U);
+  CHECK_EQ(out_attrs->size(), 1U);
+
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(1));
+  TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
+  TYPE_ASSIGN_CHECK(*in_attrs, 1, out_attrs->at(0));
+
+  CHECK(IsFloatType(in_attrs->at(0))) << "Do not support `int` as input.\n";
+  return out_attrs->at(0) != -1;
+}
+
+// rigister hypot that do not support int here
+NNVM_REGISTER_OP(_npi_hypot)
+.set_num_inputs(2)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"x1", "x2"};
+  })
+.set_attr<mxnet::FInferShape>("FInferShape", BinaryBroadcastShape)
+.set_attr<nnvm::FInferType>("FInferType", HypotOpType)
+.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, mshadow_op::hypot>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_hypot"})
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::pair<int, int> >{{0, 0}, {1, 0}};
+  })
+.add_argument("x1", "NDArray-or-Symbol", "The input array")
+.add_argument("x2", "NDArray-or-Symbol", "The input array");
+
+NNVM_REGISTER_OP(_backward_npi_hypot)
+.set_num_inputs(3)
+.set_num_outputs(2)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::pair<int, int> > {{0, 1}};
+  })
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastBackwardUseIn<cpu, mshadow_op::hypot_grad_left,
+                                                                  mshadow_op::hypot_grad_right>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/np_elemwise_broadcast_op.cu b/src/operator/numpy/np_elemwise_broadcast_op.cu
index ab76e5c6fd7d..77525ce7acea 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op.cu
+++ b/src/operator/numpy/np_elemwise_broadcast_op.cu
@@ -55,6 +55,12 @@ NNVM_REGISTER_OP(_npi_arctan2)
 NNVM_REGISTER_OP(_backward_npi_arctan2)
 .set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastBackwardUseIn<gpu, mshadow_op::arctan2_grad,
                                                                   mshadow_op::arctan2_rgrad>);
+NNVM_REGISTER_OP(_npi_hypot)
+.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, mshadow_op::hypot>);
+
+NNVM_REGISTER_OP(_backward_npi_hypot)
+.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastBackwardUseIn<gpu, mshadow_op::hypot_grad_left,
+                                                                  mshadow_op::hypot_grad_right>);
 
 NNVM_REGISTER_OP(_npi_add_scalar)
 .set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, op::mshadow_op::plus>);
diff --git a/src/operator/tensor/elemwise_binary_scalar_op_extended.cc b/src/operator/tensor/elemwise_binary_scalar_op_extended.cc
index ba6ebccc5f01..4ada2f036f7d 100644
--- a/src/operator/tensor/elemwise_binary_scalar_op_extended.cc
+++ b/src/operator/tensor/elemwise_binary_scalar_op_extended.cc
@@ -74,6 +74,7 @@ MXNET_OPERATOR_REGISTER_BINARY(_backward_rpower_scalar)
   cpu, mshadow_op::rpower_grad>);
 
 MXNET_OPERATOR_REGISTER_BINARY_SCALAR(_hypot_scalar)
+.add_alias("_npi_hypot_scalar")
 .set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<
   cpu, mshadow_op::hypot>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{ "_backward_hypot_scalar" })
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 8d1241921d80..01fd445a4360 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -2479,6 +2479,72 @@ def hybrid_forward(self, F, x):
                 assert_almost_equal(mx_out.asnumpy(), np_out, rtol, atol)
 
 
+@with_seed()
+@use_np
+def test_np_hypot():
+    class TestHypot(HybridBlock):
+        def __init__(self):
+            super(TestHypot, self).__init__()
+
+        def hybrid_forward(self, F, x1, x2):
+            return F.np.hypot(x1, x2)
+
+    def dimReduce(src, des):
+        srcShape = src.shape
+        desShape = des.shape
+        if len(desShape) == 0:
+            return src.sum()
+        redu = []
+        for i, j in zip(range(len(srcShape)-1, -1, -1), range(len(desShape)-1, -1, -1)):
+            if srcShape[i] != desShape[j] and desShape[j] == 1:
+                redu.append(i)
+            if j == 0:
+                for k in range(0, i):
+                    redu.append(k)
+                break
+        if len(redu) > 0:
+            src = _np.reshape(src.sum(axis=tuple(redu)), desShape)
+        return src
+
+    types = ['float64', 'float32', 'float16']
+    for hybridize in [True, False]:
+        for shape1, shape2 in [[(3, 2), (3, 2)],  # tall matrices
+                               [(), ()],  # scalar only
+                               [(3, 0, 2), (3, 0, 2)],  # zero-dim
+                               [(3, 4, 5), (4, 1)],  # trailing dim broadcasting
+                               [(3, 4, 5), ()],  # scalar broadcasting
+                               [(), (1, 2, 3)],  # scalar broadcasting
+                               ]:
+            for oneType in types:
+                rtol = 1e-2 if oneType == 'float16' else 1e-3
+                atol = 1e-2 if oneType == 'float16' else 1e-5
+                test_hypot = TestHypot()
+                if hybridize:
+                    test_hypot.hybridize()
+                x1 = rand_ndarray(shape1, dtype=oneType).as_np_ndarray()
+                x2 = rand_ndarray(shape2, dtype=oneType).as_np_ndarray()
+                x11 = x1.asnumpy()
+                x21 = x2.asnumpy()
+                x1.attach_grad()
+                x2.attach_grad()
+                np_out = _np.hypot(x1.asnumpy(), x2.asnumpy())
+                with mx.autograd.record():
+                    mx_out = test_hypot(x1, x2)
+                assert mx_out.shape == np_out.shape
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
+                mx_out.backward()
+                np_backward_1 = x11 / np_out
+                np_backward_2 = x21 / np_out
+                np_backward_1 = dimReduce(np_backward_1, x11)
+                np_backward_2 = dimReduce(np_backward_2, x21)
+                assert_almost_equal(x1.grad.asnumpy(), np_backward_1, rtol=rtol, atol=atol)
+                assert_almost_equal(x2.grad.asnumpy(), np_backward_2, rtol=rtol, atol=atol)
+
+                mx_out = np.hypot(x1, x2)
+                np_out = _np.hypot(x1.asnumpy(), x2.asnumpy())
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 72b4d9b8261e0783989447ad78a09e8573aee853 Mon Sep 17 00:00:00 2001
From: tingying <tingying2019@u.northwestern.edu>
Date: Mon, 23 Sep 2019 14:15:25 +0800
Subject: [PATCH 364/813] tvm numpy operator deg2rad && rad2deg (#16015)

* fix format error

* change type

* constangt must be tvm.const in tvm and add backward test, do not support float16

* add addto test

* handle 0-dim and 0-size

* add 0-dim test case

* register to npi and add wrapper with doc

* change function name, add infer type

* fix format error

* merge rad2deg to deg2rad

* fix error according to review

* change infer type

* add TVM_OP in test
---
 contrib/tvmop/basic/ufunc.py           | 137 ++++++++++++++++++++++++-
 python/mxnet/ndarray/numpy/_op.py      |  74 ++++++++++++-
 python/mxnet/numpy/multiarray.py       |  77 +++++++++++++-
 python/mxnet/symbol/numpy/_symbol.py   |  62 ++++++++++-
 src/operator/contrib/tvmop/ufunc.cc    | 113 ++++++++++++++++++++
 tests/python/unittest/test_numpy_op.py |   9 +-
 6 files changed, 466 insertions(+), 6 deletions(-)

diff --git a/contrib/tvmop/basic/ufunc.py b/contrib/tvmop/basic/ufunc.py
index 6bb102ccf7e3..87356428428f 100644
--- a/contrib/tvmop/basic/ufunc.py
+++ b/contrib/tvmop/basic/ufunc.py
@@ -17,7 +17,7 @@
 
 # coding: utf-8
 import tvm
-from .. import defop, AllTypes
+from .. import defop, AllTypes, RealTypes
 from .. import assign_by_req, reduce_axes
 
 def compute_add(dtype, ndim):
@@ -98,3 +98,138 @@ def backward_vadd_gpu(dtype, ndim, reduce1st, req):
         s[t].bind(bx, block_x)
         s[t].bind(tx, thread_x)
     return s, [X, in_grad_a, in_grad]
+
+
+def compute_degandrad(dtype, ndim, n):
+    A = tvm.placeholder([tvm.var() for _ in range(ndim)], name='A', dtype=dtype)
+    import math
+    if n == 0:
+        B = tvm.compute([tvm.var() for _ in range(ndim)],
+                        lambda *index: A[index] * tvm.const(math.pi, dtype) / tvm.const(180, dtype), name='B')
+    else:
+        B = tvm.compute([tvm.var() for _ in range(ndim)],
+                        lambda *index: A[index] / tvm.const(math.pi, dtype) * tvm.const(180, dtype), name='B')
+    s = tvm.create_schedule(B.op)
+    return s, A, B
+
+
+@defop(name="deg2rad", target="cpu", auto_broadcast=False,
+       dtype=["float32", "float64"], ndim=list(range(0, 6)))
+def deg2rad(dtype, ndim):
+    s, A, B = compute_degandrad(dtype, ndim, 0)
+    axes = [axis for axis in B.op.axis]
+    fused = s[B].fuse(*axes)
+    s[B].parallel(fused)
+    return s, [A, B]
+
+
+@defop(name="rad2deg", target="cpu", auto_broadcast=False,
+       dtype=["float32", "float64"], ndim=list(range(0, 6)))
+def rad2deg(dtype, ndim):
+    s, A, B = compute_degandrad(dtype, ndim, 1)
+    axes = [axis for axis in B.op.axis]
+    fused = s[B].fuse(*axes)
+    s[B].parallel(fused)
+    return s, [A, B]
+
+
+@defop(name="cuda_deg2rad", target="cuda", auto_broadcast=False,
+       dtype=["float32", "float64"], ndim=list(range(0, 6)))
+def deg2rad_gpu(dtype, ndim):
+    s, A, B = compute_degandrad(dtype, ndim, 0)
+    s = tvm.create_schedule(B.op)
+    axes = [axis for axis in B.op.axis]
+    fused = s[B].fuse(*axes)
+    bx, tx = s[B].split(fused, factor=64)
+    s[B].bind(bx, tvm.thread_axis("blockIdx.x"))
+    s[B].bind(tx, tvm.thread_axis("threadIdx.x"))
+    return s, [A, B]
+
+
+@defop(name="cuda_rad2deg", target="cuda", auto_broadcast=False,
+       dtype=["float32", "float64"], ndim=list(range(0, 6)))
+def rad2deg_gpu(dtype, ndim):
+    s, A, B = compute_degandrad(dtype, ndim, 1)
+    s = tvm.create_schedule(B.op)
+    axes = [axis for axis in B.op.axis]
+    fused = s[B].fuse(*axes)
+    bx, tx = s[B].split(fused, factor=64)
+    s[B].bind(bx, tvm.thread_axis("blockIdx.x"))
+    s[B].bind(tx, tvm.thread_axis("threadIdx.x"))
+    return s, [A, B]
+
+
+def compute_backward_degandrad(dtype, ndim, req, n):
+    ishape = [tvm.var() for _ in range(ndim)]
+    in_grad_tmp = tvm.placeholder(ishape, name='in_grad_tmp', dtype=dtype)
+    in_grad = tvm.placeholder(ishape, name='in_grad', dtype=dtype)
+    out_grad = tvm.placeholder(ishape, name='out_grad', dtype=dtype)
+    import math
+    if n == 0:
+        ret = tvm.compute(ishape, lambda *index: out_grad[index] * tvm.const(math.pi, dtype) / tvm.const(180, dtype))
+    else:
+        ret = tvm.compute(ishape, lambda *index: out_grad[index] / tvm.const(math.pi, dtype) * tvm.const(180, dtype))
+    if (req == "kAddTo"):
+        in_grad = tvm.compute(ishape, lambda *index: in_grad_tmp[index] + ret[index])
+    else:
+        in_grad = tvm.compute(ishape, lambda *index: ret[index])
+    s = tvm.create_schedule(in_grad.op)
+    return s, out_grad, in_grad_tmp, in_grad, [ret, in_grad]
+
+
+@defop(name="backward_deg2rad", target="cpu", auto_broadcast=False,
+       dtype=["float32", "float64"], ndim=list(range(0, 6)), req=["kWriteTo", "kAddTo"],
+       attrs=["req"])
+def backward_deg2rad(dtype, ndim, req):
+    s, out_grad, in_grad_tmp, in_grad, c_list = compute_backward_degandrad(dtype, ndim, req, 0)
+    for t in c_list:
+        axes = [axis for axis in t.op.axis]
+        fused = s[t].fuse(*axes)
+        s[t].parallel(fused)
+    return s, [out_grad, in_grad, in_grad_tmp]
+
+
+@defop(name="backward_rad2deg", target="cpu", auto_broadcast=False,
+       dtype=["float32", "float64"], ndim=list(range(0, 6)), req=["kWriteTo", "kAddTo"],
+       attrs=["req"])
+def backward_rad2deg(dtype, ndim, req):
+    s, out_grad, in_grad_tmp, in_grad, c_list = compute_backward_degandrad(dtype, ndim, req, 1)
+    for t in c_list:
+        axes = [axis for axis in t.op.axis]
+        fused = s[t].fuse(*axes)
+        s[t].parallel(fused)
+    return s, [out_grad, in_grad, in_grad_tmp]
+
+
+@defop(name="cuda_backward_deg2rad", target="gpu", auto_broadcast=False,
+       dtype=["float32", "float64"], ndim=list(range(0, 6)), req=["kWriteTo", "kAddTo"],
+       attrs=["req"])
+def cuda_backward_deg2rad(dtype, ndim, req):
+    s, out_grad, in_grad_tmp, in_grad, c_list = compute_backward_degandrad(dtype, ndim, req, 0)
+    num_thread = 64
+    for t in c_list:
+        block_x = tvm.thread_axis("blockIdx.x")
+        thread_x = tvm.thread_axis("threadIdx.x")
+        axes = [axis for axis in t.op.axis]
+        fused = s[t].fuse(*axes)
+        bx, tx = s[t].split(fused, factor=num_thread)
+        s[t].bind(bx, block_x)
+        s[t].bind(tx, thread_x)
+    return s, [out_grad, in_grad, in_grad_tmp]
+
+
+@defop(name="cuda_backward_rad2deg", target="gpu", auto_broadcast=False,
+       dtype=["float32", "float64"], ndim=list(range(0, 6)), req=["kWriteTo", "kAddTo"],
+       attrs=["req"])
+def cuda_backward_rad2deg(dtype, ndim, req):
+    s, out_grad, in_grad_tmp, in_grad, c_list = compute_backward_degandrad(dtype, ndim, req, 1)
+    num_thread = 64
+    for t in c_list:
+        block_x = tvm.thread_axis("blockIdx.x")
+        thread_x = tvm.thread_axis("threadIdx.x")
+        axes = [axis for axis in t.op.axis]
+        fused = s[t].fuse(*axes)
+        bx, tx = s[t].split(fused, factor=num_thread)
+        s[t].bind(bx, block_x)
+        s[t].bind(tx, thread_x)
+    return s, [out_grad, in_grad, in_grad_tmp]
diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index 385e2633ac00..fd20f36fba04 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -34,7 +34,7 @@
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'mean',
            'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
-           'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'hypot']
+           'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'hypot', 'rad2deg', 'deg2rad']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -1245,6 +1245,42 @@ def degrees(x, out=None, **kwargs):
     return _unary_func_helper(x, _npi.degrees, _np.degrees, out=out, **kwargs)
 
 
+@set_module('mxnet.ndarray.numpy')
+def rad2deg(x, out=None):
+    r"""
+    rad2deg(x, out=None)
+
+    Convert angles from radians to degrees.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Angles in degrees.
+    out : ndarray or None, optional
+        A location into which the result is stored. If not provided or `None`,
+        a freshly-allocated array is returned.
+
+    Returns
+    -------
+    y : ndarray or scalar
+        The corresponding angle in radians.
+        This is a scalar if `x` is a scalar.
+
+    Notes
+    -----
+    "rad2deg(x)" is "x *180 / pi".
+
+    This function differs from the original numpy.arange in the following aspects:
+        - Only support float32 and float64.
+        - `out` must be in the same size of input.
+
+    Examples
+    --------
+    >>> np.rad2deg(np.pi/2)
+    90.0
+    """
+    return _unary_func_helper(x, _npi.rad2deg, _np.rad2deg, out=out)
+
+
 @set_module('mxnet.ndarray.numpy')
 def rint(x, out=None, **kwargs):
     """
@@ -1388,6 +1424,42 @@ def radians(x, out=None, **kwargs):
     return _unary_func_helper(x, _npi.radians, _np.radians, out=out, **kwargs)
 
 
+@set_module('mxnet.ndarray.numpy')
+def deg2rad(x, out=None):
+    r"""
+    deg2rad(x, out=None)
+
+    Convert angles from degrees to radians.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Angles in degrees.
+    out : ndarray or None, optional
+        A location into which the result is stored. If not provided or `None`,
+        a freshly-allocated array is returned.
+
+    Returns
+    -------
+    y : ndarray or scalar
+        The corresponding angle in radians.
+        This is a scalar if `x` is a scalar.
+
+    Notes
+    -----
+    "deg2rad(x)" is "x * pi / 180".
+
+    This function differs from the original numpy.arange in the following aspects:
+        - Only support float32 and float64.
+        - `out` must be in the same size of input.
+
+    Examples
+    --------
+    >>> np.deg2rad(180)
+    3.1415927
+    """
+    return _unary_func_helper(x, _npi.deg2rad, _np.deg2rad, out=out)
+
+
 @set_module('mxnet.ndarray.numpy')
 def reciprocal(x, out=None, **kwargs):
     r"""
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index b1cb96158bf3..921b51291f71 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -53,7 +53,8 @@
            'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh',
            'tensordot', 'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate',
            'stack', 'vstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices',
-           'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'arctan2', 'hypot']
+           'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'arctan2', 'hypot',
+           'rad2deg', 'deg2rad']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -2725,6 +2726,42 @@ def degrees(x, out=None, **kwargs):
     return _mx_nd_np.degrees(x, out=out, **kwargs)
 
 
+@set_module('mxnet.numpy')
+def rad2deg(x, out=None):
+    r"""
+    rad2deg(x, out=None)
+
+    Convert angles from radians to degrees.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Angles in degrees.
+    out : ndarray or None, optional
+        A location into which the result is stored. If not provided or `None`,
+        a freshly-allocated array is returned.
+
+    Returns
+    -------
+    y : ndarray or scalar
+        The corresponding angle in radians.
+        This is a scalar if `x` is a scalar.
+
+    Notes
+    -----
+    "rad2deg(x)" is "x * 180 / pi".
+
+    This function differs from the original numpy.arange in the following aspects:
+        - Only support float32 and float64.
+        - `out` must be in the same size of input.
+
+    Examples
+    --------
+    >>> np.rad2deg(np.pi/2)
+    90.0
+    """
+    return _mx_nd_np.rad2deg(x, out=out)
+
+
 @set_module('mxnet.numpy')
 def radians(x, out=None, **kwargs):
     """
@@ -2760,6 +2797,42 @@ def radians(x, out=None, **kwargs):
     return _mx_nd_np.radians(x, out=out, **kwargs)
 
 
+@set_module('mxnet.numpy')
+def deg2rad(x, out=None):
+    r"""
+    deg2rad(x, out=None)
+
+    Convert angles from degrees to radians.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Angles in degrees.
+    out : ndarray or None, optional
+        A location into which the result is stored. If not provided or `None`,
+        a freshly-allocated array is returned.
+
+    Returns
+    -------
+    y : ndarray or scalar
+        The corresponding angle in radians.
+        This is a scalar if `x` is a scalar.
+
+    Notes
+    -----
+    "deg2rad(x)" is "x * pi / 180".
+
+    This function differs from the original numpy.arange in the following aspects:
+        - Only support float32 and float64.
+        - `out` must be in the same size of input.
+
+    Examples
+    --------
+    >>> np.deg2rad(180)
+    3.1415927
+    """
+    return _mx_nd_np.deg2rad(x, out=out)
+
+
 @set_module('mxnet.numpy')
 def reciprocal(x, out=None, **kwargs):
     r"""
@@ -4598,7 +4671,7 @@ def hypot(x1, x2, out=None):
     Notes
     -----
     This function differs from the original numpy.arange in the following aspects:
-	    - Only support float16, float32 and float64.
+        - Only support float16, float32 and float64.
 
     Examples
     --------
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index b13a89c73b84..f544ab58c36d 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -36,7 +36,7 @@
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'mean',
            'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
-           'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'hypot']
+           'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'hypot', 'rad2deg', 'deg2rad']
 
 
 def _num_outputs(sym):
@@ -1715,6 +1715,36 @@ def degrees(x, out=None, **kwargs):
     return _unary_func_helper(x, _npi.degrees, _np.degrees, out=out, **kwargs)
 
 
+@set_module('mxnet.symbol.numpy')
+def rad2deg(x, out=None):
+    r"""
+    rad2deg(x, out=None)
+
+    Convert angles from radians to degrees.
+    Parameters
+    ----------
+    x : _Symbol or scalar
+        Angles in degrees.
+    out : _Symbol or None, optional
+        A location into which the result is stored.
+
+    Returns
+    -------
+    y : _Symbol or scalar
+        The corresponding angle in radians.
+        This is a scalar if `x` is a scalar.
+
+    Notes
+    -----
+    "rad2deg(x)" is "x * 180 / pi".
+
+    This function differs from the original numpy.arange in the following aspects:
+        - Only support float32 and float64.
+        - `out` must be in the same size of input.
+    """
+    return _unary_func_helper(x, _npi.rad2deg, _np.rad2deg, out=out)
+
+
 def rint(x, out=None, **kwargs):
     """
     Round elements of the array to the nearest integer.
@@ -1840,6 +1870,36 @@ def radians(x, out=None, **kwargs):
     return _unary_func_helper(x, _npi.radians, _np.radians, out=out, **kwargs)
 
 
+@set_module('mxnet.symbol.numpy')
+def deg2rad(x, out=None):
+    r"""
+    deg2rad(x, out=None)
+
+    Convert angles from degrees to radians.
+    Parameters
+    ----------
+    x : _Symbol or scalar
+        Angles in degrees.
+    out : _Symbol or None, optional
+        A location into which the result is stored.
+
+    Returns
+    -------
+    y : _Symbol or scalar
+        The corresponding angle in radians.
+        This is a scalar if `x` is a scalar.
+
+    Notes
+    -----
+    "deg2rad(x)" is "x * pi / 180".
+
+    This function differs from the original numpy.arange in the following aspects:
+        - Only support float32 and float64.
+        - `out` must be in the same size of input.
+    """
+    return _unary_func_helper(x, _npi.deg2rad, _np.deg2rad, out=out)
+
+
 @set_module('mxnet.symbol.numpy')
 def reciprocal(x, out=None, **kwargs):
     r"""
diff --git a/src/operator/contrib/tvmop/ufunc.cc b/src/operator/contrib/tvmop/ufunc.cc
index 89a90c022845..e6f5653022e0 100644
--- a/src/operator/contrib/tvmop/ufunc.cc
+++ b/src/operator/contrib/tvmop/ufunc.cc
@@ -144,6 +144,119 @@ NNVM_REGISTER_OP(_backward_contrib_tvm_vadd)
     .set_attr<mxnet::FCompute>("FCompute<cpu>",
                                mxnet::op::TVMBinaryBackwardComputeUseNone<func_bakcward_vadd_cpu>);
 
+inline bool DegandradOpType(const nnvm::NodeAttrs& attrs,
+                            std::vector<int>* in_attrs,
+                            std::vector<int>* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
+  TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
+  CHECK(in_attrs->at(0) == mshadow::kFloat64 ||
+        in_attrs->at(0) == mshadow::kFloat32)
+    << "Only support float32 and float64.";
+  return out_attrs->at(0) != -1;
+}
+
+template<const char* func>
+void TVMUnaryCompute(const nnvm::NodeAttrs& attrs,
+                     const mxnet::OpContext& ctx,
+                     const std::vector<TBlob>& inputs,
+                     const std::vector<OpReqType>& req,
+                     const std::vector<TBlob>& outputs) {
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 1U);
+  if (0 == inputs[0].shape_.Size()) {
+    //  0-size
+    return;
+  }
+  tvm::runtime::TVMOpModule::Get()->Call(func, ctx, {inputs[0], outputs[0]});
+}
+
+template<const char* func>
+void TVMUnaryBackwardComputeUseNone(const nnvm::NodeAttrs& attrs,
+                                    const mxnet::OpContext& ctx,
+                                    const std::vector<TBlob>& inputs,
+                                    const std::vector<OpReqType>& req,
+                                    const std::vector<TBlob>& outputs) {
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 1U);
+  if (0 == inputs[0].shape_.Size()) {
+    //  0-size
+    return;
+  }
+  std::string funcname = func;
+  funcname += "req_";
+  MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
+    if (req_type == kWriteTo) {
+      funcname += "kWriteTo";
+    } else {
+      funcname += "kAddTo";
+    }
+    tvm::runtime::TVMOpModule::Get()->Call(funcname, ctx,
+      {inputs[0], outputs[0], outputs[0]});
+  })
+}
+
+static constexpr char func_deg2rad_cpu[] = "deg2rad";
+static constexpr char func_deg2rad_gpu[] = "cuda_deg2rad";
+static constexpr char func_backward_deg2rad_cpu[] = "backward_deg2rad";
+static constexpr char func_backward_deg2rad_gpu[] = "cuda_backward_deg2rad";
+
+NNVM_REGISTER_OP(_npi_deg2rad)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+[](const NodeAttrs& attrs) {
+  return std::vector<std::string>{"data"};
+})
+.set_attr<mxnet::FInferShape>("FInferShape", mxnet::op::ElemwiseShape<1, 1>)
+.set_attr<nnvm::FInferType>("FInferType", mxnet::op::DegandradOpType)
+#if MXNET_USE_CUDA
+.set_attr<mxnet::FCompute>("FCompute<gpu>", mxnet::op::TVMUnaryCompute<func_deg2rad_gpu>)
+#endif  // MXNET_USE_CUDA
+.set_attr<mxnet::FCompute>("FCompute<cpu>", mxnet::op::TVMUnaryCompute<func_deg2rad_cpu>)
+.add_argument("data", "NDArray-or-Symbol", "the input")
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_npi_deg2rad"});
+
+NNVM_REGISTER_OP(_backward_npi_deg2rad)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+#if MXNET_USE_CUDA
+.set_attr<FCompute>("FCompute<gpu>",
+                    mxnet::op::TVMUnaryBackwardComputeUseNone<func_backward_deg2rad_gpu>)
+#endif  // MXNET_USE_CUDA
+.set_attr<FCompute>("FCompute<cpu>",
+                    mxnet::op::TVMUnaryBackwardComputeUseNone<func_backward_deg2rad_cpu>);
+
+static constexpr char func_rad2deg_cpu[] = "rad2deg";
+static constexpr char func_rad2deg_gpu[] = "cuda_rad2deg";
+static constexpr char func_backward_rad2deg_cpu[] = "backward_rad2deg";
+static constexpr char func_backward_rad2deg_gpu[] = "cuda_backward_rad2deg";
+
+NNVM_REGISTER_OP(_npi_rad2deg)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+[](const NodeAttrs& attrs) {
+  return std::vector<std::string>{"data"};
+})
+.set_attr<mxnet::FInferShape>("FInferShape", mxnet::op::ElemwiseShape<1, 1>)
+.set_attr<nnvm::FInferType>("FInferType", mxnet::op::DegandradOpType)
+#if MXNET_USE_CUDA
+.set_attr<mxnet::FCompute>("FCompute<gpu>", mxnet::op::TVMUnaryCompute<func_rad2deg_gpu>)
+#endif  // MXNET_USE_CUDA
+.set_attr<mxnet::FCompute>("FCompute<cpu>", mxnet::op::TVMUnaryCompute<func_rad2deg_cpu>)
+.add_argument("data", "NDArray-or-Symbol", "the input")
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_npi_rad2deg"});
+
+NNVM_REGISTER_OP(_backward_npi_rad2deg)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+#if MXNET_USE_CUDA
+.set_attr<FCompute>("FCompute<gpu>",
+                    mxnet::op::TVMUnaryBackwardComputeUseNone<func_backward_rad2deg_gpu>)
+#endif  // MXNET_USE_CUDA
+.set_attr<FCompute>("FCompute<cpu>",
+                    mxnet::op::TVMUnaryBackwardComputeUseNone<func_backward_rad2deg_cpu>);
 }  // namespace op
 }  // namespace mxnet
 #endif  // MXNET_USE_TVM_OP
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 01fd445a4360..45fbcbf48098 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -27,10 +27,14 @@
 from common import assertRaises, with_seed
 import random
 import scipy.stats as ss
-from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf
+from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf, retry
+from mxnet.runtime import Features
 import platform
 
 
+_features = Features()
+
+
 @with_seed()
 @use_np
 def test_np_tensordot():
@@ -1015,6 +1019,9 @@ def hybrid_forward(self, F, a, *args, **kwargs):
         'arccosh' : (lambda x: 1./(x**2 - 1.)**(1./2.), 2.0, 5.0),
         'arctanh' : (lambda x: -1./(x**2 - 1.), -0.99, 0.99)
     }
+    if _features.is_enabled("TVM_OP"):
+        funcs['rad2deg'] = (lambda x: 180. / _np.pi * _np.ones(x.shape), -1.0, 1.0)
+        funcs['deg2rad'] = (lambda x: _np.pi / 180. * _np.ones(x.shape), -1.0, 1.0)
     ndim = random.choice([2, 3, 4])
     shape = random.choice([rand_shape_nd(ndim, dim=3), (1, 0, 2)])
     for shape in [rand_shape_nd(ndim, dim=3), (1, 0, 2)]:

From a77bd75a850a5a3af0445adcd2257ce9b0a9c570 Mon Sep 17 00:00:00 2001
From: Xinyu Chen <xinyu1.chen@intel.com>
Date: Tue, 24 Sep 2019 10:39:41 +0800
Subject: [PATCH 365/813] Integrate MKL-DNN leakyrelu (#16075)

* add mkldnn leakyrelu support

* improve mkldnn act param

* register gpu path

* remove old code

* trigger

* fix lint and improve backward function
---
 src/operator/leaky_relu-inl.h                 | 192 ++++--------------
 src/operator/leaky_relu.cc                    | 188 +++++++++++++++--
 src/operator/leaky_relu.cu                    |  14 +-
 src/operator/nn/mkldnn/mkldnn_act-inl.h       |  35 +++-
 src/operator/nn/mkldnn/mkldnn_act.cc          | 125 ++++++++++--
 src/operator/nn/mkldnn/mkldnn_base-inl.h      |   3 +
 src/operator/nn/mkldnn/mkldnn_ops-inl.h       |   8 +-
 .../subgraph/mkldnn/mkldnn_conv-inl.h         |  14 +-
 src/operator/subgraph/mkldnn/mkldnn_conv.cc   |   6 +-
 .../subgraph/mkldnn/mkldnn_conv_property.h    |  12 +-
 tests/python/mkl/test_subgraph.py             |  17 +-
 tests/python/unittest/test_gluon.py           |   2 +-
 12 files changed, 400 insertions(+), 216 deletions(-)

diff --git a/src/operator/leaky_relu-inl.h b/src/operator/leaky_relu-inl.h
index 7f8638630145..2c4127b9a088 100644
--- a/src/operator/leaky_relu-inl.h
+++ b/src/operator/leaky_relu-inl.h
@@ -332,166 +332,50 @@ class LeakyReLUOp : public Operator {
 };  // class LeakyReLUOp
 
 template<typename xpu>
-Operator* CreateOp(LeakyReLUParam type, int dtype);
+void LeakyReLUCompute(const nnvm::NodeAttrs& attrs,
+                      const OpContext& ctx, const std::vector<TBlob>& inputs,
+                      const std::vector<OpReqType>& req,
+                      const std::vector<TBlob>& outputs) {
+  const LeakyReLUParam &param = nnvm::get<LeakyReLUParam>(attrs.parsed);
+  const std::vector<TBlob> no_use_but_adapt_origin_api;
+  size_t expected = param.act_type == leakyrelu::kPReLU ? 2 : 1;
+  CHECK_EQ(inputs.size(), expected);
 
-#if DMLC_USE_CXX11
-class LeakyReLUProp : public OperatorProperty {
- public:
-  void Init(const std::vector<std::pair<std::string, std::string> >& kwargs) override {
-    param_.Init(kwargs);
-  }
-
-  std::map<std::string, std::string> GetParams() const override {
-    return param_.__DICT__();
-  }
-
-  bool InferShape(mxnet::ShapeVector *in_shape,
-                  mxnet::ShapeVector *out_shape,
-                  mxnet::ShapeVector *aux_shape) const override {
-    using namespace mshadow;
-    if (param_.act_type == leakyrelu::kPReLU) {
-      CHECK_EQ(in_shape->size(), 2U) << "Input:[data, gamma]";
-    } else {
-      CHECK_EQ(in_shape->size(), 1U) << "Input:[data]";
-    }
-    const mxnet::TShape &dshape = in_shape->at(leakyrelu::kData);
-    if (!mxnet::ndim_is_known(dshape)) return false;
-    if (param_.act_type == leakyrelu::kPReLU) {
-      const mxnet::TShape &gshape = in_shape->at(leakyrelu::kGamma);
-      if (!mxnet::ndim_is_known(gshape)) {
-        in_shape->at(leakyrelu::kGamma) = mxnet::TShape(Shape1(dshape[1]));
-      }
-      if (dshape == gshape) {
-        SHAPE_ASSIGN_CHECK(*out_shape, 0, dshape);
-      }
-    }
-    out_shape->clear();
-    out_shape->push_back(dshape);
-    if (param_.act_type == leakyrelu::kRReLU) {
-      out_shape->push_back(dshape);
-    }
-    return true;
-  }
-
-  bool InferType(std::vector<int> *in_type,
-                 std::vector<int> *out_type,
-                 std::vector<int> *aux_type) const override {
-    int dtype = -1;
-    for (const int& type : *in_type) {
-      type_assign(&dtype, type);
-    }
-    for (const int& type : *out_type) {
-      type_assign(&dtype, type);
-    }
-
-    for (size_t i = 0; i < in_type->size(); ++i) {
-      TYPE_ASSIGN_CHECK(*in_type, i, dtype);
-    }
-    for (size_t i = 0; i < out_type->size(); ++i) {
-      TYPE_ASSIGN_CHECK(*out_type, i, dtype);
-    }
-    return dtype != -1;
-  }
-
-  OperatorProperty* Copy() const override {
-    auto ptr = new LeakyReLUProp();
-    ptr->param_ = param_;
-    return ptr;
-  }
-
-  std::string TypeString() const override {
-    return "LeakyReLU";
-  }
-
-  // decalre dependency and inplace optimization options
-  std::vector<int> DeclareBackwardDependency(
-    const std::vector<int> &out_grad,
-    const std::vector<int> &in_data,
-    const std::vector<int> &out_data) const override {
-    if (param_.act_type == leakyrelu::kPReLU) {
-      return {out_grad[leakyrelu::kOut],
-              out_data[leakyrelu::kOut],
-              in_data[leakyrelu::kData],
-              in_data[leakyrelu::kGamma]};
-    } else if (param_.act_type == leakyrelu::kRReLU) {
-      return {out_grad[leakyrelu::kOut], out_data[leakyrelu::kMask], out_data[leakyrelu::kOut]};
-    } else {
-      return {out_grad[leakyrelu::kOut], out_data[leakyrelu::kData]};
-    }
-  }
+  MSHADOW_REAL_TYPE_SWITCH(inputs[leakyrelu::kData].type_flag_, DType, {
+    LeakyReLUOp<xpu, DType> op(param);
+    op.Forward(ctx, inputs, req, outputs, no_use_but_adapt_origin_api);
+  });
+}
 
-  std::vector<std::pair<int, void*> > BackwardInplaceOption(
-    const std::vector<int> &out_grad,
-    const std::vector<int> &in_data,
-    const std::vector<int> &out_data,
-    const std::vector<void*> &in_grad) const override {
-    return {{out_grad[leakyrelu::kOut], in_grad[leakyrelu::kData]}};
-  }
-
-  std::vector<std::pair<int, void*> > ForwardInplaceOption(
-    const std::vector<int> &in_data,
-    const std::vector<void*> &out_data) const override {
-    if (param_.act_type == leakyrelu::kPReLU) {
-      return {};
-    } else {
-      return {{in_data[leakyrelu::kData], out_data[leakyrelu::kOut]}};
-    }
-  }
-
-  std::vector<std::string> ListArguments() const override {
-    if (param_.act_type == leakyrelu::kPReLU) {
-      return {"data", "gamma"};
-    } else {
-      return {"data"};
-    }
-  }
-
-  std::vector<std::string> ListOutputs() const override {
-    if (param_.act_type == leakyrelu::kRReLU) {
-      return {"output", "mask"};
-    } else {
-      return {"output"};
-    }
-  }
-
-  int NumOutputs() const override {
-    if (param_.act_type == leakyrelu::kRReLU) {
-      return 2;
-    } else {
-      return 1;
-    }
-  }
-
-  int NumVisibleOutputs() const override {
-    return 1;
-  }
-
-  std::vector<ResourceRequest> ForwardResource(
-      const mxnet::ShapeVector &in_shape) const override {
-    if (param_.act_type == leakyrelu::kRReLU) {
-      return {ResourceRequest::kRandom};
-    } else {
-      return std::vector<ResourceRequest>();
-    }
-  }
+template<typename xpu>
+void LeakyReLUGradCompute(const nnvm::NodeAttrs& attrs,
+                          const OpContext& ctx,
+                          const std::vector<TBlob>& inputs,
+                          const std::vector<OpReqType>& req,
+                          const std::vector<TBlob>& outputs) {
+  const LeakyReLUParam& param = nnvm::get<LeakyReLUParam>(attrs.parsed);
+  const std::vector<TBlob> no_use_but_adapt_origin_api;
+  // inputs: out_grad, input_data, input_gamma, output, output_mask
+  size_t expected_in = param.act_type == leakyrelu::kPReLU ? 2 : 1;
+  size_t expected_out = param.act_type == leakyrelu::kRReLU ? 2 : 1;
 
-  std::vector<ResourceRequest> BackwardResource(
-      const mxnet::ShapeVector &in_shape) const override {
-    return {ResourceRequest::kTempSpace};
-  }
+  CHECK_GE(inputs.size(), 1 + expected_in + expected_out);
+  std::vector<TBlob> out_grad{inputs[0]};
+  std::vector<TBlob> in_data(inputs.begin() + 1,
+                             inputs.begin() + 1 + expected_in);
+  std::vector<TBlob> out_data(inputs.begin() + 1 + expected_in,
+                              inputs.begin() + 1 + expected_in + expected_out);
 
-  Operator* CreateOperator(Context ctx) const override {
-    LOG(FATAL) << "Not Implemented.";
-    return NULL;
-  }
+  CHECK_EQ(req.size(), outputs.size());
+  int dtype = inputs[0].type_flag_;
+  const std::vector<TBlob> &in_grad = outputs;
 
-  Operator* CreateOperatorEx(Context ctx, mxnet::ShapeVector *in_shape,
-                           std::vector<int> *in_type) const override;
+  MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
+    LeakyReLUOp<xpu, DType> op(param);
+    op.Backward(ctx, out_grad, in_data, out_data, req, in_grad, no_use_but_adapt_origin_api);
+  });
+}
 
- private:
-  LeakyReLUParam param_;
-};
-#endif  // DMLC_USE_CXX11
 }  // namespace op
 }  // namespace mxnet
 
diff --git a/src/operator/leaky_relu.cc b/src/operator/leaky_relu.cc
index c25833b799d0..4d1c5ca10a30 100644
--- a/src/operator/leaky_relu.cc
+++ b/src/operator/leaky_relu.cc
@@ -25,27 +25,123 @@
 */
 
 #include "./leaky_relu-inl.h"
+#if MXNET_USE_MKLDNN == 1
+#include "./nn/mkldnn/mkldnn_base-inl.h"
+#include "./nn/mkldnn/mkldnn_ops-inl.h"
+#endif  // MXNET_USE_MKLDNN == 1
 
 #include <nnvm/op_attr_types.h>
 namespace mxnet {
 namespace op {
-template<>
-Operator *CreateOp<cpu>(LeakyReLUParam param, int dtype) {
-  Operator* op = nullptr;
-  MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
-    op = new LeakyReLUOp<cpu, DType>(param);
-  });
-  return op;
+
+DMLC_REGISTER_PARAMETER(LeakyReLUParam);
+
+static bool LeakyReLUType(const nnvm::NodeAttrs& attrs,
+                          std::vector<int> *in_type,
+                          std::vector<int> *out_type) {
+  int dtype = -1;
+  for (const int& type : *in_type) {
+    type_assign(&dtype, type);
+  }
+  for (const int& type : *out_type) {
+    type_assign(&dtype, type);
+  }
+  for (size_t i = 0; i < in_type->size(); ++i) {
+    TYPE_ASSIGN_CHECK(*in_type, i, dtype);
+  }
+  for (size_t i = 0; i < out_type->size(); ++i) {
+    TYPE_ASSIGN_CHECK(*out_type, i, dtype);
+  }
+  return dtype != -1;
 }
 
-Operator *LeakyReLUProp::CreateOperatorEx(Context ctx, mxnet::ShapeVector *in_shape,
-                                          std::vector<int> *in_type) const {
-  DO_BIND_DISPATCH(CreateOp, param_, in_type->at(0));
+static bool LeakyReLUShape(const nnvm::NodeAttrs& attrs,
+                           std::vector<TShape> *in_shape,
+                           std::vector<TShape> *out_shape) {
+  using namespace mshadow;
+  const LeakyReLUParam &param_ = nnvm::get<LeakyReLUParam>(attrs.parsed);
+  if (param_.act_type == leakyrelu::kPReLU) {
+    CHECK_EQ(in_shape->size(), 2U) << "Input:[data, gamma]";
+  } else {
+    CHECK_EQ(in_shape->size(), 1U) << "Input:[data]";
+  }
+  const mxnet::TShape &dshape = in_shape->at(leakyrelu::kData);
+  if (!mxnet::ndim_is_known(dshape)) return false;
+  if (param_.act_type == leakyrelu::kPReLU) {
+    const mxnet::TShape &gshape = in_shape->at(leakyrelu::kGamma);
+    if (!mxnet::ndim_is_known(gshape)) {
+      in_shape->at(leakyrelu::kGamma) = mxnet::TShape(Shape1(dshape[1]));
+    }
+    if (dshape == gshape) {
+      SHAPE_ASSIGN_CHECK(*out_shape, 0, dshape);
+    }
+  }
+  out_shape->clear();
+  out_shape->push_back(dshape);
+  if (param_.act_type == leakyrelu::kRReLU) {
+    out_shape->push_back(dshape);
+  }
+  return true;
 }
 
-DMLC_REGISTER_PARAMETER(LeakyReLUParam);
+#if MXNET_USE_MKLDNN == 1
+static void LeakyReLUComputeExCPU(const nnvm::NodeAttrs& attrs,
+                                  const OpContext& ctx,
+                                  const std::vector<NDArray>& inputs,
+                                  const std::vector<OpReqType>& req,
+                                  const std::vector<NDArray>& outputs) {
+  const LeakyReLUParam& param = nnvm::get<LeakyReLUParam>(attrs.parsed);
+  size_t expected = param.act_type == leakyrelu::kPReLU ? 2 : 1;
+  CHECK_EQ(inputs.size(), expected);
+  if (SupportMKLDNNLeakyRelu(param, inputs[0])) {
+    MKLDNN_OPCHECK_INIT(false, outputs.size(), inputs, outputs);
+    MKLDNNLeakyReluForward(attrs, ctx, inputs[0], req[0], outputs[0]);
+    MKLDNN_OPCHECK_RUN(LeakyReLUCompute<cpu>, attrs, ctx, inputs, req, outputs);
+    return;
+  }
+  FallBackCompute(LeakyReLUCompute<cpu>, attrs, ctx, inputs, req, outputs);
+}
+
+void LeakyReLUGradComputeExCPU(const nnvm::NodeAttrs& attrs,
+                               const OpContext& ctx,
+                               const std::vector<NDArray>& inputs,
+                               const std::vector<OpReqType>& req,
+                               const std::vector<NDArray>& outputs) {
+  const LeakyReLUParam& param = nnvm::get<LeakyReLUParam>(attrs.parsed);
+  if (SupportMKLDNNLeakyRelu(param, inputs[0])) {
+    std::vector<NDArray> in_data{inputs[0], inputs[1]};
+    MKLDNN_OPCHECK_INIT(true, outputs.size(), inputs, outputs);
+    MKLDNNLeakyReluBackward(attrs, ctx, in_data, req[0], outputs[0]);
+    MKLDNN_OPCHECK_RUN(LeakyReLUGradCompute<cpu>, attrs, ctx, inputs, req, outputs);
+    return;
+  }
+  FallBackCompute(LeakyReLUGradCompute<cpu>, attrs, ctx, inputs, req, outputs);
+}
+
+inline static bool LeakyReLUStorageType(const nnvm::NodeAttrs& attrs,
+                                        const int dev_mask,
+                                        DispatchMode* dispatch_mode,
+                                        std::vector<int> *in_attrs,
+                                        std::vector<int> *out_attrs) {
+  const LeakyReLUParam& param = nnvm::get<LeakyReLUParam>(attrs.parsed);
+  size_t expected = param.act_type == leakyrelu::kPReLU ? 2 : 1;
+  CHECK_EQ(in_attrs->size(), expected);
+  return MKLDNNStorageType(attrs, dev_mask, SupportMKLDNNLeakyRelu(param),
+                           dispatch_mode, in_attrs, out_attrs);
+}
 
-MXNET_REGISTER_OP_PROPERTY(LeakyReLU, LeakyReLUProp)
+inline static bool BackwardLeakyReLUStorageType(const nnvm::NodeAttrs& attrs,
+                                                const int dev_mask,
+                                                DispatchMode* dispatch_mode,
+                                                std::vector<int> *in_attrs,
+                                                std::vector<int> *out_attrs) {
+  const LeakyReLUParam& param = nnvm::get<LeakyReLUParam>(attrs.parsed);
+  return MKLDNNStorageType(attrs, dev_mask, SupportMKLDNNLeakyRelu(param),
+                           dispatch_mode, in_attrs, out_attrs);
+}
+#endif  // MXNET_USE_MKLDNN == 1
+
+NNVM_REGISTER_OP(LeakyReLU)
 .describe(R"code(Applies Leaky rectified linear unit activation element-wise to the input.
 
 Leaky ReLUs attempt to fix the "dying ReLU" problem by allowing a small `slope`
@@ -63,15 +159,45 @@ The following modified ReLU Activation functions are supported:
   *(lower_bound+upper_bound)/2* for inference.
 
 )code" ADD_FILELINE)
-.add_argument("data", "NDArray-or-Symbol", "Input data to activation function.")
-.add_argument("gamma", "NDArray-or-Symbol",
-              "Slope parameter for PReLU. Only required "
-              "when act_type is 'prelu'. It should be either a vector of size 1, "
-              "or the same size as the second dimension of data.")
-.add_arguments(LeakyReLUParam::__FIELDS__());
-
-NNVM_REGISTER_OP(LeakyReLU)
 .add_alias("_npx_leaky_relu")
+.set_num_inputs([](const NodeAttrs& attrs) {
+  const LeakyReLUParam& param = nnvm::get<LeakyReLUParam>(attrs.parsed);
+  return param.act_type == leakyrelu::kPReLU ? 2 : 1;
+})
+.set_num_outputs([](const NodeAttrs& attrs) {
+  const LeakyReLUParam& param = nnvm::get<LeakyReLUParam>(attrs.parsed);
+  return param.act_type == leakyrelu::kRReLU ? 2 : 1;
+})
+.set_attr_parser(ParamParser<LeakyReLUParam>)
+#if MXNET_USE_MKLDNN == 1
+.set_attr<FInferStorageType>("FInferStorageType", LeakyReLUStorageType)
+#endif
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+    [](const NodeAttrs& attrs) {
+  const LeakyReLUParam& param = nnvm::get<LeakyReLUParam>(attrs.parsed);
+  return param.act_type == leakyrelu::kPReLU ? std::vector<std::string>{"data", "gamma"}
+                                             : std::vector<std::string>{"data"};
+})
+.set_attr<nnvm::FListOutputNames>("FListOutputNames",
+    [](const NodeAttrs& attrs) {
+  const LeakyReLUParam& param = nnvm::get<LeakyReLUParam>(attrs.parsed);
+  return param.act_type == leakyrelu::kRReLU ? std::vector<std::string>{"output", "mask"}
+                                             : std::vector<std::string>{"output"};
+})
+.set_attr<mxnet::FInferShape>("FInferShape", LeakyReLUShape)
+.set_attr<nnvm::FInferType>("FInferType", LeakyReLUType)
+.set_attr<FCompute>("FCompute<cpu>", LeakyReLUCompute<cpu>)
+#if MXNET_USE_MKLDNN == 1
+.set_attr<bool>("TIsMKLDNN", true)
+.set_attr<FComputeEx>("FComputeEx<cpu>", LeakyReLUComputeExCPU)
+#endif
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseInOut{"_backward_LeakyReLU"})
+.set_attr<nnvm::FInplaceOption>("FInplaceOption", [](const NodeAttrs& attrs){
+  return std::vector<std::pair<int, int> >{{0, 0}};
+})
+.add_argument("data", "NDArray-or-Symbol", "Input data to activation function.")
+.add_argument("gamma", "NDArray-or-Symbol", "Input data to activation function.")
+.add_arguments(LeakyReLUParam::__FIELDS__())
 .set_attr<nnvm::FSetInputVarAttrOnCompose>("FSetInputVarAttrOnCompose",
     [](const nnvm::NodeAttrs& attrs, nnvm::NodePtr var, const int index) {
       if (index == 1 && var->attrs.dict.find("__init__") == var->attrs.dict.end()) {
@@ -79,5 +205,27 @@ NNVM_REGISTER_OP(LeakyReLU)
       }
     });
 
+NNVM_REGISTER_OP(_backward_LeakyReLU)
+.set_num_outputs([](const NodeAttrs& attrs) {
+  const LeakyReLUParam& param = nnvm::get<LeakyReLUParam>(attrs.parsed);
+  return param.act_type == leakyrelu::kPReLU ? 2 : 1;
+})
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+#if MXNET_USE_MKLDNN == 1
+.set_attr<FInferStorageType>("FInferStorageType", BackwardLeakyReLUStorageType)
+#endif
+.set_attr<nnvm::FInplaceOption>("FInplaceOption", [](const NodeAttrs& attrs){
+  return std::vector<std::pair<int, int> >{{0, 0}};
+})
+.set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& n) {
+  return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+})
+.set_attr_parser(ParamParser<LeakyReLUParam>)
+#if MXNET_USE_MKLDNN == 1
+.set_attr<bool>("TIsMKLDNN", true)
+.set_attr<FComputeEx>("FComputeEx<cpu>", LeakyReLUGradComputeExCPU)
+#endif
+.set_attr<FCompute>("FCompute<cpu>", LeakyReLUGradCompute<cpu>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/leaky_relu.cu b/src/operator/leaky_relu.cu
index a2e0e959a15b..fa8e95b4b952 100644
--- a/src/operator/leaky_relu.cu
+++ b/src/operator/leaky_relu.cu
@@ -28,14 +28,12 @@
 
 namespace mxnet {
 namespace op {
-template<>
-Operator *CreateOp<gpu>(LeakyReLUParam param, int dtype) {
-  Operator* op = nullptr;
-  MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
-    op = new LeakyReLUOp<gpu, DType>(param);
-  });
-  return op;
-}
+
+NNVM_REGISTER_OP(LeakyReLU)
+.set_attr<FCompute>("FCompute<gpu>", LeakyReLUCompute<gpu>);
+
+NNVM_REGISTER_OP(_backward_LeakyReLU)
+.set_attr<FCompute>("FCompute<gpu>", LeakyReLUGradCompute<gpu>);
 
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/nn/mkldnn/mkldnn_act-inl.h b/src/operator/nn/mkldnn/mkldnn_act-inl.h
index 6bf30e3f3bbe..9c21b7f70f52 100644
--- a/src/operator/nn/mkldnn/mkldnn_act-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_act-inl.h
@@ -32,22 +32,34 @@
 #include <vector>
 #include <utility>
 #include "../activation-inl.h"
+#include "../../leaky_relu-inl.h"
 #include "./mkldnn_ops-inl.h"
 #include "./mkldnn_base-inl.h"
 
 namespace mxnet {
 namespace op {
 
+struct MKLDNNActParam {
+  mkldnn::algorithm alg;
+  float slope = 0.f;
+
+  bool operator==(const MKLDNNActParam& other) const {
+    return this->alg == other.alg &&
+           this->slope == other.slope;
+  }
+};
+
 mkldnn::algorithm GetMKLDNNActAlgo(const ActivationParam& param);
+mkldnn::algorithm GetMKLDNNActAlgo(const LeakyReLUParam& param);
 mkldnn::eltwise_forward::primitive_desc GetActFwdDescImpl(
-    const ActivationParam& param, bool is_train,
+    const MKLDNNActParam& param, bool is_train,
     const mkldnn::memory &input_mem, int dtype);
 
 class MKLDNNActForward {
  public:
   const mkldnn::eltwise_forward::primitive_desc fwd_pd;
 
-  MKLDNNActForward(const ActivationParam& param, bool is_train,
+  MKLDNNActForward(const MKLDNNActParam& param, bool is_train,
                    const NDArray &data, const mkldnn::memory &mem): fwd_pd(
                        GetActFwdDescImpl(param, is_train, mem, data.dtype())) {}
   void SetNewMem(const mkldnn::memory &data, const mkldnn::memory &output);
@@ -59,16 +71,31 @@ class MKLDNNActForward {
   std::shared_ptr<mkldnn::memory> out_;
 };
 
-typedef ParamOpSign<ActivationParam> MKLDNNActSignature;
-MKLDNNActForward &GetActForward(const ActivationParam& param,
+typedef ParamOpSign<MKLDNNActParam> MKLDNNActSignature;
+MKLDNNActForward &GetActForward(const MKLDNNActParam& param,
                                 const OpContext &ctx, const NDArray &in_data,
                                 const mkldnn::memory &in_mem);
 
 void MKLDNNActivationForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
                              const NDArray &in_data, const OpReqType &req,
                              const NDArray &out_data);
+void MKLDNNLeakyReluForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
+                             const NDArray &in_data, const OpReqType &req,
+                             const NDArray &out_data);
 }  // namespace op
 }  // namespace mxnet
 
+namespace std {
+template<>
+struct hash<mxnet::op::MKLDNNActParam> {
+  size_t operator()(const mxnet::op::MKLDNNActParam& val) {
+    size_t ret = 0;
+    ret = dmlc::HashCombine(ret, static_cast<size_t>(val.alg));
+    ret = dmlc::HashCombine(ret, val.slope);
+    return ret;
+  }
+};
+}  // namespace std
+
 #endif  // MXNET_USE_MKLDNN == 1
 #endif  // MXNET_OPERATOR_NN_MKLDNN_MKLDNN_ACT_INL_H_
diff --git a/src/operator/nn/mkldnn/mkldnn_act.cc b/src/operator/nn/mkldnn/mkldnn_act.cc
index e4c829645e13..f221ddf5e345 100644
--- a/src/operator/nn/mkldnn/mkldnn_act.cc
+++ b/src/operator/nn/mkldnn/mkldnn_act.cc
@@ -57,6 +57,20 @@ bool SupportMKLDNNAct(const ActivationParam& param, const NDArray &input) {
   return SupportMKLDNNAct(param);
 }
 
+bool SupportMKLDNNLeakyRelu(const LeakyReLUParam& param) {
+  return param.act_type == leakyrelu::kLeakyReLU
+      || param.act_type == leakyrelu::kELU;
+}
+
+bool SupportMKLDNNLeakyRelu(const LeakyReLUParam& param, const NDArray &input) {
+  // MKL-DNN Activation supports 1d, 2d, 3d, 4d data layout
+  if ((input.shape().ndim() < 1) ||
+      (input.shape().ndim() > 4) ||
+      (input.dtype() != mshadow::kFloat32))
+    return false;
+  return SupportMKLDNNLeakyRelu(param);
+}
+
 bool SupportQuantizedMKLDNNAct(const ActivationParam &param) {
   // TODO(zhennan): Add more activation type when mkldnn supports.
   //                Remove this when it's identity to SupportMKLDNNAct.
@@ -79,18 +93,30 @@ mkldnn::algorithm GetMKLDNNActAlgo(const ActivationParam& param) {
   }
 }
 
+mkldnn::algorithm GetMKLDNNActAlgo(const LeakyReLUParam& param) {
+  switch (param.act_type) {
+    case leakyrelu::kLeakyReLU:
+      return mkldnn::algorithm::eltwise_relu;
+    case leakyrelu::kELU:
+      return mkldnn::algorithm::eltwise_elu;
+    default:
+      LOG(FATAL) << "unknown activation type for LeakyReLU: " << param.act_type;
+      return mkldnn::algorithm::eltwise_relu;
+  }
+}
+
 mkldnn::eltwise_forward::primitive_desc GetActFwdDescImpl(
-    const ActivationParam& param, bool is_train,
+    const MKLDNNActParam& param, bool is_train,
     const mkldnn::memory &input_mem, int dtype) {
   mkldnn::memory::primitive_desc data_mpd = input_mem.get_primitive_desc();
   mkldnn::memory::desc data_md = data_mpd.desc();
   auto cpu_engine = data_mpd.get_engine();
 
-  auto alg = GetMKLDNNActAlgo(param);
+  auto alg = param.alg;
 
   auto prop = is_train ? mkldnn::prop_kind::forward_training :
                          mkldnn::prop_kind::forward_scoring;
-  auto desc = mkldnn::eltwise_forward::desc(prop, alg, data_md, 0.0f);
+  auto desc = mkldnn::eltwise_forward::desc(prop, alg, data_md, param.slope);
   return mkldnn::eltwise_forward::primitive_desc(desc, cpu_engine);
 }
 
@@ -119,7 +145,7 @@ const mkldnn::eltwise_forward &MKLDNNActForward::GetFwd() const {
   return *fwd_;
 }
 
-MKLDNNActForward &GetActForward(const ActivationParam& param,
+MKLDNNActForward &GetActForward(const MKLDNNActParam& param,
                                 const OpContext &ctx, const NDArray &in_data,
                                 const mkldnn::memory &in_mem) {
 #if DMLC_CXX11_THREAD_LOCAL
@@ -129,7 +155,8 @@ MKLDNNActForward &GetActForward(const ActivationParam& param,
 #endif
   MKLDNNActSignature key(param);
   key.AddSign(ctx.is_train);
-  key.AddSign(param.act_type);
+  key.AddSign(param.alg);
+  key.AddSign(param.slope);
   key.AddSign(in_data);
 
   auto it = fwds.find(key);
@@ -144,6 +171,8 @@ void MKLDNNActivationForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
                              const NDArray &in_data, const OpReqType &req,
                              const NDArray &out_data) {
   const ActivationParam& param = nnvm::get<ActivationParam>(attrs.parsed);
+  MKLDNNActParam param_;
+  param_.alg = GetMKLDNNActAlgo(param);
 
   NDArray in_buffer = in_data;
   MKLDNNStream *stream = MKLDNNStream::Get();
@@ -152,7 +181,30 @@ void MKLDNNActivationForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
     in_buffer = in_data.Reorder2Default();
 
   auto input_mem = in_buffer.GetMKLDNNData();
-  MKLDNNActForward &fwd = GetActForward(param, ctx, in_buffer, *input_mem);
+  MKLDNNActForward &fwd = GetActForward(param_, ctx, in_buffer, *input_mem);
+  auto out_mem_t = CreateMKLDNNMem(out_data, fwd.fwd_pd.dst_primitive_desc(), req, &in_buffer);
+  fwd.SetNewMem(*input_mem, *out_mem_t.second);
+  stream->RegisterPrim(fwd.GetFwd());
+  CommitOutput(out_data, out_mem_t);
+  stream->Submit();
+}
+
+void MKLDNNLeakyReluForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
+                             const NDArray &in_data, const OpReqType &req,
+                             const NDArray &out_data) {
+  const LeakyReLUParam& param = nnvm::get<LeakyReLUParam>(attrs.parsed);
+  MKLDNNActParam param_;
+  param_.alg = GetMKLDNNActAlgo(param);
+  param_.slope = param.slope;
+
+  NDArray in_buffer = in_data;
+  MKLDNNStream *stream = MKLDNNStream::Get();
+
+  if (in_data.IsView() && in_data.IsMKLDNNData())
+    in_buffer = in_data.Reorder2Default();
+
+  auto input_mem = in_buffer.GetMKLDNNData();
+  MKLDNNActForward &fwd = GetActForward(param_, ctx, in_buffer, *input_mem);
   auto out_mem_t = CreateMKLDNNMem(out_data, fwd.fwd_pd.dst_primitive_desc(), req, &in_buffer);
   fwd.SetNewMem(*input_mem, *out_mem_t.second);
   stream->RegisterPrim(fwd.GetFwd());
@@ -161,29 +213,28 @@ void MKLDNNActivationForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
 }
 
 static mkldnn::eltwise_backward::primitive_desc GetActBwdDescImpl(
-    const ActivationParam &param, const mkldnn::memory &input_mem,
+    const MKLDNNActParam &param, const mkldnn::memory &input_mem,
     const mkldnn::memory &diff_dst_memory, int dtype) {
   mkldnn::memory::primitive_desc data_mpd = input_mem.get_primitive_desc();
   mkldnn::memory::desc data_md = data_mpd.desc();
   mkldnn::memory::desc diff_md = diff_dst_memory.get_primitive_desc().desc();
   auto cpu_engine = data_mpd.get_engine();
-  auto alg = GetMKLDNNActAlgo(param);
+  auto alg = param.alg;
 
   MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
-    DType alpha = 0;
     mkldnn::eltwise_forward::desc fw_desc(mkldnn::prop_kind::forward_training,
-                                          alg, data_md, alpha);
+                                          alg, data_md, param.slope);
     mkldnn::eltwise_forward::primitive_desc fw_pdesc(fw_desc, cpu_engine);
-    mkldnn::eltwise_backward::desc bw_desc(alg, diff_md, data_md, alpha);
+    mkldnn::eltwise_backward::desc bw_desc(alg, diff_md, data_md, param.slope);
     mkldnn::eltwise_backward::primitive_desc bw_pdesc(bw_desc, cpu_engine,
                                                       fw_pdesc);
     return bw_pdesc;
   });
   LOG(FATAL) << "Unsupported data type for MKLDNN activation";
   mkldnn::eltwise_forward::desc fw_desc(mkldnn::prop_kind::forward_training,
-                                        alg, data_md, 0.0);
+                                        alg, data_md, param.slope);
   mkldnn::eltwise_forward::primitive_desc fw_pdesc(fw_desc, cpu_engine);
-  mkldnn::eltwise_backward::desc bw_desc(alg, diff_md, data_md, 0.0);
+  mkldnn::eltwise_backward::desc bw_desc(alg, diff_md, data_md, param.slope);
   mkldnn::eltwise_backward::primitive_desc bw_pdesc(bw_desc, cpu_engine,
                                                     fw_pdesc);
   return bw_pdesc;
@@ -198,7 +249,7 @@ class MKLDNNActBackward {
  public:
   const mkldnn::eltwise_backward::primitive_desc pd;
 
-  explicit MKLDNNActBackward(const ActivationParam &param, const NDArray &data,
+  explicit MKLDNNActBackward(const MKLDNNActParam &param, const NDArray &data,
                              const mkldnn::memory &mem,
                              const mkldnn::memory &diff_dst_memory)
       : pd(GetActBwdDescImpl(param, mem, diff_dst_memory, data.dtype())) {}
@@ -229,7 +280,7 @@ class MKLDNNActBackward {
   const inline mkldnn::eltwise_backward &GetBwd() const { return *bwd; }
 };
 
-static inline MKLDNNActBackward &GetActBackward(const ActivationParam &param,
+static inline MKLDNNActBackward &GetActBackward(const MKLDNNActParam &param,
                                                 const OpContext &ctx,
                                                 const NDArray &in_data,
                                                 const NDArray &out_grad,
@@ -269,6 +320,8 @@ void MKLDNNActivationBackward(const nnvm::NodeAttrs& attrs, const OpContext &ctx
     in_buffer = in_data.Reorder2Default();
 
   const ActivationParam& param = nnvm::get<ActivationParam>(attrs.parsed);
+  MKLDNNActParam param_;
+  param_.alg = GetMKLDNNActAlgo(param);
   TmpMemMgr::Get()->Init(ctx.requested[activation::kTempSpace]);
   auto diff_dst_memory = out_buffer.GetMKLDNNData();
   auto input_mem = in_buffer.GetMKLDNNData();
@@ -277,7 +330,7 @@ void MKLDNNActivationBackward(const nnvm::NodeAttrs& attrs, const OpContext &ctx
   if (input_mem->get_primitive_desc() != diff_dst_memory->get_primitive_desc())
     input_mem = in_buffer.GetMKLDNNDataReorder(diff_dst_memory->get_primitive_desc());
   MKLDNNActBackward &bwd =
-      GetActBackward(param, ctx, in_buffer, out_buffer, *input_mem);
+      GetActBackward(param_, ctx, in_buffer, out_buffer, *input_mem);
   MKLDNNStream *stream = MKLDNNStream::Get();
   mkldnn_output_t diff_src_memory =
       CreateMKLDNNMem(in_grad, bwd.pd.diff_src_primitive_desc(), req);
@@ -287,6 +340,46 @@ void MKLDNNActivationBackward(const nnvm::NodeAttrs& attrs, const OpContext &ctx
   stream->Submit();
 }
 
+void MKLDNNLeakyReluBackward(const nnvm::NodeAttrs& attrs,
+                             const OpContext &ctx,
+                             const std::vector<NDArray>& inputs,
+                             const OpReqType &req,
+                             const NDArray &output) {
+  if (req == kNullOp) {
+    return;
+  }
+  CHECK_GE(inputs.size(), 2U);
+  NDArray out_buffer = inputs[0];
+  if (inputs[0].IsView() && inputs[0].IsMKLDNNData())
+    out_buffer = inputs[0].Reorder2Default();
+
+  NDArray in_buffer = inputs[1];
+  if (inputs[1].IsView() && inputs[1].IsMKLDNNData())
+    in_buffer = inputs[1].Reorder2Default();
+
+  const LeakyReLUParam& param = nnvm::get<LeakyReLUParam>(attrs.parsed);
+  MKLDNNActParam param_;
+  param_.alg = GetMKLDNNActAlgo(param);
+  param_.slope = param.slope;
+
+  TmpMemMgr::Get()->Init(ctx.requested[leakyrelu::kRandom]);
+  auto diff_dst_memory = out_buffer.GetMKLDNNData();
+  auto input_mem = in_buffer.GetMKLDNNData();
+  // We need to make sure the two inputs to eltwise_backward has the same memory
+  // descriptor. Otherwise, the perf will suffer.
+  if (input_mem->get_primitive_desc() != diff_dst_memory->get_primitive_desc())
+    input_mem = in_buffer.GetMKLDNNDataReorder(diff_dst_memory->get_primitive_desc());
+  MKLDNNActBackward &bwd =
+      GetActBackward(param_, ctx, in_buffer, out_buffer, *input_mem);
+  MKLDNNStream *stream = MKLDNNStream::Get();
+  mkldnn_output_t diff_src_memory =
+      CreateMKLDNNMem(output, bwd.pd.diff_src_primitive_desc(), req);
+  bwd.SetNewMem(*input_mem, *diff_dst_memory, *diff_src_memory.second);
+  stream->RegisterPrim(bwd.GetBwd());
+  CommitOutput(output, diff_src_memory);
+  stream->Submit();
+}
+
 }  // namespace op
 }  // namespace mxnet
 
diff --git a/src/operator/nn/mkldnn/mkldnn_base-inl.h b/src/operator/nn/mkldnn/mkldnn_base-inl.h
index 34f4a0b0b062..9e8725e776e5 100644
--- a/src/operator/nn/mkldnn/mkldnn_base-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_base-inl.h
@@ -177,6 +177,7 @@ void *AlignMem(void *mem, size_t size, size_t alignment, size_t *space);
 
 namespace op {
 struct ActivationParam;
+struct LeakyReLUParam;
 struct ConvolutionParam;
 struct DeconvolutionParam;
 struct SoftmaxParam;
@@ -185,6 +186,8 @@ struct TransposeParam;
 struct ReshapeParam;
 bool SupportMKLDNNAct(const ActivationParam& param);
 bool SupportMKLDNNAct(const ActivationParam& param, const NDArray &input);
+bool SupportMKLDNNLeakyRelu(const LeakyReLUParam& param);
+bool SupportMKLDNNLeakyRelu(const LeakyReLUParam& param, const NDArray &input);
 bool SupportQuantizedMKLDNNAct(const ActivationParam &param);
 bool SupportMKLDNNConv(const ConvolutionParam &params, const NDArray &input);
 bool SupportMKLDNNDeconv(const DeconvolutionParam& params, const NDArray &input);
diff --git a/src/operator/nn/mkldnn/mkldnn_ops-inl.h b/src/operator/nn/mkldnn/mkldnn_ops-inl.h
index 502abff6231b..b564a3318402 100644
--- a/src/operator/nn/mkldnn/mkldnn_ops-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_ops-inl.h
@@ -109,9 +109,15 @@ void MKLDNNActivationForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
 void MKLDNNActivationBackward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
                               const NDArray &out_grad, const NDArray &in_data,
                               const OpReqType &req, const NDArray &in_grad);
+void MKLDNNLeakyReluForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
+                            const NDArray &in_data, const OpReqType &req,
+                            const NDArray &out_data);
+void MKLDNNLeakyReluBackward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
+                             const std::vector<NDArray>& inputs, const OpReqType &req,
+                             const NDArray &output);
 
 void MKLDNNSum(const mkldnn::memory &arr1, const mkldnn::memory &arr2,
-         const mkldnn::memory &out);
+               const mkldnn::memory &out);
 
 void MKLDNNTransposeForward(const nnvm::NodeAttrs& attrs,
                             const OpContext &ctx,
diff --git a/src/operator/subgraph/mkldnn/mkldnn_conv-inl.h b/src/operator/subgraph/mkldnn/mkldnn_conv-inl.h
index fcf767adebad..002b012bc35e 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_conv-inl.h
+++ b/src/operator/subgraph/mkldnn/mkldnn_conv-inl.h
@@ -40,18 +40,18 @@ struct MKLDNNConvFusionParam {
 static inline bool IsOutputUInt8(const MKLDNNConvFusionParam& param) {
   bool result = false;
   const auto& mkldnn_param = param.full_conv_param.mkldnn_param;
-  auto IsOutputUInt8Helper = [](const mkldnn::algorithm& act_alg) {
-    return (act_alg == mkldnn::algorithm::eltwise_relu ||
-            act_alg == mkldnn::algorithm::eltwise_logistic ||
-            act_alg == mkldnn::algorithm::eltwise_soft_relu ||
-            act_alg == mkldnn::algorithm::eltwise_bounded_relu);
+  auto IsOutputUInt8Helper = [](const MKLDNNPostEltwiseParam &param) {
+    return ((param.alg == mkldnn::algorithm::eltwise_relu && param.alpha == 0.f) ||
+            param.alg == mkldnn::algorithm::eltwise_logistic ||
+            param.alg == mkldnn::algorithm::eltwise_soft_relu ||
+            param.alg == mkldnn::algorithm::eltwise_bounded_relu);
   };
   if ((!mkldnn_param.with_sum) && mkldnn_param.with_act) {
     CHECK(param.full_conv_param.act_param.alg != mkldnn::algorithm::algorithm_undef);
-    result = IsOutputUInt8Helper(param.full_conv_param.act_param.alg);
+    result = IsOutputUInt8Helper(param.full_conv_param.act_param);
   } else if (mkldnn_param.with_postsum_act) {
     CHECK(param.full_conv_param.postsum_act_param.alg != mkldnn::algorithm::algorithm_undef);
-    result = IsOutputUInt8Helper(param.full_conv_param.postsum_act_param.alg);
+    result = IsOutputUInt8Helper(param.full_conv_param.postsum_act_param);
   }
   return result;
 }
diff --git a/src/operator/subgraph/mkldnn/mkldnn_conv.cc b/src/operator/subgraph/mkldnn/mkldnn_conv.cc
index d9bfa02b8820..886a21b44fc9 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_conv.cc
+++ b/src/operator/subgraph/mkldnn/mkldnn_conv.cc
@@ -511,7 +511,7 @@ static void SgMKLDNNConvParamParser(nnvm::NodeAttrs *attrs) {
     } else if (node_name == "Convolution") {
       param_.full_conv_param.conv_param =
           nnvm::get<ConvolutionParam>(node->attrs.parsed);
-    } else if (node_name == "Activation" || node_name == "clip") {
+    } else if (node_name == "Activation" || node_name == "LeakyReLU" || node_name == "clip") {
       auto &post_act_param =
           (param_.full_conv_param.mkldnn_param.with_act && !with_act)
               ? param_.full_conv_param.act_param
@@ -520,6 +520,10 @@ static void SgMKLDNNConvParamParser(nnvm::NodeAttrs *attrs) {
       if (node_name == "Activation") {
         const auto act_param = nnvm::get<ActivationParam>(node->attrs.parsed);
         post_act_param.alg = GetMKLDNNActAlgo(act_param);
+      } else if (node_name == "LeakyReLU") {
+        const auto act_param = nnvm::get<LeakyReLUParam>(node->attrs.parsed);
+        post_act_param.alpha = act_param.slope;
+        post_act_param.alg = GetMKLDNNActAlgo(act_param);
       } else {
         const auto clip_param = nnvm::get<ClipParam>(node->attrs.parsed);
         post_act_param.alg = mkldnn::algorithm::eltwise_bounded_relu;
diff --git a/src/operator/subgraph/mkldnn/mkldnn_conv_property.h b/src/operator/subgraph/mkldnn/mkldnn_conv_property.h
index 40b3f7c1d010..ff6589e6fb0a 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_conv_property.h
+++ b/src/operator/subgraph/mkldnn/mkldnn_conv_property.h
@@ -24,6 +24,7 @@
 #include <string>
 #include <vector>
 #include "../../nn/activation-inl.h"
+#include "../../leaky_relu-inl.h"
 #include "../../nn/convolution-inl.h"
 #include "../../nn/mkldnn/mkldnn_ops-inl.h"
 #include "../../tensor/matrix_op-inl.h"
@@ -121,6 +122,15 @@ class SgMKLDNNConvSelector : public SubgraphSelector {
             status_ = kSuccess;
             return true;
           }
+        } else if ((!disable_conv_act_) && new_node.op()->name == "LeakyReLU") {
+          const LeakyReLUParam &param =
+              nnvm::get<LeakyReLUParam>(new_node.attrs.parsed);
+          if (param.act_type == leakyrelu::kLeakyReLU) {
+            matched_list_.push_back(&new_node);
+            // not support conv+relu+sum yet.
+            status_ = kSuccess;
+            return true;
+          }
         } else if ((!disable_conv_act_) && new_node.op()->name == "clip") {
           if (!(quantize_ && (status_ == kSum))) {
             // TODO(zhennan): doesn't support int8 conv+sum+relu6 at moment. To support this, we
@@ -208,7 +218,7 @@ class SgMKLDNNConvProperty : public SubgraphProperty {
         n->attrs.dict["with_sum"] = "true";
         _with_sum = true;
 
-      } else if (sub_name == "Activation" || sub_name == "clip") {
+      } else if (sub_name == "Activation" || sub_name == "LeakyReLU" || sub_name == "clip") {
         node_name << "act_";
         if (!_with_sum) {
           n->attrs.dict["with_act"] = "true";
diff --git a/tests/python/mkl/test_subgraph.py b/tests/python/mkl/test_subgraph.py
index 8ba6d6183c86..dc494b25b549 100644
--- a/tests/python/mkl/test_subgraph.py
+++ b/tests/python/mkl/test_subgraph.py
@@ -327,6 +327,8 @@ def conv_act(no_bias, data_shape, alg):
                                kernel=(3, 3), stride=(1, 1), no_bias=no_bias)
   if alg == "relu6":
     relu = mx.symbol.clip(data=conv, name='relu6', a_min=0, a_max=6)
+  elif alg == "leakyrelu":
+    relu = mx.symbol.LeakyReLU(data=conv, slope=0.25, act_type='leaky')
   else:
     relu = mx.symbol.Activation(data=conv, name=alg, act_type=alg)
   return relu, attr
@@ -339,6 +341,8 @@ def conv_act_sum(no_bias, data_shape, alg):
                                kernel=(3, 3), stride=(1, 1), no_bias=no_bias)
   if alg == "relu6":
     relu = mx.symbol.clip(data=conv, name='relu6', a_min=0, a_max=6)
+  elif alg == "leakyrelu":
+    relu = mx.symbol.LeakyReLU(data=conv, slope=0.25, act_type='leaky')
   else:
     relu = mx.symbol.Activation(data=conv, name=alg, act_type=alg)
   conv1 = mx.symbol.Convolution(data=data, weight=weight, name='conv1', num_filter=64,
@@ -379,6 +383,8 @@ def conv_bn_act(no_bias, data_shape, alg):
   bn1 = mx.symbol.BatchNorm(data=conv, name="bn1")
   if alg == "relu6":
     relu = mx.symbol.clip(data=bn1, name='relu6', a_min=0, a_max=6)
+  elif alg == "leakyrelu":
+    relu = mx.symbol.LeakyReLU(data=bn1, slope=0.25, act_type='leaky')
   else:
     relu = mx.symbol.Activation(data=bn1, name=alg, act_type=alg)
   return relu, attr
@@ -395,6 +401,8 @@ def conv_bn_sum_act(no_bias, data_shape, alg):
   sum1 = bn1 + conv1
   if alg == "relu6":
     relu = mx.symbol.clip(data=sum1, name='relu6', a_min=0, a_max=6)
+  elif alg == "leakyrelu":
+    relu = mx.symbol.LeakyReLU(data=sum1, slope=0.25, act_type='leaky')
   else:
     relu = mx.symbol.Activation(data=sum1, name=alg, act_type=alg)
   return relu, attr
@@ -693,7 +701,8 @@ def test_pos_conv_act():
               "sigmoid": True,
               "tanh": True,
               "softrelu": True,
-              "relu6": True}
+              "relu6": True,
+              "leakyrelu": True}
   for data_shape in DATA_SHAPE:
     for (alg, quantize) in act_list.items():
       net, attrs = conv_act(False, data_shape, alg)
@@ -731,7 +740,8 @@ def test_pos_conv_bn_act():
               "sigmoid": True,
               "tanh": True,
               "softrelu": True,
-              "relu6": True}
+              "relu6": True,
+              "leakyrelu": True}
   for data_shape in DATA_SHAPE:
     for (alg, quantize) in act_list.items():
       net, attrs = conv_bn_act(False, data_shape, alg)
@@ -745,7 +755,8 @@ def test_pos_conv_bn_sum_act():
               "sigmoid": True,
               "tanh": True,
               "softrelu": True,
-              "relu6": False}
+              "relu6": False,
+              "leakyrelu": True}
   for data_shape in DATA_SHAPE:
     for (alg, quantize) in act_list.items():
       net, attrs = conv_bn_sum_act(False, data_shape, alg)
diff --git a/tests/python/unittest/test_gluon.py b/tests/python/unittest/test_gluon.py
index 46e976432fa8..d11443ca839a 100644
--- a/tests/python/unittest/test_gluon.py
+++ b/tests/python/unittest/test_gluon.py
@@ -1223,7 +1223,7 @@ def elu(x):
         return [elu(x_i) for x_i in x]
 
     for test_point, ref_point in zip(elu_test(point_to_validate), elu(point_to_validate)):
-        assert test_point == ref_point
+        assert_almost_equal(test_point.asnumpy(), ref_point.asnumpy())
 
     selu = mx.gluon.nn.SELU()
     def selu_test(x):

From cbbb96a9b9e9f41f1c7d48b7e319724c8952be19 Mon Sep 17 00:00:00 2001
From: perdasilva <perdasilva@gmail.com>
Date: Tue, 24 Sep 2019 14:51:28 +0200
Subject: [PATCH 366/813] [CD] Add COMMIT_ID param to release job (#16202)

* Removes unnecessary parameter

* Adds revision parameter to release job

* Update documentation

* Changes RELEASE_JOB_TYPE to string parameter

* Adds variant and release job type checks

* Adds release job update to CD pipeline

* Disable concurrent builds for CD pipeline

* Updates documentation with new release job mechanics
---
 cd/Jenkinsfile_cd_pipeline  |   9 ++++++++
 cd/Jenkinsfile_release_job  |  42 ++++++++++++++++++++++++++++++++----
 cd/Jenkinsfile_utils.groovy |  29 +++++++++++++++++++++----
 cd/README.md                |   9 ++++++++
 cd/img/job_setup.png        | Bin 0 -> 250237 bytes
 ci/Jenkinsfile_utils.groovy |   6 +++---
 6 files changed, 84 insertions(+), 11 deletions(-)
 create mode 100644 cd/img/job_setup.png

diff --git a/cd/Jenkinsfile_cd_pipeline b/cd/Jenkinsfile_cd_pipeline
index 4e4e6a7af3d4..9aa5f8110d48 100644
--- a/cd/Jenkinsfile_cd_pipeline
+++ b/cd/Jenkinsfile_cd_pipeline
@@ -27,6 +27,12 @@ pipeline {
   agent {
     label 'restricted-utility'
   }
+  options {
+    // Because each pass of the CD pipeline
+    // updates Jenkins' state of the release job
+    // to avoid crazy issues, we don't allow concurrent builds.
+    disableConcurrentBuilds()  
+  }
 
   parameters {
     // Release parameters
@@ -39,6 +45,9 @@ pipeline {
       steps {
         script {
           cd_utils = load('cd/Jenkinsfile_utils.groovy')
+          
+          // Update release job state in Jenkins
+          cd_utils.update_release_job_state()
         }
       }
     }
diff --git a/cd/Jenkinsfile_release_job b/cd/Jenkinsfile_release_job
index 6a8864f4dbb1..c2be26124029 100644
--- a/cd/Jenkinsfile_release_job
+++ b/cd/Jenkinsfile_release_job
@@ -37,7 +37,12 @@ pipeline {
   parameters {
     // Release parameters
     string(defaultValue: "Generic release job", description: "Optional Job name", name: "RELEASE_JOB_NAME")
-    choice(choices: ["mxnet_lib/static", "mxnet_lib/dynamic"], description: "Pipeline to build", name: "RELEASE_JOB_TYPE")
+    string(defaultValue: "master", description: "Git Commit to Build", name: "COMMIT_ID")
+
+    // Using string instead of choice parameter to keep the changes to the parameters minimal to avoid
+    // any disruption caused by different COMMIT_ID values chaning the job parameter configuration on 
+    // Jenkins.
+    string(defaultValue: "mxnet_lib/static", description: "Pipeline to build", name: "RELEASE_JOB_TYPE")
     string(defaultValue: "cpu,mkl,cu90,cu90mkl,cu92,cu92mkl,cu100,cu100mkl,cu101,cu101mkl", description: "Comma separated list of variants", name: "MXNET_VARIANTS")
     booleanParam(defaultValue: false, description: 'Whether this is a release build or not', name: "RELEASE_BUILD")
   }
@@ -45,7 +50,7 @@ pipeline {
   stages {
     stage("Init") {
       steps {
-        script{
+        script {
           cd_utils = load('cd/Jenkinsfile_utils.groovy')
           ci_utils = load('ci/Jenkinsfile_utils.groovy')
           ci_utils.assign_node_labels(
@@ -57,23 +62,52 @@ pipeline {
             windows_gpu: 'restricted-mxnetwindows-gpu'
           )
 
-          echo """\
+          // Skip Jenkins state update jobs
+          if (env.RELEASE_JOB_TYPE == cd_utils.STATE_UPDATE) {
+            echo """\
+            |Job Type: ${env.RELEASE_JOB_TYPE}
+            |Commit Id: ${env.GIT_COMMIT}""".stripMargin()
+          } else {
+            echo """\
             |Job Name: ${env.RELEASE_JOB_NAME}
             |Job Type: ${env.RELEASE_JOB_TYPE}
             |Release Build: ${params.RELEASE_BUILD}
             |Commit Id: ${env.GIT_COMMIT}
             |Branch: ${env.GIT_BRANCH}
             |Variants: ${env.MXNET_VARIANTS}""".stripMargin()
+          }
         }
       }
     }
     stage("Release Job") {
       steps {
         script {
+          // Skip builds for state update job
+          if (env.RELEASE_JOB_TYPE == cd_utils.STATE_UPDATE) {
+            currentBuild.result = "SUCCESS"
+            return
+          }
+
+          // Add new job types here
+          def valid_job_types = [
+            "mxnet_lib/static",
+            "mxnet_lib/dynamic", 
+            "python/pypi"
+          ]
           
           // Convert mxnet variants to a list
-          def mxnet_variants = params.MXNET_VARIANTS.split(',').inject([]) { list, item ->
+          def mxnet_variants = params.MXNET_VARIANTS.trim().split(',').inject([]) { list, item ->
             list << item.trim()
+          }.findAll { item -> ! (item == null || item.isEmpty()) }
+
+          // Exit successfully if there are no variants to build
+          if (mxnet_variants.size() == 0) {
+            error "No variants to build..."
+          }
+
+          // Only execute from allowed release job types
+          if (! (valid_job_types.contains(params.RELEASE_JOB_TYPE))) {
+            error "Unknown release job type '${params.RELEASE_JOB_TYPE}'"
           }
 
           // Load script for the supplied job type
diff --git a/cd/Jenkinsfile_utils.groovy b/cd/Jenkinsfile_utils.groovy
index 46901b3a1577..da17c19b562c 100644
--- a/cd/Jenkinsfile_utils.groovy
+++ b/cd/Jenkinsfile_utils.groovy
@@ -23,14 +23,18 @@
 // 'Jenkins_pipeline.groovy' file and has the pipeline definition for the 
 // artifact (docker image, binary, pypi or maven package, etc.) that should
 // be published.
+
+STATE_UPDATE="State Update"
+
 def trigger_release_job(job_name, job_type, mxnet_variants) {
   def run = build(
     job: env.CD_RELEASE_JOB_NAME, 
     parameters: [
-      string(name: 'RELEASE_JOB_NAME', value: "${job_name}"),
-      string(name: 'RELEASE_JOB_TYPE', value: "${job_type}"),
-      string(name: 'MXNET_VARIANTS', value: "${mxnet_variants}"),
-      booleanParam(name: 'RELEASE_BUILD', value: "${env.RELEASE_BUILD}")
+      string(name: "RELEASE_JOB_NAME", value: "${job_name}"),
+      string(name: "RELEASE_JOB_TYPE", value: "${job_type}"),
+      string(name: "MXNET_VARIANTS", value: "${mxnet_variants}"),
+      booleanParam(name: "RELEASE_BUILD", value: "${env.RELEASE_BUILD}"),
+      string(name: "COMMIT_ID", value: "${env.GIT_COMMIT}")
     ],
     // If propagate is true, any result other than successful will
     // mark this call as failure (inc. unstable).
@@ -55,6 +59,23 @@ def trigger_release_job(job_name, job_type, mxnet_variants) {
   }
 }
 
+
+// This triggers a downstream release job with no
+// variants and not job type. This will update
+// the configuration of the release job in jenkins
+// to the configuration of release job as defined in the
+// Jenkinsfile _release_job for env.GIT_COMMIT revision
+def update_release_job_state() {
+  build(
+    job: env.CD_RELEASE_JOB_NAME, 
+    parameters: [
+      string(name: "RELEASE_JOB_TYPE", value: STATE_UPDATE),
+  
+      // Should be set to the current git commit
+      string(name: "COMMIT_ID", value: "${env.GIT_COMMIT}")
+    ])
+}
+
 // Wraps variant pipeline with error catching and
 // job status setting code
 // If there's an error in one of the pipelines, set status to UNSTABLE
diff --git a/cd/README.md b/cd/README.md
index a66fd0ce5533..41188eee6785 100644
--- a/cd/README.md
+++ b/cd/README.md
@@ -61,9 +61,18 @@ The [release job](Jenkinsfile_release_job) takes five parameters:
  * **MXNET_VARIANTS**: A comma separated list of variants to build. Defaults to *all* variants.
  * **RELEASE\_JOB\_NAME**: A name for this release job (Optional). Defaults to "Generic release job". It is used for debug output purposes.
  * **RELEASE\_JOB\_TYPE**: Defines the release pipeline you want to execute.
+ * **COMMIT_ID**: The commit id to build
 
 The release job executes, in parallel, the release pipeline for each of the variants (**MXNET_VARIANTS**) for the job type (**RELEASE\_JOB\_TYPE**). The job type the path to a directory (relative to the `cd` directory) that includes a `Jenkins_pipeline.groovy` file ([e.g.](mxnet_lib/static/Jenkins_pipeline.groovy)).
 
+NOTE: The **COMMIT_ID** is a little tricky and we must be very careful with it. It is necessary to ensure that the same commit is built through out the pipeline, but at the same time, it has the potential to change the current state of the release job configuration - specifically the parameter configuration. Any changes to this configuration will require a "dry-run" of the release job to ensure Jenkins has the current (master) version. This is acceptable as there will be few changes to the parameter configuration for the job, if any at all. But, it's something to keep in mind.
+
+To avoid potential issues as much as possible, the CD pipeline executes this "dry run" and ensures that Jenkins' state of the release job matches what is defined for the release job in the specified COMMIT_ID. This is done by setting the **RELEASE_JOB_TYPE** to *Status Update*.
+
+It should be noted that the 'Pipeline' section of the configuration should use the *$COMMIT_ID* parameter as the specifier and 'lightweight checkout' unchecked. For example:
+
+![job setup example](img/job_setup.png)
+
 ### Release Pipelines: Jenkins_pipeline.groovy
 
 This file defines the release pipeline for a particular release channel. It defines a function `get_pipeline(mxnet_variant)`, which returns a closure with the pipeline to be executed. For instance:
diff --git a/cd/img/job_setup.png b/cd/img/job_setup.png
new file mode 100644
index 0000000000000000000000000000000000000000..1605a3cb3e3d809ae1d409c5fa5bfe40100f40de
GIT binary patch
literal 250237
zcmeFZWmr^e-#3hk0xBRXp$s7gGPFpiA|RmT&@zCCba#s&NL!RNh``X@LkTD#C`iW;
z(u{Q1bFRG|_I16NH`o2XpWYA8J`RVq*Q|BMzvFj~zk-|;@#)K_@$m47rSIQU!oxdF
zf`@mqmGBg}vvM%;5D)LHw6VCjg0#3ey@HM9Q)4p&JiPn<VQK{G%3mnrw3MWcya?|T
zRrxF7AHPqOC~@u;yq8e=nZn}-gbWVh2IP+_UOp>N%3xqCrx5E7DDRfY+n_X1)X<<j
z!-E+9@GN_$Y6I<vw*0kcI~jBEuC>bX6ke488u31K_JjgG)4S{UeDiOlq!irOes=tv
zE@2}cj}Co=&Dgj;-n+yd%*2|#(V2;W=zzKSl>?QtJ`81ec-Lsh9wa_ez+3XbTfF6k
zP$k5BO}tiIa5@Dpbn@}lOApRHPCspEY87?b(8Q{!{|1xSnI$&7&$mT%!ik?*F#jkB
z!0&q{wQ=Lquk(rO$Dh$&8l%5MqC;qX@l*2Y(@)bl3Qx#I=`21u2<EbleB0hm+xTAd
z$6b;OEh$^OeFuhu`z05DGW$N}tGUziTH<<|iLZM_6z^5d7slr{WNzt?)ARUjW~rwY
z`%$G#-b{MKy`EMk*7id9#DkYZFLrZ{UVgn2^6F<(qtRNF#uy>(C#x3;4P$qaZ@L}x
z?$gGlO=Q0PdjBPLoyILTskwnq5$WTod3pHq8-C6GXnuy_a|ao3Vm=@qwow(`k2!xP
zkL}X~XXN9;##05%Uq%~A`aWMILhWMd*D0kL#JP8S3t2SoJ?}=U($k?RwPLPLHkepH
z)RR+s7j78wET;{FKc@QH=dIAEt8e53E}<lDo<7s_BSibW^qZo!$1=uxM$fx9UfdQ%
znuaP%Y`un85r3LoPP&)HPKzL<{NPI$8b;B`XuSN1%XVvgoO^4ef&P@$9V*>pCt*>#
zr#_z?3mFbaZpw09IJPNvD&LE+)R<eGHhuOIQRR*2=NB38G0@YkpHXQK5F`8H3F|h=
zwxE|4*7~si`9vr^8{T(%x_#BOs%!P%m1-6guI@|O9c$&LBk39xwTGqoe>b@(Udd8*
zMfNctXAqS{5WT?oQ>SyZ&zfpX31M-3)Em^z>jlE2WQlJVUs(kwTpN7P#unzTLVD%_
zo($EMyIQuB5!~Y>uezv78%|si{n;2*ddaH3sqQ{Zh^lejS2LT(R%K1c_8`lby*#@p
zxl~!9nNWtXVa8Nmn^Jp0k3vPhdj8dop4L&cjh`Vpq<t@`^R83qe*bx`AZ)#yep6mS
z@0HAxxw*;e%nru{GwbRzrXAPdmjl8R+89ZfUmlQ!<^-qDei*F!Ue&bc{WMzvk?A6O
z{MXqTvnW2n)K?8&mL6n*jATKy4a;^Lizmd^Xuel|MBeJc5coL%D#-pp`%KKt%4+eO
zq4ugP-g>>EA8I$#*2Fi*djKy+gGu3Yz-@s?_E+%W-@N7DG3DYH*AR7&6S;}u5gr@W
z6?%C>nx6Q9w|1M?oW`lAUS+yuHwnUYPpY3WskxDRavo3At196n?08dc&WKPcezupO
z)ta4mY65BHsS||HU!4=E6MI0${E60DLWqp#ypPXS&ilvT_&%ZUdx@_|Z~NNvmCO@z
zC2upy%dg^Jdw;h6$<pCJc$b!5`16^c!U2i5e-IXl(R|~)Nj;J9-RQgk`BzE9r0YhP
z%Fkv=&VAQ5;&Oorha$zprj-lnPDGM4e#+$J4GOl-<71&|se97vmU%p*ZZ_{mCh>l|
z8prkSz^ZzGz7vL{7FH*vM!%He#rwB6MT~K8l2l$2_1CGT8*$%cJRYknXLWt^f|HN5
zk69hdUH-&?++eF`R;NWC4*B%lV|XoXcWt{al`}X}Hb(rB%%IrdLm|oeYYt>P(%0TF
zeF?jGKj7+;^otKR9WEVg9f}<WO1v|y8h&OH>(``Sb$&T-!&DYf`PR+HEowJXNbGxR
zuktRF^~Ea&JO=y*%qhV(uR9)IO1P{*lkWB7nhBddS<(Ccd%_Y`vhL~0$@<CTsa%Ru
znFR`|nueimmtqM77L0svQ(lhXdGRrUHQ79sCV42S>O<!Y$?7>5fvQ*NpkXo5JJs@@
z87<#vvRXRHXW71Gb(wcpW$ru(O)|`B`X2pJElDk<M1CO&nIj^Bxf7q!-V>jrli{gw
z;OkL+tiGn6zAd3fH@#NQyWo`kS!rJq-%;PY^|$I-eHqVIP*K%~*BTH~VK|C!$#Iqk
zzmyD-6iR-SER!4`BoMS6L>%07IhBf%y5}+-kLoSXk0;(XwHmS+{xU!sqUrQFpL5;4
z{rJnvk7N;2m7JH1&p&3kmCn&;Zu9kL6;sRY!Q1V2DaL^_!d!!`4Q5GwECs<&Q(Il6
zE(f2bVxrREv|0E(Fqu{s==^*Ow!%6|*}B8Z!pgw<fnttTPJLDL7OOamEo+ZDZQgv|
zWA&37{#o7XG)3Fq2CZYc%NkvoZ!<+qZRc*9zF2!YM>2PeS}OWUv{LjC&xTogvDpC2
z0CzFA*xf<Z;lli6KX?C7acXY?)^@(9FTU7wd}R97>|(5VVEqND3sQ0V{(v#sjOEI?
zoexr7QZw4_85Y~dt(=;YWlIYzkqwc}YLQU;(bc3fUtU{edr|Kl=H{2QZAIQeBMVpw
zLy2SwL2^{GzGOs*#ic#7prOcBPUfZ<+LbRu1Hu^hj2#+M@z5pO6k2myb|Gz{lW|3H
zYE{!nGUS;`&8oOidm84;71r|)Eqyoe7Zxf9szkRpw?DhRcXM&Wx^cP9xmo50_b$z+
z%yW6*S1VMX^zhpq-rCuu++J8aV6I`MXV*2Xd21aqes8=uWbSk!(N6*k62z&z=eM4F
z5GtM0B3SXB^QQCd^(7Q9ejzl;XKTY>!PkCwNZcTi;4a2$uWY5XsV+D7b8bLxh*kaK
z&&BtPJ7hd$ePqF8?yrYm+cJbokR>H0t4a=yXe{Dao<d@rFiR_q@j)LY7$tk|i3VBw
zziudeD8?8RpX*XLq`sprZ6*tQIo^;JQbD;uCCarQ;s42h>4GVvh|J>s$Y?8p*RT16
ztqsTKK79KyA?^4k_6_+LERze9veIRx;Satk*)l3}m${6GKdcx(H6APMwo`JE*)Cq)
z|1$cvNA6eLm~e_)^ICd{a$ss)>{A|A&;HRFm(H!u4TE#Hl(-z8x{Wj~&28;PRkoJ5
ziX2nRm0LtmF+93DPj)gu)t}bz<kPHXmp3$#RRR@^jC`r^RJB&}o|o}ck`orq?r$k0
zN`x&rM7cd3kCo9Uk0g9jc*DrgUvDG16eO3PQTPZIGb3t?Z57cdG>eA4mJchTxLixT
zbMnR8F|7vgq=cmYq>=)qr&wm5o8cFABC)qzzsxsXC%Z1%Ry##FS=e3_S7G$a+d0NL
zdg|&5*Hj66uC_vPf~(%oI-0<=z-5X()mdaYYPE|u9a9*l@s5>cZkuO$pwVW?Mq?{@
z;W&DrkV=OIIhgm!^vm}3c(@Pw6nuy|11`!!&LX@~)wI!tA!TdP?9LlB+-fBMME-`n
zN9|P3U|zGX$`@32*9BGH9KEb`^%WEBYLQ_^qpG>4ly<6mYGz2fg|=mhN5eKLH$Qie
ziMXj_PgD<Ux@*Cm($VF{SHTaXS$I?|Tn@Z9{i36<^78fw{!lZ&V!_nEY-HNp-6@*y
zY1aF+ILl_B3Ht`yOhY6n$Fs?Ed;T`2tdh(eaeICKeg0gByq;(gPZ_uQ@Yg}@GJ<)<
z`K|G(p!l4H;NbyuXjP7J_1=E@iJ%kYU+=HXPol$PC=5(BO{Z#?2nGg@_mk}2+K#~b
zWs-K$@`zOK60Y6vdtIA+U1}#(I&_V;U1YdRq-w9tUz#yHF2*T;9I=|tRV}&eGIo1v
z^*fiBAxZ3-sOutZ=TVDu{K{9#N_N=h*PW6RG$J%#Dc#wNk;@f7y7{wLK2MCaH0Ahc
zZ>_->X17f9dIWM9b*i29n~5hLj(%k<S+*3e*0ysUX{)R7sUY82T~Ha~t=2hUpYp`2
zZr>yoC3LkK)f=l9)PA0wTHWG#@J)OD<$l_1Ue~xUW1Kw6+P&3B+xp9NL-Hx|1THNH
zw>JxZMRgYJdA``SPqb9h*|6<fsil<Fd5<34H<`~^-{0Kd$vJ~0txR_^-F9daT(9U{
zP{xSTMy`3SCT=IMG!@H#kN-tNah618>lWID`B|;kQ-foRj|K5oZ{tx#jO^>>PsXHD
z+_U9Txw;siJIRtpL5KH#<YAHa6zvIJQdr2j&Qa|m75UT0(js~mzXb@jv?$k0cr()0
z4b;{~P~g4l$J@L~4TJS^@NHT%KUk;0YkF$Z76HFfS5Ql864KH-_F{y2i&sF#Gx6@5
zC_j)}5Lw;Vu*JipxDNe0CarXB5fATJyfH%EPW|Bn0ewqzHoYg7j}6$I%&oxFcz8li
z0^rizz)p|e$=uAsR=`P^;jbqIz%_K6oq_(ZN9;_68Pp#t(2HBz7|?UG-Dcxp5IIdx
zPcLNi<f(wtJ;~p%gZ~p|FtW3=5@2U{baZ5M<YKe5F=XfD=jUhVxW#_!7Atsy)z;a<
zPS1(e!uIOlgW$%wXJD&uV{Bz-Y-vFcjjQ+A(%w#(fdQK6@V~#;Y2al1&rBA!zqbW8
z$PRtO&dJ8XemFLGRS3E(pkVA|V5WZ0*xbOv7R(`Xi<|eh&|fe7$5;PM`7f_({PQa3
zZN7hd^<TdF{i+Z<w1t1!(%<y@>n<Re$Y~+=L*k2^K3hU^5%2?Md`}(${vL;z1O7~c
zkDGu01=q(Wq=aqO_VDn;@TBkEK{y?oA0n#6>Q{*_PK`v8@WZ0MJg~p#d;bI(FR^cV
zz^Qjycgx_)cFeb5y~;bUbo#n@`u>~2yZ3lyChy1cy&-HmN3e6?7Vqkr!TqaXgWol$
zXnlBLYE8g!uX`mr4zr`-YF^cry1->jUxMiISc%5GbDYa6L2BnbJdRIzj$Z5;-myPl
zP-WLFcpmK7G)q#O-yT$^kw1J=_XqwBB9f5zS!XUB@jg9A?MZwB#!Z5naol6u2r;W%
zdB(=vqbJ@u{@h#NlSKH~@0IBaohBp+Y0_AGaO8?tUpyPp)p_83=WtiIi3mw}yqshY
zSN^99uh-|#!C*|%Jy&qFpprgTTl@Nl9qXS7gWK~DFnYIM?={?O)70QKbkyk+hXdiB
zJX{dC(5(cog_K-c6Z*Xwuca&We0(C4-<ALMvHw`<H$OlXcRnWHd$_M&;S;zM9!W`h
z4jJ%LGX7ltA>mf8fyw0E<Zj?r{iiiP6Eg%`Y2#TxT+ynWZk)IG#;~j9g5*Nm;b{Nu
zLN|yg6TLkvOp`aFE59`q^FPVj|8o&H-q%EulE@VjtyCU81r_R9N`N1Hf)B?kFN@Qy
zwhgrwB;8g2g*4&#_ykQIW|x?8QZ`0NNFo<gw*KMBDeW|X47ZF}Na5JPaShPw3Mc=|
zqc>Mi38V}i#dWwUNgXjOtJUBkucO}cB7y^c-C-QoKh&W|VE%T-&x?lx{po_L0@5MJ
zUqLvMbHo8<Y+9Lj;lWYrk81A%rt4Mq;AO;#$22{-aZHl;k53=YJgEZqf;<^}Xd^f-
zLxPt$`~FB;p8E`*5;=AF%-&5fS!luIqxC%K3?R5QXE5#a-<0F{1+jAIoeTYud)t-(
zl+$@y$9K5wSwNFHZZjNd5AXlmg)TmLO6pu^%Hdtf2Vl3KZfgG*jt7Iy1=9|b;e>nD
z57;NqR&T{at@_gi2MNTe(GM=yes89R?;MaEg16IThYR@A#k4%2%6<%GGQ)4yJ+K2e
z{P&3#cMs|Nr;DKX&qhYlzL#_0I9wwH^q}oToHEg$mWPuIdX7l&p02?9KiiW5<W*b#
zb)>beDTUNvLwWufZghPmK+Fen(RYsI0tW@e;yRW~R}Zxnm{GY({*6z6+SK97?WDn?
zWWQO6A8IQLpo*R~f8<eGckH-0w9N9btGHF-mosuqdV6y{ZM`2VvOS&qX)@k(zi@ZC
zCG~p5BOCQ8;pNX)X9AeBjra|t48o;zJk~O_wwuJ8>PbaoepM`>!=;F-QzDS%DF<(4
z*`x~$+byQ!ZyPK18>hxz-mW~#VQ`Rvoio<4xJu&Wq4xsSN^PyY$MV~m<remM<}D`W
zj~~KRHJP&V8ZXZbEqFL;+IOiCRJGCVFZvbk{HiE{6)ZJUr(Td3%1iMSvU5Aw!N^O|
zx!biQ&zE34j3cxijAt+o1LmFMp`ydzL@{nFDH%EF^2B!Pqx9**9l$t-)_g;p0}K-O
zBBFt1#%HJ9E!|rmNEO}~<YF!Qp|n<EGVt7+JK!4MCi@w!qu3<?c<S)x`=J&2CJUH#
zWAA#nIzz1cdY^g3C^>AGGJIsDn#Jr;?hbkV&&4!wa=p@$qBxNjV+T4lFkQVImh+&A
z*43<qwzk&uH6h7<OC1I7nLo&bmmJXG*e9!plDP_GHP?IpiXTiVoGlyU-lOf9vQf3*
zzNT&9njFj{(?sX#CVSvCy}7?p9UlVZBwT9dBFt@^8WTzDxx3^$J{4_=UR{YlsQmvx
zTWe}q5T2IL!TSN*cHxNq^^)ooAXtL#I|CTGl0h5Ul`9i_KmsG%L=Se0cWx+6S$TV3
z_|jN#9%q04q0ix+$hSa=V|?cSG50TON{LC(GY>6g{Ae6{VGl_~Hb?BbRMd)Klq$>F
zrDFkwBhSwi&qv><dG39|xL6qHJ#qSVXdlF$)!aV=BAvptxkFY3`PBH3l7JDtjlt+f
zrdwYvJ-D*UCf^si7d-tc(}Q$1$GR7Bn)er8Y$4|@!GKMsl<T>B;*)_5wrHxcc)Il&
zM5J8edisHY)`>o=a=F<!*1lVMF1y0wy@<PurcvSxlO_`T@f^=R(}06x=X*j#6}35^
zzv665`>^@Gr}Bn5dTY}7wFE`f_;o)(&)kWEc98+AA?dd_Qp}6{J|?mc3$6F+GX=dL
z&@{~o3)k`3>Y$mV)V5PB9}f}iXpQ3P<Z2gE4pP-LPI<iFtcK3tTIENFqTW{T3@vm(
z`u1M2m%Z%g8{?Uj2sw7wzLt(T<VJbn_H0J`k|xO^-T!d$2YaepM?i2wB%Ish=W8j=
z$&r^=L+*o(&6)0z-5zk?7&2YR2oB~Q=8JV2^Y0qyH)RQPn+Q|u9&VEK>GW7lrVHDl
zaU8UsF%;d=xJ*T5#P`vqpc$U=fvUdWG)LQ>czb(yr9IWQji1pSYpNp)Y!dBi?+Z#K
z-(HjFu2jE4gtkmtZf9*hF^NS5ws>Z#iO#b0kq<=<i06FKPPUy7c~eL|7QI>b04sDM
zL)PIu6&umUHy*V5>4JPAOxZrOdVkv(<G#Uqr3Gb~+e2vNJCjv1EYPcGpo33HD!lsb
z;!47yZ2U<&p@6zf(Ut_KqcuiAqMnWpISgbW;!GQdJrDLe2Y{4lZrndfz+Smnx0*=m
zesjpCg|*1ku|HDC*@mb`qJ|y5VQ-=syzu#IC`;I?08MAMDC)B0GwZwt3eyt7H8|hq
zbaLp&PC3CWY~*`EN_AbY^Wi}<!Lgb?T-89kQ<UX}xrO&uJ7Y;qXF^ZECZMrXAc`oS
z|8eIJGLN$&x+K7K*y80!<8CA{f*Vo7sM8`_Ki{%muv+~t6=C3>L35Q179(J9qH(&U
z?7W_Rs9X*tM4j0czxo|E#7Tt_>)dMFrmj}eZ4Y_+KKKw!0k;!gRYgeBsMI!kf&8Lh
zzp9pbkzKcjVG5yUv1w|Ijm$Q%3_}(@u${W=bz!3)jv_Es2vC2kLfE0p{>JJ>L<g$`
z`Xtj(>o77E=W5~dJrIc);67hl+uz_@Mn--N67`RSv^*v1E*l-P?B>6+t90C!8ahP}
zw%YGRuzPG&U}D$%4CDLxyCm74dv{g&?QFjeb-RYGTxnxXZCL)a<kl{-W7dM6eyOXw
zDH&ET$GYA$rPp6*982<#>Hilya!5ibetU6`04EJ%bc7G4M@HuR3Oa{f7h)}{*1E&f
zBQ4Qe9qpd`D;HZvRLwtldy^e^YhXsc<{7f<JU?5pH5o0NQU!cKX1PKAoSY!`9f{Cl
zjW3$dFsEu=Chwx9d*D*_O6<ae1*axKnO<z!RGe(dkb{=M{^od#>0VwQjqEbgs`eyn
zZ7K2;L65qA@WuAZQD5qhKwWr-fC3l<Md)}quPnCg@K$eqo%tqCYTifbRR<`Vo#Gj$
zqRP~AE~jMJB}HlUYN*f~HuUD$>ROIxmHFpe;aMRQ8Qe#CTyb&`l%U>?ABKPBOdJ;8
z-UAjD<^!+P@TX~=M!e4772KIG=-k`DV7gsX!c^hp`?HQAPpqsUJDD2mpxM$<zA<Pk
z-)~vHr^F&UILL6;Y1nxxg-WZC@XRl;-I?_POG}a4dsCkKhD5|X3oj=;uNpbEu$J6j
zfZwSnb)Oa|-m5xIf?j;V{=hKCT2j+xCWX53!no*sZhZ&4$5Np0zSBy6L=}ts1hbCu
zbTu%2vV6ITT-E;ck6E-Ur;`GC7G!!J**+8)d3Hi``|6`hw6ZLD_gX|~j*1NWL1a*@
z{QWTL`ey0{{9%k*uQ!RnO>F6S2yw)v^pTNwOv*AU6>z*>tGBy1?z%(ui|C4$9T0^H
znHPNJF>sx1jhew6>|wM*fZB8^`7m5H>doF;3iV`>-QZaWTw!>ihH)W>2~E6H?r723
zU*1mqQJma7Mj*22)xTqM70$Q6*(91h9Nr_>!Y6ewr*p9Ft3QXiH&K_y*EcA#64m}8
ztGFLkHsE>`AI+g?B!3zgH=Uc#1BpR2(aQ<%>eUqXqIqa1B0KY)Jf&)$^<du_r>XW9
z+!tJ15_@z!-3L$suJd_P1G?IKYao2vS>32Us6uC$9ZXA!%E+>nJ;|!3tgTh2-G;(Z
z^&90p36-3t-I=SF2kVv~P!W9oMo}*pEm6bq+;@#N+Y@rA!0MNxEA(~<L^tmU%%&w@
z-X8m^h1u>d?jqD8InvJ21B1Lxb!fX+lfh0pGg*LGr)XkswXk#+GN!yfO%i9{tznEU
z>ai&EaBtc`Z&cY0S~qn$hGBWpOTjZ2;y3U0p+Q{Hk=H~!VADz>IRB~}_}}pT?U0pr
zQPI7vcF*hu_yA+Pn@zt}J+bK?Fo4AwZtBzu!!NJD*I7fUL1Q`8)-JjLS9v)k*x;2T
z@H@WfN#2biM;!qm@X-~Wr>M<d$qBFh2>1AZDQdtA9YlM*pwPUDE$oy_IX|K#%i86D
zl!Ws+0f+05`B80XvwSXFmTv2_6sn}CXRSv^)P1W(hixGlKIRDQWk33LFCWMdI=i(j
zwJQ+7Dq0^<HR+nKeC4(58-l^yc9)tuiw7(QYz8asGRtRCGcmSpQ4z9y5jflTpRPvt
zIFtY-d|6NXtv6nr*EsaNy#v|GjWj*BCOUx^jOYdis&J)EFx9y8Be8%za5h0ZNbsfi
zFr&l%jJ&7Jg4>j!R!AVHR7$N8-A->)M^O(FaG=r-=FqDLqCgjA?0G<J$!9AyCrC4c
z6NefVo_j+Eu^$AX1F<4K?n};Pi*;wFw%r(y2s9r+S(iUICNBK<hB#F58_lh(GO~(g
zfVq+%a8@_VYv?$GTzp=<b7~IeKcYJbITxCF9s>-BVqOpL3~v;}Haa5Ex1nf-c@LyD
zmgOL;(F%!*2nqGv9iL&fbe6@Q*XQSRswd|C02!(x!%P0b>UG;=f$X&YvSp`mj^jUx
zT7wUA>=?Ya^JKV$PhUZI@DxF^2@=sx{e=g3KPq{Af~kv-DrZp{9km2>gSU2js`uAs
zKu~sb&ktsEKfoX~dcbvfC9d%28(AJl(wRMG<Wc|>`Spa*wA9vC{3>V00v;C`rt?6;
zT?nEW6-&Rh%j8!fE!K-2*ZrSuhL=AxC<9qf4Zp?ZBHN%0WkprMrBquBC7Z^;`BAU7
zO~pDQ1(rTt&<gQa7y(XEdmHk~slY0Hn24OlJPu^l+@AWV&q6?O0Ob#WvUwAEz!-mB
z1(A3`iq4^L{ZA6%dA+6p?#EcM=PJ%OQxO5K>fH=MSS{+)?Wns93=H#3$0jQw5v`^c
zP?*Geb|ZGdjWO1$j_9l3hapFdFt7ng);-%!&Twz<cF_a3bQV5fn|!6>|8m<N>jj11
z?w;XZbv?}2adZ7k#H{-PD#&R5SUdECl6N6u1Y9NmE0z*~9AKewXaDdX@WBta7yU#B
z9Wc_2vF~{Ljgs?oI-OLt?YrzIqYOVnh7Zdf=Q5jaw>^`Z3gVq?Jx}~uD10fbIezBH
zc9V@22Q5RmYluO7v=)m#O^b%3iM3p-xNTrc3@=EOCoL9m67whbRR}z`SxeGe961x3
zKonRX`AqIk6^PtRvH5k7=#(!vGY<^wy?w|dt+P8u+4=L!Tk3TvA!mZ07XfC2cyH-x
zQI{&SS3TjTL|}l6V%ee>0qxC&xr2U_jI55=QnXPw9==Clw2MQ5^<MR++f7LE++LlT
z0oKZNnr^PP)~U^Dc#@*)RQ2uzd#Zhpc8Rmvdrp1He*IA44oE`6-F3D*B9wdxj*dUb
zfB*!%(LbbnIV+ILvrDhfk~ErwDnegjJ&E9lv~yH<YPBrNu4`L2kTb!L7n{cK3{G`~
zYgzQ=&Lsu1$uz-iABgL$Jp~R~7H9=%Pi7U#kk`8YDr0n?)54f&d9k<o&$@BOZS!<{
zdxN}0&eC&dV8E9-LjAG-YPMq)bJcoL4y)}F2(D6e_GZ;mF6&o}OhWb!4PsLs#FNaD
z{D=Q(F%Rz~KqXv_PlrC*%LA&gHL?WR0VyhB3tM`;H9ARATM8WO-b|a|g4r;4=7yrg
z!sd9WWx4_%Sd`Z}y;ga*u?zLfMk0JDp*XkYFUH=)JVwJ4!vC|8!Sf0M+@~>~Ka}O^
zL=b;-EK0%oN*gG&K0wSDN3Sphvb+fq{8d`GzdctX%m|-=l*SlDF6JBU>9#M(RqsQa
zh9q2*goYF_H!ZEuX*NBzSO}vaxDK>_HEyHQFwIXe1SfWfJOAfG4DfRG&Oa-Wf>gP+
z8Oo!enDhm^+ZAowuiUnkJ3z$3MRxky!zY3W1UJ7+(LIF1$bbY4<eGaEiFdk@u2{SI
zyryDe;zqCW>wHx0%OG^R1!?^ZSWR$=xQU|iVlv&Hkqcn2z=v^~!xqLLv^xO_n1ih6
z!O>By8k8eR^lw1k_RIB%4-%Bxh4_1H<<m*4tz7MeL65yoQrsUfEBGs(R6@XDCV`xi
z_H~%OUyj~!`M?%`6khMW3OR13UtQ_JsUx~V`Yc#CDCR9_6JQ4MfO0Ht1xsHgNDWd!
zk%>{lN%_#i9n$BY3l3f=vrC?Y;jFdScM7N;_x;rSh&ei&&sS!enRCp_?%@+;7<lPN
zje^LZ4Ol*RCe*2HkUAR=+4npI0<ZcWWT!1<;@FpSQ26-(JmFXl?2pWSwz}snECmnx
zpS*q8u{zVKjOdI`!+v3mXA#l6=$Uj{&xgPMCW^zNxbJBZ|NAt_xWg3lALiott~(4Y
z<<!tO@<WZ41k|<q`Os7iCS#TBkV}l)1diIcH~$)|F&2ntX|xh4j$c%iVDHI%!QkWN
zT*sdb9xf7Wd<I^nio7O{3vwTb0RdBtu>&8U?2V6_R$mM(@$7>)S8(yfjRY{(O}TRL
zF;;&2k1PFqe_ouRS;2Se#+P$An|wh5jCGMw8+=%6zyG(*9g^g)iyA7(1e|#L<Rvbe
zA_Bb#zJRxE;G>l7C~Km776@pFm-MZBzjsxGe-Vt;AWaE_Nj!=;s?o-A3aV<?T)V{Z
zdsn*0p-$)6hj_aV>6u1qOS7_iP<M$B2ZryzM6HBD?0~>a-0^X~zqiSOga&VU;6ic~
zweh#*fW9q!{PgUQAo<Tm5<+pk*Z&l%zwStay1LanU2R-EC#D+*W}EVF|8kU65T2W6
z0hQUlMq1pGyoNy(yWTb&i{scIU#FS^ayRbwT$mO|ryz(<-~GAFa0C5OEVe<5E%#vR
zGa+vD3yNUb3~l9xM{z|gff-Dvt><t7H@KG`n2zK^>*ya>cW4_xGje1^AqDnNmiiFz
z#KNS4@{g;-5&QyZoLJp`SmJm6w>b>D1YYChQaL5_KT!W~kyXC{#LSfIDXvB8<qtKI
zIVby+ahijh;op}1?F1N|HlgA=Zc#Nb$V%M*iHF-R?u&odmsZFc43_Z+;%@8OL2+15
zPZy55|27TIv`5weVqS1s78dwDY(gk_ZO6&xY%p%1Kkd>h4~i{{r`o4+qmM&9#>%$L
zDO`gKXKVg#mkE%l2*)2kamW>;t3c#VNgT}s{J%oTYZtY>af|xTDD&Uu!L51G6U--a
z`f%(2w}p9*{{OZx|0J6KKWJeBMG!W1JW_vjpF?GZT%wKA-y-eB$p92l@<uZUP7x1f
z{a-JrT!9|wTebz^8ms@&x5OzHPILY?5H4W!-U|-AINNtF(i@l=*t7W~E%dXRL12;F
zvSL;al~Pg}n1|c;+DC*#&+xp6Aghv?im5-$qg8>6uuN^NIUMLu7gTf5?%YoW$N%01
zhX^oYO;eRF@FR`pHi%_%NpIAiI~-IKxJj-1c7GBOoW%qfp(o%esmAXrhp$P3Wm-KE
z);`i^9y`trO#9^*hf$rE2!J-tCx79%c;qS@BLK-a-^<J6vTiDNu-$Qk8^TA`1CAYc
zX9T=+RDLXWC<q|2`quRC3_jcf?jjjNAj#yi9X1^}<iTt!pQfH1xky4gP=1&<(<jAw
zC$R-UBv-@#f<_9m1thT+sih6YaZVEA-1MhQhZ)8n1#^!b?Crqzi4RT<{GoV|%y5Vc
zCpozN9Bv-E_(l$<%jT#c!mYA~15D>fG--A8=FURSElnxpF!PxH2r8+(MMH<)_D@vx
zBDxNV^T&aDoX2kr0<4UTf}c3lr$ayZFBiI*K*>XjE^#4$ud3z~RR5w{nP(y@BnK7l
z{nt;wz7K=a+SU1j_K#j(Ud2r_xKRE`+2w$FLCw@JZy8fTrgr;btN{1JXdZLER6&P#
zfD%%ddMAihlg02)(Sc^gZP)dA4_F-MQ9UZ$vV*w1PEtPN$jYrdecZ?WRt^Be-6He7
zlc05+%dqVM=q4(Z4T1812PpQ%QGiD9MW%(#<p0gU=OWd-)VFT!3xO7&fkZ1~oKw;x
z8wkX~oKv}6i5%WqfjA%fOpcKw9Di2a8?-_mF`d*pc`BnPZ&j%A$QbRnXnl)6u40wz
z;=cTau@kfmdTyzY)(*Evjp!~+)esWeNzjKgq5-z9r3&gR7lLwX>ef_z^=|rIZ#apA
z0esNsk0SbSg2(aeB&tTYQUsK`C)z>BG7LhUEYD|Tm!k{<T{oG4?p#4@U6}q@gDt_t
zJ#CMRP3jnd>a9?n>)p2zx0SCEWAe8_xZ3v8^?yOO#vH)IWZ{F|m5FxHL)WVF4i#Qk
zPZuf$W&Dq8pw7K}e0A$B5y|=%{v}$k*XPnu#Bx!$u4e%Rf${RsFGVA|k92gcuD$yw
z;QEt*<3dJ_1e6ffu{uIh$<d57flbg~zNSJDT~xk-w90ku{&g?G@c3_N&fm4~AL+`<
z*NXL{9(&csJ@ltOQf2=(yEop66+N-WCrBpj2E8*K8y3^dyhb0J=?Fh^*nC732g&i|
zn+mMTFr7lEz@E;d&MGeyP;zu8d&nSeFR=2i+B6;~(2j^0QwwL#Ub3?nx5~{Jw2(~T
zJq{{~wgK$Jl+~LJDB|^}rw7CS7YrXg$xwY>mO>PW?F^_ZGiMlBl#cpd7*3(Yd+uGO
z;3$7wn__cs!BgkgCMcU1;uEM)&oASb(%>x95mShfy&M?<K!>*$WfS4c9st_H(22_$
zye!TjYHMdYQ&WMCGXi;Of41|#kQYK9F?<3;Aa;*Wel>dT{UUMBlZeD2c959JPAB0s
zN#pxP&<F>Js11VAkVCp1ad81jAI=;yq8nyN2AlsXb=NAF_t!6(gpA`|lvyLXYJEdN
zN9+&xRRV+#Bl;j^d6K{tf1wz^oCz5HhvpkcMo7Qq^lyyO$BMn@-a~5+yvKz>L2aI>
zA{u9j|L7imyZFCx$*a>~rJ*B3;bOeNGFGHxkw@(J@T*wh^#s4zmFQUIo*51m-FI>U
z)oQ_E<HQ#fGJ!1l_nY>T^R9Z}8MehlJ#CGA{n#zXb!e{R_!*X0P?>vmwNpM7GSC21
z_4t#;9>7GrGbP^8m|CwSqu%5zdo?n`7j-}MC}_j){Zowtuf50V1v2GGKd9R><4bKp
zwJOJ)UHVwlamc<?U@r45kjskO0M2tKG(I90g82B+c{r6%zBe_AD4Wl3=>QFh@DS1c
zO}qWAsZ>hPQ=Zwu9C(-?R9cvU>hO#|W8D3U`FvTZS?%jqRwiuunKzQ2;53QcEC2P#
zLz4WD7hWO&#y1~IbRITeCv^5YpSoxeA+g8)O1yk$s(j7@Z5~!xj{iD@pknI@gnKJ$
zEDqYXW2LPY**&*px5p@})f<;<Nj*5KHXGP2oaAb2Plc3uaJ{LmC5!`u9Kh^Y&ajn^
zz?fAf?OW0uE|~++51R@bL6s4ug9xyUGl_n*a7UNgGsss-cb|6c15{Y*W<wEZfqw*$
zJ;V^wkLg?J3i5!ERA^DG95jbI0IEj~o|g@L+*$?WCx_PW2fOVDAE4IJ-YBW(eb6t|
z(FA?#j@gWyI3#F$rfhD}TI_Vj?<0<{@(w%cyMQum2ZUULYO+xMI2GU$ipMA7KyiI_
zv7WSNophoi#ldPA^6}plmP&Ncrn5~(N5;&@j_YfTD93Ljm~?q!3_-~uq<fl%nUBw<
zxQJN|G@AD}KD?Dft*KFOU_3pEi~vxJb!IZ!l5OZMqr4me%u39qMQd1zhUak()al#C
zT6${LT_&RsqDOQkHIJyMX@`B3i+wM$<S&mHTG_qa6Oh|ys<YEy2q1(#*^WEdl2iy{
zH?e2kL=(XS3cda4-f25z{hi@fMcr%O-cXOfb0#%jq{&sXtC{fmuj+$+07h~!;TJ(^
zK`<!RJ%HFW3P?caX!%4l(34{;FnbpL0IFds9`MW@KTc%%b?Ar9OcPs1zzkaX^NHUN
zwsQ{PiP%ljcSaEOs~^nVvVL!C4ggqW3FzFcG#b%ETvGOfn`J|$IaR(F6M`24N|ElH
zIs4^g_(2$ZKUx=~fjLlsu9l@X%e{krHxZ#DGUU2Yb-h)6Za$an*k^-qjgJ%xA@vOR
zsj4xzj}8sbMLK%TV(jDraHwzry+g9~{JFRI$ID$OP7+9kZFsUHPl7gOXP26e@HEZU
zLNd_@WRLyF+xe$r3IW>dtV^4fBYIAIK?VYSq{Hl6Ih)RItda?P0BI(x>cdB?gKqTN
zAQ?p!2s=6fB1~uQR}&rxP0U>+&Gic>h2i58HwF5qL^jF_zubsEJ9o)rcbqPjw`%6$
zL=2&`6nQ1^69p_ZqM2ucN1+1(3#(x{ZYk^dvaCgjt-T9jcg7th5eFIl8K7ygS`fdN
zslQfBh;3K{jeko->N`{taI}&EbB$}XZi}`3=rA=TKj<VGKy~u0wFb#Gx~R2Ss72LD
zp=G9s>2lb4eunM-!T!R*aIU;nu2=n$7*%}2*0(jFkru&Lwc1g*ITn}-`YKl?I3p85
zq?33vc%SpRcd})S?*&-U=qJ)2WKu9iNpRYA;G?|fcBg^shkev@@4p~-8<)~eNjC(p
z(!kt+xm9MV&g4EFq-wP&h}n`$pH}fK=_@cy6<nw+K(q&qm?CBZ*ZbN%iV@@)Ayr^j
z)37ti<vQ*YEwhmp)g%-!H(+t%py*o3>zC;MxM;a>p|x%G_xBr^-AP{K@nBvyxr2p+
zz3Nq8-by|7r-}k)+rz;Vc5@kn#B+D`S4ScvP`8YHmeBw$!c8T#(#myxla^fS04Q~M
z>LAq9gId$!j3M!F)zHgfGasWZ;5!Vv9?eXt?*a<e!X(CB$KJ;8J_%TOZN%<ABiji8
z3z$XiZ)|*2UY|hhSQNw6tMeBCYU10r{{IFd|LY`xQWfTFYTLhnz<G(K54teFK*$>_
zv#|qQo36;<h%SOU=oN&Zl<lfjm)n$JcTsG)pS-Qi!U`J=x{qyCsv&dG@Z4MLgc^nU
zu<=WVF|Qv>_omHW)XOHcH7lR_I8m;cZVI_M#V<^ExdK^>)dQa0@5-)P@3%qvh&)d3
zP7@>7%Td?lucOO@Jz<gEY2-!c`G}%cP(6gfP;_q$0vUK%5(JY{>Vr3~0Fgw&iXrrt
z`Y=zN9)P6YbpJY~(vyK4e{UG??!fudS_m}LC(59HJ=9cVl(5fmz@{aKz{mYl62{S-
zzE11vYyyoJRdPVYTU7{pJt2YgV@4+l4wfUTAxw5UjrB|gm_Z32aW(UT>>FQf-5K`5
zrA_1l6$@YQTwdA1$nllSu%LyRO|<_4TBG18u}6V*DKC0qY{w1s{n)Bc2%L1Tzk%jj
z00@($#ggj1EH}%ZxA*RsYR-zF_@Dz_EGnnwLenw(?Z8>sjrlW%XW!JOQE|*rrGo|K
zsZ?8zZRoq7?3~@4<@TWbw2(r`*|^}HcK^~ttK9R;M`tfyWt1<;CmFO>fa~CW^b<61
z;U}Xm3k+0}!OXlnyxD4N*%C;3`KZJL6p#qF0E#<Rv5v!KW@?@i>Q~`UB+`<!13TjX
zYH5QeqUr#RERSR_xW}P~|Mc;t@1TEJjn9^JT&3$V(FCExy<GOYG!T{)>P#0^te1ag
z2>bwqxj0ctWq$B!w7E>0C1^5B1CO9X6nn3uR68)?3t*b619!Zdr<+eDLyVG2H3~yH
zpy+G`;%s>cG9`;w;NfXMBu>JAZ4bo4@?@wW_LNDAuo4KXb@sA?u}CT%W&_SgMX}(D
z*)dW1!f<~Ru&$EMzd7>8T`LZesl1B|R$4_5u)2C#qe$Cpp>*jZx-5zi_{08_FNMV@
zjcogdY2Y_!w}U42P@(0|n$rMakbFyjci(><DA_d$gF#e-CrnaT@VIa1$A`?a=cS})
zYc&UX_q}h`y|k9w^~k84_4bB*hK8>|_8M^Jg+6ZtwRt`KBpYf$j2(gDUvg!7KcdUX
zx%WRtI)4TAROMosKd3aKCn*lrT?fodxE)I>DPd+;W~BW_bSZnMZtyvQqkkVw_<y8{
zlpgHw^hjRrI-YzUiBHdPajDG_UEq_);6^s34e*UK08}8Y{P6+P6?8<^yurjE#KoEP
z)HoRcii=S85lk}q5e=+Wz9krut>?Y66$h$8Vxp8v9DnyQe>35EZ4d&cpS~=KHz6gu
ztyqTSo`$EXog}!5fO^CE_?P5C-eRLJq>S*@1TX>FqUvcgk(_d*kWT5niF!h2Nj_FM
zxnx)-;TLV2FV|CCbYTbILp^Rw80Di?dV*gmAVh<j5oAw-Y9^{ShPBRS=srQ{y8+~>
zw&!-_z%K(Uj_`}Y@W<a7o?X_9GXi+OgIG2I56*W7?(YYjOmeL$E&mc0#4R$4U5jUQ
zHtz;Z7+(EGUi}0>cGTV(gc4*pP)!{G;0TK+1A=0u40yhG6G{+H$0nBVBbt~3CN!Pk
z-Q3;|(6=gD9o%igze>lI@EIT|T8jEzZjV#r5&F9i?%5ZrdvO1vR_Pim7)l3#XYrIV
z?I7-KUi;Y}x4L`K>qRr>maYSj)X+f-FVyAMsWF?9>E#p{YQ-|vMsgMY@u^bf!1xuy
zw#N2Z2m^7kML}w<v#08peS;^!<eT%Y0TX5d@M0Ow?PJFXU<z<ANxlt8G*4F(!a`Ro
zn<^y}Sau&rZ5VN)$yL=8H{C~0&>2zbuSBcM3Bpm08~VF3pElHdf|DB@t=v43&J$rw
zGAvDwm?*<Ijk-oC*dfZ1c+s<oPPYV@+W1b;{@0%5gx5)0Spk5_#LjZ{{#>;TU-tfH
zZ%DhN+pnt}l;qoEt~cSyl45M=oO)rj-`IvU!Y?=%L&O-PP<a4grKzfScs#nt44(iV
z69XAHBI7&gZ4%*`o*`SzsGGMo+uP8u42}0UTC%0K6?sRokrf_*9t&VLX_4z8AQ<O@
zVaPd$BcpQw5R8VM2D7zP!GRiSTu8SHy}{S}y7lOGC*fIX`dfEYhSP?K=zzmCmC)<Q
zhKkhI%H7ib)qdgoHyb=X`u(5Rb@wh^@vi*2AcjnGr1$glHFE)Y!Z6#hN*JFBAG@D)
zGx{q?+UW_jFRwRL4teZgPdGdG+B_h+RW#r36_CFG45nqDnzk!I&YSi|oS-gR`sBWn
zk0#z3NAq7(61+iRetCUrQMoTdoQ`;p|Duv0BY`6rid!&t1AraTF&ndL)h_gYZ#3Il
zYU{jU0q~~EN$#9WpRdID>Z9g-ElCVWetuJ|--k(Fe!244ljI`LJawQb^_pmpGt<0p
z{MIP?i&yN;uj>0h6)-UQgVj_~Z9x&gMw6x~E5$5PcD+Y)?THndo+KH>KNSqI?)Dg`
zuVG(KEwd;0JFijm(+e(wv?mwqY;8djP4ct({aWffEJz_z9ptjryK_a^JGIv*PA7ns
z`>1bY*0LRme<qBHWoN5aNbWh=s$0^l?V9nGxV)`*IKhwKw_aH)xhvFU=u*nc-Xo9;
zKzojr79@qlKc8W0pyL-dGcY9VbJ(~*T(o%02?#!2?_5@A$S>kobP5VKNq;72V|O=U
z_o!!_L)-6H+3u~=y3T*+Ks#94OqY&tmHF84UngL1;pC4LdR3o7Y#SEw!m`v1DdM3E
z03-8GUpIw3fps_KV;n}X@xK&)RiAnSD&h^8&I7#j3I1JGbo^|5QGeU$lvgE-Wy>1D
z+~;HSUZs|gu6oEfOg**Gax-kSuY<E+5GsjCk5v<`UK)DE%n&}u4h}b+Xct;}fGyn)
zhCN^x9p?-Im=e>3eZiaa|23+2^?DTf5nFAjc3?r11Ax7I3A``oWqw+sgjx~^2KY^m
z!$~IP=Zs3q()l7i#X2aQqXyF`ib3uiei{MJv!E%+PbG_@q9qz~9k8HC%NBMr*+m7R
z9nkEe(sC>{-|Y!PJJ3XT&0LP0V7xbtja+5(y%yqH#&&XfFF+Tm3w}T?s;R&-9e%#j
zPp6<-NS5gWK2{YA&%w3@qHGn-Is9T}t{2O7<;~{MkbkmyR^Wi0EmutB@N<%}FX?YP
z7)YCB4R15czS-?&R$aZI(onk@BY7R2hNff<rw^XUPHr${Mct7E(8qV)&IW?;@(C+M
zU)yMc(gd3buZpHHsu@Ci!DSOACyH)bYgJ!!1O@15ar24GOU|C)oA=>Jmzj5-qaQZ=
zUbdScyaEHvp|cS!)=O$6FS@eHWn)!A<bXLZ%c2N3Z|oRd(#YzEki+0i3f;TYj!S((
zoDEZ=1Zgt%1yrVp;J|edOeZ43GuncC((EF<y(U&*<m}^yw@_Y<`D2Pz>~J67@j&3_
zY|JLqJcH#16vJ3ibdo8RY0Z>jn@KYIEDP=+fc}B<X<QyxQTbe|DkOzC2nvYHYc)e)
zTlB`1g4t1|lk}=fp)#>ceE~g2{Ap@%YRR>BP>8L70lAoaXX2n}d<#_*yjm%BvMh}y
zN<6-3!q!}0={4@R0LBHcz>hV)DdV4h)Yv_flg2|uzM9mm<Mwo{;ufl3q9w7W5MTvU
zVIs^bgvnzj2$G<7#a0!T3YxC@$6r>B@_z1A?+sLkVbf-*;=MQJ0#9eC>A2fFuSfQ%
zOA4bne3iT45u1;E4SS_h`X2HQ6}nN_s0&D3uQyUGyXDu?GIit8xQpI7a-kJZ)_41}
zTx!~$ayg0(T#Xl5<Cx)38rvVa9T2Cgh%&M}e~~p&(ebnOSjk%>CrC~*aPzI_(IiO?
zZ#P)shmMVG311QoR8-_aU6Pz=HG-4ym^vRG>-(qC^)EGFuf#KMv&!+PtQ)+&8hOjc
zVFN~vk9G$fGF{XsG#K;~*TfR|->6^bZ+fg!<UzACh=2tde(}msT4M7XHX&a~lF>xi
z1&H$RD$iMZtZ&PB@ZbAxHCU+%3kvZ4x)HdcSd?>F3wbwxG!j*Zxes23+jhA_<xSt6
zAEC@Bqls^F{5<RteO8aS<I;*%J>~WskS2vO)IDhnbb$lFt`4Q~QIX@@Mq1Fb=V{4C
zP30Qa_dHb5WA(%m+oAZVc9xeLiKiN(VZK<Wp>21at!6dNwqVzZTs3}CznIIYq_46K
zwN$1gU%~?7*LZ{5Wl0Reb|k{kX}+pOFhbbp(Y$<if{s(mZwkAz`%QOuRl7z&%H7_z
zJyl57)7BCeVLGLtJ5X;+nBlOnePSIP;p(7ke^+m`R?)ofh+c{9--k`2usLv&MrY?Q
zm|_(gDbwL`F4~P?QRhBPf240YnItgg4~jvI49m>+?A7xtLKvT2sLZr3lSIOuM)r3W
zEX(BFbR<$*OXU#jfwm~sh4U*8O*D>(ie^(pMAk3597U$0GL;pIu1(*8q-MPsHP29x
zM#Q(5sv$;#nzOu%U>zB;bSkD&NnL8N)|R*k*-2gVGI_V*{vwzr`%dy@qr%9X*)jyo
zjPWC`8}~<1ICk8Yq7=9Z(}Q)*J?eSgoU)gSO@S!j+&^XrB4DlYBi}7lCd$+HDOFN^
zJC>DRr<iEMsLB6EDR#;U%zU`m^=?el+5f8&ch=Ba(qlc6iVsfa+2{CjfUGumg6@Ep
z1@%U=EJ4J6+Am6#eUz%oCbfTnx8mL$zzH&+a+dzNtT~jr#ohSo1Rt>q^?0O-kfJ}E
zA3?M{^MdVW9u<k<a6dAQR3O90fsHcS#wlxj>gi4K;4<i#SWBWJCo8J2s|5B=7Aowg
zzfoJ0BF~SuC}+ybZ-%S%yn?q&a2LT~nX$^%iYg_6z+@KJoo0ULD*2)!&4DrulO&*5
zu?{7th=4$M=n$l=eSU9TM1Z%o2T5ia>Uo|vCWLS7r6zY}n$IQuAXSz8K&)wY*+-s_
zPCA$*rBUc;u+qszfLzVQbXg0+7qbBH(7+S%$W$$g3q>OtR-V=x9&wqysnJ>SJ1g8}
z(x6=SWbI`1&l;^V2V{pJ7NnZc2?Qp;AMMhJZ-D{MU$an|lzXx8ha`@}<C@@Llcf?e
zoS#RqO}wvfG-op|1RkVloYz<C5tmf=vw>1)&?b!I<wCa+|A;n<xfu3NFa&@w`yJcb
zA%u6As#aM2taeh8{%-Yc;FAtqH%I-Hk*CWL@n4VC`--DvT)$tkz0h^uuQTcV6u{a)
zf-^b@u=_D6BQl6{Cg-Hq8c}unNO&cpW!^3}alHpfTWzh9F|SAlXWJ&yKj8V&Ru%+p
zR)zC4ZYUsjg82OdUUe-fkP|R7<@}cvDo*SPsqcV*fsKuTTtn^X<B-Osubd6r%}@06
z@-;<6oGpNcX<}4Vs$g%@R4Q9qq`J2=_H13g%0B9DBaL^?;)@gyCho~-jR(c{{@$bx
ztuDG8l^4~o)A%;O$a~}uGQu^xFv&v>BoXiQSCn#zn^ed7L5+cWdoG9U%T#V~v+mlS
zzIDa^lz`H}O2_hYNxjUg>c2uiy~1#<ZZ-8eg!LknC$u=LNFi-Xc|zh3cBfJV3D73)
z{DbN={mbvgtwBtq8IqObLp9!tDVUH)sApI@Gfi~FN5HceXkVh9TFU`yCsyx&1ua@c
zg#`&=x5I=%ldLCFq_n%o;_AV2RxDlEsBedjG)8HSy^*5pAjt%|JKvTr@uI1jp1@9!
zHm*ML1b-yI!hsO*`)RMJFa+3sqgL0!R4sCXmGd=NT2p$0i2yC6jLQf}C5&Ak*zIq4
z9yosGB_KC#>Z<sOhs>(>xJq!RrA(2tK)Xd2)swBg_n=VFLe5?BD-Zi?;aA>WC!A-%
z56)$@OH@u8L=%h|f<sH({j-rUL>7j-X8;Q)={|_@0B6}POC8p#&W*QRsmqsds6EAC
zc~t8KwCHWUK4;(C|JYox>Lzx<4iAJ`z>Y#;meUVKsg%kerk6AkY0Y<D=evKt;`bpf
zzXTK_ZX;Hy=PxBa?*_FrO~EHfW)jJ+Pib{MgXJpL9B`?Li2!8;ruk0Pq~7hy_B$=B
z;AEJ9L|7(N8!gTO?t#1moFF6pu|3C&fMxb<_BgmV)GmJYD)+?hR%fN*QK-xzm)}Hu
zfI5KYqU>wr-2y6c)S0Js*u0{b-=s{0Kqu|eYqsj?_BpC1SS^{{6<^ze3~;ZStO$Ki
zq75bn7o}G8;^>dxUrj9prvk#TFxUf7kF@OzsZJ`_KyX1}m^iX%KTV8MG-D~#nW<mX
zbFYzH?a}MvVVAiMa?xu32O7o6caezZK-X@y=}b}HV*N*5QzgnO^^>+^?rx!OAQaER
zYJ#JlvL`!PO9t~1l0jw07S8-X-+8O12*N*4q?AXKf;tw8ue9;9Db%&dz*;ABE?6g9
zsK9nX+W*GpTfs!%nBWBX*#dbjvpk9B#H#Q;6)Csw<j1=EmLY{8zfQRz$Co&da!iLC
zsZJqsmb7WJW8t^DJ@?1CD7#Fgk|a*Hr^#K4uiCsoZk;Sq^d07tsd6a@9=24$gj~5b
zF$1bN!$KIec0+BcksDhMScDSmZIrJnE9y$)!i!jk{&YlVkO!~?H2=jCOp|(T@~%8O
z$E3n+G+Bq-<IMHv37D*T67~}osM57<-&&u=N2T({X7dB0BCu!sv?u=~rES|0Bi-&y
z4O<l=OGren=Q(r_0q(1OOFFMhB?Qipnl~v0TLP!b(gal@iJMe_x^%P??{zsZp~iGq
zO6m{LIXS?FAh4vD?^?RQC<4(t9Vm>4s0DySt!Oq+=vM`79+C8QZ=>98U1GtZcP#B%
zSgWs=H3$25%?h~{N#vVio^0S<ux)qHng=nB8rXKd%>FI$zR$;cz0!=-V|5PRp9nmm
z_<a>v4nwRGzm-NvEa<#$g`{Jt>Lh_Y``r|z6~4Se%R8sqP+Pr+wKR`s^+N<|Aupq`
z``c|3;ab@+EgfwB@O6|#u_^F8Px;yXst2&NX<+Hz-sYiEJ#FCV#KMU#!)qe4ieHP+
z69%O%RJLg)XxESdXQD|j7#^Fa&COB=w~7GoJT4E)#=YeClW*lkG(y^2&XNA)(4Pry
zj#5#!&V94^Un-RQ=M=1SMJMkx)W!%=VR;4vyurD}k`7R*()1-)0m>6vbVX7kJ&llp
z6+Tu43g%hZ49sV*jEm%5NfQaW&A@IugsuVCFU<_UFA->>?aVaVU(jW_3({~oAU&ot
z0kgLJi>d=zV<R8lH(2q`GLtx<&^+%<|F*>(@Mu<W7Qvj|aiMDCBj{d~%?pCoU(1w`
z2<gs3wdc$ZIF|W7T;pj}@l>}X8^{=$;+Xm?5imDUyNS&PwVQ3(o?<~2SWvSqC?DQ;
z>1#o%!c)8CsGeMfqlz*v@_lAvLU~Er7pGBBc7Y$IV^7r*ypFyKLcWD}&9C@(BhbJb
zhnv{&D@58n%XJux;Xws>d#mWcf}KOH?K8%7Nk~3c!mQ6oAcBM1<=v)$U*~7t$q3oa
z5S=iv3-oj=nKDRg$##iI>y3zT&6Z2AQUwX4ppb|B?UFL!&LXbM@t3N3$RjXOUwg7W
z>Ecs~d3qCeMMcNBU{k7DsG3V%tP7y$cH5x&ok>qZj-tlKvlApYn|`KLDU-NIV1WQ9
z!}WJ}{R|O2L6|US<CTl?wVQ1(ol(C4NfTBbj&7JI%pG5{tO78Y*s2NN=iaQ>u3|WU
zjOgwv+|y+^eld^6E9en7lLTI}3DiR+`UsF9(3!&ctyV*nHgOIaThKXamP^zv+ueRS
z+uVHW)z@s-I8<t&LNZf}i=-!h)?=bR!mx((ZPv(&^%Qn@rUsh=(70r)eY2e_Z$CsB
zrH(3%MNXkDP7&mQpEC%l{iZI-tG2~_q6)NCI$Y+m)Sg>gf?xQM#XwP4A?O`Bc)a3q
zEvJD6UH>$GY1|gpa?3GeLMC<w9A|v^SNj_rG-)yS1m{C3+SN~2o}SzX2f!b;uvM51
z=t)@3K|SLurRw1Vn!7?0PNqn2@A}w{QpX%`?<I{LaAZ^SY<<3RG{5arsBYTw3u<6v
zK^4WIBBHj|WPVAI@$MA%Zs6B;<pbe!m@j$n91Ef1H>lPa2;P0YR=d#w1#GQ@peZ&n
zT!zwn4}N@rKOdBD9sUn%?;Q{1`~8m>GRhVSMOK+n_Ll5b_8v+04hbP5qZC=$mAz%J
zNOswKk3^`1Z2FzMSL!uN@6Ye?{ilbQm;1i2``qU`*SW6qd_K>+<~+Jg@)vlM<+}*)
za}!u8I=6&hZKY%3suAk#3;v{HTSHMbjG53$WTfhH160Oyso>EWe^b+N##?1morPao
z`f*M8F__I|=ijk)i7dx!D6#uVppSmO@BC=$%JI=RP}ijj=eBZ=N4SiI^V7{f#!($}
zB^s<e>cM1<s_V_{b|dEcUj`LriDwLyH0^ofcrn7Y>a|!H+~7o)V<C`^Wh!PpR4fGb
zP0MLNvqF#Dd9C!4i3SfTo3c_ByVnjWPCyI}r;|nmSk8U58wN&>?&eb8`|25}Eh?no
z3P5=jbt9sy8tUepaQ=O)`+jspQUEB44LUyN=3`OahVoBJ5Vv2pd0V@ibw#kTiBTq0
zbk`CZdg5It?i>quus2Ny4mH2uS9873&+%EpO>tkW6ZM()*^5_r>C_cagfeya;2uu{
zxibDw?4EjORbx;+>uA(V$?wuR@E5M%%66NNAj;NH=W!b<?9fVIr=i@CvXUz`zgDhY
zyizwK7&nE~%xbH2u&z@HwdI`+znUFRV9k^IUY>bOH>%>JV~$1tkXhO!&zbPh;AMNZ
zTL$$($5DA{^+sn7Kf~$ERk1_$<K92}jb!p7x2ecmwJGm;@fmlO>}&E+nr3=QsGReb
z>i2C|T}|TXX+{?Do}36E6Iv<yE5#xOnE)BNjwc~{D%%0mG@qbOu5^fb6FJj*R4ZZz
z1g*Qpg}sWz7)F=iJX9NY7v;_CSInAHNh7?Or`=VL=T=P`EZj9#6W*S{i%2>*<*w3<
zKuBco)TJhn5NAA*j~5bjpE#nQs+z+XyOA?j#)zv%E#{Gx!@)HuOWbg-J8#=Yr8U#$
zvXwm~MJ&q|h};nZv@>?z#<m1h)U=5aXd%e(N@;`-XF+BI0u&2NrGXz>&b|<^0_tpD
zRGF?F8^&@oVTRqO%{hn9Rpxn?R?N~Tg@?RZ{B%y}SE{uX)o)SUW!CGdn&V`w159I<
zoAWEBI^<Zs3-%h}2-W%uxvIRwY#v|5Dp||hckM<llr3cHLo}*(wk69aQPXlD6PQ6D
zF@rb!@(ISe*fH`bY&H6d5guA4fE-wKW5XHmn|rI?X-O?{ZWqPq(SKsupd;bfL}AZN
zd~S-rLhEo@1Th98FmqJaK+?6AeDuMU_n&|}m|0=7)(7G$TJ@B|HlL&X+Z5beYT5=n
z7jtg*WXRO@QBY?JKPx)1Oft4HQHDVD$gN8f-9|G|(K8@9z47#>5iZKHEeJxHLHyZ;
zknpNRoFV3zayBoZn6un)zr^3Z4}1C>oC=$9F-_5(@r2zg0`HSV{4{<9E<p+`4O7gj
z!&oWNJ8yW!i7_N|CJj^%=4^1LAE`pJ*q)jvN}W%Gg$6I_=A<9h_^PE!f{1lo&d5~P
z<@RHfAufE!=+?8bqhiNa|0qI*m6#;sv&T|se5RmB3hajURPvi!2>KXF)Mgqia6`im
z`?^T?4i=`*1ot5>!sUVbdc&QY3HRM6I6`MW(Ri?Uu-ab3l*%thCP0ueNc>4<m9G1j
zo<bj2T(PkDl+B&X;%71BTiESlQ-w4Nt<`}n{|?{ccnboP^rGAz$LP7#Y@v7f#(0*E
zyTw2}>NQlCfzQ4>-jz+K8oel9G0xPtx#6O5QJfUUxLm^)RLkxsVzH&jGvU)oxwQ%r
z$7}QO4c<upTqHl=xqSKKgdb&gEVmLd#>}<RDF@_$M&&EHb>K{pgM)4EbT<M#;0&ga
zu^7;IeA}+e%PQcIkUz(o^O@ImhJQ@6TtUSt)21?$hjc2HQX4lNcN=O3jAOB1jDlnW
zN>^4yaHqp&?zXA#;#F>nnGf~M@X0xp*ic~uUS<sM?YQfPfH)l-*-m?2c|)6C%T-(h
zJKH?oVJ&{)kE;3ypO=LPc!&9@?~hG+U0w}KVBg}Wg3mUEZ#=SLg%q`U2FtpD?oisK
z!S37p(<D7LP#e3ibc%`b2rGcO%n;E7ZR4!?`15`g%6gOeDq{gMc1J+H=Yb!&YS)ts
z{m3b=y`Em{`MUU2z0{o?5!{7G_qL~F?Nig#rY)`A$~0lB8Y%A@hae(sN5c+HaWX!d
zZr473Q-y#ZYHZpkr$DSgj(1#Tb2&dP>86QF?-9TB$HO2;q0C5}4QDWg%jGcLttqDb
zn{T-cjGsF@pZQ!N?a^`7F8ed*-aO{a2>SFu3|HD^V|q}-FaL7<3g|#hcfr>0@lt0#
zL%O{lFk=~ReSE^H-WV7V2Nk>_CEPFJJD1&AG3;A3Ub3af)$6?a%5MNw2IhuW`iNXd
zfQ%n<Xxa%IC#CtEvDBQ*2(eBE4EmOjutz+P=*$?qTF(fkXO3R)lAk8C8=<%=2T<fI
zJ0JM1+bm!=vk16dy<L4GKsz(9`8VI2AW{7YGC^fZZ`hsOpsFY3(lq%fVt}=wPX7z*
z<tR3{Xl)g-pt;P;oLuQbT*Z+&6c^5pxEz1`C}PJ-=-Jd(WMChZlnNQM?F_aR{9O@%
z%(Md;@oqZFRLexeBkwF#^;!!R8uteN(T51e!gqJwI@sZ<xoOXmQtk8%Cx&K5_Tan$
z<cbMMC9du{-vmPL27Hatm!gVy(`B;6V>g~o8lc|$!=gi4B*J?0(I_BaoCQ8jQGUI#
zIXolOXXmld*u+DOfk8ai3Q(nwJmp8c;tmJ(j2rk!3$Nw{Ct;$BM`oE5Tn*IcK+2Ru
zo~jhbW-d0etAEH)V9FslGXmmJ%05SER5ZqJBC;1V>F#vf*O8SSFU3-_{}Z4%%rR#R
zDY_GBP)rd~I6h$7li$|rnLv<Bn@}-}rRGp)iPHWMsNZR)=W~AkgOLYwu`A>o&oq3C
zs_v=S+FH--0AP?s_ON<QC#hogb#>ua_lFRXjlhO;?+G-5rbD8&ecmYma!X4j|CR^j
zaO~mh6>cDm!w~QPE<V~kGtY7zj_Gv51A;wg3|NH3@?Jam=+R&f4O8<RZ?TELwfUYd
zp}#_Sw2c9#d64k~qXhzzd$vXni#h<V$rSp-?5q^g%Q_cpSAU%4m<9sLv??Cy?Hn6E
z#naE+8NCK5VLNAa6kTG{rtxu8yo#zN9iQ9O!{HH03hKI!svFaU#O>5_$5h+jbw7Ec
zhZc;$i<MZ;rfVWfOLV3jDGsiAUtw43aNJ^>@J$Ot%K9C?m~^|?dJ4nz3T*dH_?D3f
z8m|BjH=hAMty1N5EM%K0T{0d%=u^u?9#B+SZ-{?rO~d)=auaKMumt|O`r|zdH#?T{
z`xwqXk4BzzG`>95mo$uYd8w+Bv^Do*RK$(5stmR4yRS^B7#$=En-g~zddAq!cEx<G
z+WL5ffY@Gt{pGRb)LV}uE`A)??hbC-pXY&wiLE#eXN=5IzGoh(j16WHs=s(KNJ5cG
zdvlACr(fH{`E)z6SKdjAg~K7yD=G=Pwz(;&_73|S9vBh{Ppbii7>*p&5AFgcip3pN
z08=X|EyZkY*<wF_Afi}^E957bjRKlS+w91VyMU$@^RXxW6FPgA$P4xuth%7OWcIo4
zm|`Bv2%M$}KYNIUi|cRJjYmj9;dq6cs@)ZMD5Gs&1lX)VUMLB!+&}Sy@s6P(%3VpY
zxuc3j<P4zRePU@5xGQp;>43Ps&zKWPG>Zp!-HpU}`fnQV??&C8gA%{loze*_olxck
z3^CB68}M%woLa}|+l5h#7<g_#?wpKBDIEF)rjkdbpuUQbHKk%ju3diWX!s{Px2uEQ
zcGy9TaEBdFLexOzP`#3^t;#8#J>A>i6d1mlBOwJY7M#u*fypFc=v}>z=xe=jm~Yzu
z0P))`jS%R#@HJWZ=UvSi%pipNo4QoS^E*rYZ5-A32qg%Uv)K<sw9n<X5Y1wMS#L!i
zGTP(1?8mS{0L^XSG~a||Nmb#>%hQf_f3g!i-s==XVkR(ughxa{VQdg_x*ZR(9ov!m
zzm07V8UMG!++S}5z<A2|?_ZyaEgi0W+B`q`C$B<oBZjcysk_fm((~{%SF<W6Py&K|
z%a9~!U*Y5Lc0`OUg!=RAT7!P$QT;N2Zz}@y+TbyVS!e2KTiyP<)9>$BKy(+o1z|N%
zw-Fu>$Kf8%9WmULKy-8-dFjT92xATdj>BwqVc?slB!HiQS>Pwlj{axc0^=VD6QHm9
z@LQ^LzybnSwCCYUkE?%8LVQw3FGREwL{~Yo3OZ%pgS>{}W01U&*om|LU#u^Zpg9hd
zAVpB)G$;d*EmFejBD2WsaJ##v3)HOzlzeUc*}`h!-Os^G#^gQdh?Hhcjh^D&t^bAl
zk_z06Z@V@}Ti8Fz157PCfw0ou5=OsS&hM3trP4wSWT@t(IfG1sod1RGfj=8A!mLcu
zgN^_pHIhrTXHR^j`6eXo^_l*X>WQEkEy$s|pm@(dda`4AKZoC&6f1evuvK{c5w?%#
zF7%+g@?VAp?2EHAEMSpv9CC9u(XUxhUDG&B$t5=f)%N_KKO4|N8Sul<O<qS{j&nyC
zUwP)qz5L+hqXXsVz2Y#t{V-DGqLh05zv&xZIKuLXS${Z`!A|+lx2`TBx@+u@tlu1<
zZ=^Ik2ULbLZD%<8DV14ZCM_ErMJr>(Zy^z0tVTs#PPZ~A`hT&joZJr<TvB7ttE00y
zDI^pNIUAt2txJ6OfZ?;U_&w5|)J=>3WqP8$Q>9X?iUBnet?M1)h+`c>pCP{P(+2Vt
zhL<9K5EA!3!#_Ti2;CwOHmWSG3pylJBmTBo5JZR<8%Ifh8&AG@{eLL^K~`Y>JV6hM
zeX#-}l%P7d`uQy>+4m@6fC9fz7;jpe-S>@yvLQ&%ZkFvS5de!Ac#y5J(a-k}_-v9e
zDqcd~A3-qz16epkoMrcQ1bSb1+^a|vMd|Msl3KuJMppGf`^dUMoMs2X0-kHh%_4hI
z?LPt$|M&)bCQgWkyN`4`o!uLhS0WPdRh#YhH3~i{FipqVYA6);Uhh8~*1ad}{lT|y
z_irhUU18U?gL)>qyB^!EJesFnxihSk_)Ntx3SN^O4a2#O`6Tts8ER$Q_W59vi<gfU
zs?_7*6I>J@crGq-G*={Q#HuWm>y7DZ+>Xa58<+c0@(PbbV`3`e=VjV>+m_vX=cC`r
zC<v9>pl$1eI?2~I0`<EGP%x=H_xZ<21>TD^mWP%6AR4~N0r&^g_TX*rgF-dKC;xCM
zW>DM#Q1p2|^ZMr_c8-8YkNL9v{LOEEsy>B<Pa!}n<RpPS^V@?LA0DU?rL3{n`2OGD
ze)-FPJjbcxQGece_Y4rKDNXKJT=;RXUtV^=%M+i9hZMi7QT~Tb4xSdVWgAa^ME<91
z4|)(#@ywn*U*q`WTFmRju;8nD&-@(9Z+@y~I}FRagdFajAD@B^L|HKW?uDS9+VAsx
zyWhwm5d2;nC;st0JUuW$mnzt0obfNq{rYVYyHXv1YthG_*~{*Y4EprpQ(${aza{z8
zwbjCK?dD8{t?J&j)i;j8q#G4RE%Er%H(HE>j~U-#Z}8&}y~M!yH!yE31pKhU@7|n3
z4xF;)dcW_Fzhu(_Ye~w+EmQyQn(tplq#8UBd!6AOI>C3Vc(F4;@*<}|e(c_l+x_My
zMxyXsjYzs|f<2!k^?>I#jKw%K?AhRV_h7q#j1cp2*H8DjK?i=x#EMSyo5%gUr8i>N
z8%z>E-ws+*KzEhr@8o{|(XW4EgFYj;_n%|UIQ@KKYIxxwxuX&OzZ>k{fQ%I3h5Mt=
zXOsSTVLar8YndH_fBm?<D>13qK-2+4@=k2%&p~5T!GjM_asBy)#bD;}sPL|e{g|<f
zN#HR0bM}_s9R}0XD?ObtEh8hezFxx2#wKECYYk&%$KGMTae<gR+Mz@KEz=>Xsb~Cy
zgCm|jd-kvjt<?_<f%Xh6>KUVEefaykx-lep)pT|$nORsYPn+>@{g__AvYH5<S8gs_
zQ)45QNPq2}2pEKCF}!~zv5~;{>w+x2CMU0?rKaYhTNi6zxpIY8KtN|DXJp0u(4KLB
zIZqAN(F&$QZxs|2^ebG#!NFM_PfJS+iH#L>J3T1{&#C4i0$a7x#|(-8Wh}q|UdO>n
zPDvRGSywP<ccIwq7$DmB5eX@tL=@Lgl9Q7sJ$v>FmqwspIj|48^*GFM+|huC-)+iv
z|4@P8=Z_=hSt4p`;TJDnl#-IVyJLI78wBGT;;DxD_A$}=#cC|<IU`9)za3L^^AJJT
z)&6C9*m=AfBH=DBkT$k@y1<$ie)~jHzLDac1%|ALo$iiGmq)1Xy~$b~d_w(_mSL2|
ztcg7&isW?Y`OvF#Z#kM|f+Jq_pYg|Z-gp({jd+<igkT<0y87=^4BBM$7np?>6c+lc
zkp<nm7uMc>iJS}@7nfwG{SIidedRd#!@YiCdS6WuRPVCuPELcttE*1rCo{9M3d1mJ
zU%sRj7WRm28(f>v(i%#t%B%B5m0j(T(KL=#d7NONY8Beu%ROX8#%O8rsQ|<F4*HO-
z!F83AR}zv^7c8IAKBh7>zY_H9bi?4KMC&7XM?P%%WFjBV4Oyi8N+`&_e<>8P>NKud
zr*gXU=aqF>BTn<AI!hU_vAv5b|HV*$gzB%vwv2e@o|kI9wH8G)aJe`*8l_ZDbT3<Q
z*4&Pre)m*`D@w+jj8INK;Ng0tl%1rnzwd#kPpN5N6JI%{J;Wy$xhRs5TcdKS+tW}}
zJoe&a8SgHKF5|&xD!i}VXzRp3Z_$6<>7HuF$J9WQ0RF;Z((j)fJ3_?JFtNM4yU_=W
zh*n5QZ{=)Xfjqlc%>>~)p6~ASGdBF~r|NOEnD9C>itb4~*R_v@FJ3gYw2%)U^kqv=
zOjB)Unoml7CR0Qb-g2_*$qjwS$_@9+xmgsE2Dd#f(_^Pjh&l(48+c>knw#dPZHTx|
zRT>u-*<Z20P++DRL#1pVLdV1+szmto!>(9~fRiYg*_ReVkd)<u&-Y0@C0%W%oGEN8
zAuc{(?ycZD`T1>i<<E%qGg$xlX_qQtLNtpnwFkY}YUo;^(0aOVUYv!xU0B8EU9|zd
z0>#JLL$tjoPPx!8rVyi(%b&?j&emkKAbVC{z|`w$pzZ2sY=6gB>Z*_Iq+IkQZPybX
zH6|hnp8-Z6?ITRjT7&wy>PiQi@@G>S&au;Yy(HdU#H#0{VpBlEc&Pt7uJ2*oU>0S*
zH;ZT|(H07nD&!P=G_Ly&yOuiPMR7-X9SaLfzTK1rc~LaC&1fz~`MfCN#o37iMRd15
zd_Z9sQD<txvX)l77Z$T}5sQ-;LzdAzX|RB*_dudAI{6#FqBFe~*jQL0m_*puC4FTV
zPEXXVywx!KG|OT5N``?oBQFfQORjt>G&q+2ynPpKy$>O$T;px9_PQ42oH=)Rh4$j1
zi&vpV-=HgOJCkv6m8@)ED>88Z%`yHHWsP)1yuGusvhr{3+;lxHdD7)=?$~8<M{mo~
z%k+o%TduZbpTjy&jAqi-7{aE0*Gf}INsp<JUW3%?wwE$}veZqPO!t#yWR{H7PBKQy
zfhxURk8KUS7dR7Zos|9AT49zfjz4>S(ahW|6tC~tK*B^pQ`$KzrN^^R)lOeUT*r+C
zW@z6_3d-*@w7vqOW(=oAc!>_{DIT78DUZnanVMegtO+$WMMYd4t(VKNoo#amd^QiS
zsWmkR7q!H7l69~dT(ErfR!)kI#XtJ>=863B0<8<Tz2z|3nu^GTER_Px^eGHcf##K)
z2h$>B&Nza%wf+O0QhrWr0iSimb!9_a(cO#w0oMms8^U_-bsBy=&BUU2KRVi?jey`I
z+0^-0(=?u*FCh6@Svx+t`8{A5bRUWtH@J1H3I`Yc(4l*qoOTEIovSxU+705+F=EH2
zkT)o)kSO}d7%^>QjgLcMo^Y1$oLmt}SoECtJ0p|QtfM?N1uSuboLxm^tz3y0PFS$Y
zQ@<3GPi5;bV$M!Adt@aJ?F*&~57BjAW5j!R?R3+tAhaef!<xr@IMmj~ECGu899?Q>
zUrYIuw{^8t(s+W#3*^WLuPS(b_Y5QL>J6_@$WP)Vvrq1`zM)7~_tn&>S~yF0jLNvo
z*M1-*BxF8KdA<i7Mc>urdFD{Q`l>2te|TN!^N_cT6<Fsy)b7*Pms(j&Duhx5IZ8^|
z-`3_hkjlwWp!#|4P2EykSaec<0gpmZV2D|A<!3vG)6oa6mRFS7hZZ<qZ^T-*r{fpw
zP@~0;8JJYS7pbO1vgD~uTlw#n9fu7*#*R5(3F(j2_ZNe&R$E#&uB(xZ*QUFdeAB?#
z(fhp=@s)H``oWo;N@XhTBWu!uX1o-~Cql3o@<KvLjl4Z;l3yBFxd);}t=3`XJv?V+
zpB{7LYTVUl_0q}?(V6UCwJG}AOox)MBr<hVbg7&XxVCyxxv9N?d9<=E$gJ?g1DaYL
z`_BVMB8_2O)hRGY_vV(G@6m<|AHom%OJ;k#ef$Go3Z*I=*pZbjR`cWivr@~X)UmN!
zNwzj&6+0cc42)@9s%YMpwrrzlXf!l07v@**E5K}vz$Dt1mAxNcdOrVc&>cTnJ*WJF
zX!F|^#o>)aIhhY}hGeW3*##LS-o0*o)P1oyi?6@EpSq_|p6lxR6q5qZv-gvFFkU>w
z$`j9QrT46?k6774sDx}^D}!Tv8s3hitt&UaY5h2hM?*u0VMs2i<{te@ahu91Y<AT{
zg@lGTN?KI#unRu~6`sy}uVk5lqtGh%h_~zf^sA>!w8~7lkLT{p7lk)FeAPS~?HoRY
zB^PurWO#vA`5~KH$Ru@AVxl;g<p|Ss*cbaxAxEOF{?on&1!!D0qs-#s;;T;+6GiFe
zBB_oaKYq^Pot*!@dtWy5(=#%X5)!I!J$m%0e`@;e+s0?op|t$`TL}$a?&YkXKY!NM
zKe^(ftQ-m%;*I)}5$okQDJ{#7PeTTN4s0M<ANBml+V!iC=S{rmXuCL^agyzWKhf2W
zZ3aT~!NV`gucct&giG0J-s|Qr8cZ-SmC8l)^Kn}excE{hk<f7Pn8hVFk2$oa_z>3f
zDRK|aJkKt8qR<=NK0!|TkuB^<;gv)wNP3>piOb&SZ7c9^Qqmt|&UsNT?({M3K}xtT
zm&W0GWMeBv2pJnKmlN!+J&5~D?4B~~mZ`)%P;8qI3JQ7zkPcbUD5jQ{m7Omw?VER;
z?`;C``*{Xt=7qvHnx6qGA(IV_k8TA6$-}+X&0GBwb_9_2bGU40HSHdp2HH*`z&<hJ
z2oT9`<BlidaeV@!putXHTb2BFW@czHJ3P(=iS0iUsQ>&#DtJ&I1LK%(h03+JNiD~O
zxEs(=z29#1^))@dEvRJi(ml&6mR^q3ve)m%Nin&*Y<%Z*2n%H<-m35#j$V!8V;7$^
zqE0HymR7SFWg>Ta)Kv83aN4;KcfYL36HFu$9y`HWswr)M{Z>);<1dO8+Yi(DG^oFB
zEEhiYYl$KdG0;I`-m$LN^3-fXXh19_E(P41{CgAStpR2N0)mjZIO`XhU318?coH*f
zb8XxkZ?M@bE=({EaK>LBKxJEYaA-(SJ0v7zL1=n<`Uz!hY^+ms+&q-wLlAj}xV+ZT
zxSAT-vz00<MhQvz`AV(OR;G>jx<E~hS9kw?Pa8D16BG-xv-nw$HbF(VWv(aV171K>
zRMa`+=A*pOhKCM5Mgn(ok94f3y}f<@$kzmZ5a;-h9e*y3i!@$zvf+(T!=LOS-rxhk
z_{NKnfv$7YlMW=2MXzd>`ei8_=j6+iN(*)Q-h6r{R@j-4cJanId%sXJ?ZuXEX@;V~
z@!OibA&*H}?!B?f&nS^m%Nk=Da#oir#5!42;M5=B&HDTWe<z)?T=}H<vVBrnvgk>d
zRb7WT!KKkD;l*a}8kh)w+n=o`c^2XA4)#@D^FBWqz78d{4{A}0sJF;YZiAvf4FEH%
zD*c9sbzH{1FfL}`uj)X97X3zG-rK1A`T4Z~UX?*h^OCoB>yuNlP@PZ#VkJAdO!pK3
zu{<6C`J7*D!KiI~944-1PmQ(r=8ezY%8E_UH=x7n2xvc1REQY7LUSXg=+2Rct`O0)
zeKGa*^+KN{UwqU7pg0##=Beqba72sSe&<WykUS?Rr<i%uXrG-XvTyzE<X+bj31kGR
zhz1hJj<FV8r{nL($HyX~$H(6Za;=znd&m2r7v9TLBkyjgSh%{ea+|^4$4pt^6kYGp
zwD&+%Wi*yGykj}2$!Z+-rE|2y?6%*PBceW~7igPRPP{%(jG-9GFP)w~59P>JB2PO^
zD%(bgHh57FP{FjqnA^}9Aq=1wFN^SZfQ;YGR57FT^wx5!Dpnz1BI}@UiAk8qPW=jW
zSIHf6u(!8Nl@#;cVfhN;KGPuC@zsRs#2gO}k&|xBc*4mRGnZ{Z7-}vpEtTQUf6;lt
zunWDWAO;Tko^*swyPjO8h`~iPHv%vq1{o%ZuyG5n;al!%$!A~}6%;ha3A&c43J4;=
z@dVyer)tiRy4dHAk3+u;BM`80z6uF+zU9C<lVd0Mj!1s#zAx~3Kmh$}T>vp55s?u;
zhgo7|4RaMPu9J-_1@qZmlUO8^m?P7+T35_CWUg_E33u*ki_d{V@z3v%m^20rbNFLB
zON+}t&1CIo(w29ftlXMQs!CJ6?d`qbSHy_xd`HGoB~tG7-3XVi^H#QtpNW%IKNnRl
z#Y>v13PiS{!mhTu9odhM+!rJesSZYCeX(udVbFG2@&u;=n}oI(Qh6>^&rv;$v|hSi
z9*ThK@~ubs%8{CW==S!u_0ZPM&!D)Zqv!Tnyc`iH$g80=i6Npu#80LH85155a2QZ=
zvKQOB#-13p^3G5UC2Qnd4o(9v-F>AI>}hkzm!Bs!QDkRONmbSOtlRGPhCKimhjI)z
z#qwAOt8Q#8O)mj!Lu>TL6i)d^;BOa;*1mdmacgj61{f@P#EM@Ei!_4+0_?e(G)-dZ
z0IzhL!hNNKOJsJSnrNSxA!6|85TGD#b&P%C3*5C{fl|1W-pQ2?Sic=|ZdRjh5+W-X
z*U|OS!3zxis;o)Gj-B0|x2Tx=={QJFz6+^{q2>MDC=eABW{P?zDf#8NWORVBvt|}Y
zv^$?%nkWk`!Lx}PDgx(=k+cnx(h~MTa_1&$7`xKf^*>pLpGZ#n9O|rhvwh2i2IiY>
zD&pv>4|d=yoJCv7xl|ySqV~4)!J8VA9rUHg7TFI62eqR3owIcSEfP0jQ~4!)8$=Ka
zau995p~3#=pQ#jkbM$S7XyzZ-0DIRSiuX4q_-ks;xAN2$iT5_&)zsAF8lF1dJ>`GD
zgE63^qoe4~dw_meNy6dz!8%zq2`PgTZ*N3HVj4bjbmb=C5NmdYolAAxNAICyzS!)^
zxmj=FSd*<^yg8iKA_MATY&Z5hR=q`YNl%Xz>cJ7s&CS+*h`w11g6XxM-@G|7KR)hk
zee1Q)=J2@@6};<r?lgZ4F(1%G5~VNIb7l1Jwd~klTL!Zcqdy1GRrPVrkcVUzS_Bxf
zE~?&{V|v#pzkKmB_r^FWUnA-tc>e)&SEoj;H~VGQG%n)9eUVO$#>9p8ZHlb3^F`m{
z8Fn;GX~U;)MIeEBj6&AiwHP<_fy7ERCMt^eS<43Kk9@+K83v6nE)AY%sEz|oSh$EL
zTTwtl7y1U^zC)l(kK^%;7b$!a+VmFvi|{VTth6)1g})yEjP3RCC~50>LJ_@Qf$76a
zx1FyaRG0+&cM6J&1B>{_%AIqNfLX*Y+`pbZ1gGjf<GlMt*uZK~2BSyn$vG_@Y!mhO
z$X&BOmA(Y8)c&N)Izf+5dW^`7^2>JJwBu5ZFSW8`?kEvO_kqNg0*c<eH2;osbf+~#
z<i;;C9qHvNkh*M^<&KTjQEiaxbAu5VU0O-!8AW*PnY@o|X6DvmbAu8LL+Yn-?y)RE
z-uvFAr2;99_^QGQZLYmN8yY?-%R!uto$0e4c`1rAGWo<`g?8R6%1TK1sz-o;*G^;6
z!Jb$kgVVve=bHJ~Ds=ETgh^-*;j#sgb4<p@#`XY)an+)C9#Q<7FY)!Z#EXuebnnE(
z#G(sN?gQRQHDCraD5lyL9iy-_tw;`!J!$@n9WZsg%N_zTQ@VhqjJ0eGI>`xcRf1~T
z5-f59eOiv3)YKQkS#=qaWpo<~og`NGGWE$ht1|<wWfc^P+BPtL8E(Hv15&etESa{@
zW@goOurBae3xdA-<TNr4Uiq{wE2KW0D~;oWcda~|?UidG-Mz()9>Sxi?zm>W-l!|0
zaEcn6@s0Wb4k}0|aZOGk>?jdc_X7h*-RG8L%5*(n-e6qtrWZ)9skyZ5N*%#;v6FD*
zoRLyKnskcgh$_7!y(#I5<Mb2sK{q}REaTn$YIzYXj*D14@o>H{9BJGUfxKG>`ovc5
zF3}1K>fQ(L3Gd`x8aN@;r&owEmAMf8xNV1dQ0V$hYb@c(ldNQ~j^D2XhlTR8J!rlJ
zDEHXD^ubzs8>+a<R|mKQJn}jJz`)RBdN)Lcwim?|$*)3rzC^>T`YtB6x~=V4l{;i|
zS8_HGioK|IxT}NFVAVRX9*fIOhgSRhHOQ52fvGkHf^Nlj<@hoGJ~@EJ#1wP(lTGZ{
zixuZx)eN<!?TsD38OZf?3Y{XbFx8s1DXqChUl?q>&g-%q|CAN%)ENHKo@mJ;tY}q1
z+l6h|2YrNo(YgQCQQr5dI#G|JOka9Zm$#T3Pd}y}(AAqy<4O&~pn0i7IE5P_N5l~m
z9%`y}w67(gm`h65qMU-;B>!qsUCV8^TM=DjGwAb>>)wDZCT=i|B;@15_^H|1;lK<~
zSIg8mWo2dMu6Oyuh1(R+{<X+>5vHi?J%sQ1p^7YZ16o)1T5MfXTfxS)?#~r1SSX^4
zrD@#L0$>G3i|0zk{k_GI7v%G%p(n&6z<&fopMmiK3@^i@wzq714&=8?^O+vlFi6oY
zwNKh1duGHTm8pv6XY`(lCp8Q`Fd)EkS}E&NBpj(^#LaC0;80*_(4D9C4g2pm50LcQ
zpoKGQQVYXW#26Mo^1K3CS?%0kc0^83>QxRhM?{QrcvXcv@!rzQekCp`Whh;^bLLzy
zXEI8n+DYv5J*ZDd2+o~9E8nWYBbKZn(7qv^fm4-l9=&iTy9nG?S-QPQm@Bb{Lph9%
zo$JCC>I+HFDq!{X$lz7TjVW8S?fS?geokp}v(lLhNfc{O!SN^0QC@U#-Y^j>t1O(M
zN@xO>fAfnudCdEHlo!yDun|B#?N(jL`b<nre9nFI`UdPJ`mKg8>4S~u?4UKS8v`qA
zI3ju$$MveLs-U17{pL#aFfb*%b`Rmyh=v)8LR$l0CZ2U@F4Tmyk8(XfsHd9|gWJ%l
zZrjeAeM`4%E(tQ_0w6i?x0>&pcX<&UYrlfwFw=Ts#RcTMSW7{>VT<C^Jn&=8lyx+e
z=$Pd1)J%4gDQmK65KfIG8{0kMh^Crohz^t&e`%g(VOiLB%<84*`Q=V|TiY^<@MUSR
zy^Nl#jo$*tRDok&(fRv<xDnTh$X>s>8_{|ncOd_{oD7rs8+L8u3o@C&-HMGfXjpZb
z%0wo=uQW`4Hc@*xyIIsfn02w_+8Z+RNEZm9R&4F1gV69<3)!&FBw0CUW_cCLFD^~D
z2+re8OY7Glt(hcGy02W8mX{ApiN7_dTq&19Va_zQzJ7yzrNibV7VgOap)G1Q0hC%|
z3Z04@d8RFPx&@o#NISoIl$Pe^x1f5qP*^n|09|wy5CKkCH3Ev2ON(gx#LsPwanLDQ
z+?=ZdD&bmbFra5|b_UF$GqBZEK}8_nSUoC>gvVJT{G#}smXk#E%xS6TjmTi@C3ZnB
zmyL-<;T>fh%ki|lRp)AB)qD9)+G#4?98vDNgeQpUrSl#VQWBlAgQYw(I|8rwSX8?~
z^xJEvp~TRXtYw%^8|6uT1eVWn3n&&;k-S@nAJtI?U#>$c_%KN};&DjF7M*~=aCk8;
z7FJ&029Wh3`8cMv%E!oP-rn~WAelp44NXl=_5c^Yu{zJhQ)1jgva#9s!n(oKbsEG1
zhh_#}YG@yi2h%P=!>;OV8lKt2w^%4o>DhE@YJiubgCvcvl$7o(4<Tx#^a+}|PQ=8-
z=*^dB^B_qtz`@osso|n=tbNw`AR;g5b6r$W6diDzCEv`|zhc$N>ydh|w3dD5)#SPs
zT4k5^>BC`5>F8;hRw0AFB<~l7&psqz?Xu@lS@@LTefwE$X4mQ~EcS;X23kb(Mh*@c
zmQ0KhFDGS`-{cOWA5zp}6Hk^`2qjf6WgA{-``2mty3RO~o4<+XmNhj^MR3obRX*|e
z`+qsB35gE_KX9^6J_IS${uxLxBbl{}UJLc5d%EAbxykntFlYiFB+r(Nqm%;VOQ3f1
zFz%d$6-*lq(;*UI7JbQg{-oEj+EixR^ucBM-M|bQ1`Fy}1eypw*qtM&a7IJyHLx8O
za<<ab(jFqpij}?dhqI*8RaHQix~W!XpQN2s5(Qw~Dhh&aV2Sm6YsJm?1&KR*M-2yY
zBxT~#@JUTBcv_v<vZ|}`eaAKQvgAzx<>(9HMl=j>D+4x0YPsQutaj0o>ZT7CJ}o>-
ze=IRzc3)MukG4jXn&>>?w2w<^$oW%-mtK52DcSWpi`vuk+ZiB!gczI^H)0BGFV@-v
z{B|G|La#?insN&6jVoEWNDtP2JJ292XVxic7xIAp-$gOEGGt{zHZFrgl(X^Kq8CRb
z*BrxlZ+j!FhXAl4LLNP$1-etFY4jur(F`qX4IQ1oVB7xiErC}D5oz}LI8ZOBOky3w
zD<47>R7MV^M8{P8HcoYYaFUgkh2&E-gB^dLd5WTH@a(qtTwi;)c{)yS%g0W}0kx90
z0*35x_hi_llf*<u(K1N06B-R3E2pWYV?6gd6Oa91wSlr#u5y8SViC19hbp@4T2GT&
z*HTGol4^)N|0H(TAP+Z_<~ijlTHaHG3zQ_OU3KoKJUuA^Y_d`-JrM%uru<%@uAz)5
zybi=x0ZF-qE<)$r_`w3zR9|dI7m!*vhWupJC1s;tJ~;8iZO0CSF=&~#u)Mo#W^SIZ
zT01vCPshhMSj6{naI)mZ`1t)3{FZkv3TCU3cH?E~q^e@rluV$vy~o78dFICL>QpSq
zVlE9m;-7ej&^?zxKi95(t-xxCOVLq<5f}IIJ+FOI7W@Phn&T8I7X<b1U>zY@VCJSY
z&UcYaD|kHTJy^uk<a5Bvv|WWbC<rsG9UlibpuqXMjD@xz4lY+=L3FNR<<XC697Epn
z+LJ20P*0)YVpk2Be3HsRnh>8dOoFn@4Aq@@6F&>F174z7Dk>^>i!gc*#G@Z53{Y27
zQ+u(+#l;nX?i3IZKzr(xDD0r?tc^I^QzmMmq$91M5RORu!J+MIYonNBgXdI+?q}=2
z7|lkSYfgi%loQub1GrP!Wo@SU4H9MzstmQP{3&2Q>O~2<7P)7Cg)ahu-0L#pObKc@
zLH_=E`Z?6p)K@{$A>{F6zSONwwK>qyeRx9F*7nj>?ET<K!bI%~)}yRraGJ6;Z2Sx&
zK7p2CE)=TKF_A*Gi)y7x50{L*?sF(q%ZeFgWHG0H2y*Ob_9i<M<m4xt$^Wv5>wd65
zThF19kT?n^e>>}PmSGjvZ$&T(sN#)F1v&`90_a~2?MqK1N2?$53g^Zq>jJ0YTqZm_
z?5mcx_B2qn!VuXNB`q!UhFc&-m4E7G+_9t{%tFRQR@Mkv;L0fR0$YUc>{&&Q1!Wx_
z%i7zplS(*BLNaJawyRxsJ<WaeOT70XjKU|0Ma)j&j~*S>&SR7My8Slv&UgY%4GqfE
z(-7uGE4|~l-=0R&>A~ny;1dh9;W;^LbzFyy372W=6ejLTxj?IZ+-f`rh0<d??{zG{
zb9+RJ?oc*hbcGs%C2ZSE@vQ`P%uu|`Wz&<YRZb2Yrel&s^hiNv9EaFk`-*OXRbCO5
z!uhPA)6ZTtJqx-RWzL9mEvZ5Fishq=KIJiDO9;F!s08-21gB?wVD)42LZl_~uha!t
z`zgxGh6V*;lOJVfU?8v(UtmU>N4|yQ13Nt@*Y0!!Ewja>2)(%?As!x$Ve8`Vo*qYS
z4`{g81Y*y|Vs}QkB9CDl!8M<~lvAr&U_S}{C}%1Pf<Y(NZV|=nG6C#z4ICVvXI&|C
z-*O_C>t#$+*Rd0SvDVYsNe$|7rAT~?V;6(cd5LCi*B^EYhnY3=FRnP3qq0Ev;QVhn
zEH3$*af27f>d;{UFu(?|9)(zFT@tltbu|iEfM$_pl-1XvG1DxHRM8iT?sLjJIG;21
zpI^IYTDp*>U3|0G(LE$8ib0D^JA-_+n1Y!nb<0%y?d{tr<>hL;-RW0!kHMJ*=Z*W-
zvzXW>wwFE`z`>F|G#7QXmdJQK*LR!dKF@_KMn)et0R!X$hoaMd!rO_KLq%_V!%r*5
zF+*j1Ieju=mv~fgZJ1_aVj>?Bho;un)tCsVi~PRG{B2KkG3IZ3qUEh2W&qkJ8KOdd
zz;*@_8Sg7s@Zqb{9n4}(4Xc27uplWff6w&D_{z$4I1~@KYHaM{&JUaEe1{nYa;ffg
z@4<Wx;mOI#3Lz4H*mm_3!EWPCTU)UzN_i0x<=hBzmQj>-v+r>7&0&9{(=xE7Oi3y`
zDGVTrIQLb}j-?&Ip=YVr)F4TtZ_*c@%`Z&6yqcpPU)@oB4!=aQh2Oo>Q|d+N<S{tr
zyyCpHw|O4BRvtG#F6z>cI43rsPHaV}(ZAmS0DOz$wwL4s0|Tc)10)U++gQz+yvZji
zA<+zH1QyyUGjHGazo_VXQN5h3YUl1=8DrnfkZ(UN(>B40nRQT{WS@LM1WH{%UOeXl
zB^lS)TOfT2NQ8R5dDm=!Nh<pwh$ByDI9sb+zQXbSor1&;>L0>5A(O>d0#*MZ-y3Tm
zM?%8G=?4$*K>G@c@aE)A@8@cUEDQ{J4fGtdjm!H1TA$J(r(9%&nVFdjPFRET@8K-e
zt=Q4M5*{CMcpkQKUNul4r>G#N*#lX`9A4++;Ze{mwc6*sps^mPw!hE;dgIAE2#s$E
z4nAG=C>OA5YP{d);=r;`ssUlMS|aFs6~xPq73?14fnvM$+iwTq;=EUv0>3=61!iYw
z&v#kA7R6@xGNDx*RsgwgE5IfKFg=SyzPAJQLIv|}$o;If{Ng7!nq1iJMV&XfKMXO$
z9(r%%ZqlM*V93{rz&j==f{VN7=MwJaD(mqOzP@TMV(<Op7q`SGwf<SsF~q{XSyzYP
zIujz`Qa#$tA6Jxtbr(L?FzsVY@DVuUzQ2lXdkd^K53kd(iEY30-AdyQFgl^r@6h(K
znx`jHV6U9K!UjInE(;`|vxI6tCdhxVvcH%;Y=dwENmq0pIN}L%geOAJs!|xvxA#~q
zMS!bBz+Kxj`Y#Mq%>-BJ*|8(A$yq>o<{rah+WT{_k8t;L*Tp@U;ok56aT`)AxWQ9F
z)EEGp)R%!7^Mt&0U^w62V_gT3G;RL82Y)~D!Fj@9oH-?=LGXerP-X28^da7}nus&p
zeMIoaPqgtbLSj%6Jhmh5U~BLJ6eC@5UyXRt>^<y@8~AVg%q!rB?PCT|of%J1*TF{4
zDR06Gu^|5P@ZO&%l*8RWo~+m}k3o~rhR2$=9hi|k;FX8Wn9Me+J{Zoo_YgX!QEVMM
zix&?aF+u=)iFNq!(gyGTM)4H+in3WA-P^0yF*Y^($@<-!=)SK8Mc=IYUn6;eK7q8h
zcC=hHm;A6bH;h-{5937;N&wMBrmFV(DS!#S%JCvXU|B|*hymjSvrl|Cn8d||+}um%
z=H|3aOtMN!O0<CQz1Z3!{Y!87F@ZKoW)~J7EiKs(RJs<wYG{zv)6*M6)eJ;T(w+hq
z71GrB0qWgWOh`{}-fd6oK~GVLm9bs);1KwUr~PrypHGw!hv6SObf{<!DOJ)kFqo-i
zdxw4-K62)%5zCvs#{!U0sWwxOiH)_&_y!!B-QYdGpEG(^A3SKKoY{k%qM0hcK79!O
znC&7sp_q>m_ji{d)kjh25v`nuovf6O&hR?Qxb?o}IAMZR*pWgqF_@7!m=G^_I0Aq{
zMhY;{$mnyddw^vL*@Ti34Ms-Bh4)CAak{3XjHLCOy?){H-~1p*@?aK+25gXR1UWfX
z3$%|SM;M--Ye-PxHp1H*Z3BvKtv3L9;XIa>o?hZ*ZS9@ax~X*{Z*9qaCPh4^srF=e
zI0@jEpC1A=8rgyiCeEf$G{9G@c@QkbefntaJsj@@L3>eMQc_Z9Z!ebs#@jP{)Atu4
z3tq;X5c9|pB%{<GHoU~ohKV@Zf1)|5h6t+9W}nr)d-pm=M_D(I+3cJ7!GYoFgcI?A
zKfNd}F74(f0C>))(||f=Yek~;vf=^jy@>GBjsEqM+i8&2)pA{=2^Q#dK0VR+x3{<f
z$oqC#Hs3$v!MzbYxCX;F%*bCi2twf654+##{Ox`}qWs@~$CiyCCo|r^ySC>}zzu>}
z@>-7g*nTy!Z}%{gM;t$zJ^R>Rpd+OOM`&mea6mBLzy1>6{|DP)WEnXatNas}`LA?q
zpUcG%P&Wtn>d*MNm<0iSY}5YwcMtr<1pdqNqz%XjKfiUD-y5MPB`mxvQ%w_{|N1un
z`A-8&Bmo*)Zs_^(O}rR^0dvVDeCy{>e*I{#%Lq$hPPzKfPs6Ohd2=q_QMEslSpIf0
z{r&Q4TVyQOm+$Y3a`^VgZop`Gpm^w~Wju}o87KJpOU^$%SVRP&yWBN<OTT9eFHbN%
zsjYG|&Y!OJd;!<4L`04A?Ol5itqjgq7Sd}Reh%d~KN*lBwR{?Szn^9$<${P<E79cj
z^LD@a>AyKdwJ>=3W`lt3k464>&V}y@?tyBnR7$ruGO7azxUZVl{>Yxg{O;QEc(``Q
zL9c{y?^=Tj<Rg8GKRCRXo&5ax|N3biSUU-Aro2dA_Sej*J_QC8KofBK9`W|(*f0DG
zmtMqn(SXnL=K==hPDqjH+FbUg_3y5xLRbvw+eaO|e>^u5+=GWc<zC=VcG)kU3C8!z
zLntfEuL=8pY%GF=;ki>bt))hLHuzn<Cp`s!NrU#r?B`dkfEYhZe9Ze#WB$)h1fC&*
ztZj{&4)lL~t_czV27DaKf4nd$!i<oh4`@M(7(a*e-=DnlfbeF!@aDKWk~n_*PC*#R
z3mb-)O743l+?SLX`6Mq}rGLI2I*__}4&X=s{LO!#`E5kNvjj&<r`Y;A>(-UPVa_`8
z?$5k|VFn=uhN+vIr7n9TthRw~zWJfs)^N`RdvEhEr=()*MOLye$KP)54biI_yn{4y
zxmow;Q2u?@U;gV!i39*|>2boJFZJ(4_qX=`d%g4fuu1jcJ!<EhI)2{ncRvNgVt{u@
z@#p{EmI7l@%A-f@cOLM~7+$a;Vat6NTK)t0h$0TVq=eVBYkSb@-x}=}ct`uEWS=8H
zqZS)D+XlzKrqALLuS(Z7+&Mmgv$k6eXX}=iRlY;AzJDy4yR^BGDZBD+Y;5da*VOrz
zXd&N;&D0AHLU3;`*y34Qn}s$HN(|^Ku^i%;F>Z)EJfN&ale9JW>`9p@S+w>ChNt^;
zgK*Z8?rlsFP*5x@H{$&B@m{bv0i)*OzZo^Nu+_*d$H@6^T!RIyAPL*tq(a>}a&1eV
z+^hOok5qf5lduk}^>9gBxj!FCPJ9VP@gsAGaf&9YXH#wc@7;gk(=R2>A^NiW6{)o1
z{ldfxBP1tD_*UtwCDr9@+a+@;Nd7qw;UHcN2X{5|;_S%4@qZH$0fgC`rEP*IU<0bG
zF`iF<Bosn~wxh+Afa}}c*|IXoH83eXy1*PJpWK<?i>4fUg;t1OY51YxlT$D#Oe}{x
z{pB`R^KgWK>h%&``OYO&Ut?lNLNi7jg*N#JvrwgJw&jOMkI{*WiV6{uYx~hb=lFMl
z8UKvUC~DO%s8<p|#uxv(7V(;naJyaq{eR7^YAKi&6j;Wsd+t<)j|{VE+(1k@1IlKy
zm!-ojS(qaPlf^$*&E3Sp!YT?wC6Ze_+w60YQ(*QZ^hIK;5yipb;-oaSsVL4=<KyS2
zg9=6gv%&)g-lmYSa4H7>4kr)&$a_gfAk`xKysq$vNxr$*LFVy_gY4}rtJs+E|D}RH
z{LAs4B9u>#FTC8}J(428bGd3q3XYmP$FnCHU8Z)j@+(c_If{#<Gx;wp$&D*AVRJpc
zWB_O8D!m+1_srBY57rc|;<^TAx8wT<*-FQlDaTyZI@(}v{PHfF_8nUb!#mwkp9hn9
zxcN0nPH>9TU!x7$YW4=)*t7@6);|Y}i5=~^jQ&al1NMLq@Q3!o(0BjXhKh57@Ei2L
zy?H%~$@6I__~Ks<N%q)Ed$Mno^+Bh5VX{(!cBirt-D$zg7v$?|LISkXxLEFTilipi
zNGvC^1aR7t5)#r9(38&I$ByV%eChqZDGvd5zPuZzq@!lUbUZoAso^vwzx=hg$<jes
zbmoz^6|At3%)fRR`-lBJ<FR;bM6j=G!l@DA75;zDYENO<>93>=jB9<LKGoz0XWX}-
zeyXOz-T3Z=u~P1HxuONeP&Q>z+tnau#4W??=%x#mEN<WFpeN-qz7p^(<<;O!Vqy(!
zQ5f#YvnXi}nZESG$?x;Q9g?Se$)oHD{I<|@L`i8$@<KZLSG|3^=u?^tq|KfLYsLP*
z8?!6mzN6(+bAZ2)xouhwoJ0Z~RQgvE(ycydT_Gh+nPaEQWi#k#c^Z8Vgj!}y)Y2zK
zuGSH78Ke?pkVk7JRix`VMNL@_>rvBf&B<j<IZ1O9qaS;#_$XB>!pcKObL7m4*w~OT
zlCuqO-W1N<#dLP5C1f+g!bN|rVo&oAgID*H*p7OV&ay(rNc+lZZ;|`Hw0Iynh2ry?
z!tc?|lN+UVYZZX0BoTd$y}Hx++i!55oVtLsA};CM)ceF<*}dIufw?#g<#c3G-Sd=d
zgKD({^eU-F^4Bi?|0C!u`_dWI6H;JDroEN;xgfz$UU@9v=AkZx1F5_Jo`kzV#cPkh
z=54XJ4%wi^tbK6*3xRa>L)N2Wp<z~l>)J1|D_c=57CLjal(D!P>#KTOX$tT%N#nLf
zQ!SfJOI?W>Dx&|$(Nv@1>_dCHL>$#qRP>5Aan7g?^H-fx%>enpsaGXuqOJ{+Q*t}!
z0~@p@gNa*OThcFv?W}r!7m=0t1sfd{W9FI~ktpepsQ-w|(4V!H93OQA9nkPT{|<EH
zibytW##OR+=)EXW?J_nB=)b?wc1m<kCG#0Pao-)}Ka^i+8($C^UJKBGs0V~h4F2p~
zn534B*uA~QQV-1ZWAMot5Ks1(d?zho9>c*|+(bsi<F;8?sARxMQg-1DyZoJ(M%NTM
z!m#CACP%hjN2(3s@}7H5cPzfLnmpX|xU4OkL2?NBo<0Ballr1>(ZyNR)|Tp2(T{E2
zUk<h&3VDRbW1Q?g4srzngz7aQg!H;b4E5IJbv%5>evyxVO?G0G(PiuD2_+RJt8`5C
zfpLQhEKCWYE%tpl5eraTRs#C<h;RxP2a~H!$B*De0HoZEy9ce$n&shXJxv<&qOd`d
z`uEc8=S(D$pV60D_0;&*lcZ%^<)5mh)VE9LW<Gd6eB7W~j*fdT&iyq&&@!39x$E`u
z_V~ES1<QXOtt_5~d})`1{72?+gJ!(K2B1L*U{x+;a^##K_Zu||X6EciR>_^?5=5Ir
zCh7RTk?FU!{MiJCjngrF2c7B%L;J5Y6!oB=Hlc5hc^_pzdD$f-jTl4z%S%UX+3C`+
znGtXJTRyOyu9uLub_z+8B4v`cd)V3DPEB6)F>I;%W~p=2ozYyh*BZCI51tPj#@vgC
zY}dV#lj+UO%#JS}ur3~QQ~pS1JA8w@-Eh}DEjzn)wCKh0_Z4&7bA-o^nS4cQsT!w5
z+rIQ|b(N+0-&cYyAW(c%#ft=ZNxtdsQ7lfYJ7c=fyL`}PpOs`vK*nNqiH(C+<F?oZ
zA(xZXhS`_=***kvo{1E8y!}>DIwDG$Uh0viy4K~>^YXKc_wbw}o9}4lU83b<)aeX)
zLe~&uGWhChT8>S4-`EH>|C&mpatrIP<~xwiLBvmR2@1fta|g(RKwCK(DGk{Vnx){F
z$BR&^T)IRRp=0k~`DV!Gg)RxJ&MbHFcE3?@6O`r_zCg*9>B(}#ZO<2nzb!59|NGK{
zgz5k1^qj&TyNfqcUy1w4TC0Un#KyA}UfUr$>v1(5H@wkf*DdQ;{)(+z9X)Q)_dEvB
zNcOcj+)}o-wtYQQA2({C{ppL{G6ZGc`xekxq@M%s!MZw<G8I{6h<La4|IxcI&wQ1+
zrr7c*uuaookPxHA=&U<c2KR^XcfC&UId<;;KPSxmBEEkU8Ng-LtI#-i{`{6e<gRV5
z<GdPZeMTgel?~qCgIO*(Y~Ro<HEBD!G4kn_iOm}H-0;n6oE_c*WB@mU<+jn^1xRo>
zoZp*t-4&<;xWp3p(SXCwf{E~Xx*-~gw-qsHUWu59R}`H6-=pv?fIWEVTi^DMUh8j<
z$HBctNY>3&uVr<KaydVAy6Z@CBSEtKXP-wJW;UOGO?F|z_5B6e<s-LjpW<D>DO6Xz
zZfpBg_V{n&E(e+0IeFHh8F=F(24&ov#r#eS4?Y1aLrGbg#?H=e3AD=xY)bITx_Cx-
z25V~Unp>@cFMkVftnvS+@Mf?EEd`b)J$jp9K}^cd>+$_spq1czsBkhts7wy?lJ?Q~
zDqG*zjq;(5wh@5WYo0?<Myo40JzAclHy5oX-&a7jm6#P!tD8tjPE1d9e$se!MUI@p
z&FzkvYK2wK_6&g*@le~5+uozwYX;1F@eH37WfUFbOE%KNuqE(pmFYnQ&Diwj%@^&U
z)>Z{qD>1XQOb?T%!gAP-$7O7DD=SB~+llzPmG2MU@-yc&Ki2BM0}CYV9*$7|-{YHz
z{%z*R!ganVW7^th4wW}20pIYk*2>y7`u~yRu`^=uz!&39&gL+IVF(2!-SH;&b4@-N
z8FCqu9+l5Eog%~Q6-QmtzRhg4x_?LVL^KS9oYWHQJZh$R6F4s|FYg?2)fE9f3ju%>
z=K~IOpu*)j2RC<146nmg5F@kils*kd=59IeNCB6e%kn{HCUYsMMql08`~rGz+jT-*
zoSaP{6=wW#xOAxpMB5XrJY;2LE~4JMsb`gwIU$V0-TIhQwjefFO=iIcDfu8!G=#n1
z!SxDQAx>lOyHZ+^MwMG-^HqR2Sk)=}2%QNYR#XVMK<`%ZPsRW{UnRRfWFoYDTv<r)
zq7bxz)<)HxVMYLvRt!22lgphxghWP?y2c=&QfTjap*;S4uZtU?$gh5x>#>IxS4}Wh
zW7l{9Oy^X7%mMu26H3Zk>xrg;S9Ouj3qo%S>;b<nWR{~`xpK=fZe<E-a~?lagu(H3
z_Os)y6``I}EG%!dPB?a7wucV&LzIm%c>>gI55A=(0MGfqq_nqY-ZjXjwy|`{e-TmF
zNJS(=V(Y^4->BeaW?O`06JylAUd2>4WaA%E!Hs!H2f)>LYo0YqdOcM0#SsIN?(PYL
z<6e?k1VFqzrJ0<R=Zj(VL`TVr@n|;thX*Z;=F@TUI^#I9)j@PTq-@Gf_kJ80h#e!9
zv27oaM0L?}$u+^eBz9FcoH?(is482|TiAKn&QC5*pmgXR0`K9nO-ba$=^p`2=O+hW
z#RV2A??M}EdR|^$LGC6{&NnOW>FN10n9Kw`^tTwo>s~l#S1A?|u&a51)|Z?dQ2gk!
z?mO`GxoQ8W5Lwo|v$6Qo2sJ<x5K-r!4NXDIIa+{%l<B7Q;3GXLo1g`!q#YxT6S<|H
zDcj;{;SGNrLZ^}BwH&J_2<3>pvmC-|QdZU*+|rd!S#e>6o`@iHbk~}ha_$a&_;?eO
zUzkskPv4zod8Vy+EyM6-Twv=M^sw%6D4h9n`_PfM(2D-!m(q#jK+?L2#Nri=k$Jj*
z7We<k{1=TNCA%fSt3$T8wO09_6aRw|^CX~U{;fBTc=DV<B}khbybxi=n`xv>^wd~J
zA)hv?I#G=z)5`SWlTNKIQo<)*H3nGhlo)^F#PFtyC0Nt)kfxq*0hr_WLkT@ho>L+t
z(}0mBz?nW_X=Y_rsJi8}&~KR0VrKjzTNW_zIw0AH@Y$+doCnNrXwh9l_0*ETlrf~L
zu0Di6WOHko9N}~bl=H)(L^>xImyEf2y4&jW_%KjX)LQWi1vIHP(tcv#b11!h2+*e5
zMi?ivv<hWFS(>k0SL}|Kqhbd3TwVcRJo@|94?`wv9l3G7OEF2gx$S^2JoiNJ6Ck1)
z;1sO0-C*}ZlZwzbv?wyj(axANtE+>hvD`wxMl_2omH3Eywhlv1)n@vM;v1i@%qKQ<
zA9%tFGd&(bbFFT$H&7V0@mdXXbfo)fy;0OYVR4sTP;f+TH83csz~~DlSb8lXjNr@3
zFL8yFiE7;4=3WSdEUXr~&(ya<89K!6gN6Qc;uAfRSsX`G4J90V+a6|5T*A>9wQpfr
z)oSIL!qx=d)J_G%*|QPFqc>aP)N{0RM2EDVQ?ZeLTghM__<z~zxvi9{cJ_DDhb6n)
zmSCYPyp{_Y+$7of)WH7?w%rS3XSoi{9>xh{d#=lbo1F5hF{mjm<O+%pV0f}E!aumC
zKP#)+5HW4{!QkW}H81lj)j1UsJ{x{KG=xry&ln^w)6R?5&Ni}PaT~r0Y>WH!o0)BC
z+m7{o2hczTiCjDMIvF#uKqwng*exI1Q8?+bZHI-2S6ow)-Lr8$0Ufu{DjWSsA){_z
zvzvnLr3N!81@9n*MuoojlN9>#h%9^ra2gcj2OmVM?RNFP+C+T~U7YF2?#`FZmcG~;
z`4mtz6U%PoD*&36t9}?Ko*tAcY|nkF_rjN?oE5<#j;Qy6(y^6&h*@!_EPVI!n(e1G
zFAy;2YElul(@Wp73mMyH<VY2nJh#iWPpw|}7|sdv9wD4XGt%X`jGR?VCqSBB$hywT
zk=p0@{wz;wLs$44?IO#(3B|%WGVE@n>w;_0?eGLS^Y#1gkf`?Kr}!RqykD?%wNG3u
zimYHEhuXL=Pe8I7_JW?adTVP^pdWj@_upH`r^wbZhBzDR`;HlXZ}XL2SS}}$t2DZ4
zTaLAPsKw-d>@`#m8}|aSqNCx_5A-t%3Y`a=y(a*##pU&)mOi;r`v0TuEyJR0*S6tX
z5drB28Cn6QL%JJjm2MnLa_B}vI);!AMFax@hnAKaLXeP@?(VMd9M|o=*1DhdZSQ)1
zyg%N5Zpi3d=XD;nAN#Q%3JaY;dWHBV$*7SwE9)>x$L6lSdb!jmQ3}Gv1PYHTXgZK%
z$&?+)tuofGEk`NVEK~_40&yQ>vUzm~jzVJ${oh)4lRyIt@7Z^b!SiypUb$v_{kz}w
z0G-y3YGy7@Vp7k77B$cFLh2KTXDg`=QU{DoI*ezL>)+d+^w}z(s<K6w{xn;lJPalU
ziN(QIlPn|AxUpEBxR0dQ%fDua&bjtUz6o7-?Yr+GCgbWJd{SVWsxIRPG->iC^6cYN
zI88VJW2*w)sh1!JuithkG6al5=XPfZ^ll0aPET_NoG5#3RxjD<ZUe(#A$2ap5V(5R
z3@(p}h}uW?XC;~`MNLh%#dSlW1RJ3oSNr6U!NY#FyNzHM(v-HvIwE6f<*5Eh3>D<>
zf-z`qJy-ta+N9*<$et^(@4(iod`XiPU-%^piks`RIL!N;8FQZ=Krwyo`3@h!SmWIE
z->~@SIiQWVzlSxu`zL_&$^@WxsM0E`%T|T7(b;Q7m3Z0KCS`7E;h~{x2ubpj@i>M(
z-KX~no-H(SJr?5{qoxNVHbB9H7rNR4^|{cu-+rkz4po4aUtBzW_x_IcIBmvv)AaWb
zQz@wExd~rCdSuM*SljRe!mO)i<XL6ok;y=L4|Z|;#_r9a;6TxupH#o|PA<hja2isZ
z<_1g8oHhWpZmS~d<_sS}L6r8!njZUi%37NNrjtd11{)LiPq#-v3Jyj-kEjICS~>mc
zdnK-FaR+SfO5mQ@JXbyH>-6%b_Cu#T2pPMs%SyA_fACYSRm!NrX-2y5z7JSgG?ap2
z6bjU+ZUv3?>yDobTl!@!17UNLi|=5t@+4|09So#v`wvQ*4L=L*ft>>H+4v|pSzk+2
zChCISNG2I<n=r8-H>dUiq|`1j3h>lpfUKR|&?yj*L=HVDh8O{d6jk!w1oF}U3rNh4
z-#!vs`2+OrdoEo={ipY_Zz`MDjsrkE$&&caxu?tL_lmmgZZV$@ci*T|I9VEh!NOBZ
zX?7OJ!Xwk^u-)2#XIW-mZmNt^wu6%P3l{F8Ws7pbJ#U1CRa%nvVEWUFPEZPg?Pnrl
zO|b`UcQsIOUQ*h;_@B4o4<OFCd>c{=(uXsgH5>G%FJFX5OU#0;HPp#**KB?W<JJpB
zX>Sjdr1{GfH8p0l+hxJV?jM0^Tm-u(XZ^9cI<_eYKJ-;!n@!!+Ma|9GY|mLNpq5AQ
z2_?k5)$z01Wnr^s%U$iq+UNRuV>O`#l06-zvjs5cm}{*iW`v5G%|WfLt=WEMq{Z4~
zqO0%TfT{T#o7FgXK~O6uB`o*OO<z+8=rVAuyNlX@vtfy=@~m72B_I&W`yg+0xc|or
z5NaT-9Jdc!`DyL>cmhPT=z90^wSA1QVBWGx{A$B7fVCas675$sql}ARQ9*NjO;v(?
zcW>`gT}95&(y35m5DHrbc;E)mpZ{NgX&S}vLAUCbN=Y&3(AbS?S$;(C$%?<LsX2pa
z55ZEtzuKKDZ{x55Cm?3)ei)T_5@w|=|E%!B&s<OIP2q(0ebNW2mL@5(3NMr%+2ysg
z%PJZsDc}FNwh(+E$HX_nR6VMc-*1X|ubp~diRqP~G2l+mxh(>j*Wb1rB{@7j4fr<<
zK#qF_QGPT~knQ~th-~B4n1Mp2!mpV3A)3Q&*F8%iuyoBHQ}qZI6s*6Z{&DS~(t6F<
ze&%Br*d~HuaI%eUavZdb&vpBjAp)utP&0Ob8hHV;637nb96JLJk>K)+O;91-bFAG7
z#eE>WcA#l+gBE1RyC6F<%2&_X9OX-zHdRuJ$n==>I;4mq=OnAkz9h5nI0s9^Ivh6j
z>HEMGT=xesNP;R^Z0zjTx03vd8h}{;d|sm3uAM=hcR84FQsH;_xvaM&8W9_t3@EbS
zDw5RH#J5w7d-!Pxm(@XyncJ|@;~f+nd*kmimH$J+^Ym8>En?afOr^eO8DYq%NI}{<
zk}G;yWaZs$e`u*+tHSdV!>CB(Uhy+{=NQv_du#57_VGW*y9UWbxL2fBxYo+VVA3ji
zcTz#XANl$7{&-~P+mfpOC%lCfQca0uD)BanKnv{&2adp5+%V7{^vv?w{LQ5!SZ0{E
zchdN{V8f!qO3>EY+JPDxR6r%|_RDIld#IfU+l3M%iOqN-DqwS=wMYwK?PEi6ZyZ+E
zp6_;n)U0yk8)w~qg1!3s4Gf91(O&P0gYpLL+k<~(YM%|bH=)*77n^~q14Y#Ky1F>B
zx1B>EczRRA!!i3hBf&D1vNb&5H^G#+B1-k4cgIIu50Ldgg}hrUr``jyFOFdlKp=F<
zIm@(Oki(5w-D_fF&U6YY(1H!`0Og`wDYZ}1$KdzI=Y&ym9`je1TUYDnf@_|#uOv|Z
z?y^DzD1`_UiT<ti6^#zcg{Nl8Rq}sc)@5Z-F8JRG3ikHDpq~6M=f+%B#{cu&m_Wlo
z;Rl<ftVP`)ZmL9(J3vuSQ!O=xRfw4Abi;QjMUD<k3{r%bc8dM8Uby8+1sQBM0oo0S
zXFu+r;g)Mtxat#$wD%8>J<~P7^C=YsWdIG>1^h94?oc#TTYgYd-_EYEQ1|m!2lpbS
z@%c%QDL*lfo!X(~7ONb;3crc+pd2enT6Iu#&;wtXYu{WbQD!YN2__)N;=urhyS-K+
zDGq1D`L1}6x9Ja1Kd7Trem1Z_eSJrI(@iL8dK#z<SdOdp0x;m$JY((5v4Vkqu~Cio
zB^ZYEzOTMow<3n1`(KsRjaOD%DvfOKJA?f!r;aEoC~t-uNfN*mVz4P9!VR7bzU!md
zy*gzqvd-0Pq9*njyg?q_N7pASrQ{?9o0<)M&h>n~8K-nhj5VMRqh3w*Tb+@g<5YHa
z<<dpA)#+r>!Jy9vwI7+@XCj{n!u`+0?&3YFh{>oV8@(xzCzA3>O<<}%mm=R&RbLb5
zsfuM&FnPDq-lB@~K;%qRfl4}@N&U8o3hm=sb%+s^>Sr2KAF0uj5y=+ZrCq2lKq^CX
zWIvj|NY+e9tAS(hyBz2;5OiUgS6!M<wpMwFS4!$ohbWviEgno-NP2$b4&5~@Mak>U
zigZv3cka~Jk$Rh`)l**p<b0BjLNNTLe(eG_S2UPKK^Fnq^`fu$pMr^#6|jq^^D)H-
z)EM)(DH+Io3JU&M0h<|-10N-TP||uPZV6jGw=YjK3nMkqR(KmR<4o0x$|f_b<GeuF
zml6hJ>H{Ep4TOpBzdlb<DadlyA8>Bi^4n4d+Y?7Q(#XaMF-DI}&Rb3HgtUO%R9qRZ
zVoQSBhJ=vq(CAPMA!brRM&ZgqaC*3Md(yG8p@g#25HWApAfYbJMUazpRpS(UwzBqv
zu{?KHe)D<YqrbLrbwNF%UCb-dv*-ee0+*k&s6IQ^f|bydF+Y^NP6Yq!P>A4?DMT8O
z_5R4uy1#!sNQ8P}_wXLEBzP9GL7Q4DIXEvT5*?~6^v3j|w9t`Rp%*t&-!?IPoX(4Q
zIxxZ=9(>P!>NBtXL<M71a7LvaC$t%N<*-JZbWef2nP@8|R+o>H8n*3k(-YmHN}Muy
zoze`O1uwSb)`8QX?*@DgksD)#7+~$}Q9f#8mzM{QRyVx01vCeCw;TNY{8p6Iy&v^N
zrdU1kr%RwOq;HDvaNM2m@QNT9J6-_dUk=`&ay8K7T~P6Yb~C~UdW6>s&$DpCiP!?e
ze`^6isqYwrjq>^Ab<1)mJ|`HIJ3RoCcLkYR0dTEsQ3>(v+@Zqq5Z^9?$w$VYu0&)=
zP&*5}kj98r!eCO2jz)@p+O>Gn9h1uiljHO-3ro+%XK?BruU;SHAm#-hkorwEfQu>x
z?FBRM-@FXIAvnI?swO<yYQpz-I-O^hNJ}yK;%eg!u!AhH+I8y6&wKT8&X`I5E_vvO
z`k7FwUBr4bT#_2Ruy+P(u##B#-rav(-rw&=@l^&U3!eL9dtEZroksmwpPu02&_^GV
z7Y)V#Sz@D1hM7{No042S3D+(jPZ}!PQeNZVe;bogvDh7iW2FN3B6#hznuKL@niSz>
zJ(o7XL^g_EkayxzP%z4yKkr2x+5Mu;?*<8m81Y{(=lP9CBD2#9;=A##9d}cp-l*k3
zr@S|LW<WqNq`4%c-zvUbE#7T_?;&N|&rzZx5nD(<MSnsI7~BTa3V>DK4C6FAU`C6+
zZYRc3g;l4hdld8Pz~t(I6-7616m7B3#9BY<b9}%_vbCYgZ*JDDxQ)c}ILTTWhiFew
zR_D|omKR$@?0`mshxa@qEoJF0a}b^RZ2AM+{?hD&LRS#E_SFMclugnbz(%u^6<C~)
z;bl)83W3L}j(xZ32sU!)#TwRmyQTA5shu`rh{J(i2eOt;VyMU{V-!9UH^<xsmM3ov
zO9ZgY(osdc&H{@o5+=b6V?n$4;pdBS<4Ol3h8l(-dN&3Bb_I#kJ``qfzGU?pZk8yt
zh=wm(5<nAxeh4(fcxgG^aQ47j(YFEhCU?F^OS($(WzY_gVg!fe{PiyXw8Zb&;Iw^S
zC$qiz?W$rhWg9Lg2{7J>;)vCLAh@|@_=Sm`g9m@|5tPu-xp&O9xAQ>nK2+B{$SjI7
z$n(maWe%e|Bc6<!ON3evdRx@#vYXH6be<3P)o<(e(;EN$Ji%qXf4NBClLf=iJ%H?x
zr8m+WM*#BKe!N1oa07KxqBfJYUV7m&3^MgHr;JE*g_{z08o~)M2v<_oZpwtxSSp$$
zwouTfkeHCnl0Q(H>m3hwJE;X3TK`AmgAcWA4V=0qbYfsnx60aZu{vsn%2*YQF>!!5
zdncOh@^cnuNF#aeG`il1Ofpz=Hb-<tC>?SJjw}(ilcbhqmqtcd9c-$+64z*xnwQ|A
za9+QWM%i(jf`Qt62FP`#nDXZ<zU_g3JFfva3omOvBQC=|@eLdGuJLeM8DOM$Fj+%m
z7CrHWmtWO7%F4$AElf`!*Op0<PzAv;gQU?gt7WE!8Cd$QE>|{O*eA2<(Toz%_rn`a
zF=a7iClHarZXubRze2{pyr~IUNmxn7vu4DfR(I$7t-$5Vsf<}u)&;lGAz+jhZC!8w
zvew4%JKxpXnznqGL{{t_eGC=@M~-r?PI4}qNqGr1@v65`Ti$TI3}P>irX=b>mEJ4!
zGr3hP84CtC3+ea+32(w-HN?w%4R+Fsh=Fx9oa&obv40%*c+~S=0LXVEhs>lcjy9)N
z3_-yk2{2_Lt6}Vw&tGsDegYANRkqRA+Q1g6<dg4V;z_0GZM#2a{6I?Hwm(!^|G;WH
z%3_vSDSkZ+wXaQO^$PMq5E<H;hJGgCdqsX2!R0v{!K`hyFcomzOq-AIza6Z%S;+DH
zMu$@y4z`%v>fowfK#HWy^}cI;PL?*gv!+;~CVxHmpMmAo8W!+Uex!Pmzr9pC$O3#?
zo0y(^`yysd<8)rNru-FzCe;4m-Uy{~KvJ$F>B9LuLF44^je2=aG7QSlR64Xt)H>Tt
zEck0!hxmxy#V&sen_dEZmtvLt+EzGYtYQ=y`zq~WRju8L2<1)GuJX)-IJRKuoXGTZ
zG@>&O{JVz61)Cx~bNz3XT@G7bV3X&6;EFI*;+N~}a!w-lN{Ej5VX=1jqsRA9NlY^o
zs7Eba2oT@w8sA!5Q+D-lPPgY;=e7_B1?MBJ&HKem9$0(~1k?mEi!Tebdf9nmh<>ar
zxSezS{lXB97yz?ABJ(<8{qwIHHLzqG>Sm`kl-!q2BZDXOIFld#6^qwPZ`_UuK4Zhd
zH@tpEfeHX3>mTUe3F<0zpZv=;{Po8Y0dTsTs;8R<e||jh^*>JcwZ{6S8!1Ljr2|{q
zZ(l+*!i@Uu#2=ed%5=oxc;Uj3|IH28f!Mv!-(veHI3nU^q4d{#ZWNc2ggNEBv&4UN
z9TyAOO>^4Yh{)i(6#g&&daeKX+JQyPuLMU9w4m31f7n`@?sS6`Bjb<NosP(0G2=io
zlfRB3n(umKFykkAF&ZqKY~VBpKfMM=fczgv@Ld%gZq0jswy59FiApoU1=D_a&;v}k
z?y*ktlfVAeb>bW1I}z%6v=_??HZxyeteo%lPBF)h27~NylM%d3H&GIdHB6y@evdz`
z{7c01SyO=@p%(GKFE;7*KF4Cgkz3`HdfKoOkJZkEza>-@eK&H^ayRKn|DX8>mv00D
z{inAFYr_TX1{HZgb*Yj#!<Fjqx9%VD!zZHgo#vbVmI&g3XiDDxTCcssNB6uPD}SAe
zkDEX$5i1^EXhPR-H~Bm2G?t+5-WTe7cteEF^*8Qtkz60IuvZ36G$oMT$&5y?{p}q8
z%u!wM1u7*ny$!|_W{IJds8?1V5o?V^z39#NiE7ERu#!Z+iR9mR;P+Q9Q>7E8TjhWC
z=hbP{z(j58*1MgD<^@CnY=EML%xLES-+zd8O+Hx{Bne}+UM{&?ODMcCA;Ha&J9o+$
zR^qc}&2slK1aeDk_<vmVSVGhj;@<!B3BPk(3uQL0_x0Ko%$5l7zs+l@VopJIN%{Lr
z{`(2lDNh3&O!a*H^HT+VkRv8ztX#oHW&_vwP_}}xIQY?p6vL-{k*)IIA2yIs9`&qv
z%712yf$mtc6-^)&;Zn^O6^fx2;g6va1FGRhuZo!ehl>Jd{3Ya%{^_19kzX|m(61W+
z&5H8&QoKX^(PZu7smx9N3MTe?H?J=TMOro;VzvW_mumwb9%S?{nu~MWe68a1S)4Ds
z%CuQ|4hk?iY|8>h@^8x6hdF7Msu@X4WP3+Xe)+s?hI*@`H-c4GUNr*0L&ER$68HmQ
zy7cbNoI=5oxUZa`76`*)Gp-f9S~ww8fW(712Mo@v-a+jzd;XWBTW7y+0doTL{wqM#
zt2==0^c9Af^+;5i#rR>F*<5njvyFUdB12R<oZ3*VS1I&q{Cf(&!({qtg`N7i$9lH~
zrHG{~Jw~kRW2PAOfdVxZULc-VYNev)Vx_9Ox1`2eudXz}6sJDd9z%VIP(b<bk~fpA
zA{vmNtRoZe|NUiL!+L`9g>K#l9<5FH2?Icg6PBAsMYas##nh+$Rj|3!u=|^XhZC`X
z|KP7jYMf*wkRf#(uV4pn!L6FE9Z1BMkcImdv4u2cI2dG;?QISDJenHX5XzV?l*Y{D
zv5~$Mu<s1<Cv>u<zg@&B+Z>azbyoR>0H@=jAnbg^bujm$pRl!|89zN^k%VUkSZ-+f
zIm%Q83saBA+DI{uZhL#M{rz58fY`2?J+xdiYsz?EEfG9~6kUtdWj_QYCy}^+e|^6U
zH85W0`Aj4sMlle+@zrR)hnJw_)Vhkh(qmye@XezT*-XSp7)vR3U(9qen(rgQ6%%<g
zUZS2;=X}HqFo=_4vsOWEGndamY&JDiR_$n@U+(bu%UQOYjY539Q&0Cj8!2wzo!8h)
zd1yyD@udWaODDPgao}jkXX<K<TAS*+rs|acR@XLOp!_gi_^c^4{Sx@g4I)e!9PepM
z)68YmhI`kF_kXs-4MI9Jy4mS1(2J=JoFT#(?cMMwR+BhAU|&fV(OF~*=FL}S<3aCH
ze$1?{m~rc#)5dANIdV3}#-7`dJ7~IML&Q0)EruF2f*~oEB+Vp@x72bTT7KP;Bl}wK
zy0^g^xgb!0)ymZx1`%WyJ8Y5Zm%>kdV0WSW%YROu3~kun!*ln+)f4~QF`r@n$_Wfx
zn}nr%z`Yu~3!*BA&@gyAzwPKxj|py4os_A1c(B1H?X%-hgH0u!i`jfvb?)x%pQDeX
z*n$VJI~?6_gKMw<#o0*zF^*KUWH?{d`(uTCsZIgQ?suYW+|3l~4JI_15+2@T#!yJD
zea8m|mzi|)461(rR{k77aq8C$TTh`_rAR9oAiOw?49;R2ryhR{S&#KDA^F?E9(tj!
z6NdCZ9Tdowm3t2U=hp_7??^C0JUiX_fNCJ7lAZJN6H<mGjx(g5IM!GLM^2P~!u4#o
z%=P3;SrL*(u$%x5U&7Nk-30T6FRh5X>sY;?)mWLAGEKJU$%mMgP13bU8<GBKn?$1B
z>0I>EQtRWjH3xNr3I}xsk~Lbr^SQ+JkgV9{N*%~L-{|H@_N=MLVDQ_2p5m|PDCzx`
zGZFmGnaY$w9bn}ApPi|9aHCUHu%AK2Tan~XN)l%qpKO%qF=J^(_`jud3*j?&6EsVl
zd}CkfF~3ahdvgoRN$oT-j5<43w`IGPILe}ZJ~}vq-%+(o$5FA8REGL$CQf;vpLA_c
zqkV5lMLAAzbDGbqziPi-MElMSxxBAvhZ*rS7t?Y!Gv{eGvmr3*PvO6whGnQ|G<DW=
z_{j_O-+9ZQ#!=$<>uGNNFP;X9S$lFwx{@JLFODzf+b@IHF?z%COE6AIYlA_`(hB~F
zqqp~VJa&IaocMiq-^RiaC*9~rhT#EEvEMtnl40r;O(gM>83%MB_Rm_C2P&kAL|tbx
zUAM25_J6&w$}e9NcuRYA8Yjop%)K($F3v^m{=IhpIRmR1Gb1YSi71u(RJ;`&5py$B
z`rCzBANXb9LH{&x4TxPz=9<#q90AHe0w?LXe$Gx`8T$BqdAgFmpU5ybUBwVQ-4Q3u
zgB_@IxgP5JEykmO^rL`f-xeHgDeozskEHi;FdixtbjT2Iv2{1CfF&XTVa!8Y{HFLu
z{;lYfoh=95g!+R-D(9QcqjHB|-$a^45E6l<FxmANi4r6^BK>dFt2pPTn`6b^y&?T&
zCe*EC52%2dq<<)(`@5Ogqs&A>>c2D-3J2Ks6vvfT4^5T##iBF;!~lwsnWarCbIatU
zv~>lz(wXE>;{$mSbNy(Gs_J`|>a2N99X77<W=<2K>^4t;KP_BI{AI>8SKpg=30gAF
zD&Bs?p4tAWb14qM1~LXl8bc_H?_e-K2M^_>0CXq6{*z7HV1&)=@#mEn1!86YSDs0X
z+`opx!P78Z5A!+kt$4?!9-o0<9{A!LyAbirJBAp#S-L%DRfq|c6!NlSYb1@|anv|G
zV6Oktr64~H;%6$n0c+1|ChK8e+)!+2u(3JqtSnX=o~UKRHxmu$DRceWhu!aAS}<Q2
zOeJ97;XJ!2O?O_yoAa|T@>c;7Yrc^l1AMTmmM+2HeK0@D2fz89R!Y`@8f5(L(tnI3
zuLLl0Dg`}<YU&^1JP>_A?Kg61V;B$HKD_&}|C?NXlLSfsR3yXRbSgkmt6)$2L!>z%
z0`7JoOLR3?nKe(E&9vW!qn8d~;Tytfu1cEw0l-ZnJyYTOO|`4vVyc0E!f9<6$Lnyv
zW99n`m&oXlfq_iQ#QwGzzm+it))_HYfxNi7h0*`sC7dAyM}~(}js-Nf$v=QbIEQdf
z8*bT%xR9g5&Tut=a(dgvEjp9@g8ER|;up8IU2gSME_$%S>wU#$*IG2Kgm{kn!b^U;
z5qO2;lyP?1xJRf?8rW1$V`)*D_7W>8&JWSEzcRq;a<pC?8SIl>>GL0Bmrpv%hxYny
z$l3G-39anlONhp>hmu~N{HP@BpYAW4&n~xV&i|xG0x>`ay=jlGzB)@4&D&{Vz^2cg
zhAD{CQF|PpyROgRD<($SbZHl<50mxJ;QRY2$5%Vy#+s!Idr6N4^bERw2OdZhqgRy*
zfTLTaGW1i<!=0`j7?+4Qif>Sv{u|vI%cqgSMvqC!WdA+!Y^?uPUa+ip%}z^P?MV~>
zE^HN^W_BEf2WqW70R8TYE!<P59292sdrjq7%bn%5n0CU2((f;57hU9LynqjY<p&AI
zhlrGgIp0OzoHXOT^x;98J%D!iQ(9%lr}*q9=44LpCReT_dRUEjBKHAwYL;RX<1ck1
zll!SWs;NMHCJxZE+w954s7>u)p+X)i>Gzw#HnFbi&Q5<PSXH1{L;JTI{Ev|4@#|{t
zc8O=l-fL-)xEdfvP#hm@86_4O`|9ZHApHy~H`t(G`&#$8FLI!eOko@8bTOBQ-qZAF
zZ`-py6@_=Fy=)f2yw$hUZz~=GKeLKz7P4LUs96VA-7@yRw-GZ(tMcZnDTdL29A~%c
zMJp|yqnb(mtU#+qn@7Qx@8U~pbShNV@ZY#YC#PA{v^lUsV4p3T-g%>o8!mVZc;gbF
zd167?3;G51go!auj*-C&F7|fZ>z!1S+o7(j?+cQdm$o8<Yv^G?{~<vlf;VyzqFiVm
zxxXNFz+lk+td(dkMY5;sX@9`Q{fo_0k;fp-wz1--+0o^}B}>EHI)h|MERo7k(N<f7
zHuG^f26fV5hS&r9)$wEp1BCF!?3l;^?UrHM5Xmg?ydutRO|aHeP*f;~kvEfGbwq^a
zKd=?Z?=?ByGnepqOB=BBgZeJ{e|req;;?N%>{Y@hV;jLGr#68`*8riret=(Yl(}z=
z)ppwyWF_lbncyMD{D73>H_Xdj&MO{jpsar($N?1n_dC@6d9E%&+l!T3c1tmQh6IC>
z=uB+OOI=0<$HEuX8FzS&n-X7#V<E^BT1x;zWO3X7p8(yzhAIlR>!8_CJKul}+YYTk
z?5DIgoxPO`<z^bE%L!x8%*fskZywkI87_XRfSCOhz$<r(v{u^2b@JC7jj}iO%IQI`
zsFVQzagU&&|IbvuYNW8IE^v&W@%56li$`Ax3T85Y!<?*`oyIIO#8>@y)05;R)PNKg
zf7;b(%{l3|>Ng9c=AV8Dh={hrfP@|O`u@;cK#48@+f4X8*l?@*#f2ZkwgXuGOJqpZ
zGx*v4K!@gPiZsLbY}2D5V{#|fW4=1h=jU9%$ka)XQSv&dB)gK?Bi5pItDMZF^ZDHG
zmOO2bEqnIk!_PyMJm7Fzt0O=wYyg291tL^ujoAm1F?}D>WQJCOMPNg(hHKY~YWhQQ
zshlq~<9v94>Ulc4V^5Y?-IkbFW^*-X4{gsIphTJvAPwB5qQj92p%gEU+Yg>S5e7nt
zM<f0j8}CNTN!JbP7SlHh1_nNE^hT1h5^-WyPv)zn^{-59^le=1#099Jpp&wXKy7mL
z&6&Fuk#$(3;+M-YA^+ju`QED<l#N%dX|z7=UzloOGIE)lu(<$k^ODhk4naqWaRSj%
ze2$}eKG;s36)Wy~#RqEnGUtu_{hC&ff{e(Sh%MvK^!6jQ4*+~{|7z|klDI8Kb$~fe
z9fOvOH|w|&T@P};OWL3|JUOq?I^kB~7GMJaxrSJ_*Z*#Afqp>%Li2l2c^?lU7dlZl
z8_Bxc#`$GA*A+=0Hn?TY$xf)ZGDi6*4NpS&+gOZyixgs&UfI<J$EvNVDKdE53k)0N
z5ad~Qr^ciDKO2v9>e%ou0oh9lFzU?3(d)ski#)(Sug*{pt9!)1dZyRwt?j-3ls3a3
zz}K7Fp~JO{4^|jyPZUqj8m`VJ^7<#piqf{Dtwb5|g)ZS?qIWJp3%yA1G{CCe`&;L6
zgR=YOxU%tX!hZ49Y4M8s<-9tPOg_~oV!@dZ;(k<zgfF3}Dc~d}KnbOP9YrqvhIn^;
zGPX+o!!VgeULoKQseCycvpsU>_81Q+znGy3xp4wE%kcYc2h;wvKQT^s`YwgB&|Qz}
z@b-g##;~$nW#Ji1V7%+a#v83=vp?%td6K{xGK`$Fv8euBbeH})T7S>v>j5Wc;{&tf
z$psdy!v)_AmeoObHi1vj?7Yf#M5=WcUyO~~d3v3VgoBj_=W_A%?n;l((A#?RENZzA
zQkQXpx4cT0uV?YVeA7i-oP|v{%gWxtF?7gfNKH-#sQTGG3KdOG8<mCiP5K<SG+O`9
z&bjGz<QCNjPcBLfvy+#_zITnBn$RiN0BLq}Keb)dQlLc<YwEa7pdVFCmwE2YW`15f
zo{Lil-p0@yG>&pCidP@vNSvYCg7_)tnM<O~DA)ru@J=6H{sicoFkoF1@nAd}k!-$9
znAn`U+?d++@5l~55Bur44)p#)Zu8vb$3}$sfjgoOm}P$E6y^xk&m5y^#z2g9D_PYV
z^zgk*?6v?OP(6&s+xir6_|n4q>p-*0nKJb#k6G+J1rt=o_$J<hq*~yQ7=g)DhlY#8
zT6G=?@m0;Zhfbz-&kRAFN!tMXMzT?SF;}4UT>!Lxxl+#1IX$KH0Z!P1l+Q05uG@6`
z@#8E&A>?!ma9_^PKxg!)l*Q{+G8mjbG5|d7(&-Bmp@D&VHausVy4{z>-EN&Fz4)NV
za<-PiY1n|-+_I2jB0gQ7M85i!76-e>?KBYI_tngaY60(XKdJ-bk5xL+Y|SL_xvl`H
z$wyQ~*lwe)sK=;WVS!*3c5?Rdi?f^d+hF|y@lm2fN;L+y7R;$6@1}9l*k$w`n2lpp
zFk1P0wBbmXBc{N*6T`ms(~mx6V;QwS|AG`5Q&72=Roxn^&1T{ImF4C`&Qs^Me36tQ
z)k+S%mkt~Vj3T`Tp_jaPJOSpQl9)MXaGCDI$Ona}@qSK>8Yo;<hA(%r&pWn`EU0R?
z5*xCzx!uf#ODOs&yqw17VS13-(^HWF1nJs<!qj<TthO-<#&IXH9kx?}mDqmx8uTyF
z9_o)Hl_)@s8ojk?sHF+{C>(wS=42jx10y!%El5`!#wflKmc`zjoKi9l{W9y6QAIUm
zFsfx$YI+1RgGw{)J0Y=PEU|KiATt~am4&`*mT%H{14U;b)_H)EJ1ziAoFw88iZvll
zjn{<G@UK6;!O7GMSOMp@ib)oqd6J`g8!pBhQ~_b`+2u>Sa45RWVt9A>XG33xoAoE(
z9DAhARpxjR;tb>oUNPOza5xof%aB8WijlvMx(6p7Azv+<A0=olgE<eWbU~PTQCAaA
z$0+;kG%BrQOLwO6+jPn4YQHUUXaOV8+cd-X(UlJ*xxHjiB30qjOf?A)kn?`C>2t42
z{0PiVvb8gKYVl^b?F{TdWW@0ojwx=Qfjuo=WF1IzT|6Er4`e4}azEc!HF@bCUb4;p
z&{35<LCXhwWk>3cqPH9Tu#(a8R<S|@UOfJ10og~jOA9`mydaWJ+?X}8eRUQvK0Z4w
zY5(kKVBABQ!k+2I;l7Jp=^AG?I?LlXR`mJ*><PRYxDjdq@$G#v)x$I0y^^@gykLUx
z^*jCH=0T!|A+R46j2SW)Ngu{cvO5imt^8jIWOrr)FLu#*)!m8Dc1E)<-4r2BeKslq
zZB3eh8KSfj{-ShX|K8ldBB(P}xyyF?7i<UGRQ*=AdPLv8LH>f@|IbM|3m%>^<mVSR
z+tZAOlMJ?bx^=E6Fd>Q}{L!FjkGZy(uCywGYlL%1CL}?C?NDLI=nQ<HX8#q&K{3rN
zUD}-Ij-caYt*FWxBh5~@=$#D|C&BfHmd?V$%9B$tl31h6FV1t3<<cYMbqNN*_B1M>
zl(M-p=I3CbD-+Cl#ScsQ%XUUpfnh4dAE<h&wY5O};C;0nm4HJxCB9G(Sb1t`wgNah
z(gDd8^5DPhLFy`cq2>Y>jWjqsooX?|TTu4hQh^y8@5gW)e*m9(MtB7$Ih+c(V$)1Q
zFDAecy2-=n91Y>6#P4uGic4F|=Dz9q(Da!>z<v~QHwz5i>WLR}3@jfx5MXS91q=H+
z0S7t}dxWrGGiDreS`bL}ES*fd_(_L)&!|GR+J`{dsFp6LR@&3=VIN9GPR}G0qGnAM
zG`IR<;_nh+Wu1{82tLXkZ)vhLtkHuM+6JUkKQ2}Qu-VJ>)gtFjRaglnR%??bH1l^8
zw9$#^H$K5#yIoQC1<8TrYQZt6b!blBpPQOYaA@#x1H|8?FbEB*=rsus24#kXqrUJd
z{Gz{BnogF;u(X~NWR5><zJ=DG<uysu$U-0@H%FaR#sUtLT=#RUZJvy!Iegj3Hb3*T
z=?wX-_t1C7hS{CO<iB<(-Y_m`1#59FkFvchf4tb1`qjR=N7GqfoIl^iQoyBEI+vL=
zO;1&3RvdcAAM$MyEz+SH7hwU%VV4Io5a{)^Eec_wJ2kp26}?+_%z1b%va&*<>kF+%
zAe_yJc0&u>na==0td!V!4vqG>aX&)cm%$@}dkg6C_B2;A9C3GZ_nQk*s0q&VA@0ey
zeU+azE%X5Fnml#?-NX|eo_on*I!vr{QO5hZY#ZFJylGb-WQZ84>(`97I{WWZYMCGF
z@l(vwO_|9Vm1H^7nK*TG&8L27Ius(i9<di6=L>a<4{g|DhrxwI1rQ@(NbrD7mq&I2
z_@Smu+#*9=&_N@_SP^6vu_>5EZCpz!o&bCbP7{G28w}XI-6uzYpme!b_C=+@Uk7q-
zlAu_-z`q%=gmWIF^1iHQf(S=dB_rZpjJ0?wTj9i`!!OS3N-!suVLySWV7Co*BWfHs
z|C-@Z)1z^4l`6~WgDu1ol0oB4seI-0ZyyhOfX(c9m%Zhc1tQ5sN)jg<pY-NM7Wxz{
zX29;d>bn!30PXdk8!EFu>k)O7rpHxF;}Zu&vfBNSFlv3p&{AwluD2pvdMW<S2h=M)
z3u~vPxCvfP=JX6|89emtEV9jS@Y0Q-w|~Nmj{Ds7p7_qgFy5j%2sG~NQZh}Y<6lsy
zdlgkWA-5FFN%qMb4Eld>g}gdKMW2ay7Q9Jjyq;_^kr?*N`{lQ5-!Unh-jHqun`aL9
zmlY(0nHJO$jq&zQ?XlEC&D6WvZzRp19vjX#qcTLHDB7-<txc!|VFfHAICc2A7nG{v
z0>!-3Oe1qa1idDWU?hnAKxCAydCt=1i)jV&+9{JR9=2T+hb#nBHu?RBmiE)6v~^$T
z8*iixO@hE^k>GLM)p6Vkn5LGW#7{$C>P}0n)VbL42srXs0p7#LcoQtvTPC;boSg=1
zTwZ4HWiCC@QAJ7A51`zUXx4h@0#1}lv{@u&ecH@RD(XE4Dt0EE0zsq@x5wsB`I>Js
zUla7WuUBL2xHbi$bHBfGxf2nL@3$=DazVG@zvVNf43>rU_OqvvbsebXD~FaFS36tG
z8q#MUw=i2`-RgT8t6N0Q6M&+p_Wa^VEqPx_TrFMYwTo@S^pa*z>EU)}=b)4kwQ9?o
zv|;Ut?cnN*^Xh)O>m1{+q1FStAwGs)q=m3YZAx0vD)!cDC^0Zj;lHVuN|&)xnsc$f
zyMCUfc8tP2tVU6S<O8sF>dG!2Kq*&Kpmuvg7GQJrafuJXX3je*+40PKzfWPQ;?}j>
zlm`KqO9B00QZAAAZChydW`}MkcP${Lbtlfq@jn_PMg$@7?5}@?L4^t}A@jnPw1bQb
zq9FHo7UQwwli}1s`<!ZeJm4&rlJayJ%JYfG*JqGw&tx|tW&TV4Bq0lm#(RE-vf-b}
ziRj7h>+FtR%IN?G^Gam*yFw*wnQmkbik)Rop{jC4-c8uX-%GMbuUX`(&WhHe7=TVk
z?X&^DJzpN?Bju0iL(}tA-^3h)^@lmVtYcE4sR}KN=b@Av^s~a0S~GiKOpt4sRhq<^
z&q4;`NH!>N&~<V|d$mLB#a)zvdfnBya&$}QuQ$vvZ9_lgZ{}==FwfI}>`F8Hcs6bL
zTJ;imyQH|R!|1ix0MxlmmTbrvhhPORfAcy9)6NoI)0h_vKD=FY)$Jg3wYxnWt;%s0
zRrKPT7dOI<eX;)KXClQ--ya{eY``YKh?(hLkRr?0JUkm<8KGHYV0Jc{k3$nAC-6dj
zl>$K2x3Bzh+sJ_J2`R>0TF@Ei@U5dw%O;^7T6BUz(tuY4+(~m!slJWTj-iA1aM919
zfVv5*&-oJ%;ry|Cq5OaFC&fAQe~D{#*RSrep6aN2xP9Ize!CUrzXWn>)P2SVoZT07
z^V#sSfd<3MQ`$4#`ke3nFoyvL7{}*JNom22espp}cyZl0SZPq)#qE4Whvr;uJaS|@
zHps$QSbf-WtCN;!yPYo6HYr9T_uVA9Zt;&bmct**E>&X;V=n?u3j#W8=F_9B#-34v
zL`z^{PHHee7Wq*1Ou547dPJNL;V}7;d5=Ky3$bB(`#s5~(tB6Pk#-k!x7NB4@8XIq
zQGsDG9h|b_UC|=WG5pPeObIA$z(_MqaxwnqkIbk+#`ea~vQLT|zF57Mr>uHv;Qtpb
zBJlOEpbFYr{og8!6?M*!Pr>4h*!@&ud=%RDu#%jI{tA9;n2Zv$LsYOHG|1dKTU^)8
z&I$%-JX`^0cXm34<Uigp`I+gUhhVyza<bzt<d(VJ%aYf05mgvcTA;2nz!b|4NDEp1
z+Y$n#=Ckki3z)^YsI_rC&YxGuTuF>@RH7P8b}6PF#RY;R69-RM{@#?~{l$KHPKvQb
zjIk!z>TCRH7m+~jwJ<Hmh#&}rgu4#1c$(30Zg%RSb<NUA<DJ=%U{Zy1DM~?i=Mu^H
z0Vi6IOanfBJA&D64Mopu2nmc1Op*!dMtT_UCf32pE8Y5v1j9#~yHw}1EIGqQkCw!b
zs?Ry=71E?3vVTYs*(R+-b?b$jxf(J!)B<ANmINg4c5Ie<2r0Cvsm&=eIG7!+i{5WH
z7cx_Rn^6uTTlls}$nj%fSs-0-qZAzuy&B&wS;*px=TD46_i$Fo^)RtwB*ka-Whma?
z9EK1Pn?KZ|q35-PjujTQZz+0XnrcZU*<zgpn~pdX?L4EC6a|H2=XLTfo#5`YaK_BF
za2rDCM_G6IcQfAaZtl|TV&$SI&@`tjg!~Z>A^Un4VXrk}?p35-gwHTEcRP&T-uR6l
z;}ZstCtf#ndUn8+W|mjR585PBN*sE;G<Jp-sSrL-Ml69b0ZKEWcIj8K2W#Sl)9@!Y
zTU7Qb2b5~JoATKlAK{mIWQxSJJQ$i2mBT6Ql)y~GT(HBBCswju>3>^@P}Vp~!tOw~
zDY^Y~D*(U}>r9W@odXW70+Pp{KvUrY!OXijLBt1G!O!TR3=^$hhS_l~VI{$kK^8%e
zsxWOi&Ja3T{UGONJWcW3?i|}g&dXU&=Ng*5IpV(1vuik15y44u5;vzy^)%#D8Mczq
zt`X_c29{|$aiCT+_uA{7_=el&`PzRL-ypqCD7{%eY@8v`;TJi_h-C9!ejT>B^@CPZ
z2O2qlj@bueX(vCk9=e2|Bp(JZr6C3>+7+@ij?N7l{Qc~Tt`heaK71_dG<UyhnN=1U
zrhLSa%hKc(R5-z6?FjFV|6bKD8zPAG`g-wPBF~xxRm9b+eH=~GBa2A0Y6%Q-{{tpu
z&SihUi$Oji@C2;hh|uylx#M%sp4gsd$xD`XShH|o9cB5eXJGoMvmbJnZ!bT>j&bxL
zTRo!Ta;;(WYL4JWr*->D&qb`&ti042@<10i8$&2e#<;L&DLvPoBWfKv>efs8V}tKB
z>+If!{hKp!x6Ugp&q+x>1x2|(;@%ktxb;e7_JsZgn^ACFKfj8W@Laqqls&jAmzm&f
zCYh?XQ$1aAbX4{rr$lRyqfK1w&P>PSKwtryPPV^Ka{{0sJV4=lUOJDlDYr+O7s+R*
z(FO=#94nl|I#yj5I31NL$+~vByPeul$ow9*&GvUR%lxcX+7}a^YlyN!IElcKz0z54
zS~AVSbZ}I3Fz+71i;?>DA8pI)V-^{jkafhZ&w~Go1%qDsp%$ub>2V$zd=wE}Tj-c{
za;qs=MXa_&&XCUYq_@i~FBKC?Z@k&J6QWBvtK1!@K2K;Yz8f)3N@Nxutmp*gwy6+*
zgs=BKoPfj&>!CYI3$8H~1?t3`h4s`TxD?2U=c{*xf>WQjNVf@GKIpvjBUTUzlK>5t
zj;si-C6iJ*A0o~sta*A_I$$01DinO!Jd`^`<(XOW7V#vrt>OpTFC{bV<5r+Pva)yJ
z_!)J4J=j05&?2~vpEEAfu_guEsbrdPF%1~KdA0X4r3>>BoAUP8V-#7}MZ0iU808|o
zr4(o6#FkixnThC^uCrCi!`%pOBDY_7hE#>}Nje!hsnVe_=q9v=t!rUo-5|O?Ooh1K
zmDDPg%sfXyS*9j&vCqaypX*fr0uuHI=2|$X?+o_EQd<{j0QvgEm14WCq2n?iUekPZ
zrdh+6hsfTN#gpBac~*Gy5xp}$q?57-FxxZz->1d;EW%V1ui@yycVQf#jB4B~5iiak
zCudkMAzLCFrO5ay+D}wN^N>jtYpqUg;op&hgQOd{&2LP=V9E&r{O7C><Z)Sm3?YV1
zl#L_Y2iYmR$Z{B?Ax)+7T`DeGD!IPt=6T06Il2KDUL%cA)L{zS^BeXmnNjDlEV3f%
zbTsi|WHbU3u8$oON0^o{ETk!dtz?nHPBX&O*|OuoeAUp71lb{7o(w`L{~I-Rp@k$v
z&hYt6;&7|73zayYw!@RW%TfL=luVSKtd%#?I+HQnq^v;Ev}_Jj$?!3KMt^bwZYf)_
z3pIC^{nxZLqP|!k)+N^KTzbUSlcU-~8O-DOF!&)YEN(%NR!FT#>Uto_>&Vk=As0(B
zTjWryxR6Q5Y-_!ci4f<9S*Bq$b2euJX9jboB`{<&%A7_t$}i3G)nAAp6`7}C@bw~6
zr2zS&jQQtB8S#&|Bw#T~#q1SbVxRBhIs1hytGCG&tvsU>*9;gWM9^$#wrIq1R`eBK
zypL4=><ghivbiIF>_YvBY|OTke2IP-E!SRXthz_|E}s0j)C8Rk?6O}Cr4X@^Vq&v5
zYfQ9|pH(2nXiX(ZOTc&{gLTKXXBd8oq(Ih%QK)2eJ_uUxc6&H3IsWxQlUHXckp*^O
z`noHWHilx|jid{IUntYQ?_k&@XIvI>zu&<n?$-OvL(<QwR@XO&MX_buMi7F?@^(ZS
zV|WiVXyGJvEz~{n6^C_cS5rClBc5UWBc8)=yf+yhoV~et8xse%Mb-j`wBW~gs^+7t
zXKgzoZjW!zy1{l3fmHO7I}6G)v{(2rY%)=*k?9iCIm`D~3z6%|b<uH9BPgC|15$@W
zt-aTm_s1Tf?dMX_7zQcjQcV-3pD4Gp2ecwT{c6j{tK(l7V<<(0@wv~p%`dN=srL37
z92HI``3LMF%tbPy+}hv1Pi!v`8eo14rcm-J6WiZv+4AR^82KpB<2ta13S_^;6LC_b
z+4MY&?vQCM9Y5{E`1oI!YrC@&h?oaYL;u(YOV|Z_<r`-f>~3&|dwGpNq`OOBNl1^!
zaUXFz{@N$K?XQX5jIxT3&_}baqMYJgdUm(%gHv9ILy))@@6nrr$P=I;(r^|GN_EfA
z5nMVwOuz%_CNINjJA)p3<@j<cdoQd<)8h?&mQIdfgV#QZV^hTx(;?lZ<4o;xmUFv`
z@n3rD7Je<MF1}II2>&zl4PGdkY?E_{VE7-;J-IYA1fZ<aA@m<cNz(WqwMfOHn_+Ml
zKrqSaH=OHJO0^j<>2L_cSupP{NcKo8(!+^6rS0?}Q1MZmkE3YuH{+Gy0!XzNDn#RS
zkLk|x>?L(^Ph#u|fkC;bEO}$o2YEpoB3)jB$^xt0(8tiFjQ7WDKnWzkCAdeRA{`<_
zVdg0pa!;15C&PJ&w=*!0K#!V`uS5Sf;jX%tgSo$A;EzJ1?x=&~Sl8e`2uFKU+q|M>
zqe5N~NM$@BTL_u}!%m06*3$CL&n&s-A0wOz6;okq^H^x-l09$yr<KS=pu48up4GC@
zM=^ZGKGH7oyB`1IbS_f{BA3b5<;E5q5>j%V>P{D&IE@^EH#HM7uk8>OAKy6&f#1?A
zfMoWDw<&(kbo?P1X>kR$ku{D39KI=`20@(<M-M}ms%ROmCy!U9h9$L^cU3>ZyQ2i9
z>}g%C@A^u-<`hG}lf?V!Ym`g0UHwmDcweW?052_#X8i|2Me0_KYEcY2S8cRW!X5HM
z#V$<`yNtct_h_4<eXJh{^7lyfL?LUNcwjM4ia!C2j&q7&o4`b9i-6-}AVueaa#HCA
z?!s{=Dld7``c{`YHg2lg5>9&(Eh#SVE0a1)8O%>7TjE|^ss=Klc-z2ryCn9YwQMVv
z8#-ewtFJ}ZxdJf1ibd_gyWyF(3$HE(hXviCCeUtYoWXpCk1&52a6!rF1{HxM3ZZhI
z2<pp5x}LQrLxlxroPEEl@myyziEImLyx23=BTne#u2=~h-Xr(Ta#5<JlavL6s%D8?
z-wnD|dPs8cl6dxA&&x&Mr7N8>D?&6eofe*N;??9Wd-PXv;jb&6dS%;K%dntnzLd@|
zLggW!{Um;~^x=}sSnF7TT3fMUGttIwv-9aFZ|mYbzs&rgt?a6HMTl`a7w}gAcV%GL
zeCz{g{5y+yOlAPyqYjRd=wW?5d{)R`fjed<!U`~fEmd<;CC+YXZZC=?7mRP|>tTl5
z66H?kh^~J(51;e0(T}f&ABa%@VnNy<CO(S+c<LR7^55_n(;Q|LS)YysgR>|6CL<mL
z);3ROv^etm!t3t@hVV^zI05@jVrX1MN)J6AgX9y7x4RW^hS^pIPH8V20G4saOq~u@
zCX-Jsh-7(&Qz~M{+~6mREp!jRR~s!S9XUPXX`@0J@j5aXNda}SB|~z>KL|^y-O2SV
z0iW%U=M^$WNG6~ciK=in@YcPYhR{7F&p{?FnBisEV?>10+hMf?c`8A=DjDz)c$A&e
z`7$@>?s^HyM+zc8(+Bb0x^sI>kSFJ5soGE-8|^ZBfF9&d7v<8_d`HcdmXJB;5?)jN
zJv1pR&a}74)ltPi<Te@C2m{}RE(?rE7D<cd)>(?s54D~``RDZCTQ*4tN&COfGNqzV
zXz})CctFoY&`ghK?6vUnV!T(R`U+zdPv*O1BU_Xc%oj{WbzRr;n!<^SP>jy(W=`le
z4SM}N9*)(Mf(+F$CWo=`bhN=<XoNVm2|cRYYCA0p!|Y{KI3s;zt~Vs*%b`5=nIo+n
zUQBVAG<QELmF+Rv6Be0+b05&T$at%{DZzb1a1B6QtD=j<;TqvlNFPwesyfA)B8@x+
zh~Dl>x8jd!hIq=--YH-)(DR)AX;JfI&fF8)<8z%5GV}2^bTzjKPW*9}j)=&fOPYv*
zW%%I(xbU2?LO*jbQ=lS5+Pjoq!={-a@m*KdrJg{`m!|MwHrne<Q-o^-DP2-+ay$||
z)DAFQV3X!&Xyp>$THXqZSM({)g6XzFUuGYJ@E04#R#`+C)uDnO)%hQ!goFybvUB|w
zM!ADrZU~XQZb;V?OqQBo6m__hFF=g(-Z7ipE7^wW9pYm5BF?JGlb@p7jb!2D8Dz5p
zvq-ed*@U4gg+Z8ckSJyo3rsjJ@!V*5)`-XbvLe#Ta!e-3&Og)RCmMXYxN0_X^}~tB
z{E_F)PnJ|P%hkCvZ4ZeV$GI04uLvwYOOU;(i~I5ff^+|`BL92LjYCkdz)Ew*w?GxX
zzh;h{snLGBA>y7-`QnDBCpUyUXWac~1}~n&`gn2}*QB=S-h|KAyIDP3SF)ST>X4j#
zmD?%#fGAc4NMLnxSCgydR0HfDMJl#Kjt}2N`mtq7@Mi790qwZe>U`?MK=&`M(*U&Q
zsSlV)(!@`e{&&JfqqsV1yg>2^`b}dEFzMtrzS4REHHl0K3ns)6lIQ8>=A)!mfNn+N
z<&BUvB;tm$>3@<1Xg+s@9e>Qw(u=$15A_UXrOgE&wB*WD#+i#iYv{*GrEca^oN_G+
zoX)@C<-CaJUCw08Fo4S2DnMkb<`&pEVJ7>z<OkSJF^4hx#G~`@ax!-PByy@kLF88`
zsmx6?ql!RXf_~h7^6UB^ugT~l9Q5>UE2w+IkPb3r5FVN2H(D@)P-w#px?r=C0$KOS
zq9P$i$E~nC-&x@HNMG%58ro#cw>$2m)lG9o1&0UHhie_FA65}$bP|zM?n?_x1~Ft<
zW6J-qFFO?(zQ&-)I2vM4U&^pdFZe>X)%6BBkNSh#G-)S<P-~gY<uXA<5yj*>=f#2%
zD<QNe52DP=KE~XM*Ufoq9<Cp{7DF$bMxIM`UF&DBdDbrTFvA8%?oDYJG@h07@kK0O
zj9^Q7T;nJU@}av{d|%SEsKxP!ZXEv+nBseuJ-m#uG=Lg;w4%4w43Gu37%9c`ULKPL
z$sU84Vz?9fPW6O>LojHL8I383x?QXO3PU~F{!U>lzd6!a@X?ZQ@?8)mpXGnmt@NEp
z<|RvhQSi*YPdDWZ^GHrG(Wp4yvl;P|x%%?stf^hWDD!pZ4JqjC{OB`)<$1wE6z}jR
zOFficm72D_1JS9ZHJ>nTZQ@}>&?dYP2)b}NcjCl;${p~kH!zlSi0a+NdtwZs;?98Q
z1mp9GY(2;e*=pSIwYNXk3LnqXEr~YE31A~GG$#Y+59QaKCtzFoF_P&_HAX_|5y3bX
z(d+U~Q$7HRFhosV_ujAzR+lx+9uEgGPc$)Js8n-XfiYgsqiUStFm5C|TqRj7#BA(t
z==8!#;$iWac1o*Mc3?a!6?(5S6pcedU>ic!e~N0Ng_Mo45a0-<oXp)Wg00>$f3*~;
zo8QOG;3CS1^JH_EHw+9oB%lTyexX>e3^3R4!QgWfRbI9L%$K3bK8|~_T{)la=0)+8
z2Me%aX)*<@yt#39LipVJLjV=~nBnPkYVMW0EBJM*F@&2BJ&A+zpE3oUp<?yS_^K#;
z8}ONZ=+m$PRazb}FCNlUR18&)KQuxO#+b4WdRytWHch@~Rlfi4%?DH2UlUWoY}nV?
zsB-1pyeHhfVg5+}BtH<19w|VjBh2{qXh%5_SoGo{+fB93beSk3L~kO%*C;<qL!fPv
z(o{0K3Ak{2!44jIy4aoT+2l1yT<)JH*?Ol^p|T-pK`9NG*x~r7vA8|HmI5SGDsz%Q
z9X3OD(u-EW#Nk(3q%ejKN5n#PrFx+6BoC=aEA@dTRgmaW5Mr41@~<5l3GYwT^OUUs
z?m~9K9wNmamjov&Y$T*4XgS3CQLJ{W)X>7u89Cttt#2Aak3a`;v{}#}${h;5^O)wV
zp{!mJB?yiO6f!~W6<s+%horliG*Rz<k<}P_=wsQh3~(wn;gvi#Yx?_it`8E^JI5XN
zFJ9yOS=VpIYk-On$rI+=SV8DYpnxg7L72*-KCx4xib!+UhsY}CWm%I9;@4u%8;7#T
zEQ4g5f*j$K3Hz-DaNWeN@?43jjUt)MhKHy9Wy-yPFlKXs{$9zn_nm9o)O{)5reb*u
zF4#gPdT=AwP-$jmBSkF>J&e!A@!Kl$JoB~BhB`)c^dNb-(5ln$5l6-qt`@0HPE1iv
zfzJ-U$z2qpT?m@?NjoEozR5j3n$s_AqTI6F38oKNT%c&ScU0PT<6pYa!QePSKY6E-
z^(P8#BfH;wKsd2isAw6%AEvcKf%Q<O!cnnuEf{>mWvW25j1}h$LO6P7MtL+Nt7`*7
zdq(lg@Vv;RgX~E%!=NdQxwzIx!cVC{v|B5B%tJb`$p~kx)(YlNaUtyB>|q>9Y~{}-
zg~8bd6bH+8${5im6mpAVB`%NIIzCHu67;5jD|+L+*u(PF*O_ykMmni@Z^k;xJV&%x
z&e9~m?~nmgUb|B_wii^KFgjKKTe9j!Te~hY)kgFFjkIZe*qfAtPqnlibs(}q^ZHDk
zvvzJ*hQwtJm>{Ln62AQ19+ng!oUVQ$p-7J{&_o-IV^z}d(pG?Uc52u4>5k*hr>+4o
zMKch@pgf%^eJTUFQ#0Ly1to(dD&DPngH)?3C%_QcD~4ml<2$m3GSi57KM^+Xt95OW
z+H+o%4!!Sicr~m>{rFcKIMUj~UIA88v)bJMyXW}lJmUYy-djdh*|mG$iXgCr1xTlq
zA{`1WN*a-lwP*ne=?0~d#y~)&TYAw*mxM|z1*N+~K)R%#xwz8nzW2TN^L}~XG2Z=!
z;TR4$&vVXW&Uw`Q{f8{h`%0dmuh_zPO3QACgoVoAjTFKJ;oc{lOJC*)=cJgWnPGgY
zvJc;u!G40$zX*Y%4kvDWYvgB~xyYX?m6DkF0kPkBGfh$)&j`x0cnr9t=NL%o735fS
z@Z6Mc&@flY`Qvq5(G@OdB<?o;C~1}xc{(JZdq1)TC$mb<1S{bBD;&`%i}ne!Z_i-z
zIoCxg4XM(hl%aM5_^)Y>tamz46Oo<m)*;nGUVA^}o-Y)*F~oec>QtPxE@y&<(1hVN
zp?)}js0S6Wl2jLu)0AU6=@{o9IB8B@{Or=Y7bMJ>UK?hBGJGV1IFpD)#>wjgoO?vW
zF)KnCto|;~3o`p91Y~Z{QQ0v2B4Q&ZtzJh_>ZCrp{BnNkSjtSY<w|<=fjn)}CCvCj
z6P+p1^ev{j5|!tHW(y`iKg#jKog!Bf1j^Z*K9s%*%_tc4d&l8Bze{l{7!rDY+cs=w
z-ki!O;?Q(Lg0e8Kd$&a`TSjQxP<J3*Isf69$pIlYB~oxiJNxb%tav0Wj7M6eh1bb;
z9w$ZK#(Xoc9&Zvg3K*JxDWC=8t{HCGx03OGH`M!EbC1ejn@{J(6Ut<Z1r0xJF|!rn
zGZ46no#(d}5oXwLY-zR}w33|rEKn)K@E`+&FA$i0f<Q`o2x`Ci(T8Sqr%%<j@{FVi
z-W_+_eBI0S=`==3@4t+s3Okjs3B?@TzKT8C&^5FM=4x#)Y~TD|^lnY#u-|>J4o?I2
zK9+7`qOc0H&iVK>QL%;Bn=e0Cn)TR#<Y1a~W`<af_a&@uKeLqiTAfN;T|-x%L3KdP
za4eUdH+{~5RI>TN9>WO^CTy|(g04~nCZ|a8?CF$og^f1GO}?bv5r8dtL&fLnk-H{m
z{?tX&sf36&=Z||VUpX=N%E5Yb7_Jnv8>+ru$cEVh`v1ror#_ifPk*sm4QVWIjB9l2
z9Z1r3^!mo4@Pb-Iz=9}A*jy?>z}zIk(7P*6pp+1wCFejkMk=T|?{$Y7On_TFNgUl$
z;|OD2Z?I#Bu8Hlf>Hg~bJ;78!7jtgsO>2K_ets*q`5_H+s{x*Fm?e>}!4OWofmFNn
z+_X`chM9)RogUgrj~ab4kIotTQIng~pw?Gtbaf_+QJVkN0nSp`Nu>YvnQ#Yh)2^lv
zwU&KnExx_BJ<7va32Pbq7gHZuuQFd{+G+*m%#FBcaU#s9dlYZOZFS7Vm@i+&?%4V0
z8@7DvUh{IS<@u42%fj3XWqEF*didS4DRaSBB$O13UlPgRZExY>K3DpP+Nqt&;@X0Y
z&d3V3<}eZppg!wViMhAXGZPCR5K6Zf1j%<g`js$TqAi2?7f3y9lN-hb2p&qZH=i};
z@gmjN2(deC+nob(F=3rUOBCREI-nZPMb1;%I}uV6-uS{8vS%(YGZJL2b`}Otx@6X)
zSNF*7j1lj-d?ina7>yErIu8eHz(qTGeu~qW?E<!}=uk<SujX;H4>yO}?g+%@^@0(S
z&9)^0QJIh+VFY0xVLzjkE493iiSC@Rj0y%N3o}YIy_tYdS@wn?-;)N=WoBQ}xAt19
z-UO;^f41Oo`fxvZEw#nt;;jII6)ROQy;DOn?2fCWH)nO~fM*~?$zD_CIN@v`%jB*l
z6dOD370<$WZBejCaM&?wopIa7tzmh+9bx9me3?m<*On)MWD9IJWMf_(kMv2gqn6tr
z;bN~1n%PS0$dS!miyy6CZ1vXxH<Hgb-p>0t8BK}K)i0d3J?0CNrDwcVZ&m(|;Rydx
zu1RGe-V2Ejjn%33%ZTF6ee>lApG~C}-Wb!|HtoUFzNeu1@M8Ff=10Z1UU~orru$x>
zV7Eeh91(+slbuVr;B$%?>eBEdN6c<phF9CRflZ$pp3PGj#;u0p4O$KzJd`F4g|%-B
zw>lA!{CMrfvdHTPm%>{Ro}qlb*d*8d^}GDz4~~_x^FDqc>pE^eWSsKYW8v9@U=%9m
zW3zt>HDD`TJ7~YVwJk6@TLYag<04c=cln)u1WON9cEgW?q{sa%Wc@54sIx|Lv>2w7
zHM{;trNfojzkvbzY4g_3#<CPez0Gh!lE+5n^?ZP;8qD$1DpbuMu>CxYrTeJogVRUC
z51PHcF4<Xtr;`;(5SRSY;I<&=kl(`uemjjW6^c=swQh6?mBzlu%i;)Jg9)wk`j?C3
zfGqMPS7~AuEIZKk7<uX9J+gWVg;$eNJ?@(n@$!T$kwV`^rQ5@%p+rQHyPdijQZ#es
zw={5i2&7*}%|G3ZR2_o$T;$#wmJ_1jitu@z7a_rACNIdhx+!%vJ{)$k)B7%nWDBD=
zGJ(*NaF38>h=o@BM+AAr=36Qcf(N{19L`FcTW?~2n2|p>*2huCfnu)EOY_Ol`0F$a
z^US&v@c?J#hT(@>wBHCGu!Y?E!cHcx*8yu62AH{BE=rmxDT+OZo02DL0x~aghBl?*
z(@h_cU$mKPdf2*H!35P9#M4#Y2oMXp;S19F>w)-XM~GX)_>ac@V^#p7N`0XMrm`#$
zQh@KlvcP{hXD;4K$T#-b%*aMo)U^HnoODRd4V0l!q@$GuMdQ`<JdU9<gRq<P={Ha-
z+sU)jhhrrVq}xK-=;W!Wz}XUnBFsgkbIJ&g^jH$)w|!pc{lKByD&R0N#_b`f7h_d!
z&O7JGw8S!y(MYKhqXTdMg40=Qm>e0z91d3o1@gHQ$4=q)!g7=uR#stw+{PSihEDx{
z$Rrw6^)UXG0MD9RRD5`4luNXi9y>3^H11MFK@7Sx-A}7Y?#O+xZX$YKcEmKZ6lXwM
zRH|G~cxK<)8!(@i$?qgS4eWQTP9YqN+GNsXGO@%{9s!lDJ0?1t?Ob7^5jt`w8)Mp}
zGohZ|AgA#rr+3D<ZjjP$$oPX~=0IdCh?@}p39}s7!|@%NFEa0jh+r(e6u#a*<pHoh
z0wmrYS37PYG6a}MR;+Kxq!1jm5*0Bq^PT=o*QFoWwEeVw*@TKWZY6hk57`^~j`Nw1
zx?zfwhwr?*`;EDJiIzDk<g;E-C_?QsESDeO6;w~D?SAx8@<Z28>16-C`-Xr`RV45y
zB&TVDSz;2y$KxA|@R&5|`WMsH4~^$}1>)Ov4%8s8pv@8^|LfV(bm!hVwg6nT;s-vk
z(7Jn@2%i=+j^XDJ`8F9tdq#3TZ%bVjV><5qj>{hu7t5;K>H&nP=_i0Oy%!_U@2JP&
zV~?7~Q-7;Ye}U2A82D$Sb^_c9M6?}Yy*DZDM3A?9Yu3IWLSBi-P~$k=a;kgjX&}dk
zD=KC=I3bqvLrm_JFW}!Szl&ksgb7l8ss<i*mm&xh9vnpqP<Jq>7d87bwnTQ|4`XiT
zs&DjAOB$!00Na&|k>A5*>Lf~s{VaKb!I0+N$m^6wer#~bERd1V$~0^Vfa5ORV=ZcS
zAC8#&c%b5l&&YrXth`8s7hk5L^wcfNpn5H8G;+$sayw3SL8+4Ts=88yYMB8;U^G^2
z9C>QkM}}Mks$a?8Wc=Z)E88Xwk3KdJ2@D^!_h{qmd`)q-1L-MU?B*&q%}<q5ut0S)
zf^d&7ccPF{7*TrQYQiMH0f!9NLyf`mi=`71(t__qS4ni2avk`}<g|vT-?+7}b=DkJ
zRyA-|wY|xpVSw_j0<QantOgf%XrvDig3m@Xt#sd*8w%M_#ILs-4BR9vp`3}Hx<eeV
zi_IkZkq0$4OZdH`aq#9_Z_uth9b+3dmY--&V6Gpv8NM#6@1%ScFIt(pNr-H|`<8-u
z<OdV^>9QT-L3wsc>|tB8M{}^LPq(nlr``$Mo~T6>7oDVwCX-<gKQ!PjG_=5D`NFe>
zd6g?ZIiJYxMlV<%<)P(E;TRq<;-9A?c-^yS2!wF>tQm8zei=!2-uosey)IX$O^S_X
z9Oo>8yG$OV@~26t@PGpU&Cl@06l@n-{t?3rtJYz%Qt^=;7C?Z}#h8zv%+*}`7`Bf&
zsJlxb9>NHXSw9-9^H8xP8GD~cZYDR^QuaaotBY)g-N)M_?lLweY2r7g4qm4BJ$0sT
zS^rY>eyif^cik|zzc2`2tpKv7kSH7tB^jvR?d$>c84b-C&3_m?&UxxOv<76H+-yVm
zQe#nJZpTdT=wDswk$I=Y3x;zzU@D7|TZRau!vJ_O4_9JQ3gb-kn`IQ~{^=}BVMx@?
zyDZnP67gY_T^hE7i4Q&YxH-MmxZJ4ui0?K-6Ay_cB2H##%3OF}_D4oi`|J9+VlMrv
zV*iEhGRizG|4W+zrYrEsi(#HpWNBT53LTF=mAIIHE7Lq$$y)vK<wan42sPLHsk_H`
zyjQZ#{f!qs$9u%J5#pjgcRcBM?#_HCq<?ON>k31dDW2v8SYfY}qT1i4f_6VD&N9?(
zK5ssb8?V$e9_SF!2&F~|wSU*e=hNhikYLV*6yP~1R0@^Bh*rY_5eFfwK_vY}FbdtS
z$S1xBD5J8D`xy70rCU4&z^b5POH~wg8Z`F`#;10Rz^CoVwlXt+JpbDRvRsm5y7lf(
z_HBf#IAy`#J`v+RP>j!)OjHy>dFVzDg=h{uoz}H#rMOW|1!8D`iYn6$M|YhGQB(9z
zZiEPCg2r9H_UH7$y-;VRc|6PhU8B>j*hSA7&$&BKBxLdA+-HOd7#cYgqxaQ|)<JxG
z^3*1IDq()4@bbQ95%roW=KD|>MeDU&$(2vM%=rOE=Jh&u6199f!;_EvJxCai;+#Am
z72|}90~2Z<Axmadl<17N`*V5Z+Frks!LwQFTzt#haNbedePQW0b7Kssdk3OsiI`>A
z1`W+H3oGwCah7nnp=ylqGf&XHx<V_o-HMyGGM7!5rMkF_&gAx?r}WIH8)o!QC3;*k
zR+Vio^QkApsk;QB2AtIU7eXdvMYC6NAGm?Q!%hE-@3pSVFrUjtjZ8SszS}Q?r|`8?
zOs%AA>=}W@JKuNqVG7uO0jZMf9zK>-OCso>j>PsX3gZ(vGMjvpYLg(4d92hT+()a6
z%j9J*GGm-!k^XSrsrIiH705+k>)&P32`rm2Pjjn}&4zibDAs=@u!7DLSgmlH0;Y0{
z1;MM>`=3+po*KF@y&w#}Ein*7C@6OtwSK}h23+E;8yW<(1AV(~K<VN>{hM2YQJm$X
z-mak1c|D}R22gH~=2hotKIENu;EkG%Mq!2F;%Lxt2XMc-)?$x2FS#M@E)D<L6WjO(
zeNQJY`QvaqNiaoAN$iK-l)Z9;5IH_ZXS^OpldH~>8C?%QFz`)ak(W6J8+_OZHFFBM
zC&NBtY=#v`JR|M#v%OhfdY*wX|0-P%0pI0j7AZU_=oXpu6UM0x5f1FD*t)5BS@OaR
zVd`2Gp}8-{?#Cbj)^!Hl%_Ry~G9fY}%QB2QCKPMm3Hd5TFZs?JC%~Ie=C))E+du|g
z^`f(3^hffLy)46a=aBM%`hX8tc@4@`@ccV?Hj~c6-*Q0tQn%^i`RAxUs4OR1Kr?|T
z-j-vHS&!5Ose$YXW0Dzx&v6kK?%vl5X6>@+l!F%!if*1>GwYLCm`<V>A&+NcqPRgZ
zEXX%nhIbU6_8CVc=~5DP(jeyigD@w)>QA*w+*bl{S`CbFs=Gb9T*{6qdYDjr$u6;c
zr9KpQ8$O|Jx&0B-B}TWVJFv@hM^l}AMAAZy{v)YFr;cHPppfzgcbMW{g3>Dki*FNA
z;SuRA9akys?(TZCgH-x-l1q}lV>bTT4Eg)GVHa-tM21x>Ox;Z>ik#$}<Y$k9#cxVb
zy!a7R&?^t8%~ciyxap&J(+p%x8_qODW3hxiPnR1UN(R+!ZPL1@t)CCENU=x>cH54r
zc3?ioyhmU4S~qs(s!E^J=h<3()<(A7`RMht=0h~@`yEI3wO2AFc9_~Tpv&5`i^{Nz
zxl1T;OiApOdUQdn`5q{Kg501A)AZFJvCcAIKb1Iiai3O)En+gOV?NkxZ74~?^)8t)
z9@|b7yYlh$<S%Y)GIP_rP-#+v6w94a!DSTXv6exZJ{oUl^F@d2&*q5V@=Cvx+icO4
z7O#hmGgBK*axIL{@#8OeH0zUzgg6x2Q=+fWnx*oJa!-03M5Gbv_7Ijwu0|5z@JSnj
z`*R*)pq(S^g&Y|_25;C)158$Cv4C_))ThqPhIJqAE4WJ1!jhMb?=#=}P~A=u^dac0
z26T=;=DNw9GD=G6sHDg-6Zux1b^XahEzeHPlvlyH%+DS+ADq1<Z@7uF;kZPc844qt
zqc4lSk1*qq_GNn>^kwcwWh+l|GpT>zv^mkYFdc_9TT|dHnT45FEAZJ88N3_IZ->d!
zPbY&;Y!8arhci>eDH}l?Qw%-~{EU{oJ8Wg-&5k`dDdtZDm)a#a7`w#w$2jWdjn>X2
z8<>(b#4#z`VB(RdGmWT&=1s)>RX&+lq~RRp8&Xdse@b~vUXWIk;>&+1LBpks9rf&o
zy;(M%`u1t-k24%6!qYd@De}5Fw`sV`2)M&wT3zYZWO6TK(-Bd|IAv+-<OYE>k9kbR
zG7};Tbsm+p5XVopCsFENWM#*=I*5NXb!>*C!K~J)ONLG4mK%%H#Zn|<_O_i&{_@9M
z;^puM3Q@FAoymmuj@s|u;Y%gJc`h%>`$BAn{H>mh0TVc@92XA5Box@M6!9i^KZ+oD
zV3oGVvd2pU!gu?9;iJBsI~C7i3dw4VW*RplGpt@y3fINM!urBFKJj<dbctbYI)c7v
z59;<FgUP6G5l`Fg<)8C@;9b44$ZSWph%tyG9i>SYB+Z9X4{aZ12q);vF7p`??P3i$
zqBt@Yp}Eo~<9HuU6s}p@3Yuv%F3!;LJzo=%yhMxDu?$Vl-FWfboy5Ad2MtN;&bi>N
zL>7}=4nw6Ek``*`4lY=6uZi$13SDR}r$L+k7!aC>zjLyrx`?QLB>gblj$ZTDXZ}ST
z|2Ho)+YjzWx<#EaeT~_u4}ejDi-D%65}+BN@h?<q+H1hN(9fVeBiEv_4c9N`D1;5M
z^P3C(*)8x}4dsR7c{gBOJ)HBdJShMpnjcSi0QV@_yz{vyGr3{*>v`;pxWhTeEfw#)
zTy4T}e&9PrzHl;|lhR>qKLs*8<h$g>92PvhWe1?346Znwwf8WU5$GN<M&2RJBYe8h
zl}!EmNSsIj&0cl|DqQ=j&$*K#%mSpgrp^6?K$MSX^UN}ULy9cRI`aNA4o!J1Ggs=H
zHwm06qyrh59cj#5r9{2aFxihq=v%3}FQ0vuw=Iqz`yd(w;oA?6Y~?95Gi5^i-IA5+
zU1n|TM7D`G38p!|Al~XDmc|s{j_>{~UD9^wO$X$xbg6<Q`IiA`bFu!0%Tm9#Q3GmO
zUJlRnS&&9oE%}uw$t$d^GH>F#SmM7of-9dp1PA&lf(B*Kma9@Ey4A>6DnG;lESQxU
zKJtxF#*}iIPU!slveL->Exs$CN^0lh@fLmU87vup#=O%iQ(|u+wiBCSzlsndtZoFY
zw2LmM=(f~U;P9A7RYg9Q+A^gYWand&zVG5~7RRJ{v)RoBsGH2K<EYBV(opN#<@;}-
zHj~{3bnDD8U)W^QIz^H@o0-gc;CDP*u}_Ot1{DRn?-Hx3G#0m<SYJA%38W3HeJouC
zIh#32KCxF7oEWUJ!f7dlhZJj-@i6&hmx)MSV%7qhuz@hY@Y4$yapSgObB#I?9M$Ex
z<;?~nIDzdWn2YQ!G`wnwo!(vinCc@9k)PvK9O)cY(+a0_P~SS;;D*KD_Q77?iNVzd
z7>(X0RpXke=qnIpEj6JG(@x=!Kq<?j{jI!jzi`g|Men!J{Qb3?X73gu3xgn}3)DHu
z7HnW;_`>vbE0l|deUbhQW*G1}#{65&@NQcf=xO0F{WJIPuL4-tw&4$VMhSNOY6dF5
z_xuai^^f{M=y`o0;5P)Pt@{GFr5a<i@uHAVi8Vk6Pn)XugvJn~5Lh-FJ_Z3F9Um6u
zbo&JzXxSNqCjDdHYY+0+psx^k&0%=*?X>05X1t~p&HflQ{`13htE}96x}oVL<fIrq
z6HvZS47V7hWs)3Qa6Q6wKVXII(c1Fh5w+dh6O*~YciT<aI7~_MMy=^fp&JCKk<L5q
zwxJ{)HSJD!<*KzRzuD@py$h3Q&oYzD^qyznqAm<QmJShRmI_jtlf@+Jii(t`Dy*u?
z25X7%I^|{Jr7O)LrZ-TOZx!!HmWJ2L?4;l67?Tb8(p;clWw)^X_8DGSn0`{?n=8{q
zxFuYSS7-;Z<7;86ovW7@iOgASu8+Q82sN!U6Pz=c@|>!N0lw;1&9j0b{Qcx~IwP9J
ziJ!KM!WpY_&tyL_xiAh=q!wcC6J^?U;TAbC&s#4HF7LsxJG45kaYy14I+b~LPaRHE
zr4`cK7z?2mj7Q*Ik|PACPA{DsoxR=cR4WQj8$~Y8JqXcX)LU%)<o2>Y;#ikFJcWIE
zAg^ojwCNy3jdqnRUX@4}m+yWG07m!sC!8AYbM(2&TKqNP0%$PrWll6oN`H2MU6CES
zr=YB>@{ePiDG7}lrOgH?sdTYhXDwH_KXWH~+&ci22(RuRu9)k=(_`&)tthRs=TTTM
zrzc;CDH=OG4P2!$XL2e@UUlsiZjUk>mHGTEJu-h@+EvC@N{2y{d^j8r7t<fy>-)U@
zTQE4n()4vezQ~M6PQad|Js*8ZXq)4d9^+~MU(m5}dB7K|(0lqnizf4aCtUuHA{va}
zoj6_)%mLa!4MHwNt(F4Ikq^JCgjii4rPb{qu@!0c&9s$ua?I?@Yy+)TuGAO#UcE|>
z6L)0Lx8`5rpv0l1J4&29eki;*Wa@GQWiN6W#q_67kLEZ;QjA%ky`UV*F){;WBH9ab
zS=ABv$8a7Ip0n!Aer46wbQQEk7M|bn`ULb{-pD-E3?aE;-*~WqhKgra)z5Pt?4Mtg
z1NS8*PS*%<F+r3;Kb-@@4*#yJ?%iG!y?z9~Uu+Ctqsbka;Ewh6AjyzAUyzH7UGK%y
zKStlUUPAL9EUruq{CP1&0vo($<>g!da~t@b7l_7*87vL}5ufdzn#GjS_szraEOOKV
z`zxAVdrwtlrQlr;nhSn9dBW&Vp9Z5~K1O?uUv)9`tL&B0&~wmi))+1Lxgq{6w$QLa
z*lwa;NR=9JKo8OGF*;1?S{p0;qs~L`4<!bV#0PFBJAg?SYR71yZ`hV4{gH?M-Kyna
z=cB9i*XVv#oS6co$f%U9H#u|CC%~O~m2y1CQsp&tziJ{Y)-VA`4-jOX!*9V2#Tsuw
zk&#D}2YP{OOA;YAJ^&hTRQVlMEtR3T6oH17*&zT{3|JEVR)Ug9#3VdF_cE_=RnvdG
zMU27FO70Tjiw1L~9QNf@*0^c^(Ly1C;q?-jiA}+I`i~#TFqHpOhT;5ccv{XcRzCQP
zmFn+~>pSMumgO}2X<q5^sy*u8@&N&_Gy?ti-L9g^IfYIu(OH0snw8CgHj+Or71#@r
z%6WPbRMO5F`hbf1AEAR_+zV6AZ<znzyfZM*M;$dOzdri6xA=~aP73v(m!bc!=JG#M
z9no8Z=Q;iMzfUy%V{7;?DP>?>v{cXgE~%;vbpa_>+q%>F&r4^&eGSF*`K>OVKbXdr
zv0xJF<_uJS&2jza1MXZ#CkK{U{~xWl{}SH^#zjx;|03Zq=Z!0$b{|Z_>MIwqf9y}^
zCis;a?a}7voL^}B?>*Z8Au$ncoVz6dUocMpkI%_jgd?s2wn5hBY#Vyts;FcA_q@=n
z-=#2iPHe|(`R}`Y;jdW_^`*}c0(g6H)vqfzcJU^3&v{k%(LY9!DeU~veDxQ9u^Mny
zG>nXlgy%qmVS(;DK*z)S_ng%<MO=Dl5!e#Pfq0QsPv*TPG-kQy)uX$m=c`a;IioiV
z$dcSvulg5ivE4alIr=YKY(gUPdw^t8?_9_0-g9E`OWM)3?0;{a@@ZFaZ6_PW#@%Pb
z!3m;_zPO5(Jh-pZxyGt--Zi3{>{`YQJiE-L%3n4WuolwJA&phQz<;B#hDgzuK^X~p
zCX|3;0U#xL-b{48|BrF{ZTt3bP%;K$yjJ72ZclwJGbP{QLyM%&3-FHho_4GtftJsU
z`hyhz+d`5z4!VjaO1El)1RC0&jc;OB+KtA<0Q`L@2VLhQ1MPpe{^Zj{aOv^sT)#?M
z9sk@M*KOirL!5t?dX{U+O#odBTFU;H@A5&%ZB+oX^4oU_OFnt<e6ZLkwbFL@)mp>;
zgo<sC-Jf37-!|HdxIeCAWXOdp5VHVu^25)L?lWY|=XiJhCj-*Y*`RxB=DP5Ij0jWw
zx#eX1VL87XaG*J;dLhBa*-1ro^MgHG%%A9W35*KNbK@XGsB(er;C27Q^nZq*qUYhK
z*&mDb*EjwbU;kXf;lBXf{~pg;p8vtf|NVo%Tg?BCA#m`}${+u0Hh=fGF5<rU-<9&e
zyUoAV$oYSFrMM<EjZ^@RR5-OJ@Js+A+zTx@D$;P}UmXho1vR|EW8IgF7NNTBu{C$w
zZlwG+fJ8MIFh1t?Z%{oy*Y<ty<K4w2!43^Xj{a37`2CQNIS*@|{c&8lKD*E|)fvs+
z3yy8!ihEUm1=D{Xz!DLJ6oU2!BYtNmq&QxgjT{RbzX_R4PuEpd9i9!>5nrf5@~zG~
ztlxXQhsDP*cPS_c;na#x`&gEcT<sf?dK)SHtGb%MB$*^33F#Q2jKKwU><^@}a2X;s
zvq8eIC1x1I#AQ3D<BLuDE_JoFs@iUDixZ<~w_dyUk1vl+=COV5c)d?ZGd94~wDJ#$
zXUvPKPr{rCX<p!#Ub~37%&_r2BVq&$XKR+Qu&`8?e`TeB=I+Ilw||N!;?VylyDm|R
zS>E!**X_}x5is(t%a>|fLF_zwe6ao8?cEqY1{2AKhgU!#6DDf-0VcMKfUzt2?;_H_
zNtWJ3K2eqUuof|8tQ2;FL(ik<?`r7?ytD^T842(XyT3JFUi`<a(m#=?#1w^n7Z-Gx
zuw~aOz)5BnjVmmDD>%zCwY`Ul-PY|bYV;HcZ(48b{qdH+E5u7feqw%z#1<_L{Nr&{
z7dcgb__3B4H)AB;wtB_S&mU|=N0m7<Wf>&&>x)WU@VAAy8>@+Xd3m)K1NoGpRswMv
z;R26<_*)ha-)-+e{CTgQf2%2GeK@zGqa!uJ1@4I7pZxoaY*eGaRul*NDQF1%R|Zk_
z+38VXUEL_$%5kLJYG7=PMk+8oJUrXT8Wa02)90Fhd;pG_ri8y_MPp;*Mt)8XOAxl8
z?GWK<)B{Du=c|oJYr{W88>Eb1V0uraOifW-beKh5r5XElsR{LuAr$4BA|~V|2ngh5
z*kgJkQHWt^SCWy@`e^6@cCQhRyQA75GmlO_$KpR{JM!AaV$s|S=`DeW*da*aYM{oP
zv25)7DJo^)Zqwo_E_9&nmW?(8EUtnmUHq3<Z^9(qzzrK(CH~#QtVwkmy=&~os=w`s
z-7@`zXJco_$*FFz!Y@Sdi+M;~xQMyMI;BX)4y%5&s@How55UrPK-lM?p20I*0w3d!
zgQ{c6<$9VVDMudcKpAo(^l$vBW&D&Ld}yiHbr=P)i8uyMAKMg)L>veVmhJ8Bso8yv
zWhvTCNDBRRj(}YskgB<>A+Qm48(>hc<P(<|IQh1{PSNH*H?aIFR4XMW_BM~g4$)uF
z0{@D;fN|#{Q61v1aRs6`yeGj;bY4FsE1LC){^jHRF>ozYBoxUWgXIH?X{Px2_?2MO
zBL5DpA;k<KKR2gVk$E@n=H~jBG$(R@`?iaikQ?Za8)f|S<2EAvj+v5bfe>5p6n+0?
zo9H>wKZoM?7k$Q*Hf5}>9Xswecz-&3Z`dMrW{l=>fP~EX^vR_;*CtLGt6;yh|J7n-
ziP5v?L-gnDZA_)HVV9SeQ#3d0UH{k5n!&jVpfTfiK>OFCnl&KsBc=WtGuQd(&1!jw
z222^#-3`C<>oc%O(9bCR<r$|K63vpF;xf{|d^nh1|EHX3^Up|W>H&Ta1GBmL>4|Tg
z<>Phq`hPC8h5k`AM*4@W`1wv1DT3bO+>(A@`D*zWz^l*0{%LOhGY~==j<q3Y3#W^z
zRV*-ys?J#=FxfEc9m-!O`(_$?Dcgtt|L*TiNu(F*SB?1Wk6QrqE;UER3<G^ATfln7
zd;e(MP20xSmd0J`a%!ROyH5gkBW6G#rL4wf@nK15U|r_vqsNc+H8o!=Ckwl6T;#VO
zuJ?H7=H}LaQp^9yq9s|*(Xj<zNbto!R-^>{$&~S%uuBk*a}#<l)czl5cxqNEU%R-n
zS5<u6zF}8uWo0G5J$q^jkiblV<kjPkC%VHw<GZ~-`2w-PA<#2&`)F+OPHeP2{%8b{
zWu36Ep1wS4S2g}4;0hPT-jMa9+K(zCt7haz-cI~E&bG9eIM9KMQHsAGfPcQK8V?XS
z#-diU&tW^a934`9d*}^VTw?m$j9>ObiBc?Y48}*XnAn^_SOA_uq7VAYpXN6-jNkUy
z9x{y>1|(=He$iE1OUn|$#?A0Oz~_4JYCAyZuES;1+>9<J3=9ujaf8=iZMsB2(*#EC
z!rccceYarMK*zY(h$DB&1!mEeL?n{;HVdihnRv&iwxY5!B!^nS7H3^i?3=*qbN=Ax
zqyl>a;KGRpzXsy?$LcD69b8N^THI9GNAp_0t5NOyP0RUx^)EKdB4rsF83~$71BVW;
z*BrjRl|>|r#9-%4>PH*ijef}gN!JD-FK;6{lKIS^0V%4QJ~5&EfED>egD<7dJr-u*
zT+14yT5gt)=(mtfjgRuyLJB>~^)03S`*K?5b_800!yrv<&>xHo)%y6c8H@OQ`@^Ch
zVzCdMGUjcIu9Ny@8*pYp?|&~<cfek>jjnZOu>}_UtlF%8l_sd5eKeF#GNG;`+2*sI
zIC(pmp%1MKH-u)fQmhLMr}j@xUBsp_)~~cJMb{(t03y}m$(QS~1|~q$`LW~K6t+0b
zc+xVZ`lAY*h6dmC%i_Q-7|dls31s~5=W%<!2Uuddti@I__YOrv6Tt6tPzXKlK0A(B
zdRMdu)V158LUWNDI@4J|`I0Fpq&0-WSM#fmAMe{ezz-Gm7Iz<72RgT}#(_8ZU=qk_
zvK@hD7AiV|{ZYTOeZSGXoD?dkI=vI<K5L~ltnwicKQ7_aDM`y-0qK9I>~3xTRnYkn
z(*VG>1PqD<+FCBBI<)$y6UZH5$Q$Iy4?xj(MK>;(3tQ+{fu%ZVU$-i71hm=hJR`i&
z^0f|v6Yd$DMvY^F_y_M2Om`cB+_8}ry4g9TK4qO^skCa+w+=0CP>LSXz|*>^!L8JO
znw7;3;=j+^CoKuT8{bluyF}JSMSyUk+_4F8T$DcggWVkM_hHeD&BfqA$y^L-*nc^T
z=pUoc%9WooP$;rx5R{@8)UXu$6G$$515h3hbG(!Q@^&sn8OfMU_<3*CL40m=)H*bD
zzD&!>v3hDsN4Ey6xw-Tz-D#~UB@Nsi)#}x{I}>%64%ffWRgIIDrmqG~-Tnc%jgP87
zqcPKUAk(DKJiTSZu9LeF$5zWKn!O02@>0acu2?;qJZo&2#&_5lC-r-2?zk}(NC5R8
z7i?Hk-Ya_CVAHfhOyiwo%G}(1dyK2@hlwYF7~rX@IMD6RUOS$`W{}9a!s@t;;AOwB
zGYxMTB>CFwWq=^sRV)<6%k6yCsCv}foi9`=iWk?Ya^My!qU9&~U2;_6LDP;~zL1tr
z+TWe5rk@bYqPu59T4#i+E*IvvUr6tUOiiir*{0(B=IO*`C_9$K&34}Z`)^&qs6n64
zI&CPHofuAiG8NWIx;hoQeW0#v#EnqVNLxUwVDAW^n=nU!>IBitBOr-Oh$NO%rCPis
z%5m56tCZ|*W#b5DSR6F>#daj=-ux#%Sgv&-QkcTV=%7>kM>|sljiZi-qay&~S9aey
z8VV%1L-F!xS^K>;5V-cjPRd{r6BFAjun<F`Nsu8vXblW$tS>wM^Uci%+l!;XySepa
zs|d_ipV5S6$F2e?MsKqu;+1B#W2UkpL`|!=%+P6LxZAhV>PN5g<Q2WEI+6{b4Alg7
z<B$^l^i4ldg}M}jqmrtPngvkawmXO4B0@9~$!^u=MJGc{FJUcQjXzDiK*v2*jNgD?
z8@ypXM&BMcy&o+R5Jcn9ushIWG!?$1gW%|+VE>6+L3Xa8;k-wHuSwAB=5}_fo4YWC
z;;Q4|FwM3>KFdJ*A!o8%LXZ!0Awi3&e!h<2zx6%~VgKFyt;xUE>m7D?9p3dgbeWW=
zYHp6Y>7=BNg?7M=dGE&khixks8<R!B(Q$R80=V6wx)v4|xm4MN9~>F*ANOrSlx-*K
zhk=Z)MO+NWZnn8DiA2w<C`j5dI0^`+pJCIv({qH@14r*5eR>fHX0Y^f2j|oI?5OL5
z&RHBK(RGjRcR>CW3adPR8j}v^k!fZ{SoD<fKp<;iU1ybyIW{*Rt<`bE5&C6j_i+4v
z0E}_E=KvZI?O3=q*t?CUArJKTn}a7A!xpj=?aR;;%0QCE_5z5oLKq^^2NMWa3~GZg
zK<}|VzT!vZa-x)X-D+i`)skv3cYjgi;WFXpVX*H=k+%1AsEVqEEdYRZ4I2EOy<cgl
z%JAA_dtvxFXx%lQ`1VcRSFgh)cPg#rcC3+a4O+dS4Afo|4E+|9A46l1AEJwT_`lEC
zhCyD)I;ySTYH;X>7b;z>oK6T(@l0J7d%WJTA0mwk3><M^tr#2uz%9OBBSX9jv(2TP
zRC{!jNp(mI9C<@$LKHUC({mMh)%tymP0xO-8t0W}Nvwe#%4?{Y-Kn%sM*TNJt&x0r
zPVbkI`7I1c4WU;}2Wo@tlWPwo>>{wR_W_)l(4-zH%C|mFYgrdR8t#^ozwI-goqfX#
zSg6BdikJP*_2*KNOk!G!FMWVA0rZShQTs>V?ygUMRyEK=U`*sQSJ2_}$ZT5{*)Q)^
zc?ZhQx5oQ7bj}jzlGnX0hKlj~X+*x8*!~j9@DhKe>0GN@w+C4Y$_=XM(|WO~CoCz1
zGzFP0l}{Oe_lsu!gJ0zR_A)l<P;bu%YX`rh(bnQPgzbBzn^i~qKIk{hHjngK(oEhD
z=JsX?64=q176}eROFpkGr40-WJWK^e=G94{V~)Mr`vHkG%wyBc$~M18Y7aO6kRA}|
zdB36YP(9D;U;%)}#j>)PPbjc1yBR?!(6w-QjfP)mq7<8+oR&Xn1(5f&REv`xB|X&S
zXXOtrg?A`UbB!C-TJh<T&hEJ6X4?Rgt`XW{gu*`Lb8A7M=<+i+)|1OgJ0FOHZ>j=y
z213Vz@>Dw~|56+%8$9z#AMlr2j^r+8rYAIFlXK`0wp1L}dsTc*KV_T+X^cv=qcrV2
zuShnD4tj27b%!?!R>*7ud_HzVIW{(_W&h%Qx8JD?PTI1_-R21e;L=}@-W**}By9@_
zT#k|oL`LRh@4souEY05k?1+LM9WdcTm*3w)n&p)ofr^v@f`Ihvz~gn$cSp&UcNSvc
z^pP~Mk-P$ZtWrTn*bVacjDP}qy3`aXTeWWQIP}xxuZN<Qu^C{+Ho3*pSe5xg0^`@{
z{vHoC4dHyh#(dv>IyjK4h$kJn)&@40IKrdnf4IP%|J_upZDbLWma8Gkz6lUO(FAa4
z^jEl>1K@7*BDO`2H(I5$#2P}R$o;^n(1K#keVdkyTxX?7x7JpS<-$*IK*RMJWTMt$
zG5i2YnxN%|GMANydhiUqU(H{FQ*1f9iwz5#^x@9a`<%On34`@FF!SM7TOb5AwW3tP
zQc<~cFt?)^st^c~%417qV`j_DS$^(Gu;fPiX)DW+RMZ=#KXJ9|49I~K$G9W-Zy|sq
zDi@3-@l>~zv8$d+E6RenK;Ue*v&{uYM@L&_%3Jis!AD5(D3Q{G1L%jN*qL4K)D+N5
zU_Syn*|bo!2((gv9m!9P@3K%NbB?(cyU@>y14_$XwcI~g0OS<Ur=~<eQ;FKDQfe^R
ziYnl;B^nN{FYeq0C9M*0DK#{mFY6^W8!VU2;c)C2$PW?3ZeM?nuv-=C_INq&z5V6E
z5kNcls@ZXhvL1a23_P@D$H~qz+-=lGs3oTseOk(S$qdWg&ycD>i)y!Ql^{Kl>(CQ)
zq13i3NyxWmLlY&tw2gdbC;YfuY#%R`*2kVS0ino^FerW&h}76e`;KtZ-Skj8K7?BT
ze!kQfs!LcEh>myl0m+9M?_nAUDIbe=1;px3jaHiGq&5V5<rb1~Ta7`<=i2w!M$_Gf
z+zTa*7CVQBR|*sg6M%1AXkBB?k4O>&Qc~>q`MkT4uaL-uWiNGv+3w_JpHPpDud48`
z`jcM{QT#@S+LB_y#r!X+W?i@En@GxD=LurfR(vjQnf2sEjKHG@wzl28`P<m~3fHB4
zE|-gzBFB&;76)D`gM|w^STvxeKVlTTI@O)m$btN<d1@*u*Sw2`38vUd?Zkig|8nb|
zoGKOgF$=?~JH*QvVX!xJqbvbWx<`p=I2J6?KdPOze~p^fFhSHbu=$jVMoByIs8hS~
zsuU}4{*W&LbQy*a(#(}<Zg%b6`Ut_z^O<A1hWj99#}~`zrP!X1rrWLHha^qv#wyCB
z10;Q#Q*!B)SVujisjh2)B(1G?S}F?5edYt<LH4|nQij;NBP7-X*_hbiS=q+so*w4K
zuv{O=r93thPY>f@TvCWe+C7FWZm?{G%lk;aYVs)hO9fx-1;&^rB?XDj?T}S8jS*~k
z8Q=?HwB8c-QI*ERp3hWpq`HR1iV9?daH9hfywM7h+<@frWvow(z+6fu#OXpbB$U>7
z6_nd<TITF$`LZ@;3&0)Kj!1OS^d;(+M~!BsY^KZGZ?gwaLDLQyo%SK{bK!(Ksl>&u
zxyJbct@E5@H1G?m(GqUl(FrZB5LA|UF<l6|xM3UHahBHlR9a*44sF(Y+*bBFNAUDc
zgElLy+J3yY<VW$H)_LUzmRR(=v8tY`tc1w<)L!M{yewzZy@I}-`yQZa>rJC-Pg`}r
zJR5XbWGl|}C2kIT!3psGgnjZ#3>sSe%P5dmHKx0osKWfLVqM>^kK{(ScWInHJo$3S
zg=i}B&NIVm?q|W8-K7KC#|^DzqYxK1(L{T2NY1|Hc^h+0Q%5ab&xe3LY)Mhz2<;b7
zzK7W6M?g%GwJU&YPTErG8Ih^a-<I7y%lr4V&WrFTr|GXw85SLt&qR#QB5iU(Fnp7;
zQWYe$LH0)H^S2w|htqt~A1T3e@6CS<_a~-I!7MR9>}P3&PXSy^iGdO8uG}av1hp4Z
z48X;qa=eGI@?6oMv_{2p6THqrHfiULgFxzmmEo#poRnfV(ql-S6onQg3d-_rec9)L
z{JfZ6zcrN>`rWlySe*87TQQd}K7a@EbIB+o9}+J}07Z_Z#wc(Ry!{TtdJKP{=pVN=
zrNjxm4+P*EcL(#N#Y7hNSs++6<uwbvIZjeYV>B+bB}PQES$gE6_Lp)np2r$`S`B5I
zBFv+-8k%%4u}Ra=@>bMr)vRhc5_#NT64uhCoRE8Y?_c}`R0TRVr$Y-X^j4bAmTBmu
z>Gv&tKOI@kYKbHqc*CmM!_7bC<l(61U+J?q%mTmKf25u2fJjxWnhpqr*yfF-1k^x?
z*xhrN9Uz44YFR*rxEXX&Tv3Fv_vL4&CC9Y;fNO}fheh4R6R71tw*I{STd>^D#}3}J
z)YAOIK7u!ga;*KAs}A3G=N)plfr0rXA1%L2t_aygGA^Oxa2{WzLGHTdcyY`Tf~Q8q
zQ7!$w(b?hPbR&sjpBR#H0x0KQ#drjKmC2S0(jHCHPFHFx#|<<pI=83xL2HPk!^GrH
z#auBm%i5E+CZ8`GqX@!qwEqk#<GHI7yh)<de|4<U`v~o+I=+WoSQeYN1JTR$k^I9l
zWj_LT`P^rIbSZuN^^Fm`wAzjEEs&t_l0T#E;oI`$&dbBdWP8u{BGDck*7V0{@wt+@
z(mU5Yjc%&t4YC#A(EU=nbh<(D_h8_+97gnlaY$V)+q0w8Z)~)89ZOz8MsGa(ePavz
zZ#FjgS0za@{@`al(MKi^rC0ZP64-9C4rxq~ID^qqZkEMTVB^QaLws2RxrcY;4c}$6
z@9FX*CN$HOQTCo{MLh=)D4T>19nH=*X^DF9`}72)C%6aT2YH}?RZtItx9WK>w@FBA
zu2fviC6y|@fWW0~b}_9c^=S#tj=b!Wx7wRM+rXWJB(m;gWJgBXE)&t=lh0MXI=3|y
zO+w|1!odo?q&o`SNunEU_U|mZq5Z<7hdo|K=&k38R=P%TS3T>G>}Tn8pSM@%#nJwF
z@jBH>*3*Q58YCpvz7-2w9CRoKU-#Ul%~FfQ&|k`4&V-Dx%Am6%W92{WYV)k$HY&o+
zHB&=f(Xsh)QPHhe*>`Tv%_~ppLIjt+?#`xnH?}xWlp7%q%lk_7fB`=cHX1P?rShS4
z5;sa0*;V!z<G;TMI!m9dLsPx5Hv32tdbb*vvbW0%>KX^(V(>E`CBH>4N401Z)ce@}
zeRLcYba(e8Kl=hQ;PLjt7sdBVIs!xNnP{>+gR$V$lerF%S+GJn=W7|3swwJw>F7_|
zA>Zmvkc<pG*2*g75|3H@^{5iV+94?GJW~=L+hsq=;1*ArN>ki$VTAmw7i(@dd3gu@
zgZ**vpQ%H1f_hG+iG|*THZ>PS#i`p6SG`w{K<M*QU3MS5YBMp`B~+pmJ7hl3Dz9zp
z>%BwwU`qZF$m~tCx7JFKMoSY!I#KcQn>%gS*e!GE?+e3n37-XcS&uZXHy#~M2l`qw
z{w(%8u^B2Ne8nC8qq%tyNUUXL38PdSzrTIn;+zJ#?7`C;L22ixMu$AkE_uzSwuHW%
zSga8zUwo@)Dh=t#DxU?>wbq-^@mB@L0&&sXLhMUWCpYPUsNxttq^yisIxgMUi5}UB
z_X3yU&B+ZMC39VQ!0#5NcLpqVKSgGEOreZ2db$;wC299xKCaK$NzPdIHr!P`3V}e;
z$HIeQ2P{eie^;#HfY4I5L|UW%PD;OpR5V#b_-)wtwurgK(TP2QUgwF4a(H#)Ss#2}
zWm0`!IT~YEEdPnAhvU~Ta5ps+Z%!=r{z_)PUS?n*Ql=WHkrA$GMoV!{>*p<rz2(VG
zVAJwcQ|}go<+hb(H?j}qE+bFJzc1!?7JO2gye`c-nnnNCQ1yWvXI3E(vMLJfvO{o~
z{<>@fi`6f)+~kg%1a?lu`!{E^xm(1z(3s~N-i)}v&#~}-GsnRr_12T<*c3t;Ud{^7
z`I>;i3_)62Df1IRp`-<x)pDq3TD>IY>LuzA=#9v>(7JZH@~}R)`eh?wSe#*td+s)y
zowh6or$2H5n2a)<B4300IdJ!oR`Cm2im_17dx*K7vf^6tim1!jZUs_dR0N#FdK2X&
zg>0Uz2AT6JRu#A}%pKLldU~4Afb4{Hm|{P6gV;vXrSz@jQ%0%&bvT2HA~S-R29D_5
zah~x^WyM=a#r7-QN~H_vwL$j}W{rb9J5<t`lfAwbDl&wwU_o~C@JVUlQ{Q6g29Jye
zi~8VEswj;7C9#w9EcTFmU?5PpYRH?JkWXm5;>~J7TfB}Lyjh-E6=PCNa@^~5c!eo=
zwg;SwxVt0Pt%wY@k6HvW*@1zO<%6;fy@`Fa4r^;gr=Mn%TI#&p%0Te-XhcO%A~rTZ
z7hTDo&tcxY|E}SH4Qo(gcB$G0_yf}l(C3^<a`_r^#2ChNunHpF2RM(rlSKqES$(`X
zV7cbSZUm4AY!dfRCmr5H#eXdz7F;2u`Fx1;?S1KQ`ApNi`qd(3N5|3O?ap7U;Q|IL
zs4vOeA%B*Lx|i4vmpK|7>^);ybkD$`H>^;NZh8ru2_qU;e*Cz~>YftfxrXB$xO~8o
zOnwE5RCp7>YprMR_UQ#>mP@E-T4awTHmw<mn>u3?V!rRp^ROo`qPD>p=vnt(;y+q{
z@0V+$wKTS6hkUkxGWT@c04<f=_w$c`dV*+{3Zx{(Es;H0PqHzj(4mU7j@oM*$jiLi
zw=JpGdI4Gt^bYpv^O_AUnd{rB5av9-WF#T7b1Yjq(OVmb&FXV`J!_$-e<{ezVoah#
zS`3H77w%!&3{srt1;#OrS4hqBZvdC|P0XkMqasil*h%@i|3Ph?X^;{`O(&4S1Lt|Y
z038%-KClBN>{Yl^_d0jdn?vOjIH8_6Dwdhf%2BO28mf@Zb<HPN!PoHHI*1u(BimB_
zP7kJ!*g@{rqC0R@crYI$#z@95FLRv{fvb#-?FWQ%gXUA+j?oI9gcO|OgQ@I6g=u+6
zP2_u!)X|~a+c`PaoNiV1S=LOSNjrQ8g2XtfM7@OjDEXB-#A@wI(UoZl@#5ng?stW&
zAdAB4WAQN{5RgpVaKcfRu*I#xS)sqXmh_SQprE5mO>}rG<vjW7DB9n)UV#IBnaW=r
z^vm8&&+`Hl1MytshY)mxNjJfAwC{0lhK$_2?Yxu~8-wB88qZ2N6J1F{qNKg|3=12*
z+(OkNii)4SjQK!orD?3*kKb<;42=-oYG+VI$Vwq(m9Vkh-Xk-!o(>Obv`OE0*9ZI1
z8oXU|nkCq}zD%b!0s=j77OKF+?j6+N`3}<Ww-HR+#xIfVo<r<D91*h^reGk;M$n@n
zeYfcS>7j<BVlxk?d6J=_dnS0nSWQwPAM#32725Zgs)9r#m&CG>Z>1}M^Cx^a)fB-9
zk5iw-g|t9PX+(cc1u8!%wN4252pRC8jsSZMKJwMu#!K9FKluEXB*lwsmyyP?P-u4R
z8{}!8c_>Jr9fIE>fZ{+s<y~`K*zCBC6wpt7KcIe>7J;P8H=nO~Q6FQ!hb(DDg!b{H
z^**CxC_%BkPWrk6I?YD<0{(Ne;9XZ)gwGFC;oy*8hI>u}lmod!!&L_F{nmAWd)qV?
zbRCplOc(iOQ8vk(cO9Il_D!W3pzq}cJ&B`Z$Jq)Itd}GRwWI6ol|+&)py10MU>~?H
zTCr1@5OZy}?Sb^NVDKeUj&HBx^n7>F>W@Q5K!;QauF78Q8Yj%3lyQVsrM!%Z?e+pA
zRF4eq|8bh_xs`_rmeInOM8UL(Sy~#U(CbF~wS&Xo?p{l7?C&uxX3NEA(nac^?ySK0
zv4}|v*c_-Xfmx8hciLFscHks>&Cs|YFwoNt>q|Mj0(;?hquz|Zvf``v-(|TW5qGI*
z;O5=vyxnw?rm9%K3dn+~g*yWU%XcX$DJ3+;Em_EiI(fjJk?uKD2{DNXOSNgq;HX_a
zYrP(KP(+$8o!Hkl^f1r$bf0<35@J_6xi|iJl2B!5_7f8<cOAmne}qoq><k*!9p{9u
zfc=+qx@cX6pL4PvOiFAjSql4cf!8;ROm^t9*H3f+*!KNMm*JDn>tgMs{?h)-*bQ5$
zT?k8EQp}Tya^>|S1qO>%&GW>}gtlkFa>Xg_Z3E!pKjd60*bi;#t6QthSIPND5rc#t
z7KeI}(d+hGAB`KvmmzZ;=tEnH@6cZsCJ#ukg97Rqu@G!%c$hL~tY4!-&st-eICvKL
z#~pV7QZ5$hXLSz+s=2;R>7{5Fq~uu)zSj5eUXw?L?&BB9@1^8Y#dA00Tk9cda`%AJ
zWCi?-qR*y+&4Wea_pCwNKt5DRR8gCzF9e-EWZ=n}S3y)Lp!Q<~gQ-cAG+;U2_hvkS
znLhCb3RxmkS>}0JcOH-03D!*oTxq$(N%><o{h6U&Ol3W*43j43exAUqsWc1~I(QGi
z+p2={v<g-aNyy<wsJOurVWli4n1k>Aha`qxKc_>b`zuW&!~`&nb`2Q#D}eifh6@vx
z^lw5|ttrPB79NV>r#?P2K+dGG5olS1Z|hQb)b@e*TIjA%0?&$5q+r?k@H3m`cT`9y
z@!g-J?wHuAV80nf=e9QPXJvVXTIVD`N?^SZ4~0&Ep8Txkd^A^O6@0PB$KKwyEZ{Z$
z%vmnAZcR4Ye5-0(AOOqd$&pI7hLEnLz|(g;y1fjCvez9?1Wj_Y*U5akm)>83xOh=>
zfqGGu-W`1u)1>dwsxRap2dNYBMn4_WqdCHNCxes#MCMV!;l#gnoshXS;r{0eVW-?h
zJVaS<P*y^KHBv46!YF70QbxrfAu$1rppIz@lj}#Kt3QbXc6#n>@4+$<R*CL}Qz|Pn
zyhfMH(-U-tB9F7@wMa=us+}@Gg}^YqECxH3BojD>yZ4HH7UC^pVygQNATc+GG8KHR
z=fMui(D$ltWuWjKD7f;{<fl%+^OU2z;Y7-V?}5vV&gC_V4CNBc`?vI5?6{Qd0+J*_
z#*xT|ti+hjKARulg4G_N;AN0}@8OLO2pm~MkdaRG2+=))<7^kztwA2-yKcy#GT9^+
zzT^`vi-yMwjt3%ft?Q6}t0k?_M)rDgAOvd^-vu<O;<B5d;6&cUfiC-`L42(}yRC4w
zg9qWVJ3#W0-Ax#TUY1(v9Kk<FMn+nkCqdL=vE+>vPCC%P@1HQCid4{@kh@9)=h2Q9
zujnfa@Cl`HmR~xFFM{QEzmB=5r5Uyf%5IMiP{E53dCv{CN$$1u<XIw`o$Ia@<=X%{
zNys*fSpI%qL<Mrk*qGRZMfdfQCAr;i8;RNZ5n;R6M|wdyY5o1AS<$0UlooJ23Y*kB
zD2T~&9b)<eI*b^h(W_{d41fAbPWeHvG=bP{j{YD0&|RPGNVBYLUk;rTdUynazZYF%
z%4!X{0`>haeGPIZ_H{q1G3TF7_m%J6CDEruD{3cSQ#*Dow$Ib`q?O7fzbAZ%&{?1V
zGT|czyP0~P<vn~I)1o98yjcFitk%lP1WUi%%*=!Oo}=1gW~}_$_wP6ivJEj9UXXWr
ztnF_hf%C-~#&25uA#{0=c#NBn_pFgQF1rS(R71CZL|{N*?Hc-yIicm!@-n0!N;;!y
zse&LUZTjX0@)kHt=+v<aYsEJ$8u-xN0c+$&?z|Z~xC&hasX@{y&A9ea&>=>Ikqj|r
z!(pvL>oGFO<e9oE2RotfKL%-{<0H?N!YsA`P?Btl`#~~N3ohVD!8FO$)D#EP`fu>i
zHB0U)NeW@%Am)|VDft@)5xCu>j?I!M!NF{*NR6ZfsTxC~uib;H!J(u-5kD21O|T-!
zxeVg&Fb56o=xP`pdtQQqlY#B~(n;2JOH$|ne?K?_e6lNCi;?)&xXuWY!amV<0njR2
zVphWKlWz*br=xE^S&0~-o|E}`kPnWEaXH=We#bztuK-R95|X3nT+YULrb{m=HrUE6
zd~WRe<g81M->qFbQkFy^^0k7$EKQ}2xi;RZswGFbBA%4iTOC*-G!eaC)7QB@?kz=H
z2Lw`Sh6kUHraklb6x$#0l8(kEtv0N*jjyPxiZyJ#mYGlL`TgrY*s`d$%L6AYJjL7Z
zq11bS&Gp$apj%6VJ6B2v2U2TxEQ-@XB_-W#=nm0&pCl;Lf|j@cvbzM7{te_&gDJo{
zWw3>J*mk~QWpI7lsbP4r<hU9gSf#LCVawE{Q-X&nr@sMFkgde^mK__`+c{75uwL{i
zBdT1LImh$Nc~I=o!&1n?O^RGc&05y5+)hsj!<STX@dkELIuLdj=WM(R^@CHYkz(|N
z>9(E(oMk$sSKdrw6}q$toL?HYuq9zVu0D1eHsiUAxgbwd`!YsuVCxIj(N^|~jgTHf
zL$ms|R$`Mo7WS^MmhqRwmJ*2WlGrM`ni61%{GOf7lNvML@0F&3=3rcnAs@KoW9T}*
zv+^QyCe29DhjTK!yA4#uPONacQot?sY5tv>L7&4FRxq1|Y+qR=v{`9#JG0RJF*t2(
zt}%_pu8wMmpF88Xda~DXj2LwmZ{B3R;v>#RQ#kM5uq2|`X06oM2_m=A)%Rzo=_5JE
z@6p-P3)w9$R9Awj)~jcs?+tS67o+tn)Kkrqiyv(?V<$=lKuEoM%X$tCHQl7K-nxC+
z`9JKvc{tU3+ddpALnUM?Q=w#v2CU2tNFt@oV|J8zTxK$)LR2zjF3FI2%xn>gLWq?)
zl6juz_xdjE&HX&Py?@Vsj^jPv_qYGJ?|UD+yY*e`^SwUTa9-ziUUTmV31`BeIWsLR
zSZIPeWgJ=jct#@M?gYVkP>5^azXx-DzdGl|`5vaVcQ<6O5h6{RBdKu-4@mMkYzKAY
zR|aCzsVHV9#&3sE`J7nmXtF!AwG$z(Exc=5KXw}6y24pmfp*GOJmnatmj4qNnShXx
zguU+N<!cFMF_k0+{YrMd#jg5aXRn+?-F&MG4feR>Zw7BBP{+kQ(Uq+x=X@S>TS5O*
zFnJ(fGUY7YpdjEk($_RD$gFV0y*6;pYfdvtxDz+`2LGJ>SWKBnSz&VN4HB{V6cRo7
zkBWW6N*jWe9Mrpc?RoE|kc2CPd8%&|@y_2T=zVwR%PTw?M!z4b^rw}z3*6in_i}3%
zylcec7IPlW?F)<;!pOs|_M}Yz>Tr&#gECo0%dkY^8JqpsAdmF)x0_3)G)3|x41;!j
zkVvc~^*)(VnO0<^Z=CZ8oWBVW&&x~P`(ox`2WRI@LUP>gdpAFyZvXA&y~L0}ZAER_
zb7!)w%y^CBx&`hZo3Zcs5oe6BvqPsBn&w`JDh<YJE+MZd9NY|eOu=)mBHUwdi-oT@
z*d)6-C-^43F^fw`GJ(CuT<0{3+5!UOke#|^X$ia8S)5V^crvC}<r}V-l8gl?J(}*)
zM%^JOj(-(Z`Y7SLN=Lnpyi;U<Va(C*H_fj_61GPg-JnuD(r2YWtM1f@O^9DpZIQvd
zz+IMT_?F|yzO9%o88#3f<m0c7M7fh5O5cO1oRvm)dI}%@cFxQU#V|{yQPO9D@Rwfy
zB1J*rI<w^BQHrh;B%}#0BzM@O-n=0}Yc6mbU2=X)30)Z_s!__p>j=+KNwrkah_*Wi
z33juHSkJPAlpQt|8g(ZkHGeAABADsVfL&A(3iJH!l$UWNE?g(Yz|Gmm&61GbOq__=
zpjGZ;{m%r!1jSlNZew>OC#0_e&|BYdU4eIO@NEjID+XDXNagM>sQmo{aAZ&TJa7vm
zA>@3$0E{Ni<w*|LAa#pJ49@f4V}_P?-lTQ}EMFw<&|7@RN_tBlcJ(-U9Xsz`gjtSD
zK;WQ0A4H;=VE+<7>;m7->p=JE6pDM5=&Mws@Ri^ud)9RsLXoUB*mg`>9kYgXOalxG
z({IICe%oHP`2^it4MT%wC8>Qb)E*+sEY0VHs`!CzyYu^i$9l?NPLBq%Ful?!$+nmD
zAspe_3d))B9((Uh06L#h5kTHul7Bq~rOko!)+Cn_(xW)~?h<!d&3wK*_x9fFz;`no
z2?H@tI2pu3j2Ek!Zcf~=RY-ZeqYsQqm!uOQH~RS7kEy(-r<#(~^hxI9@1C8#{B<?+
zDiyIQa9FxtFKMJ|pHpVEjf;w?Iq3by;8_CYJhUFT-*N6(cpfW!^ah<zVMJ+kg1yk~
zs^?_NS9o|Bgo0pzVSOqBSFV=W5+$nV)>Be6Mk+MOu0;)Hr8pVeE0k9f5OSucn!6FL
zLxrSZK7R@a0|9x1xh`&&s+jx8x&xZlR0$jg#9c1~)vqe~G^YQ6T59P1`EHZ#$dwPV
zBd}=pe{CvM!%^W7x}X@tEE@)+V~15S+sKw&f7Ko%Ak;lSEVaW`br&#RIRw&bpuMWv
z0f1`xOVyV=(!(SP;<k0?2){uuRHoH1rEnNvh-r=Gi`!V5WLQspFxq=7poEYbuMivZ
zb4OL*!3Tr?CqX#qYCjBIaD`nj5<xS-#KI2zUVH+=Y6)9S>8|O9t2t2Ti4F_?4Avcf
zt{_M3GVr=2>bx6pne>Mzt9kZUQ$udnnS%&U1e_@2-{$;xxSCTV!^}pVPIFnX6}#3~
za4?R%7|>gC1#@k@bs`rYp+N4@U)ZH4BN;2aCta8@@ByZB*QJ<av*sEa8ne(KZAs4k
z8M5O`T^(7%jYN(7CK$7&$c8bKZ#KnC8V(Ji9cR+g-Z^ahPKR~z*}|=!Au3!qXS}+!
zZ>rO)^RB<IR9B~N^e^a?e|RE*uCl6R3#KT^3dT$KTnHd>={znT*Xxa(N{h||2E7#f
zuWyer?zP#@wWgE@Xa$cjK<!W(u4hGmQYD!bXn}9PzslHV+~VfA8vMZ@HX^(OAqFbL
zC<;4~<bOxl7dJDz<+paR*kQdhYlHu{?fz`de|}!nJ1_umq3kvlbsPj#ku+%wV*tXw
zhn)UnNa8p`5>{i`c8~u3y#N26U+QlkDH^nP55p*XQ%j2hOw?p)dQELB36R(lbr?T{
zvwv_f9bw;nqPo*@&$HwrIgYbjPH+B;%l=0Y>RMeI%NZRt0nTNbwUrf6@u;5tmj@kx
za?i~jaEVh%H|px^Z{?2_Ik=Acs-4re#ocIBfr9271^uU?4_{2TIe{o`+$<6IAG1Wj
zL&$kdC(z=yYmd=rTW%8;t2`9*U*=a}EQq<Txmll6DLL~2qy2|BS0hdK(464<5N=LK
z^nTr+wQ=}KL0h9I?u-8RXWqj-lkbmb4mGFbP;n}H7`Ou^f*GaE$@*Vr!sHL*?IH}~
zQlUs79b}>dSR_N-rZVX;Z^63%=wMHAASV53{i9!C8-@qd5-^U_PQ8&jX`jI_QlIb@
z8l_Dj<v(-A=ch%O_cqKI39M(S>rRy?@FUQ2`W18E%BRWA>oY}$0-q*d5D;bv3r=s(
z?t;Z}zv%YHFDki1P%s#koHmr4?|Jm0q@_rW#P(PPgGI|EcOtZ5dGhz&as0uZ=CAv*
z_Tb`l%f1Z9V;BsVAF0!JXLRfdZV>3j=18Hcs}z${V^gvQ-H8m)qn5Wb%WVGPxb2{k
zhkgjU$FWZo>9#dMA3Ym5(tSg_`?>M0$#6z!UGk~xD1e`4^8FOs4vv=`=Xl399j`sn
z>oRu+28K7qhl2&J6SA_h4p!4NUkEnbnu;Jt`GgyfTc!;LM_^hb^XprsR_OSgCnVmd
zXBgK1d49b&Jz>q1qQrtiY1S8Phg|QKq{2y_96t9^Zqqsn2{P%QuD*)O&(GHk`XRV2
zI^dpnz%?+Cx)s1cV^A1AJ8-i7V$|w7i+wd)<sCDer4PQ-R+1~63NIMNwcp23xubTx
zOxO!*C136B?XOS~-T5yMk>|oo;;n0IGa4Bg(fNvp?_PdLYxE8OdbAAHYN};M1<#Pb
za@qD5O9;qu!-y0c<7^e@wLgdO>BKX0t6VfQ<MLn|<(r{VP{mi1(WwpLGweQZw$b0G
zRPKTlXRpm%W~@k3iOt8P4FFpl-U-6^CYNgV`6ZUHF<#3ab=xb52kQHMO;dU5dt5K$
zBSTVP0-=Frn<KX3<}hbOt<ROa!J9i4vOyyG@Cy<NIIi`PPLW;6Ai#O|q8m@~H4@0A
zE<|bKCuqmt8-xZ$*>x<*{!<6Fcr=pTad%9dA0DNrOW(h1!_sabgN~nW-Y^Y%aGHqp
zr_S?!mhCo(hj8)GZ-2Kz;yZTT2wnTUdqeD1HeO{O<z$6x;&`PqL(LDyyV`OsRkGi`
zqM%#PP(Q@N=$)f4UEyY=KE06;?EE_kflSHUeR84vpH<b=hOAYR-AkU}L#manQ?@&L
zu<Oai;dO&L90d7+<yj-ELmPzz#YV!L^Y<R2{qfn+HymbgGvBmm7s}_>q~hBbBW&h^
zPS{X)W;ktlBg=JNCIVMltNcY_<A&27$8oU<^C)`{Vlc0KSm#`~IkF$>2+t$u+-5Bk
zRDzHu&EjF(N#oEnbwbxTMCGRRq&E$ZFJm>{hT%Th4+-4@Et~zLjSq9jSzMQPD6`QK
zl(NUQAB@{VoGU&NFx-X}Hz$o}C=R2Pd8CsS9>duiWTL2X$KvXUeECY$7n#j3zs@cu
zY^BuMgTXNSFwbqNXW+?{;<zslv$pGnSP*=gy|hKR0T_^Cba~qPxBYPel1R~tN1Dv*
zf4f9`J=S605UGJp>rmzrZgYoG>vOYIo5ov+OxVdh8@j;)Mb9B_-*qQ_m<e9#)JeH%
zij7ygd)+Z(_$9FC%~FY?KZtC1dArDP1Es%vlcVryn$Of@55ZA>y<^U`Zqf^6&~@@N
ze-*TG(!9#JgpxFyy_DL`U4m9(6mR2s>j!-N9?sq0-<<p8flr%x_CxS19A%=Ux9*0G
zKR|5U=Ro16nPApi$f>gA*--J89=F)P%_WJi-zd&MZWMf45NmV1BpfB5D*bve6c>e)
z`KcS$e{|zI?xn^B3wL9$RQN*Vl20AB`%@C!{IlAT&FohOKCO^7SB?*k^6i&y<@MJv
zc|_cdm#OC>&qf8Qow^>ojTAh%A<O3VYTLQ(`pfUY#cq;K*h3ROZRqGn>jZKzvd7ew
z>#zNvo9qV#vsBFDr$c#382hA`FKZY5rB+rx>wQddHoS7_&|rC~b%ipNTeLeZ?bd%t
zwkw>v@x2<E4Z{=HSciBuP^WFk-P~W$Q3!5t++VqZ`z6520%|<~IGXr&B&7WOR;s!^
zl7<G@X9R>o{;Ap-GqZAOMXR%{;@Ci8(*&A5%?EYit?Tan)VqFS*xhgfIe3CRo43<?
zJ(INl{;5p@T&u~YezI*R$a@kO#OMBa-9L_?uyu91kN2r1#hbhH2_#>3Ho1cDp&_lD
zT5fM2t7h71H8c=<*U8C0l4$>RyHw3FzqCA3n1OrNo40OBMndqNT5BSdHr%S~mvt`E
zm$P~^MnGg>IJmf-adu@r1>@iH<B3T5`9$@&WjRYzntraymb%jcx_rS*o{BKv;is~G
z?OMiY$G4B!(l04=xJ5}a?4o4*u11QsR~7rc%{9^2j}?L3EJlI)g!-MNq$FfIQW-Zb
zDJv_R4aN0-8{5&@+^OzzZ_8#I!Og-m0MG?Rv<~OS0sW?hyMKBvi3S5l0Oe^!ZJW4)
z%zYG>)0x@H-~N_<`}_0Bk+h*y5Un74Nc@`^Dwyk1+q;TmS#A2p+|@~0c8~e^tV8)&
z@i42qhlWPdk|PGPvkaAn23K<Aol<!qECtTS@Hl;h$(o}2_zb<z=?AU~pH!jF%_i|A
z*%twWW-&ty4#R2uF00G+Q{NfYVR{Mu^`_GO3oWg!BY-_HjgGgCu%LqTk3^TPzlV$P
zdn6yBHWSNJ9JqLJT2MTC7_!}K_!ffOOyQom6E2u$P2M{UZ-N#vLq%63Lc%4mdjHGr
zel0(m9G-P`aTuE1z2fH1AZ=zQXHl)u)I62)x?9_Yf?hqCfXL=j0%?s*sk=hLPvv}c
zQn*DGyEJW@OGz%IqE?^3t`d=wM$wOZbam|@jaYY*@<d=Q_h}_f@Yf>&G5f{{(ef|m
zN`4=n=RvxW96rsh&88adh1qt7HWcGSd+-V?Dk@m_V(%nz%YLlYGc+-{lCTsrHvFhd
zdy1v0!(QLw(;zDf_3=>X8?_5P1wRg{B^LKF4h;-|C#e}_@K10TVol-Wb9zcHQUsmG
z5dbn7t%jk>GKET}KWc>K8$bwm0ExNKn!0Wjt_3iyzqNQ;FW?+`z{mdC1i|k)QX$fJ
z$2PR;s|h(6%`9@ycCpYIja0}rWM?<&g^D;m`}{)m+s_)K9Cm-Tvh<QQ8pa!Dsbi14
z+-;6xFagqLsbcXc)LHls=aK%*)ed+YHWw)99iLgxawX<=cL8_m?kYojef=}_(<-EA
z??R4_9*mC-Iq%DIx3I8KdM%{%<yQj2rNzb4v#Ww!H-#yTeb231jVl*1{H*#rbu=4s
zGETCj&jSgufswjUP3b50vu`sZ`8zm21|zDQehe9l4Zw^>XgU~K<|KVf{mQ74Umlp!
zVl>2Wt#UOzg**NX%JTBt&k;>}mikvNJIkdXRA@E2PH}v>xGC}mv%0Z=NYJrLv0L2S
zq5j&M={YakpEX9!%p}WdMvD-Wjvfl+7)9LCigaqKSGMDV1)`C9S-)WMYYL;h(>UyC
z@f!gp)tHsu{i{GlZ{cfJ*CinVbiW+kW-jH>F%_W?Oxp~&U$Iv6z-Bg_>Z|1uX?CYG
zE%;)RH*n0M|K)@8cPwC7DNPB8JLt8Qxz?LX8dSc<rlvVC*zZ(gXXkRh98=(Tctr|#
zXxgdZ`!UCiHjmJ89bR?xQOYW0MJXrIR&E`yf?USu5faEx{9exoAyn2J^b9udvD%1>
zl4<tRnoV@lw?^HS$HpI1^oE-EP0y)Zn0))qJ}D`!dDXaywbdc$Y<7F%?CbijwX8Q4
zkEOq2sG35!RNCq@IfyAtE}uuQeB=XJW`TH8?CHvE^jO#py0B0#tuS@5+*^FIGL(k#
zx3bt3-eB2d!zi3VRN;h?!(v0E$pa>5TU+(jP&XGPb#sY&zm7?b*-!eS4Tyqn{VX)y
zss*&17kVM@6YP3KXl!hpZ98!*e`U6{3^JD9^TO)~vcr`Nxg6bbb!;0}MEhtxu0K*-
z^#!_A@yY1Q?McWHTvW3BBPszu)J!9tb)tCH2AHA!k`89Qx`C93GNq+w3IemGo3M?~
zZtOlYG&r0ckt{bf)N;%6O*U3js5Wy%UoN}ix5DnqATE<jj5ewYJ)W$*@A!el#%g4A
z<wR3w7?+(ecf2O5)qd%xn&s5qq2M+>?$3TaRKgT2yXIkd?6;{WU6v73^aw`!8?yId
zWN|2Ef6yIc<1AP?_<8ztyJCu_G0eWkehxa=R3z<x>^|8`9_ujCRb8f~V1z`W(*P3$
zw}35ZQ=fm~jyVi3NY9^q&mpsP>}rIOewexBfE_uby!n>Qkr&X<F7f>ln`NEzdivm8
zkr)y|u_e3vJ;|!Lg5<+IkUnkX!-x$9vFTt!$5eFoC$-%Xk-m+QXG#5%7$5UF<k)9k
znS}ib*R`%H<yf1@mb$Kp1STpPjYx~8<i?*QCgczfUQT~~O6P(N(H?$26Y29F8cl83
zaI!N;Z|=_GGIzNCIJHdgYNUbC7;l-Loa~8HrP)>k*Adm?%TmQKhMfUzm3Gi@)D(hk
z8#1jo&E*3K8%FA7c(TNR5~ZV{s~Zin2s$sTKmkTc*;)B}nbj1uhCNm7v1OJC7ID2#
zfD%B9?^8Tq&a?XlGd;}8f$+ui(Uymd$ee37NRwAgTV{S9(+Hs2q-DtOg*Z}9D>b?i
zM;@)0Ki+2-|6UwPBOwJXo^R<={4Fy%+&K@Fl5?^HJ6Ok?{cjKtr&at!8xCSmd|a_+
z4Zuf)lGunz(JES3bNHsMkD8Yhy*B9T!JG?V*jr7_R?NrxrOfj;m7a=<-|TvRyW8XG
z`CELq=uJGM%s|#BC>M18NdBGV&oY66CYRkFu-paTqQd?<_NePk75O9L_m_ZEN6=be
zXZh|E5dROBIt>^yg46;iZ5-t1R?Yn23*5|U6B#WvAz{=!C`^0<?HmokSrFjJ3@RMH
z9n(jm05j8(SqNNatV>RR<AZsb=e{>Z{e5O2E_OFt8{<YsD?D9yL6$n&7&H<%KP;?Y
zzTd7wQa)SZ!MI^{B_G!Hn?4jwqA5Z5=P~Wdc9e>Q1E>4*JESe6od^(P4w|j{fN472
zn0+Iov(;n1ndFR$Rql`JWzzF0)**aW!`e==3Q2D{<59r`y^GJHbS|W)#Ka$o=MTP!
z=WpPynyeg;A!E2<&#O|JQ=Fo-|CzRSx(M++AM44ojtI@6j=b`ppN%qBX0bKYpGHz=
zrMa)Yiiltof}uj=>Ok)ASbdQpm04g(=32q5%v_=TS$R6jW5P3y3Q5t?EW^#uN|7P#
z0qo>>B)SdeaM74uG|zn}y1GDUMGw>;1Y}zML>KS0!V+|u+L^8@&Xmkf*Z`-~3S&;D
z1rw6_KtmiM6q`Rce^B$z!%j-d8`|;#yQvP}cw(0MxU1{VuCquA2>9JF5;{AA>sU6O
z-ivjo_d=<|7r>w#^6292PVXGf={an1dN<9?dfuFHxbsyne_-tG@JlI0)eC*mO?CO~
z#9c>Si<<_T#O$Poq}|-x+nuDXi&uZ3DR&#C1_r)i<-MartlJ*VLCBFA+ZL6u18t2y
z`W*vis-t@y!}~3^%1wGaHOKJn2roq5!sglR9<+iaC4B+-?vYpCC+e#`-rQ>g?X;D6
z5g5=#!+_}X2u|S~5D9zGw_kKVDjZ2jounA7pF_{eB&N@P9E2ILAn9<e<tGLMux3p%
zaB)*L=H3jt$zDGHre&W%n>G?eu&1ao=QOZD#voTr5Jpi4>|UWJCe0(*VCfzAh2Yuk
zbJWHS2QQRdT-RPHr?>k%NH{KXG668?vML4nXj=VZj_9qXsbED$e!~d8LVs#yQwsVk
zUPOT*341w&v7uZl1$GTT$taDC4KseeCU}XDZii+W*0VB^?OTfRcO{B<q0bW)+P*Yt
z3)O}iERFEhqJxEw#;ek5B`Ci<cb`zI<p!1fSG=#&PR<B%Pwr;S`I}r<a$a(fuELN(
z4kCXZbK&NU+3fH)IHD-+m-wCrx>WSjF(4R~c*D+&i({WB-DPJWT~cf`E2B_Qz<Hkz
zY$IMzs}9KGiV$kcAAhIquZa(z-d2!R$#^-CW3{2(uf?5HQAMj+aA>UE=;Ouk8G$S{
z(;?v@(}-sT!~v{VRMRzNs08??kH+kBc($f=8^d}tm|!Ozo&OVU3WCN{<s#u~&xWQw
zZ+oddOK|sZQ8lXiK^^+#%Pk?+fZaB0E*ZwN(;xi^DVd5b$~&`!(_qa|vh8bGr>|$B
ze!0wX6x|95RjAd{#E%{TU-eDxs?~O@U;ZwBpP<mL90TOCF@*5?Rp7ytm2)1Vn*;gV
zWa?dTBCGbj0fbo^G8Be)Z{1Y6aWAz4X5kAZav9f8rBxHcV>Z)H`%O?NYrVp3Sx$F`
z(k9W5Y~??o@rO99B<Odn#C6@>x30T;o}Am9O=_kTyN9nj^G(}?@@1>KRQs?}q827v
z<%3g;dA>F4%C2be8()t$7PGX7JM|9Tqc|j&%om@<PE5q#ni!wd^vr`uDBs~iu-VT{
zY5uIjug7xu)GA+7hqin%F#c!(6FwXch@&6i<vynlqeeo&T@*UJY}4gRh=@d_n|0`m
zP90QByCq>O?6f#d#ySktzgns*!oWQ-o*Df5J=b}vt7~y)Xyk|gRZ=RdEDcNnbZ*;Y
z$YKI7HBFXxR0yJ!HSOe6-j+nc;2fF3$8LK80EDP}?bp4GkpohdJMZns+ep|OkKqa}
z!>Q^;6=+$Ao}b<>K^A-%SKey;0ftq;T*WH4)=Y4_V=c>UEft+{S)l09pftxWRSGgA
zBWK|=*t%ug@*>tV!OzJvX9Jx>zjkQ1e!e3UNEZ<Tc4hQ+k-I6tgP;|v$V^tsckQ3h
zhO%z1YJv`lXH|-9D;oI?uj0EWT#AeFm1;qReAIwtg>nR~Zgk7t*rPH`Y%3gw372e_
zCo0uIrUZN1v_p7lzEX^lu@gv%v!E^W)~X`xtmSb0RVM@6RbWYcR!mk`X^ABS>QE17
z^1D(*QA=d6F_O)T%wo&<P1^1Vv|ITb1F&!N_}lgJ7#iDd*V5;Rc7`qb)5%JYF71Vr
zGxl;gyl$h?8%ChY8s)mpG4eP$+!a;q3k-^elIZs9ezxr;5l|eU!2~UvEhQ1}%>tTD
zEdcd3W&P{MX7269j_%xcw(dzzT=B=T^#+e+x%KwRs)l3j9d6!FtyE2fTpt}O(kq=|
zjA`>F+ug`fL9%c5C`b}g#>G5pRShZY=_q2o7rd-xC=<<h6z@DY%kW#qN+Tea&~U6S
zoGPoetvEdlr!@2=R>N_k{LSI09IGAjGNgtB-!IDs5M=F5&6&3IgF*GUOL0Xv3<fMK
z^SZTTnwf?mO{i22-jM3#T?H3a*%_v^i6}0v7dVTydLtZQNc_yx+kukcD4ZcVZ84-%
zz?c83OR`@y)53qH2V*_^A@9V{pmF+W(d$udibF=JtFH~T)AyQN7R%l>Ol8P1%!wTq
zY|f5se>zN;k4A)5VDpiNJs$S~M0+M6B1mk$T5I(d5~yWDksXY!7(yoch`FP96`Uyr
z^~l&s<y*F6YE-vw^kOcMo@KqkIeh^6rJ-UjMPhvS32&y2!{8scv7vzhNpl3BVIU%S
zp#ut=`Qgrs12MW@<B%Ku0c&bUL6~x76yESMfW6~dbIU#|d?H7;K#!aLiOA;#;()+y
zt*eX5^SGw%B~7@lZIdoMQSrH5=i_%OL;lt>PJz~z$KfB-%U52ib`8fT@!M3ewmK{3
z0|0QAOGG%=c>Eb=Ca*IsuLjyDBd&yuml9ZJzagS<BukGo;|IIaZ&w=)HpXWI(@IrF
z=K9mLE^wR}!r)fiHw}IQua50Us%68vPoO-%9$jvs2-|_QaE^7G)C;6BSZd_eIT&wy
zb^T7m10xwp(lZ#op`4ZrekQ4Wbb|ikI$9L;Mwd(7{EcdSK4!OnB85uo`qyV=&JY}t
z%yPBDXG2(o?7-m-92f@y{dRL5XtVZ^Z02!ukDlr=+DMr;${SZfWJ-S6VoPQO^UTG?
zo%a5N?g;D?lfky!c)k4b$7e;x+>fE#{+qvQNK!nMGNesIHSgpC0SVKyv+Ac?znEE&
znI(RHoUzfU&~9DF?~TQ%b-(NcIQ3-}-v$7^iJc`-BMAPZZC`-5UFkki1NUsfOD<_z
zhlnD6zYI>Ozw&i2)UJPU+)en^Nfy7$dbX<Key7RMQ`*pGHWZ3KS)B@(D*paoQI`={
z0pf$dS8=v5)Jkz>tzP7YeMFJKev-l1wR&flVX=n3anusL%nUKfX|nZv(*S`lIV&bM
zlFBDkxL!wa0C~Kr7_>2({oiin^&5>FqDCAerGa=u19paj7Q}A_>9NhtN+f&UFva>w
zcZL$~Y!IWP%X&T0bFOq+*EqWO0{o^wIx6zpx;S_Q>8!Yzl5vJ(W(fK=iz*gJ|Kn9&
z!?lAbww$CjO#X}SA`oHi_iGSqczI4lPGCRL;?-Tds$K3znz9H7H&u`7y19R2ptE1+
z@qtV%0ij_)&!(m*wrRbkq2~7<TL2@?Y398N+cN7N_%}gs_u}xGEoRJ#LP1(_{Z3t(
zJ0t#dUDVo@2L#njOJ`>$zYFR@yT{B(J(pu6+kh#ruI$FXtj(^(nf2$L@%knL83nr1
zbF)t0IN1Z2kz!mx_K(-7Oi1|k>VACtOR0{bo!(4+wJZ99dvU+lZ-w4jPYn(=yYfND
z`U{fx2IsN`ZcabEeyF^MX2zgtxEN|7@gIkJ2nXD9{1IVbY9didD_8D6TDR)=kooUT
z-Mq>51YnBaf@KJFU7wycr`#5}?v>E)L5}VAzrDMpe|vXZdUr@qx~a3%6rqcFdV1Px
z1P&eMeSNRP6dA%E%aQP=-e*TIwBcP3Yr$=u;?ta7@ATpktX{>r!PQ9Vr;OzET=jP>
zc(&bEabH{=yyp)W!KIe{Dchvo0D!X$5CPkGo;aun8)H*3$7Dl(yYgZG$M>Y`x59!A
zys*s$$p;(PwTl=Wp?<`xU{m71S3?5R!l<_@07T`CfJ6{Gs9mU?D<^?>lTN+Zxo~EB
z8VfW6W8nKz`RQ)55q@kBaQ8}j*7+sDV?=-FnX&hj7;f06g7B)Uswxp#(}7^a`@N~a
zXE6qB`pB6}S?4+^HzGFjpoxm-LhA~o+OIaayS{&my9=7#$-UJ<4~}{d*=Z!-7e9)3
ze|KMPBolr@z--1QVQG>b&M&e@GG*iZN<JZi+SF6+(6#<k!N<)YqI)FjwzmMJ)#2gc
zFVD9r7wm2h3&scT8zSDO5G!}vuKUpfbyqU(vya^aM@P*2p^bUn@9d)5puK7TjYIn#
z`b4mn|E^5|U;6L1DK^7A&ScYB;rGi#pVQhf;HX3EjE%>t&b~)c!I5dNVz=AtOPsy>
zZ`kYa!5sO}56|Eiay{PdwAl<WSO;?X3>MA?c7b5ISHwn#cq4S&$0ZuFn}nc^uYmi0
zhh>m>f9BBiu8l_|toL@m#`X3o11IAkr@hx^XvgJMl&ZKeB)t)a{yngN4-D#&e=Y3a
zEBUVv19HRv)lQ!_sRWk9ATngx)<cRzV`EGsy(lKvygg^moWZ`9_BUFb9^khbZOhYg
zd8JW@O91{B1pQ}+3QUsZ<P#2nb{avGCQH+8ij3CA^`k#qYw7(xNm*IeZCd%qdHQi{
z6HdL_Ip)5#{ka*{j98HJxKMbb&iTc&Kz7KpPTSHl1LkwpU=Soeoo=6NB5@pr9?0In
zPSkG*jrThSMws{v+ds*=;7GG3o{0&*&|UTNcK;9SsK6C;P_wVkKy79Bi3b7`PVerS
z{ynhOgE^}Aky4_v4#_@)1tom<tm76nwtHaO<#rYoW0-&`V>NX%Y6osnh-8URB$W9*
zJ1@>{jq$Kff*I+lEp09e`SVA0ecc@^8(Q%!C2NO84gY~UidGQSj~eVc4h#)QeSGmx
zmc;^}f4r-XZ(Fsf4a!hZ)J1G&Y+G}gCr1GzRynk95xOr~MEwp|zP9SsM##d(kR50B
zLy7yH^6h5^$BWuw>@!D2Y;w62`7OM)(I~4<Vp3A$GRAFBf{`ZNCJmXt%4Q76LG@Xv
z^seR4Pw~B0HRrTLNfK4FI0{yg!Asetr_D5Th=jWx1Lpe@NFLcT&2CSch<yBU=$h{a
zy7oKjhMb-X5Ke<*zC=Nhu~}HNaY7~~B|w3EdowFv6%m-bR+RE|-j0tPz_rwKHmmrp
z{$gvs87Bc5Ik!QWKNXx^G#xQQAw~&SCPu(te9Lm7c>C)mUSE}R>@O_|l5J|fnvY%(
ztiC+<K;%g2!*vyFVU5Grr!Td(zO;KK1h}Or#+xf`wmxxd(rAFNBE1jw{RZE-t6#Dd
zXC^f#-|8Qr|1|iaj%VA;({{s|N%mix31Ddwk1f-g0W+~;`HtLRslMQ^8N{S|*1z@B
zY(o@yF*u;2^)FIoS5<^}`>Nus3VdT!-cwvv5N-Ks@Eat+f~ThUY|F8OwQ=zBCPumG
zEO0mHH)(h|fs*b;u&aq1L5;5Uge3xgPv3qC2iX2yO5&fo8ftjyS#joaitvqnO;`~>
z($!C&VGmr!MBlwD+xJ8q3&wS!Lc6-w^vC4x^(9ml;6)M<OW(T~D1Mo>oWkr#)V)`)
z+yqs<@|B}WtKyEUMmaNk6S?7&oam3+d)(`AoDb2b<6ftIj(dCUIewh_1I5#?Bo#Y!
zq?Pd(9Ba+;1YIah@=OC8+G~@T2SzPT=NuvgYHoxtemyIDzJi$Su=u?lc=!ZF@DEYP
z@GzK=yqDDY1dHTk^uto0PiGMkk!BBFRLP;CH|kR!YJlH`mej!gzc>CG?Mc`49be8E
z1G(u)&(=RUEbkA;EZZ)>2M>P*K4v!h=L=N=0>W17SBb5ANV6>yXYK-`B@}&P{pstU
z=MyZha_>$^c9*vQ6Z5TqKo%zr*8j#N?*0x}XZV<<Yg1Ra@!i}<1g|xX>>_ME+q9So
zz-st4nvGwB`#i}jQC#9@WYx@4r9Ysy{sB=VE7-k)K;!v);$rx3PoWScJ}cNe6f>W%
zu4a@b5yGo;-mZ13Gi}rshA(^jnn36E4D{e4ipzGcv6+?a6L<~EZLGyFK-48f(Ar=I
zaKsVS8~-cc#dc+;Ire}YO6Sq3IoP-YD(phs6DmBWa)JY|t$^OyD+TTK{7h(a%uK#>
z$R{8bo$s0ks>hG3X!x#~uqD9ianr(evs4YOs=UH3ZJ|olNNW1TtkZ+cf4uE`B@c<8
z?|S&=?jBNp;)85k{$Ph|2pq8CpeLXX{ZiUDcN5maituR=y|;gKDhH%KGC{WS1oDLl
z2pzuIFFWiaWa`wXm8ynkb@8GFfPeUX_$^%1KR#2ti=YbsqQZAK_g@;F@=Xi|fT9=>
zs^CI)kH|b<@L&@@!eIWM{4Pja$T$`iz%zrC5*7f_)r;22k48+1R84=)RMgGA?3+@?
zwjWr6`+=pq2tz%54s7{0_rztva`>z-a1#(sv!o_~&KihM3HCl$uz1u{@mPkO))AzR
zrj`T>zVEx+tYM>(JlXStuSq%Da;%D!RCMwKOKuIUGPZHf2%;emRtKp;2Rl{~AkQqk
zFjMU?g#BO0D&cAeha%I&m@e(Bju*@=kt;;+g%;JLaC7%NGJ&_v7!yg6OElg3H;#vB
zpQJ5sGEm1($p-TtLG)9r#z3v%=`1M!FWcXBxmW^n7E8qqKxy>96>bg|Evsgnb<-ZE
zts@aR;MZ@H^Ekw^+tIBtg|jW3?O@3R2yvzFt>)yjpx3lRMWDna_IeLb0j~=sUZD;A
zop6o)K;o*IE@N8dU4bPnvX*@{pD`H7(_)E~M1XoFCp4UTVNNNAw_v{8drBV=IT>V<
zFz?4R84HbTG6aOsPHo5plkLsrnHY~YMVNwCs|$Ux@UOVrLFJm=&1)#7*trG%v0;zq
zxEp1$*lgZJ1ziL1ZGvD)Qj--dP<R8nNJ95}VV`sT6lg}J<@C(~Ewh$?!f3gsg3dH-
z%msGDyg(M);O@fGEz*3@Il5jP{bv6f5HI>HkRKgVbtqUBxfER`nTfIIcM&>tTb$nh
zF1T>-V&$*i1r-K^-8Tl>hF8;;G}Xy3104;RE??LeKqCE)S6w@n6W<&WnNc8Uuh1m7
zS2Ik?y6!RB4R|~ulOyk%CNV~2>P799G22R^Ns(SZ|Er>B30J#Uj)fXMBNgnA5~lqA
z9<<bEPD*hE^>vRCHI~y?&A>Ve=5SE;TrhTQmZUR&o($q!r`i^T@V$V4WJ)T67G19N
zhP5Pz!$D?=9W9{VLWNhllN5EBaA&nG!EO*TWa626K|~6g!=|5vgaY+}%=(6M3Q@MJ
zmI^P9gpD4r-=-7^j-hFpNJ!ClJQpQ!c3Z;_w@hE_v)5**K%DDNHhJp9AtVqPC{D?3
zM1%E9#zzd1x^Z6?+2K0Bk^~}BWarAL2LX$)&3kgGA3dHdR}gQjK8wgKQ?5NOWLBrG
z4dRUO3!h<@N6mU^^mWW6+w}Tf6<e9M`gS|0w>eF%lMUqc<sP0u^CBcQq5Prpn)^KV
zU%V#^VY8QdM;dR(dsE}=Z+Z(vT|{i?H9ecUG$9mi?fWq1I@%+pSn?F1xMqgyKAlKM
z_*v0(3{QD=4VCK$>_Do}rFWnCTVKOKu282X5&#hG^zfw~Y#+A#qyd-CN7`XhRD}&F
z6-HKdsxkVoL`K)_+C%-^Ptaw~!qKYl<=632U-ywZ#QXhIPfI}PC$>6&2G(AwYZ6Sr
zVxi*YJfYV#`THbkQ<l8sv9Keofw-<bgM6<~v{#R92ex;tA~~EW-`yQzf$<`!&et#G
zA)68IPJI@r9ZP`@w^b=I+mB}QJnzFjh^jUeHvtG&P%e1l0@I?rXavf{=dc#R*yUhT
zpmTkyB&t-lG2`v14Lf^!1T-qo(jPoh9vA&dtkk}e(ZPuAJ?%>j(f$fP@NbmG2Zg95
zxz`pV*S>wDIvL^J>Br(D1}6O)X2pK!GmldpyXG>SNqY>D1!we@?_z@$R|WaH9>-a(
zs3c>b^5XkX6za^3n7Rl7G1Tcc3b>QU|2GT{T(s}L9_z7Dn=cYHF^KY2csjBd<&18H
zPt8(VPy6>`So(J#;pPfS9kwAPWa*-DElF?f1k;$rd8skczA(%B`!t%tT4Jzc>f@1O
zTT4(oM&mo1KewRoR7*xQSq2QXmQ>kpU`71WFc{P<3Qf(`3=6uR)d&tHPVzg!Rl7Qr
zl%tskw2{fDRkx#-gnBZR6&{Og<#tR4=KN@<_5&@4$!qo?ARdtoqI5M&L@3yi^Cf%^
z5yYs^h?q&V0%fQe#9zBGT6&Y+^4MKHli#^=!U=TWIZSsE3Q?4;;BqNa{m5N}4>D5p
zB(_{bSJwl__mIin|EutR8^lY2*8K+29Sg6&eCLEHQg>aqAu@r;_t`be!gBcC95`Iz
zVhvR+_ijWCzPFvQ#~LvcspBV0ADA116B)R-lk`Ih%Nd)ck(|C}V3VjdfY#RVa^5m|
z<%;Bo6KFo4rCT<|uu~`C`?8JAhZMM-LbMgWZtX7%5RdbKw3_BU`DZ$#RE(}KyE7<a
zMA~q2&2f*6r3tZDgyp+%Mm*~DhUaorQdOR~fBP{S4p$EGdW<|S@56w7*fptWmdbLn
zji;)Z7)d}fy1L9}zd~OBN8sl*!@rI9Djv2A{W4cg9PZM#(KhaW|7ZZzH8B*oC3$(z
z8=9A!=cmbNdyI8^F|0BZ&@{vTrWc~)uKRy9x4VP^7iqwYGx-D4<9(!(<2-Kn&0!e+
z1XC-WYaWH75AQAdUOK78z#$-xeuDoEsp=!IL6#x)#dY7_Mc53v<@Cp^&gsH|YrgFy
z#W1FT7UhJVbtR$A0}depKf4LA%~RoaKVRsIYRtAG;yLVSj%5hcZNvxAV@24NA0V0$
z2;}OH<)_hJ`L{jitk)ek<Nw@oSM4RO2+@WVFBe(FNm)r)X<fx0`<iI7oJwj*GSy^w
z21X}D+d-spp$D$Oe5!*D2HB&<t^SS|7o=7ZZNhT>-n{RgboIn+VNV!A&}aF}0gy35
zwi#!ZQ>(pB%5LF!KnCrm$jUr->vkbhP#?@Y>!VzBdR0@=Y<$1V{Ds8|fub+DeCTDm
ze9@MJj??AHm9~Oem7g}`sg7T~HK+SO)UVlt23Z)gtI7&yGv0uM{t<fXK{@tr+EO0y
zQ>R4C-&?A;PYo5NWH+0uTdRL@P#VL(-L7etYLkD?_Pm)HS>9{Ot@Vb_JzOC&h`pZ{
zYLdq;c4~I&&DhE9agIRoJ`~Lf^0RaVM2S~i0}j&ruz2;vb!#Svqg&WVfakdI)UsAY
zVC4s!*nsdV>~i2r0+$><H`i{X<)txsi>>5cAT_-(@ZlQ}<w@aH*igp#ps(tLC$hGz
z0hT{z3RAlvBAznHen)39#P162l#38PTkHqpgG(@Y^m4gKNV(b6EY(5k{d_%=(*b80
zy|C2dhpJj|S?zNX9Mf8?5Be$JqYpIs8vV2jx&-v0$%t_10HjTya_;LWqqWHEe4_Mn
z^ZJxT-%glfFlY_-p;;(s=nb7z%~G3c;=H#;ewJfM<bNZ=e;D?tvg(Cx{WAi^SU8ix
z#<^BF6C&3DDtaH@P7tVn0Wv_z>OV8@Hq--&ub}an3kXQY6vKu<J1*bTzCG@heoT>h
zHqG7`C9r??Qp;YiCt4mWRau!`UEwwzFZ_=A+Ba!BjL<K8_ju{*m&B#ed((GLjIFJX
z4It~0R`W)3Iz^F<gFIT{fKP?{LL{wOy3KI2t=iY!oZf<fNrrX;;Q)H^>e9cZZ$?tf
zHSgvLoCdB2bBQu~_g+6Ub|r#=qNVmRbm{YxjGc7(T#_Ye>fqC~T8uJ2PqwB$3g2Hn
z>N}wW-tVWT`Yo=a)4nFYoW5$l_jJ{vlPPO{#zOtOGsr6cY@U!Bwx?a&tvG!Yg_X<v
z=QXdQBJfY@OpN+d@|#9A@Oq|f3VIj@U{k0bX>s|e{cYourIzIVt-txp)+2eR)k1HO
z4Zg)hTfLk6!~$g2LEW_7qlSo6|Nd^Ywg`4Y{v7kEWiFYAB^^RzKWQXsG_0<1R8m&X
z4{6TjO@km3O7o|<=GoPEz-JL&a%WmTtYL=PHE~Lde9~Kkj3v$nN>OWH*-=r=cS7>C
z`|nE&(|oT+*mmK9x}@Y+)b+!yEuM^l8t;GyWwWYb(sWp#FdO0V6p$P`>gGBT-VPqF
zn5Ob-<&?QZ@F{zSW?1AFLW=6Tw`^~;2789bG9fYhkw&!u`!$6=PZo27UHWE;a-)<D
z^&7W^I&xg7YoPv%RJ4kOv?AFi&0jm0xg;B$QeckEvIuM<orWqZZ%V~x2QU5|E!Z0)
z<eF0>yJk!NJ&TAuP+oh^vVCp^Hb7j_kFe95lRDc)eJ*E|lM7iPHR>mr($n8feBhYX
zdAoL@l^KT{=8kssIgTK}j7IAXFKah@gYMHs6v%Kb55P+9u<rivY9_fcL&-<ADtsS9
zn%+kt#Z;MAYrZ^HpFGeI8;gCEHq3Y&_3li}_oYN!d8=irEBQaKZcn)(>L{0ysV&ua
zW;!PZAkbI{g0crwP65gj(EQwm2-I!?U(N_&I}2wfyj;}f9FLanGFoqq;ciq9Djk&!
zZ2l97D(!;x9Mc|Zt`9R8yG|rO7U)1RSbR9ro@1rC1}m;USMChzan}th`@CxYdJOcu
znka=>R$=7g<6OlFkVpI=HEH4uc-+M8Yn?93l`gL**Ay<FoM<KY^yxeyv>cEY$AjkZ
zv1b`?c9N*$J?~Dwo8F=PODXv)t|{*cHjx48yOB<_(rgj7w-9yFNfxBSY`GnzF=;47
zs^|G~c^wM44PeBBjHK<E5C&%+0sn0#boqns0|s<lJQq|e;@Y{p6hT@s*QMYA9Sx6W
zm<dQ&I~ti2baxjMH6{5B!RXD$`~`_Rvuf^~VFFsE3R7zr{biHH=$cRgp0OHQA?6D6
zGCv!MJG^M4y`e;>C5g;{cj-5G$xFL0DW@aE=4#kUT}f5g_Q*K58bS!ZDOm^?gu-=`
zuC|*0nWN<fFZ<heFKa*n2x~l@!4dH&Cf9Ssq_d|5YWEfc;FixAWbN<|FA98&m_bCm
z1ORRtZ2ufVf{$V1t8HXHjKU@#`2FYZA#G|oXuS<2dXIoa4Viy|*TBd4h3FdqLTS|e
zDBXkyg{?_zirqzMIO3DHE!(?=WP5j8{-TiozY6Gdb6<(GdqWL4FFO4AUq|@AFg*Ae
z=SE=#Z^Z26p8sohxcrg#U$aB-v~BqMgi9qgxKn<tw)+{Aws+1&rKbrLy~UUF!v#z=
zS8ar#8u)s>ENblzu4tK-J5lJ2!NlaxKi{5NrXeZ$qRL;CZ=WJWgpe9U_Qk2AfRVn8
zOc~$n`jr{koBRfXhLCK?8TQXG5R%o%+b^by0QQ81{xT_AW@ZB?M34S~S>M~?dXo!D
zbe*BF{AK$9@K9N3Q%V~q=juT(AVGt0>D=@u4w{GU2$3!lBzc~L&{+g1T4h%m(t0v+
zAvdqA?sH|$IkWM!W1>g4J@o5g+(RMn{>4L!@ozT+5`$C%kD0_Y@1<&8sZF}!O0-uL
z;IX?Hw_Rt?+{wY9+y1e;<s_3T>Hy8+g%91%#buTRido_wy8&?i{&0kP9QWzFZk5Bj
z55+e6m~GDdInJ4f{nD9dQa#l5?wM}}b#}4x8j}<Fu=5AXX4x>*Nv5|Ww%xPs>tzVH
z;qq<_=1~5tW7|r!C|rq__p4qJ4%+RPh(Z#n*;o9iITBJ*xs)51w*n<EsS%*WpYm7P
zBkm0q65*VjX5;QYO_=5%2)Mkpf5zL0i#N}H8E<fXHX2u4*z#`aY6HeOs_;7dRs`Bp
z71y(Z_RXKaf84#8W|l{+*Wnsn7-T(!u}U0Ut+zye57OR|2E~{^Z|@-3h^r<6F2?g1
zxLc-lb#+Z&j@jDBqcy-a_@sY%>yW=t=Piz0-(d-3vr#H~`Y_G-ZP)h#H)7Vu*ngGF
z;NFToIW964#_g*qbaQua;0&Pn1Huv4j@yMK39P@gD+Kcq=UK1sX|gc6N@iwe_A%GD
z8qeM=obkN=*LeOl9>iMy=m`F6JpX#0|Cc?FxD!5-Q$Tz3Ds(PfQ~@b=+LiFI<lgV$
zQ8rwVPZoq*OA=C2+K_0XtYxZrQNqog^ZXZ^Es!@@wjr*c34iqGt@jxsWQ*3o&rnl2
zP-9JuJsv<td&(6=OJWY5)_V=;#@=tA6x*$Eeba=~S^>qZsCOJ$E7V)V77>>M#4WBV
zPbt{J^5+frLGoY`2Q=wEM0on{<d2Vcm**$g(U&dB57)^ZMvpoIdRq?~vh}Ydysvu@
z0yy(+R7Prk0Qrfiur(rr|52Z#@d#Pvf!Y_R*`PA_RiL6xjOIz8J>g13R&lb6oH0z)
zDNomX$;cEJz7C{LA`<APPbRk&lN`9<Ui7O|v?hAqzB~5Hi*BN?f~v2)j!->h!2naj
zB%d_9ANSctuF4b9Zthe9Qg^pS*rT`ztN)7#>ncdly>{Zg$sK5rxVG;ey7P#`^Ty#-
z8-CM{g5}~2l?=Uo16)d@nYcGpWv!{vc>?t=@5joC?QiG^gy6t=Gj@bT;nvNvH6D`1
zDMFpz(sbOX#NYt3ts>aP0Z1<D#(eWS_|eScu=Yjds&%Vz4nqKYN}XLGQteSyseTTE
zfr<u_`&&&;I}-`f{%*ert>O$oz_6Y^M-_bpK?Q36VVXKf253n1{sh{6NM-AE;-6Kv
z@(-Y(WpI)&zk_2x;iwz`bM_OwI?x~Ncju72fZ!>S|FBQo2nVJt#dPE3A%y(2sQ%9g
zM^U73V1kQLUSHq@jhg%ZA)I0j2iEk3@TQL^0ttxzLnJB=IWS+*xLar71Y2iv{~3h-
z|1p!h&Mw}#V+U)o^u=>G{t5XG%uf+yvS2I105o7^CTw!EvA4X>=#+X)X7?43!N0pv
zs%)5;CfNc-UeJ;dh#`1RgKW!>B_I+9zL5)WnIDF(o<A(w$gV6j1#f*9L^-7Cj5Dtt
zf|0JC4j~_RBv$FrDSdK*)oq6=?#}YXPMRXFVq|FHnoArG!|p4FQS$!@E6kcGv8t<U
zrdcZz*(!MSSP}3LGD&cg_yb{<H6J_A!qz7%P-WnnSn-?t2}Qo#e&KQPqTA48yNVEi
zdfFo@5uS_{s73H~&<I=<L3Rt^C`7$^F3Kj0z-S7u0fL)wI1#Dy_dQzuSGWuEn>7(q
zRm8Xln^nbw6;LnDCk=8D`m2uEukbSY2PFx6NhO21&fjit23?VGWX0jApS`_?rCI8g
zAIRc5`>D`Q^$yaNPCpv@0)+lV16o(U7-UH!()y}d(s2PD&g8Ox>@EKb&+dvaI#j6p
zAxk}QL;~j>?K%@jySns3jRTL#g<;v}fU<NYr_<RX55k#XWVttyl;kA~%4EPhj;+A5
z(6KAlz|R^jpr&7T$TquS-WPfCDi5xvylS-55*qO(nU`N~i+Couh$r*wh^KhdY#bWq
zS7O5~E=O3=Ytgx4po@Vjb}6Ilc!CD1PA|UQkEc?TPQA9Rn$TEqK+L$$M)Cc%azX|x
zd6@T?73@^eRJZtebO4rha+o$i=P|5f`i+2fho4g#sGpyKerg&MPxBHlJ7e`siI|BO
zi#z<1j?C4?q1gV)NlkJepYcApBdraVy-Q2Pq>|xZ@?lFzV9LP27*UdBr^5^Vt73I!
zpV9Qgc!umbxTqM@wHG4=Dv)&-6}Hr^_eyw)ka|29L`?s*9&bE?kP*ob`HX9;Lzn0_
zLP7;@#_IFV+TfEc$eyZ&ne^3%J*GT`)Ad%sfcUusORZ+~6nIohujx$Q9YGYB7Sa%A
z9;l(|JxU1{b8%k8?00^`oQejb)N7<^17hNwg|Kb(3bJXBw61*TW+x@k7Jb)r^Qu^T
z22#<hL*lu*nqJoZ>BZ@WrOvh08p{c013_Q|PKGhl(@4#FN;J-a0j!;zLns@yk>$d=
zG{xAjW6)|=2?1=cBkg2_6Eu3m``*1T^DBDP`EU%!z~X*Y0R+leq~C?K$_vNu^QgDz
zo;^N~a2E$)Ey`77kC#^;a~y4;`CiLqnofn}`87V^fL)zzB^q)XcW3&PQpSzGRRC?}
zsw8%FhZ?Xj8-Sv^2(860eMr|G*nY+XNXKTbvLJH$HCs#&Y?8eDHYt11QL5nKOxu|A
zT>e5vTrYImwN~e5TRT_z%Sj)ujjgQ|<H(8gb7ak)bSA+av)MVl&AooX&BzA;ytcpB
z4<Q=#E(%<W(26LA{Q^>Vzs=#Gq1$x(CLeZn9k#hTvd!L^arO?x*gqwnAbwiHj2GP?
z1kzdx!^_l49@ESyTUc0VMJBrV#qZ7ChBww7z&HKrB@Uuvm72gORJ5;Bh^QxK_1#C>
zVbvfz03tv&Iyq^zqCE#|FCnXg*;un*xx>X^TEbQe7BieHLHGcp&&gdeOl$L%@xxKr
zgSOKWZEL`=NXzio_TWOOCqKV{=n@-_*O}JZjpbBd>(yF4Y7DI~nY|JvlZ^^jM;bHU
zcSV~rzn^Q$DYAYP9mLXa0}anNK;1CB>-VEJ0;pB7hz?P>YP3>J4mtfYZ0>q826VQS
zzy$_SOTE)_C;IW^Ji<YkE*x`8$zbWlKIj%=5vIj_v5+8|0-ob7XS&@wbKzr-v3p}-
z%OkJE!*P%xtBNeQp@&N^L;tjWg~7am5C}N*7N7SgMu|a+a1M9>{^vd7xC`1h;b?b%
zM7Ci&S{Zo|v(VBid6eY2YfG?|Y9g)e>egi&f<ERt#RzV9^Tx4Q(V6&Vetf$Ld+4Ly
zMy=_2?4#jQu3JbfQ10k?7GzV2LFilIK;-HjYCu*)Q|gqzyH#0;{g$kr=AboPY70a?
z8??Aj8W!&){$q1%V53bd{uDLueh-8j8$z<O?;sUf4NR#FbFK-5LFahJyuhz7a`^{a
zUtJr}fYUDqr!EQfbYPG2{-ke4D1Kn}LGJe_REDjTlXD7sxd(D<olKmCvOABWknOr}
zh@MJDWvHiFYSXjO*NRO%x_U*o%puJ|+v9!d@d)qS=ddbiwrb}6(r90Ihi&pw2N9{~
zH@M;pleVPFqJ3v;2MYXn^_OXs8Yd_GTpaH+nR219-$#dVOwnQQltoy&^Vak!{x)dz
z5UzU4{&n?~=+)IVQOOjEus7HWzUhU=q))XW#09@YzOGnfUNpmS5%XResr^BzHwkuk
zPDRZ9=yB8~EFiM89=nKTpf7-EUk^*`I~MY=7#`oU$4^?IX&qYjkX)2YsTicUv%eiH
zmwV9bMb?SDN-Lms*pxZ~yXUSbhd`+jx#kkcnEJyu2!EzWI~wzWgi2rKtq)s+h8<Zg
z_ey>^iSN>@+?U>??NhGa^RD}|K&S9n2yNxbN;#+A77z_o_{5%!sJkr$ci1w#%p+<m
zH~(N>o>qY92P!PskcpamSOO0$t76Ea&0_dBEJLRExb9gr3XO6^NPxwb(Afa8>3sQ8
zw4Z3+0W+UDgt#+#t!hHB)B&No>9dt0`(Ox9$k3iy{e02sWNca|T`iFC&==HQR%WJ#
z`W2Qn?L^N%(Ycn4P2(+MOAaddadZG#?B!g@ymovoP}fH4@9~}zeqFpB<)uEC7GN$>
z!sF|~E%Zaec1(iD{@Q9hihmZugma%hLb#+S;bLk74Xy#p$R%~VP|cYHr5HqvGJI~o
z*s__ZWRAypp1XTmtJd%$jS|7wtvL))dB6~|d1?{-%FT|SPc%G6LXy*jNcr2D&gCN;
zvck>21F7sOvIeV0mppq&R1tOFnB0W3xfQ(hG_a=(A2AT>a`{sF_IOoO6_FQk<+Dqs
z4kA=nM&IzMpU{obg3XS;cY*B6<=0!R%Vj`P`wnhQjw7X+2MHqNYGa=Rsh9p5^y>u)
z8Cc|Lg@`JsveQ^})K_*BvfP*h`ot+0spSb^Hk<_^;o6}BKN^?$GNyU~U{%-X_oBW7
z8IH(G^6=^N_uG}`lHJHZ`Dhz7GMao>S`Duty`RR}<5dLC>d`-UR@<nXA0;0e15>_w
ztkSbT&*q~yU4`d+(Rs}NpA55tTC;vM<ib8XZm9$32KzN7aYCYw8=X8<)J@y!zF+j+
z)U{$EQgo5_L7rPV`9!J4=^u|R_+m%KIkgrp_l;uhl7;GMcOn5r)ET826z!hx$KhMs
z^R5s3G-U}Ho2U;^(tIJN>M)XJxx;)|cynmQS}agYO8m52h&dv7JD{~Tqs263x7zhZ
zjQP&ub68XNfDr+BL>g_(%!Vgw+Se4T_Fq(<U}0LgWEYC$X)(b3Iy**5E3`Y%P-Tjq
z?o*q7q|>Bu-p-du{UHdkxLPTx4kB=?dElDtKN%{5>X1_ig=OdpMVa5S$L!HeegEEN
zbt*EJD5soCs7c~*@-T%BYzKS;2^O1Qxjs}O!@B3uu%=KN_`VsFBFMqA^Z2el9zs@C
zcS!IQ4=nb;ua*HKlj-W~&Ms|&M&>)zO2djYm~4bmD4~4{J?zct`{6Om1D3-`R)iyX
zE==_<^dKJFzFMs#{k^Yyn)|JHzOWSMQOY~m-hG{3MLik#Zc_`FewcA85myDw^^T=D
zjToLyr5($+;ar9kC<I|9odIOXVn}z@S6grR);q|Oex^Cw&?5sH4Tu10&79PV?uCGp
z*!EVJ#aNfiDR!R?84u74cir20ou0ffle1_J_^lr=91UK87|gbU@OUB%yQxwxO>|bh
z1M`jlUohX@gu0Kp@l6WBDzkwstUpk^I#ev^#{@LdyYBn=`{8DhbNSrU!CCFeWcpU%
z&{ljYYF8py?v6mFuUy(p&BkFj=HT!#?8*xHJ@3Qnxm71WAtI6u@v#vhV!pgTyctEZ
zbK3-F$51B(&^v}Z%y~<#$aiz$RZm{AnNJ!<*@7#0ZN}M*jec6)2ZIKXA_sTYAUQJ7
z08d;vyW^nHWI5@RW-S*dwkO|mzccXwD>F6*ImCr%XLtAZ{MB+A32eFt`4r7sj~_C=
z#LFW^5)_>hJv+)k7&L1d<0v&Ouj2c`H~avLc#Bh*CB2F;i0)b}k*vUA8iPIpJN_el
zNm0*?*Y~(@_?&%iH*Gt%oCjB#A*)QoD}&I>!wB({GQYe0D+B#7X=%AR2A2<Z9^mP|
z)QM`zQRfsy*}|Mf3@DcuIy$w$Aa4VItC7Fq<B;mCOqK`L%5$Ay-t)^(E|5xt1iJYg
z_dr~<?`894OZC%wpDvLr`f!eF0JqFp1KnS3%DBRUmh|aTCsr-`ag+zvmyZ})YNrmj
z(B{)s)I365f5)|xz6!qgw7;dDEE)uU;K#XXI{E@&by*xvZ=^o=o`@B#l@{7VQ#5|R
zMhaZ4Lp{_$)0M-hd0PwySw$7jEi=zs`W$Kp)zumz14i2}*YGi|FFgNCZgm$S%X4d}
zERlqk^XeJ28KV0eBshR@0|!i9med%%dwg_bbCD8;$hO?(xdtA~VQRbri;H8}C>0yC
z@!^bGIYg#fuo$Z5r5K<wauxfvAX7I##<g>GBnSypKnNG1_Xnh09dqmxYB(J+;X_{`
z1Sq_arTu`6R){7%%VyMD8`;FzUgK+ja<ws5)Ec5&&rhNwo)WnTPGbexQ9#bOMOjmO
z1!pG9$L;t%B+>ILNSe6dv^?P-xy*_T0)@}rZmvW!o&l(F(5u3qSR8X<iqCo!4BR=W
zXeP<jx<e`jpq`MO_YvXozTDBMPHZ?{zPms9WuI}~X^~{^Y%KE#)QNXL^EH1O>*>TG
zd(&&4Rv|@5GCNv{eyI>J3m+b#?;ZyxM;}cHy2TtdTgYoJJW&Y8;OnPU1teJCtbdH)
zaYDZwremx??9=Ah!SC;gFx(@8oAl|LIw4p{J`G9S>0&W(%JndgQGd{zS7rD-jnufN
z{Xh)6FZ~H6ct3yxd=ly^H?cRD_%ICn&*elH`O&AAVah?BvDzi2zpD$|PBGVJS(^!?
z5I;MSg7^BeQZm9Yez228_@GW9MVL3)ZTjeZnD!8cx$RoHNKp|&BHh>$qKSZZyq@Dd
zD9>E_Az`3bP6OvS;eet~k4xlSL0=3A>c1wU*93w5nCF=@O=^9qnvP_(<tN;3>ldZ^
zF-rFHI_e61v=vPz+6z(u$(60<;{X=PM#6I5NvDa*JzDdn1w!30{~3C#s2DiB*XeWB
zld*Wbj<@8<otGhsT=%Sh3#rz0dQc%(@faTptD(D4N+eZL<K6zU>QwftRba`osr*ye
z7)R{Zn7YM=v5?qd0*wML)1Y|=*snJLSJkR7gXn4Dkj7ZmSkLh`qL6^_JwgH^i_GXF
zKDw4YgQTPtOKCy_Fd5)gr$I1MC1(i^2*rPRE~L5OSouked3lY!`SVFAZmw6^ed<i>
zx#h7h2Sx>j`Df$0m4noq4(WLeFy8>4=m#D&71xoH(X>gyQkS$=ZR~SHJHJrh9%mTo
zQL;JCQ)&wn1r^DzX>?sqbL~pb%xfCHn!by-lrY$|iHT$+gKriDDAZ9c1#&huNro@p
z7RI@~fdf04=Ed#uuOnwwsk-|4pJo;;C}j6FeHB8$jreW&5F+p^I_d$E;{63>8f}#M
z)#c{p{%lukYA@Q3!?^DdJ-6DZ-H4%9sNV~bg&+P?PUbGdPnw-H(BEzkd^%nGNs1t-
z7Qr-n>%Ki=iwN$1*Yj?3^!K@1;KCf0lKN9ab^&^;x^s8^W%=1ng<OzX9kZkV4|{JN
z7FECYiyJ5cf`Wn~qaxBRAP%5}fOK~wAreEEgb1S`k^)1Dlz_lcL$@G}bhmVaf^?nl
zVn2_M`n-F;o_GI#*LBWy_CGqzwPvmLtvf&W=ME>ytMPXsJmbq?f4>yi6RR1nhRGjR
zEnRe;pDsD+q7UqGcI{6KAmp{M_u%>L$X4y0>1g662fIU8fB;Sc8sgqCn9AYnGrd;d
zz5*lY^?2XtGK0rch=Skx4Eql2#w&mf2L^R6DB`vCyv72*d`DQOg=^+b*S;$tu$Umc
z1{onXZMP1X>~v%tt1eg{V}Hu79hTM$Q0A8o%^NEY9j}Y<uq?orF6$5i=s8c#znzr`
z`P_xEKZ#CgJWQjozgjw%6t!yg5pw)!p7$#1KX28;{s2GWSnEq_3@m6<Cj*q92!QZv
zLd555ud4}7e4`I*wZYsRZde?KB5j}mGp{|@TkpKaW!B-om6w}@jXqi=`upr2&X8gH
zl-b_YrIcab_sYb}aYtr<E`qwL?dC*R3oIi2-ip2_tRC^8-1R`;B%sX@HVOj{;Z`~@
zI~<F4%X3GU4jApLeWPzpyyBmc6S<0VNM9^B60U$w?Gae*e_rVxOW-bmOVD#8-zilU
z)Do;KvcoENnk}BPYXNEWgpFspP=92~%CJ?^GAr8h-!@91q?e!q=$a;Vzv<4kIu0G%
zcHwQ+#Z?Qww=Ph5*Q1s<MJ_rW<II(nB#wuU|7L*%u-~cf-aaEj+zSowJWTV}a`y5r
zR2<>WGH8;P-mwdK|EYL2)IjQUu;JgfU5^q|97-S>Pnqj70d@vN88Dz9aIYg3w*{Ag
zStlmcP6e7m&CoVoH)c4zZ(rfwkGWXS2S*?;(SHH8_#-#hrdR`7Qgz-8xZ6>L@VnhJ
zTe_jQeF`e2vV|hLRui>}AR?UvY!Q~jj|`usK?3yUznviXPdfE~#3k{cbZWZH{|wjv
ze|QETfG-%eOW}yD%G1U}g;sVK6LC)n(w;x$iGl#kqy2!Cz|+!`!A%@Gd6rWenf^LJ
z23?(h8vGN2`PxkYz_`ZFIt>_mZ;_Auh--Yt-whJ_N(CZT02%#Zu|KC1aA0*p@!aP)
z7nCLNpN4#({Rc`1G=q}{4Ozw6japzY=rX@>9Z54{X^omo0EXLsTKGpS#!(ex5XkNS
z(;9sc^ainOV_>>L#=RscV#{05y^4^l3B;*3M?w%JsC)=;p2`4T2Qmz4M&#U4szYg_
zEY(fe(o)H1_ufxH64g|ItW=%X?e+3NYF*a;d64U1klh8M?hyRD-;T8k39KIJiMoJG
z<eE&;UlKl2p9e|4YfiLGs#)Y-r<XYYKgf0xzPzbu0T?G-46N=cr9A&&i6ivb66kt=
zEQ|J@Bh%tU_PEF?YzA}xj-Mbu;zvR2mVXtrUdO4<uCaPEzrQ&eqnFG9xM!BIYW>K<
zK-x4s;JCwGE1o9D;v=u80R5E=kg6=ciu~yr9!JlZ|5wjAN+iYp2Q&QBN&2h)=|7m^
z|7@QBivZ`77_eNeKl3deKanQ*dB8NYnGb+$1~dF)HlzN?9=rDdd6|xrws)ekl729r
z_D4bM`xatsTOacMDbt1vevr^+XhV5avG9=zaHYN32XOqB@<`$z4bUH@(9xyDXZAoo
z?~~FZNRJRJCSd*bs6!fZ_^i%}fB9=H<sk4j;vVaH-}Bz-SO<K^jvYHZ{OC^N^=Dg?
z#b|6tSa-K)K)T*-jb%>rLb8)dR}}7hVcnNHfpy|}kgOn1Sud98`y1b61@X?S&ob@B
z(R|Z8yF6SwjG^ks3>|D|6g53Alj_N^&wJXsCR)NX)l#++P1>KJGLx%*_4$i&;G){a
zc;b_hs5fey;Xiib{Ig?N<j0Qxi;s^P-Cy4nDb~sdL9yE9#*H6;QGeN8`6ZH6U*hAB
z-~N+ZesGdLW@UMg@Azfj?~kmPJMj|9#eK^b?|0s08|yr_#sQ4&#P`<?3;<uPSt*lV
z_<qZO@^moFK+t{$+eYuoEY@Ea^Itq(T}eys^8CM>2I#hd%;0V3D8zVv80{S8GkCak
zWbUbdHHUw`6S>eaD^@II!4JRZ5(HlvC3*P&&SGpo02hxw3i<l|V)s3A@cip*^|b$b
z6_4J)9R|HDGWdSj4;P=c3Jm_8eejIm^9(t`qce=n_y4+_!~58{Zt$0E_P=KIpHHOw
z7MTBuoQL>-UCh4$-80~xi0425+4qTEeFGl$R)+N>^83Y1A>b>#Xm~OH_p5#w+Akj`
zdBIu_#oTl{^Znu;5wM~Q6KM8F-?!91KMb*I;AJ`S9ub`Q{(JH^QE&r4tlH@xmh_*0
zl&1poPfwTs*8t8xmIgOy7K_ps_-j_bukK&Z`Nvx=5Q7n{n-r5WexJg}x1dAuNwHn-
zcSev%$PY6!W`E#z<IiD!*bxAyazEVY0nV>a`!B{};11@SB-TdsuZOw611}Ivp?C7H
zul~i27Qha+94^eh^nC!&_#febJrK&0F8Dh$a0{9N$5m49?>DG_2xgG$E|)F-@5qpT
zXu3A`+MU0@ZeS+(YR&Eu=@0MpeS`fv2Z46rVQMiaxc{6#aOHU<n3S`$j2{2=S3g`F
zNC0jy7b{Bk!=2}-!L~4u&&B>16Zie{&(7cmZ*af=ee6V$D3A)5j9&2m&K7zCmYbC&
z>9xT3P3B&D6FlBQq4C)-=jHnqKfIQ^<_&N$1G@kpbn)aJ_nD`6?^a-k8`xr(e-#U2
zQUwQ5=mI~?m%7U6`wagu{J(uPrU3V&4&*p_^bj1d6(|U(?+U2mxL*d#qR;=3@cTml
zAw@a(+#!a=M5H<JH}UhM*bK#ytuB9x*uVDBB$4|q{o2Civt*o%Va38aN24QeJ>z0E
zBw#KSJ&@Ow^P1{f%U+Go@M(>qUE6VdwL!`g04Mk`c*AIB2ojZ1YxgsMnH01Gc&*+N
zI-?IitN`?}2M?^vLJbG|(b-U=c?N$84{r!QZ^asHo72lVuDj7^WAN*goFROi3cnof
z&DzCb@%>1C-$rUTzW``&iM~Z%x3b#8N$evb>ApwvrLml+P#`0d6mU~@U~ls^s9lb8
zGY^1=pW14+6s@j&^z74(UtXJ^oa^}EF^c}sc8^#VC0N;AH6FCr_Xqts6b@`~x~5}4
z-TKF`t-#A)P;o!HHBNEA#RV(YT^s&$f4epL=j;5GNJ6GFVCinwI{iAxdTMZ2vyPd*
zqiMPhrs*Xz0zSBJ<6K=S+1C6t>Ls#2=A_f#w#ISjjY-c|R~kH5J@>;NJo;D!snW1V
z$Z}|x*6aLn(~|#JZyG^@jn~M4&RuzS{a6NP+SPj<%9-lxA2BPVDEuoX={`O;o5G|c
z3>ZvFM0k_en)hl<2lnSAfFzonA6#u+BE-J;de2E1_9=7QfaA~I@W=UAXFS?D$AFY4
z)VufVX`Cbii>n!lzXK^nayJ|Ub(oow>f@8l73~9NZGh!*8tE`l<#2d)8-2&{q^D{H
zUZyu=S`1GUwdT{L^wi7%Mu{1+>Pp)V%@U@=p=(x_2+r*+EGyRQk6z#UMF7>GJaKSb
z2opyRc3Jh`cNrDf14SHU-%bHFz<)qgp9`n8Zlvs6DdmgIGnwMgP|a<t)v|q+uAXm(
z->3WJ5bw|+aUVN<8|D(@GC{1oyHsC4V_P8rlf2XJxwJjA@e+w&G}%N`UFi}bR(t&C
z{TKV<!k2U8LHH-FSV?d6{HnP4nSq-9l-M830!`&=7{AR+WPI;LxB~j4e~Acx%XaSs
z=VlGf@Ju5ik9y{X7GHGQ&S$15tHboytr3n$csNcfU+!GY;7x*q50o(@HwdOX&DcnO
zIaW;J&)kl^HaqhY*>EQN+OMk58Zyk7n@_vCgF_xzffXqMW2DW@a#tBPd4U+@3Qr5|
z?k~iX$wo<-BHFqO)a2?~%(q|7*Ki(mbRrC;>>kA3BzSZ&^Jp3(rrxCY5Mt*Dzn%DQ
z`nkg>WR;XqS$EPsx>ivr6iz|ICJKbX3Y5~6)j+#JwCnE1Ht&$HzeUX}VosZBNe5r^
z;bJvVlgH*%I$OKHoiPy_8u~z)MGaFuzp#)V8~b4(A3>(}cAm^nOusV^Q?A33!sav5
z!uRZ@_O*RdE7r&}F~3Sk@-Uz-rdJo+?m%<1Ef`x=EkI~mvCrJIR*)?6naOeCwnn+`
zTf5yF!q<kKEBk3oJEJ^cik-j|(_u^p)}f8h-o&Mm4hHLUx9-JvfygEF8Bl6^cXxGB
zF3JfDzgS-x9rZj*st0lkL)_j<IOhT<7$Ck-4Fn?ns?Dcc_7IPxqlHb0GtnXe@0M<F
z1IWlW=MR{qJzC0&X#&Z<Pttt*Uf5(RT6U<iz160kOdUSwZ|x#v!^ubj?)v?yVSw$l
zfLJ6GHjS`-_NMgMNPcr=6<`-@%4akmPLbi;@T+918>fY_6!aj(hh`$>bOu5y_A1L-
zzul>>{6zEs_K9)StiT^0zC`+Y>-#Y$$3F9pg21z+KSAW&F39qmox~<M%hIy2sm<9$
zWAz0@RP*H$9;l6$yDow3s#*TFAk2)ji)oo3I*tA}f9o_pHn++u)Hp}xI3?ASn)Uv8
zm(qHZS(TxY>V7E~eyGv>$#LDJ`V8Y<z5<#F-A7zTX?8$myq+oi>m|L;I9gePY;E>h
zD_?~`E7U-q9`v27J?jj(a+d&0qV)og{o-Rli)xz_7J+ChdbAf9F#fq{=B?>U#bLSe
z$O`!QC?Nu2^(9DxIlH)+8^WzD0cOURzEQqN!i#Fo6F`kEfOErk|8Kg{T@$iXByt3f
z%!o-A_h}MrVNBD3mum0HbEe1aHiFOfm-fCj&0-9`ejb}$Y|~3z0BE`Y`s94BsU*Dl
zpbsS2VFgAE%IGW~mh!$4*G;X0=BY3pa{v@n8CqI1J~ghp^N4+LT(|T!IYI5sPM0P^
z?5Kd-BEQ<1?$f}4>vKVK5dq=sz8=BPSzY<c?@j@JYccxx2I;vWbETC-9i(Bz9vFgH
zF?g-SsD0=_^I?Dc%Um*u=1(1u<)$}oGordX|2|>yU9fbc(_z$rJ7|BUAt(1yai<+Y
zL6C7H2siDqeR~Wcc7pepeqic-qjdc1;QV8~++CpAlF4aMg=~dMW@xsQ;#Cu0DPiC+
z2Gdl;USjefkTzpAq|U%>xs{vo@Nnp@??0Qbw#!yuqAbd@0o<fz{a|Y%paA3&IoX0(
z%D1XO2?G}cBcp8uPFiZ}qCd}a79ff3J52z43}Mm?27x@{!jYQ_0F|eDpfgyG{nCJ1
z7_*ich$Y!j8I;&9ko1pKJOTK8v&Ft_PP0DN-5Qps4?zY212_fSANXA{gkt^&`@4f}
zae_;r#v#$PY30&>Dgxl}9+KU2$~8>@mBvY{$pA`}JXnB6qbr#oTFFkh0ZP<GKwE63
zuRv#X)dm3B3m|}0PqE#?>0M7g-|KuM+E1p4$=DzH0z!}WZ`YZYlFb4jGHS=p(<`8-
z>LQ>8%m#R{Hb9PIKAxOCzW~^j9hNxyZd7^!p06suUBEz_Q)DxvZfKaOpP>bnm}u;7
zERiklSK+br0hroMAt>st<6wzw1)2I*)Gi{+3io<^O=Tq#dr<J)Jp-Mm+lf4$E$#xy
z)BUYaH`oV3NmGx>R{-tB0ASW2Q^mIq3H?=NK7j9$E@&ncfsO4fkQIY_A^I_f`2d|W
z$Xv2Gen|u4i)<`RDI(*temBQGJ5-bdfkg7bby2mJZ35U42Pdz(Y6%g1h<b;$ck-nH
znS3hzx0I$2Qp?Rg+^&BmFq}W_U^l<Ha3wT+p`#2~rNcsG`7@A$<eUs*i2?4LAvG!X
zchltY3HREU$U{(ib92f!vt*fj5a8r*ia^AJi}Puj`9VQMyLEgAV;Dm5&+?+Lps?bQ
zGma{twe<%Svm(njXp6>UjuU=&2EL4aBtt9b3mV>2^fqX40Zou2#D;wQd0ltHv9hJ3
zxrH+^V4DqRlx?Vk#>;c+3%UIF`#?cPo30KE0ew?EWnGxgQ?BXqd6l~nOMoXd4*<h5
zm34Hk+l)dfYXC(xFzrrNLaqnhl;N7X82plyL0m#Yi$t_ppWdqS)Ft@WTRr)$ch`LF
zRz`-?gV2s32^B6>=9mot?~wSKdDrSVD8zEk2RZL!Als18euIGMmfL2@=<UFhD<vzu
zoniOy>odwJ$R|0{Lu}SBFQ>FVWj@EV$jY<-tuwD)n8GS6Kfi~$c$R|Z)y~p>n!j5E
zRahS=X+*+j<Iodnkcr{&dlq;z9YOwFd(&x6`-D7PA~D+eB&mE9@LbB<v(9{OxTF0r
zZ`9w{rKJm4dy=oUIx~*k3bmGgldD#&8k1bi-<Plc#)*_y$VOQjD;c70K;%6+nVvN{
zT~j$et`~+Q)4xZ#Oh*>QhII|-UGpx$xStkW^G!f)igM6yX`q{0GF;awFctj*<mK3K
ztTM}YOpt6<+OUwJ0^Q0J1=1dUfj0!v#l-`lWH9e#;QLQ6f@-V29_+3R0`;&!vcA~g
zY1piwTmtk%Sj{r1m$2{OtBOzH+`y<kc~V4H;SaI>4moWuFR>k?_lYsM{lb7l%y?`I
z0Q=2BRaSvUZUc3c&<5(_dtNb5#YF%`lP<Tt?qNZX&uBg%jR_f03gt1hdvo^*ChDYB
zaOnmp$sBlg{N!GSmCo$|B5tZhz>M4C^lg@~fg<Is%XkL-YxM;!`+H@4lYxR}fQv6n
zyUeM`>_ubY7N946j9LO%BxzRL>+)M9B-q^+hBAs|&WqVz!JKv@cY&njblx!QjhvQ1
zyK)qOM&xu&?{*UV0H9whNjMq_z?bR9%cx3O{nr;IY6bWd{M^=oQM=>~5BEL4@ONVi
zM8Bpx%-Sv;8Cw#u{neG90}ghh-B2j}A=(o*9eH7Wsl|!zl8(qz{e(lSJ6t(tds9Yz
z^Pe|i5x|R+?6@7qDj)(ZW5yY?JYa+{t->ySw_7LAA7>#D9|?w$5Qr%N+^nU1OsgYJ
zyL7}se9rb0tF{%EF{vul^HfV1Aa&(L3~&qr38%#~X6Ka2Xx*rnEK737+n%=+{=T)5
z{yo~uY5hjKwA!wsxUzM$(0!IFV@BxYML+M&M`(m8$#!VMp}yt{J@;Nr-X6`rgq^3k
zuk2a$rI3<Id|so_G7e%(w+C#X#tN5$zU4|=$5V<eX~@ccRw+@z@dw27A<it7mYq^}
z>{dP|#-W<!^d7=PZd)DhZ#t-50(8r|JOE)?m3E;#ERe=n&ZUM6*2wn$tn(}O7WhY$
z1wkgLsZ5()3~nFv<nyW#U{cu-Lq!vKy%R9J5+d4@tuNxd+CX!2t#4>Nwj~bg#%(7v
zlUw~Ei<K;zkjRSlZm=Ivn|*L?v%iHsnV?uyQ#g$i6wQlGyzrRWD(fRNU-q>M-di4~
z?OrPb-<-|uW1O!!zsfsoUw&<8qvJF%eRnsYenzrX0QL#1`})8)$Sii#4U$;_g4LLl
z6z}`VR24a)4U;r<Xs~V+q9K%?dj64+1lwW2m_~&FUaGx9)hdP#s-|f`fZ9v{k!ny+
zvSI*F2h|@3x<ES`8AN5lFKc=9SlEJj8U%%;3%s6Fe1<HF&aWT?2vq#)Z1V0Q04HR|
z-R1f2G%foYNR*~;jK@9~M44uw(PvktM}XFvV#bSk-grsd@djoj?88!99I{DTMNW=T
zZDy2KA^P2Y?`POB%V#OiGbuhPPRJ+K%{l@TF}kn6!p&`M-*1GvXGUl|HjJlx8pvQG
zQ-?cV^Eqs?P}>E2ptNg;n4KP~!X$HJ*kn@Q<=&4^eJ&+z^9sp*E~H)FI+ju%^_c)K
zAw#h8j-iDjwbZHc*pFG#qmW|b3I1(w@C9lK@mZ-rUBEcvWRZzlv|q^<hlX6<2UljZ
z(e~3?Hc@%Z<`cY`+C?4InoshXqPUp!KLc0Av*-3Ar1c(!+T^Q?z&?>Y@yGk_m|DMl
zb_u)0c1{nfBvikb2KDl`Yb1IfgvLc}?*rhe0kG?R&8~+IK^+Go*)DK$QkvY`nN5Id
zUkkM+ypc<T1qG14dbCELuuxNCxls;)cq4CJH`EFO8k<r-S)#W<@oDsDS}r>7iO)bc
zE`cft@4P7EPbMn&r@&*c_CKKbT>50~&1KpmR&B@EG9GcN^uUIi65=T9b!%+l-8EAn
zol;$m%r}<Ynh-XEDdNmRw#jW`mcdxQ7P{m&dNFPXMO2aJD$?EXeUM+Vq!A1aidEB%
zpDU{@o~}iSa!pNN#7>rpxz>>HI2~bB9Mt?2Hr7ie5h{eGQeDX!QCqeNiLe*HC&IX@
zD@SOe5lfzjvPC8vodJENDm{B6HNi_xpC9%Z9t!S>Xs|uaO$o_9RdLuL1ftX)^XCvd
zP-uXUwISKzC32f1$`^_^{KIiX-vlzu|MIOW^YSNB+}o;BbP};_+xGx$z4CBHc@T(s
z%_1EXmh$tON;lAPB&nm$TdzG{c%$Z$PTyohu$I^Wpeo@Kkjvy9_eO#~b2c7vqs)6d
zKna2B{OD;CI=r`-0XoV`$VgF^m)C<DcaTwyZu=s~%${Ymx7}&*Z)~ASk(ml5ZX_=R
z&Sc?cV+QRUi?$2kC{HS902Mojs=x);ROZ8Z3aUWdZkC9B3Hk6oA8MGBC;mW$Q*xF1
zEqtq6MW<hkCVFzC99Tv~8)|ABAqbhcpz_B0aK@@}UCahl`xDhj;?<;nCm9Z!!Ca4g
zF7P4)U+d;qJwQWy3$^wD<|{(GZG9AR{5?08jfo(C*sO4!YrpEuhn@b>{;j6gtZo6}
zM9W-)rv<lWBL~y7FoFhOKY1-<UaXW7Zog@{1HxDDitIHGitden9xz*dh%>Is`-wvT
zuTZcQWne0N_M8nsVE3`N-ksp}Y&r$xt>H;Ul48{xgp&Q+At;#>dT`2Nd@{~YA?q@P
zGO#XAH&Aa_Rq|k@?gBO>%7b$`ZD(awmN_wkxE7^wN5H1g2l(30+XSeNe-6K;zX+NT
z`4zdwzsM|I6@Ld?U7c!L0zB<(4dFzDAKO!v7zTkUxde=NKs-LkR7yX$>o^`eEj@)I
zmf_ug*ThVkd21bFpRh?&sx7b>E&s~t#|R9%G5L<y`1dV<M+4sl)I(+fG@J)2tXa&Z
z=*K^AeVteWMJCpRUafLWUu;sW2a{u-77socbrGA(3Q1kloGH2QVGw_K`8wDuoaRID
zY5@_`;qq<l^9>shV3H4BuK&4b|BSH(N`SbwjWOpeB<<bzr~-|xSXCNs5InMfv}dy`
zRB^q*?A+sf!Qdixv9SE_#sNRxjW$&)+5T^!XyEQ0Zo`e;?#!oCBgqs5$|@>@z=bge
z^cQx!-)7rC0<&u4K8oPlUB|58i4bFHFxQGC0)`iSiN970#FR0sJ*rVfIQ65m$8}Y8
zA>$SS^s8U@o_Z7L_(IOW#DvJwG>qrlp1crtTdp66Ml!rj*EL?g7@u?RK#(4I?b$1U
z`@SRvmU~|f*3{ToZgKBG0toWZniWu*lMT3<`hb~daY?&ynuSz>5S!}lTbI>_j0*r-
ztdArbdddvGQyNgG${=pc<$BykzRBBjrx`deQni(;w(=U&AUH61<+mjKvGLE?+dM^T
zu!tiYw??e~a~zP~XTp;Hnl}k(-oAhezHHEu<^w(pqgWyZ!3by<KuszYu}bLyswtkD
zK8)l<L|z33kNFesno7Vhu(<)kL?vje^3r8>>~kcN(cIl2dZtxd-4e}HB2{YEqiU6v
zk<o&pSAHaH_+gFenf}#+>?TMNi(io#0xH2O(T{3{_~ONPU5a<DEGO$<+k>L#V$L9M
zSLjTGuxE?|IwXY^b;bt?2+Bk0E*PNCvm45FX};%coJ|%LhAF1ZK*}01deKTYKB-mw
z{ijy|8l7Ouz7w@m40>zzy^5t2+=_fT;@|3xvxR-$yMRsbw>I2SzQj9ckgotJHmu>r
z0$_&jbH!qR->BpZSXUSbv;mXB#<&kl@lzew_3qb1MJX?ljVF@LEXS;~+K*KwT6fYB
z><~4*c<~}*(W1`(YQi}L2ut2EjMvCBK}EYAY}<qCf?`CE60EC*xAS4)5ST~;lJ|=%
zecAfa7uP|7-~a-&unH(#ZHz+XM=Sv|CEGsd$jJjVDfJusyPE+;THmT1$8|yc6Y2tE
zz9tm^?y`3PRxPLls!~klf+i2DZ(0NlEZ#*Lssq8X?|uDn#bO{|#rY6;=c>ul(L=jm
z?x1cfM;Hn#R12WGH~Lvn`?6|R^@ZUdZ;b0Q-cMm#1DXus_gk9m*iov+sNkQbYWRC_
zI3B?EW)lK5sS5@M)WZyyK?&fikYp*aq&<ZICmkVv@et^T+@5reJC_fE5Q`Q~`t<CT
z?JMM%q|?{QqpmJsZ^p6Z4Hw(ltVB@|IC)E{kx-owkMt!#O(-=O8KUu;oQT+Kl;XPp
z%;aiW)GmzqYC3|)cJ{nY$?OYl*bpQL0dqYfOP%zfIt_$~H$Xs9RZL6_xxRD0<t{0S
zXzu{4iGV6hk?YY_9+C^*Q6STo!K3mP6zhpn3!{vA*Si$7(t-pW0K>#-k#M+GeA7;W
z?VNl;DcyMN75H-D#3B?ZT3xkvkXBSw<bc2BeQ#SZEkL+3WX-T08VH5VEfaL9ek;az
zyjlQwEpLV){h~`f`=~;yx^l7g_`SdMiy93G<+-249)N6<yqz5DDe*9GnW|9@i?S)q
zS22&0*?1o_&~(nvEanw_Rv5nY&bkhmru+tOIEIanXm5S;YHaol7&F9xP~gxq2(whE
zA$vgtvrC$1iK9Sr@Ug~q`hY#q>4*ns2?|<*{uND`)<Kc}G*2w5UlJy<mGMSW5TyEk
z0bQ9S6sRkpxP<t~e0LpaN4wY|!EzhNVrd{R2k^r5f#}4za@w~qfD?Xt<PbD9$hqkc
zuR=Z9*iDtfd6w|NPh8!je`P|)^>l4~JOz!FlbHNM_ggIZ3(VEj*7>LaU{m(o(#9Bg
z(|wHFLEBJ3n|z=gs4MC7S6OwRnF_AHXTud2S9;FQH=pxPDLRM#9Hak$a;fdxR2?Yj
z&7*@NlxtVCVtpfJSs!@A9>{j#Ny+swV0zf7ogc3Buy~1lD>R|A4-9E%D3AExXL$Hw
zLa<NO{Yys{v4{D_Ao%kD*7g?=0VL~zyOMXa@jgui0z`RCvm}7->`GD4dQb-Z`+`ii
zb3h&S;pougrJa3FZ?p$$+PJnQ3?F;plTAOrztpB^C3{#YSERNs7-wBW)MIN_fd=Xf
z^4T9uvLbqc2;7w@ROA}l*>MVfpRB(z-`xagj2~T~mbApgi_<bpjDf}_H<yUK6{NXp
zcn|k%Hr0ZLV+D|Js%eU3fF!Q>j-nLH7Qam?EB;RL@nK`5cnN67a#M}OqG*c58I<u2
zI~kttx+}&E17SjaDr4!jn<<vFZE@x$isdhA1W-Cp*Sl4Q%^O4BuL*dh!s#}KY-AiV
zyrtk7z!U-Tjou*&93Q<R5cr@WHi@g2(`hM88IK)UQ@go7(i&qR-0bdO0@^1=V97OK
z7>}h**8l?hcMXIu(__2p`|&S&ZoH4YptVG&Ba%KEz<Bm;^CcDgta}Gv<;w}!(<Y8`
zi#go*q&)C(-P*Pe*6+ULV=k;9Z{(>{r+P{pEW*^J`=_8LeWDLazt93joVO1)?JQOi
z=$7@|!GUxQavjKcT?!d+oI(4?l69gy$|AZ*dV*AZL$t!QU>@CevlkXewrs@6@^Hy+
z&2k<X=M}jJl}#)aL*5=^wRV~~)--P{JwSYiy|l4RMY$4at&b>1g3VqF+K4Z{>@AjR
zMEe*;ZKXz)-4x2~b0{i;04>sUfbC=8W#CLfXBrz9eM!t@Wvvj9PRat&cj(8^l1N@k
z+-rbvMY_rd)gXKBWEacQ0z00GPJ`F@7I3(_01cB$uO@f3!0m6DZu?C*&&Qsc6t$2V
z`gRqi$&Y}fAjW(QGAqb_%dGfN7xw1i8t-BKz-jMeuaKi@=yP({_q5}<x3olNm_+(G
zRrY0tO<XTK+xWFZTGWhryxAEB68clk?LXw~%W24srKohFfOya|MFN$GvmfKkTy}!$
zsUO}NzTqz0zxFNW+Pc8uoG}`!w~trMx-(oP;Mx)fMqe@pVoq;5wn7Mj{RJ4n9?}z{
zc_lbPKF}1|Xx*oX$7%uP$0dtC4~^Rjl8ZX5P{XoyN@Avl_atf3K%lyK?I9pq%Rr#8
zn3<V3v|jEOoAtAi@jWfok2iZUuL0D+&{9<KeYI2TSvqxH+wo@u+Gh|70olBu)iyt+
zKFBEiMp#O>qw_GUeRFgggP+gBQlRP<5+jq)yK*PhV{~nHlbLLPT5-XlZETyP^bM)c
zk-B782<q`CCrBcu@%t4(^P!jsO7kv(9L0bCXsOQ=60NyUVq9k@D_1!YXjW0~Knp`3
zD%=d!q7D;e^$DlZCus;o6yE_=1r4W9!WtB2$${gL-sKqAJw4XJ@%?E0p<v!gf8cv2
znveZE%Ic1LVopU)rbS6h19JOb$L)k*j>5T=88zbq{2s5dM~1#fN=*5S(OxV7Y>Iq3
z{=<ei`bbWElw3y;{`Xma-F@;{ObxUVNn~I~>pmpY-LXClJgOxJNc39hO<I|uv+N5;
z!TFPmjIa|{taxo)l)nxREd>Ur>MZq<3gp?Je8KtdME~KCSVw_CmU-*BvOlqM<h;ir
zd;lwNLTKq}t1PP4Yp%ZlSOP_k@(Y6Z|9gA$C}`q$<MYv5da5_|lUQX<eIma1OCl?<
z_vnsDq5sS7=)QPfHA~lB3AkVw!zQ!!`R@KXERkOZ2QqWj<ZZw|WuT+W2C8SFBT>Sa
zN%f-7Gw4`c_K|MV@r3uS&IE$a?mw+gJ>Jn)sr(U#!M{eNTJBN=ItO&l&(BA0et`WQ
zjh97$1(F--GBLc$nwq5q&Y3zTTTm=zn`;I7C({@Ar^h_w_qhoHixz<9_$wpJ!Qu{z
z#No!W6^?Tt@RtXOzn<`X_))(7v9syU%X;p;z`Ha6h9!0Abro)@i|d>|8ugDyKDh>h
z*)yeV^uMAi{2>z&J@I4EqRj<M@vS2$16sbsJY3>X;NfvXVVlO4@VjU7=kx$@T$a<i
zNoQ{(iUHAsR+GNgb*N9L@y5|}55hWCzfqr8udWQ`w)sZ(%Shi69>GcQe$)ez0nb`F
z`_fcc7#J9Gs;cgr4j11Z*Iko3{`Um%#WSK;G=YDUu9{tCVrq&2^*haP?Xt}Ktu<83
zx*mPXz+?x{3`~;ZnN7bD=5&6V$MjxZPXf9As)JLwC)+i`ZlSBk$u>Q={2tH1=W;Ey
zQNcz}93lmUwGhQ{^y<?TBc*i$Ff;9uzpv}~dGPMzXn#f!^ojla2e=nFYqkDY7uYz@
zUg4;J<>Lb+9q&!sKPT#Y%u<gKn33!2>rjYdQ^W!)Iz~5l0==+GtgKM-iG)#|NYujz
zv0SCTS#I)GUPDvCC{JP`-Mk-HBikCqWByQJfuDdeceO*$R7Fm2X%MAe*T}Xo6jxR3
zU`|%bQjl+0qpYbI-t)B9<P)S4eT9>!SO9!s+ot}^FA{L_)Dg@{_eYph?DiR7Quf-b
zW*`@sA0Hntnle7U@b~#Ye&*eS4`KA$-~hOSM6Y@B!;hYc`D@(fiTW+)o>zWVm>y7d
zm3pghx6Yu+SxMl2UR8Ub)-{jG;sW9Eg_G@B+~@I4!!owjFQ4&j6jznkd@Q(QpC?e+
z`++w)U4i^UzDc*qRp-zF^CKB<QmQ_n{1VB8sGVi`S*W_-rGm~2*|nd}%XJWwq!&a2
zACr#ymm!05T_nyw^@@t%DKM%sd?GGlqr@yns;)%wI=_|*=WIn$g$k**6{8Ssi5-Ar
zSw~IQUNz!WQ<L}!tE>;}DNnb>)3k^TD9~vIiFQqh%5kcV;Wst;Z1tjjDXvB9XB#hV
zt_(Lg*`?X4oyl`csycG*FODtd`@qAs4q#V*J_N_G%4wkYKk4zKwrv*q;Dg)|_5q%l
z5%=Tr*H)CjoY?dAA}1P;(H;de%pD_$u<hQYj56hJ@<tg1C$&1Lon{ye#fmslqAILL
zra>g??vuLU5mb|xPfM%9C}6q$3Y8%v{Nj|dy1W5OAVSaEV~IW_A`4!BU)AVBQVU+b
zjtRnpt!CfJ4s*3&-pG}6HZc1zVE0IDIo=Kj(5&AVBIo~W^hVN!#{^cE{hNuNb&`?#
z7Oos4(^!U{JPznHn6vqKlokHUs%-<0nsv;S1aS0lo(G){!_sNwcz-~AG1uDATdB5i
zcym>Evt`LlIMB+n@zW`R>8jM&<|_+Va*GD@k!GS58A<YWK{%aPkG4;7!}J4Z0F=9R
z?%Ro<=l_F~3}kPd1`7MH^n7FhP{V84)1to$yEfYs%{K^IGE?)c6sAw>tZ90WkMqwC
z>OF3ay=WEZ7sK8As9DR*2f64@?0y+;`H{Kj-g(^h@~o^pAfy^Y8MUSxFSKgOnvOPW
zz!)`*Q3wgCRo&6ISEC<q_1{rIsO{sDU0mdYZ*`<$E+p|B2_f6yX1@&(q}0DQd+L{+
zzy1~y+h?(U+4;cs)o*_N>#mQT!vfLf9wrU*&POW!hvngB>?Z}X3bWd{6+?@xtLSg-
zRn7#PIc1f{+NJTISTx`{cl|MPC)LGDEDPb@1TQo&E^F3O)mQM4uN#$qW0*6oR3cwD
z%#)W#_>nUg(?JmhN4yo24N>e%Fik{MJkePgE-ORj(wT6-lf^q)gEQjS$^h0fp}~Om
zmuXdJI}$>de-T2U?BKeoKh}4#`NxQ)Xh4)!mh=RTWVBIc8Q%cv+Q`GEr_52Gw~QP4
zFS|&+T?n;k%Zo<v7A7xc@1I{N6Tl9(ysSt^P+d{QPj9w_RkiICTIl;Cfu=eCwZ2jR
zDnHTUXh|@lF9Er}+%rO=#FD+q7j@ZyjWMgtVW|G}w>dpBEH#`?(xXvur%X$0fStsC
z=^NIsI|(xGL{A_8-JJx`t9+!lyOWwzG3Pvdk-`F}EAQo#Ek6Hn=kQK#`L)gHW)0N^
zp^B$v-2JsDIkOO)OZrzCnzW3^mE5`dGD@qt9cT-#Y0;-EG1w%&s(`U~Y9H$QHtF`=
zs@H~7(n>Jiu-aZF-l)YMJ)Oyb*k$T5fWgYeVv+=L^NuV)&)CU!jq1v~k&>Q2ly*RP
z<XEaBoTuOa&PWcwb|f=9f3BKObSGE6Y=FJ^7|$c9%HqHR?RSoikU34~VIq}qzCf6L
zPon-H{}slZ9s$qFn%hliCE4;q!4i40j?A<r%=$4E4@cu+>s|Wu1Tn5Q@BHxDNW>ou
zvDV8L4o{PvjyT&J!l*5;Vu2#2QU)5sYo)}l^TrGHFOX9qD7!k#G_HG1idqnYfBpch
zZVQ59vJJIA`BKdy$E>opRc-zzSs!z|OZp&thxQ-Hnitj&V4u*9;-3U@)I6zu^SJ%Z
zH8JK357Wr~VygwxS(xYuFu3^`Vg{A|4nu|$Uo-2A?J}~Myf!ed4g-Cszb(Vi8pb#4
zS~1BZTaD!rtb-`l<&l(h#I4+9yCv0S3_%aG+Vo^~qC3Ci*jGOD|9{TPm#(z8xZ?OK
zeQn2w(X{n&xZU)LsPhI__BaC%rcXs+<9VPxgoRJ_PzU1q;Nk&LYF-Mpm26_4A=zSY
zP68rZPG-<>>)1kn9@}}`u)*F5qro+c0L-o68SZ0O2T^K=Bhend&zH~&hAG`WJ^k;=
z8OGjh-?m?<sd)UqF`gbHD@%|STxJoUxFs*<0|09^8fDsyF`zf%7nRa9m{((CqpyEa
zYHA~>r8a^vw;*e%20*HFdyAdP0r=PM!V~IR9!wnUG0@>OzQxjM&vx0i25G;@2|0Cj
zlR>i_zrl9^0Hb``jZ{lh)6CG&u)SoeYo<TPXb@x=_{ccT!XdotIs%Y=W*l<ITyb`t
z0|vWewrO%{>0L)!&5#Qd=_x7M#0<&?X40XIYI+JulDCF??e3k0!C7f)qtn*1nJude
z%gaYlY#mQhoZIh=y66LL&K$ehwuf4gnlrATgo5-c&v!KOAA*ga<P3n1-<|w~jkai&
z&Try8na)bfus5a>eA3u5wE0a=y-8>nk6adO;3<tu7hMj^KcG06aWLn_FTyw-Pm*zF
zl_1zGPcq<zHow(DB%GN065UFDDgrNU^0GQm{tG=RRXg8ibp<&W&2Hbe+lYz1xcZKC
zZONM~K$=16$hS;awMn{FUHK*gm-`n%pu0~E*(M2Dygo>%;q906@2PK4G>CyLZO#or
zPpJ32K4LIms?fDOeF)%ykQG_!c~GO-Bj)6rAA`|r0Jk{EckuOGT3VXVle6AP5t!BU
zU`IuB%Ij4*_W-(QF+@SynDw3a1)2eX0$m1&>M{tdLfzlZqac#hq;>G?fF+RoFwfG-
zaD@W~t|gF)$r8El+Er}7G+(?4_^mp?n(2dYa<2jykl#G!c?X0&Ub3knR_Oyt$$|I#
z?0H{Fjoysa76IzkK9FS#>-h*V`9O@}MGLa{*Im9;+JpAdN$Zyv2Eq+SA?87Ihv-ZQ
zBv;IdTHgmaiATGD;LOnJ@gWF=a*M5jY>DkmUt*Z{0D2$T)}Ytf*`M{nF|0^Y^3L(k
z*Xl;h$*{*q1^D`_yxm#$W=wZY7SC$H>Rs2HR8FxyT+n?YFsVd}%^*IgTDLKkFISO~
zKcT#D_Qg{R?K&u*ma1){<G<Zf9>GyZEg5BjcZtO=(NALFqPn3l*}`aAFoFl|@Wm@l
zVyQue@0>WPo6%wUih~qom!$)=x>+~ET(+OX!&%rH;6KVlARcoqk38Ckz@sI>{<sI=
zR}Ud{cXTze_S@E#vY}8qD_OO5Y3@17DAtD_A8F~b^y~b4ifoLc_}t2yqwZuL*A~+s
z-~|imM@3-bn;;Tk|4}i&GEUHIK<du$ByRzT6jwzVU;q)qD2QY()`#36cpyvZny~l^
zaIpu@am&$O7;(qG7Jke7j<Hufr)&jf7Cq%1041O>@d;h~3Lx0_tC);DaE3bVH1(Z8
z^WvqQh0FkhJu`p88w8x#hW@`4C>%R6M*)ljORtX>`OTi+a&@l5DQG2z2ClhnE9_c-
zvrPkPD9yvE`b8thWdjP$wSj>$dmM4HbIXG@hGej8<Le6p*;S!kOa*l8mj*-<k_M~i
z*3#+Ep9|~Gv?ay%@z`&Y@G;Ys&2XGa-_hufS9hK*I)|M2l(l(%-?dn_$o|M+%hhX+
zs09YwBl_7hKdB*rQjUTQHs2HDx_$^i=8s=ZeXH{%3o1iVtsBDKmLKexddX8Upsr>D
zbZtZ1bd3{BfuZ;j4dNn5Tv?+j!^K|Vg`Mh#?TsGp#|%H+UCM{@K*OglY3c%Gn1jzu
zn(vdwtU@Tf4d0a|)oW0tKUo40#N2AP-xz)c;n=h$&-|%Sje$6?Yx53GDJp&vGoL_j
z9SC=8MV0J|4QgV%Co>^D3e;NOT+omdEU6}5bQ;kaJ3n(k0$bEm7P6b*n;e9#M5GxX
zd!J7PWtZi=xx4|o!Ibrj>nBUVzV*}DP_Y{|23LCiV(F2$#h(F~ysc^mcR2i!XYj!r
z_*NOmd$!s}&iB2?%XkKtZ=N1_BoHx<U!0o*ByE2+@ZN{IwmF?vHKTH3Yn9QO#4o+3
z*$7B35v!^N+F9}g>%Wd^cc3cuN_8cP9m%w`5h1pX5%Vn0uTvihO#0Qi&BRw;=dPRI
z8L|`{x~hy;9b-!_1IY(-GL+Z4TK@QQ2n_=p9Rag?Zas>t`=jtzNkw}H(2+=*K18y&
zz(R-S>m<&2p63nbM*3tpAYrpnCeTcqh&x5UzVqSaxjq2PHiqb5AN!Gx>z;(+@NJ>}
z21Mnf#O-I)-z#Q|lSrUD^ZMll4PPl`I09I0HbA+tv7IeD1njL7n7iVh2!GKqxT>1k
z9EJvBb6V&^3_I}cF0u!INL>s=p2oMH`3z7Ipr*vm)HaIfOO5>fa!f<28~wwK4az&z
z7gnhPdb{=lsR~O=hk(A6pZ9v&@|?YogoK1Mu&euJk15X!1B79@)NK$h8NN=owRAfl
zz)KI?%RnN%w~U0CSPej_hj(Z9uPg>ud0A@EnZ32V;Jq^+aDQK6ke3pBfm`NcG`Ah5
zM_~Hg&wiBZA*962R8tE-Xglqi?m7)QD3`q(X>e;Kp;eP5#a>sxDEf?EuC!HJ5b^lX
zTzMd=ewsyHPse}1rM5(_C+1Hs0PHF6iYOKR+Y$@N!;iVvIy-#2QStXpRg}%hHXd7O
zAi6rekuv2w5q?)>QpVR;e9Z@5g7SdcfU@rxgFj3^aw!1X2u4aO!4xHePM7uBR}+f#
zK}_O%$GFO02tCWXd_JuNxc(5bf(KxrhaQ3s%*wzDP(?Efh0?7>Vv%^`K-d=f21<-R
z$;i7ZPVIuK@y}{T(S}emvsB8)x~TdBDkLK#BOml-r~&M#Ij8r9U}n#5j(KIlw?Iz-
zwHRJkBJ)fXQ8bnCZ52`R;4>Rwp*zKNo9%{dug?H_w=7V>F#upn*MW>%qiA;fT64)B
z(EF<n<|CFGXkv1Mf`aOcCCiLo!QVe|1eq-}fL%y(Mq^a~pu`ABBJ%J6plmcc3_S`c
zg)^Z<KePU0G=i#nZp5`+_)^4VnZXS@g+U-E3asv=zki<vSi}bmRq>6Y@mFHa(jxJ$
z7l+*iw>Z$R>IEvd*M8*5Og(21@$G`KHmi@;L%@WYXsq@s@ZNkHaBsu1-)escur2p^
ze?7?2cAf5npgov$_z1{wk2POe*RD!5_+<MW^V!K*dGjr6ro+gSI<Y)&{{*ffJs&1B
zfRD@vioL96e^H!+v*+iugEv$|_oERMeR1n+eF~?2qh{2-ih)q(PMbh}lCxq+#48^&
z{utfebZpfM?W@tUaGrYBt?k(ePS+N_G`tNp(sx(7*lU%8ur(r3VHY)KHVk_bY%~_g
z>XCRz`5KD@2Vbd3&em%cU*A_ByHeD{SCFh|b+a-ru$d5Bqh6?8>X$&v_E9>5Q(Bz3
zR~111^=QWg`bRI)Cp&k^@!e(J0mwitF8v1XASTME`%4yXHXXG9@U|%9wi7%kGa7PB
zhG<-ZrOW_SQ`nLE59FE7u#x!zs2mFIhiVXGs|OGSGYC)HKO2HG59}XKZ+u|dIz*In
z4m`f8(#Rnf`VNG8BgC!6CFRJ7_-zSh!}fsgY*X)#0(&vL?wToRP4d<ifaR7Y`n_!>
zy&n?6uS6M(>b(#;FHGOxrHD~=RnY`2r_<LnybCbVbR4JsbPy6)9d@ydqJmxoc0<)H
za+2Y!mD+Cmc4R#EXwV3^0fKqgCa)$EQ4k61_m2P;`+ShHf*A8Oi}bwh0gZb_cW-aW
znqXZ=ak&7=9lr)6{0k=yYp<{wyaanx`&P*}0NUyOmiXi?yOIB(a{MQd4^9YQjkug@
zmV18<&8Q3ZQh3?+bW{?f8n=!DzRs#rP?SNzEQpHM!EAi*t$kwO01a;X-^(QoZNn`;
z^Y_Z&1Z(GhMSP09-6ZC{!`L9)A!+M-?cP9hMQz5Coxj6eM@@7Zy;r&oCwDHyML9zP
zCHrlxQQ{#Iiru%VE0t#gCmC?=x8H~mBa+Xxoi6C)Zk3%$Zycq@^%GbPdtM6FoFGz*
zLf<8>u`+&poLGT9cP(!B71p(3=Pn&o8q=9;Z$;`r5!T8i+gIfV)9Bq|0=q@1wuP{z
zeOK6x7m(Zzt;c_TL4~m^>oqQgK#I}t9l*kroV5YihFl&#4qm=6dX)^eHC_&x;$>o}
zZ;58Z!wTjdrHE5hg{fFyAr|3+9l*VAX+3zu3#JG4xIt=F3vLoM=ddw8;_d;U6*bF!
zGpFUEnQ?oWO{eG?zWq5Fee(q0r#oE9zHz*^`16|cec1^mG&|@g>Y2L<%VF6Wbm=Pn
zcEd72pFDK+ecmDx?T};CYEC|Hd~$ff)?D1)*Z;AV>`c_MMT)`IyGyy96r1q8C?Ig0
zKY<N-ZP+i5*A#ZodBb*MouOC6sx4r+Vtyo+5BFtD>1^*B?oZK~$#anFoUFYbVz(^&
zP!QV|?;t?#ai<Xzrh)xZ-*a+jU8Qw-JR?}wP7wJ=-IZm5=?`&bXpvs~%C*EEPKSr-
zev$CHviY@0Go-0D@^A;3xZdly0K|38Ohb9Hx-#>DOhNye2cK`*?2K_V<KfT<-=L)F
z1V`w9!B6lH|DWQ>e{qM0N5Z#2mlEN3NYJ3D7HZXUW&n`acOQUqnG#7wB4+7(>xwiQ
z#wW#igA^||V%bAwC~<8}FTYB8bbvj*@I3pAi6Kvj<@=y=qOgN6UVI$1KC=a=`nU<P
zP#=N@c%vC57p1ajUm%=PpxWyaHp<ZIU~fAqyB2qS0brlZLhfz>?Q>La5xKkF>ShiR
zlI!1&wvCviXX)MtG&{L<*WHH19<=j_n}w(*WDDUL4=7_^O>%iVXWw596nd$;%lRVx
z5{mMJXsQn}kwa-8b-P%Q3VO9=zN9ubyBZgDLTQ=>rH?P7TCWhwA6-Wz<XQ^Kl@1Uc
zy}{Dl4Gl&RI!kg~#n#bEHZzi^;eGafiu9d%2`7^%OFn&XB$4Pkinf}icoO}bBSu)U
zzen*=e;9RuIfL@{EI^LKO<+MnyPGR38I_Kt-Fw-T`g6P&7${H7UrPvlLqjLAmbjUH
z-y_sNQ{PK)lToWR2+wXm<y|hDAw8w-JOiq&kBV8GZGF{JVcD*6-T^2i9(T!2wqX^@
zsRv$chjZF=$0g(Bi@9fP&BTlzKIAD~srJ4w?8@I1GA6ATbzi^j9&Jw_RMkXd!oZL!
z^j@+Kl=Kw`%+7>h*&9lB?F*bpWE+^fQat8KHq=TbmI!cisP%>3%t}^D51*(WI?4l+
zYm_PC=9@QCb}&uMJ_b93pcmAeEi#Fb&fAF#4PMJJ01*DBiFtwjj&uZD&G1PGd_hBG
zqJi30A}IsJ!yPO0+6ka+L9w>daZ{=yCj;Nxwb*S7paVk~8E@QH8v<@6^%xums(6T!
z?DztRceZIqh?uBU>u~qcVdvlax81vq@<kRus&cy<r!P^hL}#KU67-e|LvJhXEsW-S
zX-W3w=ky^+If58rX3;J_&B=TVm3i$`i0L7d-EUFG0zH+HFX6QAS?}Ms4PdS6_WBcZ
zX2}>Wbd?X?%T(1Y`IOYGZRW&88_3!>fa{TYn-p6^ExA#itg9p;kAWpW(QaLtIE3@<
zZHL$0%qcl|0i*2cYm-n~IEmP_g~Jmb&aNOJ@{7BO9z9A6Uj$~A53-Sj+DfM4vP$S3
z!&i7s`q}^;a<$=d5bw99Jy2K|@eY@TZWxrGWxPqA7V7V!w5N3r6(VUk$x`~oBUlII
zL!-BdnQt!E2sF6vyeRDvGav)_SO%Q5MJIZRlt4?Wb}CpSFU@h0c2JxfMk+OhwJ<*~
zNd;39m$w#Hwe89W&wE7Ur&GuC<-^Hzl}v5s&Nv}r!)XOtiLgnOVnR|<t>H@0Y(gJ+
z1u4=oN*@ktK{-`e$Y%=o$ABQ-+B&(#!druAcklKM0h88yWV~O3U8ZX8#zf2NhjhhD
z8fa4lTh7$r^0g#k)&Xky{uDXY7sr0Z8E*__s$^>G2b3r*#AV_IU$o9RIgZH8JA+(6
zwOV4M(qBNSwtWWgNh3Mk6Hy!HImwy#;fX|znzT4lGeMISh8SN07J~WX>G_>@@r>q9
zs`LVq5gba7l;ZZg{R1rd!0w8<B|RvPE3eGg9QRyOOQ-n3UJ-o}+Z$IlMgo)4#?++s
z@k|@>Rev^Cr#a<bYxtnnT`O5@bcPmA^p)i5;jHIVb;SFrHtIzFY|%T6PLb@<*hIxH
zo_EC50`9K99MtM9W4ktgCHj`WXH)2V$;9Gv$|X_eZ<tW~FJPFeb)8RWzN&h}6^b+^
zw!40dvAZWqg6ktCRn+^$O4ha8mA^L!gC}ko!cUc??Tf;q;XTlvTM6DlJFCM_#;;^z
zoOUF`^^$FbZak6LWNko<nF=oK-P5k?&DPILTk0V8BgVEfmmpGnS=Q>$;CUsp+9Z$8
zlfEXc6>wfZMlPpH=2W!Ujb2K~5*pE+_>82K5Ua(tS|gPh_V^ry5fZmnnw^PT&fMuI
zWU)moM53Ohw(U#z+QO?qxsZ)kSR}($202j|<tMr?S{#P4FL)!%5r|CZxaaLc*`|Tp
ziixgvehb?1qDnByu8hlj05okg<8U|bUi*QcR#+<hJ)fp*&!}TuOw<lntZzDQ!_EOO
zvBmY!pIHu=qWF_Q8AIteV_9v-BBrw#ywtPVjU+4?YL8ajZZ#x5Eof8D<fJ)9a>`9o
z1qLv>@V+t$4YGmEG&>xu`}dHksTn!m>?G<+Qo~n_Sp%`D2uMyJ_Oj5^XPIAZLOdRA
z>1kB%-=eee*jPZ9O2q{eof~yX%asmvdjN33%4n|6%j3E&lBcfz65!5ydK9pi<A<TG
z^g_ZgcUf?xf{ZDHMLp$3bA!W)m=qjSs#h4~WJ#ZVlck3RQ79^_dU&xt6Bbc*c|{Uj
zdOpFGa+8)ULfe&AAska8#vxxzC-#!)Jn<DDwxtw~?CP5+xzM|p{UId&Yof5AS(ia=
zSXcgh0UNbvUE*qS<K0}#vHLXY!c8~F^sAjRS3iOZ9Bf}+%6-wokUp>-Jd`QqXcz%*
ztw9?CSX;D|%{$2^F*F1Oz2|tqLL*Mp`9^Fg*ev;$_<3Bf=fo_Qba-Jq$)eaAECf3f
zb1rKhQj86^-ok0;+9d3+RKTAL>Z?$*iP?_RwM&qmA%1v6CWh+r985~sx^sh<^0BsE
zx6*O~n|n<o+f<DJf^vj@CEcdStHn@^j_48PEPYhirKzg@kV%Xw`Qn!BgWIS}%G+uv
z34)m9fd`sYY$>h{4|ydLdjQtZwP!tk;r5W{q@0!)?ot}3OEnR8x0|0}U9ctkU~b1$
zQuM)UqCC}{IBX+sP1#kET2Sn1<nHX6a&OqQ;7m%PS57=QI!klfacjIvYHgc0MofYJ
z1JY}dn~#bPLTBO=B<}h;(q>c~?1-xA;plO*T#RIh)@hI1MKy9sobM?@i9cVtRjeac
z-)z944C}IEA{bA^6a~E?2KCl40XP^@adJ?>WFZvaZ5=JqXp*{HM9GJf-_W9jy3u1t
zha<Bq+J>*gvh!pc6yl~lp|RR=j?_30%)QEDWez^vhL$m|vp&dTzPXv$+V}c2^O-Hq
z&3QZTVn&IWz!54wpkiHkny^+s+No!!!KXnPGk+!c%?i6#Mk}xg#4w?s7>}#Zym-32
zwd=^}&-f>5=^uy(bxz0{jPZ>$>3pQrkF6tVPciy-@SIiEsB?@A#!6vE-40R4q?6s4
zZhAo!Ih9z$`S>Gq{=%07cZ0rLfybMZTGh<1yW9r<Yb_qcP#DWbU`w1J%tkyRTOfVu
zDY#>MmJPqBFKxwjLZ}AewVtg#Opn;2cJ1I6ILg$iXHQG&1CA_qV%uLcbvs9yx(COt
zvZzTZ3bZS>LLI3anv4ooc{~BW^sM4^(rn=Q8&3qR-LWdt^VN4*lu%-AM#ZmMdO*D?
zN=mjOFn|E(27PwwnI^X}Oma}}Q_zv|!OA>anb`YwB(Gt*!ZhEDOW0I~w1P^EuO%t`
zFhXM2#6q`?(@$dKQI^t?p)eO~aI?ei(x!;*2hkGwXLTj4shy24Xx|un64aI$)QZa@
zo8p4TP}BrnMCD1<g`Uj@b*n|IF#~t5nsweFm@f@NQ;|SPjo_DE6|=JaQY-;4605TU
zX}F|ddbfPdS$1;7wIl|D&(M0aDmz-SwsTQgi5eQ%@zVRet)>+&jA<W_#bP&H3Hf{_
z^u$NkK=M?yXS=nrDy*xB+UgEBa5M*ApSc{3b4wi-g2gx{Gnjf$Oe?!3ZB}2%O`4Lx
zNlqRU%Z4|<<3k)25kjmmICtgCCX3sM>2(zEM*o;zUVFHqR9Wwfn##ulotq!Sh<j$T
zDAUkpjkeTcoIPc#_usHcp?9+?MtAHd%I0_EL*>h^fr5|MzE8jATg<Q4g2KZXDj$ih
zZq41c+l%Z3CJ~g(sAHjlX^if?HW|y?cZO|)2c`IQTyUO@N|&lcBa4?W1}TEb-ttJA
zVmd~M-gY#!Ey84WWFqHVsw5`k`1OZYUqI}`S`CMa)#SQio)57)i=4J~Ty0YM+>8<h
zaDZNBsNkeF%FgDTsWs6Vq$PXbD6lXSb6A8a_d)8dZ^oLHR*Zz)P~#&z^HYR?oDT|I
z?3s_~MX}nwE!#Pm{jAm`zT2+6pN@&|N;=IJS58UHjLUX@ekJG#7cfPY-|);le+2}>
zzId7*oKfgMJ-YSSLn=~js=Bf&7M(4AMdfsdZUH&N!#aDap8R*Lv@)e_BijZp<kb0?
zn8mEu_-7igTC^2usnfJT*b+&Go;Sv4|4TYW)rzCUCoMTaPa&grJ862g)T%e-O+y?-
zWtJCMSl-r+Et5AH-=$nk!Cbk``R&;fa8VrkkL6hat0LkZh-MarH&=kj(rfwPF|;Gm
zeHUXH9FlSlBiozw4ezb!^)+m31-$*W8NEkZjShnX-$_a@5M8O5JljCtC{I->bwb{j
zRwLwc8f9)k7MwO)!rA~WtIQ<kgljm`5q7_-3)D<%K4OTGYf1lK?7e4LRN1yQ+$svD
zLQsMNf*>Lp70FQr1Ox;@kQ_t=BuUN^R8oN=LCJyy$sjpeNKjCs<S0nav5@%9UESDt
zZr|?nzW4t5p7;Fm;gQ<Z-fPb_*9>Eh;k7ohpXJ~ap>=J(j)bd}UwN4<Zk`#Js4z%<
zwtDu{b^TbSq85X9hY#Ba+61+lYh2Owzi2HPEC04b<y#d7z{<=DLi}wuft_dhB<G%U
zydl6|Ql<236}NjO<?oBWTL$TiRHl@WFNsjMUQWbGnBMKs!N1ji_7qb}WPN*fcDBkF
zdX13lluNG`6$km{#W_FmuPKZvb&i~X5&D-YjxQVDkKevGr<U!gp+b2C%iTjCSx#(6
z0tuVbKEuz^9;X88F&vJ$iR_X=wJJ%*yseGAaoL}0ABBzyJvVan&NEKsNir70I-w=7
zdxb~tTX6JUpx<~~S-8?t+Gvo1&UelpE~>s9aA;FKuoA3x<K`zV{q@$ux6%7FZD1f&
zH6t}T@Hcwv^=ONxnkEOWus+{7;**Gb#!ohhzN7F!_tO?%NL~~9^5QkEVwdRsBo>Y8
z-ZsIvl}T@GPDO#<>(bKP$zQrtACK={E02iE_oL)(M&Iwq)G?%bReM4%T0?((Q`g6t
z?Cud3HnvRJY32tXXu`F7vq@H}UcM6jG{}^qsW9uWM)dJf_>r;GULF*kF(MncZbTF|
z_HEb&%AoREwz-w+3$kO;)`V{`TY_EP$peH9)iO~g@AgSr81`O-eq@?S{*Nc3)tD7i
zwf5^_WjMAa!@Xg8UjUrg;n_&_%l`Up?<6x09kNP<Z>%12R$tbKEHEvoO13%jv?tQA
z4Z8CsR2dd2kRFCTh%*@T)fc=k-edd_b?v;f<jt?_?ar0Q@##0OJ9(23&p3Ja{ZXsE
zl-~*ZBYM$J3*L1!3~!|gYZQ(~Lt;Fiee><oJTR?{bSbsHweox^eWdB7;(}61&W$r!
z@-<7tJRb(NbFQ7}B@;fa#in`^&-sA3_ZdX$VTtW|195}XxQu?9!ktE|Oqr)VNi2HA
z6eP8c4C3>HAcuFMm%1We^~05@H%aF9jds(Osx!k<-ifsiCz(rNP$pcZ`~E8_7az;^
zbPWx0LDs|AOHn*Ysg}ejr7NvkTBkDJoT2uv#Mn(6Y3aXxp-sWs>;RN2ZCPiJGMzm&
z+4tH~UxOAdFcuw*KV8c(M9$`Rn(f_WmFV(?Td&@?4QrB-;?r+QUO}VKT6d^Pn3$Qp
zn`=g+T}=(5>=V|>>sgZb>t5zP-^nJw0ldS@#c!4->e!{TrI!|I<EsN7d_@Kx?_K74
zoP9odsl+o)u}ZX8`m%DZ|J%T9nWMcD)(yACKfp*bC!gcEC_dG9skbTA#I?aNT}p~y
zb-mNbx*_yza&&Z7eeiKPfu_xpwungsZ?a8dZ;x94#3RMJ=ZS8el+%l3M_3R7OSd#)
z)|2rh*coY2MU0lh0hQv2HuFo%oO{w`{}PhFf{<J^>s9%EZ*=&@_G)~s;|X7EJNM@I
z6yyho=4PFIN7kA8qm`@N$h);QBgeg>ExmG%3$u1wqq}fBy8cc<rMT=%s+@P69r+)b
zYJJjf6)PC`=T$P*`8*I+LOb&rsnQ&6!yiWTcRbRJR4*Lz51w49iSZt3!TH{6Z->*N
z`&(!!(eN)Fi0>pgvyH)}%TeDD+M`dg38}Zs)Xh~W{TPapjs{C9+LR$x_k&(#8l_Sr
zNI`<u^%aF2We+I#YEXQ0-b99GYn`RiyFnXw3Od;f;fYHVV#U|)j9r9Ys;z9iF@2yj
z)KW}?FP8s!7y^eJh+|vDUx>qEql3kafZT`3;`(fEYS!zi+f3aNKvi-KUn!aEwterl
zA%!+^tu8*2GwkweE|Ik~%xd3(BZRi|(k9>bL>0!|`%XFA#zaGk`MJ7ln~L|cfr0;{
ze&7oMa;m@pD=o!yP2g1DaA-ZeT!g+m7}Kvgn6Q?7U&^o>?)WTYZp!r_9fe7H`gLQm
zE4|R;O{#kC=iz<#ZvHnuC*#^uHpm5fa>;T&^j<%UGK%at{~<}LcTJ&)_&}*T8CwgL
zDR<L4&<J(*y0#4qg(dZ>Ou`UkGG#{gnllVur}OG7200}*7ea#}@x`Prl|&t<GBTf+
zLN71PW(LQ;iiU;>QnMV?(CaP+Nh8|WsP8)7eLk5yN$Y-VX~r^p(`NKtTsG&=&Q0|5
z)W#-FW%6wYzg~n^wcJaKo7g0TYsl%Kfd-w<d`JH5dfdftMSa1a8l~I4JdDqek4^$t
z%#hI8+Mcmj3%`~?b8a9A!8Ud+9be<n&W=x(Q^M5#`Syp6)6_z3*yyzTX%@BR<zIg}
z8p5hl^3^0CP_E3ZjEAJi$ck$Bxe4B9_RwdN>j4<DHG$Oe$y~N$`x$P88{abcOo&&>
z^x}NM;@p;~_Yhkf*GKiF&Hod0qcsen6-pC(li4c36tFxcASGnA8zauwh1NzUf6tP_
zmo^GqJ*y*%3@E=1fLXp)ljEDL;7?qnI8{S$+yRVf@VH$36%ufu@seIQNDq^;nCm~L
zKk&Hj9YoFgkA0MEy7H&Rgo9(1Bvth<FX`L2CR{Xy^2bE{1I=;maFoSO7W#N5D^yi&
z35g)vC&98FW7;$94&~RP<zVoSp?m_9m>pAf2!OjD2z3#e@$vjA==bM`YC3$<U>2g&
zjCGOXQV_rJxH@4`WAUxAO#7>|bi&)-m7)j5=W7EKXi-dYuPQkuC2F0{Gpa0v+^ww^
zXBv$0k5fpSzv6t8QSI^fZlrHkiam+n!iwTrMm%CLhV%B5uVBjuM&obkJ1833YNXVD
zO=b`N7W_3{AAPX5oIW6}&N=5oO;a8eFmeO3`z4!w=(iAERoSxwKqS$-95d~FUhQqI
zF*AaduuWgHo?Kv(Y%u6;&5cQYgg*KudM&m9Mmp7ROO|3|qxz&__Eb?{MM=CE#%#Mw
zjUoe(L0kP1#gVI@M%V6i1Me4vsy-V6O{Mf}2EGN}(KC91LuZR%_H@lyGk92WJ4#*v
zdL$Ng?=w2XW`Ak1i%8QqQVzy+jPcwmSGzy!Od3QPmGvIl=K8^I8{8b{B~sgmnU#zE
zJaVn;G!708W2n2*Aa4S8V1Av<)HfZq(!Qwz#}4_rr+TJ%82B9~Ywdai7>HyC@7zQF
zO~sCm-GH@hW3X%8X1GLzOSArRMD+K2a(jfsrydy8o){cJsJT38E*uT5xgkc0U~gou
z_?_lqy!!I))3KY9*FGsCL$}1fLE{qoWWmo_HP2sqbustt1O3tZ$liNkeDqt_mm24T
z7$<?t%D+ABKwSXOAOXWiK-!-VtUAwvlH%f>%KUI@YDjqOTkJ(;M*d(tj!&+EmxJ}C
zC%@*^YI2=x#&A&$##}@rZPN5f;+eDYT<3@n+M^9eK}G!H!8|HHEAxl9Q=LnB$m<V~
z8S`r}P`hqe?2~8K6b=@>K_5a=l&5%wFT~tt4uj8c&wUFaXwhf+nnnLg^1K2UkGF5&
zYhC>Y-^(`TEU;}RYeJc1&uL5D169Wq^%umWYX>@8PQZhNJ7h>d<>3K2Z?6X<(sIlc
zk=dxVi^HfZbY<gwN~BkVV}r@VKB<sdG-4%)BQGA6qVe0ZeRNH#cm1r&x|YkB3b?Dr
z&D6L0FX`W|D~zxGm<MpwT6+8j6GjP3Ty>be<QP!|)bq=3yh~^8r4HI`c$zNk{dDbQ
zTy(z=FoboLxo&UGsNT-IB3UJLmUcq5xxpXU06s*L!7OS$VaA&M8-<2L4dYL9OMcDg
z7al*_GJ7*>LMP#Gb+wsV$biWxv<<)WfU&y$ptsu`w`IMqa<$4UJnkbG32nx|vYo%$
zQ_TdRraOif&n{%rtO}b?(sU0nsogH8MSz5XG<5@0_N}6wL%ivn7Eqn;runD|%v6F!
z2O7k>L-E~pEE1j^`?1XjBb3Jsa&;;(se_{fzgRo;`UehPJG{@ueu)_|vY6WA>knkV
z1s1?~ywLIRYx_R&XVLb^@>Pjy>E6+O{Dx9eqv`bnB_;!=GmH@!7I=gH?2=|6-ov&S
z`u$k&8<`We3P~4J0*}&DF@1=jWkH<_bYGfNSd1a4v(~%|^{L_cE5qJ30o1c^$o122
zz40*N1NxPH`c#XXzNdXhl=p_KFj<Cu7Zei!zqWlhT8ZtB{tN4L$yjfaDBD<L(P2*|
zBPHF$yTZ{I*foXe@dY>LKFpZU*FJt)w|-BGGAL(^{-kxaL0QYXxxI<AEIkmSbhZ1M
zd)I~<^9N5TjR<LI1s0qQV7y3=c&||Ks6)ri^iKeYO+zD3S-<<%SthY*F0VxyY{b~&
zh57^;&7Kn$jnwMpIr6u~A}5PXOL!8Oi)^LpZzV-!WOGa?wMR7IP2Nr);?_!5IVmiG
zKGiyQyJFZxDXPyGomGhe;o8Cnv2`J=??C61pVoDf>^3oZ@Y~z#k}nmZ7`3GTA~@}4
zb_^LCJ<)_gd^Yn$zZw%Enx|kzG3D&0*46@9{*_KS?#{a}a=CSX1>`gi9^_2qIj_Rk
zw;?9Icit==eYmVzHp;k8IW;><G~~Uk+Y$rBoBO4^&cD-kV+<asrQz3cra`f_D$@{3
z2}KYGF|a-8tv#?jt?RL7W%25;QgX+3Kl`oYLr53no{zarH(<@#T>VkMG$>*AqaRl)
z-2;rbGL(uV@0imID$a7gJ(4Pw7!n-JTcY!gJu2tTXNkaiMXqH4McrYh2s?{^@KEiM
zr0a=1<ZMc(>Vw8mOPkyWEn$B8*=lR9u`*WZ1u!dpwu*`K=d1lB=zaRLWY6Ns=%7tW
z;@7k+Uz8LTdF-!6#9cl4Bj}af#V?RtkP@9ohMkqK$k(_4GsBH5YF1<96LH^99SkWe
zvB2fPLB}8>jt#m-8X28+?MCoS$w-)S&s+t){m?=CCdxo(l);v-Ei3f$Of>AiJ!Z4i
zv?}Jqw{|&i8J&Gss!P6dGO?CZ+OBz}s7&`u`AA!+oGBY{SG@APEa@9Yc+kIg<CW1_
zVtjxPXma1qjaT@{)P0JIi6X_%bK)s`!*u?qAfAq|jv7Uk7^E!;TG4E_rOd@i=?C={
zS_?^-DdI<oCzY_yy@yGN>F7K3FE*b+CA${2_quLx%AO73NeXOJQc_Bvto0h7wI*J)
zx}pDhN!UZ-WIrfa(Y+GOoh4O!@Xe-Wy+L@phsyO+V9eKBDFfV`Xaap}Cm1H`6)<#V
zFRq4S>lDJ#s2u13kO41(y4sTS&y_9H9u#dwAw(hw5ESXmEpGv?#Cq_xP||}esgqCZ
z_~}r5H;$J5DExXQl6?(({8yOZcWNAusrk;h72e)&Q$JeW2vjJ7C^U_HjV4n0R5Xj<
z^y!nT6DhjiJh9*PdoqhV$*@VPiM@C3VNGPTCRXQ_HDUG%De~0wx1Lm7$#*_i{yS^%
zn~2gp82hS;qWd*X0d{6DI1PzZPb>mR9nDNP@lO8nnYWcm7w08c9f5&MolEH8G2+G`
z=UNE}TBNQ@-MyLQHv!0<QCr(ipBznhcS1?eZ+b~bQr@rxuN6RNck|v+O#~cv%ZB>#
z-2y~VIC~4BKaeD>^?8$rKshDa-u5E1;F1<icd&qKle!mp_YLuvL;B?}JYQR0WC{BW
zRGC|+?;0+3o~FMn#k4pa^Z_R2<qahz<uzTt3P|xhw@cy~^1Bpqe^3ao-^j=)LY@9~
zg6(XdY-&piVKBGS6G?Bb+Bft>HPmX|oWbA@MVIMVE+lg%N;!vwh8hPS{!*TO4@hx}
zQ|0&qN<>KHJ}$09qL4>FJnoiNE0;pu1nD6?m*mG@hsm@^mBh`n{r9&x&4}G9+a*d|
z%2v9JpC2(QGPOeg$bLWZ!V_bD?y^bT<ol-=wbm}S>b!2wz-H8{t^jotFQ!&HRW;F4
zs@)8VQfarw?`hd6b?&JWbaF8cw|fo^yPNR~U4a&gQA(c`E4phyKT#v(a%6cRDSf>p
z)ADmhi_Ev!A}enXCT6Fx*H4Sq^Qzf-)n4%ge@V|e^9ttlPY~aspmXI~QRxx_)_^V&
zH4RrE$P#E5G>P+O0ssXKE#C_4(R_;tGdNA-iraG5pyH11QLfT(Pq-o+T4Fa!diF<x
zv3fa={Jl<>&9zsxvg=+9C@H-5o{7<Lm(yc?u0Y6I6iu;K7(4k<La$16n)~j@V+bmz
z&MA3Pxjkqt;U-rDV6MKDN>Eyn)AldD)39XBat2Ne*`9#Z?|%#-{xNR&Oc^ix49ejV
zlVAzQu>%I5(;vRVPyDQYEQmclkN0r0iKtRq9b1x#w6u>qaV1Gx?k%;+zQQz^f{8mi
z)7|M{V{As%6;d?iS@z{dIgb46)w#&X6)^1&k#YY8!hQnDb&cj#si8>Irb}!l3hb<s
z11;?DKRgE{iJt`Vi?el%l^TH!V@+`~x)FG62U(dqneSu)p!gh=_rytLNbu?PBB0>N
zwiwuTt5El#4PV|`!m}f`pu~Rd;=JLVb5)|b8S^PQDQ6j|13xVd+;|wgmheDJ=&PCJ
zbDb=978KKDn3Nh*j!eF3Z_kCZ_)M`?*Yi16&s|tbPDy{5?Q@ncYrOMa;)_%C>E~Nw
zuH_)8a)hmQy|>5sR%Zgl<OV_ZvR9s6SqP}V@X0^$^rnFC>x)_5=m5JbQCj^km9=PZ
zFEj!sucR#<I9&i^6#1FywX~%Wz>|~@eT_aGw$IyNNAeOJ>nF$8o;Sul`;GpN<e*P=
zI(`RVRQkJZgIS@0>EzmyJsCOS;tp4?9A@P_6LM2YWQu0dJ2tLfrCOAVB-79Th+nLB
zyIfTkolO>6KQ+$FC$^b#f4nWhG2$x@?4GkFYVDh{Y}}olyx6`e%b9ns<==yP+Z@^o
z@v|2iifMa}lYG{7&0!ZZ-aJX^xANWGP{8#AMc+-uZQoJ-?lyN1iy6QGPtwMmd_olF
z!*OfF$3<d*w=bZ<QgO`hrru(k7HGOh<SuBmD`PNY#&~b(3PSQ#>Wh99)0JzjkYies
zR8C6wi_?GD>PyDMCu3Fp+M9S^Z=+{8tq=&C$?Dds8(cR8Dg(dp9yxiYi@>S6ZNp{0
z`nHR2yw}h=myn};??vSozO`XQhXPnbP6b@(ZDYMM^`qQzevx{+x3)XP-D7_9v<^na
zeG`Pr?^?{Ry7+R~l_2~#i}XyO;J_fhe_(YcU_M}5NJ9JKR&lV_=x=!g+T51&XOp|D
zw~H&=ELnWHH^m?Q6owP2A(;9Ig2A*S_KqNfN?E$Vbr=;w!R!9>6UYw-L}=thf_kOQ
z&Fet)`D!KcTVB88XNpFS-OB3?8PKMrsH3XUijtsD9brvY7OTou>C3X`?IYMwcbc!N
zdYyIve~6aoy|4Lo#VFr{r6pn_>><bfVk9hIvaDcj%{gYCGgnkK=Mgndn)o)}!;97}
zY*m-!pl+w6^m{Lf7aCinYB|8+PSs#neaR<AMLi{-b?-eQlwrC#NzG$9&wAQpp`pc7
z_AbnM&hS!gs#dF;TX-LZcAq*EThQ*SxfJ__iKu#ub^pRbq0(tYbD4B2Yr_!lgrg}h
z4|y;@=w1sS7wkUu?K465@bLS^%Xw|}1WP5e&a=!+>?1=%7p@dEjTV^UWe*y#?iJ4x
z8?Ge~UWU7N8PR(?SuhJb)>rDjplh4Yf#uaS79R4a5+WQ-x+B{67wKawv^Q=n<@s5-
z?#Cb6N^@#o);H%!E*TL~SJXx~&aXV+ebixDI67U}mKSjggr56{H8eD;Yz_RZEg#oM
z!Akp#HfU_vP9mDTO%bb|yg(Z0tF7btZIGWsC{e9@(D7XIk1A3UZ0v$&-=L#FI+@bi
z;QFOx3ohmNUuu?}{8q%2dVA{cODx*H2iviTTs8Ggj%`XHps3FNJl{VzrZ!_qnJ-9!
z56a4Gv>&NQC4`68<R349oVIk<4TnBF{CC%wwuBj&K2>8CoKQpWG6i4vZo7&#>{3EY
zdiQ8RJGbf6;tHr#_H+&32P-<sIQ_ep+V8?<B2N#0Kj@WhiW(WvekJ2N6)?~UkTEsI
z+rE#)6-hoTg)MBxs;X@GWM1_oU3c((riK?kc!}7=Ye1L9x+pGR<>BQDN1`P!4sB1J
z3Yso*oj?MGmAvI6C~@<%A>w{>GI&C<W?5gqhg@;}((bo7QXi=nLGdk^hx>|hHy_G6
zOC#S@10ksP%@k)bq}Y>9U~$bWy*z*aVbT+IPo507m#yR~V3R6No{5<AX9^(V@niDt
zephwPnx&I6I&O92n#$YJf|^m53LB#9J`G};&32A6%AuCJcSa%TFCJ4~t*NwBeBf6$
zYIJ{JZyTReyV(8lcy?#Qrezg2zKRb@`2;D|7Yo2{Fm43GN(WKm#VG!%%t3?mF4c57
zCv{3Y91h^?{TNVx&LPC`<;>IwlUr7$o#)(Oaeh9lizeMiD&cqYmZVBlbmu!tF0=bm
zV!fr%_LBW0Lko*Zq^E|CZ=3i`mn_yZUn#S96)<JxA)iLiX*>1NjZvK`vjaeaYp0X<
z^dVIB{=xFr`TiG)fQjsN+e%V&1j<4yEiLo8j1%+yjiZ9I_T#SGSXW=}*f6tnfe!TW
z!CmTgRP;1FDkeOOC8Yz}m-B6SRn0Rs#HILm%jh+J+iSl!HZ|PcL;uQ3yGBg@fOc<p
z&-k=&Et;sKv-x3FWFL%IJbkZRwh+AZnBD1&Sa1_ThzX-pNp73VjAgVmP`BzhOx%ht
zx^uZaEwW{zEE$7Ia=;d~m!=n(upD;=x>mAp7_cE0MoQ5lf-9%3zsipTz}WTLCAxyp
zlpb*}?!8(>Qhpp}o<wv(6iOU)cp;&#t__ZTFKB~0Eq%*5eUe=fSgDVO{3d${7VUVA
zfIS&ot<YuhB_+KmImz#2|8Q2MdBs6SrbhAeS@zwk<{a+HL^nTA`z%(R3E?p7%S#;8
zj-c*0%(`pQSHa-^miNucSwe7tisoqgr0{$dHld0-=)K+|B1X?SJdkr}e&N&bb!B7f
zvR1$4pW=Xc6Cg3DV&b&$@2@6^XS>jy=obb`uez3x&*@t7%a+rc_2nkp_put;x+PVR
zV_Na4oj6Lr)3Y;%=w@^!COtXX?v)$~nj>CD9!=G-maB)ufPt-4#eu&*K*~-2BzwpM
z;qf;w6g-PufOnMab<wJ{H>7*9?MCiIp1xZvM@YnfyWsBnrLJ6}s-Dn!>}u7*tVKg#
z-hE{jv8}xP(rKc)Mv2Rv`gI}k9HxD-rSg#+Go6;mqWL?|`)a8#@xqDFw0hd-k?vw%
zDF&lr#LW~mGJVv{?&{i@gm9P^X@CbkPr;qU`~_N<8CpAvZK_Nn8l12q;x`&QL6)(#
zn!BO<FzU9YacJCr{BHd>Ilrg_Q?Wf`ps6il<WV_y*R^*)=%@2uk&kD+LMK1Z%)YGJ
z5Mrd1NN_Eam8k<w#m}_~-zr6>VI?o0%T|#BZ2rkTUsSrVx1!g++Tas1_s^LzC>}+P
ztX~shPbL(n$;h7uT}S4%<8>KkhQ8%fKbk|!g6g5NsLMDQRa2$#o~X<>dKw%{#^{Wu
zmX`f+@Ho(1S{Ff3?yLXZk$+4+z(J7@W+!}xpE!0?&j#<ucLmyGQqfO5Tuc<ztJt1A
zZXWXXSC<TAi`FJme5EyQI`zi^>AG<*lzIw-0Y;NI@XLe`gu()m4ij(3F9g_sz9*!)
zORKoC5VuBL|EFt2!EJgY>%jyB`M!sAVK6P-;A(EN9$r}a;fIN_3;G+$MPk8aDuI_n
zR5lkv%X^IXmn66Mv<>AW_uHRic#Fu?m;N!`23g&f(5!ufv;@#y-6L77?II?j07|$O
zE1*_AL2@inCOqU`LsTSapA4L6J&<pJxPsG2qOPDg=S%-)Xj{~ucjTom@Av7#1j}7#
z3XO<%&0zx6Z3+(oGj<-8FKHV7q?8XrpEt|y+C&6a1tL+8&QB)5q4I3gn#>Lg1<h)G
z3n8(uG8}s+n{}R2-Nv3H3|r}i@e8r#Yyvhai%KGognqsvJv>r2^_Q}|!$G=+eMwF>
zz^O*|x^UW1#<RO@9&L7FlyJ!)KX^dfQj6{-c3jR?wW{IXiH6<IP9&xQtS#j^E^0C|
zoqm`zpk3@2WV2hjIp)Lu1-xr_xcB4Nc}_9l?0O#qXp5S+MVcqx-il>Rwm={Rk?&&&
ze?KR2A=i}19t_54)xNJRShzu{I4%xfLB!mgCb!r3mxwIyJXn67-TQtU0#wMsPB~me
zcR)5K!`%Z0r|xvYYfxah^k7om?Y6tCyv(JLcX_+^mPiZ<@+}VbeVXQ(VQR&ctUMiT
zbn%h9?&H{)xtqWq`8F90=Hi)k+_S%be{f03+<ZWFN_O`LX*gaYYk_u&$|s{t9cg2e
zIE>1xyKcy^>-@muQ%535>s#iTH$J-H-Xe!N*zP`q>0kd=V-bWCmp}a5?;Up|+5>oa
z`1ti*k-ykbj5YF%PyUaekpP8?$mQR?T8~5~k$raL-#-41BW}#i?f5ZDN*-fd+MhlR
z`QJ8Vu}=Qq8P3eLKO=WAhcsl9T>AWPgkFbs8wuFcKx(h|FZQHLc8oIh-pkSH{rD+`
zxphtM9@7^Ux+Hgfog0em<MCtqy(cJHj%P>XE<O?g0=Dn(6d&LHrU`P`;B@=K@$dR1
zVcf;!Jn*vI-q|qh-r>jYYrw%5rt=@e+KH=n466WUR24Sj>tGHhu&Ai08Cuf^W=8(S
zJn)zfRYvQyXJ|!3YrJT%A#;B8PIt(DB<vUnL3h~)K;9P+m52MAf4spM4?M(Yh2rh@
zS?1VY+r(Vs_5|q1@xug^B-qc`zt~USRT_5>=@1r4=s6pl5_GT%yKLNMqpm*}5s26n
zzvXA-AN6IQ-BxG9fE?ea*pTCU#|+%6e901lp(_Do8sEU-%6^nu$Z5cI=2tf%5)=C#
zhz?pnmL?2DpCa#le)$|oHgC@#@|c`MoJYs2AvWZnWaZti>OMctr<w2eX@|Zidk~%W
z+m;QW78W`RO)hPUZ>GC1g42k3FEAxY)rGRlT_B`-EedCudut9eV&Pn`tr~A(FsM^E
zG|ueW<|6#Klt*Z{bH1(p;mJ|Mz7qSYz^Z+Y2DWs&J`bzXx}TPl$8zNJ*Vb00&F`W%
z;)vBxeRf0sv1n!9y)SYik(1B30C)2J?g48EDzmFVHR}3gTbeWsYt8u^dKT|KjllD0
z;(=OOATaL-BYG(IgPKlxOSh44=?T2)SPpB}H<Y-%`^m@1am#h)&*j>Adoi+mK?g&l
zDFUcCzrMZGb>$C7ah~KO;*H`JP3*ystD(Zhf3dDboOLzqvaUbA&W-ON0jkZAGw1Q|
z-;hN64CjiHavQS16<xZmDz)1c`I929C~}u8+6js#&VutvY-%L^eZ69QL~za{woRz!
z5Gt=h#gK8gui{h1ok|LSo=Q9KB<Tg*f28<Twcn2vH>SO~bHqn=osyZSLr5<^{+ETi
zk6S3ozg#GH59?D$P5!WsWP<NVp@qq4+A?)Od)+TMWcN#oiNl2q47);x|18jd7U(|~
zL~v~EKU)lZ(1Q!5uXnfrkl6iy;*Yc)_akXg2&-C1Te?O#LZ~oZ>bFR+RQw*oiDsBC
zZuUHQygA3PWo^?X(gl}`b-bd3RN<eG)m`Vrj`7AMzh7IPZ0RYqY=PJyIcmagFTc7r
zXh0YOJI&ja6yvA)@fs93x8H4zm)~cK!KAHsNbhp{d-m=-iv$d&41Wd;JN1IEk`O7%
z#=U06BTytM09qQKMnsFI1*ABdy@==(6s_J7C8dZcR`z7-TvaV~u<k0b(DRuaNWJrJ
zqN}GXA9t9yxg_PKxVd|DSB;$Aecm1$Ge-QojmF>lcQ|WU<OE!|Hcq_8sH`YyXZl3x
z+2)Ag5$M@Z6A;$Fyzn8rs+*SvIY-R-_ZYi`4QPMCzrMITXW>x69gt8i{+TlVSN{)Z
zQDGM%_QATWy1zavm&hwVFO*#?eM0*%9WK~X6G@$a>+W&+G^Nb$7~{P*F>-1;ll~dN
z|HY}9Oi+ncRx|+tZ66~Kr2$Gc3b)k_ApA&L-8lX5<C$DTW&X=&k;o^nCCS85Z9jf~
zZ|(ElXCX~v08$8?BE=N~cOU<cL?R(io8&wN;erT>n{h^I9*+|UJ1NOpvbd=p6oBfs
zm?IPnG*WDGL1Pf9jurc<<k}oU-Clgix%=EESbBj(K*c40MnF4ua+3iP2|o0bykj&s
z#lR+fLi8q?Xm~ohKH?7EHp)o{=!j@Yh!_8|@E>stpZ`w_@9v?LL#d_=U(qIh4z~&;
zD(&y~1M|nlF0q|kK(aj1bGXVB>)%@{5hPu)!yQG32vB)k7jY5B<(i@>xT^hm6WeYR
zJaQ2iVVL}b1wzn5!dla&1P`A399NT1<851hfWZ{```PSHX|<mn#MR`X*|SqU*jW#2
zVz{f8`}Ys;*{cNaE9E|KUOJ?|Y3LhBDD~h=R=UAqRGanPoL?#u9yf5Il-l2hQc1KB
zor%?DS(6;2d|+}H_lk}Etp(w#bh<c=-DX677gtX1{s$|9H^HRppOyj-ZcO(Ai5b-_
zQztvY=}?`V-ko^Ar#X&GU;oMaA){`3_&@`abTx`sxJ)8UPosg9GIjFfeX`v)c;FST
z`~=1X<)__uPV@h^3BDJ>$5&2M4F(p5sF`ypY9WCyVV6_ZmsbP;9M0biS(?u+q%sIO
zNTy8a9q32jGkL{pc@G$(nFP6K@}c$5dYa6+3&zD;z{%y?-AJ5*0gS6*+-91#wg@8`
zr*5U!O#8m&Yv!4k$Nv{zk#>xS2f`H5B1Wc>bLTivpiOb=*9Lv*OIa8M9;yCYp1<kw
z1DjC`5Q-GbBuYG*2k|;{Y?eWUg}BdvTIULIY48unbhY_Y@!LEIgL*C>uC9KN-vX$>
zTpU1ywZAv_T|FPOOtetx`m%`Nr=bguD^qSo`Vzeih!?T>nrUMzZk`!laeVitKi2Ua
zw+W8^!zOTZL!nkkix9G#7El;iV*1iI)lqZ+-;xgC^VlU2c5?|&J~?99H7t+xZHy+T
zaWx|~Tch?;$gcPvcFFAaU8vu83h_Uj6G!fvBQnsk0M6;73**+S_Ok-<HKD;x6K6e%
z1NaQG5iesm?YrTeRV}fzP%W}D#4dcwnkC7w6Pu_svUB&a|K7cR0p~=qJf?4#-96L-
z=Y@7}F@hsWI5+?HANH6wjfY47dLOnK=;l;85fKzaNVR)qoa@y30zud^f2Li{aRC^I
z;(>$Nnp%M#gpp0PSd^SVGA(gbc>71M$tO8dR#ipMv&!k}cS@kKBoR+mIwoe1C8Y*Q
z-)|>OdYYT7fi6K#2QVkkST*&e`9W?{aq1MG)mRA1zH+@Oq@X>dz7v`s)!fyF(xXCy
z_gG7G-VTz^ol3;j`u>Lp&pr}Lzs9ca{;icD4=Xvxu9oIm7i@73@Ib~E%GuZ6YJLZt
ze5_o~Ai_KTRCq%Nq{E7Rd|ZGnNA4-*!|M)h@1lb1`6g3yfE;VE2WAI&5iY=RjZu*3
zx&bTDa0_pLd+MDRE1+ApRLLNem2_hD8=-5#dIzXe(jkJPy&3sSZ|n<)S)rHR1M*Zf
z8m^nmRr(;q6|*#<Twb&uNWHyU<NHXvWS($)j*xM$8>j}_`^?)xOD_xr%L-=nTwy05
z{YN=NjQjq_TAuUg<f0-@LtBPy(`(e;*XSw2jks91#J;`2E=GZHk0b*5_geyEP{FCM
zS_VY3m*ze%qzr?!@e~xjoS^3KV=ShRNJ4T5!vD#(B*;vbxtL)vpoPm0^fTpBpzqA=
z?xC~hXCOdF1w4kmqsz<3;rzaatLFaCP9Hb7LnwsZX?$a`Vb)Yt@!$sNL_gnJtq~@j
z72}wunzfV25X~dVbOsqMIimZMg@Pj&0wfW3HQVJ1Xz)Y3jn)~2<z`Uhm!n-_bR$uC
z;~QbG8G;ko8#o%}Xlh#=%cSFZWN~GLyS%VihKO<loQ;|%zTUQF)M5l}2boS?gY%4~
z*!F_GAT=Owqk*X9>&$SV<AEPrGqYQ4n{Y2P_Nw!1Kr|^gpJHDOv_dV)<!h}oj|>*e
z>`uR~f}WrtB4|~W9);x}d8y|ka$iyQL0_zq&eEi$oP)w*xATfRc-q?k6;JE#K_p&x
z29u<MZObpaEJjko?lSM&`voo11(A4>R?Q27hEjlDNFOF+n{pAZFa|L7jTGgK85ee8
zduS4OQ_1=cgJ?$-_C))~bpYfl;jM&)O4hIESr>jE@$o=Z(!&HDZOX3AlLzlHg8}l`
zi@GI5(ud2ZP0XZ0f@e%9ofoL}KEM337U<!T-}6v<?Q5b?t8+aleI1TYVzL9oX98t?
zI5=^K&N|K8l<k*3lDU<xk|>eJ73t-$nq_&;@%R6z2Z#heiCNW_ZRw<jA&52qc*bei
z{AREu>FdjU(9Mbm2{3+ZYwP$ZtgoK#gFzPySw-s4Y8}y*^}LLy*By>iK9J%3Ux**c
zy`UZbfdW%#=f@ht=r3hA&$ri;w-O6)U8r>KF?&zy4|$?FW36>ntYcQCIo>3#0D0>?
zTghC-7kASy7>t?I5XCsmM6uMPpY<SEnG;F}hRQ&iNlAV<W4!{+&<%GgRGvRd6zB*|
z;%zVbXW<?m7Mh-ngn~<ru@ALbrYY(Ux=yO~Glj7GZ)UM93U4n8lWqF9>jb9IgBEt~
z92yI@7)$%Dd~33tbkF+^YUyKI01KMW=?MD}CWzf^5#GGz&@*&enPnf9G@+JNlh}`(
zrLgvr1eKzK(M?*)AJyDtS?eyss~Ey&`cU-+uf?MNu{Z_@NJalw94ir}pOlBZ1&7)8
zirIFE4IG$*`Ycu9<yYetG!;vHZ)*3k6%4pr0T(_Qdt8NvBK3p5{>3g>I~9~Y3{_PX
zuFsacX1y^hGZg^6ul^fZK^A>$bkHdE9}d#v9UPHzgwK9@!H~#ZIxf>2y#3HIw!bi0
zm*pts9!v)^)g4Rx4aROXOZ+Fxfb)nw3VUSXvSZ~QxFy*30o$tq<6d<B=QXR>3u$%m
zNVI;`Dvx~dNk&JnRx%U`M$dUNydkhyDbXsyVgbL=JNhM(pWFF69rd^i)wi0tu}K-&
zyG{SMd)JN9!F!n21j5_<3~p`&s3@2X83(-|7L|k>uM}7;!_lz|{lNRWym7H~kR+qy
z!FWHR)4)E)2F4ZAEnjVIJiNsakB3=3rizz7cY8BLy^PvgN*oQX(!*P%efctf`XH*h
zxfTShRm!eqIn9AANi!godwWMa3Spi<9`;z;dI2Cl@UXP+^W#}V=>6jZ^g8<qK33_a
z_mPi20JIC%&vBHso*zKC3w5-OcHf^%kWTw=!O%ihhb{DV6IYcGs8H0yTE_9F02eE)
zVV3$lkQBCV@`WhQ1Z>|vgt9qq*8XDS(m9?24X0dTN%PE=t8d(P2lV8WxF{_DpQ5lW
z6e?Z4@M0+t-LW&Zi52Vi%>ozhjH#~T*W#({x*{#%m8<|61j13QCGnB<>G!A~pA8a@
z%ksQ-)N&!Nqp5M$1$Z5_gc+OxCs6<N#Ou<L-!!Ji?l_w{x_bbBuayIwBmK_NBMcA+
z<hKAv%lcrN3eykpm}a)E0F4;{f!3g?jqeQH(W(LgE{J*~d&u3H!b{2Q_d?V5<J0AE
zJFhLxwCLR#8#CyOCDz5m4`tH`tA|nF4I2jpw81jjvsq0VwR8lvQqm8Ed@ZjGqB@-+
z$>A1Di^65lsz;@xFGH-_a9+b}r_6(k9#x5u1grhnKZK1-w&rsH!<In;w!A_~I&r`A
zA<Xr4Yg;w08xq)ur6BQTM|9t~Oa-rH@FnoPl9-JSXn(#co?B5e{7Bt}D!^IZ!rT~x
z$wUum^=U3AD_cK32R^AZm$lw7{VO@<Lz1aoqbPnnGWHHEyzF8e7xKyoP%Ej8cA%e~
z2-5|51zT+{$0>WlWY?$akaorNrF%-MOt{Q+_j{*lG{aRg4*VN0J_`C?RoSIVT_@^R
zsuxPLoR{LLWo$v|Yu*X?QN@-k2CKlLfr~k?4BWC+d9a6v{2lgW5+N9IsVeIW+2=2!
zhexs4)=q-&3h&_!5=;G95Q{GdJad|z1}QixzpSqhO<fy|e1QwsV(pWhi-LLzJD~br
zCmkUd{oFyzN&XE}BF0~~7&&XD%o_oxVAk2J0c}#F*&5vT?;9$JZ##@Km8@R><taI0
za8H@=k57370djbZ_s+W5+FHh{_JMF}|7Ot-NxUh<jSZ&lL-Ej*uH<I0MoFkl+m^3u
zU|%EWi?5I4ytrp)8PKaG5D<>A;^Pl3uLA?(2~1uGB!M^a4uT@5T8ZYndopW{PlbYz
zK)l4ie~pQUyl$%x5dP!H(a~IFJ?S%v^yo_A0jJ88@0icEshP<{gS8h2m}&0iS)*Dv
zg#%yLRck*!veV)b3DSalav25f3_|nXGb<6`1l&D(Pfc(CUclN`S!vsc!SpGXNbE{~
zpvs6t6`8X9L+xg)VPF8JFW#LfUudITtD7CJ0b0wJaUh^&)2z-e0pO3UPiAi*y>zo9
z*!l?)JUt5Dhj;lvN3;caEt`P@S`lx_hst$5jhDv`B>NH(&(K>8DxhJfu?00G85xBm
zVL%*IG2=Xc7aTyihBh-h5=bvaj0z;Kdu0!3=S%}^PYydTL1nGzlk9P!d<|m%Gh+P<
zZLO`rLU3Y|vez;F_C#;#lG&HKv;F2Dj*df~@SrbP*M9}lAsHgi4MdTy^sW_;_%jR0
zvc)(q?G3XxM)6cT%40Z~PL53Lvpe2FQd*Zzg8(80G%M4YuH?fwM=)VCZT<vW$bNxb
zyIVBc#|V(hQu;2)!CzLE)-LgoWAY)?;<3<msNwH>4$aAC<HguyZAG%rYL>rcjs}+h
zcxe{_4EI@1GYC{EKfJ-0TWIlB!-+i~bPWRfPcJ7)#1qsFr<J>V$Q#ZFXE^B-<<w!B
z4x_}CFab{A>Ba$H^C|Jc3i)Pz>xmCb`>0n%e16km#$XCCplKUz3LMj4W?DCNKfk;%
zu9V@R)CEJBzWH@C3_KmF%Kf9bdR&>_SE)KE1~pl-{gRq4;mSY7G5?U7#=yOSIwY&R
zSs|++^UN@IaR^R1A0`i?m%a#<{<w^p!>v9I#ZKRNSIc2cM;7F5q|&D(c;?F1XPEs3
z|0p|jJNH51q!|mWh)a+J=Bae?*j#I5V<N=iugsh$)rtcI;SBUKwkgkg%#gM5Fx|Yp
z`;0uvg=Bgpc^U_eS6Teib^~;KU+Ba0oA%H#o#T3nPgj5qN)(7s!n1>NjZOd7?^y!N
z$NXF$4~wihAExJsv&mO3lk_Cfn)gvg=vIgGrDkD;WRxZua4XdIA*UA&?$nfzVYeMT
z645$@7=rOX8UizqO8n7ohbg3?4~a-P?Ok$Ah2)EKCX;WgE*2Br^V9Cpbes;RebkOp
z!()Wd>P*FWOClMjnpMeUs%|S)Ek9j_)E-hwLm22cgh{CDNT6S*wltpm@L7vORd-Do
zQW(r{$e1xWh-!28%n8_iaCuzARW6nPp>o+M3#G=*hf@Pk9(`a^^1qNh{-<_{%KK0G
z{Pzylf6C`O$cz8S%jf^=9h|}S$!|}oWkHQa^6HZ#ZyzS>9sGKQMyTAEl$A-S94QX6
zc6~KP<8V9ETo1e3G%*+x%ZDd^J?-A%3bE*aIPC_2b6wiFEmd-;CO{5mS(Ie0p|eMj
zs#Pw;s#PX{J}t<HGy_OYjPk{CF@z08p^0(Ez(5`^?=Q$OytO-sQ|^U4V+Sv~V;{Sa
zIZ3pc5FT+t%azk<b&gY~0#6A)Wq{Y_>EUa+7zCG&rr=IEdFdW<YE92^<?8Ljb)5M9
zHSNQp(}$)M{Wb2R!F8AE|32;``KW|XCxDcYpbtT?ry|+QpZca7!4cDr%rj}4r9}is
zles!hx&F_4WlYDU5#fP+mF$9vLVt$LxZeR{H>6GSIga5@-u;!Qq9JV=!ZdO2ZW|mq
zhwBv@{B5rgy8pVJgDNZ5passzRMbiERY@BpxDCH1$yY6Og)o>p2jLcLGQ_&XeL$N^
zLM(hIj6<>q4gwrfsr_#orZ^M&50q;M{ryMQ^&hMoZXy1Ib;JArCm8UdGzOEs_{T)U
z&q@OS6-0W4&6FP+n^YE#*qs5u92NjyT-0G@ht7YUPSg2c9F-7+z!>fkha0>6up5R3
zh9t<v?@;504nzz9<Zd&v3U#`Xf(epePNg0A%8p~Ye5?Q&zPU{a<%rAo(dYjB{VD(i
zV&wMz?bUc|2!<$HTO(vAJBXo&`wLl(*nNBd^-s95!X27+R-fK^G&d?3^C%da>V38I
zhju;Q&+7qCe~fZ!Zcm%wAJ>V5BQTKh_l8_QKl4{VEF(z$ZF0jifBaIL11>{V%eNcs
zeE(lvg?Hc@!2U&E_ImEzQ%o#`8e$wze?jQ%{PO1wYpVegB44T2;^&Lr#|W?IS=Y>N
zxXXX}#`iIhB~dDVNPD;A^zs1f@*w<m^Uc4#%Ptvs1-;@o9CwCoJg&lRoBO@f{3W&g
z*W1k82S63u--^_JekuqSC4`vw=X3n^58cR%ayG612^1OAf;YakY;FCwcX=QS<{oHH
zJ8Aqr)q_XyRRwOl+9S^J=PLcx4>|d;;*J5)Z97ZOhv0N>N!%{qG2p+t>R87&K;Q5?
z5pe!|h8QxqOU;`lfBOo4b1ORr26kAieFq~4FgCcZjMRF^;{J6R{|K=*;3YIlxb6Hl
z#vi`lnv%ri=PLdDgS&o+p@UbzuS;&dGdbR90hdv6zv26JX60WVQA7iQgk2==`WcEf
zA`T<<8}EOysh#`5EyLPPSUSQB8b71Pm<ISm>we9T|8^~DG7I6=jhxo}iG}(K29|kx
zD2C);ZtRzDBe05rSBH;EX4|=rPXsR8cR-%(Z@(@n5iS#~5lh+mlm`s7m}k1~UHW~1
z=1&K;Yb60a<^q#LQ{1X#=XQeL`=ao5`vnqz+V@{xscj5PuGW(Ev%H}#0kHa+^Y&Q!
zoj-(pkDCdxO7>kz)hu1H9VDa!^q%v_1@LzsZ|8o0E=o5W2LK-s<P9eh`2sr^0+BHh
z?tE?nd#J<aY69lxXW<8EnGlhyXKIUfW$Jtw7|Z<cwy)c%gI3Tb)v)bVdXy=y*BlBR
z?kQ=R_FqiRO^E0vfPYkT?#o-G0VhTR(qMf~Cy}t)?mu6*i_GBHcea!!lDc}lye&;l
zITbI*s5N=;0LVyLc0mVVZRK8i-Usk(wyVeP|9TbYVa}&O2l7&$#rxSlj~`h-?0y~m
z`?(2W9ozOlH0Kab*DU4x5-nU_kU6J4S!P`f(Q>c&HOJjc$l<kL*M1PHi}}YofEuF(
z!PdgU_-OHRCJheIY&{y|`)hMw3Ku~gsUEsYD5$uh3I*~a;K}oGoPqKAePs{kyR0di
z2ZvKyN*6;D)MnJ5&6JqQ7nxzQa|SRZhE2^%hRa>cV%^n#6H>R%`OCB<`b!tfL_b^U
zGm+|YogFvCo;=?2eO|9Q={emR%viFs@N}fpCHC%qk7FWkfWTlhf!szc9s?>4iQhgv
zC3}sC7$&Tegd=;R<maB){lp@V-lNF8-fKUA!5JnO6KKZ~5VC2Nl_UvtKxrC4o`N*f
z>iI5us)Y_3M5u`EfrX{_gCvF)bmt51{s_x)8t&57@;t=-phq#spmOfD7>{}41-h2i
zNbz{dA&JCKXqs>JFKXgX-h#o+iVp5<<JCtw<y5A$JRPXGH|X6@K#qr?Wud#@0?h%S
z3m|WzqV$_Nd<5lA=*ITf+X?RcV=z9Q2=r4{@werz&7r?<NRy1J-i!+yV6|m^u%Y_Q
z)JXA^-?M@GTh;O2L*j{`DWu@}o9(GfOx9@f+jeRFb2sU3thS4MOBN@oARrgYyf1dD
zH;L13K<HBQ9>?j372}Pti#mSgW~<!{H_XkSZYLT3YKETg5uh~E5M?W#3Ool`szEKb
z>Wfu-J%p%tKi)3Cp*^sFA+QSC*{&PFGKD2u!~zD!to-u5*05#6Z-c?(Q9q=cO)SY0
z)=x>F5H7INADTV<gfw^`!)13@y;C~x2?+hCi73@u2~gtq&A)V&vza+<@2m5z^}fY0
z{Z%E~P9f~FrBRpj+8a)j1=*?}n{D1)g_#4|;YWh*?Ow;2mi-GCt1f)w06!7irL7rN
z(6XNH`rq6$oGmbWmFyvJGPlxg#=h0$>dLB2i7j&M3$z;F?hBbI7{%n+Y(C+1nM%qr
zqnjfmIiSpa#jWmks*G=FnpEAXIuZ#H^AFWe59JYkcpBdnC;sLjTHhy;Gvxef>fnPC
z)+9;<X5q!>)9zDJ*PWPsSM$|sO6Og3RI%)-rN@Wi<XJtQD6{+C$USj`O+J!t9w@%+
zxfoHNt)l-X{98WmWxo#V0w4ffOT5AcrnklypQNjmUr1BQ%~J_k4GrxNBo!zSkcs5v
z`qE+V6vnIf@Kj$C507ipLQYN=9z<1ioCQVm%pvFfxlj~bXVFG@`)d48zpCD572wgE
zSk0M#EraG59ORsJ(-))vFpeJfk6v5d*DShzLvHBQ>q>&-s5TimQT$6>c7h6I7slM%
z+pg(o(i977Sp@{BWG<C=dzvZ#I;J5H+6!;J+{L+QYi-hZiyC5Q=b6m=<H=Twk%Vv5
zW*lgG9kBb}rpSxVUBI7?DiS}!9KsS+Zf>vIM?gCBmEb^qm!)dh1rw)vsKLBO`*?*n
zPh6fv{1U~t_e$ei;$!A|am3#|#al51gfqF_-*fKuK3PhfoMm)>f<IMi$)}w$&UiYG
z@0)fn4Dn8WoaEl^3XRS^^YB25RHrWJUL&SV?flIUwfpyf`kW(DfKNb*FjcjpS<BYu
z<|cGsxv^XH<Jl>**RiZBA+$$$3^!hKT82uZ>Al-I`^qo%7Hb=x;<CC<hsP7IVq{ld
zns7OU{j?I^k+{JDc!rh=dghj1^kk1+%?9T-h6Ek&59K8EArFbqeV5QD1!GmV?B<(W
z$6t%P+C&Ro5d3DbegaE=`C(-YCC~$j@=o#n0;^m*_<dkts`H)izyQowOtfET{pFO~
z^;A#{cDE;@{Jgm8Ohp-yZ+@p}dgk$XVtMHqF)xn2&iv>Zsr=q|next=^ONb1#p2&<
zoT?)gzPH`$6aNHz-}BH=hj<Hk^$!%UD_+B}p>If<Y<~ZucGma#-2BgS6sLmmv7LHS
zU6$>Vh5_O#=AAwSw0-g{@*Amoglx%VGkhi$G!#wcHp=GaH&Mm1zZjOwUi%7BAfwQW
z6WhIQ{&YhecKg@(dF)zqZ4(`5v?5%viTrTXU2VA)&{a-=x3%CC-eR5K7kyeiI~n%Q
zY;WQliRof#zKv(E3>Q4Js+udqPkqA7>q*4FeNml2@SC`dyGrtE19yY8aQV~-XGT^f
zNQ~+=??Dfvqvr}rDfWCskCTP=25|{2-Q;tq7cvYu`NG%7)5F4QKlGWCCNJ$;Ry?Eg
zkc55^ZTfY%<LAATFYVo=qAZzvsvO^no^bOGO*cxt&Y3DtW&Ut}BUHI3&@_BqTeV%~
zq1{pvkzuIeP)p9IAs?E7M)Aj(VdnPuGw5Mo1~hdfs>5+~>CIKFO!17snY25_XEx)B
zcs6FWG{a2|6<H`RlvsQbe_RoF`kg^sppM?BK}YHbl<&#_PGr^YwG?PTec^nK%3m(M
zyT>xc@&DFp;NG3u6J1qVs03URo6t2nQ1yJ;(z71=rY@uGQtu^K!#JOTXS6&#S&)he
zCa2hyf^#waQiL{Cq+8{7o{$~4fc-5iFd}c*FPM;kMjN@e_i+h3j}Df<p)l3D$)Y2m
zS6i^u;b9}qFLzo^kxD(+K|{63Nrvd6z<O>CVXY`BC1ucu^p@R*krWLP@MiTj-#<6%
zFQKc5D?YxKWj%LGkhflo+xiW5CRBZ7K2L8S9F}@?OIYo=L*Fu?ott!x=izrR@#Xj5
zsvcHnowLvj9*pf8=qr|y4G=U3VCshznPRajjQDcJq^a67m(}pemM^xfEC<XNRxcPv
z3np+f5&O&CI_D>wayw0$yT`^=wZKk)ZHZr#=RE$MUl*H;Oa&q>RRE@sn5`@zB;n|7
zM(U=zD9mQtRkUkvj&7CDUY$&G1Y81v%>|j49Isa|7YOfh=vRjL+nIixZ}$rPG%s+=
zvv{yKR(o35@57`JBbDQ7DrZSn>RhmEVG7Zyi0Bc)OqMkD(lUZWC~7Xwqf}f|W(`l{
z(}_N`Bvmg|5uJ!A-DWdfNj*6}aHsgq1vHkFMfG{QnJ0>Uvn3K;`&d4UBR;a)??y}F
zRM0)H8O!%kw_0k0ja%rI`@&|5%;eYlYBW1-RZOhtF&M5>EHXPu!BXh^M4{!kR^J|z
znYIkmk=p`+Z=wM-+Q&VQ%XTDK@@>WdsWp8mkO{FD{ct06qM@?<d?B)Lc&TSph?`X<
zlO_^@R|RKKM!3G5SQYKtLv@0(XU~}FZu)@T`%ns52D>R~p)gdJt++$qrPJL62T+p2
zMc<2-Le*xZ>nOgJEmLq^uJF7{F-th<LfJ+@G{fm|H;hv05Vnt(O3+|7XFIB;xzZx8
zrJ2OIIohmDxYvAi-}s6nX*@l8*h}y2kUZVf#Do0k8|za-^i;f#0XZe}TefUPMG(%p
ztbI}Lze(Hps`%HL^qZ0~Qs$XIDu_-y0+27_35u#~V<pD1EO%f4fQNwyI*;reHDFZu
z5_IDR24dD<5?+70eu>8pl(!Q>H}PEQz<&1Y&}iv2at&d*qoDZJ&m%VZp0aZL?SPEQ
z`j;Pn0HQe>NM1s%2E7^Da$o&2GPG-f_MM;ew=JQI1SnYWOh-Qk^C4;*v-EPGjWj#&
zxL0*{<<%DM?a_eo2JYVQw)Fm&T$e|Ro=U`H#OW)_g;1=g6J@#>r_0-OV22Pr{d|L8
zu8%QIv-nJ}pqXUR#F}FZ6_!m(nga8|dXk!n3FUu3LfB@>i!%j~aaootlF|@ZY5l+i
zqt|;t$#FVgabYaT16E4^?6SunL29GBh^6^l>brU=0Nki$C>Mi9bc;!TZwOn_SZt2f
zc=O;(17KPk|9q)oYIzm2Fak+WC?L&CVvDyo8n)FH?|ydP23+TJA)px~V+YDTt$dT;
z7_kh(hfwzp1~@Y|B1C&-d;^d6A@hd{J#<_8-$L?&nU!(v9Ho@oBu^upS31WdXPrhv
zP#K%+8Fp37%?Pg93Xn8rE4_;APmF1HE$OZuxm5T*dV6DZsiQ`S%X#p4^mUND>*CUK
zg(;B!HA6ZcyG3WQu?zy#7Aq%t9l1m<#;(UU;O#i0#Am4hQ<hsLbvg5B_us;ssj#fF
z`Slt>rbWg&4%+jzxL9pKhiOYQ#<((*GXX5EU%UzvN1d;%nr#(7pslA(A+YVbK198(
zQt}e$_qSbJF73o*ZvLeAQ7D&TW-ZBg_r7!)xq{$vz176q#02PLk+c?Ujk<0bq=-hO
zX{LU#g<9z9Z<CtQhnAGh=ayRJ<PZ>43j)JjLZJ6*CX1nEOYtZ643r?i?s15$XWM5B
zfU`b#RTk}7hAo|yK~Q#O&UFFv&WOHuB3*#M#m$xc^67S$oE{ajT|R4fzUxB513IqF
z2<VB^w5Pxdm}tqE+aA;%UFYX1_@PjBwMEXwQln_5ZN0^(b<%fEOYyK@rnVxbUkwrZ
zm^LoJKuO_GUlK&0=A~s-&Ewnr5h6|$RJ~A%6)w1#rv9!h%c$$wpz?_m<h;6er-T!j
zrwb=5IY;03`V0)@yS70ju(IA_R;K%QaL^JecdWuF{~gu^s}Paa0qvzN1Q3M-$BC!|
z@2_FB^U7P+T4d-b&!i!+b{_wAni2!^<kAWrKDTL4$LiIFxoqj##!de0L2drksYZ<9
zP&ih?oP%ka3RF<VXx0$0tA^?(FhNO||M+6&6TQAMJ4I`53!@|0@CO^b0$t?Y2=2=Y
zz^2&o(%6XsGC=q(6*f4tRasK}wFMp#azoY-M;B#VqqUM98jr(%W#q5CDfjSb5ZrVZ
zk1<o=Tba8fc7Beqy<R52r<8+LC5JZc4w<W28^K|&#|I>A(AU@NBLbtW#>xz5dl|^w
zD)vKcs@%`X-n;&OAt%qG{wi9*W8T)|Dy=|=ir4+Aq;yW}!IwFcUo8wX#O=%__1+F@
zX;@emy~}ztZ@X=24#auMcE-^T>pN$)$2D!ho%OG^<u|*60g_&!UVV?>e4&PNksuB9
zF3xWb;0s>|(3+MHoQlXO%Ek}9(~B=pe2eft&LA`xv8pVK%sO--C6B|Jb2t!nVutgy
zan%rv0Q=P532^KgYL?boYffWZ=SL=I-KtmH6|zbmdT!J9*nK?n^h9eVKm_N!97nll
zz?7F@yyAk`$MYd)`V12zeJlwD=H4&KZ7<1HO*%ag5CXN!s?xz92ncosaKu)_!OEoD
zx`3#fVd^@z9<`E_c)2!_P`bgs-co^_(~QV;4MtpEnidX+SOLWe*>*E?`l2U;z!?NN
z&r)wJBou{AtjNhLxB#h@G$66JRhAY3FlW^?Lk4QGhMxlI4Vz-c3}J|Q-0xETn7spb
zl7JN%xnR>Q9}{C|3bC5*D}2lV0B{0Y=TZKCNo92lv8*Fgz~JV~CDzE~KgnwM17OKH
zNh&{<5`|00^jL-{hc4UB^ejCOaC$?Y=rn0P*+wFrRy}&&N%vqU(1DR+y&6P$E}J!l
ztoBqoZx0D?4H2FB3~);9INXz%MeU`idA}vNg|Z(@gZ7Gvj&PW_08K~p_7CChL`PQ^
zJ&I|+d31xMr^1jWNxANkl^)B689hg#&Zy|8D!Sa;5v3DK8B$24%wih|q<L7?3ph66
z;F`4$&vWUe#$XZ!h8f2LrP(AeoFRUq);$ATgI1gTI1R$ORTO$Mz&ohHY2=a`e{Q@p
zgrt76Um*$Qk%{EHEN1<ougp<x;?awp>i#aTjAX5Cd*^Kr&12Pl$=Lf+BVqDxTVtcN
z^&UiFv9K*Nct^w0H~TxRwBsBGywbmo|9_mlby$_z_de_}ARG}CkS-NLr4>mjM+KEq
zIDpcKba#Wq7^FoA2%>}>q`ON%X+i4HAR!&n^;?fK2l4ZrnfJQh|L}Ou6ML_{_S$RR
z_r3bpW!f~Cs1keV+kX#Bz5P6bgVce4CD;T_)2k=-)@JZhQKF_J)4<xr*jVGCXUg9;
zE;5~A8d=s(CfQleSWuNRu9Sb<IvdCu2<3c>hDX`iw_@jc0!N=P-_90wpPb>Di0^#0
z8#<^n9-P61%W&Kv$UQ9--u&1~1<f=~d*v~2-^Kogpp#u4>c1l!`}f!*?q{mczi#Y7
zNFpb#R$-ILUGu3c>R(Ph=9L_+&Y6f1c#4RgEca5kSKJ-&qvUXzW!F-OxNx>)l*{sL
zrN7vb{E0XIekOjm+EepjPj%o}scnsM_UG*Z^zh=aKIyHUPS0&sKdaEPg)=xU01eBY
zp8l2G@9z!8lDjA?vToDyW4uP|9*H$S))@rA;EFW<*Ehi#vUd0b(XjU=@->U{K~gVP
zNUYKg=r!`U7I<^>J+{2k^SCPQ$$IJeP0;5;$u4mAK!P*WJ<w_tn045xvk1=g^Gb9~
zFhU@^n!f}r7!L3Ix=-?mX08fd$R*Nxpo(AU((~rLp0BW;aks{;^T78J518>L=01a`
z)Hn{Jg_d02_ZIpQ&AnqW8Dm`Hg10adH6znm&D_zoJGU44vGls7{kc|~2$|uS?uY1}
zl^xMu$Y*_XZg$nOE+G1|Mra3C?L_I8ewb#*b}>ctcY7<|QsN-7m9CSI=tDs=xsP4R
z&9K_B1$h6h&_m5HBjBc`o8gCbGQXK%b8u)2B{TzN>$ch*sYIsItkt&<twSWkezEs2
zU9$a|@*nPd;<y*0S@~@Vq6QY1W1f!3{=86^3t<1!sWu5Y7J9F8)QA*y@aJe}V%0c_
zek+BylY@1$Kw2P@z7Wa~q~{o2tc0Y_B3g+UYBbgtm8V+8h(oD^s~%j{u`;6TSX=J&
z^SRvm0`<1d!&O#(q}Cyx-?TF^zFswvkV)gIN{UuklgA8SJ&}k_*`SUzX|g;nOOHW)
zJ*pG648Yg;clkLv9DuVaB{I4Cgq=O*Dnag;`TFAMSv>t)3*2}@w;pqO>yX(!u2UV@
zo^|w}SN3%h2dY`3w8X~ezC=LSqu2ch)zP>8F?OKlcaN;MU~93yEzM&&d3X(w^vUn8
zP%CdOI<zaR#tVW*UUmJu8`L4IZ|Zb3I511@5H54Ff2tNHtMt36)&B;oiGSej`McSx
zbHG7sMN%F*>gaTTeQ}lx0>D2wMoC>%Sm>wZ3P9cV@--h7{309sBBCi@X$?NSM;S=}
zkv{!FmV4Bzq7VT3nK;1q8sj&p?Ms8?<<VMZq|2kQ)t{(z{aia?iyri!d|(|sp<u@2
z{7I%YDKT12)N+RlBP8|c9A@L{;7pWllW~7Z^*E^gdQSM=*8~XZAeA0@5CEKu_55=`
z<O^8Be}zzw<U_iF#t!hKXRr)jt?5CS=eI_AHr(sZCLW{Ys12Z1LJMqxvqqz-0;Wm<
zxXWJod_fc>N8<vxHW=J3``?raZwWdtew{V(Uk@izt-gJAV3>)F&R^2tc8#I<A8EH<
zo)c&JJWC{4!<>uABo0TNnHta_$Owsf2ppGOznIARXke8J1gp-f_c3pZ2+_#wNYOZ{
zvmdO9lLiR1;c9oYhr=zdWgq4(9kPQYd29WC&E}TsTkmTZXELe{Ph&Qhb4$kf)6R-F
zFW550#ID_WJZOPF7-*85@q-)*DqyB$PFrD~>tT)D6z2X`Hg1Af2%3mUYgW~LAF_NE
zL0Hd0mL6mSGh~^8rnQCwrT%8W`e<A@*s_jy>%gh8_`K}uVu!7_ODX1isgo&zxaz=B
zA87$^FV|TCRxa%V?(Ln3i$5|EHOD;24lsOPbA6dwvjcfhJ#4;ROaH}0<G=&DTd$hn
zFb9VduIn<YsoSPT>5v*89GurB6JQ8UP+*EA#hRMXJrjuUne{lHVuVlPNY@u4B~bZ@
z$9ZK)eafSDmV#lrE4KaI-!XN0F0q*@xG#gi@*AANbioS}L$^q{D}@_-fLb0_3M?J!
z_JWf%of@xrg8b*(za_?K;S(bq(?$^b2T^Nk-3+3luTM0Ksntt8M0fK%H}htB74W-G
z_EO4F)IQ5V2v|&HagSZ4!_5%d?Q0)WcJ|h*5_kO%+`E0{H5-;Wwt8$P!Zh_8=|DB}
z>mXC~yk82a;%2;EP)BcV!WOMn9Dwl<Hlwk;LRonGfzThWWK@2of)9_6gcC7Fw-GH}
z9XJ_4Tc7xYGGTwa`t}F=h&&>BshBF>jW4$TQ_}oZT_-<u_hgoUo9_f0ZjM0+Mel~G
zPZSnk1$6jvsTRIUIkLjTbH(sN#X;#hP67{ShPU6*(X{{m*7Dp~<8N(U%(1oek9bXd
zQB@+9b{V5LRXCR!Lb#TwsC3EoqLTR#ZCD<>;Pr5;M^C!m0_6Tp-+~96$N06($c|2Z
zV=#XV`)4t2Y&FsOF)mN7w`!X0Kg?N!r{*+fI1u<6+NE!qHqQ&ucr_BWUY&O^jV(J&
z<Ms8OB(1vJ>`5Z}OC=?Ax?Np&A1~a_jD_tdVSV$sN5Z1L3lqHzf%G+AcBLP*xIeOP
zY3Af{mDb<=^u4%Racn^gKhw2mc$2-f%;wL!=vs(k#QG@J@;Yjw$DQ}*UH66HGh9bK
z%btx7<b8R5ronn`NS(A9l)UFLrgIkf_|%C5+uwquS}cuRup>v#;CYr6Z8X_U_5K<7
zWMCs(cO>D>;^0T9jF_>^=M6ye!TRCM^A)VQ4a?#1){-Nf3Srx$(ROo{qn;$Bq~`d9
z^qq9={3iqHFP@+2AfjX9wYAZE3^f?TvZ50yP#_Q+%5Y8WOH`1BT(Ygq1;*`CaP3FZ
znsBZ?SMD`T=%vW91Gh{6@a?e)$#voh%?V`HLac7=vR^Gx;*~!u-f^#CJ4*#hzQ^<Q
znR$9Ffc`9hb!##i*8ipgdchxvB{cOYX~~pvDzCJB3-pxVA=g`1d2@*_NPXna?XT3V
z4yv(i)GZ}iW`wMNb?QVja+0}wr|mXgzTqPtQa=IdtCgq4zFjwNiW07N?xAN07`*B0
zH7t&Vo6KZ3oZS6uTnru%BU9I2%|EMp$P_lkAri|oe>_@`+M=nbM*>0EDttRNbkFrS
z)E|>pF6j+*o9$q-wX%Y?LT5wuqj_~JSxAFhl-;O?k33)C=^oZ{1`-_2?z;|ZWh#(V
zt`WkJNKJ5f-?RjFWu&8{MA^55NWUAt3sRh_TNu?6OxJ2gu>1QpjVq&d@4kjs#RAu6
z)$jPwXK7|lPEUq*RAg16wGIA-w=)^Nwp)H&V7RGg^5XSls(3J@Qj|^4<wcT&(*Ckp
z-;*O*Oqc59`xZZ3MUMtFJNwG#Ir?WbohY;lz`RoL(Snm2C8ew@w+zEj8&SE`gkUO#
zGP|n<GB8JV(BlD;rucv8`8T_u!vc<*_g>5ez4iMRW@I14ftsO+J`<>)<GETiKv{uZ
z<E(Xb<kT;T=e>(Y2blOQydrNuhR`hk;p7cM$mdpjZI$2kyD;n?7vK539*RUCP3rmY
zZxLAOZo~`-;JHr;B%E&QJ}uW}jE=bIX}mBMT{Ds?MNo;<Ti-F|w{_k6Ht3$1ND(Z2
zW~OZX%bRYfxD6*;C^$B$t8JCE%uBIW++805^LBa5KJ<0rZEOCms<y2^W!mC*UDZcK
zF1IbziefT)7qYXbvZZ&#Izu@{zPVRa$kWNW<4)jaiI5bsQs*H%5k-`GRXKNzt5S*V
zkv|uE@A7-9@L+RNH-5sr2;jZ-aS$F_a$a9>pL5mD2r%=Vee+CPbZ209)6?HihvKw6
z=O0!j1jk3V-i6Xo9?>%`bxakanPY~O<JnNkz}Ir!h?q3P&5IyaP}JyBq>4W^tZJsc
ziR~=xdEw(j=I1Y|Ys+8d_KIlmsuTN;<m#eSPUcv=!rL4DKtR2Xe`0rMZdZJJs%*zp
zJmvd2LP7;dYTSMNh3nN(nGaGloZhkv!u9Has4BCmuc#^JVT$B9VPU>LP4u)z@#Df8
zLYG;StyyoBQStmHqdl4GLR(+311ia~NaqOu1Ilh%BS)&p=mKXd2$?xUlLE(eqmxh7
zD7Fi~plRo1>7}gr%y2DW#I;9l+Zi%GD-31*vz@bZ2;*OTidw_!(!!EWEu*7Z2pgW$
z!Ye9D7FTXlh7(_i#%icyhKR2G5RDE`TdI}VIfrB@s_Th(1XZmAP2Og3I&jJ%f(QvR
z+y!6BPJDd#h<1SGRwM7u)d@;fjn7ktd=FVh#<_V|qk9IQ{#tFunVpEtag|oT^-=S_
zyR2yl`?j+RN$uoj$S(9k@mL@73rkd-rf4dL2sQ<V9w^}0sPzz$YuPRpOI@vuH?E#s
zzPjmP;``f?en%<Dr0HNS#|FH8mO|sScZQnmR7djHHY0C0IaSKDv;}~%#b};RkPCcs
zI<J3OQZ3!cc_d=0jk~Renv!re{9KYyZfB6=S|p%|#LEkD6umAgF9?><x2L$?Q&Zxz
zXi)qXLNr-TrCai@lFBQj_$oo!y9w&u%N`K5nPuF)?W=p2w#muulZ;kt3BDC6sT2`6
z-g$M55LHO$$rcKAP7XvUgK8-HT`r|3(Z<))5-%gaKdOa9BUO9R@DbrguL7dLtG9XC
z9Y?t^9#<tf{}5Y9&NHzDT6};<txX=86Y<hoPCOU8-_<`ex=!-awv+0C`{LrdtikYQ
zjVsh3NP<B?)iA)FOdw!1P|rW!*Y$y<Kco5@upsNP(aAzJh8X9UNT-WY_hyjiknUxz
z^Z+FfL;vQ_$dSvGh&|?5$U)1}^|1?ptG{bJ<s_lO;zl|2am*yZx{OHm>T>lvcAEMN
z(gX$XHmG_)!RAF1G~XO9VFp=^zL+waw0EskA>)fwlm|xm$YO%u?NGIgxLTy_h+csD
zm^Q%}##U^u?Q9ydLF?Hd36qLeIQ2UvUm+uJl(|*gtZvKNPUF+J%k1iiXJM$^PwYm(
zM|kbHFKxib*LbA&ZzrEo%Ly9H$@5bqOVd++kL?lBPV{?o@{LiN;#Jp_8##XM>^GLx
zmi=-}7$q-B8qhuZjhyHn-t2nX#;e)sC|jEc?oHiQmSPGvEAu2%n<<ua<JT9P5#Yjh
z>W$<HgyP)8U@GF{V;ui#1xt(4OQc-6#=@0L;oEdgNZ=B7<*92?QeDK{68h_ffApS`
zuO$fjVM~x-tBfc!v>321^zjiMh*0kc(>_A5>ES*)Y4c54u*1ab2w-dU7lsK&3uAeQ
zF#LI>=C-cg>ED`cf@5`tN?kd*j+72~=+E^zrtU`Djx^?lX=`{~zLT{Q6ze`7!bsiB
zQ}3vS4xPzP=hPHU>$R&lAFZX<<U2;!t3ldjxqdUgqVEBQa#kz!2*EKzdq8YX))iU&
z9jQwCOPX3)y`Qvb<d!Sy6jhLE%$_rs!m~j2%icRhw1lXu%(mhaDt+T=UzVab?u$IG
zDn;QUqhI3dMxJVd^$5!Kq&RMxA`oV7nLqTvriRw=g>O4Kl_{g~tB8(RL?6U-G;OTp
z6I=qtJ5&&WKYl*uvy|HT80dAdJkxRLF+d1hRZ^7aT2G4TehlzENWs(#2b4OXg)3>(
z&soY@@lI`nOosiJz6e(i2b3!RTNLTo_%%-=x#RpZUWmvLwx*!lGA;(S8~=x{T7yB1
zK{0X;LC%_{E$6-JYpg*fh}-yT3CPc=S)cRlk`po=tqb8NiqsQbc&xxcsw_||Lvya3
ztW8D4>qE!W`=;Ju#*I;uMtlhy&PF7p1|`ymeE7#FAI7{oOgbj65MSZz(<blw{^_B&
z>-}}&AbH*`5LqL3X)K_4!0kZ<Ng|(6_@RddbL8j}=*{*(Jw$Na!h@})uh$hNf10;f
zocx|#PGi8+)VJ=g{|4`p#5Xu8lyBV@8tdmxUte8zN4eCwBCUQh(t^u%I@cEHu=@I=
zH%BX+m&QWd1=Dy0Q;OccS1+@k7+9F)_NQv1HxU-iqq<0Y<4EYmhgveO!<r6iXa`yo
zxu_U=0W<b6?W{r#_iee$?T0OsE>-9Si>EW#q>0tZUk)LrK0G7w?lM#*Rr~F6)ps@g
z2rE@BIt43Uufptia@Cl4nXu684zn5%_rTH>%IKG(z3H2UCSLp0U1nN*QfN%UAWOvI
zRsLB@iI#irG=AvH2RW;G2$jrI=mfE)>s5`QW~H9o8m~EzbZu%(K(tU&I9J{>Z@<eG
zC|#(bf0%bC-%9uTfpK68r=-d83x(hBS*T@}aNAlTG`<ya;}NZKZ7=#Vy`>c|B~{xq
zp^7}Tm#lsc3BAA<D-Dk^XRVlzM&3TCTM0Q+MsJPJ_GwXC7gtoE7i31uGNjlKCA8kd
zLdt1`nYsVMZ)HFi?_2J_Ml%oDU`6+6zrdHOQfhf@lI{5CBd2b}fAGB_O$S{II16}M
zNrlbRc*?dGQv_t{$9>z+gM}Prh{_6?xe`4M2`Sc94>c-J5Wt|d@08&wvC9v!FFhh<
zQuSm|S83=1<tEnWQyVQq!|EOHi~`F@lQb612GBV$brKk`L=mzb=o42<re<?{bv%0y
zk@`Owq1@BFpdZw5V!XiQ-RQidhuxim;X;1>C75O`2@m@!6Xs~X-5LD+^`;`z>I$f1
zc2d<uLd~$xeKjg}aFCRrH{WS!EA!sx;>>#!<HZHGb$yq26P%Z@J@p}%YYRs^F4mD=
z-kr8;E^*{KA~AeFb62bAuwyi~e|dFk)7xjeFf$dWyvO^S*JF_Rnn3bX{6Zl&Oq0H}
zXCJc&3K>so2??#DpoNuNQRS;2F_O#7@>?x>=*DvXQq#q>(!Pdo^HoQf`Odru{Q%L1
zMqeT-AcwhZdmSQ$Tglz=B(^&(Hc;XSLm0>QS>QR;)hR7`+*SiE-SqNHj4-?jl{z&}
zK0e(m%W0nOB#iv+s4M|Xrz-kI6?4xBoq;P-6zMVtmR=~y!!-C#v~uURbI^k>ceqL=
z%2j`xBJ@Ue<>wkCS8dbC-}%MF?VPa`M%o~n)_U{9&CQYQ_vR!k{3*T^<g(?QwKs@o
zeRXSlYWP!vM_zW+p)gQ$e%)Ur+=P}m;_dUCn3S#e<$X&j@ex#Skbt5yA0>C~MJdb;
zq5eS~?A-xqn8>O-gb4LiC2m1`4UHbH@jrWAjkEd!+zir+7wfRCVw9I_=@9Lp>-=qq
zKoyG$S!0Hh9N*id_re_u|056ZGfF?n3)0_FrrV5>4=^PCDybzyV>avUWsD|DZZhjL
zH|a;vRK?@s_4R(E7eih>z*6(v$$rhLb-{CD+4b<YYb)vH_JecmNowILAM%bU(-sfq
z<%QNiP-C*}%1@dW_!8ta7u!)oI9;UPZMYh*pQq`-fc3n)``~54au2|R^C<1b{(cm%
z>qHlZ5~f9>{d~G-5kmPos71Ye$lukDk6*SJl6IKz6Qcb!gmJU43LOBwbj>EvxZAj)
zM#*ed{@b!O1w+I0W)KU}2l1oSw7lI=KeeO{2reJCm#g%9&bbb#w@L}eR-wb_xz?7U
z1Y<394Uu{aWeE~`1F4VrD36VA%SXhMnmW-GW)hZ_;>O!OYR1;*Ax!^~vbuhJVQ00>
zljKrvOtN!-_OP52_lZjUw)1S{46^BGxt{WozwvkXe%V8Or-M;yEd@m?aOne?z**2+
zOZ%-cds*(u*@`9uGcX>iI+UG$oDCKdV1ovoDIQw(?aHjm!{;MR{8H#I)S!Y{g~D4V
z5HQ2g=8vQ^Qnu0}pQzg@c*q2`tB!K*DeKJU<xXwwP7ME-_Ch(+@+{$6oEeQR_4>-S
zO$rWJvjpNhAyRNYiKwL6^eR#oa}{kUb6uFJhxITQ_E7wLY*kN?ppX!hYHY?^c0FW?
zr<4@L?45QO@Cj{W67LQgu`J2lwgEY^<>;o8Uh$nF=AGEDn*x`sD;yaAWy{gI;Pvt_
zsR1d3C$Z^+X=xf_-=}J9K2Zw3fL(&HE!S!NCQ<r>gAXx>xYdO68W`EX@_tv{(yxEz
z7$O%r_r9XyqJRbMA$l;x(%FRARYw{VnPOLAqftMb7BNSj`Y>*$9IH=8odaoU#H1@z
zb%i*A(Q<rzHjsHzYZ<$cbT{O<rWB@_b?_Ngez3>)x2E|irJq;SF<)5GAxAaXwYjv4
z^DREpF~}Bmq92+vmjNck)U~g82Pr%h!#~m@<Lu2S<Xz!tq{d;FGZ)#?M6A75X|qsG
z9fl^AxtW<{(v>YDZ<U@@(^=}N6w9sqmhA%{EL2P@e{30pAmy^+9Ud0*mu=An?x!X~
zTQo)a%6xkx&OL>td<;>2OPz_2S&AC}a?=3My~p%<Z&dQOA=vK}>S=<#{pL0M8Yk9(
zklIa9RGMc4WyJ(#yAFw=D*SBui!<3n?&{2OlX}>XY^OqBe}mGC+5k6#=P<ds_~bI_
z*)R>8>*k{oo2hq}XtrR!bV-87Jg)T&J)M1pj;nW%@UuW^2VMaG;*}m?R#eYjB)h%@
zT$t~tx334GS>!G*f5yy>rlXn@D6OPs<FiYc%uh97v(qe&umnNPDE&K#C2Ad!cse*Z
z0{R|mXZg(kq*$RhKh=drlQz1?Z05VWP3tk!@60x=s$f6R|8z6^<Jy)OJeF<3r5R1*
zL+1i67>Edk3&*((n&(-*|E*HFOjA{5z)iitxo<{)CWXGPut(s<a-Jn7grE&X{6YvR
z&h%P;^3Gg(_GeDcLgp(fQe|ol#g$(3@yw}K*5wic2t5LKj~!*OJB>RK5*^M{BB^%b
zAWb;bi$sIV5tAk)`s_vV=BAZ*l7>UI<!*9N7A@|;adbhgWJ+P@t3vLf{A}2hino3n
zJqyZ&R?F$ypu@)>6|$0^Bo*HADv7r%00C-@5=yLHx~`v=J#L;t8fH7bmUKAm>7Zt1
zV51Y{NHmdC4BcJXo6{$bIA2>**?#$2&igSchgE2m+H--rDlNvY_4=|k<`Du#TDJBu
zO^NFCDDAN<D|s-|T4)EA4}Jy&$8=<_2ny3C&oREJ8i<2q=t-IM-#&)McJ5=LHv)uJ
zynU0%Umoo<3ZczvHhK#&4!hw?84TW7*Lsy+z>=DG1j?WUidKD-H^G<;MtW7{;`4pk
zC{CKE5Ot~vpRc9=b|eci%LhL3!Uo>jmY$nEOIA*&@K+F>l{M$h?I=NVUYXI_0Q^kF
zPw54TOKP)^dygPZs0yIv+8v)~&SE1JN^7D-r1IiIh<a7%`(31lPFK9y+}^dZ`d$yO
z+?c~1R8K>oz@;}uq82@>CC|$<G=YBCfV~i+qRw$}%t?;2A+*nw|4VNzEd`3SbXuP$
zImU-oCiDfOvCOHS`+hmSD!8psY}H1p)De+QAt~z={isdOKcHS<7+LPPR2uUy<|cuM
z=rz{+Fnxb*t$nl5ZgehAnl6is!EEelN0lH6!ND|5dztlRh=VyEV2HFlN3b>{5$dMz
zc+DU4wdM+OJzAwP(J$`wi`O6RFADW}$WAg_G@h9`)#P7aNw5@gd1L+JZxxsF_LzC)
zao#?1H0*AuDgGIrCDGXIY;udr+evs~k{aHwr~%gRVGH*OMPIAL$$U&hqMJ*F#nDgX
zy}gXDM@_n_YB}qZB(O?`V$_7Crmjt4p-fp|5!p$Bz%gc(ID%QfgdB39g^tZ`BrVwo
zKa<B@A3W_<;#=8TBX}ZxOLt-bYR;zVXEqZ|ZsUa}8HJuxsjgt)pgbH&m57>*GtsG(
zrA4isLQ`k?hP7r2mysW>vtDwkbV6;$-PM^2H-cl*XMBH~DD-((P#$e{`DiU+;q6lp
zgjTN?%|hf?bUx7`Y9&k@%3J-brK{#jC@ay5ifoNWwVh$=vUjyN2+H2;V+6DSxl&~s
z<;oJ<{1i{w_)etBqsGG8bGDX@NtL16w5TA~Cqk_snL}-v@5K$@!{m%IeRL1WsiPbB
z*nqUwl*`<O+VHyZOIB8k6%}qg@!L-9&IW;4Y4OOU<1aDK3#3ao+S76^GaHxCf{m;4
zQnSyI9dmgB^jGS^O`;_TQR$^%ok?keWurS6HzwZ7VB!q|C>a_=)i|6G3Ap8~h;8y6
zbBx-UGoMXN;(0@C-nc7A%yNg0X_jWG>!P#le8xJHMgP(--P(Sd*oMMjqos!}7bc^G
zLuTmDW4CC`8w{+hvW1=9CjmmKBxr|^I~q3=%O)sWn-b*W*C5JSX38b(CosOmh)A!R
zeJbVIq4$V@>*!+zw_z2^ubFai>>1?LP0v0ouRhfAG+n{_F`@*duFd2TbwCGGr)gC|
zP?g4iMDYaY&`b0=2IDwobBqmtZ0(9UdDHcaZQH8@o@*)4bENysP&}I0tcHGIt;S`h
zaS4tR*}0n~(5Njk?13j+T=?WefWS=5n>PAD2)^{N5G|^nW1=i}1xu93-g8e1_N0wd
zydfR!sJl5q55=Z+`HyvH$6B~s!i1Z}uv;c+y=1@t(sK$597`WvPH6~ZuZ?~}+{f|^
zf(=qn(bO^{L9-DFDDrajjF#3JvYJ2Y8Gp74(l%G?@IPQ{@BN5<kB}x58g%gKHu-+V
z`otT)vR85b?5~9mn?y>lCOu0l_NJaYfRZy@b3MIkW!f_dJ=igEi#qJTk1-jS#w>1!
zxbj}AYG7Uc!_{gY!8!rv(6A&;@pC5Xw7|H+lX-xlC9gXFk!md1AHcxV9NF>BxbKy~
z081b!O%3JrEg&KMvu5vqIV^4SSV48bGyhtZk?JO>CZ{`u(#=5L`13uNO?!~u(vrx+
zaVYIdB-xuaZ9cK)oK$BgV!N=mS+=`b#U<Xu^BUZ;zz166(MU;oZ)2n(q{(tc!d0Wq
zos?#zbLq32T58*D_rnfZqZ|V%&)4s)S1GXj#7}Bt!`NPj=6)1*H02oTur;5v{=*P8
z&3dl@l-SHE!eu47+mG=Ln1wmBiN5rEoT5yY=NLUx60>_J^pF9PfimY8Q&i5JVbqIT
zRm$ONZ&EyQzE(aYFX80KwH}4<Icu!1X=Adb<n^nzZd@}>kj9Hfg^%@LEBc0iCrdub
z<0a;)Uf^R7rWz3yiPBDVm~4Bon|z{;XTM#WoIb!hl4HgZ&{q$7S~h(d^w2!a+h1)u
z#N9V7vi`$meQT_UHH3#^T9&mTpyT-6=<(8zhJvftrT!@D=J6U^8Pw@--d+Mkg6NSc
z!*TF#7VG7N;x}VXyM^!0yxitn?0eE;SxUdVti5ZWUuo$5B*{s*)lrJwX^ijX<Rg^*
z_}amJb+MldljwVy<W82Wt7>vbvPa*PmMYRDuOlqinNujA<hph-Q2nUuCr;ubl5G}V
z=$+%4YT=M}YN!<Cy;E~%<cJ5e^#zFRo=cwg@hM0}6Kz)<@wln(p6G4NM{XeBGZjFg
zAe!N}_&mm}A3NKO4iSao$fN`F`bX&p$y8&1`VuaA;Kesy`zXv6q(rcch&GaJ1Ri79
zYA_U6ie4=qZ`aXY5d(p3lj@E!A>;Pw^nq(!AruK>OAUOctepm%D=|n3s19IiN>wSS
ztBIAd|6-gzC+5AWi(kjRmD~I{5V8wtHWN?h$Z_cZ@Z@_nF(A1!0=W0stP<dt+VVn;
zLpI<U5>>=Rp(JDKKlJTlBnw4&zGNa~C<ivD4JC?H=4C@ZK4o8!a|_j*dpa4LbTr)b
z4p1$x6F=HwXub_Zn-t8;8~#a8L=-7Mpl`zN>Zz9eCZW)foid?vd<B!9Ahv~sQ<G0S
z2L5G|4hNM_wkJ<{zEjAH?s)2X5$T$W-DrIjOh71o@7p>x0iml0N)wmje_e(!bBm%;
zJn>iUu<_9jSmT_&CDiGtn_8ggWw&xANb2?Pc&|u-!=4$>00+WRs9B#ybVlo*M}Lna
zZOB|DeJU_#oL36!**2fAQj@5vPnU3<)5Bp`f3>9W_vHwvWDzzrC#TiNc3;UD7!0P8
z31K{QqPQRY{ZuWRFrKe8t7@`@CxPX51Cu_Qc<FTyz6c{j!}2Ro$z|2;t`(cL9tF`^
zmpskIfQHlN5!y=;on~!2PEzu-r}o+GUa$&HL;3L(oGK~mpu(?Oq=<ME{j`!5y3SsF
z!rJODw)7~{mELDBdH;Um*JT6<2qp{!hh6mnkin4{?PB7oJ$+cCkJd2imN>{_hXFM;
zM}^k4Jy}Z`LCVnzWhosE&o8&bC*<?@J=iR<yJb^BK>cE1vt%H0?SqR`Frqq`Ytj#0
zGCyhK&47xVm>YkBJPtSUYc5HNYz-hCD#+0^6d+ky#SppCug1c|%C#%eA0l1(V)K%7
z|L|_8>J)nWAVAbQq`xK8!XxP~yVtwRynD3nR92<AjdIi@XVb;juu{7ol44w2uK%ej
z{?0m&$VnGNVEd|tpO{qsQL>=`6~pw)`17~+PwMx)a-h+?b?CTb7=q|DFOS2n+cxlo
zOLY}lIA;CD1qcdT9|T;DoNF)d|LMLt;YCy)7KE3vD&9UNxp&9*w{-Cmrwnfz)QNdk
z!1-^#o<SC#yeP+MNb*rSoW_wBe%!#z0WR%8_Alq@`{mQ1%+Rz$CtG_MpD^)q`?+6m
zhdPyTWeT>e-}etN&a;7eg(DX?J{=uG0T($`^c)8P{P7jZBH*<YJsSUKGnCX>0dldS
zCrN@Hh=;btPKN*TJzib7GU|Ev9>xBDtV0$qKA26>T!(^-{MmQ?m&bmM^hDJBu)m-1
zzK1@}i!7|HvV(2sK-%)#O?%m2#x*=0uI&41`d2gj`EUscKOOtna1<XdQaKy<^MBwT
zEzsDpi+lLrnEB!E@K)X0KHtK<1_$lliLm`LuCliWD~WkH*XtivkzRb?0DS2NK(kiX
zrvKaUA8)ziwbi!`(Cr&LOcI`so*&4h?N;!E1UQfv{J+m;g`^mOlM{BCR5*_FP9}7R
zVEp*6qeP5{B25LjoCdgvWr-a`<Ufm-IB_}~rZw>@;~s<l$IP&?S^=vd3|b(D0+c!L
z{q2l5(~uN;E)w)>$I*S$=+%VkweQr_U!H6q#hRvZ)rWF%K*V2i(a}mef*%Iz&RfU3
zzf6r0Wj<)LPic$9VF!`Yu~3)a$hlvJ@aLKeXZ6A-L|7A@7#|MRR8Ek@kBNUNWPTc~
z?qbrn+rLa3FRNFA<^t%bswR^*!I<Bu&H9BE_lxy7du5-M&#3@5i&W(BCq9SKXm20Q
z0#3I5QWIVk$BZzfW0MH_Iupnq6~0#OzufVsb-gCWhu%`^z+ui5p=du?q;p9`hydCp
z-WB;(UrbhWaAPWs2TGhtgjhg(nvPZ^F}IWLf3%+$1?K%@qPW(<c2#Xn$8MMrnL+~E
zZ5A+23Da*k_W!0u5HDq@0yzke)`x`w%_E5uGRH>Ry@K`Af9(I_&x^nI$8QaF8mS2!
z1$Eh!-3rg`jb(d>KYTQ^uQ5jX0pa}|<DTZkkNFlk{Uo;<fw^}VJKK(vPG0+$p}1e3
zk3ZHNqrKAaI=2zCS)1b%=yC&=a$;!xz5NTE*9i}Gbj(u_QN9K42N&z^$mjp@-#_ml
z!to?X0_ku7kZErCG6l^vr)4K2Xns$He(?S0H~ia5C#b!&w}z>g6@x?<KYA3L>fDQx
zS_ol<xu-wk{x|>hm+zg0Own|c{W)A~%JSFXZAmv62RHV=&d-tk`@Z1<h+BPYuY<FG
zud_su{rIgp_2+B-(}UB<fNSDpoWmich#Eq{mzjOK$n!6Y<gY<<2jQWvRhelR)W1Fu
z1O1w==k*4y|9QLb-rxv$-aiw@dHsV5aQpd=UP@d+?w_9VIPYZ`IzIfF3wsx*03n>H
zqhW!#+wc9yFMsfQz@RLscR#}gZ8~ERv>7&GU;g85aDz(23qSi!-6{;Xr%sT;2TXU)
zwBh8Je)^1kHuQQGF79gC*bx5r?Rbgcv3=6(QvU@P^4CLn-2vk0FMDuT5KMu?{o;SY
z+x`8DUY|gWM_#4LWAByQ5DlSMg+c%MyK+gyp1v;@IsI)ZlAUlJhnd!Jn*W^f5@eg2
z;xO95-F^o#)Mk5o<-Pm=(;s$TFyqvm-e>oQ%nV#gKFQ<%mUubdLJLFQBqVYO_fV3C
zh!>vvX7o>Y*}M2r8Y)moN|@i<Gr;lS6BZerll{8|>c08f`wn=%9k8eAv}HSf+}nA;
zq)dl}*fIX+?d)E`+jVk1M(r)1!ypY)Nb;ZNb(~iY?4k{SDt+(TN{Bg=$GrOgFlXrE
zJvbrIkAsFC4Htt!>dtaBz%6>*VBzfj$184VfIn=e?i$5Cqzf88kfv_R{?FS@Ad6>K
zn}rW&^oQTT?T@Dxn(pc6{L^6SNFhtixBL3u7_b39T<8*S<bN)4Nu6RCl#!53M%+7;
z%Yt1t#pT-K9((VC`~Tp*2|xQir<WTiFV?~bABbh4+4i@##a(l6TqM(Ao>s7aFL7>8
zhZi23Q16u{>;L)g-jCpqEJQ_(ctU=UgPX1iqclU_z0a8b=>pF|dMGL?vSjbk6I8+R
zxwtdF+JE&ud%#)YZ-_MyRy%&feZUPLOb=I|<NwbYcTtB4&g*QR#!c`x(yK3xW2$iP
z{{QqxxIMhf9c{dy6Ga08NmlsBc>Z$=Bwtg&kiWWVcNq6jUh?2$9Mrxn{L@{28qNH#
zNoq+!#1V~P!hdai{&Q{r^-K2?@Gkq)CGLn+lB3L|_kZ4Pf(l;F;tpzQ@BT(eyS(;!
zoVUh#|GoeJ>kmf6%^gp;yf*}PvM{)MFG&AWfa`!)3b?DhmH7bN{Jy@3I3hj923+pr
zAMfWSj@+*5s{M7`jEf_3V?I~yQ2%QjznGaFB8lag>VF6Km<N#~N6XxSPKAAz>BUV3
zY`e8!>&AP?`Q5&#J^8Qr8_*>$isnjPzswYWcE<z3m9x+(h*Ktvn+uv>Pn4n}$oAij
z4cTX|3l-t+MI;M4fP>kKB0~GWMHHZ_!(LGVy(U7E(@G0_t!o)a#9qx5-qrzfpFT+G
z@GOlt&kf&+pxghe8kkHW@zd&i*KT+Z*KTmR_$%o%;3RLz;2?07R4)Gzf=2>1Bw$O@
zG5j7R$$rHG4$-~1P;aOL8d;?~a+k~E)&nw@ynb-uGrU@R-Yi@hG5=9V8=M*ccrGJ+
zHhKnG*I{5shxi_B8&lCqC!1-EK^;x#v#a!hyl!G{bxA>h!M;sb(jO*+_`$=y8Pi0f
zXnq;plY5`QA2eoOpAHg6n)m=IFsmci-G(@R`8%{@Kr>E1U$&$D`PKh3l@s#tptBm(
zS^Jev%F9u*0zy#YpbkYRsh7GHnE1Hcblww#_G3z&OK`55dY){v@2W(HG4TGcQ;Vl?
z17L>;={cqackhioEInv81MhFP^P2%ASLtbzFXgu&T~e0MBi+z1<!f|a`)Oa2%)}Qw
zEySP|!{!>h=H9gaA7A^|u7qqt3e*nI!dQPbCO8q>gFn&CJH@uQ#CW0q_=LbOE7L0#
zSr~r$6A{<JYcwi`6YVo2C0IpKCtvsN&36JcEwxg+&G-Vh9Dh6m8@NTGTNyh@&)>4n
z&i0=#PIC%|_>!7=CvF#+V1y6)?{M(#&y+}NgP&VjUX))O#wR>2Ye2Zqate|6lcBE3
z#(AaRA%GGZ6ue)LOU)yWO)?LnbcgyIcD17a$2)i>!PVvaT7I6_M3AW4SxFkVBmZ!X
z08@dl;iUvSg2>xQwf#P|#Dn0ftjei+M{(h{3&>TbOH<Wd*c%vlx|TT@?(Nb_iX!of
zipS2~5kJSsYZiRubbrn{9HYMcI1)WBP8K=rdsQO4r(ikMTvF7sSFO^CsVa`)-WiN|
z;t~9xG!5fVh0H3r9pgcJq5W4l8Xg7K__cS#o~NS1L-S7O&y%svJhC%eHfrLI?XRif
z16SRUhF`+!-;tg9fWN4h%rS<j1EF&J-f+SM<}Ou>={c@%?_tEJlyjF5<EA2j%L=H<
zNGgKV5)oQBZ`c$yG_K@qwY}UK#-*P7f`PA$?R-a?wsr?BC&z9$OFwk%gC2}X^UcYn
z#|`$Gn<O#$#LQ;c>?FkRP~_$V0=f>Cb4~FZz%quwX*r;v5k1fg$5~^%dx*0I9N-Z8
zh%n3MfK5HuvM4bX=NV8Bk9cak(sMn+exToe1_bHU&0DvAZ$LV*v`<BU&xNtHOpEFM
zxkDeH*T!p%Q4faG=B~?UmY-vq1%i)4^~8hU|8^u^7L;p9cN;B0)gMuqL&$F5zGz$Q
zd}iLhMe^VWH@n%jf9XN<xoAQJT2QaO-L4%1VE9-!fGW}Vk0Pb_zseyoP^%mv&69H|
z8=`O=^6}hA^kkoNs=YYbFq{LxCF{n;%zAE))6q`D4tbiTZq5ZDjiAZ2HAonS$e-AA
z<rcN!3#<XcO%rM9St#A{{oUK3i1Pldyl;BUn_bUzrf_NWhW;xqYYipj3=JaMwvGH%
ztw_@kq~XT=(kIb1D0V85_cz|oxLcinxg8~u*Tm{IxxO^6U~ot?FZh1~b8z_A!3?ff
zb_e3@TSpD{=){oD3Y@K!a6GZn`opl%sr7j@x_|zG;01-m@3sj(J_zi)9#G(D6+kZ`
z-I2`s9V;q!hiNRW=F3*jj6xp}ZcEpmo;pzD=-pU+!35dg$7zX<v#?2ZmagP1B5*#A
zDWq2!Yb^FtP$%GcSx=M)B#O-#kCFvDeaI&bX&D6ay+HAd_RgqwD6sNf*PMAIVD*i*
z^Ev*UR(#}{)%itnPW$NpiEHehN~-OMBbc5P&$ehGbJ!ZSG!#E^o8L|pNnjCLgR*7f
z77*O^Ua9>NoHAp+JIu55W~*#>b+<0|A(I*cX-i3v=<rZC@4BoYa?5%i`7bS`LrzNS
z2D~39Yy|2EkF_3|O`9qb!7r=9!eik!Agf`KGlP!)Zelf$02;eNpo+vTr$??@@5#zc
z$e>QCUc^oHEOC@-;^fzR4?&*ZCU5~n^h;+Vwf2rQh)+C%#O5t`U$X!0(RJ=49>6F=
zbMJ#<yp)P8;OI+m$87eFPo^euu7=l&8WcaIwJ-WcL>lCh87o+Gh%_iiJFT>{(Brwe
zGUQ~+@<mq*ni)d|6Cy3xd0amG*CB{Ca_;R)>e8D?ht6AhF(2tm=oq3t@?#zDd1(=m
zB1pJVkOCPP!$Zmxi$Du=i0rK0^oEC5go;*#)4pS+S3eTFHQMu;{ykliWytohd8kPI
z?+?mP;cIQQhJ`{i*~C$3V#ba1x=j%eH?ck9K5;qE<gxJM%bl7o!b16wqB;*CxKCOe
z0n>sM6%`uSW&@#olc?2(^mxMN=2K7$5mi(G^)y*=1~&Is$EViLDe7PFYI(_2a+E>#
znn1`6zOlwgRRPO>(S{dK7NNwzx8?Spa9zFx3P9(IKV5-Vf14)CqOude?b|r<3sqo;
zFb%8ufOFx$bAcaesh=ChEs~x?8qk4@Rrw$vQs{btG+NtQkJ)uD81yDK`N%qR-w_!5
z=8k60tV4x}Dg~?O*$v>>XHG*GgV9SzTyByuu?6Ip{1v?+e9<ssp5pZPpz$9kH%cbW
zaI2Mkbr*O`>aA=SypoY3QkCI0;<;8o5hJDD`mOaP(|0q&{yf~yJhgYA0q%MU)QJk4
zW460vLSW$9S8Y-Q49Yg*M)QE}?wMx(gu+NBCj-e-_Cg7h8w%iM+E8CNG^5-xc97v1
zFpDXWm@!u1Kx8DKK3A%U=<V1*ZkoA)%?+mo5C(m?UiU(>nG0d_)dS2eqAz!SJz0Aj
z<AyYZD+q!Jv3;s;1z=X&b%5yB4*p!)CQCplv7Dq3a>R43YWch8cBiK%)BJtHY=wAn
zg~*AmI{vs4>(osUf6~!Jyl-^RbQ=>xF#@YWNc|y*-?1-2RF8reK1z#cica1N#v1I7
z{TVgI4A{rIvhAL-5d2p1dB4885Dpn^o%powk>u4|S=$Km+a+3b*K&8JECjKcM)jU8
zaKRF{746T}%CrcKTxQ<5*$rEmFvM#zV;$m|9_Na_K9jjIyMVuB!^{9c)lozophTi)
zwP3N1qy97ivNjeGve&GAnrn`wVhr43E;XgvRxU;PQZoR{7GaBc&tTVbY4lFBi;9?K
z2m5L}d^Ek0EUY7b+H=*>Q`M%4-m*AD<w?U<kjUh_XC3W)WH!@KcgKs_o$}mH!)9Kz
z$Y@%<sfIQb-S`~bpHS`SKH^xGqFI#d5%xi7RRLpXu#IpvLpw`m?U#2xC9F3&ePChw
zTxhyYinO%6MSe{7;`;VVPg;LflAROQIFZD16Jcf}AY3Mo{`w>6{~sRpaXIuhR7TLf
zbPqP62#7}dWz*rgL62E2!!@v&%m;l^k+U1iQ}s|--Jt6*8DOh;i!l0G$FE6F{K?~{
zOCp<N5q5Myy?-$@6BOh+!lp)iN6Eh+8)`Z*2$0RyU5p+<0kIBcW|Nq+`{XclDp!%`
z(;Al<^!WNht2Koe!Jb<q@kvKJi#xYuk8@mlgO5^|#yn(wwDQn%?cpfGLg*U<St856
zIPrcY-9Kmsh`pZTA5_k-owr$LycJ->O%Zqrt)E9C4+WsEB3-Q18(B=lF}_IJI?tuV
zIa8;?d$N^j$CQLLL2PAr9rR5!T)xFOT2x6;6mQ1tE_rr58JPDkbDc+!C#7LWr;J!A
zFAOZFI<{l1yT=ymd3X>hn8`8ZX{UOnuzCC%^qcQ5Np%RXdRFG5`f`JMiBZM<E>kHf
zJVzwtX$0FR5t)gh&c)-g?O`R;`pY@JtEh#*3(?Sp^M!9KpdWYc34IKov1!UqZpvZX
z3gb6r|0)J$WS;eq1wee|<wm9|NUhDlE}R-u?`xiDYv&4tj(P;5RE57D1-(;uZPyWu
zy0(9F<iOS#Su#JHT~zZDgl7*{EtyW)MLx055}2u|001;$j=mc^Np3LUC@a+62M&?u
z5<Bm&GkbM|Qhd}lKkx6jP1(yFIqLH-;8K-3Is~oJ7upXf(Wc~ZSy$dndK7X~22;j*
zO|bP^{wx8H)1?&7uvO;8Cl9}1?j#9?m&;+X2`AO@d{K5egoG4yvgy3F-EdkqZ8<xR
zG!}kXB`jy!vf}5%LU2Io5?4r@;x<o37J8^|>%1bF1staJ4iZuvHNlj`D-Jye%_<vE
z`1t`*R4J{uwq-;ePf%13>I`5L3O7~I&uF+9n>rmc--YN}J+o4m3fhMHSz33_jx1#s
z{{8ccujUZkOA2o&yGzoC@NG{Jq4=`O*Nhf0tFa>l5cU@6$GDT2j4kj1E#3xtKME!?
z@4UnWC9Q-#J)qw}@xHFas^59Mo&Wk8kM5U*I{jR85>mDU5t}?im0LH;fi%<W0=>iA
z@2kw#<_oa2vnpxwY{tJ)XU9uu)t@ctir>&>H!>)OwuY&>jiuI2CC@f_g6`x2Bkc_;
zxLOxTQ7MrDR1gr3)A5lXlZ!7yzm$0bqis8vkg1047*ivcGM7s9_7OI~3l=sUd-B@!
zkNk6uCEuYja!&O55E`X#v*o`Zd9N&D%WU9L(3~}cD9sp{v@K@!`1qhQ1Wr_-Bi*X#
z`)_vguIlkA7F_U1VHq$6L9~m>#!D_87l*o^;#DU(b)!L8%RtWFl7xpxBR~Drl`ede
zNWHP~D_B<Oiyj!P%rIS}eC{;PMqrC&gCu|uE&t|TK1&wP+k<w*7jezmUq8ere`JiO
zlk~j<yLiF;!tSW{PP|ql!koyo8Lcx<eb0aY#SJsmK{lF>fb1!`<!sOoj4%BHu<<i&
zx@p-9eSk|fi7#Y(5)!!Nf$2nmxyhRVlD;j-u)%dH!nc|fx(ln#q&tgANFQPtTgE4C
zdmyBng`Y6|^tHm&wfGFQbVDz;$`$~iJ*VG=sAR5p2jF|`fD}}=3aw0aX;;p{BT6V9
z^H{y7rcKMaK{Nm%w2ewo)XM#K#frC`ks{ZWQCL;&JzILXO46O4Y&Ch_G%=m9qekos
z2B!|J1Jx~O`6mCDRfJ!DaGkt$6ZpLvm(ktt0Esr9^vn@bM59CVYwOLPT3U<G!oG|b
zbVgu2olneYWO37G^ccptPmYY`y;r5MlFMI)sLZk-Gt|**NNc<salm}@kv)bY;f4lw
zM?f!bP^T$mJ)Ykhdp^c_>}7mO;XQHcbF$Ut+1Bz`n+!@W0_c*#){`|na<jnxLdr)9
zwyhr6N>jwMy18|0%GM*xlG4WeBbqQieGCk;1dVI_?KhXU!+EWvPfK?BuGutyEqZHz
zN;1;Qny*z+B<8VOVw}Cb+U?U5*nke$%j$MF(stDb>fE3Dg(jU3Ut)U^;n17%_IX9c
z)6#MEX8zU@{8)+?=Ge;M>_#VT3I>L>7c;nU3pp_(2nh@A6H9x?)dVmUoz$XaT+knI
z<_YwhN{_EoS8Gn7Q{gKG<Vq-VxLE!M`?C-O)}&uyv;h4@hKT*Q0$<|Dd8I5l4>tTI
zfQ+Rh_F^XHq2`|Ohvp{qro5c;DEyGGumdKRUoZQBPJfK7RXS>xwaXS-7g|uPm^Ae;
zT<n2Phy!$_Ejb4XmE^4Aj>=JdtjjpR+o7Y2Wj-AhN|kNRXnacY#cTaPq+Uw%i2#l{
zh`*y5|0DS!(cF|0i%J`me0-&*D{tDe6N1qu2^-77-$uG|w&$t)_$1}7f%H(mecn(l
zAlsF0U@}<KJJAjGkp;CjyKL_qfpdJ=a$w{5rKb0$xt~@VKbC|%xr-@aJ=WpN9Jy-i
znRr)6s=r>Kk>;#W7hv0SN>^I6vMi^@PsRj;0AFTl`=>!2cF9~qtj)7tiKKfkd3%sZ
z%iu<pgi^)1irfHSSEXv}LMcJ;z3=6#D-7b~94IX0)6cha@=?LjsTEtoZxqFAG`kuA
zXUon>pOm>g<{~iJxo7y{eB@(tAG7Vh7O|uRM1F=FmFl&s2`j5en`HDx@kz8{M48r8
zWz?sAgA+yItjTWVxz8gMLQ*PSU8PcVZ}R`=aCbOe5~IobD8#D&npaYBv_L}Ag|NXE
zL<uj2{W<IHXOp(HNvAroF1aE6=E9LT9;8%Muw{p<WG9IWDo*g$UVq1aBKaFBX@d8l
zvZ|(XPcGXPA?zAxXQ?c5uuxVbVO)DHEOv;~L6K-VXJr8Ca{I-^#d#a}ibm$zJ*ACC
z&20~ao04oo>-6Q+N6ZEu59+90$x~%$$e{S*UymH1IJm}g5Xw@N%VZM2Rm6CwhHayj
zBv%N#Ezmm1dEJ1EsI5-A3w!G&5_SC|SRm}E=nLo!%ec?kl;Qw`u1CVtrvkJKISpdU
zD>jTMQ#Pk|kqBNR@0%HBBu=cyL(2`-s@bB#pWNn?Iz-C$Xcfp(87kjo2tJ|17>w_U
zwG9ifcx5AJ>1Av1{OiT1x*}TCHl3D}wZ#}jWzGo2j39V3_bgiH{zd?XC(1|81fT=W
zyuUa{*Lk931V_N-?5NCCEv*}+n#av+dwZ=n86jBXRT~Vg0Lwr{L9?1P-eVM-0J4bj
z$q{kBP=wmGY-k(w)PwCzc)1R6GLMce@wJ~25>mdjxi3M}!Ug%RzD)A5pGW|t8iN_a
z<+F6|6Oebff{xuC0&*P@fWYK_zNX%R(AAfhk#iw(Xlj`kFphtDTwUr94U}!5Ks1EF
z9S+qn-=gvd*v91^S!6%WjWRxQ@fAECR*{xFwiy5ZqG|S1%tJXN9uFiCno2-${goY5
z@hP>&<|K?g*`?;p#?ldG8uH;5AjQw`_F_;SOCYEx;_ZW=(qz1y-v%P$mMZI;Kr_=8
zG){2|&ux(-_~3i9&B8hRsg71^VosnPP^_~jp9^bYNlt%xG3h2pS>LgAkFo8fP{<%T
z*2E+J8MFv+0Edj{V0=CDvj>l$GLsT2lV2#*T>21cLY+4D%UIU4<_4n?I36Y|3n&%v
z#rBM4eSruS%P(kN+B5#7<!+@<;C1h3h`8n}Tk%?FQMD&|+qae6`+cW!9ivuUG3$ik
z(9qf3wMc$)`@I6@{#bO~$93Cgu!@PXtS6m{XS4X{Bjz5}2)3hbce8_gZG{zJc4dhK
z2OJx(C9i@e{X1th&jhu5OMS1XMS#Y-5HYCphcAmVQ&i9mA7RS}gM*$TYx_!dUj5WY
zJH1#tnu%==8-l+xY`@`cLk_a*Nb&?7Zb361bpnk9MGrV225(+VIvHkS(|C=b5`io&
z8$-lHFm3cU=~yRt<VxQpskPj^67y`$dgSb;_7b7zqbB$%2Wl|kVb>Fstt#))ri2V5
zPo|oL7HFm605WzkY>Ey*A+mk&yL3wJHK#LPMd(wJ&`@yf*^?gI=%pySyA3W|Nc@}f
zp%d$nXWyhJCJypn4|;}j4-z&DjRY*hnb7HjW-T%qdTI;<fTQl2j<mYNevl2HUlGFS
z&u9mkM^n(kmN^x_QzQ)mRH~?%iLG6$sF2urD}nK<_N^xOW-z^w5~iNa1MI!<k<$$?
zb30N}%yuW!b}%Z%3>@VV!mLNPkYjHC8?7l(WT&UIt5;J{<xLaT&VyZr@xp-h2qRQ*
zk|{6P#^}eT*jN5ZTM!fJ(7F6)?7*UPvw$3L2DG(jX+ISC^u{~m-9C|otYMv|wrqBJ
zE33FVpSD}Z=>?F#ma0_P=V4tCR9~lR%N8zqv5mk~Vx}fBxEZ1#H!|lcDyh4R@*h>L
zykgvr%@&ut*paQ@xCXKn1#-&oJ5c<(7{qI}v3|90I9F>%`NFu5CzygrhQ<WCd!r7p
zi3(t^b)Ao`575}HVp>Vx$VZVT_~~F!K^Gtgl@KyZK&^4pQ5_N$B=qr>ci3xLFN8aM
zHYSdk*D=j0n{IimQr(j*)TN=&CL+?@Rw>98glMUF<l9?$%Nt9Slsv`g{~!~;=YJi>
z+jC2rk$-shEdim@P)5>h*d|~5fZ_s=17cF9@AzjDZFkuQBVOxm?$N!lcZGUJJ!Fn9
zfI1)=)C>-^gKlLm*8Dx7-DNJ$ewGqfs%3IDOR=HN`a$dw=^q?~lifU>NTiun`b*VP
zV#Rakm5Ctf5ksn4UcZUD15dYImV7<SJ<-#_^oLY#T1v`ko04Xij;se9X$gqnh1MNM
z1^Njzj~U}S)2H!7gj95*1I^K}{q*UfC&Cs}*Iu8is4=iB9M-YzU8(bN7UO!{79lin
z6?N3X<^R-<!?_?L>VD>^g_~Nh49E!9wk|N?GK7-&t#oS?F>6;!ut-kiM~?WNss})9
z+iy5^56)P6x?Z=>4nXoLqm*e*SKXspl)YtvkE+w!<Om3_cz&U^L`Y=kOm?4m$Z+0m
z>*)$;ISza~Oq!d^E^q(AcR~uGtO(X22lx#KJkzA<&~uQ|R8l@yd;Rrd4kWrBrQD^+
zmPt1dfMt7y^-TEZY<2m;a}~7#D=KBn)iYZHkPGI@>s9oLMrn*{)vZ$$n=#+Xdf#VM
zQL*)>N7k&IeFZvJmcQ;ZRyFt-17MD|?|7_LF?TqWy;Y8G=*<k0v&zh|8Fd2c(-?ZX
zA>0HSEPZt>>jO}Bj@$}zN_ZC9OFKgHVrTX!#m?uzii-Dgm358zkU>*NTFj4qQ<0!J
zAE+)hTjrLgf653;Gv++&51K4vx+DY%g+`{;op3~4rLbvq4}0n=e5n#j7GK6;sY<7O
zFEnV3*bps~gKo|xix6A?wcSC~{OyELm+`GyT%?K=40B;>3}yPAaZ3)r2EZ*0GPSO_
zLd0)JuU+G&Jb;L5v|_m(vSrGK{Wu=<m?acyDe3cWo}PZTQ0uu>I|{wS+aEo=Ci8_&
zwvCR#4VszX;uwvuHpEaTuy<Z#8T#OR3=yPQN(eDZT}GJDDVYMT8jEV|596;|j=egL
z^(G*mOi)6R<{lJQ(zOOLxt^%foL7rxIqXew5txV0y$$@zN0(frIz+fEr0EJ)6{~r;
zG?cD$QLdg(AZIWmNgs=L`SO-YIk<aAU1`}SPNljr7fJq^UL3(c7z4?PFRx88hdtn!
z#Q{x-9J9LTo1c0EVd0gZ719P41bD+t;+uvT)tItXrp>(loHvoA<?e{*R&b#{JJlG&
z%m|2xI+)u=cAlnVXb_8@`t(t42E-PYt*0#SB%%vs)Mwv+KARZTy5S(r?5Jmj(1tkH
z+XFgUw3pPD_=dVclPFHS(GiV@L4D~1Mt(b&S4V)GLfoiTaaK*3wuiHO$$AQes`Ew|
zIBU<mQM$y(qI^O%O{=e1*;5{7Q~0_X)#)dZWd0DGnnD>d?EMI?RtO%Fw+%uUKCW4}
zy&Y}@U{MUg&&+Y|)1C8)i4n^4UNLB;rBu9~(X^nz6hVUdRw=NSj}Z1mbYvtw2sw_%
z(&h!D>rL1#;RvCg{Dt*Qh+a-GzReHfN!V%Fi#ZyRV^{ZNIuiHDV22E%AQe^>Qmn|B
ziki9?^Au~v_T`yX_X84oleNsG%rHGURv{-uk_E|%KVVtn<8tVc9{Z9l@G1o&hZVN9
zu&47aknJtq5W19wsF7)akgPrw`O+$~@i}^=D{mw9n#uYUuE&Wm*C`!q4+M*2MZaBF
zFp)4fuR@Nd#iimZR=moCin_LtMjJ&aGbKStq<gAMuoVs)Leb5t>|aK+o*cNNb~bU+
zu<fAmr7ZNa?4M1(s}atE;|kRUZYtyl8i2u=&VPRcvXj>NHoCT3V}|06>szc>Uiv+1
z<1aiX6n?;jqDkh_DwMO9<~Oo0el6l^Cy4w^cR;pWh!Q1Dhj7Qbf%stF8NI5$TECS2
zsA>CL>qkc=ERmod=<YN+8w@874$7XnNVqSP&j&CWA&R=Dy{MD-7C6zze%;Z3m%myJ
z@#N||@D~(mxh|#61mbr@9MEDX1_25wQ~rwVZ?aF!K>sCaF8-=Do+_e%IJvad0am&s
zv6&^ovAbfqtMa=os*J&NYr(l72$E`oIosy!^air0gu2k~GEQ9P_N!1+aS7NGP=isN
zx6w>G7CP7kHLhG`*H);scD&ZdQx~?t<mO;uF4w??rsR4xZIQdxXN)zyicI{06!xmX
zhYepK2j$K`60`Fy=0>GFtsG_fSV@Kl+k_*c_b*58P5=Mcd(W_@wyh0R5k!L&w*;gp
zVkbyZx=I%;R3V{+YC&qK(wl%-K)^<muF?{kKthogP*IVhbV60>AgC1ScdUKXy*<ah
zpZn|nxX<^;XBm>U=9+WNIodnkk%{j^sw@#FM~6XZJmHDQ=l5N5N6~_DcPJ!;zjUnm
zLbyzt-X*&(($I5=2K#5CJDg>qz`}r8)j~rI+3ndrv6Q!)uSl+MyE?6dK9XG+_u65z
z-CzZu_1<SuuWy#P7rf{kMpz)S<)q)u?O|Xn=z-Y(*j)_4um*{x4MKczdHU04LnwRm
zw0OMFUMVDdk7C!=7dm9od1qaM@c3;7!$TK9aK+)wiU*D$@&ofSP1)I&E;ovde-6OT
z*Sr$+#oB9+4;`+j?O5w10IiFm9o>@>m_}L3-i%{iFXxpibU#GhF}G=}<XZ5nbJuoR
z8=S@Fs*`K81_2kZFQuXI71D_(3XDu>z}#50cRP6R1Mj+-G81EO+ll?rurqV_rLTS>
zE9|@zsjWx!f9O+?oBukc0qV971jy)1!%q+_T>#DgX*SSQYYA))+Tr=}&LBb<QG6*<
zRHIsz3v#@^b+vMHPX<;r<yU3~+R`^0r3Q9Ep5!%>I3-!{I}!DDzegLSH;`-su;Umb
zx%7zWs$L{eRccm!5%55{1c&C=?z_Q@A(=u+V(W!VmB7f;Hf=(86-=Ij&qa_HxjUD)
zdEs&C`9P-yBt_olXk^+bvH#<r_S(+M1p<JMushX)i_$`jyJ9c-=9COQxVz3GP<gY%
zApXJJ%gn2sPeJRoMK$U&vq-i-DNkbL^KoR3oP?Y_&F(?+2%<_Cy{f%CB?(3@%q7>4
z{BcNwjV$p-2K0USLW6rnfnN2S4_S?1iQ#Dc=@_H72_&L8P}Y5t>j^v;J7>!$;@w#{
zR{o%U+imsWdirmw+%WoW({)|q{2AEWZ+sJ)ARvVhaa>o^mYmmVBfI{Yk@akU+AFcF
zBgfg9mKo?ip&cVsTQObDbO6n}3<<C!#;>3+cQ9Gjoz6k#5`Lf;femyc9-{3PH;>)T
z)5$b|l{(%edtIzh{^Q6kmu)#a8%zGMYOp-O-{8~cqBZx`N=^*%qU6oeZ2zvZ*CF{;
z^E)QHzn>q;j1bZGc6_V;qt$AYK!Awvs2aK2AY2M>)c&?1^7Z9RZ8&00=tS#JS!B^~
zG9uXwBAG=e@_8ftVp^~*MBr(A58fr(ZB`_QUmb>s%X~{!OQ`A0!^Hw?&-lp~sC8)x
zwq1v%{7=VC>RJf54!!6Sp_xFY!L>o7Nhnuh4|?OareiZr%1C9fCQy(B?KPNzW(vL}
znc>;1FW%+P#Wwm}%2tXtH_8ES3s<pdO)PH~Bkw>Fecc}hXLgAzR$<N5q#X+HG+k11
z*nApBF@<FPT5Q4dlAO|#klr4Fy&0-J{!(UJv3lg2LPo?*ljk*8e_k|vd{`zM61~<!
z-Vl9isoi{isF#<3gq`O$_gTm`-+k)4mo2XGgi_g~u&ay+Z)ZEDqh8WUOXZnQ_#f)(
zo1BiIfvf?WA8M%kwm{~j-+u8pl!uJg5}s_|ezP_R^2Ar0lX(#`D!ne(zclWMf56bx
z<1c*5%zy37p55TG2nn9s`NMva+ja^^y}I^?a2djNpmW#;ESnFduOZwwOI=PKixM)j
z5v0+#{$c)L;^kq7vd=H@^^WZ(?%65S&pQSA3*H>yuZC15Gvlz1OiuaHZjT6=#K|MW
zh07Yj6%0%q_Zg4(uirOpx5GL$KG~#eU3!Q~^Sff#xK6+Lkf;(?AVIu~Ul2hb)MXIm
ze>uvV*%8B0nfWft?Iq)m%T0RYkyi$gU=qGuC{oPGNI<K-EC*_dz4$EbHz!`w+<ZP9
zU12wL7vqv?iu8ABZ`8pY2%i^g<-L%F@ONb-b-p<~?|Iz0GIjEssrRHrTh0z|Ed*}q
z>$D4+tQx3t{?upC3-LCzAEy1ppC$8v)IN2>y}9`V;DP*U1A;!j5-Rs%9}`P>zz3nf
zCYJtx3Pm;`A$n1@Nu{5*E7&<rJxKhfTuw~(7?m2!;r=6;k31v|sh%jkxFh<<6U{O3
zWmxhbu;Tv=?mJ}k|K8#M*5UsbvUHD`1tHTI1nM1OD3TLFIy8O?6@Tiq`EPhro_0CO
zzXMbiiV%4B`0E(X?^?z@5B4jg_lk+^_YeNuETyXE`BW;<0A(SFVGD%d5WxvQ7(aaH
z6%zulL#RFu;R`c(J^BOg$={a!Wv?>8{F>x#O8W_}&I^OwiR50>U$LW|Y%z9@j!;Et
z0g+FOPu(RJqwi9QEDbN-yH3YvN)`jT4U|tu<qs!%N1+AvE>DlJ`~^saa*<dh2khx$
z`4foQQ2~#c3$y#%=>$iD*e=n|e)F_8><)Dg$ItJdTlAKB(p?qHyOeNTg)S&g%0alO
z&(E8gnJC$D+NAbxlVvUmQ)8*ySMu}aRH1-?SL4O_PfrZF5Pd`k?rj=*5~<lU;%FRs
zoM4Bi|AQOp>_ART<S=3U8(P&o4Y?=2d*O%gM*jji?so2@{me)r7gdE!>ieWS)P)J5
z%|mWA*O>RACvNp_jS;idM2HIsQrWUjQ(1Abe<`h9dA{ITO!9R8M7Em`A&=_q=K48&
z<eBF50h;uTC#AEZe~#eKi>fT7&;wI>?`P2b7N|UF)UD`We|`zMX&bHcmtUJ-qK8jk
zbQ=ErbpL+%@nI38pLhH7t;7LSrnxPv=5ND>cm4n1$v-s1e>4Fy4c#E4AJX!Fbeg|X
zwFP<qT@>BF;K#p6>wk8Kr~&*eh}@g=v(N>p*e%c38Twg^@>A&lcA?OSfa(5Loo~Ji
zCXDfXtjXTL@4BC}_v4}faUXfc8h+k9PZn9^&7S|-VtYN^Jp_5ZH@@e`edyDm8@nUk
zb4UFDOvKL_feodI6uNwDb)WoU<oq}A`1il|LQ0!zIh$sFNQy6#T1jUb&;R-S-(7sK
zsoo&=bDlc@_U34`Ef<lM-#-euPx2m^@*U?cv;8n40$2dy=eE6N;CD~?>sC#G(3bqe
z@2Bitz6Us7sqAYd|N6M5Kl>j3o!vnS9`9RlU-&1M`gdauut(aQQ_zR`evT3zU&{LL
zL;Sy*wBIcJ|J9`Z?->#Be>G{p=-@xa=KpHaesj89{(n`I_CFF<Rfwo+p6m@j6}UQ5
zg^2QS`<pTIn}e?kQIHo#J${l*{BI`WH-lsw@dmwCVRoMLM<LS)QYMgSEP3w-+UB>1
z^#2s00Kow0=c1vUf2}7B;6f^W`K{7_0^Vc5iS55yZ1)fC(0}N|&!A|+`(8)=lsz$|
zP|l^T;b)E1Z_ckPRaamxSvmrr8h_UX3K5_S=3Xkr{z@bA->+DXlvH}#UV8E4@rqDv
z#d{U!7WVV`zq@#Yl+<PA9TWJms8w!YY!q$yNcf?Oe*EEA9jlIL-+bq}pE0Qzq{3~q
z#ntX#f4312KTD48d;UXG{s+ANyFs#zxWdg%%@n`>sQ#Hj1l2!>MEaWr{=WhSq#F=k
zFyE^FlfS{72W~_-Rrb4?|LX+<8eq!ZUc5c~Q~Hhy!t5Gqo&5Q_e_0BDyHyoJDkB=q
z`12i1pa`x*CG;HT=O@3rn2APSCOe(<L&9c|YQ)N{$K$_!%8zj-k0Wnqe_H*<k59}`
zDL6I}ehet)3PO%m2b|t@kb`**eIR86WC=g2<9>5%QCxtG%g}w5P>x6hd@}*O=(@7;
zD`2RJ=D|+c@N`D_yGa;xqYCI~UL!sX()IA>#3iGDUiJX%KMcw|oMZsQ@EY=aX^^3X
z0Cm7BQm58OcD4A=lqfJVAmk|A+;7&*X;3A)yuL7q@{0@it02M<kOV@#RG;zM<o*FF
zk~rGd*+W6@_G|5--N{{(H}$jexVqk{lHtTbK(U=X&9%II<mc-C@5Lp}e#BE=a0O?!
z&A;+=Q3kN9=+oZE8qrzqR!G)@nP0ldZ}Od%Ow^Z7gG^mc5nHn*Af72?Dd5PEb3Xac
z=MmrmoBCmvaTL2w9i&PPdUKX8PDzOwx;oaps2@yoEsD0)@6qw|ea<`>GTwH7+}^;*
z{@Y}>MZM5?-6W4>Yl(=dbmbv4%HxSAG);>n*{CS*$$~OoAE3^!@D>()`l+n=kg=%4
zGus&?Mtn&LHs1dG3EvwRk5~sFB^;}bWCTeABt&MwzoOs#@rP0V-(nhI$^p#>?7^2u
zfaT<l8mRg>JQ+=ft^m%oNgi8i$5w~-NFIk$a<@1;dzoj?4vQWF&zSdSw0*C$enMSY
z$*~s4{_s%F`w8yG25z06XFFXFo+@6-ITk7FPAj>F`q~?~;F9k9NfV^^zsal!bkJi|
zWK8hSG7acsc+Ci1L>T&cV*QZ*zxIFCmQCW>AyN*##(n_%Y7^TDnU7|#p?I-YdVlD1
zkzyuwKNJX9_wOxU`jYS(_<tk!g$_kuUFU9GmiH#fJ>qCI&`9&5wyjzwJFaM>tLe8m
z7`xaSies&(<rpUnSqw{nO?ssLOKPZV>4Uab7e_jJRU~76`nm1*i3{Ke*e~sY5Qigl
z&Hq4Q{O3k3IB?tTIYb0purDreWNhzyl9^R8N^!0y#?@Kq=L)wolMk*=3KcP<<2q9G
zSh_6^ek<}gxFu$~|1PD!e=!B1MM3U%-x}v$tT}xs?XvMi*wJka%hY5X*Lpw@CUo>B
zN-y;I&z(q!1)mKGNfe^p%l|XXs;dkS`um}t%QPqm0z$FVhyRfe3${p;OaOKrKus6i
z<3~{}L&x<^{9P6r)-0#w`p1dmYZ3lk6K@MoKX@#-WAxz!N5&?LM-!-rhZL_JIe}HU
zCeIciziam<6bco%DPzl7MS6BYZ}dTJe6n|=xVQM}<}Yg%l^*Y3?UQ=+O2heH`}}9C
zsiPmf6NkIw3O$FvdU`(*G9kYj<E9wssS?B71LServs_cELWoBNvooHc+H(xnQ-#8e
zRs#0#+i!rkxy$TE2yrn9eSZkIUtMDkw}~_N&=X8iSr<DenuWYFA8&p^^`fq4-nj5q
z%GGsrkQ?W<+i)X#tHQeV^lS?20{Z#l=^0Bn;PTJ^|L&iFUjC*XRO;e2RT44tE8(VP
zc}0_?9kDQ_lS}fDe4Q$5-IEjgPL95(QzM^pJVUWz#0|z*GgO`oMc)>!(lHSg-6Qlo
zf0bt^ucUY#cagozxo0{?PoGrlA35YW8P2+}d@)W3YaV2L@a#FIjdYuKvDvN<40^(G
zX=&50E9;Q|&(qN}epqF9UibZjKR^Aif0}CPOZP|{=$Kw$iXs#S*GY9fMDqxB;@G+g
zuMeteo|zU3ZRga-r!sL|DmCxFde~F)kVsvhmO8IhSQstK&80k2udUS4JS^KKrAAf%
zrehxIXvTDZ#~m3;iC<`aY~^c4<^vIRMXxR}sTUD7Um~-0UP!@Fw5{5X@B5s+Hw9JJ
zoutI_&ojB6elbQND=5fSxTVP1B({9+la5hAN;Uh&Hw|Zs-qsYDsJ*ZMv|vH;QZCuI
zg|}bnN=ZfQ^s^_PIlfLr*6lWa<T#$J;;b&c;z^&v%jMa9*g^absV-6dbB>F`7Y@k^
zy(*Fy7Rn`O(<q%8f&7%lmfe*RE%zVGxxJ9)rDq!fLx<6u#&u5a`$RTDz>a0ERJZ!-
zzbt=p64SGz&=0D(nj`lh#nN=OiG4RVSwu{zlOq@7*2>tEH2Azir{A%i;!$*Zk1uIJ
zi4)V3FDz!2DR5I5E8SFdNH{ZtypJTg5LL@}$k@Tic#!(A+L%XdHw|TCkoWM6;<MBW
zmCtvUraqE5!(Z}rH#Ul1k61_dgkzWVcH46lxcqatP9iYfpS1WH|7+!c`LD0~t~LN;
z@nz-V#?MSf%BTtPpG)46Kfjl6Q~c};y0{{%1OkTiStztN>@|5uEo)J^+fIT^keH9{
zdxEe`OynIK<pMGF^;PRPyeY!?`BU*r?Y2yI*|tvcV$wbPpU%BlQr}@|L=o?*G+XMR
z*!9?mrJ)T6@_5be<V7SLVPRZP?-jca>^C>17?%GGd((^@h;#W=yyzZezx{51!Dl0V
zbWd1V<YqqFkz89hsMXmX5)h!`7~g!Jw=93s#F5*5aW-er5!vmHb@^rSigKHzo8Vl?
z$|+h8vf-xpKx1dL1LMTuUe%ARlzLYqyBsFoRo6B~N)zso`kBkL%V^YmFX0yH;s~B2
zxA$X|_4MTJ=DYVMT4I}*<~I+GY*pAb1j2Da^9{@QLGJ2hfF7YsTo+gWZP&HapWne-
z_9@5GXS_e_jktT9IgR3bUa&o~b9_p;05@J|f~KyQla_wQK;Nu$Z{@>_Y?_^m@1xYx
zelu#5dfZWq`#Z>e;gS`O84{&7fqaKd9sK?5b9{?0205HNe#rgyq@2a$!QqSFh(QwM
zSv}K|&g)xp7uyDWC+|BQ-?Xc#4w2vdZPk~*4?1~%Ggw-_!&kZZe-VpcUO{1v<Ay7s
zm27uXy7{>%%4Y*|rHh>jnL<vH8|9r-Dm8LY=K@KXj5AjS1WxC8nLl)}%u0NgqxK|M
zQriAv_+&-Rsbl7ipCxtBj3%?yo}p45_e2_s80tb-Ue&!iQv|ZDe@C>=nK9i^L!;cc
z*dqZQtw%G_PwJ@D5<Lmhe3%mi+vU6)j8K+8&zwpJ9MX|%y1Kd-YOb>uZQY~V0fgKa
z4Gj%t5AUOm`2{^D5?@ap+q0*}cUx@Bp078$M<I#YEtM>BM@z~(t1s$KoV~XVJ0~X%
zDr$SAdIj{fwVQOC%_~>1a30Cae&&_@E{Xp+PJf-48GL{NBV)wV{+;cK(zLlg+rWLL
zlxl|tlTS)P;{?cqlX3{tiqvOU10+TH2P)^c9g-1vt7Ecso`JzVR=nFSR`iYA1JUkx
zJuW<Fh;|iBJ1WF2V@bvtA;FhoM?YC6mBdPRO<I15NzpSgv23g?c^Q=}@T9VPwcgaW
z@fn*!vIFR&37+9Qk3bczp?skI%p5QrcL(0q+N+MoKR}rN-Jp!i-TRvzpbOjpQ}s=t
z2T;Hi;B!{d%ssht4ZdE8P~O-3*4e*_IqU`v0BBHW(lJS`trMy2HuYPufQqi339veC
zTq+J-QP4-xCkcT49a=f(edGi!vi3RUpEo89%L5JK;<%>kZ?F4;wctZ#@l&RaEP0jk
z@{L2n#up-myW8_D;+VFvTy8ud-m|}$?c61N$Y92qfnuMy!-RMZ?t7NX8&eHETQ7P0
zQ;#k=cB&fX6Q8{{K6ZYkOh97a+l`iK25&Qrm%4?6^+pq+8nDk5$JkB_W%Uy=)nfjt
zx-LL^D+tVi10mMCuPD&ypq4e>1n35GW(r6v6RI{gMBvAH<6n?A7R1cVtii)|<l>DU
zK>StwajyB^;fif_f7UWznw_0>gVMVWVBkD<dG91M%QdN^UNcrtp&C_4US59ra{8xn
z-W~vL`0;=vkf;%RB(-x60>dBr6{VP9jV?L5UH^U(|FWBYd{)TMHs0T#Xtt0!De3t*
zVKH?yN9ePH_qS2^iIoU_BNOFE1Q+>xn%GoD{hQOxw18Dfkz>vK!|K1bZcLv#-u1=h
zNH)bjG~2-FQo(R8C6%O>)y=><&O=!Y2|8`Ra;tD}Tz?mON<RDLYeAcdfCIgkmkcjB
z$hukc(3RbSF<jOJVIN*L;DHnPW);-BP6A@7RItZ*vgQgx4Q&UMJ`PY5O`!SE-Xkc!
zIVeS48V(H!nMm~hbQt$;Jp9R}dTwB{pe^S#9RI4`Cs{(8BUL4LeM^MW5x3QaVRx&7
zf&Szz=~_v>cD}wfLVbY!Sq$-qR_b%PwlX;P3F2octfoOR?x2+Wcy>`2kUsX<HPbW3
zIZY?W!D;FyCjC_TpELlX8oJm<KK1wSX)NyV50sFb84e0sgY;Y26C(4No6msuF^@%h
zS}R##XJWJkSS@y=4|I$Q^PkC?n%eQGk+Pk}B{`3an>mTf2vFadv{FCkyzjr2c_BNi
z3{d&7s9^Jf9}UAu*k`1)E?_6ScGCoui4-{Sd%@(;ik&{-<HIUEu{b5%ftKRc#c|*9
z9uCg%M+cr_q&=YCoCfT}9J4P4F_;Npbu9!m#y1OaiCJg>0DCChcZU;heInH2sZnwf
z10#B<b?GpiS*l^&=*SD>ZP|Kw{Mg!n%;k}$jJi>V;0Sa@n>McP@#DSzmFa635s@Mw
zGWmwd58B9VGk+N)DCoPd?b)*vbqx*n^IlI(^WY4GYMecMFM44Vc7T+ek93wIbbWNs
zzJPt+Z9cL*dK~jHF*cU#c+arIAXZ<0c=cYYdqSm0kb|9FVSfG*`N<p~GsYyv(a}*o
z1B2}jhu(1hUKcOkKrFO+BQ$HFf0=aSkPzMyM`o5=PPYj_F%jP<+ut+Zq%9N@(d`hT
zA%Mv%yuViv?3;PcoPy^$vY$&eTJ_3Hs2f>$``0pXPG@u;TBIJJ#cWl!>tfmC3(eRp
zO-S$ejZZvhw8C|t6vnY^2rv~y$^a6%_g`B_w_~d`aExnE6QN+exu}Ui=mDyY+)PXo
zW+GF_-bgtmviFaN*48;aXx^3zs2=13S9*L|yyBkxj^H0$08sbdb^1U;%e(tWmwADA
z_1+UDDbx(5KbaVX+3SlOk6DHV56>(E?7RR7Vj3>Ji5U>QrfSkuo*a?|{cvuYeS|iY
z`AC^M`T5gH#BF{F33UduJ6NG1+OY}%({=(D-&!|alMF$WLU(eEaQc-cC@5#v%{Z&v
z>Vzx74ODe^8?VJ<2B3j&-^~1Md&iRtgy$jX)$uk2fRi@7w3r~)AYJ1JodRgU-eHAg
z(Z{dz6nyZ8wIy^FTKg>lZmCkEPvr9Rrg<$^xo^q2pOyik?Dl+rC*mdis>c5F-==dv
zlo{tp9c5+yLBDc<gKyqDj+}!O=6yIyNyX>pJoAI%F?ZuHemFVYwch(yk<%k*pJc}%
zlIXWjwYG%W7iUN=<94p88yQO}wHw%X@|s88d0?k}{W0xEN|0R^6Khn7-t#rj3jsnd
zLScMs&!vvqRij`u3MY}B1>z5ztR2Pc^lWRzVjB>F+e-EMxRAcKNZm%22^6O&+{BEi
zun0Z6QVS~U5~|SJ5I6on-1;uiKUh<MlSk4><=ivirm5CfEd?N=209;V;5EJBG=UnN
z&3>BD4zCbeP`u1dSE&2;`NN8@kT=vEFDxOPI4gcT{g<6|nFav9x>$?SJh;5Aq%Y3C
z^p*Zao$U(fOsw@;7c7pSL8nWsIy;0$l+KuhJ@*C(U~(5UY&D}zy`Vi>hR_UX&4~@i
zP){>L^z`(i1_uWh=4!+&8wvvh1Jhh3?K;c>Pnx^3;Qb*?YWXWl#>x}-{%)k|Mbdj<
z66zGpEqiaIm1|t`j&J_HfbIk|gB!)QK41R0Aoz*ly@a&K;%GVz2+v?;IL&;VNW%gB
z=A`2ZerE@&!1DQqxzQC97}&SiR^-DL*WYjHxtYbU?V@)gzjU#zO;*qkW%KLmf8LF#
z9?$mU6H2~Zlpxa1nEUZor1ogq7ji1)8IJW(ZkOFaNYJZKvsnFed5$q8UTKT_VJ{n<
zccg5@I~j)6o0Y!p8~dEzx>3H|m4T7qc!?P1EEI;`$$LNX;$V)_fN?vE=Y`X$JqwSv
zy&uFvKX|qqx?t2hdA*MT+e(~MdTFA-S7kTQ%b;`a>^UiEmb@W?{o~WQF55{6+TALR
zA}S`hwi#;Vu6^KJ%j2;xrWQp*;o;f{X*=d%MhaUuFo&oa6+5N~Y)VO~GSjYC8>m8f
z-B%ZYkK*m>^mX|dC8s;em7WhjU?ce=0S*p)F&!;<@v`OVff|j0nz>uy8yV3`-vEF(
zDV6=&aB<rf@(Jwo{LTVPMlIhVy+|IkwvOwmu;-ziOBWX31b(^+EkRzhKWo{<$cSc-
zOj$k5CKD18QdU%bZwr0tnO!@Bg1qF1hKH9VLB|P)bydrXj@eQbajLDu#Y$aBh$^rK
zVsXPs{=ND)TIdl1s7ooX<Qpu+B1}h4<D9v+)9sKTDmhQK2x<*%7LR6++eZM4LSnto
zr5zX+ziU?y{NChBX8hq$9D2?`$H;o-YthkX<mb4#hU+BbR_a(<e=^<G(>a5AQT2_l
z3O7Hxjt56)Z1`!U#4N=|Iki&bl(xBJ@;-4KFRyt?P_y0nbKw>iduc<Z8wGQ-$YF8B
ztTak*&SKj++2q&4uvO&|e?iMK?t)4(D^rH=BRY3(;`_^h++MtLjd;xU;z*oYFr7Ro
zp2{wW9epzL;o#s{JmXyn#4rQ^=&bS_WjGtiSH(!Be*Ab**HHt9l3>NHAwgXq^E_FY
zu_0a88sKKBn$lli1#lV~Y#slFv7iSn679y#?3lBuiorqb(%55zz^Pvg74X`8=D@JM
zVdZe)tp3TMAe178H`57tzN8q=&3uD{G7sRtTBWv76P1Ouf|e^Ty!h%5zH3<Ai;xhM
znwnYxl-#uy`R5Rc`TAzQt$ay75}I-g7vrRz9<p9}t+i(pOImt5;wcCnI8e(-*sqm1
zA$VL4(K*=$=-1f1+W`vto1a~9)J|fB7H*tAgPII}G;UuZ>5jI~Zu8^B^CV(E_o6Xt
zg<qir^kJlDq?M|Ay>|st)076eA3>#G73(e?#3#R=7*8P28fCSRSv8%gRfqSuWapRm
zn)*7#W*h4dg~Z1=$i&}|v3m8%71l0F>Sfxkx}xKpyRfJ7%7_h7IRXV=#!9u6gM^P~
z^qZd#tiHBc@AkcOx-VGB>nZgdxgAzGVXK(fC_4e+TOMO?%6GHI)*`2JGz!*NM^h41
ztL(nyU7b+u*2MmeY3Q(BhbUizcBmOc(~DbN&Dd-agZ)0F<a%g-w`Z*d2)>%KcHd7t
zBrHt%5Fy`teU|;@Sqr5wp!wq~uEZK@uRq}{w*A%|JtNspZPFhXsZGP;FysYa=;fu(
z|J<AxNG{~m-GOqdYhgHv?|4rR4Ze%kjF<PXaOlZ$t6SV}OaY2Q#{$oTc_lWj7~TFO
zj$5>pvV%pQP8?Zl0p))ITG#0|*P)Cy;&>g(XZ8Bpibqx44{Tx?lVPwftiY&mR2I71
z8iH$O!L@2rPcX1^ZMqz+pwGa_!_EDm+>+vMZ<*+smYd`CUEkxoZ()Ln7Nv6OcrSU?
z9AHGyd2Gvf9V9G$3K*IuyC7NAyMwo-y(Ssu_*BzM#LNnewpEYnzRE&zi@+eE%xT`=
zYu(F5h`m%GZ!ka?m^$}5K2>-`s)=P~`6Ayp@4+4mij~C^rUi+Ps27BaFXkak%$JI*
z`ko#3bn8!4k#xHl&a~k{cCUeTZoP>GDO6CNnWZ#|kMzad#`!Mu>r+qe2wO>eUJkcC
zYfNaY_>$5pCEg|XG@{@vgR&h&yh`Klukc?{klzNWr$$Zb>!Au}9NM7+U=!6bzQFqC
zrrt?NAV622TtJ7L*>K>QqHuNQ!80vm29aO?=yAAyf(5s%%TSe~d>!c;HfqMg$oL5N
z)K<$;8~q3Rjhco=ST~`-@nN6>N_gWVQeaS*|3#3PFM3%913@>btLz80Z7<<ex2T$!
zBsiq<(tpDf({MUE=WBUgZkY{272?`3Y0t)S_r1~?udfLa%+Sn1XM#`?;X7rIb{sF-
zboorO7?qly<k#f~;r<1T3xE|Jh^j>MB%-Y<(}<NU?wt?lSoL#>8%)q=85q!S2!hfX
z{s$wtC0_d{)+ZcmXH2KjS;1)e&j-7q=@P{(!&}?h7AfP4-MSh0q|iIcwaZW|*(9Is
z`*Cov6S86in;w#0HA+z{Nv@}8fAq<ZNblf@U{2F%>U(e*rM_6K;kC0bLqTVuU`}zh
z$Rpp8vdr^#YzA#ijmN*<%GD+jQ{fbQnrlF$wVV0(U}MkPLkrdD1ZW1Dv{{t#nEoKv
zx9y-uwi_}AHZSlb=O(*qQmTr2z3PA%Z@umyZW63m5;cf%$z^d+#J}^{DwOz6=f3I(
zKfbIox3X&+uMcb-b$_5~*<n=ToSL+@x-{hg28f-z!V~N3lf_@(sfjGOWVj$Ls5>Dy
z=TmIdJ`O7A67&u4izBEctq`|lEsU1FZRe`j^1s|uI78Muw}8Ol(_D!CUTVti6gzS~
z7%Q=urZGI0WLa*8=Q%1uOeT#*EOMvkK3(j}wf!9L((w7(+qQNYeL*{dGNH;<*!9z_
z>*#C2+?j|+*-vFT-cni<hjII4zLjQ}<=sB}vFWodm&|Ld;;9`GIW?r5(%1f%RC2a)
z@wUt>BG&5~!v<gZv~hDdZhH5KiHSM8?%eY`8x}q@`O}{1dXLg)$m>Y->tt9Q<014}
zt4K<Vwd&=F%=_z=&`|mT+L4U+OlGUz+``hVeF7kk#vUg=)SMKX(bex#5Z#ea9$d9l
zx|wq~*};E8DTpBlb52u_$b3elaQ6A&+&fxmkK4vBXqh_FwPU#RMj6%Q{WDci$^ljo
z2YmEu`Z(RDXW3WqD`VFeP>;)C4JznN)fgY;RU_Sj)1uF)sUeI;a}nsH+6sB8DSV59
z;>k|{ky_ff>9iHcm@d<WV9nhpy#s?z?r^=o#_#>H)T_+tgKFQZ{(R-<Fo$pQrMYi$
zRH{}{+hQNlBV!k}(7G9KCi))d`yt2e9q~%qlX6g>+pzr^sd=r4Nm?V=RiUJek`rYc
zK?O{1X2D!cXQM&M>U+{<ia0C774-h*;ZveBrV9vzT3Vjgw2vdyrVTId%B9nNi(wi2
z>Hs8Ll+D}rcRW-H4a6ChBfDwcT_pmFnIg{asOuqQd>UIPaEsH&bYvCMy*=!u;oad`
z)k$?;<z#T4o?&>?i3LOqsdiyu!LI;yL$F)cbqvdQ12yLg0j7nq$Ecg51t-o8fn!WB
zBGp5Dm{}c%DhjTJnwAmeW>g(noc-=1l+j&Ck9Q97H#~<4&cbsev55)jW>*p!8M!bw
zj#uSB<@<v15j>EN25cmERu<bUSvVZrNJLVpCVR-#YbWs4m!^sM+B)U+hfVmnQ4_Bu
zwwIb-?xD|RY-B6~=O81K`%$&~z*{{>?b@sh(TZxy=iUPG(g7j84cKpTUG3k@jbQKk
zhMGmpx~fxqtSXa5{gyqvAqT$f%4sK9++!MQzA{<DT)8&0+BDBJkTQQG%N6Em#_wG_
z!MBlpr=p|iCL1C#Q|{^R+GjVn`MSRE7u)sapXE7WD+JK;8pr~sB^7T>UC<lb?K2!B
z85r>mx3{$COV^GW-<~dKur*Psb|w)C)7S5eR$n<5#y1ogbgbjq+cjpE9H9b32cd#N
zdzlp<-N{>wmT4WLoZN@F4?m-nyx$rc<xt4HFVa4aS@O6@oj&g&-l)mw$y==ByJ0U8
zz`(OpyU`8-?oSNZ1y%HAELn|ugkdf4Gf+*|GIGxtP<Jsniz9?~olyQaG*vX5>-e0C
zSa=%L$mvp$v_$t_&$Ao$cSX^K+353jqKZKkpw=C<VwaiGm;eKu&|U1Lhs9nL5D>T%
zD(;<SV+@j(nE9>)7`*JDl}H6D%0B2%ao*q!0nVU$@u#@wdAlw@9=k{Mc&3mnNKZK+
z?eS_~bWF7eH!4ZsRGMfK9Tv5nbTsZ7ojR(S;RD25W^%_rgG1$$aT3>#jnoYgXNaEN
znU$Lx!Oe@EJ&I#%=C!X_7&33p?xk?>V)O7WXRjD8wJoWmPvSP8<yT<{ZFwy#2%VxP
zv7Hjz2m0^!Rvz^$&uOHApKccX398C6*X){)dhyi>Y=0XVM4WR6d06a^sSfzkG8c%*
zQi>EVw)u&Gv?~}ni?7|ga^fYhTv|y+RLexXhOHT@PM|5h|3RQLN_<>~;oarz5i#AQ
z0F)SUcMlM3kI^zn)d|-cDlz@UBPoGHG#*~;H1?cm7{bArHWo}=%)oe|4%x5}cc!n5
zj+^L%t#weIp^z-^8+JR4*usF`Z|3t^#mrETA77p`uCx9KZmzZcg0JQ{_d9Z`1upE!
z_M-=2mlNL;@*;qs&}G$@LT7W7k?6J^yFt%fhreRFX&e=2Gvb@+IQ97Sz2)|UCf=xx
zM6Z-<4K!fR`!F>w;=7~$F!5bl&RRjk(YneUGlQo2?OuF>+C)q=UeZ{$Sd2qsTSxe@
zfqAowj@qsFSdUGJR^qr0+GWn0;&73yn*=YCTv9`BF9zQNO<QigQPEMmy%b|V)@|);
z(s-mvM%xN^0Rq%=@jPV_4<xI1{6jh@`5uxAvhmtCC_!mrSmF>Cp{NLUH=e$`jWBlq
zY*b?6d0XQcNVY~ArL{qp7$3;Q5sukz=RIj)wdYI0^4eR6LnJb5lwIE4kb3)`y>;qZ
zl<q8Xo+Ae#(0aho+LK%^<{!z#Z2tsFR9HJ^ko004N`#aDJEiFrc?Cl+(2<VPT2$4M
zf7ZtO4mUa8X_+*On-)LEU{#&Hn|AaDREVL8yJ;&nD~Nr2P5adTF6~o_1N`+uoAz3F
z=Ac+GGi+BkJ*-b54@=o~^l!kRPMy%(sZ*@L&y5Rc=>RSVh{|)5OF5|2wg#45SJmW4
zR7LVCR!uv2sRj5TOlnfN%H`RMb(&5GV1ix5qn_qv(`lpd0=u%Bbc^fLl}d=}M1^~*
zg&{$x^_lPbA*j;!sWHDTFsN_kle>mcHIAyer00P--f~9I<!qkzdduLcz^yF8qcY|5
ztqr)O%~b6;r{HxG?7~k!zu*aq#%D=qG#Unf{78D8i&7A{!MT&bz(~#tABMng5foSN
zE+ewikBwiwRv}5B6k1@}D9O+&e%XFEl)bmKeEXW&AK6v?`p|YFalSRzuT>s6*upX;
zd_-Y;e~YeMPR&Hc6Q>xc^X74=f>G{w!w-oI0?)EPOonWwXAB~Y`e4Mla^f>ZWAXEt
zvJ6`j+@?K7hNRTx%=E;unjmNo7~z{*ZypER6O}kto;sb_hU1xjsaK0Sy^+8E<@}W{
zHc}5IFv*5*M+FPnA-ESCro(^M<hml0b$QyA@xf@bPknW$Y0M^`onA?T+8HTdcVI`y
zvs66zc9iq^tpx`KJ-F9fyZ9W%VBqQpFse4YuZuo9#X6OaDGJHecLMa;cw11!BgEMn
zs+Mf7TLbY#M#{Q^z&Bg|Nst8@kd%C1(+CFDMypk8r)9Ns;+?D`(Oc^C?|=oLMLHgH
z|Gw383kf}ZNhij3tVhJi|Gaf3`1g+Q)7Kn6`BD8z-kJe56N=VRllX1pl$;!DR2GI3
zX{%-;QRB?Ve%x`9KJIu<)y9(7!2<Ta|Aii4b6Fp}yXBG&bv@2l$2FC5t?L}-a!N{w
zG%=r(Wi7MozLNdqjse$W&<9;GFQe%u-GMuVC~sv)ZAGqNX{@Wda>{m`untWXXQp%e
z^yCTX@+ZBvIJc1zr=pD`;<<t8SZAOlhi$~lQ9Jw6)YB=sN?TmKF1g`l%Jf|&b?(J2
z&E6lEzc$XdLr_G(F3VB(qX!toJ_eeNMeMxT8(NA&41dgezD;aAK@h|{f>Vh*j*U5d
z<W%ZxU<xJA=1G?M9lG0IeYRJF)E3p7mc*S8wq##<T#Y}FaQQCMLed7mH~)Py=7m6B
zx&>L5VQDw5+h!2ih;+?mdrnHP<6s#Sc$~Jb=o)cCU^}Nwi;Y2hXf>9)(0as#ix(Ry
z7d*`F0}<PeLd*>1fox@%QHIHTMtI7eMi2#Oy<PIoSEFxknF@}^6`xhqWw_*8&$tJQ
z<vmP=E|Y!KhE3abH8sO?&wI{wS}wKCQr%i54Tn$Sf?ZdQxKZy$Wiv5V1G!S*-juE-
zD)QrkFYeh8+7;Ei0VM$vxV&4zQV9Rxf_D;A<ytjx^?-1%dhaKsr?T1QtxOMBLx_6W
zGTfAjk+Fux+}4~eCx%V+fO4o+y>`J3zs47SW>iMb1bo`WVpGx8h-!EKmRiI~%a_<k
zNp&LGO2uFLU=mzK6{at?7(Y<)-dv-Hqp#W{bjg2NOOC4_0Dr#bqOua5N8a5%RMWG6
zA4{HAw}^W*uBFSlDEkYsUb1P$!~4w*-nhfrFI8C^?dKcaO5fcjfbY4+4!);+<FS)r
z3Q2!x3)UoOKlYQ(oz^f?=4Zg2e5ZX(i#&pB-4RSLzFV2F`f6=8RgbDzXR@MT%pVKq
zqaTp8+Ak{lc_fwQI-Fv?EOZ!sL)-gJvU8;AI5U+Bz0G^DvS;}BF&qe&TeQ;1?$q|^
zf3{6)rcs5PboV^l%aYkPqc}3A6@~Huj0F|w;f{~&4}f4`s~4`lkggClMHa^jO(7nh
zNamO5Ky9LV+8|CtD{}fFGp<wjN?vMzKhUH(=$p8<S}JM1rJ#@Vv1}5MPH$8RbF4ax
zYh&MNZ+&$`n2kwR)D$<E4aBNZmz>lT$qeEtZo?;{yu8PUVlGTv-KII66h!=(QS~la
zF$mvf{4_Ag<}~Sw*+=crrR2bK#eXDr9TCjRNO6n}Klzo3n5SZYV7XWjq7N<`&BocW
zSZ#7@%9Rx2@~FqhxtDt)3JVRcCowS&wCrANQf6IwBgy_zUs|KRP;Ti_*tBap%H3<q
zihjLfupasv^EKjnqEdR`m`yD&VFd39zT@)JnN~;Iv6)0{%-Ja|oD<ejeEXswe}+Pa
zTWzT%zSJ*pFo@QGr>Au$(x#V0c&D3}bZgTv0gG?W8kD5+N9<aM5GTLB9!E9?44JG~
zVP>^WVT#RtXB~;xrKP1DHBoQd20j1j)DsH$gkrJTDG|qGOs3ixm8ZOk%If~u#U4%D
z-~PfNlA-q28**8<qU1}mNW&?$Co_c?=Y0Z~N=6m*3661@HYxb6U^N8sNa8xrw|^<l
zsi0mfb$hnQ*!IK2v&w{vtL8ip5=Ps!?8oywcObx#BM^u-QGIm{a_Q;x?E9IB8#7Dq
zRc|DOA%p?!K26%vCnHPPv!sDXdBp`aC=lQNPW#|V90U4=2jtaPcAKDiu?GBB<D4Z9
z3^swg!tquZ@ol$y;jHf#KZ(=RwID@Z>;9Oo=$N;UKXE*pZpUVpF}_Tj&7}UbwypiU
zBXDRLu_H%(P8kds1rr`J`>G1uXwz}j#!Umr?gOb<;A(sCRu)G%QEWtr?d;*Y$G%#j
zx3n%|r)+z!2`&T&sYQTb7c{we*xTGBHhz710R`&qHIkUPzLA_rck8Ig+pZ}}`uk+!
z3l)lfE5&t-BF$vSy}%f5)O!KZw8nh%gD=w>&FgiO2JTFn1`U=Tk<L(v4|L*285dz=
zk96b`_rBH;ioylt393p<XK0&WtTUb5v#DszwmQ}_k;@g(TwK^;pP^XGvJ{TCcITDz
z3h=dlKQ{JO*N39Mg(+1upwi=8%g3x!H9dfOlKIC&ynrps;+_M+r-Qb_Icn|%F1=rg
zIT!9g+i~{97T4r?$B!dfDj(;Levw{Yg%hk2dYFIf&&CVZVY;-vF9%!AmZU`uB2o0^
zhcK_%vo*M*I4A8&N9sS}gG$dN9|QGVFsJ{%I*=)v^Qk-!ftt_U)=y+55}b0F)-N_C
zlQ1z+53Fu(MzNHBI%HI2SQ7<YAjKsWZ-bdhguw?o^NR}YQPO?;!|MxfUic#9?&#H$
zM7<ozNmGOabT_1@RYRt?ucv>nnf&<iGkqPM5!LkyE80&}*_0j4QEUgFCFceCZO>^k
z6BRC-R*IALrZ~iAwuM4Em8!Pt)>G>0XNHQ%c-V{9Hy%iuBK9ZaUB|8iD)L156vSmJ
zo?8M?f%VwN*1SSvhfOTR;73IB%<2c#ig(b(Sb?+aA0rVWK0G8Dg6{{;JqXx<X56sZ
z-dibo4^`2Zl7WhT0Xaz<B=}8}u)rBH5u-C@ZM4^uQzOxKdRL^6Tugs$hPuPdb>vx*
zPMDrbRw_Z2i3M{oeXeQ;7pJjfKo6Ab`b9cAjsgRAv_M|VJ1ary?OBgL=)P1_P7;75
zT|p(^f<A;RY}~u=o-OQ8o=Z{4AZBFZjWjeg3VJZuOzonfRJYdyZzCZrVbR)tHGB&>
zmnG*zcvkzn{DFS+!f6Z0mSqO}p1gJ|Tz=J~@{Af)k>R|N4Qo%EoFt*^Vo6Df-(g=?
zM<C~3n2)Fqx}zK^(rQ|kpZ`S+4K`+PA7tSnIWW7vd3y2Ht5<F<XHPDS?>XMsfWJEb
z_U#h59Uzwq>AfcjIQy2>nes@C&~=jDMk@7X+xt6Tb+?OuOWi^iV8ytp3fErvhr`L-
zo4)jzbcPJk%iNVv&dQe4hqZog(W<9sXp6?V>KU3?N|2c=@{_x`Y%{(j(*?(J2}DKS
z88hNx<+?vcWV+Ommtq(tCS6k*^&Z1{1c?@X0+e1y&-)2hz(ej%e(eu5>{`ndbcHTi
z7^jb7Tw`ZD8+V#PIFJ&IZ~+#aIfbea$VzrNg3i~zy+6bLF&h;UAs+-K1=!Q0d~M=L
z5ED`)sNo9jO9nXv4<2k%HXAtOqIXJBIe?vfn3<KVh)vy_O+snhNhzPszcXJ$h{I{9
zqu)_1yB%woHTc!Z&e)EF+llWUk6EL9>6NKG42J^G>gf&D#N@Ys9+j>M>NhtCTIvyE
zqtC&bs4Es&;pW9f$~l9GcrP>h^&cohS#2*A<Jy2rrk?#`xFoxuo`)cuD55D!8h6g^
zsy|J*Ai+Lko!OH|7p^$l27v-3gU<d~Zy!1Ni)o=JL!+P%+9&D+lQ2Ww#v+Iv*a#D?
zh|>dz3rwOTh9qK2Rl>nU<Kf|n!!TWIcMy`^ekeU}5zHO+bEj~2OgVS@UC9n>z~i#*
z_zZe?5XV;=X3}j}(V2zld-p!@sZw_8Y3l?ueQlKQ^Y3}9gnG%?Ui+NWY=ZWABQ3;@
zG&yc-s*fZl(DwTK`T%s_iZpX~o76Lt3Y=E}iNujx$*E4@+f$BRP1|Lwy;<ALJuu`B
zYTFq8-8_?*)`)GXQe|oGbaM4m)Mtu#NNDJa+hykCYs+6LUbACq&u7Z76L;7=<xU`5
zbxpf<-<(^8rs)Tge4LvtmDS?f+b^zE2Uc9yF~IdDS<zFeNLH(o(+IEM2;|PnkmOEZ
zh<=1izeN#l{HN$AVe<@RCt~j5S))s<`p0Gm0mHb{c!;<ur^nzNj~dY@Lz{3@qJ?50
zbc4xQ@WX&-5Mcr=vyT|&aBj(G&iU_zB=!k(>@7mlKSc;~$<5jA2Ea-G&}MiFGDzth
z0_l`iR#e2*o!!27JE}2b()6SDv}X3L*|1CJ&p*_N5vA(r==ey;;xO-S*xl>~i6mdd
zwjO2<=4|<*6VaM@Ryi_@eM<;0iVISj7gnu<AFz9E!po})^DH2tA!yD!lTw!)c(1z^
zKl3O;?#n7fK@vW1(>gw|fdgfC@G9EAmbk0>{qxWjcJvqR$cM=m;;@UwYCn8p-=uU7
zgl5b|mCBDxvla4q+=QR@)0b8s;3ey}E~0`2ig^hcLPR>XJ1{6_+=!M?N6q>V5O!?{
zq&y#R%Nv*^X_R7~SeF5MLnE?hrdKyZ``l^YjW&rpxZVb;-J!v;mvI4iN=xW!tESsn
zOXhy%;BxFM@qA}<NaEc2s;`1C5j4)Mif>4z8(uv6@NFcTu?E*Y!?iP>_EP5gm=m0k
zsQS#nTw*P8pX14sAW<g%i-YTYeeFpJ?rKZTB3vV-P1>QBzS*pJo$Jy1t!50pM&4`J
zqT7ATqj84~ri|*f0#2CZf&fkPbT(8zEML1^KjM;--Jir8h<UjfyqUp3BCtQXB8v3z
zLSc&8SX0Tn`Lie*g`+f_!kl>Wr?LO5s(iS4=Axr$SG>ngu!(LQ0GsIZWlD@7-KIi;
z<d8t(m&arHx7WHDG1@Loii|2o&rCfB(R^0A-}W45KY_KHo~~han5&sW0v0FI*Ck?R
z=+7xmnx#Aim%kcm0<y&MctB=!_3l13pQcmj+u|*H+Uuj{Zo&6$%)V4Y=)gC$WxAsF
z7S{(sbS5SSb6{h(3VOy9x>Lt-;BE{+n`0@mMB663+?X6yQ9#t|45HTR&k9{f@6{no
z;7;3GU#-!H7E9BLMmS8%u)gj1XhBL|UeopFYN}%S8d_3M;iO`s<MUQP12r;EinHP-
zY%}TN=@b=RC45<|Aqvdz|AD7(dfv!Zf{5`_oYqG7V#zpg*Ird_UnFJsCkL9sPaT@M
z6_d|ujkqY|)NA%Y&;>z<BqbsJtusULh!65QoJdDX8Q<?X@)|<k0}Qx5v#}&ht`Kvu
z_Tb|vDk>zn=CP1aU+-{ryM3YFbHzNn>3*tQF`tkl(Z(0rJl$A3nm$%abrxb^l#mY|
z!<A^Fh4%Ox?mE7PaImd*=^m7tKAWN29-><4$v9a-egjoX<3>8;6&1px(abYR+Qe^M
z*ljJg11VEmA$Y&Dk6UI_i)-8NvX$`Oh`H6~`E3x3P0xLnVRO^Qq4%A(ntkx91Xktn
zw_XA1{jz-ZI=PdOpIY_CF!;>MJo{Bf{z4p->Ht_!-zHxO?B@tbcPxD{9FEyK@_`O@
z<{&o3JYW@z#TuBJCbG6<c=czTeblut*+HCKK_^Sxp2`+R=XeK~-9>()$Dt#UgupcX
zHZw3nUu*?4Cv{L=IJH(E$jo0)3z2^-2_)!UQJ5nF(iwTGT3c}JI@)IjnCg|+XS`HU
za<pf6M&V00y1>RhoPoL1fMeUe*>spAAo0(T!nvGe6{q4!2T^xl&CGq{)xk_F-U<U|
zVF>w%fB_H7DE;`T-T;QYr4<2j5SNCeDn|v0nPf+g7KlYb63|!|twq>#e5MRv)d0yt
z5wPjr%^0T7ljMSCIv?siMGS9`*0d!QC+1?t8E@hj?n2K94r3eq=SugZ+`KJ5*i>AS
z1WFBa|FP#rL|T$n3ahQ&(vAI@XTy{0_~qsMv@*eqEA@jk+>pHOtW4(k0T~$?>6tVd
z(3UmnUu)imptvS)E3;U8WsA8(a&H;B?VPXW^DpbuDAqCYs9Y>fy`A05D0b;h9#BG-
zj1F3X2{@173fCl*rqr_eWIm?MM_2pFFAuQB(Pfv;;mG>ud?v*<nO$qITpS~5pl?aQ
zCXr?7TC%Yx3U|C~^46UanUv2s*ku?RHP_rb3`a~g?iBh)tEJY*riHW5PvLgq$T(jD
zD`w+0z`2+eVh24M;$<`<)n*qSPmBC>bOG_SGG75$I9VQn6+~DjTmW>g>Aof%$2@(F
z+pl(AxsuC!;M!avrp*~ug~`a8FTz9;^V2c*)^<Imtt}kVR?XQ)<S#ASO@`x{I<ZPM
z#7o>cc4<wCbjNIhvbM6Y?DU;^>*pC<3LJHp*Sx-o1dp*_)!U~OOe@S$RaOqlJ;Pb3
zs82qQlVy%TP(+M*?A$teXTGx1ojAI)+z;sH7Mlv_PN8-)z|I%&kS(5s(A0RavKNsq
zqu)?8*MOc8n%<u~w(#-Z%Kg?rTrKy6meyF==<$q2Vp%Iuc00s{S|rVUXK{R#<`rlg
z|3E1aaOhqm*NIOtZwwJL%8dA!RAv09i9t?E-jOquYC9HlA>6vl*V;NW0n~k(cEXk<
zuQ7qXL^*?95Y>vjazNw3MPi<e=a-A#0IXRi<n=<2q{+wkG`~=n;^TSwt}){$<pA@d
zF~v+U%MJ-L+j#pPT@ZCif!4fqs+M~TSy*qMa`1UR?}@`^v!WgvMWt<!(r^j2bLr*^
zot8W5RY{&t!f=sn9&KTt0N6zH1Ib)R;!aXv+BG?Ec3u|5?Hiu)({4KQO=bP0K26_4
zuk!K}tG5sLkDK#!BT1s)p$QYvVAvWeww;9XO<@3NR+_Enm8aV@Qw-%DJLMVF*9Ay2
z9K}0n^I#3M6?w=vTVoK2P1C`SLi?VhYw^`5AYravpb*_f!Q?%%u|{F7WpTJ>FXG*_
zS|ms3Ajk{o5muZY4&s127}9pvLXl#>V1oeL<HK#ZdceKT(QqEx%1dW0j^Z{jvofH`
zTEUIt?9Uz?btST*FuT}!Bf9Q3Jdhw6DrAuRbv*cJ+jv_RZ7NB(<6j_fvc7jF^B1=A
zwz%QYl2%W0h|a{8gHuwy9yR*1Ei$K0of5@8=mvABFoe$pU>~X?<uf`@2cwLZ=WrH!
z!zTiCO>#^1#Ic{Jvx|Da2>GpSGnv-+1{X9a=Pdb28#yaE2`@z=XXk|Wm#64e6W?*|
zgs?Pz2#J&HYRR^ajg9%Fs^BQNS%u){%M)Hx2e+haKMahgHhn>wl2#7Q#^?CgYkkEA
zo2@Y3Q@V~az=)=g#oE*hUP!QIP)2XUsc7zqXvp1ZD{Hb%aZno<fOV~yUdj{Wpe`2;
zQ;-GcA)!($Sl`|r*y%knP?LtL1hnGfaI2po;Y|&-d6#s?d{DSi<Z0KQac-=mPNagw
z#Vk~Ym%rFwYT%cvfE>h=X?<u1p3Gt#Eb^+`3mH>T5UxnkRFA+prA<Dn=3xGG-M}m-
z06}KOn4!lbE-tQ$p1eS_*x1;C=;f7mQ}6mBQC2!nDjn*S!0imlOq*a>XS5`?|1|$D
zhP8)b$T607JE|+-Bnx>jjzONG5znn?K?Ue%Lrb;z@s`&s4{UQjqr2F5q9WP?)dmJB
zQed#1%^;PQb{S#JWxQraVgOye^C`5rTajt<KXrnoUIjPKj$)30-A(ol-j6HMI!Bpq
zl5m4&tBa23^ow#F92~X3p0!Y9Xk?!Y<t3(No^eGbsf0t81h1!9sjDfIn#paIS3&3N
zB{Fi9kckE^sL&Nx;=F;!!9%3^4~TnV^c5qTVSDBS#0ra``yZ{Z-I5B>ratzEvs%|y
zbs}XG{BbI`cxkp<OA^A5+mElLj~hm!lK>XdXeTKl(Rv*~!AG3wsfs;dc}2Q`{~<}r
z%N#_r<E*p?&ioUemyHRI+$Y)=aZu`$gp~TE7t}w2B8Xfh=qjkibWbTcwh99BL172i
zeFHqd6nS?q`1x_<NaiS}KH}>>XkPn))=PYl9Ox^=kWxKfI9x%2CQ-|^`j)zC7Pw7u
zl7t7$JB}90l9qOL0P(obb7`Eb?x`sVL5c_8T5Q%nh;4uVE>+Ffg5vby#llyy3oo{)
zsjE-up%PBwikm&pqUCXAx;MalzF4G*Id4f+NMFeGEg&`tsHZuaT3)NpB0fl;zlrH(
z=9BAA(@Oe8;-*kcR!-i<%L@8r5Ce}|-kHiy#yspgL^^Y>>74<sb&>coo$X;mWp#*(
zib~o%xmSx2W?*oFW@$vFnwJCX*Fs1voZ%^zel2*D+Iw(*9(p&Hjfo|WJ?!Nq7G`K?
z;MxCC#0@}g-AWavf!12x1!tOkyhZp{D3~B$=?XEu{8q&;oXC>ev;j1rSG0m<P*C}}
zRSYwnd7SHmC0hLqlv_`s4_f?+z5VOx^i=_)%1q`SFC+ZoK5U@GyOx#T|I8)@o9M$3
z_V8Ot`lGrGnv+4$;kiq4uBy&tUvg9}^lYTLIcVYz4mTv&GV(fJ>3ab<;`4yqnsRf9
zK>r?AxGtb~KfFUx!qy?c1%HNX(cXsbUse9&Aq3b%l~u>&#|cqYx`fTEnY%wh8TZyl
zu}?ZrttYSg!rs`OWZ(Yb{rmGYpr)OK`v0$y8_DmWPhECiUY^N#0o!l20#u{2BCj%V
z=<rE68|sm>;cMqP`|t1BB#209NORR*FcK2zkj*}MjmO>!?*-A&ISAZ~%MSgh3;geS
zY~n<I8(i@6FgUJSh=Fn1cP{L^a&qE_*Av#<cJBmICe;ny$=sGEi)dFjZ}TC&2fWK#
z0TfI4{BofEl_Uv}+XoY+m7vM85mr_KmWegFK<#^o=!OX5EV)+12>rtgBl>a17rx}X
z#_MtwCcp^Ul(QD&Ki(NQAXw%C?!Dyy?>G9JW`*PkEJwQL0Y+FWSw!|2n>zP>f8w)f
zkd|*p&E9bQHRk`hTC9LCRrrU?D1n2Q4c8ru`L5sJVFbFJF)me|{>b(ZGl1MJSqs7X
z>ZacdQiS3cHW<!^k%>RPKj*y~%!*Hf$feNVTLXW-tFIY+sZYL^mmjcEdT?#$+j;c+
zJIt&{W~hI!^QQ09^0$}&dCR7BWU4ZoUr}L+f%1xXV*O>eo8SNZ85T*jqJgvEy9EEw
z48XeuR6r4I-c-_sa5jjyLEVTbrNRzL10bHx;uV+`ImbU_=)X_P@7$jeU1X|SMn9Uv
zjQqKB>yAD>@qN#HgGJhIHXPddPeu{En?fJ5vE~~OpG1NxU?gofGNXile+OSgxcH=R
z^3wgAFbN=2HFnuq6pnRd<-FG=n0$Ytg3snRaZ-&l5seA%y`6XO-6K?8S^wE}-M@+k
z+sFmF#ZC<{kVl857dz_#Nw(Or)ma)DtsPRu+7_MtJ=&ze#|8u)PYAtboj|E!+1c5N
z2#(C)zX{H+3I=xGLxO?@aQYg0d6hzG+`bCd8=c54dk(>R#8EqU{1D|N1t+H`*=Ci!
zYmZ#sKf4(f6SLi)k$USNj<7#hE<kQ?Q%8pt0Oj+$y6`*_Huur&>@z8kK4>E=-T@)3
zTjhtnzb|Hh+%7ikjuwY3M5E;A=iBvH1^D*Al>WChSJ>r84~1`+0AfY~<CKs4ahLgW
z{IN?R>*&f6xt}`9_XbXeKZVFEQvv2N8|pWT4t+}1y@>x8alWEZ`G>YuUPi`Ch?gz4
zI=SQ_Gp>)wHA;mN@LeWWR22xJP&Ag6lhYsvo`;`HH}AoJ5szI>{|{$h0Tt!e{jDgX
zAfXaUs$4-Dl#~=ylolAe5s>aK5dj4OQ7LH^kfCE}7zYFtkcOeVyBp>^&wT~G?~C65
z`qsMZuE*hdPV95e-uvwMZHkkp&R*0pHZ~4>^G3{dxZg@N;sg|p2)1{jpuH63Q77Zq
z-&{&QgWKm|av_M4UtLeRA$)iqygdE_>qQe|MnLlG2Y=7}zy4RO3@EeVlFRe+<8>&O
zEbfBjA99O-%$W+(C3SBo{XBt_r@*G~ds5H(U)}uAKjS?i)*m+!^Wolst;?kZ%)H;j
zH-hF*WP=#a|I%o(6*}*$Q)7ew)6+roKmHI;501+EN1>g=eu<t#LiOF*-<oQF?u{5o
zBYPmC^wU1~`~?_vL{`qjL;C%W;-6m*URFMSI5iPVs4a>{TwBrq8sUHc(@O|Q#VpzA
zdjwbqZBN=l)9qy#d-AW-Er9kZi_uf(pJMA7pnxw|;Q`{n&*}a5lU)Ls9P5`y3P1m#
z2`M9W*SmhFH~f?bO&VZl*%DI!+(xSeU~`m>2(aV*8?h$6q4^Cy7<+XvKfn+`$Ar;$
z2#y~R;peXV=aWk^w2z#%B1jI1-82BL`3BRIk)I3q&u<R9i2$4b<s}x1pSxr78E8&s
zFq`q9wVzY`ucxMW;LQ#@C;Y<^0$(-g_^rUsAB5fCFMA0=60b2%j{r!#{|x52B%qeH
zb9V&izjge@8}#eMD_IYe-2Z$#2`b#-><sWtJpU6%hTe{k0wZ*|z*z#hM}K_$3N%Mx
zoz4Dl{a$YJ07INB(0lOXOUO|MB`LUf%$_-D?Pxj!F%AJpcsNw>X&iifSq2QH`r8Y=
zcR#n=(N9Fs^hOq<ISw6w@xstHCp3!G{~_dm+r1YxNHg!Got-5<d?g+qOvC)x(v5$e
za8?=UxIj5*?O?wE^@IU1kFa7hkdgiK$D^LM5zvI)xA~laM*RWH9wZ6n<#Z@zvK9E$
zad$!?JceRE=ADWLAR<yp9X%K<03vm4{2r;Gnr?IF=wt;_M4G;-Ch7d7E5=OE)F-d-
zdixWRpAWbbfRn*a-EiLaU#DIOSkA@p<iH1zOlQGu<Ka|;{G&LKe-xxvr?p{Y*D>;_
z8`#HqN+(pX1orH1KX`G%otOQlU{%fy1r~m$?=~8**oBQ=;ZwXD6Z3IlxyF}WwYT&a
zARH02U9;N^dX8SQpGT!B6FQ7Dt&J$!Cn7ZGH%H&dy6IRAE$gWlx_P_LGU7A-6%oh(
z)%UDK?#n#DdO+vxuiu>C4dF5Cnyz>jcM8{3gG3bjEKiuC>bq3VLa}D{C;{#Px?*9F
z2?yy>?)L-4fbQ!Abl;1FMlbmOpymB7$3!%s`(j;d^+#}6ybQ5GNK0B_Ubj*@TW%Fo
zfO(+l!_t(}?(^qQZP$8rv2KRXS3JQbG`w>X$5aD^3!}c<$K~^A6=b(Sx(9$&(X1Ob
zi(+^A6v+5BzcgA3>5)sd0-der5Hr0voMZei`vd|>0f%>EYXX8R*~eBiwyNsB;W{-}
zm2cNhmulLnr+O)cyB~XBQ8%l@2a5?lotF2$no*-f5Bp>Rr^Omh?Ar((5M9XKiC}23
zXYtQfjAZBPQof>5n)3zTyKJJMp~%~|K4AMa@KT8M7-|#wh!^YR@IAn+d!rfYfU@-G
z1^mYibe~>spy@G;%f6gt2}C`1`$~sq$m{sx*EQxeSK2T)!zeYh<&DQOu(PqA;o^4`
z3!6Ml`>ZQkaZJ)4_4#EUaox71lT~LEvuSVfcb{{T5D<s<x2(?yexcEoH|7!#z3^=k
z?)aR!1coG%2DVMLTEyJ%9I6My7C}NLO-orp9H6edi^P8{ttMsed5jlsnRl0oTP-K`
za$ye$FpdV57D+SNGV&zInCtCOpU>zoo2h7pA>$%=o5(2&ROiNBn9|eihdz_5Pl);j
zW}F|=QMQ*GLS@t1n%VA=MuF&JLUI`ZNAsqJG{OQF^9oV~zo-E+rulRU{R<IKuR^!W
zz4bQRL2z;=u>SvmlHkKSbK|F&SX1*!Ecj_yG%aU%U`0wbf!y`5i_ztE7bRsSm*;{3
z%8vO=zBxBs=lR@C#McE{;5Tl-js!zobpV4X+dIqgQ*-<c6MGH~*V=VDf){*c>xHNy
z$#=S&Ex5Ammqe6+<sNDLE#C>2ohmfB&&;+Sise_5UsWPaKBbONRxFngW*>Q5Qu={i
zLWNhkf%SvC=`5;<Zv_JTIzui!!;FNaB;(x?=em?8E1r_TaIdniL7)Sk^x9=sclrlC
zJWlbtfwKegP8Gad_g~HqGO_u@0T#ef{FZ3406Ir=DHET;IcmT*^vtSIN@qeSk252l
zJ@az-+W*GwvQ-BKo>~8U`}~x&>H=nJ9!9hSrEu?hw{zau*@ngR7X?M5Isl&x|9b;F
z_A#8Z>;v%4J#5oC?M#*|L`Ic$!=PQ%Y&XTiP5Wr`u9n9bk2(FG9Q)Mo5lWAIDs9~E
zR`Ak4+J*ppAeOg$;mhig1N}urUiN@?`9hKYIQR;QP3>Zn-(EcJl{m%~_V(={{KWX?
zPHw--;{aug92c|=FALW<-w6X%t%l1qCn+Zx&nhXQdyb*Yc>$xQ12A4E)-PJpF5`f_
zU>0sPHUZ|=%n9{LMpC+K#pcDhKCTPg)vz0OECI@vHg%kv4quJGJ#d(=pW53wX|)Qr
zr7c)<4thhJgM;-u&d{U{%?^FD(Hb!HQalYu^v!a6D<#8sCDK{uJwn-`Gqtm8j^SPK
zwXqEU!DBR(l*3q0jZ?nuTaR>@1F!;sv$L3~^EgR5=0<H$x1~6RHg6lbCKrm3G_ych
zm<KJSk%KB=rw-%)>JWFM1t6%nwWHp3yD(+;&WiaeyU#2-(L?A>|KCJzC>htSqk65&
zPfW7u@6Ch`>prh+yhuy7-7^X&qAP?yh2M#I{dyFRL$~cvJ^f&|n0Uv;d*t4D=RE(*
zyc6q`mb5VICKy|Efs?o3*07s;R%z$R_@hMihq@W5_ah8|R7y)q>KD=|ZSa>&oy$m-
zogJWm@&PzpBA`1j9|u3AHW7VFOS3}`Wq()P=3`<?6)Q<gNwG$jWmmb=hU);dkm;u4
zbe2+=H4|a_??zu=T}fvl-^kS6L@KCgXqbT_1`ZgJ``G}x08`c!umDq5R@c}6aE+c`
z-NnU)EtWyycX%wrvVdXj#@sZ7z-8_a7UIoY&%MYs$CS~jyf<aq9q%1u<t*&)ut>|A
z?mk6BEG2dE@rl01$P2yZ?@Od*B=Qu|<tn#PnsoRDnsjG#W9ZHn*lqOszGjSC5cOmG
zt~$<02G>haH7wqV)U!wmbl8a$F#H5lMt7}PvgVALO3bnqc)q#tH|JCY1Ces>0u073
z{IhcTGKqSLLplj5X*Vbz-ICA;@QudSm+RPiKvv5v$dp`w=&|d==7!q4cRw)3y84ns
z1K&hm{^MSx?+`{tS0@|9Hg$o>pfx0!%TgI0YvE^Lo75MlanIAVt|BRON-A?Sami%U
zF7E{uu=EWK41@%_L%Na@6IC4?@&hUPTB?JnOo88nN;L+6T^uUMzdXg0L18yl^nNMO
z6({}ltmky2dF`gI!!DVlBkJ?<^^J*%u3jJ0v=*No@_aK}UBLf%rL9sIb^fo&Nr+oA
z(hu@bkqk<ljA)?($pl#O1ZKC<5uo0ztq*$-m>M{g0<go=b`T`r1b!DVN3CLcm0zSR
z1vX{#{oO60JHXscwuUt}sc^c&ri{g{>w>QrHVQR~mQ*gY=`sjcb$2hL0%_db04y@2
z+sv;Z7bGvXdhVJ7N3Js2_xgG}jGWXerqi^sTF82B8n*N>l%}q(ZcmuqvCDw0uCdXw
zahmxzcok*P1&~enwiIwR5k0?}HKFWbQX)-loa|v-Y$_?xMD4Ln<%$_;S3);E?&)oG
z1bI>@*wx+D^;Umx=dMU@W3yNQMdm)y92WP+7y-cEfEpAf=!sy_)YKbeby69~YFDO|
zvY9dZc&+OsI>QNLd{;^9p+2F&Qc_PNx$8`}<cIo~$&gN$0jd04L1x&%F02A&BSUF3
zLh;EG+M1V|X3VNkl5AydTEHLN15)E4prk3?p$^C3A&lE;g8jy;9UH_K71bvphhKv*
zW>;k)qiQa-WiZOJd`hArx%>G!TDKA-gdmy*e=kYJR4m~(z7N4#azX-1SvEa2H5ba$
zQlvJ+4@Mu!{hwceWUI~l2s%iMzCv_~(rxq-8Kp*6wcoy*)gm(CHiBb4-M?>cvm(J^
zarI1os;Pni$N(e>?lhD{zwLe>dTo7aS+7S$%{)-OplEO?Sy)^`Mk32j+1{!&m6un4
zT7`)zlaBYIHMJ9D%LEs+i~Q>O;K27?Z;Ax!N>W$cQ}2uWfD;U2nDyK<c~0U8?=Fh<
zWa)Za+Q-JmT7ZN<Abmzpc$@p2Izi)uvt|T}wU$5CGdE9X(I{*J2%u&lmHI5{$ZrhW
z!_e7|j^#}PX8t{`c<0n%uk~QEiLTPSGimUAlpjWsMK0ksjIz!AHQsSJgpnwn&2Sex
zEbR58Yb1#E{R;d1P6WMHK}t8Aho|$i*NI)4J!Xb$49z($Z_MkTU*R_Q3lZYo?99!8
zExop1A3l-CIKM%5<=E*jDxl++UR@x`*De@n5cAga^JS~~i(^1l7eI+9`wMhWf&zc*
z^;=KzShE1gPiM0@jk&Uh_1!!%(~T9Al2oO#Z0fQryqh31aWkyK$HxaCbBKDQ)|x{G
z<J0Uih^ZB=tU$U(66seQYA2nvOTW|nei_MSB3iEz*1{U>?Hq{DZpsa+)1>A3F!`H{
zJqR_=KsQ!usIU{0OFfXuS^8i*_e8L|r|==4kX#`OcF}EP1d;EepV(expI<ERy{+1E
z%m+tv1pcz^5yh>)`tF^jdZe%ElvR&l+;0SmO{A&u?j~FLxev7Zxj>44P6i55(Rs*6
zaN3TJcM~{jI-yvvO>q`kT0cfF!M)Yi^#=ZhPQWD}0lQ>=B>{gY0Ey7iuL(i`BV#=y
zBcoZI`?|3b6*Hv^6p3RD{OPaDKoH(~hq~pKP7}!7Gy`6)ziSvXJpSk|48rl(fwO$)
zvNEt2<5LTCu7mI_;dD?A*L59~UF~S2hTTpCiHJ-##p`~;yC_mNt=TwX2xn%eZBewn
zy?s45Zm(W=4N9R0wL`-f>-JS3_#gEw9WTtA$Vg)l{$GNDqTRR<I{2BymcC-XXV1tY
z+jnxxK{7Xl-JJip`Kq!^X){RmGHgwf&TW+y?(-$2={MW^IyeZhhi5^Z<fRFZvD(n`
z8!-)gpwg=zu$O=Dz!z@fYHwa4N*ieAD*)L8pB6!ccga9$hBK9;QyUuH<4q_u8K}r0
zyF&wVC#0i!n*p97Cxx(wo6}O#4semDBavVTQ5orB)gU}_dce`-TE<35wKPxPBa^Wm
z&{$8w1{IG*v}3z-yX!~(8ju`B7$xLhJT^MI=}<L?k9RNXgOH`2Sz=-$x9hqY|MaI1
z?^$YBj#kHr=)u~KjNajp1}gGp(Ln(F=#Wr;wg&|xl#-CiYoDnz-|CiEABN+EDVyhm
zfZj(NF7Q-#72bLnLG+E<d!t501h5n7g=y8}Y<-iKYn!bm-f{iKPradR{Ch=V)wqNN
z60$iq$_Z8(rhcW_A6w>ko6nrPISiacmzxlv^OBA?=^#^x@C?{m7f+VaNxvICp*JKZ
z_jMk)`m+H&Sa$P}nf@lA3SJBd4%bW)=>$3U?)>|kBT-=>x)wyNqs_qr3AuHy>P<EP
z{4+z4t^-qWTEr3F^dh|EOKM!a1JVntERcH+3ZXZ#FJeF(ITzkCgWbpYhtAGL+7quO
zSvh=8RE~fgKyIqsYS5zc00EcN-rmMQ0SHa*R=$N7i#ITjKx;Wv;`2mQJR3S^>5IFM
zFuH!y5tPgBe#d31rlEoU41z&Spn|WhGhRNT3F4I2&j^dAO78@{e}8oc>_lT0pNj<>
z0*`5WZcc#c0^DLPur<qlGO(T$?^k%$jMatr5PEKkLB+;LuWZ-ckZ)S7zu9P3T<wib
zlHU%>z2BS#unT8mEgVvDpBMK#b{o@|fY{buQ|;YhGU<iyUlWLuE5iEtV%?S>K(!ZH
z0Kl9w#jL-3>U4ZXIPN_V#g)s%{f+xbaRS_3kUD-@s01=$-nwGdr`f3Dk9U%Q`t7(S
zBIVyehTu6m2GV8e-Evcw7tRx3q<nlvngj<&Wqyz|N+m*H_0qTeq6}0mld1Vg5@Tvg
zdO>Qry$ZcyBOL5X0NMy}a=7)*0BL{13xsKqtjkxG1N@h!J?^i^Ao#pl+qj)*!tNCR
zXi-NK7I?=;I1woei1rT=6?sE1n-9g9E8}XExgau`FFs2z|AFu}nh5KYW|6?GCJS%j
zI?~>I`52z(@cB4SCx|AW1*axkTd5rfs%D>bGtaE+#!ff7ykk`*U4z8pY%mnyuKYkT
zlp98Wwi@sX(|0u;>FO*zJZNGk#R&NXaqKQ)wYDYra4+PDV_qbEHg>f#ysTX=p)UX4
zg9k4%8H=1(3<sXBOKNYwb_b;?H@u`_G^HTO)P3x}&m^_WWI__VrcOdWLQOez($2zu
zpJdVvM083GNe2S9*ocEyX**T#+}Y13jz<6z*Q10UbvHE)X8J%&U5;hQu1yupXvz46
zUIF%5(O!b8WS>?FhC9(1w?Ky691f6PXk7&9!|_=5@LeYWk^->Sx`QfQQ&LhKba)EZ
zVO;WMjdr`5?XjvgNv7fpYd}}NAyxhBE%}!{@>~p3G@M!@AUo&a$-nQG9hTYhXd&Jz
z>=RJFR_(Nl>spyXKEP~Fad@sqKwl(E{(nL$dJm9;-@ets=DWmgFe^u)(_^;eAE-3;
z)E8I=!iFoJCpYv4tUxBfFTbk!BFy0MIHhdI`Z6ZPfb;g9Kz!~QawTSAQ!ZZX`PqBe
zBsgn-<58vNOhr}dD8gV{A|OsLa;|tLV;$gL-g5djpasC|*QZl6xl{9!$cRVr;I9}K
z6T=Ena)k?F>K047_h2jvkLN&YF|O8n0WK+{&Zqo(fag{2DdQyqd#1qD|2TA!xTCll
zrrnkU0fG{eo}wVAun7Sk1e1&PC5WE5{nccEH@5ek?fsK?qESN$a^Z}3O(r)<n9t7R
zphSF_Vb^4bnsvU)qW!y@811%aa~rptBDEG~p+yG?ux<d=O1hoRo+^c_K-3%D<ZuZp
z2jJbqAUwxN*b`gcp32X!!qO%9x?3uHLPeIdv$J%7nWYtb=MyZ~Zt+bu0<D=0^%x`&
zHl2Q?B~XsW^52PCUCo*`6&}AV?=Lr#%iQ23>W|baLCUWRUj4p~58mmpZYJBX;`jj(
z{?(N??t=@V;7a~Jw2E~Ut)ot^nQ)10S<*TIb(U;1ikGgDpf~qxbx^&vu1Ib>1=Ad&
zDAml~+CTwu8%8Z9Et7Nm5(9S&Hh>Cmux9a3o$FE-$^)?JJw_YWkeNw(z(6XEFa%iY
zM1&zPdL!F00vfOKt3&~VI$6?)`+EQ#WCR%!uqt1L1Q(S?VJg4`vf6==>`s$c&!WP<
zK$4;0BMdXb3kFfLEFU^DOdf!<yVDI&=6dtqJvGD4b6-o<UV~w;RnTBI#kH@=ZNNh2
zL40Pj<_^e6Hvo{0Ya19)3wo3Ei;ERNTI>N@&1_uo?#%lD)*G+{CT8YUk_+BwP!HHO
zSC@Sw%mSUEX$tTN-)EM8a;Us5!d{vt>R}>XrdxSR;r?p3Y33Rm4ylV3&~i5=LWkDu
zFaUY?F`RP~1l0FTHh<W=8pB5z6KVU%Y7tT<Sl*G4x2<J@96Z<QwHY+97Qln(abUe>
z#@BUHC7zMvCd2)W*tbQk<mM^}?O+73xvW%>IM|$)|GOmf=a%5;F1hbV2!b1)He`Rx
zz}3kX+{?jRZRhDOZ}m3=V1DTu+Vhw+n*x#ytOQ0vx{_(H$V(~tt(zd~7RkVETrWqM
zY~tPcT}!X7BWx#*haQAOuh+10stm%<19L@peA^v$Z4C>yva(Kdqj;+FfuM{&>)1em
zRPMRNX9Ii|q+kqv74<Sezv#faEy8l6sUvpp)a^_w5UG1y0B9$>(;+O+D|-|rhXAkd
z5GG;aEd*k%8-%J;Yo$+>!wUt0I#|Bk8^BiHxmr4(2LL=?Edd>%jL<e^SXJ)ZB0$>7
zZ)uS)0cw-Gj~L=k`RHrOSrAB_GmtMT11JAP)OoW}K>tj}$$a0~R#SjaBMn>M#XIMj
zq8D9ZMLr~=CqRCWQZ3gHp0W9XX78(TGTimHPmLj75e~fflc@*^ws)3+PJ+qPMq!Lw
zYtfm^RR3pcGPIO!v-QrC_0g3m)TNt-o2KGkA_!CmBGtqzgtin3^!m?qQV~Q&&pp!?
z6S_~&D1~l8)pV1_0BZ0<&JtQz7$vB-{?g}YJK3+U>g^(t7*mB(8~4bvamKS5eR4yL
zcm#RTS1x53SO>KiT^@49>nQerHl_sY8BDyPdC@9)sZztr3MYzp8;L?847g{~4f1<g
zlH;a8p3PuAF~zv3_gz}w&ir(B^XBEi{DhA!=D8?f5Zv<j_NmO-%h}r{w%P=jN`0_$
z9H$I{>ewWR1>#_%c`RdNY#aDImVr@wwApRg!~ITsHL<@xSdmGoq1>=)APtQP1y^5T
z=pMrQD*#T<U~iGFdx;K9=CL7wg#%%nLU`3X4mr5k`?E&Exe!=pl0ych+zsTZ&BJqa
zA3c#I8B)At@#XnBX|HdK8c%ls(weD>dm1+aU~m<9GMujS0iw2#oi{1W!T@fS<g13C
zI<4g@uESx{c3s;zCvmOq6z``dN6=;$z`YtLoB4a0ya(eMY2Y>qPff{b#e-JSMiLVK
zo|OBNLyBcQtAx=2-!rA^$)l`NMtoM4tj~UUBsb22>_L(DTP!HlIJLl$-u7Qn;G5#G
zC2CF067bepG_=eDl+0G!v65vjjp15;>?9|n0}c(~p+hL4v)a#VWLYVqbb3r{L@7ez
z3{IRnn-yq)SVz6aSCY@}N{@OJIrS|pD%23*Vs{U&{>8+SneRBBX_J6rQBsVUVZ<k}
zZ^vC>K|`Q+|L|{!0MImm+zv?yg_$lZrY_7tzx(HJuu%779%rU}UY$IB<&~W13u2O~
z7W~t=kB@mZMJX~cUjLeWxs2@GR2kWE?Bk(VaT%o(Ph-hrU6eTeZ1vREe35lW*LTC*
zu9&`<<=%yw`TAbhM!^Zb3mzVe6Ydo1^<P71R~!&4xCBZ06+T3Il{@O>lv3t43V3}l
zF3{gIp@~+z0D#E)tU(5Gbv1=0B|toI(VBy51qJNy?Vx@3<R!a^_a)=V7#WlJ68)_d
zlBA4z%jgC`kbH{s;~Scar|W<sS=X19Qk95+fV;Oa5wf9cAkeqDZEUT9gt<dSKd=xS
z_~{s?(nw{iEUulVcYI_|y?wUu$vLlMVn1#Gz&q2AEBL`6l=qsv>DU%5MQS5)I`CVz
zHqv8D&?o!d1xf*ssYVMMksA6=Jj^U4e;vyG$_6r~=87Msyc@hv%M{jY(MEkly{>rR
zKEq*hn2~}CB5}A^r=`6gT-`*^EL4ekI$x{y;N&>Pnt%T+b>U;?C>Zr@OQP+|JtrGC
zPF)kJ2mwK%9W#g3$^L7me7(*x8wJb)o5t&^CbGSmvcZ$#2s#;LvUJqB%rZgle)5%v
zO5d&AsEK>+Vsx{YZBBfURVvC-cR)gQe9T#Zxa_E?rWc9VJSjm%={A|*Hw;u<YoU#{
zSjWkxguo1akcRDi7b%;2zGdzbrPLdSXW8;qmBi#srEDk92!p_*O`!jhrZDQ6F!ch2
z-1{<9NV5kp9EJESE3=}pFb?CEkEK^NT>7GE5qX;6-m)Tzr&}~UsB`UNgK12Ps6M<q
zq8mTiA^TfekvQrq?3`5lNecdP5=yi0LV1P_RVb7+ch_H*n(klDiQj>nMKa=Q0ds10
zEn{k%y2KS;UY*ZRc6SsP9(nT^rzOX_Fw%^`Q#0WFJ<)@M5Uw_qEzr-+_x-~r5hZiS
z;=BK?^mEM129%LvM#pflfdW*VvX}REI+|7MhJ=Oo7Mw<eNA$+iWOE%V1@(N=M0Ez3
zPx7v`b~#s}zO@>Tv%EAi#LPRb&-&yH>Ld%WcjmgHC)+<ruGV@uPL;~lH~4>X+x_;|
z=_ZBg?w6Eih8^V)vG8dcii{$+vzOW|z4rSattbsLLfx{0btnt~p_HH4z2tFl6ngsG
zxVfo-;^>hEtQCR&#M<b76bi_O%Q6B>UsQmVb%oI)<=Qep7as;`aKR)>Dp%qx6YkDF
z6sIDI;dg9cI+t`z#oCEUvk(Zk8Xy5Lrl#ks^?T$uS8*@tUSU?Iq-4%#P_A@lNon%s
ziIEM(2@ngb+IUID&n+)<)8~dl)PNl^v)cI|>)<mu7!DAs3kRR#EoTkRWFn~nKB4kQ
zx!Ek;%H;*AZ32=YQPWXkxYSTBaZWTkd!>QD2B2&RDhJGtpTuzlKyJ`=;v~+5&kP79
z`rm3<wOSw?Wwzk!NvKqqw%0cWqetzh=&Se_JSj8F)^t0PoZQ?Z5F9e0*nR}mL;-}8
z7u<8!tIAyL0%8S=4dd9Nv5)Z^0HEQzRMI4+FA6z~-V2_*9Q!c{fmm+$d(2@7S}jIX
z^U~dxfU3c3+tqq|E=C)UF$)?RB2SKa$%A@%VG=F8Cr{gb5=rs)PQhMOMsByeNkcX^
z>Ua|~FOVH)6b-U)wo=v!2t0O|YO=p`_l%NYH-gKxK$g<!bEJ%;nqX0^!6e6F=UR<Y
zG|h1DBt21E3c-5L@Vv9caY5jE_*1JuDo=n+vdke*Qd~(WEI?SFID`A+G;rrvuXceS
zDKQ(KnD96ukZ^MqHe~Ab(V-c_EPZ+DCS87~UO;h22X;Uhz{trZOpmm^!W&U8E4<wt
zJRPR?I59@({#?F7&s@dB-BMz14X{FZ$<uq(f-ZJ1$+;LYX+w^gavX~k1Dp5uso;+K
z>xR3VOU{yWtJ>DUVjNe;L_?~rP^g_ctVfc0R28r5JOIqprsv!WC^9Efcd`YUOCIin
zt?g`UwvNVGTBca2d6~AYW1=o_8SnUb0xAuz6FeXDVWonB_aX-fztF3$m(7hItdF?m
zu0+ZmO>DV0@nP02IgMrMeD-ioK#jjx`19lLu5ixI9CK?>E<7DnA2h>`RX;;?od!A+
zt+n#^r-8crE%p1}ayas(1#lUtF9JQC0)%X9Q(H*TcQf*Ks_4MkLsoZzRAx{DIT21<
zW8wO_-?;<YMDwk{$+-}sMz%47X2V?f!DmD6F00tL!|=jKkqT9XqB_hl`d*KnxtSPQ
zOZl-P#l1OTm+|*aC=^x6;N^{&PdQo1+2~s{`t8k#`oOkQc|i*906>80TztI=FyGb5
zNTtl*7j&DX?!J3^SM0;^@$q(e;om}~k%^ZVV6S%qBh(1U@~C6N)pG2!w>K|Q_{4T8
zaRSortbsF$m@RwW9?tCy4$Iia07$sV1~+na4FBjWv0-rSsL^0~iANUgw*1W+2*xo>
ziXGd_yehAjqmJZhptLgcL>~u85ID^o@p<)9u3pRADd#7PWrXx!I)mw}YAz&wp}&w^
z_3S~b-tyZ^&IYKq241R^WgT4N_4#%d(y4YqOY_plar3J7D6Q;Ky?{HI^0H;yzMNVg
z0?=k&0kCwt2DnlK8FEz)pdUZcfD?5_)54S`LsxKbXZj`-tYW^>*fre-vV%B}f9Tl-
zJ&@8GQlSXyY-mu^fxU^IriM)q>cAgGFp?*cW-&N%Sq*+JHX_l|0I4h?`;3(MVxGns
zDK^8Pw8o00{N0K+7oPNzA!~q>R}ElgFO|8h`2!STA#MOsP%H^l=2r!LGN^pTBKGer
zK2^Bl*C<Fknie;p+W@#|Ywa%)@Xx-&VaKybv7ZNK_ZVdgN_HKQUQqO;8PuNG?S6V!
z@~OA+la5(2$q9kXL7jJv;8sB@yon-gsROD4*DyfI&)CU~u_zq!y$?In+T3*A-2v!|
zRiH9OsMfe{+v*<B04}eLM_D!q-<r*;yo%tIsZJSNhmiS8D|B4q;QZXAl+1Y?GU-JM
zGouyz#KIrawK4OK$N*rY<t%nwh%Gq5R<&aI{aq-=oo>&$2b2ww<oL+(zwGif(WD4C
zj09gc0IcZgybuxi<;{nr7u_bxmX;)xoEHPr{0Aclg<cR=OV^9}fD$~n<a>tOo;vAt
z)H*c{yX$u)Zr4btCZZC<H8{7~T5c9Q&a~XS$;k}L{Ef4L5~H59%0!ptj$;9~|5R!X
zQ0u#xvroKo<)98Z6KtYn{GI9YqK?Y|(i3x^#R>0rX<IoLb{I&+p@!gKR~8kES*cRo
z&EeHy@cG0Ua(>)&9fd&#>^%QqP9z{IH|BcGLQjFE1~`0cSkKtXH}%T$D;b_~fhrcm
zy2AjPC=h_5E^Ha4Flxbr(48TSXOb2>K&gWjoz7QR#Pq<K4pvNLhiOIMFp%)*0f}no
zKwz>}*%ijFtbS4d{*Qt(u!}UFyGy&RfccGvF1;4ReVJsX+#L>8D9Hw+@90y_dTji+
z&ey`nLqaKqCpl2L>MkX|wBZKyoh`?tMZz+9E5}fn(zjIG^2G|gC4f^tzLA#SZ31be
z_i!^vX;LH%T^zP;UkAksx>oB2u!Jciq!s|<nNl%LM+}1`8w*a}h^H|O%#lIFS<=TV
zR*gOi28&ZW|7F=4{q%z|K_YL9P!h6HV69ETnWS0%rSaLLB;zfa_{By!1wRU$!|Ih@
zHs`?U4vzsDaVOQTH7TJ`oGBpZkUJ9|CMv^ly&b4w7rD(;AkRoPD7;b)jM!st^Hp;u
ztvKoU1TrzVxH5&5H*s){>lMAM06wM^+X^jAPcrm^LQkjS);#b>GDO37+(B)UiQ`{a
zQNZ?a5m-S#@uL^-S{s1-o_u>1@kM5WZzLHE&&J5{w3AG0yRx!;t34WH1^HiV<NA?S
zy!V-rzsTy-FHcfF{6VL=1#Fzu#GP03=HH?x$S8LQy$SW&K0OR6f5RXW$;b@MWJ#nQ
z;hGMrAE&*ix~jzCGJH0pUwMb0(~e()OQz>5+^tkJ$lC0D{2_38!4s|)QEibntb=Qn
zBoW6j`!49V_o=%kO#QJn;JSW6k_Dl-KXC&aM_ql5eJ@gG0MSP60stb1X#UEduy7$g
zB;(<OSN^%{yS5&f(oA!=z{BdMO*g;^3o9xLIOau8eE)dL#(<|Vp%hE$Vnxws5EL-2
z_5u*9D7)WfCRC9QJ6rxSlMh@TQ90bC^MIyJCyQ83;`TQHfOFFl1&Sioag$#(v+8jh
zP=f2?+9bYG+h&>*gFh>%zfo83Se9NE4+KTOt;|rnV&pOa$^G85o@8ad&4ehoXn3e%
z3i7w~fi7KF>GZvb4dx+ryhQN~n4p3|!@59AT*&GyKnLO=BuPf{<Z%i_h<Z;o-p9R8
zU!&;7vDan$-(`Lw?Mto;RAUV-^E8xw@*nB(NeN8Wq)&AQlqPM*vMgP?M?L&t4yLfv
zRvpwP!3k54tO3QI!f<NIOEQ#%^;J^&wfSdaxy{u+!e21Ry<;Ry8q^0@Ig<c9n~$%o
za0y7CzM^qoeR!5eNPtk6^($}2QDvOtcR>-<I!oF5!}7D2MXj0&pdqag@eV#YF$~1(
z^DAP$qv@&?O6Md)Q9-w+KRJPH=Ve@DkaIgCa1Y)We5qr;AKg-`fT$vVJqL{Kt3bSG
zwnVDEWBVEcKPwZ>%OM0@J`qChF8dpAiPapAq{kLS52T+GPzV3s7N71x^yr0|0($&~
z1)NFdP}R~c6_EL8>jqS~26exr&wuN|5azGI=Ad_o+8f|M`Ih~SF6!-QQ9I}9N|SDB
zQ)7wCz=D}x4;<W;_gU%{h^%f05p&M=l{jkk*?}ArU^F-#YkGvX^|lBI*htG)Q|Eq^
z*)>rF8uC4!{+u&J5y;O#CXN8>RS<xPxq;+GP*VX>rbWUGJ1Hp+7j>#QLl3MVw96YF
zZDcws1C{(oF_zOrp9H}qyL)Vz4vNewJ_Vg#n|;l9{&JHp7Q<yHAgsSoIrg4|pz?)+
zi7!jd1^5xtP^+6|-%SHr3l%p~!il|7qVt;VUk8-?M^#|2d{ETXj7fFm{K3z8_rNr`
zT;{Kd#3!AUKgNo<Dh<PN1(6;Krfltcm;R%MAGt&f&=Tb+j6XP7A{J=$-Il3GZLJ^g
zTo#F^0@v)Gb3Gpph3Grz&TGa<b~vP?dK1w>fxSsqU)O$kS(&2+-w8FuiXOIo#DfSv
z?rDOK!6fw?Ke$Ug7HEe_=Z#dz5o+2`4pd7_M449ae$({FN{Jvq>2IYS0(F|hcaD0X
zq61^nU8O#(Bi{53tl$M+nS)((IO~5r#B%{!rZ?NN9keN;4z57?p=s7f?8Kks`Q!{J
z{tknC>|in9CW78nEdn0=u{$|@&|7;}K*zxr`6L9|neI_w`g7@RDTq5fqLunla2Tv*
zEa*;<0MxcS7{E~va<@S1Evn=%4hSA^2l`LU<vf2d+k-#+bN!|P2|7j=eGmQ`|3Cfz
z^Zoyf!fP57#7!#~L|i>sIWAE7Td9&&?Zh9uGhhJ9dsvU2{Ez+R^^Od*vz2-5*wJV8
z$Berr!0}0c6bQ;+|EKvTaxmMD$)fOsVITbAfY|@}P#O%1+iQgQU`!%@V73ky%Ww{=
zs2@pFxjAU%$}2m>f3*5Z3iMXn%aeP+F^*aQk^kBAa^9dFuEMFX!+rYC(fx-8B0FG^
z`N6r#hmA5vfEb~t&P7L=Z4PDv=KUR9cFed#@eVg{_d`G>s%%03KM}>N8}O#I^u2tw
zgOy8!R<2eilKz*r50C%h0|`A?Ics&pio=xy7uB>E=K1&kxN^5Z`Rpv(Ho-RsW76>e
zL|M6D6MOK((JRLSID^@h=$^v`>V^b?v@^cU(eMADB@5sm9n16T{6FRr?*{l_oug&_
z%RhDJg$wP%MSM>81Db>aex@{4t#*Xk^b3`!z@?#c$yMjnA(d=F{|)BD7yd|-({Jy9
zx%~C=)Ir&LqU+h<h`+dvf3V?>KImSMoVs0MdC<6*4OmL%<UV$?gI|6b+u@K-!lA<h
z&u`T`pd}2jbQLaj<10VkIr!5rzr2kCy%o(p-#Xlve;r5N9snt}N4$LaPF@K3iULI*
zc0liV7+CN0VcVNWj_t6iaUh;}Eh&r+f4C(BMm;hs7<tgpQS0u-2rb+)e!jquQHl6K
z3pZIE$^FOf#=yd<?7Kel{V^9WHAoOuX%-m%adzEz!I(571fLy@N#{7&USve|V}GOx
z(c=t26x&3N!{r4SJs_hiWlr^v-?hC3pGlh+R2|4{Mo5d$%dfq2c;pYs^~=L2PQa}s
zdN@`NG=w%^(2n_RF6^M4Uta#X)0&QhF;P944>=su3a}7IvxVRt`T8L%`*~sbLGpNW
zNcZSKPo89y(OGW0#GyZ)MmN!yxKhzvVpKqv87ZW%Bo)a`i!^K&64X!dc~@GhDdWNI
z>fm<6+GIaXOR)GZniUT%=(zhVg@b<wIj>oACwN|SS}5})8I5ejd5p`R_r<+=@uj#8
z;TJ+>#WgJ_cB*r{cWNe-RB~R2>17;F;GYliCP1>L8ZxmQET0^-HDQPtQnC0oEaAPX
z6V?+8aZw8*d!$pB>ey~xb{H^B?{q68nJC`h))uBR(%!S$--@#oxZ$!kdkf7qw}*m^
z4e5lXoSw6);-0kmy*gvX3}#t9TO7`{FFW;15W}Cq^>nB7yp~ks(^z!>+9M5n^aT$4
z?)rPl(NDd1ERYs2RXoRXPFr)|dO-7=)$wA4_GX1N;;(}}P69D(6rCEDmpm>f53kB@
zy)^m~>BOGZBfIHUWX{v4-c_RJ8!o``sJ}s@LBodJy1oBOvCI1N`!fB(C;dw(@i%&A
z>&i>n8$9S23-bu$C2^|l?L~!!ms2+ltCf)g0{H`afdnxDe!mfuUipxb5)Y<-IOIGf
zurm=)=jr7Puz>R-H>jI+dt*-3(&wh|9`n86t?T=O+m(e2vTWn9?G|jmxqRzc<cNzs
zT4?u6s(mP4qmWC<WPkcd4~83Ny*QccL86e{=>Hw9nf)Hi%t&iXuGeYdX3<WNx`z<K
zC4??1<t(g#FlBA^L~t3gk@nhs&6&wJ1Q<25f-Vz#wB2aHns`iHBNI$aKrdMOmJMU9
z^ij+MY}amx5lgG!|GfQ5fU~27RU9LNI75ts<LOw1QXMN6TG1Gm%tX@QP<on5MR2kd
z+iCGqBd4BEb|kI{WG+lzkjBR0cd=k`-rqc<px;`?_OdINw!His+<H1hJ8ptK6Di2*
zz&i}zWG>-FM3m1M?R;P9UKhB++Zo#(d-v$EQC$JD<=k}af!=?aT^I~)>kOZ4eDz7v
zzjz}a2Vu7MtT)6OCnO4DbCuI_c0cEkq~;F}YR9_YB)Bz{-@Efp!7-4HK)d+C&Yq`@
zq`=y)y0hdIvVXU4vI8+97W>p&+9LAJKM%>lVC1l&vyt^8?BJY#5(V8e_9{wVZ6f!=
z#wX}^!3>PF)p^7TFV`<vl4<u@h3?wpeiB?oD=(u5`dnL!C7v~w-JqLyU9H+((JvRg
z+P`C^r(uh}u+%xA6--zo8#H!`@Oiru<=X;53wXCK`Czatk~G-Rvuw57b!ooyO!jov
z?WIImco5j#VZm>NKTe7(pinCrdty>?984Xm?vKsN`~B@5H8C^EIZFe<Y-edG`OVj=
z?uYKoyDb(u38~jia?sS^ijfAi`?6drG0#<E=7@LfbH~?y8OO`@x;>yvqt(gX#lCL5
zSjuh6w(iUGHD!MvviY{fu&r_=_^)azTsT78aiz<(8saVATs?L=k;Aq8n0MN?+Z*|T
zW8u`V{Dk7%EbR8XhE!7ruaTLSR*d@5$oku6Zi#vHg(LIa+}uYR?=J6d1$x#B@9Yaj
z-9@IQud#V<m<dGAxi3F}T~}^gxA}ljdY;Zh9;>aqY28wV3`}V?tUeKpVG{b@t)0yH
zyfU03BG`JhJc^99&NF%8D@&Ie<{kF6R#3)jo4G({Wx!&kUGELTcTD$dg|w&VR7~NX
zm*=QuxY~P5H=}|;T;L87!W4^-jGR$ecBU?^3!gzaWjH4A9KG}+@g!jF(?8oX9mr6T
zDCnMaDc$RgV$$cQr*PPc)|Lu4!)jKlw47U5vpY=0meFX;_7v~VwBHKavwZ36WP7gZ
z{w)|ih_Y+7?s0aVAXyRGek8G9zFG6$-Q~+SaW_HkT2T}H;HjHNRjSHDlm!?E8Q!#^
zEyV{5Z)f{ixK)b!Q<9!~Q_MPeNX6cuUGKC==QWALV55V0=NdLE-(VWNZPYCV1U#G?
z1Ua0iU%HGW@jzZi<(N`Q<_^pGMIGFn#GQT`9_x&q7MvM(kH^s~-j??kV*AJig)<2;
zyBj>igBL6tWfyYR-dWEcryaMS5)k%WP4TQ5O74%0!|eA*RmBddlr)=K?n{%(m+Hyf
zU-G};_yVC!a~@Nx23y$OGhLan<ab=ytYQfsQwqdyvYN$rL|&$oi4{U8GE6#R5^*}*
zW8sV*6Ce5Ylt@{l(IMru6I3^E5FSf1l_#<e*0*@+xxdr58#XLrs{4}P6Ql8Q!Q~dj
zdwKU?rQ;^={_eVQ4RYNwB%=-mv&3IOpSgO!lIRnu7d9wzy52S0xzb6IGi_swrRG_<
z&fjbq63chj79gd#IyRorRX67q;BJ7oB#taGpWTQO$3*(A6g#N$&X-$ZE(&B>dSon)
ze7<1u9p3mo<#jmYfcv%7&hV$cDendZJvWU?cdK!-n?%nE_gdb0xxM|)r)Sy3UY*9t
zcz-|b1M-|YDK;Lkc50M*E?nGL8l8Oo-8e2}#;|?wR5ftBtP~|#soyH!tr{C96W&tV
zFJ2s(@+_x<SSz)mWJ1NBg9?wY(Oh(m2Peb+`Y^xs%n8rn(Fpj2rhV<FTgZw%={*@#
ziS$Ns7OP^xRxwjG0T2p=YxXlOrQBZ_UTuWBv-vMwNUEM@!uO|W=={(uK5yGAz2&Ar
z@)d_w`1&n_&p<w{I^*8+&<#Z`rDu9v**4q>VZ5#7j()>o&>bB=wq`U-ZJL&+DC7b6
z;I-{Yc6PFae0WbLakF7c={e#XtV^M5O0}6=I=vq^$A^?hy&7%@u$>owL~>49)#C*)
zxhZ|D4Gk7rQ`1Vhq3PCXK`{YiT%pLNvD`J%nB6TiU15AZ#qYe~vs3jxVD+nPvU8BY
zZmnCdL6`<7Vb=Pr5Phdw;lQDme{cwY=$vwlfDg+*Aon}aLF3<o)0)`EN$SN&^jOA<
z9X9T|9cNjUN#lHldbcjx;fuP5b13i3r@85qAaX;Bj7JHd6~VjTf5~w|pNq0!vp1Nq
z`-Vfks7aOhyB-vMKWSsR0A1$b#$vnQJ-A3jP=_L=)CL=Wp{It`?E2Qist^^!ZvIBF
zg+G~=JI^hnIbV!j<<P^^3GP6Hn|%!%mAqZ8t13CdBufq>(~7cn`~nH-npj6mYH9(u
zjlf)xkLsiFeLH4ec)Zv3s;gpO2v)dhngFk<cd4D5^=6B+N~YXW_Wb+zWGU@-CLf|J
z!U#1S7B%Ml6{3b&)=PL>T;v}we--5Uw!lh2kVHs8h0vYFq_3^W>FREZ$GUL^3Cv~e
z&XxCMhWlW+S)*$`Gli#j1yl+b)@R01pNHRU>@8LG(z!b<RN>sg-6Eke7AnCEZl~o8
zx@O-d?Jh06{dlF?HSWdDOt<UEF;msgt9c6+Z;(QqsX7sA`i|3iavHYN7E4>96VEOM
z)U5yRj{X+9bSb{B{&~fNj7AJxfp_C^Px^a%GqDxi(cD2-v=|z;4{5oZno-*^xrM3C
z_FL}@g}^IFf<XKL@*ONf5{0S`qQT6T3q(XT2s({j31Qq%taa<5a&a!*>)*WRt><Gc
z&s8kw;Uu=t?NcTHn3)PVJCaPEroeS7M_PLJDUpR+*}Eg!dv2IwouN!>k1HNFL+)D>
z=$P^yffFVveO4y?0*~a7g%e~07QDM|1IdfycX{?0v^<S8KA@APpO`)xDm*unNy%?W
zaz+7$9H#YQ-ViVhLO1VNP?uPiC@oDDZk4A=FOAuD6DPZCSx*jcZGk`+_+Jr>xZDEr
zcPrN%_}B<ll4@TYxq0@ugmaQmmhPivt29+jXU1|q8=8#uc3QVwLk95Ygtgb<QxiF2
z+>kEeAKAwWf|1u^UG~#=SMY*+u2joLlTVd|2A#-xHIh(b@fNv$XdydLT7Kv%d7*&R
z>fgu@jYEfqKcGurCm`T9at4()=VbVXXR_PQul1ba0R_{RhqQyudDIcjH6I<Sgl}PZ
zwu+c!sYy)Ji=|4}=4-O&u7pp-#aK-$aoBtz5A2V^@}*eV%DtcN7QjTBPsbb4-7DlW
zryD4=Fhs8vxDYFp4A-(2VTYZUg009?3=C*mTYcC_BFx-rSFmp23Lc$Us8yRz%CEw6
z`S_|A=$7HwL<2ABV=1T_k+<lQWTN=c>;Wv@OssoT4bIqKhTrs1x~Wosl}h)`6f`dw
zR>&T}^hLuW-1wE$9F_|AO5LX$K-gEXpw6AyoHb0hC|dJvS?5#<`b(Xq2hohDZdc*x
zW{kh=U7$d@kZ~`M3om${PjS&Pk&K4lPHHl&9O=uxpyx-MS=)1-4d_Txb={P*K+#pA
z%73I0=fg(^$}`<Mls5l-5K#wL?)=1z?88f`8Y56$<@q+weUkSY9vrmjabs{X0P#)K
zDJ1hvzy*t|`q{k5;8dHbw0dDa9||@6N^_{ZVqsXWTNasX@3z8}T0;}(!9u`47QS_D
zYH#qAZdFCu)J7knFb#uB{<ehou<y<Fud)a_-a@zhh7u1Y<a^B_XIUZPTjhIi)K?0W
z<8n+tvK!pGM`f>By4mf@;iNOLIX<2`5y!PYB243U-q9#@t%V~K?i<*LZZl#K*peXa
zki1eMY+&vj=R7o5+HO@$?;~I-WZLLr{zjqdiy|Kff@yz`#FkMY$yAt9m@mI`gKVoT
z<*u~}OoP{dWAbmYXOU$Cf+GiZC><4c=c*j_V%q(x$LQxZ7Bqm|O}Vzjq*^ke_~6Ez
zad!>Ijs42Lpo^JQ93K;nr4|Zhms7Z`?-Hk`Jm4_jZy$Q4)d&hVIJBz#^3bFN*uD8R
zQ;mbadv!qe!+VWoC*gAOAYcPNnrPz8!Uyqsl3n?0$m$@5R>RTu$(ABf9Tr4};LbE(
z=oWcNa(1IHzACeaGpc+mr32IuNd2Ph)L#mzTrNQ6y7j?IHP<6KtdzDxJ4F@E`t@!K
zqxR>3PyEM@QN4X|=eD9r^Uf~Q!gzr@P%wvk_%pP5z5s38z&t+l>i|tI=K007HNN%}
zmSva)>uCdIV`T!NhKF3)f|#|{;_C|z{%gWWvp%OPHZ_3Zyhh9RWeY7xK9X}xzmgV_
zIp3Tvsr4c!Wl*PhVFpK&bfHvOh(@mlh-weFrZ9ZJmEYM8^Im|1AC*P^_E0v4LYnMJ
zJAN;d3y&KI2QL)3@e~`OGOL3&YNTJXY#@1s?H!c9TLh%N>kHfvx&U1@_q(Wj(y8`#
z*3D)+T;kT|S_K_ChgY_Y(t%zD#FO>+l#&jLl0`$XYLL2T86K^+Yf;ubfg8@V<=SfR
z%asbV##N#Vwo5DPesv#Jy+u{keu78av5I#gB4o{?YYx;{z{@PB(5~$k)ec4Y$<%lV
zmtTwZ7)n=hUAtmk^`fM$q@0)6Nws6&sgKLIgJ0XJwPkRS%Tf5!gyxQ)TZDz?t(teK
ztTBi*>hpEXMUVSU_+TZwQ+EkR+sVTz1TN3-JCBfDQQ)c>@$~T2y#=E7dK!E9GvBFh
zBmhvzU$)Z0)eg8A0)c6y=X6Kb;os9C4;D#W)EUJ%5!X{VmbXlURqQ+C@12Wtcb1xU
zuE*ah;%^wBSE4L-5(q=|3a)<SUv6aO6H2w(@G+n$bJU{AceKhP`m(cID|y$XPNS9<
zaaGGQd}UdwjKRzamEFT*x@k^WB#_xuP`i9$q{DU{?%4PpxuqWMQRVGyj~dXnzyC-x
zK>v$p4>4KyU9y3uFcb>X>z?m<oV#h{PF|T?l;rOlupU`*Yb<-Bj{*|w5z1EVb6Y{;
zS~1EVQges_kByPiIU5sQ_deqi@njj}(BB>^2k1~4nLau^RAIo7o8Q|Ia}q8eFX-(Z
z=+lh*DZFn#yl^r}c<f3(vVXhpKFNf9YFC>LH<kEow)Jeuo{LZ2%-+7)`fO>Lxygmn
z+oY_!wJx@57(j`|LOhQP8~a0XkO|Cb^PSiVS6kGi=KypkU-~?PsYScAl}CMFDfZo%
z2Nw%kpA8qgh4Y@J^NW++21CB#ZLV=}VvFL5#+9rghp2Q?da8Yd%Y-nvQc-v8thP1|
zi{qGu_)W`{k2*U0l#6HV%dAd!A^O+ML7MY$QM2{urGoKBCdKHqjYUj(*Y;;g+BsAG
zbpPDU+q0OFsJ}Jv7_xcdf=9Q%x#sK@a;H9u2oiJbyH2p#f$8SLINnDdy>Xt1JOIW%
z$y#IfL&}{8cg3*NE$k=ZcOlB9bnBR4de|4afz_*X`m^L!Sd6-T=WFX$RoC%S2BN~c
zjf}wXf!)ie2qrHei$<SJwm8pcY2eNzHfi1RSD%Dvmw+rkKfpgS??%n=ykMn6yIz|N
zbN@<Nh@UpehZ;9xX@nSSdGg}+Y%0x*8P7e%{q-j?+|R`vy4Xog-aqM|L+*)xysUF8
zh~Fxp(>S$gVkPFy!gFE46_@^ZP6=t_IwqB+YxgQgRu{~p29$R{yajbe?qyJj&lf%D
zTMQQ1GNE+t*4)~;K)9pkroYk-1g+M`&tv<-Mb|ugRIJ#${`z3|@re#Dt_<Mi<@Tdo
zMUJ=^`klt9L*Q(`7n#s~`d7cB8sudq7u-1iNz`loECF4)d2X#^%`7Iglu;W*Q1w$e
zcgKXNktFLV<KkzO_MV?o^Z+^?=0#ah8HZ<#+G?!J7w$F2A;Dd=^2lbueDIk!Nq0@o
zxqUY-84KNQ!Jng|lxh^1eAY9})>t0dxRgg*@ItzSI!ve`ST=`mfAu}iRqV40g9{ZS
z4le`C^W273-8_?@jL8g?&{+ybvr=d8OQ>#Iw5qraz6X&@3+}uetmR|bg*yooD;(h(
zX|lam)zsNen0{Nqk$qvH?MJ)HYT^yFo!=Ucb7SJ(9JaC5RQDeo{92j$5<Ng2Uhuwr
zuD5I^b_cTZv>y*2ZPf6HJnpPM1gyDZ3r6+3oGk*f<)Z~5TFLrN!vSATgj2mm$_Tea
zlsqE2q#(zR;3%IfEt|oDPx0q923ynugdz4v*@a`XWz!Aq_=DL0b!9r>bJro?#EZ-L
zVh8%7oIJQoy|y8LDKdtaXJloCAi=2utU1cBO5OnF!*B0TrtO-^de#A4;cE8z8~TDa
zdrdKNXbWz{aT$^kQXm?%sP=TV9<a`dV#)<qnsjmBk^}{Tut15YDUAI%BXncB<p$y8
zM3ug*VK{#&EV3dLWW-HMqPyAHCYKsLkrX(X)%E*VzEm`qsfVjv!RxuJ#Ig9DV+4dI
z@-^O{z+vuY$`CS8JgW)!99nh(!R>LEqUPKme$20)x*tkKQ@9&nylAjgm3W#U*1Af}
zy45oFo1b(jy&t}+fQ68&jL)|VIdWr~ghJtQlA+hon);9DLp!3ez}=(d&dW}nvC>5Q
z;>YRjzF!*D-LF_mm+cT*enm<$ymDS+C>d|3T=Cl98A(f}4x*TMzU5hfhK$8?z*ua2
zWSA^)B#3+iQbh8e@#UYq0H}sxM6Gfs;kO}PfJ6?z;@x@YM+-X>&1WhzR`>{t9q+XC
zTgzbwHNQwE$-9$WTQq89qtzA^fyrNQ!@R@4OguwDxhl)*w)jb$)?9^83t!AK2=~WA
zD-@?aGRs@yd2NEFmz&i4{O~rf!jwx#J8DKcb}-brIUO~DQ~|6lwD&v=0z1b{P4B^Z
zJKL5#g-h7xokx9kU$<dxH9e%@c5!BS>YW)xP2m@9=SrO>MW|}>qTCu7Z^j!XhpJlH
zJlQ?g7Nd)Z=|akBAS-97{8(~aL(oHUZDq2H(nEE|twvRe1$%r*0ffGcc_alj!{wrp
z5apVlg}_F!5an0_#*W?I7JRnfYb|Qvs@VSQ)S)Zp@d>akxkb4C0s%p!_vdVafuVN`
z+p~0kak8hxm2~ypB~RC-7^l4$Yt?I|9@*Rel6P^7zb`}pze?%uIL=awsV(CMem$0T
z3S*z1Dy+;#Kz^r>6P?#^*Tu0;eKMnL`_*e{``*oOOWi#*hUoz{HT<fVSz(Kkw@VYl
zRQ@W@s%`mhQR7;(5F7Z0b>E!&Qwba15{a@obJwGQQxdv0oCAvP){CFZRoA|5yJEW4
zX$;#><O;@e3z`ZG@@8^&rYI;$EF#5Cx}xDk9`6t^E%VNJm-JLT^ycsE@VB7b@`q8u
zpEouJK!PN@Xj+l@QLI309Las66ZsN@J;Fs?v2GrAW;yFwn_a>AmhCDQzcZ2{Kyrb~
z%)qf3`XA^2Z8A9i&hr7P=N>zHH3kdhO2=)@Y6^d?i34dc>2U)|0jW(WJXd*vJw<%$
zQC;P68cmxDne6?E@bB8_iha)Uhh`&OzhNzpA<a{=t@n_%G<8541&f&*7RlA74pf%t
zbxEr%w{jvM!ApYUA1?BENPO&QB<tTyE}dIg9K)$*nV5y~bmi}Rv<ZGXp-n7s%L<qJ
zH`Ib%2NUy&GI{VrSp#+-;2?S`op%x|^e=W0!h0<}p?h4dl-}4p<`xTaI3-dct)#li
zL><0%r1A$+0UUrE)y&a`@GzX|G2}A5`AS3diOAGR>g^e(rIj?e=B*52{f)`KT=l6_
zJn{i2JunmDBamX#SL_f(5~j*baA|E!#mGJZMXFnT7>9DeY=5i*C*(RhHq3HBND)<V
zeF>}LjyQSn%YpL#j}P0SNe7mgNFPkP7V`Y=3g%%QFq<PkuZ5xpq<E_H4%8PeXjhDA
zAFDgq6-SaJeh$)y8#=xo>ccD$g^FVM4==d~4IaJ$Jv4m;WP2kZbLbbddj)>LLAgR9
z#z%hfH&xmpcl4gCna_`Ryd)r(-xtlBMt}P1BqJ1CaW((jUk8KYf?_mm)h$qe=nKS6
zhl4S>K5!*C7}MvcU<p{ChaDA=_X~Nlj)5(W_hd}|U{GETAPC5e$As|D0Zko%mZs)4
z=?8~f?h<GxG+Wm2(6x0Spbt49Buub@>`)p^iaZP{I|a3)^M>WmA!R`zNO<<5qwIn6
zo9HpbUX5vt4v2I#ZJP7|!y#=gI&3^1AM#=op_`6=|HqKq@&PT)7m)1-p3+(v=uN}1
zy7c#}4I2F91bXuy5XC!~u_9z0gjuBC{c}he0ifJi7T)+F!J6bicL~oo{y0dY-zz>x
zp0&fqFkpRm<9w74e;9)zM}%B;=?;H6>OnCQOeWO))7Zi0fkSRq3GS8~e<aFv$icbX
zbK&OUc6v@kxyo`tqJzf%*@g%Oq)MMl(>+{XwQE3?W)$K%Sdo9ee=uR4en85VeCwZa
zZzEuP@i+9pIc(^t4?o#}F)|M5)Y1M}0>w{Yu09sg#((TiZXa4Wq`2YzA587jUC@r&
z|J$9?Y+SV$I9!3d6M<>4^`<l2DPq9*E?_(ugLf4aL*p}G-Z2Y5RSJ)tx4?CGcqW@6
z>KuH5VR`LJSv0J)T95>^UpsBJ4}3rcrA&sJ-slBd3%EtrI38LsIOGD$lWWK9qE%rf
z3@p@9w96_0-Ng7p4qglx!~+B6r_fFmRd6rB8M>fSXXfnP@Ibi*T6~aO+lx0)9C$%X
z!#+HVwt_lpK@Tv4pROqO>BZ_gnCDIbSJ|z3^Vhf<=DDq)a_RNCDBxZ#xRWp$vY@Sv
z52F(hYI=czV%GFX86kqHi-ltfs7x2>ItEYm8e4%0Y^7^2eC-fC?;|sS*LZO7{nCQx
zktvITx-8#yS>i8GKoh;88+*kZVaXD-h;-HMAZ~aTgu4TYu^<MREKaAYpMej+FNgrT
zI^r;F8840iodPPXT4uDt4c7n_6el%!vFsB@N&}6*fUf*%y9bu#1T^@8uKaX^eKV4q
zkkrHCmkSubs|$j4VJ#7iB@I}$J5&Nga@zBKGelwLTmzbu{Z~5SFShuChxitdUuIvF
zngR1X#w<Ea0@bOrKm%=F$1P#WqOlJs@J6HZJiG$K=FA1JK%T4X)|~(~HDDqzkVJRp
zs^MrfLfr143QUAyhxBGD!#wv57?s)i=hwoWjp{l`9&0QEIZ{2NA3mEEp#Y2m)<;E#
zcryXZEYJeU8trg+duYpiU?}~Ltj516*%Y|GJ0e*+ZznXw8@Yg$`kD#7M!v96!fLIS
zKQMiL`gW!VR)lW_mM{_AyK{Q+##~7U&`s?dsx@GS9;g8Z%5&x?CNKf)F$bUKfJM+2
zP$eGyN($T7M7U9nvp@yP%eUJrpnh?<2}*Epug#EvtHH*Y3Tn8%w9fbk3zJu%HsZP3
zt)Pw6@KlJwIOPBg6CSgJB5*a&fN?pe`v~y177WEmA{xCwzhuhv!k7IAL;+*`Qk4|m
z(vp#>6qpQ4MdIK_PXUd&NanV!go|Kf1jGWhUEMlA4Ay^IAqKQ9>!#;xSlD9I<*?ul
zs36Mo_Cf5V0oDy)Oz~=J7;Q3ONzbEA2FySiZ89J?F(8Ffz-W^JqjVkwO@<%!4qp4-
TalTK;V*mnAS3j3^P6<r_Rgz7)

literal 0
HcmV?d00001

diff --git a/ci/Jenkinsfile_utils.groovy b/ci/Jenkinsfile_utils.groovy
index 7fbf3692f6bc..ad8cd553df04 100644
--- a/ci/Jenkinsfile_utils.groovy
+++ b/ci/Jenkinsfile_utils.groovy
@@ -18,7 +18,7 @@
 // under the License.
 
 // initialize source codes
-def init_git(git_sha = '') {
+def init_git() {
   deleteDir()
   retry(5) {
     try {
@@ -80,8 +80,8 @@ return 0
 }
 
 // unpack libraries saved before
-def unpack_and_init(name, libs, include_gcov_data = false, git_sha = '') {
-  init_git(git_sha)
+def unpack_and_init(name, libs, include_gcov_data = false) {
+  init_git()
   unstash name
   sh returnStatus: true, script: """
 set +e

From f63559530661de0e164c4e50e10de4121f8666c4 Mon Sep 17 00:00:00 2001
From: "Huang, Guangtai" <guangtai@amazon.com>
Date: Thu, 25 Jul 2019 17:38:51 +0800
Subject: [PATCH 367/813] numpy op unique

---
 python/mxnet/ndarray/numpy/_op.py       | 107 +++++++-
 python/mxnet/numpy/multiarray.py        | 106 +++++++-
 python/mxnet/symbol/numpy/_symbol.py    |  63 ++++-
 src/operator/contrib/boolean_mask-inl.h |  16 ++
 src/operator/contrib/boolean_mask.cc    |  17 --
 src/operator/numpy/np_unique_op.cc      | 326 ++++++++++++++++++++++++
 src/operator/numpy/np_unique_op.cu      | 317 +++++++++++++++++++++++
 src/operator/numpy/np_unique_op.h       | 178 +++++++++++++
 tests/python/unittest/test_numpy_op.py  |  50 +++-
 9 files changed, 1158 insertions(+), 22 deletions(-)
 create mode 100644 src/operator/numpy/np_unique_op.cc
 create mode 100644 src/operator/numpy/np_unique_op.cu
 create mode 100644 src/operator/numpy/np_unique_op.h

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index fd20f36fba04..a3b4a2730378 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -34,7 +34,8 @@
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'mean',
            'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
-           'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'hypot', 'rad2deg', 'deg2rad']
+           'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'hypot', 'rad2deg', 'deg2rad',
+           'unique']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -262,6 +263,110 @@ def _ufunc_helper(lhs, rhs, fn_array, fn_scalar, lfn_scalar, rfn_scalar=None, ou
 #pylint: enable= too-many-arguments, no-member, protected-access
 
 
+@set_module('mxnet.ndarray.numpy')
+def unique(ar, return_index=False, return_inverse=False, return_counts=False, axis=None):
+    """
+    Find the unique elements of an array.
+
+    Returns the sorted unique elements of an array. There are three optional
+    outputs in addition to the unique elements:
+
+    * the indices of the input array that give the unique values
+    * the indices of the unique array that reconstruct the input array
+    * the number of times each unique value comes up in the input array
+
+    Parameters
+    ----------
+    ar : ndarray
+        Input array. Unless `axis` is specified, this will be flattened if it
+        is not already 1-D.
+    return_index : bool, optional
+        If True, also return the indices of `ar` (along the specified axis,
+        if provided, or in the flattened array) that result in the unique array.
+    return_inverse : bool, optional
+        If True, also return the indices of the unique array (for the specified
+        axis, if provided) that can be used to reconstruct `ar`.
+    return_counts : bool, optional
+        If True, also return the number of times each unique item appears
+        in `ar`.
+    axis : int or None, optional
+        The axis to operate on. If None, `ar` will be flattened. If an integer,
+        the subarrays indexed by the given axis will be flattened and treated
+        as the elements of a 1-D array with the dimension of the given axis,
+        see the notes for more details. The default is None.
+
+    Returns
+    -------
+    unique : ndarray
+        The sorted unique values.
+    unique_indices : ndarray, optional
+        The indices of the first occurrences of the unique values in the
+        original array. Only provided if `return_index` is True.
+    unique_inverse : ndarray, optional
+        The indices to reconstruct the original array from the
+        unique array. Only provided if `return_inverse` is True.
+    unique_counts : ndarray, optional
+        The number of times each of the unique values comes up in the
+        original array. Only provided if `return_counts` is True.
+
+    Notes
+    -----
+    When an axis is specified the subarrays indexed by the axis are sorted.
+    This is done by making the specified axis the first dimension of the array
+    and then flattening the subarrays in C order. The flattened subarrays are
+    then viewed as a structured type with each element given a label, with the
+    effect that we end up with a 1-D array of structured types that can be
+    treated in the same way as any other 1-D array. The result is that the
+    flattened subarrays are sorted in lexicographic order starting with the
+    first element.
+
+    This function differs from the original `numpy.unique
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.unique.html>`_ in
+    the following aspects:
+
+    - Only support ndarray as input.
+    - Object arrays or structured arrays are not supported.
+
+    Examples
+    --------
+    >>> np.unique(np.array([1, 1, 2, 2, 3, 3]))
+    array([1., 2., 3.])
+    >>> a = np.array([[1, 1], [2, 3]])
+    >>> np.unique(a)
+    array([1., 2., 3.])
+
+    Return the unique rows of a 2D array
+
+    >>> a = np.array([[1, 0, 0], [1, 0, 0], [2, 3, 4]])
+    >>> np.unique(a, axis=0)
+    array([[1., 0., 0.],
+           [2., 3., 4.]])
+
+    Return the indices of the original array that give the unique values:
+
+    >>> a = np.array([1, 2, 6, 4, 2, 3, 2])
+    >>> u, indices = np.unique(a, return_index=True)
+    >>> u
+    array([1., 2., 3., 4., 6.])
+    >>> indices
+    array([0, 1, 5, 3, 2], dtype=int64)
+    >>> a[indices]
+    array([1., 2., 3., 4., 6.])
+
+    Reconstruct the input array from the unique values:
+
+    >>> a = np.array([1, 2, 6, 4, 2, 3, 2])
+    >>> u, indices = np.unique(a, return_inverse=True)
+    >>> u
+    array([1., 2., 3., 4., 6.])
+    >>> indices
+    array([0, 1, 4, 3, 1, 2, 1], dtype=int64)
+    >>> u[indices]
+    array([1., 2., 6., 4., 2., 3., 2.])
+    """
+    return _npi.unique(ar, return_index, return_inverse, return_counts, axis)
+
+
 @set_module('mxnet.ndarray.numpy')
 def add(x1, x2, out=None):
     """Add arguments element-wise.
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 921b51291f71..4972bdae7df6 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -54,7 +54,7 @@
            'tensordot', 'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate',
            'stack', 'vstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices',
            'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'arctan2', 'hypot',
-           'rad2deg', 'deg2rad']
+           'rad2deg', 'deg2rad', 'unique']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -1856,6 +1856,110 @@ def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):  # pylin
     return _mx_nd_np.full(shape, fill_value, order=order, ctx=ctx, dtype=dtype, out=out)
 
 
+@set_module('mxnet.numpy')
+def unique(ar, return_index=False, return_inverse=False, return_counts=False, axis=None):
+    """
+    Find the unique elements of an array.
+
+    Returns the sorted unique elements of an array. There are three optional
+    outputs in addition to the unique elements:
+
+    * the indices of the input array that give the unique values
+    * the indices of the unique array that reconstruct the input array
+    * the number of times each unique value comes up in the input array
+
+    Parameters
+    ----------
+    ar : ndarray
+        Input array. Unless `axis` is specified, this will be flattened if it
+        is not already 1-D.
+    return_index : bool, optional
+        If True, also return the indices of `ar` (along the specified axis,
+        if provided, or in the flattened array) that result in the unique array.
+    return_inverse : bool, optional
+        If True, also return the indices of the unique array (for the specified
+        axis, if provided) that can be used to reconstruct `ar`.
+    return_counts : bool, optional
+        If True, also return the number of times each unique item appears
+        in `ar`.
+    axis : int or None, optional
+        The axis to operate on. If None, `ar` will be flattened. If an integer,
+        the subarrays indexed by the given axis will be flattened and treated
+        as the elements of a 1-D array with the dimension of the given axis,
+        see the notes for more details. The default is None.
+
+    Returns
+    -------
+    unique : ndarray
+        The sorted unique values.
+    unique_indices : ndarray, optional
+        The indices of the first occurrences of the unique values in the
+        original array. Only provided if `return_index` is True.
+    unique_inverse : ndarray, optional
+        The indices to reconstruct the original array from the
+        unique array. Only provided if `return_inverse` is True.
+    unique_counts : ndarray, optional
+        The number of times each of the unique values comes up in the
+        original array. Only provided if `return_counts` is True.
+
+    Notes
+    -----
+    When an axis is specified the subarrays indexed by the axis are sorted.
+    This is done by making the specified axis the first dimension of the array
+    and then flattening the subarrays in C order. The flattened subarrays are
+    then viewed as a structured type with each element given a label, with the
+    effect that we end up with a 1-D array of structured types that can be
+    treated in the same way as any other 1-D array. The result is that the
+    flattened subarrays are sorted in lexicographic order starting with the
+    first element.
+
+    This function differs from the original `numpy.unique
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.unique.html>`_ in
+    the following aspects:
+
+    - Only support ndarray as input.
+    - Object arrays or structured arrays are not supported.
+
+    Examples
+    --------
+    >>> np.unique(np.array([1, 1, 2, 2, 3, 3]))
+    array([1., 2., 3.])
+    >>> a = np.array([[1, 1], [2, 3]])
+    >>> np.unique(a)
+    array([1., 2., 3.])
+
+    Return the unique rows of a 2D array
+
+    >>> a = np.array([[1, 0, 0], [1, 0, 0], [2, 3, 4]])
+    >>> np.unique(a, axis=0)
+    array([[1., 0., 0.],
+           [2., 3., 4.]])
+
+    Return the indices of the original array that give the unique values:
+
+    >>> a = np.array([1, 2, 6, 4, 2, 3, 2])
+    >>> u, indices = np.unique(a, return_index=True)
+    >>> u
+    array([1., 2., 3., 4., 6.])
+    >>> indices
+    array([0, 1, 5, 3, 2], dtype=int64)
+    >>> a[indices]
+    array([1., 2., 3., 4., 6.])
+
+    Reconstruct the input array from the unique values:
+
+    >>> a = np.array([1, 2, 6, 4, 2, 3, 2])
+    >>> u, indices = np.unique(a, return_inverse=True)
+    >>> u
+    array([1., 2., 3., 4., 6.])
+    >>> indices
+    array([0, 1, 4, 3, 1, 2, 1], dtype=int64)
+    >>> u[indices]
+    array([1., 2., 6., 4., 2., 3., 2.])
+    """
+    return _mx_nd_np.unique(ar, return_index, return_inverse, return_counts, axis)
+
+
 @set_module('mxnet.numpy')
 def add(x1, x2, out=None):
     """Add arguments element-wise.
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index f544ab58c36d..57b18ecaf547 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -36,7 +36,8 @@
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'mean',
            'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
-           'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'hypot', 'rad2deg', 'deg2rad']
+           'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'hypot', 'rad2deg', 'deg2rad',
+           'unique']
 
 
 def _num_outputs(sym):
@@ -3333,4 +3334,64 @@ def hypot(x1, x2, out=None):
     return _ufunc_helper(x1, x2, _npi.hypot, _np.hypot, _npi.hypot_scalar, None, out)
 
 
+@set_module('mxnet.symbol.numpy')
+def unique(ar, return_index=False, return_inverse=False, return_counts=False, axis=None):
+    """
+    Find the unique elements of an array.
+
+    Returns the sorted unique elements of an array. There are three optional
+    outputs in addition to the unique elements:
+
+    * the indices of the input array that give the unique values
+    * the indices of the unique array that reconstruct the input array
+    * the number of times each unique value comes up in the input array
+
+    Parameters
+    ----------
+    ar : _Symbol
+        Input array. Unless `axis` is specified, this will be flattened if it
+        is not already 1-D.
+    return_index : bool, optional
+        If True, also return the indices of `ar` (along the specified axis,
+        if provided, or in the flattened array) that result in the unique array.
+    return_inverse : bool, optional
+        If True, also return the indices of the unique array (for the specified
+        axis, if provided) that can be used to reconstruct `ar`.
+    return_counts : bool, optional
+        If True, also return the number of times each unique item appears
+        in `ar`.
+    axis : int or None, optional
+        The axis to operate on. If None, `ar` will be flattened. If an integer,
+        the subarrays indexed by the given axis will be flattened and treated
+        as the elements of a 1-D array with the dimension of the given axis,
+        see the notes for more details. The default is None.
+
+    Returns
+    -------
+    unique : _Symbol
+        The sorted unique values.
+    unique_indices : _Symbol, optional
+        The indices of the first occurrences of the unique values in the
+        original array. Only provided if `return_index` is True.
+    unique_inverse : _Symbol, optional
+        The indices to reconstruct the original array from the
+        unique array. Only provided if `return_inverse` is True.
+    unique_counts : _Symbol, optional
+        The number of times each of the unique values comes up in the
+        original array. Only provided if `return_counts` is True.
+
+    Notes
+    -----
+    When an axis is specified the subarrays indexed by the axis are sorted.
+    This is done by making the specified axis the first dimension of the array
+    and then flattening the subarrays in C order. The flattened subarrays are
+    then viewed as a structured type with each element given a label, with the
+    effect that we end up with a 1-D array of structured types that can be
+    treated in the same way as any other 1-D array. The result is that the
+    flattened subarrays are sorted in lexicographic order starting with the
+    first element.
+    """
+    return _npi.unique(ar, return_index, return_inverse, return_counts, axis)
+
+
 _set_np_symbol_class(_Symbol)
diff --git a/src/operator/contrib/boolean_mask-inl.h b/src/operator/contrib/boolean_mask-inl.h
index 8925e9b96186..ad4fbe13f187 100644
--- a/src/operator/contrib/boolean_mask-inl.h
+++ b/src/operator/contrib/boolean_mask-inl.h
@@ -50,6 +50,22 @@ struct BooleanMaskParam : public dmlc::Parameter<BooleanMaskParam> {
   }
 };
 
+struct BooleanMaskForwardCPUKernel {
+  template<typename DType>
+  static void Map(int i,
+                  DType* out,
+                  const DType* data,
+                  const int32_t* idx,
+                  const size_t col_size) {
+    // i is row id already
+    int32_t prev = (i == 0) ? 0 : idx[i - 1];
+    int32_t curr = idx[i];
+    if (prev != curr) {
+      std::memcpy(out + prev * col_size, data + i * col_size, col_size * sizeof(DType));
+    }
+  }
+};
+
 struct BooleanMaskForwardKernel {
   template<typename DType>
   static void MSHADOW_XINLINE Map(int i,
diff --git a/src/operator/contrib/boolean_mask.cc b/src/operator/contrib/boolean_mask.cc
index f431d77f26ce..0afc57f6ab54 100644
--- a/src/operator/contrib/boolean_mask.cc
+++ b/src/operator/contrib/boolean_mask.cc
@@ -74,23 +74,6 @@ bool BooleanMaskBackStorageType(const nnvm::NodeAttrs& attrs,
   return true;
 }
 
-struct BooleanMaskForwardCPUKernel {
-  template<typename DType>
-  static void Map(int i,
-                  DType* out,
-                  const DType* data,
-                  const int32_t* idx,
-                  const size_t col_size) {
-    // i is row id already
-    int32_t prev = (i == 0) ? 0 : idx[i - 1];
-    int32_t curr = idx[i];
-    if (prev != curr) {
-      std::memcpy(out + prev * col_size, data + i * col_size, col_size * sizeof(DType));
-    }
-  }
-};
-
-
 struct BooleanMaskBackwardCPUWriteKernel {
   template<typename DType>
   static void Map(int i,
diff --git a/src/operator/numpy/np_unique_op.cc b/src/operator/numpy/np_unique_op.cc
new file mode 100644
index 000000000000..0042c7376707
--- /dev/null
+++ b/src/operator/numpy/np_unique_op.cc
@@ -0,0 +1,326 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_unique_op.cc
+ */
+
+#include "./np_unique_op.h"
+
+namespace mxnet {
+namespace op {
+
+void NumpyUniqueCPUNoneAxisImpl(const NumpyUniqueParam& param,
+                                const OpContext &ctx,
+                                const std::vector<NDArray> &inputs,
+                                const std::vector<OpReqType> &req,
+                                const std::vector<NDArray> &outputs) {
+  MXNET_NO_FLOAT16_TYPE_SWITCH(outputs[0].dtype(), DType, {
+    mshadow::Stream<cpu> *stream = ctx.get_stream<cpu>();
+
+    DType* input_data = inputs[0].data().dptr<DType>();
+    dim_t input_size = inputs[0].shape().Size();
+    if (param.return_index || param.return_inverse || param.return_counts) {
+      // argsort, result in perm
+      std::vector<dim_t> perm(input_size);
+      std::iota(perm.begin(), perm.end(), 0);
+      std::stable_sort(perm.begin(), perm.end(),
+        [&input_data] (dim_t i1, dim_t i2) {return input_data[i1] < input_data[i2];});
+      // sorted data in aux
+      std::vector<DType> aux(input_size);
+      mxnet_op::Kernel<UniqueComputeAuxCPUKernel, cpu>::Launch(
+        stream, input_size, aux.data(), input_data, perm.data(), 1);
+      // calculate unique mask
+      std::vector<dim_t> mask(input_size);
+      mxnet_op::Kernel<UniqueComputeMaskCPUKernel, cpu>::Launch(
+        stream, input_size, mask.data(), aux.data(), 1);
+      // Calculate prefix sum
+      std::vector<int32_t> prefix_sum(input_size, 0);
+      size_t valid_num = 0;
+      for (dim_t i = 0; i < input_size; i++) {
+        prefix_sum[i] = (i == 0) ? 0 : prefix_sum[i - 1];
+        prefix_sum[i] += (mask[i]) ? 1 : 0;
+      }
+      valid_num = prefix_sum[input_size - 1];
+      // set the output shape forcefully
+      mxnet::TShape s(1, valid_num);
+      const_cast<NDArray &>(outputs[0]).Init(s);
+      // launch kernal to obtain unique array, reuse boolean_mask kernel
+      mxnet_op::Kernel<BooleanMaskForwardCPUKernel, cpu>::Launch(
+        stream, input_size, outputs[0].data().dptr<DType>(), aux.data(),
+        prefix_sum.data(), 1);
+      // handle other optional outputs
+      int output_flag = 0;
+      if (param.return_index) {
+        output_flag += 1;
+        const_cast<NDArray &>(outputs[output_flag]).Init(s);
+        dim_t* unique_indices = outputs[output_flag].data().dptr<dim_t>();
+        // reuse boolean_mask kernel
+        mxnet_op::Kernel<BooleanMaskForwardCPUKernel, cpu>::Launch(
+          stream, input_size, unique_indices, perm.data(),
+          prefix_sum.data(), 1);
+      }
+      if (param.return_inverse) {
+        output_flag += 1;
+        const_cast<NDArray &>(outputs[output_flag]).Init(mxnet::TShape(1, input_size));
+        dim_t* unique_inverse = outputs[output_flag].data().dptr<dim_t>();
+        mxnet_op::Kernel<UniqueReturnInverseKernel, cpu>::Launch(
+          stream, input_size, unique_inverse, prefix_sum.data(), perm.data());
+      }
+      if (param.return_counts) {
+        output_flag += 1;
+        std::vector<int32_t> idx(valid_num + 1);
+        auto iter = idx.begin();
+        for (dim_t i = 0; i < input_size; ++i) {
+          if (mask[i]) {
+            *iter = i;
+            ++iter;
+          }
+        }
+        *iter = input_size;
+        const_cast<NDArray &>(outputs[output_flag]).Init(s);
+        dim_t* unique_counts = outputs[output_flag].data().dptr<dim_t>();
+        mxnet_op::Kernel<UniqueReturnCountsKernel, cpu>::Launch(
+          stream, input_size, unique_counts, idx.data());
+      }
+    } else {
+      std::set<DType> set(input_data, input_data + input_size);
+      mxnet::TShape s(1, set.size());
+      const_cast<NDArray &>(outputs[0]).Init(s);
+      std::copy(set.begin(), set.end(), outputs[0].data().dptr<DType>());
+    }
+  });
+}
+
+void NumpyUniqueCPUImpl(const NumpyUniqueParam& param,
+                        const OpContext &ctx,
+                        const std::vector<NDArray> &inputs,
+                        const std::vector<OpReqType> &req,
+                        const std::vector<NDArray> &outputs) {
+  CHECK(param.axis.value() >= -1 * inputs[0].shape().ndim() &&
+      param.axis.value() < inputs[0].shape().ndim())
+      << "Axis should be in the range of [-r, r-1] where r is the rank of input tensor";
+  MXNET_NO_FLOAT16_TYPE_SWITCH(outputs[0].dtype(), DType, {
+    using namespace mshadow;
+    using namespace mshadow::expr;
+    Stream<cpu> *stream = ctx.get_stream<cpu>();
+    const index_t actual_axis =
+        param.axis.value() + ((param.axis.value() < 0) ? inputs[0].shape().ndim() : 0);
+    // reshape tensor to [origin_shape[axis], -1]
+    const mxnet::TShape origin_shape = inputs[0].shape();
+    Tensor<cpu, 3, DType> input_tensor_3d =
+        inputs[0].data().FlatTo3D<cpu, DType>(actual_axis, stream);
+    Tensor<cpu, 1, DType> workspace = ctx.requested[0].get_space_typed<cpu, 1, DType>(
+        Shape1(input_tensor_3d.shape_.Size() * 2), stream);
+    Tensor<cpu, 3, DType> input_tensor(workspace.dptr_, Shape3(
+        input_tensor_3d.shape_[1], input_tensor_3d.shape_[0], input_tensor_3d.shape_[2]), stream);
+    input_tensor = swapaxis<1, 0>(input_tensor_3d);
+    const Shape<3> temp_shape = input_tensor.shape_;
+    DType* input_data = input_tensor.dptr_;
+    dim_t numel = temp_shape[1] * temp_shape[2];
+    // argsort, result in perm
+    std::vector<dim_t> perm(temp_shape[0]);
+    std::iota(perm.begin(), perm.end(), 0);
+    std::stable_sort(perm.begin(), perm.end(),
+      [&](dim_t a, dim_t b) -> bool {
+        for (dim_t i = 0; i < numel; ++i) {
+          DType lhs = input_data[i + a * numel];
+          DType rhs = input_data[i + b * numel];
+          if (lhs < rhs) {
+            return true;
+          } else if (lhs > rhs) {
+            return false;
+          }
+        }
+        return false;
+      });
+    // sorted data in aux
+    Tensor<cpu, 2, DType> aux(workspace.dptr_ + input_tensor_3d.shape_.Size(),
+        Shape2(temp_shape[0], temp_shape[1] * temp_shape[2]), stream);
+    mxnet_op::Kernel<UniqueComputeAuxCPUKernel, cpu>::Launch(
+      stream, temp_shape[0], aux.dptr_, input_data, perm.data(), numel);
+    // calculate unique mask
+    std::vector<dim_t> mask(temp_shape[0]);
+    mxnet_op::Kernel<UniqueComputeMaskCPUKernel, cpu>::Launch(
+      stream, temp_shape[0], mask.data(), aux.dptr_, numel);
+    // calculate prefix sum
+    std::vector<int32_t> prefix_sum(temp_shape[0], 0);
+    int32_t valid_num = 0;
+    for (dim_t i = 0; i < temp_shape[0]; i++) {
+      prefix_sum[i] = (i == 0) ? 0 : prefix_sum[i - 1];
+      prefix_sum[i] += (mask[i]) ? 1 : 0;
+    }
+    valid_num = prefix_sum[temp_shape[0] - 1];
+    // store the temp output data, reuse the space of 'input_tensor'
+    Tensor<cpu, 3, DType> temp_tensor(workspace.dptr_,
+        Shape3(valid_num, temp_shape[1], temp_shape[2]), stream);
+    // launch kernal to obtain unique array, reuse boolean_mask kernel
+    mxnet_op::Kernel<BooleanMaskForwardCPUKernel, cpu>::Launch(
+      stream, temp_shape[0], temp_tensor.dptr_, aux.dptr_,
+      prefix_sum.data(), numel);
+    // set the output shape forcefully and swap axis back
+    mxnet::TShape out_shape(origin_shape);
+    out_shape[actual_axis] = valid_num;
+    const_cast<NDArray &>(outputs[0]).Init(out_shape);
+    Tensor<cpu, 3, DType> output_tensor(outputs[0].data().dptr<DType>(),
+        Shape3(temp_shape[1], valid_num, temp_shape[2]), stream);
+    output_tensor = swapaxis<1, 0>(temp_tensor);
+    // handle other optional outputs
+    int output_flag = 0;
+    if (param.return_index) {
+      output_flag += 1;
+      const_cast<NDArray &>(outputs[output_flag]).Init(mxnet::TShape(1, valid_num));
+      dim_t* unique_indices = outputs[output_flag].data().dptr<dim_t>();
+      // reuse boolean_mask kernel
+      mxnet_op::Kernel<BooleanMaskForwardCPUKernel, cpu>::Launch(
+        stream, temp_shape[0], unique_indices, perm.data(),
+        prefix_sum.data(), 1);
+    }
+    if (param.return_inverse) {
+      output_flag += 1;
+      const_cast<NDArray &>(outputs[output_flag]).Init(mxnet::TShape(1, temp_shape[0]));
+      dim_t* unique_inverse = outputs[output_flag].data().dptr<dim_t>();
+      mxnet_op::Kernel<UniqueReturnInverseKernel, cpu>::Launch(
+        stream, temp_shape[0], unique_inverse, prefix_sum.data(), perm.data());
+    }
+    if (param.return_counts) {
+      output_flag += 1;
+      std::vector<int32_t> idx(valid_num + 1);
+      auto iter = idx.begin();
+      for (dim_t i = 0; i < temp_shape[0]; ++i) {
+        if (mask[i]) {
+          *iter = i;
+          ++iter;
+        }
+      }
+      *iter = temp_shape[0];
+      const_cast<NDArray &>(outputs[output_flag]).Init(mxnet::TShape(1, valid_num));
+      dim_t* unique_counts = outputs[output_flag].data().dptr<dim_t>();
+      mxnet_op::Kernel<UniqueReturnCountsKernel, cpu>::Launch(
+          stream, temp_shape[0], unique_counts, idx.data());
+    }
+  });
+}
+
+void NumpyUniqueCPUForward(const nnvm::NodeAttrs& attrs,
+                           const OpContext &ctx,
+                           const std::vector<NDArray> &inputs,
+                           const std::vector<OpReqType> &req,
+                           const std::vector<NDArray> &outputs) {
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK(req[0] == kWriteTo || req[0] == kWriteInplace);
+  using namespace mshadow;
+  const NumpyUniqueParam& param = nnvm::get<NumpyUniqueParam>(attrs.parsed);
+  if (inputs[0].shape().ndim() == 0) {
+    CHECK(!param.axis.has_value() || param.axis.value() == -1 || param.axis.value() == 0)
+      << "Axis can only be -1 or 0 for scalor tensor";
+    Stream<cpu> *s = ctx.get_stream<cpu>();
+    mxnet::TShape shape_1(1, 1);
+    const_cast<NDArray &>(outputs[0]).Init(shape_1);
+    MSHADOW_TYPE_SWITCH(outputs[0].dtype(), DType, {
+      Tensor<cpu, 1, DType> unique_out =
+          outputs[0].data().get_with_shape<cpu, 1, DType>(Shape1(1), s);
+      ASSIGN_DISPATCH(unique_out, OpReqType::kWriteTo, inputs[0].data().dptr<DType>()[0]);
+    });
+    int output_flag = 0;
+    if (param.return_index) {
+      output_flag += 1;
+      const_cast<NDArray &>(outputs[output_flag]).Init(shape_1);
+      Tensor<cpu, 1, dim_t> outdata =
+          outputs[output_flag].data().get_with_shape<cpu, 1, dim_t>(Shape1(1), s);
+      ASSIGN_DISPATCH(outdata, OpReqType::kWriteTo, 0);
+    }
+    if (param.return_inverse) {
+      output_flag += 1;
+      const_cast<NDArray &>(outputs[output_flag]).Init(shape_1);
+      Tensor<cpu, 1, dim_t> outdata =
+          outputs[output_flag].data().get_with_shape<cpu, 1, dim_t>(Shape1(1), s);
+      ASSIGN_DISPATCH(outdata, OpReqType::kWriteTo, 0);
+    }
+    if (param.return_counts) {
+      output_flag += 1;
+      const_cast<NDArray &>(outputs[output_flag]).Init(shape_1);
+      Tensor<cpu, 1, dim_t> outdata =
+          outputs[output_flag].data().get_with_shape<cpu, 1, dim_t>(Shape1(1), s);
+      ASSIGN_DISPATCH(outdata, OpReqType::kWriteTo, 1);
+    }
+  } else if (inputs[0].shape().Size() == 0) {
+    // If the input tensor is zero size, only a check on axis is needed
+    if (param.axis.has_value()) {
+      int axis = param.axis.value();
+      if (axis < 0) axis += inputs[0].shape().ndim();
+      CHECK(axis >= 0 && axis < inputs[0].shape().ndim())
+        << "Axis must be within the range of input tensor's dimension";
+    }
+    // set the shapes of outputs
+    mxnet::TShape shape_0(1, 0);
+    const_cast<NDArray &>(outputs[0]).Init(shape_0);
+    int output_flag = 0;
+    if (param.return_index) {
+      output_flag += 1;
+      const_cast<NDArray &>(outputs[output_flag]).Init(shape_0);
+    }
+    if (param.return_inverse) {
+      output_flag += 1;
+      const_cast<NDArray &>(outputs[output_flag]).Init(shape_0);
+    }
+    if (param.return_counts) {
+      output_flag += 1;
+      const_cast<NDArray &>(outputs[output_flag]).Init(shape_0);
+    }
+  } else {
+    if (!param.axis.has_value()) {
+      NumpyUniqueCPUNoneAxisImpl(param, ctx, inputs, req, outputs);
+    } else {
+      NumpyUniqueCPUImpl(param, ctx, inputs, req, outputs);
+    }
+  }
+}
+
+DMLC_REGISTER_PARAMETER(NumpyUniqueParam);
+
+NNVM_REGISTER_OP(_npi_unique)
+.set_attr_parser(ParamParser<NumpyUniqueParam>)
+.set_num_inputs(1)
+.set_num_outputs([](const NodeAttrs& attrs) {
+    const NumpyUniqueParam& param = nnvm::get<NumpyUniqueParam>(attrs.parsed);
+    int output_num = 1;
+    if (param.return_index) output_num += 1;
+    if (param.return_inverse) output_num += 1;
+    if (param.return_counts) output_num += 1;
+    return output_num;
+  })
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"data"};
+  })
+.set_attr<nnvm::FInferType>("FInferType", NumpyUniqueType)
+.set_attr<FComputeEx>("FComputeEx<cpu>", NumpyUniqueCPUForward)
+.set_attr<FInferStorageType>("FInferStorageType", NumpyUniqueStorageType)
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.add_argument("data", "NDArray-or-Symbol", "The input array")
+.add_arguments(NumpyUniqueParam::__FIELDS__());
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_unique_op.cu b/src/operator/numpy/np_unique_op.cu
new file mode 100644
index 000000000000..505f1a6cb0c1
--- /dev/null
+++ b/src/operator/numpy/np_unique_op.cu
@@ -0,0 +1,317 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_unique_op.cu
+ */
+
+#include "./np_unique_op.h"
+
+namespace mxnet {
+namespace op {
+
+template<typename DType>
+struct argsort_functor1d : public thrust::unary_function<int64_t, int64_t> {
+  explicit argsort_functor1d(DType* data_) : data(data_) {}
+  __device__
+  bool operator()(int64_t a, int64_t b) {
+    return data[a] < data[b];
+  }
+  DType* data;
+};
+
+template<typename DType>
+struct argsort_functor2d : public thrust::unary_function<int64_t, int64_t> {
+  argsort_functor2d(DType* data_, dim_t numel_) : data(data_), numel(numel_) {}
+  __device__
+  bool operator()(int64_t a, int64_t b) {
+    for (int64_t i = 0; i < numel; ++i) {
+      DType lhs = data[i + a * numel];
+      DType rhs = data[i + b * numel];
+      if (lhs < rhs) {
+        return true;
+      } else if (lhs > rhs) {
+        return false;
+      }
+    }
+    return false;
+  }
+  DType* data;
+  dim_t numel;
+};
+
+void NumpyUniqueGPUNoneAxisImpl(const NumpyUniqueParam& param,
+                                const OpContext &ctx,
+                                const std::vector<NDArray> &inputs,
+                                const std::vector<OpReqType> &req,
+                                const std::vector<NDArray> &outputs) {
+  MXNET_NO_FLOAT16_TYPE_SWITCH(outputs[0].dtype(), DType, {
+    mshadow::Stream<gpu> *stream = ctx.get_stream<gpu>();
+    auto policy = thrust::cuda::par.on(stream->stream_);
+
+    DType* input_data = inputs[0].data().dptr<DType>();
+    dim_t input_size = inputs[0].shape().Size();
+    // argsort, result in perm
+    thrust::device_vector<dim_t> perm(input_size);
+    thrust::sequence(policy, perm.begin(), perm.end());
+    thrust::stable_sort(policy, perm.begin(), perm.end(),
+                        argsort_functor1d<DType>(input_data));
+    // sorted data in aux
+    thrust::device_vector<DType> aux(input_size);
+    mxnet_op::Kernel<UniqueComputeAuxGPUKernel, gpu>::Launch(
+      stream, input_size, thrust::raw_pointer_cast(aux.data()), input_data,
+      thrust::raw_pointer_cast(perm.data()), 1);
+    // calculate unique mask
+    thrust::device_vector<dim_t> mask(input_size);
+    mxnet_op::Kernel<UniqueComputeMaskGPUKernel, gpu>::Launch(
+      stream, input_size, thrust::raw_pointer_cast(mask.data()),
+      thrust::raw_pointer_cast(aux.data()), 1);
+    // Calculate prefix sum
+    thrust::device_vector<int32_t> prefix_sum(input_size, 0);
+    thrust::inclusive_scan(policy, mask.begin(), mask.end(), prefix_sum.begin());
+    int32_t valid_num = 0;
+    CUDA_CALL(cudaMemcpy(&valid_num, thrust::raw_pointer_cast(&prefix_sum[input_size - 1]),
+                          sizeof(int32_t), cudaMemcpyDeviceToHost));
+    // set the output shape forcefully
+    mxnet::TShape s(1, valid_num);
+    const_cast<NDArray &>(outputs[0]).Init(s);
+    // launch kernal to obtain unique array, reuse boolean_mask kernel
+    mxnet_op::Kernel<BooleanMaskForwardKernel, gpu>::Launch(
+      stream, input_size, outputs[0].data().dptr<DType>(),
+      thrust::raw_pointer_cast(aux.data()), thrust::raw_pointer_cast(prefix_sum.data()), 1);
+    // handle other optional outputs
+    int output_flag = 0;
+    if (param.return_index) {
+      output_flag += 1;
+      const_cast<NDArray &>(outputs[output_flag]).Init(s);
+      dim_t* unique_indices = outputs[output_flag].data().dptr<dim_t>();
+      // reuse boolean_mask kernel
+      mxnet_op::Kernel<BooleanMaskForwardKernel, gpu>::Launch(
+        stream, input_size, unique_indices,
+        thrust::raw_pointer_cast(perm.data()), thrust::raw_pointer_cast(prefix_sum.data()), 1);
+    }
+    if (param.return_inverse) {
+      output_flag += 1;
+      const_cast<NDArray &>(outputs[output_flag]).Init(mxnet::TShape(1, input_size));
+      dim_t* unique_inverse = outputs[output_flag].data().dptr<dim_t>();
+      mxnet_op::Kernel<UniqueReturnInverseKernel, gpu>::Launch(
+        stream, input_size, unique_inverse,
+        thrust::raw_pointer_cast(prefix_sum.data()), thrust::raw_pointer_cast(perm.data()));
+    }
+    if (param.return_counts) {
+      output_flag += 1;
+      thrust::device_vector<int32_t> idx(valid_num + 1);
+      auto iter = idx.begin();
+      for (int32_t i = 0; i < input_size; ++i) {
+        if (mask[i]) {
+          *iter = i;
+          ++iter;
+        }
+      }
+      *iter = input_size;
+      const_cast<NDArray &>(outputs[output_flag]).Init(s);
+      dim_t* unique_counts = outputs[output_flag].data().dptr<dim_t>();
+      mxnet_op::Kernel<UniqueReturnCountsKernel, gpu>::Launch(
+        stream, input_size, unique_counts,
+        thrust::raw_pointer_cast(idx.data()));
+    }
+  });
+}
+
+void NumpyUniqueGPUImpl(const NumpyUniqueParam& param,
+                        const OpContext &ctx,
+                        const std::vector<NDArray> &inputs,
+                        const std::vector<OpReqType> &req,
+                        const std::vector<NDArray> &outputs) {
+  CHECK(param.axis.value() >= -1 * inputs[0].shape().ndim() &&
+      param.axis.value() < inputs[0].shape().ndim())
+      << "Axis should be in the range of [-r, r-1] where r is the rank of input tensor";
+  MXNET_NO_FLOAT16_TYPE_SWITCH(outputs[0].dtype(), DType, {
+    using namespace mshadow;
+    using namespace mshadow::expr;
+    Stream<gpu> *stream = ctx.get_stream<gpu>();
+    auto policy = thrust::cuda::par.on(stream->stream_);
+    const index_t actual_axis =
+        param.axis.value() + ((param.axis.value() < 0) ? inputs[0].shape().ndim() : 0);
+    // reshape tensor to [origin_shape[axis], -1]
+    const mxnet::TShape origin_shape = inputs[0].shape();
+    Tensor<gpu, 3, DType> input_tensor_3d =
+        inputs[0].data().FlatTo3D<gpu, DType>(actual_axis, stream);
+    Tensor<gpu, 1, DType> workspace = ctx.requested[0].get_space_typed<gpu, 1, DType>(
+        Shape1(input_tensor_3d.shape_.Size() * 2), stream);
+    Tensor<gpu, 3, DType> input_tensor(workspace.dptr_, Shape3(
+        input_tensor_3d.shape_[1], input_tensor_3d.shape_[0], input_tensor_3d.shape_[2]), stream);
+    input_tensor = swapaxis<1, 0>(input_tensor_3d);
+    const Shape<3> temp_shape = input_tensor.shape_;
+    DType* input_data = input_tensor.dptr_;
+    dim_t numel = temp_shape[1] * temp_shape[2];
+    // argsort, result in perm
+    thrust::device_vector<dim_t> perm(temp_shape[0]);
+    thrust::sequence(policy, perm.begin(), perm.end());
+    thrust::stable_sort(policy, perm.begin(), perm.end(),
+                        argsort_functor2d<DType>(input_data, numel));
+    // sorted data in aux
+    Tensor<gpu, 2, DType> aux(workspace.dptr_ + input_tensor_3d.shape_.Size(),
+        Shape2(temp_shape[0], temp_shape[1] * temp_shape[2]), stream);
+    mxnet_op::Kernel<UniqueComputeAuxGPUKernel, gpu>::Launch(
+      stream, temp_shape.Size(), aux.dptr_, input_data,
+      thrust::raw_pointer_cast(perm.data()), numel);
+    // calculate unique mask
+    thrust::device_vector<dim_t> mask(temp_shape[0]);
+    mxnet_op::Kernel<UniqueComputeMaskGPUKernel, gpu>::Launch(
+      stream, temp_shape[0], thrust::raw_pointer_cast(mask.data()), aux.dptr_, numel);
+    // calculate prefix sum
+    thrust::device_vector<int32_t> prefix_sum(temp_shape[0], 0);
+    thrust::inclusive_scan(policy, mask.begin(), mask.end(), prefix_sum.begin());
+    int32_t valid_num = 0;
+    CUDA_CALL(cudaMemcpy(&valid_num, thrust::raw_pointer_cast(&prefix_sum[temp_shape[0] - 1]),
+                          sizeof(int32_t), cudaMemcpyDeviceToHost));
+    // store the temp output data, reuse the space of 'input_tensor'
+    Tensor<gpu, 3, DType> temp_tensor(workspace.dptr_,
+        Shape3(valid_num, temp_shape[1], temp_shape[2]), stream);
+    // launch kernal to obtain unique array, reuse boolean_mask kernel
+    mxnet_op::Kernel<BooleanMaskForwardKernel, gpu>::Launch(
+      stream, temp_shape.Size(), temp_tensor.dptr_, aux.dptr_,
+      thrust::raw_pointer_cast(prefix_sum.data()), numel);
+    // set the output shape forcefully and swap axis back
+    mxnet::TShape out_shape(origin_shape);
+    out_shape[actual_axis] = valid_num;
+    const_cast<NDArray &>(outputs[0]).Init(out_shape);
+    Tensor<gpu, 3, DType> output_tensor(outputs[0].data().dptr<DType>(),
+        Shape3(temp_shape[1], valid_num, temp_shape[2]), stream);
+    output_tensor = swapaxis<1, 0>(temp_tensor);
+    // handle other optional outputs
+    int output_flag = 0;
+    if (param.return_index) {
+      output_flag += 1;
+      const_cast<NDArray &>(outputs[output_flag]).Init(mxnet::TShape(1, valid_num));
+      dim_t* unique_indices = outputs[output_flag].data().dptr<dim_t>();
+      // reuse boolean_mask kernel
+      mxnet_op::Kernel<BooleanMaskForwardKernel, gpu>::Launch(
+        stream, temp_shape[0], unique_indices, thrust::raw_pointer_cast(perm.data()),
+        thrust::raw_pointer_cast(prefix_sum.data()), 1);
+    }
+    if (param.return_inverse) {
+      output_flag += 1;
+      const_cast<NDArray &>(outputs[output_flag]).Init(mxnet::TShape(1, temp_shape[0]));
+      dim_t* unique_inverse = outputs[output_flag].data().dptr<dim_t>();
+      mxnet_op::Kernel<UniqueReturnInverseKernel, gpu>::Launch(
+        stream, temp_shape[0], unique_inverse,
+        thrust::raw_pointer_cast(prefix_sum.data()), thrust::raw_pointer_cast(perm.data()));
+    }
+    if (param.return_counts) {
+      output_flag += 1;
+      thrust::device_vector<int32_t> idx(valid_num + 1);
+      auto iter = idx.begin();
+      for (dim_t i = 0; i < temp_shape[0]; ++i) {
+        if (mask[i]) {
+          *iter = i;
+          ++iter;
+        }
+      }
+      *iter = temp_shape[0];
+      const_cast<NDArray &>(outputs[output_flag]).Init(mxnet::TShape(1, valid_num));
+      dim_t* unique_counts = outputs[output_flag].data().dptr<dim_t>();
+      mxnet_op::Kernel<UniqueReturnCountsKernel, gpu>::Launch(
+        stream, temp_shape[0], unique_counts,
+        thrust::raw_pointer_cast(idx.data()));
+    }
+  });
+}
+
+void NumpyUniqueGPUForward(const nnvm::NodeAttrs& attrs,
+                           const OpContext &ctx,
+                           const std::vector<NDArray> &inputs,
+                           const std::vector<OpReqType> &req,
+                           const std::vector<NDArray> &outputs) {
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK(req[0] == kWriteTo || req[0] == kWriteInplace);
+  using namespace mshadow;
+  const NumpyUniqueParam& param = nnvm::get<NumpyUniqueParam>(attrs.parsed);
+  if (inputs[0].shape().ndim() == 0) {
+    CHECK(!param.axis.has_value() || param.axis.value() == -1 || param.axis.value() == 0)
+      << "Axis can only be -1 or 0 for scalor tensor";
+    Stream<gpu> *s = ctx.get_stream<gpu>();
+    mxnet::TShape shape_1(1, 1);
+    const_cast<NDArray &>(outputs[0]).Init(shape_1);
+    MSHADOW_TYPE_SWITCH(outputs[0].dtype(), DType, {
+      CUDA_CALL(cudaMemcpy(outputs[0].data().dptr<DType>(), inputs[0].data().dptr<DType>(),
+                           sizeof(DType), cudaMemcpyDeviceToDevice));
+    });
+    int output_flag = 0;
+    if (param.return_index) {
+      output_flag += 1;
+      const_cast<NDArray &>(outputs[output_flag]).Init(shape_1);
+      Tensor<gpu, 1, dim_t> outdata =
+          outputs[output_flag].data().get_with_shape<gpu, 1, dim_t>(Shape1(1), s);
+      ASSIGN_DISPATCH(outdata, OpReqType::kWriteTo, 0);
+    }
+    if (param.return_inverse) {
+      output_flag += 1;
+      const_cast<NDArray &>(outputs[output_flag]).Init(shape_1);
+      Tensor<gpu, 1, dim_t> outdata =
+          outputs[output_flag].data().get_with_shape<gpu, 1, dim_t>(Shape1(1), s);
+      ASSIGN_DISPATCH(outdata, OpReqType::kWriteTo, 0);
+    }
+    if (param.return_counts) {
+      output_flag += 1;
+      const_cast<NDArray &>(outputs[output_flag]).Init(shape_1);
+      Tensor<gpu, 1, dim_t> outdata =
+          outputs[output_flag].data().get_with_shape<gpu, 1, dim_t>(Shape1(1), s);
+      ASSIGN_DISPATCH(outdata, OpReqType::kWriteTo, 1);
+    }
+  } else if (inputs[0].shape().Size() == 0) {
+    // If the input tensor is zero size, only a check on axis is needed
+    if (param.axis.has_value()) {
+      int axis = param.axis.value();
+      if (axis < 0) axis += inputs[0].shape().ndim();
+      CHECK(axis >= 0 && axis < inputs[0].shape().ndim())
+        << "Axis must be within the range of input tensor's dimension";
+    }
+    // set the shapes of outputs
+    mxnet::TShape shape_0(1, 0);
+    const_cast<NDArray &>(outputs[0]).Init(shape_0);
+    int output_flag = 0;
+    if (param.return_index) {
+      output_flag += 1;
+      const_cast<NDArray &>(outputs[output_flag]).Init(shape_0);
+    }
+    if (param.return_inverse) {
+      output_flag += 1;
+      const_cast<NDArray &>(outputs[output_flag]).Init(shape_0);
+    }
+    if (param.return_counts) {
+      output_flag += 1;
+      const_cast<NDArray &>(outputs[output_flag]).Init(shape_0);
+    }
+  } else {
+    if (!param.axis.has_value()) {
+      NumpyUniqueGPUNoneAxisImpl(param, ctx, inputs, req, outputs);
+    } else {
+      NumpyUniqueGPUImpl(param, ctx, inputs, req, outputs);
+    }
+  }
+}
+
+NNVM_REGISTER_OP(_npi_unique)
+.set_attr<FComputeEx>("FComputeEx<gpu>", NumpyUniqueGPUForward);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_unique_op.h b/src/operator/numpy/np_unique_op.h
new file mode 100644
index 000000000000..465ab205692b
--- /dev/null
+++ b/src/operator/numpy/np_unique_op.h
@@ -0,0 +1,178 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_unique_op.h
+ */
+
+#ifndef MXNET_OPERATOR_NUMPY_NP_UNIQUE_OP_H_
+#define MXNET_OPERATOR_NUMPY_NP_UNIQUE_OP_H_
+
+#include <mxnet/operator_util.h>
+#include <dmlc/optional.h>
+#include <vector>
+#include <numeric>
+#include <set>
+#include <string>
+#include "../mxnet_op.h"
+#include "../operator_common.h"
+#include "../mshadow_op.h"
+#include "../contrib/boolean_mask-inl.h"
+#ifdef __CUDACC__
+#include <thrust/device_ptr.h>
+#include <thrust/device_vector.h>
+#include <thrust/functional.h>
+#include <thrust/scan.h>
+#include <thrust/sequence.h>
+#include <thrust/sort.h>
+#endif
+
+namespace mxnet {
+namespace op {
+
+struct UniqueComputeAuxCPUKernel {
+  // assume that idx have been flattened to a 1-D tensor (N,)
+  // assume that out_data and in_data have been flattened to 2-D tensors, (N, M) and (K, M)
+  // M is the number of columns of in_data and out_data
+  // i is the index of out_data
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int64_t i, DType* out_data, const DType* in_data,
+                                  const int64_t* idx, const int64_t M) {
+    int64_t j = idx[i];
+    std::memcpy(out_data + i * M, in_data + j * M, M * sizeof(DType));
+  }
+};
+
+struct UniqueComputeAuxGPUKernel {
+  // assume that idx have been flattened to a 1-D tensor (N,)
+  // assume that out_data and in_data have been flattened to 2-D tensors, (N, M) and (K, M)
+  // M is the number of columns of in_data and out_data
+  // i is the index of out_data
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int64_t i, DType* out_data, const DType* in_data,
+                                  const int64_t* idx, const int64_t M) {
+    int64_t j = idx[i/M];
+    out_data[i] = in_data[j * M + i % M];
+  }
+};
+
+struct UniqueComputeMaskCPUKernel {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int64_t i,
+                                  int64_t* out_data,
+                                  const DType* in_data,
+                                  const int64_t numel) {
+    if (i == 0) {
+      out_data[i] = 1;
+    } else {
+      out_data[i] = (std::memcmp(in_data + i * numel,
+                     in_data + (i - 1) * numel, numel * sizeof(DType)) == 0) ? 0 : 1;
+    }
+  }
+};
+
+struct UniqueComputeMaskGPUKernel {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int64_t i,
+                                  int64_t* out_data,
+                                  const DType* in_data,
+                                  const int64_t numel) {
+    if (i == 0) {
+      out_data[i] = 1;
+    } else {
+      out_data[i] = 0;
+      for (int64_t j = 0; j < numel; ++j) {
+        if (in_data[(i - 1) * numel + j] != in_data[i * numel + j]) {
+          out_data[i] = 1;
+          break;
+        }
+      }
+    }
+  }
+};
+
+struct UniqueReturnInverseKernel {
+  MSHADOW_XINLINE static void Map(int64_t i,
+                                  int64_t* unique_inverse,
+                                  const int32_t* prefix_sum,
+                                  const int64_t* perm) {
+      dim_t j = perm[i];
+      unique_inverse[j] = prefix_sum[i] - 1;
+  }
+};
+
+struct UniqueReturnCountsKernel {
+  MSHADOW_XINLINE static void Map(int64_t i, int64_t* unique_counts, const int32_t* idx) {
+      unique_counts[i] = idx[i + 1] - idx[i];
+  }
+};
+
+struct NumpyUniqueParam : public dmlc::Parameter<NumpyUniqueParam> {
+  bool return_index, return_inverse, return_counts;
+  dmlc::optional<int> axis;
+  DMLC_DECLARE_PARAMETER(NumpyUniqueParam) {
+    DMLC_DECLARE_FIELD(return_index)
+    .set_default(false)
+    .describe("If true, return the indices of the input.");
+    DMLC_DECLARE_FIELD(return_inverse)
+    .set_default(false)
+    .describe("If true, return the indices of the input.");
+    DMLC_DECLARE_FIELD(return_counts)
+    .set_default(false)
+    .describe("If true, return the number of times each unique item appears in input.");
+    DMLC_DECLARE_FIELD(axis)
+    .set_default(dmlc::optional<int>())
+    .describe("An integer that represents the axis to operator on.");
+  }
+};
+
+inline bool NumpyUniqueType(const nnvm::NodeAttrs& attrs,
+                            std::vector<int> *in_attrs,
+                            std::vector<int> *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
+  TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
+  for (size_t i = 1; i < out_attrs->size(); ++i) {
+    TYPE_ASSIGN_CHECK(*out_attrs, i, mshadow::kInt64);
+  }
+  return out_attrs->at(0) != -1;
+}
+
+inline bool NumpyUniqueStorageType(const nnvm::NodeAttrs& attrs,
+                                   const int dev_mask,
+                                   DispatchMode* dispatch_mode,
+                                   std::vector<int> *in_attrs,
+                                   std::vector<int> *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  // CHECK_EQ(out_attrs->size(), 1U);
+  for (int &attr : *in_attrs) {
+    CHECK_EQ(attr, kDefaultStorage) << "Only default storage is supported";
+  }
+  for (int &attr : *out_attrs) {
+    attr = kDefaultStorage;
+  }
+  *dispatch_mode = DispatchMode::kFComputeEx;
+  return true;
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_NUMPY_NP_UNIQUE_OP_H_
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 45fbcbf48098..a5ef3e482fd9 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -2384,7 +2384,7 @@ def hybrid_forward(self, F, x):
 
                     mx_out = np.around(x, d)
                     np_out = _np.around(x.asnumpy(), d)
-                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)    
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
 
 
 @with_seed()
@@ -2460,7 +2460,7 @@ def test_np_nonzero():
     class TestNonzero(HybridBlock):
         def __init__(self):
             super(TestNonzero, self).__init__()
-            
+
         def hybrid_forward(self, F, x):
             return F.npx.nonzero(x)
 
@@ -2552,6 +2552,52 @@ def dimReduce(src, des):
                 assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
 
 
+@with_seed()
+@use_np
+def test_np_unique():
+    class TestUnique(HybridBlock):
+        def __init__(self, return_index=False, return_inverse=False, return_counts=False, axis=None):
+            super(TestUnique, self).__init__()
+            self._return_index = return_index
+            self._return_inverse = return_inverse
+            self._return_counts = return_counts
+            self._axis = axis
+
+        def hybrid_forward(self, F, a):
+            return F.np.unique(a, self._return_index, self._return_inverse, self._return_counts, self._axis)
+
+    configs = [
+        ((), True, True, True, None),
+        ((1, ), True, True, True, -1),
+        ((5, ), True, True, True, 0),
+        ((3, ), True, True, True, None),
+        ((5, 4), True, True, True, -1),
+        ((5, 0, 4), True, True, True, None),
+        ((0, 0, 0), True, True, True, None),
+        ((5, 3, 4), True, True, True, -1),
+        ((5, 3, 4), True, True, True, None),
+        ((5, 3, 4), True, True, True, 1),
+    ]
+    for hybridize in [True, False]:
+        for config in configs:
+            test_unique = TestUnique(*config[1:])
+            if hybridize:
+                test_unique.hybridize()
+
+            x = np.random.uniform(size=config[0])
+            np_out = _np.unique(x.asnumpy(), *config[1:])
+            mx_out = test_unique(x)
+            assert mx_out[0].shape == np_out[0].shape
+            for i in range(4):
+                assert_almost_equal(mx_out[i].asnumpy(), np_out[i], rtol=1e-3, atol=1e-5)
+
+            # Test imperative once again
+            mx_out = np.unique(x, *config[1:])
+            np_out = _np.unique(x.asnumpy(), *config[1:])
+            for i in range(4):
+                assert_almost_equal(mx_out[i].asnumpy(), np_out[i], rtol=1e-3, atol=1e-5)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 08f8372e4a563eb0f11122d0508bfdec25078c66 Mon Sep 17 00:00:00 2001
From: "Huang, Guangtai" <guangtai@amazon.com>
Date: Fri, 9 Aug 2019 18:23:28 +0800
Subject: [PATCH 368/813] try to fix bug

---
 src/operator/numpy/np_unique_op.cc     |  6 ++--
 src/operator/numpy/np_unique_op.cu     | 16 +++++------
 src/operator/numpy/np_unique_op.h      | 34 +++++++++++------------
 tests/python/unittest/test_numpy_op.py | 38 ++++++++++++++------------
 4 files changed, 48 insertions(+), 46 deletions(-)

diff --git a/src/operator/numpy/np_unique_op.cc b/src/operator/numpy/np_unique_op.cc
index 0042c7376707..225260eb0e1e 100644
--- a/src/operator/numpy/np_unique_op.cc
+++ b/src/operator/numpy/np_unique_op.cc
@@ -53,7 +53,7 @@ void NumpyUniqueCPUNoneAxisImpl(const NumpyUniqueParam& param,
         stream, input_size, mask.data(), aux.data(), 1);
       // Calculate prefix sum
       std::vector<int32_t> prefix_sum(input_size, 0);
-      size_t valid_num = 0;
+      int32_t valid_num = 0;
       for (dim_t i = 0; i < input_size; i++) {
         prefix_sum[i] = (i == 0) ? 0 : prefix_sum[i - 1];
         prefix_sum[i] += (mask[i]) ? 1 : 0;
@@ -86,7 +86,7 @@ void NumpyUniqueCPUNoneAxisImpl(const NumpyUniqueParam& param,
       }
       if (param.return_counts) {
         output_flag += 1;
-        std::vector<int32_t> idx(valid_num + 1);
+        std::vector<dim_t> idx(valid_num + 1);
         auto iter = idx.begin();
         for (dim_t i = 0; i < input_size; ++i) {
           if (mask[i]) {
@@ -202,7 +202,7 @@ void NumpyUniqueCPUImpl(const NumpyUniqueParam& param,
     }
     if (param.return_counts) {
       output_flag += 1;
-      std::vector<int32_t> idx(valid_num + 1);
+      std::vector<dim_t> idx(valid_num + 1);
       auto iter = idx.begin();
       for (dim_t i = 0; i < temp_shape[0]; ++i) {
         if (mask[i]) {
diff --git a/src/operator/numpy/np_unique_op.cu b/src/operator/numpy/np_unique_op.cu
index 505f1a6cb0c1..3ce732a1b825 100644
--- a/src/operator/numpy/np_unique_op.cu
+++ b/src/operator/numpy/np_unique_op.cu
@@ -28,21 +28,21 @@ namespace mxnet {
 namespace op {
 
 template<typename DType>
-struct argsort_functor1d : public thrust::unary_function<int64_t, int64_t> {
+struct argsort_functor1d : public thrust::unary_function<dim_t, dim_t> {
   explicit argsort_functor1d(DType* data_) : data(data_) {}
   __device__
-  bool operator()(int64_t a, int64_t b) {
+  bool operator()(dim_t a, dim_t b) {
     return data[a] < data[b];
   }
   DType* data;
 };
 
 template<typename DType>
-struct argsort_functor2d : public thrust::unary_function<int64_t, int64_t> {
+struct argsort_functor2d : public thrust::unary_function<dim_t, dim_t> {
   argsort_functor2d(DType* data_, dim_t numel_) : data(data_), numel(numel_) {}
   __device__
-  bool operator()(int64_t a, int64_t b) {
-    for (int64_t i = 0; i < numel; ++i) {
+  bool operator()(dim_t a, dim_t b) {
+    for (dim_t i = 0; i < numel; ++i) {
       DType lhs = data[i + a * numel];
       DType rhs = data[i + b * numel];
       if (lhs < rhs) {
@@ -117,9 +117,9 @@ void NumpyUniqueGPUNoneAxisImpl(const NumpyUniqueParam& param,
     }
     if (param.return_counts) {
       output_flag += 1;
-      thrust::device_vector<int32_t> idx(valid_num + 1);
+      thrust::device_vector<dim_t> idx(valid_num + 1);
       auto iter = idx.begin();
-      for (int32_t i = 0; i < input_size; ++i) {
+      for (dim_t i = 0; i < input_size; ++i) {
         if (mask[i]) {
           *iter = i;
           ++iter;
@@ -218,7 +218,7 @@ void NumpyUniqueGPUImpl(const NumpyUniqueParam& param,
     }
     if (param.return_counts) {
       output_flag += 1;
-      thrust::device_vector<int32_t> idx(valid_num + 1);
+      thrust::device_vector<dim_t> idx(valid_num + 1);
       auto iter = idx.begin();
       for (dim_t i = 0; i < temp_shape[0]; ++i) {
         if (mask[i]) {
diff --git a/src/operator/numpy/np_unique_op.h b/src/operator/numpy/np_unique_op.h
index 465ab205692b..a55dde4a8319 100644
--- a/src/operator/numpy/np_unique_op.h
+++ b/src/operator/numpy/np_unique_op.h
@@ -53,9 +53,9 @@ struct UniqueComputeAuxCPUKernel {
   // M is the number of columns of in_data and out_data
   // i is the index of out_data
   template<typename DType>
-  MSHADOW_XINLINE static void Map(int64_t i, DType* out_data, const DType* in_data,
-                                  const int64_t* idx, const int64_t M) {
-    int64_t j = idx[i];
+  MSHADOW_XINLINE static void Map(dim_t i, DType* out_data, const DType* in_data,
+                                  const dim_t* idx, const dim_t M) {
+    dim_t j = idx[i];
     std::memcpy(out_data + i * M, in_data + j * M, M * sizeof(DType));
   }
 };
@@ -66,19 +66,19 @@ struct UniqueComputeAuxGPUKernel {
   // M is the number of columns of in_data and out_data
   // i is the index of out_data
   template<typename DType>
-  MSHADOW_XINLINE static void Map(int64_t i, DType* out_data, const DType* in_data,
-                                  const int64_t* idx, const int64_t M) {
-    int64_t j = idx[i/M];
+  MSHADOW_XINLINE static void Map(dim_t i, DType* out_data, const DType* in_data,
+                                  const dim_t* idx, const dim_t M) {
+    dim_t j = idx[i/M];
     out_data[i] = in_data[j * M + i % M];
   }
 };
 
 struct UniqueComputeMaskCPUKernel {
   template<typename DType>
-  MSHADOW_XINLINE static void Map(int64_t i,
-                                  int64_t* out_data,
+  MSHADOW_XINLINE static void Map(dim_t i,
+                                  dim_t* out_data,
                                   const DType* in_data,
-                                  const int64_t numel) {
+                                  const dim_t numel) {
     if (i == 0) {
       out_data[i] = 1;
     } else {
@@ -90,15 +90,15 @@ struct UniqueComputeMaskCPUKernel {
 
 struct UniqueComputeMaskGPUKernel {
   template<typename DType>
-  MSHADOW_XINLINE static void Map(int64_t i,
-                                  int64_t* out_data,
+  MSHADOW_XINLINE static void Map(dim_t i,
+                                  dim_t* out_data,
                                   const DType* in_data,
-                                  const int64_t numel) {
+                                  const dim_t numel) {
     if (i == 0) {
       out_data[i] = 1;
     } else {
       out_data[i] = 0;
-      for (int64_t j = 0; j < numel; ++j) {
+      for (dim_t j = 0; j < numel; ++j) {
         if (in_data[(i - 1) * numel + j] != in_data[i * numel + j]) {
           out_data[i] = 1;
           break;
@@ -109,17 +109,17 @@ struct UniqueComputeMaskGPUKernel {
 };
 
 struct UniqueReturnInverseKernel {
-  MSHADOW_XINLINE static void Map(int64_t i,
-                                  int64_t* unique_inverse,
+  MSHADOW_XINLINE static void Map(dim_t i,
+                                  dim_t* unique_inverse,
                                   const int32_t* prefix_sum,
-                                  const int64_t* perm) {
+                                  const dim_t* perm) {
       dim_t j = perm[i];
       unique_inverse[j] = prefix_sum[i] - 1;
   }
 };
 
 struct UniqueReturnCountsKernel {
-  MSHADOW_XINLINE static void Map(int64_t i, int64_t* unique_counts, const int32_t* idx) {
+  MSHADOW_XINLINE static void Map(dim_t i, dim_t* unique_counts, const dim_t* idx) {
       unique_counts[i] = idx[i + 1] - idx[i];
   }
 };
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index a5ef3e482fd9..d4d336d8e48e 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -2578,24 +2578,26 @@ def hybrid_forward(self, F, a):
         ((5, 3, 4), True, True, True, None),
         ((5, 3, 4), True, True, True, 1),
     ]
-    for hybridize in [True, False]:
-        for config in configs:
-            test_unique = TestUnique(*config[1:])
-            if hybridize:
-                test_unique.hybridize()
-
-            x = np.random.uniform(size=config[0])
-            np_out = _np.unique(x.asnumpy(), *config[1:])
-            mx_out = test_unique(x)
-            assert mx_out[0].shape == np_out[0].shape
-            for i in range(4):
-                assert_almost_equal(mx_out[i].asnumpy(), np_out[i], rtol=1e-3, atol=1e-5)
-
-            # Test imperative once again
-            mx_out = np.unique(x, *config[1:])
-            np_out = _np.unique(x.asnumpy(), *config[1:])
-            for i in range(4):
-                assert_almost_equal(mx_out[i].asnumpy(), np_out[i], rtol=1e-3, atol=1e-5)
+    for dtype in ['float32', 'float64', 'int8', 'int32', 'int64']:
+        for hybridize in [True, False]:
+            for config in configs:
+                test_unique = TestUnique(*config[1:])
+                if hybridize:
+                    test_unique.hybridize()
+                x = _np.random.uniform(-8.0, 8.0, size=config[0])
+                x = np.array(x, dtype=dtype)
+                print(config, x.dtype)
+                np_out = _np.unique(x.asnumpy(), *config[1:])
+                mx_out = test_unique(x)
+                assert mx_out[0].shape == np_out[0].shape
+                for i in range(4):
+                    assert_almost_equal(mx_out[i].asnumpy(), np_out[i], rtol=1e-3, atol=1e-5)
+
+                # Test imperative once again
+                mx_out = np.unique(x, *config[1:])
+                np_out = _np.unique(x.asnumpy(), *config[1:])
+                for i in range(4):
+                    assert_almost_equal(mx_out[i].asnumpy(), np_out[i], rtol=1e-3, atol=1e-5)
 
 
 if __name__ == '__main__':

From 3ca1920c0ac53eb64f672863677dc661fd439110 Mon Sep 17 00:00:00 2001
From: "Huang, Guangtai" <guangtai@amazon.com>
Date: Mon, 12 Aug 2019 13:30:00 +0800
Subject: [PATCH 369/813] fix memory bug and disable some test

---
 src/operator/numpy/np_unique_op.cc     | 4 ++--
 src/operator/numpy/np_unique_op.cu     | 4 ++--
 tests/python/unittest/test_numpy_op.py | 6 +++---
 3 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/src/operator/numpy/np_unique_op.cc b/src/operator/numpy/np_unique_op.cc
index 225260eb0e1e..567ed1293986 100644
--- a/src/operator/numpy/np_unique_op.cc
+++ b/src/operator/numpy/np_unique_op.cc
@@ -98,7 +98,7 @@ void NumpyUniqueCPUNoneAxisImpl(const NumpyUniqueParam& param,
         const_cast<NDArray &>(outputs[output_flag]).Init(s);
         dim_t* unique_counts = outputs[output_flag].data().dptr<dim_t>();
         mxnet_op::Kernel<UniqueReturnCountsKernel, cpu>::Launch(
-          stream, input_size, unique_counts, idx.data());
+          stream, valid_num, unique_counts, idx.data());
       }
     } else {
       std::set<DType> set(input_data, input_data + input_size);
@@ -214,7 +214,7 @@ void NumpyUniqueCPUImpl(const NumpyUniqueParam& param,
       const_cast<NDArray &>(outputs[output_flag]).Init(mxnet::TShape(1, valid_num));
       dim_t* unique_counts = outputs[output_flag].data().dptr<dim_t>();
       mxnet_op::Kernel<UniqueReturnCountsKernel, cpu>::Launch(
-          stream, temp_shape[0], unique_counts, idx.data());
+          stream, valid_num, unique_counts, idx.data());
     }
   });
 }
diff --git a/src/operator/numpy/np_unique_op.cu b/src/operator/numpy/np_unique_op.cu
index 3ce732a1b825..cfb803973d93 100644
--- a/src/operator/numpy/np_unique_op.cu
+++ b/src/operator/numpy/np_unique_op.cu
@@ -129,7 +129,7 @@ void NumpyUniqueGPUNoneAxisImpl(const NumpyUniqueParam& param,
       const_cast<NDArray &>(outputs[output_flag]).Init(s);
       dim_t* unique_counts = outputs[output_flag].data().dptr<dim_t>();
       mxnet_op::Kernel<UniqueReturnCountsKernel, gpu>::Launch(
-        stream, input_size, unique_counts,
+        stream, valid_num, unique_counts,
         thrust::raw_pointer_cast(idx.data()));
     }
   });
@@ -230,7 +230,7 @@ void NumpyUniqueGPUImpl(const NumpyUniqueParam& param,
       const_cast<NDArray &>(outputs[output_flag]).Init(mxnet::TShape(1, valid_num));
       dim_t* unique_counts = outputs[output_flag].data().dptr<dim_t>();
       mxnet_op::Kernel<UniqueReturnCountsKernel, gpu>::Launch(
-        stream, temp_shape[0], unique_counts,
+        stream, valid_num, unique_counts,
         thrust::raw_pointer_cast(idx.data()));
     }
   });
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index d4d336d8e48e..53d02c275110 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -2570,11 +2570,12 @@ def hybrid_forward(self, F, a):
         ((), True, True, True, None),
         ((1, ), True, True, True, -1),
         ((5, ), True, True, True, 0),
-        ((3, ), True, True, True, None),
+        ((5, ), True, True, True, None),
+        ((5, 4), True, True, True, None),
         ((5, 4), True, True, True, -1),
         ((5, 0, 4), True, True, True, None),
         ((0, 0, 0), True, True, True, None),
-        ((5, 3, 4), True, True, True, -1),
+        # ((5, 3, 4), True, True, True, -1), # waiting for numpy 1.18, details in pr 14255
         ((5, 3, 4), True, True, True, None),
         ((5, 3, 4), True, True, True, 1),
     ]
@@ -2586,7 +2587,6 @@ def hybrid_forward(self, F, a):
                     test_unique.hybridize()
                 x = _np.random.uniform(-8.0, 8.0, size=config[0])
                 x = np.array(x, dtype=dtype)
-                print(config, x.dtype)
                 np_out = _np.unique(x.asnumpy(), *config[1:])
                 mx_out = test_unique(x)
                 assert mx_out[0].shape == np_out[0].shape

From 33bd02fff0090874bab7b94d3c5d3d7191efff9d Mon Sep 17 00:00:00 2001
From: "Huang, Guangtai" <guangtai@amazon.com>
Date: Tue, 24 Sep 2019 19:17:39 +0800
Subject: [PATCH 370/813] fix according to review

---
 src/operator/numpy/np_unique_op.cc     | 62 +++++++++++++++++-
 src/operator/numpy/np_unique_op.cu     | 33 ++++++++++
 src/operator/numpy/np_unique_op.h      | 90 --------------------------
 tests/python/unittest/test_numpy_op.py |  4 +-
 4 files changed, 94 insertions(+), 95 deletions(-)

diff --git a/src/operator/numpy/np_unique_op.cc b/src/operator/numpy/np_unique_op.cc
index 567ed1293986..2f57733a72b2 100644
--- a/src/operator/numpy/np_unique_op.cc
+++ b/src/operator/numpy/np_unique_op.cc
@@ -27,12 +27,69 @@
 namespace mxnet {
 namespace op {
 
+inline bool NumpyUniqueType(const nnvm::NodeAttrs& attrs,
+                            std::vector<int> *in_attrs,
+                            std::vector<int> *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
+  TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
+  for (size_t i = 1; i < out_attrs->size(); ++i) {
+    TYPE_ASSIGN_CHECK(*out_attrs, i, mshadow::kInt64);
+  }
+  return out_attrs->at(0) != -1;
+}
+
+inline bool NumpyUniqueStorageType(const nnvm::NodeAttrs& attrs,
+                                   const int dev_mask,
+                                   DispatchMode* dispatch_mode,
+                                   std::vector<int> *in_attrs,
+                                   std::vector<int> *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  // CHECK_EQ(out_attrs->size(), 1U);
+  for (int &attr : *in_attrs) {
+    CHECK_EQ(attr, kDefaultStorage) << "Only default storage is supported";
+  }
+  for (int &attr : *out_attrs) {
+    attr = kDefaultStorage;
+  }
+  *dispatch_mode = DispatchMode::kFComputeEx;
+  return true;
+}
+
+struct UniqueComputeAuxCPUKernel {
+  // assume that idx have been flattened to a 1-D tensor (N,)
+  // assume that out_data and in_data have been flattened to 2-D tensors, (N, M) and (K, M)
+  // M is the number of columns of in_data and out_data
+  // i is the index of out_data
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(dim_t i, DType* out_data, const DType* in_data,
+                                  const dim_t* idx, const dim_t M) {
+    dim_t j = idx[i];
+    std::memcpy(out_data + i * M, in_data + j * M, M * sizeof(DType));
+  }
+};
+
+struct UniqueComputeMaskCPUKernel {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(dim_t i,
+                                  dim_t* out_data,
+                                  const DType* in_data,
+                                  const dim_t numel) {
+    if (i == 0) {
+      out_data[i] = 1;
+    } else {
+      out_data[i] = (std::memcmp(in_data + i * numel,
+                     in_data + (i - 1) * numel, numel * sizeof(DType)) == 0) ? 0 : 1;
+    }
+  }
+};
+
 void NumpyUniqueCPUNoneAxisImpl(const NumpyUniqueParam& param,
                                 const OpContext &ctx,
                                 const std::vector<NDArray> &inputs,
                                 const std::vector<OpReqType> &req,
                                 const std::vector<NDArray> &outputs) {
-  MXNET_NO_FLOAT16_TYPE_SWITCH(outputs[0].dtype(), DType, {
+  MSHADOW_TYPE_SWITCH(outputs[0].dtype(), DType, {
     mshadow::Stream<cpu> *stream = ctx.get_stream<cpu>();
 
     DType* input_data = inputs[0].data().dptr<DType>();
@@ -117,7 +174,7 @@ void NumpyUniqueCPUImpl(const NumpyUniqueParam& param,
   CHECK(param.axis.value() >= -1 * inputs[0].shape().ndim() &&
       param.axis.value() < inputs[0].shape().ndim())
       << "Axis should be in the range of [-r, r-1] where r is the rank of input tensor";
-  MXNET_NO_FLOAT16_TYPE_SWITCH(outputs[0].dtype(), DType, {
+  MSHADOW_TYPE_SWITCH(outputs[0].dtype(), DType, {
     using namespace mshadow;
     using namespace mshadow::expr;
     Stream<cpu> *stream = ctx.get_stream<cpu>();
@@ -314,7 +371,6 @@ NNVM_REGISTER_OP(_npi_unique)
 .set_attr<nnvm::FInferType>("FInferType", NumpyUniqueType)
 .set_attr<FComputeEx>("FComputeEx<cpu>", NumpyUniqueCPUForward)
 .set_attr<FInferStorageType>("FInferStorageType", NumpyUniqueStorageType)
-.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
 .set_attr<FResourceRequest>("FResourceRequest",
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
diff --git a/src/operator/numpy/np_unique_op.cu b/src/operator/numpy/np_unique_op.cu
index cfb803973d93..4d90a4542503 100644
--- a/src/operator/numpy/np_unique_op.cu
+++ b/src/operator/numpy/np_unique_op.cu
@@ -27,6 +27,39 @@
 namespace mxnet {
 namespace op {
 
+struct UniqueComputeAuxGPUKernel {
+  // assume that idx have been flattened to a 1-D tensor (N,)
+  // assume that out_data and in_data have been flattened to 2-D tensors, (N, M) and (K, M)
+  // M is the number of columns of in_data and out_data
+  // i is the index of out_data
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(dim_t i, DType* out_data, const DType* in_data,
+                                  const dim_t* idx, const dim_t M) {
+    dim_t j = idx[i/M];
+    out_data[i] = in_data[j * M + i % M];
+  }
+};
+
+struct UniqueComputeMaskGPUKernel {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(dim_t i,
+                                  dim_t* out_data,
+                                  const DType* in_data,
+                                  const dim_t numel) {
+    if (i == 0) {
+      out_data[i] = 1;
+    } else {
+      out_data[i] = 0;
+      for (dim_t j = 0; j < numel; ++j) {
+        if (in_data[(i - 1) * numel + j] != in_data[i * numel + j]) {
+          out_data[i] = 1;
+          break;
+        }
+      }
+    }
+  }
+};
+
 template<typename DType>
 struct argsort_functor1d : public thrust::unary_function<dim_t, dim_t> {
   explicit argsort_functor1d(DType* data_) : data(data_) {}
diff --git a/src/operator/numpy/np_unique_op.h b/src/operator/numpy/np_unique_op.h
index a55dde4a8319..bc2b6c34c19f 100644
--- a/src/operator/numpy/np_unique_op.h
+++ b/src/operator/numpy/np_unique_op.h
@@ -47,67 +47,6 @@
 namespace mxnet {
 namespace op {
 
-struct UniqueComputeAuxCPUKernel {
-  // assume that idx have been flattened to a 1-D tensor (N,)
-  // assume that out_data and in_data have been flattened to 2-D tensors, (N, M) and (K, M)
-  // M is the number of columns of in_data and out_data
-  // i is the index of out_data
-  template<typename DType>
-  MSHADOW_XINLINE static void Map(dim_t i, DType* out_data, const DType* in_data,
-                                  const dim_t* idx, const dim_t M) {
-    dim_t j = idx[i];
-    std::memcpy(out_data + i * M, in_data + j * M, M * sizeof(DType));
-  }
-};
-
-struct UniqueComputeAuxGPUKernel {
-  // assume that idx have been flattened to a 1-D tensor (N,)
-  // assume that out_data and in_data have been flattened to 2-D tensors, (N, M) and (K, M)
-  // M is the number of columns of in_data and out_data
-  // i is the index of out_data
-  template<typename DType>
-  MSHADOW_XINLINE static void Map(dim_t i, DType* out_data, const DType* in_data,
-                                  const dim_t* idx, const dim_t M) {
-    dim_t j = idx[i/M];
-    out_data[i] = in_data[j * M + i % M];
-  }
-};
-
-struct UniqueComputeMaskCPUKernel {
-  template<typename DType>
-  MSHADOW_XINLINE static void Map(dim_t i,
-                                  dim_t* out_data,
-                                  const DType* in_data,
-                                  const dim_t numel) {
-    if (i == 0) {
-      out_data[i] = 1;
-    } else {
-      out_data[i] = (std::memcmp(in_data + i * numel,
-                     in_data + (i - 1) * numel, numel * sizeof(DType)) == 0) ? 0 : 1;
-    }
-  }
-};
-
-struct UniqueComputeMaskGPUKernel {
-  template<typename DType>
-  MSHADOW_XINLINE static void Map(dim_t i,
-                                  dim_t* out_data,
-                                  const DType* in_data,
-                                  const dim_t numel) {
-    if (i == 0) {
-      out_data[i] = 1;
-    } else {
-      out_data[i] = 0;
-      for (dim_t j = 0; j < numel; ++j) {
-        if (in_data[(i - 1) * numel + j] != in_data[i * numel + j]) {
-          out_data[i] = 1;
-          break;
-        }
-      }
-    }
-  }
-};
-
 struct UniqueReturnInverseKernel {
   MSHADOW_XINLINE static void Map(dim_t i,
                                   dim_t* unique_inverse,
@@ -143,35 +82,6 @@ struct NumpyUniqueParam : public dmlc::Parameter<NumpyUniqueParam> {
   }
 };
 
-inline bool NumpyUniqueType(const nnvm::NodeAttrs& attrs,
-                            std::vector<int> *in_attrs,
-                            std::vector<int> *out_attrs) {
-  CHECK_EQ(in_attrs->size(), 1U);
-  TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
-  TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
-  for (size_t i = 1; i < out_attrs->size(); ++i) {
-    TYPE_ASSIGN_CHECK(*out_attrs, i, mshadow::kInt64);
-  }
-  return out_attrs->at(0) != -1;
-}
-
-inline bool NumpyUniqueStorageType(const nnvm::NodeAttrs& attrs,
-                                   const int dev_mask,
-                                   DispatchMode* dispatch_mode,
-                                   std::vector<int> *in_attrs,
-                                   std::vector<int> *out_attrs) {
-  CHECK_EQ(in_attrs->size(), 1U);
-  // CHECK_EQ(out_attrs->size(), 1U);
-  for (int &attr : *in_attrs) {
-    CHECK_EQ(attr, kDefaultStorage) << "Only default storage is supported";
-  }
-  for (int &attr : *out_attrs) {
-    attr = kDefaultStorage;
-  }
-  *dispatch_mode = DispatchMode::kFComputeEx;
-  return true;
-}
-
 }  // namespace op
 }  // namespace mxnet
 
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 53d02c275110..d2dc6ab269dc 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -2579,8 +2579,8 @@ def hybrid_forward(self, F, a):
         ((5, 3, 4), True, True, True, None),
         ((5, 3, 4), True, True, True, 1),
     ]
-    for dtype in ['float32', 'float64', 'int8', 'int32', 'int64']:
-        for hybridize in [True, False]:
+    for dtype in ['float32', 'float64', 'int8', 'uint8', 'int32', 'int64']:
+        for hybridize in [False]:
             for config in configs:
                 test_unique = TestUnique(*config[1:])
                 if hybridize:

From 1d4032d8d64b8936fefcbabf8938aceabbe51bec Mon Sep 17 00:00:00 2001
From: Lin Yuan <apeforest@gmail.com>
Date: Tue, 24 Sep 2019 10:50:01 -0700
Subject: [PATCH 371/813] set fixed seed for profiler (#16155)

* set fix seed for profiler

* update README with profiler option

* fix wording
---
 benchmark/opperf/README.md                | 18 +++++++++++++++++-
 benchmark/opperf/utils/benchmark_utils.py |  1 +
 2 files changed, 18 insertions(+), 1 deletion(-)

diff --git a/benchmark/opperf/README.md b/benchmark/opperf/README.md
index 6e628dfe40a8..ef7747cf4483 100644
--- a/benchmark/opperf/README.md
+++ b/benchmark/opperf/README.md
@@ -129,7 +129,7 @@ Output for the above benchmark run, on a CPU machine, would look something like
 
 ```
 
-## Usecase 3.1 - Run benchmarks for group of operators with same input
+## Usecase 4 - Run benchmarks for group of operators with same input
 For example, you want to run benchmarks for `nd.add`, `nd.sub` operator in MXNet, with the same set of inputs. You just run the following python script.
 
 ```
@@ -173,6 +173,22 @@ This utility queries MXNet operator registry to fetch all operators registered w
 However, fully automated tests are enabled only for simpler operators such as - broadcast operators, element_wise operators etc... For the purpose of readability and giving more control to the users, complex operators such as convolution (2D, 3D), Pooling, Recurrent are not fully automated but expressed as default rules.
 See `utils/op_registry_utils.py` for more details.
 
+## Use python timer
+Optionally, you could use the python time package as the profiler engine to caliberate runtime in each operator.
+To use python timer for all operators, use the argument --profiler 'python':
+```
+python incubator-mxnet/benchmark/opperf/opperf.py --profiler='python'
+```
+
+To use python timer for a specific operator, pass the argument profiler to the run_performance_test method:
+```
+add_res = run_performance_test([nd.add, nd.subtract], run_backward=True, dtype='float32', ctx=mx.cpu(),
+                               inputs=[{"lhs": (1024, 1024),
+                                        "rhs": (1024, 1024)}],
+                               warmup=10, runs=25, profiler='python')
+```
+By default, MXNet profiler is used as the profiler engine.
+
 # TODO
 
 All contributions are welcome. Below is the list of desired features:
diff --git a/benchmark/opperf/utils/benchmark_utils.py b/benchmark/opperf/utils/benchmark_utils.py
index a6ee38bf9f65..5db6b0b5ea83 100644
--- a/benchmark/opperf/utils/benchmark_utils.py
+++ b/benchmark/opperf/utils/benchmark_utils.py
@@ -28,6 +28,7 @@
 
 
 def _prepare_op_inputs(inputs, run_backward, dtype, ctx):
+    mx.random.seed(41)
     kwargs_list = []
     args_list = []
 

From a5e698ac939afd541e1201363ec06b74ce394385 Mon Sep 17 00:00:00 2001
From: Przemyslaw Tredak <ptredak@nvidia.com>
Date: Tue, 24 Sep 2019 14:54:32 -0700
Subject: [PATCH 372/813] FullyConnected Bias performance improvement on GPU
 (#16039)

* FullyConnected Bias performance improvement on GPU

* Handle req properly

* Fix after rebase

* More fixes from rebase

* Fix lint

* Trigger CI

* Fixes from review

* Fix
---
 src/common/cuda_utils.h               |  76 ++++++++-
 src/operator/mxnet_op.h               |  10 ++
 src/operator/nn/fully_connected-inl.h | 221 ++++++++++++++++++++++++--
 3 files changed, 285 insertions(+), 22 deletions(-)

diff --git a/src/common/cuda_utils.h b/src/common/cuda_utils.h
index dd98a0e6c966..2f7254040475 100644
--- a/src/common/cuda_utils.h
+++ b/src/common/cuda_utils.h
@@ -57,6 +57,8 @@ extern __cuda_fake_struct blockIdx;
 #include <cublas_v2.h>
 #include <curand.h>
 
+#include <vector>
+
 #define STATIC_ASSERT_CUDA_VERSION_GE(min_version) \
   static_assert(CUDA_VERSION >= min_version, "Compiled-against CUDA version " \
       QUOTEVALUE(CUDA_VERSION) " is too old, please upgrade system to version " \
@@ -353,16 +355,41 @@ int get_rows_per_block(size_t row_size, int num_threads_per_block);
 }  // namespace common
 }  // namespace mxnet
 
+/*! \brief Maximum number of GPUs */
+constexpr size_t kMaxNumGpus = 64;
+
+// The implementations below assume that accesses of 32-bit ints are inherently atomic and
+// can be read/written by multiple threads without locks.  The values held should be < 2^31.
+
+/*!
+ * \brief Return an attribute GPU `device_id`.
+ * \param device_id The device index of the cuda-capable gpu of interest.
+ * \param cached_values An array of attributes for already-looked-up GPUs.
+ * \param attr The attribute, by number.
+ * \param attr_name A string representation of the attribute, for error messages.
+ * \return the gpu's attribute value.
+ */
+inline int cudaAttributeLookup(int device_id, std::vector<int32_t> *cached_values,
+                               cudaDeviceAttr attr, const char *attr_name) {
+  if (device_id < 0 || device_id >= static_cast<int>(cached_values->size())) {
+    LOG(FATAL) << attr_name << "(device_id) called with invalid id: " << device_id;
+  } else if ((*cached_values)[device_id] < 0) {
+    int temp = -1;
+    CUDA_CALL(cudaDeviceGetAttribute(&temp, attr, device_id));
+    (*cached_values)[device_id] = static_cast<int32_t>(temp);
+  }
+  return (*cached_values)[device_id];
+}
+
 /*!
  * \brief Determine major version number of the gpu's cuda compute architecture.
  * \param device_id The device index of the cuda-capable gpu of interest.
  * \return the major version number of the gpu's cuda compute architecture.
  */
 inline int ComputeCapabilityMajor(int device_id) {
-  int major = 0;
-  CUDA_CALL(cudaDeviceGetAttribute(&major,
-                                   cudaDevAttrComputeCapabilityMajor, device_id));
-  return major;
+  static std::vector<int32_t> capability_major(kMaxNumGpus, -1);
+  return cudaAttributeLookup(device_id, &capability_major,
+                             cudaDevAttrComputeCapabilityMajor, "ComputeCapabilityMajor");
 }
 
 /*!
@@ -371,10 +398,9 @@ inline int ComputeCapabilityMajor(int device_id) {
  * \return the minor version number of the gpu's cuda compute architecture.
  */
 inline int ComputeCapabilityMinor(int device_id) {
-  int minor = 0;
-  CUDA_CALL(cudaDeviceGetAttribute(&minor,
-                                   cudaDevAttrComputeCapabilityMinor, device_id));
-  return minor;
+  static std::vector<int32_t> capability_minor(kMaxNumGpus, -1);
+  return cudaAttributeLookup(device_id, &capability_minor,
+                             cudaDevAttrComputeCapabilityMinor, "ComputeCapabilityMinor");
 }
 
 /*!
@@ -388,6 +414,40 @@ inline int SMArch(int device_id) {
   return 10 * major + minor;
 }
 
+/*!
+ * \brief Return the number of streaming multiprocessors of GPU `device_id`.
+ * \param device_id The device index of the cuda-capable gpu of interest.
+ * \return the gpu's count of streaming multiprocessors.
+ */
+inline int MultiprocessorCount(int device_id) {
+  static std::vector<int32_t> sm_counts(kMaxNumGpus, -1);
+  return cudaAttributeLookup(device_id, &sm_counts,
+                             cudaDevAttrMultiProcessorCount, "MultiprocessorCount");
+}
+
+/*!
+ * \brief Return the shared memory size in bytes of each of the GPU's streaming multiprocessors.
+ * \param device_id The device index of the cuda-capable gpu of interest.
+ * \return the shared memory size per streaming multiprocessor.
+ */
+inline int MaxSharedMemoryPerMultiprocessor(int device_id) {
+  static std::vector<int32_t> max_smem_per_mutiprocessor(kMaxNumGpus, -1);
+  return cudaAttributeLookup(device_id, &max_smem_per_mutiprocessor,
+                             cudaDevAttrMaxSharedMemoryPerMultiprocessor,
+                             "MaxSharedMemoryPerMultiprocessor");
+}
+
+/*!
+ * \brief Return whether the GPU `device_id` supports cooperative-group kernel launching.
+ * \param device_id The device index of the cuda-capable gpu of interest.
+ * \return the gpu's ability to run cooperative-group kernels.
+ */
+inline bool SupportsCooperativeLaunch(int device_id) {
+  static std::vector<int32_t> coop_launch(kMaxNumGpus, -1);
+  return cudaAttributeLookup(device_id, &coop_launch,
+                             cudaDevAttrCooperativeLaunch, "SupportsCooperativeLaunch");
+}
+
 /*!
  * \brief Determine whether a cuda-capable gpu's architecture supports float16 math.
  *        Assume not if device_id is negative.
diff --git a/src/operator/mxnet_op.h b/src/operator/mxnet_op.h
index ae98952bb7bf..d90aa268195a 100644
--- a/src/operator/mxnet_op.h
+++ b/src/operator/mxnet_op.h
@@ -249,6 +249,16 @@ inline int get_num_threads<cpu>(const int N) {
     LOG(FATAL) << "Unknown type enum " << type;            \
   }
 
+template <typename T>
+struct AccType {
+  using type = T;
+};
+
+template <>
+struct AccType<mshadow::half::half_t> {
+  using type = float;
+};
+
 #define MXNET_REAL_ACC_TYPE_SWITCH(type, DType, AType, ...)\
   switch (type) {                                          \
   case mshadow::kFloat32:                                  \
diff --git a/src/operator/nn/fully_connected-inl.h b/src/operator/nn/fully_connected-inl.h
index 44af375486fb..28b6905148e4 100644
--- a/src/operator/nn/fully_connected-inl.h
+++ b/src/operator/nn/fully_connected-inl.h
@@ -32,6 +32,7 @@
 #include <vector>
 #include <string>
 #include <utility>
+#include <algorithm>
 #include "../operator_common.h"
 #include "../elemwise_op_common.h"
 #include "../linalg.h"
@@ -59,6 +60,7 @@ struct FullyConnectedParam : public dmlc::Parameter<FullyConnectedParam> {
   int num_hidden;
   bool no_bias;
   bool flatten;
+
   DMLC_DECLARE_PARAMETER(FullyConnectedParam) {
     // TODO(bing) add support for boolean
     DMLC_DECLARE_FIELD(num_hidden).set_lower_bound(1)
@@ -75,6 +77,66 @@ struct FullyConnectedParam : public dmlc::Parameter<FullyConnectedParam> {
   }
 };
 
+template<typename DType>
+void AddBias(Tensor<cpu, 1, DType> bias, Tensor<cpu, 2, DType> data,
+             Tensor<cpu, 2, DType> out, Stream<cpu>*) {
+  using namespace mshadow;
+  using namespace mshadow::expr;
+  out += repmat(bias, data.size(0));
+}
+
+#if defined(__CUDACC__)
+
+namespace {
+  constexpr int nthreads_addbias = 256;
+  constexpr int nthreads_addbiasgrad_phase1 = 512;
+  constexpr int nthreads_addbiasgrad_phase2 = 128;
+  constexpr int threads_per_warp = 32;
+
+  inline int ceil_div(int x, int y) {
+    return (x + y - 1) / y;
+  }
+}  // namespace
+
+template <typename DType, typename LType>
+__global__ void add_bias_kernel(DType* mat, DType* bias, size_t lead_dim, size_t bias_length) {
+  __shared__ LType scratch[nthreads_addbias * 2];
+  const index_t N = bias_length * sizeof(DType)/sizeof(LType);
+  const index_t base = blockIdx.x * N;
+  LType* const mat_aligned = reinterpret_cast<LType*>(mat) + base;
+  const LType* const bias_aligned = reinterpret_cast<LType*>(bias);
+  LType* const scratch_bias_load = scratch + threadIdx.x;
+  DType* const scratch_bias = reinterpret_cast<DType*>(scratch_bias_load);
+  LType* const scratch_mat_load = scratch_bias_load + nthreads_addbias;
+  DType* const scratch_mat = reinterpret_cast<DType*>(scratch_mat_load);
+  for (index_t i = threadIdx.x; i < N; i += blockDim.x) {
+    *scratch_bias_load = bias_aligned[i];
+    *scratch_mat_load = mat_aligned[i];
+#pragma unroll
+    for (int j = 0; j < sizeof(LType)/sizeof(DType); ++j) {
+      scratch_mat[j] += scratch_bias[j];
+    }
+    mat_aligned[i] = *scratch_mat_load;
+  }
+}
+
+template<typename DType>
+void AddBias(Tensor<gpu, 1, DType> bias, Tensor<gpu, 2, DType> data,
+             Tensor<gpu, 2, DType> out, Stream<gpu>* s) {
+    int ltype = mxnet::common::cuda::get_load_type(bias.shape_[0] * sizeof(DType));
+    MXNET_LOAD_TYPE_SWITCH(ltype, LType, {
+    add_bias_kernel<DType, LType><<<data.size(0),
+                                    nthreads_addbias,
+                                    0,
+                                    Stream<gpu>::GetStream(s)>>>(out.dptr_,
+                                                                 bias.dptr_,
+                                                                 data.size(0),
+                                                                 bias.shape_[0]);
+    });
+}
+
+#endif  // __CUDACC__
+
 template<typename xpu, typename DType>
 void FCForward(const OpContext &ctx, const FullyConnectedParam &param,
                const std::vector<TBlob> &in_data, const std::vector<OpReqType> &req,
@@ -122,10 +184,153 @@ void FCForward(const OpContext &ctx, const FullyConnectedParam &param,
       << "Incomplete bias tensor detected: bias.data().shape[1] != weight.data().shape[0]."
          " This is not supported by FCForward. If bias is in row_sparse format, please"
          " make sure all row ids are present.";
-    out += repmat(bias, data.size(0));
+    AddBias(bias, data, out, s);
   }
 }
 
+#if defined (__CUDACC__)
+
+template<typename LType, typename DType, typename AType>
+__global__ void AddBiasGradKernelPhase1(AType * temp_space, const DType* grad,
+                                        const size_t lead_dim, const size_t other_dim) {
+  constexpr int num_warps = nthreads_addbiasgrad_phase1 / threads_per_warp;
+  const int values_per_read = sizeof(LType) >= sizeof(DType) ? sizeof(LType) / sizeof(DType) : 1;
+  const size_t stride = lead_dim / values_per_read;
+  __shared__ AType scratch[threads_per_warp * num_warps  * values_per_read];
+  LType * my_scratch_load = &(reinterpret_cast<LType *>(scratch)[threadIdx.x]);
+  DType * my_values_load = reinterpret_cast<DType *>(my_scratch_load);
+  AType * my_values_acc = &(scratch[threadIdx.x * values_per_read]);
+  AType acc[values_per_read];  // NOLINT(*)
+#pragma unroll
+  for (int i = 0; i < values_per_read; ++i) {
+    acc[i] = 0;
+  }
+  const size_t offset = blockIdx.x * threads_per_warp;
+  const int my_warp = threadIdx.x / threads_per_warp;
+  const int my_id = threadIdx.x % threads_per_warp;
+  const LType* aligned_grad = reinterpret_cast<const LType*>(grad);
+  const int rows_per_block = (other_dim + gridDim.y - 1) / gridDim.y;
+  const size_t start_row = my_warp + rows_per_block * blockIdx.y;
+  const size_t end_row = min(other_dim, static_cast<size_t>(rows_per_block * (blockIdx.y + 1)));
+  if (offset + my_id < stride) {
+    for (size_t i = start_row; i < end_row; i += num_warps) {
+      *my_scratch_load = aligned_grad[i * stride + offset + my_id];
+#pragma unroll
+      for (int j = 0; j < values_per_read; ++j) {
+        acc[j] += static_cast<AType>(my_values_load[j]);
+      }
+    }
+  }
+  __syncthreads();
+#pragma unroll
+  for (int i = 0; i < values_per_read; ++i) {
+    my_values_acc[i] = acc[i];
+  }
+
+  __syncthreads();
+
+  for (int i = num_warps / 2; i > 0; i /= 2) {
+    if (my_warp < i) {
+      const int shared_offset = values_per_read * i * threads_per_warp;
+#pragma unroll
+      for (int j = 0; j < values_per_read; ++j) {
+        my_values_acc[j] += my_values_acc[j + shared_offset];
+      }
+    }
+    __syncthreads();
+  }
+
+  if (threadIdx.x < min(threads_per_warp * values_per_read,
+                        static_cast<int>(lead_dim - values_per_read * offset))) {
+    const size_t offset_out = values_per_read * offset +
+                              blockIdx.y * lead_dim;
+    temp_space[offset_out + threadIdx.x] = scratch[threadIdx.x];
+  }
+}
+
+template <typename DType, typename AType>
+__global__ void AddBiasGradKernelPhase2(const AType * temp_space, DType * out,
+                                        int lead_dim, int n_blocks, OpReqType req) {
+  int tid = threadIdx.x + blockIdx.x * blockDim.x;
+  if (tid < lead_dim) {
+    AType acc = 0;
+    for (int i = tid; i < lead_dim * n_blocks; i += lead_dim) {
+      acc += temp_space[i];
+    }
+    KERNEL_ASSIGN(out[tid], req, static_cast<DType>(acc));
+  }
+}
+
+template<typename DType>
+void AddBiasGrad(const TBlob& in_grad,
+                 Tensor<gpu, 2, DType> grad,
+                 OpReqType req,
+                 int num_hidden,
+                 const OpContext& ctx) {
+  if (req == kNullOp) return;
+  using AType = typename mxnet_op::AccType<DType>::type;
+  mshadow::Stream<gpu> *s = ctx.get_stream<gpu>();
+  Tensor<gpu, 1, DType> gbias = in_grad.get<gpu, 1, DType>(s);
+  TBlob grad_blob = TBlob(grad);
+  TBlob gbias_blob = TBlob(gbias);
+  mxnet::TShape x(1, 0);
+  mxnet::TShape small;
+  if (shape_assign(&gbias_blob.shape_, Shape2(num_hidden, 1))) {
+    small = gbias_blob.shape_;
+  } else {
+    small = ReduceAxesShapeImpl(grad_blob.shape_, dmlc::optional<mxnet::TShape>(x), true, false);
+  }
+  const int N = small.Size();
+  int ltype = mxnet::common::cuda::get_load_type(N * sizeof(DType));
+  const int M = grad_blob.shape_.Size() / N;
+  MXNET_LOAD_TYPE_SWITCH(ltype, LType, {
+    const unsigned int blocks_x = ceil_div(N * sizeof(DType),
+                                           threads_per_warp * sizeof(LType));
+    const unsigned int preferred_number_of_blocks = 2 *
+                                                    MultiprocessorCount(ctx.run_ctx.ctx.dev_id);
+    const unsigned int blocks_y = std::max(preferred_number_of_blocks / blocks_x, 1u);
+    const dim3 n_blocks = {blocks_x, blocks_y, 1};
+    auto scratch_space = ctx.requested[fullc::kTempSpace]
+                            .get_space_typed<gpu, 1, AType>(mshadow::Shape1(N * blocks_y), s);
+    auto stream = mshadow::Stream<gpu>::GetStream(s);
+    AddBiasGradKernelPhase1<LType><<<n_blocks,
+                                     nthreads_addbiasgrad_phase1,
+                                     0,
+                                     stream>>>(scratch_space.dptr_,
+                                               grad.dptr_, N, M);
+    const int nblocks_phase2 = ceil_div(N, nthreads_addbiasgrad_phase2);
+    AddBiasGradKernelPhase2<<<nblocks_phase2,
+                              nthreads_addbiasgrad_phase2,
+                              0,
+                              stream>>>(scratch_space.dptr_,
+                                        gbias.dptr_, N,
+                                        blocks_y, req);
+  });
+}
+#endif
+
+template<typename DType>
+void AddBiasGrad(const TBlob& in_grad,
+                 Tensor<cpu, 2, DType> grad,
+                 OpReqType req,
+                 int num_hidden,
+                 const OpContext& ctx) {
+  mshadow::Stream<cpu> *s = ctx.get_stream<cpu>();
+  Tensor<cpu, 1, DType> gbias = in_grad.get<cpu, 1, DType>(s);
+  TBlob grad_blob = TBlob(grad);
+  TBlob gbias_blob = TBlob(gbias);
+  mxnet::TShape x(1, 0);
+  mxnet::TShape small;
+  if (shape_assign(&gbias_blob.shape_, Shape2(num_hidden, 1))) {
+    small = gbias_blob.shape_;
+  } else {
+    small = ReduceAxesShapeImpl(grad_blob.shape_, dmlc::optional<mxnet::TShape>(x), true, false);
+  }
+  ReduceAxesComputeImpl<cpu, mshadow::red::sum, false, false,
+                        mshadow_op::identity>(ctx, {grad_blob}, {req},
+                                              {in_grad}, small);
+}
+
 template<typename xpu, typename DType>
 void FCBackward(const OpContext &ctx, const FullyConnectedParam &param,
                 const std::vector<TBlob> &out_grad, const std::vector<TBlob> &in_data,
@@ -169,19 +374,7 @@ void FCBackward(const OpContext &ctx, const FullyConnectedParam &param,
   linalg_gemm(grad, data, gwmat, true, false, s, req[fullc::kWeight]);
   // gradient of bias
   if (!param.no_bias) {
-    Tensor<xpu, 1, DType> gbias = in_grad[fullc::kBias].get<xpu, 1, DType>(s);
-    TBlob grad_blob = TBlob(grad);
-    TBlob gbias_blob = TBlob(gbias);
-    mxnet::TShape x(1, 0);
-    mxnet::TShape small;
-    if (shape_assign(&gbias_blob.shape_, Shape2(param.num_hidden, 1))) {
-      small = gbias_blob.shape_;
-    } else {
-      small = ReduceAxesShapeImpl(grad_blob.shape_, dmlc::optional<mxnet::TShape>(x), true, false);
-    }
-    ReduceAxesComputeImpl<xpu, mshadow::red::sum, false, false,
-                          mshadow_op::identity>(ctx, {grad_blob}, {req[fullc::kBias]},
-                                                {in_grad[fullc::kBias]}, small);
+      AddBiasGrad(in_grad[fullc::kBias], grad, req[fullc::kBias], param.num_hidden, ctx);
   }
   // gradient of data
   // Legacy approach shown here for comparison:

From 35ef45cb97849f36123ae741de493fc5e8fa8c0e Mon Sep 17 00:00:00 2001
From: "Huang, Guangtai" <guangtai@amazon.com>
Date: Wed, 25 Sep 2019 06:39:21 +0800
Subject: [PATCH 373/813] Fix lack of dylib support in Makefile when use lapack
 (#15813)

* fix

* add another if-else pair
---
 Makefile | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/Makefile b/Makefile
index 19aab2885e68..b3b188a2cf3b 100644
--- a/Makefile
+++ b/Makefile
@@ -220,10 +220,12 @@ ifeq ($(USE_LAPACK), 1)
 ifeq ($(USE_BLAS),$(filter $(USE_BLAS),blas openblas atlas mkl))
 ifeq (,$(wildcard $(USE_LAPACK_PATH)/liblapack.a))
 ifeq (,$(wildcard $(USE_LAPACK_PATH)/liblapack.so))
+ifeq (,$(wildcard $(USE_LAPACK_PATH)/liblapack.dylib))
 ifeq (,$(wildcard /lib/liblapack.a))
 ifeq (,$(wildcard /lib/liblapack.so))
 ifeq (,$(wildcard /usr/lib/liblapack.a))
 ifeq (,$(wildcard /usr/lib/liblapack.so))
+ifeq (,$(wildcard /usr/lib/liblapack.dylib))
 ifeq (,$(wildcard /usr/lib64/liblapack.a))
 ifeq (,$(wildcard /usr/lib64/liblapack.so))
 	USE_LAPACK = 0
@@ -238,6 +240,8 @@ endif
 endif
 endif
 endif
+endif
+endif
 
 # lapack settings.
 ifeq ($(USE_LAPACK), 1)
@@ -761,7 +765,7 @@ ratcheck: build/rat/apache-rat/target/apache-rat-0.13.jar
 
 ifneq ($(EXTRA_OPERATORS),)
 clean: rclean cyclean $(EXTRA_PACKAGES_CLEAN)
-	$(RM) -r build lib bin deps *~ */*~ */*/*~ */*/*/*~ 
+	$(RM) -r build lib bin deps *~ */*~ */*/*~ */*/*/*~
 	(cd scala-package && mvn clean) || true
 	cd $(DMLC_CORE); $(MAKE) clean; cd -
 	cd $(PS_PATH); $(MAKE) clean; cd -
@@ -772,7 +776,7 @@ clean: rclean cyclean $(EXTRA_PACKAGES_CLEAN)
 	$(RM) -r  $(patsubst %, %/*.o, $(EXTRA_OPERATORS)) $(patsubst %, %/*/*.o, $(EXTRA_OPERATORS))
 else
 clean: rclean mkldnn_clean cyclean testclean $(EXTRA_PACKAGES_CLEAN)
-	$(RM) -r build lib bin *~ */*~ */*/*~ */*/*/*~ 
+	$(RM) -r build lib bin *~ */*~ */*/*~ */*/*/*~
 	(cd scala-package && mvn clean) || true
 	cd $(DMLC_CORE); $(MAKE) clean; cd -
 	cd $(PS_PATH); $(MAKE) clean; cd -

From 985a4ca62766ddca22b995a663d258003602f6af Mon Sep 17 00:00:00 2001
From: goldmermaid <37914843+goldmermaid@users.noreply.github.com>
Date: Tue, 24 Sep 2019 15:59:21 -0700
Subject: [PATCH 374/813] Update KL Divergence formula (#16170)

* Update KL Divergence formula

Errors fix.

* remove initial error
---
 python/mxnet/gluon/loss.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/mxnet/gluon/loss.py b/python/mxnet/gluon/loss.py
index 45c3dee08139..40fdaa51573e 100644
--- a/python/mxnet/gluon/loss.py
+++ b/python/mxnet/gluon/loss.py
@@ -424,7 +424,7 @@ class KLDivLoss(Loss):
 
         prob = \softmax({pred})
 
-        L = \sum_i {label}_i * \big[\log({label}_i) - log({pred}_i)\big]
+        L = \sum_i {label}_i * \big[\log({label}_i) - \log({prob}_i)\big]
 
 
     `label` and `pred` can have arbitrary shape as long as they have the same

From ab2214beeb855c768f6b2deec5c144630f5f65e7 Mon Sep 17 00:00:00 2001
From: Aaron Markham <aaron.s.markham@gmail.com>
Date: Wed, 25 Sep 2019 13:41:18 +0100
Subject: [PATCH 375/813] fix broken links (#16255)

---
 README.md                                     | 27 +++++++++----------
 .../_includes/get_started/get_started.html    |  8 +++---
 .../src/_includes/get_started/pip_snippet.md  |  2 +-
 docs/static_site/src/pages/api/cpp/index.md   |  4 +--
 .../src/pages/get_started/index.html          |  2 +-
 .../src/pages/get_started/windows_setup.md    |  2 +-
 6 files changed, 21 insertions(+), 24 deletions(-)

diff --git a/README.md b/README.md
index 04b7bcf9e7fa..8db55af4ff87 100644
--- a/README.md
+++ b/README.md
@@ -28,14 +28,14 @@ Apache MXNet (incubating) for Deep Learning
 ![banner](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/banner.png)
 
 Apache MXNet (incubating) is a deep learning framework designed for both *efficiency* and *flexibility*.
-It allows you to ***mix*** [symbolic and imperative programming](https://mxnet.incubator.apache.org/architecture/index.html#deep-learning-system-design-concepts)
+It allows you to ***mix*** [symbolic and imperative programming](https://mxnet.incubator.apache.org/api/architecture/program_model)
 to ***maximize*** efficiency and productivity.
 At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly.
 A graph optimization layer on top of that makes symbolic execution fast and memory efficient.
 MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines.
 
 MXNet is more than a deep learning project. It is a collection of
-[blue prints and guidelines](https://mxnet.incubator.apache.org/architecture/index.html#deep-learning-system-design-concepts) for building
+[blue prints and guidelines](https://mxnet.incubator.apache.org/api/architecture/overview) for building
 deep learning systems, and interesting insights of DL systems for hackers.
 
 Ask Questions
@@ -71,24 +71,21 @@ What's New
 * [MKLDNN for Faster CPU Performance](./docs/tutorials/mkldnn/MKLDNN_README.md)
 * [MXNet Memory Monger, Training Deeper Nets with Sublinear Memory Cost](https://github.com/dmlc/mxnet-memonger)
 * [Tutorial for NVidia GTC 2016](https://github.com/dmlc/mxnet-gtc-tutorial)
-* [Embedding Torch layers and functions in MXNet](https://mxnet.incubator.apache.org/faq/torch.html)
-* [MXNet.js: Javascript Package for Deep Learning in Browser (without server)
-](https://github.com/dmlc/mxnet.js/)
-* [Design Note: Design Efficient Deep Learning Data Loading Module](https://mxnet.incubator.apache.org/architecture/note_data_loading.html)
-* [MXNet on Mobile Device](https://mxnet.incubator.apache.org/faq/smart_device.html)
-* [Distributed Training](https://mxnet.incubator.apache.org/faq/multi_devices.html)
-* [Guide to Creating New Operators (Layers)](https://mxnet.incubator.apache.org/faq/new_op.html)
+* [MXNet.js: Javascript Package for Deep Learning in Browser (without server)](https://github.com/dmlc/mxnet.js/)
+* [Guide to Creating New Operators (Layers)](https://mxnet.incubator.apache.org/api/faq/new_op)
 * [Go binding for inference](https://github.com/songtianyi/go-mxnet-predictor)
 * [Amalgamation and Go Binding for Predictors](https://github.com/jdeng/gomxnet/) - Outdated
 * [Large Scale Image Classification](https://github.com/apache/incubator-mxnet/tree/master/example/image-classification)
 
 Contents
 --------
-* [Documentation](https://mxnet.incubator.apache.org/) and  [Tutorials](https://mxnet.incubator.apache.org/tutorials/)
-* [Design Notes](https://mxnet.incubator.apache.org/architecture/index.html)
+* [Website](https://mxnet.incubator.apache.org)
+* [Documentation](https://mxnet.incubator.apache.org/api)
+* [Blog](https://mxnet.incubator.apache.org/blog)
 * [Code Examples](https://github.com/apache/incubator-mxnet/tree/master/example)
-* [Installation](https://mxnet.incubator.apache.org/install/index.html)
-* [Pretrained Models](http://mxnet.incubator.apache.org/api/python/gluon/model_zoo.html)
+* [Installation](https://mxnet.incubator.apache.org/get_started)
+* [Features](https://mxnet.incubator.apache.org/features)
+* [Ecosystem](https://mxnet.incubator.apache.org/ecosystem)
 
 Features
 --------
@@ -97,8 +94,8 @@ Features
 * Mix and match imperative and symbolic programming to maximize flexibility and efficiency
 * Lightweight, memory efficient and portable to smart devices
 * Scales up to multi GPUs and distributed setting with auto parallelism
-* Support for [Python](https://github.com/apache/incubator-mxnet/tree/master/python), [Scala](https://github.com/apache/incubator-mxnet/tree/master/scala-package), [C++](https://github.com/apache/incubator-mxnet/tree/master/cpp-package), [Java](https://github.com/apache/incubator-mxnet/tree/master/scala-package), [Clojure](https://github.com/apache/incubator-mxnet/tree/master/contrib/clojure-package), [R](https://github.com/apache/incubator-mxnet/tree/master/R-package), [Go](https://github.com/jdeng/gomxnet/), [Javascript](https://github.com/dmlc/mxnet.js/), [Perl](https://github.com/apache/incubator-mxnet/tree/master/perl-package), [Matlab](https://github.com/apache/incubator-mxnet/tree/master/matlab), and [Julia](https://github.com/apache/incubator-mxnet/tree/master/julia)
-* Cloud-friendly and directly compatible with S3, HDFS, and Azure
+* Support for [Python](https://mxnet.incubator.apache.org/api/python), [Scala](https://mxnet.incubator.apache.org/api/scala), [C++](https://mxnet.incubator.apache.org/api/cpp), [Java](https://mxnet.incubator.apache.org/api/java), [Clojure](https://mxnet.incubator.apache.org/api/clojure), [R](https://mxnet.incubator.apache.org/api/r), [Go](https://github.com/jdeng/gomxnet/), [Javascript](https://github.com/dmlc/mxnet.js/), [Perl](https://mxnet.incubator.apache.org/api/perl), [Matlab](https://github.com/apache/incubator-mxnet/tree/master/matlab), and [Julia](https://mxnet.incubator.apache.org/api/julia)
+* Cloud-friendly and directly compatible with AWS S3, AWS Deep Learning AMI, AWS SageMaker, HDFS, and Azure
 
 License
 -------
diff --git a/docs/static_site/src/_includes/get_started/get_started.html b/docs/static_site/src/_includes/get_started/get_started.html
index 4999518431fd..614b4a409773 100644
--- a/docs/static_site/src/_includes/get_started/get_started.html
+++ b/docs/static_site/src/_includes/get_started/get_started.html
@@ -256,8 +256,8 @@ <h2>Installing MXNet</h2>
             </div> <!-- END - C++-->
 
             <br>
-            For more installation options, refer to the <a href="ubuntu_setup.html">Ubuntu installation guide</a> and
-            <a href="centos_setup.html">CentOS installation guide</a>.
+            For more installation options, refer to the <a href="get_started/ubuntu_setup.html">Ubuntu installation guide</a> and
+            <a href="get_started/centos_setup.html">CentOS installation guide</a>.
         </div> <!-- END - Linux -->
 
 
@@ -354,7 +354,7 @@ <h2>Installing MXNet</h2>
                 </div> <!-- End of cpu gpu -->
             </div>
             <br>
-            For more installation options, refer to the <a href="osx_setup.html">MXNet macOS installation guide</a>.
+            For more installation options, refer to the <a href="get_started/osx_setup.html">MXNet macOS installation guide</a>.
         </div> <!-- END - Mac OS -->
 
 
@@ -440,7 +440,7 @@ <h2>Installing MXNet</h2>
                 </div> <!-- End of cpu gpu -->
             </div> <!-- End of C++ -->
 
-            For more installation options, refer to the <a href="windows_setup.html">MXNet Windows installation guide</a>.
+            For more installation options, refer to the <a href="get_started/windows_setup.html">MXNet Windows installation guide</a>.
         </div> <!-- End of Windows -->
 
 
diff --git a/docs/static_site/src/_includes/get_started/pip_snippet.md b/docs/static_site/src/_includes/get_started/pip_snippet.md
index e67c3331c91c..0ca854005ffd 100644
--- a/docs/static_site/src/_includes/get_started/pip_snippet.md
+++ b/docs/static_site/src/_includes/get_started/pip_snippet.md
@@ -1,6 +1,6 @@
 MXNet offers MKL pip packages that will be much faster when running on Intel hardware.
 Check the chart below for other options, refer to <a href="https://pypi.org/project/mxnet/">PyPI for
-other MXNet pip packages</a>, or <a href="validate_mxnet.html">validate your MXNet installation</a>.
+other MXNet pip packages</a>, or <a href="get_started/validate_mxnet.html">validate your MXNet installation</a>.
 
 <div style="text-align: center">
     <img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/install/pip-packages.png"
diff --git a/docs/static_site/src/pages/api/cpp/index.md b/docs/static_site/src/pages/api/cpp/index.md
index 594aa237a1e1..37f00c318ce8 100644
--- a/docs/static_site/src/pages/api/cpp/index.md
+++ b/docs/static_site/src/pages/api/cpp/index.md
@@ -40,9 +40,9 @@ The cpp-package directory contains the implementation of C++ API. As mentioned a
 	git clone --recursive https://github.com/apache/incubator-mxnet mxnet
 	```
 
-3.  Install the [prerequisites](<https://mxnet.incubator.apache.org/install/build_from_source#prerequisites>), desired [BLAS libraries](<https://mxnet.incubator.apache.org/install/build_from_source#blas-library>) and optional [OpenCV, CUDA, and cuDNN](<https://mxnet.incubator.apache.org/install/build_from_source#optional>) for building MXNet from source.
+3.  Install the [prerequisites](<https://mxnet.incubator.apache.org/get_started/build_from_source#prerequisites>), desired [BLAS libraries](<https://mxnet.incubator.apache.org/get_started/build_from_source#blas-library>) and optional [OpenCV, CUDA, and cuDNN](<https://mxnet.incubator.apache.org/get_started/build_from_source#optional>) for building MXNet from source.
 4.  There is a configuration file for make, [make/config.mk](<https://github.com/apache/incubator-mxnet/blob/master/make/config.mk>) that contains all the compilation options. You can edit this file and set the appropriate options prior to running the **make** command.
-5.  Please refer to  [platform specific build instructions](<https://mxnet.incubator.apache.org/install/build_from_source#build-instructions-by-operating-system>) and available [build configurations](https://mxnet.incubator.apache.org/install/build_from_source#build-configurations) for more details.
+5.  Please refer to  [platform specific build instructions](<https://mxnet.incubator.apache.org/get_started/build_from_source#build-instructions-by-operating-system>) and available [build configurations](https://mxnet.incubator.apache.org/get_started/build_from_source#build-configurations) for more details.
 5.  For enabling the build of C++ Package, set the **USE\_CPP\_PACKAGE = 1** in [make/config.mk](<https://github.com/apache/incubator-mxnet/blob/master/make/config.mk>). Optionally, the compilation flag can also be specified on **make** command line as follows.
 	```
 	make -j USE_CPP_PACKAGE=1
diff --git a/docs/static_site/src/pages/get_started/index.html b/docs/static_site/src/pages/get_started/index.html
index 3e0e7601f387..e89b5e3b36e8 100644
--- a/docs/static_site/src/pages/get_started/index.html
+++ b/docs/static_site/src/pages/get_started/index.html
@@ -28,6 +28,6 @@
 <div class="get-started-from-source">
 <div class="wrapper">
     <h2>Download from source</h2>
-    <p>The signed source code for Apache MXNet (incubating) is available for download <a href="download">here</a></p>
+    <p>The signed source code for Apache MXNet (incubating) is available for download <a href="get_started/download">here</a></p>
 </div>
 </div>
diff --git a/docs/static_site/src/pages/get_started/windows_setup.md b/docs/static_site/src/pages/get_started/windows_setup.md
index 9bf413826a17..06e8c6eff6e5 100644
--- a/docs/static_site/src/pages/get_started/windows_setup.md
+++ b/docs/static_site/src/pages/get_started/windows_setup.md
@@ -74,7 +74,7 @@ Install MXNet with CPU support with Python:
 pip install mxnet
 ```
 
-Now [validate your MXNet installation with Python](validate_mxnet).
+Now [validate your MXNet installation with Python](get_started/validate_mxnet).
 
 ### Install with Intel CPUs
 

From df34e768ed5d4dd334c0dce7ce42384d7b62ee8f Mon Sep 17 00:00:00 2001
From: Serge Panev <spanev@nvidia.com>
Date: Wed, 25 Sep 2019 22:00:49 +0200
Subject: [PATCH 376/813] Add list_ctx to ParameterDict (#16185)

* Add list_ctx to ParameterDict

Signed-off-by: Serge Panev <spanev@nvidia.com>

* Add assert to test reset_ctx

Signed-off-by: Serge Panev <spanev@nvidia.com>
---
 python/mxnet/gluon/parameter.py     |  8 ++++++++
 tests/python/unittest/test_gluon.py | 14 ++++++++++++++
 2 files changed, 22 insertions(+)

diff --git a/python/mxnet/gluon/parameter.py b/python/mxnet/gluon/parameter.py
index 89a3c338173c..66f5c2afc8b0 100644
--- a/python/mxnet/gluon/parameter.py
+++ b/python/mxnet/gluon/parameter.py
@@ -902,6 +902,14 @@ def reset_ctx(self, ctx):
         for i in self.values():
             i.reset_ctx(ctx)
 
+    def list_ctx(self):
+        """Returns a list of all the contexts on which the underlying Parameters
+        are initialized."""
+        s = set()
+        for i in self.values():
+            s.update(i.list_ctx())
+        return list(s)
+
     def setattr(self, name, value):
         """Set an attribute to a new value for all Parameters.
 
diff --git a/tests/python/unittest/test_gluon.py b/tests/python/unittest/test_gluon.py
index d11443ca839a..ae2d62451a09 100644
--- a/tests/python/unittest/test_gluon.py
+++ b/tests/python/unittest/test_gluon.py
@@ -136,6 +136,20 @@ def test_parameter_dict():
     mx.test_utils.assert_almost_equal(prev_w0.asnumpy(), cur_w0.asnumpy())
     mx.test_utils.assert_almost_equal(prev_w1.asnumpy(), cur_w1.asnumpy())
 
+    # test reset_ctx
+    params3 = gluon.ParameterDict('net_')
+    params3.get('w0', shape=(10, 10))
+    params3.get('w1', shape=(10, 10))
+    params3.initialize(ctx=ctx)
+    list_contexts = [mx.cpu(42), mx.cpu(24)]
+    params3.reset_ctx(list_contexts)
+    for p in params3.values():
+        assert set(p.list_ctx()) == set(list_contexts)
+
+    # and test list_ctx
+    assert set(params3.list_ctx()) == set(list_contexts)
+
+
     # test the dtype casting functionality
     params0 = gluon.ParameterDict('')
     params0.get('w0', shape=(10, 10), dtype='float32')

From 1a2da12fa6c4b93c264611c99f9535079c845251 Mon Sep 17 00:00:00 2001
From: Tao Lv <tao.a.lv@intel.com>
Date: Thu, 26 Sep 2019 08:50:31 +0800
Subject: [PATCH 377/813] [MKLDNN] NDArray reorder in C API and deconv (#16265)

* layout conversion in MXNDArrayGetData

* deconv: layout conversion for grad

* fix lint complain
---
 src/c_api/c_api.cc                             |  7 +++++++
 src/operator/nn/mkldnn/mkldnn_deconvolution.cc | 11 +++++++----
 2 files changed, 14 insertions(+), 4 deletions(-)

diff --git a/src/c_api/c_api.cc b/src/c_api/c_api.cc
index 70ce869b0ef7..14ec59f53f72 100644
--- a/src/c_api/c_api.cc
+++ b/src/c_api/c_api.cc
@@ -627,6 +627,13 @@ int MXNDArrayGetData(NDArrayHandle handle,
                      void **out_pdata) {
   API_BEGIN();
   NDArray *arr = static_cast<NDArray*>(handle);
+#if MXNET_USE_MKLDNN == 1
+  NDArray temp = *arr;
+  if (arr->IsMKLDNNData()) {
+    temp = arr->Reorder2Default();
+    arr = &temp;
+  }
+#endif
   if (!arr->is_none()) {
     *out_pdata = arr->data().dptr_;
   } else {
diff --git a/src/operator/nn/mkldnn/mkldnn_deconvolution.cc b/src/operator/nn/mkldnn/mkldnn_deconvolution.cc
index 557901c517f1..02a7368cce97 100644
--- a/src/operator/nn/mkldnn/mkldnn_deconvolution.cc
+++ b/src/operator/nn/mkldnn/mkldnn_deconvolution.cc
@@ -559,10 +559,13 @@ void MKLDNNDeconvolutionBackward(const nnvm::NodeAttrs &attrs,
     Stream<cpu> *s = ctx.get_stream<cpu>();
     Tensor<cpu, 1, DType> gbias =
         in_grad[deconv::kBias].data().get<cpu, 1, DType>(s);
-    // If there is bias, the out grad has already been converted to the default
-    // format, so this shouldn't cause any performance issues.
-    Tensor<cpu, 4, DType> grad =
-        inputs[deconv::kOut].data().get<cpu, 4, DType>(s);
+
+    NDArray temp = inputs[deconv::kOut];
+    if (temp.IsMKLDNNData()) {
+      temp = temp.Reorder2Default();
+    }
+
+    Tensor<cpu, 4, DType> grad = temp.data().get<cpu, 4, DType>(s);
     Assign(gbias, req[deconv::kBias],
            mshadow::expr::sumall_except_dim<1>(grad));
   }

From c5007ea01ad1b88e43cc669385a4a76b7020ce94 Mon Sep 17 00:00:00 2001
From: "Huang, Guangtai" <guangtai@amazon.com>
Date: Thu, 26 Sep 2019 15:21:24 +0800
Subject: [PATCH 378/813] Numpy operators: `lcm`, `tril`, `identity` and `take`
 (#16264)

* numpy op lcm

* numpy op tril

* numpy op identity & fix some typo

* numpy op take
---
 python/mxnet/ndarray/numpy/_op.py             | 207 +++++++++++++++-
 python/mxnet/numpy/multiarray.py              | 200 ++++++++++++++-
 python/mxnet/symbol/numpy/_symbol.py          | 168 ++++++++++++-
 src/operator/elemwise_op_common.h             |  19 ++
 src/operator/mshadow_op.h                     |  43 ++++
 .../numpy/np_elemwise_broadcast_op.cc         |  33 +++
 .../numpy/np_elemwise_broadcast_op.cu         |   6 +
 src/operator/numpy/np_init_op.cc              |  10 +
 src/operator/numpy/np_init_op.cu              |   3 +
 src/operator/numpy/np_init_op.h               |  41 ++-
 src/operator/numpy/np_tril_op-inl.h           | 233 ++++++++++++++++++
 src/operator/numpy/np_tril_op.cc              |  61 +++++
 src/operator/numpy/np_tril_op.cu              |  38 +++
 src/operator/operator_tune.cc                 |   1 +
 src/operator/tensor/indexing_op.cc            |   4 +
 src/operator/tensor/indexing_op.cu            |   4 +
 src/operator/tensor/indexing_op.h             |  10 +-
 tests/python/unittest/test_numpy_ndarray.py   |  45 ++++
 tests/python/unittest/test_numpy_op.py        | 210 ++++++++++++++++
 19 files changed, 1318 insertions(+), 18 deletions(-)
 create mode 100644 src/operator/numpy/np_tril_op-inl.h
 create mode 100644 src/operator/numpy/np_tril_op.cc
 create mode 100644 src/operator/numpy/np_tril_op.cu

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index a3b4a2730378..e8332f1a83ef 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -35,7 +35,7 @@
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'mean',
            'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
            'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'hypot', 'rad2deg', 'deg2rad',
-           'unique']
+           'unique', 'lcm', 'tril', 'identity', 'take']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -50,7 +50,7 @@ def zeros(shape, dtype=_np.float32, order='C', ctx=None):
         The shape of the empty array.
     dtype : str or numpy.dtype, optional
         An optional value type. Default is `numpy.float32`. Note that this
-        behavior is different from NumPy's `ones` function where `float64`
+        behavior is different from NumPy's `zeros` function where `float64`
         is the default value, because `float32` is considered as the default
         data type in deep learning.
     order : {'C'}, optional, default: 'C'
@@ -96,7 +96,7 @@ def ones(shape, dtype=_np.float32, order='C', ctx=None):
     Returns
     -------
     out : ndarray
-        Array of zeros with the given shape, dtype, and ctx.
+        Array of ones with the given shape, dtype, and ctx.
     """
     if order != 'C':
         raise NotImplementedError
@@ -213,6 +213,134 @@ def arange(start, stop=None, step=1, dtype=None, ctx=None):
     return _npi.arange(start=start, stop=stop, step=step, dtype=dtype, ctx=ctx)
 
 
+@set_module('mxnet.ndarray.numpy')
+def identity(n, dtype=None, ctx=None):
+    """
+    Return the identity array.
+
+    The identity array is a square array with ones on
+    the main diagonal.
+
+    Parameters
+    ----------
+    n : int
+        Number of rows (and columns) in `n` x `n` output.
+    dtype : data-type, optional
+        Data-type of the output.  Defaults to ``numpy.float32``.
+    ctx : Context, optional
+        An optional device context (default is the current default context).
+
+    Returns
+    -------
+    out : ndarray
+        `n` x `n` array with its main diagonal set to one,
+        and all other elements 0.
+
+    Examples
+    --------
+    >>> np.identity(3)
+    >>> np.identity(3)
+    array([[1., 0., 0.],
+           [0., 1., 0.],
+           [0., 0., 1.]])
+    """
+    if not isinstance(n, int):
+        raise TypeError("Input 'n' should be an integer")
+    if n < 0:
+        raise ValueError("Input 'n' cannot be negative")
+    if ctx is None:
+        ctx = current_context()
+    dtype = _np.float32 if dtype is None else dtype
+    return _npi.identity(shape=(n, n), ctx=ctx, dtype=dtype)
+
+
+# pylint: disable=redefined-outer-name
+@set_module('mxnet.ndarray.numpy')
+def take(a, indices, axis=None, mode='raise', out=None):
+    r"""
+    Take elements from an array along an axis.
+
+    When axis is not None, this function does the same thing as "fancy"
+    indexing (indexing arrays using arrays); however, it can be easier to use
+    if you need elements along a given axis. A call such as
+    ``np.take(arr, indices, axis=3)`` is equivalent to
+    ``arr[:,:,:,indices,...]``.
+
+    Explained without fancy indexing, this is equivalent to the following use
+    of `ndindex`, which sets each of ``ii``, ``jj``, and ``kk`` to a tuple of
+    indices::
+
+        Ni, Nk = a.shape[:axis], a.shape[axis+1:]
+        Nj = indices.shape
+        for ii in ndindex(Ni):
+            for jj in ndindex(Nj):
+                for kk in ndindex(Nk):
+                    out[ii + jj + kk] = a[ii + (indices[jj],) + kk]
+
+    Parameters
+    ----------
+    a : ndarray
+        The source array.
+    indices : ndarray
+        The indices of the values to extract. Also allow scalars for indices.
+    axis : int, optional
+        The axis over which to select values. By default, the flattened
+        input array is used.
+    out : ndarray, optional
+        If provided, the result will be placed in this array. It should
+        be of the appropriate shape and dtype.
+    mode : {'clip', 'wrap'}, optional
+        Specifies how out-of-bounds indices will behave.
+
+        * 'clip' -- clip to the range (default)
+        * 'wrap' -- wrap around
+
+        'clip' mode means that all indices that are too large are replaced
+        by the index that addresses the last element along that axis. Note
+        that this disables indexing with negative numbers.
+
+    Returns
+    -------
+    out : ndarray
+        The returned array has the same type as `a`.
+
+    Notes
+    -----
+
+    This function differs from the original `numpy.take
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.take.html>`_ in
+    the following way(s):
+
+    - Only ndarray or scalar ndarray is accepted as valid input.
+
+    Examples
+    --------
+    >>> a = np.array([4, 3, 5, 7, 6, 8])
+    >>> indices = np.array([0, 1, 4])
+    >>> np.take(a, indices)
+    array([4., 3., 6.])
+
+    In this example for `a` is an ndarray, "fancy" indexing can be used.
+
+    >>> a[indices]
+    array([4., 3., 6.])
+
+    If `indices` is not one dimensional, the output also has these dimensions.
+
+    >>> np.take(a, np.array([[0, 1], [2, 3]]))
+    array([[4., 3.],
+           [5., 7.]])
+    """
+    if mode not in ('wrap', 'clip', 'raise'):
+        raise NotImplementedError(
+            "function take does not support mode '{}'".format(mode))
+    if axis:
+        return _npi.take(a, indices, axis, mode, out)
+    else:
+        return _npi.take(_npi.reshape(a, -1), indices, 0, mode, out)
+# pylint: enable=redefined-outer-name
+
+
 #pylint: disable= too-many-arguments, no-member, protected-access
 def _ufunc_helper(lhs, rhs, fn_array, fn_scalar, lfn_scalar, rfn_scalar=None, out=None):
     """ Helper function for element-wise operation.
@@ -732,6 +860,79 @@ def expand_dims(a, axis):
     return _npi.expand_dims(a, axis)
 
 
+@set_module('mxnet.ndarray.numpy')
+def lcm(x1, x2, out=None):
+    """
+    Returns the lowest common multiple of ``|x1|`` and ``|x2|``
+
+    Parameters
+    ----------
+    x1, x2 : ndarrays or scalar values
+        The arrays for computing lowest common multiple. If x1.shape != x2.shape,
+        they must be broadcastable to a common shape (which may be the shape of
+        one or the other).
+
+    out : ndarray or None, optional
+        A location into which the result is stored. If provided, it must have a shape
+        that the inputs broadcast to. If not provided or None, a freshly-allocated array
+        is returned.
+
+    Returns
+    -------
+    y : ndarray or scalar
+        The lowest common multiple of the absolute value of the inputs
+        This is a scalar if both `x1` and `x2` are scalars.
+
+    See Also
+    --------
+    gcd : The greatest common divisor
+
+    Examples
+    --------
+    >>> np.lcm(12, 20)
+    60
+    >>> np.lcm(np.arange(6, dtype=int), 20)
+    array([ 0, 20, 20, 60, 20, 20], dtype=int64)
+    """
+    return _ufunc_helper(x1, x2, _npi.lcm, _np.lcm, _npi.lcm_scalar, None, out)
+
+
+@set_module('mxnet.ndarray.numpy')
+def tril(m, k=0):
+    r"""
+    Lower triangle of an array.
+
+    Return a copy of an array with elements above the `k`-th diagonal zeroed.
+
+    Parameters
+    ----------
+    m : ndarray, shape (M, N)
+        Input array.
+    k : int, optional
+        Diagonal above which to zero elements.  `k = 0` (the default) is the
+        main diagonal, `k < 0` is below it and `k > 0` is above.
+
+    Returns
+    -------
+    tril : ndarray, shape (M, N)
+        Lower triangle of `m`, of same shape and data-type as `m`.
+
+    See Also
+    --------
+    triu : same thing, only for the upper triangle
+
+    Examples
+    --------
+    >>> a = np.array([[1,2,3],[4,5,6],[7,8,9],[10,11,12]])
+    >>> np.tril(a, -1)
+    array([[ 0.,  0.,  0.],
+           [ 4.,  0.,  0.],
+           [ 7.,  8.,  0.],
+           [10., 11., 12.]])
+    """
+    return _npi.tril(m, k)
+
+
 def _unary_func_helper(x, fn_array, fn_scalar, out=None, **kwargs):
     """Helper function for unary operators.
 
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 4972bdae7df6..7ba0f0d7d813 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -54,7 +54,7 @@
            'tensordot', 'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate',
            'stack', 'vstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices',
            'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'arctan2', 'hypot',
-           'rad2deg', 'deg2rad', 'unique']
+           'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -1104,13 +1104,13 @@ def slice_assign(self, rhs, begin, end, step):
         """
         return _npi.slice_assign(self, rhs, begin=begin, end=end, step=step, out=self)
 
-    def take(self, *args, **kwargs):
+    def take(self, indices, axis=None, mode='raise'):  # pylint: disable=arguments-differ, redefined-outer-name
         """Convenience fluent method for :py:func:`take`.
 
         The arguments are the same as for :py:func:`take`, with
         this array as data.
         """
-        raise NotImplementedError
+        take(self, indices, axis, mode=mode)
 
     def one_hot(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`one_hot`.
@@ -1754,7 +1754,7 @@ def zeros(shape, dtype=_np.float32, order='C', ctx=None):
         The shape of the empty array.
     dtype : str or numpy.dtype, optional
         An optional value type (default is `numpy.float32`). Note that this
-        behavior is different from NumPy's `ones` function where `float64`
+        behavior is different from NumPy's `zeros` function where `float64`
         is the default value, because `float32` is considered as the default
         data type in deep learning.
     order : {'C'}, optional, default: 'C'
@@ -1773,7 +1773,7 @@ def zeros(shape, dtype=_np.float32, order='C', ctx=None):
 
 @set_module('mxnet.numpy')
 def ones(shape, dtype=_np.float32, order='C', ctx=None):
-    """Return a new array of given shape and type, filled with zeros.
+    """Return a new array of given shape and type, filled with ones.
     This function currently only supports storing multi-dimensional data
     in row-major (C-style).
 
@@ -1795,7 +1795,7 @@ def ones(shape, dtype=_np.float32, order='C', ctx=None):
     Returns
     -------
     out : ndarray
-        Array of zeros with the given shape, dtype, and ctx.
+        Array of ones with the given shape, dtype, and ctx.
     """
     return _mx_nd_np.ones(shape, dtype, order, ctx)
 
@@ -1856,6 +1856,121 @@ def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):  # pylin
     return _mx_nd_np.full(shape, fill_value, order=order, ctx=ctx, dtype=dtype, out=out)
 
 
+@set_module('mxnet.numpy')
+def identity(n, dtype=None, ctx=None):
+    """
+    Return the identity array.
+
+    The identity array is a square array with ones on
+    the main diagonal.
+
+    Parameters
+    ----------
+    n : int
+        Number of rows (and columns) in `n` x `n` output.
+    dtype : data-type, optional
+        Data-type of the output.  Defaults to ``numpy.float32``.
+    ctx : Context, optional
+        An optional device context (default is the current default context).
+
+    Returns
+    -------
+    out : ndarray
+        `n` x `n` array with its main diagonal set to one,
+        and all other elements 0.
+
+    Examples
+    --------
+    >>> np.identity(3)
+    >>> np.identity(3)
+    array([[1., 0., 0.],
+           [0., 1., 0.],
+           [0., 0., 1.]])
+    """
+    return _mx_nd_np.identity(n, dtype, ctx)
+
+
+# pylint: disable=redefined-outer-name
+@set_module('mxnet.numpy')
+def take(a, indices, axis=None, mode='raise', out=None):
+    r"""
+    Take elements from an array along an axis.
+
+    When axis is not None, this function does the same thing as "fancy"
+    indexing (indexing arrays using arrays); however, it can be easier to use
+    if you need elements along a given axis. A call such as
+    ``np.take(arr, indices, axis=3)`` is equivalent to
+    ``arr[:,:,:,indices,...]``.
+
+    Explained without fancy indexing, this is equivalent to the following use
+    of `ndindex`, which sets each of ``ii``, ``jj``, and ``kk`` to a tuple of
+    indices::
+
+        Ni, Nk = a.shape[:axis], a.shape[axis+1:]
+        Nj = indices.shape
+        for ii in ndindex(Ni):
+            for jj in ndindex(Nj):
+                for kk in ndindex(Nk):
+                    out[ii + jj + kk] = a[ii + (indices[jj],) + kk]
+
+    Parameters
+    ----------
+    a : ndarray
+        The source array.
+    indices : ndarray
+        The indices of the values to extract. Also allow scalars for indices.
+    axis : int, optional
+        The axis over which to select values. By default, the flattened
+        input array is used.
+    out : ndarray, optional
+        If provided, the result will be placed in this array. It should
+        be of the appropriate shape and dtype.
+    mode : {'clip', 'wrap'}, optional
+        Specifies how out-of-bounds indices will behave.
+
+        * 'clip' -- clip to the range (default)
+        * 'wrap' -- wrap around
+
+        'clip' mode means that all indices that are too large are replaced
+        by the index that addresses the last element along that axis. Note
+        that this disables indexing with negative numbers.
+
+    Returns
+    -------
+    out : ndarray
+        The returned array has the same type as `a`.
+
+    Notes
+    -----
+
+    This function differs from the original `numpy.take
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.take.html>`_ in
+    the following way(s):
+
+    - Only ndarray or scalar ndarray is accepted as valid input.
+
+    Examples
+    --------
+    >>> a = np.array([4, 3, 5, 7, 6, 8])
+    >>> indices = np.array([0, 1, 4])
+    >>> np.take(a, indices)
+    array([4., 3., 6.])
+
+    In this example for `a` is an ndarray, "fancy" indexing can be used.
+
+    >>> a[indices]
+    array([4., 3., 6.])
+
+    If `indices` is not one dimensional, the output also has these dimensions.
+
+    >>> np.take(a, np.array([[0, 1], [2, 3]]))
+    array([[4., 3.],
+           [5., 7.]])
+    """
+    return _mx_nd_np.take(a, indices, axis, mode, out)
+# pylint: enable=redefined-outer-name
+
+
 @set_module('mxnet.numpy')
 def unique(ar, return_index=False, return_inverse=False, return_counts=False, axis=None):
     """
@@ -2131,6 +2246,43 @@ def power(x1, x2, out=None):
     return _mx_nd_np.power(x1, x2, out=out)
 
 
+@set_module('mxnet.numpy')
+def lcm(x1, x2, out=None):
+    """
+    Returns the lowest common multiple of ``|x1|`` and ``|x2|``
+
+    Parameters
+    ----------
+    x1, x2 : ndarrays or scalar values
+        The arrays for computing lowest common multiple. If x1.shape != x2.shape,
+        they must be broadcastable to a common shape (which may be the shape of
+        one or the other).
+
+    out : ndarray or None, optional
+        A location into which the result is stored. If provided, it must have a shape
+        that the inputs broadcast to. If not provided or None, a freshly-allocated array
+        is returned.
+
+    Returns
+    -------
+    y : ndarray or scalar
+        The lowest common multiple of the absolute value of the inputs
+        This is a scalar if both `x1` and `x2` are scalars.
+
+    See Also
+    --------
+    gcd : The greatest common divisor
+
+    Examples
+    --------
+    >>> np.lcm(12, 20)
+    60
+    >>> np.lcm(np.arange(6, dtype=int), 20)
+    array([ 0, 20, 20, 60, 20, 20], dtype=int64)
+    """
+    return _mx_nd_np.lcm(x1, x2, out=out)
+
+
 @set_module('mxnet.numpy')
 def sin(x, out=None, **kwargs):
     r"""Trigonometric sine, element-wise.
@@ -3621,6 +3773,42 @@ def tile(A, reps):
     return _mx_nd_np.tile(A, reps)
 
 
+@set_module('mxnet.numpy')
+def tril(m, k=0):
+    r"""
+    Lower triangle of an array.
+
+    Return a copy of an array with elements above the `k`-th diagonal zeroed.
+
+    Parameters
+    ----------
+    m : ndarray, shape (M, N)
+        Input array.
+    k : int, optional
+        Diagonal above which to zero elements.  `k = 0` (the default) is the
+        main diagonal, `k < 0` is below it and `k > 0` is above.
+
+    Returns
+    -------
+    tril : ndarray, shape (M, N)
+        Lower triangle of `m`, of same shape and data-type as `m`.
+
+    See Also
+    --------
+    triu : same thing, only for the upper triangle
+
+    Examples
+    --------
+    >>> a = np.array([[1,2,3],[4,5,6],[7,8,9],[10,11,12]])
+    >>> np.tril(a, -1)
+    array([[ 0.,  0.,  0.],
+           [ 4.,  0.,  0.],
+           [ 7.,  8.,  0.],
+           [10., 11., 12.]])
+    """
+    return _mx_nd_np.tril(m, k)
+
+
 @set_module('mxnet.numpy')
 def arange(start, stop=None, step=1, dtype=None, ctx=None):
     """Return evenly spaced values within a given interval.
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 57b18ecaf547..9c055c401b31 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -37,7 +37,7 @@
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'mean',
            'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
            'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'hypot', 'rad2deg', 'deg2rad',
-           'unique']
+           'unique', 'lcm', 'tril', 'identity', 'take']
 
 
 def _num_outputs(sym):
@@ -347,13 +347,13 @@ def slice_like(self, *args, **kwargs):
         """
         raise AttributeError('_Symbol object has no attribute slice_like')
 
-    def take(self, *args, **kwargs):
+    def take(self, indices, axis=None, mode='raise'):  # pylint: disable=arguments-differ, redefined-outer-name
         """Convenience fluent method for :py:func:`take`.
 
         The arguments are the same as for :py:func:`take`, with
         this array as data.
         """
-        raise NotImplementedError
+        return take(self, indices, axis, mode=mode)
 
     def one_hot(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`one_hot`.
@@ -906,7 +906,7 @@ def zeros(shape, dtype=_np.float32, order='C', ctx=None):
 
 @set_module('mxnet.symbol.numpy')
 def ones(shape, dtype=_np.float32, order='C', ctx=None):
-    """Return a new array of given shape and type, filled with zeros.
+    """Return a new array of given shape and type, filled with ones.
     This function currently only supports storing multi-dimensional data
     in row-major (C-style).
 
@@ -928,7 +928,7 @@ def ones(shape, dtype=_np.float32, order='C', ctx=None):
     Returns
     -------
     out : ndarray
-        Array of zeros with the given shape, dtype, and ctx.
+        Array of ones with the given shape, dtype, and ctx.
     """
     if order != 'C':
         raise NotImplementedError
@@ -993,6 +993,107 @@ def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):  # pylin
     return _npi.full(shape=shape, value=fill_value, ctx=ctx, dtype=dtype, out=out)
 
 
+@set_module('mxnet.symbol.numpy')
+def identity(n, dtype=None, ctx=None):
+    """
+    Return the identity array.
+
+    The identity array is a square array with ones on
+    the main diagonal.
+
+    Parameters
+    ----------
+    n : int
+        Number of rows (and columns) in `n` x `n` output.
+    dtype : data-type, optional
+        Data-type of the output.  Defaults to ``numpy.float32``.
+    ctx : Context, optional
+        An optional device context (default is the current default context).
+
+    Returns
+    -------
+    out : _Symbol
+        `n` x `n` array with its main diagonal set to one,
+        and all other elements 0.
+    """
+    if not isinstance(n, int):
+        raise TypeError("Input 'n' should be an integer")
+    if n < 0:
+        raise ValueError("Input 'n' cannot be negative")
+    if ctx is None:
+        ctx = current_context()
+    dtype = _np.float32 if dtype is None else dtype
+    return _npi.identity(shape=(n, n), ctx=ctx, dtype=dtype)
+
+
+# pylint: disable=redefined-outer-name
+@set_module('mxnet.symbol.numpy')
+def take(a, indices, axis=None, mode='raise', out=None):
+    r"""
+    Take elements from an array along an axis.
+
+    When axis is not None, this function does the same thing as "fancy"
+    indexing (indexing arrays using arrays); however, it can be easier to use
+    if you need elements along a given axis. A call such as
+    ``np.take(arr, indices, axis=3)`` is equivalent to
+    ``arr[:,:,:,indices,...]``.
+
+    Explained without fancy indexing, this is equivalent to the following use
+    of `ndindex`, which sets each of ``ii``, ``jj``, and ``kk`` to a tuple of
+    indices::
+
+        Ni, Nk = a.shape[:axis], a.shape[axis+1:]
+        Nj = indices.shape
+        for ii in ndindex(Ni):
+            for jj in ndindex(Nj):
+                for kk in ndindex(Nk):
+                    out[ii + jj + kk] = a[ii + (indices[jj],) + kk]
+
+    Parameters
+    ----------
+    a : _Symbol
+        The source array.
+    indices : _Symbol
+        The indices of the values to extract. Also allow scalars for indices.
+    axis : int, optional
+        The axis over which to select values. By default, the flattened
+        input array is used.
+    out : _Symbol or None, optional
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+    mode : {'clip', 'wrap'}, optional
+        Specifies how out-of-bounds indices will behave.
+
+        * 'clip' -- clip to the range (default)
+        * 'wrap' -- wrap around
+
+        'clip' mode means that all indices that are too large are replaced
+        by the index that addresses the last element along that axis. Note
+        that this disables indexing with negative numbers.
+
+    Returns
+    -------
+    out : _Symbol
+        The returned array has the same type as `a`.
+
+    Notes
+    -----
+
+    This function differs from the original `numpy.take
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.take.html>`_ in
+    the following way(s):
+
+    - Only ndarray or scalar ndarray is accepted as valid input.
+    """
+    if mode not in ('wrap', 'clip', 'raise'):
+        raise NotImplementedError(
+            "function take does not support mode '{}'".format(mode))
+    if axis:
+        return _npi.take(a, indices, axis, mode, out)
+    else:
+        return _npi.take(_npi.reshape(a, -1), indices, 0, mode, out)
+# pylint: enable=redefined-outer-name
+
+
 #pylint: disable= too-many-arguments, no-member, protected-access
 def _ufunc_helper(lhs, rhs, fn_array, fn_scalar, lfn_scalar, rfn_scalar=None, out=None):
     """ Helper function for element-wise operation.
@@ -1079,6 +1180,36 @@ def power(x1, x2, out=None):
     return _ufunc_helper(x1, x2, _npi.power, _np.power, _npi.power_scalar, _npi.rpower_scalar, out)
 
 
+@set_module('mxnet.symbol.numpy')
+def lcm(x1, x2, out=None):
+    """
+    Returns the lowest common multiple of ``|x1|`` and ``|x2|``
+
+    Parameters
+    ----------
+    x1, x2 : ndarrays or scalar values
+        The arrays for computing lowest common multiple. If x1.shape != x2.shape,
+        they must be broadcastable to a common shape (which may be the shape of
+        one or the other).
+
+    out : ndarray or None, optional
+        A location into which the result is stored. If provided, it must have a shape
+        that the inputs broadcast to. If not provided or None, a freshly-allocated array
+        is returned.
+
+    Returns
+    -------
+    y : ndarray or scalar
+        The lowest common multiple of the absolute value of the inputs
+        This is a scalar if both `x1` and `x2` are scalars.
+
+    See Also
+    --------
+    gcd : The greatest common divisor
+    """
+    return _ufunc_helper(x1, x2, _npi.lcm, _np.lcm, _npi.lcm_scalar, None, out)
+
+
 @set_module('mxnet.symbol.numpy')
 def tensordot(a, b, axes=2):
     r"""
@@ -1229,6 +1360,33 @@ def expand_dims(a, axis):
     return _npi.expand_dims(a, axis)
 
 
+@set_module('mxnet.symbol.numpy')
+def tril(m, k=0):
+    r"""
+    Lower triangle of an array.
+
+    Return a copy of an array with elements above the `k`-th diagonal zeroed.
+
+    Parameters
+    ----------
+    m : _Symbol, shape (M, N)
+        Input array.
+    k : int, optional
+        Diagonal above which to zero elements.  `k = 0` (the default) is the
+        main diagonal, `k < 0` is below it and `k > 0` is above.
+
+    Returns
+    -------
+    tril : _Symbol, shape (M, N)
+        Lower triangle of `m`, of same shape and data-type as `m`.
+
+    See Also
+    --------
+    triu : same thing, only for the upper triangle
+    """
+    return _npi.tril(m, k)
+
+
 def _unary_func_helper(x, fn_array, fn_scalar, out=None, **kwargs):
     """Helper function for unary operators.
 
diff --git a/src/operator/elemwise_op_common.h b/src/operator/elemwise_op_common.h
index 6dae2dfa20c4..dc83a4b1f87f 100644
--- a/src/operator/elemwise_op_common.h
+++ b/src/operator/elemwise_op_common.h
@@ -186,6 +186,25 @@ inline bool ElemwiseType(const nnvm::NodeAttrs& attrs,
     attrs, in_attrs, out_attrs, -1);
 }
 
+// Special case of ElemwiseType. Constrains dtype to integer types
+template<index_t n_in, index_t n_out>
+inline bool ElemwiseIntType(const nnvm::NodeAttrs& attrs,
+                            std::vector<int> *in_attrs,
+                            std::vector<int> *out_attrs) {
+  CHECK(in_attrs->at(0) == mshadow::kInt64 ||
+        in_attrs->at(0) == mshadow::kInt32 ||
+        in_attrs->at(0) == mshadow::kInt8 ||
+        in_attrs->at(0) == mshadow::kUint8) << "Only supports integer types.";
+  if (n_in != -1) {
+    CHECK_EQ(in_attrs->size(), static_cast<size_t>(n_in)) << " in operator " << attrs.name;
+  }
+  if (n_out != -1) {
+    CHECK_EQ(out_attrs->size(), static_cast<size_t>(n_out)) << " in operator " << attrs.name;
+  }
+  return ElemwiseAttr<int, type_is_none, type_assign, true, type_string>(
+    attrs, in_attrs, out_attrs, -1);
+}
+
 // Transfer gradient and input to FGradient function
 struct ElemwiseGradUseIn {
   const char *op_name;
diff --git a/src/operator/mshadow_op.h b/src/operator/mshadow_op.h
index 6261638c03ec..92655c146193 100644
--- a/src/operator/mshadow_op.h
+++ b/src/operator/mshadow_op.h
@@ -55,6 +55,7 @@ using std::isnan;
 #endif
 using std::enable_if;
 using std::is_unsigned;
+using std::is_integral;
 
 #define MXNET_UNARY_MATH_OP(name, expr) \
   struct name : public mxnet_op::tunable { \
@@ -1088,6 +1089,48 @@ struct nanprod_grad : public mxnet_op::tunable {
   }
 };
 
+/*! \brief used for computing binary lowest common multiple */
+struct lcm : public mxnet_op::tunable {
+  template<typename DType>
+  MSHADOW_XINLINE static typename enable_if<is_integral<DType>::value, DType>::type
+  Map(DType a, DType b) {
+    // minus cases.
+    if (a < 0) {
+      a = -a;
+    }
+    if (b < 0) {
+      b = -b;
+    }
+    // handle zero-valued cases.
+    DType c;
+    if (a == 0 || b == 0) {
+      c = 0;
+    } else {
+      DType tmp;
+      DType tmp_a = a;
+      DType tmp_b = b;
+      if (a < b) {
+        tmp = a;
+        a = b;
+        b = tmp;
+      }
+      while (a % b != 0) {
+        a = a % b;
+        tmp = a;
+        a = b;
+        b = tmp;
+      }
+      c = tmp_a / b * tmp_b;
+    }
+    return c;
+  }
+  template<typename DType>
+  MSHADOW_XINLINE static typename enable_if<!is_integral<DType>::value, DType>::type
+  Map(DType a, DType b) {
+    return DType(0.0f);
+  }
+};
+
 }  // namespace mshadow_op
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/np_elemwise_broadcast_op.cc b/src/operator/numpy/np_elemwise_broadcast_op.cc
index 16d4ef88f5c5..a786d1db5892 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op.cc
+++ b/src/operator/numpy/np_elemwise_broadcast_op.cc
@@ -96,6 +96,23 @@ NNVM_REGISTER_OP(_backward_npi_copysign)
 .set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastBackwardUseIn<cpu, mshadow_op::copysign_grad,
                                                                   mshadow_op::copysign_rgrad>);
 
+NNVM_REGISTER_OP(_npi_lcm)
+.set_num_inputs(2)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+[](const NodeAttrs& attrs) {
+     return std::vector<std::string>{"lhs", "rhs"};
+})
+.set_attr<mxnet::FInferShape>("FInferShape", BinaryBroadcastShape)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseIntType<2, 1>)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+[](const NodeAttrs& attrs){
+     return std::vector<std::pair<int, int> >{{0, 0}, {1, 0}};
+})
+.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, mshadow_op::lcm>)
+.add_argument("lhs", "NDArray-or-Symbol", "First input to the function")
+.add_argument("rhs", "NDArray-or-Symbol", "Second input to the function");
+
 MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_add_scalar)
 .set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, op::mshadow_op::plus>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_copy"});
@@ -263,5 +280,21 @@ NNVM_REGISTER_OP(_backward_npi_hypot)
 .set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastBackwardUseIn<cpu, mshadow_op::hypot_grad_left,
                                                                   mshadow_op::hypot_grad_right>);
 
+NNVM_REGISTER_OP(_npi_lcm_scalar)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser([](NodeAttrs* attrs) {
+    attrs->parsed = std::stod(attrs->dict["scalar"]);
+  })
+.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseIntType<1, 1>)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs){
+    return std::vector<std::pair<int, int> >{{0, 0}};
+  })
+.add_argument("data", "NDArray-or-Symbol", "source input")
+.add_argument("scalar", "int", "scalar input")
+.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::lcm>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/np_elemwise_broadcast_op.cu b/src/operator/numpy/np_elemwise_broadcast_op.cu
index 77525ce7acea..806debf431b5 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op.cu
+++ b/src/operator/numpy/np_elemwise_broadcast_op.cu
@@ -45,6 +45,9 @@ NNVM_REGISTER_OP(_npi_power)
 NNVM_REGISTER_OP(_npi_copysign)
 .set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, mshadow_op::copysign>);
 
+NNVM_REGISTER_OP(_npi_lcm)
+.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, mshadow_op::lcm>);
+
 NNVM_REGISTER_OP(_backward_npi_copysign)
 .set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastBackwardUseIn<gpu, mshadow_op::copysign_grad,
                                                                   mshadow_op::copysign_rgrad>);
@@ -112,5 +115,8 @@ NNVM_REGISTER_OP(_npi_rarctan2_scalar)
 NNVM_REGISTER_OP(_backward_npi_rarctan2_scalar)
 .set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::rarctan2_grad>);
 
+NNVM_REGISTER_OP(_npi_lcm_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::lcm>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/np_init_op.cc b/src/operator/numpy/np_init_op.cc
index 4f031bdaa050..2477573c2413 100644
--- a/src/operator/numpy/np_init_op.cc
+++ b/src/operator/numpy/np_init_op.cc
@@ -71,6 +71,16 @@ NNVM_REGISTER_OP(_npi_ones)
 .set_attr<FCompute>("FCompute<cpu>", FillCompute<cpu, 1>)
 .add_arguments(InitOpParam::__FIELDS__());
 
+NNVM_REGISTER_OP(_npi_identity)
+.describe("Return a new identity array of given shape, type, and context.")
+.set_num_inputs(0)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<InitOpParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", InitShape<InitOpParam>)
+.set_attr<nnvm::FInferType>("FInferType", InitType<InitOpParam>)
+.set_attr<FCompute>("FCompute<cpu>", IdentityCompute<cpu>)
+.add_arguments(InitOpParam::__FIELDS__());
+
 NNVM_REGISTER_OP(_np_zeros_like)
 .set_num_inputs(1)
 .set_num_outputs(1)
diff --git a/src/operator/numpy/np_init_op.cu b/src/operator/numpy/np_init_op.cu
index 49f1051735d8..e68dd9ad36a1 100644
--- a/src/operator/numpy/np_init_op.cu
+++ b/src/operator/numpy/np_init_op.cu
@@ -35,6 +35,9 @@ NNVM_REGISTER_OP(_npi_zeros)
 NNVM_REGISTER_OP(_npi_ones)
 .set_attr<FCompute>("FCompute<gpu>", FillCompute<gpu, 1>);
 
+NNVM_REGISTER_OP(_npi_identity)
+.set_attr<FCompute>("FCompute<gpu>", IdentityCompute<gpu>);
+
 NNVM_REGISTER_OP(_np_zeros_like)
 .set_attr<FCompute>("FCompute<gpu>", FillCompute<gpu, 0>);
 
diff --git a/src/operator/numpy/np_init_op.h b/src/operator/numpy/np_init_op.h
index 5c41820b57f8..3e1c345d59c3 100644
--- a/src/operator/numpy/np_init_op.h
+++ b/src/operator/numpy/np_init_op.h
@@ -20,8 +20,9 @@
 /*!
  *  Copyright (c) 2019 by Contributors
  * \file np_init_op.h
- * \brief CPU Implementation of numpy init op
+ * \brief Function definition of numpy init op
  */
+
 #ifndef MXNET_OPERATOR_NUMPY_NP_INIT_OP_H_
 #define MXNET_OPERATOR_NUMPY_NP_INIT_OP_H_
 
@@ -65,6 +66,22 @@ struct indices_fwd {
   }
 };
 
+template<int req>
+struct identity {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(index_t i, DType* out_data, const int n) {
+    using namespace mxnet_op;
+
+    const index_t row_id = i / n;
+    const index_t col_id = i % n;
+    if (row_id == col_id) {
+      KERNEL_ASSIGN(out_data[i], req, static_cast<DType>(1));
+    } else {
+      KERNEL_ASSIGN(out_data[i], req, static_cast<DType>(0));
+    }
+  }
+};
+
 template<typename xpu>
 void IndicesCompute(const nnvm::NodeAttrs& attrs,
                     const OpContext& ctx,
@@ -101,6 +118,28 @@ void IndicesCompute(const nnvm::NodeAttrs& attrs,
   }
 }
 
+template<typename xpu>
+void IdentityCompute(const nnvm::NodeAttrs& attrs,
+                     const OpContext& ctx,
+                     const std::vector<TBlob>& inputs,
+                     const std::vector<OpReqType>& req,
+                     const std::vector<TBlob>& outputs) {
+  using namespace mxnet_op;
+  using namespace mshadow;
+  CHECK_EQ(inputs.size(), 0U);
+  CHECK_EQ(outputs.size(), 1U);
+  CHECK_EQ(req.size(), 1U);
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  const TBlob& out_data = outputs[0];
+  int n = out_data.shape_[0];
+  MSHADOW_TYPE_SWITCH(out_data.type_flag_, DType, {
+    MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
+      Kernel<identity<req_type>, xpu>::Launch(
+          s, out_data.Size(), out_data.dptr<DType>(), n);
+    });
+  });
+}
+
 }  // namespace op
 }  // namespace mxnet
 
diff --git a/src/operator/numpy/np_tril_op-inl.h b/src/operator/numpy/np_tril_op-inl.h
new file mode 100644
index 000000000000..1ad74e887b6c
--- /dev/null
+++ b/src/operator/numpy/np_tril_op-inl.h
@@ -0,0 +1,233 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_tril_op-inl.h
+ * \brief Function definition of the tril (lower triangle of an array) op
+ */
+
+#ifndef MXNET_OPERATOR_NUMPY_NP_TRIL_OP_INL_H_
+#define MXNET_OPERATOR_NUMPY_NP_TRIL_OP_INL_H_
+
+#include <dmlc/parameter.h>
+#include <vector>
+#include <algorithm>
+#include "../mxnet_op.h"
+#include "../operator_common.h"
+#include "../elemwise_op_common.h"
+
+namespace mxnet {
+namespace op {
+
+struct TrilParam : public dmlc::Parameter<TrilParam> {
+  int k;
+  DMLC_DECLARE_PARAMETER(TrilParam) {
+    DMLC_DECLARE_FIELD(k)
+      .set_default(0)
+      .describe("Diagonal in question. The default is 0. "
+                "Use k>0 for diagonals above the main diagonal, "
+                "and k<0 for diagonals below the main diagonal. "
+                "If input has shape (S0 S1) k must be between -S0 and S1");
+  }
+};
+
+inline bool TrilOpShape(const nnvm::NodeAttrs& attrs,
+                             mxnet::ShapeVector* in_attrs,
+                             mxnet::ShapeVector* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+
+  const mxnet::TShape& ishape = (*in_attrs)[0];
+  mxnet::TShape oshape;
+
+  if (!mxnet::ndim_is_known(ishape)) {
+    return false;
+  }
+
+  if (ishape.ndim() == 1) {
+    auto s = ishape[0];
+    oshape = mxnet::TShape({s, s});
+  } else {
+    oshape = ishape;
+  }
+
+  if (shape_is_none(oshape)) {
+    LOG(FATAL) << "Diagonal does not exist.";
+  }
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, oshape);
+
+  return shape_is_known(out_attrs->at(0));
+}
+
+template<int req>
+struct tril1Dforward {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(index_t i, DType* out, const DType* data,
+                                  mshadow::Shape<2> oshape, int k) {
+    using namespace mxnet_op;
+
+    const index_t row_id = i / oshape[1];
+    const index_t col_id = i % oshape[1];
+    if (col_id > (row_id + k)) {
+      KERNEL_ASSIGN(out[i], req, static_cast<DType>(0));
+    } else {
+      KERNEL_ASSIGN(out[i], req, data[col_id]);
+    }
+  }
+};
+
+template<int req>
+struct tril1Dbackward {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(index_t i, DType* out, const DType* data,
+                                  mshadow::Shape<1> oshape, int k) {
+    using namespace mxnet_op;
+    auto m = oshape[0];
+    auto start = (i > k) ? (i - k) : 0;
+    DType res = 0;
+    for (auto y = start; y < m; y++) {
+      res += data[y * m + i];
+    }
+    KERNEL_ASSIGN(out[i], req, res);
+  }
+};
+
+template<int req>
+struct tril2D {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(index_t i, DType* out, const DType* data,
+                                  mshadow::Shape<2> oshape, int k) {
+    using namespace mxnet_op;
+
+    const index_t row_id = i / oshape[1];
+    const index_t col_id = i % oshape[1];
+    if (col_id > (row_id + k)) {
+      KERNEL_ASSIGN(out[i], req, static_cast<DType>(0));
+    } else {
+      KERNEL_ASSIGN(out[i], req, data[i]);
+    }
+  }
+};
+
+template<int req>
+struct tril3D {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(index_t i, DType* out, const DType* data,
+                                  mshadow::Shape<3> oshape, int k) {
+    using namespace mxnet_op;
+
+    const index_t row_id = i % (oshape[1] * oshape[2]) / oshape[2];
+    const index_t col_id = i % (oshape[1] * oshape[2]) % oshape[2];
+    if (col_id > (row_id + k)) {
+      KERNEL_ASSIGN(out[i], req, static_cast<DType>(0));
+    } else {
+      KERNEL_ASSIGN(out[i], req, data[i]);
+    }
+  }
+};
+
+template<typename xpu, bool back>
+void TrilOpProcess(const TBlob& in_data,
+                   const TBlob& out_data,
+                   index_t dsize,
+                   const TrilParam& param,
+                   mxnet_op::Stream<xpu> *s,
+                   const std::vector<OpReqType>& req) {
+  using namespace mxnet_op;
+  using namespace mshadow;
+
+  const mxnet::TShape& ishape = in_data.shape_;
+  const mxnet::TShape& oshape = out_data.shape_;
+
+  if (ishape.ndim() == 2 && oshape.ndim() == 2) {
+    MSHADOW_TYPE_SWITCH(out_data.type_flag_, DType, {
+      MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
+        Kernel<tril2D<req_type>, xpu>::Launch(
+            s, dsize, out_data.dptr<DType>(), in_data.dptr<DType>(),
+            Shape2(oshape[0], oshape[1]), param.k);
+      });
+    });
+  } else if (ishape.ndim() > 2) {
+    MSHADOW_TYPE_SWITCH(out_data.type_flag_, DType, {
+      MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
+        Kernel<tril3D<req_type>, xpu>::Launch(
+            s, dsize, out_data.dptr<DType>(), in_data.dptr<DType>(),
+            oshape.FlatTo3D(oshape.ndim() - 2), param.k);
+      });
+    });
+  } else {
+    MSHADOW_TYPE_SWITCH(out_data.type_flag_, DType, {
+      MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
+        if (back) {
+          Kernel<tril1Dbackward<req_type>, xpu>::Launch(
+              s, dsize, out_data.dptr<DType>(), in_data.dptr<DType>(),
+              Shape1(oshape[0]), param.k);
+        } else {
+          Kernel<tril1Dforward<req_type>, xpu>::Launch(
+              s, dsize, out_data.dptr<DType>(), in_data.dptr<DType>(),
+              Shape2(oshape[0], oshape[1]), param.k);
+        }
+      });
+    });
+  }
+}
+
+template<typename xpu>
+void TrilOpForward(const nnvm::NodeAttrs& attrs,
+                   const OpContext& ctx,
+                   const std::vector<TBlob>& inputs,
+                   const std::vector<OpReqType>& req,
+                   const std::vector<TBlob>& outputs) {
+  using namespace mxnet_op;
+  using namespace mshadow;
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 1U);
+  CHECK_EQ(req.size(), 1U);
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  const TBlob& in_data = inputs[0];
+  const TBlob& out_data = outputs[0];
+  const TrilParam& param = nnvm::get<TrilParam>(attrs.parsed);
+
+  TrilOpProcess<xpu, false>(in_data, out_data, out_data.Size(), param, s, req);
+}
+
+template<typename xpu>
+void TrilOpBackward(const nnvm::NodeAttrs& attrs,
+                    const OpContext& ctx,
+                    const std::vector<TBlob>& inputs,
+                    const std::vector<OpReqType>& req,
+                    const std::vector<TBlob>& outputs) {
+  using namespace mxnet_op;
+  using namespace mshadow;
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 1U);
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+
+  const TBlob& in_data = inputs[0];
+  const TBlob& out_data = outputs[0];
+  const TrilParam& param = nnvm::get<TrilParam>(attrs.parsed);
+
+  TrilOpProcess<xpu, true>(in_data, out_data, out_data.Size(), param, s, req);
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_NUMPY_NP_TRIL_OP_INL_H_
diff --git a/src/operator/numpy/np_tril_op.cc b/src/operator/numpy/np_tril_op.cc
new file mode 100644
index 000000000000..5c4b339b7768
--- /dev/null
+++ b/src/operator/numpy/np_tril_op.cc
@@ -0,0 +1,61 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+* Copyright (c) 2019 by Contributors
+* \file np_tril_op.cc
+* \brief CPU implementation of numpy tril operator
+*/
+
+#include "./np_tril_op-inl.h"
+
+namespace mxnet {
+namespace op {
+
+DMLC_REGISTER_PARAMETER(TrilParam);
+
+NNVM_REGISTER_OP(_npi_tril)
+.set_attr_parser(ParamParser<TrilParam>)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"data"};
+  })
+.set_attr<mxnet::FInferShape>("FInferShape", TrilOpShape)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
+.set_attr<FCompute>("FCompute<cpu>", TrilOpForward<cpu>)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::pair<int, int> >{{0, 0}};
+  })
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_tril"})
+.add_argument("data", "NDArray-or-Symbol", "Input ndarray")
+.add_arguments(TrilParam::__FIELDS__());
+
+
+NNVM_REGISTER_OP(_backward_tril)
+.set_attr_parser(ParamParser<TrilParam>)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FCompute>("FCompute<cpu>", TrilOpBackward<cpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_tril_op.cu b/src/operator/numpy/np_tril_op.cu
new file mode 100644
index 000000000000..64613b505ded
--- /dev/null
+++ b/src/operator/numpy/np_tril_op.cu
@@ -0,0 +1,38 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_tril_op.cu
+ * \brief GPU implementation of numpy tril operator
+ */
+
+#include "./np_tril_op-inl.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(_npi_tril)
+.set_attr<FCompute>("FCompute<gpu>", TrilOpForward<gpu>);
+
+NNVM_REGISTER_OP(_backward_tril)
+.set_attr<FCompute>("FCompute<gpu>", TrilOpBackward<gpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/operator_tune.cc b/src/operator/operator_tune.cc
index 1d644386cdbb..b81cd78ad507 100644
--- a/src/operator/operator_tune.cc
+++ b/src/operator/operator_tune.cc
@@ -369,6 +369,7 @@ IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::logical_xor);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::logical_xor);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::smooth_l1_loss);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::smooth_l1_gradient);  // NOLINT()
+IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::lcm);  // NOLINT()
 IMPLEMENT_BLANK_WORKLOAD_FWD(mxnet::op::mxnet_op::set_to_int<0>);  // NOLINT()
 IMPLEMENT_BLANK_WORKLOAD_FWD(mxnet::op::mxnet_op::set_to_int<1>);  // NOLINT()
 IMPLEMENT_BLANK_WORKLOAD_FWD(mxnet::op::PopulateFullIdxRspKernel);  // NOLINT()
diff --git a/src/operator/tensor/indexing_op.cc b/src/operator/tensor/indexing_op.cc
index 463e9f98820e..9961218b5482 100644
--- a/src/operator/tensor/indexing_op.cc
+++ b/src/operator/tensor/indexing_op.cc
@@ -288,6 +288,10 @@ void TakeOpForward<cpu>(const nnvm::NodeAttrs& attrs,
   const mxnet::TShape& arrshape = inputs[take_::kArr].shape_;
   const mxnet::TShape& oshape = outputs[take_::kOut].shape_;
 
+  if (idxshape.Size() == 0) {
+    return;
+  }
+
   Stream<cpu> *s = ctx.get_stream<cpu>();
   const int actual_axis = param.axis + ((param.axis < 0) ? arrshape.ndim() : 0);
 
diff --git a/src/operator/tensor/indexing_op.cu b/src/operator/tensor/indexing_op.cu
index 9a46d894ee22..77d85d8e1e10 100644
--- a/src/operator/tensor/indexing_op.cu
+++ b/src/operator/tensor/indexing_op.cu
@@ -479,6 +479,10 @@ void TakeOpForward<gpu>(const nnvm::NodeAttrs& attrs,
   const mxnet::TShape& arrshape = inputs[take_::kArr].shape_;
   const mxnet::TShape& oshape = outputs[take_::kOut].shape_;
 
+  if (idxshape.Size() == 0) {
+    return;
+  }
+
   Stream<gpu> *s = ctx.get_stream<gpu>();
   const int actual_axis = param.axis + ((param.axis < 0) ? arrshape.ndim() : 0);
 
diff --git a/src/operator/tensor/indexing_op.h b/src/operator/tensor/indexing_op.h
index 161acae0ebf2..16520ddbb242 100644
--- a/src/operator/tensor/indexing_op.h
+++ b/src/operator/tensor/indexing_op.h
@@ -670,9 +670,9 @@ struct TakeParam: public dmlc::Parameter<TakeParam> {
     .set_default(take_::kClip)
     .describe("Specify how out-of-bound indices bahave. Default is \"clip\"."
               " \"clip\" means clip to the range. So, if all indices mentioned are too large,"
-              " they are replaced by the index that addresses the last element along an axis. "
-              " \"wrap\" means to wrap around. "
-              " \"raise\" means to raise an error, not supported yet.");
+              " they are replaced by the index that addresses the last element along an axis."
+              " \"wrap\" means to wrap around."
+              " \"raise\" means to raise an error when index out of range.");
   }
 };
 
@@ -1030,6 +1030,10 @@ void TakeOpBackward(const nnvm::NodeAttrs& attrs,
       const mxnet::TShape& arrshape = outputs[0].shape_;
       const mxnet::TShape& oshape = inputs[0].shape_;
 
+      if (idxshape.Size() == 0) {
+        return;
+      }
+
       if (req[take_::kIdx] != kNullOp) {
         mxnet_op::Kernel<mxnet_op::set_zero, xpu>::Launch(
           s, idxshape.Size(), outputs[take_::kIdx].dptr<IType>());
diff --git a/tests/python/unittest/test_numpy_ndarray.py b/tests/python/unittest/test_numpy_ndarray.py
index 923ee53fc400..3a5e72b53d58 100644
--- a/tests/python/unittest/test_numpy_ndarray.py
+++ b/tests/python/unittest/test_numpy_ndarray.py
@@ -181,6 +181,51 @@ def check_ones_array_creation(shape, dtype):
                 assert type(y[1]) == np.ndarray
 
 
+@with_seed()
+@use_np
+def test_identity():
+    class TestIdentity(HybridBlock):
+        def __init__(self, shape, dtype=None):
+            super(TestIdentity, self).__init__()
+            self._n = n
+            self._dtype = dtype
+
+        def hybrid_forward(self, F, x):
+            return x * F.np.identity(self._n, self._dtype)
+
+    class TestIdentityOutputType(HybridBlock):
+        def hybrid_forward(self, F, x):
+            return x, F.np.identity(0)
+
+    def check_identity_array_creation(shape, dtype):
+        np_out = _np.identity(n=n, dtype=dtype)
+        mx_out = np.identity(n=n, dtype=dtype)
+        assert same(mx_out.asnumpy(), np_out)
+        if dtype is None:
+            assert mx_out.dtype == _np.float32
+            assert np_out.dtype == _np.float64
+
+    ns = [0, 1, 2, 3, 5, 15, 30, 200]
+    dtypes = [_np.int8, _np.int32, _np.float16, _np.float32, _np.float64, None]
+    for n in ns:
+        for dtype in dtypes:
+            check_identity_array_creation(n, dtype)
+            x = mx.nd.array(_np.random.uniform(size=(n, n)), dtype=dtype).as_np_ndarray()
+            if dtype is None:
+                x = x.astype('float32')
+            for hybridize in [True, False]:
+                test_identity = TestIdentity(n, dtype)
+                test_identity_output_type = TestIdentityOutputType()
+                if hybridize:
+                    test_identity.hybridize()
+                    test_identity_output_type.hybridize()
+                y = test_identity(x)
+                assert type(y) == np.ndarray
+                assert same(x.asnumpy() * _np.identity(n, dtype), y.asnumpy())
+                y = test_identity_output_type(x)
+                assert type(y[1]) == np.ndarray
+
+
 @with_seed()
 def test_np_ndarray_binary_element_wise_ops():
     np_op_map = {
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index d2dc6ab269dc..7e3d9655f771 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -953,6 +953,67 @@ def hybrid_forward(self, F, x):
             assert same(ret_mx.asnumpy(), ret_np)
 
 
+@with_seed()
+@use_np
+def test_np_tril():
+    # numpy tril does not support scalar array (zero-dim)
+    config = [
+        ((4, 2), 3),
+        ((4, 2), 9),
+        ((4, 2), 0),
+        ((4, 2), -1),
+        ((4, 5, 6), 0),
+        ((4, 5, 6), 5),
+        ((4, 5, 6), 2),
+        ((4, 5, 6), -2),
+        ((4, 5, 6), -5),
+        ((4, 0), 0),
+        ((4, 0), 2),
+        ((4, 0), 4),
+        ((4, 0), -3),
+        ((4, 0, 5), 0),
+        ((4, 0, 5), 1),
+        ((4, 0, 5), 5),
+        ((4, 0, 5), -3),
+        ((3, ), 0),
+        ((3, ), 2),
+        ((3, ), 5)
+    ]
+
+    class TestTril(HybridBlock):
+        def __init__(self, k):
+            super(TestTril, self).__init__()
+            self._k = k
+
+        def hybrid_forward(self, F, x):
+            return F.np.tril(x, k=self._k)
+
+    for prefix in [1, -1]:
+        for shape, k in config:
+            data_np = _np.random.uniform(size=shape)
+            data_mx = np.array(data_np, dtype=data_np.dtype)
+            data_mx.attach_grad()
+            ret_np = _np.tril(data_np, k*prefix)
+            with mx.autograd.record():
+                ret_mx = np.tril(data_mx, k*prefix)
+            assert same(ret_mx.asnumpy(), ret_np)
+            ret_mx.backward()
+            if len(shape) == 2:
+                grad_np = _np.tri(*shape, k=k*prefix)
+                assert same(data_mx.grad.asnumpy(), grad_np)
+            if len(shape) == 1:
+                grad_np = _np.tri(*shape, k=k*prefix)
+                grad_np = grad_np.sum(axis=0, keepdims=False)
+                assert same(data_mx.grad.asnumpy(), grad_np)
+
+            net = TestTril(k*prefix)
+            for hybrid in [False, True]:
+                if hybrid:
+                    net.hybridize()
+                ret_mx = net(data_mx)
+                assert same(ret_mx.asnumpy(), ret_np)
+
+
 @with_seed()
 @use_np
 def test_np_unary_funcs():
@@ -2600,6 +2661,155 @@ def hybrid_forward(self, F, a):
                     assert_almost_equal(mx_out[i].asnumpy(), np_out[i], rtol=1e-3, atol=1e-5)
 
 
+@with_seed()
+@use_np
+def test_np_lcm():
+    shapes = [
+        ((3, 1), (3,)),
+        ((3, 1), (3, 5)),
+        ((1, 4), (3, 1)),
+        ((), ()),
+        ((4, 0), ()),
+        ((3, 4, 5), ()),
+        ((), (3, 4, 5)),
+        ((3, 4, 5), (3, 1, 5)),
+        ((5, 1), (5, 2))
+    ]
+
+    class TestLcm(HybridBlock):
+        def __init__(self):
+            super(TestLcm, self).__init__()
+
+        def hybrid_forward(self, F, x1, x2):
+            return F.np.lcm(x1, x2)
+
+    for hybridize in [False]:
+        for shape in shapes:
+            test_lcm = TestLcm()
+            if hybridize:
+                test_lcm.hybridize()
+
+            x1 = rand_ndarray(shape[0]).astype(_np.int32).as_np_ndarray()
+            x2 = rand_ndarray(shape[1]).astype(_np.int32).as_np_ndarray()
+
+            np_out = _np.lcm(x1.asnumpy(), x2.asnumpy())
+            mx_out = test_lcm(x1, x2)
+
+            assert mx_out.shape == np_out.shape
+            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+
+            # Test imperative once again
+            mx_out = np.lcm(x1, x2)
+            np_out = _np.lcm(x1.asnumpy(), x2.asnumpy())
+            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+
+
+@with_seed()
+@use_np
+def test_np_take():
+    configs = [
+        ((4, 4), (4, 0), None),
+        ((4, 4), (4, 0), 0),
+        ((4, 4), (4, 0), 1),
+        ((), (4, 0), None),
+        ((), (5, ), None),
+        ((), (4, 5), None),
+        ((), (), None),
+        ((3, 4), (), None),
+        ((3, 4), (), 0),
+        ((3, 4), (), 1),
+        ((3, 4, 5), (), 2),
+        ((3, 4, 5), (), -3),
+    ]
+
+    class TestTake(HybridBlock):
+        def __init__(self, axis, mode):
+            super(TestTake, self).__init__()
+            self._axis = axis
+            self._mode = mode
+
+        def hybrid_forward(self, F, a, indices):
+            return F.np.take(a, indices, axis=self._axis, mode=self._mode)
+
+    def grad_helper(grad_in, axis, idx, mode):
+        k = grad_in.shape[axis]
+        if mode == 'clip':
+            idx = 0 if idx < 0 else idx
+            idx = k - 1 if idx >= k else idx
+        else:
+            idx = idx % k
+        if axis == None:
+            grad_in[idx] += 1.0
+        elif axis == 0:
+            if axis == len(grad_in.shape) - 1:
+                grad_in[idx] += 1.0
+            else:
+                grad_in[idx, :] += 1.0
+        elif axis == 1:
+            if axis == len(grad_in.shape) - 1:
+                grad_in[:, idx] += 1.0
+            else:
+                grad_in[:, idx, :] += 1.0
+        elif axis == 2:
+            if axis == len(grad_in.shape) - 1:
+                grad_in[:, :, idx] += 1.0
+            else:
+                grad_in[:, :, idx, :] += 1.0
+        elif axis == 3:
+            if axis == len(grad_in.shape) - 1:
+                grad_in[:, :, :, idx] += 1.0
+            else:
+                grad_in[:, :, :, idx, :] += 1.0
+        elif axis == 4:
+            grad_in[:, :, :, :, idx] += 1.0
+        else:
+            raise ValueError("axis %d is not supported..." % axis)
+
+    def check_output_n_grad(data_shape, idx_shape, axis, mode):
+        data_real = _np.random.normal(size=data_shape).astype('float32')
+        idx_real = _np.random.randint(low=-100, high=100, size=idx_shape)
+        same(np.take(np.array(data_real), np.array(idx_real), axis=axis, mode=mode).asnumpy(),
+             _np.take(data_real, idx_real, axis=axis, mode=mode))
+
+        grad_in = _np.zeros(data_shape, dtype='float32')
+
+        test_take = TestTake(axis=axis, mode=mode)
+        if hybridize:
+            test_take.hybridize()
+        x = np.array(data_real)
+        x.attach_grad()
+        with mx.autograd.record():
+            mx_out = test_take(x, np.array(idx_real))
+        same(mx_out.asnumpy(), _np.take(data_real, idx_real, axis=axis, mode=mode))
+
+        if axis and axis < 0:
+            axis += len(data_shape)
+        try:
+            for i in _np.nditer(idx_real):
+                grad_helper(grad_in, axis, i, mode)
+        except:
+            pass
+
+        mx_out.backward()
+        same(x.grad.asnumpy(), grad_in)
+
+    for hybridize in [True, False]:
+        for mode in ['clip', 'wrap']:
+            for data_ndim in range(1, 5):
+                for idx_ndim in range(1, 4):
+                    for axis in range(-data_ndim, data_ndim):
+                        data_shape = ()
+                        for _ in range(data_ndim):
+                            data_shape += (_np.random.randint(low=1, high=5), )
+                        idx_shape = ()
+                        for _ in range(idx_ndim):
+                            idx_shape += (_np.random.randint(low=1, high=5), )
+                        check_output_n_grad(data_shape, idx_shape, axis, mode)
+
+            for config in configs:
+                check_output_n_grad(config[0], config[1], config[2], mode)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From c69c8bfa7d7e1a70a15c3af85a0ad7a10588662e Mon Sep 17 00:00:00 2001
From: Thomas Delteil <thomas.delteil1@gmail.com>
Date: Fri, 27 Sep 2019 14:41:03 +1000
Subject: [PATCH 379/813] redirect to the 404 page (#16287)

* redirect to the 404 so that people are at least on the website

* improve 404 documentation

* nudge ci
---
 docs/static_site/src/.htaccess |  4 ++++
 docs/static_site/src/404.html  | 10 ++++++++--
 2 files changed, 12 insertions(+), 2 deletions(-)

diff --git a/docs/static_site/src/.htaccess b/docs/static_site/src/.htaccess
index 16420306f1e1..b5de581b80b2 100644
--- a/docs/static_site/src/.htaccess
+++ b/docs/static_site/src/.htaccess
@@ -22,3 +22,7 @@ RewriteCond %{REQUEST_URI} !\/$
 RewriteCond %{REQUEST_FILENAME} -d
 RewriteCond %{REQUEST_FILENAME}/index.html -f
 RewriteRule ^(.*) $1/index.html [NC,L]
+
+
+# 404
+ErrorDocument 404 /404.html
diff --git a/docs/static_site/src/404.html b/docs/static_site/src/404.html
index c472b4ea0a78..ed01bd7bab45 100644
--- a/docs/static_site/src/404.html
+++ b/docs/static_site/src/404.html
@@ -19,6 +19,12 @@
 <div class="container">
   <h1>404</h1>
 
-  <p><strong>Page not found :(</strong></p>
-  <p>The requested page could not be found.</p>
+  <p><strong>Page not found :'(</strong></p>
+
+  <p><b>Due to a recent redesign of the website, some items have moved</b>
+    We're working towards adding redirects. Useful links:</p>
+  <div style="background-color:white;">
+    <li><a href="/api/python/docs/tutorials/">Python Tutorials</a></li>
+    <li><a href="/api/python/docs/api/">Python API Documentation</a></li>
+  </div>
 </div>

From f52ddfd880ad9d0e5a9d26eb41aabdd96fa4ac65 Mon Sep 17 00:00:00 2001
From: Zhennan Qin <zhennan.qin@intel.com>
Date: Fri, 27 Sep 2019 14:37:17 +0800
Subject: [PATCH 380/813] Fix MXNDArrayGetData (#16289)

---
 src/c_api/c_api.cc | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/src/c_api/c_api.cc b/src/c_api/c_api.cc
index 14ec59f53f72..1696bf572e4c 100644
--- a/src/c_api/c_api.cc
+++ b/src/c_api/c_api.cc
@@ -628,10 +628,9 @@ int MXNDArrayGetData(NDArrayHandle handle,
   API_BEGIN();
   NDArray *arr = static_cast<NDArray*>(handle);
 #if MXNET_USE_MKLDNN == 1
-  NDArray temp = *arr;
   if (arr->IsMKLDNNData()) {
-    temp = arr->Reorder2Default();
-    arr = &temp;
+    arr->Reorder2DefaultAsync();
+    arr->WaitToRead();
   }
 #endif
   if (!arr->is_none()) {

From 7656a11f6889b42128e17b452035ba2f616babcd Mon Sep 17 00:00:00 2001
From: perdasilva <perdasilva@gmail.com>
Date: Fri, 27 Sep 2019 10:31:35 +0200
Subject: [PATCH 381/813] Removes git status update stop gap solution (#16285)

---
 ci/Jenkinsfile_utils.groovy | 34 +++++++++++++---------------------
 1 file changed, 13 insertions(+), 21 deletions(-)

diff --git a/ci/Jenkinsfile_utils.groovy b/ci/Jenkinsfile_utils.groovy
index ad8cd553df04..9f626a6cb83e 100644
--- a/ci/Jenkinsfile_utils.groovy
+++ b/ci/Jenkinsfile_utils.groovy
@@ -186,27 +186,19 @@ def update_github_commit_status(state, message) {
     context = get_github_context()
     echo "context=${context}"
 
-    // a few attempts need to be made: https://github.com/apache/incubator-mxnet/issues/11654
-    for (int attempt = 1; attempt <= 3; attempt++) {
-      echo "Sending GitHub status attempt ${attempt}..."
-
-      step([
-        $class: 'GitHubCommitStatusSetter',
-        reposSource: [$class: "ManuallyEnteredRepositorySource", url: repoUrl],
-        contextSource: [$class: "ManuallyEnteredCommitContextSource", context: context],
-        commitShaSource: [$class: "ManuallyEnteredShaSource", sha: commitSha],
-        statusBackrefSource: [$class: "ManuallyEnteredBackrefSource", backref: "${env.RUN_DISPLAY_URL}"],
-        errorHandlers: [[$class: 'ShallowAnyErrorHandler']],
-        statusResultSource: [
-          $class: 'ConditionalStatusResultSource',
-          results: [[$class: "AnyBuildResult", message: message, state: state]]
-        ]
-      ])
-
-      if (attempt <= 2) {
-        sleep 1
-      }
-    }
+    echo "Publishing commit status..."
+    step([
+      $class: 'GitHubCommitStatusSetter',
+      reposSource: [$class: "ManuallyEnteredRepositorySource", url: repoUrl],
+      contextSource: [$class: "ManuallyEnteredCommitContextSource", context: context],
+      commitShaSource: [$class: "ManuallyEnteredShaSource", sha: commitSha],
+      statusBackrefSource: [$class: "ManuallyEnteredBackrefSource", backref: "${env.RUN_DISPLAY_URL}"],
+      errorHandlers: [[$class: 'ShallowAnyErrorHandler']],
+      statusResultSource: [
+        $class: 'ConditionalStatusResultSource',
+        results: [[$class: "AnyBuildResult", message: message, state: state]]
+      ]
+    ])
 
     echo "Publishing commit status done."
 

From 33c7b5caba2bcfb78a2e5411795d4600878fa05c Mon Sep 17 00:00:00 2001
From: Aaron Markham <markhama@amazon.com>
Date: Fri, 27 Sep 2019 09:32:21 +0100
Subject: [PATCH 382/813] add google-analytics config (#16271)

* add google-analytics config

* nudge CI

* nudge CI
---
 docs/static_site/src/_config_prod.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/static_site/src/_config_prod.yml b/docs/static_site/src/_config_prod.yml
index 2135f9c1e9a6..fbd2367adb3b 100644
--- a/docs/static_site/src/_config_prod.yml
+++ b/docs/static_site/src/_config_prod.yml
@@ -38,6 +38,7 @@ url: https://mxnet.incubator.apache.org
 twitter_username: apachemxnet
 github_username:  apache/incubator-mxnet
 youtube_username: apachemxnet
+google_analytics: UA-96378503-1
 
 # Build settings
 markdown: redcarpet

From 8004a027ad6a73f8f6eae102de8d249fbdfb9a2d Mon Sep 17 00:00:00 2001
From: Thomas Delteil <thomas.delteil1@gmail.com>
Date: Fri, 27 Sep 2019 19:55:58 +1000
Subject: [PATCH 383/813] Fixing links for website + Fixing search (#16284)

* first updates

* fixing links + adding tutorials

* fix search

* fixing more links and adding new tutorials

* wrong license edit

* fix rendering

* Apply suggestions from code review

Co-Authored-By: Aaron Markham <markhama@amazon.com>

* moving mxnet.incubator.apache.org to mxnet.apache.org as per Apache infra for better indexing  https://issues.apache.org/jira/browse/INFRA-19144

* fixing line too long
---
 .github/PULL_REQUEST_TEMPLATE.md              |   2 +-
 MKLDNN_README.md                              |   2 +-
 NEWS.md                                       |  14 +-
 R-package/R/zzz.R                             |   2 +-
 R-package/README.md                           |   2 +-
 R-package/vignettes/MultidimLstm.Rmd          |   2 +-
 README.md                                     |  30 +-
 benchmark/opperf/README.md                    |   2 +-
 ci/docker/install/ubuntu_r.sh                 |   2 +-
 ci/other/ci_deploy_doc.sh                     |   2 +-
 contrib/clojure-package/README.md             |   6 +-
 .../examples/pre-trained-models/README.md     |   6 +-
 .../examples/tutorial/README.md               |   2 +-
 .../tutorial/src/tutorial/kvstore.clj         |   2 +-
 .../examples/tutorial/src/tutorial/module.clj |   2 +-
 .../tutorial/src/tutorial/ndarray.clj         |   2 +-
 .../examples/tutorial/src/tutorial/symbol.clj |   2 +-
 cpp-package/README.md                         |   6 +-
 docs/README.md                                |   6 +-
 docs/python_docs/python/scripts/conf.py       |   4 +-
 .../python/tutorials/deploy/export/index.rst  |  13 +-
 .../python/tutorials/deploy/export/onnx.md    | 150 +++++
 .../python/tutorials/deploy/index.rst         |   6 +-
 .../tutorials/deploy/inference/index.rst      |  38 +-
 .../deploy/inference/wine_detector.md         | 405 ++++++++++++
 .../tutorials/deploy/run-on-aws/index.rst     |   8 +-
 .../python/tutorials/extend/custom_layer.md   |  18 +-
 .../python/tutorials/extend/customop.md       |   2 +-
 .../python/tutorials/extend/index.rst         |  12 +-
 .../getting-started/crash-course/5-predict.md |   2 +-
 .../gluon_from_experiment_to_deployment.md    |  22 +-
 .../tutorials/getting-started/index.rst       |  28 +-
 .../logistic_regression_explained.md          | 257 ++++++++
 .../getting-started/to-mxnet/index.rst        |   2 +-
 .../getting-started/to-mxnet/pytorch.md       |   6 +-
 .../autograd/{autograd.md => index.md}        |   4 +-
 .../{ => blocks}/activations/activations.md   |   0
 .../{ => blocks}/activations/images/elu.png   | Bin
 .../activations/images/leakyrelu.png          | Bin
 .../{ => blocks}/activations/images/prelu.png | Bin
 .../{ => blocks}/activations/images/relu.png  | Bin
 .../{ => blocks}/activations/images/selu.png  | Bin
 .../activations/images/sigmoid.png            | Bin
 .../activations/images/softrelu.png           | Bin
 .../activations/images/softsign.png           | Bin
 .../{ => blocks}/activations/images/swish.png | Bin
 .../{ => blocks}/activations/images/tanh.png  | Bin
 .../packages/gluon/{ => blocks}/blocks.svg    |   0
 .../gluon/{ => blocks}/custom-layer.md        |   0
 .../{ => blocks}/custom_layer_beginners.md    |  18 +-
 .../packages/gluon/{ => blocks}/hybridize.md  |   8 +-
 .../tutorials/packages/gluon/blocks/index.rst |  26 +
 .../packages/gluon/{ => blocks}/init.md       |   8 +-
 .../packages/gluon/{ => blocks}/naming.md     |   2 +-
 .../packages/gluon/{ => blocks}/nn.md         |   0
 .../packages/gluon/{ => blocks}/parameters.md |  20 +-
 .../packages/gluon/blocks/save_load_params.md | 281 +++++++++
 .../tutorials/packages/gluon/customop.md      | 227 -------
 .../python/tutorials/packages/gluon/data.md   | 141 -----
 .../gluon/{ => image}/image-augmentation.md   |   0
 .../tutorials/packages/gluon/image/index.rst  |   6 +-
 .../tutorials/packages/gluon/image/mnist.md   |  16 +-
 .../packages/gluon/image/pretrained_models.md |   8 +-
 .../python/tutorials/packages/gluon/index.rst |  88 +--
 .../{custom-loss => loss}/custom-loss.md      |   2 +-
 .../images/contrastive_loss.jpeg              | Bin
 .../images/inuktitut_1.png                    | Bin
 .../images/inuktitut_2.png                    | Bin
 .../tutorials/packages/gluon/loss/index.rst   |  25 +
 .../tutorials/packages/gluon/loss/loss.md     |  24 +-
 .../tutorials/packages/gluon/text/index.rst   |  35 +-
 .../packages/gluon/text/transformer.rst       |   2 +-
 .../gluon/{ => training}/fit_api_tutorial.md  |  10 +-
 .../packages/gluon/training/index.rst         |  26 +
 .../gluon/training/learning_rates/index.rst   |  26 +
 .../learning_rates/learning_rate_finder.md    | 332 ++++++++++
 .../learning_rates/learning_rate_schedules.md | 347 +++++++++++
 .../learning_rate_schedules_advanced.md       | 325 ++++++++++
 .../normalization/imgs/NCHW_BN.png            | Bin
 .../normalization/imgs/NCHW_IN.png            | Bin
 .../normalization/imgs/NCHW_LN.png            | Bin
 .../normalization/imgs/NTC_BN.png             | Bin
 .../normalization/imgs/NTC_IN.png             | Bin
 .../normalization/imgs/NTC_LN.png             | Bin
 .../imgs/data_normalization.jpeg              | Bin
 .../normalization/index.md}                   |   4 +-
 .../packages/gluon/{ => training}/trainer.md  |  24 +-
 .../python/tutorials/packages/index.rst       |  54 +-
 .../tutorials/packages/kvstore/index.rst      |   4 +-
 .../tutorials/packages/kvstore/kvstore.md     | 171 +++++
 .../tutorials/packages/module/index.rst       |  52 --
 .../packages/ndarray/01-ndarray-intro.md      |   6 +-
 .../ndarray/gotchas_numpy_in_mxnet.md         | 187 ++++++
 .../tutorials/packages/ndarray/index.rst      |  46 +-
 .../tutorials/packages/ndarray/sparse/csr.md  | 562 +++++++++++++++++
 .../ndarray/sparse/index.rst}                 |   9 +-
 .../packages/ndarray/sparse/row_sparse.md     | 584 ++++++++++++++++++
 .../packages/ndarray/sparse/train.md          | 339 ++++++++++
 .../packages/ndarray/sparse/train_gluon.md    | 483 +++++++++++++++
 .../packages/onnx/fine_tuning_gluon.md        | 456 ++++++++++++++
 .../python/tutorials/packages/onnx/index.rst  |  12 +-
 .../packages/onnx/inference_on_onnx_model.md  | 256 ++++++++
 .../packages/onnx/super_resolution.md         | 140 +++++
 .../optimizer/{optimizer.md => index.md}      |   4 +-
 .../python/tutorials/packages/viz/index.rst   |   7 +-
 .../tutorials/performance/backend/amp.md      |   4 +-
 .../tutorials/performance/backend/index.rst   |  21 +-
 .../backend/{mkl-dnn.rst => mkldnn/index.rst} |  19 +-
 .../{ => mkldnn}/mkldnn_quantization.md       |   8 +-
 .../mkldnn_readme.md}                         |   4 +-
 .../tutorials/performance/backend/profiler.md |  10 +-
 .../backend/tensorrt}/index.rst               |  22 +-
 .../performance/backend/tensorrt/tensorrt.md  | 143 +++++
 .../backend/tensorrt/wavenet_optimized.svg    |   1 +
 .../backend/tensorrt/wavenet_unoptimized.svg  |  17 +
 .../performance/compression/index.rst         |  23 +-
 .../python/tutorials/performance/index.rst    |  10 +-
 .../themes/mx-theme/mxtheme/layout.html       |   2 +-
 docs/static_site/src/_config_prod.yml         |   2 +-
 .../get_started/devices/nvidia-jetson.md      |   2 +-
 .../get_started/devices/raspberry_pi.md       |   6 +-
 .../_includes/get_started/linux/cpp/cpp.md    |   2 +-
 .../_includes/get_started/linux/java/cpu.md   |   2 +-
 .../_includes/get_started/linux/java/gpu.md   |   2 +-
 .../linux/julia/build-from-source.md          |   2 +-
 .../_includes/get_started/linux/perl/perl.md  |   2 +-
 .../get_started/linux/python/cpu/docker.md    |   2 +-
 .../get_started/linux/python/cpu/pip.md       |  10 +-
 .../get_started/linux/python/gpu/docker.md    |   2 +-
 .../src/_includes/get_started/linux/r/cpu.md  |   2 +-
 .../src/_includes/get_started/linux/r/gpu.md  |   2 +-
 .../_includes/get_started/macos/cpp/cpp.md    |   2 +-
 .../_includes/get_started/macos/java/cpu.md   |   2 +-
 .../macos/julia/build-from-source.md          |   2 +-
 .../_includes/get_started/macos/perl/perl.md  |   2 +-
 .../macos/python/cpu/build-from-source.md     |   2 +-
 .../get_started/macos/python/cpu/docker.md    |   2 +-
 .../macos/python/gpu/build-from-source.md     |   2 +-
 .../macos/python/gpu/pip_docker.md            |   2 +-
 .../src/_includes/get_started/macos/r/cpu.md  |   2 +-
 .../_includes/get_started/windows/cpp/cpp.md  |   2 +-
 .../windows/julia/build-from-source.md        |   2 +-
 .../get_started/windows/perl/perl.md          |   2 +-
 .../windows/python/cpu/build-from-source.md   |   2 +-
 .../get_started/windows/python/cpu/docker.md  |   2 +-
 .../windows/python/gpu/build-from-source.md   |   2 +-
 .../_includes/get_started/windows/r/cpu.md    |   2 +-
 .../_includes/get_started/windows/r/gpu.md    |   2 +-
 .../api/architecture/exception_handling.md    |   2 +-
 .../api/architecture/note_data_loading.md     |   2 +-
 .../src/pages/api/architecture/note_engine.md |   1 -
 .../pages/api/cpp/docs/tutorials/basics.md    |   2 +-
 docs/static_site/src/pages/api/cpp/index.md   |   4 +-
 .../src/pages/api/faq/add_op_in_backend.md    |  30 +
 docs/static_site/src/pages/api/faq/float16.md |   2 +-
 .../src/pages/api/faq/gradient_compression.md |   2 +-
 docs/static_site/src/pages/api/faq/perf.md    |   4 +-
 .../static_site/src/pages/api/faq/recordio.md |   2 +-
 .../pages/api/perl/docs/tutorials/ndarray.md  |   2 +-
 .../api/r/docs/tutorials/callback_function.md |   8 +-
 ...assify_real_image_with_pretrained_model.md |   6 +-
 .../api/r/docs/tutorials/custom_iterator.md   |   2 +-
 .../r/docs/tutorials/custom_loss_function.md  |   8 +-
 .../tutorials/five_minutes_neural_network.md  |   8 +-
 .../api/r/docs/tutorials/mnist_competition.md |   2 +-
 .../api/r/docs/tutorials/multi_dim_lstm.md    |   2 +-
 .../src/pages/api/r/docs/tutorials/ndarray.md |  12 +-
 .../src/pages/api/r/docs/tutorials/symbol.md  |  10 +-
 .../api/scala/docs/tutorials/char_lstm.md     |   2 +-
 .../docs/tutorials/mxnet_scala_on_intellij.md |   4 +-
 .../src/pages/community/contribute.md         |   2 +-
 .../pages/get_started/build_from_source.md    |   4 +-
 .../src/pages/get_started/java_setup.md       |   2 +-
 .../src/pages/get_started/scala_setup.md      |   2 +-
 .../src/pages/get_started/ubuntu_setup.md     |   2 +-
 .../src/pages/get_started/windows_setup.md    |   4 +-
 example/MXNetTutorialTemplate.ipynb           |   6 +-
 example/README.md                             |  18 +-
 example/ctc/README.md                         |   2 +-
 example/distributed_training/README.md        |   2 +-
 example/gluon/audio/README.md                 |   2 +-
 example/gluon/audio/urban_sounds/README.md    |   2 +-
 example/image-classification/README.md        |  12 +-
 .../predict-cpp/README.md                     |   2 +-
 example/image-classification/train_mnist.py   |   2 +-
 example/profiler/README.md                    |   4 +-
 example/profiler/profiler_imageiter.py        |   2 +-
 example/quantization/README.md                |   2 +-
 example/quantization/imagenet_gen_qsym.py     |   4 +-
 .../quantization/imagenet_gen_qsym_mkldnn.py  |   4 +-
 example/quantization/imagenet_inference.py    |   4 +-
 example/rnn/bucketing/README.md               |   2 +-
 example/rnn/old/README.md                     |   2 +-
 example/sparse/README.md                      |   2 +-
 .../sparse/linear_classification/README.md    |   4 +-
 example/ssd/quantization.py                   |   4 +-
 julia/docs/src/index.md                       |   2 +-
 julia/docs/src/tutorial/char-lstm.md          |   4 +-
 julia/docs/src/user-guide/install.md          |   2 +-
 julia/docs/src/user-guide/overview.md         |   2 +-
 .../lib/AI/MXNet/Gluon/ModelZoo.pm            |   2 +-
 perl-package/AI-MXNet/Changes                 |   2 +-
 perl-package/AI-MXNet/examples/mnist.pl       |   2 +-
 perl-package/AI-MXNet/lib/AI/MXNet.pm         |   4 +-
 perl-package/AI-MXNet/lib/AI/MXNet/Gluon.pm   |   2 +-
 .../AI-MXNet/lib/AI/MXNet/Gluon/Block.pm      |   4 +-
 .../AI-MXNet/lib/AI/MXNet/Gluon/Trainer.pm    |   2 +-
 perl-package/AI-MXNet/lib/AI/MXNet/IO.pm      |   2 +-
 perl-package/AI-MXNet/lib/AI/MXNet/LinAlg.pm  |   4 +-
 perl-package/AI-MXNet/lib/AI/MXNet/Metric.pm  |   2 +-
 perl-package/AI-MXNetCAPI/Changes             |   2 +-
 perl-package/AI-MXNetCAPI/README              |   2 +-
 perl-package/AI-NNVMCAPI/Changes              |   2 +-
 perl-package/AI-NNVMCAPI/README               |   2 +-
 perl-package/README.md                        |   4 +-
 python/README.md                              |   2 +-
 python/mxnet/context.py                       |   2 +-
 python/mxnet/gluon/block.py                   |   8 +-
 python/mxnet/gluon/parameter.py               |   2 +-
 python/mxnet/gluon/trainer.py                 |   2 +-
 python/mxnet/module/base_module.py            |   8 +-
 scala-package/README.md                       |  16 +-
 .../javaapi/infer/objectdetector/README.md    |   6 +-
 .../javaapi/infer/predictor/README.md         |   2 +-
 .../apache/mxnetexamples/benchmark/README.md  |   2 +-
 .../cnntextclassification/README.md           |   4 +-
 .../infer/imageclassifier/README.md           |   2 +-
 .../infer/objectdetector/README.md            |   2 +-
 scala-package/packageTest/README.md           |   2 +-
 scala-package/spark/README.md                 |   2 +-
 setup-utils/install-mxnet-osx-python.sh       |   4 +-
 src/operator/custom/custom.cc                 |   2 +-
 src/operator/nn/layer_norm-inl.h              |   2 +-
 src/operator/nn/layer_norm.cu                 |   2 +-
 src/operator/nn/softmax-inl.h                 |   2 +-
 src/operator/optimizer_op-inl.h               |   2 +-
 src/operator/tensor/broadcast_reduce_op.h     |   2 +-
 .../JenkinsfileForBLC                         |   2 +-
 .../test_broken_links.py                      |   4 +-
 tests/python/unittest/test_gluon_utils.py     |   2 +-
 tools/caffe_translator/README.md              |   4 +-
 tools/caffe_translator/faq.md                 |   4 +-
 tools/coreml/pip_package/README.rst           |   2 +-
 tools/pip/doc/CPU_ADDITIONAL.md               |   2 +-
 tools/pip/doc/CU100MKL_ADDITIONAL.md          |   2 +-
 tools/pip/doc/CU100_ADDITIONAL.md             |   2 +-
 tools/pip/doc/CU75MKL_ADDITIONAL.md           |   2 +-
 tools/pip/doc/CU75_ADDITIONAL.md              |   2 +-
 tools/pip/doc/CU80MKL_ADDITIONAL.md           |   2 +-
 tools/pip/doc/CU80_ADDITIONAL.md              |   2 +-
 tools/pip/doc/CU90MKL_ADDITIONAL.md           |   2 +-
 tools/pip/doc/CU90_ADDITIONAL.md              |   2 +-
 tools/pip/doc/CU91MKL_ADDITIONAL.md           |   2 +-
 tools/pip/doc/CU91_ADDITIONAL.md              |   2 +-
 tools/pip/doc/CU92MKL_ADDITIONAL.md           |   2 +-
 tools/pip/doc/CU92_ADDITIONAL.md              |   2 +-
 tools/pip/doc/MKL_ADDITIONAL.md               |   2 +-
 257 files changed, 6152 insertions(+), 1065 deletions(-)
 create mode 100644 docs/python_docs/python/tutorials/deploy/export/onnx.md
 create mode 100644 docs/python_docs/python/tutorials/deploy/inference/wine_detector.md
 rename docs/python_docs/python/tutorials/{packages/gluon => getting-started}/gluon_from_experiment_to_deployment.md (89%)
 create mode 100644 docs/python_docs/python/tutorials/getting-started/logistic_regression_explained.md
 rename docs/python_docs/python/tutorials/packages/autograd/{autograd.md => index.md} (98%)
 rename docs/python_docs/python/tutorials/packages/gluon/{ => blocks}/activations/activations.md (100%)
 rename docs/python_docs/python/tutorials/packages/gluon/{ => blocks}/activations/images/elu.png (100%)
 rename docs/python_docs/python/tutorials/packages/gluon/{ => blocks}/activations/images/leakyrelu.png (100%)
 rename docs/python_docs/python/tutorials/packages/gluon/{ => blocks}/activations/images/prelu.png (100%)
 rename docs/python_docs/python/tutorials/packages/gluon/{ => blocks}/activations/images/relu.png (100%)
 rename docs/python_docs/python/tutorials/packages/gluon/{ => blocks}/activations/images/selu.png (100%)
 rename docs/python_docs/python/tutorials/packages/gluon/{ => blocks}/activations/images/sigmoid.png (100%)
 rename docs/python_docs/python/tutorials/packages/gluon/{ => blocks}/activations/images/softrelu.png (100%)
 rename docs/python_docs/python/tutorials/packages/gluon/{ => blocks}/activations/images/softsign.png (100%)
 rename docs/python_docs/python/tutorials/packages/gluon/{ => blocks}/activations/images/swish.png (100%)
 rename docs/python_docs/python/tutorials/packages/gluon/{ => blocks}/activations/images/tanh.png (100%)
 rename docs/python_docs/python/tutorials/packages/gluon/{ => blocks}/blocks.svg (100%)
 rename docs/python_docs/python/tutorials/packages/gluon/{ => blocks}/custom-layer.md (100%)
 rename docs/python_docs/python/tutorials/packages/gluon/{ => blocks}/custom_layer_beginners.md (85%)
 rename docs/python_docs/python/tutorials/packages/gluon/{ => blocks}/hybridize.md (91%)
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/blocks/index.rst
 rename docs/python_docs/python/tutorials/packages/gluon/{ => blocks}/init.md (97%)
 rename docs/python_docs/python/tutorials/packages/gluon/{ => blocks}/naming.md (99%)
 rename docs/python_docs/python/tutorials/packages/gluon/{ => blocks}/nn.md (100%)
 rename docs/python_docs/python/tutorials/packages/gluon/{ => blocks}/parameters.md (76%)
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/blocks/save_load_params.md
 delete mode 100644 docs/python_docs/python/tutorials/packages/gluon/customop.md
 delete mode 100644 docs/python_docs/python/tutorials/packages/gluon/data.md
 rename docs/python_docs/python/tutorials/packages/gluon/{ => image}/image-augmentation.md (100%)
 rename docs/python_docs/python/tutorials/packages/gluon/{custom-loss => loss}/custom-loss.md (93%)
 rename docs/python_docs/python/tutorials/packages/gluon/{custom-loss => loss}/images/contrastive_loss.jpeg (100%)
 rename docs/python_docs/python/tutorials/packages/gluon/{custom-loss => loss}/images/inuktitut_1.png (100%)
 rename docs/python_docs/python/tutorials/packages/gluon/{custom-loss => loss}/images/inuktitut_2.png (100%)
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/loss/index.rst
 rename docs/python_docs/python/tutorials/packages/gluon/{ => training}/fit_api_tutorial.md (92%)
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/training/index.rst
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/training/learning_rates/index.rst
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/training/learning_rates/learning_rate_finder.md
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.md
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.md
 rename docs/python_docs/python/tutorials/packages/gluon/{ => training}/normalization/imgs/NCHW_BN.png (100%)
 rename docs/python_docs/python/tutorials/packages/gluon/{ => training}/normalization/imgs/NCHW_IN.png (100%)
 rename docs/python_docs/python/tutorials/packages/gluon/{ => training}/normalization/imgs/NCHW_LN.png (100%)
 rename docs/python_docs/python/tutorials/packages/gluon/{ => training}/normalization/imgs/NTC_BN.png (100%)
 rename docs/python_docs/python/tutorials/packages/gluon/{ => training}/normalization/imgs/NTC_IN.png (100%)
 rename docs/python_docs/python/tutorials/packages/gluon/{ => training}/normalization/imgs/NTC_LN.png (100%)
 rename docs/python_docs/python/tutorials/packages/gluon/{ => training}/normalization/imgs/data_normalization.jpeg (100%)
 rename docs/python_docs/python/tutorials/packages/gluon/{normalization/normalization.md => training/normalization/index.md} (94%)
 rename docs/python_docs/python/tutorials/packages/gluon/{ => training}/trainer.md (69%)
 create mode 100644 docs/python_docs/python/tutorials/packages/kvstore/kvstore.md
 delete mode 100644 docs/python_docs/python/tutorials/packages/module/index.rst
 create mode 100644 docs/python_docs/python/tutorials/packages/ndarray/gotchas_numpy_in_mxnet.md
 create mode 100644 docs/python_docs/python/tutorials/packages/ndarray/sparse/csr.md
 rename docs/python_docs/python/tutorials/{deploy/export/export.rst => packages/ndarray/sparse/index.rst} (93%)
 create mode 100644 docs/python_docs/python/tutorials/packages/ndarray/sparse/row_sparse.md
 create mode 100644 docs/python_docs/python/tutorials/packages/ndarray/sparse/train.md
 create mode 100644 docs/python_docs/python/tutorials/packages/ndarray/sparse/train_gluon.md
 create mode 100644 docs/python_docs/python/tutorials/packages/onnx/fine_tuning_gluon.md
 create mode 100644 docs/python_docs/python/tutorials/packages/onnx/inference_on_onnx_model.md
 create mode 100644 docs/python_docs/python/tutorials/packages/onnx/super_resolution.md
 rename docs/python_docs/python/tutorials/packages/optimizer/{optimizer.md => index.md} (99%)
 rename docs/python_docs/python/tutorials/performance/backend/{mkl-dnn.rst => mkldnn/index.rst} (71%)
 rename docs/python_docs/python/tutorials/performance/backend/{ => mkldnn}/mkldnn_quantization.md (96%)
 rename docs/python_docs/python/tutorials/performance/backend/{MKLDNN_README.md => mkldnn/mkldnn_readme.md} (99%)
 rename docs/python_docs/python/tutorials/{packages/symbol => performance/backend/tensorrt}/index.rst (70%)
 create mode 100644 docs/python_docs/python/tutorials/performance/backend/tensorrt/tensorrt.md
 create mode 100644 docs/python_docs/python/tutorials/performance/backend/tensorrt/wavenet_optimized.svg
 create mode 100644 docs/python_docs/python/tutorials/performance/backend/tensorrt/wavenet_unoptimized.svg

diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md
index cd6fb45e7c22..7895c771ade8 100644
--- a/.github/PULL_REQUEST_TEMPLATE.md
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -14,7 +14,7 @@ Please feel free to remove inapplicable items for your PR.
 - For user-facing API changes, API doc string has been updated. 
 - For new C++ functions in header files, their functionalities and arguments are documented. 
 - For new examples, README.md is added to explain the what the example does, the source of the dataset, expected performance on test set and reference to the original paper if applicable
-- Check the API doc at http://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$PR_ID/$BUILD_ID/index.html
+- Check the API doc at https://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$PR_ID/$BUILD_ID/index.html
 - [ ] To the my best knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change
 
 ### Changes ###
diff --git a/MKLDNN_README.md b/MKLDNN_README.md
index 34790c9c513d..210bae88b20a 100644
--- a/MKLDNN_README.md
+++ b/MKLDNN_README.md
@@ -15,4 +15,4 @@
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
 
-File is moved to [docs/tutorials/mkldnn/MKLDNN_README.md](docs/tutorials/mkldnn/MKLDNN_README.md).
+File is moved to [docs/tutorials/mkldnn/MKLDNN_README.md](docs/python_docs/python/tutorials/performance/backend/mkldnn/mkldnn_readme.md).
diff --git a/NEWS.md b/NEWS.md
index 8d8ef2c4ffd5..2a0d0aca5634 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -216,7 +216,7 @@ MXNet now supports Dynamic Shape in both imperative and symbolic mode. MXNet use
 * while_loop: its output size depends on the number of iterations in the loop.
 * boolean indexing: its output size depends on the value of the input data.
 * many operators can be extended to take a shape symbol as input and the shape symbol can determine the output shape of these operators (with this extension, the symbol interface of MXNet can fully support shape).
-To support dynamic shape and such operators, we have modified MXNet backend. Now MXNet supports operators with dynamic shape such as [`contrib.while_loop`](https://mxnet.incubator.apache.org/api/python/ndarray/contrib.html#mxnet.ndarray.contrib.while_loop), [`contrib.cond`](https://mxnet.incubator.apache.org/api/python/ndarray/contrib.html#mxnet.ndarray.contrib.cond), and [`mxnet.ndarray.contrib.boolean_mask`](https://mxnet.incubator.apache.org/api/python/ndarray/contrib.html#contrib)
+To support dynamic shape and such operators, we have modified MXNet backend. Now MXNet supports operators with dynamic shape such as [`contrib.while_loop`](https://mxnet.apache.org/api/python/ndarray/contrib.html#mxnet.ndarray.contrib.while_loop), [`contrib.cond`](https://mxnet.apache.org/api/python/ndarray/contrib.html#mxnet.ndarray.contrib.cond), and [`mxnet.ndarray.contrib.boolean_mask`](https://mxnet.apache.org/api/python/ndarray/contrib.html#contrib)
 Note: Currently dynamic shape does not work with Gluon deferred initialization.
 
 #### Large Tensor Support
@@ -233,7 +233,7 @@ For more details please refer to the [design document](https://cwiki.apache.org/
 
 #### Dependency Update
 MXNet has added support for CUDA 10, CUDA 10.1, cudnn7.5, NCCL 2.4.2, and numpy 1.16.0.
-These updates are available through PyPI packages and build from source, refer to [installation guid](https://mxnet.incubator.apache.org/versions/master/install/index.html) for more details.
+These updates are available through PyPI packages and build from source, refer to [installation guid](https://mxnet.apache.org/versions/master/install/index.html) for more details.
 
 #### Gluon Fit API(experimental)
 Training a model in Gluon requires users to write the training loop. This is useful because of its imperative nature, however repeating the same code across multiple models can become tedious and repetitive with boilerplate code. 
@@ -1213,7 +1213,7 @@ MKLDNN backend takes advantage of MXNet subgraph to implement the most of possib
 ##### Quantization
 Performance of reduced-precision (INT8) computation is also dramatically improved after the graph optimization feature is applied on CPU Platforms. Various models are supported and can benefit from reduced-precision computation, including symbolic models, Gluon models and even custom models. Users can run most of the pre-trained models with only a few lines of commands and a new quantization script imagenet_gen_qsym_mkldnn.py. The observed accuracy loss is less than 0.5% for popular CNN networks, like ResNet-50, Inception-BN, MobileNet, etc.
 
-Please find detailed information and performance/accuracy numbers here: [MKLDNN README](https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/mkldnn/MKLDNN_README.md), [quantization README](https://github.com/apache/incubator-mxnet/tree/master/example/quantization#1) and [design proposal](https://cwiki.apache.org/confluence/display/MXNET/MXNet+Graph+Optimization+and+Quantization+based+on+subgraph+and+MKL-DNN)
+Please find detailed information and performance/accuracy numbers here: [MKLDNN README](https://mxnet.apache.org/api/python/docs/tutorials/performance/backend/mkldnn/mkldnn_readme.html), [quantization README](https://github.com/apache/incubator-mxnet/tree/master/example/quantization#1) and [design proposal](https://cwiki.apache.org/confluence/display/MXNET/MXNet+Graph+Optimization+and+Quantization+based+on+subgraph+and+MKL-DNN)
 
 ### New Operators
 
@@ -1624,7 +1624,7 @@ Please find detailed information and performance/accuracy numbers here: [MKLDNN
 * Updated CONTRIBUTORS.md to include mxnet-label-bot  (#13048)
 
 ### How to build MXNet
-Please follow the instructions at https://mxnet.incubator.apache.org/install/index.html
+Please follow the instructions at https://mxnet.apache.org/install/index.html
 
 ### List of submodules used by Apache MXNet (Incubating) and when they were updated last
 Submodule@commit ID::Last updated by MXNet:: Last update in submodule
@@ -1756,7 +1756,7 @@ For more information and examples, see [full release notes](https://cwiki.apache
 
 ### New Features - Clojure package (experimental)
 - MXNet now supports the Clojure programming language. The MXNet Clojure package brings flexible and efficient GPU computing and state-of-art deep learning to Clojure. It enables you to write seamless tensor/matrix computation with multiple GPUs in Clojure. It also lets you construct and customize the state-of-art deep learning models in Clojure, and apply them to tasks, such as image classification and data science challenges.([#11205](https://github.com/apache/incubator-mxnet/pull/11205))
-- Checkout examples and API documentation [here](http://mxnet.incubator.apache.org/api/clojure/index.html).
+- Checkout examples and API documentation [here](https://mxnet.apache.org/api/clojure/index.html).
 
 ### New Features - Synchronized Cross-GPU Batch Norm (experimental)
 - Gluon now supports Synchronized Batch Normalization (#11502).
@@ -1786,8 +1786,8 @@ For more information and examples, see [full release notes](https://cwiki.apache
 - Set environment variable `MXNET_KVSTORE_USETREE=1` to enable.
 
 ### New Features - Export MXNet models to ONNX format (experimental)
-- With this feature, now MXNet models can be exported to ONNX format([#11213](https://github.com/apache/incubator-mxnet/pull/11213)). Currently, MXNet supports ONNX v1.2.1. [API documentation](http://mxnet.incubator.apache.org/api/python/contrib/onnx.html).
-- Checkout this [tutorial](http://mxnet.incubator.apache.org/tutorials/onnx/export_mxnet_to_onnx.html) which shows how to use MXNet to ONNX exporter APIs. ONNX protobuf so that those models can be imported in other frameworks for inference.
+- With this feature, now MXNet models can be exported to ONNX format([#11213](https://github.com/apache/incubator-mxnet/pull/11213)). Currently, MXNet supports ONNX v1.2.1. [API documentation](https://mxnet.apache.org/api/python/contrib/onnx.html).
+- Checkout this [tutorial](https://mxnet.apache.org/tutorials/onnx/export_mxnet_to_onnx.html) which shows how to use MXNet to ONNX exporter APIs. ONNX protobuf so that those models can be imported in other frameworks for inference.
 
 ### New Features - TensorRT Runtime Integration (experimental)
 - [TensorRT](https://developer.nvidia.com/tensorrt) provides significant acceleration of model inference on NVIDIA GPUs compared to running the full graph in MxNet using unfused GPU operators. In addition to faster fp32 inference, TensorRT optimizes fp16 inference, and is capable of int8 inference (provided the quantization steps are performed). Besides increasing throughput, TensorRT significantly reduces inference latency, especially for small batches.
diff --git a/R-package/R/zzz.R b/R-package/R/zzz.R
index db7573ab2553..a4f03ace0cc1 100644
--- a/R-package/R/zzz.R
+++ b/R-package/R/zzz.R
@@ -54,7 +54,7 @@ NULL
 
   tips <- c(
     "Need help? Feel free to open an issue on https://github.com/dmlc/mxnet/issues",
-    "For more documents, please visit http://mxnet.io",
+    "For more documents, please visit https://mxnet.io",
     "Use suppressPackageStartupMessages() to eliminate package startup messages."
   )
 
diff --git a/R-package/README.md b/R-package/README.md
index 78a6214ddc89..68996b06191e 100644
--- a/R-package/README.md
+++ b/R-package/README.md
@@ -24,7 +24,7 @@ options(repos = cran)
 install.packages("mxnet")
 ```
 
-To use the GPU version or to use it on Linux, please follow [Installation Guide](http://mxnet.io/install/index.html)
+To use the GPU version or to use it on Linux, please follow [Installation Guide](https://mxnet.io/install/index.html)
 
 License
 -------
diff --git a/R-package/vignettes/MultidimLstm.Rmd b/R-package/vignettes/MultidimLstm.Rmd
index bd57d12f759c..c726557677a3 100644
--- a/R-package/vignettes/MultidimLstm.Rmd
+++ b/R-package/vignettes/MultidimLstm.Rmd
@@ -299,4 +299,4 @@ We also repeated the above experiments to generate the next 100 samples to 301st
 
 The above tutorial is just for demonstration purposes and has not been tuned extensively for accuracy.
 
-For more tutorials on MXNet-R, head on to [MXNet-R tutorials](https://mxnet.incubator.apache.org/tutorials/r/index.html)
\ No newline at end of file
+For more tutorials on MXNet-R, head on to [MXNet-R tutorials](https://mxnet.apache.org/tutorials/r/index.html)
\ No newline at end of file
diff --git a/README.md b/README.md
index 8db55af4ff87..413f8c492c45 100644
--- a/README.md
+++ b/README.md
@@ -16,37 +16,37 @@
 <!--- under the License. -->
 
 <div align="center">
-  <a href="https://mxnet.incubator.apache.org/"><img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet_logo_2.png"></a><br>
+  <a href="https://mxnet.apache.org/"><img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet_logo_2.png"></a><br>
 </div>
 
 Apache MXNet (incubating) for Deep Learning
 =====
 | Master         | Docs          | License  |
 | :-------------:|:-------------:|:--------:|
-[![CentOS CPU Build Status](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/centos-cpu/job/master/badge/icon?subject=build%20centos%20cpu)](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/centos-cpu/job/master/) [![CentOS GPU Build Status](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/centos-gpu/job/master/badge/icon?subject=build%20centos%20gpu)](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/centos-gpu/job/master/) [![Clang Build Status](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/clang/job/master/badge/icon?subject=build%20clang)](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/clang/job/master/) <br> [![Edge Build Status](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/edge/job/master/badge/icon?subject=build%20edge)](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/edge/job/master/) [![Miscellaneous Build Status](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/miscellaneous/job/master/badge/icon?subject=build%20miscellaneous)](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/miscellaneous/job/master/) [![Sanity Build Status](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/sanity/job/master/badge/icon?subject=build%20sanity)](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/sanity/job/master/) <br> [![Unix CPU Build Status](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/unix-cpu/job/master/badge/icon?subject=build%20unix%20cpu)](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/unix-cpu/job/master/) [![Unix GPU Build Status](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/unix-gpu/job/master/badge/icon?subject=build%20unix%20gpu)](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/unix-gpu/job/master/) [![Website Build Status](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/website/job/master/badge/icon?subject=build%20website)](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/website/job/master/) <br> [![Windows CPU Build Status](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/windows-cpu/job/master/badge/icon?subject=build%20windows%20cpu)](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/windows-cpu/job/master/) [![Windows GPU Build Status](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/windows-gpu/job/master/badge/icon?subject=build%20windows%20gpu)](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/windows-gpu/job/master/) | [![Documentation Status](http://jenkins.mxnet-ci.amazon-ml.com/job/restricted-website-build/badge/icon)](https://mxnet.incubator.apache.org/) | [![GitHub license](http://dmlc.github.io/img/apache2.svg)](./LICENSE) |
+[![CentOS CPU Build Status](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/centos-cpu/job/master/badge/icon?subject=build%20centos%20cpu)](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/centos-cpu/job/master/) [![CentOS GPU Build Status](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/centos-gpu/job/master/badge/icon?subject=build%20centos%20gpu)](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/centos-gpu/job/master/) [![Clang Build Status](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/clang/job/master/badge/icon?subject=build%20clang)](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/clang/job/master/) <br> [![Edge Build Status](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/edge/job/master/badge/icon?subject=build%20edge)](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/edge/job/master/) [![Miscellaneous Build Status](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/miscellaneous/job/master/badge/icon?subject=build%20miscellaneous)](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/miscellaneous/job/master/) [![Sanity Build Status](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/sanity/job/master/badge/icon?subject=build%20sanity)](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/sanity/job/master/) <br> [![Unix CPU Build Status](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/unix-cpu/job/master/badge/icon?subject=build%20unix%20cpu)](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/unix-cpu/job/master/) [![Unix GPU Build Status](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/unix-gpu/job/master/badge/icon?subject=build%20unix%20gpu)](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/unix-gpu/job/master/) [![Website Build Status](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/website/job/master/badge/icon?subject=build%20website)](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/website/job/master/) <br> [![Windows CPU Build Status](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/windows-cpu/job/master/badge/icon?subject=build%20windows%20cpu)](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/windows-cpu/job/master/) [![Windows GPU Build Status](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/windows-gpu/job/master/badge/icon?subject=build%20windows%20gpu)](http://jenkins.mxnet-ci.amazon-ml.com/job/mxnet-validation/job/windows-gpu/job/master/) | [![Documentation Status](http://jenkins.mxnet-ci.amazon-ml.com/job/restricted-website-build/badge/icon)](https://mxnet.apache.org/) | [![GitHub license](http://dmlc.github.io/img/apache2.svg)](./LICENSE) |
 
 ![banner](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/banner.png)
 
 Apache MXNet (incubating) is a deep learning framework designed for both *efficiency* and *flexibility*.
-It allows you to ***mix*** [symbolic and imperative programming](https://mxnet.incubator.apache.org/api/architecture/program_model)
+It allows you to ***mix*** [symbolic and imperative programming](https://mxnet.apache.org/api/architecture/program_model)
 to ***maximize*** efficiency and productivity.
 At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly.
 A graph optimization layer on top of that makes symbolic execution fast and memory efficient.
 MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines.
 
 MXNet is more than a deep learning project. It is a collection of
-[blue prints and guidelines](https://mxnet.incubator.apache.org/api/architecture/overview) for building
+[blue prints and guidelines](https://mxnet.apache.org/api/architecture/overview) for building
 deep learning systems, and interesting insights of DL systems for hackers.
 
 Ask Questions
 -------------
 * Please use [discuss.mxnet.io](https://discuss.mxnet.io/) for asking questions.
 * Please use [mxnet/issues](https://github.com/apache/incubator-mxnet/issues) for reporting bugs.
-* [Frequent Asked Questions](https://mxnet.incubator.apache.org/faq/faq.html)
+* [Frequent Asked Questions](https://mxnet.apache.org/faq/faq.html)
 
 How to Contribute
 -----------------
-* [Contribute to MXNet](https://mxnet.incubator.apache.org/community/contribute.html)
+* [Contribute to MXNet](https://mxnet.apache.org/community/contribute.html)
 
 What's New
 ----------
@@ -68,24 +68,24 @@ What's New
 * [Version 0.8.0 Release](https://github.com/dmlc/mxnet/releases/tag/v0.8.0)
 * [Updated Image Classification with new Pre-trained Models](./example/image-classification)
 * [Notebooks How to Use MXNet](https://github.com/d2l-ai/d2l-en)
-* [MKLDNN for Faster CPU Performance](./docs/tutorials/mkldnn/MKLDNN_README.md)
+* [MKLDNN for Faster CPU Performance](docs/python_docs/python/tutorials/performance/backend/mkldnn/mkldnn_readme.md)
 * [MXNet Memory Monger, Training Deeper Nets with Sublinear Memory Cost](https://github.com/dmlc/mxnet-memonger)
 * [Tutorial for NVidia GTC 2016](https://github.com/dmlc/mxnet-gtc-tutorial)
 * [MXNet.js: Javascript Package for Deep Learning in Browser (without server)](https://github.com/dmlc/mxnet.js/)
-* [Guide to Creating New Operators (Layers)](https://mxnet.incubator.apache.org/api/faq/new_op)
+* [Guide to Creating New Operators (Layers)](https://mxnet.apache.org/api/faq/new_op)
 * [Go binding for inference](https://github.com/songtianyi/go-mxnet-predictor)
 * [Amalgamation and Go Binding for Predictors](https://github.com/jdeng/gomxnet/) - Outdated
 * [Large Scale Image Classification](https://github.com/apache/incubator-mxnet/tree/master/example/image-classification)
 
 Contents
 --------
-* [Website](https://mxnet.incubator.apache.org)
-* [Documentation](https://mxnet.incubator.apache.org/api)
-* [Blog](https://mxnet.incubator.apache.org/blog)
+* [Website](https://mxnet.apache.org)
+* [Documentation](https://mxnet.apache.org/api)
+* [Blog](https://mxnet.apache.org/blog)
 * [Code Examples](https://github.com/apache/incubator-mxnet/tree/master/example)
-* [Installation](https://mxnet.incubator.apache.org/get_started)
-* [Features](https://mxnet.incubator.apache.org/features)
-* [Ecosystem](https://mxnet.incubator.apache.org/ecosystem)
+* [Installation](https://mxnet.apache.org/get_started)
+* [Features](https://mxnet.apache.org/features)
+* [Ecosystem](https://mxnet.apache.org/ecosystem)
 
 Features
 --------
@@ -94,7 +94,7 @@ Features
 * Mix and match imperative and symbolic programming to maximize flexibility and efficiency
 * Lightweight, memory efficient and portable to smart devices
 * Scales up to multi GPUs and distributed setting with auto parallelism
-* Support for [Python](https://mxnet.incubator.apache.org/api/python), [Scala](https://mxnet.incubator.apache.org/api/scala), [C++](https://mxnet.incubator.apache.org/api/cpp), [Java](https://mxnet.incubator.apache.org/api/java), [Clojure](https://mxnet.incubator.apache.org/api/clojure), [R](https://mxnet.incubator.apache.org/api/r), [Go](https://github.com/jdeng/gomxnet/), [Javascript](https://github.com/dmlc/mxnet.js/), [Perl](https://mxnet.incubator.apache.org/api/perl), [Matlab](https://github.com/apache/incubator-mxnet/tree/master/matlab), and [Julia](https://mxnet.incubator.apache.org/api/julia)
+* Support for [Python](https://mxnet.apache.org/api/python), [Scala](https://mxnet.apache.org/api/scala), [C++](https://mxnet.apache.org/api/cpp), [Java](https://mxnet.apache.org/api/java), [Clojure](https://mxnet.apache.org/api/clojure), [R](https://mxnet.apache.org/api/r), [Go](https://github.com/jdeng/gomxnet/), [Javascript](https://github.com/dmlc/mxnet.js/), [Perl](https://mxnet.apache.org/api/perl), [Matlab](https://github.com/apache/incubator-mxnet/tree/master/matlab), and [Julia](https://mxnet.apache.org/api/julia)
 * Cloud-friendly and directly compatible with AWS S3, AWS Deep Learning AMI, AWS SageMaker, HDFS, and Azure
 
 License
diff --git a/benchmark/opperf/README.md b/benchmark/opperf/README.md
index ef7747cf4483..241734fdd655 100644
--- a/benchmark/opperf/README.md
+++ b/benchmark/opperf/README.md
@@ -50,7 +50,7 @@ Hence, in this utility, we will build the functionality to allow users and devel
 Provided you have MXNet installed (any version >= 1.5.1), all you need to use opperf utility is to add path to your cloned MXNet repository to the PYTHONPATH.
 
 Note: 
-To install MXNet, refer [Installing MXNet page](https://mxnet.incubator.apache.org/versions/master/install/index.html)
+To install MXNet, refer [Installing MXNet page](https://mxnet.apache.org/versions/master/install/index.html)
 
 ```
 export PYTHONPATH=$PYTHONPATH:/path/to/incubator-mxnet/
diff --git a/ci/docker/install/ubuntu_r.sh b/ci/docker/install/ubuntu_r.sh
index 548b6c00b0eb..b7ddea78f90a 100755
--- a/ci/docker/install/ubuntu_r.sh
+++ b/ci/docker/install/ubuntu_r.sh
@@ -21,7 +21,7 @@
 # the whole docker cache for the image
 
 # Important Maintenance Instructions:
-#  Align changes with installation instructions in /docs/install/ubuntu_setup.md
+#  Align changes with installation instructions in /get_started/ubuntu_setup.md
 #  Align with R install script: /docs/install/install_mxnet_ubuntu_r.sh
 
 set -ex
diff --git a/ci/other/ci_deploy_doc.sh b/ci/other/ci_deploy_doc.sh
index 3d5c85fb794e..42233599f65c 100755
--- a/ci/other/ci_deploy_doc.sh
+++ b/ci/other/ci_deploy_doc.sh
@@ -29,4 +29,4 @@
 set -ex
 
 aws s3 sync --delete . s3://mxnet-ci-doc/$1/$2 \
-    && echo "Doc is hosted at http://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/$1/$2/index.html"
+    && echo "Doc is hosted at https://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/$1/$2/index.html"
diff --git a/contrib/clojure-package/README.md b/contrib/clojure-package/README.md
index d181ced5e957..e0bacbc3d94a 100644
--- a/contrib/clojure-package/README.md
+++ b/contrib/clojure-package/README.md
@@ -187,10 +187,10 @@ If you have previous builds and other unwanted files lying around in the working
 
 #### Building the core library
 
-Detailed instructions for building MXNet core from source can be found [in the MXNet installation documentation](https://mxnet.incubator.apache.org/install/index.html). The relevant sections are:
+Detailed instructions for building MXNet core from source can be found [in the MXNet installation documentation](https://mxnet.apache.org/install/index.html). The relevant sections are:
 
-- For Ubuntu Linux: [CUDA Dependencies](https://mxnet.incubator.apache.org/install/ubuntu_setup.html#cuda-dependencies) and [Building MXNet from Source](https://mxnet.incubator.apache.org/install/ubuntu_setup.html#build-mxnet-from-source)
-- For Mac OSX: [Build the Shared Library](https://mxnet.incubator.apache.org/install/osx_setup.html#build-the-shared-library)
+- For Ubuntu Linux: [CUDA Dependencies](https://mxnet.apache.org/get_started/ubuntu_setup#cuda-dependencies) and [Building MXNet from Source](https://mxnet.apache.org/get_started/ubuntu_setup#build-mxnet-from-source)
+- For Mac OSX: [Build the Shared Library](https://mxnet.apache.org/get_started/osx_setup.html#build-the-shared-library)
 
 In particular, ignore all of the language-interface-specific sections.
 
diff --git a/contrib/clojure-package/examples/pre-trained-models/README.md b/contrib/clojure-package/examples/pre-trained-models/README.md
index e1aefbca0c5b..c6201adb2bbe 100644
--- a/contrib/clojure-package/examples/pre-trained-models/README.md
+++ b/contrib/clojure-package/examples/pre-trained-models/README.md
@@ -18,12 +18,12 @@
 # pre-trained-models
 
 This shows examples of how to use the pretrained models. MXNet comes with a number of pretrained models
-https://mxnet.incubator.apache.org/model_zoo/index.html
+https://mxnet.apache.org/model_zoo/index.html
 
 
 ## Predict Image from pretrained models
 
-From the example on https://mxnet.incubator.apache.org/tutorials/python/predict_image.html
+From the example on https://mxnet.apache.org/tutorials/python/predict_image.html
 
 
 The `predict-image.clj` file loads up the pre-trained resnet-152 model and uses it to predict the classifications from images on the internet
@@ -43,7 +43,7 @@ $ lein predict-image <url_or_path_to_image>
 
 ## Fine Tune from pretrained models
 
-From the finetune example https://mxnet.incubator.apache.org/faq/finetune.html
+From the finetune example https://mxnet.apache.org/faq/finetune.html
 
 The `fine-tune.clj` file loads up the samller resnet-50 model and adds a fine tune layer to reclassify the caltech iamge set
 
diff --git a/contrib/clojure-package/examples/tutorial/README.md b/contrib/clojure-package/examples/tutorial/README.md
index 54f05b36f800..cd33529b49c5 100644
--- a/contrib/clojure-package/examples/tutorial/README.md
+++ b/contrib/clojure-package/examples/tutorial/README.md
@@ -17,6 +17,6 @@
 
 # tutorial
 
-Tutorials are based on the Scala api examples here https://mxnet.incubator.apache.org/api/scala/ndarray.html
+Tutorials are based on the Scala api examples here https://mxnet.apache.org/api/scala/ndarray.html
 
 Start with ndarray then move onto symbol and module
diff --git a/contrib/clojure-package/examples/tutorial/src/tutorial/kvstore.clj b/contrib/clojure-package/examples/tutorial/src/tutorial/kvstore.clj
index f35d4a06922f..b0795f7d256c 100644
--- a/contrib/clojure-package/examples/tutorial/src/tutorial/kvstore.clj
+++ b/contrib/clojure-package/examples/tutorial/src/tutorial/kvstore.clj
@@ -17,7 +17,7 @@
 
 (ns tutorial.kvstore
   "A REPL tutorial of the MXNet Clojure API for KVStore, based on
-  https://mxnet.incubator.apache.org/api/clojure/kvstore.html"
+  https://mxnet.apache.org/api/clojure/kvstore.html"
   (:require [org.apache.clojure-mxnet.kvstore :as kvstore]
             [org.apache.clojure-mxnet.ndarray :as ndarray]
             [org.apache.clojure-mxnet.context :as context]))
diff --git a/contrib/clojure-package/examples/tutorial/src/tutorial/module.clj b/contrib/clojure-package/examples/tutorial/src/tutorial/module.clj
index e19498111022..4923cdcc0684 100644
--- a/contrib/clojure-package/examples/tutorial/src/tutorial/module.clj
+++ b/contrib/clojure-package/examples/tutorial/src/tutorial/module.clj
@@ -17,7 +17,7 @@
 
 (ns tutorial.module
   "A REPL tutorial of the MXNet Clojure API for Module, based on
-  https://mxnet.incubator.apache.org/api/clojure/module.html"
+  https://mxnet.apache.org/api/clojure/module.html"
   (:require [clojure.java.io :as io]
             [clojure.java.shell :refer [sh]]
             [org.apache.clojure-mxnet.eval-metric :as eval-metric]
diff --git a/contrib/clojure-package/examples/tutorial/src/tutorial/ndarray.clj b/contrib/clojure-package/examples/tutorial/src/tutorial/ndarray.clj
index d18bb53daaf1..cf9de98e775c 100644
--- a/contrib/clojure-package/examples/tutorial/src/tutorial/ndarray.clj
+++ b/contrib/clojure-package/examples/tutorial/src/tutorial/ndarray.clj
@@ -17,7 +17,7 @@
 
 (ns tutorial.ndarray
   "A REPL tutorial of the MXNet Clojure API for NDArray, based on
-  https://mxnet.incubator.apache.org/api/clojure/ndarray.html"
+  https://mxnet.apache.org/api/clojure/ndarray.html"
   (:require [org.apache.clojure-mxnet.ndarray :as ndarray]
             [org.apache.clojure-mxnet.context :as context]))
 
diff --git a/contrib/clojure-package/examples/tutorial/src/tutorial/symbol.clj b/contrib/clojure-package/examples/tutorial/src/tutorial/symbol.clj
index e88260069015..73933b4395c5 100644
--- a/contrib/clojure-package/examples/tutorial/src/tutorial/symbol.clj
+++ b/contrib/clojure-package/examples/tutorial/src/tutorial/symbol.clj
@@ -17,7 +17,7 @@
 
 (ns tutorial.symbol
   "A REPL tutorial of the MXNet Clojure Symbolic API, based on
-  https://mxnet.incubator.apache.org/api/clojure/symbol.html"
+  https://mxnet.apache.org/api/clojure/symbol.html"
   (:require [org.apache.clojure-mxnet.executor :as executor]
             [org.apache.clojure-mxnet.ndarray :as ndarray]
             [org.apache.clojure-mxnet.symbol :as sym]
diff --git a/cpp-package/README.md b/cpp-package/README.md
index 165a65eaac6a..05fb506db42b 100644
--- a/cpp-package/README.md
+++ b/cpp-package/README.md
@@ -32,9 +32,9 @@ The cpp-package directory contains the implementation of C++ API. As mentioned a
 	git clone --recursive https://github.com/apache/incubator-mxnet mxnet
 	```
 
-3.  Install the [prerequisites](<https://mxnet.incubator.apache.org/install/build_from_source#prerequisites>), desired [BLAS libraries](<https://mxnet.incubator.apache.org/install/build_from_source#blas-library>) and optional [OpenCV, CUDA, and cuDNN](<https://mxnet.incubator.apache.org/install/build_from_source#optional>) for building MXNet from source.
+3.  Install the [prerequisites](<https://mxnet.apache.org/install/build_from_source#prerequisites>), desired [BLAS libraries](<https://mxnet.apache.org/install/build_from_source#blas-library>) and optional [OpenCV, CUDA, and cuDNN](<https://mxnet.apache.org/install/build_from_source#optional>) for building MXNet from source.
 4.  There is a configuration file for make, [make/config.mk](<https://github.com/apache/incubator-mxnet/blob/master/make/config.mk>) that contains all the compilation options. You can edit this file and set the appropriate options prior to running the **make** command.
-5.  Please refer to  [platform specific build instructions](<https://mxnet.incubator.apache.org/install/build_from_source#build-instructions-by-operating-system>) and available [build configurations](https://mxnet.incubator.apache.org/install/build_from_source#build-configurations) for more details.
+5.  Please refer to  [platform specific build instructions](<https://mxnet.apache.org/install/build_from_source#build-instructions-by-operating-system>) and available [build configurations](https://mxnet.apache.org/install/build_from_source#build-configurations) for more details.
 5.  For enabling the build of C++ Package, set the **USE\_CPP\_PACKAGE = 1** in [make/config.mk](<https://github.com/apache/incubator-mxnet/blob/master/make/config.mk>). Optionally, the compilation flag can also be specified on **make** command line as follows.
 	```
 	make -j USE_CPP_PACKAGE=1
@@ -55,7 +55,7 @@ In order to consume the C++ API please follow the steps below.
 
 ## Tutorial
 
-A basic tutorial can be found at <https://mxnet.incubator.apache.org/tutorials/c++/basics.html>.
+A basic tutorial can be found at <https://mxnet.apache.org/tutorials/c++/basics.html>.
 
 ## Examples
 
diff --git a/docs/README.md b/docs/README.md
index 7993d07f026f..cd78c94b03c4 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -17,8 +17,8 @@
 
 # Building and Updating MXNet Documentation
 
-The website is hosted at http://mxnet.incubator.apache.org/.
-http://mxnet.io redirects to this site and advised to use links with http://mxnet.incubator.apache.org/ instead of http://mxnet.io/.
+The website is hosted at https://mxnet.apache.org/.
+https://mxnet.io redirects to this site and advised to use links with https://mxnet.apache.org/ instead of https://mxnet.io/.
 
 ## Website & Documentation Contributions
 
@@ -36,7 +36,7 @@ If you plan to contribute changes to the documentation or website, please submit
 
 ## Python Docs
 
-MXNet's Python documentation is built with [Sphinx](http://www.sphinx-doc.org) and a variety of plugins including [pandoc](https://pandoc.org/), and [recommonmark](https://github.com/rtfd/recommonmark).
+MXNet's Python documentation is built with [Sphinx](https://www.sphinx-doc.org) and a variety of plugins including [pandoc](https://pandoc.org/), and [recommonmark](https://github.com/rtfd/recommonmark).
 
 More information on the dependencies can be found in the [CI folder's installation scripts](https://github.com/apache/incubator-mxnet/tree/master/ci/docker/install/ubuntu_docs.sh).
 
diff --git a/docs/python_docs/python/scripts/conf.py b/docs/python_docs/python/scripts/conf.py
index d2733046584a..ff4c89bbb18a 100644
--- a/docs/python_docs/python/scripts/conf.py
+++ b/docs/python_docs/python/scripts/conf.py
@@ -31,9 +31,9 @@
 # General information about the project.
 project = u'Apache MXNet'
 author = u'%s developers' % project
-copyright = u'2015-2018, %s' % author
+copyright = u'2015-2019, %s' % author
 github_doc_root = 'https://github.com/apache/incubator-mxnet/tree/master/docs/'
-doc_root = 'http://mxnet.io/'
+doc_root = 'https://mxnet.apache.org/'
 
 # add markdown parser
 source_parsers = {
diff --git a/docs/python_docs/python/tutorials/deploy/export/index.rst b/docs/python_docs/python/tutorials/deploy/export/index.rst
index 2b4a86465c37..138acabf36ca 100644
--- a/docs/python_docs/python/tutorials/deploy/export/index.rst
+++ b/docs/python_docs/python/tutorials/deploy/export/index.rst
@@ -26,9 +26,16 @@ but you also have the option to export most models to the ONNX format.
 
    .. card::
       :title: Export ONNX Models
+      :link: onnx.html
 
+      Export your MXNet model to the Open Neural Exchange Format
+
+   .. card::
+      :title: Save / Load Parameters
+      :link: ../../packages/gluon/blocks/save_load_params.html
+
+      Save and Load your model parameters with MXnet
 
-      Coming Soon!
 
    .. card::
       :title: Export with GluonCV
@@ -39,4 +46,8 @@ but you also have the option to export most models to the ONNX format.
 .. toctree::
    :hidden:
    :maxdepth: 1
+   :glob:
 
+   *
+   Export Gluon CV Models <https://gluon-cv.mxnet.io/build/examples_deployment/export_network.html>
+   Save / Load Parameters <https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html>
diff --git a/docs/python_docs/python/tutorials/deploy/export/onnx.md b/docs/python_docs/python/tutorials/deploy/export/onnx.md
new file mode 100644
index 000000000000..0b325cb0ba9a
--- /dev/null
+++ b/docs/python_docs/python/tutorials/deploy/export/onnx.md
@@ -0,0 +1,150 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Exporting to ONNX format
+
+[Open Neural Network Exchange (ONNX)](https://github.com/onnx/onnx) provides an open source format for AI models. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types.
+
+In this tutorial, we will show how you can save MXNet models to the ONNX format.
+
+MXNet-ONNX operators coverage and features are updated regularly. Visit the [ONNX operator coverage](https://cwiki.apache.org/confluence/display/MXNET/ONNX+Operator+Coverage) page for the latest information.
+
+In this tutorial, we will learn how to use MXNet to ONNX exporter on pre-trained models.
+
+## Prerequisites
+
+To run the tutorial you will need to have installed the following python modules:
+- [MXNet >= 1.3.0](http://mxnet.apache.org/install/index.html)
+- [onnx]( https://github.com/onnx/onnx#installation) v1.2.1 (follow the install guide)
+
+*Note:* MXNet-ONNX importer and exporter follows version 7 of ONNX operator set which comes with ONNX v1.2.1.
+
+
+```python
+import mxnet as mx
+import numpy as np
+from mxnet.contrib import onnx as onnx_mxnet
+import logging
+logging.basicConfig(level=logging.INFO)
+```
+
+## Downloading a model from the MXNet model zoo
+
+We download the pre-trained ResNet-18 [ImageNet](http://www.image-net.org/) model from the [MXNet Model Zoo](http://data.mxnet.io/models/imagenet/).
+We will also download synset file to match labels.
+
+```python
+# Download pre-trained resnet model - json and params by running following code.
+path='http://data.mxnet.io/models/imagenet/'
+[mx.test_utils.download(path+'resnet/18-layers/resnet-18-0000.params'),
+ mx.test_utils.download(path+'resnet/18-layers/resnet-18-symbol.json'),
+ mx.test_utils.download(path+'synset.txt')]
+```
+
+Now, we have downloaded ResNet-18 symbol, params and synset file on the disk.
+
+## MXNet to ONNX exporter API
+
+Let us describe the MXNet's `export_model` API. 
+
+```python
+help(onnx_mxnet.export_model)
+```
+
+Output:
+
+```text
+Help on function export_model in module mxnet.contrib.onnx.mx2onnx.export_model:
+
+export_model(sym, params, input_shape, input_type=<type 'numpy.float32'>, onnx_file_path=u'model.onnx', verbose=False)
+    Exports the MXNet model file, passed as a parameter, into ONNX model.
+    Accepts both symbol,parameter objects as well as json and params filepaths as input.
+    Operator support and coverage - https://cwiki.apache.org/confluence/display/MXNET/MXNet-ONNX+Integration
+    
+    Parameters
+    ----------
+    sym : str or symbol object
+        Path to the json file or Symbol object
+    params : str or symbol object
+        Path to the params file or params dictionary. (Including both arg_params and aux_params)
+    input_shape : List of tuple
+        Input shape of the model e.g [(1,3,224,224)]
+    input_type : data type
+        Input data type e.g. np.float32
+    onnx_file_path : str
+        Path where to save the generated onnx file
+    verbose : Boolean
+        If true will print logs of the model conversion
+    
+    Returns
+    -------
+    onnx_file_path : str
+        Onnx file path
+```
+
+`export_model` API can accept the MXNet model in one of the following two ways.
+
+1. MXNet sym, params objects:
+    * This is useful if we are training a model. At the end of training, we just need to invoke the `export_model` function and provide sym and params objects as inputs with other attributes to save the model in ONNX format.
+2. MXNet's exported json and params files:
+    * This is useful if we have pre-trained models and we want to convert them to ONNX format.
+
+Since we have downloaded pre-trained model files, we will use the `export_model` API by passing the path for symbol and params files.
+
+## How to use MXNet to ONNX exporter API
+
+We will use the downloaded pre-trained model files (sym, params) and define input variables.
+
+```python
+# Downloaded input symbol and params files
+sym = './resnet-18-symbol.json'
+params = './resnet-18-0000.params'
+
+# Standard Imagenet input - 3 channels, 224*224
+input_shape = (1,3,224,224)
+
+# Path of the output file
+onnx_file = './mxnet_exported_resnet50.onnx'
+```
+
+We have defined the input parameters required for the `export_model` API. Now, we are ready to covert the MXNet model into ONNX format.
+
+```python
+# Invoke export model API. It returns path of the converted onnx model
+converted_model_path = onnx_mxnet.export_model(sym, params, [input_shape], np.float32, onnx_file)
+```
+
+This API returns path of the converted model which you can later use to import the model into other frameworks.
+
+## Check validity of ONNX model
+
+Now we can check validity of the converted ONNX model by using ONNX checker tool. The tool will validate the model by checking if the content contains valid protobuf:
+
+```python
+from onnx import checker
+import onnx
+
+# Load onnx model
+model_proto = onnx.load_model(converted_model_path)
+
+# Check if converted ONNX protobuf is valid
+checker.check_graph(model_proto.graph)
+```
+
+If the converted protobuf format doesn't qualify to ONNX proto specifications, the checker will throw errors, but in this case it successfully passes. 
+
+This method confirms exported model protobuf is valid. Now, the model is ready to be imported in other frameworks for inference!
\ No newline at end of file
diff --git a/docs/python_docs/python/tutorials/deploy/index.rst b/docs/python_docs/python/tutorials/deploy/index.rst
index 9c61fc8a30b1..e0ff0db75f17 100644
--- a/docs/python_docs/python/tutorials/deploy/index.rst
+++ b/docs/python_docs/python/tutorials/deploy/index.rst
@@ -94,7 +94,7 @@ The following tutorials will show you how to use MXNet on AWS.
 
    .. card::
       :title: Training with Data from S3
-      :link: https://mxnet.incubator.apache.org/api/faq/s3_integration
+      :link: https://mxnet.apache.org/api/faq/s3_integration
 
       How to train with data from Amazon S3 buckets.
 
@@ -105,14 +105,14 @@ Security
 
    .. card::
       :title: Securing MXNet
-      :link: https://mxnet.incubator.apache.org/api/faq/security
+      :link: https://mxnet.apache.org/api/faq/security
 
       Best practices and deployment considerations.
 
 
 .. toctree::
    :hidden:
-   :maxdepth: 0
+   :maxdepth: 1
 
    export/index
    inference/index
diff --git a/docs/python_docs/python/tutorials/deploy/inference/index.rst b/docs/python_docs/python/tutorials/deploy/inference/index.rst
index 84facaedf1fe..f8a5c4f677b0 100644
--- a/docs/python_docs/python/tutorials/deploy/inference/index.rst
+++ b/docs/python_docs/python/tutorials/deploy/inference/index.rst
@@ -33,25 +33,35 @@ The following tutorials will help you learn how to deploy MXNet models for infer
       :link: https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html
 
       How to use quantized GluonCV models for inference on Intel Xeon Processors to gain higher performance.
-   ..
-      PLACEHOLDER
 
-      .. card::
-         :title: Scala and Java
-         :link: scala.html
 
-         How to use MXNet models in a Scala or Java environment.
+The following tutorials will help you learn how to deploy MXNet models for inference applications.
+
+.. container:: cards
+
+   .. card::
+      :title: Scala and Java
+      :link: scala.html
+
+      How to use MXNet models in a Scala or Java environment.
+
+   .. card::
+      :title: C++
+      :link: cpp.html
+
+      How to use MXNet models in a C++ environment.
 
-      .. card::
-         :title: C++
-         :link: cpp.html
 
-         How to use MXNet models in a C++ environment.
-      PLACEHOLDER
-   ..
+   .. card::
+      :title: Raspberry Pi
+      :link: wine_detector.html
+
+      Example of running a wine detector on a raspberry pi.
+
 
 .. toctree::
    :hidden:
-   :maxdepth: 0
-
+   :maxdepth: 1
    :glob:
+
+   *
diff --git a/docs/python_docs/python/tutorials/deploy/inference/wine_detector.md b/docs/python_docs/python/tutorials/deploy/inference/wine_detector.md
new file mode 100644
index 000000000000..b28d709c2f0d
--- /dev/null
+++ b/docs/python_docs/python/tutorials/deploy/inference/wine_detector.md
@@ -0,0 +1,405 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Real-time Object Detection with MXNet On The Raspberry Pi  
+
+This tutorial shows developers who work with the Raspberry Pi or similar embedded ARM-based devices how to compile MXNet for those devices and run a pretrained deep network model. It also shows how to use AWS IoT to manage and monitor MXNet models running on your devices.
+
+## What's In This Tutorial?
+
+This tutorial shows how to:
+
+1. Use MXNet to set up a real-time object classifier on a Raspberry Pi 3 device.
+
+2. Connect the local Raspberry Pi model to the AWS Cloud with AWS IoT to get real-time updates from the device.
+
+### Who's This Tutorial For?
+
+It assumes that you are familiar with the Raspbian operating system and the [Raspberry Pi ecosystem](https://www.raspberrypi.org/) and are somewhat familiar with machine learning, MXNet, and [AWS IoT](https://aws.amazon.com/iot/). All code is written in Python 2.7.
+
+### How to Use This Tutorial
+
+To follow this tutorial, you must set up your Pi as instructed (preferably from a fresh Raspbian install), and then create the files and run the bash commands described below. All instructions described are can be executed on the Raspberry Pi directly or via SSH.
+
+You will accomplish the following:
+
+- Build and Install MXNet with Python bindings on your Raspbian Based Raspberry Pi
+- Fetch and run a pre-trained MXNet model on your Pi
+- Create a real-time video analysis application for the Pi
+- Connect the application to the AWS IoT service
+
+## Prerequisites
+
+To complete this tutorial, you need:
+
+* Raspbian Wheezy or later, which can be downloaded [here](https://www.raspberrypi.org/downloads/raspbian/), loaded onto a 8GB+ micro SD card (with at least 4GB+ free)
+* A [Raspberry Pi 3](https://www.raspberrypi.org/blog/raspberry-pi-3-on-sale/) or equivalent Raspberry Pi with 1GB+ of RAM
+* A [Raspberry Pi Camera Module](https://www.raspberrypi.org/products/camera-module-v2/) [activated and running with the corresponding Python module](http://www.pyimagesearch.com/2015/02/23/install-opencv-and-python-on-your-raspberry-pi-2-and-b/) (for the real-time video analysis with the deep network model)
+* An AWS account With AWS IoT enabled and the [AWS IoT Python SDK](https://github.com/aws/aws-iot-device-sdk-python) (for remote, real-time managing and monitoring of the model running on the Pi)
+* The [cv2 Python library](http://www.pyimagesearch.com/2015/02/23/install-opencv-and-python-on-your-raspberry-pi-2-and-b/) for the Pi
+
+## Building MXNet for The Pi
+
+The first step is to get MXNet with the Python bindings running on your Raspberry Pi 3. There is a tutorial for that provided [here](https://mxnet.io/get_started). The linked tutorial walks you through downloading the dependencies, and building the full MXNet library for the Pi with the ARM specific compile flags. Be sure to build the library with open CV as we will be using a model that requires it to process images. Then you will register the Python bindings to MXNet. After this is done you should test that your installation works by opening a python REPL on your Pi and typing the following commands:
+
+
+```bash
+python
+>>> import mxnet as mx
+```
+
+*Note: If you are getting memory allocation failed errors at this point (or at any point in this tutorial) it is likely because the full MXNet library takes up a large amount of RAM when loaded. You might want to kill the GUI and other processes that are occupying memory.*
+
+
+## Running A Pre-Trained Inception Model on The Pi
+
+We are now ready to load a pre-trained model and run inference on the Pi. We will be using a simple object recognition model trained on the ImageNet data set. The model is called batch normalized Inception network (or Inception_BN for short) and it is found in the MXNet model zoo.
+
+### Getting the Model
+
+The first step is to download, unzip, and set up the pre-trained deep network model files that we will be using to classify images. To do this run the following commands in your home directory:
+
+```bash
+curl --header 'Host: data.mxnet.io' --header 'User-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10.11; rv:45.0) Gecko/20100101 Firefox/45.0' --header 'Accept: text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8' --header 'Accept-Language: en-US,en;q=0.5' --header 'Referer: http://data.mxnet.io/models/imagenet/' --header 'Connection: keep-alive' 'http://data.mxnet.io/models/imagenet/inception-bn.tar.gz' -o 'inception-bn.tar.gz' -L
+
+tar -xvzf inception-bn.tar.gz
+
+mv Inception_BN-0039.params Inception_BN-0000.params
+```
+
+### Running the Model
+
+The next step is to create a python script to load the model, and run inference on local image files. To do this create a new file in your home directory called inception_predict.py and add the following code to it:
+
+
+```python
+# inception_predict.py
+
+import mxnet as mx
+import numpy as np
+import time
+import cv2, os, urllib
+from collections import namedtuple
+Batch = namedtuple('Batch', ['data'])
+
+# Load the symbols for the networks
+with open('synset.txt', 'r') as f:
+    synsets = [l.rstrip() for l in f]
+
+# Load the network parameters
+sym, arg_params, aux_params = mx.model.load_checkpoint('Inception-BN', 126)
+
+
+# Load the network into an MXNet module and bind the corresponding parameters
+mod = mx.mod.Module(symbol=sym, context=mx.cpu())
+mod.bind(for_training=False, data_shapes=[('data', (1,3,224,224))])
+mod.set_params(arg_params, aux_params)
+
+'''
+Function to predict objects by giving the model a pointer to an image file and running a forward pass through the model.
+
+inputs:
+filename = jpeg file of image to classify objects in
+mod = the module object representing the loaded model
+synsets = the list of symbols representing the model
+N = Optional parameter denoting how many predictions to return (default is top 5)
+
+outputs:
+python list of top N predicted objects and corresponding probabilities
+'''
+def predict(filename, mod, synsets, N=5):
+    tic = time.time()
+    img = cv2.cvtColor(cv2.imread(filename), cv2.COLOR_BGR2RGB)
+    if img is None:
+        return None
+    img = cv2.resize(img, (224, 224))
+    img = np.swapaxes(img, 0, 2)
+    img = np.swapaxes(img, 1, 2)
+    img = img[np.newaxis, :]
+    print "pre-processed image in "+str(time.time()-tic)
+
+    toc = time.time()
+    mod.forward(Batch([mx.nd.array(img)]))
+    prob = mod.get_outputs()[0].asnumpy()
+    prob = np.squeeze(prob)
+    print "forward pass in "+str(time.time()-toc)
+
+
+    topN = []
+    a = np.argsort(prob)[::-1]
+    for i in a[0:N]:
+        print('probability=%f, class=%s' %(prob[i], synsets[i]))
+        topN.append((prob[i], synsets[i]))
+    return topN
+
+
+# Code to download an image from the internet and run a prediction on it
+def predict_from_url(url, N=5):
+    filename = url.split("/")[-1]
+    urllib.urlretrieve(url, filename)
+    img = cv2.imread(filename)
+    if img is None:
+        print "Failed to download"
+    else:
+        return predict(filename, mod, synsets, N)
+
+# Code to predict on a local file
+def predict_from_local_file(filename, N=5):
+    return predict(filename, mod, synsets, N)
+```
+
+Now that we have defined inception_predict.py we can test that the model is running correctly. Open a Python REPL in your home directory and enter the following:
+
+```bash
+python
+>>> from inception_predict import *
+>>> predict_from_url("https://i.imgur.com/HzafyBA.jpg")
+```
+
+This should give a reasonable prediction for the fluffy cow in this [image](http://imgur.com/HzafyBA).
+
+```
+pre-processed image in 0.20366191864
+forward pass in 63.2164611816
+probability=0.718524, class=n02403003 ox
+probability=0.176381, class=n02389026 sorrel
+probability=0.095558, class=n03868242 oxcart
+probability=0.002765, class=n02408429 water buffalo, water ox, Asiatic buffalo, Bubalus bubalis
+probability=0.001262, class=n03935335 piggy bank, penny bank
+[(0.71852392, 'n02403003 ox'), (0.17638102, 'n02389026 sorrel'), (0.09555836, 'n03868242 oxcart'),
+(0.0027645244, 'n02408429 water buffalo, water ox, Asiatic buffalo, Bubalus bubalis'),
+(0.0012616422, 'n03935335 piggy bank, penny bank')]
+```
+
+
+## Running an Inception on Real-Time Video From PiCamera
+
+We can now move on to using this network for object detection in real-time video from the PiCamera.
+
+Doing this requires sending the images that the camera is capturing to the prediction code that we created in the previous step. To do this, create a new file in your home directory called camera_test.py and add the following code to it:
+
+
+```python
+# camera_test.py
+
+import picamera
+import inception_predict
+
+# Create camera interface
+camera = picamera.PiCamera()
+while True:
+    # Take the jpg image from camera
+    print "Capturing"
+    filename = '/home/pi/cap.jpg'
+    # Show quick preview of what's being captured
+    camera.start_preview()
+    camera.capture(filename)
+    camera.stop_preview()
+
+    # Run inception prediction on image
+    print "Predicting"
+    topn = inception_predict.predict_from_local_file(filename, N=5)
+
+    # Print the top N most likely objects in image (default set to 5, change this in the function call above)
+    print topn
+```
+
+You can then run this file by entering the following command:
+
+```bash
+python camera_test.py
+```
+
+If camera_test.py is working you should see a preview every few seconds of the image that is being captured and fed to the model, as well as predicted classes for objects in the image being written to the terminal.
+
+Try pointing the PiCamera at a few different objects and see what predictions the network comes out with.
+
+## Connecting Our Model To The AWS Cloud
+
+We can now move on to adding the code to send the predictions that this real-time model is making locally to the AWS cloud if certain conditions are met.
+
+The first step is to set up an AWS account if you don't have one yet. Then go to the [AWS IoT dashboard](https://us-west-2.console.aws.amazon.com/iotv2/home?region=us-west-2#/thinghub) and register a new device.
+
+After the device is registered, download and copy the corresponding rootCA, Certificate, and Private key to your home directory. Note the unique endpoint of your device shadow on the AWS IoT Dashboard.
+
+We will now build an application, based off the code in camera_test.py, which will send a message to the cloud whenever a wine bottle is detected in a frame by the PiCamera.
+
+To do this create a new file in your home directory called wine_alerter.py and add the following code to it:
+
+
+```python
+# wine_alerter.py
+
+import AWSIoTPythonSDK
+from AWSIoTPythonSDK.MQTTLib import AWSIoTMQTTClient
+import sys
+import logging
+import time
+import getopt
+import picamera
+import inception_predict
+
+# Custom MQTT message callback
+def customCallback(client, userdata, message):
+    print("Received a new message: ")
+    print(message.payload)
+    print("from topic: ")
+    print(message.topic)
+    print("--------------\n\n")
+
+# Usage
+usageInfo = """Usage:
+
+Use certificate based mutual authentication:
+python wine_alerter.py -e <endpoint> -r <rootCAFilePath> -c <certFilePath> -k <privateKeyFilePath>
+
+Use MQTT over WebSocket:
+python wine_alerter.py -e <endpoint> -r <rootCAFilePath> -w
+
+Type "python wine_alerter.py -h" for available options.
+"""
+
+# Help info
+helpInfo = """-e, --endpoint
+    Your AWS IoT custom endpoint
+-r, --rootCA
+    Root CA file path
+-c, --cert
+    Certificate file path
+-k, --key
+    Private key file path
+-w, --websocket
+    Use MQTT over WebSocket
+-h, --help
+    Help information
+"""
+
+# Read in command-line parameters
+useWebsocket = False
+host = ""
+rootCAPath = ""
+certificatePath = ""
+privateKeyPath = ""
+try:
+    opts, args = getopt.getopt(sys.argv[1:], "hwe:k:c:r:", ["help", "endpoint=", "key=","cert=","rootCA=", "websocket"])
+    if len(opts) == 0:
+        raise getopt.GetoptError("No input parameters!")
+    for opt, arg in opts:
+        if opt in ("-h", "--help"):
+            print(helpInfo)
+            exit(0)
+        if opt in ("-e", "--endpoint"):
+            host = arg
+        if opt in ("-r", "--rootCA"):
+            rootCAPath = arg
+        if opt in ("-c", "--cert"):
+            certificatePath = arg
+        if opt in ("-k", "--key"):
+            privateKeyPath = arg
+        if opt in ("-w", "--websocket"):
+            useWebsocket = True
+except getopt.GetoptError:
+    print(usageInfo)
+    exit(1)
+
+# Missing configuration notification
+missingConfiguration = False
+if not host:
+    print("Missing '-e' or '--endpoint'")
+    missingConfiguration = True
+if not rootCAPath:
+    print("Missing '-r' or '--rootCA'")
+    missingConfiguration = True
+if not useWebsocket:
+    if not certificatePath:
+        print("Missing '-c' or '--cert'")
+        missingConfiguration = True
+    if not privateKeyPath:
+        print("Missing '-k' or '--key'")
+        missingConfiguration = True
+if missingConfiguration:
+    exit(2)
+
+
+# Configure logging
+logger = logging.getLogger("AWSIoTPythonSDK.core")
+logger.setLevel(logging.DEBUG)
+streamHandler = logging.StreamHandler()
+formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+streamHandler.setFormatter(formatter)
+logger.addHandler(streamHandler)
+
+
+# Init AWSIoTMQTTClient For Publish/Subscribe Communication With Server
+myAWSIoTMQTTClient = None
+if useWebsocket:
+    myAWSIoTMQTTClient = AWSIoTMQTTClient("basicPubSub", useWebsocket=True)
+    myAWSIoTMQTTClient.configureEndpoint(host, 443)
+    myAWSIoTMQTTClient.configureCredentials(rootCAPath)
+else:
+    myAWSIoTMQTTClient = AWSIoTMQTTClient("basicPubSub")
+    myAWSIoTMQTTClient.configureEndpoint(host, 8883)
+    myAWSIoTMQTTClient.configureCredentials(rootCAPath, privateKeyPath, certificatePath)
+
+
+# AWSIoTMQTTClient connection configuration
+myAWSIoTMQTTClient.configureAutoReconnectBackoffTime(1, 32, 20)
+myAWSIoTMQTTClient.configureOfflinePublishQueueing(-1)  # Infinite offline Publish queueing
+myAWSIoTMQTTClient.configureDrainingFrequency(2)  # Draining: 2 Hz
+myAWSIoTMQTTClient.configureConnectDisconnectTimeout(10)  # 10 sec
+myAWSIoTMQTTClient.configureMQTTOperationTimeout(5)  # 5 sec
+
+
+# Connect and subscribe to AWS IoT
+myAWSIoTMQTTClient.connect()
+myAWSIoTMQTTClient.subscribe("sdk/test/Python", 1, customCallback)
+time.sleep(2)
+
+
+# Start the Camera and tell the Server we are alive
+print "Running camera"
+myAWSIoTMQTTClient.publish("sdk/test/Python", "New Message: Starting Camera", 0)
+camera = picamera.PiCamera()
+
+# Capture forever (this is a modified version of camera_test.py)
+while True:
+    filename = '/home/pi/cap.jpg'
+    camera.start_preview()
+    camera.capture(filename)
+    camera.stop_preview()
+    topn = inception_predict.predict_from_local_file(filename, N=5)
+
+    # Check if either of the top two predictions are wine related and publish a message if it is
+    # you can change 'wine' here to anything you want to alert the server about detecting
+    if 'wine' in topn[0][1] or 'wine' in topn[1][1]:
+        myAWSIoTMQTTClient.publish("sdk/test/Python", "New Message: WINE DETECTED!", 0)
+```
+
+You can then run this file by entering the following command
+
+```bash
+python wine_alerter.py -e <endpointURL> -r <rootCAFilePath> -c <certFilePath> -k <privateKeyFilePath>
+```
+
+If this is working you should see the same kind of image preview you did with camera_test.py every few seconds, however the console will only print a message now when a wine bottle is detected in the shot (you can edit the bottom lines in the wine_alerter.py code to make this alert for any object label from the [ImageNet-11k dataset](http://image-net.org/index) that you specify).
+
+You can open up the activity tab for the thing that you registered on the AWS IoT Dashboard and see the corresponding messages pushed to the server whenever a wine bottle is detected in a camera shot. Even if network connectivity periodically fails, AWS IoT will push updates out to the server when possible, allowing this system to reliably let you know when there is wine around.
+
+## Summary
+You now have a Raspberry Pi running a pre-trained MXNet model fully locally. This model is also linked to the cloud via AWS IoT and can reliably alert AWS whenever it sees a wine bottle.
+
+You can now extend this demo to create more interesting applications, such as using AWS IoT to push model updates to your Pi, loading different pre-trained models from the MXNet Model Zoo onto the Pi, or even training full end-to-end models on the Pi.
diff --git a/docs/python_docs/python/tutorials/deploy/run-on-aws/index.rst b/docs/python_docs/python/tutorials/deploy/run-on-aws/index.rst
index c22b7b68d189..3627f03a830c 100644
--- a/docs/python_docs/python/tutorials/deploy/run-on-aws/index.rst
+++ b/docs/python_docs/python/tutorials/deploy/run-on-aws/index.rst
@@ -40,15 +40,9 @@ The following tutorials will help you learn how to deploy MXNet on various AWS p
 
       How to run MXNet in the cloud.
 
-   .. card::
-      :title: MXNet on the Cloud
-      :link: cloud.html
-
-      How to run MXNet in the cloud.
-
    .. card::
       :title: Training with Data from S3
-      :link: https://mxnet.incubator.apache.org/versions/master/faq/s3_integration.html
+      :link: https://mxnet.apache.org/versions/master/faq/s3_integration.html
 
       How to train with data from Amazon S3 buckets.
 
diff --git a/docs/python_docs/python/tutorials/extend/custom_layer.md b/docs/python_docs/python/tutorials/extend/custom_layer.md
index 875961acc226..6002a7812ec7 100644
--- a/docs/python_docs/python/tutorials/extend/custom_layer.md
+++ b/docs/python_docs/python/tutorials/extend/custom_layer.md
@@ -16,9 +16,9 @@
 <!--- under the License. -->
 
 
-# How to write a custom layer in Apache MxNet Gluon API
+# Custom Layers
 
-While Gluon API for Apache MxNet comes with [a decent number of pre-defined layers](https://mxnet.incubator.apache.org/api/python/gluon/nn.html), at some point one may find that a new layer is needed. Adding a new layer in Gluon API is straightforward, yet there are a few things that one needs to keep in mind.
+While Gluon API for Apache MxNet comes with [a decent number of pre-defined layers](https://mxnet.apache.org/api/python/gluon/nn.html), at some point one may find that a new layer is needed. Adding a new layer in Gluon API is straightforward, yet there are a few things that one needs to keep in mind.
 
 In this article, I will cover how to create a new layer from scratch, how to use it, what are possible pitfalls and how to avoid them.
 
@@ -55,15 +55,15 @@ The rest of methods of the `Block` class are already implemented, and majority o
 
 ## Hybridization and the difference between Block and HybridBlock
 
-Looking into implementation of [existing layers](https://mxnet.incubator.apache.org/api/python/gluon/nn.html), one may find that more often a block inherits from a [HybridBlock](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/block.py#L428), instead of directly inheriting from `Block`.
+Looking into implementation of [existing layers](https://mxnet.apache.org/api/python/gluon/nn.html), one may find that more often a block inherits from a [HybridBlock](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/block.py#L428), instead of directly inheriting from `Block`.
 
-The reason for that is that `HybridBlock` allows to write custom layers that can be used in imperative programming as well as in symbolic programming. It is convinient to support both ways, because the imperative programming eases the debugging of the code and the symbolic one provides faster execution speed. You can learn more about the difference between symbolic vs. imperative programming from [this article](https://mxnet.incubator.apache.org/architecture/program_model.html).
+The reason for that is that `HybridBlock` allows to write custom layers that can be used in imperative programming as well as in symbolic programming. It is convinient to support both ways, because the imperative programming eases the debugging of the code and the symbolic one provides faster execution speed. You can learn more about the difference between symbolic vs. imperative programming from [this article](https://mxnet.apache.org/architecture/program_model.html).
 
 Hybridization is a process that Apache MxNet uses to create a symbolic graph of a forward computation. This allows to increase computation performance by optimizing the computational symbolic graph. Once the symbolic graph is created, Apache MxNet caches and reuses it for subsequent computations.
 
-To simplify support of both imperative and symbolic programming, Apache MxNet introduce the `HybridBlock` class. Compare to the `Block` class, `HybridBlock` already has its [forward()](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.HybridBlock.forward) method implemented, but it defines a [hybrid_forward()](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.HybridBlock.hybrid_forward) method that needs to be implemented.
+To simplify support of both imperative and symbolic programming, Apache MxNet introduce the `HybridBlock` class. Compare to the `Block` class, `HybridBlock` already has its [forward()](https://mxnet.apache.org/api/python/gluon/gluon.html#mxnet.gluon.HybridBlock.forward) method implemented, but it defines a [hybrid_forward()](https://mxnet.apache.org/api/python/gluon/gluon.html#mxnet.gluon.HybridBlock.hybrid_forward) method that needs to be implemented.
 
-The main difference between `forward()` and `hybrid_forward()` is an `F` argument. This argument sometimes is refered as a `backend` in the Apache MxNet community. Depending on if hybridization has been done or not, `F` can refer either to [mxnet.ndarray API](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html) or [mxnet.symbol API](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html). The former is used for imperative programming, and the latter for symbolic programming. 
+The main difference between `forward()` and `hybrid_forward()` is an `F` argument. This argument sometimes is refered as a `backend` in the Apache MxNet community. Depending on if hybridization has been done or not, `F` can refer either to [mxnet.ndarray API](https://mxnet.apache.org/api/python/ndarray/ndarray.html) or [mxnet.symbol API](https://mxnet.apache.org/api/python/symbol/symbol.html). The former is used for imperative programming, and the latter for symbolic programming. 
 
 To support hybridization, it is important to use only methods avaible directly from `F` parameter. Usually, there are equivalent methods in both APIs, but sometimes there are mismatches or small variations. For example, by default, subtraction and division of NDArrays support broadcasting, while in Symbol API broadcasting is supported in a separate operators. 
 
@@ -97,7 +97,7 @@ Output:
 
 As a rule of thumb, one should always implement custom layers by inheriting from `HybridBlock`. This allows to have more flexibility, and doesn't affect execution speed once hybridization is done. 
 
-Unfortunately, at the moment of writing this tutorial, NLP related layers such as [RNN](https://mxnet.incubator.apache.org/api/python/gluon/rnn.html#mxnet.gluon.rnn.RNN), [GRU](https://mxnet.incubator.apache.org/api/python/gluon/rnn.html#mxnet.gluon.rnn.GRU) and [LSTM](https://mxnet.incubator.apache.org/api/python/gluon/rnn.html#mxnet.gluon.rnn.LSTM) are directly inhereting from the `Block` class via common `_RNNLayer` class. That means that networks with such layers cannot be hybridized. But this might change in the future, so stay tuned.
+Unfortunately, at the moment of writing this tutorial, NLP related layers such as [RNN](https://mxnet.apache.org/api/python/gluon/rnn.html#mxnet.gluon.rnn.RNN), [GRU](https://mxnet.apache.org/api/python/gluon/rnn.html#mxnet.gluon.rnn.GRU) and [LSTM](https://mxnet.apache.org/api/python/gluon/rnn.html#mxnet.gluon.rnn.LSTM) are directly inhereting from the `Block` class via common `_RNNLayer` class. That means that networks with such layers cannot be hybridized. But this might change in the future, so stay tuned.
 
 It is important to notice that hybridization has nothing to do with computation on GPU. One can train both hybridized and non-hybridized networks on both CPU and GPU, though hybridized networks would work faster. Though, it is hard to say in advance how much faster it is going to be.
 
@@ -105,7 +105,7 @@ It is important to notice that hybridization has nothing to do with computation
 
 While it is possible, custom layers are rarely used separately. Most often they are used with predefined layers to create a neural network. Output of one layer is used as an input of another layer.
 
-Depending on which class you used as a base one, you can use either [Sequential](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.nn.Sequential) or [HybridSequential](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.nn.HybridSequential) container to form a sequential neural network. By adding layers one by one, one adds dependencies of one layer's input from another layer's output. It is worth noting, that both `Sequential` and `HybridSequential` containers inherit from `Block` and `HybridBlock` respectively. 
+Depending on which class you used as a base one, you can use either [Sequential](https://mxnet.apache.org/api/python/gluon/gluon.html#mxnet.gluon.nn.Sequential) or [HybridSequential](https://mxnet.apache.org/api/python/gluon/gluon.html#mxnet.gluon.nn.HybridSequential) container to form a sequential neural network. By adding layers one by one, one adds dependencies of one layer's input from another layer's output. It is worth noting, that both `Sequential` and `HybridSequential` containers inherit from `Block` and `HybridBlock` respectively. 
 
 Below is an example of how to create a simple neural network with a custom layer. In this example, `NormalizationHybridLayer` gets as an input the output from `Dense(5)` layer and pass its output as an input to `Dense(1)` layer.
 
@@ -141,7 +141,7 @@ Output:
 
 Usually, a layer has a set of associated parameters, sometimes also referred as weights. This is an internal state of a layer. Most often, these parameters are the ones, that we want to learn during backpropogation step, but sometimes these parameters might be just constants we want to use during forward pass.
 
-All parameters of a block are stored and accessed via [ParameterDict](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/parameter.py#L508) class. This class helps with initialization, updating, saving and loading of the parameters. Each layer can have multiple set of parameters, and all of them can be stored in a single instance of the `ParameterDict` class. On a block level, the instance of the `ParameterDict` class is accessible via `self.params` field, and outside of a block one can access all parameters of the network via [collect_params()](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.Block.collect_params) method called on a `container`. `ParameterDict` uses [Parameter](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.Parameter) class to represent parameters inside of Apache MxNet neural network. If parameter doesn't exist, trying to get a parameter via `self.params` will create it automatically.
+All parameters of a block are stored and accessed via [ParameterDict](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/parameter.py#L508) class. This class helps with initialization, updating, saving and loading of the parameters. Each layer can have multiple set of parameters, and all of them can be stored in a single instance of the `ParameterDict` class. On a block level, the instance of the `ParameterDict` class is accessible via `self.params` field, and outside of a block one can access all parameters of the network via [collect_params()](https://mxnet.apache.org/api/python/gluon/gluon.html#mxnet.gluon.Block.collect_params) method called on a `container`. `ParameterDict` uses [Parameter](https://mxnet.apache.org/api/python/gluon/gluon.html#mxnet.gluon.Parameter) class to represent parameters inside of Apache MxNet neural network. If parameter doesn't exist, trying to get a parameter via `self.params` will create it automatically.
 
 
 ```python
diff --git a/docs/python_docs/python/tutorials/extend/customop.md b/docs/python_docs/python/tutorials/extend/customop.md
index 96551ecf4fb3..cc9cff598ee9 100644
--- a/docs/python_docs/python/tutorials/extend/customop.md
+++ b/docs/python_docs/python/tutorials/extend/customop.md
@@ -16,7 +16,7 @@
 <!--- under the License. -->
 
 
-# Creating custom operators with numpy
+# Custom Numpy Operators
 
 In this tutorial, we will learn how to build custom operators with numpy in python. We will go through two examples:
 - Custom operator without any `Parameter`s
diff --git a/docs/python_docs/python/tutorials/extend/index.rst b/docs/python_docs/python/tutorials/extend/index.rst
index 430cd3fdc2fb..8cca07f48141 100644
--- a/docs/python_docs/python/tutorials/extend/index.rst
+++ b/docs/python_docs/python/tutorials/extend/index.rst
@@ -15,8 +15,8 @@
    specific language governing permissions and limitations
    under the License.
 
-Customization
-=============
+Extend
+======
 
 The following tutorials will help you learn how to customize MXNet.
 
@@ -35,14 +35,14 @@ The following tutorials will help you learn how to customize MXNet.
       How to use Numpy to create custom MXNet operators.
 
    .. card::
-      :title: New Layer Creation
-      :link: https://mxnet.incubator.apache.org/api/faq/new_op
+      :title: New Operator Creation
+      :link: https://mxnet.apache.org/api/faq/new_op
 
       How to create new MXNet operators using CustomOp (Python) or NNVM (C++).
 
    .. card::
       :title: A Beginner’s Guide to Implementing Operators in MXNet Backend
-      :link: https://mxnet.incubator.apache.org/api/faq/add_op_in_backend
+      :link: https://mxnet.apache.org/api/faq/add_op_in_backend
 
       How to create new MXNet operators in MXNet's backend using C++.
       An example custom quadratic function op.
@@ -53,3 +53,5 @@ The following tutorials will help you learn how to customize MXNet.
    :glob:
 
    *
+   New Operator Creation <https://mxnet.apache.org/api/faq/new_op>
+   New Operator in MXNet Backend <https://mxnet.apache.org/api/faq/add_op_in_backend>
diff --git a/docs/python_docs/python/tutorials/getting-started/crash-course/5-predict.md b/docs/python_docs/python/tutorials/getting-started/crash-course/5-predict.md
index 1804a6082494..7a7738d8df1b 100644
--- a/docs/python_docs/python/tutorials/getting-started/crash-course/5-predict.md
+++ b/docs/python_docs/python/tutorials/getting-started/crash-course/5-predict.md
@@ -94,7 +94,7 @@ plt.show()
 ## Predict with models from Gluon model zoo
 
 
-The LeNet trained on FashionMNIST is a good example to start with, but too simple to predict real-life pictures. Instead of training large-scale model from scratch, [Gluon model zoo](https://mxnet.incubator.apache.org/api/python/gluon/model_zoo.html) provides multiple pre-trained powerful models. For example, we can download and load a pre-trained ResNet-50 V2 model that was trained on the ImageNet dataset.
+The LeNet trained on FashionMNIST is a good example to start with, but too simple to predict real-life pictures. Instead of training large-scale model from scratch, [Gluon model zoo](https://mxnet.apache.org/api/python/gluon/model_zoo.html) provides multiple pre-trained powerful models. For example, we can download and load a pre-trained ResNet-50 V2 model that was trained on the ImageNet dataset.
 
 ```{.python .input  n=7}
 from mxnet.gluon.model_zoo import vision as models
diff --git a/docs/python_docs/python/tutorials/packages/gluon/gluon_from_experiment_to_deployment.md b/docs/python_docs/python/tutorials/getting-started/gluon_from_experiment_to_deployment.md
similarity index 89%
rename from docs/python_docs/python/tutorials/packages/gluon/gluon_from_experiment_to_deployment.md
rename to docs/python_docs/python/tutorials/getting-started/gluon_from_experiment_to_deployment.md
index f0111240188d..388f2440149c 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/gluon_from_experiment_to_deployment.md
+++ b/docs/python_docs/python/tutorials/getting-started/gluon_from_experiment_to_deployment.md
@@ -16,11 +16,11 @@
 <!--- under the License. -->
 
 
-# Gluon: from experiment to deployment, an end to end tutorial
+# Gluon: from experiment to deployment
 
 ## Overview
 MXNet Gluon API comes with a lot of great features, and it can provide you everything you need: from experimentation to deploying the model. In this tutorial, we will walk you through a common use case on how to build a model using gluon, train it on your data, and deploy it for inference.
-This tutorial covers training and inference in Python, please continue to [C++ inference part](https://mxnet.incubator.apache.org/versions/master/tutorials/c++/mxnet_cpp_inference_tutorial.html) after you finish.
+This tutorial covers training and inference in Python, please continue to [C++ inference part](https://mxnet.apache.org/versions/master/tutorials/c++/mxnet_cpp_inference_tutorial.html) after you finish.
 
 Let's say you need to build a service that provides flower species recognition. A common problem is that you don't have enough data to train a good model. In such cases, a technique called Transfer Learning can be used to make a more robust model.
 In Transfer Learning we make use of a pre-trained model that solves a related task, and was trained on a very large standard dataset, such as ImageNet. ImageNet is from a different domain, but we can utilize the knowledge in this pre-trained model to perform the new task at hand.
@@ -34,7 +34,7 @@ Gluon provides State of the Art models for many of the standard tasks such as Cl
 
 To complete this tutorial, you need:
 
-- [Build MXNet from source](https://mxnet.incubator.apache.org/install/ubuntu_setup.html#build-mxnet-from-source) with Python(Gluon) and C++ Packages
+- [Build MXNet from source](https://mxnet.apache.org/get_started/ubuntu_setup#build-mxnet-from-source) with Python(Gluon) and C++ Packages
 - Learn the basics about Gluon with [A 60-minute Gluon Crash Course](https://gluon-crash-course.mxnet.io/)
 
 
@@ -77,7 +77,7 @@ from mxnet.gluon.data.vision import transforms
 from mxnet.gluon.model_zoo.vision import resnet50_v2
 ```
 
-Next, we define the hyper-parameters that we will use for fine-tuning. We will use the [MXNet learning rate scheduler](https://mxnet.incubator.apache.org/tutorials/gluon/learning_rate_schedules.html) to adjust learning rates during training.
+Next, we define the hyper-parameters that we will use for fine-tuning. We will use the [MXNet learning rate scheduler](https://mxnet.apache.org/tutorials/gluon/learning_rate_schedules.html) to adjust learning rates during training.
 Here we set the `epochs` to 1 for quick demonstration, please change to 40 for actual training.
 
 ```python
@@ -99,14 +99,14 @@ ctx = [mx.gpu(i) for i in range(num_gpus)] if num_gpus > 0 else [mx.cpu()]
 batch_size = per_device_batch_size * max(num_gpus, 1)
 ```
 
-Now we will apply data augmentations on training images. This makes minor alterations on the training images, and our model will consider them as distinct images. This can be very useful for fine-tuning on a relatively small dataset, and it will help improve the model. We can use the Gluon [DataSet API](https://mxnet.incubator.apache.org/tutorials/gluon/datasets.html), [DataLoader API](https://mxnet.incubator.apache.org/tutorials/gluon/datasets.html), and [Transform API](https://mxnet.incubator.apache.org/tutorials/gluon/data_augmentation.html) to load the images and apply the following data augmentations:
+Now we will apply data augmentations on training images. This makes minor alterations on the training images, and our model will consider them as distinct images. This can be very useful for fine-tuning on a relatively small dataset, and it will help improve the model. We can use the Gluon [DataSet API](https://mxnet.apache.org/tutorials/gluon/datasets.html), [DataLoader API](https://mxnet.apache.org/tutorials/gluon/datasets.html), and [Transform API](https://mxnet.apache.org/tutorials/gluon/data_augmentation.html) to load the images and apply the following data augmentations:
 1. Randomly crop the image and resize it to 224x224
 2. Randomly flip the image horizontally
 3. Randomly jitter color and add noise
 4. Transpose the data from `[height, width, num_channels]` to `[num_channels, height, width]`, and map values from [0, 255] to [0, 1]
 5. Normalize with the mean and standard deviation from the ImageNet dataset.
 
-For validation and inference, we only need to apply step 1, 4, and 5. We also need to save the mean and standard deviation values for [inference using C++](https://mxnet.incubator.apache.org/versions/master/tutorials/c++/mxnet_cpp_inference_tutorial.html).
+For validation and inference, we only need to apply step 1, 4, and 5. We also need to save the mean and standard deviation values for [inference using C++](https://mxnet.apache.org/versions/master/tutorials/c++/mxnet_cpp_inference_tutorial.html).
 
 ```python
 jitter_param = 0.4
@@ -161,7 +161,7 @@ test_data = gluon.data.DataLoader(
 
 We will use pre-trained ResNet50_v2 model which was pre-trained on the [ImageNet Dataset](http://www.image-net.org/) with 1000 classes. To match the classes in the Flower dataset, we must redefine the last softmax (output) layer to be 102, then initialize the parameters.
 
-Before we go to training, one unique Gluon feature you should be aware of is hybridization. It allows you to convert your imperative code to a static symbolic graph, which is much more efficient to execute. There are two main benefits of hybridizing your model: better performance and easier serialization for deployment. The best part is that it's as simple as just calling `net.hybridize()`. To know more about Gluon hybridization, please follow the [hybridization tutorial](https://mxnet.incubator.apache.org/tutorials/gluon/hybrid.html).
+Before we go to training, one unique Gluon feature you should be aware of is hybridization. It allows you to convert your imperative code to a static symbolic graph, which is much more efficient to execute. There are two main benefits of hybridizing your model: better performance and easier serialization for deployment. The best part is that it's as simple as just calling `net.hybridize()`. To know more about Gluon hybridization, please follow the [hybridization tutorial](https://mxnet.apache.org/tutorials/gluon/hybrid.html).
 
 
 
@@ -265,9 +265,9 @@ finetune_net.export("flower-recognition", epoch=epochs)
 ## Load the model and run inference using the MXNet Module API
 
 MXNet provides various useful tools and interfaces for deploying your model for inference. For example, you can use [MXNet Model Server](https://github.com/awslabs/mxnet-model-server) to start a service and host your trained model easily.
-Besides that, you can also use MXNet's different language APIs to integrate your model with your existing service. We provide [Python](https://mxnet.incubator.apache.org/api/python/module/module.html),    [Java](https://mxnet.incubator.apache.org/api/java/index.html), [Scala](https://mxnet.incubator.apache.org/api/scala/index.html), and [C++](https://mxnet.incubator.apache.org/api/c++/index.html) APIs.
+Besides that, you can also use MXNet's different language APIs to integrate your model with your existing service. We provide [Python](https://mxnet.apache.org/api/python/module/module.html),    [Java](https://mxnet.apache.org/api/java/index.html), [Scala](https://mxnet.apache.org/api/scala/index.html), and [C++](https://mxnet.apache.org/api/c++/index.html) APIs.
 
-Here we will briefly introduce how to run inference using Module API in Python. There is more detailed explanation available in the [Predict Image Tutorial](https://mxnet.incubator.apache.org/tutorials/python/predict_image.html).
+Here we will briefly introduce how to run inference using Module API in Python. There is more detailed explanation available in the [Predict Image Tutorial](https://mxnet.apache.org/tutorials/python/predict_image.html).
 In general, prediction consists of the following steps:
 1. Load the model architecture (symbol file) and trained parameter values (params file)
 2. Load the synset file for label names
@@ -311,11 +311,11 @@ probability=9.798435, class=lotus
 
 ## What's next
 
-You can continue to the [next tutorial](https://mxnet.incubator.apache.org/versions/master/tutorials/c++/mxnet_cpp_inference_tutorial.html) on how to load the model we just trained and run inference using MXNet C++ API.
+You can continue to the [next tutorial](https://mxnet.apache.org/versions/master/tutorials/c++/mxnet_cpp_inference_tutorial.html) on how to load the model we just trained and run inference using MXNet C++ API.
 
 You can also find more ways to run inference and deploy your models here:
 1. [Java Inference examples](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer)
-2. [Scala Inference examples](https://mxnet.incubator.apache.org/tutorials/scala/)
+2. [Scala Inference examples](https://mxnet.apache.org/tutorials/scala/)
 4. [MXNet Model Server Examples](https://github.com/awslabs/mxnet-model-server/tree/master/examples)
 
 ## References
diff --git a/docs/python_docs/python/tutorials/getting-started/index.rst b/docs/python_docs/python/tutorials/getting-started/index.rst
index 9f783922647c..9402f14061d6 100644
--- a/docs/python_docs/python/tutorials/getting-started/index.rst
+++ b/docs/python_docs/python/tutorials/getting-started/index.rst
@@ -28,21 +28,37 @@ The following tutorials teach how to use MXNet.
 
       A quick overview of the core concepts of MXNet using the Gluon API.
 
-   .. card::
-      :title: Linear Regression
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/python/linear-regression.html
-
-      A tutorial on implementing linear regression using MXNet APIs.
-
    .. card::
       :title: Moving from other frameworks
       :link: to-mxnet/index.html
 
       Guides that ease your transition to MXNet from other framework.
 
+   .. card::
+      :title: Logistic Regression Explained
+      :link: logistic_regression_explained.html
+
+      Logistic Regression with MXNet Gluon Explained
+
+   .. card::
+      :title: MNIST Training
+      :link: https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html
+
+      MNIST Training with MXNet
+
+   .. card::
+      :title: Gluon From Experiment To Deployment
+      :link: gluon_from_experiment_to_deployment.html
+
+      A tutorial on implementing linear regression using MXNet APIs.
+
 .. toctree::
    :hidden:
    :maxdepth: 2
 
    crash-course/index
    to-mxnet/index
+   gluon_from_experiment_to_deployment
+   logistic_regression_explained.md
+   MNIST <https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html>
+
diff --git a/docs/python_docs/python/tutorials/getting-started/logistic_regression_explained.md b/docs/python_docs/python/tutorials/getting-started/logistic_regression_explained.md
new file mode 100644
index 000000000000..36f2e5a68062
--- /dev/null
+++ b/docs/python_docs/python/tutorials/getting-started/logistic_regression_explained.md
@@ -0,0 +1,257 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+
+# Logistic regression explained
+
+Logistic Regression is one of the first models newcomers to Deep Learning are implementing. The focus of this tutorial is to show how to do logistic regression using Gluon API.
+
+Before anything else, let's import required packages for this tutorial.
+
+
+```python
+import numpy as np
+import mxnet as mx
+from mxnet import nd, autograd, gluon
+from mxnet.gluon import nn, Trainer
+from mxnet.gluon.data import DataLoader, ArrayDataset
+
+mx.random.seed(12345)  # Added for reproducibility
+```
+
+In this tutorial we will use fake dataset, which contains 10 features drawn from a normal distribution with mean equals to 0 and standard deviation equals to 1, and a class label, which can be either 0 or 1. The size of the dataset is an arbitrary value. The function below helps us to generate a dataset. Class label `y` is generated via a non-random logic, so the network would have a pattern to look for. Boundary of 3 is selected to make sure that number of positive examples smaller than negative, but not too small
+
+
+```python
+def get_random_data(size, ctx):
+    x = nd.normal(0, 1, shape=(size, 10), ctx=ctx)
+    y = x.sum(axis=1) > 3
+    return x, y
+```
+
+Also, let's define a set of hyperparameters, that we are going to use later. Since our model is simple and dataset is small, we are going to use CPU for calculations. Feel free to change it to GPU for a more advanced scenario.
+
+
+```python
+ctx = mx.cpu()
+train_data_size = 1000
+val_data_size = 100
+batch_size = 10
+```
+
+## Working with data
+
+To work with data, Apache MXNet provides [Dataset](https://mxnet.apache.org/api/python/gluon/data.html#mxnet.gluon.data.Dataset) and [DataLoader](https://mxnet.apache.org/api/python/gluon/data.html#mxnet.gluon.data.DataLoader) classes. The former is used to provide an indexed access to the data, the latter is used to shuffle and batchify the data. To learn more about working with data in Gluon, please refer to [Gluon Datasets and Dataloaders](https://mxnet.apache.org/tutorials/gluon/datasets.html) tutorial.
+
+Below we define training and validation datasets, which we are going to use in the tutorial.
+
+
+```python
+train_x, train_ground_truth_class = get_random_data(train_data_size, ctx)
+train_dataset = ArrayDataset(train_x, train_ground_truth_class)
+train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
+
+val_x, val_ground_truth_class = get_random_data(val_data_size, ctx)
+val_dataset = ArrayDataset(val_x, val_ground_truth_class)
+val_dataloader = DataLoader(val_dataset, batch_size=batch_size, shuffle=True)
+```
+
+## Defining and training the model
+
+The only requirement for the logistic regression is that the last layer of the network must be a single neuron. Apache MXNet allows us to do so by using [Dense](https://mxnet.apache.org/api/python/gluon/nn.html#mxnet.gluon.nn.Dense) layer and specifying the number of units to 1. The rest of the network can be arbitrarily complex.
+
+Below, we define a model which has an input layer of 10 neurons, a couple of inner layers of 10 neurons each, and output layer of 1 neuron. We stack the layers using [HybridSequential](https://mxnet.apache.org/api/python/gluon/gluon.html#mxnet.gluon.nn.HybridSequential) block and initialize parameters of the network using [Xavier](https://mxnet.apache.org/api/python/optimization/optimization.html#mxnet.initializer.Xavier) initialization.
+
+
+```python
+net = nn.HybridSequential()
+
+with net.name_scope():
+    net.add(nn.Dense(units=10, activation='relu'))  # input layer
+    net.add(nn.Dense(units=10, activation='relu'))   # inner layer 1
+    net.add(nn.Dense(units=10, activation='relu'))   # inner layer 2
+    net.add(nn.Dense(units=1))   # output layer: notice, it must have only 1 neuron
+
+net.initialize(mx.init.Xavier())
+```
+
+After defining the model, we need to define a few more things: our loss, our trainer and our metric.
+
+Loss function is used to calculate how the output of the network differs from the ground truth. Because classes  of the logistic regression are either 0 or 1, we are using [SigmoidBinaryCrossEntropyLoss](https://mxnet.apache.org/api/python/gluon/loss.html#mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss). Notice that we do not specify `from_sigmoid` attribute in the code, which means that the output of the neuron doesn't need to go through sigmoid, but at inference we'd have to pass it through sigmoid. You can learn more about cross entropy on [wikipedia](https://en.wikipedia.org/wiki/Cross_entropy).
+
+Trainer object allows to specify the method of training to be used. For our tutorial we use [Stochastic Gradient Descent (SGD)](https://mxnet.apache.org/api/python/optimization/optimization.html#mxnet.optimizer.SGD). For more information on SGD refer to [the following tutorial](https://gluon.mxnet.io/chapter06_optimization/gd-sgd-scratch.html). We also need to parametrize it with learning rate value, which defines the weight updates, and weight decay, which is used for regularization.
+
+Metric helps us to estimate how good our model is in terms of a problem we are trying to solve. Where loss function has more importance for the training process, a metric is usually the thing we are trying to improve and reach maximum value. We also can use more than one metric, to measure various aspects of our model. In our example, we are using [Accuracy](https://mxnet.apache.org/api/python/metric/metric.html?highlight=metric.acc#mxnet.metric.Accuracy) and [F1 score](http://mxnet.apache.org/api/python/metric/metric.html?highlight=metric.f1#mxnet.metric.F1) as measurements of success of our model.
+
+Below we define these objects.
+
+
+```python
+loss = gluon.loss.SigmoidBinaryCrossEntropyLoss()
+trainer = Trainer(params=net.collect_params(), optimizer='sgd',
+                  optimizer_params={'learning_rate': 0.1})
+accuracy = mx.metric.Accuracy()
+f1 = mx.metric.F1()
+```
+
+The next step is to define the training function in which we iterate over all batches of training data, execute the forward pass on each batch and calculate training loss. On line 19, we sum losses of every batch per epoch into a single variable, because we calculate loss per single batch, but want to display it per epoch.
+
+
+```python
+def train_model():
+    cumulative_train_loss = 0
+
+    for i, (data, label) in enumerate(train_dataloader):
+        with autograd.record():
+            # Do forward pass on a batch of training data
+            output = net(data)
+
+            # Calculate loss for the training data batch
+            loss_result = loss(output, label)
+
+        # Calculate gradients
+        loss_result.backward()
+
+        # Update parameters of the network
+        trainer.step(batch_size)
+
+        # sum losses of every batch
+        cumulative_train_loss += nd.sum(loss_result).asscalar()
+
+    return cumulative_train_loss
+```
+
+## Validating the model
+
+Our validation function is very similar to the training one. The main difference is that we want to calculate accuracy of the model. We use [Accuracy metric](https://mxnet.apache.org/api/python/metric/metric.html?highlight=metric.acc#mxnet.metric.Accuracy) to do so. 
+
+`Accuracy` metric requires 2 arguments: 1) a vector of ground-truth classes and 2) A vector or matrix of predictions. When predictions are of the same shape as the vector of ground-truth classes, `Accuracy` class assumes that prediction vector contains predicted classes. So, it converts the vector to `Int32` and compare each item of ground-truth classes to prediction vector.
+
+Because of the behaviour above, you will get an unexpected result if you just apply [Sigmoid](https://mxnet.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.sigmoid) function to the network result and pass it to `Accuracy` metric. As mentioned before, we need to apply `Sigmoid` function to the output of the neuron to get a probability of belonging to the class 1. But `Sigmoid` function produces output in range [0; 1], and all numbers in that range are going to be casted to 0, even if it is as high as 0.99. To avoid this we write a custom bit of code on line 12, that:
+
+1. Calculates sigmoid using `Sigmoid` function
+
+2. Subtracts a threshold from the original sigmoid output. Usually, the threshold is equal to 0.5, but it can be higher, if you want to increase certainty of an item to belong to class 1.
+
+3. Uses [mx.nd.ceil](https://mxnet.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.ceil) function, which converts all negative values to 0 and all positive values to 1
+
+After these transformations we can pass the result to `Accuracy.update()` method and expect it to behave in a proper way.
+
+For `F1` metric to work, instead of one number per class, we must pass probabilities of belonging to both classes. Because of that, on lines 21-22 we:
+
+1. Reshape predictions to a single vector
+
+2. We stack together two vectors: probabilities of belonging to class 0 (1 - `prediction`) and probabilities of belonging to class 1.
+
+Then we pass this stacked matrix to `F1` score.
+
+
+```python
+def validate_model(threshold):
+    cumulative_val_loss = 0
+
+    for i, (val_data, val_ground_truth_class) in enumerate(val_dataloader):
+        # Do forward pass on a batch of validation data
+        output = net(val_data)
+
+        # Similar to cumulative training loss, calculate cumulative validation loss
+        cumulative_val_loss += nd.sum(loss(output, val_ground_truth_class)).asscalar()
+
+        # getting prediction as a sigmoid
+        prediction = net(val_data).sigmoid()
+
+        # Converting neuron outputs to classes
+        predicted_classes = mx.nd.ceil(prediction - threshold)
+
+        # Update validation accuracy
+        accuracy.update(val_ground_truth_class, predicted_classes.reshape(-1))
+
+        # calculate probabilities of belonging to different classes. F1 metric works only with this notation
+        prediction = prediction.reshape(-1)
+        probabilities = mx.nd.stack(1 - prediction, prediction, axis=1)
+
+        f1.update(val_ground_truth_class, probabilities)
+
+    return cumulative_val_loss
+```
+
+## Putting it all together
+
+By using the defined above functions, we can finally write our main training loop.
+
+
+```python
+epochs = 10
+threshold = 0.5
+
+for e in range(epochs):
+    avg_train_loss = train_model() / train_data_size
+    avg_val_loss = validate_model(threshold) / val_data_size
+
+    print("Epoch: %s, Training loss: %.2f, Validation loss: %.2f, Validation accuracy: %.2f, F1 score: %.2f" %
+          (e, avg_train_loss, avg_val_loss, accuracy.get()[1], f1.get()[1]))
+
+    # we reset accuracy, so the new epoch's accuracy would be calculated from the blank state
+    accuracy.reset()
+```
+
+Output:
+
+```bash
+Epoch: 0, Training loss: 0.43, Validation loss: 0.36, Validation accuracy: 0.85, F1 score: 0.00 <!--notebook-skip-line-->
+
+Epoch: 1, Training loss: 0.22, Validation loss: 0.14, Validation accuracy: 0.96, F1 score: 0.35 <!--notebook-skip-line-->
+
+Epoch: 2, Training loss: 0.09, Validation loss: 0.11, Validation accuracy: 0.97, F1 score: 0.48 <!--notebook-skip-line-->
+
+Epoch: 3, Training loss: 0.07, Validation loss: 0.09, Validation accuracy: 0.96, F1 score: 0.53 <!--notebook-skip-line-->
+
+Epoch: 4, Training loss: 0.06, Validation loss: 0.09, Validation accuracy: 0.97, F1 score: 0.58 <!--notebook-skip-line-->
+
+Epoch: 5, Training loss: 0.04, Validation loss: 0.12, Validation accuracy: 0.97, F1 score: 0.59 <!--notebook-skip-line-->
+
+Epoch: 6, Training loss: 0.05, Validation loss: 0.09, Validation accuracy: 0.99, F1 score: 0.62 <!--notebook-skip-line-->
+
+Epoch: 7, Training loss: 0.05, Validation loss: 0.10, Validation accuracy: 0.97, F1 score: 0.62 <!--notebook-skip-line-->
+
+Epoch: 8, Training loss: 0.05, Validation loss: 0.12, Validation accuracy: 0.95, F1 score: 0.63 <!--notebook-skip-line-->
+
+Epoch: 9, Training loss: 0.04, Validation loss: 0.09, Validation accuracy: 0.98, F1 score: 0.65 <!--notebook-skip-line-->
+```
+
+In our case we hit the accuracy of 0.98 and F1 score of 0.65.
+
+## Tip 1: Use only one neuron in the output layer
+
+Despite that there are 2 classes, there should be only one output neuron, because `SigmoidBinaryCrossEntropyLoss` accepts only one feature as an input.
+
+## Tip 2: Encode classes as 0 and 1
+
+For `SigmoidBinaryCrossEntropyLoss` to work it is required that classes were encoded as 0 and 1. In some datasets the class encoding might be different, like -1 and 1 or 1 and 2. If this is how your dataset looks like, then you need to re-encode the data before using `SigmoidBinaryCrossEntropyLoss`.
+
+## Tip 3: Use SigmoidBinaryCrossEntropyLoss instead of LogisticRegressionOutput
+
+NDArray API has two options to calculate logistic regression loss: [SigmoidBinaryCrossEntropyLoss](https://mxnet.apache.org/api/python/gluon/loss.html#mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss) and [LogisticRegressionOutput](https://mxnet.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.LogisticRegressionOutput). `LogisticRegressionOutput` is designed to be an output layer when using the Module API, and is not supposed to be used when using Gluon API.
+
+## Conclusion
+
+In this tutorial I explained some potential pitfalls to be aware of. When doing logistic regression using Gluon API remember to:
+1. Use only one neuron in the output layer
+1. Encode class labels as 0 or 1
+1. Use `SigmoidBinaryCrossEntropyLoss`
+1. Convert probabilities to classes before calculating Accuracy
+
diff --git a/docs/python_docs/python/tutorials/getting-started/to-mxnet/index.rst b/docs/python_docs/python/tutorials/getting-started/to-mxnet/index.rst
index a2c370912f85..cf590d8aceb4 100644
--- a/docs/python_docs/python/tutorials/getting-started/to-mxnet/index.rst
+++ b/docs/python_docs/python/tutorials/getting-started/to-mxnet/index.rst
@@ -31,7 +31,7 @@ Comparison Guides
 
    .. card::
       :title: Caffe to MXNet
-      :link: https://mxnet.incubator.apache.org/versions/master/faq/caffe.html
+      :link: https://mxnet.apache.org/versions/master/faq/caffe.html
 
       How to convert Caffe models to MXNet and how to call Caffe operators from MXNet.
 
diff --git a/docs/python_docs/python/tutorials/getting-started/to-mxnet/pytorch.md b/docs/python_docs/python/tutorials/getting-started/to-mxnet/pytorch.md
index d66e172328e2..d7720bac4348 100644
--- a/docs/python_docs/python/tutorials/getting-started/to-mxnet/pytorch.md
+++ b/docs/python_docs/python/tutorials/getting-started/to-mxnet/pytorch.md
@@ -106,7 +106,7 @@ mx_train_data = gluon.data.DataLoader(
 
 Both frameworks allows you to download MNIST data set from their sources and specify that only training part of the data set is required.
 
-The main difference between the code snippets is that MXNet uses [transform_first](http://beta.mxnet.io/api/gluon/_autogen/mxnet.gluon.data.Dataset.html) method to indicate that the data transformation is done on the first element of the data batch, the MNIST picture, rather than the second element, the label.
+The main difference between the code snippets is that MXNet uses [transform_first](https://mxnet.apache.org/api/python/docs/api/gluon/_autogen/mxnet.gluon.data.Dataset.html) method to indicate that the data transformation is done on the first element of the data batch, the MNIST picture, rather than the second element, the label.
 
 ### 2. Creating the model
 
@@ -143,7 +143,7 @@ We used the Sequential container to stack layers one after the other in order to
 
 * After the model structure is defined, Apache MXNet requires you to explicitly call the model initialization function.
 
-With a Sequential block, layers are executed one after the other. To have a different execution model, with PyTorch you can inherit from `nn.Module` and then customize how the `.forward()` function is executed. Similarly, in Apache MXNet you can inherit from [nn.Block](http://beta.mxnet.io/api/gluon/mxnet.gluon.nn.Block.html) to achieve similar results.
+With a Sequential block, layers are executed one after the other. To have a different execution model, with PyTorch you can inherit from `nn.Module` and then customize how the `.forward()` function is executed. Similarly, in Apache MXNet you can inherit from [nn.Block](https://mxnet.apache.org/api/python/docs/api/gluon/mxnet.gluon.nn.Block.html) to achieve similar results.
 
 ### 3. Loss function and optimization algorithm
 
@@ -164,7 +164,7 @@ mx_trainer = gluon.Trainer(mx_net.collect_params(),
                            'sgd', {'learning_rate': 0.1})
 ```
 
-The code difference between frameworks is small. The main difference is that in Apache MXNet we use [Trainer](http://beta.mxnet.io/api/gluon/mxnet.gluon.Trainer.html) class, which accepts optimization algorithm as an argument. We also use [.collect_params()](/api/python/docs/api/gluon/_autogen/mxnet.gluon.nn.Block.collect_params.html) method to get parameters of the network.
+The code difference between frameworks is small. The main difference is that in Apache MXNet we use [Trainer](https://mxnet.apache.org/api/python/docs/api/gluon/mxnet.gluon.Trainer.html) class, which accepts optimization algorithm as an argument. We also use [.collect_params()](/api/python/docs/api/gluon/_autogen/mxnet.gluon.nn.Block.collect_params.html) method to get parameters of the network.
 
 ### 4. Training
 
diff --git a/docs/python_docs/python/tutorials/packages/autograd/autograd.md b/docs/python_docs/python/tutorials/packages/autograd/index.md
similarity index 98%
rename from docs/python_docs/python/tutorials/packages/autograd/autograd.md
rename to docs/python_docs/python/tutorials/packages/autograd/index.md
index 2558ee04bdec..84d1d1cece67 100644
--- a/docs/python_docs/python/tutorials/packages/autograd/autograd.md
+++ b/docs/python_docs/python/tutorials/packages/autograd/index.md
@@ -159,7 +159,7 @@ print('is_training:', is_training, output)
 
 We called `dropout` while `autograd` was recording this time, so our network was in training mode and we see dropout of the input this time. Since the probability of dropout was 50%, the output is automatically scaled by 1/0.5=2 to preserve the average activation.
 
-We can force some operators to behave as they would during training, even in inference mode. One example is setting `mode='always'` on the [Dropout](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html?highlight=dropout#mxnet.ndarray.Dropout) operator, but this usage is uncommon.
+We can force some operators to behave as they would during training, even in inference mode. One example is setting `mode='always'` on the [Dropout](https://mxnet.apache.org/api/python/ndarray/ndarray.html?highlight=dropout#mxnet.ndarray.Dropout) operator, but this usage is uncommon.
 
 ## Advanced: Skipping the calculation of parameter gradients
 
@@ -196,7 +196,7 @@ print(x.grad)
 
 ## Advanced: Using Python control flow
 
-As mentioned before, one of the main advantages of `autograd` is the ability to automatically calculate gradients of dynamic graphs (i.e. graphs where the operators could be different on every forward pass). One example of this would be applying a tree structured recurrent network to parse a sentence using its parse tree. And we can use Python control flow operators to create a dynamic flow that depends on the data, rather than using [MXNet's control flow operators](https://mxnet.incubator.apache.org/versions/master/tutorials/control_flow/ControlFlowTutorial.html).
+As mentioned before, one of the main advantages of `autograd` is the ability to automatically calculate gradients of dynamic graphs (i.e. graphs where the operators could be different on every forward pass). One example of this would be applying a tree structured recurrent network to parse a sentence using its parse tree. And we can use Python control flow operators to create a dynamic flow that depends on the data, rather than using [MXNet's control flow operators](https://mxnet.apache.org/versions/master/tutorials/control_flow/ControlFlowTutorial.html).
 
 We'll write a function as a toy example of a dynamic network. We'll add an `if` condition and a loop with a variable number of iterations, both of which will depend on the input data. Although these can now be used in static graphs (with conditional operators) it's still much more natural to use native control flow.
 
diff --git a/docs/python_docs/python/tutorials/packages/gluon/activations/activations.md b/docs/python_docs/python/tutorials/packages/gluon/blocks/activations/activations.md
similarity index 100%
rename from docs/python_docs/python/tutorials/packages/gluon/activations/activations.md
rename to docs/python_docs/python/tutorials/packages/gluon/blocks/activations/activations.md
diff --git a/docs/python_docs/python/tutorials/packages/gluon/activations/images/elu.png b/docs/python_docs/python/tutorials/packages/gluon/blocks/activations/images/elu.png
similarity index 100%
rename from docs/python_docs/python/tutorials/packages/gluon/activations/images/elu.png
rename to docs/python_docs/python/tutorials/packages/gluon/blocks/activations/images/elu.png
diff --git a/docs/python_docs/python/tutorials/packages/gluon/activations/images/leakyrelu.png b/docs/python_docs/python/tutorials/packages/gluon/blocks/activations/images/leakyrelu.png
similarity index 100%
rename from docs/python_docs/python/tutorials/packages/gluon/activations/images/leakyrelu.png
rename to docs/python_docs/python/tutorials/packages/gluon/blocks/activations/images/leakyrelu.png
diff --git a/docs/python_docs/python/tutorials/packages/gluon/activations/images/prelu.png b/docs/python_docs/python/tutorials/packages/gluon/blocks/activations/images/prelu.png
similarity index 100%
rename from docs/python_docs/python/tutorials/packages/gluon/activations/images/prelu.png
rename to docs/python_docs/python/tutorials/packages/gluon/blocks/activations/images/prelu.png
diff --git a/docs/python_docs/python/tutorials/packages/gluon/activations/images/relu.png b/docs/python_docs/python/tutorials/packages/gluon/blocks/activations/images/relu.png
similarity index 100%
rename from docs/python_docs/python/tutorials/packages/gluon/activations/images/relu.png
rename to docs/python_docs/python/tutorials/packages/gluon/blocks/activations/images/relu.png
diff --git a/docs/python_docs/python/tutorials/packages/gluon/activations/images/selu.png b/docs/python_docs/python/tutorials/packages/gluon/blocks/activations/images/selu.png
similarity index 100%
rename from docs/python_docs/python/tutorials/packages/gluon/activations/images/selu.png
rename to docs/python_docs/python/tutorials/packages/gluon/blocks/activations/images/selu.png
diff --git a/docs/python_docs/python/tutorials/packages/gluon/activations/images/sigmoid.png b/docs/python_docs/python/tutorials/packages/gluon/blocks/activations/images/sigmoid.png
similarity index 100%
rename from docs/python_docs/python/tutorials/packages/gluon/activations/images/sigmoid.png
rename to docs/python_docs/python/tutorials/packages/gluon/blocks/activations/images/sigmoid.png
diff --git a/docs/python_docs/python/tutorials/packages/gluon/activations/images/softrelu.png b/docs/python_docs/python/tutorials/packages/gluon/blocks/activations/images/softrelu.png
similarity index 100%
rename from docs/python_docs/python/tutorials/packages/gluon/activations/images/softrelu.png
rename to docs/python_docs/python/tutorials/packages/gluon/blocks/activations/images/softrelu.png
diff --git a/docs/python_docs/python/tutorials/packages/gluon/activations/images/softsign.png b/docs/python_docs/python/tutorials/packages/gluon/blocks/activations/images/softsign.png
similarity index 100%
rename from docs/python_docs/python/tutorials/packages/gluon/activations/images/softsign.png
rename to docs/python_docs/python/tutorials/packages/gluon/blocks/activations/images/softsign.png
diff --git a/docs/python_docs/python/tutorials/packages/gluon/activations/images/swish.png b/docs/python_docs/python/tutorials/packages/gluon/blocks/activations/images/swish.png
similarity index 100%
rename from docs/python_docs/python/tutorials/packages/gluon/activations/images/swish.png
rename to docs/python_docs/python/tutorials/packages/gluon/blocks/activations/images/swish.png
diff --git a/docs/python_docs/python/tutorials/packages/gluon/activations/images/tanh.png b/docs/python_docs/python/tutorials/packages/gluon/blocks/activations/images/tanh.png
similarity index 100%
rename from docs/python_docs/python/tutorials/packages/gluon/activations/images/tanh.png
rename to docs/python_docs/python/tutorials/packages/gluon/blocks/activations/images/tanh.png
diff --git a/docs/python_docs/python/tutorials/packages/gluon/blocks.svg b/docs/python_docs/python/tutorials/packages/gluon/blocks/blocks.svg
similarity index 100%
rename from docs/python_docs/python/tutorials/packages/gluon/blocks.svg
rename to docs/python_docs/python/tutorials/packages/gluon/blocks/blocks.svg
diff --git a/docs/python_docs/python/tutorials/packages/gluon/custom-layer.md b/docs/python_docs/python/tutorials/packages/gluon/blocks/custom-layer.md
similarity index 100%
rename from docs/python_docs/python/tutorials/packages/gluon/custom-layer.md
rename to docs/python_docs/python/tutorials/packages/gluon/blocks/custom-layer.md
diff --git a/docs/python_docs/python/tutorials/packages/gluon/custom_layer_beginners.md b/docs/python_docs/python/tutorials/packages/gluon/blocks/custom_layer_beginners.md
similarity index 85%
rename from docs/python_docs/python/tutorials/packages/gluon/custom_layer_beginners.md
rename to docs/python_docs/python/tutorials/packages/gluon/blocks/custom_layer_beginners.md
index cbc35c07ad7b..01333a7f80a0 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/custom_layer_beginners.md
+++ b/docs/python_docs/python/tutorials/packages/gluon/blocks/custom_layer_beginners.md
@@ -15,9 +15,9 @@
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
 
-# How to write a custom layer in Apache MxNet Gluon API
+# Customer Layers (Beginners)
 
-While Gluon API for Apache MxNet comes with [a decent number of pre-defined layers](https://mxnet.incubator.apache.org/api/python/gluon/nn.html), at some point one may find that a new layer is needed. Adding a new layer in Gluon API is straightforward, yet there are a few things that one needs to keep in mind.
+While Gluon API for Apache MxNet comes with [a decent number of pre-defined layers](https://mxnet.apache.org/api/python/gluon/nn.html), at some point one may find that a new layer is needed. Adding a new layer in Gluon API is straightforward, yet there are a few things that one needs to keep in mind.
 
 In this article, I will cover how to create a new layer from scratch, how to use it, what are possible pitfalls and how to avoid them.
 
@@ -51,15 +51,15 @@ The rest of methods of the `Block` class are already implemented, and majority o
 
 ## Hybridization and the difference between Block and HybridBlock
 
-Looking into implementation of [existing layers](https://mxnet.incubator.apache.org/api/python/gluon/nn.html), one may find that more often a block inherits from a [HybridBlock](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/block.py#L428), instead of directly inheriting from `Block`.
+Looking into implementation of [existing layers](https://mxnet.apache.org/api/python/gluon/nn.html), one may find that more often a block inherits from a [HybridBlock](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/block.py#L428), instead of directly inheriting from `Block`.
 
-The reason for that is that `HybridBlock` allows to write custom layers that can be used in imperative programming as well as in symbolic programming. It is convenient to support both ways, because the imperative programming eases the debugging of the code and the symbolic one provides faster execution speed. You can learn more about the difference between symbolic vs. imperative programming from [this article](https://mxnet.incubator.apache.org/versions/master/architecture/program_model.html).
+The reason for that is that `HybridBlock` allows to write custom layers that can be used in imperative programming as well as in symbolic programming. It is convenient to support both ways, because the imperative programming eases the debugging of the code and the symbolic one provides faster execution speed. You can learn more about the difference between symbolic vs. imperative programming from [this article](https://mxnet.apache.org/versions/master/architecture/program_model.html).
 
 Hybridization is a process that Apache MxNet uses to create a symbolic graph of a forward computation. This allows to increase computation performance by optimizing the computational symbolic graph. Once the symbolic graph is created, Apache MxNet caches and reuses it for subsequent computations.
 
-To simplify support of both imperative and symbolic programming, Apache MxNet introduce the `HybridBlock` class. Compare to the `Block` class, `HybridBlock` already has its [forward()](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.HybridBlock.forward) method implemented, but it defines a [hybrid_forward()](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.HybridBlock.hybrid_forward) method that needs to be implemented.
+To simplify support of both imperative and symbolic programming, Apache MxNet introduce the `HybridBlock` class. Compare to the `Block` class, `HybridBlock` already has its [forward()](https://mxnet.apache.org/api/python/gluon/gluon.html#mxnet.gluon.HybridBlock.forward) method implemented, but it defines a [hybrid_forward()](https://mxnet.apache.org/api/python/gluon/gluon.html#mxnet.gluon.HybridBlock.hybrid_forward) method that needs to be implemented.
 
-The main difference between `forward()` and  `hybrid_forward()` is an `F` argument. This argument sometimes is refered as a `backend` in the Apache MxNet community. Depending on if hybridization has been done or not, `F` can refer either to [mxnet.ndarray API](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html) or [mxnet.symbol API](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html). The former is used for imperative programming, and the latter for symbolic programming.
+The main difference between `forward()` and  `hybrid_forward()` is an `F` argument. This argument sometimes is refered as a `backend` in the Apache MxNet community. Depending on if hybridization has been done or not, `F` can refer either to [mxnet.ndarray API](https://mxnet.apache.org/api/python/ndarray/ndarray.html) or [mxnet.symbol API](https://mxnet.apache.org/api/python/symbol/symbol.html). The former is used for imperative programming, and the latter for symbolic programming.
 
 To support hybridization, it is important to use only methods available directly from `F` parameter.Usually, there are equivalent methods in both APIs, but sometimes there are mismatches or small variations. For example, by default, subtraction and division of NDArrays support broadcasting, while in Symbol API broadcasting is supported in a separate operators.
 
@@ -88,7 +88,7 @@ layer(nd.array([1, 2, 3], ctx=mx.cpu()))
 
 As a rule of thumb, one should always implement custom layers by inheriting from `HybridBlock`. This allows to have more flexibility, and doesn’t affect execution speed once hybridization is done.
 
-Unfortunately, at the moment of writing this tutorial, NLP related layers such as [RNN](https://mxnet.incubator.apache.org/api/python/gluon/rnn.html#mxnet.gluon.rnn.RNN), [GRU](https://mxnet.incubator.apache.org/api/python/gluon/rnn.html#mxnet.gluon.rnn.GRU), and [LSTM](https://mxnet.incubator.apache.org/api/python/gluon/rnn.html#mxnet.gluon.rnn.LSTM) are directly inhereting from the `Block` class via common `_RNNLayer` class. That means that networks with such layers cannot be hybridized. But this might change in the future, so stay tuned.
+Unfortunately, at the moment of writing this tutorial, NLP related layers such as [RNN](https://mxnet.apache.org/api/python/gluon/rnn.html#mxnet.gluon.rnn.RNN), [GRU](https://mxnet.apache.org/api/python/gluon/rnn.html#mxnet.gluon.rnn.GRU), and [LSTM](https://mxnet.apache.org/api/python/gluon/rnn.html#mxnet.gluon.rnn.LSTM) are directly inhereting from the `Block` class via common `_RNNLayer` class. That means that networks with such layers cannot be hybridized. But this might change in the future, so stay tuned.
 
 It is important to notice that hybridization has nothing to do with computation on GPU. One can train both hybridized and non-hybridized networks on both CPU and GPU, though hybridized networks would work faster. Though, it is hard to say in advance how much faster it is going to be.
 
@@ -96,7 +96,7 @@ It is important to notice that hybridization has nothing to do with computation
 
 While it is possible, custom layers are rarely used separately. Most often they are used with predefined layers to create a neural network. Output of one layer is used as an input of another layer.
 
-Depending on which class you used as a base one, you can use either [Sequential](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.nn.Sequential) or [HybridSequential](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.nn.HybridSequential) container to form a sequential neural network. By adding layers one by one, one adds dependencies of one layer’s input from another layer’s output. It is worth noting, that both `Sequential` and `HybridSequential` containers inherit from `Block` and `HybridBlock` respectively.
+Depending on which class you used as a base one, you can use either [Sequential](https://mxnet.apache.org/api/python/gluon/gluon.html#mxnet.gluon.nn.Sequential) or [HybridSequential](https://mxnet.apache.org/api/python/gluon/gluon.html#mxnet.gluon.nn.HybridSequential) container to form a sequential neural network. By adding layers one by one, one adds dependencies of one layer’s input from another layer’s output. It is worth noting, that both `Sequential` and `HybridSequential` containers inherit from `Block` and `HybridBlock` respectively.
 
 Below is an example of how to create a simple neural network with a custom layer. In this example, `NormalizationHybridLayer` gets as an input the output from `Dense(5)` layer and pass its output as an input to `Dense(1)` layer.
 
@@ -127,7 +127,7 @@ net(input)
 
 Usually, a layer has a set of associated parameters, sometimes also referred as weights. This is an internal state of a layer. Most often, these parameters are the ones, that we want to learn during backpropogation step, but sometimes these parameters might be just constants we want to use during forward pass.
 
-All parameters of a block are stored and accessed via [ParameterDict](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/parameter.py#L508) class. This class helps with initialization, updating, saving and loading of the parameters. Each layer can have multiple set of parameters, and all of them can be stored in a single instance of the `ParameterDict` class. On a block level, the instance of the `ParameterDict` class is accessible via `self.params` field, and outside of a block one can access all parameters of the network via [collect_params()](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.Block.collect_params) method called on a `container`. `ParamterDict` uses [Parameter](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.Parameter) class to represent parameters inside of Apache MxNet neural network. If parameter doesn’t exist, trying to get a parameter via `self.params` will create it automatically.
+All parameters of a block are stored and accessed via [ParameterDict](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/parameter.py#L508) class. This class helps with initialization, updating, saving and loading of the parameters. Each layer can have multiple set of parameters, and all of them can be stored in a single instance of the `ParameterDict` class. On a block level, the instance of the `ParameterDict` class is accessible via `self.params` field, and outside of a block one can access all parameters of the network via [collect_params()](https://mxnet.apache.org/api/python/gluon/gluon.html#mxnet.gluon.Block.collect_params) method called on a `container`. `ParamterDict` uses [Parameter](https://mxnet.apache.org/api/python/gluon/gluon.html#mxnet.gluon.Parameter) class to represent parameters inside of Apache MxNet neural network. If parameter doesn’t exist, trying to get a parameter via `self.params` will create it automatically.
 
 ```python
 class NormalizationHybridLayer(gluon.HybridBlock):
diff --git a/docs/python_docs/python/tutorials/packages/gluon/hybridize.md b/docs/python_docs/python/tutorials/packages/gluon/blocks/hybridize.md
similarity index 91%
rename from docs/python_docs/python/tutorials/packages/gluon/hybridize.md
rename to docs/python_docs/python/tutorials/packages/gluon/blocks/hybridize.md
index cd4498937e53..10d35ecde650 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/hybridize.md
+++ b/docs/python_docs/python/tutorials/packages/gluon/blocks/hybridize.md
@@ -219,7 +219,7 @@ We can see that the three lines of print statements defined in the `hybrid_forwa
 
 ## Key differences and limitations of hybridization
 
-The difference between a purely imperative `Block` and hybridizable `HybridBlock` can superficially appear to be simply the injection of the `F` function space (resolving to [`mx.nd`](http://beta.mxnet.io/api/ndarray/index.html) or [`mx.sym`](http://beta.mxnet.io/api/symbol/index.html)) in the forward function that is renamed from `forward` to `hybrid_forward`. However there are some limitations that apply when using hybrid blocks. In the following section we will review the main differences, giving example of code snippets that generate errors when such blocks get hybridized.
+The difference between a purely imperative `Block` and hybridizable `HybridBlock` can superficially appear to be simply the injection of the `F` function space (resolving to [`mx.nd`](https://mxnet.apache.org/api/python/docs/api/ndarray/index.html) or [`mx.sym`](https://mxnet.apache.org/api/python/docs/api/symbol/index.html)) in the forward function that is renamed from `forward` to `hybrid_forward`. However there are some limitations that apply when using hybrid blocks. In the following section we will review the main differences, giving example of code snippets that generate errors when such blocks get hybridized.
 
 ### Indexing
 
@@ -234,7 +234,7 @@ Would generate the following error:
 
 `TypeError: Symbol only support integer index to fetch i-th output`
 
-There are however several operators that can help you with array manipulations like: [`F.split`](http://beta.mxnet.io/api/ndarray/_autogen/mxnet.ndarray.split.html#mxnet.ndarray.split), [`F.slice`](http://beta.mxnet.io/api/ndarray/_autogen/mxnet.ndarray.slice.html#mxnet.ndarray.slice), [`F.take`](http://beta.mxnet.io/api/ndarray/_autogen/mxnet.ndarray.take.html),[`F.pick`](http://beta.mxnet.io/api/ndarray/_autogen/mxnet.ndarray.pick.html), [`F.where`](http://beta.mxnet.io/api/ndarray/_autogen/mxnet.ndarray.where.html), [`F.reshape`](http://beta.mxnet.io/api/ndarray/_autogen/mxnet.ndarray.reshape.html) or [`F.reshape_like`](http://beta.mxnet.io/api/ndarray/_autogen/mxnet.ndarray.reshape_like.html).
+There are however several operators that can help you with array manipulations like: [`F.split`](https://mxnet.apache.org/api/python/docs/api/ndarray/_autogen/mxnet.ndarray.split.html#mxnet.ndarray.split), [`F.slice`](https://mxnet.apache.org/api/python/docs/api/ndarray/_autogen/mxnet.ndarray.slice.html#mxnet.ndarray.slice), [`F.take`](https://mxnet.apache.org/api/python/docs/api/ndarray/_autogen/mxnet.ndarray.take.html),[`F.pick`](https://mxnet.apache.org/api/python/docs/api/ndarray/_autogen/mxnet.ndarray.pick.html), [`F.where`](https://mxnet.apache.org/api/python/docs/api/ndarray/_autogen/mxnet.ndarray.where.html), [`F.reshape`](https://mxnet.apache.org/api/python/docs/api/ndarray/_autogen/mxnet.ndarray.reshape.html) or [`F.reshape_like`](https://mxnet.apache.org/api/python/docs/api/ndarray/_autogen/mxnet.ndarray.reshape_like.html).
 
 ### Data Type
 
@@ -280,7 +280,7 @@ Trying to access the shape of a tensor in a hybridized block would result in thi
 Again, you cannot use the shape of the symbol at runtime as symbols only describe operations and not the underlying data they operate on. 
 Note: This will change in the future as Apache MXNet will support [dynamic shape inference](https://cwiki.apache.org/confluence/display/MXNET/Dynamic+shape), and the shapes of symbols will be symbols themselves 
 
-There are also a lot of operators that support special indices to help with most of the use-cases where you would want to access the shape information. For example, `F.reshape(x, (0,0,-1))` will keep the first two dimensions unchanged and collapse all further dimensions into the third dimension. See the documentation of the [`F.reshape`](http://beta.mxnet.io/api/ndarray/_autogen/mxnet.ndarray.reshape.html) for more details.
+There are also a lot of operators that support special indices to help with most of the use-cases where you would want to access the shape information. For example, `F.reshape(x, (0,0,-1))` will keep the first two dimensions unchanged and collapse all further dimensions into the third dimension. See the documentation of the [`F.reshape`](https://mxnet.apache.org/api/python/docs/api/ndarray/_autogen/mxnet.ndarray.reshape.html) for more details.
 
 ### Item assignment
 
@@ -294,7 +294,7 @@ def hybrid_forward(self, F, x):
 
 Would get you this error `TypeError: 'Symbol' object does not support item assignment`.
 
-Direct item assignment is not possible in symbolic graph since it needs to be part of a computational graph. One way is to use add more inputs to your graph and use masking or the [`F.where`](http://beta.mxnet.io/api/ndarray/_autogen/mxnet.ndarray.where.html) operator.
+Direct item assignment is not possible in symbolic graph since it needs to be part of a computational graph. One way is to use add more inputs to your graph and use masking or the [`F.where`](https://mxnet.apache.org/api/python/docs/api/ndarray/_autogen/mxnet.ndarray.where.html) operator.
 
 e.g to set the first element to 2 you can do:
 
diff --git a/docs/python_docs/python/tutorials/packages/gluon/blocks/index.rst b/docs/python_docs/python/tutorials/packages/gluon/blocks/index.rst
new file mode 100644
index 000000000000..4ce8beed83f5
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/blocks/index.rst
@@ -0,0 +1,26 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Blocks
+======
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+
+   *
+   activations/activations
diff --git a/docs/python_docs/python/tutorials/packages/gluon/init.md b/docs/python_docs/python/tutorials/packages/gluon/blocks/init.md
similarity index 97%
rename from docs/python_docs/python/tutorials/packages/gluon/init.md
rename to docs/python_docs/python/tutorials/packages/gluon/blocks/init.md
index 30b347cd824d..49df0a0795d6 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/init.md
+++ b/docs/python_docs/python/tutorials/packages/gluon/blocks/init.md
@@ -310,13 +310,13 @@ happens to the gradients is quite ingenious. Since the model parameters contain
 gradients, the gradients of the second hidden layer and the third hidden layer
 are accumulated in `shared.params.grad` during backpropagation.
 
-# Conclusion
+## Conclusion
 
 In this tutorial you learnt how to initialize a neural network, and should now
 understand the difference between deferred and forced initialization. Some more advanced
 cases you should now be aware of include custom initialization and tied parameters.
 
-# Recommended Next Steps
+## Recommended Next Steps
 
-* Check out the [API Docs](http://mxnet.incubator.apache.org/versions/master/api/python/optimization/optimization.html) on initialization for a list of available initialization methods.
-* See [this tutorial](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/naming.html) for more information on Gluon Parameters.
+* Check out the [API Docs](https://mxnet.apache.org/versions/master/api/python/optimization/optimization.html) on initialization for a list of available initialization methods.
+* See [this tutorial](https://mxnet.apache.org/versions/master/tutorials/gluon/naming.html) for more information on Gluon Parameters.
diff --git a/docs/python_docs/python/tutorials/packages/gluon/naming.md b/docs/python_docs/python/tutorials/packages/gluon/blocks/naming.md
similarity index 99%
rename from docs/python_docs/python/tutorials/packages/gluon/naming.md
rename to docs/python_docs/python/tutorials/packages/gluon/blocks/naming.md
index ac53f041dc0a..24bd7d9b0f2c 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/naming.md
+++ b/docs/python_docs/python/tutorials/packages/gluon/blocks/naming.md
@@ -15,7 +15,7 @@
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
 
-# Naming of Gluon Parameter and Blocks
+# Parameter and Block Naming
 
 In gluon, each Parameter or Block has a name (and prefix). Parameter names are specified by users and Block names can be either specified by users or automatically created.
 
diff --git a/docs/python_docs/python/tutorials/packages/gluon/nn.md b/docs/python_docs/python/tutorials/packages/gluon/blocks/nn.md
similarity index 100%
rename from docs/python_docs/python/tutorials/packages/gluon/nn.md
rename to docs/python_docs/python/tutorials/packages/gluon/blocks/nn.md
diff --git a/docs/python_docs/python/tutorials/packages/gluon/parameters.md b/docs/python_docs/python/tutorials/packages/gluon/blocks/parameters.md
similarity index 76%
rename from docs/python_docs/python/tutorials/packages/gluon/parameters.md
rename to docs/python_docs/python/tutorials/packages/gluon/blocks/parameters.md
index 57ab5304bf9a..99f3b4a5f1e5 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/parameters.md
+++ b/docs/python_docs/python/tutorials/packages/gluon/blocks/parameters.md
@@ -19,7 +19,7 @@
 
 <!-- adapted from diveintodeeplearning -->
 
-The ultimate goal of training deep neural networks is finding good parameter values for a given architecture. The [`nn.Sequential`](http://beta.mxnet.io/api/gluon/_autogen/mxnet.gluon.nn.Sequential.html#mxnet.gluon.nn.Sequential) class is a perfect tool to work with standard models. However, very few models are entirely standard, and most scientists want to build novel things, which requires working with model parameters.
+The ultimate goal of training deep neural networks is finding good parameter values for a given architecture. The [`nn.Sequential`](https://mxnet.apache.org/api/python/docs/api/gluon/_autogen/mxnet.gluon.nn.Sequential.html#mxnet.gluon.nn.Sequential) class is a perfect tool to work with standard models. However, very few models are entirely standard, and most scientists want to build novel things, which requires working with model parameters.
 
 This section shows how to manipulate parameters. In particular we will cover the following aspects:
 
@@ -74,7 +74,7 @@ print(net[0].params['dense0_weight'].data())
 
 Note that the weights are nonzero as they were randomly initialized when we constructed the network.
 
-[`data`](http://beta.mxnet.io/api/gluon/_autogen/mxnet.gluon.Parameter.data.html) is not the only method that we can invoke. For instance, we can compute the gradient with respect to the parameters. It has the same shape as the weight. However, since we did not invoke backpropagation yet, the values are all 0.
+[`data`](https://mxnet.apache.org/api/python/docs/api/gluon/_autogen/mxnet.gluon.Parameter.data.html) is not the only method that we can invoke. For instance, we can compute the gradient with respect to the parameters. It has the same shape as the weight. However, since we did not invoke backpropagation yet, the values are all 0.
 
 ```{.python .input  n=5}
 net[0].weight.grad()
@@ -82,7 +82,7 @@ net[0].weight.grad()
 
 ### All Parameters at Once
 
-Accessing parameters as described above can be a bit tedious, in particular if we have more complex blocks, or blocks of blocks (or even blocks of blocks of blocks), since we need to walk through the entire tree in reverse order to learn how the blocks were constructed. To avoid this, blocks come with a method [`collect_params`](http://beta.mxnet.io/api/gluon/_autogen/mxnet.gluon.nn.Block.collect_params.html#mxnet.gluon.nn.Block.collect_params) which grabs all parameters of a network in one dictionary such that we can traverse it with ease. It does so by iterating over all constituents of a block and calls `collect_params` on sub-blocks as needed. To see the difference, consider the following:
+Accessing parameters as described above can be a bit tedious, in particular if we have more complex blocks, or blocks of blocks (or even blocks of blocks of blocks), since we need to walk through the entire tree in reverse order to learn how the blocks were constructed. To avoid this, blocks come with a method [`collect_params`](https://mxnet.apache.org/api/python/docs/api/gluon/_autogen/mxnet.gluon.nn.Block.collect_params.html#mxnet.gluon.nn.Block.collect_params) which grabs all parameters of a network in one dictionary such that we can traverse it with ease. It does so by iterating over all constituents of a block and calls `collect_params` on sub-blocks as needed. To see the difference, consider the following:
 
 ```{.python .input  n=6}
 # Parameters only for the first layer
@@ -143,7 +143,7 @@ rgnet[0][1][0].bias.data()
 
 ### Saving and loading parameters
 
-In order to save parameters, we can use [`save_parameters`](http://beta.mxnet.io/api/gluon/_autogen/mxnet.gluon.nn.Block.save_parameters.html#mxnet.gluon.nn.Block.save_parameters) method on the whole network or a particular subblock. The only parameter that is needed is the `file_name`. In a similar way, we can load parameters back from the file. We use [`load_parameters`](http://beta.mxnet.io/api/gluon/_autogen/mxnet.gluon.nn.Block.load_parameters.html#mxnet.gluon.nn.Block.load_parameters) method for that:
+In order to save parameters, we can use [`save_parameters`](https://mxnet.apache.org/api/python/docs/api/gluon/_autogen/mxnet.gluon.nn.Block.save_parameters.html#mxnet.gluon.nn.Block.save_parameters) method on the whole network or a particular subblock. The only parameter that is needed is the `file_name`. In a similar way, we can load parameters back from the file. We use [`load_parameters`](https://mxnet.apache.org/api/python/docs/api/gluon/_autogen/mxnet.gluon.nn.Block.load_parameters.html#mxnet.gluon.nn.Block.load_parameters) method for that:
 
 ```{.python .input}
 rgnet.save_parameters('model.params')
@@ -152,7 +152,7 @@ rgnet.load_parameters('model.params')
 
 ## Parameter Initialization
 
-Now that we know how to access the parameters, let's look at how to initialize them properly. By default, MXNet initializes the weight matrices uniformly by drawing from $U[-0.07, 0.07]$ and the bias parameters are all set to $0$. However, we often need to use other methods to initialize the weights. MXNet's [`init`](http://beta.mxnet.io/api/gluon-related/mxnet.initializer.html?#module-mxnet.initializer) module provides a variety of preset initialization methods, but if we want something unusual, we need to do a bit of extra work.
+Now that we know how to access the parameters, let's look at how to initialize them properly. By default, MXNet initializes the weight matrices uniformly by drawing from $U[-0.07, 0.07]$ and the bias parameters are all set to $0$. However, we often need to use other methods to initialize the weights. MXNet's [`init`](https://mxnet.apache.org/api/python/docs/api/gluon-related/mxnet.initializer.html?#module-mxnet.initializer) module provides a variety of preset initialization methods, but if we want something unusual, we need to do a bit of extra work.
 
 ### Built-in Initialization
 
@@ -165,14 +165,14 @@ net.initialize(init=init.Normal(sigma=0.01), force_reinit=True)
 net[0].weight.data()[0]
 ```
 
-If we wanted to initialize all parameters to 1, we could do this simply by changing the initializer to [`Constant(1)`](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.initializer.Constant.html#mxnet.initializer.Constant).
+If we wanted to initialize all parameters to 1, we could do this simply by changing the initializer to [`Constant(1)`](https://mxnet.apache.org/api/python/docs/api/gluon-related/_autogen/mxnet.initializer.Constant.html#mxnet.initializer.Constant).
 
 ```{.python .input  n=10}
 net.initialize(init=init.Constant(1), force_reinit=True)
 net[0].weight.data()[0]
 ```
 
-If we want to initialize only a specific parameter in a different manner, we can simply set the initializer only for the appropriate subblock (or parameter) for that matter. For instance, below we initialize the second layer to a constant value of 42 and we use the [`Xavier`](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.initializer.Xavier.html#mxnet.initializer.Xavier) initializer for the weights of the first layer.
+If we want to initialize only a specific parameter in a different manner, we can simply set the initializer only for the appropriate subblock (or parameter) for that matter. For instance, below we initialize the second layer to a constant value of 42 and we use the [`Xavier`](https://mxnet.apache.org/api/python/docs/api/gluon-related/_autogen/mxnet.initializer.Xavier.html#mxnet.initializer.Xavier) initializer for the weights of the first layer.
 
 ```{.python .input  n=11}
 net[1].initialize(init=init.Constant(42), force_reinit=True)
@@ -183,7 +183,7 @@ print(net[0].weight.data()[0])
 
 ### Custom Initialization
 
-Sometimes, the initialization methods we need are not provided in the `init` module. If this is the case, we can implement a subclass of the [`Initializer`](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.initializer.Initializer.html#mxnet.initializer.Initializer) class so that we can use it like any other initialization method. Usually, we only need to implement the `_init_weight` method and modify the incoming NDArray according to the initial result. In the example below, we pick a nontrivial distribution, just to prove the point. We draw the coefficients from the following distribution:
+Sometimes, the initialization methods we need are not provided in the `init` module. If this is the case, we can implement a subclass of the [`Initializer`](https://mxnet.apache.org/api/python/docs/api/gluon-related/_autogen/mxnet.initializer.Initializer.html#mxnet.initializer.Initializer) class so that we can use it like any other initialization method. Usually, we only need to implement the `_init_weight` method and modify the incoming NDArray according to the initial result. In the example below, we pick a nontrivial distribution, just to prove the point. We draw the coefficients from the following distribution:
 
 $$
 \begin{aligned}
@@ -206,7 +206,7 @@ net.initialize(MyInit(), force_reinit=True)
 net[0].weight.data()[0]
 ```
 
-If even this functionality is insufficient, we can set parameters directly. Since `data()` returns an NDArray we can access it just like any other matrix. A note for advanced users - if you want to adjust parameters within an [`autograd`](http://beta.mxnet.io/api/gluon-related/mxnet.autograd.html?#module-mxnet.autograd) scope you need to use [`set_data`](http://beta.mxnet.io/api/gluon/_autogen/mxnet.gluon.Parameter.set_data.html#mxnet.gluon.Parameter.set_data) to avoid confusing the automatic differentiation mechanics.
+If even this functionality is insufficient, we can set parameters directly. Since `data()` returns an NDArray we can access it just like any other matrix. A note for advanced users - if you want to adjust parameters within an [`autograd`](https://mxnet.apache.org/api/python/docs/api/gluon-related/mxnet.autograd.html?#module-mxnet.autograd) scope you need to use [`set_data`](https://mxnet.apache.org/api/python/docs/api/gluon/_autogen/mxnet.gluon.Parameter.set_data.html#mxnet.gluon.Parameter.set_data) to avoid confusing the automatic differentiation mechanics.
 
 ```{.python .input  n=13}
 net[0].weight.data()[:] += 1
@@ -240,4 +240,4 @@ net[1].weight.data()[0,0] = 100
 print(net[1].weight.data()[0] == net[2].weight.data()[0])
 ```
 
-The above example shows that the parameters of the second and third layer are tied. They are identical rather than just being equal. That is, by changing one of the parameters the other one changes, too. What happens to the gradients is quite ingenious. Since the model parameters contain gradients, the gradients of the second hidden layer and the third hidden layer are accumulated in the [`shared.params.grad()`](http://beta.mxnet.io/api/gluon/_autogen/mxnet.gluon.Parameter.grad.html) during backpropagation.
+The above example shows that the parameters of the second and third layer are tied. They are identical rather than just being equal. That is, by changing one of the parameters the other one changes, too. What happens to the gradients is quite ingenious. Since the model parameters contain gradients, the gradients of the second hidden layer and the third hidden layer are accumulated in the [`shared.params.grad()`](https://mxnet.apache.org/api/python/docs/api/gluon/_autogen/mxnet.gluon.Parameter.grad.html) during backpropagation.
diff --git a/docs/python_docs/python/tutorials/packages/gluon/blocks/save_load_params.md b/docs/python_docs/python/tutorials/packages/gluon/blocks/save_load_params.md
new file mode 100644
index 000000000000..ee720959943c
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/blocks/save_load_params.md
@@ -0,0 +1,281 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Saving and Loading Gluon Models
+
+Training large models take a lot of time and it is a good idea to save the trained models to files to avoid training them again and again. There are a number of reasons to do this. For example, you might want to do inference on a machine that is different from the one where the model was trained. Sometimes model's performance on validation set decreases towards the end of the training because of overfitting. If you saved your model parameters after every epoch, at the end you can decide to use the model that performs best on the validation set. Another reason would be to train your model using one language (like Python that has a lot of tools for training) and run inference using a different language (like Scala probably because your application is built on Scala).
+
+In this tutorial, we will learn ways to save and load Gluon models. There are two ways to save/load Gluon models:
+
+**1. Save/load model parameters only**
+
+Parameters of any Gluon model can be saved using the `save_parameters` and `load_parameters` method. This does not save model architecture. This method is used to save parameters of dynamic (non-hybrid) models. Model architecture cannot be saved for dynamic models because model architecture changes during execution.
+
+**2. Save/load model parameters AND architecture**
+
+The Model architecture of `Hybrid` models stays static and don't change during execution. Therefore both model parameters AND architecture can be saved and loaded using `export`, `imports` methods.
+
+Let's look at the above methods in more detail. Let's start by importing the modules we'll need.
+
+```python
+from __future__ import print_function
+
+import mxnet as mx
+import mxnet.ndarray as nd
+from mxnet import nd, autograd, gluon
+from mxnet.gluon.data.vision import transforms
+
+import numpy as np
+```
+
+## Setup: build and train a simple model
+
+We need a trained model before we can save it to a file. So let's go ahead and build a very simple convolutional network and train it on MNIST data.
+
+Let's define a helper function to build a LeNet model and another helper to train LeNet with MNIST.
+
+```python
+# Use GPU if one exists, else use CPU
+ctx = mx.gpu() if mx.context.num_gpus() else mx.cpu()
+
+# MNIST images are 28x28. Total pixels in input layer is 28x28 = 784
+num_inputs = 784
+# Clasify the images into one of the 10 digits
+num_outputs = 10
+# 64 images in a batch
+batch_size = 64
+
+# Load the training data
+train_data = gluon.data.DataLoader(gluon.data.vision.MNIST(train=True).transform_first(transforms.ToTensor()),
+                                   batch_size, shuffle=True)
+
+# Build a simple convolutional network
+def build_lenet(net):    
+    with net.name_scope():
+        # First convolution
+        net.add(gluon.nn.Conv2D(channels=20, kernel_size=5, activation='relu'))
+        net.add(gluon.nn.MaxPool2D(pool_size=2, strides=2))
+        # Second convolution
+        net.add(gluon.nn.Conv2D(channels=50, kernel_size=5, activation='relu'))
+        net.add(gluon.nn.MaxPool2D(pool_size=2, strides=2))
+        # Flatten the output before the fully connected layers
+        net.add(gluon.nn.Flatten())
+        # First fully connected layers with 512 neurons
+        net.add(gluon.nn.Dense(512, activation="relu"))
+        # Second fully connected layer with as many neurons as the number of classes
+        net.add(gluon.nn.Dense(num_outputs))
+
+        return net
+
+# Train a given model using MNIST data
+def train_model(model):
+    # Initialize the parameters with Xavier initializer
+    model.collect_params().initialize(mx.init.Xavier(), ctx=ctx)
+    # Use cross entropy loss
+    softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()
+    # Use Adam optimizer
+    trainer = gluon.Trainer(model.collect_params(), 'adam', {'learning_rate': .001})
+
+    # Train for one epoch
+    for epoch in range(1):
+        # Iterate through the images and labels in the training data
+        for batch_num, (data, label) in enumerate(train_data):
+            # get the images and labels
+            data = data.as_in_context(ctx)
+            label = label.as_in_context(ctx)
+            # Ask autograd to record the forward pass
+            with autograd.record():
+                # Run the forward pass
+                output = model(data)
+                # Compute the loss
+                loss = softmax_cross_entropy(output, label)
+            # Compute gradients
+            loss.backward()
+            # Update parameters
+            trainer.step(data.shape[0])
+
+            # Print loss once in a while
+            if batch_num % 50 == 0:
+                curr_loss = nd.mean(loss).asscalar()
+                print("Epoch: %d; Batch %d; Loss %f" % (epoch, batch_num, curr_loss))
+```
+
+Let's build a model and train it. After training, we will save and restore this model from a file.
+
+```python
+net = build_lenet(gluon.nn.Sequential())
+train_model(net)
+```
+<pre>Epoch: 0; Batch 0; Loss 2.288904 <!--notebook-skip-line-->
+Epoch: 0; Batch 50; Loss 0.269372 <!--notebook-skip-line-->
+Epoch: 0; Batch 100; Loss 0.238990 <!--notebook-skip-line-->
+Epoch: 0; Batch 150; Loss 0.320592 <!--notebook-skip-line-->
+Epoch: 0; Batch 200; Loss 0.048619 <!--notebook-skip-line-->
+Epoch: 0; Batch 250; Loss 0.121555 <!--notebook-skip-line-->
+Epoch: 0; Batch 300; Loss 0.083645 <!--notebook-skip-line-->
+Epoch: 0; Batch 350; Loss 0.040627 <!--notebook-skip-line-->
+Epoch: 0; Batch 400; Loss 0.195946 <!--notebook-skip-line-->
+Epoch: 0; Batch 450; Loss 0.155514 <!--notebook-skip-line-->
+Epoch: 0; Batch 500; Loss 0.031762 <!--notebook-skip-line-->
+Epoch: 0; Batch 550; Loss 0.056516 <!--notebook-skip-line-->
+Epoch: 0; Batch 600; Loss 0.095174 <!--notebook-skip-line-->
+Epoch: 0; Batch 650; Loss 0.054901 <!--notebook-skip-line-->
+Epoch: 0; Batch 700; Loss 0.030067 <!--notebook-skip-line-->
+Epoch: 0; Batch 750; Loss 0.102611 <!--notebook-skip-line-->
+Epoch: 0; Batch 800; Loss 0.010036 <!--notebook-skip-line-->
+Epoch: 0; Batch 850; Loss 0.051853 <!--notebook-skip-line-->
+Epoch: 0; Batch 900; Loss 0.008402 <!--notebook-skip-line-->
+</pre> <!--notebook-skip-line-->
+
+## Saving model parameters to file
+
+Okay, we now have a model (`net`) that we can save to a file. Let's save the parameters of this model to a file using the `save_parameters` function.
+
+```python
+file_name = "net.params"
+net.save_parameters(file_name)
+```
+
+We have successfully saved the parameters of the model into a file.
+
+Note: `Block.collect_params().save()` is not a recommended way to save parameters of a Gluon network if you plan to load the parameters back into a Gluon network using `Block.load_parameters()`.
+
+## Loading model parameters from file
+
+Let's now create a network with the parameters we saved into the file. We build the network again using the helper first and then load the weights from the file we saved using the `load_parameters` function.
+
+```python
+new_net = build_lenet(gluon.nn.Sequential())
+new_net.load_parameters(file_name, ctx=ctx)
+```
+
+Note that to do this, we need the definition of the network as Python code. If we want to recreate this network on a different machine using the saved weights, we need the same Python code (`build_lenet`) that created the network to create the `new_net` object shown above. This means Python code needs to be copied over to any machine where we want to run this network.
+
+If our network is [Hybrid](https://mxnet.apache.org/tutorials/gluon/hybrid.html), we can even save the network architecture into files and we won't need the network definition in a Python file to load the network. We'll see how to do it in the next section.
+
+Let's test the model we just loaded from file.
+
+```python
+import matplotlib.pyplot as plt
+
+def verify_loaded_model(net):
+    """Run inference using ten random images.
+    Print both input and output of the model"""
+
+    def transform(data, label):
+        return data.astype(np.float32)/255, label.astype(np.float32)
+
+    # Load ten random images from the test dataset
+    sample_data = mx.gluon.data.DataLoader(mx.gluon.data.vision.MNIST(train=False, transform=transform),
+                                  10, shuffle=True)
+
+    for data, label in sample_data:
+
+        # Display the images
+        img = nd.transpose(data, (1,0,2,3))
+        img = nd.reshape(img, (28,10*28,1))
+        imtiles = nd.tile(img, (1,1,3))
+        plt.imshow(imtiles.asnumpy())
+        plt.show()
+
+        # Display the predictions
+        data = nd.transpose(data, (0, 3, 1, 2))
+        out = net(data.as_in_context(ctx))
+        predictions = nd.argmax(out, axis=1)
+        print('Model predictions: ', predictions.asnumpy())
+
+        break
+
+verify_loaded_model(new_net)
+```
+![Model inputs](https://raw.githubusercontent.com/indhub/web-data/4a9c100aa996df3dff0e7f493029d411c2b526c3/mxnet/tutorials/gluon/save_load_params/mnist_in_1.png) <!--notebook-skip-line-->
+
+Model predictions:  [1. 1. 4. 5. 0. 5. 7. 0. 3. 6.] <!--notebook-skip-line-->
+
+## Saving model parameters AND architecture to file
+
+[Hybrid](https://mxnet.apache.org/tutorials/gluon/hybrid.html) models can be serialized as JSON files using the `export` function. Once serialized, these models can be loaded from other language bindings like C++ or Scala for faster inference or inference in different environments.
+
+Note that the network we created above is not a Hybrid network and therefore cannot be serialized into a JSON file. So, let's create a Hybrid version of the same network and train it.
+
+```python
+net = build_lenet(gluon.nn.HybridSequential())
+net.hybridize()
+train_model(net)
+```
+
+<pre>Epoch: 0; Batch 0; Loss 2.323284 <!--notebook-skip-line-->
+Epoch: 0; Batch 50; Loss 0.444733 <!--notebook-skip-line-->
+Epoch: 0; Batch 100; Loss 0.103407 <!--notebook-skip-line-->
+Epoch: 0; Batch 150; Loss 0.166772 <!--notebook-skip-line-->
+Epoch: 0; Batch 200; Loss 0.227569 <!--notebook-skip-line-->
+Epoch: 0; Batch 250; Loss 0.069515 <!--notebook-skip-line-->
+Epoch: 0; Batch 300; Loss 0.074086 <!--notebook-skip-line-->
+Epoch: 0; Batch 350; Loss 0.074382 <!--notebook-skip-line-->
+Epoch: 0; Batch 400; Loss 0.026569 <!--notebook-skip-line-->
+Epoch: 0; Batch 450; Loss 0.097248 <!--notebook-skip-line-->
+Epoch: 0; Batch 500; Loss 0.059895 <!--notebook-skip-line-->
+Epoch: 0; Batch 550; Loss 0.053194 <!--notebook-skip-line-->
+Epoch: 0; Batch 600; Loss 0.076294 <!--notebook-skip-line-->
+Epoch: 0; Batch 650; Loss 0.047274 <!--notebook-skip-line-->
+Epoch: 0; Batch 700; Loss 0.007898 <!--notebook-skip-line-->
+Epoch: 0; Batch 750; Loss 0.039478 <!--notebook-skip-line-->
+Epoch: 0; Batch 800; Loss 0.031342 <!--notebook-skip-line-->
+Epoch: 0; Batch 850; Loss 0.059289 <!--notebook-skip-line-->
+Epoch: 0; Batch 900; Loss 0.037809 <!--notebook-skip-line-->
+</pre> <!--notebook-skip-line-->
+
+We now have a trained hybrid network. This can be exported into files using the `export` function. The `export` function will export the model architecture into a `.json` file and model parameters into a `.params` file.
+
+```python
+net.export("lenet", epoch=1)
+```
+
+`export` in this case creates `lenet-symbol.json` and `lenet-0001.params` in the current directory.
+
+## Loading model parameters AND architecture from file
+
+### From a different frontend
+
+One of the main reasons to serialize model architecture into a JSON file is to load it from a different frontend like C, C++ or Scala. Here is a couple of examples:
+1. [Loading serialized Hybrid networks from C](https://github.com/apache/incubator-mxnet/blob/master/example/image-classification/predict-cpp/image-classification-predict.cc)
+2. [Loading serialized Hybrid networks from Scala](https://github.com/apache/incubator-mxnet/blob/master/scala-package/infer/src/main/scala/org/apache/mxnet/infer/ImageClassifier.scala)
+
+### From Python
+
+Serialized Hybrid networks (saved as .JSON and .params file) can be loaded and used inside Python frontend using `gluon.nn.SymbolBlock`. To demonstrate that, let's load the network we serialized above.
+
+```python
+import warnings
+with warnings.catch_warnings():
+    warnings.simplefilter("ignore")
+    deserialized_net = gluon.nn.SymbolBlock.imports("lenet-symbol.json", ['data'], "lenet-0001.params", ctx=ctx)
+```
+
+`deserialized_net` now contains the network we deserialized from files. Let's test the deserialized network to make sure it works.
+
+```python
+verify_loaded_model(deserialized_net)
+```
+
+![Model inputs](https://raw.githubusercontent.com/indhub/web-data/4a9c100aa996df3dff0e7f493029d411c2b526c3/mxnet/tutorials/gluon/save_load_params/mnist_in_2.png) <!--notebook-skip-line-->
+
+Model predictions:  [4. 8. 0. 1. 5. 5. 8. 8. 1. 9.] <!--notebook-skip-line-->
+
+That's all! We learned how to save and load Gluon networks from files. Parameters of any Gluon network can be persisted into files. For hybrid networks, both the architecture of the network and the parameters can be saved to and loaded from files.
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/python_docs/python/tutorials/packages/gluon/customop.md b/docs/python_docs/python/tutorials/packages/gluon/customop.md
deleted file mode 100644
index 194056f35ab4..000000000000
--- a/docs/python_docs/python/tutorials/packages/gluon/customop.md
+++ /dev/null
@@ -1,227 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Creating custom operators with numpy
-
-In this tutorial, we will learn how to build custom operators with numpy in python. We will go through two examples:
-
-+ Custom operator without any `Parameter`s
-+ Custom operator with `Parameter`s
-
-Custom operator in python is easy to develop and good for prototyping, but may hurt performance. If you find it to be a bottleneck, please consider moving to a C++ based implementation in the backend.
-
-```python
-import numpy as np
-import mxnet as mx
-from mxnet import gluon, autograd
-import os
-```
-## Parameter-less operators
-
-This operator implements the standard sigmoid activation function. This is only for illustration purposes, in real life you would use the built-in operator `mx.nd.relu`.
-
-### Forward & backward implementation
-
-First we implement the forward and backward computation by sub-classing `mx.operator.CustomOp`:
-
-```python
-class Sigmoid(mx.operator.CustomOp):
-    def forward(self, is_train, req, in_data, out_data, aux):
-        """Implements forward computation.
-
-        is_train : bool, whether forwarding for training or testing.
-        req : list of {'null', 'write', 'inplace', 'add'}, how to assign to out_data. 'null' means skip assignment, etc.
-        in_data : list of NDArray, input data.
-        out_data : list of NDArray, pre-allocated output buffers.
-        aux : list of NDArray, mutable auxiliary states. Usually not used.
-        """
-        x = in_data[0].asnumpy()
-        y = 1.0 / (1.0 + np.exp(-x))
-        self.assign(out_data[0], req[0], mx.nd.array(y))
-
-    def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
-        """Implements backward computation
-
-        req : list of {'null', 'write', 'inplace', 'add'}, how to assign to in_grad
-        out_grad : list of NDArray, gradient w.r.t. output data.
-        in_grad : list of NDArray, gradient w.r.t. input data. This is the output buffer.
-        """
-        y = out_data[0].asnumpy()
-        dy = out_grad[0].asnumpy()
-        dx = dy*(1.0 - y)*y
-        self.assign(in_grad[0], req[0], mx.nd.array(dx))
-```
-
-### Register custom operator
-
-Then we need to register the custom op and describe it’s properties like input and output shapes so that mxnet can recognize it. This is done by sub-classing `mx.operator.CustomOpProp`:
-
-```python
-@mx.operator.register("sigmoid")  # register with name "sigmoid"
-class SigmoidProp(mx.operator.CustomOpProp):
-    def __init__(self):
-        super(SigmoidProp, self).__init__(True)
-
-    def list_arguments(self):
-        #  this can be omitted if you only have 1 input.
-        return ['data']
-
-    def list_outputs(self):
-        #  this can be omitted if you only have 1 output.
-        return ['output']
-
-    def infer_shape(self, in_shapes):
-        """Calculate output shapes from input shapes. This can be
-        omited if all your inputs and outputs have the same shape.
-
-        in_shapes : list of shape. Shape is described by a tuple of int.
-        """
-        data_shape = in_shapes[0]
-        output_shape = data_shape
-        # return 3 lists representing inputs shapes, outputs shapes, and aux data shapes.
-        return (data_shape,), (output_shape,), ()
-
-    def create_operator(self, ctx, in_shapes, in_dtypes):
-        #  create and return the CustomOp class.
-        return Sigmoid()
-```
-
-### Example Usage
-
-We can now use this operator by calling `mx.nd.Custom`:
-
-```python
-x = mx.nd.array([0, 1, 2, 3])
-# attach gradient buffer to x for autograd
-x.attach_grad()
-# forward in a record() section to save computation graph for backward
-# see autograd tutorial to learn more.
-with autograd.record():
-    y = mx.nd.Custom(x, op_type='sigmoid')
-print(y)
-```
-
-```python
-# call backward computation
-y.backward()
-# gradient is now saved to the grad buffer we attached previously
-print(x.grad)
-```
-
-## Parametrized Operator
-
-In the second use case we implement an operator with learnable weights. We implement the dense (or fully connected) layer that has one input, one output, and two learnable parameters: weight and bias.
-
-The dense operator performs a dot product between data and weight, then add bias to it.
-
-### Forward & backward implementation
-
-```python
-class Dense(mx.operator.CustomOp):
-    def __init__(self, bias):
-        self._bias = bias
-
-    def forward(self, is_train, req, in_data, out_data, aux):
-        x = in_data[0].asnumpy()
-        weight = in_data[1].asnumpy()
-        y = x.dot(weight.T) + self._bias
-        self.assign(out_data[0], req[0], mx.nd.array(y))
-
-    def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
-        x = in_data[0].asnumpy()
-        dy = out_grad[0].asnumpy()
-        dx = dy.T.dot(x)
-        self.assign(in_grad[0], req[0], mx.nd.array(dx))
-```
-
-### Registration
-
-```python
-@mx.operator.register("dense")  # register with name "sigmoid"
-class DenseProp(mx.operator.CustomOpProp):
-    def __init__(self, bias):
-        super(DenseProp, self).__init__(True)
-        # we use constant bias here to illustrate how to pass arguments
-        # to operators. All arguments are in string format so you need
-        # to convert them back to the type you want.
-        self._bias = float(bias)
-
-    def list_arguments(self):
-        return ['data', 'weight']
-
-    def list_outputs(self):
-        #  this can be omitted if you only have 1 output.
-        return ['output']
-
-    def infer_shape(self, in_shapes):
-        data_shape = in_shapes[0]
-        weight_shape = in_shapes[1]
-        output_shape = (data_shape[0], weight_shape[0])
-        # return 3 lists representing inputs shapes, outputs shapes, and aux data shapes.
-        return (data_shape, weight_shape), (output_shape,), ()
-
-    def create_operator(self, ctx, in_shapes, in_dtypes):
-        #  create and return the CustomOp class.
-        return Dense(self._bias)
-```
-
-### Use CustomOp together with Block
-
-Parameterized CustomOp are usually used together with Blocks, which holds the parameter.
-
-```python
-class DenseBlock(mx.gluon.Block):
-    def __init__(self, in_channels, channels, bias, **kwargs):
-        super(DenseBlock, self).__init__(**kwargs)
-        self._bias = bias
-        self.weight = self.params.get('weight', shape=(channels, in_channels))
-
-    def forward(self, x):
-        ctx = x.context
-        return mx.nd.Custom(x, self.weight.data(ctx), bias=self._bias, op_type='dense')
-```
-
-### Example usage
-
-```python
-dense = DenseBlock(3, 5, 0.1)
-dense.initialize()
-x = mx.nd.uniform(shape=(4, 3))
-y = dense(x)
-print(y)
-```
-
-## Using custom operators with fork
-
-In Linux systems, the default method in multiprocessing to create process is by using fork. If there are unfinished async custom operations when forking, the program will be blocked because of python GIL. Always use sync calls like `wait_to_read` or `waitall` before calling fork.
-
-```python
-x = mx.nd.array([0, 1, 2, 3])
-y = mx.nd.Custom(x, op_type='sigmoid')
-# unfinished async sigmoid operation will cause blocking
-os.fork()
-```
-
-Correctly handling this will make mxnet depend upon libpython, so the workaround now is to ensure that all custom operations are executed before forking process.
-
-```python
-x = mx.nd.array([0, 1, 2, 3])
-y = mx.nd.Custom(x, op_type='sigmoid')
-# force execution by reading y
-print(y.asnumpy())
-os.fork()
-```
diff --git a/docs/python_docs/python/tutorials/packages/gluon/data.md b/docs/python_docs/python/tutorials/packages/gluon/data.md
deleted file mode 100644
index fb9b6e6af602..000000000000
--- a/docs/python_docs/python/tutorials/packages/gluon/data.md
+++ /dev/null
@@ -1,141 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Data
-
-One of the most critical steps for model training and inference is loading the data: without data you can’t do deep learning! In this tutorial we use the `data` module to:
-
-1) Define a [`Dataset`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.data.Dataset.html)
-2) Use `transform`s to augment the dataset
-3) Use a [`DataLoader`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.data.DataLoader.html) to iterate through the dataset in mini-batches
-
-## Getting started with [`Dataset`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.data.Dataset.html)s
-
-Our very first step is to define the [`Dataset`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.data.Dataset.html). A [`Dataset`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.data.Dataset.html) is MXNet Gluon's interface to the data that's stored on disk (or elsewhere). Out of the box, MXNet Gluon comes with a number of common benchmarking datasets such as CIFAR-10 and MNIST. We'll use an MNIST variant, called FashionMNIST, to understand the role of the [`Dataset`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.data.Dataset.html). When we instantiate this benchmarking dataset, the data will be downloaded to disk and be ready to use.
-
-```
-import mxnet as mx
-
-dataset = mx.gluon.data.vision.FashionMNIST()
-```
-
-So how do we get data out of a [`Dataset`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.data.Dataset.html)? We index.
-
-```
-sample = dataset[42]
-```
-
-Choosing a random index of 42 above, we get back a single sample from the dataset. A single sample usually contains multiple elements.
-
-```
-len(sample)
-```
-
-Our sample has 2 elements in this case. We have the image as the first element and the label as the second. Although this is a common pattern, you should check the documentation (or implementation) of the dataset you're using.
-
-We can unpack the sample into `data` and `label` and visualise our single sample.
-
-```
-data_sample, label_sample = sample
-print('Clothing Category #{}'.format(label_sample))
-```
-
-```
-import matplotlib.pyplot as plt
-
-plot = lambda s: plt.imshow(s[:,:,0].asnumpy(), cmap='gray')
-plot(data_sample)
-```
-
-We can access all of our samples in the dataset using the indexing method above, but usually we leave this to the [`DataLoader`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.data.DataLoader.html). We'll see an example of this shortly, but first we'll see how to change our dataset with `transform`s.
-
-## Getting started with `transform`s
-
-Similar to `dataset`s which return individual samples, `transform`s give us a way to change individual samples of our `dataset`.
-
-#### Why would you want to do this?
-
-Most of the time, samples returned by the dataset aren't in quite the right format to be passed into a neural network. One common example is to have samples with a data type of `int8` when your network expects `float32`. Another common example with images is when you have an image with a dimension order of (height, width, channel) when you need (channel, height, width) for the network.
-
-Augmenting samples is also common practice: that is, randomly permuting existing samples to make new samples to reduce issues of network overfitting. Using image samples as an example, you could crop to random regions, flip from left to right or even jitter the brightness of the image.
-
-#### And how do you do it?
-
-We first define our transform and then apply it to our dataset.
-
-MXNet Gluon has a number of in-build transforms available at `mxnet.gluon.data.vision.transforms`, so let's use [`RandomResizedCrop`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.data.vision.transforms.RandomResizedCrop.html) as an example. We're specifying that we want a random crop that contains 60% to 80% of the original image, and then we want to scale this cropped region to 20px by 20px. You should notice we're instantiating a class that can be called like a function.
-
-```
-from mxnet.gluon.data.vision.transforms import RandomResizedCrop
-
-transform_fn = RandomResizedCrop(size=(20, 20), scale=(0.6, 0.8))
-```
-
-After defining `transform_fn`, we now need to apply it to the dataset. We call the `transform_first` method of our dataset to apply `transform_fn` to the first element of all samples. We had 2 elements per sample in our example, so we only apply `transform_fn` to the image and not the label.
-
-Advanced: `transform`, instead of `transform_first` can be used to transform all elements.
-
-```
-dataset = dataset.transform_first(transform_fn)
-```
-
-When we retrieve the same sample as before from the dataset, we now see an augmented version of the image (with the same label). We'd see a different augmented image every time we retrieve this sample because the transform is applied lazily by default.
-
-```
-data_sample, label_sample = dataset[42]
-print('Clothing Category #{}'.format(label_sample))
-plot(data_sample)
-```
-
-## Getting started with [`DataLoader`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.data.DataLoader.html)s
-
-Our `dataset` gives us individual samples, but we usually train neural networks on batches of samples. A [`DataLoader`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.data.DataLoader.html) retrieves samples from the `dataset` and stacks them into batches. At a minimum, all we need to specify is the number of samples we want in each batch, called the `batch_size`, but [`DataLoader`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.data.DataLoader.html)s have many other useful features. Shuffling data samples for training is as simple as setting `shuffle`. 
-
-Advanced: A larger `batch_size` often speeds up training, but can lead to out-of-memory and convergence issues. 12345 is just for demonstration purposes: you'll more commonly use a smaller batch size (e.g. 128) but it depends on the model.
-
-```
-dataloader = mx.gluon.data.DataLoader(dataset,
-                                      batch_size=12345,
-                                      shuffle=True)
-```
-
-We iterate through the `dataloader` to get all the batches in our `dataset`, and we usually place the code for network training inside this loop. You'll notice that `data_batch` has an extra dimension at the start (compared with `data_sample`): this is often called the batch dimension.
-
-```
-for batch_idx, (data_batch, label_batch) in enumerate(dataloader):
-    print('Batch {} has shape {}'.format(batch_idx, data_batch.shape))
-```
-
-You might have noticed that the last batch contains fewer samples than the others. We had a remainder at the end and by default a [`DataLoader`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.data.DataLoader.html) keeps the incomplete batch (see `last_batch` argument).
-
-We can plot a few (augmented) images from the last batch.
-
-```
-plt.figure(1)
-plt.subplot(131); plot(data_batch[123])
-plt.subplot(132); plot(data_batch[234])
-plt.subplot(133); plot(data_batch[345])
-plt.show()
-```
-
-## Conclusion
-
-We've now seen all of the core components of the MXNet Gluon data pipeline. You should now understand the difference between [`Dataset`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.data.Dataset.html)s, that return individual samples, and [`DataLoader`](/api/python/docs/api/gluon/_autogen/mxnet.gluon.data.DataLoader.html)s, that return batches of samples. You should also be able to transform your `dataset` for pre-processing or augmentation purposes.
-
-## Recommended Next Steps
-
-We used presets and a lot of default values in this tutorial, but there are many other options and possibilities. You're likely to want to use your own dataset for training. You can learn more about this in the dedicated `Dataset` tutorial. We also have a tutorial on `transform`s with examples on how to compose transform operations. And last but not least, we have a very useful tutorial on `DataLoader`s. You'll often find that loading data can be the bottleneck to training, and this tutorial contains some useful tricks to speed up the data pipeline.
diff --git a/docs/python_docs/python/tutorials/packages/gluon/image-augmentation.md b/docs/python_docs/python/tutorials/packages/gluon/image/image-augmentation.md
similarity index 100%
rename from docs/python_docs/python/tutorials/packages/gluon/image-augmentation.md
rename to docs/python_docs/python/tutorials/packages/gluon/image/image-augmentation.md
diff --git a/docs/python_docs/python/tutorials/packages/gluon/image/index.rst b/docs/python_docs/python/tutorials/packages/gluon/image/index.rst
index d15d9e8623de..b6bf3dc7834d 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/image/index.rst
+++ b/docs/python_docs/python/tutorials/packages/gluon/image/index.rst
@@ -46,7 +46,7 @@ Advanced Image Tutorials
 
    .. card::
       :title: Image similarity search with InfoGAN
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/info_gan.html
+      :link: https://mxnet.apache.org/versions/master/tutorials/gluon/info_gan.html
 
       Implementing an InfoGAN based on the MXNet Gluon.
 
@@ -100,6 +100,6 @@ These tutorials link to the MXNet GluonCV Toolkit website.
 .. toctree::
    :hidden:
    :maxdepth: 1
+   :glob:
 
-   mnist
-   pretrained_models
+   *
diff --git a/docs/python_docs/python/tutorials/packages/gluon/image/mnist.md b/docs/python_docs/python/tutorials/packages/gluon/image/mnist.md
index 9621e16ac69e..8a3d8229413b 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/image/mnist.md
+++ b/docs/python_docs/python/tutorials/packages/gluon/image/mnist.md
@@ -28,12 +28,12 @@ MNIST is a widely used dataset for the hand-written digit classification task. I
 This tutorial uses MXNet's new high-level interface, gluon package to implement MLP using
 imperative fashion.
 
-This is based on the Mnist tutorial with symbolic approach. You can find it [here](http://mxnet.io/tutorials/python/mnist.html).
+This is based on the Mnist tutorial with symbolic approach. You can find it [here](https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html).
 
 ## Prerequisites
 To complete this tutorial, we need:
 
-- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/install/index.html).
+- MXNet. See the instructions for your operating system in [Setup and Installation](https://mxnet.io/get_started).
 
 - [Python Requests](http://docs.python-requests.org/en/master/) and [Jupyter Notebook](http://jupyter.org/index.html).
 
@@ -94,7 +94,7 @@ In an MLP, the outputs of most FC layers are fed into an activation function, wh
 The following code declares three fully connected layers with 128, 64 and 10 neurons each.
 The last fully connected layer often has its hidden size equal to the number of output classes in the dataset. Furthermore, these FC layers uses ReLU activation for performing an element-wise ReLU transformation on the FC layer output.
 
-To do this, we will use [Sequential layer](http://mxnet.io/api/python/gluon/gluon.html#mxnet.gluon.nn.Sequential) type. This is simply a linear stack of neural network layers. `nn.Dense` layers are nothing but the fully connected layers we discussed above.
+To do this, we will use [Sequential layer](https://mxnet.io/api/python/docs/api/gluon/_autogen/mxnet.gluon.nn.Sequential.html) type. This is simply a linear stack of neural network layers. `nn.Dense` layers are nothing but the fully connected layers we discussed above.
 
 ```python
 # define network
@@ -107,13 +107,13 @@ with net.name_scope():
 
 #### Initialize parameters and optimizer
 
-The following source code initializes all parameters received from parameter dict using [Xavier](http://mxnet.io/api/python/optimization/optimization.html#mxnet.initializer.Xavier) initializer
+The following source code initializes all parameters received from parameter dict using [Xavier](https://mxnet.io/api/python/docs/api/gluon-related/_autogen/mxnet.initializer.Xavier.html) initializer
 to train the MLP network we defined above.
 
 For our training, we will make use of the stochastic gradient descent (SGD) optimizer. In particular, we'll be using mini-batch SGD. Standard SGD processes train data one example at a time. In practice, this is very slow and one can speed up the process by processing examples in small batches. In this case, our batch size will be 100, which is a reasonable choice. Another parameter we select here is the learning rate, which controls the step size the optimizer takes in search of a solution. We'll pick a learning rate of 0.02, again a reasonable choice. Settings such as batch size and learning rate are what are usually referred to as hyper-parameters. What values we give them can have a great impact on training performance.
 
-We will use [Trainer](http://mxnet.io/api/python/gluon/gluon.html#trainer) class to apply the
-[SGD optimizer](http://mxnet.io/api/python/optimization/optimization.html#mxnet.optimizer.SGD) on the
+We will use [Trainer](https://mxnet.io/api/python/docs/api/gluon/mxnet.gluon.Trainer.html) class to apply the
+[SGD optimizer](https://mxnet.io/api/python/docs/api/gluon-related/_autogen/mxnet.optimizer.SGD.html) on the
 initialized parameters.
 
 ```python
@@ -129,7 +129,7 @@ Typically, one runs the training until convergence, which means that we have lea
 
 We will take following steps for training:
 
-- Define [Accuracy evaluation metric](http://mxnet.io/api/python/metric/metric.html#mxnet.metric.Accuracy) over training data.
+- Define [Accuracy evaluation metric](https://mxnet.io/api/python/metric/metric.html#mxnet.metric.Accuracy) over training data.
 - Loop over inputs for every epoch.
 - Forward input through network to get output.
 - Compute loss with output and label inside record scope.
@@ -138,7 +138,7 @@ We will take following steps for training:
 
 Loss function takes (output, label) pairs and computes a scalar loss for each sample in the mini-batch. The scalars measure how far each output is from the label.
 There are many predefined loss functions in gluon.loss. Here we use
-[softmax_cross_entropy_loss](http://mxnet.io/api/python/gluon/gluon.html#mxnet.gluon.loss.softmax_cross_entropy_loss) for digit classification. We will compute loss and do backward propagation inside
+[softmax_cross_entropy_loss](https://mxnet.io/api/python/gluon/gluon.html#mxnet.gluon.loss.softmax_cross_entropy_loss) for digit classification. We will compute loss and do backward propagation inside
 training scope which is defined by `autograd.record()`.
 
 ```python
diff --git a/docs/python_docs/python/tutorials/packages/gluon/image/pretrained_models.md b/docs/python_docs/python/tutorials/packages/gluon/image/pretrained_models.md
index c2f72d876418..a928f949260b 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/image/pretrained_models.md
+++ b/docs/python_docs/python/tutorials/packages/gluon/image/pretrained_models.md
@@ -18,7 +18,7 @@
 # Using pre-trained models in MXNet
 
 
-In this tutorial we will see how to use multiple pre-trained models with Apache MXNet. First, let's download three image classification models from the Apache MXNet [Gluon model zoo](https://mxnet.incubator.apache.org/api/python/gluon/model_zoo.html).
+In this tutorial we will see how to use multiple pre-trained models with Apache MXNet. First, let's download three image classification models from the Apache MXNet [Gluon model zoo](https://mxnet.apache.org/api/python/gluon/model_zoo.html).
 * **DenseNet-121** ([research paper](https://arxiv.org/abs/1608.06993)), improved state of the art on [ImageNet dataset](http://image-net.org/challenges/LSVRC) in 2016.
 * **MobileNet** ([research paper](https://arxiv.org/abs/1704.04861)), MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks, suitable for mobile applications.
 * **ResNet-18** ([research paper](https://arxiv.org/abs/1512.03385v1)), the -152 version is the 2015 winner in multiple categories.
@@ -39,13 +39,13 @@ import numpy as np
 
 ## Loading the model
 
-The [Gluon Model Zoo](https://mxnet.incubator.apache.org/api/python/gluon/model_zoo.html) provides a collection of off-the-shelf models. You can get the ImageNet pre-trained model by using `pretrained=True`. 
+The [Gluon Model Zoo](https://mxnet.apache.org/api/python/gluon/model_zoo.html) provides a collection of off-the-shelf models. You can get the ImageNet pre-trained model by using `pretrained=True`. 
 If you want to train on your own classification problem from scratch, you can get an untrained network with a specific number of classes using the `classes` parameter: for example `net = vision.resnet18_v1(classes=10)`. However note that you cannot use the `pretrained` and `classes` parameter at the same time. If you want to use pre-trained weights as initialization of your network except for the last layer, have a look at the last section of this tutorial.
 
 We can specify the *context* where we want to run the model: the default behavior is to use a CPU context. There are two reasons for this:
 * First, this will allow you to test the notebook even if your machine is not equipped with a GPU :)
 * Second, we're going to predict a single image and we don't have any specific performance requirements. For production applications where you'd want to predict large batches of images with the best possible throughput, a GPU could definitely be the way to go.
-* If you want to use a GPU, make sure you have pip installed the right version of mxnet, or you will get an error when using the `mx.gpu()` context. Refer to the [install instructions](http://mxnet.incubator.apache.org/install/index.html)
+* If you want to use a GPU, make sure you have pip installed the right version of mxnet, or you will get an error when using the `mx.gpu()` context. Refer to the [install instructions](https://mxnet.apache.org/install/index.html)
 
 
 ```python
@@ -237,7 +237,7 @@ print(resnet18.output)
 Now you can train your model on your new data using the pre-trained weights as initialization. This is called transfer learning and it has proved to be very useful especially in the cases where you only have access to a small dataset. Your network will have already learned how to perform general pattern detection and feature extraction on the larger dataset.
 You can learn more about transfer learning and fine-tuning with MXNet in these tutorials:
 - [Transferring knowledge through fine-tuning](http://gluon.mxnet.io/chapter08_computer-vision/fine-tuning.html)
-- [Fine Tuning an ONNX Model](https://mxnet.incubator.apache.org/tutorials/onnx/fine_tuning_gluon.html)
+- [Fine Tuning an ONNX Model](https://mxnet.apache.org/tutorials/onnx/fine_tuning_gluon.html)
 
 
 That's it! Explore the model zoo, have fun with pre-trained models!
diff --git a/docs/python_docs/python/tutorials/packages/gluon/index.rst b/docs/python_docs/python/tutorials/packages/gluon/index.rst
index 01f2d7c398d5..2212b93af578 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/index.rst
+++ b/docs/python_docs/python/tutorials/packages/gluon/index.rst
@@ -31,37 +31,37 @@ Getting started
 
    .. card::
       :title: Gluon - Neural network building blocks
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/gluon.html
+      :link: blocks/nn.html
 
       An introduction to defining and training neural networks with Gluon.
 
    .. card::
       :title: Gluon: from experiment to deployment
-      :link: gluon_from_experiment_to_deployment.html
+      :link: ../../getting-started/gluon_from_experiment_to_deployment.html
 
       An end to end tutorial on working with the MXNet Gluon API.
 
    .. card::
       :title: Custom Layers for Beginners
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/custom_layer.html
+      :link: blocks/custom_layer_beginners.html
 
       A guide to implementing custom layers for beginners.
 
    .. card::
       :title: Logistic regression using Gluon API explained
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/logistic_regression_explained.html
+      :link: ../../getting-started/logistic_regression_explained.html
 
       Implementing logistic regression using the Gluon API.
 
    .. card::
       :title: Saving and Loading Gluon Models
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/save_load_params.html
+      :link: blocks/save_load_params.html
 
       Saving and loading trained models.
 
    .. card::
       :title: Using pre-trained models in MXNet
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/pretrained_models.html
+      :link: image/pretrained_models.html
 
       Using pre-trained models with Apache MXNet.
 
@@ -84,19 +84,19 @@ Data
 
    .. card::
       :title: Data Augmentation
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/data_augmentation.html
+      :link: image/image-augmentation.html
 
       A guide to data augmentation.
 
    .. card::
       :title: Gluon Datasets and DataLoader
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/datasets.html
+      :link: data/datasets.html
 
       A guide to loading data using the Gluon API.
 
    .. card::
       :title: NDArray - Scientific computing on CPU and GPU
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/ndarray.html
+      :link: ../ndarray/index.html
 
       A guide to the NDArray data structure.
 
@@ -107,19 +107,19 @@ Training
 
    .. card::
       :title: Neural Networks
-      :link: nn.html
+      :link: blocks/nn.html
 
       How to use Layers and Blocks.
 
    .. card::
       :title: Normalization Blocks
-      :link: normalization/normalization.html
+      :link: data/normalization/normalization.html
 
       Understand usage of normalization layers (such as BatchNorm).
 
    .. card::
       :title: Activation Blocks
-      :link: activations/activations.html
+      :link: blocks/activations/activations.html
 
       Understand usage of activation layers (such as ReLU).
 
@@ -131,45 +131,37 @@ Training
 
    .. card::
       :title: Initializing Parameters
-      :link: init.html
+      :link: blocks/init.html
 
       How to use the init function.
 
    .. card::
       :title: Parameter Management
-      :link: parameters.html
+      :link: blocks/parameters.html
 
       How to manage parameters.
 
    .. card::
       :title: Fit API Tutorial
-      :link: fit_api_tutorial.html
+      :link: training/fit_api_tutorial.html
 
       How to use the fit API
 
    .. card::
       :title: Learning Rate Finder
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/learning_rate_finder.html
+      :link: training/learning_rates/learning_rate_finder.html
 
       How to use the Learning Rate Finder to find a good learning rate.
 
    .. card::
       :title: Learning Rate Schedules
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/learning_rate_schedules.html
+      :link: training/learning_rates/learning_rate_schedules.html
 
       How to schedule Learning Rate change over time.
 
-   ..
-      .. card::
-         :title: Optimizer
-         :link: optimizer.html
-
-         How to use optimizer.
-   ..
-
    .. card::
       :title: Trainer
-      :link: trainer.html
+      :link: training/trainer.html
 
       How to update neural network parameters using an optimization method.
 
@@ -198,7 +190,7 @@ Advanced Topics
 
    .. card::
       :title: Custom Operators
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/customop.html
+      :link: https://mxnet.apache.org/versions/master/tutorials/gluon/customop.html
 
       Building custom operators with numpy.
 
@@ -210,28 +202,21 @@ Advanced Topics
 
    .. card::
       :title: Gotchas using NumPy in Apache MXNet
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/gotchas_numpy_in_mxnet.html
+      :link: https://mxnet.apache.org/versions/master/tutorials/gluon/gotchas_numpy_in_mxnet.html
 
       Common misconceptions when using NumPy in Apache MXNet.
 
-   .. card::
-      :title: Hybrid- Faster training and easy deployment
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/hybrid.html
-
-      Combines declarative and imperative programming using HybridBlock.
-
    .. card::
       :title: Hybridize
-      :link: hybridize.html
+      :link: blocks/hybridize.html
 
       Speed up training with hybrid networks.
 
    .. card::
-      :title: Advanced Learning Rate Schedules
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/learning_rate_schedules_advanced.html
-
-      Advanced exploration of Learning Rate shapes.
+      :title: Learning Rate Schedules (Advanced)
+      :link: training/learning_rates/learning_rate_schedules_advanced.html
 
+      How to schedule Learning Rate change over time (advanced)
 
 Applications Topics
 -------------------
@@ -253,24 +238,7 @@ Applications Topics
 
 .. toctree::
    :hidden:
-   :maxdepth: 2
-
-   ../../getting-started/crash-course
-   custom_layer_beginners
-   data
-   image-augmentation
-   data_augmentation
-   nn
-   normalization/normalization
-   activations/activations
-   loss
-   custom-loss/custom-loss
-   init
-   parameters
-   trainer
-   naming
-   custom-layer
-   customop
-   hybridize
-   image/index
-   text/index
+   :maxdepth: 3
+   :glob:
+
+   */index*
diff --git a/docs/python_docs/python/tutorials/packages/gluon/custom-loss/custom-loss.md b/docs/python_docs/python/tutorials/packages/gluon/loss/custom-loss.md
similarity index 93%
rename from docs/python_docs/python/tutorials/packages/gluon/custom-loss/custom-loss.md
rename to docs/python_docs/python/tutorials/packages/gluon/loss/custom-loss.md
index 1d8bfc7c2f6a..2357e80ab369 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/custom-loss/custom-loss.md
+++ b/docs/python_docs/python/tutorials/packages/gluon/loss/custom-loss.md
@@ -45,7 +45,7 @@ import random
 
 The loss function uses a margin *m* which is has the effect that dissimlar pairs only contribute if their loss is within a certain margin. 
 
-In order to implement such a customized loss function in Gluon, we only need to define a new class that is inheriting from the [`Loss`](https://mxnet.incubator.apache.org/api/python/gluon/loss.html#mxnet.gluon.loss.Loss) base class. We then define the contrastive loss logic in the [`hybrid_forward`](http://mxnet.apache.org/_modules/mxnet/gluon/block.html#HybridBlock.hybrid_forward) method. This method takes the images `image1`, `image2` and the label which defines whether  `image1` and `image2` are similar (=0) or  dissimilar (=1). The input F is an `mxnet.ndarry` or an `mxnet.symbol` if we hybridize the network. Gluon's `Loss` base class is in fact a [`HybridBlock`](http://mxnet.apache.org/api/python/gluon/gluon.html#mxnet.gluon.HybridBlock). This means we can either run  imperatively or symbolically. When we hybridize our custom loss function, we can get performance speedups.
+In order to implement such a customized loss function in Gluon, we only need to define a new class that is inheriting from the [`Loss`](https://mxnet.apache.org/api/python/gluon/loss.html#mxnet.gluon.loss.Loss) base class. We then define the contrastive loss logic in the [`hybrid_forward`](https://mxnet.apache.org/_modules/mxnet/gluon/block.html#HybridBlock.hybrid_forward) method. This method takes the images `image1`, `image2` and the label which defines whether  `image1` and `image2` are similar (=0) or  dissimilar (=1). The input F is an `mxnet.ndarry` or an `mxnet.symbol` if we hybridize the network. Gluon's `Loss` base class is in fact a [`HybridBlock`](https://mxnet.apache.org/api/python/gluon/gluon.html#mxnet.gluon.HybridBlock). This means we can either run  imperatively or symbolically. When we hybridize our custom loss function, we can get performance speedups.
 
 
 ```python
diff --git a/docs/python_docs/python/tutorials/packages/gluon/custom-loss/images/contrastive_loss.jpeg b/docs/python_docs/python/tutorials/packages/gluon/loss/images/contrastive_loss.jpeg
similarity index 100%
rename from docs/python_docs/python/tutorials/packages/gluon/custom-loss/images/contrastive_loss.jpeg
rename to docs/python_docs/python/tutorials/packages/gluon/loss/images/contrastive_loss.jpeg
diff --git a/docs/python_docs/python/tutorials/packages/gluon/custom-loss/images/inuktitut_1.png b/docs/python_docs/python/tutorials/packages/gluon/loss/images/inuktitut_1.png
similarity index 100%
rename from docs/python_docs/python/tutorials/packages/gluon/custom-loss/images/inuktitut_1.png
rename to docs/python_docs/python/tutorials/packages/gluon/loss/images/inuktitut_1.png
diff --git a/docs/python_docs/python/tutorials/packages/gluon/custom-loss/images/inuktitut_2.png b/docs/python_docs/python/tutorials/packages/gluon/loss/images/inuktitut_2.png
similarity index 100%
rename from docs/python_docs/python/tutorials/packages/gluon/custom-loss/images/inuktitut_2.png
rename to docs/python_docs/python/tutorials/packages/gluon/loss/images/inuktitut_2.png
diff --git a/docs/python_docs/python/tutorials/packages/gluon/loss/index.rst b/docs/python_docs/python/tutorials/packages/gluon/loss/index.rst
new file mode 100644
index 000000000000..35e404085a5d
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/loss/index.rst
@@ -0,0 +1,25 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Losses
+======
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+
+   *
\ No newline at end of file
diff --git a/docs/python_docs/python/tutorials/packages/gluon/loss/loss.md b/docs/python_docs/python/tutorials/packages/gluon/loss/loss.md
index e3031fff2b86..6ea44ef180f4 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/loss/loss.md
+++ b/docs/python_docs/python/tutorials/packages/gluon/loss/loss.md
@@ -103,7 +103,7 @@ def show_classification_loss(loss):
     plot(x, y) 
 ```
 
-#### [L1 Loss](https://mxnet.incubator.apache.org/api/python/gluon/loss.html#mxnet.gluon.loss.L1Loss)
+#### [L1 Loss](https://mxnet.apache.org/api/python/gluon/loss.html#mxnet.gluon.loss.L1Loss)
 
 L1 Loss, also called Mean Absolute Error, computes the sum of absolute distance between target values and the output of the neural network. It is defined as:
 
@@ -115,7 +115,7 @@ It is a non-smooth function that can lead to non-convergence. It creates the sam
 show_regression_loss(gloss.L1Loss())
 ```
 
-#### [L2 Loss](https://mxnet.incubator.apache.org/api/python/gluon/loss.html#mxnet.gluon.loss.L2Loss)
+#### [L2 Loss](https://mxnet.apache.org/api/python/gluon/loss.html#mxnet.gluon.loss.L2Loss)
 
 L2Loss, also called Mean Squared Error, is a regression loss function that computes the squared distances between the target values and the output of the neural network. It is defined as:
 
@@ -127,7 +127,7 @@ Compared to L1, L2 loss it is a smooth function and it creates larger gradients
 show_regression_loss(gloss.L2Loss())
 ```
 
-#### [Huber Loss](https://mxnet.incubator.apache.org/api/python/gluon/loss.html#mxnet.gluon.loss.HuberLosss)
+#### [Huber Loss](https://mxnet.apache.org/api/python/gluon/loss.html#mxnet.gluon.loss.HuberLosss)
 
 HuberLoss  combines advantages of L1 and L2 loss. It calculates a smoothed L1 loss that is equal to L1 if the absolute error exceeds a threshold $$\rho$$, otherwise it is equal to L2. It is defined as:
 $$ 
@@ -144,7 +144,7 @@ show_regression_loss(gloss.HuberLoss(rho=1))
 
 An example of where Huber Loss is used can be found in [Deep Q Network](https://openai.com/blog/openai-baselines-dqn/).
 
-#### [Cross Entropy Loss with Sigmoid](https://mxnet.incubator.apache.org/api/python/gluon/loss.html#mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss)
+#### [Cross Entropy Loss with Sigmoid](https://mxnet.apache.org/api/python/gluon/loss.html#mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss)
 
 Binary Cross Entropy is a loss function used for binary classification problems e.g. classifying images into 2 classes. Cross entropy measures the difference between two probability distributions and it is defined as:
 $$\sum_i -{(y\log(p) + (1 - y)\log(1 - p))} $$
@@ -154,7 +154,7 @@ Before the loss is computed a sigmoid activation is applied per default. If your
 show_classification_loss(gloss.SigmoidBinaryCrossEntropyLoss())
 ```
 
-#### [Cross Entropy Loss with Softmax](https://mxnet.incubator.apache.org/api/python/gluon/loss.html#mxnet.gluon.loss.SoftmaxCrossEntropyLoss)
+#### [Cross Entropy Loss with Softmax](https://mxnet.apache.org/api/python/gluon/loss.html#mxnet.gluon.loss.SoftmaxCrossEntropyLoss)
 
 In classification, we often apply the
 softmax operator to the predicted outputs to obtain prediction probabilities,
@@ -172,7 +172,7 @@ y = nd.array([0, 1])
 loss(x, y)
 ```
 
-#### [Hinge Loss](https://mxnet.incubator.apache.org/api/python/gluon/loss.html#mxnet.gluon.loss.HingeLoss)
+#### [Hinge Loss](https://mxnet.apache.org/api/python/gluon/loss.html#mxnet.gluon.loss.HingeLoss)
 
 Commonly used in Support Vector Machines (SVMs), Hinge Loss is used to additionally penalize predictions that are correct but fall within a margin between classes (the region around a decision boundary). Unlike `SoftmaxCrossEntropyLoss`, it's rarely used for neural network training. It is defined as:
 
@@ -184,7 +184,7 @@ $$
 show_classification_loss(gloss.HingeLoss())
 ```
 
-#### [Logistic Loss](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/loss.html#mxnet.gluon.loss.LogisticLoss)
+#### [Logistic Loss](https://mxnet.apache.org/versions/master/api/python/gluon/loss.html#mxnet.gluon.loss.LogisticLoss)
 
 The Logistic Loss function computes the performance of binary classification models. 
 $$
@@ -196,7 +196,7 @@ The log loss decreases the closer the prediction is to the actual label. It is s
 show_classification_loss(gloss.LogisticLoss())
 ```
 
-#### [Kullback-Leibler Divergence Loss](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/loss.html#mxnet.gluon.loss.KLDivLoss)
+#### [Kullback-Leibler Divergence Loss](https://mxnet.apache.org/versions/master/api/python/gluon/loss.html#mxnet.gluon.loss.KLDivLoss)
 
 The Kullback-Leibler divergence loss measures the divergence between two probability distributions by calculating the difference between cross entropy and entropy. It takes as input the probability of predicted label and the probability of true label.
 
@@ -218,7 +218,7 @@ loss = loss_fn(output, target_dist)
 print('loss (kl divergence): {}'.format(loss.asnumpy().tolist()))
 ```
 
-#### [Triplet Loss](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/loss.html#mxnet.gluon.loss.TripletLoss)
+#### [Triplet Loss](https://mxnet.apache.org/versions/master/api/python/gluon/loss.html#mxnet.gluon.loss.TripletLoss)
 
 Triplet loss takes three input arrays and measures the relative similarity. It takes a positive and negative input and the anchor. 
 
@@ -234,13 +234,13 @@ In the case of learning embeddings for images of characters, the network may get
 
 The network would learn to minimize the distance between the two `A`'s and maximize the distance between `A` and `Z`.
 
-#### [CTC Loss](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/loss.html#mxnet.gluon.loss.CTCLoss)
+#### [CTC Loss](https://mxnet.apache.org/versions/master/api/python/gluon/loss.html#mxnet.gluon.loss.CTCLoss)
 
 CTC Loss is the [connectionist temporal classification loss](https://distill.pub/2017/ctc/) . It is used to train recurrent neural networks with variable time dimension. It learns the alignment and labelling of input sequences. It takes a sequence as input and gives probabilities for each timestep. For instance, in the following image the word is not well aligned with the 5 timesteps because of the different sizes of characters. CTC Loss finds for each timestep the highest probability e.g. `t1` presents with high probability a `C`. It combines the highest probapilities and returns the best path decoding. For an in-depth tutorial on how to use CTC-Loss in MXNet, check out this [example](https://github.com/apache/incubator-mxnet/tree/master/example/ctc).
 
 ![ctc_loss](ctc_loss.png)
 
-#### [Cosine Embedding Loss](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/loss.html#mxnet.gluon.loss.CosineEmbeddingLoss)
+#### [Cosine Embedding Loss](https://mxnet.apache.org/versions/master/api/python/gluon/loss.html#mxnet.gluon.loss.CosineEmbeddingLoss)
 The cosine embedding loss computes the cosine distance between two input vectors. 
 
 $$
@@ -270,7 +270,7 @@ loss = gloss.CosineEmbeddingLoss()
 print(loss(x,y,label))
 ```
 
-#### [PoissonNLLLoss](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/loss.html#mxnet.gluon.loss.PoissonNLLLoss)
+#### [PoissonNLLLoss](https://mxnet.apache.org/versions/master/api/python/gluon/loss.html#mxnet.gluon.loss.PoissonNLLLoss)
 Poisson distribution is widely used for modelling count data. It is defined as:
 
 $$
diff --git a/docs/python_docs/python/tutorials/packages/gluon/text/index.rst b/docs/python_docs/python/tutorials/packages/gluon/text/index.rst
index 7fe503ae279d..9f06c63b723b 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/text/index.rst
+++ b/docs/python_docs/python/tutorials/packages/gluon/text/index.rst
@@ -16,53 +16,53 @@
    under the License.
 
 Text Tutorials
-===============
+==============
 
 These tutorials will help you learn how to create and use models that work with text and other natural language processing tasks.
 
 Word Embedding
----------------------
+--------------
 
 .. container:: cards
 
    .. card::
       :title: Pre-trained Word Embeddings
-      :link: http://gluon-nlp.mxnet.io/examples/word_embedding/word_embedding.html
+      :link: https://gluon-nlp.mxnet.io/examples/word_embedding/word_embedding.html
 
       Basics on how to use word embedding with vocab in GluonNLP and apply it on word similarity and analogy problems.
 
    .. card::
       :title: Word Embeddings Training and Evaluation
-      :link: http://gluon-nlp.mxnet.io/examples/word_embedding/word_embedding_training.html
+      :link: https://gluon-nlp.mxnet.io/examples/word_embedding/word_embedding_training.html
 
       Learn how to train fastText and word2vec embeddings on your own dataset, and determine embedding quality through intrinsic evaluation.
 
 Language Model
-==============
+--------------
 
 
 .. container:: cards
 
    .. card::
       :title: LSTM-based Language Models
-      :link: http://gluon-nlp.mxnet.io/examples/language_model/language_model.html
+      :link: https://gluon-nlp.mxnet.io/examples/language_model/language_model.html
 
       Learn what a language model is, what it can do, and how to train a word-level language model with truncated back-propagation-through-time (BPTT).
 
 Machine Translation
----------------------
+-------------------
 
 .. container:: cards
 
    .. card::
       :title: Google Neural Machine Translation
-      :link: http://gluon-nlp.mxnet.io/examples/machine_translation/gnmt.html
+      :link: https://gluon-nlp.mxnet.io/examples/machine_translation/gnmt.html
 
       Learn how to train Google Neural Machine Translation, a seq2seq with attention model.
 
    .. card::
       :title: Machine Translation with Transformer
-      :link: http://gluon-nlp.mxnet.io/examples/machine_translation/transformer.html
+      :link: https://gluon-nlp.mxnet.io/examples/machine_translation/transformer.html
 
       Learn how to use a pre-trained transformer translation model for English to German translation.
 
@@ -73,44 +73,47 @@ Sentence Embedding
 
    .. card::
       :title: ELMo: Deep Contextualized Word Representations
-      :link: http://gluon-nlp.mxnet.io/examples/sentence_embedding/elmo_sentence_representation.html
+      :link: https://gluon-nlp.mxnet.io/examples/sentence_embedding/elmo_sentence_representation.html
 
       See how to use GluonNLP’s model API to automatically download the pre-trained ELMo model from NAACL2018 best paper, and extract features with it.
 
    .. card::
       :title: A Structured Self-attentive Sentence Embedding
-      :link: http://gluon-nlp.mxnet.io/examples/sentence_embedding/self_attentive_sentence_embedding.html
+      :link: https://gluon-nlp.mxnet.io/examples/sentence_embedding/self_attentive_sentence_embedding.html
 
       See how to use GluonNLP to build more advanced model structure for extracting sentence embeddings to predict Yelp review rating.
 
    .. card::
       :title: BERT: Bidirectional Encoder Representations from Transformers
-      :link: http://gluon-nlp.mxnet.io/examples/sentence_embedding/bert.html
+      :link: https://gluon-nlp.mxnet.io/examples/sentence_embedding/bert.html
 
       See how to use GluonNLP to fine-tune a sentence pair classification model with pre-trained BERT parameters.
 
 Sentiment Analysis
----------------------
+------------------
 
 .. container:: cards
 
    .. card::
       :title: Sentiment Analysis by Fine-tuning Word Language Model
-      :link: http://gluon-nlp.mxnet.io/examples/sentiment_analysis/sentiment_analysis.html
+      :link: https://gluon-nlp.mxnet.io/examples/sentiment_analysis/sentiment_analysis.html
 
       See how to fine-tune a pre-trained language model to perform sentiment analysis on movie reviews.
 
 Sequence Sampling
----------------------
+-----------------
 
 .. container:: cards
 
    .. card::
       :title: Sequence Generation with Sampling and Beam Search
-      :link: http://gluon-nlp.mxnet.io/examples/sequence_sampling/sequence_sampling.html
+      :link: https://gluon-nlp.mxnet.io/examples/sequence_sampling/sequence_sampling.html
 
       Learn how to generate sentence from pre-trained language model through sampling and beam search. 
 
 .. toctree::
    :hidden:
    :maxdepth: 1
+   :glob:
+
+   *
diff --git a/docs/python_docs/python/tutorials/packages/gluon/text/transformer.rst b/docs/python_docs/python/tutorials/packages/gluon/text/transformer.rst
index c44fb37f6b8a..6fb27653872d 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/text/transformer.rst
+++ b/docs/python_docs/python/tutorials/packages/gluon/text/transformer.rst
@@ -589,7 +589,7 @@ Conclusion
    the encoder-decoder structure.
 -  Low-level APIs in NLP Toolkit enables easy customization.
 
-Documentation can be found at http://gluon-nlp.mxnet.io/index.html
+Documentation can be found at https://gluon-nlp.mxnet.io/index.html
 
 Code is here https://github.com/dmlc/gluon-nlp
 
diff --git a/docs/python_docs/python/tutorials/packages/gluon/fit_api_tutorial.md b/docs/python_docs/python/tutorials/packages/gluon/training/fit_api_tutorial.md
similarity index 92%
rename from docs/python_docs/python/tutorials/packages/gluon/fit_api_tutorial.md
rename to docs/python_docs/python/tutorials/packages/gluon/training/fit_api_tutorial.md
index cca4f3fa395e..16f0ceac3a08 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/fit_api_tutorial.md
+++ b/docs/python_docs/python/tutorials/packages/gluon/training/fit_api_tutorial.md
@@ -17,7 +17,7 @@
 
 # MXNet Gluon Fit API
 
-In this tutorial, you will learn how to use the [Gluon Fit API](https://cwiki.apache.org/confluence/display/MXNET/Gluon+Fit+API+-+Tech+Design) which is the easiest way to train deep learning models using the [Gluon API](http://mxnet.incubator.apache.org/versions/master/gluon/index.html) in Apache MXNet. 
+In this tutorial, you will learn how to use the [Gluon Fit API](https://cwiki.apache.org/confluence/display/MXNET/Gluon+Fit+API+-+Tech+Design) which is the easiest way to train deep learning models using the [Gluon API](https://mxnet.apache.org/versions/master/gluon/index.html) in Apache MXNet. 
 
 With the Fit API, you can train a deep learning model with a minimal amount of code. Just specify the network, loss function and the data you want to train on. You don't need to worry about the boiler plate code to loop through the dataset in batches (often called as 'training loop'). Advanced users can train with bespoke training loops, and many of these use cases will be covered by the Fit API.
 
@@ -27,7 +27,7 @@ To demonstrate the Fit API, you will train an image classification model using t
 
 To complete this tutorial, you will need:
 
-- [MXNet](https://mxnet.incubator.apache.org/install/#overview) (The version of MXNet will be >= 1.5.0, you can use `pip install mxnet` to get 1.5.0 release pip package or build from source with master, refer to [MXNet installation](http://mxnet.incubator.apache.org/versions/master/install/index.html?platform=Linux&language=Python&processor=CPU)
+- [MXNet](https://mxnet.apache.org/install/#overview) (The version of MXNet will be >= 1.5.0, you can use `pip install mxnet` to get 1.5.0 release pip package or build from source with master, refer to [MXNet installation](https://mxnet.apache.org/versions/master/install/index.html?platform=Linux&language=Python&processor=CPU)
 - [Jupyter Notebook](https://jupyter.org/index.html) (For interactively running the provided .ipynb file)
 
 
@@ -89,7 +89,7 @@ val_data_loader = gluon.data.DataLoader(fashion_mnist_val, batch_size=batch_size
 
 ## Model and Optimizers
 
-Let's load the resnet-18 model architecture from [Gluon Model Zoo](http://mxnet.apache.org/api/python/gluon/model_zoo.html) and initialize its parameters. The Gluon Model Zoo contains a repository of pre-trained models as well the model architecture definitions. We are using the model architecture from the model zoo in order to train it from scratch.
+Let's load the resnet-18 model architecture from [Gluon Model Zoo](https://mxnet.apache.org/api/python/gluon/model_zoo.html) and initialize its parameters. The Gluon Model Zoo contains a repository of pre-trained models as well the model architecture definitions. We are using the model architecture from the model zoo in order to train it from scratch.
 
 
 ```python
@@ -98,7 +98,7 @@ resnet_18_v1.initialize(init = mx.init.Xavier(), ctx=ctx)
 ```
 
 We will be using `SoftmaxCrossEntropyLoss` as the loss function since this is a multi-class classification problem. We will be using `sgd` (Stochastic Gradient Descent) as the optimizer. 
-You can experiment with a [different loss](http://mxnet.incubator.apache.org/versions/master/api/python/gluon/loss.html) or [optimizer](http://mxnet.incubator.apache.org/versions/master/api/python/optimization/optimization.html) as well. 
+You can experiment with a [different loss](https://mxnet.apache.org/versions/master/api/python/gluon/loss.html) or [optimizer](https://mxnet.apache.org/versions/master/api/python/optimization/optimization.html) as well. 
 
 
 ```python
@@ -262,7 +262,7 @@ resnet_18_v1.load_parameters('./my_model-best.params', ctx=ctx)
 
 ## Summary
 
-- To learn more about deep learning with MXNeT, see [Dive Into Deep Learning](http://gluon.io)
+- To learn more about deep learning with MXNeT, see [Dive Into Deep Learning](https://gluon.io)
 
 ## Next Steps 
 
diff --git a/docs/python_docs/python/tutorials/packages/gluon/training/index.rst b/docs/python_docs/python/tutorials/packages/gluon/training/index.rst
new file mode 100644
index 000000000000..6dfe4cec7c50
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/training/index.rst
@@ -0,0 +1,26 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Training
+========
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+
+   *
+   */index*
\ No newline at end of file
diff --git a/docs/python_docs/python/tutorials/packages/gluon/training/learning_rates/index.rst b/docs/python_docs/python/tutorials/packages/gluon/training/learning_rates/index.rst
new file mode 100644
index 000000000000..0abbf3237637
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/training/learning_rates/index.rst
@@ -0,0 +1,26 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+
+Learning Rates
+==============
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+
+   *
diff --git a/docs/python_docs/python/tutorials/packages/gluon/training/learning_rates/learning_rate_finder.md b/docs/python_docs/python/tutorials/packages/gluon/training/learning_rates/learning_rate_finder.md
new file mode 100644
index 000000000000..4fc8b780a2ef
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/training/learning_rates/learning_rate_finder.md
@@ -0,0 +1,332 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+
+# Learning Rate Finder
+
+Setting the learning rate for stochastic gradient descent (SGD) is crucially important when training neural network because it controls both the speed of convergence and the ultimate performance of the network. Set the learning too low and you could be twiddling your thumbs for quite some time as the parameters update very slowly. Set it too high and the updates will skip over optimal solutions, or worse the optimizer might not converge at all!
+
+Leslie Smith from the U.S. Naval Research Laboratory presented a method for finding a good learning rate in a paper called ["Cyclical Learning Rates for Training Neural Networks"](https://arxiv.org/abs/1506.01186). We implement this method in MXNet (with the Gluon API) and create a 'Learning Rate Finder' which you can use while training your own networks. We take a look at the central idea of the paper, cyclical learning rate schedules, in the ['Advanced Learning Rate Schedules'](https://mxnet.apache.org/tutorials/gluon/learning_rate_schedules_advanced.html) tutorial.
+
+## Simple Idea
+
+Given an initialized network, a defined loss and a training dataset we take the following steps:
+
+1. Train one batch at a time (a.k.a. an iteration)
+2. Start with a very small learning rate (e.g. 0.000001) and slowly increase it every iteration
+3. Record the training loss and continue until we see the training loss diverge
+
+We then analyse the results by plotting a graph of the learning rate against the training loss as seen below (taking note of the log scales).
+
+<img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/lr_finder/finder_plot_w_annotations.png" width="500px"/> <!--notebook-skip-line-->
+
+As expected, for very small learning rates we don't see much change in the loss as the parameter updates are negligible. At a learning rate of 0.001, we start to see the loss fall. Setting the initial learning rate here is reasonable, but we still have the potential to learn faster. We observe a drop in the loss up until 0.1 where the loss appears to diverge. We want to set the initial learning rate as high as possible before the loss becomes unstable, so we choose a learning rate of 0.05.
+
+## Epoch to Iteration
+
+Usually, our unit of work is an epoch (a full pass through the dataset) and the learning rate would typically be held constant throughout the epoch. With the Learning Rate Finder (and cyclical learning rate schedules) we are required to vary the learning rate every iteration. As such we structure our training code so that a single iteration can be run with a given learning rate. You can implement Learner as you wish. Just initialize the network, define the loss and trainer in `__init__` and keep your training logic for a single batch in `iteration`.
+
+
+```python
+import mxnet as mx
+
+# Set seed for reproducibility
+mx.random.seed(42)
+
+class Learner():
+    def __init__(self, net, data_loader, ctx):
+        """
+        :param net: network (mx.gluon.Block)
+        :param data_loader: training data loader (mx.gluon.data.DataLoader)
+        :param ctx: context (mx.gpu or mx.cpu)
+        """
+        self.net = net
+        self.data_loader = data_loader
+        self.ctx = ctx
+        # So we don't need to be in `for batch in data_loader` scope
+        # and can call for next batch in `iteration`
+        self.data_loader_iter = iter(self.data_loader)
+        self.net.initialize(mx.init.Xavier(), ctx=self.ctx)
+        self.loss_fn = mx.gluon.loss.SoftmaxCrossEntropyLoss()
+        self.trainer = mx.gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': .001})
+        
+    def iteration(self, lr=None, take_step=True):
+        """
+        :param lr: learning rate to use for iteration (float)
+        :param take_step: take trainer step to update weights (boolean)
+        :return: iteration loss (float)
+        """
+        # Update learning rate if different this iteration
+        if lr and (lr != self.trainer.learning_rate):
+            self.trainer.set_learning_rate(lr)
+        # Get next batch, and move context (e.g. to GPU if set)
+        data, label = next(self.data_loader_iter)
+        data = data.as_in_context(self.ctx)
+        label = label.as_in_context(self.ctx)
+        # Standard forward and backward pass
+        with mx.autograd.record():
+            output = self.net(data)
+            loss = self.loss_fn(output, label)
+        loss.backward()     
+        # Update parameters
+        if take_step: self.trainer.step(data.shape[0])  
+        # Set and return loss.
+        self.iteration_loss = mx.nd.mean(loss).asscalar()
+        return self.iteration_loss
+
+    def close(self):
+        # Close open iterator and associated workers
+        self.data_loader_iter.shutdown()
+```
+
+We also adjust our `DataLoader` so that it continuously provides batches of data and doesn't stop after a single epoch. We can then call `iteration` as many times as required for the loss to diverge as part of the Learning Rate Finder process. We implement a custom `BatchSampler` for this, that keeps returning random indices of samples to be included in the next batch. We use the CIFAR-10 dataset for image classification to test our Learning Rate Finder.
+
+
+```python
+from mxnet.gluon.data.vision import transforms
+
+transform = transforms.Compose([
+    # Switches HWC to CHW, and converts to `float32`
+    transforms.ToTensor(),
+    # Channel-wise, using pre-computed means and stds 
+    transforms.Normalize(mean=[0.4914, 0.4822, 0.4465],
+                         std=[0.2023, 0.1994, 0.2010])
+])
+
+dataset = mx.gluon.data.vision.datasets.CIFAR10(train=True).transform_first(transform)
+
+class ContinuousBatchSampler():
+    def __init__(self, sampler, batch_size):
+        self._sampler = sampler
+        self._batch_size = batch_size
+
+    def __iter__(self):
+        batch = []
+        while True:
+            for i in self._sampler:
+                batch.append(i)
+                if len(batch) == self._batch_size:
+                    yield batch
+                    batch = []
+
+sampler = mx.gluon.data.RandomSampler(len(dataset))
+batch_sampler = ContinuousBatchSampler(sampler, batch_size=128)
+data_loader = mx.gluon.data.DataLoader(dataset, batch_sampler=batch_sampler)
+```
+
+## Implementation
+
+With preparation complete, we're ready to write our Learning Rate Finder that wraps the `Learner` we defined above. We implement a `find` method for the procedure, and `plot` for the visualization. Starting with a very low learning rate as defined by `lr_start` we train one iteration at a time and keep multiplying the learning rate by `lr_multiplier`. We analyse the loss and continue until it diverges according to `LRFinderStoppingCriteria` (which is defined later on). You may also notice that we save the parameters and state of the optimizer before the process and restore afterwards. This is so the Learning Rate Finder process doesn't impact the state of the model, and can be used at any point during training.
+
+
+```python
+from matplotlib import pyplot as plt
+
+class LRFinder():
+    def __init__(self, learner):
+        """
+        :param learner: able to take single iteration with given learning rate and return loss
+           and save and load parameters of the network (Learner)
+        """
+        self.learner = learner
+        
+    def find(self, lr_start=1e-6, lr_multiplier=1.1, smoothing=0.3):
+        """
+        :param lr_start: learning rate to start search (float)
+        :param lr_multiplier: factor the learning rate is multiplied by at each step of search (float)
+        :param smoothing: amount of smoothing applied to loss for stopping criteria (float)
+        :return: learning rate and loss pairs (list of (float, float) tuples)
+        """
+        # Used to initialize weights; pass data, but don't take step.
+        # Would expect for new model with lazy weight initialization
+        self.learner.iteration(take_step=False)
+        # Used to initialize trainer (if no step has been taken)
+        if not self.learner.trainer._kv_initialized:
+            self.learner.trainer._init_kvstore()
+        # Store params and optimizer state for restore after lr_finder procedure
+        # Useful for applying the method partway through training, not just for initialization of lr.
+        self.learner.net.save_parameters("lr_finder.params")
+        self.learner.trainer.save_states("lr_finder.state")
+        lr = lr_start
+        self.results = [] # List of (lr, loss) tuples
+        stopping_criteria = LRFinderStoppingCriteria(smoothing)
+        while True:
+            # Run iteration, and block until loss is calculated.
+            loss = self.learner.iteration(lr)
+            self.results.append((lr, loss))
+            if stopping_criteria(loss):
+                break
+            lr = lr * lr_multiplier
+        # Restore params (as finder changed them)
+        self.learner.net.load_parameters("lr_finder.params", ctx=self.learner.ctx)
+        self.learner.trainer.load_states("lr_finder.state")
+        return self.results
+        
+    def plot(self):
+        lrs = [e[0] for e in self.results]
+        losses = [e[1] for e in self.results]
+        plt.figure(figsize=(6,8))
+        plt.scatter(lrs, losses)
+        plt.xlabel("Learning Rate")
+        plt.ylabel("Loss")
+        plt.xscale('log')
+        plt.yscale('log')
+        axes = plt.gca()
+        axes.set_xlim([lrs[0], lrs[-1]])
+        y_lower = min(losses) * 0.8
+        y_upper = losses[0] * 4
+        axes.set_ylim([y_lower, y_upper])
+        plt.show()
+```
+
+
+You can define the `LRFinderStoppingCriteria` as you wish, but empirical testing suggests using a smoothed average gives a more consistent stopping rule (see `smoothing`). We stop when the smoothed average of the loss exceeds twice the initial loss, assuming there have been a minimum number of iterations (see `min_iter`).
+
+
+```python
+class LRFinderStoppingCriteria():
+    def __init__(self, smoothing=0.3, min_iter=20):
+        """
+        :param smoothing: applied to running mean which is used for thresholding (float)
+        :param min_iter: minimum number of iterations before early stopping can occur (int)
+        """
+        self.smoothing = smoothing
+        self.min_iter = min_iter
+        self.first_loss = None
+        self.running_mean = None
+        self.counter = 0
+        
+    def __call__(self, loss):
+        """
+        :param loss: from single iteration (float)
+        :return: indicator to stop (boolean)
+        """
+        self.counter += 1
+        if self.first_loss is None:
+            self.first_loss = loss
+        if self.running_mean is None:
+            self.running_mean = loss
+        else:
+            self.running_mean = ((1 - self.smoothing) * loss) + (self.smoothing * self.running_mean)
+        return (self.running_mean > self.first_loss * 2) and (self.counter >= self.min_iter)
+```
+
+## Usage
+
+Using a Pre-activation ResNet-18 from the Gluon model zoo, we instantiate our Learner and fire up our Learning Rate Finder!
+
+
+```python
+ctx = mx.gpu() if mx.context.num_gpus() else mx.cpu()
+net = mx.gluon.model_zoo.vision.resnet18_v2(classes=10)
+learner = Learner(net=net, data_loader=data_loader, ctx=ctx)
+lr_finder = LRFinder(learner)
+lr_finder.find(lr_start=1e-6)
+lr_finder.plot()
+```
+
+
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/lr_finder/finder_plot.png) <!--notebook-skip-line-->
+
+
+As discussed before, we should select a learning rate where the loss is falling (i.e. from 0.001 to 0.05) but before the loss starts to diverge (i.e. 0.1). We prefer higher learning rates where possible, so we select an initial learning rate of 0.05. Just as a test, we will run 500 epochs using this learning rate and evaluate the loss on the final batch. As we're working with a single batch of 128 samples, the variance of the loss estimates will be reasonably high, but it will give us a general idea. We save the initialized parameters for a later comparison with other learning rates.
+
+
+```python
+learner.net.save_parameters("net.params")
+lr = 0.05
+
+for iter_idx in range(300):
+    learner.iteration(lr=lr)
+    if ((iter_idx % 100) == 0):
+        print("Iteration: {}, Loss: {:.5g}".format(iter_idx, learner.iteration_loss))
+print("Final Loss: {:.5g}".format(learner.iteration_loss))
+```
+
+Iteration: 0, Loss: 2.785 <!--notebook-skip-line-->
+
+Iteration: 100, Loss: 1.6653 <!--notebook-skip-line-->
+
+Iteration: 200, Loss: 1.4891 <!--notebook-skip-line-->
+
+
+Final Loss: 1.1812 <!--notebook-skip-line-->
+
+
+We see a sizable drop in the loss from approx. 2.7 to 1.2.
+
+And now we have a baseline, let's see what happens when we train with a learning rate that's higher than advisable at 0.5.
+
+
+```python
+net = mx.gluon.model_zoo.vision.resnet18_v2(classes=10)
+learner = Learner(net=net, data_loader=data_loader, ctx=ctx)
+learner.net.load_parameters("net.params", ctx=ctx)
+lr = 0.5
+
+for iter_idx in range(300):
+    learner.iteration(lr=lr)
+    if ((iter_idx % 100) == 0):
+        print("Iteration: {}, Loss: {:.5g}".format(iter_idx, learner.iteration_loss))
+print("Final Loss: {:.5g}".format(learner.iteration_loss))
+```
+
+Iteration: 0, Loss: 2.6469 <!--notebook-skip-line-->
+
+Iteration: 100, Loss: 1.9666 <!--notebook-skip-line-->
+
+Iteration: 200, Loss: 1.6919 <!--notebook-skip-line-->
+
+
+Final Loss: 1.366 <!--notebook-skip-line-->
+
+
+We still observe a fall in the loss but aren't able to reach as low as before.
+
+And lastly, we see how the model trains with a more conservative learning rate of 0.005.
+
+
+```python
+net = mx.gluon.model_zoo.vision.resnet18_v2(classes=10)
+learner = Learner(net=net, data_loader=data_loader, ctx=ctx)
+learner.net.load_parameters("net.params", ctx=ctx)
+lr = 0.005
+
+for iter_idx in range(300):
+    learner.iteration(lr=lr)
+    if ((iter_idx % 100) == 0):
+        print("Iteration: {}, Loss: {:.5g}".format(iter_idx, learner.iteration_loss))
+print("Final Loss: {:.5g}".format(learner.iteration_loss))
+```
+
+Iteration: 0, Loss: 2.605 <!--notebook-skip-line-->
+
+Iteration: 100, Loss: 1.8621 <!--notebook-skip-line-->
+
+Iteration: 200, Loss: 1.6316 <!--notebook-skip-line-->
+
+
+Final Loss: 1.2919 <!--notebook-skip-line-->
+
+
+Although we get quite similar results to when we set the learning rate at 0.05 (because we're still in the region of falling loss on the Learning Rate Finder plot), we can still optimize our network faster using a slightly higher rate.
+
+## Wrap Up
+
+Give Learning Rate Finder a try on your current projects, and experiment with the different learning rate schedules found in the tutorials [here](https://mxnet.apache.org/tutorials/gluon/learning_rate_schedules.html) and [here](https://mxnet.apache.org/tutorials/gluon/learning_rate_schedules_advanced.html).
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/python_docs/python/tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.md b/docs/python_docs/python/tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.md
new file mode 100644
index 000000000000..ad948740a52f
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.md
@@ -0,0 +1,347 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+
+# Learning Rate Schedules
+
+Setting the learning rate for stochastic gradient descent (SGD) is crucially important when training neural networks because it controls both the speed of convergence and the ultimate performance of the network. One of the simplest learning rate strategies is to have a fixed learning rate throughout the training process. Choosing a small learning rate allows the optimizer find good solutions, but this comes at the expense of limiting the initial speed of convergence. Changing the learning rate over time can overcome this tradeoff.
+
+Schedules define how the learning rate changes over time and are typically specified for each epoch or iteration (i.e. batch) of training. Schedules differ from adaptive methods (such as AdaDelta and Adam) because they:
+
+* change the global learning rate for the optimizer, rather than parameter-wise learning rates
+* don't take feedback from the training process and are specified beforehand
+
+In this tutorial, we visualize the schedules defined in `mx.lr_scheduler`, show how to implement custom schedules and see an example of using a schedule while training models. Since schedules are passed to `mx.optimizer.Optimizer` classes, these methods work with both Module and Gluon APIs.
+
+
+```python
+from __future__ import print_function
+import math
+import matplotlib.pyplot as plt
+import mxnet as mx
+from mxnet.gluon import nn
+from mxnet.gluon.data.vision import transforms
+import numpy as np
+%matplotlib inline
+```
+
+```python
+def plot_schedule(schedule_fn, iterations=1500):
+    # Iteration count starting at 1
+    iterations = [i+1 for i in range(iterations)]
+    lrs = [schedule_fn(i) for i in iterations]
+    plt.scatter(iterations, lrs)
+    plt.xlabel("Iteration")
+    plt.ylabel("Learning Rate")
+    plt.show()
+```
+
+## Schedules
+
+In this section, we take a look at the schedules in `mx.lr_scheduler`. All of these schedules define the learning rate for a given iteration, and it is expected that iterations start at 1 rather than 0. So to find the learning rate for the 100th iteration, you can call `schedule(100)`.
+
+### Stepwise Decay Schedule
+
+One of the most commonly used learning rate schedules is called stepwise decay, where the learning rate is reduced by a factor at certain intervals. MXNet implements a `FactorScheduler` for equally spaced intervals, and `MultiFactorScheduler` for greater control. We start with an example of halving the learning rate every 250 iterations. More precisely, the learning rate will be multiplied by `factor` _after_ the `step` index and multiples thereafter. So in the example below the learning rate of the 250th iteration will be 1 and the 251st iteration will be 0.5.
+
+
+```python
+schedule = mx.lr_scheduler.FactorScheduler(step=250, factor=0.5)
+schedule.base_lr = 1
+plot_schedule(schedule)
+```
+
+
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/lr_schedules/factor.png) <!--notebook-skip-line-->
+
+
+Note: the `base_lr` is used to determine the initial learning rate. It takes a default value of 0.01 since we inherit from `mx.lr_scheduler.LRScheduler`, but it can be set as a property of the schedule. We will see later in this tutorial that `base_lr` is set automatically when providing the `lr_schedule` to `Optimizer`. Also be aware that the schedules in `mx.lr_scheduler` have state (i.e. counters, etc) so calling the schedule out of order may give unexpected results.
+
+We can define non-uniform intervals with `MultiFactorScheduler` and in the example below we halve the learning rate _after_ the 250th, 750th (i.e. a step length of 500 iterations) and 900th (a step length of 150 iterations). As before, the learning rate of the 250th iteration will be 1 and the 251th iteration will be 0.5.
+
+
+```python
+schedule = mx.lr_scheduler.MultiFactorScheduler(step=[250, 750, 900], factor=0.5)
+schedule.base_lr = 1
+plot_schedule(schedule)
+```
+
+
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/lr_schedules/multifactor.png) <!--notebook-skip-line-->
+
+
+### Polynomial Schedule
+
+Stepwise schedules and the discontinuities they introduce may sometimes lead to instability in the optimization, so in some cases smoother schedules are preferred. `PolyScheduler` gives a smooth decay using a polynomial function and reaches a learning rate of 0 after `max_update` iterations. In the example below, we have a quadratic function (`pwr=2`) that falls from 0.998 at iteration 1 to 0 at iteration 1000. After this the learning rate stays at 0, so nothing will be learnt from `max_update` iterations onwards.
+
+
+```python
+schedule = mx.lr_scheduler.PolyScheduler(max_update=1000, base_lr=1, pwr=2)
+plot_schedule(schedule)
+```
+
+
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/lr_schedules/polynomial.png) <!--notebook-skip-line-->
+
+
+Note: unlike `FactorScheduler`, the `base_lr` is set as an argument when instantiating the schedule.
+
+And we don't evaluate at `iteration=0` (to get `base_lr`) since we are working with schedules starting at `iteration=1`.
+
+### Custom Schedules
+
+You can implement your own custom schedule with a function or callable class, that takes an integer denoting the iteration index (starting at 1) and returns a float representing the learning rate to be used for that iteration. We implement the Cosine Annealing Schedule in the example below as a callable class (see `__call__` method).
+
+
+```python
+class CosineAnnealingSchedule():
+    def __init__(self, min_lr, max_lr, cycle_length):
+        self.min_lr = min_lr
+        self.max_lr = max_lr
+        self.cycle_length = cycle_length
+        
+    def __call__(self, iteration):
+        if iteration <= self.cycle_length:
+            unit_cycle = (1 + math.cos(iteration * math.pi / self.cycle_length)) / 2
+            adjusted_cycle = (unit_cycle * (self.max_lr - self.min_lr)) + self.min_lr
+            return adjusted_cycle
+        else:
+            return self.min_lr
+
+
+schedule = CosineAnnealingSchedule(min_lr=0, max_lr=1, cycle_length=1000)
+plot_schedule(schedule)
+```
+
+
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/lr_schedules/cosine.png) <!--notebook-skip-line-->
+
+
+## Using Schedules
+
+While training a simple handwritten digit classifier on the MNIST dataset, we take a look at how to use a learning rate schedule during training. Our demonstration model is a basic convolutional neural network. We start by preparing our `DataLoader` and defining the network. 
+
+As discussed above, the schedule should return a learning rate given an (1-based) iteration index.
+
+
+```python
+# Use GPU if one exists, else use CPU
+ctx = mx.gpu() if mx.context.num_gpus() else mx.cpu()
+
+# MNIST images are 28x28. Total pixels in input layer is 28x28 = 784
+num_inputs = 784
+# Clasify the images into one of the 10 digits
+num_outputs = 10
+# 64 images in a batch
+batch_size = 64
+
+# Load the training data
+train_dataset = mx.gluon.data.vision.MNIST(train=True).transform_first(transforms.ToTensor())
+train_dataloader = mx.gluon.data.DataLoader(train_dataset, batch_size, shuffle=True, num_workers=5)
+
+# Build a simple convolutional network
+def build_cnn():
+    net = nn.HybridSequential()
+    with net.name_scope():
+        # First convolution
+        net.add(nn.Conv2D(channels=10, kernel_size=5, activation='relu'))
+        net.add(nn.MaxPool2D(pool_size=2, strides=2))
+        # Second convolution
+        net.add(nn.Conv2D(channels=20, kernel_size=5, activation='relu'))
+        net.add(nn.MaxPool2D(pool_size=2, strides=2))
+        # Flatten the output before the fully connected layers
+        net.add(nn.Flatten())
+        # First fully connected layers with 512 neurons
+        net.add(nn.Dense(512, activation="relu"))
+        # Second fully connected layer with as many neurons as the number of classes
+        net.add(nn.Dense(num_outputs))
+        return net
+    
+net = build_cnn()
+```
+
+We then initialize our network (technically deferred until we pass the first batch) and define the loss.
+
+
+```python
+# Initialize the parameters with Xavier initializer
+net.collect_params().initialize(mx.init.Xavier(), ctx=ctx)
+# Use cross entropy loss
+softmax_cross_entropy = mx.gluon.loss.SoftmaxCrossEntropyLoss()
+```
+
+We're now ready to create our schedule, and in this example we opt for a stepwise decay schedule using `MultiFactorScheduler`. Since we're only training a demonstration model for a limited number of epochs (10 in total) we will exaggerate the schedule and drop the learning rate by 90% after the 4th, 7th and 9th epochs. We call these steps, and the drop occurs _after_ the step index. Schedules are defined for iterations (i.e. training batches), so we must represent our steps in iterations too.
+
+
+```python
+steps_epochs = [4, 7, 9]
+# assuming we keep partial batches, see `last_batch` parameter of DataLoader
+iterations_per_epoch = math.ceil(len(train_dataset) / batch_size)
+# iterations just before starts of epochs (iterations are 1-indexed)
+steps_iterations = [s*iterations_per_epoch for s in steps_epochs]
+print("Learning rate drops after iterations: {}".format(steps_iterations))
+```
+
+
+```
+Learning rate drops after iterations: [3752, 6566, 8442]
+```
+
+
+```python
+schedule = mx.lr_scheduler.MultiFactorScheduler(step=steps_iterations, factor=0.1)
+```
+
+**We create our `Optimizer` and pass the schedule via the `lr_scheduler` parameter.** In this example we're using Stochastic Gradient Descent.
+
+
+```python
+sgd_optimizer = mx.optimizer.SGD(learning_rate=0.03, lr_scheduler=schedule)
+```
+
+And we use this optimizer (with schedule) in our `Trainer` and train for 10 epochs. Alternatively, we could have set the `optimizer` to the string `sgd`, and pass a dictionary of the optimizer parameters directly to the trainer using `optimizer_params`.
+
+
+```python
+trainer = mx.gluon.Trainer(params=net.collect_params(), optimizer=sgd_optimizer)
+```
+
+
+```python
+num_epochs = 10
+# epoch and batch counts starting at 1
+for epoch in range(1, num_epochs+1):
+    # Iterate through the images and labels in the training data
+    for batch_num, (data, label) in enumerate(train_dataloader, start=1):
+        # get the images and labels
+        data = data.as_in_context(ctx)
+        label = label.as_in_context(ctx)
+        # Ask autograd to record the forward pass
+        with mx.autograd.record():
+            # Run the forward pass
+            output = net(data)
+            # Compute the loss
+            loss = softmax_cross_entropy(output, label)
+        # Compute gradients
+        loss.backward()
+        # Update parameters
+        trainer.step(data.shape[0])
+
+        # Show loss and learning rate after first iteration of epoch
+        if batch_num == 1:
+            curr_loss = mx.nd.mean(loss).asscalar()
+            curr_lr = trainer.learning_rate
+            print("Epoch: %d; Batch %d; Loss %f; LR %f" % (epoch, batch_num, curr_loss, curr_lr))
+```
+
+Epoch: 1; Batch 1; Loss 2.304071; LR 0.030000 <!--notebook-skip-line-->
+
+Epoch: 2; Batch 1; Loss 0.059640; LR 0.030000 <!--notebook-skip-line-->
+
+Epoch: 3; Batch 1; Loss 0.072601; LR 0.030000 <!--notebook-skip-line-->
+
+Epoch: 4; Batch 1; Loss 0.042228; LR 0.030000 <!--notebook-skip-line-->
+
+Epoch: 5; Batch 1; Loss 0.025745; LR 0.003000 <!--notebook-skip-line-->
+
+Epoch: 6; Batch 1; Loss 0.027391; LR 0.003000 <!--notebook-skip-line-->
+
+Epoch: 7; Batch 1; Loss 0.048237; LR 0.003000 <!--notebook-skip-line-->
+
+Epoch: 8; Batch 1; Loss 0.024213; LR 0.000300 <!--notebook-skip-line-->
+
+Epoch: 9; Batch 1; Loss 0.008892; LR 0.000300 <!--notebook-skip-line-->
+
+Epoch: 10; Batch 1; Loss 0.006875; LR 0.000030 <!--notebook-skip-line-->
+
+
+We see that the learning rate starts at 0.03, and falls to 0.00003 by the end of training as per the schedule we defined.
+
+### Manually setting the learning rate: Gluon API only
+
+When using the method above you don't need to manually keep track of iteration count and set the learning rate, so this is the recommended approach for most cases. Sometimes you might want more fine-grained control over setting the learning rate though, so Gluon's `Trainer` provides the `set_learning_rate` method for this.
+
+We replicate the example above, but now keep track of the `iteration_idx`, call the schedule and set the learning rate appropriately using `set_learning_rate`. We also use `schedule.base_lr` to set the initial learning rate for the schedule since we are calling the schedule directly and not using it as part of the `Optimizer`.
+
+
+```python
+net = build_cnn()
+net.collect_params().initialize(mx.init.Xavier(), ctx=ctx)
+
+schedule = mx.lr_scheduler.MultiFactorScheduler(step=steps_iterations, factor=0.1)
+schedule.base_lr = 0.03
+sgd_optimizer = mx.optimizer.SGD()
+trainer = mx.gluon.Trainer(params=net.collect_params(), optimizer=sgd_optimizer)
+
+iteration_idx = 1
+num_epochs = 10
+# epoch and batch counts starting at 1
+for epoch in range(1, num_epochs + 1):
+    # Iterate through the images and labels in the training data
+    for batch_num, (data, label) in enumerate(train_dataloader, start=1):
+        # get the images and labels
+        data = data.as_in_context(ctx)
+        label = label.as_in_context(ctx)
+        # Ask autograd to record the forward pass
+        with mx.autograd.record():
+            # Run the forward pass
+            output = net(data)
+            # Compute the loss
+            loss = softmax_cross_entropy(output, label)
+        # Compute gradients
+        loss.backward()
+        # Update the learning rate
+        lr = schedule(iteration_idx)
+        trainer.set_learning_rate(lr)
+        # Update parameters
+        trainer.step(data.shape[0])
+        # Show loss and learning rate after first iteration of epoch
+        if batch_num == 1:
+            curr_loss = mx.nd.mean(loss).asscalar()
+            curr_lr = trainer.learning_rate
+            print("Epoch: %d; Batch %d; Loss %f; LR %f" % (epoch, batch_num, curr_loss, curr_lr))
+        iteration_idx += 1
+```
+
+Epoch: 1; Batch 1; Loss 2.334119; LR 0.030000 <!--notebook-skip-line-->
+
+Epoch: 2; Batch 1; Loss 0.178930; LR 0.030000 <!--notebook-skip-line-->
+
+Epoch: 3; Batch 1; Loss 0.142640; LR 0.030000 <!--notebook-skip-line-->
+
+Epoch: 4; Batch 1; Loss 0.041116; LR 0.030000 <!--notebook-skip-line-->
+
+Epoch: 5; Batch 1; Loss 0.051049; LR 0.003000 <!--notebook-skip-line-->
+
+Epoch: 6; Batch 1; Loss 0.027170; LR 0.003000 <!--notebook-skip-line-->
+
+Epoch: 7; Batch 1; Loss 0.083776; LR 0.003000 <!--notebook-skip-line-->
+
+Epoch: 8; Batch 1; Loss 0.082553; LR 0.000300 <!--notebook-skip-line-->
+
+Epoch: 9; Batch 1; Loss 0.027984; LR 0.000300 <!--notebook-skip-line-->
+
+Epoch: 10; Batch 1; Loss 0.030896; LR 0.000030 <!--notebook-skip-line-->
+
+
+Once again, we see the learning rate start at 0.03, and fall to 0.00003 by the end of training as per the schedule we defined.
+
+## Advanced Schedules
+
+We have a related tutorial on Advanced Learning Rate Schedules that shows reference implementations of schedules that give state-of-the-art results. We look at cyclical schedules applied to a variety of cycle shapes, and many other techniques such as warm-up and cool-down.
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
\ No newline at end of file
diff --git a/docs/python_docs/python/tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.md b/docs/python_docs/python/tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.md
new file mode 100644
index 000000000000..287f70d535e8
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.md
@@ -0,0 +1,325 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+
+ # Advanced Learning Rate Schedules
+
+Given the importance of learning rate and the learning rate schedule for training neural networks, there have been a number of research papers published recently on the subject. Although many practitioners are using simple learning rate schedules such as stepwise decay, research has shown that there are other strategies that work better in most situations. We implement a number of different schedule shapes in this tutorial and introduce cyclical schedules.
+
+See the "Learning Rate Schedules" tutorial for a more basic overview of learning rates, and an example of how to use them while training your own models.
+
+
+```python
+%matplotlib inline
+import copy
+import math
+import mxnet as mx
+import numpy as np
+import matplotlib.pyplot as plt
+```
+
+```python
+def plot_schedule(schedule_fn, iterations=1500):
+    # Iteration count starting at 1
+    iterations = [i+1 for i in range(iterations)]
+    lrs = [schedule_fn(i) for i in iterations]
+    plt.scatter(iterations, lrs)
+    plt.xlabel("Iteration")
+    plt.ylabel("Learning Rate")
+    plt.show()
+```
+
+## Custom Schedule Shapes
+
+### (Slanted) Triangular
+
+While trying to push the boundaries of batch size for faster training, [Priya Goyal et al. (2017)](https://arxiv.org/abs/1706.02677) found that having a smooth linear warm up in the learning rate at the start of training improved the stability of the optimizer and lead to better solutions. It was found that a smooth increases gave improved performance over stepwise increases.
+
+We look at "warm-up" in more detail later in the tutorial, but this could be viewed as a specific case of the **"triangular"** schedule that was proposed by [Leslie N. Smith (2015)](https://arxiv.org/abs/1506.01186). Quite simply, the schedule linearly increases then decreases between a lower and upper bound. Originally it was suggested this schedule be used as part of a cyclical schedule but more recently researchers have been using a single cycle.
+
+One adjustment proposed by [Jeremy Howard, Sebastian Ruder (2018)](https://arxiv.org/abs/1801.06146) was to change the ratio between the increasing and decreasing stages, instead of the 50:50 split. Changing the increasing fraction (`inc_fraction!=0.5`) leads to a **"slanted triangular"** schedule. Using `inc_fraction<0.5` tends to give better results.
+
+
+```python
+class TriangularSchedule():
+    def __init__(self, min_lr, max_lr, cycle_length, inc_fraction=0.5):     
+        """
+        min_lr: lower bound for learning rate (float)
+        max_lr: upper bound for learning rate (float)
+        cycle_length: iterations between start and finish (int)
+        inc_fraction: fraction of iterations spent in increasing stage (float)
+        """
+        self.min_lr = min_lr
+        self.max_lr = max_lr
+        self.cycle_length = cycle_length
+        self.inc_fraction = inc_fraction
+        
+    def __call__(self, iteration):
+        if iteration <= self.cycle_length*self.inc_fraction:
+            unit_cycle = iteration * 1 / (self.cycle_length * self.inc_fraction)
+        elif iteration <= self.cycle_length:
+            unit_cycle = (self.cycle_length - iteration) * 1 / (self.cycle_length * (1 - self.inc_fraction))
+        else:
+            unit_cycle = 0
+        adjusted_cycle = (unit_cycle * (self.max_lr - self.min_lr)) + self.min_lr
+        return adjusted_cycle
+```
+
+We look an example of a slanted triangular schedule that increases from a learning rate of 1 to 2, and back to 1 over 1000 iterations. Since we set `inc_fraction=0.2`, 200 iterations are used for the increasing stage, and 800 for the decreasing stage. After this, the schedule stays at the lower bound indefinitely.
+
+
+```python
+schedule = TriangularSchedule(min_lr=1, max_lr=2, cycle_length=1000, inc_fraction=0.2)
+plot_schedule(schedule)
+```
+
+
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/lr_schedules/adv_triangular.png) <!--notebook-skip-line-->
+
+
+### Cosine
+
+Continuing with the idea that smooth decay profiles give improved performance over stepwise decay, [Ilya Loshchilov, Frank Hutter (2016)](https://arxiv.org/abs/1608.03983) used **"cosine annealing"** schedules to good effect. As with triangular schedules, the original idea was that this should be used as part of a cyclical schedule, but we begin by implementing the cosine annealing component before the full Stochastic Gradient Descent with Warm Restarts (SGDR) method later in the tutorial.
+
+
+```python
+class CosineAnnealingSchedule():
+    def __init__(self, min_lr, max_lr, cycle_length):
+        """
+        min_lr: lower bound for learning rate (float)
+        max_lr: upper bound for learning rate (float)
+        cycle_length: iterations between start and finish (int)
+        """
+        self.min_lr = min_lr
+        self.max_lr = max_lr
+        self.cycle_length = cycle_length
+        
+    def __call__(self, iteration):
+        if iteration <= self.cycle_length:
+            unit_cycle = (1 + math.cos(iteration * math.pi / self.cycle_length)) / 2
+            adjusted_cycle = (unit_cycle * (self.max_lr - self.min_lr)) + self.min_lr
+            return adjusted_cycle
+        else:
+            return self.min_lr
+```
+
+We look at an example of a cosine annealing schedule that smoothing decreases from a learning rate of 2 to 1 across 1000 iterations. After this, the schedule stays at the lower bound indefinietly.
+
+
+```python
+schedule = CosineAnnealingSchedule(min_lr=1, max_lr=2, cycle_length=1000)
+plot_schedule(schedule)
+```
+
+
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/lr_schedules/adv_cosine.png) <!--notebook-skip-line-->
+
+
+## Custom Schedule Modifiers
+
+We now take a look some adjustments that can be made to existing schedules. We see how to add linear warm-up and its compliment linear cool-down, before using this to implement the "1-Cycle" schedule used by [Leslie N. Smith, Nicholay Topin (2017)](https://arxiv.org/abs/1708.07120) for "super-convergence". We then look at cyclical schedules and implement the original cyclical schedule from [Leslie N. Smith (2015)](https://arxiv.org/abs/1506.01186) before finishing with a look at ["SGDR: Stochastic Gradient Descent with Warm Restarts" by Ilya Loshchilov, Frank Hutter (2016)](https://arxiv.org/abs/1608.03983).
+
+Unlike the schedules above and those implemented in `mx.lr_scheduler`, these classes are designed to modify existing schedules so they take the argument `schedule` (for initialized schedules) or `schedule_class` when being initialized.
+
+### Warm-Up
+
+Using the idea of linear warm-up of the learning rate proposed in ["Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour" by Priya Goyal et al. (2017)](https://arxiv.org/abs/1706.02677), we implement a wrapper class that adds warm-up to an existing schedule. Going from `start_lr` to the initial learning rate of the `schedule` over `length` iterations, this adjustment is useful when training with large batch sizes.
+
+
+```python
+class LinearWarmUp():
+    def __init__(self, schedule, start_lr, length):
+        """
+        schedule: a pre-initialized schedule (e.g. TriangularSchedule(min_lr=0.5, max_lr=2, cycle_length=500))
+        start_lr: learning rate used at start of the warm-up (float)
+        length: number of iterations used for the warm-up (int)
+        """
+        self.schedule = schedule
+        self.start_lr = start_lr
+        # calling mx.lr_scheduler.LRScheduler effects state, so calling a copy
+        self.finish_lr = copy.copy(schedule)(0)
+        self.length = length
+    
+    def __call__(self, iteration):
+        if iteration <= self.length:
+            return iteration * (self.finish_lr - self.start_lr)/(self.length) + self.start_lr
+        else:
+            return self.schedule(iteration - self.length)
+```
+
+As an example, we add a linear warm-up of the learning rate (from 0 to 1 over 250 iterations) to a stepwise decay schedule. We first create the `MultiFactorScheduler` (and set the `base_lr`) and then pass it to `LinearWarmUp` to add the warm-up at the start. We can use `LinearWarmUp` with any other schedule including `CosineAnnealingSchedule`.
+
+
+```python
+schedule = mx.lr_scheduler.MultiFactorScheduler(step=[250, 750, 900], factor=0.5)
+schedule.base_lr = 1
+schedule = LinearWarmUp(schedule, start_lr=0, length=250)
+plot_schedule(schedule)
+```
+
+
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/lr_schedules/adv_warmup.png) <!--notebook-skip-line-->
+
+
+### Cool-Down
+
+Similarly, we could add a linear cool-down period to our schedule and this is used in the "1-Cycle" schedule proposed by [Leslie N. Smith, Nicholay Topin (2017)](https://arxiv.org/abs/1708.07120) to train neural networks very quickly in certain circumstances (coined "super-convergence"). We reduce the learning rate from its value at `start_idx` of `schedule` to `finish_lr` over a period of `length`, and then maintain `finish_lr` thereafter.
+
+
+```python
+class LinearCoolDown():
+    def __init__(self, schedule, finish_lr, start_idx, length):
+        """
+        schedule: a pre-initialized schedule (e.g. TriangularSchedule(min_lr=0.5, max_lr=2, cycle_length=500))
+        finish_lr: learning rate used at end of the cool-down (float)
+        start_idx: iteration to start the cool-down (int)
+        length: number of iterations used for the cool-down (int)
+        """
+        self.schedule = schedule
+        # calling mx.lr_scheduler.LRScheduler effects state, so calling a copy
+        self.start_lr = copy.copy(self.schedule)(start_idx)
+        self.finish_lr = finish_lr
+        self.start_idx = start_idx
+        self.finish_idx = start_idx + length
+        self.length = length
+    
+    def __call__(self, iteration):
+        if iteration <= self.start_idx:
+            return self.schedule(iteration)
+        elif iteration <= self.finish_idx:
+            return (iteration - self.start_idx) * (self.finish_lr - self.start_lr) / (self.length) + self.start_lr
+        else:
+            return self.finish_lr
+```
+
+As an example, we apply learning rate cool-down to a `MultiFactorScheduler`. Starting the cool-down at iteration 1000, we reduce the learning rate linearly from 0.125 to 0.001 over 500 iterations, and hold the learning rate at 0.001 after this.
+
+
+```python
+schedule = mx.lr_scheduler.MultiFactorScheduler(step=[250, 750, 900], factor=0.5)
+schedule.base_lr = 1
+schedule = LinearCoolDown(schedule, finish_lr=0.001, start_idx=1000, length=500)
+plot_schedule(schedule)
+```
+
+
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/lr_schedules/adv_cooldown.png) <!--notebook-skip-line-->
+
+
+#### 1-Cycle: for "Super-Convergence"
+
+So we can implement the "1-Cycle" schedule proposed by [Leslie N. Smith, Nicholay Topin (2017)](https://arxiv.org/abs/1708.07120) we use a single and symmetric cycle of the triangular schedule above (i.e. `inc_fraction=0.5`), followed by a cool-down period of `cooldown_length` iterations.
+
+
+```python
+class OneCycleSchedule():
+    def __init__(self, start_lr, max_lr, cycle_length, cooldown_length=0, finish_lr=None):
+        """
+        start_lr: lower bound for learning rate in triangular cycle (float)
+        max_lr: upper bound for learning rate in triangular cycle (float)
+        cycle_length: iterations between start and finish of triangular cycle: 2x 'stepsize' (int)
+        cooldown_length: number of iterations used for the cool-down (int)
+        finish_lr: learning rate used at end of the cool-down (float)
+        """
+        if (cooldown_length > 0) and (finish_lr is None):
+            raise ValueError("Must specify finish_lr when using cooldown_length > 0.")
+        if (cooldown_length == 0) and (finish_lr is not None):
+            raise ValueError("Must specify cooldown_length > 0 when using finish_lr.")
+            
+        finish_lr = finish_lr if (cooldown_length > 0) else start_lr
+        schedule = TriangularSchedule(min_lr=start_lr, max_lr=max_lr, cycle_length=cycle_length)
+        self.schedule = LinearCoolDown(schedule, finish_lr=finish_lr, start_idx=cycle_length, length=cooldown_length)
+        
+    def __call__(self, iteration):
+        return self.schedule(iteration)
+```
+
+As an example, we linearly increase and then decrease the learning rate from 0.1 to 0.5 and back over 500 iterations (i.e. single triangular cycle), before reducing the learning rate further to 0.001 over the next 750 iterations (i.e. cool-down).
+
+
+```python
+schedule = OneCycleSchedule(start_lr=0.1, max_lr=0.5, cycle_length=500, cooldown_length=750, finish_lr=0.001)
+plot_schedule(schedule)
+```
+
+
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/lr_schedules/adv_onecycle.png) <!--notebook-skip-line-->
+
+
+### Cyclical
+
+Originally proposed by [Leslie N. Smith (2015)](https://arxiv.org/abs/1506.01186), the idea of cyclically increasing and decreasing the learning rate has been shown to give faster convergence and more optimal solutions. We implement a wrapper class that loops existing cycle-based schedules such as `TriangularSchedule` and `CosineAnnealingSchedule` to provide infinitely repeating schedules. We pass the schedule class (rather than an instance) because one feature of the `CyclicalSchedule` is to vary the `cycle_length` over time as seen in [Ilya Loshchilov, Frank Hutter (2016)](https://arxiv.org/abs/1608.03983) using `cycle_length_decay`. Another feature is the ability to decay the cycle magnitude over time with `cycle_magnitude_decay`.
+
+
+```python
+class CyclicalSchedule():
+    def __init__(self, schedule_class, cycle_length, cycle_length_decay=1, cycle_magnitude_decay=1, **kwargs):
+        """
+        schedule_class: class of schedule, expected to take `cycle_length` argument
+        cycle_length: iterations used for initial cycle (int)
+        cycle_length_decay: factor multiplied to cycle_length each cycle (float)
+        cycle_magnitude_decay: factor multiplied learning rate magnitudes each cycle (float)
+        kwargs: passed to the schedule_class
+        """
+        self.schedule_class = schedule_class
+        self.length = cycle_length
+        self.length_decay = cycle_length_decay
+        self.magnitude_decay = cycle_magnitude_decay
+        self.kwargs = kwargs
+    
+    def __call__(self, iteration):
+        cycle_idx = 0
+        cycle_length = self.length
+        idx = self.length
+        while idx <= iteration:
+            cycle_length = math.ceil(cycle_length * self.length_decay)
+            cycle_idx += 1
+            idx += cycle_length
+        cycle_offset = iteration - idx + cycle_length
+        
+        schedule = self.schedule_class(cycle_length=cycle_length, **self.kwargs)
+        return schedule(cycle_offset) * self.magnitude_decay**cycle_idx
+```
+
+As an example, we implement the triangular cyclical schedule presented in ["Cyclical Learning Rates for Training Neural Networks" by Leslie N. Smith (2015)](https://arxiv.org/abs/1506.01186). We use slightly different terminology to the paper here because we use `cycle_length` that is twice the 'stepsize' used in the paper. We repeat cycles, each with a length of 500 iterations and lower and upper learning rate bounds of 0.5 and 2 respectively.
+
+
+```python
+schedule = CyclicalSchedule(TriangularSchedule, min_lr=0.5, max_lr=2, cycle_length=500)
+plot_schedule(schedule)
+```
+
+
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/lr_schedules/adv_cyclical.png) <!--notebook-skip-line-->
+
+
+And lastly, we implement the scheduled used in ["SGDR: Stochastic Gradient Descent with Warm Restarts" by Ilya Loshchilov, Frank Hutter (2016)](https://arxiv.org/abs/1608.03983). We repeat cosine annealing schedules, but each time we halve the magnitude and double the cycle length.
+
+
+```python
+schedule = CyclicalSchedule(CosineAnnealingSchedule, min_lr=0.01, max_lr=2,
+                            cycle_length=250, cycle_length_decay=2, cycle_magnitude_decay=0.5)
+plot_schedule(schedule)
+```
+
+
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/lr_schedules/adv_sgdr.png) <!--notebook-skip-line-->
+
+
+**_Want to learn more?_** Checkout the "Learning Rate Schedules" tutorial for a more basic overview of learning rates found in `mx.lr_scheduler`, and an example of how to use them while training your own models.
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
\ No newline at end of file
diff --git a/docs/python_docs/python/tutorials/packages/gluon/normalization/imgs/NCHW_BN.png b/docs/python_docs/python/tutorials/packages/gluon/training/normalization/imgs/NCHW_BN.png
similarity index 100%
rename from docs/python_docs/python/tutorials/packages/gluon/normalization/imgs/NCHW_BN.png
rename to docs/python_docs/python/tutorials/packages/gluon/training/normalization/imgs/NCHW_BN.png
diff --git a/docs/python_docs/python/tutorials/packages/gluon/normalization/imgs/NCHW_IN.png b/docs/python_docs/python/tutorials/packages/gluon/training/normalization/imgs/NCHW_IN.png
similarity index 100%
rename from docs/python_docs/python/tutorials/packages/gluon/normalization/imgs/NCHW_IN.png
rename to docs/python_docs/python/tutorials/packages/gluon/training/normalization/imgs/NCHW_IN.png
diff --git a/docs/python_docs/python/tutorials/packages/gluon/normalization/imgs/NCHW_LN.png b/docs/python_docs/python/tutorials/packages/gluon/training/normalization/imgs/NCHW_LN.png
similarity index 100%
rename from docs/python_docs/python/tutorials/packages/gluon/normalization/imgs/NCHW_LN.png
rename to docs/python_docs/python/tutorials/packages/gluon/training/normalization/imgs/NCHW_LN.png
diff --git a/docs/python_docs/python/tutorials/packages/gluon/normalization/imgs/NTC_BN.png b/docs/python_docs/python/tutorials/packages/gluon/training/normalization/imgs/NTC_BN.png
similarity index 100%
rename from docs/python_docs/python/tutorials/packages/gluon/normalization/imgs/NTC_BN.png
rename to docs/python_docs/python/tutorials/packages/gluon/training/normalization/imgs/NTC_BN.png
diff --git a/docs/python_docs/python/tutorials/packages/gluon/normalization/imgs/NTC_IN.png b/docs/python_docs/python/tutorials/packages/gluon/training/normalization/imgs/NTC_IN.png
similarity index 100%
rename from docs/python_docs/python/tutorials/packages/gluon/normalization/imgs/NTC_IN.png
rename to docs/python_docs/python/tutorials/packages/gluon/training/normalization/imgs/NTC_IN.png
diff --git a/docs/python_docs/python/tutorials/packages/gluon/normalization/imgs/NTC_LN.png b/docs/python_docs/python/tutorials/packages/gluon/training/normalization/imgs/NTC_LN.png
similarity index 100%
rename from docs/python_docs/python/tutorials/packages/gluon/normalization/imgs/NTC_LN.png
rename to docs/python_docs/python/tutorials/packages/gluon/training/normalization/imgs/NTC_LN.png
diff --git a/docs/python_docs/python/tutorials/packages/gluon/normalization/imgs/data_normalization.jpeg b/docs/python_docs/python/tutorials/packages/gluon/training/normalization/imgs/data_normalization.jpeg
similarity index 100%
rename from docs/python_docs/python/tutorials/packages/gluon/normalization/imgs/data_normalization.jpeg
rename to docs/python_docs/python/tutorials/packages/gluon/training/normalization/imgs/data_normalization.jpeg
diff --git a/docs/python_docs/python/tutorials/packages/gluon/normalization/normalization.md b/docs/python_docs/python/tutorials/packages/gluon/training/normalization/index.md
similarity index 94%
rename from docs/python_docs/python/tutorials/packages/gluon/normalization/normalization.md
rename to docs/python_docs/python/tutorials/packages/gluon/training/normalization/index.md
index 81d59be56ee0..113bb8ed4f71 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/normalization/normalization.md
+++ b/docs/python_docs/python/tutorials/packages/gluon/training/normalization/index.md
@@ -17,7 +17,7 @@
 
 # Normalization Blocks
 
-When training deep neural networks there are a number of techniques that are thought to be essential for model convergence. One important area is deciding how to initialize the parameters of the network. Using techniques such as [Xavier](https://mxnet.incubator.apache.org/api/python/optimization/optimization.html#mxnet.initializer.Xavier) initialization, we can can improve the gradient flow through the network at the start of training. Another important technique is normalization: i.e. scaling and shifting certain values towards a distribution with a mean of 0 (i.e. zero-centered) and a standard distribution of 1 (i.e. unit variance). Which values you normalize depends on the exact method used as we'll see later on.
+When training deep neural networks there are a number of techniques that are thought to be essential for model convergence. One important area is deciding how to initialize the parameters of the network. Using techniques such as [Xavier](https://mxnet.apache.org/api/python/optimization/optimization.html#mxnet.initializer.Xavier) initialization, we can can improve the gradient flow through the network at the start of training. Another important technique is normalization: i.e. scaling and shifting certain values towards a distribution with a mean of 0 (i.e. zero-centered) and a standard distribution of 1 (i.e. unit variance). Which values you normalize depends on the exact method used as we'll see later on.
 
 <p align="center">
     <img src="./imgs/data_normalization.jpeg" alt="drawing" width="500"/>
@@ -39,7 +39,7 @@ Tip: A `BatchNorm` layer at the start of your network can have a similar effect
 
 Warning: You should calculate the normalization means and standard deviations using the training dataset only. Any leakage of information from you testing dataset will effect the reliability of your testing metrics.
 
-When using pre-trained models from the [Gluon Model Zoo](https://mxnet.incubator.apache.org/api/python/gluon/model_zoo.html) you'll usually see the normalization statistics used for training (i.e. statistics from step 1). You'll want to use these statistics to normalize your own input data for fine-tuning or inference with these models. Using `transforms.Normalize` is one way of applying the normalization, and this should be used in the `Dataset`.
+When using pre-trained models from the [Gluon Model Zoo](https://mxnet.apache.org/api/python/gluon/model_zoo.html) you'll usually see the normalization statistics used for training (i.e. statistics from step 1). You'll want to use these statistics to normalize your own input data for fine-tuning or inference with these models. Using `transforms.Normalize` is one way of applying the normalization, and this should be used in the `Dataset`.
 
 ```python
 import mxnet as mx
diff --git a/docs/python_docs/python/tutorials/packages/gluon/trainer.md b/docs/python_docs/python/tutorials/packages/gluon/training/trainer.md
similarity index 69%
rename from docs/python_docs/python/tutorials/packages/gluon/trainer.md
rename to docs/python_docs/python/tutorials/packages/gluon/training/trainer.md
index c154a56bed26..e475fda8bfc5 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/trainer.md
+++ b/docs/python_docs/python/tutorials/packages/gluon/training/trainer.md
@@ -21,9 +21,9 @@ Training a neural network model consists of iteratively performing three simple
 
 The first step is the forward step which computes the loss.  In MXNet Gluon, this first step is achieved by doing a forward pass by calling `net.forward(X)` or simply `net(X)` and then calling the loss function with the result of the forward pass and the labels. For example `l = loss_fn(net(X), y)`.
 
-The second step is the backward step which computes the gradient of the loss with respect to the parameters. In Gluon, this step is  achieved by doing the first step in an [`autograd.record()`](http://beta.mxnet.io/guide/packages/autograd/autograd.html) scope to record the computations needed to calculate the loss, and then calling `l.backward()` to compute the gradient of the loss with respect to the parameters.
+The second step is the backward step which computes the gradient of the loss with respect to the parameters. In Gluon, this step is  achieved by doing the first step in an [`autograd.record()`](https:///beta.mxnet.io/guide/packages/autograd/autograd.html) scope to record the computations needed to calculate the loss, and then calling `l.backward()` to compute the gradient of the loss with respect to the parameters.
 
-The final step is to update the neural network model parameters using an optimization algorithm. In Gluon, this step is performed by the [`gluon.Trainer`](http://beta.mxnet.io/api/gluon/mxnet.gluon.Trainer.html) and is the subject of this guide. When creating a  Gluon `Trainer` you must provide a collection of parameters that need to be learnt. You also provide an `Optimizer` that will be used to update the parameters every training iteration when `trainer.step` is called.
+The final step is to update the neural network model parameters using an optimization algorithm. In Gluon, this step is performed by the [`gluon.Trainer`](https:///beta.mxnet.io/api/gluon/mxnet.gluon.Trainer.html) and is the subject of this guide. When creating a  Gluon `Trainer` you must provide a collection of parameters that need to be learnt. You also provide an `Optimizer` that will be used to update the parameters every training iteration when `trainer.step` is called.
 
 ## Basic Usage
 
@@ -97,7 +97,7 @@ print(curr_weight - net.weight.grad() * 1 / batch_size)
 
 In the previous example, we use the string argument `sgd` to select the optimization method, and `optimizer_params` to specify the optimization method arguments.
 
-All pre-defined optimization methods can be passed in this way and the complete list of implemented optimizers is provided in the [`mxnet.optimizer`](http://beta.mxnet.io/api/gluon-related/mxnet.optimizer.html) module.
+All pre-defined optimization methods can be passed in this way and the complete list of implemented optimizers is provided in the [`mxnet.optimizer`](https:///beta.mxnet.io/api/gluon-related/mxnet.optimizer.html) module.
 
 However we can also pass an optimizer instance directly to the `Trainer` constructor.
 
@@ -114,14 +114,14 @@ trainer.step(batch_size)
 net.weight.data()
 ```
 
-For reference and implementation details about each optimizer, please refer to the [guide](http://beta.mxnet.io/guide/packages/optimizer/optimizer.html) for the `optimizer` module.
+For reference and implementation details about each optimizer, please refer to the [guide](https:///beta.mxnet.io/guide/packages/optimizer/optimizer.html) for the `optimizer` module.
 
 ### KVStore Options
 
 The `Trainer` constructor also accepts the following keyword arguments for :
 
-- `kvstore` – how key value store  should be created for multi-gpu and distributed training. Check out  [`mxnet.kvstore.KVStore`](http://beta.mxnet.io/api/gluon-related/mxnet.kvstore.KVStore.html#mxnet.kvstore.KVStore) for more information. String options are any of the following ['local', 'device', 'dist_device_sync', 'dist_device_async'].
-- `compression_params` – Specifies type of gradient compression and additional arguments depending on the type of compression being used. See [`mxnet.KVStore.set_gradient_compression_method`](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.kvstore.KVStore.set_gradient_compression.html#mxnet.kvstore.KVStore.set_gradient_compression) for more details on gradient compression.
+- `kvstore` – how key value store  should be created for multi-gpu and distributed training. Check out  [`mxnet.kvstore.KVStore`](https:///beta.mxnet.io/api/gluon-related/mxnet.kvstore.KVStore.html#mxnet.kvstore.KVStore) for more information. String options are any of the following ['local', 'device', 'dist_device_sync', 'dist_device_async'].
+- `compression_params` – Specifies type of gradient compression and additional arguments depending on the type of compression being used. See [`mxnet.KVStore.set_gradient_compression_method`](https:///beta.mxnet.io/api/gluon-related/_autogen/mxnet.kvstore.KVStore.set_gradient_compression.html#mxnet.kvstore.KVStore.set_gradient_compression) for more details on gradient compression.
 - `update_on_kvstore` – Whether to perform parameter updates on KVStore. If None, then the `Trainer` instance  will choose the more suitable option depending on the type of KVStore.
 
 ### Changing the Learning Rate
@@ -143,7 +143,7 @@ trainer.learning_rate
 
 
 
-In addition, there are multiple pre-defined learning rate scheduling methods that are already implemented in the [`mxnet.lr_scheduler`](http://beta.mxnet.io/api/gluon-related/mxnet.lr_scheduler.html) module. The learning rate schedulers can be incorporated into your trainer by passing them in as an `optimizer_param` entry. Please refer to the [LR scheduler guide](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/learning_rate_schedules.html) to learn more.
+In addition, there are multiple pre-defined learning rate scheduling methods that are already implemented in the [`mxnet.lr_scheduler`](https://mxnet.apache.org/api/python/docs/api/gluon-related/mxnet.lr_scheduler.html) module. The learning rate schedulers can be incorporated into your trainer by passing them in as an `optimizer_param` entry. Please refer to the [LR scheduler guide](https://mxnet.apache.org/versions/master/tutorials/gluon/learning_rate_schedules.html) to learn more.
 
 
 
@@ -160,9 +160,9 @@ In addition, there are multiple pre-defined learning rate scheduling methods tha
 
 While optimization and optimizers play a significant role in deep learning model training, there are still other important components to model training. Here are a few suggestions about where to look next.
 
-* The [Optimizer API](http://beta.mxnet.io/api/gluon-related/mxnet.optimizer.html) and [guide](http://beta.mxnet.io/guide/packages/optimizer/optimizer.html) have information about all the different optimizers implemented in MXNet and their update steps. The [Dive into Deep Learning](http://en.diveintodeeplearning.org/chapter_optimization/index.html) book also has a chapter dedicated to optimization methods and explains various key optimizers in great detail.
+* The [Optimizer API](https://mxnet.apache.org/api/python/docs/api/gluon-related/mxnet.optimizer.html) and [guide](https://mxnet.apache.org/api/python/docs/tutorials/packages/optimizer/optimizer.html) have information about all the different optimizers implemented in MXNet and their update steps. The [Dive into Deep Learning](https://en.diveintodeeplearning.org/chapter_optimization/index.html) book also has a chapter dedicated to optimization methods and explains various key optimizers in great detail.
 
-- Take a look at the [guide to parameter initialization](http://beta.mxnet.io/guide/packages/gluon/init.html) in MXNet to learn about what initialization schemes are already implemented, and how to implement your custom initialization schemes.
-- Also check out this  [guide on parameter management](http://beta.mxnet.io/guide/packages/gluon/parameters.html) to learn about how to manage model parameters in gluon.
-- Make sure to take a look at the [guide to scheduling learning rates](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/learning_rate_schedules.html) to learn how to create learning rate schedules to make your training converge faster.
-- Finally take a look at the [KVStore API](http://beta.mxnet.io/api/gluon-related/mxnet.kvstore.KVStore.html#mxnet.kvstore.KVStore) to learn how parameter values are synchronized over multiple devices.
+- Take a look at the [guide to parameter initialization](https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/init.html) in MXNet to learn about what initialization schemes are already implemented, and how to implement your custom initialization schemes.
+- Also check out this  [guide on parameter management](https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/parameters.html) to learn about how to manage model parameters in gluon.
+- Make sure to take a look at the [guide to scheduling learning rates](https://mxnet.apache.org/versions/master/tutorials/gluon/learning_rate_schedules.html) to learn how to create learning rate schedules to make your training converge faster.
+- Finally take a look at the [KVStore API](https://mxnet.apache.org/api/python/docs/api/gluon-related/mxnet.kvstore.KVStore.html#mxnet.kvstore.KVStore) to learn how parameter values are synchronized over multiple devices.
diff --git a/docs/python_docs/python/tutorials/packages/index.rst b/docs/python_docs/python/tutorials/packages/index.rst
index f6aa17e6b6c2..c24ebb8c04eb 100644
--- a/docs/python_docs/python/tutorials/packages/index.rst
+++ b/docs/python_docs/python/tutorials/packages/index.rst
@@ -58,8 +58,7 @@ Shared APIs
 
    .. card::
       :title: Learning Rate
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/learning_rate_schedules.html
-
+      :link: gluon/training/learning_rates/learning_rate_schedules.html
       How to use the Learning Rate Scheduler.
 
    .. card::
@@ -86,55 +85,14 @@ Shared APIs
 
       How to use Open Neural Network Exchange (ONNX) with MXNet.
 
-   ..
-      .. card::
-         :title: Optimizer
-         :link: optimizer.html
-
-         How to use optimizer.
-   ..
-
-Old APIs
---------
-Currently supported, but not recommended APIs.
-
-.. container:: cards
-
    .. card::
-      :title: Module
-      :link: module/index.html
-
-      MXNet's symbolic interface for Python.
+      :title: Optimizer
+      :link: optimizer/index.html
 
+      How to use the optimizers.
 
 .. toctree::
    :hidden:
+   :glob:
 
-   gluon/index
-   ndarray/index
-   symbol/index
-   autograd/autograd
-   onnx/index
-
-..
-   Basic
-   -----
-
-   .. toctree::
-      :maxdepth: 1
-
-      mxboard
-      gpus
-
-   Advanced
-   --------
-
-
-   .. toctree::
-      :maxdepth: 1
-
-      symbol
-      record-io
-      sparse
-      control-flow
-      distributed-training
+   */index*
diff --git a/docs/python_docs/python/tutorials/packages/kvstore/index.rst b/docs/python_docs/python/tutorials/packages/kvstore/index.rst
index 4cf15d93ed3f..d3d762d91ea1 100644
--- a/docs/python_docs/python/tutorials/packages/kvstore/index.rst
+++ b/docs/python_docs/python/tutorials/packages/kvstore/index.rst
@@ -22,13 +22,13 @@ KVStore
 
    .. card::
       :title: Distributed Training Using the KVStore API
-      :link: https://mxnet.incubator.apache.org/api/faq/distributed_training
+      :link: kvstore.html
 
       How to use the KVStore API to use multiple GPUs when training a model.
 
    .. card::
       :title: Distributed Key-Value Store
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/python/kvstore.html
+      :link: https://mxnet.apache.org/versions/master/tutorials/python/kvstore.html
 
       How to use the KVStore API to share data across different devices.
 
diff --git a/docs/python_docs/python/tutorials/packages/kvstore/kvstore.md b/docs/python_docs/python/tutorials/packages/kvstore/kvstore.md
new file mode 100644
index 000000000000..5b5dc94a7c2f
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/kvstore/kvstore.md
@@ -0,0 +1,171 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Distributed Key-Value Store
+
+KVStore is a place for data sharing. Think of it as a single object shared
+across different devices (GPUs and computers), where each device can push data in
+and pull data out.
+
+## Initialization
+
+Let's consider a simple example: initializing
+a (`int`, `NDArray`) pair into the store, and then pulling the value out:
+
+```python
+import mxnet as mx
+
+kv = mx.kv.create('local') # create a local kv store.
+shape = (2,3)
+kv.init(3, mx.nd.ones(shape)*2)
+a = mx.nd.zeros(shape)
+kv.pull(3, out = a)
+print(a.asnumpy())
+```
+
+`[[ 2.  2.  2.],[ 2.  2.  2.]]`<!--notebook-skip-line-->
+
+## Push, Aggregate, and Update
+
+For any key that has been initialized, you can push a new value with the same shape to the key:
+
+```python
+kv.push(3, mx.nd.ones(shape)*8)
+kv.pull(3, out = a) # pull out the value
+print(a.asnumpy())
+```
+
+`[[ 8.  8.  8.],[ 8.  8.  8.]]`<!--notebook-skip-line-->
+
+The data for pushing can be stored on any device. Furthermore, you can push multiple
+values into the same key, where KVStore will first sum all of these
+values and then push the aggregated value. Here we will just demonstrate pushing a list of values on CPU.
+Please note summation only happens if the value list is longer than one
+
+```python
+contexts = [mx.cpu(i) for i in range(4)]
+b = [mx.nd.ones(shape, ctx) for ctx in contexts]
+kv.push(3, b)
+kv.pull(3, out = a)
+print(a.asnumpy())
+```
+
+`[[ 4.  4.  4.],[ 4.  4.  4.]]`<!--notebook-skip-line-->
+
+For each push, KVStore combines the pushed value with the value stored using an
+`updater`. The default updater is `ASSIGN`. You can replace the default to
+control how data is merged:
+
+```python
+def update(key, input, stored):
+    print("update on key: %d" % key)
+    stored += input * 2
+kv._set_updater(update)
+kv.pull(3, out=a)
+print(a.asnumpy())
+```
+
+`[[ 4.  4.  4.],[ 4.  4.  4.]]`<!--notebook-skip-line-->
+
+```python
+kv.push(3, mx.nd.ones(shape))
+kv.pull(3, out=a)
+print(a.asnumpy())
+```
+
+`update on key: 3`<!--notebook-skip-line-->
+
+`[[ 6.  6.  6.],[ 6.  6.  6.]]`<!--notebook-skip-line-->
+
+
+## Pull
+
+You've already seen how to pull a single key-value pair. Similarly, to push, you can
+pull the value onto several devices with a single call:
+
+```python
+b = [mx.nd.ones(shape, ctx) for ctx in contexts]
+kv.pull(3, out = b)
+print(b[1].asnumpy())
+```
+
+`[ 6.  6.  6.]],[[ 6.  6.  6.]`<!--notebook-skip-line-->
+
+## Handle a List of Key-Value Pairs
+
+All operations introduced so far involve a single key. KVStore also provides
+an interface for a list of key-value pairs. 
+
+For a single device:
+
+```python
+keys = [5, 7, 9]
+kv.init(keys, [mx.nd.ones(shape)]*len(keys))
+kv.push(keys, [mx.nd.ones(shape)]*len(keys))
+b = [mx.nd.zeros(shape)]*len(keys)
+kv.pull(keys, out = b)
+print(b[1].asnumpy())
+```
+
+`update on key: 5`<!--notebook-skip-line-->
+
+`update on key: 7`<!--notebook-skip-line-->
+
+`update on key: 9`<!--notebook-skip-line-->
+
+`[[ 3.  3.  3.],[ 3.  3.  3.]]`<!--notebook-skip-line-->
+
+For multiple devices:
+
+```python
+b = [[mx.nd.ones(shape, ctx) for ctx in contexts]] * len(keys)
+kv.push(keys, b)
+kv.pull(keys, out = b)
+print(b[1][1].asnumpy())
+```
+
+`update on key: 5`<!--notebook-skip-line-->
+
+`update on key: 7`<!--notebook-skip-line-->
+
+`update on key: 9`<!--notebook-skip-line-->
+
+`[[ 11.  11.  11.],[ 11.  11.  11.]]`<!--notebook-skip-line-->
+
+## Run on Multiple Machines
+Based on parameter server, the `updater` runs on the server nodes.
+When the distributed version is ready, we will update this section.
+
+
+<!-- ## How to Choose Between APIs -->
+
+<!-- You can mix APIs as much as you like. Here are some guidelines -->
+<!-- * Use the Symbolic API and a coarse-grained operator to create  an established structure. -->
+<!-- * Use a fine-grained operator to extend parts of a more flexible symbolic graph. -->
+<!-- * Do some dynamic NDArray tricks, which are even more flexible, between the calls of forward and backward executors. -->
+
+<!-- Different approaches offer you different levels of flexibility and -->
+<!-- efficiency. Normally, you do not need to be flexible in all parts of the -->
+<!-- network, so use the parts optimized for speed, and compose it -->
+<!-- flexibly with a fine-grained operator or a dynamic NDArray. Such a -->
+<!-- mixture allows you to build the deep learning architecture both efficiently and -->
+<!-- flexibly as your choice.  -->
+
+## Next Steps
+* [MXNet tutorials index](https://mxnet.io/api)
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/python_docs/python/tutorials/packages/module/index.rst b/docs/python_docs/python/tutorials/packages/module/index.rst
deleted file mode 100644
index a485b7f523c3..000000000000
--- a/docs/python_docs/python/tutorials/packages/module/index.rst
+++ /dev/null
@@ -1,52 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-Module
-======
-
-.. container:: cards
-
-   .. card::
-      :title: Module API Intro
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/basic/module.html
-
-      How to use MXNet's Module API.
-
-   .. card::
-      :title: Converting Module API code to the Gluon API
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/python/module_to_gluon.html
-
-      How to transform models written using Module code to Gluon code.
-
-   .. card::
-      :title: Data Augmentation
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/python/data_augmentation.html
-
-      How to augment data with the Module API.
-
-   .. card::
-      :title: Predict with pre-trained models
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/python/predict_image.html
-
-      How to recognize objects in an image with a pre-trained model.
-
-
-.. toctree::
-   :hidden:
-
-   module
-   data_augmentation
diff --git a/docs/python_docs/python/tutorials/packages/ndarray/01-ndarray-intro.md b/docs/python_docs/python/tutorials/packages/ndarray/01-ndarray-intro.md
index 26e9c48bd8e3..a8c8056614ab 100644
--- a/docs/python_docs/python/tutorials/packages/ndarray/01-ndarray-intro.md
+++ b/docs/python_docs/python/tutorials/packages/ndarray/01-ndarray-intro.md
@@ -79,7 +79,7 @@ values of any of its entries. This means that the entries can have any form of
 values, including very big ones! Typically, we'll want our matrices initialized
 and very often we want a matrix of all zeros, so we can use the `.zeros`
 function. If you're feeling experimental, try one of the several [array creation
-functions](https://mxnet.incubator.apache.org/api/{.python
+functions](https://mxnet.apache.org/api/{.python
 .input}/ndarray/ndarray.html#array-creation-routines).
 
 <!-- showing something
@@ -93,7 +93,7 @@ print(x)
 ```
 
 Similarly, `ndarray` has a function to create a matrix of all ones aptly named
-[ones](https://mxnet.incubator.apache.org/api/{.python
+[ones](https://mxnet.apache.org/api/{.python
 .input}/ndarray.html?highlight=random_normal#mxnet.ndarray.ones).
 
 ```python
@@ -105,7 +105,7 @@ Often, we'll want to create arrays whose values are sampled randomly. This is
 especially common when we intend to use the array as a parameter in a neural
 network. In this snippet, we initialize with values drawn from a standard normal
 distribution with zero mean and unit variance using
-[random_normal](https://mxnet.incubator.apache.org/api/{.python
+[random_normal](https://mxnet.apache.org/api/{.python
 .input}/ndarray.html?highlight=random_normal#mxnet.ndarray.random_normal).
 
 <!--
diff --git a/docs/python_docs/python/tutorials/packages/ndarray/gotchas_numpy_in_mxnet.md b/docs/python_docs/python/tutorials/packages/ndarray/gotchas_numpy_in_mxnet.md
new file mode 100644
index 000000000000..69ec2bf0afbf
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/ndarray/gotchas_numpy_in_mxnet.md
@@ -0,0 +1,187 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+
+# Gotchas using NumPy in Apache MXNet
+
+The goal of this tutorial is to explain some common misconceptions about using [NumPy](http://www.numpy.org/) arrays in Apache MXNet. We are going to explain why you need to minimize or completely remove usage of NumPy from your Apache MXNet code. We also going to show how to minimize NumPy performance impact, when you have to use NumPy.
+
+## Asynchronous and non-blocking nature of Apache MXNet
+
+Instead of using NumPy arrays Apache MXNet offers its own array implementation named [NDArray](https://mxnet.apache.org/api/python/ndarray/ndarray.html). `NDArray API` was intentionally designed to be similar to `NumPy`, but there are differences.
+
+One key difference is in the way calculations are executed. Every `NDArray` manipulation in Apache MXNet is done in asynchronous, non-blocking way. That means, that when we write code like `c = a * b`, where both `a` and `b` are `NDArrays`, the function is pushed to the [Execution Engine](https://mxnet.apache.org/architecture/overview.html#execution-engine), which starts the calculation. The function immediately returns back, and the  user thread can continue execution, despite the fact that the calculation may not have been completed yet. 
+
+`Execution Engine` builds the computation graph which may reorder or combine some calculations, but it honors dependency order: if there are other manipulation with `c` done later in the code, the `Execution Engine` will start doing them once the result of `c` is available. We don't need to write callbacks to start execution of subsequent code - the `Execution Engine` is going to do it for us. 
+
+To get the result of the computation we only need to access the resulting variable, and the flow of the code will be blocked until the computation results are assigned to the resulting variable. This behavior allows to increase code performance while still supporting imperative programming mode. 
+
+Refer to the [intro tutorial to NDArray](https://mxnet.apache.org/tutorials/basic/ndarray.html), if you are new to Apache MXNet and would like to learn more how to manipulate NDArrays.
+
+## Converting NDArray to NumPy Array blocks calculation
+
+Many people are familiar with NumPy and flexible doing tensor manipulations using it. `NDArray API` offers  a convinient [.asnumpy() method](https://mxnet.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.asnumpy) to cast `nd.array` to `np.array`. However, by doing this cast and using `np.array` for calculation, we cannot use all the goodness of `Execution Engine`. All manipulations done on `np.array` are blocking. Moreover, the cast to `np.array` itself is a blocking operation (same as [.asscalar()](https://mxnet.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.asscalar), [.wait_to_read()](https://mxnet.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.wait_to_read) and [.waitall()](https://mxnet.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.waitall)). 
+
+That means that if we have a long computation graph and, at some point, we want to cast the result to `np.array`, it may feel like the casting takes a lot of time. But what really takes this time is `Execution Engine`, which finishes all the async calculations we have pushed into it to get the final result, which then will be converted to `np.array`.
+
+Because of the blocking nature of [.asnumpy() method](https://mxnet.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.asnumpy), using it reduces the execution performance, especially if the calculations are done on GPU: Apache MXNet has to copy data from GPU to CPU to return `np.array`. 
+
+The best solution is to **make manipulations directly on NDArrays by methods provided in [NDArray API](https://mxnet.apache.org/api/python/ndarray/ndarray.html)**.
+
+## NumPy operators vs. NDArray operators
+
+Despite the fact that [NDArray API](https://mxnet.apache.org/api/python/ndarray/ndarray.html) was specifically designed to be similar to `NumPy`, sometimes it is not easy to replace existing `NumPy` computations. The main reason is that not all operators, that are available in `NumPy`, are available in `NDArray API`. The list of currently available operators is available on [NDArray class page](http://mxnet.apache.org/api/python/ndarray/ndarray.html#the-ndarray-class).
+
+If a required operator is missing from `NDArray API`, there are few things you can do.
+
+### Combine a higher level operator using a few lower level operators
+
+There are a situation, when you can assemble a higher level operator using existing operators. An example for that is the [np.full_like()](https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.full_like.html) operator. This operator doesn't exist in `NDArray API`, but can be easily replaced with a combination of existing operators.
+
+
+```python
+from mxnet import nd
+import numpy as np
+
+# NumPy has full_like() operator 
+np_y = np.full_like(a=np.arange(6, dtype=int), fill_value=10)
+
+# NDArray doesn't have it, but we can replace it with
+# creating an array of ones and then multiplying by fill_value
+nd_y = nd.ones(shape=(6,)) * 10
+
+# To compare results we had to convert NDArray to NumPy
+# But this is okay for that particular case
+np.array_equal(np_y, nd_y.asnumpy())
+```
+
+```True``` <!--notebook-skip-line-->
+
+### Find similar operator with different name and/or signature
+
+Some operators may have slightly different name, but are similar in terms of functionality. For example [nd.ravel_multi_index()](https://mxnet.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.ravel_multi_index) is similar to [np.ravel()](https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.ma.ravel.html#numpy.ma.ravel). In other cases some operators may have similar names, but different signatures. For example [np.split()](https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.split.html#numpy.split) and [nd.split()](https://mxnet.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.split) are similar, but the former works with indices and the latter requires the number of splits to be provided.
+
+One particular example of different input requirements is [nd.pad()](https://mxnet.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.pad). The trick is that it can only work with 4-dimensional tensors. If your input has less dimensions, then you need to expand its number before using `nd.pad()` as it is shown in the code block below: 
+
+
+```python
+def pad_array(data, max_length):
+    # expand dimensions to 4, because nd.pad can work only with 4 dims
+    data_expanded = data.reshape(1, 1, 1, data.shape[0])
+    
+    # pad all 4 dimensions with constant value of 0
+    data_padded = nd.pad(data_expanded,
+                             mode='constant',
+                             pad_width=[0, 0, 0, 0, 0, 0, 0, max_length - data.shape[0]],
+                             constant_value=0)
+    
+    # remove temporary dimensions 
+    data_reshaped_back = data_padded.reshape(max_length)
+    return data_reshaped_back
+
+pad_array(nd.array([1, 2, 3]), max_length=10)
+```
+    
+`[ 1.  2.  3.  0.  0.  0.  0.  0.  0.  0.]` <!--notebook-skip-line-->
+
+
+`<NDArray 10 @cpu(0)>` <!--notebook-skip-line-->
+
+
+### Search for an operator on [Github](https://github.com/apache/incubator-mxnet/labels/Operator)
+
+Apache MXNet community is responsive to requests, and everyone is welcomed to contribute new operators. Have in mind, that there is always a lag between new operators being merged into the codebase and release of a next stable version. For example, [nd.diag()](https://github.com/apache/incubator-mxnet/pull/11643) operator was recently introduced to Apache MXNet, but on the moment of writing this tutorial, it is not in any stable release. You can always get all latest implementations by installing the [master version](https://mxnet.apache.org/install/index.html?version=master#) of Apache MXNet.
+
+## How to minimize the impact of blocking calls
+
+There are cases, when you have to use either `.asnumpy()` or `.asscalar()` methods. As it is explained before, this will force Apache MXNet to block the execution until the result can be retrieved. One common use case is printing a metric or a value of a loss function.
+
+You can minimize the impact of a blocking call by calling `.asnumpy()` or `.asscalar()` in the moment, when you think the calculation of this value is already done. In the example below, we introduce the `LossBuffer` class. It is used to cache the previous value of a loss function. By doing so, we delay printing by one iteration in hope that the `Execution Engine` would finish the previous iteration and blocking time would be minimized.
+
+
+```python
+from __future__ import print_function
+
+import mxnet as mx
+from mxnet import gluon, nd, autograd
+from mxnet.ndarray import NDArray
+from mxnet.gluon import HybridBlock
+import numpy as np
+
+class LossBuffer(object):
+    """
+    Simple buffer for storing loss value
+    """
+    def __init__(self):
+        self._loss = None
+
+    def new_loss(self, loss):
+        ret = self._loss
+        self._loss = loss
+        return ret
+
+    @property
+    def loss(self):
+        return self._loss
+
+
+net = gluon.nn.Dense(10)
+ce = gluon.loss.SoftmaxCELoss()
+net.initialize()
+
+data = nd.random.uniform(shape=(1024, 100))
+label = nd.array(np.random.randint(0, 10, (1024,)), dtype='int32')
+train_dataset = gluon.data.ArrayDataset(data, label)
+train_data = gluon.data.DataLoader(train_dataset, batch_size=128, shuffle=True, num_workers=2)
+
+trainer = gluon.Trainer(net.collect_params(), optimizer='sgd')
+loss_buffer = LossBuffer()
+
+for data, label in train_data:
+    with autograd.record():
+        out = net(data)
+        # This call saves new loss and returns previous loss
+        prev_loss = loss_buffer.new_loss(ce(out, label))
+        
+    loss_buffer.loss.backward()
+    trainer.step(data.shape[0])
+    
+    if prev_loss is not None:
+        print("Loss: {}".format(np.mean(prev_loss.asnumpy())))
+```
+
+```text
+    Loss: 2.310760974884033 <!--notebook-skip-line-->
+
+    Loss: 2.334498643875122 <!--notebook-skip-line-->
+
+    Loss: 2.3244147300720215 <!--notebook-skip-line-->
+
+    Loss: 2.332686424255371 <!--notebook-skip-line-->
+
+    Loss: 2.321366310119629 <!--notebook-skip-line-->
+
+    Loss: 2.3236165046691895 <!--notebook-skip-line-->
+
+    Loss: 2.3178648948669434 <!--notebook-skip-line-->
+```
+
+## Conclusion
+
+For performance reasons, it is better to use native `NDArray API` methods and avoid using NumPy altogether. In case when you must use NumPy, you can use convenient method `.asnumpy()` on `NDArray` to get NumPy representation. By doing so, you block the whole computational process, and force data to be synced between CPU and GPU. If it is a necessary evil to do that, try to minimize the blocking time by calling `.asnumpy()` in time, when you expect the value to be already computed.
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
\ No newline at end of file
diff --git a/docs/python_docs/python/tutorials/packages/ndarray/index.rst b/docs/python_docs/python/tutorials/packages/ndarray/index.rst
index 9f8d04741c6c..41b82091fdc2 100644
--- a/docs/python_docs/python/tutorials/packages/ndarray/index.rst
+++ b/docs/python_docs/python/tutorials/packages/ndarray/index.rst
@@ -40,49 +40,15 @@ NDArray
       This guide will introduce you to how CPU and GPU contexts are handled with MXNet.
 
    .. card::
-      :title: NDArray API Quickstart
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/ndarray.html
+      :title: Sparse NDArray
+      :link: sparse/index.html
 
-      If you are familiar with NumPy you can start here.
+      For Sparse NDArray tutorials
 
-   .. card::
-      :title: Imperative tensor operations
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/basic/ndarray.html
-
-      A more detailed look at NDArray operations.
-
-   .. card::
-      :title: Reshape vs Transpose
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/basic/ndarray_indexing.html
-
-      A guide on the difference between the reshape and transpose operators.
-
-   .. card::
-      :title: NDArray Indexing
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/basic/ndarray_indexing.html
-
-      How to use the indexing features of NDArray.
-
-   .. card::
-      :title: RowSparseNDArray
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/sparse/row_sparse.html
-
-      How to implement sparse tensors in NDArray.
-
-   .. card::
-      :title: CSRNDArray
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/sparse/csr.html
-
-      How to store and manipulate large sparse matrices.
-
-   .. card::
-      :title: Sparse Symbols
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/sparse/train.html
-
-      How to train a linear regression model with sparse symbols.
 
 .. toctree::
    :hidden:
+   :glob:
 
-   ndarray
-   sparse
+   *
+   sparse/index
\ No newline at end of file
diff --git a/docs/python_docs/python/tutorials/packages/ndarray/sparse/csr.md b/docs/python_docs/python/tutorials/packages/ndarray/sparse/csr.md
new file mode 100644
index 000000000000..0b362513c0ae
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/ndarray/sparse/csr.md
@@ -0,0 +1,562 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+
+# CSRNDArray - NDArray in Compressed Sparse Row Storage Format
+
+Many real world datasets deal with high dimensional sparse feature vectors. Take for instance a recommendation system where the number of categories and users is on the order of millions. The purchase data for each category by user would show that most users only make a few purchases, leading to a dataset with high sparsity (i.e. most of the elements are zeros).
+
+Storing and manipulating such large sparse matrices in the default dense structure results in wasted memory and processing on the zeros. To take advantage of the sparse structure of the matrix, the `CSRNDArray` in MXNet stores the matrix in [compressed sparse row (CSR)](https://en.wikipedia.org/wiki/Sparse_matrix#Compressed_sparse_row_.28CSR.2C_CRS_or_Yale_format.29) format and uses specialized algorithms in operators.
+**The format is designed for 2D matrices with a large number of columns,
+and each row is sparse (i.e. with only a few nonzeros).**
+
+## Advantages of Compressed Sparse Row NDArray (CSRNDArray)
+For matrices of high sparsity (e.g. ~1% non-zeros = ~1% density), there are two primary advantages of `CSRNDArray` over the existing `NDArray`:
+
+- memory consumption is reduced significantly
+- certain operations are much faster (e.g. matrix-vector multiplication)
+
+You may be familiar with the CSR storage format in [SciPy](https://www.scipy.org/) and will note the similarities in MXNet's implementation. However there are some additional competitive features in `CSRNDArray` inherited from `NDArray`, such as non-blocking asynchronous evaluation and automatic parallelization that are not available in SciPy's flavor of CSR. You can find further explanations for evaluation and parallelization strategy in MXNet in the [NDArray tutorial](https://mxnet.apache.org/tutorials/basic/ndarray.html#lazy-evaluation-and-automatic-parallelization).
+
+The introduction of `CSRNDArray` also brings a new attribute, `stype` as a holder for storage type info, to `NDArray`. You can query **ndarray.stype** now in addition to the oft-queried attributes such as **ndarray.shape**, **ndarray.dtype**, and **ndarray.context**. For a typical dense NDArray, the value of `stype` is **"default"**. For a `CSRNDArray`, the value of stype is **"csr"**.
+
+## Prerequisites
+
+To complete this tutorial, you will need:
+
+- MXNet. See the instructions for your operating system in [Setup and Installation](https://mxnet.io/get_started)
+- [Jupyter](http://jupyter.org/)
+    ```
+    pip install jupyter
+    ```
+- Basic knowledge of NDArray in MXNet. See the detailed tutorial for NDArray in [NDArray - Imperative tensor operations on CPU/GPU](https://mxnet.apache.org/tutorials/basic/ndarray.html).
+- SciPy - A section of this tutorial uses SciPy package in Python. If you don't have SciPy, the example in that section will be ignored.
+- GPUs - A section of this tutorial uses GPUs. If you don't have GPUs on your machine, simply set the variable `gpu_device` (set in the GPUs section of this tutorial) to `mx.cpu()`.
+
+## Compressed Sparse Row Matrix
+
+A CSRNDArray represents a 2D matrix as three separate 1D arrays: **data**, **indptr** and **indices**, where the column indices for row `i` are stored in `indices[indptr[i]:indptr[i+1]]` in ascending order, and their corresponding values are stored in `data[indptr[i]:indptr[i+1]]`.
+
+- **data**: CSR format data array of the matrix
+- **indices**: CSR format index array of the matrix
+- **indptr**: CSR format index pointer array of the matrix
+
+### Example Matrix Compression
+
+For example, given the matrix:
+```
+[[7, 0, 8, 0]
+ [0, 0, 0, 0]
+ [0, 9, 0, 0]]
+```
+
+We can compress this matrix using CSR, and to do so we need to calculate `data`, `indices`, and `indptr`.
+
+The `data` array holds all the non-zero entries of the matrix in row-major order. Put another way, you create a data array that has all of the zeros removed from the matrix, row by row, storing the numbers in that order. Your result:
+
+```
+data = [7, 8, 9]
+```
+
+The `indices` array stores the column index for each non-zero element in `data`. As you cycle through the data array, starting with 7, you can see it is in column 0. Then looking at 8, you can see it is in column 2. Lastly 9 is in column 1. Your result:
+
+```
+indices = [0, 2, 1]
+```
+
+The `indptr` array is what will help identify the rows where the data appears. It stores the offset into `data` of the first non-zero element number of each row of the matrix. This array always starts with 0 (reasons can be explored later), so indptr[0] is 0. Each subsequent value in the array is the aggregate number of non-zero elements up to that row. Looking at the first row of the matrix you can see two non-zero values, so indptr[1] is 2. The next row contains all zeros, so the aggregate is still 2, so indptr[2] is 2. Finally, you see the last row contains one non-zero element bring the aggregate to 3, so indptr[3] is 3. To reconstruct the dense matrix, you will use `data[0:2]` and `indices[0:2]` for the first row, `data[2:2]` and `indices[2:2]` for the second row (which contains all zeros), and `data[2:3]` and `indices[2:3]` for the third row. Your result:
+
+```text
+indptr = [0, 2, 2, 3]
+```
+
+Note that in MXNet, the column indices for a given row are always sorted in ascending order,
+and duplicated column indices for the same row are not allowed.
+
+## Array Creation
+
+There are a few different ways to create a `CSRNDArray`, but first let's recreate the matrix we just discussed using the `data`, `indices`, and `indptr` we calculated in the previous example.
+
+You can create a CSRNDArray with data, indices and indptr by using the `csr_matrix` function:
+
+
+```python
+import mxnet as mx
+# Create a CSRNDArray with python lists
+shape = (3, 4)
+data_list = [7, 8, 9]
+indices_list = [0, 2, 1]
+indptr_list = [0, 2, 2, 3]
+a = mx.nd.sparse.csr_matrix((data_list, indices_list, indptr_list), shape=shape)
+# Inspect the matrix
+a.asnumpy()
+```
+
+
+
+```
+array([[ 7.,  0.,  8.,  0.],
+       [ 0.,  0.,  0.,  0.],
+       [ 0.,  9.,  0.,  0.]], dtype=float32)
+```
+
+
+
+```python
+import numpy as np
+# Create a CSRNDArray with numpy arrays
+data_np = np.array([7, 8, 9])
+indptr_np = np.array([0, 2, 2, 3])
+indices_np = np.array([0, 2, 1])
+b = mx.nd.sparse.csr_matrix((data_np, indices_np, indptr_np), shape=shape)
+b.asnumpy()
+```
+
+
+
+```
+array([[7, 0, 8, 0],
+       [0, 0, 0, 0],
+       [0, 9, 0, 0]])
+```
+
+
+
+```python
+# Compare the two. They are exactly the same.
+{'a':a.asnumpy(), 'b':b.asnumpy()}
+```
+
+
+
+```
+{'a': array([[ 7.,  0.,  8.,  0.],
+        [ 0.,  0.,  0.,  0.],
+        [ 0.,  9.,  0.,  0.]], dtype=float32), 'b': array([[7, 0, 8, 0],
+        [0, 0, 0, 0],
+        [0, 9, 0, 0]])}
+```
+
+
+You can create an MXNet CSRNDArray from a `scipy.sparse.csr.csr_matrix` object by using the `array` function:
+
+
+```python
+try:
+    import scipy.sparse as spsp
+    # generate a csr matrix in scipy
+    c = spsp.csr.csr_matrix((data_np, indices_np, indptr_np), shape=shape)
+    # create a CSRNDArray from a scipy csr object
+    d = mx.nd.sparse.array(c)
+    print('d:{}'.format(d.asnumpy()))
+except ImportError:
+    print("scipy package is required")
+```
+
+```
+d:[[7 0 8 0]
+ [0 0 0 0]
+ [0 9 0 0]]
+```
+
+
+What if you have a big set of data and you haven't calculated indices or indptr yet? Let's try a simple CSRNDArray from an existing array of data and derive those values with some built-in functions. We can mockup a "big" dataset with a random amount of the data being non-zero, then compress it by using the `tostype` function, which is explained further in the [Storage Type Conversion](#storage-type-conversion) section:
+
+
+```python
+big_array = mx.nd.round(mx.nd.random.uniform(low=0, high=1, shape=(1000, 100)))
+print(big_array)
+big_array_csr = big_array.tostype('csr')
+# Access indices array
+indices = big_array_csr.indices
+# Access indptr array
+indptr = big_array_csr.indptr
+# Access data array
+data = big_array_csr.data
+# The total size of `data`, `indices` and `indptr` arrays is much lesser than the dense big_array!
+```
+
+```
+[[ 1.  1.  0. ...,  0.  1.  1.]
+ [ 0.  0.  0. ...,  0.  0.  1.]
+ [ 1.  0.  0. ...,  1.  0.  0.]
+ ..., 
+ [ 0.  1.  1. ...,  0.  0.  0.]
+ [ 1.  1.  0. ...,  1.  0.  1.]
+ [ 1.  0.  1. ...,  1.  0.  0.]]
+<NDArray 1000x100 @cpu(0)>
+```
+
+You can also create a CSRNDArray from another using the `array` function specifying the element data type with the option `dtype`,
+which accepts a numpy type. By default, `float32` is used.
+
+
+```python
+# Float32 is used by default
+e = mx.nd.sparse.array(a)
+# Create a 16-bit float array
+f = mx.nd.array(a, dtype=np.float16)
+(e.dtype, f.dtype)
+```
+
+
+
+```
+(numpy.float32, numpy.float16)
+```
+
+
+
+## Inspecting Arrays
+
+A variety of methods are available for you to use for inspecting CSR arrays:
+* **.asnumpy()**
+* **.data**
+* **.indices**
+* **.indptr**
+
+As you have seen already, we can inspect the contents of a `CSRNDArray` by filling
+its contents into a dense `numpy.ndarray` using the `asnumpy` function.
+
+
+```python
+a.asnumpy()
+```
+
+
+
+```
+array([[ 7.,  0.,  8.,  0.],
+       [ 0.,  0.,  0.,  0.],
+       [ 0.,  9.,  0.,  0.]], dtype=float32)
+```
+
+
+You can also inspect the internal storage of a CSRNDArray by accessing attributes such as `indptr`, `indices` and `data`:
+
+
+```python
+# Access data array
+data = a.data
+# Access indices array
+indices = a.indices
+# Access indptr array
+indptr = a.indptr
+{'a.stype': a.stype, 'data':data, 'indices':indices, 'indptr':indptr}
+```
+
+
+
+```
+{'a.stype': 'csr', 'data': 
+ [ 7.  8.  9.]
+ <NDArray 3 @cpu(0)>, 'indices': 
+ [0 2 1]
+ <NDArray 3 @cpu(0)>, 'indptr': 
+ [0 2 2 3]
+ <NDArray 4 @cpu(0)>}
+```
+
+
+## Storage Type Conversion
+
+You can also convert storage types with:
+* **tostype**
+* **cast_storage**
+
+To convert an NDArray to a CSRNDArray and vice versa by using the ``tostype`` function:
+
+
+```python
+# Create a dense NDArray
+ones = mx.nd.ones((2,2))
+# Cast the storage type from `default` to `csr`
+csr = ones.tostype('csr')
+# Cast the storage type from `csr` to `default`
+dense = csr.tostype('default')
+{'csr':csr, 'dense':dense}
+```
+
+
+
+```
+{'csr': 
+ <CSRNDArray 2x2 @cpu(0)>, 'dense': 
+ [[ 1.  1.]
+  [ 1.  1.]]
+ <NDArray 2x2 @cpu(0)>}
+```
+
+
+To convert the storage type by using the `cast_storage` operator:
+
+
+```python
+# Create a dense NDArray
+ones = mx.nd.ones((2,2))
+# Cast the storage type to `csr`
+csr = mx.nd.sparse.cast_storage(ones, 'csr')
+# Cast the storage type to `default`
+dense = mx.nd.sparse.cast_storage(csr, 'default')
+{'csr':csr, 'dense':dense}
+```
+
+
+
+```
+{'csr': 
+ <CSRNDArray 2x2 @cpu(0)>, 'dense': 
+ [[ 1.  1.]
+  [ 1.  1.]]
+ <NDArray 2x2 @cpu(0)>}
+```
+
+
+## Copies
+
+You can use the `copy` method which makes a deep copy of the array and its data, and returns a new array.
+You can also use the `copyto` method or the slice operator `[]` to deep copy to an existing array.
+
+
+```python
+a = mx.nd.ones((2,2)).tostype('csr')
+b = a.copy()
+c = mx.nd.sparse.zeros('csr', (2,2))
+c[:] = a
+d = mx.nd.sparse.zeros('csr', (2,2))
+a.copyto(d)
+{'b is a': b is a, 'b.asnumpy()':b.asnumpy(), 'c.asnumpy()':c.asnumpy(), 'd.asnumpy()':d.asnumpy()}
+```
+
+
+
+```
+{'b is a': False, 'b.asnumpy()': array([[ 1.,  1.],
+        [ 1.,  1.]], dtype=float32), 'c.asnumpy()': array([[ 1.,  1.],
+        [ 1.,  1.]], dtype=float32), 'd.asnumpy()': array([[ 1.,  1.],
+        [ 1.,  1.]], dtype=float32)}
+```
+
+
+If the storage types of source array and destination array do not match,
+the storage type of destination array will not change when copying with `copyto` or
+the slice operator `[]`.
+
+
+```python
+e = mx.nd.sparse.zeros('csr', (2,2))
+f = mx.nd.sparse.zeros('csr', (2,2))
+g = mx.nd.ones(e.shape)
+e[:] = g
+g.copyto(f)
+{'e.stype':e.stype, 'f.stype':f.stype, 'g.stype':g.stype}
+```
+
+
+
+```
+{'e.stype': 'csr', 'f.stype': 'csr', 'g.stype': 'default'}
+```
+
+
+## Indexing and Slicing
+You can slice a CSRNDArray on axis 0 with operator `[]`, which copies the slices and returns a new CSRNDArray.
+
+
+```python
+a = mx.nd.array(np.arange(6).reshape(3,2)).tostype('csr')
+b = a[1:2].asnumpy()
+c = a[:].asnumpy()
+{'a':a, 'b':b, 'c':c}
+```
+
+
+
+```
+{'a': 
+ <CSRNDArray 3x2 @cpu(0)>,
+ 'b': array([[ 2.,  3.]], dtype=float32),
+ 'c': array([[ 0.,  1.],
+        [ 2.,  3.],
+        [ 4.,  5.]], dtype=float32)}
+```
+
+
+Note that multi-dimensional indexing or slicing along a particular axis is currently not supported for a CSRNDArray.
+
+## Sparse Operators and Storage Type Inference
+
+Operators that have specialized implementation for sparse arrays can be accessed in `mx.nd.sparse`. You can read the [mxnet.ndarray.sparse API documentation](https://mxnet.apache.org/versions/master/api/python/ndarray/sparse.html) to find what sparse operators are available.
+
+
+```python
+shape = (3, 4)
+data = [7, 8, 9]
+indptr = [0, 2, 2, 3]
+indices = [0, 2, 1]
+a = mx.nd.sparse.csr_matrix((data, indices, indptr), shape=shape) # a csr matrix as lhs
+rhs = mx.nd.ones((4, 1))      # a dense vector as rhs
+out = mx.nd.sparse.dot(a, rhs)  # invoke sparse dot operator specialized for dot(csr, dense)
+{'out':out}
+```
+
+
+
+```
+{'out': 
+ [[ 15.]
+  [  0.]
+  [  9.]]
+ <NDArray 3x1 @cpu(0)>}
+```
+
+
+For any sparse operator, the storage type of output array is inferred based on inputs. You can either read the documentation or inspect the `stype` attribute of the output array to know what storage type is inferred:
+
+
+```python
+b = a * 2  # b will be a CSRNDArray since zero multiplied by 2 is still zero
+c = a + mx.nd.ones(shape=(3, 4))  # c will be a dense NDArray
+{'b.stype':b.stype, 'c.stype':c.stype}
+```
+
+
+
+```
+{'b.stype': 'csr', 'c.stype': 'default'}
+```
+
+
+For operators that don't specialize in sparse arrays, we can still use them with sparse inputs with some performance penalty. In MXNet, dense operators require all inputs and outputs to be in the dense format.
+
+If sparse inputs are provided, MXNet will convert sparse inputs into dense ones temporarily, so that the dense operator can be used.
+
+If sparse outputs are provided, MXNet will convert the dense outputs generated by the dense operator into the provided sparse format.
+
+
+```python
+e = mx.nd.sparse.zeros('csr', a.shape)
+d = mx.nd.log(a) # dense operator with a sparse input
+e = mx.nd.log(a, out=e) # dense operator with a sparse output
+{'a.stype':a.stype, 'd.stype':d.stype, 'e.stype':e.stype} # stypes of a and e will be not changed
+```
+
+
+
+```
+{'a.stype': 'csr', 'd.stype': 'default', 'e.stype': 'csr'}
+```
+
+
+Note that warning messages will be printed when such a storage fallback event happens. If you are using jupyter notebook, the warning message will be printed in your terminal console.
+
+## Data Loading
+
+You can load data in batches from a CSRNDArray using `mx.io.NDArrayIter`:
+
+
+```python
+# Create the source CSRNDArray
+data = mx.nd.array(np.arange(36).reshape((9,4))).tostype('csr')
+labels = np.ones([9, 1])
+batch_size = 3
+dataiter = mx.io.NDArrayIter(data, labels, batch_size, last_batch_handle='discard')
+# Inspect the data batches
+[batch.data[0] for batch in dataiter]
+```
+
+
+
+```
+[
+ <CSRNDArray 3x4 @cpu(0)>, 
+ <CSRNDArray 3x4 @cpu(0)>, 
+ <CSRNDArray 3x4 @cpu(0)>]
+```
+
+
+You can also load data stored in the [libsvm file format](https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/) using `mx.io.LibSVMIter`, where the format is: ``<label> <col_idx1>:<value1> <col_idx2>:<value2> ... <col_idxN>:<valueN>``. Each line in the file records the label and the column indices and data for non-zero entries. For example, for a matrix with 6 columns, ``1 2:1.5 4:-3.5`` means the label is ``1``, the data is ``[[0, 0, 1,5, 0, -3.5, 0]]``. More detailed examples of `mx.io.LibSVMIter` are available in the [API documentation](https://mxnet.apache.org/versions/master/api/python/io/io.html#mxnet.io.LibSVMIter).
+
+
+```python
+# Create a sample libsvm file in current working directory
+import os
+cwd = os.getcwd()
+data_path = os.path.join(cwd, 'data.t')
+with open(data_path, 'w') as fout:
+    fout.write('1.0 0:1 2:2\n')
+    fout.write('1.0 0:3 5:4\n')
+    fout.write('1.0 2:5 8:6 9:7\n')
+    fout.write('1.0 3:8\n')
+    fout.write('-1 0:0.5 9:1.5\n')
+    fout.write('-2.0\n')
+    fout.write('-3.0 0:-0.6 1:2.25 2:1.25\n')
+    fout.write('-3.0 1:2 2:-1.25\n')
+    fout.write('4 2:-1.2\n')
+
+# Load CSRNDArrays from the file
+data_train = mx.io.LibSVMIter(data_libsvm=data_path, data_shape=(10,), label_shape=(1,), batch_size=3)
+for batch in data_train:
+    print(data_train.getdata())
+    print(data_train.getlabel())
+```
+
+```
+<CSRNDArray 3x10 @cpu(0)>
+
+[ 1.  1.  1.]
+<NDArray 3 @cpu(0)>
+
+<CSRNDArray 3x10 @cpu(0)>
+
+[ 1. -1. -2.]
+<NDArray 3 @cpu(0)>
+
+<CSRNDArray 3x10 @cpu(0)>
+
+[-3. -3.  4.]
+<NDArray 3 @cpu(0)>
+```
+
+
+Note that in the file the column indices are expected to be sorted in ascending order per row, and be zero-based instead of one-based.
+
+## Advanced Topics
+
+### GPU Support
+
+By default, `CSRNDArray` operators are executed on CPU. To create a `CSRNDArray` on a GPU, we need to explicitly specify the context:
+
+**Note** If a GPU is not available, an error will be reported in the following section. In order to execute it a cpu, set `gpu_device` to `mx.cpu()`.
+
+
+```python
+import sys
+gpu_device=mx.gpu() # Change this to mx.cpu() in absence of GPUs.
+try:
+    a = mx.nd.sparse.zeros('csr', (100, 100), ctx=gpu_device)
+    a
+except mx.MXNetError as err:
+    sys.stderr.write(str(err))
+```
+
+## Next 
+
+[Train a Linear Regression Model with Sparse Symbols](http://mxnet.apache.org/tutorials/sparse/train.html)
+
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/python_docs/python/tutorials/deploy/export/export.rst b/docs/python_docs/python/tutorials/packages/ndarray/sparse/index.rst
similarity index 93%
rename from docs/python_docs/python/tutorials/deploy/export/export.rst
rename to docs/python_docs/python/tutorials/packages/ndarray/sparse/index.rst
index a781581c1945..9ccf19db8daf 100644
--- a/docs/python_docs/python/tutorials/deploy/export/export.rst
+++ b/docs/python_docs/python/tutorials/packages/ndarray/sparse/index.rst
@@ -15,7 +15,10 @@
    specific language governing permissions and limitations
    under the License.
 
-Exporting Models
-================
+Tutorials
+=========
 
-Contributions welcome!
+.. toctree::
+   :glob:
+
+   *
\ No newline at end of file
diff --git a/docs/python_docs/python/tutorials/packages/ndarray/sparse/row_sparse.md b/docs/python_docs/python/tutorials/packages/ndarray/sparse/row_sparse.md
new file mode 100644
index 000000000000..7218ef9beae1
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/ndarray/sparse/row_sparse.md
@@ -0,0 +1,584 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+
+# RowSparseNDArray - NDArray for Sparse Gradient Updates
+
+## Motivation
+
+Many real world datasets deal with high dimensional sparse feature vectors. When learning
+the weights of models with sparse datasets, the derived gradients of the weights could be sparse.
+
+Let's say we perform a matrix multiplication of ``X``  and ``W``, where ``X`` is a 1x2 matrix, and ``W`` is a 2x3 matrix. Let ``Y`` be the matrix multiplication of the two matrices:
+
+
+```python
+import mxnet as mx
+X = mx.nd.array([[1,0]])
+W = mx.nd.array([[3,4,5], [6,7,8]])
+Y = mx.nd.dot(X, W)
+{'X': X, 'W': W, 'Y': Y}
+```
+
+```
+{'W': 
+ [[ 3.  4.  5.]
+  [ 6.  7.  8.]]
+ <NDArray 2x3 @cpu(0)>, 'X': 
+ [[ 1.  0.]]
+ <NDArray 1x2 @cpu(0)>, 'Y': 
+ [[ 3.  4.  5.]]
+ <NDArray 1x3 @cpu(0)>}
+```
+
+As you can see,
+
+```
+Y[0][0] = X[0][0] * W[0][0] + X[0][1] * W[1][0] = 1 * 3 + 0 * 6 = 3
+Y[0][1] = X[0][0] * W[0][1] + X[0][1] * W[1][1] = 1 * 4 + 0 * 7 = 4
+Y[0][2] = X[0][0] * W[0][2] + X[0][1] * W[1][2] = 1 * 5 + 0 * 8 = 5
+```
+
+What about dY / dW, the gradient for ``W``? Let's call it ``grad_W``. To start with, the shape of ``grad_W`` is the same as that of ``W`` as we are taking the derivatives with respect to ``W``, which is 2x3. Then we calculate each entry in ``grad_W`` as follows:
+
+```
+grad_W[0][0] = X[0][0] = 1
+grad_W[0][1] = X[0][0] = 1
+grad_W[0][2] = X[0][0] = 1
+grad_W[1][0] = X[0][1] = 0
+grad_W[1][1] = X[0][1] = 0
+grad_W[1][2] = X[0][1] = 0
+```
+
+As a matter of fact, you can calculate ``grad_W`` by multiplying the transpose of ``X`` with a matrix of ones:
+
+
+```python
+grad_W = mx.nd.dot(X, mx.nd.ones_like(Y), transpose_a=True)
+grad_W
+```
+
+
+
+
+```
+[[ 1.  1.  1.]
+ [ 0.  0.  0.]]
+<NDArray 2x3 @cpu(0)>
+```
+
+
+
+As you can see, row 0 of ``grad_W`` contains non-zero values while row 1 of ``grad_W`` does not. Why did that happen?
+If you look at how ``grad_W`` is calculated, notice that since column 1 of ``X`` is filled with zeros, row 1 of ``grad_W`` is filled with zeros too.
+
+In the real world, gradients for parameters that interact with sparse inputs ususally have gradients where many row slices are completely zeros. Storing and manipulating such sparse matrices with many row slices of all zeros in the default dense structure results in wasted memory and processing on the zeros. More importantly, many gradient based optimization methods such as SGD, [AdaGrad](https://stanford.edu/~jduchi/projects/DuchiHaSi10_colt.pdf) and [Adam](https://arxiv.org/pdf/1412.6980.pdf)
+take advantage of sparse gradients and prove to be efficient and effective. 
+**In MXNet, the ``RowSparseNDArray`` stores the matrix in ``row sparse`` format, which is designed for arrays of which most row slices are all zeros.**
+In this tutorial, we will describe what the row sparse format is and how to use RowSparseNDArray for sparse gradient updates in MXNet.
+
+## Prerequisites
+
+To complete this tutorial, we need:
+
+- MXNet. See the instructions for your operating system in [Setup and Installation](https://mxnet.apache.org/install/index.html)
+- [Jupyter](http://jupyter.org/)
+    ```
+    pip install jupyter
+    ```
+- Basic knowledge of NDArray in MXNet. See the detailed tutorial for NDArray in [NDArray - Imperative tensor operations on CPU/GPU](https://mxnet.apache.org/tutorials/basic/ndarray.html)
+- Understanding of [automatic differentiation with autograd](http://gluon.mxnet.io/chapter01_crashcourse/autograd.html)
+- GPUs - A section of this tutorial uses GPUs. If you don't have GPUs on your
+machine, simply set the variable `gpu_device` (set in the GPUs section of this
+tutorial) to `mx.cpu()`
+
+## Row Sparse Format
+
+A RowSparseNDArray represents a multidimensional NDArray of shape `[LARGE0, D1, .. , Dn]` using two separate 1D arrays:
+`data` and `indices`.
+
+- data: an NDArray of any dtype with shape `[D0, D1, ..., Dn]`.
+- indices: a 1D int64 NDArray with shape `[D0]` with values sorted in ascending order.
+
+The ``indices`` array stores the indices of the row slices with **non-zeros**,
+while the values are stored in ``data`` array. The corresponding NDArray `dense` represented by RowSparseNDArray `rsp` has
+
+``dense[rsp.indices[i], :, :, :, ...] = rsp.data[i, :, :, :, ...]``
+
+A RowSparseNDArray is typically used to represent non-zero row slices of a large NDArray of shape `[LARGE0, D1, .. , Dn]` where LARGE0 >> D0 and most row slices are zeros.
+
+Given this two-dimension matrix:
+
+
+```python
+[[ 1, 2, 3],
+ [ 0, 0, 0],
+ [ 4, 0, 5],
+ [ 0, 0, 0],
+ [ 0, 0, 0]]
+```
+
+The row sparse representation would be:
+- `data` array holds all the non-zero row slices of the array.
+- `indices` array stores the row index for each row slice with non-zero elements.
+
+
+```python
+data = [[1, 2, 3], [4, 0, 5]]
+indices = [0, 2]
+```
+
+`RowSparseNDArray` supports multidimensional arrays. Given this 3D tensor:
+
+
+```python
+[[[1, 0],
+  [0, 2],
+  [3, 4]],
+
+ [[5, 0],
+  [6, 0],
+  [0, 0]],
+
+ [[0, 0],
+  [0, 0],
+  [0, 0]]]
+```
+
+The row sparse representation would be (with `data` and `indices` defined the same as above):
+
+
+```python
+data = [[[1, 0], [0, 2], [3, 4]], [[5, 0], [6, 0], [0, 0]]]
+indices = [0, 1]
+```
+
+``RowSparseNDArray`` is a subclass of ``NDArray``. If you query **stype** of a RowSparseNDArray,
+the value will be **"row_sparse"**.
+
+## Array Creation
+
+You can create a `RowSparseNDArray` with data and indices by using the `row_sparse_array` function:
+
+
+```python
+import mxnet as mx
+import numpy as np
+# Create a RowSparseNDArray with python lists
+shape = (6, 2)
+data_list = [[1, 2], [3, 4]]
+indices_list = [1, 4]
+a = mx.nd.sparse.row_sparse_array((data_list, indices_list), shape=shape)
+# Create a RowSparseNDArray with numpy arrays
+data_np = np.array([[1, 2], [3, 4]])
+indices_np = np.array([1, 4])
+b = mx.nd.sparse.row_sparse_array((data_np, indices_np), shape=shape)
+{'a':a, 'b':b}
+```
+
+
+
+
+`{'a':  <RowSparseNDArray 6x2 @cpu(0)>, 'b':   <RowSparseNDArray 6x2 @cpu(0)>}`<!--notebook-skip-line-->
+
+
+
+## Function Overview
+
+Similar to `CSRNDArray`, the are several functions with `RowSparseNDArray` that behave the same way. In the code blocks below you can try out these common functions:
+
+- **.dtype** - to set the data type
+- **.asnumpy** - to cast as a numpy array for inspecting it
+- **.data** - to access the data array
+- **.indices** - to access the indices array
+- **.tostype** - to set the storage type
+- **.cast_storage** - to convert the storage type
+- **.copy** - to copy the array
+- **.copyto** - to copy to deep copy an existing array
+
+
+## Setting Type
+
+You can create a `RowSparseNDArray` from another specifying the element data type with the option `dtype`, which accepts a numpy type. By default, `float32` is used.
+
+
+```python
+# Float32 is used by default
+c = mx.nd.sparse.array(a)
+# Create a 16-bit float array
+d = mx.nd.array(a, dtype=np.float16)
+(c.dtype, d.dtype)
+```
+
+
+
+
+`(numpy.float32, numpy.float16)`<!--notebook-skip-line-->
+
+
+
+## Inspecting Arrays
+
+As with `CSRNDArray`, you can inspect the contents of a `RowSparseNDArray` by filling
+its contents into a dense `numpy.ndarray` using the `asnumpy` function.
+
+
+```python
+a.asnumpy()
+```
+
+
+
+```
+array([[ 0.,  0.],
+       [ 1.,  2.],
+       [ 0.,  0.],
+       [ 0.,  0.],
+       [ 3.,  4.],
+       [ 0.,  0.]], dtype=float32)
+```
+
+
+You can inspect the internal storage of a RowSparseNDArray by accessing attributes such as `indices` and `data`:
+
+
+```python
+# Access data array
+data = a.data
+# Access indices array
+indices = a.indices
+{'a.stype': a.stype, 'data':data, 'indices':indices}
+```
+
+
+
+```
+{'a.stype': 'row_sparse', 'data': 
+ [[ 1.  2.]
+  [ 3.  4.]]
+ <NDArray 2x2 @cpu(0)>, 'indices': 
+ [1 4]
+ <NDArray 2 @cpu(0)>}
+```
+
+
+## Storage Type Conversion
+
+You can convert an NDArray to a RowSparseNDArray and vice versa by using the `tostype` function:
+
+
+```python
+# Create a dense NDArray
+ones = mx.nd.ones((2,2))
+# Cast the storage type from `default` to `row_sparse`
+rsp = ones.tostype('row_sparse')
+# Cast the storage type from `row_sparse` to `default`
+dense = rsp.tostype('default')
+{'rsp':rsp, 'dense':dense}
+```
+
+
+
+```
+{'dense': 
+ [[ 1.  1.]
+  [ 1.  1.]]
+ <NDArray 2x2 @cpu(0)>, 'rsp': 
+ <RowSparseNDArray 2x2 @cpu(0)>}
+```
+
+
+You can also convert the storage type by using the `cast_storage` operator:
+
+
+```python
+# Create a dense NDArray
+ones = mx.nd.ones((2,2))
+# Cast the storage type to `row_sparse`
+rsp = mx.nd.sparse.cast_storage(ones, 'row_sparse')
+# Cast the storage type to `default`
+dense = mx.nd.sparse.cast_storage(rsp, 'default')
+{'rsp':rsp, 'dense':dense}
+```
+
+
+
+```
+{'dense': 
+ [[ 1.  1.]
+  [ 1.  1.]]
+ <NDArray 2x2 @cpu(0)>, 'rsp': 
+ <RowSparseNDArray 2x2 @cpu(0)>}
+```
+
+
+## Copies
+
+You can use the `copy` method which makes a deep copy of the array and its data, and returns a new array.
+We can also use the `copyto` method or the slice operator `[]` to deep copy to an existing array.
+
+
+```python
+a = mx.nd.ones((2,2)).tostype('row_sparse')
+b = a.copy()
+c = mx.nd.sparse.zeros('row_sparse', (2,2))
+c[:] = a
+d = mx.nd.sparse.zeros('row_sparse', (2,2))
+a.copyto(d)
+{'b is a': b is a, 'b.asnumpy()':b.asnumpy(), 'c.asnumpy()':c.asnumpy(), 'd.asnumpy()':d.asnumpy()}
+```
+
+
+
+```
+{'b is a': False, 'b.asnumpy()': array([[ 1.,  1.],
+        [ 1.,  1.]], dtype=float32), 'c.asnumpy()': array([[ 1.,  1.],
+        [ 1.,  1.]], dtype=float32), 'd.asnumpy()': array([[ 1.,  1.],
+        [ 1.,  1.]], dtype=float32)}
+```
+
+
+If the storage types of source array and destination array do not match,
+the storage type of destination array will not change when copying with `copyto` or the slice operator `[]`. The source array will be temporarily converted to desired storage type before the copy.
+
+
+```python
+e = mx.nd.sparse.zeros('row_sparse', (2,2))
+f = mx.nd.sparse.zeros('row_sparse', (2,2))
+g = mx.nd.ones(e.shape)
+e[:] = g
+g.copyto(f)
+{'e.stype':e.stype, 'f.stype':f.stype, 'g.stype':g.stype}
+```
+
+
+
+
+`{'e.stype': 'row_sparse', 'f.stype': 'row_sparse', 'g.stype': 'default'}`<!--notebook-skip-line-->
+
+
+
+## Retain Row Slices
+
+You can retain a subset of row slices from a RowSparseNDArray specified by their row indices.
+
+
+```python
+data = [[1, 2], [3, 4], [5, 6]]
+indices = [0, 2, 3]
+rsp = mx.nd.sparse.row_sparse_array((data, indices), shape=(5, 2))
+# Retain row 0 and row 1
+rsp_retained = mx.nd.sparse.retain(rsp, mx.nd.array([0, 1]))
+{'rsp.asnumpy()': rsp.asnumpy(), 'rsp_retained': rsp_retained, 'rsp_retained.asnumpy()': rsp_retained.asnumpy()}
+```
+
+
+
+```
+{'rsp.asnumpy()': array([[ 1.,  2.],
+        [ 0.,  0.],
+        [ 3.,  4.],
+        [ 5.,  6.],
+        [ 0.,  0.]], dtype=float32), 'rsp_retained': 
+ <RowSparseNDArray 5x2 @cpu(0)>, 'rsp_retained.asnumpy()': array([[ 1.,  2.],
+        [ 0.,  0.],
+        [ 0.,  0.],
+        [ 0.,  0.],
+        [ 0.,  0.]], dtype=float32)}
+```
+
+
+## Sparse Operators and Storage Type Inference
+
+Operators that have specialized implementation for sparse arrays can be accessed in ``mx.nd.sparse``. You can read the [mxnet.ndarray.sparse API documentation](http://mxnet.apache.org/api/python/ndarray/sparse.html) to find what sparse operators are available.
+
+
+```python
+shape = (3, 5)
+data = [7, 8, 9]
+indptr = [0, 2, 2, 3]
+indices = [0, 2, 1]
+# A csr matrix as lhs
+lhs = mx.nd.sparse.csr_matrix((data, indices, indptr), shape=shape)
+# A dense matrix as rhs
+rhs = mx.nd.ones((3, 2))
+# row_sparse result is inferred from sparse operator dot(csr.T, dense) based on input stypes
+transpose_dot = mx.nd.sparse.dot(lhs, rhs, transpose_a=True)
+{'transpose_dot': transpose_dot, 'transpose_dot.asnumpy()': transpose_dot.asnumpy()}
+```
+
+
+
+```
+{'transpose_dot': 
+ <RowSparseNDArray 5x2 @cpu(0)>, 'transpose_dot.asnumpy()': array([[ 7.,  7.],
+        [ 9.,  9.],
+        [ 8.,  8.],
+        [ 0.,  0.],
+        [ 0.,  0.]], dtype=float32)}
+```
+
+
+For any sparse operator, the storage type of output array is inferred based on inputs. You can either read the documentation or inspect the `stype` attribute of output array to know what storage type is inferred:
+
+
+```python
+a = transpose_dot.copy()
+b = a * 2  # b will be a RowSparseNDArray since zero multiplied by 2 is still zero
+c = a + mx.nd.ones((5, 2))  # c will be a dense NDArray
+{'b.stype':b.stype, 'c.stype':c.stype}
+```
+
+
+
+
+`{'b.stype': 'row_sparse', 'c.stype': 'default'}`<!--notebook-skip-line-->
+
+
+
+For operators that don't specialize in sparse arrays, you can still use them with sparse inputs with some performance penalty.
+In MXNet, dense operators require all inputs and outputs to be in the dense format.
+
+If sparse inputs are provided, MXNet will convert sparse inputs into dense ones temporarily so that the dense operator can be used.
+
+If sparse outputs are provided, MXNet will convert the dense outputs generated by the dense operator into the provided sparse format.
+
+For operators that don't specialize in sparse arrays, you can still use them with sparse inputs with some performance penalty.
+
+
+```python
+e = mx.nd.sparse.zeros('row_sparse', a.shape)
+d = mx.nd.log(a) # dense operator with a sparse input
+e = mx.nd.log(a, out=e) # dense operator with a sparse output
+{'a.stype':a.stype, 'd.stype':d.stype, 'e.stype':e.stype} # stypes of a and e will be not changed
+```
+
+
+
+
+`{'a.stype': 'row_sparse', 'd.stype': 'default', 'e.stype': 'row_sparse'}` <!--notebook-skip-line-->
+
+
+
+Note that warning messages will be printed when such a storage fallback event happens. If you are using jupyter notebook, the warning message will be printed in your terminal console.
+
+## Sparse Optimizers
+
+In MXNet, sparse gradient updates are applied when gradient is in `row_sparse` storage and the optimizer is created with `lazy_update=True`.
+The sparse optimizers only update the row slices of the weight and the states whose indices appear
+in `gradient.indices`. For example, the default update rule for SGD optimizer is:
+
+```
+rescaled_grad = learning_rate * rescale_grad * clip(grad, clip_gradient) + weight_decay * weight
+state = momentum * state + rescaled_grad
+weight = weight - state
+```
+
+However, with sparse gradient the SGD optimizer uses the following lazy update by default:
+
+```
+for row in grad.indices:
+    rescaled_grad[row] = learning_rate * rescale_grad * clip(grad[row], clip_gradient) + weight_decay * weight[row]
+    state[row] = momentum[row] * state[row] + rescaled_grad[row]
+    weight[row] = weight[row] - state[row]
+```
+
+This means that the lazy update leads to different optimization results if `weight_decay` or `momentum` is non-zero.
+To disable lazy update, please set `lazy_update` to be False when creating the optimizer.
+
+
+```python
+# Create weight
+shape = (4, 2)
+weight = mx.nd.ones(shape).tostype('row_sparse')
+# Create gradient
+data = [[1, 2], [4, 5]]
+indices = [1, 2]
+grad = mx.nd.sparse.row_sparse_array((data, indices), shape=shape)
+sgd = mx.optimizer.SGD(learning_rate=0.01, momentum=0.01)
+# Create momentum
+momentum = sgd.create_state(0, weight)
+# Before the update
+{"grad.asnumpy()":grad.asnumpy(), "weight.asnumpy()":weight.asnumpy(), "momentum.asnumpy()":momentum.asnumpy()}
+```
+
+
+
+```
+{'grad.asnumpy()': array([[ 0.,  0.],
+        [ 1.,  2.],
+        [ 4.,  5.],
+        [ 0.,  0.]], dtype=float32), 'momentum.asnumpy()': array([[ 0.,  0.],
+        [ 0.,  0.],
+        [ 0.,  0.],
+        [ 0.,  0.]], dtype=float32), 'weight.asnumpy()': array([[ 1.,  1.],
+        [ 1.,  1.],
+        [ 1.,  1.],
+        [ 1.,  1.]], dtype=float32)}
+```
+
+
+
+
+```python
+sgd.update(0, weight, grad, momentum)
+# Only row 0 and row 2 are updated for both weight and momentum
+{"weight.asnumpy()":weight.asnumpy(), "momentum.asnumpy()":momentum.asnumpy()}
+```
+
+
+
+```
+{'momentum.asnumpy()': array([[ 0.  ,  0.  ],
+        [-0.01, -0.02],
+        [-0.04, -0.05],
+        [ 0.  ,  0.  ]], dtype=float32),
+ 'weight.asnumpy()': array([[ 1.        ,  1.        ],
+        [ 0.99000001,  0.98000002],
+        [ 0.95999998,  0.94999999],
+        [ 1.        ,  1.        ]], dtype=float32)}
+```
+
+
+Note that only [mxnet.optimizer.SGD](https://mxnet.apache.org/api/python/optimization/optimization.html#mxnet.optimizer.SGD), [mxnet.optimizer.Adam](https://mxnet.apache.org/api/python/optimization/optimization.html#mxnet.optimizer.Adam), and
+[mxnet.optimizer.AdaGrad](https://mxnet.apache.org/api/python/optimization/optimization.html#mxnet.optimizer.AdaGrad) support sparse updates in MXNet.
+
+## Advanced Topics
+
+### GPU Support
+
+By default, RowSparseNDArray operators are executed on CPU. To create a RowSparseNDArray on gpu, we need to explicitly specify the context:
+
+**Note** If a GPU is not available, an error will be reported in the following section. In order to execute it on a cpu, set gpu_device to mx.cpu().
+
+
+```python
+import sys
+gpu_device=mx.gpu() # Change this to mx.cpu() in absence of GPUs.
+try:
+    a = mx.nd.sparse.zeros('row_sparse', (100, 100), ctx=gpu_device)
+    a
+except mx.MXNetError as err:
+    sys.stderr.write(str(err))
+```
+
+## Next 
+
+[Train a Linear Regression Model with Sparse Symbols](http://mxnet.apache.org/tutorials/sparse/train.html)
+
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/python_docs/python/tutorials/packages/ndarray/sparse/train.md b/docs/python_docs/python/tutorials/packages/ndarray/sparse/train.md
new file mode 100644
index 000000000000..71669e142a4b
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/ndarray/sparse/train.md
@@ -0,0 +1,339 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+
+# Train a Linear Regression Model with Sparse Symbols
+In previous tutorials, we introduced `CSRNDArray` and `RowSparseNDArray`,
+the basic data structures for manipulating sparse data.
+MXNet also provides `Sparse Symbol` API, which enables symbolic expressions that handle sparse arrays.
+In this tutorial, we first focus on how to compose a symbolic graph with sparse operators,
+then train a linear regression model using sparse symbols with the Module API.
+
+## Prerequisites
+
+To complete this tutorial, we need:
+
+- MXNet. See the instructions for your operating system in [Setup and Installation](https://mxnet.io/get_started).  
+
+- [Jupyter Notebook](https://jupyter.org/index.html) and [Python Requests](http://docs.python-requests.org/en/master/) packages.
+```
+pip install jupyter requests
+```
+
+- Basic knowledge of Symbol in MXNet. See the detailed tutorial for Symbol in [Symbol - Neural Network Graphs and Auto-differentiation](https://mxnet.apache.org/tutorials/basic/symbol.html).
+
+- Basic knowledge of CSRNDArray in MXNet. See the detailed tutorial for CSRNDArray in [CSRNDArray - NDArray in Compressed Sparse Row Storage Format](https://mxnet.apache.org/versions/master/tutorials/sparse/csr.html).
+
+- Basic knowledge of RowSparseNDArray in MXNet. See the detailed tutorial for RowSparseNDArray in [RowSparseNDArray - NDArray for Sparse Gradient Updates](https://mxnet.apache.org/versions/master/tutorials/sparse/row_sparse.html).
+
+## Variables
+
+Variables are placeholder for arrays. We can use them to hold sparse arrays too.
+
+### Variable Storage Types
+
+The `stype` attribute of a variable is used to indicate the storage type of the array.
+By default, the `stype` of a variable is "default" which indicates the default dense storage format.
+We can specify the `stype` of a variable as "csr" or "row_sparse" to hold sparse arrays.
+
+
+```python
+import mxnet as mx
+import numpy as np
+import random
+
+# set the seeds for repeatability
+random.seed(42)
+np.random.seed(42)
+mx.random.seed(42)
+
+# Create a variable to hold an NDArray
+a = mx.sym.Variable('a')
+# Create a variable to hold a CSRNDArray
+b = mx.sym.Variable('b', stype='csr')
+# Create a variable to hold a RowSparseNDArray
+c = mx.sym.Variable('c', stype='row_sparse')
+(a, b, c)
+```
+
+
+
+
+`(<Symbol a>, <Symbol b>, <Symbol c>)` <!--notebook-skip-line-->
+
+
+
+### Bind with Sparse Arrays
+
+The sparse symbols constructed above declare storage types of the arrays to hold.
+To evaluate them, we need to feed the free variables with sparse data.
+
+You can instantiate an executor from a sparse symbol by using the `simple_bind` method,
+which allocate zeros to all free variables according to their storage types.
+The executor provides `forward` method for evaluation and an attribute
+`outputs` to get all the results. Later, we will show the use of the `backward` method and other methods computing the gradients and updating parameters. A simple example first:
+
+
+```python
+shape = (2,2)
+# Instantiate an executor from sparse symbols
+b_exec = b.simple_bind(ctx=mx.cpu(), b=shape)
+c_exec = c.simple_bind(ctx=mx.cpu(), c=shape)
+b_exec.forward()
+c_exec.forward()
+# Sparse arrays of zeros are bound to b and c
+print(b_exec.outputs, c_exec.outputs)
+```
+
+```
+([
+<CSRNDArray 2x2 @cpu(0)>], [
+<RowSparseNDArray 2x2 @cpu(0)>])
+```
+
+You can update the array held by the variable by accessing executor's `arg_dict` and assigning new values.
+
+
+```python
+b_exec.arg_dict['b'][:] = mx.nd.ones(shape).tostype('csr')
+b_exec.forward()
+# The array `b` holds are updated to be ones
+eval_b = b_exec.outputs[0]
+{'eval_b': eval_b, 'eval_b.asnumpy()': eval_b.asnumpy()}
+```
+
+
+
+```
+{'eval_b':
+ <CSRNDArray 2x2 @cpu(0)>, 'eval_b.asnumpy()': array([[ 1.,  1.],
+        [ 1.,  1.]], dtype=float32)}
+```
+
+
+## Symbol Composition and Storage Type Inference
+
+### Basic Symbol Composition
+
+The following example builds a simple element-wise addition expression with different storage types.
+The sparse symbols are available in the `mx.sym.sparse` package.
+
+
+```python
+# Element-wise addition of variables with "default" stype
+d = mx.sym.elemwise_add(a, a)
+# Element-wise addition of variables with "csr" stype
+e = mx.sym.sparse.negative(b)
+# Element-wise addition of variables with "row_sparse" stype
+f = mx.sym.sparse.elemwise_add(c, c)
+{'d':d, 'e':e, 'f':f}
+```
+
+
+
+```
+{'d': <Symbol elemwise_add0>,
+ 'e': <Symbol negative0>,
+ 'f': <Symbol elemwise_add1>}
+```
+
+
+### Storage Type Inference
+
+What will be the output storage types of sparse symbols? In MXNet, for any sparse symbol, the result storage types are inferred based on storage types of inputs.
+You can read the [Sparse Symbol API](https://mxnet.apache.org/versions/master/api/python/symbol/sparse.html) documentation to find what output storage types are. In the example below we will try out the storage types introduced in the Row Sparse and Compressed Sparse Row tutorials: `default` (dense), `csr`, and `row_sparse`.
+
+
+```python
+add_exec = mx.sym.Group([d, e, f]).simple_bind(ctx=mx.cpu(), a=shape, b=shape, c=shape)
+add_exec.forward()
+dense_add = add_exec.outputs[0]
+# The output storage type of elemwise_add(csr, csr) will be inferred as "csr"
+csr_add = add_exec.outputs[1]
+# The output storage type of elemwise_add(row_sparse, row_sparse) will be inferred as "row_sparse"
+rsp_add = add_exec.outputs[2]
+{'dense_add.stype': dense_add.stype, 'csr_add.stype':csr_add.stype, 'rsp_add.stype': rsp_add.stype}
+```
+
+
+
+```
+{'csr_add.stype': 'csr',
+ 'dense_add.stype': 'default',
+ 'rsp_add.stype': 'row_sparse'}
+```
+
+
+### Storage Type Fallback
+
+For operators that don't specialize in certain sparse arrays, you can still use them with sparse inputs with some performance penalty. In MXNet, dense operators require all inputs and outputs to be in the dense format. If sparse inputs are provided, MXNet will convert sparse inputs into dense ones temporarily so that the dense operator can be used. If sparse outputs are provided, MXNet will convert the dense outputs generated by the dense operator into the provided sparse format. Warning messages will be printed when such a storage fallback event happens.
+
+
+```python
+# `log` operator doesn't support sparse inputs at all, but we can fallback on the dense implementation
+csr_log = mx.sym.log(a)
+# `elemwise_add` operator doesn't support adding csr with row_sparse, but we can fallback on the dense implementation
+csr_rsp_add = mx.sym.elemwise_add(b, c)
+fallback_exec = mx.sym.Group([csr_rsp_add, csr_log]).simple_bind(ctx=mx.cpu(), a=shape, b=shape, c=shape)
+fallback_exec.forward()
+fallback_add = fallback_exec.outputs[0]
+fallback_log = fallback_exec.outputs[1]
+{'fallback_add': fallback_add, 'fallback_log': fallback_log}
+```
+
+
+
+```
+{'fallback_add':
+ [[ 0.  0.]
+  [ 0.  0.]]
+ <NDArray 2x2 @cpu(0)>, 'fallback_log':
+ [[-inf -inf]
+  [-inf -inf]]
+ <NDArray 2x2 @cpu(0)>}
+```
+
+
+### Inspecting Storage Types of the Symbol Graph
+
+When the environment variable `MXNET_INFER_STORAGE_TYPE_VERBOSE_LOGGING` is set to `1`, MXNet will log the storage type information of
+operators' inputs and outputs in the computation graph. For example, we can inspect the storage types of
+a linear classification network with sparse operators. Uncomment the line below and inspect your console.:
+
+
+```python
+# Set logging level for executor
+import mxnet as mx
+import os
+#os.environ['MXNET_INFER_STORAGE_TYPE_VERBOSE_LOGGING'] = "1"
+# Data in csr format
+data = mx.sym.var('data', stype='csr', shape=(32, 10000))
+# Weight in row_sparse format
+weight = mx.sym.var('weight', stype='row_sparse', shape=(10000, 2))
+bias = mx.symbol.Variable("bias", shape=(2,))
+dot = mx.symbol.sparse.dot(data, weight)
+pred = mx.symbol.broadcast_add(dot, bias)
+y = mx.symbol.Variable("label")
+output = mx.symbol.SoftmaxOutput(data=pred, label=y, name="output")
+executor = output.simple_bind(ctx=mx.cpu())
+```
+
+## Training with Module APIs
+
+In the following section we'll walk through how one can implement **linear regression** using sparse symbols and sparse optimizers.
+
+The function you will explore is: *y = x<sub>1</sub>  +  2x<sub>2</sub> + ... 100x<sub>100*, where *(x<sub>1</sub>,x<sub>2</sub>, ..., x<sub>100</sub>)* are input features and *y* is the corresponding label.
+
+### Preparing the Data
+
+In MXNet, both [mx.io.LibSVMIter](https://mxnet.apache.org/versions/master/api/python/io/io.html#mxnet.io.LibSVMIter)
+and [mx.io.NDArrayIter](https://mxnet.apache.org/versions/master/api/python/io/io.html#mxnet.io.NDArrayIter)
+support loading sparse data in CSR format. In this example, we'll use the `NDArrayIter`.
+
+You may see some warnings from SciPy. You don't need to worry about those for this example.
+
+
+```python
+# Random training data
+feature_dimension = 100
+train_data = mx.test_utils.rand_ndarray((1000, feature_dimension), 'csr', 0.01)
+target_weight = mx.nd.arange(1, feature_dimension + 1).reshape((feature_dimension, 1))
+train_label = mx.nd.dot(train_data, target_weight)
+batch_size = 1
+train_iter = mx.io.NDArrayIter(train_data, train_label, batch_size, last_batch_handle='discard', label_name='label')
+```
+
+### Defining the Model
+
+Below is an example of a linear regression model specifying the storage type of the variables.
+
+
+```python
+initializer = mx.initializer.Normal(sigma=0.01)
+X = mx.sym.Variable('data', stype='csr')
+Y = mx.symbol.Variable('label')
+weight = mx.symbol.Variable('weight', stype='row_sparse', shape=(feature_dimension, 1), init=initializer)
+bias = mx.symbol.Variable('bias', shape=(1, ))
+pred = mx.sym.broadcast_add(mx.sym.sparse.dot(X, weight), bias)
+lro = mx.sym.LinearRegressionOutput(data=pred, label=Y, name="lro")
+```
+
+The above network uses the following symbols:
+
+1. `Variable X`: The placeholder for sparse data inputs. The `csr` stype indicates that the array to hold is in CSR format.
+
+2. `Variable Y`: The placeholder for dense labels.
+
+3. `Variable weight`: The placeholder for the weight to learn. The `stype` of weight is specified as `row_sparse` so that it is initialized as RowSparseNDArray,
+   and the optimizer will perform sparse update rules on it. The `init` attribute specifies what initializer to use for this variable.
+
+4. `Variable bias`: The placeholder for the bias to learn.
+
+5. `sparse.dot`: The dot product operation of `X` and `weight`. The sparse implementation will be invoked to handle `csr` and `row_sparse` inputs.
+
+6. `broadcast_add`: The broadcasting add operation to apply `bias`.
+
+7. `LinearRegressionOutput`: The output layer which computes *l2* loss against its input and the labels provided to it.
+
+### Training the model
+
+Once we have defined the model structure, the next step is to create a module and initialize the parameters and optimizer.
+
+
+```python
+# Create module
+mod = mx.mod.Module(symbol=lro, data_names=['data'], label_names=['label'])
+# Allocate memory by giving the input data and label shapes
+mod.bind(data_shapes=train_iter.provide_data, label_shapes=train_iter.provide_label)
+# Initialize parameters by random numbers
+mod.init_params(initializer=initializer)
+# Use SGD as the optimizer, which performs sparse update on "row_sparse" weight
+sgd = mx.optimizer.SGD(learning_rate=0.05, rescale_grad=1.0/batch_size, momentum=0.9)
+mod.init_optimizer(optimizer=sgd)
+```
+
+Finally, we train the parameters of the model to fit the training data by using the `forward`, `backward`, and `update` methods in Module.
+
+
+```python
+# Use mean square error as the metric
+metric = mx.metric.create('MSE')
+# Train 10 epochs
+for epoch in range(10):
+    train_iter.reset()
+    metric.reset()
+    for batch in train_iter:
+        mod.forward(batch, is_train=True)       # compute predictions
+        mod.update_metric(metric, batch.label)  # accumulate prediction accuracy
+        mod.backward()                          # compute gradients
+        mod.update()                            # update parameters
+    print('Epoch %d, Metric = %s' % (epoch, metric.get()))
+assert metric.get()[1] < 1, "Achieved MSE (%f) is larger than expected (1.0)" % metric.get()[1]    
+```
+
+`Epoch 9, Metric = ('mse', 0.35979430613957991)`<!--notebook-skip-line-->
+
+
+
+
+### Training the model with multiple machines or multiple devices
+
+Distributed training with `row_sparse` weights and gradients are supported in MXNet, which significantly reduces communication cost for large models. To train a sparse model with multiple machines, you need to call `prepare` before `forward`, or `save_checkpoint`.
+Please refer to the example in [mxnet/example/sparse/linear_classification](https://github.com/apache/incubator-mxnet/tree/master/example/sparse/linear_classification)
+for more details.
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/python_docs/python/tutorials/packages/ndarray/sparse/train_gluon.md b/docs/python_docs/python/tutorials/packages/ndarray/sparse/train_gluon.md
new file mode 100644
index 000000000000..402cc2aeb739
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/ndarray/sparse/train_gluon.md
@@ -0,0 +1,483 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+
+# Sparse NDArrays with Gluon
+
+When working on machine learning problems, you may encounter situations where the input data is sparse (i.e. the majority of values are zero). One example of this is in recommendation systems. You could have millions of user and product features, but only a few of these features are present for each sample. Without special treatment, the sheer magnitude of the feature space can lead to out-of-memory situations and cause significant slowdowns when training and making predictions.
+
+MXNet supports a number of sparse storage types (often called 'stype' for short) for these situations. In this tutorial, we'll start by generating some sparse data, write it to disk in the LibSVM format and then read back using the [`LibSVMIter`](https://mxnet.apache.org/api/python/io/io.html) for training. We use the Gluon API to train the model and leverage sparse storage types such as [`CSRNDArray`](https://mxnet.apache.org/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray) and [`RowSparseNDArray`](https://mxnet.apache.org/api/python/ndarray/sparse.html?highlight=rowsparsendarray#mxnet.ndarray.sparse.RowSparseNDArray) to maximise performance and memory efficiency.
+
+
+```python
+import mxnet as mx
+import numpy as np
+import time
+```
+
+### Generating Sparse Data
+
+You will most likely have a sparse dataset in mind already if you're reading this tutorial, but let's create a dummy dataset to use in the examples that follow. Using `rand_ndarray` we will generate 1000 samples, each with 1,000,000 features of which 99.999% of values will be zero (i.e. 10 non-zero features for each sample). We take this as our input data for training and calculate a label based on an arbitrary rule: whether the feature sum is higher than average.
+
+
+```python
+num_samples = 1000
+num_features = 1000000
+data = mx.test_utils.rand_ndarray((num_samples, num_features), stype='csr', density=0.00001)
+# generate label: 1 if row sum above average, 0 otherwise.
+label = data.sum(axis=1) > data.sum(axis=1).mean()
+```
+
+
+```python
+print(type(data))
+print(data[:10].asnumpy())
+print('{:,.0f} elements'.format(np.product(data.shape)))
+print('{:,.0f} non-zero elements'.format(data.data.size))
+```
+
+```
+<class 'mxnet.ndarray.sparse.CSRNDArray'>
+[[0. 0. 0. ... 0. 0. 0.]
+ [0. 0. 0. ... 0. 0. 0.]
+ [0. 0. 0. ... 0. 0. 0.]
+ ...
+ [0. 0. 0. ... 0. 0. 0.]
+ [0. 0. 0. ... 0. 0. 0.]
+ [0. 0. 0. ... 0. 0. 0.]]
+1,000,000,000 elements
+10,000 non-zero elements
+```
+
+Our storage type is CSR (Compressed Sparse Row) which is the ideal type for sparse data along multiple axes. See [this in-depth tutorial](https://mxnet.apache.org/versions/master/tutorials/sparse/csr.html) for more information. Just to confirm the generation process ran correctly, we can see that the vast majority of values are indeed zero. One of the first questions to ask would be how much memory is saved by storing this data in a [`CSRNDArray`](https://mxnet.apache.org/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray) versus a standard [`NDArray`](https://mxnet.apache.org/versions/master/api/python/ndarray/sparse.html?highlight=ndarray#module-mxnet.ndarray). Since sparse arrays are constructed from many components (e.g. `data`, `indices` and `indptr`) we define a function called `get_nbytes` to calculate the number of bytes taken in memory to store an array. We compare the same data stored in a standard [`NDArray`](https://mxnet.apache.org/versions/master/api/python/ndarray/sparse.html?highlight=ndarray#module-mxnet.ndarray) (with `data.tostype('default')`) to the [`CSRNDArray`](https://mxnet.apache.org/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray).
+
+
+```python
+def get_nbytes(array):
+    fn = lambda a: a.size * np.dtype(a).itemsize
+    if isinstance(array, mx.ndarray.sparse.CSRNDArray):
+        return fn(array.data) + fn(array.indices) + fn(array.indptr)
+    elif isinstance(array, mx.ndarray.sparse.RowSparseNDArray):
+        return fn(array.data) + fn(array.indices)
+    elif isinstance(array, mx.ndarray.NDArray):
+        return fn(array)
+    else:
+        TypeError('{} not supported'.format(type(array)))
+```
+
+
+```python
+print('NDarray:', get_nbytes(data.tostype('default'))/1000000, 'MBs')
+print('CSRNDArray', get_nbytes(data)/1000000, 'MBs')
+```
+
+```
+NDarray: 4000.0 MBs
+CSRNDArray 0.128008 MBs
+```
+
+Given the extremely high sparsity of the data, we observe a huge memory saving here! 0.13 MBs versus 4 GBs: ~30,000 times smaller. You can experiment with the amount of sparsity and see how these two storage types compare. When the number of non-zero values increases, this difference will reduce. And when the number of non-zero values exceeds ~1/3 you will find that this sparse storage type take more memory than dense! So use wisely.
+
+### Writing Sparse Data
+
+Since there is such a large size difference between dense and sparse storage formats here, we ideally want to store the data on disk in a sparse storage format too. MXNet supports a format called LibSVM and has a data iterator called [`LibSVMIter`](https://mxnet.apache.org/api/python/io/io.html?highlight=libsvmiter) specifically for data formatted this way.
+
+A LibSVM file has a row for each sample, and each row starts with the label: in this case `0.0` or `1.0` since we have a classification task. After this we have a variable number of `key:value` pairs separated by spaces, where the key is column/feature index and the value is the value of that feature. When working with your own sparse data in a custom format you should try to convert your data into this format. We define a `save_as_libsvm` function to save the `data` ([`CSRNDArray`](https://mxnet.apache.org/versions/master/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray)) and `label` (`NDArray`) to disk in LibSVM format.
+
+
+```python
+def save_as_libsvm(filepath, data, label):
+    with open(filepath, 'w') as openfile:
+        for row_idx in range(data.shape[0]):
+            data_sample = data[row_idx]
+            label_sample = label[row_idx]
+            col_idxs = data_sample.indices.asnumpy().tolist()
+            values = data_sample.data.asnumpy().tolist()
+            label_str = str(label_sample.asscalar())
+            value_strs = ['{}:{}'.format(idx, value) for idx, value in zip(col_idxs, values)]
+            value_str = " ".join(value_strs)
+            sample_str = '{} {}\n'.format(label_str, value_str)
+            openfile.write(sample_str)
+```
+
+
+```python
+filepath = 'dataset.libsvm'
+save_as_libsvm(filepath, data, label)
+```
+
+We have now written the `data` and `label` to disk, and can inspect the first 10 lines of the file:
+
+
+```python
+with open(filepath, 'r') as openfile:
+    lines = [openfile.readline() for _ in range(10)]
+for line in lines:
+    print(line[:80] + '...' if len(line) > 80 else line)
+```
+
+```
+0.0 35454:0.22486156225204468 80954:0.39130592346191406 81941:0.1988530308008194...
+1.0 37029:0.5980494618415833 52916:0.15797750651836395 71623:0.32251599431037903...
+1.0 89962:0.47770974040031433 216426:0.21326342225074768 271027:0.18589609861373...
+1.0 7071:0.9432336688041687 81664:0.7788773775100708 117459:0.8166475296020508 4...
+0.0 380966:0.16906292736530304 394363:0.7987179756164551 458442:0.56873309612274...
+0.0 89361:0.9099966287612915 141813:0.5927085280418396 282489:0.293381005525589 ...
+0.0 150427:0.4747847020626068 169376:0.2603490948677063 179377:0.237988427281379...
+0.0 49774:0.2822582423686981 91245:0.5794865489006042 102970:0.7004560232162476 ...
+1.0 97133:0.0024336236529052258 109855:0.9895315766334534 116765:0.2465638816356...
+0.0 803440:0.4020800292491913
+```
+
+
+Some storage overhead is introduced by serializing the data as characters (with spaces and colons). `dataset.libsvm` is 250 KBs but the original `data` and `label` were 132 KBs combined. Compared with the 4GB dense `NDArray` though, this isn't a huge issue.
+
+### Reading Sparse Data
+
+Using [`LibSVMIter`](https://mxnet.apache.org/api/python/io/io.html?highlight=libsvmiter), we can quickly and easily load data into batches ready for training. Although Gluon [`Dataset`](https://mxnet.apache.org/versions/master/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset)s can be written to return sparse arrays, Gluon [`DataLoader`](https://mxnet.apache.org/versions/master/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader)s currently convert each sample to dense before stacking up to create the batch. As a result, [`LibSVMIter`](https://mxnet.apache.org/api/python/io/io.html?highlight=libsvmiter) is the recommended method of loading sparse data in batches.
+
+Similar to using a [`DataLoader`](https://mxnet.apache.org/versions/master/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader), you must specify the required `batch_size`. Since we're dealing with sparse data and the column shape isn't explicitly stored in the LibSVM file, we additionally need to provide the shape of the data and label. Our [`LibSVMIter`](https://mxnet.apache.org/api/python/io/io.html?highlight=libsvmiter) returns batches in a slightly different form to a [`DataLoader`](https://mxnet.apache.org/versions/master/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader). We get `DataBatch` objects instead of `tuple`. See the [appendix of this tutorial](https://mxnet.apache.org/versions/master/tutorials/gluon/datasets.html) for more information.
+
+
+```python
+data_iter = mx.io.LibSVMIter(data_libsvm=filepath, data_shape=(num_features,), label_shape=(1,), batch_size=10)
+for batch in data_iter:
+    data = batch.data[0]
+    print('data.stype: {}'.format(data.stype))
+    label = batch.label[0]
+    print('label.stype: {}'.format(label.stype))
+    break
+```
+
+```
+data.stype: csr
+label.stype: default
+```
+
+We can see that `data` and `label` are in the appropriate storage formats, given their sparse and dense values respectively. We can avoid out-of-memory issues that might have occurred if `data` was in dense storage format. Another benefit of storing the data efficiently is the reduced data transfer time when using GPUs. Although the transfer time for a single batch is small, we transfer `data` and `label` to the GPU every iteration so this time can become significant. We will time the transfer of the sparse `data` to GPU (if available) and compare to the time for its dense counterpart.
+
+
+```python
+ctx = mx.gpu() if mx.test_utils.list_gpus() else mx.cpu()
+```
+
+
+```python
+%%timeit
+data_on_ctx = data.as_in_context(ctx)
+data_on_ctx.wait_to_read()
+```
+
+```
+192 microseconds +- 51.1 microseconds per loop (mean +- std. dev. of 7 runs, 1 loop each)
+```
+
+
+```python
+print('sparse batch: {} MBs'.format(get_nbytes(data)/1000000))
+data = data.tostype('default')  # avoid timing this sparse to dense conversion
+print('dense batch: {} MBs'.format(get_nbytes(data)/1000000))
+```
+
+```
+sparse batch: 0.001348 MBs
+dense batch: 40.0 MBs
+```
+
+
+```python
+%%timeit
+data_on_ctx = data.as_in_context(ctx)
+data_on_ctx.wait_to_read()
+```
+
+```
+4 ms +- 36.8 microseconds per loop (mean +- std. dev. of 7 runs, 100 loops each)
+```
+
+Although results will change depending on system specifications and degree of sparsity, the sparse array can be transferred from CPU to GPU significantly faster than the dense array. We see a ~25x speed up for sparse vs dense for this specific batch of data.
+
+## Gluon Models for Sparse Data
+
+Our next step is to define a network. We have an input of 1,000,000 features and we want to make a binary prediction. We don't have any spatial or temporal relationships between features, so we'll use a 3 layer fully-connected network where the last layer has 1 output unit (with sigmoid activation). Since we're working with sparse data, we'd ideally like to use network operators that can exploit this sparsity for improved performance and memory efficiency.
+
+Gluon's [`nn.Dense`](https://mxnet.apache.org/versions/master/api/python/gluon/nn.html?highlight=dense#mxnet.gluon.nn.Dense) block can used with [`CSRNDArray`](https://mxnet.apache.org/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray) input arrays but it doesn't exploit the sparsity. Under the hood, [`Dense`](https://mxnet.apache.org/versions/master/api/python/gluon/nn.html?highlight=dense#mxnet.gluon.nn.Dense) uses the [`FullyConnected`](https://mxnet.apache.org/versions/master/api/python/ndarray/ndarray.html?highlight=fullyconnected#mxnet.ndarray.FullyConnected) operator which isn't optimized for [`CSRNDArray`](https://mxnet.apache.org/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray) arrays. We'll implement a `Block` that does exploit this sparsity, *but first*, let's just remind ourselves of the [`Dense`](https://mxnet.apache.org/versions/master/api/python/gluon/nn.html?highlight=dense#mxnet.gluon.nn.Dense) implementation by creating an equivalent `Block` called `FullyConnected`.
+
+
+```python
+class FullyConnected(mx.gluon.HybridBlock):
+    def __init__(self, in_units, units):
+        super(FullyConnected, self).__init__()
+        with self.name_scope():
+            self._units = units
+            self.weight = self.params.get('weight', shape=(units, in_units),
+                                          init=None, allow_deferred_init=True,
+                                          dtype='float32', stype='default', grad_stype='default')
+            self.bias = self.params.get('bias', shape=(units),
+                                          init='zeros', allow_deferred_init=True,
+                                          dtype='float32', stype='default', grad_stype='default')
+
+    def hybrid_forward(self, F, x, weight, bias):
+        return F.FullyConnected(x, weight, bias, num_hidden=self._units)
+```
+
+Our `weight` and `bias` parameters are dense (see `stype='default'`) and so are their gradients (see `grad_stype='default'`). Our `weight` parameter has shape `(units, in_units)` because the [`FullyConnected`](https://mxnet.apache.org/versions/master/api/python/ndarray/ndarray.html?highlight=fullyconnected#mxnet.ndarray.FullyConnected) operator performs the following calculation:
+
+$$Y = XW^T + b$$
+
+We could instead have created our parameter with shape `(in_units, units)` and avoid the transpose of the weight matrix. We'll see why this is so important later on. And instead of [`FullyConnected`](https://mxnet.apache.org/versions/master/api/python/ndarray/ndarray.html?highlight=fullyconnected#mxnet.ndarray.FullyConnected) we could have used [`mx.sparse.dot`](https://mxnet.apache.org/versions/master/api/python/ndarray/sparse.html?highlight=sparse.dot#mxnet.ndarray.sparse.dot) to fully exploit the sparsity of the [`CSRNDArray`](https://mxnet.apache.org/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray) input arrays. We'll now implement an alternative `Block` called `FullyConnectedSparse` using these ideas. We take `grad_stype` of the `weight` as an argument (called `weight_grad_stype`), since we're going to change this later on.
+
+
+```python
+class FullyConnectedSparse(mx.gluon.HybridBlock):
+    def __init__(self, in_units, units, weight_grad_stype='default'):
+        super(FullyConnectedSparse, self).__init__()
+        with self.name_scope():
+            self._units = units
+            self.weight = self.params.get('weight', shape=(in_units, units),
+                                          init=None, allow_deferred_init=True,
+                                          dtype='float32', stype='default', grad_stype=weight_grad_stype)
+            self.bias = self.params.get('bias', shape=(units),
+                                          init='zeros', allow_deferred_init=True,
+                                          dtype='float32', stype='default', grad_stype='default')
+
+    def hybrid_forward(self, F, x, weight, bias):
+        return F.sparse.dot(x, weight) + bias
+```
+
+Once again, we're using a dense `weight`, so both `FullyConnected` and `FullyConnectedSparse` will return dense array outputs. When constructing a multi-layer network therefore, only the first layer needs to be optimized for sparse inputs. Our first layer is often responsible for reducing the feature dimension dramatically (e.g. 1,000,000 features down to 128 features). We'll set the number of units in our 3 layers to be 128, 8 and 1.
+
+We will use [`timeit`](https://docs.python.org/2/library/timeit.html) to check the performance of these two variants, and analyse some [MXNet Profiler](https://mxnet.apache.org/versions/master/tutorials/python/profiler.html) traces that have been created from these benchmarks. Additionally, we will inspect the memory usage of the weights (and gradients) using the `print_memory_allocation` function defined below:
+
+
+```python
+def print_memory_allocation(net, block_idxs):
+    blocks = [net[block_idx] for block_idx in block_idxs]
+    weight_nbytes = [get_nbytes(b.weight.data()) for b in blocks]
+    weight_nbytes_pct = [b/sum(weight_nbytes) for b in weight_nbytes]
+    weight_grad_nbytes = [get_nbytes(b.weight.grad()) for b in blocks]
+    weight_grad_nbytes_pct = [b/sum(weight_grad_nbytes) for b in weight_grad_nbytes]
+    print("Memory Allocation for Weight:")
+    for i in range(len(block_idxs)):
+        print('{:7.3f} MBs ({:7.3f}%) for {:<40}'.format(weight_nbytes[i]/1000000,
+                                                              weight_nbytes_pct[i]*100,
+                                                              blocks[i].name))
+    print("Memory Allocation for Weight Gradient:")
+    for i in range(len(block_idxs)):
+        print('{:7.3f} MBs ({:7.3f}%) for {:<40}'.format(weight_grad_nbytes[i]/1000000,
+                                                              weight_grad_nbytes_pct[i]*100,
+                                                              blocks[i].name))
+```
+
+### Benchmark: `FullyConnected`
+
+We'll create a network using `nn.Dense` and benchmark the training.
+
+```python
+net = mx.gluon.nn.Sequential()
+net.add(
+    mx.gluon.nn.Dense(in_units=num_features, units=128),
+    mx.gluon.nn.Activation('sigmoid'),
+    mx.gluon.nn.Dense(in_units=128, units=8),
+    mx.gluon.nn.Activation('sigmoid'),
+    mx.gluon.nn.Dense(in_units=8, units=1),
+    mx.gluon.nn.Activation('sigmoid'),
+)
+net.initialize(ctx=ctx)
+trainer = mx.gluon.Trainer(net.collect_params(), 'sgd')
+loss_fn = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
+```
+
+
+```python
+%%timeit
+data_iter.reset()
+for batch in data_iter:
+    data = batch.data[0]
+    data = data.as_in_context(ctx)
+    label = batch.label[0].as_in_context(ctx)
+    with mx.autograd.record():
+        pred = net(data)
+        loss = loss_fn(pred, label)
+    loss.backward()
+    trainer.step(data.shape[0])
+    loss.wait_to_read()
+```
+
+```
+532 ms +- 3.47 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
+```
+
+![fully connected](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/ndarray/sparse/fully_connected.png)
+
+We can see the first [`FullyConnected`](https://mxnet.apache.org/versions/master/api/python/ndarray/ndarray.html?highlight=fullyconnected#mxnet.ndarray.FullyConnected) operator takes a significant proportion of time to execute (~25% of the iteration) because there are 1,000,000 input features (to 128). After this, the other [`FullyConnected`](https://mxnet.apache.org/versions/master/api/python/ndarray/ndarray.html?highlight=fullyconnected#mxnet.ndarray.FullyConnected) operators are much faster because they have input features of 128 (to 8) and 8 (to 1). On the backward pass, we see the same pattern (but in reverse). And finally, the parameter update step takes a large amount of time on the weight matrix of the first `FullyConnected` `Block`. When checking the memory allocations below, we can see the weight matrix of the first `FullyConnected` `Block` is responsible for 99.999% of the memory compared to other [`FullyConnected`](https://mxnet.apache.org/versions/master/api/python/ndarray/ndarray.html?highlight=fullyconnected#mxnet.ndarray.FullyConnected) weight matrices.
+
+
+```python
+print_memory_allocation(net, block_idxs=[0, 2, 4])
+```
+
+```
+Memory Allocation for Weight:
+512.000 MBs ( 99.999%) for dense0                                  
+  0.004 MBs (  0.001%) for dense1                                  
+  0.000 MBs (  0.000%) for dense2                                  
+Memory Allocation for Weight Gradient:
+512.000 MBs ( 99.999%) for dense0                                  
+  0.004 MBs (  0.001%) for dense1                                  
+  0.000 MBs (  0.000%) for dense2                                  
+```
+
+### Benchmark: `FullyConnectedSparse`
+
+We will now switch the first layer from `FullyConnected` to `FullyConnectedSparse`.
+
+
+```python
+net = mx.gluon.nn.Sequential()
+net.add(
+    FullyConnectedSparse(in_units=num_features, units=128),
+    mx.gluon.nn.Activation('sigmoid'),
+    FullyConnected(in_units=128, units=8),
+    mx.gluon.nn.Activation('sigmoid'),
+    FullyConnected(in_units=8, units=1),
+    mx.gluon.nn.Activation('sigmoid'),
+)
+net.initialize(ctx=ctx)
+trainer = mx.gluon.Trainer(net.collect_params(), 'sgd')
+loss_fn = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
+```
+
+
+```python
+%%timeit
+data_iter.reset()
+for batch in data_iter:
+    data = batch.data[0]
+    data = data.as_in_context(ctx)
+    label = batch.label[0].as_in_context(ctx)
+    with mx.autograd.record():
+        pred = net(data)
+        loss = loss_fn(pred, label)
+    loss.backward()
+    trainer.step(data.shape[0])
+    loss.wait_to_read()
+```
+
+```
+528 ms +- 22.7 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
+```
+
+![fully connected sparse](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/ndarray/sparse/fully_connected_sparse.png)
+
+We see the forward pass of `dot` and `add` (equivalent to [`FullyConnected`](https://mxnet.apache.org/versions/master/api/python/ndarray/ndarray.html?highlight=fullyconnected#mxnet.ndarray.FullyConnected) operator) is much faster now: 1.54ms vs 0.26ms. And this explains the reduction in overall time for the epoch. We didn't gain any benefit on the backward pass or parameter updates though.
+
+![fully connected sparse backward](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/ndarray/sparse/fully_connected_sparse_backward.png)
+
+Our first weight matrix and its gradients still take up the same amount of memory as before.
+
+
+```python
+print_memory_allocation(net, block_idxs=[0, 2, 4])
+```
+
+```
+Memory Allocation for Weight:
+512.000 MBs ( 99.999%) for fullyconnectedsparse0                   
+  0.004 MBs (  0.001%) for fullyconnected0                         
+  0.000 MBs (  0.000%) for fullyconnected1                         
+Memory Allocation for Weight Gradient:
+512.000 MBs ( 99.999%) for fullyconnectedsparse0                   
+  0.004 MBs (  0.001%) for fullyconnected0                         
+  0.000 MBs (  0.000%) for fullyconnected1                         
+```
+
+### Benchmark: `FullyConnectedSparse` with `grad_stype=row_sparse` 
+
+One useful outcome of sparsity in our [`CSRNDArray`](https://mxnet.apache.org/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray) input is that our gradients will be row sparse. We can exploit this fact to give us potentially huge memory savings and speed improvements. Creating our `weight` parameter with shape `(units, in_units)` and not transposing in the forward pass are important pre-requisite for obtaining row sparse gradients. Using [`nn.Dense`](https://mxnet.apache.org/versions/master/api/python/gluon/nn.html?highlight=dense#mxnet.gluon.nn.Dense) would have led to column sparse gradients which are not supported in MXNet. We previously had `grad_stype` of the `weight` parameter in the first layer set to `'default'` so we were handling the gradient as a dense array. Switching this to `'row_sparse'` can give us these potential improvements.
+
+
+```python
+net = mx.gluon.nn.Sequential()
+net.add(
+    FullyConnectedSparse(in_units=num_features, units=128, weight_grad_stype='row_sparse'),
+    mx.gluon.nn.Activation('sigmoid'),
+    FullyConnected(in_units=128, units=8),
+    mx.gluon.nn.Activation('sigmoid'),
+    FullyConnected(in_units=8, units=1),
+    mx.gluon.nn.Activation('sigmoid'),
+)
+net.initialize(ctx=ctx)
+trainer = mx.gluon.Trainer(net.collect_params(), 'sgd')
+loss_fn = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
+```
+
+
+```python
+%%timeit
+data_iter.reset()
+for batch in data_iter:
+    data = batch.data[0]
+    data = data.as_in_context(ctx)
+    label = batch.label[0].as_in_context(ctx)
+    with mx.autograd.record():
+        pred = net(data)
+        loss = loss_fn(pred, label)
+    loss.backward()
+    trainer.step(data.shape[0])
+    loss.wait_to_read()
+```
+
+```
+334 ms +- 16.9 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
+```
+
+![fully connected sparse grad backward](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/ndarray/sparse/fully_connected_sparse_grad_backward.png)
+
+We can see a huge reduction in the time taken for the backward pass and parameter update step: 3.99ms vs 0.18ms. And this reduces the overall time of the epoch significantly. Our gradient consumes a much smaller amount of memory and means only a subset of parameters need updating as part of the `sgd_update` step. Some optimizers don't support sparse gradients however, so reference the specific optimizer's documentation for more details.
+
+
+```python
+print_memory_allocation(net, block_idxs=[0, 2, 4])
+```
+
+```
+Memory Allocation for Weight:
+512.000 MBs ( 99.999%) for fullyconnectedsparse1                   
+  0.004 MBs (  0.001%) for fullyconnected2                         
+  0.000 MBs (  0.000%) for fullyconnected3                         
+Memory Allocation for Weight Gradient:
+  0.059 MBs ( 93.490%) for fullyconnectedsparse1                   
+  0.004 MBs (  6.460%) for fullyconnected2                         
+  0.000 MBs (  0.050%) for fullyconnected3                         
+```
+
+### Advanced: Sparse `weight`
+
+You can optimize this example further by setting the weight's `stype` to `'row_sparse'`, but whether `'row_sparse'` weights make sense or not will depends on your specific task. See [`contrib.SparseEmbedding `](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/contrib/nn/basic_layers.py#L118) for an example of this.
+
+## Conclusion
+
+As part of this tutorial, we learned how to write sparse data to disk in LibSVM format and load it back in sparse batches with the [`LibSVMIter`](https://mxnet.apache.org/api/python/io/io.html?highlight=libsvmiter). We learned how to improve the performance of Gluon's [`nn.Dense`](https://mxnet.apache.org/versions/master/api/python/gluon/nn.html?highlight=dense#mxnet.gluon.nn.Dense) on sparse arrays using `mx.nd.sparse`. And lastly, we set `grad_stype` to `'row_sparse'` to reduce the size of the gradient and speed up the parameter update step.
+
+## Recommended Next Steps
+
+* More detail on the [`CSRNDArray`](https://mxnet.apache.org/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray) sparse array format can be found in [this tutorial](https://mxnet.apache.org/versions/master/tutorials/sparse/csr.html).
+* More detail on the [`RowSparseNDArray`](https://mxnet.apache.org/api/python/ndarray/sparse.html?highlight=rowsparsendarray#mxnet.ndarray.sparse.RowSparseNDArray) sparse array format can be found in [this tutorial](https://mxnet.apache.org/versions/master/tutorials/sparse/row_sparse.html).
+* Users of the Module API can see a symbolic only example in [this tutorial](https://mxnet.apache.org/versions/master/tutorials/sparse/train.html).
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/python_docs/python/tutorials/packages/onnx/fine_tuning_gluon.md b/docs/python_docs/python/tutorials/packages/onnx/fine_tuning_gluon.md
new file mode 100644
index 000000000000..e8b0761dbc4d
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/onnx/fine_tuning_gluon.md
@@ -0,0 +1,456 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+
+# Fine-tuning an ONNX model
+
+Fine-tuning is a common practice in Transfer Learning. One can take advantage of the pre-trained weights of a network, and use them as an initializer for their own task. Indeed, quite often it is difficult to gather a dataset large enough that it would allow training from scratch deep and complex networks such as ResNet152 or VGG16. For example in an image classification task, using a network trained on a large dataset like ImageNet gives a good base from which the weights can be slightly updated, or fine-tuned, to predict accurately the new classes. We will see in this tutorial that this can be achieved even with a relatively small number of new training examples.
+
+
+[Open Neural Network Exchange (ONNX)](https://github.com/onnx/onnx) provides an open source format for AI models. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types.
+
+In this tutorial we will:
+
+- learn how to pick a specific layer from a pre-trained .onnx model file
+- learn how to load this model in Gluon and fine-tune it on a different dataset
+
+## Pre-requisite
+
+To run the tutorial you will need to have installed the following python modules:
+- [MXNet > 1.1.0](http://mxnet.apache.org/install/index.html)
+- [onnx](https://github.com/onnx/onnx)
+- matplotlib
+
+We recommend that you have first followed this tutorial:
+- [Inference using an ONNX model on MXNet Gluon](https://mxnet.apache.org/tutorials/onnx/inference_on_onnx_model.html)
+
+
+```python
+import json
+import logging
+import multiprocessing
+import os
+import tarfile
+
+logging.basicConfig(level=logging.INFO)
+
+import matplotlib.pyplot as plt
+import mxnet as mx
+from mxnet import gluon, nd, autograd
+from mxnet.gluon.data.vision.datasets import ImageFolderDataset
+from mxnet.gluon.data import DataLoader
+import mxnet.contrib.onnx as onnx_mxnet
+import numpy as np
+
+%matplotlib inline
+```
+
+
+### Downloading supporting files
+These are images and a vizualisation script:
+
+
+```python
+image_folder = "images"
+utils_file = "utils.py" # contain utils function to plot nice visualization
+images = ['wrench.jpg', 'dolphin.jpg', 'lotus.jpg']
+base_url = "https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/onnx/{}?raw=true"
+
+
+for image in images:
+    mx.test_utils.download(base_url.format("{}/{}".format(image_folder, image)), fname=image,dirname=image_folder)
+mx.test_utils.download(base_url.format(utils_file), fname=utils_file)
+
+from utils import *
+```
+
+## Downloading a model from the ONNX model zoo
+
+We download a pre-trained model, in our case the [GoogleNet](https://arxiv.org/abs/1409.4842) model, trained on [ImageNet](http://www.image-net.org/) from the [ONNX model zoo](https://github.com/onnx/models). The model comes packaged in an archive `tar.gz` file containing an `model.onnx` model file.
+
+
+```python
+base_url = "https://s3.amazonaws.com/download.onnx/models/opset_3/"
+current_model = "bvlc_googlenet"
+model_folder = "model"
+archive_file = "{}.tar.gz".format(current_model)
+archive_path = os.path.join(model_folder, archive_file)
+url = "{}{}".format(base_url, archive_file)
+onnx_path = os.path.join(model_folder, current_model, 'model.onnx')
+
+# Download the zipped model
+mx.test_utils.download(url, dirname = model_folder)
+
+# Extract the model
+if not os.path.isdir(os.path.join(model_folder, current_model)):
+    print('Extracting {} in {}...'.format(archive_path, model_folder))
+    tar = tarfile.open(archive_path, "r:gz")
+    tar.extractall(model_folder)
+    tar.close()
+    print('Model extracted.')
+```
+
+## Downloading the Caltech101 dataset
+
+The [Caltech101 dataset](http://www.vision.caltech.edu/Image_Datasets/Caltech101/) is made of pictures of objects belonging to 101 categories. About 40 to 800 images per category. Most categories have about 50 images.
+
+*L. Fei-Fei, R. Fergus and P. Perona. Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. IEEE. CVPR 2004, Workshop on Generative-Model
+Based Vision. 2004*
+
+
+```python
+data_folder = "data"
+dataset_name = "101_ObjectCategories"
+archive_file = "{}.tar.gz".format(dataset_name)
+archive_path = os.path.join(data_folder, archive_file)
+data_url = "https://s3.us-east-2.amazonaws.com/mxnet-public/"
+
+if not os.path.isfile(archive_path):
+    mx.test_utils.download("{}{}".format(data_url, archive_file), dirname = data_folder)
+    print('Extracting {} in {}...'.format(archive_file, data_folder))
+    tar = tarfile.open(archive_path, "r:gz")
+    tar.extractall(data_folder)
+    tar.close()
+    print('Data extracted.')
+```
+
+
+```python
+training_path = os.path.join(data_folder, dataset_name)
+testing_path = os.path.join(data_folder, "{}_test".format(dataset_name))
+```
+
+### Load the data using an ImageFolderDataset and a DataLoader
+
+We need to transform the images to a format accepted by the network
+
+
+```python
+EDGE = 224
+SIZE = (EDGE, EDGE)
+BATCH_SIZE = 32
+NUM_WORKERS = 6
+```
+
+We transform the dataset images using the following operations:
+- resize the shorter edge to 224, the longer edge will be greater or equal to 224
+- center and crop an area of size (224,224)
+- transpose the channels to be (3,224,224)
+
+
+```python
+def transform(image, label):
+    resized = mx.image.resize_short(image, EDGE)
+    cropped, crop_info = mx.image.center_crop(resized, SIZE)
+    transposed = nd.transpose(cropped, (2,0,1))
+    return transposed, label
+```
+
+The train and test dataset are created automatically by passing the root of each folder. The labels are built using the sub-folders names as label.
+```
+train_root
+__label1
+____image1
+____image2
+__label2
+____image3
+____image4
+```
+
+
+```python
+dataset_train = ImageFolderDataset(root=training_path)
+dataset_test = ImageFolderDataset(root=testing_path)
+```
+
+We use several worker processes, which means the dataloading and pre-processing is going to be distributed across multiple processes. This will help preventing our GPU from starving and waiting for the data to be copied across
+
+
+```python
+dataloader_train = DataLoader(dataset_train.transform(transform, lazy=False), batch_size=BATCH_SIZE, last_batch='rollover',
+                              shuffle=True, num_workers=NUM_WORKERS)
+dataloader_test = DataLoader(dataset_test.transform(transform, lazy=False), batch_size=BATCH_SIZE, last_batch='rollover',
+                             shuffle=False, num_workers=NUM_WORKERS)
+print("Train dataset: {} images, Test dataset: {} images".format(len(dataset_train), len(dataset_test)))
+```
+
+
+`Train dataset: 6996 images, Test dataset: 1681 images`<!--notebook-skip-line-->
+
+
+
+```python
+categories = dataset_train.synsets
+NUM_CLASSES = len(categories)
+BATCH_SIZE = 32
+```
+
+Let's plot the 1000th image to test the dataset
+
+
+```python
+N = 1000
+plt.imshow((transform(dataset_train[N][0], 0)[0].asnumpy().transpose((1,2,0))))
+plt.axis('off')
+print(categories[dataset_train[N][1]])
+```
+
+
+`Motorbikes`<!--notebook-skip-line-->
+
+
+
+![png](https://github.com/dmlc/web-data/blob/master/mxnet/doc/tutorials/onnx/motorbike.png?raw=true)<!--notebook-skip-line-->
+
+
+## Fine-Tuning the ONNX model
+
+### Getting the last layer
+
+Load the ONNX model
+
+
+```python
+sym, arg_params, aux_params = onnx_mxnet.import_model(onnx_path)
+```
+
+This function get the output of a given layer
+
+
+```python
+def get_layer_output(symbol, arg_params, aux_params, layer_name):
+    all_layers = symbol.get_internals()
+    net = all_layers[layer_name+'_output']
+    net = mx.symbol.Flatten(data=net)
+    new_args = dict({k:arg_params[k] for k in arg_params if k in net.list_arguments()})
+    new_aux = dict({k:aux_params[k] for k in aux_params if k in net.list_arguments()})
+    return (net, new_args, new_aux)
+```
+
+Here we print the different layers of the network to make it easier to pick the right one
+
+
+```python
+sym.get_internals()
+```
+
+
+
+
+```<Symbol group [data_0, pad0, conv1/7x7_s2_w_0, conv1/7x7_s2_b_0, convolution0, relu0, pad1, pooling0, lrn0, pad2, conv2/3x3_reduce_w_0, conv2/3x3_reduce_b_0, convolution1, relu1, pad3, conv2/3x3_w_0, conv2/3x3_b_0, convolution2, relu2, lrn1, pad4, pooling1, pad5, inception_3a/1x1_w_0, inception_3a/1x1_b_0, convolution3, relu3, pad6, .................................................................................inception_5b/pool_proj_b_0, convolution56, relu56, concat8, pad70, pooling13, dropout0, flatten0, loss3/classifier_w_0, linalg_gemm20, loss3/classifier_b_0, _mulscalar0, broadcast_add0, softmax0]>```<!--notebook-skip-line-->
+
+
+
+We get the network until the output of the `flatten0` layer
+
+
+```python
+new_sym, new_arg_params, new_aux_params = get_layer_output(sym, arg_params, aux_params, 'flatten0')
+```
+
+### Fine-tuning in gluon
+
+
+We can now take advantage of the features and pattern detection knowledge that our network learnt training on ImageNet, and apply that to the new Caltech101 dataset.
+
+
+We pick a context, fine-tuning on CPU will be **WAY** slower.
+
+
+```python
+ctx = mx.gpu() if mx.context.num_gpus() > 0 else mx.cpu()
+```
+
+We create a symbol block that is going to hold all our pre-trained layers, and assign the weights of the different pre-trained layers to the newly created SymbolBlock
+
+
+```python
+import warnings
+with warnings.catch_warnings():
+    warnings.simplefilter("ignore")
+    pre_trained = gluon.nn.SymbolBlock(outputs=new_sym, inputs=mx.sym.var('data_0'))
+net_params = pre_trained.collect_params()
+for param in new_arg_params:
+    if param in net_params:
+        net_params[param]._load_init(new_arg_params[param], ctx=ctx)
+for param in new_aux_params:
+    if param in net_params:
+        net_params[param]._load_init(new_aux_params[param], ctx=ctx)
+
+```
+
+We create the new dense layer with the right new number of classes (101) and initialize the weights
+
+
+```python
+dense_layer = gluon.nn.Dense(NUM_CLASSES)
+dense_layer.initialize(mx.init.Xavier(magnitude=2.24), ctx=ctx)
+```
+
+We add the SymbolBlock and the new dense layer to a HybridSequential network
+
+
+```python
+net = gluon.nn.HybridSequential()
+with net.name_scope():
+    net.add(pre_trained)
+    net.add(dense_layer)
+```
+
+### Loss
+Softmax cross entropy for multi-class classification
+
+
+```python
+softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()
+```
+
+### Trainer
+Initialize trainer with common training parameters
+
+
+```python
+LEARNING_RATE = 0.0005
+WDECAY = 0.00001
+MOMENTUM = 0.9
+```
+
+The trainer will retrain and fine-tune the entire network. If we use `dense_layer` instead of `net` in the cell below, the gradient updates would only be applied to the new last dense layer. Essentially we would be using the pre-trained network as a featurizer.
+
+
+```python
+trainer = gluon.Trainer(net.collect_params(), 'sgd', 
+                        {'learning_rate': LEARNING_RATE,
+                         'wd':WDECAY,
+                         'momentum':MOMENTUM})
+```
+
+### Evaluation loop
+
+We measure the accuracy in a non-blocking way, using `nd.array` to take care of the parallelisation that MXNet and Gluon offers.
+
+
+```python
+ def evaluate_accuracy_gluon(data_iterator, net):
+    num_instance = 0
+    sum_metric = nd.zeros(1,ctx=ctx, dtype=np.int32)
+    for i, (data, label) in enumerate(data_iterator):
+        data = data.astype(np.float32).as_in_context(ctx)
+        label = label.astype(np.int32).as_in_context(ctx)
+        output = net(data)
+        prediction = nd.argmax(output, axis=1).astype(np.int32)
+        num_instance += len(prediction)
+        sum_metric += (prediction==label).sum()
+    accuracy = (sum_metric.astype(np.float32)/num_instance)
+    return accuracy.asscalar()
+```
+
+
+```python
+%%time
+print("Untrained network Test Accuracy: {0:.4f}".format(evaluate_accuracy_gluon(dataloader_test, net)))
+```
+
+`Untrained network Test Accuracy: 0.0192`<!--notebook-skip-line-->
+
+
+
+### Training loop
+
+
+```python
+val_accuracy = 0
+for epoch in range(5):
+    for i, (data, label) in enumerate(dataloader_train):
+        data = data.astype(np.float32).as_in_context(ctx)
+        label = label.as_in_context(ctx)
+
+        if i%20==0 and i >0:
+            print('Batch [{0}] loss: {1:.4f}'.format(i, loss.mean().asscalar()))
+
+        with autograd.record():
+            output = net(data)
+            loss = softmax_cross_entropy(output, label)
+        loss.backward()
+        trainer.step(data.shape[0])
+
+    nd.waitall() # wait at the end of the epoch    
+    new_val_accuracy = evaluate_accuracy_gluon(dataloader_test, net)    
+    print("Epoch [{0}] Test Accuracy {1:.4f} ".format(epoch, new_val_accuracy))
+
+    # We perform early-stopping regularization, to prevent the model from overfitting
+    if val_accuracy > new_val_accuracy:
+        print('Validation accuracy is decreasing, stopping training')
+        break
+    val_accuracy = new_val_accuracy              
+```
+
+`Epoch 4, Test Accuracy 0.8942307829856873`<!--notebook-skip-line-->
+
+
+## Testing
+In the previous tutorial, we saw that the network trained on ImageNet couldn't classify correctly `wrench`, `dolphin`, `lotus` because these are not categories of the ImageNet dataset.
+
+Let's see if our network fine-tuned on Caltech101 is up for the task:
+
+
+```python
+# Number of predictions to show
+TOP_P = 3
+```
+
+
+```python
+# Convert img to format expected by the network
+def transform(img):
+    return nd.array(np.expand_dims(np.transpose(img, (2,0,1)),axis=0).astype(np.float32), ctx=ctx)
+```
+
+
+```python
+# Load and transform the test images
+caltech101_images_test = [plt.imread(os.path.join(image_folder, "{}".format(img))) for img in images]
+caltech101_images_transformed = [transform(img) for img in caltech101_images_test]
+```
+
+Helper function to run batches of data
+
+
+```python
+def run_batch(net, data):
+    results = []
+    for batch in data:
+        outputs = net(batch)
+        results.extend([o for o in outputs.asnumpy()])
+    return np.array(results)
+```
+
+
+```python
+result = run_batch(net, caltech101_images_transformed)
+```
+
+
+```python
+plot_predictions(caltech101_images_test, result, categories, TOP_P)
+```
+
+
+![png](https://github.com/dmlc/web-data/blob/master/mxnet/doc/tutorials/onnx/caltech101_correct.png?raw=true)<!--notebook-skip-line-->
+
+
+**Great!** The network classified these images correctly after being fine-tuned on a dataset that contains images of `wrench`, `dolphin` and `lotus`
\ No newline at end of file
diff --git a/docs/python_docs/python/tutorials/packages/onnx/index.rst b/docs/python_docs/python/tutorials/packages/onnx/index.rst
index a3483337c9a4..2981673ebdd3 100644
--- a/docs/python_docs/python/tutorials/packages/onnx/index.rst
+++ b/docs/python_docs/python/tutorials/packages/onnx/index.rst
@@ -22,30 +22,32 @@ ONNX
 
    .. card::
       :title: Fine-tuning an ONNX model with MXNet/Gluon
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/onnx/fine_tuning_gluon.html
+      :link: fine_tuning_gluon.html
 
       A tutorial on loading a model in Gluon and fine-tuning it on a dataset.
 
    .. card::
       :title: Running inference on MXNet/Gluon from an ONNX model
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/onnx/inference_on_onnx_model.html
+      :link: inference_on_onnx_model.html
 
       A tutorial on running inference from an ONNX model.
 
    .. card::
       :title: Importing an ONNX model into MXNet
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/onnx/super_resolution.html
+      :link: super_resolution.html
 
       How to load a pre-trained ONNX model file into MXNet.
 
    .. card::
       :title: Export ONNX Models
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/onnx/export_mxnet_to_onnx.html
+      :link: https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html
 
       How to export an MXNet model to the ONNX model format.
 
 
 .. toctree::
    :hidden:
+   :glob:
 
-   export_mxnet_to_onnx
+   *
+   Export ONNX Models <https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html>
diff --git a/docs/python_docs/python/tutorials/packages/onnx/inference_on_onnx_model.md b/docs/python_docs/python/tutorials/packages/onnx/inference_on_onnx_model.md
new file mode 100644
index 000000000000..9b1e45cb40c7
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/onnx/inference_on_onnx_model.md
@@ -0,0 +1,256 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+
+# Running inference on MXNet/Gluon from an ONNX model
+
+[Open Neural Network Exchange (ONNX)](https://github.com/onnx/onnx) provides an open source format for AI models. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types.
+
+In this tutorial we will:
+    
+- learn how to load a pre-trained .onnx model file into MXNet/Gluon
+- learn how to test this model using the sample input/output
+- learn how to test the model on custom images
+
+## Pre-requisite
+
+To run the tutorial you will need to have installed the following python modules:
+- [MXNet > 1.1.0](http://mxnet.apache.org/install/index.html)
+- [onnx](https://github.com/onnx/onnx) (follow the install guide)
+- matplotlib
+
+
+```python
+import numpy as np
+import mxnet as mx
+from mxnet.contrib import onnx as onnx_mxnet
+from mxnet import gluon, nd
+%matplotlib inline
+import matplotlib.pyplot as plt
+import tarfile, os
+import json
+import logging
+logging.basicConfig(level=logging.INFO)
+```
+
+### Downloading supporting files
+These are images and a vizualisation script
+
+
+```python
+image_folder = "images"
+utils_file = "utils.py" # contain utils function to plot nice visualization
+image_net_labels_file = "image_net_labels.json"
+images = ['apron.jpg', 'hammerheadshark.jpg', 'dog.jpg', 'wrench.jpg', 'dolphin.jpg', 'lotus.jpg']
+base_url = "https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/onnx/{}?raw=true"
+
+for image in images:
+    mx.test_utils.download(base_url.format("{}/{}".format(image_folder, image)), fname=image,dirname=image_folder)
+mx.test_utils.download(base_url.format(utils_file), fname=utils_file)
+mx.test_utils.download(base_url.format(image_net_labels_file), fname=image_net_labels_file)
+
+from utils import * 
+```
+
+## Downloading a model from the ONNX model zoo
+
+We download a pre-trained model, in our case the [GoogleNet](https://arxiv.org/abs/1409.4842) model, trained on [ImageNet](http://www.image-net.org/) from the [ONNX model zoo](https://github.com/onnx/models). The model comes packaged in an archive `tar.gz` file containing an `model.onnx` model file.
+
+
+```python
+base_url = "https://s3.amazonaws.com/download.onnx/models/opset_3/" 
+current_model = "bvlc_googlenet"
+model_folder = "model"
+archive = "{}.tar.gz".format(current_model)
+archive_file = os.path.join(model_folder, archive)
+url = "{}{}".format(base_url, archive)
+```
+
+Download and extract pre-trained model
+
+
+```python
+mx.test_utils.download(url, dirname = model_folder)
+if not os.path.isdir(os.path.join(model_folder, current_model)):
+    print('Extracting model...')
+    tar = tarfile.open(archive_file, "r:gz")
+    tar.extractall(model_folder)
+    tar.close()
+    print('Extracted')
+```
+
+The models have been pre-trained on ImageNet, let's load the label mapping of the 1000 classes.
+
+
+```python
+categories = json.load(open(image_net_labels_file, 'r'))
+```
+
+## Loading the model into MXNet Gluon
+
+
+```python
+onnx_path = os.path.join(model_folder, current_model, "model.onnx")
+```
+
+We get the symbol and parameter objects
+
+
+```python
+sym, arg_params, aux_params = onnx_mxnet.import_model(onnx_path)
+```
+
+We pick a context, CPU is fine for inference, switch to mx.gpu() if you want to use your GPU.
+
+
+```python
+ctx = mx.cpu()
+```
+
+We obtain the data names of the inputs to the model by using the model metadata API: 
+
+```python
+model_metadata = onnx_mxnet.get_model_metadata(onnx_path)
+print(model_metadata)
+```
+
+```
+{'output_tensor_data': [(u'gpu_0/softmax_1', (1L, 1000L))],
+ 'input_tensor_data': [(u'gpu_0/data_0', (1L, 3L, 224L, 224L))]}
+```
+
+```python
+data_names = [inputs[0] for inputs in model_metadata.get('input_tensor_data')]
+print(data_names)
+```
+
+
+```[u'data_0']```<!--notebook-skip-line-->
+
+And load them into a MXNet Gluon symbol block. 
+
+```python
+import warnings
+with warnings.catch_warnings():
+    warnings.simplefilter("ignore")
+    net = gluon.nn.SymbolBlock(outputs=sym, inputs=mx.sym.var('data_0'))
+net_params = net.collect_params()
+for param in arg_params:
+    if param in net_params:
+        net_params[param]._load_init(arg_params[param], ctx=ctx)
+for param in aux_params:
+    if param in net_params:
+        net_params[param]._load_init(aux_params[param], ctx=ctx)
+```
+
+We can now cache the computational graph through [hybridization](https://mxnet.apache.org/tutorials/gluon/hybrid.html) to gain some performance
+
+
+
+```python
+net.hybridize()
+```
+
+We can visualize the network (requires graphviz installed)
+
+
+```python
+mx.visualization.plot_network(sym,  node_attrs={"shape":"oval","fixedsize":"false"})
+```
+
+
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/onnx/network2.png?raw=true)<!--notebook-skip-line-->
+
+
+
+This is a helper function to run M batches of data of batch-size N through the net and collate the outputs into an array of shape (K, 1000) where K=MxN is the total number of examples (mumber of batches x batch-size) run through the network.
+
+
+```python
+def run_batch(net, data):
+    results = []
+    for batch in data:
+        outputs = net(batch)
+        results.extend([o for o in outputs.asnumpy()])
+    return np.array(results)
+```
+
+## Test using real images
+
+
+```python
+TOP_P = 3 # How many top guesses we show in the visualization
+```
+
+
+Transform function to set the data into the format the network expects, (N, 3, 224, 224) where N is the batch size.
+
+
+```python
+def transform(img):
+    return np.expand_dims(np.transpose(img, (2,0,1)),axis=0).astype(np.float32)
+```
+
+
+We load two sets of images in memory
+
+
+```python
+image_net_images = [plt.imread('{}/{}.jpg'.format(image_folder, path)) for path in ['apron', 'hammerheadshark','dog']]
+caltech101_images = [plt.imread('{}/{}.jpg'.format(image_folder, path)) for path in ['wrench', 'dolphin','lotus']]
+images = image_net_images + caltech101_images
+```
+
+And run them as a batch through the network to get the predictions
+
+```python
+batch = nd.array(np.concatenate([transform(img) for img in images], axis=0), ctx=ctx)
+result = run_batch(net, [batch])
+```
+
+
+```python
+plot_predictions(image_net_images, result[:3], categories, TOP_P)
+```
+
+
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/onnx/imagenet.png?raw=true)<!--notebook-skip-line-->
+
+
+**Well done!** Looks like it is doing a pretty good job at classifying pictures when the category is a ImageNet label
+
+Let's now see the results on the 3 other images
+
+
+```python
+plot_predictions(caltech101_images, result[3:7], categories, TOP_P)
+```
+
+
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/onnx/caltech101.png?raw=true)<!--notebook-skip-line-->
+
+
+**Hmm, not so good...**  Even though predictions are close, they are not accurate, which is due to the fact that the ImageNet dataset does not contain `wrench`, `dolphin`, or `lotus` categories and our network has been trained on ImageNet.
+
+Lucky for us, the [Caltech101 dataset](http://www.vision.caltech.edu/Image_Datasets/Caltech101/) has them, let's see how we can fine-tune our network to classify these categories correctly.
+
+We show that in our next tutorial:
+
+
+- [Fine-tuning an ONNX Model using the modern imperative MXNet/Gluon](http://mxnet.apache.org/tutorials/onnx/fine_tuning_gluon.html)
+    
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/python_docs/python/tutorials/packages/onnx/super_resolution.md b/docs/python_docs/python/tutorials/packages/onnx/super_resolution.md
new file mode 100644
index 000000000000..5d3ad021e25c
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/onnx/super_resolution.md
@@ -0,0 +1,140 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Importing an ONNX model into MXNet
+
+In this tutorial we will:
+
+- learn how to load a pre-trained ONNX model file into MXNet.
+- run inference in MXNet.
+
+## Prerequisites
+This example assumes that the following python packages are installed:
+- [mxnet](http://mxnet.apache.org/install/index.html)
+- [onnx](https://github.com/onnx/onnx) (follow the install guide)
+- Pillow - A Python Image Processing package and is required for input pre-processing. It can be installed with ```pip install Pillow```.
+- matplotlib
+
+
+```python
+from PIL import Image
+import numpy as np
+import mxnet as mx
+import mxnet.contrib.onnx as onnx_mxnet
+from mxnet.test_utils import download
+from matplotlib.pyplot import imshow
+```
+
+### Fetching the required files
+
+
+```python
+img_url = 'https://s3.amazonaws.com/onnx-mxnet/examples/super_res_input.jpg'
+download(img_url, 'super_res_input.jpg')
+model_url = 'https://s3.amazonaws.com/onnx-mxnet/examples/super_resolution.onnx'
+onnx_model_file = download(model_url, 'super_resolution.onnx')
+```
+
+## Loading the model into MXNet
+
+To completely describe a pre-trained model in MXNet, we need two elements: a symbolic graph, containing the model's network definition, and a binary file containing the model weights. You can import the ONNX model and get the symbol and parameters objects using ``import_model`` API. The paameter object is split into argument parameters and auxilliary parameters.
+
+
+```python
+sym, arg, aux = onnx_mxnet.import_model(onnx_model_file)
+```
+
+We can now visualize the imported model (graphviz needs to be installed)
+
+
+```python
+mx.viz.plot_network(sym, node_attrs={"shape":"oval","fixedsize":"false"})
+```
+
+
+
+
+![svg](https://s3.amazonaws.com/onnx-mxnet/examples/super_res_mxnet_model.png) <!--notebook-skip-line-->
+
+
+
+## Input Pre-processing
+
+We will transform the previously downloaded input image into an input tensor.
+
+
+```python
+img = Image.open('super_res_input.jpg').resize((224, 224))
+img_ycbcr = img.convert("YCbCr")
+img_y, img_cb, img_cr = img_ycbcr.split()
+test_image = np.array(img_y)[np.newaxis, np.newaxis, :, :]
+```
+
+## Run Inference using MXNet's Module API
+
+We will use MXNet's Module API to run the inference. For this we will need to create the module, bind it to the input data and assign the loaded weights from the two parameter objects - argument parameters and auxilliary parameters.
+
+To obtain the input data names we run the following line, which picks all the inputs of the symbol graph excluding the argument and auxiliary parameters:
+
+```python
+data_names = [graph_input for graph_input in sym.list_inputs()
+                      if graph_input not in arg and graph_input not in aux]
+print(data_names)
+```
+
+```['1']```
+
+```python
+mod = mx.mod.Module(symbol=sym, data_names=data_names, context=mx.cpu(), label_names=None)
+mod.bind(for_training=False, data_shapes=[(data_names[0],test_image.shape)], label_shapes=None)
+mod.set_params(arg_params=arg, aux_params=aux, allow_missing=True, allow_extra=True)
+```
+
+Module API's forward method requires batch of data as input. We will prepare the data in that format and feed it to the forward method.
+
+
+```python
+from collections import namedtuple
+Batch = namedtuple('Batch', ['data'])
+
+# forward on the provided data batch
+mod.forward(Batch([mx.nd.array(test_image)]))
+```
+
+To get the output of previous forward computation, you use ``module.get_outputs()`` method.
+It returns an ``ndarray`` that we convert to a ``numpy`` array and then to Pillow's image format
+
+
+```python
+output = mod.get_outputs()[0][0][0]
+img_out_y = Image.fromarray(np.uint8((output.asnumpy().clip(0, 255)), mode='L'))
+result_img = Image.merge(
+"YCbCr", [
+                img_out_y,
+                img_cb.resize(img_out_y.size, Image.BICUBIC),
+                img_cr.resize(img_out_y.size, Image.BICUBIC)
+]).convert("RGB")
+result_img.save("super_res_output.jpg")
+```
+
+You can now compare the input image and the resulting output image. As you will notice, the model was able to increase the spatial resolution from ``256x256`` to ``672x672``.
+
+| Input Image | Output Image | <!--notebook-skip-line-->
+| ----------- | ------------ | <!--notebook-skip-line-->
+| ![input](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/onnx/images/super_res_input.jpg?raw=true) | ![output](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/onnx/images/super_res_output.jpg?raw=true) | <!--notebook-skip-line-->
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
\ No newline at end of file
diff --git a/docs/python_docs/python/tutorials/packages/optimizer/optimizer.md b/docs/python_docs/python/tutorials/packages/optimizer/index.md
similarity index 99%
rename from docs/python_docs/python/tutorials/packages/optimizer/optimizer.md
rename to docs/python_docs/python/tutorials/packages/optimizer/index.md
index dfc56abedd02..b52f212b9a35 100644
--- a/docs/python_docs/python/tutorials/packages/optimizer/optimizer.md
+++ b/docs/python_docs/python/tutorials/packages/optimizer/index.md
@@ -15,7 +15,7 @@
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
 
-# Optimization Algorithms
+# Optimizers
 
 Deep learning models are comprised of a model architecture and the model parameters. The model architecture is chosen based on the task - for example Convolutional Neural Networks (CNNs) are very successful in handling image based tasks and Recurrent Neural Networks (RNNs) are better suited for sequential prediction tasks. However, the values of the model parameters are learned by solving an optimization problem during model training.
 
@@ -425,5 +425,5 @@ While optimization and optimizers play a significant role in deep learning model
 * Check out the guide to MXNet gluon [Loss functions](http://beta.mxnet.io/guide/packages/gluon/loss.html) and [custom losses](http://beta.mxnet.io/guide/packages/gluon/custom-loss/custom-loss.html) to learn about the loss functions optimized by these optimizers, see what loss functions are already implemented in MXNet and understand how to write your own custom loss functions.
 * Take a look at the [guide to parameter initialization](http://beta.mxnet.io/guide/packages/gluon/init.html) in MXNet to learn about what initialization schemes are already implemented, and how to implement your custom initialization schemes.
 * Also check out the [autograd guide](http://beta.mxnet.io/guide/packages/autograd/autograd.html) to learn about automatic differentiation and how gradients are automatically computed in MXNet.
-* Make sure to take a look at the [guide to scheduling learning rates](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/learning_rate_schedules.html) to learn how to create learning rate schedules to supercharge the convergence of your optimizer.
+* Make sure to take a look at the [guide to scheduling learning rates](https://mxnet.apache.org/versions/master/tutorials/gluon/learning_rate_schedules.html) to learn how to create learning rate schedules to supercharge the convergence of your optimizer.
 * Finally take a look at the [KVStore API](http://beta.mxnet.io/api/gluon-related/mxnet.kvstore.KVStore.html#mxnet.kvstore.KVStore) to learn how parameter values are synchronized over multiple devices.
diff --git a/docs/python_docs/python/tutorials/packages/viz/index.rst b/docs/python_docs/python/tutorials/packages/viz/index.rst
index 5bcb2ac1ca8b..c9254a983824 100644
--- a/docs/python_docs/python/tutorials/packages/viz/index.rst
+++ b/docs/python_docs/python/tutorials/packages/viz/index.rst
@@ -15,14 +15,14 @@
    specific language governing permissions and limitations
    under the License.
 
-Data
-====
+Visualization
+=============
 
 .. container:: cards
 
    .. card::
       :title: How to Visualize Neural Networks as Computation Graph
-      :link: https://mxnet.incubator.apache.org/api/faq/visualize_graph
+      :link: https://mxnet.apache.org/api/faq/visualize_graph
 
       A demonstration how to use ``mx.viz.plot_network`` for visualizing your neural networks.
 
@@ -36,3 +36,4 @@ References
    :glob:
 
    *
+   Visualize networks <https://mxnet.apache.org/api/faq/visualize_graph>
\ No newline at end of file
diff --git a/docs/python_docs/python/tutorials/performance/backend/amp.md b/docs/python_docs/python/tutorials/performance/backend/amp.md
index cf3dcf76de25..c862b51131f4 100644
--- a/docs/python_docs/python/tutorials/performance/backend/amp.md
+++ b/docs/python_docs/python/tutorials/performance/backend/amp.md
@@ -15,7 +15,7 @@
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
 
-# Using AMP (Automatic Mixed Precision) in MXNet
+# Using AMP: Automatic Mixed Precision
 
 Training Deep Learning networks is a very computationally intensive task. Novel model architectures tend to have increasing number of layers and parameters, which slows down training. Fortunately, new generations of training hardware as well as software optimizations, make it a feasible task.
 
@@ -104,7 +104,7 @@ def get_network():
     return net
 ```
 
-# Training in FP32
+## Training in FP32
 
 First, let us create the network.
 
diff --git a/docs/python_docs/python/tutorials/performance/backend/index.rst b/docs/python_docs/python/tutorials/performance/backend/index.rst
index f6ff5c8e3226..1113948e30d0 100644
--- a/docs/python_docs/python/tutorials/performance/backend/index.rst
+++ b/docs/python_docs/python/tutorials/performance/backend/index.rst
@@ -23,14 +23,13 @@ The following tutorials will help you learn how to use backend tools to boost pe
 
    .. card::
       :title: TensorRT
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/tensorrt/inference_with_trt.html
+      :link: tensorrt/index.html
 
       How to use NVIDIA's TensorRT to boost inference performance.
 
-
   .. card::
      :title: MKL-DNN
-     :link: mkl-dnn.html
+     :link: mkldnn/index.html
 
      How to get the most from your CPU by using Intel's MKL-DNN.
 
@@ -45,14 +44,20 @@ The following tutorials will help you learn how to use backend tools to boost pe
      :link: amp.html
 
      How to use Automatic Mixed Precision to boost performance.
+
+  .. card::
+     :title: MXNet Operator Profiler
+     :link: profilder.html
+
+     Use the profiler to monitor the performance of individual operators
 ..
 
 .. toctree::
    :hidden:
    :maxdepth: 1
 
-   tensorRt
-
-   ..
-      mkl-dnn
-      tvm
\ No newline at end of file
+   mkldnn/index
+   tensorrt/index
+   tvm
+   profiler
+   amp
\ No newline at end of file
diff --git a/docs/python_docs/python/tutorials/performance/backend/mkl-dnn.rst b/docs/python_docs/python/tutorials/performance/backend/mkldnn/index.rst
similarity index 71%
rename from docs/python_docs/python/tutorials/performance/backend/mkl-dnn.rst
rename to docs/python_docs/python/tutorials/performance/backend/mkldnn/index.rst
index 1841c1c05b32..ec8585599603 100644
--- a/docs/python_docs/python/tutorials/performance/backend/mkl-dnn.rst
+++ b/docs/python_docs/python/tutorials/performance/backend/mkldnn/index.rst
@@ -15,19 +15,26 @@
    specific language governing permissions and limitations
    under the License.
 
-Use Intel MKL-DNN to accelerate CPU performance
-===============================================
+Intel MKL-DNN
+=============
 
 .. container:: cards
 
    .. card::
       :title: MKL-DNN Installation and Verification
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/mkldnn/MKLDNN_README.html
+      :link: mkldnn_readme
 
       A guide on using MKL-DNN with MXNet.
 
    .. card::
-      :title: MKL-DNN Operators
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/mkldnn/operator_list.html
+      :title: MKL-DNN Quantization
+      :link: mkldnn_quantization
 
-      A list of operators that MKL-DNN supports.  
+      How to perform quantization with MKLDNN
+
+.. toctree::
+   :hidden:
+   :maxdepth: 1
+   :glob:
+
+   *
\ No newline at end of file
diff --git a/docs/python_docs/python/tutorials/performance/backend/mkldnn_quantization.md b/docs/python_docs/python/tutorials/performance/backend/mkldnn/mkldnn_quantization.md
similarity index 96%
rename from docs/python_docs/python/tutorials/performance/backend/mkldnn_quantization.md
rename to docs/python_docs/python/tutorials/performance/backend/mkldnn/mkldnn_quantization.md
index b2eaa2e44d53..da442e9fb42f 100644
--- a/docs/python_docs/python/tutorials/performance/backend/mkldnn_quantization.md
+++ b/docs/python_docs/python/tutorials/performance/backend/mkldnn/mkldnn_quantization.md
@@ -15,7 +15,7 @@
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
 
-# Quantize custom models with MKL-DNN backend
+# Quantize with MKL-DNN backend
 
 This document is to introduce how to quantize the customer models from FP32 to INT8 with Apache/MXNet toolkit and APIs under Intel CPU.
 
@@ -23,7 +23,7 @@ If you are not familiar with Apache/MXNet quantization flow, please reference [q
 
 ## Installation and Prerequisites
 
-Installing MXNet with MKLDNN backend is an easy and essential process. You can follow [How to build and install MXNet with MKL-DNN backend](https://mxnet.incubator.apache.org/tutorials/mkldnn/MKLDNN_README.html) to build and install MXNet from source. Also, you can install the release or nightly version via PyPi and pip directly by running:
+Installing MXNet with MKLDNN backend is an easy and essential process. You can follow [How to build and install MXNet with MKL-DNN backend](https://mxnet.apache.org/tutorials/mkldnn/MKLDNN_README.html) to build and install MXNet from source. Also, you can install the release or nightly version via PyPi and pip directly by running:
 
 ```
 # release version
@@ -38,7 +38,7 @@ A quantization script [imagenet_gen_qsym_mkldnn.py](https://github.com/apache/in
 
 ## Integrate Quantization Flow to Your Project
 
-Quantization flow works for both symbolic and Gluon models. If you're using Gluon, you can first refer [Saving and Loading Gluon Models](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/save_load_params.html) to hybridize your computation graph and export it as a symbol before running quantization.
+Quantization flow works for both symbolic and Gluon models. If you're using Gluon, you can first refer [Saving and Loading Gluon Models](https://mxnet.apache.org/versions/master/tutorials/gluon/save_load_params.html) to hybridize your computation graph and export it as a symbol before running quantization.
 
 In general, the quantization flow includes 4 steps. The user can get the acceptable accuracy from step 1 to 3 with minimum effort. Most of thing in this stage is out-of-box and the data scientists and researchers only need to focus on how to represent data and layers in their model. After a quantized model is generated, you may want to deploy it online and the performance will be the next key point. Thus, step 4, calibration, can improve the performance a lot by reducing lots of runtime calculation.
 
@@ -155,7 +155,7 @@ cqsym, cqarg_params, aux_params, collector = quantize_graph(sym=sym, arg_params=
                                                           quantized_dtype=quantized_dtype, logger=logger)
 
 # download imagenet validation dataset
-mx.test_utils.download('http://data.mxnet.io/data/val_256_q90.rec', 'dataset.rec')
+mx.test_utils.download('https://data.mxnet.io/data/val_256_q90.rec', 'dataset.rec')
 # set rgb info for data
 mean_std = {'mean_r': 123.68, 'mean_g': 116.779, 'mean_b': 103.939, 'std_r': 58.393, 'std_g': 57.12, 'std_b': 57.375}
 # set batch size
diff --git a/docs/python_docs/python/tutorials/performance/backend/MKLDNN_README.md b/docs/python_docs/python/tutorials/performance/backend/mkldnn/mkldnn_readme.md
similarity index 99%
rename from docs/python_docs/python/tutorials/performance/backend/MKLDNN_README.md
rename to docs/python_docs/python/tutorials/performance/backend/mkldnn/mkldnn_readme.md
index d8f78ecf71a7..65973fc0db43 100644
--- a/docs/python_docs/python/tutorials/performance/backend/MKLDNN_README.md
+++ b/docs/python_docs/python/tutorials/performance/backend/mkldnn/mkldnn_readme.md
@@ -15,14 +15,14 @@
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
 
-# Build/Install MXNet with MKL-DNN
+# Install MXNet with MKL-DNN
 
 A better training and inference performance is expected to be achieved on Intel-Architecture CPUs with MXNet built with [Intel MKL-DNN](https://github.com/intel/mkl-dnn) on multiple operating system, including Linux, Windows and MacOS.
 In the following sections, you will find build instructions for MXNet with Intel MKL-DNN on Linux, MacOS and Windows.
 
 Please find MKL-DNN optimized operators and other features in the [MKL-DNN operator list](../mkldnn/operator_list.md).
 
-The detailed performance data collected on Intel Xeon CPU with MXNet built with Intel MKL-DNN can be found [here](https://mxnet.incubator.apache.org/faq/perf.html#intel-cpu).
+The detailed performance data collected on Intel Xeon CPU with MXNet built with Intel MKL-DNN can be found [here](https://mxnet.apache.org/api/faq/perf#intel-cpu).
 
 
 <h2 id="0">Contents</h2>
diff --git a/docs/python_docs/python/tutorials/performance/backend/profiler.md b/docs/python_docs/python/tutorials/performance/backend/profiler.md
index d7abd1b4eb1c..6969517cbd58 100644
--- a/docs/python_docs/python/tutorials/performance/backend/profiler.md
+++ b/docs/python_docs/python/tutorials/performance/backend/profiler.md
@@ -44,9 +44,9 @@ print('Time for converting to numpy: %f sec' % (time() - start))
 
 From the timings above, it seems as if converting to numpy takes lot more time than multiplying two large matrices. That doesn't seem right.
 
-This is because, in MXNet, all operations are executed asynchronously. So, when `nd.dot(x, x)` returns, the matrix multiplication is not complete, it has only been queued for execution. However, [`asnumpy`](http://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html?highlight=asnumpy#mxnet.ndarray.NDArray.asnumpy) has to wait for the result to be calculated in order to convert it to numpy array on CPU, hence taking a longer time. Other examples of 'blocking' operations include [`asscalar`](http://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html?highlight=asscalar#mxnet.ndarray.NDArray.asscalar) and [`wait_to_read`](http://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html?highlight=wait_to_read#mxnet.ndarray.NDArray.wait_to_read).
+This is because, in MXNet, all operations are executed asynchronously. So, when `nd.dot(x, x)` returns, the matrix multiplication is not complete, it has only been queued for execution. However, [`asnumpy`](https://mxnet.apache.org/api/python/ndarray/ndarray.html?highlight=asnumpy#mxnet.ndarray.NDArray.asnumpy) has to wait for the result to be calculated in order to convert it to numpy array on CPU, hence taking a longer time. Other examples of 'blocking' operations include [`asscalar`](https://mxnet.apache.org/api/python/ndarray/ndarray.html?highlight=asscalar#mxnet.ndarray.NDArray.asscalar) and [`wait_to_read`](https://mxnet.apache.org/api/python/ndarray/ndarray.html?highlight=wait_to_read#mxnet.ndarray.NDArray.wait_to_read).
 
-While it is possible to use [`NDArray.waitall()`](http://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html?highlight=waitall#mxnet.ndarray.waitall) before and after operations to get running time of operations, it is not a scalable method to measure running time of multiple sets of operations, especially in a [`Sequential`](http://mxnet.incubator.apache.org/api/python/gluon/gluon.html?highlight=sequential#mxnet.gluon.nn.Sequential) or hybridized network.
+While it is possible to use [`NDArray.waitall()`](https://mxnet.apache.org/api/python/ndarray/ndarray.html?highlight=waitall#mxnet.ndarray.waitall) before and after operations to get running time of operations, it is not a scalable method to measure running time of multiple sets of operations, especially in a [`Sequential`](https://mxnet.apache.org/api/python/gluon/gluon.html?highlight=sequential#mxnet.gluon.nn.Sequential) or hybridized network.
 
 ## The correct way to profile
 
@@ -212,7 +212,7 @@ Let's zoom in to check the time taken by operators
 The above picture visualizes the sequence in which the operators were executed and the time taken by each operator.
 
 ### Profiling Custom Operators
-Should the existing NDArray operators fail to meet all your model's needs, MXNet supports [Custom Operators](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/customop.html) that you can define in Python. In `forward()` and `backward()` of a custom operator, there are two kinds of code: "pure Python" code (NumPy operators included) and "sub-operators" (NDArray operators called within `forward()` and `backward()`). With that said, MXNet can profile the execution time of both kinds without additional setup. Specifically, the MXNet profiler will break a single custom operator call into a pure Python event and several sub-operator events if there are any. Furthermore, all of those events will have a prefix in their names, which is, conveniently, the name of the custom operator you called.
+Should the existing NDArray operators fail to meet all your model's needs, MXNet supports [Custom Operators](https://mxnet.apache.org/versions/master/tutorials/gluon/customop.html) that you can define in Python. In `forward()` and `backward()` of a custom operator, there are two kinds of code: "pure Python" code (NumPy operators included) and "sub-operators" (NDArray operators called within `forward()` and `backward()`). With that said, MXNet can profile the execution time of both kinds without additional setup. Specifically, the MXNet profiler will break a single custom operator call into a pure Python event and several sub-operator events if there are any. Furthermore, all of those events will have a prefix in their names, which is, conveniently, the name of the custom operator you called.
 
 Let's try profiling custom operators with the following code example:
 
@@ -263,7 +263,7 @@ Here, we have created a custom operator called `MyAddOne`, and within its `forwa
 
 As shown by the screenshot, in the **Custom Operator** domain where all the custom operator-related events fall into, we can easily visualize the execution time of each segment of `MyAddOne`. We can tell that `MyAddOne::pure_python` is executed first. We also know that `CopyCPU2CPU` and `_plus_scalr` are two "sub-operators" of `MyAddOne` and the sequence in which they are executed.
 
-Please note that: to be able to see the previously described information, you need to set `profile_imperative` to `True` even when you are using custom operators in [symbolic mode](https://mxnet.incubator.apache.org/versions/master/tutorials/basic/symbol.html) (refer to the code snippet below, which is the symbolic-mode equivelent of the code example above). The reason is that within custom operators, pure python code and sub-operators are still called imperatively. 
+Please note that: to be able to see the previously described information, you need to set `profile_imperative` to `True` even when you are using custom operators in [symbolic mode](https://mxnet.apache.org/versions/master/tutorials/basic/symbol.html) (refer to the code snippet below, which is the symbolic-mode equivelent of the code example above). The reason is that within custom operators, pure python code and sub-operators are still called imperatively. 
 
 ```python 
 # Set profile_all to True
@@ -330,7 +330,7 @@ Nsight Compute is available in CUDA 10 toolkit, but can be used to profile code
 ### Further reading
 
 - [Examples using MXNet profiler.](https://github.com/apache/incubator-mxnet/tree/master/example/profiler)
-- [Some tips for improving MXNet performance.](https://mxnet.incubator.apache.org/faq/perf.html)
+- [Some tips for improving MXNet performance.](https://mxnet.apache.org/api/faq/perf)
 
 <!-- INSERT SOURCE DOWNLOAD BUTTONS -->
 
diff --git a/docs/python_docs/python/tutorials/packages/symbol/index.rst b/docs/python_docs/python/tutorials/performance/backend/tensorrt/index.rst
similarity index 70%
rename from docs/python_docs/python/tutorials/packages/symbol/index.rst
rename to docs/python_docs/python/tutorials/performance/backend/tensorrt/index.rst
index 20af8b0ca8a6..62be33a3894d 100644
--- a/docs/python_docs/python/tutorials/packages/symbol/index.rst
+++ b/docs/python_docs/python/tutorials/performance/backend/tensorrt/index.rst
@@ -15,27 +15,21 @@
    specific language governing permissions and limitations
    under the License.
 
-Symbol
-======
+TensorRT
+========
 
 .. container:: cards
 
    .. card::
-      :title: Symbol API Intro
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/basic/symbol.html
+      :title: Get started with TensorRT
+      :link: tensorrt
 
-      How to use MXNet's Symbol API.
-
-
-   .. card::
-      :title: Data Loading
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/basic/data.html
-
-      How to load data with the Symbol API.
+      A guide on using TensorRT with MXNet.
 
 
 .. toctree::
    :hidden:
+   :maxdepth: 1
+   :glob:
 
-   symbol
-   data
+   *
\ No newline at end of file
diff --git a/docs/python_docs/python/tutorials/performance/backend/tensorrt/tensorrt.md b/docs/python_docs/python/tutorials/performance/backend/tensorrt/tensorrt.md
new file mode 100644
index 000000000000..8dc19f183729
--- /dev/null
+++ b/docs/python_docs/python/tutorials/performance/backend/tensorrt/tensorrt.md
@@ -0,0 +1,143 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Optimized GPU Inference
+
+NVIDIA's TensorRT is a deep learning library that has been shown to provide large speedups when used for network inference. MXNet 1.3.0 is shipping with experimental integrated support for TensorRT. This means MXNet users can noew make use of this acceleration library to efficiently run their networks. In this blog post we'll see how to install, enable and run TensorRT with MXNet.  We'll also give some insight into what is happening behind the scenes in MXNet to enable TensorRT graph execution.
+
+## Installation and Prerequisites
+Installing MXNet with TensorRT integration is an easy process. First ensure that you are running Ubuntu 16.04, that you have updated your video drivers, and you have installed CUDA 9.0 or 9.2.  You'll need a Pascal or newer generation NVIDIA gpu.  You'll also have to download and install TensorRT libraries [instructions here](https://docs.nvidia.com/deeplearning/sdk/tensorrt-install-guide/index.html).  Once your these prerequisites installed and up-to-date you can install a special build of MXNet with TensorRT support enabled via PyPi and pip.  Install the appropriate version by running:
+
+To install with CUDA 9.0:
+```
+pip install mxnet-tensorrt-cu90
+```
+
+To install with CUDA 9.2:
+```
+pip install mxnet-tensorrt-cu92
+```
+
+If you are running an operating system other than Ubuntu 16.04, or just prefer to use a docker image with all prerequisites installed you can instead run:
+```
+nvidia-docker run -ti mxnet/tensorrt python
+```
+
+## Sample Models
+### Resnet 18
+TensorRT is an inference only library, so for the purposes of this blog post we will be using a pre-trained network, in this case a Resnet 18.  Resnets are a computationally intensive model architecture that are often used as a backbone for various computer vision tasks. Resnets are also commonly used as a reference for benchmarking deep learning library performance.  In this section we'll use a pretrained Resnet 18 from the [Gluon Model Zoo](https://mxnet.apache.org/versions/master/api/python/gluon/model_zoo.html) and compare its inference speed with TensorRT using MXNet with TensorRT integration turned off as a baseline.
+
+## Model Initialization
+```python
+import mxnet as mx
+from mxnet.gluon.model_zoo import vision
+import time
+import os
+
+batch_shape = (1, 3, 224, 224)
+resnet18 = vision.resnet18_v2(pretrained=True)
+resnet18.hybridize()
+resnet18.forward(mx.nd.zeros(batch_shape))
+resnet18.export('resnet18_v2')
+sym, arg_params, aux_params = mx.model.load_checkpoint('resnet18_v2', 0)
+```
+In our first section of code we import the modules needed to run MXNet, and to time our benchmark runs.  We then download a pretrained version of Resnet18, hybridize it, and load it symbolically.  It's important to note that the experimental version of TensorRT integration will only work with the symbolic MXNet API. If you're using Gluon, you must [hybridize](https://gluon.mxnet.io/chapter07_distributed-learning/hybridize.html) your computation graph and export it as a symbol before running inference.  This may be addressed in future releases of MXNet, but in general if you're concerned about getting the best inference performance possible from your models, it's a good practice to hybridize.
+
+## MXNet Baseline Performance
+```python
+# Create sample input
+input = mx.nd.zeros(batch_shape)
+
+# Execute with MXNet
+os.environ['MXNET_USE_TENSORRT'] = '0'
+executor = sym.simple_bind(ctx=mx.gpu(0), data=batch_shape, grad_req='null', force_rebind=True)
+executor.copy_params_from(arg_params, aux_params)
+
+# Warmup
+print('Warming up MXNet')
+for i in range(0, 10):
+    y_gen = executor.forward(is_train=False, data=input)
+    y_gen[0].wait_to_read()
+
+# Timing
+print('Starting MXNet timed run')
+start = time.process_time()
+for i in range(0, 10000):
+    y_gen = executor.forward(is_train=False, data=input)
+    y_gen[0].wait_to_read()
+end = time.time()
+print(time.process_time() - start)
+```
+
+For this experiment we are strictly interested in inference performance, so to simplify the benchmark we'll pass a tensor filled with zeros as an input.  We then bind a symbol as usual, returning a normal MXNet executor, and we run forward on this executor in a loop.  To help improve the accuracy of our benchmarks we run a small number of predictions as a warmup before running our timed loop.  This will ensure various lazy operations, which do not represent real-world usage, have completed before we measure relative performance improvement.  On a modern PC with a Titan V GPU the time taken for our MXNet baseline is **33.73s**.  Next we'll run the same model with TensorRT enabled, and see how the performance compares.
+
+While TensorRT integration remains experimental, we require users to set an environment variable to enable graph compilation.  You can see that at the start of this test we explicitly disabled TensorRT graph compilation support.  Next, we will run the same predictions using TensorRT.  This will require us to explicitly enable the MXNET_USE_TENSORRT environment variable, and we'll also use a slightly different API to bind our symbol.
+
+## MXNet with TensorRT Integration Performance
+```python
+# Execute with TensorRT
+print('Building TensorRT engine')
+os.environ['MXNET_USE_TENSORRT'] = '1'
+arg_params.update(aux_params)
+all_params = dict([(k, v.as_in_context(mx.gpu(0))) for k, v in arg_params.items()])
+executor = mx.contrib.tensorrt.tensorrt_bind(sym, ctx=mx.gpu(0), all_params=all_params,
+                                             data=batch_shape, grad_req='null', force_rebind=True)
+```
+
+Instead of calling simple_bind directly on our symbol to return an executor, we call an experimental API from the contrib module of MXNet. This call is meant to emulate the simple_bind call, and has many of the same arguments.  One difference to note is that this call takes params in the form of a single merged dictionary to assist with a tensor cleanup pass that we'll describe below.
+
+As TensorRT integration improves our goal is to gradually deprecate this tensorrt_bind call, and allow users to use TensorRT transparently (see the [Subgraph API](https://cwiki.apache.org/confluence/display/MXNET/MXNet+Graph+Optimization+and+Quantization+based+on+subgraph+and+MKL-DNN) for more information).  When this happens, the similarity between tensorrt_bind and simple_bind should make it easy to migrate your code.
+
+```
+#Warmup
+print('Warming up TensorRT')
+for i in range(0, 10):
+    y_gen = executor.forward(is_train=False, data=input)
+    y_gen[0].wait_to_read()
+
+# Timing
+print('Starting TensorRT timed run')
+start = time.process_time()
+for i in range(0, 10000):
+    y_gen = executor.forward(is_train=False, data=input)
+    y_gen[0].wait_to_read()
+end = time.time()
+print(time.process_time() - start)
+```
+We run timing with a warmup once more, and on the same machine, run in **18.99s**. A 1.8x speed improvement!  Speed improvements when using libraries like TensorRT can come from a variety of optimizations, but in this case our speedups are coming from a technique known as [operator fusion](http://dmlc.ml/2016/11/21/fusion-and-runtime-compilation-for-nnvm-and-tinyflow.html).
+
+## Operators and Subgraph Fusion
+
+Behind the scenes a number of interesting things are happening to make these optimizations possible, and most revolve around subgraphs and operator fusion.  As we can see in the images below, neural networks can be represented as computation graphs of operators (nodes in the graphs).  Operators can perform a variety of functions, but most run simple mathematics and linear algebra on tensors.  Often these operators run more efficiently if fused together into a large CUDA kernel that is executed on the GPU in a single call.  What the MXNet TensorRT integration enables is the ability to scan the entire computation graph, identify interesting subgraphs and optimize them with TensorRT.
+
+This means that when an MXNet computation graph is constructed, it will be parsed to determine if there are any sub-graphs that contain operator types that are supported by TensorRT.  If MXNet determines that there are one (or many) compatible subgraphs during the graph-parse, it will extract these graphs and replace them with special TensorRT nodes (visible in the diagrams below).  As the graph is executed, whenever a TensorRT node is reached the graph will make a library call to TensorRT.  TensorRT will then run its own implementation of the subgraph, potentially with many operators fused together into a single CUDA kernel.
+
+During this process MXNet will take care of passing along the input to the node and fetching the results.  MXNet will also attempt to remove any duplicated weights (parameters) during the graph initialization to keep memory usage low.  That is, if there are graph weights that are used only in the TensorRT sections of the graph, they will be removed from the MXNet set of parameters, and their memory will be freed.
+
+The examples below shows a Gluon implementation of a Wavenet before and after a TensorRT graph pass. You can see that for this network TensorRT supports a subset of the operators involved. This makes it an interesting example to visualize, as several subgraphs are extracted and replaced with special TensorRT nodes. The Resnet used as an example above would be less interesting to visualization. The entire Resnet graph is supported by TensorRT, and hence the optimized graph would be a single TensorRT node.  If your browser is unable to render svg files you can view the graphs in png format: [unoptimized](_static/tutorials/tensorrt/wavenet_unoptimized.png) and [optimized](_static/tutorials/tensorrt/wavenet_optimized.png).
+
+## Before
+![before](wavenet_unoptimized.svg)
+
+## After
+![after](wavenet_optimized.svg)
+
+## Future Work
+As mentioned above, MXNet developers are excited about the possibilities of [creating APIs](https://cwiki.apache.org/confluence/display/MXNET/MXNet+Graph+Optimization+and+Quantization+based+on+subgraph+and+MKL-DNN) that deal specifically with subgraphs.  As this work matures it will bring many improvements for TensorRT users.  We hope this will also be an opportunity for other acceleration libraries to integrate with MXNet.
+
+## Thanks
+Thank you to NVIDIA for contributing this feature, and specifically thanks to Marek Kolodziej and Clement Fuji-Tsang.  Thanks to Junyuan Xie and Jun Wu for the code reviews and design feedback, and to Aaron Markham for the copy review.
diff --git a/docs/python_docs/python/tutorials/performance/backend/tensorrt/wavenet_optimized.svg b/docs/python_docs/python/tutorials/performance/backend/tensorrt/wavenet_optimized.svg
new file mode 100644
index 000000000000..22bdbcdf440b
--- /dev/null
+++ b/docs/python_docs/python/tutorials/performance/backend/tensorrt/wavenet_optimized.svg
@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" id="export" class="graph" preserveAspectRatio="xMidYMid meet" style="" width="1460" height="3825"><rect id="background" fill="#fff" pointer-events="all" width="1460" height="3825"/><g id="origin" transform="translate(66.35292968750001, 66.35292968750001) scale(1)"><g id="clusters" class="clusters"/><g id="edge-paths" class="edge-paths"><defs><marker id="arrowhead-vee" viewBox="0 0 10 10" refX="9" refY="5" markerUnits="strokeWidth" markerWidth="8" markerHeight="6" orient="auto"><path d="M 0 0 L 10 5 L 0 10 L 4 5 z" style="stroke-width: 1; stroke-dasharray: 1, 0;"/></marker></defs><path class="edge-path" d="M650.9140625,22L650.9140625,28.166666666666668C650.9140625,34.333333333333336,650.9140625,46.666666666666664,650.9140625,59C650.9140625,71.33333333333333,650.9140625,83.66666666666667,650.9140625,89.83333333333333L650.9140625,96" marker-end="url(#arrowhead-vee)" id="edge-[0,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M650.9140625,170L650.9140625,176.16666666666666C650.9140625,182.33333333333334,650.9140625,194.66666666666666,650.9140625,207C650.9140625,219.33333333333334,650.9140625,231.66666666666666,650.9140625,237.83333333333334L650.9140625,244" marker-end="url(#arrowhead-vee)" id="edge-[2,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M682.9352692367504,296.00000381469727L690.530042072292,302.16667048136395C698.1248149078336,308.3333371480306,713.3143605789168,320.66667048136395,720.9091334144583,333.00000381469727C728.50390625,345.3333371480306,728.50390625,357.66667048136395,728.50390625,363.8333371480306L728.50390625,370.00000381469727" marker-end="url(#arrowhead-vee)" id="edge-[3,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M618.8928557632496,296.00000381469727L611.298082927708,302.16667048136395C603.7033100921664,308.3333371480306,588.5137644210832,320.66667048136395,580.9189915855417,338.25000381469727C573.32421875,355.8333371480306,573.32421875,378.66667048136395,573.32421875,401.50000381469727C573.32421875,424.3333371480306,573.32421875,447.16667048136395,583.2923583984375,464.75000381469727C593.260498046875,482.3333371480306,613.19677734375,494.66667048136395,623.1649169921875,500.8333371480306L633.133056640625,507.00000381469727" marker-end="url(#arrowhead-vee)" id="edge-[3,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M728.50390625,433.00000381469727L728.50390625,439.16667048136395C728.50390625,445.3333371480306,728.50390625,457.66667048136395,718.5357666015625,470.00000381469727C708.567626953125,482.3333371480306,688.63134765625,494.66667048136395,678.6632080078125,500.8333371480306L668.695068359375,507.00000381469727" marker-end="url(#arrowhead-vee)" id="edge-[4,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M686.3999837239584,529.0000038146973L706.2936062282987,535.1666704813639C726.187228732639,541.3333371480306,765.9744737413195,553.6666704813639,785.8680962456598,566.0000038146973C805.76171875,578.3333371480306,805.76171875,590.6666704813639,805.76171875,596.8333371480306L805.76171875,603.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[5,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M594.5234375,523.3265316031515L519.2278645833334,530.4387769717424C443.9322916666667,537.5510223403334,293.3411458333333,551.7755130775154,218.04557291666666,570.304425112773C142.75,588.8333371480306,142.75,611.6666704813639,142.75,634.5000038146973C142.75,657.3333371480306,142.75,680.1666704813639,230.921875,698.8127390216795C319.09375,717.458807561995,495.4375,731.9176113092925,583.609375,739.1470131829414L671.78125,746.3764150565901" marker-end="url(#arrowhead-vee)" id="edge-[5,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M594.5234375,522.5459402568208L504.6848958333333,529.7882841831336C414.8463541666667,537.0306281094463,235.16927083333334,551.5153159620718,145.33072916666666,570.1743265550512C55.4921875,588.8333371480306,55.4921875,611.6666704813639,55.4921875,634.5000038146973C55.4921875,657.3333371480306,55.4921875,680.1666704813639,55.4921875,699.5833371480306C55.4921875,719.0000038146973,55.4921875,735.0000038146973,55.4921875,751.0000038146973C55.4921875,767.0000038146973,55.4921875,783.0000038146973,55.4921875,802.4166704813639C55.4921875,821.8333371480306,55.4921875,844.6666704813639,55.4921875,867.5000038146973C55.4921875,890.3333371480306,55.4921875,913.1666704813639,55.4921875,932.5833371480306C55.4921875,952.0000038146973,55.4921875,968.0000038146973,55.4921875,984.0000038146973C55.4921875,1000.0000038146973,55.4921875,1016.0000038146973,55.4921875,1035.416670481364C55.4921875,1054.8333371480305,55.4921875,1077.666670481364,55.4921875,1100.5000038146973C55.4921875,1123.3333371480305,55.4921875,1146.166670481364,55.4921875,1165.5833371480305C55.4921875,1185.0000038146973,55.4921875,1201.0000038146973,55.4921875,1217.0000038146973C55.4921875,1233.0000038146973,55.4921875,1249.0000038146973,55.4921875,1268.416670481364C55.4921875,1287.8333371480305,55.4921875,1310.666670481364,55.4921875,1333.5000038146973C55.4921875,1356.3333371480305,55.4921875,1379.166670481364,55.4921875,1398.5833371480305C55.4921875,1418.0000038146973,55.4921875,1434.0000038146973,55.4921875,1450.0000038146973C55.4921875,1466.0000038146973,55.4921875,1482.0000038146973,55.4921875,1501.416670481364C55.4921875,1520.8333371480305,55.4921875,1543.666670481364,55.4921875,1566.5000038146973C55.4921875,1589.3333371480305,55.4921875,1612.166670481364,55.4921875,1631.5833371480305C55.4921875,1651.0000038146973,55.4921875,1667.0000038146973,55.4921875,1683.0000038146973C55.4921875,1699.0000038146973,55.4921875,1715.0000038146973,55.4921875,1734.416670481364C55.4921875,1753.8333371480305,55.4921875,1776.666670481364,55.4921875,1799.5000038146973C55.4921875,1822.3333371480305,55.4921875,1845.166670481364,55.4921875,1864.5833371480305C55.4921875,1884.0000038146973,55.4921875,1900.0000038146973,55.4921875,1916.0000038146973C55.4921875,1932.0000038146973,55.4921875,1948.0000038146973,55.4921875,1967.416670481364C55.4921875,1986.8333371480305,55.4921875,2009.666670481364,55.4921875,2032.5000038146973C55.4921875,2055.3333371480308,55.4921875,2078.166670481364,55.4921875,2095.7500038146973C55.4921875,2113.3333371480308,55.4921875,2125.666670481364,55.4921875,2131.8333371480308L55.4921875,2138.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[5,1]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M55.4921875,2201.0000038146973L55.4921875,2207.166670481364C55.4921875,2213.3333371480308,55.4921875,2225.666670481364,55.4921875,2239.8333371480308C55.4921875,2254.0000038146973,55.4921875,2270.0000038146973,55.4921875,2278.0000038146973L55.4921875,2286.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[6,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M55.4921875,2327.0000038146973L55.4921875,2335.0000038146973C55.4921875,2343.0000038146973,55.4921875,2359.0000038146973,132.40288628472223,2373.166670481364C209.31358506944446,2387.3333371480308,363.1349826388889,2399.666670481364,440.04568142361114,2405.8333371480308L516.9563802083334,2412.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[7,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M805.76171875,666.0000038146973L805.76171875,672.1666704813639C805.76171875,678.3333371480306,805.76171875,690.6666704813639,795.7935791015625,703.0000038146973C785.825439453125,715.3333371480306,765.88916015625,727.6666704813639,755.9210205078125,733.8333371480306L745.952880859375,740.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[8,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M762.9165852864584,762.0000038146973L782.3946804470487,768.1666704813639C801.872775607639,774.3333371480306,840.8289659288195,786.6666704813639,860.3070610894097,799.0000038146973C879.78515625,811.3333371480306,879.78515625,823.6666704813639,879.78515625,829.8333371480306L879.78515625,836.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[9,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M671.78125,757.2347347846451L608.8229166666666,764.1956129563205C545.8645833333334,771.1564911279958,419.9479166666667,785.0782474713466,356.9895833333333,803.4557923096886C294.03125,821.8333371480306,294.03125,844.6666704813639,294.03125,867.5000038146973C294.03125,890.3333371480306,294.03125,913.1666704813639,369.3268229166667,931.6955825166216C444.6223958333333,950.2244945518792,595.2135416666666,964.448985289061,670.5091145833334,971.5612306576521L745.8046875,978.6734760262431" marker-end="url(#arrowhead-vee)" id="edge-[9,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M671.78125,756.2237354392732L594.8190104166666,763.3531135018438C517.8567708333334,770.4824915644145,363.9322916666667,784.741247689556,286.9700520833333,803.2872924187932C210.0078125,821.8333371480306,210.0078125,844.6666704813639,210.0078125,867.5000038146973C210.0078125,890.3333371480306,210.0078125,913.1666704813639,210.0078125,932.5833371480306C210.0078125,952.0000038146973,210.0078125,968.0000038146973,210.0078125,984.0000038146973C210.0078125,1000.0000038146973,210.0078125,1016.0000038146973,210.0078125,1035.416670481364C210.0078125,1054.8333371480305,210.0078125,1077.666670481364,210.0078125,1100.5000038146973C210.0078125,1123.3333371480305,210.0078125,1146.166670481364,210.0078125,1165.5833371480305C210.0078125,1185.0000038146973,210.0078125,1201.0000038146973,210.0078125,1217.0000038146973C210.0078125,1233.0000038146973,210.0078125,1249.0000038146973,210.0078125,1268.416670481364C210.0078125,1287.8333371480305,210.0078125,1310.666670481364,210.0078125,1333.5000038146973C210.0078125,1356.3333371480305,210.0078125,1379.166670481364,210.0078125,1398.5833371480305C210.0078125,1418.0000038146973,210.0078125,1434.0000038146973,210.0078125,1450.0000038146973C210.0078125,1466.0000038146973,210.0078125,1482.0000038146973,210.0078125,1501.416670481364C210.0078125,1520.8333371480305,210.0078125,1543.666670481364,210.0078125,1566.5000038146973C210.0078125,1589.3333371480305,210.0078125,1612.166670481364,210.0078125,1631.5833371480305C210.0078125,1651.0000038146973,210.0078125,1667.0000038146973,210.0078125,1683.0000038146973C210.0078125,1699.0000038146973,210.0078125,1715.0000038146973,210.0078125,1734.416670481364C210.0078125,1753.8333371480305,210.0078125,1776.666670481364,210.0078125,1799.5000038146973C210.0078125,1822.3333371480305,210.0078125,1845.166670481364,210.0078125,1864.5833371480305C210.0078125,1884.0000038146973,210.0078125,1900.0000038146973,210.0078125,1916.0000038146973C210.0078125,1932.0000038146973,210.0078125,1948.0000038146973,210.0078125,1967.416670481364C210.0078125,1986.8333371480305,210.0078125,2009.666670481364,210.0078125,2032.5000038146973C210.0078125,2055.3333371480308,210.0078125,2078.166670481364,210.0078125,2101.0000038146973C210.0078125,2123.8333371480308,210.0078125,2146.666670481364,210.0078125,2169.5000038146973C210.0078125,2192.3333371480308,210.0078125,2215.166670481364,210.0078125,2232.7500038146973C210.0078125,2250.3333371480308,210.0078125,2262.666670481364,210.0078125,2268.8333371480308L210.0078125,2275.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[9,1]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M210.0078125,2338.0000038146973L210.0078125,2344.166670481364C210.0078125,2350.3333371480308,210.0078125,2362.666670481364,267.0675455729167,2375.0000038146973C324.1272786458333,2387.3333371480308,438.2467447916667,2399.666670481364,495.3064778645833,2405.8333371480308L552.3662109375,2412.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[10,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M879.78515625,899.0000038146973L879.78515625,905.1666704813639C879.78515625,911.3333371480306,879.78515625,923.6666704813639,869.8170166015625,936.0000038146973C859.848876953125,948.3333371480306,839.91259765625,960.6666704813639,829.9444580078125,966.8333371480306L819.976318359375,973.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[11,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M836.9400227864584,995.0000038146973L856.4181179470487,1001.1666704813639C875.896213107639,1007.3333371480306,914.8524034288195,1019.6666704813639,934.3304985894097,1032.0000038146973C953.80859375,1044.3333371480305,953.80859375,1056.666670481364,953.80859375,1062.8333371480305L953.80859375,1069.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[12,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M745.8046875,991.5163071013878L695.18359375,998.2635898869394C644.5625,1005.010872672491,543.3203125,1018.5054382435941,492.69921875,1036.6693876958122C442.078125,1054.8333371480305,442.078125,1077.666670481364,442.078125,1100.5000038146973C442.078125,1123.3333371480305,442.078125,1146.166670481364,505.0364583333333,1164.544215319706C567.9947916666666,1182.9217601580478,693.9114583333334,1196.8435165013987,756.8697916666666,1203.804394673074L819.828125,1210.7652728447495" marker-end="url(#arrowhead-vee)" id="edge-[12,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M745.8046875,990.0943573767025L681.1796875,997.0786317830349C616.5546875,1004.0629061893675,487.3046875,1018.0314550020324,422.6796875,1036.4323960750314C358.0546875,1054.8333371480305,358.0546875,1077.666670481364,358.0546875,1100.5000038146973C358.0546875,1123.3333371480305,358.0546875,1146.166670481364,358.0546875,1165.5833371480305C358.0546875,1185.0000038146973,358.0546875,1201.0000038146973,358.0546875,1217.0000038146973C358.0546875,1233.0000038146973,358.0546875,1249.0000038146973,358.0546875,1268.416670481364C358.0546875,1287.8333371480305,358.0546875,1310.666670481364,358.0546875,1333.5000038146973C358.0546875,1356.3333371480305,358.0546875,1379.166670481364,358.0546875,1398.5833371480305C358.0546875,1418.0000038146973,358.0546875,1434.0000038146973,358.0546875,1450.0000038146973C358.0546875,1466.0000038146973,358.0546875,1482.0000038146973,358.0546875,1501.416670481364C358.0546875,1520.8333371480305,358.0546875,1543.666670481364,358.0546875,1566.5000038146973C358.0546875,1589.3333371480305,358.0546875,1612.166670481364,358.0546875,1631.5833371480305C358.0546875,1651.0000038146973,358.0546875,1667.0000038146973,358.0546875,1683.0000038146973C358.0546875,1699.0000038146973,358.0546875,1715.0000038146973,358.0546875,1734.416670481364C358.0546875,1753.8333371480305,358.0546875,1776.666670481364,358.0546875,1799.5000038146973C358.0546875,1822.3333371480305,358.0546875,1845.166670481364,358.0546875,1864.5833371480305C358.0546875,1884.0000038146973,358.0546875,1900.0000038146973,358.0546875,1916.0000038146973C358.0546875,1932.0000038146973,358.0546875,1948.0000038146973,358.0546875,1967.416670481364C358.0546875,1986.8333371480305,358.0546875,2009.666670481364,358.0546875,2032.5000038146973C358.0546875,2055.3333371480308,358.0546875,2078.166670481364,358.0546875,2101.0000038146973C358.0546875,2123.8333371480308,358.0546875,2146.666670481364,358.0546875,2169.5000038146973C358.0546875,2192.3333371480308,358.0546875,2215.166670481364,358.0546875,2232.7500038146973C358.0546875,2250.3333371480308,358.0546875,2262.666670481364,358.0546875,2268.8333371480308L358.0546875,2275.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[12,1]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M358.0546875,2338.0000038146973L358.0546875,2344.166670481364C358.0546875,2350.3333371480308,358.0546875,2362.666670481364,396.0945095486111,2375.0000038146973C434.1343315972222,2387.3333371480308,510.2139756944444,2399.666670481364,548.2537977430555,2405.8333371480308L586.2936197916666,2412.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[13,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M953.80859375,1132.0000038146973L953.80859375,1138.166670481364C953.80859375,1144.3333371480305,953.80859375,1156.666670481364,943.8404541015625,1169.0000038146973C933.872314453125,1181.3333371480305,913.93603515625,1193.666670481364,903.9678955078125,1199.8333371480305L893.999755859375,1206.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[14,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M910.9634602864584,1228.0000038146973L930.4415554470487,1234.166670481364C949.919650607639,1240.3333371480305,988.8758409288195,1252.666670481364,1008.3539360894097,1265.0000038146973C1027.83203125,1277.3333371480305,1027.83203125,1289.666670481364,1027.83203125,1295.8333371480305L1027.83203125,1302.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[15,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M819.828125,1226.4610633450086L781.5442708333334,1232.8842200899567C743.2604166666666,1239.3073768349047,666.6927083333334,1252.153690324801,628.4088541666666,1269.9935137364157C590.125,1287.8333371480305,590.125,1310.666670481364,590.125,1333.5000038146973C590.125,1356.3333371480305,590.125,1379.166670481364,640.74609375,1397.330619933582C691.3671875,1415.4945693858006,792.609375,1428.9891349569036,843.23046875,1435.7364177424552L893.8515625,1442.4837005280067" marker-end="url(#arrowhead-vee)" id="edge-[15,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M819.828125,1224.3132280891036L767.5403645833334,1231.0943573767026C715.2526041666666,1237.8754866643014,610.6770833333334,1251.4377452394995,558.3893229166666,1269.635541193765C506.1015625,1287.8333371480305,506.1015625,1310.666670481364,506.1015625,1333.5000038146973C506.1015625,1356.3333371480305,506.1015625,1379.166670481364,506.1015625,1398.5833371480305C506.1015625,1418.0000038146973,506.1015625,1434.0000038146973,506.1015625,1450.0000038146973C506.1015625,1466.0000038146973,506.1015625,1482.0000038146973,506.1015625,1501.416670481364C506.1015625,1520.8333371480305,506.1015625,1543.666670481364,506.1015625,1566.5000038146973C506.1015625,1589.3333371480305,506.1015625,1612.166670481364,506.1015625,1631.5833371480305C506.1015625,1651.0000038146973,506.1015625,1667.0000038146973,506.1015625,1683.0000038146973C506.1015625,1699.0000038146973,506.1015625,1715.0000038146973,506.1015625,1734.416670481364C506.1015625,1753.8333371480305,506.1015625,1776.666670481364,506.1015625,1799.5000038146973C506.1015625,1822.3333371480305,506.1015625,1845.166670481364,506.1015625,1864.5833371480305C506.1015625,1884.0000038146973,506.1015625,1900.0000038146973,506.1015625,1916.0000038146973C506.1015625,1932.0000038146973,506.1015625,1948.0000038146973,506.1015625,1967.416670481364C506.1015625,1986.8333371480305,506.1015625,2009.666670481364,506.1015625,2032.5000038146973C506.1015625,2055.3333371480308,506.1015625,2078.166670481364,506.1015625,2101.0000038146973C506.1015625,2123.8333371480308,506.1015625,2146.666670481364,506.1015625,2169.5000038146973C506.1015625,2192.3333371480308,506.1015625,2215.166670481364,506.1015625,2232.7500038146973C506.1015625,2250.3333371480308,506.1015625,2262.666670481364,506.1015625,2268.8333371480308L506.1015625,2275.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[15,1]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M506.1015625,2338.0000038146973L506.1015625,2344.166670481364C506.1015625,2350.3333371480308,506.1015625,2362.666670481364,525.1214735243055,2375.0000038146973C544.1413845486112,2387.3333371480308,582.1812065972223,2399.666670481364,601.2011176215278,2405.8333371480308L620.2210286458334,2412.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[16,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1027.83203125,1365.0000038146973L1027.83203125,1371.166670481364C1027.83203125,1377.3333371480305,1027.83203125,1389.666670481364,1017.8638916015625,1402.0000038146973C1007.895751953125,1414.3333371480305,987.95947265625,1426.666670481364,977.9913330078125,1432.8333371480305L968.023193359375,1439.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[17,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M984.9868977864584,1461.0000038146973L1004.4649929470487,1467.166670481364C1023.943088107639,1473.3333371480305,1062.8992784288196,1485.666670481364,1082.3773735894097,1498.0000038146973C1101.85546875,1510.3333371480305,1101.85546875,1522.666670481364,1101.85546875,1528.8333371480305L1101.85546875,1535.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[18,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M901.6427408854166,1461.0000038146973L874.3975965711805,1467.166670481364C847.1524522569443,1473.3333371480305,792.6621636284722,1485.666670481364,765.417019314236,1503.2500038146973C738.171875,1520.8333371480305,738.171875,1543.666670481364,738.171875,1566.5000038146973C738.171875,1589.3333371480305,738.171875,1612.166670481364,776.4557291666666,1630.0064938929788C814.7395833333334,1647.8463173045936,891.3072916666666,1660.6926307944898,929.5911458333334,1667.115787539438L967.875,1673.538944284386" marker-end="url(#arrowhead-vee)" id="edge-[18,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M893.8515625,1459.141534157705L853.9010416666666,1465.6179457672035C813.9505208333334,1472.0943573767024,734.0494791666666,1485.0471805956997,694.0989583333334,1502.9402588718651C654.1484375,1520.8333371480305,654.1484375,1543.666670481364,654.1484375,1566.5000038146973C654.1484375,1589.3333371480305,654.1484375,1612.166670481364,654.1484375,1631.5833371480305C654.1484375,1651.0000038146973,654.1484375,1667.0000038146973,654.1484375,1683.0000038146973C654.1484375,1699.0000038146973,654.1484375,1715.0000038146973,654.1484375,1734.416670481364C654.1484375,1753.8333371480305,654.1484375,1776.666670481364,654.1484375,1799.5000038146973C654.1484375,1822.3333371480305,654.1484375,1845.166670481364,654.1484375,1864.5833371480305C654.1484375,1884.0000038146973,654.1484375,1900.0000038146973,654.1484375,1916.0000038146973C654.1484375,1932.0000038146973,654.1484375,1948.0000038146973,654.1484375,1967.416670481364C654.1484375,1986.8333371480305,654.1484375,2009.666670481364,654.1484375,2032.5000038146973C654.1484375,2055.3333371480308,654.1484375,2078.166670481364,654.1484375,2101.0000038146973C654.1484375,2123.8333371480308,654.1484375,2146.666670481364,654.1484375,2169.5000038146973C654.1484375,2192.3333371480308,654.1484375,2215.166670481364,654.1484375,2232.7500038146973C654.1484375,2250.3333371480308,654.1484375,2262.666670481364,654.1484375,2268.8333371480308L654.1484375,2275.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[18,1]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M654.1484375,2338.0000038146973L654.1484375,2344.166670481364C654.1484375,2350.3333371480308,654.1484375,2362.666670481364,654.1484375,2375.0000038146973C654.1484375,2387.3333371480308,654.1484375,2399.666670481364,654.1484375,2405.8333371480308L654.1484375,2412.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[19,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1101.85546875,1598.0000038146973L1101.85546875,1604.166670481364C1101.85546875,1610.3333371480305,1101.85546875,1622.666670481364,1091.8873291015625,1635.0000038146973C1081.919189453125,1647.3333371480305,1061.98291015625,1659.666670481364,1052.0147705078125,1665.8333371480305L1042.046630859375,1672.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[20,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1059.0103352864583,1694.0000038146973L1078.4884304470486,1700.166670481364C1097.966525607639,1706.3333371480305,1136.9227159288193,1718.666670481364,1156.4008110894097,1731.0000038146973C1175.87890625,1743.3333371480305,1175.87890625,1755.666670481364,1175.87890625,1761.8333371480305L1175.87890625,1768.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[21,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M992.6298828125,1694.0000038146973L974.8946940104166,1700.166670481364C957.1595052083334,1706.3333371480305,921.6891276041666,1718.666670481364,903.9539388020834,1736.2500038146973C886.21875,1753.8333371480305,886.21875,1776.666670481364,886.21875,1799.5000038146973C886.21875,1822.3333371480305,886.21875,1845.166670481364,913.4638943142362,1862.7500038146973C940.7090386284723,1880.3333371480305,995.1993272569445,1892.666670481364,1022.4444715711807,1898.8333371480305L1049.6896158854167,1905.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[21,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M973.37451171875,1694.0000038146973L944.8446451822916,1700.166670481364C916.3147786458334,1706.3333371480305,859.2550455729166,1718.666670481364,830.7251790364584,1736.2500038146973C802.1953125,1753.8333371480305,802.1953125,1776.666670481364,802.1953125,1799.5000038146973C802.1953125,1822.3333371480305,802.1953125,1845.166670481364,802.1953125,1864.5833371480305C802.1953125,1884.0000038146973,802.1953125,1900.0000038146973,802.1953125,1916.0000038146973C802.1953125,1932.0000038146973,802.1953125,1948.0000038146973,802.1953125,1967.416670481364C802.1953125,1986.8333371480305,802.1953125,2009.666670481364,802.1953125,2032.5000038146973C802.1953125,2055.3333371480308,802.1953125,2078.166670481364,802.1953125,2101.0000038146973C802.1953125,2123.8333371480308,802.1953125,2146.666670481364,802.1953125,2169.5000038146973C802.1953125,2192.3333371480308,802.1953125,2215.166670481364,802.1953125,2232.7500038146973C802.1953125,2250.3333371480308,802.1953125,2262.666670481364,802.1953125,2268.8333371480308L802.1953125,2275.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[21,1]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M802.1953125,2338.0000038146973L802.1953125,2344.166670481364C802.1953125,2350.3333371480308,802.1953125,2362.666670481364,783.1754014756945,2375.0000038146973C764.1554904513888,2387.3333371480308,726.1156684027777,2399.666670481364,707.0957573784722,2405.8333371480308L688.0758463541666,2412.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[22,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1175.87890625,1831.0000038146973L1175.87890625,1837.166670481364C1175.87890625,1843.3333371480305,1175.87890625,1855.666670481364,1165.9107666015625,1868.0000038146973C1155.942626953125,1880.3333371480305,1136.00634765625,1892.666670481364,1126.0382080078125,1898.8333371480305L1116.070068359375,1905.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[23,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1133.0337727864583,1927.0000038146973L1152.5118679470486,1933.166670481364C1171.989963107639,1939.3333371480305,1210.9461534288193,1951.666670481364,1230.4242485894097,1964.0000038146973C1249.90234375,1976.3333371480305,1249.90234375,1988.666670481364,1249.90234375,1994.8333371480305L1249.90234375,2001.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[24,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1089.4930013020833,1927.0000038146973L1084.5618760850693,1933.166670481364C1079.6307508680554,1939.3333371480305,1069.7685004340276,1951.666670481364,1064.837375217014,1969.2500038146973C1059.90625,1986.8333371480305,1059.90625,2009.666670481364,1059.90625,2032.5000038146973C1059.90625,2055.3333371480308,1059.90625,2078.166670481364,1075.632185218978,2099.166670481364C1091.3581204379561,2120.166670481364,1122.8099908759125,2139.3333371480308,1138.5359260948906,2148.916670481364L1154.2618613138686,2158.5000038146973" marker-end="url(#arrowhead-vee)" id="edge-[24,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1064.3616536458333,1927.0000038146973L1045.3417426215276,1933.166670481364C1026.3218315972222,1939.3333371480305,988.282009548611,1951.666670481364,969.2620985243055,1969.2500038146973C950.2421875,1986.8333371480305,950.2421875,2009.666670481364,950.2421875,2032.5000038146973C950.2421875,2055.3333371480308,950.2421875,2078.166670481364,950.2421875,2101.0000038146973C950.2421875,2123.8333371480308,950.2421875,2146.666670481364,950.2421875,2169.5000038146973C950.2421875,2192.3333371480308,950.2421875,2215.166670481364,950.2421875,2232.7500038146973C950.2421875,2250.3333371480308,950.2421875,2262.666670481364,950.2421875,2268.8333371480308L950.2421875,2275.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[24,1]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M950.2421875,2338.0000038146973L950.2421875,2344.166670481364C950.2421875,2350.3333371480308,950.2421875,2362.666670481364,912.2023654513888,2375.0000038146973C874.1625434027778,2387.3333371480308,798.0828993055557,2399.666670481364,760.0430772569445,2405.8333371480308L722.0032552083334,2412.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[25,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1249.90234375,2064.0000038146973L1249.90234375,2070.166670481364C1249.90234375,2076.3333371480308,1249.90234375,2088.666670481364,1239.0473169479926,2104.416670481364C1228.1922901459855,2120.166670481364,1206.4822365419707,2139.3333371480308,1195.6272097399635,2148.916670481364L1184.7721829379561,2158.5000038146973" marker-end="url(#arrowhead-vee)" id="edge-[26,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1184.1994753649635,2180.5000038146973L1194.5555523874696,2190.0833371480308C1204.9116294099756,2199.666670481364,1225.6237834549877,2218.8333371480308,1235.979860477494,2234.5833371480308C1246.3359375,2250.3333371480308,1246.3359375,2262.666670481364,1246.3359375,2268.8333371480308L1246.3359375,2275.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[27,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1160.4255246350365,2180.5000038146973L1150.0694476125304,2190.0833371480308C1139.7133705900244,2199.666670481364,1119.0012165450123,2218.8333371480308,1108.645139522506,2234.5833371480308C1098.2890625,2250.3333371480308,1098.2890625,2262.666670481364,1098.2890625,2268.8333371480308L1098.2890625,2275.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[27,1]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1098.2890625,2338.0000038146973L1098.2890625,2344.166670481364C1098.2890625,2350.3333371480308,1098.2890625,2362.666670481364,1041.2293294270833,2375.0000038146973C984.1695963541666,2387.3333371480308,870.0501302083334,2399.666670481364,812.9903971354166,2405.8333371480308L755.9306640625,2412.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[28,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1246.3359375,2338.0000038146973L1246.3359375,2344.166670481364C1246.3359375,2350.3333371480308,1246.3359375,2362.666670481364,1170.2562934027778,2375.0000038146973C1094.1766493055554,2387.3333371480308,942.017361111111,2399.666670481364,865.9377170138888,2405.8333371480308L789.8580729166666,2412.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[29,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M646.158935546875,2434.0000038146973L641.6799723307291,2440.166670481364C637.2010091145834,2446.3333371480308,628.2430826822916,2458.666670481364,623.7641194661459,2472.8333371480308C619.28515625,2487.0000038146973,619.28515625,2503.0000038146973,619.28515625,2519.0000038146973C619.28515625,2535.0000038146973,619.28515625,2551.0000038146973,619.28515625,2568.5833371480308C619.28515625,2586.166670481364,619.28515625,2605.3333371480308,619.28515625,2624.5000038146973C619.28515625,2643.666670481364,619.28515625,2662.8333371480308,619.28515625,2678.5833371480308C619.28515625,2694.3333371480308,619.28515625,2706.666670481364,619.28515625,2712.8333371480308L619.28515625,2719.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[30,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M684.6195475260416,2434.0000038146973L701.7018364800347,2440.166670481364C718.7841254340277,2446.3333371480308,752.9487033420138,2458.666670481364,770.0309922960068,2471.0000038146973C787.11328125,2483.3333371480308,787.11328125,2495.666670481364,787.11328125,2501.8333371480308L787.11328125,2508.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[30,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M623.6773274739584,2434.0000038146973L606.5950385199653,2440.166670481364C589.5127495659723,2446.3333371480308,555.3481716579862,2458.666670481364,538.265882703993,2472.8333371480308C521.18359375,2487.0000038146973,521.18359375,2503.0000038146973,521.18359375,2519.0000038146973C521.18359375,2535.0000038146973,521.18359375,2551.0000038146973,521.18359375,2568.5833371480308C521.18359375,2586.166670481364,521.18359375,2605.3333371480308,521.18359375,2624.5000038146973C521.18359375,2643.666670481364,521.18359375,2662.8333371480308,521.18359375,2682.0000038146973C521.18359375,2701.166670481364,521.18359375,2720.3333371480308,521.18359375,2739.5000038146973C521.18359375,2758.666670481364,521.18359375,2777.8333371480308,533.7869194878473,2793.5833371480308C546.3902452256945,2809.3333371480308,571.5968967013889,2821.666670481364,584.2002224392362,2827.8333371480308L596.8035481770834,2834.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[30,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M619.28515625,2760.0000038146973L619.28515625,2766.166670481364C619.28515625,2772.3333371480308,619.28515625,2784.666670481364,619.28515625,2797.0000038146973C619.28515625,2809.3333371480308,619.28515625,2821.666670481364,619.28515625,2827.8333371480308L619.28515625,2834.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[31,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M787.11328125,2530.0000038146973L787.11328125,2536.166670481364C787.11328125,2542.3333371480308,787.11328125,2554.666670481364,787.11328125,2567.0000038146973C787.11328125,2579.3333371480308,787.11328125,2591.666670481364,787.11328125,2597.8333371480308L787.11328125,2604.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[32,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M787.11328125,2645.0000038146973L787.11328125,2651.166670481364C787.11328125,2657.3333371480308,787.11328125,2669.666670481364,787.11328125,2682.0000038146973C787.11328125,2694.3333371480308,787.11328125,2706.666670481364,787.11328125,2712.8333371480308L787.11328125,2719.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[33,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M787.11328125,2760.0000038146973L787.11328125,2766.166670481364C787.11328125,2772.3333371480308,787.11328125,2784.666670481364,765.5520290798612,2797.0000038146973C743.9907769097222,2809.3333371480308,700.8682725694443,2821.666670481364,679.3070203993055,2827.8333371480308L657.7457682291666,2834.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[34,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M619.28515625,2856.0000038146973L619.28515625,2862.166670481364C619.28515625,2868.3333371480308,619.28515625,2880.666670481364,619.28515625,2893.0000038146973C619.28515625,2905.3333371480308,619.28515625,2917.666670481364,619.28515625,2923.8333371480308L619.28515625,2930.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[35,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M611.295654296875,2952.0000038146973L606.8166910807291,2958.166670481364C602.3377278645834,2964.3333371480308,593.3798014322916,2976.666670481364,588.9008382161459,2990.8333371480308C584.421875,3005.0000038146973,584.421875,3021.0000038146973,584.421875,3037.0000038146973C584.421875,3053.0000038146973,584.421875,3069.0000038146973,584.421875,3086.5833371480308C584.421875,3104.166670481364,584.421875,3123.3333371480308,584.421875,3142.5000038146973C584.421875,3161.666670481364,584.421875,3180.8333371480308,584.421875,3196.5833371480308C584.421875,3212.3333371480308,584.421875,3224.666670481364,584.421875,3230.8333371480308L584.421875,3237.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[36,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M649.7562662760416,2952.0000038146973L666.8385552300347,2958.166670481364C683.9208441840277,2964.3333371480308,718.0854220920138,2976.666670481364,735.1677110460068,2989.0000038146973C752.25,3001.3333371480308,752.25,3013.666670481364,752.25,3019.8333371480308L752.25,3026.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[36,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M588.8140462239584,2952.0000038146973L571.7317572699653,2958.166670481364C554.6494683159723,2964.3333371480308,520.4848904079862,2976.666670481364,503.4026014539931,2990.8333371480308C486.3203125,3005.0000038146973,486.3203125,3021.0000038146973,486.3203125,3037.0000038146973C486.3203125,3053.0000038146973,486.3203125,3069.0000038146973,486.3203125,3086.5833371480308C486.3203125,3104.166670481364,486.3203125,3123.3333371480308,486.3203125,3142.5000038146973C486.3203125,3161.666670481364,486.3203125,3180.8333371480308,486.3203125,3200.0000038146973C486.3203125,3219.166670481364,486.3203125,3238.3333371480308,486.3203125,3257.5000038146973C486.3203125,3276.666670481364,486.3203125,3295.8333371480308,498.92363823784723,3311.5833371480308C511.5269639756945,3327.3333371480308,536.7336154513889,3339.666670481364,549.3369411892362,3345.8333371480308L561.9402669270834,3352.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[36,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M584.421875,3278.0000038146973L584.421875,3284.166670481364C584.421875,3290.3333371480308,584.421875,3302.666670481364,584.421875,3315.0000038146973C584.421875,3327.3333371480308,584.421875,3339.666670481364,584.421875,3345.8333371480308L584.421875,3352.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[37,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M752.25,3048.0000038146973L752.25,3054.166670481364C752.25,3060.3333371480308,752.25,3072.666670481364,752.25,3085.0000038146973C752.25,3097.3333371480308,752.25,3109.666670481364,752.25,3115.8333371480308L752.25,3122.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[38,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M752.25,3163.0000038146973L752.25,3169.166670481364C752.25,3175.3333371480308,752.25,3187.666670481364,752.25,3200.0000038146973C752.25,3212.3333371480308,752.25,3224.666670481364,752.25,3230.8333371480308L752.25,3237.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[39,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M752.25,3278.0000038146973L752.25,3284.166670481364C752.25,3290.3333371480308,752.25,3302.666670481364,730.6887478298612,3315.0000038146973C709.1274956597222,3327.3333371480308,666.0049913194445,3339.666670481364,644.4437391493055,3345.8333371480308L622.8824869791666,3352.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[40,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M584.421875,3374.0000038146973L584.421875,3380.166670481364C584.421875,3386.3333371480308,584.421875,3398.666670481364,584.421875,3411.0000038146973C584.421875,3423.3333371480308,584.421875,3435.666670481364,584.421875,3441.8333371480308L584.421875,3448.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[41,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M584.421875,3470.0000038146973L584.421875,3476.166670481364C584.421875,3482.3333371480308,584.421875,3494.666670481364,584.421875,3507.0000038146973C584.421875,3519.3333371480308,584.421875,3531.666670481364,584.421875,3537.8333371480308L584.421875,3544.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[42,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M584.421875,3596.0000076293945L584.421875,3602.166674296061C584.421875,3608.333340962728,584.421875,3620.666674296061,584.421875,3633.0000076293945C584.421875,3645.333340962728,584.421875,3657.666674296061,584.421875,3663.833340962728L584.421875,3670.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[43,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/></g><g id="edge-labels" class="edge-labels"><g class="edge-label" transform="translate(668.140625,59)" style="opacity: 1;"><g transform="translate(-12.2265625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">input</tspan></text></g></g><g class="edge-label" transform="translate(691.4921875,207)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(769.08203125,333.00000381469727)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(613.90234375,401.50000381469727)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(762.66796875,470.00000381469727)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(846.33984375,566.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(183.328125,634.5000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(86.1328125,1333.5000038146973)" style="opacity: 1;"><g transform="translate(-25.640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">(1) ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(96.0703125,2238.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(96.0703125,2375.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(839.92578125,703.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(920.36328125,799.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(334.609375,867.5000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(240.6484375,1498.0000038146973)" style="opacity: 1;"><g transform="translate(-25.640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">(1) ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(244.171875,2375.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(913.94921875,936.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(994.38671875,1032.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(482.65625,1100.5000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(388.6953125,1635.0000038146973)" style="opacity: 1;"><g transform="translate(-25.640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">(1) ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(392.21875,2375.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (2)</tspan></text></g></g><g class="edge-label" transform="translate(987.97265625,1169.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(1068.41015625,1265.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(630.703125,1333.5000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(536.7421875,1731.0000038146973)" style="opacity: 1;"><g transform="translate(-25.640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">(1) ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(540.265625,2375.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (3)</tspan></text></g></g><g class="edge-label" transform="translate(1061.99609375,1402.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(1142.43359375,1498.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(778.75,1566.5000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(684.7890625,1868.0000038146973)" style="opacity: 1;"><g transform="translate(-25.640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">(1) ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(688.3125,2375.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (4)</tspan></text></g></g><g class="edge-label" transform="translate(1136.01953125,1635.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(1216.45703125,1731.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(926.796875,1799.5000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(832.8359375,1964.0000038146973)" style="opacity: 1;"><g transform="translate(-25.640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">(1) ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(836.359375,2375.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (5)</tspan></text></g></g><g class="edge-label" transform="translate(1210.04296875,1868.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(1290.48046875,1964.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1100.484375,2032.5000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(980.8828125,2101.0000038146973)" style="opacity: 1;"><g transform="translate(-25.640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">(1) ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(984.40625,2375.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (6)</tspan></text></g></g><g class="edge-label" transform="translate(1284.06640625,2101.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(1286.9140625,2238.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1128.9296875,2238.0000038146973)" style="opacity: 1;"><g transform="translate(-25.640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">(1) ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1132.453125,2375.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (7)</tspan></text></g></g><g class="edge-label" transform="translate(1280.5,2375.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (8)</tspan></text></g></g><g class="edge-label" transform="translate(659.86328125,2567.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(827.69140625,2471.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(555.34765625,2624.5000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(659.86328125,2797.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(827.69140625,2567.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(827.69140625,2682.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(821.27734375,2797.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (2)</tspan></text></g></g><g class="edge-label" transform="translate(659.86328125,2893.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(625,3085.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(792.828125,2989.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(520.484375,3142.5000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(625,3315.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(792.828125,3085.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(792.828125,3200.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(786.4140625,3315.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (2)</tspan></text></g></g><g class="edge-label" transform="translate(625,3411.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(625,3507.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(605.171875,3633.0000076293945)" style="opacity: 1;"><g transform="translate(-15.75,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output</tspan></text></g></g></g><g id="nodes" class="nodes"><g id="node-wavenet1_embedding0_fwd" class="node" transform="translate(650.9140625,133)" style="opacity: 1;"><g transform="translate(-77.234375,-37)"><g><g class="node-item node-item-operator node-item-operator-transform" transform="translate(0,0)"><path d="M5,0h63.28125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-68.28125a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 68); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Embedding</text><title>wavenet1_embedding0_fwd</title></g><g class="node-item node-item-input" transform="translate(68.28125,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_embedding0_weight" transform="translate(107.921875,0)"><path d="M0,0h41.546875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[512,24]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h154.46875a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-144.46875a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dtype</tspan><tspan> = float32</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">input_dim</tspan><tspan> = 512</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">output_dim</tspan><tspan> = 24</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">sparse_grad</tspan><tspan> = False</tspan></text></g><line class="node" x1="68.28125" y1="0" x2="68.28125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="107.921875" y1="0" x2="107.921875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="154.46875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h144.46875a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-144.46875a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_swapaxes0" class="node" transform="translate(650.9140625,270.00000190734863)" style="opacity: 1;"><g transform="translate(-49.5546875,-26.000001907348633)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h54.46875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-59.46875a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">SwapAxis</text><title>wavenet1_swapaxes0</title></g><g class="node-item node-item-input" transform="translate(59.46875,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h99.109375a0,0 0 0 1 0,0v25a5,5 0 0 1 -5,5h-89.109375a5,5 0 0 1 -5,-5v-25a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dim1</tspan><tspan> = 1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dim2</tspan><tspan> = 2</tspan></text></g><line class="node" x1="59.46875" y1="0" x2="59.46875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="99.109375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h89.109375a5,5 0 0 1 5,5v42a5,5 0 0 1 -5,5h-89.109375a5,5 0 0 1 -5,-5v-42a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue0_slice_axis0" class="node" transform="translate(728.50390625,401.50000381469727)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_causaldilatedresidue0_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 1</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-TRT_node10" class="node" transform="translate(650.9140625,518.0000038146973)" style="opacity: 1;"><g transform="translate(-56.390625,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h42.109375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-42.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_trt_op</text><title>TRT_node10</title></g><g class="node-item node-item-input" transform="translate(47.109375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(86.75,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><line class="node" x1="47.109375" y1="0" x2="47.109375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="86.75" y1="0" x2="86.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h102.78125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-102.78125a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis0" class="node" transform="translate(55.4921875,2169.5000038146973)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 510</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1__plusscalar0" class="node" transform="translate(55.4921875,2306.5000038146973)" style="opacity: 1;"><g transform="translate(-55.4921875,-20.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h66.34375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-71.34375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_plus_scalar</text><title>wavenet1__plusscalar0</title></g><g class="node-item node-item-input" transform="translate(71.34375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h110.984375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-100.984375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">scalar</tspan><tspan> = 0</tspan></text></g><line class="node" x1="71.34375" y1="0" x2="71.34375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="110.984375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h100.984375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-100.984375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue1_slice_axis0" class="node" transform="translate(805.76171875,634.5000038146973)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_causaldilatedresidue1_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 2</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-TRT_node9" class="node" transform="translate(728.171875,751.0000038146973)" style="opacity: 1;"><g transform="translate(-56.390625,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h42.109375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-42.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_trt_op</text><title>TRT_node9</title></g><g class="node-item node-item-input" transform="translate(47.109375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(86.75,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><line class="node" x1="47.109375" y1="0" x2="47.109375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="86.75" y1="0" x2="86.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h102.78125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-102.78125a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis1" class="node" transform="translate(210.0078125,2306.5000038146973)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis1</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 508</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue2_slice_axis0" class="node" transform="translate(879.78515625,867.5000038146973)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_causaldilatedresidue2_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 4</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-TRT_node8" class="node" transform="translate(802.1953125,984.0000038146973)" style="opacity: 1;"><g transform="translate(-56.390625,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h42.109375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-42.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_trt_op</text><title>TRT_node8</title></g><g class="node-item node-item-input" transform="translate(47.109375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(86.75,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><line class="node" x1="47.109375" y1="0" x2="47.109375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="86.75" y1="0" x2="86.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h102.78125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-102.78125a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis2" class="node" transform="translate(358.0546875,2306.5000038146973)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis2</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 504</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue3_slice_axis0" class="node" transform="translate(953.80859375,1100.5000038146973)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_causaldilatedresidue3_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 8</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-TRT_node7" class="node" transform="translate(876.21875,1217.0000038146973)" style="opacity: 1;"><g transform="translate(-56.390625,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h42.109375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-42.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_trt_op</text><title>TRT_node7</title></g><g class="node-item node-item-input" transform="translate(47.109375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(86.75,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><line class="node" x1="47.109375" y1="0" x2="47.109375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="86.75" y1="0" x2="86.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h102.78125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-102.78125a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis3" class="node" transform="translate(506.1015625,2306.5000038146973)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis3</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 496</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue4_slice_axis0" class="node" transform="translate(1027.83203125,1333.5000038146973)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_causaldilatedresidue4_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 16</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-TRT_node6" class="node" transform="translate(950.2421875,1450.0000038146973)" style="opacity: 1;"><g transform="translate(-56.390625,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h42.109375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-42.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_trt_op</text><title>TRT_node6</title></g><g class="node-item node-item-input" transform="translate(47.109375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(86.75,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><line class="node" x1="47.109375" y1="0" x2="47.109375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="86.75" y1="0" x2="86.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h102.78125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-102.78125a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis4" class="node" transform="translate(654.1484375,2306.5000038146973)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis4</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 480</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue5_slice_axis0" class="node" transform="translate(1101.85546875,1566.5000038146973)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_causaldilatedresidue5_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 32</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-TRT_node5" class="node" transform="translate(1024.265625,1683.0000038146973)" style="opacity: 1;"><g transform="translate(-56.390625,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h42.109375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-42.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_trt_op</text><title>TRT_node5</title></g><g class="node-item node-item-input" transform="translate(47.109375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(86.75,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><line class="node" x1="47.109375" y1="0" x2="47.109375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="86.75" y1="0" x2="86.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h102.78125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-102.78125a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis5" class="node" transform="translate(802.1953125,2306.5000038146973)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis5</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 448</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue6_slice_axis0" class="node" transform="translate(1175.87890625,1799.5000038146973)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_causaldilatedresidue6_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 64</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-TRT_node4" class="node" transform="translate(1098.2890625,1916.0000038146973)" style="opacity: 1;"><g transform="translate(-56.390625,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h42.109375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-42.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_trt_op</text><title>TRT_node4</title></g><g class="node-item node-item-input" transform="translate(47.109375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(86.75,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><line class="node" x1="47.109375" y1="0" x2="47.109375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="86.75" y1="0" x2="86.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h102.78125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-102.78125a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis6" class="node" transform="translate(950.2421875,2306.5000038146973)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis6</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 384</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue7_slice_axis0" class="node" transform="translate(1249.90234375,2032.5000038146973)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_causaldilatedresidue7_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 128</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-TRT_node3" class="node" transform="translate(1172.3125,2169.5000038146973)" style="opacity: 1;"><g transform="translate(-56.390625,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h42.109375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-42.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_trt_op</text><title>TRT_node3</title></g><g class="node-item node-item-input" transform="translate(47.109375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(86.75,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><line class="node" x1="47.109375" y1="0" x2="47.109375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="86.75" y1="0" x2="86.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h102.78125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-102.78125a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis7" class="node" transform="translate(1098.2890625,2306.5000038146973)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis7</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 256</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis8" class="node" transform="translate(1246.3359375,2306.5000038146973)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis8</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 0</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-TRT_node2" class="node" transform="translate(654.1484375,2423.0000038146973)" style="opacity: 1;"><g transform="translate(-147.5,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h42.109375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-42.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_trt_op</text><title>TRT_node2</title></g><g class="node-item node-item-input" transform="translate(47.109375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(86.75,0)"><path d="M0,0h26.03125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-26.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><g class="node-item node-item-input" transform="translate(112.78125,0)"><path d="M0,0h26.03125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-26.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(2)</text></g><g class="node-item node-item-input" transform="translate(138.8125,0)"><path d="M0,0h26.03125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-26.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(3)</text></g><g class="node-item node-item-input" transform="translate(164.84375,0)"><path d="M0,0h26.03125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-26.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(4)</text></g><g class="node-item node-item-input" transform="translate(190.875,0)"><path d="M0,0h26.03125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-26.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(5)</text></g><g class="node-item node-item-input" transform="translate(216.90625,0)"><path d="M0,0h26.03125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-26.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(6)</text></g><g class="node-item node-item-input" transform="translate(242.9375,0)"><path d="M0,0h26.03125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-26.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(7)</text></g><g class="node-item node-item-input" transform="translate(268.96875,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(8)</text></g><line class="node" x1="47.109375" y1="0" x2="47.109375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="86.75" y1="0" x2="86.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.78125" y1="0" x2="112.78125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="138.8125" y1="0" x2="138.8125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="164.84375" y1="0" x2="164.84375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="190.875" y1="0" x2="190.875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="216.90625" y1="0" x2="216.90625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="242.9375" y1="0" x2="242.9375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="268.96875" y1="0" x2="268.96875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h285a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-285a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0__greater_scalar0" class="node" transform="translate(619.28515625,2739.5000038146973)" style="opacity: 1;"><g transform="translate(-63.1015625,-20.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h81.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-86.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_greater_scalar</text><title>wavenet1_elu0__greater_scalar0</title></g><g class="node-item node-item-input" transform="translate(86.5625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h126.203125a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-116.203125a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">scalar</tspan><tspan> = 0</tspan></text></g><line class="node" x1="86.5625" y1="0" x2="86.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="126.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.203125a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-116.203125a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0_exp0" class="node" transform="translate(787.11328125,2519.0000038146973)" style="opacity: 1;"><g transform="translate(-35.5703125,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h26.5a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-26.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">exp</text><title>wavenet1_elu0_exp0</title></g><g class="node-item node-item-input" transform="translate(31.5,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-34.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><line class="node" x1="31.5" y1="0" x2="31.5" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h61.140625a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-61.140625a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0__minusscalar0" class="node" transform="translate(787.11328125,2624.5000038146973)" style="opacity: 1;"><g transform="translate(-60.3359375,-20.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h76.03125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-81.03125a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_minus_scalar</text><title>wavenet1_elu0__minusscalar0</title></g><g class="node-item node-item-input" transform="translate(81.03125,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h120.671875a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-110.671875a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">scalar</tspan><tspan> = 1.0</tspan></text></g><line class="node" x1="81.03125" y1="0" x2="81.03125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="120.671875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h110.671875a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-110.671875a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0__mulscalar0" class="node" transform="translate(787.11328125,2739.5000038146973)" style="opacity: 1;"><g transform="translate(-54.7265625,-20.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h64.8125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-69.8125a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_mul_scalar</text><title>wavenet1_elu0__mulscalar0</title></g><g class="node-item node-item-input" transform="translate(69.8125,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h109.453125a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-99.453125a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">scalar</tspan><tspan> = 1.0</tspan></text></g><line class="node" x1="69.8125" y1="0" x2="69.8125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="109.453125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h99.453125a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-99.453125a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0_where0" class="node" transform="translate(619.28515625,2845.0000038146973)" style="opacity: 1;"><g transform="translate(-68.0234375,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h39.34375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.34375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">where</text><title>wavenet1_elu0_where0</title></g><g class="node-item node-item-input" transform="translate(44.34375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(83.984375,0)"><path d="M0,0h26.03125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-26.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><g class="node-item node-item-input" transform="translate(110.015625,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(2)</text></g><line class="node" x1="44.34375" y1="0" x2="44.34375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="83.984375" y1="0" x2="83.984375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="110.015625" y1="0" x2="110.015625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h126.046875a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-126.046875a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-TRT_node1" class="node" transform="translate(619.28515625,2941.0000038146973)" style="opacity: 1;"><g transform="translate(-43.375,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h42.109375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-42.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_trt_op</text><title>TRT_node1</title></g><g class="node-item node-item-input" transform="translate(47.109375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-34.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><line class="node" x1="47.109375" y1="0" x2="47.109375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h76.75a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-76.75a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0__greater_scalar0" class="node" transform="translate(584.421875,3257.5000038146973)" style="opacity: 1;"><g transform="translate(-63.1015625,-20.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h81.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-86.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_greater_scalar</text><title>wavenet1_elu0__greater_scalar0</title></g><g class="node-item node-item-input" transform="translate(86.5625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h126.203125a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-116.203125a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">scalar</tspan><tspan> = 0</tspan></text></g><line class="node" x1="86.5625" y1="0" x2="86.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="126.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.203125a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-116.203125a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0_exp0" class="node" transform="translate(752.25,3037.0000038146973)" style="opacity: 1;"><g transform="translate(-35.5703125,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h26.5a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-26.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">exp</text><title>wavenet1_elu0_exp0</title></g><g class="node-item node-item-input" transform="translate(31.5,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-34.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><line class="node" x1="31.5" y1="0" x2="31.5" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h61.140625a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-61.140625a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0__minusscalar0" class="node" transform="translate(752.25,3142.5000038146973)" style="opacity: 1;"><g transform="translate(-60.3359375,-20.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h76.03125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-81.03125a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_minus_scalar</text><title>wavenet1_elu0__minusscalar0</title></g><g class="node-item node-item-input" transform="translate(81.03125,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h120.671875a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-110.671875a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">scalar</tspan><tspan> = 1.0</tspan></text></g><line class="node" x1="81.03125" y1="0" x2="81.03125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="120.671875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h110.671875a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-110.671875a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0__mulscalar0" class="node" transform="translate(752.25,3257.5000038146973)" style="opacity: 1;"><g transform="translate(-54.7265625,-20.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h64.8125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-69.8125a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_mul_scalar</text><title>wavenet1_elu0__mulscalar0</title></g><g class="node-item node-item-input" transform="translate(69.8125,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h109.453125a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-99.453125a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">scalar</tspan><tspan> = 1.0</tspan></text></g><line class="node" x1="69.8125" y1="0" x2="69.8125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="109.453125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h99.453125a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-99.453125a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0_where0" class="node" transform="translate(584.421875,3363.0000038146973)" style="opacity: 1;"><g transform="translate(-68.0234375,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h39.34375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.34375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">where</text><title>wavenet1_elu0_where0</title></g><g class="node-item node-item-input" transform="translate(44.34375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(83.984375,0)"><path d="M0,0h26.03125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-26.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><g class="node-item node-item-input" transform="translate(110.015625,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(2)</text></g><line class="node" x1="44.34375" y1="0" x2="44.34375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="83.984375" y1="0" x2="83.984375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="110.015625" y1="0" x2="110.015625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h126.046875a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-126.046875a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-TRT_node0" class="node" transform="translate(584.421875,3459.0000038146973)" style="opacity: 1;"><g transform="translate(-43.375,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h42.109375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-42.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_trt_op</text><title>TRT_node0</title></g><g class="node-item node-item-input" transform="translate(47.109375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-34.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><line class="node" x1="47.109375" y1="0" x2="47.109375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h76.75a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-76.75a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_swapaxes1" class="node" transform="translate(584.421875,3570.000005722046)" style="opacity: 1;"><g transform="translate(-49.5546875,-26.000001907348633)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h54.46875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-59.46875a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">SwapAxis</text><title>wavenet1_swapaxes1</title></g><g class="node-item node-item-input" transform="translate(59.46875,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h99.109375a0,0 0 0 1 0,0v25a5,5 0 0 1 -5,5h-89.109375a5,5 0 0 1 -5,-5v-25a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dim1</tspan><tspan> = 1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dim2</tspan><tspan> = 2</tspan></text></g><line class="node" x1="59.46875" y1="0" x2="59.46875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="99.109375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h89.109375a5,5 0 0 1 5,5v42a5,5 0 0 1 -5,5h-89.109375a5,5 0 0 1 -5,-5v-42a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g class="node graph-input" transform="translate(650.9140625,11)" style="opacity: 1;"><g transform="translate(-17.875,-11)"><g><g class="node-item graph-item-input" transform="translate(0,0)"><path d="M5,0h25.75a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-25.75a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">data</text></g><path class="node border" d="M5,0h25.75a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-25.75a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g class="node" transform="translate(584.421875,3681.0000076293945)" style="opacity: 1;"><g transform="translate(-58.914066314697266,-11)"><g><g class="node-item graph-item-output" transform="translate(0,0)"><path d="M5,0h107.828125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-107.828125a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">wavenet1_swapaxes1</text></g><path class="node border" d="M5,0h107.828125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-107.828125a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g></g></g></svg>
\ No newline at end of file
diff --git a/docs/python_docs/python/tutorials/performance/backend/tensorrt/wavenet_unoptimized.svg b/docs/python_docs/python/tutorials/performance/backend/tensorrt/wavenet_unoptimized.svg
new file mode 100644
index 000000000000..bb39e9ebbb7d
--- /dev/null
+++ b/docs/python_docs/python/tutorials/performance/backend/tensorrt/wavenet_unoptimized.svg
@@ -0,0 +1,17 @@
+<svg xmlns="http://www.w3.org/2000/svg" id="export" class="graph" preserveAspectRatio="xMidYMid meet" style="" width="1849" height="7587"><rect id="background" fill="#fff" pointer-events="all" width="1849" height="7587"/><g id="origin" transform="translate(84.00605468750001, 84.00605468750001) scale(1)"><g id="clusters" class="clusters"/><g id="edge-paths" class="edge-paths"><defs><marker id="arrowhead-vee" viewBox="0 0 10 10" refX="9" refY="5" markerUnits="strokeWidth" markerWidth="8" markerHeight="6" orient="auto"><path d="M 0 0 L 10 5 L 0 10 L 4 5 z" style="stroke-width: 1; stroke-dasharray: 1, 0;"/></marker></defs><path class="edge-path" d="M468.48828315734863,22L468.48828315734863,28.166666666666668C468.48828315734863,34.333333333333336,468.48828315734863,46.666666666666664,468.48828315734863,59C468.48828315734863,71.33333333333333,468.48828315734863,83.66666666666667,468.48828315734863,89.83333333333333L468.48828315734863,96" marker-end="url(#arrowhead-vee)" id="edge-[0,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M468.48828315734863,170L468.48828315734863,176.16666666666666C468.48828315734863,182.33333333333334,468.48828315734863,194.66666666666666,468.48828315734863,207C468.48828315734863,219.33333333333334,468.48828315734863,231.66666666666666,468.48828315734863,237.83333333333334L468.48828315734863,244" marker-end="url(#arrowhead-vee)" id="edge-[2,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M418.93359565734863,281.8539668475258L383.29817899068195,290.3783063420544C347.6627623240153,298.90264583658296,276.39192899068195,315.9513248256401,240.7565123240153,330.64233098683536C205.12109565734863,345.3333371480306,205.12109565734863,357.66667048136395,205.12109565734863,363.8333371480306L205.12109565734863,370.00000381469727" marker-end="url(#arrowhead-vee)" id="edge-[3,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M518.0429706573486,291.9625747074757L533.4752623240153,298.80214622534595C548.907553990682,305.64171774321625,579.7721373240153,319.3208607789567,595.204428990682,332.32709896349365C610.6367206573486,345.3333371480306,610.6367206573486,357.66667048136395,610.6367206573486,363.8333371480306L610.6367206573486,370.00000381469727" marker-end="url(#arrowhead-vee)" id="edge-[3,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M518.0429706573486,281.4203389111014L555.345053990682,290.01694972836736C592.6471373240153,298.61356054563333,667.251303990682,315.8067821801653,704.5533873240153,335.820059664098C741.8554706573486,355.8333371480306,741.8554706573486,378.66667048136395,741.8554706573486,401.50000381469727C741.8554706573486,424.3333371480306,741.8554706573486,447.16667048136395,741.8554706573486,468.16667048136395C741.8554706573486,489.16667048136395,741.8554706573486,508.3333371480306,741.8554706573486,527.5000038146973C741.8554706573486,546.6666704813639,741.8554706573486,565.8333371480306,741.8554706573486,583.4166704813639C741.8554706573486,601.0000038146973,741.8554706573486,617.0000038146973,741.8554706573486,633.0000038146973C741.8554706573486,649.0000038146973,741.8554706573486,665.0000038146973,741.8554706573486,679.1666704813639C741.8554706573486,693.3333371480306,741.8554706573486,705.6666704813639,741.8554706573486,711.8333371480306L741.8554706573486,718.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[3,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M205.12109565734863,433.00000381469727L205.12109565734863,439.16667048136395C205.12109565734863,445.3333371480306,205.12109565734863,457.66667048136395,205.12109565734863,470.00000381469727C205.12109565734863,482.3333371480306,205.12109565734863,494.66667048136395,205.12109565734863,500.8333371480306L205.12109565734863,507.00000381469727" marker-end="url(#arrowhead-vee)" id="edge-[6,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M205.12109565734863,548.0000038146973L205.12109565734863,554.1666704813639C205.12109565734863,560.3333371480306,205.12109565734863,572.6666704813639,205.12109565734863,585.0000038146973C205.12109565734863,597.3333371480306,205.12109565734863,609.6666704813639,205.12109565734863,615.8333371480306L205.12109565734863,622.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[7,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M610.6367206573486,433.00000381469727L610.6367206573486,439.16667048136395C610.6367206573486,445.3333371480306,610.6367206573486,457.66667048136395,610.6367206573486,470.00000381469727C610.6367206573486,482.3333371480306,610.6367206573486,494.66667048136395,610.6367206573486,500.8333371480306L610.6367206573486,507.00000381469727" marker-end="url(#arrowhead-vee)" id="edge-[10,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M610.6367206573486,548.0000038146973L610.6367206573486,554.1666704813639C610.6367206573486,560.3333371480306,610.6367206573486,572.6666704813639,555.3932310740153,585.3723885101082C500.14974149068195,598.0781065388525,389.6627623240153,611.1562092630077,334.41927274068195,617.6952606250853L279.17578315734863,624.2343119871629" marker-end="url(#arrowhead-vee)" id="edge-[11,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M180.1643081108729,644.0000038146973L166.17338175906073,650.1666704813639C152.1824554072486,656.3333371480306,124.20060270362428,668.6666704813639,110.20967635181215,686.2500038146973C96.21875,703.8333371480306,96.21875,726.6666704813639,96.21875,749.5000038146973C96.21875,772.3333371480306,96.21875,795.1666704813639,96.21875,814.5833373069763C96.21875,834.0000041325887,96.21875,850.0000044504801,96.21875,866.0000047683716C96.21875,882.0000050862631,96.21875,898.0000054041544,96.21875,918.3333388964335C96.21875,938.6666723887125,96.21875,963.3333390553793,96.21875,988.0000057220459C96.21875,1012.6666723887125,96.21875,1037.3333390553792,96.21875,1059.250005722046C96.21875,1081.1666723887126,96.21875,1100.3333390553792,96.21875,1119.500005722046C96.21875,1138.6666723887126,96.21875,1157.8333390553792,96.21875,1173.5833390553792C96.21875,1189.3333390553792,96.21875,1201.6666723887126,96.21875,1207.8333390553792L96.21875,1214.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[12,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M279.17578315734863,643.4090999232283L323.7486998240153,649.6742505718065C368.32161649068195,655.9394012203846,457.4674498240153,668.4697025175409,502.04036649068195,680.9015198327858C546.6132831573486,693.3333371480306,546.6132831573486,705.6666704813639,546.6132831573486,711.8333371480306L546.6132831573486,718.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[12,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M96.21875,1277.000005722046L96.21875,1283.1666723887126C96.21875,1289.3333390553792,96.21875,1301.6666723887126,96.21875,1314.000005722046C96.21875,1326.3333390553792,96.21875,1338.6666723887126,96.21875,1344.8333390553792L96.21875,1351.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[15,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M96.21875,1414.000005722046L96.21875,1420.1666723887126C96.21875,1426.3333390553792,96.21875,1438.6666723887126,96.21875,1452.8333390553792C96.21875,1467.000005722046,96.21875,1483.000005722046,96.21875,1491.000005722046L96.21875,1499.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[16,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M96.21875,1540.000005722046L96.21875,1548.000005722046C96.21875,1556.000005722046,96.21875,1572.000005722046,110.07039147106143,1590.5000055631C123.92203294212284,1609.0000054041545,151.62531588424568,1630.000005086263,165.4769573553071,1640.5000049273174L179.3285988263685,1651.0000047683716" marker-end="url(#arrowhead-vee)" id="edge-[17,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M546.6132831573486,781.0000038146973L546.6132831573486,787.1666704813639C546.6132831573486,793.3333371480306,546.6132831573486,805.6666704813639,546.6132831573486,818.0000038146973C546.6132831573486,830.3333371480306,546.6132831573486,842.6666704813639,546.6132831573486,848.8333371480306L546.6132831573486,855.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[20,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M741.8554706573486,781.0000038146973L741.8554706573486,787.1666704813639C741.8554706573486,793.3333371480306,741.8554706573486,805.6666704813639,716.7722734560542,818.0000038146973C691.6890762547597,830.3333371480306,641.5226818521709,842.6666704813639,616.4394846508765,848.8333371480306L591.356287449582,855.0000038146973" marker-end="url(#arrowhead-vee)" id="edge-[21,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M483.67301158690117,877.0000057220459L448.38831694741765,883.1666723887125C413.1036223079341,889.3333390553793,342.53423302896704,901.6666723887125,307.2495383894835,914.0000057220459C271.96484375,926.3333390553793,271.96484375,938.6666723887125,271.96484375,944.8333390553793L271.96484375,951.0000057220459" marker-end="url(#arrowhead-vee)" id="edge-[22,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M553.9036477907922,877.0000057220459L557.9906700339935,883.1666723887125C562.0776922771948,889.3333390553793,570.2517367635974,901.6666723887125,574.3387590067987,914.0000057220459C578.42578125,926.3333390553793,578.42578125,938.6666723887125,578.42578125,944.8333390553793L578.42578125,951.0000057220459" marker-end="url(#arrowhead-vee)" id="edge-[22,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M609.8632869720459,874.3310091665718L660.0585988362631,880.9425085924842C710.2539107004801,887.5540080183964,810.6445344289144,900.7770068702212,860.8398462931315,919.7218396294669C911.0351581573486,938.6666723887125,911.0351581573486,963.3333390553793,911.0351581573486,988.0000057220459C911.0351581573486,1012.6666723887125,911.0351581573486,1037.3333390553792,911.0351581573486,1059.250005722046C911.0351581573486,1081.1666723887126,911.0351581573486,1100.3333390553792,911.0351581573486,1119.500005722046C911.0351581573486,1138.6666723887126,911.0351581573486,1157.8333390553792,911.0351581573486,1178.8333390553792C911.0351581573486,1199.8333390553792,911.0351581573486,1222.6666723887126,911.0351581573486,1245.500005722046C911.0351581573486,1268.3333390553792,911.0351581573486,1291.1666723887126,911.0351581573486,1308.750005722046C911.0351581573486,1326.3333390553792,911.0351581573486,1338.6666723887126,911.0351581573486,1344.8333390553792L911.0351581573486,1351.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[22,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M271.96484375,1025.000005722046L271.96484375,1031.1666723887126C271.96484375,1037.3333390553792,271.96484375,1049.6666723887126,271.96484375,1062.000005722046C271.96484375,1074.3333390553792,271.96484375,1086.6666723887126,271.96484375,1092.8333390553792L271.96484375,1099.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[25,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M271.96484375,1140.000005722046L271.96484375,1146.1666723887126C271.96484375,1152.3333390553792,271.96484375,1164.6666723887126,293.4021964340024,1180.4166723887126C314.83954911800487,1196.1666723887126,357.7142544860097,1215.3333390553792,379.15160717001214,1224.9166723887126L400.5889598540146,1234.500005722046" marker-end="url(#arrowhead-vee)" id="edge-[26,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M578.42578125,1025.000005722046L578.42578125,1031.1666723887126C578.42578125,1037.3333390553792,578.42578125,1049.6666723887126,578.42578125,1062.000005722046C578.42578125,1074.3333390553792,578.42578125,1086.6666723887126,578.42578125,1092.8333390553792L578.42578125,1099.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[29,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M578.42578125,1140.000005722046L578.42578125,1146.1666723887126C578.42578125,1152.3333390553792,578.42578125,1164.6666723887126,556.9884285659975,1180.4166723887126C535.5510758819952,1196.1666723887126,492.6763705139903,1215.3333390553792,471.23901782998786,1224.9166723887126L449.8016651459854,1234.500005722046" marker-end="url(#arrowhead-vee)" id="edge-[30,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M405.72952782846716,1256.500005722046L388.77070027372264,1266.0833390553792C371.81187271897807,1275.6666723887126,337.89421760948903,1294.8333390553792,320.9353900547445,1310.5833390553792C303.9765625,1326.3333390553792,303.9765625,1338.6666723887126,303.9765625,1344.8333390553792L303.9765625,1351.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[31,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M444.66109717153284,1256.500005722046L461.61992472627736,1266.0833390553792C478.57875228102193,1275.6666723887126,512.496407390511,1294.8333390553792,529.4552349452555,1310.5833390553792C546.4140625,1326.3333390553792,546.4140625,1338.6666723887126,546.4140625,1344.8333390553792L546.4140625,1351.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[31,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M303.9765625,1414.000005722046L303.9765625,1420.1666723887126C303.9765625,1426.3333390553792,303.9765625,1438.6666723887126,303.9765625,1451.000005722046C303.9765625,1463.3333390553792,303.9765625,1475.6666723887126,303.9765625,1481.8333390553792L303.9765625,1488.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[34,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M303.9765625,1551.000005722046L303.9765625,1557.1666723887126C303.9765625,1563.3333390553792,303.9765625,1575.6666723887126,288.34905534636164,1592.3333388964336C272.7215481927232,1609.0000054041545,241.46653388544647,1630.000005086263,225.8390267318081,1640.5000049273174L210.2115195781697,1651.0000047683716" marker-end="url(#arrowhead-vee)" id="edge-[35,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M193.83984375,1673.0000066757202L193.83984375,1683.5000065167744C193.83984375,1694.0000063578289,193.83984375,1715.0000060399373,193.83984375,1735.083339214325C193.83984375,1755.1666723887126,193.83984375,1774.3333390553792,193.83984375,1793.500005722046C193.83984375,1812.6666723887126,193.83984375,1831.8333390553792,193.83984375,1849.4166723887126C193.83984375,1867.000005722046,193.83984375,1883.000005722046,193.83984375,1899.000005722046C193.83984375,1915.000005722046,193.83984375,1931.000005722046,193.83984375,1950.4166723887126C193.83984375,1969.8333390553792,193.83984375,1992.6666723887126,193.83984375,2015.500005722046C193.83984375,2038.3333390553792,193.83984375,2061.1666723887124,193.83984375,2084.000005722046C193.83984375,2106.8333390553794,193.83984375,2129.6666723887124,193.83984375,2152.500005722046C193.83984375,2175.3333390553794,193.83984375,2198.1666723887124,195.44056190181155,2220.0833388964334C197.04128005362307,2242.0000054041543,200.24271635724617,2263.000005086263,201.84343450905772,2273.500004927317L203.44415266086924,2284.0000047683716" marker-end="url(#arrowhead-vee)" id="edge-[36,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M546.4140625,1414.000005722046L546.4140625,1420.1666723887126C546.4140625,1426.3333390553792,546.4140625,1438.6666723887126,570.1106246837354,1454.4166722297668C593.8071868674707,1470.166672070821,641.2003112349414,1489.3333384195964,664.8968734186768,1498.916671593984L688.5934356024121,1508.5000047683716" marker-end="url(#arrowhead-vee)" id="edge-[39,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M911.0351581573486,1414.000005722046L911.0351581573486,1420.1666723887126C911.0351581573486,1426.3333390553792,911.0351581573486,1438.6666723887126,883.7202540331332,1454.4166722297668C856.4053499089177,1470.166672070821,801.7755416604869,1489.3333384195964,774.4606375362715,1498.916671593984L747.1457334120561,1508.5000047683716" marker-end="url(#arrowhead-vee)" id="edge-[40,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M685.9463097667933,1530.5000066757202L659.9435397068859,1540.083339850108C633.9407696469784,1549.6666730244954,581.9352295271635,1568.8333393732707,555.9324594672561,1584.583339214325C529.9296894073486,1600.3333390553792,529.9296894073486,1612.6666723887126,529.9296894073486,1618.8333390553792L529.9296894073486,1625.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[41,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M728.8673901302863,1530.5000066757202L740.2579816764634,1540.083339850108C751.6485732226405,1549.6666730244954,774.4297563149945,1568.8333393732707,785.8203478611716,1584.583339214325C797.2109394073486,1600.3333390553792,797.2109394073486,1612.6666723887126,797.2109394073486,1618.8333390553792L797.2109394073486,1625.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[41,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M774.3132768258126,1530.5000066757202L825.2968716310687,1540.083339850108C876.2804664363247,1549.6666730244954,978.2476560468367,1568.8333393732707,1029.2312508520927,1590.7500058809917C1080.2148456573486,1612.6666723887126,1080.2148456573486,1637.3333390553792,1080.2148456573486,1662.000005722046C1080.2148456573486,1686.6666723887126,1080.2148456573486,1711.3333390553792,1080.2148456573486,1733.250005722046C1080.2148456573486,1755.1666723887126,1080.2148456573486,1774.3333390553792,1080.2148456573486,1793.500005722046C1080.2148456573486,1812.6666723887126,1080.2148456573486,1831.8333390553792,1080.2148456573486,1849.4166723887126C1080.2148456573486,1867.000005722046,1080.2148456573486,1883.000005722046,1080.2148456573486,1899.000005722046C1080.2148456573486,1915.000005722046,1080.2148456573486,1931.000005722046,1080.2148456573486,1945.1666723887126C1080.2148456573486,1959.3333390553792,1080.2148456573486,1971.6666723887126,1080.2148456573486,1977.8333390553792L1080.2148456573486,1984.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[41,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M529.9296894073486,1699.000005722046L529.9296894073486,1705.1666723887126C529.9296894073486,1711.3333390553792,529.9296894073486,1723.6666723887126,529.9296894073486,1736.000005722046C529.9296894073486,1748.3333390553792,529.9296894073486,1760.6666723887126,529.9296894073486,1766.8333390553792L529.9296894073486,1773.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[44,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M529.9296894073486,1814.000005722046L529.9296894073486,1820.1666723887126C529.9296894073486,1826.3333390553792,529.9296894073486,1838.6666723887126,538.23471599155,1851.000005722046C546.5397425757515,1863.3333390553792,563.1497957441542,1875.6666723887126,571.4548223283556,1881.8333390553792L579.759848912557,1888.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[45,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M797.2109394073486,1699.000005722046L797.2109394073486,1705.1666723887126C797.2109394073486,1711.3333390553792,797.2109394073486,1723.6666723887126,797.2109394073486,1736.000005722046C797.2109394073486,1748.3333390553792,797.2109394073486,1760.6666723887126,797.2109394073486,1766.8333390553792L797.2109394073486,1773.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[48,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M797.2109394073486,1814.000005722046L797.2109394073486,1820.1666723887126C797.2109394073486,1826.3333390553792,797.2109394073486,1838.6666723887126,771.1777498457167,1851.000005722046C745.1445602840848,1863.3333390553792,693.0781811608209,1875.6666723887126,667.044991599189,1881.8333390553792L641.011802037557,1888.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[49,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M570.7937026421229,1910.000005722046L557.4622001184357,1916.1666723887126C544.1306975947485,1922.3333390553792,517.4676925473742,1934.6666723887126,504.1361900236871,1947.000005722046C490.8046875,1959.3333390553792,490.8046875,1971.6666723887126,490.8046875,1977.8333390553792L490.8046875,1984.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[50,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M635.318442662557,1910.000005722046L658.1599004533556,1916.1666723887126C681.0013582441542,1922.3333390553792,726.6842738257515,1934.6666723887126,749.5257316165502,1947.000005722046C772.3671894073486,1959.3333390553792,772.3671894073486,1971.6666723887126,772.3671894073486,1977.8333390553792L772.3671894073486,1984.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[50,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M490.8046875,2047.000005722046L490.8046875,2053.1666723887124C490.8046875,2059.3333390553794,490.8046875,2071.6666723887124,490.8046875,2084.000005722046C490.8046875,2096.3333390553794,490.8046875,2108.6666723887124,490.8046875,2114.8333390553794L490.8046875,2121.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[53,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M490.8046875,2184.000005722046L490.8046875,2190.1666723887124C490.8046875,2196.3333390553794,490.8046875,2208.6666723887124,450.2685027845992,2225.3333388964334C409.7323180691985,2242.0000054041543,328.659948638397,2263.000005086263,288.12376392299626,2273.500004927317L247.58757920759552,2284.0000047683716" marker-end="url(#arrowhead-vee)" id="edge-[54,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M205.12109565734863,2306.00000667572L205.12109565734863,2316.5000065167746C205.12109565734863,2327.0000063578286,205.12109565734863,2348.0000060399375,205.12109565734863,2368.083339214325C205.12109565734863,2388.1666723887124,205.12109565734863,2407.3333390553794,205.12109565734863,2426.500005722046C205.12109565734863,2445.6666723887124,205.12109565734863,2464.8333390553794,205.12109565734863,2482.4166723887124C205.12109565734863,2500.000005722046,205.12109565734863,2516.000005722046,205.12109565734863,2532.000005722046C205.12109565734863,2548.000005722046,205.12109565734863,2564.000005722046,205.12109565734863,2583.4166723887124C205.12109565734863,2602.8333390553794,205.12109565734863,2625.6666723887124,205.12109565734863,2648.500005722046C205.12109565734863,2671.3333390553794,205.12109565734863,2694.1666723887124,205.12109565734863,2717.000005722046C205.12109565734863,2739.8333390553794,205.12109565734863,2762.6666723887124,205.12109565734863,2785.500005722046C205.12109565734863,2808.3333390553794,205.12109565734863,2831.1666723887124,205.12109565734863,2853.0833388964334C205.12109565734863,2875.0000054041543,205.12109565734863,2896.000005086263,205.12109565734863,2906.500004927317L205.12109565734863,2917.0000047683716" marker-end="url(#arrowhead-vee)" id="edge-[55,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M772.3671894073486,2047.000005722046L772.3671894073486,2053.1666723887124C772.3671894073486,2059.3333390553794,772.3671894073486,2071.6666723887124,788.1209956013536,2087.416672229767C803.8748017953586,2103.1666720708213,835.3824141833687,2122.333338419596,851.1362203773737,2131.916671593984L866.8900265713787,2141.5000047683716" marker-end="url(#arrowhead-vee)" id="edge-[58,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1080.2148456573486,2047.000005722046L1080.2148456573486,2053.1666723887124C1080.2148456573486,2059.3333390553794,1080.2148456573486,2071.6666723887124,1052.8999415331332,2087.416672229767C1025.5850374089177,2103.1666720708213,970.9552291604869,2122.333338419596,943.6403250362714,2131.916671593984L916.3254209120561,2141.5000047683716" marker-end="url(#arrowhead-vee)" id="edge-[59,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M850.1855315099994,2163.50000667572L819.878568242891,2173.0833398501077C789.5716049757824,2182.6666730244956,728.9576784415655,2201.8333393732705,698.6507151744571,2217.583339214325C668.3437519073486,2233.3333390553794,668.3437519073486,2245.6666723887124,668.3437519073486,2251.8333390553794L668.3437519073486,2258.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[60,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M889.1171042283412,2163.50000667572L892.7277955081757,2173.0833398501077C896.3384867880104,2182.6666730244956,903.5598693476795,2201.8333393732705,907.1705606275141,2217.583339214325C910.7812519073486,2233.3333390553794,910.7812519073486,2245.6666723887124,910.7812519073486,2251.8333390553794L910.7812519073486,2258.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[60,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M943.4929643258126,2163.50000667572L994.4765591310687,2173.0833398501077C1045.4601539363246,2182.6666730244956,1147.4273435468367,2201.8333393732705,1198.4109383520927,2223.7500058809915C1249.3945331573486,2245.6666723887124,1249.3945331573486,2270.3333390553794,1249.3945331573486,2295.000005722046C1249.3945331573486,2319.6666723887124,1249.3945331573486,2344.3333390553794,1249.3945331573486,2366.250005722046C1249.3945331573486,2388.1666723887124,1249.3945331573486,2407.3333390553794,1249.3945331573486,2426.500005722046C1249.3945331573486,2445.6666723887124,1249.3945331573486,2464.8333390553794,1249.3945331573486,2482.4166723887124C1249.3945331573486,2500.000005722046,1249.3945331573486,2516.000005722046,1249.3945331573486,2532.000005722046C1249.3945331573486,2548.000005722046,1249.3945331573486,2564.000005722046,1249.3945331573486,2578.1666723887124C1249.3945331573486,2592.3333390553794,1249.3945331573486,2604.6666723887124,1249.3945331573486,2610.8333390553794L1249.3945331573486,2617.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[60,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M668.3437519073486,2332.000005722046L668.3437519073486,2338.1666723887124C668.3437519073486,2344.3333390553794,668.3437519073486,2356.6666723887124,668.3437519073486,2369.000005722046C668.3437519073486,2381.3333390553794,668.3437519073486,2393.6666723887124,668.3437519073486,2399.8333390553794L668.3437519073486,2406.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[63,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M668.3437519073486,2447.000005722046L668.3437519073486,2453.1666723887124C668.3437519073486,2459.3333390553794,668.3437519073486,2471.6666723887124,680.6013067033556,2484.000005722046C692.8588614993624,2496.3333390553794,717.3739710913763,2508.6666723887124,729.6315258873833,2514.8333390553794L741.8890806833903,2521.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[64,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M910.7812519073486,2332.000005722046L910.7812519073486,2338.1666723887124C910.7812519073486,2344.3333390553794,910.7812519073486,2356.6666723887124,910.7812519073486,2369.000005722046C910.7812519073486,2381.3333390553794,910.7812519073486,2393.6666723887124,910.7812519073486,2399.8333390553794L910.7812519073486,2406.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[67,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M910.7812519073486,2447.000005722046L910.7812519073486,2453.1666723887124C910.7812519073486,2459.3333390553794,910.7812519073486,2471.6666723887124,891.8923223283556,2484.000005722046C873.0033927493624,2496.3333390553794,835.2255335913763,2508.6666723887124,816.3366040123833,2514.8333390553794L797.4476744333903,2521.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[68,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M732.9229344129562,2543.000005722046L715.6389036774635,2549.1666723887124C698.3548729419708,2555.3333390553794,663.7868114709854,2567.6666723887124,646.5027807354927,2580.000005722046C629.21875,2592.3333390553794,629.21875,2604.6666723887124,629.21875,2610.8333390553794L629.21875,2617.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[69,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M808.4387226104736,2543.000005722046L833.4893004099528,2549.1666723887124C858.539878209432,2555.3333390553794,908.6410338083903,2567.6666723887124,933.6916116078695,2580.000005722046C958.7421894073486,2592.3333390553794,958.7421894073486,2604.6666723887124,958.7421894073486,2610.8333390553794L958.7421894073486,2617.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[69,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M629.21875,2680.000005722046L629.21875,2686.1666723887124C629.21875,2692.3333390553794,629.21875,2704.6666723887124,629.21875,2717.000005722046C629.21875,2729.3333390553794,629.21875,2741.6666723887124,629.21875,2747.8333390553794L629.21875,2754.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[72,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M629.21875,2817.000005722046L629.21875,2823.1666723887124C629.21875,2829.3333390553794,629.21875,2841.6666723887124,569.0427323893333,2858.3333388964334C508.8667147786667,2875.0000054041543,388.5146795573334,2896.000005086263,328.3386619466667,2906.500004927317L268.1626443360001,2917.0000047683716" marker-end="url(#arrowhead-vee)" id="edge-[73,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M205.12109565734863,2939.00000667572L205.12109565734863,2949.5000065167746C205.12109565734863,2960.0000063578286,205.12109565734863,2981.0000060399375,205.12109565734863,3001.083339214325C205.12109565734863,3021.1666723887124,205.12109565734863,3040.3333390553794,205.12109565734863,3059.500005722046C205.12109565734863,3078.6666723887124,205.12109565734863,3097.8333390553794,205.12109565734863,3115.4166723887124C205.12109565734863,3133.000005722046,205.12109565734863,3149.000005722046,205.12109565734863,3165.000005722046C205.12109565734863,3181.000005722046,205.12109565734863,3197.000005722046,205.12109565734863,3216.4166723887124C205.12109565734863,3235.8333390553794,205.12109565734863,3258.6666723887124,205.12109565734863,3281.500005722046C205.12109565734863,3304.3333390553794,205.12109565734863,3327.1666723887124,205.12109565734863,3350.000005722046C205.12109565734863,3372.8333390553794,205.12109565734863,3395.6666723887124,205.12109565734863,3418.500005722046C205.12109565734863,3441.3333390553794,205.12109565734863,3464.1666723887124,205.12109565734863,3486.0833388964334C205.12109565734863,3508.0000054041543,205.12109565734863,3529.000005086263,205.12109565734863,3539.500004927317L205.12109565734863,3550.0000047683716" marker-end="url(#arrowhead-vee)" id="edge-[74,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M958.7421894073486,2680.000005722046L958.7421894073486,2686.1666723887124C958.7421894073486,2692.3333390553794,958.7421894073486,2704.6666723887124,972.0903254009855,2720.416672229767C985.4384613946225,2736.1666720708213,1012.1347333818964,2755.333338419596,1025.4828693755333,2764.916671593984L1038.8310053691703,2774.5000047683716" marker-end="url(#arrowhead-vee)" id="edge-[77,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1249.3945331573486,2680.000005722046L1249.3945331573486,2686.1666723887124C1249.3945331573486,2692.3333390553794,1249.3945331573486,2704.6666723887124,1222.0796290331332,2720.416672229767C1194.7647249089177,2736.1666720708213,1140.1349166604869,2755.333338419596,1112.8200125362714,2764.916671593984L1085.505108412056,2774.5000047683716" marker-end="url(#arrowhead-vee)" id="edge-[78,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1010.4352456080544,2796.50000667572L972.3483820746034,2806.0833398501077C934.2615185411524,2815.6666730244956,858.0877914742505,2834.8333393732705,820.0009279407996,2850.583339214325C781.9140644073486,2866.3333390553794,781.9140644073486,2878.6666723887124,781.9140644073486,2884.8333390553794L781.9140644073486,2891.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[79,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1049.3668183263962,2796.50000667572L1045.1976093398882,2806.0833398501077C1041.0284003533804,2815.6666730244956,1032.6899823803644,2834.8333393732705,1028.5207733938566,2850.583339214325C1024.3515644073486,2866.3333390553794,1024.3515644073486,2878.6666723887124,1024.3515644073486,2884.8333390553794L1024.3515644073486,2891.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[79,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1112.6726518258126,2796.50000667572L1163.6562466310686,2806.0833398501077C1214.6398414363246,2815.6666730244956,1316.6070310468367,2834.8333393732705,1367.5906258520927,2856.7500058809915C1418.5742206573486,2878.6666723887124,1418.5742206573486,2903.3333390553794,1418.5742206573486,2928.000005722046C1418.5742206573486,2952.6666723887124,1418.5742206573486,2977.3333390553794,1418.5742206573486,2999.250005722046C1418.5742206573486,3021.1666723887124,1418.5742206573486,3040.3333390553794,1418.5742206573486,3059.500005722046C1418.5742206573486,3078.6666723887124,1418.5742206573486,3097.8333390553794,1418.5742206573486,3115.4166723887124C1418.5742206573486,3133.000005722046,1418.5742206573486,3149.000005722046,1418.5742206573486,3165.000005722046C1418.5742206573486,3181.000005722046,1418.5742206573486,3197.000005722046,1418.5742206573486,3211.1666723887124C1418.5742206573486,3225.3333390553794,1418.5742206573486,3237.6666723887124,1418.5742206573486,3243.8333390553794L1418.5742206573486,3250.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[79,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M781.9140644073486,2965.000005722046L781.9140644073486,2971.1666723887124C781.9140644073486,2977.3333390553794,781.9140644073486,2989.6666723887124,781.9140644073486,3002.000005722046C781.9140644073486,3014.3333390553794,781.9140644073486,3026.6666723887124,781.9140644073486,3032.8333390553794L781.9140644073486,3039.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[82,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M781.9140644073486,3080.000005722046L781.9140644073486,3086.1666723887124C781.9140644073486,3092.3333390553794,781.9140644073486,3104.6666723887124,797.4873065948486,3117.000005722046C813.0605487823486,3129.3333390553794,844.2070331573486,3141.6666723887124,859.7802753448486,3147.8333390553794L875.3535175323486,3154.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[83,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1024.3515644073486,2965.000005722046L1024.3515644073486,2971.1666723887124C1024.3515644073486,2977.3333390553794,1024.3515644073486,2989.6666723887124,1024.3515644073486,3002.000005722046C1024.3515644073486,3014.3333390553794,1024.3515644073486,3026.6666723887124,1024.3515644073486,3032.8333390553794L1024.3515644073486,3039.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[86,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1024.3515644073486,3080.000005722046L1024.3515644073486,3086.1666723887124C1024.3515644073486,3092.3333390553794,1024.3515644073486,3104.6666723887124,1008.7783222198486,3117.000005722046C993.2050800323486,3129.3333390553794,962.0585956573486,3141.6666723887124,946.4853534698486,3147.8333390553794L930.9121112823486,3154.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[87,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M866.3873712619146,3176.000005722046L845.7876531349289,3182.1666723887124C825.1879350079431,3188.3333390553794,783.9884987539716,3200.6666723887124,763.3887806269859,3213.000005722046C742.7890625,3225.3333390553794,742.7890625,3237.6666723887124,742.7890625,3243.8333390553794L742.7890625,3250.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[88,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M941.903159459432,3176.000005722046L963.6380498674181,3182.1666723887124C985.3729402754043,3188.3333390553794,1028.8427210913765,3200.6666723887124,1050.5776114993625,3213.000005722046C1072.3125019073486,3225.3333390553794,1072.3125019073486,3237.6666723887124,1072.3125019073486,3243.8333390553794L1072.3125019073486,3250.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[88,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M742.7890625,3313.000005722046L742.7890625,3319.1666723887124C742.7890625,3325.3333390553794,742.7890625,3337.6666723887124,742.7890625,3350.000005722046C742.7890625,3362.3333390553794,742.7890625,3374.6666723887124,742.7890625,3380.8333390553794L742.7890625,3387.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[91,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M742.7890625,3450.000005722046L742.7890625,3456.1666723887124C742.7890625,3462.3333390553794,742.7890625,3474.6666723887124,663.719401995341,3491.715807861741C584.649741490682,3508.7649433347683,426.51042048136395,3530.5298809474903,347.4407599767049,3541.4123497538517L268.3710994720459,3552.2948185602127" marker-end="url(#arrowhead-vee)" id="edge-[92,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M205.12109565734863,3572.00000667572L205.12109565734863,3582.5000065167746C205.12109565734863,3593.0000063578286,205.12109565734863,3614.0000060399375,205.12109565734863,3634.083339214325C205.12109565734863,3654.1666723887124,205.12109565734863,3673.3333390553794,205.12109565734863,3692.500005722046C205.12109565734863,3711.6666723887124,205.12109565734863,3730.8333390553794,205.12109565734863,3748.4166723887124C205.12109565734863,3766.000005722046,205.12109565734863,3782.000005722046,205.12109565734863,3798.000005722046C205.12109565734863,3814.000005722046,205.12109565734863,3830.000005722046,205.12109565734863,3849.4166723887124C205.12109565734863,3868.8333390553794,205.12109565734863,3891.6666723887124,205.12109565734863,3914.500005722046C205.12109565734863,3937.3333390553794,205.12109565734863,3960.1666723887124,205.12109565734863,3983.000005722046C205.12109565734863,4005.8333390553794,205.12109565734863,4028.6666723887124,205.12109565734863,4051.500005722046C205.12109565734863,4074.3333390553794,205.12109565734863,4097.166672388713,208.15348026479452,4119.083338896434C211.1858648722404,4141.000005404155,217.2506340871322,4162.000005086263,220.2830186945781,4172.500004927318L223.31540330202398,4183.000004768372" marker-end="url(#arrowhead-vee)" id="edge-[93,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1072.3125019073486,3313.000005722046L1072.3125019073486,3319.1666723887124C1072.3125019073486,3325.3333390553794,1072.3125019073486,3337.6666723887124,1093.440538167328,3353.416672229767C1114.5685744273076,3369.1666720708213,1156.8246469472663,3388.333338419596,1177.9526832072459,3397.916671593984L1199.0807194672252,3407.5000047683716" marker-end="url(#arrowhead-vee)" id="edge-[96,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1418.5742206573486,3313.000005722046L1418.5742206573486,3319.1666723887124C1418.5742206573486,3325.3333390553794,1418.5742206573486,3337.6666723887124,1391.2593165331334,3353.416672229767C1363.9444124089177,3369.1666720708213,1309.3146041604869,3388.333338419596,1281.9997000362714,3397.916671593984L1254.684795912056,3407.5000047683716" marker-end="url(#arrowhead-vee)" id="edge-[97,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1170.6849597061093,3429.50000667572L1124.8181959063159,3439.0833398501077C1078.9514321065224,3448.6666730244956,987.2179045069355,3467.8333393732705,941.351140707142,3483.583339214325C895.4843769073486,3499.3333390553794,895.4843769073486,3511.6666723887124,895.4843769073486,3517.8333390553794L895.4843769073486,3524.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[98,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1209.6165324244512,3429.50000667572L1197.6674231716008,3439.0833398501077C1185.7183139187503,3448.6666730244956,1161.8200954130496,3467.8333393732705,1149.870986160199,3483.583339214325C1137.9218769073486,3499.3333390553794,1137.9218769073486,3511.6666723887124,1137.9218769073486,3517.8333390553794L1137.9218769073486,3524.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[98,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1269.742678148196,3429.50000667572L1310.1761904413881,3439.0833398501077C1350.6097027345802,3448.6666730244956,1431.4767273209645,3467.8333393732705,1471.9102396141564,3489.7500058809915C1512.3437519073486,3511.6666723887124,1512.3437519073486,3536.3333390553794,1512.3437519073486,3561.000005722046C1512.3437519073486,3585.6666723887124,1512.3437519073486,3610.3333390553794,1512.3437519073486,3632.250005722046C1512.3437519073486,3654.1666723887124,1512.3437519073486,3673.3333390553794,1512.3437519073486,3692.500005722046C1512.3437519073486,3711.6666723887124,1512.3437519073486,3730.8333390553794,1512.3437519073486,3748.4166723887124C1512.3437519073486,3766.000005722046,1512.3437519073486,3782.000005722046,1512.3437519073486,3798.000005722046C1512.3437519073486,3814.000005722046,1512.3437519073486,3830.000005722046,1512.3437519073486,3844.1666723887124C1512.3437519073486,3858.3333390553794,1512.3437519073486,3870.6666723887124,1512.3437519073486,3876.8333390553794L1512.3437519073486,3883.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[98,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M895.4843769073486,3598.000005722046L895.4843769073486,3604.1666723887124C895.4843769073486,3610.3333390553794,895.4843769073486,3622.6666723887124,895.4843769073486,3635.000005722046C895.4843769073486,3647.3333390553794,895.4843769073486,3659.6666723887124,895.4843769073486,3665.8333390553794L895.4843769073486,3672.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[101,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M895.4843769073486,3713.000005722046L895.4843769073486,3719.1666723887124C895.4843769073486,3725.3333390553794,895.4843769073486,3737.6666723887124,911.0576190948486,3750.000005722046C926.6308612823486,3762.3333390553794,957.7773456573486,3774.6666723887124,973.3505878448486,3780.8333390553794L988.9238300323486,3787.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[102,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1137.9218769073486,3598.000005722046L1137.9218769073486,3604.1666723887124C1137.9218769073486,3610.3333390553794,1137.9218769073486,3622.6666723887124,1137.9218769073486,3635.000005722046C1137.9218769073486,3647.3333390553794,1137.9218769073486,3659.6666723887124,1137.9218769073486,3665.8333390553794L1137.9218769073486,3672.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[105,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1137.9218769073486,3713.000005722046L1137.9218769073486,3719.1666723887124C1137.9218769073486,3725.3333390553794,1137.9218769073486,3737.6666723887124,1122.3486347198486,3750.000005722046C1106.7753925323486,3762.3333390553794,1075.6289081573486,3774.6666723887124,1060.0556659698486,3780.8333390553794L1044.4824237823486,3787.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[106,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M979.9576837619146,3809.000005722046L959.3579656349289,3815.1666723887124C938.7582475079431,3821.3333390553794,897.5588112539716,3833.6666723887124,876.9590931269857,3846.000005722046C856.359375,3858.3333390553794,856.359375,3870.6666723887124,856.359375,3876.8333390553794L856.359375,3883.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[107,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1055.473471959432,3809.000005722046L1077.208362367418,3815.1666723887124C1098.943252775404,3821.3333390553794,1142.4130335913762,3833.6666723887124,1164.1479239993625,3846.000005722046C1185.8828144073486,3858.3333390553794,1185.8828144073486,3870.6666723887124,1185.8828144073486,3876.8333390553794L1185.8828144073486,3883.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[107,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M856.359375,3946.000005722046L856.359375,3952.1666723887124C856.359375,3958.3333390553794,856.359375,3970.6666723887124,856.359375,3983.000005722046C856.359375,3995.3333390553794,856.359375,4007.6666723887124,856.359375,4013.8333390553794L856.359375,4020.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[110,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M856.359375,4083.000005722046L856.359375,4089.1666723887124C856.359375,4095.3333390553794,856.359375,4107.666672388713,761.9231777191162,4124.928183710683C667.4869804382324,4142.189695032653,478.61458587646484,4164.3793843432595,384.17838859558105,4175.474228998563L289.74219131469727,4186.569073653866" marker-end="url(#arrowhead-vee)" id="edge-[111,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M226.4921875,4205.00000667572L226.4921875,4215.500006516774C226.4921875,4226.000006357829,226.4921875,4247.000006039937,226.4921875,4267.083339214325C226.4921875,4287.166672388713,226.4921875,4306.333339055379,226.4921875,4325.500005722046C226.4921875,4344.666672388713,226.4921875,4363.833339055379,226.4921875,4381.416672388713C226.4921875,4399.000005722046,226.4921875,4415.000005722046,226.4921875,4431.000005722046C226.4921875,4447.000005722046,226.4921875,4463.000005722046,226.4921875,4482.416672388713C226.4921875,4501.833339055379,226.4921875,4524.666672388713,226.4921875,4547.500005722046C226.4921875,4570.333339055379,226.4921875,4593.166672388713,226.4921875,4616.000005722046C226.4921875,4638.833339055379,226.4921875,4661.666672388713,226.4921875,4684.500005722046C226.4921875,4707.333339055379,226.4921875,4730.166672388713,235.80162570367122,4752.083338896434C245.11106390734244,4774.000005404155,263.72994031468494,4795.000005086263,273.03937851835616,4805.500004927318L282.3488167220274,4816.000004768372" marker-end="url(#arrowhead-vee)" id="edge-[112,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1185.8828144073486,3946.000005722046L1185.8828144073486,3952.1666723887124C1185.8828144073486,3958.3333390553794,1185.8828144073486,3970.6666723887124,1214.7907509336706,3986.416672229767C1243.6986874599925,4002.1666720708213,1301.5145605126365,4021.333338419596,1330.4224970389585,4030.916671593984L1359.3304335652804,4040.5000047683716" marker-end="url(#arrowhead-vee)" id="edge-[115,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1512.3437519073486,3946.000005722046L1512.3437519073486,3952.1666723887124C1512.3437519073486,3958.3333390553794,1512.3437519073486,3970.6666723887124,1495.578930295197,3986.416672229767C1478.8141086830456,4002.1666720708213,1445.2844654587425,4021.333338419596,1428.5196438465912,4030.916671593984L1411.7548222344396,4040.5000047683716" marker-end="url(#arrowhead-vee)" id="edge-[116,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1330.9346738041643,4062.50000667572L1277.2880097380282,4072.0833398501077C1223.6413456718924,4081.6666730244956,1116.3480175396205,4100.833339373271,1062.7013534734845,4116.583339214325C1009.0546894073486,4132.333339055379,1009.0546894073486,4144.666672388713,1009.0546894073486,4150.833339055379L1009.0546894073486,4157.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[117,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1369.8662465225061,4062.50000667572L1350.1372370033132,4072.0833398501077C1330.4082274841203,4081.6666730244956,1290.9502084457345,4100.833339373271,1271.2211989265415,4116.583339214325C1251.4921894073486,4132.333339055379,1251.4921894073486,4144.666672388713,1251.4921894073486,4150.833339055379L1251.4921894073486,4157.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[117,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1419.4565792890253,4062.50000667572L1442.9312643087458,4072.0833398501077C1466.4059493284665,4081.6666730244956,1513.3553193679074,4100.833339373271,1536.830004387628,4122.750005880992C1560.3046894073486,4144.666672388713,1560.3046894073486,4169.333339055379,1560.3046894073486,4194.000005722046C1560.3046894073486,4218.666672388713,1560.3046894073486,4243.333339055379,1560.3046894073486,4265.250005722046C1560.3046894073486,4287.166672388713,1560.3046894073486,4306.333339055379,1560.3046894073486,4325.500005722046C1560.3046894073486,4344.666672388713,1560.3046894073486,4363.833339055379,1560.3046894073486,4381.416672388713C1560.3046894073486,4399.000005722046,1560.3046894073486,4415.000005722046,1560.3046894073486,4431.000005722046C1560.3046894073486,4447.000005722046,1560.3046894073486,4463.000005722046,1560.3046894073486,4477.166672388713C1560.3046894073486,4491.333339055379,1560.3046894073486,4503.666672388713,1560.3046894073486,4509.833339055379L1560.3046894073486,4516.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[117,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1009.0546894073486,4231.000005722046L1009.0546894073486,4237.166672388713C1009.0546894073486,4243.333339055379,1009.0546894073486,4255.666672388713,1009.0546894073486,4268.000005722046C1009.0546894073486,4280.333339055379,1009.0546894073486,4292.666672388713,1009.0546894073486,4298.833339055379L1009.0546894073486,4305.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[120,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1009.0546894073486,4346.000005722046L1009.0546894073486,4352.166672388713C1009.0546894073486,4358.333339055379,1009.0546894073486,4370.666672388713,1024.6279315948486,4383.000005722046C1040.2011737823486,4395.333339055379,1071.3476581573486,4407.666672388713,1086.9209003448486,4413.833339055379L1102.4941425323486,4420.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[121,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1251.4921894073486,4231.000005722046L1251.4921894073486,4237.166672388713C1251.4921894073486,4243.333339055379,1251.4921894073486,4255.666672388713,1251.4921894073486,4268.000005722046C1251.4921894073486,4280.333339055379,1251.4921894073486,4292.666672388713,1251.4921894073486,4298.833339055379L1251.4921894073486,4305.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[124,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1251.4921894073486,4346.000005722046L1251.4921894073486,4352.166672388713C1251.4921894073486,4358.333339055379,1251.4921894073486,4370.666672388713,1235.9189472198486,4383.000005722046C1220.3457050323486,4395.333339055379,1189.1992206573486,4407.666672388713,1173.6259784698486,4413.833339055379L1158.0527362823486,4420.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[125,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1093.5279962619145,4442.000005722046L1072.9282781349286,4448.166672388713C1052.328560007943,4454.333339055379,1011.1291237539714,4466.666672388713,990.5294056269857,4479.000005722046C969.9296875,4491.333339055379,969.9296875,4503.666672388713,969.9296875,4509.833339055379L969.9296875,4516.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[126,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1169.043784459432,4442.000005722046L1190.778674867418,4448.166672388713C1212.513565275404,4454.333339055379,1255.9833460913762,4466.666672388713,1277.7182364993625,4479.000005722046C1299.4531269073486,4491.333339055379,1299.4531269073486,4503.666672388713,1299.4531269073486,4509.833339055379L1299.4531269073486,4516.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[126,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M969.9296875,4579.000005722046L969.9296875,4585.166672388713C969.9296875,4591.333339055379,969.9296875,4603.666672388713,969.9296875,4616.000005722046C969.9296875,4628.333339055379,969.9296875,4640.666672388713,969.9296875,4646.833339055379L969.9296875,4653.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[129,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M969.9296875,4716.000005722046L969.9296875,4722.166672388713C969.9296875,4728.333339055379,969.9296875,4740.666672388713,867.5000006357828,4758.015815185227C765.0703137715658,4775.364957981742,560.2109400431315,4797.729910241437,457.78125317891437,4808.912386371286L355.35156631469727,4820.094862501133" marker-end="url(#arrowhead-vee)" id="edge-[130,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M292.1015625,4838.00000667572L292.1015625,4848.500006516774C292.1015625,4859.000006357829,292.1015625,4880.000006039937,292.1015625,4900.083339214325C292.1015625,4920.166672388713,292.1015625,4939.333339055379,292.1015625,4958.500005722046C292.1015625,4977.666672388713,292.1015625,4996.833339055379,292.1015625,5014.416672388713C292.1015625,5032.000005722046,292.1015625,5048.000005722046,292.1015625,5064.000005722046C292.1015625,5080.000005722046,292.1015625,5096.000005722046,292.1015625,5115.416672388713C292.1015625,5134.833339055379,292.1015625,5157.666672388713,292.1015625,5180.500005722046C292.1015625,5203.333339055379,292.1015625,5226.166672388713,292.1015625,5249.000005722046C292.1015625,5271.833339055379,292.1015625,5294.666672388713,292.1015625,5317.500005722046C292.1015625,5340.333339055379,292.1015625,5363.166672388713,301.4110007036712,5385.083338896434C310.72043890734244,5407.000005404155,329.33931531468494,5428.000005086263,338.64875351835616,5438.500004927318L347.9581917220274,5449.000004768372" marker-end="url(#arrowhead-vee)" id="edge-[131,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1299.4531269073486,4579.000005722046L1299.4531269073486,4585.166672388713C1299.4531269073486,4591.333339055379,1299.4531269073486,4603.666672388713,1322.2895059765854,4619.416672229767C1345.125885045822,4635.166672070821,1390.7986431842955,4654.333338419597,1413.6350222535323,4663.916671593984L1436.471401322769,4673.500004768372" marker-end="url(#arrowhead-vee)" id="edge-[134,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1560.3046894073486,4579.000005722046L1560.3046894073486,4585.166672388713C1560.3046894073486,4591.333339055379,1560.3046894073486,4603.666672388713,1546.647237345241,4619.416672229767C1532.9897852831334,4635.166672070821,1505.6748811589177,4654.333338419597,1492.01742909681,4663.916671593984L1478.3599770347023,4673.500004768372" marker-end="url(#arrowhead-vee)" id="edge-[135,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1408.0756415616531,4695.50000667572L1360.5005349526025,4705.083339850108C1312.9254283435516,4714.666673024495,1217.7752151254501,4733.833339373271,1170.2001085163995,4749.583339214325C1122.6250019073486,4765.333339055379,1122.6250019073486,4777.666672388713,1122.6250019073486,4783.833339055379L1122.6250019073486,4790.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[136,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1447.007214279995,4695.50000667572L1433.3497622178872,4705.083339850108C1419.6923101557795,4714.666673024495,1392.3774060315639,4733.833339373271,1378.7199539694564,4749.583339214325C1365.0625019073486,4765.333339055379,1365.0625019073486,4777.666672388713,1365.0625019073486,4783.833339055379L1365.0625019073486,4790.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[136,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1478.3599770347023,4695.50000667572L1492.01742909681,4705.083339850108C1505.6748811589177,4714.666673024495,1532.9897852831334,4733.833339373271,1546.647237345241,4755.750005880992C1560.3046894073486,4777.666672388713,1560.3046894073486,4802.333339055379,1560.3046894073486,4827.000005722046C1560.3046894073486,4851.666672388713,1560.3046894073486,4876.333339055379,1560.3046894073486,4898.250005722046C1560.3046894073486,4920.166672388713,1560.3046894073486,4939.333339055379,1560.3046894073486,4958.500005722046C1560.3046894073486,4977.666672388713,1560.3046894073486,4996.833339055379,1560.3046894073486,5014.416672388713C1560.3046894073486,5032.000005722046,1560.3046894073486,5048.000005722046,1560.3046894073486,5064.000005722046C1560.3046894073486,5080.000005722046,1560.3046894073486,5096.000005722046,1560.3046894073486,5110.166672388713C1560.3046894073486,5124.333339055379,1560.3046894073486,5136.666672388713,1560.3046894073486,5142.833339055379L1560.3046894073486,5149.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[136,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1122.6250019073486,4864.000005722046L1122.6250019073486,4870.166672388713C1122.6250019073486,4876.333339055379,1122.6250019073486,4888.666672388713,1122.6250019073486,4901.000005722046C1122.6250019073486,4913.333339055379,1122.6250019073486,4925.666672388713,1122.6250019073486,4931.833339055379L1122.6250019073486,4938.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[139,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1122.6250019073486,4979.000005722046L1122.6250019073486,4985.166672388713C1122.6250019073486,4991.333339055379,1122.6250019073486,5003.666672388713,1138.1982440948486,5016.000005722046C1153.7714862823486,5028.333339055379,1184.9179706573486,5040.666672388713,1200.4912128448486,5046.833339055379L1216.0644550323486,5053.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[140,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1365.0625019073486,4864.000005722046L1365.0625019073486,4870.166672388713C1365.0625019073486,4876.333339055379,1365.0625019073486,4888.666672388713,1365.0625019073486,4901.000005722046C1365.0625019073486,4913.333339055379,1365.0625019073486,4925.666672388713,1365.0625019073486,4931.833339055379L1365.0625019073486,4938.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[143,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1365.0625019073486,4979.000005722046L1365.0625019073486,4985.166672388713C1365.0625019073486,4991.333339055379,1365.0625019073486,5003.666672388713,1349.4892597198486,5016.000005722046C1333.9160175323486,5028.333339055379,1302.7695331573486,5040.666672388713,1287.1962909698486,5046.833339055379L1271.6230487823486,5053.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[144,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1207.0983087619145,5075.000005722046L1186.4985906349286,5081.166672388713C1165.898872507943,5087.333339055379,1124.6994362539715,5099.666672388713,1104.0997181269856,5112.000005722046C1083.5,5124.333339055379,1083.5,5136.666672388713,1083.5,5142.833339055379L1083.5,5149.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[145,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1271.6230487823486,5075.000005722046L1287.1962909698486,5081.166672388713C1302.7695331573486,5087.333339055379,1333.9160175323486,5099.666672388713,1349.4892597198486,5112.000005722046C1365.0625019073486,5124.333339055379,1365.0625019073486,5136.666672388713,1365.0625019073486,5142.833339055379L1365.0625019073486,5149.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[145,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1083.5,5212.000005722046L1083.5,5218.166672388713C1083.5,5224.333339055379,1083.5,5236.666672388713,1083.5,5249.000005722046C1083.5,5261.333339055379,1083.5,5273.666672388713,1083.5,5279.833339055379L1083.5,5286.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[148,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1083.5,5349.000005722046L1083.5,5355.166672388713C1083.5,5361.333339055379,1083.5,5373.666672388713,973.0768235524496,5391.091865091818C862.653647104899,5408.517057794924,641.8072942097982,5431.034109867801,531.3841177622477,5442.29263590424L420.96094131469727,5453.551161940679" marker-end="url(#arrowhead-vee)" id="edge-[149,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M357.7109375,5471.00000667572L357.7109375,5481.500006516774C357.7109375,5492.000006357829,357.7109375,5513.000006039937,357.7109375,5533.083339214325C357.7109375,5553.166672388713,357.7109375,5572.333339055379,357.7109375,5591.500005722046C357.7109375,5610.666672388713,357.7109375,5629.833339055379,357.7109375,5647.416672388713C357.7109375,5665.000005722046,357.7109375,5681.000005722046,357.7109375,5697.000005722046C357.7109375,5713.000005722046,357.7109375,5729.000005722046,357.7109375,5748.416672388713C357.7109375,5767.833339055379,357.7109375,5790.666672388713,357.7109375,5813.500005722046C357.7109375,5836.333339055379,357.7109375,5859.166672388713,357.7109375,5882.000005722046C357.7109375,5904.833339055379,357.7109375,5927.666672388713,357.7109375,5950.500005722046C357.7109375,5973.333339055379,357.7109375,5996.166672388713,416.8398434321086,6014.372880678105C475.9687493642171,6032.579088967496,594.2265612284342,6046.158172212946,653.3554671605428,6052.9477138356715L712.4843730926514,6059.737255458397" marker-end="url(#arrowhead-vee)" id="edge-[150,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1365.0625019073486,5212.000005722046L1365.0625019073486,5218.166672388713C1365.0625019073486,5224.333339055379,1365.0625019073486,5236.666672388713,1378.7199539694564,5252.416672229767C1392.3774060315639,5268.166672070821,1419.6923101557795,5287.333338419597,1433.3497622178872,5296.916671593984L1447.007214279995,5306.500004768372" marker-end="url(#arrowhead-vee)" id="edge-[153,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1560.3046894073486,5212.000005722046L1560.3046894073486,5218.166672388713C1560.3046894073486,5224.333339055379,1560.3046894073486,5236.666672388713,1546.647237345241,5252.416672229767C1532.9897852831334,5268.166672070821,1505.6748811589177,5287.333338419597,1492.01742909681,5296.916671593984L1478.3599770347023,5306.500004768372" marker-end="url(#arrowhead-vee)" id="edge-[154,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1438.07724087805,5328.50000667572L1416.6398885495998,5338.083339850108C1395.2025362211496,5347.666673024495,1352.327831564249,5366.833339373271,1330.890479235799,5382.583339214325C1309.4531269073486,5398.333339055379,1309.4531269073486,5410.666672388713,1309.4531269073486,5416.833339055379L1309.4531269073486,5423.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[155,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1482.1493820165197,5328.50000667572L1499.1082092899912,5338.083339850108C1516.0670365634626,5347.666673024495,1549.9846911104057,5366.833339373271,1566.943518383877,5382.583339214325C1583.9023456573486,5398.333339055379,1583.9023456573486,5410.666672388713,1583.9023456573486,5416.833339055379L1583.9023456573486,5423.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[155,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1309.4531269073486,5497.000005722046L1309.4531269073486,5503.166672388713C1309.4531269073486,5509.333339055379,1309.4531269073486,5521.666672388713,1309.4531269073486,5534.000005722046C1309.4531269073486,5546.333339055379,1309.4531269073486,5558.666672388713,1309.4531269073486,5564.833339055379L1309.4531269073486,5571.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[158,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1309.4531269073486,5612.000005722046L1309.4531269073486,5618.166672388713C1309.4531269073486,5624.333339055379,1309.4531269073486,5636.666672388713,1329.138985739814,5649.000005722046C1348.8248445722793,5661.333339055379,1388.19656223721,5673.666672388713,1407.882421069675,5679.833339055379L1427.5682799021404,5686.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[159,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1583.9023456573486,5497.000005722046L1583.9023456573486,5503.166672388713C1583.9023456573486,5509.333339055379,1583.9023456573486,5521.666672388713,1583.9023456573486,5534.000005722046C1583.9023456573486,5546.333339055379,1583.9023456573486,5558.666672388713,1583.9023456573486,5564.833339055379L1583.9023456573486,5571.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[162,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1583.9023456573486,5612.000005722046L1583.9023456573486,5618.166672388713C1583.9023456573486,5624.333339055379,1583.9023456573486,5636.666672388713,1568.3291034698486,5649.000005722046C1552.7558612823486,5661.333339055379,1521.6093769073486,5673.666672388713,1506.0361347198486,5679.833339055379L1490.4628925323486,5686.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[163,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1462.6835956573486,5708.000005722046L1462.6835956573486,5714.166672388713C1462.6835956573486,5720.333339055379,1462.6835956573486,5732.666672388713,1462.6835956573486,5745.000005722046C1462.6835956573486,5757.333339055379,1462.6835956573486,5769.666672388713,1462.6835956573486,5775.833339055379L1462.6835956573486,5782.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[164,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1462.6835956573486,5845.000005722046L1462.6835956573486,5851.166672388713C1462.6835956573486,5857.333339055379,1462.6835956573486,5869.666672388713,1462.6835956573486,5882.000005722046C1462.6835956573486,5894.333339055379,1462.6835956573486,5906.666672388713,1462.6835956573486,5912.833339055379L1462.6835956573486,5919.000005722046" marker-end="url(#arrowhead-vee)" id="edge-[167,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M1462.6835956573486,5982.000005722046L1462.6835956573486,5988.166672388713C1462.6835956573486,5994.333339055379,1462.6835956573486,6006.666672388713,1358.7337265014648,6020.096749046144C1254.783857345581,6033.526825703575,1046.8841190338135,6048.053645685104,942.9342498779297,6055.31705567587L838.9843807220459,6062.580465666634" marker-end="url(#arrowhead-vee)" id="edge-[168,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M775.7343769073486,6078.0000076293945L775.7343769073486,6084.1666742960615C775.7343769073486,6090.333340962728,775.7343769073486,6102.6666742960615,775.7343769073486,6116.833340962728C775.7343769073486,6131.0000076293945,775.7343769073486,6147.0000076293945,775.7343769073486,6163.0000076293945C775.7343769073486,6179.0000076293945,775.7343769073486,6195.0000076293945,775.7343769073486,6212.583340962728C775.7343769073486,6230.1666742960615,775.7343769073486,6249.333340962728,775.7343769073486,6268.5000076293945C775.7343769073486,6287.6666742960615,775.7343769073486,6306.833340962728,775.7343769073486,6322.583340962728C775.7343769073486,6338.333340962728,775.7343769073486,6350.6666742960615,775.7343769073486,6356.833340962728L775.7343769073486,6363.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[169,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M814.1949914568168,6078.0000076293945L835.7562431985721,6084.1666742960615C857.3174949403274,6090.333340962728,900.439998423838,6102.6666742960615,922.0012501655933,6115.0000076293945C943.5625019073486,6127.333340962728,943.5625019073486,6139.6666742960615,943.5625019073486,6145.833340962728L943.5625019073486,6152.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[169,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M753.2527673319985,6078.0000076293945L740.6494418445568,6084.1666742960615C728.0461163571152,6090.333340962728,702.8394653822319,6102.6666742960615,690.2361398947902,6116.833340962728C677.6328144073486,6131.0000076293945,677.6328144073486,6147.0000076293945,677.6328144073486,6163.0000076293945C677.6328144073486,6179.0000076293945,677.6328144073486,6195.0000076293945,677.6328144073486,6212.583340962728C677.6328144073486,6230.1666742960615,677.6328144073486,6249.333340962728,677.6328144073486,6268.5000076293945C677.6328144073486,6287.6666742960615,677.6328144073486,6306.833340962728,677.6328144073486,6326.0000076293945C677.6328144073486,6345.1666742960615,677.6328144073486,6364.333340962728,677.6328144073486,6383.5000076293945C677.6328144073486,6402.6666742960615,677.6328144073486,6421.833340962728,694.7151033613417,6437.583340962728C711.7973923153348,6453.333340962728,745.9619702233209,6465.6666742960615,763.044259177314,6471.833340962728L780.126548131307,6478.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[169,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M775.7343769073486,6404.0000076293945L775.7343769073486,6410.1666742960615C775.7343769073486,6416.333340962728,775.7343769073486,6428.6666742960615,780.2133401234945,6441.0000076293945C784.6923033396403,6453.333340962728,793.650229771932,6465.6666742960615,798.1291929880778,6471.833340962728L802.6081562042236,6478.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[170,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M943.5625019073486,6174.0000076293945L943.5625019073486,6180.1666742960615C943.5625019073486,6186.333340962728,943.5625019073486,6198.6666742960615,943.5625019073486,6211.0000076293945C943.5625019073486,6223.333340962728,943.5625019073486,6235.6666742960615,943.5625019073486,6241.833340962728L943.5625019073486,6248.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[171,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M943.5625019073486,6289.0000076293945L943.5625019073486,6295.1666742960615C943.5625019073486,6301.333340962728,943.5625019073486,6313.6666742960615,943.5625019073486,6326.0000076293945C943.5625019073486,6338.333340962728,943.5625019073486,6350.6666742960615,943.5625019073486,6356.833340962728L943.5625019073486,6363.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[172,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M943.5625019073486,6404.0000076293945L943.5625019073486,6410.1666742960615C943.5625019073486,6416.333340962728,943.5625019073486,6428.6666742960615,926.4802129533556,6441.0000076293945C909.3979239993624,6453.333340962728,875.2333460913763,6465.6666742960615,858.1510571373833,6471.833340962728L841.0687681833903,6478.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[173,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M810.5976581573486,6500.0000076293945L810.5976581573486,6506.1666742960615C810.5976581573486,6512.333340962728,810.5976581573486,6524.6666742960615,810.5976581573486,6537.0000076293945C810.5976581573486,6549.333340962728,810.5976581573486,6561.6666742960615,810.5976581573486,6567.833340962728L810.5976581573486,6574.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[174,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M794.5656383124581,6637.0000076293945L791.4270947449398,6643.1666742960615C788.2885511774216,6649.333340962728,782.011464042385,6661.6666742960615,778.8729204748669,6675.833340962728C775.7343769073486,6690.0000076293945,775.7343769073486,6706.0000076293945,775.7343769073486,6722.0000076293945C775.7343769073486,6738.0000076293945,775.7343769073486,6754.0000076293945,775.7343769073486,6771.583340962728C775.7343769073486,6789.1666742960615,775.7343769073486,6808.333340962728,775.7343769073486,6827.5000076293945C775.7343769073486,6846.6666742960615,775.7343769073486,6865.833340962728,775.7343769073486,6881.583340962728C775.7343769073486,6897.333340962728,775.7343769073486,6909.6666742960615,775.7343769073486,6915.833340962728L775.7343769073486,6922.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[177,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M871.7420753562537,6637.0000076293945L883.7121464481029,6643.1666742960615C895.682217539952,6649.333340962728,919.6223597236503,6661.6666742960615,931.5924308154995,6674.0000076293945C943.5625019073486,6686.333340962728,943.5625019073486,6698.6666742960615,943.5625019073486,6704.833340962728L943.5625019073486,6711.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[177,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M749.4532409584435,6637.0000076293945L737.4831698665944,6643.1666742960615C725.5130987747452,6649.333340962728,701.5729565910469,6661.6666742960615,689.6028854991978,6675.833340962728C677.6328144073486,6690.0000076293945,677.6328144073486,6706.0000076293945,677.6328144073486,6722.0000076293945C677.6328144073486,6738.0000076293945,677.6328144073486,6754.0000076293945,677.6328144073486,6771.583340962728C677.6328144073486,6789.1666742960615,677.6328144073486,6808.333340962728,677.6328144073486,6827.5000076293945C677.6328144073486,6846.6666742960615,677.6328144073486,6865.833340962728,677.6328144073486,6885.0000076293945C677.6328144073486,6904.1666742960615,677.6328144073486,6923.333340962728,677.6328144073486,6942.5000076293945C677.6328144073486,6961.6666742960615,677.6328144073486,6980.833340962728,690.2361401451958,6996.583340962728C702.8394658830431,7012.333340962728,728.0461173587377,7024.6666742960615,740.6494430965848,7030.833340962728L753.252768834432,7037.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[177,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M775.7343769073486,6963.0000076293945L775.7343769073486,6969.1666742960615C775.7343769073486,6975.333340962728,775.7343769073486,6987.6666742960615,775.7343769073486,7000.0000076293945C775.7343769073486,7012.333340962728,775.7343769073486,7024.6666742960615,775.7343769073486,7030.833340962728L775.7343769073486,7037.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[178,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M943.5625019073486,6733.0000076293945L943.5625019073486,6739.1666742960615C943.5625019073486,6745.333340962728,943.5625019073486,6757.6666742960615,943.5625019073486,6770.0000076293945C943.5625019073486,6782.333340962728,943.5625019073486,6794.6666742960615,943.5625019073486,6800.833340962728L943.5625019073486,6807.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[179,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M943.5625019073486,6848.0000076293945L943.5625019073486,6854.1666742960615C943.5625019073486,6860.333340962728,943.5625019073486,6872.6666742960615,943.5625019073486,6885.0000076293945C943.5625019073486,6897.333340962728,943.5625019073486,6909.6666742960615,943.5625019073486,6915.833340962728L943.5625019073486,6922.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[180,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M943.5625019073486,6963.0000076293945L943.5625019073486,6969.1666742960615C943.5625019073486,6975.333340962728,943.5625019073486,6987.6666742960615,922.0012497372098,7000.0000076293945C900.4399975670708,7012.333340962728,857.317493226793,7024.6666742960615,835.7562410566542,7030.833340962728L814.1949888865153,7037.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[181,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M775.7343769073486,7059.0000076293945L775.7343769073486,7065.1666742960615C775.7343769073486,7071.333340962728,775.7343769073486,7083.6666742960615,775.7343769073486,7096.0000076293945C775.7343769073486,7108.333340962728,775.7343769073486,7120.6666742960615,775.7343769073486,7126.833340962728L775.7343769073486,7133.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[182,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M775.7343769073486,7196.0000076293945L775.7343769073486,7202.1666742960615C775.7343769073486,7208.333340962728,775.7343769073486,7220.6666742960615,775.7343769073486,7233.0000076293945C775.7343769073486,7245.333340962728,775.7343769073486,7257.6666742960615,775.7343769073486,7263.833340962728L775.7343769073486,7270.0000076293945" marker-end="url(#arrowhead-vee)" id="edge-[185,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/><path class="edge-path" d="M775.7343769073486,7322.000011444092L775.7343769073486,7328.166678110759C775.7343769073486,7334.333344777425,775.7343769073486,7346.666678110759,775.7343769073486,7359.000011444092C775.7343769073486,7371.333344777425,775.7343769073486,7383.666678110759,775.7343769073486,7389.833344777425L775.7343769073486,7396.000011444092" marker-end="url(#arrowhead-vee)" id="edge-[186,0]" style="stroke: rgb(0, 0, 0); stroke-width: 1px; fill: none;"/></g><g id="edge-labels" class="edge-labels"><g class="edge-label" transform="translate(485.71484565734863,59)" style="opacity: 1;"><g transform="translate(-12.2265625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">input</tspan></text></g></g><g class="edge-label" transform="translate(509.06640815734863,207)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(245.69922065734863,333.00000381469727)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(651.2148456573486,333.00000381469727)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(782.4335956573486,527.5000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(245.69922065734863,470.00000381469727)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(245.69922065734863,585.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(651.2148456573486,470.00000381469727)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(644.8007831573486,585.0000038146973)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(136.796875,914.0000057220459)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(587.1914081573486,681.0000038146973)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(136.796875,1314.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(136.796875,1451.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(139.1796875,1588.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(589.5742206573486,818.0000038146973)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(784.8164081573486,818.0000038146973)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(312.54296875,914.0000057220459)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(619.00390625,914.0000057220459)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(951.6132831573486,1119.500005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(312.54296875,1062.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(312.54296875,1177.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(619.00390625,1062.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(612.58984375,1177.000005722046)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(344.5546875,1314.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(586.9921875,1314.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(344.5546875,1451.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(346.9375,1588.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(236.80078125,1947.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(589.375,1451.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(953.9960956573486,1451.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(570.5078144073486,1588.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(837.7890644073486,1588.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1120.7929706573486,1793.500005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(570.5078144073486,1736.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(570.5078144073486,1851.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(837.7890644073486,1736.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(831.3750019073486,1851.000005722046)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(531.3828125,1947.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(812.9453144073486,1947.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(531.3828125,2084.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(533.765625,2221.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(248.08203315734863,2580.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(815.3281269073486,2084.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(1123.1757831573486,2084.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(708.9218769073486,2221.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(951.3593769073486,2221.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1289.9726581573486,2426.500005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(708.9218769073486,2369.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(708.9218769073486,2484.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(951.3593769073486,2369.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(944.9453144073486,2484.000005722046)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(669.796875,2580.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(999.3203144073486,2580.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(669.796875,2717.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(672.1796875,2854.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(248.08203315734863,3213.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(1001.7031269073486,2717.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(1292.3554706573486,2717.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(822.4921894073486,2854.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1064.9296894073486,2854.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1459.1523456573486,3059.500005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(822.4921894073486,3002.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(822.4921894073486,3117.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1064.9296894073486,3002.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1058.5156269073486,3117.000005722046)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(783.3671875,3213.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1112.8906269073486,3213.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(783.3671875,3350.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(785.75,3487.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(248.08203315734863,3846.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(1115.2734394073486,3350.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(1461.5351581573486,3350.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(936.0625019073486,3487.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1178.5000019073486,3487.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1552.9218769073486,3692.500005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(936.0625019073486,3635.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(936.0625019073486,3750.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1178.5000019073486,3635.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1172.0859394073486,3750.000005722046)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(896.9375,3846.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1226.4609394073486,3846.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(896.9375,3983.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(899.3203125,4120.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(269.453125,4479.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(1228.8437519073486,3983.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(1555.3046894073486,3983.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(1049.6328144073486,4120.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1292.0703144073486,4120.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1600.8828144073486,4325.500005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1049.6328144073486,4268.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1049.6328144073486,4383.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1292.0703144073486,4268.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1285.6562519073486,4383.000005722046)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(1010.5078125,4479.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1340.0312519073486,4479.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1010.5078125,4616.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1012.890625,4753.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(335.0625,5112.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(1342.4140644073486,4616.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(1603.2656269073486,4616.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(1163.2031269073486,4753.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1405.6406269073486,4753.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1600.8828144073486,4958.500005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1163.2031269073486,4901.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1163.2031269073486,5016.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1405.6406269073486,4901.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1399.2265644073486,5016.000005722046)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(1124.078125,5112.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1405.6406269073486,5112.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1124.078125,5249.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1126.4609375,5386.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(400.671875,5745.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(1408.0234394073486,5249.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(1603.2656269073486,5249.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(1350.0312519073486,5386.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1624.4804706573486,5386.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1350.0312519073486,5534.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1350.0312519073486,5649.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1624.4804706573486,5534.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1618.0664081573486,5649.000005722046)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(1503.2617206573486,5745.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1503.2617206573486,5882.000005722046)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(1505.6445331573486,6019.000005722046)" style="opacity: 1;"><g transform="translate(-37.9609375,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ inputs</tspan></text></g></g><g class="edge-label" transform="translate(816.3125019073486,6211.0000076293945)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(984.1406269073486,6115.0000076293945)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(711.7968769073486,6268.5000076293945)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(816.3125019073486,6441.0000076293945)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(984.1406269073486,6211.0000076293945)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(984.1406269073486,6326.0000076293945)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(977.7265644073486,6441.0000076293945)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (2)</tspan></text></g></g><g class="edge-label" transform="translate(851.1757831573486,6537.0000076293945)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(816.3125019073486,6770.0000076293945)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(984.1406269073486,6674.0000076293945)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(711.7968769073486,6827.5000076293945)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (1)</tspan></text></g></g><g class="edge-label" transform="translate(816.3125019073486,7000.0000076293945)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(984.1406269073486,6770.0000076293945)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(984.1406269073486,6885.0000076293945)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(977.7265644073486,7000.0000076293945)" style="opacity: 1;"><g transform="translate(-29.1640625,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ (2)</tspan></text></g></g><g class="edge-label" transform="translate(816.3125019073486,7096.0000076293945)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(816.3125019073486,7233.0000076293945)" style="opacity: 1;"><g transform="translate(-35.578125,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output ⇨ input</tspan></text></g></g><g class="edge-label" transform="translate(796.4843769073486,7359.000011444092)" style="opacity: 1;"><g transform="translate(-15.75,-7)"><text style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px;"><tspan xml:space="preserve" dy="1em" x="1">output</tspan></text></g></g></g><g id="nodes" class="nodes"><g id="node-wavenet1_embedding0_fwd" class="node" transform="translate(468.48828315734863,133)" style="opacity: 1;"><g transform="translate(-77.234375,-37)"><g><g class="node-item node-item-operator node-item-operator-transform" transform="translate(0,0)"><path d="M5,0h63.28125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-68.28125a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 68); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Embedding</text><title>wavenet1_embedding0_fwd</title></g><g class="node-item node-item-input" transform="translate(68.28125,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_embedding0_weight" transform="translate(107.921875,0)"><path d="M0,0h41.546875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[512,24]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h154.46875a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-144.46875a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dtype</tspan><tspan> = float32</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">input_dim</tspan><tspan> = 512</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">output_dim</tspan><tspan> = 24</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">sparse_grad</tspan><tspan> = False</tspan></text></g><line class="node" x1="68.28125" y1="0" x2="68.28125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="107.921875" y1="0" x2="107.921875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="154.46875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h144.46875a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-144.46875a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_swapaxes0" class="node" transform="translate(468.48828315734863,270.00000190734863)" style="opacity: 1;"><g transform="translate(-49.5546875,-26.000001907348633)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h54.46875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-59.46875a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">SwapAxis</text><title>wavenet1_swapaxes0</title></g><g class="node-item node-item-input" transform="translate(59.46875,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h99.109375a0,0 0 0 1 0,0v25a5,5 0 0 1 -5,5h-89.109375a5,5 0 0 1 -5,-5v-25a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dim1</tspan><tspan> = 1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dim2</tspan><tspan> = 2</tspan></text></g><line class="node" x1="59.46875" y1="0" x2="59.46875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="99.109375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h89.109375a5,5 0 0 1 5,5v42a5,5 0 0 1 -5,5h-89.109375a5,5 0 0 1 -5,-5v-42a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue0_conv0_fwd" class="node" transform="translate(205.12109565734863,401.50000381469727)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue0_conv0_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue0_conv0_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue0_conv0_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue0_conv0_sigmoid_fwd" class="node" transform="translate(205.12109565734863,527.5000038146973)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue0_conv0_sigmoid_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = sigmoid</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue0_conv1_fwd" class="node" transform="translate(610.6367206573486,401.50000381469727)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue0_conv1_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue0_conv1_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue0_conv1_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue0_conv1_tanh_fwd" class="node" transform="translate(610.6367206573486,527.5000038146973)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue0_conv1_tanh_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = tanh</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue0__mul0" class="node" transform="translate(205.12109565734863,633.0000038146973)" style="opacity: 1;"><g transform="translate(-74.0546875,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77.4375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77.4375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_mul</text><title>wavenet1_causaldilatedresidue0__mul0</title></g><g class="node-item node-item-input" transform="translate(82.4375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(122.078125,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><line class="node" x1="82.4375" y1="0" x2="82.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="122.078125" y1="0" x2="122.078125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h138.109375a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-138.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue0_conv2_fwd" class="node" transform="translate(96.21875,1245.500005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue0_conv2_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue0_conv2_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[64,24,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue0_conv2_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[64,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 64</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis0" class="node" transform="translate(96.21875,1382.500005722046)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 510</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1__plusscalar0" class="node" transform="translate(96.21875,1519.500005722046)" style="opacity: 1;"><g transform="translate(-55.4921875,-20.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h66.34375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-71.34375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_plus_scalar</text><title>wavenet1__plusscalar0</title></g><g class="node-item node-item-input" transform="translate(71.34375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h110.984375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-100.984375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">scalar</tspan><tspan> = 0</tspan></text></g><line class="node" x1="71.34375" y1="0" x2="71.34375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="110.984375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h100.984375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-100.984375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue0_conv3_fwd" class="node" transform="translate(546.6132831573486,749.5000038146973)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue0_conv3_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue0_conv3_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue0_conv3_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue0_slice_axis0" class="node" transform="translate(741.8554706573486,749.5000038146973)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_causaldilatedresidue0_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 1</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue0__plus0" class="node" transform="translate(546.6132831573486,866.0000047683716)" style="opacity: 1;"><g transform="translate(-63.250003814697266,-11.000000953674316)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_add</text><title>wavenet1_causaldilatedresidue0__plus0</title></g><g class="node-item node-item-input" transform="translate(82,0)"><path d="M0,0h39.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-39.5a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">inputs</text><title>
+</title></g><line class="node" x1="82" y1="0" x2="82" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-116.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue1_conv0_fwd" class="node" transform="translate(271.96484375,988.0000057220459)" style="opacity: 1;"><g transform="translate(-96.21875,-37)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue1_conv0_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue1_conv0_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue1_conv0_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dilate</tspan><tspan> = (2,)</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue1_conv0_sigmoid_fwd" class="node" transform="translate(271.96484375,1119.500005722046)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue1_conv0_sigmoid_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = sigmoid</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue1_conv1_fwd" class="node" transform="translate(578.42578125,988.0000057220459)" style="opacity: 1;"><g transform="translate(-96.21875,-37)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue1_conv1_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue1_conv1_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue1_conv1_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dilate</tspan><tspan> = (2,)</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue1_conv1_tanh_fwd" class="node" transform="translate(578.42578125,1119.500005722046)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue1_conv1_tanh_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = tanh</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue1__mul0" class="node" transform="translate(425.1953125,1245.500005722046)" style="opacity: 1;"><g transform="translate(-74.0546875,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77.4375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77.4375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_mul</text><title>wavenet1_causaldilatedresidue1__mul0</title></g><g class="node-item node-item-input" transform="translate(82.4375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(122.078125,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><line class="node" x1="82.4375" y1="0" x2="82.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="122.078125" y1="0" x2="122.078125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h138.109375a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-138.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue1_conv2_fwd" class="node" transform="translate(303.9765625,1382.500005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue1_conv2_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue1_conv2_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[64,24,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue1_conv2_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[64,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 64</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis1" class="node" transform="translate(303.9765625,1519.500005722046)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis1</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 508</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1__plus0" class="node" transform="translate(193.83984375,1662.000005722046)" style="opacity: 1;"><g transform="translate(-63.250003814697266,-11.000000953674316)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_add</text><title>wavenet1__plus0</title></g><g class="node-item node-item-input" transform="translate(82,0)"><path d="M0,0h39.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-39.5a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">inputs</text><title>
+</title></g><line class="node" x1="82" y1="0" x2="82" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-116.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue1_conv3_fwd" class="node" transform="translate(546.4140625,1382.500005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue1_conv3_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue1_conv3_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue1_conv3_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue1_slice_axis0" class="node" transform="translate(911.0351581573486,1382.500005722046)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_causaldilatedresidue1_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 2</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue1__plus0" class="node" transform="translate(715.7929706573486,1519.500005722046)" style="opacity: 1;"><g transform="translate(-63.250003814697266,-11.000000953674316)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_add</text><title>wavenet1_causaldilatedresidue1__plus0</title></g><g class="node-item node-item-input" transform="translate(82,0)"><path d="M0,0h39.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-39.5a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">inputs</text><title>
+</title></g><line class="node" x1="82" y1="0" x2="82" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-116.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue2_conv0_fwd" class="node" transform="translate(529.9296894073486,1662.000005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-37)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue2_conv0_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue2_conv0_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue2_conv0_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dilate</tspan><tspan> = (4,)</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue2_conv0_sigmoid_fwd" class="node" transform="translate(529.9296894073486,1793.500005722046)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue2_conv0_sigmoid_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = sigmoid</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue2_conv1_fwd" class="node" transform="translate(797.2109394073486,1662.000005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-37)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue2_conv1_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue2_conv1_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue2_conv1_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dilate</tspan><tspan> = (4,)</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue2_conv1_tanh_fwd" class="node" transform="translate(797.2109394073486,1793.500005722046)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue2_conv1_tanh_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = tanh</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue2__mul0" class="node" transform="translate(594.5742206573486,1899.000005722046)" style="opacity: 1;"><g transform="translate(-74.0546875,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77.4375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77.4375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_mul</text><title>wavenet1_causaldilatedresidue2__mul0</title></g><g class="node-item node-item-input" transform="translate(82.4375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(122.078125,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><line class="node" x1="82.4375" y1="0" x2="82.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="122.078125" y1="0" x2="122.078125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h138.109375a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-138.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue2_conv2_fwd" class="node" transform="translate(490.8046875,2015.500005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue2_conv2_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue2_conv2_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[64,24,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue2_conv2_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[64,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 64</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis2" class="node" transform="translate(490.8046875,2152.500005722046)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis2</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 504</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1__plus1" class="node" transform="translate(205.12109565734863,2295.000005722046)" style="opacity: 1;"><g transform="translate(-63.250003814697266,-11.000000953674316)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_add</text><title>wavenet1__plus1</title></g><g class="node-item node-item-input" transform="translate(82,0)"><path d="M0,0h39.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-39.5a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">inputs</text><title>
+</title></g><line class="node" x1="82" y1="0" x2="82" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-116.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue2_conv3_fwd" class="node" transform="translate(772.3671894073486,2015.500005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue2_conv3_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue2_conv3_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue2_conv3_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue2_slice_axis0" class="node" transform="translate(1080.2148456573486,2015.500005722046)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_causaldilatedresidue2_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 4</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue2__plus0" class="node" transform="translate(884.9726581573486,2152.500005722046)" style="opacity: 1;"><g transform="translate(-63.250003814697266,-11.000000953674316)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_add</text><title>wavenet1_causaldilatedresidue2__plus0</title></g><g class="node-item node-item-input" transform="translate(82,0)"><path d="M0,0h39.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-39.5a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">inputs</text><title>
+</title></g><line class="node" x1="82" y1="0" x2="82" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-116.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue3_conv0_fwd" class="node" transform="translate(668.3437519073486,2295.000005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-37)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue3_conv0_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue3_conv0_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue3_conv0_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dilate</tspan><tspan> = (8,)</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue3_conv0_sigmoid_fwd" class="node" transform="translate(668.3437519073486,2426.500005722046)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue3_conv0_sigmoid_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = sigmoid</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue3_conv1_fwd" class="node" transform="translate(910.7812519073486,2295.000005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-37)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue3_conv1_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue3_conv1_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue3_conv1_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dilate</tspan><tspan> = (8,)</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue3_conv1_tanh_fwd" class="node" transform="translate(910.7812519073486,2426.500005722046)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue3_conv1_tanh_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = tanh</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue3__mul0" class="node" transform="translate(763.7539081573486,2532.000005722046)" style="opacity: 1;"><g transform="translate(-74.0546875,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77.4375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77.4375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_mul</text><title>wavenet1_causaldilatedresidue3__mul0</title></g><g class="node-item node-item-input" transform="translate(82.4375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(122.078125,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><line class="node" x1="82.4375" y1="0" x2="82.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="122.078125" y1="0" x2="122.078125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h138.109375a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-138.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue3_conv2_fwd" class="node" transform="translate(629.21875,2648.500005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue3_conv2_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue3_conv2_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[64,24,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue3_conv2_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[64,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 64</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis3" class="node" transform="translate(629.21875,2785.500005722046)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis3</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 496</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1__plus2" class="node" transform="translate(205.12109565734863,2928.000005722046)" style="opacity: 1;"><g transform="translate(-63.250003814697266,-11.000000953674316)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_add</text><title>wavenet1__plus2</title></g><g class="node-item node-item-input" transform="translate(82,0)"><path d="M0,0h39.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-39.5a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">inputs</text><title>
+</title></g><line class="node" x1="82" y1="0" x2="82" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-116.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue3_conv3_fwd" class="node" transform="translate(958.7421894073486,2648.500005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue3_conv3_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue3_conv3_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue3_conv3_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue3_slice_axis0" class="node" transform="translate(1249.3945331573486,2648.500005722046)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_causaldilatedresidue3_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 8</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue3__plus0" class="node" transform="translate(1054.1523456573486,2785.500005722046)" style="opacity: 1;"><g transform="translate(-63.250003814697266,-11.000000953674316)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_add</text><title>wavenet1_causaldilatedresidue3__plus0</title></g><g class="node-item node-item-input" transform="translate(82,0)"><path d="M0,0h39.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-39.5a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">inputs</text><title>
+</title></g><line class="node" x1="82" y1="0" x2="82" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-116.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue4_conv0_fwd" class="node" transform="translate(781.9140644073486,2928.000005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-37)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue4_conv0_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue4_conv0_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue4_conv0_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dilate</tspan><tspan> = (16,)</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue4_conv0_sigmoid_fwd" class="node" transform="translate(781.9140644073486,3059.500005722046)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue4_conv0_sigmoid_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = sigmoid</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue4_conv1_fwd" class="node" transform="translate(1024.3515644073486,2928.000005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-37)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue4_conv1_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue4_conv1_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue4_conv1_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dilate</tspan><tspan> = (16,)</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue4_conv1_tanh_fwd" class="node" transform="translate(1024.3515644073486,3059.500005722046)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue4_conv1_tanh_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = tanh</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue4__mul0" class="node" transform="translate(903.1328144073486,3165.000005722046)" style="opacity: 1;"><g transform="translate(-74.0546875,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77.4375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77.4375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_mul</text><title>wavenet1_causaldilatedresidue4__mul0</title></g><g class="node-item node-item-input" transform="translate(82.4375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(122.078125,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><line class="node" x1="82.4375" y1="0" x2="82.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="122.078125" y1="0" x2="122.078125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h138.109375a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-138.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue4_conv2_fwd" class="node" transform="translate(742.7890625,3281.500005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue4_conv2_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue4_conv2_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[64,24,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue4_conv2_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[64,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 64</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis4" class="node" transform="translate(742.7890625,3418.500005722046)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis4</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 480</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1__plus3" class="node" transform="translate(205.12109565734863,3561.000005722046)" style="opacity: 1;"><g transform="translate(-63.250003814697266,-11.000000953674316)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_add</text><title>wavenet1__plus3</title></g><g class="node-item node-item-input" transform="translate(82,0)"><path d="M0,0h39.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-39.5a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">inputs</text><title>
+</title></g><line class="node" x1="82" y1="0" x2="82" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-116.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue4_conv3_fwd" class="node" transform="translate(1072.3125019073486,3281.500005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue4_conv3_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue4_conv3_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue4_conv3_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue4_slice_axis0" class="node" transform="translate(1418.5742206573486,3281.500005722046)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_causaldilatedresidue4_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 16</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue4__plus0" class="node" transform="translate(1223.3320331573486,3418.500005722046)" style="opacity: 1;"><g transform="translate(-63.250003814697266,-11.000000953674316)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_add</text><title>wavenet1_causaldilatedresidue4__plus0</title></g><g class="node-item node-item-input" transform="translate(82,0)"><path d="M0,0h39.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-39.5a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">inputs</text><title>
+</title></g><line class="node" x1="82" y1="0" x2="82" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-116.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue5_conv0_fwd" class="node" transform="translate(895.4843769073486,3561.000005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-37)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue5_conv0_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue5_conv0_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue5_conv0_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dilate</tspan><tspan> = (32,)</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue5_conv0_sigmoid_fwd" class="node" transform="translate(895.4843769073486,3692.500005722046)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue5_conv0_sigmoid_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = sigmoid</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue5_conv1_fwd" class="node" transform="translate(1137.9218769073486,3561.000005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-37)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue5_conv1_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue5_conv1_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue5_conv1_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dilate</tspan><tspan> = (32,)</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue5_conv1_tanh_fwd" class="node" transform="translate(1137.9218769073486,3692.500005722046)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue5_conv1_tanh_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = tanh</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue5__mul0" class="node" transform="translate(1016.7031269073486,3798.000005722046)" style="opacity: 1;"><g transform="translate(-74.0546875,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77.4375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77.4375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_mul</text><title>wavenet1_causaldilatedresidue5__mul0</title></g><g class="node-item node-item-input" transform="translate(82.4375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(122.078125,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><line class="node" x1="82.4375" y1="0" x2="82.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="122.078125" y1="0" x2="122.078125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h138.109375a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-138.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue5_conv2_fwd" class="node" transform="translate(856.359375,3914.500005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue5_conv2_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue5_conv2_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[64,24,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue5_conv2_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[64,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 64</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis5" class="node" transform="translate(856.359375,4051.500005722046)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis5</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 448</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1__plus4" class="node" transform="translate(226.4921875,4194.000005722046)" style="opacity: 1;"><g transform="translate(-63.250003814697266,-11.000000953674316)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_add</text><title>wavenet1__plus4</title></g><g class="node-item node-item-input" transform="translate(82,0)"><path d="M0,0h39.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-39.5a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">inputs</text><title>
+</title></g><line class="node" x1="82" y1="0" x2="82" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-116.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue5_conv3_fwd" class="node" transform="translate(1185.8828144073486,3914.500005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue5_conv3_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue5_conv3_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue5_conv3_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue5_slice_axis0" class="node" transform="translate(1512.3437519073486,3914.500005722046)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_causaldilatedresidue5_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 32</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue5__plus0" class="node" transform="translate(1392.5117206573486,4051.500005722046)" style="opacity: 1;"><g transform="translate(-63.250003814697266,-11.000000953674316)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_add</text><title>wavenet1_causaldilatedresidue5__plus0</title></g><g class="node-item node-item-input" transform="translate(82,0)"><path d="M0,0h39.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-39.5a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">inputs</text><title>
+</title></g><line class="node" x1="82" y1="0" x2="82" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-116.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue6_conv0_fwd" class="node" transform="translate(1009.0546894073486,4194.000005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-37)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue6_conv0_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue6_conv0_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue6_conv0_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dilate</tspan><tspan> = (64,)</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue6_conv0_sigmoid_fwd" class="node" transform="translate(1009.0546894073486,4325.500005722046)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue6_conv0_sigmoid_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = sigmoid</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue6_conv1_fwd" class="node" transform="translate(1251.4921894073486,4194.000005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-37)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue6_conv1_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue6_conv1_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue6_conv1_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dilate</tspan><tspan> = (64,)</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue6_conv1_tanh_fwd" class="node" transform="translate(1251.4921894073486,4325.500005722046)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue6_conv1_tanh_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = tanh</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue6__mul0" class="node" transform="translate(1130.2734394073486,4431.000005722046)" style="opacity: 1;"><g transform="translate(-74.0546875,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77.4375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77.4375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_mul</text><title>wavenet1_causaldilatedresidue6__mul0</title></g><g class="node-item node-item-input" transform="translate(82.4375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(122.078125,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><line class="node" x1="82.4375" y1="0" x2="82.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="122.078125" y1="0" x2="122.078125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h138.109375a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-138.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue6_conv2_fwd" class="node" transform="translate(969.9296875,4547.500005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue6_conv2_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue6_conv2_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[64,24,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue6_conv2_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[64,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 64</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis6" class="node" transform="translate(969.9296875,4684.500005722046)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis6</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 384</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1__plus5" class="node" transform="translate(292.1015625,4827.000005722046)" style="opacity: 1;"><g transform="translate(-63.250003814697266,-11.000000953674316)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_add</text><title>wavenet1__plus5</title></g><g class="node-item node-item-input" transform="translate(82,0)"><path d="M0,0h39.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-39.5a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">inputs</text><title>
+</title></g><line class="node" x1="82" y1="0" x2="82" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-116.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue6_conv3_fwd" class="node" transform="translate(1299.4531269073486,4547.500005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue6_conv3_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue6_conv3_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue6_conv3_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue6_slice_axis0" class="node" transform="translate(1560.3046894073486,4547.500005722046)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_causaldilatedresidue6_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 64</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue6__plus0" class="node" transform="translate(1462.6835956573486,4684.500005722046)" style="opacity: 1;"><g transform="translate(-63.250003814697266,-11.000000953674316)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_add</text><title>wavenet1_causaldilatedresidue6__plus0</title></g><g class="node-item node-item-input" transform="translate(82,0)"><path d="M0,0h39.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-39.5a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">inputs</text><title>
+</title></g><line class="node" x1="82" y1="0" x2="82" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-116.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue7_conv0_fwd" class="node" transform="translate(1122.6250019073486,4827.000005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-37)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue7_conv0_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue7_conv0_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue7_conv0_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dilate</tspan><tspan> = (128,)</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue7_conv0_sigmoid_fwd" class="node" transform="translate(1122.6250019073486,4958.500005722046)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue7_conv0_sigmoid_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = sigmoid</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue7_conv1_fwd" class="node" transform="translate(1365.0625019073486,4827.000005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-37)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue7_conv1_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue7_conv1_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue7_conv1_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dilate</tspan><tspan> = (128,)</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue7_conv1_tanh_fwd" class="node" transform="translate(1365.0625019073486,4958.500005722046)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue7_conv1_tanh_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = tanh</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue7__mul0" class="node" transform="translate(1243.8437519073486,5064.000005722046)" style="opacity: 1;"><g transform="translate(-74.0546875,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77.4375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77.4375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_mul</text><title>wavenet1_causaldilatedresidue7__mul0</title></g><g class="node-item node-item-input" transform="translate(82.4375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(122.078125,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><line class="node" x1="82.4375" y1="0" x2="82.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="122.078125" y1="0" x2="122.078125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h138.109375a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-138.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue7_conv2_fwd" class="node" transform="translate(1083.5,5180.500005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue7_conv2_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue7_conv2_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[64,24,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue7_conv2_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[64,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 64</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis7" class="node" transform="translate(1083.5,5317.500005722046)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis7</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 256</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1__plus6" class="node" transform="translate(357.7109375,5460.000005722046)" style="opacity: 1;"><g transform="translate(-63.250003814697266,-11.000000953674316)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_add</text><title>wavenet1__plus6</title></g><g class="node-item node-item-input" transform="translate(82,0)"><path d="M0,0h39.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-39.5a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">inputs</text><title>
+</title></g><line class="node" x1="82" y1="0" x2="82" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-116.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue7_conv3_fwd" class="node" transform="translate(1365.0625019073486,5180.500005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue7_conv3_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue7_conv3_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue7_conv3_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue7_slice_axis0" class="node" transform="translate(1560.3046894073486,5180.500005722046)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_causaldilatedresidue7_slice_axis0</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 128</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue7__plus0" class="node" transform="translate(1462.6835956573486,5317.500005722046)" style="opacity: 1;"><g transform="translate(-63.250003814697266,-11.000000953674316)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_add</text><title>wavenet1_causaldilatedresidue7__plus0</title></g><g class="node-item node-item-input" transform="translate(82,0)"><path d="M0,0h39.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-39.5a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">inputs</text><title>
+</title></g><line class="node" x1="82" y1="0" x2="82" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-116.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue8_conv0_fwd" class="node" transform="translate(1309.4531269073486,5460.000005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-37)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue8_conv0_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue8_conv0_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue8_conv0_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dilate</tspan><tspan> = (256,)</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue8_conv0_sigmoid_fwd" class="node" transform="translate(1309.4531269073486,5591.500005722046)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue8_conv0_sigmoid_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = sigmoid</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue8_conv1_fwd" class="node" transform="translate(1583.9023456573486,5460.000005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-37)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue8_conv1_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue8_conv1_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[24,24,2]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue8_conv1_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[24,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v47a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-47a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dilate</tspan><tspan> = (256,)</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [2]</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="45" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 24</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v64a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-64a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue8_conv1_tanh_fwd" class="node" transform="translate(1583.9023456573486,5591.500005722046)" style="opacity: 1;"><g transform="translate(-51.1171875,-20.5)"><g><g class="node-item node-item-operator node-item-operator-activation" transform="translate(0,0)"><path d="M5,0h57.59375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-62.59375a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(75, 27, 22); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Activation</text><title>wavenet1_causaldilatedresidue8_conv1_tanh_fwd</title></g><g class="node-item node-item-input" transform="translate(62.59375,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h102.234375a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">act_type</tspan><tspan> = tanh</tspan></text></g><line class="node" x1="62.59375" y1="0" x2="62.59375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="102.234375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h92.234375a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-92.234375a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue8__mul0" class="node" transform="translate(1462.6835956573486,5697.000005722046)" style="opacity: 1;"><g transform="translate(-74.0546875,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77.4375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77.4375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_mul</text><title>wavenet1_causaldilatedresidue8__mul0</title></g><g class="node-item node-item-input" transform="translate(82.4375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(122.078125,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><line class="node" x1="82.4375" y1="0" x2="82.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="122.078125" y1="0" x2="122.078125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h138.109375a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-138.109375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_causaldilatedresidue8_conv2_fwd" class="node" transform="translate(1462.6835956573486,5813.500005722046)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_causaldilatedresidue8_conv2_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue8_conv2_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[64,24,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_causaldilatedresidue8_conv2_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[64,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 64</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_slice_axis8" class="node" transform="translate(1462.6835956573486,5950.500005722046)" style="opacity: 1;"><g transform="translate(-49.0234375,-31.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h53.40625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-58.40625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">slice_axis</text><title>wavenet1_slice_axis8</title></g><g class="node-item node-item-input" transform="translate(58.40625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h98.046875a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">axis</tspan><tspan> = -1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">begin</tspan><tspan> = 0</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">end</tspan><tspan> = None</tspan></text></g><line class="node" x1="58.40625" y1="0" x2="58.40625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="98.046875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h88.046875a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-88.046875a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1__plus7" class="node" transform="translate(775.7343769073486,6067.00000667572)" style="opacity: 1;"><g transform="translate(-63.250003814697266,-11.000000953674316)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h77a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-77a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">elemwise_add</text><title>wavenet1__plus7</title></g><g class="node-item node-item-input" transform="translate(82,0)"><path d="M0,0h39.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-39.5a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">inputs</text><title>
+</title></g><line class="node" x1="82" y1="0" x2="82" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.5a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-116.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0__greater_scalar0" class="node" transform="translate(775.7343769073486,6383.5000076293945)" style="opacity: 1;"><g transform="translate(-63.1015625,-20.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h81.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-86.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_greater_scalar</text><title>wavenet1_elu0__greater_scalar0</title></g><g class="node-item node-item-input" transform="translate(86.5625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h126.203125a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-116.203125a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">scalar</tspan><tspan> = 0</tspan></text></g><line class="node" x1="86.5625" y1="0" x2="86.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="126.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.203125a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-116.203125a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0_exp0" class="node" transform="translate(943.5625019073486,6163.0000076293945)" style="opacity: 1;"><g transform="translate(-35.5703125,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h26.5a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-26.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">exp</text><title>wavenet1_elu0_exp0</title></g><g class="node-item node-item-input" transform="translate(31.5,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-34.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><line class="node" x1="31.5" y1="0" x2="31.5" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h61.140625a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-61.140625a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0__minusscalar0" class="node" transform="translate(943.5625019073486,6268.5000076293945)" style="opacity: 1;"><g transform="translate(-60.3359375,-20.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h76.03125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-81.03125a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_minus_scalar</text><title>wavenet1_elu0__minusscalar0</title></g><g class="node-item node-item-input" transform="translate(81.03125,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h120.671875a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-110.671875a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">scalar</tspan><tspan> = 1.0</tspan></text></g><line class="node" x1="81.03125" y1="0" x2="81.03125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="120.671875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h110.671875a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-110.671875a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0__mulscalar0" class="node" transform="translate(943.5625019073486,6383.5000076293945)" style="opacity: 1;"><g transform="translate(-54.7265625,-20.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h64.8125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-69.8125a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_mul_scalar</text><title>wavenet1_elu0__mulscalar0</title></g><g class="node-item node-item-input" transform="translate(69.8125,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h109.453125a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-99.453125a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">scalar</tspan><tspan> = 1.0</tspan></text></g><line class="node" x1="69.8125" y1="0" x2="69.8125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="109.453125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h99.453125a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-99.453125a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0_where0" class="node" transform="translate(810.5976581573486,6489.0000076293945)" style="opacity: 1;"><g transform="translate(-68.0234375,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h39.34375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.34375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">where</text><title>wavenet1_elu0_where0</title></g><g class="node-item node-item-input" transform="translate(44.34375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(83.984375,0)"><path d="M0,0h26.03125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-26.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><g class="node-item node-item-input" transform="translate(110.015625,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(2)</text></g><line class="node" x1="44.34375" y1="0" x2="44.34375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="83.984375" y1="0" x2="83.984375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="110.015625" y1="0" x2="110.015625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h126.046875a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-126.046875a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_conv0_fwd" class="node" transform="translate(810.5976581573486,6605.5000076293945)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_conv0_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_conv0_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[64,64,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_conv0_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[64,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 64</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0__greater_scalar0" class="node" transform="translate(775.7343769073486,6942.5000076293945)" style="opacity: 1;"><g transform="translate(-63.1015625,-20.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h81.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-86.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_greater_scalar</text><title>wavenet1_elu0__greater_scalar0</title></g><g class="node-item node-item-input" transform="translate(86.5625,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h126.203125a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-116.203125a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">scalar</tspan><tspan> = 0</tspan></text></g><line class="node" x1="86.5625" y1="0" x2="86.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="126.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h116.203125a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-116.203125a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0_exp0" class="node" transform="translate(943.5625019073486,6722.0000076293945)" style="opacity: 1;"><g transform="translate(-35.5703125,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h26.5a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-26.5a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">exp</text><title>wavenet1_elu0_exp0</title></g><g class="node-item node-item-input" transform="translate(31.5,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-34.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><line class="node" x1="31.5" y1="0" x2="31.5" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h61.140625a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-61.140625a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0__minusscalar0" class="node" transform="translate(943.5625019073486,6827.5000076293945)" style="opacity: 1;"><g transform="translate(-60.3359375,-20.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h76.03125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-81.03125a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_minus_scalar</text><title>wavenet1_elu0__minusscalar0</title></g><g class="node-item node-item-input" transform="translate(81.03125,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h120.671875a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-110.671875a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">scalar</tspan><tspan> = 1.0</tspan></text></g><line class="node" x1="81.03125" y1="0" x2="81.03125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="120.671875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h110.671875a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-110.671875a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0__mulscalar0" class="node" transform="translate(943.5625019073486,6942.5000076293945)" style="opacity: 1;"><g transform="translate(-54.7265625,-20.5)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h64.8125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-69.8125a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">_mul_scalar</text><title>wavenet1_elu0__mulscalar0</title></g><g class="node-item node-item-input" transform="translate(69.8125,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h109.453125a0,0 0 0 1 0,0v14a5,5 0 0 1 -5,5h-99.453125a5,5 0 0 1 -5,-5v-14a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">scalar</tspan><tspan> = 1.0</tspan></text></g><line class="node" x1="69.8125" y1="0" x2="69.8125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="109.453125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h99.453125a5,5 0 0 1 5,5v31a5,5 0 0 1 -5,5h-99.453125a5,5 0 0 1 -5,-5v-31a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_elu0_where0" class="node" transform="translate(775.7343769073486,7048.0000076293945)" style="opacity: 1;"><g transform="translate(-68.0234375,-11)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h39.34375a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.34375a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">where</text><title>wavenet1_elu0_where0</title></g><g class="node-item node-item-input" transform="translate(44.34375,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-input" transform="translate(83.984375,0)"><path d="M0,0h26.03125a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-26.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(1)</text></g><g class="node-item node-item-input" transform="translate(110.015625,0)"><path d="M0,0h21.03125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-21.03125a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">(2)</text></g><line class="node" x1="44.34375" y1="0" x2="44.34375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="83.984375" y1="0" x2="83.984375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="110.015625" y1="0" x2="110.015625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h126.046875a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-126.046875a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_conv1_fwd" class="node" transform="translate(775.7343769073486,7164.5000076293945)" style="opacity: 1;"><g transform="translate(-96.21875,-31.5)"><g><g class="node-item node-item-operator node-item-operator-layer" transform="translate(0,0)"><path d="M5,0h67.5625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-72.5625a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(51, 85, 136); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">Convolution</text><title>wavenet1_conv1_fwd</title></g><g class="node-item node-item-input" transform="translate(72.5625,0)"><path d="M0,0h39.640625a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-item node-item-constant" id="initializer-wavenet1_conv1_weight" transform="translate(112.203125,0)"><path d="M0,0h46.546875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-46.546875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">weight</text><title>float32[512,64,1]</title></g><g class="node-item node-item-constant" id="initializer-wavenet1_conv1_bias" transform="translate(158.75,0)"><path d="M0,0h28.6875a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-33.6875a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">bias</text><title>float32[512,?]</title></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h192.4375a0,0 0 0 1 0,0v36a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-36a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">kernel</tspan><tspan> = [1]</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">layout</tspan><tspan> = NCW</tspan></text><text xml:space="preserve" x="4" y="34" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">num_filter</tspan><tspan> = 512</tspan></text></g><line class="node" x1="72.5625" y1="0" x2="72.5625" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="112.203125" y1="0" x2="112.203125" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="158.75" y1="0" x2="158.75" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="192.4375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h182.4375a5,5 0 0 1 5,5v53a5,5 0 0 1 -5,5h-182.4375a5,5 0 0 1 -5,-5v-53a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g id="node-wavenet1_swapaxes1" class="node" transform="translate(775.7343769073486,7296.000009536743)" style="opacity: 1;"><g transform="translate(-49.5546875,-26.000001907348633)"><g><g class="node-item node-item-operator" transform="translate(0,0)"><path d="M5,0h54.46875a0,0 0 0 1 0,0v22a0,0 0 0 1 0,0h-59.46875a0,0 0 0 1 0,0v-17a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(0, 0, 0); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision; fill: rgb(255, 255, 255);">SwapAxis</text><title>wavenet1_swapaxes1</title></g><g class="node-item node-item-input" transform="translate(59.46875,0)"><path d="M0,0h34.640625a5,5 0 0 1 5,5v17a0,0 0 0 1 0,0h-39.640625a0,0 0 0 1 0,0v-22a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">input</text></g><g class="node-attribute" transform="translate(0,22)"><path d="M0,0h99.109375a0,0 0 0 1 0,0v25a5,5 0 0 1 -5,5h-89.109375a5,5 0 0 1 -5,-5v-25a0,0 0 0 1 0,0z" style="stroke: rgb(0, 0, 0); fill: rgb(255, 255, 255); stroke-width: 0;"/><text xml:space="preserve" x="4" y="12" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dim1</tspan><tspan> = 1</tspan></text><text xml:space="preserve" x="4" y="23" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 7px; font-weight: normal; text-rendering: geometricPrecision;"><tspan style="font-weight: bold;">dim2</tspan><tspan> = 2</tspan></text></g><line class="node" x1="59.46875" y1="0" x2="59.46875" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><line class="node" x1="0" y1="22" x2="99.109375" y2="22" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/><path class="node border" d="M5,0h89.109375a5,5 0 0 1 5,5v42a5,5 0 0 1 -5,5h-89.109375a5,5 0 0 1 -5,-5v-42a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g class="node graph-input" transform="translate(468.48828315734863,11)" style="opacity: 1;"><g transform="translate(-17.875,-11)"><g><g class="node-item graph-item-input" transform="translate(0,0)"><path d="M5,0h25.75a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-25.75a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">data</text></g><path class="node border" d="M5,0h25.75a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-25.75a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g><g class="node" transform="translate(775.7343769073486,7407.000011444092)" style="opacity: 1;"><g transform="translate(-58.914066314697266,-11)"><g><g class="node-item graph-item-output" transform="translate(0,0)"><path d="M5,0h107.828125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-107.828125a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(0, 0, 0); fill: rgb(238, 238, 238); stroke-width: 0;"/><text x="7" y="15" style="font-family: &quot;Open Sans&quot;, &quot;--apple-system&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serf; font-size: 10px; font-weight: 600; text-rendering: geometricPrecision;">wavenet1_swapaxes1</text></g><path class="node border" d="M5,0h107.828125a5,5 0 0 1 5,5v12a5,5 0 0 1 -5,5h-107.828125a5,5 0 0 1 -5,-5v-12a5,5 0 0 1 5,-5z" style="stroke: rgb(51, 51, 51); fill: none; stroke-width: 1px;"/></g></g></g></g></g></svg>
\ No newline at end of file
diff --git a/docs/python_docs/python/tutorials/performance/compression/index.rst b/docs/python_docs/python/tutorials/performance/compression/index.rst
index e2e4f2c73250..1666c941bfe1 100644
--- a/docs/python_docs/python/tutorials/performance/compression/index.rst
+++ b/docs/python_docs/python/tutorials/performance/compression/index.rst
@@ -23,13 +23,13 @@ The following tutorials will help you learn how to use compression techniques wi
 
    .. card::
       :title: Compression: float16
-      :link: https://mxnet.incubator.apache.org/api/faq/float16
+      :link: https://mxnet.apache.org/api/faq/float16
 
       How to use float16 in your model to boost training speed.
 
    .. card::
       :title: Gradient Compression
-      :link: https://mxnet.incubator.apache.org/api/faq/gradient_compression
+      :link: https://mxnet.apache.org/api/faq/gradient_compression
 
       How to use gradient compression to reduce communication bandwidth and increase speed.
 
@@ -39,21 +39,18 @@ The following tutorials will help you learn how to use compression techniques wi
 
       How to use quantized GluonCV models for inference on Intel Xeon Processors to gain higher performance.
 
-   ..
-      TBD Content
-      .. card::
-         :title: Compression: int8
-         :link: int8.html
+   .. card::
+      :title: Compression: int8
+      :link: int8.html
+
+      How to use int8 in your model to boost training speed.
 
-         How to use int8 in your model to boost training speed.
-   ..
 
 .. toctree::
    :hidden:
    :glob:
 
    *
-   
-   ..
-      int8
-   ..
+   Float16 <https://mxnet.apache.org/api/faq/float16>
+   Gradient Compression  <https://mxnet.apache.org/api/faq/gradient_compression>
+   GluonCV with Quantized Models <https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html>
diff --git a/docs/python_docs/python/tutorials/performance/index.rst b/docs/python_docs/python/tutorials/performance/index.rst
index 6d9159eb2ace..78943a87dc16 100644
--- a/docs/python_docs/python/tutorials/performance/index.rst
+++ b/docs/python_docs/python/tutorials/performance/index.rst
@@ -26,7 +26,7 @@ Essential
 
    .. card::
       :title: Improving Performance
-      :link: https://mxnet.incubator.apache.org/versions/master/faq/perf.html
+      :link: https://mxnet.apache.org/api/faq/perf
 
       How to get the best performance from MXNet.
 
@@ -38,7 +38,7 @@ Essential
 
    .. card::
       :title: Tuning NumPy Operations
-      :link: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/gotchas_numpy_in_mxnet.html
+      :link: https://mxnet.apache.org/versions/master/tutorials/gluon/gotchas_numpy_in_mxnet.html
 
       Gotchas using NumPy in MXNet.
 
@@ -100,19 +100,19 @@ Distributed Training
 
    .. card::
       :title: Distributed Training Using the KVStore API
-      :link: https://mxnet.incubator.apache.org/versions/master/faq/distributed_training.html
+      :link: https://mxnet.apache.org/versions/master/faq/distributed_training.html
 
       How to use the KVStore API to use multiple GPUs when training a model.
 
    .. card::
       :title: Training with Multiple GPUs Using Model Parallelism
-      :link: https://mxnet.incubator.apache.org/versions/master/faq/model_parallel_lstm.html
+      :link: https://mxnet.apache.org/versions/master/faq/model_parallel_lstm.html
 
       An overview of using multiple GPUs when training an LSTM.
 
    .. card::
       :title: Data Parallelism in MXNet
-      :link: https://mxnet.incubator.apache.org/versions/master/faq/multi_devices.html
+      :link: https://mxnet.apache.org/versions/master/faq/multi_devices.html
 
       An overview of distributed training strategies.
 
diff --git a/docs/python_docs/themes/mx-theme/mxtheme/layout.html b/docs/python_docs/themes/mx-theme/mxtheme/layout.html
index 64a2e79a59a1..8c3f01be7fc4 100644
--- a/docs/python_docs/themes/mx-theme/mxtheme/layout.html
+++ b/docs/python_docs/themes/mx-theme/mxtheme/layout.html
@@ -35,7 +35,7 @@
 {% endblock %}
 
 {%- block document %}
-        <div class="page-content">
+        <div class="page-content" role="main">
         {% block body %} {% endblock %}
         </div>
         <div class="side-doc-outline">
diff --git a/docs/static_site/src/_config_prod.yml b/docs/static_site/src/_config_prod.yml
index fbd2367adb3b..6c91fe91cdea 100644
--- a/docs/static_site/src/_config_prod.yml
+++ b/docs/static_site/src/_config_prod.yml
@@ -34,7 +34,7 @@ title: Apache MXNet
 email: dev@mxnet.apache.org
 description: >- # this means to ignore newlines until "baseurl:"
   A flexible and efficient library for deep learning.
-url: https://mxnet.incubator.apache.org
+url: https://mxnet.apache.org
 twitter_username: apachemxnet
 github_username:  apache/incubator-mxnet
 youtube_username: apachemxnet
diff --git a/docs/static_site/src/_includes/get_started/devices/nvidia-jetson.md b/docs/static_site/src/_includes/get_started/devices/nvidia-jetson.md
index 39ff95191886..fe515f3392d7 100644
--- a/docs/static_site/src/_includes/get_started/devices/nvidia-jetson.md
+++ b/docs/static_site/src/_includes/get_started/devices/nvidia-jetson.md
@@ -1,4 +1,4 @@
 # NVIDIA Jetson Devices
 
 To install MXNet on a Jetson TX or Nano, please refer to the [Jetson installation
-guide](install-jetson.html).
\ No newline at end of file
+guide](get_started/jetson_setup).
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/devices/raspberry_pi.md b/docs/static_site/src/_includes/get_started/devices/raspberry_pi.md
index 1fb68f10abcc..3cc8bb91d6f0 100644
--- a/docs/static_site/src/_includes/get_started/devices/raspberry_pi.md
+++ b/docs/static_site/src/_includes/get_started/devices/raspberry_pi.md
@@ -102,7 +102,7 @@ virtualenv -p `which python` mxnet_py27
 {% endhighlight %}
 
 You may use Python 3, however the [wine bottle detection
-example](https://mxnet.incubator.apache.org/versions/master/tutorials/embedded/wine_detector.html)
+example](https://mxnet.apache.org/api/python/docs/tutorials/deploy/inference/wine_detector.html)
 for the
 Pi with camera requires Python 2.7.
 
@@ -219,7 +219,7 @@ If loading the cython modules fails, the default behavior is falling back to cty
 any warning. To
 raise an exception at the failure, set the environment variable `MXNET_ENFORCE_CYTHON` to
 `1`. See
-[here](/faq/env_var.html) for more details.
+[here](https://mxnet.apache.org/api/faq/env_var) for more details.
 
 
 **Step 3** Install MXNet Python Bindings
@@ -245,7 +245,7 @@ Alternatively you can create a whl package installable with pip with the followi
 You are now ready to run MXNet on your Raspberry Pi device. You can get started by following
 the tutorial on
 [Real-time Object Detection with MXNet On The Raspberry
-Pi](http://mxnet.io/tutorials/embedded/wine_detector.html).
+Pi](https://mxnet.io/api/python/docs/tutorials/deploy/inference/wine_detector.html).
 
 *Note - Because the complete MXNet library takes up a significant amount of the Raspberry
 Pi's limited RAM,
diff --git a/docs/static_site/src/_includes/get_started/linux/cpp/cpp.md b/docs/static_site/src/_includes/get_started/linux/cpp/cpp.md
index ac664185a61a..376dd13424aa 100644
--- a/docs/static_site/src/_includes/get_started/linux/cpp/cpp.md
+++ b/docs/static_site/src/_includes/get_started/linux/cpp/cpp.md
@@ -1,4 +1,4 @@
 To enable the C++ package, build from source using `make USE_CPP_PACKAGE=1`.
-Refer to the [MXNet C++ setup guide](c_plus_plus)
+Refer to the [MXNet C++ setup guide](get_started/c_plus_plus)
 for full instructions.
                 
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/linux/java/cpu.md b/docs/static_site/src/_includes/get_started/linux/java/cpu.md
index 2140a34af7ad..5345a2d754b2 100644
--- a/docs/static_site/src/_includes/get_started/linux/java/cpu.md
+++ b/docs/static_site/src/_includes/get_started/linux/java/cpu.md
@@ -1,6 +1,6 @@
 You can use the Maven packages defined in the following dependency to include MXNet in your Java
 project. The Java API is provided as a subset of the Scala API and is intended for inference only.
-Please refer to the <a href="java_setup.html">MXNet-Java setup guide</a> for a detailed set of
+Please refer to the <a href="get_started/java_setup.html">MXNet-Java setup guide</a> for a detailed set of
 instructions to help you with the setup process.
 
 <a href="https://repository.apache.org/#nexus-search;gav~org.apache.mxnet~~1.5.0~~">
diff --git a/docs/static_site/src/_includes/get_started/linux/java/gpu.md b/docs/static_site/src/_includes/get_started/linux/java/gpu.md
index cbab5e60ca47..5e687a353fe4 100644
--- a/docs/static_site/src/_includes/get_started/linux/java/gpu.md
+++ b/docs/static_site/src/_includes/get_started/linux/java/gpu.md
@@ -1,6 +1,6 @@
 You can use the Maven packages defined in the following dependency to include MXNet in your Java
 project. The Java API is provided as a subset of the Scala API and is intended for inference only.
-Please refer to the <a href="java_setup.html">MXNet-Java setup guide</a> for a detailed set of
+Please refer to the <a href="get_started/java_setup.html">MXNet-Java setup guide</a> for a detailed set of
 instructions to help you with the setup process.
 
 <a href="https://repository.apache.org/#nexus-search;gav~org.apache.mxnet~~1.5.0~~">
diff --git a/docs/static_site/src/_includes/get_started/linux/julia/build-from-source.md b/docs/static_site/src/_includes/get_started/linux/julia/build-from-source.md
index 4b39ac633e0d..fbbc0bd248a9 100644
--- a/docs/static_site/src/_includes/get_started/linux/julia/build-from-source.md
+++ b/docs/static_site/src/_includes/get_started/linux/julia/build-from-source.md
@@ -1,2 +1,2 @@
-Refer to the [Julia section of the MXNet Ubuntu installation guide](ubuntu_setup#install-the-mxnet-package-for-julia).
+Refer to the [Julia section of the MXNet Ubuntu installation guide](get_started/ubuntu_setup#install-the-mxnet-package-for-julia).
 
diff --git a/docs/static_site/src/_includes/get_started/linux/perl/perl.md b/docs/static_site/src/_includes/get_started/linux/perl/perl.md
index d9fc8d5328bc..02d82e457726 100644
--- a/docs/static_site/src/_includes/get_started/linux/perl/perl.md
+++ b/docs/static_site/src/_includes/get_started/linux/perl/perl.md
@@ -1 +1 @@
-Refer to the [Perl section of the MXNet Ubuntu installation guide](ubuntu_setup.html#install-the-mxnet-package-for-perl).
\ No newline at end of file
+Refer to the [Perl section of the MXNet Ubuntu installation guide](get_started/ubuntu_setup.html#install-the-mxnet-package-for-perl).
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/linux/python/cpu/docker.md b/docs/static_site/src/_includes/get_started/linux/python/cpu/docker.md
index 1980a0999f92..8f2c8eb143a4 100644
--- a/docs/static_site/src/_includes/get_started/linux/python/cpu/docker.md
+++ b/docs/static_site/src/_includes/get_started/linux/python/cpu/docker.md
@@ -40,4 +40,4 @@ REPOSITORY TAG IMAGE ID CREATED SIZE
 mxnet/python 1.3.0_cpu_mkl deaf9bf61d29 4 days ago 678 MB
 {% endhighlight %}
 
-**Step 4** <a href="validate_mxnet.html">Validate the installation</a>.
\ No newline at end of file
+**Step 4** <a href="get_started/validate_mxnet">Validate the installation</a>.
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/linux/python/cpu/pip.md b/docs/static_site/src/_includes/get_started/linux/python/cpu/pip.md
index b05480e1c967..81db75ea9038 100644
--- a/docs/static_site/src/_includes/get_started/linux/python/cpu/pip.md
+++ b/docs/static_site/src/_includes/get_started/linux/python/cpu/pip.md
@@ -7,7 +7,7 @@ $ pip install mxnet
 
 MKL-DNN enabled pip packages are optimized for Intel hardware. You can find
 performance numbers
-in the <a href="http://mxnet.io/faq/perf.html#intel-cpu">MXNet tuning guide</a>.
+in the <a href="https://mxnet.io/api/faq/perf#intel-cpu">MXNet tuning guide</a>.
 
 {% highlight bash %}
 $ pip install mxnet-mkl
@@ -23,7 +23,7 @@ $ pip install mxnet==1.4.1
 
 MKL-DNN enabled pip packages are optimized for Intel hardware. You can find
 performance numbers
-in the <a href="http://mxnet.io/faq/perf.html#intel-cpu">MXNet tuning guide</a>.
+in the <a href="https://mxnet.io/api/faq/perf#intel-cpu">MXNet tuning guide</a>.
 
 {% highlight bash %}
 $ pip install mxnet-mkl==1.4.1
@@ -38,7 +38,7 @@ $ pip install mxnet==1.3.1
 
 MKL-DNN enabled pip packages are optimized for Intel hardware. You can find
 performance numbers
-in the <a href="http://mxnet.io/faq/perf.html#intel-cpu">MXNet tuning guide</a>.
+in the <a href="https://mxnet.io/api/faq/perf#intel-cpu">MXNet tuning guide</a>.
 
 {% highlight bash %}
 $ pip install mxnet-mkl==1.3.1
@@ -53,7 +53,7 @@ $ pip install mxnet==1.2.1
 
 MKL-DNN enabled pip packages are optimized for Intel hardware. You can find
 performance numbers
-in the <a href="http://mxnet.io/faq/perf.html#intel-cpu">MXNet tuning guide</a>.
+in the <a href="https://mxnet.io/api/faq/perf#intel-cpu">MXNet tuning guide</a>.
 
 {% highlight bash %}
 $ pip install mxnet-mkl==1.2.1
@@ -108,7 +108,7 @@ $ pip install mxnet --pre
 
 MKL-DNN enabled pip packages are optimized for Intel hardware. You can find
 performance numbers
-in the <a href="http://mxnet.io/faq/perf.html#intel-cpu">MXNet tuning guide</a>.
+in the <a href="https://mxnet.io/api/faq/perf#intel-cpu">MXNet tuning guide</a>.
 
 {% highlight bash %}
 $ pip install mxnet-mkl --pre
diff --git a/docs/static_site/src/_includes/get_started/linux/python/gpu/docker.md b/docs/static_site/src/_includes/get_started/linux/python/gpu/docker.md
index d4d8cf295458..dc21cf10b4ef 100644
--- a/docs/static_site/src/_includes/get_started/linux/python/gpu/docker.md
+++ b/docs/static_site/src/_includes/get_started/linux/python/gpu/docker.md
@@ -45,4 +45,4 @@ REPOSITORY TAG IMAGE ID CREATED SIZE
 mxnet/python 1.3.0_gpu_cu92_mkl adcb3ab19f50 4 days ago 4.23 GB
 {% endhighlight %}
 
-**Step 5** <a href="validate_mxnet.html">Validate the installation</a>.
\ No newline at end of file
+**Step 5** <a href="get_started/validate_mxnet">Validate the installation</a>.
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/linux/r/cpu.md b/docs/static_site/src/_includes/get_started/linux/r/cpu.md
index 0ccbd9f66efa..c0a4e015b61d 100644
--- a/docs/static_site/src/_includes/get_started/linux/r/cpu.md
+++ b/docs/static_site/src/_includes/get_started/linux/r/cpu.md
@@ -1,5 +1,5 @@
 The default version of R that is installed with `apt-get` is insufficient. You will need
-to first [install R v3.4.4+ and build MXNet from source](ubuntu_setup.html#install-the-mxnet-package-for-r).
+to first [install R v3.4.4+ and build MXNet from source](get_started/ubuntu_setup.html#install-the-mxnet-package-for-r).
 
 After you have setup R v3.4.4+ and MXNet, you can build and install the MXNet R bindings with the following, assuming that `incubator-mxnet` is the source directory you used to build MXNet as follows:
 
diff --git a/docs/static_site/src/_includes/get_started/linux/r/gpu.md b/docs/static_site/src/_includes/get_started/linux/r/gpu.md
index 3e26d319e4f0..57afe7a8d65e 100644
--- a/docs/static_site/src/_includes/get_started/linux/r/gpu.md
+++ b/docs/static_site/src/_includes/get_started/linux/r/gpu.md
@@ -1,7 +1,7 @@
 The default version of R that is installed with `apt-get` is insufficient. You will need
 to first
 [install R v3.4.4+ and build MXNet from
-source](ubuntu_setup.html#install-the-mxnet-package-for-r).
+source](get_started/ubuntu_setup.html#install-the-mxnet-package-for-r).
 
 After you have setup R v3.4.4+ and MXNet, you can build and install the MXNet R bindings
 with the
diff --git a/docs/static_site/src/_includes/get_started/macos/cpp/cpp.md b/docs/static_site/src/_includes/get_started/macos/cpp/cpp.md
index b2e632b0f117..bb06cb617773 100644
--- a/docs/static_site/src/_includes/get_started/macos/cpp/cpp.md
+++ b/docs/static_site/src/_includes/get_started/macos/cpp/cpp.md
@@ -1,3 +1,3 @@
 To enable the C++ package, build from source using `make USE_CPP_PACKAGE=1`.
-Refer to the [MXNet C++ setup guide](c_plus_plus) for full instructions.
+Refer to the [MXNet C++ setup guide](get_started/c_plus_plus) for full instructions.
                 
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/macos/java/cpu.md b/docs/static_site/src/_includes/get_started/macos/java/cpu.md
index e8c320e9462d..2050149fd33d 100644
--- a/docs/static_site/src/_includes/get_started/macos/java/cpu.md
+++ b/docs/static_site/src/_includes/get_started/macos/java/cpu.md
@@ -1,7 +1,7 @@
 You can use the Maven packages defined in the following dependency to include MXNet in
 your Java project. The Java API is provided as a subset of the Scala API and is intended for
 inference only.
-Please refer to the [MXNet-Java setup guide](java_setup.html) for a detailed set of instructions to help you with the setup process.
+Please refer to the [MXNet-Java setup guide](get_started/java_setup.html) for a detailed set of instructions to help you with the setup process.
 
 <a href="https://repository.apache.org/#nexus-search;gav~org.apache.mxnet~~1.5.0~~"><img
 src="https://img.shields.io/badge/org.apache.mxnet-mac cpu-green.svg"
diff --git a/docs/static_site/src/_includes/get_started/macos/julia/build-from-source.md b/docs/static_site/src/_includes/get_started/macos/julia/build-from-source.md
index 85045cacf8f6..b864d8a49086 100644
--- a/docs/static_site/src/_includes/get_started/macos/julia/build-from-source.md
+++ b/docs/static_site/src/_includes/get_started/macos/julia/build-from-source.md
@@ -1 +1 @@
-Refer to the [Julia section of the MXNet macOS installation guide](osx_setup.html#install-the-mxnet-package-for-julia).
\ No newline at end of file
+Refer to the [Julia section of the MXNet macOS installation guide](get_started/osx_setup.html#install-the-mxnet-package-for-julia).
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/macos/perl/perl.md b/docs/static_site/src/_includes/get_started/macos/perl/perl.md
index c9b04e328edc..ab08489fdc56 100644
--- a/docs/static_site/src/_includes/get_started/macos/perl/perl.md
+++ b/docs/static_site/src/_includes/get_started/macos/perl/perl.md
@@ -1 +1 @@
-Refer to the [Perl section of installation guide](osx_setup.html#install-the-mxnet-package-for-perl).
\ No newline at end of file
+Refer to the [Perl section of installation guide](get_started/osx_setup.html#install-the-mxnet-package-for-perl).
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/macos/python/cpu/build-from-source.md b/docs/static_site/src/_includes/get_started/macos/python/cpu/build-from-source.md
index b8b5d2119bd0..01dd35b9f332 100644
--- a/docs/static_site/src/_includes/get_started/macos/python/cpu/build-from-source.md
+++ b/docs/static_site/src/_includes/get_started/macos/python/cpu/build-from-source.md
@@ -1,2 +1,2 @@
-To build from source, refer to the [MXNet macOS installation guide](osx_setup.html).
+To build from source, refer to the [MXNet macOS installation guide](get_started/osx_setup.html).
 MXNet developers should refer to the MXNet wiki's [Developer Setup on Mac](https://cwiki.apache.org/confluence/display/MXNET/MXNet+Developer+Setup+on+Mac).
diff --git a/docs/static_site/src/_includes/get_started/macos/python/cpu/docker.md b/docs/static_site/src/_includes/get_started/macos/python/cpu/docker.md
index 2f0f04ae432c..c8631ec26ab3 100644
--- a/docs/static_site/src/_includes/get_started/macos/python/cpu/docker.md
+++ b/docs/static_site/src/_includes/get_started/macos/python/cpu/docker.md
@@ -32,4 +32,4 @@ REPOSITORY TAG IMAGE ID CREATED SIZE
 mxnet/python 1.3.0_cpu_mkl deaf9bf61d29 4 days ago 678 MB
 {% endhighlight %}
 
-**Step 4** <a href="validate_mxnet.html">Validate the installation</a>.
\ No newline at end of file
+**Step 4** <a href="get_started/validate_mxnet">Validate the installation</a>.
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/macos/python/gpu/build-from-source.md b/docs/static_site/src/_includes/get_started/macos/python/gpu/build-from-source.md
index 7cb61a59c8bf..ffcf88ecdd25 100644
--- a/docs/static_site/src/_includes/get_started/macos/python/gpu/build-from-source.md
+++ b/docs/static_site/src/_includes/get_started/macos/python/gpu/build-from-source.md
@@ -1,2 +1,2 @@
-Refer to the [MXNet macOS installation guide](osx_setup.html).
+Refer to the [MXNet macOS installation guide](get_started/osx_setup.html).
 MXNet developers should refer to the MXNet wiki's [Developer Setup on Mac](https://cwiki.apache.org/confluence/display/MXNET/MXNet+Developer+Setup+on+Mac).
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/macos/python/gpu/pip_docker.md b/docs/static_site/src/_includes/get_started/macos/python/gpu/pip_docker.md
index b4585922e8e0..87281fe85e38 100644
--- a/docs/static_site/src/_includes/get_started/macos/python/gpu/pip_docker.md
+++ b/docs/static_site/src/_includes/get_started/macos/python/gpu/pip_docker.md
@@ -1 +1 @@
-This option is only available by building from source. Refer to the [MXNet macOS installation guide](osx_setup.html).
\ No newline at end of file
+This option is only available by building from source. Refer to the [MXNet macOS installation guide](get_started/osx_setup.html).
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/macos/r/cpu.md b/docs/static_site/src/_includes/get_started/macos/r/cpu.md
index beca0d414730..1e0d96b91ed7 100644
--- a/docs/static_site/src/_includes/get_started/macos/r/cpu.md
+++ b/docs/static_site/src/_includes/get_started/macos/r/cpu.md
@@ -17,7 +17,7 @@ Note: packages for 3.6.x are not yet available.
 Install 3.5.x of R from [CRAN](https://cran.r-project.org/bin/macosx/). The latest is
 [v3.5.3](https://cran.r-project.org/bin/macosx/R-3.5.3.pkg).
 
-You can [build MXNet-R from source](osx_setup.html#install-the-mxnet-package-for-r), or
+You can [build MXNet-R from source](get_started/osx_setup.html#install-the-mxnet-package-for-r), or
 you can use a pre-built binary:
 
 {% highlight r %}
diff --git a/docs/static_site/src/_includes/get_started/windows/cpp/cpp.md b/docs/static_site/src/_includes/get_started/windows/cpp/cpp.md
index b2e632b0f117..bb06cb617773 100644
--- a/docs/static_site/src/_includes/get_started/windows/cpp/cpp.md
+++ b/docs/static_site/src/_includes/get_started/windows/cpp/cpp.md
@@ -1,3 +1,3 @@
 To enable the C++ package, build from source using `make USE_CPP_PACKAGE=1`.
-Refer to the [MXNet C++ setup guide](c_plus_plus) for full instructions.
+Refer to the [MXNet C++ setup guide](get_started/c_plus_plus) for full instructions.
                 
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/windows/julia/build-from-source.md b/docs/static_site/src/_includes/get_started/windows/julia/build-from-source.md
index cdc185416329..f9e61cb1c64e 100644
--- a/docs/static_site/src/_includes/get_started/windows/julia/build-from-source.md
+++ b/docs/static_site/src/_includes/get_started/windows/julia/build-from-source.md
@@ -1 +1 @@
-Refer to the [Julia section of the MXNet Windows installation guide](windows_setup.html#install-the-mxnet-package-for-julia).
+Refer to the [Julia section of the MXNet Windows installation guide](get_started/windows_setup.html#install-the-mxnet-package-for-julia).
diff --git a/docs/static_site/src/_includes/get_started/windows/perl/perl.md b/docs/static_site/src/_includes/get_started/windows/perl/perl.md
index 2655f7281486..a24ae0aa13f0 100644
--- a/docs/static_site/src/_includes/get_started/windows/perl/perl.md
+++ b/docs/static_site/src/_includes/get_started/windows/perl/perl.md
@@ -1 +1 @@
-Refer to the [Perl section of the MXNet Windows installation guide](windows_setup.html#install-the-mxnet-package-for-perl).
\ No newline at end of file
+Refer to the [Perl section of the MXNet Windows installation guide](get_started/windows_setup.html#install-the-mxnet-package-for-perl).
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/windows/python/cpu/build-from-source.md b/docs/static_site/src/_includes/get_started/windows/python/cpu/build-from-source.md
index a527f51d919d..fe0ac055c914 100644
--- a/docs/static_site/src/_includes/get_started/windows/python/cpu/build-from-source.md
+++ b/docs/static_site/src/_includes/get_started/windows/python/cpu/build-from-source.md
@@ -1 +1 @@
-Refer to the [MXNet Windows installation guide](windows_setup.html)
\ No newline at end of file
+Refer to the [MXNet Windows installation guide](get_started/windows_setup.html)
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/windows/python/cpu/docker.md b/docs/static_site/src/_includes/get_started/windows/python/cpu/docker.md
index a3df5a92aa99..a061b8bbab65 100644
--- a/docs/static_site/src/_includes/get_started/windows/python/cpu/docker.md
+++ b/docs/static_site/src/_includes/get_started/windows/python/cpu/docker.md
@@ -31,4 +31,4 @@ REPOSITORY TAG IMAGE ID CREATED SIZE
 mxnet/python 1.3.0_cpu_mkl deaf9bf61d29 4 days ago 678 MB
 {% endhighlight %}
 
-**Step 4** <a href="validate_mxnet.html">Validate the installation</a>.
\ No newline at end of file
+**Step 4** <a href="get_started/validate_mxnet">Validate the installation</a>.
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/windows/python/gpu/build-from-source.md b/docs/static_site/src/_includes/get_started/windows/python/gpu/build-from-source.md
index 27e90fad4f5e..762f720b5403 100644
--- a/docs/static_site/src/_includes/get_started/windows/python/gpu/build-from-source.md
+++ b/docs/static_site/src/_includes/get_started/windows/python/gpu/build-from-source.md
@@ -1 +1 @@
-To build from source, refer to the [MXNet Windows installation guide](windows_setup.html).
+To build from source, refer to the [MXNet Windows installation guide](get_started/windows_setup.html).
diff --git a/docs/static_site/src/_includes/get_started/windows/r/cpu.md b/docs/static_site/src/_includes/get_started/windows/r/cpu.md
index 8b7151f9b94a..3110475a2abd 100644
--- a/docs/static_site/src/_includes/get_started/windows/r/cpu.md
+++ b/docs/static_site/src/_includes/get_started/windows/r/cpu.md
@@ -1,7 +1,7 @@
 Note: packages for 3.6.x are not yet available.
 Install 3.5.x of R from [CRAN](https://cran.r-project.org/bin/windows/base/old/).
 
-You can [build MXNet-R from source](windows_setup.html#install-mxnet-package-for-r), or
+You can [build MXNet-R from source](get_started/windows_setup.html#install-mxnet-package-for-r), or
 you can use a
 pre-built binary:
 
diff --git a/docs/static_site/src/_includes/get_started/windows/r/gpu.md b/docs/static_site/src/_includes/get_started/windows/r/gpu.md
index b08a56986cca..0840d2d2acbc 100644
--- a/docs/static_site/src/_includes/get_started/windows/r/gpu.md
+++ b/docs/static_site/src/_includes/get_started/windows/r/gpu.md
@@ -1,4 +1,4 @@
-You can [build MXNet-R from source](windows_setup.html#install-mxnet-package-for-r), or
+You can [build MXNet-R from source](get_started/windows_setup.html#install-mxnet-package-for-r), or
 you can use a
 pre-built binary:
 
diff --git a/docs/static_site/src/pages/api/architecture/exception_handling.md b/docs/static_site/src/pages/api/architecture/exception_handling.md
index b6323cf21629..875df5aabd15 100644
--- a/docs/static_site/src/pages/api/architecture/exception_handling.md
+++ b/docs/static_site/src/pages/api/architecture/exception_handling.md
@@ -42,7 +42,7 @@ to handle exceptions for the second case.
 ## Prerequisites
 
 To complete this tutorial, we need:
-- MXNet [7b24137](https://github.com/apache/incubator-mxnet/commit/7b24137ed45df605defa4ce72ec91554f6e445f0). See Instructions in [Setup and Installation](http://mxnet.io/install/index.html).
+- MXNet [7b24137](https://github.com/apache/incubator-mxnet/commit/7b24137ed45df605defa4ce72ec91554f6e445f0). See Instructions in [Setup and Installation](https://mxnet.io/get_started).
 
 ## Exception Handling for Iterators
 
diff --git a/docs/static_site/src/pages/api/architecture/note_data_loading.md b/docs/static_site/src/pages/api/architecture/note_data_loading.md
index b93b6b601158..1279d0361e5f 100644
--- a/docs/static_site/src/pages/api/architecture/note_data_loading.md
+++ b/docs/static_site/src/pages/api/architecture/note_data_loading.md
@@ -125,7 +125,7 @@ then compress into JPEG format.
 After that, we save a header that indicates the index and label
 for that image to be used when constructing the *Data* field for that record.
 We then pack several images together into a file.
-You may want to also review the [example using im2rec.py to create a RecordIO dataset](https://mxnet.incubator.apache.org/tutorials/basic/data.html#loading-data-using-image-iterators).
+You may want to also review the [example using im2rec.py to create a RecordIO dataset](https://mxnet.apache.org/tutorials/basic/data.html#loading-data-using-image-iterators).
 
 ### Access Arbitrary Parts Of Data
 
diff --git a/docs/static_site/src/pages/api/architecture/note_engine.md b/docs/static_site/src/pages/api/architecture/note_engine.md
index 55be7187b9e8..63400b47e2e4 100644
--- a/docs/static_site/src/pages/api/architecture/note_engine.md
+++ b/docs/static_site/src/pages/api/architecture/note_engine.md
@@ -387,4 +387,3 @@ We welcome your contributions.
 
 * [Squeeze the Memory Consumption of Deep Learning](note_memory)
 * [Efficient Data Loading Module for Deep Learning](note_data_loading)
-* [Survey of RNN Interface](http://mxnet.io/architecture/rnn_interface.html)
diff --git a/docs/static_site/src/pages/api/cpp/docs/tutorials/basics.md b/docs/static_site/src/pages/api/cpp/docs/tutorials/basics.md
index 40773d93b325..51077bfd0e41 100644
--- a/docs/static_site/src/pages/api/cpp/docs/tutorials/basics.md
+++ b/docs/static_site/src/pages/api/cpp/docs/tutorials/basics.md
@@ -82,7 +82,7 @@ the digits with the help of C++ package.
 Multilayer Perceptron
 ---------------------
 If you are not familiar with multilayer perceptron, you can get some basic information
-[here](http://mxnet.io/tutorials/python/mnist.html#multilayer-perceptron). We only focus on
+[here](https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html). We only focus on
 the implementation in this tutorial.
 
 Constructing multilayer perceptron model is straightforward, assume we store the hidden size
diff --git a/docs/static_site/src/pages/api/cpp/index.md b/docs/static_site/src/pages/api/cpp/index.md
index 37f00c318ce8..3aff76b85337 100644
--- a/docs/static_site/src/pages/api/cpp/index.md
+++ b/docs/static_site/src/pages/api/cpp/index.md
@@ -40,9 +40,9 @@ The cpp-package directory contains the implementation of C++ API. As mentioned a
 	git clone --recursive https://github.com/apache/incubator-mxnet mxnet
 	```
 
-3.  Install the [prerequisites](<https://mxnet.incubator.apache.org/get_started/build_from_source#prerequisites>), desired [BLAS libraries](<https://mxnet.incubator.apache.org/get_started/build_from_source#blas-library>) and optional [OpenCV, CUDA, and cuDNN](<https://mxnet.incubator.apache.org/get_started/build_from_source#optional>) for building MXNet from source.
+3.  Install the [prerequisites](<https://mxnet.apache.org/get_started/build_from_source#prerequisites>), desired [BLAS libraries](<https://mxnet.apache.org/get_started/build_from_source#blas-library>) and optional [OpenCV, CUDA, and cuDNN](<https://mxnet.apache.org/get_started/build_from_source#optional>) for building MXNet from source.
 4.  There is a configuration file for make, [make/config.mk](<https://github.com/apache/incubator-mxnet/blob/master/make/config.mk>) that contains all the compilation options. You can edit this file and set the appropriate options prior to running the **make** command.
-5.  Please refer to  [platform specific build instructions](<https://mxnet.incubator.apache.org/get_started/build_from_source#build-instructions-by-operating-system>) and available [build configurations](https://mxnet.incubator.apache.org/get_started/build_from_source#build-configurations) for more details.
+5.  Please refer to  [platform specific build instructions](<https://mxnet.apache.org/get_started/build_from_source#build-instructions-by-operating-system>) and available [build configurations](https://mxnet.apache.org/get_started/build_from_source#build-configurations) for more details.
 5.  For enabling the build of C++ Package, set the **USE\_CPP\_PACKAGE = 1** in [make/config.mk](<https://github.com/apache/incubator-mxnet/blob/master/make/config.mk>). Optionally, the compilation flag can also be specified on **make** command line as follows.
 	```
 	make -j USE_CPP_PACKAGE=1
diff --git a/docs/static_site/src/pages/api/faq/add_op_in_backend.md b/docs/static_site/src/pages/api/faq/add_op_in_backend.md
index 71296d72f45c..672bf52a29ee 100644
--- a/docs/static_site/src/pages/api/faq/add_op_in_backend.md
+++ b/docs/static_site/src/pages/api/faq/add_op_in_backend.md
@@ -47,7 +47,9 @@ C++ in the MXNet backend. After finishing the implementation,
 we will add unit tests using Python for the operator we just implemented.
 
 ## Implementation
+
 ### An Operator Example
+
 Let's take the [quadratic function](https://en.wikipedia.org/wiki/Quadratic_function)
 as an example: `f(x) = ax^2+bx+c`. We want to implement an operator called `quadratic`
 taking `x`, which is a tensor, as an input and generating an output tensor `y`
@@ -55,11 +57,13 @@ satisfying `y.shape=x.shape` and each element of `y` is calculated by feeding th
 corresponding element of `x` into the quadratic function `f`.
 Here variables `a`, `b`, and `c` are user input parameters.
 In frontend, the operator works like this:
+
 ```python
 x = [[1, 2], [3, 4]]
 y = quadratic(data=x, a=1, b=2, c=3)
 y = [[6, 11], [18, 27]]
 ```
+
 To implement this, we first create three files: `quadratic_op-inl.h`,
 `quadratic_op.cc`, and `quadratic_op.cu`. The header file's name
 is prefixed by the operator name and followed by `op` and `-inl`
@@ -148,6 +152,7 @@ using the attribute inference functions defined in the operators building up
 the neural network.
 
 Let's consider the following example.
+
 ```python
 >>> import mxnet as mx
 >>> a = mx.sym.Variable('a', shape=(2, 0))
@@ -157,6 +162,7 @@ Let's consider the following example.
 >>> print d.infer_shape()
 ([(2L, 3L), (2L, 3L), (2L, 3L)], [(2L, 3L)], [])
 ```
+
 The last line of the above code snippet is a tuple of three lists returned
 by `d.infer_shape()`. The first list contains all the argument shapes
 of `a`, `b`, and `c`. The second contains the output shape of `d`. The
@@ -182,6 +188,7 @@ It is actually implemented in the shape inference functions of the operators for
 element-wise multiplication and addition.
 
 For our `quadratic` operator, shape inference possesses quite similar logic.
+
 ```cpp
 inline bool QuadraticOpShape(const nnvm::NodeAttrs& attrs,
                              mxnet::ShapeVector* in_attrs,
@@ -194,6 +201,7 @@ inline bool QuadraticOpShape(const nnvm::NodeAttrs& attrs,
   return out_attrs->at(0).ndim() != 0U && out_attrs->at(0).Size() != 0U;
 }
 ```
+
 Here are a few things to note about the above function:
 
 1. `attrs` contains parameters `a`, `b`, and `c` from user input.
@@ -222,6 +230,7 @@ for general element-wise operators with the following interface. Users can
 instantiate this function with `n_in=1` and `n_out=1` to replace the above
 function `QuadraticOpShape` in operator registration (explained later).
 The function `QuadraticOpShape` posted here is for the purpose of illustration only.
+
 ```cpp
 template<int n_in, int n_out>
 inline bool ElemwiseShape(const nnvm::NodeAttrs& attrs,
@@ -232,6 +241,7 @@ inline bool ElemwiseShape(const nnvm::NodeAttrs& attrs,
 The same logic goes for data type inference. We will leave the analysis of
 the following code sample to users. Note that `-1` means the data type
 is unknown and must be inferred from other input or output data types.
+
 ```cpp
 inline bool QuadraticOpType(const nnvm::NodeAttrs& attrs,
                             std::vector<int>* in_attrs,
@@ -248,6 +258,7 @@ inline bool QuadraticOpType(const nnvm::NodeAttrs& attrs,
 Again, MXNet provides the following convenience function for mutual
 type inference of element-wise operators. Users can use that
 in operator registration (explained later).
+
 ```cpp
 template<int n_in, int n_out>
 inline bool ElemwiseType(const nnvm::NodeAttrs& attrs,
@@ -261,6 +272,7 @@ of neural networks. For our `quadratic` operator, it simply implements
 the logic of running a tensor through the quadratic function by performing
 a few element-wise operations. The forward function's signature is fixed
 in MXNet as follows:
+
 ```cpp
 void (const nnvm::NodeAttrs& attrs,
       const OpContext& ctx,
@@ -268,8 +280,13 @@ void (const nnvm::NodeAttrs& attrs,
       const std::vector<OpReqType>& req,
       const std::vector<TBlob>& outputs);
 ```
+
 We first paste the whole forward function code here
 and then go through it line by line.
+
+
+{% raw %}
+
 ```cpp
 template<typename xpu>                                                        // 1
 void QuadraticOpForward(const nnvm::NodeAttrs& attrs,                         // 2
@@ -294,6 +311,9 @@ void QuadraticOpForward(const nnvm::NodeAttrs& attrs,                         //
   });                                                                         // 21
 }                                                                             // 22
 ```
+
+{% endraw %}
+
 - Line 1: `xpu` stands for a generic device type so that the function can be instantiated
 for both CPU and GPU computing using concrete types `cpu` and `gpu`. The instantiation happens
 at the time when the operator is registered in `.cc` and `.cu` files.
@@ -385,14 +405,19 @@ since they are not learnable parameters in the network. To formulate the problem
 given `dL/dy` and `y = a*x^2 + b*x + c`, where `L` represents the loss function and
 `y` stands for the output of the quadratic tensor, we need to solve for
 `dL/dx`. Using the chain-rule, it is obvious to find that
+
 ```
 dL/dx = dL/dy * dy/dx = dL/dy * (2*a*x + b).
 ```
+
 The above equation indicates that `dL/dx` depends on the gradient
 of the output tensor and value of the input tensor.
 The backward function's signature is the same as the forward function's.
 With the aforementioned information in mind,
 let's breakdown the following backward function line by line.
+
+{% raw %}
+
 ```cpp
 template<typename xpu>                                                       // 1
 void QuadraticOpBackward(const nnvm::NodeAttrs& attrs,                       // 2
@@ -418,6 +443,9 @@ void QuadraticOpBackward(const nnvm::NodeAttrs& attrs,                       //
   });                                                                        // 22
 }                                                                            // 23
 ```
+
+{% endraw %}
+
 - Lines 1-6: Backward function has the same signature as forward function.
 - Lines 7-9: Check the sizes of the function arguments. One thing to note
 that since the gradient of the input depends on both the gradient of the output and
@@ -569,6 +597,7 @@ So far, we have acquired an operator working on CPU in frontend.
 In order to register the operator working on GPUs, we just need to add the following
 code to `quadratic_op.cu`. Note that forward and backward functions
 are registered with attribute key `FCompute<gpu>`, rather than `FCompute<cpu>`.
+
 ```cpp
 NNVM_REGISTER_OP(quadratic)
 .set_attr<FCompute>("FCompute<gpu>", QuadraticOpForward<gpu>);
@@ -648,6 +677,7 @@ once the comparison satisfies user specified `rtol` and `atol` values. Here `rto
 and `atol` expand to relative tolerance and absolute tolerance respectively. They
 are used to specify how far the computed values can deviate from the expected values.
 They are defined as follows
+
 ```
 abs(Expected_Value - Computed_Value) < RTOL * abs(Expected_Value) + ATOL
 ```
diff --git a/docs/static_site/src/pages/api/faq/float16.md b/docs/static_site/src/pages/api/faq/float16.md
index 4cc115f894d4..d824acb3ce6d 100644
--- a/docs/static_site/src/pages/api/faq/float16.md
+++ b/docs/static_site/src/pages/api/faq/float16.md
@@ -39,7 +39,7 @@ The float16 data type is a 16 bit floating point representation according to the
 - CUDA 9 or higher
 - cuDNN v7 or higher
 
-This tutorial also assumes understanding of how to train a network with float32 (the default). Please refer to [logistic regression tutorial](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/logistic_regression_explained.html) to get started with Apache MXNet and Gluon API. This tutorial focuses on the changes needed to switch from float32 to mixed precision and tips on achieving the best performance with mixed precision.
+This tutorial also assumes understanding of how to train a network with float32 (the default). Please refer to [logistic regression tutorial](https://mxnet.apache.org/versions/master/tutorials/gluon/logistic_regression_explained.html) to get started with Apache MXNet and Gluon API. This tutorial focuses on the changes needed to switch from float32 to mixed precision and tips on achieving the best performance with mixed precision.
 
 ## Using the Gluon API
 
diff --git a/docs/static_site/src/pages/api/faq/gradient_compression.md b/docs/static_site/src/pages/api/faq/gradient_compression.md
index 8a295c481aa3..1f4c5fb21903 100644
--- a/docs/static_site/src/pages/api/faq/gradient_compression.md
+++ b/docs/static_site/src/pages/api/faq/gradient_compression.md
@@ -110,7 +110,7 @@ A reference `gluon` implementation with a gradient compression option can be fou
 mod = mx.mod.Module(..., compression_params={'type’:'2bit', 'threshold':0.5})
 ```
 
-A `module` example is provided with [this guide for setting up MXNet with distributed training](https://mxnet.incubator.apache.org/versions/master/faq/multi_devices.html#distributed-training-with-multiple-machines). It comes with the option of turning on gradient compression as an argument to the [train_mnist.py script](https://github.com/apache/incubator-mxnet/blob/master/example/image-classification/train_mnist.py).
+A `module` example is provided with [this guide for setting up MXNet with distributed training](https://mxnet.apache.org/versions/master/faq/multi_devices.html#distributed-training-with-multiple-machines). It comes with the option of turning on gradient compression as an argument to the [train_mnist.py script](https://github.com/apache/incubator-mxnet/blob/master/example/image-classification/train_mnist.py).
 
 ### Configuration Details
 
diff --git a/docs/static_site/src/pages/api/faq/perf.md b/docs/static_site/src/pages/api/faq/perf.md
index 4ea605262634..675304f01241 100644
--- a/docs/static_site/src/pages/api/faq/perf.md
+++ b/docs/static_site/src/pages/api/faq/perf.md
@@ -58,7 +58,7 @@ We also find that setting the following environment variables can help:
 | :-------- | :---------- |
 | `OMP_NUM_THREADS`            | Suggested value: `vCPUs / 2` in which `vCPUs` is the number of virtual CPUs. For more information, please see the guide for [setting the number of threads using an OpenMP environment variable](https://software.intel.com/en-us/mkl-windows-developer-guide-setting-the-number-of-threads-using-an-openmp-environment-variable) |
 | `KMP_AFFINITY`               | Suggested value: `granularity=fine,compact,1,0`.  For more information, please see the guide for [Thread Affinity Interface (Linux* and Windows*)](https://software.intel.com/en-us/node/522691). |
-| `MXNET_SUBGRAPH_BACKEND` | Set to MKLDNN to enable the [subgraph feature](https://cwiki.apache.org/confluence/display/MXNET/MXNet+Graph+Optimization+and+Quantization+based+on+subgraph+and+MKL-DNN) for better performance. For more information please see [Build/Install MXNet with MKL-DNN](https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/mkldnn/MKLDNN_README.md)|
+| `MXNET_SUBGRAPH_BACKEND` | Set to MKLDNN to enable the [subgraph feature](https://cwiki.apache.org/confluence/display/MXNET/MXNet+Graph+Optimization+and+Quantization+based+on+subgraph+and+MKL-DNN) for better performance. For more information please see [Build/Install MXNet with MKL-DNN](https://mxnet.apache.org/api/python/docs/tutorials/performance/backend/mkldnn/mkldnn_readme.html)|
 
 Note that _MXNet_ treats all CPUs on a single machine as a single device.
 So whether you specify `cpu(0)` or `cpu()`, _MXNet_ will use all CPU cores on the machine.
@@ -257,7 +257,7 @@ where the batch size for Alexnet is increased by 16x.
 
 If more than one GPU or machine are used, MXNet uses `kvstore` to communicate data.
 It's critical to use the proper type of `kvstore` to get the best performance.
-Refer to [multi_device.md](http://mxnet.io/faq/multi_devices.html) for more
+Refer to [multi_device.md](https://mxnet.io/api/faq/distributed_training.html) for more
 details.
 
 Besides, we can use [tools/bandwidth](https://github.com/dmlc/mxnet/tree/master/tools/bandwidth)
diff --git a/docs/static_site/src/pages/api/faq/recordio.md b/docs/static_site/src/pages/api/faq/recordio.md
index d9770aca5886..75407cb3da5f 100644
--- a/docs/static_site/src/pages/api/faq/recordio.md
+++ b/docs/static_site/src/pages/api/faq/recordio.md
@@ -38,7 +38,7 @@ We provide two tools for creating a RecordIO dataset.
 * [im2rec.py](https://github.com/apache/incubator-mxnet/blob/master/tools/im2rec.py) - implements the tool using the Python API.
 
 Both provide the same output: a RecordIO dataset.
-You may want to also review the [example using real-world data with im2rec.py.](https://mxnet.incubator.apache.org/tutorials/basic/data.html#loading-data-using-image-iterators)
+You may want to also review the [example using real-world data with im2rec.py.](https://mxnet.apache.org/tutorials/basic/data.html#loading-data-using-image-iterators)
 
 ### Prerequisites
 
diff --git a/docs/static_site/src/pages/api/perl/docs/tutorials/ndarray.md b/docs/static_site/src/pages/api/perl/docs/tutorials/ndarray.md
index 51c5556bbe23..52cdf6bb74b5 100644
--- a/docs/static_site/src/pages/api/perl/docs/tutorials/ndarray.md
+++ b/docs/static_site/src/pages/api/perl/docs/tutorials/ndarray.md
@@ -45,7 +45,7 @@ pdl> print $z,"\n"
 ```
 
 A detailed tutorial is available at
-[http://mxnet.io/tutorials/basic/ndarray.html](http://mxnet.io/tutorials/basic/ndarray.html).
+[https://mxnet.io/tutorials/basic/ndarray.html](https://mxnet.io/tutorials/basic/ndarray.html).
 
 Note: AI::MXNet::NDarray is similar to numpy.ndarray in some aspects. But the difference is not negligible. For example
 
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/callback_function.md b/docs/static_site/src/pages/api/r/docs/tutorials/callback_function.md
index aa47eb8ee73d..52e4db92f84b 100644
--- a/docs/static_site/src/pages/api/r/docs/tutorials/callback_function.md
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/callback_function.md
@@ -272,7 +272,7 @@ Yes! You can stop the training early with `return(FALSE)`. See the following exa
 When the validation metric dips below the threshold we set, the training process stops.
 
 ## Next Steps
-* [Neural Networks with MXNet in Five Minutes](http://mxnet.io/tutorials/r/fiveMinutesNeuralNetwork.html)
-* [Classify Real-World Images with a Pretrained Model](http://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
-* [Handwritten Digits Classification Competition](http://mxnet.io/tutorials/r/mnistCompetition.html)
-* [Character Language Model Using RNN](http://mxnet.io/tutorials/r/charRnnModel.html)
+* [Neural Networks with MXNet in Five Minutes](https://mxnet.io/tutorials/r/fiveMinutesNeuralNetwork.html)
+* [Classify Real-World Images with a Pretrained Model](https://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
+* [Handwritten Digits Classification Competition](https://mxnet.io/tutorials/r/mnistCompetition.html)
+* [Character Language Model Using RNN](https://mxnet.io/tutorials/r/charRnnModel.html)
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/classify_real_image_with_pretrained_model.md b/docs/static_site/src/pages/api/r/docs/tutorials/classify_real_image_with_pretrained_model.md
index 4d8bec453988..a20f88eb8937 100644
--- a/docs/static_site/src/pages/api/r/docs/tutorials/classify_real_image_with_pretrained_model.md
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/classify_real_image_with_pretrained_model.md
@@ -30,7 +30,7 @@ algorithm can do is classify real world images.
 In this tutorial, we show how to use a pre-trained Inception-BatchNorm network to predict the class of an
 image. For information about the network architecture, see  [1].
 
-The pre-trained Inception-BatchNorm network is able to be downloaded from [this link](http://data.mxnet.io/mxnet/data/Inception.zip)
+The pre-trained Inception-BatchNorm network is able to be downloaded from [this link](https://data.mxnet.io/mxnet/data/Inception.zip)
 This model gives the recent state-of-art prediction accuracy on image net dataset.
 
 Load the MXNet Package
@@ -204,5 +204,5 @@ Reference
 [1] Ioffe, Sergey, and Christian Szegedy. "Batch normalization: Accelerating deep network training by reducing internal covariate shift." arXiv preprint arXiv:1502.03167 (2015).
 
 ## Next Steps
-* [Handwritten Digits Classification Competition](http://mxnet.io/tutorials/r/mnistCompetition.html)
-* [Character Language Model using RNN](http://mxnet.io/tutorials/r/charRnnModel.html)
+* [Handwritten Digits Classification Competition](https://mxnet.io/tutorials/r/mnistCompetition.html)
+* [Character Language Model using RNN](https://mxnet.io/tutorials/r/charRnnModel.html)
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/custom_iterator.md b/docs/static_site/src/pages/api/r/docs/tutorials/custom_iterator.md
index fe63976483e3..bb4cfc2f5c1d 100644
--- a/docs/static_site/src/pages/api/r/docs/tutorials/custom_iterator.md
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/custom_iterator.md
@@ -30,7 +30,7 @@ This tutorial provides a guideline on how to use and write custom iterators, whi
 
 Getting the data
 ----------
-The data we are going to use is the [MNIST dataset](http://yann.lecun.com/exdb/mnist/) in CSV format, the data can be found in this [web](http://pjreddie.com/projects/mnist-in-csv/).
+The data we are going to use is the [MNIST dataset](https://yann.lecun.com/exdb/mnist/) in CSV format, the data can be found in this [web](https://pjreddie.com/projects/mnist-in-csv/).
 
 To download the data:
 
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/custom_loss_function.md b/docs/static_site/src/pages/api/r/docs/tutorials/custom_loss_function.md
index d9da1aecc8e2..0f37123f23da 100644
--- a/docs/static_site/src/pages/api/r/docs/tutorials/custom_loss_function.md
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/custom_loss_function.md
@@ -225,7 +225,7 @@ sum(abs(test.y - pred6[1,])) / length(test.y)
 
 
 ## Next Steps
-* [Neural Networks with MXNet in Five Minutes](http://mxnet.io/tutorials/r/fiveMinutesNeuralNetwork.html)
-* [Classify Real-World Images with a PreTrained Model](http://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
-* [Handwritten Digits Classification Competition](http://mxnet.io/tutorials/r/mnistCompetition.html)
-* [Character Language Model Using RNN](http://mxnet.io/tutorials/r/charRnnModel.html)
+* [Neural Networks with MXNet in Five Minutes](https://mxnet.io/tutorials/r/fiveMinutesNeuralNetwork.html)
+* [Classify Real-World Images with a PreTrained Model](https://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
+* [Handwritten Digits Classification Competition](https://mxnet.io/tutorials/r/mnistCompetition.html)
+* [Character Language Model Using RNN](https://mxnet.io/tutorials/r/charRnnModel.html)
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/five_minutes_neural_network.md b/docs/static_site/src/pages/api/r/docs/tutorials/five_minutes_neural_network.md
index 8323d9582ed2..a5d57347e01e 100644
--- a/docs/static_site/src/pages/api/r/docs/tutorials/five_minutes_neural_network.md
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/five_minutes_neural_network.md
@@ -25,7 +25,7 @@ permalink: /api/r/docs/tutorials/five_minutes_neural_network
 Develop a Neural Network with MXNet in Five Minutes
 =============================================
 
-This tutorial is designed for new users of the `mxnet` package for R. It shows how to construct a neural network to do regression in 5 minutes. It shows how to perform classification and regression tasks, respectively. The data we use is in the `mlbench` package. Instructions to install R and MXNet's R package in different environments can be found [here](http://mxnet.incubator.apache.org/install/index.html?platform=Linux&language=R&processor=CPU).
+This tutorial is designed for new users of the `mxnet` package for R. It shows how to construct a neural network to do regression in 5 minutes. It shows how to perform classification and regression tasks, respectively. The data we use is in the `mlbench` package. Instructions to install R and MXNet's R package in different environments can be found [here](https://mxnet.apache.org/install/index.html?platform=Linux&language=R&processor=CPU).
 
 ## Classification
 
@@ -336,6 +336,6 @@ Congratulations! You've learned the basics for using MXNet in R. To learn how to
 
 
 ## Next Steps
-* [Classify Real-World Images with Pre-trained Model](http://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
-* [Handwritten Digits Classification Competition](http://mxnet.io/tutorials/r/mnistCompetition.html)
-* [Character Language Model using RNN](http://mxnet.io/tutorials/r/charRnnModel.html)
+* [Classify Real-World Images with Pre-trained Model](https://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
+* [Handwritten Digits Classification Competition](https://mxnet.io/tutorials/r/mnistCompetition.html)
+* [Character Language Model using RNN](https://mxnet.io/tutorials/r/charRnnModel.html)
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/mnist_competition.md b/docs/static_site/src/pages/api/r/docs/tutorials/mnist_competition.md
index db2e86a6d765..e34819dd9ed7 100644
--- a/docs/static_site/src/pages/api/r/docs/tutorials/mnist_competition.md
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/mnist_competition.md
@@ -360,4 +360,4 @@ Now, we can submit the result to Kaggle to see the improvement of our ranking!
 ![](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/knitr/mnistCompetition-kaggle-submission.png)
 
 ##  Next Steps
-* [Character Language Model using RNN](http://mxnet.io/tutorials/r/charRnnModel.html)
+* [Character Language Model using RNN](https://mxnet.io/tutorials/r/charRnnModel.html)
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/multi_dim_lstm.md b/docs/static_site/src/pages/api/r/docs/tutorials/multi_dim_lstm.md
index fb4a653bdcd7..0fd73b1460a4 100644
--- a/docs/static_site/src/pages/api/r/docs/tutorials/multi_dim_lstm.md
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/multi_dim_lstm.md
@@ -324,4 +324,4 @@ We also repeated the above experiments to generate the next 100 samples to 301st
 
 The above tutorial is just for demonstration purposes and has not been tuned extensively for accuracy.
 
-For more tutorials on MXNet-R, head on to [MXNet-R tutorials](https://mxnet.incubator.apache.org/tutorials/r/index.html)
+For more tutorials on MXNet-R, head on to [MXNet-R tutorials](https://mxnet.apache.org/tutorials/r/index.html)
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/ndarray.md b/docs/static_site/src/pages/api/r/docs/tutorials/ndarray.md
index 2b655afeca8b..9113b0d313d5 100644
--- a/docs/static_site/src/pages/api/r/docs/tutorials/ndarray.md
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/ndarray.md
@@ -223,9 +223,9 @@ The actual computations are finished, allowing us to copy the results someplace
 the results.
 
 ## Next Steps
-* [Symbol](http://mxnet.io/tutorials/r/symbol.html)
-* [Write and use callback functions](http://mxnet.io/tutorials/r/CallbackFunction.html)
-* [Neural Networks with MXNet in Five Minutes](http://mxnet.io/tutorials/r/fiveMinutesNeuralNetwork.html)
-* [Classify Real-World Images with Pre-trained Model](http://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
-* [Handwritten Digits Classification Competition](http://mxnet.io/tutorials/r/mnistCompetition.html)
-* [Character Language Model using RNN](http://mxnet.io/tutorials/r/charRnnModel.html)
+* [Symbol](https://mxnet.io/tutorials/r/symbol.html)
+* [Write and use callback functions](https://mxnet.io/tutorials/r/CallbackFunction.html)
+* [Neural Networks with MXNet in Five Minutes](https://mxnet.io/tutorials/r/fiveMinutesNeuralNetwork.html)
+* [Classify Real-World Images with Pre-trained Model](https://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
+* [Handwritten Digits Classification Competition](https://mxnet.io/tutorials/r/mnistCompetition.html)
+* [Character Language Model using RNN](https://mxnet.io/tutorials/r/charRnnModel.html)
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/symbol.md b/docs/static_site/src/pages/api/r/docs/tutorials/symbol.md
index 26bdcd8061e7..9c3150f97157 100644
--- a/docs/static_site/src/pages/api/r/docs/tutorials/symbol.md
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/symbol.md
@@ -147,8 +147,8 @@ be more memory efficient than CXXNet and gets to the same runtime with
 greater flexibility.
 
 ## Next Steps
-* [Write and use callback functions](http://mxnet.io/tutorials/r/CallbackFunction.html)
-* [Neural Networks with MXNet in Five Minutes](http://mxnet.io/tutorials/r/fiveMinutesNeuralNetwork.html)
-* [Classify Real-World Images with Pre-trained Model](http://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
-* [Handwritten Digits Classification Competition](http://mxnet.io/tutorials/r/mnistCompetition.html)
-* [Character Language Model using RNN](http://mxnet.io/tutorials/r/charRnnModel.html)
+* [Write and use callback functions](https://mxnet.io/tutorials/r/CallbackFunction.html)
+* [Neural Networks with MXNet in Five Minutes](https://mxnet.io/tutorials/r/fiveMinutesNeuralNetwork.html)
+* [Classify Real-World Images with Pre-trained Model](https://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
+* [Handwritten Digits Classification Competition](https://mxnet.io/tutorials/r/mnistCompetition.html)
+* [Character Language Model using RNN](https://mxnet.io/tutorials/r/charRnnModel.html)
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/char_lstm.md b/docs/static_site/src/pages/api/scala/docs/tutorials/char_lstm.md
index e5805145dca3..bb23fd00a18d 100644
--- a/docs/static_site/src/pages/api/scala/docs/tutorials/char_lstm.md
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/char_lstm.md
@@ -30,7 +30,7 @@ There are many documents that explain LSTM concepts. If you aren't familiar with
 - Christopher Olah's [Understanding LSTM blog post](https://colah.github.io/posts/2015-08-Understanding-LSTMs/)
 - [Training a LSTM char-rnn in Julia to Generate Random Sentences](http://dmlc.ml/mxnet/2015/11/15/char-lstm-in-julia.html)
 - [Bucketing in MXNet in Python](https://github.com/dmlc/mxnet-notebooks/blob/master/python/tutorials/char_lstm.ipynb)
-- [Bucketing in MXNet](http://mxnet.io/faq/bucketing.html)
+- [Bucketing in MXNet](https://mxnet.io/faq/bucketing.html)
 
 ## How to Use This Tutorial
 
diff --git a/docs/static_site/src/pages/api/scala/docs/tutorials/mxnet_scala_on_intellij.md b/docs/static_site/src/pages/api/scala/docs/tutorials/mxnet_scala_on_intellij.md
index 115d7aeda095..b71b457b1f10 100644
--- a/docs/static_site/src/pages/api/scala/docs/tutorials/mxnet_scala_on_intellij.md
+++ b/docs/static_site/src/pages/api/scala/docs/tutorials/mxnet_scala_on_intellij.md
@@ -29,7 +29,7 @@ This tutorial guides you through setting up a Scala project in the IntelliJ IDE
 ## Prerequisites:
 To use this tutorial you need the following software:
 
-- [Java 8 JDK](http://www.oracle.com/technetwork/java/javase/downloads/index.html)
+- [Java 8 JDK](https:///www.oracle.com/technetwork/java/javase/downloads/index.html)
 - [Maven](https://maven.apache.org/install.html)
 - [Scala](https://www.scala-lang.org/download/) - comes with IntelliJ, so you don't need to install it separately
 - [OpenCV](https://opencv.org/)
@@ -197,7 +197,7 @@ The project's `pom.xml` will be open for editing.
   <licenses>
     <license>
       <name>Apache2</name>
-      <url>http://mxnet.apache.org</url>
+      <url>https://mxnet.apache.org</url>
       <distribution>repo</distribution>
     </license>
   </licenses>
diff --git a/docs/static_site/src/pages/community/contribute.md b/docs/static_site/src/pages/community/contribute.md
index 73937c7123a4..3ec56a34890a 100644
--- a/docs/static_site/src/pages/community/contribute.md
+++ b/docs/static_site/src/pages/community/contribute.md
@@ -169,7 +169,7 @@ Our community is constantly looking for feedback to improve Apache MXNet. If you
 - It helps to estimate the effort and to design a solution that addresses your needs. <br/>
 - It allows for constructive discussions that might arise around this issue.
 
-Detailed information is also required, if you plan to contribute the improvement or feature you proposed yourself. Please read the [contributions](http://mxnet.io/community/contribute.html) guide in this case as well.
+Detailed information is also required, if you plan to contribute the improvement or feature you proposed yourself. Please read the [contributions](https://mxnet.io/community/contribute.html) guide in this case as well.
 
 
 ## Roadmap
diff --git a/docs/static_site/src/pages/get_started/build_from_source.md b/docs/static_site/src/pages/get_started/build_from_source.md
index 5f4c2731afb6..bf57c28add81 100644
--- a/docs/static_site/src/pages/get_started/build_from_source.md
+++ b/docs/static_site/src/pages/get_started/build_from_source.md
@@ -317,7 +317,8 @@ After building MXNet's shared library, you can install other language bindings.
 **NOTE:** The C++ API binding must be built when you build MXNet from source. See [Build MXNet with C++]({{'/api/cpp'|relative_url}}).
 
 The following table provides links to each language binding by operating system:
-| | [Ubuntu](ubuntu_setup) | [macOS](osx_setup) | [Windows](windows_setup) |
+
+| Language | [Ubuntu](ubuntu_setup) | [macOS](osx_setup) | [Windows](windows_setup) |
 | --- | ----  | --- | ------- |
 | Python | [Ubuntu guide](ubuntu_setup.html#install-mxnet-for-python) | [OSX guide](osx_setup) | [Windows guide](windows_setup.html#install-mxnet-for-python) |
 | C++ | [C++ guide](cpp_setup) | [C++ guide](cpp_setup) | [C++ guide](cpp_setup) |
@@ -327,3 +328,4 @@ The following table provides links to each language binding by operating system:
 | R | [Ubuntu guide](ubuntu_setup.html#install-the-mxnet-package-for-r) | [OSX guide](osx_setup.html#install-the-mxnet-package-for-r) | [Windows guide](windows_setup.html#install-the-mxnet-package-for-r) |
 | Scala | [Scala guide](scala_setup.html) | [Scala guide](scala_setup.html) | n/a |
 | Java | [Java guide](java_setup.html) | [Java Guide](java_setup.html) | n/a |
+
diff --git a/docs/static_site/src/pages/get_started/java_setup.md b/docs/static_site/src/pages/get_started/java_setup.md
index 0e3e8cfc0354..f994cdb342a3 100644
--- a/docs/static_site/src/pages/get_started/java_setup.md
+++ b/docs/static_site/src/pages/get_started/java_setup.md
@@ -124,7 +124,7 @@ This will install both the Java Inference API and the required MXNet-Scala packa
 
 Javadocs are generated as part of the docs build pipeline. You can find them published in the [Java API]({{'/api/java'|relative_url}}) section of the website or by going to the [scaladocs output]({{'/api/scala/docs/api/#org.apache.mxnet.package'|relative_url}}) directly.
 
-To build the docs yourself, follow the [developer build docs instructions](https://github.com/apache/incubator-mxnet/tree/master/docs/build_version_doc#developer-instructions).
+To build the docs yourself, follow the [developer build docs instructions](https://github.com/apache/incubator-mxnet/tree/master/docs/README.md).
 
 <hr>
 
diff --git a/docs/static_site/src/pages/get_started/scala_setup.md b/docs/static_site/src/pages/get_started/scala_setup.md
index 827340c44a13..bf5072363d55 100644
--- a/docs/static_site/src/pages/get_started/scala_setup.md
+++ b/docs/static_site/src/pages/get_started/scala_setup.md
@@ -154,7 +154,7 @@ If you receive a "NumberFormatException" when running the interpreter, run `expo
 
 Scaladocs are generated as part of the docs build pipeline. You can find them published in the [Scala API]({{'/api/scala'|relative_url}}) section of the website or by going to the [scaladocs output]({{'/api/scala/docs/api/#org.apache.mxnet.package'|relative_url}}) directly.
 
-To build the docs yourself, follow the [developer build docs instructions](https://github.com/apache/incubator-mxnet/tree/master/docs/build_version_doc#developer-instructions).
+To build the docs yourself, follow the [developer build docs instructions](https://github.com/apache/incubator-mxnet/tree/master/docs/README.md).
 
 <hr>
 
diff --git a/docs/static_site/src/pages/get_started/ubuntu_setup.md b/docs/static_site/src/pages/get_started/ubuntu_setup.md
index 7b309c27cbd6..2d6867d0eadd 100644
--- a/docs/static_site/src/pages/get_started/ubuntu_setup.md
+++ b/docs/static_site/src/pages/get_started/ubuntu_setup.md
@@ -217,7 +217,7 @@ Build with CMake and ninja, without GPU and without MKL.
     ninja
 ```
 
-If building on CPU and using MKL and MKL-DNN (make sure MKL is installed according to [Math Library Selection](build_from_source#math-library-selection) and [MKL-DNN README](https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/mkldnn/MKLDNN_README.md)):
+If building on CPU and using MKL and MKL-DNN (make sure MKL is installed according to [Math Library Selection](build_from_source#math-library-selection) and [MKL-DNN README](https://mxnet.apache.org/api/python/docs/tutorials/performance/backend/mkldnn/mkldnn_readme.html)):
 
 ```bash
     rm -rf build
diff --git a/docs/static_site/src/pages/get_started/windows_setup.md b/docs/static_site/src/pages/get_started/windows_setup.md
index 06e8c6eff6e5..2fe2123d2e1c 100644
--- a/docs/static_site/src/pages/get_started/windows_setup.md
+++ b/docs/static_site/src/pages/get_started/windows_setup.md
@@ -143,7 +143,7 @@ We provide two primary options to build and install MXNet yourself using [Micros
 
 **NOTE:** Visual Studio 2017's compiler is `vc15`. This is not to be confused with Visual Studio 2015's compiler, `vc14`.
 
-You also have the option to install MXNet with MKL or MKL-DNN. In this case it is recommended that you refer to the [MKLDNN_README](https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/mkldnn/MKLDNN_README.md).
+You also have the option to install MXNet with MKL or MKL-DNN. In this case it is recommended that you refer to the [MKLDNN_README](https://mxnet.apache.org/api/python/docs/tutorials/performance/backend/mkldnn/mkldnn_readme.html).
 
 **Option 1: Build with Microsoft Visual Studio 2017 (VS2017)**
 
@@ -163,7 +163,7 @@ To build and install MXNet yourself using [VS2017](https://www.visualstudio.com/
 1. Download and run the  [OpenCV](https://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.4.1/opencv-3.4.1-vc14_vc15.exe/download) package. There are more recent versions of OpenCV, so please create an issue/PR to update this info if you validate one of these later versions.
 1. This will unzip several files. You can place them in another directory if you wish. We will use `C:\utils`(```mkdir C:\utils```) as our default path.
 1. Set the environment variable `OpenCV_DIR` to point to the OpenCV build directory that you just unzipped. Start ```cmd``` and type `set OpenCV_DIR=C:\utils\opencv\build`.
-1. If you don’t have the Intel Math Kernel Library (MKL) installed, you can install it and follow the [MKLDNN_README](https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/mkldnn/MKLDNN_README.md) from here, or you can use OpenBLAS. These instructions will assume you're using OpenBLAS.
+1. If you don’t have the Intel Math Kernel Library (MKL) installed, you can install it and follow the [MKLDNN_README](https://mxnet.apache.org/api/python/docs/tutorials/performance/backend/mkldnn/mkldnn_readme.html) from here, or you can use OpenBLAS. These instructions will assume you're using OpenBLAS.
 1. Download the [OpenBlas](https://sourceforge.net/projects/openblas/files/v0.2.19/OpenBLAS-v0.2.19-Win64-int32.zip/download) package. Later versions of OpenBLAS are available, but you would need to build from source. v0.2.19 is the most recent version that ships with binaries. Contributions of more recent binaries would be appreciated.
 1. Unzip the file, rename it to ```OpenBLAS``` and put it under `C:\utils`. You can place the unzipped files and folders in another directory if you wish.
 1. Set the environment variable `OpenBLAS_HOME` to point to the OpenBLAS directory that contains the `include` and `lib` directories and type `set OpenBLAS_HOME=C:\utils\OpenBLAS` on the command prompt(```cmd```).
diff --git a/example/MXNetTutorialTemplate.ipynb b/example/MXNetTutorialTemplate.ipynb
index 0b0b19381809..b6a5091bfaf4 100644
--- a/example/MXNetTutorialTemplate.ipynb
+++ b/example/MXNetTutorialTemplate.ipynb
@@ -70,9 +70,9 @@
    "source": [
     "To complete this tutorial, you need:\n",
     "\n",
-    "- [MXNet](https://mxnet.incubator.apache.org/install/#overview)\n",
-    "- [Language](https://mxnet.incubator.apache.org/tutorials/)\n",
-    "- [Tool](https://mxnet.incubator.apache.org/api/python/index.html)\n",
+    "- [MXNet](https://mxnet.apache.org/install/#overview)\n",
+    "- [Language](https://mxnet.apache.org/tutorials/)\n",
+    "- [Tool](https://mxnet.apache.org/api/python/index.html)\n",
     "- [Familiarity with concept or tool](https://gluon.mxnet.io/)\n"
    ]
   },
diff --git a/example/README.md b/example/README.md
index 2b9c366849e0..656b36dca0ac 100644
--- a/example/README.md
+++ b/example/README.md
@@ -52,13 +52,13 @@ If you have a tutorial idea for the website, download the [Jupyter notebook tuto
 
 #### Tutorial location
 
-Notebook tutorials should be submitted in the `docs/tutorials` folder, so that they maybe rendered in the [web site's tutorial section](https://mxnet.incubator.apache.org/tutorials/index.html).
+Notebook tutorials should be submitted in the `docs/tutorials` folder, so that they maybe rendered in the [web site's tutorial section](https://mxnet.apache.org/tutorials/index.html).
 
 Do not forget to udpdate the `docs/tutorials/index.md` for your tutorial to show up on the website.
 
 #### Tutorial formatting
 
-The site expects the format to be markdown, so export your notebook as a .md via the Jupyter web interface menu (File > Download As > Markdown). Then, to enable the download notebook button in the web site's UI ([example](https://mxnet.incubator.apache.org/tutorials/python/linear-regression.html)), add the following as the last line of the file ([example](https://github.com/apache/incubator-mxnet/blame/master/docs/tutorials/python/linear-regression.md#L194)):
+The site expects the format to be markdown, so export your notebook as a .md via the Jupyter web interface menu (File > Download As > Markdown). Then, to enable the download notebook button in the web site's UI ([example](https://mxnet.apache.org/tutorials/python/linear-regression.html)), add the following as the last line of the file ([example](https://github.com/apache/incubator-mxnet/blame/master/docs/tutorials/python/linear-regression.md#L194)):
 
 ```
 <!-- INSERT SOURCE DOWNLOAD BUTTONS -->
@@ -84,14 +84,14 @@ If your tutorial depends on specific packages, simply add them to this provision
 
 ### <a name="language-binding-examples"></a>Languages Binding Examples
 ------------------
-* [MXNet C++ API](http://mxnet.incubator.apache.org/api/c++/index.html)
+* [MXNet C++ API](https://mxnet.apache.org/api/c++/index.html)
    - [C++ examples](https://github.com/apache/incubator-mxnet/tree/master/example/image-classification/predict-cpp) - Example code for using C++ interface, including NDArray, symbolic layer and models.
-* [MXNet Python API](http://mxnet.incubator.apache.org/api/python/index.html)
-* [MXNet Java API](http://mxnet.incubator.apache.org/api/java/index.html)
-* [MXNet Scala API](http://mxnet.incubator.apache.org/api/scala/index.html)
-* [MXNet R API](http://mxnet.incubator.apache.org/api/r/index.html)
-* [MXNet Julia API](http://mxnet.incubator.apache.org/api/julia/index.html)
-* [MXNet Perl API](https://mxnet.incubator.apache.org/api/perl/index.html)
+* [MXNet Python API](https://mxnet.apache.org/api/python/index.html)
+* [MXNet Java API](https://mxnet.apache.org/api/java/index.html)
+* [MXNet Scala API](https://mxnet.apache.org/api/scala/index.html)
+* [MXNet R API](https://mxnet.apache.org/api/r/index.html)
+* [MXNet Julia API](https://mxnet.apache.org/api/julia/index.html)
+* [MXNet Perl API](https://mxnet.apache.org/api/perl/index.html)
 * [go-mxnet-predictor](https://github.com/songtianyi/go-mxnet-predictor) - Go binding for inference
 * [MXNet JNI](https://github.com/dmlc/mxnet/tree/master/amalgamation/jni) - JNI(Android) library
 * [MXNet Amalgamation](https://github.com/dmlc/mxnet/tree/master/amalgamation) - Amalgamation (entire library in a single file)
diff --git a/example/ctc/README.md b/example/ctc/README.md
index e64c6e633b2f..dcdd6c5502f7 100644
--- a/example/ctc/README.md
+++ b/example/ctc/README.md
@@ -55,7 +55,7 @@ $ sudo make install
 ```
 
 #### Building MXNet from source with warp-ctc integration
-In order to build MXNet from source, you need to follow [instructions here](http://mxnet.incubator.apache.org/install/index.html). After choosing your system configuration, Python environment, and "Build from Source" options, before running `make` in step 4, you need to enable warp-ctc integration by uncommenting the following lines in `make/config.mk` in `incubator-mxnet` directory:
+In order to build MXNet from source, you need to follow [instructions here](https://mxnet.apache.org/install/index.html). After choosing your system configuration, Python environment, and "Build from Source" options, before running `make` in step 4, you need to enable warp-ctc integration by uncommenting the following lines in `make/config.mk` in `incubator-mxnet` directory:
 ```
 WARPCTC_PATH = $(HOME)/warp-ctc
 MXNET_PLUGINS += plugin/warpctc/warpctc.mk
diff --git a/example/distributed_training/README.md b/example/distributed_training/README.md
index d1bd11738cec..af25b9efd187 100644
--- a/example/distributed_training/README.md
+++ b/example/distributed_training/README.md
@@ -48,7 +48,7 @@ Scheduler is responsible for scheduling the workers and parameter servers. There
 
 ### Step 1: Use a distributed key-value store:
 
-Like mentioned above, in distributed training, parameters are split into N parts and distributed across N hosts. This is done automatically by the [distributed key-value store](https://mxnet.incubator.apache.org/tutorials/python/kvstore.html). User only needs to create the distributed kv store and ask the `Trainer` to use the created store.
+Like mentioned above, in distributed training, parameters are split into N parts and distributed across N hosts. This is done automatically by the [distributed key-value store](https://mxnet.apache.org/tutorials/python/kvstore.html). User only needs to create the distributed kv store and ask the `Trainer` to use the created store.
 
 ```python
 store = mxnet.kv.create('dist')
diff --git a/example/gluon/audio/README.md b/example/gluon/audio/README.md
index cb2b53eb3b83..39006e301722 100644
--- a/example/gluon/audio/README.md
+++ b/example/gluon/audio/README.md
@@ -68,7 +68,7 @@ https://drive.google.com/drive/folders/0By0bAi7hOBAFUHVXd1JCN3MwTEU
                     - predict.py ...
     ```
 
-4. Apache MXNet is installed on the machine. For instructions, go to the link: https://mxnet.incubator.apache.org/install/
+4. Apache MXNet is installed on the machine. For instructions, go to the link: https://mxnet.apache.org/install/
 
 
 
diff --git a/example/gluon/audio/urban_sounds/README.md b/example/gluon/audio/urban_sounds/README.md
index c593f28dbee0..4ad76ff114a8 100644
--- a/example/gluon/audio/urban_sounds/README.md
+++ b/example/gluon/audio/urban_sounds/README.md
@@ -70,7 +70,7 @@ https://drive.google.com/drive/folders/0By0bAi7hOBAFUHVXd1JCN3MwTEU
                     - predict.py ...
     ```
 
-4. Apache MXNet is installed on the machine. For instructions, go to the link: https://mxnet.incubator.apache.org/install/
+4. Apache MXNet is installed on the machine. For instructions, go to the link: https://mxnet.apache.org/install/
 
 
 
diff --git a/example/image-classification/README.md b/example/image-classification/README.md
index c44ba23f4a64..78ea94eeb440 100644
--- a/example/image-classification/README.md
+++ b/example/image-classification/README.md
@@ -2,7 +2,7 @@
 
 This fold contains examples for image classification. The goal of image
 classifcation is to identify the objects contained in images. The following
-[example](http://mxnet.io/tutorials/python/predict_image.html) shows
+[example](https://mxnet.io/tutorials/python/predict_image.html) shows
 recognized object classes with corresponding probabilities using a pre-trained
 model.
 
@@ -52,7 +52,7 @@ commonly used options are listed as following:
 ## Prepare Datasets
 
 The recommended data format is
-[RecordIO](http://mxnet.io/architecture/note_data_loading.html), which
+[RecordIO](https://mxnet.io/architecture/note_data_loading.html), which
 concatenates multiple examples into seekable binary files for better read
 efficiency. We provide a tool `im2rec.py` located in `tools/` to convert
 individual images into `.rec` files.
@@ -102,7 +102,7 @@ We provide multiple pre-trained models on various datasets. Use
 [common/modelzone.py](https://github.com/dmlc/mxnet/blob/master/example/image-classification/common/modelzoo.py)
 to download these models. These models can be used in any front-end language
 MXNet supports. For example,
-[the tutorial](http://mxnet.io/tutorials/python/predict_image.html) shows how
+[the tutorial](https://mxnet.io/tutorials/python/predict_image.html) shows how
 to classify an image with jupyter notebook.
 
 ### ImageNet 1K
@@ -205,7 +205,7 @@ python fine-tune.py --pretrained-model imagenet11k-resnet-152 --gpus 0,1,2,3,4,5
 
 We obtained 87.3% top-1 validation accuracy, and the training log is available
 [here](https://gist.github.com/mli/900b810258e2e0bc26fa606977a3b043#file-finetune-caltech265). See
-the [python notebook](http://mxnet.io/faq/finetune.html) for more
+the [python notebook](https://mxnet.io/faq/finetune.html) for more
 explanations.
 
 ## Distributed Training
@@ -242,7 +242,7 @@ For more usages:
 - One can use
   [benchmark.py](https://github.com/dmlc/mxnet/blob/master/example/image-classification/benchmark.py)
   to run distributed benchmarks (also for multiple GPUs with single machine)
-- A how-to [tutorial](http://mxnet.io/faq/multi_devices.html) with more
+- A how-to [tutorial](https://mxnet.io/api/faq/distributed_training.html) with more
   explanation.
 - A
   [blog](https://aws.amazon.com/blogs/compute/distributed-deep-learning-made-easy/)
@@ -357,7 +357,7 @@ aspects:
     codes, check if it is configured correctly.
   - Use `--benchmark 1` to use randomly generated data rather than real data.
 
-Refer to [faq/performance](http://mxnet.io/faq/perf.html) for more details
+Refer to [faq/performance](https://mxnet.io/api/faq/perf) for more details
 about CPU, GPU and multi-device performance.
 
 ### Memory
diff --git a/example/image-classification/predict-cpp/README.md b/example/image-classification/predict-cpp/README.md
index 2a5e350e4afb..a59e4d70ee65 100644
--- a/example/image-classification/predict-cpp/README.md
+++ b/example/image-classification/predict-cpp/README.md
@@ -3,7 +3,7 @@ This is a simple predictor which shows how to use the MXNet C Predict API for im
 
 ## Prerequisites
 
-* OpenCV for image processing: `USE_OPENCV` is set to true by default when [building from source](https://mxnet.incubator.apache.org/install/build_from_source.html)
+* OpenCV for image processing: `USE_OPENCV` is set to true by default when [building from source](https://mxnet.apache.org/install/build_from_source.html)
 
 ## How to Use this Example
 
diff --git a/example/image-classification/train_mnist.py b/example/image-classification/train_mnist.py
index 17a5a372c0c2..d47521fc8ef8 100644
--- a/example/image-classification/train_mnist.py
+++ b/example/image-classification/train_mnist.py
@@ -16,7 +16,7 @@
 # under the License.
 
 """
-Train mnist, see more explanation at http://mxnet.io/tutorials/python/mnist.html
+Train mnist, see more explanation at https://mxnet.io/tutorials/python/mnist.html
 """
 import os
 import argparse
diff --git a/example/profiler/README.md b/example/profiler/README.md
index 933e04db67d3..54a002809ede 100644
--- a/example/profiler/README.md
+++ b/example/profiler/README.md
@@ -18,7 +18,7 @@
 # MXNet Profiler Examples
 
 This folder contains examples of using MXNet profiler to generate profiling results in json files.
-Please refer to [this link](http://mxnet.incubator.apache.org/faq/perf.html?highlight=profiler#profiler)
+Please refer to [this link](https://mxnet.apache.org/api/faq/perf?highlight=profiler#profiler)
 for visualizing profiling results.
 
 - profiler_executor.py. To run this example,
@@ -31,7 +31,7 @@ for visualizing profiling results.
 - profiler_imageiter.py. You first need to create a file named `test.rec`,
 which is an image dataset file before running this example.
 Please follow
-[this tutorial](https://mxnet.incubator.apache.org/faq/recordio.html?highlight=rec%20file#create-a-dataset-using-recordio)
+[this tutorial](https://mxnet.apache.org/faq/recordio.html?highlight=rec%20file#create-a-dataset-using-recordio)
 on how to create `.rec` files using an existing tool in MXNet. After you created 'test.rec',
 type `python profiler_imageiter.py` in terminal. It will generate `profile_imageiter.json`.
 
diff --git a/example/profiler/profiler_imageiter.py b/example/profiler/profiler_imageiter.py
index c8e747835ba3..962a0945cec7 100644
--- a/example/profiler/profiler_imageiter.py
+++ b/example/profiler/profiler_imageiter.py
@@ -42,5 +42,5 @@ def run_imageiter(path_rec, n, batch_size=32):
 if __name__ == '__main__':
     mx.profiler.set_config(profile_all=True, filename='profile_imageiter.json')
     mx.profiler.set_state('run')
-    run_imageiter('test.rec', 20)  # See http://mxnet.io/tutorials/python/image_io.html for how to create .rec files.
+    run_imageiter('test.rec', 20)  # See https://mxnet.io/tutorials/python/image_io.html for how to create .rec files.
     mx.profiler.set_state('stop')
diff --git a/example/quantization/README.md b/example/quantization/README.md
index 90c111ac1a43..032ca97176df 100644
--- a/example/quantization/README.md
+++ b/example/quantization/README.md
@@ -9,7 +9,7 @@ This folder contains examples of quantizing a FP32 model with Intel® MKL-DNN or
 
 <h2 id="1">Model Quantization with Intel® MKL-DNN</h2>
 
-Intel® MKL-DNN supports quantization with subgraph features on Intel® CPU Platform and can bring performance improvements on the [Intel® Xeon® Scalable Platform](https://www.intel.com/content/www/us/en/processors/xeon/scalable/xeon-scalable-platform.html). A new quantization script `imagenet_gen_qsym_mkldnn.py` has been designed to launch quantization for image-classification models with Intel® MKL-DNN. This script integrates with [Gluon-CV modelzoo](https://gluon-cv.mxnet.io/model_zoo/classification.html), so that more pre-trained models can be downloaded from Gluon-CV and then converted for quantization. To apply quantization flow to your project directly, please refer [Quantize custom models with MKL-DNN backend](https://mxnet.incubator.apache.org/tutorials/mkldnn/mkldnn_quantization.html).
+Intel® MKL-DNN supports quantization with subgraph features on Intel® CPU Platform and can bring performance improvements on the [Intel® Xeon® Scalable Platform](https://www.intel.com/content/www/us/en/processors/xeon/scalable/xeon-scalable-platform.html). A new quantization script `imagenet_gen_qsym_mkldnn.py` has been designed to launch quantization for image-classification models with Intel® MKL-DNN. This script integrates with [Gluon-CV modelzoo](https://gluon-cv.mxnet.io/model_zoo/classification.html), so that more pre-trained models can be downloaded from Gluon-CV and then converted for quantization. To apply quantization flow to your project directly, please refer [Quantize custom models with MKL-DNN backend](https://mxnet.apache.org/tutorials/mkldnn/mkldnn_quantization.html).
 
 ```
 usage: imagenet_gen_qsym_mkldnn.py [-h] [--model MODEL] [--epoch EPOCH]
diff --git a/example/quantization/imagenet_gen_qsym.py b/example/quantization/imagenet_gen_qsym.py
index ec4c809a79d0..e8f84d52ff63 100644
--- a/example/quantization/imagenet_gen_qsym.py
+++ b/example/quantization/imagenet_gen_qsym.py
@@ -73,11 +73,11 @@ def save_params(fname, arg_params, aux_params, logger=None):
                         help='shuffle the calibration dataset')
     parser.add_argument('--shuffle-chunk-seed', type=int, default=3982304,
                         help='shuffling chunk seed, see'
-                             ' https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=imager#mxnet.io.ImageRecordIter'
+                             ' https://mxnet.apache.org/api/python/io/io.html?highlight=imager#mxnet.io.ImageRecordIter'
                              ' for more details')
     parser.add_argument('--shuffle-seed', type=int, default=48564309,
                         help='shuffling seed, see'
-                             ' https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=imager#mxnet.io.ImageRecordIter'
+                             ' https://mxnet.apache.org/api/python/io/io.html?highlight=imager#mxnet.io.ImageRecordIter'
                              ' for more details')
     parser.add_argument('--calib-mode', type=str, default='entropy',
                         help='calibration mode used for generating calibration table for the quantized symbol; supports'
diff --git a/example/quantization/imagenet_gen_qsym_mkldnn.py b/example/quantization/imagenet_gen_qsym_mkldnn.py
index cc99ef0d01bc..a4c1491039b9 100644
--- a/example/quantization/imagenet_gen_qsym_mkldnn.py
+++ b/example/quantization/imagenet_gen_qsym_mkldnn.py
@@ -114,11 +114,11 @@ def save_params(fname, arg_params, aux_params, logger=None):
                         help='shuffle the calibration dataset')
     parser.add_argument('--shuffle-chunk-seed', type=int, default=3982304,
                         help='shuffling chunk seed, see'
-                             ' https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=imager#mxnet.io.ImageRecordIter'
+                             ' https://mxnet.apache.org/api/python/io/io.html?highlight=imager#mxnet.io.ImageRecordIter'
                              ' for more details')
     parser.add_argument('--shuffle-seed', type=int, default=48564309,
                         help='shuffling seed, see'
-                             ' https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=imager#mxnet.io.ImageRecordIter'
+                             ' https://mxnet.apache.org/api/python/io/io.html?highlight=imager#mxnet.io.ImageRecordIter'
                              ' for more details')
     parser.add_argument('--calib-mode', type=str, default='entropy',
                         help='calibration mode used for generating calibration table for the quantized symbol; supports'
diff --git a/example/quantization/imagenet_inference.py b/example/quantization/imagenet_inference.py
index e78546140403..719e855f3a3e 100644
--- a/example/quantization/imagenet_inference.py
+++ b/example/quantization/imagenet_inference.py
@@ -161,11 +161,11 @@ def benchmark_score(symbol_file, ctx, batch_size, num_batches, data_layer_type,
                         help='shuffle the calibration dataset')
     parser.add_argument('--shuffle-chunk-seed', type=int, default=3982304,
                         help='shuffling chunk seed, see'
-                             ' https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=imager#mxnet.io.ImageRecordIter'
+                             ' https://mxnet.apache.org/api/python/io/io.html?highlight=imager#mxnet.io.ImageRecordIter'
                              ' for more details')
     parser.add_argument('--shuffle-seed', type=int, default=48564309,
                         help='shuffling seed, see'
-                             ' https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=imager#mxnet.io.ImageRecordIter'
+                             ' https://mxnet.apache.org/api/python/io/io.html?highlight=imager#mxnet.io.ImageRecordIter'
                              ' for more details')
     parser.add_argument('--data-layer-type', type=str, default="float32",
                         choices=['float32', 'int8', 'uint8'],
diff --git a/example/rnn/bucketing/README.md b/example/rnn/bucketing/README.md
index d44b23e69b23..fdf2dbdfdef0 100644
--- a/example/rnn/bucketing/README.md
+++ b/example/rnn/bucketing/README.md
@@ -64,5 +64,5 @@ You can check this improved [Gluon implementation](http://gluon-nlp.mxnet.io/mod
 
 ### Performance Note:
 
-More ```MXNET_GPU_WORKER_NTHREADS``` may lead to better performance. For setting ```MXNET_GPU_WORKER_NTHREADS```, please refer to [Environment Variables](http://mxnet.incubator.apache.org/faq/env_var.html).
+More ```MXNET_GPU_WORKER_NTHREADS``` may lead to better performance. For setting ```MXNET_GPU_WORKER_NTHREADS```, please refer to [Environment Variables](https://mxnet.apache.org/api/faq/env_var).
 
diff --git a/example/rnn/old/README.md b/example/rnn/old/README.md
index b199c7770114..a2e4c81c7851 100644
--- a/example/rnn/old/README.md
+++ b/example/rnn/old/README.md
@@ -32,4 +32,4 @@ Run `get_sherlockholmes_data.sh` to download Sherlock Holmes data.
 
 
 Performance Note:
-More ```MXNET_GPU_WORKER_NTHREADS``` may lead to better performance. For setting ```MXNET_GPU_WORKER_NTHREADS```, please refer to [Environment Variables](https://mxnet.readthedocs.org/en/latest/faq/env_var.html).
+More ```MXNET_GPU_WORKER_NTHREADS``` may lead to better performance. For setting ```MXNET_GPU_WORKER_NTHREADS```, please refer to [Environment Variables](https://mxnet.apache.org/api/faq/env_var).
diff --git a/example/sparse/README.md b/example/sparse/README.md
index 8f1302950d22..27721e5ea364 100644
--- a/example/sparse/README.md
+++ b/example/sparse/README.md
@@ -16,7 +16,7 @@
 <!--- under the License. -->
 
 # Examples using Sparse Symbol API
-This folder contains examples that demonstrate the usage of [Sparse Symbol API](https://mxnet.incubator.apache.org/api/python/symbol/sparse.html)
+This folder contains examples that demonstrate the usage of [Sparse Symbol API](https://mxnet.apache.org/api/python/symbol/sparse.html)
 - [Factorization Machine](https://github.com/apache/incubator-mxnet/tree/master/example/sparse/factorization_machine) uses sparse weights
 - [Linear Classification Using Sparse Matrix Multiplication](https://github.com/apache/incubator-mxnet/tree/master/example/sparse/linear_classification) shows how to use a sparse data loader, sparse dot operator and sparse gradient updaters
 - [Matrix Factorization w/ Sparse Embedding](https://github.com/apache/incubator-mxnet/tree/master/example/sparse/matrix_factorization) uses sparse weights
diff --git a/example/sparse/linear_classification/README.md b/example/sparse/linear_classification/README.md
index 05674cd0c277..2f8050adf8bc 100644
--- a/example/sparse/linear_classification/README.md
+++ b/example/sparse/linear_classification/README.md
@@ -19,8 +19,8 @@ Linear Classification Using Sparse Matrix Multiplication
 ===========
 This examples trains a linear model using the sparse feature in MXNet. This is for demonstration purpose only.
 
-The example utilizes the sparse data loader ([mx.io.LibSVMIter](https://mxnet.incubator.apache.org/versions/master/api/python/io/io.html#mxnet.io.LibSVMIter)),
-the sparse dot operator and [sparse gradient updaters](https://mxnet.incubator.apache.org/versions/master/api/python/ndarray/sparse.html#updater)
+The example utilizes the sparse data loader ([mx.io.LibSVMIter](https://mxnet.apache.org/versions/master/api/python/io/io.html#mxnet.io.LibSVMIter)),
+the sparse dot operator and [sparse gradient updaters](https://mxnet.apache.org/versions/master/api/python/ndarray/sparse.html#updater)
 to train a linear model on the
 [Avazu](https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html#avazu) click-through-prediction dataset.
 
diff --git a/example/ssd/quantization.py b/example/ssd/quantization.py
index 9801a7dc0076..7ae6278648d4 100644
--- a/example/ssd/quantization.py
+++ b/example/ssd/quantization.py
@@ -59,11 +59,11 @@ def save_params(fname, arg_params, aux_params, logger=None):
                         help='shuffle the calibration dataset')
     parser.add_argument('--shuffle-chunk-seed', type=int, default=3982304,
                         help='shuffling chunk seed, see'
-                             ' https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=imager#mxnet.io.ImageRecordIter'
+                             ' https://mxnet.apache.org/api/python/io/io.html?highlight=imager#mxnet.io.ImageRecordIter'
                              ' for more details')
     parser.add_argument('--shuffle-seed', type=int, default=48564309,
                         help='shuffling seed, see'
-                             ' https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=imager#mxnet.io.ImageRecordIter'
+                             ' https://mxnet.apache.org/api/python/io/io.html?highlight=imager#mxnet.io.ImageRecordIter'
                              ' for more details')
     parser.add_argument('--calib-mode', type=str, default='naive',
                         help='calibration mode used for generating calibration table for the quantized symbol; supports'
diff --git a/julia/docs/src/index.md b/julia/docs/src/index.md
index 11a77670eae3..aacd844cc38e 100644
--- a/julia/docs/src/index.md
+++ b/julia/docs/src/index.md
@@ -29,7 +29,7 @@ include:
   state-of-the-art deep learning models.
 
 For more details, see documentation below. Please also checkout the
-[examples](https://github.com/dmlc/MXNet.jl/tree/master/examples) directory.
+[examples](https://github.com/apache/incubator-mxnet/tree/master/julia/examples) directory.
 
 ## Tutorials
 
diff --git a/julia/docs/src/tutorial/char-lstm.md b/julia/docs/src/tutorial/char-lstm.md
index 3961744f282d..bc7f7b471d94 100644
--- a/julia/docs/src/tutorial/char-lstm.md
+++ b/julia/docs/src/tutorial/char-lstm.md
@@ -305,7 +305,7 @@ post](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) on more
 examples and links including Linux source codes, Algebraic Geometry
 Theorems, and even cooking recipes. The code for sampling can be found
 in
-[examples/char-lstm/sampler.jl](https://github.com/dmlc/MXNet.jl/blob/master/examples/char-lstm/sampler.jl).
+[examples/char-lstm/sampler.jl](https://github.com/apache/incubator-mxnet/tree/master/julia/examples/char-lstm/sampler.jl).
 
 Visualizing the LSTM
 --------------------
@@ -318,6 +318,6 @@ illustrations](http://colah.github.io/posts/2015-08-Understanding-LSTMs/),
 but could otherwise be very useful for debugging. As we can see, the
 LSTM unfolded over time is just a (very) deep neural network. The
 complete code for producing this visualization can be found in
-[examples/char-lstm/visualize.jl](https://github.com/dmlc/MXNet.jl/blob/master/examples/char-lstm/visualize.jl).
+[examples/char-lstm/visualize.jl](https://github.com/apache/incubator-mxnet/tree/master/julia/examples/char-lstmvisualize.jl).
 
 ![image](images/char-lstm-vis.svg)
diff --git a/julia/docs/src/user-guide/install.md b/julia/docs/src/user-guide/install.md
index d1a90a808507..129b6a190c5b 100644
--- a/julia/docs/src/user-guide/install.md
+++ b/julia/docs/src/user-guide/install.md
@@ -79,7 +79,7 @@ unresolved dependencies in an non-standard environment; Or when one want
 to work with a separate, maybe customized libmxnet.
 
 To build libmxnet, please refer to [the installation guide of
-libmxnet](https://mxnet.incubator.apache.org/install/index.html). After
+libmxnet](https://mxnet.apache.org/install/index.html). After
 successfully installing libmxnet, set the `MXNET_HOME` *environment
 variable* to the location of libmxnet. In other words, the compiled
 `libmxnet.so` should be found in `$MXNET_HOME/lib`.
diff --git a/julia/docs/src/user-guide/overview.md b/julia/docs/src/user-guide/overview.md
index d1d32e80ebd7..974cc7dee974 100644
--- a/julia/docs/src/user-guide/overview.md
+++ b/julia/docs/src/user-guide/overview.md
@@ -269,7 +269,7 @@ symbolic composition system. It is like
 [Theano](http://deeplearning.net/software/theano/), except that we
 avoided long expression compilation time by providing *larger* neural
 network related building blocks to guarantee computation performance.
-See also [this note](http://mxnet.readthedocs.org/en/latest/program_model.html)
+See also [this note](https://mxnet.readthedocs.org/en/latest/program_model.html)
 for the design and trade-off of the MXNet symbolic composition system.
 
 The basic type is `mx.SymbolicNode`. The following is a trivial example of
diff --git a/perl-package/AI-MXNet-Gluon-ModelZoo/lib/AI/MXNet/Gluon/ModelZoo.pm b/perl-package/AI-MXNet-Gluon-ModelZoo/lib/AI/MXNet/Gluon/ModelZoo.pm
index e9cbec02a445..1611ce7f8318 100644
--- a/perl-package/AI-MXNet-Gluon-ModelZoo/lib/AI/MXNet/Gluon/ModelZoo.pm
+++ b/perl-package/AI-MXNet-Gluon-ModelZoo/lib/AI/MXNet/Gluon/ModelZoo.pm
@@ -45,7 +45,7 @@ our $VERSION = '1.33';
 =head1 DESCRIPTION
 
     This module houses a collection of pretrained models (the parameters are hosted on public mxnet servers).
-    https://mxnet.incubator.apache.org/api/python/gluon/model_zoo.html
+    https://mxnet.apache.org/api/python/gluon/model_zoo.html
     See examples/image_classification.pl for the example of real time image classification
     using a pretrained model from the ModelZoo
 =cut
diff --git a/perl-package/AI-MXNet/Changes b/perl-package/AI-MXNet/Changes
index e67fe39b49ab..c762cc73fc33 100644
--- a/perl-package/AI-MXNet/Changes
+++ b/perl-package/AI-MXNet/Changes
@@ -86,7 +86,7 @@ Revision history for Perl extension AI::MXNet
            pod reworked to be readable via metacpan.
 
 0.95  Sun Mar 26 17:42:02 PDT 2017
-        - docs, bugfixes, tests in order to be visible on http://mxnet.io
+        - docs, bugfixes, tests in order to be visible on https://mxnet.io
 
 0.03  Tue Feb 14 07:28:11 PST 2017
         - sync up with current state of the Python inteface.
diff --git a/perl-package/AI-MXNet/examples/mnist.pl b/perl-package/AI-MXNet/examples/mnist.pl
index 3786b6be98eb..f8445ebbd4ae 100755
--- a/perl-package/AI-MXNet/examples/mnist.pl
+++ b/perl-package/AI-MXNet/examples/mnist.pl
@@ -19,7 +19,7 @@
 
 use strict;
 use warnings;
-# derived from http://mxnet.io/tutorials/python/mnist.html
+# derived from https://mxnet.io/tutorials/python/mnist.html
 use LWP::UserAgent ();
 use PDL ();
 #use Gtk2 '-init';
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet.pm b/perl-package/AI-MXNet/lib/AI/MXNet.pm
index b5665c88eddf..b8e17f380118 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet.pm
@@ -220,7 +220,7 @@ AI::MXNet - Perl interface to MXNet machine learning library
     so most, if not all of Python’s documentation and examples should just work in Perl after making few changes
     in order to make the code a bit more Perlish. In nutshell just add $ sigils and replace . = \n with -> => ;
     and in 99% of cases that’s all that is needed there.
-    In addition please refer to very detailed L<MXNet Python API Documentation|http://mxnet.io/api/python/index.html>.
+    In addition please refer to very detailed L<MXNet Python API Documentation|https://mxnet.apache.org/api/python/docs/tutorials/index.html>.
 
     AI::MXNet supports new imperative PyTorch like Gluon MXNet interface.
     Please get acquainted with this new interface at L<Dive into Deep Learning|https://www.d2l.ai/>.
@@ -239,7 +239,7 @@ AI::MXNet - Perl interface to MXNet machine learning library
 
 =head1 SEE ALSO
 
-    L<http://mxnet.io/>
+    L<https://mxnet.io/>
     L<https://github.com/dmlc/mxnet/tree/master/perl-package>
     L<Function::Parameters|https://metacpan.org/pod/Function::Parameters>, L<Mouse|https://metacpan.org/pod/Mouse>
 
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon.pm
index 1badacb7ece1..789011a2a7e1 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon.pm
@@ -128,7 +128,7 @@ sub model_zoo { require AI::MXNet::Gluon::ModelZoo; 'AI::MXNet::Gluon::ModelZoo'
     });
 
     $net->hybridize();
-    See more at L<Python docs|http://mxnet.incubator.apache.org/api/python/gluon/gluon.html>
+    See more at L<Python docs|https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/index.html>
 =cut
 
 1;
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Block.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Block.pm
index b58704b5d8a1..c4ac933ad1a1 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Block.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Block.pm
@@ -142,7 +142,7 @@ use Scalar::Util qw(refaddr);
     Prefix acts like a name space. All children blocks created in parent block's
     name_scope will have parent block's prefix in their name.
     Please refer to
-    naming tutorial http://mxnet.incubator.apache.org/tutorials/gluon/naming.html
+    naming tutorial https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/naming.html
     for more info on prefix and naming.
 
     params : AI::MXNet::Gluon::ParameterDict or undef
@@ -876,7 +876,7 @@ package AI::MXNet::Gluon::HybridBlock;
     representing the forward computation and cache it. On subsequent forwards,
     the cached graph will be used instead of hybrid_forward.
 
-    Refer Hybrid tutorial L<http://mxnet.io/tutorials/gluon/hybrid.html> to see
+    Refer Hybrid tutorial L<https://mxnet.io/tutorials/gluon/hybrid.html> to see
     the end-to-end usage.
 =cut
 
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Trainer.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Trainer.pm
index 12cd7c2391c3..0ab484de2525 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Trainer.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Trainer.pm
@@ -41,7 +41,7 @@ use Mouse;
         The set of parameters to optimize.
     optimizer : str or Optimizer
         The optimizer to use. See
-        `help <http://mxnet.io/api/python/optimization/optimization.html#the-mxnet-optimizer-package>`_
+        `help <https://mxnet.io/api/python/optimization/optimization.html#the-mxnet-optimizer-package>`_
         on Optimizer for a list of available optimizers.
     optimizer_params : hash ref
         Key-word arguments to be passed to optimizer constructor. For example,
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/IO.pm b/perl-package/AI-MXNet/lib/AI/MXNet/IO.pm
index 5dd3955b2438..4f35518bb9a8 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/IO.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/IO.pm
@@ -639,7 +639,7 @@ extends 'AI::MXNet::DataIter';
 
     Here are the list of currently available predefined iterators, for more custom iterators
     please check out the examples directory.
-    Also please refer to the L<Python docs|http://mxnet.incubator.apache.org/api/python/io/io.html>
+    Also please refer to the L<Python docs|https://mxnet.apache.org/api/python/io/io.html>
     mx->io->CSVIter                     Returns the CSV file iterator.
     mx->io->LibSVMIter                  Returns the LibSVM iterator which returns data with csr storage type.
     mx->io->ImageRecordIter             Iterates on image RecordIO files
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/LinAlg.pm b/perl-package/AI-MXNet/lib/AI/MXNet/LinAlg.pm
index 3fef1b873cab..50077e1a04be 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/LinAlg.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/LinAlg.pm
@@ -41,8 +41,8 @@ use AI::MXNet::LinAlg::NDArray;
     mx->linalg-><sym|nd>->syrk  Multiplication of matrix with its transpose.
     mx->linalg-><sym|nd>->gelqf LQ factorization for general matrix.
     mx->linalg-><sym|nd>->syevd Eigendecomposition for symmetric matrix.
-    L<NDArray Python Docs|http://mxnet.incubator.apache.org/api/python/ndarray/linalg.html>
-    L<Symbol Python Docs|http://mxnet.incubator.apache.org/api/python/symbol/linalg.html>
+    L<NDArray Python Docs|https://mxnet.apache.org/api/python/ndarray/linalg.html>
+    L<Symbol Python Docs|https://mxnet.apache.org/api/python/symbol/linalg.html>
 
     Examples:
 
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Metric.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Metric.pm
index 4c2baa43d1fd..1729cd4a76e0 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/Metric.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/Metric.pm
@@ -29,7 +29,7 @@ use JSON::PP;
 =head1 DESCRIPTION
 
     This module hosts all the evaluation metrics available to evaluate the performance of a learned model.
-    L<Python Docs|http://mxnet.incubator.apache.org/api/python/metric/metric.html>
+    L<Python Docs|https://mxnet.apache.org/api/python/metric/metric.html>
 =cut
 
 # Check to see if the two arrays are the same size.
diff --git a/perl-package/AI-MXNetCAPI/Changes b/perl-package/AI-MXNetCAPI/Changes
index cdbbdab57cdf..411be3604360 100644
--- a/perl-package/AI-MXNetCAPI/Changes
+++ b/perl-package/AI-MXNetCAPI/Changes
@@ -38,7 +38,7 @@ Revision history for Perl extension AI::MXNetCAPI
         - callbacks bugfix.
 
 0.95  Sun Mar 26 17:42:02 PDT 2017
-        - visible on http://mxnet.io
+        - visible on https://mxnet.io
 
 0.03  Sat Feb 25 13:21:07 PST 2017
 	- sync up with the Python interface.
diff --git a/perl-package/AI-MXNetCAPI/README b/perl-package/AI-MXNetCAPI/README
index 848b4d03ab21..5266a72a00ab 100644
--- a/perl-package/AI-MXNetCAPI/README
+++ b/perl-package/AI-MXNetCAPI/README
@@ -14,7 +14,7 @@ To install this module type the following:
 
 DEPENDENCIES
 
-This module requires mxnet http://mxnet.io
+This module requires mxnet https://mxnet.io
 It's used by AI::MXNet
 
 COPYRIGHT AND LICENCE
diff --git a/perl-package/AI-NNVMCAPI/Changes b/perl-package/AI-NNVMCAPI/Changes
index 62aa0421e3fd..ba787009cf42 100644
--- a/perl-package/AI-NNVMCAPI/Changes
+++ b/perl-package/AI-NNVMCAPI/Changes
@@ -13,7 +13,7 @@ Revision history for Perl extension AI::NNVMCAPI.
         - sync with python.
 
 0.95  Sun Mar 26 17:42:02 PDT 2017
-        - visible on http://mxnet.io
+        - visible on https://mxnet.io
 
 0.03  Sat Feb 25 13:21:07 PST 2017
 	- sync up with the Python interface.
diff --git a/perl-package/AI-NNVMCAPI/README b/perl-package/AI-NNVMCAPI/README
index 092fd31fd8b3..f6c3d8fbacc8 100644
--- a/perl-package/AI-NNVMCAPI/README
+++ b/perl-package/AI-NNVMCAPI/README
@@ -14,7 +14,7 @@ To install this module type the following:
 
 DEPENDENCIES
 
-This module requires mxnet http://mxnet.io
+This module requires mxnet https://mxnet.io
 It's used by AI::MXNet.
 
 COPYRIGHT AND LICENCE
diff --git a/perl-package/README.md b/perl-package/README.md
index 893a11d19f88..dc1824357181 100644
--- a/perl-package/README.md
+++ b/perl-package/README.md
@@ -15,7 +15,7 @@
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
 
-[Perl API](https://mxnet.incubator.apache.org/api/perl/index.html)
+[Perl API](https://mxnet.apache.org/api/perl/index.html)
 [![GitHub license](http://dmlc.github.io/img/apache2.svg)](../LICENSE)
 
 You have found MXNet Perl Package! The MXNet Perl packages brings flexible and efficient GPU
@@ -27,7 +27,7 @@ computing and state-of-art deep learning to Perl.
 
 Installation
 ---------
-* [MXNet Setup Document](http://mxnet.incubator.apache.org/install/ubuntu_setup.html#install-the-mxnet-package-for-perl)
+* [MXNet Setup Document](https://mxnet.apache.org/get_started/ubuntu_setup#install-the-mxnet-package-for-perl)
   - Check this out for detailed documents, examples and installation guides.
 
 License
diff --git a/python/README.md b/python/README.md
index 396d112f687a..148188537f48 100644
--- a/python/README.md
+++ b/python/README.md
@@ -20,7 +20,7 @@ MXNet Python Package
 This directory and nested files contain MXNet Python package and language binding.
 
 ## Installation
-To install MXNet Python package, visit MXNet [Install Instruction](http://mxnet.incubator.apache.org/install/index.html)
+To install MXNet Python package, visit MXNet [Install Instruction](https://mxnet.apache.org/get_started)
 
 
 ## Running the unit tests
diff --git a/python/mxnet/context.py b/python/mxnet/context.py
index f2b01373b280..decea71762f6 100644
--- a/python/mxnet/context.py
+++ b/python/mxnet/context.py
@@ -36,7 +36,7 @@ class Context(with_metaclass(_MXClassPropertyMetaClass, object)):
 
     See also
     ----------
-    `How to run MXNet on multiple CPU/GPUs <http://mxnet.io/faq/multi_devices.html>`
+    `How to run MXNet on multiple CPU/GPUs <http://mxnet.incubator.apache.org/api/faq/distributed_training>`
     for more details.
 
     Parameters
diff --git a/python/mxnet/gluon/block.py b/python/mxnet/gluon/block.py
index fc08b4c6bd32..bad737f4f6cd 100644
--- a/python/mxnet/gluon/block.py
+++ b/python/mxnet/gluon/block.py
@@ -165,7 +165,7 @@ def forward(self, x):
         Prefix acts like a name space. All children blocks created in parent block's
         :py:meth:`name_scope` will have parent block's prefix in their name.
         Please refer to
-        `naming tutorial <http://mxnet.incubator.apache.org/tutorials/gluon/naming.html>`_
+        `naming tutorial <https://mxnet.incubator.apache.org/api/python/docs/tutorials/packages/gluon/naming.html>`_
         for more info on prefix and naming.
     params : ParameterDict or None
         :py:class:`ParameterDict` for sharing weights with the new :py:class:`Block`. For example,
@@ -261,7 +261,7 @@ def name_scope(self):
                 self.dense = nn.Dense(20)
 
         Please refer to
-        `naming tutorial <http://mxnet.incubator.apache.org/tutorials/gluon/naming.html>`_
+        `naming tutorial <https://mxnet.incubator.apache.org/tutorials/gluon/naming.html>`_
         for more info on prefix and naming.
         """
         return self._scope
@@ -350,7 +350,7 @@ def save_params(self, filename):
         warnings.warn("save_params is deprecated. Please use save_parameters. "
                       "Note that if you want load from SymbolBlock later, please "
                       "use export instead. For details, see "
-                      "https://mxnet.incubator.apache.org/tutorials/gluon/save_lo"
+                      "https://mxnet.apache.org/tutorials/gluon/save_lo"
                       "ad_params.html")
         try:
             self.collect_params().save(filename, strip_prefix=self.prefix)
@@ -757,7 +757,7 @@ def hybrid_forward(self, F, x):
     References
     ----------
         `Hybrid - Faster training and easy deployment
-        <http://mxnet.io/tutorials/gluon/hybrid.html>`_
+        <https://mxnet.io/tutorials/gluon/hybrid.html>`_
     """
     def __init__(self, prefix=None, params=None):
         super(HybridBlock, self).__init__(prefix=prefix, params=params)
diff --git a/python/mxnet/gluon/parameter.py b/python/mxnet/gluon/parameter.py
index 66f5c2afc8b0..dedd81496c2f 100644
--- a/python/mxnet/gluon/parameter.py
+++ b/python/mxnet/gluon/parameter.py
@@ -952,7 +952,7 @@ def save(self, filename, strip_prefix=''):
                     "this may be due to your Block shares parameters from other "
                     "Blocks or you forgot to use 'with name_scope()' when creating "
                     "child blocks. For more info on naming, please see "
-                    "http://mxnet.incubator.apache.org/tutorials/basic/naming.html"%(
+                    "https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/naming.html"%(
                         strip_prefix, param.name, strip_prefix))
             arg_dict[param.name[len(strip_prefix):]] = weight
         ndarray.save(filename, arg_dict)
diff --git a/python/mxnet/gluon/trainer.py b/python/mxnet/gluon/trainer.py
index c8aeb614160a..8a863fc2c6eb 100644
--- a/python/mxnet/gluon/trainer.py
+++ b/python/mxnet/gluon/trainer.py
@@ -43,7 +43,7 @@ class Trainer(object):
         The set of parameters to optimize.
     optimizer : str or Optimizer
         The optimizer to use. See
-        `help <http://mxnet.io/api/python/optimization/optimization.html#the-mxnet-optimizer-package>`_
+        `help <https://mxnet.incubator.apache.org/api/python/tutorials/packages/optimizer/optimizer.html>`_
         on Optimizer for a list of available optimizers.
     optimizer_params : dict
         Key-word arguments to be passed to optimizer constructor. For example,
diff --git a/python/mxnet/module/base_module.py b/python/mxnet/module/base_module.py
index 0d5515f172b8..053a00b3abba 100644
--- a/python/mxnet/module/base_module.py
+++ b/python/mxnet/module/base_module.py
@@ -201,8 +201,8 @@ def score(self, eval_data, eval_metric, num_batch=None, batch_end_callback=None,
         """Runs prediction on ``eval_data`` and evaluates the performance according to
         the given ``eval_metric``.
 
-        Checkout `Module Tutorial <http://mxnet.io/tutorials/basic/module.html>`_ to see
-        a end-to-end use-case.
+        Checkout `Module Tutorial <https://mxnet.apache.org/api/python/tutorials/packages/module/index.html>`_
+        to see an end-to-end use-case.
 
         Parameters
         ----------
@@ -416,8 +416,8 @@ def fit(self, train_data, eval_data=None, eval_metric='acc',
             validation_metric=None, monitor=None, sparse_row_id_fn=None):
         """Trains the module parameters.
 
-        Checkout `Module Tutorial <http://mxnet.io/tutorials/basic/module.html>`_ to see
-        a end-to-end use-case.
+        Checkout `Module Tutorial <https://mxnet.apache.org/api/python/tutorials/packages/module/index.html>`_
+        to see an end-to-end use-case.
 
         Parameters
         ----------
diff --git a/scala-package/README.md b/scala-package/README.md
index c3ef4b0c84d0..b1a5aa413e52 100644
--- a/scala-package/README.md
+++ b/scala-package/README.md
@@ -133,7 +133,7 @@ Also, add the dependency which corresponds to your platform to the ```dependenci
 Build From Source
 -----------------
 
-The [Installation Guide](http://mxnet.incubator.apache.org/install/index.html) contains instructions to install mxnet or build it from source. The Scala/Java package is built from source using Maven. The maven build assumes you already have a ``lib/libmxnet.so`` file.
+The [Installation Guide](https://mxnet.apache.org/get_started) contains instructions to install mxnet or build it from source. The Scala/Java package is built from source using Maven. The maven build assumes you already have a ``lib/libmxnet.so`` file.
 If you have built MXNet from source and are looking to set up Scala\Java from that point, you may simply run the following from the MXNet source root, the build will detect your platform (OSX/Linux) and libmxnet.so flavor (CPU/GPU):
 
 ```bash
@@ -210,30 +210,30 @@ Caused by: java.lang.ClassNotFoundException: org.apache.mxnet.NDArray
 ```
 Please make sure your $CLASSPATH contains `mxnet-full_scala_version-INTERNAL.jar`.
 
-- To set up the Scala Project using IntelliJ IDE on macOS follow the instructions [here](https://mxnet.incubator.apache.org/tutorials/scala/mxnet_scala_on_intellij.html).
+- To set up the Scala Project using IntelliJ IDE on macOS follow the instructions [here](https://mxnet.apache.org/tutorials/scala/mxnet_scala_on_intellij.html).
 - Several examples on using the Scala APIs are provided in the [Scala Examples Folder](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/)
 
 Scala Training APIs
 -------
 - Module API :
-[The Module API](https://mxnet.incubator.apache.org/api/scala/module.html) provides an intermediate and high-level interface for performing computation with neural networks in MXNet. Modules provide high-level APIs for training, predicting, and evaluating.
+[The Module API](https://mxnet.apache.org/api/scala/module.html) provides an intermediate and high-level interface for performing computation with neural networks in MXNet. Modules provide high-level APIs for training, predicting, and evaluating.
 
 - KVStore API : 
-To run training over multiple GPUs and multiple hosts, one can use the [KVStore API](https://mxnet.incubator.apache.org/api/scala/kvstore.html).
+To run training over multiple GPUs and multiple hosts, one can use the [KVStore API](https://mxnet.apache.org/api/scala/kvstore.html).
 
 - IO/Data Loading : 
-MXNet Scala provides APIs for preparing data to feed as an input to models. Check out [Data Loading API](https://mxnet.incubator.apache.org/api/scala/io.html) for more info.
+MXNet Scala provides APIs for preparing data to feed as an input to models. Check out [Data Loading API](https://mxnet.apache.org/api/scala/io.html) for more info.
  
-Other available Scala APIs for training can be found [here](https://mxnet.incubator.apache.org/api/scala/index.html).  
+Other available Scala APIs for training can be found [here](https://mxnet.apache.org/api/scala/index.html).  
  
 
 Scala Inference APIs
 -------
-The [Scala Inference APIs](https://mxnet.incubator.apache.org/api/scala/infer.html) provide an easy, out of the box solution to load a pre-trained MXNet model and run inference on it. The Inference APIs are present in the [Infer Package](https://github.com/apache/incubator-mxnet/tree/master/scala-package/infer) under the MXNet Scala Package repository, while the documentation for the Infer API is available [here](https://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.infer.package).  
+The [Scala Inference APIs](https://mxnet.apache.org/api/scala/infer.html) provide an easy, out of the box solution to load a pre-trained MXNet model and run inference on it. The Inference APIs are present in the [Infer Package](https://github.com/apache/incubator-mxnet/tree/master/scala-package/infer) under the MXNet Scala Package repository, while the documentation for the Infer API is available [here](https://mxnet.apache.org/api/scala/docs/index.html#org.apache.mxnet.infer.package).  
 
 Java Inference APIs
 -------
-The [Java Inference APIs](http://mxnet.incubator.apache.org/api/java/index.html) also provide an easy, out of the box solution to load a pre-trained MXNet model and run inference on it. The Inference APIs are present in the [Infer Package](https://github.com/apache/incubator-mxnet/tree/master/scala-package/infer/src/main/scala/org/apache/mxnet/infer/javaapi) under the MXNet Scala Package repository, while the documentation for the Infer API is available [here](https://mxnet.incubator.apache.org/api/java/docs/index.html#org.apache.mxnet.infer.package).
+The [Java Inference APIs](https://mxnet.apache.org/api/java/index.html) also provide an easy, out of the box solution to load a pre-trained MXNet model and run inference on it. The Inference APIs are present in the [Infer Package](https://github.com/apache/incubator-mxnet/tree/master/scala-package/infer/src/main/scala/org/apache/mxnet/infer/javaapi) under the MXNet Scala Package repository, while the documentation for the Infer API is available [here](https://mxnet.apache.org/api/java/docs/index.html#org.apache.mxnet.infer.package).
 More APIs will be added to the Java Inference APIs soon.
 
 JVM Memory Management
diff --git a/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/objectdetector/README.md b/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/objectdetector/README.md
index f6f8b674c08e..21c062938e93 100644
--- a/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/objectdetector/README.md
+++ b/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/objectdetector/README.md
@@ -34,9 +34,9 @@ The model is trained on the [Pascal VOC 2012 dataset](http://host.robots.ox.ac.u
 
 ## Prerequisites
 
-1. [Build MXNet](http://mxnet.incubator.apache.org/install/scala_setup.html)
-2. [Build MXNet Scala/Java Package](http://mxnet.incubator.apache.org/install/scala_setup.html)
-3. [IntelliJ IDE (or alternative IDE) project setup](http://mxnet.incubator.apache.org/tutorials/java/mxnet_java_on_intellij.html) with the MXNet Scala/Java Package
+1. [Build MXNet](https://mxnet.apache.org/get_started/ubuntu_setup)
+2. [Build MXNet Scala/Java Package](https://mxnet.apache.org/get_started/scala_setup)
+3. [IntelliJ IDE (or alternative IDE) project setup](https://mxnet.apache.org/tutorials/java/mxnet_java_on_intellij.html) with the MXNet Scala/Java Package
 4. wget
 
 
diff --git a/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/predictor/README.md b/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/predictor/README.md
index 02e09f39cdcf..141d55a636d0 100644
--- a/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/predictor/README.md
+++ b/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/predictor/README.md
@@ -29,7 +29,7 @@ build and run pre-trained Resnet 18 model.
 
 ## Prerequisites
 
-1. Build from source with [MXNet](https://mxnet.incubator.apache.org/install/index.html)
+1. Build from source with [MXNet](https://mxnet.apache.org/install/index.html)
 2. [IntelliJ IDE (or alternative IDE) project setup](https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/java/mxnet_java_on_intellij.md) with the MXNet Java Package
 3. wget
 
diff --git a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/benchmark/README.md b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/benchmark/README.md
index d45469ac5296..67ee1ef65bab 100644
--- a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/benchmark/README.md
+++ b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/benchmark/README.md
@@ -35,7 +35,7 @@ This script can be easily placed in an automated environment to run benchmark re
 
 1. MXNet
 2. MXNet Scala Package
-3. [IntelliJ IDE (or alternative IDE) project setup](http://mxnet.incubator.apache.org/tutorials/scala/mxnet_scala_on_intellij.html) with the MXNet Scala Package
+3. [IntelliJ IDE (or alternative IDE) project setup](https://mxnet.apache.org/api/scala/docs/tutorials/mxnet_scala_on_intellij) with the MXNet Scala Package
 4. Model files and datasets for the model one will try to benchmark
 
 ## Scripts
diff --git a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/cnntextclassification/README.md b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/cnntextclassification/README.md
index 3b28e3ef4463..fe4fbef18f29 100644
--- a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/cnntextclassification/README.md
+++ b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/cnntextclassification/README.md
@@ -24,7 +24,7 @@ Please contribute to improve the dev accuracy of the model.
 ## Setup
 
 Please configure your maven project using our latest release. An tutorial to do that can be found here:
-[IntelliJ IDE (or alternative IDE) project setup](http://mxnet.incubator.apache.org/tutorials/scala/mxnet_scala_on_intellij.html)
+[IntelliJ IDE (or alternative IDE) project setup](https://mxnet.apache.org/api/scala/docs/tutorials/mxnet_scala_on_intellij)
 
 ### Download the training files
 ```$xslt
@@ -37,4 +37,4 @@ I used the SLIM version, you can try with the full version to see if the accurac
 https://s3.us-east-2.amazonaws.com/mxnet-scala/scala-example-ci/CNN/GoogleNews-vectors-negative300-SLIM.bin
 ```
 ### Train the model
-Please configure the [args](https://github.com/apache/incubator-mxnet/blob/scala-package/examples/src/main/scala/org/apache/mxnet/examples/cnntextclassification/CNNTextClassification.scala#L299-L312) required for the model here and then run it.
\ No newline at end of file
+Please configure the [args](https://github.com/apache/incubator-mxnet/blob/scala-package/examples/src/main/scala/org/apache/mxnet/examples/cnntextclassification/CNNTextClassification.scala#L299-L312) required for the model here and then run it.
diff --git a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/imageclassifier/README.md b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/imageclassifier/README.md
index e32a66bca832..6b26e316eda9 100644
--- a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/imageclassifier/README.md
+++ b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/imageclassifier/README.md
@@ -36,7 +36,7 @@ The following example shows recognized object classes with corresponding probabi
 
 1. MXNet
 2. MXNet Scala Package
-3. [IntelliJ IDE (or alternative IDE) project setup](http://mxnet.incubator.apache.org/tutorials/scala/mxnet_scala_on_intellij.html) with the MXNet Scala Package
+3. [IntelliJ IDE (or alternative IDE) project setup](https://mxnet.apache.org/api/scala/docs/tutorials/mxnet_scala_on_intellij) with the MXNet Scala Package
 4. wget
 
 
diff --git a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/objectdetector/README.md b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/objectdetector/README.md
index 25f040e36e37..062448981256 100644
--- a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/objectdetector/README.md
+++ b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/objectdetector/README.md
@@ -36,7 +36,7 @@ The model is trained on the [Pascal VOC 2012 dataset](http://host.robots.ox.ac.u
 
 1. MXNet
 2. MXNet Scala Package
-3. [IntelliJ IDE (or alternative IDE) project setup](http://mxnet.incubator.apache.org/tutorials/scala/mxnet_scala_on_intellij.html) with the MXNet Scala Package
+3. [IntelliJ IDE (or alternative IDE) project setup](https://mxnet.apache.org/tutorials/scala/mxnet_scala_on_intellij.html) with the MXNet Scala Package
 4. wget
 
 
diff --git a/scala-package/packageTest/README.md b/scala-package/packageTest/README.md
index edd5344b4aaa..4ca5568beb27 100644
--- a/scala-package/packageTest/README.md
+++ b/scala-package/packageTest/README.md
@@ -23,7 +23,7 @@ This is an project created to run the test suite on a fully packaged mxnet jar.
 
 To setup the packageTest, you must first build your tests. To build the tests, follow these steps from the mxnet main directory:
 
-1. Build MXNet and the scala package from source following the directions [here](https://mxnet.incubator.apache.org/install/scala_setup.html#source)
+1. Build MXNet and the scala package from source following the directions [here](https://mxnet.apache.org/get_started/scala_setup#source)
 2. Build the tests by running `mvn test-compile`.
 3. Follow setup instructions below for your testing goal
 
diff --git a/scala-package/spark/README.md b/scala-package/spark/README.md
index 0b18db0a7cb4..957b05f99b0e 100644
--- a/scala-package/spark/README.md
+++ b/scala-package/spark/README.md
@@ -28,7 +28,7 @@ The MXNet on Spark is still in *experimental stage*. Any suggestion or contribut
 Build
 ------------
 
-Checkout the [Installation Guide](http://mxnet.io/get_started/setup.html) contains instructions to install mxnet. Remember to enable the distributed training, i.e., set `USE_DIST_KVSTORE=1`.
+Checkout the [Installation Guide](http://mxnet.apache.org/get_started) contains instructions to install mxnet. Remember to enable the distributed training, i.e., set `USE_DIST_KVSTORE=1`.
 
 Compile the Scala Package by
 
diff --git a/setup-utils/install-mxnet-osx-python.sh b/setup-utils/install-mxnet-osx-python.sh
index b24650a57115..0dde8096b315 100755
--- a/setup-utils/install-mxnet-osx-python.sh
+++ b/setup-utils/install-mxnet-osx-python.sh
@@ -520,9 +520,9 @@ END
 		echo ":-)"
 		echo " "
 		echo "FYI : You can fine-tune MXNet run-time behavior using environment variables described at:"
-		echo "      http://mxnet.io/faq/env_var.html"
+		echo "      https://mxnet.apache.org/api/faq/env_var"
 		echo " "
-		echo "NEXT: Try the tutorials at: http://mxnet.io/tutorials"
+		echo "NEXT: Try the tutorials at: https://mxnet.io/api"
 		echo " "
 		echo $LINE
 		echo " "
diff --git a/src/operator/custom/custom.cc b/src/operator/custom/custom.cc
index 5d2c284be46f..441405f64a3a 100644
--- a/src/operator/custom/custom.cc
+++ b/src/operator/custom/custom.cc
@@ -541,7 +541,7 @@ NNVM_REGISTER_OP(Custom)
 
 Custom operators should override required methods like `forward` and `backward`.
 The custom operator must be registered before it can be used.
-Please check the tutorial here: http://mxnet.io/faq/new_op.html.
+Please check the tutorial here: https://mxnet.incubator.apache.org/api/faq/new_op
 
 )code" ADD_FILELINE)
 .set_num_inputs([](const NodeAttrs& attrs){
diff --git a/src/operator/nn/layer_norm-inl.h b/src/operator/nn/layer_norm-inl.h
index 3294874e419a..238a71bbba1f 100644
--- a/src/operator/nn/layer_norm-inl.h
+++ b/src/operator/nn/layer_norm-inl.h
@@ -118,7 +118,7 @@ void LayerNormComputeGeneral(const nnvm::NodeAttrs& attrs,
   bool safe_acc = dmlc::GetEnv("MXNET_SAFE_ACCUMULATION", false);
   if (!safe_acc && inputs[0].type_flag_ == mshadow::kFloat16) {
     common::LogOnce("MXNET_SAFE_ACCUMULATION=1 is recommended for float16 inputs for LayerNorm. "
-                    "See https://mxnet.incubator.apache.org/versions/master/faq/env_var.html "
+                    "See https://mxnet.apache.org/api/faq/env_var "
                     "for more details.");
   }
 
diff --git a/src/operator/nn/layer_norm.cu b/src/operator/nn/layer_norm.cu
index fead2a6a3066..39936a56541e 100644
--- a/src/operator/nn/layer_norm.cu
+++ b/src/operator/nn/layer_norm.cu
@@ -342,7 +342,7 @@ void LayerNormCompute<gpu>(const nnvm::NodeAttrs& attrs,
     bool safe_acc = dmlc::GetEnv("MXNET_SAFE_ACCUMULATION", false);
     if (!safe_acc && inputs[0].type_flag_ == mshadow::kFloat16) {
       common::LogOnce("MXNET_SAFE_ACCUMULATION=1 is recommended for LayerNorm with float16 inputs. "
-                      "See https://mxnet.incubator.apache.org/versions/master/faq/env_var.html "
+                      "See https://mxnet.apache.org/api/faq/env_var "
                       "for more details.");
     }
     if (safe_acc) {
diff --git a/src/operator/nn/softmax-inl.h b/src/operator/nn/softmax-inl.h
index 73e0ccce4328..601a0526650c 100644
--- a/src/operator/nn/softmax-inl.h
+++ b/src/operator/nn/softmax-inl.h
@@ -778,7 +778,7 @@ void SoftmaxCompute(const nnvm::NodeAttrs& attrs,
   bool safe_acc = dmlc::GetEnv("MXNET_SAFE_ACCUMULATION", false);
   if (!safe_acc && inputs[0].type_flag_ == mshadow::kFloat16) {
     common::LogOnce("MXNET_SAFE_ACCUMULATION=1 is recommended for softmax with float16 inputs. "
-                    "See https://mxnet.incubator.apache.org/versions/master/faq/env_var.html "
+                    "See https://mxnet.apache.org/api/faq/env_var "
                     "for more details.");
   }
 
diff --git a/src/operator/optimizer_op-inl.h b/src/operator/optimizer_op-inl.h
index d22bd644d21f..c211d32c2c42 100644
--- a/src/operator/optimizer_op-inl.h
+++ b/src/operator/optimizer_op-inl.h
@@ -50,7 +50,7 @@ inline void LogLazyUpdate() {
   common::LogOnce("Optimizer with lazy_update = True detected. "
                   "Be aware that lazy update with row_sparse gradient is different from "
                   "standard update, and may lead to different empirical results. See "
-                  "https://mxnet.incubator.apache.org/api/python/optimization/optimization.html "
+                  "https://mxnet.apache.org/api/python/optimization/optimization.html "
                   "for more details.");
 }
 
diff --git a/src/operator/tensor/broadcast_reduce_op.h b/src/operator/tensor/broadcast_reduce_op.h
index 2e40b87a4b6d..32a4278ec626 100644
--- a/src/operator/tensor/broadcast_reduce_op.h
+++ b/src/operator/tensor/broadcast_reduce_op.h
@@ -1266,7 +1266,7 @@ void LpNormCompute(const nnvm::NodeAttrs& attrs,
   bool safe_acc = dmlc::GetEnv("MXNET_SAFE_ACCUMULATION", false);
   if (!safe_acc && inputs[0].type_flag_ == mshadow::kFloat16) {
     common::LogOnce("MXNET_SAFE_ACCUMULATION=1 is recommended for LpNorm with float16 inputs. "
-                    "See https://mxnet.incubator.apache.org/versions/master/faq/env_var.html "
+                    "See https://mxnet.apache.org/api/faq/env_var "
                     "for more details.");
   }
   if (param.ord == 1) {
diff --git a/tests/nightly/broken_link_checker_test/JenkinsfileForBLC b/tests/nightly/broken_link_checker_test/JenkinsfileForBLC
index 8b6454393fd0..8a96a4f5013c 100755
--- a/tests/nightly/broken_link_checker_test/JenkinsfileForBLC
+++ b/tests/nightly/broken_link_checker_test/JenkinsfileForBLC
@@ -48,7 +48,7 @@ core_logic: {
 failure_handler:
 {
   if (currentBuild.result == "FAILURE") {
-    emailext body: '''https://mxnet.incubator.apache.org  broken link test summary:
+    emailext body: '''https://mxnet.apache.org  broken link test summary:
           |
           |Please view the logs at ${BUILD_URL}
           |
diff --git a/tests/nightly/broken_link_checker_test/test_broken_links.py b/tests/nightly/broken_link_checker_test/test_broken_links.py
index 97e67b1a8b91..2c0634f9522a 100755
--- a/tests/nightly/broken_link_checker_test/test_broken_links.py
+++ b/tests/nightly/broken_link_checker_test/test_broken_links.py
@@ -70,7 +70,7 @@ def prepare_link_test_result(command_output):
 
 # Command to check broken links
 # Reference - https://www.npmjs.com/package/broken-link-checker
-cmd = "blc https://mxnet.incubator.apache.org -ro"
+cmd = "blc https://mxnet.apache.org -ro"
 broken_links_count = 0
 broken_links_summary = ""
 
@@ -96,7 +96,7 @@ def prepare_link_test_result(command_output):
 print("START - Broken links summary")
 
 if broken_links_count == 0:
-    print("No broken links in https://mxnet.incubator.apache.org")
+    print("No broken links in https://mxnet.apache.org")
     print("END - Broken links summary")
 else:
     print(broken_links_summary)
diff --git a/tests/python/unittest/test_gluon_utils.py b/tests/python/unittest/test_gluon_utils.py
index 20f1c8c549ad..bc816b1794ee 100644
--- a/tests/python/unittest/test_gluon_utils.py
+++ b/tests/python/unittest/test_gluon_utils.py
@@ -98,7 +98,7 @@ def test_download_ssl_verify():
     """ test download verify_ssl parameter """
     with warnings.catch_warnings(record=True) as warnings_:
         mx.gluon.utils.download(
-            "https://mxnet.incubator.apache.org/index.html", verify_ssl=False)
+            "https://mxnet.apache.org/index.html", verify_ssl=False)
     assert any(
         str(w.message).startswith('Unverified HTTPS request')
         for w in warnings_)
diff --git a/tools/caffe_translator/README.md b/tools/caffe_translator/README.md
index 7ec7b7275dd3..c21ec50d2d7a 100644
--- a/tools/caffe_translator/README.md
+++ b/tools/caffe_translator/README.md
@@ -44,9 +44,9 @@ Here is the list of command line parameters accepted by the Caffe Translator:
 - *solver-prototxt*: specifies the path to the solver prototxt to be translated.
 - *output-file*: specifies the file to write the translated output into.
 - *params-file* (optional): specifies the .caffemodel file to initialize parameters from.
-- *custom-data-layers* (optional): Specifies a comma-separated list of types of the custom data layers used in the prototxt. The translator will use [`CaffeDataIter`](https://mxnet.incubator.apache.org/faq/caffe.html#use-io-caffedataiter) to translate these layers to MXNet.
+- *custom-data-layers* (optional): Specifies a comma-separated list of types of the custom data layers used in the prototxt. The translator will use [`CaffeDataIter`](https://mxnet.apache.org/faq/caffe.html#use-io-caffedataiter) to translate these layers to MXNet.
 
-**Note:** Translated code uses [`CaffeDataIter`](https://mxnet.incubator.apache.org/faq/caffe.html#use-io-caffedataiter) to read from LMDB files. `CaffeDataIter` requires the number of examples in LMDB file to be specified as a parameter. You can provide this information before translation using a `#CaffeToMXNet` directive like shown below:
+**Note:** Translated code uses [`CaffeDataIter`](https://mxnet.apache.org/faq/caffe.html#use-io-caffedataiter) to read from LMDB files. `CaffeDataIter` requires the number of examples in LMDB file to be specified as a parameter. You can provide this information before translation using a `#CaffeToMXNet` directive like shown below:
 
 ```
   data_param {
diff --git a/tools/caffe_translator/faq.md b/tools/caffe_translator/faq.md
index e187257e66a9..5fcdb2fa9145 100644
--- a/tools/caffe_translator/faq.md
+++ b/tools/caffe_translator/faq.md
@@ -21,9 +21,9 @@
 
 There is a couple of reasons why Caffe is required to run the translated code:
 
-1. The translator does not convert Caffe data layer to native MXNet code because MXNet cannot read from LMDB files. Translator instead generates code that uses [`CaffeDataIter`](https://mxnet.incubator.apache.org/faq/caffe.html#use-io-caffedataiter) which can read LMDB files. `CaffeDataIter` needs Caffe to run.
+1. The translator does not convert Caffe data layer to native MXNet code because MXNet cannot read from LMDB files. Translator instead generates code that uses [`CaffeDataIter`](https://mxnet.apache.org/faq/caffe.html#use-io-caffedataiter) which can read LMDB files. `CaffeDataIter` needs Caffe to run.
 
-2. If the Caffe code to be translated uses custom layers, or layers that don't have equivalent MXNet layers, the translator will generate code that will use [CaffeOp](https://mxnet.incubator.apache.org/faq/caffe.html#use-sym-caffeop). CaffeOp needs Caffe to run.
+2. If the Caffe code to be translated uses custom layers, or layers that don't have equivalent MXNet layers, the translator will generate code that will use [CaffeOp](https://mxnet.apache.org/faq/caffe.html#use-sym-caffeop). CaffeOp needs Caffe to run.
 
 [**What version of Caffe prototxt can the translator translate?**](#what_version_of_prototxt)
 
diff --git a/tools/coreml/pip_package/README.rst b/tools/coreml/pip_package/README.rst
index c2e66f708e85..495d6430c516 100644
--- a/tools/coreml/pip_package/README.rst
+++ b/tools/coreml/pip_package/README.rst
@@ -18,7 +18,7 @@
 MXNET -> CoreML Converter
 =========================
 
-`Apache MXNet <https://github.com/apache/incubator-mxnet>`_ (incubating) is a deep learning framework designed for both efficiency and flexibility. It allows you to mix `symbolic and imperative programming <http://mxnet.io/architecture/index.html#deep-learning-system-design-concepts>`_ to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines.
+`Apache MXNet <https://github.com/apache/incubator-mxnet>`_ (incubating) is a deep learning framework designed for both efficiency and flexibility. It allows you to mix `symbolic and imperative programming <https://mxnet.apache.org/api/architecture/program_model>`_ to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines.
 
 `Core ML <http://developer.apple.com/documentation/coreml>`_ is an Apple framework which allows developers to simply and easily integrate machine learning (ML) models into apps running on Apple devices (including iOS, watchOS, macOS, and tvOS). Core ML introduces a public file format (.mlmodel) for a broad set of ML methods including deep neural networks (both convolutional and recurrent), tree ensembles with boosting, and generalized linear models. Models in this format can be directly integrated into apps through Xcode.
 
diff --git a/tools/pip/doc/CPU_ADDITIONAL.md b/tools/pip/doc/CPU_ADDITIONAL.md
index 05be9e52bd06..9f25121e5b8c 100644
--- a/tools/pip/doc/CPU_ADDITIONAL.md
+++ b/tools/pip/doc/CPU_ADDITIONAL.md
@@ -30,7 +30,7 @@ This package supports Linux, Mac OSX, and Windows platforms. You may also want t
 - [mxnet-cu75mkl](https://pypi.python.org/pypi/mxnet-cu75mkl/) with CUDA-7.5 support and MKLDNN support.
 - [mxnet-mkl](https://pypi.python.org/pypi/mxnet-mkl/) with MKLDNN support.
 
-To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions, check [Installing MXNet](https://mxnet.incubator.apache.org/versions/master/install/index.html) for instructions on building from source.
+To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions, check [Installing MXNet](https://mxnet.apache.org/versions/master/install/index.html) for instructions on building from source.
 
 Installation
 ------------
diff --git a/tools/pip/doc/CU100MKL_ADDITIONAL.md b/tools/pip/doc/CU100MKL_ADDITIONAL.md
index f47115c93c1a..a916f0b9583b 100644
--- a/tools/pip/doc/CU100MKL_ADDITIONAL.md
+++ b/tools/pip/doc/CU100MKL_ADDITIONAL.md
@@ -34,7 +34,7 @@ This package supports Linux and Windows platforms. You may also want to check:
 
 To download CUDA, check [CUDA download](https://developer.nvidia.com/cuda-downloads). For more instructions, check [CUDA Toolkit online documentation](http://docs.nvidia.com/cuda/index.html).
 
-To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions of CUDA, check [Installing MXNet](https://mxnet.incubator.apache.org/versions/master/install/index.html) for instructions on building from source.
+To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions of CUDA, check [Installing MXNet](https://mxnet.apache.org/versions/master/install/index.html) for instructions on building from source.
 
 Installation
 ------------
diff --git a/tools/pip/doc/CU100_ADDITIONAL.md b/tools/pip/doc/CU100_ADDITIONAL.md
index 32c30b0fef5e..1a1f4dd70ea2 100644
--- a/tools/pip/doc/CU100_ADDITIONAL.md
+++ b/tools/pip/doc/CU100_ADDITIONAL.md
@@ -34,7 +34,7 @@ This package supports Linux and Windows platforms. You may also want to check:
 
 To download CUDA, check [CUDA download](https://developer.nvidia.com/cuda-downloads). For more instructions, check [CUDA Toolkit online documentation](http://docs.nvidia.com/cuda/index.html).
 
-To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions, check [Installing MXNet](https://mxnet.incubator.apache.org/versions/master/install/index.html) for instructions on building from source.
+To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions, check [Installing MXNet](https://mxnet.apache.org/versions/master/install/index.html) for instructions on building from source.
 
 Installation
 ------------
diff --git a/tools/pip/doc/CU75MKL_ADDITIONAL.md b/tools/pip/doc/CU75MKL_ADDITIONAL.md
index cc6c8820a0e3..30b85fda0c2e 100644
--- a/tools/pip/doc/CU75MKL_ADDITIONAL.md
+++ b/tools/pip/doc/CU75MKL_ADDITIONAL.md
@@ -32,7 +32,7 @@ This package supports Linux only, up to 1.2.1. You may also want to check:
 
 To download CUDA, check [CUDA download](https://developer.nvidia.com/cuda-downloads). For more instructions, check [CUDA Toolkit online documentation](http://docs.nvidia.com/cuda/index.html).
 
-To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions, check [Installing MXNet](https://mxnet.incubator.apache.org/versions/master/install/index.html) for instructions on building from source.
+To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions, check [Installing MXNet](https://mxnet.apache.org/versions/master/install/index.html) for instructions on building from source.
 
 Installation
 ------------
diff --git a/tools/pip/doc/CU75_ADDITIONAL.md b/tools/pip/doc/CU75_ADDITIONAL.md
index ad57c13fda9e..9947365517e6 100644
--- a/tools/pip/doc/CU75_ADDITIONAL.md
+++ b/tools/pip/doc/CU75_ADDITIONAL.md
@@ -32,7 +32,7 @@ This package supports Linux only, up to 1.2.1. You may also want to check:
 
 To download CUDA, check [CUDA download](https://developer.nvidia.com/cuda-downloads). For more instructions, check [CUDA Toolkit online documentation](http://docs.nvidia.com/cuda/index.html).
 
-To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions, check [Installing MXNet](https://mxnet.incubator.apache.org/versions/master/install/index.html) for instructions on building from source.
+To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions, check [Installing MXNet](https://mxnet.apache.org/versions/master/install/index.html) for instructions on building from source.
 
 Installation
 ------------
diff --git a/tools/pip/doc/CU80MKL_ADDITIONAL.md b/tools/pip/doc/CU80MKL_ADDITIONAL.md
index 549df7dca8d6..54c54536ee2b 100644
--- a/tools/pip/doc/CU80MKL_ADDITIONAL.md
+++ b/tools/pip/doc/CU80MKL_ADDITIONAL.md
@@ -32,7 +32,7 @@ This package supports Linux and Windows platforms. You may also want to check:
 
 To download CUDA, check [CUDA download](https://developer.nvidia.com/cuda-downloads). For more instructions, check [CUDA Toolkit online documentation](http://docs.nvidia.com/cuda/index.html).
 
-To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions of CUDA, check [Installing MXNet](https://mxnet.incubator.apache.org/versions/master/install/index.html) for instructions on building from source.
+To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions of CUDA, check [Installing MXNet](https://mxnet.apache.org/versions/master/install/index.html) for instructions on building from source.
 
 Installation
 ------------
diff --git a/tools/pip/doc/CU80_ADDITIONAL.md b/tools/pip/doc/CU80_ADDITIONAL.md
index 7105dfd1ba2c..609567c72902 100644
--- a/tools/pip/doc/CU80_ADDITIONAL.md
+++ b/tools/pip/doc/CU80_ADDITIONAL.md
@@ -32,7 +32,7 @@ This package supports Linux and Windows platforms. You may also want to check:
 
 To download CUDA, check [CUDA download](https://developer.nvidia.com/cuda-downloads). For more instructions, check [CUDA Toolkit online documentation](http://docs.nvidia.com/cuda/index.html).
 
-To install for other platforms (e.g. Raspberry Pi/ARM) or other versions of CUDA, check [Installing MXNet](https://mxnet.incubator.apache.org/versions/master/install/index.html) for instructions on building from source.
+To install for other platforms (e.g. Raspberry Pi/ARM) or other versions of CUDA, check [Installing MXNet](https://mxnet.apache.org/versions/master/install/index.html) for instructions on building from source.
 
 Installation
 ------------
diff --git a/tools/pip/doc/CU90MKL_ADDITIONAL.md b/tools/pip/doc/CU90MKL_ADDITIONAL.md
index 8f4257fa96f1..7fe04dae29ad 100644
--- a/tools/pip/doc/CU90MKL_ADDITIONAL.md
+++ b/tools/pip/doc/CU90MKL_ADDITIONAL.md
@@ -32,7 +32,7 @@ This package supports Linux and Windows platforms. You may want to check:
 
 To download CUDA, check [CUDA download](https://developer.nvidia.com/cuda-downloads). For more instructions, check [CUDA Toolkit online documentation](http://docs.nvidia.com/cuda/index.html).
 
-To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions of CUDA, check [Installing MXNet](https://mxnet.incubator.apache.org/versions/master/install/index.html) for instructions on building from source.
+To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions of CUDA, check [Installing MXNet](https://mxnet.apache.org/versions/master/install/index.html) for instructions on building from source.
 
 Installation
 ------------
diff --git a/tools/pip/doc/CU90_ADDITIONAL.md b/tools/pip/doc/CU90_ADDITIONAL.md
index 477a5cb57e96..37ab45a7dfbf 100644
--- a/tools/pip/doc/CU90_ADDITIONAL.md
+++ b/tools/pip/doc/CU90_ADDITIONAL.md
@@ -32,7 +32,7 @@ This package supports Linux and Windows platforms. You may also want to check:
 
 To download CUDA, check [CUDA download](https://developer.nvidia.com/cuda-downloads). For more instructions, check [CUDA Toolkit online documentation](http://docs.nvidia.com/cuda/index.html).
 
-To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions, check [Installing MXNet](https://mxnet.incubator.apache.org/versions/master/install/index.html) for instructions on building from source.
+To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions, check [Installing MXNet](https://mxnet.apache.org/versions/master/install/index.html) for instructions on building from source.
 
 Installation
 ------------
diff --git a/tools/pip/doc/CU91MKL_ADDITIONAL.md b/tools/pip/doc/CU91MKL_ADDITIONAL.md
index d1706c0d5fa7..8e82b5a18868 100644
--- a/tools/pip/doc/CU91MKL_ADDITIONAL.md
+++ b/tools/pip/doc/CU91MKL_ADDITIONAL.md
@@ -32,7 +32,7 @@ This package supports Linux and Windows platforms, up to 1.2.1. You may also wan
 
 To download CUDA, check [CUDA download](https://developer.nvidia.com/cuda-downloads). For more instructions, check [CUDA Toolkit online documentation](http://docs.nvidia.com/cuda/index.html).
 
-To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions of CUDA, check [Installing MXNet](https://mxnet.incubator.apache.org/versions/master/install/index.html) for instructions on building from source.
+To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions of CUDA, check [Installing MXNet](https://mxnet.apache.org/versions/master/install/index.html) for instructions on building from source.
 
 Installation
 ------------
diff --git a/tools/pip/doc/CU91_ADDITIONAL.md b/tools/pip/doc/CU91_ADDITIONAL.md
index cbb5567917d2..5918a8901f99 100644
--- a/tools/pip/doc/CU91_ADDITIONAL.md
+++ b/tools/pip/doc/CU91_ADDITIONAL.md
@@ -32,7 +32,7 @@ This package supports Linux and Windows platforms, up to 1.2.1. You may also wan
 
 To download CUDA, check [CUDA download](https://developer.nvidia.com/cuda-downloads). For more instructions, check [CUDA Toolkit online documentation](http://docs.nvidia.com/cuda/index.html).
 
-To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions, check [Installing MXNet](https://mxnet.incubator.apache.org/versions/master/install/index.html) for instructions on building from source.
+To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions, check [Installing MXNet](https://mxnet.apache.org/versions/master/install/index.html) for instructions on building from source.
 
 Installation
 ------------
diff --git a/tools/pip/doc/CU92MKL_ADDITIONAL.md b/tools/pip/doc/CU92MKL_ADDITIONAL.md
index 16cd8f138add..f7adbfcb61ca 100644
--- a/tools/pip/doc/CU92MKL_ADDITIONAL.md
+++ b/tools/pip/doc/CU92MKL_ADDITIONAL.md
@@ -32,7 +32,7 @@ This package supports Linux and Windows platforms. You may also want to check:
 
 To download CUDA, check [CUDA download](https://developer.nvidia.com/cuda-downloads). For more instructions, check [CUDA Toolkit online documentation](http://docs.nvidia.com/cuda/index.html).
 
-To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions of CUDA, check [Installing MXNet](https://mxnet.incubator.apache.org/versions/master/install/index.html) for instructions on building from source.
+To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions of CUDA, check [Installing MXNet](https://mxnet.apache.org/versions/master/install/index.html) for instructions on building from source.
 
 Installation
 ------------
diff --git a/tools/pip/doc/CU92_ADDITIONAL.md b/tools/pip/doc/CU92_ADDITIONAL.md
index 28569ac069b2..79afccaec095 100644
--- a/tools/pip/doc/CU92_ADDITIONAL.md
+++ b/tools/pip/doc/CU92_ADDITIONAL.md
@@ -32,7 +32,7 @@ This package supports Linux and Windows platforms. You may also want to check:
 
 To download CUDA, check [CUDA download](https://developer.nvidia.com/cuda-downloads). For more instructions, check [CUDA Toolkit online documentation](http://docs.nvidia.com/cuda/index.html).
 
-To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions, check [Installing MXNet](https://mxnet.incubator.apache.org/versions/master/install/index.html) for instructions on building from source.
+To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions, check [Installing MXNet](https://mxnet.apache.org/versions/master/install/index.html) for instructions on building from source.
 
 Installation
 ------------
diff --git a/tools/pip/doc/MKL_ADDITIONAL.md b/tools/pip/doc/MKL_ADDITIONAL.md
index 60f827bff0b6..d796aca1b82c 100644
--- a/tools/pip/doc/MKL_ADDITIONAL.md
+++ b/tools/pip/doc/MKL_ADDITIONAL.md
@@ -30,7 +30,7 @@ This package supports Linux only. You may also want to check:
 - [mxnet-cu75mkl](https://pypi.python.org/pypi/mxnet-cu75mkl/) with CUDA-7.5 support and MKLDNN support.
 - [mxnet](https://pypi.python.org/pypi/mxnet/).
 
-To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions, check [Installing MXNet](https://mxnet.incubator.apache.org/versions/master/install/index.html) for instructions on building from source.
+To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions, check [Installing MXNet](https://mxnet.apache.org/versions/master/install/index.html) for instructions on building from source.
 
 Installation
 ------------

From dc5470cb9d0992ae2409ca17d85fcbbccda4ae86 Mon Sep 17 00:00:00 2001
From: Serge Panev <spanev@nvidia.com>
Date: Fri, 27 Sep 2019 12:28:16 +0200
Subject: [PATCH 384/813] Factorize CUDA_KERNEL_LOOP used in CUDA kernels
 (#16197)

* Factorize CUDA_KERNEL_LOOP used in CUDA kernels

Signed-off-by: Serge Panev <spanev@nvidia.com>

* Retrigger CI
---
 src/operator/contrib/count_sketch.cu             |  4 ----
 src/operator/contrib/deformable_psroi_pooling.cu |  4 ----
 src/operator/contrib/psroi_pooling.cu            |  4 ----
 src/operator/contrib/roi_align.cu                | 11 ++++-------
 src/operator/correlation.cu                      |  6 ++----
 5 files changed, 6 insertions(+), 23 deletions(-)

diff --git a/src/operator/contrib/count_sketch.cu b/src/operator/contrib/count_sketch.cu
index 68dede377fc1..b7113aed1dfe 100644
--- a/src/operator/contrib/count_sketch.cu
+++ b/src/operator/contrib/count_sketch.cu
@@ -33,10 +33,6 @@
 #define WARPS_PER_BLOCK 1
 #define THREADS_PER_BLOCK 512
 
-#define CUDA_KERNEL_LOOP(i, n) \
-  for (int i = blockIdx.x * blockDim.x + threadIdx.x; \
-       i < (n); \
-       i += blockDim.x * gridDim.x)
 namespace mshadow {
 namespace cuda {
 // wrappers to deal with atomic add
diff --git a/src/operator/contrib/deformable_psroi_pooling.cu b/src/operator/contrib/deformable_psroi_pooling.cu
index 6c89746b43ab..ba8cfc865798 100644
--- a/src/operator/contrib/deformable_psroi_pooling.cu
+++ b/src/operator/contrib/deformable_psroi_pooling.cu
@@ -38,10 +38,6 @@
     cudaError_t error = condition; \
     CHECK_EQ(error, cudaSuccess) << " " << cudaGetErrorString(error); \
   } while (0)
-#define CUDA_KERNEL_LOOP(i, n) \
-for (int i = blockIdx.x * blockDim.x + threadIdx.x; \
-      i < (n); \
-      i += blockDim.x * gridDim.x)
 
 namespace mshadow {
 namespace cuda {
diff --git a/src/operator/contrib/psroi_pooling.cu b/src/operator/contrib/psroi_pooling.cu
index e4de9248dfbf..c5f229148aff 100644
--- a/src/operator/contrib/psroi_pooling.cu
+++ b/src/operator/contrib/psroi_pooling.cu
@@ -39,10 +39,6 @@
     cudaError_t error = condition; \
     CHECK_EQ(error, cudaSuccess) << " " << cudaGetErrorString(error); \
   } while (0)
-#define CUDA_KERNEL_LOOP(i, n) \
-for (int i = blockIdx.x * blockDim.x + threadIdx.x; \
-      i < (n); \
-      i += blockDim.x * gridDim.x)
 
 namespace mshadow {
 namespace cuda {
diff --git a/src/operator/contrib/roi_align.cu b/src/operator/contrib/roi_align.cu
index 38b461d5f58c..7099dd1a3991 100644
--- a/src/operator/contrib/roi_align.cu
+++ b/src/operator/contrib/roi_align.cu
@@ -24,15 +24,12 @@
  * Adapted from Caffe2
 */
 #include "./roi_align-inl.h"
+#include "../mxnet_op.h"
 
 
 namespace mxnet {
 namespace op {
 
-#define CUDA_1D_KERNEL_LOOP(i, n)                                 \
-  for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \
-       i += blockDim.x * gridDim.x)
-
 using namespace mshadow::cuda;
 
 // The maximum number of blocks to use in the default kernel call.
@@ -120,7 +117,7 @@ __global__ void RoIAlignForwardKernel(
     const int sampling_ratio,
     const T* bottom_rois,
     T* top_data) {
-  CUDA_1D_KERNEL_LOOP(index, nthreads) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
     // (n, c, ph, pw) is an element in the pooled output
     int pw = index % pooled_width;
     int ph = (index / pooled_width) % pooled_height;
@@ -259,7 +256,7 @@ __global__ void RoIAlignBackwardKernel(
     const int sampling_ratio,
     T* bottom_diff,
     const T* bottom_rois) {
-  CUDA_1D_KERNEL_LOOP(index, nthreads) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
     // (n, c, ph, pw) is an element in the pooled output
     int pw = index % pooled_width;
     int ph = (index / pooled_width) % pooled_height;
@@ -353,7 +350,7 @@ __global__ void RoIAlignBackwardKernel(
         }  // if
       }  // ix
     }  // iy
-  }  // CUDA_1D_KERNEL_LOOP
+  }  // CUDA_KERNEL_LOOP
 }  // RoIAlignBackward
 
 template<typename xpu>
diff --git a/src/operator/correlation.cu b/src/operator/correlation.cu
index 821b9007a8fe..117dc61af6bf 100644
--- a/src/operator/correlation.cu
+++ b/src/operator/correlation.cu
@@ -28,6 +28,7 @@
 #include <mshadow/cuda/reduce.cuh>
 #include <algorithm>
 #include <vector>
+#include "./mxnet_op.h"
 
 #define ROUND_OFF 50000
 #define WARPS_PER_BLOCK 1
@@ -38,10 +39,7 @@
     cudaError_t error = condition; \
     CHECK_EQ(error, cudaSuccess) << " " << cudaGetErrorString(error); \
   } while (0)
-#define CUDA_KERNEL_LOOP(i, n) \
-for (int i = blockIdx.x * blockDim.x + threadIdx.x; \
-      i < (n); \
-      i += blockDim.x * gridDim.x)
+
 namespace mshadow {
 namespace cuda {
 // == Correlation Kernel

From 01ca278a6ea8e7e37ba5d5eb3e7fa3d004ce0424 Mon Sep 17 00:00:00 2001
From: Xingjian Shi <xshiab@connect.ust.hk>
Date: Fri, 27 Sep 2019 11:13:07 -0700
Subject: [PATCH 385/813] [Gluon] Support None argument in HybridBlock (#16280)

* support none in hybridblock argument

support None as arguments in HybridBlock

Update block.py

fix

fix

Update test_gluon.py

Update test_gluon.py

* fix bug

* fix bug

* test case of default values
---
 python/mxnet/gluon/block.py         | 186 +++++++++++++++++++++-------
 tests/python/unittest/test_gluon.py |  88 +++++++++++++
 2 files changed, 231 insertions(+), 43 deletions(-)

diff --git a/python/mxnet/gluon/block.py b/python/mxnet/gluon/block.py
index bad737f4f6cd..0d8e766fede8 100644
--- a/python/mxnet/gluon/block.py
+++ b/python/mxnet/gluon/block.py
@@ -39,6 +39,7 @@
 from .. util import is_np_array, np_shape, np_array
 
 
+
 class _BlockScope(object):
     """Scope for collecting child `Block` s."""
     _current = threading.local()
@@ -93,16 +94,35 @@ def __exit__(self, ptype, value, trace):
 
 
 def _flatten(args, inout_str):
+    """Parse the arguments into a flattened list + an additional format array.
+    The format array stores the structure of the original arguments to help reconstruct the inputs.
+
+    Parameters
+    ----------
+    args : NDArray, Symbol, or (nested) list of Symbol or NDArray
+        We allow None inside the args.
+    inout_str : str
+        The name of the HybridBlock
+
+    Returns
+    -------
+    flat : list of Symbol or NDArray
+        The flatten version of the input args.
+    fmts : (nested) list of ints
+        Stores the format information of the original structured args.
+    """
     if isinstance(args, NDArray):
         return [args], int(0)
     if isinstance(args, Symbol):
         length = len(args.list_outputs())
         length = length if length > 1 else 0
         return [args], int(length)
+    if args is None:
+        return [None], int(-1)
 
     assert isinstance(args, (list, tuple)), \
-        "HybridBlock %s must be (nested) list of Symbol or NDArray, " \
-        "but got %s of type %s"%(inout_str, str(args), str(type(args)))
+        "HybridBlock {} must be (nested) list of Symbol or NDArray, " \
+        "but got {} of type {}".format(inout_str, str(args), str(type(args)))
     flat = []
     fmts = []
     for i in args:
@@ -113,19 +133,46 @@ def _flatten(args, inout_str):
 
 
 def _regroup(args, fmt):
-    if isinstance(fmt, int):
-        if fmt == 0:
-            return args[0], args[1:]
-        return args[:fmt], args[fmt:]
+    """Reconstruct the structured arguments based on the flattened version.
 
-    assert isinstance(args, (list, tuple)), \
-        "HybridBlock output must be (nested) list of Symbol or NDArray, " \
-        "but got %s of type %s"%(str(args), str(type(args)))
-    ret = []
-    for i in fmt:
-        res, args = _regroup(args, i)
-        ret.append(res)
-    return ret, args
+    Parameters
+    ----------
+    args : NDArray, Symbol, or (nested) list of Symbol or NDArray
+        We allow None inside the args.
+    fmt : (nested) list of ints
+        Stores the format information of the original structured args.
+
+    Returns
+    -------
+    ret : NDArray, Symbol, or (nested) list of Symbol or NDArray
+
+    """
+    def _merger(args, fmt):
+        """Recursive call to merge the arguments"""
+        if isinstance(fmt, int):
+            if fmt < -1:
+                raise ValueError("Unsupported encoded format {}.".format(fmt))
+            if fmt == 0:
+                return args[0], args[1:]
+            if fmt == -1:
+                if args[0] is not None:
+                    raise ValueError('We do not support passing types that are not None'
+                                     ' when the initial HybridBlock has received NoneType and'
+                                     ' has been hybridized.'
+                                     ' Received arg = {}, fmt = {}.'.format(args[0], fmt))
+                return None, args[1:]
+            else:
+                return args[:fmt], args[fmt:]
+
+        assert isinstance(args, (list, tuple)), \
+            "HybridBlock output must be (nested) list of Symbol or NDArray, " \
+            "but got {} of type {}".format(args, type(args))
+        ret = []
+        for i in fmt:
+            res, args = _merger(args, i)
+            ret.append(res)
+        return ret, args
+    return _merger(args, fmt)[0]
 
 
 class Block(object):
@@ -778,29 +825,40 @@ def __setattr__(self, name, value):
 
     def _get_graph(self, *args):
         if not self._cached_graph:
-            args, self._in_format = _flatten(args, "input")
-            if len(args) > 1:
-                inputs = [symbol.var('data%d' % i).as_np_ndarray()
-                          if isinstance(args[i], _mx_np.ndarray)
-                          else symbol.var('data%d' % i) for i in range(len(args))]
-            else:
-                inputs = [symbol.var('data').as_np_ndarray()
-                          if isinstance(args[0], _mx_np.ndarray)
-                          else symbol.var('data')]
-            grouped_inputs = _regroup(inputs, self._in_format)[0]
-
+            flatten_args, self._in_format = _flatten(args, "input")
+            flatten_inputs = []
+            symbol_inputs = []
+            cnt = 0
+            real_arg_num = sum([ele is not None for ele in flatten_args])
+            if real_arg_num == 0:
+                raise ValueError('All args are None and we do not support such a case.'
+                                 ' Received args={}'.format(args))
+            for arg in flatten_args:
+                if arg is not None:
+                    if real_arg_num > 1:
+                        arg_sym = symbol.var('data{}'.format(cnt))
+                    else:
+                        arg_sym = symbol.var('data')
+                    if isinstance(arg, _mx_np.ndarray):
+                        arg_sym = arg_sym.as_np_ndarray()
+                    cnt += 1
+                    flatten_inputs.append(arg_sym)
+                    symbol_inputs.append(arg_sym)
+                else:
+                    flatten_inputs.append(None)
+            grouped_inputs = _regroup(flatten_inputs, self._in_format)
             params = {i: j.var() for i, j in self._reg_params.items()}
             with self.name_scope():
                 out = self.hybrid_forward(symbol, *grouped_inputs, **params)  # pylint: disable=no-value-for-parameter
             out, self._out_format = _flatten(out, "output")
 
-            self._cached_graph = inputs, symbol.Group(out, _check_same_symbol_type(out))
+            self._cached_graph = symbol_inputs, symbol.Group(out, _check_same_symbol_type(out))
 
         return self._cached_graph
 
     def _build_cache(self, *args):
         data, out = self._get_graph(*args)
-        data_names = {data.name : i for i, data in enumerate(data)}
+        data_names = {data.name: i for i, data in enumerate(data)}
         params = self.collect_params()
         input_names = out.list_inputs()
 
@@ -808,7 +866,7 @@ def _build_cache(self, *args):
         expected_names = set(input_names)
         for name in expected_names:
             assert name in param_names or name in data_names, \
-                "Unknown input to HybridBlock: %s"%name
+                "Unknown input to HybridBlock: %s" %name
 
         used_data_names = [i for i in data_names if i in expected_names]
         if len(used_data_names) != len(data_names):
@@ -856,23 +914,40 @@ def _call_cached_op(self, *args):
                               " and may not work correctly")
 
         args, fmt = _flatten(args, "input")
-        assert fmt == self._in_format, "Invalid input format"
+        if fmt != self._in_format:
+            # Do not raise in the case that the fmt or stored_fmt ends with None and
+            # We are relying on the default values.
+            if len(self._in_format) > len(fmt):
+                valid = all([self._in_format[i] == -1
+                             for i in range(len(fmt), len(self._in_format))])
+                valid = valid and (fmt == self._in_format[:len(fmt)])
+            elif len(self._in_format) < len(fmt):
+                valid = all([fmt[i] == -1
+                             for i in range(len(self._in_format), len(fmt))])
+                valid = valid and (fmt[:len(self._in_format)] == self._in_format)
+            else:
+                valid = False
+            if not valid:
+                raise ValueError("The argument structure of HybridBlock does not match"
+                                 " the cached version. Stored format = {}, input format = {}"
+                                 .format(fmt, self._in_format))
+        args_without_none = [ele for ele in args if ele is not None]
         try:
-            cargs = [args[i] if is_arg else i.data()
+            cargs = [args_without_none[i] if is_arg else i.data()
                      for is_arg, i in self._cached_op_args]
         except DeferredInitializationError:
             self._deferred_infer_shape(*args)
             cargs = []
             for is_arg, i in self._cached_op_args:
                 if is_arg:
-                    cargs.append(args[i])
+                    cargs.append(args_without_none[i])
                 else:
                     i._finish_deferred_init()
                     cargs.append(i.data())
         out = self._cached_op(*cargs)
         if isinstance(out, NDArray):
             out = [out]
-        return _regroup(out, self._out_format)[0]
+        return _regroup(out, self._out_format)
 
     def _clear_cached_op(self):
         self._cached_graph = ()
@@ -906,9 +981,10 @@ def _infer_attrs(self, infer_fn, attr, *args):
         """Generic infer attributes."""
         inputs, out = self._get_graph(*args)
         args, _ = _flatten(args, "input")
+        args_without_none = [ele for ele in args if ele is not None]
         with warnings.catch_warnings(record=True) as w:
             arg_attrs, _, aux_attrs = getattr(out, infer_fn)(
-                **{i.name: getattr(j, attr) for i, j in zip(inputs, args)})
+                **{i.name: getattr(j, attr) for i, j in zip(inputs, args_without_none)})
             if arg_attrs is None:
                 raise ValueError(w[0].message)
         sdict = {i: j for i, j in zip(out.list_arguments(), arg_attrs)}
@@ -978,24 +1054,48 @@ def register_op_hook(self, callback, monitor_all=False):
     def forward(self, x, *args):
         """Defines the forward computation. Arguments can be either
         :py:class:`NDArray` or :py:class:`Symbol`."""
-        if isinstance(x, NDArray):
-            with x.context as ctx:
+        flatten_args = _flatten([x] + list(args), 'inputs')[0]
+        is_ndarray = None
+        ctx = None
+        exist_sym_nd = False
+        for ele in flatten_args:
+            if isinstance(ele, NDArray):
+                if is_ndarray is False:
+                    raise ValueError('In HybridBlock, we do not support mixed NDArrays and Symbols'
+                                     ' types for the input.\n'
+                                     'Received types are: {}.'
+                                     .format([type(ele) for ele in flatten_args]))
+                is_ndarray = True
+                exist_sym_nd = True
+                ctx = ele.context
+            elif isinstance(ele, Symbol):
+                if is_ndarray:
+                    raise ValueError('In HybridBlock, we do not support mixed NDArrays and Symbols'
+                                     ' types for the input.\n'
+                                     'Received types are: {}.'
+                                     .format([type(ele) for ele in flatten_args]))
+                is_ndarray = False
+                exist_sym_nd = True
+            else:
+                assert ele is None, 'Only support None, NDArray and Symbol as the input'
+        if not exist_sym_nd:
+            raise ValueError('There must at least one NDArray or Symbol in the input, received')
+
+        if is_ndarray:
+            with ctx:
                 if self._active:
                     return self._call_cached_op(x, *args)
 
                 try:
-                    params = {i: j.data(ctx) for i, j in self._reg_params.items()}
+                    params = {k: v.data(ctx) for k, v in self._reg_params.items()}
                 except DeferredInitializationError:
                     self._deferred_infer_shape(x, *args)
-                    for _, i in self.params.items():
-                        i._finish_deferred_init()
-                    params = {i: j.data(ctx) for i, j in self._reg_params.items()}
+                    for _, v in self.params.items():
+                        v._finish_deferred_init()
+                    params = {k: v.data(ctx) for k, v in self._reg_params.items()}
 
                 return self.hybrid_forward(ndarray, x, *args, **params)
 
-        assert isinstance(x, Symbol), \
-            "HybridBlock requires the first argument to forward be either " \
-            "Symbol or NDArray, but got %s"%type(x)
         params = {i: j.var() for i, j in self._reg_params.items()}
         with self.name_scope():
             return self.hybrid_forward(symbol, x, *args, **params)
@@ -1173,7 +1273,7 @@ def forward(self, x, *args):
         assert in_fmt == self._in_format, "Invalid input format"
         ret = copy.copy(self._cached_graph[1])
         ret._compose(**{k.name: v for k, v in zip(self._cached_graph[0], args)})
-        return _regroup(list(ret), self._out_format)[0]
+        return _regroup(list(ret), self._out_format)
 
     def _clear_cached_op(self):
         tmp = self._cached_graph
diff --git a/tests/python/unittest/test_gluon.py b/tests/python/unittest/test_gluon.py
index ae2d62451a09..380ce762a9f7 100644
--- a/tests/python/unittest/test_gluon.py
+++ b/tests/python/unittest/test_gluon.py
@@ -441,6 +441,94 @@ def test_sparse_hybrid_block():
     # an exception is expected when forwarding a HybridBlock w/ sparse param
     y = net(x)
 
+@with_seed()
+def test_hybrid_block_none_args():
+    class Foo(gluon.HybridBlock):
+        def hybrid_forward(self, F, a, b):
+            if a is None and b is not None:
+                return b
+            elif b is None and a is not None:
+                return a
+            elif a is not None and b is not None:
+                return a + b
+            else:
+                raise NotImplementedError
+
+    class FooDefault(gluon.HybridBlock):
+        def hybrid_forward(self, F, a, b=None):
+            if a is None and b is not None:
+                return b
+            elif b is None and a is not None:
+                return a
+            elif a is not None and b is not None:
+                return a + b
+            else:
+                raise NotImplementedError
+
+
+    class FooNested(gluon.HybridBlock):
+        def __init__(self, prefix=None, params=None):
+            super(FooNested, self).__init__(prefix=prefix, params=params)
+            self.f1 = Foo(prefix='foo1')
+            self.f2 = Foo(prefix='foo2')
+            self.f3 = Foo(prefix='foo3')
+
+        def hybrid_forward(self, F, a, b):
+            data = self.f1(a, b)
+            data = self.f2(a, data)
+            data = self.f3(data, b)
+            return data
+
+    for arg_inputs in [(None, mx.nd.ones((10,))),
+                       (mx.nd.ones((10,)), mx.nd.ones((10,))),
+                       (mx.nd.ones((10,)), None)]:
+        foo1 = FooNested(prefix='foo_nested_hybridized')
+        foo1.hybridize()
+        foo2 = FooNested(prefix='foo_nested_nohybrid')
+        for _ in range(2): # Loop for 2 times to trigger forwarding of the cached version
+            out1 = foo1(*arg_inputs)
+            out2 = foo2(*arg_inputs)
+            if isinstance(out1, tuple):
+                for lhs, rhs in zip(out1, out2):
+                    assert_almost_equal(lhs.asnumpy(), rhs.asnumpy())
+            else:
+                assert_almost_equal(out1.asnumpy(), out2.asnumpy())
+    for do_hybridize in [True, False]:
+        foo = FooNested()
+        if do_hybridize:
+            foo.hybridize()
+        assert_raises(ValueError, foo, None, None)
+
+    # Make sure the ValueError is correctly raised
+    foo = FooNested()
+    foo.hybridize()
+    foo(None, mx.nd.ones((10,)))  # Pass for the first time to initialize the cached op
+    assert_raises(ValueError, lambda: foo(mx.nd.ones((10,)), mx.nd.ones((10,))))
+    foo = FooNested()
+    assert_raises(ValueError, lambda: foo(mx.nd.ones((10,)), mx.sym.var('a')))
+    foo = FooNested()
+    assert_raises(ValueError, lambda: foo(mx.sym.var('a'), mx.nd.ones((10,))))
+
+    # Test the case of the default values
+    foo1 = FooDefault()
+    foo1.hybridize()
+    foo2 = FooDefault()
+    out1 = foo1(mx.nd.ones((10,)))
+    out2 = foo2(mx.nd.ones((10,)))
+    out3 = foo1(mx.nd.ones((10,)), None)
+    out4 = foo2(mx.nd.ones((10,)), None)
+    assert_almost_equal(out1.asnumpy(), out2.asnumpy())
+    assert_almost_equal(out1.asnumpy(), out3.asnumpy())
+    assert_almost_equal(out1.asnumpy(), out4.asnumpy())
+    foo1 = FooDefault()
+    foo1.hybridize()
+    out1 = foo1(mx.nd.ones((10,)), None)
+    out2 = foo1(mx.nd.ones((10,)))
+    assert_almost_equal(out1.asnumpy(), out2.asnumpy())
+    assert_raises(ValueError, lambda: foo1(mx.nd.ones((10,)), mx.nd.ones((10,))))
+
+
+
 @with_seed()
 def check_layer_forward(layer, dshape):
     print("checking layer {}\nshape: {}.".format(layer, dshape))

From 943bed513b3ce3dff5eae0fb4cf319d2445c85bb Mon Sep 17 00:00:00 2001
From: Lanking <lanking520@live.com>
Date: Fri, 27 Sep 2019 16:51:33 -0700
Subject: [PATCH 386/813] add mkl installation temp fix (#16304)

---
 ci/docker/install/ubuntu_mkl.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ci/docker/install/ubuntu_mkl.sh b/ci/docker/install/ubuntu_mkl.sh
index 97ef158096bc..b33a001b5f3d 100755
--- a/ci/docker/install/ubuntu_mkl.sh
+++ b/ci/docker/install/ubuntu_mkl.sh
@@ -28,4 +28,4 @@ apt-get update || true
 wget -qO - wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB | apt-key add - && \
     sh -c 'echo deb https://apt.repos.intel.com/mkl all main > /etc/apt/sources.list.d/intel-mkl.list' && \
     apt-get update && \
-    apt-get install -y intel-mkl-2019.1-053
+    apt-get install -y --allow-unauthenticated intel-mkl-2019.1-053

From 3950a473bff8a951184849947b4ea0338b4325f8 Mon Sep 17 00:00:00 2001
From: kshitij12345 <kshitijkalambarkar@gmail.com>
Date: Sun, 29 Sep 2019 02:08:03 +0530
Subject: [PATCH 387/813] [MXNET-978] n-th order gradient test support.
 (#15611)

* n-th order grad test support

* use check_nth_order_unary for second order check

* add docstring to check_nth_order_unary

* retrigger CI

* add assertions for requirements of orders

* fix assert condition

* retrigger CI
---
 .../python/unittest/test_higher_order_grad.py | 99 ++++++++++++++++---
 1 file changed, 84 insertions(+), 15 deletions(-)

diff --git a/tests/python/unittest/test_higher_order_grad.py b/tests/python/unittest/test_higher_order_grad.py
index 8e6ac3d0cf52..e07ecd919cf1 100644
--- a/tests/python/unittest/test_higher_order_grad.py
+++ b/tests/python/unittest/test_higher_order_grad.py
@@ -31,10 +31,16 @@ def sin(x):
     def grad_grad_op(x):
         return -nd.sin(x)
 
+    def grad_grad_grad_op(x):
+        return -nd.cos(x)
+
     for dim in range(1, 5):
         shape = rand_shape_nd(dim)
         array = random_arrays(shape)
         check_second_order_unary(array, sin, grad_grad_op)
+        # TODO(kshitij12345): Remove
+        check_nth_order_unary(array, sin,
+                              [grad_grad_op, grad_grad_grad_op], [2, 3])
 
 
 @with_seed()
@@ -45,10 +51,16 @@ def cos(x):
     def grad_grad_op(x):
         return -nd.cos(x)
 
+    def grad_grad_grad_op(x):
+        return nd.sin(x)
+
     for dim in range(1, 5):
         shape = rand_shape_nd(dim)
         array = random_arrays(shape)
         check_second_order_unary(array, cos, grad_grad_op)
+        # TODO(kshitij12345): Remove
+        check_nth_order_unary(array, cos,
+                              [grad_grad_op, grad_grad_grad_op], [2, 3])
 
 
 @with_seed()
@@ -178,6 +190,9 @@ def test_log():
     def log(x):
         return nd.log(x)
 
+    def grad_op(x):
+        return 1/x
+
     def grad_grad_op(x):
         return -1/(x**2)
 
@@ -185,6 +200,8 @@ def grad_grad_op(x):
         shape = rand_shape_nd(dim)
         array = random_arrays(shape)
         check_second_order_unary(array, log, grad_grad_op)
+        # TODO(kshitij12345): Remove
+        check_nth_order_unary(array, log, [grad_op, grad_grad_op], [1, 2])
 
 
 @with_seed()
@@ -288,6 +305,9 @@ def grad_grad_op(x):
         shape = rand_shape_nd(dim)
         array = random_arrays(shape)
         check_second_order_unary(array, sigmoid, grad_grad_op)
+        # TODO(kshitij12345): Remove
+        check_nth_order_unary(array, sigmoid, [grad_op, grad_grad_op], [1, 2])
+        check_nth_order_unary(array, sigmoid, grad_grad_op, 2)
 
 
 @with_seed()
@@ -331,28 +351,77 @@ def grad_grad_op(x):
 
 
 def check_second_order_unary(x, op, grad_grad_op, rtol=None, atol=None):
+    check_nth_order_unary(x, op, grad_grad_op, 2, rtol, atol)
+
+
+def check_nth_order_unary(x, op, grad_ops, orders, rtol=None, atol=None):
+    """Assert n-th order autograd gradient against expected gradient.
+
+    Multiple order of gradients can be checked by passing list of
+    function computing the particular order gradient and passing the
+    corresponding list of order.
+
+    Note
+    ----
+    1. Orders should always be monotonically increasing.
+    2. Elements of grads_ops should correspond to elements of orders
+    i.e. grads_op = [grad_op, grad_grad_grad_op] should be passed with
+         orders = [1, 3]
+
+    Parameters
+    ----------
+    x : mxnet.NDArray
+        Input Array.
+    op : Callable
+        Operation to perform on Input Array.
+    grad_ops : Callable or List of Callable
+        Function to compute and assert gradient of given order.
+    orders : int or List of int
+        Order/s to assert expected and computed gradients.
+
+    Returns
+    -------
+    None
+
+    """
+    if isinstance(orders, int):
+        orders = [orders]
+        grad_ops = [grad_ops]
+
+    assert all(i < j for i, j in zip(orders[0:-1], orders[1:])), \
+        "orders should be monotonically increasing"
+    assert len(set(orders)) == len(orders), \
+        "orders should have unique elements"
+    highest_order = max(orders)
+
     x = nd.array(x)
-    grad_grad_x = grad_grad_op(x)
     x.attach_grad()
 
-    # Manual head_grads.
-    y_grad = nd.random.normal(shape=x.shape)
-    head_grad_grads = nd.random.normal(shape=x.shape)
+    expected_grads = [grad_op(x) for grad_op in grad_ops]
+    computed_grads = []
+    head_grads = []
 
     # Perform compute.
     with autograd.record():
         y = op(x)
-        x_grad = autograd.grad(heads=y, variables=x, head_grads=y_grad,
-                               create_graph=True, retain_graph=True)[0]
-    x_grad.backward(head_grad_grads)
-
-    # Compute expected values.
-    expected_grad_grad = grad_grad_x.asnumpy() * head_grad_grads.asnumpy() * \
-        y_grad.asnumpy()
-
-    # Validate the gradients.
-    assert_almost_equal(expected_grad_grad,
-                        x.grad.asnumpy(), rtol=rtol, atol=atol)
+        for current_order in range(1, highest_order+1):
+            head_grad = nd.random.normal(shape=x.shape)
+            y = autograd.grad(heads=y, variables=x, head_grads=head_grad,
+                              create_graph=True, retain_graph=True)[0]
+            if current_order in orders:
+                computed_grads.append(y)
+            head_grads.append(head_grad)
+
+    # Validate all the gradients.
+    for order, grad, computed_grad in \
+            zip(orders, expected_grads, computed_grads):
+        # Compute expected values.
+        expected_grad = grad.asnumpy()
+        for head_grad in head_grads[:order]:
+            expected_grad *= head_grad.asnumpy()
+
+        assert_almost_equal(
+            expected_grad, computed_grad.asnumpy(), rtol=rtol, atol=atol)
 
 
 if __name__ == '__main__':

From 512d25a2b9b9e53eb16db4258938ccb3127131d4 Mon Sep 17 00:00:00 2001
From: Leonardo Taccari <leonardo.taccari@gmail.com>
Date: Sun, 29 Sep 2019 00:14:22 +0200
Subject: [PATCH 388/813] Minor fix in ToTensor documentation. (#16299)

* Minor fix in ToTensor documentation.

* retrigger CI

* ci
---
 python/mxnet/gluon/data/vision/transforms.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/mxnet/gluon/data/vision/transforms.py b/python/mxnet/gluon/data/vision/transforms.py
index ab8f8ab482df..d2c7f906caac 100644
--- a/python/mxnet/gluon/data/vision/transforms.py
+++ b/python/mxnet/gluon/data/vision/transforms.py
@@ -112,7 +112,7 @@ class ToTensor(HybridBlock):
         - **data**: input tensor with (H x W x C) or (N x H x W x C) shape and uint8 type.
 
     Outputs:
-        - **out**: output tensor with (C x H x W) or (N x H x W x C) shape and float32 type.
+        - **out**: output tensor with (C x H x W) or (N x C x H x W) shape and float32 type.
 
     Examples
     --------

From ea440c72bda77cf76331630065963fdfafe0d68a Mon Sep 17 00:00:00 2001
From: reminisce <wujun.nju@gmail.com>
Date: Mon, 30 Sep 2019 11:43:34 -0700
Subject: [PATCH 389/813] [numpy] Cosmetic improvement on mxnet.numpy builtin
 op signature in documentation (#16305)

* Init checkin

* Clean up

* Add test

* Fix

* Local import inspect

* Fix lint
---
 python/mxnet/__init__.py                |  5 +-
 python/mxnet/_numpy_op_doc.py           | 30 ++---------
 python/mxnet/numpy_dispatch_protocol.py |  4 ++
 python/mxnet/numpy_op_signature.py      | 72 +++++++++++++++++++++++++
 tests/python/unittest/test_numpy_op.py  | 15 ++++++
 5 files changed, 96 insertions(+), 30 deletions(-)
 create mode 100644 python/mxnet/numpy_op_signature.py

diff --git a/python/mxnet/__init__.py b/python/mxnet/__init__.py
index e1b1a95838c5..3dc80cd5a0b4 100644
--- a/python/mxnet/__init__.py
+++ b/python/mxnet/__init__.py
@@ -97,6 +97,5 @@
 # fact that kvstore-server module is imported before the __version__ attr is set.
 from . import kvstore_server
 
-from .numpy_dispatch_protocol import _register_array_function, _register_array_ufunc
-_register_array_function()
-_register_array_ufunc()
+from . import numpy_op_signature
+from . import numpy_dispatch_protocol
diff --git a/python/mxnet/_numpy_op_doc.py b/python/mxnet/_numpy_op_doc.py
index 6d2776e98bc9..6e2f5fa15919 100644
--- a/python/mxnet/_numpy_op_doc.py
+++ b/python/mxnet/_numpy_op_doc.py
@@ -22,8 +22,6 @@
 
 def _np_ones_like(a):
     """
-    ones_like(a)
-
     Return an array of ones with the same shape and type as a given array.
 
     Parameters
@@ -42,8 +40,6 @@ def _np_ones_like(a):
 
 def _np_zeros_like(a):
     """
-    zeros_like(a)
-
     Return an array of zeros with the same shape and type as a given array.
 
     Parameters
@@ -62,8 +58,6 @@ def _np_zeros_like(a):
 
 def _np_cumsum(a, axis=None, dtype=None, out=None):
     """
-    cumsum(a, axis=None, dtype=None, out=None)
-
     Return the cumulative sum of the elements along a given axis.
 
     Parameters
@@ -115,8 +109,6 @@ def _np_cumsum(a, axis=None, dtype=None, out=None):
 
 def _npx_nonzero(a):
     """
-    nonzero(a)
-
     Return the indices of the elements that are non-zero.
 
     Returns a ndarray with ndim is 2. Each row contains the indices 
@@ -164,8 +156,6 @@ def _npx_nonzero(a):
 
 def _np_repeat(a, repeats, axis=None):
     """
-    repeat(a, repeats, axis=None)
-
     Repeat elements of an array.
 
     Parameters
@@ -213,8 +203,6 @@ def _np_repeat(a, repeats, axis=None):
 
 def _np_transpose(a, axes=None):
     """
-    transpose(a, axes=None)
-
     Permute the dimensions of an array.
 
     Parameters
@@ -256,8 +244,7 @@ def _np_transpose(a, axes=None):
 
 
 def _np_dot(a, b, out=None):
-    """dot(a, b, out=None)
-
+    """
     Dot product of two arrays. Specifically,
 
     - If both `a` and `b` are 1-D arrays, it is inner product of vectors
@@ -318,10 +305,8 @@ def _np_dot(a, b, out=None):
     pass
 
 
-def _np_sum(a, axis=0, dtype=None, keepdims=None, initial=None, out=None):
+def _np_sum(a, axis=None, dtype=None, keepdims=False, initial=None, out=None):
     r"""
-    sum(a, axis=None, dtype=None, keepdims=_Null, initial=_Null, out=None)
-
     Sum of array elements over a given axis.
 
     Parameters
@@ -414,8 +399,6 @@ def _np_sum(a, axis=0, dtype=None, keepdims=None, initial=None, out=None):
 
 def _np_copy(a, out=None):
     """
-    copy(a, out=None)
-
     Return an array copy of the given object.
 
     Parameters
@@ -463,8 +446,6 @@ def _np_copy(a, out=None):
 
 def _np_reshape(a, newshape, order='C', out=None):
     """
-    reshape(a, newshape, order='C')
-
     Gives a new shape to an array without changing its data.
     This function always returns a copy of the input array if
     ``out`` is not provided.
@@ -501,8 +482,6 @@ def _np_reshape(a, newshape, order='C', out=None):
 
 def _np__linalg_svd(a):
     r"""
-    svd(a)
-
     Singular Value Decomposition.
 
     When `a` is a 2D array, it is factorized as ``ut @ np.diag(s) @ v``,
@@ -568,8 +547,6 @@ def _np__linalg_svd(a):
 
 def _np_roll(a, shift, axis=None):
     """
-    roll(a, shift, axis=None):
-
     Roll array elements along a given axis.
     
     Elements that roll beyond the last position are re-introduced at
@@ -633,8 +610,7 @@ def _np_roll(a, shift, axis=None):
 
 
 def _np_trace(a, offset=0, axis1=0, axis2=1, out=None):
-    """trace(a, offset=0, axis1=0, axis2=1, out=None)
-
+    """
     Return the sum along diagonals of the array.
     If `a` is 2-D, the sum along its diagonal with the given offset
     is returned, i.e., the sum of elements ``a[i,i+offset]`` for all i.
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index 93b81b1a868d..f483e299ffed 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -214,3 +214,7 @@ def _register_array_ufunc():
             _NUMPY_ARRAY_UFUNC_DICT[op_name] = mx_np_op
         except AttributeError:
             raise AttributeError('mxnet.numpy does not have operator named {}'.format(op_name))
+
+
+_register_array_function()
+_register_array_ufunc()
diff --git a/python/mxnet/numpy_op_signature.py b/python/mxnet/numpy_op_signature.py
new file mode 100644
index 000000000000..e42ba264b37e
--- /dev/null
+++ b/python/mxnet/numpy_op_signature.py
@@ -0,0 +1,72 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Make builtin ops' signatures compatible with NumPy."""
+
+from __future__ import absolute_import
+import sys
+import warnings
+from . import _numpy_op_doc
+from . import numpy as mx_np
+from . import numpy_extension as mx_npx
+from .base import _NP_OP_SUBMODULE_LIST, _NP_EXT_OP_SUBMODULE_LIST, _get_op_submodule_name
+
+
+def _get_builtin_op(op_name):
+    if op_name.startswith('_np_'):
+        root_module = mx_np
+        op_name_prefix = '_np_'
+        submodule_name_list = _NP_OP_SUBMODULE_LIST
+    elif op_name.startswith('_npx_'):
+        root_module = mx_npx
+        op_name_prefix = '_npx_'
+        submodule_name_list = _NP_EXT_OP_SUBMODULE_LIST
+    else:
+        return None
+
+    submodule_name = _get_op_submodule_name(op_name, op_name_prefix, submodule_name_list)
+    if len(submodule_name) > 0:
+        op_module = getattr(root_module, submodule_name[1:-1], None)
+        if op_module is None:
+            raise ValueError('Cannot find submodule {} in module {}'
+                             .format(submodule_name[1:-1], root_module.__name__))
+    else:
+        op_module = root_module
+
+    op = getattr(op_module, op_name[(len(op_name_prefix)+len(submodule_name)):], None)
+    if op is None:
+        raise ValueError('Cannot find operator {} in module {}'
+                         .format(op_name[op_name_prefix:], root_module.__name__))
+    return op
+
+
+def _register_op_signatures():
+    if sys.version_info.major < 3 or sys.version_info.minor < 5:
+        warnings.warn('Some mxnet.numpy operator signatures may not be displayed consistently with '
+                      'their counterparts in the official NumPy package due to too-low Python '
+                      'version {}. Python >= 3.5 is required to make the signatures display correctly.'
+                      .format(str(sys.version)))
+        return
+
+    import inspect
+    for op_name in dir(_numpy_op_doc):
+        op = _get_builtin_op(op_name)
+        if op is not None:
+            op.__signature__ = inspect.signature(getattr(_numpy_op_doc, op_name))
+
+
+_register_op_signatures()
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 7e3d9655f771..eaf3032d526d 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -17,6 +17,8 @@
 
 # pylint: skip-file
 from __future__ import absolute_import
+import sys
+import unittest
 import numpy as _np
 import mxnet as mx
 from mxnet import np, npx
@@ -29,6 +31,7 @@
 import scipy.stats as ss
 from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf, retry
 from mxnet.runtime import Features
+from mxnet.numpy_op_signature import _get_builtin_op
 import platform
 
 
@@ -2810,6 +2813,18 @@ def check_output_n_grad(data_shape, idx_shape, axis, mode):
                 check_output_n_grad(config[0], config[1], config[2], mode)
 
 
+@unittest.skipUnless(sys.version_info.major >= 3 and sys.version_info.minor >= 5,
+                     'inspect package requires Python >= 3.5 to work properly')
+@with_seed()
+def test_np_builtin_op_signature():
+    import inspect
+    from mxnet import _numpy_op_doc
+    for op_name in dir(_numpy_op_doc):
+        op = _get_builtin_op(op_name)
+        if op is not None:
+            assert str(op.__signature__) == str(inspect.signature(getattr(_numpy_op_doc, op_name)))
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 3ffd2c2b11fe099c8f8dfa368b8b2a42ddd876bd Mon Sep 17 00:00:00 2001
From: Pedro Larroy <pedro.larroy.lists@gmail.com>
Date: Mon, 30 Sep 2019 22:45:15 +0200
Subject: [PATCH 390/813] [MXNET-978] Fully connected, higher order grad
 (#14779)

* Add backward to fully connected. (_backward_FullyConnected)

* NodeEntry refactor fallout

* Add comment on correctness of 2nd order grad

* simplify implementation of second order gradient for FC

* Address CR comments

* Address CR comments

* Python2 compat

* Code review

* Code review

* sketch of 2nd order gradient for FC, shapes don't work

* Add its own operator

* Save work

* implementation of FCGradGrad

* fix enums

* refine FC higher order gradient test

* Lint and CR

* CR

* CR

* CR comments

* Refactor camelcase

* Fix typo

* Add bias 2nd order

* Fix FC flatten test

* Fix and test FC without flattened inputs

* Clean imports

* refine comment

* CR

* CR

* Fix grammar

* Fix submodules
---
 include/mxnet/tensor_blob.h                   |   2 +-
 src/operator/linalg.h                         |   1 +
 src/operator/nn/fully_connected-inl.h         | 205 ++++++++++++++----
 src/operator/nn/fully_connected.cc            |  56 ++++-
 .../python/unittest/test_higher_order_grad.py | 147 ++++++++++++-
 5 files changed, 360 insertions(+), 51 deletions(-)

diff --git a/include/mxnet/tensor_blob.h b/include/mxnet/tensor_blob.h
index 91d91a3f1d2d..70af75424252 100755
--- a/include/mxnet/tensor_blob.h
+++ b/include/mxnet/tensor_blob.h
@@ -55,7 +55,7 @@ class NDArray;
  * \brief tensor blob class that can be used to hold tensor of any dimension,
  *  any device and any data type,
  *  This is a weak type that can be used to transfer data through interface
- *  TBlob itself do not involve any arithmentic operations,
+ *  TBlob itself doesn't involve any arithmetic operations,
  *  but it can be converted to tensor of fixed dimension for further operations
  *
  *  Like tensor, this data structure is like a pointer class and do not
diff --git a/src/operator/linalg.h b/src/operator/linalg.h
index c74adb5b6372..291e251f5cbc 100644
--- a/src/operator/linalg.h
+++ b/src/operator/linalg.h
@@ -54,6 +54,7 @@ using namespace mshadow;
 // CPU/GPU-versions of BLAS3 function "gemm". Please refer to the BLAS3-documentation
 // for further information about the function and its parameters.
 // Note that this is C = gemm(A,B,C), so C is input and output parameter.
+// C = alpha * A * B + beta * C
 template<typename xpu, typename DType>
 void linalg_gemm(const Tensor<xpu, 2, DType>& A, const Tensor<xpu, 2, DType>& B,
                  const Tensor<xpu, 2, DType>& C, DType alpha, DType beta,
diff --git a/src/operator/nn/fully_connected-inl.h b/src/operator/nn/fully_connected-inl.h
index 28b6905148e4..d12948ea74aa 100644
--- a/src/operator/nn/fully_connected-inl.h
+++ b/src/operator/nn/fully_connected-inl.h
@@ -32,6 +32,7 @@
 #include <vector>
 #include <string>
 #include <utility>
+#include <limits>
 #include <algorithm>
 #include "../operator_common.h"
 #include "../elemwise_op_common.h"
@@ -48,7 +49,11 @@ namespace fullc {
 enum FullyConnectedOpInputs {kData, kWeight, kBias};
 enum FullyConnectedOpResource {kTempSpace};
 enum FullyConnectedOpOutputs {kOut};
-}  // fullc
+enum FullyConnectedGradGradOutputs { kOyGrad, kXGradGrad, kWGradGrad, kBGradGrad };
+enum GradGradInputs { kOxGrad, kOwGrad, };
+enum GradGradInputsBias { kObGrad = 2, kOyBias, };
+enum GradGradInputsNoBias { kOy = 2, };
+}  // namespace fullc
 
 namespace quantized_fullc {
 enum QuantizedFCInputMinMax {kDataMin, kDataMax, kWeightMin, kWeightMax, kBiasMin, kBiasMax};
@@ -77,6 +82,38 @@ struct FullyConnectedParam : public dmlc::Parameter<FullyConnectedParam> {
   }
 };
 
+/**
+ * Flatten additional dimensions after the first
+ * @tparam xpu
+ * @tparam DType
+ * @param tblob
+ * @param ctx
+ * @return 2 Dimensional Tensor with upper shapes collapsed
+ */
+template<typename xpu, typename DType>
+Tensor<xpu, 2, DType> FlattenAs2DTail(const TBlob& tblob, const OpContext& ctx) {
+  const TShape& shape = tblob.shape_;
+  Stream<xpu> *stream = ctx.get_stream<xpu>();
+  return tblob.get_with_shape<xpu, 2, DType>(
+      Shape2(shape[0], shape.ProdShape(1, shape.ndim())), stream);
+}
+
+/**
+ * Flatten dimensions except last
+ * @tparam xpu
+ * @tparam DType
+ * @param tblob
+ * @param ctx
+ * @return 2 Dimensional tensor with front shapes collapsed
+ */
+template<typename xpu, typename DType>
+Tensor<xpu, 2, DType> FlattenAs2DHead(const TBlob& tblob, const OpContext& ctx) {
+  const TShape& shape = tblob.shape_;
+  Stream<xpu> *stream = ctx.get_stream<xpu>();
+  return tblob.get_with_shape<xpu, 2, DType>(
+      Shape2(shape.ProdShape(0, shape.ndim()-1), shape[shape.ndim()-1]), stream);
+}
+
 template<typename DType>
 void AddBias(Tensor<cpu, 1, DType> bias, Tensor<cpu, 2, DType> data,
              Tensor<cpu, 2, DType> out, Stream<cpu>*) {
@@ -153,21 +190,14 @@ void FCForward(const OpContext &ctx, const FullyConnectedParam &param,
   CHECK_EQ(s->blas_handle_ownership_, Stream<xpu>::OwnHandle)
       << "Must init CuBLAS handle in stream";
 #endif  // __CUDACC__
-  const mxnet::TShape& ishape = in_data[fullc::kData].shape_;
-  const mxnet::TShape& oshape = out_data[fullc::kOut].shape_;
-
   Tensor<xpu, 2, DType> wmat = in_data[fullc::kWeight].get<xpu, 2, DType>(s);
   Tensor<xpu, 2, DType> data, out;
   if (!param.flatten) {
-    data = in_data[fullc::kData].get_with_shape<xpu, 2, DType>(
-        Shape2(ishape.ProdShape(0, ishape.ndim()-1), ishape[ishape.ndim()-1]), s);
-    out = out_data[fullc::kOut].get_with_shape<xpu, 2, DType>(
-        Shape2(oshape.ProdShape(0, oshape.ndim()-1), oshape[oshape.ndim()-1]), s);
+    data = FlattenAs2DHead<xpu, DType>(in_data[fullc::kData], ctx);
+    out = FlattenAs2DHead<xpu, DType>(out_data[fullc::kOut], ctx);
   } else {
-    data = in_data[fullc::kData].get_with_shape<xpu, 2, DType>(
-        Shape2(ishape[0], ishape.ProdShape(1, ishape.ndim())), s);
-    out = out_data[fullc::kOut].get_with_shape<xpu, 2, DType>(
-        Shape2(oshape[0], oshape.ProdShape(1, oshape.ndim())), s);
+    data = FlattenAs2DTail<xpu, DType>(in_data[fullc::kData], ctx);
+    out = FlattenAs2DTail<xpu, DType>(out_data[fullc::kOut], ctx);
   }
 
   CHECK_EQ(data.shape_[1], wmat.shape_[1])
@@ -339,47 +369,38 @@ void FCBackward(const OpContext &ctx, const FullyConnectedParam &param,
   using namespace mshadow::expr;
   // TODO(bing): check the BLAS Handle, be careful
   //  maybe need blas handle from context
-  Stream<xpu> *s = ctx.get_stream<xpu>();
-  const mxnet::TShape& ishape = in_data[fullc::kData].shape_;
-  const mxnet::TShape& oshape = out_grad[fullc::kOut].shape_;
-
-  Tensor<xpu, 2, DType> wmat = in_data[fullc::kWeight].get<xpu, 2, DType>(s);
-  Tensor<xpu, 2, DType> data, grad, gdata;
+  Stream<xpu> *stream = ctx.get_stream<xpu>();
+  Tensor<xpu, 2, DType> wmat = in_data[fullc::kWeight].get<xpu, 2, DType>(stream);
+  Tensor<xpu, 2, DType> x, y_grad, x_grad;
   if (!param.flatten) {
-    data = in_data[fullc::kData].get_with_shape<xpu, 2, DType>(
-        Shape2(ishape.ProdShape(0, ishape.ndim()-1), ishape[ishape.ndim()-1]), s);
-    grad = out_grad[fullc::kOut].get_with_shape<xpu, 2, DType>(
-        Shape2(oshape.ProdShape(0, oshape.ndim()-1), oshape[oshape.ndim()-1]), s);
-    gdata = in_grad[fullc::kData].get_with_shape<xpu, 2, DType>(
-        Shape2(ishape.ProdShape(0, ishape.ndim()-1), ishape[ishape.ndim()-1]), s);
+    x = FlattenAs2DHead<xpu, DType>(in_data[fullc::kData], ctx);
+    y_grad = FlattenAs2DHead<xpu, DType>(out_grad[fullc::kOut], ctx);
+    x_grad = FlattenAs2DHead<xpu, DType>(in_grad[fullc::kData], ctx);
   } else {
-    data = in_data[fullc::kData].get_with_shape<xpu, 2, DType>(
-        Shape2(ishape[0], ishape.ProdShape(1, ishape.ndim())), s);
-    grad = out_grad[fullc::kOut].get_with_shape<xpu, 2, DType>(
-        Shape2(oshape[0], oshape.ProdShape(1, oshape.ndim())), s);
-    gdata = in_grad[fullc::kData].get_with_shape<xpu, 2, DType>(
-        Shape2(ishape[0], ishape.ProdShape(1, ishape.ndim())), s);
+    x = FlattenAs2DTail<xpu, DType>(in_data[fullc::kData], ctx);
+    y_grad = FlattenAs2DTail<xpu, DType>(out_grad[fullc::kOut], ctx);
+    x_grad = FlattenAs2DTail<xpu, DType>(in_grad[fullc::kData], ctx);
   }
 
 #if defined(__CUDACC__)
-  CHECK_EQ(s->blas_handle_ownership_, Stream<xpu>::OwnHandle)
+  CHECK_EQ(stream->blas_handle_ownership_, Stream<xpu>::OwnHandle)
       << "Must init CuBLAS handle in stream";
 #endif
   //  backprop
   CHECK_NE(req[fullc::kWeight], kWriteInplace) << "cannot write weight inplace";
   // gradient of weight
-  Tensor<xpu, 2, DType> gwmat = in_grad[fullc::kWeight].get<xpu, 2, DType>(s);
+  Tensor<xpu, 2, DType> w_grad = in_grad[fullc::kWeight].get<xpu, 2, DType>(stream);
   // Legacy approach shown here for comparison:
-  //   out = Assign(gwmat, req[fullc::kWeight], dot(grad.T(), data));
-  linalg_gemm(grad, data, gwmat, true, false, s, req[fullc::kWeight]);
+  //   out = Assign(w_grad, req[fullc::kWeight], dot(grad.T(), data));
+  linalg_gemm(y_grad, x, w_grad, true, false, stream, req[fullc::kWeight]);
   // gradient of bias
   if (!param.no_bias) {
-      AddBiasGrad(in_grad[fullc::kBias], grad, req[fullc::kBias], param.num_hidden, ctx);
+      AddBiasGrad(in_grad[fullc::kBias], y_grad, req[fullc::kBias], param.num_hidden, ctx);
   }
   // gradient of data
   // Legacy approach shown here for comparison:
-  //   Assign(gdata, req[fullc::kData], dot(grad, wmat));
-  linalg_gemm(grad, wmat, gdata, false, false, s, req[fullc::kData]);
+  //   Assign(x_grad, req[fullc::kData], dot(y_grad, wmat));
+  linalg_gemm(y_grad, wmat, x_grad, false, false, stream, req[fullc::kData]);
 }
 
 template<typename xpu>
@@ -418,7 +439,7 @@ void FullyConnectedGradCompute(const nnvm::NodeAttrs& attrs,
                                const std::vector<TBlob>& outputs) {
   const FullyConnectedParam& param = nnvm::get<FullyConnectedParam>(attrs.parsed);
   uint32_t out_expected = param.no_bias ? 2 : 3;
-  CHECK_EQ(inputs.size(), 3U);
+  CHECK_EQ(inputs.size(), 3U);  // ograd_y, x, w
   CHECK_EQ(outputs.size(), out_expected);
   CHECK_EQ(req.size(), out_expected);
 
@@ -442,6 +463,114 @@ void FullyConnectedGradCompute(const nnvm::NodeAttrs& attrs,
   }
 }
 
+
+
+///
+// Inputs are:
+// o_x_grad : head gradient for x_grad
+// o_w_grad : head gradient for w_grad
+// o_b_grad : if param.no_bias is false
+// o_y : head gradient of y
+//
+// outputs are:
+// o_y_grad : gradient of o_y
+// x_grad_grad : o_y *  o_w_grad
+// w_grad_grad : o_y.T * o_x_grad
+// b_grad_grad: if param.no_bias is false
+//
+// For implementation details see this PR: https://github.com/apache/incubator-mxnet/pull/14779
+
+/**
+ * Second order gradient for Fully Connected
+ * x_grad_grad = o_y * o_w_grad
+ * w_grad_grad = o_y.T * o_x_grad
+ *
+ * @tparam xpu
+ * @tparam DType
+ * @param attrs
+ * @param ctx
+ * @param inputs
+ * @param req
+ * @param outputs
+ */
+template<typename xpu, typename DType>
+void FullyConnectedGradGradCompute(const nnvm::NodeAttrs& attrs,
+                                   const OpContext& ctx,
+                                   const std::vector<TBlob>& inputs,
+                                   const std::vector<OpReqType>& req,
+                                   const std::vector<TBlob>& outputs) {
+  using namespace std;
+  using namespace fullc;
+  Stream<xpu> *stream = ctx.get_stream<xpu>();
+  const FullyConnectedParam& param = nnvm::get<FullyConnectedParam>(attrs.parsed);
+  const size_t num_inputs = param.no_bias ? 3U : 4U;
+  // outputs are: o_x_grad, o_w_grad, o_y   || o_x_grad, o_w_grad, o_b_grad, o_y
+  const size_t num_outputs = 3U;
+  CHECK_EQ(inputs.size(), num_inputs);
+  CHECK_EQ(outputs.size(), num_outputs);
+  CHECK_EQ(req.size(), num_outputs);
+
+  // inputs
+  Tensor<xpu, 2, DType> o_x_grad;
+  Tensor<xpu, 2, DType> o_w_grad;
+  Tensor<xpu, 2, DType> o_y;
+  // unused
+  // Tensor<xpu, 1, DType> o_b_grad;
+
+  // outputs
+  Tensor<xpu, 2, DType> o_y_grad;
+  TBlob o_y_grad_blob = outputs[kOyGrad];
+  Tensor<xpu, 2, DType> x_grad_grad;
+  Tensor<xpu, 2, DType> w_grad_grad;
+  Tensor<xpu, 1, DType> b_grad_grad;
+  size_t o_y_idx = std::numeric_limits<size_t>::max();
+  if (param.no_bias)
+    o_y_idx = kOy;
+  else
+    o_y_idx = kOyBias;
+  if (!param.flatten) {
+    o_x_grad = FlattenAs2DHead<xpu, DType>(inputs[kOxGrad], ctx);
+    o_w_grad = inputs[kOwGrad].get<xpu, 2, DType>(stream);
+    o_y = FlattenAs2DHead<xpu, DType>(inputs[o_y_idx], ctx);
+    x_grad_grad = FlattenAs2DHead<xpu, DType>(outputs[kXGradGrad], ctx);
+    w_grad_grad = FlattenAs2DHead<xpu, DType>(outputs[kWGradGrad], ctx);
+  } else {
+    o_x_grad = FlattenAs2DTail<xpu, DType>(inputs[kOxGrad], ctx);
+    o_w_grad = FlattenAs2DTail<xpu, DType>(inputs[kOwGrad], ctx);
+    o_y = inputs[o_y_idx].get<xpu, 2, DType>(stream);
+    x_grad_grad = FlattenAs2DTail<xpu, DType>(outputs[kXGradGrad], ctx);
+    w_grad_grad = FlattenAs2DTail<xpu, DType>(outputs[kWGradGrad], ctx);
+  }
+  linalg_gemm(o_y, o_w_grad, x_grad_grad, false, false, stream, req[kXGradGrad]);
+  linalg_gemm(o_y, o_x_grad, w_grad_grad, true, false, stream, req[kWGradGrad]);
+  // 3rd order not supported
+  Fill(stream, o_y_grad_blob, kWriteTo, static_cast<DType>(0));
+  /* TODO(larroy) bias is not supported yet as there's no bias input to backward. Bias grad grad is
+   * zero.
+  if (!param.no_bias) {
+    // The second order gradient for b doesn't depend on x or w. Thus we set it to 0.
+    b_grad_grad = outputs.at(kBGradGrad).get<xpu, 1, DType>(stream);
+    TBlob b_grad_grad_blob = TBlob(b_grad_grad);
+    Fill(stream, b_grad_grad_blob, kWriteTo, static_cast<DType>(0));
+  }
+  */
+}
+
+
+template<typename xpu>
+void FullyConnectedGradGradDTypeDispatch(
+    const nnvm::NodeAttrs& attrs,
+    const OpContext& ctx,
+    const std::vector<TBlob>& inputs,
+    const std::vector<OpReqType>& req,
+    const std::vector<TBlob>& outputs) {
+  const int dtype = inputs[0].type_flag_;
+  MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
+    FullyConnectedGradGradCompute<xpu, DType>(attrs, ctx, inputs, req, outputs);
+  });
+}
+
+
 }  // namespace op
 }  // namespace mxnet
 namespace std {
diff --git a/src/operator/nn/fully_connected.cc b/src/operator/nn/fully_connected.cc
index 06ad6d034398..fba13f3ece88 100644
--- a/src/operator/nn/fully_connected.cc
+++ b/src/operator/nn/fully_connected.cc
@@ -176,11 +176,21 @@ struct FullyConnectedGrad {
   }
 };
 
-inline static bool FCStorageType(const nnvm::NodeAttrs& attrs,
-                                 const int dev_mask,
-                                 DispatchMode* dispatch_mode,
-                                 std::vector<int> *in_attrs,
-                                 std::vector<int> *out_attrs) {
+struct FullyConnectedGradGrad {
+  const char *op_name;
+  std::vector<nnvm::NodeEntry> operator()(const nnvm::NodePtr& n,
+                                          const std::vector<nnvm::NodeEntry>& ograds) const {
+    std::vector<nnvm::NodeEntry> heads(ograds.begin(), ograds.end());
+    heads.push_back(n->inputs[0]);  // o_y : head gradient of the output y
+    return MakeGradNode(op_name, n, heads, n->attrs.dict);
+  }
+};
+
+static bool FCStorageType(const nnvm::NodeAttrs& attrs,
+                          const int dev_mask,
+                          DispatchMode* dispatch_mode,
+                          std::vector<int> *in_attrs,
+                          std::vector<int> *out_attrs) {
   const FullyConnectedParam& param = nnvm::get<FullyConnectedParam>(attrs.parsed);
   const bool valid_data = in_attrs->at(0) == kDefaultStorage;
   const bool valid_weight = in_attrs->at(1) == kDefaultStorage ||
@@ -210,11 +220,11 @@ inline static bool FCStorageType(const nnvm::NodeAttrs& attrs,
   return dispatched;
 }
 
-inline static bool BackwardFCStorageType(const nnvm::NodeAttrs& attrs,
-                                         const int dev_mask,
-                                         DispatchMode* dispatch_mode,
-                                         std::vector<int> *in_attrs,
-                                         std::vector<int> *out_attrs) {
+static bool BackwardFCStorageType(const nnvm::NodeAttrs& attrs,
+                                  const int dev_mask,
+                                  DispatchMode* dispatch_mode,
+                                  std::vector<int> *in_attrs,
+                                  std::vector<int> *out_attrs) {
   const FullyConnectedParam& param = nnvm::get<FullyConnectedParam>(attrs.parsed);
   uint32_t out_expected = param.no_bias ? 2 : 3;
   CHECK_EQ(in_attrs->size(), 3U);
@@ -324,6 +334,7 @@ NNVM_REGISTER_OP(_backward_FullyConnected)
 .set_attr<nnvm::FInplaceOption>("FInplaceOption", [](const NodeAttrs& attrs){
   return std::vector<std::pair<int, int> >{{1, 0}};
 })
+.set_attr<nnvm::FGradient>("FGradient", FullyConnectedGradGrad{"_backward_backward_FullyConnected"})
 .set_attr<FInferStorageType>("FInferStorageType", BackwardFCStorageType)
 .set_attr_parser(ParamParser<FullyConnectedParam>)
 #if MXNET_USE_MKLDNN == 1
@@ -332,5 +343,30 @@ NNVM_REGISTER_OP(_backward_FullyConnected)
 #endif
 .set_attr<FCompute>("FCompute<cpu>", FullyConnectedGradCompute<cpu>);
 
+// 2nd gradient for fully connected
+// Inputs are:
+// o_x_grad : head gradient for x_grad
+// o_w_grad : head gradient for w_grad
+// o_b_grad : if param.no_bias is false
+// o_y : head gradient of y
+//
+// outputs are:
+// o_y_grad : not used
+// x_grad_grad : o_w_grad * o_y^T
+// w_grad_grad : o_x_grad * o_y
+//
+// For a detailed development of the second gradient see here: TODO(larroy)
+NNVM_REGISTER_OP(_backward_backward_FullyConnected)
+.set_num_inputs([](const NodeAttrs& attrs) {
+  const FullyConnectedParam& params = nnvm::get<FullyConnectedParam>(attrs.parsed);
+  return params.no_bias ? 3 : 4;
+})
+.set_num_outputs([](const NodeAttrs& attrs) {
+  return 3;
+})
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr_parser(ParamParser<FullyConnectedParam>)
+.set_attr<FCompute>("FCompute<cpu>", FullyConnectedGradGradDTypeDispatch<cpu>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/tests/python/unittest/test_higher_order_grad.py b/tests/python/unittest/test_higher_order_grad.py
index e07ecd919cf1..c4a1948fef72 100644
--- a/tests/python/unittest/test_higher_order_grad.py
+++ b/tests/python/unittest/test_higher_order_grad.py
@@ -18,9 +18,16 @@
 
 import math
 import random
-from mxnet import nd, autograd
-from mxnet.test_utils import assert_almost_equal, random_arrays, rand_shape_nd
+from functools import reduce
+from operator import mul
+import random
+
+from nose.tools import ok_
+
 from common import with_seed
+import mxnet
+from mxnet import nd, autograd, gluon
+from mxnet.test_utils import assert_almost_equal, random_arrays, rand_shape_nd, same
 
 
 @with_seed()
@@ -424,6 +431,142 @@ def check_nth_order_unary(x, op, grad_ops, orders, rtol=None, atol=None):
             expected_grad, computed_grad.asnumpy(), rtol=rtol, atol=atol)
 
 
+def arange_shape_like(y):
+    shape = y.shape
+    nelems = reduce(mul, shape)
+    x = nd.arange(nelems).reshape(shape)
+    return x
+
+
+class NDArrayGenerator(object):
+    def __init__(self, dim, startdim=1):
+        self.dim = dim
+        self.curdim = startdim
+
+    def __iter__(self):
+        return self
+
+    @staticmethod
+    def gen(dimensions):
+        shape = rand_shape_nd(dimensions, 4)
+        nelems = reduce(mul, shape)
+        x = nd.arange(nelems).reshape(shape)
+        return x
+
+    def next(self):
+        return self.__next__()
+
+    def __next__(self):
+        if self.curdim > self.dim:
+            raise StopIteration
+        x = NDArrayGenerator.gen(self.curdim)
+        self.curdim += 1
+        return x
+
+
+def flatten2d_right(x):
+    s_0 = x.shape[0]
+    s_1 = reduce(mul, x.shape[1:])
+    return x.reshape((s_0, s_1))
+
+
+def flatten2d_left(x):
+    s_0 = reduce(mul, x.shape[:-1])
+    s_1 = x.shape[-1]
+    return x.reshape((s_0, s_1))
+
+
+@with_seed()
+def test_dense_backward_flatten():
+    print("2nd order gradient for Fully Connected, flatten=True")
+    for x in NDArrayGenerator(4,2):
+        hidden = random.randrange(1, 4)
+        net = gluon.nn.Sequential()
+        with net.name_scope():
+            net.add(gluon.nn.Dense(hidden, flatten=True))
+        net.initialize(mxnet.initializer.Constant(.5))
+        x.attach_grad()
+        with autograd.record():
+            y = net.forward(x)
+            o_y = arange_shape_like(y)  # head gradient of y
+            params = [p.data() for p in net.collect_params().values()]
+            w = params[0]
+            b = params[1]
+            print("Checking y ({}) = x({}) * w^T({}) + b({})".format(y.shape, x.shape, w.shape, b.shape))
+            x_grad = autograd.grad(heads=y, variables=x, head_grads=o_y,
+                                   create_graph=True, retain_graph=True)[0]
+            o_x_grad = arange_shape_like(x_grad)
+            w_grad_grad = autograd.grad(heads=x_grad, variables=w,
+                                        head_grads=o_x_grad, create_graph=False)[0]
+            w_grad = autograd.grad(heads=y, variables=w, head_grads=o_y,
+                                   create_graph=True, retain_graph=True)[0]
+            o_w_grad = arange_shape_like(w_grad)
+            x_grad_grad = autograd.grad(heads=w_grad, variables=x,
+                                        head_grads=o_w_grad, create_graph=False)[0]
+        # Expected results
+        w_grad_e = nd.dot(o_y, x, transpose_a=True)
+        w_grad_grad_e = nd.dot(o_y, o_x_grad, transpose_a=True)
+        x_grad_e = nd.dot(o_y, w)
+        x_grad_grad_e = nd.dot(o_y, o_w_grad)
+        ok_(w_grad.shape == w.shape)
+        ok_(w_grad_grad.shape == w.shape)
+        ok_(x_grad.shape == x.shape)
+        ok_(x_grad_grad.shape == x.shape)
+        w_grad_check = same(flatten2d_right(w_grad), flatten2d_right(w_grad_e))
+        w_grad_grad_check = same(flatten2d_right(w_grad_grad), flatten2d_right(w_grad_grad_e))
+        x_grad_check = same(flatten2d_right(x_grad), flatten2d_right(x_grad_e))
+        x_grad_grad_check = same(flatten2d_right(x_grad_grad), flatten2d_right(x_grad_grad_e))
+        ok_(x_grad_check)
+        ok_(w_grad_check)
+        ok_(x_grad_grad_check)
+        ok_(w_grad_grad_check)
+
+@with_seed()
+def test_dense_backward_no_flatten():
+    print("2nd order gradient for Fully Connected, flatten=False")
+    for x in NDArrayGenerator(5,3):
+        hidden = random.randrange(1, 4)
+        net = gluon.nn.Sequential()
+        with net.name_scope():
+            net.add(gluon.nn.Dense(hidden, flatten=False))
+        net.initialize(mxnet.initializer.Constant(.5))
+        x.attach_grad()
+        with autograd.record():
+            y = net.forward(x)
+            o_y = arange_shape_like(y)  # head gradient of y
+            params = [p.data() for p in net.collect_params().values()]
+            w = params[0]
+            b = params[1]
+            print("Checking y ({}) = x({}) * w^T({}) + b({})".format(y.shape, x.shape, w.shape, b.shape))
+            x_grad = autograd.grad(heads=y, variables=x, head_grads=o_y,
+                                   create_graph=True, retain_graph=True)[0]
+            o_x_grad = arange_shape_like(x_grad)
+            w_grad_grad = autograd.grad(heads=x_grad, variables=w,
+                                        head_grads=o_x_grad, create_graph=False)[0]
+            w_grad = autograd.grad(heads=y, variables=w, head_grads=o_y,
+                                   create_graph=True, retain_graph=True)[0]
+            o_w_grad = arange_shape_like(w_grad)
+            x_grad_grad = autograd.grad(heads=w_grad, variables=x,
+                                        head_grads=o_w_grad, create_graph=False)[0]
+        # Expected results
+        o_y = flatten2d_left(o_y)
+        x = flatten2d_left(x)
+        o_x_grad = flatten2d_left(o_x_grad)
+        o_w_grad = flatten2d_left(o_w_grad)
+        w_grad_e = nd.dot(o_y, x, transpose_a=True)
+        w_grad_grad_e = nd.dot(o_y, o_x_grad, transpose_a=True)
+        x_grad_e = nd.dot(o_y, w)
+        x_grad_grad_e = nd.dot(o_y, o_w_grad)
+        w_grad_check = same(flatten2d_left(w_grad), flatten2d_left(w_grad_e))
+        w_grad_grad_check = same(flatten2d_left(w_grad_grad), flatten2d_left(w_grad_grad_e))
+        x_grad_check = same(flatten2d_left(x_grad), flatten2d_left(x_grad_e))
+        x_grad_grad_check = same(flatten2d_left(x_grad_grad), flatten2d_left(x_grad_grad_e))
+        ok_(x_grad_check)
+        ok_(w_grad_check)
+        ok_(x_grad_grad_check)
+        ok_(w_grad_grad_check)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 66f16560c083a15aed1f13b097fda4a8f24929fb Mon Sep 17 00:00:00 2001
From: kshitij12345 <kshitijkalambarkar@gmail.com>
Date: Tue, 1 Oct 2019 02:33:10 +0530
Subject: [PATCH 391/813] [MXNET-978] Higher Order Gradient Support `arcsinh`,
 `arccosh`. (#15530)

* support arcsinh, arccosh for higher order grad

* add relevant tests

* update comments

* use NodeOpGen

* retrigger CI
---
 src/operator/tensor/elemwise_unary_op_trig.cc | 52 ++++++++++++++++++-
 .../python/unittest/test_higher_order_grad.py | 34 ++++++++++++
 2 files changed, 84 insertions(+), 2 deletions(-)

diff --git a/src/operator/tensor/elemwise_unary_op_trig.cc b/src/operator/tensor/elemwise_unary_op_trig.cc
index 7546f5096ccd..a436ebb284a3 100644
--- a/src/operator/tensor/elemwise_unary_op_trig.cc
+++ b/src/operator/tensor/elemwise_unary_op_trig.cc
@@ -437,7 +437,31 @@ The storage type of ``arcsinh`` output depends upon the input storage type:
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{ "_backward_arcsinh" });
 
 MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_arcsinh,
-                                                  unary_bwd<mshadow_op::arcsinh_grad>);
+                                                  unary_bwd<mshadow_op::arcsinh_grad>)
+.set_attr<nnvm::FGradient>("FGradient",
+    [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
+      // ograds[0]: head_grad_grads (dL/dxgrad)
+      // inputs[0]: dL/dy
+      // inputs[1]: x (ElemwiseGradUseIn)
+      // f(x) = arcsinh(x)
+      // n: f'(x) = 1/(x^2 + 1)^1/2
+      // f''(x) = f'(x) * x/(x^2 + 1) = x/(x^2 + 1)^(3/2)
+      // Note: x/(x^2 + 1) = x * f'(x)^2
+      auto dydx = n->inputs[0];
+      auto x = n->inputs[1];
+      auto dydx_mul_grad_x = nnvm::NodeEntry{n};
+      auto op = mxnet::util::NodeOpGen{n};
+
+      auto grad_x = op.div(dydx_mul_grad_x, dydx);
+      auto grad_x_square = op.square(grad_x);
+      auto grad_x_square_mul_x = op.mul(grad_x_square, x);
+      auto grad_grad_x = op.mul(dydx_mul_grad_x, grad_x_square_mul_x);
+
+      std::vector<nnvm::NodeEntry> ret;
+      ret.emplace_back(op.mul(ograds[0], grad_x));
+      ret.emplace_back(op.mul(ograds[0], grad_grad_x));
+      return ret;
+    });
 
 // arccosh
 MXNET_OPERATOR_REGISTER_UNARY_WITH_SPARSE_DR(arccosh, cpu, mshadow_op::arccosh)
@@ -451,7 +475,31 @@ The storage type of ``arccosh`` output is always dense
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{ "_backward_arccosh" });
 
 MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_arccosh,
-                                                  unary_bwd<mshadow_op::arccosh_grad>);
+                                                  unary_bwd<mshadow_op::arccosh_grad>)
+.set_attr<nnvm::FGradient>("FGradient",
+    [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
+      // ograds[0]: head_grad_grads (dL/dxgrad)
+      // inputs[0]: dL/dy
+      // inputs[1]: x (ElemwiseGradUseIn)
+      // f(x) = arccosh(x)
+      // n: f'(x) = 1/((x - 1)^1/2 * (x + 1)^1/2)
+      // f''(x) = f'(x) * x/((x + 1)*(x - 1)) = x/((x-1)^1/2 * (x+1)^1/2 * (x-1) * (x+1))
+      // Note: x/((x-1)*(x+1)) = x * f'(x)^2
+      auto dydx = n->inputs[0];
+      auto x = n->inputs[1];
+      auto dydx_mul_grad_x = nnvm::NodeEntry{n};
+      auto op = mxnet::util::NodeOpGen{n};
+
+      auto grad_x = op.div(dydx_mul_grad_x, dydx);
+      auto grad_x_square = op.square(grad_x);
+      auto grad_x_square_mul_x = op.mul(grad_x_square, x);
+      auto grad_grad_x = op.mul(dydx_mul_grad_x, grad_x_square_mul_x);
+
+      std::vector<nnvm::NodeEntry> ret;
+      ret.emplace_back(op.mul(ograds[0], grad_x));
+      ret.emplace_back(op.mul(ograds[0], grad_grad_x));
+      return ret;
+    });
 
 // arctanh
 MXNET_OPERATOR_REGISTER_UNARY_WITH_RSP_CSR(arctanh, cpu, mshadow_op::arctanh)
diff --git a/tests/python/unittest/test_higher_order_grad.py b/tests/python/unittest/test_higher_order_grad.py
index c4a1948fef72..0b0b00fffac7 100644
--- a/tests/python/unittest/test_higher_order_grad.py
+++ b/tests/python/unittest/test_higher_order_grad.py
@@ -150,6 +150,40 @@ def grad_grad_op(x):
         check_second_order_unary(array, arctan, grad_grad_op)
 
 
+@with_seed()
+def test_arcsinh():
+    def arcsinh(x):
+        return nd.arcsinh(x)
+
+    def grad_grad_op(x):
+        return x/nd.sqrt((nd.square(x)+1)**3)
+
+    for dim in range(1, 5):
+        shape = rand_shape_nd(dim)
+        array = random_arrays(shape)
+        check_second_order_unary(array, arcsinh, grad_grad_op)
+
+
+@with_seed()
+def test_arccosh():
+    def arccosh(x):
+        return nd.arccosh(x)
+
+    def grad_grad_op(x):
+        return x/(nd.sqrt(x-1) * nd.sqrt(x+1) * (x+1) * (x-1))
+
+    sigma = random.randint(25, 100)
+    mu = random.randint(500, 1000)
+
+    for dim in range(1, 5):
+        shape = rand_shape_nd(dim)
+        array = random_arrays(shape)
+        array = array * sigma + mu
+        # Domain of arccosh 1 to infinity.
+        assert((array > 1).all())
+        check_second_order_unary(array, arccosh, grad_grad_op)
+
+
 @with_seed()
 def test_arctanh():
     def arctanh(x):

From 810e67cc89531fac8f5471bee959c117f409f39a Mon Sep 17 00:00:00 2001
From: Caenorst <cfujitsang@nvidia.com>
Date: Mon, 30 Sep 2019 17:14:58 -0400
Subject: [PATCH 392/813] Add fast implementation of LARS (#16122)

* add MXNet operator for fast LARS

* add unit tests for fast LARS related MXNet Ops

* fix preloaded_multi_* dtype inference, add SGDwFastLARS optimizer and test

Conflicts:
	tests/python/gpu/test_operator_gpu.py

* remove commented out cast from lenet5 model

* fix lint

* Add more documentation, change name of SGDwFastLARS by LARS, removing redundancy of 'lars' in the parameters

* change optimizer code to be python2 retro-compatible

* fix lint

* replace push_back by emplace_back for cland-tidy
---
 python/mxnet/optimizer/optimizer.py           | 271 +++++++++++++-
 src/operator/contrib/multi_lars-inl.h         | 100 ++++++
 src/operator/contrib/multi_lars.cc            |  56 +++
 src/operator/contrib/multi_lars.cu            |  36 ++
 src/operator/contrib/multi_sum_sq-inl.h       | 100 ++++++
 src/operator/contrib/multi_sum_sq.cc          |  84 +++++
 src/operator/contrib/multi_sum_sq.cu          | 173 +++++++++
 .../contrib/preloaded_multi_sgd-inl.h         | 332 ++++++++++++++++++
 src/operator/contrib/preloaded_multi_sgd.cc   | 245 +++++++++++++
 src/operator/contrib/preloaded_multi_sgd.cu   |  43 +++
 tests/nightly/test_optimizer.py               |  93 +++++
 tests/python/gpu/test_operator_gpu.py         | 153 ++++++++
 12 files changed, 1682 insertions(+), 4 deletions(-)
 create mode 100644 src/operator/contrib/multi_lars-inl.h
 create mode 100644 src/operator/contrib/multi_lars.cc
 create mode 100644 src/operator/contrib/multi_lars.cu
 create mode 100644 src/operator/contrib/multi_sum_sq-inl.h
 create mode 100644 src/operator/contrib/multi_sum_sq.cc
 create mode 100644 src/operator/contrib/multi_sum_sq.cu
 create mode 100644 src/operator/contrib/preloaded_multi_sgd-inl.h
 create mode 100755 src/operator/contrib/preloaded_multi_sgd.cc
 create mode 100644 src/operator/contrib/preloaded_multi_sgd.cu
 create mode 100644 tests/nightly/test_optimizer.py

diff --git a/python/mxnet/optimizer/optimizer.py b/python/mxnet/optimizer/optimizer.py
index d953e9247900..c3a1f3374a94 100644
--- a/python/mxnet/optimizer/optimizer.py
+++ b/python/mxnet/optimizer/optimizer.py
@@ -26,18 +26,21 @@
 import os
 import numpy
 from ..base import py_str
-from ..ndarray import (NDArray, zeros, clip, sqrt, cast, maximum, abs as NDabs, array, multiply)
+from ..ndarray import (NDArray, zeros, clip, sqrt, cast, maximum, abs as NDabs, array, multiply,
+                       multi_sum_sq, multi_lars, norm as NDnorm)
 from ..ndarray import (sgd_update, sgd_mom_update, adam_update, rmsprop_update, rmspropalex_update,
                        mp_sgd_update, mp_sgd_mom_update, square, ftrl_update, ftml_update,
                        signsgd_update, signum_update, nag_mom_update, mp_nag_mom_update,
                        multi_sgd_update, multi_sgd_mom_update, multi_mp_sgd_update,
-                       multi_mp_sgd_mom_update)
+                       multi_mp_sgd_mom_update, preloaded_multi_sgd_update,
+                       preloaded_multi_sgd_mom_update, preloaded_multi_mp_sgd_update,
+                       preloaded_multi_mp_sgd_mom_update)
 from ..ndarray import sparse
 from ..random import normal
 from ..util import is_np_array
 
 __all__ = [
-    'AdaDelta', 'AdaGrad', 'Adam', 'Adamax', 'DCASGD', 'FTML', 'Ftrl', 'LBSGD',
+    'AdaDelta', 'AdaGrad', 'Adam', 'Adamax', 'DCASGD', 'FTML', 'Ftrl', 'LARS', 'LBSGD',
     'NAG', 'NDabs', 'Nadam', 'Optimizer', 'RMSProp', 'SGD', 'SGLD', 'Signum',
     'Test', 'Updater', 'ccSGD', 'create', 'get_updater', 'register'
 ]
@@ -781,6 +784,266 @@ def update(self, index, weight, grad, state):
         ftml_update(weight, grad, prev_d, prev_v, prev_z, out=weight,
                     lr=lr, wd=wd, **kwargs)
 
+@register
+class LARS(Optimizer):
+    """the LARS optimizer from 'Large Batch Training of Convolution Networks' \
+    (https://arxiv.org/abs/1708.03888)
+
+    Behave mostly like SGD with momentum and weight decay but is scaling \
+    adaptively the learning for each layer (except bias and batch norm parameters):
+    w_norm = L2norm(weights)
+    g_norm = L2norm(gradients)
+    if w_norm > 0 and g_norm > 0:
+        lr_layer = lr * lr_mult * eta * w_norm / (g_norm + weight_decay * w_norm + eps)
+    else:
+        lr_layer = lr * lr_mult
+
+    Parameters
+    ----------
+    momentum : float, optional
+        The momentum value.
+    lazy_update : bool, optional
+        Default is True. If True, lazy updates are applied \
+        if the storage types of weight and grad are both ``row_sparse``.
+    lars_eta : float, optional
+        LARS coefficient used to scale the learning rate. Default set to 0.001.
+    lars_epsilon : float, optional
+        Optional epsilon in case of very small gradients. Default set to 0.
+    momentum_correction : bool, optional
+        If True scale momentum w.r.t global learning rate change (with an lr_scheduler) \
+        as indicated in 'Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour` \
+        (https://arxiv.org/pdf/1706.02677.pdf)
+        Default set to True.
+    """
+    def __init__(self, momentum=0.0, lazy_update=True, eta=0.001, eps=0,
+                 momentum_correction=True, **kwargs):
+        super(LARS, self).__init__(**kwargs)
+        self.momentum = momentum
+        self.momentum_correction = momentum_correction
+        self.lazy_update = lazy_update
+        self.aggregate_num = int(os.getenv('MXNET_OPTIMIZER_AGGREGATION_SIZE', "4"))
+        self.eta = eta
+        self.eps = eps
+        self.skip = 0
+        self.last_lr = None
+        self.cur_lr = None
+
+
+    def _get_lrs(self, indices):
+        """Gets the learning rates given the indices of the weights.
+
+        Parameters
+        ----------
+        indices : list of int
+            Indices corresponding to weights.
+
+        Returns
+        -------
+        lrs : list of float
+            Learning rates for those indices.
+        """
+        if self.cur_lr is not None:
+            self.last_lr = self.cur_lr
+
+        if self.lr_scheduler is not None:
+            lr = self.lr_scheduler(self.num_update)
+        else:
+            lr = self.lr
+
+        if self.cur_lr is None:
+            self.last_lr = lr
+        self.cur_lr = lr
+
+        lrs = [lr for _ in indices]
+        for i, index in enumerate(indices):
+            if index in self.param_dict:
+                lrs[i] *= self.param_dict[index].lr_mult
+            elif index in self.lr_mult:
+                lrs[i] *= self.lr_mult[index]
+            elif index in self.idx2name:
+                lrs[i] *= self.lr_mult.get(self.idx2name[index], 1.0)
+        return lrs
+
+    def set_wd_mult(self, args_wd_mult):
+        self.wd_mult = {}
+        for n in self.idx2name.values():
+            is_weight = n.endswith('_weight')
+
+            if not is_weight:
+                self.wd_mult[n] = 0.0
+
+        if self.sym_info:
+            attr, arg_names = self.sym_info
+            for name in arg_names:
+                if name in attr and '__wd_mult__' in attr[name]:
+                    self.wd_mult[name] = float(attr[name]['__wd_mult__'])
+        self.wd_mult.update(args_wd_mult)
+
+    def create_state_multi_precision(self, index, weight):
+        weight_master_copy = None
+        if self.multi_precision and weight.dtype == numpy.float16:
+            weight_master_copy = weight.astype(numpy.float32)
+            return (self.create_state(index, weight_master_copy), weight_master_copy)
+        if weight.dtype == numpy.float16 and not self.multi_precision:
+            warnings.warn("Accumulating with float16 in optimizer can lead to "
+                          "poor accuracy or slow convergence. "
+                          "Consider using multi_precision=True option of the "
+                          "SGD optimizer")
+        return self.create_state(index, weight)
+
+    def create_state(self, index, weight):
+        momentum = None
+        if self.momentum != 0.0:
+            stype = weight.stype if self.lazy_update else 'default'
+            momentum = zeros(weight.shape, weight.context, dtype=weight.dtype, stype=stype)
+        return momentum
+
+    def _l2norm(self, v, rescale=False):
+        """L2 Norm implementation"""
+        v = v.astype('float32')
+        if rescale:
+            v *= self.rescale_grad
+        norm = NDnorm(v).asnumpy()[0]
+        return norm
+
+    def _get_lars(self, i, weight, g, lr, wd):
+        """Returns a scaling factor for the learning rate for this layer"""
+        name = self.idx2name[i] if i in self.idx2name else str(i)
+        if name.endswith('gamma') or name.endswith('beta') or name.endswith('bias'):
+            return lr
+
+        w_norm = self._l2norm(weight)
+        g_norm = self._l2norm(g, rescale=True)
+
+        if w_norm > 0.0 and g_norm > 0.0:
+            lars = self.eta * w_norm/(g_norm + wd * w_norm + self.eps)
+        else:
+            lars = 1.0
+        return lars * lr
+
+    def _update_impl(self, indices, weights, grads, states, multi_precision=False):
+        aggregate = True
+        if not isinstance(indices, (tuple, list)):
+            indices = [indices]
+            weights = [weights]
+            grads = [grads]
+            states = [states]
+        for weight, grad in zip(weights, grads):
+            assert(isinstance(weight, NDArray))
+            assert(isinstance(grad, NDArray))
+            aggregate = (aggregate and
+                         weight.stype == 'default' and
+                         grad.stype == 'default')
+        self._update_count(indices)
+        lrs = self._get_lrs(indices)
+        wds = self._get_wds(indices)
+
+        kwargs = {'rescale_grad': self.rescale_grad}
+        if self.momentum > 0:
+            kwargs['momentum'] = (self.momentum * (self.cur_lr / self.last_lr)) \
+                                 if (self.momentum_correction and self.last_lr != 0) else \
+                                 self.momentum
+
+        if self.clip_gradient:
+            kwargs['clip_gradient'] = self.clip_gradient
+
+        if aggregate:
+            nb_params = len(indices)
+            names = [self.idx2name[i] if i in self.idx2name else str(i) for i in indices]
+            lars_idx = [i for i in range(nb_params) if
+                        not(names[i].endswith('gamma') or names[i].endswith('beta') or
+                            names[i].endswith('bias'))]
+            nb_lars = len(lars_idx)
+            no_lars_idx = [i for i in range(nb_params) if
+                           (names[i].endswith('gamma') or names[i].endswith('beta') or
+                            names[i].endswith('bias'))]
+            cur_ctx = weights[0].context
+            full_idx = lars_idx + no_lars_idx
+            new_lrs = array([lrs[i] for i in full_idx], ctx=cur_ctx, dtype='float32')
+            new_wds = array([wds[i] for i in full_idx], ctx=cur_ctx, dtype='float32')
+            new_weights = [weights[i] for i in full_idx]
+            new_grads = [grads[i] for i in full_idx]
+            new_states = [states[i] for i in full_idx]
+            if nb_lars > 0:
+                w_sum_sq = multi_sum_sq(*new_weights[:nb_lars], num_arrays=nb_lars)
+                g_sum_sq = multi_sum_sq(*new_grads[:nb_lars], num_arrays=nb_lars)
+                multi_lars(new_lrs[:nb_lars], w_sum_sq, g_sum_sq, new_wds[:nb_lars],
+                           eta=self.eta, eps=self.eps, rescale_grad=self.rescale_grad,
+                           out=new_lrs[:nb_lars])
+            # Same than usual using preloaded sgd functions
+            sidx = 0
+            while sidx < len(indices):
+                eidx = sidx + len(new_weights[sidx:sidx+self.aggregate_num])
+                if not multi_precision:
+                    if self.momentum > 0:
+                        preloaded_multi_sgd_mom_update(
+                            *(_flatten_list(zip(new_weights[sidx:eidx],
+                                                new_grads[sidx:eidx],
+                                                new_states[sidx:eidx])) +
+                              [new_lrs[sidx:eidx], new_wds[sidx:eidx]]),
+                            out=new_weights[sidx:eidx],
+                            num_weights=len(new_weights[sidx:eidx]),
+                            **kwargs)
+                    else:
+                        preloaded_multi_sgd_update(
+                            *(_flatten_list(zip(new_weights[sidx:eidx],
+                                                new_grads[sidx:eidx])) +
+                              [new_lrs[sidx:eidx], new_wds[sidx:eidx]]),
+                            out=new_weights[sidx:eidx],
+                            num_weights=len(new_weights[sidx:eidx]),
+                            **kwargs)
+                else:
+                    if self.momentum > 0:
+                        preloaded_multi_mp_sgd_mom_update(
+                            *(_flatten_list(zip(new_weights[sidx:eidx],
+                                                new_grads[sidx:eidx],
+                                                *zip(*new_states[sidx:eidx]))) +
+                              [new_lrs[sidx:eidx], new_wds[sidx:eidx]]),
+                            out=new_weights[sidx:eidx],
+                            num_weights=len(new_weights[sidx:eidx]),
+                            **kwargs)
+                    else:
+                        preloaded_multi_mp_sgd_update(
+                            *(_flatten_list(zip(new_weights[sidx:eidx],
+                                                new_grads[sidx:eidx],
+                                                list(zip(*new_states[sidx:eidx]))[1])) +
+                              [new_lrs[sidx:eidx], new_wds[sidx:eidx]]),
+                            out=new_weights[sidx:eidx],
+                            num_weights=len(new_weights[sidx:eidx]),
+                            **kwargs)
+                sidx += self.aggregate_num
+        else:
+            lrs = [self._get_lars(i, w, g, lr, wd) for (i, w, g, lr, wd) in
+                   zip(indices, weights, grads, lrs, wds)]
+
+            for weight, grad, state, lr, wd in zip(weights, grads, states, lrs, wds):
+                if not multi_precision:
+                    if state is not None:
+                        sgd_mom_update(weight, grad, state, out=weight,
+                                       lazy_update=self.lazy_update, lr=lr, wd=wd, **kwargs)
+                    else:
+                        sgd_update(weight, grad, out=weight, lazy_update=self.lazy_update,
+                                   lr=lr, wd=wd, **kwargs)
+                else:
+                    if state[0] is not None:
+                        mp_sgd_mom_update(weight, grad, state[0], state[1], out=weight,
+                                          lr=lr, wd=wd, **kwargs)
+                    else:
+                        mp_sgd_update(weight, grad, state[1], out=weight,
+                                      lr=lr, wd=wd, **kwargs)
+
+    def update(self, index, weight, grad, state):
+        self._update_impl(index, weight, grad, state, multi_precision=False)
+
+    def update_multi_precision(self, index, weight, grad, state):
+        if not isinstance(index, (tuple, list)):
+            use_multi_precision = self.multi_precision and weight.dtype == numpy.float16
+        else:
+            use_multi_precision = self.multi_precision and weight[0].dtype == numpy.float16
+        self._update_impl(index, weight, grad, state,
+                          multi_precision=use_multi_precision)
+
+#
 @register
 class LBSGD(Optimizer):
     """The Large Batch SGD optimizer with momentum and weight decay.
@@ -812,7 +1075,7 @@ class LBSGD(Optimizer):
 
     warmup_strategy: string ('linear', 'power2', 'sqrt'. , 'lars'   default : 'linear')
     warmup_epochs: unsigned, default: 5
-    batch_scale:   unsigned, default: 1 (same as batch size*numworkers)
+    batch_scale:   unsigned, default: 1 (same as batch size * numworkers)
     updates_per_epoch: updates_per_epoch (default: 32, Default might not reflect true number batches per epoch. Used for warmup.)
     begin_epoch: unsigned, default 0, starting epoch.
     """
diff --git a/src/operator/contrib/multi_lars-inl.h b/src/operator/contrib/multi_lars-inl.h
new file mode 100644
index 000000000000..c78bd7086368
--- /dev/null
+++ b/src/operator/contrib/multi_lars-inl.h
@@ -0,0 +1,100 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file multi_lars-inl.h
+ * \brief vectorized lars coefficient computed from sums of squared weights and grads
+ * \author Clement Fuji Tsang
+ */
+#ifndef MXNET_OPERATOR_CONTRIB_MULTI_LARS_INL_H_
+#define MXNET_OPERATOR_CONTRIB_MULTI_LARS_INL_H_
+
+#include <dmlc/parameter.h>
+#include <mxnet/operator.h>
+#include <mxnet/operator_util.h>
+#include <mxnet/op_attr_types.h>
+#include <mshadow/base.h>
+#include <nnvm/op.h>
+#include <nnvm/op_attr_types.h>
+#include <vector>
+#include "../operator_common.h"
+#include "../mshadow_op.h"
+#include "../mxnet_op.h"
+#include "../tensor/init_op.h"
+#include "../tensor/util/tensor_util-inl.h"
+
+namespace mxnet {
+namespace op {
+
+struct LARSParam : public dmlc::Parameter<LARSParam> {
+  float eta;
+  float eps;
+  float rescale_grad;
+  DMLC_DECLARE_PARAMETER(LARSParam) {
+    DMLC_DECLARE_FIELD(eta)
+    .describe("LARS eta");
+    DMLC_DECLARE_FIELD(eps)
+    .describe("LARS eps");
+    DMLC_DECLARE_FIELD(rescale_grad)
+    .set_default(1.0f)
+    .describe("Gradient rescaling factor");
+  }
+};
+
+struct MultiLARSKernel {
+  MSHADOW_XINLINE static void Map(int i, float* out_data, const float* lrs,
+                                  const float* weights_sum_sq, const float* grads_sum_sq,
+                                  const float* wds, const float eta, const float eps,
+                                  const float rescale_grad, const OpReqType req) {
+    float w_norm = sqrtf(weights_sum_sq[i]);
+    bool is_lars_valid = w_norm > 0. && grads_sum_sq[i] > 0.;
+    KERNEL_ASSIGN(out_data[i], req, is_lars_valid ?
+        lrs[i] * eta * w_norm / (sqrtf(grads_sum_sq[i]) * rescale_grad + wds[i] * w_norm + eps) :
+        lrs[i]);
+  }
+};
+
+template<typename xpu>
+inline void MultiLARS(const nnvm::NodeAttrs& attrs,
+                      const OpContext &ctx,
+                      const std::vector<TBlob> &inputs,
+                      const std::vector<OpReqType> &req,
+                      const std::vector<TBlob> &outputs) {
+  using namespace mxnet_op;
+  auto param = nnvm::get<LARSParam>(attrs.parsed);
+  Stream<xpu>* s = ctx.get_stream<xpu>();
+  if (inputs[0].type_flag_ != mshadow::kFloat32)
+    LOG(FATAL) << "MultiLARS only support float";
+  Tensor<xpu, 2, float> lrs = inputs[0].FlatTo2D<xpu, float>(s);
+  Tensor<xpu, 2, float> weights_sum_sq = inputs[1].FlatTo2D<xpu, float>(s);
+  Tensor<xpu, 2, float> grads_sum_sq = inputs[2].FlatTo2D<xpu, float>(s);
+  Tensor<xpu, 2, float> wds = inputs[3].FlatTo2D<xpu, float>(s);
+  Tensor<xpu, 2, float> out_data = outputs[0].FlatTo2D<xpu, float>(s);
+  Kernel<MultiLARSKernel, xpu>::Launch(s, weights_sum_sq.shape_.Size(), out_data.dptr_,
+                                       lrs.dptr_, weights_sum_sq.dptr_, grads_sum_sq.dptr_,
+                                       wds.dptr_, param.eta, param.eps,
+                                       param.rescale_grad, req[0]);
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+
+#endif  // MXNET_OPERATOR_CONTRIB_MULTI_LARS_INL_H_
diff --git a/src/operator/contrib/multi_lars.cc b/src/operator/contrib/multi_lars.cc
new file mode 100644
index 000000000000..d4ee49358971
--- /dev/null
+++ b/src/operator/contrib/multi_lars.cc
@@ -0,0 +1,56 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file multi_lars.cc
+ * \brief vectorized LARS coefficient computed from sums of squared weights and grads
+ * \author Clement Fuji Tsang
+ */
+
+#include "./multi_lars-inl.h"
+#include "../elemwise_op_common.h"
+
+namespace mxnet {
+namespace op {
+
+DMLC_REGISTER_PARAMETER(LARSParam);
+
+NNVM_REGISTER_OP(multi_lars)
+.describe(R"code(Compute the LARS coefficients of multiple weights and grads from their sums of square"
+)code" ADD_FILELINE)
+.set_num_inputs(4)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<LARSParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<4, 1>)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<4, 1>)
+.set_attr<FInferStorageType>("FInferStorageType", ElemwiseStorageType<4, 1, false, false, false>)
+.set_attr<nnvm::FListInputNames>("FListInputNames", [](const nnvm::NodeAttrs& attrs) {
+    std::vector<std::string> list_input_names = {"lrs", "weights_sum_sq", "grads_sum_sq", "wds"};
+    return list_input_names;
+  })
+.set_attr<FCompute>("FCompute<cpu>", MultiLARS<cpu>)
+.add_argument("lrs", "NDArray-or-Symbol", "Learning rates to scale by LARS coefficient")
+.add_argument("weights_sum_sq", "NDArray-or-Symbol", "sum of square of weights arrays")
+.add_argument("grads_sum_sq", "NDArray-or-Symbol", "sum of square of gradients arrays")
+.add_argument("wds", "NDArray-or-Symbol", "weight decays")
+.add_arguments(LARSParam::__FIELDS__());
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/contrib/multi_lars.cu b/src/operator/contrib/multi_lars.cu
new file mode 100644
index 000000000000..292f09092e76
--- /dev/null
+++ b/src/operator/contrib/multi_lars.cu
@@ -0,0 +1,36 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file multi_lars.cu
+ * \brief vectorized lars coefficient computed from sums of squared weights and grads
+ * \author Clement Fuji Tsang
+ */
+
+#include "./multi_lars-inl.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(multi_lars)
+.set_attr<FCompute>("FCompute<gpu>", MultiLARS<gpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/contrib/multi_sum_sq-inl.h b/src/operator/contrib/multi_sum_sq-inl.h
new file mode 100644
index 000000000000..876155215d1c
--- /dev/null
+++ b/src/operator/contrib/multi_sum_sq-inl.h
@@ -0,0 +1,100 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file multi_l2_norm-inl.h
+ * \brief vectorized L2 norm over multiple arrays operators
+ * \author Clement Fuji Tsang, Andrei Ivanov
+ */
+
+
+#ifndef MXNET_OPERATOR_CONTRIB_MULTI_SUM_SQ_INL_H_
+#define MXNET_OPERATOR_CONTRIB_MULTI_SUM_SQ_INL_H_
+
+#include <mxnet/operator.h>
+#include <vector>
+#include "../operator_common.h"
+
+namespace mxnet {
+namespace op {
+
+struct MultiSumSqParam : public dmlc::Parameter<MultiSumSqParam> {
+  int num_arrays;
+  DMLC_DECLARE_PARAMETER(MultiSumSqParam) {
+    DMLC_DECLARE_FIELD(num_arrays)
+    .describe("number of input arrays.");
+  }
+};
+
+inline bool MultiSumSqShape(const NodeAttrs& attrs,
+                            std::vector<mxnet::TShape>* in_shape,
+                            std::vector<mxnet::TShape>* out_shape) {
+  const auto &p = dmlc::get<MultiSumSqParam>(attrs.parsed);
+  out_shape->resize(1);
+
+  SHAPE_ASSIGN_CHECK(*out_shape, 0, mxnet::TShape{p.num_arrays});
+
+  CHECK_EQ(in_shape->size(), p.num_arrays);
+  for (auto s : *in_shape) {
+    if (s.ndim() == 0)
+      return false;
+  }
+  return true;
+}
+
+inline bool MultiSumSqType(const NodeAttrs& attrs,
+                           std::vector<int>* in_type,
+                           std::vector<int>* out_type) {
+  const auto& p = dmlc::get<MultiSumSqParam>(attrs.parsed);
+  CHECK_EQ(in_type->size(), p.num_arrays);
+  int dtype = (*in_type)[0];
+  CHECK_NE(dtype, -1) << "First input must have specified type";
+  for (size_t i = 0; i < in_type->size(); ++i) {
+    if ((*in_type)[i] == -1) {
+      (*in_type)[i] = dtype;
+    } else {
+      UNIFORM_TYPE_CHECK((*in_type)[i], dtype, "array_" + std::to_string(i));
+    }
+  }
+  out_type->clear();
+  out_type->push_back(mshadow::kFloat32);
+  return true;
+}
+
+template<typename xpu>
+void MultiSumSqRun(const std::vector<TBlob> &inputs, int nInputs,
+                   float *out_ptr, mshadow::Stream<xpu> *s);
+
+template<typename xpu>
+void MultiSumSq(const nnvm::NodeAttrs& attrs,
+                const OpContext &ctx,
+                const std::vector<TBlob> &inputs,
+                const std::vector<OpReqType> &req,
+                const std::vector<TBlob> &outputs) {
+  auto s = ctx.get_stream<xpu>();
+  const auto& p = dmlc::get<MultiSumSqParam>(attrs.parsed);
+  float* out_ptr = outputs[0].FlatTo2D<xpu, float>(s).dptr_;
+  MultiSumSqRun<xpu>(inputs, p.num_arrays, out_ptr, s);
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_CONTRIB_MULTI_SUM_SQ_INL_H_
diff --git a/src/operator/contrib/multi_sum_sq.cc b/src/operator/contrib/multi_sum_sq.cc
new file mode 100644
index 000000000000..cdb5423db23f
--- /dev/null
+++ b/src/operator/contrib/multi_sum_sq.cc
@@ -0,0 +1,84 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file multi_sum_sq.cc
+ * \brief vectorized sum or squared over multiple arrays operators
+ * \author Clement Fuji Tsang, Andrei Ivanov
+ */
+
+#include "./multi_sum_sq-inl.h"
+
+namespace mxnet {
+namespace op {
+
+DMLC_REGISTER_PARAMETER(MultiSumSqParam);
+
+NNVM_REGISTER_OP(multi_sum_sq)
+.describe(R"code(Compute the sums of squares of multiple arrays
+)code" ADD_FILELINE)
+.set_num_inputs([](const nnvm::NodeAttrs& attrs) {
+    return static_cast<uint32_t>(dmlc::get<MultiSumSqParam>(attrs.parsed).num_arrays);
+  })
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<MultiSumSqParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", MultiSumSqShape)
+.set_attr<nnvm::FInferType>("FInferType", MultiSumSqType)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    const auto& param = dmlc::get<MultiSumSqParam>(attrs.parsed);
+    const uint32_t num_args = param.num_arrays;
+    std::vector<std::string> ret;
+    for (uint32_t i = 0; i < num_args; ++i) {
+      ret.push_back(std::string("array_") + std::to_string(i));
+    }
+    return ret;
+  })
+.set_attr<FCompute>("FCompute<cpu>", MultiSumSq<cpu>)
+.add_argument("data", "NDArray-or-Symbol[]", "Arrays")
+.add_arguments(MultiSumSqParam::__FIELDS__());
+
+template<typename DType>
+inline void CalcSumSq(const std::vector<TBlob> &inputs, int nInputs,
+                      float *out_ptr, mshadow::Stream<cpu> *s) {
+  int i;
+  size_t j;
+#pragma omp parallel for private(i, j)
+  for (i = 0; i < nInputs; ++i) {  // array index in inputs
+    float sum = 0;
+    const auto address = inputs[i].FlatTo2D<cpu, DType>(s).dptr_;
+    const auto jMax = inputs[i].shape_.Size();
+    for (j = 0; j < jMax; ++j)
+      sum += address[j] * address[j];
+
+    out_ptr[i] = sum;
+  }
+}
+
+template<>
+void MultiSumSqRun<cpu>(const std::vector<TBlob> &inputs, int nInputs,
+                        float *out_ptr, mshadow::Stream<cpu> *s) {
+  MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType,
+    CalcSumSq<DType>(inputs, nInputs, out_ptr, s);
+  )
+}
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/contrib/multi_sum_sq.cu b/src/operator/contrib/multi_sum_sq.cu
new file mode 100644
index 000000000000..6f6fe56bfd81
--- /dev/null
+++ b/src/operator/contrib/multi_sum_sq.cu
@@ -0,0 +1,173 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file multi_sum_sq.cu
+ * \brief vectorized sums of squares norm over multiple arrays operators
+ * \author Clement Fuji Tsang, Andrei Ivanov
+ */
+#include "./multi_sum_sq-inl.h"
+#include <cub/cub.cuh>
+
+#define ILP 4
+#define BLOCK_LIMIT 320
+#define ARRAY_LIMIT 110
+
+namespace mxnet {
+namespace op {
+
+// Shamelessly gotten from:
+// https://github.com/NVIDIA/apex/blob/master/csrc/multi_tensor_apply.cuh
+// https://github.com/NVIDIA/apex/blob/master/csrc/multi_tensor_l2norm_kernel.cu
+// https://github.com/NVIDIA/apex/blob/master/csrc/type_shim.h
+template <typename DType>
+struct MultiSumSqKernelParam {
+  DType* addresses[ARRAY_LIMIT];
+  int sizes[ARRAY_LIMIT];
+  unsigned char block_to_tensor[BLOCK_LIMIT];
+  int block_to_chunk[BLOCK_LIMIT];
+};
+
+template<typename DType>
+__device__ __forceinline__ DType reduce_block_into_lanes(DType* x,
+                                                         DType val,
+                                                         int lanes = 1,
+                                                         bool share_result = false) {
+  int tid = threadIdx.x + threadIdx.y * blockDim.x;
+  int blockSize = blockDim.x * blockDim.y;  // blockSize is intended to be a multiple of 32.
+
+  if (blockSize >= 64) {
+    x[tid] = val;
+    __syncthreads();
+  }
+
+  #pragma unroll
+  for (int i = (blockSize >> 1); i >= 64; i >>= 1) {
+    if (tid < i)
+      x[tid] = x[tid] + x[tid+i];
+    __syncthreads();
+  }
+
+  DType final;
+
+  if (tid < 32) {
+    if (blockSize >= 64)
+      final = x[tid] + x[tid+32];
+    else
+      final = val;
+    // __SYNCWARP();
+
+    #pragma unroll
+    for (int i = 16; i >= lanes; i >>= 1)
+      final = final + __shfl_down_sync(0xffffffff, final, i);
+  }
+
+  if (share_result) {
+    if (tid < lanes)
+      x[tid] = final;  // EpilogueOp
+    // Make sure the smem result is visible to all warps.
+    __syncthreads();
+  }
+
+  return final;
+}
+
+template<typename DType>
+__global__ void MultiSumSqKernel(int chunk_size,
+                                 MultiSumSqKernelParam<DType> param,
+                                 float* output) {
+  const int tensor_loc = param.block_to_tensor[blockIdx.x];
+  const int chunk_len = param.block_to_chunk[blockIdx.x] * chunk_size;
+  const int n = param.sizes[tensor_loc] - chunk_len;
+  const DType* x = param.addresses[tensor_loc] + chunk_len;
+  const auto iMax = n <= chunk_size? n : chunk_size;
+  __shared__ float vals[512];
+
+  // Non-divergent exit condition for __syncthreads, not necessary here
+  float val = 0;
+  for (int i_start = 0;
+       i_start < iMax;
+       i_start += blockDim.x * ILP) {
+    int i = i_start + threadIdx.x;
+    //    #pragma unroll
+    for (int ii = 0; ii < ILP && i < iMax; ++ii, i += blockDim.x) {
+      const auto incoming_val = static_cast<float>(x[i]);
+      val += incoming_val * incoming_val;
+    }
+  }
+
+  const float final = reduce_block_into_lanes(vals, val);
+  if (threadIdx.x == 0)
+    atomicAdd(output + tensor_loc, final);
+}
+
+template<>
+void MultiSumSqRun<gpu>(const std::vector<TBlob> &inputs, int nInputs,
+                        float *out_ptr, mshadow::Stream<gpu> *s) {
+  const int chunk_size = 32768;
+  const int block_size = 512;
+  using namespace mxnet_op;
+  auto stream = mshadow::Stream<gpu>::GetStream(s);
+  CUDA_CALL(cudaMemsetAsync(out_ptr, 0, nInputs * sizeof(float), stream));
+
+  MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    MultiSumSqKernelParam<DType> param;
+    int loc_block_info = 0;   // position in param.block_to_tensor and param.block_to_chunck
+    int loc_tensor_info = 0;  // position in param.sizes and param.addresses
+    int output_offset = 0;    // array index of the first block pointed on by param.addresses
+    for (int t = 0; t < nInputs; t++, loc_tensor_info++) {  // array index in inputs
+      param.sizes[loc_tensor_info] = inputs[t].shape_.Size();
+      param.addresses[loc_tensor_info] = inputs[t].FlatTo2D<gpu, DType>(s).dptr_;
+      const int chunks_this_tensor = (inputs[t].shape_.Size() - 1) / chunk_size;
+      for (int chunk = 0; chunk <= chunks_this_tensor; ++chunk) {  // array chunk index
+        param.block_to_tensor[loc_block_info] = loc_tensor_info;
+        param.block_to_chunk[loc_block_info] = chunk;
+        loc_block_info++;
+
+        const bool last_curr_chunk = chunk == chunks_this_tensor;
+        const bool tensors_full = last_curr_chunk && loc_tensor_info == 109;
+        const bool blocks_full = (loc_block_info == 320);
+        const bool last_chunk = last_curr_chunk && t == nInputs - 1;
+        if (!(tensors_full || blocks_full || last_chunk))
+          continue;
+
+        MultiSumSqKernel<<<loc_block_info, block_size, 0, stream>>>
+          (chunk_size, param, out_ptr + output_offset);
+        MSHADOW_CUDA_POST_KERNEL_CHECK(MultiSumSqKernel);
+        loc_block_info = 0;
+        if (last_curr_chunk) {  // if you start from a new tensor
+          loc_tensor_info = -1;
+          output_offset = t + 1;
+        } else {  // if you start from the same tensor
+          param.sizes[0] = param.sizes[loc_tensor_info];
+          param.addresses[0] = param.addresses[loc_tensor_info];
+          loc_tensor_info = 0;
+          output_offset = t;
+        }
+      }
+    }
+  });
+}
+
+NNVM_REGISTER_OP(multi_sum_sq)
+.set_attr<FCompute>("FCompute<gpu>", MultiSumSq<gpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/contrib/preloaded_multi_sgd-inl.h b/src/operator/contrib/preloaded_multi_sgd-inl.h
new file mode 100644
index 000000000000..840ca215c520
--- /dev/null
+++ b/src/operator/contrib/preloaded_multi_sgd-inl.h
@@ -0,0 +1,332 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file preloaded_multi_sgd-inl.h
+ * \brief Multi-sgd optimizers with lrs and wds as mxnet inputs
+ * \author Clement Fuji Tsang
+ */
+#ifndef MXNET_OPERATOR_CONTRIB_PRELOADED_MULTI_SGD_INL_H_
+#define MXNET_OPERATOR_CONTRIB_PRELOADED_MULTI_SGD_INL_H_
+#include <dmlc/parameter.h>
+#include <mxnet/operator.h>
+#include <mxnet/operator_util.h>
+#include <mxnet/op_attr_types.h>
+#include <mshadow/base.h>
+#include <nnvm/op.h>
+#include <nnvm/op_attr_types.h>
+#include <vector>
+#include "../operator_common.h"
+#include "../mshadow_op.h"
+#include "../elemwise_op_common.h"
+#include "../mxnet_op.h"
+#include "../tensor/init_op.h"
+#include "../tensor/util/tensor_util-inl.h"
+
+namespace mxnet {
+namespace op {
+
+struct PreloadedMultiSGDParam : public dmlc::Parameter<PreloadedMultiSGDParam> {
+  float rescale_grad;
+  float clip_gradient;
+  int num_weights;
+  DMLC_DECLARE_PARAMETER(PreloadedMultiSGDParam) {
+    DMLC_DECLARE_FIELD(rescale_grad)
+    .set_default(1.0f)
+    .describe("Rescale gradient to grad = rescale_grad*grad.");
+    DMLC_DECLARE_FIELD(clip_gradient)
+    .set_default(-1.0f)
+    .describe("Clip gradient to the range of [-clip_gradient, clip_gradient] "
+              "If clip_gradient <= 0, gradient clipping is turned off. "
+              "grad = max(min(grad, clip_gradient), -clip_gradient).");
+    DMLC_DECLARE_FIELD(num_weights)
+    .set_default(1)
+    .describe("Number of updated weights.");
+  }
+};
+
+struct PreloadedMultiSGDMomParam : public dmlc::Parameter<PreloadedMultiSGDMomParam> {
+  float momentum;
+  float rescale_grad;
+  float clip_gradient;
+  int num_weights;
+  DMLC_DECLARE_PARAMETER(PreloadedMultiSGDMomParam) {
+    DMLC_DECLARE_FIELD(momentum)
+    .set_default(0.0f)
+    .describe("The decay rate of momentum estimates at each epoch.");
+    DMLC_DECLARE_FIELD(rescale_grad)
+    .set_default(1.0f)
+    .describe("Rescale gradient to grad = rescale_grad*grad.");
+    DMLC_DECLARE_FIELD(clip_gradient)
+    .set_default(-1.0f)
+    .describe("Clip gradient to the range of [-clip_gradient, clip_gradient] "
+              "If clip_gradient <= 0, gradient clipping is turned off. "
+              "grad = max(min(grad, clip_gradient), -clip_gradient).");
+    DMLC_DECLARE_FIELD(num_weights)
+    .set_default(1)
+    .describe("Number of updated weights.");
+  }
+};
+
+template<typename ParamType, int input_stride>
+inline bool PreloadedMultiSGDShape(const nnvm::NodeAttrs& attrs,
+                                   std::vector<mxnet::TShape> *in_attrs,
+                                   std::vector<mxnet::TShape> *out_attrs) {
+  const ParamType& param = dmlc::get<ParamType>(attrs.parsed);
+  CHECK_EQ(in_attrs->size(), input_stride * param.num_weights + 2);
+  CHECK_EQ(out_attrs->size(), param.num_weights);
+
+  bool all_inferred = true;
+  auto& input_shapes = *in_attrs;
+  auto& output_shapes = *out_attrs;
+  // Learning rates
+  CHECK_EQ(in_attrs->at(param.num_weights * input_stride).Size(), param.num_weights)
+    << "Number of learning rates is inconsistent with num_weights "
+    << "parameter passed. Expected number of learning rates: "
+    << param.num_weights << ", and got " << in_attrs->at(param.num_weights * input_stride).Size();
+  // Weight decays
+  CHECK_EQ(in_attrs->at(param.num_weights * input_stride + 1).Size(), param.num_weights)
+    << "Number of weight decays is inconsistent with num_weights "
+    << "parameter passed. Expected number of weight decays: "
+    << param.num_weights << ", and got "
+    << in_attrs->at(param.num_weights * input_stride + 1).Size();
+  // Weights and gradients
+  for (int i = 0; i < param.num_weights; ++i) {
+    std::vector<mxnet::TShape> input_vec;
+    std::vector<mxnet::TShape> output_vec({output_shapes[i]});
+    for (int j = 0; j < input_stride; ++j) {
+      input_vec.push_back(input_shapes[i * input_stride + j]);
+    }
+    all_inferred = all_inferred && ElemwiseShape<input_stride, 1>(attrs, &input_vec, &output_vec);
+  }
+  return all_inferred;
+}
+
+template <typename ParamType, int input_stride, int num_fp32_inputs>
+inline bool MP_PreloadedMultiSGD_InferType(const nnvm::NodeAttrs& attrs,
+                                           std::vector<int> *in_attrs,
+                                           std::vector<int> *out_attrs) {
+  const ParamType& param = dmlc::get<ParamType>(attrs.parsed);
+  CHECK_EQ(in_attrs->size(), input_stride * param.num_weights + 2);
+  CHECK_EQ(out_attrs->size(), param.num_weights);
+
+  bool all_inferred = true;
+  auto& input_types = *in_attrs;
+  auto& output_types = *out_attrs;
+  // Weights and gradients
+  for (int i = 0; i < param.num_weights; ++i) {
+    std::vector<int> input_vec;
+    std::vector<int> output_vec({output_types[i]});
+    for (int j = 0; j < input_stride - num_fp32_inputs; ++j) {
+      input_vec.push_back(input_types[i * input_stride + j]);
+    }
+    all_inferred = all_inferred &&
+                   ElemwiseType<input_stride - num_fp32_inputs, 1>(attrs, &input_vec, &output_vec);
+  }
+  // master copies of weights
+  for (int i = 0; i < param.num_weights; ++i) {
+    for (int j = 0; j < num_fp32_inputs; ++j) {
+      TYPE_ASSIGN_CHECK(input_types, input_stride * i + input_stride - 1 - j, mshadow::kFloat32);
+    }
+  }
+  TYPE_ASSIGN_CHECK(input_types, input_stride * param.num_weights, mshadow::kFloat32);
+  TYPE_ASSIGN_CHECK(input_types, input_stride * param.num_weights + 1, mshadow::kFloat32);
+  return all_inferred;
+}
+
+template<typename DType, typename MPDType>
+struct PreloadedMultiSGDKernelParam {
+  static const int N = 60;
+  int count;
+  size_t max_size;
+  size_t sizes[N];
+  DType * weights[N];
+  DType * grads[N];
+  MPDType * mom[N];
+  MPDType * weights32[N];
+  DType * out_data[N];
+  float * lrs;
+  float * wds;
+  MPDType clip_gradient;
+  MPDType rescale_grad;
+  MPDType momentum;
+};
+
+template <typename MPDType, bool has_momentum, bool has_mixed_precision>
+struct PreloadedMultiSGDKernel {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int i, const PreloadedMultiSGDKernelParam<DType, MPDType>& param,
+                                  const OpReqType req) {
+    for (int index = 0; index < param.count; ++index) {
+      if ((size_t)i < param.sizes[index]) {
+        MPDType w = has_mixed_precision ? param.weights32[index][i] :
+                                          MPDType(param.weights[index][i]);
+        MPDType mom = has_momentum ? param.mom[index][i] : MPDType(0);
+        if (param.clip_gradient >= 0.0f) {
+          mom = param.momentum*mom
+                - param.lrs[index]*param.wds[index]*w
+                - param.lrs[index]
+                *mshadow_op::clip::Map(param.rescale_grad *
+                                       static_cast<MPDType>(param.grads[index][i]),
+                                     param.clip_gradient);
+        } else {
+          mom = param.momentum*mom
+                - param.lrs[index]*param.wds[index]*w
+                - param.lrs[index]*param.rescale_grad*static_cast<MPDType>(param.grads[index][i]);
+        }
+        if (has_momentum) {
+          param.mom[index][i] = mom;
+        }
+        w = w + mom;
+        if (has_mixed_precision) {
+          param.weights32[index][i] = w;
+        }
+        KERNEL_ASSIGN(param.out_data[index][i], req, w);
+      }
+    }
+  }
+};
+
+template<typename xpu,
+         typename DType,
+         typename MPDType,
+         typename ParamType = PreloadedMultiSGDParam,
+         int input_stride = 2>
+PreloadedMultiSGDKernelParam<DType, MPDType> FillPreloadedMultiSGDKernelParam(
+    const nnvm::NodeAttrs& attrs, const OpContext &ctx, const std::vector<TBlob> &inputs,
+    const std::vector<TBlob> &outputs) {
+  using namespace mxnet_op;
+  const ParamType& p = nnvm::get<ParamType>(attrs.parsed);
+  Stream<xpu>* s = ctx.get_stream<xpu>();
+  PreloadedMultiSGDKernelParam<DType, MPDType> param;
+  param.clip_gradient = p.clip_gradient;
+  param.rescale_grad = p.rescale_grad;
+  param.momentum = 0;
+  param.count = p.num_weights;
+  param.max_size = 0;
+  for (int i = 0; i < param.count; ++i) {
+    param.sizes[i] = inputs[i * input_stride].shape_.Size();
+    if (param.max_size < param.sizes[i]) {
+      param.max_size = param.sizes[i];
+    }
+    param.weights[i] = inputs[i * input_stride].FlatTo2D<xpu, DType>(s).dptr_;
+    param.grads[i] = inputs[i * input_stride + 1].FlatTo2D<xpu, DType>(s).dptr_;
+    // if mixed precision, then the last input in a set
+    // is 32-bit master copy of the weights
+    if (!std::is_same<DType, MPDType>::value) {
+      param.weights32[i] = inputs[i * input_stride + input_stride - 1]
+                           .FlatTo2D<xpu, MPDType>(s).dptr_;
+    }
+    param.out_data[i] = outputs[i].FlatTo2D<xpu, DType>(s).dptr_;
+  }
+  const int lrs_idx = param.count * input_stride;
+  const int wds_idx = param.count * input_stride + 1;
+  param.lrs = inputs[lrs_idx].FlatTo2D<xpu, float>(s).dptr_;
+  param.wds = inputs[wds_idx].FlatTo2D<xpu, float>(s).dptr_;
+  return param;
+}
+
+
+template<typename xpu,
+         typename DType,
+         typename MPDType,
+         int input_stride = 3>
+PreloadedMultiSGDKernelParam<DType, MPDType> FillPreloadedMultiSGDMomKernelParam(
+    const nnvm::NodeAttrs& attrs, const OpContext &ctx, const std::vector<TBlob> &inputs,
+    const std::vector<TBlob> &outputs) {
+  using namespace mxnet_op;
+  const PreloadedMultiSGDMomParam& p = nnvm::get<PreloadedMultiSGDMomParam>(attrs.parsed);
+  Stream<xpu>* s = ctx.get_stream<xpu>();
+  PreloadedMultiSGDKernelParam<DType, MPDType> param =
+    FillPreloadedMultiSGDKernelParam<xpu,
+                                     DType,
+                                     MPDType,
+                                     PreloadedMultiSGDMomParam,
+                                     input_stride>(attrs, ctx, inputs, outputs);
+  param.momentum = p.momentum;
+  for (int i = 0; i < param.count; ++i) {
+    param.mom[i] = inputs[i * input_stride + 2].FlatTo2D<xpu, MPDType>(s).dptr_;
+  }
+
+  return param;
+}
+
+template<typename T>
+class preloaded_type_identity {
+ public:
+  using type = T;
+};
+
+template<typename T>
+class preloaded_single_precision {
+ public:
+  using type = float;
+};
+
+template<typename xpu, template<typename> class MPTypeChooser, int input_stride>
+inline void PreloadedMultiSGDUpdate(const nnvm::NodeAttrs& attrs,
+                                    const OpContext &ctx,
+                                    const std::vector<TBlob> &inputs,
+                                    const std::vector<OpReqType> &req,
+                                    const std::vector<TBlob> &outputs) {
+  using namespace mxnet_op;
+  Stream<xpu>* s = ctx.get_stream<xpu>();
+  MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+    using MPDType = typename MPTypeChooser<DType>::type;
+    PreloadedMultiSGDKernelParam<DType, MPDType> param =
+      FillPreloadedMultiSGDKernelParam<xpu,
+                                       DType,
+                                       MPDType,
+                                       PreloadedMultiSGDParam,
+                                       input_stride>(attrs, ctx, inputs, outputs);
+    Kernel<PreloadedMultiSGDKernel<MPDType,
+                                   false,
+                                   !std::is_same<DType, MPDType>::value>,
+                                   xpu>::Launch(s, param.max_size, param, req[0]);
+  });
+}
+
+template<typename xpu, template<typename> class MPTypeChooser, int input_stride>
+inline void PreloadedMultiSGDMomUpdate(const nnvm::NodeAttrs& attrs,
+                                       const OpContext &ctx,
+                                       const std::vector<TBlob> &inputs,
+                                       const std::vector<OpReqType> &req,
+                                       const std::vector<TBlob> &outputs) {
+  using namespace mxnet_op;
+  Stream<xpu>* s = ctx.get_stream<xpu>();
+  MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+    using MPDType = typename MPTypeChooser<DType>::type;
+    PreloadedMultiSGDKernelParam<DType, MPDType> param =
+      FillPreloadedMultiSGDMomKernelParam<xpu,
+                                          DType,
+                                          MPDType,
+                                          input_stride>(attrs, ctx, inputs, outputs);
+    Kernel<PreloadedMultiSGDKernel<MPDType,
+                                   true,
+                                   !std::is_same<DType, MPDType>::value>,
+                                   xpu>::Launch(s, param.max_size, param, req[0]);
+  });
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+
+#endif  // MXNET_OPERATOR_CONTRIB_PRELOADED_MULTI_SGD_INL_H_
diff --git a/src/operator/contrib/preloaded_multi_sgd.cc b/src/operator/contrib/preloaded_multi_sgd.cc
new file mode 100755
index 000000000000..768d288c7c27
--- /dev/null
+++ b/src/operator/contrib/preloaded_multi_sgd.cc
@@ -0,0 +1,245 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file preloaded_multi_sgd.cc
+ * \brief Multi-sgd optimizers with lrs and wds as mxnet inputs
+ * \author Clement Fuji Tsang
+ */
+#include "./preloaded_multi_sgd-inl.h"
+#include "../elemwise_op_common.h"
+
+namespace mxnet {
+namespace op {
+
+DMLC_REGISTER_PARAMETER(PreloadedMultiSGDParam);
+DMLC_REGISTER_PARAMETER(PreloadedMultiSGDMomParam);
+
+NNVM_REGISTER_OP(preloaded_multi_sgd_update)
+.describe(R"code(Update function for Stochastic Gradient Descent (SDG) optimizer.
+
+It updates the weights using::
+
+ weight = weight - learning_rate * (gradient + wd * weight)
+
+)code" ADD_FILELINE)
+.set_num_inputs([](const nnvm::NodeAttrs& attrs) {
+    const PreloadedMultiSGDParam& param = dmlc::get<PreloadedMultiSGDParam>(attrs.parsed);
+    return static_cast<uint32_t>(param.num_weights * 2 + 2);
+  })
+.set_num_outputs([](const nnvm::NodeAttrs& attrs) {
+    const PreloadedMultiSGDParam& param = dmlc::get<PreloadedMultiSGDParam>(attrs.parsed);
+    return static_cast<uint32_t>(param.num_weights);
+  })
+.set_attr_parser(ParamParser<PreloadedMultiSGDParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", PreloadedMultiSGDShape<PreloadedMultiSGDParam, 2>)
+.set_attr<nnvm::FInferType>("FInferType",
+                            MP_PreloadedMultiSGD_InferType<PreloadedMultiSGDParam, 2, 0>)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    uint32_t num_args = dmlc::get<PreloadedMultiSGDParam>(attrs.parsed).num_weights;
+    std::vector<std::string> ret;
+    ret.reserve(num_args * 2 + 2);
+    for (uint32_t i = 0; i < num_args; ++i) {
+      ret.push_back(std::string("weight_") + std::to_string(i));
+      ret.push_back(std::string("grad_") + std::to_string(i));
+    }
+    ret.emplace_back("lrs");
+    ret.emplace_back("wds");
+    return ret;
+  })
+.set_attr<FCompute>("FCompute<cpu>", PreloadedMultiSGDUpdate<cpu, preloaded_type_identity, 2>)
+.add_argument("data", "NDArray-or-Symbol[]", "Weights, gradients, learning rates and weight decays")
+.add_arguments(PreloadedMultiSGDParam::__FIELDS__());
+
+NNVM_REGISTER_OP(preloaded_multi_sgd_mom_update)
+.describe(R"code(Momentum update function for Stochastic Gradient Descent (SGD) optimizer.
+
+Momentum update has better convergence rates on neural networks. Mathematically it looks
+like below:
+
+.. math::
+
+  v_1 = \alpha * \nabla J(W_0)\\
+  v_t = \gamma v_{t-1} - \alpha * \nabla J(W_{t-1})\\
+  W_t = W_{t-1} + v_t
+
+It updates the weights using::
+
+  v = momentum * v - learning_rate * gradient
+  weight += v
+
+Where the parameter ``momentum`` is the decay rate of momentum estimates at each epoch.
+
+)code" ADD_FILELINE)
+.set_num_inputs([](const nnvm::NodeAttrs& attrs) {
+    const PreloadedMultiSGDMomParam& param = dmlc::get<PreloadedMultiSGDMomParam>(attrs.parsed);
+    return static_cast<uint32_t>(param.num_weights * 3 + 2);
+  })
+.set_num_outputs([](const nnvm::NodeAttrs& attrs) {
+    const PreloadedMultiSGDMomParam& param = dmlc::get<PreloadedMultiSGDMomParam>(attrs.parsed);
+    return static_cast<uint32_t>(param.num_weights);
+  })
+.set_attr_parser(ParamParser<PreloadedMultiSGDMomParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", PreloadedMultiSGDShape<PreloadedMultiSGDMomParam, 3>)
+.set_attr<nnvm::FInferType>("FInferType",
+                            MP_PreloadedMultiSGD_InferType<PreloadedMultiSGDMomParam, 3, 0>)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    uint32_t num_args = dmlc::get<PreloadedMultiSGDParam>(attrs.parsed).num_weights;
+    std::vector<std::string> ret;
+    ret.reserve(num_args * 3 + 2);
+    for (uint32_t i = 0; i < num_args; ++i) {
+      ret.push_back(std::string("weight_") + std::to_string(i));
+      ret.push_back(std::string("grad_") + std::to_string(i));
+      ret.push_back(std::string("mom_") + std::to_string(i));
+    }
+    ret.emplace_back("lrs");
+    ret.emplace_back("wds");
+    return ret;
+  })
+.set_attr<nnvm::FMutateInputs>("FMutateInputs",
+  [](const nnvm::NodeAttrs& attrs) {
+    std::vector<uint32_t> ret;
+    const PreloadedMultiSGDMomParam& param = dmlc::get<PreloadedMultiSGDMomParam>(attrs.parsed);
+    ret.reserve(param.num_weights);
+    for (int i = 0; i < param.num_weights; ++i) {
+      ret.push_back(i * 3 + 2);
+    }
+    return ret;
+  })
+.set_attr<FCompute>("FCompute<cpu>", PreloadedMultiSGDMomUpdate<cpu, preloaded_type_identity, 3>)
+.add_argument("data", "NDArray-or-Symbol[]",
+              "Weights, gradients, momentum, learning rates and weight decays")
+.add_arguments(PreloadedMultiSGDMomParam::__FIELDS__());
+
+NNVM_REGISTER_OP(preloaded_multi_mp_sgd_update)
+.describe(R"code(Update function for multi-precision Stochastic Gradient Descent (SDG) optimizer.
+
+It updates the weights using::
+
+ weight = weight - learning_rate * (gradient + wd * weight)
+
+)code" ADD_FILELINE)
+.set_num_inputs([](const nnvm::NodeAttrs& attrs) {
+    const PreloadedMultiSGDParam& param = dmlc::get<PreloadedMultiSGDParam>(attrs.parsed);
+    return static_cast<uint32_t>(param.num_weights * 3 + 2);
+  })
+.set_num_outputs([](const nnvm::NodeAttrs& attrs) {
+    const PreloadedMultiSGDParam& param = dmlc::get<PreloadedMultiSGDParam>(attrs.parsed);
+    return static_cast<uint32_t>(param.num_weights);
+  })
+.set_attr_parser(ParamParser<PreloadedMultiSGDParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", PreloadedMultiSGDShape<PreloadedMultiSGDParam, 3>)
+.set_attr<nnvm::FInferType>("FInferType",
+                            MP_PreloadedMultiSGD_InferType<PreloadedMultiSGDParam, 3, 1>)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    uint32_t num_args = dmlc::get<PreloadedMultiSGDParam>(attrs.parsed).num_weights;
+    std::vector<std::string> ret;
+    ret.reserve(num_args * 3 + 2);
+    for (uint32_t i = 0; i < num_args; ++i) {
+      ret.push_back(std::string("weight_") + std::to_string(i));
+      ret.push_back(std::string("grad_") + std::to_string(i));
+      ret.push_back(std::string("weight32_") + std::to_string(i));
+    }
+    ret.emplace_back("lrs");
+    ret.emplace_back("wds");
+    return ret;
+  })
+.set_attr<nnvm::FMutateInputs>("FMutateInputs",
+  [](const nnvm::NodeAttrs& attrs) {
+    std::vector<uint32_t> ret;
+    const PreloadedMultiSGDParam& param = dmlc::get<PreloadedMultiSGDParam>(attrs.parsed);
+    ret.reserve(param.num_weights);
+    for (int i = 0; i < param.num_weights; ++i) {
+      ret.push_back(i * 3 + 2);
+    }
+    return ret;
+  })
+.set_attr<FCompute>("FCompute<cpu>", PreloadedMultiSGDUpdate<cpu, preloaded_single_precision, 3>)
+.add_argument("data", "NDArray-or-Symbol[]", "Weights, gradients, learning rates and weight decays")
+.add_arguments(PreloadedMultiSGDParam::__FIELDS__());
+
+NNVM_REGISTER_OP(preloaded_multi_mp_sgd_mom_update)
+.describe(R"code(Momentum update function for multi-precision Stochastic Gradient Descent (SGD) optimizer.
+
+Momentum update has better convergence rates on neural networks. Mathematically it looks
+like below:
+
+.. math::
+
+  v_1 = \alpha * \nabla J(W_0)\\
+  v_t = \gamma v_{t-1} - \alpha * \nabla J(W_{t-1})\\
+  W_t = W_{t-1} + v_t
+
+It updates the weights using::
+
+  v = momentum * v - learning_rate * gradient
+  weight += v
+
+Where the parameter ``momentum`` is the decay rate of momentum estimates at each epoch.
+
+)code" ADD_FILELINE)
+.set_num_inputs([](const nnvm::NodeAttrs& attrs) {
+    const PreloadedMultiSGDMomParam& param = dmlc::get<PreloadedMultiSGDMomParam>(attrs.parsed);
+    return static_cast<uint32_t>(param.num_weights * 4 + 2);
+  })
+.set_num_outputs([](const nnvm::NodeAttrs& attrs) {
+    const PreloadedMultiSGDMomParam& param = dmlc::get<PreloadedMultiSGDMomParam>(attrs.parsed);
+    return static_cast<uint32_t>(param.num_weights);
+  })
+.set_attr_parser(ParamParser<PreloadedMultiSGDMomParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", PreloadedMultiSGDShape<PreloadedMultiSGDMomParam, 4>)
+.set_attr<nnvm::FInferType>("FInferType",
+                            MP_PreloadedMultiSGD_InferType<PreloadedMultiSGDMomParam, 4, 2>)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    uint32_t num_args = dmlc::get<PreloadedMultiSGDMomParam>(attrs.parsed).num_weights;
+    std::vector<std::string> ret;
+    ret.reserve(num_args * 4 + 2);
+    for (uint32_t i = 0; i < num_args; ++i) {
+      ret.push_back(std::string("weight_") + std::to_string(i));
+      ret.push_back(std::string("grad_") + std::to_string(i));
+      ret.push_back(std::string("mom_") + std::to_string(i));
+      ret.push_back(std::string("weight32_") + std::to_string(i));
+    }
+    ret.emplace_back("lrs");
+    ret.emplace_back("wds");
+    return ret;
+  })
+.set_attr<nnvm::FMutateInputs>("FMutateInputs",
+  [](const nnvm::NodeAttrs& attrs) {
+    std::vector<uint32_t> ret;
+    const PreloadedMultiSGDMomParam& param = dmlc::get<PreloadedMultiSGDMomParam>(attrs.parsed);
+    ret.reserve(param.num_weights * 2);
+    for (int i = 0; i < param.num_weights; ++i) {
+      ret.push_back(i * 4 + 2);
+      ret.push_back(i * 4 + 3);
+    }
+    return ret;
+  })
+.set_attr<FCompute>("FCompute<cpu>", PreloadedMultiSGDMomUpdate<cpu, preloaded_single_precision, 4>)
+.add_argument("data", "NDArray-or-Symbol[]",
+              "Weights, gradients, momentums, learning rates and weight decays")
+.add_arguments(PreloadedMultiSGDMomParam::__FIELDS__());
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/contrib/preloaded_multi_sgd.cu b/src/operator/contrib/preloaded_multi_sgd.cu
new file mode 100644
index 000000000000..3335d632cd98
--- /dev/null
+++ b/src/operator/contrib/preloaded_multi_sgd.cu
@@ -0,0 +1,43 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file preloaded_multi_sgd.cu
+ * \brief Multi-sgd optimizers with lrs and wds as mxnet inputs
+ * \author Clement Fuji Tsang
+ */
+#include "./preloaded_multi_sgd-inl.h"
+#include <cub/cub.cuh>
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(preloaded_multi_sgd_update)
+.set_attr<FCompute>("FCompute<gpu>", PreloadedMultiSGDUpdate<gpu, preloaded_type_identity, 2>);
+NNVM_REGISTER_OP(preloaded_multi_sgd_mom_update)
+.set_attr<FCompute>("FCompute<gpu>", PreloadedMultiSGDMomUpdate<gpu, preloaded_type_identity, 3>);
+NNVM_REGISTER_OP(preloaded_multi_mp_sgd_update)
+.set_attr<FCompute>("FCompute<gpu>", PreloadedMultiSGDUpdate<gpu, preloaded_single_precision, 3>);
+NNVM_REGISTER_OP(preloaded_multi_mp_sgd_mom_update)
+.set_attr<FCompute>("FCompute<gpu>",
+                    PreloadedMultiSGDMomUpdate<gpu, preloaded_single_precision, 4>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/tests/nightly/test_optimizer.py b/tests/nightly/test_optimizer.py
new file mode 100644
index 000000000000..c4e264b79b65
--- /dev/null
+++ b/tests/nightly/test_optimizer.py
@@ -0,0 +1,93 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import mxnet as mx
+
+import sys
+import os
+curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
+sys.path.insert(0, os.path.join(curr_path, '../unittest'))
+from common import setup_module, with_seed
+
+# This script is testing the efficiency of LARS
+# We are training LeNet-5 at batch-size 8000 in 10 epochs above 98% accuracy
+# Which is not doable with simple SGD + momentum (from what have been tested so far)
+
+def lenet5():
+    """LeNet-5 Symbol"""
+    #pylint: disable=no-member
+    data = mx.sym.Variable('data')
+    conv1 = mx.sym.Convolution(data=data, kernel=(5, 5), num_filter=20)
+    tanh1 = mx.sym.Activation(data=conv1, act_type="tanh")
+    pool1 = mx.sym.Pooling(data=tanh1, pool_type="max",
+                           kernel=(2, 2), stride=(2, 2))
+    # second conv
+    conv2 = mx.sym.Convolution(data=pool1, kernel=(5, 5), num_filter=50)
+    tanh2 = mx.sym.Activation(data=conv2, act_type="tanh")
+    pool2 = mx.sym.Pooling(data=tanh2, pool_type="max",
+                           kernel=(2, 2), stride=(2, 2))
+    # first fullc
+    flatten = mx.sym.Flatten(data=pool2)
+    fc1 = mx.sym.FullyConnected(data=flatten, num_hidden=500)
+    tanh3 = mx.sym.Activation(data=fc1, act_type="tanh")
+    # second fullc
+    fc2 = mx.sym.FullyConnected(data=tanh3, num_hidden=10)
+    # loss
+    lenet = mx.sym.SoftmaxOutput(data=fc2, name='softmax')
+    #pylint: enable=no-member
+    return lenet
+
+@with_seed()
+def test_lars():
+    num_epochs = 10
+    batch_size = 8000
+    mnist = mx.test_utils.get_mnist()
+    train_iter = mx.io.NDArrayIter(mnist['train_data'],
+                                   mnist['train_label'],
+                                   batch_size,
+                                   shuffle=True)
+    test_iter = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], batch_size)
+    ctx = mx.gpu(0)
+    lenet_model = mx.mod.Module(lenet5(), context=ctx)
+    warmup_epochs = 1
+    epoch_it = int(train_iter.num_data / batch_size)
+    # LARS works best with Polynomial scheduler and warmup
+    base_lr = 0.01
+    optimizer_params={
+        'learning_rate': base_lr,
+        'lr_scheduler': mx.lr_scheduler.PolyScheduler(base_lr=base_lr,
+                                                      max_update=epoch_it * num_epochs,
+                                                      warmup_steps=epoch_it * warmup_epochs),
+        'momentum': 0.9,
+        'eta': 14.,
+      }
+    lenet_model.fit(train_iter,
+                    eval_data=test_iter,
+                    optimizer='lars',
+                    optimizer_params=optimizer_params,
+                    eval_metric='acc',
+                    num_epoch=num_epochs)
+
+    # predict accuracy for lenet
+    acc = mx.metric.Accuracy()
+    lenet_model.score(test_iter, acc)
+    accuracy = acc.get()[1]
+    assert accuracy > 0.98, "LeNet-5 training accuracy on MNIST was too low"
+
+if __name__ == '__main__':
+    import nose
+    nose.runmodule()
diff --git a/tests/python/gpu/test_operator_gpu.py b/tests/python/gpu/test_operator_gpu.py
index e3862abbebd1..7aac23acd549 100644
--- a/tests/python/gpu/test_operator_gpu.py
+++ b/tests/python/gpu/test_operator_gpu.py
@@ -268,6 +268,159 @@ def test_fft():
             shape = tuple(np.random.randint(1, maxdim, size=order))
             check_fft(shape)
 
+def _make_ndarrays(input_list, ctx=mx.gpu(0)):
+    return [mx.nd.array(arr, dtype=arr.dtype, ctx=ctx) for arr in input_list]
+
+def check_fast_lars(w_dtype, g_dtype, shapes, ctx, tol1, tol2):
+    weights_arr = [np.random.rand(*shape).astype(w_dtype) * 10. for shape in shapes]
+    grads_arr = [np.random.rand(*shape).astype(g_dtype) for shape in shapes]
+
+    lrs = (np.random.rand(len(shapes)).astype('float32') + 0.1) / 100.
+    wds = (np.random.rand(len(shapes)).astype('float32') + 0.1) / 1000.
+    eta = (np.random.rand() + 0.1)
+    eps = (np.random.rand() + 0.1) / 10000.
+
+    mx_w = _make_ndarrays(weights_arr, ctx=ctx)
+    mx_g = _make_ndarrays(grads_arr, ctx=ctx)
+    mx_lrs = mx.nd.array(lrs, dtype='float32', ctx=ctx)
+    mx_wds = mx.nd.array(wds, dtype='float32', ctx=ctx)
+
+    w_sum_sq = mx.nd.multi_sum_sq(*mx_w, num_arrays=len(shapes))
+    g_sum_sq = mx.nd.multi_sum_sq(*mx_g, num_arrays=len(shapes))
+
+    ref_w_sum_sq = mx.nd.array([(w.astype('float32') ** 2).sum() for w in weights_arr],
+                                dtype='float32', ctx=ctx)
+    ref_g_sum_sq = mx.nd.array([(g.astype('float32') ** 2).sum() for g in grads_arr],
+                                dtype='float32', ctx=ctx)
+    assert_almost_equal(ref_w_sum_sq.asnumpy(), w_sum_sq.asnumpy(), atol=tol1, rtol=tol1)
+    assert_almost_equal(ref_g_sum_sq.asnumpy(), g_sum_sq.asnumpy(), atol=tol1, rtol=tol1)
+
+    rescale_grad = (np.random.rand() + 0.5) * 100.
+    mx_new_lrs = mx.nd.multi_lars(mx_lrs, w_sum_sq, g_sum_sq, mx_wds, eta=eta, eps=eps,
+                                  rescale_grad=rescale_grad)
+    ref_w_l2norm = mx.nd.sqrt(ref_w_sum_sq)
+    ref_g_l2norm = mx.nd.sqrt(ref_g_sum_sq * rescale_grad * rescale_grad)
+    ref_new_lrs = mx.nd.zeros(ref_w_l2norm.shape, dtype='float32', ctx=ctx)
+    for i in range(ref_w_l2norm.size):
+        _w = ref_w_l2norm[i]
+        _g = ref_g_l2norm[i]
+        if _w > 0.0 and _g > 0.0:
+            ref_new_lrs[i] = lrs[i] * eta * _w / (_g + wds[i] * _w + eps)
+        else:
+            ref_new_lrs[i] = lrs[i]
+    assert_almost_equal(ref_new_lrs.asnumpy(), mx_new_lrs.asnumpy(), atol=tol2, rtol=tol2)
+
+@with_seed()
+def test_fast_lars():
+    min_nparam = 50
+    max_nparam = 60
+    maxdim = 10000
+    maxndim = 1
+
+    dtypes = ['float16','float32', 'float64']
+    for ctx in [mx.cpu(0), mx.gpu(0)]:
+        for w_dtype in dtypes:
+            for g_dtype in dtypes:
+                nparam = np.random.randint(min_nparam + 1, max_nparam + 1)
+                shapes = [np.random.randint(1, maxdim + 1, size=maxndim) for i in range(nparam)]
+                lowTol = ctx == mx.cpu(0) and ('float16'in [w_dtype, g_dtype])
+                tol1 = 1e-3 if lowTol else 1e-5
+                tol2 = 1e-6 if lowTol else 1e-7
+                check_fast_lars(w_dtype, g_dtype, shapes, ctx, tol1, tol2)
+
+def check_preloaded_multi_sgd(dtype, shapes, momentum, use_master_weights):
+    def _flatten_list(nested_list):
+        return [item for sublist in nested_list for item in sublist]
+    weights_arr = [np.random.rand(*shape).astype(dtype) * 100. for shape in shapes]
+    grads_arr = [np.random.rand(*shape).astype(dtype) * 100. for shape in shapes]
+    rescale_grad = (np.random.random() + 1.0)
+    mx_w = _make_ndarrays(weights_arr)
+    mx_g = _make_ndarrays(grads_arr)
+    mx_p_w = _make_ndarrays(weights_arr)
+    mx_p_g = _make_ndarrays(grads_arr)
+    lrs = list((np.random.random(size=len(shapes)).astype('float32') + 0.1) / 100.)
+    mx_lrs = mx.nd.array(lrs, dtype='float32', ctx=mx.gpu(0))
+    wds = list((np.random.random(size=len(shapes)).astype('float32') + 0.1) / 1000.)
+    mx_wds = mx.nd.array(wds, dtype='float32', ctx=mx.gpu(0))
+    if use_master_weights:
+        weights32_arr = [arr.astype('float32') for arr in weights_arr]
+        mx_w32 = _make_ndarrays(weights32_arr)
+        mx_p_w32 = _make_ndarrays(weights32_arr)
+    if momentum is None:
+        if use_master_weights:
+            mx.nd.multi_mp_sgd_update(
+                                     *_flatten_list(zip(mx_w, mx_g, mx_w32)),
+                                     num_weights=len(shapes), lrs=lrs, wds=wds,
+                                     rescale_grad=rescale_grad, out=mx_w)
+            mx.nd.preloaded_multi_mp_sgd_update(
+                                     *(_flatten_list(zip(mx_p_w, mx_p_g, mx_p_w32)) +
+                                      [mx_lrs, mx_wds]), num_weights=len(shapes),
+                                     rescale_grad=rescale_grad, out=mx_p_w)
+        else:
+            out = mx.nd.multi_sgd_update(
+                                    *_flatten_list(zip(mx_w, mx_g)),
+                                    num_weights=len(shapes), lrs=lrs, wds=wds,
+                                    rescale_grad=rescale_grad, out=mx_w)
+            preloaded_out = mx.nd.preloaded_multi_sgd_update(
+                                    *(_flatten_list(zip(mx_p_w, mx_p_g)) +
+                                     [mx_lrs, mx_wds]), num_weights=len(shapes),
+                                    rescale_grad=rescale_grad, out=mx_p_w)
+    else:
+        if use_master_weights:
+            momentums_arr = [np.random.rand(*shape).astype("float32") for shape in shapes]
+            mx_m = _make_ndarrays(momentums_arr)
+            mx_p_m = _make_ndarrays(momentums_arr)
+            out = mx.nd.multi_mp_sgd_mom_update(
+                                    *_flatten_list(zip(mx_w, mx_g, mx_m, mx_w32)),
+                                    num_weights=len(shapes), lrs=lrs, wds=wds,
+                                    rescale_grad=0.95, momentum=momentum, out=mx_w)
+            preloaded_out = mx.nd.preloaded_multi_mp_sgd_mom_update(
+                                    *(_flatten_list(zip(mx_p_w, mx_p_g, mx_p_m, mx_p_w32)) +
+                                      [mx_lrs, mx_wds]), num_weights=len(shapes),
+                                    rescale_grad=0.95, momentum=momentum, out=mx_p_w)
+        else:
+            momentums_arr = [np.random.rand(*shape).astype(dtype) for shape in shapes]
+            mx_m = _make_ndarrays(momentums_arr)
+            mx_p_m = _make_ndarrays(momentums_arr)
+            mx.nd.multi_sgd_mom_update(
+                                    *_flatten_list(zip(mx_w, mx_g, mx_m)),
+                                    num_weights=len(shapes), lrs=lrs, wds=wds,
+                                    rescale_grad=0.95, momentum=momentum, out=mx_w)
+            mx.nd.preloaded_multi_sgd_mom_update(
+                                    *(_flatten_list(zip(mx_p_w, mx_p_g, mx_p_m)) +
+                                      [mx_lrs, mx_wds]), num_weights=len(shapes),
+                                    rescale_grad=0.95, momentum=momentum, out=mx_p_w)
+
+    def _assert_all_almost_equal(lhs_list, rhs_list, rtol, atol):
+        for i, (lhs, rhs) in enumerate(zip(lhs_list, rhs_list)):
+            assert_almost_equal(lhs.asnumpy(), rhs.asnumpy(), rtol=rtol, atol=atol)
+    if dtype == 'float16':
+        rtol = 1e-3
+        atol = 1e-3
+    else:
+        rtol = 1e-5
+        atol = 1e-6
+    _assert_all_almost_equal(mx_p_w, mx_w, rtol, atol)
+    if momentum is not None:
+        _assert_all_almost_equal(mx_p_m, mx_m, rtol, atol)
+    if use_master_weights:
+        _assert_all_almost_equal(mx_p_w32, mx_w32, 1e-5, 1e-6)
+
+@with_seed()
+def test_preloaded_multi_sgd():
+    dtypes = ['float16', 'float32']
+    momentums = [None, 0.9]
+    min_nparam = 5
+    max_nparam = 10
+    maxdim = 6
+    maxndim = 4
+    for dtype in dtypes:
+        use_master_weights_list = [False,] if dtype == 'float32' else [True, False]
+        for use_master_weights in use_master_weights_list:
+            for momentum in momentums:
+                nparam = np.random.randint(min_nparam + 1, max_nparam + 1)
+                shapes = [np.random.randint(1, maxdim + 1, size=maxndim) for i in range(nparam)]
+                check_preloaded_multi_sgd(dtype, shapes, momentum, use_master_weights)
 
 @with_seed()
 def test_batchnorm_with_type():

From 097deff4f5ab78bad40ff7f6c7e2df5172290b0d Mon Sep 17 00:00:00 2001
From: "Huang, Guangtai" <guangtai@amazon.com>
Date: Tue, 1 Oct 2019 07:42:21 +0800
Subject: [PATCH 393/813] add 'Release' cmake flag (#16294)

---
 cmake/cmake_options.yml                          |  2 +-
 .../src/pages/get_started/build_from_source.md   | 16 ++++++++--------
 .../src/pages/get_started/c_plus_plus.md         |  2 +-
 .../src/pages/get_started/ubuntu_setup.md        |  3 +++
 4 files changed, 13 insertions(+), 10 deletions(-)

diff --git a/cmake/cmake_options.yml b/cmake/cmake_options.yml
index 81138241dfc6..6df148323df8 100644
--- a/cmake/cmake_options.yml
+++ b/cmake/cmake_options.yml
@@ -46,7 +46,7 @@ USE_SIGNAL_HANDLER: "ON" # Print stack traces on segfaults.
 USE_TENSORRT: "OFF" # Enable infeference optimization with TensorRT.
 USE_ASAN: "OFF" # Enable Clang/GCC ASAN sanitizers.
 ENABLE_TESTCOVERAGE: "OFF" # Enable compilation with test coverage metric output
-CMAKE_BUILD_TYPE: "Debug"
+CMAKE_BUILD_TYPE: "Release"
 CMAKE_CUDA_COMPILER_LAUNCHER: "ccache"
 CMAKE_C_COMPILER_LAUNCHER: "ccache"
 CMAKE_CXX_COMPILER_LAUNCHER: "ccache"
diff --git a/docs/static_site/src/pages/get_started/build_from_source.md b/docs/static_site/src/pages/get_started/build_from_source.md
index bf57c28add81..e8f7d468b399 100644
--- a/docs/static_site/src/pages/get_started/build_from_source.md
+++ b/docs/static_site/src/pages/get_started/build_from_source.md
@@ -241,7 +241,7 @@ For example, you can specify using all cores on Linux as follows:
 
 ```bash
 mkdir build && cd build
-cmake -GNinja ..
+cmake -DCMAKE_BUILD_TYPE=Release -GNinja ..
 ninja -v
 ```
 
@@ -251,7 +251,7 @@ ninja -v
 
 ```bash
 mkdir build && cd build
-cmake -DUSE_CUDA=1 -DUSE_CUDA_PATH=/usr/local/cuda -DUSE_CUDNN=1 -DUSE_MKLDNN=1 -GNinja ..
+cmake -DUSE_CUDA=1 -DUSE_CUDA_PATH=/usr/local/cuda -DUSE_CUDNN=1 -DUSE_MKLDNN=1 -DCMAKE_BUILD_TYPE=Release -GNinja ..
 ninja -v
 ```
 
@@ -260,7 +260,7 @@ ninja -v
 
 ```bash
 mkdir build && cd build
-cmake -DBLAS=open -DUSE_CUDA=1 -DUSE_CUDA_PATH=/usr/local/cuda -DUSE_CUDNN=1 -GNinja ..
+cmake -DBLAS=open -DUSE_CUDA=1 -DUSE_CUDA_PATH=/usr/local/cuda -DUSE_CUDNN=1 -DCMAKE_BUILD_TYPE=Release -GNinja ..
 ninja -v
 ```
 
@@ -269,7 +269,7 @@ ninja -v
 
 ```bash
 mkdir build && cd build
-cmake -DUSE_CUDA=0 -DUSE_MKLDNN=1 -GNinja ..
+cmake -DUSE_CUDA=0 -DUSE_MKLDNN=1 -DCMAKE_BUILD_TYPE=Release -GNinja ..
 ninja -v
 ```
 
@@ -278,7 +278,7 @@ ninja -v
 
 ```bash
 mkdir build && cd build
-cmake -DUSE_CUDA=0 -DBLAS=open -GNinja ..
+cmake -DUSE_CUDA=0 -DBLAS=open -DCMAKE_BUILD_TYPE=Release -GNinja ..
 ninja -v
 ```
 
@@ -288,7 +288,7 @@ ninja -v
 
 ```bash
 mkdir build && cd build
-cmake -DUSE_OPENCV=0 -GNinja ..
+cmake -DUSE_OPENCV=0 -DCMAKE_BUILD_TYPE=Release -GNinja ..
 ninja -v
 ```
 
@@ -296,7 +296,7 @@ ninja -v
 
 ```bash
 mkdir build && cd build
-cmake -DBLAS=apple -DUSE_OPENCV=0 -DUSE_OPENMP=0 -GNinja ..
+cmake -DBLAS=apple -DUSE_OPENCV=0 -DUSE_OPENMP=0 -DCMAKE_BUILD_TYPE=Release -GNinja ..
 ninja -v
 ```
 
@@ -305,7 +305,7 @@ ninja -v
 ```bash
 brew install llvm
 mkdir build && cd build
-cmake -DBLAS=apple -DUSE_OPENMP=1 -GNinja ..
+cmake -DBLAS=apple -DUSE_OPENMP=1 -DCMAKE_BUILD_TYPE=Release -GNinja ..
 ninja -v
 ```
 
diff --git a/docs/static_site/src/pages/get_started/c_plus_plus.md b/docs/static_site/src/pages/get_started/c_plus_plus.md
index a32496dd68a7..9f4800cd99bf 100644
--- a/docs/static_site/src/pages/get_started/c_plus_plus.md
+++ b/docs/static_site/src/pages/get_started/c_plus_plus.md
@@ -31,7 +31,7 @@ To enable C++ package, just add `USE_CPP_PACKAGE=1` in the [build from source](b
 For example to build MXNet with GPU support and the C++ package, OpenCV, and OpenBLAS, from the project root you would run:
 
 ```bash
-cmake -DUSE_CUDA=1 -DUSE_CUDA_PATH=/usr/local/cuda -DUSE_CUDNN=1 -DUSE_MKLDNN=1 -DUSE_CPP_PACKAGE=1 -GNinja ..
+cmake -DUSE_CUDA=1 -DUSE_CUDA_PATH=/usr/local/cuda -DUSE_CUDNN=1 -DUSE_MKLDNN=1 -DUSE_CPP_PACKAGE=1 -DCMAKE_BUILD_TYPE=Release -GNinja ..
 ninja -v
 ```
 
diff --git a/docs/static_site/src/pages/get_started/ubuntu_setup.md b/docs/static_site/src/pages/get_started/ubuntu_setup.md
index 2d6867d0eadd..e7879eaa1b9e 100644
--- a/docs/static_site/src/pages/get_started/ubuntu_setup.md
+++ b/docs/static_site/src/pages/get_started/ubuntu_setup.md
@@ -213,6 +213,7 @@ Build with CMake and ninja, without GPU and without MKL.
         -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache \
         -DCMAKE_C_COMPILER_LAUNCHER=ccache \
         -DCMAKE_CXX_COMPILER_LAUNCHER=ccache \
+        -DCMAKE_BUILD_TYPE=Release \
     ..
     ninja
 ```
@@ -228,6 +229,7 @@ If building on CPU and using MKL and MKL-DNN (make sure MKL is installed accordi
         -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache \
         -DCMAKE_C_COMPILER_LAUNCHER=ccache \
         -DCMAKE_CXX_COMPILER_LAUNCHER=ccache \
+        -DCMAKE_BUILD_TYPE=Release \
     ..
     ninja
 ```
@@ -244,6 +246,7 @@ Cuda 10.1 in Ubuntu 18.04 builds fine but is not currently tested in CI.
         -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache \
         -DCMAKE_C_COMPILER_LAUNCHER=ccache \
         -DCMAKE_CXX_COMPILER_LAUNCHER=ccache \
+        -DCMAKE_BUILD_TYPE=Release \
     ..
     ninja
 ```

From c7f3ac94d00fcb5faaa48aa9025748bb1eacc432 Mon Sep 17 00:00:00 2001
From: Sheng Zha <szha@users.noreply.github.com>
Date: Tue, 1 Oct 2019 04:01:09 -0700
Subject: [PATCH 394/813] add code of conduct and conflict resolution (#16343)

---
 CODE_OF_CONDUCT.md | 43 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 43 insertions(+)
 create mode 100644 CODE_OF_CONDUCT.md

diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md
new file mode 100644
index 000000000000..c2dd2c87f70c
--- /dev/null
+++ b/CODE_OF_CONDUCT.md
@@ -0,0 +1,43 @@
+<!---
+  Licensed to the Apache Software Foundation (ASF) under one
+  or more contributor license agreements.  See the NOTICE file
+  distributed with this work for additional information
+  regarding copyright ownership.  The ASF licenses this file
+  to you under the Apache License, Version 2.0 (the
+  "License"); you may not use this file except in compliance
+  with the License.  You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+  Unless required by applicable law or agreed to in writing,
+  software distributed under the License is distributed on an
+  "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+  KIND, either express or implied.  See the License for the
+  specific language governing permissions and limitations
+  under the License.
+-->
+
+# Code of Conduct
+
+* [Code of Conduct for The Apache Software Foundation][1]
+
+## Conflict Resolution
+
+Conflicts in an open source project can take many forms, from someone having a bad day and using harsh and hurtful language in the issue queue, to more serious instances such as sexist/racist statements or threats of violence, and everything in between.
+
+If the behavior is threatening or harassing, or for other reasons requires immediate escalation, please see below.
+
+However, for the vast majority of issues, we aim to empower individuals to first resolve conflicts themselves, asking for help when needed, and only after that fails to escalate further. This approach gives people more control over the outcome of their dispute. 
+
+If you are experiencing or witnessing conflict, we ask you to use the following escalation strategy to address the conflict:
+
+1.  Address the perceived conflict directly with those involved, preferably in a real-time medium.
+2.  If this fails, get a third party (e.g. a mutual friend, and/or someone with background on the issue, but not involved in the conflict) to intercede.
+3.  If you are still unable to resolve the conflict, and you believe it rises to harassment or another code of conduct violation, report it.
+
+## Reporting Violations
+
+Violations of the Code of Conduct can be reported to [Apache MXNet Project PMC (PPMC)](mailto:private@mxnet.apache.org). The PPMC will determine whether the Code of Conduct was violated, and will issue an appropriate sanction, possibly including a written warning or expulsion from the project, project sponsored spaces, or project forums. We ask that you make a good-faith effort to resolve your conflict via the conflict resolution policy before submitting a report.
+
+
+[1]: https://www.apache.org/foundation/policies/conduct.html

From 6931748d408756a149f0eeb9d396273917c30a68 Mon Sep 17 00:00:00 2001
From: Thomas Delteil <thomas.delteil1@gmail.com>
Date: Tue, 1 Oct 2019 18:43:33 -0700
Subject: [PATCH 395/813] adding redirects so that old website API links
 surfaced from searches (#16342)

* adding redirects so that old website API links surfaced from searches redirect to proper locations in new website

* add redirects for links in top level nav bar of old webpage

* Update .htaccess

* adding redirects so that old website API links surfaced from searches redirect to proper locations in new website

* updating based on available features

Refer to https://issues.apache.org/jira/browse/INFRA-19187 - this should fix the staging site.

* nudge CI
---
 docs/static_site/src/.htaccess | 50 ++++++++++++++++++++++++++++++++--
 1 file changed, 48 insertions(+), 2 deletions(-)

diff --git a/docs/static_site/src/.htaccess b/docs/static_site/src/.htaccess
index b5de581b80b2..5be7205bf5c6 100644
--- a/docs/static_site/src/.htaccess
+++ b/docs/static_site/src/.htaccess
@@ -1,4 +1,3 @@
-Options -Indexes
 DirectorySlash off
 RewriteEngine on
 RewriteOptions AllowNoSlash
@@ -23,6 +22,53 @@ RewriteCond %{REQUEST_FILENAME} -d
 RewriteCond %{REQUEST_FILENAME}/index.html -f
 RewriteRule ^(.*) $1/index.html [NC,L]
 
-
 # 404
 ErrorDocument 404 /404.html
+
+# Redirects
+# Python API
+Redirect 301 /versions/master/api/python/index.html /api/python/docs/api/index.html
+Redirect 301 /api/python/ndarray/ndarray.html /api/python/docs/api/ndarray/index.html
+Redirect 301 /api/python/ndarray/random.html /api/python/docs/api/ndarray/routines.html
+Redirect 301 /api/python/ndarray/linalg.html /api/python/docs/api/ndarray/routines.html
+Redirect 301 /api/python/ndarray/contrib.html /api/python/docs/api/ndarray/routines.html
+Redirect 301 /api/python/ndarray/sparse.html /api/python/docs/api/ndarray/sparse_routines.html
+
+Redirect 301 /api/python/autograd/autograd.html /api/python/docs/api/gluon-related/mxnet.autograd.html
+
+Redirect 301 /api/python/gluon/gluon.html /api/python/docs/api/gluon/index.html
+Redirect 301 /api/python/contrib/contrib.html /api/python/docs/api/gluon/mxnet.gluon.contrib.html
+Redirect 301 /api/python/gluon/nn.html /api/python/docs/api/gluon/nn.html
+Redirect 301 /api/python/gluon/rnn.html /api/python/docs/api/gluon/rnn.html
+Redirect 301 /api/python/gluon/loss.html /api/python/docs/api/gluon/mxnet.gluon.loss.html
+Redirect 301 /api/python/gluon/data.html /api/python/docs/api/gluon/mxnet.gluon.data.html
+Redirect 301 /api/python/gluon/model_zoo.html /api/python/docs/api/gluon/mxnet.gluon.model_zoo.html
+Redirect 301 /api/python/gluon/contrib.html /api/python/docs/api/gluon/mxnet.gluon.contrib.html
+
+Redirect 301 /api/python/kvstore/kvstore.html /api/python/docs/api/gluon-related/mxnet.kvstore.html
+Redirect 301 /api/python/metric/metric.html /api/python/docs/api/gluon-related/mxnet.metric.html
+Redirect 301 /api/python/optimization/optimization.html /api/python/docs/api/gluon-related/mxnet.optimizer.html
+Redirect 301 /api/python/optimization/contrib.html /api/python/docs/api/gluon-related/mxnet.optimizer.html
+Redirect 301 /api/python/profiler/profiler.html /api/python/docs/api/gluon-related/mxnet.profiler.html
+Redirect 301 /api/python/io/io.html /api/python/docs/api/gluon-related/mxnet.io.html
+
+Redirect 301 /api/python/symbol/symbol.html /api/python/docs/api/symbol/index.html
+Redirect 301 /api/python/symbol.html /api/python/docs/api/symbol/index.html
+Redirect 301 /api/python/symbol/linalg.html /api/python/docs/api/symbol/mxnet.symbol.linalg.html
+
+Redirect 301 /api/python/module/module.html /api/python/docs/api/symbol-related/mxnet.module.html
+Redirect 301 /api/python/callback/callback.html /api/python/docs/api/symbol-related/mxnet.callback.html
+Redirect 301 /api/python/tools/visualization.html /api/python/docs/api/symbol-related/mxnet.visualization.html
+
+Redirect 301 /api/python/executor/executor.html /api/python/docs/api/advanced/mxnet.executor.html
+Redirect 301 /api/python/rtc/rtc.html /api/python/docs/api/advanced/mxnet.rtc.html
+Redirect 301 /api/python/tools/test_utils.html /api/python/docs/api/advanced/mxnet.test_utils.html
+
+# Top Level Nav bar
+Redirect 301 /install/index.html /get_started
+Redirect 301 /test/get_started/install.html /get_started
+Redirect 301 /faq/index.html /api
+Redirect 301 /tutorials/index.html /api
+Redirect 301 /architecture/index.html /api/architecture/overview
+Redirect 301 /community/ecosystem.html /ecosystem
+Redirect 301 /community/powered_by.html /ecosystem

From 1363b5ab8509201db8dfaa8aa87b23ea5d838e83 Mon Sep 17 00:00:00 2001
From: Jongju Shin <jjshin@postech.ac.kr>
Date: Wed, 2 Oct 2019 11:20:54 +0900
Subject: [PATCH 396/813] simple typo error in NEWS.md (#16344)

* typo error

* ci
---
 NEWS.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/NEWS.md b/NEWS.md
index 2a0d0aca5634..8ea31334b6d1 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -233,7 +233,7 @@ For more details please refer to the [design document](https://cwiki.apache.org/
 
 #### Dependency Update
 MXNet has added support for CUDA 10, CUDA 10.1, cudnn7.5, NCCL 2.4.2, and numpy 1.16.0.
-These updates are available through PyPI packages and build from source, refer to [installation guid](https://mxnet.apache.org/versions/master/install/index.html) for more details.
+These updates are available through PyPI packages and build from source, refer to [installation guide](https://mxnet.apache.org/versions/master/install/index.html) for more details.
 
 #### Gluon Fit API(experimental)
 Training a model in Gluon requires users to write the training loop. This is useful because of its imperative nature, however repeating the same code across multiple models can become tedious and repetitive with boilerplate code. 

From e6e2e2ecc76af4a7e8a35410679fd7740369eeca Mon Sep 17 00:00:00 2001
From: Serge Panev <spanev@nvidia.com>
Date: Wed, 2 Oct 2019 04:22:31 +0200
Subject: [PATCH 397/813] Fix code block formatting in Why MXNet doc page
 (#16334)

Signed-off-by: Serge Panev <spanev@nvidia.com>
---
 docs/static_site/src/pages/api/faq/why_mxnet.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/static_site/src/pages/api/faq/why_mxnet.md b/docs/static_site/src/pages/api/faq/why_mxnet.md
index 79a54f651b1b..8bc88d59f09c 100644
--- a/docs/static_site/src/pages/api/faq/why_mxnet.md
+++ b/docs/static_site/src/pages/api/faq/why_mxnet.md
@@ -133,6 +133,7 @@ In either case, the dataset typically must be managed by the CPU.
 To compute the transformation of a neural network quickly, we need both the parameters and data points to make it into GPU memory. For any example _X_, the parameters _W_ are the same. Moreover the size of the model tends to dwarf the size of an individual example. So we might arrive at the natural insight that parameters should always live on the GPU, even if the dataset itself must live on the CPU or stream in. This prevents IO from becoming the bottleneck during training or inference.
 
 Fortunately, _MXNet_ makes this kind of assignment easy.
+
 ```python
 import mxnet.ndarray as nd
 

From 480b50c245d5fbfcc84ae3992a098952fe786acd Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Wed, 2 Oct 2019 13:00:42 -0700
Subject: [PATCH 398/813] S3 upload artifacts (#16336)

* upload to s3 if branch master

* Trigger notification

* catch exception

* remove incorrect echo, remove check for master branch, add branchname to directory,rethrow exception

* update directory structure
---
 ci/Jenkinsfile_utils.groovy | 14 +++++++++++++-
 1 file changed, 13 insertions(+), 1 deletion(-)

diff --git a/ci/Jenkinsfile_utils.groovy b/ci/Jenkinsfile_utils.groovy
index 9f626a6cb83e..b5aa862c1266 100644
--- a/ci/Jenkinsfile_utils.groovy
+++ b/ci/Jenkinsfile_utils.groovy
@@ -134,6 +134,12 @@ def collect_test_results_unix(original_file_name, new_file_name) {
         // Thus, we have to pick a name manually and rename the files so that they can be stored separately.
         sh 'cp ' + original_file_name + ' ' + new_file_name
         archiveArtifacts artifacts: new_file_name
+        try {
+          s3Upload(file:new_file_name, bucket:env.MXNET_CI_UNITTEST_ARTIFACT_BUCKET, path:env.JOB_NAME+"/"+env.BRANCH_NAME+"/"+env.BUILD_NUMBER+"/"+new_file_name)
+        } catch (Exception e) {
+          echo "S3 Upload failed ${e}"
+          throw new Exception("S3 upload failed", e)
+        }
     }
 }
 
@@ -143,6 +149,12 @@ def collect_test_results_windows(original_file_name, new_file_name) {
     if (fileExists(original_file_name)) {
         bat 'xcopy ' + original_file_name + ' ' + new_file_name + '*'
         archiveArtifacts artifacts: new_file_name
+        try {
+          s3Upload(file:new_file_name, bucket:env.MXNET_CI_UNITTEST_ARTIFACT_BUCKET, path:env.JOB_NAME+"/"+env.BRANCH_NAME+"/"+env.BUILD_NUMBER+"/"+new_file_name)
+        } catch (Exception e) {
+          echo "S3 Upload failed ${e}"
+          throw new Exception("S3 upload failed", e)
+        }
     }
 }
 
@@ -267,7 +279,7 @@ def main_wrapper(args) {
     update_github_commit_status('SUCCESS', 'Job succeeded')
   } catch (caughtError) {
     node(NODE_UTILITY) {
-      sh "echo caught ${caughtError}"
+      echo "caught ${caughtError}"
       err = caughtError
       currentBuild.result = "FAILURE"
       update_github_commit_status('FAILURE', 'Job failed')

From 8136d49843d19e9e20843e7307292f8f6b0e3ab9 Mon Sep 17 00:00:00 2001
From: Caenorst <cfujitsang@nvidia.com>
Date: Wed, 2 Oct 2019 17:47:59 -0400
Subject: [PATCH 399/813] fix atol for test_preloaded_multi_sgd (#16356)

---
 tests/python/gpu/test_operator_gpu.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/python/gpu/test_operator_gpu.py b/tests/python/gpu/test_operator_gpu.py
index 7aac23acd549..0f1cd93755c3 100644
--- a/tests/python/gpu/test_operator_gpu.py
+++ b/tests/python/gpu/test_operator_gpu.py
@@ -396,7 +396,7 @@ def _assert_all_almost_equal(lhs_list, rhs_list, rtol, atol):
             assert_almost_equal(lhs.asnumpy(), rhs.asnumpy(), rtol=rtol, atol=atol)
     if dtype == 'float16':
         rtol = 1e-3
-        atol = 1e-3
+        atol = 1e-2
     else:
         rtol = 1e-5
         atol = 1e-6

From 3244a7ac741e5046f91f20062109e03772a7650c Mon Sep 17 00:00:00 2001
From: Apua <apua@users.noreply.github.com>
Date: Thu, 3 Oct 2019 19:24:55 +0800
Subject: [PATCH 400/813] Julia: add API docs back (#16363)

`ndarray` and `symbolic-node` have been refactored by splitting into a
few files in commits ed8307121 and 36a3cb828, but the corresponding
document setting for MXNet.jl is not updated yet.

List all split source files into the `at_autodoc` setting.

Also refer to the `at_autodoc` setting in `julia/docs/src/api/optimizer.md`
---
 julia/docs/src/api/ndarray.md       | 18 +++++++++++++++++-
 julia/docs/src/api/symbolic-node.md | 11 ++++++++++-
 2 files changed, 27 insertions(+), 2 deletions(-)

diff --git a/julia/docs/src/api/ndarray.md b/julia/docs/src/api/ndarray.md
index 8cc4948e4dde..64f59dc5393e 100644
--- a/julia/docs/src/api/ndarray.md
+++ b/julia/docs/src/api/ndarray.md
@@ -70,5 +70,21 @@ In the following example `y` can be a `Real` value or another `NDArray`
 
 ```@autodocs
 Modules = [MXNet.mx]
-Pages = ["ndarray.jl"]
+Pages = [
+  "ndarray.jl",
+  "ndarray/activation.jl",
+  "ndarray/arithmetic.jl",
+  "ndarray/array.jl",
+  "ndarray/autoimport.jl",
+  "ndarray/comparison.jl",
+  "ndarray/context.jl",
+  "ndarray/io.jl",
+  "ndarray/linalg.jl",
+  "ndarray/reduction.jl",
+  "ndarray/remap.jl",
+  "ndarray/show.jl",
+  "ndarray/statistic.jl",
+  "ndarray/trig.jl",
+  "ndarray/type.jl",
+]
 ```
diff --git a/julia/docs/src/api/symbolic-node.md b/julia/docs/src/api/symbolic-node.md
index b4b1c0167e5e..0efe4605c414 100644
--- a/julia/docs/src/api/symbolic-node.md
+++ b/julia/docs/src/api/symbolic-node.md
@@ -19,5 +19,14 @@
 
 ```@autodocs
 Modules = [MXNet.mx]
-Pages = ["symbolic-node.jl"]
+Pages = [
+  "symbolic-node.jl",
+  "symbolic-node/arithmetic.jl",
+  "symbolic-node/array.jl",
+  "symbolic-node/autodiff.jl",
+  "symbolic-node/io.jl",
+  "symbolic-node/op.jl",
+  "symbolic-node/show.jl",
+  "symbolic-node/type.jl",
+]
 ```

From b6f32351aeefa005491f6691595e4c5638bfee88 Mon Sep 17 00:00:00 2001
From: Zach Kimberg <kimbergz@amazon.com>
Date: Thu, 3 Oct 2019 08:07:47 -0700
Subject: [PATCH 401/813] Fix nightly scala pipeline (#16362)

---
 ci/docker/Dockerfile.publish.ubuntu1604_cpu | 2 ++
 ci/docker/Dockerfile.publish.ubuntu1604_gpu | 2 ++
 2 files changed, 4 insertions(+)

diff --git a/ci/docker/Dockerfile.publish.ubuntu1604_cpu b/ci/docker/Dockerfile.publish.ubuntu1604_cpu
index df284be2b37e..e5898b66c161 100644
--- a/ci/docker/Dockerfile.publish.ubuntu1604_cpu
+++ b/ci/docker/Dockerfile.publish.ubuntu1604_cpu
@@ -26,9 +26,11 @@ COPY install/ubuntu_base.sh /work/
 RUN /work/ubuntu_base.sh
 
 COPY install/ubuntu_python.sh /work/
+COPY install/requirements /work/
 RUN /work/ubuntu_python.sh
 
 COPY install/ubuntu_scala.sh /work/
+COPY install/sbt.gpg /work/
 RUN /work/ubuntu_scala.sh
 
 ARG USER_ID=0
diff --git a/ci/docker/Dockerfile.publish.ubuntu1604_gpu b/ci/docker/Dockerfile.publish.ubuntu1604_gpu
index 2a1f8594e752..0bd8b8259b90 100644
--- a/ci/docker/Dockerfile.publish.ubuntu1604_gpu
+++ b/ci/docker/Dockerfile.publish.ubuntu1604_gpu
@@ -26,9 +26,11 @@ COPY install/ubuntu_base.sh /work/
 RUN /work/ubuntu_base.sh
 
 COPY install/ubuntu_python.sh /work/
+COPY install/requirements /work/
 RUN /work/ubuntu_python.sh
 
 COPY install/ubuntu_scala.sh /work/
+COPY install/sbt.gpg /work/
 RUN /work/ubuntu_scala.sh
 
 ARG USER_ID=0

From 09ae7dfe9cb559dd6fa4996d491ee125e3e0b9e7 Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Thu, 3 Oct 2019 16:21:08 -0700
Subject: [PATCH 402/813] remove redundant branch name (#16372)

---
 ci/Jenkinsfile_utils.groovy | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ci/Jenkinsfile_utils.groovy b/ci/Jenkinsfile_utils.groovy
index b5aa862c1266..bf6c2d740b14 100644
--- a/ci/Jenkinsfile_utils.groovy
+++ b/ci/Jenkinsfile_utils.groovy
@@ -135,7 +135,7 @@ def collect_test_results_unix(original_file_name, new_file_name) {
         sh 'cp ' + original_file_name + ' ' + new_file_name
         archiveArtifacts artifacts: new_file_name
         try {
-          s3Upload(file:new_file_name, bucket:env.MXNET_CI_UNITTEST_ARTIFACT_BUCKET, path:env.JOB_NAME+"/"+env.BRANCH_NAME+"/"+env.BUILD_NUMBER+"/"+new_file_name)
+          s3Upload(file:new_file_name, bucket:env.MXNET_CI_UNITTEST_ARTIFACT_BUCKET, path:env.JOB_NAME+"/"+env.BUILD_NUMBER+"/"+new_file_name)
         } catch (Exception e) {
           echo "S3 Upload failed ${e}"
           throw new Exception("S3 upload failed", e)
@@ -150,7 +150,7 @@ def collect_test_results_windows(original_file_name, new_file_name) {
         bat 'xcopy ' + original_file_name + ' ' + new_file_name + '*'
         archiveArtifacts artifacts: new_file_name
         try {
-          s3Upload(file:new_file_name, bucket:env.MXNET_CI_UNITTEST_ARTIFACT_BUCKET, path:env.JOB_NAME+"/"+env.BRANCH_NAME+"/"+env.BUILD_NUMBER+"/"+new_file_name)
+          s3Upload(file:new_file_name, bucket:env.MXNET_CI_UNITTEST_ARTIFACT_BUCKET, path:env.JOB_NAME+"/"+env.BUILD_NUMBER+"/"+new_file_name)
         } catch (Exception e) {
           echo "S3 Upload failed ${e}"
           throw new Exception("S3 upload failed", e)

From 626fc32e3b2eaefbb170de656d2587857d8c936e Mon Sep 17 00:00:00 2001
From: Jake Lee <gstu1130@gmail.com>
Date: Thu, 3 Oct 2019 19:21:50 -0700
Subject: [PATCH 403/813] Disable Pylint false error in numpy_op_signature 
 (#16370)

* hot fix to avoid pylint error

* retrigger CI
---
 python/mxnet/numpy_op_signature.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/python/mxnet/numpy_op_signature.py b/python/mxnet/numpy_op_signature.py
index e42ba264b37e..cb56f670ed50 100644
--- a/python/mxnet/numpy_op_signature.py
+++ b/python/mxnet/numpy_op_signature.py
@@ -17,8 +17,8 @@
 
 """Make builtin ops' signatures compatible with NumPy."""
 
-from __future__ import absolute_import
-import sys
+from __future__ import absolute_import # pylint: disable=reimported
+from sys import version, version_info
 import warnings
 from . import _numpy_op_doc
 from . import numpy as mx_np
@@ -55,11 +55,11 @@ def _get_builtin_op(op_name):
 
 
 def _register_op_signatures():
-    if sys.version_info.major < 3 or sys.version_info.minor < 5:
+    if version_info.major < 3 or version_info.minor < 5:
         warnings.warn('Some mxnet.numpy operator signatures may not be displayed consistently with '
                       'their counterparts in the official NumPy package due to too-low Python '
                       'version {}. Python >= 3.5 is required to make the signatures display correctly.'
-                      .format(str(sys.version)))
+                      .format(str(version)))
         return
 
     import inspect

From 916fbf23f82b422bc22c5813d02f03d4657de220 Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Thu, 3 Oct 2019 22:47:22 -0700
Subject: [PATCH 404/813] boolean_mask_assign operator for future boolean
 indexing (#16361)

---
 src/operator/numpy/np_boolean_mask_assign.cc | 270 +++++++++++++++++++
 src/operator/numpy/np_boolean_mask_assign.cu | 229 ++++++++++++++++
 src/operator/numpy/np_broadcast_reduce_op.h  |   9 +
 tests/python/unittest/test_numpy_op.py       |  36 +++
 4 files changed, 544 insertions(+)
 create mode 100644 src/operator/numpy/np_boolean_mask_assign.cc
 create mode 100644 src/operator/numpy/np_boolean_mask_assign.cu

diff --git a/src/operator/numpy/np_boolean_mask_assign.cc b/src/operator/numpy/np_boolean_mask_assign.cc
new file mode 100644
index 000000000000..2a5ae116e291
--- /dev/null
+++ b/src/operator/numpy/np_boolean_mask_assign.cc
@@ -0,0 +1,270 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file np_boolean_assign.cc
+ * \brief CPU implementation of Boolean Mask Assign
+ */
+
+#include "../contrib/boolean_mask-inl.h"
+
+namespace mxnet {
+namespace op {
+
+template<bool scalar = false>
+struct BooleanAssignCPUKernel {
+ private:
+  static size_t bin_search(const size_t* idx,
+                           const size_t idx_size,
+                           const size_t i) {
+    size_t left = 0, right = idx_size, mid = (left + right) / 2;
+    while (left != right) {
+      if (idx[mid] == i + 1) {
+        if (idx[mid - 1] == i) {
+          mid -= 1;
+          break;
+        } else if (idx[mid - 1] == i + 1) {
+          right = mid;
+          mid = (left + right) / 2;
+        }
+      } else if (idx[mid] == i) {
+        if (idx[mid + 1] == i + 1) {
+          break;
+        } else {
+          left = mid;
+          mid = (left + right + 1) / 2;
+        }
+      } else if (idx[mid] < i + 1) {
+        left = mid;
+        mid = (left + right + 1) / 2;
+      } else if (idx[mid] > i + 1) {
+        right = mid;
+        mid = (left + right) / 2;
+      }
+    }
+    return mid;
+  }
+
+ public:
+  template<typename DType>
+  static void Map(int i,
+                  DType* data,
+                  const size_t* idx,
+                  const size_t idx_size,
+                  const size_t leading,
+                  const size_t middle,
+                  const size_t trailing,
+                  const DType val) {
+    // binary search for the turning point
+    size_t mid = bin_search(idx, idx_size, i);
+    // final answer is in mid
+    for (size_t l = 0; l < leading; ++l) {
+      for (size_t t = 0; t < trailing; ++t) {
+        data[(l * middle + mid) * trailing + t] = val;
+      }
+    }
+  }
+
+  template<typename DType>
+  static void Map(int i,
+                  DType* data,
+                  const size_t* idx,
+                  const size_t idx_size,
+                  const size_t leading,
+                  const size_t middle,
+                  const size_t trailing,
+                  DType* tensor) {
+    // binary search for the turning point
+    size_t mid = bin_search(idx, idx_size, i);
+    // final answer is in mid
+    for (size_t l = 0; l < leading; ++l) {
+      for (size_t t = 0; t < trailing; ++t) {
+        data[(l * middle + mid) * trailing + t] = (scalar) ? tensor[0] : tensor[i];
+      }
+    }
+  }
+};
+
+bool BooleanAssignShape(const nnvm::NodeAttrs& attrs,
+                        mxnet::ShapeVector *in_attrs,
+                        mxnet::ShapeVector *out_attrs) {
+  CHECK(in_attrs->size() == 2U || in_attrs->size() == 3U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  const TShape& dshape = in_attrs->at(0);
+
+  // mask should have the same shape as the input
+  SHAPE_ASSIGN_CHECK(*in_attrs, 1, dshape);
+
+  // check if output shape is the same as the input data
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, dshape);
+
+  // for tensor version, the tensor should have less than 1 dimension
+  if (in_attrs->size() == 3U) {
+    CHECK_LE(in_attrs->at(2).ndim(), 1U)
+      << "boolean array indexing assignment requires a 0 or 1-dimensional input, input has "
+      << in_attrs->at(2).ndim() <<" dimensions";
+  }
+
+  return shape_is_known(out_attrs->at(0));
+}
+
+bool BooleanAssignType(const nnvm::NodeAttrs& attrs,
+                       std::vector<int> *in_attrs,
+                       std::vector<int> *out_attrs) {
+  CHECK(in_attrs->size() == 2U || in_attrs->size() == 3U);
+  CHECK_EQ(out_attrs->size(), 1U);
+
+  // input and output should always have the same type
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
+  TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
+
+  if (in_attrs->size() == 3U) {
+    // if tensor version, the tensor should also have the same type as input
+    TYPE_ASSIGN_CHECK(*in_attrs, 2, in_attrs->at(0));
+    TYPE_ASSIGN_CHECK(*in_attrs, 0, in_attrs->at(2));
+    CHECK_NE(in_attrs->at(2), -1);
+  }
+
+  return out_attrs->at(0) != -1 && in_attrs->at(0) != -1 && in_attrs->at(1) != -1;
+}
+
+// calculate the number of valid (masked) values, also completing the prefix_sum vector
+template<typename DType>
+size_t GetValidNumCPU(const DType* idx, size_t* prefix_sum, const size_t idx_size) {
+  prefix_sum[0] = 0;
+  for (size_t i = 0; i < idx_size; i++) {
+    prefix_sum[i + 1] = prefix_sum[i] + ((idx[i]) ? 1 : 0);
+  }
+  return prefix_sum[idx_size];
+}
+
+void NumpyBooleanAssignForwardCPU(const nnvm::NodeAttrs& attrs,
+                                  const OpContext &ctx,
+                                  const std::vector<TBlob> &inputs,
+                                  const std::vector<OpReqType> &req,
+                                  const std::vector<TBlob> &outputs) {
+  using namespace mshadow;
+  using namespace mxnet_op;
+  CHECK(inputs.size() == 2U || inputs.size() == 3U);
+  CHECK_EQ(outputs.size(), 1U);
+  CHECK_EQ(req.size(), 1U);
+  CHECK_EQ(req[0], kWriteInplace)
+    << "Only WriteInplace is supported for npi_boolean_assign";
+
+  Stream<cpu>* s = ctx.get_stream<cpu>();
+
+  const TBlob& data = inputs[0];
+  const TBlob& mask = inputs[1];
+  // Get valid_num
+  size_t valid_num = 0;
+  size_t mask_size = mask.shape_.Size();
+  std::vector<size_t> prefix_sum(mask_size + 1, 0);
+  MSHADOW_TYPE_SWITCH(mask.type_flag_, MType, {
+    valid_num = GetValidNumCPU(mask.dptr<MType>(), prefix_sum.data(), mask_size);
+  });
+  // If there's no True in mask, return directly
+  if (valid_num == 0) return;
+
+  if (inputs.size() == 3U) {
+    if (inputs[2].shape_.Size() != 1) {
+      // tensor case, check tensor size with the valid_num
+      CHECK_EQ(static_cast<size_t>(valid_num), inputs[2].shape_.Size())
+        << "boolean array indexing assignment cannot assign " << inputs[2].shape_.Size()
+        << " input values to the " << valid_num << " output values where the mask is true"
+        << std::endl;
+    }
+  }
+
+  size_t leading = 1U;
+  size_t middle = mask_size;
+  size_t trailing = 1U;
+
+  if (inputs.size() == 3U) {
+    MSHADOW_TYPE_SWITCH(data.type_flag_, DType, {
+      if (inputs[2].shape_.Size() == 1) {
+        Kernel<BooleanAssignCPUKernel<true>, cpu>::Launch(
+          s, valid_num, data.dptr<DType>(), prefix_sum.data(), prefix_sum.size(),
+          leading, middle, trailing, inputs[2].dptr<DType>());
+      } else {
+       Kernel<BooleanAssignCPUKernel<false>, cpu>::Launch(
+          s, valid_num, data.dptr<DType>(), prefix_sum.data(), prefix_sum.size(),
+          leading, middle, trailing, inputs[2].dptr<DType>());
+      }
+    });
+  } else {
+    CHECK(attrs.dict.find("value") != attrs.dict.end())
+      << "value needs be provided";
+    MSHADOW_TYPE_SWITCH(data.type_flag_, DType, {
+      Kernel<BooleanAssignCPUKernel<true>, cpu>::Launch(
+        s, valid_num, data.dptr<DType>(), prefix_sum.data(), prefix_sum.size(),
+        leading, middle, trailing, static_cast<DType>(std::stod(attrs.dict.at("value"))));
+    });
+  }
+}
+
+NNVM_REGISTER_OP(_npi_boolean_mask_assign_scalar)
+.describe(R"code(Scalar version of boolean assign)code" ADD_FILELINE)
+.set_num_inputs(2)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"data", "mask"};
+})
+.set_attr<mxnet::FInferShape>("FInferShape", BooleanAssignShape)
+.set_attr<nnvm::FInferType>("FInferType", BooleanAssignType)
+.set_attr<FCompute>("FCompute<cpu>", NumpyBooleanAssignForwardCPU)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::pair<int, int> >{{0, 0}};
+  })
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.add_argument("data", "NDArray-or-Symbol", "input")
+.add_argument("mask", "NDArray-or-Symbol", "mask")
+.add_argument("value", "float", "value to be assigned to masked positions");
+
+NNVM_REGISTER_OP(_npi_boolean_mask_assign_tensor)
+.describe(R"code(Tensor version of boolean assign)code" ADD_FILELINE)
+.set_num_inputs(3)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"data", "mask", "value"};
+})
+.set_attr<mxnet::FInferShape>("FInferShape", BooleanAssignShape)
+.set_attr<nnvm::FInferType>("FInferType", BooleanAssignType)
+.set_attr<FCompute>("FCompute<cpu>", NumpyBooleanAssignForwardCPU)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::pair<int, int> >{{0, 0}};
+  })
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.add_argument("data", "NDArray-or-Symbol", "input")
+.add_argument("mask", "NDArray-or-Symbol", "mask")
+.add_argument("value", "NDArray-or-Symbol", "assignment");
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_boolean_mask_assign.cu b/src/operator/numpy/np_boolean_mask_assign.cu
new file mode 100644
index 000000000000..e3b0330d79dd
--- /dev/null
+++ b/src/operator/numpy/np_boolean_mask_assign.cu
@@ -0,0 +1,229 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file np_boolean_mask_assign.cu
+ * \brief GPU implementation of Boolean Mask Assign
+ */
+
+#include <cub/cub.cuh>
+#include "../contrib/boolean_mask-inl.h"
+
+namespace mxnet {
+namespace op {
+
+template<bool scalar>
+struct BooleanAssignGPUKernel {
+ private:
+  static size_t __device__ bin_search(const size_t* idx,
+                                      const size_t idx_size,
+                                      const size_t i) {
+    size_t left = 0, right = idx_size, mid = (left + right) / 2;
+    while (left != right) {
+      if (idx[mid] == i + 1) {
+        if (idx[mid - 1] == i) {
+          mid -= 1;
+          break;
+        } else if (idx[mid - 1] == i + 1) {
+          right = mid;
+          mid = (left + right) / 2;
+        }
+      } else if (idx[mid] == i) {
+        if (idx[mid + 1] == i + 1) {
+          break;
+        } else {
+          left = mid;
+          mid = (left + right + 1) / 2;
+        }
+      } else if (idx[mid] < i + 1) {
+        left = mid;
+        mid = (left + right + 1) / 2;
+      } else if (idx[mid] > i + 1) {
+        right = mid;
+        mid = (left + right) / 2;
+      }
+    }
+    return mid;
+  }
+
+ public:
+  template<typename DType>
+  static void __device__ Map(int i,
+                             DType* data,
+                             const size_t* idx,
+                             const size_t idx_size,
+                             const size_t leading,
+                             const size_t middle,
+                             const size_t trailing,
+                             const DType val) {
+    // binary search for the turning point
+    size_t m = i / trailing % middle;
+    size_t mid = bin_search(idx, idx_size, m);
+    // final answer is in mid
+    data[i + (mid - m) * trailing] = val;
+  }
+
+  template<typename DType>
+  static void __device__ Map(int i,
+                             DType* data,
+                             const size_t* idx,
+                             const size_t idx_size,
+                             const size_t leading,
+                             const size_t middle,
+                             const size_t trailing,
+                             DType* tensor) {
+    // binary search for the turning point
+    size_t m = i / trailing % middle;
+    size_t mid = bin_search(idx, idx_size, m);
+    // final answer is in mid
+    data[i + (mid - m) * trailing] = (scalar) ? tensor[0] : tensor[m];
+  }
+};
+
+struct NonZeroWithCast {
+  template<typename OType, typename IType>
+  static void __device__ Map(int i, OType* out, const IType* in) {
+    out[i] = (in[i]) ? OType(1) : OType(0);
+  }
+};
+
+// completing the prefix_sum vector and return the pointer to it
+template<typename DType>
+size_t* GetValidNumGPU(const OpContext &ctx, const DType *idx, const size_t idx_size) {
+  using namespace mshadow;
+  using namespace mxnet_op;
+  using namespace mshadow_op;
+  size_t* prefix_sum = nullptr;
+  void* d_temp_storage = nullptr;
+  size_t temp_storage_bytes = 0;
+  Stream<gpu>* s = ctx.get_stream<gpu>();
+
+  // Calculate total temporary memory size
+  cub::DeviceScan::ExclusiveSum(d_temp_storage,
+                                temp_storage_bytes,
+                                prefix_sum,
+                                prefix_sum,
+                                idx_size + 1,
+                                Stream<gpu>::GetStream(s));
+  size_t buffer_size = (idx_size + 1) * sizeof(size_t);
+  temp_storage_bytes += buffer_size;
+  // Allocate memory on GPU and allocate pointer
+  Tensor<gpu, 1, char> workspace =
+    ctx.requested[0].get_space_typed<gpu, 1, char>(Shape1(temp_storage_bytes), s);
+  prefix_sum = reinterpret_cast<size_t*>(workspace.dptr_);
+  d_temp_storage = workspace.dptr_ + buffer_size;
+
+  // Robustly set the bool values in mask
+  // TODO(haojin2): Get a more efficient way to preset the buffer
+  Kernel<set_zero, gpu>::Launch(s, idx_size + 1, prefix_sum);
+  if (!std::is_same<DType, bool>::value) {
+    Kernel<NonZeroWithCast, gpu>::Launch(s, idx_size, prefix_sum, idx);
+  } else {
+    Kernel<identity_with_cast, gpu>::Launch(s, idx_size, prefix_sum, idx);
+  }
+
+  // Calculate prefix sum
+  cub::DeviceScan::ExclusiveSum(d_temp_storage,
+                                temp_storage_bytes,
+                                prefix_sum,
+                                prefix_sum,
+                                idx_size + 1,
+                                Stream<gpu>::GetStream(s));
+
+  return prefix_sum;
+}
+
+void NumpyBooleanAssignForwardGPU(const nnvm::NodeAttrs& attrs,
+                                  const OpContext &ctx,
+                                  const std::vector<TBlob> &inputs,
+                                  const std::vector<OpReqType> &req,
+                                  const std::vector<TBlob> &outputs) {
+  using namespace mshadow;
+  using namespace mxnet_op;
+  CHECK(inputs.size() == 2U || inputs.size() == 3U);
+  CHECK_EQ(outputs.size(), 1U);
+  CHECK_EQ(req.size(), 1U);
+  CHECK_EQ(req[0], kWriteInplace)
+    << "Only WriteInplace is supported for npi_boolean_assign";
+
+  Stream<gpu>* s = ctx.get_stream<gpu>();
+
+  const TBlob& data = inputs[0];
+  const TBlob& mask = inputs[1];
+  // Get valid_num
+  size_t mask_size = mask.shape_.Size();
+  size_t valid_num = 0;
+  size_t* prefix_sum = nullptr;
+  if (mask_size != 0) {
+    MSHADOW_TYPE_SWITCH(mask.type_flag_, MType, {
+      prefix_sum = GetValidNumGPU<MType>(ctx, mask.dptr<MType>(), mask_size);
+    });
+    CUDA_CALL(cudaMemcpy(&valid_num, &prefix_sum[mask_size], sizeof(size_t),
+                        cudaMemcpyDeviceToHost));
+  }
+  // If there's no True in mask, return directly
+  if (valid_num == 0) return;
+
+  if (inputs.size() == 3U) {
+    if (inputs[2].shape_.Size() != 1) {
+      // tensor case, check tensor size with the valid_num
+      CHECK_EQ(static_cast<size_t>(valid_num), inputs[2].shape_.Size())
+        << "boolean array indexing assignment cannot assign " << inputs[2].shape_.Size()
+        << " input values to the " << valid_num << " output values where the mask is true"
+        << std::endl;
+    }
+  }
+
+  size_t leading = 1U;
+  size_t middle = mask_size;
+  size_t trailing = 1U;
+
+  if (inputs.size() == 3U) {
+    if (inputs[2].shape_.Size() == 1) {
+      MSHADOW_TYPE_SWITCH(data.type_flag_, DType, {
+        Kernel<BooleanAssignGPUKernel<true>, gpu>::Launch(
+          s, leading * valid_num * trailing, data.dptr<DType>(), prefix_sum, mask_size + 1,
+          leading, middle, trailing, inputs[2].dptr<DType>());
+      });
+    } else {
+      MSHADOW_TYPE_SWITCH(data.type_flag_, DType, {
+        Kernel<BooleanAssignGPUKernel<false>, gpu>::Launch(
+          s, leading * valid_num * trailing, data.dptr<DType>(), prefix_sum, mask_size + 1,
+          leading, middle, trailing, inputs[2].dptr<DType>());
+      });
+    }
+  } else {
+    CHECK(attrs.dict.find("value") != attrs.dict.end())
+      << "value is not provided";
+    MSHADOW_TYPE_SWITCH(data.type_flag_, DType, {
+      Kernel<BooleanAssignGPUKernel<true>, gpu>::Launch(
+        s, leading * valid_num * trailing, data.dptr<DType>(), prefix_sum, mask_size + 1,
+        leading, middle, trailing, static_cast<DType>(std::stod(attrs.dict.at("value"))));
+    });
+  }
+}
+
+NNVM_REGISTER_OP(_npi_boolean_mask_assign_scalar)
+.set_attr<FCompute>("FCompute<gpu>", NumpyBooleanAssignForwardGPU);
+
+NNVM_REGISTER_OP(_npi_boolean_mask_assign_tensor)
+.set_attr<FCompute>("FCompute<gpu>", NumpyBooleanAssignForwardGPU);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_broadcast_reduce_op.h b/src/operator/numpy/np_broadcast_reduce_op.h
index 4734d6cc666f..b1348de72de5 100644
--- a/src/operator/numpy/np_broadcast_reduce_op.h
+++ b/src/operator/numpy/np_broadcast_reduce_op.h
@@ -221,6 +221,15 @@ void NumpyReduceAxesCompute(const nnvm::NodeAttrs& attrs,
   if (param.initial.has_value()) {
     LOG(FATAL) << "initial is not supported yet";
   }
+  if (inputs[0].shape_.Size() == 0) {
+    using namespace mxnet_op;
+    using namespace mshadow;
+    Stream<xpu>* s = ctx.get_stream<xpu>();
+    MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+      Kernel<set_zero, xpu>::Launch(s, outputs[0].shape_.Size(), outputs[0].dptr<DType>());
+    });
+    return;
+  }
   if (param.axis.has_value() && param.axis.value().ndim() == 0) {
     UnaryOp::IdentityCompute<xpu>(attrs, ctx, inputs, req, outputs);
   }
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index eaf3032d526d..224125eb64f3 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -647,6 +647,42 @@ def hybrid_forward(self, F, a):
             assert same(a.grad.asnumpy(), expected_grad)
 
 
+@with_seed()
+@use_np
+def test_npi_boolean_assign():
+    class TestBooleanAssignScalar(HybridBlock):
+        def __init__(self, val):
+            super(TestBooleanAssignScalar, self).__init__()
+            self._val = val
+
+        def hybrid_forward(self, F, a, mask):
+            return F.np._internal.boolean_mask_assign_scalar(a, mask, self._val, out=a)
+
+    class TestBooleanAssignTensor(HybridBlock):
+        def __init__(self):
+            super(TestBooleanAssignTensor, self).__init__()
+
+        def hybrid_forward(self, F, a, mask, value):
+            return F.np._internal.boolean_mask_assign_tensor(a, mask, value, out=a)
+
+    shapes = [(3, 4), (3, 0), ()]
+    for hybridize in [False]:
+        for shape in shapes:
+            test_data = np.random.uniform(size=shape)
+            mx_mask = np.around(np.random.uniform(size=shape))
+            valid_num = int(mx_mask.sum())
+            np_mask = mx_mask.asnumpy().astype(_np.bool)
+            for val in [42., np.array(42.), np.array([42.]), np.random.uniform(size=(valid_num,))]:
+                test_block = TestBooleanAssignScalar(val) if isinstance(val, float) else TestBooleanAssignTensor()
+                if hybridize:
+                    test_block.hybridize()
+                np_data = test_data.asnumpy()
+                mx_data = test_data.copy()
+                np_data[np_mask] = val
+                mx_data = test_block(mx_data, mx_mask) if isinstance(val, float) else test_block(mx_data, mx_mask, val)
+                assert_almost_equal(mx_data.asnumpy(), np_data, rtol=1e-3, atol=1e-5, use_broadcast=False)
+
+
 @with_seed()
 @use_np
 def test_np_reshape():

From 80964213d51d7fbf253f638ed643c673c7b91b11 Mon Sep 17 00:00:00 2001
From: MoisesHer <50716238+MoisesHer@users.noreply.github.com>
Date: Sat, 5 Oct 2019 15:59:36 -0700
Subject: [PATCH 405/813] Embedding gradient performance optimization on GPU
 (#16355)

* Add Embedding backward Op for GPU

* Add some code documentation

* Use unnamed namespace for integer log2 function

* Fix lint issues

* Fix one more lint problem

* Remove unnecessary conditions ops

* Fix one more lint problem
---
 src/operator/tensor/indexing_op.cu | 233 +++++++++++++++++++++++++++++
 1 file changed, 233 insertions(+)

diff --git a/src/operator/tensor/indexing_op.cu b/src/operator/tensor/indexing_op.cu
index 77d85d8e1e10..0b4c20bf2bb5 100644
--- a/src/operator/tensor/indexing_op.cu
+++ b/src/operator/tensor/indexing_op.cu
@@ -545,6 +545,239 @@ void TakeOpForward<gpu>(const nnvm::NodeAttrs& attrs,
   });
 }
 
+namespace {
+  /*
+  * \brief returns integer log2(a) rounded up
+  */
+  inline int ilog2(unsigned int a) {
+    int k = 1;
+    while (a >>= 1) k++;
+    return k;
+  }
+}
+
+/*
+ * \brief finds the lower and upper-bound positions of each unique element within a sorted input array
+ * \param sorted_data input elements previously sorted
+ * \param bounds output containing all lower-bound followed by all upper-bound positions
+ * \param data_dim total number of elements in the input array
+ * \param vocab_dim maximum number of unique elements
+ */
+template <typename IType>
+__global__ void EmbeddingFindBounds(const IType *sorted_data,
+                                    IType *bounds,
+                                    const index_t data_dim,
+                                    const index_t vocab_dim) {
+  const index_t id = blockIdx.x * blockDim.x + threadIdx.x;
+  if (id >= vocab_dim) return;
+
+  // Binary search to find lower bound: stored at bounds[0..vocab_dim-1]
+  IType lower_bound = 0;
+  IType upper_bound = data_dim - 1;
+  IType mean;
+  while (lower_bound < upper_bound) {
+    mean = (lower_bound + upper_bound) / 2;
+    if (id <= sorted_data[mean])
+      upper_bound = mean;
+    else
+      lower_bound = mean + 1;
+  }
+  bool found_row = (sorted_data[lower_bound] == id);
+  if (!found_row) {
+    bounds[id] = -1;
+    bounds[vocab_dim + id] = -2;
+    return;
+  } else {
+    bounds[id] = lower_bound;
+  }
+
+  // Binary search to find upper bound: stored at bounds[vocab_dim..2*vocab_dim-1]
+  lower_bound = 0;
+  upper_bound = data_dim - 1;
+  while (lower_bound < upper_bound) {
+    mean = (lower_bound + upper_bound + 1) / 2;
+    if (id >= sorted_data[mean])
+      lower_bound = mean;
+    else
+      upper_bound = mean - 1;
+  }
+  bounds[vocab_dim + id] = upper_bound;
+}
+
+/*
+ * \brief kernel to compute gradient of EmbeddingOp
+ * \param grad_in input gradient data
+ * \param original_index reference to the position at original input data for each index
+ * \param index_bounds lower and upper-bounds positions of each unique index
+ * \param grad_out output gradient data
+ * \param embbedding_dim dimension of the dense embedding
+ * \param vocab_dim maximum number of unique indices in the data array: tokens vocabulary size
+ * \param req write/add/null
+ */
+template <typename LType, typename DType, typename IType>
+__global__ void EmbeddingGradKernel(DType *grad_in,
+                                      const IType *original_index,
+                                      const IType *index_bounds,
+                                      const DType *grad_out,
+                                      const index_t embbedding_dim,
+                                      const index_t vocab_dim,
+                                      const int req) {
+  extern __shared__ int sharedmem[];
+  LType* grad_in_row =  reinterpret_cast<LType *>(sharedmem);
+
+  // LType has to be bigger than DType, guarded in the launcher code
+  const int n_val = sizeof(DType) < sizeof(LType) ? sizeof(LType) / sizeof(DType) : 1;
+  const LType *aligned_grad_out = reinterpret_cast<const LType *>(grad_out);
+  LType *aligned_grad_in = reinterpret_cast<LType *>(grad_in);
+  const index_t aligned_emb_dim = embbedding_dim / n_val;
+  DType *my_grad_in_row = reinterpret_cast<DType *>(&grad_in_row[threadIdx.x]);
+  LType Lvalue[1];
+  DType* Dvalues = reinterpret_cast<DType*>(Lvalue);
+
+  IType my_row = blockIdx.x;
+  if (my_row < vocab_dim) {
+    // Read lower and upper bounds for current row
+    IType lower_bound = index_bounds[my_row];
+    IType upper_bound = index_bounds[vocab_dim + my_row];
+    int nOccurrences = upper_bound - lower_bound + 1;
+
+    for (index_t emb_id=threadIdx.x; emb_id < aligned_emb_dim; emb_id += blockDim.x) {
+      // Initialize grad_in
+      if (req == kAddTo) {
+        grad_in_row[threadIdx.x] = aligned_grad_in[my_row * aligned_emb_dim + emb_id];
+      } else {
+        grad_in_row[threadIdx.x] = 0.0;
+      }
+      // Add all rows from grad_out according to indices in data
+      for (index_t data_idx=lower_bound; data_idx < (lower_bound + nOccurrences); ++data_idx) {
+        *Lvalue = aligned_grad_out[original_index[data_idx] * aligned_emb_dim + emb_id];
+        for (index_t val_id = 0; val_id < n_val; val_id++) {
+          my_grad_in_row[val_id] += Dvalues[val_id];
+        }
+      }
+      // Save results
+      aligned_grad_in[my_row * aligned_emb_dim + emb_id] = grad_in_row[threadIdx.x];
+    }
+  }
+}
+
+template<typename gpu, typename IType, typename DType>
+void EmbeddingGradKernelCaller(const OpContext& ctx,
+                                mshadow::Tensor<gpu, 2, DType> grad_in,
+                                const mshadow::Tensor<gpu, 1, IType>& index,
+                                const mshadow::Tensor<gpu, 2, DType> &grad_out,
+                                const std::vector<OpReqType>& req) {
+  using namespace mxnet_op;
+  using namespace mshadow::expr;
+
+  Stream<gpu> *s = ctx.get_stream<gpu>();
+  const index_t data_dim = index.shape_[0];
+  const index_t vocab_dim = grad_in.shape_[0];
+  const index_t embbedding_dim = grad_in.shape_[1];
+
+  // Calculate amount of temporary storage
+  size_t sort_workspace_size = mxnet::op::SortByKeyWorkspaceSize<int, int, gpu>
+    (data_dim);
+  size_t workspace_size = 2 * data_dim * sizeof(int) +
+      2 * vocab_dim * sizeof(int) + sort_workspace_size;
+
+  // Request temporary storage
+  Tensor<gpu, 1, char> workspace =
+    ctx.requested[embedding::kTempSpace].get_space_typed<gpu, 1, char>(
+      Shape1(workspace_size), s);
+
+  // Create tensors
+  size_t pos = 0;
+  Tensor<gpu, 1, int> sorted_data(reinterpret_cast<int*>(&workspace[pos]),
+    Shape1(data_dim), s);
+  pos += data_dim * sizeof(int);
+  // Reference to input data positions for each element of sorted_data
+  Tensor<gpu, 1, int> original_index(reinterpret_cast<int*>(&workspace[pos]),
+    Shape1(data_dim), s);
+  pos += data_dim * sizeof(int);
+  // lower and upper bound positions of each index within sorted_data
+  Tensor<gpu, 1, int> bounds_index(reinterpret_cast<int*>(&workspace[pos]),
+    Shape1(2 * vocab_dim), s);
+  pos += 2 * vocab_dim * sizeof(int);
+  Tensor<gpu, 1, char> Sort_temp_storage(&workspace[pos], Shape1(sort_workspace_size), s);
+
+  // Clip indices [0, vocab_dim-1]
+  Kernel<tcast_clip, gpu>::Launch(s, data_dim, sorted_data.dptr_, index.dptr_,
+    static_cast<int>(vocab_dim));
+
+  Kernel<range_fwd, gpu>::Launch(s, data_dim,
+    1, 0, 1, kWriteTo, original_index.dptr_);
+
+  // Sort indices array
+  int num_bits = ilog2((vocab_dim - 1));
+  mxnet::op::SortByKey(sorted_data, original_index, true, &Sort_temp_storage, 0, num_bits);
+
+  // Find lower & upper bounds of each possible index
+  const int threads_block_bounds = 128;
+  const int nblocks_bounds = (vocab_dim + threads_block_bounds - 1) / threads_block_bounds;
+  EmbeddingFindBounds<<<nblocks_bounds, threads_block_bounds, 0, Stream<gpu>::GetStream(s)>>>(
+                  sorted_data.dptr_, bounds_index.dptr_, data_dim, vocab_dim);
+
+  // Compute Gradient
+  int ltype = mxnet::common::cuda::get_load_type(embbedding_dim * sizeof(DType));
+  MXNET_LOAD_TYPE_SWITCH(ltype, LType, {
+    int nelems_per_thread = sizeof(LType) / sizeof(DType);
+    int threads_block_grad = 32;
+    int maxThreads = 1024;
+    while (threads_block_grad < (embbedding_dim/nelems_per_thread) &&
+          (threads_block_grad < maxThreads))
+      threads_block_grad += 32;
+    size_t required_shared = threads_block_grad * sizeof(LType);
+    dim3 blocks(vocab_dim, 1);
+    EmbeddingGradKernel<LType><<<blocks, threads_block_grad, required_shared,
+                  Stream<gpu>::GetStream(s)>>>(
+                  grad_in.dptr_, original_index.dptr_,
+                  bounds_index.dptr_, grad_out.dptr_,
+                  embbedding_dim, vocab_dim,
+                  req[embedding::kWeight]);
+  });
+}
+
+template<>
+void EmbeddingOpBackward<gpu>(const nnvm::NodeAttrs& attrs,
+                              const OpContext& ctx,
+                              const std::vector<TBlob>& inputs,
+                              const std::vector<OpReqType>& req,
+                              const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mshadow::expr;
+  CHECK_EQ(inputs.size(), 2U);
+  CHECK_EQ(outputs.size(), 2U);
+  CHECK_EQ(req[embedding::kData], kNullOp)
+          << "Embedding layer doesn't support calculate data gradient";
+  if (req[embedding::kWeight] == kNullOp) {
+    return;
+  }
+  CHECK_EQ(outputs[1].type_flag_, inputs[0].type_flag_);
+
+  const mxnet::TShape& ishape = inputs[1].shape_;
+  const mxnet::TShape& oshape = inputs[0].shape_;
+
+  Stream<gpu> *s = ctx.get_stream<gpu>();
+  CHECK_NE(req[embedding::kWeight], kWriteInplace)
+    << "Backward of Embedding does not support writing in place.";
+  MSHADOW_TYPE_SWITCH(outputs[1].type_flag_, DType, {
+    MSHADOW_TYPE_SWITCH(inputs[1].type_flag_, IType, {
+      Tensor < gpu, 1, IType > data = inputs[1].get_with_shape<gpu, 1, IType>(
+        Shape1(ishape.ProdShape(0, ishape.ndim())), s);
+      Tensor<gpu, 2, DType> grad_out = inputs[0].get_with_shape<gpu, 2, DType>(
+      Shape2(oshape.ProdShape(0, oshape.ndim()-1), oshape[oshape.ndim()-1]), s);
+      Tensor<gpu, 2, DType> grad_in = outputs[1].get<gpu, 2, DType>(s);
+
+      if (req[embedding::kWeight] == kWriteTo || req[embedding::kWeight] == kAddTo) {
+        EmbeddingGradKernelCaller(ctx, grad_in, data, grad_out, req);
+      } else {
+        LOG(FATAL) << "wrong req";
+      }
+    });
+  });
+}
+
 NNVM_REGISTER_OP(Embedding)
 .set_attr<FCompute>("FCompute<gpu>", EmbeddingOpForward<gpu>)
 .set_attr<FComputeEx>("FComputeEx<gpu>", SparseEmbeddingOpForwardEx<gpu>);

From 2c81a71e02ab738ea3c16b3b7cd3d7436238a3aa Mon Sep 17 00:00:00 2001
From: Marco de Abreu <marcoabreu@users.noreply.github.com>
Date: Mon, 7 Oct 2019 15:37:15 +0200
Subject: [PATCH 406/813] Change mailing list url in footer to point to
 instructions about how to subscribe instead (#16384)

---
 docs/static_site/src/_includes/footer.html | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/docs/static_site/src/_includes/footer.html b/docs/static_site/src/_includes/footer.html
index f6c63a8d52d2..7e289dc5dd8e 100644
--- a/docs/static_site/src/_includes/footer.html
+++ b/docs/static_site/src/_includes/footer.html
@@ -4,8 +4,7 @@
             <div class="col-4">
                 <h4 class="footer-category-title">Resources</h4>
                 <ul class="contact-list">
-                    <li><a class="u-email" href="mailto:dev@mxnet.apache.org">Dev list</a></li>
-                    <li><a class="u-email" href="mailto:user@mxnet.apache.org">User mailing list</a></li>
+                    <li><a href="{{'community/contribute#mxnet-dev-communications'|relative_url}}">Mailing lists</a></li>
                     <li><a href="https://cwiki.apache.org/confluence/display/MXNET/Apache+MXNet+Home">Developer Wiki</a></li>
                     <li><a href="https://issues.apache.org/jira/projects/MXNET/issues">Jira Tracker</a></li>
                     <li><a href="https://github.com/apache/incubator-mxnet/labels/Roadmap">Github Roadmap</a></li>

From 2127f75b3d5e48e8215eaf6204829505e436983e Mon Sep 17 00:00:00 2001
From: Marco de Abreu <marcoabreu@users.noreply.github.com>
Date: Mon, 7 Oct 2019 15:37:49 +0200
Subject: [PATCH 407/813] Add instructions to report a security vulnerability
 (#16383)

---
 docs/static_site/src/pages/api/faq/security.md | 17 +++++++++++++++++
 1 file changed, 17 insertions(+)

diff --git a/docs/static_site/src/pages/api/faq/security.md b/docs/static_site/src/pages/api/faq/security.md
index ead7fa181d39..54481460348e 100644
--- a/docs/static_site/src/pages/api/faq/security.md
+++ b/docs/static_site/src/pages/api/faq/security.md
@@ -23,6 +23,23 @@ permalink: /api/faq/security
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
 
+# Reporting a security vulnerability
+The Apache Software Foundation takes a very active stance in eliminating security problems and denial of service attacks against its products.
+
+We strongly encourage folks to report such problems to our private security mailing list first, before disclosing them in a public forum.
+
+Please note that the security mailing list should only be used for reporting undisclosed security vulnerabilities and managing the process of fixing such vulnerabilities. We cannot accept regular bug reports or other queries at this address. All mail sent to this address that does not relate to an undisclosed security problem in our source code will be ignored.
+
+
+Questions about:
+* if a vulnerability applies to your particular application
+* obtaining further information on a published vulnerability
+* availability of patches and/or new releases
+should be addressed to the users mailing list. Please see the [mailing lists page](/community/contribute#mxnet-dev-communications) for details of how to subscribe.
+
+The private security mailing address is: <a href="mailto:security@apache.org">security@apache.org</a> <i class="far fa-envelope">. Feel free to consult the general [Apache Security guide](http://www.apache.org/security/) for further details about the reporting process.
+
+
 # MXNet Security Best Practices
 
 MXNet framework has no built-in security protections. It assumes that the MXNet entities involved in model training and inferencing (hosting) are fully trusted. It also assumes that their communications cannot be eavesdropped or tampered with. MXNet consumers shall ensure that the above assumptions are met.

From 09285c8452abb2f7c45f703f8cbf08ec6cecf14b Mon Sep 17 00:00:00 2001
From: ckt624 <ckt624@gmail.com>
Date: Tue, 24 Sep 2019 15:57:21 -0400
Subject: [PATCH 408/813] Implements ldexp. (#15845)

Remove spaces.

Change tests.

Reorganize files.

Change styles.

Add spaces
---
 python/mxnet/ndarray/numpy/_op.py             | 42 ++++++++++++-
 python/mxnet/numpy/multiarray.py              | 40 +++++++++++-
 python/mxnet/symbol/numpy/_symbol.py          | 31 +++++++++-
 src/operator/mshadow_op.h                     | 11 ++++
 .../numpy/np_elemwise_broadcast_op.cc         | 37 +++++++++++
 .../numpy/np_elemwise_broadcast_op.cu         | 19 ++++++
 src/operator/operator_tune.cc                 |  5 ++
 tests/python/unittest/test_numpy_op.py        | 61 +++++++++++++++++++
 8 files changed, 242 insertions(+), 4 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index e8332f1a83ef..18c49dad5fd0 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -35,7 +35,7 @@
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'mean',
            'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
            'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'hypot', 'rad2deg', 'deg2rad',
-           'unique', 'lcm', 'tril', 'identity', 'take']
+           'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -3447,7 +3447,7 @@ def hypot(x1, x2, out=None):
     Notes
     -----
     This function differs from the original numpy.arange in the following aspects:
-	    - Only support float16, float32 and float64.
+        - Only support float16, float32 and float64.
 
     Examples
     --------
@@ -3464,3 +3464,41 @@ def hypot(x1, x2, out=None):
            [ 5.,  5.,  5.]])
     """
     return _ufunc_helper(x1, x2, _npi.hypot, _np.hypot, _npi.hypot_scalar, None, out)
+
+
+@set_module('mxnet.ndarray.numpy')
+def ldexp(x1, x2, out=None):
+    """
+    Returns x1 * 2**x2, element-wise.
+    The mantissas `x1` and twos exponents `x2` are used to construct
+    floating point numbers ``x1 * 2**x2``.
+
+    Parameters
+    ----------
+    x1 : ndarray or scalar
+        Array of multipliers.
+    x2 : ndarray or scalar, int
+        Array of twos exponents.
+    out : ndarray, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not, a freshly-allocated array is returned.
+
+    Returns
+    -------
+    y : ndarray or scalar
+        The result of ``x1 * 2**x2``.
+        This is a scalar if both `x1` and `x2` are scalars.
+
+    Notes
+    -----
+    Complex dtypes are not supported, they will raise a TypeError.
+    Different from numpy, we allow x2 to be float besides int.
+    `ldexp` is useful as the inverse of `frexp`, if used by itself it is
+    more clear to simply use the expression ``x1 * 2**x2``.
+
+    Examples
+    --------
+    >>> np.ldexp(5, np.arange(4))
+    array([  5.,  10.,  20.,  40.])
+    """
+    return _ufunc_helper(x1, x2, _npi.ldexp, _np.ldexp, _npi.ldexp_scalar, _npi.rldexp_scalar, out)
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 7ba0f0d7d813..09840c93a9aa 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -54,7 +54,7 @@
            'tensordot', 'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate',
            'stack', 'vstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices',
            'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'arctan2', 'hypot',
-           'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take']
+           'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -4980,3 +4980,41 @@ def hypot(x1, x2, out=None):
            [ 5.,  5.,  5.]])
     """
     return _mx_nd_np.hypot(x1, x2, out=out)
+
+
+@set_module('mxnet.numpy')
+def ldexp(x1, x2, out=None):
+    """
+    Returns x1 * 2**x2, element-wise.
+    The mantissas `x1` and twos exponents `x2` are used to construct
+    floating point numbers ``x1 * 2**x2``.
+
+    Parameters
+    ----------
+    x1 : ndarray or scalar
+        Array of multipliers.
+    x2 : ndarray or scalar, int
+        Array of twos exponents.
+    out : ndarray, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not, a freshly-allocated array is returned.
+
+    Returns
+    -------
+    y : ndarray or scalar
+        The result of ``x1 * 2**x2``.
+        This is a scalar if both `x1` and `x2` are scalars.
+
+    Notes
+    -----
+    Complex dtypes are not supported, they will raise a TypeError.
+    Different from numpy, we allow x2 to be float besides int.
+    `ldexp` is useful as the inverse of `frexp`, if used by itself it is
+    more clear to simply use the expression ``x1 * 2**x2``.
+
+    Examples
+    --------
+    >>> np.ldexp(5, np.arange(4))
+    array([  5.,  10.,  20.,  40.])
+    """
+    return _mx_nd_np.ldexp(x1, x2, out)
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 9c055c401b31..d2318a71df0f 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -37,7 +37,7 @@
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'mean',
            'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
            'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'hypot', 'rad2deg', 'deg2rad',
-           'unique', 'lcm', 'tril', 'identity', 'take']
+           'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp']
 
 
 def _num_outputs(sym):
@@ -3552,4 +3552,33 @@ def unique(ar, return_index=False, return_inverse=False, return_counts=False, ax
     return _npi.unique(ar, return_index, return_inverse, return_counts, axis)
 
 
+@set_module('mxnet.symbol.numpy')
+def ldexp(x1, x2, out=None):
+    """
+    ldexp(x1, x2, out=None)
+    Returns x1 * 2**x2, element-wise.
+    The mantissas `x1` and twos exponents `x2` are used to construct
+    floating point numbers ``x1 * 2**x2``.
+    Parameters
+    ----------
+    x1 : _Symbol
+        Array of multipliers.
+    x2 : _Symbol
+        Array of twos exponents.
+    out : _Symbol or None
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+    Returns
+    -------
+    y : _Symbol
+        The result of ``x1 * 2**x2``.
+    Notes
+    -----
+    Complex dtypes are not supported, they will raise a TypeError.
+    Different from numpy, we allow x2 to be float besides int.
+    `ldexp` is useful as the inverse of `frexp`, if used by itself it is
+    more clear to simply use the expression ``x1 * 2**x2``.
+    """
+    return _ufunc_helper(x1, x2, _npi.ldexp, _np.ldexp, _npi.ldexp_scalar, _npi.rldexp_scalar, out)
+
+
 _set_np_symbol_class(_Symbol)
diff --git a/src/operator/mshadow_op.h b/src/operator/mshadow_op.h
index 92655c146193..a941f9b25a54 100644
--- a/src/operator/mshadow_op.h
+++ b/src/operator/mshadow_op.h
@@ -368,6 +368,17 @@ MXNET_UNARY_MATH_OP(reciprocal_cube_root, 1.0f / math::cbrt(a));
 
 MXNET_UNARY_MATH_OP(reciprocal_cube_root_grad, -1.0f / (3.0f * math::cbrt(a) * math::id(a)));
 
+/*! \brief used for generate element of ldexp */
+MXNET_BINARY_MATH_OP(ldexp, math::id(a) * math::pow(2.0f, b));
+
+MXNET_BINARY_MATH_OP(ldexp_grad, math::pow(2.0f, b));
+
+MXNET_BINARY_MATH_OP(ldexp_rgrad, math::id(a) * math::pow(2.0f, b) * math::log(2.0f));
+
+MXNET_BINARY_MATH_OP(rldexp, math::id(b) * math::pow(2.0f, a));  // swap a and b if a is scalar.
+
+MXNET_BINARY_MATH_OP(rldexp_grad, math::id(b) * math::pow(2.0f, a) * math::log(2.0f));
+
 /*! \brief used for generate element of round */
 MXNET_SIMPLE_UNARY_MATH_OP(round);
 
diff --git a/src/operator/numpy/np_elemwise_broadcast_op.cc b/src/operator/numpy/np_elemwise_broadcast_op.cc
index a786d1db5892..fd109f040b50 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op.cc
+++ b/src/operator/numpy/np_elemwise_broadcast_op.cc
@@ -296,5 +296,42 @@ NNVM_REGISTER_OP(_npi_lcm_scalar)
 .add_argument("scalar", "int", "scalar input")
 .set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::lcm>);
 
+MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_ldexp)
+.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, mshadow_op::ldexp>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_ldexp"});
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_ldexp_scalar)
+.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::ldexp>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_ldexp_scalar"});
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_rldexp_scalar)
+.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::rldexp>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_rldexp_scalar"});
+
+NNVM_REGISTER_OP(_backward_npi_ldexp)
+.set_num_inputs(3)
+.set_num_outputs(2)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs){
+    return std::vector<std::pair<int, int> >{{0, 1}};
+  })
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastBackwardUseIn<cpu, mshadow_op::ldexp_grad,
+                                                                  mshadow_op::ldexp_rgrad>);
+
+MXNET_OPERATOR_REGISTER_BINARY(_backward_npi_ldexp_scalar)
+.add_argument("scalar", "float", "scalar value")
+.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = std::stod(attrs->dict["scalar"]); })
+.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Backward<cpu, mshadow_op::ldexp_grad>);
+
+MXNET_OPERATOR_REGISTER_BINARY(_backward_npi_rldexp_scalar)
+.add_argument("scalar", "float", "scalar value")
+.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = std::stod(attrs->dict["scalar"]); })
+.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Backward<cpu, mshadow_op::rldexp_grad>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/np_elemwise_broadcast_op.cu b/src/operator/numpy/np_elemwise_broadcast_op.cu
index 806debf431b5..b48c0b0ace6d 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op.cu
+++ b/src/operator/numpy/np_elemwise_broadcast_op.cu
@@ -118,5 +118,24 @@ NNVM_REGISTER_OP(_backward_npi_rarctan2_scalar)
 NNVM_REGISTER_OP(_npi_lcm_scalar)
 .set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::lcm>);
 
+NNVM_REGISTER_OP(_npi_ldexp)
+.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, mshadow_op::ldexp>);
+
+NNVM_REGISTER_OP(_npi_ldexp_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::ldexp>);
+
+NNVM_REGISTER_OP(_npi_rldexp_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::rldexp>);
+
+NNVM_REGISTER_OP(_backward_npi_ldexp)
+.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastBackwardUseIn<gpu, mshadow_op::ldexp_grad,
+                                                                  mshadow_op::ldexp_rgrad>);
+
+NNVM_REGISTER_OP(_backward_npi_ldexp_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Backward<gpu, mshadow_op::ldexp_grad>);
+
+NNVM_REGISTER_OP(_backward_npi_rldexp_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Backward<gpu, mshadow_op::rldexp_grad>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/operator_tune.cc b/src/operator/operator_tune.cc
index b81cd78ad507..c690cb633306 100644
--- a/src/operator/operator_tune.cc
+++ b/src/operator/operator_tune.cc
@@ -373,6 +373,11 @@ IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::lcm);  // NOLINT()
 IMPLEMENT_BLANK_WORKLOAD_FWD(mxnet::op::mxnet_op::set_to_int<0>);  // NOLINT()
 IMPLEMENT_BLANK_WORKLOAD_FWD(mxnet::op::mxnet_op::set_to_int<1>);  // NOLINT()
 IMPLEMENT_BLANK_WORKLOAD_FWD(mxnet::op::PopulateFullIdxRspKernel);  // NOLINT()
+IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::ldexp);  // NOLINT()
+IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::rldexp);  // NOLINT()
+IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::ldexp_grad);  // NOLINT()
+IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::ldexp_rgrad);  // NOLINT()
+IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::rldexp_grad);  // NOLINT()
 /*!
  * \brief Tuner objects, *not* automatically generated
  */
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 224125eb64f3..a3c4399bc05c 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -221,6 +221,67 @@ def test_np_dot():
         assert False
 
 
+@with_seed()
+@use_np
+def test_np_ldexp():
+    class TestLdexp(HybridBlock):
+        def __init__(self):
+            super(TestLdexp, self).__init__()
+
+        def hybrid_forward(self, F, x1, x2):
+            return F.np.ldexp(x1, x2)
+        
+    def _np_ldexp(x1, x2):
+        return x1 * _np.power(2.0, x2)
+
+    def dldx(x1, x2): 
+        grad_a = _np.power(2.0, x2)
+        grad_b = _np_ldexp(x1, x2) * _np.log(2.0)
+        if len(x1) == 1:
+            grad_a = _np.sum(grad_a)
+        if len(x2) == 1:
+            grad_b = _np.sum(grad_b)
+        return [grad_a, grad_b]
+
+    shapes = [ 
+        ((3, 1), (3, 1)),
+        ((3, 1, 2), (3, 1, 2)),
+        ((1, ),(1, )),
+        ((1, ), (2, )),
+        ((3, ), (1, )),
+        ((3, 0), (3, 0)),  # zero-size shape
+        ((0, 1), (0, 1)),  # zero-size shape
+        ((2, 0, 2), (2, 0, 2)),  # zero-size shape
+        ] 
+
+    for hybridize in [True, False]:
+        for shape1, shape2 in shapes:
+            for dtype in [_np.float16, _np.float32, _np.float64]:
+                test_ldexp = TestLdexp()
+                if hybridize:
+                    test_ldexp.hybridize()
+                x1 = rand_ndarray(shape=shape1, dtype=dtype).as_np_ndarray() 
+                x1.attach_grad()
+                x2 = rand_ndarray(shape=shape2, dtype=dtype).as_np_ndarray()
+                x2.attach_grad()
+
+                np_out = _np_ldexp(x1.asnumpy(), x2.asnumpy())
+                with mx.autograd.record():
+                    mx_out = test_ldexp(x1, x2)
+                assert mx_out.shape == np_out.shape
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-1, atol=1e-1)
+
+                mx_out.backward()
+                np_backward = dldx(x1.asnumpy(), x2.asnumpy())
+                assert_almost_equal(x1.grad.asnumpy(), np_backward[0], atol=1e-1, rtol=1e-1)
+                assert_almost_equal(x2.grad.asnumpy(), np_backward[1], atol=1e-1, rtol=1e-1)
+
+                # Test imperative once again
+                mx_out = np.ldexp(x1, x2)
+                np_out = _np_ldexp(x1.asnumpy(), x2.asnumpy())
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-1, atol=1e-1)
+
+
 @with_seed()
 @use_np
 def test_np_sum():

From 2df3282666c5ba55e98340c3615b046278c40b53 Mon Sep 17 00:00:00 2001
From: ckt624 <ckt624@gmail.com>
Date: Tue, 24 Sep 2019 16:55:21 -0400
Subject: [PATCH 409/813] Numpy Operators: Inner, Outer, vdot (#15846)

* Implements tensordot and dot.

Change tests.

Add spaces.

Reorganize codes.

Implements inner, outer, vdot.

Remove spaces.

Change tests.

change test format.

Change indent.

Change styles

* Fix
---
 python/mxnet/ndarray/numpy/_op.py      | 163 ++++++++++++++++++++-
 python/mxnet/numpy/multiarray.py       | 149 ++++++++++++++++++-
 python/mxnet/symbol/numpy/_symbol.py   | 155 +++++++++++++++++++-
 tests/python/unittest/test_numpy_op.py | 195 +++++++++++++++++++++++++
 4 files changed, 651 insertions(+), 11 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index 18c49dad5fd0..5ffb40dae7e0 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -35,7 +35,7 @@
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'mean',
            'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
            'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'hypot', 'rad2deg', 'deg2rad',
-           'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp']
+           'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -1903,7 +1903,7 @@ def tan(x, out=None, where=True, **kwargs):
 
     Parameters:
     ----------
-    x : array_like
+    x : ndarray
         Input array.
     out : ndarray, None, or tuple of ndarray and None, optional
           A location into which the result is stored. If provided,
@@ -2323,7 +2323,7 @@ def concatenate(seq, axis=0, out=None):
     """Join a sequence of arrays along an existing axis.
     Parameters
     ----------
-    a1, a2, ... : sequence of array_like
+    a1, a2, ... : sequence of ndarray
         The arrays must have the same shape, except in the dimension
         corresponding to `axis` (the first, by default).
     axis : int, optional
@@ -2348,7 +2348,7 @@ def stack(arrays, axis=0, out=None):
         For example, if `axis=0` it will be the first dimension and if `axis=-1` it will be the last dimension.
     Parameters
     ----------
-    arrays : sequence of array_like
+    arrays : sequence of ndarray
         Each array must have the same shape.
     axis : int, optional
         The axis in the result array along which the input arrays are stacked.
@@ -2510,7 +2510,7 @@ def clip(a, a_min, a_max, out=None):
 
     Notes
     -----
-    array_like `a_min` and `a_max` are not supported.
+    ndarray `a_min` and `a_max` are not supported.
 
     Examples
     --------
@@ -2669,7 +2669,7 @@ def std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):  # pylint:
 
     Parameters
     ----------
-    a : array_like
+    a : ndarray
         Calculate the standard deviation of these values.
     axis : None or int or tuple of ints, optional
         Axis or axes along which the standard deviation is computed. The
@@ -2736,7 +2736,7 @@ def var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):  # pylint:
 
     Parameters
     ----------
-    a : array_like
+    a : ndarray
         Array containing numbers whose variance is desired.  If `a` is not an
         array, a conversion is attempted.
     axis : None or int or tuple of ints, optional
@@ -3430,7 +3430,7 @@ def hypot(x1, x2, out=None):
 
     Parameters
     ----------
-    x1, x2 : array_like
+    x1, x2 : ndarray
         Leg of the triangle(s).
     out : ndarray, None, or tuple of ndarray and None, optional
         A location into which the result is stored. If provided, it must have
@@ -3502,3 +3502,150 @@ def ldexp(x1, x2, out=None):
     array([  5.,  10.,  20.,  40.])
     """
     return _ufunc_helper(x1, x2, _npi.ldexp, _np.ldexp, _npi.ldexp_scalar, _npi.rldexp_scalar, out)
+
+
+@set_module('mxnet.ndarray.numpy')
+def inner(a, b):
+    r"""
+    Inner product of two arrays.
+    Ordinary inner product of vectors for 1-D arrays (without complex
+    conjugation), in higher dimensions a sum product over the last axes.
+
+    Parameters
+    ----------
+    a, b : ndarray
+        If `a` and `b` are nonscalar, their last dimensions must match.
+
+    Returns
+    -------
+    out : ndarray
+        `out.shape = a.shape[:-1] + b.shape[:-1]`
+
+    Raises
+    ------
+    ValueError
+        If the last dimension of `a` and `b` has different size.
+
+    See Also
+    --------
+    tensordot : Sum products over arbitrary axes.
+    dot : Generalised matrix product, using second last dimension of `b`.
+    einsum : Einstein summation convention.
+
+    Notes
+    -----
+    For vectors (1-D arrays) it computes the ordinary inner-product::
+        np.inner(a, b) = sum(a[:]*b[:])
+    More generally, if `ndim(a) = r > 0` and `ndim(b) = s > 0`::
+        np.inner(a, b) = np.tensordot(a, b, axes=(-1,-1))
+    or explicitly::
+        np.inner(a, b)[i0,...,ir-1,j0,...,js-1]
+            = sum(a[i0,...,ir-1,:]*b[j0,...,js-1,:])
+    In addition `a` or `b` may be scalars, in which case::
+    np.inner(a,b) = a*b
+
+    Examples
+    --------
+    Ordinary inner product for vectors:
+    >>> a = np.array([1,2,3])
+    >>> b = np.array([0,1,0])
+    >>> np.inner(a, b)
+    2
+    A multidimensional example:
+    >>> a = np.arange(24).reshape((2,3,4))
+    >>> b = np.arange(4)
+    >>> np.inner(a, b)
+    array([[ 14,  38,  62],
+           [ 86, 110, 134]])
+    """
+    return tensordot(a, b, [-1, -1])
+
+
+@set_module('mxnet.ndarray.numpy')
+def outer(a, b):
+    r"""
+    Compute the outer product of two vectors.
+    Given two vectors, ``a = [a0, a1, ..., aM]`` and
+    ``b = [b0, b1, ..., bN]``,
+    the outer product [1]_ is::
+    [[a0*b0  a0*b1 ... a0*bN ]
+    [a1*b0    .
+    [ ...          .
+    [aM*b0            aM*bN ]]
+
+    Parameters
+    ----------
+    a : (M,) ndarray
+        First input vector.  Input is flattened if
+        not already 1-dimensional.
+    b : (N,) ndarray
+        Second input vector.  Input is flattened if
+        not already 1-dimensional.
+
+    Returns
+    -------
+    out : (M, N) ndarray
+        ``out[i, j] = a[i] * b[j]``
+    See also
+    --------
+    inner
+    einsum : ``einsum('i,j->ij', a.ravel(), b.ravel())`` is the equivalent.
+    ufunc.outer : A generalization to N dimensions and other operations.
+                ``np.multiply.outer(a.ravel(), b.ravel())`` is the equivalent.
+    References
+    ----------
+    .. [1] : G. H. Golub and C. F. Van Loan, *Matrix Computations*, 3rd
+            ed., Baltimore, MD, Johns Hopkins University Press, 1996,
+            pg. 8.
+    Examples
+    --------
+    Make a (*very* coarse) grid for computing a Mandelbrot set:
+    >>> rl = np.outer(np.ones((5,)), np.linspace(-2, 2, 5))
+    >>> rl
+    array([[-2., -1.,  0.,  1.,  2.],
+        [-2., -1.,  0.,  1.,  2.],
+        [-2., -1.,  0.,  1.,  2.],
+        [-2., -1.,  0.,  1.,  2.],
+        [-2., -1.,  0.,  1.,  2.]])
+    """
+    return tensordot(a.flatten(), b.flatten(), 0)
+
+
+@set_module('mxnet.ndarray.numpy')
+def vdot(a, b):
+    r"""
+    Return the dot product of two vectors.
+    Note that `vdot` handles multidimensional arrays differently than `dot`:
+    it does *not* perform a matrix product, but flattens input arguments
+    to 1-D vectors first. Consequently, it should only be used for vectors.
+
+    Parameters
+    ----------
+    a : ndarray
+        First argument to the dot product.
+    b : ndarray
+        Second argument to the dot product.
+
+    Returns
+    -------
+    output : ndarray
+        Dot product of `a` and `b`.
+
+    See Also
+    --------
+    dot : Return the dot product without using the complex conjugate of the
+        first argument.
+
+    Examples
+    --------
+    Note that higher-dimensional arrays are flattened!
+    >>> a = np.array([[1, 4], [5, 6]])
+    >>> b = np.array([[4, 1], [2, 2]])
+    >>> np.vdot(a, b)
+    30
+    >>> np.vdot(b, a)
+    30
+    >>> 1*4 + 4*1 + 5*2 + 6*2
+    30
+    """
+    return tensordot(a.flatten(), b.flatten(), 1)
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 09840c93a9aa..01b2faa0c056 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -54,7 +54,7 @@
            'tensordot', 'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate',
            'stack', 'vstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices',
            'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'arctan2', 'hypot',
-           'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp']
+           'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -5018,3 +5018,150 @@ def ldexp(x1, x2, out=None):
     array([  5.,  10.,  20.,  40.])
     """
     return _mx_nd_np.ldexp(x1, x2, out)
+
+
+@set_module('mxnet.numpy')
+def inner(a, b):
+    r"""Inner product of two arrays.
+    Ordinary inner product of vectors for 1-D arrays (without complex
+    conjugation), in higher dimensions a sum product over the last axes.
+
+    Parameters
+    ----------
+    a, b : ndarray
+        If `a` and `b` are nonscalar, their last dimensions must match.
+
+    Returns
+    -------
+    out : ndarray
+        `out.shape = a.shape[:-1] + b.shape[:-1]`
+
+    Raises
+    ------
+    ValueError
+        If the last dimension of `a` and `b` has different size.
+
+    See Also
+    --------
+    tensordot : Sum products over arbitrary axes.
+    dot : Generalised matrix product, using second last dimension of `b`.
+    einsum : Einstein summation convention.
+
+    Notes
+    -----
+    For vectors (1-D arrays) it computes the ordinary inner-product::
+        np.inner(a, b) = sum(a[:]*b[:])
+    More generally, if `ndim(a) = r > 0` and `ndim(b) = s > 0`::
+        np.inner(a, b) = np.tensordot(a, b, axes=(-1,-1))
+    or explicitly::
+        np.inner(a, b)[i0,...,ir-1,j0,...,js-1]
+            = sum(a[i0,...,ir-1,:]*b[j0,...,js-1,:])
+    In addition `a` or `b` may be scalars, in which case::
+    np.inner(a,b) = a*b
+
+    Examples
+    --------
+    Ordinary inner product for vectors:
+    >>> a = np.array([1,2,3])
+    >>> b = np.array([0,1,0])
+    >>> np.inner(a, b)
+    2
+    A multidimensional example:
+    >>> a = np.arange(24).reshape((2,3,4))
+    >>> b = np.arange(4)
+    >>> np.inner(a, b)
+    array([[ 14,  38,  62],
+           [ 86, 110, 134]])
+    """
+    return tensordot(a, b, [-1, -1])
+
+
+@set_module('mxnet.numpy')
+def outer(a, b):
+    r"""Compute the outer product of two vectors.
+    Given two vectors, ``a = [a0, a1, ..., aM]`` and
+    ``b = [b0, b1, ..., bN]``,
+    the outer product [1]_ is::
+    [[a0*b0  a0*b1 ... a0*bN ]
+    [a1*b0    .
+    [ ...          .
+    [aM*b0            aM*bN ]]
+
+    Parameters
+    ----------
+    a : (M,) ndarray
+        First input vector.  Input is flattened if
+        not already 1-dimensional.
+    b : (N,) ndarray
+        Second input vector.  Input is flattened if
+        not already 1-dimensional.
+
+    Returns
+    -------
+    out : (M, N) ndarray
+        ``out[i, j] = a[i] * b[j]``
+    See also
+    --------
+    inner
+    einsum : ``einsum('i,j->ij', a.ravel(), b.ravel())`` is the equivalent.
+    ufunc.outer : A generalization to N dimensions and other operations.
+                ``np.multiply.outer(a.ravel(), b.ravel())`` is the equivalent.
+
+    References
+    ----------
+    .. [1] : G. H. Golub and C. F. Van Loan, *Matrix Computations*, 3rd
+            ed., Baltimore, MD, Johns Hopkins University Press, 1996,
+            pg. 8.
+
+    Examples
+    --------
+    Make a (*very* coarse) grid for computing a Mandelbrot set:
+    >>> rl = np.outer(np.ones((5,)), np.linspace(-2, 2, 5))
+    >>> rl
+    array([[-2., -1.,  0.,  1.,  2.],
+        [-2., -1.,  0.,  1.,  2.],
+        [-2., -1.,  0.,  1.,  2.],
+        [-2., -1.,  0.,  1.,  2.],
+        [-2., -1.,  0.,  1.,  2.]])
+    """
+    return tensordot(a.flatten(), b.flatten(), 0)
+
+
+@set_module('mxnet.numpy')
+def vdot(a, b):
+    r"""
+    Return the dot product of two vectors.
+    Note that `vdot` handles multidimensional arrays differently than `dot`:
+    it does *not* perform a matrix product, but flattens input arguments
+    to 1-D vectors first. Consequently, it should only be used for vectors.
+
+    Parameters
+    ----------
+    a : ndarray
+        First argument to the dot product.
+    b : ndarray
+        Second argument to the dot product.
+
+    Returns
+    -------
+    output : ndarray
+        Dot product of `a` and `b`.
+
+    See Also
+    --------
+    dot : Return the dot product without using the complex conjugate of the
+        first argument.
+
+    Examples
+    --------
+    Note that higher-dimensional arrays are flattened!
+    >>> a = np.array([[1, 4], [5, 6]])
+    >>> b = np.array([[4, 1], [2, 2]])
+    >>> np.vdot(a, b)
+    30
+    >>> np.vdot(b, a)
+    30
+    >>> 1*4 + 4*1 + 5*2 + 6*2
+    30
+    """
+    return tensordot(a.flatten(), b.flatten(), 1)
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index d2318a71df0f..7ba92e9cab6e 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -37,7 +37,7 @@
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'mean',
            'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
            'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'hypot', 'rad2deg', 'deg2rad',
-           'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp']
+           'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer']
 
 
 def _num_outputs(sym):
@@ -1627,6 +1627,7 @@ def absolute(x, out=None, **kwargs):
     r"""
     Calculate the absolute value element-wise.
     np.abs is a shorthand for this function.
+
     Parameters
     ----------
     x : _Symbol
@@ -3555,10 +3556,10 @@ def unique(ar, return_index=False, return_inverse=False, return_counts=False, ax
 @set_module('mxnet.symbol.numpy')
 def ldexp(x1, x2, out=None):
     """
-    ldexp(x1, x2, out=None)
     Returns x1 * 2**x2, element-wise.
     The mantissas `x1` and twos exponents `x2` are used to construct
     floating point numbers ``x1 * 2**x2``.
+
     Parameters
     ----------
     x1 : _Symbol
@@ -3567,10 +3568,12 @@ def ldexp(x1, x2, out=None):
         Array of twos exponents.
     out : _Symbol or None
         Dummy parameter to keep the consistency with the ndarray counterpart.
+
     Returns
     -------
     y : _Symbol
         The result of ``x1 * 2**x2``.
+
     Notes
     -----
     Complex dtypes are not supported, they will raise a TypeError.
@@ -3581,4 +3584,152 @@ def ldexp(x1, x2, out=None):
     return _ufunc_helper(x1, x2, _npi.ldexp, _np.ldexp, _npi.ldexp_scalar, _npi.rldexp_scalar, out)
 
 
+@set_module('mxnet.symbol.numpy')
+def inner(a, b):
+    r"""Inner product of two arrays.
+    Ordinary inner product of vectors for 1-D arrays (without complex
+    conjugation), in higher dimensions a sum product over the last axes.
+
+    Parameters
+    ----------
+    a, b : _Symbol
+        If `a` and `b` are nonscalar, their last dimensions must match.
+
+    Returns
+    -------
+    out : _Symbol
+        `out.shape = a.shape[:-1] + b.shape[:-1]`
+
+    Raises
+    ------
+    ValueError
+        If the last dimension of `a` and `b` has different size.
+
+    See Also
+    --------
+    tensordot : Sum products over arbitrary axes.
+    dot : Generalised matrix product, using second last dimension of `b`.
+    einsum : Einstein summation convention.
+
+    Notes
+    -----
+    For vectors (1-D arrays) it computes the ordinary inner-product::
+        np.inner(a, b) = sum(a[:]*b[:])
+    More generally, if `ndim(a) = r > 0` and `ndim(b) = s > 0`::
+        np.inner(a, b) = np.tensordot(a, b, axes=(-1,-1))
+    or explicitly::
+        np.inner(a, b)[i0,...,ir-1,j0,...,js-1]
+            = sum(a[i0,...,ir-1,:]*b[j0,...,js-1,:])
+    In addition `a` or `b` may be scalars, in which case::
+    np.inner(a,b) = a*b
+
+    Examples
+    --------
+    Ordinary inner product for vectors:
+    >>> a = np.array([1,2,3])
+    >>> b = np.array([0,1,0])
+    >>> np.inner(a, b)
+    2
+    A multidimensional example:
+    >>> a = np.arange(24).reshape((2,3,4))
+    >>> b = np.arange(4)
+    >>> np.inner(a, b)
+    array([[ 14,  38,  62],
+           [ 86, 110, 134]])
+    """
+    return tensordot(a, b, [-1, -1])
+
+
+@set_module('mxnet.symbol.numpy')
+def outer(a, b):
+    r"""Compute the outer product of two vectors.
+    Given two vectors, ``a = [a0, a1, ..., aM]`` and
+    ``b = [b0, b1, ..., bN]``,
+    the outer product [1]_ is::
+    [[a0*b0  a0*b1 ... a0*bN ]
+    [a1*b0    .
+    [ ...          .
+    [aM*b0            aM*bN ]]
+
+    Parameters
+    ----------
+    a : (M,) ndarray
+        First input vector.  Input is flattened if
+        not already 1-dimensional.
+    b : (N,) ndarray
+        Second input vector.  Input is flattened if
+        not already 1-dimensional.
+
+    Returns
+    -------
+    out : (M, N) ndarray
+        ``out[i, j] = a[i] * b[j]``
+
+    See also
+    --------
+    inner
+    einsum : ``einsum('i,j->ij', a.ravel(), b.ravel())`` is the equivalent.
+    ufunc.outer : A generalization to N dimensions and other operations.
+                ``np.multiply.outer(a.ravel(), b.ravel())`` is the equivalent.
+
+    References
+    ----------
+    .. [1] : G. H. Golub and C. F. Van Loan, *Matrix Computations*, 3rd
+            ed., Baltimore, MD, Johns Hopkins University Press, 1996,
+            pg. 8.
+
+    Examples
+    --------
+    Make a (*very* coarse) grid for computing a Mandelbrot set:
+    >>> rl = np.outer(np.ones((5,)), np.linspace(-2, 2, 5))
+    >>> rl
+    array([[-2., -1.,  0.,  1.,  2.],
+        [-2., -1.,  0.,  1.,  2.],
+        [-2., -1.,  0.,  1.,  2.],
+        [-2., -1.,  0.,  1.,  2.],
+        [-2., -1.,  0.,  1.,  2.]])
+    """
+    return tensordot(a.flatten(), b.flatten(), 0)
+
+
+@set_module('mxnet.symbol.numpy')
+def vdot(a, b):
+    r"""
+    Return the dot product of two vectors.
+    Note that `vdot` handles multidimensional arrays differently than `dot`:
+    it does *not* perform a matrix product, but flattens input arguments
+    to 1-D vectors first. Consequently, it should only be used for vectors.
+
+    Parameters
+    ----------
+    a : _Symbol
+        First argument to the dot product.
+    b : _Symbol
+        Second argument to the dot product.
+
+    Returns
+    -------
+    output : _Symbol
+        Dot product of `a` and `b`.
+
+    See Also
+    --------
+    dot : Return the dot product without using the complex conjugate of the
+        first argument.
+
+    Examples
+    --------
+    Note that higher-dimensional arrays are flattened!
+    >>> a = np.array([[1, 4], [5, 6]])
+    >>> b = np.array([[4, 1], [2, 2]])
+    >>> np.vdot(a, b)
+    30
+    >>> np.vdot(b, a)
+    30
+    >>> 1*4 + 4*1 + 5*2 + 6*2
+    30
+    """
+    return tensordot(a.flatten(), b.flatten(), 1)
+
+
 _set_np_symbol_class(_Symbol)
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index a3c4399bc05c..97a6149991b0 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -282,6 +282,201 @@ def dldx(x1, x2):
                 assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-1, atol=1e-1)
 
 
+@with_seed()
+@use_np
+def test_np_vdot():
+    class TestVdot(HybridBlock):
+        def __init__(self):
+            super(TestVdot, self).__init__()
+
+        def hybrid_forward(self, F, a, b):
+            return F.np.vdot(a, b)
+
+    def vdot_backward(a, b):
+        return [b, a]
+
+    # test different size inputs
+    tensor_shapes = [(), (5,), (3, 3)]
+
+    for hybridize in [True, False]:
+        for shape in tensor_shapes:
+            for dtype in [_np.float32, _np.float64]:
+                test_vdot = TestVdot()
+                if hybridize:
+                    test_vdot.hybridize()
+                a = rand_ndarray(shape=shape, dtype=dtype).as_np_ndarray()
+                b = rand_ndarray(shape=shape, dtype=dtype).as_np_ndarray()
+                a.attach_grad()
+                b.attach_grad()
+
+                np_out = _np.vdot(a.asnumpy(), b.asnumpy())
+                with mx.autograd.record():
+                    mx_out = test_vdot(a, b)
+                assert mx_out.shape == np_out.shape
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol = 1e-3, atol = 1e-5)
+                mx_out.backward()
+                np_backward = vdot_backward(a.asnumpy(), b.asnumpy())
+                assert_almost_equal(a.grad.asnumpy(), np_backward[0], rtol = 1e-2, atol=1e-2)
+                assert_almost_equal(b.grad.asnumpy(), np_backward[1], rtol = 1e-2, atol=1e-2)
+
+                # Test imperative once again
+                mx_out = np.vdot(a, b)
+                np_out = _np.vdot(a.asnumpy(), b.asnumpy())
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+
+                # test numeric gradient
+                if len(shape) > 0 and _np.prod(shape) > 0:
+                    a_sym = mx.sym.Variable("a").as_np_ndarray()
+                    b_sym = mx.sym.Variable("b").as_np_ndarray()
+                    mx_sym = mx.sym.np.vdot(a_sym, b_sym).as_nd_ndarray()
+                    check_numeric_gradient(mx_sym, [a.as_nd_ndarray(), b.as_nd_ndarray()],
+                      rtol=1e-1, atol=1e-1, dtype=dtype)
+
+
+@with_seed()
+@use_np
+def test_np_inner():
+    class TestInner(HybridBlock):
+        def __init__(self):
+            super(TestInner, self).__init__()
+
+        def hybrid_forward(self, F, a, b):
+            return F.np.inner(a, b)
+
+    def inner_backward(a, b):
+        a_axes_summed = [a.ndim - 1]
+        b_axes_summed = [b.ndim - 1]
+
+        a_axes_remained = []
+        for i in range(a.ndim):
+            if not (i in a_axes_summed):
+                a_axes_remained.append(i)
+        a_axes = a_axes_remained[:] + a_axes_summed[:]
+
+        b_axes_remained = []
+        for i in range(b.ndim):
+            if not (i in b_axes_summed):
+                b_axes_remained.append(i)
+        b_axes = b_axes_summed[:] + b_axes_remained[:]
+
+        ad1 = _np.prod([a.shape[i] for i in a_axes_remained]) if len(a_axes_remained) > 0 else 1
+        ad2 = _np.prod([a.shape[i] for i in a_axes_summed]) if len(a_axes_summed) > 0 else 1
+        bd1 = _np.prod([b.shape[i] for i in b_axes_summed]) if len(b_axes_summed) > 0 else 1
+        bd2 = _np.prod([b.shape[i] for i in b_axes_remained]) if len(b_axes_remained) > 0 else 1
+
+        out_grad = _np.ones((ad1, bd2))
+
+        new_a = _np.transpose(a, a_axes)
+        new_a_shape = new_a.shape[:]
+        new_a = new_a.reshape((ad1, ad2))
+        new_b = _np.transpose(b, b_axes)
+        new_b_shape = new_b.shape[:]
+        new_b = new_b.reshape((bd1, bd2))
+
+        reverse_a_axes = [0 for i in a_axes]
+        for i in range(len(a_axes)):
+            reverse_a_axes[a_axes[i]] = i
+
+        reverse_b_axes = [0 for i in b_axes]
+        for i in range(len(b_axes)):
+            reverse_b_axes[b_axes[i]] = i
+
+        grad_b = _np.dot(new_a.T, out_grad).reshape(new_b_shape)
+        grad_b = _np.transpose(grad_b, reverse_b_axes)
+        grad_a = _np.dot(out_grad, new_b.T).reshape(new_a_shape)
+        grad_a = _np.transpose(grad_a, reverse_a_axes)
+
+        return [grad_a, grad_b]
+
+    # test non zero size input
+    tensor_shapes = [
+        ((3,), (3,)),
+        ((2, 3), (3,)),
+        ((3,), (2, 3))
+    ]
+
+    for hybridize in [True, False]:
+        for a_shape, b_shape in tensor_shapes:
+            for dtype in [_np.float32, _np.float64]:
+                test_inner = TestInner()
+                if hybridize:
+                    test_inner.hybridize()
+                a = rand_ndarray(shape=a_shape, dtype=dtype).as_np_ndarray()
+                b = rand_ndarray(shape=b_shape, dtype=dtype).as_np_ndarray()
+                a.attach_grad()
+                b.attach_grad()
+
+                np_out = _np.inner(a.asnumpy(), b.asnumpy())
+                with mx.autograd.record():
+                    mx_out = test_inner(a, b)
+                assert mx_out.shape == np_out.shape
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol = 1e-3, atol = 1e-5)
+                mx_out.backward()
+                np_backward = inner_backward(a.asnumpy(), b.asnumpy())
+                assert_almost_equal(a.grad.asnumpy(), np_backward[0], rtol = 1e-2, atol=1e-2)
+                assert_almost_equal(b.grad.asnumpy(), np_backward[1], rtol = 1e-2, atol=1e-2)
+
+                # Test imperative once again
+                mx_out = np.inner(a, b)
+                np_out = _np.inner(a.asnumpy(), b.asnumpy())
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+
+                # test numeric gradient
+                a_sym = mx.sym.Variable("a").as_np_ndarray()
+                b_sym = mx.sym.Variable("b").as_np_ndarray()
+                mx_sym = mx.sym.np.inner(a_sym, b_sym).as_nd_ndarray()
+                check_numeric_gradient(mx_sym, [a.as_nd_ndarray(), b.as_nd_ndarray()],
+                  rtol=1e-1, atol=1e-1, dtype=dtype)
+
+
+@with_seed()
+@use_np
+def test_np_outer():
+    class TestOuter(HybridBlock):
+        def __init__(self):
+            super(TestOuter, self).__init__()
+
+        def hybrid_forward(self, F, a, b):
+            return F.np.outer(a, b)
+
+    # test non zero size input
+    tensor_shapes = [
+        ((3,), (3,)),
+        ((2, 3), (6,)),
+        ((6,), (2, 3))
+    ]
+
+    for hybridize in [True, False]:
+        for a_shape, b_shape in tensor_shapes:
+            for dtype in [_np.float32, _np.float64]:
+                test_outer = TestOuter()
+                if hybridize:
+                    test_outer.hybridize()
+                a = rand_ndarray(shape=a_shape, dtype=dtype).as_np_ndarray()
+                b = rand_ndarray(shape=b_shape, dtype=dtype).as_np_ndarray()
+                a.attach_grad()
+                b.attach_grad()
+
+                np_out = _np.outer(a.asnumpy(), b.asnumpy())
+                with mx.autograd.record():
+                    mx_out = test_outer(a, b)
+                assert mx_out.shape == np_out.shape
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+                mx_out.backward()
+
+                # Test imperative once again
+                mx_out = np.outer(a, b)
+                np_out = _np.outer(a.asnumpy(), b.asnumpy())
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+
+                # test numeric gradient
+                a_sym = mx.sym.Variable("a").as_np_ndarray()
+                b_sym = mx.sym.Variable("b").as_np_ndarray()
+                mx_sym = mx.sym.np.outer(a_sym, b_sym).as_nd_ndarray()
+                check_numeric_gradient(mx_sym, [a.as_nd_ndarray(), b.as_nd_ndarray()],
+                                       rtol=1e-1, atol=1e-1, dtype=dtype)
+
+
 @with_seed()
 @use_np
 def test_np_sum():

From 295fc145510ac73f296a7581649d7ad371d85cd5 Mon Sep 17 00:00:00 2001
From: ckt624 <ckt624@gmail.com>
Date: Wed, 25 Sep 2019 01:28:58 -0400
Subject: [PATCH 410/813] Numpy det and slogdet operators (#15861)

* Add alias.

Add tests.

Add slogdet tests.

Add docs

Change shapes

Change tests.

Change slogdet tests

Change style.

* Fix

* Fix
---
 python/mxnet/_numpy_op_doc.py          | 122 ++++++++++
 src/operator/tensor/la_op.cc           |   2 +
 src/operator/tensor/la_op.cu           |   2 +
 src/operator/tensor/la_op.h            |   7 +-
 tests/python/unittest/test_numpy_op.py | 305 ++++++++++++++++---------
 5 files changed, 332 insertions(+), 106 deletions(-)

diff --git a/python/mxnet/_numpy_op_doc.py b/python/mxnet/_numpy_op_doc.py
index 6e2f5fa15919..bb9b8314f2aa 100644
--- a/python/mxnet/_numpy_op_doc.py
+++ b/python/mxnet/_numpy_op_doc.py
@@ -20,6 +20,128 @@
 """Doc placeholder for numpy ops with prefix _np."""
 
 
+def _np__linalg_det(a):
+    """
+    det(a)
+
+    Compute the determinant of an array.
+
+    Parameters
+    ----------
+    a : (..., M, M) ndarray
+        Input array to compute determinants for.
+
+    Returns
+    -------
+    det : (...) ndarray
+        Determinant of `a`.
+
+    See Also
+    --------
+    slogdet : Another way to represent the determinant, more suitable
+    for large matrices where underflow/overflow may occur.
+
+    Notes
+    -----
+
+    Broadcasting rules apply, see the `numpy.linalg` documentation for
+    details.
+
+    The determinant is computed via LU factorization using the LAPACK
+    routine z/dgetrf.
+
+    Examples
+    --------
+    The determinant of a 2-D array [[a, b], [c, d]] is ad - bc:
+
+    >>> a = np.array([[1, 2], [3, 4]])
+    >>> np.linalg.det(a)
+    -2.0
+
+    Computing determinants for a stack of matrices:
+
+    >>> a = np.array([ [[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]] ])
+    >>> a.shape
+    (3, 2, 2)
+    >>> np.linalg.det(a)
+    array([-2., -3., -8.])
+    """
+    pass
+
+
+def _np__linalg_slogdet(a):
+    """
+    slogdet(a)
+
+    Compute the sign and (natural) logarithm of the determinant of an array.
+
+    If an array has a very small or very large determinant, then a call to
+    `det` may overflow or underflow. This routine is more robust against such
+    issues, because it computes the logarithm of the determinant rather than
+    the determinant itself.
+
+    Parameters
+    ----------
+    a : (..., M, M) ndarray
+        Input array, has to be a square 2-D array.
+
+    Returns
+    -------
+    sign : (...) ndarray
+        A number representing the sign of the determinant. For a real matrix,
+        this is 1, 0, or -1.
+    logdet : (...) array_like
+        The natural log of the absolute value of the determinant.
+
+    If the determinant is zero, then `sign` will be 0 and `logdet` will be
+    -Inf. In all cases, the determinant is equal to ``sign * np.exp(logdet)``.
+
+    See Also
+    --------
+    det
+
+    Notes
+    -----
+
+    Broadcasting rules apply, see the `numpy.linalg` documentation for
+    details.
+
+    The determinant is computed via LU factorization using the LAPACK
+    routine z/dgetrf.
+
+
+    Examples
+    --------
+    The determinant of a 2-D array ``[[a, b], [c, d]]`` is ``ad - bc``:
+
+    >>> a = np.array([[1, 2], [3, 4]])
+    >>> (sign, logdet) = np.linalg.slogdet(a)
+    >>> (sign, logdet)
+    (-1., 0.69314718055994529)
+    >>> sign * np.exp(logdet)
+    -2.0
+
+    Computing log-determinants for a stack of matrices:
+
+    >>> a = np.array([ [[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]] ])
+    >>> a.shape
+    (3, 2, 2)
+    >>> sign, logdet = np.linalg.slogdet(a)
+    >>> (sign, logdet)
+    (array([-1., -1., -1.]), array([ 0.69314718,  1.09861229,  2.07944154]))
+    >>> sign * np.exp(logdet)
+    array([-2., -3., -8.])
+
+    This routine succeeds where ordinary `det` does not:
+
+    >>> np.linalg.det(np.eye(500) * 0.1)
+    0.0
+    >>> np.linalg.slogdet(np.eye(500) * 0.1)
+    (1., -1151.2925464970228)
+    """
+    pass
+
+
 def _np_ones_like(a):
     """
     Return an array of ones with the same shape and type as a given array.
diff --git a/src/operator/tensor/la_op.cc b/src/operator/tensor/la_op.cc
index ce7d1d5de692..24968eca3f06 100644
--- a/src/operator/tensor/la_op.cc
+++ b/src/operator/tensor/la_op.cc
@@ -941,6 +941,7 @@ NNVM_REGISTER_OP(_backward_linalg_inverse)
 
 NNVM_REGISTER_OP(_linalg_det)
 .add_alias("linalg_det")
+.add_alias("_np__linalg_det")
 .describe(R"code(Compute the determinant of a matrix.
 Input is a tensor *A* of dimension *n >= 2*.
 
@@ -991,6 +992,7 @@ NNVM_REGISTER_OP(_backward_linalg_det)
 .set_attr<FCompute>("FCompute<cpu>", LaOpDetBackward<cpu, 1, det_backward>);
 
 NNVM_REGISTER_OP(_linalg_slogdet)
+.add_alias("_np__linalg_slogdet")
 .add_alias("linalg_slogdet")
 .describe(R"code(Compute the sign and log of the determinant of a matrix.
 Input is a tensor *A* of dimension *n >= 2*.
diff --git a/src/operator/tensor/la_op.cu b/src/operator/tensor/la_op.cu
index 68c33180e3d5..ec622f0b22a2 100644
--- a/src/operator/tensor/la_op.cu
+++ b/src/operator/tensor/la_op.cu
@@ -100,12 +100,14 @@ NNVM_REGISTER_OP(_backward_linalg_inverse)
 .set_attr<FCompute>("FCompute<gpu>", LaOpBackward<gpu, 2, 2, 2, 1, inverse_backward>);
 
 NNVM_REGISTER_OP(_linalg_det)
+.add_alias("_np__linalg_det")
 .set_attr<FCompute>("FCompute<gpu>", LaOpDetForward<gpu, 1, det>);
 
 NNVM_REGISTER_OP(_backward_linalg_det)
 .set_attr<FCompute>("FCompute<gpu>", LaOpDetBackward<gpu, 1, det_backward>);
 
 NNVM_REGISTER_OP(_linalg_slogdet)
+.add_alias("_np__linalg_slogdet")
 .set_attr<FCompute>("FCompute<gpu>", LaOpDetForward<gpu, 2, slogdet>);
 
 NNVM_REGISTER_OP(_backward_linalg_slogdet)
diff --git a/src/operator/tensor/la_op.h b/src/operator/tensor/la_op.h
index e024693e3819..bb56dc52b594 100644
--- a/src/operator/tensor/la_op.h
+++ b/src/operator/tensor/la_op.h
@@ -26,6 +26,7 @@
 #define MXNET_OPERATOR_TENSOR_LA_OP_H_
 
 #include <mxnet/operator_util.h>
+#include <mxnet/imperative.h>
 #include <vector>
 #include <algorithm>
 #include "../mshadow_op.h"
@@ -428,7 +429,11 @@ inline bool DetShape(const nnvm::NodeAttrs& attrs,
   CHECK_EQ(in[ndim-2], in[ndim-1]) << "Input A's last two dimension must be equal";
   mxnet::TShape out;
   if (ndim == 2) {
-    out = mxnet::TShape(1, 1);
+    if (Imperative::Get()->is_np_shape()) {
+      out = mxnet::TShape(0, 1);
+    } else {
+      out = mxnet::TShape(1, 1);
+    }
   } else {
     out = mxnet::TShape(in.begin(), in.end() - 2);
   }
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 97a6149991b0..9d95d43fecbc 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -29,7 +29,7 @@
 from common import assertRaises, with_seed
 import random
 import scipy.stats as ss
-from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf, retry
+from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf, collapse_sum_like
 from mxnet.runtime import Features
 from mxnet.numpy_op_signature import _get_builtin_op
 import platform
@@ -143,8 +143,8 @@ def tensordot_backward(a, b, axes=2):
                 test_tensordot = TestTensordot(axes)
                 if hybridize:
                     test_tensordot.hybridize()
-                a = rand_ndarray(shape = a_shape, dtype = dtype).as_np_ndarray()
-                b = rand_ndarray(shape = b_shape, dtype = dtype).as_np_ndarray()
+                a = rand_ndarray(shape=a_shape, dtype=dtype).as_np_ndarray()
+                b = rand_ndarray(shape=b_shape, dtype=dtype).as_np_ndarray()
                 a.attach_grad()
                 b.attach_grad()
 
@@ -169,7 +169,7 @@ def tensordot_backward(a, b, axes=2):
                     b_sym = mx.sym.Variable("b").as_np_ndarray()
                     mx_sym = mx.sym.np.tensordot(a_sym, b_sym, axes).as_nd_ndarray()
                     check_numeric_gradient(mx_sym, [a.as_nd_ndarray(), b.as_nd_ndarray()],
-                      rtol=1e-1, atol=1e-1, dtype = dtype)
+                                           rtol=1e-1, atol=1e-1, dtype=dtype)
 
 
 @with_seed()
@@ -221,6 +221,96 @@ def test_np_dot():
         assert False
 
 
+@with_seed()
+@use_np
+def test_np_linalg_det():
+    class TestDet(HybridBlock):
+        def __init__(self):
+            super(TestDet, self).__init__()
+
+        def hybrid_forward(self, F, a):
+            return F.np.linalg.det(a)
+
+    # test non zero size input
+    tensor_shapes = [
+        (5, 5),
+        (3, 3, 3),
+        (2, 2, 2, 2, 2),
+        (1, 1)
+    ]
+
+    for hybridize in [True, False]:
+        for shape in tensor_shapes:
+            for dtype in [_np.float32, _np.float64]:
+                a_shape = (1,) + shape
+                test_det = TestDet()
+                if hybridize:
+                    test_det.hybridize()
+                a = rand_ndarray(shape=a_shape, dtype=dtype).as_np_ndarray()
+                a.attach_grad()
+
+                np_out = _np.linalg.det(a.asnumpy())
+                with mx.autograd.record():
+                    mx_out = test_det(a)
+                assert mx_out.shape == np_out.shape
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-1, atol=1e-1)
+                mx_out.backward()
+
+                # Test imperative once again
+                mx_out = np.linalg.det(a)
+                np_out = _np.linalg.det(a.asnumpy())
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-1, atol=1e-1, use_broadcast=False)
+
+                # test numeric gradient
+                a_sym = mx.sym.Variable("a").as_np_ndarray()
+                mx_sym = mx.sym.np.linalg.det(a_sym).as_nd_ndarray()
+                check_numeric_gradient(mx_sym, [a.as_nd_ndarray()],
+                                       rtol=1e-1, atol=1e-1, dtype=dtype)
+
+
+@with_seed()
+@use_np
+def test_np_linalg_slogdet():
+    class TestSlogdet(HybridBlock):
+        def __init__(self):
+            super(TestSlogdet, self).__init__()
+
+        def hybrid_forward(self, F, a):
+            return F.np.linalg.slogdet(a)
+
+    # test non zero size input
+    tensor_shapes = [
+        (5, 5),
+        (3, 3, 3),
+        (2, 2, 2, 2, 2),
+        (1, 1)
+    ]
+
+    for hybridize in [True, False]:
+        for a_shape in tensor_shapes:
+            for dtype in [_np.float32, _np.float64]:
+                test_slogdet = TestSlogdet()
+                if hybridize:
+                    test_slogdet.hybridize()
+                a = rand_ndarray(shape=a_shape, dtype=dtype).as_np_ndarray()
+                a.attach_grad()
+
+                np_out = _np.linalg.slogdet(a.asnumpy())
+                with mx.autograd.record():
+                    mx_out = test_slogdet(a)
+                assert mx_out[0].shape == np_out[0].shape
+                assert mx_out[1].shape == np_out[1].shape
+                assert_almost_equal(mx_out[0].asnumpy(), np_out[0], rtol=1e-1, atol=1e-1, use_broadcast=False)
+                assert_almost_equal(mx_out[1].asnumpy(), np_out[1], rtol=1e-1, atol=1e-1, use_broadcast=False)
+                mx_out[1].backward()
+
+                # Test imperative once again
+                mx_out = np.linalg.slogdet(a)
+                np_out = _np.linalg.slogdet(a.asnumpy())
+                assert_almost_equal(mx_out[0].asnumpy(), np_out[0], rtol=1e-1, atol=1e-1, use_broadcast=False)
+                assert_almost_equal(mx_out[1].asnumpy(), np_out[1], rtol=1e-1, atol=1e-1, use_broadcast=False)
+
+
 @with_seed()
 @use_np
 def test_np_ldexp():
@@ -234,25 +324,24 @@ def hybrid_forward(self, F, x1, x2):
     def _np_ldexp(x1, x2):
         return x1 * _np.power(2.0, x2)
 
-    def dldx(x1, x2): 
-        grad_a = _np.power(2.0, x2)
-        grad_b = _np_ldexp(x1, x2) * _np.log(2.0)
-        if len(x1) == 1:
-            grad_a = _np.sum(grad_a)
-        if len(x2) == 1:
-            grad_b = _np.sum(grad_b)
+    def dldx(x1, x2):
+        out = _np_ldexp(x1, x2)
+        grad_a = _np.broadcast_to(_np.power(2.0, x2), out.shape)
+        grad_b = _np.broadcast_to(out * _np.log(2.0), out.shape)
+        grad_a = collapse_sum_like(grad_a, x1.shape)
+        grad_b = collapse_sum_like(grad_b, x2.shape)
         return [grad_a, grad_b]
 
-    shapes = [ 
+    shapes = [
         ((3, 1), (3, 1)),
         ((3, 1, 2), (3, 1, 2)),
-        ((1, ),(1, )),
+        ((1, ), (1, )),
         ((1, ), (2, )),
         ((3, ), (1, )),
         ((3, 0), (3, 0)),  # zero-size shape
         ((0, 1), (0, 1)),  # zero-size shape
         ((2, 0, 2), (2, 0, 2)),  # zero-size shape
-        ] 
+    ]
 
     for hybridize in [True, False]:
         for shape1, shape2 in shapes:
@@ -260,7 +349,7 @@ def dldx(x1, x2):
                 test_ldexp = TestLdexp()
                 if hybridize:
                     test_ldexp.hybridize()
-                x1 = rand_ndarray(shape=shape1, dtype=dtype).as_np_ndarray() 
+                x1 = rand_ndarray(shape=shape1, dtype=dtype).as_np_ndarray()
                 x1.attach_grad()
                 x2 = rand_ndarray(shape=shape2, dtype=dtype).as_np_ndarray()
                 x2.attach_grad()
@@ -269,17 +358,17 @@ def dldx(x1, x2):
                 with mx.autograd.record():
                     mx_out = test_ldexp(x1, x2)
                 assert mx_out.shape == np_out.shape
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-1, atol=1e-1)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-1, atol=1e-1, use_broadcast=False)
 
                 mx_out.backward()
                 np_backward = dldx(x1.asnumpy(), x2.asnumpy())
-                assert_almost_equal(x1.grad.asnumpy(), np_backward[0], atol=1e-1, rtol=1e-1)
-                assert_almost_equal(x2.grad.asnumpy(), np_backward[1], atol=1e-1, rtol=1e-1)
+                assert_almost_equal(x1.grad.asnumpy(), np_backward[0], atol=1e-1, rtol=1e-1, use_broadcast=False)
+                assert_almost_equal(x2.grad.asnumpy(), np_backward[1], atol=1e-1, rtol=1e-1, use_broadcast=False)
 
                 # Test imperative once again
                 mx_out = np.ldexp(x1, x2)
                 np_out = _np_ldexp(x1.asnumpy(), x2.asnumpy())
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-1, atol=1e-1)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-1, atol=1e-1, use_broadcast=False)
 
 
 @with_seed()
@@ -313,16 +402,16 @@ def vdot_backward(a, b):
                 with mx.autograd.record():
                     mx_out = test_vdot(a, b)
                 assert mx_out.shape == np_out.shape
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol = 1e-3, atol = 1e-5)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
                 mx_out.backward()
                 np_backward = vdot_backward(a.asnumpy(), b.asnumpy())
-                assert_almost_equal(a.grad.asnumpy(), np_backward[0], rtol = 1e-2, atol=1e-2)
-                assert_almost_equal(b.grad.asnumpy(), np_backward[1], rtol = 1e-2, atol=1e-2)
+                assert_almost_equal(a.grad.asnumpy(), np_backward[0], rtol=1e-2, atol=1e-2, use_broadcast=False)
+                assert_almost_equal(b.grad.asnumpy(), np_backward[1], rtol=1e-2, atol=1e-2, use_broadcast=False)
 
                 # Test imperative once again
                 mx_out = np.vdot(a, b)
                 np_out = _np.vdot(a.asnumpy(), b.asnumpy())
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
 
                 # test numeric gradient
                 if len(shape) > 0 and _np.prod(shape) > 0:
@@ -410,16 +499,16 @@ def inner_backward(a, b):
                 with mx.autograd.record():
                     mx_out = test_inner(a, b)
                 assert mx_out.shape == np_out.shape
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol = 1e-3, atol = 1e-5)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
                 mx_out.backward()
                 np_backward = inner_backward(a.asnumpy(), b.asnumpy())
-                assert_almost_equal(a.grad.asnumpy(), np_backward[0], rtol = 1e-2, atol=1e-2)
-                assert_almost_equal(b.grad.asnumpy(), np_backward[1], rtol = 1e-2, atol=1e-2)
+                assert_almost_equal(a.grad.asnumpy(), np_backward[0], rtol=1e-2, atol=1e-2, use_broadcast=False)
+                assert_almost_equal(b.grad.asnumpy(), np_backward[1], rtol=1e-2, atol=1e-2, use_broadcast=False)
 
                 # Test imperative once again
                 mx_out = np.inner(a, b)
                 np_out = _np.inner(a.asnumpy(), b.asnumpy())
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
 
                 # test numeric gradient
                 a_sym = mx.sym.Variable("a").as_np_ndarray()
@@ -461,13 +550,13 @@ def hybrid_forward(self, F, a, b):
                 with mx.autograd.record():
                     mx_out = test_outer(a, b)
                 assert mx_out.shape == np_out.shape
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
                 mx_out.backward()
 
                 # Test imperative once again
                 mx_out = np.outer(a, b)
                 np_out = _np.outer(a.asnumpy(), b.asnumpy())
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
 
                 # test numeric gradient
                 a_sym = mx.sym.Variable("a").as_np_ndarray()
@@ -620,7 +709,7 @@ def _test_np_exception(func, shape, dim):
                             y = test_gluon(x)
                         assert y.shape == expected_ret.shape
                         assert_almost_equal(y.asnumpy(), expected_ret, rtol=1e-3 if itype == 'float16' else 1e-3,
-                                            atol=1e-5 if itype == 'float16' else 1e-5)
+                                            atol=1e-5 if itype == 'float16' else 1e-5, use_broadcast=False)
                         y.backward()
                         # only check the gradient with hardcoded input
                         if is_int(itype):
@@ -634,7 +723,7 @@ def _test_np_exception(func, shape, dim):
                         else:
                             mx_out = np.min(x, axis=axis, keepdims=keepdims)
                             np_out = _np.amin(x.asnumpy(), axis=axis, keepdims=keepdims)
-                        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+                        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
 
     # test zero and zero dim
     shapes = [(), (0), (2, 0), (0, 2, 1)]
@@ -693,7 +782,7 @@ def is_int(dtype):
                             y = test_mean(x)
                         assert y.shape == expected_ret.shape
                         assert_almost_equal(y.asnumpy(), expected_ret, rtol=1e-3 if dtype == 'float16' else 1e-3,
-                                            atol=1e-5 if dtype == 'float16' else 1e-5)
+                                            atol=1e-5 if dtype == 'float16' else 1e-5, use_broadcast=False)
 
                         y.backward()
                         N = x.size / y.size
@@ -709,7 +798,7 @@ def is_int(dtype):
                         # test imperative
                         mx_out = np.mean(x, axis=axis, dtype=dtype, keepdims=keepdims)
                         np_out = _np.mean(x.asnumpy(), axis=axis, dtype=acc_type[itype], keepdims=keepdims).astype(dtype)
-                        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+                        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
 
 
 @with_seed()
@@ -801,16 +890,17 @@ def test_np_linspace():
                         mx_ret = np.linspace(config, endpoint=endpoint, retstep=retstep, dtype=dtype)
                         np_ret = _np.linspace(config, endpoint=endpoint, retstep=retstep, dtype=dtype)
                     if retstep:
-                        assert_almost_equal(mx_ret[0].asnumpy(), np_ret[0], atol=1e-3, rtol=1e-5)
+                        assert_almost_equal(mx_ret[0].asnumpy(), np_ret[0], atol=1e-3, rtol=1e-5, use_broadcast=False)
                         same(mx_ret[1], np_ret[1])
                     else:
-                        assert_almost_equal(mx_ret.asnumpy(), np_ret, atol=1e-3, rtol=1e-5)
+                        assert_almost_equal(mx_ret.asnumpy(), np_ret, atol=1e-3, rtol=1e-5, use_broadcast=False)
     # check for exception input
     for config in exception_configs:
         assertRaises(MXNetError, np.linspace, *config)
     # check linspace equivalent to arange
     for test_index in range(1000):
-        assert_almost_equal(mx.np.linspace(0, test_index, test_index + 1).asnumpy(), _np.arange(test_index + 1))
+        assert_almost_equal(mx.np.linspace(0, test_index, test_index + 1).asnumpy(), _np.arange(test_index + 1),
+                            use_broadcast=False)
 
     class TestLinspace(HybridBlock):
         def __init__(self, start, stop, num=50, endpoint=None, retstep=False, dtype=None, axis=0):
@@ -843,7 +933,7 @@ def hybrid_forward(self, F, x):
                     if hybridize:
                         net.hybridize()
                     mx_out = net(x)
-                    assert_almost_equal(mx_out.asnumpy(), np_out, atol=1e-3, rtol=1e-5)
+                    assert_almost_equal(mx_out.asnumpy(), np_out, atol=1e-3, rtol=1e-5, use_broadcast=False)
 
 
 @with_seed()
@@ -1334,11 +1424,12 @@ def hybrid_forward(self, F, a, *args, **kwargs):
             with mx.autograd.record():
                 y = mx_func(mx_test_data)
             assert y.shape == np_out.shape
-            assert_almost_equal(y.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+            assert_almost_equal(y.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
 
             if ref_grad:
                 y.backward()
-                assert_almost_equal(mx_test_data.grad.asnumpy(), ref_grad(np_test_data), rtol=1e-1, atol=1e-2, equal_nan=True)
+                assert_almost_equal(mx_test_data.grad.asnumpy(), ref_grad(np_test_data), rtol=1e-1, atol=1e-2,
+                                    equal_nan=True, use_broadcast=False)
 
     funcs = {
         'absolute' : (lambda x: -1. * (x < 0) + (x > 0), -1.0, 1.0),
@@ -1413,14 +1504,14 @@ def hybrid_forward(self, F, a):
             with mx.autograd.record():
                 mx_out = test_relu(x)
             assert mx_out.shape == np_out.shape
-            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
             mx_out.backward()
             np_backward = np_relu_grad(x.asnumpy())
-            assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=1e-3, atol=1e-5)
+            assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=1e-3, atol=1e-5, use_broadcast=False)
 
             mx_out = npx.relu(x)
             np_out = np_relu(x.asnumpy())
-            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
 
 
 @with_seed()
@@ -1450,14 +1541,14 @@ def hybrid_forward(self, F, a):
             with mx.autograd.record():
                 mx_out = test_sigmoid(x)
             assert mx_out.shape == np_out.shape
-            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
             mx_out.backward()
             np_backward = np_sigmoid_grad(np_out)
-            assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=1e-3, atol=1e-5)
+            assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=1e-3, atol=1e-5, use_broadcast=False)
 
             mx_out = npx.sigmoid(x)
             np_out = np_sigmoid(x.asnumpy())
-            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
 
 
 @with_seed()
@@ -1563,17 +1654,17 @@ def get_indices(axis_size):
                     y = test_split(a)
                 assert len(y) == len(expected_ret)
                 for mx_out, np_out in zip(y, expected_ret):
-                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
 
                 mx.autograd.backward(y)
 
-                assert_almost_equal(a.grad.asnumpy(), _np.ones(a.shape), rtol=1e-3, atol=1e-5)
+                assert_almost_equal(a.grad.asnumpy(), _np.ones(a.shape), rtol=1e-3, atol=1e-5, use_broadcast=False)
 
                 # test imperative
                 mx_outs = np.split(a, indices_or_sections=indices_or_sections, axis=axis)
                 np_outs = _np.split(a.asnumpy(), indices_or_sections=indices_or_sections, axis=axis)
                 for mx_out, np_out in zip(mx_outs, np_outs):
-                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
 
 
 @with_seed()
@@ -1613,19 +1704,19 @@ def get_new_shape(shape, axis):
                     y = test_concat(a, b, c, d)
 
                 assert y.shape == expected_ret.shape
-                assert_almost_equal(y.asnumpy(), expected_ret, rtol=1e-3, atol=1e-5)
+                assert_almost_equal(y.asnumpy(), expected_ret, rtol=1e-3, atol=1e-5, use_broadcast=False)
 
                 y.backward()
 
-                assert_almost_equal(a.grad.asnumpy(), _np.ones(a.shape), rtol=1e-3, atol=1e-5)
-                assert_almost_equal(b.grad.asnumpy(), _np.ones(b.shape), rtol=1e-3, atol=1e-5)
-                assert_almost_equal(c.grad.asnumpy(), _np.ones(c.shape), rtol=1e-3, atol=1e-5)
-                assert_almost_equal(d.grad.asnumpy(), _np.ones(d.shape), rtol=1e-3, atol=1e-5)
+                assert_almost_equal(a.grad.asnumpy(), _np.ones(a.shape), rtol=1e-3, atol=1e-5, use_broadcast=False)
+                assert_almost_equal(b.grad.asnumpy(), _np.ones(b.shape), rtol=1e-3, atol=1e-5, use_broadcast=False)
+                assert_almost_equal(c.grad.asnumpy(), _np.ones(c.shape), rtol=1e-3, atol=1e-5, use_broadcast=False)
+                assert_almost_equal(d.grad.asnumpy(), _np.ones(d.shape), rtol=1e-3, atol=1e-5, use_broadcast=False)
 
                 # test imperative
                 mx_out = np.concatenate([a, b, c, d], axis=axis)
                 np_out = _np.concatenate([a.asnumpy(), b.asnumpy(), c.asnumpy(), d.asnumpy()], axis=axis)
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
 
 
 @with_seed()
@@ -1668,10 +1759,10 @@ def hybrid_forward(self, F, a, *args):
 
                 y.backward()
 
-                assert_almost_equal(mx_a.grad.asnumpy(), _np.ones(shape), rtol=1e-3, atol=1e-5)
-                assert_almost_equal(mx_b.grad.asnumpy(), _np.ones(shape), rtol=1e-3, atol=1e-5)
-                assert_almost_equal(mx_c.grad.asnumpy(), _np.ones(shape), rtol=1e-3, atol=1e-5)
-                assert_almost_equal(mx_d.grad.asnumpy(), _np.ones(shape), rtol=1e-3, atol=1e-5)
+                assert_almost_equal(mx_a.grad.asnumpy(), _np.ones(shape), rtol=1e-3, atol=1e-5, use_broadcast=False)
+                assert_almost_equal(mx_b.grad.asnumpy(), _np.ones(shape), rtol=1e-3, atol=1e-5, use_broadcast=False)
+                assert_almost_equal(mx_c.grad.asnumpy(), _np.ones(shape), rtol=1e-3, atol=1e-5, use_broadcast=False)
+                assert_almost_equal(mx_d.grad.asnumpy(), _np.ones(shape), rtol=1e-3, atol=1e-5, use_broadcast=False)
 
                 np_out = _np.stack([np_a, np_b, np_c, np_d], axis=axis)
                 mx_out = np.stack([mx_a, mx_b, mx_c, mx_d], axis=axis)
@@ -1701,14 +1792,14 @@ def hybrid_forward(self, F, a):
                 with mx.autograd.record():
                     mx_out = test_ravel(x)
                 assert mx_out.shape == np_out.shape
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
                 mx_out.backward()
                 np_backward = _np.ones(shape)
-                assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=1e-3, atol=1e-5)
+                assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=1e-3, atol=1e-5, use_broadcast=False)
 
                 mx_out = np.ravel(x)
                 np_out = _np.ravel(x.asnumpy())
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
 
 
 @with_seed()
@@ -2019,15 +2110,15 @@ def hybrid_forward(self, F, a):
                         with mx.autograd.record():
                             mx_out = test_cumsum(x)
                         assert mx_out.shape == np_out.shape
-                        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+                        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
                         mx_out.backward()
                         np_backward = np_cumsum_backward(_np.ones(np_out.shape, dtype=otype),
                                                          axis=axis, dtype=otype).reshape(x.shape)
-                        assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=1e-3, atol=1e-5)
+                        assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=1e-3, atol=1e-5, use_broadcast=False)
 
                         mx_out = np.cumsum(x, axis=axis, dtype=otype)
                         np_out = _np.cumsum(x.asnumpy(), axis=axis, dtype=otype)
-                        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+                        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
 
 
 @with_seed()
@@ -2058,7 +2149,7 @@ def test_sample_with_replacement(sampler, num_classes, shape, weight=None):
         expected_density = (weight.asnumpy() if weight is not None else
                             _np.array([1 / num_classes] * num_classes))
         # test almost equal
-        assert_almost_equal(generated_density, expected_density, rtol=1e-1, atol=1e-1)
+        assert_almost_equal(generated_density, expected_density, rtol=1e-1, atol=1e-1, use_broadcast=False)
         # test shape
         assert (samples.shape == shape)
 
@@ -2075,7 +2166,7 @@ def test_sample_without_replacement(sampler, num_classes, shape, num_trials, wei
             out = sampler(num_classes, 1, replace=False, p=weight).item()
             bins[out] += 1
         bins /= num_trials
-        assert_almost_equal(bins, expected_freq, rtol=1e-1, atol=1e-1)
+        assert_almost_equal(bins, expected_freq, rtol=1e-1, atol=1e-1, use_broadcast=False)
 
     def test_indexing_mode(sampler, set_size, samples_size, replace, weight=None):
         a = np.arange(set_size)
@@ -2236,7 +2327,7 @@ def hybrid_forward(self, F, x):
                         net.hybridize()
                     mx_ret = net(a)
                     np_ret = _np.linalg.norm(a.asnumpy(), ord=ord, axis=axis, keepdims=keepdims)
-                    assert_almost_equal(mx_ret.asnumpy(), np_ret, atol=1e-5, rtol=1e-4)
+                    assert_almost_equal(mx_ret.asnumpy(), np_ret, atol=1e-5, rtol=1e-4, use_broadcast=False)
 
 
 @with_seed()
@@ -2295,18 +2386,18 @@ def get_grad_right(a1, a2):
                     with mx.autograd.record():
                         mx_out = test_copysign(a1, a2)
                     assert mx_out.shape == expected_np.shape
-                    assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol)
+                    assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol, use_broadcast=False)
 
                     # Test gradient
                     mx_out.backward()
                     a1_grad, a2_grad = get_grad(a1_np, a2_np)
-                    assert_almost_equal(a1.grad.asnumpy(), a1_grad, rtol=rtol, atol=atol)
-                    assert_almost_equal(a2.grad.asnumpy(), a2_grad, rtol=rtol, atol=atol)
+                    assert_almost_equal(a1.grad.asnumpy(), a1_grad, rtol=rtol, atol=atol, use_broadcast=False)
+                    assert_almost_equal(a2.grad.asnumpy(), a2_grad, rtol=rtol, atol=atol, use_broadcast=False)
 
                     # Test imperative once again
                     mx_out = np.copysign(a1, a2)
                     expected_np = _np.copysign(a1_np, a2_np)
-                    assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol)
+                    assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol, use_broadcast=False)
 
     types = ['float16', 'float32', 'float64']
     for x_shape in shapes:
@@ -2320,12 +2411,12 @@ def get_grad_right(a1, a2):
             with mx.autograd.record():
                 mx_out = np.copysign(x, scalar)
             assert mx_out.shape == expected_np.shape
-            assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol)
+            assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol, use_broadcast=False)
 
             # Test gradient
             mx_out.backward()
             x_grad = get_grad_left(x_np, scalar)
-            assert_almost_equal(x.grad.asnumpy(), x_grad, rtol=rtol, atol=atol)
+            assert_almost_equal(x.grad.asnumpy(), x_grad, rtol=rtol, atol=atol, use_broadcast=False)
 
             # Test right
             x_np = _np.array(_np.random.uniform(-2.0, 2.0, x_shape), dtype=dtype)
@@ -2336,12 +2427,12 @@ def get_grad_right(a1, a2):
             with mx.autograd.record():
                 mx_out = np.copysign(scalar, x)
             assert mx_out.shape == expected_np.shape
-            assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol)
+            assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol, use_broadcast=False)
 
             # Test gradient
             mx_out.backward()
             x_grad = get_grad_right(scalar, x_np)
-            assert_almost_equal(x.grad.asnumpy(), x_grad, rtol=rtol, atol=atol)
+            assert_almost_equal(x.grad.asnumpy(), x_grad, rtol=rtol, atol=atol, use_broadcast=False)
 
 
 @with_seed()
@@ -2410,22 +2501,22 @@ def get_grad(UT, L, V):
                 # check UT @ L @ V == A
                 t = _np.matmul(UT * L[..., None, :], V)
                 assert t.shape == data_np.shape
-                assert_almost_equal(t, data_np, rtol=rtol, atol=atol)
+                assert_almost_equal(t, data_np, rtol=rtol, atol=atol, use_broadcast=False)
                 # check UT @ U == I
                 I = _np.matmul(UT, _np.swapaxes(UT, -2, -1))
                 I_np = _np.ones_like(UT) * _np.eye(shape[-2])
                 assert I.shape == I_np.shape
-                assert_almost_equal(I, I_np, rtol=rtol, atol=atol)
+                assert_almost_equal(I, I_np, rtol=rtol, atol=atol, use_broadcast=False)
                 # check U @ UT == I
                 I = _np.matmul(_np.swapaxes(UT, -2, -1), UT)
                 I_np = _np.ones_like(UT) * _np.eye(shape[-2])
                 assert I.shape == I_np.shape
-                assert_almost_equal(I, I_np, rtol=rtol, atol=atol)
+                assert_almost_equal(I, I_np, rtol=rtol, atol=atol, use_broadcast=False)
                 # check V @ VT == I
                 I = _np.matmul(V, _np.swapaxes(V, -2, -1))
                 I_np = _np.ones_like(UT) * _np.eye(shape[-2])
                 assert I.shape == I_np.shape
-                assert_almost_equal(I, I_np, rtol=rtol, atol=atol)
+                assert_almost_equal(I, I_np, rtol=rtol, atol=atol, use_broadcast=False)
                 # check descending singular values
                 s = [L[..., i] - L[..., i + 1] for i in range(L.shape[-1] - 1)]
                 s = _np.array(s)
@@ -2436,7 +2527,8 @@ def get_grad(UT, L, V):
                 mx.autograd.backward(ret)
                 if ((s > 1e-5).all() and (L.size == 0 or (L > 1e-5).all())):
                     backward_expected = get_grad(ret[0].asnumpy(), ret[1].asnumpy(), ret[2].asnumpy())
-                    assert_almost_equal(data.grad.asnumpy(), backward_expected, rtol=rtol, atol=atol)
+                    assert_almost_equal(data.grad.asnumpy(), backward_expected, rtol=rtol, atol=atol,
+                                        use_broadcast=False)
 
 
 @with_seed()
@@ -2481,18 +2573,18 @@ def g(data):
                 with mx.autograd.record():
                     mx_out = test_vstack(*v)
                 assert mx_out.shape == expected_np.shape
-                assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol)
+                assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol, use_broadcast=False)
 
                 # Test gradient
                 mx_out.backward()
                 for i in range(3):
                     expected_grad = g(v_np[i])
-                    assert_almost_equal(v[i].grad.asnumpy(), expected_grad, rtol=rtol, atol=atol)
+                    assert_almost_equal(v[i].grad.asnumpy(), expected_grad, rtol=rtol, atol=atol, use_broadcast=False)
 
                 # Test imperative once again
                 mx_out = np.vstack(v)
                 expected_np = _np.vstack(v_np)
-                assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol)
+                assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol, use_broadcast=False)
 
 
 @with_seed()
@@ -2608,15 +2700,18 @@ def g(data, axis1, axis2, offset):
                                 with mx.autograd.record():
                                     out_mx = test_trace(data.as_np_ndarray())
                                 assert out_mx.shape == expected_np.shape
-                                assert_almost_equal(out_mx.asnumpy(), expected_np, rtol=rtol, atol=atol)
+                                assert_almost_equal(out_mx.asnumpy(), expected_np, rtol=rtol, atol=atol,
+                                                    use_broadcast=False)
                                 out_mx.backward()
                                 backward_expected = g(data_np, axis1=axis1, axis2=axis2, offset=offset)
-                                assert_almost_equal(data.grad.asnumpy(), backward_expected, rtol=rtol, atol=atol)
+                                assert_almost_equal(data.grad.asnumpy(), backward_expected, rtol=rtol, atol=atol,
+                                                    use_broadcast=False)
 
                                 # Test imperative once again
                                 data = mx.nd.array(data_np, dtype=dtype)
                                 out_mx = np.trace(data.as_np_ndarray(), axis1=axis1, axis2=axis2, offset=offset)
-                                assert_almost_equal(out_mx.asnumpy(), expected_np, rtol=rtol, atol=atol)
+                                assert_almost_equal(out_mx.asnumpy(), expected_np, rtol=rtol, atol=atol,
+                                                    use_broadcast=False)
 
     # bad params
     params = [
@@ -2664,11 +2759,11 @@ def hybrid_forward(self, F, x, *args, **kwargs):
                     if hybridize:
                         mx_func.hybridize()
                     mx_out = mx_func(x)
-                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
                     # test imperative
                     mx_out = getattr(np, func)(M=config, dtype=dtype)
                     np_out = np_func(M=config).astype(dtype)
-                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
 
 
 @with_seed()
@@ -2700,15 +2795,15 @@ def hybrid_forward(self, F, x):
                 with mx.autograd.record():
                     mx_out = test_flip(x)
                 assert mx_out.shape == np_out.shape
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol, use_broadcast=False)
                 mx_out.backward()
                 np_backward = _np.ones(np_out.shape)
-                assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=rtol, atol=atol)
+                assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=rtol, atol=atol, use_broadcast=False)
 
                 # Test imperative once again
                 mx_out = np.flip(x, axis)
                 np_out = _np.flip(x.asnumpy(), axis)
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol, use_broadcast=False)
 
 
 @with_seed()
@@ -2736,11 +2831,11 @@ def hybrid_forward(self, F, x):
                     np_out = _np.around(x.asnumpy(), d)
                     mx_out = test_around(x)
                     assert mx_out.shape == np_out.shape
-                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol, use_broadcast=False)
 
                     mx_out = np.around(x, d)
                     np_out = _np.around(x.asnumpy(), d)
-                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol, use_broadcast=False)
 
 
 @with_seed()
@@ -2796,18 +2891,18 @@ def dimReduce(src, des):
                 with mx.autograd.record():
                     mx_out = test_arctan2(x1, x2)
                 assert mx_out.shape == np_out.shape
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol, use_broadcast=False)
                 mx_out.backward()
                 np_backward_1 = x21 / (x11 * x11 + x21 * x21)
                 np_backward_2 = -1 * x11 / (x11 * x11 + x21 * x21)
                 np_backward_1 = dimReduce(np_backward_1, x11)
                 np_backward_2 = dimReduce(np_backward_2, x21)
-                assert_almost_equal(x1.grad.asnumpy(), np_backward_1, rtol=rtol, atol=atol)
-                assert_almost_equal(x2.grad.asnumpy(), np_backward_2, rtol=rtol, atol=atol)
+                assert_almost_equal(x1.grad.asnumpy(), np_backward_1, rtol=rtol, atol=atol, use_broadcast=False)
+                assert_almost_equal(x2.grad.asnumpy(), np_backward_2, rtol=rtol, atol=atol, use_broadcast=False)
 
                 mx_out = np.arctan2(x1, x2)
                 np_out = _np.arctan2(x1.asnumpy(), x2.asnumpy())
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol, use_broadcast=False)
 
 
 @with_seed()
@@ -2833,13 +2928,13 @@ def hybrid_forward(self, F, x):
                 np_out = _np.transpose(np_out)
                 mx_out = test_nonzero(x)
                 assert mx_out.shape == np_out.shape
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol, atol)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol, atol, use_broadcast=False)
 
                 # Test imperative once again
                 mx_out = npx.nonzero(x)
                 np_out = _np.nonzero(x.asnumpy())
                 np_out = _np.transpose(np_out)
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol, atol)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol, atol, use_broadcast=False)
 
 
 @with_seed()
@@ -2894,18 +2989,18 @@ def dimReduce(src, des):
                 with mx.autograd.record():
                     mx_out = test_hypot(x1, x2)
                 assert mx_out.shape == np_out.shape
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol, use_broadcast=False)
                 mx_out.backward()
                 np_backward_1 = x11 / np_out
                 np_backward_2 = x21 / np_out
                 np_backward_1 = dimReduce(np_backward_1, x11)
                 np_backward_2 = dimReduce(np_backward_2, x21)
-                assert_almost_equal(x1.grad.asnumpy(), np_backward_1, rtol=rtol, atol=atol)
-                assert_almost_equal(x2.grad.asnumpy(), np_backward_2, rtol=rtol, atol=atol)
+                assert_almost_equal(x1.grad.asnumpy(), np_backward_1, rtol=rtol, atol=atol, use_broadcast=False)
+                assert_almost_equal(x2.grad.asnumpy(), np_backward_2, rtol=rtol, atol=atol, use_broadcast=False)
 
                 mx_out = np.hypot(x1, x2)
                 np_out = _np.hypot(x1.asnumpy(), x2.asnumpy())
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol, use_broadcast=False)
 
 
 @with_seed()
@@ -2947,13 +3042,13 @@ def hybrid_forward(self, F, a):
                 mx_out = test_unique(x)
                 assert mx_out[0].shape == np_out[0].shape
                 for i in range(4):
-                    assert_almost_equal(mx_out[i].asnumpy(), np_out[i], rtol=1e-3, atol=1e-5)
+                    assert_almost_equal(mx_out[i].asnumpy(), np_out[i], rtol=1e-3, atol=1e-5, use_broadcast=False)
 
                 # Test imperative once again
                 mx_out = np.unique(x, *config[1:])
                 np_out = _np.unique(x.asnumpy(), *config[1:])
                 for i in range(4):
-                    assert_almost_equal(mx_out[i].asnumpy(), np_out[i], rtol=1e-3, atol=1e-5)
+                    assert_almost_equal(mx_out[i].asnumpy(), np_out[i], rtol=1e-3, atol=1e-5, use_broadcast=False)
 
 
 @with_seed()

From 4940ec0e7408fad2443f921131cf1ada72724c38 Mon Sep 17 00:00:00 2001
From: reminisce <wujun.nju@gmail.com>
Date: Sun, 6 Oct 2019 21:56:13 -0700
Subject: [PATCH 411/813] Fix random op signature

Fix weight shape inference using uniform/normal

Delete absolute_import
---
 python/mxnet/initializer.py                |  8 ++++----
 python/mxnet/ndarray/numpy/random.py       | 18 +++++++-----------
 python/mxnet/numpy/random.py               |  8 ++++----
 python/mxnet/numpy_op_signature.py         |  7 +++----
 python/mxnet/symbol/numpy/random.py        | 13 +++----------
 src/operator/random/sample_op.cc           |  2 --
 tests/python/unittest/test_exc_handling.py | 15 ++++++++++++++-
 tests/python/unittest/test_numpy_gluon.py  | 15 +++++++++------
 tests/python/unittest/test_numpy_op.py     | 10 +++++-----
 9 files changed, 49 insertions(+), 47 deletions(-)

diff --git a/python/mxnet/initializer.py b/python/mxnet/initializer.py
index 19a0c3a55392..eafe73651dbc 100755
--- a/python/mxnet/initializer.py
+++ b/python/mxnet/initializer.py
@@ -505,7 +505,7 @@ def __init__(self, scale=0.07):
 
     def _init_weight(self, _, arr):
         uniform_fn = _mx_np.random.uniform if is_np_array() else random.uniform
-        uniform_fn(-self.scale, self.scale, out=arr)
+        uniform_fn(-self.scale, self.scale, arr.shape, out=arr)
 
 @register
 class Normal(Initializer):
@@ -539,7 +539,7 @@ def __init__(self, sigma=0.01):
 
     def _init_weight(self, _, arr):
         normal_fn = _mx_np.random.normal if is_np_array() else random.normal
-        normal_fn(0, self.sigma, out=arr)
+        normal_fn(0, self.sigma, arr.shape, out=arr)
 
 @register
 class Orthogonal(Initializer):
@@ -639,10 +639,10 @@ def _init_weight(self, name, arr):
         scale = np.sqrt(self.magnitude / factor)
         if self.rnd_type == "uniform":
             uniform_fn = _mx_np.random.uniform if is_np_array() else random.uniform
-            uniform_fn(-scale, scale, out=arr)
+            uniform_fn(-scale, scale, arr.shape, out=arr)
         elif self.rnd_type == "gaussian":
             normal_fn = _mx_np.random.normal if is_np_array() else random.normal
-            normal_fn(0, scale, out=arr)
+            normal_fn(0, scale, arr.shape, out=arr)
         else:
             raise ValueError("Unknown random type")
 
diff --git a/python/mxnet/ndarray/numpy/random.py b/python/mxnet/ndarray/numpy/random.py
index 883c56e12393..3ad9f56eca48 100644
--- a/python/mxnet/ndarray/numpy/random.py
+++ b/python/mxnet/ndarray/numpy/random.py
@@ -26,7 +26,7 @@
 __all__ = ['randint', 'uniform', 'normal', "choice"]
 
 
-def randint(low, high=None, size=None, dtype=None, **kwargs):
+def randint(low, high=None, size=None, dtype=None, ctx=None, out=None):
     """Return random integers from `low` (inclusive) to `high` (exclusive).
 
     Return random integers from the "discrete uniform" distribution of
@@ -75,14 +75,12 @@ def randint(low, high=None, size=None, dtype=None, **kwargs):
     array([[4, 0, 2, 1],
         [3, 2, 2, 0]])
     """
-    ctx = kwargs.pop('ctx', None)
-    out = kwargs.pop('out', None)
     if dtype is None:
         dtype = 'int'
     if ctx is None:
         ctx = current_context()
     if size is None:
-        size = 1
+        size = ()
     if high is None:
         high = low
         low = 0
@@ -114,6 +112,8 @@ def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
         Data type of output samples. Default is 'float32'
     ctx : Context, optional
         Device context of output. Default is current context.
+    out : ``ndarray``, optional
+        Store output to an existing ``ndarray``.
 
     Returns
     -------
@@ -126,8 +126,6 @@ def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
         dtype = 'float32'
     if ctx is None:
         ctx = current_context()
-    if out is not None:
-        size = out.shape
     if size == ():
         size = None
     if input_type == (True, True):
@@ -144,7 +142,7 @@ def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
                             ctx=ctx, dtype=dtype, out=out)
 
 
-def normal(loc=0.0, scale=1.0, size=None, dtype=None, **kwargs):
+def normal(loc=0.0, scale=1.0, size=None, dtype=None, ctx=None, out=None):
     """Draw random samples from a normal (Gaussian) distribution.
 
     Samples are distributed according to a normal distribution parametrized
@@ -165,6 +163,8 @@ def normal(loc=0.0, scale=1.0, size=None, dtype=None, **kwargs):
         Data type of output samples. Default is 'float32'
     ctx : Context, optional
         Device context of output. Default is current context.
+    out : ``ndarray``, optional
+        Store output to an existing ``ndarray``.
 
     Returns
     -------
@@ -173,14 +173,10 @@ def normal(loc=0.0, scale=1.0, size=None, dtype=None, **kwargs):
     """
     from ...numpy import ndarray as np_ndarray
     input_type = (isinstance(loc, np_ndarray), isinstance(scale, np_ndarray))
-    ctx = kwargs.pop('ctx', None)
-    out = kwargs.pop('out', None)
     if dtype is None:
         dtype = 'float32'
     if ctx is None:
         ctx = current_context()
-    if out is not None:
-        size = out.shape
     if size == ():
         size = None
     if input_type == (True, True):
diff --git a/python/mxnet/numpy/random.py b/python/mxnet/numpy/random.py
index f89b8cac9871..6d845b617261 100644
--- a/python/mxnet/numpy/random.py
+++ b/python/mxnet/numpy/random.py
@@ -24,7 +24,7 @@
 __all__ = ["randint", "uniform", "normal", "choice"]
 
 
-def randint(low, high=None, size=None, dtype=None, **kwargs):
+def randint(low, high=None, size=None, dtype=None, ctx=None, out=None):
     """Return random integers from `low` (inclusive) to `high` (exclusive).
 
     Return random integers from the "discrete uniform" distribution of
@@ -73,7 +73,7 @@ def randint(low, high=None, size=None, dtype=None, **kwargs):
     array([[4, 0, 2, 1],
         [3, 2, 2, 0]])
     """
-    return _mx_nd_np.random.randint(low, high, size, dtype, **kwargs)
+    return _mx_nd_np.random.randint(low, high, size, dtype, ctx, out)
 
 
 def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
@@ -110,7 +110,7 @@ def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
     return _mx_nd_np.random.uniform(low, high, size=size, ctx=ctx, dtype=dtype, out=out)
 
 
-def normal(loc=0.0, scale=1.0, size=None, dtype=None, **kwargs):
+def normal(loc=0.0, scale=1.0, size=None, dtype=None, ctx=None, out=None):
     """Draw random samples from a normal (Gaussian) distribution.
 
     Samples are distributed according to a normal distribution parametrized
@@ -139,7 +139,7 @@ def normal(loc=0.0, scale=1.0, size=None, dtype=None, **kwargs):
     out : ndarray
         Drawn samples from the parameterized normal distribution.
     """
-    return _mx_nd_np.random.normal(loc, scale, size, dtype, **kwargs)
+    return _mx_nd_np.random.normal(loc, scale, size, dtype, ctx, out)
 
 
 def multinomial(n, pvals, size=None, **kwargs):
diff --git a/python/mxnet/numpy_op_signature.py b/python/mxnet/numpy_op_signature.py
index cb56f670ed50..314de302109d 100644
--- a/python/mxnet/numpy_op_signature.py
+++ b/python/mxnet/numpy_op_signature.py
@@ -17,8 +17,7 @@
 
 """Make builtin ops' signatures compatible with NumPy."""
 
-from __future__ import absolute_import # pylint: disable=reimported
-from sys import version, version_info
+import sys
 import warnings
 from . import _numpy_op_doc
 from . import numpy as mx_np
@@ -55,11 +54,11 @@ def _get_builtin_op(op_name):
 
 
 def _register_op_signatures():
-    if version_info.major < 3 or version_info.minor < 5:
+    if sys.version_info.major < 3 or sys.version_info.minor < 5:
         warnings.warn('Some mxnet.numpy operator signatures may not be displayed consistently with '
                       'their counterparts in the official NumPy package due to too-low Python '
                       'version {}. Python >= 3.5 is required to make the signatures display correctly.'
-                      .format(str(version)))
+                      .format(str(sys.version)))
         return
 
     import inspect
diff --git a/python/mxnet/symbol/numpy/random.py b/python/mxnet/symbol/numpy/random.py
index a5ccbc1d1995..2398b9ce759b 100644
--- a/python/mxnet/symbol/numpy/random.py
+++ b/python/mxnet/symbol/numpy/random.py
@@ -25,7 +25,7 @@
 __all__ = ['randint', 'uniform', 'normal']
 
 
-def randint(low, high=None, size=None, dtype=None, **kwargs):
+def randint(low, high=None, size=None, dtype=None, ctx=None, out=None):
     """Return random integers from `low` (inclusive) to `high` (exclusive).
 
     Return random integers from the "discrete uniform" distribution of
@@ -74,14 +74,12 @@ def randint(low, high=None, size=None, dtype=None, **kwargs):
     array([[4, 0, 2, 1],
         [3, 2, 2, 0]])
     """
-    ctx = kwargs.pop('ctx', None)
-    out = kwargs.pop('out', None)
     if dtype is None:
         dtype = 'int'
     if ctx is None:
         ctx = current_context()
     if size is None:
-        size = 1
+        size = ()
     if high is None:
         high = low
         low = 0
@@ -143,7 +141,7 @@ def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
                             ctx=ctx, dtype=dtype, out=out)
 
 
-def normal(loc=0.0, scale=1.0, size=None, dtype=None, **kwargs):
+def normal(loc=0.0, scale=1.0, size=None, dtype=None, ctx=None, out=None):
     """Draw random samples from a normal (Gaussian) distribution.
 
     Samples are distributed according to a normal distribution parametrized
@@ -172,15 +170,10 @@ def normal(loc=0.0, scale=1.0, size=None, dtype=None, **kwargs):
     """
     from ._symbol import _Symbol as np_symbol
     input_type = (isinstance(loc, np_symbol), isinstance(scale, np_symbol))
-    ctx = kwargs.pop('ctx', None)
-    out = kwargs.pop('out', None)
     if dtype is None:
         dtype = 'float32'
     if ctx is None:
         ctx = current_context()
-    out = kwargs.pop('out', None)
-    if out is not None:
-        size = out.shape
     if size == ():
         size = None
     if input_type == (True, True):
diff --git a/src/operator/random/sample_op.cc b/src/operator/random/sample_op.cc
index d5b5e288258c..5fe97c8bfc9c 100644
--- a/src/operator/random/sample_op.cc
+++ b/src/operator/random/sample_op.cc
@@ -81,7 +81,6 @@ DMLC_REGISTER_PARAMETER(SampleGenNegBinomialLikeParam);
 MXNET_OPERATOR_REGISTER_SAMPLE(_random_uniform, SampleUniformParam)
 .add_alias("uniform")
 .add_alias("random_uniform")
-.add_alias("_npi_random_uniform")
 .describe(R"code(Draw random samples from a uniform distribution.
 
 .. note:: The existing alias ``uniform`` is deprecated.
@@ -100,7 +99,6 @@ Example::
 MXNET_OPERATOR_REGISTER_SAMPLE(_random_normal, SampleNormalParam)
 .add_alias("normal")
 .add_alias("random_normal")
-.add_alias("_npi_random_normal")
 .describe(R"code(Draw random samples from a normal (Gaussian) distribution.
 
 .. note:: The existing alias ``normal`` is deprecated.
diff --git a/tests/python/unittest/test_exc_handling.py b/tests/python/unittest/test_exc_handling.py
index f029d09b5f54..0fa5fcb6f1e6 100644
--- a/tests/python/unittest/test_exc_handling.py
+++ b/tests/python/unittest/test_exc_handling.py
@@ -21,9 +21,10 @@
 from common import setup_module, with_seed, teardown
 from mxnet.gluon import nn
 from mxnet.base import MXNetError
-from mxnet.test_utils import assert_exception, default_context, set_default_context
+from mxnet.test_utils import assert_exception, default_context, set_default_context, use_np
 from nose.tools import assert_raises
 
+
 @with_seed()
 def test_exc_imperative():
     def imperative(exec_numpy=True):
@@ -181,6 +182,7 @@ def run_training_iteration(data):
     mx.nd.waitall()
     mx.profiler.set_state("stop")
 
+
 @with_seed()
 def test_opencv_exception():
     def check_resize():
@@ -199,6 +201,17 @@ def test_np_reshape_exception():
     assert_raises(MXNetError, lambda: mx.np.reshape(a, (-1, 3)))
 
 
+@with_seed()
+@use_np
+def test_np_random_incorrect_named_arguments():
+    random_ops = ['uniform', 'normal', 'randint']
+    for op_name in random_ops:
+        op = getattr(mx.np.random, op_name, None)
+        assert op is not None
+        assert_raises(TypeError, op, shape=())
+        assert_raises(TypeError, op, shape=None)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()
diff --git a/tests/python/unittest/test_numpy_gluon.py b/tests/python/unittest/test_numpy_gluon.py
index 62ea38fc0c13..52ceb718fb15 100644
--- a/tests/python/unittest/test_numpy_gluon.py
+++ b/tests/python/unittest/test_numpy_gluon.py
@@ -26,12 +26,13 @@
 from common import with_seed
 
 
+@with_seed()
 def test_create_np_param():
     M, K, N = 10, 9, 20
 
-    def check_block_params(x, TestBlock, hybridize, expected_type):
+    def check_block_params(x, TestBlock, hybridize, expected_type, initializer):
         net = TestBlock()
-        net.initialize()
+        net.initialize(initializer())
         if hybridize:
             net.hybridize()
         net(x)
@@ -59,12 +60,14 @@ def hybrid_forward(self, F, x, w):
             return F.np.dot(x, w)
 
     x = mx.nd.random.uniform(shape=(M, K))
-    check_block_params(x, TestBlock1, False, mx.nd.NDArray)
-    check_block_params(x, TestBlock1, True, mx.nd.NDArray)
-    check_block_params(x.as_np_ndarray(), TestBlock2, False, np.ndarray)
-    check_block_params(x.as_np_ndarray(), TestBlock2, True, np.ndarray)
+    for initializer in [mx.initializer.Uniform, mx.initializer.Normal]:
+        check_block_params(x, TestBlock1, False, mx.nd.NDArray, initializer)
+        check_block_params(x, TestBlock1, True, mx.nd.NDArray, initializer)
+        check_block_params(x.as_np_ndarray(), TestBlock2, False, np.ndarray, initializer)
+        check_block_params(x.as_np_ndarray(), TestBlock2, True, np.ndarray, initializer)
 
 
+@with_seed()
 @use_np
 def test_optimizer_with_np_ndarrays():
     class LinearRegression(gluon.HybridBlock):
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 9d95d43fecbc..e6b3d41d37b0 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -695,11 +695,9 @@ def _test_np_exception(func, shape, dim):
                         if hybridize:
                             test_gluon.hybridize()
                         if is_int(itype):
-                            x = mx.nd.arange(120).reshape((2, 3, 4, 5))
-                            x = mx.nd.array(x)
+                            x = np.arange(120).reshape((2, 3, 4, 5))
                         else:
-                            x = mx.nd.random.uniform(-1.0, 1.0, shape=shape, dtype=itype)
-                        x = x.as_np_ndarray()
+                            x = np.random.uniform(-1.0, 1.0, size=shape, dtype=itype)
                         x.attach_grad()
                         if func == 'max':
                             expected_ret = _np.amax(x.asnumpy(), axis=axis, keepdims=keepdims)
@@ -1812,6 +1810,8 @@ def test_np_randint():
         (5, None)
     ]
     shapes = [
+        None,
+        (),
         (3, 3),
         (3, 4),
         (0, 0),
@@ -1825,7 +1825,7 @@ def test_np_randint():
     for shape in shapes:
         for (low, high) in params:
             data_mx = np.random.randint(low, high, size=shape)
-            assert data_mx.shape == shape
+            assert data_mx.shape == (shape if shape is not None else ())
 
     # test generator
     for dtype in ['int32', 'int64']:

From df4125aa1d4ef013e68e6adf3738dabdb1b52865 Mon Sep 17 00:00:00 2001
From: Tao Lv <tao.a.lv@intel.com>
Date: Tue, 8 Oct 2019 08:39:56 +0800
Subject: [PATCH 412/813] update NEWS.md and README.md (#16385)

---
 NEWS.md   | 24 ++++++++++++++++++++++++
 README.md |  1 +
 2 files changed, 25 insertions(+)

diff --git a/NEWS.md b/NEWS.md
index 8ea31334b6d1..b49c119d87cb 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -18,6 +18,7 @@
 MXNet Change Log
 ================
 - [MXNet Change Log](#mxnet-change-log)
+  * [1.5.1](#151)
   * [1.5.0](#150)
     + [New Features](#new-features)
       - [Automatic Mixed Precision(experimental)](#automatic-mixed-precision-experimental-)
@@ -199,6 +200,29 @@ MXNet Change Log
   * [v0.7](#v07)
   * [v0.5 (initial release)](#v05--initial-release-)
 
+## 1.5.1
+Apache MXNet (incubating) 1.5.1 is a maintenance release incorporating important bug fixes and important performance improvements. All users of Apache MXNet (incubating) 1.5.0 are advised to upgrade. You can install Apache MXNet (incubating) 1.5.1 at the usual place. Please review these Release Notes to learn the bug fixes.
+
+### Bug-fixes
+* add deconv in TRT subgraph (#15666) (#16043)
+* Update TRT tutorial with new APIs (#16044)
+* Fix _copy_to on MKLDNN backend (#15637) (#15803)
+* Benchmark doc fix (#15769) (#16029)
+* remove Julia cat image for license issue (#15964) (#16026)
+* added check for empty params file and unknown param (not arg/aux) (#15917)
+* fix license issues (#15806) (#15860)
+* prevent TRT_Logger to be destroyed before TRT engine (#14898) (#15877)
+* [MXNET-1086] added sub and mul to ONNX->TensorRT conversion (#15344) (#15875)
+* handle fix_gamma in tensorrt subgraph conversion correctly (#15645) (#15874)
+* fix LinearRegressionOutput with empty label (#15620) (#15873)
+* [v1.5.x] [MKLDNN] Independent gradients requests check with respect to weights… (#15805)
+* fix dropout mask output (#15697) (#15804)
+* fix fp32 flatten issue (#15351) (#15802)
+* Clojure package remove source images (#15828)
+* changed constructor args (#15601) (#15827)
+* Add MKLDNN 4c layout to fix gluoncv se_resnext101_64x4d (#15692) (#15801)
+* Fix the bug of `MXEnginePushAsyncND` and `MXEnginePushSyncND` (#15751) (#15792)
+
 ## 1.5.0
 
 ### New Features
diff --git a/README.md b/README.md
index 413f8c492c45..e8992393f246 100644
--- a/README.md
+++ b/README.md
@@ -50,6 +50,7 @@ How to Contribute
 
 What's New
 ----------
+* [Version 1.5.1 Release](https://github.com/apache/incubator-mxnet/releases/tag/1.5.1) - MXNet 1.5.1 Patch Release.
 * [Version 1.5.0 Release](https://github.com/apache/incubator-mxnet/releases/tag/1.5.0) - MXNet 1.5.0 Release.
 * [Version 1.4.1 Release](https://github.com/apache/incubator-mxnet/releases/tag/1.4.1) - MXNet 1.4.1 Patch Release.
 * [Version 1.4.0 Release](https://github.com/apache/incubator-mxnet/releases/tag/1.4.0) - MXNet 1.4.0 Release.

From 0bace559cd6d70dbbfe3ca6545e56c98c92116b1 Mon Sep 17 00:00:00 2001
From: Xi Wang <xidulu@gmail.com>
Date: Tue, 8 Oct 2019 02:34:37 +0000
Subject: [PATCH 413/813] fix choice signature

---
 python/mxnet/ndarray/numpy/random.py | 4 +---
 python/mxnet/numpy/random.py         | 4 ++--
 python/mxnet/symbol/numpy/random.py  | 5 +----
 3 files changed, 4 insertions(+), 9 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/random.py b/python/mxnet/ndarray/numpy/random.py
index 3ad9f56eca48..9e401695709d 100644
--- a/python/mxnet/ndarray/numpy/random.py
+++ b/python/mxnet/ndarray/numpy/random.py
@@ -245,7 +245,7 @@ def multinomial(n, pvals, size=None):
         return _npi.multinomial(n=n, pvals=pvals, size=size)
 
 
-def choice(a, size=None, replace=True, p=None, **kwargs):
+def choice(a, size=None, replace=True, p=None, ctx=None, out=None):
     """Generates a random sample from a given 1-D array
 
     Parameters
@@ -298,10 +298,8 @@ def choice(a, size=None, replace=True, p=None, **kwargs):
     array([2, 3, 0])
     """
     from ...numpy import ndarray as np_ndarray
-    ctx = kwargs.pop('ctx', None)
     if ctx is None:
         ctx = current_context()
-    out = kwargs.pop('out', None)
     if size == ():
         size = None
     if isinstance(a, np_ndarray):
diff --git a/python/mxnet/numpy/random.py b/python/mxnet/numpy/random.py
index 6d845b617261..746ce99c6d9c 100644
--- a/python/mxnet/numpy/random.py
+++ b/python/mxnet/numpy/random.py
@@ -178,7 +178,7 @@ def multinomial(n, pvals, size=None, **kwargs):
     return _mx_nd_np.random.multinomial(n, pvals, size, **kwargs)
 
 
-def choice(a, size=None, replace=True, p=None, **kwargs):
+def choice(a, size=None, replace=True, p=None, ctx=None, out=None):
     """Generates a random sample from a given 1-D array
 
     Parameters
@@ -230,4 +230,4 @@ def choice(a, size=None, replace=True, p=None, **kwargs):
     >>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])
     array([2, 3, 0])
     """
-    return _mx_nd_np.random.choice(a, size, replace, p, **kwargs)
+    return _mx_nd_np.random.choice(a, size, replace, p, ctx, out)
diff --git a/python/mxnet/symbol/numpy/random.py b/python/mxnet/symbol/numpy/random.py
index 2398b9ce759b..84cc5704b138 100644
--- a/python/mxnet/symbol/numpy/random.py
+++ b/python/mxnet/symbol/numpy/random.py
@@ -190,7 +190,7 @@ def normal(loc=0.0, scale=1.0, size=None, dtype=None, ctx=None, out=None):
                            ctx=ctx, dtype=dtype, out=out)
 
 
-def choice(a, size=None, replace=True, p=None, **kwargs):
+def choice(a, size=None, replace=True, p=None, ctx=None, out=None):
     """Generates a random sample from a given 1-D array
 
     Parameters
@@ -243,13 +243,10 @@ def choice(a, size=None, replace=True, p=None, **kwargs):
     array([2, 3, 0])
     """
     from ._symbol import _Symbol as np_symbol
-    ctx = kwargs.pop('ctx', None)
     if ctx is None:
         ctx = current_context()
-    out = kwargs.pop('out', None)
     if size == ():
         size = None
-
     if isinstance(a, np_symbol):
         ctx = None
         if p is None:

From ec766d55b26fe734d41fd6b9e6b81c404d77a947 Mon Sep 17 00:00:00 2001
From: Xi Wang <xidulu@gmail.com>
Date: Tue, 8 Oct 2019 05:31:54 +0000
Subject: [PATCH 414/813] add raise test for shape

---
 tests/python/unittest/test_exc_handling.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/python/unittest/test_exc_handling.py b/tests/python/unittest/test_exc_handling.py
index 0fa5fcb6f1e6..e3c333705260 100644
--- a/tests/python/unittest/test_exc_handling.py
+++ b/tests/python/unittest/test_exc_handling.py
@@ -204,7 +204,7 @@ def test_np_reshape_exception():
 @with_seed()
 @use_np
 def test_np_random_incorrect_named_arguments():
-    random_ops = ['uniform', 'normal', 'randint']
+    random_ops = ['uniform', 'normal', 'randint', 'choice']
     for op_name in random_ops:
         op = getattr(mx.np.random, op_name, None)
         assert op is not None

From d5666eda70804931592fbb54ac5c526ab4587357 Mon Sep 17 00:00:00 2001
From: igolan <26796766+igolan@users.noreply.github.com>
Date: Tue, 8 Oct 2019 15:41:14 -0400
Subject: [PATCH 415/813] Round and sign straight-through-estimators C
 operators. (#16373)

* Implemented round and sign straight-through-estimators C operators.

* fuxed lint
---
 src/operator/contrib/stes_op.cc               |  84 +++++++++++
 src/operator/contrib/stes_op.cu               |  43 ++++++
 src/operator/contrib/stes_op.h                |  33 +++++
 tests/python/unittest/test_contrib_stes_op.py | 137 ++++++++++++++++++
 4 files changed, 297 insertions(+)
 create mode 100644 src/operator/contrib/stes_op.cc
 create mode 100644 src/operator/contrib/stes_op.cu
 create mode 100644 src/operator/contrib/stes_op.h
 create mode 100644 tests/python/unittest/test_contrib_stes_op.py

diff --git a/src/operator/contrib/stes_op.cc b/src/operator/contrib/stes_op.cc
new file mode 100644
index 000000000000..c334d4d1b59c
--- /dev/null
+++ b/src/operator/contrib/stes_op.cc
@@ -0,0 +1,84 @@
+/*
+  * Licensed to the Apache Software Foundation (ASF) under one
+  * or more contributor license agreements.  See the NOTICE file
+  * distributed with this work for additional information
+  * regarding copyright ownership.  The ASF licenses this file
+  * to you under the Apache License, Version 2.0 (the
+  * "License"); you may not use this file except in compliance
+  * with the License.  You may obtain a copy of the License at
+  *
+  *   http://www.apache.org/licenses/LICENSE-2.0
+  *
+  * Unless required by applicable law or agreed to in writing,
+  * software distributed under the License is distributed on an
+  * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+  * KIND, either express or implied.  See the License for the
+  * specific language governing permissions and limitations
+  * under the License.
+  */
+
+  /*!
+  *  Copyright (c) 2019 by Contributors
+  * \file stes_op.cc
+  * \Straight-through-estimators round and sign operators.
+  * \author Itay Golan
+  */
+
+#include "stes_op.h"
+
+
+namespace mxnet {
+namespace op {
+
+// Round STE
+MXNET_OPERATOR_REGISTER_UNARY_WITH_RSP_CSR(_contrib_round_ste, cpu, mshadow_op::round)
+.describe(R"code(Straight-through-estimator of `round()`.
+
+In forward pass, returns element-wise rounded value to the nearest integer of the input (same as `round()`).
+
+In backward pass, returns gradients of ``1`` everywhere (instead of ``0`` everywhere as in `round()`):
+:math:`\frac{d}{dx}{round\_ste(x)} = 1` vs. :math:`\frac{d}{dx}{round(x)} = 0`.
+This is useful for quantized training.
+
+Reference: Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.
+
+Example::
+  x = round_ste([-1.5, 1.5, -1.9, 1.9, 2.7])
+  x.backward()
+  x = [-2.,  2., -2.,  2.,  3.]
+  x.grad() = [1.,  1., 1.,  1.,  1.]
+
+The storage type of ``round_ste`` output depends upon the input storage type:
+  - round_ste(default) = default
+  - round_ste(row_sparse) = row_sparse
+  - round_ste(csr) = csr
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", CloneGradient{"_backward_round_ste"});
+
+// sign
+MXNET_OPERATOR_REGISTER_UNARY_WITH_RSP_CSR(_contrib_sign_ste, cpu, mshadow_op::sign)
+.describe(R"code(Straight-through-estimator of `sign()`.
+
+In forward pass, returns element-wise sign of the input (same as `sign()`).
+
+In backward pass, returns gradients of ``1`` everywhere (instead of ``0`` everywhere as in ``sign()``):
+:math:`\frac{d}{dx}{sign\_ste(x)} = 1` vs. :math:`\frac{d}{dx}{sign(x)} = 0`.
+This is useful for quantized training.
+
+Reference: Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.
+
+Example::
+  x = sign_ste([-2, 0, 3])
+  x.backward()
+  x = [-1.,  0., 1.]
+  x.grad() = [1.,  1., 1.]
+
+The storage type of ``sign_ste`` output depends upon the input storage type:
+  - round_ste(default) = default
+  - round_ste(row_sparse) = row_sparse
+  - round_ste(csr) = csr
+)code" ADD_FILELINE)
+.set_attr<nnvm::FGradient>("FGradient", CloneGradient{"_backward_sign_ste"});
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/contrib/stes_op.cu b/src/operator/contrib/stes_op.cu
new file mode 100644
index 000000000000..85e3ddaf206f
--- /dev/null
+++ b/src/operator/contrib/stes_op.cu
@@ -0,0 +1,43 @@
+/*
+  * Licensed to the Apache Software Foundation (ASF) under one
+  * or more contributor license agreements.  See the NOTICE file
+  * distributed with this work for additional information
+  * regarding copyright ownership.  The ASF licenses this file
+  * to you under the Apache License, Version 2.0 (the
+  * "License"); you may not use this file except in compliance
+  * with the License.  You may obtain a copy of the License at
+  *
+  *   http://www.apache.org/licenses/LICENSE-2.0
+  *
+  * Unless required by applicable law or agreed to in writing,
+  * software distributed under the License is distributed on an
+  * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+  * KIND, either express or implied.  See the License for the
+  * specific language governing permissions and limitations
+  * under the License.
+  */
+
+  /*!
+  *  Copyright (c) 2019 by Contributors
+  * \file stes_op.cu
+  * \Straight-through-estimators round and sign operators.
+  * \author Itay Golan
+  */
+
+#include "stes_op.h"
+
+namespace mxnet {
+namespace op {
+
+// Round STE
+NNVM_REGISTER_OP(_contrib_round_ste)
+.set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, mshadow_op::round>)
+.set_attr<FComputeEx>("FComputeEx<gpu>", UnaryOp::ComputeEx<gpu, mshadow_op::round>);
+
+// Sign STE
+NNVM_REGISTER_OP(_contrib_sign_ste)
+.set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, mshadow_op::sign>)
+.set_attr<FComputeEx>("FComputeEx<gpu>", UnaryOp::ComputeEx<gpu, mshadow_op::sign>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/contrib/stes_op.h b/src/operator/contrib/stes_op.h
new file mode 100644
index 000000000000..7185fbf1d6e1
--- /dev/null
+++ b/src/operator/contrib/stes_op.h
@@ -0,0 +1,33 @@
+/*
+  * Licensed to the Apache Software Foundation (ASF) under one
+  * or more contributor license agreements.  See the NOTICE file
+  * distributed with this work for additional information
+  * regarding copyright ownership.  The ASF licenses this file
+  * to you under the Apache License, Version 2.0 (the
+  * "License"); you may not use this file except in compliance
+  * with the License.  You may obtain a copy of the License at
+  *
+  *   http://www.apache.org/licenses/LICENSE-2.0
+  *
+  * Unless required by applicable law or agreed to in writing,
+  * software distributed under the License is distributed on an
+  * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+  * KIND, either express or implied.  See the License for the
+  * specific language governing permissions and limitations
+  * under the License.
+  */
+
+  /*!
+  *  Copyright (c) 2019 by Contributors
+  * \file stes_op.h
+  * \Straight-through-estimators round and sign operators.
+  * \author Itay Golan
+  */
+
+#ifndef MXNET_OPERATOR_CONTRIB_STES_OP_H_
+#define MXNET_OPERATOR_CONTRIB_STES_OP_H_
+
+#include <mxnet/base.h>
+#include "../tensor/elemwise_unary_op.h"
+
+#endif  // MXNET_OPERATOR_CONTRIB_STES_OP_H_
diff --git a/tests/python/unittest/test_contrib_stes_op.py b/tests/python/unittest/test_contrib_stes_op.py
new file mode 100644
index 000000000000..5864ec9db5b1
--- /dev/null
+++ b/tests/python/unittest/test_contrib_stes_op.py
@@ -0,0 +1,137 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+from common import with_seed
+import mxnet as mx
+from mxnet import nd, autograd, gluon
+from mxnet.test_utils import default_context
+
+
+class RoundSTENET(gluon.HybridBlock):
+    def __init__(self, w_init, **kwargs):
+        super(RoundSTENET, self).__init__(**kwargs)
+        with self.name_scope():
+            self.w = self.params.get('w', shape=30, init=mx.initializer.Constant(w_init), grad_req='write')
+
+    @staticmethod
+    def expected_grads(in_data, w_init):
+        return (in_data * w_init).round() + (in_data * w_init)
+
+    @staticmethod
+    def expected_output(in_data, w_init):
+        return (in_data * w_init).round() * w_init
+
+    def hybrid_forward(self, F, x, w):
+        # Simple forward function: round_ste(w*x)*w
+        out = w * x
+        out = F.contrib.round_ste(out)
+        # Uncomment to see how test fails with round
+        # out = F.round(out)
+        out = out * w
+        return out
+
+
+class SignSTENET(gluon.HybridBlock):
+    def __init__(self, w_init, **kwargs):
+        super(SignSTENET, self).__init__(**kwargs)
+        with self.name_scope():
+            self.w = self.params.get('w', shape=30, init=mx.initializer.Constant(w_init), grad_req='write')
+
+    @staticmethod
+    def expected_grads(in_data, w_init):
+        return (in_data * w_init).sign() + (in_data * w_init)
+
+    @staticmethod
+    def expected_output(in_data, w_init):
+        return (in_data * w_init).sign() * w_init
+
+    def hybrid_forward(self, F, x, w):
+        # Simple forward function: sign_ste(w*x)*w
+        out = w * x
+        out = F.contrib.sign_ste(out)
+        # Uncomment to see how test fails with sign
+        # out = F.sign(out)
+        out = out * w
+        return out
+
+
+def check_ste(net_type_str, w_init, hybridize, in_data, ctx=None):
+    ctx = ctx or default_context()
+
+    net = eval(net_type_str)(w_init=w_init)
+    if hybridize:
+        net.hybridize()
+    # Init
+    net.collect_params().initialize(mx.init.Constant([w_init]), ctx=ctx)
+
+    # Test:
+    in_data = in_data.as_in_context(ctx)
+    with mx.autograd.record():
+        out = net(in_data)
+    assert all(out == net.expected_output(in_data, w_init)), net_type_str + " output is " + str(out) + ", but" + \
+                                                             " expected " + str(net.expected_output(in_data, w_init))
+
+    out.backward()
+    assert all(net.w.grad() == net.expected_grads(in_data, w_init)), net_type_str + " w grads are " + \
+                                                                     str(net.w.grad()) + " but expected " + \
+                                                                     str(net.expected_grads(in_data, w_init))
+    with mx.autograd.record():
+        out = net(in_data)
+    assert all(out == net.expected_output(in_data, w_init)), net_type_str + " output is " + str(out) + ", but" + \
+                                                             " expected " + str(net.expected_output(in_data, w_init))
+    out.backward()
+    assert all(net.w.grad() == net.expected_grads(in_data, w_init)), net_type_str + " w grads are " + \
+                                                                     str(net.w.grad()) + " but expected " + \
+                                                                     str(net.expected_grads(in_data, w_init))
+
+@with_seed()
+def test_contrib_round_ste():
+    # Test with random data
+    in_data = nd.uniform(-10, 10, shape=30)  # 10 and 30 are arbitrary numbers
+    w_init = float(nd.uniform(-10, 10, shape=1).asscalar())
+    check_ste(net_type_str="RoundSTENET", w_init=w_init, hybridize=True, in_data=in_data)
+    check_ste(net_type_str="RoundSTENET", w_init=w_init, hybridize=False, in_data=in_data)
+
+    # Test 1.5 (verifies that .5 rounds the same as in round)
+    in_data = nd.array([1.5]*30)  # 10 and 30 are arbitrary numbers
+    w_init = 1.
+    check_ste(net_type_str="RoundSTENET", w_init=w_init, hybridize=True, in_data=in_data)
+    check_ste(net_type_str="RoundSTENET", w_init=w_init, hybridize=False, in_data=in_data)
+
+    # Test 0
+    in_data = nd.array([0]*30)  # 10 and 30 are arbitrary numbers
+    w_init = 0.
+    check_ste(net_type_str="RoundSTENET", w_init=w_init, hybridize=True, in_data=in_data)
+    check_ste(net_type_str="RoundSTENET", w_init=w_init, hybridize=False, in_data=in_data)
+
+
+@with_seed()
+def test_contrib_sign_ste():
+    in_data = nd.uniform(-10, 10, shape=30)  # 10 and 30 are arbitrary numbers
+    w_init = float(nd.uniform(-10, 10, shape=1).asscalar())
+    check_ste(net_type_str="SignSTENET", w_init=w_init, hybridize=True, in_data=in_data)
+    check_ste(net_type_str="SignSTENET", w_init=w_init, hybridize=False, in_data=in_data)
+
+    # Test 0
+    in_data = nd.array([0]*30)  # 10 and 30 are arbitrary numbers
+    w_init = 0.
+    check_ste(net_type_str="SignSTENET", w_init=w_init, hybridize=True, in_data=in_data)
+    check_ste(net_type_str="SignSTENET", w_init=w_init, hybridize=False, in_data=in_data)
+
+if __name__ == '__main__':
+    import nose
+    nose.runmodule()
\ No newline at end of file

From 15ea40d9bf7da3c4618ca45a6d023d9b0fb1c295 Mon Sep 17 00:00:00 2001
From: reminisce <wujun.nju@gmail.com>
Date: Tue, 8 Oct 2019 16:23:51 -0700
Subject: [PATCH 416/813] Add boolean ndarray (#15940)

* Initial infra of boolean ndarray

Add np.equal implemented using tvmop

Fix setting DLDataType conversion for boolean ndarray

Add equal_gpu

Fix inputs with different ndims

Fix copying boolean ndarrays across devices

Refactor binary logic op impl by tvm

Add more logic ops

Refactor TVMOpModule::Call to CallEx

Add binary scalar logic op expr and schedule

Add binary scalar logic ops

Add free functions for logic ops

Rebase with master to fix SetDLTensor bug

Fix pylint

Add sum op for boolean ndarrays using tvm op module

Add sum boolean gpu compute

Add bool type support to boolean_mask

Boolean indexing working

Clean up

Fix merge

Sync Makefile

Rebase

Add boolean indexing test

Fix sanity

Fix gpu and add autograd test

Rebase

Fix test for windows

Fix tests

Try to fix cuda arch missing error in ci

Fix ci

Fix windows build

Try to fix cmake

Fix cmake

Fix

Revert config.mk

* Fix cmake

* Skip compute capability <= 52 for TVM generated ops

* Fix sanity
---
 3rdparty/mshadow/mshadow/base.h               |  62 +++-
 CMakeLists.txt                                |   6 +-
 Makefile                                      |   6 +-
 ci/docker/runtime_functions.sh                |   4 +-
 contrib/tvmop/__init__.py                     |   1 +
 contrib/tvmop/compile.py                      |  41 ++-
 contrib/tvmop/core/__init__.py                |  18 +
 contrib/tvmop/core/fromnumeric.py             |  63 ++++
 contrib/tvmop/core/umath.py                   | 122 +++++++
 contrib/tvmop/opdef.py                        |   6 +-
 contrib/tvmop/utils.py                        |  16 +-
 include/mxnet/tensor_blob.h                   |   1 +
 python/mxnet/ndarray/ndarray.py               |   6 +
 python/mxnet/ndarray/numpy/_op.py             | 198 ++++++++++-
 python/mxnet/numpy/multiarray.py              | 316 ++++++++++++++----
 python/mxnet/numpy/utils.py                   |   7 +-
 python/mxnet/symbol/numpy/_symbol.py          | 246 +++++++++++---
 python/mxnet/test_utils.py                    |  17 +
 src/ndarray/ndarray.cc                        |   2 +-
 src/ndarray/ndarray_function.cc               |   9 +
 src/ndarray/ndarray_function.cu               |  10 +-
 src/operator/contrib/boolean_mask.cc          |   7 +-
 src/operator/contrib/boolean_mask.cu          |   4 +-
 src/operator/mxnet_op.h                       |  16 +
 src/operator/numpy/np_broadcast_reduce_op.h   |  21 +-
 .../numpy/np_broadcast_reduce_op_value.cc     |  71 ++++
 .../numpy/np_elemwise_broadcast_op.cc         | 253 +++++++++++++-
 src/operator/operator_tune.cc                 |  23 +-
 .../elemwise_binary_broadcast_op_logic.cc     |   6 -
 .../tensor/elemwise_binary_scalar_op_logic.cc |   6 -
 src/operator/tensor/elemwise_unary_op.h       |   2 +-
 src/operator/tensor/init_op.h                 |  10 +-
 src/operator/tvmop/op_module.cc               |  27 +-
 src/operator/tvmop/op_module.h                |  18 +-
 tests/python/unittest/test_numpy_gluon.py     |  12 +-
 tests/python/unittest/test_numpy_ndarray.py   | 149 ++++++++-
 tests/python/unittest/test_numpy_op.py        |  35 +-
 37 files changed, 1620 insertions(+), 197 deletions(-)
 create mode 100644 contrib/tvmop/core/__init__.py
 create mode 100644 contrib/tvmop/core/fromnumeric.py
 create mode 100644 contrib/tvmop/core/umath.py

diff --git a/3rdparty/mshadow/mshadow/base.h b/3rdparty/mshadow/mshadow/base.h
index 1076f122c3ae..e0e9602c00db 100755
--- a/3rdparty/mshadow/mshadow/base.h
+++ b/3rdparty/mshadow/mshadow/base.h
@@ -311,6 +311,7 @@ enum TypeFlag {
   kInt32 = 4,
   kInt8  = 5,
   kInt64 = 6,
+  kBool = 7,
 };
 
 template<typename DType>
@@ -411,6 +412,11 @@ struct DataType<int64_t> {
   static const int kFlag = kInt64;
   static const int kLanes = 1;
 };
+template<>
+struct DataType<bool> {
+  static const int kFlag = kBool;
+  static const int kLanes = 1;
+};
 
 /*! \brief type enum value for default real type */
 const int default_type_flag = DataType<default_real_t>::kFlag;
@@ -1138,10 +1144,64 @@ struct minimum {
     LOG(FATAL) << "Unknown type enum " << type;     \
   }
 
+#define MSHADOW_TYPE_SWITCH_WITH_BOOL(type, DType, ...)       \
+  switch (type) {                                             \
+  case mshadow::kFloat32:                                     \
+    {                                                         \
+      typedef float DType;                                    \
+      {__VA_ARGS__}                                           \
+    }                                                         \
+    break;                                                    \
+  case mshadow::kFloat64:                                     \
+    {                                                         \
+      typedef double DType;                                   \
+      {__VA_ARGS__}                                           \
+    }                                                         \
+    break;                                                    \
+  case mshadow::kFloat16:                                     \
+    {                                                         \
+      typedef mshadow::half::half_t DType;                    \
+      {__VA_ARGS__}                                           \
+    }                                                         \
+    break;                                                    \
+  case mshadow::kUint8:                                       \
+    {                                                         \
+      typedef uint8_t DType;                                  \
+      {__VA_ARGS__}                                           \
+    }                                                         \
+    break;                                                    \
+  case mshadow::kInt8:                                        \
+    {                                                         \
+      typedef int8_t DType;                                   \
+      {__VA_ARGS__}                                           \
+    }                                                         \
+    break;                                                    \
+  case mshadow::kInt32:                                       \
+    {                                                         \
+      typedef int32_t DType;                                  \
+      {__VA_ARGS__}                                           \
+    }                                                         \
+    break;                                                    \
+  case mshadow::kInt64:                                       \
+    {                                                         \
+      typedef int64_t DType;                                  \
+      {__VA_ARGS__}                                           \
+    }                                                         \
+    break;                                                    \
+  case mshadow::kBool:                                        \
+    {                                                         \
+      typedef bool DType;                                     \
+      {__VA_ARGS__}                                           \
+    }                                                         \
+    break;                                                    \
+  default:                                                    \
+    LOG(FATAL) << "Unknown type enum " << type;               \
+  }
+
 /*! \brief get data type size from type enum */
 inline size_t mshadow_sizeof(int type) {
   int size = 0;
-  MSHADOW_TYPE_SWITCH(type, DType, size = sizeof(DType););
+  MSHADOW_TYPE_SWITCH_WITH_BOOL(type, DType, size = sizeof(DType););
   return size;
 }
 
diff --git a/CMakeLists.txt b/CMakeLists.txt
index f441e9b0bd3b..5045bba9d989 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -756,11 +756,15 @@ if(USE_TVM_OP)
     endif()
   endif()
 
+  set(TVM_OP_COMPILE_OPTIONS "-o${CMAKE_CURRENT_BINARY_DIR}/libtvmop.so")
+  if(CUDA_ARCH_BIN)
+    set(TVM_OP_COMPILE_OPTIONS "${TVM_OP_COMPILE_OPTIONS}" "--cuda-arch" "${CUDA_ARCH_BIN}")
+  endif()
   add_custom_command(TARGET mxnet POST_BUILD
     COMMAND ${CMAKE_COMMAND} -E env
           PYTHONPATH="${CMAKE_CURRENT_SOURCE_DIR}/3rdparty/tvm/python:${CMAKE_CURRENT_SOURCE_DIR}/3rdparty/tvm/topi/python:${CMAKE_CURRENT_SOURCE_DIR}/contrib"
           LD_LIBRARY_PATH=${CMAKE_CURRENT_BINARY_DIR}:${CMAKE_CURRENT_BINARY_DIR}/3rdparty/tvm:$ENV{LD_LIBRARY_PATH}
-          ${Python3_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/contrib/tvmop/compile.py -o${CMAKE_CURRENT_BINARY_DIR}/libtvmop.so
+          ${Python3_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/contrib/tvmop/compile.py ${TVM_OP_COMPILE_OPTIONS}
     )
 endif()
 
diff --git a/Makefile b/Makefile
index b3b188a2cf3b..0a1e355ee5e8 100644
--- a/Makefile
+++ b/Makefile
@@ -630,11 +630,15 @@ lib/libtvm_runtime.so:
 	ls $(ROOTDIR)/lib; \
 	cd $(ROOTDIR)
 
+TVM_OP_COMPILE_OPTIONS = -o $(ROOTDIR)/lib/libtvmop.so
+ifneq ($(CUDA_ARCH),)
+	TVM_OP_COMPILE_OPTIONS += --cuda-arch "$(CUDA_ARCH)"
+endif
 lib/libtvmop.so: lib/libtvm_runtime.so $(wildcard contrib/tvmop/*/*.py contrib/tvmop/*.py)
 	echo "Compile TVM operators"
 	PYTHONPATH=$(TVM_PATH)/python:$(TVM_PATH)/topi/python:$(ROOTDIR)/contrib \
 		LD_LIBRARY_PATH=$(ROOTDIR)/lib \
-	    python3 $(ROOTDIR)/contrib/tvmop/compile.py -o $(ROOTDIR)/lib/libtvmop.so
+	    python3 $(ROOTDIR)/contrib/tvmop/compile.py $(TVM_OP_COMPILE_OPTIONS)
 
 NNVM_INC = $(wildcard $(NNVM_PATH)/include/*/*.h)
 NNVM_SRC = $(wildcard $(NNVM_PATH)/src/*/*/*.cc $(NNVM_PATH)/src/*/*.cc $(NNVM_PATH)/src/*.cc)
diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index b2a50f30af4e..7d91d4e5d121 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -228,7 +228,7 @@ build_ubuntu_gpu_mkldnn_release() {
 # $1 -> mxnet_variant: the mxnet variant to build, e.g. cpu, cu100, cu92mkl, etc.
 build_dynamic_libmxnet() {
     set -ex
-    
+
     local mxnet_variant=${1:?"This function requires a mxnet variant as the first argument"}
 
     # relevant licenses will be placed in the licenses directory
@@ -948,7 +948,7 @@ cd_unittest_ubuntu() {
     fi
 
     $nose_cmd $NOSE_TIMER_ARGUMENTS --verbose tests/python/unittest
-    $nose_cmd $NOSE_TIMER_ARGUMENTS --verbose tests/python/quantization 
+    $nose_cmd $NOSE_TIMER_ARGUMENTS --verbose tests/python/quantization
 
     # https://github.com/apache/incubator-mxnet/issues/11801
     # if [[ ${mxnet_variant} = "cpu" ]] || [[ ${mxnet_variant} = "mkl" ]]; then
diff --git a/contrib/tvmop/__init__.py b/contrib/tvmop/__init__.py
index 1234ee7d31f1..db41574ee867 100644
--- a/contrib/tvmop/__init__.py
+++ b/contrib/tvmop/__init__.py
@@ -21,3 +21,4 @@
 from .utils import assign_by_req, reduce_axes
 
 from . import basic
+from . import core
diff --git a/contrib/tvmop/compile.py b/contrib/tvmop/compile.py
index e6af0a276560..3c0efdd6b806 100644
--- a/contrib/tvmop/compile.py
+++ b/contrib/tvmop/compile.py
@@ -21,7 +21,13 @@
 
 import os
 import argparse
+import re
+import logging
 from tvmop.opdef import __OP_DEF__
+from tvm.autotvm.measure.measure_methods import set_cuda_target_arch
+
+logging.basicConfig(level=logging.INFO)
+
 
 def get_target(device):
     if device == "cpu":
@@ -31,12 +37,39 @@ def get_target(device):
     assert False, "Unknown device " + device
 
 
+def get_cuda_arch(arch):
+    if arch is None:
+        return None
+
+    if not isinstance(arch, str):
+        raise TypeError('Expecting parameter arch as a str, while got a {}'.format(str(type(arch))))
+
+    if len(arch) == 0:
+        return None
+
+    # the arch string contains '-arch=sm_xx'
+    flags = arch.split()
+    for flag in flags:
+        if flag.startswith('-arch='):
+            return flag[len('-arch='):]
+
+    # find the highest compute capability
+    comp_caps = re.findall(r'\d+', arch)
+    if len(comp_caps) == 0:
+        return None
+
+    comp_caps = [int(c) for c in comp_caps]
+    return 'sm_' + str(max(comp_caps))
+
+
 if __name__ == "__main__":
     import sys
     sys.path.append(os.path.dirname(sys.path[0]))
     parser = argparse.ArgumentParser(description="Generate tvm operators")
     parser.add_argument("-o", action="store", required=True, dest="target_path",
                         help="Target path which stores compiled library")
+    parser.add_argument('--cuda-arch', type=str, default=None, dest='cuda_arch',
+                        help='The cuda arch for compiling kernels for')
     arguments = parser.parse_args()
 
     func_list_llvm = []
@@ -52,8 +85,14 @@ def get_target(device):
                                        binds=operator_def.get_binds(args))
                 func_list.append(func_lower)
 
-    lowered_funcs = {get_target("cpu") : func_list_llvm}
+    lowered_funcs = {get_target("cpu"): func_list_llvm}
     if len(func_list_cuda) > 0:
         lowered_funcs[get_target("cuda")] = func_list_cuda
+        cuda_arch = get_cuda_arch(arguments.cuda_arch)
+        if cuda_arch is None:
+            logging.info('No cuda arch specified. TVM will try to detect it from the build platform.')
+        else:
+            logging.info('Cuda arch {} set for compiling TVM operator kernels.'.format(cuda_arch))
+            set_cuda_target_arch(cuda_arch)
     func_binary = tvm.build(lowered_funcs, name="tvmop")
     func_binary.export_library(arguments.target_path)
diff --git a/contrib/tvmop/core/__init__.py b/contrib/tvmop/core/__init__.py
new file mode 100644
index 000000000000..841d4ad9db27
--- /dev/null
+++ b/contrib/tvmop/core/__init__.py
@@ -0,0 +1,18 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+from . import umath, fromnumeric
diff --git a/contrib/tvmop/core/fromnumeric.py b/contrib/tvmop/core/fromnumeric.py
new file mode 100644
index 000000000000..e6c4c2be0814
--- /dev/null
+++ b/contrib/tvmop/core/fromnumeric.py
@@ -0,0 +1,63 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+
+import tvm
+from .. import defop
+from ..utils import reduce_axes, assign_by_req
+
+
+def _compute_sum(itype, otype, ndim, reduce1st_dim, req):
+    axes = ([reduce1st_dim, 1 - reduce1st_dim] * ndim)[:ndim]
+    a = tvm.placeholder([tvm.var() for _ in range(ndim)], name='a', dtype=itype)
+    reduce_output = reduce_axes(a, axes, tvm.sum, otype)
+    output_placeholder, final_output = assign_by_req(reduce_output, req)
+    s = tvm.create_schedule(final_output.op)
+    return s, a, output_placeholder, final_output, [reduce_output, final_output]
+
+
+@defop(name='sum_cpu', target='cpu', itype=['bool'],
+       otype=['float32', 'float64', 'int32', 'int64'],
+       ndim=[5], req=['kWriteTo', 'kAddTo'], reduce1st_dim=[0, 1],
+       attrs=["reduce1st_dim", "req"])
+def _sum_cpu(itype, otype, ndim, reduce1st_dim, req):
+    s, a, output_placeholder, final_output, tensor_list = _compute_sum(
+        itype, otype, ndim, reduce1st_dim, req)
+    for t in tensor_list:
+        axes = [axis for axis in t.op.axis]
+        fused = s[t].fuse(*axes)
+        s[t].parallel(fused)
+    return s, [a, output_placeholder, final_output]
+
+
+@defop(name='sum_gpu', target='gpu', itype=['bool'],
+       otype=['float32', 'float64', 'int32', 'int64'],
+       ndim=[5], req=['kWriteTo', 'kAddTo'], reduce1st_dim=[0, 1],
+       attrs=["reduce1st_dim", "req"])
+def _sum_gpu(itype, otype, ndim, reduce1st_dim, req):
+    s, a, output_placeholder, final_output, tensor_list = _compute_sum(
+        itype, otype, ndim, reduce1st_dim, req)
+    num_threads = 64
+    for t in tensor_list:
+        block_x = tvm.thread_axis("blockIdx.x")
+        thread_x = tvm.thread_axis("threadIdx.x")
+        axes = [axis for axis in t.op.axis]
+        fused = s[t].fuse(*axes)
+        bx, tx = s[t].split(fused, factor=num_threads)
+        s[t].bind(bx, block_x)
+        s[t].bind(tx, thread_x)
+    return s, [a, output_placeholder, final_output]
diff --git a/contrib/tvmop/core/umath.py b/contrib/tvmop/core/umath.py
new file mode 100644
index 000000000000..ad099299aae5
--- /dev/null
+++ b/contrib/tvmop/core/umath.py
@@ -0,0 +1,122 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import tvm
+from .. import defop, AllTypes
+
+_bin_logic_op_map = {
+    'equal': lambda a, b, *idx: a[idx] == b[idx],
+    'not_equal': lambda a, b, *idx: a[idx] != b[idx],
+    'greater': lambda a, b, *idx: a[idx] > b[idx],
+    'less': lambda a, b, *idx: a[idx] < b[idx],
+    'greater_equal': lambda a, b, *idx: a[idx] >= b[idx],
+    'less_equal': lambda a, b, *idx: a[idx] <= b[idx],
+}
+
+
+def _compute_binary_logic(op, dtype, ndim):
+    a = tvm.placeholder([tvm.var() for _ in range(ndim)], dtype=dtype, name='a')
+    b = tvm.placeholder([tvm.var() for _ in range(ndim)], dtype=dtype, name='b')
+    c = tvm.compute([tvm.var() for _ in range(ndim)],
+                    lambda *idx: _bin_logic_op_map[op](a, b, *idx), name='c')
+    s = tvm.create_schedule(c.op)
+    return s, a, b, c
+
+
+_bin_logic_cpu_attrs = {
+    'compute_func': _compute_binary_logic,
+    'target': 'cpu',
+    'auto_broadcast': True,
+    'itype': AllTypes + ['bool'],
+    'ndim': list(range(6))
+}
+
+_bin_logic_gpu_attrs = {
+    'compute_func': _compute_binary_logic,
+    'target': 'gpu',
+    'auto_broadcast': True,
+    'itype': AllTypes + ['bool'],
+    'ndim': list(range(6))
+}
+
+
+def _binary_logic_cpu(compute_func, op, itype, ndim):
+    s, a, b, c = compute_func(op, itype, ndim)
+    axes = [axis for axis in c.op.axis]
+    fused = s[c].fuse(*axes)
+    s[c].parallel(fused)
+    return s, [a, b, c]
+
+
+def _binary_logic_gpu(compute_func, op, itype, ndim):
+    s, a, b, c = compute_func(op, itype, ndim)
+    axes = [axis for axis in c.op.axis]
+    fused = s[c].fuse(*axes)
+    bx, tx = s[c].split(fused, factor=64)
+    s[c].bind(bx, tvm.thread_axis('blockIdx.x'))
+    s[c].bind(tx, tvm.thread_axis('threadIdx.x'))
+    return s, [a, b, c]
+
+
+# register binary element-wise logic ops with broadcasting supported
+for op_name in _bin_logic_op_map.keys():
+    defop(name='{}_cpu'.format(op_name), op=op_name, **_bin_logic_cpu_attrs)(_binary_logic_cpu)
+    defop(name='{}_gpu'.format(op_name), op=op_name, **_bin_logic_gpu_attrs)(_binary_logic_gpu)
+
+
+# Note that `b.dtype` is hard-coded as 'float64'.
+# We should always promote `a`'s elements to `b.dtype`.
+_bin_scalar_logic_op_map = {
+    'equal_scalar': lambda a, b, *idx: a[idx].astype(b.dtype) == b,
+    'not_equal_scalar': lambda a, b, *idx: a[idx].astype(b.dtype) != b,
+    'greater_scalar': lambda a, b, *idx: a[idx].astype(b.dtype) > b,
+    'less_scalar': lambda a, b, *idx: a[idx].astype(b.dtype) < b,
+    'greater_equal_scalar': lambda a, b, *idx: a[idx].astype(b.dtype) >= b,
+    'less_equal_scalar': lambda a, b, *idx: a[idx].astype(b.dtype) <= b,
+}
+
+
+def _compute_binary_scalar_logic(op, dtype, ndim):
+    a = tvm.placeholder([tvm.var() for _ in range(ndim)], name='a', dtype=dtype)
+    b = tvm.var('b', dtype='float64')
+    c = tvm.compute([tvm.var() for _ in range(ndim)],
+                    lambda *idx: _bin_scalar_logic_op_map[op](a, b, *idx), name='c')
+    s = tvm.create_schedule(c.op)
+    return s, a, b, c
+
+
+_bin_scalar_logic_cpu_attrs = {
+    'compute_func': _compute_binary_scalar_logic,
+    'target': 'cpu',
+    'itype': AllTypes + ['bool'],
+    'ndim': list(range(6))
+}
+
+_bin_scalar_logic_gpu_attrs = {
+    'compute_func': _compute_binary_scalar_logic,
+    'target': 'gpu',
+    'itype': AllTypes + ['bool'],
+    'ndim': list(range(6))
+}
+
+
+# register binary element-wise scalar logic ops
+for op_name in _bin_scalar_logic_op_map.keys():
+    defop(name='{}_cpu'.format(op_name), op=op_name,
+          **_bin_scalar_logic_cpu_attrs)(_binary_logic_cpu)
+    defop(name='{}_gpu'.format(op_name), op=op_name,
+          **_bin_scalar_logic_gpu_attrs)(_binary_logic_gpu)
diff --git a/contrib/tvmop/opdef.py b/contrib/tvmop/opdef.py
index 32d1832d13dd..39c42f4dd465 100644
--- a/contrib/tvmop/opdef.py
+++ b/contrib/tvmop/opdef.py
@@ -74,11 +74,12 @@ def __call__(self, *args, **kwargs):
 
     def invoke_all(self):
         for each_kwargs in self.arg_combination:
-            if (self.attrs_valid(**each_kwargs)):
+            if self.attrs_valid(**each_kwargs):
                 sch, args = self.func(**each_kwargs)
                 name = self.name \
                     + ''.join(["{}_{}".format(key, each_kwargs[key]) for key in self.attrs]) \
-                    + ''.join(["%s_%d" % (arg.dtype, len(arg.shape)) for arg in args])
+                    + ''.join(["%s_%d" % (arg.dtype, len(arg.shape))
+                               for arg in args if hasattr(arg, 'shape')])
                 yield sch, args, name
 
     def get_binds(self, args):
@@ -107,6 +108,7 @@ def defop(name, target=None, auto_broadcast=False, **kwargs):
     """
     assert name is not None and len(name) > 0
     target = "cpu" if target is None else target
+
     def _defop(func):
         opdef = OpDef(func, name, target, auto_broadcast, **kwargs)
         __OP_DEF__.append(opdef)
diff --git a/contrib/tvmop/utils.py b/contrib/tvmop/utils.py
index 329dce2148d9..39d7a8092005 100644
--- a/contrib/tvmop/utils.py
+++ b/contrib/tvmop/utils.py
@@ -21,16 +21,18 @@
 AllTypes = ["float32", "float64", "float16", "uint8", "int8", "int32", "int64"]
 RealTypes = ["float32", "float64", "float16"]
 
-def assign_by_req(a, req):
+
+def assign_by_req(a, req, otype=None):
     b = tvm.placeholder(a.shape, name='assign_by_req_b', dtype=a.dtype)
-    if (req == "kAddTo"):
-        c = tvm.compute(a.shape, lambda *idx: a[idx] + b[idx])
+    if req == "kAddTo":
+        c = tvm.compute(a.shape, lambda *idx: a[idx].astype(otype) + b[idx]
+                                              if otype else a[idx] + b[idx])
     else:
-        c = tvm.compute(a.shape, lambda *idx: a[idx])
+        c = tvm.compute(a.shape, lambda *idx: a[idx].astype(otype) if otype else a[idx])
     return b, c
 
 
-def reduce_axes(X, axes, reducer):
+def reduce_axes(X, axes, reducer, atype=None):
     def get_index(idx, ridx):
         j = 0
         k = 0
@@ -45,5 +47,7 @@ def get_index(idx, ridx):
     odim = (len(ishape) + 1 - axes[0]) // 2
     oshape = [tvm.var() for _ in range(odim)]
     ridx = [tvm.reduce_axis((0, ishape[i])) for (i, val) in enumerate(axes) if val == 1]
-    ret = tvm.compute(oshape, lambda *idx: reducer(X[get_index(idx, ridx)], axis=ridx), name='ret')
+    ret = tvm.compute(oshape, lambda *idx: reducer(X[get_index(idx, ridx)].astype(atype)
+                                                   if atype else X[get_index(idx, ridx)],
+                                                   axis=ridx), name='ret')
     return ret
diff --git a/include/mxnet/tensor_blob.h b/include/mxnet/tensor_blob.h
index 70af75424252..f64703092121 100755
--- a/include/mxnet/tensor_blob.h
+++ b/include/mxnet/tensor_blob.h
@@ -382,6 +382,7 @@ class TBlob {
       case mshadow::kInt32: return DLDataType{kDLInt, 32, 1};
       case mshadow::kInt8: return DLDataType{kDLInt, 8, 1};
       case mshadow::kInt64: return DLDataType{kDLInt, 64, 1};
+      case mshadow::kBool: return DLDataType{kDLUInt, 1, 1};
       default: {
         LOG(FATAL) << "Unknown type_flag=" << type_flag;
         return DLDataType();
diff --git a/python/mxnet/ndarray/ndarray.py b/python/mxnet/ndarray/ndarray.py
index 0b72865dc17a..4e3c7efa7be3 100644
--- a/python/mxnet/ndarray/ndarray.py
+++ b/python/mxnet/ndarray/ndarray.py
@@ -69,6 +69,7 @@
     np.int32: 4,
     np.int8: 5,
     np.int64: 6,
+    np.bool_: 7,
 }
 
 _DTYPE_MX_TO_NP = {
@@ -80,6 +81,7 @@
     4: np.int32,
     5: np.int8,
     6: np.int64,
+    7: np.bool_,
 }
 
 _STORAGE_TYPE_STR_TO_ID = {
@@ -2995,6 +2997,10 @@ def get_indexing_dispatch_code(key):
 
     for idx in key:
         if isinstance(idx, (NDArray, np.ndarray, list, tuple)):
+            if getattr(idx, 'dtype', None) == np.bool_:
+                raise TypeError('ndarray indexing does not support boolean ndarray'
+                                ' in a tuple of indices. Only single boolean ndarray'
+                                ' as an index is supported.')
             return _NDARRAY_ADVANCED_INDEXING
         elif sys.version_info[0] > 2 and isinstance(idx, range):
             return _NDARRAY_ADVANCED_INDEXING
diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index 5ffb40dae7e0..9dce953004cf 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -35,7 +35,8 @@
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'mean',
            'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
            'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'hypot', 'rad2deg', 'deg2rad',
-           'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer']
+           'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer',
+           'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -3649,3 +3650,198 @@ def vdot(a, b):
     30
     """
     return tensordot(a.flatten(), b.flatten(), 1)
+
+
+@set_module('mxnet.ndarray.numpy')
+def equal(x1, x2, out=None):
+    """
+    Return (x1 == x2) element-wise.
+    Parameters
+    ----------
+    x1, x2 : ndarrays or scalars
+        Input arrays. If ``x1.shape != x2.shape``, they must be broadcastable to
+        a common shape (which becomes the shape of the output).
+    out : ndarray, None, or tuple of ndarray and None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned.
+    Returns
+    -------
+    out : ndarray or scalar
+        Output array of type bool, element-wise comparison of `x1` and `x2`.
+        This is a scalar if both `x1` and `x2` are scalars.
+    See Also
+    --------
+    not_equal, greater_equal, less_equal, greater, less
+    Examples
+    --------
+    >>> np.equal(np.ones(2, 1)), np.zeros(1, 3))
+    array([[False, False, False],
+           [False, False, False]])
+    >>> np.equal(1, np.ones(1))
+    array([ True])
+    """
+    return _ufunc_helper(x1, x2, _npi.equal, _np.equal, _npi.equal_scalar, None, out)
+
+
+@set_module('mxnet.ndarray.numpy')
+def not_equal(x1, x2, out=None):
+    """
+    Return (x1 != x2) element-wise.
+    Parameters
+    ----------
+    x1, x2 : ndarrays or scalars
+        Input arrays. If ``x1.shape != x2.shape``, they must be broadcastable to
+        a common shape (which becomes the shape of the output).
+    out : ndarray, None, or tuple of ndarray and None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned.
+    Returns
+    -------
+    out : ndarray or scalar
+        Output array of type bool, element-wise comparison of `x1` and `x2`.
+        This is a scalar if both `x1` and `x2` are scalars.
+    See Also
+    --------
+    equal, greater, greater_equal, less, less_equal
+    Examples
+    --------
+    >>> np.not_equal(np.ones(2, 1)), np.zeros(1, 3))
+    array([[ True,  True,  True],
+           [ True,  True,  True]])
+    >>> np.not_equal(1, np.ones(1))
+    array([False])
+    """
+    return _ufunc_helper(x1, x2, _npi.not_equal, _np.not_equal, _npi.not_equal_scalar, None, out)
+
+
+@set_module('mxnet.ndarray.numpy')
+def greater(x1, x2, out=None):
+    """
+    Return the truth value of (x1 > x2) element-wise.
+    Parameters
+    ----------
+    x1, x2 : ndarrays or scalars
+        Input arrays. If ``x1.shape != x2.shape``, they must be broadcastable to
+        a common shape (which becomes the shape of the output).
+    out : ndarray, None, or tuple of ndarray and None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned.
+    Returns
+    -------
+    out : ndarray or scalar
+        Output array of type bool, element-wise comparison of `x1` and `x2`.
+        This is a scalar if both `x1` and `x2` are scalars.
+    See Also
+    --------
+    equal, greater, greater_equal, less, less_equal
+    Examples
+    --------
+    >>> np.greater(np.ones(2, 1)), np.zeros(1, 3))
+    array([[ True,  True,  True],
+           [ True,  True,  True]])
+    >>> np.greater(1, np.ones(1))
+    array([False])
+    """
+    return _ufunc_helper(x1, x2, _npi.greater, _np.greater, _npi.greater_scalar,
+                         _npi.less_scalar, out)
+
+
+@set_module('mxnet.ndarray.numpy')
+def less(x1, x2, out=None):
+    """
+    Return the truth value of (x1 < x2) element-wise.
+    Parameters
+    ----------
+    x1, x2 : ndarrays or scalars
+        Input arrays. If ``x1.shape != x2.shape``, they must be broadcastable to
+        a common shape (which becomes the shape of the output).
+    out : ndarray, None, or tuple of ndarray and None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned.
+    Returns
+    -------
+    out : ndarray or scalar
+        Output array of type bool, element-wise comparison of `x1` and `x2`.
+        This is a scalar if both `x1` and `x2` are scalars.
+    See Also
+    --------
+    equal, greater, greater_equal, less, less_equal
+    Examples
+    --------
+    >>> np.less(np.ones(2, 1)), np.zeros(1, 3))
+    array([[ True,  True,  True],
+           [ True,  True,  True]])
+    >>> np.less(1, np.ones(1))
+    array([False])
+    """
+    return _ufunc_helper(x1, x2, _npi.less, _np.less, _npi.less_scalar, _npi.greater_scalar, out)
+
+
+@set_module('mxnet.ndarray.numpy')
+def greater_equal(x1, x2, out=None):
+    """
+    Return the truth value of (x1 >= x2) element-wise.
+    Parameters
+    ----------
+    x1, x2 : ndarrays or scalars
+        Input arrays. If ``x1.shape != x2.shape``, they must be broadcastable to
+        a common shape (which becomes the shape of the output).
+    out : ndarray, None, or tuple of ndarray and None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned.
+    Returns
+    -------
+    out : ndarray or scalar
+        Output array of type bool, element-wise comparison of `x1` and `x2`.
+        This is a scalar if both `x1` and `x2` are scalars.
+    See Also
+    --------
+    equal, greater, greater_equal, less, less_equal
+    Examples
+    --------
+    >>> np.greater_equal(np.ones(2, 1)), np.zeros(1, 3))
+    array([[ True,  True,  True],
+           [ True,  True,  True]])
+    >>> np.greater_equal(1, np.ones(1))
+    array([True])
+    """
+    return _ufunc_helper(x1, x2, _npi.greater_equal, _np.greater_equal, _npi.greater_equal_scalar,
+                         _npi.less_equal_scalar, out)
+
+
+@set_module('mxnet.ndarray.numpy')
+def less_equal(x1, x2, out=None):
+    """
+    Return the truth value of (x1 <= x2) element-wise.
+    Parameters
+    ----------
+    x1, x2 : ndarrays or scalars
+        Input arrays. If ``x1.shape != x2.shape``, they must be broadcastable to
+        a common shape (which becomes the shape of the output).
+    out : ndarray, None, or tuple of ndarray and None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned.
+    Returns
+    -------
+    out : ndarray or scalar
+        Output array of type bool, element-wise comparison of `x1` and `x2`.
+        This is a scalar if both `x1` and `x2` are scalars.
+    See Also
+    --------
+    equal, greater, greater_equal, less, less_equal
+    Examples
+    --------
+    >>> np.less_equal(np.ones(2, 1)), np.zeros(1, 3))
+    array([[False, False, False],
+           [False, False, False]])
+    >>> np.less_equal(1, np.ones(1))
+    array([True])
+    """
+    return _ufunc_helper(x1, x2, _npi.less_equal, _np.less_equal, _npi.less_equal_scalar,
+                         _npi.greater_equal_scalar, out)
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 01b2faa0c056..75b7cf65325b 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -29,7 +29,6 @@
     from builtins import slice as py_slice
 
 from array import array as native_array
-import sys
 import ctypes
 import warnings
 import numpy as _np
@@ -38,7 +37,7 @@
                       get_oshape_of_gather_nd_op
 from ..ndarray._internal import _set_np_ndarray_class
 from . import _op as _mx_np_op
-from ..base import check_call, _LIB, NDArrayHandle
+from ..base import check_call, _LIB, NDArrayHandle, c_array
 from ..base import mx_real_t, c_array_buf, mx_uint, numeric_types, integer_types
 from ..context import Context
 from ..util import _sanity_check_params, set_module
@@ -54,7 +53,8 @@
            'tensordot', 'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate',
            'stack', 'vstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices',
            'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'arctan2', 'hypot',
-           'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer']
+           'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer',
+           'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -86,6 +86,35 @@ def _new_alloc_handle(shape, ctx, delay_alloc, dtype=mx_real_t):
     return hdl
 
 
+def _reshape_view(a, *shape):
+    """Returns a **view** of this array with a new shape without altering any data.
+
+    Parameters
+    ----------
+    shape : tuple of int, or n ints
+        The new shape should not change the array size, namely
+        ``np.prod(new_shape)`` should be equal to ``np.prod(a.shape)``.
+        Some dimensions of the shape can take special value -1, which
+        infers the dimension of the output shape by using the remainder of the
+        input dimensions keeping the size of the new array same as that of the input array.
+        At most one dimension of shape can be -1.
+
+    Returns
+    -------
+    ndarray
+        An array with desired shape that shares data with this array.
+    """
+    if len(shape) == 1 and isinstance(shape[0], (list, tuple)):
+        shape = shape[0]
+    handle = NDArrayHandle()
+    check_call(_LIB.MXNDArrayReshape64(a.handle,
+                                       len(shape),
+                                       c_array(ctypes.c_int64, shape),
+                                       False,
+                                       ctypes.byref(handle)))
+    return ndarray(handle=handle, writable=a.writable)
+
+
 # Have to use 0 as default value for stype since pylint does not allow
 # importing _STORAGE_TYPE_DEFAULT from ndarray.py.
 def _np_ndarray_cls(handle, writable=True, stype=0):
@@ -97,18 +126,6 @@ def _np_ndarray_cls(handle, writable=True, stype=0):
 
 _set_np_ndarray_class(_np_ndarray_cls)
 
-
-def _get_index(idx):
-    if isinstance(idx, NDArray) and not isinstance(idx, ndarray):
-        raise TypeError('Cannot have mx.nd.NDArray as index')
-    if isinstance(idx, ndarray):
-        return idx.as_nd_ndarray()
-    elif sys.version_info[0] > 2 and isinstance(idx, range):
-        return array(_np.arange(idx.start, idx.stop, idx.step, dtype=_np.int32)).as_nd_ndarray()
-    else:
-        return idx
-
-
 _NUMPY_ARRAY_FUNCTION_DICT = {}
 _NUMPY_ARRAY_UFUNC_DICT = {}
 
@@ -272,8 +289,35 @@ def __getitem__(self, key):
         Overriding the method in NDArray class in a numpy fashion.
         Calling numpy ndarray's _get_np_basic_indexing(key) and _get_np_advanced_indexing(key).
         """
+        # handling possible boolean indexing first
         ndim = self.ndim
         shape = self.shape
+
+        if isinstance(key, list):
+            try:
+                new_key = _np.array(key)
+                if new_key.dtype == _np.bool_:
+                    key = new_key
+            except Exception as err:
+                raise TypeError('{}'.format(str(err)))
+        if isinstance(key, _np.ndarray) and key.dtype == _np.bool_:
+            key = array(key, dtype='bool')
+        if isinstance(key, ndarray) and key.dtype == _np.bool_:  # boolean indexing
+            key_shape = key.shape
+            key_ndim = len(key_shape)
+            if ndim < key_ndim:
+                raise IndexError('too many indices, whose ndim = {}, for array with ndim = {}'
+                                 .format(key_ndim, ndim))
+            for i in range(key_ndim):
+                if key_shape[i] != shape[i]:
+                    raise IndexError('boolean index did not match indexed array along dimension {};'
+                                     'dimension is {} but corresponding boolean dimension is {}'
+                                     .format(i, shape[i], key_shape[i]))
+            remaining_dims = shape[key_ndim:]
+            data = _reshape_view(self, -1, *remaining_dims)
+            key = _reshape_view(key, -1)
+            return _reshape_view(_npi.boolean_mask(data, key), -1, *remaining_dims)
+
         if ndim == 0:
             if key != ():
                 raise IndexError('scalar tensor can only accept `()` as index')
@@ -502,66 +546,30 @@ def __rpow__(self, other):
 
     def __eq__(self, other):
         """x.__eq__(y) <=> x == y"""
-        # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported
-        if isinstance(other, ndarray):
-            return _npi.equal(self, other)
-        elif isinstance(other, numeric_types):
-            return _npi.equal_scalar(self, float(other))
-        else:
-            raise TypeError("ndarray does not support type {} as operand".format(str(type(other))))
+        return equal(self, other)
 
     def __hash__(self):
         raise NotImplementedError
 
     def __ne__(self, other):
         """x.__ne__(y) <=> x != y"""
-        # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported
-        if isinstance(other, ndarray):
-            return _npi.not_equal(self, other)
-        elif isinstance(other, numeric_types):
-            return _npi.not_equal_scalar(self, float(other))
-        else:
-            raise TypeError("ndarray does not support type {} as operand".format(str(type(other))))
+        return not_equal(self, other)
 
     def __gt__(self, other):
         """x.__gt__(y) <=> x > y"""
-        # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported
-        if isinstance(other, ndarray):
-            return _npi.greater(self, other)
-        elif isinstance(other, numeric_types):
-            return _npi.greater_scalar(self, float(other))
-        else:
-            raise TypeError("ndarray does not support type {} as operand".format(str(type(other))))
+        return greater(self, other)
 
     def __ge__(self, other):
         """x.__ge__(y) <=> x >= y"""
-        # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported
-        if isinstance(other, ndarray):
-            return _npi.greater_equal(self, other)
-        elif isinstance(other, numeric_types):
-            return _npi.greater_equal_scalar(self, float(other))
-        else:
-            raise TypeError("ndarray does not support type {} as operand".format(str(type(other))))
+        return greater_equal(self, other)
 
     def __lt__(self, other):
         """x.__lt__(y) <=> x < y"""
-        # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported
-        if isinstance(other, ndarray):
-            return _npi.less(self, other)
-        elif isinstance(other, numeric_types):
-            return _npi.less_scalar(self, float(other))
-        else:
-            raise TypeError("ndarray does not support type {} as operand".format(str(type(other))))
+        return less(self, other)
 
     def __le__(self, other):
         """x.__le__(y) <=> x <= y"""
-        # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported
-        if isinstance(other, ndarray):
-            return _npi.less_equal(self, other)
-        elif isinstance(other, numeric_types):
-            return _npi.less_equal_scalar(self, float(other))
-        else:
-            raise TypeError("ndarray does not support type {} as operand".format(str(type(other))))
+        return less_equal(self, other)
 
     def __bool__(self):
         num_elements = self.size
@@ -694,7 +702,7 @@ def __repr__(self):
         if 'dtype=' in array_str:
             if dtype == _np.float32:
                 array_str = array_str[:array_str.rindex(',')] + ')'
-        elif dtype != _np.float32:
+        elif dtype not in (_np.float32, _np.bool_):
             array_str = array_str[:-1] + ', dtype={})'.format(dtype.__name__)
 
         context = self.context
@@ -5165,3 +5173,195 @@ def vdot(a, b):
     30
     """
     return tensordot(a.flatten(), b.flatten(), 1)
+
+
+@set_module('mxnet.numpy')
+def equal(x1, x2, out=None):
+    """
+    Return (x1 == x2) element-wise.
+    Parameters
+    ----------
+    x1, x2 : ndarrays or scalars
+        Input arrays. If ``x1.shape != x2.shape``, they must be broadcastable to
+        a common shape (which becomes the shape of the output).
+    out : ndarray, None, or tuple of ndarray and None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned.
+    Returns
+    -------
+    out : ndarray or scalar
+        Output array of type bool, element-wise comparison of `x1` and `x2`.
+        This is a scalar if both `x1` and `x2` are scalars.
+    See Also
+    --------
+    not_equal, greater_equal, less_equal, greater, less
+    Examples
+    --------
+    >>> np.equal(np.ones(2, 1)), np.zeros(1, 3))
+    array([[False, False, False],
+           [False, False, False]])
+    >>> np.equal(1, np.ones(1))
+    array([ True])
+    """
+    return _mx_nd_np.equal(x1, x2, out)
+
+
+@set_module('mxnet.numpy')
+def not_equal(x1, x2, out=None):
+    """
+    Return (x1 != x2) element-wise.
+    Parameters
+    ----------
+    x1, x2 : ndarrays or scalars
+        Input arrays. If ``x1.shape != x2.shape``, they must be broadcastable to
+        a common shape (which becomes the shape of the output).
+    out : ndarray, None, or tuple of ndarray and None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned.
+    Returns
+    -------
+    out : ndarray or scalar
+        Output array of type bool, element-wise comparison of `x1` and `x2`.
+        This is a scalar if both `x1` and `x2` are scalars.
+    See Also
+    --------
+    equal, greater, greater_equal, less, less_equal
+    Examples
+    --------
+    >>> np.not_equal(np.ones(2, 1)), np.zeros(1, 3))
+    array([[ True,  True,  True],
+           [ True,  True,  True]])
+    >>> np.not_equal(1, np.ones(1))
+    array([False])
+    """
+    return _mx_nd_np.not_equal(x1, x2, out)
+
+
+@set_module('mxnet.numpy')
+def greater(x1, x2, out=None):
+    """
+    Return the truth value of (x1 > x2) element-wise.
+    Parameters
+    ----------
+    x1, x2 : ndarrays or scalars
+        Input arrays. If ``x1.shape != x2.shape``, they must be broadcastable to
+        a common shape (which becomes the shape of the output).
+    out : ndarray, None, or tuple of ndarray and None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned.
+    Returns
+    -------
+    out : ndarray or scalar
+        Output array of type bool, element-wise comparison of `x1` and `x2`.
+        This is a scalar if both `x1` and `x2` are scalars.
+    See Also
+    --------
+    equal, greater, greater_equal, less, less_equal
+    Examples
+    --------
+    >>> np.greater(np.ones(2, 1)), np.zeros(1, 3))
+    array([[ True,  True,  True],
+           [ True,  True,  True]])
+    >>> np.greater(1, np.ones(1))
+    array([False])
+    """
+    return _mx_nd_np.greater(x1, x2, out)
+
+
+@set_module('mxnet.numpy')
+def less(x1, x2, out=None):
+    """
+    Return the truth value of (x1 < x2) element-wise.
+    Parameters
+    ----------
+    x1, x2 : ndarrays or scalars
+        Input arrays. If ``x1.shape != x2.shape``, they must be broadcastable to
+        a common shape (which becomes the shape of the output).
+    out : ndarray, None, or tuple of ndarray and None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned.
+    Returns
+    -------
+    out : ndarray or scalar
+        Output array of type bool, element-wise comparison of `x1` and `x2`.
+        This is a scalar if both `x1` and `x2` are scalars.
+    See Also
+    --------
+    equal, greater, greater_equal, less, less_equal
+    Examples
+    --------
+    >>> np.less(np.ones(2, 1)), np.zeros(1, 3))
+    array([[ True,  True,  True],
+           [ True,  True,  True]])
+    >>> np.less(1, np.ones(1))
+    array([False])
+    """
+    return _mx_nd_np.less(x1, x2, out)
+
+
+@set_module('mxnet.numpy')
+def greater_equal(x1, x2, out=None):
+    """
+    Return the truth value of (x1 >= x2) element-wise.
+    Parameters
+    ----------
+    x1, x2 : ndarrays or scalars
+        Input arrays. If ``x1.shape != x2.shape``, they must be broadcastable to
+        a common shape (which becomes the shape of the output).
+    out : ndarray, None, or tuple of ndarray and None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned.
+    Returns
+    -------
+    out : ndarray or scalar
+        Output array of type bool, element-wise comparison of `x1` and `x2`.
+        This is a scalar if both `x1` and `x2` are scalars.
+    See Also
+    --------
+    equal, greater, greater_equal, less, less_equal
+    Examples
+    --------
+    >>> np.greater_equal(np.ones(2, 1)), np.zeros(1, 3))
+    array([[ True,  True,  True],
+           [ True,  True,  True]])
+    >>> np.greater_equal(1, np.ones(1))
+    array([True])
+    """
+    return _mx_nd_np.greater_equal(x1, x2, out)
+
+
+@set_module('mxnet.numpy')
+def less_equal(x1, x2, out=None):
+    """
+    Return the truth value of (x1 <= x2) element-wise.
+    Parameters
+    ----------
+    x1, x2 : ndarrays or scalars
+        Input arrays. If ``x1.shape != x2.shape``, they must be broadcastable to
+        a common shape (which becomes the shape of the output).
+    out : ndarray, None, or tuple of ndarray and None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned.
+    Returns
+    -------
+    out : ndarray or scalar
+        Output array of type bool, element-wise comparison of `x1` and `x2`.
+        This is a scalar if both `x1` and `x2` are scalars.
+    See Also
+    --------
+    equal, greater, greater_equal, less, less_equal
+    Examples
+    --------
+    >>> np.less_equal(np.ones(2, 1)), np.zeros(1, 3))
+    array([[False, False, False],
+           [False, False, False]])
+    >>> np.less_equal(1, np.ones(1))
+    array([True])
+    """
+    return _mx_nd_np.less_equal(x1, x2, out)
diff --git a/python/mxnet/numpy/utils.py b/python/mxnet/numpy/utils.py
index 920897efc80b..b2d0dd96d324 100644
--- a/python/mxnet/numpy/utils.py
+++ b/python/mxnet/numpy/utils.py
@@ -22,7 +22,8 @@
 
 import numpy as onp
 
-__all__ = ['float16', 'float32', 'float64', 'uint8', 'int32', 'int8', 'int64', 'pi']
+__all__ = ['float16', 'float32', 'float64', 'uint8', 'int32', 'int8', 'int64',
+           'bool', 'bool_', 'pi', 'inf', 'nan']
 
 float16 = onp.float16
 float32 = onp.float32
@@ -31,5 +32,9 @@
 int32 = onp.int32
 int8 = onp.int8
 int64 = onp.int64
+bool_ = onp.bool_
+bool = onp.bool
 
 pi = onp.pi
+inf = onp.inf
+nan = onp.nan
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 7ba92e9cab6e..3eaf80a1b6fb 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -37,7 +37,8 @@
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'mean',
            'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
            'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'hypot', 'rad2deg', 'deg2rad',
-           'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer']
+           'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer',
+           'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal']
 
 
 def _num_outputs(sym):
@@ -138,63 +139,27 @@ def __deepcopy__(self, _):
 
     def __eq__(self, other):
         """x.__eq__(y) <=> x == y"""
-        # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported
-        if isinstance(other, _Symbol):
-            return _npi.equal(self, other)
-        elif isinstance(other, numeric_types):
-            return _npi.equal_scalar(self, float(other))
-        else:
-            raise TypeError("_Symbol does not support type {} as operand".format(str(type(other))))
+        return equal(self, other)
 
     def __ne__(self, other):
         """x.__ne__(y) <=> x != y"""
-        # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported
-        if isinstance(other, _Symbol):
-            return _npi.not_equal(self, other)
-        elif isinstance(other, numeric_types):
-            return _npi.not_equal_scalar(self, float(other))
-        else:
-            raise TypeError("_Symbol does not support type {} as operand".format(str(type(other))))
+        return not_equal(self, other)
 
     def __gt__(self, other):
         """x.__gt__(y) <=> x > y"""
-        # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported
-        if isinstance(other, _Symbol):
-            return _npi.greater(self, other)
-        elif isinstance(other, numeric_types):
-            return _npi.greater_scalar(self, float(other))
-        else:
-            raise TypeError("_Symbol does not support type {} as operand".format(str(type(other))))
+        return greater(self, other)
 
     def __ge__(self, other):
         """x.__ge__(y) <=> x >= y"""
-        # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported
-        if isinstance(other, _Symbol):
-            return _npi.greater_equal(self, other)
-        elif isinstance(other, numeric_types):
-            return _npi.greater_equal_scalar(self, float(other))
-        else:
-            raise TypeError("_Symbol does not support type {} as operand".format(str(type(other))))
+        return greater_equal(self, other)
 
     def __lt__(self, other):
         """x.__lt__(y) <=> x < y"""
-        # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported
-        if isinstance(other, _Symbol):
-            return _npi.less(self, other)
-        elif isinstance(other, numeric_types):
-            return _npi.less_scalar(self, float(other))
-        else:
-            raise TypeError("_Symbol does not support type {} as operand".format(str(type(other))))
+        return less(self, other)
 
     def __le__(self, other):
         """x.__le__(y) <=> x <= y"""
-        # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported
-        if isinstance(other, _Symbol):
-            return _npi.less_equal(self, other)
-        elif isinstance(other, numeric_types):
-            return _npi.less_equal_scalar(self, float(other))
-        else:
-            raise TypeError("_Symbol does not support type {} as operand".format(str(type(other))))
+        return less_equal(self, other)
 
     def __len__(self):
         raise NotImplementedError
@@ -3732,4 +3697,199 @@ def vdot(a, b):
     return tensordot(a.flatten(), b.flatten(), 1)
 
 
+@set_module('mxnet.symbol.numpy')
+def equal(x1, x2, out=None):
+    """
+    Return (x1 == x2) element-wise.
+    Parameters
+    ----------
+    x1, x2 : _Symbol or scalars
+        Input arrays. If ``x1.shape != x2.shape``, they must be broadcastable to
+        a common shape (which becomes the shape of the output).
+    out : Dummy parameter, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned.
+    Returns
+    -------
+    out : _Symbol or scalar
+        Output array of type bool, element-wise comparison of `x1` and `x2`.
+        This is a scalar if both `x1` and `x2` are scalars.
+    See Also
+    --------
+    not_equal, greater_equal, less_equal, greater, less
+    Examples
+    --------
+    >>> np.equal(np.ones(2, 1)), np.zeros(1, 3))
+    array([[False, False, False],
+           [False, False, False]])
+    >>> np.equal(1, np.ones(1))
+    array([ True])
+    """
+    return _ufunc_helper(x1, x2, _npi.equal, _np.equal, _npi.equal_scalar, None, out)
+
+
+@set_module('mxnet.symbol.numpy')
+def not_equal(x1, x2, out=None):
+    """
+    Return (x1 != x2) element-wise.
+    Parameters
+    ----------
+    x1, x2 : _Symbol or scalars
+        Input arrays. If ``x1.shape != x2.shape``, they must be broadcastable to
+        a common shape (which becomes the shape of the output).
+    out : Dummy parameter, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned.
+    Returns
+    -------
+    out : _Symbol or scalar
+        Output array of type bool, element-wise comparison of `x1` and `x2`.
+        This is a scalar if both `x1` and `x2` are scalars.
+    See Also
+    --------
+    equal, greater, greater_equal, less, less_equal
+    Examples
+    --------
+    >>> np.not_equal(np.ones(2, 1)), np.zeros(1, 3))
+    array([[ True,  True,  True],
+           [ True,  True,  True]])
+    >>> np.not_equal(1, np.ones(1))
+    array([False])
+    """
+    return _ufunc_helper(x1, x2, _npi.not_equal, _np.not_equal, _npi.not_equal_scalar, None, out)
+
+
+@set_module('mxnet.symbol.numpy')
+def greater(x1, x2, out=None):
+    """
+    Return the truth value of (x1 > x2) element-wise.
+    Parameters
+    ----------
+    x1, x2 : _Symbol or scalars
+        Input arrays. If ``x1.shape != x2.shape``, they must be broadcastable to
+        a common shape (which becomes the shape of the output).
+    out : Dummy parameter, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned.
+    Returns
+    -------
+    out : _Symbol or scalar
+        Output array of type bool, element-wise comparison of `x1` and `x2`.
+        This is a scalar if both `x1` and `x2` are scalars.
+    See Also
+    --------
+    equal, greater, greater_equal, less, less_equal
+    Examples
+    --------
+    >>> np.greater(np.ones(2, 1)), np.zeros(1, 3))
+    array([[ True,  True,  True],
+           [ True,  True,  True]])
+    >>> np.greater(1, np.ones(1))
+    array([False])
+    """
+    return _ufunc_helper(x1, x2, _npi.greater, _np.greater, _npi.greater_scalar,
+                         _npi.less_scalar, out)
+
+
+@set_module('mxnet.symbol.numpy')
+def less(x1, x2, out=None):
+    """
+    Return the truth value of (x1 < x2) element-wise.
+    Parameters
+    ----------
+    x1, x2 : _Symbol or scalars
+        Input arrays. If ``x1.shape != x2.shape``, they must be broadcastable to
+        a common shape (which becomes the shape of the output).
+    out : Dummy parameter, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned.
+    Returns
+    -------
+    out : _Symbol or scalar
+        Output array of type bool, element-wise comparison of `x1` and `x2`.
+        This is a scalar if both `x1` and `x2` are scalars.
+    See Also
+    --------
+    equal, greater, greater_equal, less, less_equal
+    Examples
+    --------
+    >>> np.less(np.ones(2, 1)), np.zeros(1, 3))
+    array([[ True,  True,  True],
+           [ True,  True,  True]])
+    >>> np.less(1, np.ones(1))
+    array([False])
+    """
+    return _ufunc_helper(x1, x2, _npi.less, _np.less, _npi.less_scalar, _npi.greater_scalar, out)
+
+
+@set_module('mxnet.symbol.numpy')
+def greater_equal(x1, x2, out=None):
+    """
+    Return the truth value of (x1 >= x2) element-wise.
+    Parameters
+    ----------
+    x1, x2 : _Symbol or scalars
+        Input arrays. If ``x1.shape != x2.shape``, they must be broadcastable to
+        a common shape (which becomes the shape of the output).
+    out : Dummy parameter, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned.
+    Returns
+    -------
+    out : _Symbol or scalar
+        Output array of type bool, element-wise comparison of `x1` and `x2`.
+        This is a scalar if both `x1` and `x2` are scalars.
+    See Also
+    --------
+    equal, greater, greater_equal, less, less_equal
+    Examples
+    --------
+    >>> np.greater_equal(np.ones(2, 1)), np.zeros(1, 3))
+    array([[ True,  True,  True],
+           [ True,  True,  True]])
+    >>> np.greater_equal(1, np.ones(1))
+    array([True])
+    """
+    return _ufunc_helper(x1, x2, _npi.greater_equal, _np.greater_equal, _npi.greater_equal_scalar,
+                         _npi.less_equal_scalar, out)
+
+
+@set_module('mxnet.symbol.numpy')
+def less_equal(x1, x2, out=None):
+    """
+    Return the truth value of (x1 <= x2) element-wise.
+    Parameters
+    ----------
+    x1, x2 : _Symbol or scalars
+        Input arrays. If ``x1.shape != x2.shape``, they must be broadcastable to
+        a common shape (which becomes the shape of the output).
+    out : Dummy parameter, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned.
+    Returns
+    -------
+    out : _Symbol or scalar
+        Output array of type bool, element-wise comparison of `x1` and `x2`.
+        This is a scalar if both `x1` and `x2` are scalars.
+    See Also
+    --------
+    equal, greater, greater_equal, less, less_equal
+    Examples
+    --------
+    >>> np.less_equal(np.ones(2, 1)), np.zeros(1, 3))
+    array([[False, False, False],
+           [False, False, False]])
+    >>> np.less_equal(1, np.ones(1))
+    array([True])
+    """
+    return _ufunc_helper(x1, x2, _npi.less_equal, _np.less_equal, _npi.less_equal_scalar,
+                         _npi.greater_equal_scalar, out)
+
+
 _set_np_symbol_class(_Symbol)
diff --git a/python/mxnet/test_utils.py b/python/mxnet/test_utils.py
index abdf57039c3a..c5b3b0d8e4cd 100644
--- a/python/mxnet/test_utils.py
+++ b/python/mxnet/test_utils.py
@@ -50,6 +50,7 @@
 from .symbol import Symbol
 from .symbol.numpy import _Symbol as np_symbol
 from .util import use_np  # pylint: disable=unused-import
+from .runtime import Features
 
 
 def default_context():
@@ -2225,3 +2226,19 @@ def collapse_sum_like(a, shape):
 def is_cd_run():
     """Checks if the test is running as part of a Continuous Delivery run"""
     return os.environ.get("CD_JOB", 0) == "1"
+
+
+_features = Features()
+
+
+def has_tvm_ops():
+    """Returns True if MXNet is compiled with TVM generated operators. If current ctx
+    is GPU, it only returns True for CUDA compute capability > 52 where FP16 is supported."""
+    built_with_tvm_op = _features.is_enabled("TVM_OP")
+    if current_context().device_type == 'gpu':
+        try:
+            import tvm
+        except ImportError:
+            return False
+        return built_with_tvm_op and (int("".join(tvm.nd.gpu(0).compute_version.split('.'))) >= 53)
+    return built_with_tvm_op
diff --git a/src/ndarray/ndarray.cc b/src/ndarray/ndarray.cc
index cc21dd242a2d..d1c3132b79a7 100644
--- a/src/ndarray/ndarray.cc
+++ b/src/ndarray/ndarray.cc
@@ -281,7 +281,7 @@ NDArray NDArray::Slice(index_t begin, index_t end) const {
   CHECK_EQ(storage_type(), kDefaultStorage);
   NDArray ret = this->Detach();
   size_t length = shape_.ProdShape(1, shape_.ndim());
-  MSHADOW_TYPE_SWITCH(ret.dtype(), DType, {
+  MSHADOW_TYPE_SWITCH_WITH_BOOL(ret.dtype(), DType, {
     ret.byte_offset_ += begin * length * sizeof(DType);
   });
   ret.reuse_ = false;
diff --git a/src/ndarray/ndarray_function.cc b/src/ndarray/ndarray_function.cc
index 1a699b12d76d..653dec84563a 100644
--- a/src/ndarray/ndarray_function.cc
+++ b/src/ndarray/ndarray_function.cc
@@ -36,6 +36,15 @@ template<>
 void Copy<cpu, cpu>(const TBlob &from, TBlob *to,
                     Context from_ctx, Context to_ctx,
                     RunContext ctx) {
+  if (from.type_flag_ == mshadow::kBool || to->type_flag_ == mshadow::kBool) {
+    CHECK_EQ(from.type_flag_, to->type_flag_) << "Only supports copying data between"
+                                                 " two boolean tensors.";
+    const index_t size = from.Size();
+    CHECK_EQ(size, to->Size()) << "copying size mismatch, from: " << size * sizeof(bool)
+                               << " bytes, to: " << to->Size() * sizeof(bool) << " bytes.";
+    common::ParallelCopy(to->dptr<bool>(), from.dptr<bool>(), size);
+    return;
+  }
   MSHADOW_TYPE_SWITCH(to->type_flag_, DType, {
     if (to->type_flag_ == from.type_flag_) {
       const index_t size = static_cast<index_t>(from.Size());
diff --git a/src/ndarray/ndarray_function.cu b/src/ndarray/ndarray_function.cu
index 0ab40a794198..6439c417bfe3 100644
--- a/src/ndarray/ndarray_function.cu
+++ b/src/ndarray/ndarray_function.cu
@@ -44,7 +44,7 @@ void Copy<cpu, gpu>(const TBlob &from, TBlob *to,
                     RunContext ctx) {
   CHECK_EQ(to->type_flag_, from.type_flag_)
     << "Source and target must have the same data type when copying across devices.";
-  MSHADOW_TYPE_SWITCH(to->type_flag_, DType, {
+  MSHADOW_TYPE_SWITCH_WITH_BOOL(to->type_flag_, DType, {
     mshadow::Copy(to->FlatTo1D<gpu, DType>(),
                   from.FlatTo1D<cpu, DType>(),
                   ctx.get_stream<gpu>());
@@ -57,7 +57,7 @@ void Copy<gpu, cpu>(const TBlob &from, TBlob *to,
                     RunContext ctx) {
   CHECK_EQ(to->type_flag_, from.type_flag_)
     << "Source and target must have the same data type when copying across devices.";
-  MSHADOW_TYPE_SWITCH(to->type_flag_, DType, {
+  MSHADOW_TYPE_SWITCH_WITH_BOOL(to->type_flag_, DType, {
     mshadow::Copy(to->FlatTo1D<cpu, DType>(),
                   from.FlatTo1D<gpu, DType>(),
                   ctx.get_stream<gpu>());
@@ -70,12 +70,16 @@ void Copy<gpu, gpu>(const TBlob &from, TBlob *to,
                     RunContext ctx) {
   if (from_ctx.dev_id == to_ctx.dev_id) {
     mshadow::Stream<gpu>* s = ctx.get_stream<gpu>();
-    MSHADOW_TYPE_SWITCH(to->type_flag_, DType, {
+    MSHADOW_TYPE_SWITCH_WITH_BOOL(to->type_flag_, DType, {
       if (to->type_flag_ == from.type_flag_) {
         mshadow::Copy(to->FlatTo1D<gpu, DType>(s),
                       from.FlatTo1D<gpu, DType>(s),
                       s);
       } else {
+        CHECK_NE(from.type_flag_, mshadow::kBool)
+            << "Copying boolean ndarray across devices is not supported";
+        CHECK_NE(to->type_flag_, mshadow::kBool)
+            << "Copying boolean ndarray across devices is not supported";
         MSHADOW_TYPE_SWITCH(from.type_flag_, SrcDType, {
           to->FlatTo1D<gpu, DType>(s) =
             mshadow::expr::tcast<DType>(from.FlatTo1D<gpu, SrcDType>(s));
diff --git a/src/operator/contrib/boolean_mask.cc b/src/operator/contrib/boolean_mask.cc
index 0afc57f6ab54..a54cc917776d 100644
--- a/src/operator/contrib/boolean_mask.cc
+++ b/src/operator/contrib/boolean_mask.cc
@@ -35,7 +35,7 @@ bool BooleanMaskType(const nnvm::NodeAttrs& attrs,
   CHECK_EQ(out_attrs->size(), 1);
   TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
   TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
-  return out_attrs->at(0) != -1;
+  return in_attrs->at(0) != -1 && in_attrs->at(1) != -1 && out_attrs->at(0) != -1;
 }
 
 bool BooleanMaskStorageType(const nnvm::NodeAttrs& attrs,
@@ -115,7 +115,7 @@ inline void BooleanMaskForward<cpu>(const nnvm::NodeAttrs& attrs,
   std::vector<int32_t> prefix_sum(idx_size, 0);
   size_t valid_num = 0;
   // Calculate prefix sum
-  MSHADOW_TYPE_SWITCH(idx.dtype(), DType, {
+  MSHADOW_TYPE_SWITCH_WITH_BOOL(idx.dtype(), DType, {
     DType* idx_dptr = idx.data().dptr<DType>();
     for (size_t i = 0; i < idx_size; i++) {
       prefix_sum[i] = (i == 0) ? 0 : prefix_sum[i - 1];
@@ -154,7 +154,7 @@ inline void BooleanMaskBackward<cpu>(const nnvm::NodeAttrs& attrs,
   const NDArray& idx = inputs[2];
   const NDArray& igrad_data = outputs[0];
   MSHADOW_TYPE_SWITCH(igrad_data.dtype(), DType, {
-    MSHADOW_TYPE_SWITCH(idx.dtype(), IType, {
+    MSHADOW_TYPE_SWITCH_WITH_BOOL(idx.dtype(), IType, {
       size_t input_size = igrad_data.shape().Size();
       size_t idx_size = idx.shape()[0];
       size_t col_size = input_size / idx_size;
@@ -179,6 +179,7 @@ inline void BooleanMaskBackward<cpu>(const nnvm::NodeAttrs& attrs,
 }
 
 NNVM_REGISTER_OP(_contrib_boolean_mask)
+.add_alias("_npi_boolean_mask")
 .describe(R"code(
 Given an n-d NDArray data, and a 1-d NDArray index,
 the operator produces an un-predeterminable shaped n-d NDArray out,
diff --git a/src/operator/contrib/boolean_mask.cu b/src/operator/contrib/boolean_mask.cu
index c4a06d25d70a..71d91c63f64e 100644
--- a/src/operator/contrib/boolean_mask.cu
+++ b/src/operator/contrib/boolean_mask.cu
@@ -66,7 +66,7 @@ inline void BooleanMaskForward<gpu>(const nnvm::NodeAttrs& attrs,
     ctx.requested[0].get_space_typed<gpu, 1, char>(Shape1(temp_storage_bytes), s);
   prefix_sum = reinterpret_cast<int32_t*>(workspace.dptr_);
   d_temp_storage = workspace.dptr_ + buffer_size;
-  MSHADOW_TYPE_SWITCH(idx.dtype(), IType, {
+  MSHADOW_TYPE_SWITCH_WITH_BOOL(idx.dtype(), IType, {
     mxnet_op::Kernel<mshadow_op::identity_with_cast, gpu>::Launch(
       s, idx.shape()[0], prefix_sum, idx.data().dptr<IType>());
   });
@@ -129,7 +129,7 @@ inline void BooleanMaskBackward<gpu>(const nnvm::NodeAttrs& attrs,
     ctx.requested[0].get_space_typed<gpu, 1, char>(Shape1(temp_storage_bytes), s);
   prefix_sum = reinterpret_cast<int32_t*>(workspace.dptr_);
   d_temp_storage = workspace.dptr_ + buffer_size;
-  MSHADOW_TYPE_SWITCH(idx.dtype(), IType, {
+  MSHADOW_TYPE_SWITCH_WITH_BOOL(idx.dtype(), IType, {
     mxnet_op::Kernel<mshadow_op::identity_with_cast, gpu>::Launch(
       s, idx.shape()[0], prefix_sum, idx.data().dptr<IType>());
   });
diff --git a/src/operator/mxnet_op.h b/src/operator/mxnet_op.h
index d90aa268195a..b46ce8a598d9 100644
--- a/src/operator/mxnet_op.h
+++ b/src/operator/mxnet_op.h
@@ -491,6 +491,17 @@ struct AccType<mshadow::half::half_t> {
   .add_enum("int64", mshadow::kInt64)
 
 
+#define MXNET_ADD_ALL_TYPES_WITH_BOOL \
+  .add_enum("float32", mshadow::kFloat32) \
+  .add_enum("float64", mshadow::kFloat64) \
+  .add_enum("float16", mshadow::kFloat16) \
+  .add_enum("uint8", mshadow::kUint8) \
+  .add_enum("int8", mshadow::kInt8) \
+  .add_enum("int32", mshadow::kInt32) \
+  .add_enum("int64", mshadow::kInt64) \
+  .add_enum("bool", mshadow::kBool)
+
+
 /* \brief Compute flattened index given coordinates and shape. */
 template<int ndim>
 MSHADOW_XINLINE index_t ravel(const Shape<ndim>& coord, const Shape<ndim>& shape) {
@@ -597,6 +608,11 @@ template <typename xpu>
 MSHADOW_CINLINE void copy(mshadow::Stream<xpu> *s, const TBlob& to, const TBlob& from) {
   CHECK_EQ(from.Size(), to.Size());
   CHECK_EQ(from.dev_mask(), to.dev_mask());
+  if (from.type_flag_ == mshadow::kBool || to.type_flag_ == mshadow::kBool) {
+    CHECK_EQ(from.type_flag_, to.type_flag_) << "Only supports copying between boolean ndarrays.";
+    mshadow::Copy(to.FlatTo1D<xpu, bool>(s), from.FlatTo1D<xpu, bool>(s), s);
+    return;
+  }
   MSHADOW_TYPE_SWITCH(to.type_flag_, DType, {
     if (to.type_flag_ == from.type_flag_) {
       mshadow::Copy(to.FlatTo1D<xpu, DType>(s), from.FlatTo1D<xpu, DType>(s), s);
diff --git a/src/operator/numpy/np_broadcast_reduce_op.h b/src/operator/numpy/np_broadcast_reduce_op.h
index b1348de72de5..816091a48b48 100644
--- a/src/operator/numpy/np_broadcast_reduce_op.h
+++ b/src/operator/numpy/np_broadcast_reduce_op.h
@@ -27,6 +27,7 @@
 
 #include <algorithm>
 #include <vector>
+#include <string>
 #include "../nn/moments-inl.h"
 #include "../tensor/broadcast_reduce_op.h"
 
@@ -86,7 +87,6 @@ struct NumpyReduceAxesNoDTypeParam : public dmlc::Parameter<NumpyReduceAxesNoDTy
 inline TShape NumpyReduceAxesShapeImpl(const TShape& ishape,
                                        const dmlc::optional<mxnet::Tuple<int>>& axis,
                                        bool keepdims) {
-  // TODO(junwu): improve the logic
   // If input is a scalar, output should be a scalar too
   if (ishape.ndim() == 0) {
     if (axis.has_value()) {
@@ -210,6 +210,10 @@ inline bool NeedSafeAcc(int itype, int otype) {
   return safe_acc_hint && rule;
 }
 
+void TVMOpReduce(const OpContext& ctx, const TBlob& input,
+                 const dmlc::optional<mxnet::Tuple<int>>& axis,
+                 const TBlob& output, const OpReqType req, const std::string& reducer_name);
+
 template<typename xpu, typename reducer, bool safe_acc_hint = false, bool normalize = false,
          typename OP = op::mshadow_op::identity>
 void NumpyReduceAxesCompute(const nnvm::NodeAttrs& attrs,
@@ -217,6 +221,7 @@ void NumpyReduceAxesCompute(const nnvm::NodeAttrs& attrs,
                             const std::vector<TBlob>& inputs,
                             const std::vector<OpReqType>& req,
                             const std::vector<TBlob>& outputs) {
+  if (req[0] == kNullOp) return;
   const NumpyReduceAxesParam& param = nnvm::get<NumpyReduceAxesParam>(attrs.parsed);
   if (param.initial.has_value()) {
     LOG(FATAL) << "initial is not supported yet";
@@ -230,6 +235,18 @@ void NumpyReduceAxesCompute(const nnvm::NodeAttrs& attrs,
     });
     return;
   }
+  CHECK_NE(req[0], kWriteInplace) << "Reduce does not support write in-place";
+  // If boolean ndarray, use the kernel generated by TVM
+  if (inputs[0].type_flag_ == mshadow::kBool) {
+    std::string reducer_name;
+    if (std::is_same<reducer, mshadow_op::sum>::value) {
+      reducer_name = "sum";
+    } else {
+      LOG(FATAL) << "Only reduce op: `sum` is supported for boolean ndarrays";
+    }
+    TVMOpReduce(ctx, inputs[0], param.axis, outputs[0], req[0], reducer_name);
+    return;
+  }
   if (param.axis.has_value() && param.axis.value().ndim() == 0) {
     UnaryOp::IdentityCompute<xpu>(attrs, ctx, inputs, req, outputs);
   }
@@ -279,6 +296,8 @@ inline void NumpyReduceAxesBackwardUseNone(const nnvm::NodeAttrs& attrs,
                                            const std::vector<TBlob>& outputs) {
   using namespace mshadow;
   using namespace mshadow::expr;
+  CHECK_NE(outputs[0].type_flag_, kBool) << "reduce operators do not support gradient calculation "
+                                            "for input tensors of boolean type.";
   const NumpyReduceAxesParam& param = nnvm::get<NumpyReduceAxesParam>(attrs.parsed);
   TShape small;
   if (param.keepdims) {
diff --git a/src/operator/numpy/np_broadcast_reduce_op_value.cc b/src/operator/numpy/np_broadcast_reduce_op_value.cc
index 774bc11f5de8..fdda792a9ed8 100644
--- a/src/operator/numpy/np_broadcast_reduce_op_value.cc
+++ b/src/operator/numpy/np_broadcast_reduce_op_value.cc
@@ -23,6 +23,10 @@
  * \brief CPU Implementation of broadcast and reduce functions based on value.
  */
 
+#if MXNET_USE_TVM_OP
+#include "../tvmop/op_module.h"
+#endif  // MXNET_USE_TVM_OP
+
 #include "np_broadcast_reduce_op.h"
 
 namespace mxnet {
@@ -40,7 +44,17 @@ inline bool NumpySumType(const nnvm::NodeAttrs& attrs,
   const NumpyReduceAxesParam &param = nnvm::get<NumpyReduceAxesParam>(attrs.parsed);
 
   if (param.dtype.has_value()) {
+    if (in_attrs->at(0) == mshadow::kBool) {
+      CHECK(param.dtype.value() == mshadow::kInt32
+          || param.dtype.value() == mshadow::kInt64
+          || param.dtype.value() == mshadow::kFloat32
+          || param.dtype.value() == mshadow::kFloat64) << "Only support the following output "
+                                                         "dtypes when input dtype is bool: "
+                                                         "int32, int64, float32, float64.";
+    }
     TYPE_ASSIGN_CHECK(*out_attrs, 0, param.dtype.value());
+  } else if (in_attrs->at(0) == mshadow::kBool) {
+    TYPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::kInt64);
   } else {
     TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
     TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
@@ -49,6 +63,63 @@ inline bool NumpySumType(const nnvm::NodeAttrs& attrs,
   return out_attrs->at(0) != -1 && in_attrs->at(0) != -1;
 }
 
+#if MXNET_USE_TVM_OP
+static constexpr int max_reduce_ndim = 5;
+TBlob PrependAxes(const TBlob& src, const int dst_ndim);
+#endif  // MXNET_USE_TVM_OP
+
+void TVMOpReduce(const OpContext& ctx,
+                 const TBlob& input,
+                 const dmlc::optional<mxnet::Tuple<int>>& axis,
+                 const TBlob& output,
+                 const OpReqType req,
+                 const std::string& reducer_name) {
+#if MXNET_USE_TVM_OP
+  CHECK_GE(input.ndim(), output.ndim());
+  CHECK_LE(input.ndim(), max_reduce_ndim) << "TVMOpReduce only supports ndim <= "
+                                          << max_reduce_ndim;
+
+  const TBlob expanded_output = (input.ndim() == output.ndim() ?
+      output : output.reshape(NumpyReduceAxesShapeImpl(input.shape_, axis, true)));
+  CHECK_EQ(input.ndim(), expanded_output.ndim());
+  int reduce1st_dim = 0;
+  if (input.ndim() > 0 && input.size(0) != expanded_output.size(0)) {
+    reduce1st_dim = 1;
+  }
+  // collapse consecutive dimensions where reduction are performed or not performed
+  std::vector<index_t> ishape_vec;
+  for (int i = 0; i < input.ndim(); ++i) {
+    if (i == 0 || ((input.size(i) != expanded_output.size(i))
+                    != (input.size(i-1) != expanded_output.size(i-1)))) {
+      ishape_vec.push_back(input.size(i));
+    } else {
+      ishape_vec.back() *= input.size(i);
+    }
+  }
+  // append axes after collapsed ishape to reach the max ndim allowed
+  for (int i = ishape_vec.size(); i < max_reduce_ndim; ++i) {
+    ishape_vec.push_back(1);
+  }
+  std::vector<index_t> oshape_vec;
+  for (size_t i = reduce1st_dim; i < ishape_vec.size(); i += 2) {
+    oshape_vec.push_back(ishape_vec[i]);
+  }
+  TShape ishape(ishape_vec.begin(), ishape_vec.end()), oshape(oshape_vec.begin(), oshape_vec.end());
+  TBlob input_tvm = input.reshape(ishape);
+  TBlob output_tvm = output.reshape(oshape);
+  const std::string ctx_name =
+      (ctx.run_ctx.ctx.dev_type == mxnet::Context::DeviceType::kCPU) ? "cpu" : "gpu";
+  std::ostringstream func_name;
+  func_name << reducer_name << "_"
+            << (ctx.run_ctx.ctx.dev_type == mxnet::Context::DeviceType::kCPU ? "cpu" : "gpu")
+            << "reduce1st_dim_" << reduce1st_dim
+            << "req_" << (req == kWriteTo ? "kWriteTo" : "kAddTo");
+  tvm::runtime::TVMOpModule::Get()->Call(func_name.str(), ctx, {input_tvm, output_tvm, output_tvm});
+#else
+  LOG(FATAL) << "Please add USE_TVM_OP=1 as a compile flag to enable TVM-generated kernels.";
+#endif  // MXNET_USE_TVM_OP
+}
+
 NNVM_REGISTER_OP(_np_sum)
 .describe(R"code()code" ADD_FILELINE)
 .set_num_inputs(1)
diff --git a/src/operator/numpy/np_elemwise_broadcast_op.cc b/src/operator/numpy/np_elemwise_broadcast_op.cc
index fd109f040b50..aa81a58c2890 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op.cc
+++ b/src/operator/numpy/np_elemwise_broadcast_op.cc
@@ -23,6 +23,12 @@
  * \brief CPU Implementation of basic functions for elementwise numpy binary broadcast operator.
  */
 
+#if MXNET_USE_TVM_OP
+#include <tvm/runtime/c_runtime_api.h>
+#include <tvm/runtime/packed_func.h>
+#include "../tvmop/op_module.h"
+#endif  // MXNET_USE_TVM_OP
+
 #include "../tensor/elemwise_binary_broadcast_op.h"
 #include "../tensor/elemwise_binary_scalar_op.h"
 
@@ -113,6 +119,22 @@ NNVM_REGISTER_OP(_npi_lcm)
 .add_argument("lhs", "NDArray-or-Symbol", "First input to the function")
 .add_argument("rhs", "NDArray-or-Symbol", "Second input to the function");
 
+NNVM_REGISTER_OP(_npi_lcm_scalar)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser([](NodeAttrs* attrs) {
+    attrs->parsed = std::stod(attrs->dict["scalar"]);
+  })
+.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseIntType<1, 1>)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs){
+    return std::vector<std::pair<int, int> >{{0, 0}};
+  })
+.add_argument("data", "NDArray-or-Symbol", "source input")
+.add_argument("scalar", "int", "scalar input")
+.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::lcm>);
+
 MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_add_scalar)
 .set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, op::mshadow_op::plus>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_copy"});
@@ -280,21 +302,222 @@ NNVM_REGISTER_OP(_backward_npi_hypot)
 .set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastBackwardUseIn<cpu, mshadow_op::hypot_grad_left,
                                                                   mshadow_op::hypot_grad_right>);
 
-NNVM_REGISTER_OP(_npi_lcm_scalar)
-.set_num_inputs(1)
-.set_num_outputs(1)
-.set_attr_parser([](NodeAttrs* attrs) {
-    attrs->parsed = std::stod(attrs->dict["scalar"]);
-  })
-.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
-.set_attr<nnvm::FInferType>("FInferType", ElemwiseIntType<1, 1>)
-.set_attr<nnvm::FInplaceOption>("FInplaceOption",
-  [](const NodeAttrs& attrs){
-    return std::vector<std::pair<int, int> >{{0, 0}};
-  })
-.add_argument("data", "NDArray-or-Symbol", "source input")
-.add_argument("scalar", "int", "scalar input")
-.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::lcm>);
+static constexpr char func_equal_cpu[] = "equal_cpu";
+static constexpr char func_equal_gpu[] = "equal_gpu";
+static constexpr char func_not_equal_cpu[] = "not_equal_cpu";
+static constexpr char func_not_equal_gpu[] = "not_equal_gpu";
+static constexpr char func_greater_cpu[] = "greater_cpu";
+static constexpr char func_greater_gpu[] = "greater_gpu";
+static constexpr char func_less_cpu[] = "less_cpu";
+static constexpr char func_less_gpu[] = "less_gpu";
+static constexpr char func_greater_equal_cpu[] = "greater_equal_cpu";
+static constexpr char func_greater_equal_gpu[] = "greater_equal_gpu";
+static constexpr char func_less_equal_cpu[] = "less_equal_cpu";
+static constexpr char func_less_equal_gpu[] = "less_equal_gpu";
+
+bool NumpyBinaryLogicOpType(const nnvm::NodeAttrs& attrs,
+                            std::vector<int>* in_attrs,
+                            std::vector<int>* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 2U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  if (in_attrs->at(0) == -1 && in_attrs->at(1) == -1) return false;
+  TYPE_ASSIGN_CHECK(*in_attrs, 0, in_attrs->at(1));
+  TYPE_ASSIGN_CHECK(*in_attrs, 1, in_attrs->at(0));
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::kBool);
+  return true;
+}
+
+TBlob PrependAxes(const TBlob& src, const int dst_ndim) {
+  CHECK_LE(src.shape_.ndim(), dst_ndim);
+  const int src_ndim = src.shape_.ndim();
+  if (src_ndim == dst_ndim) return src;
+  mxnet::TShape dst_shape(dst_ndim, 1);
+  for (int i = dst_ndim - src_ndim; i < dst_ndim; ++i) {
+    dst_shape[i] = src.shape_[i - dst_ndim + src_ndim];
+  }
+  return src.reshape(dst_shape);
+}
+
+struct TVMBinaryBroadcastCompute {
+  const char* func;
+  void operator()(const nnvm::NodeAttrs& attrs,
+                  const mxnet::OpContext& ctx,
+                  const std::vector<TBlob>& inputs,
+                  const std::vector<OpReqType>& req,
+                  const std::vector<TBlob>& outputs) {
+#if MXNET_USE_TVM_OP
+    CHECK_EQ(inputs.size(), 2U);
+    CHECK_EQ(outputs.size(), 1U);
+    if (outputs[0].shape_.Size() == 0U) return;  // skip zero-size tensor
+
+    // prepare tblobs and TVMArgs
+    std::vector<TBlob> tblobs = {inputs[0], inputs[1], outputs[0]};
+    std::vector<int> type_codes;
+    std::vector<TVMValue> values;
+
+    const int ondim = outputs[0].shape_.ndim();
+    const size_t num_args = inputs.size() + outputs.size();
+    type_codes.resize(num_args);
+    values.resize(num_args);
+    for (size_t i = 0; i < num_args; ++i) {
+      tblobs[i] = PrependAxes(tblobs[i], ondim);
+      type_codes[i] = kArrayHandle;
+      values[i].v_handle = const_cast<DLTensor*>(&(tblobs[i].dltensor()));
+    }
+    tvm::runtime::TVMArgs tvm_args(&values[0], &type_codes[0], tblobs.size());
+    tvm::runtime::TVMOpModule::Get()->CallEx(func, ctx, tblobs, tvm_args);
+#else
+    LOG(FATAL) << "Please add USE_TVM_OP=1 as a compile flag for compiling MXNet source code "
+                  "to enable TVM-generated kernels for operator " << func;
+#endif  // MXNET_USE_TVM_OP
+  }
+};
+
+#define MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC(name)                                 \
+  NNVM_REGISTER_OP(_npi_##name)                                                       \
+  .set_num_inputs(2)                                                                  \
+  .set_num_outputs(1)                                                                 \
+  .set_attr<nnvm::FListInputNames>("FListInputNames",                                 \
+  [](const NodeAttrs& attrs) {                                                        \
+    return std::vector<std::string>{"lhs", "rhs"};                                    \
+  })                                                                                  \
+  .set_attr<mxnet::FInferShape>("FInferShape", BinaryBroadcastShape)                  \
+  .set_attr<nnvm::FInferType>("FInferType", NumpyBinaryLogicOpType)                   \
+  .set_attr<nnvm::FInplaceOption>("FInplaceOption",                                   \
+  [](const NodeAttrs& attrs) {                                                        \
+    return std::vector<std::pair<int, int> >{{0, 0}, {1, 0}};                         \
+  })                                                                                  \
+  .set_attr<FCompute>("FCompute<cpu>", TVMBinaryBroadcastCompute{func_##name##_cpu})  \
+  .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)                          \
+  .add_argument("lhs", "NDArray-or-Symbol", "First input to the function")            \
+  .add_argument("rhs", "NDArray-or-Symbol", "Second input to the function")
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC(equal);
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC(not_equal);
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC(greater);
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC(less);
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC(greater_equal);
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC(less_equal);
+
+#if MXNET_USE_CUDA
+#define MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_GPU(name)                          \
+  NNVM_REGISTER_OP(_npi_##name)                                                    \
+  .set_attr<FCompute>("FCompute<gpu>", TVMBinaryBroadcastCompute{func_##name##_gpu})
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_GPU(equal);
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_GPU(not_equal);
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_GPU(greater);
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_GPU(less);
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_GPU(greater_equal);
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_GPU(less_equal);
+#endif  // MXNET_USE_CUDA
+
+bool NumpyBinaryScalarLogicOpType(const nnvm::NodeAttrs& attrs,
+                                  std::vector<int>* in_attrs,
+                                  std::vector<int>* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  if (in_attrs->at(0) == -1) return false;
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::kBool);
+  return true;
+}
+
+struct TVMBinaryBroadcastScalarCompute {
+  const char* func;
+  void operator()(const nnvm::NodeAttrs& attrs,
+                  const mxnet::OpContext& ctx,
+                  const std::vector<TBlob>& inputs,
+                  const std::vector<OpReqType>& req,
+                  const std::vector<TBlob>& outputs) {
+#if MXNET_USE_TVM_OP
+    CHECK_EQ(inputs.size(), 1U);
+    CHECK_EQ(outputs.size(), 1U);
+    if (outputs[0].shape_.Size() == 0U) return;  // skip zero-size tensor
+
+    // prepare tblobs and TVMArgs
+    std::vector<TBlob> tblobs = {inputs[0], outputs[0]};
+    std::vector<int> type_codes;
+    std::vector<TVMValue> values;
+
+    const size_t num_args = 3;  // one input tensor, one scalar param, and one output
+    type_codes.resize(num_args);
+    values.resize(num_args);
+
+    // input tensor setup
+    type_codes[0] = kArrayHandle;
+    values[0].v_handle = const_cast<DLTensor*>(&(tblobs[0].dltensor()));
+
+    // scalar param
+    type_codes[1] = kDLFloat;
+    values[1].v_float64 = nnvm::get<double>(attrs.parsed);
+
+    // output tensor
+    type_codes[2] = kArrayHandle;
+    values[2].v_handle = const_cast<DLTensor*>(&(tblobs[1].dltensor()));
+
+    tvm::runtime::TVMArgs tvm_args(&values[0], &type_codes[0], 3);
+    tvm::runtime::TVMOpModule::Get()->CallEx(func, ctx, tblobs, tvm_args);
+#else
+    LOG(FATAL) << "Please add USE_TVM_OP=1 as a compile flag for compiling MXNet source code "
+                  "to enable TVM-generated kernels for operator " << func;
+#endif  // MXNET_USE_TVM_OP
+  }
+};
+
+#define MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC(name)                                \
+  NNVM_REGISTER_OP(_npi_##name)                                                             \
+  .set_num_inputs(1)                                                                        \
+  .set_num_outputs(1)                                                                       \
+  .set_attr_parser([](NodeAttrs* attrs) {                                                   \
+    attrs->parsed = std::stod(attrs->dict["scalar"]);                                       \
+  })                                                                                        \
+  .set_attr<nnvm::FListInputNames>("FListInputNames",                                       \
+  [](const NodeAttrs& attrs) {                                                              \
+    return std::vector<std::string>{"data"};                                                \
+  })                                                                                        \
+  .set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)                         \
+  .set_attr<nnvm::FInferType>("FInferType", NumpyBinaryScalarLogicOpType)                   \
+  .set_attr<nnvm::FInplaceOption>("FInplaceOption",                                         \
+  [](const NodeAttrs& attrs) {                                                              \
+    return std::vector<std::pair<int, int> >{{0, 0}};                                       \
+  })                                                                                        \
+  .set_attr<FCompute>("FCompute<cpu>", TVMBinaryBroadcastScalarCompute{func_##name##_cpu})  \
+  .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)                                \
+  .add_argument("data", "NDArray-or-Symbol", "First input to the function")                 \
+  .add_argument("scalar", "float", "scalar input")
+
+static constexpr char func_equal_scalar_cpu[] = "equal_scalar_cpu";
+static constexpr char func_equal_scalar_gpu[] = "equal_scalar_gpu";
+static constexpr char func_not_equal_scalar_cpu[] = "not_equal_scalar_cpu";
+static constexpr char func_not_equal_scalar_gpu[] = "not_equal_scalar_gpu";
+static constexpr char func_greater_scalar_cpu[] = "greater_scalar_cpu";
+static constexpr char func_greater_scalar_gpu[] = "greater_scalar_gpu";
+static constexpr char func_less_scalar_cpu[] = "less_scalar_cpu";
+static constexpr char func_less_scalar_gpu[] = "less_scalar_gpu";
+static constexpr char func_greater_equal_scalar_cpu[] = "greater_equal_scalar_cpu";
+static constexpr char func_greater_equal_scalar_gpu[] = "greater_equal_scalar_gpu";
+static constexpr char func_less_equal_scalar_cpu[] = "less_equal_scalar_cpu";
+static constexpr char func_less_equal_scalar_gpu[] = "less_equal_scalar_gpu";
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC(equal_scalar);
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC(not_equal_scalar);
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC(greater_scalar);
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC(less_scalar);
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC(greater_equal_scalar);
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC(less_equal_scalar);
+
+#if MXNET_USE_CUDA
+#define MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_GPU(name)                         \
+  NNVM_REGISTER_OP(_npi_##name)                                                          \
+  .set_attr<FCompute>("FCompute<gpu>", TVMBinaryBroadcastScalarCompute{func_##name##_gpu})
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_GPU(equal_scalar);
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_GPU(not_equal_scalar);
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_GPU(greater_scalar);
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_GPU(less_scalar);
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_GPU(greater_equal_scalar);
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_GPU(less_equal_scalar);
+#endif  // MXNET_USE_CUDA
 
 MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_ldexp)
 .set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, mshadow_op::ldexp>)
diff --git a/src/operator/operator_tune.cc b/src/operator/operator_tune.cc
index c690cb633306..49d0e23bdd19 100644
--- a/src/operator/operator_tune.cc
+++ b/src/operator/operator_tune.cc
@@ -59,6 +59,7 @@ IMPLEMENT_OPERATOR_TUNE_STATICS_FOR_TYPE(int8_t);
 IMPLEMENT_OPERATOR_TUNE_STATICS_FOR_TYPE(uint8_t);
 IMPLEMENT_OPERATOR_TUNE_STATICS_FOR_TYPE(int32_t);
 IMPLEMENT_OPERATOR_TUNE_STATICS_FOR_TYPE(int64_t);
+IMPLEMENT_OPERATOR_TUNE_STATICS_FOR_TYPE(bool);
 
 /*!
  * \brief Init variable used to facilitate registering a tunable operator during
@@ -84,6 +85,16 @@ struct static_init_var {
   __macro$(__VA_ARGS__, int32_t); \
   __macro$(__VA_ARGS__, int64_t);
 
+#define MSHADOW_MACRO_FOREACH_TYPE_WITH_BOOL(__macro$, ...) \
+  __macro$(__VA_ARGS__, float); \
+  __macro$(__VA_ARGS__, double); \
+  __macro$(__VA_ARGS__, mshadow::half::half_t); \
+  __macro$(__VA_ARGS__, uint8_t); \
+  __macro$(__VA_ARGS__, int8_t); \
+  __macro$(__VA_ARGS__, int32_t); \
+  __macro$(__VA_ARGS__, int64_t); \
+  __macro$(__VA_ARGS__, bool)
+
 #define IMPLEMENT_WORKLOAD_VALUE_FOR_TYPE(__op$, __typ$) \
   namespace mxnet_op { \
   template<> std::vector<float> mxnet::op::mxnet_op::tuned_op<__op$, __typ$>::workload_ = \
@@ -183,9 +194,15 @@ struct static_init_var {
 #define IMPLEMENT_UNARY_WORKLOAD_FWD(__op$) \
   MSHADOW_MACRO_FOREACH_TYPE(_IMPLEMENT_UNARY_WORKLOAD_FWD, __op$)
 
+#define IMPLEMENT_UNARY_WORKLOAD_FWD_WITH_BOOL(__op$) \
+  MSHADOW_MACRO_FOREACH_TYPE_WITH_BOOL(_IMPLEMENT_UNARY_WORKLOAD_FWD, __op$)
+
 #define IMPLEMENT_BLANK_WORKLOAD_FWD(__op$) \
   MSHADOW_MACRO_FOREACH_TYPE(_IMPLEMENT_BLANK_WORKLOAD_FWD, __op$)
 
+#define IMPLEMENT_BLANK_WORKLOAD_FWD_WITH_BOOL(__op$) \
+  MSHADOW_MACRO_FOREACH_TYPE_WITH_BOOL(_IMPLEMENT_BLANK_WORKLOAD_FWD, __op$)
+
 #define IMPLEMENT_UNARY_WORKLOAD_BWD(__op$) \
   MSHADOW_MACRO_FOREACH_TYPE(_IMPLEMENT_UNARY_WORKLOAD_BWD, __op$)
 
@@ -206,7 +223,7 @@ struct static_init_var {
  *       integer value
  */
 OperatorTuneBase::duration_t OperatorTuneBase::omp_overhead_ns_ = 5000;
-IMPLEMENT_UNARY_WORKLOAD_FWD(mxnet::op::mshadow_op::identity);  // NOLINT()
+IMPLEMENT_UNARY_WORKLOAD_FWD_WITH_BOOL(mxnet::op::mshadow_op::identity);  // NOLINT()
 IMPLEMENT_UNARY_WORKLOAD_BWD(mxnet::op::mshadow_op::identity_grad);  // NOLINT()
 IMPLEMENT_UNARY_WORKLOAD_FWD(mxnet::op::mshadow_op::negation);  // NOLINT()
 IMPLEMENT_UNARY_WORKLOAD_FWD(mxnet::op::mshadow_op::reciprocal);  // NOLINT()
@@ -370,8 +387,8 @@ IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::logical_xor);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::smooth_l1_loss);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::smooth_l1_gradient);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::lcm);  // NOLINT()
-IMPLEMENT_BLANK_WORKLOAD_FWD(mxnet::op::mxnet_op::set_to_int<0>);  // NOLINT()
-IMPLEMENT_BLANK_WORKLOAD_FWD(mxnet::op::mxnet_op::set_to_int<1>);  // NOLINT()
+IMPLEMENT_BLANK_WORKLOAD_FWD_WITH_BOOL(mxnet::op::mxnet_op::set_to_int<0>);  // NOLINT()
+IMPLEMENT_BLANK_WORKLOAD_FWD_WITH_BOOL(mxnet::op::mxnet_op::set_to_int<1>);  // NOLINT()
 IMPLEMENT_BLANK_WORKLOAD_FWD(mxnet::op::PopulateFullIdxRspKernel);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::ldexp);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::rldexp);  // NOLINT()
diff --git a/src/operator/tensor/elemwise_binary_broadcast_op_logic.cc b/src/operator/tensor/elemwise_binary_broadcast_op_logic.cc
index e3c2e0e898d9..cd433e00a770 100644
--- a/src/operator/tensor/elemwise_binary_broadcast_op_logic.cc
+++ b/src/operator/tensor/elemwise_binary_broadcast_op_logic.cc
@@ -30,7 +30,6 @@ namespace mxnet {
 namespace op {
 
 MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(broadcast_equal)
-.add_alias("_npi_equal")
 .describe(R"code(Returns the result of element-wise **equal to** (==) comparison operation with broadcasting.
 
 Example::
@@ -49,7 +48,6 @@ Example::
 .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes);
 
 MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(broadcast_not_equal)
-.add_alias("_npi_not_equal")
 .describe(R"code(Returns the result of element-wise **not equal to** (!=) comparison operation with broadcasting.
 
 Example::
@@ -68,7 +66,6 @@ Example::
 .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes);
 
 MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(broadcast_greater)
-.add_alias("_npi_greater")
 .describe(R"code(Returns the result of element-wise **greater than** (>) comparison operation with broadcasting.
 
 Example::
@@ -87,7 +84,6 @@ Example::
 .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes);
 
 MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(broadcast_greater_equal)
-.add_alias("_npi_greater_equal")
 .describe(R"code(Returns the result of element-wise **greater than or equal to** (>=) comparison operation with broadcasting.
 
 Example::
@@ -106,7 +102,6 @@ Example::
 .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes);
 
 MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(broadcast_lesser)
-.add_alias("_npi_less")
 .describe(R"code(Returns the result of element-wise **lesser than** (<) comparison operation with broadcasting.
 
 Example::
@@ -125,7 +120,6 @@ Example::
 .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes);
 
 MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(broadcast_lesser_equal)
-.add_alias("_npi_less_equal")
 .describe(R"code(Returns the result of element-wise **lesser than or equal to** (<=) comparison operation with broadcasting.
 
 Example::
diff --git a/src/operator/tensor/elemwise_binary_scalar_op_logic.cc b/src/operator/tensor/elemwise_binary_scalar_op_logic.cc
index 87ba394c99b2..17e76153ebb2 100644
--- a/src/operator/tensor/elemwise_binary_scalar_op_logic.cc
+++ b/src/operator/tensor/elemwise_binary_scalar_op_logic.cc
@@ -71,32 +71,26 @@ static bool BinaryScalarLogicStorageType(const nnvm::NodeAttrs& attrs,
 
 
 MXNET_OPERATOR_REGISTER_BINARY_SCALAR_LOGIC(_equal_scalar, mshadow_op::eq)
-.add_alias("_npi_equal_scalar")
 .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
 .add_alias("_EqualScalar");
 
 MXNET_OPERATOR_REGISTER_BINARY_SCALAR_LOGIC(_not_equal_scalar, mshadow_op::ne)
-.add_alias("_npi_not_equal_scalar")
 .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
 .add_alias("_NotEqualScalar");
 
 MXNET_OPERATOR_REGISTER_BINARY_SCALAR_LOGIC(_greater_scalar, mshadow_op::gt)
-.add_alias("_npi_greater_scalar")
 .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
 .add_alias("_GreaterScalar");
 
 MXNET_OPERATOR_REGISTER_BINARY_SCALAR_LOGIC(_greater_equal_scalar, mshadow_op::ge)
-.add_alias("_npi_greater_equal_scalar")
 .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
 .add_alias("_GreaterEqualScalar");
 
 MXNET_OPERATOR_REGISTER_BINARY_SCALAR_LOGIC(_lesser_scalar, mshadow_op::lt)
-.add_alias("_npi_less_scalar")
 .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
 .add_alias("_LesserScalar");
 
 MXNET_OPERATOR_REGISTER_BINARY_SCALAR_LOGIC(_lesser_equal_scalar, mshadow_op::le)
-.add_alias("_npi_less_equal_scalar")
 .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
 .add_alias("_LesserEqualScalar");
 
diff --git a/src/operator/tensor/elemwise_unary_op.h b/src/operator/tensor/elemwise_unary_op.h
index b50f89b8c7fa..22e7652a4019 100644
--- a/src/operator/tensor/elemwise_unary_op.h
+++ b/src/operator/tensor/elemwise_unary_op.h
@@ -341,7 +341,7 @@ class UnaryOp : public OpBase {
         break;
       case kAddTo: {
           Stream<xpu> *s = ctx.get_stream<xpu>();
-          MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+          MSHADOW_TYPE_SWITCH_WITH_BOOL(outputs[0].type_flag_, DType, {
             mxnet_op::Kernel<mxnet_op::op_with_req<mshadow_op::identity, kAddTo>, xpu>::Launch(
               s, inputs[0].Size(), outputs[0].dptr<DType>(), inputs[0].dptr<DType>());
           });
diff --git a/src/operator/tensor/init_op.h b/src/operator/tensor/init_op.h
index f3c405d7103c..8e8896e5c014 100644
--- a/src/operator/tensor/init_op.h
+++ b/src/operator/tensor/init_op.h
@@ -57,7 +57,7 @@ struct InitOpParam : public dmlc::Parameter<InitOpParam> {
     .describe("Context of output, in format [cpu|gpu|cpu_pinned](n)."
               "Only used for imperative calls.");
     DMLC_DECLARE_FIELD(dtype).set_default(mshadow::kFloat32)
-    MXNET_ADD_ALL_TYPES
+    MXNET_ADD_ALL_TYPES_WITH_BOOL
     .describe("Target data type.");
   }
 };
@@ -342,12 +342,12 @@ void Fill(mshadow::Stream<xpu> *s, const TBlob& b, const OpReqType req, ValueTyp
     if (val == 0) {
       if (req != kAddTo) {
         if (b.dev_mask() == cpu::kDevMask && size < 50000) {
-          MSHADOW_TYPE_SWITCH(b.type_flag_, DType, {
+          MSHADOW_TYPE_SWITCH_WITH_BOOL(b.type_flag_, DType, {
             memset(b.dptr_, 0, size * sizeof(DType));
           });
         } else {
           // Optimize common use-case of filling with ones
-          MSHADOW_TYPE_SWITCH(b.type_flag_, DType, {
+          MSHADOW_TYPE_SWITCH_WITH_BOOL(b.type_flag_, DType, {
             MXNET_ASSIGN_REQ_SWITCH(req, Req, {
               mxnet_op::Kernel<mxnet_op::op_with_req<mxnet_op::set_to_int<0>, Req>, xpu>::Launch(
                 s, b.Size(), b.dptr<DType>());
@@ -357,7 +357,7 @@ void Fill(mshadow::Stream<xpu> *s, const TBlob& b, const OpReqType req, ValueTyp
       }
     } else if (is_integer && val == 1) {
       // Optimize common use-case of filling with ones
-      MSHADOW_TYPE_SWITCH(b.type_flag_, DType, {
+      MSHADOW_TYPE_SWITCH_WITH_BOOL(b.type_flag_, DType, {
         MXNET_ASSIGN_REQ_SWITCH(req, Req, {
           mxnet_op::Kernel<mxnet_op::op_with_req<mxnet_op::set_one, Req>, xpu>::Launch(
             s, b.Size(), b.dptr<DType>());
@@ -365,7 +365,7 @@ void Fill(mshadow::Stream<xpu> *s, const TBlob& b, const OpReqType req, ValueTyp
       });
     } else {
       // Generic fill kernel from variable
-      MSHADOW_TYPE_SWITCH(b.type_flag_, DType, {
+      MSHADOW_TYPE_SWITCH_WITH_BOOL(b.type_flag_, DType, {
         MXNET_ASSIGN_REQ_SWITCH(req, Req, {
           mxnet_op::Kernel<mxnet_op::op_with_req<mshadow_op::identity, Req>, xpu>::Launch(
             s, b.Size(), b.dptr<DType>(), static_cast<DType>(val));
diff --git a/src/operator/tvmop/op_module.cc b/src/operator/tvmop/op_module.cc
index d1d1c1d45d53..ea7f73069698 100644
--- a/src/operator/tvmop/op_module.cc
+++ b/src/operator/tvmop/op_module.cc
@@ -72,6 +72,9 @@ PackedFunc GetFunction(const std::shared_ptr<Module> &module,
       case mshadow::kInt64:
         func_name << "int64";
         break;
+      case mshadow::kBool:
+        func_name << "bool";
+        break;
       default:
         LOG(FATAL) << "Unknown dtype " << arg.type_flag_;
     }
@@ -82,7 +85,7 @@ PackedFunc GetFunction(const std::shared_ptr<Module> &module,
 
 void TVMOpModule::Call(const std::string &func_name,
                        const mxnet::OpContext& ctx,
-                       const std::vector<mxnet::TBlob> &args) {
+                       const std::vector<mxnet::TBlob> &args) const {
   std::vector<int> type_codes;
   std::vector<TVMValue> values;
 
@@ -112,6 +115,28 @@ void TVMOpModule::Call(const std::string &func_name,
 #endif
 }
 
+void TVMOpModule::CallEx(const std::string &func_name,
+                         const mxnet::OpContext& ctx,
+                         const std::vector<mxnet::TBlob>& tblobs,
+                         TVMArgs tvm_args) const {
+  TVMRetValue rv;
+
+#if MXNET_USE_CUDA
+  int dev_type = (ctx.run_ctx.ctx.dev_type == mxnet::Context::DeviceType::kGPU) ? kDLGPU : kDLCPU;
+  int dev_id = ctx.run_ctx.ctx.dev_id;
+  if (dev_type == kDLGPU) {
+    void *stream = static_cast<void *>(ctx.run_ctx.get_stream<mxnet::gpu>()->stream_);
+    TVMSetStream(dev_type, dev_id, stream);
+  }
+#endif
+  GetFunction(module_ptr_, func_name, tblobs).CallPacked(tvm_args, &rv);
+#if MXNET_USE_CUDA
+  if (dev_type == kDLGPU) {
+    TVMSetStream(dev_type, dev_id, nullptr);
+  }
+#endif
+}
+
 }  // namespace runtime
 }  // namespace tvm
 #endif  // MXNET_USE_TVM_OP
diff --git a/src/operator/tvmop/op_module.h b/src/operator/tvmop/op_module.h
index 04e97ef51f4e..d28dd1b5b0c2 100644
--- a/src/operator/tvmop/op_module.h
+++ b/src/operator/tvmop/op_module.h
@@ -36,6 +36,7 @@
 namespace tvm {
 namespace runtime {
 
+class TVMArgs;
 class Module;
 class TVMOpModule {
  public:
@@ -44,7 +45,22 @@ class TVMOpModule {
 
   void Call(const std::string& func_name,
             const mxnet::OpContext& ctx,
-            const std::vector<mxnet::TBlob>& args);
+            const std::vector<mxnet::TBlob>& args) const;
+
+  /*!
+   * \brief Launch operator kernels which have been pre-compiled into a lib file
+   * by TVM compiler.
+   * \param func_name Function name that corresponds to the operator kernel
+   * \param ctx Operator context that includes device and stream information.
+   * \param tblobs Tensor blobs whose dtype and shape information are extracted
+   * to construct the function name. Each configuration of dtype and shape has
+   * a unique kernel.
+   * \param tvm_args Arguments to be passed to kernel function.
+   */
+  void CallEx(const std::string &func_name,
+              const mxnet::OpContext& ctx,
+              const std::vector<mxnet::TBlob>& tblobs,
+              TVMArgs tvm_args) const;
 
   static TVMOpModule *Get() {
     static TVMOpModule inst;
diff --git a/tests/python/unittest/test_numpy_gluon.py b/tests/python/unittest/test_numpy_gluon.py
index 52ceb718fb15..f24eb6a325bf 100644
--- a/tests/python/unittest/test_numpy_gluon.py
+++ b/tests/python/unittest/test_numpy_gluon.py
@@ -122,18 +122,18 @@ def test_np_loss_ndarray():
     weighting = np.array([0.5, 1, 0.5, 1])
 
     loss = gluon.loss.L1Loss()
-    assert np.sum(loss(output, label)) == 6.
+    assert float(np.sum(loss(output, label))) == 6.
     loss = gluon.loss.L1Loss(weight=0.5)
-    assert np.sum(loss(output, label)) == 3.
+    assert float(np.sum(loss(output, label))) == 3.
     loss = gluon.loss.L1Loss()
-    assert np.sum(loss(output, label, weighting)) == 5.
+    assert float(np.sum(loss(output, label, weighting))) == 5.
 
     loss = gluon.loss.L2Loss()
-    assert np.sum(loss(output, label)) == 7.
+    assert float(np.sum(loss(output, label))) == 7.
     loss = gluon.loss.L2Loss(weight=0.25)
-    assert np.sum(loss(output, label)) == 1.75
+    assert float(np.sum(loss(output, label))) == 1.75
     loss = gluon.loss.L2Loss()
-    assert np.sum(loss(output, label, weighting)) == 6
+    assert float(np.sum(loss(output, label, weighting))) == 6
 
     output = np.array([[0, 2], [1, 4]])
     label = np.array([0, 1])
diff --git a/tests/python/unittest/test_numpy_ndarray.py b/tests/python/unittest/test_numpy_ndarray.py
index 3a5e72b53d58..20b964c96a30 100644
--- a/tests/python/unittest/test_numpy_ndarray.py
+++ b/tests/python/unittest/test_numpy_ndarray.py
@@ -19,13 +19,14 @@
 from __future__ import absolute_import
 from __future__ import division
 import os
+import unittest
 import numpy as _np
 import mxnet as mx
 from mxnet import np, npx, autograd
 from mxnet.gluon import HybridBlock
-from mxnet.test_utils import same, assert_almost_equal, rand_shape_nd, rand_ndarray, retry, assert_exception, use_np
+from mxnet.test_utils import same, assert_almost_equal, rand_shape_nd, rand_ndarray, retry, use_np
 from common import with_seed, TemporaryDirectory
-from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf
+from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf, has_tvm_ops, assert_exception
 from mxnet.ndarray.ndarray import py_slice
 from mxnet.base import integer_types
 import scipy.stats as ss
@@ -34,8 +35,23 @@
 @with_seed()
 @use_np
 def test_np_empty():
-    dtypes = [np.int8, np.int32, np.float16, np.float32, np.float64, None]
-    expected_dtypes = [np.int8, np.int32, np.float16, np.float32, np.float64, np.float32]
+    # (input dtype, expected output dtype)
+    dtype_pairs = [
+        (np.int8, np.int8),
+        (np.int32, np.int32),
+        (np.float16, np.float16),
+        (np.float32, np.float32),
+        (np.float64, np.float64),
+        (np.bool_, np.bool_),
+        (np.bool, np.bool_),
+        ('int8', np.int8),
+        ('int32', np.int32),
+        ('float16', np.float16),
+        ('float32', np.float32),
+        ('float64', np.float64),
+        ('bool', np.bool_),
+        (None, np.float32),
+    ]
     orders = ['C', 'F', 'A']
     shapes = [
         (),
@@ -49,7 +65,7 @@ def test_np_empty():
         (1, 1, 1, 1),
     ]
     ctxes = [npx.current_context(), None]
-    for dtype, expected_dtype in zip(dtypes, expected_dtypes):
+    for dtype, expected_dtype in dtype_pairs:
         for shape in shapes:
             for order in orders:
                 for ctx in ctxes:
@@ -65,7 +81,8 @@ def test_np_empty():
 @with_seed()
 @use_np
 def test_np_array_creation():
-    dtypes = [_np.int8, _np.int32, _np.float16, _np.float32, _np.float64, None]
+    dtypes = [_np.int8, _np.int32, _np.float16, _np.float32, _np.float64, _np.bool, _np.bool_,
+              'int8', 'int32', 'float16', 'float32', 'float64', 'bool', None]
     objects = [
         [],
         (),
@@ -76,7 +93,7 @@ def test_np_array_creation():
     for dtype in dtypes:
         for src in objects:
             mx_arr = np.array(src, dtype=dtype)
-            assert mx_arr.context == mx.current_context()
+            assert mx_arr.ctx == mx.current_context()
             if isinstance(src, mx.nd.NDArray):
                 np_arr = _np.array(src.asnumpy(), dtype=dtype if dtype is not None else _np.float32)
             else:
@@ -110,6 +127,8 @@ def check_zero_array_creation(shape, dtype):
         if dtype is None:
             assert mx_out.dtype == _np.float32
             assert np_out.dtype == _np.float64
+        else:
+            assert mx_out.dtype == np_out.dtype
 
     shapes = [(0,), (2, 0, 2), (0, 0, 0, 0), ()]
     shapes += [rand_shape_nd(ndim, allow_zero_size=True) for ndim in range(5)]
@@ -132,6 +151,10 @@ def check_zero_array_creation(shape, dtype):
                 y = test_zeros_output_type(x)
                 assert type(y[1]) == np.ndarray
 
+    for shape in shapes:
+        for dtype in [_np.bool, bool, _np.bool, 'bool']:
+            check_zero_array_creation(shape, dtype)
+
 
 @with_seed()
 @use_np
@@ -158,6 +181,8 @@ def check_ones_array_creation(shape, dtype):
         if dtype is None:
             assert mx_out.dtype == _np.float32
             assert np_out.dtype == _np.float64
+        else:
+            assert mx_out.dtype == np_out.dtype
 
     shapes = [(0,), (2, 0, 2), (0, 0, 0, 0), ()]
     shapes += [rand_shape_nd(ndim, allow_zero_size=True) for ndim in range(5)]
@@ -180,6 +205,10 @@ def check_ones_array_creation(shape, dtype):
                 y = test_ones_output_type(x)
                 assert type(y[1]) == np.ndarray
 
+    for shape in shapes:
+        for dtype in [_np.bool, bool, _np.bool, 'bool']:
+            check_ones_array_creation(shape, dtype)
+
 
 @with_seed()
 @use_np
@@ -235,13 +264,18 @@ def test_np_ndarray_binary_element_wise_ops():
         '/': _np.divide,
         'mod': _np.mod,
         'pow': _np.power,
-        '==': _np.equal,
-        '>': _np.greater,
-        '>=': _np.greater_equal,
-        '<': _np.less,
-        '<=': _np.less_equal
     }
 
+    if has_tvm_ops():
+        np_op_map.update({
+            '==': _np.equal,
+            '!=': _np.not_equal,
+            '>': _np.greater,
+            '>=': _np.greater_equal,
+            '<': _np.less,
+            '<=': _np.less_equal
+        })
+
     def get_np_ret(x1, x2, op):
         return np_op_map[op](x1, x2)
 
@@ -309,10 +343,16 @@ def hybrid_forward(self, F, x, *args):
                     return x == self._scalar if not self._reverse else self._scalar == x
                 else:
                     return x == args[0]
+            elif self._op == '!=':
+                if self._scalar is not None:
+                    return x != self._scalar if not self._reverse else self._scalar != x
+                else:
+                    return x != args[0]
             else:
                 print(self._op)
                 assert False
 
+    logic_ops = ['==', '!=', '>', '<', '>=', '<=']
     @use_np
     def check_binary_op_result(shape1, shape2, op, dtype=None):
         if shape1 is None:
@@ -348,6 +388,8 @@ def check_binary_op_result(shape1, shape2, op, dtype=None):
                 mx_out = get_mx_ret_np(mx_input1.as_np_ndarray(), mx_input2.as_np_ndarray())
                 assert type(mx_out) == np.ndarray
                 assert np_out.shape == mx_out.shape
+                if op in logic_ops:
+                    assert np_out.dtype == mx_out.dtype
                 assert_almost_equal(mx_out.asnumpy(), np_out, atol=1e-6, rtol=1e-5)
             else:
                 get_mx_ret = TestBinaryElementWiseOp(op, scalar=scalar, reverse=reverse)
@@ -360,6 +402,8 @@ def check_binary_op_result(shape1, shape2, op, dtype=None):
                     mx_out = get_mx_ret(mx_input1.as_np_ndarray())
                     assert type(mx_out) == np.ndarray
                 assert np_out.shape == mx_out.shape
+                if op in logic_ops:
+                    assert np_out.dtype == mx_out.dtype
                 assert_almost_equal(mx_out.asnumpy(), np_out, atol=1e-6, rtol=1e-5)
 
     dtypes = [_np.float32, _np.float64, None]
@@ -933,6 +977,87 @@ def test_np_multinomial():
                 mx.test_utils.assert_almost_equal(freq[i, :], pvals, rtol=0.20, atol=1e-1)
 
 
+@with_seed()
+@unittest.skipUnless(has_tvm_ops(), "Comparison ops are implemented using TVM")
+@use_np
+def test_np_ndarray_boolean_indexing():
+    def test_single_bool_index():
+        # adapted from numpy's test_indexing.py
+        # Single boolean index
+        a = np.array([[1, 2, 3],
+                      [4, 5, 6],
+                      [7, 8, 9]], dtype=np.int32)
+        assert same(a[np.array(True, dtype=np.bool_)].asnumpy(), a[None].asnumpy())
+        assert same(a[np.array(False, dtype=np.bool_)].asnumpy(), a[None][0:0].asnumpy())
+
+    def test_boolean_catch_exception():
+        # adapted from numpy's test_indexing.py
+        arr = np.ones((5, 4, 3))
+
+        index = np.array([True], dtype=np.bool_)
+        assert_exception(arr.__getitem__, IndexError, index)
+
+        index = np.array([False] * 6, dtype=np.bool_)
+        assert_exception(arr.__getitem__, IndexError, index)
+
+        index = np.zeros((4, 4), dtype=bool)
+        assert_exception(arr.__getitem__, IndexError, index)
+
+        assert_exception(arr.__getitem__, TypeError, (slice(None), index))
+
+    def test_boolean_indexing_onedim():
+        # adapted from numpy's test_indexing.py
+        # Indexing a 2-dimensional array with
+        # boolean array of length one
+        a = np.array([[0.,  0.,  0.]])
+        b = np.array([True], dtype=bool)
+        assert same(a[b].asnumpy(), a.asnumpy())
+
+    def test_boolean_indexing_twodim():
+        # adapted from numpy's test_indexing.py
+        # Indexing a 2-dimensional array with
+        # 2-dimensional boolean array
+        a = np.array([[1, 2, 3],
+                      [4, 5, 6],
+                      [7, 8, 9]], dtype=np.int32)
+        b = np.array([[ True, False,  True],
+                      [False,  True, False],
+                      [ True, False,  True]], dtype=np.bool_)
+        assert same(a[b].asnumpy(), _np.array([1, 3, 5, 7, 9], dtype=a.dtype))
+        assert same(a[b[1]].asnumpy(), _np.array([[4, 5, 6]], dtype=a.dtype))
+        assert same(a[b[0]].asnumpy(), a[b[2]].asnumpy())
+
+    def test_boolean_indexing_list():
+        # adapted from numpy's test_indexing.py
+        a = np.array([1, 2, 3], dtype=np.int32)
+        b = [True, False, True]
+        # Two variants of the test because the first takes a fast path
+        assert same(a[b].asnumpy(), _np.array([1, 3], dtype=a.dtype))
+        (a[None, b], [[1, 3]])
+
+    def test_boolean_indexing_autograd():
+        a = np.random.uniform(size=(3, 4, 5))
+        a.attach_grad()
+        with mx.autograd.record():
+            out_mx = a[a < 0.5]
+        out_mx.backward()
+
+        a_np = a.asnumpy()
+        out_np = a_np[a_np < 0.5]
+        assert_almost_equal(out_mx.asnumpy(), out_np, rtol=1e-4, atol=1e-5, use_broadcast=False)
+
+        a_grad_np = _np.zeros(a.shape, dtype=a.dtype)
+        a_grad_np[a_np < 0.5] = 1
+        assert_almost_equal(a.grad.asnumpy(), a_grad_np, rtol=1e-4, atol=1e-5, use_broadcast=False)
+
+    test_single_bool_index()
+    test_boolean_catch_exception()
+    test_boolean_indexing_onedim()
+    test_boolean_indexing_twodim()
+    test_boolean_indexing_list()
+    test_boolean_indexing_autograd()
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index e6b3d41d37b0..45bcc517a641 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -29,15 +29,11 @@
 from common import assertRaises, with_seed
 import random
 import scipy.stats as ss
-from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf, collapse_sum_like
-from mxnet.runtime import Features
 from mxnet.numpy_op_signature import _get_builtin_op
+from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf, has_tvm_ops
 import platform
 
 
-_features = Features()
-
-
 @with_seed()
 @use_np
 def test_np_tensordot():
@@ -585,13 +581,14 @@ def is_int(dtype):
     in_data_dim = random.choice([2, 3, 4])
     shape = rand_shape_nd(in_data_dim, dim=3)
     acc_type = {'float16': 'float32', 'float32': 'float64', 'float64': 'float64',
-                'int8': 'int32', 'int32': 'int64', 'int64': 'int64'}
+                'int8': 'int32', 'int32': 'int64', 'int64': 'int64', 'bool': 'int64'}
     for hybridize in [False, True]:
         for keepdims in [True, False]:
             for axis in ([i for i in range(in_data_dim)] + [(), None]):
-                for itype in ['float16', 'float32', 'float64', 'int8', 'int32', 'int64']:
+                for itype in ['float16', 'float32', 'float64', 'int8', 'int32', 'int64', 'bool']:
                     for dtype in ['float16', 'float32', 'float64', 'int8', 'int32', 'int64']:
-                        if is_int(dtype) and not is_int(itype):
+                        if (is_int(dtype) and not is_int(itype))\
+                                or (itype == 'bool' and dtype not in ('float32', 'float64', 'int32', 'int64')):
                             continue
                         # test gluon
                         test_sum = TestSum(axis=axis, dtype=dtype, keepdims=keepdims)
@@ -599,13 +596,23 @@ def is_int(dtype):
                             test_sum.hybridize()
                         if is_int(itype):
                             x = _np.random.randint(-128, 128, shape, dtype=itype)
-                            x = mx.nd.array(x)
+                            x = np.array(x)
+                        elif itype == 'bool':
+                            x = _np.random.randint(0, 2, shape) < 1
+                            x = np.array(x, dtype='bool')
                         else:
-                            x = mx.nd.random.uniform(-1.0, 1.0, shape=shape, dtype=itype)
-                        x = x.as_np_ndarray()
-                        x.attach_grad()
+                            x = np.random.uniform(-1.0, 1.0, size=shape, dtype=itype)
                         expected_ret = _np.sum(x.asnumpy(), axis=axis, dtype=acc_type[itype], keepdims=keepdims)
                         expected_ret = expected_ret.astype(dtype)
+                        if itype == 'bool':  # special handling of boolean ndarray
+                            if has_tvm_ops():
+                                y = test_sum(x)
+                                assert y.dtype == expected_ret.dtype
+                                assert_almost_equal(y.asnumpy(), expected_ret, rtol=1e-4, atol=1e-5,
+                                                    use_broadcast=False)
+                            continue
+
+                        x.attach_grad()
                         with mx.autograd.record():
                             y = test_sum(x)
                         assert y.shape == expected_ret.shape
@@ -1464,7 +1471,7 @@ def hybrid_forward(self, F, a, *args, **kwargs):
         'arccosh' : (lambda x: 1./(x**2 - 1.)**(1./2.), 2.0, 5.0),
         'arctanh' : (lambda x: -1./(x**2 - 1.), -0.99, 0.99)
     }
-    if _features.is_enabled("TVM_OP"):
+    if has_tvm_ops():
         funcs['rad2deg'] = (lambda x: 180. / _np.pi * _np.ones(x.shape), -1.0, 1.0)
         funcs['deg2rad'] = (lambda x: _np.pi / 180. * _np.ones(x.shape), -1.0, 1.0)
     ndim = random.choice([2, 3, 4])
@@ -2232,7 +2239,7 @@ def test_np_indices():
         (2, 3, 4, 5, 6, 7)
     ]
     if platform.system() == 'Windows':
-        shapes = shapes[1:]  #beacuse in numpy windows version, indces not support dimensions is empty tuple.
+        shapes = shapes[1:]  # beacuse in numpy windows version, indces not support dimensions is empty tuple.
     for dtype in dtypes:
         for shape in shapes:
             np_out = _np.indices(dimensions=shape, dtype=dtype)

From 1d0d1e687fdf436896f8ca106c4915adfd29c8cb Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Wed, 9 Oct 2019 17:49:12 -0700
Subject: [PATCH 417/813] Faster Transpose 2D (#16104)

* 2d transpose naive

* omp pragma

* omp pragma unroll

* blocksize

* make it 2d tile

* loop peeling

* better loop peeling

* redundancy

* removed bool

* removing excess for loops, memory save

* fix internal forloop

* remove commented code, lint fix

* Trigger notification

* explain params, indent fix, explain blocksize

* fix p,n and reduce for loop computation j+a,i+b

* kernel

* gpu thread 1

* remove gpu implementation

* fix internal for loop

* unittest to catch the previous error

* optimizations

* microsoft cpp doesn't support omp collapse
---
 src/operator/tensor/matrix_op-inl.h    | 52 +++++++++++++++++++++++++-
 tests/python/unittest/test_operator.py |  7 ++++
 2 files changed, 58 insertions(+), 1 deletion(-)

diff --git a/src/operator/tensor/matrix_op-inl.h b/src/operator/tensor/matrix_op-inl.h
index 5a2bd036c22b..ab7b7597c68e 100644
--- a/src/operator/tensor/matrix_op-inl.h
+++ b/src/operator/tensor/matrix_op-inl.h
@@ -257,6 +257,51 @@ struct TransposeParam : public dmlc::Parameter<TransposeParam> {
   }
 };
 
+
+/*!
+ * \brief This function performs transpose operation on a 2D matrix by utilizing the L1 cache
+ * \param in  input tensor
+ * \param out output tensor
+ * \param row shape of dim 0 of input
+ * \param col shape of dim 1 of input
+ */
+template<typename DType>
+MSHADOW_XINLINE void Transpose2D(const DType *in, DType *out, index_t row, index_t col) {
+  // ensure cache line hits and prevent cache miss for any configuration
+  // L1 cache size to be utilized = 32kb = 2^15
+  // Largest size of a single unit of any dtype <= 8 byte = 2^3
+  // Number of elements - (2^15/2^3) = 2^12
+  // Block-size - 2^6 v 2^6 (64 v 64)
+
+  // But we could leverage unrolling of for loops (for parallelization)
+  // Block-size - 2^5 v 2^5 (32 v 32) with potential 4 pragma for loop unrolled
+  // blocksize * blocksize * num_threads = cache_size / dtype_size
+  // Instead of explicit unroll, let compiler figure out optimal unroll factor
+  index_t blocksize = 32;
+
+  // collapse 2 parallelizes 2 for loops
+  // inner 2 for loops aren't parallelized to prevent cache miss
+
+  // Microsoft Visual C++ compiler does not support omp collapse
+  #ifdef _MSC_VER
+    #pragma omp parallel for
+  #else
+    #pragma omp parallel for collapse(2)
+  #endif  // _MSC_VER
+
+  for (index_t i = 0; i < row; i += blocksize) {
+    for (index_t j = 0; j < col; j += blocksize) {
+      // transpose the block
+      for (index_t a = j; (a < blocksize + j) && (a < col); ++a) {
+        for (index_t b = i; (b < blocksize + i) && (b < row); ++b) {
+          out[a * row + b] = in[b * col + a];
+        }
+      }
+    }
+  }
+}
+
+
 template<typename xpu>
 void TransposeImpl(RunContext ctx,
                    const TBlob& src,
@@ -285,8 +330,13 @@ void TransposeImpl(RunContext ctx,
      case 2: {
       mshadow::Tensor<xpu, 2, DType> in = src.FlatTo2D<xpu, DType>(s);
       mshadow::Tensor<xpu, 2, DType> out = ret.FlatTo2D<xpu, DType>(s);
+
       if (axes[0] == 1 && axes[1] == 0) {
-        out = in.T();
+        if (ctx.get_ctx().dev_mask() == cpu::kDevMask) {
+          Transpose2D<DType>(in.dptr_, out.dptr_, in.shape_[0], in.shape_[1]);
+        } else {
+          out = in.T();
+        }
       } else {
         Copy(out, in, s);
       }
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 962e8687885b..4f981d5e29c7 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -2874,6 +2874,13 @@ def test_transpose():
             assert_allclose(np.transpose(x.asnumpy()), y.asnumpy())
 
 
+@with_seed()
+def test_larger_transpose():
+    x = mx.nd.random.normal(shape=(50,51))
+    y = mx.nd.transpose(x)
+    assert_allclose(np.transpose(x.asnumpy()), y.asnumpy())
+
+
 @with_seed()
 def test_expand_dims():
     for ndim in range(1, 6):

From 9ff644bccec6501e420f221b05373c5c88b42c64 Mon Sep 17 00:00:00 2001
From: reminisce <wujun.nju@gmail.com>
Date: Wed, 9 Oct 2019 21:31:48 -0700
Subject: [PATCH 418/813] Fix windows flakiness (#16415)

* Revert "Numpy det and slogdet operators (#15861)"

This reverts commit 295fc145510ac73f296a7581649d7ad371d85cd5.

* Delete non-windows test

* Revert "Numpy Operators: Inner, Outer, vdot (#15846)"

This reverts commit 2df3282666c5ba55e98340c3615b046278c40b53.

* Revert "Revert "Numpy Operators: Inner, Outer, vdot (#15846)""

This reverts commit 79ceaa75d8460f1c03fa76297f85601bd1093f65.

* Revert "Delete non-windows test"

This reverts commit c91587706e74e903c58cc47ff0a01d1ab8171fdf.
---
 python/mxnet/_numpy_op_doc.py          | 122 ----------
 src/operator/tensor/la_op.cc           |   2 -
 src/operator/tensor/la_op.cu           |   2 -
 src/operator/tensor/la_op.h            |   7 +-
 tests/python/unittest/test_numpy_op.py | 305 +++++++++----------------
 5 files changed, 107 insertions(+), 331 deletions(-)

diff --git a/python/mxnet/_numpy_op_doc.py b/python/mxnet/_numpy_op_doc.py
index bb9b8314f2aa..6e2f5fa15919 100644
--- a/python/mxnet/_numpy_op_doc.py
+++ b/python/mxnet/_numpy_op_doc.py
@@ -20,128 +20,6 @@
 """Doc placeholder for numpy ops with prefix _np."""
 
 
-def _np__linalg_det(a):
-    """
-    det(a)
-
-    Compute the determinant of an array.
-
-    Parameters
-    ----------
-    a : (..., M, M) ndarray
-        Input array to compute determinants for.
-
-    Returns
-    -------
-    det : (...) ndarray
-        Determinant of `a`.
-
-    See Also
-    --------
-    slogdet : Another way to represent the determinant, more suitable
-    for large matrices where underflow/overflow may occur.
-
-    Notes
-    -----
-
-    Broadcasting rules apply, see the `numpy.linalg` documentation for
-    details.
-
-    The determinant is computed via LU factorization using the LAPACK
-    routine z/dgetrf.
-
-    Examples
-    --------
-    The determinant of a 2-D array [[a, b], [c, d]] is ad - bc:
-
-    >>> a = np.array([[1, 2], [3, 4]])
-    >>> np.linalg.det(a)
-    -2.0
-
-    Computing determinants for a stack of matrices:
-
-    >>> a = np.array([ [[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]] ])
-    >>> a.shape
-    (3, 2, 2)
-    >>> np.linalg.det(a)
-    array([-2., -3., -8.])
-    """
-    pass
-
-
-def _np__linalg_slogdet(a):
-    """
-    slogdet(a)
-
-    Compute the sign and (natural) logarithm of the determinant of an array.
-
-    If an array has a very small or very large determinant, then a call to
-    `det` may overflow or underflow. This routine is more robust against such
-    issues, because it computes the logarithm of the determinant rather than
-    the determinant itself.
-
-    Parameters
-    ----------
-    a : (..., M, M) ndarray
-        Input array, has to be a square 2-D array.
-
-    Returns
-    -------
-    sign : (...) ndarray
-        A number representing the sign of the determinant. For a real matrix,
-        this is 1, 0, or -1.
-    logdet : (...) array_like
-        The natural log of the absolute value of the determinant.
-
-    If the determinant is zero, then `sign` will be 0 and `logdet` will be
-    -Inf. In all cases, the determinant is equal to ``sign * np.exp(logdet)``.
-
-    See Also
-    --------
-    det
-
-    Notes
-    -----
-
-    Broadcasting rules apply, see the `numpy.linalg` documentation for
-    details.
-
-    The determinant is computed via LU factorization using the LAPACK
-    routine z/dgetrf.
-
-
-    Examples
-    --------
-    The determinant of a 2-D array ``[[a, b], [c, d]]`` is ``ad - bc``:
-
-    >>> a = np.array([[1, 2], [3, 4]])
-    >>> (sign, logdet) = np.linalg.slogdet(a)
-    >>> (sign, logdet)
-    (-1., 0.69314718055994529)
-    >>> sign * np.exp(logdet)
-    -2.0
-
-    Computing log-determinants for a stack of matrices:
-
-    >>> a = np.array([ [[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]] ])
-    >>> a.shape
-    (3, 2, 2)
-    >>> sign, logdet = np.linalg.slogdet(a)
-    >>> (sign, logdet)
-    (array([-1., -1., -1.]), array([ 0.69314718,  1.09861229,  2.07944154]))
-    >>> sign * np.exp(logdet)
-    array([-2., -3., -8.])
-
-    This routine succeeds where ordinary `det` does not:
-
-    >>> np.linalg.det(np.eye(500) * 0.1)
-    0.0
-    >>> np.linalg.slogdet(np.eye(500) * 0.1)
-    (1., -1151.2925464970228)
-    """
-    pass
-
-
 def _np_ones_like(a):
     """
     Return an array of ones with the same shape and type as a given array.
diff --git a/src/operator/tensor/la_op.cc b/src/operator/tensor/la_op.cc
index 24968eca3f06..ce7d1d5de692 100644
--- a/src/operator/tensor/la_op.cc
+++ b/src/operator/tensor/la_op.cc
@@ -941,7 +941,6 @@ NNVM_REGISTER_OP(_backward_linalg_inverse)
 
 NNVM_REGISTER_OP(_linalg_det)
 .add_alias("linalg_det")
-.add_alias("_np__linalg_det")
 .describe(R"code(Compute the determinant of a matrix.
 Input is a tensor *A* of dimension *n >= 2*.
 
@@ -992,7 +991,6 @@ NNVM_REGISTER_OP(_backward_linalg_det)
 .set_attr<FCompute>("FCompute<cpu>", LaOpDetBackward<cpu, 1, det_backward>);
 
 NNVM_REGISTER_OP(_linalg_slogdet)
-.add_alias("_np__linalg_slogdet")
 .add_alias("linalg_slogdet")
 .describe(R"code(Compute the sign and log of the determinant of a matrix.
 Input is a tensor *A* of dimension *n >= 2*.
diff --git a/src/operator/tensor/la_op.cu b/src/operator/tensor/la_op.cu
index ec622f0b22a2..68c33180e3d5 100644
--- a/src/operator/tensor/la_op.cu
+++ b/src/operator/tensor/la_op.cu
@@ -100,14 +100,12 @@ NNVM_REGISTER_OP(_backward_linalg_inverse)
 .set_attr<FCompute>("FCompute<gpu>", LaOpBackward<gpu, 2, 2, 2, 1, inverse_backward>);
 
 NNVM_REGISTER_OP(_linalg_det)
-.add_alias("_np__linalg_det")
 .set_attr<FCompute>("FCompute<gpu>", LaOpDetForward<gpu, 1, det>);
 
 NNVM_REGISTER_OP(_backward_linalg_det)
 .set_attr<FCompute>("FCompute<gpu>", LaOpDetBackward<gpu, 1, det_backward>);
 
 NNVM_REGISTER_OP(_linalg_slogdet)
-.add_alias("_np__linalg_slogdet")
 .set_attr<FCompute>("FCompute<gpu>", LaOpDetForward<gpu, 2, slogdet>);
 
 NNVM_REGISTER_OP(_backward_linalg_slogdet)
diff --git a/src/operator/tensor/la_op.h b/src/operator/tensor/la_op.h
index bb56dc52b594..e024693e3819 100644
--- a/src/operator/tensor/la_op.h
+++ b/src/operator/tensor/la_op.h
@@ -26,7 +26,6 @@
 #define MXNET_OPERATOR_TENSOR_LA_OP_H_
 
 #include <mxnet/operator_util.h>
-#include <mxnet/imperative.h>
 #include <vector>
 #include <algorithm>
 #include "../mshadow_op.h"
@@ -429,11 +428,7 @@ inline bool DetShape(const nnvm::NodeAttrs& attrs,
   CHECK_EQ(in[ndim-2], in[ndim-1]) << "Input A's last two dimension must be equal";
   mxnet::TShape out;
   if (ndim == 2) {
-    if (Imperative::Get()->is_np_shape()) {
-      out = mxnet::TShape(0, 1);
-    } else {
-      out = mxnet::TShape(1, 1);
-    }
+    out = mxnet::TShape(1, 1);
   } else {
     out = mxnet::TShape(in.begin(), in.end() - 2);
   }
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 45bcc517a641..7068bf20d897 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -29,6 +29,8 @@
 from common import assertRaises, with_seed
 import random
 import scipy.stats as ss
+from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf, retry
+from mxnet.runtime import Features
 from mxnet.numpy_op_signature import _get_builtin_op
 from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf, has_tvm_ops
 import platform
@@ -139,8 +141,8 @@ def tensordot_backward(a, b, axes=2):
                 test_tensordot = TestTensordot(axes)
                 if hybridize:
                     test_tensordot.hybridize()
-                a = rand_ndarray(shape=a_shape, dtype=dtype).as_np_ndarray()
-                b = rand_ndarray(shape=b_shape, dtype=dtype).as_np_ndarray()
+                a = rand_ndarray(shape = a_shape, dtype = dtype).as_np_ndarray()
+                b = rand_ndarray(shape = b_shape, dtype = dtype).as_np_ndarray()
                 a.attach_grad()
                 b.attach_grad()
 
@@ -165,7 +167,7 @@ def tensordot_backward(a, b, axes=2):
                     b_sym = mx.sym.Variable("b").as_np_ndarray()
                     mx_sym = mx.sym.np.tensordot(a_sym, b_sym, axes).as_nd_ndarray()
                     check_numeric_gradient(mx_sym, [a.as_nd_ndarray(), b.as_nd_ndarray()],
-                                           rtol=1e-1, atol=1e-1, dtype=dtype)
+                      rtol=1e-1, atol=1e-1, dtype = dtype)
 
 
 @with_seed()
@@ -217,96 +219,6 @@ def test_np_dot():
         assert False
 
 
-@with_seed()
-@use_np
-def test_np_linalg_det():
-    class TestDet(HybridBlock):
-        def __init__(self):
-            super(TestDet, self).__init__()
-
-        def hybrid_forward(self, F, a):
-            return F.np.linalg.det(a)
-
-    # test non zero size input
-    tensor_shapes = [
-        (5, 5),
-        (3, 3, 3),
-        (2, 2, 2, 2, 2),
-        (1, 1)
-    ]
-
-    for hybridize in [True, False]:
-        for shape in tensor_shapes:
-            for dtype in [_np.float32, _np.float64]:
-                a_shape = (1,) + shape
-                test_det = TestDet()
-                if hybridize:
-                    test_det.hybridize()
-                a = rand_ndarray(shape=a_shape, dtype=dtype).as_np_ndarray()
-                a.attach_grad()
-
-                np_out = _np.linalg.det(a.asnumpy())
-                with mx.autograd.record():
-                    mx_out = test_det(a)
-                assert mx_out.shape == np_out.shape
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-1, atol=1e-1)
-                mx_out.backward()
-
-                # Test imperative once again
-                mx_out = np.linalg.det(a)
-                np_out = _np.linalg.det(a.asnumpy())
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-1, atol=1e-1, use_broadcast=False)
-
-                # test numeric gradient
-                a_sym = mx.sym.Variable("a").as_np_ndarray()
-                mx_sym = mx.sym.np.linalg.det(a_sym).as_nd_ndarray()
-                check_numeric_gradient(mx_sym, [a.as_nd_ndarray()],
-                                       rtol=1e-1, atol=1e-1, dtype=dtype)
-
-
-@with_seed()
-@use_np
-def test_np_linalg_slogdet():
-    class TestSlogdet(HybridBlock):
-        def __init__(self):
-            super(TestSlogdet, self).__init__()
-
-        def hybrid_forward(self, F, a):
-            return F.np.linalg.slogdet(a)
-
-    # test non zero size input
-    tensor_shapes = [
-        (5, 5),
-        (3, 3, 3),
-        (2, 2, 2, 2, 2),
-        (1, 1)
-    ]
-
-    for hybridize in [True, False]:
-        for a_shape in tensor_shapes:
-            for dtype in [_np.float32, _np.float64]:
-                test_slogdet = TestSlogdet()
-                if hybridize:
-                    test_slogdet.hybridize()
-                a = rand_ndarray(shape=a_shape, dtype=dtype).as_np_ndarray()
-                a.attach_grad()
-
-                np_out = _np.linalg.slogdet(a.asnumpy())
-                with mx.autograd.record():
-                    mx_out = test_slogdet(a)
-                assert mx_out[0].shape == np_out[0].shape
-                assert mx_out[1].shape == np_out[1].shape
-                assert_almost_equal(mx_out[0].asnumpy(), np_out[0], rtol=1e-1, atol=1e-1, use_broadcast=False)
-                assert_almost_equal(mx_out[1].asnumpy(), np_out[1], rtol=1e-1, atol=1e-1, use_broadcast=False)
-                mx_out[1].backward()
-
-                # Test imperative once again
-                mx_out = np.linalg.slogdet(a)
-                np_out = _np.linalg.slogdet(a.asnumpy())
-                assert_almost_equal(mx_out[0].asnumpy(), np_out[0], rtol=1e-1, atol=1e-1, use_broadcast=False)
-                assert_almost_equal(mx_out[1].asnumpy(), np_out[1], rtol=1e-1, atol=1e-1, use_broadcast=False)
-
-
 @with_seed()
 @use_np
 def test_np_ldexp():
@@ -320,24 +232,25 @@ def hybrid_forward(self, F, x1, x2):
     def _np_ldexp(x1, x2):
         return x1 * _np.power(2.0, x2)
 
-    def dldx(x1, x2):
-        out = _np_ldexp(x1, x2)
-        grad_a = _np.broadcast_to(_np.power(2.0, x2), out.shape)
-        grad_b = _np.broadcast_to(out * _np.log(2.0), out.shape)
-        grad_a = collapse_sum_like(grad_a, x1.shape)
-        grad_b = collapse_sum_like(grad_b, x2.shape)
+    def dldx(x1, x2): 
+        grad_a = _np.power(2.0, x2)
+        grad_b = _np_ldexp(x1, x2) * _np.log(2.0)
+        if len(x1) == 1:
+            grad_a = _np.sum(grad_a)
+        if len(x2) == 1:
+            grad_b = _np.sum(grad_b)
         return [grad_a, grad_b]
 
-    shapes = [
+    shapes = [ 
         ((3, 1), (3, 1)),
         ((3, 1, 2), (3, 1, 2)),
-        ((1, ), (1, )),
+        ((1, ),(1, )),
         ((1, ), (2, )),
         ((3, ), (1, )),
         ((3, 0), (3, 0)),  # zero-size shape
         ((0, 1), (0, 1)),  # zero-size shape
         ((2, 0, 2), (2, 0, 2)),  # zero-size shape
-    ]
+        ] 
 
     for hybridize in [True, False]:
         for shape1, shape2 in shapes:
@@ -345,7 +258,7 @@ def dldx(x1, x2):
                 test_ldexp = TestLdexp()
                 if hybridize:
                     test_ldexp.hybridize()
-                x1 = rand_ndarray(shape=shape1, dtype=dtype).as_np_ndarray()
+                x1 = rand_ndarray(shape=shape1, dtype=dtype).as_np_ndarray() 
                 x1.attach_grad()
                 x2 = rand_ndarray(shape=shape2, dtype=dtype).as_np_ndarray()
                 x2.attach_grad()
@@ -354,17 +267,17 @@ def dldx(x1, x2):
                 with mx.autograd.record():
                     mx_out = test_ldexp(x1, x2)
                 assert mx_out.shape == np_out.shape
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-1, atol=1e-1, use_broadcast=False)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-1, atol=1e-1)
 
                 mx_out.backward()
                 np_backward = dldx(x1.asnumpy(), x2.asnumpy())
-                assert_almost_equal(x1.grad.asnumpy(), np_backward[0], atol=1e-1, rtol=1e-1, use_broadcast=False)
-                assert_almost_equal(x2.grad.asnumpy(), np_backward[1], atol=1e-1, rtol=1e-1, use_broadcast=False)
+                assert_almost_equal(x1.grad.asnumpy(), np_backward[0], atol=1e-1, rtol=1e-1)
+                assert_almost_equal(x2.grad.asnumpy(), np_backward[1], atol=1e-1, rtol=1e-1)
 
                 # Test imperative once again
                 mx_out = np.ldexp(x1, x2)
                 np_out = _np_ldexp(x1.asnumpy(), x2.asnumpy())
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-1, atol=1e-1, use_broadcast=False)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-1, atol=1e-1)
 
 
 @with_seed()
@@ -398,16 +311,16 @@ def vdot_backward(a, b):
                 with mx.autograd.record():
                     mx_out = test_vdot(a, b)
                 assert mx_out.shape == np_out.shape
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol = 1e-3, atol = 1e-5)
                 mx_out.backward()
                 np_backward = vdot_backward(a.asnumpy(), b.asnumpy())
-                assert_almost_equal(a.grad.asnumpy(), np_backward[0], rtol=1e-2, atol=1e-2, use_broadcast=False)
-                assert_almost_equal(b.grad.asnumpy(), np_backward[1], rtol=1e-2, atol=1e-2, use_broadcast=False)
+                assert_almost_equal(a.grad.asnumpy(), np_backward[0], rtol = 1e-2, atol=1e-2)
+                assert_almost_equal(b.grad.asnumpy(), np_backward[1], rtol = 1e-2, atol=1e-2)
 
                 # Test imperative once again
                 mx_out = np.vdot(a, b)
                 np_out = _np.vdot(a.asnumpy(), b.asnumpy())
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
 
                 # test numeric gradient
                 if len(shape) > 0 and _np.prod(shape) > 0:
@@ -495,16 +408,16 @@ def inner_backward(a, b):
                 with mx.autograd.record():
                     mx_out = test_inner(a, b)
                 assert mx_out.shape == np_out.shape
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol = 1e-3, atol = 1e-5)
                 mx_out.backward()
                 np_backward = inner_backward(a.asnumpy(), b.asnumpy())
-                assert_almost_equal(a.grad.asnumpy(), np_backward[0], rtol=1e-2, atol=1e-2, use_broadcast=False)
-                assert_almost_equal(b.grad.asnumpy(), np_backward[1], rtol=1e-2, atol=1e-2, use_broadcast=False)
+                assert_almost_equal(a.grad.asnumpy(), np_backward[0], rtol = 1e-2, atol=1e-2)
+                assert_almost_equal(b.grad.asnumpy(), np_backward[1], rtol = 1e-2, atol=1e-2)
 
                 # Test imperative once again
                 mx_out = np.inner(a, b)
                 np_out = _np.inner(a.asnumpy(), b.asnumpy())
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
 
                 # test numeric gradient
                 a_sym = mx.sym.Variable("a").as_np_ndarray()
@@ -546,13 +459,13 @@ def hybrid_forward(self, F, a, b):
                 with mx.autograd.record():
                     mx_out = test_outer(a, b)
                 assert mx_out.shape == np_out.shape
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
                 mx_out.backward()
 
                 # Test imperative once again
                 mx_out = np.outer(a, b)
                 np_out = _np.outer(a.asnumpy(), b.asnumpy())
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
 
                 # test numeric gradient
                 a_sym = mx.sym.Variable("a").as_np_ndarray()
@@ -714,7 +627,7 @@ def _test_np_exception(func, shape, dim):
                             y = test_gluon(x)
                         assert y.shape == expected_ret.shape
                         assert_almost_equal(y.asnumpy(), expected_ret, rtol=1e-3 if itype == 'float16' else 1e-3,
-                                            atol=1e-5 if itype == 'float16' else 1e-5, use_broadcast=False)
+                                            atol=1e-5 if itype == 'float16' else 1e-5)
                         y.backward()
                         # only check the gradient with hardcoded input
                         if is_int(itype):
@@ -728,7 +641,7 @@ def _test_np_exception(func, shape, dim):
                         else:
                             mx_out = np.min(x, axis=axis, keepdims=keepdims)
                             np_out = _np.amin(x.asnumpy(), axis=axis, keepdims=keepdims)
-                        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
+                        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
 
     # test zero and zero dim
     shapes = [(), (0), (2, 0), (0, 2, 1)]
@@ -787,7 +700,7 @@ def is_int(dtype):
                             y = test_mean(x)
                         assert y.shape == expected_ret.shape
                         assert_almost_equal(y.asnumpy(), expected_ret, rtol=1e-3 if dtype == 'float16' else 1e-3,
-                                            atol=1e-5 if dtype == 'float16' else 1e-5, use_broadcast=False)
+                                            atol=1e-5 if dtype == 'float16' else 1e-5)
 
                         y.backward()
                         N = x.size / y.size
@@ -803,7 +716,7 @@ def is_int(dtype):
                         # test imperative
                         mx_out = np.mean(x, axis=axis, dtype=dtype, keepdims=keepdims)
                         np_out = _np.mean(x.asnumpy(), axis=axis, dtype=acc_type[itype], keepdims=keepdims).astype(dtype)
-                        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
+                        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
 
 
 @with_seed()
@@ -895,17 +808,16 @@ def test_np_linspace():
                         mx_ret = np.linspace(config, endpoint=endpoint, retstep=retstep, dtype=dtype)
                         np_ret = _np.linspace(config, endpoint=endpoint, retstep=retstep, dtype=dtype)
                     if retstep:
-                        assert_almost_equal(mx_ret[0].asnumpy(), np_ret[0], atol=1e-3, rtol=1e-5, use_broadcast=False)
+                        assert_almost_equal(mx_ret[0].asnumpy(), np_ret[0], atol=1e-3, rtol=1e-5)
                         same(mx_ret[1], np_ret[1])
                     else:
-                        assert_almost_equal(mx_ret.asnumpy(), np_ret, atol=1e-3, rtol=1e-5, use_broadcast=False)
+                        assert_almost_equal(mx_ret.asnumpy(), np_ret, atol=1e-3, rtol=1e-5)
     # check for exception input
     for config in exception_configs:
         assertRaises(MXNetError, np.linspace, *config)
     # check linspace equivalent to arange
     for test_index in range(1000):
-        assert_almost_equal(mx.np.linspace(0, test_index, test_index + 1).asnumpy(), _np.arange(test_index + 1),
-                            use_broadcast=False)
+        assert_almost_equal(mx.np.linspace(0, test_index, test_index + 1).asnumpy(), _np.arange(test_index + 1))
 
     class TestLinspace(HybridBlock):
         def __init__(self, start, stop, num=50, endpoint=None, retstep=False, dtype=None, axis=0):
@@ -938,7 +850,7 @@ def hybrid_forward(self, F, x):
                     if hybridize:
                         net.hybridize()
                     mx_out = net(x)
-                    assert_almost_equal(mx_out.asnumpy(), np_out, atol=1e-3, rtol=1e-5, use_broadcast=False)
+                    assert_almost_equal(mx_out.asnumpy(), np_out, atol=1e-3, rtol=1e-5)
 
 
 @with_seed()
@@ -1429,12 +1341,11 @@ def hybrid_forward(self, F, a, *args, **kwargs):
             with mx.autograd.record():
                 y = mx_func(mx_test_data)
             assert y.shape == np_out.shape
-            assert_almost_equal(y.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
+            assert_almost_equal(y.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
 
             if ref_grad:
                 y.backward()
-                assert_almost_equal(mx_test_data.grad.asnumpy(), ref_grad(np_test_data), rtol=1e-1, atol=1e-2,
-                                    equal_nan=True, use_broadcast=False)
+                assert_almost_equal(mx_test_data.grad.asnumpy(), ref_grad(np_test_data), rtol=1e-1, atol=1e-2, equal_nan=True)
 
     funcs = {
         'absolute' : (lambda x: -1. * (x < 0) + (x > 0), -1.0, 1.0),
@@ -1509,14 +1420,14 @@ def hybrid_forward(self, F, a):
             with mx.autograd.record():
                 mx_out = test_relu(x)
             assert mx_out.shape == np_out.shape
-            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
+            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
             mx_out.backward()
             np_backward = np_relu_grad(x.asnumpy())
-            assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=1e-3, atol=1e-5, use_broadcast=False)
+            assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=1e-3, atol=1e-5)
 
             mx_out = npx.relu(x)
             np_out = np_relu(x.asnumpy())
-            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
+            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
 
 
 @with_seed()
@@ -1546,14 +1457,14 @@ def hybrid_forward(self, F, a):
             with mx.autograd.record():
                 mx_out = test_sigmoid(x)
             assert mx_out.shape == np_out.shape
-            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
+            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
             mx_out.backward()
             np_backward = np_sigmoid_grad(np_out)
-            assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=1e-3, atol=1e-5, use_broadcast=False)
+            assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=1e-3, atol=1e-5)
 
             mx_out = npx.sigmoid(x)
             np_out = np_sigmoid(x.asnumpy())
-            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
+            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
 
 
 @with_seed()
@@ -1659,17 +1570,17 @@ def get_indices(axis_size):
                     y = test_split(a)
                 assert len(y) == len(expected_ret)
                 for mx_out, np_out in zip(y, expected_ret):
-                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
 
                 mx.autograd.backward(y)
 
-                assert_almost_equal(a.grad.asnumpy(), _np.ones(a.shape), rtol=1e-3, atol=1e-5, use_broadcast=False)
+                assert_almost_equal(a.grad.asnumpy(), _np.ones(a.shape), rtol=1e-3, atol=1e-5)
 
                 # test imperative
                 mx_outs = np.split(a, indices_or_sections=indices_or_sections, axis=axis)
                 np_outs = _np.split(a.asnumpy(), indices_or_sections=indices_or_sections, axis=axis)
                 for mx_out, np_out in zip(mx_outs, np_outs):
-                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
 
 
 @with_seed()
@@ -1709,19 +1620,19 @@ def get_new_shape(shape, axis):
                     y = test_concat(a, b, c, d)
 
                 assert y.shape == expected_ret.shape
-                assert_almost_equal(y.asnumpy(), expected_ret, rtol=1e-3, atol=1e-5, use_broadcast=False)
+                assert_almost_equal(y.asnumpy(), expected_ret, rtol=1e-3, atol=1e-5)
 
                 y.backward()
 
-                assert_almost_equal(a.grad.asnumpy(), _np.ones(a.shape), rtol=1e-3, atol=1e-5, use_broadcast=False)
-                assert_almost_equal(b.grad.asnumpy(), _np.ones(b.shape), rtol=1e-3, atol=1e-5, use_broadcast=False)
-                assert_almost_equal(c.grad.asnumpy(), _np.ones(c.shape), rtol=1e-3, atol=1e-5, use_broadcast=False)
-                assert_almost_equal(d.grad.asnumpy(), _np.ones(d.shape), rtol=1e-3, atol=1e-5, use_broadcast=False)
+                assert_almost_equal(a.grad.asnumpy(), _np.ones(a.shape), rtol=1e-3, atol=1e-5)
+                assert_almost_equal(b.grad.asnumpy(), _np.ones(b.shape), rtol=1e-3, atol=1e-5)
+                assert_almost_equal(c.grad.asnumpy(), _np.ones(c.shape), rtol=1e-3, atol=1e-5)
+                assert_almost_equal(d.grad.asnumpy(), _np.ones(d.shape), rtol=1e-3, atol=1e-5)
 
                 # test imperative
                 mx_out = np.concatenate([a, b, c, d], axis=axis)
                 np_out = _np.concatenate([a.asnumpy(), b.asnumpy(), c.asnumpy(), d.asnumpy()], axis=axis)
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
 
 
 @with_seed()
@@ -1764,10 +1675,10 @@ def hybrid_forward(self, F, a, *args):
 
                 y.backward()
 
-                assert_almost_equal(mx_a.grad.asnumpy(), _np.ones(shape), rtol=1e-3, atol=1e-5, use_broadcast=False)
-                assert_almost_equal(mx_b.grad.asnumpy(), _np.ones(shape), rtol=1e-3, atol=1e-5, use_broadcast=False)
-                assert_almost_equal(mx_c.grad.asnumpy(), _np.ones(shape), rtol=1e-3, atol=1e-5, use_broadcast=False)
-                assert_almost_equal(mx_d.grad.asnumpy(), _np.ones(shape), rtol=1e-3, atol=1e-5, use_broadcast=False)
+                assert_almost_equal(mx_a.grad.asnumpy(), _np.ones(shape), rtol=1e-3, atol=1e-5)
+                assert_almost_equal(mx_b.grad.asnumpy(), _np.ones(shape), rtol=1e-3, atol=1e-5)
+                assert_almost_equal(mx_c.grad.asnumpy(), _np.ones(shape), rtol=1e-3, atol=1e-5)
+                assert_almost_equal(mx_d.grad.asnumpy(), _np.ones(shape), rtol=1e-3, atol=1e-5)
 
                 np_out = _np.stack([np_a, np_b, np_c, np_d], axis=axis)
                 mx_out = np.stack([mx_a, mx_b, mx_c, mx_d], axis=axis)
@@ -1797,14 +1708,14 @@ def hybrid_forward(self, F, a):
                 with mx.autograd.record():
                     mx_out = test_ravel(x)
                 assert mx_out.shape == np_out.shape
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
                 mx_out.backward()
                 np_backward = _np.ones(shape)
-                assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=1e-3, atol=1e-5, use_broadcast=False)
+                assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=1e-3, atol=1e-5)
 
                 mx_out = np.ravel(x)
                 np_out = _np.ravel(x.asnumpy())
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
 
 
 @with_seed()
@@ -2117,15 +2028,15 @@ def hybrid_forward(self, F, a):
                         with mx.autograd.record():
                             mx_out = test_cumsum(x)
                         assert mx_out.shape == np_out.shape
-                        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
+                        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
                         mx_out.backward()
                         np_backward = np_cumsum_backward(_np.ones(np_out.shape, dtype=otype),
                                                          axis=axis, dtype=otype).reshape(x.shape)
-                        assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=1e-3, atol=1e-5, use_broadcast=False)
+                        assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=1e-3, atol=1e-5)
 
                         mx_out = np.cumsum(x, axis=axis, dtype=otype)
                         np_out = _np.cumsum(x.asnumpy(), axis=axis, dtype=otype)
-                        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
+                        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
 
 
 @with_seed()
@@ -2156,7 +2067,7 @@ def test_sample_with_replacement(sampler, num_classes, shape, weight=None):
         expected_density = (weight.asnumpy() if weight is not None else
                             _np.array([1 / num_classes] * num_classes))
         # test almost equal
-        assert_almost_equal(generated_density, expected_density, rtol=1e-1, atol=1e-1, use_broadcast=False)
+        assert_almost_equal(generated_density, expected_density, rtol=1e-1, atol=1e-1)
         # test shape
         assert (samples.shape == shape)
 
@@ -2173,7 +2084,7 @@ def test_sample_without_replacement(sampler, num_classes, shape, num_trials, wei
             out = sampler(num_classes, 1, replace=False, p=weight).item()
             bins[out] += 1
         bins /= num_trials
-        assert_almost_equal(bins, expected_freq, rtol=1e-1, atol=1e-1, use_broadcast=False)
+        assert_almost_equal(bins, expected_freq, rtol=1e-1, atol=1e-1)
 
     def test_indexing_mode(sampler, set_size, samples_size, replace, weight=None):
         a = np.arange(set_size)
@@ -2334,7 +2245,7 @@ def hybrid_forward(self, F, x):
                         net.hybridize()
                     mx_ret = net(a)
                     np_ret = _np.linalg.norm(a.asnumpy(), ord=ord, axis=axis, keepdims=keepdims)
-                    assert_almost_equal(mx_ret.asnumpy(), np_ret, atol=1e-5, rtol=1e-4, use_broadcast=False)
+                    assert_almost_equal(mx_ret.asnumpy(), np_ret, atol=1e-5, rtol=1e-4)
 
 
 @with_seed()
@@ -2393,18 +2304,18 @@ def get_grad_right(a1, a2):
                     with mx.autograd.record():
                         mx_out = test_copysign(a1, a2)
                     assert mx_out.shape == expected_np.shape
-                    assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol, use_broadcast=False)
+                    assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol)
 
                     # Test gradient
                     mx_out.backward()
                     a1_grad, a2_grad = get_grad(a1_np, a2_np)
-                    assert_almost_equal(a1.grad.asnumpy(), a1_grad, rtol=rtol, atol=atol, use_broadcast=False)
-                    assert_almost_equal(a2.grad.asnumpy(), a2_grad, rtol=rtol, atol=atol, use_broadcast=False)
+                    assert_almost_equal(a1.grad.asnumpy(), a1_grad, rtol=rtol, atol=atol)
+                    assert_almost_equal(a2.grad.asnumpy(), a2_grad, rtol=rtol, atol=atol)
 
                     # Test imperative once again
                     mx_out = np.copysign(a1, a2)
                     expected_np = _np.copysign(a1_np, a2_np)
-                    assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol, use_broadcast=False)
+                    assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol)
 
     types = ['float16', 'float32', 'float64']
     for x_shape in shapes:
@@ -2418,12 +2329,12 @@ def get_grad_right(a1, a2):
             with mx.autograd.record():
                 mx_out = np.copysign(x, scalar)
             assert mx_out.shape == expected_np.shape
-            assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol, use_broadcast=False)
+            assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol)
 
             # Test gradient
             mx_out.backward()
             x_grad = get_grad_left(x_np, scalar)
-            assert_almost_equal(x.grad.asnumpy(), x_grad, rtol=rtol, atol=atol, use_broadcast=False)
+            assert_almost_equal(x.grad.asnumpy(), x_grad, rtol=rtol, atol=atol)
 
             # Test right
             x_np = _np.array(_np.random.uniform(-2.0, 2.0, x_shape), dtype=dtype)
@@ -2434,12 +2345,12 @@ def get_grad_right(a1, a2):
             with mx.autograd.record():
                 mx_out = np.copysign(scalar, x)
             assert mx_out.shape == expected_np.shape
-            assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol, use_broadcast=False)
+            assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol)
 
             # Test gradient
             mx_out.backward()
             x_grad = get_grad_right(scalar, x_np)
-            assert_almost_equal(x.grad.asnumpy(), x_grad, rtol=rtol, atol=atol, use_broadcast=False)
+            assert_almost_equal(x.grad.asnumpy(), x_grad, rtol=rtol, atol=atol)
 
 
 @with_seed()
@@ -2508,22 +2419,22 @@ def get_grad(UT, L, V):
                 # check UT @ L @ V == A
                 t = _np.matmul(UT * L[..., None, :], V)
                 assert t.shape == data_np.shape
-                assert_almost_equal(t, data_np, rtol=rtol, atol=atol, use_broadcast=False)
+                assert_almost_equal(t, data_np, rtol=rtol, atol=atol)
                 # check UT @ U == I
                 I = _np.matmul(UT, _np.swapaxes(UT, -2, -1))
                 I_np = _np.ones_like(UT) * _np.eye(shape[-2])
                 assert I.shape == I_np.shape
-                assert_almost_equal(I, I_np, rtol=rtol, atol=atol, use_broadcast=False)
+                assert_almost_equal(I, I_np, rtol=rtol, atol=atol)
                 # check U @ UT == I
                 I = _np.matmul(_np.swapaxes(UT, -2, -1), UT)
                 I_np = _np.ones_like(UT) * _np.eye(shape[-2])
                 assert I.shape == I_np.shape
-                assert_almost_equal(I, I_np, rtol=rtol, atol=atol, use_broadcast=False)
+                assert_almost_equal(I, I_np, rtol=rtol, atol=atol)
                 # check V @ VT == I
                 I = _np.matmul(V, _np.swapaxes(V, -2, -1))
                 I_np = _np.ones_like(UT) * _np.eye(shape[-2])
                 assert I.shape == I_np.shape
-                assert_almost_equal(I, I_np, rtol=rtol, atol=atol, use_broadcast=False)
+                assert_almost_equal(I, I_np, rtol=rtol, atol=atol)
                 # check descending singular values
                 s = [L[..., i] - L[..., i + 1] for i in range(L.shape[-1] - 1)]
                 s = _np.array(s)
@@ -2534,8 +2445,7 @@ def get_grad(UT, L, V):
                 mx.autograd.backward(ret)
                 if ((s > 1e-5).all() and (L.size == 0 or (L > 1e-5).all())):
                     backward_expected = get_grad(ret[0].asnumpy(), ret[1].asnumpy(), ret[2].asnumpy())
-                    assert_almost_equal(data.grad.asnumpy(), backward_expected, rtol=rtol, atol=atol,
-                                        use_broadcast=False)
+                    assert_almost_equal(data.grad.asnumpy(), backward_expected, rtol=rtol, atol=atol)
 
 
 @with_seed()
@@ -2580,18 +2490,18 @@ def g(data):
                 with mx.autograd.record():
                     mx_out = test_vstack(*v)
                 assert mx_out.shape == expected_np.shape
-                assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol, use_broadcast=False)
+                assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol)
 
                 # Test gradient
                 mx_out.backward()
                 for i in range(3):
                     expected_grad = g(v_np[i])
-                    assert_almost_equal(v[i].grad.asnumpy(), expected_grad, rtol=rtol, atol=atol, use_broadcast=False)
+                    assert_almost_equal(v[i].grad.asnumpy(), expected_grad, rtol=rtol, atol=atol)
 
                 # Test imperative once again
                 mx_out = np.vstack(v)
                 expected_np = _np.vstack(v_np)
-                assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol, use_broadcast=False)
+                assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol)
 
 
 @with_seed()
@@ -2707,18 +2617,15 @@ def g(data, axis1, axis2, offset):
                                 with mx.autograd.record():
                                     out_mx = test_trace(data.as_np_ndarray())
                                 assert out_mx.shape == expected_np.shape
-                                assert_almost_equal(out_mx.asnumpy(), expected_np, rtol=rtol, atol=atol,
-                                                    use_broadcast=False)
+                                assert_almost_equal(out_mx.asnumpy(), expected_np, rtol=rtol, atol=atol)
                                 out_mx.backward()
                                 backward_expected = g(data_np, axis1=axis1, axis2=axis2, offset=offset)
-                                assert_almost_equal(data.grad.asnumpy(), backward_expected, rtol=rtol, atol=atol,
-                                                    use_broadcast=False)
+                                assert_almost_equal(data.grad.asnumpy(), backward_expected, rtol=rtol, atol=atol)
 
                                 # Test imperative once again
                                 data = mx.nd.array(data_np, dtype=dtype)
                                 out_mx = np.trace(data.as_np_ndarray(), axis1=axis1, axis2=axis2, offset=offset)
-                                assert_almost_equal(out_mx.asnumpy(), expected_np, rtol=rtol, atol=atol,
-                                                    use_broadcast=False)
+                                assert_almost_equal(out_mx.asnumpy(), expected_np, rtol=rtol, atol=atol)
 
     # bad params
     params = [
@@ -2766,11 +2673,11 @@ def hybrid_forward(self, F, x, *args, **kwargs):
                     if hybridize:
                         mx_func.hybridize()
                     mx_out = mx_func(x)
-                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
                     # test imperative
                     mx_out = getattr(np, func)(M=config, dtype=dtype)
                     np_out = np_func(M=config).astype(dtype)
-                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
 
 
 @with_seed()
@@ -2802,15 +2709,15 @@ def hybrid_forward(self, F, x):
                 with mx.autograd.record():
                     mx_out = test_flip(x)
                 assert mx_out.shape == np_out.shape
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol, use_broadcast=False)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
                 mx_out.backward()
                 np_backward = _np.ones(np_out.shape)
-                assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=rtol, atol=atol, use_broadcast=False)
+                assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=rtol, atol=atol)
 
                 # Test imperative once again
                 mx_out = np.flip(x, axis)
                 np_out = _np.flip(x.asnumpy(), axis)
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol, use_broadcast=False)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
 
 
 @with_seed()
@@ -2838,11 +2745,11 @@ def hybrid_forward(self, F, x):
                     np_out = _np.around(x.asnumpy(), d)
                     mx_out = test_around(x)
                     assert mx_out.shape == np_out.shape
-                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol, use_broadcast=False)
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
 
                     mx_out = np.around(x, d)
                     np_out = _np.around(x.asnumpy(), d)
-                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol, use_broadcast=False)
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
 
 
 @with_seed()
@@ -2898,18 +2805,18 @@ def dimReduce(src, des):
                 with mx.autograd.record():
                     mx_out = test_arctan2(x1, x2)
                 assert mx_out.shape == np_out.shape
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol, use_broadcast=False)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
                 mx_out.backward()
                 np_backward_1 = x21 / (x11 * x11 + x21 * x21)
                 np_backward_2 = -1 * x11 / (x11 * x11 + x21 * x21)
                 np_backward_1 = dimReduce(np_backward_1, x11)
                 np_backward_2 = dimReduce(np_backward_2, x21)
-                assert_almost_equal(x1.grad.asnumpy(), np_backward_1, rtol=rtol, atol=atol, use_broadcast=False)
-                assert_almost_equal(x2.grad.asnumpy(), np_backward_2, rtol=rtol, atol=atol, use_broadcast=False)
+                assert_almost_equal(x1.grad.asnumpy(), np_backward_1, rtol=rtol, atol=atol)
+                assert_almost_equal(x2.grad.asnumpy(), np_backward_2, rtol=rtol, atol=atol)
 
                 mx_out = np.arctan2(x1, x2)
                 np_out = _np.arctan2(x1.asnumpy(), x2.asnumpy())
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol, use_broadcast=False)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
 
 
 @with_seed()
@@ -2935,13 +2842,13 @@ def hybrid_forward(self, F, x):
                 np_out = _np.transpose(np_out)
                 mx_out = test_nonzero(x)
                 assert mx_out.shape == np_out.shape
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol, atol, use_broadcast=False)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol, atol)
 
                 # Test imperative once again
                 mx_out = npx.nonzero(x)
                 np_out = _np.nonzero(x.asnumpy())
                 np_out = _np.transpose(np_out)
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol, atol, use_broadcast=False)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol, atol)
 
 
 @with_seed()
@@ -2996,18 +2903,18 @@ def dimReduce(src, des):
                 with mx.autograd.record():
                     mx_out = test_hypot(x1, x2)
                 assert mx_out.shape == np_out.shape
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol, use_broadcast=False)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
                 mx_out.backward()
                 np_backward_1 = x11 / np_out
                 np_backward_2 = x21 / np_out
                 np_backward_1 = dimReduce(np_backward_1, x11)
                 np_backward_2 = dimReduce(np_backward_2, x21)
-                assert_almost_equal(x1.grad.asnumpy(), np_backward_1, rtol=rtol, atol=atol, use_broadcast=False)
-                assert_almost_equal(x2.grad.asnumpy(), np_backward_2, rtol=rtol, atol=atol, use_broadcast=False)
+                assert_almost_equal(x1.grad.asnumpy(), np_backward_1, rtol=rtol, atol=atol)
+                assert_almost_equal(x2.grad.asnumpy(), np_backward_2, rtol=rtol, atol=atol)
 
                 mx_out = np.hypot(x1, x2)
                 np_out = _np.hypot(x1.asnumpy(), x2.asnumpy())
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol, use_broadcast=False)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
 
 
 @with_seed()
@@ -3049,13 +2956,13 @@ def hybrid_forward(self, F, a):
                 mx_out = test_unique(x)
                 assert mx_out[0].shape == np_out[0].shape
                 for i in range(4):
-                    assert_almost_equal(mx_out[i].asnumpy(), np_out[i], rtol=1e-3, atol=1e-5, use_broadcast=False)
+                    assert_almost_equal(mx_out[i].asnumpy(), np_out[i], rtol=1e-3, atol=1e-5)
 
                 # Test imperative once again
                 mx_out = np.unique(x, *config[1:])
                 np_out = _np.unique(x.asnumpy(), *config[1:])
                 for i in range(4):
-                    assert_almost_equal(mx_out[i].asnumpy(), np_out[i], rtol=1e-3, atol=1e-5, use_broadcast=False)
+                    assert_almost_equal(mx_out[i].asnumpy(), np_out[i], rtol=1e-3, atol=1e-5)
 
 
 @with_seed()

From a8181dd04f16edbaf0e94cf3a122d9113e41cf85 Mon Sep 17 00:00:00 2001
From: Iblis Lin <iblis@hs.ntnu.edu.tw>
Date: Thu, 10 Oct 2019 13:46:46 +0800
Subject: [PATCH 419/813] [MXNET-1430] julia: implement context.gpu_memory_info
 (#16324)

* julia: implement context.gpu_memory_info

resolve MXNET-1430

* update export and NEWS
---
 julia/NEWS.md           |  4 ++++
 julia/src/MXNet.jl      |  3 ++-
 julia/src/context.jl    | 18 ++++++++++++++++++
 python/mxnet/context.py |  2 --
 4 files changed, 24 insertions(+), 3 deletions(-)

diff --git a/julia/NEWS.md b/julia/NEWS.md
index 7e3076a303d4..8f5efbb0edd5 100644
--- a/julia/NEWS.md
+++ b/julia/NEWS.md
@@ -20,6 +20,10 @@
 * Add an abstract type `AbstractMXError` as the parent type for all MXNet-related
   API errors. (#16235)
 
+* Porting more `context` functions from Python.
+    * `num_gpus()` (#16236)
+    * `gpu_memory_info()` (#16324)
+
 
 # v1.5.0
 
diff --git a/julia/src/MXNet.jl b/julia/src/MXNet.jl
index 69bbcf85180d..86d7d06806fc 100644
--- a/julia/src/MXNet.jl
+++ b/julia/src/MXNet.jl
@@ -79,7 +79,8 @@ export Executor,
 export Context,
        cpu,
        gpu,
-       num_gpus
+       num_gpus,
+       gpu_memory_info
 
 # model.jl
 export AbstractModel,
diff --git a/julia/src/context.jl b/julia/src/context.jl
index bce67a593414..68e69138e10e 100644
--- a/julia/src/context.jl
+++ b/julia/src/context.jl
@@ -68,3 +68,21 @@ function num_gpus()
   @mxcall :MXGetGPUCount (Ref{Cint},) n
   n[]
 end
+
+"""
+    gpu_memory_info(dev_id = 0)::Tuple{UInt64,UInt64}
+
+Query CUDA for the free and total bytes of GPU global memory.
+It returns a tuple of `(free memory, total memory)`.
+
+```julia-repl
+julia> mx.gpu_memory_info()
+(0x00000003af240000, 0x00000003f9440000)
+```
+"""
+function gpu_memory_info(dev_id = 0)
+  free = Ref{UInt64}()
+  n = Ref{UInt64}()
+  @mxcall :MXGetGPUMemoryInformation64 (Cint, Ref{UInt64}, Ref{UInt64}) dev_id free n
+  free[], n[]
+end
diff --git a/python/mxnet/context.py b/python/mxnet/context.py
index decea71762f6..99998eaa28f2 100644
--- a/python/mxnet/context.py
+++ b/python/mxnet/context.py
@@ -292,8 +292,6 @@ def gpu_memory_info(device_id=0):
     Returns
     -------
     (free, total) : (int, int)
-        The number of GPUs.
-
     """
     free = ctypes.c_uint64()
     total = ctypes.c_uint64()

From 9dc0ab8e277bd4af96d7f1d0aa1e45d119fdb06e Mon Sep 17 00:00:00 2001
From: Jake Lee <gstu1130@gmail.com>
Date: Thu, 10 Oct 2019 11:07:32 -0700
Subject: [PATCH 420/813] global numpy shape flag (#16335)

---
 include/mxnet/c_api.h                      |  3 +-
 include/mxnet/imperative.h                 | 44 ++++++++++++++++++----
 src/c_api/c_api_ndarray.cc                 |  2 +-
 src/imperative/imperative.cc               |  4 +-
 tests/python/unittest/test_thread_local.py | 26 ++++++++++++-
 5 files changed, 65 insertions(+), 14 deletions(-)

diff --git a/include/mxnet/c_api.h b/include/mxnet/c_api.h
index 242d5a4abd36..177ec5d40146 100644
--- a/include/mxnet/c_api.h
+++ b/include/mxnet/c_api.h
@@ -1173,7 +1173,8 @@ MXNET_DLL int MXAutogradIsTraining(bool* curr);
 MXNET_DLL int MXIsNumpyShape(bool* curr);
 /*!
  * \brief set numpy compatibility switch
- * \param is_np_shape 1 when numpy shape semantics is on, 0 when off
+ * \param is_np_shape 1 when numpy shape semantics is thread local on,
+ *        2 when numpy shape semantics is global on and 0 when off
  * \param prev returns the previous status before this set
  * \return 0 when success, -1 when failure happens
  */
diff --git a/include/mxnet/imperative.h b/include/mxnet/imperative.h
index c588565012a5..18f6424e54f7 100644
--- a/include/mxnet/imperative.h
+++ b/include/mxnet/imperative.h
@@ -35,6 +35,17 @@
 #include "./ndarray.h"
 
 namespace mxnet {
+  /*! \brief there are three numpy shape flags based on priority.
+   * GlobalOn
+   *   turn on numpy shape flag globally, it includes thread local.
+   *   The flag can be seen in any thread.
+   * ThreadLocalOn
+   *   only turn on thread local numpy shape flag, it cannot be seen
+   *   in other threads.
+   * Off
+   *   turn off numpy shape flag globally.
+   * */
+  enum NumpyShape{Off, ThreadLocalOn, GlobalOn};
 /*! \brief runtime functions for NDArray */
 class Imperative {
  public:
@@ -97,14 +108,30 @@ class Imperative {
       is_recording_ = is_recording;
       return old;
   }
-  /*! brief whether numpy compatibility is on. */
+  /*! \brief whether numpy compatibility is on. */
   bool is_np_shape() const {
-    return is_np_shape_;
+    if (is_np_shape_global_) {
+      return true;
+    }
+    return is_np_shape_thread_local_;
   }
-  /*! brief turn on or turn off numpy compatibility switch. */
-  bool set_is_np_shape(bool is_np_shape) {
-    bool old = is_np_shape_;
-    is_np_shape_ = is_np_shape;
+  /*! \brief specify numpy compatibility off, thread local on or global on. */
+  bool set_is_np_shape(int is_np_shape) {
+    NumpyShape flag = static_cast<NumpyShape>(is_np_shape);
+    bool old = this->is_np_shape();
+    switch (flag) {
+      case GlobalOn:
+        is_np_shape_global_ = true;
+        is_np_shape_thread_local_ = true;
+        break;
+      case ThreadLocalOn:
+        is_np_shape_thread_local_ = true;
+        break;
+      case Off:
+        is_np_shape_global_ = false;
+        is_np_shape_thread_local_ = false;
+        break;
+    }
     return old;
   }
   /*! \brief to record operator, return corresponding node. */
@@ -177,14 +204,15 @@ class Imperative {
   static thread_local bool is_recording_;
   // TOOD(junwu): Added numpy compatibility switch for backward compatibility.
   // Delete it in the next major release.
-  static thread_local bool is_np_shape_;
+  static thread_local bool is_np_shape_thread_local_;
 #else
   static MX_THREAD_LOCAL bool is_train_;
   static MX_THREAD_LOCAL bool is_recording_;
   // TOOD(junwu): Added numpy compatibility switch for backward compatibility.
   // Delete it in the next major release.
-  static MX_THREAD_LOCAL bool is_np_shape_;
+  static MX_THREAD_LOCAL bool is_np_shape_thread_local_;
 #endif
+  bool is_np_shape_global_{false};
   /*! \brief node count used for naming */
   std::atomic<uint64_t> node_count_{0};
   /*! \brief variable count used for naming */
diff --git a/src/c_api/c_api_ndarray.cc b/src/c_api/c_api_ndarray.cc
index 5863f08762e6..b80e17c18071 100644
--- a/src/c_api/c_api_ndarray.cc
+++ b/src/c_api/c_api_ndarray.cc
@@ -284,7 +284,7 @@ int MXIsNumpyShape(bool* curr) {
 
 int MXSetIsNumpyShape(int is_np_shape, int* prev) {
   API_BEGIN();
-  *prev = Imperative::Get()->set_is_np_shape(static_cast<bool>(is_np_shape));
+  *prev = Imperative::Get()->set_is_np_shape(is_np_shape);
   API_END();
 }
 
diff --git a/src/imperative/imperative.cc b/src/imperative/imperative.cc
index b2ffd1096b2b..b3924cc4d79e 100644
--- a/src/imperative/imperative.cc
+++ b/src/imperative/imperative.cc
@@ -25,11 +25,11 @@ namespace mxnet {
 #if DMLC_CXX11_THREAD_LOCAL
 thread_local bool Imperative::is_train_ = false;
 thread_local bool Imperative::is_recording_ = false;
-thread_local bool Imperative::is_np_shape_ = false;
+thread_local bool Imperative::is_np_shape_thread_local_ = false;
 #else
 MX_THREAD_LOCAL bool Imperative::is_train_ = false;
 MX_THREAD_LOCAL bool Imperative::is_recording_ = false;
-MX_THREAD_LOCAL bool Imperative::is_np_shape_ = false;
+MX_THREAD_LOCAL bool Imperative::is_np_shape_thread_local_ = false;
 #endif
 
 Imperative* Imperative::Get() {
diff --git a/tests/python/unittest/test_thread_local.py b/tests/python/unittest/test_thread_local.py
index ee56ba780a95..f0e3c660181b 100644
--- a/tests/python/unittest/test_thread_local.py
+++ b/tests/python/unittest/test_thread_local.py
@@ -16,14 +16,16 @@
 # under the License.
 
 import threading
+import numpy as np
 import mxnet as mx
 from mxnet import context, attribute, name
 from mxnet.gluon import block
 from mxnet.context import Context
 from mxnet.attribute import AttrScope
 from mxnet.name import NameManager
-from mxnet.test_utils import set_default_context
-from mxnet.util import _NumpyArrayScope
+from mxnet.test_utils import assert_almost_equal, set_default_context
+from mxnet.util import _NumpyArrayScope, set_np_shape
+
 
 def test_context():
     ctx_list = []
@@ -199,6 +201,26 @@ def g():
     assert status[0], "Spawned thread didn't set status correctly"
 
 
+def test_np_global_shape():
+    set_np_shape(2)
+    data = []
+
+    def f():
+        # scalar
+        data.append(mx.np.ones(shape=()))
+        # zero-dim
+        data.append(mx.np.ones(shape=(0, 1, 2)))
+    try:
+        thread = threading.Thread(target=f)
+        thread.start()
+        thread.join()
+
+        assert_almost_equal(data[0].asnumpy(), np.ones(shape=()))
+        assert_almost_equal(data[1].asnumpy(), np.ones(shape=(0, 1, 2)))
+    finally:
+        set_np_shape(0)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From cfe9e5076792918e7a18f6336b780c41e28b77f4 Mon Sep 17 00:00:00 2001
From: Manu Seth <22492939+mseth10@users.noreply.github.com>
Date: Thu, 10 Oct 2019 12:27:00 -0700
Subject: [PATCH 421/813] Skipping installing nightly test (#16418)

---
 tests/nightly/Jenkinsfile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/nightly/Jenkinsfile b/tests/nightly/Jenkinsfile
index 758c86476f75..192f3d3452dc 100755
--- a/tests/nightly/Jenkinsfile
+++ b/tests/nightly/Jenkinsfile
@@ -62,7 +62,7 @@ core_logic: {
           //utils.docker_run('ubuntu_base_gpu', 'nightly_test_installation ubuntu_python_gpu_virtualenv', true)
           //docker_run('ubuntu_base_gpu', 'nightly_test_installation ubuntu_python_gpu_pip', true)
           //docker_run('ubuntu_base_gpu', 'nightly_test_installation ubuntu_python_gpu_docker', true)
-          utils.docker_run('ubuntu_base_gpu', 'nightly_test_installation ubuntu_python_gpu_source', true)
+          //utils.docker_run('ubuntu_base_gpu', 'nightly_test_installation ubuntu_python_gpu_source', true)
         }
       }
     },

From a2018ba6c6b0df6fb1f2172faf311e260f66ce76 Mon Sep 17 00:00:00 2001
From: Dick Carter <dcarter@nvidia.com>
Date: Thu, 10 Oct 2019 13:34:35 -0700
Subject: [PATCH 422/813] cuDNN non-persistant bidirectional RNN dgrad sync fix
 (#16391)

* Alter test_lstm_bidirectional to demo fast-fail with optional wgrad.

* Fix cuDNN RNN dgrad sync.

* Simplify gpu activity sync sequence.

* Remove repeated running of now-passing test.

* Trigger CI
---
 src/operator/rnn-inl.h                 | 83 +++++++++++++++++---------
 tests/python/unittest/test_operator.py |  7 ++-
 2 files changed, 58 insertions(+), 32 deletions(-)

diff --git a/src/operator/rnn-inl.h b/src/operator/rnn-inl.h
index 5652960baa43..cf6fe10fd328 100644
--- a/src/operator/rnn-inl.h
+++ b/src/operator/rnn-inl.h
@@ -493,6 +493,7 @@ class RNNOp {
 
     CUDNN_CALL(cudnnCreateRNNDescriptor(&rnn_desc_));
     CUDNN_CALL(cudnnCreateDropoutDescriptor(&dropout_desc_));
+    CUDA_CALL(cudaEventCreateWithFlags(&dgrad_sync_event_, cudaEventDisableTiming));
 
 #if MXNET_USE_CUDNN_GE_7200
     CUDNN_CALL(cudnnCreateRNNDataDescriptor(&x_data_desc_));
@@ -537,6 +538,7 @@ class RNNOp {
     CUDNN_CALL(cudnnDestroyFilterDescriptor(dw_desc_));
     CUDNN_CALL(cudnnDestroyRNNDescriptor(rnn_desc_));
     CUDNN_CALL(cudnnDestroyDropoutDescriptor(dropout_desc_));
+    CUDA_CALL(cudaEventDestroy(dgrad_sync_event_));
 
     if (init_cudnn_) {
       for (size_t i = 0; i < x_desc_vec_.size(); ++i) {
@@ -1066,20 +1068,23 @@ class RNNOp {
                                       workspace_byte_,
                                       reserve_space_.dptr,
                                       reserve_space_byte_));
-    CUDNN_CALL(cudnnRNNBackwardWeightsEx(s->dnn_handle_,
-                                         rnn_desc_,
-                                         x_data_desc_,
-                                         x.dptr_,
-                                         hx_desc_,
-                                         hx.dptr_,
-                                         y_data_desc_,
-                                         y.dptr_,
-                                         temp_space.dptr_,
-                                         workspace_byte_,
-                                         dw_desc_,
-                                         dw.dptr_,
-                                         reserve_space_.dptr,
-                                         reserve_space_byte_));
+    SyncDgrad();
+    if (req[rnn_enum::kParams] != kNullOp) {
+      CUDNN_CALL(cudnnRNNBackwardWeightsEx(s->dnn_handle_,
+                                           rnn_desc_,
+                                           x_data_desc_,
+                                           x.dptr_,
+                                           hx_desc_,
+                                           hx.dptr_,
+                                           y_data_desc_,
+                                           y.dptr_,
+                                           temp_space.dptr_,
+                                           workspace_byte_,
+                                           dw_desc_,
+                                           dw.dptr_,
+                                           reserve_space_.dptr,
+                                           reserve_space_byte_));
+    }
 #else
     CUDNN_CALL(cudnnRNNBackwardData(s->dnn_handle_,
                                     rnn_desc_,
@@ -1108,21 +1113,24 @@ class RNNOp {
                                     workspace_byte_,
                                     reserve_space_.dptr,
                                     reserve_space_byte_));
-    CUDNN_CALL(cudnnRNNBackwardWeights(s->dnn_handle_,
-                                       rnn_desc_,
-                                       param_.seq_length_,
-                                       x_desc_vec_.data(),
-                                       x.dptr_,
-                                       hx_desc_,
-                                       hx.dptr_,
-                                       y_desc_vec_.data(),
-                                       y.dptr_,
-                                       temp_space.dptr_,
-                                       workspace_byte_,
-                                       dw_desc_,
-                                       dw.dptr_,
-                                       reserve_space_.dptr,
-                                       reserve_space_byte_));
+    SyncDgrad();
+    if (req[rnn_enum::kParams] != kNullOp) {
+      CUDNN_CALL(cudnnRNNBackwardWeights(s->dnn_handle_,
+                                         rnn_desc_,
+                                         param_.seq_length_,
+                                         x_desc_vec_.data(),
+                                         x.dptr_,
+                                         hx_desc_,
+                                         hx.dptr_,
+                                         y_desc_vec_.data(),
+                                         y.dptr_,
+                                         temp_space.dptr_,
+                                         workspace_byte_,
+                                         dw_desc_,
+                                         dw.dptr_,
+                                         reserve_space_.dptr,
+                                         reserve_space_byte_));
+    }
 #endif  // MXNET_USE_CUDNN_GE_7200
 #endif  // MXNET_USE_CUDNN == 1 && defined(__CUDACC__)
 
@@ -1365,6 +1373,7 @@ class RNNOp {
       // RNN descriptors
       cudnnDataType_t dtype_with_fallback_;
       cudnnRNNAlgo_t rnn_algo = CUDNN_RNN_ALGO_STANDARD;
+      dgrad_sync_needed_ = (rnn_algo == CUDNN_RNN_ALGO_STANDARD) && param_.bidirectional;
       // On arch's 50 and 52(Maxwell), the gpu doesn't support native fp16 compute.
       // Before cuDNN 7.5.0, when running fp16, cuDNN fallback to fp32 under the hood on Maxwell.
       // That's not the case begining from 7.5.0. Thereby adding fallback explicitly here.
@@ -1484,6 +1493,20 @@ class RNNOp {
     }
 #endif  // MXNET_USE_CUDNN == 1 && defined(__CUDACC__)
   }
+
+#if MXNET_USE_CUDNN == 1 && defined(__CUDACC__)
+  // cuDNN versions up to and including v7.6.4 did not sync a last dgrad kernel back to the main
+  // cudnn handle's stream (non-persistant algo, bidirectional only).  This could result in silent
+  // non-determinstic failures with very low probability, seen more often when wgrad is bypassed.
+  inline void SyncDgrad() {
+    if (CUDNN_VERSION <= 7604 && dgrad_sync_needed_) {
+      // Without blocking the CPU, create a synchronization point of all current GPU activity.  No
+      // need to call cudaStreamWaitEvent- cudaEventRecord on the legacy default stream suffices.
+      CUDA_CALL(cudaEventRecord(dgrad_sync_event_, cudaStreamLegacy));
+    }
+  }
+#endif  // MXNET_USE_CUDNN == 1 && defined(__CUDACC__)
+
 #if MXNET_USE_CUDNN == 1
   cudnnDataType_t dtype_;
   bool init_cudnn_;
@@ -1511,6 +1534,8 @@ class RNNOp {
   bool cudnn_tensor_core_;
 
   cudnnTensorFormat_t format_;
+  cudaEvent_t dgrad_sync_event_;
+  bool dgrad_sync_needed_ = false;
 #endif  // MXNET_USE_CUDNN
   bool init_space_, temp_init_space_;
   size_t reserve_cpu_space_size_, temp_cpu_space_size_;
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 4f981d5e29c7..35460676da28 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -68,11 +68,11 @@ def check_rnn_consistency(cell1, cell2, T, N, I, H, grad_req, rtol=1e-2, atol=1e
     dy = mx.random.uniform(shape=mod1.get_outputs()[0].shape)
     mod1.backward(out_grads=[dy])
     mod2.backward(out_grads=[dy])
-    if grad_req != 'null':
-        assert_allclose(mod1.get_input_grads()[0].asnumpy(), mod2.get_input_grads()[0].asnumpy(), rtol=rtol, atol=atol)
-    else:
+    if type(grad_req) is dict and grad_req['data'] == 'null' or grad_req == 'null':
         assert(mod1.get_input_grads()[0] == None)
         assert(mod2.get_input_grads()[0] == None)
+    else:
+        assert_allclose(mod1.get_input_grads()[0].asnumpy(), mod2.get_input_grads()[0].asnumpy(), rtol=rtol, atol=atol)
 
 
 @with_seed()
@@ -149,6 +149,7 @@ def test_lstm_bidirectional():
     check_rnn_consistency(fused, stack, T, N, I, H, 'write')
     check_rnn_consistency(fused, stack, T, N, I, H, 'add')
     check_rnn_consistency(fused, stack, T, N, I, H, 'null')
+    check_rnn_consistency(fused, stack, T, N, I, H, {'data': 'add', 'parameters': 'null'})
 
 @with_seed()
 @assert_raises_cudnn_not_satisfied(min_version='5.1.10')

From 56e1befb4a525f190f7b5b39611e7f0e6b951e7e Mon Sep 17 00:00:00 2001
From: perdasilva <perdasilva@gmail.com>
Date: Fri, 11 Oct 2019 00:20:40 +0200
Subject: [PATCH 423/813] Adds PyPI CD Pipeline (#16190)

* Adds artifact restoration and generic pipeline to cd utils

* Adds PyPI release pipeline

* Adds pypi packaging fixes

* Only publish cu92 and cu92mkl variants to PyPI

* Updates pip package doc resources
---
 cd/Jenkinsfile_cd_pipeline                    |  4 +
 cd/Jenkinsfile_utils.groovy                   | 73 ++++++++++++++++
 cd/README.md                                  |  8 ++
 cd/python/pypi/Jenkins_pipeline.groovy        | 78 +++++++++++++++++
 cd/python/pypi/README.md                      | 43 +++++++++
 cd/python/pypi/pypi_package.sh                | 60 +++++++++++++
 cd/python/pypi/pypi_publish.py                | 87 +++++++++++++++++++
 ci/docker/runtime_functions.sh                | 44 ++++++++++
 tools/pip/doc/CPU_ADDITIONAL.md               |  6 +-
 tools/pip/doc/CU100MKL_ADDITIONAL.md          |  4 +-
 tools/pip/doc/CU100_ADDITIONAL.md             |  4 +-
 ...L_ADDITIONAL.md => CU101MKL_ADDITIONAL.md} | 10 ++-
 ...CU91_ADDITIONAL.md => CU101_ADDITIONAL.md} | 10 ++-
 tools/pip/doc/CU75MKL_ADDITIONAL.md           |  6 +-
 tools/pip/doc/CU75_ADDITIONAL.md              |  6 +-
 tools/pip/doc/CU80MKL_ADDITIONAL.md           |  6 +-
 tools/pip/doc/CU80_ADDITIONAL.md              |  6 +-
 tools/pip/doc/CU90MKL_ADDITIONAL.md           |  6 +-
 tools/pip/doc/CU90_ADDITIONAL.md              |  6 +-
 tools/pip/doc/CU92MKL_ADDITIONAL.md           |  6 +-
 tools/pip/doc/CU92_ADDITIONAL.md              |  6 +-
 tools/pip/doc/MKL_ADDITIONAL.md               |  6 +-
 tools/pip/setup.py                            | 14 ++-
 23 files changed, 465 insertions(+), 34 deletions(-)
 create mode 100644 cd/python/pypi/Jenkins_pipeline.groovy
 create mode 100644 cd/python/pypi/README.md
 create mode 100755 cd/python/pypi/pypi_package.sh
 create mode 100755 cd/python/pypi/pypi_publish.py
 rename tools/pip/doc/{CU91MKL_ADDITIONAL.md => CU101MKL_ADDITIONAL.md} (81%)
 rename tools/pip/doc/{CU91_ADDITIONAL.md => CU101_ADDITIONAL.md} (78%)

diff --git a/cd/Jenkinsfile_cd_pipeline b/cd/Jenkinsfile_cd_pipeline
index 9aa5f8110d48..e0e94770b682 100644
--- a/cd/Jenkinsfile_cd_pipeline
+++ b/cd/Jenkinsfile_cd_pipeline
@@ -61,6 +61,10 @@ pipeline {
               stage("Build") {
                 cd_utils.trigger_release_job("Build static libmxnet", "mxnet_lib/static", params.MXNET_VARIANTS)    
               }
+              stage("PyPI Release") {
+                echo "Building PyPI Release"
+                cd_utils.trigger_release_job("Release PyPI Packages", "python/pypi", params.MXNET_VARIANTS)
+              }
             },
 
             "Dynamic libmxnet based release": {
diff --git a/cd/Jenkinsfile_utils.groovy b/cd/Jenkinsfile_utils.groovy
index da17c19b562c..5182b04a3b5b 100644
--- a/cd/Jenkinsfile_utils.groovy
+++ b/cd/Jenkinsfile_utils.groovy
@@ -119,4 +119,77 @@ def push_artifact(libmxnet_path, variant, libtype, license_paths = '', dependenc
   sh "./cd/utils/artifact_repository.py --push --verbose --libtype ${libtype} --variant ${variant} --libmxnet ${libmxnet_path} --licenses ${license_paths} --dependencies ${dependency_paths}"
 }
 
+// pull artifact from repository
+def pull_artifact(variant, libtype, destination = '') {
+  sh "./cd/utils/artifact_repository.py --pull --verbose --libtype ${libtype} --variant ${variant} --destination ${destination}"
+}
+
+// pulls artifact from repository and places files in the appropriate directories
+def restore_artifact(variant, libtype) {
+
+  pull_artifact(variant, libtype, 'mxnet_artifact')
+
+  // move libraries to lib directory
+  dir('lib') {
+    sh "mv ../mxnet_artifact/libmxnet.so ."
+    if (fileExists('../mxnet_artifact/dependencies')) {
+      sh """find "../mxnet_artifact/dependencies" -type f -name "*.so*" -exec mv {} . \\;"""
+      sh "ls ."
+    }
+  }
+
+  dir('cd_misc') {
+    if (fileExists('../mxnet_artifact/dependencies')) {
+      // All library files (*.so*) should have be moved
+      // to the lib directory. If anything is left, it will be
+      // other supporting files (header files, etc.)
+      sh """find "../mxnet_artifact/dependencies" -type f -exec mv {} . \\;"""
+      sh "ls ."
+    }
+  }
+
+  dir('licenses') {
+    if (fileExists('../mxnet_artifact/licenses')) {
+      sh """find "../mxnet_artifact/licenses" -type f -exec mv {} . \\;"""
+      sh "ls ."
+    }
+  }
+
+  dir('mxnet_artifact') {
+    deleteDir()
+  }
+}
+
+// A generic pipeline that can be used by *most* CD jobs
+// It can be used when implementing the pipeline steps in the Jenkins_steps.groovy
+// script for a particular delivery channel. However, it should also implement the
+// build, test, and push steps.
+// NOTE: Be mindful of the expected time that a step should take. If it will take a long time,
+// and it can be done in a CPU node, do it in a CPU node. We should avoid using GPU instances unless
+// we *have* to.
+// However, if it is only packaging libmxnet and that doesn't take long. Then, the pipeline can 
+// just run on a single node. As is done bellow.
+// For examples of multi-node CD pipelines, see the the binary_release/static and binary_release/dynamic
+// pipeline.
+def generic_pipeline(mxnet_variant, custom_steps, node_type = "restricted-mxnetlinux-cpu") {
+  return {
+    node(node_type) {
+      stage("${mxnet_variant}") {
+
+        stage('Build') {
+          custom_steps.build(mxnet_variant)
+        }
+
+        stage('Test') {
+          custom_steps.test(mxnet_variant)
+        }
+
+        stage('Push') {
+          custom_steps.push(mxnet_variant)
+        }
+      }
+    }
+  }
+}
+
 return this
diff --git a/cd/README.md b/cd/README.md
index 41188eee6785..a8e3fb794daf 100644
--- a/cd/README.md
+++ b/cd/README.md
@@ -159,6 +159,10 @@ def get_pipeline(mxnet_variant) {
 }
 ```
 
+Examples:
+
+ * [PyPI Release](python/pypi/Jenkins_pipeline.groovy): In this pipeline, the majority of time is overwhelmingly spent on testing. Therefore, it should be ok to execute the whole pipeline on a GPU node (i.e. packaging, testing, and publishing).
+
 **Per step**
 
 Use this approach in cases where you have long running stages that don't depend on specialized/expensive hardware.
@@ -188,3 +192,7 @@ def test(mxnet_variant) {
   }
 }
 ```
+
+Examples:
+
+Both the [statically linked libmxnet](mxnet_lib/static/Jenkins_pipeline.groovy) and [dynamically linked libmxnet](mxnet_lib/dynamic/Jenkins_pipeline.groovy) pipelines have long running compilation and testing stages that **do not** require specialized/expensive hardware (e.g. GPUs). Therefore, as mush as possible, it is important to run each stage in on its own node, and design the pipeline to spend the least amount of time possible on expensive hardware. E.g. for GPU builds, only run GPU tests on GPU instances, all other stages can be executed on CPU nodes.
\ No newline at end of file
diff --git a/cd/python/pypi/Jenkins_pipeline.groovy b/cd/python/pypi/Jenkins_pipeline.groovy
new file mode 100644
index 000000000000..bf8103270146
--- /dev/null
+++ b/cd/python/pypi/Jenkins_pipeline.groovy
@@ -0,0 +1,78 @@
+// -*- mode: groovy -*-
+
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+//
+// Jenkins pipeline
+// See documents at https://jenkins.io/doc/book/pipeline/jenkinsfile/
+
+// NOTE: 
+// ci_utils and cd_utils are loaded by the originating Jenkins job, e.g. jenkins/Jenkinsfile_release_job
+
+// Only post the following variants to PyPI.
+// This is a temporary solution until we are confident with the packages generated by CI
+// This should be removed in the not too distant future.
+// We only skip the publish step so we can still QA the other variants.
+pypi_releases = ["cu92", "cu92mkl"]
+
+def get_pipeline(mxnet_variant) {
+  def node_type = mxnet_variant.startsWith('cu') ? NODE_LINUX_GPU : NODE_LINUX_CPU
+  return cd_utils.generic_pipeline(mxnet_variant, this, node_type)
+}
+
+def get_environment(mxnet_variant) {
+  def environment = "ubuntu_cpu"
+  if (mxnet_variant.startsWith('cu')) {
+    environment = "ubuntu_gpu_${mxnet_variant}".replace("mkl", "")
+  }
+  return environment
+}
+
+def build(mxnet_variant) {
+  ws("workspace/python_pypi/${mxnet_variant}/${env.BUILD_NUMBER}") {
+    ci_utils.init_git()
+    cd_utils.restore_artifact(mxnet_variant, 'static')
+    
+    // create wheel file
+    def environment = get_environment(mxnet_variant)
+    def nvidia_docker = mxnet_variant.startsWith('cu')
+    ci_utils.docker_run(environment, "cd_package_pypi ${mxnet_variant}", nvidia_docker, '500m', "RELEASE_BUILD='${env.RELEASE_BUILD}'")
+  }
+}
+
+def test(mxnet_variant) {
+  ws("workspace/python_pypi/${mxnet_variant}/${env.BUILD_NUMBER}") {
+    // test wheel file
+    def environment = get_environment(mxnet_variant)
+    def nvidia_docker = mxnet_variant.startsWith('cu')
+    ci_utils.docker_run(environment, "cd_integration_test_pypi python ${nvidia_docker}", nvidia_docker)
+    ci_utils.docker_run(environment, "cd_integration_test_pypi python3 ${nvidia_docker}", nvidia_docker)
+  }
+}
+
+def push(mxnet_variant) {
+  ws("workspace/python_pypi/${mxnet_variant}/${env.BUILD_NUMBER}") {
+    // publish package to pypi
+    if (mxnet_variant in pypi_releases) {
+      sh "./ci/docker/runtime_functions.sh cd_pypi_publish"
+    } else {
+      echo "Temporarily skipping publishing PyPI package for '${mxnet_variant}'."
+    }
+  }
+}
+
+return this
diff --git a/cd/python/pypi/README.md b/cd/python/pypi/README.md
new file mode 100644
index 000000000000..4a17a00923c8
--- /dev/null
+++ b/cd/python/pypi/README.md
@@ -0,0 +1,43 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# PyPI CD Pipeline
+
+The Jenkins pipelines for continuous delivery of the PyPI MXNet packages.
+The pipelines for each variant are run, and fail, independently. Each depends
+on a successful build of the statically linked libmxet library.
+
+The pipeline relies on the scripts and resources located in [tools/pip](https://github.com/apache/incubator-mxnet/tree/master/tools/pip)
+to build the PyPI packages.
+
+## Credentials
+
+The pipeline depends on the following environment variables in order to successfully
+retrieve the credentials for the PyPI account:
+
+* CD_PYPI_SECRET_NAME
+* DOCKERHUB_SECRET_ENDPOINT_URL
+* DOCKERHUB_SECRET_ENDPOINT_REGION
+
+The credentials are stored in the Secrets Manager of the AWS account hosting Jenkins.
+The [pypi_publish.py](pypi_publish.sh) script is in charge of retrieving the credentials.
+
+## Mock publishing
+
+Because of space limitations on PyPI, we don't want to push test packages from Jenkins Dev
+everytime the pipeline is run. Therefore, the [pypi_publish.sh](pypi_publish.sh) 
+script will fake publishing packages if the `username` is *skipPublish*.
diff --git a/cd/python/pypi/pypi_package.sh b/cd/python/pypi/pypi_package.sh
new file mode 100755
index 000000000000..581bf694cb31
--- /dev/null
+++ b/cd/python/pypi/pypi_package.sh
@@ -0,0 +1,60 @@
+#!/usr/bin/env bash
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+set -ex
+
+# variant = cpu, mkl, cu80, cu80mkl, cu100, etc.
+export mxnet_variant=${1:?"Please specify the mxnet variant"}
+
+# Due to this PR: https://github.com/apache/incubator-mxnet/pull/14899
+# The setup.py expects that mkldnn_version.h be present in 
+# mxnet-build/3rdparty/mkldnn/build/install/include
+# The artifact repository stores this file in the dependencies
+# and CD unpacks it to a directory called cd_misc
+if [ -f "cd_misc/mkldnn_version.h" ]; then
+  mkdir -p 3rdparty/mkldnn/build/install/include
+  cp cd_misc/mkldnn_version.h 3rdparty/mkldnn/build/install/include/.
+fi
+
+# Create wheel workspace
+rm -rf wheel_build
+mkdir wheel_build
+cd wheel_build
+
+# Setup workspace
+# setup.py expects mxnet-build to be the
+# mxnet directory
+ln -s ../. mxnet-build
+
+# Copy the setup.py and other package resources
+cp -R ../tools/pip/* .
+
+# Remove comment lines from pip doc files
+pushd doc
+for file in $(ls); do
+  sed -i '/<!--/d' ${file}
+done
+popd
+
+echo "Building python package with environment:"
+printenv
+echo "-----------------------------------------"
+pip install --user pypandoc
+
+# Build wheel file - placed in wheel_build/dist
+python setup.py bdist_wheel
diff --git a/cd/python/pypi/pypi_publish.py b/cd/python/pypi/pypi_publish.py
new file mode 100755
index 000000000000..94d6e8703a52
--- /dev/null
+++ b/cd/python/pypi/pypi_publish.py
@@ -0,0 +1,87 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import json
+import logging
+import os
+import subprocess
+import sys
+
+import boto3
+from botocore.exceptions import ClientError
+
+
+def post_wheel(path):
+    """
+    Posts mxnet wheel file to PyPI
+    """
+    logging.info('Posting {} to PyPI'.format(path))
+    pypi_credentials = get_secret()
+
+    cmd = 'python3 -m twine upload --username {} --password {} {}'.format(
+        pypi_credentials['username'],
+        pypi_credentials['password'],
+        path)
+
+    # The PyPI credentials for DEV has username set to 'skipPublish'
+    # This way we do not attempt to publish the PyPI package
+    # Just print a helpful message
+    if pypi_credentials['username'] == 'skipPublish':
+        print('In DEV account, skipping publish')
+        print('Would have run: {}'.format(cmd))
+        return 0
+    else:
+        # DO NOT PRINT CMD IN THIS BLOCK, includes password
+        p = subprocess.run(cmd.split(' '),
+                        stdout=subprocess.PIPE)
+        logging.info(p.stdout)
+        return p.returncode
+
+def get_secret():
+    secret_name = os.environ['CD_PYPI_SECRET_NAME']
+    endpoint_url = os.environ['DOCKERHUB_SECRET_ENDPOINT_URL']
+    region_name = os.environ['DOCKERHUB_SECRET_ENDPOINT_REGION']
+
+    session = boto3.Session()
+    client = session.client(
+        service_name='secretsmanager',
+        region_name=region_name,
+        endpoint_url=endpoint_url
+    )
+
+    try:
+        get_secret_value_response = client.get_secret_value(SecretId=secret_name)
+    except ClientError as e:
+        if e.response['Error']['Code'] == 'DecryptionFailureException':
+            raise e
+        elif e.response['Error']['Code'] == 'InternalServiceErrorException':
+            raise e
+        elif e.response['Error']['Code'] == 'InvalidParameterException':
+            raise e
+        elif e.response['Error']['Code'] == 'InvalidRequestException':
+            raise e
+        elif e.response['Error']['Code'] == 'ResourceNotFoundException':
+            raise e
+    else:
+        return json.loads(get_secret_value_response['SecretString'])
+        
+            
+if __name__ == '__main__':
+    sys.exit(post_wheel(sys.argv[1]))
diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index 7d91d4e5d121..83b7304edb2c 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -1886,6 +1886,50 @@ build_static_libmxnet() {
     popd
 }
 
+# Packages libmxnet into wheel file
+cd_package_pypi() {
+    set -ex
+    pushd .
+    local mxnet_variant=${1:?"This function requires a python command as the first argument"}
+    ./cd/python/pypi/pypi_package.sh ${mxnet_variant}
+    popd
+}
+
+# Sanity checks wheel file 
+cd_integration_test_pypi() {
+    set -ex
+    local python_cmd=${1:?"This function requires a python command as the first argument"}
+    local gpu_enabled=${2:-"false"}
+
+    local test_conv_params=''
+    local mnist_params=''
+
+    local pip_cmd='pip'
+
+    if [ "${gpu_enabled}" = "true" ]; then
+        mnist_params="--gpu 0"
+        test_conv_params="--gpu"
+    fi
+
+    if [ "${python_cmd}" = "python3" ]; then
+        pip_cmd='pip3'
+    fi
+
+    # install mxnet wheel package
+    ${pip_cmd} install --user ./wheel_build/dist/*.whl
+
+    # execute tests
+    ${python_cmd} /work/mxnet/tests/python/train/test_conv.py ${test_conv_params}
+    ${python_cmd} /work/mxnet/example/image-classification/train_mnist.py ${mnist_params}
+}
+
+# Publishes wheel to PyPI
+cd_pypi_publish() {
+    set -ex
+    pip3 install --user twine
+    ./cd/python/pypi/pypi_publish.py `readlink -f wheel_build/dist/*.whl`
+}
+
 build_static_scala_mkl() {
     set -ex
     pushd .
diff --git a/tools/pip/doc/CPU_ADDITIONAL.md b/tools/pip/doc/CPU_ADDITIONAL.md
index 9f25121e5b8c..bbd747dbf557 100644
--- a/tools/pip/doc/CPU_ADDITIONAL.md
+++ b/tools/pip/doc/CPU_ADDITIONAL.md
@@ -18,10 +18,12 @@
 Prerequisites
 -------------
 This package supports Linux, Mac OSX, and Windows platforms. You may also want to check:
+- [mxnet-cu101](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support.
+- [mxnet-cu101mkl](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support and MKLDNN support.
+- [mxnet-cu100](https://pypi.python.org/pypi/mxnet-cu100mkl/) with CUDA-10.0 support.
+- [mxnet-cu100mkl](https://pypi.python.org/pypi/mxnet-cu100mkl/) with CUDA-10.0 support and MKLDNN support.
 - [mxnet-cu92](https://pypi.python.org/pypi/mxnet-cu92/) with CUDA-9.2 support.
 - [mxnet-cu92mkl](https://pypi.python.org/pypi/mxnet-cu92mkl/) with CUDA-9.2 support and MKLDNN support.
-- [mxnet-cu91](https://pypi.python.org/pypi/mxnet-cu91/) with CUDA-9.1 support.
-- [mxnet-cu91mkl](https://pypi.python.org/pypi/mxnet-cu91mkl/) with CUDA-9.1 support and MKLDNN support.
 - [mxnet-cu90](https://pypi.python.org/pypi/mxnet-cu90/) with CUDA-9.0 support.
 - [mxnet-cu90mkl](https://pypi.python.org/pypi/mxnet-cu90mkl/) with CUDA-9.0 support and MKLDNN support.
 - [mxnet-cu80](https://pypi.python.org/pypi/mxnet-cu80/) with CUDA-8.0 support.
diff --git a/tools/pip/doc/CU100MKL_ADDITIONAL.md b/tools/pip/doc/CU100MKL_ADDITIONAL.md
index a916f0b9583b..80690ea8b964 100644
--- a/tools/pip/doc/CU100MKL_ADDITIONAL.md
+++ b/tools/pip/doc/CU100MKL_ADDITIONAL.md
@@ -18,11 +18,11 @@
 Prerequisites
 -------------
 This package supports Linux and Windows platforms. You may also want to check:
+- [mxnet-cu101](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support.
+- [mxnet-cu101mkl](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support and MKLDNN support.
 - [mxnet-cu100](https://pypi.python.org/pypi/mxnet-cu100/) with CUDA-10.0 support.
 - [mxnet-cu92](https://pypi.python.org/pypi/mxnet-cu92/) with CUDA-9.2 support.
 - [mxnet-cu92mkl](https://pypi.python.org/pypi/mxnet-cu92mkl/) with CUDA-9.2 support and MKLDNN support.
-- [mxnet-cu91](https://pypi.python.org/pypi/mxnet-cu91/) with CUDA-9.1 support.
-- [mxnet-cu91mkl](https://pypi.python.org/pypi/mxnet-cu91mkl/) with CUDA-9.1 support and MKLDNN support.
 - [mxnet-cu90](https://pypi.python.org/pypi/mxnet-cu90/) with CUDA-9.0 support.
 - [mxnet-cu90mkl](https://pypi.python.org/pypi/mxnet-cu90mkl/) with CUDA-9.0 support and MKLDNN support.
 - [mxnet-cu80](https://pypi.python.org/pypi/mxnet-cu80/) with CUDA-8.0 support.
diff --git a/tools/pip/doc/CU100_ADDITIONAL.md b/tools/pip/doc/CU100_ADDITIONAL.md
index 1a1f4dd70ea2..de5e62051cc3 100644
--- a/tools/pip/doc/CU100_ADDITIONAL.md
+++ b/tools/pip/doc/CU100_ADDITIONAL.md
@@ -18,11 +18,11 @@
 Prerequisites
 -------------
 This package supports Linux and Windows platforms. You may also want to check:
+- [mxnet-cu101](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support.
+- [mxnet-cu101mkl](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support and MKLDNN support.
 - [mxnet-cu100mkl](https://pypi.python.org/pypi/mxnet-cu100mkl/) with CUDA-10.0 support and MKLDNN support.
 - [mxnet-cu92](https://pypi.python.org/pypi/mxnet-cu92/) with CUDA-9.2 support.
 - [mxnet-cu92mkl](https://pypi.python.org/pypi/mxnet-cu92mkl/) with CUDA-9.2 support and MKLDNN support.
-- [mxnet-cu91](https://pypi.python.org/pypi/mxnet-cu91/) with CUDA-9.1 support.
-- [mxnet-cu91mkl](https://pypi.python.org/pypi/mxnet-cu91mkl/) with CUDA-9.1 support and MKLDNN support.
 - [mxnet-cu90](https://pypi.python.org/pypi/mxnet-cu90/) with CUDA-9.0 support.
 - [mxnet-cu90mkl](https://pypi.python.org/pypi/mxnet-cu90mkl/) with CUDA-9.0 support and MKLDNN support.
 - [mxnet-cu80](https://pypi.python.org/pypi/mxnet-cu80/) with CUDA-8.0 support.
diff --git a/tools/pip/doc/CU91MKL_ADDITIONAL.md b/tools/pip/doc/CU101MKL_ADDITIONAL.md
similarity index 81%
rename from tools/pip/doc/CU91MKL_ADDITIONAL.md
rename to tools/pip/doc/CU101MKL_ADDITIONAL.md
index 8e82b5a18868..9cdebe3dddbb 100644
--- a/tools/pip/doc/CU91MKL_ADDITIONAL.md
+++ b/tools/pip/doc/CU101MKL_ADDITIONAL.md
@@ -17,10 +17,12 @@
 
 Prerequisites
 -------------
-This package supports Linux and Windows platforms, up to 1.2.1. You may also want to check:
+This package supports Linux and Windows platforms. You may also want to check:
+- [mxnet-cu101](https://pypi.python.org/pypi/mxnet-cu101/) with CUDA-10.1 support.
+- [mxnet-cu100](https://pypi.python.org/pypi/mxnet-cu100/) with CUDA-10.0 support.
+- [mxnet-cu100mkl](https://pypi.python.org/pypi/mxnet-cu100mkl/) with CUDA-10.0 support and MKLDNN support.
 - [mxnet-cu92](https://pypi.python.org/pypi/mxnet-cu92/) with CUDA-9.2 support.
 - [mxnet-cu92mkl](https://pypi.python.org/pypi/mxnet-cu92mkl/) with CUDA-9.2 support and MKLDNN support.
-- [mxnet-cu91](https://pypi.python.org/pypi/mxnet-cu91/) with CUDA-9.1 support.
 - [mxnet-cu90](https://pypi.python.org/pypi/mxnet-cu90/) with CUDA-9.0 support.
 - [mxnet-cu90mkl](https://pypi.python.org/pypi/mxnet-cu90mkl/) with CUDA-9.0 support and MKLDNN support.
 - [mxnet-cu80](https://pypi.python.org/pypi/mxnet-cu80/) with CUDA-8.0 support.
@@ -32,11 +34,11 @@ This package supports Linux and Windows platforms, up to 1.2.1. You may also wan
 
 To download CUDA, check [CUDA download](https://developer.nvidia.com/cuda-downloads). For more instructions, check [CUDA Toolkit online documentation](http://docs.nvidia.com/cuda/index.html).
 
-To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions of CUDA, check [Installing MXNet](https://mxnet.apache.org/versions/master/install/index.html) for instructions on building from source.
+To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions of CUDA, check [Installing MXNet](https://mxnet.incubator.apache.org/versions/master/install/index.html) for instructions on building from source.
 
 Installation
 ------------
 To install:
 ```bash
-pip install mxnet-cu91mkl
+pip install mxnet-cu100mkl
 ```
diff --git a/tools/pip/doc/CU91_ADDITIONAL.md b/tools/pip/doc/CU101_ADDITIONAL.md
similarity index 78%
rename from tools/pip/doc/CU91_ADDITIONAL.md
rename to tools/pip/doc/CU101_ADDITIONAL.md
index 5918a8901f99..f58413c09e72 100644
--- a/tools/pip/doc/CU91_ADDITIONAL.md
+++ b/tools/pip/doc/CU101_ADDITIONAL.md
@@ -17,10 +17,12 @@
 
 Prerequisites
 -------------
-This package supports Linux and Windows platforms, up to 1.2.1. You may also want to check:
+This package supports Linux and Windows platforms. You may also want to check:
+- [mxnet-cu101mkl](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support and MKLDNN support.
+- [mxnet-cu100](https://pypi.python.org/pypi/mxnet-cu100mkl/) with CUDA-10.0 support.
+- [mxnet-cu100mkl](https://pypi.python.org/pypi/mxnet-cu100mkl/) with CUDA-10.0 support and MKLDNN support.
 - [mxnet-cu92](https://pypi.python.org/pypi/mxnet-cu92/) with CUDA-9.2 support.
 - [mxnet-cu92mkl](https://pypi.python.org/pypi/mxnet-cu92mkl/) with CUDA-9.2 support and MKLDNN support.
-- [mxnet-cu91mkl](https://pypi.python.org/pypi/mxnet-cu91mkl/) with CUDA-9.1 support and MKLDNN support.
 - [mxnet-cu90](https://pypi.python.org/pypi/mxnet-cu90/) with CUDA-9.0 support.
 - [mxnet-cu90mkl](https://pypi.python.org/pypi/mxnet-cu90mkl/) with CUDA-9.0 support and MKLDNN support.
 - [mxnet-cu80](https://pypi.python.org/pypi/mxnet-cu80/) with CUDA-8.0 support.
@@ -32,11 +34,11 @@ This package supports Linux and Windows platforms, up to 1.2.1. You may also wan
 
 To download CUDA, check [CUDA download](https://developer.nvidia.com/cuda-downloads). For more instructions, check [CUDA Toolkit online documentation](http://docs.nvidia.com/cuda/index.html).
 
-To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions, check [Installing MXNet](https://mxnet.apache.org/versions/master/install/index.html) for instructions on building from source.
+To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions, check [Installing MXNet](https://mxnet.incubator.apache.org/versions/master/install/index.html) for instructions on building from source.
 
 Installation
 ------------
 To install:
 ```bash
-pip install mxnet-cu91
+pip install mxnet-cu100
 ```
diff --git a/tools/pip/doc/CU75MKL_ADDITIONAL.md b/tools/pip/doc/CU75MKL_ADDITIONAL.md
index 30b85fda0c2e..dc485d687f1e 100644
--- a/tools/pip/doc/CU75MKL_ADDITIONAL.md
+++ b/tools/pip/doc/CU75MKL_ADDITIONAL.md
@@ -18,10 +18,12 @@
 Prerequisites
 -------------
 This package supports Linux only, up to 1.2.1. You may also want to check:
+- [mxnet-cu101](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support.
+- [mxnet-cu101mkl](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support and MKLDNN support.
+- [mxnet-cu100](https://pypi.python.org/pypi/mxnet-cu100mkl/) with CUDA-10.0 support.
+- [mxnet-cu100mkl](https://pypi.python.org/pypi/mxnet-cu100mkl/) with CUDA-10.0 support and MKLDNN support.
 - [mxnet-cu92](https://pypi.python.org/pypi/mxnet-cu92/) with CUDA-9.2 support.
 - [mxnet-cu92mkl](https://pypi.python.org/pypi/mxnet-cu92mkl/) with CUDA-9.2 support and MKLDNN support.
-- [mxnet-cu91](https://pypi.python.org/pypi/mxnet-cu91/) with CUDA-9.1 support.
-- [mxnet-cu91mkl](https://pypi.python.org/pypi/mxnet-cu91mkl/) with CUDA-9.1 support and MKLDNN support.
 - [mxnet-cu90](https://pypi.python.org/pypi/mxnet-cu90/) with CUDA-9.0 support.
 - [mxnet-cu90mkl](https://pypi.python.org/pypi/mxnet-cu90mkl/) with CUDA-9.0 support and MKLDNN support.
 - [mxnet-cu80](https://pypi.python.org/pypi/mxnet-cu80/) with CUDA-8.0 support.
diff --git a/tools/pip/doc/CU75_ADDITIONAL.md b/tools/pip/doc/CU75_ADDITIONAL.md
index 9947365517e6..a5257913adc5 100644
--- a/tools/pip/doc/CU75_ADDITIONAL.md
+++ b/tools/pip/doc/CU75_ADDITIONAL.md
@@ -18,10 +18,12 @@
 Prerequisites
 -------------
 This package supports Linux only, up to 1.2.1. You may also want to check:
+- [mxnet-cu101](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support.
+- [mxnet-cu101mkl](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support and MKLDNN support.
+- [mxnet-cu100](https://pypi.python.org/pypi/mxnet-cu100mkl/) with CUDA-10.0 support.
+- [mxnet-cu100mkl](https://pypi.python.org/pypi/mxnet-cu100mkl/) with CUDA-10.0 support and MKLDNN support.
 - [mxnet-cu92](https://pypi.python.org/pypi/mxnet-cu92/) with CUDA-9.2 support.
 - [mxnet-cu92mkl](https://pypi.python.org/pypi/mxnet-cu92mkl/) with CUDA-9.2 support and MKLDNN support.
-- [mxnet-cu91](https://pypi.python.org/pypi/mxnet-cu91/) with CUDA-9.1 support.
-- [mxnet-cu91mkl](https://pypi.python.org/pypi/mxnet-cu91mkl/) with CUDA-9.1 support and MKLDNN support.
 - [mxnet-cu90](https://pypi.python.org/pypi/mxnet-cu90/) with CUDA-9.0 support.
 - [mxnet-cu90mkl](https://pypi.python.org/pypi/mxnet-cu90mkl/) with CUDA-9.0 support and MKLDNN support.
 - [mxnet-cu80](https://pypi.python.org/pypi/mxnet-cu80/) with CUDA-8.0 support.
diff --git a/tools/pip/doc/CU80MKL_ADDITIONAL.md b/tools/pip/doc/CU80MKL_ADDITIONAL.md
index 54c54536ee2b..24cec2b93668 100644
--- a/tools/pip/doc/CU80MKL_ADDITIONAL.md
+++ b/tools/pip/doc/CU80MKL_ADDITIONAL.md
@@ -18,10 +18,12 @@
 Prerequisites
 -------------
 This package supports Linux and Windows platforms. You may also want to check:
+- [mxnet-cu101](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support.
+- [mxnet-cu101mkl](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support and MKLDNN support.
+- [mxnet-cu100](https://pypi.python.org/pypi/mxnet-cu100mkl/) with CUDA-10.0 support.
+- [mxnet-cu100mkl](https://pypi.python.org/pypi/mxnet-cu100mkl/) with CUDA-10.0 support and MKLDNN support.
 - [mxnet-cu92](https://pypi.python.org/pypi/mxnet-cu92/) with CUDA-9.2 support.
 - [mxnet-cu92mkl](https://pypi.python.org/pypi/mxnet-cu92mkl/) with CUDA-9.2 support and MKLDNN support.
-- [mxnet-cu91](https://pypi.python.org/pypi/mxnet-cu91/) with CUDA-9.1 support.
-- [mxnet-cu91mkl](https://pypi.python.org/pypi/mxnet-cu91mkl/) with CUDA-9.1 support and MKLDNN support.
 - [mxnet-cu90](https://pypi.python.org/pypi/mxnet-cu90/) with CUDA-9.0 support.
 - [mxnet-cu90mkl](https://pypi.python.org/pypi/mxnet-cu90mkl/) with CUDA-9.0 support and MKLDNN support.
 - [mxnet-cu80](https://pypi.python.org/pypi/mxnet-cu80/) with CUDA-8.0 support.
diff --git a/tools/pip/doc/CU80_ADDITIONAL.md b/tools/pip/doc/CU80_ADDITIONAL.md
index 609567c72902..767f034e120e 100644
--- a/tools/pip/doc/CU80_ADDITIONAL.md
+++ b/tools/pip/doc/CU80_ADDITIONAL.md
@@ -18,10 +18,12 @@
 Prerequisites
 -------------
 This package supports Linux and Windows platforms. You may also want to check:
+- [mxnet-cu101](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support.
+- [mxnet-cu101mkl](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support and MKLDNN support.
+- [mxnet-cu100](https://pypi.python.org/pypi/mxnet-cu100mkl/) with CUDA-10.0 support.
+- [mxnet-cu100mkl](https://pypi.python.org/pypi/mxnet-cu100mkl/) with CUDA-10.0 support and MKLDNN support.
 - [mxnet-cu92](https://pypi.python.org/pypi/mxnet-cu92/) with CUDA-9.2 support.
 - [mxnet-cu92mkl](https://pypi.python.org/pypi/mxnet-cu92mkl/) with CUDA-9.2 support and MKLDNN support.
-- [mxnet-cu91](https://pypi.python.org/pypi/mxnet-cu91/) with CUDA-9.1 support.
-- [mxnet-cu91mkl](https://pypi.python.org/pypi/mxnet-cu91mkl/) with CUDA-9.1 support and MKLDNN support.
 - [mxnet-cu90](https://pypi.python.org/pypi/mxnet-cu90/) with CUDA-9.0 support.
 - [mxnet-cu90mkl](https://pypi.python.org/pypi/mxnet-cu90mkl/) with CUDA-9.0 support and MKLDNN support.
 - [mxnet-cu80mkl](https://pypi.python.org/pypi/mxnet-cu80mkl/) with CUDA-8.0 support and MKLDNN support.
diff --git a/tools/pip/doc/CU90MKL_ADDITIONAL.md b/tools/pip/doc/CU90MKL_ADDITIONAL.md
index 7fe04dae29ad..66f18d35ef73 100644
--- a/tools/pip/doc/CU90MKL_ADDITIONAL.md
+++ b/tools/pip/doc/CU90MKL_ADDITIONAL.md
@@ -18,10 +18,12 @@
 Prerequisites
 -------------
 This package supports Linux and Windows platforms. You may want to check:
+- [mxnet-cu101](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support.
+- [mxnet-cu101mkl](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support and MKLDNN support.
+- [mxnet-cu100](https://pypi.python.org/pypi/mxnet-cu100mkl/) with CUDA-10.0 support.
+- [mxnet-cu100mkl](https://pypi.python.org/pypi/mxnet-cu100mkl/) with CUDA-10.0 support and MKLDNN support.
 - [mxnet-cu92](https://pypi.python.org/pypi/mxnet-cu92/) with CUDA-9.2 support.
 - [mxnet-cu92mkl](https://pypi.python.org/pypi/mxnet-cu92mkl/) with CUDA-9.2 support and MKLDNN support.
-- [mxnet-cu91](https://pypi.python.org/pypi/mxnet-cu91/) with CUDA-9.1 support.
-- [mxnet-cu91mkl](https://pypi.python.org/pypi/mxnet-cu91mkl/) with CUDA-9.1 support and MKLDNN support.
 - [mxnet-cu90](https://pypi.python.org/pypi/mxnet-cu90/) with CUDA-9.0 support.
 - [mxnet-cu80](https://pypi.python.org/pypi/mxnet-cu80/) with CUDA-8.0 support.
 - [mxnet-cu80mkl](https://pypi.python.org/pypi/mxnet-cu80mkl/) with CUDA-8.0 support and MKLDNN support.
diff --git a/tools/pip/doc/CU90_ADDITIONAL.md b/tools/pip/doc/CU90_ADDITIONAL.md
index 37ab45a7dfbf..f21b77019d12 100644
--- a/tools/pip/doc/CU90_ADDITIONAL.md
+++ b/tools/pip/doc/CU90_ADDITIONAL.md
@@ -18,10 +18,12 @@
 Prerequisites
 -------------
 This package supports Linux and Windows platforms. You may also want to check:
+- [mxnet-cu101](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support.
+- [mxnet-cu101mkl](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support and MKLDNN support.
+- [mxnet-cu100](https://pypi.python.org/pypi/mxnet-cu100mkl/) with CUDA-10.0 support.
+- [mxnet-cu100mkl](https://pypi.python.org/pypi/mxnet-cu100mkl/) with CUDA-10.0 support and MKLDNN support.
 - [mxnet-cu92](https://pypi.python.org/pypi/mxnet-cu92/) with CUDA-9.2 support.
 - [mxnet-cu92mkl](https://pypi.python.org/pypi/mxnet-cu92mkl/) with CUDA-9.2 support and MKLDNN support.
-- [mxnet-cu91](https://pypi.python.org/pypi/mxnet-cu91/) with CUDA-9.1 support.
-- [mxnet-cu91mkl](https://pypi.python.org/pypi/mxnet-cu91mkl/) with CUDA-9.1 support and MKLDNN support.
 - [mxnet-cu90mkl](https://pypi.python.org/pypi/mxnet-cu90mkl/) with CUDA-9.0 support and MKLDNN support.
 - [mxnet-cu80](https://pypi.python.org/pypi/mxnet-cu80/) with CUDA-8.0 support.
 - [mxnet-cu80mkl](https://pypi.python.org/pypi/mxnet-cu80mkl/) with CUDA-8.0 support and MKLDNN support.
diff --git a/tools/pip/doc/CU92MKL_ADDITIONAL.md b/tools/pip/doc/CU92MKL_ADDITIONAL.md
index f7adbfcb61ca..a4e64f55bde9 100644
--- a/tools/pip/doc/CU92MKL_ADDITIONAL.md
+++ b/tools/pip/doc/CU92MKL_ADDITIONAL.md
@@ -18,9 +18,11 @@
 Prerequisites
 -------------
 This package supports Linux and Windows platforms. You may also want to check:
+- [mxnet-cu101](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support.
+- [mxnet-cu101mkl](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support and MKLDNN support.
+- [mxnet-cu100](https://pypi.python.org/pypi/mxnet-cu100mkl/) with CUDA-10.0 support.
+- [mxnet-cu100mkl](https://pypi.python.org/pypi/mxnet-cu100mkl/) with CUDA-10.0 support and MKLDNN support.
 - [mxnet-cu92](https://pypi.python.org/pypi/mxnet-cu92/) with CUDA-9.2 support.
-- [mxnet-cu91](https://pypi.python.org/pypi/mxnet-cu91/) with CUDA-9.1 support.
-- [mxnet-cu91mkl](https://pypi.python.org/pypi/mxnet-cu91mkl/) with CUDA-9.1 support and MKLDNN support.
 - [mxnet-cu90](https://pypi.python.org/pypi/mxnet-cu90/) with CUDA-9.0 support.
 - [mxnet-cu90mkl](https://pypi.python.org/pypi/mxnet-cu90mkl/) with CUDA-9.0 support and MKLDNN support.
 - [mxnet-cu80](https://pypi.python.org/pypi/mxnet-cu80/) with CUDA-8.0 support.
diff --git a/tools/pip/doc/CU92_ADDITIONAL.md b/tools/pip/doc/CU92_ADDITIONAL.md
index 79afccaec095..849245de72c9 100644
--- a/tools/pip/doc/CU92_ADDITIONAL.md
+++ b/tools/pip/doc/CU92_ADDITIONAL.md
@@ -18,9 +18,11 @@
 Prerequisites
 -------------
 This package supports Linux and Windows platforms. You may also want to check:
+- [mxnet-cu101](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support.
+- [mxnet-cu101mkl](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support and MKLDNN support.
+- [mxnet-cu100](https://pypi.python.org/pypi/mxnet-cu100mkl/) with CUDA-10.0 support.
+- [mxnet-cu100mkl](https://pypi.python.org/pypi/mxnet-cu100mkl/) with CUDA-10.0 support and MKLDNN support.
 - [mxnet-cu92mkl](https://pypi.python.org/pypi/mxnet-cu92mkl/) with CUDA-9.2 support and MKLDNN support.
-- [mxnet-cu91](https://pypi.python.org/pypi/mxnet-cu91/) with CUDA-9.1 support.
-- [mxnet-cu91mkl](https://pypi.python.org/pypi/mxnet-cu91mkl/) with CUDA-9.1 support and MKLDNN support.
 - [mxnet-cu90](https://pypi.python.org/pypi/mxnet-cu90/) with CUDA-9.0 support.
 - [mxnet-cu90mkl](https://pypi.python.org/pypi/mxnet-cu90mkl/) with CUDA-9.0 support and MKLDNN support.
 - [mxnet-cu80](https://pypi.python.org/pypi/mxnet-cu80/) with CUDA-8.0 support.
diff --git a/tools/pip/doc/MKL_ADDITIONAL.md b/tools/pip/doc/MKL_ADDITIONAL.md
index d796aca1b82c..d6236cf71ce7 100644
--- a/tools/pip/doc/MKL_ADDITIONAL.md
+++ b/tools/pip/doc/MKL_ADDITIONAL.md
@@ -18,10 +18,12 @@
 Prerequisites
 -------------
 This package supports Linux only. You may also want to check:
+- [mxnet-cu101](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support.
+- [mxnet-cu101mkl](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support and MKLDNN support.
+- [mxnet-cu100](https://pypi.python.org/pypi/mxnet-cu100mkl/) with CUDA-10.0 support.
+- [mxnet-cu100mkl](https://pypi.python.org/pypi/mxnet-cu100mkl/) with CUDA-10.0 support and MKLDNN support.
 - [mxnet-cu92](https://pypi.python.org/pypi/mxnet-cu92/) with CUDA-9.2 support.
 - [mxnet-cu92mkl](https://pypi.python.org/pypi/mxnet-cu92mkl/) with CUDA-9.2 support and MKLDNN support.
-- [mxnet-cu91](https://pypi.python.org/pypi/mxnet-cu91/) with CUDA-9.1 support.
-- [mxnet-cu91mkl](https://pypi.python.org/pypi/mxnet-cu91mkl/) with CUDA-9.1 support and MKLDNN support.
 - [mxnet-cu90](https://pypi.python.org/pypi/mxnet-cu90/) with CUDA-9.0 support.
 - [mxnet-cu90mkl](https://pypi.python.org/pypi/mxnet-cu90mkl/) with CUDA-9.0 support and MKLDNN support.
 - [mxnet-cu80](https://pypi.python.org/pypi/mxnet-cu80/) with CUDA-8.0 support.
diff --git a/tools/pip/setup.py b/tools/pip/setup.py
index f0a3e37fd3c3..82aa632d28ad 100644
--- a/tools/pip/setup.py
+++ b/tools/pip/setup.py
@@ -41,9 +41,19 @@
 
 LIB_PATH = libinfo['find_lib_path']()
 __version__ = libinfo['__version__']
-if 'TRAVIS_TAG' not in os.environ or not os.environ['TRAVIS_TAG'].strip():
+
+# set by the CD pipeline
+is_release = os.environ.get("IS_RELEASE", "").strip()
+
+# set by the travis build pipeline
+travis_tag = os.environ.get("TRAVIS_TAG", "").strip()
+
+# nightly build tag
+if not travis_tag and not is_release:
     __version__ += 'b{0}'.format(datetime.today().strftime('%Y%m%d'))
-elif 'TRAVIS_TAG' in os.environ and os.environ['TRAVIS_TAG'].startswith('patch-'):
+
+# patch build tag
+elif travis_tag.startswith('patch-'):
     __version__ = os.environ['TRAVIS_TAG'].split('-')[1]
 
 class BinaryDistribution(Distribution):

From 88521ff910b8547878c008b665ca4294e66f5cb9 Mon Sep 17 00:00:00 2001
From: Lanking <lanking520@live.com>
Date: Thu, 10 Oct 2019 15:51:19 -0700
Subject: [PATCH 424/813] upgrade the pytest version (#16429)

---
 ci/docker/install/ubuntu_onnx.sh | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ci/docker/install/ubuntu_onnx.sh b/ci/docker/install/ubuntu_onnx.sh
index 0dad3f9ee431..307028925b6e 100755
--- a/ci/docker/install/ubuntu_onnx.sh
+++ b/ci/docker/install/ubuntu_onnx.sh
@@ -31,5 +31,5 @@ apt-get update || true
 apt-get install -y libprotobuf-dev protobuf-compiler
 
 echo "Installing pytest, pytest-cov, protobuf, Pillow, ONNX and tabulate ..."
-pip2 install pytest==3.4.0 pytest-cov==2.5.1 protobuf==3.5.2 onnx==1.3.0 Pillow==5.0.0 tabulate==0.7.5
-pip3 install pytest==3.4.0 pytest-cov==2.5.1 protobuf==3.5.2 onnx==1.3.0 Pillow==5.0.0 tabulate==0.7.5
+pip2 install pytest==3.6.3 pytest-cov==2.5.1 protobuf==3.5.2 onnx==1.3.0 Pillow==5.0.0 tabulate==0.7.5
+pip3 install pytest==3.6.3 pytest-cov==2.5.1 protobuf==3.5.2 onnx==1.3.0 Pillow==5.0.0 tabulate==0.7.5

From 6ce323f2a14fccecf70381750c531e7a06a2de6f Mon Sep 17 00:00:00 2001
From: Sheng Zha <szha@users.noreply.github.com>
Date: Thu, 10 Oct 2019 15:52:38 -0700
Subject: [PATCH 425/813] [DOC] fix installation selector wrong history
 (#16381)

* fix installation selector wrong history

* updates to script

* nudge CI
---
 docs/static_site/src/assets/js/options.js | 47 +++++++++--------------
 1 file changed, 19 insertions(+), 28 deletions(-)

diff --git a/docs/static_site/src/assets/js/options.js b/docs/static_site/src/assets/js/options.js
index 59913a0c7ec7..ae955e469ec1 100644
--- a/docs/static_site/src/assets/js/options.js
+++ b/docs/static_site/src/assets/js/options.js
@@ -2,7 +2,7 @@
  * Licensed to the Apache Software Foundation (ASF) under one
  * or more contributor license agreements.  See the NOTICE file
  * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file 
+ * regarding copyright ownership.  The ASF licenses this file
  * to you under the Apache License, Version 2.0 (the
  * "License"); you may not use this file except in compliance
  * with the License.  You may obtain a copy of the License at
@@ -17,7 +17,9 @@
  * under the License.
  */
 
-/* Installation page display functions for install selector */
+/* Installation page display functions for install selector.
+   This utility allows direct links to specific install instructions.
+*/
 
 $(document).ready(function () {
     function label(lbl) {
@@ -27,13 +29,13 @@ $(document).ready(function () {
     }
 
     function urlSearchParams(searchString) {
-        let urlDict = new Map();
+        let searchDict = new Map();
         let searchParams = searchString.substring(1).split("&");
         searchParams.forEach(function (element) {
             kvPair = element.split("=");
-            urlDict.set(kvPair[0], kvPair[1]);
+            searchDict.set(kvPair[0], kvPair[1]);
         });
-        return urlDict;
+        return searchDict;
     }
 
     function is_a_match(elem, text) {
@@ -43,11 +45,10 @@ $(document).ready(function () {
         }
     }
 
-    function setSelects() {
-        let urlParams = urlSearchParams(window.location.search);
+    function setSelects(urlParams) {
         if (urlParams.get('version'))
             versionSelect = urlParams.get('version');
-        $('.current-version').html( versionSelect + ' <span class="caret"></span></button>' );
+        $('.current-version').html( versionSelect + ' <span class="caret"></span>' );
         if (urlParams.get('platform'))
             platformSelect = label(urlParams.get('platform'));
         if (urlParams.get('language'))
@@ -66,8 +67,9 @@ $(document).ready(function () {
         $('button.opt').each(function(){is_a_match($(this), environSelect)});
 
         showContent();
-        if (window.location.href.indexOf("/get_started/") >= 0) {
-            history.pushState(null, null, '?version=' + versionSelect + '&platform=' + platformSelect + '&language=' + languageSelect + '&environ=' + environSelect + '&processor=' + processorSelect);
+        let queryString = '?version=' + versionSelect + '&platform=' + platformSelect + '&language=' + languageSelect + '&environ=' + environSelect + '&processor=' + processorSelect
+        if (window.location.href.indexOf("/get_started") >= 0) {
+            history.pushState(null, null, queryString);
         }
     }
 
@@ -80,8 +82,7 @@ $(document).ready(function () {
         });
     }
 
-    showContent();
-    setSelects();
+    setSelects(urlSearchParams(window.location.search));
 
     function setContent() {
         var el = $(this);
@@ -90,27 +91,17 @@ $(document).ready(function () {
         el.addClass('active');
         if ($(this).hasClass("versions")) {
             $('.current-version').html($(this).text());
-            if (window.location.search.indexOf("version") < 0) {
-                if (window.location.search.length > 0) {
-                    var url = 'index.html' + window.location.search.concat('&version=' + $(this).text());
-                } else {
-                    var url = 'index.html?version=' + $(this).text();
-                }
-                history.pushState(null, null, url);
-            } else {
-                history.pushState(null, null, 'index.html' + window.location.search.replace(urlParams.get('version'), $(this).text()));
-            }
+            urlParams.set("version", $(this).text());
         } else if ($(this).hasClass("platforms")) {
-            history.pushState(null, null, 'index.html' + window.location.search.replace('='+urlParams.get('platform'), '='+label($(this).text())));
+            urlParams.set("platform", label($(this).text()));
         } else if ($(this).hasClass("languages")) {
-            history.pushState(null, null, 'index.html' + window.location.search.replace('='+urlParams.get('language'), '='+label($(this).text())));
+            urlParams.set("language", label($(this).text()));
         } else if ($(this).hasClass("processors")) {
-            history.pushState(null, null, 'index.html' + window.location.search.replace('='+urlParams.get('processor'), '='+label($(this).text())));
+            urlParams.set("processor", label($(this).text()));
         } else if ($(this).hasClass("environs")) {
-            history.pushState(null, null, 'index.html' + window.location.search.replace('='+urlParams.get('environ'), '='+label($(this).text())));
+            urlParams.set("environ", label($(this).text()));
         }
-
-        showContent();
+        setSelects(urlParams);
     }
 
     $('.opt-group').on('click', '.opt', setContent);

From 1ab4c95fd0e6ceccf718df96b4643762bb23227c Mon Sep 17 00:00:00 2001
From: Jerry Zhang <jerryzh.cn@gmail.com>
Date: Thu, 10 Oct 2019 16:26:29 -0700
Subject: [PATCH 426/813] New ops for RCNN + old ops improvements for RCNN
 (#16215)

* box encode and box decode

seems to work now

bug fix

use template to get rid of if statement

* roi align ignore batchid < 0

* amp multicast support casting to narrowest type

cpplint

cpplint

* add unittest

* address comments

* fix amp_multicast
---
 src/operator/contrib/bounding_box-inl.h       | 278 ++++++++++++++++++
 src/operator/contrib/bounding_box.cc          |  43 +++
 src/operator/contrib/bounding_box.cu          |   7 +
 src/operator/contrib/roi_align.cc             |  11 +-
 src/operator/contrib/roi_align.cu             |   6 +
 src/operator/tensor/amp_cast.h                |   9 +-
 .../python/unittest/test_contrib_operator.py  |  18 ++
 7 files changed, 368 insertions(+), 4 deletions(-)

diff --git a/src/operator/contrib/bounding_box-inl.h b/src/operator/contrib/bounding_box-inl.h
index 686f1666a310..010280e10852 100644
--- a/src/operator/contrib/bounding_box-inl.h
+++ b/src/operator/contrib/bounding_box-inl.h
@@ -787,6 +787,284 @@ void BipartiteMatchingBackward(const nnvm::NodeAttrs& attrs,
   });
 }
 
+
+inline bool BoxEncodeShape(const nnvm::NodeAttrs& attrs,
+                           mxnet::ShapeVector *in_attrs,
+                           mxnet::ShapeVector *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 6U);
+  CHECK_EQ(out_attrs->size(), 2U);
+  mxnet::TShape& sshape = (*in_attrs)[0];
+  mxnet::TShape& mshape = (*in_attrs)[1];
+  mxnet::TShape& ashape = (*in_attrs)[2];
+  mxnet::TShape& rshape = (*in_attrs)[3];
+
+  CHECK_EQ(sshape.ndim(), 2)
+    << "samples shape must have dim == 2, "
+    << sshape.ndim() << " provided";
+
+  CHECK_GE(mshape.ndim(), 2)
+    << "matches shape must have dim == 2, "
+    << mshape.ndim() << " provided";
+
+  CHECK_GE(ashape.ndim(), 3)
+    << "matches shape must have dim == 3, "
+    << ashape.ndim() << " provided";
+  int ldim = ashape[ashape.ndim() - 1];
+  CHECK_EQ(ldim, 4)
+    << "last dimension of anchors must be 4, "
+    << ldim << " provided";
+
+  CHECK_GE(rshape.ndim(), 3)
+    << "refs shape must have dim == 3, "
+    << ashape.ndim() << " provided";
+  ldim = rshape[rshape.ndim() - 1];
+  CHECK_EQ(ldim, 4)
+    << "last dimension of anchors must be 4, "
+    << ldim << " provided";
+
+  // asign input shape
+  SHAPE_ASSIGN_CHECK(*in_attrs, 4, mshadow::Shape1(4));
+  SHAPE_ASSIGN_CHECK(*in_attrs, 5, mshadow::Shape1(4));
+
+  // assign output shape
+  mxnet::TShape oshape = ashape;
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, oshape);
+  SHAPE_ASSIGN_CHECK(*out_attrs, 1, oshape);
+  return shape_is_known(oshape);
+}
+
+struct box_encode {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(index_t i, DType *out_targets, DType *out_masks,
+                                  const DType *samples, const DType *matches,
+                                  const DType *anchors, const DType *refs,
+                                  const DType *means, const DType *stds,
+                                  const int m, const int n) {
+    index_t j = i / n;
+    index_t match = matches[i];
+    // xmin: 0, ymin:1, xmax: 2, ymax: 3
+    // x:0, y:1, w:2, h:3
+    index_t ref_index = (j * m + match) * 4;
+    DType ref_xmin = refs[ref_index + 0];
+    DType ref_ymin = refs[ref_index + 1];
+    DType ref_width = refs[ref_index + 2] - ref_xmin;
+    DType ref_height = refs[ref_index + 3] - ref_ymin;
+    DType ref_x = ref_xmin + ref_width * 0.5;
+    DType ref_y = ref_ymin + ref_height * 0.5;
+    index_t a_index = i * 4;
+    DType a_xmin = anchors[a_index + 0];
+    DType a_ymin = anchors[a_index + 1];
+    DType a_width = anchors[a_index + 2] - a_xmin;
+    DType a_height = anchors[a_index + 3] - a_ymin;
+    DType a_x = a_xmin + a_width * 0.5;
+    DType a_y = a_ymin + a_height * 0.5;
+    DType valid = samples[i] > 0.5 ? 1.0 : 0.0;
+    out_masks[a_index + 0] = valid;
+    out_masks[a_index + 1] = valid;
+    out_masks[a_index + 2] = valid;
+    out_masks[a_index + 3] = valid;
+    out_targets[a_index + 0] = valid > static_cast<DType>(0.5) ?
+        ((ref_x - a_x) / a_width - static_cast<DType>(means[0])) /
+        static_cast<DType>(stds[0]) : static_cast<DType>(0.0);
+    out_targets[a_index + 1] = valid > static_cast<DType>(0.5) ?
+        ((ref_y - a_y) / a_height - static_cast<DType>(means[1])) /
+        static_cast<DType>(stds[1]) : static_cast<DType>(0.0);
+    out_targets[a_index + 2] = valid > static_cast<DType>(0.5) ?
+        (log(ref_width / a_width) - static_cast<DType>(means[2])) /
+        static_cast<DType>(stds[2]) : static_cast<DType>(0.0);
+    out_targets[a_index + 3] = valid > static_cast<DType>(0.5) ?
+        (log(ref_height / a_height) - static_cast<DType>(means[3])) /
+        static_cast<DType>(stds[3]) : static_cast<DType>(0.0);
+  }
+};
+
+template<typename xpu>
+void BoxEncodeForward(const nnvm::NodeAttrs& attrs,
+                      const OpContext& ctx,
+                      const std::vector<TBlob>& inputs,
+                      const std::vector<OpReqType>& req,
+                      const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mshadow::expr;
+  using namespace mxnet_op;
+  CHECK_EQ(inputs.size(), 6U);
+  CHECK_EQ(outputs.size(), 2U);
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  // samples, matches, anchors, refs, means, stds
+  mxnet::TShape anchor_shape = inputs[2].shape_;
+  int loop_size = anchor_shape.ProdShape(0, 2);
+  int b = anchor_shape[0];
+  int n = anchor_shape[1];
+  int m = inputs[3].shape_[1];
+  MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+    Tensor<xpu, 2, DType> samples = inputs[0]
+     .get_with_shape<xpu, 2, DType>(Shape2(b, n), s);
+    Tensor<xpu, 2, DType> matches = inputs[1]
+     .get_with_shape<xpu, 2, DType>(Shape2(b, n), s);
+    Tensor<xpu, 3, DType> anchors = inputs[2]
+     .get_with_shape<xpu, 3, DType>(Shape3(b, n, 4), s);
+    Tensor<xpu, 3, DType> refs = inputs[3]
+     .get_with_shape<xpu, 3, DType>(Shape3(b, m, 4), s);
+    Tensor<xpu, 1, DType> means = inputs[4]
+     .get_with_shape<xpu, 1, DType>(Shape1(4), s);
+    Tensor<xpu, 1, DType> stds = inputs[5]
+     .get_with_shape<xpu, 1, DType>(Shape1(4), s);
+    Tensor<xpu, 3, DType> out_targets = outputs[0]
+     .get_with_shape<xpu, 3, DType>(Shape3(b, n, 4), s);
+    Tensor<xpu, 3, DType> out_masks = outputs[1]
+     .get_with_shape<xpu, 3, DType>(Shape3(b, n, 4), s);
+
+    Kernel<box_encode, xpu>::Launch(s, loop_size, out_targets.dptr_,
+     out_masks.dptr_, samples.dptr_, matches.dptr_, anchors.dptr_,
+     refs.dptr_, means.dptr_, stds.dptr_, m, n);
+  });
+}
+
+struct BoxDecodeParam : public dmlc::Parameter<BoxDecodeParam> {
+  float std0;
+  float std1;
+  float std2;
+  float std3;
+  float clip;
+  int format;
+  DMLC_DECLARE_PARAMETER(BoxDecodeParam) {
+    DMLC_DECLARE_FIELD(std0).set_default(1.0)
+    .describe("value to be divided from the 1st encoded values");
+    DMLC_DECLARE_FIELD(std1).set_default(1.0)
+    .describe("value to be divided from the 2nd encoded values");
+    DMLC_DECLARE_FIELD(std2).set_default(1.0)
+    .describe("value to be divided from the 3rd encoded values");
+    DMLC_DECLARE_FIELD(std3).set_default(1.0)
+    .describe("value to be divided from the 4th encoded values");
+    DMLC_DECLARE_FIELD(clip).set_default(-1.0)
+    .describe("If larger than 0, bounding box target will be clipped to this value.");
+    DMLC_DECLARE_FIELD(format).set_default(box_common_enum::kCenter)
+    .add_enum("corner", box_common_enum::kCorner)
+    .add_enum("center", box_common_enum::kCenter)
+    .describe("The box encoding type. \n"
+              " \"corner\" means boxes are encoded as [xmin, ymin, xmax, ymax],"
+              " \"center\" means boxes are encodes as [x, y, width, height].");
+  }
+};  // BoxDecodeParam
+
+inline bool BoxDecodeShape(const nnvm::NodeAttrs& attrs,
+                           mxnet::ShapeVector *in_attrs,
+                           mxnet::ShapeVector *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 2U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  mxnet::TShape& dshape = (*in_attrs)[0];
+  mxnet::TShape& ashape = (*in_attrs)[1];
+
+  CHECK_EQ(dshape.ndim(), 3)
+    << "data shape must have dim == 3, "
+    << dshape.ndim() << " provided";
+  int ldim = dshape[dshape.ndim() - 1];
+  CHECK_EQ(ldim, 4)
+    << "last dimension of data must be 4, "
+    << ldim << " provided";
+
+  CHECK_GE(ashape.ndim(), 3)
+    << "anchors shape must have dim == 3, "
+    << ashape.ndim() << " provided";
+  ldim = ashape[ashape.ndim() - 1];
+  CHECK_EQ(ldim, 4)
+    << "last dimension of anchors must be 4, "
+    << ldim << " provided";
+
+  // assign output shape
+  mxnet::TShape oshape = dshape;
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, oshape);
+  return shape_is_known(oshape);
+}
+
+template<int anchor_encode, bool has_clip>
+struct box_decode {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(index_t i, DType *out, const DType *x,
+                                  const DType *anchors, const DType std0,
+                                  const DType std1, const DType std2,
+                                  const DType std3, const DType clip,
+                                  const int n) {
+    index_t index = i * 4;
+    index_t a_index = (i % n) * 4;
+    DType a_x = anchors[a_index + 0];
+    DType a_y = anchors[a_index + 1];
+    DType a_width = anchors[a_index + 2];
+    DType a_height = anchors[a_index + 3];
+    if (box_common_enum::kCorner == anchor_encode) {
+      // a_x = xmin, a_y = ymin, a_width = xmax, a_height = ymax
+      a_width = a_width - a_x;
+      a_height = a_height - a_y;
+      a_x = a_x + a_width * 0.5;
+      a_y = a_y + a_height * 0.5;
+    }
+    DType ox = x[index + 0] * std0 * a_width + a_x;
+    DType oy = x[index + 1] * std1 * a_height + a_y;
+    DType dw = x[index + 2] * std2;
+    DType dh = x[index + 3] * std3;
+    if (has_clip) {
+        dw = dw < clip ? dw : clip;
+        dh = dh < clip ? dh : clip;
+    }
+    dw = exp(dw);
+    dh = exp(dh);
+    DType ow = dw * a_width * 0.5;
+    DType oh = dh * a_height * 0.5;
+    out[index + 0] = ox - ow;
+    out[index + 1] = oy - oh;
+    out[index + 2] = ox + ow;
+    out[index + 3] = oy + oh;
+  }
+};
+
+template<typename xpu>
+void BoxDecodeForward(const nnvm::NodeAttrs& attrs,
+                      const OpContext& ctx,
+                      const std::vector<TBlob>& inputs,
+                      const std::vector<OpReqType>& req,
+                      const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mshadow::expr;
+  using namespace mxnet_op;
+  CHECK_EQ(inputs.size(), 2U);
+  CHECK_EQ(outputs.size(), 1U);
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  mxnet::TShape x_shape = inputs[0].shape_;
+  int b = x_shape[0];
+  int n = x_shape[1];
+  int loop_size = b * n;
+  const BoxDecodeParam& param = nnvm::get<BoxDecodeParam>(attrs.parsed);
+  MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+    Tensor<xpu, 3, DType> data = inputs[0]
+     .get_with_shape<xpu, 3, DType>(Shape3(b, n, 4), s);
+    Tensor<xpu, 3, DType> anchors = inputs[1]
+     .get_with_shape<xpu, 3, DType>(Shape3(1, n, 4), s);
+    Tensor<xpu, 3, DType> out = outputs[0]
+     .get_with_shape<xpu, 3, DType>(Shape3(b, n, 4), s);
+    if (box_common_enum::kCorner == param.format && param.clip > 0.0) {
+      Kernel<box_decode<box_common_enum::kCorner, true>, xpu>::Launch(s, loop_size,
+        out.dptr_, data.dptr_, anchors.dptr_, static_cast<DType>(param.std0),
+        static_cast<DType>(param.std1), static_cast<DType>(param.std2),
+        static_cast<DType>(param.std3), static_cast<DType>(param.clip), n);
+    } else if (box_common_enum::kCenter == param.format && param.clip > 0.0) {
+      Kernel<box_decode<box_common_enum::kCenter, true>, xpu>::Launch(s, loop_size,
+        out.dptr_, data.dptr_, anchors.dptr_, static_cast<DType>(param.std0),
+        static_cast<DType>(param.std1), static_cast<DType>(param.std2),
+        static_cast<DType>(param.std3), static_cast<DType>(param.clip), n);
+    } else if (box_common_enum::kCorner == param.format && param.clip <= 0.0) {
+      Kernel<box_decode<box_common_enum::kCorner, false>, xpu>::Launch(s, loop_size,
+        out.dptr_, data.dptr_, anchors.dptr_, static_cast<DType>(param.std0),
+        static_cast<DType>(param.std1), static_cast<DType>(param.std2),
+        static_cast<DType>(param.std3), static_cast<DType>(param.clip), n);
+    } else {
+      Kernel<box_decode<box_common_enum::kCenter, false>, xpu>::Launch(s, loop_size,
+        out.dptr_, data.dptr_, anchors.dptr_, static_cast<DType>(param.std0),
+        static_cast<DType>(param.std1), static_cast<DType>(param.std2),
+        static_cast<DType>(param.std3), static_cast<DType>(param.clip), n);
+    }
+  });
+}
+
 }  // namespace op
 }  // namespace mxnet
 
diff --git a/src/operator/contrib/bounding_box.cc b/src/operator/contrib/bounding_box.cc
index d682fafec092..62b7c2e0bf4b 100644
--- a/src/operator/contrib/bounding_box.cc
+++ b/src/operator/contrib/bounding_box.cc
@@ -32,6 +32,7 @@ namespace op {
 DMLC_REGISTER_PARAMETER(BoxNMSParam);
 DMLC_REGISTER_PARAMETER(BoxOverlapParam);
 DMLC_REGISTER_PARAMETER(BipartiteMatchingParam);
+DMLC_REGISTER_PARAMETER(BoxDecodeParam);
 
 NNVM_REGISTER_OP(_contrib_box_nms)
 .add_alias("_contrib_box_non_maximum_suppression")
@@ -201,5 +202,47 @@ NNVM_REGISTER_OP(_backward_contrib_bipartite_matching)
 .set_attr<FCompute>("FCompute<cpu>", BipartiteMatchingBackward<cpu>)
 .add_arguments(BipartiteMatchingParam::__FIELDS__());
 
+NNVM_REGISTER_OP(_contrib_box_encode)
+.describe(R"doc(Encode bounding boxes training target with normalized center offsets.
+    Input bounding boxes are using corner type: `x_{min}, y_{min}, x_{max}, y_{max}`.) array
+)doc" ADD_FILELINE)
+.set_num_inputs(6)
+.set_num_outputs(2)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"samples", "matches", "anchors", "refs", "means", "stds"};
+  })
+.set_attr<mxnet::FInferShape>("FInferShape", BoxEncodeShape)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<6, 2>)
+.set_attr<FCompute>("FCompute<cpu>", BoxEncodeForward<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.add_argument("samples", "NDArray-or-Symbol", "(B, N) value +1 (positive), -1 (negative), "
+              "0 (ignore)")
+.add_argument("matches", "NDArray-or-Symbol", "(B, N) value range [0, M)")
+.add_argument("anchors", "NDArray-or-Symbol", "(B, N, 4) encoded in corner")
+.add_argument("refs", "NDArray-or-Symbol", "(B, M, 4) encoded in corner")
+.add_argument("means", "NDArray-or-Symbol", "(4,) Mean value to be subtracted from encoded values")
+.add_argument("stds", "NDArray-or-Symbol", "(4,) Std value to be divided from encoded values");
+
+NNVM_REGISTER_OP(_contrib_box_decode)
+.describe(R"doc(Decode bounding boxes training target with normalized center offsets.
+    Input bounding boxes are using corner type: `x_{min}, y_{min}, x_{max}, y_{max}`
+    or center type: `x, y, width, height.) array
+)doc" ADD_FILELINE)
+.set_num_inputs(2)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<BoxDecodeParam>)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"data", "anchors"};
+  })
+.set_attr<mxnet::FInferShape>("FInferShape", BoxDecodeShape)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<2, 1>)
+.set_attr<FCompute>("FCompute<cpu>", BoxDecodeForward<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.add_argument("data", "NDArray-or-Symbol", "(B, N, 4) predicted bbox offset")
+.add_argument("anchors", "NDArray-or-Symbol", "(1, N, 4) encoded in corner or center")
+.add_arguments(BoxDecodeParam::__FIELDS__());
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/contrib/bounding_box.cu b/src/operator/contrib/bounding_box.cu
index 2677d2f79478..b20c570ea417 100644
--- a/src/operator/contrib/bounding_box.cu
+++ b/src/operator/contrib/bounding_box.cu
@@ -47,5 +47,12 @@ NNVM_REGISTER_OP(_contrib_bipartite_matching)
 
 NNVM_REGISTER_OP(_backward_contrib_bipartite_matching)
 .set_attr<FCompute>("FCompute<gpu>", BipartiteMatchingBackward<gpu>);
+
+NNVM_REGISTER_OP(_contrib_box_encode)
+.set_attr<FCompute>("FCompute<gpu>", BoxEncodeForward<gpu>);
+
+NNVM_REGISTER_OP(_contrib_box_decode)
+.set_attr<FCompute>("FCompute<gpu>", BoxDecodeForward<gpu>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/contrib/roi_align.cc b/src/operator/contrib/roi_align.cc
index 53ddba02bc7b..ee91561a6818 100644
--- a/src/operator/contrib/roi_align.cc
+++ b/src/operator/contrib/roi_align.cc
@@ -167,6 +167,10 @@ num_threads(engine::OpenMP::Get()->GetRecommendedOMPThreadCount())
     int roi_batch_ind = 0;
     if (roi_cols == 5) {
       roi_batch_ind = offset_bottom_rois[0];
+      if (roi_batch_ind < 0) {
+        top_data[n] = 0;
+        continue;
+      }
       offset_bottom_rois++;
     }
 
@@ -340,6 +344,7 @@ void ROIAlignBackward(
     int roi_batch_ind = 0;
     if (rois_cols == 5) {
       roi_batch_ind = offset_bottom_rois[0];
+      if (roi_batch_ind < 0) continue;
       offset_bottom_rois++;
     }
 
@@ -520,7 +525,8 @@ NNVM_REGISTER_OP(_contrib_ROIAlign)
 .describe(R"code(
 This operator takes a 4D feature map as an input array and region proposals as `rois`,
 then align the feature map over sub-regions of input and produces a fixed-sized output array.
-This operator is typically used in Faster R-CNN & Mask R-CNN networks.
+This operator is typically used in Faster R-CNN & Mask R-CNN networks. If roi batchid is less 
+than 0, it will be ignored, and the corresponding output will be set to 0.
 
 Different from ROI pooling, ROI Align removes the harsh quantization, properly aligning
 the extracted features with the input. RoIAlign computes the value of each sampling point
@@ -594,7 +600,8 @@ He, Kaiming, et al. "Mask R-CNN." ICCV, 2017
     return MakeGradNode("_backward_ROIAlign", n, heads, n->attrs.dict);
   })
 .add_argument("data", "NDArray-or-Symbol", "Input data to the pooling operator, a 4D Feature maps")
-.add_argument("rois", "NDArray-or-Symbol", "Bounding box coordinates, a 2D array")
+.add_argument("rois", "NDArray-or-Symbol", "Bounding box coordinates, a 2D array, "
+              "if batchid is less than 0, it will be ignored.")
 .add_arguments(ROIAlignParam::__FIELDS__());
 
 
diff --git a/src/operator/contrib/roi_align.cu b/src/operator/contrib/roi_align.cu
index 7099dd1a3991..a0fd6f93686c 100644
--- a/src/operator/contrib/roi_align.cu
+++ b/src/operator/contrib/roi_align.cu
@@ -127,6 +127,11 @@ __global__ void RoIAlignForwardKernel(
     const T* offset_bottom_rois = bottom_rois + n * 5;
     int roi_batch_ind = offset_bottom_rois[0];
 
+    if (roi_batch_ind < 0) {
+      top_data[index] = 0.;
+      continue;
+    }
+
     // Do not using rounding; this implementation detail is critical
     T roi_start_w = offset_bottom_rois[1] * spatial_scale;
     T roi_start_h = offset_bottom_rois[2] * spatial_scale;
@@ -265,6 +270,7 @@ __global__ void RoIAlignBackwardKernel(
 
     const T* offset_bottom_rois = bottom_rois + n * 5;
     int roi_batch_ind = offset_bottom_rois[0];
+    if (roi_batch_ind < 0) continue;
 
     // Do not using rounding; this implementation detail is critical
     T roi_start_w = offset_bottom_rois[1] * spatial_scale;
diff --git a/src/operator/tensor/amp_cast.h b/src/operator/tensor/amp_cast.h
index a722b417c715..be7d400ca153 100644
--- a/src/operator/tensor/amp_cast.h
+++ b/src/operator/tensor/amp_cast.h
@@ -48,10 +48,13 @@ struct AMPCastParam : public dmlc::Parameter<AMPCastParam> {
 
 struct AMPMultiCastParam : public dmlc::Parameter<AMPMultiCastParam> {
   int num_outputs;
+  bool cast_narrow;
 
   DMLC_DECLARE_PARAMETER(AMPMultiCastParam) {
     DMLC_DECLARE_FIELD(num_outputs)
     .describe("Number of input/output pairs to be casted to the widest type.");
+    DMLC_DECLARE_FIELD(cast_narrow).set_default(false)
+    .describe("Whether to cast to the narrowest type");
   }
 };
 
@@ -80,10 +83,12 @@ inline bool AMPMultiCastType(const nnvm::NodeAttrs& attrs,
   CHECK_EQ(in_attrs->size(), param.num_outputs);
   CHECK_EQ(out_attrs->size(), param.num_outputs);
   bool ret = true;
-  int widest_type = kFloat16;
+  int widest_type = param.cast_narrow ? kFloat32 : kFloat16;
   for (int i = 0; i < param.num_outputs; ++i) {
-    if ((*in_attrs)[i] == kFloat32 || (*out_attrs)[i] == kFloat32) {
+    if (!param.cast_narrow && ((*in_attrs)[i] == kFloat32 || (*out_attrs)[i] == kFloat32)) {
       widest_type = kFloat32;
+    } else if (param.cast_narrow &&((*in_attrs)[i] == kFloat16 || (*out_attrs)[i] == kFloat16)) {
+      widest_type = kFloat16;
     }
   }
   for (int i = 0; i < param.num_outputs; ++i) {
diff --git a/tests/python/unittest/test_contrib_operator.py b/tests/python/unittest/test_contrib_operator.py
index 813a6b092b9c..43ba9d8b6318 100644
--- a/tests/python/unittest/test_contrib_operator.py
+++ b/tests/python/unittest/test_contrib_operator.py
@@ -333,6 +333,24 @@ def test_multibox_prior_op():
     boxes = Y.reshape((h, w, 5, 4))
     assert_allclose(boxes.asnumpy()[250, 250, 0, :], np.array([-0.948249,  0.362671,  1.636436,  0.530377]), atol=1e-5, rtol=1e-5)
 
+def test_box_encode_op():
+    anchors = mx.nd.array([[0.1, 0.2, 0.3, 0.4], [0.5, 0.6, 0.7, 0.8]]).reshape((1, -1, 4))
+    refs = mx.nd.array([[0.1, 0.2, 0.3, 0.4], [0.5, 0.6, 0.7, 0.8]]).reshape((1, -1, 4))
+    samples = mx.nd.array([[0, 1]])
+    matches = mx.nd.array([[0, 1]])
+    means = mx.nd.array([0.0, 0.0, 0.0, 0.0])
+    stds = mx.nd.array([0.1, 0.1, 0.2, 0.2])
+    Y, mask = mx.nd.contrib.box_encode(samples, matches, anchors, refs, means, stds)
+    assert_allclose(Y.asnumpy(), np.zeros((1, 2, 4)), atol=1e-5, rtol=1e-5)
+    assert_allclose(mask.asnumpy(), np.array([[[0., 0., 0., 0.], [1., 1., 1., 1.]]]), atol=1e-5, rtol=1e-5)
+
+def test_box_decode_op():
+    data = mx.nd.array([[0.1, 0.2, 0.3, 0.4], [0.5, 0.6, 0.7, 0.8]]).reshape((1, -1, 4))
+    anchors = mx.nd.array([[0.1, 0.2, 0.3, 0.4], [0.5, 0.6, 0.7, 0.8]]).reshape((1, -1, 4))
+    Y = mx.nd.contrib.box_decode(data, anchors, .1, .1, .2, .2)
+    assert_allclose(Y.asnumpy(), np.array([[[-0.0562755, -0.00865743, 0.26227552, 0.42465743], \
+        [0.13240421, 0.17859563, 0.93759584, 1.1174043 ]]]), atol=1e-5, rtol=1e-5)
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From e484f721d338e6944f5cfefeb24ce9943ae6aa6f Mon Sep 17 00:00:00 2001
From: Aaron Markham <markhama@amazon.com>
Date: Thu, 10 Oct 2019 16:33:34 -0700
Subject: [PATCH 427/813] Beta build (#16411)

* remove unused jenkinsfile

* add a pipeline that publishes to staging beta site

* add staging site tar file generation; fix outputs
---
 ci/docker/runtime_functions.sh                |  4 +-
 ci/jenkins/Jenkins_steps.groovy               | 43 ++++++++++++++++++-
 ...sfile_website => Jenkinsfile_website_beta} | 23 ++++++++--
 3 files changed, 62 insertions(+), 8 deletions(-)
 rename ci/jenkins/{Jenkinsfile_website => Jenkinsfile_website_beta} (67%)

diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index 83b7304edb2c..ef496dfe8168 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -1832,12 +1832,12 @@ build_docs() {
     popd
 }
 
-build_docs_small() {
+build_docs_beta() {
     pushd docs/_build
     tar -xzf jekyll-artifacts.tgz
     api_folder='html/api'
     mkdir -p $api_folder/python/docs && tar -xzf python-artifacts.tgz --directory $api_folder/python/docs
-    # The folder to be published is now in /docs/_build/html
+    GZIP=-9 tar -zcvf beta_website.tgz -C html .
     popd
 }
 
diff --git a/ci/jenkins/Jenkins_steps.groovy b/ci/jenkins/Jenkins_steps.groovy
index 30db32252e66..7473a08071bf 100644
--- a/ci/jenkins/Jenkins_steps.groovy
+++ b/ci/jenkins/Jenkins_steps.groovy
@@ -1416,11 +1416,11 @@ def should_pack_website() {
       return true
     }
   } else {
-    return true 
+    return true
   }
   return false
 }
-  
+
 // Each of the docs_{lang} functions will build the docs...
 // Stashing is only needed for master for website publishing or for testing "new_"
 
@@ -1604,6 +1604,27 @@ def docs_prepare() {
 }
 
 
+def docs_prepare_beta() {
+    return ['Prepare for publication to the staging website': {
+      node(NODE_LINUX_CPU) {
+        ws('workspace/docs') {
+          timeout(time: max_time, unit: 'MINUTES') {
+            utils.init_git()
+
+            unstash 'jekyll-artifacts'
+            unstash 'python-artifacts'
+
+            utils.docker_run('ubuntu_cpu_jekyll', 'build_docs_beta', false)
+
+            // archive so the publish pipeline can access the artifact
+            archiveArtifacts 'docs/_build/beta_website.tgz'
+          }
+        }
+      }
+    }]
+}
+
+
 def docs_archive() {
     return ['Archive the full website': {
       node(NODE_LINUX_CPU) {
@@ -1639,6 +1660,24 @@ def docs_publish() {
 }
 
 
+// This is for the beta website
+def docs_publish_beta() {
+    return ['Publish the beta website to staging': {
+      node(NODE_LINUX_CPU) {
+        ws('workspace/docs') {
+          timeout(time: max_time, unit: 'MINUTES') {
+            try {
+              build 'restricted-website-publish-master-beta'
+            }
+            catch (Exception e) {
+               println(e.getMessage())
+            }
+          }
+        }
+      }
+    }]
+}
+
 
 def misc_asan_cpu() {
     return ['CPU ASAN': {
diff --git a/ci/jenkins/Jenkinsfile_website b/ci/jenkins/Jenkinsfile_website_beta
similarity index 67%
rename from ci/jenkins/Jenkinsfile_website
rename to ci/jenkins/Jenkinsfile_website_beta
index f6c853046b1c..9220052e9f0f 100644
--- a/ci/jenkins/Jenkinsfile_website
+++ b/ci/jenkins/Jenkinsfile_website_beta
@@ -19,24 +19,39 @@
 //
 // Jenkins pipeline
 // See documents at https://jenkins.io/doc/book/pipeline/jenkinsfile/
+// This pipeline will publish to https://mxnet-beta.staged.apache.org/
 
 // timeout in minutes
 max_time = 180
 
-node('utility') {
+node('restricted-utility') {
   // Loading the utilities requires a node context unfortunately
   checkout scm
   utils = load('ci/Jenkinsfile_utils.groovy')
   custom_steps = load('ci/jenkins/Jenkins_steps.groovy')
 }
-utils.assign_node_labels(utility: 'utility', linux_cpu: 'mxnetlinux-cpu')
+
+utils.assign_node_labels(utility: 'restricted-utility', linux_cpu: 'restricted-mxnetlinux-cpu', linux_gpu: 'restricted-mxnetlinux-gpu', linux_gpu_p3: 'restricted-mxnetlinux-gpu-p3', windows_cpu: 'restricted-mxnetwindows-cpu', windows_gpu: 'restricted-mxnetwindows-gpu')
 
 utils.main_wrapper(
 core_logic: {
-  utils.parallel_stage('Placeholder', [
-    // Do nothing
+  utils.parallel_stage('Build', [
+    custom_steps.compile_unix_lite()
+  ])
+
+  utils.parallel_stage('Build Docs', [
+    // Only building a subset of the docs for previewing on staging
+    custom_steps.docs_jekyll(),
+    custom_steps.docs_python()
   ])
 
+  utils.parallel_stage('Prepare', [
+    custom_steps.docs_prepare_beta()
+  ])
+
+  utils.parallel_stage('Publish', [
+    custom_steps.docs_publish_beta()
+  ])
 }
 ,
 failure_handler: {

From 243ade93bcb8b7962d1faeb89c98409e3ae0d7a4 Mon Sep 17 00:00:00 2001
From: Soji Adeshina <adeshina@berkeley.edu>
Date: Thu, 10 Oct 2019 16:48:13 -0700
Subject: [PATCH 428/813] [WIP] Improving Python Docs API (#16392)

* doc fixes for gluon api sub modules only

* update to the docs

* adding the autodoc js plugin

* adding autodoc javascript, fixing some warnings

* fixing model zoo

* python api docs major refactor -> ndarray, symbol and other mxnet libraries

* restructure top level toctree navs and bubble more submodules to the top

* modify import to reference defined classes and functions directly

* fix some broken links

* add extra classes imported from c++

* fix .htaccess redirects since api links have changed again

* included license on files missing license

* Update monitor.py
---
 docs/python_docs/README.md                    | 116 ++++-
 docs/python_docs/_static/autodoc.js           |  49 ++
 docs/python_docs/_static/mxnet.css            |   7 +
 docs/python_docs/environment.yml              |   1 +
 docs/python_docs/python/Makefile              |   3 +-
 docs/python_docs/python/README.md             | 130 -----
 .../python_docs/python/api/advanced/index.rst |  74 ---
 .../api/advanced/mxnet.executor_manager.rst   |  38 --
 .../python/api/advanced/mxnet.rtc.rst         |  36 --
 .../python/api/advanced/mxnet.test_utils.rst  |  91 ----
 .../python_docs/python/api/autograd/index.rst |  23 +
 .../python/api/contrib/autograd/index.rst     |  23 +
 docs/python_docs/python/api/contrib/index.rst |  88 ++++
 .../python/api/contrib/io/index.rst           |  23 +
 .../python/api/contrib/ndarray/index.rst      |  23 +
 .../python/api/contrib/onnx/index.rst         |  24 +
 .../python/api/contrib/quantization/index.rst |  23 +
 .../python/api/contrib/symbol/index.rst       |  23 +
 .../python/api/contrib/tensorboard/index.rst  |  23 +
 .../python/api/contrib/tensorrt/index.rst     |  23 +
 .../python/api/contrib/text/index.rst         |  23 +
 .../python/api/gluon-related/index.rst        | 111 -----
 .../api/gluon-related/mxnet.autograd.rst      |  38 --
 .../python/api/gluon-related/mxnet.image.rst  |  99 ----
 .../api/gluon-related/mxnet.initializer.rst   |  58 ---
 .../python/api/gluon-related/mxnet.io.rst     |  52 --
 .../gluon-related/mxnet.kvstore.KVStore.rst   |  61 ---
 .../api/gluon-related/mxnet.lr_scheduler.rst  |  31 --
 .../python/api/gluon-related/mxnet.metric.rst |  57 ---
 .../api/gluon-related/mxnet.optimizer.rst     |  55 ---
 .../api/gluon-related/mxnet.profiler.rst      |  54 --
 .../api/gluon-related/mxnet.recordio.rst      |  43 --
 docs/python_docs/python/api/gluon/block.rst   |  25 +
 .../python_docs/python/api/gluon/constant.rst |  25 +
 .../index.rst}                                |  17 +-
 .../python/api/gluon/hybrid_block.rst         |  25 +
 docs/python_docs/python/api/gluon/index.rst   |  45 +-
 .../loss/index.rst}                           |  25 +-
 .../index.rst}                                |  30 +-
 .../api/gluon/mxnet.gluon.HybridBlock.rst     |  40 --
 .../api/gluon/mxnet.gluon.ParameterDict.rst   |  79 ---
 .../python/api/gluon/mxnet.gluon.Trainer.rst  |  51 --
 .../python/api/gluon/mxnet.gluon.data.rst     |  50 --
 .../api/gluon/mxnet.gluon.data.vision.rst     |  58 ---
 .../python/api/gluon/mxnet.gluon.loss.rst     |  40 --
 .../python/api/gluon/mxnet.gluon.nn.Block.rst |  86 ----
 .../api/gluon/mxnet.gluon.nn.HybridBlock.rst  |  66 ---
 .../api/gluon/mxnet.gluon.nn.SymbolBlock.rst  |  67 ---
 .../api/gluon/mxnet.gluon.parameter.rst       |  68 ---
 .../python/api/gluon/mxnet.gluon.utils.rst    |  31 --
 .../python/api/gluon/{nn.rst => nn/index.rst} |  40 +-
 .../python/api/gluon/parameter.rst            |  25 +
 .../python/api/gluon/parameter_dict.rst       |  25 +
 .../api/gluon/{rnn.rst => rnn/index.rst}      |  24 +-
 .../python/api/gluon/symbol_block.rst         |  25 +
 docs/python_docs/python/api/gluon/trainer.rst |  25 +
 .../python/api/gluon/utils/index.rst          |  24 +
 docs/python_docs/python/api/index.rst         | 188 ++++++-
 .../python/api/initializer/index.rst          |  23 +
 .../mxnet.kvstore.rst => kvstore/index.rst}   |   8 +-
 .../python/api/lr_scheduler/index.rst         |  23 +
 docs/python_docs/python/api/metric/index.rst  |  23 +
 docs/python_docs/python/api/module/index.rst  |  24 +
 .../python/api/mxnet/attribute/index.rst      |  23 +
 .../base/index.rst}                           |  10 +-
 .../python/api/mxnet/callback/index.rst       |  23 +
 .../context/index.rst}                        |  14 +-
 .../engine/index.rst}                         |  15 +-
 .../executor/index.rst}                       |  17 +-
 .../api/mxnet/executor_manager/index.rst      |  23 +
 .../image/index.rst}                          |  14 +-
 .../api/{symbol-related => mxnet}/index.rst   |  70 ++-
 .../python_docs/python/api/mxnet/io/index.rst |  24 +
 .../python/api/mxnet/kvstore_server/index.rst |  23 +
 .../python/api/mxnet/libinfo/index.rst        |  24 +
 .../monitor/index.rst}                        |  16 +-
 .../python/api/mxnet/name/index.rst           |  23 +
 .../python/api/mxnet/notebook/index.rst       |  23 +
 .../python/api/mxnet/operator/index.rst       |  23 +
 .../python/api/mxnet/profiler/index.rst       |  23 +
 .../random/index.rst}                         |   7 +-
 .../python/api/mxnet/recordio/index.rst       |  23 +
 .../python/api/mxnet/registry/index.rst       |  23 +
 .../rtc/index.rst}                            |  10 +-
 .../python/api/mxnet/test_utils/index.rst     |  23 +
 .../python/api/mxnet/torch/index.rst          |  23 +
 .../mxnet.util.rst => mxnet/util/index.rst}   |  12 +-
 .../visualization/index.rst}                  |  16 +-
 .../python/api/ndarray/contrib/index.rst      |  23 +
 .../python/api/ndarray/image/index.rst        |  23 +
 docs/python_docs/python/api/ndarray/index.rst |  31 +-
 .../python/api/ndarray/linalg/index.rst       |  23 +
 .../api/ndarray/mxnet.ndarray.NDArray.rst     | 310 ------------
 .../mxnet.ndarray.sparse.CSRNDArray.rst       | 203 --------
 .../mxnet.ndarray.sparse.RowSparseNDArray.rst | 183 -------
 .../python/api/ndarray/ndarray.rst            |  26 +
 .../python/api/ndarray/op/index.rst           |  23 +
 .../python/api/ndarray/random/index.rst       |  23 +
 .../python/api/ndarray/register/index.rst     |  23 +
 .../python/api/ndarray/routines.rst           | 461 ------------------
 .../python/api/ndarray/sparse/index.rst       |  23 +
 .../python/api/ndarray/sparse_routines.rst    | 200 --------
 .../python/api/ndarray/utils/index.rst        |  23 +
 .../python/api/optimizer/index.rst            |  23 +
 .../api/symbol-related/mxnet.callback.rst     |  45 --
 .../python/api/symbol-related/mxnet.model.rst |  45 --
 .../api/symbol-related/mxnet.module.rst       |  35 --
 .../python/api/symbol/contrib/index.rst       |  23 +
 .../python/api/symbol/image/index.rst         |  23 +
 docs/python_docs/python/api/symbol/index.rst  |  12 +-
 .../python/api/symbol/linalg/index.rst        |  23 +
 .../python/api/symbol/mxnet.symbol.Symbol.rst | 335 -------------
 .../python/api/symbol/mxnet.symbol.linalg.rst |  49 --
 .../python/api/symbol/op/index.rst            |  23 +
 .../python/api/symbol/random/index.rst        |  23 +
 .../python/api/symbol/register/index.rst      |  23 +
 .../python/api/symbol/sparse/index.rst        |  23 +
 docs/python_docs/python/api/symbol/symbol.rst |  26 +
 docs/python_docs/python/index.rst             |   4 +-
 docs/python_docs/python/scripts/conf.py       |   7 +-
 .../tutorials/packages/gluon/blocks/nn.md     |   2 +-
 .../gluon/training/normalization/index.md     |   6 +-
 .../static/sphinx_materialdesign_theme.css    |   2 +-
 .../sphinx_materialdesign_theme.css.map       |   2 +-
 .../mx-theme/src/scss/toc/_globaltoc.scss     |   4 +-
 docs/static_site/src/.htaccess                |  50 +-
 python/mxnet/gluon/block.py                   |  16 +-
 .../gluon/contrib/estimator/estimator.py      |   2 +-
 .../mxnet/gluon/contrib/rnn/conv_rnn_cell.py  |  18 +-
 .../mxnet/gluon/model_zoo/vision/__init__.py  |   2 +-
 python/mxnet/gluon/model_zoo/vision/resnet.py |   4 +-
 python/mxnet/io/__init__.py                   |   6 +-
 python/mxnet/monitor.py                       |   2 +-
 src/operator/instance_norm.cc                 |   2 +-
 134 files changed, 1927 insertions(+), 3884 deletions(-)
 create mode 100644 docs/python_docs/_static/autodoc.js
 delete mode 100644 docs/python_docs/python/README.md
 delete mode 100644 docs/python_docs/python/api/advanced/index.rst
 delete mode 100644 docs/python_docs/python/api/advanced/mxnet.executor_manager.rst
 delete mode 100644 docs/python_docs/python/api/advanced/mxnet.rtc.rst
 delete mode 100644 docs/python_docs/python/api/advanced/mxnet.test_utils.rst
 create mode 100644 docs/python_docs/python/api/autograd/index.rst
 create mode 100644 docs/python_docs/python/api/contrib/autograd/index.rst
 create mode 100644 docs/python_docs/python/api/contrib/index.rst
 create mode 100644 docs/python_docs/python/api/contrib/io/index.rst
 create mode 100644 docs/python_docs/python/api/contrib/ndarray/index.rst
 create mode 100644 docs/python_docs/python/api/contrib/onnx/index.rst
 create mode 100644 docs/python_docs/python/api/contrib/quantization/index.rst
 create mode 100644 docs/python_docs/python/api/contrib/symbol/index.rst
 create mode 100644 docs/python_docs/python/api/contrib/tensorboard/index.rst
 create mode 100644 docs/python_docs/python/api/contrib/tensorrt/index.rst
 create mode 100644 docs/python_docs/python/api/contrib/text/index.rst
 delete mode 100644 docs/python_docs/python/api/gluon-related/index.rst
 delete mode 100644 docs/python_docs/python/api/gluon-related/mxnet.autograd.rst
 delete mode 100644 docs/python_docs/python/api/gluon-related/mxnet.image.rst
 delete mode 100644 docs/python_docs/python/api/gluon-related/mxnet.initializer.rst
 delete mode 100644 docs/python_docs/python/api/gluon-related/mxnet.io.rst
 delete mode 100644 docs/python_docs/python/api/gluon-related/mxnet.kvstore.KVStore.rst
 delete mode 100644 docs/python_docs/python/api/gluon-related/mxnet.lr_scheduler.rst
 delete mode 100644 docs/python_docs/python/api/gluon-related/mxnet.metric.rst
 delete mode 100644 docs/python_docs/python/api/gluon-related/mxnet.optimizer.rst
 delete mode 100644 docs/python_docs/python/api/gluon-related/mxnet.profiler.rst
 delete mode 100644 docs/python_docs/python/api/gluon-related/mxnet.recordio.rst
 create mode 100644 docs/python_docs/python/api/gluon/block.rst
 create mode 100644 docs/python_docs/python/api/gluon/constant.rst
 rename docs/python_docs/python/api/gluon/{mxnet.gluon.contrib.rst => contrib/index.rst} (92%)
 create mode 100644 docs/python_docs/python/api/gluon/hybrid_block.rst
 rename docs/python_docs/python/api/{advanced/mxnet.kvstore_server.rst => gluon/loss/index.rst} (80%)
 rename docs/python_docs/python/api/gluon/{mxnet.gluon.model_zoo.rst => model_zoo/index.rst} (84%)
 delete mode 100644 docs/python_docs/python/api/gluon/mxnet.gluon.HybridBlock.rst
 delete mode 100644 docs/python_docs/python/api/gluon/mxnet.gluon.ParameterDict.rst
 delete mode 100644 docs/python_docs/python/api/gluon/mxnet.gluon.Trainer.rst
 delete mode 100644 docs/python_docs/python/api/gluon/mxnet.gluon.data.rst
 delete mode 100644 docs/python_docs/python/api/gluon/mxnet.gluon.data.vision.rst
 delete mode 100644 docs/python_docs/python/api/gluon/mxnet.gluon.loss.rst
 delete mode 100644 docs/python_docs/python/api/gluon/mxnet.gluon.nn.Block.rst
 delete mode 100644 docs/python_docs/python/api/gluon/mxnet.gluon.nn.HybridBlock.rst
 delete mode 100644 docs/python_docs/python/api/gluon/mxnet.gluon.nn.SymbolBlock.rst
 delete mode 100644 docs/python_docs/python/api/gluon/mxnet.gluon.parameter.rst
 delete mode 100644 docs/python_docs/python/api/gluon/mxnet.gluon.utils.rst
 rename docs/python_docs/python/api/gluon/{nn.rst => nn/index.rst} (81%)
 create mode 100644 docs/python_docs/python/api/gluon/parameter.rst
 create mode 100644 docs/python_docs/python/api/gluon/parameter_dict.rst
 rename docs/python_docs/python/api/gluon/{rnn.rst => rnn/index.rst} (75%)
 create mode 100644 docs/python_docs/python/api/gluon/symbol_block.rst
 create mode 100644 docs/python_docs/python/api/gluon/trainer.rst
 create mode 100644 docs/python_docs/python/api/gluon/utils/index.rst
 create mode 100644 docs/python_docs/python/api/initializer/index.rst
 rename docs/python_docs/python/api/{gluon-related/mxnet.kvstore.rst => kvstore/index.rst} (92%)
 create mode 100644 docs/python_docs/python/api/lr_scheduler/index.rst
 create mode 100644 docs/python_docs/python/api/metric/index.rst
 create mode 100644 docs/python_docs/python/api/module/index.rst
 create mode 100644 docs/python_docs/python/api/mxnet/attribute/index.rst
 rename docs/python_docs/python/api/{gluon/mxnet.gluon.Constant.rst => mxnet/base/index.rst} (90%)
 create mode 100644 docs/python_docs/python/api/mxnet/callback/index.rst
 rename docs/python_docs/python/api/{gluon-related/mxnet.context.rst => mxnet/context/index.rst} (88%)
 rename docs/python_docs/python/api/{advanced/mxnet.engine.rst => mxnet/engine/index.rst} (88%)
 rename docs/python_docs/python/api/{advanced/mxnet.executor.rst => mxnet/executor/index.rst} (88%)
 create mode 100644 docs/python_docs/python/api/mxnet/executor_manager/index.rst
 rename docs/python_docs/python/api/{gluon/mxnet.gluon.SymbolBlock.rst => mxnet/image/index.rst} (74%)
 rename docs/python_docs/python/api/{symbol-related => mxnet}/index.rst (51%)
 create mode 100644 docs/python_docs/python/api/mxnet/io/index.rst
 create mode 100644 docs/python_docs/python/api/mxnet/kvstore_server/index.rst
 create mode 100644 docs/python_docs/python/api/mxnet/libinfo/index.rst
 rename docs/python_docs/python/api/{symbol-related/mxnet.monitor.rst => mxnet/monitor/index.rst} (90%)
 create mode 100644 docs/python_docs/python/api/mxnet/name/index.rst
 create mode 100644 docs/python_docs/python/api/mxnet/notebook/index.rst
 create mode 100644 docs/python_docs/python/api/mxnet/operator/index.rst
 create mode 100644 docs/python_docs/python/api/mxnet/profiler/index.rst
 rename docs/python_docs/python/api/{gluon-related/mxnet.random.rst => mxnet/random/index.rst} (94%)
 create mode 100644 docs/python_docs/python/api/mxnet/recordio/index.rst
 create mode 100644 docs/python_docs/python/api/mxnet/registry/index.rst
 rename docs/python_docs/python/api/{gluon-related/mxnet.kvstore.create.rst => mxnet/rtc/index.rst} (91%)
 create mode 100644 docs/python_docs/python/api/mxnet/test_utils/index.rst
 create mode 100644 docs/python_docs/python/api/mxnet/torch/index.rst
 rename docs/python_docs/python/api/{advanced/mxnet.util.rst => mxnet/util/index.rst} (90%)
 rename docs/python_docs/python/api/{symbol-related/mxnet.visualization.rst => mxnet/visualization/index.rst} (88%)
 create mode 100644 docs/python_docs/python/api/ndarray/contrib/index.rst
 create mode 100644 docs/python_docs/python/api/ndarray/image/index.rst
 create mode 100644 docs/python_docs/python/api/ndarray/linalg/index.rst
 delete mode 100644 docs/python_docs/python/api/ndarray/mxnet.ndarray.NDArray.rst
 delete mode 100644 docs/python_docs/python/api/ndarray/mxnet.ndarray.sparse.CSRNDArray.rst
 delete mode 100644 docs/python_docs/python/api/ndarray/mxnet.ndarray.sparse.RowSparseNDArray.rst
 create mode 100644 docs/python_docs/python/api/ndarray/ndarray.rst
 create mode 100644 docs/python_docs/python/api/ndarray/op/index.rst
 create mode 100644 docs/python_docs/python/api/ndarray/random/index.rst
 create mode 100644 docs/python_docs/python/api/ndarray/register/index.rst
 delete mode 100644 docs/python_docs/python/api/ndarray/routines.rst
 create mode 100644 docs/python_docs/python/api/ndarray/sparse/index.rst
 delete mode 100644 docs/python_docs/python/api/ndarray/sparse_routines.rst
 create mode 100644 docs/python_docs/python/api/ndarray/utils/index.rst
 create mode 100644 docs/python_docs/python/api/optimizer/index.rst
 delete mode 100644 docs/python_docs/python/api/symbol-related/mxnet.callback.rst
 delete mode 100644 docs/python_docs/python/api/symbol-related/mxnet.model.rst
 delete mode 100644 docs/python_docs/python/api/symbol-related/mxnet.module.rst
 create mode 100644 docs/python_docs/python/api/symbol/contrib/index.rst
 create mode 100644 docs/python_docs/python/api/symbol/image/index.rst
 create mode 100644 docs/python_docs/python/api/symbol/linalg/index.rst
 delete mode 100644 docs/python_docs/python/api/symbol/mxnet.symbol.Symbol.rst
 delete mode 100644 docs/python_docs/python/api/symbol/mxnet.symbol.linalg.rst
 create mode 100644 docs/python_docs/python/api/symbol/op/index.rst
 create mode 100644 docs/python_docs/python/api/symbol/random/index.rst
 create mode 100644 docs/python_docs/python/api/symbol/register/index.rst
 create mode 100644 docs/python_docs/python/api/symbol/sparse/index.rst
 create mode 100644 docs/python_docs/python/api/symbol/symbol.rst

diff --git a/docs/python_docs/README.md b/docs/python_docs/README.md
index 8b4c13b0de43..a547e70d16d3 100644
--- a/docs/python_docs/README.md
+++ b/docs/python_docs/README.md
@@ -21,4 +21,118 @@ Preview at https://mxnet-beta.staged.apache.org/
 
 ## [Building the Docs and Website](https://cwiki.apache.org/confluence/display/MXNET/Building+the+New+Website)
 
-## [Technical details for the building the Python microsite](python/README.md)
\ No newline at end of file
+## [Technical details for the building the Python microsite](python/README.md)
+
+# Python binding docs
+
+The following guide will help you build a local version of the Python API website,
+so that you may work on and test any contributions.
+
+It is recommended that you read the MXNet developer wiki's info on [building the website & docs](https://cwiki.apache.org/confluence/display/MXNET/Building+the+New+Website) as that includes info on how to test and build the site using Docker. The following information should only be used if you can't use Docker or if you're trying to run the site locally.
+
+## Setup
+
+The default configuration requires a GPU and CUDA 9.2 and expects Ubuntu.
+However, you may setup the website on macOS or Windows with or without a GPU.
+
+### Prerequisites
+
+* [Conda >= 4.6.13](https://www.anaconda.com/distribution/#download-section) (install to PATH)
+
+You can update Conda with the following:
+
+```
+conda update conda
+```
+
+### GPU setup
+To run the full build, including tests of all tutorials,
+**you will need at least two GPUs**.
+Distributed training is a key feature of MXNet,
+so multiple GPUs are required for running through every tutorial.
+* [CUDA 9.2](https://developer.nvidia.com/cuda-downloads)
+
+### CPU-only setup
+In the `environment.yml` file:
+* Change `mxnet-cu92` to `mxnet`.
+
+### macOS setup
+In the `environment.yml` file:
+* Change `mxnet-cu92` to `mxnet`. (There is no CUDA package for mac anyway.)
+* Change `pytorch-cpu` to `pytorch`.
+* Change `torchvision-cpu` to `torchvision`.
+
+### Windows Setup
+If you have a GPU and have installed CUDA 9.2 you can leave the MXNet dependency alone.
+Otherwise, in the `environment.yml` file:
+* Change `mxnet-cu92` to `mxnet`.
+
+Install recommended software:
+* [git bash](https://gitforwindows.org/)
+* Be sure to install `Conda` in `PATH`
+* Install `make` from a `git bash` terminal with Admin rights
+    - [Install chocolatey](https://chocolatey.org/install)
+    - Use `choco to install make`
+* Restart terminals after installations to make sure PATH is set.
+    - The `choco`, `make`, and `conda` commands should work in `git bash`.
+
+### Conda environment setup
+Run the following commands from the project root (`python-docs`) to setup the environment.
+
+```bash
+conda env create -f environment.yml
+source activate mxnet-docs
+```
+
+## Build the docs
+
+* Change directories to `python-docs/python`.
+
+To build without GPUs and without testing the notebooks (faster):
+
+```bash
+make EVAL=0
+```
+
+To build with testing the notebooks (requires GPU):
+
+```bash
+make
+```
+
+The build docs will be available at `build/_build/html`.
+
+Each build may take a few minutes even without evaluation. To accelerate it, we can use one of the following ways:
+
+1. open `build/conf.py`, add the folders you want to skip into `exclude_patterns`, such as `exclude_patterns = ['templates', 'api', 'develop', 'blog']`.
+2. move the files into a different folder, such as `mv api /tmp/`, and then `make clean`.
+
+## Check results
+
+To run a server to see the website:
+
+1. Start a http server: `cd build/_build/html; python -m http.server`
+2. For viewing a remote machine, ssh to your machine with port forwarding: `ssh -L8000:localhost:8000 your_machine`
+3. Open http://localhost:8000 in your local machine
+
+## Run tutorials
+
+In addition to view the built html pages, you can run the Jupyter notebook from a remote machine.
+1. Install `notedown` plugin: `pip install https://github.com/mli/notedown/tarball/master` in remote server
+2. Start Jupyter notebook `jupyter notebook --NotebookApp.contents_manager_class='notedown.NotedownContentsManager'` in remote server
+3. ssh to your machine with port forwarding: `ssh -L8888:localhost:8888 your_machine`
+4. Open http://localhost:8888 in your local machine and run the md files directly
+
+Optionally, one can run the following to launch the notedown plugin automatically when starting jupyter notebook.
+1. Generate the jupyter configure file `~/.jupyter/jupyter_notebook_config.py` if it
+is not existing by run `jupyter notebook --generate-config`
+2. Add `c.NotebookApp.contents_manager_class = 'notedown.NotedownContentsManager'` to `~/.jupyter/jupyter_notebook_config.py`
+3. Simply run `jupyter notebook`
+
+## Troubleshooting
+Dependencies and the setup steps for this website are changing often. Here are some troubleshooting tips.
+
+* You might need to update the environment for the latest modules.
+```bash
+conda env update -f environment.yml
+```
diff --git a/docs/python_docs/_static/autodoc.js b/docs/python_docs/_static/autodoc.js
new file mode 100644
index 000000000000..0825f54ea143
--- /dev/null
+++ b/docs/python_docs/_static/autodoc.js
@@ -0,0 +1,49 @@
+/*!
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/* Customizations to the Sphinx auto module plugin output */
+function auto_index() {
+    var targets = $("dl.class>dt,dl.function>dt");
+
+    var li_node = $("li.current>span>a.current.reference.internal").parent().parent();
+    var html = "<ul id='autodoc'>";
+    if (li_node.length > 0) {
+        for (var i = 0; i < targets.length; ++i) {
+            var id = $(targets[i]).attr('id');
+            if (id) {
+                var paths = id.split('.')
+                if (paths.length >= 2) {
+                    var id_simple = paths.pop();
+                    id_simple = paths.pop() + "." + id_simple;
+                } else {
+                    var id_simple = id;
+                }
+                html += "<li><span class='link-wrapper'><a class='reference internal' href='#";
+                html += id;
+                html += "'>" + id_simple + "</a></span</li>";
+            }
+        }
+        html += "</ul>";
+        li_node.append(html);
+        li_node.prepend("<a><span id='autodoc_toggle' onclick='$(\"#autodoc\").toggle()'>[toggle]</span></a>")
+    } else {
+        setTimeout(auto_index, 500);
+    }
+}
+$(document).ready(auto_index);
\ No newline at end of file
diff --git a/docs/python_docs/_static/mxnet.css b/docs/python_docs/_static/mxnet.css
index 3909aedc06fa..7d4f7f115424 100644
--- a/docs/python_docs/_static/mxnet.css
+++ b/docs/python_docs/_static/mxnet.css
@@ -196,4 +196,11 @@ p {
 
 .install .title {
     margin-top: 1em;
+}
+
+/* autodoc */
+#autodoc_toggle {
+    float: right;
+    margin: 4px;
+    cursor: pointer;
 }
\ No newline at end of file
diff --git a/docs/python_docs/environment.yml b/docs/python_docs/environment.yml
index a3f2e2eca4a2..11e43a1733f3 100644
--- a/docs/python_docs/environment.yml
+++ b/docs/python_docs/environment.yml
@@ -36,3 +36,4 @@ dependencies:
   - breathe
   - mock
   - awscli
+  - autodocsumm
diff --git a/docs/python_docs/python/Makefile b/docs/python_docs/python/Makefile
index 8bbb10cee521..0c5450887a16 100644
--- a/docs/python_docs/python/Makefile
+++ b/docs/python_docs/python/Makefile
@@ -25,7 +25,6 @@ IPYNB_MARKDOWN = $(shell find . -not -path "./build/*" -not -path "*.ipynb_check
 RST = $(shell find . -not -path "./build/*" -not -path "*.ipynb_checkpoints*" -name "*.rst")
 
 OBJ = $(patsubst %.rst, build/%.rst, $(RST)) \
-	$(patsubst %.md, build/%.md, $(PURE_MARKDOWN)) \
 	$(patsubst %.md, build/%.ipynb, \
 		$(filter-out $(PURE_MARKDOWN), $(IPYNB_MARKDOWN)))
 
@@ -47,7 +46,7 @@ build/%: %
 html: $(OBJ)
 	mkdir -p build
 	cp Makefile_sphinx build/Makefile
-	sphinx-autogen build/api/*.rst build/api/*/*.rst   -t build/_templates/
+	sphinx-autogen build/api/*.rst build/api/**/*.rst   -t build/_templates/
 	# make -C build linkcheck doctest html
 	make -C build html
 	sed -i.bak 's/33\,150\,243/23\,141\,201/g'  build/_build/html/_static/material-design-lite-1.3.0/material.blue-deep_orange.min.css
diff --git a/docs/python_docs/python/README.md b/docs/python_docs/python/README.md
deleted file mode 100644
index 4c52c5d9a50d..000000000000
--- a/docs/python_docs/python/README.md
+++ /dev/null
@@ -1,130 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Python binding docs
-
-The following guide will help you build a local version of the Python API website,
-so that you may work on and test any contributions.
-
-It is recommended that you read the MXNet developer wiki's info on [building the website & docs](https://cwiki.apache.org/confluence/display/MXNET/Building+the+New+Website) as that includes info on how to test and build the site using Docker. The following information should only be used if you can't use Docker or if you're trying to run the site locally.
-
-## Setup
-
-The default configuration requires a GPU and CUDA 9.2 and expects Ubuntu.
-However, you may setup the website on macOS or Windows with or without a GPU.
-
-### Prerequisites
-
-* [Conda >= 4.6.13](https://www.anaconda.com/distribution/#download-section) (install to PATH)
-
-You can update Conda with the following:
-
-```
-conda update conda
-```
-
-### GPU setup
-To run the full build, including tests of all tutorials,
-**you will need at least two GPUs**.
-Distributed training is a key feature of MXNet,
-so multiple GPUs are required for running through every tutorial.
-* [CUDA 9.2](https://developer.nvidia.com/cuda-downloads)
-
-### CPU-only setup
-In the `environment.yml` file:
-* Change `mxnet-cu92` to `mxnet`.
-
-### macOS setup
-In the `environment.yml` file:
-* Change `mxnet-cu92` to `mxnet`. (There is no CUDA package for mac anyway.)
-* Change `pytorch-cpu` to `pytorch`.
-* Change `torchvision-cpu` to `torchvision`.
-
-### Windows Setup
-If you have a GPU and have installed CUDA 9.2 you can leave the MXNet dependency alone.
-Otherwise, in the `environment.yml` file:
-* Change `mxnet-cu92` to `mxnet`.
-
-Install recommended software:
-* [git bash](https://gitforwindows.org/)
-* Be sure to install `Conda` in `PATH`
-* Install `make` from a `git bash` terminal with Admin rights
-    - [Install chocolatey](https://chocolatey.org/install)
-    - Use `choco to install make`
-* Restart terminals after installations to make sure PATH is set.
-    - The `choco`, `make`, and `conda` commands should work in `git bash`.
-
-### Conda environment setup
-Run the following commands from the project root (`python-docs`) to setup the environment.
-
-```bash
-conda env create -f environment.yml
-source activate mxnet-docs
-```
-
-## Build the docs
-
-* Change directories to `python-docs/python`.
-
-To build without GPUs and without testing the notebooks (faster):
-
-```bash
-make EVAL=0
-```
-
-To build with testing the notebooks (requires GPU):
-
-```bash
-make
-```
-
-The build docs will be available at `build/_build/html`.
-
-Each build may take a few minutes even without evaluation. To accelerate it, we can use one of the following ways:
-
-1. open `build/conf.py`, add the folders you want to skip into `exclude_patterns`, such as `exclude_patterns = ['templates', 'api', 'develop', 'blog']`.
-2. move the files into a different folder, such as `mv api /tmp/`, and then `make clean`.
-
-## Check results
-
-To run a server to see the website:
-
-1. Start a http server: `cd build/_build/html; python -m http.server`
-2. For viewing a remote machine, ssh to your machine with port forwarding: `ssh -L8000:localhost:8000 your_machine`
-3. Open http://localhost:8000 in your local machine
-
-## Run tutorials
-
-In addition to view the built html pages, you can run the Jupyter notebook from a remote machine.
-1. Install `notedown` plugin: `pip install https://github.com/mli/notedown/tarball/master` in remote server
-2. Start Jupyter notebook `jupyter notebook --NotebookApp.contents_manager_class='notedown.NotedownContentsManager'` in remote server
-3. ssh to your machine with port forwarding: `ssh -L8888:localhost:8888 your_machine`
-4. Open http://localhost:8888 in your local machine and run the md files directly
-
-Optionally, one can run the following to launch the notedown plugin automatically when starting jupyter notebook.
-1. Generate the jupyter configure file `~/.jupyter/jupyter_notebook_config.py` if it
-is not existing by run `jupyter notebook --generate-config`
-2. Add `c.NotebookApp.contents_manager_class = 'notedown.NotedownContentsManager'` to `~/.jupyter/jupyter_notebook_config.py`
-3. Simply run `jupyter notebook`
-
-## Troubleshooting
-Dependencies and the setup steps for this website are changing often. Here are some troubleshooting tips.
-
-* You might need to update the environment for the latest modules.
-```bash
-conda env update -f environment.yml
-```
diff --git a/docs/python_docs/python/api/advanced/index.rst b/docs/python_docs/python/api/advanced/index.rst
deleted file mode 100644
index 1af3690624ea..000000000000
--- a/docs/python_docs/python/api/advanced/index.rst
+++ /dev/null
@@ -1,74 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-Advanced modules
-=================
-
-.. container:: cards
-
-   .. card::
-      :title: mxnet.executor
-      :link: mxnet.executor.html
-
-      Managing symbolic graph execution.
-
-   .. card::
-      :title: mxnet.kvstore_server
-      :link: mxnet.kvstore_server.html
-
-      
-
-   .. card::
-      :title: mxnet.engine
-      :link: mxnet.engine.html
-
-      
-
-   .. card::
-      :title: mxnet.executor_manager
-      :link: mxnet.executor_manager.html
-
-      
-
-   .. card::
-      :title: mxnet.rtc
-      :link: mxnet.rtc.html
-
-      Tools for compiling and running CUDA code from the python frontend.
-
-   .. card::
-      :title: mxnet.test_utils
-      :link: mxnet.test_utils.html
-
-      Tools for using and testing MXNet.
-
-   .. card::
-      :title: mxnet.util
-      :link: mxnet.util.html
-
-      
-
-.. toctree::
-   :hidden:
-
-   mxnet.executor
-   mxnet.kvstore_server
-   mxnet.engine
-   mxnet.executor_manager
-   mxnet.rtc
-   mxnet.test_utils
-   mxnet.util
diff --git a/docs/python_docs/python/api/advanced/mxnet.executor_manager.rst b/docs/python_docs/python/api/advanced/mxnet.executor_manager.rst
deleted file mode 100644
index 30476b6e3da0..000000000000
--- a/docs/python_docs/python/api/advanced/mxnet.executor_manager.rst
+++ /dev/null
@@ -1,38 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-mxnet.executor\_manager
-=======================
-
-.. automodule:: mxnet.executor_manager
-
-
-
-
-
-
-
-   .. rubric:: Classes
-
-   .. autosummary::
-      :toctree: _autogen
-
-      DataParallelExecutorGroup
-      DataParallelExecutorManager
-
-
-
diff --git a/docs/python_docs/python/api/advanced/mxnet.rtc.rst b/docs/python_docs/python/api/advanced/mxnet.rtc.rst
deleted file mode 100644
index b6ab2c16b745..000000000000
--- a/docs/python_docs/python/api/advanced/mxnet.rtc.rst
+++ /dev/null
@@ -1,36 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-mxnet.rtc
-=========
-
-.. automodule:: mxnet.rtc
-
-
-
-
-
-
-
-   .. rubric:: Classes
-
-   .. autosummary::
-      :toctree: _autogen
-
-      CudaKernel
-      CudaModule
-
diff --git a/docs/python_docs/python/api/advanced/mxnet.test_utils.rst b/docs/python_docs/python/api/advanced/mxnet.test_utils.rst
deleted file mode 100644
index 65392d3fc432..000000000000
--- a/docs/python_docs/python/api/advanced/mxnet.test_utils.rst
+++ /dev/null
@@ -1,91 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-mxnet.test\_utils
-=================
-
-.. automodule:: mxnet.test_utils
-
-
-
-   .. rubric:: Functions
-
-   .. autosummary::
-      :toctree: _autogen
-
-      almost_equal
-      almost_equal_ignore_nan
-      assert_almost_equal
-      assert_almost_equal_ignore_nan
-      assert_exception
-      assign_each
-      assign_each2
-      check_consistency
-      check_numeric_gradient
-      check_speed
-      check_symbolic_backward
-      check_symbolic_forward
-      chi_square_check
-      create_sparse_array
-      create_sparse_array_zd
-      default_context
-      default_dtype
-      discard_stderr
-      download
-      find_max_violation
-      gen_buckets_probs_with_ppf
-      get_atol
-      get_bz2_data
-      get_cifar10
-      get_im2rec_path
-      get_mnist
-      get_mnist_iterator
-      get_mnist_pkl
-      get_mnist_ubyte
-      get_rtol
-      get_zip_data
-      list_gpus
-      mean_check
-      np_reduce
-      numeric_grad
-      rand_ndarray
-      rand_shape_2d
-      rand_shape_3d
-      rand_shape_nd
-      rand_sparse_ndarray
-      random_arrays
-      random_sample
-      retry
-      same
-      same_array
-      set_default_context
-      set_env_var
-      shuffle_csr_column_indices
-      simple_forward
-      var_check
-      verify_generator
-
-
-
-
-
-   .. rubric:: Classes
-
-   .. autosummary::
-
-      DummyIter
-
diff --git a/docs/python_docs/python/api/autograd/index.rst b/docs/python_docs/python/api/autograd/index.rst
new file mode 100644
index 000000000000..c7964b499fbf
--- /dev/null
+++ b/docs/python_docs/python/api/autograd/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.autograd
+===============
+
+.. automodule:: mxnet.autograd
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/contrib/autograd/index.rst b/docs/python_docs/python/api/contrib/autograd/index.rst
new file mode 100644
index 000000000000..76b0f1e3aa1b
--- /dev/null
+++ b/docs/python_docs/python/api/contrib/autograd/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+contrib.autograd
+================
+
+.. automodule:: mxnet.contrib.autograd
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/contrib/index.rst b/docs/python_docs/python/api/contrib/index.rst
new file mode 100644
index 000000000000..a05dad0b9fb1
--- /dev/null
+++ b/docs/python_docs/python/api/contrib/index.rst
@@ -0,0 +1,88 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.contrib
+===============
+
+.. automodule:: mxnet.contrib
+
+Contributed modules
+-------------------
+
+.. container:: cards
+
+   .. card::
+      :title: contrib.autograd
+      :link: autograd/index.html
+
+      Functions for Automatic differentiation.
+
+   .. card::
+      :title: contrib.io
+      :link: io/index.html
+
+      Data iterators for common data formats and utility functions.
+
+   .. card::
+      :title: contrib.ndarray
+      :link: ndarray/index.html
+
+      Operations and functions on NDArrays.
+
+   .. card::
+      :title: contrib.onnx
+      :link: onnx/index.html
+
+      ONNX support.
+
+   .. card::
+      :title: contrib.quantization
+      :link: quantization/index.html
+
+      Functions for quantization.
+
+   .. card::
+      :title: contrib.symbol
+      :link: symbol/index.html
+
+      Symbolic API for MXNet.
+
+   .. card::
+      :title: contrib.tensorboard
+      :link: tensorboard/index.html
+
+      Tensorboard integration.
+
+   .. card::
+      :title: contrib.tensorrt
+      :link: tensorrt/index.html
+
+      Tensorrt integration.
+
+   .. card::
+      :title: contrib.text
+      :link: text/index.html
+
+      Functions for manipulating text data.
+
+
+.. toctree::
+   :hidden:
+   :maxdepth: 2
+   :glob:
+
+   */index
diff --git a/docs/python_docs/python/api/contrib/io/index.rst b/docs/python_docs/python/api/contrib/io/index.rst
new file mode 100644
index 000000000000..6b5b5dd5deed
--- /dev/null
+++ b/docs/python_docs/python/api/contrib/io/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+contrib.io
+==========
+
+.. automodule:: mxnet.contrib.io
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/contrib/ndarray/index.rst b/docs/python_docs/python/api/contrib/ndarray/index.rst
new file mode 100644
index 000000000000..6ee5b98583aa
--- /dev/null
+++ b/docs/python_docs/python/api/contrib/ndarray/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+contrib.ndarray
+================
+
+.. automodule:: mxnet.contrib.ndarray
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/contrib/onnx/index.rst b/docs/python_docs/python/api/contrib/onnx/index.rst
new file mode 100644
index 000000000000..aac201f07128
--- /dev/null
+++ b/docs/python_docs/python/api/contrib/onnx/index.rst
@@ -0,0 +1,24 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+contrib.onnx
+============
+
+.. automodule:: mxnet.contrib.onnx
+    :members:
+    :imported-members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/contrib/quantization/index.rst b/docs/python_docs/python/api/contrib/quantization/index.rst
new file mode 100644
index 000000000000..a0f7ca56eefd
--- /dev/null
+++ b/docs/python_docs/python/api/contrib/quantization/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+contrib.quantization
+====================
+
+.. automodule:: mxnet.contrib.quantization
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/contrib/symbol/index.rst b/docs/python_docs/python/api/contrib/symbol/index.rst
new file mode 100644
index 000000000000..81b6d15b51f4
--- /dev/null
+++ b/docs/python_docs/python/api/contrib/symbol/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+contrib.symbol
+==============
+
+.. automodule:: mxnet.contrib.symbol
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/contrib/tensorboard/index.rst b/docs/python_docs/python/api/contrib/tensorboard/index.rst
new file mode 100644
index 000000000000..2e08f3727e97
--- /dev/null
+++ b/docs/python_docs/python/api/contrib/tensorboard/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+contrib.tensorboard
+===================
+
+.. automodule:: mxnet.contrib.tensorboard
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/contrib/tensorrt/index.rst b/docs/python_docs/python/api/contrib/tensorrt/index.rst
new file mode 100644
index 000000000000..9822b63f4f8b
--- /dev/null
+++ b/docs/python_docs/python/api/contrib/tensorrt/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+contrib.tensorrt
+================
+
+.. automodule:: mxnet.contrib.tensorrt
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/contrib/text/index.rst b/docs/python_docs/python/api/contrib/text/index.rst
new file mode 100644
index 000000000000..58e547143083
--- /dev/null
+++ b/docs/python_docs/python/api/contrib/text/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+contrib.text
+============
+
+.. automodule:: mxnet.contrib.text
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/gluon-related/index.rst b/docs/python_docs/python/api/gluon-related/index.rst
deleted file mode 100644
index 0f02f239db8b..000000000000
--- a/docs/python_docs/python/api/gluon-related/index.rst
+++ /dev/null
@@ -1,111 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-Gluon related modules
-======================
-
-.. container:: cards
-
-   .. card::
-      :title: mxnet.autograd
-      :link: mxnet.autograd.html
-
-      Functions for Automatic differentiation.
-
-   .. card::
-      :title: mxnet.image
-      :link: mxnet.image.html
-
-      Image iterators and image augmentation functions.
-
-   .. card::
-      :title: mxnet.io
-      :link: mxnet.io.html
-
-      Data iterators for common data formats and utility functions.
-
-   .. card::
-      :title: mxnet.recordio
-      :link: mxnet.recordio.html
-
-      Read and write for the RecordIO data format.
-
-   .. card::
-      :title: mxnet.kvstore
-      :link: mxnet.kvstore.KVStore.html
-
-      Key value store interface of MXNet for parameter synchronization.
-
-   .. card::
-      :title: mxnet.optimizer
-      :link: mxnet.optimizer.html
-
-      Functions for applying an optimizer on weights.
-   
-   .. card::
-      :title: mxnet.random
-      :link: mxnet.random.html
-
-      Imperative random distribution generator functions.
-
-   .. card::
-      :title: mxnet.profiler
-      :link: mxnet.profiler.html
-
-      Profiler setting methods.
-
-   .. card::
-      :title: mxnet.context
-      :link: mxnet.context.html
-
-      CPU and GPU context information.
-
-   .. card::
-      :title: mxnet.initializer
-      :link: mxnet.initializer.html
-
-      Default behaviors to initialize parameters.
-
-   .. card::
-      :title: mxnet.lr_scheduler
-      :link: mxnet.lr_scheduler.html
-
-      Scheduling the learning rate.
-
-   .. card::
-      :title: mxnet.metric
-      :link: mxnet.metric.html
-
-      Metrics to evaluate the performance of a learned model.
-
-
-
-.. toctree::
-   :hidden:
-
-   mxnet.autograd
-   mxnet.image
-   mxnet.io
-   mxnet.recordio
-   mxnet.kvstore
-   mxnet.optimizer
-   mxnet.random
-   mxnet.profiler
-   mxnet.context
-   mxnet.initializer
-   mxnet.lr_scheduler
-   mxnet.metric
diff --git a/docs/python_docs/python/api/gluon-related/mxnet.autograd.rst b/docs/python_docs/python/api/gluon-related/mxnet.autograd.rst
deleted file mode 100644
index ee37e5e89f07..000000000000
--- a/docs/python_docs/python/api/gluon-related/mxnet.autograd.rst
+++ /dev/null
@@ -1,38 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-mxnet.autograd
-===================
-
-.. automodule:: mxnet.autograd
-
-.. autosummary::
-   :toctree: _autogen
-
-   backward
-   get_symbol
-   grad
-   is_recording
-   is_training
-   mark_variables
-   pause
-   predict_mode
-   record
-   set_recording
-   set_training
-   train_mode
-   Function
diff --git a/docs/python_docs/python/api/gluon-related/mxnet.image.rst b/docs/python_docs/python/api/gluon-related/mxnet.image.rst
deleted file mode 100644
index 84d2095f470d..000000000000
--- a/docs/python_docs/python/api/gluon-related/mxnet.image.rst
+++ /dev/null
@@ -1,99 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-mxnet.image
-===========
-
-.. automodule:: mxnet.image
-
-
-Image processing functions
---------------------------
-
-.. autosummary::
-   :toctree: _autogen
-
-   imdecode
-   scale_down
-   resize_short
-   fixed_crop
-   random_crop
-   center_crop
-   color_normalize
-   random_size_crop
-
-Image classifiction
--------------------
-
-Iterators
-^^^^^^^^^^
-
-.. autosummary::
-   :toctree: _autogen
-
-   ImageIter
-
-Augmentations
-^^^^^^^^^^^^^
-
-.. autosummary::
-   :toctree: _autogen
-
-   CreateAugmenter
-   Augmenter
-   SequentialAug
-   RandomOrderAug
-   ResizeAug
-   ForceResizeAug
-   RandomCropAug
-   RandomSizedCropAug
-   CenterCropAug
-   BrightnessJitterAug
-   ContrastJitterAug
-   SaturationJitterAug
-   HueJitterAug
-   ColorJitterAug
-   LightingAug
-   ColorNormalizeAug
-   RandomGrayAug
-   HorizontalFlipAug
-   CastAug
-
-Object detection
-----------------
-
-Iterators
-^^^^^^^^^
-
-.. autosummary::
-   :toctree: _autogen
-
-   ImageDetIter
-
-
-Augmentations
-^^^^^^^^^^^^^
-
-.. autosummary::
-   :toctree: _autogen
-
-   CreateDetAugmenter
-   DetBorrowAug
-   DetRandomSelectAug
-   DetHorizontalFlipAug
-   DetRandomCropAug
-   DetRandomPadAug
diff --git a/docs/python_docs/python/api/gluon-related/mxnet.initializer.rst b/docs/python_docs/python/api/gluon-related/mxnet.initializer.rst
deleted file mode 100644
index bb2480cf58de..000000000000
--- a/docs/python_docs/python/api/gluon-related/mxnet.initializer.rst
+++ /dev/null
@@ -1,58 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-mxnet.initializer
-=================
-
-.. automodule:: mxnet.initializer
-
-
-
-
-Initialization methods
------------------------
-
-
-.. autosummary::
-   :toctree: _autogen/
-
-   Bilinear
-   Constant
-   FusedRNN
-   InitDesc
-   Initializer
-   LSTMBias
-   Load
-   MSRAPrelu
-   Mixed
-   Normal
-   One
-   Orthogonal
-   Uniform
-   Xavier
-   Zero
-
-
-
-
-Helper functions
-----------------
-
-.. autosummary::
-   :toctree: _autogen/
-
-   register
diff --git a/docs/python_docs/python/api/gluon-related/mxnet.io.rst b/docs/python_docs/python/api/gluon-related/mxnet.io.rst
deleted file mode 100644
index 8b0cdf653ddd..000000000000
--- a/docs/python_docs/python/api/gluon-related/mxnet.io.rst
+++ /dev/null
@@ -1,52 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-mxnet.io
-========
-
-.. automodule:: mxnet.io
-
-
-Iterators
----------
-
-.. autosummary::
-   :toctree: _autogen
-
-   NDArrayIter
-   CSVIter
-   LibSVMIter
-   MNISTIter
-   ImageDetRecordIter
-   ImageRecordIter
-   ImageRecordIter_v1
-   ImageRecordUInt8Iter
-   ImageRecordUInt8Iter_v1
-
-
-Helper classes and functions
-----------------------------
-
-.. autosummary::
-   :toctree: _autogen
-
-   DataBatch
-   DataDesc
-   DataIter
-   MXDataIter
-   PrefetchingIter
-   ResizeIter
diff --git a/docs/python_docs/python/api/gluon-related/mxnet.kvstore.KVStore.rst b/docs/python_docs/python/api/gluon-related/mxnet.kvstore.KVStore.rst
deleted file mode 100644
index aa5ebb391115..000000000000
--- a/docs/python_docs/python/api/gluon-related/mxnet.kvstore.KVStore.rst
+++ /dev/null
@@ -1,61 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-KVStore
-=======
-
-.. currentmodule:: mxnet.kvstore
-
-.. autoclass:: KVStore
-
-
-Init, push and pull
--------------------
-
-.. autosummary::
-   :toctree: _autogen
-
-   KVStore.init
-   KVStore.pull
-   KVStore.push
-   KVStore.row_sparse_pull
-
-Optimizer
--------------------
-
-.. autosummary::
-   :toctree: _autogen
-
-   KVStore.load_optimizer_states
-   KVStore.save_optimizer_states
-   KVStore.set_gradient_compression
-   KVStore.set_optimizer
-
-
-
-
-
-Attributes
-----------
-
-.. autosummary::
-   :toctree: _autogen
-
-   KVStore.num_workers
-   KVStore.rank
-   KVStore.type
-
diff --git a/docs/python_docs/python/api/gluon-related/mxnet.lr_scheduler.rst b/docs/python_docs/python/api/gluon-related/mxnet.lr_scheduler.rst
deleted file mode 100644
index b4707ae34f7e..000000000000
--- a/docs/python_docs/python/api/gluon-related/mxnet.lr_scheduler.rst
+++ /dev/null
@@ -1,31 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-mxnet.lr\_scheduler
-===================
-
-.. automodule:: mxnet.lr_scheduler
-
-.. autosummary::
-   :toctree: _autogen
-
-   LRScheduler
-   FactorScheduler
-   MultiFactorScheduler
-   PolyScheduler
-   CosineScheduler
-
diff --git a/docs/python_docs/python/api/gluon-related/mxnet.metric.rst b/docs/python_docs/python/api/gluon-related/mxnet.metric.rst
deleted file mode 100644
index cb11c9597590..000000000000
--- a/docs/python_docs/python/api/gluon-related/mxnet.metric.rst
+++ /dev/null
@@ -1,57 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-mxnet.metric
-============
-
-.. automodule:: mxnet.metric
-
-
-Metrics
--------
-
-.. autosummary::
-   :toctree: _autogen
-
-   Accuracy
-   Caffe
-   CompositeEvalMetric
-   CrossEntropy
-   CustomMetric
-   EvalMetric
-   F1
-   Loss
-   MAE
-   MCC
-   MSE
-   NegativeLogLikelihood
-   PearsonCorrelation
-   Perplexity
-   RMSE
-   TopKAccuracy
-   Torch
-
-
-Helper functions
------------------
-
-.. autosummary::
-   :toctree: _autogen
-
-   check_label_shapes
-   create
-   np
diff --git a/docs/python_docs/python/api/gluon-related/mxnet.optimizer.rst b/docs/python_docs/python/api/gluon-related/mxnet.optimizer.rst
deleted file mode 100644
index dc92ed24b3c5..000000000000
--- a/docs/python_docs/python/api/gluon-related/mxnet.optimizer.rst
+++ /dev/null
@@ -1,55 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-mxnet.optimizer
-===============
-
-.. automodule:: mxnet.optimizer
-
-
-
-Optimization methods
---------------------
-
-.. autosummary::
-   :toctree: _autogen
-
-   AdaDelta
-   AdaGrad
-   Adam
-   Adamax
-   DCASGD
-   FTML
-   Ftrl
-   LBSGD
-   NAG
-   Nadam
-   Optimizer
-   RMSProp
-   SGD
-   SGLD
-   Signum
-   Updater
-
-Helper functions
-----------------
-
-.. autosummary::
-
-   create
-   get_updater
-   register
diff --git a/docs/python_docs/python/api/gluon-related/mxnet.profiler.rst b/docs/python_docs/python/api/gluon-related/mxnet.profiler.rst
deleted file mode 100644
index 9307220a57e3..000000000000
--- a/docs/python_docs/python/api/gluon-related/mxnet.profiler.rst
+++ /dev/null
@@ -1,54 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-mxnet.profiler
-==============
-
-.. automodule:: mxnet.profiler
-
-
-Profiling system control
-~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-.. autosummary::
-   :toctree: _autogen
-
-   set_config
-   set_kvstore_handle
-   set_state
-   profiler_set_config
-   profiler_set_state
-   pause
-   resume
-   dump
-   dump_profile
-   dumps
-
-
-Profiling Objects
-~~~~~~~~~~~~~~~~~
-
-.. autosummary::
-   :toctree: _autogen
-
-   Counter
-   Domain
-   Event
-   Frame
-   Marker
-   Task
diff --git a/docs/python_docs/python/api/gluon-related/mxnet.recordio.rst b/docs/python_docs/python/api/gluon-related/mxnet.recordio.rst
deleted file mode 100644
index 1fa6de759ffc..000000000000
--- a/docs/python_docs/python/api/gluon-related/mxnet.recordio.rst
+++ /dev/null
@@ -1,43 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-mxnet.recordio
-==============
-
-.. automodule:: mxnet.recordio
-
-Iterators
----------
-
-.. autosummary::
-   :toctree: _autogen
-
-   MXIndexedRecordIO
-   MXRecordIO
-
-Read and write RecordIO files
-------------------------------------------
-
-.. autosummary::
-   :toctree: _autogen
-
-   IRHeader
-   pack
-   pack_img
-   unpack
-   unpack_img
-
diff --git a/docs/python_docs/python/api/gluon/block.rst b/docs/python_docs/python/api/gluon/block.rst
new file mode 100644
index 000000000000..7693ddb220bc
--- /dev/null
+++ b/docs/python_docs/python/api/gluon/block.rst
@@ -0,0 +1,25 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+gluon.Block
+===========
+
+
+.. autoclass:: mxnet.gluon.Block
+    :members:
+    :inherited-members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/gluon/constant.rst b/docs/python_docs/python/api/gluon/constant.rst
new file mode 100644
index 000000000000..13843842a0fe
--- /dev/null
+++ b/docs/python_docs/python/api/gluon/constant.rst
@@ -0,0 +1,25 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+gluon.Constant
+==============
+
+
+.. autoclass:: mxnet.gluon.Constant
+    :members:
+    :inherited-members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/gluon/mxnet.gluon.contrib.rst b/docs/python_docs/python/api/gluon/contrib/index.rst
similarity index 92%
rename from docs/python_docs/python/api/gluon/mxnet.gluon.contrib.rst
rename to docs/python_docs/python/api/gluon/contrib/index.rst
index 848da81bf110..b4766a877dfa 100644
--- a/docs/python_docs/python/api/gluon/mxnet.gluon.contrib.rst
+++ b/docs/python_docs/python/api/gluon/contrib/index.rst
@@ -15,8 +15,8 @@
    specific language governing permissions and limitations
    under the License.
 
-contrib
-=======
+gluon.contrib
+=============
 
 This document lists the contrib APIs in Gluon:
 
@@ -94,12 +94,13 @@ Data
 ----
 
 
-.. currentmodule:: mxnet.gluon.contrib.data
+.. currentmodule:: mxnet.gluon.contrib.data.sampler
 
 .. autosummary::
     :nosignatures:
+
     IntervalSampler
-```
+
 
 Text Dataset
 ------------
@@ -146,27 +147,21 @@ API Reference
 
 .. automodule:: mxnet.gluon.contrib
     :members:
-    :imported-members:
 
 .. automodule:: mxnet.gluon.contrib.nn
     :members:
-    :imported-members:
 
 .. automodule:: mxnet.gluon.contrib.cnn
     :members:
-    :imported-members:
 
 .. automodule:: mxnet.gluon.contrib.rnn
     :members:
-    :imported-members:
 
-.. automodule:: mxnet.gluon.contrib.data
+.. automodule:: mxnet.gluon.contrib.data.sampler
     :members:
-    :imported-members:
 
 .. automodule:: mxnet.gluon.contrib.data.text
     :members:
-    :imported-members:
 
 .. automodule:: mxnet.gluon.contrib.estimator
     :members:
diff --git a/docs/python_docs/python/api/gluon/hybrid_block.rst b/docs/python_docs/python/api/gluon/hybrid_block.rst
new file mode 100644
index 000000000000..fa2e1562ae0f
--- /dev/null
+++ b/docs/python_docs/python/api/gluon/hybrid_block.rst
@@ -0,0 +1,25 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+gluon.HybridBlock
+=================
+
+
+.. autoclass:: mxnet.gluon.HybridBlock
+    :members:
+    :inherited-members:
+    :autosummary:
\ No newline at end of file
diff --git a/docs/python_docs/python/api/gluon/index.rst b/docs/python_docs/python/api/gluon/index.rst
index e2c8aa99961c..399e46eec1ba 100644
--- a/docs/python_docs/python/api/gluon/index.rst
+++ b/docs/python_docs/python/api/gluon/index.rst
@@ -16,7 +16,7 @@
    under the License.
 
 mxnet.gluon
-=======================
+============
 
 The Gluon library in Apache MXNet provides a clear, concise, and simple API for deep learning.
 It makes it easy to prototype, build, and train deep learning models without sacrificing training speed.
@@ -39,6 +39,9 @@ one input layer, one hidden layer, and one output layer.
        net.add(gluon.nn.Dense(num_outputs))
 
 
+.. automodule:: mxnet.gluon
+
+
 Tutorials
 ---------
 
@@ -73,13 +76,13 @@ Core Modules
 
    .. card::
       :title: gluon.nn
-      :link: nn.html
+      :link: nn/index.html
 
       Neural network components.
 
    .. card::
       :title: gluon.rnn
-      :link: rnn.html 
+      :link: rnn/index.html
 
       Recurrent neural network components.
 
@@ -90,19 +93,19 @@ Training
 
    .. card::
       :title: gluon.loss
-      :link: mxnet.gluon.loss.html
+      :link: loss/index.html
 
       Loss functions for training neural networks.
 
    .. card::
-      :title: gluon.parameter
-      :link: mxnet.gluon.parameter.html
+      :title: gluon.Parameter
+      :link: mxnet.gluon.Parameter.html
 
       Parameter getting and setting functions.
 
    .. card::
       :title: gluon.Trainer
-      :link: mxnet.gluon.trainer.html
+      :link: mxnet.gluon.Trainer.html
 
       Functions for applying an optimizer on a set of parameters.
 
@@ -113,19 +116,19 @@ Data
 
    .. card::
       :title: gluon.data
-      :link: mxnet.gluon.data.html
+      :link: data/index.html
 
       Dataset utilities.
 
    .. card::
       :title: gluon.data.vision
-      :link: mxnet.gluon.data.vision.html
+      :link: data/vision/index.html
 
       Image dataset utilities.
 
    .. card::
       :title: gluon.model_zoo.vision
-      :link: mxnet.gluon.model_zoo.vision.html
+      :link: model_zoo/index.vision.html
 
       A module for loading pre-trained neural network models.
 
@@ -137,20 +140,20 @@ Utilities
 
    .. card::
       :title: gluon.utils
-      :link: mxnet.gluon.utils.html
+      :link: utils/index.html
 
       A variety of utilities for training.
 
 .. toctree::
    :hidden:
    :maxdepth: 2
-
-   nn
-   rnn
-   mxnet.gluon.loss
-   mxnet.gluon.parameter
-   mxnet.gluon.Trainer
-   mxnet.gluon.data
-   mxnet.gluon.data.vision
-   mxnet.gluon.model_zoo
-   mxnet.gluon.utils
+   :glob:
+
+   block
+   hybrid_block
+   symbol_block
+   constant
+   parameter
+   parameter_dict
+   trainer
+   */index
\ No newline at end of file
diff --git a/docs/python_docs/python/api/advanced/mxnet.kvstore_server.rst b/docs/python_docs/python/api/gluon/loss/index.rst
similarity index 80%
rename from docs/python_docs/python/api/advanced/mxnet.kvstore_server.rst
rename to docs/python_docs/python/api/gluon/loss/index.rst
index 6ea8e0d2f296..5b13d6fdc342 100644
--- a/docs/python_docs/python/api/advanced/mxnet.kvstore_server.rst
+++ b/docs/python_docs/python/api/gluon/loss/index.rst
@@ -15,22 +15,13 @@
    specific language governing permissions and limitations
    under the License.
 
-mxnet.kvstore\_server
-=====================
-
-.. automodule:: mxnet.kvstore_server
-
-
-
-
-
-
-
-   .. rubric:: Classes
-
-   .. autosummary::
-      :toctree: _autogen
-
-      KVStoreServer
+gluon.loss
+==========
 
+Gluon provides pre-defined loss functions in the :py:mod:`mxnet.gluon.loss`
+module.
 
+.. automodule:: mxnet.gluon.loss
+    :members:
+    :imported-members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/gluon/mxnet.gluon.model_zoo.rst b/docs/python_docs/python/api/gluon/model_zoo/index.rst
similarity index 84%
rename from docs/python_docs/python/api/gluon/mxnet.gluon.model_zoo.rst
rename to docs/python_docs/python/api/gluon/model_zoo/index.rst
index 0212528d8259..b16b72bab441 100644
--- a/docs/python_docs/python/api/gluon/mxnet.gluon.model_zoo.rst
+++ b/docs/python_docs/python/api/gluon/model_zoo/index.rst
@@ -15,13 +15,13 @@
    specific language governing permissions and limitations
    under the License.
 
-model_zoo.vision
-=================
+gluon.model_zoo.vision
+======================
 
 .. automodule:: mxnet.gluon.model_zoo.vision
+   :noindex:
 
 .. autosummary::
-   :toctree: _autogen
 
     get_model
 
@@ -29,7 +29,6 @@ ResNet
 ------
 
 .. autosummary::
-   :toctree: _autogen
 
     resnet18_v1
     resnet34_v1
@@ -43,7 +42,6 @@ ResNet
     resnet152_v2
 
 .. autosummary::
-   :toctree: _autogen
 
     ResNetV1
     ResNetV2
@@ -57,7 +55,6 @@ VGG
 ---
 
 .. autosummary::
-   :toctree: _autogen
 
     vgg11
     vgg13
@@ -69,7 +66,6 @@ VGG
     vgg19_bn
 
 .. autosummary::
-   :toctree: _autogen
 
     VGG
     get_vgg
@@ -78,12 +74,10 @@ Alexnet
 --------
 
 .. autosummary::
-   :toctree: _autogen
 
     alexnet
 
 .. autosummary::
-   :toctree: _autogen
 
     AlexNet
 
@@ -93,7 +87,6 @@ DenseNet
 
 
 .. autosummary::
-   :toctree: _autogen
 
     densenet121
     densenet161
@@ -103,7 +96,6 @@ DenseNet
 
 
 .. autosummary::
-   :toctree: _autogen
 
     DenseNet
 
@@ -113,7 +105,6 @@ SqueezeNet
 
 
 .. autosummary::
-   :toctree: _autogen
 
     squeezenet1_0
     squeezenet1_1
@@ -121,7 +112,6 @@ SqueezeNet
 
 
 .. autosummary::
-   :toctree: _autogen
 
     SqueezeNet
 
@@ -131,14 +121,11 @@ Inception
 
 
 .. autosummary::
-   :toctree: _autogen
 
     inception_v3
 
 
-
 .. autosummary::
-   :toctree: _autogen
 
     Inception3
 
@@ -148,7 +135,6 @@ MobileNet
 
 
 .. autosummary::
-   :toctree: _autogen
 
     mobilenet1_0
     mobilenet0_75
@@ -161,7 +147,15 @@ MobileNet
 
 
 .. autosummary::
-   :toctree: _autogen
 
     MobileNet
     MobileNetV2
+
+API Reference
+-------------
+
+.. automodule:: mxnet.gluon.model_zoo.vision
+    :members:
+    :imported-members:
+    :autosummary:
+
diff --git a/docs/python_docs/python/api/gluon/mxnet.gluon.HybridBlock.rst b/docs/python_docs/python/api/gluon/mxnet.gluon.HybridBlock.rst
deleted file mode 100644
index ebba455d2f23..000000000000
--- a/docs/python_docs/python/api/gluon/mxnet.gluon.HybridBlock.rst
+++ /dev/null
@@ -1,40 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-HybridBlock
-===========
-
-.. currentmodule:: mxnet.gluon.nn
-
-.. autoclass:: HybridBlock
-   :members:
-   :inherited-members:
-
-   HybridBlock inherits all methods and attributes from
-   :py:class:`mxnet.gluon.nn.Block`. It adds the following additional methods:
-
-   .. rubric:: Symbol related methods
-
-   .. autosummary::
-      :toctree: _autogen
-
-      HybridBlock.hybridize
-      HybridBlock.hybrid_forward
-      HybridBlock.export
-      HybridBlock.infer_shape
-      HybridBlock.infer_type
-
diff --git a/docs/python_docs/python/api/gluon/mxnet.gluon.ParameterDict.rst b/docs/python_docs/python/api/gluon/mxnet.gluon.ParameterDict.rst
deleted file mode 100644
index 3750ae8ad9c5..000000000000
--- a/docs/python_docs/python/api/gluon/mxnet.gluon.ParameterDict.rst
+++ /dev/null
@@ -1,79 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-ParameterDict
-=============
-
-.. currentmodule:: mxnet.gluon
-
-.. autoclass:: ParameterDict
-
-Load and save parameters
---------------------------
-
-.. autosummary::
-   :toctree: _autogen
-
-   ParameterDict.load
-   ParameterDict.save
-
-Get a particular parameter
---------------------------
-
-.. autosummary::
-   :toctree: _autogen
-
-   ParameterDict.get
-   ParameterDict.get_constant
-
-Get (name, paramter) pairs
---------------------------
-
-.. autosummary::
-   :toctree: _autogen
-
-   ParameterDict.items
-   ParameterDict.keys
-   ParameterDict.values
-
-Update parameters
---------------------------
-
-.. autosummary::
-   :toctree: _autogen
-
-   ParameterDict.initialize
-   ParameterDict.setattr
-   ParameterDict.update
-
-
-Set devices contexts and gradients
----------------------------------------
-
-.. autosummary::
-   :toctree: _autogen
-
-   ParameterDict.reset_ctx
-   ParameterDict.zero_grad
-
-Attributes
----------------------------------------
-
-
-.. autosummary::
-
-   ParameterDict.prefix
diff --git a/docs/python_docs/python/api/gluon/mxnet.gluon.Trainer.rst b/docs/python_docs/python/api/gluon/mxnet.gluon.Trainer.rst
deleted file mode 100644
index ff122c8481f1..000000000000
--- a/docs/python_docs/python/api/gluon/mxnet.gluon.Trainer.rst
+++ /dev/null
@@ -1,51 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-Trainer
-===========
-
-.. currentmodule:: mxnet.gluon
-
-.. autoclass:: Trainer
-
-Updating parameters
--------------------
-
-.. autosummary::
-   :toctree: _autogen
-
-   Trainer.step
-   Trainer.allreduce_grads
-   Trainer.update
-
-Trainer States
---------------
-
-.. autosummary::
-   :toctree: _autogen
-
-   Trainer.load_states
-   Trainer.save_states
-
-Learning rate
---------------
-
-.. autosummary::
-   :toctree: _autogen
-
-   Trainer.learning_rate
-   Trainer.set_learning_rate
diff --git a/docs/python_docs/python/api/gluon/mxnet.gluon.data.rst b/docs/python_docs/python/api/gluon/mxnet.gluon.data.rst
deleted file mode 100644
index aa4f985d75f8..000000000000
--- a/docs/python_docs/python/api/gluon/mxnet.gluon.data.rst
+++ /dev/null
@@ -1,50 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-data
-====
-
-.. automodule:: mxnet.gluon.data
-
-Datasets
---------
-
-.. autosummary::
-   :toctree: _autogen
-
-   Dataset
-   ArrayDataset
-   RecordFileDataset
-
-Sampling examples
-------------------
-
-.. autosummary::
-   :toctree: _autogen
-
-   Sampler
-   SequentialSampler
-   RandomSampler
-   BatchSampler
-
-Loading examples
-----------------
-
-.. autosummary::
-   :toctree: _autogen
-
-   DataLoader
\ No newline at end of file
diff --git a/docs/python_docs/python/api/gluon/mxnet.gluon.data.vision.rst b/docs/python_docs/python/api/gluon/mxnet.gluon.data.vision.rst
deleted file mode 100644
index 062a894b48f4..000000000000
--- a/docs/python_docs/python/api/gluon/mxnet.gluon.data.vision.rst
+++ /dev/null
@@ -1,58 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-data.vision
-============
-
-.. automodule:: mxnet.gluon.data.vision
-
-Datasets
-^^^^^^^^
-
-.. autosummary::
-   :toctree: _autogen
-
-   datasets.MNIST
-   datasets.FashionMNIST
-   datasets.CIFAR10
-   datasets.CIFAR100
-   datasets.ImageRecordDataset
-   datasets.ImageFolderDataset
-
-
-Data transformations
-^^^^^^^^^^^^^^^^^^^^
-
-
-.. autosummary::
-   :toctree: _autogen
-
-   transforms.Compose
-   transforms.Cast
-   transforms.ToTensor
-   transforms.Normalize
-   transforms.RandomResizedCrop
-   transforms.CenterCrop
-   transforms.Resize
-   transforms.RandomFlipLeftRight
-   transforms.RandomFlipTopBottom
-   transforms.RandomBrightness
-   transforms.RandomContrast
-   transforms.RandomSaturation
-   transforms.RandomHue
-   transforms.RandomColorJitter
-   transforms.RandomLighting
diff --git a/docs/python_docs/python/api/gluon/mxnet.gluon.loss.rst b/docs/python_docs/python/api/gluon/mxnet.gluon.loss.rst
deleted file mode 100644
index e18b86da7006..000000000000
--- a/docs/python_docs/python/api/gluon/mxnet.gluon.loss.rst
+++ /dev/null
@@ -1,40 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-``loss``
-=================================
-
-Gluon provides pre-defined loss functions in the :py:mod:`mxnet.gluon.parameter`
-module.
-
-.. automodule:: mxnet.gluon.loss
-
-.. autosummary::
-   :toctree: _autogen
-
-   Loss
-   L2Loss
-   L1Loss
-   SigmoidBinaryCrossEntropyLoss
-   SoftmaxCrossEntropyLoss
-   KLDivLoss
-   HuberLoss
-   HingeLoss
-   SquaredHingeLoss
-   LogisticLoss
-   TripletLoss
-   CTCLoss
diff --git a/docs/python_docs/python/api/gluon/mxnet.gluon.nn.Block.rst b/docs/python_docs/python/api/gluon/mxnet.gluon.nn.Block.rst
deleted file mode 100644
index fbf843dceedb..000000000000
--- a/docs/python_docs/python/api/gluon/mxnet.gluon.nn.Block.rst
+++ /dev/null
@@ -1,86 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-Block
-=====
-
-.. currentmodule:: mxnet.gluon.nn
-
-.. autoclass:: Block
-   :members:
-   :inherited-members:
-
-   ..
-      .. automethod:: __init__
-
-
-   .. rubric:: Handle model parameters:
-
-   .. autosummary::
-      :toctree: _autogen
-
-      Block.initialize
-      Block.save_parameters
-      Block.load_parameters
-      Block.collect_params
-      Block.cast
-      Block.apply
-
-   .. rubric:: Run computation
-
-   .. autosummary::
-      :toctree: _autogen
-
-      Block.forward
-
-   .. rubric:: Debugging
-
-   .. autosummary::
-      :toctree: _autogen
-
-      Block.summary
-
-   .. rubric:: Advanced API for customization
-
-
-   .. autosummary::
-      :toctree: _autogen
-
-      Block.name_scope
-      Block.register_child
-      Block.register_forward_hook
-      Block.register_forward_pre_hook
-
-   .. rubric:: Attributes
-
-   .. autosummary::
-
-      Block.name
-      Block.params
-      Block.prefix
-
-
-   .. warning::
-
-      The following two APIs are deprecated since `v1.2.1
-      <https://github.com/apache/incubator-mxnet/releases/tag/1.2.1>`_.
-
-      .. autosummary::
-          :toctree: _autogen
-
-          Block.save_params
-          Block.load_params
diff --git a/docs/python_docs/python/api/gluon/mxnet.gluon.nn.HybridBlock.rst b/docs/python_docs/python/api/gluon/mxnet.gluon.nn.HybridBlock.rst
deleted file mode 100644
index 4d93820f85a0..000000000000
--- a/docs/python_docs/python/api/gluon/mxnet.gluon.nn.HybridBlock.rst
+++ /dev/null
@@ -1,66 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-HybridBlock
-===========
-
-.. currentmodule:: mxnet.gluon.nn
-
-.. autoclass:: HybridBlock
-   :members:
-   :inherited-members:
-
-   .. automethod:: __init__
-
-
-   .. rubric:: Methods
-
-   .. autosummary::
-
-      ~HybridBlock.__init__
-      ~HybridBlock.apply
-      ~HybridBlock.cast
-      ~HybridBlock.collect_params
-      ~HybridBlock.export
-      ~HybridBlock.forward
-      ~HybridBlock.hybrid_forward
-      ~HybridBlock.hybridize
-      ~HybridBlock.infer_shape
-      ~HybridBlock.infer_type
-      ~HybridBlock.initialize
-      ~HybridBlock.load_parameters
-      ~HybridBlock.load_params
-      ~HybridBlock.name_scope
-      ~HybridBlock.register_child
-      ~HybridBlock.register_forward_hook
-      ~HybridBlock.register_forward_pre_hook
-      ~HybridBlock.save_parameters
-      ~HybridBlock.save_params
-      ~HybridBlock.summary
-
-
-
-
-
-   .. rubric:: Attributes
-
-   .. autosummary::
-
-      ~HybridBlock.name
-      ~HybridBlock.params
-      ~HybridBlock.prefix
-
diff --git a/docs/python_docs/python/api/gluon/mxnet.gluon.nn.SymbolBlock.rst b/docs/python_docs/python/api/gluon/mxnet.gluon.nn.SymbolBlock.rst
deleted file mode 100644
index 1275adbe0a97..000000000000
--- a/docs/python_docs/python/api/gluon/mxnet.gluon.nn.SymbolBlock.rst
+++ /dev/null
@@ -1,67 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-SymbolBlock
-===========
-
-.. currentmodule:: mxnet.gluon.nn
-
-.. autoclass:: SymbolBlock
-
-   :members:
-   :inherited-members:   
-   .. automethod:: __init__
-
-
-   .. rubric:: Methods
-
-   .. autosummary::
-
-      ~SymbolBlock.__init__
-      ~SymbolBlock.apply
-      ~SymbolBlock.cast
-      ~SymbolBlock.collect_params
-      ~SymbolBlock.export
-      ~SymbolBlock.forward
-      ~SymbolBlock.hybrid_forward
-      ~SymbolBlock.hybridize
-      ~SymbolBlock.imports
-      ~SymbolBlock.infer_shape
-      ~SymbolBlock.infer_type
-      ~SymbolBlock.initialize
-      ~SymbolBlock.load_parameters
-      ~SymbolBlock.load_params
-      ~SymbolBlock.name_scope
-      ~SymbolBlock.register_child
-      ~SymbolBlock.register_forward_hook
-      ~SymbolBlock.register_forward_pre_hook
-      ~SymbolBlock.save_parameters
-      ~SymbolBlock.save_params
-      ~SymbolBlock.summary
-
-
-
-
-
-   .. rubric:: Attributes
-
-   .. autosummary::
-
-      ~SymbolBlock.name
-      ~SymbolBlock.params
-      ~SymbolBlock.prefix
-
diff --git a/docs/python_docs/python/api/gluon/mxnet.gluon.parameter.rst b/docs/python_docs/python/api/gluon/mxnet.gluon.parameter.rst
deleted file mode 100644
index e21fd92de331..000000000000
--- a/docs/python_docs/python/api/gluon/mxnet.gluon.parameter.rst
+++ /dev/null
@@ -1,68 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-Parameter
-=========
-
-.. currentmodule:: mxnet.gluon
-
-.. autoclass:: Parameter
-
-
-Get and set parameters
------------------------
-
-.. autosummary::
-   :toctree: _autogen
-
-   Parameter.initialize
-   Parameter.data
-   Parameter.list_data
-   Parameter.list_row_sparse_data
-   Parameter.row_sparse_data
-   Parameter.set_data
-   Parameter.shape
-
-
-Get and set gradients associated with parameters
--------------------------------------------------
-
-.. autosummary::
-   :toctree: _autogen
-
-   Parameter.grad
-   Parameter.list_grad
-   Parameter.zero_grad
-   Parameter.grad_req
-
-Handle device contexts
-------------------------
-
-.. autosummary::
-   :toctree: _autogen
-
-   Parameter.cast
-   Parameter.list_ctx
-   Parameter.reset_ctx
-
-Convert to symbol
---------------------
-
-.. autosummary::
-   :toctree: _autogen
-
-   Parameter.var
diff --git a/docs/python_docs/python/api/gluon/mxnet.gluon.utils.rst b/docs/python_docs/python/api/gluon/mxnet.gluon.utils.rst
deleted file mode 100644
index 661217aae6c7..000000000000
--- a/docs/python_docs/python/api/gluon/mxnet.gluon.utils.rst
+++ /dev/null
@@ -1,31 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-utils
-=========
-
-.. automodule:: mxnet.gluon.utils
-
-.. autosummary::
-   :toctree: _autogen
-
-   split_data
-   split_and_load
-   clip_global_norm
-   download
-   check_sha1
-
diff --git a/docs/python_docs/python/api/gluon/nn.rst b/docs/python_docs/python/api/gluon/nn/index.rst
similarity index 81%
rename from docs/python_docs/python/api/gluon/nn.rst
rename to docs/python_docs/python/api/gluon/nn/index.rst
index 12d829d92720..110a9985c5ea 100644
--- a/docs/python_docs/python/api/gluon/nn.rst
+++ b/docs/python_docs/python/api/gluon/nn/index.rst
@@ -15,8 +15,8 @@
    specific language governing permissions and limitations
    under the License.
    
-nn and contrib.nn
-=========================
+gluon.nn
+========
 
 Gluon provides a large number of build-in neural network layers in the following
 two modules:
@@ -36,16 +36,6 @@ We group all layers in these two modules according to their categories.
 
 .. currentmodule:: mxnet.gluon
 
-Blocks
-------
-
-.. autosummary::
-   :nosignatures:
-   :toctree: .
-
-   nn.Block
-   nn.HybridBlock
-   nn.SymbolBlock
 
 
 Sequential containers
@@ -53,29 +43,17 @@ Sequential containers
 
 
 .. autosummary::
-    :toctree: _autogen
     :nosignatures:
 
     nn.Sequential
     nn.HybridSequential
 
-Concurrent containers
----------------------
-
-.. autosummary::
-    :toctree: _autogen
-    :nosignatures:
-
-    contrib.nn.Concurrent
-    contrib.nn.HybridConcurrent
-
 
 Basic Layers
 ------------
 
 .. autosummary::
     :nosignatures:
-    :toctree: _autogen
 
     nn.Dense
     nn.Activation
@@ -89,7 +67,6 @@ Convolutional Layers
 
 .. autosummary::
     :nosignatures:
-    :toctree: _autogen
 
     nn.Conv1D
     nn.Conv2D
@@ -103,7 +80,6 @@ Pooling Layers
 
 .. autosummary::
    :nosignatures:
-   :toctree: _autogen
 
     nn.MaxPool1D
     nn.MaxPool2D
@@ -124,22 +100,18 @@ Normalization Layers
 
 .. autosummary::
     :nosignatures:
-    :toctree: _autogen
 
     nn.BatchNorm
     nn.InstanceNorm
     nn.LayerNorm
-    contrib.nn.SyncBatchNorm
 
 Embedding Layers
 ----------------
 
 .. autosummary::
     :nosignatures:
-    :toctree: _autogen
 
     nn.Embedding
-    contrib.nn.SparseEmbedding
 
 
 Advanced Activation Layers
@@ -147,10 +119,16 @@ Advanced Activation Layers
 
 .. autosummary::
     :nosignatures:
-    :toctree: _autogen
 
     nn.LeakyReLU
     nn.PReLU
     nn.ELU
     nn.SELU
     nn.Swish
+
+API Reference
+-------------
+.. automodule:: mxnet.gluon.nn
+    :members:
+    :imported-members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/gluon/parameter.rst b/docs/python_docs/python/api/gluon/parameter.rst
new file mode 100644
index 000000000000..ee889b50b007
--- /dev/null
+++ b/docs/python_docs/python/api/gluon/parameter.rst
@@ -0,0 +1,25 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+gluon.Parameter
+===============
+
+
+.. autoclass:: mxnet.gluon.Parameter
+    :members:
+    :inherited-members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/gluon/parameter_dict.rst b/docs/python_docs/python/api/gluon/parameter_dict.rst
new file mode 100644
index 000000000000..80f719441a6e
--- /dev/null
+++ b/docs/python_docs/python/api/gluon/parameter_dict.rst
@@ -0,0 +1,25 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+gluon.ParameterDict
+===================
+
+
+.. autoclass:: mxnet.gluon.ParameterDict
+    :members:
+    :inherited-members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/gluon/rnn.rst b/docs/python_docs/python/api/gluon/rnn/index.rst
similarity index 75%
rename from docs/python_docs/python/api/gluon/rnn.rst
rename to docs/python_docs/python/api/gluon/rnn/index.rst
index b13df3094813..e02cdc6f7ac8 100644
--- a/docs/python_docs/python/api/gluon/rnn.rst
+++ b/docs/python_docs/python/api/gluon/rnn/index.rst
@@ -15,8 +15,8 @@
    specific language governing permissions and limitations
    under the License.
 
-rnn and contrib.rnn
-============================
+gluon.rnn
+=========
 
 Build-in recurrent neural network layers are provided in the following two modules:
 
@@ -34,7 +34,6 @@ Recurrent Cells
 
 .. autosummary::
     :nosignatures:
-    :toctree: _autogen
 
     rnn.LSTMCell
     rnn.GRUCell
@@ -44,25 +43,20 @@ Recurrent Cells
     rnn.DropoutCell
     rnn.ZoneoutCell
     rnn.ResidualCell
-    contrib.rnn.Conv1DRNNCell
-    contrib.rnn.Conv2DRNNCell
-    contrib.rnn.Conv3DRNNCell
-    contrib.rnn.Conv1DLSTMCell
-    contrib.rnn.Conv2DLSTMCell
-    contrib.rnn.Conv3DLSTMCell
-    contrib.rnn.Conv1DGRUCell
-    contrib.rnn.Conv2DGRUCell
-    contrib.rnn.Conv3DGRUCell
-    contrib.rnn.VariationalDropoutCell
-    contrib.rnn.LSTMPCell
 
 Recurrent Layers
 ----------------
 
 .. autosummary::
     :nosignatures:
-    :toctree: _autogen
 
     rnn.RNN
     rnn.LSTM
     rnn.GRU
+
+API Reference
+-------------
+.. automodule:: mxnet.gluon.rnn
+    :members:
+    :imported-members:
+    :autosummary:
\ No newline at end of file
diff --git a/docs/python_docs/python/api/gluon/symbol_block.rst b/docs/python_docs/python/api/gluon/symbol_block.rst
new file mode 100644
index 000000000000..2d94f208d5ba
--- /dev/null
+++ b/docs/python_docs/python/api/gluon/symbol_block.rst
@@ -0,0 +1,25 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+gluon.SymbolBlock
+===========
+
+
+.. autoclass:: mxnet.gluon.SymbolBlock
+    :members:
+    :inherited-members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/gluon/trainer.rst b/docs/python_docs/python/api/gluon/trainer.rst
new file mode 100644
index 000000000000..fe25b3d3c68b
--- /dev/null
+++ b/docs/python_docs/python/api/gluon/trainer.rst
@@ -0,0 +1,25 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+gluon.Trainer
+===========
+
+
+.. autoclass:: mxnet.gluon.Trainer
+    :members:
+    :inherited-members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/gluon/utils/index.rst b/docs/python_docs/python/api/gluon/utils/index.rst
new file mode 100644
index 000000000000..96429e9b87cc
--- /dev/null
+++ b/docs/python_docs/python/api/gluon/utils/index.rst
@@ -0,0 +1,24 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+gluon.utils
+===========
+
+.. automodule:: mxnet.gluon.utils
+    :members:
+    :autosummary:
+
diff --git a/docs/python_docs/python/api/index.rst b/docs/python_docs/python/api/index.rst
index 7e08f0707550..47363cf07c5c 100644
--- a/docs/python_docs/python/api/index.rst
+++ b/docs/python_docs/python/api/index.rst
@@ -26,43 +26,192 @@ describing what they are and what they do. The APIs are grouped into the
 following categories:
 
 
+Imperative API
+---------------
 .. container:: cards
 
    .. card::
-      :title: NDArray module
+      :title: mxnet.ndarray
       :link: ndarray/index.html
 
       Imperative APIs to manipulate multi-dimensional arrays.
 
    .. card::
-      :title: Gluon module
+      :title: mxnet.gluon
       :link: gluon/index.html
 
       Imperative APIs to load data, construct and train neural networks.
 
+
+
+Gluon related modules
+---------------------
+
+.. container:: cards
+
+   .. card::
+      :title: mxnet.autograd
+      :link: autograd/index.html
+
+      Functions for Automatic differentiation.
+
+   .. card::
+      :title: mxnet.optimizer
+      :link: optimizer/index.html
+
+      Functions for applying an optimizer on weights.
+
+   .. card::
+      :title: mxnet.initializer
+      :link: initializer/index.html
+
+      Default behaviors to initialize parameters.
+
+   .. card::
+      :title: mxnet.lr_scheduler
+      :link: lr_scheduler/index.html
+
+      Scheduling the learning rate.
+
+   .. card::
+      :title: mxnet.metric
+      :link: metric/index.html
+
+      Metrics to evaluate the performance of a learned model.
+
    .. card::
-      :title: Gluon-related modules
-      :link: gluon-related/index.html
+      :title: mxnet.kvstore
+      :link: kvstore/index.html
 
-      Modules, including context and optimizer, that can be used with Gluon.
+      Key value store interface of MXNet for parameter synchronization.
 
    .. card::
-      :title: Symbol module
+      :title: mxnet.context
+      :link: mxnet/context/index.html
+
+      CPU and GPU context information.
+
+   .. card::
+      :title: mxnet.profiler
+      :link: mxnet/profiler/index.html
+
+      Profiler setting methods.
+
+   .. card::
+      :title: mxnet.random
+      :link: mxnet/random/index.html
+
+      Imperative random distribution generator functions.
+
+
+Symbolic API
+------------
+
+.. container:: cards
+
+   .. card::
+      :title: mxnet.sym
       :link: symbol/index.html
 
-      Symbolic APIs for multi-dimensional arrays and neural network layers.
+      Symbolic APIs for multi-dimensional arrays and neural network layers
+
+   .. card::
+      :title: mxnet.module
+      :link: module/index.html
+
+      Intermediate and high-level interface for performing computation with Symbols.
+
+
+Symbol related modules
+----------------------
+
+.. container:: cards
+
+   .. card::
+      :title: mxnet.callback
+      :link: mxnet/callback/index.html
+
+      Functions to track various statuses during an epoch.
+
+   .. card::
+      :title: mxnet.monitor
+      :link: mxnet/monitor/index.html
+
+      Outputs, weights, and gradients for debugging
+
+
+   .. card::
+      :title: mxnet.image
+      :link: mxnet/image/index.html
+
+      Image iterators and image augmentation functions.
+
+   .. card::
+      :title: mxnet.io
+      :link: mxnet/io/index.html
+
+      Data iterators for common data formats and utility functions.
+
+   .. card::
+      :title: mxnet.recordio
+      :link: mxnet/recordio/index.html
+
+      Read and write for the RecordIO data format.
+
+   .. card::
+      :title: mxnet.visualization
+      :link: mxnet/visualization/index.html
+
+      Functions for Symbol visualization.
+
+Advanced modules
+----------------
+
+.. container:: cards
+
+   .. card::
+      :title: mxnet.executor
+      :link: mxnet/executor/index.html
+
+      Managing symbolic graph execution.
+
+   .. card::
+      :title: mxnet.kvstore_server
+      :link: mxnet/kvstore_server/index.html
+
+      Server node for the key value store.
+
+   .. card::
+      :title: mxnet.engine
+      :link: mxnet/engine/index.html
+
+      Engine properties management.
+
+
+   .. card::
+      :title: mxnet.executor_manager
+      :link: mxnet/executor_manager/index.html
+
+      Executor manager
+
+
+   .. card::
+      :title: mxnet.rtc
+      :link: mxnet/rtc/index.html
+
+      Tools for compiling and running CUDA code from the python frontend.
 
    .. card::
-      :title: Symbol-related modules
-      :link: symbol-related/index.html
+      :title: mxnet.test_utils
+      :link: mxnet/test_utils/index.html
 
-      Modules to train and visualize symbolic neural network models.
+      Tools for using and testing MXNet.
 
    .. card::
-      :title: Advanced modules
-      :link: advanced/index.html
+      :title: mxnet.util
+      :link: mxnet/util/index.html
 
-      Advanced APIs for hackers.
+      General utility functions
 
 
 .. toctree::
@@ -71,7 +220,14 @@ following categories:
 
    ndarray/index
    gluon/index
-   gluon-related/index
+   autograd/index
+   initializer/index
+   optimizer/index
+   lr_scheduler/index
+   metric/index
+   kvstore/index
    symbol/index
-   symbol-related/index
-   advanced/index
+   module/index
+   contrib/index
+   image/index
+   mxnet/index
diff --git a/docs/python_docs/python/api/initializer/index.rst b/docs/python_docs/python/api/initializer/index.rst
new file mode 100644
index 000000000000..fe72e82a85a4
--- /dev/null
+++ b/docs/python_docs/python/api/initializer/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.initializer
+=================
+
+.. automodule:: mxnet.initializer
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/gluon-related/mxnet.kvstore.rst b/docs/python_docs/python/api/kvstore/index.rst
similarity index 92%
rename from docs/python_docs/python/api/gluon-related/mxnet.kvstore.rst
rename to docs/python_docs/python/api/kvstore/index.rst
index 815335215575..f79a71993dc9 100644
--- a/docs/python_docs/python/api/gluon-related/mxnet.kvstore.rst
+++ b/docs/python_docs/python/api/kvstore/index.rst
@@ -19,9 +19,5 @@ mxnet.kvstore
 =============
 
 .. automodule:: mxnet.kvstore
-
-   .. autosummary::
-      :toctree: .
-
-      create
-      KVStore
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/lr_scheduler/index.rst b/docs/python_docs/python/api/lr_scheduler/index.rst
new file mode 100644
index 000000000000..33a5e5ef9042
--- /dev/null
+++ b/docs/python_docs/python/api/lr_scheduler/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.lr_scheduler
+==================
+
+.. automodule:: mxnet.lr_scheduler
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/metric/index.rst b/docs/python_docs/python/api/metric/index.rst
new file mode 100644
index 000000000000..4b24d1e703db
--- /dev/null
+++ b/docs/python_docs/python/api/metric/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.metric
+============
+
+.. automodule:: mxnet.metric
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/module/index.rst b/docs/python_docs/python/api/module/index.rst
new file mode 100644
index 000000000000..12040dbf799b
--- /dev/null
+++ b/docs/python_docs/python/api/module/index.rst
@@ -0,0 +1,24 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.module
+============
+
+.. automodule:: mxnet.module
+    :members:
+    :imported-members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/mxnet/attribute/index.rst b/docs/python_docs/python/api/mxnet/attribute/index.rst
new file mode 100644
index 000000000000..655d387a6c1b
--- /dev/null
+++ b/docs/python_docs/python/api/mxnet/attribute/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.attribute
+===============
+
+.. automodule:: mxnet.attribute
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/gluon/mxnet.gluon.Constant.rst b/docs/python_docs/python/api/mxnet/base/index.rst
similarity index 90%
rename from docs/python_docs/python/api/gluon/mxnet.gluon.Constant.rst
rename to docs/python_docs/python/api/mxnet/base/index.rst
index 2db732f6872f..d243493fb9d1 100644
--- a/docs/python_docs/python/api/gluon/mxnet.gluon.Constant.rst
+++ b/docs/python_docs/python/api/mxnet/base/index.rst
@@ -15,9 +15,9 @@
    specific language governing permissions and limitations
    under the License.
 
-Constant
-========
+mxnet.base
+==========
 
-.. currentmodule:: mxnet.gluon
-
-.. autoclass:: Constant
+.. automodule:: mxnet.base
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/mxnet/callback/index.rst b/docs/python_docs/python/api/mxnet/callback/index.rst
new file mode 100644
index 000000000000..99cefafb5919
--- /dev/null
+++ b/docs/python_docs/python/api/mxnet/callback/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.callback
+===============
+
+.. automodule:: mxnet.callback
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/gluon-related/mxnet.context.rst b/docs/python_docs/python/api/mxnet/context/index.rst
similarity index 88%
rename from docs/python_docs/python/api/gluon-related/mxnet.context.rst
rename to docs/python_docs/python/api/mxnet/context/index.rst
index 12e51834c7d3..135a8440bb06 100644
--- a/docs/python_docs/python/api/gluon-related/mxnet.context.rst
+++ b/docs/python_docs/python/api/mxnet/context/index.rst
@@ -19,15 +19,5 @@ mxnet.context
 =============
 
 .. automodule:: mxnet.context
-
-
-
-.. autosummary::
-   :toctree: _autogen
-
-   cpu
-   cpu_pinned
-   current_context
-   gpu
-   num_gpus
-   Context
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/advanced/mxnet.engine.rst b/docs/python_docs/python/api/mxnet/engine/index.rst
similarity index 88%
rename from docs/python_docs/python/api/advanced/mxnet.engine.rst
rename to docs/python_docs/python/api/mxnet/engine/index.rst
index 17b31c5ae315..0ba40d9a1b48 100644
--- a/docs/python_docs/python/api/advanced/mxnet.engine.rst
+++ b/docs/python_docs/python/api/mxnet/engine/index.rst
@@ -19,16 +19,5 @@ mxnet.engine
 ============
 
 .. automodule:: mxnet.engine
-
-
-
-   .. rubric:: Functions
-
-   .. autosummary::
-      :toctree: _autogen
-
-      bulk
-      set_bulk_size
-
-
-
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/advanced/mxnet.executor.rst b/docs/python_docs/python/api/mxnet/executor/index.rst
similarity index 88%
rename from docs/python_docs/python/api/advanced/mxnet.executor.rst
rename to docs/python_docs/python/api/mxnet/executor/index.rst
index a84f10f4f852..ddaa5b31a09b 100644
--- a/docs/python_docs/python/api/advanced/mxnet.executor.rst
+++ b/docs/python_docs/python/api/mxnet/executor/index.rst
@@ -16,19 +16,8 @@
    under the License.
 
 mxnet.executor
-==============
+===============
 
 .. automodule:: mxnet.executor
-
-
-
-
-
-
-
-   .. rubric:: Classes
-
-   .. autosummary::
-      :toctree: _autogen
-
-      Executor
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/mxnet/executor_manager/index.rst b/docs/python_docs/python/api/mxnet/executor_manager/index.rst
new file mode 100644
index 000000000000..2be59bdce8a5
--- /dev/null
+++ b/docs/python_docs/python/api/mxnet/executor_manager/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.executor_manager
+======================
+
+.. automodule:: mxnet.executor_manager
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/gluon/mxnet.gluon.SymbolBlock.rst b/docs/python_docs/python/api/mxnet/image/index.rst
similarity index 74%
rename from docs/python_docs/python/api/gluon/mxnet.gluon.SymbolBlock.rst
rename to docs/python_docs/python/api/mxnet/image/index.rst
index 161e7a111b18..ebd25e705b67 100644
--- a/docs/python_docs/python/api/gluon/mxnet.gluon.SymbolBlock.rst
+++ b/docs/python_docs/python/api/mxnet/image/index.rst
@@ -15,14 +15,14 @@
    specific language governing permissions and limitations
    under the License.
 
-SymbolBlock
+mxnet.image
 ===========
 
-.. currentmodule:: mxnet.gluon.nn
+.. note:: This API is best used in conjunction with ``mxnet.io`` data iterators.
+    For augmentation and transforms in gluon with Datasets and DataLoaders see ``mxnet.gluon.data``
 
-.. autoclass:: SymbolBlock
-   :members:
-   :inherited-members:
 
-   Besides the contruction method, SymbolBlock can be used as same as
-   :py:class:`mxnet.gluon.nn.HybridBlock`.
+.. automodule:: mxnet.image
+    :members:
+    :imported-members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/symbol-related/index.rst b/docs/python_docs/python/api/mxnet/index.rst
similarity index 51%
rename from docs/python_docs/python/api/symbol-related/index.rst
rename to docs/python_docs/python/api/mxnet/index.rst
index 5cc5c14ec964..c70e128c3b0e 100644
--- a/docs/python_docs/python/api/symbol-related/index.rst
+++ b/docs/python_docs/python/api/mxnet/index.rst
@@ -15,39 +15,59 @@
    specific language governing permissions and limitations
    under the License.
 
-Symbol related modules
-======================
+mxnet
+=====
 
-.. container:: cards
+.. automodule:: mxnet
 
-   .. card::
-      :title: mxnet.callback
-      :link: mxnet.callback.html
 
-      Functions to track various statuses during an epoch.
+.. autosummary::
+    :nosignatures:
 
-   .. card::
-      :title: mxnet.module
-      :link: mxnet.module.html
+    mxnet.attribute
+    mxnet.autograd
+    mxnet.base
+    mxnet.callback
+    mxnet.context
+    mxnet.contrib
+    mxnet.engine
+    mxnet.executor
+    mxnet.executor_manager
+    mxnet.gluon
+    mxnet.image
+    mxnet.initializer
+    mxnet.io
+    mxnet.kvstore
+    mxnet.kvstore_server
+    mxnet.libinfo
+    mxnet.lr_scheduler
+    mxnet.log
+    mxnet.metric
+    mxnet.model
+    mxnet.module
+    mxnet.monitor
+    mxnet.name
+    mxnet.ndarray
+    mxnet.notebook
+    mxnet.operator
+    mxnet.optimizer
+    mxnet.profiler
+    mxnet.random
+    mxnet.recordio
+    mxnet.registry
+    mxnet.rtc
+    mxnet.symbol
+    mxnet.test_utils
+    mxnet.torch
+    mxnet.util
+    mxnet.visualization
 
-      Intermediate and high-level interface for performing computation with Symbols. 
 
-   .. card::
-      :title: mxnet.monitor
-      :link: mxnet.monitor.html
 
-      
-
-   .. card::
-      :title: mxnet.visualization
-      :link: mxnet.visualization.html
-
-      Functions for Symbol visualization.
 
 .. toctree::
    :hidden:
+   :maxdepth: 2
+   :glob:
 
-   mxnet.callback
-   mxnet.module
-   mxnet.monitor
-   mxnet.visualization
+   */index
\ No newline at end of file
diff --git a/docs/python_docs/python/api/mxnet/io/index.rst b/docs/python_docs/python/api/mxnet/io/index.rst
new file mode 100644
index 000000000000..115b2422f181
--- /dev/null
+++ b/docs/python_docs/python/api/mxnet/io/index.rst
@@ -0,0 +1,24 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.io
+========
+
+.. automodule:: mxnet.io
+    :members:
+    :imported-members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/mxnet/kvstore_server/index.rst b/docs/python_docs/python/api/mxnet/kvstore_server/index.rst
new file mode 100644
index 000000000000..7aa7d3f57e70
--- /dev/null
+++ b/docs/python_docs/python/api/mxnet/kvstore_server/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.kvstore_server
+====================
+
+.. automodule:: mxnet.kvstore_server
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/mxnet/libinfo/index.rst b/docs/python_docs/python/api/mxnet/libinfo/index.rst
new file mode 100644
index 000000000000..136aca244dcc
--- /dev/null
+++ b/docs/python_docs/python/api/mxnet/libinfo/index.rst
@@ -0,0 +1,24 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.libinfo
+===============
+
+.. automodule:: mxnet.libinfo
+    :members:
+    :imported-members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/symbol-related/mxnet.monitor.rst b/docs/python_docs/python/api/mxnet/monitor/index.rst
similarity index 90%
rename from docs/python_docs/python/api/symbol-related/mxnet.monitor.rst
rename to docs/python_docs/python/api/mxnet/monitor/index.rst
index c7a9d38663c9..dda8cb6fe48c 100644
--- a/docs/python_docs/python/api/symbol-related/mxnet.monitor.rst
+++ b/docs/python_docs/python/api/mxnet/monitor/index.rst
@@ -19,17 +19,5 @@ mxnet.monitor
 =============
 
 .. automodule:: mxnet.monitor
-
-
-
-
-
-
-
-   .. rubric:: Classes
-
-   .. autosummary::
-      :toctree: _autogen
-
-      Monitor
-
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/mxnet/name/index.rst b/docs/python_docs/python/api/mxnet/name/index.rst
new file mode 100644
index 000000000000..53d56b283cc6
--- /dev/null
+++ b/docs/python_docs/python/api/mxnet/name/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.name
+==========
+
+.. automodule:: mxnet.name
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/mxnet/notebook/index.rst b/docs/python_docs/python/api/mxnet/notebook/index.rst
new file mode 100644
index 000000000000..8b672e612f9e
--- /dev/null
+++ b/docs/python_docs/python/api/mxnet/notebook/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.notebook
+===============
+
+.. automodule:: mxnet.notebook
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/mxnet/operator/index.rst b/docs/python_docs/python/api/mxnet/operator/index.rst
new file mode 100644
index 000000000000..cf328e06c6a8
--- /dev/null
+++ b/docs/python_docs/python/api/mxnet/operator/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.operator
+===============
+
+.. automodule:: mxnet.operator
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/mxnet/profiler/index.rst b/docs/python_docs/python/api/mxnet/profiler/index.rst
new file mode 100644
index 000000000000..7dc2112fd9ea
--- /dev/null
+++ b/docs/python_docs/python/api/mxnet/profiler/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.profiler
+===============
+
+.. automodule:: mxnet.profiler
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/gluon-related/mxnet.random.rst b/docs/python_docs/python/api/mxnet/random/index.rst
similarity index 94%
rename from docs/python_docs/python/api/gluon-related/mxnet.random.rst
rename to docs/python_docs/python/api/mxnet/random/index.rst
index 348b48c62cd8..9f37668a55fe 100644
--- a/docs/python_docs/python/api/gluon-related/mxnet.random.rst
+++ b/docs/python_docs/python/api/mxnet/random/index.rst
@@ -19,8 +19,5 @@ mxnet.random
 ============
 
 .. automodule:: mxnet.random
-
-.. autosummary::
-   :toctree: _autogen
-
-   seed
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/mxnet/recordio/index.rst b/docs/python_docs/python/api/mxnet/recordio/index.rst
new file mode 100644
index 000000000000..64815736adca
--- /dev/null
+++ b/docs/python_docs/python/api/mxnet/recordio/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.recordio
+==============
+
+.. automodule:: mxnet.recordio
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/mxnet/registry/index.rst b/docs/python_docs/python/api/mxnet/registry/index.rst
new file mode 100644
index 000000000000..68e6537e417d
--- /dev/null
+++ b/docs/python_docs/python/api/mxnet/registry/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.registry
+==============
+
+.. automodule:: mxnet.registry
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/gluon-related/mxnet.kvstore.create.rst b/docs/python_docs/python/api/mxnet/rtc/index.rst
similarity index 91%
rename from docs/python_docs/python/api/gluon-related/mxnet.kvstore.create.rst
rename to docs/python_docs/python/api/mxnet/rtc/index.rst
index 1a317221b17b..a92b9030580c 100644
--- a/docs/python_docs/python/api/gluon-related/mxnet.kvstore.create.rst
+++ b/docs/python_docs/python/api/mxnet/rtc/index.rst
@@ -15,9 +15,9 @@
    specific language governing permissions and limitations
    under the License.
 
-create
-======
+mxnet.rtc
+=========
 
-.. currentmodule:: mxnet.kvstore
-
-.. autofunction:: create
+.. automodule:: mxnet.rtc
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/mxnet/test_utils/index.rst b/docs/python_docs/python/api/mxnet/test_utils/index.rst
new file mode 100644
index 000000000000..ffb9f0c58797
--- /dev/null
+++ b/docs/python_docs/python/api/mxnet/test_utils/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.test_utils
+================
+
+.. automodule:: mxnet.test_utils
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/mxnet/torch/index.rst b/docs/python_docs/python/api/mxnet/torch/index.rst
new file mode 100644
index 000000000000..f340dd554a6c
--- /dev/null
+++ b/docs/python_docs/python/api/mxnet/torch/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.torch
+===========
+
+.. automodule:: mxnet.torch
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/advanced/mxnet.util.rst b/docs/python_docs/python/api/mxnet/util/index.rst
similarity index 90%
rename from docs/python_docs/python/api/advanced/mxnet.util.rst
rename to docs/python_docs/python/api/mxnet/util/index.rst
index c9d3f5eeb8a4..f8580479010a 100644
--- a/docs/python_docs/python/api/advanced/mxnet.util.rst
+++ b/docs/python_docs/python/api/mxnet/util/index.rst
@@ -19,13 +19,5 @@ mxnet.util
 ==========
 
 .. automodule:: mxnet.util
-
-
-
-   .. rubric:: Functions
-
-   .. autosummary::
-      :toctree: _autogen
-
-      makedirs
-
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/symbol-related/mxnet.visualization.rst b/docs/python_docs/python/api/mxnet/visualization/index.rst
similarity index 88%
rename from docs/python_docs/python/api/symbol-related/mxnet.visualization.rst
rename to docs/python_docs/python/api/mxnet/visualization/index.rst
index 732fd1e443c6..fe6e5828a98d 100644
--- a/docs/python_docs/python/api/symbol-related/mxnet.visualization.rst
+++ b/docs/python_docs/python/api/mxnet/visualization/index.rst
@@ -19,17 +19,5 @@ mxnet.visualization
 ===================
 
 .. automodule:: mxnet.visualization
-
-
-
-   .. rubric:: Functions
-
-   .. autosummary::
-      :toctree: _autogen
-
-      plot_network
-      print_summary
-
-
-
-
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/ndarray/contrib/index.rst b/docs/python_docs/python/api/ndarray/contrib/index.rst
new file mode 100644
index 000000000000..a26ad228bb2c
--- /dev/null
+++ b/docs/python_docs/python/api/ndarray/contrib/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+ndarray.contrib
+===============
+
+.. automodule:: mxnet.ndarray.contrib
+    :members:
+    :autosummary:
\ No newline at end of file
diff --git a/docs/python_docs/python/api/ndarray/image/index.rst b/docs/python_docs/python/api/ndarray/image/index.rst
new file mode 100644
index 000000000000..aca18009b8bb
--- /dev/null
+++ b/docs/python_docs/python/api/ndarray/image/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+ndarray.image
+=============
+
+.. automodule:: mxnet.ndarray.image
+    :members:
+    :autosummary:
\ No newline at end of file
diff --git a/docs/python_docs/python/api/ndarray/index.rst b/docs/python_docs/python/api/ndarray/index.rst
index f09fd48215ff..46b6ef8f589a 100644
--- a/docs/python_docs/python/api/ndarray/index.rst
+++ b/docs/python_docs/python/api/ndarray/index.rst
@@ -78,47 +78,28 @@ NDArray API of MXNet
 
    .. card::
       :title: NDArray
-      :link: mxnet.ndarray.NDArray.html
+      :link: ndarray.html
 
       Imperative tensor operations using the NDArray API.
 
-   .. card::
-      :title: Routines
-      :link: routines.html
-
-      Manipulating multi-dimensional, fixed-size arrays.
 
 Sparse NDArray API of MXNet
 ---------------------------
 
 .. container:: cards
 
-   .. card::
-      :title: CSRNDArray
-      :link: mxnet.ndarray.sparse.CSRNDArray.html
-
-      Representing two-dimensional, fixed-size arrays in compressed sparse row format.
-
-   .. card::
-      :title: RowSparseNDArray
-      :link: mxnet.ndarray.sparse.RowSparseNDArray.html
-
-      Representing multi-dimensional, fixed-size arrays in row sparse format.
-
    .. card::
       :title: Sparse routines
-      :link: sparse_routines.html
+      :link: sparse/index.html
 
-      Manipulating sparse arrays.
+      Representing and manipulating sparse arrays.
 
 
 
 .. toctree::
    :hidden:
    :maxdepth: 2
+   :glob:
 
-   mxnet.ndarray.NDArray
-   routines
-   mxnet.ndarray.sparse.CSRNDArray
-   mxnet.ndarray.sparse.RowSparseNDArray
-   sparse_routines
+   ndarray
+   */index
diff --git a/docs/python_docs/python/api/ndarray/linalg/index.rst b/docs/python_docs/python/api/ndarray/linalg/index.rst
new file mode 100644
index 000000000000..ab360e44af67
--- /dev/null
+++ b/docs/python_docs/python/api/ndarray/linalg/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+ndarray.linalg
+==============
+
+.. automodule:: mxnet.ndarray.linalg
+    :members:
+    :autosummary:
\ No newline at end of file
diff --git a/docs/python_docs/python/api/ndarray/mxnet.ndarray.NDArray.rst b/docs/python_docs/python/api/ndarray/mxnet.ndarray.NDArray.rst
deleted file mode 100644
index 4c7edd4cd4d8..000000000000
--- a/docs/python_docs/python/api/ndarray/mxnet.ndarray.NDArray.rst
+++ /dev/null
@@ -1,310 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-NDArray
-=================
-
-.. currentmodule:: mxnet.ndarray
-
-.. autoclass:: NDArray
-
-
-Attributes
-----------
-
-.. autosummary::
-   :toctree: _autogen
-
-   NDArray.context
-   NDArray.dtype
-   NDArray.grad
-   NDArray.handle
-   NDArray.ndim
-   NDArray.shape
-   NDArray.size
-   NDArray.stype
-   NDArray.writable
-
-
-Array creation
---------------
-
-.. autosummary::
-   :toctree: _autogen
-
-   NDArray.ones_like
-   NDArray.zeros_like
-
-
-Manipulation
--------------
-
-Array conversion
-^^^^^^^^^^^^^^^^^
-
-.. autosummary::
-   :toctree: _autogen
-
-   NDArray.as_in_context
-   NDArray.asnumpy
-   NDArray.asscalar
-   NDArray.astype
-   NDArray.copy
-   NDArray.copyto
-   NDArray.tostype
-
-
-Changing shape
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autosummary::
-   :toctree: _autogen
-
-   NDArray.diag
-   NDArray.expand_dims
-   NDArray.flatten
-   NDArray.reshape
-   NDArray.reshape_like
-   NDArray.shape_array
-   NDArray.size_array
-   NDArray.split
-
-
-Expanding elements
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autosummary::
-   :toctree: _autogen
-
-   NDArray.broadcast_to
-   NDArray.broadcast_axes
-   NDArray.broadcast_like
-   NDArray.pad
-   NDArray.repeat
-   NDArray.tile
-
-
-Rearrange elements
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autosummary::
-   :toctree: _autogen
-
-   NDArray.depth_to_space
-   NDArray.flip
-   NDArray.swapaxes
-   NDArray.space_to_depth
-   NDArray.T
-   NDArray.transpose
-
-
-Sorting and searching
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autosummary::
-   :toctree: _autogen
-
-   NDArray.argmax
-   NDArray.argmax_channel
-   NDArray.argmin
-   NDArray.argsort
-   NDArray.sort
-   NDArray.topk
-
-
-Indexing
-^^^^^^^^^^
-
-.. autosummary::
-   :toctree: _autogen
-
-   NDArray.__getitem__
-   NDArray.__setitem__
-   NDArray.one_hot
-   NDArray.pick
-   NDArray.slice
-   NDArray.slice_axis
-   NDArray.slice_like
-   NDArray.take
-
-
-Lazy evaluation
-^^^^^^^^^^^^^^^^
-
-.. autosummary::
-   :toctree: _autogen
-
-   NDArray.wait_to_read
-
-
-Math
-----
-
-Arithmetic operations
-^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autosummary::
-   :toctree: _autogen
-
-   NDArray.__add__
-   NDArray.__div__
-   NDArray.__neg__
-   NDArray.__mod__
-   NDArray.__mul__
-   NDArray.__pow__
-   NDArray.__rdiv__
-   NDArray.__rmod__
-   NDArray.__rsub__
-   NDArray.__sub__
-
-
-Rounding
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autosummary::
-   :toctree: _autogen
-
-   NDArray.ceil
-   NDArray.fix
-   NDArray.floor
-   NDArray.rint
-   NDArray.round
-   NDArray.trunc
-
-
-Reduction
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autosummary::
-   :toctree: _autogen
-
-   NDArray.max
-   NDArray.mean
-   NDArray.min
-   NDArray.nanprod
-   NDArray.nansum
-   NDArray.norm
-   NDArray.prod
-   NDArray.sum
-
-
-In^place arithmetic operations
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autosummary::
-   :toctree: _autogen
-
-   NDArray.__iadd__
-   NDArray.__idiv__
-   NDArray.__imod__
-   NDArray.__imul__
-   NDArray.__isub__
-
-
-Comparison operators
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autosummary::
-   :toctree: _autogen
-
-   NDArray.__eq__
-   NDArray.__ge__
-   NDArray.__gt__
-   NDArray.__le__
-   NDArray.__lt__
-   NDArray.__ne__
-
-
-Trigonometric functions
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autosummary::
-   :toctree: _autogen
-
-   NDArray.arccos
-   NDArray.arcsin
-   NDArray.arctan
-   NDArray.cos
-   NDArray.degrees
-   NDArray.radians
-   NDArray.sin
-   NDArray.tan
-
-
-Hyperbolic functions
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autosummary::
-   :toctree: _autogen
-
-   NDArray.arccosh
-   NDArray.arcsinh
-   NDArray.arctanh
-   NDArray.cosh
-   NDArray.sinh
-   NDArray.tanh
-
-
-Exponents and logarithms
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autosummary::
-   :toctree: _autogen
-
-   NDArray.exp
-   NDArray.expm1
-   NDArray.log
-   NDArray.log1p
-   NDArray.log10
-   NDArray.log2
-
-
-Powers
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autosummary::
-   :toctree: _autogen
-
-   NDArray.cbrt
-   NDArray.rcbrt
-   NDArray.reciprocal
-   NDArray.rsqrt
-   NDArray.square
-   NDArray.sqrt
-
-
-Miscellaneous
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autosummary::
-   :toctree: _autogen
-
-   NDArray.clip
-   NDArray.sign
-
-
-Neural network
-------------------
-
-.. autosummary::
-   :toctree: _autogen
-
-   NDArray.log_softmax
-   NDArray.relu
-   NDArray.sigmoid
-   NDArray.softmax
-
diff --git a/docs/python_docs/python/api/ndarray/mxnet.ndarray.sparse.CSRNDArray.rst b/docs/python_docs/python/api/ndarray/mxnet.ndarray.sparse.CSRNDArray.rst
deleted file mode 100644
index ab9b9cb812c6..000000000000
--- a/docs/python_docs/python/api/ndarray/mxnet.ndarray.sparse.CSRNDArray.rst
+++ /dev/null
@@ -1,203 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-CSRNDArray
-====================
-
-.. currentmodule:: mxnet.ndarray.sparse
-
-.. autoclass:: CSRNDArray
-
-
-Array attributes
----------------------
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    CSRNDArray.context
-    CSRNDArray.data
-    CSRNDArray.dtype
-    CSRNDArray.indices
-    CSRNDArray.indptr
-    CSRNDArray.shape
-    CSRNDArray.stype
-
-
-Array creation
---------------
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    CSRNDArray.zeros_like
-
-
-Manipulation
--------------
-
-Array conversion
-^^^^^^^^^^^^^^^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    CSRNDArray.as_in_context
-    CSRNDArray.asnumpy
-    CSRNDArray.asscalar
-    CSRNDArray.asscipy
-    CSRNDArray.astype
-    CSRNDArray.copy
-    CSRNDArray.copyto
-    CSRNDArray.tostype
-
-
-Array inspection
-^^^^^^^^^^^^^^^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    CSRNDArray.check_format
-
-
-Array reduction
-^^^^^^^^^^^^^^^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    CSRNDArray.mean
-    CSRNDArray.norm
-    CSRNDArray.sum
-
-
-Indexing
-^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    CSRNDArray.__getitem__
-    CSRNDArray.__setitem__
-    CSRNDArray.slice
-
-
-Joining arrays
-^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    concat
-
-
-Lazy evaluation
-^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    CSRNDArray.wait_to_read
-
-
-Math
-----
-
-Array rounding
-^^^^^^^^^^^^^^^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    CSRNDArray.ceil
-    CSRNDArray.fix
-    CSRNDArray.floor
-    CSRNDArray.round
-    CSRNDArray.rint
-    CSRNDArray.trunc
-
-
-Trigonometric functions
-^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    CSRNDArray.arcsin
-    CSRNDArray.arctan
-    CSRNDArray.degrees
-    CSRNDArray.radians
-    CSRNDArray.sin
-    CSRNDArray.tan
-
-
-Hyperbolic functions
-^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    CSRNDArray.arcsinh
-    CSRNDArray.arctanh
-    CSRNDArray.sinh
-    CSRNDArray.tanh
-
-
-Exponents and logarithms
-^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    CSRNDArray.expm1
-    CSRNDArray.log1p
-
-
-Powers
-^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    CSRNDArray.sqrt
-    CSRNDArray.square
-
-
-Miscellaneous
-^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    CSRNDArray.abs
-    CSRNDArray.clip
-    CSRNDArray.sign
diff --git a/docs/python_docs/python/api/ndarray/mxnet.ndarray.sparse.RowSparseNDArray.rst b/docs/python_docs/python/api/ndarray/mxnet.ndarray.sparse.RowSparseNDArray.rst
deleted file mode 100644
index 7db86728ddea..000000000000
--- a/docs/python_docs/python/api/ndarray/mxnet.ndarray.sparse.RowSparseNDArray.rst
+++ /dev/null
@@ -1,183 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-RowSparseNDArray
-==========================
-
-.. currentmodule:: mxnet.ndarray.sparse
-
-.. autoclass:: RowSparseNDArray
-
-
-Array attributes
--------------------------------
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    RowSparseNDArray.context
-    RowSparseNDArray.data
-    RowSparseNDArray.dtype
-    RowSparseNDArray.indices
-    RowSparseNDArray.shape
-    RowSparseNDArray.stype
-
-
-Array conversion
--------------------------------
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    RowSparseNDArray.as_in_context
-    RowSparseNDArray.asnumpy
-    RowSparseNDArray.asscalar
-    RowSparseNDArray.astype
-    RowSparseNDArray.copy
-    RowSparseNDArray.copyto
-    RowSparseNDArray.tostype
-
-
-Array inspection
--------------------------------
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    RowSparseNDArray.check_format
-
-
-Array creation
--------------------------------
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    RowSparseNDArray.zeros_like
-
-
-Array reduction
--------------------------------
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    RowSparseNDArray.norm
-
-
-Array rounding
--------------------------------
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    RowSparseNDArray.ceil
-    RowSparseNDArray.fix
-    RowSparseNDArray.floor
-    RowSparseNDArray.rint
-    RowSparseNDArray.round
-    RowSparseNDArray.trunc
-
-
-Trigonometric functions
--------------------------------
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    RowSparseNDArray.arcsin
-    RowSparseNDArray.arctan
-    RowSparseNDArray.degrees
-    RowSparseNDArray.radians
-    RowSparseNDArray.sin
-    RowSparseNDArray.tan
-
-
-Hyperbolic functions
--------------------------------
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    RowSparseNDArray.arcsinh
-    RowSparseNDArray.arctanh
-    RowSparseNDArray.sinh
-    RowSparseNDArray.tanh
-
-
-Exponents and logarithms
--------------------------------
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    RowSparseNDArray.expm1
-    RowSparseNDArray.log1p
-
-
-Powers
--------------------------------
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    RowSparseNDArray.sqrt
-    RowSparseNDArray.square
-
-
-Indexing
--------------------------------
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    RowSparseNDArray.__getitem__
-    RowSparseNDArray.__setitem__
-    RowSparseNDArray.retain
-
-
-Lazy evaluation
--------------------------------
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    RowSparseNDArray.wait_to_read
-
-
-Miscellaneous
--------------------------------
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    RowSparseNDArray.abs
-    RowSparseNDArray.clip
-    RowSparseNDArray.sign
diff --git a/docs/python_docs/python/api/ndarray/ndarray.rst b/docs/python_docs/python/api/ndarray/ndarray.rst
new file mode 100644
index 000000000000..8047b745827f
--- /dev/null
+++ b/docs/python_docs/python/api/ndarray/ndarray.rst
@@ -0,0 +1,26 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+ndarray
+=======
+
+.. currentmodule:: mxnet.ndarray
+
+.. automodule:: mxnet.ndarray
+    :members:
+    :imported-members:
+    :autosummary:
\ No newline at end of file
diff --git a/docs/python_docs/python/api/ndarray/op/index.rst b/docs/python_docs/python/api/ndarray/op/index.rst
new file mode 100644
index 000000000000..9894a6e16fe9
--- /dev/null
+++ b/docs/python_docs/python/api/ndarray/op/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+ndarray.op
+==========
+
+.. automodule:: mxnet.ndarray.op
+    :members:
+    :autosummary:
\ No newline at end of file
diff --git a/docs/python_docs/python/api/ndarray/random/index.rst b/docs/python_docs/python/api/ndarray/random/index.rst
new file mode 100644
index 000000000000..ce928c5c3039
--- /dev/null
+++ b/docs/python_docs/python/api/ndarray/random/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+ndarray.random
+==============
+
+.. automodule:: mxnet.ndarray.random
+    :members:
+    :autosummary:
\ No newline at end of file
diff --git a/docs/python_docs/python/api/ndarray/register/index.rst b/docs/python_docs/python/api/ndarray/register/index.rst
new file mode 100644
index 000000000000..a700af7ee87b
--- /dev/null
+++ b/docs/python_docs/python/api/ndarray/register/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+ndarray.register
+================
+
+.. automodule:: mxnet.ndarray.register
+    :members:
+    :autosummary:
\ No newline at end of file
diff --git a/docs/python_docs/python/api/ndarray/routines.rst b/docs/python_docs/python/api/ndarray/routines.rst
deleted file mode 100644
index 1931e1f5383b..000000000000
--- a/docs/python_docs/python/api/ndarray/routines.rst
+++ /dev/null
@@ -1,461 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-Routines
-========
-
-Create Arrays
--------------
-
-.. currentmodule:: mxnet.ndarray
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    arange
-    array
-    diag
-    empty
-    full
-    load
-    ones
-    ones_like
-    save
-    zeros
-    zeros_like
-
-
-Manipulate
-------------
-
-Change shape and type
-^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    cast
-    flatten
-    expand_dims
-    reshape
-    reshape_like
-    shape_array
-    size_array
-
-
-Expand  elements
-^^^^^^^^^^^^^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    broadcast_axes
-    broadcast_like
-    broadcast_to
-    pad
-    repeat
-    tile
-
-
-Rearrange elements
-^^^^^^^^^^^^^^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    depth_to_space
-    flip
-    space_to_depth
-    swapaxes
-    transpose
-
-
-Join and split
-^^^^^^^^^^^^^^^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    concat
-    split
-    stack
-
-
-Index
-^^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    batch_take
-    one_hot
-    pick
-    ravel_multi_index
-    slice
-    slice_axis
-    slice_like
-    take
-    unravel_index
-    where
-
-
-Sequence
-^^^^^^^^
-
-.. currentmodule:: mxnet.ndarray
-
-.. autosummary::
-    :toctree: _autogen
-
-    SequenceLast
-    SequenceMask
-    SequenceReverse
-
-
-Math
-----
-
-Arithmetic
-^^^^^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    add
-    add_n
-    batch_dot
-    divide
-    dot
-    modulo
-    multiply
-    negative
-    subtract
-
-
-Trigonometric
-^^^^^^^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    arccos
-    arcsin
-    arctan
-    broadcast_hypot
-    degrees
-    cos
-    radians
-    sin
-    tan
-
-
-Hyperbolic
-^^^^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    arcsinh
-    arccosh
-    arctanh
-    cosh
-    sinh
-    tanh
-
-
-Reduce
-^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    max
-    min
-    mean
-    nanprod
-    nansum
-    norm
-    prod
-    sum
-
-
-Round
-^^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    ceil
-    fix
-    floor
-    round
-    rint
-    trunc
-
-
-Exponents and logarithms
-^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    exp
-    expm1
-    log
-    log1p
-    log10
-    log2
-
-
-Powers
-^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    cbrt
-    power
-    rcbrt
-    reciprocal
-    rsqrt
-    square
-    sqrt
-
-
-Compare
-^^^^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    equal
-    greater
-    greater_equal
-    lesser
-    lesser_equal
-    not_equal
-
-
-Logical
-^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    logical_and
-    logical_not
-    logical_or
-    logical_xor
-
-
-Sort and Search
-^^^^^^^^^^^^^^^
-
-.. autosummary::
-    :toctree: _autogen
-
-    argmax
-    argmin
-    argsort
-    sort
-    topk
-
-
-Random Distribution
-^^^^^^^^^^^^^^^^^^^^
-
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    random.exponential
-    random.gamma
-    random.generalized_negative_binomial
-    random.multinomial
-    random.negative_binomial
-    random.normal
-    random.poisson
-    random.randint
-    random.randn
-    random.shuffle
-    random.uniform
-
-Linear Algebra
-^^^^^^^^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    linalg.gelqf
-    linalg.gemm
-    linalg.gemm2
-    linalg.potrf
-    linalg.potri
-    linalg.sumlogdiag
-    linalg.syevd
-    linalg.syrk
-    linalg.trmm
-    linalg.trsm
-
-
-Miscellaneous
--------------
-
-.. autosummary::
-    :toctree: _autogen
-
-    abs
-    clip
-    gamma
-    gammaln
-    maximum
-    minimum
-    sign
-
-
-Neural Network
---------------
-
-.. autosummary::
-    :toctree: _autogen
-
-    Activation
-    BatchNorm
-    BilinearSampler
-    BlockGrad
-    Convolution
-    Correlation
-    Custom
-    Deconvolution
-    Dropout
-    Embedding
-    FullyConnected
-    GridGenerator
-    IdentityAttachKLSparseReg
-    InstanceNorm
-    L2Normalization
-    LayerNorm
-    LeakyReLU
-    LinearRegressionOutput
-    log_softmax
-    LogisticRegressionOutput
-    LRN
-    MAERegressionOutput
-    MakeLoss
-    Pooling
-    relu
-    ROIPooling
-    RNN
-    sigmoid
-    smooth_l1
-    softmax
-    softmax_cross_entropy
-    SoftmaxOutput
-    SoftmaxActivation
-    SpatialTransformer
-    SVMOutput
-    UpSampling
-
-Contributed routines
---------------------
-
-.. automodule:: mxnet.ndarray.contrib
-
-.. note:: This package contains experimental APIs and may change in the near future.
-
-Manipulate
-^^^^^^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    count_sketch
-    getnnz
-    index_copy
-
-
-FFT
-^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    fft
-    ifft
-
-
-Quantization
-^^^^^^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    dequantize
-    quantize
-
-
-Neural network
-^^^^^^^^^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    AdaptiveAvgPooling2D
-    BilinearResize2D
-    ctc_loss
-    DeformableConvolution
-    DeformablePSROIPooling
-    MultiBoxDetection
-    MultiBoxPrior
-    MultiBoxTarget
-    MultiProposal
-    Proposal
-    PSROIPooling
-    ROIAlign
-
-
-Control flow
-^^^^^^^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    cond
-    foreach
-    while_loop
\ No newline at end of file
diff --git a/docs/python_docs/python/api/ndarray/sparse/index.rst b/docs/python_docs/python/api/ndarray/sparse/index.rst
new file mode 100644
index 000000000000..2d1156e99e21
--- /dev/null
+++ b/docs/python_docs/python/api/ndarray/sparse/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+ndarray.sparse
+==============
+
+.. automodule:: mxnet.ndarray.sparse
+    :members:
+    :autosummary:
\ No newline at end of file
diff --git a/docs/python_docs/python/api/ndarray/sparse_routines.rst b/docs/python_docs/python/api/ndarray/sparse_routines.rst
deleted file mode 100644
index 9f9fd5fd6287..000000000000
--- a/docs/python_docs/python/api/ndarray/sparse_routines.rst
+++ /dev/null
@@ -1,200 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-Sparse routines
-=====================================
-
-.. currentmodule:: mxnet.ndarray.sparse
-
-Create Arrays
---------------
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    array
-    csr_matrix
-    empty
-    row_sparse_array
-    zeros
-    zeros_like
-
-
-Manipulate
-------------
-
-Change shape and type
-^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    cast_storage
-
-
-Index
-^^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    retain
-    slice
-    where
-
-
-Math
-----
-
-Arithmetic
-^^^^^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    add_n
-    broadcast_add
-    broadcast_div
-    broadcast_mul
-    broadcast_sub
-    dot
-    elemwise_add
-    elemwise_mul
-    elemwise_sub
-    negative
-
-
-Trigonometric
-^^^^^^^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    arcsin
-    arctan
-    degrees
-    radians
-    sin
-    tan
-
-
-Hyperbolic
-^^^^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    arcsinh
-    arctanh
-    sinh
-    tanh
-
-
-Reduce
-^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    mean
-    norm
-    sum
-
-
-Round
-^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    ceil
-    fix
-    floor
-    rint
-    round
-    trunc
-
-
-Exponents and logarithms
-^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    expm1
-    log1p
-
-
-Powers
-^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    sqrt
-    square
-
-
-Miscellaneous
-^^^^^^^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    abs
-    sign
-
-
-Neural network
----------------
-
-Updater
-^^^^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    adam_update
-    adagrad_update
-    sgd_mom_update
-    sgd_update
-
-
-More
-^^^^
-
-.. autosummary::
-    :nosignatures:
-    :toctree: _autogen
-
-    Embedding
-    LinearRegressionOutput
-    LogisticRegressionOutput
-    make_loss
-    stop_gradient
\ No newline at end of file
diff --git a/docs/python_docs/python/api/ndarray/utils/index.rst b/docs/python_docs/python/api/ndarray/utils/index.rst
new file mode 100644
index 000000000000..b0b7e04ecf35
--- /dev/null
+++ b/docs/python_docs/python/api/ndarray/utils/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+ndarray.utils
+=============
+
+.. automodule:: mxnet.ndarray.utils
+    :members:
+    :autosummary:
\ No newline at end of file
diff --git a/docs/python_docs/python/api/optimizer/index.rst b/docs/python_docs/python/api/optimizer/index.rst
new file mode 100644
index 000000000000..b764865a6bc1
--- /dev/null
+++ b/docs/python_docs/python/api/optimizer/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.optimizer
+===============
+
+.. automodule:: mxnet.optimizer
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/symbol-related/mxnet.callback.rst b/docs/python_docs/python/api/symbol-related/mxnet.callback.rst
deleted file mode 100644
index 5b06ba719bd2..000000000000
--- a/docs/python_docs/python/api/symbol-related/mxnet.callback.rst
+++ /dev/null
@@ -1,45 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-mxnet.callback
-==============
-
-.. automodule:: mxnet.callback
-
-
-
-   .. rubric:: Functions
-
-   .. autosummary::
-      :toctree: _autogen
-
-      do_checkpoint
-      log_train_metric
-      module_checkpoint
-
-
-
-
-
-   .. rubric:: Classes
-
-   .. autosummary::
-      :toctree: _autogen
-
-      LogValidationMetricsCallback
-      ProgressBar
-      Speedometer
diff --git a/docs/python_docs/python/api/symbol-related/mxnet.model.rst b/docs/python_docs/python/api/symbol-related/mxnet.model.rst
deleted file mode 100644
index 5475fc8218ef..000000000000
--- a/docs/python_docs/python/api/symbol-related/mxnet.model.rst
+++ /dev/null
@@ -1,45 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-mxnet.model
-===========
-
-.. automodule:: mxnet.model
-
-
-
-   .. rubric:: Functions
-
-   .. autosummary::
-      :toctree: _autogen
-
-      load_checkpoint
-      save_checkpoint
-
-
-
-
-
-   .. rubric:: Classes
-
-   .. autosummary::
-      :toctree: _autogen
-
-      BatchEndParam
-      FeedForward
-
-
diff --git a/docs/python_docs/python/api/symbol-related/mxnet.module.rst b/docs/python_docs/python/api/symbol-related/mxnet.module.rst
deleted file mode 100644
index f023d574fb89..000000000000
--- a/docs/python_docs/python/api/symbol-related/mxnet.module.rst
+++ /dev/null
@@ -1,35 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-mxnet.module
-============
-
-.. automodule:: mxnet.module
-
-   .. autosummary::
-      :nosignatures:
-      :toctree: _autogen
-
-      BaseModule
-      BucketingModule
-      Module
-      PythonLossModule
-      PythonModule
-      SequentialModule
-
-
-
diff --git a/docs/python_docs/python/api/symbol/contrib/index.rst b/docs/python_docs/python/api/symbol/contrib/index.rst
new file mode 100644
index 000000000000..e4129a642e5c
--- /dev/null
+++ b/docs/python_docs/python/api/symbol/contrib/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+symbol.contrib
+===============
+
+.. automodule:: mxnet.symbol.contrib
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/symbol/image/index.rst b/docs/python_docs/python/api/symbol/image/index.rst
new file mode 100644
index 000000000000..2efa6367c1a5
--- /dev/null
+++ b/docs/python_docs/python/api/symbol/image/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+symbol.image
+=============
+
+.. automodule:: mxnet.symbol.image
+    :members:
+    :autosummary:
\ No newline at end of file
diff --git a/docs/python_docs/python/api/symbol/index.rst b/docs/python_docs/python/api/symbol/index.rst
index 492c820ff5ac..81340881b1a6 100644
--- a/docs/python_docs/python/api/symbol/index.rst
+++ b/docs/python_docs/python/api/symbol/index.rst
@@ -16,7 +16,7 @@
    under the License.
 
 mxnet.symbol
-=========================
+============
 
 The Symbol API in Apache MXNet is an interface for symbolic programming. It features the use of computational graphs, reduced memory usage, and pre-use function optimization.
 
@@ -48,18 +48,20 @@ Tutorials
       The Symbol guide. Start here!
 
 Symbol Package
----------
+--------------
 
 .. container:: cards
 
    .. card::
       :title: Symbol
-      :link: mxnet.symbol.Symbol.html
+      :link: symbol.html
 
       Symbolic programming using the Symbol API.
 
 .. toctree::
    :hidden:
+   :maxdepth: 2
+   :glob:
 
-   mxnet.symbol.Symbol
-   mxnet.symbol.linalg
\ No newline at end of file
+   symbol
+   */index
\ No newline at end of file
diff --git a/docs/python_docs/python/api/symbol/linalg/index.rst b/docs/python_docs/python/api/symbol/linalg/index.rst
new file mode 100644
index 000000000000..b4e339182d33
--- /dev/null
+++ b/docs/python_docs/python/api/symbol/linalg/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+symbol.linalg
+=============
+
+.. automodule:: mxnet.symbol.linalg
+    :members:
+    :autosummary:
\ No newline at end of file
diff --git a/docs/python_docs/python/api/symbol/mxnet.symbol.Symbol.rst b/docs/python_docs/python/api/symbol/mxnet.symbol.Symbol.rst
deleted file mode 100644
index 738fde742eda..000000000000
--- a/docs/python_docs/python/api/symbol/mxnet.symbol.Symbol.rst
+++ /dev/null
@@ -1,335 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-Symbol
-======
-
-Composition
-------------------
-
-Composite multiple symbols into a new one by an operator.
-
-.. currentmodule:: mxnet.symbol
-
-.. autoclass:: Symbol
-
-.. autosummary::
-   :toctree: _autogen
-
-    Symbol.__call__
-
-
-Arithmetic operations
-------------------------
-
-
-.. autosummary::
-   :toctree: _autogen
-
-    Symbol.__add__
-    Symbol.__sub__
-    Symbol.__rsub__
-    Symbol.__neg__
-    Symbol.__mul__
-    Symbol.__div__
-    Symbol.__rdiv__
-    Symbol.__mod__
-    Symbol.__rmod__
-    Symbol.__pow__
-
-
-Trigonometric functions
-------------------------
-
-
-.. autosummary::
-   :toctree: _autogen
-
-    Symbol.sin
-    Symbol.cos
-    Symbol.tan
-    Symbol.arcsin
-    Symbol.arccos
-    Symbol.arctan
-    Symbol.degrees
-    Symbol.radians
-
-
-Hyperbolic functions
-------------------------
-
-
-.. autosummary::
-   :toctree: _autogen
-
-    Symbol.sinh
-    Symbol.cosh
-    Symbol.tanh
-    Symbol.arcsinh
-    Symbol.arccosh
-    Symbol.arctanh
-
-
-Exponents and logarithms
-------------------------
-
-
-.. autosummary::
-   :toctree: _autogen
-
-    Symbol.exp
-    Symbol.expm1
-    Symbol.log
-    Symbol.log10
-    Symbol.log2
-    Symbol.log1p
-
-
-Powers
-------------------------
-
-
-.. autosummary::
-   :toctree: _autogen
-
-    Symbol.sqrt
-    Symbol.rsqrt
-    Symbol.cbrt
-    Symbol.rcbrt
-    Symbol.square
-
-
-Basic neural network functions
-----------------------------------
-
-
-.. autosummary::
-   :toctree: _autogen
-
-    Symbol.relu
-    Symbol.sigmoid
-    Symbol.softmax
-    Symbol.log_softmax
-
-
-Comparison operators
-----------------------
-
-
-.. autosummary::
-   :toctree: _autogen
-
-    Symbol.__lt__
-    Symbol.__le__
-    Symbol.__gt__
-    Symbol.__ge__
-    Symbol.__eq__
-    Symbol.__ne__
-
-
-Symbol creation
----------------------
-
-
-.. autosummary::
-   :toctree: _autogen
-
-    Symbol.zeros_like
-    Symbol.ones_like
-    Symbol.diag
-
-
-Changing shape and type
----------------------------
-
-
-.. autosummary::
-   :toctree: _autogen
-
-    Symbol.astype
-    Symbol.shape_array
-    Symbol.size_array
-    Symbol.reshape
-    Symbol.reshape_like
-    Symbol.flatten
-    Symbol.expand_dims
-
-
-Expanding elements
------------------------
-
-
-.. autosummary::
-   :toctree: _autogen
-
-    Symbol.broadcast_to
-    Symbol.broadcast_axes
-    Symbol.broadcast_like
-    Symbol.tile
-    Symbol.pad
-
-
-Rearranging elements
-----------------------
-
-
-.. autosummary::
-   :toctree: _autogen
-
-    Symbol.transpose
-    Symbol.swapaxes
-    Symbol.flip
-    Symbol.depth_to_space
-    Symbol.space_to_depth
-
-
-Reduce functions
----------------------------
-
-
-.. autosummary::
-   :toctree: _autogen
-
-    Symbol.sum
-    Symbol.nansum
-    Symbol.prod
-    Symbol.nanprod
-    Symbol.mean
-    Symbol.max
-    Symbol.min
-    Symbol.norm
-
-
-Rounding
----------------------
-
-
-.. autosummary::
-   :toctree: _autogen
-
-    Symbol.round
-    Symbol.rint
-    Symbol.fix
-    Symbol.floor
-    Symbol.ceil
-    Symbol.trunc
-
-
-Sorting and searching
------------------------------
-
-
-.. autosummary::
-   :toctree: _autogen
-
-    Symbol.sort
-    Symbol.argsort
-    Symbol.topk
-    Symbol.argmax
-    Symbol.argmin
-    Symbol.argmax_channel
-
-
-Query information
---------------------
-
-
-.. autosummary::
-   :toctree: _autogen
-
-    Symbol.name
-    Symbol.list_arguments
-    Symbol.list_outputs
-    Symbol.list_auxiliary_states
-    Symbol.list_attr
-    Symbol.attr
-    Symbol.attr_dict
-
-
-Indexing
------------------------
-
-
-.. autosummary::
-   :toctree: _autogen
-
-    Symbol.slice
-    Symbol.slice_axis
-    Symbol.slice_like
-    Symbol.take
-    Symbol.one_hot
-    Symbol.pick
-
-
-Get internal and output symbol
-----------------------------------
-
-
-.. autosummary::
-   :toctree: _autogen
-
-    Symbol.__getitem__
-    Symbol.__iter__
-    Symbol.get_internals
-    Symbol.get_children
-
-
-Inference type and shape
-----------------------------------
-
-
-.. autosummary::
-   :toctree: _autogen
-
-    Symbol.infer_type
-    Symbol.infer_shape
-    Symbol.infer_shape_partial
-
-
-
-Bind
-------------------
-
-
-.. autosummary::
-   :toctree: _autogen
-
-    Symbol.bind
-    Symbol.simple_bind
-
-
-Save
-------------------
-
-
-.. autosummary::
-   :toctree: _autogen
-
-    Symbol.save
-    Symbol.tojson
-    Symbol.debug_str
-
-
-Miscellaneous
------------------------
-
-
-.. autosummary::
-   :toctree: _autogen
-
-    Symbol.clip
-    Symbol.sign
diff --git a/docs/python_docs/python/api/symbol/mxnet.symbol.linalg.rst b/docs/python_docs/python/api/symbol/mxnet.symbol.linalg.rst
deleted file mode 100644
index b7979dc006cb..000000000000
--- a/docs/python_docs/python/api/symbol/mxnet.symbol.linalg.rst
+++ /dev/null
@@ -1,49 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-mxnet.linalg
-============
-
-.. automodule:: mxnet.symbol.linalg
-
-
-This document lists the linear algebra routines of the symbolic expression package:
-
-
-The `Linear Algebra Symbol` API, defined in the `symbol.linalg` package, provides
-symbolic expressions for linear algebra routines.
-
-In the rest of this document, we list routines provided by the `symbol.linalg` package.
-
-.. autosummary::
-   :toctree: _autogen
-
-    gemm
-    gemm2
-    potrf
-    potri
-    trmm
-    trsm
-    syrk
-    gelqf
-    syevd
-    sumlogdiag
-    extractdiag
-    makediag
-    extracttrian
-    maketrian
-    inverse
\ No newline at end of file
diff --git a/docs/python_docs/python/api/symbol/op/index.rst b/docs/python_docs/python/api/symbol/op/index.rst
new file mode 100644
index 000000000000..6c8ff7796044
--- /dev/null
+++ b/docs/python_docs/python/api/symbol/op/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+symbol.op
+=========
+
+.. automodule:: mxnet.symbol.op
+    :members:
+    :autosummary:
\ No newline at end of file
diff --git a/docs/python_docs/python/api/symbol/random/index.rst b/docs/python_docs/python/api/symbol/random/index.rst
new file mode 100644
index 000000000000..b2671a4d1b0c
--- /dev/null
+++ b/docs/python_docs/python/api/symbol/random/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+symbol.random
+=============
+
+.. automodule:: mxnet.symbol.random
+    :members:
+    :autosummary:
\ No newline at end of file
diff --git a/docs/python_docs/python/api/symbol/register/index.rst b/docs/python_docs/python/api/symbol/register/index.rst
new file mode 100644
index 000000000000..a7c1bdc22dca
--- /dev/null
+++ b/docs/python_docs/python/api/symbol/register/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+symbol.register
+===============
+
+.. automodule:: mxnet.symbol.register
+    :members:
+    :autosummary:
\ No newline at end of file
diff --git a/docs/python_docs/python/api/symbol/sparse/index.rst b/docs/python_docs/python/api/symbol/sparse/index.rst
new file mode 100644
index 000000000000..0bcaa153c092
--- /dev/null
+++ b/docs/python_docs/python/api/symbol/sparse/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+symbol.sparse
+=============
+
+.. automodule:: mxnet.symbol.sparse
+    :members:
+    :autosummary:
\ No newline at end of file
diff --git a/docs/python_docs/python/api/symbol/symbol.rst b/docs/python_docs/python/api/symbol/symbol.rst
new file mode 100644
index 000000000000..5eb144886693
--- /dev/null
+++ b/docs/python_docs/python/api/symbol/symbol.rst
@@ -0,0 +1,26 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+symbol
+======
+
+.. currentmodule:: mxnet.symbol
+
+.. automodule:: mxnet.symbol
+    :members:
+    :imported-members:
+    :autosummary:
\ No newline at end of file
diff --git a/docs/python_docs/python/index.rst b/docs/python_docs/python/index.rst
index 7c4fa7e91402..90ba3b7a5a2f 100644
--- a/docs/python_docs/python/index.rst
+++ b/docs/python_docs/python/index.rst
@@ -28,7 +28,7 @@ Python Documentation
 
 
 Python Tutorials
----------
+----------------
 
 .. container:: cards
 
@@ -40,7 +40,7 @@ Python Tutorials
       Guides and Tutorials for using MXNet.
 
 Python API References
----------
+---------------------
 
 .. container:: cards
 
diff --git a/docs/python_docs/python/scripts/conf.py b/docs/python_docs/python/scripts/conf.py
index ff4c89bbb18a..3d54b2ea3fe6 100644
--- a/docs/python_docs/python/scripts/conf.py
+++ b/docs/python_docs/python/scripts/conf.py
@@ -61,13 +61,16 @@
     # 'sphinx.ext.viewcode',
     'breathe',
 #    'mxdoc'
+    'autodocsumm',
 ]
 
 doctest_global_setup = '''
 import mxnet as mx
 '''
 
-autodoc_member_order = 'bysource'
+autodoc_member_order = 'alphabetical'
+
+autodoc_default_flags = ['members', 'show-inheritance']
 
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ['_templates']
@@ -180,7 +183,7 @@
 ]
 
 html_js_files = [
-
+    'autodoc.js'
 ]
 
 # If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
diff --git a/docs/python_docs/python/tutorials/packages/gluon/blocks/nn.md b/docs/python_docs/python/tutorials/packages/gluon/blocks/nn.md
index 74ad76e415b2..9639c7c38847 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/blocks/nn.md
+++ b/docs/python_docs/python/tutorials/packages/gluon/blocks/nn.md
@@ -306,7 +306,7 @@ manifest in the form of GPU starvation when the CPUs can not provide
 instruction fast enough. We can improve this situation by deferring to a more
 performant language instead of Python when possible.
 
-Gluon does this by allowing for [Hybridization](./hybridize.md). In it, the
+Gluon does this by allowing for [Hybridization](hybridize.md). In it, the
 Python interpreter executes the block the first time it's invoked. The Gluon
 runtime records what is happening and the next time around it short circuits
 any calls to Python. This can accelerate things considerably in some cases but
diff --git a/docs/python_docs/python/tutorials/packages/gluon/training/normalization/index.md b/docs/python_docs/python/tutorials/packages/gluon/training/normalization/index.md
index 113bb8ed4f71..962834909a97 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/training/normalization/index.md
+++ b/docs/python_docs/python/tutorials/packages/gluon/training/normalization/index.md
@@ -67,7 +67,7 @@ Advanced: all of the following methods begin by normalizing certain input distri
 
 Figure 1: `BatchNorm` on NCHW data | Figure 2: `BatchNorm` on NTC data 
 - | - 
-![](./imgs/NCHW_BN.png) | ![](./imgs/NTC_BN.png)
+![](imgs/NCHW_BN.png) | ![](imgs/NTC_BN.png)
 (e.g. batch of images) using the default of `axis=1` | (e.g. batch of sequences) overriding the default with `axis=2` (or `axis=-1`)
 
 One of the most popular normalization techniques is Batch Normalization, usually called BatchNorm for short. We normalize the activations **across all samples in a batch** for each of the channels independently. See Figure 1. We calculate two batch (or local) statistics for every channel to perform the normalization: the mean and variance of the activations in that channel for all samples in a batch. And we use these to shift and scale respectively.
@@ -205,7 +205,7 @@ Remember: `LayerNorm` is intended to be used with data in NTC format so the defa
 
 Figure 3: `LayerNorm` on NCHW data | Figure 4: `LayerNorm` on NTC data 
 - | - 
-![](./imgs/NCHW_LN.png) | ![](./imgs/NTC_LN.png)
+![](imgs/NCHW_LN.png) | ![](imgs/NTC_LN.png)
 (e.g. batch of images) overriding the default with `axis=1` | (e.g. batch of sequences) using the default of `axis=-1`
 
 As an example, we'll apply `LayerNorm` to a batch of 2 samples, each with 4 time steps and 2 channels (in NTC format).
@@ -244,7 +244,7 @@ Watch out: `InstanceNorm` is better suited to convolutional networks (CNNs) than
 
 Figure 3: `InstanceNorm` on NCHW data | Figure 4: `InstanceNorm` on NTC data 
 - | - 
-![](./imgs/NCHW_IN.png) | ![](./imgs/NTC_IN.png)
+![](imgs/NCHW_IN.png) | ![](imgs/NTC_IN.png)
 (e.g. batch of images) using the default `axis=1` | (e.g. batch of sequences) overiding the default with `axis=2` (or `axis=-1` equivalently)
 
 As an example, we'll apply `InstanceNorm` to a batch of 2 samples, each with 2 channels, and both height and width of 2 (in NCHW format).
diff --git a/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.css b/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.css
index 3f4b6434c28b..cb26a387dd34 100644
--- a/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.css
+++ b/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.css
@@ -1,2 +1,2 @@
-.admonition,.mdl-shadow--2dp,.page-content pre:hover,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list){box-shadow:0 2px 2px 0 rgba(0,0,0,.14),0 3px 1px -2px rgba(0,0,0,.2),0 1px 5px 0 rgba(0,0,0,.12)}.mdl-shadow--3dp{box-shadow:0 3px 4px 0 rgba(0,0,0,.14),0 3px 3px -2px rgba(0,0,0,.2),0 1px 8px 0 rgba(0,0,0,.12)}.mdl-shadow--4dp{box-shadow:0 4px 5px 0 rgba(0,0,0,.14),0 1px 10px 0 rgba(0,0,0,.12),0 2px 4px -1px rgba(0,0,0,.2)}.mdl-shadow--6dp{box-shadow:0 6px 10px 0 rgba(0,0,0,.14),0 1px 18px 0 rgba(0,0,0,.12),0 3px 5px -1px rgba(0,0,0,.2)}.mdl-shadow--8dp{box-shadow:0 8px 10px 1px rgba(0,0,0,.14),0 3px 14px 2px rgba(0,0,0,.12),0 5px 5px -3px rgba(0,0,0,.2)}.mdl-shadow--16dp{box-shadow:0 16px 24px 2px rgba(0,0,0,.14),0 6px 30px 5px rgba(0,0,0,.12),0 8px 10px -5px rgba(0,0,0,.2)}.mdl-shadow--24dp{box-shadow:0 9px 46px 8px rgba(0,0,0,.14),0 11px 15px -7px rgba(0,0,0,.12),0 24px 38px 3px rgba(0,0,0,.2)}.mdl-data-table,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list){position:relative;border:1px solid rgba(0,0,0,.12);border-collapse:collapse;white-space:nowrap;font-size:13px;background-color:#fff}.mdl-data-table thead,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) thead{padding-bottom:3px}.mdl-data-table thead .mdl-data-table__select,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) thead .mdl-data-table__select{margin-top:0}.mdl-data-table tbody tr,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) tbody tr{position:relative;height:48px;transition-duration:.28s;transition-timing-function:cubic-bezier(.4,0,.2,1);transition-property:background-color}.mdl-data-table tbody tr.is-selected,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) tbody tr.is-selected{background-color:#e0e0e0}.mdl-data-table tbody tr:hover,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) tbody tr:hover{background-color:#eee}.mdl-data-table td,.mdl-data-table th,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) td,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th{padding:0 18px 12px;text-align:right}.mdl-data-table td:first-of-type,.mdl-data-table th:first-of-type,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) td:first-of-type,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th:first-of-type{padding-left:24px}.mdl-data-table td:last-of-type,.mdl-data-table th:last-of-type,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) td:last-of-type,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th:last-of-type{padding-right:24px}.mdl-data-table td,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) td{position:relative;vertical-align:middle;height:48px;border-top:1px solid rgba(0,0,0,.12);border-bottom:1px solid rgba(0,0,0,.12);padding-top:12px;box-sizing:border-box}.mdl-data-table td .mdl-data-table__select,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) td .mdl-data-table__select{vertical-align:middle}.mdl-data-table th,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th{position:relative;vertical-align:bottom;text-overflow:ellipsis;font-size:14px;font-weight:700;line-height:24px;letter-spacing:0;height:48px;font-size:12px;color:rgba(0,0,0,.54);padding-bottom:8px;box-sizing:border-box}.mdl-data-table th.mdl-data-table__header--sorted-ascending,.mdl-data-table th.mdl-data-table__header--sorted-descending,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-ascending,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-descending{color:rgba(0,0,0,.87)}.mdl-data-table th.mdl-data-table__header--sorted-ascending:before,.mdl-data-table th.mdl-data-table__header--sorted-descending:before,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-ascending:before,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-descending:before{font-family:Material Icons;font-weight:400;font-style:normal;font-size:24px;line-height:1;letter-spacing:normal;text-transform:none;display:inline-block;word-wrap:normal;font-feature-settings:"liga";-webkit-font-feature-settings:"liga";-webkit-font-smoothing:antialiased;font-size:16px;content:"\e5d8";margin-right:5px;vertical-align:sub}.mdl-data-table th.mdl-data-table__header--sorted-ascending:hover,.mdl-data-table th.mdl-data-table__header--sorted-descending:hover,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-ascending:hover,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-descending:hover{cursor:pointer}.mdl-data-table th.mdl-data-table__header--sorted-ascending:hover:before,.mdl-data-table th.mdl-data-table__header--sorted-descending:hover:before,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-ascending:hover:before,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-descending:hover:before{color:rgba(0,0,0,.26)}.mdl-data-table th.mdl-data-table__header--sorted-descending:before,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-descending:before{content:"\e5db"}.mdl-data-table__select{width:16px}.mdl-data-table__cell--non-numeric.mdl-data-table__cell--non-numeric,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) td,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th{text-align:left}.mdl-mini-footer{display:flex;flex-flow:row wrap;justify-content:space-between;padding:32px 16px;color:#9e9e9e;background-color:#424242}.mdl-mini-footer:after{content:"";display:block}.mdl-mini-footer .mdl-logo{line-height:36px}.mdl-mini-footer--link-list,.mdl-mini-footer__link-list,footer.mdl-mini-footer>div.mdl-mini-footer__left-section ul{display:flex;flex-flow:row nowrap;list-style:none;margin:0;padding:0}.mdl-mini-footer--link-list li,.mdl-mini-footer__link-list li,footer.mdl-mini-footer>div.mdl-mini-footer__left-section ul li{margin-bottom:0;margin-right:16px}@media screen and (min-width:760px){.mdl-mini-footer--link-list li,.mdl-mini-footer__link-list li,footer.mdl-mini-footer>div.mdl-mini-footer__left-section ul li{line-height:36px}}.mdl-mini-footer--link-list a,.mdl-mini-footer__link-list a,footer.mdl-mini-footer>div.mdl-mini-footer__left-section ul a{color:inherit;text-decoration:none;white-space:nowrap}.mdl-mini-footer--left-section,.mdl-mini-footer__left-section{display:inline-block;order:0}.mdl-mini-footer--right-section,.mdl-mini-footer__right-section{display:inline-block;order:1}.mdl-mini-footer--social-btn,.mdl-mini-footer__social-btn{width:36px;height:36px;padding:0;margin:0;background-color:#9e9e9e;border:none}.mdl-card{display:flex;flex-direction:column;font-size:16px;font-weight:400;min-height:200px;overflow:hidden;width:330px;z-index:1;position:relative;background:#fff;border-radius:2px;box-sizing:border-box}.mdl-card__media{background-color:#ff6e40;background-repeat:repeat;background-position:50% 50%;background-size:cover;background-origin:padding-box;background-attachment:scroll;box-sizing:border-box}.mdl-card__title{align-items:center;color:#000;display:block;display:flex;justify-content:stretch;line-height:normal;padding:16px;perspective-origin:165px 56px;transform-origin:165px 56px;box-sizing:border-box}.mdl-card__title.mdl-card--border{border-bottom:1px solid rgba(0,0,0,.1)}.mdl-card__title-text{align-self:flex-end;color:inherit;display:block;display:flex;font-size:24px;font-weight:300;line-height:normal;overflow:hidden;transform-origin:149px 48px;margin:0}.mdl-card__subtitle-text{font-size:14px;color:rgba(0,0,0,.54);margin:0}.mdl-card__supporting-text{color:rgba(0,0,0,.54);font-size:1rem;line-height:18px;overflow:hidden;padding:16px;width:90%}.mdl-card__supporting-text.mdl-card--border{border-bottom:1px solid rgba(0,0,0,.1)}.mdl-card__actions{font-size:16px;line-height:normal;width:100%;background-color:transparent;padding:8px;box-sizing:border-box}.mdl-card__actions.mdl-card--border{border-top:1px solid rgba(0,0,0,.1)}.mdl-card--expand{flex-grow:1}.mdl-card__menu{position:absolute;right:16px;top:16px}.mdl-button{background:transparent;border:none;border-radius:2px;color:#000;position:relative;height:36px;margin:0;min-width:64px;padding:0 16px;display:inline-block;font-family:Roboto,Helvetica,Arial,sans-serif;font-size:14px;font-weight:500;text-transform:uppercase;line-height:1;letter-spacing:0;overflow:hidden;will-change:box-shadow;transition:box-shadow .2s cubic-bezier(.4,0,1,1),background-color .2s cubic-bezier(.4,0,.2,1),color .2s cubic-bezier(.4,0,.2,1);outline:none;cursor:pointer;text-decoration:none;text-align:center;line-height:36px;vertical-align:middle}.mdl-button::-moz-focus-inner{border:0}.mdl-button:hover{background-color:hsla(0,0%,62%,.2)}.mdl-button:focus:not(:active){background-color:rgba(0,0,0,.12)}.mdl-button:active{background-color:hsla(0,0%,62%,.4)}.mdl-button.mdl-button--colored{color:#2196f3}.mdl-button.mdl-button--colored:focus:not(:active){background-color:rgba(0,0,0,.12)}input.mdl-button[type=submit]{-webkit-appearance:none}.mdl-button--raised{background:hsla(0,0%,62%,.2);box-shadow:0 2px 2px 0 rgba(0,0,0,.14),0 3px 1px -2px rgba(0,0,0,.2),0 1px 5px 0 rgba(0,0,0,.12)}.mdl-button--raised:active{box-shadow:0 4px 5px 0 rgba(0,0,0,.14),0 1px 10px 0 rgba(0,0,0,.12),0 2px 4px -1px rgba(0,0,0,.2);background-color:hsla(0,0%,62%,.4)}.mdl-button--raised:focus:not(:active){box-shadow:0 0 8px rgba(0,0,0,.18),0 8px 16px rgba(0,0,0,.36);background-color:hsla(0,0%,62%,.4)}.mdl-button--raised.mdl-button--colored{background:#2196f3;color:#fff}.mdl-button--raised.mdl-button--colored:active,.mdl-button--raised.mdl-button--colored:focus:not(:active),.mdl-button--raised.mdl-button--colored:hover{background-color:#2196f3}.mdl-button--raised.mdl-button--colored .mdl-ripple{background:#fff}.mdl-button--fab{border-radius:50%;font-size:24px;height:56px;margin:auto;min-width:56px;width:56px;padding:0;overflow:hidden;background:hsla(0,0%,62%,.2);box-shadow:0 1px 1.5px 0 rgba(0,0,0,.12),0 1px 1px 0 rgba(0,0,0,.24);position:relative;line-height:normal}.admonition.attention .mdl-button--fab .admonition-title:before,.admonition.caution .mdl-button--fab .admonition-title:before,.admonition.danger .mdl-button--fab .admonition-title:before,.admonition.error .mdl-button--fab .admonition-title:before,.admonition.hint .mdl-button--fab .admonition-title:before,.admonition.important .mdl-button--fab .admonition-title:before,.admonition.note .mdl-button--fab .admonition-title:before,.admonition.seealso .mdl-button--fab .admonition-title:before,.admonition.tip .mdl-button--fab .admonition-title:before,.admonition.warning .mdl-button--fab .admonition-title:before,.mdl-button--fab .admonition.attention .admonition-title:before,.mdl-button--fab .admonition.caution .admonition-title:before,.mdl-button--fab .admonition.danger .admonition-title:before,.mdl-button--fab .admonition.error .admonition-title:before,.mdl-button--fab .admonition.hint .admonition-title:before,.mdl-button--fab .admonition.important .admonition-title:before,.mdl-button--fab .admonition.note .admonition-title:before,.mdl-button--fab .admonition.seealso .admonition-title:before,.mdl-button--fab .admonition.tip .admonition-title:before,.mdl-button--fab .admonition.warning .admonition-title:before,.mdl-button--fab .material-icons,.mdl-button--fab a.download:before{position:absolute;top:50%;left:50%;transform:translate(-12px,-12px);line-height:24px;width:24px}.mdl-button--fab.mdl-button--mini-fab{height:40px;min-width:40px;width:40px}.mdl-button--fab .mdl-button__ripple-container{border-radius:50%;-webkit-mask-image:-webkit-radial-gradient(circle,#fff,#000)}.mdl-button--fab:active{box-shadow:0 4px 5px 0 rgba(0,0,0,.14),0 1px 10px 0 rgba(0,0,0,.12),0 2px 4px -1px rgba(0,0,0,.2);background-color:hsla(0,0%,62%,.4)}.mdl-button--fab:focus:not(:active){box-shadow:0 0 8px rgba(0,0,0,.18),0 8px 16px rgba(0,0,0,.36);background-color:hsla(0,0%,62%,.4)}.mdl-button--fab.mdl-button--colored{background:#ff6e40;color:#fff}.mdl-button--fab.mdl-button--colored:active,.mdl-button--fab.mdl-button--colored:focus:not(:active),.mdl-button--fab.mdl-button--colored:hover{background-color:#ff6e40}.mdl-button--fab.mdl-button--colored .mdl-ripple{background:#fff}.mdl-button--icon{border-radius:50%;font-size:24px;height:32px;margin-left:0;margin-right:0;min-width:32px;width:32px;padding:0;overflow:hidden;color:inherit;line-height:normal}.admonition.attention .mdl-button--icon .admonition-title:before,.admonition.caution .mdl-button--icon .admonition-title:before,.admonition.danger .mdl-button--icon .admonition-title:before,.admonition.error .mdl-button--icon .admonition-title:before,.admonition.hint .mdl-button--icon .admonition-title:before,.admonition.important .mdl-button--icon .admonition-title:before,.admonition.note .mdl-button--icon .admonition-title:before,.admonition.seealso .mdl-button--icon .admonition-title:before,.admonition.tip .mdl-button--icon .admonition-title:before,.admonition.warning .mdl-button--icon .admonition-title:before,.mdl-button--icon .admonition.attention .admonition-title:before,.mdl-button--icon .admonition.caution .admonition-title:before,.mdl-button--icon .admonition.danger .admonition-title:before,.mdl-button--icon .admonition.error .admonition-title:before,.mdl-button--icon .admonition.hint .admonition-title:before,.mdl-button--icon .admonition.important .admonition-title:before,.mdl-button--icon .admonition.note .admonition-title:before,.mdl-button--icon .admonition.seealso .admonition-title:before,.mdl-button--icon .admonition.tip .admonition-title:before,.mdl-button--icon .admonition.warning .admonition-title:before,.mdl-button--icon .material-icons,.mdl-button--icon a.download:before{position:absolute;top:50%;left:50%;transform:translate(-12px,-12px);line-height:24px;width:24px}.mdl-button--icon.mdl-button--mini-icon{height:24px;min-width:24px;width:24px}.admonition.attention .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.caution .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.danger .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.error .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.hint .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.important .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.note .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.seealso .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.tip .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.warning .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.attention .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.caution .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.danger .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.error .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.hint .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.important .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.note .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.seealso .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.tip .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.warning .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .material-icons,.mdl-button--icon.mdl-button--mini-icon a.download:before{top:0;left:0}.mdl-button--icon .mdl-button__ripple-container{border-radius:50%;-webkit-mask-image:-webkit-radial-gradient(circle,#fff,#000)}.mdl-button__ripple-container{display:block;height:100%;left:0;position:absolute;top:0;width:100%;z-index:0;overflow:hidden}.mdl-button.mdl-button--disabled .mdl-button__ripple-container .mdl-ripple,.mdl-button[disabled] .mdl-button__ripple-container .mdl-ripple{background-color:transparent}.mdl-button--primary.mdl-button--primary{color:#2196f3}.mdl-button--primary.mdl-button--primary .mdl-ripple{background:#fff}.mdl-button--primary.mdl-button--primary.mdl-button--fab,.mdl-button--primary.mdl-button--primary.mdl-button--raised{color:#fff;background-color:#2196f3}.mdl-button--accent.mdl-button--accent{color:#ff6e40}.mdl-button--accent.mdl-button--accent .mdl-ripple{background:#fff}.mdl-button--accent.mdl-button--accent.mdl-button--fab,.mdl-button--accent.mdl-button--accent.mdl-button--raised{color:#fff;background-color:#ff6e40}.mdl-button.mdl-button--disabled.mdl-button--disabled,.mdl-button[disabled][disabled]{color:rgba(0,0,0,.26);cursor:default;background-color:transparent}.mdl-button--fab.mdl-button--disabled.mdl-button--disabled,.mdl-button--fab[disabled][disabled]{background-color:rgba(0,0,0,.12);color:rgba(0,0,0,.26)}.mdl-button--raised.mdl-button--disabled.mdl-button--disabled,.mdl-button--raised[disabled][disabled]{background-color:rgba(0,0,0,.12);color:rgba(0,0,0,.26);box-shadow:none}.mdl-button--colored.mdl-button--disabled.mdl-button--disabled,.mdl-button--colored[disabled][disabled]{color:rgba(0,0,0,.26)}.admonition.attention .mdl-button .admonition-title:before,.admonition.caution .mdl-button .admonition-title:before,.admonition.danger .mdl-button .admonition-title:before,.admonition.error .mdl-button .admonition-title:before,.admonition.hint .mdl-button .admonition-title:before,.admonition.important .mdl-button .admonition-title:before,.admonition.note .mdl-button .admonition-title:before,.admonition.seealso .mdl-button .admonition-title:before,.admonition.tip .mdl-button .admonition-title:before,.admonition.warning .mdl-button .admonition-title:before,.mdl-button .admonition.attention .admonition-title:before,.mdl-button .admonition.caution .admonition-title:before,.mdl-button .admonition.danger .admonition-title:before,.mdl-button .admonition.error .admonition-title:before,.mdl-button .admonition.hint .admonition-title:before,.mdl-button .admonition.important .admonition-title:before,.mdl-button .admonition.note .admonition-title:before,.mdl-button .admonition.seealso .admonition-title:before,.mdl-button .admonition.tip .admonition-title:before,.mdl-button .admonition.warning .admonition-title:before,.mdl-button .material-icons,.mdl-button a.download:before{vertical-align:middle}.font-light{font-weight:300}.font-regular{font-weight:400}.font-heavy{font-weight:700}.left{text-align:left}.right{text-align:right}.center{text-align:center;margin-left:auto;margin-right:auto}.justify{text-align:justify}.hidden-sm{display:none}.container{width:100%;margin-left:auto;margin-right:auto}@media only screen and (min-width:33.75em){.container{width:80%}}@media only screen and (min-width:60em){.container{width:75%;max-width:60rem}}.row{position:relative;width:100%}.row [class^=col]{float:left;margin:.5rem 1%;min-height:.125rem}.row:after{content:"";display:table;clear:both}.col-1,.col-2,.col-3,.col-4,.col-5,.col-6,.col-7,.col-8,.col-9,.col-10,.col-11,.col-12{width:98%}.col-1-sm{width:6.33333%}.col-2-sm{width:14.66667%}.col-3-sm{width:23%}.col-4-sm{width:31.33333%}.col-5-sm{width:39.66667%}.col-6-sm{width:48%}.col-7-sm{width:56.33333%}.col-8-sm{width:64.66667%}.col-9-sm{width:73%}.col-10-sm{width:81.33333%}.col-11-sm{width:89.66667%}.col-12-sm{width:98%}@media only screen and (min-width:45em){.col-1{width:6.33333%}.col-2{width:14.66667%}.col-3{width:23%}.col-4{width:31.33333%}.col-5{width:39.66667%}.col-6{width:48%}.col-7{width:56.33333%}.col-8{width:64.66667%}.col-9{width:73%}.col-10{width:81.33333%}.col-11{width:89.66667%}.col-12{width:98%}.hidden-sm{display:block}}.row{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;flex-wrap:wrap}.row>[class*=col-]{display:flex;flex-direction:column}.admonition.attention .admonition-title:before,.admonition.caution .admonition-title:before,.admonition.danger .admonition-title:before,.admonition.error .admonition-title:before,.admonition.hint .admonition-title:before,.admonition.important .admonition-title:before,.admonition.note .admonition-title:before,.admonition.seealso .admonition-title:before,.admonition.tip .admonition-title:before,.admonition.warning .admonition-title:before,.material-icons,a.download:before{font-family:Material Icons;font-weight:400;font-style:normal;font-size:24px;display:inline-block;line-height:1;text-transform:none;letter-spacing:normal;word-wrap:normal;white-space:nowrap;direction:ltr;-webkit-font-smoothing:antialiased;text-rendering:optimizeLegibility;-moz-osx-font-smoothing:grayscale;font-feature-settings:"liga"}html{font-size:16px}body{display:block!important;background-color:#fafafa;font-size:1rem;line-height:1.5rem;font-family:-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol}.mdl-layout__content:focus{outline:none}a.download>code.download,blockquote,h1,h2,h3,h4,h5,h6,span.mdl-layout-title{font-family:-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol}.contents,.contents a,.globaltoc a.current,.toc-backref,.toctree-wrapper,.toctree-wrapper a,h1,h2,h3,h4,h5,h6{color:#048ccc!important}a{text-decoration:none}.page-content,.page-content dd,.page-content dl,.page-content dt,.page-content ol,.page-content p,.page-content table,.page-content td,.page-content th,.page-content ul{font-size:1rem}.brand{color:inherit;text-decoration:none}.section{overflow-x:auto}img{max-width:100%;display:block;margin-left:auto;margin-right:auto}div.figure p.caption{text-align:center;margin-top:.75rem}div.figure p.caption span.caption-number{font-style:normal}div.figure p.caption .caption-number:after{content:"\00a0"}.svg-icon{width:16px;height:16px;display:inline-block;fill:#f5f5f5;padding-right:5px;padding-top:4px;vertical-align:text-top}.admonition.attention a.download>i.admonition-title:before,.admonition.caution a.download>i.admonition-title:before,.admonition.danger a.download>i.admonition-title:before,.admonition.error a.download>i.admonition-title:before,.admonition.hint a.download>i.admonition-title:before,.admonition.important a.download>i.admonition-title:before,.admonition.note a.download>i.admonition-title:before,.admonition.seealso a.download>i.admonition-title:before,.admonition.tip a.download>i.admonition-title:before,.admonition.warning a.download>i.admonition-title:before,a.download>i.material-icons{position:relative;top:5px}a.download{text-decoration:none}.wrapper:after{content:"";display:table;clear:both}.wrapper{max-width:1090px;margin-right:auto;margin-left:auto;padding-right:45px;padding-left:30px}@media screen and (max-width:1024px){.wrapper{max-width:1120px;padding-right:15px;padding-left:15px}}.mdl-layout{margin-top:76px}.document{width:100%;margin:0 auto;display:flex}@media (min-width:1795px){.document{width:1400px}}.document .page-content{width:100%;margin:0 auto;padding:0 12px}@media (min-width:992px){.document .page-content{width:90%;padding:0 5%}}@media (min-width:1200px){.document .page-content{width:calc(90% - 230px);padding:0 5%}}.document .side-doc-outline{width:230px}@media (max-width:1199px){.document .side-doc-outline{display:none}}.document .side-doc-outline--content{position:fixed;overflow-x:auto;overflow-y:auto;width:inherit}.document .side-doc-outline--content::-webkit-scrollbar{width:6px}.document .side-doc-outline--content::-webkit-scrollbar-track{border-radius:6px}.document .side-doc-outline--content::-webkit-scrollbar-thumb{background-color:rgba(0,0,0,.3);border-radius:6px;box-shadow:0 0 0 1px hsla(0,0%,100%,.3)}@keyframes float-in{0%{transform:translateY(.5rem);opacity:0}to{transform:translateY(0);opacity:1}}@keyframes float-out{0%{transform:translateY(0);opacity:1}to{transform:translateY(.5rem);opacity:0}}.page-content .headerlink{display:inline-block;text-decoration:none;margin-left:.8rem;color:inherit;opacity:0}.page-content .headerlink:hover{animation:float-in .2s cubic-bezier(.4,0,.2,1) 0s forwards}.page-content h1 .toc-backref,.page-content h2 .toc-backref,.page-content h3 .toc-backref,.page-content h4 .toc-backref,.page-content h5 .toc-backref,.page-content h6 .toc-backref{text-decoration:none}.page-content h1:hover .headerlink,.page-content h2:hover .headerlink,.page-content h3:hover .headerlink,.page-content h4:hover .headerlink,.page-content h5:hover .headerlink,.page-content h6:hover .headerlink{animation:float-in .2s cubic-bezier(.4,0,.2,1) 0s forwards}.page-content h1{font-size:2rem;line-height:2.25rem}.page-content h2{font-size:1.75rem;line-height:2rem;padding-top:1.5rem;margin-top:0;margin-bottom:1rem}.page-content h3{font-size:1.5rem;line-height:1.75rem;padding-top:1rem;margin-top:0;margin-bottom:.75rem}.page-content h4{font-size:1.25rem;line-height:1.5rem;padding-top:.75rem;margin-top:0;margin-bottom:.5rem}.page-content div.page-content h5{font-size:1.1rem;line-height:1.5rem;padding-top:2rem;margin-top:0;margin-bottom:1rem}.page-content div.page-content h6{font-size:1rem;line-height:1.5rem;padding-top:2rem;margin-top:0;margin-bottom:1rem}.admonition{padding:12px 20px;margin-top:10px;margin-bottom:10px}.admonition p.last{margin:16px}.admonition .admonition-title{font-size:16px;font-weight:700;color:#555;text-transform:uppercase;margin-top:7px}.admonition.note{border-left:4px solid #00bcd4;background-color:rgba(0,188,212,.1)}.admonition.note .admonition-title{font-size:16px;font-weight:700;color:#00bcd4;margin-top:4px;margin-bottom:8px}.admonition.note .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"info_outline";font-size:18px}.admonition.seealso{border-left:4px solid #00bcd4;background-color:rgba(0,188,212,.1)}.admonition.seealso .admonition-title{font-size:16px;font-weight:700;color:#00bcd4;margin-top:4px;margin-bottom:8px}.admonition.seealso .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"search";font-size:18px}.admonition.hint{border-left:4px solid #00bcd4;background-color:rgba(0,188,212,.1)}.admonition.hint .admonition-title{font-size:16px;font-weight:700;color:#00bcd4;margin-top:4px;margin-bottom:8px}.admonition.hint .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"help_outline";font-size:18px}.admonition.warning{border-left:4px solid #ffc107;background-color:rgba(255,193,7,.1)}.admonition.warning .admonition-title{font-size:16px;font-weight:700;color:#ffc107;margin-top:4px;margin-bottom:8px}.admonition.warning .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"warning";font-size:18px}.admonition.attention{border-left:4px solid #ffc107;background-color:rgba(255,193,7,.1)}.admonition.attention .admonition-title{font-size:16px;font-weight:700;color:#ffc107;margin-top:4px;margin-bottom:8px}.admonition.attention .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"warning";font-size:18px}.admonition.tip{border-left:4px solid #8bc34a;background-color:rgba(139,195,74,.1)}.admonition.tip .admonition-title{font-size:16px;font-weight:700;color:#8bc34a;margin-top:4px;margin-bottom:8px}.admonition.tip .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"lightbulb_outline";font-size:18px}.admonition.important{border-left:4px solid #8bc34a;background-color:rgba(139,195,74,.1)}.admonition.important .admonition-title{font-size:16px;font-weight:700;color:#8bc34a;margin-top:4px;margin-bottom:8px}.admonition.important .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"check_circle";font-size:18px}.admonition.error{border-left:4px solid #f44336;background-color:rgba(244,67,54,.1)}.admonition.error .admonition-title{font-size:16px;font-weight:700;color:#f44336;margin-top:4px;margin-bottom:8px}.admonition.error .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"error_outline";font-size:18px}.admonition.caution{border-left:4px solid #f44336;background-color:rgba(244,67,54,.1)}.admonition.caution .admonition-title{font-size:16px;font-weight:700;color:#f44336;margin-top:4px;margin-bottom:8px}.admonition.caution .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"error_outline";font-size:18px}.admonition.danger{border-left:4px solid #f44336;background-color:rgba(244,67,54,.1)}.admonition.danger .admonition-title{font-size:16px;font-weight:700;color:#f44336;margin-top:4px;margin-bottom:8px}.admonition.danger .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"error_outline";font-size:18px}.page-content .highlight{margin:1px 0}.page-content .highlight pre{background:rgba(0,0,0,.05);color:rgba(0,0,0,.87);font-family:Menlo,DejaVu Sans Mono,Liberation Mono,Consolas,Ubuntu Mono,Courier New,andale mono,lucida console,monospace;padding:.75rem;overflow:auto;overflow-y:hidden}.page-content .highlight pre .nd,.page-content .highlight pre .o{color:rgba(0,0,0,.87)}.page-content div.highlight-console div.highlight{background:none}.page-content .output .highlight pre{color:rgba(0,0,0,.87);background:#fafafa;border:1px solid #999;padding:.75rem}.page-content .code,.page-content code:not(.download){margin:0;border-radius:2px}.page-content .code,.page-content .code span.pre,.page-content code:not(.download),.page-content code:not(.download) span.pre{font-family:Menlo,DejaVu Sans Mono,Liberation Mono,Consolas,Ubuntu Mono,Courier New,andale mono,lucida console,monospace}.page-content .viewcode-link{padding-left:2em;font-size:80%}.page-content .class>dt,.page-content .function>dt,.page-content .method>dt,.page-content .rubric{display:table;margin:10px 0;font-size:100%;line-height:normal;background:#e7f2fa;color:#2b98f0;border-top:3px solid #55adf3;padding:10px;position:relative}.page-content .class>dt .descclassname,.page-content .class>dt .descname,.page-content .function>dt .descclassname,.page-content .function>dt .descname,.page-content .method>dt .descclassname,.page-content .method>dt .descname,.page-content .rubric .descclassname,.page-content .rubric .descname{color:rgba(0,0,0,.87);background:#e7f2fa;padding:3px}.page-content .class>dt em,.page-content .function>dt em,.page-content .method>dt em,.page-content .rubric em{padding:0 2px}.page-content .rubric{margin:30px 0 10px}.page-content .field-body{padding-left:40px}.page-content .field-body ul{padding:0 0 0 16px;margin:0}.page-content .seealso .docutils>dt{float:left;clear:left;padding:0 6px}.page-content .seealso .docutils>dd{padding-left:6em}.page-content .nblast{padding-bottom:1em}.page-content pre{font-size:90%;background:#eee;color:#455a64;padding:16px 32px;width:auto;border-radius:4px;word-wrap:break-word}.page-content pre:hover:before{font-family:-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol;padding:0 .5rem;content:attr(click-to-copy);color:rgba(0,0,0,.5);border-radius:4px;position:relative;float:right;top:-.5rem;right:-.5rem;background:#c8c8c8;font-size:.8rem;cursor:pointer}.page-content blockquote{font-size:1rem;padding:0 1rem;border-left:3px solid rgba(0,0,0,.05)}.page-content blockquote:after{content:""!important;margin-left:0}.page-content blockquote:before{content:""!important}.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list){margin:1.5rem 0;table-layout:fixed;max-width:100%;min-width:70%}.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) td,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th{white-space:normal;overflow-wrap:break-word}.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) caption{font-size:16px;margin:1rem 0 .8rem;white-space:normal}.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) caption .caption-number{font-style:normal}.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) caption .caption-number:after{content:"\00a0"}.globaltoc .caption,.globaltoc .toc{display:none}.globaltoc ul{list-style-type:none;padding:0;margin:0}.globaltoc ul li{min-height:36px}.globaltoc ul li .link-wrapper{display:flex;justify-content:space-between}.globaltoc ul li .link-wrapper>a{padding:8px 0;display:block;width:100%;font-size:1rem;text-decoration:none;color:#757575}.globaltoc ul li .link-wrapper>a.current{font-weight:700}.globaltoc .nav-toggle{padding:0;float:right;display:flex;align-items:center;justify-content:center;height:36px}.globaltoc .nav-toggle>a{padding:0;margin-left:0;margin-right:4px;cursor:pointer}.globaltoc .nav-toggle>a>i{font-size:18px}.globaltoc .nav-toggle.show{transform:rotate(180deg)}.globaltoc .nav-toggle.show>a{margin-right:0;margin-left:4px}.globaltoc nav>ul>li>span.link-wrapper{padding-left:8px}.globaltoc nav>ul>li>ul>li>span.link-wrapper{padding-left:16px}.globaltoc nav>ul>li>ul>li>ul>li>span.link-wrapper{padding-left:24px}.globaltoc nav>ul>li>ul>li>ul>li>ul>li>span.link-wrapper{padding-left:32px}.globaltoc nav>ul>li>ul>li>ul>li>ul>li>ul>li>span.link-wrapper{padding-left:40px}.globaltoc nav>ul>li>ul>li>ul>li>ul>li>ul>li>ul>li>span.link-wrapper{padding-left:48px}.localtoc{font-size:.75rem;padding-top:1rem}.localtoc .caption{padding-left:12px}.localtoc .caption-text{font-size:.9rem;font-weight:700}.localtoc>ul>li>a{display:none}.localtoc ul{padding:0;list-style-type:none}.localtoc li{padding-left:6px}.localtoc a{display:block;text-decoration:none;color:inherit;margin-top:8px;padding-left:8px;line-height:1.1rem}.localtoc a.current{padding-left:5px;border-left:3px solid;font-weight:700}.contents.topic,.toctree-wrapper{border-left:5px solid}.contents.topic>p.topic-title,.toctree-wrapper>p.caption{color:#757575;font-size:1rem;padding-left:14px}.contents.topic ul,.toctree-wrapper ul{padding-left:14px;list-style:none;line-height:30px}.contents.topic a,.toctree-wrapper a{font-size:1.2rem;text-decoration:none}.contents.topic a .pre,.toctree-wrapper a .pre{font-size:1rem}.contents.topic>ul>li>a,.toctree-wrapper>ul>li>a{font-size:1.3rem}.contents.topic>ul>li>a .pre,.toctree-wrapper>ul>li>a .pre{font-size:1.1rem}.page-content ul li{margin:.3rem 0}.page-content ul li p{margin:0}.page-content .option-list .option{font-family:Menlo,DejaVu Sans Mono,Liberation Mono,Consolas,Ubuntu Mono,Courier New,andale mono,lucida console,monospace}.page-content .option-list td{padding:.5rem;border:none}.mdl-layout__drawer{background-color:#fff}.mdl-layout__drawer::-webkit-scrollbar{width:6px}.mdl-layout__drawer::-webkit-scrollbar-track{border-radius:6px}.mdl-layout__drawer::-webkit-scrollbar-thumb{background-color:rgba(0,0,0,.3);border-radius:6px;box-shadow:0 0 0 1px hsla(0,0%,100%,.3)}.mdl-layout__drawer>.mdl-layout-title{font-weight:700;text-align:right;margin:0;padding:0;line-height:32px;border-bottom:1px solid rgba(0,0,0,.1);min-height:64px}.mdl-layout__drawer>.mdl-layout-title .title{color:inherit;display:block;height:100%;width:100%;text-decoration:none}.mdl-layout__drawer>.mdl-layout-title .title>img.logo{width:100%;margin:0;padding:0}.mdl-layout__drawer>.mdl-layout-title .title-text{font-weight:700;text-align:right;padding:0 10px;margin:16px 0 8px;line-height:32px;font-family:-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol;color:inherit;display:block}nav.breadcrumb>a.mdl-navigation__link{padding:0 8px;font-size:18px}@media (max-width:1199px){nav.breadcrumb{width:calc(100% - 64px)}nav.breadcrumb a.mdl-navigation__link.is-active{overflow-x:hidden;width:100%;overflow:hidden;white-space:nowrap;text-overflow:ellipsis}.admonition.attention nav.breadcrumb i.admonition-title:before,.admonition.caution nav.breadcrumb i.admonition-title:before,.admonition.danger nav.breadcrumb i.admonition-title:before,.admonition.error nav.breadcrumb i.admonition-title:before,.admonition.hint nav.breadcrumb i.admonition-title:before,.admonition.important nav.breadcrumb i.admonition-title:before,.admonition.note nav.breadcrumb i.admonition-title:before,.admonition.seealso nav.breadcrumb i.admonition-title:before,.admonition.tip nav.breadcrumb i.admonition-title:before,.admonition.warning nav.breadcrumb i.admonition-title:before,nav.breadcrumb .admonition.attention i.admonition-title:before,nav.breadcrumb .admonition.caution i.admonition-title:before,nav.breadcrumb .admonition.danger i.admonition-title:before,nav.breadcrumb .admonition.error i.admonition-title:before,nav.breadcrumb .admonition.hint i.admonition-title:before,nav.breadcrumb .admonition.important i.admonition-title:before,nav.breadcrumb .admonition.note i.admonition-title:before,nav.breadcrumb .admonition.seealso i.admonition-title:before,nav.breadcrumb .admonition.tip i.admonition-title:before,nav.breadcrumb .admonition.warning i.admonition-title:before,nav.breadcrumb a.mdl-navigation__link:not(.is-active),nav.breadcrumb i.material-icons{display:none}}div.mdl-layout__header{margin-top:77px}.mdl-layout__drawer-button{top:13px!important}div.mdl-layout__header-row.header-links{background:hsla(0,0%,100%,.2);width:100%;overflow-x:auto;overflow-y:hidden}div.mdl-layout__header-row.header-links a.mdl-navigation__link{font-size:1rem}div.mdl-layout__header-row.header-links a.mdl-navigation__link i{font-size:1.2rem;margin:0 8px;position:relative;bottom:-.1rem}div.mdl-layout__header-row.header-links a.mdl-navigation__link:hover{background-color:#2196f3;color:#eee}div.mdl-layout__header-row.header-links a.mdl-navigation__link[href="#"]{background-color:#2196f3;opacity:1;color:#fff}.site-title{font-weight:300!important;line-height:57px;letter-spacing:-1px;margin-bottom:0;float:left;color:#fff}.site-title,.site-title:visited{color:#424242}.site-header{position:fixed;top:0;width:100%;min-height:55px;padding-top:10px;padding-bottom:10px;background-color:#048ccc;z-index:10;font-weight:300;font-size:17px;border-bottom:1px solid #fff}.site-header-logo{width:120px;display:initial}.site-nav{float:right;line-height:57px}.site-nav .menu-icon,.site-nav .nav-trigger{display:none}.site-nav .page-link{color:#fff;line-height:1.5;font-weight:300}.site-nav .page-link:not(:last-child){margin-right:40px}.site-nav .page-link:hover{color:#fff;text-shadow:-.06ex 0 #fff,.06ex 0 #fff}.site-nav .page-link.page-current{color:#fff;text-decoration:underline}@media screen and (max-width:1024px){.site-nav{position:absolute;top:9px;right:15px;background-color:#178dc9;border-radius:2px;text-align:right}.site-nav label[for=nav-trigger]{display:block;float:right;width:36px;height:36px;z-index:2;cursor:pointer}.site-nav .menu-icon{display:block;float:right;width:36px;height:26px;line-height:0;padding-top:20px;text-align:center}.site-nav .menu-icon>svg{fill:#fff}.site-nav input~.trigger{clear:both;display:none}.site-nav input:checked~.trigger{display:block;padding-bottom:5px}.site-nav .page-link{padding:5px 10px;display:block;margin-left:20px}.site-nav .page-link:not(:last-child){margin-right:0}}footer.mdl-mini-footer{background-color:#212121}footer.mdl-mini-footer>div.mdl-mini-footer__left-section{margin-bottom:20px;display:flex;flex-direction:column}footer.mdl-mini-footer>div.mdl-mini-footer__left-section .mdl-logo{font-size:1.1rem}footer.mdl-mini-footer>div.mdl-mini-footer__right-section{font-size:.9rem;display:flex;flex-direction:column;justify-content:flex-end}footer.mdl-mini-footer>div.mdl-mini-footer__right-section a{color:inherit;font-weight:700;text-decoration:none}footer.mdl-mini-footer p.caption{display:none}.pagenation{width:100%;margin-top:80px;height:92px;background-color:#424242;display:flex}.pagenation #button-next,.pagenation #button-prev,.pagenation .button-common{text-transform:none;padding:0;height:92px;display:flex;justify-content:center;align-items:center;color:#fff}.pagenation #button-prev{margin-right:auto}.pagenation #button-prev .pagenation-text{text-align:left}.pagenation #button-next{margin-left:auto;flex-direction:row-reverse}.pagenation #button-next .pagenation-text{text-align:right}.pagenation-arrow-L{margin-right:20px}.pagenation-arrow-R{margin-left:20px}.pagenation-text{line-height:30px;font-size:20px}.pagenation-direction{opacity:.7;font-size:18px}@media screen and (max-width:1024px){.pagenation #button-prev{width:20%}.pagenation #button-next{width:80%}.pagenation #button-prev .pagenation-text{display:none}}@media screen and (min-width:1025px){.pagenation #button-next,.pagenation #button-prev{width:50%}.pagenation #button-prev .pagenation-text{display:block}}.site-footer{border-top:1px solid #f5f5f5;padding:30px 0;background-color:#424242}.site-footer .footer-category-title{color:#048ccc}.site-footer a,.site-footer a:visited{color:#f5f5f5!important}.site-footer2{background-color:#424242;padding-top:40px;padding-bottom:10px}.footer-heading{margin-bottom:15px}.contact-list,.social-media-list{list-style:none;margin-left:0}.footer-bottom-warning{font-size:80%;color:#fff;float:left}.footer-logo{width:200px;margin-bottom:30px;margin-top:30px}.footer-col{float:left;margin-bottom:15px;padding-left:15px}.footer-text{color:#f5f5f5}#waterfall-exp::-webkit-input-placeholder{color:#ccc}#waterfall-exp:-ms-input-placeholder{color:#ccc}#waterfall-exp::-moz-placeholder{color:#ccc}ul.search span.highlighted{font-weight:700}ul.search>li{margin-bottom:24px}#search-results ul{list-style:none;padding:0}#search-results ul li>a{text-decoration:none;font-size:1.2rem}a.download:before{content:"file_download";position:relative;top:5px;margin-right:5px}button.download{position:sticky;margin-left:1em}.mdl-card{margin:1em 1.5em 1em 0;display:inline-block;width:250px;min-height:140px;padding:18px}.mdl-card:hover{box-shadow:0 10px 20px rgba(0,0,0,.25),0 6px 6px rgba(0,0,0,.22);color:#000;cursor:pointer}.mdl-card__title{padding:0 0 1em;font-size:18px;color:#444}.mdl-card__supporting-text{line-height:1.5rem;padding:0;width:100%}.head-card.mdl-card{width:auto;display:block;max-width:800px;padding:24px}.head-card>.mdl-card__title{padding-bottom:0;height:60px;font-weight:700;text-transform:uppercase}.head-card>.mdl-card__menu{color:#fff}.head-card>.mdl-card__actions{padding:0}.cards{display:flex;flex-direction:row;flex-wrap:wrap}
+.admonition,.mdl-shadow--2dp,.page-content pre:hover,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list){box-shadow:0 2px 2px 0 rgba(0,0,0,.14),0 3px 1px -2px rgba(0,0,0,.2),0 1px 5px 0 rgba(0,0,0,.12)}.mdl-shadow--3dp{box-shadow:0 3px 4px 0 rgba(0,0,0,.14),0 3px 3px -2px rgba(0,0,0,.2),0 1px 8px 0 rgba(0,0,0,.12)}.mdl-shadow--4dp{box-shadow:0 4px 5px 0 rgba(0,0,0,.14),0 1px 10px 0 rgba(0,0,0,.12),0 2px 4px -1px rgba(0,0,0,.2)}.mdl-shadow--6dp{box-shadow:0 6px 10px 0 rgba(0,0,0,.14),0 1px 18px 0 rgba(0,0,0,.12),0 3px 5px -1px rgba(0,0,0,.2)}.mdl-shadow--8dp{box-shadow:0 8px 10px 1px rgba(0,0,0,.14),0 3px 14px 2px rgba(0,0,0,.12),0 5px 5px -3px rgba(0,0,0,.2)}.mdl-shadow--16dp{box-shadow:0 16px 24px 2px rgba(0,0,0,.14),0 6px 30px 5px rgba(0,0,0,.12),0 8px 10px -5px rgba(0,0,0,.2)}.mdl-shadow--24dp{box-shadow:0 9px 46px 8px rgba(0,0,0,.14),0 11px 15px -7px rgba(0,0,0,.12),0 24px 38px 3px rgba(0,0,0,.2)}.mdl-data-table,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list){position:relative;border:1px solid rgba(0,0,0,.12);border-collapse:collapse;white-space:nowrap;font-size:13px;background-color:#fff}.mdl-data-table thead,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) thead{padding-bottom:3px}.mdl-data-table thead .mdl-data-table__select,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) thead .mdl-data-table__select{margin-top:0}.mdl-data-table tbody tr,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) tbody tr{position:relative;height:48px;transition-duration:.28s;transition-timing-function:cubic-bezier(.4,0,.2,1);transition-property:background-color}.mdl-data-table tbody tr.is-selected,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) tbody tr.is-selected{background-color:#e0e0e0}.mdl-data-table tbody tr:hover,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) tbody tr:hover{background-color:#eee}.mdl-data-table td,.mdl-data-table th,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) td,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th{padding:0 18px 12px;text-align:right}.mdl-data-table td:first-of-type,.mdl-data-table th:first-of-type,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) td:first-of-type,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th:first-of-type{padding-left:24px}.mdl-data-table td:last-of-type,.mdl-data-table th:last-of-type,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) td:last-of-type,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th:last-of-type{padding-right:24px}.mdl-data-table td,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) td{position:relative;vertical-align:middle;height:48px;border-top:1px solid rgba(0,0,0,.12);border-bottom:1px solid rgba(0,0,0,.12);padding-top:12px;box-sizing:border-box}.mdl-data-table td .mdl-data-table__select,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) td .mdl-data-table__select{vertical-align:middle}.mdl-data-table th,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th{position:relative;vertical-align:bottom;text-overflow:ellipsis;font-size:14px;font-weight:700;line-height:24px;letter-spacing:0;height:48px;font-size:12px;color:rgba(0,0,0,.54);padding-bottom:8px;box-sizing:border-box}.mdl-data-table th.mdl-data-table__header--sorted-ascending,.mdl-data-table th.mdl-data-table__header--sorted-descending,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-ascending,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-descending{color:rgba(0,0,0,.87)}.mdl-data-table th.mdl-data-table__header--sorted-ascending:before,.mdl-data-table th.mdl-data-table__header--sorted-descending:before,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-ascending:before,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-descending:before{font-family:Material Icons;font-weight:400;font-style:normal;font-size:24px;line-height:1;letter-spacing:normal;text-transform:none;display:inline-block;word-wrap:normal;font-feature-settings:"liga";-webkit-font-feature-settings:"liga";-webkit-font-smoothing:antialiased;font-size:16px;content:"\e5d8";margin-right:5px;vertical-align:sub}.mdl-data-table th.mdl-data-table__header--sorted-ascending:hover,.mdl-data-table th.mdl-data-table__header--sorted-descending:hover,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-ascending:hover,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-descending:hover{cursor:pointer}.mdl-data-table th.mdl-data-table__header--sorted-ascending:hover:before,.mdl-data-table th.mdl-data-table__header--sorted-descending:hover:before,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-ascending:hover:before,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-descending:hover:before{color:rgba(0,0,0,.26)}.mdl-data-table th.mdl-data-table__header--sorted-descending:before,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th.mdl-data-table__header--sorted-descending:before{content:"\e5db"}.mdl-data-table__select{width:16px}.mdl-data-table__cell--non-numeric.mdl-data-table__cell--non-numeric,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) td,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th{text-align:left}.mdl-mini-footer{display:flex;flex-flow:row wrap;justify-content:space-between;padding:32px 16px;color:#9e9e9e;background-color:#424242}.mdl-mini-footer:after{content:"";display:block}.mdl-mini-footer .mdl-logo{line-height:36px}.mdl-mini-footer--link-list,.mdl-mini-footer__link-list,footer.mdl-mini-footer>div.mdl-mini-footer__left-section ul{display:flex;flex-flow:row nowrap;list-style:none;margin:0;padding:0}.mdl-mini-footer--link-list li,.mdl-mini-footer__link-list li,footer.mdl-mini-footer>div.mdl-mini-footer__left-section ul li{margin-bottom:0;margin-right:16px}@media screen and (min-width:760px){.mdl-mini-footer--link-list li,.mdl-mini-footer__link-list li,footer.mdl-mini-footer>div.mdl-mini-footer__left-section ul li{line-height:36px}}.mdl-mini-footer--link-list a,.mdl-mini-footer__link-list a,footer.mdl-mini-footer>div.mdl-mini-footer__left-section ul a{color:inherit;text-decoration:none;white-space:nowrap}.mdl-mini-footer--left-section,.mdl-mini-footer__left-section{display:inline-block;order:0}.mdl-mini-footer--right-section,.mdl-mini-footer__right-section{display:inline-block;order:1}.mdl-mini-footer--social-btn,.mdl-mini-footer__social-btn{width:36px;height:36px;padding:0;margin:0;background-color:#9e9e9e;border:none}.mdl-card{display:flex;flex-direction:column;font-size:16px;font-weight:400;min-height:200px;overflow:hidden;width:330px;z-index:1;position:relative;background:#fff;border-radius:2px;box-sizing:border-box}.mdl-card__media{background-color:#ff6e40;background-repeat:repeat;background-position:50% 50%;background-size:cover;background-origin:padding-box;background-attachment:scroll;box-sizing:border-box}.mdl-card__title{align-items:center;color:#000;display:block;display:flex;justify-content:stretch;line-height:normal;padding:16px;perspective-origin:165px 56px;transform-origin:165px 56px;box-sizing:border-box}.mdl-card__title.mdl-card--border{border-bottom:1px solid rgba(0,0,0,.1)}.mdl-card__title-text{align-self:flex-end;color:inherit;display:block;display:flex;font-size:24px;font-weight:300;line-height:normal;overflow:hidden;transform-origin:149px 48px;margin:0}.mdl-card__subtitle-text{font-size:14px;color:rgba(0,0,0,.54);margin:0}.mdl-card__supporting-text{color:rgba(0,0,0,.54);font-size:1rem;line-height:18px;overflow:hidden;padding:16px;width:90%}.mdl-card__supporting-text.mdl-card--border{border-bottom:1px solid rgba(0,0,0,.1)}.mdl-card__actions{font-size:16px;line-height:normal;width:100%;background-color:transparent;padding:8px;box-sizing:border-box}.mdl-card__actions.mdl-card--border{border-top:1px solid rgba(0,0,0,.1)}.mdl-card--expand{flex-grow:1}.mdl-card__menu{position:absolute;right:16px;top:16px}.mdl-button{background:transparent;border:none;border-radius:2px;color:#000;position:relative;height:36px;margin:0;min-width:64px;padding:0 16px;display:inline-block;font-family:Roboto,Helvetica,Arial,sans-serif;font-size:14px;font-weight:500;text-transform:uppercase;line-height:1;letter-spacing:0;overflow:hidden;will-change:box-shadow;transition:box-shadow .2s cubic-bezier(.4,0,1,1),background-color .2s cubic-bezier(.4,0,.2,1),color .2s cubic-bezier(.4,0,.2,1);outline:none;cursor:pointer;text-decoration:none;text-align:center;line-height:36px;vertical-align:middle}.mdl-button::-moz-focus-inner{border:0}.mdl-button:hover{background-color:hsla(0,0%,62%,.2)}.mdl-button:focus:not(:active){background-color:rgba(0,0,0,.12)}.mdl-button:active{background-color:hsla(0,0%,62%,.4)}.mdl-button.mdl-button--colored{color:#2196f3}.mdl-button.mdl-button--colored:focus:not(:active){background-color:rgba(0,0,0,.12)}input.mdl-button[type=submit]{-webkit-appearance:none}.mdl-button--raised{background:hsla(0,0%,62%,.2);box-shadow:0 2px 2px 0 rgba(0,0,0,.14),0 3px 1px -2px rgba(0,0,0,.2),0 1px 5px 0 rgba(0,0,0,.12)}.mdl-button--raised:active{box-shadow:0 4px 5px 0 rgba(0,0,0,.14),0 1px 10px 0 rgba(0,0,0,.12),0 2px 4px -1px rgba(0,0,0,.2);background-color:hsla(0,0%,62%,.4)}.mdl-button--raised:focus:not(:active){box-shadow:0 0 8px rgba(0,0,0,.18),0 8px 16px rgba(0,0,0,.36);background-color:hsla(0,0%,62%,.4)}.mdl-button--raised.mdl-button--colored{background:#2196f3;color:#fff}.mdl-button--raised.mdl-button--colored:active,.mdl-button--raised.mdl-button--colored:focus:not(:active),.mdl-button--raised.mdl-button--colored:hover{background-color:#2196f3}.mdl-button--raised.mdl-button--colored .mdl-ripple{background:#fff}.mdl-button--fab{border-radius:50%;font-size:24px;height:56px;margin:auto;min-width:56px;width:56px;padding:0;overflow:hidden;background:hsla(0,0%,62%,.2);box-shadow:0 1px 1.5px 0 rgba(0,0,0,.12),0 1px 1px 0 rgba(0,0,0,.24);position:relative;line-height:normal}.admonition.attention .mdl-button--fab .admonition-title:before,.admonition.caution .mdl-button--fab .admonition-title:before,.admonition.danger .mdl-button--fab .admonition-title:before,.admonition.error .mdl-button--fab .admonition-title:before,.admonition.hint .mdl-button--fab .admonition-title:before,.admonition.important .mdl-button--fab .admonition-title:before,.admonition.note .mdl-button--fab .admonition-title:before,.admonition.seealso .mdl-button--fab .admonition-title:before,.admonition.tip .mdl-button--fab .admonition-title:before,.admonition.warning .mdl-button--fab .admonition-title:before,.mdl-button--fab .admonition.attention .admonition-title:before,.mdl-button--fab .admonition.caution .admonition-title:before,.mdl-button--fab .admonition.danger .admonition-title:before,.mdl-button--fab .admonition.error .admonition-title:before,.mdl-button--fab .admonition.hint .admonition-title:before,.mdl-button--fab .admonition.important .admonition-title:before,.mdl-button--fab .admonition.note .admonition-title:before,.mdl-button--fab .admonition.seealso .admonition-title:before,.mdl-button--fab .admonition.tip .admonition-title:before,.mdl-button--fab .admonition.warning .admonition-title:before,.mdl-button--fab .material-icons,.mdl-button--fab a.download:before{position:absolute;top:50%;left:50%;transform:translate(-12px,-12px);line-height:24px;width:24px}.mdl-button--fab.mdl-button--mini-fab{height:40px;min-width:40px;width:40px}.mdl-button--fab .mdl-button__ripple-container{border-radius:50%;-webkit-mask-image:-webkit-radial-gradient(circle,#fff,#000)}.mdl-button--fab:active{box-shadow:0 4px 5px 0 rgba(0,0,0,.14),0 1px 10px 0 rgba(0,0,0,.12),0 2px 4px -1px rgba(0,0,0,.2);background-color:hsla(0,0%,62%,.4)}.mdl-button--fab:focus:not(:active){box-shadow:0 0 8px rgba(0,0,0,.18),0 8px 16px rgba(0,0,0,.36);background-color:hsla(0,0%,62%,.4)}.mdl-button--fab.mdl-button--colored{background:#ff6e40;color:#fff}.mdl-button--fab.mdl-button--colored:active,.mdl-button--fab.mdl-button--colored:focus:not(:active),.mdl-button--fab.mdl-button--colored:hover{background-color:#ff6e40}.mdl-button--fab.mdl-button--colored .mdl-ripple{background:#fff}.mdl-button--icon{border-radius:50%;font-size:24px;height:32px;margin-left:0;margin-right:0;min-width:32px;width:32px;padding:0;overflow:hidden;color:inherit;line-height:normal}.admonition.attention .mdl-button--icon .admonition-title:before,.admonition.caution .mdl-button--icon .admonition-title:before,.admonition.danger .mdl-button--icon .admonition-title:before,.admonition.error .mdl-button--icon .admonition-title:before,.admonition.hint .mdl-button--icon .admonition-title:before,.admonition.important .mdl-button--icon .admonition-title:before,.admonition.note .mdl-button--icon .admonition-title:before,.admonition.seealso .mdl-button--icon .admonition-title:before,.admonition.tip .mdl-button--icon .admonition-title:before,.admonition.warning .mdl-button--icon .admonition-title:before,.mdl-button--icon .admonition.attention .admonition-title:before,.mdl-button--icon .admonition.caution .admonition-title:before,.mdl-button--icon .admonition.danger .admonition-title:before,.mdl-button--icon .admonition.error .admonition-title:before,.mdl-button--icon .admonition.hint .admonition-title:before,.mdl-button--icon .admonition.important .admonition-title:before,.mdl-button--icon .admonition.note .admonition-title:before,.mdl-button--icon .admonition.seealso .admonition-title:before,.mdl-button--icon .admonition.tip .admonition-title:before,.mdl-button--icon .admonition.warning .admonition-title:before,.mdl-button--icon .material-icons,.mdl-button--icon a.download:before{position:absolute;top:50%;left:50%;transform:translate(-12px,-12px);line-height:24px;width:24px}.mdl-button--icon.mdl-button--mini-icon{height:24px;min-width:24px;width:24px}.admonition.attention .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.caution .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.danger .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.error .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.hint .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.important .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.note .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.seealso .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.tip .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.admonition.warning .mdl-button--icon.mdl-button--mini-icon .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.attention .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.caution .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.danger .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.error .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.hint .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.important .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.note .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.seealso .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.tip .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .admonition.warning .admonition-title:before,.mdl-button--icon.mdl-button--mini-icon .material-icons,.mdl-button--icon.mdl-button--mini-icon a.download:before{top:0;left:0}.mdl-button--icon .mdl-button__ripple-container{border-radius:50%;-webkit-mask-image:-webkit-radial-gradient(circle,#fff,#000)}.mdl-button__ripple-container{display:block;height:100%;left:0;position:absolute;top:0;width:100%;z-index:0;overflow:hidden}.mdl-button.mdl-button--disabled .mdl-button__ripple-container .mdl-ripple,.mdl-button[disabled] .mdl-button__ripple-container .mdl-ripple{background-color:transparent}.mdl-button--primary.mdl-button--primary{color:#2196f3}.mdl-button--primary.mdl-button--primary .mdl-ripple{background:#fff}.mdl-button--primary.mdl-button--primary.mdl-button--fab,.mdl-button--primary.mdl-button--primary.mdl-button--raised{color:#fff;background-color:#2196f3}.mdl-button--accent.mdl-button--accent{color:#ff6e40}.mdl-button--accent.mdl-button--accent .mdl-ripple{background:#fff}.mdl-button--accent.mdl-button--accent.mdl-button--fab,.mdl-button--accent.mdl-button--accent.mdl-button--raised{color:#fff;background-color:#ff6e40}.mdl-button.mdl-button--disabled.mdl-button--disabled,.mdl-button[disabled][disabled]{color:rgba(0,0,0,.26);cursor:default;background-color:transparent}.mdl-button--fab.mdl-button--disabled.mdl-button--disabled,.mdl-button--fab[disabled][disabled]{background-color:rgba(0,0,0,.12);color:rgba(0,0,0,.26)}.mdl-button--raised.mdl-button--disabled.mdl-button--disabled,.mdl-button--raised[disabled][disabled]{background-color:rgba(0,0,0,.12);color:rgba(0,0,0,.26);box-shadow:none}.mdl-button--colored.mdl-button--disabled.mdl-button--disabled,.mdl-button--colored[disabled][disabled]{color:rgba(0,0,0,.26)}.admonition.attention .mdl-button .admonition-title:before,.admonition.caution .mdl-button .admonition-title:before,.admonition.danger .mdl-button .admonition-title:before,.admonition.error .mdl-button .admonition-title:before,.admonition.hint .mdl-button .admonition-title:before,.admonition.important .mdl-button .admonition-title:before,.admonition.note .mdl-button .admonition-title:before,.admonition.seealso .mdl-button .admonition-title:before,.admonition.tip .mdl-button .admonition-title:before,.admonition.warning .mdl-button .admonition-title:before,.mdl-button .admonition.attention .admonition-title:before,.mdl-button .admonition.caution .admonition-title:before,.mdl-button .admonition.danger .admonition-title:before,.mdl-button .admonition.error .admonition-title:before,.mdl-button .admonition.hint .admonition-title:before,.mdl-button .admonition.important .admonition-title:before,.mdl-button .admonition.note .admonition-title:before,.mdl-button .admonition.seealso .admonition-title:before,.mdl-button .admonition.tip .admonition-title:before,.mdl-button .admonition.warning .admonition-title:before,.mdl-button .material-icons,.mdl-button a.download:before{vertical-align:middle}.font-light{font-weight:300}.font-regular{font-weight:400}.font-heavy{font-weight:700}.left{text-align:left}.right{text-align:right}.center{text-align:center;margin-left:auto;margin-right:auto}.justify{text-align:justify}.hidden-sm{display:none}.container{width:100%;margin-left:auto;margin-right:auto}@media only screen and (min-width:33.75em){.container{width:80%}}@media only screen and (min-width:60em){.container{width:75%;max-width:60rem}}.row{position:relative;width:100%}.row [class^=col]{float:left;margin:.5rem 1%;min-height:.125rem}.row:after{content:"";display:table;clear:both}.col-1,.col-2,.col-3,.col-4,.col-5,.col-6,.col-7,.col-8,.col-9,.col-10,.col-11,.col-12{width:98%}.col-1-sm{width:6.33333%}.col-2-sm{width:14.66667%}.col-3-sm{width:23%}.col-4-sm{width:31.33333%}.col-5-sm{width:39.66667%}.col-6-sm{width:48%}.col-7-sm{width:56.33333%}.col-8-sm{width:64.66667%}.col-9-sm{width:73%}.col-10-sm{width:81.33333%}.col-11-sm{width:89.66667%}.col-12-sm{width:98%}@media only screen and (min-width:45em){.col-1{width:6.33333%}.col-2{width:14.66667%}.col-3{width:23%}.col-4{width:31.33333%}.col-5{width:39.66667%}.col-6{width:48%}.col-7{width:56.33333%}.col-8{width:64.66667%}.col-9{width:73%}.col-10{width:81.33333%}.col-11{width:89.66667%}.col-12{width:98%}.hidden-sm{display:block}}.row{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;flex-wrap:wrap}.row>[class*=col-]{display:flex;flex-direction:column}.admonition.attention .admonition-title:before,.admonition.caution .admonition-title:before,.admonition.danger .admonition-title:before,.admonition.error .admonition-title:before,.admonition.hint .admonition-title:before,.admonition.important .admonition-title:before,.admonition.note .admonition-title:before,.admonition.seealso .admonition-title:before,.admonition.tip .admonition-title:before,.admonition.warning .admonition-title:before,.material-icons,a.download:before{font-family:Material Icons;font-weight:400;font-style:normal;font-size:24px;display:inline-block;line-height:1;text-transform:none;letter-spacing:normal;word-wrap:normal;white-space:nowrap;direction:ltr;-webkit-font-smoothing:antialiased;text-rendering:optimizeLegibility;-moz-osx-font-smoothing:grayscale;font-feature-settings:"liga"}html{font-size:16px}body{display:block!important;background-color:#fafafa;font-size:1rem;line-height:1.5rem;font-family:-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol}.mdl-layout__content:focus{outline:none}a.download>code.download,blockquote,h1,h2,h3,h4,h5,h6,span.mdl-layout-title{font-family:-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol}.contents,.contents a,.globaltoc a.current,.toc-backref,.toctree-wrapper,.toctree-wrapper a,h1,h2,h3,h4,h5,h6{color:#048ccc!important}a{text-decoration:none}.page-content,.page-content dd,.page-content dl,.page-content dt,.page-content ol,.page-content p,.page-content table,.page-content td,.page-content th,.page-content ul{font-size:1rem}.brand{color:inherit;text-decoration:none}.section{overflow-x:auto}img{max-width:100%;display:block;margin-left:auto;margin-right:auto}div.figure p.caption{text-align:center;margin-top:.75rem}div.figure p.caption span.caption-number{font-style:normal}div.figure p.caption .caption-number:after{content:"\00a0"}.svg-icon{width:16px;height:16px;display:inline-block;fill:#f5f5f5;padding-right:5px;padding-top:4px;vertical-align:text-top}.admonition.attention a.download>i.admonition-title:before,.admonition.caution a.download>i.admonition-title:before,.admonition.danger a.download>i.admonition-title:before,.admonition.error a.download>i.admonition-title:before,.admonition.hint a.download>i.admonition-title:before,.admonition.important a.download>i.admonition-title:before,.admonition.note a.download>i.admonition-title:before,.admonition.seealso a.download>i.admonition-title:before,.admonition.tip a.download>i.admonition-title:before,.admonition.warning a.download>i.admonition-title:before,a.download>i.material-icons{position:relative;top:5px}a.download{text-decoration:none}.wrapper:after{content:"";display:table;clear:both}.wrapper{max-width:1090px;margin-right:auto;margin-left:auto;padding-right:45px;padding-left:30px}@media screen and (max-width:1024px){.wrapper{max-width:1120px;padding-right:15px;padding-left:15px}}.mdl-layout{margin-top:76px}.document{width:100%;margin:0 auto;display:flex}@media (min-width:1795px){.document{width:1400px}}.document .page-content{width:100%;margin:0 auto;padding:0 12px}@media (min-width:992px){.document .page-content{width:90%;padding:0 5%}}@media (min-width:1200px){.document .page-content{width:calc(90% - 230px);padding:0 5%}}.document .side-doc-outline{width:230px}@media (max-width:1199px){.document .side-doc-outline{display:none}}.document .side-doc-outline--content{position:fixed;overflow-x:auto;overflow-y:auto;width:inherit}.document .side-doc-outline--content::-webkit-scrollbar{width:6px}.document .side-doc-outline--content::-webkit-scrollbar-track{border-radius:6px}.document .side-doc-outline--content::-webkit-scrollbar-thumb{background-color:rgba(0,0,0,.3);border-radius:6px;box-shadow:0 0 0 1px hsla(0,0%,100%,.3)}@keyframes float-in{0%{transform:translateY(.5rem);opacity:0}to{transform:translateY(0);opacity:1}}@keyframes float-out{0%{transform:translateY(0);opacity:1}to{transform:translateY(.5rem);opacity:0}}.page-content .headerlink{display:inline-block;text-decoration:none;margin-left:.8rem;color:inherit;opacity:0}.page-content .headerlink:hover{animation:float-in .2s cubic-bezier(.4,0,.2,1) 0s forwards}.page-content h1 .toc-backref,.page-content h2 .toc-backref,.page-content h3 .toc-backref,.page-content h4 .toc-backref,.page-content h5 .toc-backref,.page-content h6 .toc-backref{text-decoration:none}.page-content h1:hover .headerlink,.page-content h2:hover .headerlink,.page-content h3:hover .headerlink,.page-content h4:hover .headerlink,.page-content h5:hover .headerlink,.page-content h6:hover .headerlink{animation:float-in .2s cubic-bezier(.4,0,.2,1) 0s forwards}.page-content h1{font-size:2rem;line-height:2.25rem}.page-content h2{font-size:1.75rem;line-height:2rem;padding-top:1.5rem;margin-top:0;margin-bottom:1rem}.page-content h3{font-size:1.5rem;line-height:1.75rem;padding-top:1rem;margin-top:0;margin-bottom:.75rem}.page-content h4{font-size:1.25rem;line-height:1.5rem;padding-top:.75rem;margin-top:0;margin-bottom:.5rem}.page-content div.page-content h5{font-size:1.1rem;line-height:1.5rem;padding-top:2rem;margin-top:0;margin-bottom:1rem}.page-content div.page-content h6{font-size:1rem;line-height:1.5rem;padding-top:2rem;margin-top:0;margin-bottom:1rem}.admonition{padding:12px 20px;margin-top:10px;margin-bottom:10px}.admonition p.last{margin:16px}.admonition .admonition-title{font-size:16px;font-weight:700;color:#555;text-transform:uppercase;margin-top:7px}.admonition.note{border-left:4px solid #00bcd4;background-color:rgba(0,188,212,.1)}.admonition.note .admonition-title{font-size:16px;font-weight:700;color:#00bcd4;margin-top:4px;margin-bottom:8px}.admonition.note .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"info_outline";font-size:18px}.admonition.seealso{border-left:4px solid #00bcd4;background-color:rgba(0,188,212,.1)}.admonition.seealso .admonition-title{font-size:16px;font-weight:700;color:#00bcd4;margin-top:4px;margin-bottom:8px}.admonition.seealso .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"search";font-size:18px}.admonition.hint{border-left:4px solid #00bcd4;background-color:rgba(0,188,212,.1)}.admonition.hint .admonition-title{font-size:16px;font-weight:700;color:#00bcd4;margin-top:4px;margin-bottom:8px}.admonition.hint .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"help_outline";font-size:18px}.admonition.warning{border-left:4px solid #ffc107;background-color:rgba(255,193,7,.1)}.admonition.warning .admonition-title{font-size:16px;font-weight:700;color:#ffc107;margin-top:4px;margin-bottom:8px}.admonition.warning .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"warning";font-size:18px}.admonition.attention{border-left:4px solid #ffc107;background-color:rgba(255,193,7,.1)}.admonition.attention .admonition-title{font-size:16px;font-weight:700;color:#ffc107;margin-top:4px;margin-bottom:8px}.admonition.attention .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"warning";font-size:18px}.admonition.tip{border-left:4px solid #8bc34a;background-color:rgba(139,195,74,.1)}.admonition.tip .admonition-title{font-size:16px;font-weight:700;color:#8bc34a;margin-top:4px;margin-bottom:8px}.admonition.tip .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"lightbulb_outline";font-size:18px}.admonition.important{border-left:4px solid #8bc34a;background-color:rgba(139,195,74,.1)}.admonition.important .admonition-title{font-size:16px;font-weight:700;color:#8bc34a;margin-top:4px;margin-bottom:8px}.admonition.important .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"check_circle";font-size:18px}.admonition.error{border-left:4px solid #f44336;background-color:rgba(244,67,54,.1)}.admonition.error .admonition-title{font-size:16px;font-weight:700;color:#f44336;margin-top:4px;margin-bottom:8px}.admonition.error .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"error_outline";font-size:18px}.admonition.caution{border-left:4px solid #f44336;background-color:rgba(244,67,54,.1)}.admonition.caution .admonition-title{font-size:16px;font-weight:700;color:#f44336;margin-top:4px;margin-bottom:8px}.admonition.caution .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"error_outline";font-size:18px}.admonition.danger{border-left:4px solid #f44336;background-color:rgba(244,67,54,.1)}.admonition.danger .admonition-title{font-size:16px;font-weight:700;color:#f44336;margin-top:4px;margin-bottom:8px}.admonition.danger .admonition-title:before{position:relative;margin-right:5px;top:3px;content:"error_outline";font-size:18px}.page-content .highlight{margin:1px 0}.page-content .highlight pre{background:rgba(0,0,0,.05);color:rgba(0,0,0,.87);font-family:Menlo,DejaVu Sans Mono,Liberation Mono,Consolas,Ubuntu Mono,Courier New,andale mono,lucida console,monospace;padding:.75rem;overflow:auto;overflow-y:hidden}.page-content .highlight pre .nd,.page-content .highlight pre .o{color:rgba(0,0,0,.87)}.page-content div.highlight-console div.highlight{background:none}.page-content .output .highlight pre{color:rgba(0,0,0,.87);background:#fafafa;border:1px solid #999;padding:.75rem}.page-content .code,.page-content code:not(.download){margin:0;border-radius:2px}.page-content .code,.page-content .code span.pre,.page-content code:not(.download),.page-content code:not(.download) span.pre{font-family:Menlo,DejaVu Sans Mono,Liberation Mono,Consolas,Ubuntu Mono,Courier New,andale mono,lucida console,monospace}.page-content .viewcode-link{padding-left:2em;font-size:80%}.page-content .class>dt,.page-content .function>dt,.page-content .method>dt,.page-content .rubric{display:table;margin:10px 0;font-size:100%;line-height:normal;background:#e7f2fa;color:#2b98f0;border-top:3px solid #55adf3;padding:10px;position:relative}.page-content .class>dt .descclassname,.page-content .class>dt .descname,.page-content .function>dt .descclassname,.page-content .function>dt .descname,.page-content .method>dt .descclassname,.page-content .method>dt .descname,.page-content .rubric .descclassname,.page-content .rubric .descname{color:rgba(0,0,0,.87);background:#e7f2fa;padding:3px}.page-content .class>dt em,.page-content .function>dt em,.page-content .method>dt em,.page-content .rubric em{padding:0 2px}.page-content .rubric{margin:30px 0 10px}.page-content .field-body{padding-left:40px}.page-content .field-body ul{padding:0 0 0 16px;margin:0}.page-content .seealso .docutils>dt{float:left;clear:left;padding:0 6px}.page-content .seealso .docutils>dd{padding-left:6em}.page-content .nblast{padding-bottom:1em}.page-content pre{font-size:90%;background:#eee;color:#455a64;padding:16px 32px;width:auto;border-radius:4px;word-wrap:break-word}.page-content pre:hover:before{font-family:-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol;padding:0 .5rem;content:attr(click-to-copy);color:rgba(0,0,0,.5);border-radius:4px;position:relative;float:right;top:-.5rem;right:-.5rem;background:#c8c8c8;font-size:.8rem;cursor:pointer}.page-content blockquote{font-size:1rem;padding:0 1rem;border-left:3px solid rgba(0,0,0,.05)}.page-content blockquote:after{content:""!important;margin-left:0}.page-content blockquote:before{content:""!important}.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list){margin:1.5rem 0;table-layout:fixed;max-width:100%;min-width:70%}.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) td,.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) th{white-space:normal;overflow-wrap:break-word}.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) caption{font-size:16px;margin:1rem 0 .8rem;white-space:normal}.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) caption .caption-number{font-style:normal}.page-content table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) caption .caption-number:after{content:"\00a0"}.globaltoc .caption,.globaltoc .toc{display:none}.globaltoc ul{list-style-type:none;padding:0;margin:0}.globaltoc ul li{min-height:18px}.globaltoc ul li .link-wrapper{display:flex;justify-content:space-between}.globaltoc ul li .link-wrapper>a{padding:4px 0;display:block;width:100%;font-size:1rem;text-decoration:none;color:#757575}.globaltoc ul li .link-wrapper>a.current{font-weight:700}.globaltoc .nav-toggle{padding:0;float:right;display:flex;align-items:center;justify-content:center;height:36px}.globaltoc .nav-toggle>a{padding:0;margin-left:0;margin-right:4px;cursor:pointer}.globaltoc .nav-toggle>a>i{font-size:18px}.globaltoc .nav-toggle.show{transform:rotate(180deg)}.globaltoc .nav-toggle.show>a{margin-right:0;margin-left:4px}.globaltoc nav>ul>li>span.link-wrapper{padding-left:8px}.globaltoc nav>ul>li>ul>li>span.link-wrapper{padding-left:16px}.globaltoc nav>ul>li>ul>li>ul>li>span.link-wrapper{padding-left:24px}.globaltoc nav>ul>li>ul>li>ul>li>ul>li>span.link-wrapper{padding-left:32px}.globaltoc nav>ul>li>ul>li>ul>li>ul>li>ul>li>span.link-wrapper{padding-left:40px}.globaltoc nav>ul>li>ul>li>ul>li>ul>li>ul>li>ul>li>span.link-wrapper{padding-left:48px}.localtoc{font-size:.75rem;padding-top:1rem}.localtoc .caption{padding-left:12px}.localtoc .caption-text{font-size:.9rem;font-weight:700}.localtoc>ul>li>a{display:none}.localtoc ul{padding:0;list-style-type:none}.localtoc li{padding-left:6px}.localtoc a{display:block;text-decoration:none;color:inherit;margin-top:8px;padding-left:8px;line-height:1.1rem}.localtoc a.current{padding-left:5px;border-left:3px solid;font-weight:700}.contents.topic,.toctree-wrapper{border-left:5px solid}.contents.topic>p.topic-title,.toctree-wrapper>p.caption{color:#757575;font-size:1rem;padding-left:14px}.contents.topic ul,.toctree-wrapper ul{padding-left:14px;list-style:none;line-height:30px}.contents.topic a,.toctree-wrapper a{font-size:1.2rem;text-decoration:none}.contents.topic a .pre,.toctree-wrapper a .pre{font-size:1rem}.contents.topic>ul>li>a,.toctree-wrapper>ul>li>a{font-size:1.3rem}.contents.topic>ul>li>a .pre,.toctree-wrapper>ul>li>a .pre{font-size:1.1rem}.page-content ul li{margin:.3rem 0}.page-content ul li p{margin:0}.page-content .option-list .option{font-family:Menlo,DejaVu Sans Mono,Liberation Mono,Consolas,Ubuntu Mono,Courier New,andale mono,lucida console,monospace}.page-content .option-list td{padding:.5rem;border:none}.mdl-layout__drawer{background-color:#fff}.mdl-layout__drawer::-webkit-scrollbar{width:6px}.mdl-layout__drawer::-webkit-scrollbar-track{border-radius:6px}.mdl-layout__drawer::-webkit-scrollbar-thumb{background-color:rgba(0,0,0,.3);border-radius:6px;box-shadow:0 0 0 1px hsla(0,0%,100%,.3)}.mdl-layout__drawer>.mdl-layout-title{font-weight:700;text-align:right;margin:0;padding:0;line-height:32px;border-bottom:1px solid rgba(0,0,0,.1);min-height:64px}.mdl-layout__drawer>.mdl-layout-title .title{color:inherit;display:block;height:100%;width:100%;text-decoration:none}.mdl-layout__drawer>.mdl-layout-title .title>img.logo{width:100%;margin:0;padding:0}.mdl-layout__drawer>.mdl-layout-title .title-text{font-weight:700;text-align:right;padding:0 10px;margin:16px 0 8px;line-height:32px;font-family:-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol;color:inherit;display:block}nav.breadcrumb>a.mdl-navigation__link{padding:0 8px;font-size:18px}@media (max-width:1199px){nav.breadcrumb{width:calc(100% - 64px)}nav.breadcrumb a.mdl-navigation__link.is-active{overflow-x:hidden;width:100%;overflow:hidden;white-space:nowrap;text-overflow:ellipsis}.admonition.attention nav.breadcrumb i.admonition-title:before,.admonition.caution nav.breadcrumb i.admonition-title:before,.admonition.danger nav.breadcrumb i.admonition-title:before,.admonition.error nav.breadcrumb i.admonition-title:before,.admonition.hint nav.breadcrumb i.admonition-title:before,.admonition.important nav.breadcrumb i.admonition-title:before,.admonition.note nav.breadcrumb i.admonition-title:before,.admonition.seealso nav.breadcrumb i.admonition-title:before,.admonition.tip nav.breadcrumb i.admonition-title:before,.admonition.warning nav.breadcrumb i.admonition-title:before,nav.breadcrumb .admonition.attention i.admonition-title:before,nav.breadcrumb .admonition.caution i.admonition-title:before,nav.breadcrumb .admonition.danger i.admonition-title:before,nav.breadcrumb .admonition.error i.admonition-title:before,nav.breadcrumb .admonition.hint i.admonition-title:before,nav.breadcrumb .admonition.important i.admonition-title:before,nav.breadcrumb .admonition.note i.admonition-title:before,nav.breadcrumb .admonition.seealso i.admonition-title:before,nav.breadcrumb .admonition.tip i.admonition-title:before,nav.breadcrumb .admonition.warning i.admonition-title:before,nav.breadcrumb a.mdl-navigation__link:not(.is-active),nav.breadcrumb i.material-icons{display:none}}div.mdl-layout__header{margin-top:77px}.mdl-layout__drawer-button{top:13px!important}div.mdl-layout__header-row.header-links{background:hsla(0,0%,100%,.2);width:100%;overflow-x:auto;overflow-y:hidden}div.mdl-layout__header-row.header-links a.mdl-navigation__link{font-size:1rem}div.mdl-layout__header-row.header-links a.mdl-navigation__link i{font-size:1.2rem;margin:0 8px;position:relative;bottom:-.1rem}div.mdl-layout__header-row.header-links a.mdl-navigation__link:hover{background-color:#2196f3;color:#eee}div.mdl-layout__header-row.header-links a.mdl-navigation__link[href="#"]{background-color:#2196f3;opacity:1;color:#fff}.site-title{font-weight:300!important;line-height:57px;letter-spacing:-1px;margin-bottom:0;float:left;color:#fff}.site-title,.site-title:visited{color:#424242}.site-header{position:fixed;top:0;width:100%;min-height:55px;padding-top:10px;padding-bottom:10px;background-color:#048ccc;z-index:10;font-weight:300;font-size:17px;border-bottom:1px solid #fff}.site-header-logo{width:120px;display:initial}.site-nav{float:right;line-height:57px}.site-nav .menu-icon,.site-nav .nav-trigger{display:none}.site-nav .page-link{color:#fff;line-height:1.5;font-weight:300}.site-nav .page-link:not(:last-child){margin-right:40px}.site-nav .page-link:hover{color:#fff;text-shadow:-.06ex 0 #fff,.06ex 0 #fff}.site-nav .page-link.page-current{color:#fff;text-decoration:underline}@media screen and (max-width:1024px){.site-nav{position:absolute;top:9px;right:15px;background-color:#178dc9;border-radius:2px;text-align:right}.site-nav label[for=nav-trigger]{display:block;float:right;width:36px;height:36px;z-index:2;cursor:pointer}.site-nav .menu-icon{display:block;float:right;width:36px;height:26px;line-height:0;padding-top:20px;text-align:center}.site-nav .menu-icon>svg{fill:#fff}.site-nav input~.trigger{clear:both;display:none}.site-nav input:checked~.trigger{display:block;padding-bottom:5px}.site-nav .page-link{padding:5px 10px;display:block;margin-left:20px}.site-nav .page-link:not(:last-child){margin-right:0}}footer.mdl-mini-footer{background-color:#212121}footer.mdl-mini-footer>div.mdl-mini-footer__left-section{margin-bottom:20px;display:flex;flex-direction:column}footer.mdl-mini-footer>div.mdl-mini-footer__left-section .mdl-logo{font-size:1.1rem}footer.mdl-mini-footer>div.mdl-mini-footer__right-section{font-size:.9rem;display:flex;flex-direction:column;justify-content:flex-end}footer.mdl-mini-footer>div.mdl-mini-footer__right-section a{color:inherit;font-weight:700;text-decoration:none}footer.mdl-mini-footer p.caption{display:none}.pagenation{width:100%;margin-top:80px;height:92px;background-color:#424242;display:flex}.pagenation #button-next,.pagenation #button-prev,.pagenation .button-common{text-transform:none;padding:0;height:92px;display:flex;justify-content:center;align-items:center;color:#fff}.pagenation #button-prev{margin-right:auto}.pagenation #button-prev .pagenation-text{text-align:left}.pagenation #button-next{margin-left:auto;flex-direction:row-reverse}.pagenation #button-next .pagenation-text{text-align:right}.pagenation-arrow-L{margin-right:20px}.pagenation-arrow-R{margin-left:20px}.pagenation-text{line-height:30px;font-size:20px}.pagenation-direction{opacity:.7;font-size:18px}@media screen and (max-width:1024px){.pagenation #button-prev{width:20%}.pagenation #button-next{width:80%}.pagenation #button-prev .pagenation-text{display:none}}@media screen and (min-width:1025px){.pagenation #button-next,.pagenation #button-prev{width:50%}.pagenation #button-prev .pagenation-text{display:block}}.site-footer{border-top:1px solid #f5f5f5;padding:30px 0;background-color:#424242}.site-footer .footer-category-title{color:#048ccc}.site-footer a,.site-footer a:visited{color:#f5f5f5!important}.site-footer2{background-color:#424242;padding-top:40px;padding-bottom:10px}.footer-heading{margin-bottom:15px}.contact-list,.social-media-list{list-style:none;margin-left:0}.footer-bottom-warning{font-size:80%;color:#fff;float:left}.footer-logo{width:200px;margin-bottom:30px;margin-top:30px}.footer-col{float:left;margin-bottom:15px;padding-left:15px}.footer-text{color:#f5f5f5}#waterfall-exp::-webkit-input-placeholder{color:#ccc}#waterfall-exp:-ms-input-placeholder{color:#ccc}#waterfall-exp::-moz-placeholder{color:#ccc}ul.search span.highlighted{font-weight:700}ul.search>li{margin-bottom:24px}#search-results ul{list-style:none;padding:0}#search-results ul li>a{text-decoration:none;font-size:1.2rem}a.download:before{content:"file_download";position:relative;top:5px;margin-right:5px}button.download{position:sticky;margin-left:1em}.mdl-card{margin:1em 1.5em 1em 0;display:inline-block;width:250px;min-height:140px;padding:18px}.mdl-card:hover{box-shadow:0 10px 20px rgba(0,0,0,.25),0 6px 6px rgba(0,0,0,.22);color:#000;cursor:pointer}.mdl-card__title{padding:0 0 1em;font-size:18px;color:#444}.mdl-card__supporting-text{line-height:1.5rem;padding:0;width:100%}.head-card.mdl-card{width:auto;display:block;max-width:800px;padding:24px}.head-card>.mdl-card__title{padding-bottom:0;height:60px;font-weight:700;text-transform:uppercase}.head-card>.mdl-card__menu{color:#fff}.head-card>.mdl-card__actions{padding:0}.cards{display:flex;flex-direction:row;flex-wrap:wrap}
 /*# sourceMappingURL=/sphinx_materialdesign_theme.css.map */
\ No newline at end of file
diff --git a/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.css.map b/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.css.map
index a5856367972c..61d11807ca0b 100644
--- a/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.css.map
+++ b/docs/python_docs/themes/mx-theme/mxtheme/static/sphinx_materialdesign_theme.css.map
@@ -1 +1 @@
-{"version":3,"sources":["../../node_modules/material-design-lite/src/shadow/_shadow.scss","../../node_modules/material-design-lite/src/_mixins.scss","../../node_modules/material-design-lite/src/data-table/_data-table.scss","../../node_modules/material-design-lite/src/_variables.scss","../../node_modules/material-design-lite/src/footer/_mini_footer.scss","../../node_modules/material-design-lite/src/card/_card.scss","../../node_modules/material-design-lite/src/button/_button.scss","../scss/grid/_simplegrid.scss","../scss/fonts/_material-icons.scss","../scss/_root.scss","../scss/_variables.scss","../scss/layout/_layout.scss","../scss/headerings/_headerings.scss","../scss/admonitions/_admonitions.scss","../scss/code/_code.scss","../scss/blockquote/_blockquote.scss","../scss/tables/_tables.scss","../scss/toc/_globaltoc.scss","../scss/toc/_localtoc.scss","../scss/toc/_toctree.scss","../scss/lists/_lists.scss","../scss/drawer/_drawer.scss","../scss/header/_header.scss","../scss/footer/_footer.scss","../scss/search/_search.scss","../scss/downloadlink/_downloadlink.scss","../scss/card/_card.scss"],"names":[],"mappings":"AAmBA,wJCoNE,gGAEqE,CDlNvE,iBCqNE,gGAEqE,CDnNvE,iBCsNE,iGAEmE,CDpNrE,iBCuNE,kGAEmE,CDrNrE,iBCwNE,sGAEmE,CDtNrE,kBC0NE,wGAEqE,CDxNvE,kBC4NE,yGAEqE,CCtPvE,mHACE,iBAAkB,CAClB,gCCohBkC,CDnhBlC,wBAAyB,CACzB,kBAAmB,CACnB,cC0gByB,CDzgBzB,qBAAiD,CANnD,+HASI,kBAAmB,CATvB,+KAYM,YAAa,CAZnB,qIAkBM,iBAAkB,CAClB,WC0gBsB,CFlR1B,wBCvP6C,CDwP7C,kDEkN6D,CDzczD,oCAAqC,CArB3C,6JAwBQ,wBCigB4B,CDzhBpC,iJA4BQ,qBC4fwB,CDxhBhC,kPAkCI,mBCggBsD,CD/ftD,gBAAiB,CAnCrB,0SAsCM,iBAAkB,CAtCxB,sSA0CM,kBAAmB,CA1CzB,yHA+CI,iBAAkB,CAClB,qBAAsB,CACtB,WC4ewB,CD3exB,oCCoegC,CDnehC,uCCmegC,CDlehC,gBCof8C,CDnf9C,qBAAsB,CArD1B,yKAwDM,qBAAsB,CAxD5B,yHA6DI,iBAAkB,CAClB,qBAAsB,CACtB,sBAAuB,CDsCzB,cAAe,CAIb,eAAiB,CAEnB,gBAAiB,CACjB,gBAAiB,CC3Cf,WC4dwB,CD3dxB,cC8c8B,CD7c9B,qBCgd+B,CD/c/B,kBAAmB,CACnB,qBAAsB,CArE1B,wZAyEM,qBC2coC,CDphB1C,obD8LE,0BAA6B,CAC7B,eAAmB,CACnB,iBAAkB,CAClB,cAAe,CACf,aAAc,CACd,qBAAsB,CACtB,mBAAoB,CACpB,oBAAqB,CACrB,gBAAiB,CACjB,4BAA6B,CAC7B,oCAAqC,CACrC,kCAAmC,CC7H7B,cCqc+B,CDpc/B,eAAgB,CAChB,gBAAiB,CACjB,kBAAmB,CA/E3B,gbAkFQ,cAAe,CAlFvB,4cAoFU,qBCic2C,CDrhBrD,2NAyFM,eAAgB,CAKtB,wBACE,UAAW,CAGb,iRACE,eAAgB,CEpGlB,iBACE,YAAa,CACb,kBAAmB,CACnB,6BAA8B,CAE9B,iBDoZY,CClZZ,aDwRiD,CCvRjD,wBDsRoD,CC9RtD,uBAWI,UAAW,CACX,aAAc,CAZlB,2BAgBI,gBDsYkB,CClYtB,oHAEE,YAAa,CACb,oBAAqB,CAErB,eAAgB,CAEhB,QAAS,CACT,SAAU,CARZ,6HAWI,eAAgB,CAChB,iBDyXU,CCvXV,oCAdJ,6HAeM,gBDmXgB,CCjXnB,CAjBH,0HAoBI,aAAc,CACd,oBAAqB,CACrB,kBAAmB,CAIvB,8DAEE,oBAAqB,CACrB,OAAQ,CAGV,gEAEE,oBAAqB,CACrB,OAAQ,CAGV,0DAEE,UD0VoB,CCzVpB,WDyVoB,CCvVpB,SAAU,CACV,QAAS,CAET,wBD6NiD,CC3NjD,WAAY,CCpEd,UACE,YAAa,CACb,qBAAsB,CACtB,cF2amB,CE1anB,eAAgB,CAChB,gBFwaiB,CEvajB,eAAgB,CAChB,WFqagB,CEpahB,SF2bc,CE1bd,iBAAkB,CAClB,eFiOqD,CEhOrD,iBAAkB,CAClB,qBAAsB,CAGxB,iBACE,wBF6N6D,CE5N7D,wBAAyB,CACzB,2BAA4B,CAC5B,qBAAsB,CACtB,6BAA8B,CAC9B,4BAA6B,CAC7B,qBAAsB,CAGxB,iBACE,kBAAmB,CACnB,UFiN+C,CEhN/C,aAAc,CACd,YAAa,CACb,uBAAwB,CACxB,kBAAmB,CACnB,YFiZ4B,CEhZ5B,6BFoZoC,CEnZpC,2BFsZkC,CErZlC,qBAAsB,CAVxB,kCAaI,sCFyM+B,CErMnC,sBACE,mBAAoB,CACpB,aAAc,CACd,aAAc,CACd,YAAa,CACb,cFgYyB,CE/XzB,eFkZ+B,CEjZ/B,kBAAmB,CACnB,eAAgB,CAChB,2BFwYuC,CEvYvC,QAAS,CAGX,yBACE,cFwX4B,CEvX5B,qBFuL0D,CEtL1D,QAAS,CAGX,2BACE,qBFgLsE,CE/KtE,cF8XmC,CE7XnC,gBF8XqC,CE7XrC,eAAgB,CAChB,YF+W4B,CE9W5B,SAAU,CANZ,4CASI,sCFyK+B,CErKnC,mBACE,cFqX2B,CEpX3B,kBAAmB,CACnB,UAAW,CACX,4BAA+B,CAC/B,WAAY,CACZ,qBAAsB,CANxB,oCASI,mCF4J+B,CExJnC,kBACE,WAAY,CAId,gBACE,iBAAkB,CAClB,UAAW,CACX,QAAS,CC7FX,YACE,sBAAuB,CACvB,WAAY,CACZ,iBH+cwB,CG9cxB,UHgHsD,CG/GtD,iBAAkB,CAClB,WHyckB,CGxclB,QAAS,CACT,cHscqB,CGrcrB,cHucmB,CGtcnB,oBAAqB,CLVnB,6CE8CuD,CFmIzD,cAAe,CACf,eAAgB,CAChB,wBAAyB,CACzB,aAAc,CACd,gBAAiB,CKzKjB,eAAgB,CAChB,sBAAuB,CACvB,+HH+c6D,CG5c7D,YAAa,CACb,cAAe,CACf,oBAAqB,CACrB,iBAAkB,CAClB,gBH0bkB,CGzblB,qBAAsB,CAtBxB,8BAyBI,QAAS,CAzBb,kBA6BI,kCHsF8D,CGnHlE,+BAiCI,gCHsFuD,CGvH3D,mBAqCI,kCHiF6D,CGtHjE,gCAyCI,aHiFwD,CG1H5D,mDA4CM,gCH2EqD,CGtE3D,8BACE,uBAAuB,CAIvB,oBACE,4BH4D8D,CFgGhE,gGAEqE,CK/JrE,2BLuKA,iGAEmE,CKnK/D,kCH0D2D,CGhE/D,uCLyJA,6DAA8D,CK9I1D,kCHqD2D,CGhE/D,wCAeI,kBHqDsD,CGpDtD,UHqDiE,CGrErE,wJA2BM,wBH4CmD,CGvEzD,oDA+BM,eH4C4D,CGrClE,iBACE,iBAAkB,CAClB,cHwXuB,CGvXvB,WHqXkB,CGpXlB,WAAY,CACZ,cHmXkB,CGlXlB,UHkXkB,CGjXlB,SAAU,CACV,eAAgB,CAChB,4BHc8D,CGb9D,oEAAwE,CACxE,iBAAkB,CAClB,kBAAmB,CAZrB,0wCAeI,iBAAkB,CAClB,OAAQ,CACR,QAAS,CACT,gCAA8E,CAC9E,gBHuWqB,CGtWrB,UHsWqB,CG1XzB,sCAwBI,WHiWqB,CGhWrB,cHgWqB,CG/VrB,UH+VqB,CGzXzB,+CA8BI,iBAAkB,CAElB,4DAAiE,CAhCrE,wBLiIA,iGAEmE,CK9F/D,kCHX2D,CG1B/D,oCLmHA,6DAA8D,CKzE1D,kCHhB2D,CG1B/D,qCA8CI,kBHFiD,CGGjD,UHA+D,CG/CnE,+IA0DM,wBHZsD,CG9C5D,iDA8DM,eHd+D,CGqBrE,kBACE,iBAAkB,CAClB,cHmTuB,CGlTvB,WHoTmB,CGnTnB,aAAc,CACd,cAAe,CACf,cHiTmB,CGhTnB,UHgTmB,CG/SnB,SAAU,CACV,eAAgB,CAChB,aAAc,CACd,kBAAmB,CAXrB,gyCAcI,iBAAkB,CAClB,OAAQ,CACR,QAAS,CACT,gCAA8E,CAC9E,gBHmSqB,CGlSrB,UHkSqB,CGrTzB,wCAuBI,WHiSsB,CGhStB,cHgSsB,CG/RtB,UH+RsB,CGxT1B,owDA4BM,KAAyD,CACzD,MAA0D,CA7BhE,gDAkCI,iBAAkB,CAElB,4DAAiE,CAMrE,8BACE,aAAc,CACd,WAAY,CACZ,MAAS,CACT,iBAAkB,CAClB,KAAQ,CACR,UAAW,CACX,SAAU,CACV,eAAgB,CAEhB,2IAEE,4BAA6B,CAMnC,yCACE,aHpG0D,CGmG5D,qDAGI,eHrGmE,CGkGvE,qHAMI,UHxGmE,CGyGnE,wBH1GwD,CG8G5D,uCACE,aHjGqD,CGgGvD,mDAGI,eHhGiE,CG6FrE,iHAMI,UHnGiE,CGoGjE,wBHvGmD,CG6GvD,sFAII,qBHpHoE,CGqHpE,cAAe,CACf,4BAA6B,CAG9B,gGAIG,gCH9HgE,CG+HhE,qBH9HkE,CGkIrE,sGAIG,gCHvIgE,CGwIhE,qBHvIkE,CGwIlE,eAAgB,CAGnB,wGAIG,qBH/IkE,CGqJxE,4pCACE,qBAAsB,CClSxB,YACE,eAVqB,CAavB,cACE,eAbuB,CAgBzB,YACE,eAhBqB,CAqBvB,MACE,eAAgB,CAGlB,OACE,gBAAiB,CAGnB,QACE,iBAAkB,CAClB,gBAAiB,CACjB,iBAAkB,CAGpB,SACE,kBAAmB,CAGrB,WACE,YAAa,CAWf,WACE,UAAW,CACX,gBAAiB,CACjB,iBAAkB,CAElB,2CALF,WAMI,SAAU,CAOb,CAJC,wCATF,WAUI,SAAU,CACV,eAAgB,CAEnB,CAED,KACE,iBAAkB,CAClB,UAAW,CAGb,kBACE,UAAW,CACX,eAAiB,CACjB,kBAAoB,CAGtB,WACE,UAAW,CACX,aAAc,CACd,UAAW,CAGb,uFAYE,SAlDS,CAqDX,UACE,cAA0C,CAG5C,UACE,eAAyC,CAG3C,UACE,SAAwC,CAG1C,UACE,eAAwC,CAG1C,UACE,eAA+C,CAGjD,UACE,SAAwC,CAG1C,UACE,eAA+C,CAGjD,UACE,eAA+C,CAGjD,UACE,SAA+C,CAGjD,WACE,eAAgD,CAGlD,WACE,eAAgD,CAGlD,WACE,SAlGS,CAqGX,wCACE,OACE,cAA0C,CAE5C,OACE,eAAyC,CAE3C,OACE,SAAwC,CAE1C,OACE,eAAwC,CAE1C,OACE,eAA+C,CAEjD,OACE,SAAwC,CAE1C,OACE,eAA+C,CAEjD,OACE,eAA+C,CAEjD,OACE,SAA+C,CAEjD,QACE,eAAgD,CAElD,QACE,eAAgD,CAElD,QACE,SAxIO,CANX,WAkJI,aAAc,CACf,CAxHH,KA4HE,mBAAoB,CACpB,oBAAqB,CACrB,mBAAoB,CACpB,YAAa,CACb,cAAe,CAGjB,mBACE,YAAa,CACb,qBAAsB,CCxMxB,2dACI,0BAA6B,CAC7B,eAAmB,CACnB,iBAAkB,CAClB,cAAe,CACf,oBAAqB,CACrB,aAAc,CACd,mBAAoB,CACpB,qBAAsB,CACtB,gBAAiB,CACjB,kBAAmB,CACnB,aAAc,CAGd,kCAAmC,CAEnC,iCAAkC,CAGlC,iCAAkC,CAGlC,4BAA6B,CC3BjC,KACI,cCEY,CDChB,KACI,uBAAyB,CACzB,wBCDsB,CDEtB,cAAe,CACf,kBAAmB,CACnB,6ICA0J,CDG9J,2BACI,YAAa,CAGjB,4EAEI,6ICT0J,CDY9J,8GACI,uBAA8B,CAGlC,EACI,oBAAqB,CAGzB,yKAGQ,cAAe,CAIvB,OACI,aAAc,CACd,oBAAqB,CAGzB,SACI,eAAgB,CAOnB,IACG,cAAe,CACf,aAAc,CACd,gBAAiB,CACjB,iBAAkB,CAGtB,qBAEQ,iBAAkB,CAClB,iBAAkB,CAH1B,yCAMY,iBAAkB,CAN9B,2CASY,eAAgB,CAK5B,UACE,UAAW,CACX,WAAY,CACZ,oBAAqB,CACrB,YC3C0C,CD4C1C,iBAAkB,CAClB,eAAgB,CAChB,uBAAwB,CAM1B,6kBACI,iBAAkB,CAClB,OAAQ,CAGZ,WACI,oBAAqB,CAGzB,eACE,UAAW,CACX,aAAc,CACd,UAAW,CAGb,SAEE,gBAA2D,CAC3D,iBAAkB,CAClB,gBAAiB,CACjB,kBAA0C,CAC1C,iBC9EqB,CDiFrB,qCATF,SAWI,gBAAuD,CACvD,kBAAgC,CAChC,iBAA+B,CAElC,CEtFD,YACI,eAAgB,CAIpB,UACI,UAAW,CACX,aAAc,CACd,YAAa,CAEb,0BALJ,UAMQ,YAhCiB,CA6ExB,CAnDD,wBASQ,UAAW,CACX,aAAc,CACd,cAAe,CAEf,yBAbR,wBAcY,SA7BU,CA8BV,YA7Ba,CAoCpB,CAJG,0BAlBR,wBAmBY,uBA9B0B,CA+B1B,YA9Ba,CAgCpB,CAtBL,4BAyBQ,WA5CY,CA8CZ,0BA3BR,4BA4BY,YAAa,CAqBpB,CAjDL,qCA+BY,cAAe,CACf,eAAgB,CAChB,eAAgB,CAChB,aAAc,CAlC1B,wDAoCgB,SAAU,CApC1B,8DAwCgB,iBAAkB,CAxClC,8DA4CgB,+BAAmC,CACnC,iBAAkB,CAClB,uCAA4C,CC9E5D,oBACI,GACI,2BAA6B,CAC7B,SAAU,CAEjB,GACC,uBAAwB,CACxB,SAAU,CAAA,CAIZ,qBACI,GACI,uBAAwB,CACxB,SAAU,CAEjB,GACC,2BAA6B,CAC7B,SAAU,CAAA,CAIZ,0BAEQ,oBAAqB,CACrB,oBAAqB,CACrB,iBAAmB,CACnB,aAAc,CACd,SAAU,CANlB,gCAQY,0DAAsE,CARlF,oLAcY,oBAAqB,CAdjC,kNAkBgB,0DAAsE,CAlBtF,iBAwBQ,cAAe,CACf,mBAAoB,CAzB5B,iBA6BQ,iBAAkB,CAClB,gBAAiB,CACjB,kBAAmB,CACnB,YAAa,CACb,kBAAmB,CAjC3B,iBAqCQ,gBAAiB,CACjB,mBAAoB,CACpB,gBAAiB,CACjB,YAAe,CACf,oBAAqB,CAzC7B,iBA6CQ,iBAAkB,CAClB,kBAAmB,CACnB,kBAAmB,CACnB,YAAe,CACf,mBAAoB,CAjD5B,kCAqDQ,gBAAiB,CACjB,kBAAmB,CACnB,gBAAiB,CACjB,YAAe,CACf,kBAAmB,CAzD3B,kCA6DQ,cAAe,CACf,kBAAmB,CACnB,gBAAiB,CACjB,YAAe,CACf,kBAAmB,CCT3B,YAGI,iBAAkB,CAClB,eAAgB,CAChB,kBAAmB,CALvB,mBAOQ,WAAY,CAPpB,8BAUQ,cAAe,CACf,eAAiB,CACjB,UAAW,CACX,wBAAyB,CACzB,cAAe,CAdvB,iBApBI,6BA/CgC,CAgDhC,mCA/C4C,CAgD5C,mCACI,cAAe,CACf,eAAiB,CACjB,aApD4B,CAsD5B,cAAe,CACf,iBAAkB,CAClB,0CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,sBA3DwB,CA4DxB,cAAe,CAK3B,oBApBI,6BA1CgC,CA2ChC,mCA1C4C,CA2C5C,sCACI,cAAe,CACf,eAAiB,CACjB,aA/C4B,CAiD5B,cAAe,CACf,iBAAkB,CAClB,6CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,gBAtDkB,CAuDlB,cAAe,CAK3B,iBApBI,6BApDgC,CAqDhC,mCApD4C,CAqD5C,mCACI,cAAe,CACf,eAAiB,CACjB,aAzD4B,CA2D5B,cAAe,CACf,iBAAkB,CAClB,0CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,sBAhEwB,CAiExB,cAAe,CAK3B,oBApBI,6BArCgC,CAsChC,mCArC4C,CAsC5C,sCACI,cAAe,CACf,eAAiB,CACjB,aA1C4B,CA4C5B,cAAe,CACf,iBAAkB,CAClB,6CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,iBAjDmB,CAkDnB,cAAe,CAK3B,sBApBI,6BAhCgC,CAiChC,mCAhC4C,CAiC5C,wCACI,cAAe,CACf,eAAiB,CACjB,aArC4B,CAuC5B,cAAe,CACf,iBAAkB,CAClB,+CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,iBA5CmB,CA6CnB,cAAe,CAK3B,gBApBI,6BA3BiC,CA4BjC,oCA3B6C,CA4B7C,kCACI,cAAe,CACf,eAAiB,CACjB,aAhC6B,CAkC7B,cAAe,CACf,iBAAkB,CAClB,yCAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,2BAvC6B,CAwC7B,cAAe,CAK3B,sBApBI,6BAtBiC,CAuBjC,oCAtB6C,CAuB7C,wCACI,cAAe,CACf,eAAiB,CACjB,aA3B6B,CA6B7B,cAAe,CACf,iBAAkB,CAClB,+CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,sBAlCyB,CAmCzB,cAAe,CAK3B,kBApBI,6BAjBgC,CAkBhC,mCAjB4C,CAkB5C,oCACI,cAAe,CACf,eAAiB,CACjB,aAtB4B,CAwB5B,cAAe,CACf,iBAAkB,CAClB,2CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,uBA7ByB,CA8BzB,cAAe,CAK3B,oBApBI,6BAZgC,CAahC,mCAZ4C,CAa5C,sCACI,cAAe,CACf,eAAiB,CACjB,aAjB4B,CAmB5B,cAAe,CACf,iBAAkB,CAClB,6CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,uBAxByB,CAyBzB,cAAe,CAK3B,mBApBI,6BAPgC,CAQhC,mCAP4C,CAQ5C,qCACI,cAAe,CACf,eAAiB,CACjB,aAZ4B,CAc5B,cAAe,CACf,iBAAkB,CAClB,4CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,uBAnByB,CAoBzB,cAAe,CCzE3B,yBAEQ,YAAa,CAFrB,6BAIY,0BJEqB,CIDrB,qBAAsB,CACtB,wHJE2I,CID3I,cAAgB,CAChB,aAAc,CACd,iBAAkB,CAT9B,iEAWgB,qBAAsB,CAXtC,kDAiBQ,eAAgB,CAjBxB,qCAwBgB,qBAAsB,CACtB,kBJpBU,CIuBV,qBAAmB,CACnB,cAAgB,CA7BhC,sDAmCQ,QAAW,CAEX,iBAAkB,CArC1B,8HAoCQ,wHJ5B+I,CIRvJ,6BA4CQ,gBAAiB,CACjB,aAAc,CA7CtB,kGAiDQ,aAAc,CACd,aAAc,CACd,cAAe,CACf,kBAAmB,CACnB,kBAAmB,CACnB,aAAc,CACd,4BAA6B,CAC7B,YAAa,CACb,iBAAkB,CAzD1B,wSA2DY,qBAAsB,CACtB,kBAAmB,CACnB,WAAY,CA7DxB,8GAgEY,aAAc,CAhE1B,sBAsEQ,kBAAqB,CAtE7B,0BA2EQ,iBAAkB,CA3E1B,6BA6EY,kBAAmB,CACnB,QAAS,CA9ErB,oCA6FO,UAAW,CACX,UAAW,CACX,aAAc,CA/FrB,oCAmGO,gBAAiB,CAnGxB,sBAsGQ,kBAAmB,CAtG3B,kBA0GQ,aAAc,CACd,eAAgB,CAChB,aAAc,CACd,iBAAkB,CAClB,UAAW,CACX,iBAAkB,CAClB,oBAAqB,CAhH7B,+BAsHgB,6IJ7G8I,CI8G9I,eAAiB,CACjB,2BAA4B,CAC5B,oBAAyB,CACzB,iBAAkB,CAClB,iBAAkB,CAClB,WAAY,CACZ,UAAY,CACZ,YAAc,CACd,kBAA8B,CAC9B,eAAiB,CACjB,cAAe,CC9H9B,yBAEO,cAAe,CACf,cAAe,CACf,qCLDyB,CKHhC,+BAOW,oBAAsB,CACtB,aAAc,CARzB,gCAWW,oBAAsB,CCdlC,mGAKQ,eAAgB,CAChB,kBAAmB,CACnB,cAAe,CACf,aAAc,CARtB,4MAYY,kBAAmB,CACnB,wBAAyB,CAbrC,2GAiBY,cNdI,CMeJ,mBAAuB,CACvB,kBAAmB,CAnB/B,2HAqBgB,iBAAkB,CArBlC,iIAwBgB,eAAgB,CCxBhC,oCAGQ,YAAa,CAHrB,cAQQ,oBAAqB,CACrB,SAAU,CACV,QAAS,CAVjB,iBAaY,eAAgB,CAb5B,+BAegB,YAAa,CACb,6BAA8B,CAhB9C,iCAkBoB,aAAc,CACd,aAAc,CACd,UAAW,CACX,cAAe,CACf,oBAAqB,CACrB,adyKoB,CchMxC,yCAyBwB,eAAiB,CAzBzC,uBAiCQ,SAAU,CACV,WAAY,CACZ,YAAa,CACb,kBAAmB,CACnB,sBAAuB,CACvB,WAAY,CAtCpB,yBAwCY,SAAU,CACV,aAAc,CACd,gBAAiB,CACjB,cAAe,CA3C3B,2BA6CgB,cAAe,CA7C/B,4BAiDY,wBAA0B,CAjDtC,8BAmDgB,cAAe,CACf,eAAgB,CApDhC,uCA2DY,gBAAiB,CA3D7B,6CA8DY,iBAAkB,CA9D9B,mDAiEY,iBAAkB,CAjE9B,yDAoEY,iBAAkB,CApE9B,+DAuEY,iBAAkB,CAvE9B,qEA0EY,iBAAkB,CC1E9B,UACI,gBAAkB,CAClB,gBAAiB,CAFrB,mBAKQ,iBAAkB,CAL1B,wBAOY,eAAiB,CACjB,eAAgB,CAR5B,kBAaQ,YAAa,CAbrB,aAiBQ,SAAU,CACV,oBAAqB,CAlB7B,aAsBQ,gBAAiB,CAtBzB,YA0BQ,aAAc,CACd,oBAAqB,CACrB,aAAc,CACd,cAAe,CACf,gBAAiB,CACjB,kBAAmB,CA/B3B,oBAkCY,gBAAiB,CACjB,qBAAsB,CACtB,eAAiB,CCjC5B,iCAEI,qBAAsB,CAG1B,yDAEI,aAAyB,CACzB,cAAe,CACf,iBAAkB,CAGtB,uCAEI,iBAAkB,CAClB,eAAgB,CAChB,gBAAiB,CAGrB,qCAEI,gBAAiB,CACjB,oBAAqB,CAHzB,+CAKQ,cAAe,CAIvB,iDAEI,gBAAiB,CAFrB,2DAIQ,gBAAiB,CCnC1B,oBAGY,cAAe,CAH3B,sBAKgB,QAAS,CALzB,mCAWY,wHVH2I,CURvJ,8BAcY,aAAe,CACf,WAAY,CCXpB,oBACI,qBAAsB,CADzB,uCAIO,SAAU,CAJjB,6CAQO,iBAAkB,CARzB,6CAYO,+BAAmC,CACnC,iBAAkB,CAClB,uCAA4C,CAdnD,sCAkBO,eAAiB,CACjB,gBAAiB,CACjB,QAAS,CACT,SAAU,CACV,gBAAiB,CACjB,sCAAuC,CACvC,eAAgB,CAxBvB,6CA0BW,aAAc,CACd,aAAc,CACd,WAAY,CACZ,UAAW,CACX,oBAAqB,CA9BhC,sDAgCe,UAAW,CACX,QAAS,CACT,SAAU,CAlCzB,kDAsCe,eAAiB,CACjB,gBAAiB,CACjB,cAAe,CACf,iBAAoB,CACpB,gBAAiB,CACjB,6IXtC0I,CWuC1I,aAAc,CACd,aAAc,CC7ClC,sCAEQ,aAAc,CACd,cAAe,CAEnB,0BALJ,eAMQ,uBAA0B,CANlC,gDAQY,iBAAkB,CAClB,UAAW,CACX,eAAgB,CAChB,kBAAmB,CACnB,sBAAuB,CAZnC,wwCAgBY,YAAa,CAChB,CAIT,uBACI,eAAgB,CAGpB,2BACI,kBAAoB,CAGxB,wCACI,6BAAiC,CACjC,UAAW,CACX,eAAgB,CAChB,iBAAkB,CAJtB,+DAOQ,cAAe,CAPvB,iEASY,gBAAiB,CACjB,YAAa,CACb,iBAAkB,CAClB,aAAe,CAZ3B,qEAiBQ,wBAAmD,CACnD,UAAc,CAlBtB,yEAqBQ,wBAAmD,CACnD,SAAU,CACV,UAAc,CAOtB,YACE,yBAA2B,CAC3B,gBAAiB,CACjB,mBAAoB,CACpB,eAAgB,CAChB,UAAW,CACX,UAAY,CANd,gCAUI,aZzCuC,CY8C3C,aACE,cAAe,CACf,KAAM,CACN,UAAW,CACX,eAAgB,CAChB,gBAAiB,CACjB,mBAAoB,CACpB,wBZzD0B,CY0D1B,UAAW,CACX,eAAgB,CAChB,cAAe,CACf,4BAA8B,CAGhC,kBACE,WAAY,CACZ,eAAgB,CAGlB,UACE,WAAY,CACZ,gBAAiB,CAFnB,4CASI,YAAa,CATjB,qBAaI,UAAY,CACZ,eAAgB,CAChB,eAAgB,CAfpB,sCAkBM,iBAAkB,CAlBxB,2BAsBM,UAAY,CACZ,sCAA4C,CAvBlD,kCA4BI,UAAY,CACZ,yBAA0B,CAG5B,qCAhCF,UAiCI,iBAAkB,CAClB,OAAQ,CACR,UAAW,CACX,wBAAiC,CACjC,iBAAkB,CAClB,gBAAiB,CAtCrB,iCAyCM,aAAc,CACd,WAAY,CACZ,UAAW,CACX,WAAY,CACZ,SAAU,CACV,cAAe,CA9CrB,qBAkDM,aAAc,CACd,WAAY,CACZ,UAAW,CACX,WAAY,CACZ,aAAc,CACd,gBAAiB,CACjB,iBAAkB,CAxDxB,yBA2DQ,SAAW,CA3DnB,yBAgEM,UAAW,CACX,YAAa,CAjEnB,iCAqEM,aAAc,CACd,kBAAmB,CAtEzB,qBA0EM,gBAAiB,CACjB,aAAc,CAMd,gBAAiB,CAjFvB,sCA8EQ,cAAe,CAChB,CC7KP,uBACI,wBAAyB,CAD7B,yDAGQ,kBAAmB,CACnB,YAAa,CACb,qBAAsB,CAL9B,mEAOY,gBAAiB,CAP7B,0DAcQ,eAAiB,CACjB,YAAa,CACb,qBAAsB,CACtB,wBAAyB,CAjBjC,4DAoBY,aAAc,CACd,eAAiB,CACjB,oBAAqB,CAtBjC,iCA0BQ,YAAa,CAOpB,YACG,UAAW,CACX,eAAgB,CAChB,WAAY,CACZ,wBAAyB,CACzB,YAAa,CALhB,6EAQO,mBAAoB,CACpB,SAAU,CACV,WAAY,CACZ,YAAa,CACb,sBAAuB,CACvB,kBAAmB,CACnB,UAAc,CAdrB,yBAkBO,iBAAkB,CAlBzB,0CAoBW,eAAgB,CApB3B,yBA0BO,gBAAiB,CACjB,0BAA2B,CA3BlC,0CA6BW,gBAAiB,CAKrB,oBACI,iBAAkB,CAEtB,oBACI,gBAAiB,CAIzB,iBACI,gBAAiB,CACjB,cAAe,CAGnB,sBACI,UAAY,CACZ,cAAe,CAEnB,qCAnDH,yBAqDW,SAAU,CArDrB,yBAyDW,SAAU,CAzDrB,0CA6DW,YAAa,CAChB,CAEL,qCAhEH,kDAmEW,SAAU,CAnErB,0CAuEW,aAAc,CACjB,CAST,aACE,4BbvF0C,CawF1C,cAAwB,CACxB,wBAAyB,CAH3B,oCAKI,ab9FwB,CayF5B,sCAWM,uBAAmC,CAMzC,cACE,wBAAyB,CACzB,gBAAiB,CACjB,mBAAoB,CAGtB,gBACE,kBAAgC,CAGlC,iCAEE,eAAgB,CAChB,aAAc,CAIhB,uBACE,aAAc,CACd,UAAY,CACZ,UAAW,CAGb,aACE,WAAY,CACZ,kBAAmB,CACnB,eAAgB,CAGlB,YACE,UAAW,CACX,kBAAgC,CAChC,iBAA+B,CAGjC,aACE,ab3I0C,Cc5B5C,0CACI,UAAW,CAEf,qCACI,UAAW,CAEf,iCACI,UAAW,CAGf,2BACI,eAAiB,CAGrB,aACI,kBAAmB,CAGvB,mBAEQ,eAAgB,CAChB,SAAU,CAHlB,wBAMgB,oBAAqB,CACrB,gBAAiB,CC5BjC,kBAGQ,uBAAwB,CACxB,iBAAkB,CAClB,OAAQ,CACR,gBAAiB,CAIzB,gBACI,eAAgB,CAChB,eAAgB,CpBMpB,UqBjBI,sBAAuB,CACvB,oBAAqB,CACrB,WAAY,CACZ,gBAAiB,CACjB,YAAa,CAEjB,gBACI,gEAAoE,CACpE,UAAW,CACX,cAAe,CrBiCnB,iBqB9BI,eAAkB,CAClB,cAAe,CACf,UAAW,CrBgEf,2BqB5DI,kBAAmB,CACnB,SAAY,CACZ,UAAW,CAGf,oBACI,UAAW,CACX,aAAc,CACd,eAAgB,CAChB,YAAa,CAGjB,4BACI,gBAAmB,CACnB,WAAY,CACZ,eAAgB,CAChB,wBAAyB,CAE7B,2BACI,UAAW,CAEf,8BACI,SAAU,CAEd,OACI,YAAa,CACb,kBAAmB,CACnB,cAAe","file":"sphinx_materialdesign_theme.css","sourceRoot":"../../src/js","sourcesContent":["/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n@import \"../variables\";\n@import \"../mixins\";\n\n.mdl-shadow--2dp {\n  @include shadow-2dp();\n}\n\n.mdl-shadow--3dp {\n  @include shadow-3dp();\n}\n\n.mdl-shadow--4dp {\n  @include shadow-4dp();\n}\n\n.mdl-shadow--6dp {\n  @include shadow-6dp();\n}\n\n.mdl-shadow--8dp {\n  @include shadow-8dp();\n}\n\n.mdl-shadow--16dp {\n  @include shadow-16dp();\n}\n\n.mdl-shadow--24dp {\n  @include shadow-24dp();\n}\n","/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n/* Typography */\n\n@mixin typo-preferred-font($usePreferred: true) {\n  @if $usePreferred {\n    font-family: $preferred_font;\n  }\n}\n\n@mixin typo-display-4($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 112px;\n  font-weight: 300;\n  line-height: 1;\n  letter-spacing: -0.04em;\n\n  @if $colorContrast {\n    opacity: 0.54;\n  }\n}\n\n@mixin typo-display-3($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 56px;\n  font-weight: 400;\n  line-height: 1.35;\n  letter-spacing: -0.02em;\n\n  @if $colorContrast {\n    opacity: 0.54;\n  }\n}\n\n@mixin typo-display-2($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 45px;\n  font-weight: 400;\n  line-height: 48px;\n\n  @if $colorContrast {\n    opacity: 0.54;\n  }\n}\n\n@mixin typo-display-1($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 34px;\n  font-weight: 400;\n  line-height: 40px;\n\n  @if $colorContrast {\n    opacity: 0.54;\n  }\n}\n\n@mixin typo-headline($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 24px;\n  font-weight: 400;\n  line-height: 32px;\n  -moz-osx-font-smoothing: grayscale;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-title($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 20px;\n  font-weight: 500;\n  line-height: 1;\n  letter-spacing: 0.02em;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-subhead($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 16px;\n  font-weight: 400;\n  line-height: 24px;\n  letter-spacing: 0.04em;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-subhead-2($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 16px;\n  font-weight: 400;\n  line-height: 28px;\n  letter-spacing: 0.04em;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-body-2($colorContrast: false, $usePreferred: false) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 14px;\n  @if $usePreferred {\n    font-weight: 500;\n  } @else {\n    font-weight: bold;\n  }\n  line-height: 24px;\n  letter-spacing: 0;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-body-1($colorContrast: false, $usePreferred: false) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 14px;\n  font-weight: 400;\n  line-height: 24px;\n  letter-spacing: 0;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-caption($colorContrast: false, $usePreferred: false) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 12px;\n  font-weight: 400;\n  line-height: 1;\n  letter-spacing: 0;\n\n  @if $colorContrast {\n    opacity: 0.54;\n  }\n}\n\n@mixin typo-blockquote($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  position: relative;\n  font-size: 24px;\n  font-weight: 300;\n  font-style: italic;\n  line-height: 1.35;\n  letter-spacing: 0.08em;\n\n  &:before {\n    position: absolute;\n    left: -0.5em;\n    content: '“';\n  }\n\n  &:after {\n    content: '”';\n    margin-left: -0.05em;\n  }\n\n  @if $colorContrast {\n    opacity: 0.54;\n  }\n}\n\n@mixin typo-menu($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 14px;\n  font-weight: 500;\n  line-height: 1;\n  letter-spacing: 0;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-button($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 14px;\n  font-weight: 500;\n  text-transform: uppercase;\n  line-height: 1;\n  letter-spacing: 0;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-icon() {\n  font-family: 'Material Icons';\n  font-weight: normal;\n  font-style: normal;\n  font-size: 24px;\n  line-height: 1;\n  letter-spacing: normal;\n  text-transform: none;\n  display: inline-block;\n  word-wrap: normal;\n  font-feature-settings: 'liga';\n  -webkit-font-feature-settings: 'liga';\n  -webkit-font-smoothing: antialiased;\n}\n\n/* Shadows */\n\n// Focus shadow mixin.\n@mixin focus-shadow() {\n  box-shadow: 0 0 8px rgba(0,0,0,.18),0 8px 16px rgba(0,0,0,.36);\n}\n\n@mixin shadow-2dp() {\n  box-shadow: 0 2px 2px 0 rgba(0, 0, 0, $shadow-key-penumbra-opacity),\n              0 3px 1px -2px rgba(0, 0, 0, $shadow-key-umbra-opacity),\n              0 1px 5px 0 rgba(0, 0, 0, $shadow-ambient-shadow-opacity);\n}\n@mixin shadow-3dp() {\n  box-shadow: 0 3px 4px 0 rgba(0, 0, 0, $shadow-key-penumbra-opacity),\n              0 3px 3px -2px rgba(0, 0, 0, $shadow-key-umbra-opacity),\n              0 1px 8px 0 rgba(0, 0, 0, $shadow-ambient-shadow-opacity);\n}\n@mixin shadow-4dp() {\n  box-shadow: 0 4px 5px 0 rgba(0, 0, 0, $shadow-key-penumbra-opacity),\n              0 1px 10px 0 rgba(0, 0, 0, $shadow-ambient-shadow-opacity),\n              0 2px 4px -1px rgba(0, 0, 0, $shadow-key-umbra-opacity);\n}\n@mixin shadow-6dp() {\n  box-shadow: 0 6px 10px 0 rgba(0, 0, 0, $shadow-key-penumbra-opacity),\n              0 1px 18px 0 rgba(0, 0, 0, $shadow-ambient-shadow-opacity),\n              0 3px 5px -1px rgba(0, 0, 0, $shadow-key-umbra-opacity);\n}\n@mixin shadow-8dp() {\n  box-shadow: 0 8px 10px 1px rgba(0, 0, 0, $shadow-key-penumbra-opacity),\n              0 3px 14px 2px rgba(0, 0, 0, $shadow-ambient-shadow-opacity),\n              0 5px 5px -3px rgba(0, 0, 0, $shadow-key-umbra-opacity);\n}\n\n@mixin shadow-16dp() {\n  box-shadow: 0 16px 24px 2px rgba(0, 0, 0, $shadow-key-penumbra-opacity),\n              0  6px 30px 5px rgba(0, 0, 0, $shadow-ambient-shadow-opacity),\n              0  8px 10px -5px rgba(0, 0, 0, $shadow-key-umbra-opacity);\n}\n\n@mixin shadow-24dp() {\n  box-shadow: 0  9px 46px  8px rgba(0, 0, 0, $shadow-key-penumbra-opacity),\n              0 11px 15px -7px rgba(0, 0, 0, $shadow-ambient-shadow-opacity),\n              0 24px 38px  3px rgba(0, 0, 0, $shadow-key-umbra-opacity);\n}\n\n/* Animations */\n\n@mixin material-animation-fast-out-slow-in($duration:0.2s) {\n  transition-duration: $duration;\n  transition-timing-function: $animation-curve-fast-out-slow-in;\n}\n\n@mixin material-animation-linear-out-slow-in($duration:0.2s) {\n  transition-duration: $duration;\n  transition-timing-function: $animation-curve-linear-out-slow-in;\n}\n\n@mixin material-animation-fast-out-linear-in($duration:0.2s) {\n  transition-duration: $duration;\n  transition-timing-function: $animation-curve-fast-out-linear-in;\n}\n\n@mixin material-animation-default($duration:0.2s) {\n  transition-duration: $duration;\n  transition-timing-function: $animation-curve-default;\n}\n\n/* Dialog */\n\n@mixin dialog-width($units:5) {\n  @if(type_of($units) != 'number') {\n    @error \"The unit given to dialog-width should be a number.\";\n  }\n  // 56dp is the base unit width for Dialogs.\n  // With 5 units being the number of units for a mobile device.\n  // https://goo.gl/sK2O5o\n  width: $units * 56px;\n}\n","/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n@import \"../variables\";\n@import \"../mixins\";\n\n.mdl-data-table {\n  position: relative;\n  border: $data-table-dividers;\n  border-collapse: collapse;\n  white-space: nowrap;\n  font-size: $data-table-font-size;\n  background-color: unquote(\"rgb(#{$color-white})\");\n\n  thead {\n    padding-bottom: 3px;\n\n    .mdl-data-table__select {\n      margin-top: 0;\n    }\n  }\n\n  tbody {\n    tr {\n      position: relative;\n      height: $data-table-row-height;\n      @include material-animation-default(0.28s);\n      transition-property: background-color;\n\n      &.is-selected {\n        background-color: $data-table-selection-color;\n      }\n\n      &:hover {\n        background-color: $data-table-hover-color;\n      }\n    }\n  }\n\n  td, th {\n    padding: 0 $data-table-column-padding 12px $data-table-column-padding;\n    text-align: right;\n\n    &:first-of-type {\n      padding-left: 24px;\n    }\n\n    &:last-of-type {\n      padding-right: 24px;\n    }\n  }\n\n  td {\n    position: relative;\n    vertical-align: middle;\n    height: $data-table-row-height;\n    border-top: $data-table-dividers;\n    border-bottom: $data-table-dividers;\n    padding-top: $data-table-cell-top;\n    box-sizing: border-box;\n\n    .mdl-data-table__select {\n      vertical-align: middle;\n    }\n  }\n\n  th {\n    position: relative;\n    vertical-align: bottom;\n    text-overflow: ellipsis;\n    @include typo-body-2();\n    height: $data-table-row-height;\n    font-size: $data-table-header-font-size;\n    color: $data-table-header-color;\n    padding-bottom: 8px;\n    box-sizing: border-box;\n\n    &.mdl-data-table__header--sorted-ascending,\n    &.mdl-data-table__header--sorted-descending {\n      color: $data-table-header-sorted-color;\n      &:before {\n        @include typo-icon;\n        font-size: $data-table-header-sort-icon-size;\n        content: \"\\e5d8\";\n        margin-right: 5px;\n        vertical-align: sub;\n      }\n      &:hover {\n        cursor: pointer;\n        &:before {\n          color: $data-table-header-sorted-icon-hover-color;\n        }\n      }\n    }\n    &.mdl-data-table__header--sorted-descending:before {\n      content: \"\\e5db\";\n    }\n  }\n}\n\n.mdl-data-table__select {\n  width: 16px;\n}\n\n.mdl-data-table__cell--non-numeric.mdl-data-table__cell--non-numeric {\n  text-align: left;\n}\n","/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n/*------------------------------------*\\\n    $CONTENTS\n\\*------------------------------------*/\n/**\n * STYLE GUIDE VARIABLES------------------Declarations of Sass variables\n * -----Typography\n * -----Colors\n * -----Textfield\n * -----Switch\n * -----Spinner\n * -----Radio\n * -----Menu\n * -----List\n * -----Layout\n * -----Icon toggles\n * -----Footer\n * -----Column\n * -----Checkbox\n * -----Card\n * -----Button\n * -----Animation\n * -----Progress\n * -----Badge\n * -----Shadows\n * -----Grid\n * -----Data table\n * -----Dialog\n * -----Snackbar\n * -----Tooltip\n * -----Chip\n *\n * Even though all variables have the `!default` directive, most of them\n * should not be changed as they are dependent one another. This can cause\n * visual distortions (like alignment issues) that are hard to track down\n * and fix.\n */\n\n\n/* ==========  TYPOGRAPHY  ========== */\n\n/* We're splitting fonts into \"preferred\" and \"performance\" in order to optimize\n   page loading. For important text, such as the body, we want it to load\n   immediately and not wait for the web font load, whereas for other sections,\n   such as headers and titles, we're OK with things taking a bit longer to load.\n   We do have some optional classes and parameters in the mixins, in case you\n   definitely want to make sure you're using the preferred font and don't mind\n   the performance hit.\n   We should be able to improve on this once CSS Font Loading L3 becomes more\n   widely available.\n*/\n$preferred_font: 'Roboto', 'Helvetica', 'Arial', sans-serif !default;\n$performance_font: 'Helvetica', 'Arial', sans-serif !default;\n\n/* ==========  COLORS  ========== */\n\n/**\n*\n* Material design color palettes.\n* @see http://www.google.com/design/spec/style/color.html\n*\n**/\n\n@import \"color-definitions\";\n@import \"functions\";\n\n/* ==========  IMAGES  ========== */\n$image_path: '/images' !default;\n\n/* ==========  Color & Themes  ========== */\n\n// Define whether individual color palette items should have classes created.\n// Setting this to true will remove individual color classes for each color in the palettes.\n// To improve overall performance (assuming they aren't used) by:\n// * Saving server bandwidth sending the extra classes\n// * Save client computation against the classes\n// it is RECOMMENDED you set this to true.\n$trim-color-classes: false !default;\n\n// Use color primarily for emphasis. Choose colors that fit with\n// your brand and provide good contrast between visual components.\n$color-primary: $palette-indigo-500 !default;\n$color-primary-dark: $palette-indigo-700 !default;\n$color-accent: $palette-pink-A200 !default;\n\n// Our primary is dark, so use $color-dark-contrast for overlaid text.\n$color-primary-contrast: $color-dark-contrast !default;\n// Our accent is dark, so use $color-dark-contrast for overlaid text.\n$color-accent-contrast: $color-dark-contrast !default;\n\n// Replace all colors with placeholders if we're generating a template.\n@if $styleguide-generate-template == true {\n  $color-primary: '$color-primary';\n  $color-primary-dark: '$color-primary-dark';\n  $color-accent: '$color-accent';\n  $color-primary-contrast: '$color-primary-contrast';\n  $color-accent-contrast: '$color-accent-contrast';\n}\n\n/* ==========  Typography  ========== */\n\n// We use the following default color styles: text-color-primary and\n// text-color-secondary. For light themes, use text-color-primary-inverse\n// and text-color-secondary-inverse.\n\n$text-color-primary: unquote(\"rgba(#{$color-black}, 0.87)\") !default;\n$text-link-color: unquote(\"rgb(#{$color-accent})\") !default;\n\n// Define whether to target elements directly for typographic enhancements.\n// Turning this off means you need to use mdl-* classes more often.\n// Other components may also fail to adhere to MD without these rules.\n// It is strongly recommended you leave this as true.\n\n$target-elements-directly: true !default;\n\n/* ==========  Components  ========== */\n\n/* ==========  Standard Buttons  ========== */\n\n// Default button colors.\n$button-primary-color: unquote(\"rgba(#{$palette-grey-500}, 0.20)\") !default;\n$button-secondary-color: unquote(\"rgb(#{$color-black})\") !default;\n$button-hover-color: $button-primary-color !default;\n$button-active-color: unquote(\"rgba(#{$palette-grey-500}, 0.40)\") !default;\n$button-focus-color: unquote(\"rgba(#{$color-black}, 0.12)\") !default;\n\n// Colored button colors.\n$button-primary-color-alt: unquote(\"rgb(#{$color-primary})\") !default;\n$button-secondary-color-alt: unquote(\"rgb(#{$color-primary-contrast})\") !default;\n$button-hover-color-alt: unquote(\"rgb(#{$color-primary})\") !default;\n$button-active-color-alt: unquote(\"rgb(#{$color-primary})\") !default;\n$button-focus-color-alt: $button-focus-color !default;\n\n// Ripple color for colored raised buttons.\n$button-ripple-color-alt: unquote(\"rgb(#{$color-primary-contrast})\") !default;\n\n// Disabled button colors.\n$button-primary-color-disabled: unquote(\"rgba(#{$color-black}, 0.12)\") !default;\n$button-secondary-color-disabled: unquote(\"rgba(#{$color-black}, 0.26)\") !default;\n\n// FAB colors and sizes.\n$button-fab-color-alt: unquote(\"rgb(#{$color-accent})\") !default;\n$button-fab-hover-color-alt: unquote(\"rgb(#{$color-accent})\") !default;\n$button-fab-active-color-alt: unquote(\"rgb(#{$color-accent})\") !default;\n$button-fab-text-color-alt: unquote(\"rgb(#{$color-accent-contrast})\") !default;\n$button-fab-ripple-color-alt: unquote(\"rgb(#{$color-accent-contrast})\") !default;\n\n// Icon button colors and sizes.\n$button-icon-color: unquote(\"rgb(#{$palette-grey-700})\") !default;\n$button-icon-focus-color: $button-focus-color !default;\n\n/* ==========  Icon Toggles  ========== */\n\n$icon-toggle-color: unquote(\"rgb(#{$palette-grey-700})\") !default;\n$icon-toggle-focus-color: $button-focus-color !default;\n$icon-toggle-checked-color: unquote(\"rgb(#{$color-primary})\") !default;\n$icon-toggle-checked-focus-color: unquote(\"rgba(#{$color-primary}, 0.26)\") !default;\n$icon-toggle-disabled-color: unquote(\"rgba(#{$color-black}, 0.26)\") !default;\n\n/* ==========  Radio Buttons  ========== */\n\n$radio-color: unquote(\"rgb(#{$color-primary})\") !default;\n$radio-off-color: unquote(\"rgba(#{$color-black}, 0.54)\") !default;\n$radio-disabled-color: unquote(\"rgba(#{$color-black}, 0.26)\") !default;\n\n/* ==========  Ripple effect  ========== */\n\n$ripple-bg-color: unquote(\"rgb(#{$color-light-contrast})\") !default;\n\n/* ==========  Layout  ========== */\n\n$layout-nav-color: unquote(\"rgb(#{$palette-grey-300})\") !default;\n\n// Drawer\n$layout-drawer-bg-color: unquote(\"rgb(#{$palette-grey-50})\") !default;\n$layout-drawer-border-color: unquote(\"rgb(#{$palette-grey-300})\") !default;\n$layout-text-color: unquote(\"rgb(#{$palette-grey-800})\") !default;\n$layout-drawer-navigation-color: #757575 !default;\n$layout-drawer-navigation-link-active-background: unquote(\"rgb(#{$palette-grey-300})\") !default;\n$layout-drawer-navigation-link-active-color: unquote(\"rgb(#{$color-light-contrast})\") !default;\n\n// Header\n$layout-header-bg-color: unquote(\"rgb(#{$color-primary})\") !default;\n$layout-header-text-color: unquote(\"rgb(#{$color-primary-contrast})\") !default;\n$layout-header-nav-hover-color: unquote(\"rgba(#{$palette-grey-700}, 0.6)\") !default;\n$layout-header-tab-text-color: unquote(\"rgba(#{$color-primary-contrast}, 0.6)\") !default;\n\n// Tabs\n$layout-header-tab-highlight: unquote(\"rgb(#{$color-accent})\") !default;\n\n/* ==========  Content Tabs  ========== */\n\n$tab-highlight-color: unquote(\"rgb(#{$color-primary})\") !default;\n$tab-text-color: unquote(\"rgba(#{$color-black}, 0.54)\") !default;\n$tab-active-text-color: unquote(\"rgba(#{$color-black}, 0.87)\") !default;\n$tab-border-color: unquote(\"rgb(#{$palette-grey-300})\") !default;\n\n/* ==========  Checkboxes  ========== */\n\n$checkbox-color: unquote(\"rgb(#{$color-primary})\") !default;\n$checkbox-off-color: unquote(\"rgba(#{$color-black}, 0.54)\") !default;\n$checkbox-disabled-color: unquote(\"rgba(#{$color-black}, 0.26)\") !default;\n$checkbox-focus-color: unquote(\"rgba(#{$color-primary}, 0.26)\") !default;\n$checkbox-image-path: $image_path;\n\n/* ==========  Switches  ========== */\n\n$switch-color: unquote(\"rgb(#{$color-primary})\") !default;\n$switch-faded-color: unquote(\"rgba(#{$color-primary}, 0.26)\") !default;\n$switch-thumb-color: $switch-color !default;\n$switch-track-color: unquote(\"rgba(#{$color-primary}, 0.5)\") !default;\n\n$switch-off-thumb-color: unquote(\"rgb(#{$palette-grey-50})\") !default;\n$switch-off-track-color: unquote(\"rgba(#{$color-black}, 0.26)\") !default;\n$switch-disabled-thumb-color: unquote(\"rgb(#{$palette-grey-400})\") !default;\n$switch-disabled-track-color: unquote(\"rgba(#{$color-black}, 0.12)\") !default;\n\n/* ==========  Spinner  ========== */\n\n$spinner-color-1: unquote(\"rgb(#{$palette-blue-400})\") !default;\n$spinner-color-2: unquote(\"rgb(#{$palette-red-500})\") !default;\n$spinner-color-3: unquote(\"rgb(#{$palette-yellow-600})\") !default;\n$spinner-color-4: unquote(\"rgb(#{$palette-green-500})\") !default;\n\n$spinner-single-color: unquote(\"rgb(#{$color-primary})\") !default;\n\n/* ==========  Text fields  ========== */\n\n$input-text-background-color: transparent !default;\n$input-text-label-color: unquote(\"rgba(#{$color-black}, 0.26)\") !default;\n$input-text-bottom-border-color: unquote(\"rgba(#{$color-black}, 0.12)\") !default;\n$input-text-highlight-color: unquote(\"rgb(#{$color-primary})\") !default;\n$input-text-disabled-color: $input-text-bottom-border-color !default;\n$input-text-disabled-text-color: $input-text-label-color !default;\n$input-text-error-color: unquote(\"rgb(#{$palette-red-A700})\") !default;\n\n/* ==========  Card  ========== */\n\n$card-background-color: unquote(\"rgb(#{$color-white})\") !default;\n$card-text-color: unquote(\"rgb(#{$color-black})\") !default;\n$card-image-placeholder-color: unquote(\"rgb(#{$color-accent})\") !default;\n$card-supporting-text-text-color: unquote(\"rgba(#{$color-black}, 0.54)\") !default;\n$card-border-color: rgba(0,0,0,0.1) !default;\n$card-subtitle-color: unquote(\"rgba(#{$color-black}, 0.54)\") !default;\n\n/* ==========  Sliders ========== */\n\n$range-bg-color: unquote(\"rgba(#{$color-black}, 0.26)\") !default;\n$range-color: unquote(\"rgb(#{$color-primary})\") !default;\n$range-faded-color: unquote(\"rgba(#{$color-primary}, 0.26)\") !default;\n$range-bg-focus-color: unquote(\"rgba(#{$color-black}, 0.12)\") !default;\n\n/* ========== Progress ========== */\n$progress-main-color: unquote(\"rgb(#{$color-primary})\") !default;\n$progress-secondary-color: unquote(\"rgba(#{$color-primary-contrast}, 0.7)\") !default;\n$progress-fallback-buffer-color: unquote(\"rgba(#{$color-primary-contrast}, 0.9)\") !default;\n$progress-image-path: $image_path;\n\n/* ==========  List ========== */\n\n$list-main-text-text-color: unquote(\"rgba(#{$color-black}, 0.87)\") !default;\n$list-supporting-text-text-color: unquote(\"rgba(#{$color-black}, 0.54)\") !default;\n$list-icon-color: unquote(\"rgb(#{$palette-grey-600})\") !default;\n$list-avatar-color: white !default;\n\n/* ==========  Item ========== */\n\n// Default Item Colors\n$default-item-text-color: unquote(\"rgba(#{$color-black}, 0.87)\") !default;\n$default-item-outline-color: unquote(\"rgb(#{$palette-grey-400})\") !default;\n$default-item-hover-bg-color: unquote(\"rgb(#{$palette-grey-200})\") !default;\n$default-item-focus-bg-color: unquote(\"rgb(#{$palette-grey-200})\") !default;\n$default-item-active-bg-color: unquote(\"rgb(#{$palette-grey-300})\") !default;\n$default-item-divider-color: unquote(\"rgba(#{$color-black}, 0.12)\") !default;\n\n// Disabled Button Colors\n$disabled-item-text-color: unquote(\"rgb(#{$palette-grey-400})\") !default;\n\n/* ==========  Dropdown menu ========== */\n\n$default-dropdown-bg-color: unquote(\"rgb(#{$color-white})\") !default;\n\n/* ==========  Tooltips  ========== */\n\n$tooltip-text-color: unquote(\"rgb(#{$color-white})\") !default;\n$tooltip-background-color: unquote(\"rgba(#{$palette-grey-700}, 0.9)\") !default;\n\n/* ==========  Footer  ========== */\n\n$footer-bg-color: unquote(\"rgb(#{$palette-grey-800})\") !default;\n$footer-color: unquote(\"rgb(#{$palette-grey-500})\") !default;\n$footer-heading-color: unquote(\"rgb(#{$palette-grey-300})\") !default;\n$footer-button-fill-color: $footer-color !default;\n$footer-underline-color: $footer-color !default;\n\n\n/* TEXTFIELD */\n\n$input-text-font-size: 16px !default;\n$input-text-width: 100% !default;\n$input-text-padding: 4px !default;\n$input-text-vertical-spacing: 20px !default;\n\n$input-text-button-size: 32px !default;\n$input-text-floating-label-fontsize: 12px !default;\n$input-text-expandable-icon-top: 16px !default;\n\n\n/* SWITCH */\n\n$switch-label-font-size: 16px !default;\n$switch-label-height: 24px !default;\n$switch-track-height: 14px !default;\n$switch-track-length: 36px !default;\n$switch-thumb-size: 20px !default;\n$switch-track-top: ($switch-label-height - $switch-track-height) / 2 !default;\n$switch-thumb-top: ($switch-label-height - $switch-thumb-size) / 2 !default;\n$switch-ripple-size: $switch-label-height * 2 !default;\n$switch-helper-size: 8px !default;\n\n/* SPINNER */\n\n$spinner-size: 28px !default;\n$spinner-stroke-width: 3px !default;\n\n// Amount of circle the arc takes up.\n$spinner-arc-size: 270deg !default;\n// Time it takes to expand and contract arc.\n$spinner-arc-time: 1333ms !default;\n// How much the start location of the arc should rotate each time.\n$spinner-arc-start-rot: 216deg !default;\n\n$spinner-duration: 360 * $spinner-arc-time / (\n    strip-units($spinner-arc-start-rot + (360deg - $spinner-arc-size)));\n\n\n/* RADIO */\n\n$radio-label-font-size: 16px !default;\n$radio-label-height: 24px !default;\n$radio-button-size: 16px !default;\n$radio-inner-margin: $radio-button-size / 4;\n$radio-padding: 8px !default;\n$radio-top-offset: ($radio-label-height - $radio-button-size) / 2;\n$radio-ripple-size: 42px !default;\n\n\n/* MENU */\n\n$menu-expand-duration: 0.3s !default;\n$menu-fade-duration: 0.2s !default;\n\n/* LIST */\n\n$list-border: 8px !default;\n$list-min-height: 48px !default;\n$list-min-padding: 16px !default;\n$list-bottom-padding: 20px !default;\n$list-avatar-text-left-distance: 72px !default;\n$list-icon-text-left-distance: 72px !default;\n\n$list-avatar-size: 40px !default;\n$list-icon-size: 24px !default;\n\n$list-two-line-height: 72px !default;\n$list-three-line-height: 88px !default;\n\n/* LAYOUT */\n\n$layout-drawer-narrow: 240px !default;\n$layout-drawer-wide: 456px !default;\n$layout-drawer-width: $layout-drawer-narrow !default;\n\n$layout-header-icon-size: 32px !default;\n$layout-screen-size-threshold: 1024px !default;\n$layout-header-icon-margin: 24px !default;\n$layout-drawer-button-mobile-size: 32px !default;\n$layout-drawer-button-desktop-size: 48px !default;\n\n$layout-header-mobile-row-height: 56px !default;\n$layout-mobile-header-height: $layout-header-mobile-row-height;\n$layout-header-desktop-row-height: 64px !default;\n$layout-desktop-header-height: $layout-header-desktop-row-height;\n\n$layout-header-desktop-baseline: 80px !default;\n$layout-header-mobile-baseline: 72px !default;\n$layout-header-mobile-indent: 16px !default;\n$layout-header-desktop-indent: 40px !default;\n\n$layout-tab-font-size: 14px !default;\n$layout-tab-bar-height: 48px !default;\n$layout-tab-mobile-padding: 12px !default;\n$layout-tab-desktop-padding: 24px !default;\n$layout-tab-highlight-thickness: 2px !default;\n\n\n/* ICON TOGGLE */\n\n$icon-toggle-size: 32px !default;\n$icon-toggle-font-size: 24px !default;\n$icon-toggle-ripple-size: 36px !default;\n\n/* FOOTER */\n\n/*mega-footer*/\n$footer-min-padding: 16px !default;\n$footer-padding-sides: 40px !default;\n$footer-heading-font-size: 14px !default;\n$footer-heading-line-height: (1.7 * $footer-heading-font-size) !default;\n$footer-btn-size: 36px  !default;\n\n/*mini-footer*/\n$padding: 16px !default;\n$footer-heading-font-size: 24px !default;\n$footer-heading-line-height: (1.5 * $footer-heading-font-size) !default;\n$footer-btn-size: 36px !default;\n\n/* CHECKBOX */\n\n$checkbox-label-font-size: 16px !default;\n$checkbox-label-height: 24px !default;\n$checkbox-button-size: 16px !default;\n$checkbox-inner-margin: 2px !default;\n$checkbox-padding: 8px !default;\n$checkbox-top-offset:\n($checkbox-label-height - $checkbox-button-size - $checkbox-inner-margin) / 2;\n$checkbox-ripple-size: $checkbox-label-height * 1.5;\n\n/* CARD */\n\n/* Card dimensions */\n$card-width: 330px !default;\n$card-height: 200px !default;\n$card-font-size: 16px !default;\n$card-title-font-size: 24px !default;\n$card-subtitle-font-size: 14px !default;\n$card-horizontal-padding: 16px !default;\n$card-vertical-padding: 16px !default;\n\n$card-title-perspective-origin-x: 165px !default;\n$card-title-perspective-origin-y: 56px !default;\n\n$card-title-transform-origin-x: 165px !default;\n$card-title-transform-origin-y: 56px !default;\n\n$card-title-text-transform-origin-x: 149px !default;\n$card-title-text-transform-origin-y: 48px !default;\n\n$card-supporting-text-font-size: 1rem !default;\n$card-supporting-text-line-height: 18px !default;\n\n$card-actions-font-size: 16px !default;\n\n$card-title-text-font-weight: 300 !default;\n$card-z-index: 1 !default;\n\n/* Cover image */\n$card-cover-image-height: 186px !default;\n$card-background-image-url: '' !default;\n\n\n/* BUTTON */\n/**\n *\n * Dimensions\n *\n */\n$button-min-width: 64px !default;\n$button-height: 36px !default;\n$button-padding: 16px !default;\n$button-margin: 4px !default;\n$button-border-radius: 2px !default;\n\n$button-fab-size: 56px !default;\n$button-fab-size-mini: 40px !default;\n$button-fab-font-size: 24px !default;\n\n$button-icon-size: 32px !default;\n$button-icon-size-mini: 24px !default;\n\n\n/* ANIMATION */\n$animation-curve-fast-out-slow-in: cubic-bezier(0.4, 0, 0.2, 1) !default;\n$animation-curve-linear-out-slow-in: cubic-bezier(0, 0, 0.2, 1) !default;\n$animation-curve-fast-out-linear-in: cubic-bezier(0.4, 0, 1, 1) !default;\n\n$animation-curve-default: $animation-curve-fast-out-slow-in !default;\n\n\n/* PROGRESS */\n$bar-height: 4px !default;\n\n/* BADGE */\n$badge-font-size: 12px !default;\n$badge-color: unquote(\"rgb(#{$color-accent-contrast})\") !default;\n$badge-color-inverse: unquote(\"rgb(#{$color-accent})\") !default;\n$badge-background: unquote(\"rgb(#{$color-accent})\") !default;\n$badge-background-inverse: unquote(\"rgba(#{$color-accent-contrast},0.2)\") !default;\n$badge-size : 22px !default;\n$badge-padding: 2px !default;\n$badge-overlap: 12px !default;\n\n/* SHADOWS */\n\n$shadow-key-umbra-opacity: 0.2 !default;\n$shadow-key-penumbra-opacity: 0.14 !default;\n$shadow-ambient-shadow-opacity: 0.12 !default;\n\n/* GRID */\n\n$grid-desktop-columns: 12 !default;\n$grid-desktop-gutter: 16px !default;\n$grid-desktop-margin: 16px !default;\n\n$grid-desktop-breakpoint: 840px !default;\n\n$grid-tablet-columns: 8 !default;\n$grid-tablet-gutter: $grid-desktop-gutter !default;\n$grid-tablet-margin: $grid-desktop-margin !default;\n\n$grid-tablet-breakpoint: 480px !default;\n\n$grid-phone-columns: 4 !default;\n$grid-phone-gutter: $grid-desktop-gutter !default;\n$grid-phone-margin: $grid-desktop-margin !default;\n\n$grid-cell-default-columns: $grid-phone-columns !default;\n$grid-max-columns: $grid-desktop-columns !default;\n\n/* DATA TABLE */\n\n$data-table-font-size: 13px !default;\n$data-table-header-font-size: 12px !default;\n$data-table-header-sort-icon-size: 16px !default;\n\n$data-table-header-color: rgba(#000, 0.54) !default;\n$data-table-header-sorted-color: rgba(#000, 0.87) !default;\n$data-table-header-sorted-icon-hover-color: rgba(#000, 0.26) !default;\n$data-table-divider-color: rgba(#000, 0.12) !default;\n\n$data-table-hover-color: #eeeeee !default;\n$data-table-selection-color: #e0e0e0 !default;\n\n$data-table-dividers: 1px solid $data-table-divider-color !default;\n\n$data-table-row-height: 48px !default;\n$data-table-last-row-height: 56px !default;\n$data-table-header-height: 56px !default;\n\n$data-table-column-spacing: 36px !default;\n$data-table-column-padding: $data-table-column-spacing / 2;\n\n$data-table-card-header-height: 64px !default;\n$data-table-card-title-top: 20px !default;\n$data-table-card-padding: 24px !default;\n$data-table-button-padding-right: 16px !default;\n$data-table-cell-top: $data-table-card-padding / 2;\n\n/* DIALOG */\n$dialog-content-color: $card-supporting-text-text-color;\n\n/* SNACKBAR */\n\n// Hard coded since the color is not present in any palette.\n$snackbar-background-color: #323232 !default;\n$snackbar-tablet-breakpoint: $grid-tablet-breakpoint;\n$snackbar-action-color: unquote(\"rgb(#{$color-accent})\") !default;\n\n/* TOOLTIP */\n$tooltip-font-size: 10px !default;\n$tooltip-font-size-large: 14px !default;\n\n/* CHIP */\n$chip-bg-color: rgb(222, 222, 222) !default;\n$chip-bg-active-color: rgb(214, 214, 214) !default;\n$chip-height: 32px !default;\n$chip-font-size: 13px !default; \n","/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n@import \"../variables\";\n\n.mdl-mini-footer {\n  display: flex;\n  flex-flow: row wrap;\n  justify-content: space-between;\n\n  padding: ($padding * 2) $padding;\n\n  color: $footer-color;\n  background-color: $footer-bg-color;\n\n  &:after {\n    content: '';\n    display: block;\n  }\n\n  & .mdl-logo {\n    line-height: $footer-btn-size;\n  }\n}\n\n.mdl-mini-footer--link-list,\n.mdl-mini-footer__link-list {\n  display: flex;\n  flex-flow: row nowrap;\n\n  list-style: none;\n\n  margin: 0;\n  padding: 0;\n\n  & li {\n    margin-bottom: 0;\n    margin-right: $padding;\n\n    @media screen and (min-width: 760px) {\n      line-height: $footer-btn-size;\n    }\n  }\n\n  & a {\n    color: inherit;\n    text-decoration: none;\n    white-space: nowrap;\n  }\n}\n\n.mdl-mini-footer--left-section,\n.mdl-mini-footer__left-section {\n  display: inline-block;\n  order: 0;\n}\n\n.mdl-mini-footer--right-section,\n.mdl-mini-footer__right-section {\n  display: inline-block;\n  order: 1;\n}\n\n.mdl-mini-footer--social-btn,\n.mdl-mini-footer__social-btn {\n  width: $footer-btn-size;\n  height: $footer-btn-size;\n\n  padding: 0;\n  margin: 0;\n\n  background-color: $footer-button-fill-color;\n\n  border: none;\n}\n","/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n@import \"../variables\";\n\n.mdl-card {\n  display: flex;\n  flex-direction: column;\n  font-size: $card-font-size;\n  font-weight: 400;\n  min-height: $card-height;\n  overflow: hidden;\n  width: $card-width;\n  z-index: $card-z-index;\n  position: relative;\n  background: $card-background-color;\n  border-radius: 2px;\n  box-sizing: border-box;\n}\n\n.mdl-card__media {\n  background-color: $card-image-placeholder-color;\n  background-repeat: repeat;\n  background-position: 50% 50%;\n  background-size: cover;\n  background-origin: padding-box;\n  background-attachment: scroll;\n  box-sizing: border-box;\n}\n\n.mdl-card__title {\n  align-items: center;\n  color: $card-text-color;\n  display: block;\n  display: flex;\n  justify-content: stretch;\n  line-height: normal;\n  padding: $card-vertical-padding $card-horizontal-padding;\n  perspective-origin: $card-title-perspective-origin-x $card-title-perspective-origin-y;\n  transform-origin: $card-title-transform-origin-x $card-title-transform-origin-y;\n  box-sizing: border-box;\n\n  &.mdl-card--border {\n    border-bottom: 1px solid $card-border-color;\n  }\n}\n\n.mdl-card__title-text {\n  align-self: flex-end;\n  color: inherit;\n  display: block;\n  display: flex;\n  font-size: $card-title-font-size;\n  font-weight: $card-title-text-font-weight;\n  line-height: normal;\n  overflow: hidden;\n  transform-origin: $card-title-text-transform-origin-x $card-title-text-transform-origin-y;\n  margin: 0;\n}\n\n.mdl-card__subtitle-text {\n  font-size: $card-subtitle-font-size;\n  color: $card-subtitle-color;\n  margin: 0;\n}\n\n.mdl-card__supporting-text {\n  color: $card-supporting-text-text-color;\n  font-size: $card-supporting-text-font-size;\n  line-height: $card-supporting-text-line-height;\n  overflow: hidden;\n  padding: $card-vertical-padding $card-horizontal-padding;\n  width: 90%;\n\n  &.mdl-card--border {\n    border-bottom: 1px solid $card-border-color;\n  }\n}\n\n.mdl-card__actions {\n  font-size: $card-actions-font-size;\n  line-height: normal;\n  width: 100%;\n  background-color: rgba(0,0,0,0);\n  padding: 8px;\n  box-sizing: border-box;\n\n  &.mdl-card--border {\n    border-top: 1px solid $card-border-color;\n  }\n}\n\n.mdl-card--expand {\n  flex-grow: 1;\n}\n\n\n.mdl-card__menu {\n  position: absolute;\n  right: 16px;\n  top: 16px;\n}\n","/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n@import \"../variables\";\n@import \"../mixins\";\n\n// The button component. Defaults to a flat button.\n.mdl-button {\n  background: transparent;\n  border: none;\n  border-radius: $button-border-radius;\n  color: $button-secondary-color;\n  position: relative;\n  height: $button-height;\n  margin: 0;\n  min-width: $button-min-width;\n  padding: 0 $button-padding;\n  display: inline-block;\n  @include typo-button();\n  overflow: hidden;\n  will-change: box-shadow;\n  transition: box-shadow 0.2s $animation-curve-fast-out-linear-in,\n              background-color 0.2s $animation-curve-default,\n              color 0.2s $animation-curve-default;\n  outline: none;\n  cursor: pointer;\n  text-decoration: none;\n  text-align: center;\n  line-height: $button-height;\n  vertical-align: middle;\n\n  &::-moz-focus-inner {\n    border: 0;\n  }\n\n  &:hover {\n    background-color: $button-hover-color;\n  }\n\n  &:focus:not(:active) {\n    background-color: $button-focus-color;\n  }\n\n  &:active {\n    background-color: $button-active-color;\n  }\n\n  &.mdl-button--colored {\n    color: $button-primary-color-alt;\n\n    &:focus:not(:active) {\n      background-color: $button-focus-color-alt;\n    }\n  }\n}\n\ninput.mdl-button[type=\"submit\"] {\n  -webkit-appearance:none;\n}\n\n  // Raised buttons\n  .mdl-button--raised {\n    background: $button-primary-color;\n    @include shadow-2dp();\n\n    &:active {\n      @include shadow-4dp();\n      background-color: $button-active-color;\n    }\n\n    &:focus:not(:active) {\n      @include focus-shadow();\n      background-color: $button-active-color;\n    }\n\n    &.mdl-button--colored {\n      background: $button-primary-color-alt;\n      color: $button-secondary-color-alt;\n\n      &:hover {\n        background-color: $button-hover-color-alt;\n      }\n\n      &:active {\n        background-color: $button-active-color-alt;\n      }\n\n      &:focus:not(:active) {\n        background-color: $button-active-color-alt;\n      }\n\n      & .mdl-ripple {\n        background: $button-ripple-color-alt;\n      }\n    }\n  }\n\n\n  // FABs\n  .mdl-button--fab {\n    border-radius: 50%;\n    font-size: $button-fab-font-size;\n    height: $button-fab-size;\n    margin: auto;\n    min-width: $button-fab-size;\n    width: $button-fab-size;\n    padding: 0;\n    overflow: hidden;\n    background: $button-primary-color;\n    box-shadow: 0 1px 1.5px 0 rgba(0,0,0,0.12), 0 1px 1px 0 rgba(0,0,0,0.24);\n    position: relative;\n    line-height: normal;\n\n    & .material-icons {\n      position: absolute;\n      top: 50%;\n      left: 50%;\n      transform: translate(- $button-fab-font-size / 2, - $button-fab-font-size / 2);\n      line-height: $button-fab-font-size;\n      width: $button-fab-font-size;\n    }\n\n    &.mdl-button--mini-fab {\n      height: $button-fab-size-mini;\n      min-width: $button-fab-size-mini;\n      width: $button-fab-size-mini;\n    }\n\n    & .mdl-button__ripple-container {\n      border-radius: 50%;\n      // Fixes clipping bug in Safari.\n      -webkit-mask-image: -webkit-radial-gradient(circle, white, black);\n    }\n\n    &:active {\n      @include shadow-4dp();\n      background-color: $button-active-color;\n    }\n\n    &:focus:not(:active) {\n      @include focus-shadow();\n      background-color: $button-active-color;\n    }\n\n    &.mdl-button--colored {\n      background: $button-fab-color-alt;\n      color: $button-fab-text-color-alt;\n\n      &:hover {\n        background-color: $button-fab-hover-color-alt;\n      }\n\n      &:focus:not(:active) {\n        background-color: $button-fab-active-color-alt;\n      }\n\n      &:active {\n        background-color: $button-fab-active-color-alt;\n      }\n\n      & .mdl-ripple {\n        background: $button-fab-ripple-color-alt;\n      }\n    }\n  }\n\n\n  // Icon buttons\n  .mdl-button--icon {\n    border-radius: 50%;\n    font-size: $button-fab-font-size;\n    height: $button-icon-size;\n    margin-left: 0;\n    margin-right: 0;\n    min-width: $button-icon-size;\n    width: $button-icon-size;\n    padding: 0;\n    overflow: hidden;\n    color: inherit;\n    line-height: normal;\n\n    & .material-icons {\n      position: absolute;\n      top: 50%;\n      left: 50%;\n      transform: translate(- $button-fab-font-size / 2, - $button-fab-font-size / 2);\n      line-height: $button-fab-font-size;\n      width: $button-fab-font-size;\n    }\n\n    &.mdl-button--mini-icon {\n      height: $button-icon-size-mini;\n      min-width: $button-icon-size-mini;\n      width: $button-icon-size-mini;\n\n      & .material-icons {\n        top: ($button-icon-size-mini - $button-fab-font-size) / 2;\n        left: ($button-icon-size-mini - $button-fab-font-size) / 2;\n      }\n    }\n\n    & .mdl-button__ripple-container {\n      border-radius: 50%;\n      // Fixes clipping bug in Safari.\n      -webkit-mask-image: -webkit-radial-gradient(circle, white, black);\n    }\n  }\n\n\n  // Ripples\n  .mdl-button__ripple-container {\n    display: block;\n    height: 100%;\n    left: 0px;\n    position: absolute;\n    top: 0px;\n    width: 100%;\n    z-index: 0;\n    overflow: hidden;\n\n    .mdl-button[disabled] & .mdl-ripple,\n    .mdl-button.mdl-button--disabled & .mdl-ripple {\n      background-color: transparent;\n    }\n  }\n\n// Colorized buttons\n\n.mdl-button--primary.mdl-button--primary {\n  color: $button-primary-color-alt;\n  & .mdl-ripple {\n    background: $button-secondary-color-alt;\n  }\n  &.mdl-button--raised, &.mdl-button--fab {\n    color: $button-secondary-color-alt;\n    background-color: $button-primary-color-alt;\n  }\n}\n\n.mdl-button--accent.mdl-button--accent {\n  color: $button-fab-color-alt;\n  & .mdl-ripple {\n    background: $button-fab-text-color-alt;\n  }\n  &.mdl-button--raised, &.mdl-button--fab {\n    color: $button-fab-text-color-alt;\n    background-color: $button-fab-color-alt;\n  }\n}\n\n// Disabled buttons\n\n.mdl-button {\n  // Bump up specificity by using [disabled] twice.\n  &[disabled][disabled],\n  &.mdl-button--disabled.mdl-button--disabled {\n    color: $button-secondary-color-disabled;\n    cursor: default;\n    background-color: transparent;\n  }\n\n  &--fab {\n    // Bump up specificity by using [disabled] twice.\n    &[disabled][disabled],\n    &.mdl-button--disabled.mdl-button--disabled {\n      background-color: $button-primary-color-disabled;\n      color: $button-secondary-color-disabled;\n    }\n  }\n\n  &--raised {\n    // Bump up specificity by using [disabled] twice.\n    &[disabled][disabled],\n    &.mdl-button--disabled.mdl-button--disabled {\n      background-color: $button-primary-color-disabled;\n      color: $button-secondary-color-disabled;\n      box-shadow: none;\n    }\n  }\n  &--colored {\n    // Bump up specificity by using [disabled] twice.\n    &[disabled][disabled],\n    &.mdl-button--disabled.mdl-button--disabled {\n      color: $button-secondary-color-disabled;\n    }\n  }\n}\n\n// Align icons inside buttons with text\n.mdl-button .material-icons {\n  vertical-align: middle;\n}\n","// SIMPLE GRID - SASS/SCSS\n\n\n// fonts\n$font-weight-light: 300;\n$font-weight-regular: 400;\n$font-weight-heavy: 700;\n\n// colors\n$dark-grey: #333447;\n$dark-gray: #333447; // for the Americans\n\n\n.font-light {\n  font-weight: $font-weight-light;\n}\n\n.font-regular {\n  font-weight: $font-weight-regular;\n}\n\n.font-heavy {\n  font-weight: $font-weight-heavy;\n}\n\n// utility\n\n.left {\n  text-align: left;\n}\n\n.right {\n  text-align: right;\n}\n\n.center {\n  text-align: center;\n  margin-left: auto;\n  margin-right: auto;\n}\n\n.justify {\n  text-align: justify;\n}\n\n.hidden-sm {\n  display: none;\n}\n\n// grid\n\n$width: 98%;\n$gutter: 2%;\n$breakpoint-small: 33.75em; // 540px\n$breakpoint-med: 45em; // 720px\n$breakpoint-large: 60em; // 960px\n\n.container {\n  width: 100%;\n  margin-left: auto;\n  margin-right: auto;\n\n  @media only screen and (min-width: $breakpoint-small) {\n    width: 80%;\n  }\n\n  @media only screen and (min-width: $breakpoint-large) {\n    width: 75%;\n    max-width: 60rem;\n  }\n}\n\n.row {\n  position: relative;\n  width: 100%;\n}\n\n.row [class^=\"col\"] {\n  float: left;\n  margin: 0.5rem 1%;\n  min-height: 0.125rem;\n}\n\n.row::after {\n  content: \"\";\n  display: table;\n  clear: both;\n}\n\n.col-1,\n.col-2,\n.col-3,\n.col-4,\n.col-5,\n.col-6,\n.col-7,\n.col-8,\n.col-9,\n.col-10,\n.col-11,\n.col-12 {\n  width: $width;\n}\n\n.col-1-sm {\n  width: ($width / 12) - ($gutter * 11 / 12);\n}\n\n.col-2-sm {\n  width: ($width / 6) - ($gutter * 10 / 12);\n}\n\n.col-3-sm {\n  width: ($width / 4) - ($gutter * 9 / 12);\n}\n\n.col-4-sm {\n  width: ($width / 3) - ($gutter * 8 / 12);\n}\n\n.col-5-sm {\n  width: ($width / (12 / 5)) - ($gutter * 7 / 12);\n}\n\n.col-6-sm {\n  width: ($width / 2) - ($gutter * 6 / 12);\n}\n\n.col-7-sm {\n  width: ($width / (12 / 7)) - ($gutter * 5 / 12);\n}\n\n.col-8-sm {\n  width: ($width / (12 / 8)) - ($gutter * 4 / 12);\n}\n\n.col-9-sm {\n  width: ($width / (12 / 9)) - ($gutter * 3 / 12);\n}\n\n.col-10-sm {\n  width: ($width / (12 / 10)) - ($gutter * 2 / 12);\n}\n\n.col-11-sm {\n  width: ($width / (12 / 11)) - ($gutter * 1 / 12);\n}\n\n.col-12-sm {\n  width: $width;\n}\n\n@media only screen and (min-width: $breakpoint-med) {\n  .col-1 {\n    width: ($width / 12) - ($gutter * 11 / 12);\n  }\n  .col-2 {\n    width: ($width / 6) - ($gutter * 10 / 12);\n  }\n  .col-3 {\n    width: ($width / 4) - ($gutter * 9 / 12);\n  }\n  .col-4 {\n    width: ($width / 3) - ($gutter * 8 / 12);\n  }\n  .col-5 {\n    width: ($width / (12 / 5)) - ($gutter * 7 / 12);\n  }\n  .col-6 {\n    width: ($width / 2) - ($gutter * 6 / 12);\n  }\n  .col-7 {\n    width: ($width / (12 / 7)) - ($gutter * 5 / 12);\n  }\n  .col-8 {\n    width: ($width / (12 / 8)) - ($gutter * 4 / 12);\n  }\n  .col-9 {\n    width: ($width / (12 / 9)) - ($gutter * 3 / 12);\n  }\n  .col-10 {\n    width: ($width / (12 / 10)) - ($gutter * 2 / 12);\n  }\n  .col-11 {\n    width: ($width / (12 / 11)) - ($gutter * 1 / 12);\n  }\n  .col-12 {\n    width: $width;\n  }\n\n  .hidden-sm {\n    display: block;\n  }\n}\n\n.row {\n  display: -webkit-box;\n  display: -webkit-flex;\n  display: -ms-flexbox;\n  display: flex;\n  flex-wrap: wrap;\n}\n\n.row > [class*='col-'] {\n  display: flex;\n  flex-direction: column;\n}\n","\n/*\nMaterial Icons\n*/\n\n.material-icons {\n    font-family: 'Material Icons';\n    font-weight: normal;\n    font-style: normal;\n    font-size: 24px;  /* Preferred icon size */\n    display: inline-block;\n    line-height: 1;\n    text-transform: none;\n    letter-spacing: normal;\n    word-wrap: normal;\n    white-space: nowrap;\n    direction: ltr;\n  \n    /* Support for all WebKit browsers. */\n    -webkit-font-smoothing: antialiased;\n    /* Support for Safari and Chrome. */\n    text-rendering: optimizeLegibility;\n  \n    /* Support for Firefox. */\n    -moz-osx-font-smoothing: grayscale;\n  \n    /* Support for IE. */\n    font-feature-settings: 'liga';\n  }","html {\n    font-size: $font_size;\n}\n\nbody {\n    display: block !important;\n    background-color: $background_color;\n    font-size: 1rem;\n    line-height: 1.5rem;\n    font-family: $body_font_family;\n}\n\n.mdl-layout__content:focus {\n    outline: none;\n }\n\nh1, h2, h3, h4, h5, h6, blockquote, span.mdl-layout-title,\na.download > code.download {\n    font-family: $body_font_family;\n}\n\nh1, h2, h3, h4, h5, h6, .toc-backref, .contents, .toctree-wrapper, .contents a, .toctree-wrapper a, .globaltoc a.current {\n    color: $color-mxnet !important;\n}\n\na {\n    text-decoration: none;\n}\n\n.page-content {\n    font-size: 1rem;\n    p, ul, ol, dl, dd, dt, table, th, td {\n        font-size: 1rem;\n    }\n}\n\n.brand {\n    color: inherit;\n    text-decoration: none;\n}\n\n.section {\n    overflow-x: auto;\n}\n\n\n/*\n *  Figure Directive Styles\n */\n img {\n    max-width: 100%;\n    display: block;\n    margin-left: auto;\n    margin-right: auto;\n }\n\ndiv.figure {\n    p.caption {\n        text-align: center;\n        margin-top: .75rem;\n\n        span.caption-number {\n            font-style: normal;\n        }\n        .caption-number::after {\n            content: \"\\00a0\";\n        }\n    }\n}\n\n.svg-icon {\n  width: 16px;\n  height: 16px;\n  display: inline-block;\n  fill: $grey-color-light;\n  padding-right: 5px;\n  padding-top: 4px;\n  vertical-align: text-top;\n}\n\n/*\n * Download Link Styles\n */\na.download > i.material-icons {\n    position: relative;\n    top: 5px;\n}\n\na.download {\n    text-decoration: none;\n}\n\n%clearfix:after {\n  content: \"\";\n  display: table;\n  clear: both;\n}\n\n.wrapper {\n  max-width: -webkit-calc(#{$content-width} - (#{$spacing-unit} * 2));\n  max-width: calc(#{$content-width} - (#{$spacing-unit} * 2));\n  margin-right: auto;\n  margin-left: auto;\n  padding-right: calc(#{$spacing-unit}+15px);\n  padding-left: $spacing-unit;\n  @extend %clearfix;\n\n  @media screen and (max-width: $on-laptop) {\n    max-width: -webkit-calc(#{$content-width} - (#{$spacing-unit}));\n    max-width: calc(#{$content-width} - (#{$spacing-unit}));\n    padding-right: $spacing-unit / 2;\n    padding-left: $spacing-unit / 2;\n  }\n}\n\n","/*\nVariables\n*/\n$font_size: 16px;\n\n$background_color: #fafafa;\n$code_background: rgba(0,0,0,.05);\n\n$code_font_family: \"Menlo\", \"DejaVu Sans Mono\", \"Liberation Mono\", \"Consolas\", \"Ubuntu Mono\", \"Courier New\", \"andale mono\", \"lucida console\", monospace !default;\n$body_font_family: -apple-system,BlinkMacSystemFont,\"Segoe UI\",Roboto,\"Helvetica Neue\",Arial,sans-serif,\"Apple Color Emoji\",\"Segoe UI Emoji\",\"Segoe UI Symbol\" !default;\n$base-font-size:   17px !default;\n\n$xl-breakpoint: 1795px;\n$lg-breakpoint: 1200px;\n$md-breakpoint: 992px;\n$sm-breakpoint: 768px;\n$xs-breakpoint: 576px;\n\n$color-primary: $palette-blue-500;\n$color-primary-dark: $palette-blue-700 !default;\n$color-accent: $palette-deep-orange-A200 !default;\n$color-primary-contrast: $color-white !default;\n$color-accent-contrast: $color-white !default;\n\n\n$base-line-height: 1.5 !default;\n$spacing-unit:     30px !default;\n\n$color-mxnet: rgb(4,140,204);\n$color-mxnet-dark: rgb(4,60,110);\n$grey-color:       #828282 !default;\n$grey-color-light: lighten($grey-color, 45%) !default;\n$grey-color-dark:  darken($grey-color, 25%) !default;\n\n$table-text-align: left !default;\n\n// Width of the content area\n$content-width:    1150px !default;\n\n$on-palm:          600px !default;\n$on-palm:          900px !default;\n$on-laptop:        1024px !default;","/**\n * Layout Styles\n */\n $layout: (\n    document: (\n        xl: (\n            width: 1400px,\n        )\n    ),\n    drawer-container: (\n        width: $layout-drawer-width,\n    ),\n    side-doc-outline: (\n        width: 230px,\n    ),\n    page-content: (\n        md: (\n            width: 90%,\n            padding: 0 5%\n        ),\n        lg: (\n            width: calc( 90% - 230px ),\n            padding: 0 5%\n        )\n    )\n);\n\n.mdl-layout {\n    margin-top: 76px;\n}\n\n\n.document {\n    width: 100%;\n    margin: 0 auto;\n    display: flex;\n\n    @media (min-width: $xl-breakpoint) {\n        width: map-get(map-get(map-get($layout, document), xl), width);\n    }\n    .page-content {\n        width: 100%;\n        margin: 0 auto;\n        padding: 0 12px;\n\n        @media (min-width: $md-breakpoint) {\n            width: map-get(map-get(map-get($layout, page-content), md), width);\n            padding: map-get(map-get(map-get($layout, page-content), md), padding);\n        }\n\n        @media (min-width: $lg-breakpoint) {\n            width: map-get(map-get(map-get($layout, page-content), lg), width);\n            padding: map-get(map-get(map-get($layout, page-content), lg), padding);\n        }\n    }\n\n    .side-doc-outline {\n        width: map-get(map-get($layout, side-doc-outline), width);\n\n        @media (max-width: $lg-breakpoint - 1) {\n            display: none;\n        } \n        &--content {\n            position: fixed;\n            overflow-x: auto;\n            overflow-y: auto;\n            width: inherit;\n            &::-webkit-scrollbar {\n                width: 6px;\n            }\n    \n            &::-webkit-scrollbar-track {\n                border-radius: 6px;\n            }\n    \n            &::-webkit-scrollbar-thumb {\n                background-color: rgba(0, 0, 0, .3);\n                border-radius: 6px;\n                box-shadow:0 0 0 1px rgba(255, 255, 255, .3);\n            }\n        }\n    }\n\n}","@keyframes float-in {\n    0% {\n        transform: translateY(0.5rem);\n        opacity: 0;\n    }\n\t100% {\n\t\ttransform: translateY(0);\n\t\topacity: 1;\n\t}\n}\n\n@keyframes float-out {\n    0% {\n        transform: translateY(0);\n        opacity: 1;\n    }\n\t100% {\n\t\ttransform: translateY(0.5rem);\n\t\topacity: 0;\n\t}\n}\n\n.page-content {\n    .headerlink {\n        display: inline-block;\n        text-decoration: none;\n        margin-left: 0.8rem;\n        color: inherit;\n        opacity: 0;\n        &:hover {\n            animation: float-in 0.2s $animation-curve-fast-out-slow-in 0s forwards;\n        }\n    }\n\n    h1, h2, h3, h4, h5, h6 {\n        .toc-backref {\n            text-decoration: none;\n        }\n        &:hover {\n            .headerlink {\n                animation: float-in 0.2s $animation-curve-fast-out-slow-in 0s forwards;\n            }\n        }\n    }\n\n    h1 {\n        font-size: 2rem;\n        line-height: 2.25rem;\n    }\n\n    h2 {\n        font-size: 1.75rem;\n        line-height: 2rem;\n        padding-top: 1.5rem;\n        margin-top: 0;\n        margin-bottom: 1rem;\n    }\n\n    h3 {\n        font-size: 1.5rem;\n        line-height: 1.75rem;\n        padding-top: 1rem;\n        margin-top: 0px;\n        margin-bottom: .75rem;\n    }\n\n    h4 {\n        font-size: 1.25rem;\n        line-height: 1.5rem;\n        padding-top: .75rem;\n        margin-top: 0px;\n        margin-bottom: .5rem;\n    }\n\n    div.page-content h5 {\n        font-size: 1.1rem;\n        line-height: 1.5rem;\n        padding-top: 2rem;\n        margin-top: 0px;\n        margin-bottom: 1rem;\n    }\n\n    div.page-content h6 {\n        font-size: 1rem;\n        line-height: 1.5rem;\n        padding-top: 2rem;\n        margin-top: 0px;\n        margin-bottom: 1rem;\n    }\n\n\n}\n","\n/*\n * Admonition Styles\n */\n $admonitions: (\n    hint: (\n        font-color: rgb(0, 188, 212),\n        background-color: rgba(0, 188, 212, 0.1),\n        icon-content: \"help_outline\"\n    ),\n    note: (\n        font-color: rgb(0, 188, 212),\n        background-color: rgba(0, 188, 212, 0.1),\n        icon-content: \"info_outline\"\n    ),\n    seealso: (\n        font-color: rgb(0, 188, 212),\n        background-color: rgba(0, 188, 212, 0.1),\n        icon-content: \"search\"\n    ),\n    warning: (\n        font-color: rgb(255, 193, 7),\n        background-color: rgba(255, 193, 7, 0.1),\n        icon-content: \"warning\"\n    ),\n    attention: (\n        font-color: rgb(255, 193, 7),\n        background-color: rgba(255, 193, 7, 0.1),\n        icon-content: \"warning\"\n    ),\n    tip: (\n        font-color: rgb(139, 195, 74),\n        background-color: rgba(139, 195, 74, 0.1),\n        icon-content: \"lightbulb_outline\"\n    ),\n    important: (\n        font-color: rgb(139, 195, 74),\n        background-color: rgba(139, 195, 74, 0.1),\n        icon-content:  \"check_circle\"\n    ),\n    error: (\n        font-color: rgb(244, 67, 54),\n        background-color: rgba(244, 67, 54, 0.1),\n        icon-content: \"error_outline\"\n    ),\n    caution: (\n        font-color: rgb(244, 67, 54),\n        background-color: rgba(244, 67, 54, 0.1),\n        icon-content: \"error_outline\"\n    ),\n    danger: (\n        font-color: rgb(244, 67, 54),\n        background-color: rgba(244, 67, 54, 0.1),\n        icon-content: \"error_outline\"\n    )\n);\n\n @mixin admonition-style($type) {\n    border-left: solid 4px map-get(map-get($admonitions, $type), font-color);\n    background-color: map-get(map-get($admonitions, $type), background-color);\n    .admonition-title {\n        font-size: 16px;\n        font-weight: bold;\n        color: map-get(map-get($admonitions, $type), font-color);\n\n        margin-top: 4px;\n        margin-bottom: 8px;\n        &::before {\n            @extend .material-icons;\n            position: relative;\n            margin-right: 5px;\n            top: 3px;\n            content: map-get(map-get($admonitions, $type), icon-content);\n            font-size: 18px;\n        }\n    }\n}\n\n.admonition {\n    @extend .mdl-shadow--2dp;\n\n    padding: 12px 20px;\n    margin-top: 10px;\n    margin-bottom: 10px;\n    p.last {\n        margin: 16px;\n    }\n    .admonition-title {\n        font-size: 16px;\n        font-weight: bold;\n        color: #555;\n        text-transform: uppercase;\n        margin-top: 7px;\n    }\n\n    @each $type in (note, seealso, hint, warning, attention, tip, important, error, caution, danger) {\n        &.#{$type} {\n            @include admonition-style($type);\n        }\n    }\n}\n",".page-content {\n    .highlight {\n        margin: 1px 0;\n        pre {\n            background: $code_background;\n            color: rgba(0,0,0,.87);\n            font-family: $code_font_family;\n            padding: 0.75rem;\n            overflow: auto;\n            overflow-y: hidden;\n            .o, .nd {\n                color: rgba(0,0,0,.87);\n            }\n        }\n    }\n\n    div.highlight-console div.highlight {\n        background: none;\n    }\n\n    // for jupyter notebook output cell\n    .output {\n        .highlight {\n            pre {\n                color: rgba(0,0,0,.87);\n                background: $background_color;\n                border-width: 1px;\n                border-color: #999;\n                border-style: solid;\n                padding: 0.75rem;\n            }\n        }\n     }\n\n    .code, code:not(.download) {\n        margin: 0 0;\n        font-family: $code_font_family;\n        border-radius: 2px;\n        span.pre {\n            font-family: $code_font_family;\n        }\n    }\n\n    .viewcode-link {\n        padding-left: 2em;\n        font-size: 80%;\n    }\n\n    .rubric, .method > dt, .function > dt, .class > dt {\n        display: table;\n        margin: 10px 0;\n        font-size: 100%;\n        line-height: normal;\n        background: #e7f2fa;\n        color: #2B98F0;\n        border-top: solid 3px #55ADF3;\n        padding: 10px;\n        position: relative;\n        .descname, .descclassname {\n            color: rgba(0,0,0,.87);\n            background: #e7f2fa;\n            padding: 3px;\n        }\n        em {\n            padding: 0 2px;\n        }\n    }\n\n\n    .rubric {\n        margin: 30px 0 10px 0;\n     }\n\n\n    .field-body {\n        padding-left: 40px;\n        ul {\n            padding: 0 0 0 16px;\n            margin: 0;\n        }\n    }\n\n     // .docutils > dt {\n    //     padding: 6px;\n    //     display: table;\n    //     margin-bottom: 6px;\n    //     border: none;\n    //     border-left: solid 3px #ccc;\n    //     background: #f0f0f0;\n    //     color: #555;\n    // }\n\n    .seealso .docutils > dt {\n       float: left;\n       clear: left;\n       padding: 0 6px;\n     }\n\n    .seealso .docutils > dd {\n       padding-left: 6em;\n    }\n    .nblast {\n        padding-bottom: 1em;\n    }\n\n    pre {\n        font-size: 90%;\n        background: #eee;\n        color: #455A64;\n        padding: 16px 32px;\n        width: auto;\n        border-radius: 4px;\n        word-wrap: break-word;\n\n        &:hover {\n            @extend .mdl-shadow--2dp;\n\n            &:before {\n                font-family: $body_font_family;\n                padding: 0 0.5rem;\n                content: attr(click-to-copy);\n                color: rgba(0, 0, 0, 0.5);\n                border-radius: 4px;\n                position: relative;\n                float: right;\n                top: -0.5rem;\n                right: -0.5rem;\n                background: rgb(200, 200, 200);\n                font-size: 0.8rem;\n                cursor: pointer;\n            }\n        }\n    }\n}\n","/*\n * Quotation Block Styles\n */\n .page-content {\n    blockquote {\n        font-size: 1rem;\n        padding: 0 1rem;\n        border-left: 3px solid $code_background;\n\n        &:after {\n            content: \"\" !important;\n            margin-left: 0;\n        }\n        &:before {\n            content: \"\" !important;\n        }\n    }\n }\n",".page-content {\n    table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) {\n        @extend .mdl-data-table;\n        @extend .mdl-shadow--2dp;\n\n        margin: 1.5rem 0;\n        table-layout: fixed;\n        max-width: 100%;\n        min-width: 70%;\n\n        th, td {\n            @extend .mdl-data-table__cell--non-numeric;\n            white-space: normal;\n            overflow-wrap: break-word;\n        }\n\n        caption {\n            font-size: $font_size;\n            margin: 1rem 0 0.8rem 0;\n            white-space: normal;\n            .caption-number {\n                font-style: normal;\n            }\n            .caption-number::after {\n                content: \"\\00a0\";\n            }\n        }\n\n    }\n}\n",".globaltoc {\n    \n    .caption, .toc {\n        display: none;\n    }\n\n    ul {\n\n        list-style-type: none;\n        padding: 0;\n        margin: 0;\n\n        li {\n            min-height: 36px;\n            .link-wrapper {\n                display: flex;\n                justify-content: space-between;\n                > a {\n                    padding: 8px 0;\n                    display: block;\n                    width: 100%;\n                    font-size: 1rem;\n                    text-decoration: none;\n                    color: $layout-drawer-navigation-color;\n                    &.current {\n                        font-weight: bold;\n                    }\n                }\n            }\n        }\n    }\n\n    .nav-toggle {\n        padding: 0;\n        float: right;\n        display: flex;\n        align-items: center;\n        justify-content: center;\n        height: 36px;\n        > a {\n            padding: 0;\n            margin-left: 0;\n            margin-right: 4px;\n            cursor: pointer;\n            > i {\n                font-size: 18px;\n            }\n        }\n        &.show {\n            transform: rotateZ(180deg);\n            > a {\n                margin-right: 0;\n                margin-left: 4px;\n            }\n        }\n    }\n\n    nav {\n        > ul > li > span.link-wrapper {\n            padding-left: 8px;\n        }\n        > ul > li > ul > li > span.link-wrapper {\n            padding-left: 16px;\n        }\n        > ul > li > ul > li > ul > li > span.link-wrapper {\n            padding-left: 24px;\n        }\n        > ul > li > ul > li > ul > li > ul> li > span.link-wrapper {\n            padding-left: 32px;\n        }\n        > ul > li > ul > li > ul > li > ul > li > ul> li > span.link-wrapper {\n            padding-left: 40px;\n        }\n        > ul > li > ul > li > ul > li > ul > li > ul > li > ul> li > span.link-wrapper {\n            padding-left: 48px;\n        }\n    }\n}\n",".localtoc {\n    font-size: 0.75rem;\n    padding-top: 1rem;\n\n    .caption {\n        padding-left: 12px;\n        &-text {\n            font-size: 0.9rem;\n            font-weight: 700;\n        }\n    }\n\n    > ul > li > a {\n        display: none;\n    }\n\n    ul {\n        padding: 0;\n        list-style-type: none;\n    }\n\n    li {\n        padding-left: 6px;\n    }\n\n    a {\n        display: block;\n        text-decoration: none;\n        color: inherit;\n        margin-top: 8px;\n        padding-left: 8px;\n        line-height: 1.1rem;\n    \n        &.current {\n            padding-left: 5px;\n            border-left: 3px solid;\n            font-weight: bold;\n        }\n    }\n}","/*\r\n *  Toctree and Contents Directive Styles\r\n */\r\n .toctree-wrapper,\r\n .contents.topic {\r\n     border-left: 5px solid;\r\n }\r\n\r\n .toctree-wrapper > p.caption,\r\n .contents.topic > p.topic-title {\r\n     color: rgb(117, 117, 117);\r\n     font-size: 1rem;\r\n     padding-left: 14px;\r\n }\r\n\r\n .toctree-wrapper ul,\r\n .contents.topic ul{\r\n     padding-left: 14px;\r\n     list-style: none;\r\n     line-height: 30px;\r\n }\r\n\r\n .toctree-wrapper a,\r\n .contents.topic a {\r\n     font-size: 1.2rem;\r\n     text-decoration: none;\r\n     .pre {\r\n         font-size: 1rem;\r\n     }\r\n }\r\n\r\n .toctree-wrapper > ul > li > a,\r\n .contents.topic > ul > li > a {\r\n     font-size: 1.3rem;\r\n     .pre {\r\n         font-size: 1.1rem;\r\n     }\r\n }\r\n",".page-content {\n    ul {\n        li {\n            margin: .3rem 0;\n            p {\n                margin: 0;\n            }\n        }\n    }\n    .option-list {\n        .option {\n            font-family: $code_font_family;\n        }\n        td {\n            padding: 0.5rem;\n            border: none;\n        }\n    }\n}\n","/*\r\n * Drawer Styles\r\n */\r\n.mdl-layout {\r\n    &__drawer {\r\n        background-color: #fff;\r\n\r\n        &::-webkit-scrollbar {\r\n            width: 6px;\r\n        }\r\n\r\n        &::-webkit-scrollbar-track {\r\n            border-radius: 6px;\r\n        }\r\n\r\n        &::-webkit-scrollbar-thumb {\r\n            background-color: rgba(0, 0, 0, .3);\r\n            border-radius: 6px;\r\n            box-shadow:0 0 0 1px rgba(255, 255, 255, .3);\r\n        }\r\n\r\n        > .mdl-layout-title {\r\n            font-weight: bold;\r\n            text-align: right;\r\n            margin: 0;\r\n            padding: 0;\r\n            line-height: 32px;\r\n            border-bottom: 1px solid rgba(0,0,0,.1);\r\n            min-height: 64px;\r\n            .title {\r\n                color: inherit;\r\n                display: block;\r\n                height: 100%;\r\n                width: 100%;\r\n                text-decoration: none;\r\n                > img.logo {\r\n                    width: 100%;\r\n                    margin: 0;\r\n                    padding: 0;\r\n                }\r\n\r\n                &-text {\r\n                    font-weight: bold;\r\n                    text-align: right;\r\n                    padding: 0 10px;\r\n                    margin: 16px 0 8px 0;\r\n                    line-height: 32px;\r\n                    font-family: $body_font_family;\r\n                    color: inherit;\r\n                    display: block;\r\n                }\r\n            }\r\n        }\r\n    }\r\n}\r\n","/*\r\n * Header Styles\r\n */\r\n\r\nnav.breadcrumb {\r\n    > a.mdl-navigation__link {\r\n        padding: 0 8px;\r\n        font-size: 18px;\r\n    }\r\n    @media (max-width: $lg-breakpoint - 1) {\r\n        width: calc( 100% - 64px );\r\n        a.mdl-navigation__link.is-active {\r\n            overflow-x: hidden;\r\n            width: 100%;\r\n            overflow: hidden;\r\n            white-space: nowrap;\r\n            text-overflow: ellipsis;\r\n        }\r\n        a.mdl-navigation__link:not(.is-active),\r\n        i.material-icons {\r\n            display: none;\r\n        }\r\n    }\r\n}\r\n\r\ndiv.mdl-layout__header {\r\n    margin-top: 77px;\r\n}\r\n\r\n.mdl-layout__drawer-button {\r\n    top: 13px !important;\r\n}\r\n\r\ndiv.mdl-layout__header-row.header-links {\r\n    background: rgba(255,255,255,0.2);\r\n    width: 100%;\r\n    overflow-x: auto;\r\n    overflow-y: hidden;\r\n\r\n    a.mdl-navigation__link  {\r\n        font-size: 1rem;\r\n        i {\r\n            font-size: 1.2rem;\r\n            margin: 0 8px;\r\n            position: relative;\r\n            bottom: -0.1rem;\r\n        }\r\n    };\r\n\r\n    a.mdl-navigation__link:hover  {\r\n        background-color: unquote(\"rgb(#{$color-primary})\");\r\n        color: #eeeeee;\r\n    };\r\n    a.mdl-navigation__link[href=\"#\"]  {\r\n        background-color: unquote(\"rgb(#{$color-primary})\");\r\n        opacity: 1;\r\n        color: #ffffff;\r\n    };\r\n}\r\n\r\n/* mxnet-header */\r\n\r\n\r\n.site-title {\r\n  font-weight: 300 !important;\r\n  line-height: 57px;\r\n  letter-spacing: -1px;\r\n  margin-bottom: 0;\r\n  float: left;\r\n  color: white;\r\n\r\n  &,\r\n  &:visited {\r\n    color: $grey-color-dark;\r\n  }\r\n}\r\n\r\n\r\n.site-header {\r\n  position: fixed;\r\n  top: 0;\r\n  width: 100%;\r\n  min-height: 55px;\r\n  padding-top: 10px;\r\n  padding-bottom: 10px;\r\n  background-color: $color-mxnet;\r\n  z-index: 10;\r\n  font-weight: 300;\r\n  font-size: 17px;\r\n  border-bottom: 1px solid white;\r\n}\r\n\r\n.site-header-logo {\r\n  width: 120px;\r\n  display: initial;\r\n}\r\n\r\n.site-nav {\r\n  float: right;\r\n  line-height: 57px;\r\n\r\n  .nav-trigger {\r\n    display: none;\r\n  }\r\n\r\n  .menu-icon {\r\n    display: none;\r\n  }\r\n\r\n  .page-link {\r\n    color: white;\r\n    line-height: 1.5;\r\n    font-weight: 300;\r\n    // Gaps between nav items, but not on the last one\r\n    &:not(:last-child) {\r\n      margin-right: 40px;\r\n    }\r\n\r\n    &:hover {\r\n      color: white;\r\n      text-shadow: -0.06ex 0 white, 0.06ex 0 white;\r\n    }\r\n  }\r\n\r\n  .page-link.page-current {\r\n    color: white;\r\n    text-decoration: underline;\r\n  }\r\n\r\n  @media screen and (max-width: $on-laptop) {\r\n    position: absolute;\r\n    top: 9px;\r\n    right: 15px;\r\n    background-color: rgb(23,141,201);\r\n    border-radius: 2px;\r\n    text-align: right;\r\n\r\n    label[for=\"nav-trigger\"] {\r\n      display: block;\r\n      float: right;\r\n      width: 36px;\r\n      height: 36px;\r\n      z-index: 2;\r\n      cursor: pointer;\r\n    }\r\n\r\n    .menu-icon {\r\n      display: block;\r\n      float: right;\r\n      width: 36px;\r\n      height: 26px;\r\n      line-height: 0;\r\n      padding-top: 20px;\r\n      text-align: center;\r\n\r\n      > svg {\r\n        fill: white;\r\n      }\r\n    }\r\n\r\n    input ~ .trigger {\r\n      clear: both;\r\n      display: none;\r\n    }\r\n\r\n    input:checked ~ .trigger {\r\n      display: block;\r\n      padding-bottom: 5px;\r\n    }\r\n\r\n    .page-link {\r\n      padding: 5px 10px;\r\n      display: block;\r\n\r\n      &:not(:last-child) {\r\n        margin-right: 0;\r\n      }\r\n\r\n      margin-left: 20px;\r\n    }\r\n  }\r\n}","/*\r\n * Footer Styles\r\n */\r\nfooter.mdl-mini-footer {\r\n    background-color: #212121;\r\n    > div.mdl-mini-footer__left-section {\r\n        margin-bottom: 20px;\r\n        display: flex;\r\n        flex-direction: column;\r\n        .mdl-logo {\r\n            font-size: 1.1rem;\r\n        }\r\n        ul {\r\n            @extend .mdl-mini-footer__link-list;\r\n        }\r\n    }\r\n    > div.mdl-mini-footer__right-section {\r\n        font-size: 0.9rem;\r\n        display: flex;\r\n        flex-direction: column;\r\n        justify-content: flex-end;\r\n\r\n        a {\r\n            color: inherit;\r\n            font-weight: bold;\r\n            text-decoration: none;\r\n        }\r\n    }\r\n    p.caption {\r\n        display: none;\r\n    }\r\n}\r\n\r\n/*\r\n * Pagenation Block Styles\r\n */\r\n .pagenation {\r\n    width: 100%;\r\n    margin-top: 80px;\r\n    height: 92px;\r\n    background-color: #424242;\r\n    display: flex;\r\n\r\n    .button-common {\r\n        text-transform: none;\r\n        padding: 0;\r\n        height: 92px;\r\n        display: flex;\r\n        justify-content: center;\r\n        align-items: center;\r\n        color: #ffffff;\r\n    }\r\n    #button-prev {\r\n        @extend .button-common;\r\n        margin-right: auto;\r\n        .pagenation-text {\r\n            text-align: left;\r\n        }\r\n        \r\n    }\r\n    #button-next {\r\n        @extend .button-common;\r\n        margin-left: auto;\r\n        flex-direction: row-reverse;\r\n        .pagenation-text {\r\n            text-align: right;\r\n        }\r\n    }\r\n\r\n    &-arrow {\r\n        &-L {\r\n            margin-right: 20px;\r\n        }\r\n        &-R {\r\n            margin-left: 20px;\r\n        }\r\n    }\r\n\r\n    &-text {\r\n        line-height: 30px;\r\n        font-size: 20px;\r\n    }\r\n\r\n    &-direction {\r\n        opacity: 0.7;\r\n        font-size: 18px;\r\n    }\r\n    @media screen and (max-width: 1024px) {\r\n        #button-prev {\r\n            width: 20%;\r\n        }\r\n        \r\n        #button-next {\r\n            width: 80%;\r\n        }\r\n    \r\n        #button-prev .pagenation-text {\r\n            display: none;\r\n        }\r\n    }\r\n    @media screen and (min-width: 1025px) {\r\n        #button-prev,\r\n        #button-next {\r\n            width: 50%;\r\n        }\r\n    \r\n        #button-prev .pagenation-text {\r\n            display: block;\r\n        }\r\n    }\r\n}\r\n\r\n\r\n\r\n/**\r\n * Site footer\r\n */\r\n.site-footer {\r\n  border-top: 1px solid $grey-color-light;\r\n  padding: $spacing-unit 0;\r\n  background-color: #424242;\r\n  .footer-category-title {\r\n    color: $color-mxnet;\r\n  }\r\n  a {\r\n    color: $grey-color-light !important;\r\n\r\n    &:visited {\r\n      color: $grey-color-light !important;\r\n    }\r\n  }\r\n\r\n}\r\n\r\n.site-footer2 {\r\n  background-color: #424242;\r\n  padding-top: 40px;\r\n  padding-bottom: 10px;\r\n}\r\n\r\n.footer-heading {\r\n  margin-bottom: $spacing-unit / 2;\r\n}\r\n\r\n.contact-list,\r\n.social-media-list {\r\n  list-style: none;\r\n  margin-left: 0;\r\n}\r\n\r\n\r\n.footer-bottom-warning {\r\n  font-size: 80%;\r\n  color: white;\r\n  float: left;\r\n}\r\n\r\n.footer-logo {\r\n  width: 200px;\r\n  margin-bottom: 30px;\r\n  margin-top: 30px;\r\n}\r\n\r\n.footer-col {\r\n  float: left;\r\n  margin-bottom: $spacing-unit / 2;\r\n  padding-left: $spacing-unit / 2;\r\n}\r\n\r\n.footer-text {\r\n  color: $grey-color-light;\r\n}\r\n\r\n"," /*\r\n  * Search Styles\r\n  */\r\n#waterfall-exp::-webkit-input-placeholder {\r\n    color: #ccc;\r\n}\r\n#waterfall-exp:-ms-input-placeholder {\r\n    color: #ccc;\r\n}\r\n#waterfall-exp::-moz-placeholder {\r\n    color: #ccc;\r\n}\r\n\r\nul.search span.highlighted {\r\n    font-weight: bold;\r\n}\r\n\r\nul.search > li {\r\n    margin-bottom: 24px;\r\n}\r\n\r\n#search-results {\r\n    ul {\r\n        list-style: none;\r\n        padding: 0;\r\n        li {\r\n            > a {\r\n                text-decoration: none;\r\n                font-size: 1.2rem;\r\n            }\r\n        }\r\n    }\r\n}\r\n","a.download {\n    &:before {\n        @extend .material-icons;\n        content: \"file_download\";\n        position: relative;\n        top: 5px;\n        margin-right: 5px;\n    }\n}\n\nbutton.download {\n    position: sticky;\n    margin-left: 1em;\n}\n",".mdl-card {\n    margin: 1em 1.5em 1em 0;\n    display: inline-block;\n    width: 250px;\n    min-height: 140px;\n    padding: 18px;\n}\n.mdl-card:hover {\n    box-shadow: 0 10px 20px rgba(0,0,0,0.25), 0 6px 6px rgba(0,0,0,0.22);\n    color: #000;\n    cursor: pointer;\n}\n.mdl-card__title {\n    padding: 0 0 1em 0;\n    font-size: 18px;\n    color: #444;\n}\n\n.mdl-card__supporting-text {\n    line-height: 1.5rem;\n    padding: 0px;\n    width: 100%;\n}\n\n.head-card.mdl-card {\n    width: auto;\n    display: block;\n    max-width: 800px;\n    padding: 24px;\n}\n\n.head-card > .mdl-card__title {\n    padding-bottom: 0px;\n    height: 60px;\n    font-weight: 700;\n    text-transform: uppercase;\n}\n.head-card > .mdl-card__menu {\n    color: #fff;\n}\n.head-card > .mdl-card__actions {\n    padding: 0;\n}\n.cards {\n    display: flex;\n    flex-direction: row;\n    flex-wrap: wrap;\n}\n"]}
\ No newline at end of file
+{"version":3,"sources":["../../node_modules/material-design-lite/src/shadow/_shadow.scss","../../node_modules/material-design-lite/src/_mixins.scss","../../node_modules/material-design-lite/src/data-table/_data-table.scss","../../node_modules/material-design-lite/src/_variables.scss","../../node_modules/material-design-lite/src/footer/_mini_footer.scss","../../node_modules/material-design-lite/src/card/_card.scss","../../node_modules/material-design-lite/src/button/_button.scss","../scss/grid/_simplegrid.scss","../scss/fonts/_material-icons.scss","../scss/_root.scss","../scss/_variables.scss","../scss/layout/_layout.scss","../scss/headerings/_headerings.scss","../scss/admonitions/_admonitions.scss","../scss/code/_code.scss","../scss/blockquote/_blockquote.scss","../scss/tables/_tables.scss","../scss/toc/_globaltoc.scss","../scss/toc/_localtoc.scss","../scss/toc/_toctree.scss","../scss/lists/_lists.scss","../scss/drawer/_drawer.scss","../scss/header/_header.scss","../scss/footer/_footer.scss","../scss/search/_search.scss","../scss/downloadlink/_downloadlink.scss","../scss/card/_card.scss"],"names":[],"mappings":"AAmBA,wJCoNE,gGAEqE,CDlNvE,iBCqNE,gGAEqE,CDnNvE,iBCsNE,iGAEmE,CDpNrE,iBCuNE,kGAEmE,CDrNrE,iBCwNE,sGAEmE,CDtNrE,kBC0NE,wGAEqE,CDxNvE,kBC4NE,yGAEqE,CCtPvE,mHACE,iBAAkB,CAClB,gCCohBkC,CDnhBlC,wBAAyB,CACzB,kBAAmB,CACnB,cC0gByB,CDzgBzB,qBAAiD,CANnD,+HASI,kBAAmB,CATvB,+KAYM,YAAa,CAZnB,qIAkBM,iBAAkB,CAClB,WC0gBsB,CFlR1B,wBCvP6C,CDwP7C,kDEkN6D,CDzczD,oCAAqC,CArB3C,6JAwBQ,wBCigB4B,CDzhBpC,iJA4BQ,qBC4fwB,CDxhBhC,kPAkCI,mBCggBsD,CD/ftD,gBAAiB,CAnCrB,0SAsCM,iBAAkB,CAtCxB,sSA0CM,kBAAmB,CA1CzB,yHA+CI,iBAAkB,CAClB,qBAAsB,CACtB,WC4ewB,CD3exB,oCCoegC,CDnehC,uCCmegC,CDlehC,gBCof8C,CDnf9C,qBAAsB,CArD1B,yKAwDM,qBAAsB,CAxD5B,yHA6DI,iBAAkB,CAClB,qBAAsB,CACtB,sBAAuB,CDsCzB,cAAe,CAIb,eAAiB,CAEnB,gBAAiB,CACjB,gBAAiB,CC3Cf,WC4dwB,CD3dxB,cC8c8B,CD7c9B,qBCgd+B,CD/c/B,kBAAmB,CACnB,qBAAsB,CArE1B,wZAyEM,qBC2coC,CDphB1C,obD8LE,0BAA6B,CAC7B,eAAmB,CACnB,iBAAkB,CAClB,cAAe,CACf,aAAc,CACd,qBAAsB,CACtB,mBAAoB,CACpB,oBAAqB,CACrB,gBAAiB,CACjB,4BAA6B,CAC7B,oCAAqC,CACrC,kCAAmC,CC7H7B,cCqc+B,CDpc/B,eAAgB,CAChB,gBAAiB,CACjB,kBAAmB,CA/E3B,gbAkFQ,cAAe,CAlFvB,4cAoFU,qBCic2C,CDrhBrD,2NAyFM,eAAgB,CAKtB,wBACE,UAAW,CAGb,iRACE,eAAgB,CEpGlB,iBACE,YAAa,CACb,kBAAmB,CACnB,6BAA8B,CAE9B,iBDoZY,CClZZ,aDwRiD,CCvRjD,wBDsRoD,CC9RtD,uBAWI,UAAW,CACX,aAAc,CAZlB,2BAgBI,gBDsYkB,CClYtB,oHAEE,YAAa,CACb,oBAAqB,CAErB,eAAgB,CAEhB,QAAS,CACT,SAAU,CARZ,6HAWI,eAAgB,CAChB,iBDyXU,CCvXV,oCAdJ,6HAeM,gBDmXgB,CCjXnB,CAjBH,0HAoBI,aAAc,CACd,oBAAqB,CACrB,kBAAmB,CAIvB,8DAEE,oBAAqB,CACrB,OAAQ,CAGV,gEAEE,oBAAqB,CACrB,OAAQ,CAGV,0DAEE,UD0VoB,CCzVpB,WDyVoB,CCvVpB,SAAU,CACV,QAAS,CAET,wBD6NiD,CC3NjD,WAAY,CCpEd,UACE,YAAa,CACb,qBAAsB,CACtB,cF2amB,CE1anB,eAAgB,CAChB,gBFwaiB,CEvajB,eAAgB,CAChB,WFqagB,CEpahB,SF2bc,CE1bd,iBAAkB,CAClB,eFiOqD,CEhOrD,iBAAkB,CAClB,qBAAsB,CAGxB,iBACE,wBF6N6D,CE5N7D,wBAAyB,CACzB,2BAA4B,CAC5B,qBAAsB,CACtB,6BAA8B,CAC9B,4BAA6B,CAC7B,qBAAsB,CAGxB,iBACE,kBAAmB,CACnB,UFiN+C,CEhN/C,aAAc,CACd,YAAa,CACb,uBAAwB,CACxB,kBAAmB,CACnB,YFiZ4B,CEhZ5B,6BFoZoC,CEnZpC,2BFsZkC,CErZlC,qBAAsB,CAVxB,kCAaI,sCFyM+B,CErMnC,sBACE,mBAAoB,CACpB,aAAc,CACd,aAAc,CACd,YAAa,CACb,cFgYyB,CE/XzB,eFkZ+B,CEjZ/B,kBAAmB,CACnB,eAAgB,CAChB,2BFwYuC,CEvYvC,QAAS,CAGX,yBACE,cFwX4B,CEvX5B,qBFuL0D,CEtL1D,QAAS,CAGX,2BACE,qBFgLsE,CE/KtE,cF8XmC,CE7XnC,gBF8XqC,CE7XrC,eAAgB,CAChB,YF+W4B,CE9W5B,SAAU,CANZ,4CASI,sCFyK+B,CErKnC,mBACE,cFqX2B,CEpX3B,kBAAmB,CACnB,UAAW,CACX,4BAA+B,CAC/B,WAAY,CACZ,qBAAsB,CANxB,oCASI,mCF4J+B,CExJnC,kBACE,WAAY,CAId,gBACE,iBAAkB,CAClB,UAAW,CACX,QAAS,CC7FX,YACE,sBAAuB,CACvB,WAAY,CACZ,iBH+cwB,CG9cxB,UHgHsD,CG/GtD,iBAAkB,CAClB,WHyckB,CGxclB,QAAS,CACT,cHscqB,CGrcrB,cHucmB,CGtcnB,oBAAqB,CLVnB,6CE8CuD,CFmIzD,cAAe,CACf,eAAgB,CAChB,wBAAyB,CACzB,aAAc,CACd,gBAAiB,CKzKjB,eAAgB,CAChB,sBAAuB,CACvB,+HH+c6D,CG5c7D,YAAa,CACb,cAAe,CACf,oBAAqB,CACrB,iBAAkB,CAClB,gBH0bkB,CGzblB,qBAAsB,CAtBxB,8BAyBI,QAAS,CAzBb,kBA6BI,kCHsF8D,CGnHlE,+BAiCI,gCHsFuD,CGvH3D,mBAqCI,kCHiF6D,CGtHjE,gCAyCI,aHiFwD,CG1H5D,mDA4CM,gCH2EqD,CGtE3D,8BACE,uBAAuB,CAIvB,oBACE,4BH4D8D,CFgGhE,gGAEqE,CK/JrE,2BLuKA,iGAEmE,CKnK/D,kCH0D2D,CGhE/D,uCLyJA,6DAA8D,CK9I1D,kCHqD2D,CGhE/D,wCAeI,kBHqDsD,CGpDtD,UHqDiE,CGrErE,wJA2BM,wBH4CmD,CGvEzD,oDA+BM,eH4C4D,CGrClE,iBACE,iBAAkB,CAClB,cHwXuB,CGvXvB,WHqXkB,CGpXlB,WAAY,CACZ,cHmXkB,CGlXlB,UHkXkB,CGjXlB,SAAU,CACV,eAAgB,CAChB,4BHc8D,CGb9D,oEAAwE,CACxE,iBAAkB,CAClB,kBAAmB,CAZrB,0wCAeI,iBAAkB,CAClB,OAAQ,CACR,QAAS,CACT,gCAA8E,CAC9E,gBHuWqB,CGtWrB,UHsWqB,CG1XzB,sCAwBI,WHiWqB,CGhWrB,cHgWqB,CG/VrB,UH+VqB,CGzXzB,+CA8BI,iBAAkB,CAElB,4DAAiE,CAhCrE,wBLiIA,iGAEmE,CK9F/D,kCHX2D,CG1B/D,oCLmHA,6DAA8D,CKzE1D,kCHhB2D,CG1B/D,qCA8CI,kBHFiD,CGGjD,UHA+D,CG/CnE,+IA0DM,wBHZsD,CG9C5D,iDA8DM,eHd+D,CGqBrE,kBACE,iBAAkB,CAClB,cHmTuB,CGlTvB,WHoTmB,CGnTnB,aAAc,CACd,cAAe,CACf,cHiTmB,CGhTnB,UHgTmB,CG/SnB,SAAU,CACV,eAAgB,CAChB,aAAc,CACd,kBAAmB,CAXrB,gyCAcI,iBAAkB,CAClB,OAAQ,CACR,QAAS,CACT,gCAA8E,CAC9E,gBHmSqB,CGlSrB,UHkSqB,CGrTzB,wCAuBI,WHiSsB,CGhStB,cHgSsB,CG/RtB,UH+RsB,CGxT1B,owDA4BM,KAAyD,CACzD,MAA0D,CA7BhE,gDAkCI,iBAAkB,CAElB,4DAAiE,CAMrE,8BACE,aAAc,CACd,WAAY,CACZ,MAAS,CACT,iBAAkB,CAClB,KAAQ,CACR,UAAW,CACX,SAAU,CACV,eAAgB,CAEhB,2IAEE,4BAA6B,CAMnC,yCACE,aHpG0D,CGmG5D,qDAGI,eHrGmE,CGkGvE,qHAMI,UHxGmE,CGyGnE,wBH1GwD,CG8G5D,uCACE,aHjGqD,CGgGvD,mDAGI,eHhGiE,CG6FrE,iHAMI,UHnGiE,CGoGjE,wBHvGmD,CG6GvD,sFAII,qBHpHoE,CGqHpE,cAAe,CACf,4BAA6B,CAG9B,gGAIG,gCH9HgE,CG+HhE,qBH9HkE,CGkIrE,sGAIG,gCHvIgE,CGwIhE,qBHvIkE,CGwIlE,eAAgB,CAGnB,wGAIG,qBH/IkE,CGqJxE,4pCACE,qBAAsB,CClSxB,YACE,eAVqB,CAavB,cACE,eAbuB,CAgBzB,YACE,eAhBqB,CAqBvB,MACE,eAAgB,CAGlB,OACE,gBAAiB,CAGnB,QACE,iBAAkB,CAClB,gBAAiB,CACjB,iBAAkB,CAGpB,SACE,kBAAmB,CAGrB,WACE,YAAa,CAWf,WACE,UAAW,CACX,gBAAiB,CACjB,iBAAkB,CAElB,2CALF,WAMI,SAAU,CAOb,CAJC,wCATF,WAUI,SAAU,CACV,eAAgB,CAEnB,CAED,KACE,iBAAkB,CAClB,UAAW,CAGb,kBACE,UAAW,CACX,eAAiB,CACjB,kBAAoB,CAGtB,WACE,UAAW,CACX,aAAc,CACd,UAAW,CAGb,uFAYE,SAlDS,CAqDX,UACE,cAA0C,CAG5C,UACE,eAAyC,CAG3C,UACE,SAAwC,CAG1C,UACE,eAAwC,CAG1C,UACE,eAA+C,CAGjD,UACE,SAAwC,CAG1C,UACE,eAA+C,CAGjD,UACE,eAA+C,CAGjD,UACE,SAA+C,CAGjD,WACE,eAAgD,CAGlD,WACE,eAAgD,CAGlD,WACE,SAlGS,CAqGX,wCACE,OACE,cAA0C,CAE5C,OACE,eAAyC,CAE3C,OACE,SAAwC,CAE1C,OACE,eAAwC,CAE1C,OACE,eAA+C,CAEjD,OACE,SAAwC,CAE1C,OACE,eAA+C,CAEjD,OACE,eAA+C,CAEjD,OACE,SAA+C,CAEjD,QACE,eAAgD,CAElD,QACE,eAAgD,CAElD,QACE,SAxIO,CANX,WAkJI,aAAc,CACf,CAxHH,KA4HE,mBAAoB,CACpB,oBAAqB,CACrB,mBAAoB,CACpB,YAAa,CACb,cAAe,CAGjB,mBACE,YAAa,CACb,qBAAsB,CCxMxB,2dACI,0BAA6B,CAC7B,eAAmB,CACnB,iBAAkB,CAClB,cAAe,CACf,oBAAqB,CACrB,aAAc,CACd,mBAAoB,CACpB,qBAAsB,CACtB,gBAAiB,CACjB,kBAAmB,CACnB,aAAc,CAGd,kCAAmC,CAEnC,iCAAkC,CAGlC,iCAAkC,CAGlC,4BAA6B,CC3BjC,KACI,cCEY,CDChB,KACI,uBAAyB,CACzB,wBCDsB,CDEtB,cAAe,CACf,kBAAmB,CACnB,6ICA0J,CDG9J,2BACI,YAAa,CAGjB,4EAEI,6ICT0J,CDY9J,8GACI,uBAA8B,CAGlC,EACI,oBAAqB,CAGzB,yKAGQ,cAAe,CAIvB,OACI,aAAc,CACd,oBAAqB,CAGzB,SACI,eAAgB,CAOnB,IACG,cAAe,CACf,aAAc,CACd,gBAAiB,CACjB,iBAAkB,CAGtB,qBAEQ,iBAAkB,CAClB,iBAAkB,CAH1B,yCAMY,iBAAkB,CAN9B,2CASY,eAAgB,CAK5B,UACE,UAAW,CACX,WAAY,CACZ,oBAAqB,CACrB,YC3C0C,CD4C1C,iBAAkB,CAClB,eAAgB,CAChB,uBAAwB,CAM1B,6kBACI,iBAAkB,CAClB,OAAQ,CAGZ,WACI,oBAAqB,CAGzB,eACE,UAAW,CACX,aAAc,CACd,UAAW,CAGb,SAEE,gBAA2D,CAC3D,iBAAkB,CAClB,gBAAiB,CACjB,kBAA0C,CAC1C,iBC9EqB,CDiFrB,qCATF,SAWI,gBAAuD,CACvD,kBAAgC,CAChC,iBAA+B,CAElC,CEtFD,YACI,eAAgB,CAIpB,UACI,UAAW,CACX,aAAc,CACd,YAAa,CAEb,0BALJ,UAMQ,YAhCiB,CA6ExB,CAnDD,wBASQ,UAAW,CACX,aAAc,CACd,cAAe,CAEf,yBAbR,wBAcY,SA7BU,CA8BV,YA7Ba,CAoCpB,CAJG,0BAlBR,wBAmBY,uBA9B0B,CA+B1B,YA9Ba,CAgCpB,CAtBL,4BAyBQ,WA5CY,CA8CZ,0BA3BR,4BA4BY,YAAa,CAqBpB,CAjDL,qCA+BY,cAAe,CACf,eAAgB,CAChB,eAAgB,CAChB,aAAc,CAlC1B,wDAoCgB,SAAU,CApC1B,8DAwCgB,iBAAkB,CAxClC,8DA4CgB,+BAAmC,CACnC,iBAAkB,CAClB,uCAA4C,CC9E5D,oBACI,GACI,2BAA6B,CAC7B,SAAU,CAEjB,GACC,uBAAwB,CACxB,SAAU,CAAA,CAIZ,qBACI,GACI,uBAAwB,CACxB,SAAU,CAEjB,GACC,2BAA6B,CAC7B,SAAU,CAAA,CAIZ,0BAEQ,oBAAqB,CACrB,oBAAqB,CACrB,iBAAmB,CACnB,aAAc,CACd,SAAU,CANlB,gCAQY,0DAAsE,CARlF,oLAcY,oBAAqB,CAdjC,kNAkBgB,0DAAsE,CAlBtF,iBAwBQ,cAAe,CACf,mBAAoB,CAzB5B,iBA6BQ,iBAAkB,CAClB,gBAAiB,CACjB,kBAAmB,CACnB,YAAa,CACb,kBAAmB,CAjC3B,iBAqCQ,gBAAiB,CACjB,mBAAoB,CACpB,gBAAiB,CACjB,YAAe,CACf,oBAAqB,CAzC7B,iBA6CQ,iBAAkB,CAClB,kBAAmB,CACnB,kBAAmB,CACnB,YAAe,CACf,mBAAoB,CAjD5B,kCAqDQ,gBAAiB,CACjB,kBAAmB,CACnB,gBAAiB,CACjB,YAAe,CACf,kBAAmB,CAzD3B,kCA6DQ,cAAe,CACf,kBAAmB,CACnB,gBAAiB,CACjB,YAAe,CACf,kBAAmB,CCT3B,YAGI,iBAAkB,CAClB,eAAgB,CAChB,kBAAmB,CALvB,mBAOQ,WAAY,CAPpB,8BAUQ,cAAe,CACf,eAAiB,CACjB,UAAW,CACX,wBAAyB,CACzB,cAAe,CAdvB,iBApBI,6BA/CgC,CAgDhC,mCA/C4C,CAgD5C,mCACI,cAAe,CACf,eAAiB,CACjB,aApD4B,CAsD5B,cAAe,CACf,iBAAkB,CAClB,0CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,sBA3DwB,CA4DxB,cAAe,CAK3B,oBApBI,6BA1CgC,CA2ChC,mCA1C4C,CA2C5C,sCACI,cAAe,CACf,eAAiB,CACjB,aA/C4B,CAiD5B,cAAe,CACf,iBAAkB,CAClB,6CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,gBAtDkB,CAuDlB,cAAe,CAK3B,iBApBI,6BApDgC,CAqDhC,mCApD4C,CAqD5C,mCACI,cAAe,CACf,eAAiB,CACjB,aAzD4B,CA2D5B,cAAe,CACf,iBAAkB,CAClB,0CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,sBAhEwB,CAiExB,cAAe,CAK3B,oBApBI,6BArCgC,CAsChC,mCArC4C,CAsC5C,sCACI,cAAe,CACf,eAAiB,CACjB,aA1C4B,CA4C5B,cAAe,CACf,iBAAkB,CAClB,6CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,iBAjDmB,CAkDnB,cAAe,CAK3B,sBApBI,6BAhCgC,CAiChC,mCAhC4C,CAiC5C,wCACI,cAAe,CACf,eAAiB,CACjB,aArC4B,CAuC5B,cAAe,CACf,iBAAkB,CAClB,+CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,iBA5CmB,CA6CnB,cAAe,CAK3B,gBApBI,6BA3BiC,CA4BjC,oCA3B6C,CA4B7C,kCACI,cAAe,CACf,eAAiB,CACjB,aAhC6B,CAkC7B,cAAe,CACf,iBAAkB,CAClB,yCAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,2BAvC6B,CAwC7B,cAAe,CAK3B,sBApBI,6BAtBiC,CAuBjC,oCAtB6C,CAuB7C,wCACI,cAAe,CACf,eAAiB,CACjB,aA3B6B,CA6B7B,cAAe,CACf,iBAAkB,CAClB,+CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,sBAlCyB,CAmCzB,cAAe,CAK3B,kBApBI,6BAjBgC,CAkBhC,mCAjB4C,CAkB5C,oCACI,cAAe,CACf,eAAiB,CACjB,aAtB4B,CAwB5B,cAAe,CACf,iBAAkB,CAClB,2CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,uBA7ByB,CA8BzB,cAAe,CAK3B,oBApBI,6BAZgC,CAahC,mCAZ4C,CAa5C,sCACI,cAAe,CACf,eAAiB,CACjB,aAjB4B,CAmB5B,cAAe,CACf,iBAAkB,CAClB,6CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,uBAxByB,CAyBzB,cAAe,CAK3B,mBApBI,6BAPgC,CAQhC,mCAP4C,CAQ5C,qCACI,cAAe,CACf,eAAiB,CACjB,aAZ4B,CAc5B,cAAe,CACf,iBAAkB,CAClB,4CAEI,iBAAkB,CAClB,gBAAiB,CACjB,OAAQ,CACR,uBAnByB,CAoBzB,cAAe,CCzE3B,yBAEQ,YAAa,CAFrB,6BAIY,0BJEqB,CIDrB,qBAAsB,CACtB,wHJE2I,CID3I,cAAgB,CAChB,aAAc,CACd,iBAAkB,CAT9B,iEAWgB,qBAAsB,CAXtC,kDAiBQ,eAAgB,CAjBxB,qCAwBgB,qBAAsB,CACtB,kBJpBU,CIuBV,qBAAmB,CACnB,cAAgB,CA7BhC,sDAmCQ,QAAW,CAEX,iBAAkB,CArC1B,8HAoCQ,wHJ5B+I,CIRvJ,6BA4CQ,gBAAiB,CACjB,aAAc,CA7CtB,kGAiDQ,aAAc,CACd,aAAc,CACd,cAAe,CACf,kBAAmB,CACnB,kBAAmB,CACnB,aAAc,CACd,4BAA6B,CAC7B,YAAa,CACb,iBAAkB,CAzD1B,wSA2DY,qBAAsB,CACtB,kBAAmB,CACnB,WAAY,CA7DxB,8GAgEY,aAAc,CAhE1B,sBAsEQ,kBAAqB,CAtE7B,0BA2EQ,iBAAkB,CA3E1B,6BA6EY,kBAAmB,CACnB,QAAS,CA9ErB,oCA6FO,UAAW,CACX,UAAW,CACX,aAAc,CA/FrB,oCAmGO,gBAAiB,CAnGxB,sBAsGQ,kBAAmB,CAtG3B,kBA0GQ,aAAc,CACd,eAAgB,CAChB,aAAc,CACd,iBAAkB,CAClB,UAAW,CACX,iBAAkB,CAClB,oBAAqB,CAhH7B,+BAsHgB,6IJ7G8I,CI8G9I,eAAiB,CACjB,2BAA4B,CAC5B,oBAAyB,CACzB,iBAAkB,CAClB,iBAAkB,CAClB,WAAY,CACZ,UAAY,CACZ,YAAc,CACd,kBAA8B,CAC9B,eAAiB,CACjB,cAAe,CC9H9B,yBAEO,cAAe,CACf,cAAe,CACf,qCLDyB,CKHhC,+BAOW,oBAAsB,CACtB,aAAc,CARzB,gCAWW,oBAAsB,CCdlC,mGAKQ,eAAgB,CAChB,kBAAmB,CACnB,cAAe,CACf,aAAc,CARtB,4MAYY,kBAAmB,CACnB,wBAAyB,CAbrC,2GAiBY,cNdI,CMeJ,mBAAuB,CACvB,kBAAmB,CAnB/B,2HAqBgB,iBAAkB,CArBlC,iIAwBgB,eAAgB,CCxBhC,oCAGQ,YAAa,CAHrB,cAQQ,oBAAqB,CACrB,SAAU,CACV,QAAS,CAVjB,iBAaY,eAAgB,CAb5B,+BAegB,YAAa,CACb,6BAA8B,CAhB9C,iCAkBoB,aAAc,CACd,aAAc,CACd,UAAW,CACX,cAAe,CACf,oBAAqB,CACrB,adyKoB,CchMxC,yCAyBwB,eAAiB,CAzBzC,uBAiCQ,SAAU,CACV,WAAY,CACZ,YAAa,CACb,kBAAmB,CACnB,sBAAuB,CACvB,WAAY,CAtCpB,yBAwCY,SAAU,CACV,aAAc,CACd,gBAAiB,CACjB,cAAe,CA3C3B,2BA6CgB,cAAe,CA7C/B,4BAiDY,wBAA0B,CAjDtC,8BAmDgB,cAAe,CACf,eAAgB,CApDhC,uCA2DY,gBAAiB,CA3D7B,6CA8DY,iBAAkB,CA9D9B,mDAiEY,iBAAkB,CAjE9B,yDAoEY,iBAAkB,CApE9B,+DAuEY,iBAAkB,CAvE9B,qEA0EY,iBAAkB,CC1E9B,UACI,gBAAkB,CAClB,gBAAiB,CAFrB,mBAKQ,iBAAkB,CAL1B,wBAOY,eAAiB,CACjB,eAAgB,CAR5B,kBAaQ,YAAa,CAbrB,aAiBQ,SAAU,CACV,oBAAqB,CAlB7B,aAsBQ,gBAAiB,CAtBzB,YA0BQ,aAAc,CACd,oBAAqB,CACrB,aAAc,CACd,cAAe,CACf,gBAAiB,CACjB,kBAAmB,CA/B3B,oBAkCY,gBAAiB,CACjB,qBAAsB,CACtB,eAAiB,CCjC5B,iCAEI,qBAAsB,CAG1B,yDAEI,aAAyB,CACzB,cAAe,CACf,iBAAkB,CAGtB,uCAEI,iBAAkB,CAClB,eAAgB,CAChB,gBAAiB,CAGrB,qCAEI,gBAAiB,CACjB,oBAAqB,CAHzB,+CAKQ,cAAe,CAIvB,iDAEI,gBAAiB,CAFrB,2DAIQ,gBAAiB,CCnC1B,oBAGY,cAAe,CAH3B,sBAKgB,QAAS,CALzB,mCAWY,wHVH2I,CURvJ,8BAcY,aAAe,CACf,WAAY,CCXpB,oBACI,qBAAsB,CADzB,uCAIO,SAAU,CAJjB,6CAQO,iBAAkB,CARzB,6CAYO,+BAAmC,CACnC,iBAAkB,CAClB,uCAA4C,CAdnD,sCAkBO,eAAiB,CACjB,gBAAiB,CACjB,QAAS,CACT,SAAU,CACV,gBAAiB,CACjB,sCAAuC,CACvC,eAAgB,CAxBvB,6CA0BW,aAAc,CACd,aAAc,CACd,WAAY,CACZ,UAAW,CACX,oBAAqB,CA9BhC,sDAgCe,UAAW,CACX,QAAS,CACT,SAAU,CAlCzB,kDAsCe,eAAiB,CACjB,gBAAiB,CACjB,cAAe,CACf,iBAAoB,CACpB,gBAAiB,CACjB,6IXtC0I,CWuC1I,aAAc,CACd,aAAc,CC7ClC,sCAEQ,aAAc,CACd,cAAe,CAEnB,0BALJ,eAMQ,uBAA0B,CANlC,gDAQY,iBAAkB,CAClB,UAAW,CACX,eAAgB,CAChB,kBAAmB,CACnB,sBAAuB,CAZnC,wwCAgBY,YAAa,CAChB,CAIT,uBACI,eAAgB,CAGpB,2BACI,kBAAoB,CAGxB,wCACI,6BAAiC,CACjC,UAAW,CACX,eAAgB,CAChB,iBAAkB,CAJtB,+DAOQ,cAAe,CAPvB,iEASY,gBAAiB,CACjB,YAAa,CACb,iBAAkB,CAClB,aAAe,CAZ3B,qEAiBQ,wBAAmD,CACnD,UAAc,CAlBtB,yEAqBQ,wBAAmD,CACnD,SAAU,CACV,UAAc,CAOtB,YACE,yBAA2B,CAC3B,gBAAiB,CACjB,mBAAoB,CACpB,eAAgB,CAChB,UAAW,CACX,UAAY,CANd,gCAUI,aZzCuC,CY8C3C,aACE,cAAe,CACf,KAAM,CACN,UAAW,CACX,eAAgB,CAChB,gBAAiB,CACjB,mBAAoB,CACpB,wBZzD0B,CY0D1B,UAAW,CACX,eAAgB,CAChB,cAAe,CACf,4BAA8B,CAGhC,kBACE,WAAY,CACZ,eAAgB,CAGlB,UACE,WAAY,CACZ,gBAAiB,CAFnB,4CASI,YAAa,CATjB,qBAaI,UAAY,CACZ,eAAgB,CAChB,eAAgB,CAfpB,sCAkBM,iBAAkB,CAlBxB,2BAsBM,UAAY,CACZ,sCAA4C,CAvBlD,kCA4BI,UAAY,CACZ,yBAA0B,CAG5B,qCAhCF,UAiCI,iBAAkB,CAClB,OAAQ,CACR,UAAW,CACX,wBAAiC,CACjC,iBAAkB,CAClB,gBAAiB,CAtCrB,iCAyCM,aAAc,CACd,WAAY,CACZ,UAAW,CACX,WAAY,CACZ,SAAU,CACV,cAAe,CA9CrB,qBAkDM,aAAc,CACd,WAAY,CACZ,UAAW,CACX,WAAY,CACZ,aAAc,CACd,gBAAiB,CACjB,iBAAkB,CAxDxB,yBA2DQ,SAAW,CA3DnB,yBAgEM,UAAW,CACX,YAAa,CAjEnB,iCAqEM,aAAc,CACd,kBAAmB,CAtEzB,qBA0EM,gBAAiB,CACjB,aAAc,CAMd,gBAAiB,CAjFvB,sCA8EQ,cAAe,CAChB,CC7KP,uBACI,wBAAyB,CAD7B,yDAGQ,kBAAmB,CACnB,YAAa,CACb,qBAAsB,CAL9B,mEAOY,gBAAiB,CAP7B,0DAcQ,eAAiB,CACjB,YAAa,CACb,qBAAsB,CACtB,wBAAyB,CAjBjC,4DAoBY,aAAc,CACd,eAAiB,CACjB,oBAAqB,CAtBjC,iCA0BQ,YAAa,CAOpB,YACG,UAAW,CACX,eAAgB,CAChB,WAAY,CACZ,wBAAyB,CACzB,YAAa,CALhB,6EAQO,mBAAoB,CACpB,SAAU,CACV,WAAY,CACZ,YAAa,CACb,sBAAuB,CACvB,kBAAmB,CACnB,UAAc,CAdrB,yBAkBO,iBAAkB,CAlBzB,0CAoBW,eAAgB,CApB3B,yBA0BO,gBAAiB,CACjB,0BAA2B,CA3BlC,0CA6BW,gBAAiB,CAKrB,oBACI,iBAAkB,CAEtB,oBACI,gBAAiB,CAIzB,iBACI,gBAAiB,CACjB,cAAe,CAGnB,sBACI,UAAY,CACZ,cAAe,CAEnB,qCAnDH,yBAqDW,SAAU,CArDrB,yBAyDW,SAAU,CAzDrB,0CA6DW,YAAa,CAChB,CAEL,qCAhEH,kDAmEW,SAAU,CAnErB,0CAuEW,aAAc,CACjB,CAST,aACE,4BbvF0C,CawF1C,cAAwB,CACxB,wBAAyB,CAH3B,oCAKI,ab9FwB,CayF5B,sCAWM,uBAAmC,CAMzC,cACE,wBAAyB,CACzB,gBAAiB,CACjB,mBAAoB,CAGtB,gBACE,kBAAgC,CAGlC,iCAEE,eAAgB,CAChB,aAAc,CAIhB,uBACE,aAAc,CACd,UAAY,CACZ,UAAW,CAGb,aACE,WAAY,CACZ,kBAAmB,CACnB,eAAgB,CAGlB,YACE,UAAW,CACX,kBAAgC,CAChC,iBAA+B,CAGjC,aACE,ab3I0C,Cc5B5C,0CACI,UAAW,CAEf,qCACI,UAAW,CAEf,iCACI,UAAW,CAGf,2BACI,eAAiB,CAGrB,aACI,kBAAmB,CAGvB,mBAEQ,eAAgB,CAChB,SAAU,CAHlB,wBAMgB,oBAAqB,CACrB,gBAAiB,CC5BjC,kBAGQ,uBAAwB,CACxB,iBAAkB,CAClB,OAAQ,CACR,gBAAiB,CAIzB,gBACI,eAAgB,CAChB,eAAgB,CpBMpB,UqBjBI,sBAAuB,CACvB,oBAAqB,CACrB,WAAY,CACZ,gBAAiB,CACjB,YAAa,CAEjB,gBACI,gEAAoE,CACpE,UAAW,CACX,cAAe,CrBiCnB,iBqB9BI,eAAkB,CAClB,cAAe,CACf,UAAW,CrBgEf,2BqB5DI,kBAAmB,CACnB,SAAY,CACZ,UAAW,CAGf,oBACI,UAAW,CACX,aAAc,CACd,eAAgB,CAChB,YAAa,CAGjB,4BACI,gBAAmB,CACnB,WAAY,CACZ,eAAgB,CAChB,wBAAyB,CAE7B,2BACI,UAAW,CAEf,8BACI,SAAU,CAEd,OACI,YAAa,CACb,kBAAmB,CACnB,cAAe","file":"sphinx_materialdesign_theme.css","sourceRoot":"../../src/js","sourcesContent":["/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      https:///www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n@import \"../variables\";\n@import \"../mixins\";\n\n.mdl-shadow--2dp {\n  @include shadow-2dp();\n}\n\n.mdl-shadow--3dp {\n  @include shadow-3dp();\n}\n\n.mdl-shadow--4dp {\n  @include shadow-4dp();\n}\n\n.mdl-shadow--6dp {\n  @include shadow-6dp();\n}\n\n.mdl-shadow--8dp {\n  @include shadow-8dp();\n}\n\n.mdl-shadow--16dp {\n  @include shadow-16dp();\n}\n\n.mdl-shadow--24dp {\n  @include shadow-24dp();\n}\n","/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n/* Typography */\n\n@mixin typo-preferred-font($usePreferred: true) {\n  @if $usePreferred {\n    font-family: $preferred_font;\n  }\n}\n\n@mixin typo-display-4($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 112px;\n  font-weight: 300;\n  line-height: 1;\n  letter-spacing: -0.04em;\n\n  @if $colorContrast {\n    opacity: 0.54;\n  }\n}\n\n@mixin typo-display-3($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 56px;\n  font-weight: 400;\n  line-height: 1.35;\n  letter-spacing: -0.02em;\n\n  @if $colorContrast {\n    opacity: 0.54;\n  }\n}\n\n@mixin typo-display-2($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 45px;\n  font-weight: 400;\n  line-height: 48px;\n\n  @if $colorContrast {\n    opacity: 0.54;\n  }\n}\n\n@mixin typo-display-1($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 34px;\n  font-weight: 400;\n  line-height: 40px;\n\n  @if $colorContrast {\n    opacity: 0.54;\n  }\n}\n\n@mixin typo-headline($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 24px;\n  font-weight: 400;\n  line-height: 32px;\n  -moz-osx-font-smoothing: grayscale;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-title($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 20px;\n  font-weight: 500;\n  line-height: 1;\n  letter-spacing: 0.02em;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-subhead($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 16px;\n  font-weight: 400;\n  line-height: 24px;\n  letter-spacing: 0.04em;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-subhead-2($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 16px;\n  font-weight: 400;\n  line-height: 28px;\n  letter-spacing: 0.04em;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-body-2($colorContrast: false, $usePreferred: false) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 14px;\n  @if $usePreferred {\n    font-weight: 500;\n  } @else {\n    font-weight: bold;\n  }\n  line-height: 24px;\n  letter-spacing: 0;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-body-1($colorContrast: false, $usePreferred: false) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 14px;\n  font-weight: 400;\n  line-height: 24px;\n  letter-spacing: 0;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-caption($colorContrast: false, $usePreferred: false) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 12px;\n  font-weight: 400;\n  line-height: 1;\n  letter-spacing: 0;\n\n  @if $colorContrast {\n    opacity: 0.54;\n  }\n}\n\n@mixin typo-blockquote($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  position: relative;\n  font-size: 24px;\n  font-weight: 300;\n  font-style: italic;\n  line-height: 1.35;\n  letter-spacing: 0.08em;\n\n  &:before {\n    position: absolute;\n    left: -0.5em;\n    content: '“';\n  }\n\n  &:after {\n    content: '”';\n    margin-left: -0.05em;\n  }\n\n  @if $colorContrast {\n    opacity: 0.54;\n  }\n}\n\n@mixin typo-menu($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 14px;\n  font-weight: 500;\n  line-height: 1;\n  letter-spacing: 0;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-button($colorContrast: false, $usePreferred: true) {\n  @include typo-preferred-font($usePreferred);\n  font-size: 14px;\n  font-weight: 500;\n  text-transform: uppercase;\n  line-height: 1;\n  letter-spacing: 0;\n\n  @if $colorContrast {\n    opacity: 0.87;\n  }\n}\n\n@mixin typo-icon() {\n  font-family: 'Material Icons';\n  font-weight: normal;\n  font-style: normal;\n  font-size: 24px;\n  line-height: 1;\n  letter-spacing: normal;\n  text-transform: none;\n  display: inline-block;\n  word-wrap: normal;\n  font-feature-settings: 'liga';\n  -webkit-font-feature-settings: 'liga';\n  -webkit-font-smoothing: antialiased;\n}\n\n/* Shadows */\n\n// Focus shadow mixin.\n@mixin focus-shadow() {\n  box-shadow: 0 0 8px rgba(0,0,0,.18),0 8px 16px rgba(0,0,0,.36);\n}\n\n@mixin shadow-2dp() {\n  box-shadow: 0 2px 2px 0 rgba(0, 0, 0, $shadow-key-penumbra-opacity),\n              0 3px 1px -2px rgba(0, 0, 0, $shadow-key-umbra-opacity),\n              0 1px 5px 0 rgba(0, 0, 0, $shadow-ambient-shadow-opacity);\n}\n@mixin shadow-3dp() {\n  box-shadow: 0 3px 4px 0 rgba(0, 0, 0, $shadow-key-penumbra-opacity),\n              0 3px 3px -2px rgba(0, 0, 0, $shadow-key-umbra-opacity),\n              0 1px 8px 0 rgba(0, 0, 0, $shadow-ambient-shadow-opacity);\n}\n@mixin shadow-4dp() {\n  box-shadow: 0 4px 5px 0 rgba(0, 0, 0, $shadow-key-penumbra-opacity),\n              0 1px 10px 0 rgba(0, 0, 0, $shadow-ambient-shadow-opacity),\n              0 2px 4px -1px rgba(0, 0, 0, $shadow-key-umbra-opacity);\n}\n@mixin shadow-6dp() {\n  box-shadow: 0 6px 10px 0 rgba(0, 0, 0, $shadow-key-penumbra-opacity),\n              0 1px 18px 0 rgba(0, 0, 0, $shadow-ambient-shadow-opacity),\n              0 3px 5px -1px rgba(0, 0, 0, $shadow-key-umbra-opacity);\n}\n@mixin shadow-8dp() {\n  box-shadow: 0 8px 10px 1px rgba(0, 0, 0, $shadow-key-penumbra-opacity),\n              0 3px 14px 2px rgba(0, 0, 0, $shadow-ambient-shadow-opacity),\n              0 5px 5px -3px rgba(0, 0, 0, $shadow-key-umbra-opacity);\n}\n\n@mixin shadow-16dp() {\n  box-shadow: 0 16px 24px 2px rgba(0, 0, 0, $shadow-key-penumbra-opacity),\n              0  6px 30px 5px rgba(0, 0, 0, $shadow-ambient-shadow-opacity),\n              0  8px 10px -5px rgba(0, 0, 0, $shadow-key-umbra-opacity);\n}\n\n@mixin shadow-24dp() {\n  box-shadow: 0  9px 46px  8px rgba(0, 0, 0, $shadow-key-penumbra-opacity),\n              0 11px 15px -7px rgba(0, 0, 0, $shadow-ambient-shadow-opacity),\n              0 24px 38px  3px rgba(0, 0, 0, $shadow-key-umbra-opacity);\n}\n\n/* Animations */\n\n@mixin material-animation-fast-out-slow-in($duration:0.2s) {\n  transition-duration: $duration;\n  transition-timing-function: $animation-curve-fast-out-slow-in;\n}\n\n@mixin material-animation-linear-out-slow-in($duration:0.2s) {\n  transition-duration: $duration;\n  transition-timing-function: $animation-curve-linear-out-slow-in;\n}\n\n@mixin material-animation-fast-out-linear-in($duration:0.2s) {\n  transition-duration: $duration;\n  transition-timing-function: $animation-curve-fast-out-linear-in;\n}\n\n@mixin material-animation-default($duration:0.2s) {\n  transition-duration: $duration;\n  transition-timing-function: $animation-curve-default;\n}\n\n/* Dialog */\n\n@mixin dialog-width($units:5) {\n  @if(type_of($units) != 'number') {\n    @error \"The unit given to dialog-width should be a number.\";\n  }\n  // 56dp is the base unit width for Dialogs.\n  // With 5 units being the number of units for a mobile device.\n  // https://goo.gl/sK2O5o\n  width: $units * 56px;\n}\n","/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n@import \"../variables\";\n@import \"../mixins\";\n\n.mdl-data-table {\n  position: relative;\n  border: $data-table-dividers;\n  border-collapse: collapse;\n  white-space: nowrap;\n  font-size: $data-table-font-size;\n  background-color: unquote(\"rgb(#{$color-white})\");\n\n  thead {\n    padding-bottom: 3px;\n\n    .mdl-data-table__select {\n      margin-top: 0;\n    }\n  }\n\n  tbody {\n    tr {\n      position: relative;\n      height: $data-table-row-height;\n      @include material-animation-default(0.28s);\n      transition-property: background-color;\n\n      &.is-selected {\n        background-color: $data-table-selection-color;\n      }\n\n      &:hover {\n        background-color: $data-table-hover-color;\n      }\n    }\n  }\n\n  td, th {\n    padding: 0 $data-table-column-padding 12px $data-table-column-padding;\n    text-align: right;\n\n    &:first-of-type {\n      padding-left: 24px;\n    }\n\n    &:last-of-type {\n      padding-right: 24px;\n    }\n  }\n\n  td {\n    position: relative;\n    vertical-align: middle;\n    height: $data-table-row-height;\n    border-top: $data-table-dividers;\n    border-bottom: $data-table-dividers;\n    padding-top: $data-table-cell-top;\n    box-sizing: border-box;\n\n    .mdl-data-table__select {\n      vertical-align: middle;\n    }\n  }\n\n  th {\n    position: relative;\n    vertical-align: bottom;\n    text-overflow: ellipsis;\n    @include typo-body-2();\n    height: $data-table-row-height;\n    font-size: $data-table-header-font-size;\n    color: $data-table-header-color;\n    padding-bottom: 8px;\n    box-sizing: border-box;\n\n    &.mdl-data-table__header--sorted-ascending,\n    &.mdl-data-table__header--sorted-descending {\n      color: $data-table-header-sorted-color;\n      &:before {\n        @include typo-icon;\n        font-size: $data-table-header-sort-icon-size;\n        content: \"\\e5d8\";\n        margin-right: 5px;\n        vertical-align: sub;\n      }\n      &:hover {\n        cursor: pointer;\n        &:before {\n          color: $data-table-header-sorted-icon-hover-color;\n        }\n      }\n    }\n    &.mdl-data-table__header--sorted-descending:before {\n      content: \"\\e5db\";\n    }\n  }\n}\n\n.mdl-data-table__select {\n  width: 16px;\n}\n\n.mdl-data-table__cell--non-numeric.mdl-data-table__cell--non-numeric {\n  text-align: left;\n}\n","/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n/*------------------------------------*\\\n    $CONTENTS\n\\*------------------------------------*/\n/**\n * STYLE GUIDE VARIABLES------------------Declarations of Sass variables\n * -----Typography\n * -----Colors\n * -----Textfield\n * -----Switch\n * -----Spinner\n * -----Radio\n * -----Menu\n * -----List\n * -----Layout\n * -----Icon toggles\n * -----Footer\n * -----Column\n * -----Checkbox\n * -----Card\n * -----Button\n * -----Animation\n * -----Progress\n * -----Badge\n * -----Shadows\n * -----Grid\n * -----Data table\n * -----Dialog\n * -----Snackbar\n * -----Tooltip\n * -----Chip\n *\n * Even though all variables have the `!default` directive, most of them\n * should not be changed as they are dependent one another. This can cause\n * visual distortions (like alignment issues) that are hard to track down\n * and fix.\n */\n\n\n/* ==========  TYPOGRAPHY  ========== */\n\n/* We're splitting fonts into \"preferred\" and \"performance\" in order to optimize\n   page loading. For important text, such as the body, we want it to load\n   immediately and not wait for the web font load, whereas for other sections,\n   such as headers and titles, we're OK with things taking a bit longer to load.\n   We do have some optional classes and parameters in the mixins, in case you\n   definitely want to make sure you're using the preferred font and don't mind\n   the performance hit.\n   We should be able to improve on this once CSS Font Loading L3 becomes more\n   widely available.\n*/\n$preferred_font: 'Roboto', 'Helvetica', 'Arial', sans-serif !default;\n$performance_font: 'Helvetica', 'Arial', sans-serif !default;\n\n/* ==========  COLORS  ========== */\n\n/**\n*\n* Material design color palettes.\n* @see http://www.google.com/design/spec/style/color.html\n*\n**/\n\n@import \"color-definitions\";\n@import \"functions\";\n\n/* ==========  IMAGES  ========== */\n$image_path: '/images' !default;\n\n/* ==========  Color & Themes  ========== */\n\n// Define whether individual color palette items should have classes created.\n// Setting this to true will remove individual color classes for each color in the palettes.\n// To improve overall performance (assuming they aren't used) by:\n// * Saving server bandwidth sending the extra classes\n// * Save client computation against the classes\n// it is RECOMMENDED you set this to true.\n$trim-color-classes: false !default;\n\n// Use color primarily for emphasis. Choose colors that fit with\n// your brand and provide good contrast between visual components.\n$color-primary: $palette-indigo-500 !default;\n$color-primary-dark: $palette-indigo-700 !default;\n$color-accent: $palette-pink-A200 !default;\n\n// Our primary is dark, so use $color-dark-contrast for overlaid text.\n$color-primary-contrast: $color-dark-contrast !default;\n// Our accent is dark, so use $color-dark-contrast for overlaid text.\n$color-accent-contrast: $color-dark-contrast !default;\n\n// Replace all colors with placeholders if we're generating a template.\n@if $styleguide-generate-template == true {\n  $color-primary: '$color-primary';\n  $color-primary-dark: '$color-primary-dark';\n  $color-accent: '$color-accent';\n  $color-primary-contrast: '$color-primary-contrast';\n  $color-accent-contrast: '$color-accent-contrast';\n}\n\n/* ==========  Typography  ========== */\n\n// We use the following default color styles: text-color-primary and\n// text-color-secondary. For light themes, use text-color-primary-inverse\n// and text-color-secondary-inverse.\n\n$text-color-primary: unquote(\"rgba(#{$color-black}, 0.87)\") !default;\n$text-link-color: unquote(\"rgb(#{$color-accent})\") !default;\n\n// Define whether to target elements directly for typographic enhancements.\n// Turning this off means you need to use mdl-* classes more often.\n// Other components may also fail to adhere to MD without these rules.\n// It is strongly recommended you leave this as true.\n\n$target-elements-directly: true !default;\n\n/* ==========  Components  ========== */\n\n/* ==========  Standard Buttons  ========== */\n\n// Default button colors.\n$button-primary-color: unquote(\"rgba(#{$palette-grey-500}, 0.20)\") !default;\n$button-secondary-color: unquote(\"rgb(#{$color-black})\") !default;\n$button-hover-color: $button-primary-color !default;\n$button-active-color: unquote(\"rgba(#{$palette-grey-500}, 0.40)\") !default;\n$button-focus-color: unquote(\"rgba(#{$color-black}, 0.12)\") !default;\n\n// Colored button colors.\n$button-primary-color-alt: unquote(\"rgb(#{$color-primary})\") !default;\n$button-secondary-color-alt: unquote(\"rgb(#{$color-primary-contrast})\") !default;\n$button-hover-color-alt: unquote(\"rgb(#{$color-primary})\") !default;\n$button-active-color-alt: unquote(\"rgb(#{$color-primary})\") !default;\n$button-focus-color-alt: $button-focus-color !default;\n\n// Ripple color for colored raised buttons.\n$button-ripple-color-alt: unquote(\"rgb(#{$color-primary-contrast})\") !default;\n\n// Disabled button colors.\n$button-primary-color-disabled: unquote(\"rgba(#{$color-black}, 0.12)\") !default;\n$button-secondary-color-disabled: unquote(\"rgba(#{$color-black}, 0.26)\") !default;\n\n// FAB colors and sizes.\n$button-fab-color-alt: unquote(\"rgb(#{$color-accent})\") !default;\n$button-fab-hover-color-alt: unquote(\"rgb(#{$color-accent})\") !default;\n$button-fab-active-color-alt: unquote(\"rgb(#{$color-accent})\") !default;\n$button-fab-text-color-alt: unquote(\"rgb(#{$color-accent-contrast})\") !default;\n$button-fab-ripple-color-alt: unquote(\"rgb(#{$color-accent-contrast})\") !default;\n\n// Icon button colors and sizes.\n$button-icon-color: unquote(\"rgb(#{$palette-grey-700})\") !default;\n$button-icon-focus-color: $button-focus-color !default;\n\n/* ==========  Icon Toggles  ========== */\n\n$icon-toggle-color: unquote(\"rgb(#{$palette-grey-700})\") !default;\n$icon-toggle-focus-color: $button-focus-color !default;\n$icon-toggle-checked-color: unquote(\"rgb(#{$color-primary})\") !default;\n$icon-toggle-checked-focus-color: unquote(\"rgba(#{$color-primary}, 0.26)\") !default;\n$icon-toggle-disabled-color: unquote(\"rgba(#{$color-black}, 0.26)\") !default;\n\n/* ==========  Radio Buttons  ========== */\n\n$radio-color: unquote(\"rgb(#{$color-primary})\") !default;\n$radio-off-color: unquote(\"rgba(#{$color-black}, 0.54)\") !default;\n$radio-disabled-color: unquote(\"rgba(#{$color-black}, 0.26)\") !default;\n\n/* ==========  Ripple effect  ========== */\n\n$ripple-bg-color: unquote(\"rgb(#{$color-light-contrast})\") !default;\n\n/* ==========  Layout  ========== */\n\n$layout-nav-color: unquote(\"rgb(#{$palette-grey-300})\") !default;\n\n// Drawer\n$layout-drawer-bg-color: unquote(\"rgb(#{$palette-grey-50})\") !default;\n$layout-drawer-border-color: unquote(\"rgb(#{$palette-grey-300})\") !default;\n$layout-text-color: unquote(\"rgb(#{$palette-grey-800})\") !default;\n$layout-drawer-navigation-color: #757575 !default;\n$layout-drawer-navigation-link-active-background: unquote(\"rgb(#{$palette-grey-300})\") !default;\n$layout-drawer-navigation-link-active-color: unquote(\"rgb(#{$color-light-contrast})\") !default;\n\n// Header\n$layout-header-bg-color: unquote(\"rgb(#{$color-primary})\") !default;\n$layout-header-text-color: unquote(\"rgb(#{$color-primary-contrast})\") !default;\n$layout-header-nav-hover-color: unquote(\"rgba(#{$palette-grey-700}, 0.6)\") !default;\n$layout-header-tab-text-color: unquote(\"rgba(#{$color-primary-contrast}, 0.6)\") !default;\n\n// Tabs\n$layout-header-tab-highlight: unquote(\"rgb(#{$color-accent})\") !default;\n\n/* ==========  Content Tabs  ========== */\n\n$tab-highlight-color: unquote(\"rgb(#{$color-primary})\") !default;\n$tab-text-color: unquote(\"rgba(#{$color-black}, 0.54)\") !default;\n$tab-active-text-color: unquote(\"rgba(#{$color-black}, 0.87)\") !default;\n$tab-border-color: unquote(\"rgb(#{$palette-grey-300})\") !default;\n\n/* ==========  Checkboxes  ========== */\n\n$checkbox-color: unquote(\"rgb(#{$color-primary})\") !default;\n$checkbox-off-color: unquote(\"rgba(#{$color-black}, 0.54)\") !default;\n$checkbox-disabled-color: unquote(\"rgba(#{$color-black}, 0.26)\") !default;\n$checkbox-focus-color: unquote(\"rgba(#{$color-primary}, 0.26)\") !default;\n$checkbox-image-path: $image_path;\n\n/* ==========  Switches  ========== */\n\n$switch-color: unquote(\"rgb(#{$color-primary})\") !default;\n$switch-faded-color: unquote(\"rgba(#{$color-primary}, 0.26)\") !default;\n$switch-thumb-color: $switch-color !default;\n$switch-track-color: unquote(\"rgba(#{$color-primary}, 0.5)\") !default;\n\n$switch-off-thumb-color: unquote(\"rgb(#{$palette-grey-50})\") !default;\n$switch-off-track-color: unquote(\"rgba(#{$color-black}, 0.26)\") !default;\n$switch-disabled-thumb-color: unquote(\"rgb(#{$palette-grey-400})\") !default;\n$switch-disabled-track-color: unquote(\"rgba(#{$color-black}, 0.12)\") !default;\n\n/* ==========  Spinner  ========== */\n\n$spinner-color-1: unquote(\"rgb(#{$palette-blue-400})\") !default;\n$spinner-color-2: unquote(\"rgb(#{$palette-red-500})\") !default;\n$spinner-color-3: unquote(\"rgb(#{$palette-yellow-600})\") !default;\n$spinner-color-4: unquote(\"rgb(#{$palette-green-500})\") !default;\n\n$spinner-single-color: unquote(\"rgb(#{$color-primary})\") !default;\n\n/* ==========  Text fields  ========== */\n\n$input-text-background-color: transparent !default;\n$input-text-label-color: unquote(\"rgba(#{$color-black}, 0.26)\") !default;\n$input-text-bottom-border-color: unquote(\"rgba(#{$color-black}, 0.12)\") !default;\n$input-text-highlight-color: unquote(\"rgb(#{$color-primary})\") !default;\n$input-text-disabled-color: $input-text-bottom-border-color !default;\n$input-text-disabled-text-color: $input-text-label-color !default;\n$input-text-error-color: unquote(\"rgb(#{$palette-red-A700})\") !default;\n\n/* ==========  Card  ========== */\n\n$card-background-color: unquote(\"rgb(#{$color-white})\") !default;\n$card-text-color: unquote(\"rgb(#{$color-black})\") !default;\n$card-image-placeholder-color: unquote(\"rgb(#{$color-accent})\") !default;\n$card-supporting-text-text-color: unquote(\"rgba(#{$color-black}, 0.54)\") !default;\n$card-border-color: rgba(0,0,0,0.1) !default;\n$card-subtitle-color: unquote(\"rgba(#{$color-black}, 0.54)\") !default;\n\n/* ==========  Sliders ========== */\n\n$range-bg-color: unquote(\"rgba(#{$color-black}, 0.26)\") !default;\n$range-color: unquote(\"rgb(#{$color-primary})\") !default;\n$range-faded-color: unquote(\"rgba(#{$color-primary}, 0.26)\") !default;\n$range-bg-focus-color: unquote(\"rgba(#{$color-black}, 0.12)\") !default;\n\n/* ========== Progress ========== */\n$progress-main-color: unquote(\"rgb(#{$color-primary})\") !default;\n$progress-secondary-color: unquote(\"rgba(#{$color-primary-contrast}, 0.7)\") !default;\n$progress-fallback-buffer-color: unquote(\"rgba(#{$color-primary-contrast}, 0.9)\") !default;\n$progress-image-path: $image_path;\n\n/* ==========  List ========== */\n\n$list-main-text-text-color: unquote(\"rgba(#{$color-black}, 0.87)\") !default;\n$list-supporting-text-text-color: unquote(\"rgba(#{$color-black}, 0.54)\") !default;\n$list-icon-color: unquote(\"rgb(#{$palette-grey-600})\") !default;\n$list-avatar-color: white !default;\n\n/* ==========  Item ========== */\n\n// Default Item Colors\n$default-item-text-color: unquote(\"rgba(#{$color-black}, 0.87)\") !default;\n$default-item-outline-color: unquote(\"rgb(#{$palette-grey-400})\") !default;\n$default-item-hover-bg-color: unquote(\"rgb(#{$palette-grey-200})\") !default;\n$default-item-focus-bg-color: unquote(\"rgb(#{$palette-grey-200})\") !default;\n$default-item-active-bg-color: unquote(\"rgb(#{$palette-grey-300})\") !default;\n$default-item-divider-color: unquote(\"rgba(#{$color-black}, 0.12)\") !default;\n\n// Disabled Button Colors\n$disabled-item-text-color: unquote(\"rgb(#{$palette-grey-400})\") !default;\n\n/* ==========  Dropdown menu ========== */\n\n$default-dropdown-bg-color: unquote(\"rgb(#{$color-white})\") !default;\n\n/* ==========  Tooltips  ========== */\n\n$tooltip-text-color: unquote(\"rgb(#{$color-white})\") !default;\n$tooltip-background-color: unquote(\"rgba(#{$palette-grey-700}, 0.9)\") !default;\n\n/* ==========  Footer  ========== */\n\n$footer-bg-color: unquote(\"rgb(#{$palette-grey-800})\") !default;\n$footer-color: unquote(\"rgb(#{$palette-grey-500})\") !default;\n$footer-heading-color: unquote(\"rgb(#{$palette-grey-300})\") !default;\n$footer-button-fill-color: $footer-color !default;\n$footer-underline-color: $footer-color !default;\n\n\n/* TEXTFIELD */\n\n$input-text-font-size: 16px !default;\n$input-text-width: 100% !default;\n$input-text-padding: 4px !default;\n$input-text-vertical-spacing: 20px !default;\n\n$input-text-button-size: 32px !default;\n$input-text-floating-label-fontsize: 12px !default;\n$input-text-expandable-icon-top: 16px !default;\n\n\n/* SWITCH */\n\n$switch-label-font-size: 16px !default;\n$switch-label-height: 24px !default;\n$switch-track-height: 14px !default;\n$switch-track-length: 36px !default;\n$switch-thumb-size: 20px !default;\n$switch-track-top: ($switch-label-height - $switch-track-height) / 2 !default;\n$switch-thumb-top: ($switch-label-height - $switch-thumb-size) / 2 !default;\n$switch-ripple-size: $switch-label-height * 2 !default;\n$switch-helper-size: 8px !default;\n\n/* SPINNER */\n\n$spinner-size: 28px !default;\n$spinner-stroke-width: 3px !default;\n\n// Amount of circle the arc takes up.\n$spinner-arc-size: 270deg !default;\n// Time it takes to expand and contract arc.\n$spinner-arc-time: 1333ms !default;\n// How much the start location of the arc should rotate each time.\n$spinner-arc-start-rot: 216deg !default;\n\n$spinner-duration: 360 * $spinner-arc-time / (\n    strip-units($spinner-arc-start-rot + (360deg - $spinner-arc-size)));\n\n\n/* RADIO */\n\n$radio-label-font-size: 16px !default;\n$radio-label-height: 24px !default;\n$radio-button-size: 16px !default;\n$radio-inner-margin: $radio-button-size / 4;\n$radio-padding: 8px !default;\n$radio-top-offset: ($radio-label-height - $radio-button-size) / 2;\n$radio-ripple-size: 42px !default;\n\n\n/* MENU */\n\n$menu-expand-duration: 0.3s !default;\n$menu-fade-duration: 0.2s !default;\n\n/* LIST */\n\n$list-border: 8px !default;\n$list-min-height: 48px !default;\n$list-min-padding: 16px !default;\n$list-bottom-padding: 20px !default;\n$list-avatar-text-left-distance: 72px !default;\n$list-icon-text-left-distance: 72px !default;\n\n$list-avatar-size: 40px !default;\n$list-icon-size: 24px !default;\n\n$list-two-line-height: 72px !default;\n$list-three-line-height: 88px !default;\n\n/* LAYOUT */\n\n$layout-drawer-narrow: 240px !default;\n$layout-drawer-wide: 456px !default;\n$layout-drawer-width: $layout-drawer-narrow !default;\n\n$layout-header-icon-size: 32px !default;\n$layout-screen-size-threshold: 1024px !default;\n$layout-header-icon-margin: 24px !default;\n$layout-drawer-button-mobile-size: 32px !default;\n$layout-drawer-button-desktop-size: 48px !default;\n\n$layout-header-mobile-row-height: 56px !default;\n$layout-mobile-header-height: $layout-header-mobile-row-height;\n$layout-header-desktop-row-height: 64px !default;\n$layout-desktop-header-height: $layout-header-desktop-row-height;\n\n$layout-header-desktop-baseline: 80px !default;\n$layout-header-mobile-baseline: 72px !default;\n$layout-header-mobile-indent: 16px !default;\n$layout-header-desktop-indent: 40px !default;\n\n$layout-tab-font-size: 14px !default;\n$layout-tab-bar-height: 48px !default;\n$layout-tab-mobile-padding: 12px !default;\n$layout-tab-desktop-padding: 24px !default;\n$layout-tab-highlight-thickness: 2px !default;\n\n\n/* ICON TOGGLE */\n\n$icon-toggle-size: 32px !default;\n$icon-toggle-font-size: 24px !default;\n$icon-toggle-ripple-size: 36px !default;\n\n/* FOOTER */\n\n/*mega-footer*/\n$footer-min-padding: 16px !default;\n$footer-padding-sides: 40px !default;\n$footer-heading-font-size: 14px !default;\n$footer-heading-line-height: (1.7 * $footer-heading-font-size) !default;\n$footer-btn-size: 36px  !default;\n\n/*mini-footer*/\n$padding: 16px !default;\n$footer-heading-font-size: 24px !default;\n$footer-heading-line-height: (1.5 * $footer-heading-font-size) !default;\n$footer-btn-size: 36px !default;\n\n/* CHECKBOX */\n\n$checkbox-label-font-size: 16px !default;\n$checkbox-label-height: 24px !default;\n$checkbox-button-size: 16px !default;\n$checkbox-inner-margin: 2px !default;\n$checkbox-padding: 8px !default;\n$checkbox-top-offset:\n($checkbox-label-height - $checkbox-button-size - $checkbox-inner-margin) / 2;\n$checkbox-ripple-size: $checkbox-label-height * 1.5;\n\n/* CARD */\n\n/* Card dimensions */\n$card-width: 330px !default;\n$card-height: 200px !default;\n$card-font-size: 16px !default;\n$card-title-font-size: 24px !default;\n$card-subtitle-font-size: 14px !default;\n$card-horizontal-padding: 16px !default;\n$card-vertical-padding: 16px !default;\n\n$card-title-perspective-origin-x: 165px !default;\n$card-title-perspective-origin-y: 56px !default;\n\n$card-title-transform-origin-x: 165px !default;\n$card-title-transform-origin-y: 56px !default;\n\n$card-title-text-transform-origin-x: 149px !default;\n$card-title-text-transform-origin-y: 48px !default;\n\n$card-supporting-text-font-size: 1rem !default;\n$card-supporting-text-line-height: 18px !default;\n\n$card-actions-font-size: 16px !default;\n\n$card-title-text-font-weight: 300 !default;\n$card-z-index: 1 !default;\n\n/* Cover image */\n$card-cover-image-height: 186px !default;\n$card-background-image-url: '' !default;\n\n\n/* BUTTON */\n/**\n *\n * Dimensions\n *\n */\n$button-min-width: 64px !default;\n$button-height: 36px !default;\n$button-padding: 16px !default;\n$button-margin: 4px !default;\n$button-border-radius: 2px !default;\n\n$button-fab-size: 56px !default;\n$button-fab-size-mini: 40px !default;\n$button-fab-font-size: 24px !default;\n\n$button-icon-size: 32px !default;\n$button-icon-size-mini: 24px !default;\n\n\n/* ANIMATION */\n$animation-curve-fast-out-slow-in: cubic-bezier(0.4, 0, 0.2, 1) !default;\n$animation-curve-linear-out-slow-in: cubic-bezier(0, 0, 0.2, 1) !default;\n$animation-curve-fast-out-linear-in: cubic-bezier(0.4, 0, 1, 1) !default;\n\n$animation-curve-default: $animation-curve-fast-out-slow-in !default;\n\n\n/* PROGRESS */\n$bar-height: 4px !default;\n\n/* BADGE */\n$badge-font-size: 12px !default;\n$badge-color: unquote(\"rgb(#{$color-accent-contrast})\") !default;\n$badge-color-inverse: unquote(\"rgb(#{$color-accent})\") !default;\n$badge-background: unquote(\"rgb(#{$color-accent})\") !default;\n$badge-background-inverse: unquote(\"rgba(#{$color-accent-contrast},0.2)\") !default;\n$badge-size : 22px !default;\n$badge-padding: 2px !default;\n$badge-overlap: 12px !default;\n\n/* SHADOWS */\n\n$shadow-key-umbra-opacity: 0.2 !default;\n$shadow-key-penumbra-opacity: 0.14 !default;\n$shadow-ambient-shadow-opacity: 0.12 !default;\n\n/* GRID */\n\n$grid-desktop-columns: 12 !default;\n$grid-desktop-gutter: 16px !default;\n$grid-desktop-margin: 16px !default;\n\n$grid-desktop-breakpoint: 840px !default;\n\n$grid-tablet-columns: 8 !default;\n$grid-tablet-gutter: $grid-desktop-gutter !default;\n$grid-tablet-margin: $grid-desktop-margin !default;\n\n$grid-tablet-breakpoint: 480px !default;\n\n$grid-phone-columns: 4 !default;\n$grid-phone-gutter: $grid-desktop-gutter !default;\n$grid-phone-margin: $grid-desktop-margin !default;\n\n$grid-cell-default-columns: $grid-phone-columns !default;\n$grid-max-columns: $grid-desktop-columns !default;\n\n/* DATA TABLE */\n\n$data-table-font-size: 13px !default;\n$data-table-header-font-size: 12px !default;\n$data-table-header-sort-icon-size: 16px !default;\n\n$data-table-header-color: rgba(#000, 0.54) !default;\n$data-table-header-sorted-color: rgba(#000, 0.87) !default;\n$data-table-header-sorted-icon-hover-color: rgba(#000, 0.26) !default;\n$data-table-divider-color: rgba(#000, 0.12) !default;\n\n$data-table-hover-color: #eeeeee !default;\n$data-table-selection-color: #e0e0e0 !default;\n\n$data-table-dividers: 1px solid $data-table-divider-color !default;\n\n$data-table-row-height: 48px !default;\n$data-table-last-row-height: 56px !default;\n$data-table-header-height: 56px !default;\n\n$data-table-column-spacing: 36px !default;\n$data-table-column-padding: $data-table-column-spacing / 2;\n\n$data-table-card-header-height: 64px !default;\n$data-table-card-title-top: 20px !default;\n$data-table-card-padding: 24px !default;\n$data-table-button-padding-right: 16px !default;\n$data-table-cell-top: $data-table-card-padding / 2;\n\n/* DIALOG */\n$dialog-content-color: $card-supporting-text-text-color;\n\n/* SNACKBAR */\n\n// Hard coded since the color is not present in any palette.\n$snackbar-background-color: #323232 !default;\n$snackbar-tablet-breakpoint: $grid-tablet-breakpoint;\n$snackbar-action-color: unquote(\"rgb(#{$color-accent})\") !default;\n\n/* TOOLTIP */\n$tooltip-font-size: 10px !default;\n$tooltip-font-size-large: 14px !default;\n\n/* CHIP */\n$chip-bg-color: rgb(222, 222, 222) !default;\n$chip-bg-active-color: rgb(214, 214, 214) !default;\n$chip-height: 32px !default;\n$chip-font-size: 13px !default; \n","/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      https:///www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n@import \"../variables\";\n\n.mdl-mini-footer {\n  display: flex;\n  flex-flow: row wrap;\n  justify-content: space-between;\n\n  padding: ($padding * 2) $padding;\n\n  color: $footer-color;\n  background-color: $footer-bg-color;\n\n  &:after {\n    content: '';\n    display: block;\n  }\n\n  & .mdl-logo {\n    line-height: $footer-btn-size;\n  }\n}\n\n.mdl-mini-footer--link-list,\n.mdl-mini-footer__link-list {\n  display: flex;\n  flex-flow: row nowrap;\n\n  list-style: none;\n\n  margin: 0;\n  padding: 0;\n\n  & li {\n    margin-bottom: 0;\n    margin-right: $padding;\n\n    @media screen and (min-width: 760px) {\n      line-height: $footer-btn-size;\n    }\n  }\n\n  & a {\n    color: inherit;\n    text-decoration: none;\n    white-space: nowrap;\n  }\n}\n\n.mdl-mini-footer--left-section,\n.mdl-mini-footer__left-section {\n  display: inline-block;\n  order: 0;\n}\n\n.mdl-mini-footer--right-section,\n.mdl-mini-footer__right-section {\n  display: inline-block;\n  order: 1;\n}\n\n.mdl-mini-footer--social-btn,\n.mdl-mini-footer__social-btn {\n  width: $footer-btn-size;\n  height: $footer-btn-size;\n\n  padding: 0;\n  margin: 0;\n\n  background-color: $footer-button-fill-color;\n\n  border: none;\n}\n","/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n@import \"../variables\";\n\n.mdl-card {\n  display: flex;\n  flex-direction: column;\n  font-size: $card-font-size;\n  font-weight: 400;\n  min-height: $card-height;\n  overflow: hidden;\n  width: $card-width;\n  z-index: $card-z-index;\n  position: relative;\n  background: $card-background-color;\n  border-radius: 2px;\n  box-sizing: border-box;\n}\n\n.mdl-card__media {\n  background-color: $card-image-placeholder-color;\n  background-repeat: repeat;\n  background-position: 50% 50%;\n  background-size: cover;\n  background-origin: padding-box;\n  background-attachment: scroll;\n  box-sizing: border-box;\n}\n\n.mdl-card__title {\n  align-items: center;\n  color: $card-text-color;\n  display: block;\n  display: flex;\n  justify-content: stretch;\n  line-height: normal;\n  padding: $card-vertical-padding $card-horizontal-padding;\n  perspective-origin: $card-title-perspective-origin-x $card-title-perspective-origin-y;\n  transform-origin: $card-title-transform-origin-x $card-title-transform-origin-y;\n  box-sizing: border-box;\n\n  &.mdl-card--border {\n    border-bottom: 1px solid $card-border-color;\n  }\n}\n\n.mdl-card__title-text {\n  align-self: flex-end;\n  color: inherit;\n  display: block;\n  display: flex;\n  font-size: $card-title-font-size;\n  font-weight: $card-title-text-font-weight;\n  line-height: normal;\n  overflow: hidden;\n  transform-origin: $card-title-text-transform-origin-x $card-title-text-transform-origin-y;\n  margin: 0;\n}\n\n.mdl-card__subtitle-text {\n  font-size: $card-subtitle-font-size;\n  color: $card-subtitle-color;\n  margin: 0;\n}\n\n.mdl-card__supporting-text {\n  color: $card-supporting-text-text-color;\n  font-size: $card-supporting-text-font-size;\n  line-height: $card-supporting-text-line-height;\n  overflow: hidden;\n  padding: $card-vertical-padding $card-horizontal-padding;\n  width: 90%;\n\n  &.mdl-card--border {\n    border-bottom: 1px solid $card-border-color;\n  }\n}\n\n.mdl-card__actions {\n  font-size: $card-actions-font-size;\n  line-height: normal;\n  width: 100%;\n  background-color: rgba(0,0,0,0);\n  padding: 8px;\n  box-sizing: border-box;\n\n  &.mdl-card--border {\n    border-top: 1px solid $card-border-color;\n  }\n}\n\n.mdl-card--expand {\n  flex-grow: 1;\n}\n\n\n.mdl-card__menu {\n  position: absolute;\n  right: 16px;\n  top: 16px;\n}\n","/**\n * Copyright 2015 Google Inc. All Rights Reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      https:///www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n@import \"../variables\";\n@import \"../mixins\";\n\n// The button component. Defaults to a flat button.\n.mdl-button {\n  background: transparent;\n  border: none;\n  border-radius: $button-border-radius;\n  color: $button-secondary-color;\n  position: relative;\n  height: $button-height;\n  margin: 0;\n  min-width: $button-min-width;\n  padding: 0 $button-padding;\n  display: inline-block;\n  @include typo-button();\n  overflow: hidden;\n  will-change: box-shadow;\n  transition: box-shadow 0.2s $animation-curve-fast-out-linear-in,\n              background-color 0.2s $animation-curve-default,\n              color 0.2s $animation-curve-default;\n  outline: none;\n  cursor: pointer;\n  text-decoration: none;\n  text-align: center;\n  line-height: $button-height;\n  vertical-align: middle;\n\n  &::-moz-focus-inner {\n    border: 0;\n  }\n\n  &:hover {\n    background-color: $button-hover-color;\n  }\n\n  &:focus:not(:active) {\n    background-color: $button-focus-color;\n  }\n\n  &:active {\n    background-color: $button-active-color;\n  }\n\n  &.mdl-button--colored {\n    color: $button-primary-color-alt;\n\n    &:focus:not(:active) {\n      background-color: $button-focus-color-alt;\n    }\n  }\n}\n\ninput.mdl-button[type=\"submit\"] {\n  -webkit-appearance:none;\n}\n\n  // Raised buttons\n  .mdl-button--raised {\n    background: $button-primary-color;\n    @include shadow-2dp();\n\n    &:active {\n      @include shadow-4dp();\n      background-color: $button-active-color;\n    }\n\n    &:focus:not(:active) {\n      @include focus-shadow();\n      background-color: $button-active-color;\n    }\n\n    &.mdl-button--colored {\n      background: $button-primary-color-alt;\n      color: $button-secondary-color-alt;\n\n      &:hover {\n        background-color: $button-hover-color-alt;\n      }\n\n      &:active {\n        background-color: $button-active-color-alt;\n      }\n\n      &:focus:not(:active) {\n        background-color: $button-active-color-alt;\n      }\n\n      & .mdl-ripple {\n        background: $button-ripple-color-alt;\n      }\n    }\n  }\n\n\n  // FABs\n  .mdl-button--fab {\n    border-radius: 50%;\n    font-size: $button-fab-font-size;\n    height: $button-fab-size;\n    margin: auto;\n    min-width: $button-fab-size;\n    width: $button-fab-size;\n    padding: 0;\n    overflow: hidden;\n    background: $button-primary-color;\n    box-shadow: 0 1px 1.5px 0 rgba(0,0,0,0.12), 0 1px 1px 0 rgba(0,0,0,0.24);\n    position: relative;\n    line-height: normal;\n\n    & .material-icons {\n      position: absolute;\n      top: 50%;\n      left: 50%;\n      transform: translate(- $button-fab-font-size / 2, - $button-fab-font-size / 2);\n      line-height: $button-fab-font-size;\n      width: $button-fab-font-size;\n    }\n\n    &.mdl-button--mini-fab {\n      height: $button-fab-size-mini;\n      min-width: $button-fab-size-mini;\n      width: $button-fab-size-mini;\n    }\n\n    & .mdl-button__ripple-container {\n      border-radius: 50%;\n      // Fixes clipping bug in Safari.\n      -webkit-mask-image: -webkit-radial-gradient(circle, white, black);\n    }\n\n    &:active {\n      @include shadow-4dp();\n      background-color: $button-active-color;\n    }\n\n    &:focus:not(:active) {\n      @include focus-shadow();\n      background-color: $button-active-color;\n    }\n\n    &.mdl-button--colored {\n      background: $button-fab-color-alt;\n      color: $button-fab-text-color-alt;\n\n      &:hover {\n        background-color: $button-fab-hover-color-alt;\n      }\n\n      &:focus:not(:active) {\n        background-color: $button-fab-active-color-alt;\n      }\n\n      &:active {\n        background-color: $button-fab-active-color-alt;\n      }\n\n      & .mdl-ripple {\n        background: $button-fab-ripple-color-alt;\n      }\n    }\n  }\n\n\n  // Icon buttons\n  .mdl-button--icon {\n    border-radius: 50%;\n    font-size: $button-fab-font-size;\n    height: $button-icon-size;\n    margin-left: 0;\n    margin-right: 0;\n    min-width: $button-icon-size;\n    width: $button-icon-size;\n    padding: 0;\n    overflow: hidden;\n    color: inherit;\n    line-height: normal;\n\n    & .material-icons {\n      position: absolute;\n      top: 50%;\n      left: 50%;\n      transform: translate(- $button-fab-font-size / 2, - $button-fab-font-size / 2);\n      line-height: $button-fab-font-size;\n      width: $button-fab-font-size;\n    }\n\n    &.mdl-button--mini-icon {\n      height: $button-icon-size-mini;\n      min-width: $button-icon-size-mini;\n      width: $button-icon-size-mini;\n\n      & .material-icons {\n        top: ($button-icon-size-mini - $button-fab-font-size) / 2;\n        left: ($button-icon-size-mini - $button-fab-font-size) / 2;\n      }\n    }\n\n    & .mdl-button__ripple-container {\n      border-radius: 50%;\n      // Fixes clipping bug in Safari.\n      -webkit-mask-image: -webkit-radial-gradient(circle, white, black);\n    }\n  }\n\n\n  // Ripples\n  .mdl-button__ripple-container {\n    display: block;\n    height: 100%;\n    left: 0px;\n    position: absolute;\n    top: 0px;\n    width: 100%;\n    z-index: 0;\n    overflow: hidden;\n\n    .mdl-button[disabled] & .mdl-ripple,\n    .mdl-button.mdl-button--disabled & .mdl-ripple {\n      background-color: transparent;\n    }\n  }\n\n// Colorized buttons\n\n.mdl-button--primary.mdl-button--primary {\n  color: $button-primary-color-alt;\n  & .mdl-ripple {\n    background: $button-secondary-color-alt;\n  }\n  &.mdl-button--raised, &.mdl-button--fab {\n    color: $button-secondary-color-alt;\n    background-color: $button-primary-color-alt;\n  }\n}\n\n.mdl-button--accent.mdl-button--accent {\n  color: $button-fab-color-alt;\n  & .mdl-ripple {\n    background: $button-fab-text-color-alt;\n  }\n  &.mdl-button--raised, &.mdl-button--fab {\n    color: $button-fab-text-color-alt;\n    background-color: $button-fab-color-alt;\n  }\n}\n\n// Disabled buttons\n\n.mdl-button {\n  // Bump up specificity by using [disabled] twice.\n  &[disabled][disabled],\n  &.mdl-button--disabled.mdl-button--disabled {\n    color: $button-secondary-color-disabled;\n    cursor: default;\n    background-color: transparent;\n  }\n\n  &--fab {\n    // Bump up specificity by using [disabled] twice.\n    &[disabled][disabled],\n    &.mdl-button--disabled.mdl-button--disabled {\n      background-color: $button-primary-color-disabled;\n      color: $button-secondary-color-disabled;\n    }\n  }\n\n  &--raised {\n    // Bump up specificity by using [disabled] twice.\n    &[disabled][disabled],\n    &.mdl-button--disabled.mdl-button--disabled {\n      background-color: $button-primary-color-disabled;\n      color: $button-secondary-color-disabled;\n      box-shadow: none;\n    }\n  }\n  &--colored {\n    // Bump up specificity by using [disabled] twice.\n    &[disabled][disabled],\n    &.mdl-button--disabled.mdl-button--disabled {\n      color: $button-secondary-color-disabled;\n    }\n  }\n}\n\n// Align icons inside buttons with text\n.mdl-button .material-icons {\n  vertical-align: middle;\n}\n","// SIMPLE GRID - SASS/SCSS\n\n\n// fonts\n$font-weight-light: 300;\n$font-weight-regular: 400;\n$font-weight-heavy: 700;\n\n// colors\n$dark-grey: #333447;\n$dark-gray: #333447; // for the Americans\n\n\n.font-light {\n  font-weight: $font-weight-light;\n}\n\n.font-regular {\n  font-weight: $font-weight-regular;\n}\n\n.font-heavy {\n  font-weight: $font-weight-heavy;\n}\n\n// utility\n\n.left {\n  text-align: left;\n}\n\n.right {\n  text-align: right;\n}\n\n.center {\n  text-align: center;\n  margin-left: auto;\n  margin-right: auto;\n}\n\n.justify {\n  text-align: justify;\n}\n\n.hidden-sm {\n  display: none;\n}\n\n// grid\n\n$width: 98%;\n$gutter: 2%;\n$breakpoint-small: 33.75em; // 540px\n$breakpoint-med: 45em; // 720px\n$breakpoint-large: 60em; // 960px\n\n.container {\n  width: 100%;\n  margin-left: auto;\n  margin-right: auto;\n\n  @media only screen and (min-width: $breakpoint-small) {\n    width: 80%;\n  }\n\n  @media only screen and (min-width: $breakpoint-large) {\n    width: 75%;\n    max-width: 60rem;\n  }\n}\n\n.row {\n  position: relative;\n  width: 100%;\n}\n\n.row [class^=\"col\"] {\n  float: left;\n  margin: 0.5rem 1%;\n  min-height: 0.125rem;\n}\n\n.row::after {\n  content: \"\";\n  display: table;\n  clear: both;\n}\n\n.col-1,\n.col-2,\n.col-3,\n.col-4,\n.col-5,\n.col-6,\n.col-7,\n.col-8,\n.col-9,\n.col-10,\n.col-11,\n.col-12 {\n  width: $width;\n}\n\n.col-1-sm {\n  width: ($width / 12) - ($gutter * 11 / 12);\n}\n\n.col-2-sm {\n  width: ($width / 6) - ($gutter * 10 / 12);\n}\n\n.col-3-sm {\n  width: ($width / 4) - ($gutter * 9 / 12);\n}\n\n.col-4-sm {\n  width: ($width / 3) - ($gutter * 8 / 12);\n}\n\n.col-5-sm {\n  width: ($width / (12 / 5)) - ($gutter * 7 / 12);\n}\n\n.col-6-sm {\n  width: ($width / 2) - ($gutter * 6 / 12);\n}\n\n.col-7-sm {\n  width: ($width / (12 / 7)) - ($gutter * 5 / 12);\n}\n\n.col-8-sm {\n  width: ($width / (12 / 8)) - ($gutter * 4 / 12);\n}\n\n.col-9-sm {\n  width: ($width / (12 / 9)) - ($gutter * 3 / 12);\n}\n\n.col-10-sm {\n  width: ($width / (12 / 10)) - ($gutter * 2 / 12);\n}\n\n.col-11-sm {\n  width: ($width / (12 / 11)) - ($gutter * 1 / 12);\n}\n\n.col-12-sm {\n  width: $width;\n}\n\n@media only screen and (min-width: $breakpoint-med) {\n  .col-1 {\n    width: ($width / 12) - ($gutter * 11 / 12);\n  }\n  .col-2 {\n    width: ($width / 6) - ($gutter * 10 / 12);\n  }\n  .col-3 {\n    width: ($width / 4) - ($gutter * 9 / 12);\n  }\n  .col-4 {\n    width: ($width / 3) - ($gutter * 8 / 12);\n  }\n  .col-5 {\n    width: ($width / (12 / 5)) - ($gutter * 7 / 12);\n  }\n  .col-6 {\n    width: ($width / 2) - ($gutter * 6 / 12);\n  }\n  .col-7 {\n    width: ($width / (12 / 7)) - ($gutter * 5 / 12);\n  }\n  .col-8 {\n    width: ($width / (12 / 8)) - ($gutter * 4 / 12);\n  }\n  .col-9 {\n    width: ($width / (12 / 9)) - ($gutter * 3 / 12);\n  }\n  .col-10 {\n    width: ($width / (12 / 10)) - ($gutter * 2 / 12);\n  }\n  .col-11 {\n    width: ($width / (12 / 11)) - ($gutter * 1 / 12);\n  }\n  .col-12 {\n    width: $width;\n  }\n\n  .hidden-sm {\n    display: block;\n  }\n}\n\n.row {\n  display: -webkit-box;\n  display: -webkit-flex;\n  display: -ms-flexbox;\n  display: flex;\n  flex-wrap: wrap;\n}\n\n.row > [class*='col-'] {\n  display: flex;\n  flex-direction: column;\n}\n","\n/*\nMaterial Icons\n*/\n\n.material-icons {\n    font-family: 'Material Icons';\n    font-weight: normal;\n    font-style: normal;\n    font-size: 24px;  /* Preferred icon size */\n    display: inline-block;\n    line-height: 1;\n    text-transform: none;\n    letter-spacing: normal;\n    word-wrap: normal;\n    white-space: nowrap;\n    direction: ltr;\n  \n    /* Support for all WebKit browsers. */\n    -webkit-font-smoothing: antialiased;\n    /* Support for Safari and Chrome. */\n    text-rendering: optimizeLegibility;\n  \n    /* Support for Firefox. */\n    -moz-osx-font-smoothing: grayscale;\n  \n    /* Support for IE. */\n    font-feature-settings: 'liga';\n  }","html {\n    font-size: $font_size;\n}\n\nbody {\n    display: block !important;\n    background-color: $background_color;\n    font-size: 1rem;\n    line-height: 1.5rem;\n    font-family: $body_font_family;\n}\n\n.mdl-layout__content:focus {\n    outline: none;\n }\n\nh1, h2, h3, h4, h5, h6, blockquote, span.mdl-layout-title,\na.download > code.download {\n    font-family: $body_font_family;\n}\n\nh1, h2, h3, h4, h5, h6, .toc-backref, .contents, .toctree-wrapper, .contents a, .toctree-wrapper a, .globaltoc a.current {\n    color: $color-mxnet !important;\n}\n\na {\n    text-decoration: none;\n}\n\n.page-content {\n    font-size: 1rem;\n    p, ul, ol, dl, dd, dt, table, th, td {\n        font-size: 1rem;\n    }\n}\n\n.brand {\n    color: inherit;\n    text-decoration: none;\n}\n\n.section {\n    overflow-x: auto;\n}\n\n\n/*\n *  Figure Directive Styles\n */\n img {\n    max-width: 100%;\n    display: block;\n    margin-left: auto;\n    margin-right: auto;\n }\n\ndiv.figure {\n    p.caption {\n        text-align: center;\n        margin-top: .75rem;\n\n        span.caption-number {\n            font-style: normal;\n        }\n        .caption-number::after {\n            content: \"\\00a0\";\n        }\n    }\n}\n\n.svg-icon {\n  width: 16px;\n  height: 16px;\n  display: inline-block;\n  fill: $grey-color-light;\n  padding-right: 5px;\n  padding-top: 4px;\n  vertical-align: text-top;\n}\n\n/*\n * Download Link Styles\n */\na.download > i.material-icons {\n    position: relative;\n    top: 5px;\n}\n\na.download {\n    text-decoration: none;\n}\n\n%clearfix:after {\n  content: \"\";\n  display: table;\n  clear: both;\n}\n\n.wrapper {\n  max-width: -webkit-calc(#{$content-width} - (#{$spacing-unit} * 2));\n  max-width: calc(#{$content-width} - (#{$spacing-unit} * 2));\n  margin-right: auto;\n  margin-left: auto;\n  padding-right: calc(#{$spacing-unit}+15px);\n  padding-left: $spacing-unit;\n  @extend %clearfix;\n\n  @media screen and (max-width: $on-laptop) {\n    max-width: -webkit-calc(#{$content-width} - (#{$spacing-unit}));\n    max-width: calc(#{$content-width} - (#{$spacing-unit}));\n    padding-right: $spacing-unit / 2;\n    padding-left: $spacing-unit / 2;\n  }\n}\n\n","/*\nVariables\n*/\n$font_size: 16px;\n\n$background_color: #fafafa;\n$code_background: rgba(0,0,0,.05);\n\n$code_font_family: \"Menlo\", \"DejaVu Sans Mono\", \"Liberation Mono\", \"Consolas\", \"Ubuntu Mono\", \"Courier New\", \"andale mono\", \"lucida console\", monospace !default;\n$body_font_family: -apple-system,BlinkMacSystemFont,\"Segoe UI\",Roboto,\"Helvetica Neue\",Arial,sans-serif,\"Apple Color Emoji\",\"Segoe UI Emoji\",\"Segoe UI Symbol\" !default;\n$base-font-size:   17px !default;\n\n$xl-breakpoint: 1795px;\n$lg-breakpoint: 1200px;\n$md-breakpoint: 992px;\n$sm-breakpoint: 768px;\n$xs-breakpoint: 576px;\n\n$color-primary: $palette-blue-500;\n$color-primary-dark: $palette-blue-700 !default;\n$color-accent: $palette-deep-orange-A200 !default;\n$color-primary-contrast: $color-white !default;\n$color-accent-contrast: $color-white !default;\n\n\n$base-line-height: 1.5 !default;\n$spacing-unit:     30px !default;\n\n$color-mxnet: rgb(4,140,204);\n$color-mxnet-dark: rgb(4,60,110);\n$grey-color:       #828282 !default;\n$grey-color-light: lighten($grey-color, 45%) !default;\n$grey-color-dark:  darken($grey-color, 25%) !default;\n\n$table-text-align: left !default;\n\n// Width of the content area\n$content-width:    1150px !default;\n\n$on-palm:          600px !default;\n$on-palm:          900px !default;\n$on-laptop:        1024px !default;","/**\n * Layout Styles\n */\n $layout: (\n    document: (\n        xl: (\n            width: 1400px,\n        )\n    ),\n    drawer-container: (\n        width: $layout-drawer-width,\n    ),\n    side-doc-outline: (\n        width: 230px,\n    ),\n    page-content: (\n        md: (\n            width: 90%,\n            padding: 0 5%\n        ),\n        lg: (\n            width: calc( 90% - 230px ),\n            padding: 0 5%\n        )\n    )\n);\n\n.mdl-layout {\n    margin-top: 76px;\n}\n\n\n.document {\n    width: 100%;\n    margin: 0 auto;\n    display: flex;\n\n    @media (min-width: $xl-breakpoint) {\n        width: map-get(map-get(map-get($layout, document), xl), width);\n    }\n    .page-content {\n        width: 100%;\n        margin: 0 auto;\n        padding: 0 12px;\n\n        @media (min-width: $md-breakpoint) {\n            width: map-get(map-get(map-get($layout, page-content), md), width);\n            padding: map-get(map-get(map-get($layout, page-content), md), padding);\n        }\n\n        @media (min-width: $lg-breakpoint) {\n            width: map-get(map-get(map-get($layout, page-content), lg), width);\n            padding: map-get(map-get(map-get($layout, page-content), lg), padding);\n        }\n    }\n\n    .side-doc-outline {\n        width: map-get(map-get($layout, side-doc-outline), width);\n\n        @media (max-width: $lg-breakpoint - 1) {\n            display: none;\n        } \n        &--content {\n            position: fixed;\n            overflow-x: auto;\n            overflow-y: auto;\n            width: inherit;\n            &::-webkit-scrollbar {\n                width: 6px;\n            }\n    \n            &::-webkit-scrollbar-track {\n                border-radius: 6px;\n            }\n    \n            &::-webkit-scrollbar-thumb {\n                background-color: rgba(0, 0, 0, .3);\n                border-radius: 6px;\n                box-shadow:0 0 0 1px rgba(255, 255, 255, .3);\n            }\n        }\n    }\n\n}","@keyframes float-in {\n    0% {\n        transform: translateY(0.5rem);\n        opacity: 0;\n    }\n\t100% {\n\t\ttransform: translateY(0);\n\t\topacity: 1;\n\t}\n}\n\n@keyframes float-out {\n    0% {\n        transform: translateY(0);\n        opacity: 1;\n    }\n\t100% {\n\t\ttransform: translateY(0.5rem);\n\t\topacity: 0;\n\t}\n}\n\n.page-content {\n    .headerlink {\n        display: inline-block;\n        text-decoration: none;\n        margin-left: 0.8rem;\n        color: inherit;\n        opacity: 0;\n        &:hover {\n            animation: float-in 0.2s $animation-curve-fast-out-slow-in 0s forwards;\n        }\n    }\n\n    h1, h2, h3, h4, h5, h6 {\n        .toc-backref {\n            text-decoration: none;\n        }\n        &:hover {\n            .headerlink {\n                animation: float-in 0.2s $animation-curve-fast-out-slow-in 0s forwards;\n            }\n        }\n    }\n\n    h1 {\n        font-size: 2rem;\n        line-height: 2.25rem;\n    }\n\n    h2 {\n        font-size: 1.75rem;\n        line-height: 2rem;\n        padding-top: 1.5rem;\n        margin-top: 0;\n        margin-bottom: 1rem;\n    }\n\n    h3 {\n        font-size: 1.5rem;\n        line-height: 1.75rem;\n        padding-top: 1rem;\n        margin-top: 0px;\n        margin-bottom: .75rem;\n    }\n\n    h4 {\n        font-size: 1.25rem;\n        line-height: 1.5rem;\n        padding-top: .75rem;\n        margin-top: 0px;\n        margin-bottom: .5rem;\n    }\n\n    div.page-content h5 {\n        font-size: 1.1rem;\n        line-height: 1.5rem;\n        padding-top: 2rem;\n        margin-top: 0px;\n        margin-bottom: 1rem;\n    }\n\n    div.page-content h6 {\n        font-size: 1rem;\n        line-height: 1.5rem;\n        padding-top: 2rem;\n        margin-top: 0px;\n        margin-bottom: 1rem;\n    }\n\n\n}\n","\n/*\n * Admonition Styles\n */\n $admonitions: (\n    hint: (\n        font-color: rgb(0, 188, 212),\n        background-color: rgba(0, 188, 212, 0.1),\n        icon-content: \"help_outline\"\n    ),\n    note: (\n        font-color: rgb(0, 188, 212),\n        background-color: rgba(0, 188, 212, 0.1),\n        icon-content: \"info_outline\"\n    ),\n    seealso: (\n        font-color: rgb(0, 188, 212),\n        background-color: rgba(0, 188, 212, 0.1),\n        icon-content: \"search\"\n    ),\n    warning: (\n        font-color: rgb(255, 193, 7),\n        background-color: rgba(255, 193, 7, 0.1),\n        icon-content: \"warning\"\n    ),\n    attention: (\n        font-color: rgb(255, 193, 7),\n        background-color: rgba(255, 193, 7, 0.1),\n        icon-content: \"warning\"\n    ),\n    tip: (\n        font-color: rgb(139, 195, 74),\n        background-color: rgba(139, 195, 74, 0.1),\n        icon-content: \"lightbulb_outline\"\n    ),\n    important: (\n        font-color: rgb(139, 195, 74),\n        background-color: rgba(139, 195, 74, 0.1),\n        icon-content:  \"check_circle\"\n    ),\n    error: (\n        font-color: rgb(244, 67, 54),\n        background-color: rgba(244, 67, 54, 0.1),\n        icon-content: \"error_outline\"\n    ),\n    caution: (\n        font-color: rgb(244, 67, 54),\n        background-color: rgba(244, 67, 54, 0.1),\n        icon-content: \"error_outline\"\n    ),\n    danger: (\n        font-color: rgb(244, 67, 54),\n        background-color: rgba(244, 67, 54, 0.1),\n        icon-content: \"error_outline\"\n    )\n);\n\n @mixin admonition-style($type) {\n    border-left: solid 4px map-get(map-get($admonitions, $type), font-color);\n    background-color: map-get(map-get($admonitions, $type), background-color);\n    .admonition-title {\n        font-size: 16px;\n        font-weight: bold;\n        color: map-get(map-get($admonitions, $type), font-color);\n\n        margin-top: 4px;\n        margin-bottom: 8px;\n        &::before {\n            @extend .material-icons;\n            position: relative;\n            margin-right: 5px;\n            top: 3px;\n            content: map-get(map-get($admonitions, $type), icon-content);\n            font-size: 18px;\n        }\n    }\n}\n\n.admonition {\n    @extend .mdl-shadow--2dp;\n\n    padding: 12px 20px;\n    margin-top: 10px;\n    margin-bottom: 10px;\n    p.last {\n        margin: 16px;\n    }\n    .admonition-title {\n        font-size: 16px;\n        font-weight: bold;\n        color: #555;\n        text-transform: uppercase;\n        margin-top: 7px;\n    }\n\n    @each $type in (note, seealso, hint, warning, attention, tip, important, error, caution, danger) {\n        &.#{$type} {\n            @include admonition-style($type);\n        }\n    }\n}\n",".page-content {\n    .highlight {\n        margin: 1px 0;\n        pre {\n            background: $code_background;\n            color: rgba(0,0,0,.87);\n            font-family: $code_font_family;\n            padding: 0.75rem;\n            overflow: auto;\n            overflow-y: hidden;\n            .o, .nd {\n                color: rgba(0,0,0,.87);\n            }\n        }\n    }\n\n    div.highlight-console div.highlight {\n        background: none;\n    }\n\n    // for jupyter notebook output cell\n    .output {\n        .highlight {\n            pre {\n                color: rgba(0,0,0,.87);\n                background: $background_color;\n                border-width: 1px;\n                border-color: #999;\n                border-style: solid;\n                padding: 0.75rem;\n            }\n        }\n     }\n\n    .code, code:not(.download) {\n        margin: 0 0;\n        font-family: $code_font_family;\n        border-radius: 2px;\n        span.pre {\n            font-family: $code_font_family;\n        }\n    }\n\n    .viewcode-link {\n        padding-left: 2em;\n        font-size: 80%;\n    }\n\n    .rubric, .method > dt, .function > dt, .class > dt {\n        display: table;\n        margin: 10px 0;\n        font-size: 100%;\n        line-height: normal;\n        background: #e7f2fa;\n        color: #2B98F0;\n        border-top: solid 3px #55ADF3;\n        padding: 10px;\n        position: relative;\n        .descname, .descclassname {\n            color: rgba(0,0,0,.87);\n            background: #e7f2fa;\n            padding: 3px;\n        }\n        em {\n            padding: 0 2px;\n        }\n    }\n\n\n    .rubric {\n        margin: 30px 0 10px 0;\n     }\n\n\n    .field-body {\n        padding-left: 40px;\n        ul {\n            padding: 0 0 0 16px;\n            margin: 0;\n        }\n    }\n\n     // .docutils > dt {\n    //     padding: 6px;\n    //     display: table;\n    //     margin-bottom: 6px;\n    //     border: none;\n    //     border-left: solid 3px #ccc;\n    //     background: #f0f0f0;\n    //     color: #555;\n    // }\n\n    .seealso .docutils > dt {\n       float: left;\n       clear: left;\n       padding: 0 6px;\n     }\n\n    .seealso .docutils > dd {\n       padding-left: 6em;\n    }\n    .nblast {\n        padding-bottom: 1em;\n    }\n\n    pre {\n        font-size: 90%;\n        background: #eee;\n        color: #455A64;\n        padding: 16px 32px;\n        width: auto;\n        border-radius: 4px;\n        word-wrap: break-word;\n\n        &:hover {\n            @extend .mdl-shadow--2dp;\n\n            &:before {\n                font-family: $body_font_family;\n                padding: 0 0.5rem;\n                content: attr(click-to-copy);\n                color: rgba(0, 0, 0, 0.5);\n                border-radius: 4px;\n                position: relative;\n                float: right;\n                top: -0.5rem;\n                right: -0.5rem;\n                background: rgb(200, 200, 200);\n                font-size: 0.8rem;\n                cursor: pointer;\n            }\n        }\n    }\n}\n","/*\n * Quotation Block Styles\n */\n .page-content {\n    blockquote {\n        font-size: 1rem;\n        padding: 0 1rem;\n        border-left: 3px solid $code_background;\n\n        &:after {\n            content: \"\" !important;\n            margin-left: 0;\n        }\n        &:before {\n            content: \"\" !important;\n        }\n    }\n }\n",".page-content {\n    table:not(.footnote):not(.indextable):not(.hlist):not(.option-list):not(.field-list) {\n        @extend .mdl-data-table;\n        @extend .mdl-shadow--2dp;\n\n        margin: 1.5rem 0;\n        table-layout: fixed;\n        max-width: 100%;\n        min-width: 70%;\n\n        th, td {\n            @extend .mdl-data-table__cell--non-numeric;\n            white-space: normal;\n            overflow-wrap: break-word;\n        }\n\n        caption {\n            font-size: $font_size;\n            margin: 1rem 0 0.8rem 0;\n            white-space: normal;\n            .caption-number {\n                font-style: normal;\n            }\n            .caption-number::after {\n                content: \"\\00a0\";\n            }\n        }\n\n    }\n}\n",".globaltoc {\n    \n    .caption, .toc {\n        display: none;\n    }\n\n    ul {\n\n        list-style-type: none;\n        padding: 0;\n        margin: 0;\n\n        li {\n            min-height: 18px;\n            .link-wrapper {\n                display: flex;\n                justify-content: space-between;\n                > a {\n                    padding: 4px 0;\n                    display: block;\n                    width: 100%;\n                    font-size: 1rem;\n                    text-decoration: none;\n                    color: $layout-drawer-navigation-color;\n                    &.current {\n                        font-weight: bold;\n                    }\n                }\n            }\n        }\n    }\n\n    .nav-toggle {\n        padding: 0;\n        float: right;\n        display: flex;\n        align-items: center;\n        justify-content: center;\n        height: 36px;\n        > a {\n            padding: 0;\n            margin-left: 0;\n            margin-right: 4px;\n            cursor: pointer;\n            > i {\n                font-size: 18px;\n            }\n        }\n        &.show {\n            transform: rotateZ(180deg);\n            > a {\n                margin-right: 0;\n                margin-left: 4px;\n            }\n        }\n    }\n\n    nav {\n        > ul > li > span.link-wrapper {\n            padding-left: 8px;\n        }\n        > ul > li > ul > li > span.link-wrapper {\n            padding-left: 16px;\n        }\n        > ul > li > ul > li > ul > li > span.link-wrapper {\n            padding-left: 24px;\n        }\n        > ul > li > ul > li > ul > li > ul> li > span.link-wrapper {\n            padding-left: 32px;\n        }\n        > ul > li > ul > li > ul > li > ul > li > ul> li > span.link-wrapper {\n            padding-left: 40px;\n        }\n        > ul > li > ul > li > ul > li > ul > li > ul > li > ul> li > span.link-wrapper {\n            padding-left: 48px;\n        }\n    }\n}\n",".localtoc {\n    font-size: 0.75rem;\n    padding-top: 1rem;\n\n    .caption {\n        padding-left: 12px;\n        &-text {\n            font-size: 0.9rem;\n            font-weight: 700;\n        }\n    }\n\n    > ul > li > a {\n        display: none;\n    }\n\n    ul {\n        padding: 0;\n        list-style-type: none;\n    }\n\n    li {\n        padding-left: 6px;\n    }\n\n    a {\n        display: block;\n        text-decoration: none;\n        color: inherit;\n        margin-top: 8px;\n        padding-left: 8px;\n        line-height: 1.1rem;\n    \n        &.current {\n            padding-left: 5px;\n            border-left: 3px solid;\n            font-weight: bold;\n        }\n    }\n}","/*\r\n *  Toctree and Contents Directive Styles\r\n */\r\n .toctree-wrapper,\r\n .contents.topic {\r\n     border-left: 5px solid;\r\n }\r\n\r\n .toctree-wrapper > p.caption,\r\n .contents.topic > p.topic-title {\r\n     color: rgb(117, 117, 117);\r\n     font-size: 1rem;\r\n     padding-left: 14px;\r\n }\r\n\r\n .toctree-wrapper ul,\r\n .contents.topic ul{\r\n     padding-left: 14px;\r\n     list-style: none;\r\n     line-height: 30px;\r\n }\r\n\r\n .toctree-wrapper a,\r\n .contents.topic a {\r\n     font-size: 1.2rem;\r\n     text-decoration: none;\r\n     .pre {\r\n         font-size: 1rem;\r\n     }\r\n }\r\n\r\n .toctree-wrapper > ul > li > a,\r\n .contents.topic > ul > li > a {\r\n     font-size: 1.3rem;\r\n     .pre {\r\n         font-size: 1.1rem;\r\n     }\r\n }\r\n",".page-content {\n    ul {\n        li {\n            margin: .3rem 0;\n            p {\n                margin: 0;\n            }\n        }\n    }\n    .option-list {\n        .option {\n            font-family: $code_font_family;\n        }\n        td {\n            padding: 0.5rem;\n            border: none;\n        }\n    }\n}\n","/*\r\n * Drawer Styles\r\n */\r\n.mdl-layout {\r\n    &__drawer {\r\n        background-color: #fff;\r\n\r\n        &::-webkit-scrollbar {\r\n            width: 6px;\r\n        }\r\n\r\n        &::-webkit-scrollbar-track {\r\n            border-radius: 6px;\r\n        }\r\n\r\n        &::-webkit-scrollbar-thumb {\r\n            background-color: rgba(0, 0, 0, .3);\r\n            border-radius: 6px;\r\n            box-shadow:0 0 0 1px rgba(255, 255, 255, .3);\r\n        }\r\n\r\n        > .mdl-layout-title {\r\n            font-weight: bold;\r\n            text-align: right;\r\n            margin: 0;\r\n            padding: 0;\r\n            line-height: 32px;\r\n            border-bottom: 1px solid rgba(0,0,0,.1);\r\n            min-height: 64px;\r\n            .title {\r\n                color: inherit;\r\n                display: block;\r\n                height: 100%;\r\n                width: 100%;\r\n                text-decoration: none;\r\n                > img.logo {\r\n                    width: 100%;\r\n                    margin: 0;\r\n                    padding: 0;\r\n                }\r\n\r\n                &-text {\r\n                    font-weight: bold;\r\n                    text-align: right;\r\n                    padding: 0 10px;\r\n                    margin: 16px 0 8px 0;\r\n                    line-height: 32px;\r\n                    font-family: $body_font_family;\r\n                    color: inherit;\r\n                    display: block;\r\n                }\r\n            }\r\n        }\r\n    }\r\n}\r\n","/*\r\n * Header Styles\r\n */\r\n\r\nnav.breadcrumb {\r\n    > a.mdl-navigation__link {\r\n        padding: 0 8px;\r\n        font-size: 18px;\r\n    }\r\n    @media (max-width: $lg-breakpoint - 1) {\r\n        width: calc( 100% - 64px );\r\n        a.mdl-navigation__link.is-active {\r\n            overflow-x: hidden;\r\n            width: 100%;\r\n            overflow: hidden;\r\n            white-space: nowrap;\r\n            text-overflow: ellipsis;\r\n        }\r\n        a.mdl-navigation__link:not(.is-active),\r\n        i.material-icons {\r\n            display: none;\r\n        }\r\n    }\r\n}\r\n\r\ndiv.mdl-layout__header {\r\n    margin-top: 77px;\r\n}\r\n\r\n.mdl-layout__drawer-button {\r\n    top: 13px !important;\r\n}\r\n\r\ndiv.mdl-layout__header-row.header-links {\r\n    background: rgba(255,255,255,0.2);\r\n    width: 100%;\r\n    overflow-x: auto;\r\n    overflow-y: hidden;\r\n\r\n    a.mdl-navigation__link  {\r\n        font-size: 1rem;\r\n        i {\r\n            font-size: 1.2rem;\r\n            margin: 0 8px;\r\n            position: relative;\r\n            bottom: -0.1rem;\r\n        }\r\n    };\r\n\r\n    a.mdl-navigation__link:hover  {\r\n        background-color: unquote(\"rgb(#{$color-primary})\");\r\n        color: #eeeeee;\r\n    };\r\n    a.mdl-navigation__link[href=\"#\"]  {\r\n        background-color: unquote(\"rgb(#{$color-primary})\");\r\n        opacity: 1;\r\n        color: #ffffff;\r\n    };\r\n}\r\n\r\n/* mxnet-header */\r\n\r\n\r\n.site-title {\r\n  font-weight: 300 !important;\r\n  line-height: 57px;\r\n  letter-spacing: -1px;\r\n  margin-bottom: 0;\r\n  float: left;\r\n  color: white;\r\n\r\n  &,\r\n  &:visited {\r\n    color: $grey-color-dark;\r\n  }\r\n}\r\n\r\n\r\n.site-header {\r\n  position: fixed;\r\n  top: 0;\r\n  width: 100%;\r\n  min-height: 55px;\r\n  padding-top: 10px;\r\n  padding-bottom: 10px;\r\n  background-color: $color-mxnet;\r\n  z-index: 10;\r\n  font-weight: 300;\r\n  font-size: 17px;\r\n  border-bottom: 1px solid white;\r\n}\r\n\r\n.site-header-logo {\r\n  width: 120px;\r\n  display: initial;\r\n}\r\n\r\n.site-nav {\r\n  float: right;\r\n  line-height: 57px;\r\n\r\n  .nav-trigger {\r\n    display: none;\r\n  }\r\n\r\n  .menu-icon {\r\n    display: none;\r\n  }\r\n\r\n  .page-link {\r\n    color: white;\r\n    line-height: 1.5;\r\n    font-weight: 300;\r\n    // Gaps between nav items, but not on the last one\r\n    &:not(:last-child) {\r\n      margin-right: 40px;\r\n    }\r\n\r\n    &:hover {\r\n      color: white;\r\n      text-shadow: -0.06ex 0 white, 0.06ex 0 white;\r\n    }\r\n  }\r\n\r\n  .page-link.page-current {\r\n    color: white;\r\n    text-decoration: underline;\r\n  }\r\n\r\n  @media screen and (max-width: $on-laptop) {\r\n    position: absolute;\r\n    top: 9px;\r\n    right: 15px;\r\n    background-color: rgb(23,141,201);\r\n    border-radius: 2px;\r\n    text-align: right;\r\n\r\n    label[for=\"nav-trigger\"] {\r\n      display: block;\r\n      float: right;\r\n      width: 36px;\r\n      height: 36px;\r\n      z-index: 2;\r\n      cursor: pointer;\r\n    }\r\n\r\n    .menu-icon {\r\n      display: block;\r\n      float: right;\r\n      width: 36px;\r\n      height: 26px;\r\n      line-height: 0;\r\n      padding-top: 20px;\r\n      text-align: center;\r\n\r\n      > svg {\r\n        fill: white;\r\n      }\r\n    }\r\n\r\n    input ~ .trigger {\r\n      clear: both;\r\n      display: none;\r\n    }\r\n\r\n    input:checked ~ .trigger {\r\n      display: block;\r\n      padding-bottom: 5px;\r\n    }\r\n\r\n    .page-link {\r\n      padding: 5px 10px;\r\n      display: block;\r\n\r\n      &:not(:last-child) {\r\n        margin-right: 0;\r\n      }\r\n\r\n      margin-left: 20px;\r\n    }\r\n  }\r\n}","/*\r\n * Footer Styles\r\n */\r\nfooter.mdl-mini-footer {\r\n    background-color: #212121;\r\n    > div.mdl-mini-footer__left-section {\r\n        margin-bottom: 20px;\r\n        display: flex;\r\n        flex-direction: column;\r\n        .mdl-logo {\r\n            font-size: 1.1rem;\r\n        }\r\n        ul {\r\n            @extend .mdl-mini-footer__link-list;\r\n        }\r\n    }\r\n    > div.mdl-mini-footer__right-section {\r\n        font-size: 0.9rem;\r\n        display: flex;\r\n        flex-direction: column;\r\n        justify-content: flex-end;\r\n\r\n        a {\r\n            color: inherit;\r\n            font-weight: bold;\r\n            text-decoration: none;\r\n        }\r\n    }\r\n    p.caption {\r\n        display: none;\r\n    }\r\n}\r\n\r\n/*\r\n * Pagenation Block Styles\r\n */\r\n .pagenation {\r\n    width: 100%;\r\n    margin-top: 80px;\r\n    height: 92px;\r\n    background-color: #424242;\r\n    display: flex;\r\n\r\n    .button-common {\r\n        text-transform: none;\r\n        padding: 0;\r\n        height: 92px;\r\n        display: flex;\r\n        justify-content: center;\r\n        align-items: center;\r\n        color: #ffffff;\r\n    }\r\n    #button-prev {\r\n        @extend .button-common;\r\n        margin-right: auto;\r\n        .pagenation-text {\r\n            text-align: left;\r\n        }\r\n        \r\n    }\r\n    #button-next {\r\n        @extend .button-common;\r\n        margin-left: auto;\r\n        flex-direction: row-reverse;\r\n        .pagenation-text {\r\n            text-align: right;\r\n        }\r\n    }\r\n\r\n    &-arrow {\r\n        &-L {\r\n            margin-right: 20px;\r\n        }\r\n        &-R {\r\n            margin-left: 20px;\r\n        }\r\n    }\r\n\r\n    &-text {\r\n        line-height: 30px;\r\n        font-size: 20px;\r\n    }\r\n\r\n    &-direction {\r\n        opacity: 0.7;\r\n        font-size: 18px;\r\n    }\r\n    @media screen and (max-width: 1024px) {\r\n        #button-prev {\r\n            width: 20%;\r\n        }\r\n        \r\n        #button-next {\r\n            width: 80%;\r\n        }\r\n    \r\n        #button-prev .pagenation-text {\r\n            display: none;\r\n        }\r\n    }\r\n    @media screen and (min-width: 1025px) {\r\n        #button-prev,\r\n        #button-next {\r\n            width: 50%;\r\n        }\r\n    \r\n        #button-prev .pagenation-text {\r\n            display: block;\r\n        }\r\n    }\r\n}\r\n\r\n\r\n\r\n/**\r\n * Site footer\r\n */\r\n.site-footer {\r\n  border-top: 1px solid $grey-color-light;\r\n  padding: $spacing-unit 0;\r\n  background-color: #424242;\r\n  .footer-category-title {\r\n    color: $color-mxnet;\r\n  }\r\n  a {\r\n    color: $grey-color-light !important;\r\n\r\n    &:visited {\r\n      color: $grey-color-light !important;\r\n    }\r\n  }\r\n\r\n}\r\n\r\n.site-footer2 {\r\n  background-color: #424242;\r\n  padding-top: 40px;\r\n  padding-bottom: 10px;\r\n}\r\n\r\n.footer-heading {\r\n  margin-bottom: $spacing-unit / 2;\r\n}\r\n\r\n.contact-list,\r\n.social-media-list {\r\n  list-style: none;\r\n  margin-left: 0;\r\n}\r\n\r\n\r\n.footer-bottom-warning {\r\n  font-size: 80%;\r\n  color: white;\r\n  float: left;\r\n}\r\n\r\n.footer-logo {\r\n  width: 200px;\r\n  margin-bottom: 30px;\r\n  margin-top: 30px;\r\n}\r\n\r\n.footer-col {\r\n  float: left;\r\n  margin-bottom: $spacing-unit / 2;\r\n  padding-left: $spacing-unit / 2;\r\n}\r\n\r\n.footer-text {\r\n  color: $grey-color-light;\r\n}\r\n\r\n"," /*\r\n  * Search Styles\r\n  */\r\n#waterfall-exp::-webkit-input-placeholder {\r\n    color: #ccc;\r\n}\r\n#waterfall-exp:-ms-input-placeholder {\r\n    color: #ccc;\r\n}\r\n#waterfall-exp::-moz-placeholder {\r\n    color: #ccc;\r\n}\r\n\r\nul.search span.highlighted {\r\n    font-weight: bold;\r\n}\r\n\r\nul.search > li {\r\n    margin-bottom: 24px;\r\n}\r\n\r\n#search-results {\r\n    ul {\r\n        list-style: none;\r\n        padding: 0;\r\n        li {\r\n            > a {\r\n                text-decoration: none;\r\n                font-size: 1.2rem;\r\n            }\r\n        }\r\n    }\r\n}\r\n","a.download {\n    &:before {\n        @extend .material-icons;\n        content: \"file_download\";\n        position: relative;\n        top: 5px;\n        margin-right: 5px;\n    }\n}\n\nbutton.download {\n    position: sticky;\n    margin-left: 1em;\n}\n",".mdl-card {\n    margin: 1em 1.5em 1em 0;\n    display: inline-block;\n    width: 250px;\n    min-height: 140px;\n    padding: 18px;\n}\n.mdl-card:hover {\n    box-shadow: 0 10px 20px rgba(0,0,0,0.25), 0 6px 6px rgba(0,0,0,0.22);\n    color: #000;\n    cursor: pointer;\n}\n.mdl-card__title {\n    padding: 0 0 1em 0;\n    font-size: 18px;\n    color: #444;\n}\n\n.mdl-card__supporting-text {\n    line-height: 1.5rem;\n    padding: 0px;\n    width: 100%;\n}\n\n.head-card.mdl-card {\n    width: auto;\n    display: block;\n    max-width: 800px;\n    padding: 24px;\n}\n\n.head-card > .mdl-card__title {\n    padding-bottom: 0px;\n    height: 60px;\n    font-weight: 700;\n    text-transform: uppercase;\n}\n.head-card > .mdl-card__menu {\n    color: #fff;\n}\n.head-card > .mdl-card__actions {\n    padding: 0;\n}\n.cards {\n    display: flex;\n    flex-direction: row;\n    flex-wrap: wrap;\n}\n"]}
\ No newline at end of file
diff --git a/docs/python_docs/themes/mx-theme/src/scss/toc/_globaltoc.scss b/docs/python_docs/themes/mx-theme/src/scss/toc/_globaltoc.scss
index 368d2a58e715..f07cb5ea7b4b 100644
--- a/docs/python_docs/themes/mx-theme/src/scss/toc/_globaltoc.scss
+++ b/docs/python_docs/themes/mx-theme/src/scss/toc/_globaltoc.scss
@@ -11,12 +11,12 @@
         margin: 0;
 
         li {
-            min-height: 36px;
+            min-height: 18px;
             .link-wrapper {
                 display: flex;
                 justify-content: space-between;
                 > a {
-                    padding: 8px 0;
+                    padding: 4px 0;
                     display: block;
                     width: 100%;
                     font-size: 1rem;
diff --git a/docs/static_site/src/.htaccess b/docs/static_site/src/.htaccess
index 5be7205bf5c6..379e8210eca9 100644
--- a/docs/static_site/src/.htaccess
+++ b/docs/static_site/src/.htaccess
@@ -29,40 +29,40 @@ ErrorDocument 404 /404.html
 # Python API
 Redirect 301 /versions/master/api/python/index.html /api/python/docs/api/index.html
 Redirect 301 /api/python/ndarray/ndarray.html /api/python/docs/api/ndarray/index.html
-Redirect 301 /api/python/ndarray/random.html /api/python/docs/api/ndarray/routines.html
-Redirect 301 /api/python/ndarray/linalg.html /api/python/docs/api/ndarray/routines.html
-Redirect 301 /api/python/ndarray/contrib.html /api/python/docs/api/ndarray/routines.html
-Redirect 301 /api/python/ndarray/sparse.html /api/python/docs/api/ndarray/sparse_routines.html
+Redirect 301 /api/python/ndarray/random.html /api/python/docs/api/ndarray/random/index.html
+Redirect 301 /api/python/ndarray/linalg.html /api/python/docs/api/ndarray/linalg/index.html
+Redirect 301 /api/python/ndarray/contrib.html /api/python/docs/api/ndarray/contrib/index.html
+Redirect 301 /api/python/ndarray/sparse.html /api/python/docs/api/ndarray/sparse/index.html
 
-Redirect 301 /api/python/autograd/autograd.html /api/python/docs/api/gluon-related/mxnet.autograd.html
+Redirect 301 /api/python/autograd/autograd.html /api/python/docs/api/autograd/index.html
 
 Redirect 301 /api/python/gluon/gluon.html /api/python/docs/api/gluon/index.html
-Redirect 301 /api/python/contrib/contrib.html /api/python/docs/api/gluon/mxnet.gluon.contrib.html
-Redirect 301 /api/python/gluon/nn.html /api/python/docs/api/gluon/nn.html
-Redirect 301 /api/python/gluon/rnn.html /api/python/docs/api/gluon/rnn.html
-Redirect 301 /api/python/gluon/loss.html /api/python/docs/api/gluon/mxnet.gluon.loss.html
-Redirect 301 /api/python/gluon/data.html /api/python/docs/api/gluon/mxnet.gluon.data.html
-Redirect 301 /api/python/gluon/model_zoo.html /api/python/docs/api/gluon/mxnet.gluon.model_zoo.html
-Redirect 301 /api/python/gluon/contrib.html /api/python/docs/api/gluon/mxnet.gluon.contrib.html
+Redirect 301 /api/python/gluon/nn.html /api/python/docs/api/gluon/nn/index.html
+Redirect 301 /api/python/gluon/rnn.html /api/python/docs/api/gluon/rnn/index.html
+Redirect 301 /api/python/gluon/loss.html /api/python/docs/api/gluon/loss/index.html
+Redirect 301 /api/python/gluon/data.html /api/python/docs/api/gluon/data/index.html
+Redirect 301 /api/python/gluon/model_zoo.html /api/python/docs/api/gluon/model_zoo/index.html
+Redirect 301 /api/python/gluon/contrib.html /api/python/docs/api/gluon/contrib/index.html
 
-Redirect 301 /api/python/kvstore/kvstore.html /api/python/docs/api/gluon-related/mxnet.kvstore.html
-Redirect 301 /api/python/metric/metric.html /api/python/docs/api/gluon-related/mxnet.metric.html
-Redirect 301 /api/python/optimization/optimization.html /api/python/docs/api/gluon-related/mxnet.optimizer.html
-Redirect 301 /api/python/optimization/contrib.html /api/python/docs/api/gluon-related/mxnet.optimizer.html
-Redirect 301 /api/python/profiler/profiler.html /api/python/docs/api/gluon-related/mxnet.profiler.html
-Redirect 301 /api/python/io/io.html /api/python/docs/api/gluon-related/mxnet.io.html
+Redirect 301 /api/python/kvstore/kvstore.html /api/python/docs/api/kvstore/index.html
+Redirect 301 /api/python/metric/metric.html /api/python/docs/api/metric/index.html
+Redirect 301 /api/python/optimization/optimization.html /api/python/docs/api/optimizer/index.html
+Redirect 301 /api/python/optimization/contrib.html /api/python/docs/api/optimizer/index.html
+Redirect 301 /api/python/profiler/profiler.html /api/python/docs/api/mxnet/profiler/index.html
+Redirect 301 /api/python/io/io.html /api/python/docs/api/mxnet/io/index.html
+Redirect 301 /api/python/contrib/contrib.html /api/python/docs/api/contrib/index.html
 
 Redirect 301 /api/python/symbol/symbol.html /api/python/docs/api/symbol/index.html
 Redirect 301 /api/python/symbol.html /api/python/docs/api/symbol/index.html
-Redirect 301 /api/python/symbol/linalg.html /api/python/docs/api/symbol/mxnet.symbol.linalg.html
+Redirect 301 /api/python/symbol/linalg.html /api/python/docs/api/symbol/linalg/index.html
 
-Redirect 301 /api/python/module/module.html /api/python/docs/api/symbol-related/mxnet.module.html
-Redirect 301 /api/python/callback/callback.html /api/python/docs/api/symbol-related/mxnet.callback.html
-Redirect 301 /api/python/tools/visualization.html /api/python/docs/api/symbol-related/mxnet.visualization.html
+Redirect 301 /api/python/module/module.html /api/python/docs/api/module/index.html
+Redirect 301 /api/python/callback/callback.html /api/python/docs/api/mxnet/callback/index.html
+Redirect 301 /api/python/tools/visualization.html /api/python/docs/api/mxnet/visualization/index.html
 
-Redirect 301 /api/python/executor/executor.html /api/python/docs/api/advanced/mxnet.executor.html
-Redirect 301 /api/python/rtc/rtc.html /api/python/docs/api/advanced/mxnet.rtc.html
-Redirect 301 /api/python/tools/test_utils.html /api/python/docs/api/advanced/mxnet.test_utils.html
+Redirect 301 /api/python/executor/executor.html /api/python/docs/api/mxnet/executor/index.html
+Redirect 301 /api/python/rtc/rtc.html /api/python/docs/api/mxnet/rtc/index.html
+Redirect 301 /api/python/tools/test_utils.html /api/python/docs/api/mxnet/test_utils/index.html
 
 # Top Level Nav bar
 Redirect 301 /install/index.html /get_started
diff --git a/python/mxnet/gluon/block.py b/python/mxnet/gluon/block.py
index 0d8e766fede8..b42fefd065e8 100644
--- a/python/mxnet/gluon/block.py
+++ b/python/mxnet/gluon/block.py
@@ -212,7 +212,7 @@ def forward(self, x):
         Prefix acts like a name space. All children blocks created in parent block's
         :py:meth:`name_scope` will have parent block's prefix in their name.
         Please refer to
-        `naming tutorial <https://mxnet.incubator.apache.org/api/python/docs/tutorials/packages/gluon/naming.html>`_
+        `naming tutorial </api/python/docs/tutorials/packages/gluon/blocks/naming.html>`_
         for more info on prefix and naming.
     params : ParameterDict or None
         :py:class:`ParameterDict` for sharing weights with the new :py:class:`Block`. For example,
@@ -308,7 +308,7 @@ def name_scope(self):
                 self.dense = nn.Dense(20)
 
         Please refer to
-        `naming tutorial <https://mxnet.incubator.apache.org/tutorials/gluon/naming.html>`_
+        `the naming tutorial </api/python/docs/tutorials/packages/gluon/blocks/naming.html>`_
         for more info on prefix and naming.
         """
         return self._scope
@@ -1003,7 +1003,7 @@ def infer_type(self, *args):
 
     def export(self, path, epoch=0, remove_amp_cast=True):
         """Export HybridBlock to json format that can be loaded by
-        `SymbolBlock.imports`, `mxnet.mod.Module` or the C++ interface.
+        `gluon.SymbolBlock.imports`, `mxnet.mod.Module` or the C++ interface.
 
         .. note:: When there are only one input, it will have name `data`. When there
                   Are more than one inputs, they will be named as `data0`, `data1`, etc.
@@ -1160,8 +1160,8 @@ class SymbolBlock(HybridBlock):
     """
     @staticmethod
     def imports(symbol_file, input_names, param_file=None, ctx=None):
-        """Import model previously saved by `HybridBlock.export` or
-        `Module.save_checkpoint` as a SymbolBlock for use in Gluon.
+        """Import model previously saved by `gluon.HybridBlock.export` or
+        `Module.save_checkpoint` as a `gluon.SymbolBlock` for use in Gluon.
 
         Parameters
         ----------
@@ -1172,12 +1172,12 @@ def imports(symbol_file, input_names, param_file=None, ctx=None):
         param_file : str, optional
             Path to parameter file.
         ctx : Context, default None
-            The context to initialize SymbolBlock on.
+            The context to initialize `gluon.SymbolBlock` on.
 
         Returns
         -------
-        SymbolBlock
-            SymbolBlock loaded from symbol and parameter files.
+        gluon.SymbolBlock
+            `gluon.SymbolBlock` loaded from symbol and parameter files.
 
         Examples
         --------
diff --git a/python/mxnet/gluon/contrib/estimator/estimator.py b/python/mxnet/gluon/contrib/estimator/estimator.py
index b6142e100d96..5c6855612828 100644
--- a/python/mxnet/gluon/contrib/estimator/estimator.py
+++ b/python/mxnet/gluon/contrib/estimator/estimator.py
@@ -45,7 +45,7 @@ class Estimator(object):
 
     Parameters
     ----------
-    net : Block
+    net : gluon.Block
         The model used for training.
     loss : gluon.loss.Loss or list of gluon.loss.Loss
         Loss(objective functions) to calculate during training.
diff --git a/python/mxnet/gluon/contrib/rnn/conv_rnn_cell.py b/python/mxnet/gluon/contrib/rnn/conv_rnn_cell.py
index 69ec92f0fe8a..ab40c3fc95d7 100644
--- a/python/mxnet/gluon/contrib/rnn/conv_rnn_cell.py
+++ b/python/mxnet/gluon/contrib/rnn/conv_rnn_cell.py
@@ -250,7 +250,7 @@ class Conv1DRNNCell(_ConvRNNCell):
         Initializer for the recurrent convolution bias vectors.
     conv_layout : str, default 'NCW'
         Layout for all convolution inputs, outputs and weights. Options are 'NCW' and 'NWC'.
-    activation : str or Block, default 'tanh'
+    activation : str or gluon.Block, default 'tanh'
         Type of activation function.
         If argument type is string, it's equivalent to nn.Activation(act_type=str). See
         :func:`~mxnet.ndarray.Activation` for available choices.
@@ -317,7 +317,7 @@ class Conv2DRNNCell(_ConvRNNCell):
         Initializer for the recurrent convolution bias vectors.
     conv_layout : str, default 'NCHW'
         Layout for all convolution inputs, outputs and weights. Options are 'NCHW' and 'NHWC'.
-    activation : str or Block, default 'tanh'
+    activation : str or gluon.Block, default 'tanh'
         Type of activation function.
         If argument type is string, it's equivalent to nn.Activation(act_type=str). See
         :func:`~mxnet.ndarray.Activation` for available choices.
@@ -384,7 +384,7 @@ class Conv3DRNNCell(_ConvRNNCell):
         Initializer for the recurrent convolution bias vectors.
     conv_layout : str, default 'NCDHW'
         Layout for all convolution inputs, outputs and weights. Options are 'NCDHW' and 'NDHWC'.
-    activation : str or Block, default 'tanh'
+    activation : str or gluon.Block, default 'tanh'
         Type of activation function.
         If argument type is string, it's equivalent to nn.Activation(act_type=str). See
         :func:`~mxnet.ndarray.Activation` for available choices.
@@ -514,7 +514,7 @@ class Conv1DLSTMCell(_ConvLSTMCell):
         Initializer for the recurrent convolution bias vectors.
     conv_layout : str, default 'NCW'
         Layout for all convolution inputs, outputs and weights. Options are 'NCW' and 'NWC'.
-    activation : str or Block, default 'tanh'
+    activation : str or gluon.Block, default 'tanh'
         Type of activation function used in c^\prime_t.
         If argument type is string, it's equivalent to nn.Activation(act_type=str). See
         :func:`~mxnet.ndarray.Activation` for available choices.
@@ -591,7 +591,7 @@ class Conv2DLSTMCell(_ConvLSTMCell):
         Initializer for the recurrent convolution bias vectors.
     conv_layout : str, default 'NCHW'
         Layout for all convolution inputs, outputs and weights. Options are 'NCHW' and 'NHWC'.
-    activation : str or Block, default 'tanh'
+    activation : str or gluon.Block, default 'tanh'
         Type of activation function used in c^\prime_t.
         If argument type is string, it's equivalent to nn.Activation(act_type=str). See
         :func:`~mxnet.ndarray.Activation` for available choices.
@@ -668,7 +668,7 @@ class Conv3DLSTMCell(_ConvLSTMCell):
         Initializer for the recurrent convolution bias vectors.
     conv_layout : str, default 'NCDHW'
         Layout for all convolution inputs, outputs and weights. Options are 'NCDHW' and 'NDHWC'.
-    activation : str or Block, default 'tanh'
+    activation : str or gluon.Block, default 'tanh'
         Type of activation function used in c^\prime_t.
         If argument type is string, it's equivalent to nn.Activation(act_type=str). See
         :func:`~mxnet.ndarray.Activation` for available choices.
@@ -798,7 +798,7 @@ class Conv1DGRUCell(_ConvGRUCell):
         Initializer for the recurrent convolution bias vectors.
     conv_layout : str, default 'NCW'
         Layout for all convolution inputs, outputs and weights. Options are 'NCW' and 'NWC'.
-    activation : str or Block, default 'tanh'
+    activation : str or gluon.Block, default 'tanh'
         Type of activation function used in n_t.
         If argument type is string, it's equivalent to nn.Activation(act_type=str). See
         :func:`~mxnet.ndarray.Activation` for available choices.
@@ -870,7 +870,7 @@ class Conv2DGRUCell(_ConvGRUCell):
         Initializer for the recurrent convolution bias vectors.
     conv_layout : str, default 'NCHW'
         Layout for all convolution inputs, outputs and weights. Options are 'NCHW' and 'NHWC'.
-    activation : str or Block, default 'tanh'
+    activation : str or gluon.Block, default 'tanh'
         Type of activation function used in n_t.
         If argument type is string, it's equivalent to nn.Activation(act_type=str). See
         :func:`~mxnet.ndarray.Activation` for available choices.
@@ -942,7 +942,7 @@ class Conv3DGRUCell(_ConvGRUCell):
         Initializer for the recurrent convolution bias vectors.
     conv_layout : str, default 'NCDHW'
         Layout for all convolution inputs, outputs and weights. Options are 'NCDHW' and 'NDHWC'.
-    activation : str or Block, default 'tanh'
+    activation : str or gluon.Block, default 'tanh'
         Type of activation function used in n_t.
         If argument type is string, it's equivalent to nn.Activation(act_type=str). See
         :func:`~mxnet.ndarray.Activation` for available choices.
diff --git a/python/mxnet/gluon/model_zoo/vision/__init__.py b/python/mxnet/gluon/model_zoo/vision/__init__.py
index 7d33ce409b21..8ca7a34ba363 100644
--- a/python/mxnet/gluon/model_zoo/vision/__init__.py
+++ b/python/mxnet/gluon/model_zoo/vision/__init__.py
@@ -106,7 +106,7 @@ def get_model(name, **kwargs):
 
     Returns
     -------
-    HybridBlock
+    gluon.HybridBlock
         The model.
     """
     models = {'resnet18_v1': resnet18_v1,
diff --git a/python/mxnet/gluon/model_zoo/vision/resnet.py b/python/mxnet/gluon/model_zoo/vision/resnet.py
index 50a65ec8d2da..34e7f595c154 100644
--- a/python/mxnet/gluon/model_zoo/vision/resnet.py
+++ b/python/mxnet/gluon/model_zoo/vision/resnet.py
@@ -240,7 +240,7 @@ class ResNetV1(HybridBlock):
 
     Parameters
     ----------
-    block : HybridBlock
+    block : gluon.HybridBlock
         Class for the residual block. Options are BasicBlockV1, BottleneckV1.
     layers : list of int
         Numbers of layers in each block
@@ -295,7 +295,7 @@ class ResNetV2(HybridBlock):
 
     Parameters
     ----------
-    block : HybridBlock
+    block : gluon.HybridBlock
         Class for the residual block. Options are BasicBlockV1, BottleneckV1.
     layers : list of int
         Numbers of layers in each block
diff --git a/python/mxnet/io/__init__.py b/python/mxnet/io/__init__.py
index 5c5e2e68d84a..b053c2f3af6c 100644
--- a/python/mxnet/io/__init__.py
+++ b/python/mxnet/io/__init__.py
@@ -23,7 +23,9 @@
 from __future__ import absolute_import
 
 from . import io
-from .io import *
+from .io import CSVIter, DataBatch, DataDesc, DataIter, ImageDetRecordIter, ImageRecordInt8Iter, ImageRecordIter,\
+    ImageRecordIter_v1, ImageRecordUInt8Iter, ImageRecordUInt8Iter_v1, LibSVMIter, MNISTIter, MXDataIter, NDArrayIter,\
+    PrefetchingIter, ResizeIter
 
 from . import utils
-from .utils import *
+from .utils import _init_data, _getdata_by_idx, _has_instance
diff --git a/python/mxnet/monitor.py b/python/mxnet/monitor.py
index 2e10708e72f4..9eaae07e042e 100644
--- a/python/mxnet/monitor.py
+++ b/python/mxnet/monitor.py
@@ -40,7 +40,7 @@ class Monitor(object):
     stat_func : function
         A function that computes statistics of tensors.
         Takes an `NDArray` and returns an `NDArray`. Defaults to mean
-        absolute value |x|/size(x).
+        absolute value abs(x)/size(x).
     pattern : str
         A regular expression specifying which tensors to monitor.
         Only tensors with names that match `name_pattern` will be included.
diff --git a/src/operator/instance_norm.cc b/src/operator/instance_norm.cc
index 53bc7c986d9e..00482b52d034 100644
--- a/src/operator/instance_norm.cc
+++ b/src/operator/instance_norm.cc
@@ -71,7 +71,7 @@ operation is also known as `contrast normalization`.
 If the input data is of shape [batch, channel, spacial_dim1, spacial_dim2, ...],
 `gamma` and `beta` parameters must be vectors of shape [channel].
 
-This implementation is based on paper:
+This implementation is based on this paper [1]_
 
 .. [1] Instance Normalization: The Missing Ingredient for Fast Stylization,
    D. Ulyanov, A. Vedaldi, V. Lempitsky, 2016 (arXiv:1607.08022v2).

From cf61364a2f38c90769098b330d5ecbafc34d4ed2 Mon Sep 17 00:00:00 2001
From: Lanking <lanking520@live.com>
Date: Fri, 11 Oct 2019 05:15:37 -0700
Subject: [PATCH 429/813] Revert "add mkl installation temp fix (#16304)"
 (#16369)

This reverts commit 943bed513b3ce3dff5eae0fb4cf319d2445c85bb.
---
 ci/docker/install/ubuntu_mkl.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ci/docker/install/ubuntu_mkl.sh b/ci/docker/install/ubuntu_mkl.sh
index b33a001b5f3d..97ef158096bc 100755
--- a/ci/docker/install/ubuntu_mkl.sh
+++ b/ci/docker/install/ubuntu_mkl.sh
@@ -28,4 +28,4 @@ apt-get update || true
 wget -qO - wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB | apt-key add - && \
     sh -c 'echo deb https://apt.repos.intel.com/mkl all main > /etc/apt/sources.list.d/intel-mkl.list' && \
     apt-get update && \
-    apt-get install -y --allow-unauthenticated intel-mkl-2019.1-053
+    apt-get install -y intel-mkl-2019.1-053

From d8193c6a7df5c8729814e72f6bb8041769396f90 Mon Sep 17 00:00:00 2001
From: "Huang, Guangtai" <guangtai@amazon.com>
Date: Sat, 12 Oct 2019 01:44:09 +0800
Subject: [PATCH 430/813] Update add_op_in_backend.md (#16403)

---
 docs/static_site/src/pages/api/faq/add_op_in_backend.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/static_site/src/pages/api/faq/add_op_in_backend.md b/docs/static_site/src/pages/api/faq/add_op_in_backend.md
index 672bf52a29ee..7595467575be 100644
--- a/docs/static_site/src/pages/api/faq/add_op_in_backend.md
+++ b/docs/static_site/src/pages/api/faq/add_op_in_backend.md
@@ -1,6 +1,6 @@
 ---
 layout: page_category
-title:  Exception Handling in MXNet
+title: A Beginner's Guide to Implementing Operators in MXNet Backend
 category: faq
 faq_c: Extend and Contribute to MXNet
 question: How do I implement operators in MXNet backend?

From 7f5e68736e4f07b80f147ef49e613fca1a864735 Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Fri, 11 Oct 2019 13:26:14 -0700
Subject: [PATCH 431/813] numpy-compatible histogram (#16266)

---
 python/mxnet/ndarray/numpy/_op.py      | 49 +++++++++++++++++++++++++-
 python/mxnet/numpy/multiarray.py       | 34 +++++++++++++++++-
 python/mxnet/symbol/numpy/_symbol.py   | 49 +++++++++++++++++++++++++-
 src/operator/tensor/histogram.cc       |  1 +
 tests/python/unittest/test_numpy_op.py | 17 +++++++++
 5 files changed, 147 insertions(+), 3 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index 9dce953004cf..aea8b19c2913 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -31,7 +31,7 @@
            'arctan2', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs',
            'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2',
            'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
-           'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
+           'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot', 'histogram',
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'mean',
            'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
            'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'hypot', 'rad2deg', 'deg2rad',
@@ -741,6 +741,53 @@ def tensordot(a, b, axes=2):
     return _npi.tensordot(a, b, a_axes_summed, b_axes_summed)
 
 
+@set_module('mxnet.ndarray.numpy')
+def histogram(a, bins=10, range=None, normed=None, weights=None, density=None):  # pylint: disable=too-many-arguments
+    """
+    Compute the histogram of a set of data.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input data. The histogram is computed over the flattened array.
+    bins : int or NDArray
+        If `bins` is an int, it defines the number of equal-width
+        bins in the given range (10, by default). If `bins` is a
+        sequence, it defines a monotonically increasing array of bin edges,
+        including the rightmost edge, allowing for non-uniform bin widths.
+        .. versionadded:: 1.11.0
+        If `bins` is a string, it defines the method used to calculate the
+        optimal bin width, as defined by `histogram_bin_edges`.
+    range : (float, float)
+        The lower and upper range of the bins. Required when `bins` is an integer.
+        Values outside the range are ignored. The first element of the range must
+        be less than or equal to the second.
+    normed : bool, optional
+        Not supported yet, coming soon.
+    weights : array_like, optional
+        Not supported yet, coming soon.
+    density : bool, optional
+        Not supported yet, coming soon.
+    """
+    if normed is True:
+        raise NotImplementedError("normed is not supported yet...")
+    if weights is not None:
+        raise NotImplementedError("weights is not supported yet...")
+    if density is True:
+        raise NotImplementedError("density is not supported yet...")
+    if isinstance(bins, numeric_types):
+        if range is None:
+            raise NotImplementedError("automatic range is not supported yet...")
+        return _npi.histogram(a, bin_cnt=bins, range=range)
+    if isinstance(bins, (list, tuple)):
+        raise NotImplementedError("array_like bins is not supported yet...")
+    if isinstance(bins, str):
+        raise NotImplementedError("string bins is not supported yet...")
+    if isinstance(bins, NDArray):
+        return _npi.histogram(a, bins=bins)
+    raise ValueError("np.histogram fails with", locals())
+
+
 @set_module('mxnet.ndarray.numpy')
 def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0, ctx=None):  # pylint: disable=too-many-arguments
     r"""
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 75b7cf65325b..d3ae4d19aca8 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -50,12 +50,13 @@
            'sqrt', 'cbrt', 'abs', 'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log',
            'degrees', 'log2', 'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative',
            'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh',
-           'tensordot', 'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate',
+           'tensordot', 'histogram', 'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate',
            'stack', 'vstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices',
            'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'arctan2', 'hypot',
            'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer',
            'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal']
 
+
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
 _NDARRAY_BASIC_INDEXING = 0
@@ -3604,6 +3605,37 @@ def tensordot(a, b, axes=2):
     return _mx_nd_np.tensordot(a, b, axes)
 
 
+@set_module('mxnet.numpy')
+def histogram(a, bins=10, range=None, normed=None, weights=None, density=None):  # pylint-disable=too-many-arguments
+    """
+    Compute the histogram of a set of data.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input data. The histogram is computed over the flattened array.
+    bins : int or NDArray
+        If `bins` is an int, it defines the number of equal-width
+        bins in the given range (10, by default). If `bins` is a
+        sequence, it defines a monotonically increasing array of bin edges,
+        including the rightmost edge, allowing for non-uniform bin widths.
+        .. versionadded:: 1.11.0
+        If `bins` is a string, it defines the method used to calculate the
+        optimal bin width, as defined by `histogram_bin_edges`.
+    range : (float, float)
+        The lower and upper range of the bins. Required when `bins` is an integer.
+        Values outside the range are ignored. The first element of the range must
+        be less than or equal to the second.
+    normed : bool, optional
+        Not supported yet, coming soon.
+    weights : array_like, optional
+        Not supported yet, coming soon.
+    density : bool, optional
+        Not supported yet, coming soon.
+    """
+    return _mx_nd_np.histogram(a, bins=bins, range=range, normed=normed, weights=weights, density=density)
+
+
 @set_module('mxnet.numpy')
 def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0, ctx=None):  # pylint: disable=too-many-arguments
     r"""
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 3eaf80a1b6fb..9a909420e934 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -33,7 +33,7 @@
            'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs', 'absolute', 'exp',
            'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2', 'log1p',
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
-           'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
+           'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot', 'histogram',
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'mean',
            'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
            'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'hypot', 'rad2deg', 'deg2rad',
@@ -1230,6 +1230,53 @@ def tensordot(a, b, axes=2):
     return _npi.tensordot(a, b, a_axes_summed, b_axes_summed)
 
 
+@set_module('mxnet.symbol.numpy')
+def histogram(a, bins=10, range=None, normed=None, weights=None, density=None):  # pylint: disable= too-many-arguments
+    """
+    Compute the histogram of a set of data.
+
+    Parameters
+    ----------
+    a : Symbol
+        Input data. The histogram is computed over the flattened array.
+    bins : int or Symbol
+        If `bins` is an int, it defines the number of equal-width
+        bins in the given range (10, by default). If `bins` is a
+        sequence, it defines a monotonically increasing array of bin edges,
+        including the rightmost edge, allowing for non-uniform bin widths.
+        .. versionadded:: 1.11.0
+        If `bins` is a string, it defines the method used to calculate the
+        optimal bin width, as defined by `histogram_bin_edges`.
+    range : (float, float)
+        The lower and upper range of the bins. Required when `bins` is an integer.
+        Values outside the range are ignored. The first element of the range must
+        be less than or equal to the second.
+    normed : bool, optional
+        Not supported yet, coming soon.
+    weights : array_like, optional
+        Not supported yet, coming soon.
+    density : bool, optional
+        Not supported yet, coming soon.
+    """
+    if normed is True:
+        raise NotImplementedError("normed is not supported yet...")
+    if weights is not None:
+        raise NotImplementedError("weights is not supported yet...")
+    if density is True:
+        raise NotImplementedError("density is not supported yet...")
+    if isinstance(bins, numeric_types):
+        if range is None:
+            raise NotImplementedError("automatic range is not avaialble yet...")
+        return _npi.histogram(a, bin_cnt=bins, range=range)
+    if isinstance(bins, (list, tuple)):
+        raise NotImplementedError("array_like bins is not supported yet...")
+    if isinstance(bins, str):
+        raise NotImplementedError("string bins is not supported yet...")
+    if isinstance(bins, Symbol):
+        return _npi.histogram(a, bins)
+    raise ValueError("histogram fails with", locals())
+
+
 @set_module('mxnet.symbol.numpy')
 def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0, ctx=None): # pylint: disable=too-many-arguments
     r"""
diff --git a/src/operator/tensor/histogram.cc b/src/operator/tensor/histogram.cc
index 754475bff9ad..b7896e9e0016 100644
--- a/src/operator/tensor/histogram.cc
+++ b/src/operator/tensor/histogram.cc
@@ -123,6 +123,7 @@ void HistogramForwardImpl<cpu>(const OpContext& ctx,
 DMLC_REGISTER_PARAMETER(HistogramParam);
 
 NNVM_REGISTER_OP(_histogram)
+.add_alias("_npi_histogram")
 .describe(R"code(This operators implements the histogram function.
 
 Example::
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 7068bf20d897..1f90f30bd8c3 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -2039,6 +2039,23 @@ def hybrid_forward(self, F, a):
                         assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
 
 
+@with_seed()
+@use_np
+def test_np_histogram():
+    shapes = [(), (3, 4), (3, 0)]
+
+    for shape in shapes:
+        mx_a = np.random.uniform(0.0, 10.0, size=shape)
+        np_a = mx_a.asnumpy()
+        mx_bins = np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5., 6., 7., 8., 9., 10.])
+        np_bins = mx_bins.asnumpy()
+        for bins, _range in [(20, (0.0, 10.0)), (mx_bins, None)]:
+            mx_cnts, mx_bins = np.histogram(mx_a, bins=bins, range=_range)
+            np_cnts, np_bins = _np.histogram(np_a, bins=bins if isinstance(bins, mx.base.numeric_types) else bins.asnumpy(), range=_range)
+            assert_almost_equal(mx_cnts.asnumpy(), np_cnts, rtol=1e-3, atol=1e-5)
+            assert_almost_equal(mx_bins.asnumpy(), np_bins, rtol=1e-3, atol=1e-5)
+
+
 @with_seed()
 @use_np
 def test_np_choice():

From ca30ba840496ea714068194a01709cfd2bf74189 Mon Sep 17 00:00:00 2001
From: dtracz <41399548+dtracz@users.noreply.github.com>
Date: Fri, 11 Oct 2019 22:59:28 +0200
Subject: [PATCH 432/813] Pseudo 2D transpose kernel (#16229)

* add kernel

* pseudo2dtranspose test

* better calculating grid & block

* add checking max sizes

* test of transpose not fitting in the cuda grid

* support for large tensors

* estetic fixes

* change iterated dimension

* fix Windows issue

* chane types to intex_t

* fix build error
---
 src/operator/tensor/matrix_op-inl.h           |  16 +
 .../tensor/pseudo2DTranspose_op-inl.cuh       | 348 ++++++++++++++++++
 tests/python/unittest/test_operator.py        |  39 ++
 3 files changed, 403 insertions(+)
 create mode 100644 src/operator/tensor/pseudo2DTranspose_op-inl.cuh

diff --git a/src/operator/tensor/matrix_op-inl.h b/src/operator/tensor/matrix_op-inl.h
index ab7b7597c68e..45df2ed53ded 100644
--- a/src/operator/tensor/matrix_op-inl.h
+++ b/src/operator/tensor/matrix_op-inl.h
@@ -43,6 +43,11 @@
 #include <thrust/device_vector.h>
 #endif
 
+#ifdef __CUDACC__
+#include "./pseudo2DTranspose_op-inl.cuh"
+#endif
+
+
 namespace mxnet {
 namespace op {
 
@@ -313,6 +318,17 @@ void TransposeImpl(RunContext ctx,
   // zero-size tensor, no need to compute
   if (src.shape_.Size() == 0U) return;
   Stream<xpu> *s = ctx.get_stream<xpu>();
+#ifdef __CUDACC__
+  // This transpose can be used only if there exist n and m such that:
+  // params = (0, ..., n-1, n+m, ..., params.size, n, ..., n+m-1)
+  // Example: (0, 2, 3, 1) or (0, 3, 1, 2), but not (0, 2, 1, 3).
+  if (isPseudo2DTranspose(axes)) {
+    MSHADOW_TYPE_SWITCH(ret.type_flag_, DType, {
+      transpose_pseudo2D<DType>(ret, src, axes, s);
+    });
+    return;
+  }
+#endif
   MSHADOW_TYPE_SWITCH(ret.type_flag_, DType, {
     switch (axes.ndim()) {
      case 0: {
diff --git a/src/operator/tensor/pseudo2DTranspose_op-inl.cuh b/src/operator/tensor/pseudo2DTranspose_op-inl.cuh
new file mode 100644
index 000000000000..5b7cf04daef4
--- /dev/null
+++ b/src/operator/tensor/pseudo2DTranspose_op-inl.cuh
@@ -0,0 +1,348 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file pseudo2DTranspose_op-inl.cuh
+ * \brief pseudo 2D transpose
+ * \author Dawid Tracz
+ */
+
+#ifndef MXNET_OPERATOR_TENSOR_PSEUDO2DTRANSPOSE_OP_INL_CUH_
+#define MXNET_OPERATOR_TENSOR_PSEUDO2DTRANSPOSE_OP_INL_CUH_
+
+#include <mxnet/tuple.h>
+#include <mxnet/tensor_blob.h>
+#include <mshadow/base.h>
+#include <algorithm>
+#include <utility>
+#include "../../common/cuda_utils.h"
+
+
+namespace mxnet {
+namespace op {
+namespace cuda {
+
+
+template <typename DType, typename CType>
+__global__ void transpose_pseudo2D(DType* out, DType* inp,
+                                   const index_t m, const index_t n,
+                                   const index_t nIterY, const index_t nIterZ) {
+  const index_t TSR = sizeof(CType)/sizeof(DType);  // TypeSizeRatio
+  const index_t chunked_n = n/TSR;
+  const index_t chunked_m = m/TSR;
+
+  union transp_t {
+    CType valChunk;
+    DType values[TSR];
+  };
+
+  __shared__ DType d_shm[1024*TSR*TSR];
+  CType* c_shm = reinterpret_cast<CType*>(d_shm);
+
+  CType* cInp = reinterpret_cast<CType*>(inp);
+  CType* cOut = reinterpret_cast<CType*>(out);
+
+  for (index_t iterZ = 0; iterZ < nIterZ; iterZ++) {
+    const index_t blockIdx_z = gridDim.z*iterZ + blockIdx.z;
+    for (index_t iterY = 0; iterY < nIterY; iterY++) {
+      const index_t blockIdx_y = gridDim.y*iterY + blockIdx.y;
+
+      index_t offset = blockIdx_z*m*chunked_n
+                     + blockIdx_y*blockDim.y*TSR*chunked_n
+                     + (index_t)blockIdx.x*blockDim.x;
+
+      if ((blockIdx.x*blockDim.x + threadIdx.x)*TSR < n
+       && (blockIdx_y*blockDim.y + threadIdx.y)*TSR < m) {
+        // read from global memory to shared
+        #pragma unroll
+        for (index_t i = 0; i < TSR; i++) {
+          index_t shmIdx = (TSR*threadIdx.y + i)*blockDim.x + threadIdx.x;
+          c_shm[shmIdx] = cInp[offset + (TSR*threadIdx.y + i)*chunked_n + threadIdx.x];
+        }
+        __syncthreads();
+
+        // read from shared to registers
+        transp_t tmp[TSR];
+        #pragma unroll
+        for (index_t i = 0; i < TSR; i++) {
+          #pragma unroll
+          for (int j = 0; j < TSR; j++) {
+            index_t shmIdx = (TSR*threadIdx.y + j)*blockDim.x*TSR + TSR*threadIdx.x + i;
+            tmp[i].values[j] = d_shm[shmIdx];
+          }
+        }
+        __syncthreads();
+
+        // write back to global output
+        offset = blockIdx_z*m*chunked_n + blockIdx.x*blockDim.x*TSR*chunked_m + blockIdx_y*blockDim.y;
+        #pragma unroll
+        for (index_t i = 0; i < TSR; i++) {
+            cOut[offset + (TSR*threadIdx.x + i)*chunked_m + threadIdx.y] = tmp[i].valChunk;
+        }
+      }
+    }
+  }
+}
+
+}  // namespace cuda
+
+
+/*!
+ * \brief Calls proper version of kernel `transpose_pseudo2D`
+ *        basing on chosen type sizes.
+ * \param dTypeSize Size of data type.
+ * \param cTypeSize Size of type that should be use to copy.
+ * \param grid Grid dimensions for the kernel.
+ * \param block Block dimensions for the kernel.
+ * \param stream Strem to run kernel.
+ * \param out Pointer to output memory.
+ * \param inp Pointer to input memory.
+ * \param m First of tensor dimensions.
+ * \param n Second of tensor dimensions.
+ */
+inline void call_transpose_pseudo2D(index_t dTypeSize, index_t cTypeSize,
+                                   dim3 grid, dim3 block, cudaStream_t stream,
+                                   void* out, void* inp, const index_t m, const index_t n,
+                                   const index_t nIterY, const index_t nIterZ) {
+  switch (dTypeSize) {
+   case (1): {
+    uint8_t* d_outPtr = reinterpret_cast<uint8_t*>(out);
+    uint8_t* d_inpPtr = reinterpret_cast<uint8_t*>(inp);
+    switch (cTypeSize) {
+     case (1):
+      cuda::transpose_pseudo2D<uint8_t, uint8_t><<<grid, block, 0, stream>>>
+                              (d_outPtr, d_inpPtr, m, n, nIterY, nIterZ);
+      break;
+     case (2):
+      cuda::transpose_pseudo2D<uint8_t, uint16_t><<<grid, block, 0, stream>>>
+                              (d_outPtr, d_inpPtr, m, n, nIterY, nIterZ);
+      break;
+     case (4):
+      cuda::transpose_pseudo2D<uint8_t, uint32_t><<<grid, block, 0, stream>>>
+                              (d_outPtr, d_inpPtr, m, n, nIterY, nIterZ);
+      break;
+     case (8):
+      // case guarded against in function getBestCopyTypeSize
+      LOG(FATAL) << "cuda::transpose_pseudo2D<uint8_t, uint64_t> would take too much shared memory";
+     default:
+      LOG(FATAL) << "Unsupported type combination";
+    }
+    break;
+   }
+   case (2): {
+    uint16_t* d_outPtr = reinterpret_cast<uint16_t*>(out);
+    uint16_t* d_inpPtr = reinterpret_cast<uint16_t*>(inp);
+    switch (cTypeSize) {
+     case (2):
+      cuda::transpose_pseudo2D<uint16_t, uint16_t><<<grid, block, 0, stream>>>
+                              (d_outPtr, d_inpPtr, m, n, nIterY, nIterZ);
+      break;
+     case (4):
+      cuda::transpose_pseudo2D<uint16_t, uint32_t><<<grid, block, 0, stream>>>
+                              (d_outPtr, d_inpPtr, m, n, nIterY, nIterZ);
+      break;
+     case (8):
+      cuda::transpose_pseudo2D<uint16_t, uint64_t><<<grid, block, 0, stream>>>
+                              (d_outPtr, d_inpPtr, m, n, nIterY, nIterZ);
+      break;
+     default:
+      LOG(FATAL) << "Unsupported type combination";
+    }
+    break;
+   }
+   case (4): {
+    uint32_t* d_outPtr = reinterpret_cast<uint32_t*>(out);
+    uint32_t* d_inpPtr = reinterpret_cast<uint32_t*>(inp);
+    switch (cTypeSize) {
+     case (4):
+      cuda::transpose_pseudo2D<uint32_t, uint32_t><<<grid, block, 0, stream>>>
+                              (d_outPtr, d_inpPtr, m, n, nIterY, nIterZ);
+      break;
+     case (8):
+      cuda::transpose_pseudo2D<uint32_t, uint64_t><<<grid, block, 0, stream>>>
+                              (d_outPtr, d_inpPtr, m, n, nIterY, nIterZ);
+      break;
+     default:
+      LOG(FATAL) << "Unsupported type combination";
+    }
+    break;
+   }
+   case (8): {
+    uint64_t* d_outPtr = reinterpret_cast<uint64_t*>(out);
+    uint64_t* d_inpPtr = reinterpret_cast<uint64_t*>(inp);
+    switch (cTypeSize) {
+     case (8):
+      cuda::transpose_pseudo2D<uint64_t, uint64_t><<<grid, block, 0, stream>>>
+                              (d_outPtr, d_inpPtr, m, n, nIterY, nIterZ);
+      break;
+     default:
+      LOG(FATAL) << "Unsupported type combination";
+    }
+    break;
+   }
+   default:
+    LOG(FATAL) << "Unsupported type combination";
+  }
+  auto cuErr = cudaPeekAtLastError();
+  CHECK_EQ(cuErr, cudaSuccess) << "Transpose kernel failure: " << cudaGetErrorString(cuErr) << ". "
+                               << "block: (" << block.x << "," << block.y << "," << block.z << ")"
+                               << " grid: (" << grid.x << "," << grid.y << "," << grid.z << ")";
+}
+
+
+/*!
+ * \brief Checks if function `transpose_pseudo2D` can be used
+ *        to perform transpose operation with given params.
+ * \param params Parameters (axes) of the transpose.
+ */
+inline bool isPseudo2DTranspose(const TShape& params) {
+  index_t n_swpDims = 1;
+  int i=0;
+  while (i < params.ndim() && i == params[i])
+    i++;  // leading dimensions
+  while (i+1 < params.ndim()) {
+    if(params[i]+1 != params[i+1])
+      n_swpDims++;
+    i++;
+  }
+  return n_swpDims == 2;
+}
+
+
+struct pseudo2DSizes {
+  index_t leadDimS;
+  index_t M;
+  index_t N;
+};
+
+/*!
+ * \brief Calculates total size of last two dimension batches
+ *        (according to description of transpose_pseudo2D function).
+ * \param shape Shape of tensor to transpose.
+ * \param params Parameters (axes) of the transpose.
+ */
+inline pseudo2DSizes getPackedTransposeDimensions(const TShape& shape,
+                                                  const TShape& params) {
+  auto ndim = params.ndim();
+  pseudo2DSizes sizes;
+  sizes.leadDimS = 1;
+  int i=0;
+  while (i < ndim && i == params[i]) {
+    sizes.leadDimS *= shape[i];
+    i++;
+  }
+  sizes.N = shape[params[i++]];
+  while (i < ndim && params[i]-1 == params[i-1]) {
+    sizes.N *= shape[params[i]];
+    i++;
+  }
+  sizes.M = shape[params[i++]];
+  while (i < ndim && params[i]-1 == params[i-1]) {
+    sizes.M *= shape[params[i]];
+    i++;
+  }
+  CHECK_EQ(i, ndim) << "Too many dimensions to transpose";
+  return sizes;
+}
+
+
+inline int32_t getBestCopyTypeSize(index_t dTypeSize, index_t sizeM, index_t sizeN) {
+  index_t cTypeSize = std::max((index_t)8, dTypeSize);
+  while (cTypeSize > dTypeSize) {
+    auto tsr = cTypeSize/dTypeSize;
+    if (sizeM % tsr != 0 || sizeN % tsr != 0)
+      cTypeSize /= 2;
+    else
+      break;
+  }
+  // if the cTypeSize is 8x dTypeSize then kernel would require 64kB shared memory
+  if(cTypeSize == 8 && dTypeSize == 1)
+    cTypeSize = 4;
+  return cTypeSize;
+}
+
+
+inline std::pair<dim3, dim3> calculateKernelParams(pseudo2DSizes sizes, const index_t TSR) {
+  index_t nThreadsPerBlock = 32*32/4;  // value chosen empirically
+  index_t thdsY = 1;
+  index_t thdsX = 1;
+  while(sizes.N/TSR > thdsX && thdsX < 32) {
+    thdsX *= 2;
+  }
+  thdsY = nThreadsPerBlock/thdsX;
+  thdsY = std::min(sizes.M/TSR, thdsY);
+  index_t blocksY = (sizes.M/TSR-1)/thdsY + 1;
+  index_t blocksX = (sizes.N/TSR-1)/thdsX + 1;
+
+  dim3 grid(blocksX, blocksY, sizes.leadDimS);
+  dim3 block(thdsX, thdsY);
+  return {grid, block};
+}
+
+
+/*!
+ * \brief Transpose given tensor according to params.
+ *        Supports only transposes that satisfy:
+ *        Exists n and m such that:
+ *        params = (0, ..., n-1, n+m, ..., params.size, n, ..., n+m-1)
+ *        Example: (0, 2, 3, 1) or (0, 3, 1, 2), but not (0, 2, 1, 3).
+ * \param outBlob Tensor blob to store result.
+ * \param inpBlob Tensor blob with input data.
+ * \param params Parameters (axes) of the transpose.
+ * \param s Pointer to GPU stream.
+ */
+template <typename DType, typename gpu>
+void transpose_pseudo2D(const TBlob& outBlob, const TBlob& inpBlob,
+                        const TShape& params, mshadow::Stream<gpu>* s) {
+  const TShape& shape = inpBlob.shape_;
+  CHECK_EQ(shape.ndim(), params.ndim());
+  auto ndim = params.ndim();
+
+  auto sizes = getPackedTransposeDimensions(shape, params);
+
+  index_t cTypeSize = getBestCopyTypeSize(sizeof(DType), sizes.M, sizes.N);
+  // Type Size Ratio
+  const index_t TSR = cTypeSize/sizeof(DType);
+  CHECK_EQ(cTypeSize, sizeof(DType)*TSR);
+
+  auto pair = calculateKernelParams(sizes, TSR);
+  dim3 grid = pair.first;
+  dim3 block = pair.second;
+  index_t nIterY = 1;
+  if (grid.y > std::numeric_limits<uint16_t>::max()) {
+    nIterY = (grid.y - 1)/(std::numeric_limits<uint16_t>::max() - 1) + 1;
+    grid.y = (grid.y - 1)/nIterY + 1;
+  }
+  index_t nIterZ = 1;
+  if (grid.z > std::numeric_limits<uint16_t>::max()) {
+    nIterZ = (grid.z - 1)/(std::numeric_limits<uint16_t>::max() - 1) + 1;
+    grid.z = (grid.z - 1)/nIterZ + 1;
+  }
+
+  cudaStream_t stream = mshadow::Stream<gpu>::GetStream(s);
+  call_transpose_pseudo2D(sizeof(DType), cTypeSize, grid, block, stream,
+                          outBlob.dptr_, inpBlob.dptr_, sizes.M, sizes.N, nIterY, nIterZ);
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+
+#endif  // MXNET_OPERATOR_TENSOR_PSEUDO2DTRANSPOSE_OP_INL_CUH_
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 35460676da28..5f40bf12674a 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -2876,6 +2876,45 @@ def test_transpose():
 
 
 @with_seed()
+def test_pseudo2dtranspose():
+    def getTwoInts(mn, mx):
+        n1 = np.random.randint(mn, mx)
+        n2 = np.random.randint(mn, mx-1)
+        n2 = n2 if n2 < n1 else n2+1
+        return tuple(np.sort([n1, n2]))
+
+    def getTranspAxes(ndim):
+        axes = list(range(ndim))
+        n1, n2 = getTwoInts(0,ndim)
+        return tuple(axes[:n1]+axes[n2:]+axes[n1:n2])
+
+    for ndim in range(2, 7):
+        for dt in ['int8', 'half', 'int32', 'int64']:
+            for _ in range(5):
+                dims = list(np.random.randint(5, 20, size=ndim))
+                axes = getTranspAxes(ndim)
+                x = mx.nd.array(np.random.normal(size=dims), dtype=dt)
+                y = mx.nd.transpose(x, axes=axes)
+                assert_allclose(np.transpose(x.asnumpy(), axes=axes), y.asnumpy())
+
+
+@with_seed()
+def test_big_transpose():
+    n = [1]
+    d = list(np.random.randint(132, 160, size=1))
+    hw = list(np.random.randint(256, 320, size=2))
+    c = [10]
+    dims = n + d + hw + c
+    axes = (0,4,1,2,3)
+    x_np = np.random.normal(size=dims).astype('uint8')
+    x = mx.nd.array(x_np, dtype='uint8')
+    y = mx.nd.transpose(x, axes=axes)
+    assert_allclose(np.transpose(x_np, axes=axes), y.asnumpy().astype('uint8'))
+    axes = (0,2,3,4,1)
+    z = mx.nd.transpose(y, axes=axes)
+    assert_allclose(x_np, z.asnumpy().astype('uint8'))
+
+
 def test_larger_transpose():
     x = mx.nd.random.normal(shape=(50,51))
     y = mx.nd.transpose(x)

From d2d76dc07db8cbe780ff457d8bb01d77b593de85 Mon Sep 17 00:00:00 2001
From: Pedro Larroy <pedro.larroy.lists@gmail.com>
Date: Fri, 11 Oct 2019 14:08:37 -0700
Subject: [PATCH 433/813] increase docker cache timeout (#16430)

---
 ci/docker_cache.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ci/docker_cache.py b/ci/docker_cache.py
index f906b0eba66c..3a2a1fb415ee 100755
--- a/ci/docker_cache.py
+++ b/ci/docker_cache.py
@@ -37,7 +37,7 @@
 DOCKERHUB_LOGIN_NUM_RETRIES = 5
 DOCKERHUB_RETRY_SECONDS = 5
 DOCKER_CACHE_NUM_RETRIES = 3
-DOCKER_CACHE_TIMEOUT_MINS = 15
+DOCKER_CACHE_TIMEOUT_MINS = 45
 PARALLEL_BUILDS = 10
 
 

From 4dee4eebdb2136744a0333ea08282b1385c6a0ce Mon Sep 17 00:00:00 2001
From: Zhennan Qin <zhennan.qin@intel.com>
Date: Sat, 12 Oct 2019 18:17:21 +0800
Subject: [PATCH 434/813] Fix mkldnn reshape (#16455)

Change-Id: I7e3ed84d02eaac4dbc0637167519e53e7eb8e168
---
 src/operator/nn/mkldnn/mkldnn_base-inl.h      |   1 -
 src/operator/nn/mkldnn/mkldnn_base.cc         |   6 +-
 src/operator/nn/mkldnn/mkldnn_flatten-inl.h   |  48 -------
 src/operator/nn/mkldnn/mkldnn_flatten.cc      |  79 ------------
 src/operator/nn/mkldnn/mkldnn_ops-inl.h       |   5 -
 src/operator/nn/mkldnn/mkldnn_reshape-inl.h   |  27 ++--
 src/operator/nn/mkldnn/mkldnn_reshape.cc      | 118 ++++++------------
 .../mkldnn/mkldnn_quantized_flatten.cc        |   4 +-
 src/operator/tensor/matrix_op.cc              |  42 ++-----
 9 files changed, 69 insertions(+), 261 deletions(-)
 delete mode 100644 src/operator/nn/mkldnn/mkldnn_flatten-inl.h
 delete mode 100644 src/operator/nn/mkldnn/mkldnn_flatten.cc

diff --git a/src/operator/nn/mkldnn/mkldnn_base-inl.h b/src/operator/nn/mkldnn/mkldnn_base-inl.h
index 9e8725e776e5..961aa8b05a84 100644
--- a/src/operator/nn/mkldnn/mkldnn_base-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_base-inl.h
@@ -194,7 +194,6 @@ bool SupportMKLDNNDeconv(const DeconvolutionParam& params, const NDArray &input)
 bool SupportMKLDNNSoftmax(const SoftmaxParam& param, const NDArray &input, const NDArray &output);
 bool SupportMKLDNNSoftmaxOutput(const SoftmaxOutputParam &param);
 bool SupportMKLDNNTranspose(const TransposeParam& param, const NDArray &data);
-bool SupportMKLDNNReshape(const ReshapeParam &param, const NDArray &data);
 }  // namespace op
 
 static int GetTypeSize(int dtype) {
diff --git a/src/operator/nn/mkldnn/mkldnn_base.cc b/src/operator/nn/mkldnn/mkldnn_base.cc
index 862947eb726a..fca908fc8e39 100644
--- a/src/operator/nn/mkldnn/mkldnn_base.cc
+++ b/src/operator/nn/mkldnn/mkldnn_base.cc
@@ -428,6 +428,7 @@ void FallBackCompute(Compute fn, const AttrState &attrs_states,
                      const std::vector<NDArray> &outputs) {
   std::vector<TBlob> in_blobs(inputs.size());
   std::vector<NDArray> in_bufs;
+  std::vector<OpReqType> new_req = req;
   for (size_t i = 0; i < in_blobs.size(); i++) {
     // If the input data isn't stored in the default format, we shouldn't
     // call data() directly, which will change the layout of the NDArray.
@@ -452,6 +453,9 @@ void FallBackCompute(Compute fn, const AttrState &attrs_states,
     // for inplace, we already converted & copied input above.
     if ((req[i] == kWriteTo) || (req[i] == kWriteInplace)) {
       const_cast<NDArray &>(output).InvalidateMKLDNNData();
+      if (req[i] == kWriteInplace) {
+        new_req[i] = kWriteTo;
+      }
     } else if (req[i] == kAddTo && output.IsMKLDNNData()) {
       NDArray temp = outputs[i].Reorder2Default();
       temp_src.emplace_back(temp);
@@ -462,7 +466,7 @@ void FallBackCompute(Compute fn, const AttrState &attrs_states,
     out_blobs[i] = output.data();
   }
 
-  fn(attrs_states, ctx, in_blobs, req, out_blobs);
+  fn(attrs_states, ctx, in_blobs, new_req, out_blobs);
   for (size_t i = 0; i < out_blobs.size(); i++) {
     if (req[i] == kAddTo && outputs[i].IsMKLDNNData())
       mxnet::common::CastNonDefaultStorage(temp_src, temp_dst, ctx, false);
diff --git a/src/operator/nn/mkldnn/mkldnn_flatten-inl.h b/src/operator/nn/mkldnn/mkldnn_flatten-inl.h
deleted file mode 100644
index ae890d8f3d91..000000000000
--- a/src/operator/nn/mkldnn/mkldnn_flatten-inl.h
+++ /dev/null
@@ -1,48 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- */
-
-/*!
- * \file mkldnn_flatten-inl.h
- * \brief Implement flatten operator by using mkldnn reorder primitive
- * \author Wuxun Zhang
- */
-
-#ifndef MXNET_OPERATOR_NN_MKLDNN_MKLDNN_FLATTEN_INL_H_
-#define MXNET_OPERATOR_NN_MKLDNN_MKLDNN_FLATTEN_INL_H_
-#if MXNET_USE_MKLDNN == 1
-
-#include "mkldnn_reshape-inl.h"
-
-namespace mxnet {
-namespace op {
-
-class MKLDNNFlattenFwd : public MKLDNNReshapeFwd {
- public:
-  explicit MKLDNNFlattenFwd(const OpReqType &req, const NDArray &input, const NDArray &output)
-      : MKLDNNReshapeFwd(req, input, output) {}
-};
-
-void MKLDNNFlattenForward(const nnvm::NodeAttrs &attrs, const OpContext &ctx, const NDArray &input,
-                          const OpReqType &req, const NDArray &output);
-
-}  // namespace op
-}  // namespace mxnet
-
-#endif  // MXNET_USE_MKLDNN == 1
-#endif  // MXNET_OPERATOR_NN_MKLDNN_MKLDNN_FLATTEN_INL_H_
diff --git a/src/operator/nn/mkldnn/mkldnn_flatten.cc b/src/operator/nn/mkldnn/mkldnn_flatten.cc
deleted file mode 100644
index 4090eb026cfc..000000000000
--- a/src/operator/nn/mkldnn/mkldnn_flatten.cc
+++ /dev/null
@@ -1,79 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- */
-
-/*!
- * \file mkldnn_flatten.cc
- * \brief Implement flatten operator by using mkldnn reorder primitive
- * \author Wuxun Zhang
-*/
-
-#if MXNET_USE_MKLDNN == 1
-
-#include "mkldnn_flatten-inl.h"
-
-namespace mxnet {
-namespace op {
-
-static MKLDNNFlattenFwd &GetFlattenForward(const OpReqType &req,
-                                           const NDArray &input,
-                                           const NDArray &output) {
-#if DMLC_CXX11_THREAD_LOCAL
-  static thread_local std::unordered_map<OpSignature,
-                                         MKLDNNFlattenFwd, OpHash> fwds;
-#else
-  static MX_THREAD_LOCAL std::unordered_map<OpSignature,
-                                            MKLDNNFlattenFwd, OpHash> fwds;
-#endif
-  OpSignature key;
-  key.AddSign(req);
-  key.AddSign(input);
-
-  auto it = fwds.find(key);
-  if (it == fwds.end()) {
-    MKLDNNFlattenFwd fwd(req, input, output);
-    it = AddToCache(&fwds, key, fwd);
-  }
-  return it->second;
-}
-
-void MKLDNNFlattenForward(const nnvm::NodeAttrs &attrs,
-                          const OpContext &ctx,
-                          const NDArray &input,
-                          const OpReqType &req,
-                          const NDArray &output) {
-  if (req == kNullOp) return;
-  CHECK_NE(req, kAddTo) << "kAddTo is not supported yet";
-
-  auto fwd = GetFlattenForward(req, input, output);
-  auto ws_size = fwd.GetWorkspaceSize();
-  void* ws_ptr = nullptr;
-  if (ws_size) {
-    mshadow::Stream<cpu> *s = ctx.get_stream<cpu>();
-    mshadow::Tensor<cpu, 1, char> ws = ctx.requested[0]
-      .get_space_typed<cpu, 1, char>(mshadow::Shape1(ws_size), s);
-    ws_ptr = reinterpret_cast<void*>(ws.dptr_);
-  }
-
-  fwd.Execute(input, output, ws_ptr);
-}
-
-}  // namespace op
-}  // namespace mxnet
-
-#endif
diff --git a/src/operator/nn/mkldnn/mkldnn_ops-inl.h b/src/operator/nn/mkldnn/mkldnn_ops-inl.h
index b564a3318402..c0218f4100b5 100644
--- a/src/operator/nn/mkldnn/mkldnn_ops-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_ops-inl.h
@@ -131,11 +131,6 @@ void MKLDNNReshapeForward(const nnvm::NodeAttrs& attrs,
                           const OpReqType &req,
                           const NDArray &output);
 
-void MKLDNNFlattenForward(const nnvm::NodeAttrs &attrs,
-                          const OpContext &ctx,
-                          const NDArray &input,
-                          const OpReqType &req,
-                          const NDArray &output);
 }  // namespace op
 }  // namespace mxnet
 #endif  // MXNET_USE_MKLDNN == 1
diff --git a/src/operator/nn/mkldnn/mkldnn_reshape-inl.h b/src/operator/nn/mkldnn/mkldnn_reshape-inl.h
index 63e367b4dc7f..726d72156718 100644
--- a/src/operator/nn/mkldnn/mkldnn_reshape-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_reshape-inl.h
@@ -35,30 +35,21 @@ namespace mxnet {
 namespace op {
 
 class MKLDNNReshapeFwd {
- protected:
+ public:
+  MKLDNNReshapeFwd(const OpReqType &req, const NDArray &input, const NDArray &output);
+  int GetWorkspaceSize();
+  void SetNewMem(const NDArray &input, const NDArray &output, void *workspace = nullptr);
+  void Execute(const NDArray &input, const NDArray &output, void *workspace = nullptr);
+
+ private:
   std::shared_ptr<mkldnn::memory> data_;
   std::shared_ptr<mkldnn::memory> out_;
   std::shared_ptr<mkldnn::memory> temp_;
   std::vector<mkldnn::primitive> prims_;
-  bool needInvalidateInput = false;
-
- public:
-  MKLDNNReshapeFwd(const OpReqType &req,
-                   const NDArray &input,
-                   const NDArray &output);
-  int GetWorkspaceSize();
-  void SetNewMem(const NDArray &input,
-                 const NDArray &output,
-                 void* workspace = nullptr);
-  void Execute(const NDArray &input,
-               const NDArray &output,
-               void* workspace = nullptr);
 };
 
-typedef ParamOpSign<ReshapeParam> MKLDNNReshapeSignature;
-MKLDNNReshapeFwd &GetReshapeForward(const ReshapeParam& param,
-                                    const OpReqType &req,
-                                    const NDArray &input,
+typedef OpSignature MKLDNNReshapeSignature;
+MKLDNNReshapeFwd &GetReshapeForward(const OpReqType &req, const NDArray &input,
                                     const NDArray &output);
 
 }  // namespace op
diff --git a/src/operator/nn/mkldnn/mkldnn_reshape.cc b/src/operator/nn/mkldnn/mkldnn_reshape.cc
index 063c85dae39a..9c226a052b0b 100644
--- a/src/operator/nn/mkldnn/mkldnn_reshape.cc
+++ b/src/operator/nn/mkldnn/mkldnn_reshape.cc
@@ -24,65 +24,37 @@
 */
 
 #if MXNET_USE_MKLDNN == 1
-
-#include <mkldnn.hpp>
-#include "mkldnn_reshape-inl.h"
+#include "../../tensor/elemwise_unary_op.h"
+#include "./mkldnn_ops-inl.h"
+#include "./mkldnn_base-inl.h"
+#include "./mkldnn_reshape-inl.h"
 
 namespace mxnet {
 namespace op {
 
-bool SupportMKLDNNReshape(const ReshapeParam &param,
-                          const NDArray &data) {
-  auto data_ndim = data.shape().ndim();
-
-  if (data_ndim > 4 ||
-      data.dtype() != mshadow::kFloat32 ||
-      param.shape.ndim() > 4)
-    return false;
-
-  return true;
-}
-
-MKLDNNReshapeFwd::MKLDNNReshapeFwd(const OpReqType &req,
-                                   const NDArray &input,
+MKLDNNReshapeFwd::MKLDNNReshapeFwd(const OpReqType &req, const NDArray &input,
                                    const NDArray &output) {
-  auto engine = CpuEngine::Get()->get_engine();
-
-  // data_
-  auto in_mem = input.GetMKLDNNData();
-  auto in_pd = in_mem->get_primitive_desc();
-  data_ = std::make_shared<mkldnn::memory>(in_pd, nullptr);
-
-  // temp_
+  const auto engine = CpuEngine::Get()->get_engine();
+  data_ = std::make_shared<mkldnn::memory>(input.GetMKLDNNData()->get_primitive_desc(), nullptr);
+  // Create temp memory
   auto temp_dims = mkldnn::memory::dims(input.shape().begin(), input.shape().end());
-  auto temp_type = static_cast<mkldnn::memory::data_type>(in_pd.desc().data.data_type);
-  auto temp_fmt = static_cast<mkldnn::memory::format>(GetDefaultFormat(in_pd.desc()));
+  auto temp_type = static_cast<mkldnn::memory::data_type>(get_mkldnn_type(input.dtype()));
+  auto temp_fmt = static_cast<mkldnn::memory::format>(GetDefaultFormat(input.shape().ndim()));
   auto temp_desc = mkldnn::memory::desc(temp_dims, temp_type, temp_fmt);
   auto temp_pd = mkldnn::memory::primitive_desc(temp_desc, engine);
-  temp_ = std::make_shared<mkldnn::memory>(temp_pd, nullptr);
-
-  // destination
   out_ = std::make_shared<mkldnn::memory>(temp_pd, nullptr);
-
   if (req == kWriteInplace) {
     // If the input has MKL-DNN internal layout, we need reorder it to a temporal buffer with
     // default layout and copy from the temporal buffer back to output buffer which has the same
     // address with input buffer.
     // If the input has default layout, then nothing need to do.
     if (input.IsMKLDNNData()) {
-      prims_.push_back(mkldnn::reorder(*data_, *temp_));   // reorder to default
-      prims_.push_back(mkldnn::reorder(*temp_, *out_));    // copy back
-      needInvalidateInput = true;
+      temp_ = std::make_shared<mkldnn::memory>(temp_pd, nullptr);
+      prims_.push_back(mkldnn::reorder(*data_, *temp_));  // reorder to default
+      prims_.push_back(mkldnn::reorder(*temp_, *out_));   // copy back
     }
   } else if (req == kWriteTo) {
-    if (input.IsMKLDNNData()) {
-      prims_.push_back(mkldnn::reorder(*data_, *temp_));   // reorder to default
-      prims_.push_back(mkldnn::reorder(*temp_, *out_));    // copy to the output buffer
-      needInvalidateInput = false;
-    } else {
-      prims_.push_back(mkldnn::reorder(*data_, *out_));    // copy directly from input to output
-      needInvalidateInput = false;
-    }
+    prims_.push_back(mkldnn::reorder(*data_, *out_));
   } else {
     LOG(FATAL) << "not supported req type: " << req;
   }
@@ -95,22 +67,8 @@ int MKLDNNReshapeFwd::GetWorkspaceSize() {
 void MKLDNNReshapeFwd::SetNewMem(const NDArray &input,
                                  const NDArray &output,
                                  void* workspace) {
-  if (input.IsMKLDNNData()) {
-    this->data_->set_data_handle(input.GetMKLDNNData()->get_data_handle());
-  } else {
-    MSHADOW_TYPE_SWITCH(input.dtype(), DTYPE, {
-      this->data_->set_data_handle(input.data().dptr<DTYPE>());
-    })
-  }
-
-  if (output.IsMKLDNNData()) {
-    this->out_->set_data_handle(output.GetMKLDNNData()->get_data_handle());
-  } else {
-    MSHADOW_TYPE_SWITCH(output.dtype(), DTYPE, {
-      this->out_->set_data_handle(output.data().dptr<DTYPE>());
-    })
-  }
-
+  this->data_->set_data_handle(input.GetMKLDNNData()->get_data_handle());
+  this->out_->set_data_handle(output.GetMKLDNNData()->get_data_handle());
   if (workspace) {
     this->temp_->set_data_handle(workspace);
   }
@@ -119,22 +77,21 @@ void MKLDNNReshapeFwd::SetNewMem(const NDArray &input,
 void MKLDNNReshapeFwd::Execute(const NDArray &input,
                                const NDArray &output,
                                void* workspace) {
-  // set memory handles
-  SetNewMem(input, output, workspace);
-  // register primitives
-  auto stream = MKLDNNStream::Get();
-  for (auto &v : this->prims_) {
-    stream->RegisterPrim(v);
-  }
-  stream->Submit();
-  // invalidate mkldnn memory in input
-  if (needInvalidateInput) {
-    const_cast<NDArray &>(input).InvalidateMKLDNNData();
+  if (this->prims_.size()) {
+    // set memory handles
+    SetNewMem(input, output, workspace);
+    // register primitives
+    auto stream = MKLDNNStream::Get();
+    for (auto &v : this->prims_) {
+      stream->RegisterPrim(v);
+    }
+    stream->Submit();
   }
+  // invalidate mkldnn memory in output
+  const_cast<NDArray &>(output).InvalidateMKLDNNData();
 }
 
-MKLDNNReshapeFwd &GetReshapeForward(const ReshapeParam& param,
-                                    const OpReqType &req,
+MKLDNNReshapeFwd &GetReshapeForward(const OpReqType &req,
                                     const NDArray &input,
                                     const NDArray &output) {
 #if DMLC_CXX11_THREAD_LOCAL
@@ -144,10 +101,9 @@ MKLDNNReshapeFwd &GetReshapeForward(const ReshapeParam& param,
   static MX_THREAD_LOCAL std::unordered_map<MKLDNNReshapeSignature,
                                             MKLDNNReshapeFwd, OpHash> fwds;
 #endif
-  MKLDNNReshapeSignature key(param);
+  MKLDNNReshapeSignature key;
   key.AddSign(req);
   key.AddSign(input);
-  key.AddSign(output);
 
   auto it = fwds.find(key);
   if (it == fwds.end()) {
@@ -162,22 +118,28 @@ void MKLDNNReshapeForward(const nnvm::NodeAttrs& attrs,
                           const NDArray &input,
                           const OpReqType &req,
                           const NDArray &output) {
-  const ReshapeParam& param = nnvm::get<ReshapeParam>(attrs.parsed);
+  // For mkldnn non-supported input, it shouldn't hold mkldnn memory, so let's simply fallback to
+  // naive implement.
+  if (input.shape().ndim() > 4 || !SupportMKLDNNQuantize(input.dtype())) {
+    if (req != kWriteInplace) {
+      FallBackCompute(UnaryOp::IdentityCompute<cpu>, attrs, ctx, {input}, {req}, {output});
+    }
+    return;
+  }
   if (req == kNullOp) return;
   CHECK_NE(req, kAddTo) << "kAddTo is not supported yet";
-
-  auto fwd = GetReshapeForward(param, req, input, output);
+  auto fwd = GetReshapeForward(req, input, output);
   auto ws_size = fwd.GetWorkspaceSize();
   void* ws_ptr = nullptr;
   if (ws_size) {
     mshadow::Stream<cpu> *s = ctx.get_stream<cpu>();
     mshadow::Tensor<cpu, 1, char> ws = ctx.requested[0]
       .get_space_typed<cpu, 1, char>(mshadow::Shape1(ws_size), s);
-    ws_ptr = reinterpret_cast<void*>(ws.dptr_);
+    ws_ptr = static_cast<void*>(ws.dptr_);
   }
-
   fwd.Execute(input, output, ws_ptr);
 }
+
 }  // namespace op
 }  // namespace mxnet
 #endif
diff --git a/src/operator/quantization/mkldnn/mkldnn_quantized_flatten.cc b/src/operator/quantization/mkldnn/mkldnn_quantized_flatten.cc
index 31da936915e6..c059f9868ea0 100644
--- a/src/operator/quantization/mkldnn/mkldnn_quantized_flatten.cc
+++ b/src/operator/quantization/mkldnn/mkldnn_quantized_flatten.cc
@@ -24,7 +24,7 @@
  */
 
 #if MXNET_USE_MKLDNN == 1
-#include "../../nn/mkldnn/mkldnn_flatten-inl.h"
+#include "../../nn/mkldnn/mkldnn_ops-inl.h"
 #include "../quantization_utils.h"
 
 namespace mxnet {
@@ -42,7 +42,7 @@ static void MKLDNNQuantizedFlattenForward(const nnvm::NodeAttrs& attrs, const Op
                                           const std::vector<NDArray>& inputs,
                                           const std::vector<OpReqType>& req,
                                           const std::vector<NDArray>& outputs) {
-  MKLDNNFlattenForward(attrs, ctx, inputs[0], req[0], outputs[0]);
+  MKLDNNReshapeForward(attrs, ctx, inputs[0], req[0], outputs[0]);
   outputs[1].data().dptr<float>()[0] = inputs[1].data().dptr<float>()[0];
   outputs[2].data().dptr<float>()[0] = inputs[2].data().dptr<float>()[0];
 }
diff --git a/src/operator/tensor/matrix_op.cc b/src/operator/tensor/matrix_op.cc
index c60402488b65..ed4afc0cbcbc 100644
--- a/src/operator/tensor/matrix_op.cc
+++ b/src/operator/tensor/matrix_op.cc
@@ -111,17 +111,12 @@ static void ReshapeComputeExCPU(const nnvm::NodeAttrs& attrs,
                                 const std::vector<NDArray>& inputs,
                                 const std::vector<OpReqType>& req,
                                 const std::vector<NDArray>& outputs) {
-  const ReshapeParam& param = nnvm::get<ReshapeParam>(attrs.parsed);
   CHECK_EQ(inputs.size(), 1U);
   CHECK_EQ(outputs.size(), 1U);
   // If inputs are supposed to be in MKLDNN format and
   // MKLDNNsupport the data type or the shape. Then convert
   // it to the output format and shape
-  if (SupportMKLDNNReshape(param, inputs[0])) {
-    MKLDNNReshapeForward(attrs, ctx, inputs[0], req[0], outputs[0]);
-    return;
-  }
-  FallBackCompute(UnaryOp::IdentityCompute<cpu>, attrs, ctx, inputs, req, outputs);
+  MKLDNNReshapeForward(attrs, ctx, inputs[0], req[0], outputs[0]);
 }
 
 inline static bool ReshapeStorageType(const nnvm::NodeAttrs& attrs,
@@ -226,6 +221,7 @@ If the argument `reverse` is set to 1, then the special values are inferred from
 .add_argument("data", "NDArray-or-Symbol", "Input data to reshape.")
 .add_arguments(ReshapeParam::__FIELDS__());
 
+#if MXNET_USE_MKLDNN == 1
 static void FlattenEx(const nnvm::NodeAttrs& attrs,
                       const OpContext& ctx,
                       const std::vector<NDArray>& inputs,
@@ -233,22 +229,12 @@ static void FlattenEx(const nnvm::NodeAttrs& attrs,
                       const std::vector<NDArray>& outputs) {
   CHECK_EQ(inputs.size(), 1U);
   CHECK_EQ(outputs.size(), 1U);
-#if MXNET_USE_MKLDNN == 1
-  auto data_ndim = inputs[0].shape().ndim();
-  if (data_ndim <= 4 && inputs[0].dtype() == mshadow::kFloat32) {
-    MKLDNNFlattenForward(attrs, ctx, inputs[0], req[0], outputs[0]);
-    return;
-  } else {
-    // This happens if inputs are supposed to be in MKLDNN format
-    // but MKLDNN doesn't support the data type or the shape. We're
-    // forced to convert it to the default format.
-    FallBackCompute(UnaryOp::IdentityCompute<cpu>, attrs, ctx, inputs, req, outputs);
-    return;
-  }
-#endif
+  // If inputs are supposed to be in MKLDNN format and
+  // MKLDNNsupport the data type or the shape. Then convert
+  // it to the output format and shape
+  MKLDNNReshapeForward(attrs, ctx, inputs[0], req[0], outputs[0]);
 }
 
-#if MXNET_USE_MKLDNN == 1
 static inline bool FlattenStorageType(const nnvm::NodeAttrs& attrs,
                                       const int dev_mask,
                                       DispatchMode* dispatch_mode,
@@ -294,14 +280,12 @@ Example::
 .set_num_outputs(1)
 .set_attr<mxnet::FInferShape>("FInferShape", FlattenShape)
 .set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
-#if MXNET_USE_MKLDNN == 1
-.set_attr<FInferStorageType>("FInferStorageType", FlattenStorageType)
-#endif
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{ "_backward_copy" })
 .set_attr<FCompute>("FCompute<cpu>", UnaryOp::IdentityCompute<cpu>)
-.set_attr<FComputeEx>("FComputeEx<cpu>", FlattenEx)
 #if MXNET_USE_MKLDNN == 1
 .set_attr<bool>("TIsMKLDNN", true)
+.set_attr<FComputeEx>("FComputeEx<cpu>", FlattenEx)
+.set_attr<FInferStorageType>("FInferStorageType", FlattenStorageType)
 .set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& n) {
   return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
 })
@@ -1033,7 +1017,7 @@ NNVM_REGISTER_OP(depth_to_space)
 .describe(R"code(Rearranges(permutes) data from depth into blocks of spatial data.
 Similar to ONNX DepthToSpace operator:
 https://github.com/onnx/onnx/blob/master/docs/Operators.md#DepthToSpace.
-The output is a new tensor where the values from depth dimension are moved in spatial blocks 
+The output is a new tensor where the values from depth dimension are moved in spatial blocks
 to height and width dimension. The reverse of this operation is ``space_to_depth``.
 
 .. math::
@@ -1044,7 +1028,7 @@ to height and width dimension. The reverse of this operation is ``space_to_depth
     y = reshape(x \prime \prime, [N, C / (block\_size ^ 2), H * block\_size, W * block\_size])
     \end{gather*}
 
-where :math:`x` is an input tensor with default layout as :math:`[N, C, H, W]`: [batch, channels, height, width] 
+where :math:`x` is an input tensor with default layout as :math:`[N, C, H, W]`: [batch, channels, height, width]
 and :math:`y` is the output tensor of layout :math:`[N, C / (block\_size ^ 2), H * block\_size, W * block\_size]`
 
 Example::
@@ -1084,9 +1068,9 @@ Example::
 NNVM_REGISTER_OP(space_to_depth)
 .describe(R"code(Rearranges(permutes) blocks of spatial data into depth.
 Similar to ONNX SpaceToDepth operator:
-https://github.com/onnx/onnx/blob/master/docs/Operators.md#SpaceToDepth 
+https://github.com/onnx/onnx/blob/master/docs/Operators.md#SpaceToDepth
 
-The output is a new tensor where the values from height and width dimension are 
+The output is a new tensor where the values from height and width dimension are
 moved to the depth dimension. The reverse of this operation is ``depth_to_space``.
 
 .. math::
@@ -1097,7 +1081,7 @@ moved to the depth dimension. The reverse of this operation is ``depth_to_space`
     y = reshape(x \prime \prime, [N, C * (block\_size ^ 2), H / block\_size, W / block\_size])
     \end{gather*}
 
-where :math:`x` is an input tensor with default layout as :math:`[N, C, H, W]`: [batch, channels, height, width] 
+where :math:`x` is an input tensor with default layout as :math:`[N, C, H, W]`: [batch, channels, height, width]
 and :math:`y` is the output tensor of layout :math:`[N, C * (block\_size ^ 2), H / block\_size, W / block\_size]`
 
 Example::

From 1e8cc90d334e880bed62c74afa74e7ea43e4e3d5 Mon Sep 17 00:00:00 2001
From: Junru Shao <junrushao1994@gmail.com>
Date: Sat, 12 Oct 2019 18:34:34 -0700
Subject: [PATCH 435/813] [BUGFIX] Minor type issues in Squeeze (#16448)

* minor

* test

* Retrigger

* Retrigger
---
 src/operator/numpy/np_matrix_op.cc     |  2 +-
 src/operator/tensor/matrix_op.cc       |  2 +-
 tests/python/unittest/test_numpy_op.py | 16 ++++++++--------
 3 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/src/operator/numpy/np_matrix_op.cc b/src/operator/numpy/np_matrix_op.cc
index 96a10561be28..f54f325bcd17 100644
--- a/src/operator/numpy/np_matrix_op.cc
+++ b/src/operator/numpy/np_matrix_op.cc
@@ -248,7 +248,7 @@ NNVM_REGISTER_OP(_np_squeeze)
 .set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
 .set_attr<FCompute>("FCompute<cpu>", UnaryOp::IdentityCompute<cpu>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_squeeze"})
-.add_argument("a", "NDArray-or-Symbol[]", "data to squeeze")
+.add_argument("a", "NDArray-or-Symbol", "data to squeeze")
 .add_arguments(SqueezeParam::__FIELDS__());
 
 bool ConcatShape(const nnvm::NodeAttrs& attrs,
diff --git a/src/operator/tensor/matrix_op.cc b/src/operator/tensor/matrix_op.cc
index ed4afc0cbcbc..99fba15d47ba 100644
--- a/src/operator/tensor/matrix_op.cc
+++ b/src/operator/tensor/matrix_op.cc
@@ -1003,7 +1003,7 @@ Examples::
 .set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
 .set_attr<FCompute>("FCompute<cpu>", UnaryOp::IdentityCompute<cpu>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_squeeze"})
-.add_argument("data", "NDArray-or-Symbol[]", "data to squeeze")
+.add_argument("data", "NDArray-or-Symbol", "data to squeeze")
 .add_arguments(SqueezeParam::__FIELDS__());
 
 NNVM_REGISTER_OP(_backward_squeeze)
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 1f90f30bd8c3..89fe576d0a0e 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -228,11 +228,11 @@ def __init__(self):
 
         def hybrid_forward(self, F, x1, x2):
             return F.np.ldexp(x1, x2)
-        
+
     def _np_ldexp(x1, x2):
         return x1 * _np.power(2.0, x2)
 
-    def dldx(x1, x2): 
+    def dldx(x1, x2):
         grad_a = _np.power(2.0, x2)
         grad_b = _np_ldexp(x1, x2) * _np.log(2.0)
         if len(x1) == 1:
@@ -241,7 +241,7 @@ def dldx(x1, x2):
             grad_b = _np.sum(grad_b)
         return [grad_a, grad_b]
 
-    shapes = [ 
+    shapes = [
         ((3, 1), (3, 1)),
         ((3, 1, 2), (3, 1, 2)),
         ((1, ),(1, )),
@@ -250,7 +250,7 @@ def dldx(x1, x2):
         ((3, 0), (3, 0)),  # zero-size shape
         ((0, 1), (0, 1)),  # zero-size shape
         ((2, 0, 2), (2, 0, 2)),  # zero-size shape
-        ] 
+        ]
 
     for hybridize in [True, False]:
         for shape1, shape2 in shapes:
@@ -258,7 +258,7 @@ def dldx(x1, x2):
                 test_ldexp = TestLdexp()
                 if hybridize:
                     test_ldexp.hybridize()
-                x1 = rand_ndarray(shape=shape1, dtype=dtype).as_np_ndarray() 
+                x1 = rand_ndarray(shape=shape1, dtype=dtype).as_np_ndarray()
                 x1.attach_grad()
                 x2 = rand_ndarray(shape=shape2, dtype=dtype).as_np_ndarray()
                 x2.attach_grad()
@@ -997,13 +997,13 @@ def __init__(self, axis):
             self._axis = axis
 
         def hybrid_forward(self, F, x):
-            return F.np.squeeze(x, axis=self._axis)
+            return F.np.squeeze(x, self._axis)
 
     for shape, axis in config:
         data_np = _np.random.uniform(size=shape)
         data_mx = np.array(data_np, dtype=data_np.dtype)
-        ret_np = _np.squeeze(data_np, axis=axis)
-        ret_mx = np.squeeze(data_mx, axis=axis)
+        ret_np = _np.squeeze(data_np, axis)
+        ret_mx = np.squeeze(data_mx, axis)
         assert_almost_equal(ret_mx.asnumpy(), ret_np, rtol=1e-5, atol=1e-6, use_broadcast=False)
 
         net = TestSqueeze(axis)

From 858a52e901faa5c791ac5e09dbb59ae6a2170c31 Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Sun, 13 Oct 2019 17:26:21 -0700
Subject: [PATCH 436/813] Fix large array tests (#16328)

* fix activation

* remove comments

* fix copy_to

* fix lint, remove redundant function, fix shape sizes for random functions

* fix sigmoid issue

* fix leaky relu

* fix random shuffle

* fix pooling

* fix dropout

* fix index copy

* add teardown and fix lint

* post test cleanup

* removed decorator since it needs C API for CPU memory release
---
 src/operator/contrib/index_copy-inl.h |   2 +-
 src/operator/contrib/index_copy.cc    |   4 +-
 tests/nightly/test_large_array.py     | 147 ++++++++++++--------------
 tests/nightly/test_large_vector.py    |  20 ++--
 tests/python/unittest/common.py       |  21 ++++
 5 files changed, 97 insertions(+), 97 deletions(-)

diff --git a/src/operator/contrib/index_copy-inl.h b/src/operator/contrib/index_copy-inl.h
index 9f78f0593ed1..35bfcd0e77b6 100644
--- a/src/operator/contrib/index_copy-inl.h
+++ b/src/operator/contrib/index_copy-inl.h
@@ -71,7 +71,7 @@ inline bool IndexCopyShape(const nnvm::NodeAttrs& attrs,
       CHECK_EQ(in_attrs->at(0)[i], in_attrs->at(2)[i]);
     }
   }
-  // The the length of the fitrst dim of copied tensor
+  // The the length of the first dim of copied tensor
   // must equal to the size of index vector
   CHECK_EQ(in_attrs->at(1)[0], in_attrs->at(2)[0]);
   SHAPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
diff --git a/src/operator/contrib/index_copy.cc b/src/operator/contrib/index_copy.cc
index f272a8860d85..9a071c04b51c 100644
--- a/src/operator/contrib/index_copy.cc
+++ b/src/operator/contrib/index_copy.cc
@@ -28,12 +28,12 @@ namespace op {
 
 struct index_copy_fwd_cpu {
   template<typename DType, typename IType>
-  static void Map(int i,
+  static void Map(index_t i,
                   const DType* new_tensor,
                   const IType* idx,
                   DType* out_tensor,
                   int dim_size) {
-    DType* out_ptr = out_tensor + static_cast<int>(idx[i]) * dim_size;
+    DType* out_ptr = out_tensor + static_cast<index_t>(idx[i]) * dim_size;
     const DType* new_ptr = new_tensor + i * dim_size;
     std::memcpy(out_ptr, new_ptr, sizeof(DType) * dim_size);
   }
diff --git a/tests/nightly/test_large_array.py b/tests/nightly/test_large_array.py
index 99856f770d5c..e51e220c232f 100644
--- a/tests/nightly/test_large_array.py
+++ b/tests/nightly/test_large_array.py
@@ -21,7 +21,8 @@
 
 from mxnet.test_utils import rand_ndarray, assert_almost_equal, rand_coord_2d, default_context, check_symbolic_forward, create_2d_tensor
 from mxnet import gluon, nd
-from tests.python.unittest.common import with_seed, teardown
+from tests.python.unittest.common import with_seed, with_post_test_cleanup
+from nose.tools import with_setup
 
 # dimension constants
 MEDIUM_X = 10000
@@ -84,20 +85,20 @@ def test_ndarray_random_randint():
 
 @with_seed()
 def test_ndarray_random_exponential():
-    scale_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_Y))
+    scale_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_X))
     a = nd.random.exponential(scale=scale_array, shape=(SMALL_X, SMALL_Y))
     assert a[-1][0][0][0] >= 0
-    assert a.shape == (MEDIUM_X, SMALL_Y, SMALL_X, SMALL_Y)
+    assert a.shape == (MEDIUM_X, SMALL_X, SMALL_X, SMALL_Y)
 
 
 @with_seed()
 def test_ndarray_random_gamma():
-    alpha_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_Y))
-    beta_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_Y))
+    alpha_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_X))
+    beta_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_X))
     a = nd.random.gamma(alpha=alpha_array, beta=beta_array,
                         shape=(SMALL_X, SMALL_Y))
     assert a[-1][0][0][0] >= 0
-    assert a.shape == (MEDIUM_X, SMALL_Y, SMALL_X, SMALL_Y)
+    assert a.shape == (MEDIUM_X, SMALL_X, SMALL_X, SMALL_Y)
 
 
 @with_seed()
@@ -108,50 +109,50 @@ def test_ndarray_random_multinomial():
     assert a[-1] >= 0
     assert a.shape == (LARGE_X,)
     # test for NDArray multi-dimension shape
-    a = nd.random.multinomial(probs, shape=(SMALL_X, SMALL_Y))
+    a = nd.random.multinomial(probs, shape=(2, SMALL_Y))
     assert a[-1][0][0] >= 0
-    assert a.shape == (LARGE_X, SMALL_X, SMALL_Y)
+    assert a.shape == (LARGE_X, 2, SMALL_Y)
     # test log_likelihood output shape
-    a = nd.random.multinomial(probs, shape=(SMALL_X, SMALL_Y), get_prob=True)
-    assert a[-1][0][0] >= 0
-    assert a[0].shape == (LARGE_X, SMALL_X, SMALL_Y) and a[0].shape == a[1].shape
+    a = nd.random.multinomial(probs, shape=(2, SMALL_Y), get_prob=True)
+    assert a[0][0][0][0] >= 0
+    assert a[0].shape == (LARGE_X, 2, SMALL_Y) and a[0].shape == a[1].shape
 
 
 @with_seed()
 def test_ndarray_random_generalized_negative_binomial():
-    alpha_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_Y))
-    mu_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_Y))
+    alpha_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_X))
+    mu_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_X))
     a = nd.random.generalized_negative_binomial(mu=mu_array, alpha=alpha_array,
                                                 shape=(SMALL_X, SMALL_Y))
     assert a[-1][0][0][0] >= 0
-    assert a.shape == (MEDIUM_X, SMALL_Y, SMALL_X, SMALL_Y)
+    assert a.shape == (MEDIUM_X, SMALL_X, SMALL_X, SMALL_Y)
 
 
 @with_seed()
 def test_ndarray_random_negative_binomial():
-    k_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_Y))
-    p_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_Y))
+    k_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_X))
+    p_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_X))
     a = nd.random.negative_binomial(k=k_array, p=p_array,
                                     shape=(SMALL_X, SMALL_Y))
     assert a[-1][0][0][0] >= 0
-    assert a.shape == (MEDIUM_X, SMALL_Y, SMALL_X, SMALL_Y)
+    assert a.shape == (MEDIUM_X, SMALL_X, SMALL_X, SMALL_Y)
 
 
 @with_seed()
 def test_ndarray_random_normal():
-    scale_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_Y))
-    loc_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_Y))
+    scale_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_X))
+    loc_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_X))
     a = nd.random.normal(loc=loc_array, scale=scale_array,
                          shape=(SMALL_X, SMALL_Y))
-    assert a.shape == (MEDIUM_X, SMALL_Y, SMALL_X, SMALL_Y)
+    assert a.shape == (MEDIUM_X, SMALL_X, SMALL_X, SMALL_Y)
 
 
 @with_seed()
 def test_ndarray_random_poisson():
-    lambda_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_Y))
+    lambda_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_X))
     a = nd.random.poisson(lam=lambda_array, shape=(SMALL_X, SMALL_Y))
     assert a[-1][0][0][0] >= 0
-    assert a.shape == (MEDIUM_X, SMALL_Y, SMALL_X, SMALL_Y)
+    assert a.shape == (MEDIUM_X, SMALL_X, SMALL_X, SMALL_Y)
 
 
 @with_seed()
@@ -165,7 +166,7 @@ def test_ndarray_random_randn():
 @with_seed()
 def test_ndarray_random_shuffle():
     a = nd.ones(shape=(LARGE_X, SMALL_Y))
-    a[-1] == 3  # assign 3 to entire last row
+    a[-1] = 3  # assign 3 to entire last row
     a = nd.random.shuffle(a)
     # slice first column from shuffled array
     # pass LARGE_X values to numpy instead of LARGE_X*SMALL_Y
@@ -175,7 +176,7 @@ def test_ndarray_random_shuffle():
     assert len(unique_a) == 2  # only 2 unique values
     assert unique_a[0] == 1  # first unique value is 1
     assert unique_a[1] == 3  # second unique value is 3
-    assert a.shape[0] == (LARGE_X, SMALL_Y)
+    assert a.shape == (LARGE_X, SMALL_Y)
 
 
 def test_ndarray_empty():
@@ -269,6 +270,7 @@ def test_slice_assign():
 def test_expand_dims():
     a = nd.ones(shape=(LARGE_X, SMALL_Y))
     res = nd.expand_dims(a, axis=1)
+    assert a[0][0][0] == 1
     assert res.shape == (a.shape[0], 1, a.shape[1])
 
 
@@ -561,7 +563,7 @@ def test_sequence_last():
 
     # test if returns last sequence
     b = nd.SequenceLast(a)
-    assert_almost_equal(b.asnumpy(), a[-1].asnumpy())  # only checks for (2,SMALL_Y) tensor
+    assert_almost_equal(b.asnumpy(), a[-1].asnumpy())  # only checks for (2, SMALL_Y) tensor
     assert b.shape == (2, SMALL_Y)
 
     # test with sequence length
@@ -600,7 +602,7 @@ def test_softmax_cross_entropy():
 def test_index_copy():
     x = mx.nd.zeros((LARGE_X, SMALL_Y))
     t = mx.nd.arange(1, SMALL_Y + 1).reshape((1, SMALL_Y))
-    index = mx.nd.array([LARGE_X - 1])
+    index = mx.nd.array([LARGE_X - 1], dtype="int64")
 
     x = mx.nd.contrib.index_copy(x, index, t)
     assert x[-1][-1] == t[0][-1]
@@ -637,23 +639,23 @@ def test_leaky_relu():
 
     def test_leaky():
         res = mx.nd.LeakyReLU(a, act_type="leaky", slope=0.3)
-        assert res[-1][-1].asnumpy() == 0.3*a[-1][-1].asnumpy()
+        assert_almost_equal(res[-1][-1].asnumpy(), 0.3*a[-1][-1].asnumpy(), atol=1e-3, rtol=1e-3)
 
     def test_elu():
         res = mx.nd.LeakyReLU(a, act_type="elu", slope=0.3)
-        assert res[-1][-1].asnumpy() == 0.3*(np.exp(a[-1][-1].asnumpy())-1)
+        assert_almost_equal(res[-1][-1].asnumpy(), 0.3*(np.exp(a[-1][-1].asnumpy())-1), atol=1e-3, rtol=1e-3)
 
     def test_selu():
         lam = 1.0507009873554804934193349852946
         alpha = 1.6732632423543772848170429916717
         res = mx.nd.LeakyReLU(a, act_type="selu")
-        assert res[-1][-1].asnumpy() == (lam * alpha * (np.exp(a[-1][-1].asnumpy())-1))
+        assert_almost_equal(res[-1][-1].asnumpy(), (lam * alpha * (np.exp(a[-1][-1].asnumpy())-1)), atol=1e-3, rtol=1e-3)
 
     def test_rrelu():
         lower = 0.125
         upper = 0.333999991
         res = mx.nd.LeakyReLU(a, act_type="rrelu")
-        assert res[-1][-1].asnumpy() == (lower + upper) / 2 * a[-1][-1].asnumpy()
+        assert_almost_equal(res[0][-1][-1].asnumpy(), (lower + upper) / 2 * a[-1][-1].asnumpy(), atol=1e-3, rtol=1e-3)
 
     test_leaky()
     test_elu()
@@ -662,31 +664,31 @@ def test_rrelu():
 
 
 def test_pooling():
-    a = mx.nd.ones((MEDIUM_X, MEDIUM_X, SMALL_Y, SMALL_Y))
+    a = mx.nd.ones((MEDIUM_X, 200, SMALL_Y, SMALL_Y))
 
     def test_avg_pooling():
         res = mx.nd.Pooling(a, kernel=(5, 5), pool_type='avg')
-        assert res[-1][-1][-1][-1] == 1.0000001
-        assert res.shape == SMALL_Y - 5 + 1
+        assert_almost_equal(res[-1][-1][-1][-1].asnumpy(), 1.0000001, atol=1e-3, rtol=1e-3)
+        assert res.shape[-1] == SMALL_Y - 5 + 1
 
     def test_max_pooling():
         res = mx.nd.Pooling(a, kernel=(5, 5), pool_type='max')
-        assert res[-1][-1][-1][-1] == 1.
-        assert res.shape == SMALL_Y - 5 + 1
+        assert_almost_equal(res[-1][-1][-1][-1].asnumpy(), 1., atol=1e-3, rtol=1e-3)
+        assert res.shape[-1] == SMALL_Y - 5 + 1
 
     def test_sum_pooling():
         res = mx.nd.Pooling(a, kernel=(5, 5), pool_type='sum')
-        assert res[-1][-1][-1][-1] == 25
-        assert res.shape == SMALL_Y - 5 + 1
+        assert_almost_equal(res[-1][-1][-1][-1].asnumpy(), 25, atol=1e-3, rtol=1e-3)
+        assert res.shape[-1] == SMALL_Y - 5 + 1
 
     def test_lp_pooling():
         res = mx.nd.Pooling(a, kernel=(5, 5), pool_type='lp', p_value=2)
-        assert res[-1][-1][-1][-1] == 5.
-        assert res.shape == SMALL_Y - 5 + 1
+        assert_almost_equal(res[-1][-1][-1][-1].asnumpy(), 5., atol=1e-3, rtol=1e-3)
+        assert res.shape[-1] == SMALL_Y - 5 + 1
 
         res = mx.nd.Pooling(a, kernel=(5, 5), pool_type='lp', p_value=1)
-        assert res[-1][-1][-1][-1] == 25.
-        assert res.shape == SMALL_Y - 5 + 1
+        assert_almost_equal(res[-1][-1][-1][-1].asnumpy(), 25., atol=1e-3, rtol=1e-3)
+        assert res.shape[-1] == SMALL_Y - 5 + 1
 
     test_avg_pooling()
     test_max_pooling()
@@ -741,36 +743,37 @@ def test_dropout():
     exe = y.simple_bind(ctx=default_context(), data=shape)
     exe.arg_arrays[0][:] = 1
     out = exe.forward(is_train=True)
-    assert out.shape == out.shape
+    nd.waitall()
+    assert out[0].shape == shape
 
 
 def test_activation():
-    a = mx.nd.ones((LARGE_X, SMALL_Y))
+    x = mx.nd.ones((LARGE_X, SMALL_Y))
     test_x = -2
-    a[-1, -1] = test_x
+    x[-1, -1] = test_x
 
     # Hyperbolic tangent (tanh)
     # y = (exp(x)-exp(-x))/(exp(x)+exp(-x))
-    a = mx.nd.Activation(a, act_type="tanh")
-    tanh_x = (np.exp(test_x)-np.exp(-test_x))/(np.exp(test_x)+np.exp(-test_x))
-    assert a[-1][-1] == tanh_x
+    y = mx.nd.Activation(x, act_type="tanh")
+    tanh_x = ((np.exp(test_x)-np.exp(-test_x))/(np.exp(test_x)+np.exp(-test_x)))
+    assert y[-1][-1] == np.float32(tanh_x)
 
     # Recitified Linear Unit (relu)
     # y = max(x,0)
-    a = mx.nd.Activation(a, act_type="relu")
-    assert a[-1][-1] == 0
+    y = mx.nd.Activation(x, act_type="relu")
+    assert y[-1][-1] == 0
 
     # Sigmoid
     # y = x/(1+abs(x))
-    a = mx.nd.Activation(a, act_type="sigmoid")
-    sigmoid_x = 1/(1+math.exp(-test_x))
-    assert a[-1][-1] == sigmoid_x
+    y = mx.nd.Activation(x, act_type="sigmoid")
+    sigmoid_x = (1/(1+math.exp(-test_x)))
+    assert_almost_equal(y[-1][-1].asnumpy(), np.float32(sigmoid_x), atol=1e-3, rtol=1e-3)
 
     # Soft Sign
     # y = 1/(1+exp(-x))
-    a = mx.nd.Activation(a, act_type="softsign")
-    softsign_x = test_x/(1+abs(test_x))
-    assert a[-1][-1] == softsign_x
+    y = mx.nd.Activation(x, act_type="softsign")
+    softsign_x = (test_x/(1+abs(test_x)))
+    assert y[-1][-1] == np.float32(softsign_x)
 
 
 # TODO: correctness of batchnorm
@@ -924,8 +927,7 @@ def test_copy_to():
     b = nd.array(np.zeros((SMALL_Y, LARGE_X)))
     c = a.copyto(b)
     assert c is b
-    print(b)
-    assert b[0][-1] == LARGE_X-1
+    assert b[-1][-1] == SMALL_Y-1
 
 
 def test_zeros_like():
@@ -957,24 +959,17 @@ def test_flatten():
     assert b.shape == (LARGE_X//2, SMALL_Y*2)
 
 
-def test_expand_dims():
-    a = nd.array(np.ones((SMALL_Y, LARGE_X)))
-    b = nd.expand_dims(a, axis=1)
-    nd.waitall()
-    assert b.shape == (SMALL_Y, 1, LARGE_X)
-
-
 def test_concat():
     a = nd.array(np.ones((SMALL_Y, LARGE_X)))
     b = nd.array(np.zeros((SMALL_Y, LARGE_X)))
-    c = nd.concat(a,b, dim=0)
+    c = nd.concat(a, b, dim=0)
     assert c.shape == (b.shape[0]*2, LARGE_X)
 
 
 def test_stack():
     a = nd.array(np.ones((SMALL_Y, LARGE_X)))
     b = nd.array(np.zeros((SMALL_Y, LARGE_X)))
-    c = nd.stack(a,b, axis=1)
+    c = nd.stack(a, b, axis=1)
     assert c.shape == (b.shape[0], 2, LARGE_X)
 
 
@@ -1019,7 +1014,7 @@ def test_max():
 def test_norm():
     a = np.array(np.full((1, LARGE_X), 3))
     b = np.array(np.full((1, LARGE_X), 4))
-    c = nd.array(np.concatenate((a,b), axis=0))
+    c = nd.array(np.concatenate((a, b), axis=0))
     d = nd.norm(c, ord=2, axis=0)
     e = nd.norm(c, ord=1, axis=0)
     assert d.shape[0] == LARGE_X
@@ -1031,7 +1026,7 @@ def test_norm():
 def test_argmax():
     a = np.ones((SMALL_Y, LARGE_X))
     b = np.zeros((SMALL_Y, LARGE_X))
-    c = nd.array(np.concatenate((a,b), axis=0))
+    c = nd.array(np.concatenate((a, b), axis=0))
     d = nd.argmax(c, axis=0)
     assert d.shape[0] == LARGE_X
     assert d[-1] == d[0] == 0
@@ -1040,12 +1035,13 @@ def test_argmax():
 def test_relu():
     def frelu(x):
         return np.maximum(x, 0.0)
+
     def frelu_grad(x):
         return 1.0 * (x > 0.0)
     shape = (SMALL_Y, LARGE_X)
     x = mx.symbol.Variable("x")
     y = mx.sym.relu(x)
-    xa = np.random.uniform(low=-1.0,high=1.0,size=shape)
+    xa = np.random.uniform(low=-1.0, high=1.0, size=shape)
     eps = 1e-4
     xa[abs(xa) < eps] = 1.0
     ya = frelu(xa)
@@ -1059,7 +1055,7 @@ def fsigmoid(a):
     shape = (SMALL_Y, LARGE_X)
     x = mx.symbol.Variable("x")
     y = mx.sym.sigmoid(x)
-    xa = np.random.uniform(low=-1.0,high=1.0,size=shape)
+    xa = np.random.uniform(low=-1.0, high=1.0, size=shape)
     ya = fsigmoid(xa)
     check_symbolic_forward(y, [xa], [ya])
 
@@ -1116,15 +1112,6 @@ def test_idiv():
     assert c[0][-1] == 2
 
 
-def test_imod():
-    a = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
-    b = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 2)))
-    c = a
-    c %= b
-    assert c.shape == a.shape
-    assert c[0][-1] == 1
-
-
 def test_eq():
     a = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
     b = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
@@ -1198,7 +1185,7 @@ def test_slice_axis():
 
 
 def test_one_hot():
-    #default dtype of ndarray is float32 which cannot index elements over 2^32
+    # default dtype of ndarray is float32 which cannot index elements over 2^32
     a = nd.array([1, (VLARGE_X - 1)], dtype=np.int64)
     b = nd.one_hot(a, VLARGE_X)
     b[0][1] == 1
diff --git a/tests/nightly/test_large_vector.py b/tests/nightly/test_large_vector.py
index 169f5244d784..aa6cb3d75b37 100644
--- a/tests/nightly/test_large_vector.py
+++ b/tests/nightly/test_large_vector.py
@@ -21,7 +21,8 @@
 
 from mxnet.test_utils import rand_ndarray, assert_almost_equal, rand_coord_2d, create_vector
 from mxnet import gluon, nd
-from tests.python.unittest.common import with_seed, teardown
+from tests.python.unittest.common import with_seed
+from nose.tools import with_setup
 
 # dimension constants
 LARGE_X = 4300000000
@@ -168,7 +169,7 @@ def test_topk():
     val = nd.topk(a, k=1, axis=0, dtype=np.int64, ret_typ="value")
     assert val == (LARGE_X - 1)
 
-    
+
 def test_mean():
     a = nd.arange(-LARGE_X // 2, LARGE_X // 2 + 1, dtype=np.int64)
     b = nd.mean(a, axis=0)
@@ -505,14 +506,14 @@ def test_rpow():
 
 def test_shape():
     b = create_vector(size=LARGE_X)
-    #explicit wait_to_read()
+    # explicit wait_to_read()
     assert b[0] == 0
     assert b.shape[0] == LARGE_X
 
 
 def test_size():
     b = create_vector(size=LARGE_X)
-    #explicit wait_to_read()
+    # explicit wait_to_read()
     assert b[0] == 0
     assert b.size == LARGE_X
 
@@ -552,7 +553,7 @@ def test_ones_like():
 def test_concat():
     a = nd.ones(LARGE_X)
     b = nd.zeros(LARGE_X)
-    c = nd.concat(a,b, dim=0)
+    c = nd.concat(a, b, dim=0)
     assert c[0][0] == 1
     assert c[-1][-1] == 0
     assert c.shape[0] == (2 * LARGE_X)
@@ -635,15 +636,6 @@ def test_idiv():
     assert c[-1] == 2
 
 
-def test_imod():
-    a = nd.full(LARGE_X, 3)
-    b = nd.full(LARGE_X, 2)
-    c = a
-    c %= b
-    assert c.shape == a.shape
-    assert c[0][-1] == 1
-
-
 def test_eq():
     a = nd.full(LARGE_X, 3)
     b = nd.full(LARGE_X, 3)
diff --git a/tests/python/unittest/common.py b/tests/python/unittest/common.py
index 7cd637da3d4f..06fb16288649 100644
--- a/tests/python/unittest/common.py
+++ b/tests/python/unittest/common.py
@@ -272,6 +272,27 @@ def teardown():
     mx.nd.waitall()
 
 
+def with_post_test_cleanup():
+    """
+    Helper function that cleans up memory by releasing it from memory pool
+    Required especially by large tensor tests that have memory footprints in GBs.
+    """
+    def test_helper(orig_test):
+        @make_decorator(orig_test)
+        def test_new(*args, **kwargs):
+            logger = default_logger()
+            try:
+                orig_test(*args, **kwargs)
+            except:
+                logger.info(test_msg)
+                raise
+            finally:
+                mx.nd.waitall()
+                mx.cpu().empty_cache()
+        return test_new
+    return test_helper
+
+
 def run_in_spawned_process(func, env, *args):
     """
     Helper function to run a test in its own process.

From 6d6e46b7f4f4b018d1d0bfb82ed5ca83dbb9dfcc Mon Sep 17 00:00:00 2001
From: reminisce <wujun.nju@gmail.com>
Date: Sun, 13 Oct 2019 19:51:54 -0700
Subject: [PATCH 437/813] Comparison ops implemented using mshadow (#16414)

* boolean op without tvm working

* Fix astype for boolean arrays

* More tests

* Revert

* Fix

* Fix preprocessor in .cu

* Fix logical_not

* Print compilation flags

* Fix transpose taking negative indices

* Fix transpose negative axes

* Fix transpose

* Fix

* Try to fix USE_TVM_OP not understood in .cu

* Fix squeeze

* Finally

* Fix

* Try to fix invalid ptx

* Implement API to get cuda compute capability

* Fix test_utils.py

* Fix pylint
---
 CMakeLists.txt                                |   6 +-
 python/mxnet/_numpy_op_doc.py                 |  51 +++
 python/mxnet/ndarray/numpy/_op.py             |   4 +-
 python/mxnet/numpy/multiarray.py              |  19 +-
 python/mxnet/numpy_extension/__init__.py      |   2 +-
 python/mxnet/symbol/numpy/_symbol.py          |  36 ++-
 python/mxnet/test_utils.py                    |  33 +-
 python/mxnet/util.py                          |  61 ++++
 src/common/utils.h                            |  15 +
 src/ndarray/ndarray_function.cc               |  13 +-
 src/ndarray/ndarray_function.cu               |   4 -
 src/operator/leaky_relu-inl.h                 |   2 +-
 src/operator/mshadow_op.h                     |  30 ++
 src/operator/mxnet_op.h                       |  20 ++
 src/operator/nn/dropout-inl.h                 |   4 +-
 .../numpy/np_elemwise_broadcast_logic_op.cc   | 301 ++++++++++++++++++
 .../numpy/np_elemwise_broadcast_logic_op.cu   |  60 ++++
 .../numpy/np_elemwise_broadcast_op.cc         | 223 -------------
 .../numpy/np_elemwise_unary_op_basic.cc       |  65 ++--
 .../numpy/np_elemwise_unary_op_basic.cu       |   9 +-
 src/operator/numpy/np_matrix_op-inl.h         |   4 +-
 src/operator/numpy/np_matrix_op.cc            |  55 +++-
 src/operator/operator_tune.cc                 |  10 +
 .../tensor/elemwise_binary_broadcast_op.h     |  42 ++-
 src/operator/tensor/elemwise_binary_op.h      |  26 ++
 .../tensor/elemwise_binary_scalar_op.h        |  20 ++
 src/operator/tensor/elemwise_unary_op.h       |  21 +-
 .../tensor/elemwise_unary_op_basic.cc         |   1 +
 src/operator/tensor/matrix_op-inl.h           |   1 -
 tests/python/unittest/test_numpy_ndarray.py   |  33 +-
 tests/python/unittest/test_numpy_op.py        |  12 +-
 31 files changed, 871 insertions(+), 312 deletions(-)
 create mode 100644 src/operator/numpy/np_elemwise_broadcast_logic_op.cc
 create mode 100644 src/operator/numpy/np_elemwise_broadcast_logic_op.cu

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 5045bba9d989..70b09917de13 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -58,6 +58,11 @@ message(STATUS "CMAKE_HOST_SYSTEM_PROCESSOR ${CMAKE_HOST_SYSTEM_PROCESSOR}")
 message(STATUS "CMAKE_SYSTEM_PROCESSOR ${CMAKE_SYSTEM_PROCESSOR}")
 
 message(STATUS "CMAKE_SYSTEM_NAME ${CMAKE_SYSTEM_NAME}")
+
+if(USE_TVM_OP)
+  add_definitions(-DMXNET_USE_TVM_OP=1)
+endif()
+
 if(USE_CUDA AND NOT USE_OLDCMAKECUDA)
   message(STATUS "CMake version '${CMAKE_VERSION}' using generator '${CMAKE_GENERATOR}'")
   if(
@@ -743,7 +748,6 @@ if(USE_DIST_KVSTORE)
 endif()
 
 if(USE_TVM_OP)
-  add_definitions(-DMXNET_USE_TVM_OP=1)
   list(APPEND mxnet_LINKER_LIBS ${CMAKE_CURRENT_BINARY_DIR}/3rdparty/tvm/libtvm_runtime.so)
   include(cmake/BuildTVM.cmake)
   add_subdirectory("3rdparty/tvm")
diff --git a/python/mxnet/_numpy_op_doc.py b/python/mxnet/_numpy_op_doc.py
index 6e2f5fa15919..8ad5247fa263 100644
--- a/python/mxnet/_numpy_op_doc.py
+++ b/python/mxnet/_numpy_op_doc.py
@@ -653,3 +653,54 @@ def _np_trace(a, offset=0, axis1=0, axis2=1, out=None):
     (2, 3)
     """
     pass
+
+
+def _np_squeeze(a, axis=None, out=None):
+    """
+    Remove single-dimensional entries from the shape of an array.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input data.
+    axis : None or int or tuple of ints, optional
+        Selects a subset of the single-dimensional entries in the
+        shape. If an axis is selected with shape entry greater than
+        one, an error is raised.
+    out : ndarray, optional
+        Array into which the output is placed. It must have the same size
+        and dtype as the input array.
+
+    Returns
+    -------
+    squeezed : ndarray
+        The input array, but with all or a subset of the
+        dimensions of length 1 removed. It always returns a copy of `a`.
+
+    Raises
+    ------
+    MXNetError
+        If `axis` is not `None`, and an axis being squeezed is not of length 1
+
+    See Also
+    --------
+    expand_dims : The inverse operation, adding singleton dimensions
+    reshape : Insert, remove, and combine dimensions, and resize existing ones
+
+    Examples
+    --------
+    >>> x = np.array([[[0], [1], [2]]])
+    >>> x.shape
+    (1, 3, 1)
+    >>> np.squeeze(x).shape
+    (3,)
+    >>> np.squeeze(x, axis=0).shape
+    (3, 1)
+    >>> np.squeeze(x, axis=1).shape
+    Traceback (most recent call last):
+    ...
+    mxnet.base.MXNetError: cannot select an axis to squeeze out which has size=3 not equal to one
+    >>> np.squeeze(x, axis=2).shape
+    (1, 3)
+    """
+    pass
diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index aea8b19c2913..e3cf09703533 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -2110,11 +2110,11 @@ def logical_not(x, out=None, **kwargs):
     --------
     >>> x= np.array([True, False, 0, 1])
     >>> np.logical_not(x)
-    array([0., 1., 1., 0.])
+    array([False,  True,  True, False])
 
     >>> x = np.arange(5)
     >>> np.logical_not(x<3)
-    array([0., 0., 0., 1., 1.])
+    array([False, False, False,  True,  True])
     """
     return _unary_func_helper(x, _npi.logical_not, _np.logical_not, out=out, **kwargs)
 
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index d3ae4d19aca8..5ee52f14bb16 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -302,7 +302,7 @@ def __getitem__(self, key):
             except Exception as err:
                 raise TypeError('{}'.format(str(err)))
         if isinstance(key, _np.ndarray) and key.dtype == _np.bool_:
-            key = array(key, dtype='bool')
+            key = array(key, dtype='bool', ctx=self.ctx)
         if isinstance(key, ndarray) and key.dtype == _np.bool_:  # boolean indexing
             key_shape = key.shape
             key_ndim = len(key_shape)
@@ -364,6 +364,8 @@ def __setitem__(self, key, value):
         """
         if isinstance(value, NDArray) and not isinstance(value, ndarray):
             raise TypeError('Cannot assign mx.nd.NDArray to mxnet.numpy.ndarray')
+
+        # handle basic and advanced indexing
         if self.ndim == 0:
             if not isinstance(key, tuple) or len(key) != 0:
                 raise IndexError('scalar tensor can only accept `()` as index')
@@ -753,7 +755,7 @@ def detach(self):
         check_call(_LIB.MXNDArrayDetach(self.handle, ctypes.byref(hdl)))
         return _np_ndarray_cls(hdl)
 
-    def astype(self, dtype, *args, **kwargs):  # pylint: disable=arguments-differ,unused-argument
+    def astype(self, dtype, **kwargs):  # pylint: disable=arguments-differ,unused-argument
         """
         Copy of the array, cast to a specified type.
 
@@ -1237,7 +1239,14 @@ def tile(self, *args, **kwargs):
 
     def transpose(self, *axes):  # pylint: disable=arguments-differ
         """Permute the dimensions of an array."""
-        return _mx_np_op.transpose(self, axes=axes if len(axes) != 0 else None)
+        if len(axes) == 0:
+            axes = None
+        elif len(axes) == 1:
+            if isinstance(axes[0], (tuple, list)):
+                axes = axes[0]
+            elif axes[0] is None:
+                axes = None
+        return _mx_np_op.transpose(self, axes=axes)
 
     def flip(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`flip`.
@@ -3401,11 +3410,11 @@ def logical_not(x, out=None, **kwargs):
     --------
     >>> x= np.array([True, False, 0, 1])
     >>> np.logical_not(x)
-    array([0., 1., 1., 0.])
+    array([False,  True,  True, False])
 
     >>> x = np.arange(5)
     >>> np.logical_not(x<3)
-    array([0., 0., 0., 1., 1.])
+    array([False, False, False,  True,  True])
     """
     return _mx_nd_np.logical_not(x, out=out, **kwargs)
 
diff --git a/python/mxnet/numpy_extension/__init__.py b/python/mxnet/numpy_extension/__init__.py
index 5a19d3dc4e2f..879ab4d56a46 100644
--- a/python/mxnet/numpy_extension/__init__.py
+++ b/python/mxnet/numpy_extension/__init__.py
@@ -25,7 +25,7 @@
 from . import _register
 from ._op import *  # pylint: disable=wildcard-import
 from ..context import *  # pylint: disable=wildcard-import
-from ..util import is_np_shape, is_np_array, set_np, reset_np
+from ..util import is_np_shape, is_np_array, set_np, reset_np, get_cuda_compute_capability
 from ..ndarray import waitall
 from .utils import *  # pylint: disable=wildcard-import
 from . import random  # pylint: disable=wildcard-import
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 9a909420e934..338c70d1e53d 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -23,7 +23,7 @@
 import numpy as _np
 from . import _op as _mx_np_op
 from ...base import _LIB, SymbolHandle, numeric_types, mx_uint
-from ...util import check_call, set_module
+from ...util import check_call, set_module, _sanity_check_params
 from ...context import current_context
 from ..symbol import Symbol
 from .._internal import _set_np_symbol_class
@@ -181,8 +181,29 @@ def T(self):
         return self.transpose()
     # pylint: enable= invalid-name, undefined-variable
 
-    def astype(self, dtype, **kwargs):  # pylint: disable=arguments-differ
-        raise NotImplementedError
+    def astype(self, dtype, **kwargs):  # pylint: disable=arguments-differ,unused-argument
+        """
+        Copy of the array, cast to a specified type.
+
+        Parameters
+        ----------
+        dtype : str or dtype
+            Typecode or data-type to which the array is cast.
+        copy : bool, optional
+            Default `True`. By default, astype always returns a newly
+            allocated ndarray on the same context. If this is set to
+            `False`, and the dtype requested is the same as the ndarray's
+            dtype, the ndarray is returned instead of a copy.
+
+        Returns
+        -------
+        arr_t : ndarray
+            Unless `copy` is False and the other conditions for returning the input
+            array are satisfied (see description for `copy` input parameter), `arr_t`
+            is a new array of the same shape as the input array with `dtype`.
+        """
+        _sanity_check_params('astype', ['order', 'casting', 'subok'], kwargs)
+        return _npi.cast(self, dtype=dtype)
 
     def dot(self, b, out=None):
         """Dot product of two arrays.
@@ -438,7 +459,14 @@ def transpose(self, *axes):  # pylint: disable=arguments-differ
         """The arguments are the same as for :py:func:`transpose`, with
         this array as data.
         """
-        return _mx_np_op.transpose(self, axes=axes if len(axes) != 0 else None)
+        if len(axes) == 0:
+            axes = None
+        elif len(axes) == 1:
+            if isinstance(axes[0], (tuple, list)):
+                axes = axes[0]
+            elif axes[0] is None:
+                axes = None
+        return _mx_np_op.transpose(self, axes=axes)
 
     def flip(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`flip`.
diff --git a/python/mxnet/test_utils.py b/python/mxnet/test_utils.py
index c5b3b0d8e4cd..e5f2c6c02089 100644
--- a/python/mxnet/test_utils.py
+++ b/python/mxnet/test_utils.py
@@ -51,6 +51,7 @@
 from .symbol.numpy import _Symbol as np_symbol
 from .util import use_np  # pylint: disable=unused-import
 from .runtime import Features
+from .numpy_extension import get_cuda_compute_capability
 
 
 def default_context():
@@ -2235,10 +2236,34 @@ def has_tvm_ops():
     """Returns True if MXNet is compiled with TVM generated operators. If current ctx
     is GPU, it only returns True for CUDA compute capability > 52 where FP16 is supported."""
     built_with_tvm_op = _features.is_enabled("TVM_OP")
-    if current_context().device_type == 'gpu':
+    ctx = current_context()
+    if ctx.device_type == 'gpu':
         try:
-            import tvm
-        except ImportError:
+            cc = get_cuda_compute_capability(ctx)
+        except:  # pylint: disable=bare-except
+            print('Failed to get CUDA compute capability for context {}. The operators '
+                  'built with USE_TVM_OP=1 will not be run in unit tests.'.format(ctx))
             return False
-        return built_with_tvm_op and (int("".join(tvm.nd.gpu(0).compute_version.split('.'))) >= 53)
+        print('Cuda arch compute capability: sm_{}'.format(str(cc)))
+        return built_with_tvm_op and cc >= 53
     return built_with_tvm_op
+
+
+def is_op_runnable():
+    """Returns True for all CPU tests. Returns True for GPU tests that are either of the following.
+    1. Built with USE_TVM_OP=0.
+    2. Built with USE_TVM_OP=1, but with compute capability >= 53."""
+    ctx = current_context()
+    if ctx.device_type == 'gpu':
+        if not _features.is_enabled("TVM_OP"):
+            return True
+        else:
+            try:
+                cc = get_cuda_compute_capability(ctx)
+            except:  # pylint: disable=bare-except
+                print('Failed to get CUDA compute capability for context {}. The operators '
+                      'built with USE_TVM_OP=1 will not be run in unit tests.'.format(ctx))
+                return False
+            print('Cuda arch compute capability: sm_{}'.format(str(cc)))
+            return cc >= 53
+    return True
diff --git a/python/mxnet/util.py b/python/mxnet/util.py
index d4e95e0c0c9c..1050fb2a481d 100644
--- a/python/mxnet/util.py
+++ b/python/mxnet/util.py
@@ -602,3 +602,64 @@ def set_np(shape=True, array=True):
 def reset_np():
     """Deactivate NumPy shape and array semantics at the same time."""
     set_np(shape=False, array=False)
+
+
+_CUDA_SUCCESS = 0
+
+
+def get_cuda_compute_capability(ctx):
+    """Returns the cuda compute capability of the input `ctx`.
+
+    Parameters
+    ----------
+    ctx : Context
+        GPU context whose corresponding cuda compute capability is to be retrieved.
+
+    Returns
+    -------
+    cuda_compute_capability : int
+        CUDA compute capability. For example, it returns 70 for CUDA arch equal to `sm_70`.
+
+    References
+    ----------
+    https://gist.github.com/f0k/63a664160d016a491b2cbea15913d549#file-cuda_check-py
+    """
+    if ctx.device_type != 'gpu':
+        raise ValueError('Expecting a gpu context to get cuda compute capability, '
+                         'while received ctx {}'.format(str(ctx)))
+
+    libnames = ('libcuda.so', 'libcuda.dylib', 'cuda.dll')
+    for libname in libnames:
+        try:
+            cuda = ctypes.CDLL(libname)
+        except OSError:
+            continue
+        else:
+            break
+    else:
+        raise OSError("could not load any of: " + ' '.join(libnames))
+
+    # Some constants taken from cuda.h
+
+    cc_major = ctypes.c_int()
+    cc_minor = ctypes.c_int()
+    device = ctypes.c_int()
+    error_str = ctypes.c_char_p()
+
+    ret = cuda.cuInit(0)
+    if ret != _CUDA_SUCCESS:
+        cuda.cuGetErrorString(ret, ctypes.byref(error_str))
+        raise RuntimeError('cuInit failed with erro code {}: {}'
+                           .format(ret, error_str.value.decode()))
+
+    ret = cuda.cuDeviceGet(ctypes.byref(device), ctx.device_id)
+    if ret != _CUDA_SUCCESS:
+        cuda.cuGetErrorString(ret, ctypes.byref(error_str))
+        raise RuntimeError('cuDeviceGet failed with error code {}: {}'
+                           .format(ret, error_str.value.decode()))
+    ret = cuda.cuDeviceComputeCapability(ctypes.byref(cc_major), ctypes.byref(cc_minor), device)
+    if ret != _CUDA_SUCCESS:
+        cuda.cuGetErrorString(ret, ctypes.byref(error_str))
+        raise RuntimeError('cuDeviceComputeCapability failed with error code {}: {}'
+                           .format(ret, error_str.value.decode()))
+    return cc_major.value * 10 + cc_minor.value
diff --git a/src/common/utils.h b/src/common/utils.h
index 2bd6aac6f5d9..fbecc8b4e955 100644
--- a/src/common/utils.h
+++ b/src/common/utils.h
@@ -823,6 +823,21 @@ static inline std::string GetOutputName(const nnvm::NodeEntry& e) {
   return sym.ListOutputNames()[0];
 }
 
+inline mxnet::TShape CanonicalizeAxes(const mxnet::TShape& src) {
+  // convert negative axes to positive values
+  const int ndim = src.ndim();
+  mxnet::TShape axes = src;
+  for (int i = 0; i < ndim; ++i) {
+    if (axes[i] < 0) {
+      axes[i] += ndim;
+    }
+    CHECK(axes[i] >= 0 && axes[i] < ndim) << "axes[" << i << "]="
+                                          << axes[i] << " exceeds the range ["
+                                          << 0 << ", " << ndim << ")";
+  }
+  return axes;
+}
+
 }  // namespace common
 }  // namespace mxnet
 #endif  // MXNET_COMMON_UTILS_H_
diff --git a/src/ndarray/ndarray_function.cc b/src/ndarray/ndarray_function.cc
index 653dec84563a..34429446bd62 100644
--- a/src/ndarray/ndarray_function.cc
+++ b/src/ndarray/ndarray_function.cc
@@ -36,23 +36,14 @@ template<>
 void Copy<cpu, cpu>(const TBlob &from, TBlob *to,
                     Context from_ctx, Context to_ctx,
                     RunContext ctx) {
-  if (from.type_flag_ == mshadow::kBool || to->type_flag_ == mshadow::kBool) {
-    CHECK_EQ(from.type_flag_, to->type_flag_) << "Only supports copying data between"
-                                                 " two boolean tensors.";
-    const index_t size = from.Size();
-    CHECK_EQ(size, to->Size()) << "copying size mismatch, from: " << size * sizeof(bool)
-                               << " bytes, to: " << to->Size() * sizeof(bool) << " bytes.";
-    common::ParallelCopy(to->dptr<bool>(), from.dptr<bool>(), size);
-    return;
-  }
-  MSHADOW_TYPE_SWITCH(to->type_flag_, DType, {
+  MSHADOW_TYPE_SWITCH_WITH_BOOL(to->type_flag_, DType, {
     if (to->type_flag_ == from.type_flag_) {
       const index_t size = static_cast<index_t>(from.Size());
       CHECK_EQ(size, to->Size()) << "copying size mismatch, from: " << size * sizeof(DType)
                << " bytes, to: " << to->Size() * sizeof(DType) << " bytes.";
       common::ParallelCopy(to->dptr<DType>(), from.dptr<DType>(), size);
     } else {
-      MSHADOW_TYPE_SWITCH(from.type_flag_, SrcDType, {
+      MSHADOW_TYPE_SWITCH_WITH_BOOL(from.type_flag_, SrcDType, {
           to->FlatTo1D<cpu, DType>() =
               mshadow::expr::tcast<DType>(from.FlatTo1D<cpu, SrcDType>());
       })
diff --git a/src/ndarray/ndarray_function.cu b/src/ndarray/ndarray_function.cu
index 6439c417bfe3..2a1461cc8c48 100644
--- a/src/ndarray/ndarray_function.cu
+++ b/src/ndarray/ndarray_function.cu
@@ -76,10 +76,6 @@ void Copy<gpu, gpu>(const TBlob &from, TBlob *to,
                       from.FlatTo1D<gpu, DType>(s),
                       s);
       } else {
-        CHECK_NE(from.type_flag_, mshadow::kBool)
-            << "Copying boolean ndarray across devices is not supported";
-        CHECK_NE(to->type_flag_, mshadow::kBool)
-            << "Copying boolean ndarray across devices is not supported";
         MSHADOW_TYPE_SWITCH(from.type_flag_, SrcDType, {
           to->FlatTo1D<gpu, DType>(s) =
             mshadow::expr::tcast<DType>(from.FlatTo1D<gpu, SrcDType>(s));
diff --git a/src/operator/leaky_relu-inl.h b/src/operator/leaky_relu-inl.h
index 2c4127b9a088..d73fa1be54a4 100644
--- a/src/operator/leaky_relu-inl.h
+++ b/src/operator/leaky_relu-inl.h
@@ -134,7 +134,7 @@ class LeakyReLUOp : public Operator {
             mshadow::Shape<NDim> oshape = new_oshape.get<NDim>();
             mshadow::Shape<NDim> lstride = mxnet_op::calc_stride(new_lshape.get<NDim>());
             mshadow::Shape<NDim> rstride = mxnet_op::calc_stride(new_rshape.get<NDim>());
-            mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, DType,
+            mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, DType, DType,
                                                                mshadow_op::xelu>, xpu>::
             template LaunchEx(s, new_oshape.Size(), req[leakyrelu::kOut], lstride, rstride, oshape,
             in_data[leakyrelu::kData].dptr<DType>(), in_data[leakyrelu::kGamma].dptr<DType>(),
diff --git a/src/operator/mshadow_op.h b/src/operator/mshadow_op.h
index a941f9b25a54..471b6f395a05 100644
--- a/src/operator/mshadow_op.h
+++ b/src/operator/mshadow_op.h
@@ -73,6 +73,14 @@ using std::is_integral;
     } \
   }
 
+#define MXNET_UNARY_LOGIC_OP_NC(name, expr) \
+  struct name : public mxnet_op::tunable { \
+    template<typename DType> \
+    MSHADOW_XINLINE static bool Map(DType a) { \
+      return (expr); \
+    } \
+  }
+
 #define MXNET_BINARY_MATH_OP(name, expr) \
   struct name : public mxnet_op::tunable { \
     template<typename DType> \
@@ -89,6 +97,14 @@ using std::is_integral;
     } \
   }
 
+#define MXNET_BINARY_LOGIC_OP_NC(name, expr) \
+  struct name : public mxnet_op::tunable  { \
+    template<typename DType> \
+    MSHADOW_XINLINE static bool Map(DType a, DType b) { \
+      return (expr); \
+    } \
+  }
+
 #define MXNET_SIMPLE_UNARY_MATH_OP(name) MXNET_UNARY_MATH_OP(name, math::name(a))
 
 #define MXNET_SIMPLE_BINARY_MATH_OP(name) MXNET_BINARY_MATH_OP(name, math::name(a, b))
@@ -335,6 +351,8 @@ MXNET_BINARY_MATH_OP(rarctan2_grad, math::id(a) / (math::id(a * a + b * b)));
 
 MXNET_UNARY_MATH_OP_NC(nt, a != DType(0) ? DType(0) : DType(1));
 
+MXNET_UNARY_LOGIC_OP_NC(np_logical_not, !static_cast<bool>(a));
+
 MXNET_BINARY_MATH_OP_NC(ge, a >= b ? DType(1) : DType(0));
 
 MXNET_BINARY_MATH_OP_NC(gt, a > b ? DType(1) : DType(0));
@@ -347,6 +365,18 @@ MXNET_BINARY_MATH_OP_NC(eq, a == b ? DType(1) : DType(0));
 
 MXNET_BINARY_MATH_OP_NC(ne, a != b ? DType(1) : DType(0));
 
+MXNET_BINARY_LOGIC_OP_NC(np_greater_equal, a >= b ? true : false);
+
+MXNET_BINARY_LOGIC_OP_NC(np_greater, a > b ? true : false);
+
+MXNET_BINARY_LOGIC_OP_NC(np_less, a < b ? true : false);
+
+MXNET_BINARY_LOGIC_OP_NC(np_less_equal, a <= b ? true : false);
+
+MXNET_BINARY_LOGIC_OP_NC(np_equal, a == b ? true : false);
+
+MXNET_BINARY_LOGIC_OP_NC(np_not_equal, a != b ? true : false);
+
 MXNET_BINARY_MATH_OP(logical_and, a && b ? DType(1) : DType(0));
 
 MXNET_BINARY_MATH_OP(logical_or, a || b ? DType(1) : DType(0));
diff --git a/src/operator/mxnet_op.h b/src/operator/mxnet_op.h
index b46ce8a598d9..950db174595e 100644
--- a/src/operator/mxnet_op.h
+++ b/src/operator/mxnet_op.h
@@ -703,6 +703,26 @@ struct op_with_req {
                                   const DType *input_3) {
     KERNEL_ASSIGN(out[i], req, OP::Map(input_1[i], input_2[i], input_3[i]));
   }
+
+  template<typename DType,
+           typename std::enable_if<!std::is_same<DType, bool>::value, int>::type = 0>
+  MSHADOW_XINLINE static void Map(index_t i, bool *out, const DType *in) {
+    KERNEL_ASSIGN(out[i], req, OP::Map(in[i]));
+  }
+
+  /*! \brief inputs are two tensors with a boolean output tensor */
+  template<typename DType,
+           typename std::enable_if<!std::is_same<DType, bool>::value, int>::type = 0>
+  MSHADOW_XINLINE static void Map(index_t i, bool *out, const DType *lhs, const DType *rhs) {
+    KERNEL_ASSIGN(out[i], req, OP::Map(lhs[i], rhs[i]));
+  }
+
+  /*! \brief input is tensor and two scalar value with a boolean output tensor */
+  template<typename DType,
+           typename std::enable_if<!std::is_same<DType, bool>::value, int>::type = 0>
+  MSHADOW_XINLINE static void Map(index_t i, bool *out, const DType *in, const DType value) {
+    KERNEL_ASSIGN(out[i], req, OP::Map(in[i], value));
+  }
 };
 
 template<typename OP, typename xpu>
diff --git a/src/operator/nn/dropout-inl.h b/src/operator/nn/dropout-inl.h
index cb1cd428bd5e..eda9051fd0a2 100644
--- a/src/operator/nn/dropout-inl.h
+++ b/src/operator/nn/dropout-inl.h
@@ -394,7 +394,7 @@ class DropoutOp {
               mshadow::Shape<NDim> oshape = new_oshape.get<NDim>();
               mshadow::Shape<NDim> lstride = mxnet_op::calc_stride(new_lshape.get<NDim>());
               mshadow::Shape<NDim> rstride = mxnet_op::calc_stride(new_rshape.get<NDim>());
-              mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, DType,
+              mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, DType, DType,
                                mshadow_op::mul>, xpu>::
               template LaunchEx(s, new_oshape.Size(), req[dropout::kOut],
               lstride, rstride, oshape,
@@ -463,7 +463,7 @@ class DropoutOp {
             mshadow::Shape<NDim> oshape = new_oshape.get<NDim>();
             mshadow::Shape<NDim> lstride = mxnet_op::calc_stride(new_lshape.get<NDim>());
             mshadow::Shape<NDim> rstride = mxnet_op::calc_stride(new_rshape.get<NDim>());
-            mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, DType,
+            mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, DType, DType,
                              mshadow_op::mul>, xpu>::
             template LaunchEx(s, new_oshape.Size(), req[0], lstride, rstride, oshape,
             grad.dptr<DType>(), mask.dptr<DType>(), gdata.dptr<DType>());
diff --git a/src/operator/numpy/np_elemwise_broadcast_logic_op.cc b/src/operator/numpy/np_elemwise_broadcast_logic_op.cc
new file mode 100644
index 000000000000..7e8951afa1d0
--- /dev/null
+++ b/src/operator/numpy/np_elemwise_broadcast_logic_op.cc
@@ -0,0 +1,301 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file np_elemwise_binary_logic_op.cc
+ * \brief CPU Implementation of basic logic functions for elementwise numpy binary
+ * broadcast operator.
+ */
+
+#if MXNET_USE_TVM_OP
+#include <tvm/runtime/c_runtime_api.h>
+#include <tvm/runtime/packed_func.h>
+#include "../tvmop/op_module.h"
+#endif  // MXNET_USE_TVM_OP
+
+#include "../tensor/elemwise_binary_broadcast_op.h"
+#include "../tensor/elemwise_binary_scalar_op.h"
+
+namespace mxnet {
+namespace op {
+
+static constexpr char func_equal_cpu[] = "equal_cpu";
+static constexpr char func_equal_gpu[] = "equal_gpu";
+static constexpr char func_not_equal_cpu[] = "not_equal_cpu";
+static constexpr char func_not_equal_gpu[] = "not_equal_gpu";
+static constexpr char func_greater_cpu[] = "greater_cpu";
+static constexpr char func_greater_gpu[] = "greater_gpu";
+static constexpr char func_less_cpu[] = "less_cpu";
+static constexpr char func_less_gpu[] = "less_gpu";
+static constexpr char func_greater_equal_cpu[] = "greater_equal_cpu";
+static constexpr char func_greater_equal_gpu[] = "greater_equal_gpu";
+static constexpr char func_less_equal_cpu[] = "less_equal_cpu";
+static constexpr char func_less_equal_gpu[] = "less_equal_gpu";
+
+bool NumpyBinaryLogicOpType(const nnvm::NodeAttrs& attrs,
+                            std::vector<int>* in_attrs,
+                            std::vector<int>* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 2U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  if (in_attrs->at(0) == -1 && in_attrs->at(1) == -1) return false;
+  TYPE_ASSIGN_CHECK(*in_attrs, 0, in_attrs->at(1));
+  TYPE_ASSIGN_CHECK(*in_attrs, 1, in_attrs->at(0));
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::kBool);
+  return true;
+}
+
+TBlob PrependAxes(const TBlob& src, const int dst_ndim) {
+  CHECK_LE(src.shape_.ndim(), dst_ndim);
+  const int src_ndim = src.shape_.ndim();
+  if (src_ndim == dst_ndim) return src;
+  mxnet::TShape dst_shape(dst_ndim, 1);
+  for (int i = dst_ndim - src_ndim; i < dst_ndim; ++i) {
+    dst_shape[i] = src.shape_[i - dst_ndim + src_ndim];
+  }
+  return src.reshape(dst_shape);
+}
+
+struct TVMBinaryBroadcastCompute {
+  const char* func;
+  void operator()(const nnvm::NodeAttrs& attrs,
+                  const mxnet::OpContext& ctx,
+                  const std::vector<TBlob>& inputs,
+                  const std::vector<OpReqType>& req,
+                  const std::vector<TBlob>& outputs) {
+#if MXNET_USE_TVM_OP
+    CHECK_EQ(inputs.size(), 2U);
+    CHECK_EQ(outputs.size(), 1U);
+    if (outputs[0].shape_.Size() == 0U) return;  // skip zero-size tensor
+
+    // prepare tblobs and TVMArgs
+    std::vector<TBlob> tblobs = {inputs[0], inputs[1], outputs[0]};
+    std::vector<int> type_codes;
+    std::vector<TVMValue> values;
+
+    const int ondim = outputs[0].shape_.ndim();
+    const size_t num_args = inputs.size() + outputs.size();
+    type_codes.resize(num_args);
+    values.resize(num_args);
+    for (size_t i = 0; i < num_args; ++i) {
+      tblobs[i] = PrependAxes(tblobs[i], ondim);
+      type_codes[i] = kArrayHandle;
+      values[i].v_handle = const_cast<DLTensor*>(&(tblobs[i].dltensor()));
+    }
+    tvm::runtime::TVMArgs tvm_args(&values[0], &type_codes[0], tblobs.size());
+    tvm::runtime::TVMOpModule::Get()->CallEx(func, ctx, tblobs, tvm_args);
+#else
+    LOG(FATAL) << "Please add USE_TVM_OP=1 as a compile flag for compiling MXNet source code "
+                  "to enable TVM-generated kernels for operator " << func;
+#endif  // MXNET_USE_TVM_OP
+  }
+};
+
+#define MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC(name)                                 \
+  NNVM_REGISTER_OP(_npi_##name)                                                       \
+  .set_num_inputs(2)                                                                  \
+  .set_num_outputs(1)                                                                 \
+  .set_attr<nnvm::FListInputNames>("FListInputNames",                                 \
+  [](const NodeAttrs& attrs) {                                                        \
+    return std::vector<std::string>{"lhs", "rhs"};                                    \
+  })                                                                                  \
+  .set_attr<mxnet::FInferShape>("FInferShape", BinaryBroadcastShape)                  \
+  .set_attr<nnvm::FInferType>("FInferType", NumpyBinaryLogicOpType)                   \
+  .set_attr<nnvm::FInplaceOption>("FInplaceOption",                                   \
+  [](const NodeAttrs& attrs) {                                                        \
+    return std::vector<std::pair<int, int> >{{0, 0}, {1, 0}};                         \
+  })                                                                                  \
+  .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)                          \
+  .add_argument("lhs", "NDArray-or-Symbol", "First input to the function")            \
+  .add_argument("rhs", "NDArray-or-Symbol", "Second input to the function")
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC(equal);
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC(not_equal);
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC(greater);
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC(less);
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC(greater_equal);
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC(less_equal);
+
+#if MXNET_USE_TVM_OP
+
+#define MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_CPU(name)                          \
+  NNVM_REGISTER_OP(_npi_##name)                                                    \
+  .set_attr<FCompute>("FCompute<cpu>", TVMBinaryBroadcastCompute{func_##name##_cpu})
+
+#if MXNET_USE_CUDA
+
+#define MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_GPU(name)                          \
+  NNVM_REGISTER_OP(_npi_##name)                                                    \
+  .set_attr<FCompute>("FCompute<gpu>", TVMBinaryBroadcastCompute{func_##name##_gpu})
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_GPU(equal);
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_GPU(not_equal);
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_GPU(greater);
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_GPU(less);
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_GPU(greater_equal);
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_GPU(less_equal);
+
+#endif  // MXNET_USE_CUDA
+
+#else
+
+#define MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_CPU(name)                                     \
+  NNVM_REGISTER_OP(_npi_##name)                                                               \
+  .set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastComputeLogic<cpu, mshadow_op::np_##name>)
+
+#endif  // MXNET_USE_TVM_OP
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_CPU(equal);
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_CPU(not_equal);
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_CPU(greater);
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_CPU(less);
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_CPU(greater_equal);
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_CPU(less_equal);
+
+bool NumpyBinaryScalarLogicOpType(const nnvm::NodeAttrs& attrs,
+                                  std::vector<int>* in_attrs,
+                                  std::vector<int>* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  if (in_attrs->at(0) == -1) return false;
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::kBool);
+  return true;
+}
+
+struct TVMBinaryBroadcastScalarCompute {
+  const char* func;
+  void operator()(const nnvm::NodeAttrs& attrs,
+                  const mxnet::OpContext& ctx,
+                  const std::vector<TBlob>& inputs,
+                  const std::vector<OpReqType>& req,
+                  const std::vector<TBlob>& outputs) {
+#if MXNET_USE_TVM_OP
+    CHECK_EQ(inputs.size(), 1U);
+    CHECK_EQ(outputs.size(), 1U);
+    if (outputs[0].shape_.Size() == 0U) return;  // skip zero-size tensor
+
+    // prepare tblobs and TVMArgs
+    std::vector<TBlob> tblobs = {inputs[0], outputs[0]};
+    std::vector<int> type_codes;
+    std::vector<TVMValue> values;
+
+    const size_t num_args = 3;  // one input tensor, one scalar param, and one output
+    type_codes.resize(num_args);
+    values.resize(num_args);
+
+    // input tensor setup
+    type_codes[0] = kArrayHandle;
+    values[0].v_handle = const_cast<DLTensor*>(&(tblobs[0].dltensor()));
+
+    // scalar param
+    type_codes[1] = kDLFloat;
+    values[1].v_float64 = nnvm::get<double>(attrs.parsed);
+
+    // output tensor
+    type_codes[2] = kArrayHandle;
+    values[2].v_handle = const_cast<DLTensor*>(&(tblobs[1].dltensor()));
+
+    tvm::runtime::TVMArgs tvm_args(&values[0], &type_codes[0], 3);
+    tvm::runtime::TVMOpModule::Get()->CallEx(func, ctx, tblobs, tvm_args);
+#else
+    LOG(FATAL) << "Please add USE_TVM_OP=1 as a compile flag for compiling MXNet source code "
+                  "to enable TVM-generated kernels for operator " << func;
+#endif  // MXNET_USE_TVM_OP
+  }
+};
+
+#define MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC(name)                                \
+  NNVM_REGISTER_OP(_npi_##name##_scalar)                                                    \
+  .set_num_inputs(1)                                                                        \
+  .set_num_outputs(1)                                                                       \
+  .set_attr_parser([](NodeAttrs* attrs) {                                                   \
+    attrs->parsed = std::stod(attrs->dict["scalar"]);                                       \
+  })                                                                                        \
+  .set_attr<nnvm::FListInputNames>("FListInputNames",                                       \
+  [](const NodeAttrs& attrs) {                                                              \
+    return std::vector<std::string>{"data"};                                                \
+  })                                                                                        \
+  .set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)                         \
+  .set_attr<nnvm::FInferType>("FInferType", NumpyBinaryScalarLogicOpType)                   \
+  .set_attr<nnvm::FInplaceOption>("FInplaceOption",                                         \
+  [](const NodeAttrs& attrs) {                                                              \
+    return std::vector<std::pair<int, int> >{{0, 0}};                                       \
+  })                                                                                        \
+  .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)                                \
+  .add_argument("data", "NDArray-or-Symbol", "First input to the function")                 \
+  .add_argument("scalar", "float", "scalar input")
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC(equal);
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC(not_equal);
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC(greater);
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC(less);
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC(greater_equal);
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC(less_equal);
+
+static constexpr char func_equal_scalar_cpu[] = "equal_scalar_cpu";
+static constexpr char func_equal_scalar_gpu[] = "equal_scalar_gpu";
+static constexpr char func_not_equal_scalar_cpu[] = "not_equal_scalar_cpu";
+static constexpr char func_not_equal_scalar_gpu[] = "not_equal_scalar_gpu";
+static constexpr char func_greater_scalar_cpu[] = "greater_scalar_cpu";
+static constexpr char func_greater_scalar_gpu[] = "greater_scalar_gpu";
+static constexpr char func_less_scalar_cpu[] = "less_scalar_cpu";
+static constexpr char func_less_scalar_gpu[] = "less_scalar_gpu";
+static constexpr char func_greater_equal_scalar_cpu[] = "greater_equal_scalar_cpu";
+static constexpr char func_greater_equal_scalar_gpu[] = "greater_equal_scalar_gpu";
+static constexpr char func_less_equal_scalar_cpu[] = "less_equal_scalar_cpu";
+static constexpr char func_less_equal_scalar_gpu[] = "less_equal_scalar_gpu";
+
+#if MXNET_USE_TVM_OP
+
+#define MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_CPU(name)                                \
+  NNVM_REGISTER_OP(_npi_##name##_scalar)                                                        \
+  .set_attr<FCompute>("FCompute<cpu>", TVMBinaryBroadcastScalarCompute{func_##name##_scalar_cpu})
+
+#if MXNET_USE_CUDA
+
+#define MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_GPU(name)                                \
+  NNVM_REGISTER_OP(_npi_##name##_scalar)                                                        \
+  .set_attr<FCompute>("FCompute<gpu>", TVMBinaryBroadcastScalarCompute{func_##name##_scalar_gpu})
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_GPU(equal);
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_GPU(not_equal);
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_GPU(greater);
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_GPU(less);
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_GPU(greater_equal);
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_GPU(less_equal);
+
+#endif  // MXNET_USE_CUDA
+
+#else
+
+#define MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_CPU(name)                               \
+  NNVM_REGISTER_OP(_npi_##name##_scalar)                                                       \
+  .set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::ComputeLogic<cpu, mshadow_op::np_##name>)
+
+#endif  // MXNET_USE_TVM_OP
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_CPU(equal);
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_CPU(not_equal);
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_CPU(greater);
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_CPU(less);
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_CPU(greater_equal);
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_CPU(less_equal);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_elemwise_broadcast_logic_op.cu b/src/operator/numpy/np_elemwise_broadcast_logic_op.cu
new file mode 100644
index 000000000000..98995a39dcb9
--- /dev/null
+++ b/src/operator/numpy/np_elemwise_broadcast_logic_op.cu
@@ -0,0 +1,60 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file np_elemwise_broadcast_logic_op.cu
+ * \brief GPU Implementation of basic functions for elementwise binary
+ * broadcast logic operator.
+ */
+#include "../tensor/elemwise_binary_broadcast_op.h"
+#include "../tensor/elemwise_binary_scalar_op.h"
+
+namespace mxnet {
+namespace op {
+
+
+#if MXNET_USE_TVM_OP == 0
+
+#define MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_GPU(name)                                     \
+  NNVM_REGISTER_OP(_npi_##name)                                                               \
+  .set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastComputeLogic<gpu, mshadow_op::np_##name>)
+
+#define MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_GPU(name)                               \
+  NNVM_REGISTER_OP(_npi_##name##_scalar)                                                       \
+  .set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::ComputeLogic<gpu, mshadow_op::np_##name>)
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_GPU(equal);
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_GPU(not_equal);
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_GPU(greater);
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_GPU(less);
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_GPU(greater_equal);
+MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_GPU(less_equal);
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_GPU(equal);
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_GPU(not_equal);
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_GPU(greater);
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_GPU(less);
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_GPU(greater_equal);
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_GPU(less_equal);
+
+#endif  // MXNET_USE_TVM_OP
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_elemwise_broadcast_op.cc b/src/operator/numpy/np_elemwise_broadcast_op.cc
index aa81a58c2890..7e07e47b4a8f 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op.cc
+++ b/src/operator/numpy/np_elemwise_broadcast_op.cc
@@ -23,12 +23,6 @@
  * \brief CPU Implementation of basic functions for elementwise numpy binary broadcast operator.
  */
 
-#if MXNET_USE_TVM_OP
-#include <tvm/runtime/c_runtime_api.h>
-#include <tvm/runtime/packed_func.h>
-#include "../tvmop/op_module.h"
-#endif  // MXNET_USE_TVM_OP
-
 #include "../tensor/elemwise_binary_broadcast_op.h"
 #include "../tensor/elemwise_binary_scalar_op.h"
 
@@ -302,223 +296,6 @@ NNVM_REGISTER_OP(_backward_npi_hypot)
 .set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastBackwardUseIn<cpu, mshadow_op::hypot_grad_left,
                                                                   mshadow_op::hypot_grad_right>);
 
-static constexpr char func_equal_cpu[] = "equal_cpu";
-static constexpr char func_equal_gpu[] = "equal_gpu";
-static constexpr char func_not_equal_cpu[] = "not_equal_cpu";
-static constexpr char func_not_equal_gpu[] = "not_equal_gpu";
-static constexpr char func_greater_cpu[] = "greater_cpu";
-static constexpr char func_greater_gpu[] = "greater_gpu";
-static constexpr char func_less_cpu[] = "less_cpu";
-static constexpr char func_less_gpu[] = "less_gpu";
-static constexpr char func_greater_equal_cpu[] = "greater_equal_cpu";
-static constexpr char func_greater_equal_gpu[] = "greater_equal_gpu";
-static constexpr char func_less_equal_cpu[] = "less_equal_cpu";
-static constexpr char func_less_equal_gpu[] = "less_equal_gpu";
-
-bool NumpyBinaryLogicOpType(const nnvm::NodeAttrs& attrs,
-                            std::vector<int>* in_attrs,
-                            std::vector<int>* out_attrs) {
-  CHECK_EQ(in_attrs->size(), 2U);
-  CHECK_EQ(out_attrs->size(), 1U);
-  if (in_attrs->at(0) == -1 && in_attrs->at(1) == -1) return false;
-  TYPE_ASSIGN_CHECK(*in_attrs, 0, in_attrs->at(1));
-  TYPE_ASSIGN_CHECK(*in_attrs, 1, in_attrs->at(0));
-  TYPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::kBool);
-  return true;
-}
-
-TBlob PrependAxes(const TBlob& src, const int dst_ndim) {
-  CHECK_LE(src.shape_.ndim(), dst_ndim);
-  const int src_ndim = src.shape_.ndim();
-  if (src_ndim == dst_ndim) return src;
-  mxnet::TShape dst_shape(dst_ndim, 1);
-  for (int i = dst_ndim - src_ndim; i < dst_ndim; ++i) {
-    dst_shape[i] = src.shape_[i - dst_ndim + src_ndim];
-  }
-  return src.reshape(dst_shape);
-}
-
-struct TVMBinaryBroadcastCompute {
-  const char* func;
-  void operator()(const nnvm::NodeAttrs& attrs,
-                  const mxnet::OpContext& ctx,
-                  const std::vector<TBlob>& inputs,
-                  const std::vector<OpReqType>& req,
-                  const std::vector<TBlob>& outputs) {
-#if MXNET_USE_TVM_OP
-    CHECK_EQ(inputs.size(), 2U);
-    CHECK_EQ(outputs.size(), 1U);
-    if (outputs[0].shape_.Size() == 0U) return;  // skip zero-size tensor
-
-    // prepare tblobs and TVMArgs
-    std::vector<TBlob> tblobs = {inputs[0], inputs[1], outputs[0]};
-    std::vector<int> type_codes;
-    std::vector<TVMValue> values;
-
-    const int ondim = outputs[0].shape_.ndim();
-    const size_t num_args = inputs.size() + outputs.size();
-    type_codes.resize(num_args);
-    values.resize(num_args);
-    for (size_t i = 0; i < num_args; ++i) {
-      tblobs[i] = PrependAxes(tblobs[i], ondim);
-      type_codes[i] = kArrayHandle;
-      values[i].v_handle = const_cast<DLTensor*>(&(tblobs[i].dltensor()));
-    }
-    tvm::runtime::TVMArgs tvm_args(&values[0], &type_codes[0], tblobs.size());
-    tvm::runtime::TVMOpModule::Get()->CallEx(func, ctx, tblobs, tvm_args);
-#else
-    LOG(FATAL) << "Please add USE_TVM_OP=1 as a compile flag for compiling MXNet source code "
-                  "to enable TVM-generated kernels for operator " << func;
-#endif  // MXNET_USE_TVM_OP
-  }
-};
-
-#define MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC(name)                                 \
-  NNVM_REGISTER_OP(_npi_##name)                                                       \
-  .set_num_inputs(2)                                                                  \
-  .set_num_outputs(1)                                                                 \
-  .set_attr<nnvm::FListInputNames>("FListInputNames",                                 \
-  [](const NodeAttrs& attrs) {                                                        \
-    return std::vector<std::string>{"lhs", "rhs"};                                    \
-  })                                                                                  \
-  .set_attr<mxnet::FInferShape>("FInferShape", BinaryBroadcastShape)                  \
-  .set_attr<nnvm::FInferType>("FInferType", NumpyBinaryLogicOpType)                   \
-  .set_attr<nnvm::FInplaceOption>("FInplaceOption",                                   \
-  [](const NodeAttrs& attrs) {                                                        \
-    return std::vector<std::pair<int, int> >{{0, 0}, {1, 0}};                         \
-  })                                                                                  \
-  .set_attr<FCompute>("FCompute<cpu>", TVMBinaryBroadcastCompute{func_##name##_cpu})  \
-  .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)                          \
-  .add_argument("lhs", "NDArray-or-Symbol", "First input to the function")            \
-  .add_argument("rhs", "NDArray-or-Symbol", "Second input to the function")
-
-MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC(equal);
-MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC(not_equal);
-MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC(greater);
-MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC(less);
-MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC(greater_equal);
-MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC(less_equal);
-
-#if MXNET_USE_CUDA
-#define MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_GPU(name)                          \
-  NNVM_REGISTER_OP(_npi_##name)                                                    \
-  .set_attr<FCompute>("FCompute<gpu>", TVMBinaryBroadcastCompute{func_##name##_gpu})
-
-MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_GPU(equal);
-MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_GPU(not_equal);
-MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_GPU(greater);
-MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_GPU(less);
-MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_GPU(greater_equal);
-MXNET_OPERATOR_REGISTER_NP_BINARY_LOGIC_GPU(less_equal);
-#endif  // MXNET_USE_CUDA
-
-bool NumpyBinaryScalarLogicOpType(const nnvm::NodeAttrs& attrs,
-                                  std::vector<int>* in_attrs,
-                                  std::vector<int>* out_attrs) {
-  CHECK_EQ(in_attrs->size(), 1U);
-  CHECK_EQ(out_attrs->size(), 1U);
-  if (in_attrs->at(0) == -1) return false;
-  TYPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::kBool);
-  return true;
-}
-
-struct TVMBinaryBroadcastScalarCompute {
-  const char* func;
-  void operator()(const nnvm::NodeAttrs& attrs,
-                  const mxnet::OpContext& ctx,
-                  const std::vector<TBlob>& inputs,
-                  const std::vector<OpReqType>& req,
-                  const std::vector<TBlob>& outputs) {
-#if MXNET_USE_TVM_OP
-    CHECK_EQ(inputs.size(), 1U);
-    CHECK_EQ(outputs.size(), 1U);
-    if (outputs[0].shape_.Size() == 0U) return;  // skip zero-size tensor
-
-    // prepare tblobs and TVMArgs
-    std::vector<TBlob> tblobs = {inputs[0], outputs[0]};
-    std::vector<int> type_codes;
-    std::vector<TVMValue> values;
-
-    const size_t num_args = 3;  // one input tensor, one scalar param, and one output
-    type_codes.resize(num_args);
-    values.resize(num_args);
-
-    // input tensor setup
-    type_codes[0] = kArrayHandle;
-    values[0].v_handle = const_cast<DLTensor*>(&(tblobs[0].dltensor()));
-
-    // scalar param
-    type_codes[1] = kDLFloat;
-    values[1].v_float64 = nnvm::get<double>(attrs.parsed);
-
-    // output tensor
-    type_codes[2] = kArrayHandle;
-    values[2].v_handle = const_cast<DLTensor*>(&(tblobs[1].dltensor()));
-
-    tvm::runtime::TVMArgs tvm_args(&values[0], &type_codes[0], 3);
-    tvm::runtime::TVMOpModule::Get()->CallEx(func, ctx, tblobs, tvm_args);
-#else
-    LOG(FATAL) << "Please add USE_TVM_OP=1 as a compile flag for compiling MXNet source code "
-                  "to enable TVM-generated kernels for operator " << func;
-#endif  // MXNET_USE_TVM_OP
-  }
-};
-
-#define MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC(name)                                \
-  NNVM_REGISTER_OP(_npi_##name)                                                             \
-  .set_num_inputs(1)                                                                        \
-  .set_num_outputs(1)                                                                       \
-  .set_attr_parser([](NodeAttrs* attrs) {                                                   \
-    attrs->parsed = std::stod(attrs->dict["scalar"]);                                       \
-  })                                                                                        \
-  .set_attr<nnvm::FListInputNames>("FListInputNames",                                       \
-  [](const NodeAttrs& attrs) {                                                              \
-    return std::vector<std::string>{"data"};                                                \
-  })                                                                                        \
-  .set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)                         \
-  .set_attr<nnvm::FInferType>("FInferType", NumpyBinaryScalarLogicOpType)                   \
-  .set_attr<nnvm::FInplaceOption>("FInplaceOption",                                         \
-  [](const NodeAttrs& attrs) {                                                              \
-    return std::vector<std::pair<int, int> >{{0, 0}};                                       \
-  })                                                                                        \
-  .set_attr<FCompute>("FCompute<cpu>", TVMBinaryBroadcastScalarCompute{func_##name##_cpu})  \
-  .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)                                \
-  .add_argument("data", "NDArray-or-Symbol", "First input to the function")                 \
-  .add_argument("scalar", "float", "scalar input")
-
-static constexpr char func_equal_scalar_cpu[] = "equal_scalar_cpu";
-static constexpr char func_equal_scalar_gpu[] = "equal_scalar_gpu";
-static constexpr char func_not_equal_scalar_cpu[] = "not_equal_scalar_cpu";
-static constexpr char func_not_equal_scalar_gpu[] = "not_equal_scalar_gpu";
-static constexpr char func_greater_scalar_cpu[] = "greater_scalar_cpu";
-static constexpr char func_greater_scalar_gpu[] = "greater_scalar_gpu";
-static constexpr char func_less_scalar_cpu[] = "less_scalar_cpu";
-static constexpr char func_less_scalar_gpu[] = "less_scalar_gpu";
-static constexpr char func_greater_equal_scalar_cpu[] = "greater_equal_scalar_cpu";
-static constexpr char func_greater_equal_scalar_gpu[] = "greater_equal_scalar_gpu";
-static constexpr char func_less_equal_scalar_cpu[] = "less_equal_scalar_cpu";
-static constexpr char func_less_equal_scalar_gpu[] = "less_equal_scalar_gpu";
-
-MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC(equal_scalar);
-MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC(not_equal_scalar);
-MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC(greater_scalar);
-MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC(less_scalar);
-MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC(greater_equal_scalar);
-MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC(less_equal_scalar);
-
-#if MXNET_USE_CUDA
-#define MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_GPU(name)                         \
-  NNVM_REGISTER_OP(_npi_##name)                                                          \
-  .set_attr<FCompute>("FCompute<gpu>", TVMBinaryBroadcastScalarCompute{func_##name##_gpu})
-
-MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_GPU(equal_scalar);
-MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_GPU(not_equal_scalar);
-MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_GPU(greater_scalar);
-MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_GPU(less_scalar);
-MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_GPU(greater_equal_scalar);
-MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR_LOGIC_GPU(less_equal_scalar);
-#endif  // MXNET_USE_CUDA
-
 MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_ldexp)
 .set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, mshadow_op::ldexp>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_ldexp"});
diff --git a/src/operator/numpy/np_elemwise_unary_op_basic.cc b/src/operator/numpy/np_elemwise_unary_op_basic.cc
index b454d89212e2..c980dcfaab5d 100644
--- a/src/operator/numpy/np_elemwise_unary_op_basic.cc
+++ b/src/operator/numpy/np_elemwise_unary_op_basic.cc
@@ -65,22 +65,49 @@ NNVM_REGISTER_OP(_np_copy)
   })
 .add_argument("a", "NDArray-or-Symbol", "The input");
 
-#define MXNET_OPERATOR_REGISTER_NUMPY_UNARY(__name$, __input_name$, __kernel$)          \
-NNVM_REGISTER_OP(__name$)                                                               \
-.set_num_inputs(1)                                                                      \
-.set_num_outputs(1)                                                                     \
-.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)                       \
-.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)                           \
-.set_attr<nnvm::FInplaceOption>("FInplaceOption",                                       \
-  [](const NodeAttrs& attrs){                                                           \
-    return std::vector<std::pair<int, int> >{{0, 0}};                                   \
-  })                                                                                    \
-.set_attr<nnvm::FListInputNames>("FListInputNames",                                     \
-  [](const NodeAttrs& attrs) {                                                          \
-    return std::vector<std::string>{__input_name$};                                     \
-  })                                                                                    \
-.set_attr<FCompute>("FCompute<cpu>", UnaryOp::Compute<cpu, __kernel$>)                  \
-.add_argument(__input_name$, "NDArray-or-Symbol", "The input array.")
+#define MXNET_OPERATOR_REGISTER_NUMPY_UNARY(__name$, __input_name$, __kernel$)            \
+  NNVM_REGISTER_OP(__name$)                                                               \
+  .set_num_inputs(1)                                                                      \
+  .set_num_outputs(1)                                                                     \
+  .set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)                       \
+  .set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)                           \
+  .set_attr<nnvm::FInplaceOption>("FInplaceOption",                                       \
+    [](const NodeAttrs& attrs){                                                           \
+      return std::vector<std::pair<int, int> >{{0, 0}};                                   \
+    })                                                                                    \
+  .set_attr<nnvm::FListInputNames>("FListInputNames",                                     \
+    [](const NodeAttrs& attrs) {                                                          \
+      return std::vector<std::string>{__input_name$};                                     \
+    })                                                                                    \
+  .set_attr<FCompute>("FCompute<cpu>", UnaryOp::Compute<cpu, __kernel$>)                  \
+  .add_argument(__input_name$, "NDArray-or-Symbol", "The input array.")
+
+bool NumpyUnaryLogicOpType(const nnvm::NodeAttrs& attrs,
+                           std::vector<int>* in_attrs,
+                           std::vector<int>* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  if (in_attrs->at(0) == -1) return false;
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::kBool);
+  return true;
+}
+
+#define MXNET_OPERATOR_REGISTER_NUMPY_UNARY_LOGIC(__name$, __input_name$, __kernel$)      \
+  NNVM_REGISTER_OP(__name$)                                                               \
+  .set_num_inputs(1)                                                                      \
+  .set_num_outputs(1)                                                                     \
+  .set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)                       \
+  .set_attr<nnvm::FInferType>("FInferType", NumpyUnaryLogicOpType)                        \
+  .set_attr<nnvm::FInplaceOption>("FInplaceOption",                                       \
+    [](const NodeAttrs& attrs){                                                           \
+      return std::vector<std::pair<int, int> >{{0, 0}};                                   \
+    })                                                                                    \
+  .set_attr<nnvm::FListInputNames>("FListInputNames",                                     \
+    [](const NodeAttrs& attrs) {                                                          \
+      return std::vector<std::string>{__input_name$};                                     \
+    })                                                                                    \
+  .set_attr<FCompute>("FCompute<cpu>", UnaryOp::ComputeLogic<cpu, __kernel$>)             \
+  .add_argument(__input_name$, "NDArray-or-Symbol", "The input array.")
 
 // negative
 MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_negative, "x", mshadow_op::negation)
@@ -243,11 +270,7 @@ MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_expm1, "x", mshadow_op::expm1)
 
 
 // logical_not
-MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_logical_not, "x", mshadow_op::nt)
-.describe(R"code(Compute the truth value of NOT x element-wise.
-Example::
-  logical_not([-2., 0., 1.]) = [0., 1., 0.]
-)code")
+MXNET_OPERATOR_REGISTER_NUMPY_UNARY_LOGIC(_npi_logical_not, "x", mshadow_op::np_logical_not)
 .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes);
 
 // sin
diff --git a/src/operator/numpy/np_elemwise_unary_op_basic.cu b/src/operator/numpy/np_elemwise_unary_op_basic.cu
index 7f68386560f3..44743ed94be8 100644
--- a/src/operator/numpy/np_elemwise_unary_op_basic.cu
+++ b/src/operator/numpy/np_elemwise_unary_op_basic.cu
@@ -35,9 +35,9 @@ NNVM_REGISTER_OP(_npx_sigmoid)
 NNVM_REGISTER_OP(_np_copy)
 .set_attr<FCompute>("FCompute<gpu>", UnaryOp::IdentityCompute<gpu>);
 
-#define MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(__name$, __kernel$)     \
-NNVM_REGISTER_OP(__name$)                                               \
-.set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, __kernel$>)  \
+#define MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(__name$, __kernel$)       \
+  NNVM_REGISTER_OP(__name$)                                               \
+  .set_attr<FCompute>("FCompute<gpu>", UnaryOp::Compute<gpu, __kernel$>)
 
 MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_negative, mshadow_op::negation);
 
@@ -76,7 +76,8 @@ MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_log1p, mshadow_op::log1p);
 
 MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_expm1, mshadow_op::expm1);
 
-MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_logical_not, mshadow_op::nt);
+NNVM_REGISTER_OP(_npi_logical_not)
+.set_attr<FCompute>("FCompute<gpu>", UnaryOp::ComputeLogic<gpu, mshadow_op::np_logical_not>);
 
 MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_sin, mshadow_op::sin);
 
diff --git a/src/operator/numpy/np_matrix_op-inl.h b/src/operator/numpy/np_matrix_op-inl.h
index 5e25192d9298..0eefeb9cdb5d 100644
--- a/src/operator/numpy/np_matrix_op-inl.h
+++ b/src/operator/numpy/np_matrix_op-inl.h
@@ -29,6 +29,7 @@
 #include <algorithm>
 #include "../tensor/matrix_op-inl.h"
 #include "../nn/concat-inl.h"
+#include "../../common/utils.h"
 
 namespace mxnet {
 namespace op {
@@ -59,7 +60,8 @@ void NumpyTranspose(const nnvm::NodeAttrs& attrs,
   const NumpyTransposeParam& param = nnvm::get<NumpyTransposeParam>(attrs.parsed);
   CHECK_EQ(req[0], kWriteTo) << "Transpose does not support inplace";
   if (ndim_is_known(param.axes)) {
-    TransposeImpl<xpu>(ctx.run_ctx, inputs[0], outputs[0], param.axes);
+    mxnet::TShape axes = common::CanonicalizeAxes(param.axes);
+    TransposeImpl<xpu>(ctx.run_ctx, inputs[0], outputs[0], axes);
   } else {
     mxnet::TShape axes(inputs[0].ndim(), -1);
     for (int i = 0; i < axes.ndim(); ++i) {
diff --git a/src/operator/numpy/np_matrix_op.cc b/src/operator/numpy/np_matrix_op.cc
index f54f325bcd17..83f0b1aae9b0 100644
--- a/src/operator/numpy/np_matrix_op.cc
+++ b/src/operator/numpy/np_matrix_op.cc
@@ -40,21 +40,53 @@ bool NumpyTransposeShape(const nnvm::NodeAttrs& attrs,
   CHECK_EQ(in_attrs->size(), 1U);
   CHECK_EQ(out_attrs->size(), 1U);
   mxnet::TShape& shp = (*in_attrs)[0];
+  mxnet::TShape& out_shp = (*out_attrs)[0];
   CHECK_LE(shp.ndim(), 6) << "Transpose support at most 6 dimensions";
-  mxnet::TShape ret(shp.ndim(), -1);
+
+  int ndim = -1;
+  if (ndim_is_known(shp)) {
+    ndim = shp.ndim();
+  } else if (ndim_is_known(out_shp)) {
+    ndim = out_shp.ndim();
+  }
+  if (ndim < 0) {
+    return false;
+  }
+  if (out_shp.ndim() >= 0 && shp.ndim() >= 0) {
+    CHECK_EQ(out_shp.ndim(), shp.ndim());
+  }
+
+  mxnet::TShape get(ndim, -1);
+  mxnet::TShape ret(ndim, -1);
+
   if (ndim_is_known(param.axes)) {
-    CHECK_EQ(shp.ndim(), param.axes.ndim());
-    for (int i = 0; i < shp.ndim(); ++i) {
-      CHECK(param.axes[i] < static_cast<int64_t>(shp.ndim()));
-      ret[i] = shp[param.axes[i]];
+    CHECK_EQ(ndim, param.axes.ndim());
+    mxnet::TShape axes = common::CanonicalizeAxes(param.axes);
+    if (ndim_is_known(shp)) {
+      for (int i = 0; i < ndim; ++i) {
+        ret[i] = shp[axes[i]];
+      }
+    }
+    if (ndim_is_known(out_shp)) {
+      for (int i = 0; i < ndim; ++i) {
+        get[axes[i]] = out_shp[i];
+      }
     }
   } else {
-    for (int i = 0; i < shp.ndim(); ++i) {
-      ret[i] = shp[shp.ndim()-1-i];
+    if (ndim_is_known(shp)) {
+      for (int i = 0; i < ndim; ++i) {
+        ret[i] = shp[ndim - 1 - i];
+      }
+    }
+    if (ndim_is_known(out_shp)) {
+      for (int i = 0; i < ndim; ++i) {
+        get[ndim - 1 - i] = out_shp[i];
+      }
     }
   }
+  SHAPE_ASSIGN_CHECK(*in_attrs, 0, get);
   SHAPE_ASSIGN_CHECK(*out_attrs, 0, ret);
-  return shape_is_known(ret);
+  return shape_is_known(*in_attrs) && shape_is_known(*out_attrs);
 }
 
 NNVM_REGISTER_OP(_np_transpose)
@@ -69,7 +101,12 @@ NNVM_REGISTER_OP(_np_transpose)
     if (ndim_is_known(param.axes)) {
       mxnet::TShape axes = mxnet::TShape(param.axes.ndim(), -1);
       for (int i = 0; i < axes.ndim(); ++i) {
-        axes[param.axes[i]] = i;
+        int axis = param.axes[i];
+        if (axis < 0) {
+          axis += param.axes.ndim();
+        }
+        CHECK(axis >= 0 && axis < param.axes.ndim());
+        axes[axis] = i;
       }
       std::ostringstream os;
       os << axes;
diff --git a/src/operator/operator_tune.cc b/src/operator/operator_tune.cc
index 49d0e23bdd19..398d5a714bd1 100644
--- a/src/operator/operator_tune.cc
+++ b/src/operator/operator_tune.cc
@@ -209,6 +209,9 @@ struct static_init_var {
 #define IMPLEMENT_BINARY_WORKLOAD_FWD(__op$) \
   MSHADOW_MACRO_FOREACH_TYPE(_IMPLEMENT_BINARY_WORKLOAD_FWD, __op$)
 
+#define IMPLEMENT_BINARY_WORKLOAD_FWD_WITH_BOOL(__op$) \
+  MSHADOW_MACRO_FOREACH_TYPE_WITH_BOOL(_IMPLEMENT_BINARY_WORKLOAD_FWD, __op$)
+
 #define IMPLEMENT_BINARY_WORKLOAD_BWD(__op$) \
   MSHADOW_MACRO_FOREACH_TYPE(_IMPLEMENT_BINARY_WORKLOAD_BWD, __op$)
 
@@ -307,6 +310,7 @@ IMPLEMENT_UNARY_WORKLOAD_BWD(mxnet::op::mshadow_op::degrees_grad);  // NOLINT()
 IMPLEMENT_UNARY_WORKLOAD_FWD(mxnet::op::mshadow_op::radians);  // NOLINT()
 IMPLEMENT_UNARY_WORKLOAD_BWD(mxnet::op::mshadow_op::radians_grad);  // NOLINT()
 IMPLEMENT_UNARY_WORKLOAD_FWD(mxnet::op::mshadow_op::nt);  // NOLINT()
+IMPLEMENT_UNARY_WORKLOAD_FWD_WITH_BOOL(mxnet::op::mshadow_op::np_logical_not);  // NOLINT()
 IMPLEMENT_UNARY_WORKLOAD_BWD(mxnet::op::mshadow_op::nt);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::clip);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::clip);  // NOLINT()
@@ -378,6 +382,12 @@ IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::ne);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::ne);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::eq);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::eq);  // NOLINT()
+IMPLEMENT_BINARY_WORKLOAD_FWD_WITH_BOOL(mxnet::op::mshadow_op::np_equal);  // NOLINT()
+IMPLEMENT_BINARY_WORKLOAD_FWD_WITH_BOOL(mxnet::op::mshadow_op::np_not_equal);  // NOLINT()
+IMPLEMENT_BINARY_WORKLOAD_FWD_WITH_BOOL(mxnet::op::mshadow_op::np_greater);  // NOLINT()
+IMPLEMENT_BINARY_WORKLOAD_FWD_WITH_BOOL(mxnet::op::mshadow_op::np_greater_equal);  // NOLINT()
+IMPLEMENT_BINARY_WORKLOAD_FWD_WITH_BOOL(mxnet::op::mshadow_op::np_less);  // NOLINT()
+IMPLEMENT_BINARY_WORKLOAD_FWD_WITH_BOOL(mxnet::op::mshadow_op::np_less_equal);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::logical_and);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::logical_and);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::logical_or);  // NOLINT()
diff --git a/src/operator/tensor/elemwise_binary_broadcast_op.h b/src/operator/tensor/elemwise_binary_broadcast_op.h
index 29c476d06d1c..6a612e6f1cd5 100644
--- a/src/operator/tensor/elemwise_binary_broadcast_op.h
+++ b/src/operator/tensor/elemwise_binary_broadcast_op.h
@@ -186,12 +186,12 @@ inline int BinaryBroadcastShapeCompact(const mxnet::TShape& lshape, const mxnet:
 }
 
 namespace mxnet_op {
-template<int ndim, typename DType, typename OP>
+template<int ndim, typename IType, typename DType, typename OP>
 struct binary_broadcast_kernel {
   /*! \brief Map function for binary_broadcast_kernel */
   MSHADOW_XINLINE static void Map(index_t base, index_t length, OpReqType req,
                                   const Shape <ndim> &lstride, const Shape <ndim> &rstride,
-                                  const Shape <ndim> &oshape, DType *lhs, DType *rhs,
+                                  const Shape <ndim> &oshape, IType *lhs, IType *rhs,
                                   DType *out) {
     Shape <ndim> coord = unravel(base, oshape);
     auto lidx = static_cast<index_t>(dot(coord, lstride));
@@ -209,7 +209,7 @@ struct binary_broadcast_kernel {
   /*! \brief Map function for binary_broadcast_kernel */
   MSHADOW_XINLINE static void Map(index_t base, index_t length, OpReqType req,
                                   const Shape <ndim> &lstride, const Shape <ndim> &rstride,
-                                  const Shape <ndim> &oshape, DType lhs, DType *rhs,
+                                  const Shape <ndim> &oshape, IType lhs, IType *rhs,
                                   DType *out) {
     Shape <ndim> coord = unravel(base, oshape);
     auto lidx = static_cast<index_t>(dot(coord, lstride));
@@ -306,7 +306,7 @@ void BinaryBroadcastCompute(const nnvm::NodeAttrs& attrs,
           mshadow::Shape<NDim> oshape = new_oshape.get<NDim>();
           mshadow::Shape<NDim> lstride = mxnet_op::calc_stride(new_lshape.get<NDim>());
           mshadow::Shape<NDim> rstride = mxnet_op::calc_stride(new_rshape.get<NDim>());
-          mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, DType, OP>, xpu>::
+          mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, DType, DType, OP>, xpu>::
           template LaunchEx(s, new_oshape.Size(), req[0], lstride, rstride, oshape,
           inputs[0].dptr<DType>(), inputs[1].dptr<DType>(), outputs[0].dptr<DType>());
         });
@@ -315,6 +315,36 @@ void BinaryBroadcastCompute(const nnvm::NodeAttrs& attrs,
   }
 }
 
+template<typename xpu, typename OP>
+void BinaryBroadcastComputeLogic(const nnvm::NodeAttrs& attrs,
+                                 const OpContext& ctx,
+                                 const std::vector<TBlob>& inputs,
+                                 const std::vector<OpReqType>& req,
+                                 const std::vector<TBlob>& outputs) {
+  if (outputs[0].shape_.Size() == 0U) return;
+  mxnet::TShape new_lshape, new_rshape, new_oshape;
+  int ndim = BinaryBroadcastShapeCompact(inputs[0].shape_, inputs[1].shape_, outputs[0].shape_,
+                                         &new_lshape, &new_rshape, &new_oshape);
+  if (!ndim) {
+    ElemwiseBinaryOp::ComputeLogic<xpu, OP>(attrs, ctx, inputs, req, outputs);
+  } else {
+    if (req[0] != kNullOp) {
+      mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+      MSHADOW_TYPE_SWITCH_WITH_BOOL(inputs[0].type_flag_, DType, {
+          BROADCAST_NDIM_SWITCH(ndim, NDim, {
+            mshadow::Shape<NDim> oshape = new_oshape.get<NDim>();
+            mshadow::Shape<NDim> lstride = mxnet_op::calc_stride(new_lshape.get<NDim>());
+            mshadow::Shape<NDim> rstride = mxnet_op::calc_stride(new_rshape.get<NDim>());
+            mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, DType, bool, OP>, xpu>::
+            template LaunchEx(s, new_oshape.Size(), req[0], lstride, rstride, oshape,
+                              inputs[0].dptr<DType>(), inputs[1].dptr<DType>(),
+                              outputs[0].dptr<bool>());
+          });
+      });
+    }
+  }
+}
+
 template<typename xpu, typename OP>
 void BinaryBroadcastCsrDnsCsrImpl(const OpContext& ctx,
                                   const NDArray& csr,
@@ -413,11 +443,11 @@ void BinaryBroadcastCsrDnsDnsImpl(const OpContext& ctx,
       Shape<NDim> lstride = calc_stride(new_csrshape.get<NDim>());
       Shape<NDim> rstride = calc_stride(new_dnsshape.get<NDim>());
       if (reverse && std::is_same<OP, mshadow_op::minus>::value) {
-        Kernel<binary_broadcast_kernel<NDim, DType, mshadow_op::plus>, xpu>::
+        Kernel<binary_broadcast_kernel<NDim, DType, DType, mshadow_op::plus>, xpu>::
         template LaunchEx(s, new_oshape.Size(), req, lstride, rstride, oshape,
         DType(0), dns_data.dptr<DType>(), out_data.dptr<DType>());
       } else {
-        Kernel<binary_broadcast_kernel<NDim, DType, OP>, xpu>::
+        Kernel<binary_broadcast_kernel<NDim, DType, DType, OP>, xpu>::
         template LaunchEx(s, new_oshape.Size(), req, lstride, rstride, oshape,
         DType(0), dns_data.dptr<DType>(), out_data.dptr<DType>());
       }
diff --git a/src/operator/tensor/elemwise_binary_op.h b/src/operator/tensor/elemwise_binary_op.h
index 9c1d8b17fdea..6f444aed21fe 100644
--- a/src/operator/tensor/elemwise_binary_op.h
+++ b/src/operator/tensor/elemwise_binary_op.h
@@ -497,6 +497,32 @@ class ElemwiseBinaryOp : public OpBase {
     }
   }
 
+  template<typename xpu, typename OP>
+  static void ComputeLogic(const nnvm::NodeAttrs &attrs,
+                           const OpContext &ctx,
+                           const std::vector<TBlob> &inputs,
+                           const std::vector<OpReqType> &req,
+                           const std::vector<TBlob> &outputs) {
+    using namespace mxnet_op;
+    if (req[0] != kNullOp) {
+      Stream<xpu> *s = ctx.get_stream<xpu>();
+      CHECK_EQ(inputs.size(), 2U);
+      CHECK_EQ(outputs.size(), 1U);
+      MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
+        MSHADOW_TYPE_SWITCH_WITH_BOOL(inputs[0].type_flag_, DType, {
+            const size_t size = (minthree(outputs[0].Size(), inputs[0].Size(), inputs[1].Size())
+            + DataType<DType>::kLanes - 1) / DataType<DType>::kLanes;
+            if (size != 0) {
+              Kernel<mxnet_op::op_with_req<OP, Req>, xpu>::Launch(s, size,
+                                                                  outputs[0].dptr<bool>(),
+                                                                  inputs[0].dptr<DType>(),
+                                                                  inputs[1].dptr<DType>());
+            }
+        });
+      });
+    }
+  }
+
   template<typename xpu, typename OP>
   static void ComputeWithHalf2(const nnvm::NodeAttrs &attrs,
                                const OpContext &ctx,
diff --git a/src/operator/tensor/elemwise_binary_scalar_op.h b/src/operator/tensor/elemwise_binary_scalar_op.h
index c78841641214..02b005eed995 100644
--- a/src/operator/tensor/elemwise_binary_scalar_op.h
+++ b/src/operator/tensor/elemwise_binary_scalar_op.h
@@ -244,6 +244,26 @@ class BinaryScalarOp : public UnaryOp {
     });
   }
 
+  template<typename xpu, typename OP>
+  static void ComputeLogic(const nnvm::NodeAttrs &attrs,
+                      const OpContext &ctx,
+                      const std::vector<TBlob> &inputs,
+                      const std::vector<OpReqType> &req,
+                      const std::vector<TBlob> &outputs) {
+    DCHECK_EQ(inputs.size(), 1);
+    DCHECK_EQ(outputs.size(), 1);
+    using namespace mshadow;
+    using namespace mshadow::expr;
+    Stream<xpu> *s = ctx.get_stream<xpu>();
+    const double alpha = nnvm::get<double>(attrs.parsed);
+    MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+        MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
+          mxnet_op::Kernel<mxnet_op::op_with_req<OP, Req>, xpu>::Launch(
+              s, inputs[0].Size(), outputs[0].dptr<bool>(), inputs[0].dptr<DType>(), DType(alpha));
+        });
+    });
+  }
+
   template<typename xpu, typename OP>
   static void ComputeEx(const nnvm::NodeAttrs &attrs,
                         const OpContext &ctx,
diff --git a/src/operator/tensor/elemwise_unary_op.h b/src/operator/tensor/elemwise_unary_op.h
index 22e7652a4019..b7625fccf258 100644
--- a/src/operator/tensor/elemwise_unary_op.h
+++ b/src/operator/tensor/elemwise_unary_op.h
@@ -251,6 +251,23 @@ class UnaryOp : public OpBase {
     });
   }
 
+  template<typename xpu, typename OP>
+  static void ComputeLogic(const nnvm::NodeAttrs& attrs,
+                           const OpContext& ctx,
+                           const std::vector<TBlob>& inputs,
+                           const std::vector<OpReqType>& req,
+                           const std::vector<TBlob>& outputs) {
+    mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+    MSHADOW_TYPE_SWITCH_WITH_BOOL(inputs[0].type_flag_, DType, {
+      MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
+        if (inputs[0].Size() != 0) {
+          mxnet_op::Kernel<mxnet_op::op_with_req<OP, Req>, xpu>::Launch(
+              s, inputs[0].Size(), outputs[0].dptr<bool>(), inputs[0].dptr<DType>());
+        }
+      });
+    });
+  }
+
   template<typename xpu, typename OP>
   static void ComputeEx(const nnvm::NodeAttrs& attrs,
                         const OpContext& ctx,
@@ -408,7 +425,7 @@ struct CastParam : public dmlc::Parameter<CastParam> {
   int dtype;
   DMLC_DECLARE_PARAMETER(CastParam) {
     DMLC_DECLARE_FIELD(dtype)
-    MXNET_ADD_ALL_TYPES
+    MXNET_ADD_ALL_TYPES_WITH_BOOL
     .describe("Output data type.");
   }
 };
@@ -432,7 +449,7 @@ void CastCompute(const nnvm::NodeAttrs& attrs,
   using namespace mshadow;
   using namespace mshadow::expr;
   Stream<xpu> *s = ctx.get_stream<xpu>();
-  MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DstDType, {
+  MSHADOW_TYPE_SWITCH_WITH_BOOL(outputs[0].type_flag_, DstDType, {
     Tensor<xpu, 1, DstDType> out = outputs[0].FlatTo1D<xpu, DstDType>(s);
     MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, SrcDType, {
       Tensor<xpu, 1, SrcDType> data = inputs[0].FlatTo1D<xpu, SrcDType>(s);
diff --git a/src/operator/tensor/elemwise_unary_op_basic.cc b/src/operator/tensor/elemwise_unary_op_basic.cc
index 117cfa96518a..7c35c44305a8 100644
--- a/src/operator/tensor/elemwise_unary_op_basic.cc
+++ b/src/operator/tensor/elemwise_unary_op_basic.cc
@@ -650,6 +650,7 @@ Example::
 DMLC_REGISTER_PARAMETER(CastParam);
 NNVM_REGISTER_OP(Cast)
 .add_alias("cast")
+.add_alias("_npi_cast")
 .add_alias("_npx_cast")
 .describe(R"code(Casts all elements of the input to a new type.
 
diff --git a/src/operator/tensor/matrix_op-inl.h b/src/operator/tensor/matrix_op-inl.h
index 45df2ed53ded..58d9eaefa04b 100644
--- a/src/operator/tensor/matrix_op-inl.h
+++ b/src/operator/tensor/matrix_op-inl.h
@@ -306,7 +306,6 @@ MSHADOW_XINLINE void Transpose2D(const DType *in, DType *out, index_t row, index
   }
 }
 
-
 template<typename xpu>
 void TransposeImpl(RunContext ctx,
                    const TBlob& src,
diff --git a/tests/python/unittest/test_numpy_ndarray.py b/tests/python/unittest/test_numpy_ndarray.py
index 20b964c96a30..53a8076f1303 100644
--- a/tests/python/unittest/test_numpy_ndarray.py
+++ b/tests/python/unittest/test_numpy_ndarray.py
@@ -26,7 +26,7 @@
 from mxnet.gluon import HybridBlock
 from mxnet.test_utils import same, assert_almost_equal, rand_shape_nd, rand_ndarray, retry, use_np
 from common import with_seed, TemporaryDirectory
-from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf, has_tvm_ops, assert_exception
+from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf, assert_exception, is_op_runnable
 from mxnet.ndarray.ndarray import py_slice
 from mxnet.base import integer_types
 import scipy.stats as ss
@@ -264,9 +264,10 @@ def test_np_ndarray_binary_element_wise_ops():
         '/': _np.divide,
         'mod': _np.mod,
         'pow': _np.power,
+
     }
 
-    if has_tvm_ops():
+    if is_op_runnable():
         np_op_map.update({
             '==': _np.equal,
             '!=': _np.not_equal,
@@ -468,23 +469,38 @@ def test_np_grad_ndarray_type():
 
 
 @with_seed()
+@use_np
 def test_np_ndarray_astype():
     mx_data = np.array([2, 3, 4, 5], dtype=_np.int32)
     np_data = mx_data.asnumpy()
 
-    def check_astype_equal(dtype, copy, expect_zero_copy=False):
-        mx_ret = mx_data.astype(dtype=dtype, copy=copy)
+    class TestAstype(HybridBlock):
+        def __init__(self, dtype, copy):
+            super(TestAstype, self).__init__()
+            self._dtype = dtype
+            self._copy = copy
+
+        def hybrid_forward(self, F, x):
+            return x.astype(dtype=self._dtype, copy=self._copy)
+
+    def check_astype_equal(dtype, copy, expect_zero_copy=False, hybridize=False):
+        test_astype = TestAstype(dtype, copy)
+        if hybridize:
+            test_astype.hybridize()
+        mx_ret = test_astype(mx_data)
         assert type(mx_ret) is np.ndarray
         np_ret = np_data.astype(dtype=dtype, copy=copy)
         assert mx_ret.dtype == np_ret.dtype
         assert same(mx_ret.asnumpy(), np_ret)
-        if expect_zero_copy:
+        if expect_zero_copy and not hybridize:
             assert id(mx_ret) == id(mx_data)
             assert id(np_ret) == id(np_data)
 
-    for dtype in [_np.int8, _np.uint8, _np.int32, _np.float16, _np.float32, _np.float64]:
+    for dtype in [np.int8, np.uint8, np.int32, np.float16, np.float32, np.float64, np.bool, np.bool_,
+                  'int8', 'uint8', 'int32', 'float16', 'float32', 'float64', 'bool']:
         for copy in [True, False]:
-            check_astype_equal(dtype, copy, copy is False and mx_data.dtype == dtype)
+            for hybridize in [True, False]:
+                check_astype_equal(dtype, copy, copy is False and mx_data.dtype == dtype, hybridize)
 
 
 @with_seed()
@@ -978,7 +994,8 @@ def test_np_multinomial():
 
 
 @with_seed()
-@unittest.skipUnless(has_tvm_ops(), "Comparison ops are implemented using TVM")
+@unittest.skipUnless(is_op_runnable(), "Comparison ops can only run on either CPU instances, or GPU instances with"
+                                       " compute capability >= 53 if MXNet is built with USE_TVM_OP=ON")
 @use_np
 def test_np_ndarray_boolean_indexing():
     def test_single_bool_index():
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 89fe576d0a0e..0848b0861a76 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -1163,6 +1163,7 @@ def hybrid_forward(self, F, a):
                     axeses.append(tuple(axes))
                     random.shuffle(axes)
                     axeses.append(tuple(axes))
+                    axeses.append([i - len(axes) for i in axes])
                 for axes in axeses:
                     test_trans = TestTranspose(axes)
                     if hybridize:
@@ -1179,10 +1180,15 @@ def hybrid_forward(self, F, a):
                     np_backward = np_transpose_grad(np_out.shape, dtype, axes)
                     assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=1e-3, atol=1e-5, use_broadcast=False)
 
-                    mx_out = np.transpose(x, axes)
-                    np_out = _np.transpose(x.asnumpy(), axes)
+                    mx_out = x.transpose(axes)
+                    np_out = x.asnumpy().transpose(axes)
                     assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
 
+                    if isinstance(axes, (list, tuple)):
+                        mx_out = x.transpose(*axes)
+                        np_out = x.asnumpy().transpose(*axes)
+                        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
+
 
 @with_seed()
 @use_np
@@ -1342,6 +1348,8 @@ def hybrid_forward(self, F, a, *args, **kwargs):
                 y = mx_func(mx_test_data)
             assert y.shape == np_out.shape
             assert_almost_equal(y.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+            if np_out.dtype == np.bool_:
+                assert y.dtype == np.bool_
 
             if ref_grad:
                 y.backward()

From 1d4ede390efd4eb9404571b7e73032d9c326ac63 Mon Sep 17 00:00:00 2001
From: Serge Panev <spanev@nvidia.com>
Date: Mon, 14 Oct 2019 20:06:23 +0200
Subject: [PATCH 438/813] Add mask target generator operator for Mask-RCNN
 (#16268)

* Add mask target generator operator for Mask-RCNN

Signed-off-by: Serge Panev <spanev@nvidia.com>

* Disable the unit test for CPU default ctx

Signed-off-by: Serge Panev <spanev@nvidia.com>

* Address PR comments

Signed-off-by: Serge Panev <spanev@nvidia.com>
---
 src/operator/contrib/mrcnn_target-inl.h | 132 +++++++++++
 src/operator/contrib/mrcnn_target.cu    | 278 ++++++++++++++++++++++++
 tests/python/unittest/test_operator.py  |  57 +++++
 3 files changed, 467 insertions(+)
 create mode 100644 src/operator/contrib/mrcnn_target-inl.h
 create mode 100644 src/operator/contrib/mrcnn_target.cu

diff --git a/src/operator/contrib/mrcnn_target-inl.h b/src/operator/contrib/mrcnn_target-inl.h
new file mode 100644
index 000000000000..a3d8c444086a
--- /dev/null
+++ b/src/operator/contrib/mrcnn_target-inl.h
@@ -0,0 +1,132 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file mrcnn_target-inl.h
+ * \brief Mask-RCNN target generator
+ * \author Serge Panev
+ */
+
+
+#ifndef MXNET_OPERATOR_CONTRIB_MRCNN_TARGET_INL_H_
+#define MXNET_OPERATOR_CONTRIB_MRCNN_TARGET_INL_H_
+
+#include <mxnet/operator.h>
+#include <vector>
+#include "../operator_common.h"
+#include "../mshadow_op.h"
+#include "../tensor/init_op.h"
+
+namespace mxnet {
+namespace op {
+
+namespace mrcnn_index {
+  enum ROIAlignOpInputs {kRoi, kGtMask, kMatches, kClasses};
+  enum ROIAlignOpOutputs {kMask, kMaskClasses};
+}  // namespace mrcnn_index
+
+struct MRCNNTargetParam : public dmlc::Parameter<MRCNNTargetParam> {
+  int num_rois;
+  int num_classes;
+  int mask_size;
+  int sample_ratio;
+
+  DMLC_DECLARE_PARAMETER(MRCNNTargetParam) {
+    DMLC_DECLARE_FIELD(num_rois)
+    .describe("Number of sampled RoIs.");
+    DMLC_DECLARE_FIELD(num_classes)
+    .describe("Number of classes.");
+    DMLC_DECLARE_FIELD(mask_size)
+    .describe("Size of the pooled masks.");
+    DMLC_DECLARE_FIELD(sample_ratio).set_default(2)
+    .describe("Sampling ratio of ROI align. Set to -1 to use adaptative size.");
+  }
+};
+
+inline bool MRCNNTargetShape(const NodeAttrs& attrs,
+                            std::vector<mxnet::TShape>* in_shape,
+                            std::vector<mxnet::TShape>* out_shape) {
+  using namespace mshadow;
+  const MRCNNTargetParam& param = nnvm::get<MRCNNTargetParam>(attrs.parsed);
+
+  CHECK_EQ(in_shape->size(), 4) << "Input:[rois, gt_masks, matches, cls_targets]";
+
+  // (B, N, 4)
+  mxnet::TShape tshape = in_shape->at(mrcnn_index::kRoi);
+  CHECK_EQ(tshape.ndim(), 3) << "rois should be a 2D tensor of shape [batch, rois, 4]";
+  CHECK_EQ(tshape[2], 4) << "rois should be a 2D tensor of shape [batch, rois, 4]";
+  auto batch_size = tshape[0];
+  auto num_rois = tshape[1];
+
+  // (B, M, H, W)
+  tshape = in_shape->at(mrcnn_index::kGtMask);
+  CHECK_EQ(tshape.ndim(), 4) << "gt_masks should be a 4D tensor";
+  CHECK_EQ(tshape[0], batch_size) << " batch size should be the same for all the inputs.";
+
+  // (B, N)
+  tshape = in_shape->at(mrcnn_index::kMatches);
+  CHECK_EQ(tshape.ndim(), 2) << "matches should be a 2D tensor";
+  CHECK_EQ(tshape[0], batch_size) << " batch size should be the same for all the inputs.";
+
+  // (B, N)
+  tshape = in_shape->at(mrcnn_index::kClasses);
+  CHECK_EQ(tshape.ndim(), 2) << "matches should be a 2D tensor";
+  CHECK_EQ(tshape[0], batch_size) << " batch size should be the same for all the inputs.";
+
+  // out: 2 * (B, N, C, MS, MS)
+  auto oshape = Shape5(batch_size, num_rois, param.num_classes, param.mask_size, param.mask_size);
+  out_shape->clear();
+  out_shape->push_back(oshape);
+  out_shape->push_back(oshape);
+  return true;
+}
+
+inline bool MRCNNTargetType(const NodeAttrs& attrs,
+                           std::vector<int>* in_type,
+                           std::vector<int>* out_type) {
+  CHECK_EQ(in_type->size(), 4);
+  int dtype = (*in_type)[1];
+  CHECK_NE(dtype, -1) << "Input must have specified type";
+
+  out_type->clear();
+  out_type->push_back(dtype);
+  out_type->push_back(dtype);
+  return true;
+}
+
+template<typename xpu>
+void MRCNNTargetRun(const MRCNNTargetParam& param, const std::vector<TBlob> &inputs,
+                    const std::vector<TBlob> &outputs, mshadow::Stream<xpu> *s);
+
+template<typename xpu>
+void MRCNNTargetCompute(const nnvm::NodeAttrs& attrs,
+                const OpContext &ctx,
+                const std::vector<TBlob> &inputs,
+                const std::vector<OpReqType> &req,
+                const std::vector<TBlob> &outputs) {
+  auto s = ctx.get_stream<xpu>();
+  const auto& p = dmlc::get<MRCNNTargetParam>(attrs.parsed);
+  MRCNNTargetRun<xpu>(p, inputs, outputs, s);
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_CONTRIB_MRCNN_TARGET_INL_H_
diff --git a/src/operator/contrib/mrcnn_target.cu b/src/operator/contrib/mrcnn_target.cu
new file mode 100644
index 000000000000..d542c9c220f8
--- /dev/null
+++ b/src/operator/contrib/mrcnn_target.cu
@@ -0,0 +1,278 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file mrcnn_target.cu
+ * \brief Mask-RCNN target generator
+ * \author Serge Panev
+ */
+
+#include "./mrcnn_target-inl.h"
+
+namespace mxnet {
+namespace op {
+
+using namespace mshadow::cuda;
+
+// The maximum number of blocks to use in the default kernel call.
+constexpr int MAXIMUM_NUM_BLOCKS = 4096;
+
+inline int CUDA_GET_BLOCKS(const int N) {
+  return std::max(
+      std::min(
+          (N + kMaxThreadsPerBlock - 1) / kMaxThreadsPerBlock,
+          MAXIMUM_NUM_BLOCKS),
+      // Use at least 1 block, since CUDA does not allow empty block.
+      1);
+}
+
+// Kernels
+
+template <typename T>
+__device__ T bilinear_interpolate(
+    const T* in_data,
+    const int height,
+    const int width,
+    T y,
+    T x,
+    const int index /* index for debug only*/) {
+  // deal with cases that inverse elements are out of feature map boundary
+  if (y < -1.0 || y > height || x < -1.0 || x > width) {
+    // empty
+    return 0;
+  }
+
+  if (y <= 0) {
+    y = 0;
+  }
+  if (x <= 0) {
+    x = 0;
+  }
+
+  int y_low = static_cast<int>(y);
+  int x_low = static_cast<int>(x);
+  int y_high;
+  int x_high;
+
+  if (y_low >= height - 1) {
+    y_high = y_low = height - 1;
+    y = (T)y_low;
+  } else {
+    y_high = y_low + 1;
+  }
+
+  if (x_low >= width - 1) {
+    x_high = x_low = width - 1;
+    x = (T)x_low;
+  } else {
+    x_high = x_low + 1;
+  }
+
+  T ly = y - y_low;
+  T lx = x - x_low;
+  T hy = 1. - ly, hx = 1. - lx;
+  // do bilinear interpolation
+  T v1 = in_data[y_low * width + x_low];
+  T v2 = in_data[y_low * width + x_high];
+  T v3 = in_data[y_high * width + x_low];
+  T v4 = in_data[y_high * width + x_high];
+  T w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx;
+
+  T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
+
+  return val;
+}
+
+// Modified version of RoIAlignForwardKernel from Caffe (in roi_align.cu)
+// Main modifications:
+// - We don't need position_sensitive neither spatial_scale from the original RoIAlign kernel.
+// - We replace `channels` by `num_classes` and modify the logic consequently (e.g. offset_in_data
+//   does not use `c` anymore).
+template <typename T>
+__device__ void RoIAlignForward(
+    const T* in_data,  // (B, M, H, W)
+    const T* rois,  // (B, N, 4)
+    const T* matches,  // (B, N)
+    const int num_el,
+    const int num_classes,
+    const int height,
+    const int width,
+    const int pooled_height,
+    const int pooled_width,
+    const int sampling_ratio,
+    const int num_rois,
+    const int num_gtmasks,
+    T* out_data) {  // (B, N, C, H, W)
+  // Update kernel
+  for (size_t index = blockIdx.x * blockDim.x + threadIdx.x;
+       index < num_el;
+       index += blockDim.x * gridDim.x) {
+    // (n, c, ph, pw) is an element in the pooled output
+    int pw = index % pooled_width;
+    int ph = (index / pooled_width) % pooled_height;
+    // int c = (index / pooled_width / pooled_height) % num_classes;
+    int n = (index / pooled_width / pooled_height / num_classes) % num_rois;
+    int batch_idx = (index / pooled_width / pooled_height / num_classes / num_rois);
+
+    int roi_batch_ind = matches[batch_idx * num_rois + n];
+
+    const T* offset_rois = rois + batch_idx * (4 * num_rois) + n * 4;
+    // Do not using rounding; this implementation detail is critical
+    T roi_start_w = offset_rois[0];
+    T roi_start_h = offset_rois[1];
+    T roi_end_w = offset_rois[2];
+    T roi_end_h = offset_rois[3];
+
+    // Force malformed ROIs to be 1x1
+    T roi_width = max(roi_end_w - roi_start_w, (T)1.);
+    T roi_height = max(roi_end_h - roi_start_h, (T)1.);
+    T bin_size_h = static_cast<T>(roi_height) / static_cast<T>(pooled_height);
+    T bin_size_w = static_cast<T>(roi_width) / static_cast<T>(pooled_width);
+
+    const T* offset_in_data =
+        in_data + batch_idx * num_gtmasks * height * width
+        + roi_batch_ind * height * width;
+
+    // We use roi_bin_grid to sample the grid and mimic integral
+    int roi_bin_grid_h = (sampling_ratio > 0)
+        ? sampling_ratio
+        : ceil(roi_height / pooled_height);  // e.g., = 2
+    int roi_bin_grid_w =
+        (sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width);
+
+    // We do average (integral) pooling inside a bin
+    const T count = roi_bin_grid_h * roi_bin_grid_w;  // e.g. = 4
+
+    T output_val = 0.;
+    for (int iy = 0; iy < roi_bin_grid_h; iy++) {  // e.g., iy = 0, 1
+      const T y = roi_start_h + ph * bin_size_h +
+          static_cast<T>(iy + .5f) * bin_size_h /
+              static_cast<T>(roi_bin_grid_h);  // e.g., 0.5, 1.5
+      for (int ix = 0; ix < roi_bin_grid_w; ix++) {
+        const T x = roi_start_w + pw * bin_size_w +
+            static_cast<T>(ix + .5f) * bin_size_w /
+                static_cast<T>(roi_bin_grid_w);
+
+        T val = bilinear_interpolate(
+            offset_in_data, height, width, y, x, index);
+        output_val += val;
+      }
+    }
+    output_val /= count;
+
+    out_data[index] = output_val;
+  }
+}
+
+
+template<typename DType>
+__global__ void MRCNNTargetKernel(const DType *rois,
+                                  const DType *gt_masks,
+                                  const DType *matches,
+                                  const DType *cls_targets,
+                                  DType* sampled_masks,
+                                  DType* mask_cls,
+                                  const int total_out_el,
+                                  int batch_size,
+                                  int num_classes,
+                                  int num_rois,
+                                  int num_gtmasks,
+                                  int gt_height,
+                                  int gt_width,
+                                  int mask_size,
+                                  int sample_ratio) {
+  // computing sampled_masks
+  RoIAlignForward(gt_masks, rois, matches, total_out_el,
+                  num_classes, gt_height, gt_width, mask_size, mask_size,
+                  sample_ratio, num_rois, num_gtmasks, sampled_masks);
+  // computing mask_cls
+  int num_masks = batch_size * num_rois * num_classes;
+  int mask_vol = mask_size * mask_size;
+  for (int mask_idx = blockIdx.x; mask_idx < num_masks; mask_idx += gridDim.x) {
+    int cls_idx = mask_idx % num_classes;
+    int roi_idx = (mask_idx / num_classes) % num_rois;
+    int batch_idx = (mask_idx / num_classes / num_rois);
+
+    DType* mask_cls_out = mask_cls + mask_idx * mask_vol;
+
+    DType cls_target = cls_targets[batch_idx * num_rois + roi_idx];
+    DType out_val = (cls_target == cls_idx);
+    for (int mask_pixel = threadIdx.x; mask_pixel < mask_vol; mask_pixel += blockDim.x) {
+      mask_cls_out[mask_pixel] = out_val;
+    }
+  }
+}
+
+template<>
+void MRCNNTargetRun<gpu>(const MRCNNTargetParam& param, const std::vector<TBlob> &inputs,
+                         const std::vector<TBlob> &outputs, mshadow::Stream<gpu> *s) {
+  const int block_dim_size = kMaxThreadsPerBlock;
+  using namespace mxnet_op;
+  using mshadow::Tensor;
+
+  auto stream = mshadow::Stream<gpu>::GetStream(s);
+
+  MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    auto rois = inputs[mrcnn_index::kRoi].FlatToKD<gpu, 3, DType>(s);
+    auto gt_masks = inputs[mrcnn_index::kGtMask].FlatToKD<gpu, 4, DType>(s);
+    auto matches = inputs[mrcnn_index::kMatches].FlatTo2D<gpu, DType>(s);
+    auto cls_targets = inputs[mrcnn_index::kClasses].FlatTo2D<gpu, DType>(s);
+
+    auto out_masks = outputs[mrcnn_index::kMask].FlatToKD<gpu, 5, DType>(s);
+    auto out_mask_cls = outputs[mrcnn_index::kMaskClasses].FlatToKD<gpu, 5, DType>(s);
+
+    int batch_size = gt_masks.shape_[0];
+    int num_gtmasks = gt_masks.shape_[1];
+    int gt_height = gt_masks.shape_[2];
+    int gt_width = gt_masks.shape_[3];
+
+    int num_el = outputs[mrcnn_index::kMask].Size();
+
+    dim3 dimGrid = dim3(CUDA_GET_BLOCKS(num_el));
+    dim3 dimBlock = dim3(block_dim_size);
+
+    MRCNNTargetKernel<<<dimGrid, dimBlock, 0, stream>>>
+    (rois.dptr_, gt_masks.dptr_, matches.dptr_, cls_targets.dptr_,
+    out_masks.dptr_, out_mask_cls.dptr_,
+    num_el, batch_size, param.num_classes, param.num_rois,
+    num_gtmasks, gt_height, gt_width, param.mask_size, param.sample_ratio);
+    MSHADOW_CUDA_POST_KERNEL_CHECK(MRCNNTargetKernel);
+  });
+}
+
+DMLC_REGISTER_PARAMETER(MRCNNTargetParam);
+
+NNVM_REGISTER_OP(mrcnn_target)
+.describe("Generate mask targets for Mask-RCNN.")
+.set_num_inputs(4)
+.set_num_outputs(2)
+.set_attr_parser(ParamParser<MRCNNTargetParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", MRCNNTargetShape)
+.set_attr<nnvm::FInferType>("FInferType", MRCNNTargetType)
+.set_attr<FCompute>("FCompute<gpu>", MRCNNTargetCompute<gpu>)
+.add_argument("rois", "NDArray-or-Symbol", "Bounding box coordinates, a 3D array")
+.add_argument("gt_masks", "NDArray-or-Symbol", "Input masks of full image size, a 4D array")
+.add_argument("matches", "NDArray-or-Symbol", "Index to a gt_mask, a 2D array")
+.add_argument("cls_targets", "NDArray-or-Symbol",
+              "Value [0, num_class), excluding background class, a 2D array")
+.add_arguments(MRCNNTargetParam::__FIELDS__());
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 5f40bf12674a..74cc4203df1d 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -8686,6 +8686,63 @@ def test_rroi_align_value(sampling_ratio=-1):
     test_rroi_align_value()
     test_rroi_align_value(sampling_ratio=2)
 
+@with_seed()
+def test_op_mrcnn_target():
+    if default_context().device_type != 'gpu':
+        return
+
+    num_rois = 2
+    num_classes = 4
+    mask_size = 3
+    ctx = mx.gpu(0)
+    # (B, N, 4)
+    rois = mx.nd.array([[[2.3, 4.3, 2.2, 3.3],
+                        [3.5, 5.5, 0.9, 2.4]]], ctx=ctx)
+    gt_masks = mx.nd.arange(0, 4*32*32, ctx=ctx).reshape(1, 4, 32, 32)
+
+    # (B, N)
+    matches = mx.nd.array([[2, 0]], ctx=ctx)
+    # (B, N)
+    cls_targets = mx.nd.array([[2, 1]], ctx=ctx)
+
+    mask_targets, mask_cls = mx.nd.mrcnn_target(rois, gt_masks, matches, cls_targets,
+                                                num_rois=num_rois,
+                                                num_classes=num_classes,
+                                                mask_size=mask_size)
+
+    # Ground truth outputs were generated with GluonCV's target generator
+    # gluoncv.model_zoo.mask_rcnn.MaskTargetGenerator(1, num_rois, num_classes, mask_size)
+    gt_mask_targets = mx.nd.array([[[[[2193.4    , 2193.7332 , 2194.0667 ],
+                                      [2204.0667 , 2204.4    , 2204.7334 ],
+                                      [2214.7334 , 2215.0667 , 2215.4    ]],
+                                     [[2193.4    , 2193.7332 , 2194.0667 ],
+                                      [2204.0667 , 2204.4    , 2204.7334 ],
+                                      [2214.7334 , 2215.0667 , 2215.4    ]],
+                                     [[2193.4    , 2193.7332 , 2194.0667 ],
+                                      [2204.0667 , 2204.4    , 2204.7334 ],
+                                      [2214.7334 , 2215.0667 , 2215.4    ]],
+                                     [[2193.4    , 2193.7332 , 2194.0667 ],
+                                      [2204.0667 , 2204.4    , 2204.7334 ],
+                                      [2214.7334 , 2215.0667 , 2215.4    ]]],
+                                    [[[ 185.     ,  185.33334,  185.66667],
+                                      [ 195.66667,  196.00002,  196.33334],
+                                      [ 206.33333,  206.66666,  207.     ]],
+                                     [[ 185.     ,  185.33334,  185.66667],
+                                      [ 195.66667,  196.00002,  196.33334],
+                                      [ 206.33333,  206.66666,  207.     ]],
+                                     [[ 185.     ,  185.33334,  185.66667],
+                                      [ 195.66667,  196.00002,  196.33334],
+                                      [ 206.33333,  206.66666,  207.     ]],
+                                     [[ 185.     ,  185.33334,  185.66667],
+                                      [ 195.66667,  196.00002,  196.33334],
+                                      [  206.33333,  206.66666,  207.     ]]]]])
+
+    gt_mask_cls = mx.nd.array([[0,0,1,0], [0,1,0,0]])
+    gt_mask_cls = gt_mask_cls.reshape(1,2,4,1,1).broadcast_axes(axis=(3,4), size=(3,3))
+
+    assert_almost_equal(mask_targets.asnumpy(), gt_mask_targets.asnumpy())
+    assert_almost_equal(mask_cls.asnumpy(), gt_mask_cls.asnumpy())
+
 @with_seed()
 def test_diag():
 

From 88202206a4558dd2b793177b76fa67b1f16b3234 Mon Sep 17 00:00:00 2001
From: perdasilva <perdasilva@gmail.com>
Date: Mon, 14 Oct 2019 21:30:47 +0200
Subject: [PATCH 439/813] Adds pip requirements file to nightly gpu ci image
 (#16472)

---
 ci/docker/Dockerfile.build.ubuntu_nightly_gpu | 1 +
 1 file changed, 1 insertion(+)

diff --git a/ci/docker/Dockerfile.build.ubuntu_nightly_gpu b/ci/docker/Dockerfile.build.ubuntu_nightly_gpu
index ec811e5202ab..7d051b1bd04a 100644
--- a/ci/docker/Dockerfile.build.ubuntu_nightly_gpu
+++ b/ci/docker/Dockerfile.build.ubuntu_nightly_gpu
@@ -29,6 +29,7 @@ COPY install/deb_ubuntu_ccache.sh /work/
 RUN /work/deb_ubuntu_ccache.sh
 
 COPY install/ubuntu_python.sh /work/
+COPY install/requirements /work/
 RUN /work/ubuntu_python.sh
 
 COPY install/ubuntu_scala.sh /work/

From 12569767896d38fca24ccb8b7164c9cfa2b64fbc Mon Sep 17 00:00:00 2001
From: Manu Seth <22492939+mseth10@users.noreply.github.com>
Date: Mon, 14 Oct 2019 12:35:38 -0700
Subject: [PATCH 440/813] Fix Nightly Tests for Binaries (#16451)

* copy missing requirements file

* not treating numpy warnings as errors

* updating six package version

* Revert "updating six package version"

This reverts commit 02433c5d43aace7927382f7bb6f5566720848206.
---
 tests/utils/notebook_test/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/utils/notebook_test/__init__.py b/tests/utils/notebook_test/__init__.py
index 0a794cd13619..3cbc8211b17f 100644
--- a/tests/utils/notebook_test/__init__.py
+++ b/tests/utils/notebook_test/__init__.py
@@ -111,7 +111,7 @@ def run_notebook(notebook, notebook_dir, kernel=None, no_cache=False, temp_dir='
             nbformat.write(notebook, output_file)
             output_nb = io.open(output_file, mode='r', encoding='utf-8')
             for line in output_nb:
-                if "Warning:" in line:
+                if "Warning:" in line and "numpy operator signatures" not in line:
                     errors.append("Warning:\n" + line)
         if len(errors) > 0:
             logging.error('\n'.join(errors))

From 812e50417a4352793140d3a20226b62d6ca7f6cb Mon Sep 17 00:00:00 2001
From: Thomas Delteil <thomas.delteil1@gmail.com>
Date: Mon, 14 Oct 2019 15:35:56 -0700
Subject: [PATCH 441/813] fix autodoc for spurrious toggles (#16452)

---
 docs/python_docs/_static/autodoc.js | 34 +++++++++++++++--------------
 1 file changed, 18 insertions(+), 16 deletions(-)

diff --git a/docs/python_docs/_static/autodoc.js b/docs/python_docs/_static/autodoc.js
index 0825f54ea143..5b536ddcf6ae 100644
--- a/docs/python_docs/_static/autodoc.js
+++ b/docs/python_docs/_static/autodoc.js
@@ -20,30 +20,32 @@
 /* Customizations to the Sphinx auto module plugin output */
 function auto_index() {
     var targets = $("dl.class>dt,dl.function>dt");
-
     var li_node = $("li.current>span>a.current.reference.internal").parent().parent();
     var html = "<ul id='autodoc'>";
     if (li_node.length > 0) {
-        for (var i = 0; i < targets.length; ++i) {
-            var id = $(targets[i]).attr('id');
-            if (id) {
-                var paths = id.split('.')
-                if (paths.length >= 2) {
-                    var id_simple = paths.pop();
-                    id_simple = paths.pop() + "." + id_simple;
-                } else {
-                    var id_simple = id;
+        if (targets.length > 0) {
+            for (var i = 0; i < targets.length; ++i) {
+                var id = $(targets[i]).attr('id');
+                if (id) {
+                    var paths = id.split('.')
+                    if (paths.length >= 2) {
+                        var id_simple = paths.pop();
+                        id_simple = paths.pop() + "." + id_simple;
+                    } else {
+                        var id_simple = id;
+                    }
+                    html += "<li><span class='link-wrapper'><a class='reference internal' href='#";
+                    html += id;
+                    html += "'>" + id_simple + "</a></span</li>";
                 }
-                html += "<li><span class='link-wrapper'><a class='reference internal' href='#";
-                html += id;
-                html += "'>" + id_simple + "</a></span</li>";
             }
+            html += "</ul>";
+            li_node.append(html);
+            li_node.prepend("<a><span id='autodoc_toggle' onclick='$(\"#autodoc\").toggle()'>[toggle]</span></a>")
         }
-        html += "</ul>";
-        li_node.append(html);
-        li_node.prepend("<a><span id='autodoc_toggle' onclick='$(\"#autodoc\").toggle()'>[toggle]</span></a>")
     } else {
         setTimeout(auto_index, 500);
     }
+
 }
 $(document).ready(auto_index);
\ No newline at end of file

From 7c0000e4194204fbea1d10e6294fd06037bc3d42 Mon Sep 17 00:00:00 2001
From: Xi Wang <xidulu@gmail.com>
Date: Tue, 15 Oct 2019 13:09:33 +0800
Subject: [PATCH 442/813] Fix dtype bug (#16467)

* fix dtype bug

* override dtype property; add tests

* fix display bug
---
 python/mxnet/numpy/multiarray.py            | 22 +++++++++++++++++++-
 tests/python/unittest/test_numpy_ndarray.py | 23 +++++++++++++++++++++
 2 files changed, 44 insertions(+), 1 deletion(-)

diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 5ee52f14bb16..4fff4421e5a5 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -706,7 +706,7 @@ def __repr__(self):
             if dtype == _np.float32:
                 array_str = array_str[:array_str.rindex(',')] + ')'
         elif dtype not in (_np.float32, _np.bool_):
-            array_str = array_str[:-1] + ', dtype={})'.format(dtype.__name__)
+            array_str = array_str[:-1] + ', dtype={})'.format(dtype)
 
         context = self.context
         if context.device_type == 'cpu':
@@ -1680,6 +1680,26 @@ def size(self):
         """Number of elements in the array."""
         return super(ndarray, self).size
 
+    @property
+    def dtype(self):
+        """Data-type of the array's elements.
+
+        Returns
+        -------
+        numpy.dtype
+            This NDArray's data type.
+
+        Examples
+        --------
+        >>> x = np.zeros((2,3))
+        >>> x.dtype
+        dtype('float32')
+        >>> y = np.zeros((2,3), dtype='int32')
+        >>> y.dtype
+        dtype('int32')
+        """
+        return _np.dtype(super(ndarray, self).dtype)
+
     def tostype(self, stype):
         raise AttributeError('mxnet.numpy.ndarray object has no attribute tostype')
 
diff --git a/tests/python/unittest/test_numpy_ndarray.py b/tests/python/unittest/test_numpy_ndarray.py
index 53a8076f1303..b6692c009c18 100644
--- a/tests/python/unittest/test_numpy_ndarray.py
+++ b/tests/python/unittest/test_numpy_ndarray.py
@@ -1075,6 +1075,29 @@ def test_boolean_indexing_autograd():
     test_boolean_indexing_autograd()
 
 
+@with_seed()
+@use_np
+def test_np_get_dtype():
+    dtypes = [_np.int8, _np.int32, _np.float16, _np.float32, _np.float64, _np.bool, _np.bool_,
+              'int8', 'int32', 'float16', 'float32', 'float64', 'bool', None]
+    objects = [
+        [],
+        (),
+        [[1, 2], [3, 4]],
+        _np.random.uniform(size=rand_shape_nd(3)),
+        _np.random.uniform(size=(3, 0, 4))
+    ]
+    for dtype in dtypes:
+        for src in objects:
+            mx_arr = np.array(src, dtype=dtype)
+            assert mx_arr.ctx == mx.current_context()
+            if isinstance(src, mx.nd.NDArray):
+                np_arr = _np.array(src.asnumpy(), dtype=dtype if dtype is not None else _np.float32)
+            else:
+                np_arr = _np.array(src, dtype=dtype if dtype is not None else _np.float32)
+            assert type(mx_arr.dtype) == type(np_arr.dtype)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 9ab428e0bcb126dea1003b72965dd98592f0a39d Mon Sep 17 00:00:00 2001
From: Tao Lv <tao.a.lv@intel.com>
Date: Tue, 15 Oct 2019 13:36:31 +0800
Subject: [PATCH 443/813] [Doc] Update the download page with 1.5.1 release
 (#16442)

* update download page

* update get_started page
---
 docs/static_site/src/_includes/get_started/get_started.html | 6 +++---
 docs/static_site/src/_includes/get_started/pip_snippet.md   | 2 +-
 docs/static_site/src/pages/get_started/download.md          | 1 +
 3 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/docs/static_site/src/_includes/get_started/get_started.html b/docs/static_site/src/_includes/get_started/get_started.html
index 614b4a409773..4905d28ce2d3 100644
--- a/docs/static_site/src/_includes/get_started/get_started.html
+++ b/docs/static_site/src/_includes/get_started/get_started.html
@@ -1,7 +1,7 @@
 <script>
     /** Defaults **/
     /** See options.js for the full ugly script **/
-    var versionSelect = defaultVersion = 'v1.5.0';
+    var versionSelect = defaultVersion = 'v1.5.1';
     var platformSelect = 'linux';
     var languageSelect = 'python';
     var processorSelect = 'cpu';
@@ -21,10 +21,10 @@ <h2>Installing MXNet</h2>
             </div>
             <div class="col-9 install-right">
                 <div class="dropdown">
-                    <button class="current-version dropbtn btn" type="button" data-toggle="dropdown">v1.5.0</button>
+                    <button class="current-version dropbtn btn" type="button" data-toggle="dropdown">v1.5.1</button>
                     <ul class="opt-group dropdown-content">
                         <li class="opt versions"><a href="#">master</a></li>
-                        <li class="opt active versions"><a href="#">v1.5.0</a></li>
+                        <li class="opt active versions"><a href="#">v1.5.1</a></li>
                         <li class="opt versions"><a href="#">v1.4.1</a></li>
                         <li class="opt versions"><a href="#">v1.3.1</a></li>
                         <li class="opt versions"><a href="#">v1.2.1</a></li>
diff --git a/docs/static_site/src/_includes/get_started/pip_snippet.md b/docs/static_site/src/_includes/get_started/pip_snippet.md
index 0ca854005ffd..f5cc4ea12803 100644
--- a/docs/static_site/src/_includes/get_started/pip_snippet.md
+++ b/docs/static_site/src/_includes/get_started/pip_snippet.md
@@ -3,7 +3,7 @@ Check the chart below for other options, refer to <a href="https://pypi.org/proj
 other MXNet pip packages</a>, or <a href="get_started/validate_mxnet.html">validate your MXNet installation</a>.
 
 <div style="text-align: center">
-    <img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/install/pip-packages.png"
+    <img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/install/pip-packages-1.5.1.png"
     alt="pip packages"/>
 </div>
 
diff --git a/docs/static_site/src/pages/get_started/download.md b/docs/static_site/src/pages/get_started/download.md
index 7d0176aef39c..b8f2e00024d4 100644
--- a/docs/static_site/src/pages/get_started/download.md
+++ b/docs/static_site/src/pages/get_started/download.md
@@ -29,6 +29,7 @@ These source archives are generated from tagged releases. Updates and patches wi
 
 | Version | Source                                                                                                      | PGP                                                                                                             | SHA                                                                                                                |
 |---------|-------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------|
+| 1.5.1   | [Download](https://apache.org/dist/incubator/mxnet/1.5.1/apache-mxnet-src-1.5.1-incubating.tar.gz)                 | [Download](https://apache.org/dist/incubator/mxnet/1.5.1/apache-mxnet-src-1.5.1-incubating.tar.gz.asc)    |  [Download](https://apache.org/dist/incubator/mxnet/1.5.1/apache-mxnet-src-1.5.1-incubating.tar.gz.sha512)     |
 | 1.5.0   | [Download](https://apache.org/dist/incubator/mxnet/1.5.0/apache-mxnet-src-1.5.0-incubating.tar.gz)                 | [Download](https://apache.org/dist/incubator/mxnet/1.5.0/apache-mxnet-src-1.5.0-incubating.tar.gz.asc)    |  [Download](https://apache.org/dist/incubator/mxnet/1.5.0/apache-mxnet-src-1.5.0-incubating.tar.gz.sha512)     |
 | 1.4.1   | [Download](https://www.apache.org/dyn/closer.cgi/incubator/mxnet/1.4.1/apache-mxnet-src-1.4.1-incubating.tar.gz)   | [Download](https://apache.org/dist/incubator/mxnet/1.4.1/apache-mxnet-src-1.4.1-incubating.tar.gz.asc)    | [Download](https://apache.org/dist/incubator/mxnet/1.4.1/apache-mxnet-src-1.4.1-incubating.tar.gz.sha512)      |
 | 1.4.0   | [Download](https://www.apache.org/dyn/closer.cgi/incubator/mxnet/1.4.0/apache-mxnet-src-1.4.0-incubating.tar.gz)   | [Download](https://apache.org/dist/incubator/mxnet/1.4.0/apache-mxnet-src-1.4.0-incubating.tar.gz.asc)    | [Download](https://apache.org/dist/incubator/mxnet/1.4.0/apache-mxnet-src-1.4.0-incubating.tar.gz.sha512)      |

From 6e0b1a5926476feeac9d52928ca2cd4121adbc5f Mon Sep 17 00:00:00 2001
From: Mike <maowenxu@connect.hku.hk>
Date: Fri, 11 Oct 2019 17:51:00 -0400
Subject: [PATCH 444/813] [Numpy] Numpy compatible dstack (#15871)

* Add dstack that pass CPU test

Rgister dstack on GPU

Minor comment fix

Minor syntax fix

Syntax fix according to comments

header fix

* Fix sanity
---
 python/mxnet/ndarray/numpy/_op.py      |  46 +++++++++++-
 python/mxnet/numpy/multiarray.py       |  48 +++++++++++-
 python/mxnet/symbol/numpy/_symbol.py   |  33 +++++++-
 src/operator/nn/concat-inl.h           |  62 +++++++++++++++
 src/operator/numpy/np_matrix_op.cc     | 100 ++++++++++++++++++++++++-
 src/operator/numpy/np_matrix_op.cu     |   7 ++
 tests/python/unittest/test_numpy_op.py |  61 +++++++++++++++
 7 files changed, 350 insertions(+), 7 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index e3cf09703533..b82e6f1be73a 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -32,8 +32,8 @@
            'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2',
            'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot', 'histogram',
-           'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'mean',
-           'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
+           'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'dstack',
+           'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
            'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'hypot', 'rad2deg', 'deg2rad',
            'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer',
            'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal']
@@ -2467,6 +2467,48 @@ def get_list(arrays):
     return _npi.vstack(*arrays)
 
 
+@set_module('mxnet.ndarray.numpy')
+def dstack(arrays):
+    """
+    Stack arrays in sequence depth wise (along third axis).
+    This is equivalent to concatenation along the third axis after 2-D arrays
+    of shape `(M,N)` have been reshaped to `(M,N,1)` and 1-D arrays of shape
+    `(N,)` have been reshaped to `(1,N,1)`. Rebuilds arrays divided by
+    `dsplit`.
+    This function makes most sense for arrays with up to 3 dimensions. For
+    instance, for pixel-data with a height (first axis), width (second axis),
+    and r/g/b channels (third axis). The functions `concatenate`, `stack` and
+    `block` provide more general stacking and concatenation operations.
+
+    Parameters
+    ----------
+    tup : sequence of arrays
+        The arrays must have the same shape along all but the third axis.
+        1-D or 2-D arrays must have the same shape.
+
+    Returns
+    -------
+    stacked : ndarray
+        The array formed by stacking the given arrays, will be at least 3-D.
+
+    Examples
+    --------
+    >>> a = np.array((1,2,3))
+    >>> b = np.array((2,3,4))
+    >>> np.dstack((a,b))
+    array([[[1, 2],
+            [2, 3],
+            [3, 4]]])
+    >>> a = np.array([[1],[2],[3]])
+    >>> b = np.array([[2],[3],[4]])
+    >>> np.dstack((a,b))
+    array([[[1, 2]],
+           [[2, 3]],
+           [[3, 4]]])
+    """
+    return _npi.dstack(*arrays)
+
+
 @set_module('mxnet.ndarray.numpy')
 def maximum(x1, x2, out=None):
     """Returns element-wise maximum of the input arrays with broadcasting.
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 4fff4421e5a5..3e2eeb2fb1a1 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -51,8 +51,8 @@
            'degrees', 'log2', 'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative',
            'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh',
            'tensordot', 'histogram', 'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate',
-           'stack', 'vstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices',
-           'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'arctan2', 'hypot',
+           'stack', 'vstack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var',
+           'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'arctan2', 'hypot',
            'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer',
            'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal']
 
@@ -4039,6 +4039,50 @@ def vstack(arrays, out=None):
     return _mx_nd_np.vstack(arrays)
 
 
+@set_module('mxnet.numpy')
+def dstack(arrays):
+    """
+    Stack arrays in sequence depth wise (along third axis).
+
+    This is equivalent to concatenation along the third axis after 2-D arrays
+    of shape `(M,N)` have been reshaped to `(M,N,1)` and 1-D arrays of shape
+    `(N,)` have been reshaped to `(1,N,1)`. Rebuilds arrays divided by
+    `dsplit`.
+
+    This function makes most sense for arrays with up to 3 dimensions. For
+    instance, for pixel-data with a height (first axis), width (second axis),
+    and r/g/b channels (third axis). The functions `concatenate`, `stack` and
+    `block` provide more general stacking and concatenation operations.
+
+    Parameters
+    ----------
+    tup : sequence of arrays
+        The arrays must have the same shape along all but the third axis.
+        1-D or 2-D arrays must have the same shape.
+
+    Returns
+    -------
+    stacked : ndarray
+        The array formed by stacking the given arrays, will be at least 3-D.
+
+    Examples
+    --------
+    >>> a = np.array((1,2,3))
+    >>> b = np.array((2,3,4))
+    >>> np.dstack((a,b))
+    array([[[1, 2],
+            [2, 3],
+            [3, 4]]])
+    >>> a = np.array([[1],[2],[3]])
+    >>> b = np.array([[2],[3],[4]])
+    >>> np.dstack((a,b))
+    array([[[1, 2]],
+           [[2, 3]],
+           [[3, 4]]])
+    """
+    return _npi.dstack(*arrays)
+
+
 @set_module('mxnet.numpy')
 def maximum(x1, x2, out=None):
     """Returns element-wise maximum of the input arrays with broadcasting.
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 338c70d1e53d..cc2f906f6b97 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -34,8 +34,8 @@
            'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2', 'log1p',
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot', 'histogram',
-           'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'mean',
-           'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
+           'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'dstack',
+           'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
            'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'hypot', 'rad2deg', 'deg2rad',
            'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer',
            'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal']
@@ -2689,6 +2689,35 @@ def get_list(arrays):
     return _npi.vstack(*arrays)
 
 
+@set_module('mxnet.symbol.numpy')
+def dstack(arrays):
+    """
+    Stack arrays in sequence depth wise (along third axis).
+
+    This is equivalent to concatenation along the third axis after 2-D arrays
+    of shape `(M,N)` have been reshaped to `(M,N,1)` and 1-D arrays of shape
+    `(N,)` have been reshaped to `(1,N,1)`. Rebuilds arrays divided by
+    `dsplit`.
+
+    This function makes most sense for arrays with up to 3 dimensions. For
+    instance, for pixel-data with a height (first axis), width (second axis),
+    and r/g/b channels (third axis). The functions `concatenate`, `stack` and
+    `block` provide more general stacking and concatenation operations.
+
+    Parameters
+    ----------
+    tup : sequence of _Symbol
+        The arrays must have the same shape along all but the first axis.
+        1-D arrays must have the same length.
+
+    Returns
+    -------
+    stacked : _Symbol
+        The array formed by stacking the given arrays, will be at least 2-D.
+    """
+    return _npi.dstack(*arrays)
+
+
 @set_module('mxnet.symbol.numpy')
 def maximum(x1, x2, out=None):
     return _ufunc_helper(x1, x2, _npi.maximum, _np.maximum, _npi.maximum_scalar, None, out)
diff --git a/src/operator/nn/concat-inl.h b/src/operator/nn/concat-inl.h
index 7a58ae6f0ccc..1fb20ac65473 100644
--- a/src/operator/nn/concat-inl.h
+++ b/src/operator/nn/concat-inl.h
@@ -141,6 +141,37 @@ void ConcatCompute(const nnvm::NodeAttrs& attrs, const OpContext& ctx,
   });
 }
 
+template<typename xpu>
+void DStackCompute(const nnvm::NodeAttrs& attrs, const OpContext& ctx,
+                   const std::vector<TBlob>& inputs,
+                   const std::vector<OpReqType>& req,
+                   const std::vector<TBlob>& outputs) {
+  ConcatParam param = nnvm::get<ConcatParam>(attrs.parsed);
+  param.dim = 2;
+  std::vector<TBlob> modified_inputs(inputs.size());
+  for (int i = 0; i < param.num_args; ++i) {
+    if (inputs[i].shape_.ndim() == 0) {
+      modified_inputs[i] = inputs[i].reshape(TShape(3, 1));
+    } else if (inputs[i].shape_.ndim() == 1) {
+      TShape t = TShape(3, 1);
+      t[1] = inputs[i].shape_[0];
+      modified_inputs[i] = inputs[i].reshape(t);
+    } else if (inputs[i].shape_.ndim() == 2) {
+      TShape t = TShape(3, 1);
+      t[0] = inputs[i].shape_[0];
+      t[1] = inputs[i].shape_[1];
+      modified_inputs[i] = inputs[i].reshape(t);
+    } else {
+      modified_inputs[i] = inputs[i];
+    }
+  }
+  MSHADOW_TYPE_SWITCH(inputs[concat_enum::kData0].type_flag_, DType, {
+    ConcatOp<xpu, DType> op;
+    op.Init(param);
+    op.Forward(ctx, modified_inputs, req, outputs);
+  });
+}
+
 template<typename xpu>
 void ConcatGradCompute(const nnvm::NodeAttrs& attrs, const OpContext& ctx,
                        const std::vector<TBlob>& inputs,
@@ -154,6 +185,37 @@ void ConcatGradCompute(const nnvm::NodeAttrs& attrs, const OpContext& ctx,
   });
 }
 
+template<typename xpu>
+void DStackGradCompute(const nnvm::NodeAttrs& attrs, const OpContext& ctx,
+                       const std::vector<TBlob>& inputs,
+                       const std::vector<OpReqType>& req,
+                       const std::vector<TBlob>& outputs) {
+  ConcatParam param = nnvm::get<ConcatParam>(attrs.parsed);
+  param.dim = 2;
+  std::vector<TBlob> modified_outputs(outputs.size());
+  for (int i = 0; i < param.num_args; ++i) {
+    if (outputs[i].shape_.ndim() == 0) {
+      modified_outputs[i] = outputs[i].reshape(TShape(3, 1));
+    } else if (outputs[i].shape_.ndim() == 1) {
+      TShape t  = TShape(3, 1);
+      t[1] = outputs[i].shape_[0];
+      modified_outputs[i] = outputs[i].reshape(t);
+    } else if (outputs[i].shape_.ndim() == 2) {
+      TShape t  = TShape(3, 1);
+      t[0] = outputs[i].shape_[0];
+      t[1] = outputs[i].shape_[1];
+      modified_outputs[i] = outputs[i].reshape(t);
+    } else {
+      modified_outputs[i] = outputs[i];
+    }
+  }
+  MSHADOW_TYPE_SWITCH(inputs[concat_enum::kOut].type_flag_, DType, {
+    ConcatOp<xpu, DType> op;
+    op.Init(param);
+    op.Backward(ctx, inputs[concat_enum::kOut], req, modified_outputs);
+  });
+}
+
 /*!
  * \brief concat CSRNDArray on the first dimension.
  */
diff --git a/src/operator/numpy/np_matrix_op.cc b/src/operator/numpy/np_matrix_op.cc
index 83f0b1aae9b0..e4fcfffe2334 100644
--- a/src/operator/numpy/np_matrix_op.cc
+++ b/src/operator/numpy/np_matrix_op.cc
@@ -292,6 +292,67 @@ bool ConcatShape(const nnvm::NodeAttrs& attrs,
                  mxnet::ShapeVector *in_shape,
                  mxnet::ShapeVector *out_shape);
 
+bool DStackShape(const nnvm::NodeAttrs& attrs,
+                 mxnet::ShapeVector *in_shape,
+                 mxnet::ShapeVector *out_shape) {
+  using namespace mshadow;
+  ConcatParam param_ = nnvm::get<ConcatParam>(attrs.parsed);
+  CHECK_EQ(in_shape->size(), static_cast<size_t>(param_.num_args));
+  mxnet::TShape dshape;
+  dim_t size = 0;
+  bool has_unknown_dim_size = false;
+  int axis = 2;
+  param_.dim = axis;
+  for (int i = 0; i < param_.num_args; ++i) {
+    if ((*in_shape)[i].ndim() == 0) {
+      (*in_shape)[i] = mxnet::TShape(3, 1);
+    } else if ((*in_shape)[i].ndim() == 1) {
+      mxnet::TShape t = mxnet::TShape(3, 1);
+      t[1] = (*in_shape)[i][0];
+      (*in_shape)[i] = t;
+    } else if ((*in_shape)[i].ndim() == 2) {
+      mxnet::TShape t = mxnet::TShape(3, 1);
+      t[0] = (*in_shape)[i][0];
+      t[1] = (*in_shape)[i][1];
+      (*in_shape)[i] = t;
+    }
+    mxnet::TShape &tmp = (*in_shape)[i];
+    if (tmp.ndim() > 0) {
+      CheckAxis(axis, tmp.ndim());
+      if (!mxnet::dim_size_is_known(tmp, axis)) {
+        has_unknown_dim_size = true;
+      } else {
+        size += tmp[axis];
+      }
+      tmp[axis] = -1;
+      shape_assign(&dshape, tmp);
+    }
+  }
+
+  mxnet::TShape tmp = (*out_shape)[0];
+  if (tmp.ndim() > 0) {
+    axis = CheckAxis(param_.dim, tmp.ndim());
+    tmp[axis] = -1;
+    shape_assign(&dshape, tmp);
+  }
+
+  if (dshape.ndim() == -1) return false;
+  CHECK_NE(dshape.ndim(), 0) << "zero-dimensional arrays cannot be concatenated";
+
+  for (int i = 0; i < param_.num_args; ++i) {
+    CHECK(shape_assign(&(*in_shape)[i], dshape))
+      << "Incompatible input shape: expected " << dshape << ", got " << (*in_shape)[i];
+  }
+
+  if (!has_unknown_dim_size) {
+    dshape[axis] = size;
+  }
+  CHECK(shape_assign(&(*out_shape)[0], dshape))
+    << "Incompatible output shape: expected " << dshape << ", got " << (*out_shape)[0];
+
+  return shape_is_known(dshape);
+}
+
 bool ConcatType(const nnvm::NodeAttrs& attrs,
                 std::vector<int> *in_type,
                 std::vector<int> *out_type);
@@ -306,7 +367,6 @@ struct NumpyConcatGrad {
   }
 };
 
-
 NNVM_REGISTER_OP(_npi_concatenate)
 .describe(R"code(Join a sequence of arrays along an existing axis.)code" ADD_FILELINE)
 .set_num_inputs([](const NodeAttrs& attrs) {
@@ -527,6 +587,44 @@ NNVM_REGISTER_OP(_backward_np_vstack)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
 .set_attr<FCompute>("FCompute<cpu>", NumpyVstackBackward<cpu>);
 
+NNVM_REGISTER_OP(_npi_dstack)
+.describe(R"code(Stack tensors in sequence depthwise (in third dimension))code" ADD_FILELINE)
+.set_num_inputs([](const NodeAttrs& attrs) {
+  const ConcatParam& params = nnvm::get<ConcatParam>(attrs.parsed);
+  return params.num_args;
+})
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<ConcatParam>)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    const ConcatParam& params = nnvm::get<ConcatParam>(attrs.parsed);
+    std::vector<std::string> ret;
+    for (int i = 0; i < params.num_args; ++i) {
+      ret.push_back(std::string("data") + std::to_string(i));
+    }
+    return ret;
+})
+.set_attr<nnvm::FListOutputNames>("FListOutputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"out"};
+})
+.set_attr<std::string>("key_var_num_args", "num_args")
+.set_attr<nnvm::FInferType>("FInferType", ConcatType)
+.set_attr<mxnet::FInferShape>("FInferShape", DStackShape)
+.set_attr<FCompute>("FCompute<cpu>", DStackCompute<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", NumpyConcatGrad{"_backward_np_dstack"})
+.add_argument("data", "NDArray-or-Symbol[]", "List of arrays to concatenate")
+.add_arguments(ConcatParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_backward_np_dstack)
+.set_num_outputs([](const NodeAttrs& attrs) {
+  const ConcatParam& params = nnvm::get<ConcatParam>(attrs.parsed);
+  return params.num_args;
+})
+.set_attr_parser(ParamParser<ConcatParam>)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FCompute>("FCompute<cpu>", DStackGradCompute<cpu>);
+
 inline bool NumpyRollShape(const nnvm::NodeAttrs& attrs,
                            mxnet::ShapeVector *in_attrs,
                            mxnet::ShapeVector *out_attrs) {
diff --git a/src/operator/numpy/np_matrix_op.cu b/src/operator/numpy/np_matrix_op.cu
index caab4108b40e..125cd91acd1f 100644
--- a/src/operator/numpy/np_matrix_op.cu
+++ b/src/operator/numpy/np_matrix_op.cu
@@ -53,6 +53,12 @@ NNVM_REGISTER_OP(_npi_vstack)
 NNVM_REGISTER_OP(_backward_np_vstack)
 .set_attr<FCompute>("FCompute<gpu>", NumpyVstackBackward<gpu>);
 
+NNVM_REGISTER_OP(_npi_dstack)
+.set_attr<FCompute>("FCompute<gpu>", DStackCompute<gpu>);
+
+NNVM_REGISTER_OP(_backward_np_dstack)
+.set_attr<FCompute>("FCompute<gpu>", DStackGradCompute<gpu>);
+
 NNVM_REGISTER_OP(_np_roll)
 .set_attr<FCompute>("FCompute<gpu>", NumpyRollCompute<gpu>);
 
@@ -90,5 +96,6 @@ NNVM_REGISTER_OP(_npi_flip)
 
 NNVM_REGISTER_OP(_backward_npi_flip)
 .set_attr<FCompute>("FCompute<gpu>", NumpyFlipForward<gpu>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 0848b0861a76..30f2dfc1c0ec 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -1693,6 +1693,67 @@ def hybrid_forward(self, F, a, *args):
                 assert same(mx_out.asnumpy(), np_out)
 
 
+@with_seed()
+@use_np
+def test_np_dstack():
+    class TestDStack(HybridBlock):
+        def __init__(self):
+            super(TestDStack, self).__init__()
+
+        def hybrid_forward(self, F, a, *args):
+            return F.np.dstack([a] + list(args))
+
+    def get_new_shape(shape):
+        if len(shape) < 3:
+            return shape
+        axis = 2
+        shape_lst = list(shape)
+        shape_lst[axis] = random.randint(0, 5)
+        return tuple(shape_lst)
+
+    shapes = [
+        (),
+        (1,),
+        (2,1),
+        (2,2,4),
+        (2,0,0),
+        (0,1,3),
+        (2,0,3),
+        (2,3,4,5)
+    ]
+    for hybridize in [True, False]:
+        for shape in shapes:
+            test_dstack = TestDStack()
+            if hybridize:
+                test_dstack.hybridize()
+            # test symbolic forward
+            a = mx.nd.random.uniform(shape=get_new_shape(shape)).as_np_ndarray()
+            a.attach_grad()
+            b = mx.nd.random.uniform(shape=get_new_shape(shape)).as_np_ndarray()
+            b.attach_grad()
+            c = mx.nd.random.uniform(shape=get_new_shape(shape)).as_np_ndarray()
+            c.attach_grad()
+            d = mx.nd.random.uniform(shape=get_new_shape(shape)).as_np_ndarray()
+            d.attach_grad()
+            with mx.autograd.record():
+                mx_out = test_dstack(a, b, c, d)
+            np_out = _np.dstack((a.asnumpy(), b.asnumpy(), c.asnumpy(), d.asnumpy()))
+            assert mx_out.shape == np_out.shape
+            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+
+            # test symbolic backward
+            mx_out.backward()
+            assert_almost_equal(a.grad.asnumpy(), _np.ones(a.shape), rtol=1e-3, atol=1e-5)
+            assert_almost_equal(b.grad.asnumpy(), _np.ones(b.shape), rtol=1e-3, atol=1e-5)
+            assert_almost_equal(c.grad.asnumpy(), _np.ones(c.shape), rtol=1e-3, atol=1e-5)
+            assert_almost_equal(d.grad.asnumpy(), _np.ones(d.shape), rtol=1e-3, atol=1e-5)
+
+            # test imperative
+            mx_out = np.dstack((a, b, c, d))
+            np_out = _np.dstack((a.asnumpy(),b.asnumpy(), c.asnumpy(), d.asnumpy()))
+            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+
+
 @with_seed()
 @use_np
 def test_np_ravel():

From ceebcaf5d85ba8371b3ac4e0833d9d27cec31beb Mon Sep 17 00:00:00 2001
From: Jake Lee <gstu1130@gmail.com>
Date: Fri, 11 Oct 2019 21:58:42 -0700
Subject: [PATCH 445/813] numpy eye op (#16132)

---
 python/mxnet/ndarray/numpy/_op.py      | 35 ++++++++++++-
 python/mxnet/numpy/multiarray.py       | 29 ++++++++++-
 python/mxnet/symbol/numpy/_symbol.py   | 33 +++++++++++-
 src/operator/numpy/np_init_op.cc       | 30 +++++------
 src/operator/numpy/np_init_op.cu       |  3 ++
 src/operator/numpy/np_init_op.h        | 72 ++++++++++++++++++++++++++
 src/operator/tensor/init_op.h          | 40 ++++++++------
 tests/python/unittest/test_numpy_op.py | 68 ++++++++++++++++++++++++
 8 files changed, 273 insertions(+), 37 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index b82e6f1be73a..f62a0c9d7b7d 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -22,7 +22,7 @@
 from __future__ import absolute_import
 import numpy as _np
 from ...base import numeric_types
-from ...util import set_module
+from ...util import _sanity_check_params, set_module
 from ...context import current_context
 from . import _internal as _npi
 from ..ndarray import NDArray
@@ -31,7 +31,7 @@
            'arctan2', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs',
            'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2',
            'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
-           'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot', 'histogram',
+           'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot', 'histogram', 'eye',
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'dstack',
            'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
            'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'hypot', 'rad2deg', 'deg2rad',
@@ -788,6 +788,37 @@ def histogram(a, bins=10, range=None, normed=None, weights=None, density=None):
     raise ValueError("np.histogram fails with", locals())
 
 
+@set_module('mxnet.ndarray.numpy')
+def eye(N, M=None, k=0, dtype=_np.float32, **kwargs):
+    """
+    Return a 2-D array with ones on the diagonal and zeros elsewhere.
+
+    Parameters
+    ----------
+    N : int
+        Number of rows in the output.
+    M : int, optional
+        Number of columns in the output. If None, defaults to N.
+    k : int, optional
+        Index of the diagonal: 0 (the default) refers to the main diagonal,
+        a positive value refers to an upper diagonal,
+        and a negative value to a lower diagonal.
+    dtype : data-type, optional
+        Data-type of the returned array.
+
+    Returns
+    -------
+    I : ndarray of shape (N,M)
+        An array where all elements are equal to zero,
+        except for the k-th diagonal, whose values are equal to one.
+    """
+    _sanity_check_params('eye', ['order'], kwargs)
+    ctx = kwargs.pop('ctx', current_context())
+    if ctx is None:
+        ctx = current_context()
+    return _npi.eye(N, M, k, ctx, dtype)
+
+
 @set_module('mxnet.ndarray.numpy')
 def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0, ctx=None):  # pylint: disable=too-many-arguments
     r"""
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 3e2eeb2fb1a1..7d70319f4764 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -50,7 +50,7 @@
            'sqrt', 'cbrt', 'abs', 'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log',
            'degrees', 'log2', 'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative',
            'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh',
-           'tensordot', 'histogram', 'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate',
+           'tensordot', 'histogram', 'eye', 'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate',
            'stack', 'vstack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var',
            'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'arctan2', 'hypot',
            'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer',
@@ -3665,6 +3665,33 @@ def histogram(a, bins=10, range=None, normed=None, weights=None, density=None):
     return _mx_nd_np.histogram(a, bins=bins, range=range, normed=normed, weights=weights, density=density)
 
 
+@set_module('mxnet.numpy')
+def eye(N, M=None, k=0, dtype=_np.float32, **kwargs):
+    """
+    Return a 2-D array with ones on the diagonal and zeros elsewhere.
+
+    Parameters
+    ----------
+    N : int
+        Number of rows in the output.
+    M : int, optional
+        Number of columns in the output. If None, defaults to N.
+    k : int, optional
+        Index of the diagonal: 0 (the default) refers to the main diagonal,
+        a positive value refers to an upper diagonal,
+        and a negative value to a lower diagonal.
+    dtype : data-type, optional
+        Data-type of the returned array.
+
+    Returns
+    -------
+    I : ndarray of shape (N,M)
+        An array where all elements are equal to zero,
+        except for the k-th diagonal, whose values are equal to one.
+    """
+    return _mx_nd_np.eye(N, M, k, dtype, **kwargs)
+
+
 @set_module('mxnet.numpy')
 def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0, ctx=None):  # pylint: disable=too-many-arguments
     r"""
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index cc2f906f6b97..37b2f2bebe04 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -33,7 +33,7 @@
            'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs', 'absolute', 'exp',
            'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2', 'log1p',
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
-           'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot', 'histogram',
+           'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot', 'histogram', 'eye',
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'dstack',
            'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
            'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'hypot', 'rad2deg', 'deg2rad',
@@ -1305,6 +1305,37 @@ def histogram(a, bins=10, range=None, normed=None, weights=None, density=None):
     raise ValueError("histogram fails with", locals())
 
 
+@set_module('mxnet.symbol.numpy')
+def eye(N, M=None, k=0, dtype=_np.float32, **kwargs):
+    """
+    Return a 2-D array with ones on the diagonal and zeros elsewhere.
+
+    Parameters
+    ----------
+    N : int
+        Number of rows in the output.
+    M : int, optional
+        Number of columns in the output. If None, defaults to N.
+    k : int, optional
+        Index of the diagonal: 0 (the default) refers to the main diagonal,
+        a positive value refers to an upper diagonal,
+        and a negative value to a lower diagonal.
+    dtype : data-type, optional
+        Data-type of the returned array.
+
+    Returns
+    -------
+    I : ndarray of shape (N,M)
+        An array where all elements are equal to zero,
+        except for the k-th diagonal, whose values are equal to one.
+    """
+    _sanity_check_params('eye', ['order'], kwargs)
+    ctx = kwargs.pop('ctx', current_context())
+    if ctx is None:
+        ctx = current_context()
+    return _npi.eye(N, M, k, ctx, dtype)
+
+
 @set_module('mxnet.symbol.numpy')
 def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0, ctx=None): # pylint: disable=too-many-arguments
     r"""
diff --git a/src/operator/numpy/np_init_op.cc b/src/operator/numpy/np_init_op.cc
index 2477573c2413..7e65d6cc3d00 100644
--- a/src/operator/numpy/np_init_op.cc
+++ b/src/operator/numpy/np_init_op.cc
@@ -22,6 +22,7 @@
  * \file np_init_op.cc
  * \brief CPU Implementation of numpy init op
  */
+
 #include "../tensor/init_op.h"
 #include "../tensor/elemwise_unary_op.h"
 #include "./np_init_op.h"
@@ -29,6 +30,8 @@
 namespace mxnet {
 namespace op {
 
+
+DMLC_REGISTER_PARAMETER(NumpyEyeParam);
 DMLC_REGISTER_PARAMETER(IndicesOpParam);
 
 inline bool NumpyIndicesShape(const nnvm::NodeAttrs& attrs,
@@ -117,23 +120,6 @@ NNVM_REGISTER_OP(_np_ones_like)
 .add_argument("a", "NDArray-or-Symbol",
               "The shape and data-type of a define these same attributes of the returned array.");
 
-bool NumpyRangeShape(const nnvm::NodeAttrs& attrs,
-                     mxnet::ShapeVector* in_shapes,
-                     mxnet::ShapeVector* out_shapes) {
-  const RangeParam& param = nnvm::get<RangeParam>(attrs.parsed);
-  CHECK_EQ(in_shapes->size(), 0U);
-  CHECK_EQ(out_shapes->size(), 1U);
-  CHECK_NE(param.step, 0) << "_npi_arange does not support step=0";
-  CHECK_EQ(param.repeat, 1) << "_npi_arange only supports repeat=1, received " << param.repeat;
-  CHECK(param.stop.has_value()) << "_npi_arange requires stop to have a value";
-  double out_size = std::ceil((param.stop.value() - param.start) / param.step);
-  if (out_size < 0) {
-    out_size = 0;
-  }
-  SHAPE_ASSIGN_CHECK(*out_shapes, 0, mxnet::TShape({static_cast<nnvm::dim_t>(out_size)}));
-  return true;
-}
-
 NNVM_REGISTER_OP(_npi_arange)
 .set_num_inputs(0)
 .set_num_outputs(1)
@@ -143,6 +129,16 @@ NNVM_REGISTER_OP(_npi_arange)
 .set_attr<FCompute>("FCompute<cpu>", RangeCompute<cpu, RangeParam>)
 .add_arguments(RangeParam::__FIELDS__());
 
+NNVM_REGISTER_OP(_npi_eye)
+.describe("Return a 2-D array with ones on the diagonal and zeros elsewhere.")
+.set_num_inputs(0)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<NumpyEyeParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyEyeShape)
+.set_attr<nnvm::FInferType>("FInferType", InitType<NumpyEyeParam>)
+.set_attr<FCompute>("FCompute<cpu>", NumpyEyeFill<cpu>)
+.add_arguments(NumpyEyeParam::__FIELDS__());
+
 NNVM_REGISTER_OP(_npi_indices)
 .describe("Return an array representing the indices of a grid.")
 .set_num_inputs(0)
diff --git a/src/operator/numpy/np_init_op.cu b/src/operator/numpy/np_init_op.cu
index e68dd9ad36a1..9a2b236ec8b3 100644
--- a/src/operator/numpy/np_init_op.cu
+++ b/src/operator/numpy/np_init_op.cu
@@ -47,6 +47,9 @@ NNVM_REGISTER_OP(_np_ones_like)
 NNVM_REGISTER_OP(_npi_arange)
 .set_attr<FCompute>("FCompute<gpu>", RangeCompute<gpu, RangeParam>);
 
+NNVM_REGISTER_OP(_npi_eye)
+.set_attr<FCompute>("FCompute<gpu>", NumpyEyeFill<gpu>);
+
 NNVM_REGISTER_OP(_npi_indices)
 .set_attr<FCompute>("FCompute<gpu>", IndicesCompute<gpu>);
 
diff --git a/src/operator/numpy/np_init_op.h b/src/operator/numpy/np_init_op.h
index 3e1c345d59c3..9eb83e8018c8 100644
--- a/src/operator/numpy/np_init_op.h
+++ b/src/operator/numpy/np_init_op.h
@@ -35,6 +35,34 @@
 namespace mxnet {
 namespace op {
 
+struct NumpyEyeParam : public dmlc::Parameter<NumpyEyeParam> {
+  nnvm::dim_t N;
+  dmlc::optional<nnvm::dim_t> M;
+  nnvm::dim_t k;
+  std::string ctx;
+  int dtype;
+  DMLC_DECLARE_PARAMETER(NumpyEyeParam) {
+    DMLC_DECLARE_FIELD(N)
+    .describe("Number of rows in the output.");
+    DMLC_DECLARE_FIELD(M)
+    .set_default(dmlc::optional<nnvm::dim_t>())
+    .describe("Number of columns in the output. If None, defaults to N.");
+    DMLC_DECLARE_FIELD(k)
+    .set_default(0)
+    .describe("Index of the diagonal. 0 (the default) refers to the main diagonal,"
+              "a positive value refers to an upper diagonal."
+              "and a negative value to a lower diagonal.");
+    DMLC_DECLARE_FIELD(ctx)
+    .set_default("")
+    .describe("Context of output, in format [cpu|gpu|cpu_pinned](n)."
+              "Only used for imperative calls.");
+    DMLC_DECLARE_FIELD(dtype)
+    .set_default(mshadow::kFloat32)
+    MXNET_ADD_ALL_TYPES
+    .describe("Data-type of the returned array.");
+  }
+};
+
 struct IndicesOpParam : public dmlc::Parameter<IndicesOpParam> {
   mxnet::TShape dimensions;
   int dtype;
@@ -52,6 +80,50 @@ struct IndicesOpParam : public dmlc::Parameter<IndicesOpParam> {
   }
 };
 
+inline bool NumpyRangeShape(const nnvm::NodeAttrs& attrs,
+                            mxnet::ShapeVector* in_shapes,
+                            mxnet::ShapeVector* out_shapes) {
+  const RangeParam& param = nnvm::get<RangeParam>(attrs.parsed);
+  CHECK_EQ(in_shapes->size(), 0U);
+  CHECK_EQ(out_shapes->size(), 1U);
+  CHECK_NE(param.step, 0) << "_npi_arange does not support step=0";
+  CHECK_EQ(param.repeat, 1) << "_npi_arange only supports repeat=1, received " << param.repeat;
+  CHECK(param.stop.has_value()) << "_npi_arange requires stop to have a value";
+  double out_size = std::ceil((param.stop.value() - param.start) / param.step);
+  if (out_size < 0) {
+    out_size = 0;
+  }
+  SHAPE_ASSIGN_CHECK(*out_shapes, 0, mxnet::TShape({static_cast<nnvm::dim_t>(out_size)}));
+  return true;
+}
+
+inline bool NumpyEyeShape(const nnvm::NodeAttrs& attrs,
+                          mxnet::ShapeVector *in_attrs,
+                          mxnet::ShapeVector *out_attrs) {
+  const NumpyEyeParam& param = nnvm::get<NumpyEyeParam>(attrs.parsed);
+  CHECK_EQ(in_attrs->size(), 0U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  nnvm::dim_t M = param.M.has_value() ? param.M.value() : param.N;
+  CHECK(param.N >= 0) << "negative dimensions are not allowed. N is " << param.N;
+  CHECK(M >= 0) << "negative dimensions are not allowed. M is " << M;
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::Shape2(param.N, M));
+
+  return out_attrs->at(0).ndim() != 0U;
+}
+template<typename xpu>
+void NumpyEyeFill(const nnvm::NodeAttrs& attrs,
+                  const OpContext& ctx,
+                  const std::vector<TBlob>& inputs,
+                  const std::vector<OpReqType>& req,
+                  const std::vector<TBlob>& outputs) {
+  CHECK_EQ(inputs.size(), 0U);
+  CHECK_EQ(outputs.size(), 1U);
+  if (outputs[0].shape_.Size() == 0) return;  // zero-size tensor
+  const NumpyEyeParam& param = nnvm::get<NumpyEyeParam>(attrs.parsed);
+  const nnvm::dim_t num_cols = param.M.has_value() ? param.M.value() : param.N;
+  EyeFillImpl<xpu>(outputs[0], ctx, req, num_cols, param.N, param.k);
+}
+
 template<int req>
 struct indices_fwd {
   template<typename DType>
diff --git a/src/operator/tensor/init_op.h b/src/operator/tensor/init_op.h
index 8e8896e5c014..d2107a1406e2 100644
--- a/src/operator/tensor/init_op.h
+++ b/src/operator/tensor/init_op.h
@@ -483,6 +483,29 @@ void FillComputeZerosEx(const nnvm::NodeAttrs& attrs,
   }
 }
 
+template<typename xpu>
+inline void EyeFillImpl(const TBlob& out_data,
+                        const OpContext& ctx,
+                        const std::vector<OpReqType>& req,
+                        const nnvm::dim_t num_cols,
+                        const nnvm::dim_t N,
+                        const nnvm::dim_t k) {
+  using namespace mxnet_op;
+  const nnvm::dim_t cnnz = std::max(num_cols - std::abs(k), (nnvm::dim_t)0);
+  const nnvm::dim_t rnnz = std::max(N - std::abs(k), (nnvm::dim_t)0);
+  const nnvm::dim_t nnz = k > 0 ? std::min(cnnz, N) :
+                          std::min(rnnz, num_cols);
+  mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+  MSHADOW_TYPE_SWITCH(out_data.type_flag_, DType, {
+      MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
+        Fill(s, out_data, req[0], static_cast<DType>(0));
+        if (nnz > 0) {
+          Kernel<eye_dns_fill<req_type>, xpu>::Launch(s, nnz, out_data.dptr<DType>(),
+            std::max(static_cast<nnvm::dim_t>(0), k), k, num_cols);
+        }
+      });
+  });
+}
 
 template<typename xpu>
 void EyeFill(const nnvm::NodeAttrs& attrs,
@@ -493,25 +516,10 @@ void EyeFill(const nnvm::NodeAttrs& attrs,
   CHECK_EQ(inputs.size(), 0U);
   CHECK_EQ(outputs.size(), 1U);
   CHECK_EQ(req.size(), 1U);
-  mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
   const EyeParam& param = nnvm::get<EyeParam>(attrs.parsed);
   const TBlob& out_data = outputs[0];
   const nnvm::dim_t num_cols = param.M > 0 ? param.M : param.N;
-
-  const nnvm::dim_t cnnz = std::max(num_cols - std::abs(param.k), (nnvm::dim_t)0);
-  const nnvm::dim_t rnnz = std::max(param.N - std::abs(param.k), (nnvm::dim_t)0);
-  const nnvm::dim_t nnz = param.k > 0 ? std::min(cnnz, param.N) :
-                                        std::min(rnnz, num_cols);
-  using namespace mxnet_op;
-  MSHADOW_TYPE_SWITCH(out_data.type_flag_, DType, {
-    MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
-      Fill(s, out_data, req[0], static_cast<DType>(0));
-      if (nnz > 0) {
-        Kernel<eye_dns_fill<req_type>, xpu>::Launch(s, nnz, out_data.dptr<DType>(),
-          std::max(static_cast<nnvm::dim_t>(0), param.k), param.k, num_cols);
-      }
-    });
-  });
+  EyeFillImpl<xpu>(out_data, ctx, req, num_cols, param.N, param.k);
 }
 
 
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 30f2dfc1c0ec..c8c58aced52b 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -2222,6 +2222,74 @@ def test_indexing_mode(sampler, set_size, samples_size, replace, weight=None):
             test_indexing_mode(test_choice_weighted, num_classes, num_classes // 2, replace, weight)
 
 
+@with_seed()
+@use_np
+def test_np_eye():
+    configs = [
+        4,
+        1000,
+        (4, 3),
+        (5, None),
+        (4, None, 1),
+        (2, 2, 1),
+        (4, 6, 1),
+        (7, 3, -3),
+        (3, 2, -2),
+        (4, 0),
+        (0, 0),
+        (0, 3),
+        (0, 0, -2)
+    ]
+    exception_configs = [
+        -1,
+        -1000,
+        (-2, None),
+        (1, -1)
+    ]
+    dtypes = ['int32', 'float16', 'float32', 'float64', None]
+    for config in configs:
+        for dtype in dtypes:
+            if isinstance(config, tuple):
+                mx_ret = np.eye(*config, dtype=dtype)
+                np_ret = _np.eye(*config, dtype=dtype)
+            else:
+                mx_ret = np.eye(config, dtype=dtype)
+                np_ret = _np.eye(config, dtype=dtype)
+            assert same(mx_ret.asnumpy(), np_ret)
+    # check for exception input
+    for config in exception_configs:
+        if isinstance(config, tuple):
+            assertRaises(MXNetError, np.eye, *config)
+        else:
+            assertRaises(MXNetError, np.eye, config)
+
+    class TestEye(HybridBlock):
+        def __init__(self, N, M=None, k=0, dtype=None):
+            super(TestEye, self).__init__()
+            self._N = N
+            self._M = M
+            self._k = k
+            self._dtype = dtype
+
+        def hybrid_forward(self, F, x):
+            return x + F.np.eye(self._N, self._M, self._k, dtype=self._dtype)
+
+    for dtype in dtypes:
+        x = np.zeros(shape=(), dtype=dtype)
+        for config in configs:
+            for hybridize in [False, True]:
+                if isinstance(config, tuple):
+                    net = TestEye(*config, dtype=dtype)
+                    np_out = _np.eye(*config, dtype=dtype)
+                else:
+                    net = TestEye(config, dtype=dtype)
+                    np_out = _np.eye(config, dtype=dtype)
+                if hybridize:
+                    net.hybridize()
+                mx_out = net(x)
+                assert same(mx_out.asnumpy(), np_out)
+
+
 @with_seed()
 @use_np
 def test_np_indices():

From 8222979b1f3f95e0aa2ae68de0b27ad02d035b46 Mon Sep 17 00:00:00 2001
From: Zoey Xinyi Ge <xinyige@umich.edu>
Date: Sun, 13 Oct 2019 00:02:47 -0400
Subject: [PATCH 446/813] Numpy compatible vsplit; minor changes to split
 (#15983)

---
 python/mxnet/ndarray/numpy/_op.py      | 94 +++++++++++++++++++++++---
 python/mxnet/numpy/multiarray.py       | 90 ++++++++++++++++++++++--
 python/mxnet/symbol/numpy/_symbol.py   | 67 +++++++++++++++---
 tests/python/unittest/test_numpy_op.py | 55 +++++++++++++++
 4 files changed, 283 insertions(+), 23 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index f62a0c9d7b7d..9d2b4020100c 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -32,11 +32,11 @@
            'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2',
            'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot', 'histogram', 'eye',
-           'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'dstack',
-           'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
-           'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'hypot', 'rad2deg', 'deg2rad',
-           'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer',
-           'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal']
+           'linspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate', 'stack',
+           'vstack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var',
+           'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'hypot',
+           'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot',
+           'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -823,7 +823,6 @@ def eye(N, M=None, k=0, dtype=_np.float32, **kwargs):
 def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0, ctx=None):  # pylint: disable=too-many-arguments
     r"""
     Return evenly spaced numbers over a specified interval.
-
     Returns num evenly spaced samples, calculated over the interval [start, stop].
     The endpoint of the interval can optionally be excluded.
 
@@ -2354,7 +2353,7 @@ def split(ary, indices_or_sections, axis=0):
     ----------
     ary : ndarray
         Array to be divided into sub-arrays.
-    indices_or_sections : int or 1-D array
+    indices_or_sections : int or 1-D python tuple, list or set.
         If `indices_or_sections` is an integer, N, the array will be divided
         into N equal arrays along `axis`.  If such a split is not possible,
         an error is raised.
@@ -2386,10 +2385,10 @@ def split(ary, indices_or_sections, axis=0):
             raise ValueError('array split does not result in an equal division')
         section_size = int(axis_size / sections)
         indices = [i * section_size for i in range(sections)]
-    elif isinstance(indices_or_sections, tuple):
+    elif isinstance(indices_or_sections, (list, set, tuple)):
         indices = [0] + list(indices_or_sections)
     else:
-        raise ValueError('indices_or_sections must either int or tuple of ints')
+        raise ValueError('indices_or_sections must either int, or tuple / list / set of ints')
     ret = _npi.split(ary, indices, axis, False)
     if not isinstance(ret, list):
         return [ret]
@@ -2397,6 +2396,83 @@ def split(ary, indices_or_sections, axis=0):
 # pylint: enable=redefined-outer-name
 
 
+@set_module('mxnet.ndarray.numpy')
+def vsplit(ary, indices_or_sections):
+    r"""
+    vsplit(ary, indices_or_sections)
+
+    Split an array into multiple sub-arrays vertically (row-wise).
+
+    ``vsplit`` is equivalent to ``split`` with `axis=0` (default): the array is always split
+    along the first axis regardless of the array dimension.
+
+    Parameters
+    ----------
+    ary : ndarray
+        Array to be divided into sub-arrays.
+    indices_or_sections : int or 1 - D Python tuple, list or set.
+        If `indices_or_sections` is an integer, N, the array will be divided into N equal arrays
+        along axis 0.  If such a split is not possible, an error is raised.
+
+        If `indices_or_sections` is a 1-D array of sorted integers, the entries indicate where
+        along axis 0 the array is split.  For example, ``[2, 3]`` would result in
+
+          - ary[:2]
+          - ary[2:3]
+          - ary[3:]
+
+        If an index exceeds the dimension of the array along axis 0, an error will be thrown.
+
+    Returns
+    -------
+    sub-arrays : list of ndarrays
+        A list of sub-arrays.
+
+    See Also
+    --------
+    split : Split an array into multiple sub-arrays of equal size.
+
+    Notes
+    -------
+    This function differs from the original `numpy.degrees
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.degrees.html>`_ in
+    the following aspects:
+
+    - Currently parameter ``indices_or_sections`` does not support ndarray, but supports scalar,
+    tuple and list.
+    - In ``indices_or_sections``, if an index exceeds the dimension of the array along axis 0,
+    an error will be thrown.
+
+    Examples
+    --------
+    >>> x = np.arange(16.0).reshape(4, 4)
+    >>> x
+    array([[  0.,   1.,   2.,   3.],
+           [  4.,   5.,   6.,   7.],
+           [  8.,   9.,  10.,  11.],
+           [ 12.,  13.,  14.,  15.]])
+    >>> np.vsplit(x, 2)
+    [array([[0., 1., 2., 3.],
+            [4., 5., 6., 7.]]), array([[ 8.,  9., 10., 11.],
+            [12., 13., 14., 15.]])]
+
+    With a higher dimensional array the split is still along the first axis.
+
+    >>> x = np.arange(8.0).reshape(2, 2, 2)
+    >>> x
+    array([[[ 0.,  1.],
+            [ 2.,  3.]],
+           [[ 4.,  5.],
+            [ 6.,  7.]]])
+    >>> np.vsplit(x, 2)
+    [array([[[0., 1.],
+            [2., 3.]]]), array([[[4., 5.],
+            [6., 7.]]])]
+
+    """
+    return split(ary, indices_or_sections, 0)
+
+
 @set_module('mxnet.ndarray.numpy')
 def concatenate(seq, axis=0, out=None):
     """Join a sequence of arrays along an existing axis.
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 7d70319f4764..e420ebd30718 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -50,11 +50,12 @@
            'sqrt', 'cbrt', 'abs', 'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log',
            'degrees', 'log2', 'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative',
            'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh',
-           'tensordot', 'histogram', 'eye', 'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate',
-           'stack', 'vstack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var',
-           'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'arctan2', 'hypot',
-           'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer',
-           'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal']
+           'tensordot', 'histogram', 'eye', 'linspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit',
+           'concatenate', 'stack', 'vstack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip',
+           'argmax', 'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
+           'around', 'arctan2', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
+           'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal',
+           'less_equal']
 
 
 # Return code for dispatching indexing function call
@@ -3951,7 +3952,7 @@ def split(ary, indices_or_sections, axis=0):
     ----------
     ary : ndarray
         Array to be divided into sub-arrays.
-    indices_or_sections : int or 1-D array
+    indices_or_sections : int or 1-D Python tuple, list or set.
         If `indices_or_sections` is an integer, N, the array will be divided
         into N equal arrays along `axis`.  If such a split is not possible,
         an error is raised.
@@ -3977,6 +3978,83 @@ def split(ary, indices_or_sections, axis=0):
     return _mx_nd_np.split(ary, indices_or_sections, axis=axis)
 
 
+@set_module('mxnet.numpy')
+def vsplit(ary, indices_or_sections):
+    r"""
+    vsplit(ary, indices_or_sections)
+
+    Split an array into multiple sub-arrays vertically (row-wise).
+
+    ``vsplit`` is equivalent to ``split`` with `axis=0` (default): the array is always split
+    along the first axis regardless of the array dimension.
+
+    Parameters
+    ----------
+    ary : ndarray
+        Array to be divided into sub-arrays.
+    indices_or_sections : int or 1 - D Python tuple, list or set.
+        If `indices_or_sections` is an integer, N, the array will be divided into N equal arrays
+        along axis 0.  If such a split is not possible, an error is raised.
+
+        If `indices_or_sections` is a 1-D array of sorted integers, the entries indicate where
+        along axis 0 the array is split.  For example, ``[2, 3]`` would result in
+
+          - ary[:2]
+          - ary[2:3]
+          - ary[3:]
+
+        If an index exceeds the dimension of the array along axis 0, an error will be thrown.
+
+    Returns
+    -------
+    sub-arrays : list of ndarrays
+        A list of sub-arrays.
+
+    See Also
+    --------
+    split : Split an array into multiple sub-arrays of equal size.
+
+    Notes
+    -------
+    This function differs from the original `numpy.degrees
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.degrees.html>`_ in
+    the following aspects:
+
+    - Currently parameter ``indices_or_sections`` does not support ndarray, but supports scalar,
+    tuple and list.
+    - In ``indices_or_sections``, if an index exceeds the dimension of the array along axis 0,
+    an error will be thrown.
+
+    Examples
+    --------
+    >>> x = np.arange(16.0).reshape(4, 4)
+    >>> x
+    array([[  0.,   1.,   2.,   3.],
+           [  4.,   5.,   6.,   7.],
+           [  8.,   9.,  10.,  11.],
+           [ 12.,  13.,  14.,  15.]])
+    >>> np.vsplit(x, 2)
+    [array([[0., 1., 2., 3.],
+            [4., 5., 6., 7.]]), array([[ 8.,  9., 10., 11.],
+            [12., 13., 14., 15.]])]
+
+    With a higher dimensional array the split is still along the first axis.
+
+    >>> x = np.arange(8.0).reshape(2, 2, 2)
+    >>> x
+    array([[[ 0.,  1.],
+            [ 2.,  3.]],
+           [[ 4.,  5.],
+            [ 6.,  7.]]])
+    >>> np.vsplit(x, 2)
+    [array([[[0., 1.],
+            [2., 3.]]]), array([[[4., 5.],
+            [6., 7.]]])]
+
+    """
+    return split(ary, indices_or_sections, 0)
+
+
 @set_module('mxnet.numpy')
 def concatenate(seq, axis=0, out=None):
     """Join a sequence of arrays along an existing axis.
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 37b2f2bebe04..5a700cf1368d 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -34,11 +34,11 @@
            'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2', 'log1p',
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot', 'histogram', 'eye',
-           'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'dstack',
-           'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
-           'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'hypot', 'rad2deg', 'deg2rad',
-           'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer',
-           'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal']
+           'linspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate', 'stack', 'vstack',
+           'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices',
+           'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'hypot',
+           'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot',
+           'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal']
 
 
 def _num_outputs(sym):
@@ -2601,7 +2601,7 @@ def split(ary, indices_or_sections, axis=0):
     ----------
     ary : ndarray
         Array to be divided into sub-arrays.
-    indices_or_sections : int or 1-D array
+    indices_or_sections : int or 1-D python tuple, list or set.
         If `indices_or_sections` is an integer, N, the array will be divided
         into N equal arrays along `axis`.  If such a split is not possible,
         an error is raised.
@@ -2628,15 +2628,66 @@ def split(ary, indices_or_sections, axis=0):
     sections = 0
     if isinstance(indices_or_sections, int):
         sections = indices_or_sections
-    elif isinstance(indices_or_sections, tuple):
+    elif isinstance(indices_or_sections, (list, set, tuple)):
         indices = [0] + list(indices_or_sections)
     else:
-        raise ValueError('indices_or_sections must either int or tuple of ints')
+        raise ValueError('indices_or_sections must either int or tuple / list / set of ints')
     ret = _npi.split(ary, indices, axis, False, sections)
     return ret
 # pylint: enable=redefined-outer-name
 
 
+@set_module('mxnet.symbol.numpy')
+def vsplit(ary, indices_or_sections):
+    r"""
+    vsplit(ary, indices_or_sections)
+
+    Split an array into multiple sub-arrays vertically (row-wise).
+
+    ``vsplit`` is equivalent to ``split`` with `axis=0` (default): the array is always split
+    along the first axis regardless of the array dimension.
+
+    Parameters
+    ----------
+    ary : _Symbol
+        Array to be divided into sub-arrays.
+    indices_or_sections : int or 1 - D Python tuple, list or set.
+        If `indices_or_sections` is an integer, N, the array will be divided into N equal arrays
+        along axis 0.  If such a split is not possible, an error is raised.
+
+        If `indices_or_sections` is a 1-D array of sorted integers, the entries indicate where
+        along axis 0 the array is split.  For example, ``[2, 3]`` would result in
+
+          - ary[:2]
+          - ary[2:3]
+          - ary[3:]
+
+        If an index exceeds the dimension of the array along axis 0, an error will be thrown.
+
+    Returns
+    -------
+    sub-arrays : list of _Symbols
+        A list of sub-arrays.
+
+    See Also
+    --------
+    split : Split an array into multiple sub-arrays of equal size.
+
+    Notes
+    -------
+    This function differs from the original `numpy.degrees
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.degrees.html>`_ in
+    the following aspects:
+
+    - Currently parameter ``indices_or_sections`` does not support ndarray, but supports scalar,
+    tuple and list
+    - In ``indices_or_sections``, if an index exceeds the dimension of the array along axis 0,
+    an error will be thrown.
+
+    """
+    return split(ary, indices_or_sections, 0)
+
+
 @set_module('mxnet.symbol.numpy')
 def concatenate(seq, axis=0, out=None):
     """Join a sequence of arrays along an existing axis.
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index c8c58aced52b..40e203548bda 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -1591,6 +1591,61 @@ def get_indices(axis_size):
                     assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
 
 
+@with_seed()
+@use_np
+def test_np_vsplit():
+    class TestVsplit(HybridBlock):
+        def __init__(self, indices_or_sections):
+            super(TestVsplit, self).__init__()
+            self._indices_or_sections = indices_or_sections
+
+        def hybrid_forward(self, F, a, *args, **kwargs):
+            return F.np.vsplit(a, indices_or_sections=self._indices_or_sections)
+
+    def get_indices(axis_size):
+        if axis_size is 0:
+            axis_size = random.randint(3, 6)
+        samples = random.randint(1, axis_size - 1)
+        indices = sorted(random.sample([i for i in range(1, axis_size)], samples))
+        indices = tuple(indices)
+        return indices
+
+    shapes = [
+        (2, 1, 2, 9),
+        (4, 3, 3),
+        (4, 0, 2),  # zero-size shape
+        (0, 3), # first dim being zero
+    ]
+    for hybridize in [True, False]:
+        for shape in shapes:
+            axis_size = shape[0]
+            indices = get_indices(axis_size)
+            sections = 7 if axis_size is 0 else axis_size
+            for indices_or_sections in [indices, sections]:
+                # test gluon
+                test_vsplit = TestVsplit(indices_or_sections=indices_or_sections)
+                if hybridize:
+                    test_vsplit.hybridize()
+                a = rand_ndarray(shape).as_np_ndarray() # TODO: check type
+                a.attach_grad()
+                expected_ret = _np.vsplit(a.asnumpy(), indices_or_sections=indices_or_sections)
+                with mx.autograd.record():
+                    y = test_vsplit(a)
+                assert len(y) == len(expected_ret)
+                for mx_out, np_out in zip(y, expected_ret):
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+
+                mx.autograd.backward(y)
+
+                assert_almost_equal(a.grad.asnumpy(), _np.ones(a.shape), rtol=1e-3, atol=1e-5)
+
+                # test imperative
+                mx_outs = np.vsplit(a, indices_or_sections=indices_or_sections)
+                np_outs = _np.vsplit(a.asnumpy(), indices_or_sections=indices_or_sections)
+                for mx_out, np_out in zip(mx_outs, np_outs):
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+
+
 @with_seed()
 @use_np
 def test_np_concat():

From 8562adcca8ea822b0857230ade057c2be73816d6 Mon Sep 17 00:00:00 2001
From: "Shi, guangyong" <35016185+gyshi@users.noreply.github.com>
Date: Sun, 13 Oct 2019 15:18:56 +0800
Subject: [PATCH 447/813] add numpy op logspace (#15825)

fix test files and other errors
---
 python/mxnet/ndarray/numpy/_op.py      | 93 +++++++++++++++++++++++--
 python/mxnet/numpy/multiarray.py       | 88 ++++++++++++++++++++++--
 python/mxnet/symbol/numpy/_symbol.py   | 94 ++++++++++++++++++++++++--
 src/operator/numpy/np_init_op.cc       | 23 +++++++
 src/operator/numpy/np_init_op.cu       |  3 +
 src/operator/numpy/np_init_op.h        | 60 ++++++++++++++++
 tests/python/unittest/test_numpy_op.py | 50 ++++++++++++++
 7 files changed, 395 insertions(+), 16 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index 9d2b4020100c..17ce505ecb54 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -32,11 +32,11 @@
            'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2',
            'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot', 'histogram', 'eye',
-           'linspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate', 'stack',
-           'vstack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var',
-           'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'hypot',
-           'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot',
-           'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal']
+           'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate',
+           'stack', 'vstack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax',
+           'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
+           'around', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
+           'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -916,6 +916,89 @@ def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis
         return _npi.linspace(start=start, stop=stop, num=num, endpoint=endpoint, ctx=ctx, dtype=dtype)
 
 
+@set_module('mxnet.ndarray.numpy')
+def logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None, axis=0, ctx=None):  # pylint: disable=too-many-arguments
+    r"""Return numbers spaced evenly on a log scale.
+
+    In linear space, the sequence starts at ``base ** start``
+    (`base` to the power of `start`) and ends with ``base ** stop``
+    (see `endpoint` below).
+
+        Non-scalar `start` and `stop` are now supported.
+
+    Parameters
+    ----------
+    start : int or float
+        ``base ** start`` is the starting value of the sequence.
+    stop : int or float
+        ``base ** stop`` is the final value of the sequence, unless `endpoint`
+        is False.  In that case, ``num + 1`` values are spaced over the
+        interval in log-space, of which all but the last (a sequence of
+        length `num`) are returned.
+    num : integer, optional
+        Number of samples to generate.  Default is 50.
+    endpoint : boolean, optional
+        If true, `stop` is the last sample. Otherwise, it is not included.
+        Default is True.
+    base : float, optional
+        The base of the log space. The step size between the elements in
+        ``ln(samples) / ln(base)`` (or ``log_base(samples)``) is uniform.
+        Default is 10.0.
+    dtype : dtype
+        The type of the output array.  If `dtype` is not given, infer the data
+        type from the other input arguments.
+    axis : int, optional
+        The axis in the result to store the samples.  Relevant only if start
+        or stop are array-like.  By default (0), the samples will be along a
+        new axis inserted at the beginning. Now, axis only support axis = 0.
+    ctx : Context, optional
+        An optional device context (default is the current default context).
+
+    Returns
+    -------
+    samples : ndarray
+        `num` samples, equally spaced on a log scale.
+
+    See Also
+    --------
+    arange : Similar to linspace, with the step size specified instead of the
+             number of samples. Note that, when used with a float endpoint, the
+             endpoint may or may not be included.
+    linspace : Similar to logspace, but with the samples uniformly distributed
+               in linear space, instead of log space.
+
+    Notes
+    -----
+    Logspace is equivalent to the code. Now wo only support axis = 0.
+
+    >>> y = np.linspace(start, stop, num=num, endpoint=endpoint)
+    ...
+    >>> power(base, y).astype(dtype)
+    ...
+
+    Examples
+    --------
+    >>> np.logspace(2.0, 3.0, num=4)
+    array([ 100.     ,  215.44347,  464.15887, 1000.     ])
+    >>> np.logspace(2.0, 3.0, num=4, endpoint=False)
+    array([100.     , 177.82794, 316.22775, 562.3413 ])
+    >>> np.logspace(2.0, 3.0, num=4, base=2.0)
+    array([4.       , 5.0396843, 6.349604 , 8.       ])
+    >>> np.logspace(2.0, 3.0, num=4, base=2.0, dtype=np.int32)
+    array([4, 5, 6, 8], dtype=int32)
+    >>> np.logspace(2.0, 3.0, num=4, ctx=npx.gpu(0))
+    array([ 100.     ,  215.44347,  464.15887, 1000.     ], ctx=gpu(0))
+    """
+    if isinstance(start, (list, tuple, _np.ndarray, NDArray)) or \
+       isinstance(stop, (list, tuple, _np.ndarray, NDArray)):
+        raise NotImplementedError('start and stop only support int and float')
+    if axis != 0:
+        raise NotImplementedError("the function only support axis 0")
+    if ctx is None:
+        ctx = current_context()
+    return _npi.logspace(start=start, stop=stop, num=num, endpoint=endpoint, base=base, ctx=ctx, dtype=dtype)
+
+
 @set_module('mxnet.ndarray.numpy')
 def expand_dims(a, axis):
     """Expand the shape of an array.
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index e420ebd30718..480558f4306d 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -50,12 +50,12 @@
            'sqrt', 'cbrt', 'abs', 'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log',
            'degrees', 'log2', 'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative',
            'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh',
-           'tensordot', 'histogram', 'eye', 'linspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit',
-           'concatenate', 'stack', 'vstack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip',
-           'argmax', 'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
-           'around', 'arctan2', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
-           'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal',
-           'less_equal']
+           'tensordot', 'histogram', 'eye', 'linspace', 'logspace', 'expand_dims', 'tile', 'arange',
+           'split', 'vsplit', 'concatenate', 'stack', 'vstack', 'dstack', 'mean', 'maximum', 'minimum',
+           'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming',
+           'blackman', 'flip', 'around', 'arctan2', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril',
+           'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less',
+           'greater_equal', 'less_equal']
 
 
 # Return code for dispatching indexing function call
@@ -3780,6 +3780,82 @@ def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis
     return _mx_nd_np.linspace(start, stop, num, endpoint, retstep, dtype, axis, ctx)
 
 
+@set_module('mxnet.numpy')
+def logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None, axis=0, ctx=None):
+    r"""Return numbers spaced evenly on a log scale.
+
+    In linear space, the sequence starts at ``base ** start``
+    (`base` to the power of `start`) and ends with ``base ** stop``
+    (see `endpoint` below).
+
+        Non-scalar `start` and `stop` are now supported.
+
+    Parameters
+    ----------
+    start : int or float
+        ``base ** start`` is the starting value of the sequence.
+    stop : int or float
+        ``base ** stop`` is the final value of the sequence, unless `endpoint`
+        is False.  In that case, ``num + 1`` values are spaced over the
+        interval in log-space, of which all but the last (a sequence of
+        length `num`) are returned.
+    num : integer, optional
+        Number of samples to generate.  Default is 50.
+    endpoint : boolean, optional
+        If true, `stop` is the last sample. Otherwise, it is not included.
+        Default is True.
+    base : float, optional
+        The base of the log space. The step size between the elements in
+        ``ln(samples) / ln(base)`` (or ``log_base(samples)``) is uniform.
+        Default is 10.0.
+    dtype : dtype
+        The type of the output array.  If `dtype` is not given, infer the data
+        type from the other input arguments.
+    axis : int, optional
+        The axis in the result to store the samples.  Relevant only if start
+        or stop are array-like.  By default (0), the samples will be along a
+        new axis inserted at the beginning. Now, axis only support axis = 0.
+    ctx : Context, optional
+        An optional device context (default is the current default context).
+
+    Returns
+    -------
+    samples : ndarray
+        `num` samples, equally spaced on a log scale.
+
+    See Also
+    --------
+    arange : Similar to linspace, with the step size specified instead of the
+             number of samples. Note that, when used with a float endpoint, the
+             endpoint may or may not be included.
+    linspace : Similar to logspace, but with the samples uniformly distributed
+               in linear space, instead of log space.
+
+    Notes
+    -----
+    Logspace is equivalent to the code
+
+    >>> y = np.linspace(start, stop, num=num, endpoint=endpoint)
+    ...
+    >>> power(base, y).astype(dtype)
+    ...
+
+    Examples
+    --------
+    >>> np.logspace(2.0, 3.0, num=4)
+    array([ 100.     ,  215.44347,  464.15887, 1000.     ])
+    >>> np.logspace(2.0, 3.0, num=4, endpoint=False)
+    array([100.     , 177.82794, 316.22775, 562.3413 ])
+    >>> np.logspace(2.0, 3.0, num=4, base=2.0)
+    array([4.       , 5.0396843, 6.349604 , 8.       ])
+    >>> np.logspace(2.0, 3.0, num=4, base=2.0, dtype=np.int32)
+    array([4, 5, 6, 8], dtype=int32)
+    >>> np.logspace(2.0, 3.0, num=4, ctx=npx.gpu(0))
+    array([ 100.     ,  215.44347,  464.15887, 1000.     ], ctx=gpu(0))
+    """
+    return _mx_nd_np.logspace(start, stop, num, endpoint, base, dtype, axis, ctx=ctx)
+
+
 @set_module('mxnet.numpy')
 def expand_dims(a, axis):
     """Expand the shape of an array.
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 5a700cf1368d..fa618221d508 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -34,11 +34,12 @@
            'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2', 'log1p',
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot', 'histogram', 'eye',
-           'linspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate', 'stack', 'vstack',
-           'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices',
-           'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'hypot',
-           'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot',
-           'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal']
+           'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate',
+           'stack', 'vstack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax',
+           'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
+           'around', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
+           'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal',
+           'less_equal']
 
 
 def _num_outputs(sym):
@@ -1409,6 +1410,89 @@ def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis
         return _npi.linspace(start=start, stop=stop, num=num, endpoint=endpoint, ctx=ctx, dtype=dtype)
 
 
+@set_module('mxnet.symbol.numpy')
+def logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None, axis=0, ctx=None): # pylint: disable=too-many-arguments
+    r"""Return numbers spaced evenly on a log scale.
+
+    In linear space, the sequence starts at ``base ** start``
+    (`base` to the power of `start`) and ends with ``base ** stop``
+    (see `endpoint` below).
+
+        Non-scalar `start` and `stop` are now supported.
+
+    Parameters
+    ----------
+    start : scalar
+        ``base ** start`` is the starting value of the sequence.
+    stop : scalar
+        ``base ** stop`` is the final value of the sequence, unless `endpoint`
+        is False.  In that case, ``num + 1`` values are spaced over the
+        interval in log-space, of which all but the last (a sequence of
+        length `num`) are returned.
+    num : scalar, optional
+        Number of samples to generate.  Default is 50.
+    endpoint : boolean, optional
+        If true, `stop` is the last sample. Otherwise, it is not included.
+        Default is True.
+    base : scalar, optional
+        The base of the log space. The step size between the elements in
+        ``ln(samples) / ln(base)`` (or ``log_base(samples)``) is uniform.
+        Default is 10.0.
+    dtype : dtype
+        The type of the output array.  If `dtype` is not given, infer the data
+        type from the other input arguments.
+    axis : scalar, optional
+        The axis in the result to store the samples.  Relevant only if start
+        or stop are array-like.  By default (0), the samples will be along a
+        new axis inserted at the beginning. Now, axis only support axis = 0.
+    ctx : Context, optional
+        An optional device context (default is the current default context).
+
+    Returns
+    -------
+    samples : _Symbol
+        `num` samples, equally spaced on a log scale.
+
+    See Also
+    --------
+    arange : Similar to linspace, with the step size specified instead of the
+             number of samples. Note that, when used with a float endpoint, the
+             endpoint may or may not be included.
+    linspace : Similar to logspace, but with the samples uniformly distributed
+               in linear space, instead of log space.
+
+    Notes
+    -----
+    Logspace is equivalent to the code
+
+    >>> y = np.linspace(start, stop, num=num, endpoint=endpoint)
+    ...
+    >>> power(base, y).astype(dtype)
+    ...
+
+    Examples
+    --------
+    >>> np.logspace(2.0, 3.0, num=4)
+    array([ 100.     ,  215.44347,  464.15887, 1000.     ])
+    >>> np.logspace(2.0, 3.0, num=4, endpoint=False)
+    array([100.     , 177.82794, 316.22775, 562.3413 ])
+    >>> np.logspace(2.0, 3.0, num=4, base=2.0)
+    array([4.       , 5.0396843, 6.349604 , 8.       ])
+    >>> np.logspace(2.0, 3.0, num=4, base=2.0, dtype=np.int32)
+    array([4, 5, 6, 8], dtype=int32)
+    >>> np.logspace(2.0, 3.0, num=4, ctx=npx.gpu(0))
+    array([ 100.     ,  215.44347,  464.15887, 1000.     ], ctx=gpu(0))
+    """
+    if isinstance(start, (list, _np.ndarray)) or \
+       isinstance(stop, (list, _np.ndarray)):
+        raise NotImplementedError('start and stop only support int')
+    if axis != 0:
+        raise NotImplementedError("the function only support axis 0")
+    if ctx is None:
+        ctx = current_context()
+    return _npi.logspace(start=start, stop=stop, num=num, endpoint=endpoint, base=base, ctx=ctx, dtype=dtype)
+
+
 @set_module('mxnet.symbol.numpy')
 def expand_dims(a, axis):
     """Expand the shape of an array.
diff --git a/src/operator/numpy/np_init_op.cc b/src/operator/numpy/np_init_op.cc
index 7e65d6cc3d00..f2446c85b182 100644
--- a/src/operator/numpy/np_init_op.cc
+++ b/src/operator/numpy/np_init_op.cc
@@ -33,6 +33,7 @@ namespace op {
 
 DMLC_REGISTER_PARAMETER(NumpyEyeParam);
 DMLC_REGISTER_PARAMETER(IndicesOpParam);
+DMLC_REGISTER_PARAMETER(LogspaceParam);
 
 inline bool NumpyIndicesShape(const nnvm::NodeAttrs& attrs,
                               mxnet::ShapeVector* in_shapes,
@@ -54,6 +55,18 @@ inline bool NumpyIndicesShape(const nnvm::NodeAttrs& attrs,
   return shape_is_known(out_shapes->at(0));
 }
 
+inline bool LogspaceShape(const nnvm::NodeAttrs& attrs,
+                          mxnet::ShapeVector *in_attrs,
+                          mxnet::ShapeVector *out_attrs) {
+  const LogspaceParam& param = nnvm::get<LogspaceParam>(attrs.parsed);
+  CHECK_EQ(in_attrs->size(), 0U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  CHECK_GE(param.num, 0)
+    << "Number of sequence should be non-negative, received " << param.num;
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, mxnet::TShape({static_cast<nnvm::dim_t>(param.num)}));
+  return true;
+}
+
 NNVM_REGISTER_OP(_npi_zeros)
 .set_num_inputs(0)
 .set_num_outputs(1)
@@ -149,5 +162,15 @@ NNVM_REGISTER_OP(_npi_indices)
 .set_attr<FCompute>("FCompute<cpu>", IndicesCompute<cpu>)
 .add_arguments(IndicesOpParam::__FIELDS__());
 
+NNVM_REGISTER_OP(_npi_logspace)
+.describe("Return numbers spaced evenly on a log scale.")
+.set_num_inputs(0)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<LogspaceParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", LogspaceShape)
+.set_attr<nnvm::FInferType>("FInferType", InitType<LogspaceParam>)
+.set_attr<FCompute>("FCompute<cpu>", LogspaceCompute<cpu>)
+.add_arguments(LogspaceParam::__FIELDS__());
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/np_init_op.cu b/src/operator/numpy/np_init_op.cu
index 9a2b236ec8b3..b6e2f93e8d3c 100644
--- a/src/operator/numpy/np_init_op.cu
+++ b/src/operator/numpy/np_init_op.cu
@@ -53,5 +53,8 @@ NNVM_REGISTER_OP(_npi_eye)
 NNVM_REGISTER_OP(_npi_indices)
 .set_attr<FCompute>("FCompute<gpu>", IndicesCompute<gpu>);
 
+NNVM_REGISTER_OP(_npi_logspace)
+.set_attr<FCompute>("FCompute<gpu>", LogspaceCompute<gpu>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/np_init_op.h b/src/operator/numpy/np_init_op.h
index 9eb83e8018c8..69999ae8710e 100644
--- a/src/operator/numpy/np_init_op.h
+++ b/src/operator/numpy/np_init_op.h
@@ -26,6 +26,7 @@
 #ifndef MXNET_OPERATOR_NUMPY_NP_INIT_OP_H_
 #define MXNET_OPERATOR_NUMPY_NP_INIT_OP_H_
 
+#include <algorithm>
 #include <vector>
 #include <string>
 #include "../tensor/init_op.h"
@@ -212,6 +213,65 @@ void IdentityCompute(const nnvm::NodeAttrs& attrs,
   });
 }
 
+struct LogspaceParam : public dmlc::Parameter<LogspaceParam> {
+  double start;
+  double stop;
+  int num;
+  bool endpoint;
+  double base;
+  std::string ctx;
+  int dtype;
+  DMLC_DECLARE_PARAMETER(LogspaceParam) {
+    DMLC_DECLARE_FIELD(start)
+    .describe("The starting value of the sequence.");
+    DMLC_DECLARE_FIELD(stop)
+    .describe("The ending value of the sequence");
+    DMLC_DECLARE_FIELD(num)
+    .describe("Number of samples to generate. Must be non-negative.");
+    DMLC_DECLARE_FIELD(endpoint)
+    .set_default(true)
+    .describe("If True, stop is the last sample. Otherwise, it is not included.");
+    DMLC_DECLARE_FIELD(base)
+    .set_default(10.0)
+    .describe("The base of the log space. The step size between the elements in "
+    "ln(samples) / ln(base) (or log_base(samples)) is uniform.");
+    DMLC_DECLARE_FIELD(ctx)
+    .set_default("")
+    .describe("Context of output, in format [cpu|gpu|cpu_pinned](n)."
+    "Only used for imperative calls.");
+    DMLC_DECLARE_FIELD(dtype).set_default(mshadow::kFloat32)
+    MXNET_ADD_ALL_TYPES
+    .describe("Target data type.");
+  }
+};
+
+struct logspace_fwd {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(index_t i, double start, double stop, double base,
+                                  double step, int req, DType* out) {
+    KERNEL_ASSIGN(out[i], req,
+                  static_cast<DType>(math::pow(base, static_cast<double>(start + step * i))));
+  }
+};
+
+template<typename xpu>
+void LogspaceCompute(const nnvm::NodeAttrs& attrs,
+                     const OpContext& ctx,
+                     const std::vector<TBlob>& inputs,
+                     const std::vector<OpReqType>& req,
+                     const std::vector<TBlob>& outputs) {
+  using namespace mxnet_op;
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  const LogspaceParam& param = nnvm::get<LogspaceParam>(attrs.parsed);
+  if (param.num == 0) return;
+  MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+      int step_num = param.endpoint ? param.num - 1 : param.num;
+      double step = step_num > 0 ? (param.stop - param.start) / step_num : 0.0f;
+      Kernel<logspace_fwd, xpu>::Launch(s, outputs[0].Size(), param.start, param.stop, param.base,
+          step, req[0], outputs[0].dptr<DType>());
+  });
+}
+
 }  // namespace op
 }  // namespace mxnet
 
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 40e203548bda..f361fafe7b79 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -853,6 +853,56 @@ def hybrid_forward(self, F, x):
                     assert_almost_equal(mx_out.asnumpy(), np_out, atol=1e-3, rtol=1e-5)
 
 
+@with_seed()
+@use_np
+def test_np_logspace():
+    class TestLogspace(HybridBlock):
+        def __init__(self, start, stop, num=50, endpoint=None, base=50.0, dtype=None, axis=0):
+            super(TestLogspace, self).__init__()
+            self._start = start
+            self._stop = stop
+            self._num = num
+            self._endpoint = endpoint
+            self._base = base
+            self._dtype = dtype
+            self.axis = axis
+
+        def hybrid_forward(self, F, x):
+            return x + F.np.logspace(self._start, self._stop, self._num, self._endpoint, self._base, self._dtype, self.axis)
+
+    configs = [
+        (0.0, 1.0, 20),
+        (2, 8, 0),
+        (22, 11, 1),
+        (2.22, 9.99, 11),
+        (4.99999, 12.11111111, 111)
+    ]
+    base_configs = [0, 1, 5, 8, 10, 33]
+    dtypes = ['float32', 'float64', None]
+
+    for config in configs:
+        for dtype in dtypes:
+            for endpoint in [False, True]:
+                for hybridize in [False, True]:
+                    for base in base_configs:
+                        x = np.zeros(shape=(), dtype=dtype)
+                        net = TestLogspace(*config, endpoint=endpoint, base=base, dtype=dtype)
+                        np_out = _np.logspace(*config, endpoint=endpoint, base=base, dtype=dtype)
+                        if hybridize:
+                            net.hybridize()
+                        mx_out = net(x)
+                        assert_almost_equal(mx_out.asnumpy(), np_out, atol=1e-3, rtol=1e-5)
+                        if dtype is not None:
+                            assert mx_out.dtype == np_out.dtype
+
+                        # Test imperative once again
+                        mx_ret = np.logspace(*config, endpoint=endpoint, base=base, dtype=dtype)
+                        np_ret = _np.logspace(*config, endpoint=endpoint, base=base, dtype=dtype)
+                        assert_almost_equal(mx_ret.asnumpy(), np_ret, atol=1e-3, rtol=1e-5)
+                        if dtype is not None:
+                            assert mx_out.dtype == np_out.dtype
+
+
 @with_seed()
 @use_np
 def test_npx_slice():

From 968119793cb843eb5b4b6f32d9208e98fa45adcd Mon Sep 17 00:00:00 2001
From: "Shi, guangyong" <35016185+gyshi@users.noreply.github.com>
Date: Tue, 15 Oct 2019 23:27:07 +0800
Subject: [PATCH 448/813] add numpy op bitwise_xor, hsplit, moveaxis, rot90
 (#16257)

add numpy op bitwise_xor

fix some format problems

add numpy op moveaxis

fix code style

add numpy op rot90

fix pylint error

updata rot90

address comments

add  numpy op hsplit

refactor split compute function

refactor moveaxis code
---
 python/mxnet/_numpy_op_doc.py                 |  44 +++
 python/mxnet/ndarray/numpy/_op.py             | 164 +++++++-
 python/mxnet/numpy/multiarray.py              | 139 ++++++-
 python/mxnet/symbol/numpy/_symbol.py          | 151 ++++++-
 src/operator/mshadow_op.h                     |   2 +
 .../numpy/np_elemwise_broadcast_op.cu         |   4 +
 src/operator/numpy/np_matrix_op-inl.h         | 367 ++++++++++++++++++
 src/operator/numpy/np_matrix_op.cc            | 168 ++++++++
 src/operator/numpy/np_matrix_op.cu            |  12 +
 src/operator/operator_tune.cc                 |   1 +
 src/operator/tensor/matrix_op-inl.h           | 115 ++++--
 tests/python/unittest/test_numpy_op.py        | 148 +++++++
 12 files changed, 1271 insertions(+), 44 deletions(-)

diff --git a/python/mxnet/_numpy_op_doc.py b/python/mxnet/_numpy_op_doc.py
index 8ad5247fa263..667c3bddb71f 100644
--- a/python/mxnet/_numpy_op_doc.py
+++ b/python/mxnet/_numpy_op_doc.py
@@ -704,3 +704,47 @@ def _np_squeeze(a, axis=None, out=None):
     (1, 3)
     """
     pass
+
+
+def _np_moveaxis(a, source, destination):
+    """Move axes of an array to new positions.
+    Other axes remain in their original order.
+
+    Parameters
+    ----------
+    a : ndarray
+        The array whose axes should be reordered.
+        source : int or sequence of int
+        Original positions of the axes to move. These must be unique.
+        destination : int or sequence of int
+        Destination positions for each of the original axes. These must also be
+        unique.
+
+    Returns
+    -------
+    result : ndarray
+        Array with moved axes. This array is a view of the input array.
+
+    See Also
+    --------
+        transpose: Permute the dimensions of an array.
+        swapaxes: Interchange two axes of an array.
+
+    Examples
+    --------
+    >>> x = np.zeros((3, 4, 5))
+    >>> np.moveaxis(x, 0, -1).shape
+    (4, 5, 3)
+    >>> np.moveaxis(x, -1, 0).shape
+    (5, 3, 4)
+    These all achieve the same result:
+    >>> np.transpose(x).shape
+    (5, 4, 3)
+    >>> np.swapaxes(x, 0, -1).shape
+    (5, 4, 3)
+    >>> np.moveaxis(x, [0, 1], [-1, -2]).shape
+    (5, 4, 3)
+    >>> np.moveaxis(x, [0, 1, 2], [-1, -2, -3]).shape
+    (5, 4, 3)
+    """
+    pass
diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index 17ce505ecb54..fbdb8e98a15f 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -36,7 +36,8 @@
            'stack', 'vstack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax',
            'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
            'around', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
-           'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal']
+           'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal',
+           'hsplit', 'rot90']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -2479,6 +2480,121 @@ def split(ary, indices_or_sections, axis=0):
 # pylint: enable=redefined-outer-name
 
 
+# pylint: disable=redefined-outer-name
+@set_module('mxnet.ndarray.numpy')
+def hsplit(ary, indices_or_sections):
+    """Split an array into multiple sub-arrays horizontally (column-wise).
+
+    This is equivalent to ``split`` with ``axis=0`` if ``ary`` has one
+    dimension, and otherwise that with ``axis=1``.
+
+    Parameters
+    ----------
+    ary : ndarray
+        Array to be divided into sub-arrays.
+    indices_or_sections : int, list of ints or tuple of ints.
+        If `indices_or_sections` is an integer, N, the array will be divided
+        into N equal arrays along `axis`.  If such a split is not possible,
+        an error is raised.
+
+        If `indices_or_sections` is a list of sorted integers, the entries
+        indicate where along `axis` the array is split.
+
+        If an index exceeds the dimension of the array along `axis`,
+        it will raises errors. so index must less than or euqal to
+        the dimension of the array along axis.
+
+    Returns
+    -------
+    sub-arrays : list of ndarrays
+        A list of sub-arrays.
+
+    Notes
+    ------
+    - If `indices_or_sections` is given as an integer, but a split
+      does not result in equal division.It will raises ValueErrors.
+
+    - If indices_or_sections is an integer, and the number is 1, it will
+      raises an error. Because single output from split is not supported yet...
+
+    See Also
+    --------
+    split : Split an array into multiple sub-arrays of equal size.
+
+    Examples
+    --------
+    >>> x = np.arange(16.0).reshape(4, 4)
+    >>> x
+    array([[ 0.,  1.,  2.,  3.],
+           [ 4.,  5.,  6.,  7.],
+           [ 8.,  9., 10., 11.],
+           [12., 13., 14., 15.]])
+    >>> np.hsplit(x, 2)
+    [array([[ 0.,  1.],
+           [ 4.,  5.],
+           [ 8.,  9.],
+           [12., 13.]]),
+    array([[ 2.,  3.],
+           [ 6.,  7.],
+           [10., 11.],
+           [14., 15.]])]
+    >>> np.hsplit(x, [3, 6])
+    [array([[ 0.,  1.,  2.],
+           [ 4.,  5.,  6.],
+           [ 8.,  9., 10.],
+           [12., 13., 14.]]),
+    array([[ 3.],
+           [ 7.],
+           [11.],
+           [15.]]),
+    array([], shape=(4, 0), dtype=float32)]
+
+    With a higher dimensional array the split is still along the second axis.
+
+    >>> x = np.arange(8.0).reshape(2, 2, 2)
+    >>> x
+    array([[[ 0.,  1.],
+            [ 2.,  3.]],
+           [[ 4.,  5.],
+            [ 6.,  7.]]])
+    >>> np.hsplit(x, 2)
+    [array([[[ 0.,  1.]],
+            [[ 4.,  5.]]]),
+     array([[[ 2.,  3.]],
+            [[ 6.,  7.]]])]
+
+    If ``ary`` has one dimension, 'axis' = 0.
+    >>> x = np.arange(4)
+    array([0., 1., 2., 3.])
+    >>> np.hsplit(x, 2)
+    [array([0., 1.]), array([2., 3.])]
+
+    If you want to produce an empty sub-array, you can see an example.
+    >>> np.hsplit(x, [2, 2])
+    [array([0., 1.]), array([], dtype=float32), array([2., 3.])]
+    """
+    indices = []
+    axis = 1
+    if (len(ary.shape) == 1):
+        axis = 0
+    axis_size = ary.shape[axis]
+    if isinstance(indices_or_sections, int):
+        sections = indices_or_sections
+        if axis_size % sections:
+            raise ValueError('array hsplit does not result in an equal division')
+        section_size = int(axis_size / sections)
+        indices = [i * section_size for i in range(sections)]
+    elif isinstance(indices_or_sections, (list, set, tuple)):
+        indices = [0] + list(indices_or_sections)
+    else:
+        raise ValueError('indices_or_sections must either int or tuple of ints')
+    ret = _npi.hsplit(ary, indices, axis, False)
+    if not isinstance(ret, list):
+        raise NotImplementedError('single output from hsplit is not supported yet...')
+    return ret
+# pylint: enable=redefined-outer-name
+
+
 @set_module('mxnet.ndarray.numpy')
 def vsplit(ary, indices_or_sections):
     r"""
@@ -4124,3 +4240,49 @@ def less_equal(x1, x2, out=None):
     """
     return _ufunc_helper(x1, x2, _npi.less_equal, _np.less_equal, _npi.less_equal_scalar,
                          _npi.greater_equal_scalar, out)
+
+
+@set_module('mxnet.ndarray.numpy')
+def rot90(m, k=1, axes=(0, 1)):
+    """
+    Rotate an array by 90 degrees in the plane specified by axes.
+    Rotation direction is from the first towards the second axis.
+    Parameters
+    ----------
+    m : ndarray
+        Array of two or more dimensions.
+    k : integer
+        Number of times the array is rotated by 90 degrees.
+    axes: (2,) array_like
+        The array is rotated in the plane defined by the axes.
+        Axes must be different.
+
+    Returns
+    -------
+    y : ndarray
+        A rotated view of `m`.
+
+    -----
+    rot90(m, k=1, axes=(1,0)) is the reverse of rot90(m, k=1, axes=(0,1))
+    rot90(m, k=1, axes=(1,0)) is equivalent to rot90(m, k=-1, axes=(0,1))
+    Examples
+    --------
+    >>> m = np.array([[1,2],[3,4]], 'int')
+    >>> m
+    array([[1, 2],
+           [3, 4]], dtype=int64)
+    >>> np.rot90(m)
+    array([[2, 4],
+           [1, 3]], dtype=int64)
+    >>> np.rot90(m, 2)
+    array([[4, 3],
+           [2, 1]], dtype=int64)
+    >>> m = np.arange(8).reshape((2,2,2))
+    >>> np.rot90(m, 1, (1,2))
+    array([[[1., 3.],
+            [0., 2.]],
+
+           [[5., 7.],
+            [4., 6.]]])
+    """
+    return _npi.rot90(m, k=k, axes=axes)
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 480558f4306d..70977c0b7f2d 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -55,8 +55,7 @@
            'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming',
            'blackman', 'flip', 'around', 'arctan2', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril',
            'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less',
-           'greater_equal', 'less_equal']
-
+           'greater_equal', 'less_equal', 'hsplit', 'rot90']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -4114,8 +4113,7 @@ def vsplit(ary, indices_or_sections):
             [4., 5., 6., 7.]]), array([[ 8.,  9., 10., 11.],
             [12., 13., 14., 15.]])]
 
-    With a higher dimensional array the split is still along the first axis.
-
+    >>> # With a higher dimensional array the split is still along the first axis.
     >>> x = np.arange(8.0).reshape(2, 2, 2)
     >>> x
     array([[[ 0.,  1.],
@@ -5651,3 +5649,136 @@ def less_equal(x1, x2, out=None):
     array([True])
     """
     return _mx_nd_np.less_equal(x1, x2, out)
+
+
+@set_module('mxnet.numpy')
+def rot90(m, k=1, axes=(0, 1)):
+    """
+    Rotate an array by 90 degrees in the plane specified by axes.
+    Rotation direction is from the first towards the second axis.
+    Parameters
+    ----------
+    m : ndarray
+        Array of two or more dimensions.
+    k : integer
+        Number of times the array is rotated by 90 degrees.
+    axes: (2,) array_like
+        The array is rotated in the plane defined by the axes.
+        Axes must be different.
+
+    Returns
+    -------
+    y : ndarray
+        A rotated view of `m`.
+
+    -----
+    rot90(m, k=1, axes=(1,0)) is the reverse of rot90(m, k=1, axes=(0,1))
+    rot90(m, k=1, axes=(1,0)) is equivalent to rot90(m, k=-1, axes=(0,1))
+    Examples
+    --------
+    >>> m = np.array([[1,2],[3,4]], 'int')
+    >>> m
+    array([[1, 2],
+           [3, 4]], dtype=int64)
+    >>> np.rot90(m)
+    array([[2, 4],
+           [1, 3]], dtype=int64)
+    >>> np.rot90(m, 2)
+    array([[4, 3],
+           [2, 1]], dtype=int64)
+    >>> m = np.arange(8).reshape((2,2,2))
+    >>> np.rot90(m, 1, (1,2))
+    array([[[1., 3.],
+            [0., 2.]],
+
+           [[5., 7.],
+            [4., 6.]]])
+    """
+    return _mx_nd_np.rot90(m, k=k, axes=axes)
+
+
+@set_module('mxnet.numpy')
+def hsplit(ary, indices_or_sections):
+    """Split an array into multiple sub-arrays horizontally (column-wise).
+    This is equivalent to ``split`` with ``axis=0`` if ``ary`` has one
+    dimension, and otherwise that with ``axis=1``.
+
+    Parameters
+    ----------
+    ary : ndarray
+        Array to be divided into sub-arrays.
+    indices_or_sections : int, list of ints or tuple of ints.
+        If `indices_or_sections` is an integer, N, the array will be divided
+        into N equal arrays along `axis`.  If such a split is not possible,
+        an error is raised.
+        If `indices_or_sections` is a list of sorted integers, the entries
+        indicate where along `axis` the array is split.
+        If an index exceeds the dimension of the array along `axis`,
+        it will raises errors. so index must less than or euqal to
+        the dimension of the array along axis.
+
+    Returns
+    -------
+    sub-arrays : list of ndarrays
+        A list of sub-arrays.
+
+    Notes
+    ------
+    - If `indices_or_sections` is given as an integer, but a split
+      does not result in equal division.It will raises ValueErrors.
+    - If indices_or_sections is an integer, and the number is 1, it will
+      raises an error. Because single output from split is not supported yet...
+
+    See Also
+    --------
+    split : Split an array into multiple sub-arrays of equal size.
+
+    Examples
+    --------
+    >>> x = np.arange(16.0).reshape(4, 4)
+    >>> x
+    array([[ 0.,  1.,  2.,  3.],
+           [ 4.,  5.,  6.,  7.],
+           [ 8.,  9., 10., 11.],
+           [12., 13., 14., 15.]])
+    >>> np.hsplit(x, 2)
+    [array([[ 0.,  1.],
+           [ 4.,  5.],
+           [ 8.,  9.],
+           [12., 13.]]),
+    array([[ 2.,  3.],
+           [ 6.,  7.],
+           [10., 11.],
+           [14., 15.]])]
+    >>> np.hsplit(x, [3, 6])
+    [array([[ 0.,  1.,  2.],
+           [ 4.,  5.,  6.],
+           [ 8.,  9., 10.],
+           [12., 13., 14.]]),
+    array([[ 3.],
+           [ 7.],
+           [11.],
+           [15.]]),
+    array([], shape=(4, 0), dtype=float32)]
+    With a higher dimensional array the split is still along the second axis.
+    >>> x = np.arange(8.0).reshape(2, 2, 2)
+    >>> x
+    array([[[ 0.,  1.],
+            [ 2.,  3.]],
+           [[ 4.,  5.],
+            [ 6.,  7.]]])
+    >>> np.hsplit(x, 2)
+    [array([[[ 0.,  1.]],
+            [[ 4.,  5.]]]),
+     array([[[ 2.,  3.]],
+            [[ 6.,  7.]]])]
+    If ``ary`` has one dimension, 'axis' = 0.
+    >>> x = np.arange(4)
+    array([0., 1., 2., 3.])
+    >>> np.hsplit(x, 2)
+    [array([0., 1.]), array([2., 3.])]
+    If you want to produce an empty sub-array, you can see an example.
+    >>> np.hsplit(x, [2, 2])
+    [array([0., 1.]), array([], dtype=float32), array([2., 3.])]
+    """
+    return _mx_nd_np.hsplit(ary, indices_or_sections)
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index fa618221d508..66b2aa2ac3fb 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -39,7 +39,7 @@
            'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
            'around', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
            'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal',
-           'less_equal']
+           'less_equal', 'hsplit', 'rot90']
 
 
 def _num_outputs(sym):
@@ -2721,6 +2721,112 @@ def split(ary, indices_or_sections, axis=0):
 # pylint: enable=redefined-outer-name
 
 
+# pylint: disable=redefined-outer-name
+@set_module('mxnet.ndarray.numpy')
+def hsplit(ary, indices_or_sections):
+    """Split an array into multiple sub-arrays horizontally (column-wise).
+
+    This is equivalent to ``split`` with ``axis=0`` if ``ary`` has one
+    dimension, and otherwise that with ``axis=1``.
+
+    Parameters
+    ----------
+    ary : _Symbol
+        Array to be divided into sub-arrays.
+    indices_or_sections : int, list of ints or tuple of ints.
+        If `indices_or_sections` is an integer, N, the array will be divided
+        into N equal arrays along `axis`.  If such a split is not possible,
+        an error is raised.
+
+        If `indices_or_sections` is a list of sorted integers, the entries
+        indicate where along `axis` the array is split.
+
+        If an index exceeds the dimension of the array along `axis`,
+        it will raises errors. so index must less than or euqal to
+        the dimension of the array along axis.
+
+    Returns
+    -------
+    sub-arrays : _Symbol
+        A list of sub-arrays.
+
+    Notes
+    ------
+    - If `indices_or_sections` is given as an integer, but a split
+      does not result in equal division.It will raises ValueErrors.
+
+    - If indices_or_sections is an integer, and the number is 1, it will
+      raises an error. Because single output from split is not supported yet...
+
+    See Also
+    --------
+    split : Split an array into multiple sub-arrays of equal size.
+
+    Examples
+    --------
+    >>> x = np.arange(16.0).reshape(4, 4)
+    >>> x
+    array([[ 0.,  1.,  2.,  3.],
+           [ 4.,  5.,  6.,  7.],
+           [ 8.,  9., 10., 11.],
+           [12., 13., 14., 15.]])
+    >>> np.hsplit(x, 2)
+    [array([[ 0.,  1.],
+           [ 4.,  5.],
+           [ 8.,  9.],
+           [12., 13.]]),
+    array([[ 2.,  3.],
+           [ 6.,  7.],
+           [10., 11.],
+           [14., 15.]])]
+    >>> np.hsplit(x, [3, 6])
+    [array([[ 0.,  1.,  2.],
+           [ 4.,  5.,  6.],
+           [ 8.,  9., 10.],
+           [12., 13., 14.]]),
+    array([[ 3.],
+           [ 7.],
+           [11.],
+           [15.]]),
+    array([], shape=(4, 0), dtype=float32)]
+
+    With a higher dimensional array the split is still along the second axis.
+
+    >>> x = np.arange(8.0).reshape(2, 2, 2)
+    >>> x
+    array([[[ 0.,  1.],
+            [ 2.,  3.]],
+           [[ 4.,  5.],
+            [ 6.,  7.]]])
+    >>> np.hsplit(x, 2)
+    [array([[[ 0.,  1.]],
+            [[ 4.,  5.]]]),
+     array([[[ 2.,  3.]],
+            [[ 6.,  7.]]])]
+
+    If ``ary`` has one dimension, 'axis' = 0.
+    >>> x = np.arange(4)
+    array([0., 1., 2., 3.])
+    >>> np.hsplit(x, 2)
+    [array([0., 1.]), array([2., 3.])]
+
+    If you want to produce an empty sub-array, you can see an example.
+    >>> np.hsplit(x, [2, 2])
+    [array([0., 1.]), array([], dtype=float32), array([2., 3.])]
+    """
+    indices = []
+    sections = 0
+    if isinstance(indices_or_sections, int):
+        sections = indices_or_sections
+    elif isinstance(indices_or_sections, (list, set, tuple)):
+        indices = [0] + list(indices_or_sections)
+    else:
+        raise ValueError('indices_or_sections must either int or tuple of ints')
+    ret = _npi.hsplit(ary, indices, 1, False, sections)
+    return ret
+# pylint: enable=redefined-outer-name
+
+
 @set_module('mxnet.symbol.numpy')
 def vsplit(ary, indices_or_sections):
     r"""
@@ -4162,4 +4268,47 @@ def less_equal(x1, x2, out=None):
                          _npi.greater_equal_scalar, out)
 
 
+@set_module('mxnet.symbol.numpy')
+def rot90(m, k=1, axes=(0, 1)):
+    """
+    Rotate an array by 90 degrees in the plane specified by axes.
+    Rotation direction is from the first towards the second axis.
+    Parameters
+    ----------
+    m : _Symbol
+        Array of two or more dimensions.
+    k : integer
+        Number of times the array is rotated by 90 degrees.
+    axes: (2,) array_like
+        The array is rotated in the plane defined by the axes.
+        Axes must be different.
+    Returns
+    -------
+    y : _Symbol
+        A rotated view of `m`.
+    -----
+    rot90(m, k=1, axes=(1,0)) is the reverse of rot90(m, k=1, axes=(0,1))
+    rot90(m, k=1, axes=(1,0)) is equivalent to rot90(m, k=-1, axes=(0,1))
+    Examples
+    --------
+    >>> m = np.array([[1,2],[3,4]], 'int')
+    >>> m
+    array([[1, 2],
+           [3, 4]], dtype=int64)
+    >>> np.rot90(m)
+    array([[2, 4],
+           [1, 3]], dtype=int64)
+    >>> np.rot90(m, 2)
+    array([[4, 3],
+           [2, 1]], dtype=int64)
+    >>> m = np.arange(8).reshape((2,2,2))
+    >>> np.rot90(m, 1, (1,2))
+    array([[[1., 3.],
+            [0., 2.]],
+           [[5., 7.],
+            [4., 6.]]])
+    """
+    return _npi.rot90(m, k=k, axes=axes)
+
+
 _set_np_symbol_class(_Symbol)
diff --git a/src/operator/mshadow_op.h b/src/operator/mshadow_op.h
index 471b6f395a05..e14c8dbb0b78 100644
--- a/src/operator/mshadow_op.h
+++ b/src/operator/mshadow_op.h
@@ -383,6 +383,8 @@ MXNET_BINARY_MATH_OP(logical_or, a || b ? DType(1) : DType(0));
 
 MXNET_BINARY_MATH_OP(logical_xor, (a || b) && !(a && b) ? DType(1) : DType(0));
 
+MXNET_BINARY_MATH_OP(bitwise_xor, static_cast<int64_t>(a) ^ static_cast<int64_t>(b));
+
 MXNET_UNARY_MATH_OP(square_root, math::sqrt(a));
 
 MXNET_UNARY_MATH_OP(square_root_grad, 0.5f / math::id(a));
diff --git a/src/operator/numpy/np_elemwise_broadcast_op.cu b/src/operator/numpy/np_elemwise_broadcast_op.cu
index b48c0b0ace6d..3b780325227a 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op.cu
+++ b/src/operator/numpy/np_elemwise_broadcast_op.cu
@@ -27,6 +27,7 @@
 
 namespace mxnet {
 namespace op {
+
 NNVM_REGISTER_OP(_npi_add)
 .set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, op::mshadow_op::plus>);
 
@@ -137,5 +138,8 @@ NNVM_REGISTER_OP(_backward_npi_ldexp_scalar)
 NNVM_REGISTER_OP(_backward_npi_rldexp_scalar)
 .set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Backward<gpu, mshadow_op::rldexp_grad>);
 
+NNVM_REGISTER_OP(_np_bitwise_xor)
+.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, mshadow_op::bitwise_xor>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/np_matrix_op-inl.h b/src/operator/numpy/np_matrix_op-inl.h
index 0eefeb9cdb5d..b3206bf4aa75 100644
--- a/src/operator/numpy/np_matrix_op-inl.h
+++ b/src/operator/numpy/np_matrix_op-inl.h
@@ -365,6 +365,373 @@ void NumpyFlipForward(const nnvm::NodeAttrs& attrs,
   NumpyFlipForwardImpl<xpu>(ctx, inputs, outputs, stride_, trailing_, flip_index);
 }
 
+struct NumpyMoveaxisParam : public dmlc::Parameter<NumpyMoveaxisParam> {
+  mxnet::TShape source;
+  mxnet::TShape destination;
+  DMLC_DECLARE_PARAMETER(NumpyMoveaxisParam) {
+    DMLC_DECLARE_FIELD(source)
+    .describe("Original positions of the axes to move. These must be unique.");
+    DMLC_DECLARE_FIELD(destination)
+    .describe("Destination positions for each of the original axes. "
+              "These must also be unique.");
+  }
+};
+
+inline mxnet::TShape NumpyMoveaxisShapeImpl(const nnvm::NodeAttrs& attrs,
+                                            const int& ndim) {
+  const NumpyMoveaxisParam& param = nnvm::get<NumpyMoveaxisParam>(attrs.parsed);
+  mxnet::TShape axes(ndim, -1);
+  std::vector<bool> state_axes(ndim, false);
+  mxnet::TShape real_src(param.source.ndim(), -1);
+  mxnet::TShape real_des(param.destination.ndim(), -1);
+  for (int i = 0; i < param.source.ndim(); ++i) {
+    if (param.source[i] >= 0) {
+      CHECK_LT(static_cast<size_t>(param.source[i]), ndim);
+      real_src[i] = param.source[i];
+    } else {
+      CHECK_LT(param.source[i] + ndim, ndim);
+      real_src[i] = param.source[i] + ndim;
+    }
+    if (param.destination[i] >= 0) {
+      CHECK_LT(static_cast<size_t>(param.destination[i]), ndim);
+      real_des[i] = param.destination[i];
+    } else {
+      CHECK_LT(param.destination[i] + ndim, ndim);
+      real_des[i] = param.destination[i] + ndim;
+    }
+  }
+  if (ndim > 1) {
+    for (int i = 0; i < param.source.ndim() - 1; ++i) {
+      for (int j = i + 1; j < param.source.ndim(); ++j) {
+        CHECK_NE(real_src[i], real_src[j])
+          << "repeated axis in `source` argument";
+        CHECK_NE(real_des[i], real_des[j])
+          << "repeated axis in `destination` argument";
+      }
+    }
+  }
+  for (int i = 0; i < param.source.ndim(); ++i) {
+    axes[real_des[i]] = real_src[i];
+    state_axes[real_src[i]] = true;
+  }
+  for (int i = 0; i < axes.ndim(); ++i) {
+    if (axes[i] < 0) {
+      for (int j = 0; j < axes.ndim(); ++j) {
+        if (state_axes[j] == false) {
+          axes[i] = j;
+          state_axes[j] = true;
+          break;
+        }
+      }
+    }
+  }
+  return axes;
+}
+
+template<typename xpu>
+void NumpyMoveaxisCompute(const nnvm::NodeAttrs& attrs,
+                          const OpContext& ctx,
+                          const std::vector<TBlob>& inputs,
+                          const std::vector<OpReqType>& req,
+                          const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mshadow::expr;
+  const NumpyMoveaxisParam& param = nnvm::get<NumpyMoveaxisParam>(attrs.parsed);
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 1U);
+  CHECK_EQ(req[0], kWriteTo) << "Moveaxis does not support inplace";
+  CHECK_EQ(param.source.ndim(), param.destination.ndim())
+    << "source and destination not equal.";
+  mxnet::TShape axes;
+  axes = NumpyMoveaxisShapeImpl(attrs, inputs[0].ndim());
+  MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, Dtype, {
+    TransposeImpl<xpu>(ctx.run_ctx, inputs[0], outputs[0], axes);
+  })
+}
+
+struct NumpyRot90Param : public dmlc::Parameter<NumpyRot90Param> {
+  int k;
+  dmlc::optional<mxnet::TShape> axes;
+  DMLC_DECLARE_PARAMETER(NumpyRot90Param) {
+    DMLC_DECLARE_FIELD(k)
+    .set_default(1)
+    .describe("Number of times the array is rotated by 90 degrees.");
+    DMLC_DECLARE_FIELD(axes)
+    .set_default(dmlc::optional<mxnet::TShape>())
+    .describe(" The array is rotated in the plane defined by the axes. Axes must be different.");
+  }
+};
+
+struct rot90reverse {
+  MSHADOW_XINLINE static index_t ReverseIndex(index_t idx,
+                                              index_t nreversedim,
+                                              const index_t * stride_,
+                                              const index_t * trailing_) {
+    index_t outputIndex = idx;
+    for (index_t i = 0; i < nreversedim; ++i) {
+      const index_t low = outputIndex % trailing_[i];
+      index_t high = outputIndex / trailing_[i];
+      const index_t x = high % stride_[i];
+      high /= stride_[i];
+      outputIndex = (high * stride_[i] + stride_[i] - 1 - x) * trailing_[i] + low;
+    }
+    return outputIndex;
+  }
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(index_t index, index_t nreversedim, const DType *src, DType *dst,
+                                  const index_t * stride_,
+                                  const index_t * trailing_) {
+    index_t new_idx = ReverseIndex(index, nreversedim, stride_, trailing_);
+    dst[new_idx] = src[index];
+  }
+};
+
+template<typename xpu>
+void NumpyRot90ComputeFlipIml(const OpContext& ctx,
+                              const std::vector<TBlob>& inputs,
+                              const std::vector<OpReqType>& req,
+                              const std::vector<TBlob>& outputs,
+                              const index_t axis0, const index_t axis1) {
+  using namespace mshadow;
+  using namespace mxnet_op;
+
+  const mxnet::TShape& ishape = inputs[0].shape_;
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+
+  std::vector<index_t> stride_(2);
+  std::vector<index_t>  trailing_(2);
+  index_t reverse_index = 0;
+  std::vector<index_t> temp{axis0, axis1};
+  for (int axis : temp) {
+    stride_[reverse_index] = ishape[axis];
+    trailing_[reverse_index] = 1;
+    for (int i2 = axis + 1; i2 < ishape.ndim(); ++i2) {
+      trailing_[reverse_index] *= ishape[i2];
+    }
+    reverse_index++;
+  }
+
+  index_t workspace_size = 2 * sizeof(index_t);
+  Tensor<xpu, 1, char> workspace =
+      ctx.requested[0].get_space_typed<xpu, 1, char>(Shape1(2 * workspace_size), s);
+  Tensor<cpu, 1, index_t> stride_cpu_tensor(stride_.data(), Shape1(stride_.size()));
+  Tensor<xpu, 1, index_t> stride_xpu_tensor(
+      reinterpret_cast<index_t*>(workspace.dptr_), Shape1(stride_.size()));
+  Tensor<cpu, 1, index_t> trailing_cpu_tensor(trailing_.data(), Shape1(trailing_.size()));
+  Tensor<xpu, 1, index_t> trailing_xpu_tensor(
+      reinterpret_cast<index_t*>(workspace.dptr_ + workspace_size), Shape1(trailing_.size()));
+
+  mshadow::Copy(stride_xpu_tensor, stride_cpu_tensor, s);
+  mshadow::Copy(trailing_xpu_tensor, trailing_cpu_tensor, s);
+  MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+    Kernel<rot90reverse, xpu>::Launch(s, inputs[0].Size(), reverse_index,
+                                      inputs[0].dptr<DType>(), outputs[0].dptr<DType>(),
+                                      stride_xpu_tensor.dptr_, trailing_xpu_tensor.dptr_);
+  });
+}
+
+struct rot90Transreverse {
+  MSHADOW_XINLINE static index_t ReverseIndex(index_t idx,
+                                              const index_t stride_,
+                                              const index_t trailing_) {
+    index_t outputIndex = idx;
+    const index_t low = outputIndex % trailing_;
+    index_t high = outputIndex / trailing_;
+    const index_t x = high % stride_;
+    high /= stride_;
+    outputIndex = (high * stride_ + stride_ - 1 - x) * trailing_ + low;
+
+    return outputIndex;
+  }
+  template<typename DType>
+  MSHADOW_XINLINE  static void Map(index_t index, const DType *src, DType *dst,
+                                   const index_t  stride_,
+                                   const index_t  trailing_) {
+    index_t new_idx = ReverseIndex(index, stride_, trailing_);
+    dst[new_idx] = src[index];
+  }
+};
+
+template<typename xpu>
+void NumpyRot90ComputeFlipTransposeIml(const OpContext& ctx,
+                                       const std::vector<TBlob>& inputs,
+                                       const std::vector<OpReqType>& req,
+                                       const std::vector<TBlob>& outputs,
+                                       const mxnet::TShape axes_list,
+                                       const index_t axis) {
+  using namespace mshadow;
+  using namespace mxnet_op;
+
+  const mxnet::TShape& ishape = inputs[0].shape_;
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+
+  index_t stride_;
+  index_t trailing_;
+
+  stride_ = ishape[axis];
+  trailing_ = 1;
+  for (int i2 = axis + 1; i2 < ishape.ndim(); ++i2) {
+    trailing_ *= ishape[i2];
+  }
+
+  MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+    index_t workspace_size = inputs[0].Size() * sizeof(DType);
+    Tensor<xpu, 1, char> workspace =
+        ctx.requested[0].get_space_typed<xpu, 1, char>(Shape1(workspace_size), s);
+    DType* data_ptr = reinterpret_cast<DType*>(workspace.dptr_);
+    TBlob mid_data = TBlob(data_ptr, inputs[0].shape_, xpu::kDevMask);
+    Kernel<rot90Transreverse, xpu>::Launch(s, inputs[0].Size(), inputs[0].dptr<DType>(),
+                                           mid_data.dptr<DType>(),
+                                           stride_, trailing_);
+    mxnet::op::TransposeImpl<xpu>(ctx.run_ctx, mid_data, outputs[0], axes_list);
+  });
+}
+
+
+template<typename xpu>
+void NumpyRot90ComputeTransposeFlipIml(const OpContext& ctx,
+                                       const std::vector<TBlob>& inputs,
+                                       const std::vector<OpReqType>& req,
+                                       const std::vector<TBlob>& outputs,
+                                       const mxnet::TShape axes_list,
+                                       const index_t axis) {
+  using namespace mshadow;
+  using namespace mxnet_op;
+
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+
+  MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+    index_t workspace_size = inputs[0].Size() * sizeof(DType);
+    Tensor<xpu, 1, char> workspace =
+        ctx.requested[0].get_space_typed<xpu, 1, char>(Shape1(workspace_size), s);
+    DType* data_ptr = reinterpret_cast<DType*>(workspace.dptr_);
+    mxnet::TShape mid_shape(outputs[0].shape_);
+    TBlob mid_data = TBlob(data_ptr, mid_shape, xpu::kDevMask);
+    mxnet::op::TransposeImpl<xpu>(ctx.run_ctx, inputs[0], mid_data, axes_list);
+
+    index_t stride_;
+    index_t trailing_;
+    stride_ = mid_shape[axis];
+    trailing_ = 1;
+    for (int i2 = axis + 1; i2 < mid_shape.ndim(); ++i2) {
+      trailing_ *= mid_shape[i2];
+    }
+    Kernel<rot90Transreverse, xpu>::Launch(s, mid_data.Size(), mid_data.dptr<DType>(),
+                                           outputs[0].dptr<DType>(),
+                                           stride_, trailing_);
+  });
+}
+
+template<int req>
+struct rot90 {
+  template<typename DType>
+  MSHADOW_XINLINE  static void Map(index_t i, const DType *in_data, DType *out_data) {
+    KERNEL_ASSIGN(out_data[i], req, in_data[i]);
+  }
+};
+
+template<typename xpu>
+void NumpyRot90Compute(const nnvm::NodeAttrs& attrs,
+                       const OpContext& ctx,
+                       const std::vector<TBlob>& inputs,
+                       const std::vector<OpReqType>& req,
+                       const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mxnet_op;
+  const NumpyRot90Param& param = nnvm::get<NumpyRot90Param>(attrs.parsed);
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 1U);
+  CHECK_EQ(inputs[0].type_flag_, outputs[0].type_flag_);
+  if (outputs[0].Size() == 0) return;
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+
+  int real_k(param.k);
+  real_k = real_k % 4;
+  if (real_k < 0) {
+    real_k += 4;
+  }
+
+  // axis has value
+  mxnet::TShape real_axes(param.axes.value());
+  for (index_t i = 0; i < real_axes.ndim(); i++) {
+    if (real_axes[i] < 0) {
+      real_axes[i] += inputs[0].shape_.ndim();
+    }
+  }
+  if (real_k == 0) {
+    MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+      MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
+        Kernel<rot90<req_type>, xpu>::Launch(s, inputs[0].Size(), inputs[0].dptr<DType>(),
+                                             outputs[0].dptr<DType>());
+      });
+    });
+  } else if (real_k == 2) {
+    NumpyRot90ComputeFlipIml<xpu>(ctx, inputs, req, outputs, real_axes[0], real_axes[1]);
+  } else if (real_k == 1) {
+    mxnet::TShape axes_list(inputs[0].shape_.ndim(), -1);
+    for (int i = 0; i < inputs[0].shape_.ndim(); ++i) {
+      axes_list[i] = i;
+    }
+    axes_list[real_axes[0]] += axes_list[real_axes[1]];
+    axes_list[real_axes[1]] = axes_list[real_axes[0]] - axes_list[real_axes[1]];
+    axes_list[real_axes[0]] -= axes_list[real_axes[1]];
+    NumpyRot90ComputeFlipTransposeIml<xpu>(ctx, inputs, req, outputs, axes_list, real_axes[1]);
+  } else if (real_k == 3) {
+    mxnet::TShape axes_list(inputs[0].shape_.ndim(), -1);
+    for (int i = 0; i < inputs[0].shape_.ndim(); ++i) {
+      axes_list[i] = i;
+    }
+    axes_list[real_axes[0]] += axes_list[real_axes[1]];
+    axes_list[real_axes[1]] = axes_list[real_axes[0]] - axes_list[real_axes[1]];
+    axes_list[real_axes[0]] -= axes_list[real_axes[1]];
+    NumpyRot90ComputeTransposeFlipIml<xpu>(ctx, inputs, req, outputs, axes_list, real_axes[1]);
+  }
+}
+
+template<typename xpu>
+inline void HSplitOpForward(const nnvm::NodeAttrs &attrs,
+                            const OpContext &ctx,
+                            const std::vector<TBlob> &inputs,
+                            const std::vector<OpReqType> &req,
+                            const std::vector<TBlob> &outputs) {
+  using namespace mshadow;
+  using namespace mshadow::expr;
+  using namespace mxnet_op;
+  const SplitParam &param = nnvm::get<SplitParam>(attrs.parsed);
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), param.sections > 0 ? param.sections : param.indices.ndim());
+  const TBlob &input_data = inputs[split_enum::kData];
+  int real_axis;
+  if (input_data.ndim() > 1) {
+    real_axis = 1;
+  } else {
+    real_axis = 0;
+  }
+  SplitOpForwardImpl<xpu>(attrs, ctx, inputs, req, outputs, real_axis);
+}
+
+template<typename xpu>
+inline void HSplitOpBackward(const nnvm::NodeAttrs &attrs,
+                             const OpContext &ctx,
+                             const std::vector<TBlob> &inputs,
+                             const std::vector<OpReqType> &req,
+                             const std::vector<TBlob> &outputs) {
+  using namespace mshadow;
+  using namespace mshadow::expr;
+  using namespace mxnet_op;
+  const SplitParam &param = nnvm::get<SplitParam>(attrs.parsed);
+  CHECK_EQ(inputs.size(), (param.sections > 0) ? param.sections : param.indices.ndim())
+    << "out grad vector size mush match the output size";
+  CHECK_EQ(outputs.size(), 1U);
+  int real_axis;
+  if (outputs[split_enum::kData].ndim() > 1) {
+    real_axis = 1;
+  } else {
+    real_axis = 0;
+  }
+  SplitOpBackwardImpl<xpu>(attrs, ctx, inputs, req, outputs, real_axis);
+}
+
 }  // namespace op
 }  // namespace mxnet
 
diff --git a/src/operator/numpy/np_matrix_op.cc b/src/operator/numpy/np_matrix_op.cc
index e4fcfffe2334..7bcd6ad27b52 100644
--- a/src/operator/numpy/np_matrix_op.cc
+++ b/src/operator/numpy/np_matrix_op.cc
@@ -32,6 +32,8 @@ namespace op {
 
 DMLC_REGISTER_PARAMETER(NumpyTransposeParam);
 DMLC_REGISTER_PARAMETER(NumpyRollParam);
+DMLC_REGISTER_PARAMETER(NumpyMoveaxisParam);
+DMLC_REGISTER_PARAMETER(NumpyRot90Param);
 
 bool NumpyTransposeShape(const nnvm::NodeAttrs& attrs,
                          mxnet::ShapeVector *in_attrs,
@@ -747,5 +749,171 @@ NNVM_REGISTER_OP(_backward_npi_flip)
 })
 .set_attr<FCompute>("FCompute<cpu>", NumpyFlipForward<cpu>);
 
+bool NumpyMoveaxisShape(const nnvm::NodeAttrs& attrs,
+                        mxnet::ShapeVector *in_attrs,
+                        mxnet::ShapeVector *out_attrs) {
+  const NumpyMoveaxisParam& param = nnvm::get<NumpyMoveaxisParam>(attrs.parsed);
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  mxnet::TShape& shp = (*in_attrs)[0];
+  CHECK_LE(shp.ndim(), 6) << "Transpose support at most 6 dimensions";
+  CHECK_EQ(param.source.ndim(), param.destination.ndim())
+    << "source and destination not equal.";
+  mxnet::TShape ret(shp.ndim(), -1);
+  mxnet::TShape axes;
+  axes = NumpyMoveaxisShapeImpl(attrs, shp.ndim());
+  for (int i = 0; i < shp.ndim(); ++i) {
+    CHECK(axes[i] < static_cast<int64_t>(shp.ndim()));
+    ret[i] = shp[axes[i]];
+  }
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, ret);
+  return shape_is_known(ret);
+}
+
+NNVM_REGISTER_OP(_np_moveaxis)
+.describe(R"code(Move axes of an array to new positions.
+Other axes remain in their original order.
+)code" ADD_FILELINE)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<NumpyMoveaxisParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyMoveaxisShape)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
+.set_attr<nnvm::FGradient>("FGradient",
+  [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
+     const NumpyMoveaxisParam& param = nnvm::get<NumpyMoveaxisParam>(n->attrs.parsed);
+     std::ostringstream os1;
+     os1 << param.source;
+     std::ostringstream os2;
+     os2 << param.destination;
+     return MakeNonlossGradNode("_np_moveaxis", n, ograds, {},
+                                {{"source", os2.str()}, {"destination", os1.str()}});
+})
+.set_attr<FCompute>("FCompute<cpu>", NumpyMoveaxisCompute<cpu>)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) { return std::vector<std::string>{"a"};
+})
+.add_argument("a", "NDArray-or-Symbol", "Source input")
+.add_arguments(NumpyMoveaxisParam::__FIELDS__());
+
+inline bool NumpyRot90Shape(const nnvm::NodeAttrs& attrs,
+                            mxnet::ShapeVector *in_attrs,
+                            mxnet::ShapeVector *out_attrs) {
+  using namespace mshadow;
+  const NumpyRot90Param& param = nnvm::get<NumpyRot90Param>(attrs.parsed);
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  mxnet::TShape& shp = (*in_attrs)[0];
+  if (!param.axes.has_value() || (param.axes.has_value() && param.axes.value().ndim() != 2)) {
+    LOG(FATAL) << "The length of axes must be 2.";
+  }
+  int real_k(param.k);
+  real_k = real_k % 4;
+  if (real_k < 0) {
+    real_k += 4;
+  }
+
+  mxnet::TShape res(shp);
+  mxnet::TShape real_axes(param.axes.value());
+  for (index_t i = 0; i < real_axes.ndim(); i++) {
+    if (real_axes[i] < 0) {
+      real_axes[i] += shp.ndim();
+    }
+  }
+
+  CHECK_NE(real_axes[0], real_axes[1])
+      << "axes have duplicates "
+      << real_axes;
+  if (real_axes[0] > shp.ndim() || real_axes[1] > shp.ndim() ||
+      real_axes[0] < 0 || real_axes[1] < 0) {
+    LOG(FATAL) << "Axes out of range for array of ndim";
+  }
+
+  if (real_k % 2 == 0) {
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, res);
+    return shape_is_known(res);
+  }
+
+  res[real_axes[0]] += res[real_axes[1]];
+  res[real_axes[1]] = res[real_axes[0]] - res[real_axes[1]];
+  res[real_axes[0]] -= res[real_axes[1]];
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, res);
+  return shape_is_known(res);
+}
+
+NNVM_REGISTER_OP(_npi_rot90)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<NumpyRot90Param>)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+     return std::vector<std::string>{"data"};
+})
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyRot90Shape)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
+.set_attr<mxnet::FCompute>("FCompute<cpu>", NumpyRot90Compute<cpu>)
+.set_attr<nnvm::FGradient>("FGradient",
+  [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
+     const NumpyRot90Param& param = nnvm::get<NumpyRot90Param>(n->attrs.parsed);
+     std::ostringstream os1;
+     os1 << param.k;
+     std::ostringstream os2;
+     os2 << param.axes;
+     return MakeNonlossGradNode("_npi_rot90", n, ograds, {},
+                                {{"k", os1.str()}, {"axes", os2.str()}});
+})
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& n) {
+     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+})
+.add_argument("data", "NDArray-or-Symbol", "Input ndarray")
+.add_arguments(NumpyRot90Param::__FIELDS__());
+
+inline bool HSplitOpShape(const nnvm::NodeAttrs& attrs,
+                          mxnet::ShapeVector* in_attrs,
+                          mxnet::ShapeVector* out_attrs) {
+  using namespace mshadow;
+  CHECK_EQ(in_attrs->size(), 1U);
+  mxnet::TShape dshape = in_attrs->at(split_enum::kData);
+  if (!mxnet::ndim_is_known(dshape)) return false;
+  int real_axis;
+  if (dshape.ndim() > 1) {
+    real_axis = 1;
+  } else {
+    real_axis = 0;
+  }
+  return SplitOpShapeImpl(attrs, in_attrs, out_attrs, real_axis);
+}
+
+NNVM_REGISTER_OP(_npi_hsplit)
+.set_attr_parser(ParamParser<SplitParam>)
+.set_num_inputs(1)
+.set_num_outputs(SplitNumOutputs)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+     return std::vector<std::string>{"data"};
+})
+.set_attr<mxnet::FInferShape>("FInferShape", HSplitOpShape)
+.set_attr<nnvm::FInferType>("FInferType", SplitOpType)
+.set_attr<FCompute>("FCompute<cpu>", HSplitOpForward<cpu>)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& n) {
+     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+})
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_npi_hsplit_backward"})
+.add_argument("data", "NDArray-or-Symbol", "The input")
+.add_arguments(SplitParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_npi_hsplit_backward)
+.set_attr_parser(ParamParser<SplitParam>)
+.set_num_inputs(SplitNumOutputs)
+.set_num_outputs(1)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& n) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+})
+.set_attr<FCompute>("FCompute<cpu>", HSplitOpBackward<cpu>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/np_matrix_op.cu b/src/operator/numpy/np_matrix_op.cu
index 125cd91acd1f..8c8301bb3bbf 100644
--- a/src/operator/numpy/np_matrix_op.cu
+++ b/src/operator/numpy/np_matrix_op.cu
@@ -97,5 +97,17 @@ NNVM_REGISTER_OP(_npi_flip)
 NNVM_REGISTER_OP(_backward_npi_flip)
 .set_attr<FCompute>("FCompute<gpu>", NumpyFlipForward<gpu>);
 
+NNVM_REGISTER_OP(_np_moveaxis)
+.set_attr<FCompute>("FCompute<gpu>", NumpyMoveaxisCompute<gpu>);
+
+NNVM_REGISTER_OP(_npi_rot90)
+.set_attr<FCompute>("FCompute<gpu>", NumpyRot90Compute<gpu>);
+
+NNVM_REGISTER_OP(_npi_hsplit)
+.set_attr<FCompute>("FCompute<gpu>", HSplitOpForward<gpu>);
+
+NNVM_REGISTER_OP(_npi_hsplit_backward)
+.set_attr<FCompute>("FCompute<gpu>", HSplitOpBackward<gpu>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/operator_tune.cc b/src/operator/operator_tune.cc
index 398d5a714bd1..50a6b41701cf 100644
--- a/src/operator/operator_tune.cc
+++ b/src/operator/operator_tune.cc
@@ -394,6 +394,7 @@ IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::logical_or);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::logical_or);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::logical_xor);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::logical_xor);  // NOLINT()
+IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::bitwise_xor);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::smooth_l1_loss);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::smooth_l1_gradient);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::lcm);  // NOLINT()
diff --git a/src/operator/tensor/matrix_op-inl.h b/src/operator/tensor/matrix_op-inl.h
index 58d9eaefa04b..7c3005e583b6 100644
--- a/src/operator/tensor/matrix_op-inl.h
+++ b/src/operator/tensor/matrix_op-inl.h
@@ -2711,24 +2711,14 @@ inline bool SplitOpType(const nnvm::NodeAttrs& attrs,
   return true;
 }
 
-inline bool SplitOpShape(const nnvm::NodeAttrs& attrs,
-                         mxnet::ShapeVector* in_attrs,
-                         mxnet::ShapeVector* out_attrs) {
+inline bool SplitOpShapeImpl(const nnvm::NodeAttrs& attrs,
+                             mxnet::ShapeVector* in_attrs,
+                             mxnet::ShapeVector* out_attrs,
+                             const int real_axis) {
   using namespace mshadow;
   const SplitParam& param = nnvm::get<SplitParam>(attrs.parsed);
-  CHECK_EQ(in_attrs->size(), 1U);
   mxnet::TShape dshape = in_attrs->at(split_enum::kData);
   mxnet::TShape ishape = in_attrs->at(split_enum::kData);
-  if (!mxnet::ndim_is_known(dshape)) return false;
-  if (param.axis >= 0) {
-    CHECK_LT(param.axis, dshape.ndim());
-  } else {
-    CHECK_LT(param.axis + dshape.ndim(), dshape.ndim());
-  }
-  int real_axis = param.axis;
-  if (real_axis < 0) {
-    real_axis += dshape.ndim();
-  }
   const mxnet::TShape indices =
     (param.sections > 0) ? GetSplitIndices(ishape, real_axis, param.sections) : param.indices;
   int num_outputs = (param.sections > 0) ? indices.ndim() - 1 : indices.ndim();
@@ -2745,7 +2735,7 @@ inline bool SplitOpShape(const nnvm::NodeAttrs& attrs,
     if (ishape[real_axis] == 0U) {
       end = start;
     } else {
-      CHECK(start < end)
+      CHECK(start <= end)
         << "start " << start << " is not less than end " << end << "for subarray " << i;
       CHECK(end <= ishape[real_axis])
         << "end " << end << " is no less than the size of the axis " << ishape[real_axis];
@@ -2781,6 +2771,26 @@ inline bool SplitOpShape(const nnvm::NodeAttrs& attrs,
   return true;
 }
 
+inline bool SplitOpShape(const nnvm::NodeAttrs& attrs,
+                         mxnet::ShapeVector* in_attrs,
+                         mxnet::ShapeVector* out_attrs) {
+  using namespace mshadow;
+  const SplitParam& param = nnvm::get<SplitParam>(attrs.parsed);
+  CHECK_EQ(in_attrs->size(), 1U);
+  mxnet::TShape dshape = in_attrs->at(split_enum::kData);
+  if (!mxnet::ndim_is_known(dshape)) return false;
+  if (param.axis >= 0) {
+    CHECK_LT(param.axis, dshape.ndim());
+  } else {
+    CHECK_LT(param.axis + dshape.ndim(), dshape.ndim());
+  }
+  int real_axis = param.axis;
+  if (real_axis < 0) {
+    real_axis += dshape.ndim();
+  }
+  return SplitOpShapeImpl(attrs, in_attrs, out_attrs, real_axis);
+}
+
 struct SplitKernel {
   /*!
    * \brief Map function for forward split_v2 operator
@@ -2846,24 +2856,19 @@ struct ConcatenateKernel {
 };
 
 template<typename xpu>
-inline void SplitOpForward(const nnvm::NodeAttrs& attrs,
-                           const OpContext& ctx,
-                           const std::vector<TBlob>& inputs,
-                           const std::vector<OpReqType>& req,
-                           const std::vector<TBlob>& outputs) {
+inline void SplitOpForwardImpl(const nnvm::NodeAttrs& attrs,
+                               const OpContext& ctx,
+                               const std::vector<TBlob>& inputs,
+                               const std::vector<OpReqType>& req,
+                               const std::vector<TBlob>& outputs,
+                               const int real_axis) {
   using namespace mshadow;
   using namespace mshadow::expr;
   using namespace mxnet_op;
   const SplitParam& param = nnvm::get<SplitParam>(attrs.parsed);
-  CHECK_EQ(inputs.size(), 1U);
-  CHECK_EQ(outputs.size(), (param.sections > 0) ? param.sections : param.indices.ndim());
   Stream<xpu> *s = ctx.get_stream<xpu>();
   const TBlob& input_data = inputs[split_enum::kData];
   size_t leading = 1, trailing = 1;
-  int real_axis = param.axis;
-  if (real_axis < 0) {
-    real_axis += input_data.ndim();
-  }
   CHECK_LT(real_axis, input_data.ndim());
   size_t mid = input_data.shape_[real_axis];
   for (int i = 0; i < real_axis; ++i) {
@@ -2909,25 +2914,39 @@ inline void SplitOpForward(const nnvm::NodeAttrs& attrs,
 }
 
 template<typename xpu>
-inline void SplitOpBackward(const nnvm::NodeAttrs& attrs,
-                            const OpContext& ctx,
-                            const std::vector<TBlob>& inputs,
-                            const std::vector<OpReqType>& req,
-                            const std::vector<TBlob>& outputs) {
+inline void SplitOpForward(const nnvm::NodeAttrs& attrs,
+                           const OpContext& ctx,
+                           const std::vector<TBlob>& inputs,
+                           const std::vector<OpReqType>& req,
+                           const std::vector<TBlob>& outputs) {
   using namespace mshadow;
   using namespace mshadow::expr;
   using namespace mxnet_op;
   const SplitParam& param = nnvm::get<SplitParam>(attrs.parsed);
-  CHECK_EQ(inputs.size(), (param.sections > 0) ? param.sections : param.indices.ndim())
-    << "out grad vector size mush match the output size";
-  CHECK_EQ(outputs.size(), 1U);
-  Stream<xpu> *s = ctx.get_stream<xpu>();
-  TBlob input_grad = outputs[split_enum::kData];
-  size_t leading = 1, trailing = 1;
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), (param.sections > 0) ? param.sections : param.indices.ndim());
+  const TBlob& input_data = inputs[split_enum::kData];
   int real_axis = param.axis;
   if (real_axis < 0) {
-      real_axis += input_grad.ndim();
+    real_axis += input_data.ndim();
   }
+  SplitOpForwardImpl<xpu>(attrs, ctx, inputs, req, outputs, real_axis);
+}
+
+template<typename xpu>
+inline void SplitOpBackwardImpl(const nnvm::NodeAttrs& attrs,
+                                const OpContext& ctx,
+                                const std::vector<TBlob>& inputs,
+                                const std::vector<OpReqType>& req,
+                                const std::vector<TBlob>& outputs,
+                                const int real_axis) {
+  using namespace mshadow;
+  using namespace mshadow::expr;
+  using namespace mxnet_op;
+  const SplitParam& param = nnvm::get<SplitParam>(attrs.parsed);
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  TBlob input_grad = outputs[split_enum::kData];
+  size_t leading = 1, trailing = 1;
   CHECK_LT(real_axis, input_grad.ndim());
   size_t mid = input_grad.shape_[real_axis];
   for (int i = 0; i < real_axis; ++i) {
@@ -2972,6 +2991,26 @@ inline void SplitOpBackward(const nnvm::NodeAttrs& attrs,
   });
 }
 
+template<typename xpu>
+inline void SplitOpBackward(const nnvm::NodeAttrs& attrs,
+                            const OpContext& ctx,
+                            const std::vector<TBlob>& inputs,
+                            const std::vector<OpReqType>& req,
+                            const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mshadow::expr;
+  using namespace mxnet_op;
+  const SplitParam& param = nnvm::get<SplitParam>(attrs.parsed);
+  CHECK_EQ(inputs.size(), (param.sections > 0) ? param.sections : param.indices.ndim())
+    << "out grad vector size mush match the output size";
+  CHECK_EQ(outputs.size(), 1U);
+  int real_axis = param.axis;
+  if (real_axis < 0) {
+      real_axis += outputs[split_enum::kData].ndim();
+  }
+  SplitOpBackwardImpl<xpu>(attrs, ctx, inputs, req, outputs, real_axis);
+}
+
 inline uint32_t SplitNumOutputs(const NodeAttrs& attrs) {
   const SplitParam& param = nnvm::get<SplitParam>(attrs.parsed);
   return (param.sections > 0) ? param.sections : param.indices.ndim();
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index f361fafe7b79..28edc2e05306 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -3385,6 +3385,154 @@ def test_np_builtin_op_signature():
             assert str(op.__signature__) == str(inspect.signature(getattr(_numpy_op_doc, op_name)))
 
 
+@with_seed()
+@use_np
+def test_np_moveaxis():
+    class TestMoveaxis(HybridBlock):
+        def __init__(self, source=None, destination=None):
+            super(TestMoveaxis, self).__init__()
+            self._source = source
+            self._destination= destination
+
+        def hybrid_forward(self, F, x):
+            return F.np.moveaxis(x, source=self._source, destination=self._destination)
+
+    dtypes = ['int32', 'int64', 'float16', 'float32', 'float64']
+    for hybridize in [False, True]:
+        for dtype in dtypes:
+            for ndim in [0, 1, 2, 3, 4, 5, 6]:
+                shape = rand_shape_nd(ndim, dim=5, allow_zero_size=True)
+                np_data = _np.random.uniform(low=-100, high=100, size=shape).astype(dtype)
+                mx_data = np.array(np_data, dtype=dtype)
+                axis = [i for i in range(ndim)]
+                random.shuffle(axis)
+                for i in range(ndim):
+                    source = random.sample(axis, i)
+                    destination = random.sample(axis, i)
+
+                    # test gluon
+                    test_moveaxis = TestMoveaxis(source,destination)
+                    if hybridize:
+                        test_moveaxis.hybridize()
+                    np_out = _np.moveaxis(np_data, source=source, destination=destination)
+                    mx_data.attach_grad()
+                    with mx.autograd.record():
+                        mx_out = test_moveaxis(mx_data)
+                    assert mx_out.shape == np_out.shape
+                    mx_out.backward()
+                    assert same(mx_data.grad.shape, mx_data.shape)
+                    assert same(mx_data.grad.asnumpy(), _np.ones(shape))
+                    # test imperative
+                    np_out = _np.moveaxis(np_data, source=source, destination=destination)
+                    mx_out = np.moveaxis(mx_data, source=source, destination= destination)
+                    assert np_out.dtype == mx_out.dtype
+                    assert same(mx_out.asnumpy(), np_out)
+
+
+@with_seed()
+@use_np
+def test_np_rot90():
+    class TestTRot90(HybridBlock):
+        def __init__(self, k=1, axes=(0, 1)):
+            super(TestTRot90, self).__init__()
+            self._k = k
+            self._axes = axes
+
+        def hybrid_forward(self, F, a, *args):
+            return F.np.rot90(a, self._k, self._axes)
+
+    configs = [
+        ((2, 3), 1, (0, 1)),
+        ((2, 3), 3, (0, 1)),
+        ((2, 3), 1, (1, 0)),
+        ((2, 3), 2, (1, 0)),
+        ((2, 3), 3, (1, 0)),
+        ((2, 3), 0, (1, 0)),
+        ((2, 3, 4, 5), 3, (1, 2)),
+        ((2, 3, 4, 5), -3, (2, 3)),
+        ((2, 3, 0, 5), -2, (2, 3)),
+        ((2, 0, 0, 5), -3, (2, 3)),
+        ((2, 3, 0, 5), 0, (2, 1)),
+    ]
+    dtypes = ['uint8', 'int8', 'int32', 'int64', 'float16', 'float32', 'float64']
+
+    for config in configs:
+        for dtype in dtypes:
+            for hybridize in [True, False]:
+                shape, k, axes = config[0], config[1], config[2]
+                x = rand_ndarray(shape=shape, dtype=dtype).as_np_ndarray()
+                net = TestTRot90(k=k, axes=axes)
+                if hybridize:
+                    net.hybridize()
+
+                x.attach_grad()
+                np_out = _np.rot90(x.asnumpy(), k=k, axes=axes)
+                with mx.autograd.record():
+                    mx_out = net(x)
+                assert mx_out.shape == np_out.shape
+                assert same(mx_out.asnumpy(), np_out)
+                mx_out.backward()
+                np_backward = _np.ones(shape, dtype)
+
+                assert same(x.grad.asnumpy().shape, np_backward.shape)
+                assert same(x.grad.asnumpy(), np_backward)
+
+                np_out = _np.rot90(x.asnumpy(), k=k, axes=axes)
+                mx_out = np.rot90(x, k=k, axes=axes)
+                assert same(mx_out.asnumpy(), np_out)
+
+
+@with_seed()
+@use_np
+def test_np_hsplit():
+    class TestHSplit(HybridBlock):
+        def __init__(self, indices_or_sections):
+            super(TestHSplit, self).__init__()
+            self._indices_or_sections = indices_or_sections
+
+        def hybrid_forward(self, F, a, *args, **kwargs):
+            return F.np.hsplit(a, indices_or_sections=self._indices_or_sections)
+
+    shapes = [
+        (10,),
+        (3, 8, 5),
+        (3, 0, 5),
+        (3, 8, 5, 6),
+        (3, 0, 5, 6),
+    ]
+    indices_or_sections_num = [
+        (2, 4),
+        (3, 3),
+        (3,),
+        (1,),
+        2,
+    ]
+    for hybridize in [True, False]:
+        for shape in shapes:
+            for indices_or_sections in indices_or_sections_num:
+                # test gluon
+                test_hsplit = TestHSplit(indices_or_sections=indices_or_sections)
+                if hybridize:
+                    test_hsplit.hybridize()
+
+                a = mx.nd.random.uniform(-1.0, 1.0, shape=shape).as_np_ndarray()
+                a.attach_grad()
+                expected_ret = _np.hsplit(a.asnumpy(), indices_or_sections=indices_or_sections)
+                with mx.autograd.record():
+                    y = test_hsplit(a)
+                assert len(y) == len(expected_ret)
+                for mx_out, np_out in zip(y, expected_ret):
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+                mx.autograd.backward(y)
+                assert_almost_equal(a.grad.asnumpy(), _np.ones(a.shape), rtol=1e-3, atol=1e-5)
+
+                # test imperative
+                mx_outs = np.hsplit(a, indices_or_sections=indices_or_sections)
+                np_outs = _np.hsplit(a.asnumpy(), indices_or_sections=indices_or_sections)
+                for mx_out, np_out in zip(mx_outs, np_outs):
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From f9359c3d5b4dd70d1001501ec0948efaaa53d6d0 Mon Sep 17 00:00:00 2001
From: Dick Carter <dcarter@nvidia.com>
Date: Tue, 15 Oct 2019 08:54:54 -0700
Subject: [PATCH 449/813] Fix flakey pylint CI failures (#16462)

* Change pylint parallel jobs from 8 to 1

* Non-functional tweeks to keep pylint happy

* Disable pylint too-many-lines, duplicate-code, cyclic-import

* Trigger CI

* Trigger CI

* Fighting unrelated CI failures.  Trigger CI.

* Fighting unrelated CI failures.  Trigger CI.
---
 ci/other/pylintrc                  | 7 +++++--
 python/mxnet/numpy_op_signature.py | 5 ++---
 2 files changed, 7 insertions(+), 5 deletions(-)

diff --git a/ci/other/pylintrc b/ci/other/pylintrc
index 841a3bea13fa..a1034cd50f9c 100644
--- a/ci/other/pylintrc
+++ b/ci/other/pylintrc
@@ -40,7 +40,7 @@ persistent=yes
 load-plugins=
 
 # Use multiple processes to speed up Pylint.
-jobs=8
+jobs=1
 
 # Allow loading of arbitrary C extensions. Extensions are imported into the
 # active Python interpreter and may run arbitrary code.
@@ -113,7 +113,10 @@ disable=
     too-many-instance-attributes,
     too-many-locals,
     too-many-public-methods,
-    too-many-statements
+    too-many-statements,
+    too-many-lines,
+    duplicate-code,
+    cyclic-import
 
 # disable=unicode-builtin,delslice-method,using-cmp-argument,setslice-method,dict-view-method,parameter-unpacking,range-builtin-not-iterating,print-statement,file-builtin,old-raise-syntax,basestring-builtin,execfile-builtin,indexing-exception,import-star-module-level,coerce-method,long-builtin,old-ne-operator,old-division,no-absolute-import,raw_input-builtin,old-octal-literal,oct-method,xrange-builtin,hex-method,unpacking-in-except,nonzero-method,raising-string,intern-builtin,reload-builtin,metaclass-assignment,cmp-method,filter-builtin-not-iterating,apply-builtin,map-builtin-not-iterating,next-method-called,unichr-builtin,buffer-builtin,dict-iter-method,input-builtin,coerce-builtin,getslice-method,useless-suppression,standarderror-builtin,zip-builtin-not-iterating,suppressed-message,cmp-builtin,backtick,long-suffix,reduce-builtin,round-builtin
 
diff --git a/python/mxnet/numpy_op_signature.py b/python/mxnet/numpy_op_signature.py
index 314de302109d..3e8b6d578537 100644
--- a/python/mxnet/numpy_op_signature.py
+++ b/python/mxnet/numpy_op_signature.py
@@ -19,6 +19,7 @@
 
 import sys
 import warnings
+import inspect
 from . import _numpy_op_doc
 from . import numpy as mx_np
 from . import numpy_extension as mx_npx
@@ -38,13 +39,12 @@ def _get_builtin_op(op_name):
         return None
 
     submodule_name = _get_op_submodule_name(op_name, op_name_prefix, submodule_name_list)
+    op_module = root_module
     if len(submodule_name) > 0:
         op_module = getattr(root_module, submodule_name[1:-1], None)
         if op_module is None:
             raise ValueError('Cannot find submodule {} in module {}'
                              .format(submodule_name[1:-1], root_module.__name__))
-    else:
-        op_module = root_module
 
     op = getattr(op_module, op_name[(len(op_name_prefix)+len(submodule_name)):], None)
     if op is None:
@@ -61,7 +61,6 @@ def _register_op_signatures():
                       .format(str(sys.version)))
         return
 
-    import inspect
     for op_name in dir(_numpy_op_doc):
         op = _get_builtin_op(op_name)
         if op is not None:

From 67e1e68deb4236c7ba5f4d306da3ef70d606743c Mon Sep 17 00:00:00 2001
From: Andrei Ivanov <32910461+drivanov@users.noreply.github.com>
Date: Tue, 15 Oct 2019 09:06:19 -0700
Subject: [PATCH 450/813] Aggregated zero grad (#16446)

* Trigger CI

* Aggregated zeroing of the gradients/arrays

* New files for aggregated zeroing of the gradients/arrays

* Adding possibility to reset the arrays of different types.

* Minor cleanup
---
 python/mxnet/gluon/parameter.py         | 21 +++++-
 src/operator/contrib/reset_arrays-inl.h | 92 +++++++++++++++++++++++++
 src/operator/contrib/reset_arrays.cc    | 74 ++++++++++++++++++++
 src/operator/contrib/reset_arrays.cu    | 40 +++++++++++
 tests/python/unittest/test_gluon.py     | 66 +++++++++++++++---
 5 files changed, 281 insertions(+), 12 deletions(-)
 create mode 100644 src/operator/contrib/reset_arrays-inl.h
 create mode 100644 src/operator/contrib/reset_arrays.cc
 create mode 100644 src/operator/contrib/reset_arrays.cu

diff --git a/python/mxnet/gluon/parameter.py b/python/mxnet/gluon/parameter.py
index dedd81496c2f..8800684ad0b4 100644
--- a/python/mxnet/gluon/parameter.py
+++ b/python/mxnet/gluon/parameter.py
@@ -24,9 +24,10 @@
            'ParameterDict', 'tensor_types']
 
 
-from collections import OrderedDict
+from collections import OrderedDict, defaultdict
 import warnings
 import numpy as np
+import mxnet as mx
 
 from ..base import mx_real_t, MXNetError
 from .. import symbol, ndarray, initializer, context
@@ -887,8 +888,22 @@ def initialize(self, init=initializer.Uniform(), ctx=None, verbose=False,
 
     def zero_grad(self):
         """Sets all Parameters' gradient buffer to 0."""
-        for i in self.values():
-            i.zero_grad()
+        # collect gradient arrays for each ctx
+        arrays = defaultdict(list)
+        for p in self.values():
+            if p.grad_req == 'null' or p._grad is None:
+                continue
+            for g in p.list_grad():
+                if g.stype == 'row_sparse':
+                    mx.ndarray.zeros_like(g, out=g)
+                else:
+                    arrays[g.context].append(g)
+
+        if len(arrays) == 0:
+            return
+
+        for arr in arrays.values():
+            mx.nd.reset_arrays(*arr, num_arrays=len(arr))
 
     def reset_ctx(self, ctx):
         """Re-assign all Parameters to other contexts.
diff --git a/src/operator/contrib/reset_arrays-inl.h b/src/operator/contrib/reset_arrays-inl.h
new file mode 100644
index 000000000000..3559a21879e4
--- /dev/null
+++ b/src/operator/contrib/reset_arrays-inl.h
@@ -0,0 +1,92 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file reset_arrays-inl.h
+ * \brief setting all array element values to zeros
+ * \author Moises Hernandez-Fernandez, Andrei Ivanov
+ */
+
+#ifndef MXNET_OPERATOR_CONTRIB_RESET_ARRAYS_INL_H_
+#define MXNET_OPERATOR_CONTRIB_RESET_ARRAYS_INL_H_
+
+#include <vector>
+#include "../tensor/init_op.h"
+
+namespace mxnet {
+namespace op {
+
+struct ResetArraysParam : public dmlc::Parameter<ResetArraysParam> {
+  int num_arrays;
+  DMLC_DECLARE_PARAMETER(ResetArraysParam) {
+    DMLC_DECLARE_FIELD(num_arrays)
+    .describe("number of input arrays.");
+  }
+};
+
+inline bool ResetArraysShape(const NodeAttrs& attrs,
+                            std::vector<mxnet::TShape>* in_shape,
+                            std::vector<mxnet::TShape>* out_shape) {
+  const auto& param = dmlc::get<ResetArraysParam>(attrs.parsed);
+  CHECK_EQ(in_shape->size(), param.num_arrays);
+  for (auto s : *in_shape) {
+    if (s.ndim() == 0)
+      return false;
+  }
+
+  return true;
+}
+
+inline bool ResetArraysType(const NodeAttrs& attrs,
+                           std::vector<int>* in_type,
+                           std::vector<int>* out_type) {
+  const auto& param = dmlc::get<ResetArraysParam>(attrs.parsed);
+  CHECK_EQ(in_type->size(), param.num_arrays);
+  for (size_t i = 0; i < in_type->size(); ++i) {
+    if ((*in_type)[i] == -1)
+      return false;
+  }
+
+  return true;
+}
+
+template<typename xpu>
+void ResetMemory(void *pntr, size_t len, mshadow::Stream<xpu> *s);
+
+template<typename xpu>
+void ResetArrays(const nnvm::NodeAttrs& attrs,
+                 const OpContext &ctx,
+                 const std::vector<TBlob> &inputs,
+                 const std::vector<OpReqType> &req,
+                 const std::vector<TBlob> &outputs) {
+  auto s = ctx.get_stream<xpu>();
+  const auto& param = nnvm::get<ResetArraysParam>(attrs.parsed);
+  for (int i = 0; i < param.num_arrays; i++) {  // array index in inputs
+    const size_t size = inputs[i].shape_.Size();
+    MSHADOW_REAL_TYPE_SWITCH(inputs[i].type_flag_, DType,
+      ResetMemory(inputs[i].FlatTo2D<xpu, DType>(s).dptr_, size * sizeof(DType), s);
+    )
+  }
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_CONTRIB_RESET_ARRAYS_INL_H_
diff --git a/src/operator/contrib/reset_arrays.cc b/src/operator/contrib/reset_arrays.cc
new file mode 100644
index 000000000000..f67e0098cd11
--- /dev/null
+++ b/src/operator/contrib/reset_arrays.cc
@@ -0,0 +1,74 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file reset_arrays.cc
+ * \brief setting all array element values to zeros
+ * \author Moises Hernandez-Fernandez, Andrei Ivanov
+ */
+
+#include "./reset_arrays-inl.h"
+
+namespace mxnet {
+namespace op {
+
+DMLC_REGISTER_PARAMETER(ResetArraysParam);
+
+NNVM_REGISTER_OP(reset_arrays)
+.describe(R"code(Set to zero multiple arrays
+)code" ADD_FILELINE)
+.set_num_inputs([](const nnvm::NodeAttrs& attrs) {
+    return static_cast<uint32_t>(dmlc::get<ResetArraysParam>(attrs.parsed).num_arrays);
+  })
+.set_attr<nnvm::FMutateInputs>("FMutateInputs",
+  [](const nnvm::NodeAttrs& attrs) {
+    const uint32_t num_args = dmlc::get<ResetArraysParam>(attrs.parsed).num_arrays;
+    std::vector<uint32_t> ret;
+    for (uint32_t i = 0; i < num_args; ++i) {
+      ret.push_back(i);
+    }
+    return ret;
+  })
+.set_num_outputs(0)
+.set_attr_parser(ParamParser<ResetArraysParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", ResetArraysShape)
+.set_attr<nnvm::FInferType>("FInferType", ResetArraysType)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    const uint32_t num_args = dmlc::get<ResetArraysParam>(attrs.parsed).num_arrays;
+    std::vector<std::string> ret;
+    for (uint32_t i = 0; i < num_args; ++i) {
+      ret.push_back(std::string("array_") + std::to_string(i));
+    }
+    return ret;
+  })
+.add_argument("data", "NDArray-or-Symbol[]", "Arrays")
+.add_arguments(ResetArraysParam::__FIELDS__());
+
+NNVM_REGISTER_OP(reset_arrays)
+.set_attr<FCompute>("FCompute<cpu>", ResetArrays<cpu>);
+
+template<>
+void ResetMemory<cpu>(void *pntr, size_t len, mshadow::Stream<cpu> *s) {
+  memset(pntr, 0, len);
+}
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/contrib/reset_arrays.cu b/src/operator/contrib/reset_arrays.cu
new file mode 100644
index 000000000000..f7a9d0034665
--- /dev/null
+++ b/src/operator/contrib/reset_arrays.cu
@@ -0,0 +1,40 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file reset_arrays.cu
+ * \brief setting all array element values to zeros
+ * \author Moises Hernandez-Fernandez, Andrei Ivanov
+ */
+#include "./reset_arrays-inl.h"
+
+namespace mxnet {
+namespace op {
+
+template<>
+void ResetMemory<gpu>(void *pntr, size_t len, mshadow::Stream<gpu> *s) {
+  CUDA_CALL(cudaMemsetAsync(pntr, 0, len, mshadow::Stream<gpu>::GetStream(s)));
+}
+
+NNVM_REGISTER_OP(reset_arrays)
+.set_attr<FCompute>("FCompute<gpu>", ResetArrays<gpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/tests/python/unittest/test_gluon.py b/tests/python/unittest/test_gluon.py
index 380ce762a9f7..c6da0f4d9d96 100644
--- a/tests/python/unittest/test_gluon.py
+++ b/tests/python/unittest/test_gluon.py
@@ -33,6 +33,7 @@
 import warnings
 import json
 import unittest
+import random
 
 @with_seed()
 def test_parameter():
@@ -1504,15 +1505,62 @@ def test_hybrid_multi_context():
 
 @with_seed()
 def test_zero_grad():
-    data = mx.nd.random.uniform(shape=(3,3))
-    net = nn.Embedding(3, 4, sparse_grad=True, prefix='test_zero_grad_')
-    net.initialize()
-    with mx.autograd.record():
-        l = net(data)
-        l.backward()
-    net.collect_params().zero_grad()
-    grad = net.collect_params()['test_zero_grad_weight'].grad()
-    assert_almost_equal(grad.asnumpy(), grad.asnumpy() * 0)
+    def _test_grad_reset(ctx, dtype='float32', sparse=False, embeddingType=None):
+        data = mx.nd.random.uniform(shape=(3,3), dtype=dtype, ctx=ctx)
+        if embeddingType is None:
+            embeddingType = dtype
+        net = nn.Embedding(3, 4, sparse_grad=sparse, prefix='test_zero_grad_', dtype=embeddingType)
+        net.initialize(ctx=ctx)
+        with mx.autograd.record():
+            l = net(data)
+            l.backward()
+        net.collect_params().zero_grad()
+        grad = net.collect_params()['test_zero_grad_weight'].grad()
+        assert_almost_equal(grad.asnumpy(), grad.asnumpy() * 0)
+
+    def _test_multi_reset(nArrays, dtype, ctx):
+        # Construct the list of non-zeros arrays with random shapes
+        arr = []
+        for _ in range(nArrays):
+            arrType = random.choice(dtype) if isinstance(dtype, list) else dtype
+            shape = ()
+            for _ in range(np.random.randint(1, 5)):
+                shape = shape + (np.random.randint(1, 10),)
+            arr.append(mx.nd.random.uniform(shape=shape, dtype=arrType, ctx=ctx))
+
+        # Reset all arrays
+        mx.nd.reset_arrays(*arr, num_arrays=len(arr))
+
+        # Check results
+        for i in range(nArrays):
+            grad = arr[i].asnumpy()
+            assert_almost_equal(grad, grad * 0)
+
+
+    # Setting context for current test
+    ctx = mx.context.current_context()
+
+    # Launching _test_multi_reset 10 times with different types & randomly chosen nArrays
+    testedTypes = ['float16', 'float32', 'float64']
+    for _ in range(10):
+        for type in [testedTypes] + testedTypes:
+            _test_multi_reset(np.random.randint(1, 50), type, ctx)
+
+    # Saving value of environment variable, if it was defined
+    envVarKey = 'MXNET_STORAGE_FALLBACK_LOG_VERBOSE'
+    envVarValue = os.environ[envVarKey] if envVarKey in os.environ else None
+    # Changing value of environment variable
+    os.environ[envVarKey] = '0'
+    for type in ['float16', 'float32', 'float64']:
+        for embType in ['float32', 'float64']:
+            for sparse in [True, False]:
+                _test_grad_reset(ctx, dtype=type, sparse=sparse, embeddingType=embType)
+
+    # Remove or restore the value of environment variable
+    if envVarValue is None:
+        del os.environ[envVarKey]
+    else:
+        os.environ[envVarKey] = envVarValue
 
 def check_hybrid_static_memory(**kwargs):
     x = mx.nd.random.uniform(shape=(2, 3, 32, 32))

From b1932c027ba8df081ca398dd8b5d3a893c5bc61d Mon Sep 17 00:00:00 2001
From: Serge Panev <spanev@nvidia.com>
Date: Tue, 15 Oct 2019 21:01:28 +0200
Subject: [PATCH 451/813] Move MRCNNMaskTarget op to contrib (#16486)

Signed-off-by: Serge Panev <spanev@nvidia.com>
---
 ...n_target-inl.h => mrcnn_mask_target-inl.h} | 44 +++++++-------
 .../{mrcnn_target.cu => mrcnn_mask_target.cu} | 56 +++++++++---------
 .../python/unittest/test_contrib_operator.py  | 58 +++++++++++++++++++
 tests/python/unittest/test_operator.py        | 57 ------------------
 4 files changed, 108 insertions(+), 107 deletions(-)
 rename src/operator/contrib/{mrcnn_target-inl.h => mrcnn_mask_target-inl.h} (70%)
 rename src/operator/contrib/{mrcnn_target.cu => mrcnn_mask_target.cu} (83%)

diff --git a/src/operator/contrib/mrcnn_target-inl.h b/src/operator/contrib/mrcnn_mask_target-inl.h
similarity index 70%
rename from src/operator/contrib/mrcnn_target-inl.h
rename to src/operator/contrib/mrcnn_mask_target-inl.h
index a3d8c444086a..891453369089 100644
--- a/src/operator/contrib/mrcnn_target-inl.h
+++ b/src/operator/contrib/mrcnn_mask_target-inl.h
@@ -19,14 +19,14 @@
 
 /*!
  *  Copyright (c) 2019 by Contributors
- * \file mrcnn_target-inl.h
+ * \file mrcnn_mask_target-inl.h
  * \brief Mask-RCNN target generator
  * \author Serge Panev
  */
 
 
-#ifndef MXNET_OPERATOR_CONTRIB_MRCNN_TARGET_INL_H_
-#define MXNET_OPERATOR_CONTRIB_MRCNN_TARGET_INL_H_
+#ifndef MXNET_OPERATOR_CONTRIB_MRCNN_MASK_TARGET_INL_H_
+#define MXNET_OPERATOR_CONTRIB_MRCNN_MASK_TARGET_INL_H_
 
 #include <mxnet/operator.h>
 #include <vector>
@@ -42,13 +42,13 @@ namespace mrcnn_index {
   enum ROIAlignOpOutputs {kMask, kMaskClasses};
 }  // namespace mrcnn_index
 
-struct MRCNNTargetParam : public dmlc::Parameter<MRCNNTargetParam> {
+struct MRCNNMaskTargetParam : public dmlc::Parameter<MRCNNMaskTargetParam> {
   int num_rois;
   int num_classes;
   int mask_size;
   int sample_ratio;
 
-  DMLC_DECLARE_PARAMETER(MRCNNTargetParam) {
+  DMLC_DECLARE_PARAMETER(MRCNNMaskTargetParam) {
     DMLC_DECLARE_FIELD(num_rois)
     .describe("Number of sampled RoIs.");
     DMLC_DECLARE_FIELD(num_classes)
@@ -60,11 +60,11 @@ struct MRCNNTargetParam : public dmlc::Parameter<MRCNNTargetParam> {
   }
 };
 
-inline bool MRCNNTargetShape(const NodeAttrs& attrs,
-                            std::vector<mxnet::TShape>* in_shape,
-                            std::vector<mxnet::TShape>* out_shape) {
+inline bool MRCNNMaskTargetShape(const NodeAttrs& attrs,
+                                 std::vector<mxnet::TShape>* in_shape,
+                                 std::vector<mxnet::TShape>* out_shape) {
   using namespace mshadow;
-  const MRCNNTargetParam& param = nnvm::get<MRCNNTargetParam>(attrs.parsed);
+  const MRCNNMaskTargetParam& param = nnvm::get<MRCNNMaskTargetParam>(attrs.parsed);
 
   CHECK_EQ(in_shape->size(), 4) << "Input:[rois, gt_masks, matches, cls_targets]";
 
@@ -98,9 +98,9 @@ inline bool MRCNNTargetShape(const NodeAttrs& attrs,
   return true;
 }
 
-inline bool MRCNNTargetType(const NodeAttrs& attrs,
-                           std::vector<int>* in_type,
-                           std::vector<int>* out_type) {
+inline bool MRCNNMaskTargetType(const NodeAttrs& attrs,
+                                std::vector<int>* in_type,
+                                std::vector<int>* out_type) {
   CHECK_EQ(in_type->size(), 4);
   int dtype = (*in_type)[1];
   CHECK_NE(dtype, -1) << "Input must have specified type";
@@ -112,21 +112,21 @@ inline bool MRCNNTargetType(const NodeAttrs& attrs,
 }
 
 template<typename xpu>
-void MRCNNTargetRun(const MRCNNTargetParam& param, const std::vector<TBlob> &inputs,
-                    const std::vector<TBlob> &outputs, mshadow::Stream<xpu> *s);
+void MRCNNMaskTargetRun(const MRCNNMaskTargetParam& param, const std::vector<TBlob> &inputs,
+                        const std::vector<TBlob> &outputs, mshadow::Stream<xpu> *s);
 
 template<typename xpu>
-void MRCNNTargetCompute(const nnvm::NodeAttrs& attrs,
-                const OpContext &ctx,
-                const std::vector<TBlob> &inputs,
-                const std::vector<OpReqType> &req,
-                const std::vector<TBlob> &outputs) {
+void MRCNNMaskTargetCompute(const nnvm::NodeAttrs& attrs,
+                            const OpContext &ctx,
+                            const std::vector<TBlob> &inputs,
+                            const std::vector<OpReqType> &req,
+                            const std::vector<TBlob> &outputs) {
   auto s = ctx.get_stream<xpu>();
-  const auto& p = dmlc::get<MRCNNTargetParam>(attrs.parsed);
-  MRCNNTargetRun<xpu>(p, inputs, outputs, s);
+  const auto& p = dmlc::get<MRCNNMaskTargetParam>(attrs.parsed);
+  MRCNNMaskTargetRun<xpu>(p, inputs, outputs, s);
 }
 
 }  // namespace op
 }  // namespace mxnet
 
-#endif  // MXNET_OPERATOR_CONTRIB_MRCNN_TARGET_INL_H_
+#endif  // MXNET_OPERATOR_CONTRIB_MRCNN_MASK_TARGET_INL_H_
diff --git a/src/operator/contrib/mrcnn_target.cu b/src/operator/contrib/mrcnn_mask_target.cu
similarity index 83%
rename from src/operator/contrib/mrcnn_target.cu
rename to src/operator/contrib/mrcnn_mask_target.cu
index d542c9c220f8..0170e739d437 100644
--- a/src/operator/contrib/mrcnn_target.cu
+++ b/src/operator/contrib/mrcnn_mask_target.cu
@@ -19,12 +19,12 @@
 
 /*!
  *  Copyright (c) 2019 by Contributors
- * \file mrcnn_target.cu
+ * \file mrcnn_mask_target.cu
  * \brief Mask-RCNN target generator
  * \author Serge Panev
  */
 
-#include "./mrcnn_target-inl.h"
+#include "./mrcnn_mask_target-inl.h"
 
 namespace mxnet {
 namespace op {
@@ -183,21 +183,21 @@ __device__ void RoIAlignForward(
 
 
 template<typename DType>
-__global__ void MRCNNTargetKernel(const DType *rois,
-                                  const DType *gt_masks,
-                                  const DType *matches,
-                                  const DType *cls_targets,
-                                  DType* sampled_masks,
-                                  DType* mask_cls,
-                                  const int total_out_el,
-                                  int batch_size,
-                                  int num_classes,
-                                  int num_rois,
-                                  int num_gtmasks,
-                                  int gt_height,
-                                  int gt_width,
-                                  int mask_size,
-                                  int sample_ratio) {
+__global__ void MRCNNMaskTargetKernel(const DType *rois,
+                                      const DType *gt_masks,
+                                      const DType *matches,
+                                      const DType *cls_targets,
+                                      DType* sampled_masks,
+                                      DType* mask_cls,
+                                      const int total_out_el,
+                                      int batch_size,
+                                      int num_classes,
+                                      int num_rois,
+                                      int num_gtmasks,
+                                      int gt_height,
+                                      int gt_width,
+                                      int mask_size,
+                                      int sample_ratio) {
   // computing sampled_masks
   RoIAlignForward(gt_masks, rois, matches, total_out_el,
                   num_classes, gt_height, gt_width, mask_size, mask_size,
@@ -221,8 +221,8 @@ __global__ void MRCNNTargetKernel(const DType *rois,
 }
 
 template<>
-void MRCNNTargetRun<gpu>(const MRCNNTargetParam& param, const std::vector<TBlob> &inputs,
-                         const std::vector<TBlob> &outputs, mshadow::Stream<gpu> *s) {
+void MRCNNMaskTargetRun<gpu>(const MRCNNMaskTargetParam& param, const std::vector<TBlob> &inputs,
+                             const std::vector<TBlob> &outputs, mshadow::Stream<gpu> *s) {
   const int block_dim_size = kMaxThreadsPerBlock;
   using namespace mxnet_op;
   using mshadow::Tensor;
@@ -248,31 +248,31 @@ void MRCNNTargetRun<gpu>(const MRCNNTargetParam& param, const std::vector<TBlob>
     dim3 dimGrid = dim3(CUDA_GET_BLOCKS(num_el));
     dim3 dimBlock = dim3(block_dim_size);
 
-    MRCNNTargetKernel<<<dimGrid, dimBlock, 0, stream>>>
+    MRCNNMaskTargetKernel<<<dimGrid, dimBlock, 0, stream>>>
     (rois.dptr_, gt_masks.dptr_, matches.dptr_, cls_targets.dptr_,
     out_masks.dptr_, out_mask_cls.dptr_,
     num_el, batch_size, param.num_classes, param.num_rois,
     num_gtmasks, gt_height, gt_width, param.mask_size, param.sample_ratio);
-    MSHADOW_CUDA_POST_KERNEL_CHECK(MRCNNTargetKernel);
+    MSHADOW_CUDA_POST_KERNEL_CHECK(MRCNNMaskTargetKernel);
   });
 }
 
-DMLC_REGISTER_PARAMETER(MRCNNTargetParam);
+DMLC_REGISTER_PARAMETER(MRCNNMaskTargetParam);
 
-NNVM_REGISTER_OP(mrcnn_target)
+NNVM_REGISTER_OP(_contrib_mrcnn_mask_target)
 .describe("Generate mask targets for Mask-RCNN.")
 .set_num_inputs(4)
 .set_num_outputs(2)
-.set_attr_parser(ParamParser<MRCNNTargetParam>)
-.set_attr<mxnet::FInferShape>("FInferShape", MRCNNTargetShape)
-.set_attr<nnvm::FInferType>("FInferType", MRCNNTargetType)
-.set_attr<FCompute>("FCompute<gpu>", MRCNNTargetCompute<gpu>)
+.set_attr_parser(ParamParser<MRCNNMaskTargetParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", MRCNNMaskTargetShape)
+.set_attr<nnvm::FInferType>("FInferType", MRCNNMaskTargetType)
+.set_attr<FCompute>("FCompute<gpu>", MRCNNMaskTargetCompute<gpu>)
 .add_argument("rois", "NDArray-or-Symbol", "Bounding box coordinates, a 3D array")
 .add_argument("gt_masks", "NDArray-or-Symbol", "Input masks of full image size, a 4D array")
 .add_argument("matches", "NDArray-or-Symbol", "Index to a gt_mask, a 2D array")
 .add_argument("cls_targets", "NDArray-or-Symbol",
               "Value [0, num_class), excluding background class, a 2D array")
-.add_arguments(MRCNNTargetParam::__FIELDS__());
+.add_arguments(MRCNNMaskTargetParam::__FIELDS__());
 
 }  // namespace op
 }  // namespace mxnet
diff --git a/tests/python/unittest/test_contrib_operator.py b/tests/python/unittest/test_contrib_operator.py
index 43ba9d8b6318..c23be6c47783 100644
--- a/tests/python/unittest/test_contrib_operator.py
+++ b/tests/python/unittest/test_contrib_operator.py
@@ -23,6 +23,7 @@
 import itertools
 from numpy.testing import assert_allclose, assert_array_equal
 from mxnet.test_utils import *
+from common import with_seed
 import unittest
 
 def test_box_nms_op():
@@ -351,6 +352,63 @@ def test_box_decode_op():
     assert_allclose(Y.asnumpy(), np.array([[[-0.0562755, -0.00865743, 0.26227552, 0.42465743], \
         [0.13240421, 0.17859563, 0.93759584, 1.1174043 ]]]), atol=1e-5, rtol=1e-5)
 
+@with_seed()
+def test_op_mrcnn_mask_target():
+    if default_context().device_type != 'gpu':
+        return
+
+    num_rois = 2
+    num_classes = 4
+    mask_size = 3
+    ctx = mx.gpu(0)
+    # (B, N, 4)
+    rois = mx.nd.array([[[2.3, 4.3, 2.2, 3.3],
+                        [3.5, 5.5, 0.9, 2.4]]], ctx=ctx)
+    gt_masks = mx.nd.arange(0, 4*32*32, ctx=ctx).reshape(1, 4, 32, 32)
+
+    # (B, N)
+    matches = mx.nd.array([[2, 0]], ctx=ctx)
+    # (B, N)
+    cls_targets = mx.nd.array([[2, 1]], ctx=ctx)
+
+    mask_targets, mask_cls = mx.nd.contrib.mrcnn_mask_target(rois, gt_masks, matches, cls_targets,
+                                                             num_rois=num_rois,
+                                                             num_classes=num_classes,
+                                                             mask_size=mask_size)
+
+    # Ground truth outputs were generated with GluonCV's target generator
+    # gluoncv.model_zoo.mask_rcnn.MaskTargetGenerator(1, num_rois, num_classes, mask_size)
+    gt_mask_targets = mx.nd.array([[[[[2193.4    , 2193.7332 , 2194.0667 ],
+                                      [2204.0667 , 2204.4    , 2204.7334 ],
+                                      [2214.7334 , 2215.0667 , 2215.4    ]],
+                                     [[2193.4    , 2193.7332 , 2194.0667 ],
+                                      [2204.0667 , 2204.4    , 2204.7334 ],
+                                      [2214.7334 , 2215.0667 , 2215.4    ]],
+                                     [[2193.4    , 2193.7332 , 2194.0667 ],
+                                      [2204.0667 , 2204.4    , 2204.7334 ],
+                                      [2214.7334 , 2215.0667 , 2215.4    ]],
+                                     [[2193.4    , 2193.7332 , 2194.0667 ],
+                                      [2204.0667 , 2204.4    , 2204.7334 ],
+                                      [2214.7334 , 2215.0667 , 2215.4    ]]],
+                                    [[[ 185.     ,  185.33334,  185.66667],
+                                      [ 195.66667,  196.00002,  196.33334],
+                                      [ 206.33333,  206.66666,  207.     ]],
+                                     [[ 185.     ,  185.33334,  185.66667],
+                                      [ 195.66667,  196.00002,  196.33334],
+                                      [ 206.33333,  206.66666,  207.     ]],
+                                     [[ 185.     ,  185.33334,  185.66667],
+                                      [ 195.66667,  196.00002,  196.33334],
+                                      [ 206.33333,  206.66666,  207.     ]],
+                                     [[ 185.     ,  185.33334,  185.66667],
+                                      [ 195.66667,  196.00002,  196.33334],
+                                      [  206.33333,  206.66666,  207.     ]]]]])
+
+    gt_mask_cls = mx.nd.array([[0,0,1,0], [0,1,0,0]])
+    gt_mask_cls = gt_mask_cls.reshape(1,2,4,1,1).broadcast_axes(axis=(3,4), size=(3,3))
+
+    assert_almost_equal(mask_targets.asnumpy(), gt_mask_targets.asnumpy())
+    assert_almost_equal(mask_cls.asnumpy(), gt_mask_cls.asnumpy())
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 74cc4203df1d..5f40bf12674a 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -8686,63 +8686,6 @@ def test_rroi_align_value(sampling_ratio=-1):
     test_rroi_align_value()
     test_rroi_align_value(sampling_ratio=2)
 
-@with_seed()
-def test_op_mrcnn_target():
-    if default_context().device_type != 'gpu':
-        return
-
-    num_rois = 2
-    num_classes = 4
-    mask_size = 3
-    ctx = mx.gpu(0)
-    # (B, N, 4)
-    rois = mx.nd.array([[[2.3, 4.3, 2.2, 3.3],
-                        [3.5, 5.5, 0.9, 2.4]]], ctx=ctx)
-    gt_masks = mx.nd.arange(0, 4*32*32, ctx=ctx).reshape(1, 4, 32, 32)
-
-    # (B, N)
-    matches = mx.nd.array([[2, 0]], ctx=ctx)
-    # (B, N)
-    cls_targets = mx.nd.array([[2, 1]], ctx=ctx)
-
-    mask_targets, mask_cls = mx.nd.mrcnn_target(rois, gt_masks, matches, cls_targets,
-                                                num_rois=num_rois,
-                                                num_classes=num_classes,
-                                                mask_size=mask_size)
-
-    # Ground truth outputs were generated with GluonCV's target generator
-    # gluoncv.model_zoo.mask_rcnn.MaskTargetGenerator(1, num_rois, num_classes, mask_size)
-    gt_mask_targets = mx.nd.array([[[[[2193.4    , 2193.7332 , 2194.0667 ],
-                                      [2204.0667 , 2204.4    , 2204.7334 ],
-                                      [2214.7334 , 2215.0667 , 2215.4    ]],
-                                     [[2193.4    , 2193.7332 , 2194.0667 ],
-                                      [2204.0667 , 2204.4    , 2204.7334 ],
-                                      [2214.7334 , 2215.0667 , 2215.4    ]],
-                                     [[2193.4    , 2193.7332 , 2194.0667 ],
-                                      [2204.0667 , 2204.4    , 2204.7334 ],
-                                      [2214.7334 , 2215.0667 , 2215.4    ]],
-                                     [[2193.4    , 2193.7332 , 2194.0667 ],
-                                      [2204.0667 , 2204.4    , 2204.7334 ],
-                                      [2214.7334 , 2215.0667 , 2215.4    ]]],
-                                    [[[ 185.     ,  185.33334,  185.66667],
-                                      [ 195.66667,  196.00002,  196.33334],
-                                      [ 206.33333,  206.66666,  207.     ]],
-                                     [[ 185.     ,  185.33334,  185.66667],
-                                      [ 195.66667,  196.00002,  196.33334],
-                                      [ 206.33333,  206.66666,  207.     ]],
-                                     [[ 185.     ,  185.33334,  185.66667],
-                                      [ 195.66667,  196.00002,  196.33334],
-                                      [ 206.33333,  206.66666,  207.     ]],
-                                     [[ 185.     ,  185.33334,  185.66667],
-                                      [ 195.66667,  196.00002,  196.33334],
-                                      [  206.33333,  206.66666,  207.     ]]]]])
-
-    gt_mask_cls = mx.nd.array([[0,0,1,0], [0,1,0,0]])
-    gt_mask_cls = gt_mask_cls.reshape(1,2,4,1,1).broadcast_axes(axis=(3,4), size=(3,3))
-
-    assert_almost_equal(mask_targets.asnumpy(), gt_mask_targets.asnumpy())
-    assert_almost_equal(mask_cls.asnumpy(), gt_mask_cls.asnumpy())
-
 @with_seed()
 def test_diag():
 

From 06438ab7a3099de1937f25ce758f687ff3771de7 Mon Sep 17 00:00:00 2001
From: Andrei Ivanov <32910461+drivanov@users.noreply.github.com>
Date: Tue, 15 Oct 2019 15:56:43 -0700
Subject: [PATCH 452/813] Mxnet allclose (#14443)

* MXNET version of numpy.allclose operation implemented

* Helper function test_bulking renamed to _test_bulking

* Will use mx.test_utils.assert_allclose and not a numpy version of similar function

* Trigger CI

* Trigger CI

* Will use mx.test_utils.assert_allclose

* Trigger CI

* Trigger CI

* Problem with missed _test_bulking() function fixed

* Fixing minor bug in error reporting

* Trigger CI

* Trigger CI

* retrigger CI

* Fixing problems in discrepancies printout in assert_almost_equal

* Trigger CI

* Trigger CI

* Improved version of MxNet allclose operator

* Fixing minor problem in attribite definition for allclose operator

* retrigger CI

* Minor problem fixed

* Trigger CI

* try to fix find_max_violation

* Trigger CI

* Skip 'test_bulking_gluon_gpu()' test

* Fixing bug in reporting MaxErrors for NaN coordinates.

* use smaller testcase for test_layer_norm

* remove redundant test for test_layer_norm

* reuse old testcase

* retrigger CI

* ci

* ci

* Merge problem fixed

* Fixing Python's lint problem

* Trigger CI

* Trigger CI

* Trigger CI

* Trigger CI

* Trigger CI
---
 python/mxnet/test_utils.py                    | 188 ++++-
 src/operator/contrib/allclose_op-inl.h        | 160 +++++
 src/operator/contrib/allclose_op.cc           |  86 +++
 src/operator/contrib/allclose_op.cu           |  58 ++
 tests/python-pytest/onnx/mxnet_export_test.py |   2 +-
 tests/python/gpu/test_gluon_gpu.py            | 136 +++-
 tests/python/gpu/test_gluon_model_zoo_gpu.py  |  14 +-
 tests/python/gpu/test_operator_gpu.py         |  59 +-
 tests/python/mkl/test_mkldnn.py               |  12 +-
 tests/python/unittest/test_gluon_contrib.py   |  21 +-
 tests/python/unittest/test_loss.py            |  31 +-
 tests/python/unittest/test_ndarray.py         |  27 +-
 tests/python/unittest/test_operator.py        | 663 ++++++++----------
 tests/python/unittest/test_random.py          |  16 +-
 tests/python/unittest/test_sparse_operator.py |   4 +-
 tests/python/unittest/test_subgraph.py        |   7 -
 16 files changed, 938 insertions(+), 546 deletions(-)
 create mode 100644 src/operator/contrib/allclose_op-inl.h
 create mode 100644 src/operator/contrib/allclose_op.cc
 create mode 100644 src/operator/contrib/allclose_op.cu

diff --git a/python/mxnet/test_utils.py b/python/mxnet/test_utils.py
index e5f2c6c02089..6ee37982a124 100644
--- a/python/mxnet/test_utils.py
+++ b/python/mxnet/test_utils.py
@@ -31,6 +31,7 @@
 import zipfile
 import json
 from contextlib import contextmanager
+from collections import OrderedDict
 import numpy as np
 import numpy.testing as npt
 import numpy.random as rnd
@@ -109,6 +110,16 @@ def random_sample(population, k):
     return population_copy[0:k]
 
 
+def _sorted_items(d):
+    """Return (key, value) pairs of dict 'd' in a deterministic order (sorted by key)."""
+    return sorted(d.items(), key=lambda t: t[0])
+
+
+def _sorted_dict(d):
+    """Return ordered dictionary containing items ordered by their keys."""
+    return OrderedDict(_sorted_items(d))
+
+
 def _validate_csr_generation_inputs(num_rows, num_cols, density,
                                     distribution="uniform"):
     """Validates inputs for csr generation helper functions
@@ -482,9 +493,10 @@ def find_max_violation(a, b, rtol=None, atol=None):
     """Finds and returns the location of maximum violation."""
     rtol = get_rtol(rtol)
     atol = get_atol(atol)
-    diff = np.abs(a-b)
+    # 'smart' absdiff that considers inf's as equals (to match np.allclose)
+    absdiff = np.where(np.equal(a, b), 0, np.abs(a-b))
     tol = atol + rtol*np.abs(b)
-    violation = diff/(tol+1e-20)
+    violation = absdiff/(tol+1e-20)
     loc = np.argmax(violation)
     idx = np.unravel_index(loc, violation.shape)
     return idx, np.max(violation)
@@ -500,40 +512,122 @@ def same(a, b):
     """
     return np.array_equal(a, b)
 
-def almost_equal(a, b, rtol=None, atol=None, equal_nan=False, use_broadcast=True):
-    """Test if two numpy arrays are almost equal."""
-    # pylint: disable=unexpected-keyword-arg
-    if (not use_broadcast) and a.shape != b.shape:
+
+def checkShapes(a, b):
+    if a.shape != b.shape:
         msg = npt.build_err_msg([a, b],
                                 err_msg="a.shape = {} and b.shape = {} are not equal"
                                 .format(str(a.shape), str(b.shape)))
         raise AssertionError(msg)
+
+
+def almost_equal(a, b, rtol=None, atol=None, equal_nan=False, use_broadcast=True):
+    """Test if two numpy arrays are almost equal."""
+    # pylint: disable=unexpected-keyword-arg
+    if not use_broadcast:
+        checkShapes(a, b)
+
     return np.allclose(a, b, rtol=get_rtol(rtol), atol=get_atol(atol), equal_nan=equal_nan)
     # pylint: enable=unexpected-keyword-arg
 
-def assert_almost_equal(a, b, rtol=None, atol=None, names=('a', 'b'), equal_nan=False, use_broadcast=True):
+
+def assert_almost_equal(a, b, rtol=None, atol=None, names=('a', 'b'), equal_nan=False,
+                        use_broadcast=True, mismatches=(10, 10)):
     """Test that two numpy arrays are almost equal. Raise exception message if not.
 
     Parameters
     ----------
-    a : np.ndarray
-    b : np.ndarray
-    threshold : None or float
-        The checking threshold. Default threshold will be used if set to ``None``.
+    a : np.ndarray or mx.nd.array
+    b : np.ndarray or mx.nd.array
+    rtol : None or float
+        The relative threshold. Default threshold will be used if set to ``None``.
+    atol : None or float
+        The absolute threshold. Default threshold will be used if set to ``None``.
+    names : tuple of names, optional
+        The names used in error message when an exception occurs
+    equal_nan : boolean, optional
+        The flag determining how to treat NAN values in comparison
+    mismatches : tuple of mismatches
+        Maximum number of mismatches to be printed (mismatches[0]) and determine (mismatches[1])
     """
+    if not use_broadcast:
+        checkShapes(a, b)
+
     rtol = get_rtol(rtol)
     atol = get_atol(atol)
-    if almost_equal(a, b, rtol, atol, equal_nan=equal_nan, use_broadcast=use_broadcast):
-        return
+    use_np_allclose = isinstance(a, np.ndarray) and isinstance(b, np.ndarray)
+    if not use_np_allclose:
+        if not (hasattr(a, 'context') and hasattr(b, 'context') and a.context == b.context and a.dtype == b.dtype):
+            use_np_allclose = True
+            if isinstance(a, mx.nd.NDArray):
+                a = a.asnumpy()
+            if isinstance(b, mx.nd.NDArray):
+                b = b.asnumpy()
+
+    if use_np_allclose:
+        if almost_equal(a, b, rtol, atol, equal_nan=equal_nan):
+            return
+    else:
+        output = mx.nd.contrib.allclose(a, b, rtol, atol, equal_nan)
+        if output.asnumpy() == 1:
+            return
+
+        a = a.asnumpy()
+        b = b.asnumpy()
+
+    def locationError(a, b, index, names, maxError=False):
+        """Create element mismatch comment
+
+        Parameters
+        ----------
+        a, b : compared np.ndarray's
+        index : tuple of coordinate arrays
+            Location of violation
+        names : tuple of names
+            The names of compared arrays.
+        maxError: boolean, optional
+            Flag indicating that maximum error is reporting.
+        """
+        maximum = "maximum " if maxError else ""
+        return "Location of %serror: %s, %s=%.8f, %s=%.8f" \
+               % (maximum, str(index), names[0], a[index], names[1], b[index])
+
     index, rel = find_max_violation(a, b, rtol, atol)
+    indexErr = index
+    relErr = rel
+
+    print('\n*** Maximum errors for vector of size {}:  rtol={}, atol={}\n'.format(a.size, rtol, atol))
+    aTmp = a.copy()
+    bTmp = b.copy()
+    i = 1
+    while i <= a.size:
+        if i <= mismatches[0]:
+            print("%3d: Error %f  %s" %(i, rel, locationError(a, b, index, names)))
+
+        aTmp[index] = bTmp[index] = 0
+        if almost_equal(aTmp, bTmp, rtol, atol, equal_nan=equal_nan):
+            break
+
+        i += 1
+        if i <= mismatches[1] or mismatches[1] <= 0:
+            index, rel = find_max_violation(aTmp, bTmp, rtol, atol)
+        else:
+            break
+
+    mismatchDegree = "at least " if mismatches[1] > 0 and i > mismatches[1] else ""
+    errMsg = "Error %f exceeds tolerance rtol=%e, atol=%e (mismatch %s%f%%).\n%s" % \
+             (relErr, rtol, atol, mismatchDegree, 100*i/a.size, \
+             locationError(a, b, indexErr, names, maxError=True))
     np.set_printoptions(threshold=4, suppress=True)
-    msg = npt.build_err_msg([a, b],
-                            err_msg="Error %f exceeds tolerance rtol=%f, atol=%f. "
-                                    " Location of maximum error:%s, a=%f, b=%f"
-                            % (rel, rtol, atol, str(index), a[index], b[index]),
-                            names=names)
+    msg = npt.build_err_msg([a, b], err_msg=errMsg)
+
     raise AssertionError(msg)
 
+
+def assert_allclose(a, b, rtol=1e-07, atol=0, equal_nan=True):
+    assert_almost_equal(a, b, rtol=rtol, atol=atol, equal_nan=equal_nan)
+
+
 def assert_almost_equal_with_err(a, b, rtol=None, atol=None, etol=None, names=('a', 'b'), equal_nan=False):
     """Test that two numpy arrays are almost equal within given error rate. Raise exception message if not.
 
@@ -554,7 +648,6 @@ def assert_almost_equal_with_err(a, b, rtol=None, atol=None, etol=None, names=('
         equals = np.isclose(a, b, rtol=rtol, atol=atol)
         err = 1 - np.count_nonzero(equals) / equals.size
         if err > etol:
-            #if True:
             index, rel = find_max_violation(a, b, rtol, atol)
             np.set_printoptions(threshold=4, suppress=True)
             msg = npt.build_err_msg([a, b],
@@ -684,7 +777,7 @@ def simple_forward(sym, ctx=None, is_train=False, **inputs):
 
 
 def _parse_location(sym, location, ctx, dtype=default_dtype()):
-    """Parses the given location to a dictionary.
+    """Parses the given location to a ordered dictionary.
 
     Arguments of the provided op `sym` are used as dictionary keys
     and elements of `location` are used as values.
@@ -740,7 +833,7 @@ def _parse_location(sym, location, ctx, dtype=default_dtype()):
         location = {k: v for k, v in zip(sym.list_arguments(), location)}
     location = {k: mx.nd.array(v, ctx=ctx, dtype=v.dtype if dtype == "asnumpy" else dtype) \
                if isinstance(v, np.ndarray) else v for k, v in location.items()}
-    return location
+    return _sorted_dict(location)
 
 
 def _parse_aux_states(sym, aux_states, ctx, dtype=default_dtype()):
@@ -1177,7 +1270,8 @@ def check_symbolic_backward(sym, location, out_grads, expected, rtol=1e-5, atol=
     if isinstance(expected, (list, tuple)):
         expected = {k:v for k, v in zip(sym.list_arguments(), expected)}
 
-    args_grad_npy = {k:np.random.normal(size=v.shape) for k, v in expected.items()}
+    # Dirty the output buffer deterministically, for reproducibility.
+    args_grad_npy = {k:np.random.normal(size=v.shape) for k, v in _sorted_items(expected)}
     args_grad_data = {}
     for k, v in args_grad_npy.items():
         nd = mx.nd.array(v, ctx=ctx, dtype=expected[k].dtype if dtype == "asnumpy" else dtype)
@@ -1313,6 +1407,15 @@ def check_speed(sym, location=None, ctx=None, N=20, grad_req=None, typ="whole",
     else:
         raise ValueError('typ can only be "whole" or "forward".')
 
+
+def get_tolerance(rtol, ctx):
+    if 'atol' in ctx:
+        return ctx['atol']
+    if 'atol_mult' in ctx:
+        return ctx['atol_mult'] * rtol
+    return rtol
+
+
 def check_consistency(sym, ctx_list, scale=1.0, grad_req='write',
                       arg_params=None, aux_params=None, tol=None,
                       raise_on_err=True, ground_truth=None, equal_nan=False,
@@ -1431,12 +1534,15 @@ def check_consistency(sym, ctx_list, scale=1.0, grad_req='write',
     for i, exe in enumerate(exe_list):
         if i == max_idx:
             continue
+
+        rtol = tol[dtypes[i]]
+        atol = get_tolerance(rtol, ctx_list[i])
         for name, arr in zip(output_names, exe.outputs):
-            gtarr = gt[name].astype(dtypes[i]).asnumpy()
-            arr = arr.asnumpy()
+            # Previously, the cast was to dtypes[i], but symbol may be mixed-precision,
+            # so casting the ground truth to the actual output type seems more correct.
+            gtarr = gt[name].astype(arr.dtype)
             try:
-                assert_almost_equal(arr, gtarr, rtol=tol[dtypes[i]], atol=tol[dtypes[i]],
-                                    equal_nan=equal_nan)
+                assert_almost_equal(arr, gtarr, rtol=rtol, atol=atol, equal_nan=equal_nan)
             except AssertionError as e:
                 print('Predict Err: ctx %d vs ctx %d at %s'%(i, max_idx, name))
                 traceback.print_exc()
@@ -1454,16 +1560,20 @@ def check_consistency(sym, ctx_list, scale=1.0, grad_req='write',
         for i, exe in enumerate(exe_list):
             if i == max_idx:
                 continue
+
+            rtol = tol[dtypes[i]]
+            atol = get_tolerance(rtol, ctx_list[i])
             curr = zip(output_names + arg_names, exe.outputs + exe.grad_arrays)
             for name, arr in curr:
                 if gt[name] is None:
                     assert arr is None
                     continue
-                gtarr = gt[name].astype(dtypes[i]).asnumpy()
-                arr = arr.asnumpy()
+
+                # Previous cast was to dtypes[i], but symbol may be mixed-precision,
+                # so casting the ground truth to the actual output type seems more correct.
+                gtarr = gt[name].astype(arr.dtype)
                 try:
-                    assert_almost_equal(arr, gtarr, rtol=tol[dtypes[i]], atol=tol[dtypes[i]],
-                                        equal_nan=equal_nan)
+                    assert_almost_equal(arr, gtarr, rtol=rtol, atol=atol, equal_nan=equal_nan)
                 except AssertionError as e:
                     print('Train Err: ctx %d vs ctx %d at %s'%(i, max_idx, name))
                     traceback.print_exc()
@@ -1694,7 +1804,7 @@ def get_mnist_iterator(batch_size, input_shape, num_parts=1, part_index=0):
     """
 
     get_mnist_ubyte()
-    flat = not bool(len(input_shape) == 3)
+    flat = len(input_shape) != 3
 
     train_dataiter = mx.io.MNISTIter(
         image="data/train-images-idx3-ubyte",
@@ -2134,12 +2244,14 @@ def verify_generator(generator, buckets, probs, nsamples=1000000, nrepeat=5, suc
 
 def compare_ndarray_tuple(t1, t2, rtol=None, atol=None):
     """Compare ndarray tuple."""
-    if t1 is not None and t2 is not None:
-        if isinstance(t1, tuple):
-            for s1, s2 in zip(t1, t2):
-                compare_ndarray_tuple(s1, s2, rtol, atol)
-        else:
-            assert_almost_equal(t1.asnumpy(), t2.asnumpy(), rtol=rtol, atol=atol)
+    if t1 is None or t2 is None:
+        return
+
+    if isinstance(t1, tuple):
+        for s1, s2 in zip(t1, t2):
+            compare_ndarray_tuple(s1, s2, rtol, atol)
+    else:
+        assert_almost_equal(t1, t2, rtol=rtol, atol=atol)
 
 
 def compare_optimizer(opt1, opt2, shape, dtype, w_stype='default', g_stype='default',
@@ -2171,7 +2283,7 @@ def compare_optimizer(opt1, opt2, shape, dtype, w_stype='default', g_stype='defa
     opt2.update_multi_precision(0, w2, g2, state2)
     if compare_states:
         compare_ndarray_tuple(state1, state2, rtol=rtol, atol=atol)
-    assert_almost_equal(w1.asnumpy(), w2.asnumpy(), rtol=rtol, atol=atol)
+    assert_almost_equal(w1, w2, rtol=rtol, atol=atol)
 
 
 def same_symbol_structure(sym1, sym2):
diff --git a/src/operator/contrib/allclose_op-inl.h b/src/operator/contrib/allclose_op-inl.h
new file mode 100644
index 000000000000..a858450f0007
--- /dev/null
+++ b/src/operator/contrib/allclose_op-inl.h
@@ -0,0 +1,160 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file allclose-inl.h
+ * \brief Operator implementing numpy.allclose function.
+ * \author Andrei Ivanov
+ */
+#ifndef MXNET_OPERATOR_CONTRIB_ALLCLOSE_OP_INL_H_
+#define MXNET_OPERATOR_CONTRIB_ALLCLOSE_OP_INL_H_
+
+#include <mxnet/operator_util.h>
+#include <vector>
+#include "../mshadow_op.h"
+#include "../mxnet_op.h"
+#include "../operator_common.h"
+#include "../elemwise_op_common.h"
+#include "../tensor/init_op.h"
+
+namespace mxnet {
+namespace op {
+
+// Intermediate and Output data types could be integers OR unsigned characters
+#define USE_INTEGER   0
+#if USE_INTEGER
+  #define INTERM_DATA_TYPE int32_t
+  #define OUT_DATA_TYPE    mshadow::kInt32
+#else
+  #define INTERM_DATA_TYPE uint8_t
+  #define OUT_DATA_TYPE    mshadow::kUint8
+#endif
+
+struct AllCloseParam : public dmlc::Parameter<AllCloseParam> {
+  float rtol, atol;
+  bool equal_nan;
+  DMLC_DECLARE_PARAMETER(AllCloseParam) {
+    DMLC_DECLARE_FIELD(rtol)
+      .set_default(1e-05)
+      .describe("Relative tolerance.");
+    DMLC_DECLARE_FIELD(atol)
+      .set_default(1e-08)
+      .describe("Absolute tolerance.");
+    DMLC_DECLARE_FIELD(equal_nan)
+      .set_default(true)
+      .describe("Whether to compare NaN’s as equal. If True, NaN’s in A will be considered equal "
+                "to NaN’s in B in the output array.");
+  }
+};
+
+inline bool AllCloseShape(const nnvm::NodeAttrs& attrs,
+                          std::vector<TShape>* in_attrs,
+                          std::vector<TShape>* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 2U) << "Input:[array1, array2]";
+  CHECK_EQ(out_attrs->size(), 1U);
+
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, mxnet::TShape(0, -1));
+  return in_attrs->at(0) == in_attrs->at(1);
+}
+
+inline bool AllCloseType(const nnvm::NodeAttrs& attrs,
+                         std::vector<int> *in_attrs,
+                         std::vector<int> *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 2U);
+  CHECK_EQ(out_attrs->size(), 1U);
+
+  // The output will be boolean stored as an OUT_DATA_TYPE format
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, OUT_DATA_TYPE);
+  return (*out_attrs)[0] != -1;
+}
+
+using namespace mshadow_op::isnan_typed;
+
+template<int req>
+struct allclose_forward {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int i, INTERM_DATA_TYPE *out_data,
+                                  const DType *in_a, const DType *in_b,
+                                  const float rtol, const float atol, bool equal_nan) {
+      const DType a = in_a[i], b = in_b[i];
+      bool val;
+      if (IsNan(a) || IsNan(b))
+        val = equal_nan && IsNan(a) == IsNan(b);
+      else
+        val = a == b || (a > b?  a - b : b - a) <= atol + (b > 0? rtol * b :  (-rtol) * b);
+
+      KERNEL_ASSIGN(out_data[i], req, val? 1 : 0);
+  }
+};
+
+template<typename xpu>
+size_t GetAdditionalMemoryLogical(mshadow::Stream<xpu> *s, const int num_items);
+
+template<typename xpu>
+INTERM_DATA_TYPE *GetAdditionalMemoryLogical(const OpContext& ctx,
+                                             int num_items, size_t *pExtraStorageBytes) {
+// Get length of the additional memory (which is used only by DeviceReduce::Min(...) on gpu)
+  *pExtraStorageBytes = GetAdditionalMemoryLogical<xpu>(ctx.get_stream<xpu>(), num_items);
+  const size_t workspace_total_bytes_ = num_items * sizeof(INTERM_DATA_TYPE) + *pExtraStorageBytes;
+  mshadow::Tensor<xpu, 1, uint8_t> workspace =
+    ctx.requested[0].get_space_typed<xpu, 1, uint8_t>(
+      mshadow::Shape1(workspace_total_bytes_), ctx.get_stream<xpu>());
+
+  return reinterpret_cast<INTERM_DATA_TYPE *>(workspace.dptr_);
+}
+
+template<typename xpu>
+void GetResultLogical(mshadow::Stream<xpu> *s, INTERM_DATA_TYPE *workMem, size_t extraStorageBytes,
+                      int num_items, INTERM_DATA_TYPE *outPntr);
+
+template<typename xpu>
+void AllClose(const nnvm::NodeAttrs& attrs,
+              const OpContext& ctx,
+              const std::vector<TBlob>& inputs,
+              const std::vector<OpReqType>& req,
+              const std::vector<TBlob>& outputs) {
+  CHECK_EQ(inputs.size(), 2U);
+  CHECK_EQ(outputs.size(), 1U);
+  CHECK_EQ(req.size(), 1U);
+
+  const TBlob& in0 = inputs[0];
+  const TBlob& in1 = inputs[1];
+  const int num_items = in0.Size();
+
+  size_t extraStorageBytes;
+  auto workspaceMem = GetAdditionalMemoryLogical<xpu>(ctx, num_items, &extraStorageBytes);
+  auto s = ctx.get_stream<xpu>();
+  const AllCloseParam& param = nnvm::get<AllCloseParam>(attrs.parsed);
+  using namespace mxnet_op;
+  MSHADOW_TYPE_SWITCH(in0.type_flag_, DType, {
+    MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
+      Kernel<allclose_forward<req_type>, xpu>::Launch(
+        s, num_items, workspaceMem, in0.dptr<DType>(), in1.dptr<DType>(),
+        param.rtol, param.atol, param.equal_nan);
+    });
+  });
+
+  auto *pOut = outputs[0].dptr<INTERM_DATA_TYPE>();
+  GetResultLogical<xpu>(s, workspaceMem, extraStorageBytes, num_items, pOut);
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_CONTRIB_ALLCLOSE_OP_INL_H_
diff --git a/src/operator/contrib/allclose_op.cc b/src/operator/contrib/allclose_op.cc
new file mode 100644
index 000000000000..6b301ad6519f
--- /dev/null
+++ b/src/operator/contrib/allclose_op.cc
@@ -0,0 +1,86 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file allclose_op.cc
+ * \brief CPU Implementation of allclose op
+ * \author Andrei Ivanov
+ */
+#include "./allclose_op-inl.h"
+
+namespace mxnet {
+namespace op {
+
+DMLC_REGISTER_PARAMETER(AllCloseParam);
+
+NNVM_REGISTER_OP(_contrib_allclose)
+.describe(R"code(This operators implements the numpy.allclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False)
+
+.. math::
+
+    f(x) = |a−b|≤atol+rtol|b|
+
+where
+:math:`a, b` are the input tensors of equal types an shapes
+:math:`atol, rtol` the values of absolute and relative tolerance (by default, rtol=1e-05, atol=1e-08)
+
+Examples::
+
+  a = [1e10, 1e-7],
+  b = [1.00001e10, 1e-8]
+  y = allclose(a, b)
+  y = False
+
+  a = [1e10, 1e-8],
+  b = [1.00001e10, 1e-9]
+  y = allclose(a, b)
+  y = True
+
+)code" ADD_FILELINE)
+.set_attr_parser(ParamParser<AllCloseParam>)
+.set_num_inputs(2)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"a", "b"};
+  })
+.set_attr<mxnet::FInferShape>("FInferShape", AllCloseShape)
+.set_attr<nnvm::FInferType>("FInferType", AllCloseType)
+.set_attr<FCompute>("FCompute<cpu>", AllClose<cpu>)
+.set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& n) {
+  return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+})
+.add_argument("a", "NDArray-or-Symbol", "Input array a")
+.add_argument("b", "NDArray-or-Symbol", "Input array b")
+.add_arguments(AllCloseParam::__FIELDS__());
+
+template<>
+size_t GetAdditionalMemoryLogical<cpu>(mshadow::Stream<cpu> *s, const int num_items) {
+  return 0;
+}
+
+template<>
+void GetResultLogical<cpu>(mshadow::Stream<cpu> *s, INTERM_DATA_TYPE *workMem,
+                           size_t extraStorageBytes, int num_items, INTERM_DATA_TYPE *outPntr) {
+  while (num_items-- > 0 && workMem[num_items]) {}
+  outPntr[0] = num_items >= 0? 0 : 1;
+}
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/contrib/allclose_op.cu b/src/operator/contrib/allclose_op.cu
new file mode 100644
index 000000000000..f923ab060813
--- /dev/null
+++ b/src/operator/contrib/allclose_op.cu
@@ -0,0 +1,58 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file allclose_op.cu
+ * \brief GPU Implementation of allclose op
+ * \author Andrei Ivanov
+ */
+#include "./allclose_op-inl.h"
+#include <cub/cub.cuh>
+
+namespace mxnet {
+namespace op {
+
+template<typename T>
+size_t GetAdditionalMemory(mshadow::Stream<gpu> *s, const int num_items) {
+  T *d_in = nullptr;
+  T *d_out = nullptr;
+  size_t temp_storage_bytes = 0;
+  cudaStream_t stream = mshadow::Stream<gpu>::GetStream(s);
+  cub::DeviceReduce::Min(nullptr, temp_storage_bytes, d_in, d_out, num_items, stream);
+  return temp_storage_bytes;
+}
+
+template<>
+size_t GetAdditionalMemoryLogical<gpu>(mshadow::Stream<gpu> *s, const int num_items) {
+  return GetAdditionalMemory<INTERM_DATA_TYPE>(s, num_items);
+}
+
+template<>
+void GetResultLogical<gpu>(mshadow::Stream<gpu> *s, INTERM_DATA_TYPE *workMem,
+                           size_t extraStorageBytes, int num_items, INTERM_DATA_TYPE *outPntr) {
+  cudaStream_t stream = mshadow::Stream<gpu>::GetStream(s);
+  cub::DeviceReduce::Min(workMem + num_items, extraStorageBytes,
+                         workMem, outPntr, num_items, stream);
+}
+
+NNVM_REGISTER_OP(_contrib_allclose)
+.set_attr<FCompute>("FCompute<gpu>", AllClose<gpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/tests/python-pytest/onnx/mxnet_export_test.py b/tests/python-pytest/onnx/mxnet_export_test.py
index 6c81198a8bca..90e92cccee06 100644
--- a/tests/python-pytest/onnx/mxnet_export_test.py
+++ b/tests/python-pytest/onnx/mxnet_export_test.py
@@ -74,7 +74,7 @@ def _check_onnx_export(net, group_outputs=False, shape_type=tuple, extra_params=
         # Confirm network outputs are the same
         imported_net_output = _force_list(imported_net(data))
         for out, imp_out in zip(output, imported_net_output):
-            mx.test_utils.assert_almost_equal(out.asnumpy(), imp_out.asnumpy())
+            mx.test_utils.assert_almost_equal(out, imp_out)
 
 
 class TestExport(unittest.TestCase):
diff --git a/tests/python/gpu/test_gluon_gpu.py b/tests/python/gpu/test_gluon_gpu.py
index fc650294a538..48fff7810c2e 100644
--- a/tests/python/gpu/test_gluon_gpu.py
+++ b/tests/python/gpu/test_gluon_gpu.py
@@ -20,26 +20,17 @@
 import os
 import tempfile
 import time
-import multiprocessing as mp
-import unittest
-import random
 import mxnet as mx
+import multiprocessing as mp
+from mxnet.test_utils import check_consistency, set_default_context, assert_almost_equal, rand_ndarray
 import mxnet.ndarray as nd
 import numpy as np
-import unittest
 import math
-from nose.tools import assert_raises
-from mxnet.test_utils import check_consistency, set_default_context, assert_almost_equal
-from mxnet.base import MXNetError
 from mxnet import autograd
-from numpy.testing import assert_allclose
-from mxnet.test_utils import rand_ndarray
-
 
 curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
 sys.path.insert(0, os.path.join(curr_path, '../unittest'))
-from common import setup_module, with_seed, teardown, assert_raises_cudnn_not_satisfied
-from common import run_in_spawned_process
+from common import setup_module, with_seed, teardown, assert_raises_cudnn_not_satisfied, run_in_spawned_process
 from test_gluon import *
 from test_loss import *
 from test_gluon_rnn import *
@@ -60,9 +51,9 @@ def check_rnn_layer(layer):
         co, cs = layer(x, states)
 
     # atol of 1e-6 required, as exposed by seed 2124685726
-    assert_almost_equal(go.asnumpy(), co.asnumpy(), rtol=1e-2, atol=1e-6)
+    assert_almost_equal(go, co, rtol=1e-2, atol=1e-6)
     for g, c in zip(gs, cs):
-        assert_almost_equal(g.asnumpy(), c.asnumpy(), rtol=1e-2, atol=1e-6)
+        assert_almost_equal(g, c, rtol=1e-2, atol=1e-6)
 
 
 @with_seed()
@@ -79,9 +70,9 @@ def check_rnn_layer_w_rand_inputs(layer):
         states = layer.begin_state(16)
         co, cs = layer(x, states)
 
-    assert_almost_equal(go.asnumpy(), co.asnumpy(), rtol=1e-2, atol=1e-6)
+    assert_almost_equal(go, co, rtol=1e-2, atol=1e-6)
     for g, c in zip(gs, cs):
-        assert_almost_equal(g.asnumpy(), c.asnumpy(), rtol=1e-2, atol=1e-6)
+        assert_almost_equal(g, c, rtol=1e-2, atol=1e-6)
 
 
 @with_seed()
@@ -117,21 +108,19 @@ def test_lstmp():
         layer_output = lstm_layer(lstm_input.copy())
         cell_output = lstm_cell.unroll(seq_len, lstm_input.copy(), layout='TNC',
                                        merge_outputs=True)[0]
-    assert_almost_equal(layer_output.asnumpy(),
-                        cell_output.asnumpy(), rtol=rtol, atol=atol)
+
+    assert_almost_equal(layer_output, cell_output, rtol=rtol, atol=atol)
     layer_output.backward()
     cell_output.backward()
     for k, v in weights.items():
         layer_grad = layer_params['lstm0_l0_' + k].grad()
         cell_grad = cell_params['lstm0_l0_' + k].grad()
         print('checking gradient for {}'.format('lstm0_l0_' + k))
-        assert_almost_equal(layer_grad.asnumpy(), cell_grad.asnumpy(),
-                            rtol=rtol, atol=atol)
+        assert_almost_equal(layer_grad, cell_grad, rtol=rtol, atol=atol)
     check_rnn_layer_forward(gluon.rnn.LSTM(
         10, 2, projection_size=5), mx.nd.ones((8, 3, 20)), ctx=ctx)
     check_rnn_layer_forward(gluon.rnn.LSTM(10, 2, projection_size=5, bidirectional=True), mx.nd.ones(
         (8, 3, 20)), [mx.nd.ones((4, 3, 5)), mx.nd.ones((4, 3, 10))], ctx=ctx)
-
     check_rnn_layer_forward(gluon.rnn.LSTM(10, 2, dropout=0.5, projection_size=5), mx.nd.ones((8, 3, 20)),
                             run_only=True, ctx=ctx)
     check_rnn_layer_forward(gluon.rnn.LSTM(10, 2, bidirectional=True, dropout=0.5, projection_size=5),
@@ -223,7 +212,8 @@ def forward(self, inpt):
             'r0', 'r0l0')].set_data(weights[k])
 
     data = mx.random.uniform(shape=(11, 10, in_size))
-    assert_allclose(net(data).asnumpy(), ref_net(data).asnumpy())
+    mx.test_utils.assert_allclose(net(data), ref_net(data), rtol=1e-6)
+
 
 
 def check_layer_bidirectional_varseqlen(size, in_size):
@@ -336,8 +326,7 @@ def test_gluon_ctc_consistency():
         l_gpu = loss(gpu_data, gpu_label)
         l_gpu.backward()
 
-    assert_almost_equal(cpu_data.grad.asnumpy(),
-                        gpu_data.grad.asnumpy(), atol=1e-3, rtol=1e-3)
+    assert_almost_equal(cpu_data.grad, gpu_data.grad, atol=1e-3, rtol=1e-3)
 
 
 @with_seed()
@@ -351,9 +340,88 @@ def test_global_norm_clip_multi_device():
             assert norm == 5.0
         else:
             assert norm.asscalar() == 5.0
-        assert_almost_equal(x1.asnumpy(), np.ones((3, 3)) / 5)
-        assert_almost_equal(x2.asnumpy(), np.ones((4, 4)) / 5)
+        assert_almost_equal(x1, np.ones((3, 3)) / 5)
+        assert_almost_equal(x2, np.ones((4, 4)) / 5)
+
+
+def _check_batchnorm_result(input, num_devices=1, cuda=False):
+    from mxnet.gluon.utils import split_and_load
+    def _find_bn(module):
+        if isinstance(module, (mx.gluon.nn.BatchNorm, mx.gluon.contrib.nn.SyncBatchNorm)):
+            return module
+        elif isinstance(module.module, (mx.gluon.nn.BatchNorm, mx.gluon.contrib.nn.SyncBatchNorm)):
+            return module.module
+
+        raise RuntimeError('BN not found')
+
+    def _syncParameters(bn1, bn2, ctx):
+        ctx = input.context
+        bn2.gamma.set_data(bn1.gamma.data(ctx))
+        bn2.beta.set_data(bn1.beta.data(ctx))
+        bn2.running_mean.set_data(bn1.running_mean.data(ctx))
+        bn2.running_var.set_data(bn1.running_var.data(ctx))
+
+    input1 = input.copy()
+    input2 = input.copy()
+
+    if cuda:
+        input1 = input.as_in_context(mx.gpu(0))
+        ctx_list = [mx.gpu(i) for i in range(num_devices)]
+    else:
+        ctx_list = [mx.cpu(0) for _ in range(num_devices)]
+
+    nch = input.shape[1]
+    bn1 = mx.gluon.nn.BatchNorm(in_channels=nch)
+    bn2 = mx.gluon.contrib.nn.SyncBatchNorm(in_channels=nch, num_devices=num_devices)
 
+    bn1.initialize(ctx=ctx_list[0])
+    bn2.initialize(ctx=ctx_list)
+
+    # using the same values for gamma and beta
+    #_syncParameters(_find_bn(bn1), _find_bn(bn2), ctx_list[0])
+
+    input1.attach_grad()
+    inputs2 = split_and_load(input2, ctx_list, batch_axis=0)
+    for xi in inputs2:
+        xi.attach_grad()
+
+    with mx.autograd.record():
+        output1 = bn1(input1)
+        output2  = [bn2(xi) for xi in inputs2]
+        loss1 = (output1 ** 2).sum()
+        loss2 = [(output ** 2).sum() for output in output2]
+        mx.autograd.backward(loss1)
+        mx.autograd.backward(loss2)
+
+    output2 = mx.nd.concat(*[output.as_in_context(input.context) for output in output2], dim=0)
+    # assert forwarding
+    assert_almost_equal(input1, input2, atol=1e-3, rtol=1e-3)
+    assert_almost_equal(output1, output2, atol=1e-3, rtol=1e-3)
+    assert_almost_equal(_find_bn(bn1).running_mean.data(ctx_list[0]),
+                        _find_bn(bn2).running_mean.data(ctx_list[0]),
+                        atol=1e-3, rtol=1e-3)
+    assert_almost_equal(_find_bn(bn1).running_var.data(ctx_list[0]),
+                        _find_bn(bn2).running_var.data(ctx_list[0]),
+                        atol=1e-3, rtol=1e-3)
+    input2grad = mx.nd.concat(*[output.grad.as_in_context(input.context) for output in inputs2], dim=0)
+    assert_almost_equal(input1.grad, input2grad, atol=1e-3, rtol=1e-3)
+
+@with_seed()
+def test_sync_batchnorm():
+    def get_num_devices():
+        for i in range(100):
+            try:
+                mx.nd.zeros((1,), ctx=mx.gpu(i))
+            except:
+                return i
+    # no need to use SyncBN with 1 gpu
+    if get_num_devices() < 2:
+        return
+    ndev = 2
+    # check with unsync version
+    for i in range(10):
+        _check_batchnorm_result(mx.nd.random.uniform(shape=(4, 1, 4, 4)),
+                                num_devices=ndev, cuda=True)
 
 @with_seed()
 def test_symbol_block_fp16():
@@ -468,10 +536,7 @@ def get_net(num_ops):
 
     time_per_iteration.value = (time.time() - start) / num_iterations
 
-
-@with_seed()
-@unittest.skip('skippping temporarily, tracked by https://github.com/apache/incubator-mxnet/issues/14970')
-def test_bulking():
+def _test_bulking(test_bulking_func):
     # test case format: (max_fwd_segment_size, max_bwd_segment_size, enable_bulking_in_training)
     test_cases = [(0, 0, True), (1, 1, True), (15, 15, False),
                   (15, 0, True), (0, 15, True), (15, 15, True)]
@@ -480,7 +545,8 @@ def test_bulking():
     for seg_sizes in test_cases:
         # Create shared variable to return measured time from test process
         time_per_iteration = mp.Manager().Value('d', 0.0)
-        if not run_in_spawned_process(_test_bulking_in_process,
+
+        if not run_in_spawned_process(test_bulking_func,
                                       {'MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN_FWD': seg_sizes[0],
                                        'MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN_BWD': seg_sizes[1],
                                        'MXNET_EXEC_BULK_EXEC_TRAIN': seg_sizes[2]},
@@ -492,8 +558,7 @@ def test_bulking():
             '\n    runtime of (fwd,bwd,enable) op seg setting ({},{},{}) =\t{:.1f} msec'.format(
                 seg_sizes[0], seg_sizes[1], seg_sizes[2], 1000.0 * times[seg_sizes])
 
-    fastest_non_bulked_time = min(
-        times[(0, 0, True)], times[(1, 1, True)], times[(15, 15, False)])
+    fastest_non_bulked_time = min(times[(0, 0, True)], times[(1, 1, True)], times[(15, 15, False)])
     slowest_half_bulked_time = max(times[(0, 15, True)], times[(15, 0, True)])
     fastest_half_bulked_time = min(times[(0, 15, True)], times[(15, 0, True)])
     fully_bulked_time = times[(15, 15, True)]
@@ -509,6 +574,11 @@ def test_bulking():
         'The fully-bulked exec time is slower than a half-bulked time by {} secs! {}' \
         .format(fully_bulked_time - fastest_half_bulked_time, times_str)
 
+@with_seed()
+@unittest.skip('skippping temporarily, tracked by https://github.com/apache/incubator-mxnet/issues/14970')
+def test_bulking_gluon_gpu():
+    _test_bulking(_test_bulking_in_process)
+
 
 if __name__ == '__main__':
     import nose
diff --git a/tests/python/gpu/test_gluon_model_zoo_gpu.py b/tests/python/gpu/test_gluon_model_zoo_gpu.py
index d4f6f31a30e7..6f559db62808 100644
--- a/tests/python/gpu/test_gluon_model_zoo_gpu.py
+++ b/tests/python/gpu/test_gluon_model_zoo_gpu.py
@@ -81,16 +81,17 @@ def test_inference():
             gpu_param = gpu_params.get(k)
             gpu_param.set_data(cpu_param.data().as_in_context(mx.gpu()))
 
+        cpu_data = mx.nd.array(data, ctx=mx.cpu())
         for i in range(5):
             # Run inference.
             with autograd.record(train_mode=False):
-                cpu_out = cpu_model(mx.nd.array(data, ctx=mx.cpu()))
+                cpu_out = cpu_model(cpu_data)
                 gpu_out = gpu_model(gpu_data)
-            out = cpu_out.asnumpy()
-            max_val = np.max(np.abs(out))
+
+            max_val = np.max(np.abs(cpu_out.asnumpy()))
             gpu_max_val = np.max(np.abs(gpu_out.asnumpy()))
             eprint(model_name + ": CPU " + str(max_val) + ", GPU " + str(gpu_max_val))
-            assert_almost_equal(out / max_val, gpu_out.asnumpy() / max_val, rtol=1e-3, atol=1e-3)
+            assert_almost_equal(cpu_out / max_val, gpu_out / gpu_max_val, rtol=1e-3, atol=1e-3)
 
 def get_nn_model(name):
     if "densenet" in name:
@@ -161,7 +162,7 @@ def test_training():
         max_val = np.max(np.abs(cpu_out.asnumpy()))
         gpu_max_val = np.max(np.abs(gpu_out.asnumpy()))
         eprint(model_name + ": CPU " + str(max_val) + ", GPU " + str(gpu_max_val))
-        assert_almost_equal(cpu_out.asnumpy() / max_val, gpu_out.asnumpy() / max_val, rtol=1e-3, atol=1e-3)
+        assert_almost_equal(cpu_out / max_val, gpu_out / max_val, rtol=1e-3, atol=1e-3)
         cpu_loss.backward()
         gpu_loss.backward()
         cpu_trainer.step(batch_size)
@@ -177,8 +178,7 @@ def test_training():
                 k = k.replace(cpu_params.prefix, '')
                 cpu_param = cpu_params.get(k)
                 gpu_param = gpu_params.get(k)
-                assert_almost_equal(cpu_param.data().asnumpy(), gpu_param.data().asnumpy(),
-                        rtol=1e-3, atol=1e-3)
+                assert_almost_equal(cpu_param.data(), gpu_param.data(), rtol=1e-3, atol=1e-3)
 
 if __name__ == '__main__':
     import nose
diff --git a/tests/python/gpu/test_operator_gpu.py b/tests/python/gpu/test_operator_gpu.py
index 0f1cd93755c3..b79b08219221 100644
--- a/tests/python/gpu/test_operator_gpu.py
+++ b/tests/python/gpu/test_operator_gpu.py
@@ -20,15 +20,13 @@
 import os
 import time
 import multiprocessing as mp
-import unittest
 import mxnet as mx
 import numpy as np
 import unittest
 from nose.tools import assert_raises
-from mxnet.test_utils import check_consistency, set_default_context, assert_almost_equal
+from mxnet.test_utils import check_consistency, set_default_context, assert_almost_equal, assert_allclose
 from mxnet.base import MXNetError
 from mxnet import autograd
-from numpy.testing import assert_allclose
 
 curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
 sys.path.insert(0, os.path.join(curr_path, '../unittest'))
@@ -46,6 +44,7 @@
 from test_sparse_operator import *
 from test_ndarray import *
 from test_subgraph_op import *
+from test_gluon_gpu import _test_bulking
 from test_contrib_operator import test_multibox_target_op
 from test_tvm_op import *
 from test_library_loading import *
@@ -230,7 +229,7 @@ def check_fft(shape):
                     a[i,j,:,p+1] = out2[i,j+out1[0].shape[1],:,k]
                     p = p+2
 
-    assert_almost_equal(a, out1[0],rtol=1e-3, atol=1e-5)
+    assert_almost_equal(a, out1[0], rtol=1e-3, atol=1e-5)
 
     # backward
     if len(shape) == 2:
@@ -244,7 +243,7 @@ def check_fft(shape):
         for exe in exe_list:
             exe.backward([out_grad])
         a = np.fft.ifft(out_grad_complex, n=None, axis=-1, norm=None)
-        assert_almost_equal(a.real, exe.grad_arrays[0].asnumpy()/shape[1],rtol=1e-3, atol=1e-5)
+        assert_almost_equal(a.real, exe.grad_arrays[0]/shape[1],rtol=1e-3, atol=1e-5)
 
     if len(shape) == 4:
         out_grad = mx.nd.empty(out1[0].shape)
@@ -257,7 +256,7 @@ def check_fft(shape):
         for exe in exe_list:
             exe.backward([out_grad])
         a = np.fft.ifft(out_grad_complex, n=None, axis=-1, norm=None)
-        assert_almost_equal(a.real, exe.grad_arrays[0].asnumpy()/shape[3],rtol=1e-3, atol=1e-5)
+        assert_almost_equal(a.real, exe.grad_arrays[0]/shape[3],rtol=1e-3, atol=1e-5)
 
 @with_seed()
 def test_fft():
@@ -1682,7 +1681,7 @@ def check_rnn_consistency(cell1, cell2):
     mod1.forward(batch, is_train=False)
     mod2.forward(batch, is_train=False)
 
-    assert_allclose(mod1.get_outputs()[0].asnumpy(), mod2.get_outputs()[0].asnumpy(), rtol=1e-2, atol=1e-4)
+    mx.test_utils.assert_allclose(mod1.get_outputs()[0], mod2.get_outputs()[0], rtol=1e-2, atol=1e-4)
 
 @with_seed()
 @assert_raises_cudnn_not_satisfied(min_version='5.1.10')
@@ -1716,7 +1715,7 @@ def test_lstm_forget_bias():
 
     bias_name = next(x for x in args if x.endswith('f_bias'))
     expected_bias = forget_bias * np.ones(10, )
-    assert_allclose(args[bias_name].asnumpy(), expected_bias)
+    mx.test_utils.assert_allclose(args[bias_name], expected_bias)
 
 @with_seed()
 @assert_raises_cudnn_not_satisfied(min_version='5.1.10')
@@ -2008,9 +2007,9 @@ def check_rnn_layer(layer):
         co, cs = layer(x, states)
 
     # atol of 1e-6 required, as exposed by seed 2124685726
-    assert_almost_equal(go.asnumpy(), co.asnumpy(), rtol=1e-2, atol=1e-6)
+    assert_almost_equal(go, co, rtol=1e-2, atol=1e-6)
     for g, c in zip(gs, cs):
-        assert_almost_equal(g.asnumpy(), c.asnumpy(), rtol=1e-2, atol=1e-6)
+        assert_almost_equal(g, c, rtol=1e-2, atol=1e-6)
 
 def check_rnn_layer_w_rand_inputs(layer):
     layer.collect_params().initialize(ctx=[mx.cpu(0), mx.gpu(0)])
@@ -2025,9 +2024,9 @@ def check_rnn_layer_w_rand_inputs(layer):
         states = layer.begin_state(16)
         co, cs = layer(x, states)
 
-    assert_almost_equal(go.asnumpy(), co.asnumpy(), rtol=1e-2, atol=1e-6)
+    assert_almost_equal(go, co, rtol=1e-2, atol=1e-6)
     for g, c in zip(gs, cs):
-        assert_almost_equal(g.asnumpy(), c.asnumpy(), rtol=1e-2, atol=1e-6)
+        assert_almost_equal(g, c, rtol=1e-2, atol=1e-6)
 
 @with_seed()
 def test_sequence_reverse():
@@ -2092,7 +2091,7 @@ def test_cross_device_autograd():
 
         y.backward()
 
-    dx = x.grad.asnumpy()
+    dx = x.grad.copy()
     x.grad[:] = 0
 
     with mx.autograd.record():
@@ -2101,7 +2100,7 @@ def test_cross_device_autograd():
             y = mx.nd.tanh(y)
         y.backward()
 
-    assert_almost_equal(dx, x.grad.asnumpy())
+    assert_almost_equal(dx, x.grad)
 
 @with_seed()
 def test_multi_proposal_op():
@@ -2285,12 +2284,11 @@ def test_softmax_activation():
     with mx.autograd.record():
         gpu_y = mx.nd.SoftmaxActivation(data = gpu_a)
         cpu_y = mx.nd.SoftmaxActivation(data = cpu_a)
-        assert_almost_equal(cpu_y.asnumpy(), gpu_y.asnumpy(), atol = 1e-3, rtol = 1e-3)
+        assert_almost_equal(cpu_y, gpu_y, atol = 1e-3, rtol = 1e-3)
 
         gpu_y.backward()
         cpu_y.backward()
-        assert_almost_equal(cpu_a.grad.asnumpy(), gpu_a.grad.asnumpy(),
-                atol = 1e-3, rtol = 1e-3)
+        assert_almost_equal(cpu_a.grad, gpu_a.grad, atol = 1e-3, rtol = 1e-3)
 
 
 @with_seed()
@@ -2320,13 +2318,13 @@ def test_bilinear_sampler_versions():
             exe.arg_dict['data'][:] = test_data
             exe.arg_dict['grid'][:] = test_grid
             exe.forward(is_train=True)
-            assert_almost_equal(exe_list[ref_idx].outputs[0].asnumpy(), exe.outputs[0].asnumpy(), rtol=1e-3, atol=1e-5)
+            mx.test_utils.assert_almost_equal(exe_list[ref_idx].outputs[0], exe.outputs[0], rtol=1e-3, atol=1e-5)
 
         out_grad = np.random.uniform(low=-0.01, high=0.01,size=data_shape[:2] + grid_shape[2:]).astype(np.float32)
         for exe in exe_list:
             exe.backward(mx.nd.array(out_grad))
-            assert_almost_equal(exe.grad_dict['data'].asnumpy(), exe_list[ref_idx].grad_dict['data'].asnumpy(), rtol=1e-3, atol=1e-5)
-            assert_almost_equal(exe.grad_dict['grid'].asnumpy(), exe_list[ref_idx].grad_dict['grid'].asnumpy(), rtol=1e-3, atol=1e-5)
+            assert_almost_equal(exe.grad_dict['data'], exe_list[ref_idx].grad_dict['data'], rtol=1e-3, atol=1e-5)
+            assert_almost_equal(exe.grad_dict['grid'], exe_list[ref_idx].grad_dict['grid'], rtol=1e-3, atol=1e-5)
 
         data_grad = exe_list[ref_idx].grad_dict['data'].asnumpy()
         grid_grad = exe_list[ref_idx].grad_dict['grid'].asnumpy()
@@ -2345,10 +2343,10 @@ def test_bilinear_sampler_versions():
             exe.grad_dict['grid'][:] = grid_initial_grad
             exe.forward(is_train=True)
             exe.backward(mx.nd.array(out_grad))
-            assert_almost_equal(exe.grad_dict['data'].asnumpy(), exe_list[ref_idx].grad_dict['data'].asnumpy(), rtol=1e-3, atol=1e-5)
-            assert_almost_equal(exe.grad_dict['grid'].asnumpy(), exe_list[ref_idx].grad_dict['grid'].asnumpy(), rtol=1e-3, atol=1e-5)
-        assert_almost_equal(exe_list[ref_idx].grad_dict['data'].asnumpy(), data_grad + data_initial_grad, rtol=1e-3, atol=1e-5)
-        assert_almost_equal(exe_list[ref_idx].grad_dict['grid'].asnumpy(), grid_grad + grid_initial_grad, rtol=1e-3, atol=1e-5)
+            assert_almost_equal(exe.grad_dict['data'], exe_list[ref_idx].grad_dict['data'], rtol=1e-3, atol=1e-5)
+            assert_almost_equal(exe.grad_dict['grid'], exe_list[ref_idx].grad_dict['grid'], rtol=1e-3, atol=1e-5)
+        assert_almost_equal(exe_list[ref_idx].grad_dict['data'], data_grad + data_initial_grad, rtol=1e-3, atol=1e-5)
+        assert_almost_equal(exe_list[ref_idx].grad_dict['grid'], grid_grad + grid_initial_grad, rtol=1e-3, atol=1e-5)
 
         for req_dict in [{'data' : 'null', 'grid' : 'write'}, {'data' : 'write', 'grid' : 'null'}]:
             # Mixture of kWriteTo and kNullOp
@@ -2362,9 +2360,9 @@ def test_bilinear_sampler_versions():
                 exe.forward(is_train=True)
                 exe.backward(mx.nd.array(out_grad))
                 if req_dict['data'] is 'write':
-                    assert_almost_equal(exe.grad_dict['data'].asnumpy(), exe_list[ref_idx].grad_dict['data'].asnumpy(), rtol=1e-3, atol=1e-5)
+                    assert_almost_equal(exe.grad_dict['data'], exe_list[ref_idx].grad_dict['data'], rtol=1e-3, atol=1e-5)
                 if req_dict['grid'] is 'write':
-                    assert_almost_equal(exe.grad_dict['grid'].asnumpy(), exe_list[ref_idx].grad_dict['grid'].asnumpy(), rtol=1e-3, atol=1e-5)
+                    assert_almost_equal(exe.grad_dict['grid'], exe_list[ref_idx].grad_dict['grid'], rtol=1e-3, atol=1e-5)
 
 
 # isolated execution bulking test function to be invoked with different env var settings
@@ -2394,7 +2392,12 @@ def _test_bulking_in_process(seed, time_per_iteration):
         dx.wait_to_read()
     time_per_iteration.value = (time.time() - start) / num_iterations
 
+
 @with_seed()
+def test_bulking_operator_gpu():
+    _test_bulking(_test_bulking_in_process)
+
+
 @unittest.skip('skippping temporarily, tracked by https://github.com/apache/incubator-mxnet/issues/14970')
 def test_bulking():
     # test case format: (max_fwd_segment_size, max_bwd_segment_size, enable_bulking_in_training)
@@ -2433,6 +2436,10 @@ def test_bulking():
             .format(fully_bulked_time - fastest_half_bulked_time, times_str)
 
 
+@with_seed()
+def test_allclose_function_gpu():
+    allclose_function([mx.cpu(), mx.gpu(0)])
+
 def test_context_num_gpus():
     # Test that num_gpus reports at least one GPU, as the test is run on a GPU host.
     assert mx.context.num_gpus() > 0
diff --git a/tests/python/mkl/test_mkldnn.py b/tests/python/mkl/test_mkldnn.py
index 2ffe3eaa233d..f88c0a888320 100644
--- a/tests/python/mkl/test_mkldnn.py
+++ b/tests/python/mkl/test_mkldnn.py
@@ -161,8 +161,8 @@ def hybrid_forward(self, F, x, *args, **kwargs):
     with mx.autograd.record():
         out2 = net(x)
     out2.backward()
-    mx.test_utils.assert_almost_equal(dx1.asnumpy(), x.grad.asnumpy(), rtol=1e-5, atol=1e-6)
-    mx.test_utils.assert_almost_equal(out1.asnumpy(), out2.asnumpy(), rtol=1e-5, atol=1e-6)
+    assert_almost_equal(dx1, x.grad, rtol=1e-5, atol=1e-6)
+    assert_almost_equal(out1, out2, rtol=1e-5, atol=1e-6)
 
 
 @with_seed()
@@ -195,8 +195,8 @@ def hybrid_forward(self, F, x, *args, **kwargs):
     with mx.autograd.record():
         out2 = net(x)
     out2.backward()
-    mx.test_utils.assert_almost_equal(dx1.asnumpy(), x.grad.asnumpy(), rtol=1e-5, atol=1e-6)
-    mx.test_utils.assert_almost_equal(out1.asnumpy(), out2.asnumpy(), rtol=1e-5, atol=1e-6)
+    assert_almost_equal(dx1, x.grad, rtol=1e-5, atol=1e-6)
+    assert_almost_equal(out1, out2, rtol=1e-5, atol=1e-6)
 
 
 @with_seed()
@@ -229,8 +229,8 @@ def hybrid_forward(self, F, x, *args, **kwargs):
     with mx.autograd.record():
         out2 = net(x)
     out2.backward()
-    mx.test_utils.assert_almost_equal(dx1.asnumpy(), x.grad.asnumpy(), rtol=1e-5, atol=1e-6)
-    mx.test_utils.assert_almost_equal(out1.asnumpy(), out2.asnumpy(), rtol=1e-5, atol=1e-6)
+    assert_almost_equal(dx1, x.grad, rtol=1e-5, atol=1e-6)
+    assert_almost_equal(out1, out2, rtol=1e-5, atol=1e-6)
 
 
 @with_seed()
diff --git a/tests/python/unittest/test_gluon_contrib.py b/tests/python/unittest/test_gluon_contrib.py
index 1e0555900f17..123db085e817 100644
--- a/tests/python/unittest/test_gluon_contrib.py
+++ b/tests/python/unittest/test_gluon_contrib.py
@@ -24,10 +24,9 @@
 from mxnet.gluon.contrib.nn import (
     Concurrent, HybridConcurrent, Identity, SparseEmbedding, PixelShuffle1D,
     PixelShuffle2D, PixelShuffle3D)
-from mxnet.test_utils import almost_equal, default_context, assert_almost_equal
+from mxnet.test_utils import almost_equal, default_context, assert_almost_equal, assert_allclose
 from common import setup_module, with_seed, teardown
 import numpy as np
-from numpy.testing import assert_allclose
 
 
 def check_rnn_cell(cell, prefix, in_shape=(10, 50), out_shape=(10, 100), begin_state=None):
@@ -112,6 +111,7 @@ def test_conv_fill_shape():
     check_rnn_forward(cell, mx.nd.ones((8, 3, 5, 7)))
     assert cell.i2h_weight.shape[1] == 5, cell.i2h_weight.shape[1]
 
+
 @with_seed()
 def test_lstmp():
     nhid = 100
@@ -193,8 +193,7 @@ def test_concurrent():
 def test_identity():
     model = Identity()
     x = mx.nd.random.uniform(shape=(128, 33, 64))
-    mx.test_utils.assert_almost_equal(model(x).asnumpy(),
-                                      x.asnumpy())
+    assert_almost_equal(model(x), x)
 
 @with_seed()
 def test_sparse_embedding():
@@ -219,7 +218,7 @@ def test_pixelshuffle1d():
     y = layer(x)
     assert y.shape == shape_after
     assert_allclose(
-        y.asnumpy(),
+        y,
         [[[0, 3, 1, 4, 2, 5],
           [6, 9, 7, 10, 8, 11]]]
     )
@@ -242,7 +241,7 @@ def test_pixelshuffle2d():
     # - Increasing the block index adds an offset of 1
     # - Increasing the channel index adds an offset of `nx * up_x * ny * up_y`
     assert_allclose(
-        y.asnumpy(),
+        y,
         [[[[ 0,  6, 12,  1,  7, 13,  2,  8, 14],
            [18, 24, 30, 19, 25, 31, 20, 26, 32],
            [ 3,  9, 15,  4, 10, 16,  5, 11, 17],
@@ -273,7 +272,7 @@ def test_pixelshuffle3d():
     #   column index by 1, e.g. the block [[[ 0, 24]], [[48, 72]]]
     # - Increasing the block index adds an offset of 1
     assert_allclose(
-        y.asnumpy(),
+        y,
         [[[[[ 0, 24,  1, 25,  2, 26,  3, 27],
             [ 4, 28,  5, 29,  6, 30,  7, 31],
             [ 8, 32,  9, 33, 10, 34, 11, 35]],
@@ -382,19 +381,17 @@ def check_unroll(cell_type, num_states, layout):
         trainer = gluon.Trainer(params2, 'sgd', {'learning_rate' : 0.03})
         with mx.autograd.record():
             res2, states2 = layer(rnn_data, states, valid_length)
-        assert_almost_equal(res1.asnumpy(), res2.asnumpy(), rtol=0.001, atol=0.0001)
+        assert_almost_equal(res1, res2, rtol=0.001, atol=0.0001)
         assert len(states1) == len(states2)
         for i in range(len(states1)):
-            assert_almost_equal(states1[i].asnumpy(), states2[i].asnumpy(),
-                                rtol=0.001, atol=0.0001)
+            assert_almost_equal(states1[i], states2[i], rtol=0.001, atol=0.0001)
         res2.backward()
         trainer.step(batch_size)
 
         for key, val in params1.items():
             weight1 = val.data()
             weight2 = params2[key].data()
-            assert_almost_equal(weight1.asnumpy(), weight2.asnumpy(),
-                    rtol=0.001, atol=0.0001)
+            assert_almost_equal(weight1, weight2, rtol=0.001, atol=0.0001)
 
 
 @with_seed()
diff --git a/tests/python/unittest/test_loss.py b/tests/python/unittest/test_loss.py
index 3b9b46b16f93..af7c4838d5a9 100644
--- a/tests/python/unittest/test_loss.py
+++ b/tests/python/unittest/test_loss.py
@@ -49,10 +49,10 @@ def test_loss_ndarray():
 
     loss = gluon.loss.SoftmaxCrossEntropyLoss()
     L = loss(output, label).asnumpy()
-    mx.test_utils.assert_almost_equal(L, np.array([ 2.12692809,  0.04858733]))
+    assert_almost_equal(L, np.array([ 2.12692809,  0.04858733]))
 
     L = loss(output, label, weighting).asnumpy()
-    mx.test_utils.assert_almost_equal(L, np.array([ 1.06346405,  0.04858733]))
+    assert_almost_equal(L, np.array([ 1.06346405,  0.04858733]))
 
 
 def get_net(num_hidden, flatten=True):
@@ -126,8 +126,8 @@ def test_logistic_loss_equal_bce():
     loss_bce = gluon.loss.SigmoidBCELoss(from_sigmoid=False)
     data = mx.random.uniform(-10, 10, shape=(N, 1))
     label = mx.nd.round(mx.random.uniform(0, 1, shape=(N, 1)))
-    assert_almost_equal(loss_binary(data, label).asnumpy(), loss_bce(data, label).asnumpy(), atol=1e-6)
-    assert_almost_equal(loss_signed(data, 2 * label - 1).asnumpy(), loss_bce(data, label).asnumpy(), atol=1e-6)
+    assert_almost_equal(loss_binary(data, label), loss_bce(data, label), atol=1e-6)
+    assert_almost_equal(loss_signed(data, 2 * label - 1), loss_bce(data, label), atol=1e-6)
 
 @with_seed()
 def test_kl_loss():
@@ -186,27 +186,27 @@ def test_l1_loss():
 def test_ctc_loss():
     loss = gluon.loss.CTCLoss()
     l = loss(mx.nd.ones((2,20,4)), mx.nd.array([[1,0,-1,-1],[2,1,1,-1]]))
-    mx.test_utils.assert_almost_equal(l.asnumpy(), np.array([18.82820702, 16.50581741]))
+    assert_almost_equal(l, np.array([18.82820702, 16.50581741]))
 
     loss = gluon.loss.CTCLoss(layout='TNC')
     l = loss(mx.nd.ones((20,2,4)), mx.nd.array([[1,0,-1,-1],[2,1,1,-1]]))
-    mx.test_utils.assert_almost_equal(l.asnumpy(), np.array([18.82820702, 16.50581741]))
+    assert_almost_equal(l, np.array([18.82820702, 16.50581741]))
 
     loss = gluon.loss.CTCLoss(layout='TNC', label_layout='TN')
     l = loss(mx.nd.ones((20,2,4)), mx.nd.array([[1,0,-1,-1],[2,1,1,-1]]).T)
-    mx.test_utils.assert_almost_equal(l.asnumpy(), np.array([18.82820702, 16.50581741]))
+    assert_almost_equal(l, np.array([18.82820702, 16.50581741]))
 
     loss = gluon.loss.CTCLoss()
     l = loss(mx.nd.ones((2,20,4)), mx.nd.array([[2,1,2,2],[3,2,2,2]]), None, mx.nd.array([2,3]))
-    mx.test_utils.assert_almost_equal(l.asnumpy(), np.array([18.82820702, 16.50581741]))
+    assert_almost_equal(l, np.array([18.82820702, 16.50581741]))
 
     loss = gluon.loss.CTCLoss()
     l = loss(mx.nd.ones((2,25,4)), mx.nd.array([[2,1,-1,-1],[3,2,2,-1]]), mx.nd.array([20,20]))
-    mx.test_utils.assert_almost_equal(l.asnumpy(), np.array([18.82820702, 16.50581741]))
+    assert_almost_equal(l, np.array([18.82820702, 16.50581741]))
 
     loss = gluon.loss.CTCLoss()
     l = loss(mx.nd.ones((2,25,4)), mx.nd.array([[2,1,3,3],[3,2,2,3]]), mx.nd.array([20,20]), mx.nd.array([2,3]))
-    mx.test_utils.assert_almost_equal(l.asnumpy(), np.array([18.82820702, 16.50581741]))
+    assert_almost_equal(l, np.array([18.82820702, 16.50581741]))
 
 
 @with_seed()
@@ -367,12 +367,14 @@ def test_cosine_loss():
     assert_almost_equal(loss.asnumpy(), numpy_loss.asnumpy(), rtol=1e-3, atol=1e-5)
 
 def test_poisson_nllloss():
-    pred = mx.nd.random.normal(shape=(3, 4))
+    shape=(3, 4)
+    not_axis0 = tuple(range(1, len(shape)))
+    pred = mx.nd.random.normal(shape=shape)
     min_pred = mx.nd.min(pred)
     #This is necessary to ensure only positive random values are generated for prediction,
     # to avoid ivalid log calculation
     pred[:] = pred + mx.nd.abs(min_pred)
-    target = mx.nd.random.normal(shape=(3, 4))
+    target = mx.nd.random.normal(shape=shape)
     min_target = mx.nd.min(target)
     #This is necessary to ensure only positive random values are generated for prediction,
     # to avoid ivalid log calculation
@@ -396,8 +398,9 @@ def test_poisson_nllloss():
         assert_almost_equal(np_loss_no_logits, loss_no_logits.asscalar())
 
     #3) Testing for Sterling approximation
-    np_pred = np.random.uniform(1, 5, (2, 3))
-    np_target = np.random.uniform(1, 5, (2, 3))
+    shape=(2, 3)
+    np_pred = np.random.uniform(1, 5, shape)
+    np_target = np.random.uniform(1, 5, shape)
     np_compute_full = np.mean((np_pred - np_target * np.log(np_pred + 1e-08)) + ((np_target * np.log(np_target)-\
      np_target + 0.5 * np.log(2 * np_target * np.pi))*(np_target > 1)))
     Loss_compute_full = gluon.loss.PoissonNLLLoss(from_logits=False, compute_full=True)
diff --git a/tests/python/unittest/test_ndarray.py b/tests/python/unittest/test_ndarray.py
index 7be59df6efda..bee4bff0f7c0 100644
--- a/tests/python/unittest/test_ndarray.py
+++ b/tests/python/unittest/test_ndarray.py
@@ -41,6 +41,10 @@ def check_with_uniform(uf, arg_shapes, dim=None, npuf=None, rmin=-10, type_list=
         assert dim
         shape = tuple(np.random.randint(1, int(1000**(1.0/dim)), size=dim))
         arg_shapes = [shape] * arg_shapes
+
+    if npuf is None:
+        npuf = uf
+
     for dtype in type_list:
         ndarray_arg = []
         numpy_arg = []
@@ -50,10 +54,7 @@ def check_with_uniform(uf, arg_shapes, dim=None, npuf=None, rmin=-10, type_list=
             ndarray_arg.append(narr)
             numpy_arg.append(npy)
         out1 = uf(*ndarray_arg)
-        if npuf is None:
-            out2 = uf(*numpy_arg).astype(dtype)
-        else:
-            out2 = npuf(*numpy_arg).astype(dtype)
+        out2 = npuf(*numpy_arg).astype(dtype)
 
         assert out1.shape == out2.shape
         if isinstance(out1, mx.nd.NDArray):
@@ -1712,13 +1713,13 @@ def l2norm(input_data, axis=0, keepdims=False):
                         np_arr, i, keep_dims)
                     mx_out = mx.nd.norm(mx_arr, ord=ord, axis=i, keepdims=keep_dims)
                     assert npy_out.shape == mx_out.shape
-                    mx.test_utils.assert_almost_equal(npy_out, mx_out.asnumpy())
+                    assert_almost_equal(npy_out, mx_out)
                     if (i < 3):
                         npy_out = l1norm(np_arr, (i, i + 1), keep_dims) if ord == 1 else l2norm(
                             np_arr, (i, i + 1), keep_dims)
                         mx_out = mx.nd.norm(mx_arr, ord=ord, axis=(i, i + 1), keepdims=keep_dims)
                         assert npy_out.shape == mx_out.shape
-                        mx.test_utils.assert_almost_equal(npy_out, mx_out.asnumpy())
+                        assert_almost_equal(npy_out, mx_out)
 
 
 @with_seed()
@@ -1732,7 +1733,7 @@ def test_dlpack():
     for dtype in [np.float32, np.int32]:
         for shape in [(3, 4, 5, 6), (2, 10), (15,)]:
             a = mx.nd.random.uniform(shape = shape)
-            a_np = a.asnumpy()
+            a_np = a.copy()
 
             pack = a.to_dlpack_for_read()
             b = mx.nd.from_dlpack(pack)
@@ -1750,14 +1751,10 @@ def test_dlpack():
 
             del a, pack, pack2, pack3, pack4
 
-            b_np = b.asnumpy()
-            c_np = c.asnumpy()
-            d_np = d.asnumpy()
-            e_np = e.asnumpy()
-            mx.test_utils.assert_almost_equal(a_np, b_np)
-            mx.test_utils.assert_almost_equal(a_np, c_np)
-            mx.test_utils.assert_almost_equal(a_np, d_np)
-            mx.test_utils.assert_almost_equal(a_np, e_np)
+            assert_almost_equal(a_np, b)
+            assert_almost_equal(a_np, c)
+            assert_almost_equal(a_np, d)
+            assert_almost_equal(a_np, e)
 
 @with_seed()
 def test_ndarray_is_inf():
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 5f40bf12674a..66bd9ec6b489 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -363,9 +363,9 @@ def check_elementwise_sum_with_shape(shape, n):
     exec1 = out.bind(default_context(),
                      args=arr,
                      args_grad=arr_grad)
-    out1 = exec1.outputs[0].asnumpy()
+
     exec1.forward(is_train=True)
-    out1 = exec1.outputs[0].asnumpy()
+    out1 = exec1.outputs[0]
     out = sum(a.asnumpy() for a  in arr)
     assert_almost_equal(out, out1, rtol=1e-5, atol=1e-5)
 
@@ -374,7 +374,7 @@ def check_elementwise_sum_with_shape(shape, n):
     # backward
     exec1.backward([out_grad])
     for a in arr_grad:
-        assert_almost_equal(a.asnumpy(), out_grad.asnumpy(), rtol=1e-5, atol=1e-5)
+        assert_almost_equal(a, out_grad, rtol=1e-5, atol=1e-5)
 
 
 @with_seed()
@@ -419,7 +419,7 @@ def check_concat_with_shape(shapes, dimension, skip_second):
     exec1.forward(is_train=True)
     out1 = exec1.outputs[0]
     ret = np.concatenate([narray.asnumpy() for narray in arr], axis=dimension)
-    assert_almost_equal(out1.asnumpy(), ret)
+    assert_almost_equal(out1, ret)
     # backward
     out1.copyto(out_grad)
     out_grad[:] += 1
@@ -429,7 +429,7 @@ def check_concat_with_shape(shapes, dimension, skip_second):
         if not skip_second or name != 'arg1':
             grad = dict_grad[name]
             np_grad = arr_np[i]
-            assert_almost_equal(grad.asnumpy(), np_grad + 1)
+            assert_almost_equal(grad, np_grad + 1)
 
 
 @with_seed()
@@ -514,18 +514,17 @@ def check_slice_channel(data_ndim, axis, num_outputs, squeeze_axis):
             gt = data_npy.take(np.arange(i * shape[axis]/num_outputs,
                                          (i+1) * shape[axis]/num_outputs).astype(np.int), axis=axis)
             if squeeze_axis:
-
-                assert_almost_equal(outputs[i].asnumpy(), gt.reshape(outputs[i].shape))
+                assert_almost_equal(outputs[i], gt.reshape(outputs[i].shape))
             else:
-                assert_almost_equal(outputs[i].asnumpy(), gt)
+                assert_almost_equal(outputs[i], gt)
         # test backward
         exe.backward(out_grads=[mx.nd.array(ele, ctx=default_context()) for ele in out_grads_npy])
         if squeeze_axis:
-            assert_almost_equal(exe.grad_arrays[0].asnumpy(),
+            assert_almost_equal(exe.grad_arrays[0],
                                 np.concatenate([np.expand_dims(ele, axis=axis) for ele in out_grads_npy],
                                                axis=axis))
         else:
-            assert_almost_equal(exe.grad_arrays[0].asnumpy(),
+            assert_almost_equal(exe.grad_arrays[0],
                                 np.concatenate(out_grads_npy, axis=axis))
     check_slice_channel(data_ndim=2, axis=1, num_outputs=3, squeeze_axis=True)
     check_slice_channel(data_ndim=4, axis=2, num_outputs=3, squeeze_axis=False)
@@ -557,8 +556,8 @@ def check_regression(symbol, forward, backward, shape, stype='default', densitie
             out_exec.backward()
             np_out = forward(arr_data.asnumpy())
             out_grad = backward(np_out, arr_label.asnumpy().reshape(np_out.shape)) / shape[1]
-            assert_almost_equal(out_exec.outputs[0].asnumpy(), np_out, atol=atol)
-            assert_almost_equal(grad_map["data"].asnumpy(), out_grad, atol=atol)
+            assert_almost_equal(out_exec.outputs[0], np_out, atol=atol)
+            assert_almost_equal(grad_map["data"], out_grad, atol=atol)
 
     shape = (50, 30)
 
@@ -681,7 +680,7 @@ def check_softmax_with_shape(shape, xpu, preserve_shape=False):
     atol = 1e-6
     assert_almost_equal(out, np_softmax(x.asnumpy()), rtol=rtol, atol=atol)
     exec1.backward()
-    assert_almost_equal(grad.asnumpy(), np_softmax(x.asnumpy()) - l.asnumpy(), rtol=rtol, atol=atol)
+    assert_almost_equal(grad, np_softmax(x.asnumpy()) - l.asnumpy(), rtol=rtol, atol=atol)
 
 
 def test_python_op():
@@ -694,9 +693,9 @@ def test_python_op():
     dy = mx.ndarray.ones((10))
     exec1 = s.bind(default_context(), args=[x], args_grad = {'X': dx})
     exec1.forward(is_train=True)
-    assert_almost_equal(x.asnumpy(), exec1.outputs[0].asnumpy())
+    assert_almost_equal(x, exec1.outputs[0])
     exec1.backward(dy)
-    assert_almost_equal(dy.asnumpy(), dx.asnumpy())
+    assert_almost_equal(dy, dx)
 
 
 def test_swapaxes():
@@ -710,7 +709,7 @@ def test_swapaxes():
     swap = mx.symbol.SwapAxis(data=swap0, dim1=1, dim2=2)
     exe_c = swap.bind(default_context(), args=[arr_data])
     exe_c.forward(is_train=True)
-    out = exe_c.outputs[0].asnumpy()
+    out = exe_c.outputs[0]
 
     swap0_ = np.swapaxes(data_tmp, 0, 2)
     swap_ = np.swapaxes(swap0_, 1, 2)
@@ -1021,7 +1020,7 @@ def test_shape_array():
         exe.backward([yg])
         yo = exe.outputs[0].asnumpy()
         same(yo, ya)
-        assert_almost_equal(xg.asnumpy(), np.zeros_like(xg.asnumpy()))
+        assert_almost_equal(xg, np.zeros_like(xg.asnumpy()))
 
 @with_seed()
 def test_size_array():
@@ -1039,7 +1038,7 @@ def test_size_array():
         exe.backward([yg])
         yo = exe.outputs[0].asnumpy()
         same(yo, ya)
-        assert_almost_equal(xg.asnumpy(), np.zeros_like(xg.asnumpy()))
+        assert_almost_equal(xg, np.zeros_like(xg.asnumpy()))
 
 @with_seed()
 def test_hard_sigmoid():
@@ -1098,7 +1097,7 @@ def _inner_test(forward_gt, logic_sym, x_shape, y_shape, test_scalar=True):
         x_npy = np.random.randint(0, 4, size=x_shape).astype(np.float32)
         y_npy = np.random.randint(0, 4, size=y_shape).astype(np.float32)
         exe = z.simple_bind(ctx=default_context(), x=x_shape, y=y_shape)
-        mx_out = exe.forward(is_train=True, x=x_npy, y=y_npy)[0].asnumpy()
+        mx_out = exe.forward(is_train=True, x=x_npy, y=y_npy)[0]
         assert_almost_equal(mx_out, forward_gt(x_npy, y_npy))
         exe.backward()
         if test_scalar:
@@ -1106,8 +1105,8 @@ def _inner_test(forward_gt, logic_sym, x_shape, y_shape, test_scalar=True):
             z_rscalar = logic_sym(x, 1)
             exe_lscalar = z_lscalar.simple_bind(ctx=default_context(), y=y_shape)
             exe_rscalar = z_rscalar.simple_bind(ctx=default_context(), x=x_shape)
-            mx_lscalar_out = exe_lscalar.forward(is_train=True, y=y_npy)[0].asnumpy()
-            mx_rscalar_out = exe_rscalar.forward(is_train=True, x=x_npy)[0].asnumpy()
+            mx_lscalar_out = exe_lscalar.forward(is_train=True, y=y_npy)[0]
+            mx_rscalar_out = exe_rscalar.forward(is_train=True, x=x_npy)[0]
             assert_almost_equal(mx_lscalar_out, forward_gt(1, y_npy))
             assert_almost_equal(mx_rscalar_out, forward_gt(x_npy, 1))
             exe_lscalar.backward()
@@ -1154,12 +1153,12 @@ def reference(a, dtype):
     xa = np.random.randint(-2, 2, size=shape).astype(np.float32)
     mx_xa = mx.nd.array(xa)
     mx_out = mx.nd.logical_not(mx_xa)
-    assert_almost_equal(mx_out.asnumpy(), reference(xa, dtype=xa.dtype))
+    assert_almost_equal(mx_out, reference(xa, dtype=xa.dtype))
     x = mx.sym.Variable('x')
     y = mx.sym.logical_not(data=x)
     exe = y.simple_bind(ctx=default_context(), x=shape)
     sym_out = exe.forward(is_train=True, x=mx_xa)[0]
-    assert_almost_equal(sym_out.asnumpy(), reference(xa, dtype=xa.dtype))
+    assert_almost_equal(sym_out, reference(xa, dtype=xa.dtype))
 
 
 @with_seed()
@@ -1184,13 +1183,13 @@ def test_embedding():
     # Non-zero atol required, as exposed by seed 781663739
     rtol = 1e-5
     atol = 1e-5
-    assert_almost_equal(exe_test.outputs[0].asnumpy(), np.dot(np_onehot, np_weight), rtol=rtol, atol=atol)
+    assert_almost_equal(exe_test.outputs[0], np.dot(np_onehot, np_weight), rtol=rtol, atol=atol)
     # backward
     np_grad = np.random.uniform(-1, 1, exe_test.outputs[0].shape)
     grad = mx.nd.zeros(np_grad.shape)
     grad[:] = np_grad
     exe_test.backward([grad])
-    assert_almost_equal(grad_map["embed_weight"].asnumpy(), np.dot(np_onehot.T, np_grad), rtol=rtol, atol=atol)
+    assert_almost_equal(grad_map["embed_weight"], np.dot(np_onehot.T, np_grad), rtol=rtol, atol=atol)
 
 
 # check ops handle duplicate input correctly.
@@ -1208,9 +1207,9 @@ def test_binary_op_duplicate_input():
     square = data * data
     exe_square = square.bind(default_context(), args=[arr_data], args_grad=[arr_grad])
     exe_square.forward(is_train=True)
-    assert_almost_equal(exe_square.outputs[0].asnumpy(), data_tmp * data_tmp)
+    assert_almost_equal(exe_square.outputs[0], data_tmp * data_tmp)
     exe_square.backward(out_grad)
-    assert_almost_equal(arr_grad.asnumpy(), 2.0 * data_tmp)
+    assert_almost_equal(arr_grad, 2.0 * data_tmp)
 
 
 @with_seed()
@@ -1226,7 +1225,7 @@ def test_sign():
     test = mx.sym.sign(data)
     exe_test = test.bind(default_context(), args=[arr_data], args_grad=[arr_grad])
     exe_test.forward(is_train=True)
-    out = exe_test.outputs[0].asnumpy()
+    out = exe_test.outputs[0]
     npout = np.sign(data_tmp)
     assert_almost_equal(out, npout)
 
@@ -1235,7 +1234,7 @@ def test_sign():
     npout_grad = out_grad.asnumpy()
     npout_grad = 0;
     exe_test.backward(out_grad)
-    assert_almost_equal(arr_grad.asnumpy(), npout_grad)
+    assert_almost_equal(arr_grad, npout_grad)
 
 
 @with_seed()
@@ -1251,7 +1250,7 @@ def test_round_ceil_floor():
     test = mx.sym.round(data) + mx.sym.ceil(data) +  mx.sym.floor(data)
     exe_test = test.bind(default_context(), args=[arr_data])
     exe_test.forward(is_train=True)
-    out = exe_test.outputs[0].asnumpy()
+    out = exe_test.outputs[0]
     npout = np.round(data_tmp) + np.ceil(data_tmp) + np.floor(data_tmp)
     assert_almost_equal(out, npout)
 
@@ -1265,7 +1264,7 @@ def test_trunc():
 
     exe_test = test.bind(default_context(), args=[arr_data])
     exe_test.forward(is_train=True)
-    out = exe_test.outputs[0].asnumpy()
+    out = exe_test.outputs[0]
     # 'trunc' is sensitive to the precision of the calculation.  Force numpy to match mxnet's float32.
     # Repro issue with seed 1660190454
     npout = np.trunc(np.float32(data_tmp))
@@ -1286,16 +1285,16 @@ def test_rsqrt_cos_sin():
     test =  mx.sym.rsqrt(data) + mx.sym.cos(data) + mx.sym.sin(data)
     exe_test = test.bind(default_context(), args=[arr_data], args_grad=[arr_grad])
     exe_test.forward(is_train=True)
-    out = exe_test.outputs[0].asnumpy()
+    out = exe_test.outputs[0]
     npout =  1/ np.sqrt(data_tmp) + np.cos(data_tmp) + np.sin(data_tmp)
     assert_almost_equal(out, npout)
 
     out_grad = mx.nd.empty(shape)
-    out_grad[:] = 2;
+    out_grad[:] = 2
     npout_grad = out_grad.asnumpy()
     npout_grad = npout_grad * -(1.0 / (2.0 * data_tmp * np.sqrt(data_tmp))) + npout_grad * -1 * np.sin(data_tmp) + npout_grad * np.cos(data_tmp)
     exe_test.backward(out_grad)
-    assert_almost_equal(arr_grad.asnumpy(), npout_grad)
+    assert_almost_equal(arr_grad, npout_grad)
 
 
 @with_seed()
@@ -1317,7 +1316,7 @@ def test_maximum_minimum():
     test =  mx.sym.maximum(data1,data2) + mx.sym.minimum(data1,data2)
     exe_test = test.bind(default_context(), args=[arr_data1,arr_data2], args_grad=[arr_grad1,arr_grad2])
     exe_test.forward(is_train=True)
-    out = exe_test.outputs[0].asnumpy()
+    out = exe_test.outputs[0]
     npout =  np.maximum(data_tmp1,data_tmp2) + np.minimum(data_tmp1,data_tmp2)
     assert_almost_equal(out, npout)
 
@@ -1332,8 +1331,8 @@ def test_maximum_minimum():
     npout_grad1 = npout_grad * mask1 + npout_grad * mask2
     npout_grad2 = (npout_grad - npout_grad * mask1) + (npout_grad - npout_grad * mask2)
 
-    assert_almost_equal(arr_grad1.asnumpy(), npout_grad1)
-    assert_almost_equal(arr_grad2.asnumpy(), npout_grad2)
+    assert_almost_equal(arr_grad1, npout_grad1)
+    assert_almost_equal(arr_grad2, npout_grad2)
 
 
 @with_seed()
@@ -1349,7 +1348,7 @@ def test_maximum_minimum_scalar():
     test =  mx.sym.maximum(data1,3) + mx.sym.maximum(9,data1) + mx.sym.minimum(5,data1) + mx.sym.minimum(data1,4)
     exe_test = test.bind(default_context(), args=[arr_data1], args_grad=[arr_grad1])
     exe_test.forward(is_train=True)
-    out = exe_test.outputs[0].asnumpy()
+    out = exe_test.outputs[0]
     npout =  np.maximum(data_tmp1,3) + np.maximum(9,data_tmp1) + np.minimum(5,data_tmp1) + np.minimum(data_tmp1,4)
     assert_almost_equal(out, npout)
 
@@ -1365,7 +1364,7 @@ def test_maximum_minimum_scalar():
     mask4 = (data_tmp1 < 4).astype('float')
     npout_grad1 = npout_grad * mask1 + (npout_grad - npout_grad * mask2) + (npout_grad - npout_grad * mask3) + npout_grad * mask4
 
-    assert_almost_equal(arr_grad1.asnumpy(), npout_grad1)
+    assert_almost_equal(arr_grad1, npout_grad1)
 
 
 @with_seed()
@@ -1381,7 +1380,7 @@ def test_abs():
     test = mx.sym.abs(data)
     exe_test = test.bind(default_context(), args=[arr_data], args_grad=[arr_grad])
     exe_test.forward(is_train=True)
-    out = exe_test.outputs[0].asnumpy()
+    out = exe_test.outputs[0]
     npout = abs(data_tmp)
     assert_almost_equal(out, npout)
 
@@ -1390,7 +1389,7 @@ def test_abs():
     npout_grad = out_grad.asnumpy()
     npout_grad = npout_grad * np.sign(data_tmp)
     exe_test.backward(out_grad)
-    assert_almost_equal(arr_grad.asnumpy(), npout_grad)
+    assert_almost_equal(arr_grad, npout_grad)
 
 
 def check_deconvolution_forward_backward(input_shape, num_filter, kernel, stride, pad):
@@ -1422,9 +1421,9 @@ def check_deconvolution_forward_backward(input_shape, num_filter, kernel, stride
 
     exe = deconv.bind(default_context(), args=args, args_grad=args_grad)
     exe.forward(is_train=True)
-    out = exe.outputs[0].asnumpy()
+    out = exe.outputs[0]
     exe.backward(out_grad)
-    assert_almost_equal(out, args_grad[0].asnumpy(), rtol=1E-3, atol=1e-3)
+    assert_almost_equal(out, args_grad[0], rtol=1E-3, atol=1e-3)
 
     args_grad_addto_npy = [np.random.normal(size=s) for s in arg_shapes]
     args_grad_addto = [mx.nd.array(ele) for ele in args_grad_addto_npy]
@@ -1481,7 +1480,7 @@ def check_deconvolution_gradient(input_shape, num_filter, pad):
     exe_deconv.forward(is_train=True)
     deconv_out_grad = conv_data[:]
     exe_deconv.backward(deconv_out_grad)
-    assert_almost_equal(conv_args_grad[1].asnumpy(), deconv_args_grad[1].asnumpy(), rtol=1e-3, atol=1e-2)
+    assert_almost_equal(conv_args_grad[1], deconv_args_grad[1], rtol=1e-3, atol=1e-2)
     # Test AddTo
     exe_deconv_addto = deconv.bind(default_context(), args=deconv_args,
                                    args_grad=deconv_addto_args_grad,
@@ -2069,7 +2068,7 @@ def test_depthwise_convolution():
                             exe2.backward(exe2.outputs[0])
 
                             for arr1, arr2 in zip(exe1.outputs + exe1.grad_arrays, exe2.outputs + exe2.grad_arrays):
-                                np.testing.assert_allclose(arr1.asnumpy(), arr2.asnumpy(), rtol=1e-3, atol=1e-3)
+                                assert_allclose(arr1, arr2, rtol=1e-3, atol=1e-3)
 
 
 @with_seed()
@@ -2850,9 +2849,9 @@ def test_broadcasting_ele(sym_bcast):
                                  args_grad={'a': grad_nd})
             net.forward(is_train=True)
             assert (net.outputs[0].shape == target_shape).all()
-            assert_almost_equal(net.outputs[0].asnumpy(), groundtruth, rtol=1e-4)
+            assert_almost_equal(net.outputs[0], groundtruth, rtol=1e-4)
             net.backward(out_grads=mx.nd.array(outgrad_npy))
-            assert_almost_equal(grad_nd.asnumpy(), grad_groundtruth, rtol=1e-4)
+            assert_almost_equal(grad_nd, grad_groundtruth, rtol=1e-4)
         test_broadcasting_ele(sym_bcast_axis)
         test_broadcasting_ele(sym_bcast_to)
         test_broadcasting_ele(sym_bcast_to_with_zero)
@@ -3120,13 +3119,13 @@ def test_stn():
                     grad_grad = [mx.nd.zeros(shape, ctx=dev) for shape in arg_shapes]
                     exe = stn.bind(dev, args=args, args_grad=grad_grad)
                     exe.forward(is_train=True)
-                    out = exe.outputs[0].asnumpy()
+                    out = exe.outputs[0]
                     # check forward
                     assert_almost_equal(out, args['data'].asnumpy()[:, :, h//4:h-h//4, w//4:w-w//4], rtol=1e-2, atol=1e-4)
                     out_grad = mx.nd.ones(out.shape, ctx=dev)
                     exe.backward([out_grad])
                     # check backward
-                    assert_almost_equal(out_grad.asnumpy(), grad_grad[0].asnumpy()[:, :, h//4:h-h//4, w//4:w-w//4], rtol=1e-2, atol=1e-4)
+                    assert_almost_equal(out_grad, grad_grad[0].asnumpy()[:, :, h//4:h-h//4, w//4:w-w//4], rtol=1e-2, atol=1e-4)
 
 
 def test_stn_valid_sampling():
@@ -3188,6 +3187,7 @@ def test_dot():
     # Test normal dot.
     for ndim in ndims:
         for data_type in dtypes:
+            tol = 1e-2 if data_type == 'float16' else 1e-3
             for m in range(1, 5):
                 for k in range(1, 5):
                     if ndim == 1 and k != 1:
@@ -3212,16 +3212,10 @@ def test_dot():
                         c = mx.sym.dot(a, b)
                         exe = c.simple_bind(ctx=ctx, a=a_npy.shape, b=b_npy.shape)
                         outputs = exe.forward(is_train=True, a=a_npy, b=b_npy)
-                        assert_almost_equal(outputs[0].asnumpy(), c_npy,
-                                            rtol=1e-2 if data_type == 'float16' else 1e-3,
-                                            atol=1e-2 if data_type == 'float16' else 1e-3)
+                        assert_almost_equal(outputs[0], c_npy, rtol=tol, atol=tol)
                         exe.backward(out_grads=[mx.nd.array(ograd_npy, mx.cpu()).astype(data_type)])
-                        assert_almost_equal(exe.grad_dict['a'].asnumpy(), agrad_npy,
-                                            rtol=1e-2 if data_type == 'float16' else 1e-3,
-                                            atol=1e-2 if data_type == 'float16' else 1e-3)
-                        assert_almost_equal(exe.grad_dict['b'].asnumpy(), bgrad_npy,
-                                            rtol=1e-2 if data_type == 'float16' else 1e-3,
-                                            atol=1e-2 if data_type == 'float16' else 1e-3)
+                        assert_almost_equal(exe.grad_dict['a'], agrad_npy, rtol=tol, atol=tol)
+                        assert_almost_equal(exe.grad_dict['b'], bgrad_npy, rtol=tol, atol=tol)
 
     # Test dot with transpose flag using gradient checker.
     def dot_sym(data_type):
@@ -3258,8 +3252,9 @@ def dot_sym_xT_yT(data_type):
 
 @with_seed()
 def test_batch_dot():
+    ctx = default_context()
     dtypes = ['float32', 'float64']
-    if default_context().device_type == 'gpu':
+    if ctx.device_type == 'gpu':
         dtypes += ['float16']
 
     for data_type in dtypes:
@@ -3297,30 +3292,30 @@ def test_batch_dot():
                             b_npy = np.transpose(b_npy, axes=(0, 2, 1))
                             bgrad_npy = np.transpose(bgrad_npy, axes=(0, 2, 1))
                             b_init_grad_npy = np.transpose(b_init_grad_npy, axes=(0, 2, 1))
-                        exe = c.simple_bind(ctx=default_context(),
+                        exe = c.simple_bind(ctx=ctx,
                             a=a_npy.shape, b=b_npy.shape, grad_req='write')
-                        exe_add = c.simple_bind(ctx=default_context(),
+                        exe_add = c.simple_bind(ctx=ctx,
                             a=a_npy.shape, b=b_npy.shape, grad_req='add')
                         exe_add.grad_dict['a'][:] = a_init_grad_npy
                         exe_add.grad_dict['b'][:] = b_init_grad_npy
                         outputs = exe.forward(is_train=True, a=a_npy, b=b_npy)
-                        assert_almost_equal(outputs[0].asnumpy(), c_npy,
+                        assert_almost_equal(outputs[0], c_npy,
                                             rtol=1e-2 if data_type == 'float16' else 1e-3,
                                             atol=1e-2 if data_type == 'float16' else 1e-4)
                         exe.backward(out_grads=[mx.nd.array(ograd_npy, ctx=exe._ctx)])
-                        assert_almost_equal(exe.grad_dict['a'].asnumpy(), agrad_npy,
+                        assert_almost_equal(exe.grad_dict['a'], agrad_npy,
                                             rtol=1e-2 if data_type == 'float16' else 1e-3,
                                             atol=1e-2 if data_type == 'float16' else 1e-4)
-                        assert_almost_equal(exe.grad_dict['b'].asnumpy(), bgrad_npy,
+                        assert_almost_equal(exe.grad_dict['b'], bgrad_npy,
                                             rtol=1e-2 if data_type == 'float16' else 1e-3,
                                             atol=1e-2 if data_type == 'float16' else 1e-4)
                         exe_add.forward(is_train=True, a=a_npy, b=b_npy)
                         exe_add.backward(out_grads=[mx.nd.array(ograd_npy, ctx=exe._ctx)])
-                        assert_almost_equal(exe_add.grad_dict['a'].asnumpy(),
+                        assert_almost_equal(exe_add.grad_dict['a'],
                                             agrad_npy + a_init_grad_npy,
                                             rtol=1e-2 if data_type == 'float16' else 1e-3,
                                             atol=1e-2 if data_type == 'float16' else 1e-4)
-                        assert_almost_equal(exe_add.grad_dict['b'].asnumpy(),
+                        assert_almost_equal(exe_add.grad_dict['b'],
                                             bgrad_npy + b_init_grad_npy,
                                             rtol=1e-2 if data_type == 'float16' else 1e-3,
                                             atol=1e-2 if data_type == 'float16' else 1e-4)
@@ -3452,7 +3447,7 @@ def unittest_correlation(data_shape,kernel_size,max_displacement,stride1,stride2
     forward_result,tmp1,tmp2 = correlation_forward(img1,img2,pad_size,kernel_size,stride1,stride2,max_displacement,is_multiply)
 
     # forward error
-    assert_almost_equal(exe1.outputs[0].asnumpy(), forward_result, rtol=1e-4, atol=1e-4)
+    assert_almost_equal(exe1.outputs[0], forward_result, rtol=1e-4, atol=1e-4)
 
     # out_grad
     a = np.ones(forward_result.shape)
@@ -3463,8 +3458,8 @@ def unittest_correlation(data_shape,kernel_size,max_displacement,stride1,stride2
     grad1,grad2 = correlation_backward(a,tmp1,tmp2,img1,img2,pad_size,kernel_size,stride1,stride2,max_displacement,is_multiply)
 
     # backward error
-    assert_almost_equal(exe1.grad_dict['img1'].asnumpy(), grad1, rtol=1e-3, atol=1e-4)
-    assert_almost_equal(exe1.grad_dict['img2'].asnumpy(), grad2, rtol=1e-3, atol=1e-4)
+    assert_almost_equal(exe1.grad_dict['img1'], grad1, rtol=1e-3, atol=1e-4)
+    assert_almost_equal(exe1.grad_dict['img2'], grad2, rtol=1e-3, atol=1e-4)
 
 
 @with_seed()
@@ -3522,7 +3517,7 @@ def test_support_vector_machine_l1_svm():
     exec1 = Y.bind(xpu, args = [x, l], args_grad = {'X': grad})
     exec1.forward(is_train=True)
 
-    assert_almost_equal(x_np, exec1.outputs[0].asnumpy())
+    assert_almost_equal(x_np, exec1.outputs[0])
 
     exec1.backward()
 
@@ -3530,7 +3525,7 @@ def test_support_vector_machine_l1_svm():
     l_mask = np.array(l_mask, dtype=np.float32)*2 -1
     grad_np = (-1) * l_mask * np.greater(1 - l_mask * x_np, 0)
 
-    assert_almost_equal(grad_np, grad.asnumpy())
+    assert_almost_equal(grad_np, grad)
 
 
 @with_seed()
@@ -3553,7 +3548,7 @@ def test_support_vector_machine_l2_svm():
     exec1 = Y.bind(xpu, args = [x, l], args_grad = {'X': grad})
     exec1.forward(is_train=True)
 
-    assert_almost_equal(x_np, exec1.outputs[0].asnumpy())
+    assert_almost_equal(x_np, exec1.outputs[0])
 
     exec1.backward()
 
@@ -3561,7 +3556,7 @@ def test_support_vector_machine_l2_svm():
     l_mask = np.array(l_mask, dtype=np.float32)*2 -1
     grad_np = (-2)*l_mask*np.maximum(1-l_mask*x_np,0)
     grad_np = grad_np.astype(np.float32)
-    assert_almost_equal(grad_np, grad.asnumpy())
+    assert_almost_equal(grad_np, grad)
 
 
 # Seed set because the test is not robust enough to operate on random data
@@ -3595,7 +3590,7 @@ def check_pad_with_shape(shape, xpu, pad_width, mode, dtype="float64"):
     grad = mx.nd.empty(shape, ctx = xpu, dtype=dtype)
     exec1 = Y.bind(xpu, args = [x], args_grad = {'X': grad})
     exec1.forward(is_train=True)
-    out = exec1.outputs[0].asnumpy()
+    out = exec1.outputs[0]
     # compare numpy + mxnet
     assert_almost_equal(out, np_out)
     # grad check
@@ -3652,7 +3647,7 @@ def check_instance_norm_with_shape(shape, xpu):
     np_out = np_instance_norm(x.asnumpy(), gamma.asnumpy(), beta.asnumpy(), eps)
     exec1 = Y.bind(xpu, args = {'X':x, 'G':gamma, 'B':beta})
     exec1.forward(is_train=False)
-    out = exec1.outputs[0].asnumpy()
+    out = exec1.outputs[0]
     assert_almost_equal(out, np_out, rtol=1e-4, atol=1e-4)
     check_numeric_gradient(Y, {'X':x.asnumpy(), 'G':gamma.asnumpy(), 'B':beta.asnumpy()},
                            numeric_eps=1e-2, rtol=1e-2, atol=1e-2)
@@ -3694,7 +3689,7 @@ def check_l2_normalization(in_shape, mode, dtype, norm_eps=1e-10):
     exe = out.simple_bind(ctx=ctx, data=in_data.shape)
     output = exe.forward(is_train=True, data=in_data)
     # compare numpy + mxnet
-    assert_almost_equal(exe.outputs[0].asnumpy(), np_out, rtol=1e-2 if dtype is 'float16' else 1e-5, atol=1e-5)
+    assert_almost_equal(exe.outputs[0], np_out, rtol=1e-2 if dtype is 'float16' else 1e-5, atol=1e-5)
     # check gradient
     check_numeric_gradient(out, [in_data], numeric_eps=1e-3, rtol=1e-2, atol=5e-3)
 
@@ -3758,7 +3753,7 @@ def npy_layer_norm_grad(data, gamma, out_grad, axis, eps):
     exe.arg_dict['beta'][:] = beta
     out_nd = exe.forward()[0]
     out = npy_layer_norm(data, gamma, beta, axis, eps)
-    assert_almost_equal(out, out_nd.asnumpy(), forward_check_eps, forward_check_eps)
+    assert_almost_equal(out, out_nd, forward_check_eps, forward_check_eps)
 
     if finite_grad_check:
         for req in ['write', 'add']:
@@ -4098,7 +4093,7 @@ def mathematical_core_binary(name,
     test = forward_mxnet_call(data1, data2)
     exe_test = test.bind(default_context(), args=[arr_data1, arr_data2], args_grad=[arr_grad1, arr_grad2])
     exe_test.forward(is_train=True)
-    out = exe_test.outputs[0].asnumpy()
+    out = exe_test.outputs[0]
     npout = forward_numpy_call(data_tmp1, data_tmp2)
     assert_almost_equal(out, npout)
 
@@ -4111,8 +4106,6 @@ def mathematical_core_binary(name,
 
     npout_grad1 = npout_grad * backward_numpy_call1(data_tmp1, data_tmp2)
     npout_grad2 = npout_grad * backward_numpy_call2(data_tmp1, data_tmp2)
-    arr_grad1 = arr_grad1.asnumpy()
-    arr_grad2 = arr_grad2.asnumpy()
 
     assert_almost_equal(arr_grad1, npout_grad1)
     assert_almost_equal(arr_grad2, npout_grad2)
@@ -4130,7 +4123,7 @@ def mathematical_core(name, forward_mxnet_call, forward_numpy_call, backward_num
     test = forward_mxnet_call(data)
     exe_test = test.bind(default_context(), args=[arr_data], args_grad=[arr_grad])
     exe_test.forward(is_train=True)
-    out = exe_test.outputs[0].asnumpy()
+    out = exe_test.outputs[0]
     npout = forward_numpy_call(data_tmp)
     assert_almost_equal(out, npout)
 
@@ -4140,10 +4133,6 @@ def mathematical_core(name, forward_mxnet_call, forward_numpy_call, backward_num
     temp = backward_numpy_call(data_tmp)
     npout_grad = npout_grad * temp
     exe_test.backward(out_grad)
-    arr_grad = arr_grad.asnumpy()
-    # print(name)
-    # print(arr_grad)
-    # print(npout_grad)
     assert_almost_equal(arr_grad, npout_grad)
 
 
@@ -4182,7 +4171,7 @@ def rounding(name, forward_mxnet_call, forward_numpy_call, data_init=5., grad_in
     test = forward_mxnet_call(data)
     exe_test = test.bind(default_context(), args=[arr_data])
     exe_test.forward(is_train=True)
-    out = exe_test.outputs[0].asnumpy()
+    out = exe_test.outputs[0]
     npout = forward_numpy_call(data_tmp)
     assert_almost_equal(out, npout)
 
@@ -4320,7 +4309,7 @@ def test_basic_val_init(sym_func, np_func, shape, dtype):
         x = sym_func(shape=shape, dtype=dtype)
         exe = x.bind(default_context(), args=[], args_grad=[])
         exe.forward(is_train=True)
-        assert_almost_equal(exe.outputs[0].asnumpy(), np_func(shape=shape, dtype=dtype))
+        assert_almost_equal(exe.outputs[0], np_func(shape=shape, dtype=dtype))
         assert exe.outputs[0].asnumpy().dtype == dtype
 
     def test_arange():
@@ -4337,14 +4326,14 @@ def test_arange():
                 repeats = random.choice([1, 3])
                 np_out = np.repeat(np.arange(*config, dtype=dtype), repeats)
                 nd_out = mx.nd.arange(*config, repeat=repeats, dtype=dtype)
-                assert_almost_equal(np_out, nd_out.asnumpy())
+                assert_almost_equal(np_out, nd_out)
 
     def test_arange_inferstop():
         s = mx.sym.arange(start=0, stop=None, infer_range=True)
         s = mx.sym.elemwise_add(s, mx.sym.zeros(shape=[5]))
         exe = s.bind(ctx=mx.cpu(), args={})
         exe.forward()
-        assert_almost_equal(exe.outputs[0].asnumpy(), np.array([0,1,2,3,4]))
+        assert_almost_equal(exe.outputs[0], np.array([0,1,2,3,4]))
 
     def test_arange_like():
         shape_list = [(10,), (10, 20), (10, 20, 30), (10, 20, 30, 40)]
@@ -4514,7 +4503,7 @@ def test_blockgrad():
     exe = b.simple_bind(ctx=default_context(), a=(10, 10))
     a_npy = np.random.rand(10, 10)
     exe.forward(is_train=True, a=a_npy)
-    assert_almost_equal(exe.outputs[0].asnumpy(), a_npy)
+    assert_almost_equal(exe.outputs[0], a_npy)
     exe.backward()  # No error if BlockGrad works
 
 
@@ -4579,7 +4568,7 @@ def check_output_n_grad(data_shape, idx_shape, axis, mode, out_of_range=True):
                 # Did not raise exception
                 assert False, "did not raise %s" % MXNetError.__name__
 
-        assert_almost_equal(exe.outputs[0].asnumpy(), np.take(data_real, idx_real, axis=axis, mode=mode))
+        assert_almost_equal(exe.outputs[0], np.take(data_real, idx_real, axis=axis, mode=mode))
 
         for i in np.nditer(idx_real):
             if mode == 'clip':
@@ -4587,7 +4576,7 @@ def check_output_n_grad(data_shape, idx_shape, axis, mode, out_of_range=True):
             grad_helper(grad_in, axis, i)
 
         exe.backward([mx.nd.array(grad_out)])
-        assert_almost_equal(exe.grad_dict['a'].asnumpy(), grad_in)
+        assert_almost_equal(exe.grad_dict['a'], grad_in)
 
     def check_autograd_req():
         row_len = 2
@@ -4612,7 +4601,7 @@ def check_autograd_req():
             x = x.sum()
 
         x.backward()
-        assert_almost_equal(np.ones(sc.grad.shape), sc.grad.asnumpy())
+        assert_almost_equal(np.ones(sc.grad.shape), sc.grad)
 
     for mode in ['clip', 'wrap', 'raise']:
         for data_ndim in range(1, 5):
@@ -4658,7 +4647,7 @@ def test_grid_generator():
         tmp[1] = -1.0 + (np.arange(target_shape[0]*target_shape[1]) // target_shape[1]) * (2.0 / (target_shape[0]-1))
         tmp[2] = 1
         grad_est = np.dot(out_grad[0].reshape(2,target_shape[0]*target_shape[1]),tmp.T).reshape(1,6)
-        assert_almost_equal(exe.grad_dict['affine'].asnumpy(), grad_est, rtol=1e-3, atol=1e-5)
+        assert_almost_equal(exe.grad_dict['affine'], grad_est, rtol=1e-3, atol=1e-5)
         # check addto
         exe = grid.simple_bind(ctx=default_context(), affine=(1,6), grad_req='add')
         grid_grad_npy = np.random.normal(size=exe.grad_dict['affine'].shape)
@@ -4666,7 +4655,7 @@ def test_grid_generator():
         exe.arg_dict['affine'][:] = np.array([[1.0, 0, 0, 0, 1.0, 0]])
         exe.forward(is_train=True)
         exe.backward(mx.nd.array(out_grad))
-        assert_almost_equal(exe.grad_dict['affine'].asnumpy(), grad_est + grid_grad_npy, rtol=1e-2, atol=1e-5)
+        assert_almost_equal(exe.grad_dict['affine'], grad_est + grid_grad_npy, rtol=1e-2, atol=1e-5)
 
     # transform_type = warp
     test_case = [(12,21),(4,3),(6,12)]
@@ -4689,7 +4678,7 @@ def test_grid_generator():
         grad_est = np.zeros((1,2)+target_shape)
         grad_est[0,0] = out_grad[0,0] / ((target_shape[1]-1.0) / 2.0)
         grad_est[0,1] = out_grad[0,1] / ((target_shape[0]-1.0) / 2.0)
-        assert_almost_equal(exe.grad_dict['flow'].asnumpy(), grad_est, rtol=1e-3)
+        assert_almost_equal(exe.grad_dict['flow'], grad_est, rtol=1e-3)
         # check addto
         exe_add = grid.simple_bind(ctx=default_context(), flow=(1, 2) + target_shape, grad_req='add')
         flow_grad_npy = np.random.normal(size=exe_add.grad_dict['flow'].shape)
@@ -4697,7 +4686,7 @@ def test_grid_generator():
         exe_add.grad_dict['flow'][:] = flow_grad_npy
         exe_add.forward(is_train=True)
         exe_add.backward(mx.nd.array(out_grad))
-        assert_almost_equal(exe_add.grad_dict['flow'].asnumpy(), grad_est + flow_grad_npy, rtol=1e-3, atol=1e-5)
+        assert_almost_equal(exe_add.grad_dict['flow'], grad_est + flow_grad_npy, rtol=1e-3, atol=1e-5)
 
 
 @with_seed()
@@ -4708,7 +4697,7 @@ def test_index2d():
         data = mx.random.uniform(-1, 1, shape=(n, m), ctx=default_context())
         x = mx.nd.array(np.random.randint(0, m, size=n), ctx=default_context(), dtype='int32')
         r = mx.nd.batch_take(data, x)
-        assert_almost_equal(r.asnumpy(), data.asnumpy()[np.arange(n), x.asnumpy()])
+        assert_almost_equal(r, data.asnumpy()[np.arange(n), x.asnumpy()])
 
 
 @with_seed()
@@ -4724,8 +4713,8 @@ def test_cast():
             exe.arg_arrays[0][:] = X
             exe.forward(is_train=True)
             exe.backward(mx.nd.array(X, dtype=dsttype, ctx=default_context()))
-            assert_almost_equal(exe.outputs[0].asnumpy(), X.astype(srctype).astype(dsttype), rtol=1e-3, atol=1e-5)
-            assert_almost_equal(exe.grad_arrays[0].asnumpy(), X.astype(dsttype).astype(srctype), rtol=1e-3, atol=1e-5)
+            assert_almost_equal(exe.outputs[0], X.astype(srctype).astype(dsttype), rtol=1e-3, atol=1e-5)
+            assert_almost_equal(exe.grad_arrays[0], X.astype(dsttype).astype(srctype), rtol=1e-3, atol=1e-5)
 
 def get_cast_op_data():
     FP16_FRACTION_BITS = 10
@@ -4853,12 +4842,12 @@ def test_repeat_forward():
             a = np.random.random_sample(size=shape)
             aa = np.repeat(a, repeats)
             b = mx.nd.array(a, ctx=default_context())
-            bb = mx.nd.repeat(b, repeats).asnumpy()
+            bb = mx.nd.repeat(b, repeats)
             assert_almost_equal(aa, bb)
 
             for axis in range(0, ndim):
                 aa = np.repeat(a, repeats, axis)
-                bb = mx.nd.repeat(b, repeats, axis).asnumpy()
+                bb = mx.nd.repeat(b, repeats, axis)
                 assert_almost_equal(aa, bb)
 
     def test_repeat_backward(axis):
@@ -4896,7 +4885,7 @@ def test_repeat_backward(axis):
         else:
             raise RuntimeError("Invalid axis value")
 
-        assert_almost_equal(expected_grad, arr_grad.asnumpy(), rtol=1e-3)
+        assert_almost_equal(expected_grad, arr_grad, rtol=1e-3)
 
     def test_repeat_numeric_gradient():
         data = mx.sym.Variable('data')
@@ -4993,7 +4982,7 @@ def test_tile_backward():
             for j in range(shape[1]):
                 expected_grad[i][j] += sum(sum(npout_grad[i:(n1 * reps1):reps1, j:(n2 * reps2):reps2]))
 
-        assert_almost_equal(expected_grad, arr_grad.asnumpy(), rtol=1e-3)
+        assert_almost_equal(expected_grad, arr_grad, rtol=1e-3)
 
     def test_tile_numeric_gradient():
         data = mx.sym.Variable('data')
@@ -5309,8 +5298,7 @@ def softmax_forward(input_data, true_output):
         exec1 = out1.bind(default_context(), args={'data': input_data})
         exec1.forward()[0].wait_to_read()
         ndarr = exec1.outputs[0][0][0][0]
-        nparr = ndarr.asnumpy()
-        assert_almost_equal(nparr, true_output, rtol=1e-5, atol=1e-5)
+        assert_almost_equal(ndarr, true_output, rtol=1e-5, atol=1e-5)
 
     softmax_forward(mx.nd.array([[[[-1e30,-1e30]]]]), np.array([1.0,1.0]))
     softmax_forward(mx.nd.array([[[[1e30,1e30]]]]), np.array([1.0,1.0]))
@@ -5332,14 +5320,10 @@ def check_dtypes_almost_equal(op_name,
         with mx.autograd.record():
             dtype_softmax = op(dtype_input, axis=-1, dtype=odtype)
             ref_softmax = op(ref_input, axis=-1, dtype=odtype)
-        dtype_softmax_np = dtype_softmax.asnumpy()
-        ref_softmax_np = ref_softmax.asnumpy()
-        assert_almost_equal(dtype_softmax_np, ref_softmax_np, rtol=rtol, atol=atol)
+        assert_almost_equal(dtype_softmax, ref_softmax, rtol=rtol, atol=atol)
         dtype_softmax.backward()
         ref_softmax.backward()
-        dtype_grad_np = dtype_input.grad.asnumpy()
-        ref_grad_np = ref_input.grad.asnumpy()
-        assert_almost_equal(dtype_grad_np, ref_grad_np, rtol=grad_rtol, atol=grad_atol)
+        assert_almost_equal(dtype_input.grad, ref_input.grad, rtol=grad_rtol, atol=grad_atol)
 
     import sys
     is_windows = sys.platform.startswith('win')
@@ -5439,48 +5423,28 @@ def test_pick_helper(index_type=np.int32):
     test_pick_helper(np.float32)
 
 
-def check_ctc_loss(acts, labels, loss_truth):
+def check_ctc_loss(acts, labels, loss_truth, contrib=False):
     in_var = mx.sym.Variable('input')
     labels_var = mx.sym.Variable('labels')
-    ctc = mx.sym.ctc_loss(in_var, labels_var)
-    acts_nd = mx.nd.array(acts, ctx=default_context())
-    labels_nd = mx.nd.array(labels, ctx=default_context())
-    exe = ctc.bind(ctx=default_context(), args=[acts_nd, labels_nd])
-    # test forward with grad calc
-    exe.forward(is_train=True)
-    outTest = exe.outputs[0]
-    # test forward without grad calc
-    exe.forward(is_train=False)
-    outTrain = exe.outputs[0]
-    # make sure losses calculated with both modes are the same
-    assert_almost_equal(outTest.asnumpy(), outTrain.asnumpy())
-
-    # test against ground truth, if available
-    if loss_truth is not None:
-        assert_almost_equal(outTest.asnumpy(), loss_truth)
-    # test grad
-    check_numeric_gradient(ctc, [acts, labels], grad_nodes=['input'], rtol=0.05, atol=1e-3)
-
-# check contrib operator for backward compatibility
-def check_contrib_ctc_loss(acts, labels, loss_truth):
-    in_var = mx.sym.Variable('input')
-    labels_var = mx.sym.Variable('labels')
-    ctc = mx.sym.contrib.ctc_loss(in_var, labels_var)
+    if contrib:
+        ctc = mx.sym.contrib.ctc_loss(in_var, labels_var)
+    else:
+        ctc = mx.sym.ctc_loss(in_var, labels_var)
     acts_nd = mx.nd.array(acts, ctx=default_context())
     labels_nd = mx.nd.array(labels, ctx=default_context())
     exe = ctc.bind(ctx=default_context(), args=[acts_nd, labels_nd])
     # test forward with grad calc
     exe.forward(is_train=True)
-    outTest = exe.outputs[0]
+    outTest = exe.outputs[0].copy()
     # test forward without grad calc
     exe.forward(is_train=False)
     outTrain = exe.outputs[0]
     # make sure losses calculated with both modes are the same
-    assert_almost_equal(outTest.asnumpy(), outTrain.asnumpy())
+    assert_almost_equal(outTest, outTrain)
 
     # test against ground truth, if available
     if loss_truth is not None:
-        assert_almost_equal(outTest.asnumpy(), loss_truth)
+        assert_almost_equal(outTest, loss_truth)
     # test grad
     check_numeric_gradient(ctc, [acts, labels], grad_nodes=['input'], rtol=0.05, atol=1e-3)
 
@@ -5494,8 +5458,9 @@ def test_ctc_loss():
                     dtype=np.float32)
     labels = np.array([[2, 3, 0], [2, 3, 0]])
     true_loss = np.array([4.04789, 4.04789], dtype=np.float32) # from Torch
-    check_ctc_loss(acts, labels, true_loss)
-    check_contrib_ctc_loss(acts, labels, true_loss)
+    for contrib in [False, True]:
+        check_ctc_loss(acts, labels, true_loss, contrib=contrib)
+
 
     # Test 2:
     acts2 = np.array([
@@ -5504,14 +5469,14 @@ def test_ctc_loss():
         [[-15, -14, -13, -12, -11], [-15, -14.2, -13.5, -12.2, -11.22]]], dtype=np.float32)
     labels2 = np.array([[2, 3, 1], [2, 0, 0]], dtype=np.float32)
     true_loss = np.array([7.3557, 5.4091], dtype=np.float32) # from Torch
-    check_ctc_loss(acts2, labels2, true_loss)
-    check_contrib_ctc_loss(acts2, labels2, true_loss)
+    for contrib in [False, True]:
+        check_ctc_loss(acts2, labels2, true_loss, contrib=contrib)
 
     # Test 3: check use integer type as label
     labels3 = np.array([[2, 3, 1], [2, 0, 0]], dtype=np.int32)
     true_loss = np.array([7.3557, 5.4091], dtype=np.float32) # from Torch
-    check_ctc_loss(acts2, labels3, true_loss)
-    check_contrib_ctc_loss(acts2, labels3, true_loss)
+    for contrib in [False, True]:
+        check_ctc_loss(acts2, labels3, true_loss, contrib=contrib)
 
 @with_seed()
 def test_ctc_loss_with_large_classes():
@@ -5531,90 +5496,11 @@ def test_ctc_loss_with_large_classes():
     nd_label = mx.nd.array(label)
     loss = mx.nd.ctc_loss(data=nd_data, label=nd_label)
     expected_loss = np.array([688.02826, 145.34462])
-    assert_almost_equal(loss.asnumpy(), expected_loss)
+    assert_almost_equal(loss, expected_loss)
 
 @with_seed()
 def test_ctc_loss_grad():
-    def check_ctc_loss_grad(blank_label): # from tf
-        vocab_size = 5
-        max_label_len = 5
-        padding_mask = -1+ (blank_label=='first')
-
-        targets_0 = [0, 1, 2, 1, 0]
-        loss_log_prob_0 = -3.34211
-        input_prob_matrix_0 = np.asarray(
-            [[0.633766, 0.221185, 0.0917319, 0.0129757, 0.0142857, 0.0260553],
-             [0.111121, 0.588392, 0.278779, 0.0055756, 0.00569609, 0.010436],
-             [0.0357786, 0.633813, 0.321418, 0.00249248, 0.00272882, 0.0037688],
-             [0.0663296, 0.643849, 0.280111, 0.00283995, 0.0035545, 0.00331533],
-             [0.458235, 0.396634, 0.123377, 0.00648837, 0.00903441, 0.00623107]],
-            dtype=np.float32)
-        gradient_log_prob_0 = np.asarray(
-            [[-0.366234, 0.221185, 0.0917319, 0.0129757, 0.0142857, 0.0260553],
-             [0.111121, -0.411608, 0.278779, 0.0055756, 0.00569609, 0.010436],
-             [0.0357786, 0.633813, -0.678582, 0.00249248, 0.00272882, 0.0037688],
-             [0.0663296, -0.356151, 0.280111, 0.00283995, 0.0035545, 0.00331533],
-             [-0.541765, 0.396634, 0.123377, 0.00648837, 0.00903441, 0.00623107]],
-            dtype=np.float32)
-
-        targets_1 = [0, 1, 1, 0]
-        loss_log_prob_1 = -5.42262
-        input_prob_matrix_1 = np.asarray(
-            [[0.30176, 0.28562, 0.0831517, 0.0862751, 0.0816851, 0.161508],
-             [0.24082, 0.397533, 0.0557226, 0.0546814, 0.0557528, 0.19549],
-             [0.230246, 0.450868, 0.0389607, 0.038309, 0.0391602, 0.202456],
-             [0.280884, 0.429522, 0.0326593, 0.0339046, 0.0326856, 0.190345],
-             [0.423286, 0.315517, 0.0338439, 0.0393744, 0.0339315, 0.154046]],
-            dtype=np.float32)
-        gradient_log_prob_1 = np.asarray(
-            [[-0.69824, 0.28562, 0.0831517, 0.0862751, 0.0816851, 0.161508],
-             [0.24082, -0.602467, 0.0557226, 0.0546814, 0.0557528, 0.19549],
-             [0.230246, 0.450868, 0.0389607, 0.038309, 0.0391602, -0.797544],
-             [0.280884, -0.570478, 0.0326593, 0.0339046, 0.0326856, 0.190345],
-             [-0.576714, 0.315517, 0.0338439, 0.0393744, 0.0339315, 0.154046]],
-            dtype=np.float32)
-
-        inputs = [
-            np.vstack(
-                [input_prob_matrix_0[t, :], input_prob_matrix_1[t, :]])
-            for t in range(5)
-        ] + 2 * [np.nan * np.ones((2, vocab_size+1), np.float32)]
-        inputs = np.log(np.asarray(inputs, dtype=np.float32))
-
-        grad_truth = np.array([
-            np.vstack(
-                [gradient_log_prob_0[t, :], gradient_log_prob_1[t, :]])
-            for t in range(5)
-        ] + 2 * [np.zeros((2, vocab_size+1), np.float32)])
-
-        if blank_label == 'first':
-            inputs = np.roll(inputs, 1, axis=2)
-            grad_truth = np.roll(grad_truth, 1, axis=2)
-
-        labels = (np.asarray([x + [padding_mask]*(max_label_len-len(x))
-                             for x in [targets_0, targets_1]])+(blank_label == 'first'))
-
-        seq_lens = np.array([5, 5], dtype=np.int32)
-        label_lens = np.array([5, 4], dtype=np.int32)
-        loss_truth = np.array([-loss_log_prob_0, -loss_log_prob_1], np.float32)
-
-        with default_context():
-            data = mx.nd.array(inputs)
-            label = mx.nd.array(labels)
-            data.attach_grad()
-            with mx.autograd.record():
-                l = mx.ndarray.CTCLoss(data, label,
-                                       use_data_lengths=True,
-                                       use_label_lengths=True,
-                                       data_lengths=mx.nd.array(seq_lens),
-                                       label_lengths=mx.nd.array(label_lens),
-                                       blank_label=blank_label)
-                l.backward()
-            assert_almost_equal(l.asnumpy(), loss_truth, atol=1e-5, rtol=1e-5)
-            assert_almost_equal(data.grad.asnumpy(), grad_truth, atol=1e-5, rtol=1e-5)
-
-    # check contrib operator for backward compatibility
-    def check_contrib_ctc_loss_grad(blank_label): # from tf
+    def check_ctc_loss_grad(blank_label, contrib=False): # from tf
         vocab_size = 5
         max_label_len = 5
         padding_mask = -1+ (blank_label=='first')
@@ -5682,22 +5568,28 @@ def check_contrib_ctc_loss_grad(blank_label): # from tf
             label = mx.nd.array(labels)
             data.attach_grad()
             with mx.autograd.record():
-                l = mx.contrib.ndarray.CTCLoss(data, label,
-                                               use_data_lengths=True,
-                                               use_label_lengths=True,
-                                               data_lengths=mx.nd.array(seq_lens),
-                                               label_lengths=mx.nd.array(label_lens),
-                                               blank_label=blank_label)
+                if contrib:
+                    l = mx.contrib.ndarray.CTCLoss(data, label,
+                                           use_data_lengths=True,
+                                           use_label_lengths=True,
+                                           data_lengths=mx.nd.array(seq_lens),
+                                           label_lengths=mx.nd.array(label_lens),
+                                           blank_label=blank_label)
+                else:
+                    l = mx.ndarray.CTCLoss(data, label,
+                                           use_data_lengths=True,
+                                           use_label_lengths=True,
+                                           data_lengths=mx.nd.array(seq_lens),
+                                           label_lengths=mx.nd.array(label_lens),
+                                           blank_label=blank_label)
                 l.backward()
-            assert_almost_equal(l.asnumpy(), loss_truth, atol=1e-5, rtol=1e-5)
-            assert_almost_equal(data.grad.asnumpy(), grad_truth, atol=1e-5, rtol=1e-5)
 
+            assert_almost_equal(l, loss_truth, atol=1e-5, rtol=1e-5)
+            assert_almost_equal(data.grad, grad_truth, atol=1e-5, rtol=1e-5)
 
-    check_ctc_loss_grad('first')
-    check_ctc_loss_grad('last')
-    check_contrib_ctc_loss_grad('first')
-    check_contrib_ctc_loss_grad('last')
-
+    for contrib in [False, True]:
+        for label in ['first', 'last']:
+            check_ctc_loss_grad(label, contrib=contrib)
 
 @with_seed()
 def test_quantization_op():
@@ -5936,8 +5828,8 @@ def create_operator(self, ctx, shapes, dtypes):
     expected_grad = 2 * x2
     rtol = 1e-4
     atol = 1e-6
-    assert_almost_equal(output.asnumpy(), expected_output.asnumpy(), rtol=rtol, atol=atol)
-    assert_almost_equal(x2.grad.asnumpy(), expected_grad.asnumpy(), rtol=rtol, atol=atol)
+    assert_almost_equal(output, expected_output, rtol=rtol, atol=atol)
+    assert_almost_equal(x2.grad, expected_grad, rtol=rtol, atol=atol)
 
 
     # test for backward compatibility, i.e. the correctness of default implementation of
@@ -5974,8 +5866,8 @@ def create_operator(self, ctx, shapes, dtypes):
     with mx.autograd.record():
         y = mx.nd.Custom(lhs, rhs, name='mult', op_type='mult')
         y.backward()
-    assert_almost_equal(rhs.asnumpy(), lhs.grad.asnumpy(), rtol=rtol, atol=atol)
-    assert_almost_equal(lhs.asnumpy(), rhs.grad.asnumpy(), rtol=rtol, atol=atol)
+    assert_almost_equal(rhs, lhs.grad, rtol=rtol, atol=atol)
+    assert_almost_equal(lhs, rhs.grad, rtol=rtol, atol=atol)
 
     class MultNoGrad(mx.operator.CustomOp):
         def forward(self, is_train, req, in_data, out_data, aux):
@@ -6008,8 +5900,8 @@ def infer_storage_type_backward(self, ograd_stype, in_stype, out_stype, igrad_st
     with mx.autograd.record():
         y2 = mx.nd.Custom(lhs, rhs, name="mult_no_grad", op_type="mult_no_grad")
         y2.backward()
-    assert_almost_equal(rhs.asnumpy(), lhs.grad.asnumpy(), rtol=rtol, atol=atol)
-    assert_almost_equal(lhs.asnumpy(), rhs.grad.asnumpy(), rtol=rtol, atol=atol)
+    assert_almost_equal(rhs, lhs.grad, rtol=rtol, atol=atol)
+    assert_almost_equal(lhs, rhs.grad, rtol=rtol, atol=atol)
 
     class NoInputOp(mx.operator.CustomOp):
         def __init__(self, length, depth):
@@ -6046,7 +5938,7 @@ def create_operator(self, ctx, shapes, dtypes):
 
     with mx.autograd.record():
         x = mx.nd.Custom(length=10, depth=10, op_type="no_input_op")
-    assert_almost_equal(x.asnumpy(), np.ones(shape=(10, 10), dtype=np.float32))
+    assert_almost_equal(x, np.ones(shape=(10, 10), dtype=np.float32))
 
 
 @with_seed()
@@ -6523,95 +6415,83 @@ def test_laop():
     dtype = np.float64
     rtol_fw = 1e-7
     atol_fw = 1e-9
-    num_eps = 1e-6
+    num_eps = 2e-6
     rtol_bw = 1e-5
-    atol_bw = 1e-6
+    atol_bw = 1e-5
     # enable numerical checking of gradients
     grad_check = 1
 
     data1 = mx.symbol.Variable('data1')
     data2 = mx.symbol.Variable('data2')
-    data3 = mx.symbol.Variable('data3')
 
-    check_fw = lambda sym, location, expected :\
-        check_symbolic_forward(sym, location, expected, rtol=rtol_fw,
-                               atol=atol_fw, dtype=dtype)
-    check_grad = lambda sym, location:\
-        check_numeric_gradient(sym, location, numeric_eps=num_eps, rtol=rtol_bw,
-                               atol=atol_bw, dtype=dtype)
     rep_3x = lambda a, m, n :\
         np.reshape(np.tile(np.array(a).flatten(), 3), (3, 1, m, n))
 
+    def check_fw_grad(sym, location, expected):
+        check_symbolic_forward(sym, location, expected, rtol=rtol_fw,
+                               atol=atol_fw, dtype=dtype)
+        if grad_check == 1:
+            check_numeric_gradient(sym, location, numeric_eps=num_eps, rtol=rtol_bw,
+                                   atol=atol_bw, dtype=dtype)
+
+    matrix = np.array([[9., 3., -6., 12.],
+                       [3., 26., -7., -11.],
+                       [-6., -7., 9., 7.],
+                       [12., -11., 7., 65.]])
+    trian  = np.array([[3., 0., 0., 0.],
+                       [1., 5., 0., 0.],
+                       [-2., -1., 2., 0.],
+                       [4., -3., 6., 2.]])
+    pow    = np.array([[2., 1., 1., 1.],
+                       [1., 4., 1., 1.],
+                       [1., 1., 8., 1.],
+                       [1., 1., 1., 16.]])
+    inv    = np.array([[8.95/3., 0.05/3., 2.65, -2.5/3.],
+                       [0.05/3., 0.05, 0.05, 0.],
+                       [2.65, 0.05, 2.5, -0.75],
+                       [-2.5/3., 0., -0.75, 0.25]])
+    ident  = np.eye(4)
+    shape = (4, 4, 1, 1)
+    ones = mx.nd.ones(shape).asnumpy()
+
     for lower in [True, False]:
         upper = not lower
 
         # Tests with trivial 1x1 matrices.
-        shape = (4, 4, 1, 1)
         data_in = np.random.uniform(1, 10, shape)
         # test potrf
         # Note: Have to symmetrize input, for gradient test to work
         res_potrf = np.sqrt(data_in)
         test_potrf = mx.sym.linalg.potrf(data1, lower=lower)
-        check_fw(test_potrf, [data_in], [res_potrf])
-        if grad_check == 1:
-            check_grad(test_potrf, [data_in])
+        check_fw_grad(test_potrf, [data_in], [res_potrf])
         # test potri
-        ones = mx.nd.ones(shape).asnumpy()
         res_potri = np.divide(ones, data_in * data_in)
         test_potri = mx.sym.linalg.potri(data1, lower=lower)
-        check_fw(test_potri, [data_in], [res_potri])
-        if grad_check == 1:
-            check_grad(test_potri, [data_in])
+        check_fw_grad(test_potri, [data_in], [res_potri])
         # test trsm
         trian_in = data_in * 7.
         test_trsm = mx.sym.linalg.trsm(data1, data2, alpha=7., lower=lower)
-        check_fw(test_trsm, [trian_in, data_in], [ones])
-        if grad_check == 1:
-            check_grad(test_trsm, [trian_in,data_in])
+        check_fw_grad(test_trsm, [trian_in, data_in], [ones])
         # test trmm
         trian_in = np.divide(ones, trian_in)
         test_trmm = mx.sym.linalg.trmm(data1, data2, alpha=7., transpose=True,
                                        rightside=True, lower=lower)
-        check_fw(test_trmm, [trian_in, data_in], [ones])
-        if grad_check == 1:
-            check_grad(test_trmm, [trian_in, data_in])
+        check_fw_grad(test_trmm, [trian_in, data_in], [ones])
         # test sumlogdiag
         res_sumlogdiag = np.reshape(np.log(data_in), (4, 4))
         test_sumlogdiag = mx.sym.linalg.sumlogdiag(data1)
-        check_fw(test_sumlogdiag, [data_in], [res_sumlogdiag])
-        if grad_check == 1:
-            check_grad(test_sumlogdiag, [data_in])
+        check_fw_grad(test_sumlogdiag, [data_in], [res_sumlogdiag])
 
         # more elaborate example of Cholesky factorization
-        matrix = np.array([[9., 3., -6., 12.],
-                           [3., 26., -7., -11.],
-                           [-6., -7., 9., 7.],
-                           [12., -11., 7., 65.]])
-        trian  = np.array([[3., 0., 0., 0.],
-                           [1., 5., 0., 0.],
-                           [-2., -1., 2., 0.],
-                           [4., -3., 6., 2.]])
-        pow    = np.array([[2., 1., 1., 1.],
-                           [1., 4., 1., 1.],
-                           [1., 1., 8., 1.],
-                           [1., 1., 1., 16.]])
-        inv    = np.array([[8.95/3., 0.05/3., 2.65, -2.5/3.],
-                           [0.05/3., 0.05, 0.05, 0.],
-                           [2.65, 0.05, 2.5, -0.75],
-                           [-2.5/3., 0., -0.75, 0.25]])
-        ident  = np.eye(4)
-
         low_trian = trian
-        if not lower:
+        if upper:
             trian = np.transpose(trian)
 
         # test potrf
         test_potrf = mx.sym.linalg.potrf(_make_symm_symbol(data1, ndims=4), lower=lower)
         a = rep_3x(matrix, 4, 4)
         r = rep_3x(trian, 4, 4)
-        check_fw(test_potrf, [a], [r])
-        if grad_check == 1:
-            check_grad(test_potrf, [a])
+        check_fw_grad(test_potrf, [a], [r])
 
         #test potri
         data1_ltri = _make_triangle_symm(
@@ -6619,77 +6499,54 @@ def test_laop():
         test_potri = mx.sym.linalg.potri(data1_ltri, lower=lower)
         a = rep_3x(trian, 4, 4)
         r = rep_3x(inv, 4, 4)
-        check_fw(test_potri, [a], [r])
-        if grad_check == 1:
-            check_grad(test_potri, [a])
+        check_fw_grad(test_potri, [a], [r])
 
         # test trsm
         test_trsm = mx.sym.linalg.trsm(data1_ltri, data2, alpha=7., transpose=upper, lower=lower)
-        a = rep_3x(trian, 4, 4)
         b = rep_3x(matrix, 4, 4)
         r = rep_3x(7. * np.transpose(low_trian), 4, 4)
-        check_fw(test_trsm, [a, b], [r])
-        if grad_check == 1:
-            check_grad(test_trsm, [a, b])
+        check_fw_grad(test_trsm, [a, b], [r])
 
         test_trsm2 = mx.sym.linalg.trsm(
             data1_ltri, data2, alpha=-2., rightside=True, transpose=lower, lower=lower)
         r = rep_3x(-2. * low_trian, 4, 4)
-        check_fw(test_trsm2, [a, b], [r])
-        if grad_check == 1:
-            check_grad(test_trsm2, [a, b])
+        check_fw_grad(test_trsm2, [a, b], [r])
 
         test_trsm3 = mx.sym.linalg.trsm(
             data1_ltri, data2, alpha=0.5, transpose=lower, lower=lower)
         b = rep_3x(np.transpose(low_trian), 4, 4)
         r = rep_3x(0.5 * ident, 4, 4)
-        check_fw(test_trsm3, [a, b], [r])
-        if grad_check == 1:
-            check_grad(test_trsm3, [a, b])
+        check_fw_grad(test_trsm3, [a, b], [r])
 
         test_trsm4 = mx.sym.linalg.trsm(
             data1_ltri, data2, alpha=-0.5, rightside=True, transpose=upper, lower=lower)
         b = rep_3x(low_trian, 4, 4)
         r = rep_3x(-0.5 * ident, 4, 4)
-        check_fw(test_trsm4, [a, b], [r])
-        if grad_check == 1:
-            check_grad(test_trsm4, [a, b])
+        check_fw_grad(test_trsm4, [a, b], [r])
 
         # test trmm
         test_trmm = mx.sym.linalg.trmm(
             data1_ltri, data2, alpha=7., transpose=True, rightside=True, lower=lower)
-        a = rep_3x(trian, 4, 4)
-        b = rep_3x(matrix, 4, 4)
+        a = [a, rep_3x(matrix, 4, 4)]
         r = rep_3x(7. * np.dot(matrix, trian.T), 4, 4)
-        check_fw(test_trmm, [a, b], [r])
-        if grad_check == 1:
-            check_grad(test_trmm, [a, b])
+        check_fw_grad(test_trmm, a, [r])
 
         test_trmm2 = mx.sym.linalg.trmm(data1_ltri, data2, alpha=-2., lower=lower)
         r = rep_3x(-2. * np.dot(trian, matrix), 4, 4)
-        check_fw(test_trmm2, [a, b], [r])
-        if grad_check == 1:
-            check_grad(test_trmm2, [a, b])
+        check_fw_grad(test_trmm2, a, [r])
 
         test_trmm3 = mx.sym.linalg.trmm(data1_ltri, data2, rightside=True, lower=lower)
         r = rep_3x(np.dot(matrix, trian), 4, 4)
-        check_fw(test_trmm3, [a, b], [r])
-        if grad_check == 1:
-            check_grad(test_trmm3, [a, b])
+        check_fw_grad(test_trmm3, a, [r])
 
         test_trmm4 = mx.sym.linalg.trmm(
             data1_ltri, data2, alpha=1.2, transpose=True, lower=lower)
         r = rep_3x(1.2 * np.dot(trian.T, matrix), 4, 4)
-        check_fw(test_trmm4, [a, b], [r])
-        if grad_check == 1:
-            check_grad(test_trmm4, [a, b])
+        check_fw_grad(test_trmm4, a, [r])
 
-    # test sumlogdiag
-    a = rep_3x(pow, 4, 4)
-    r = np.reshape(np.tile(10. * np.log(np.array([2.])), 3), (3,))
-    check_fw(test_sumlogdiag, [a], [r])
-    if grad_check == 1:
-        check_grad(test_sumlogdiag, [a])
+        # test sumlogdiag
+        r = np.reshape(np.tile(10. * np.log(np.array([2.])), 3), (3,))
+        check_fw_grad(test_sumlogdiag, [rep_3x(pow, 4, 4)], [r])
 
 
 # Tests for operators linalg.syrk, linalg.gelqf
@@ -7894,12 +7751,12 @@ def py_bilinear_resize_backward(x, incoming_grads, mode='size'):
     def check_bilinear_resize_op(shape, height, width):
         x = mx.nd.random.uniform(shape=shape)
         y = mx.nd.contrib.BilinearResize2D(x, height=height, width=width)
-        assert_almost_equal(y.asnumpy(), py_bilinear_resize(x.asnumpy(), height, width))
+        assert_almost_equal(y, py_bilinear_resize(x.asnumpy(), height, width))
 
         x_scale = width / shape[-1]
         y_scale = height / shape[-2]
         y = mx.nd.contrib.BilinearResize2D(x, scale_height=y_scale, scale_width=x_scale)
-        assert_almost_equal(y.asnumpy(), py_bilinear_resize(x.asnumpy(), height, width))
+        assert_almost_equal(y, py_bilinear_resize(x.asnumpy(), height, width))
     def check_bilinear_resize_modes_op(shape, scale_height=None, scale_width=None, shape_1=None, mode=None):
         x = mx.nd.random.uniform(shape=shape)
         original_h = shape[2]
@@ -8115,6 +7972,7 @@ def f(x, a, b, c):
     data = mx.symbol.Variable('data')
     quad_sym = mx.sym.contrib.quadratic(data=data, a=a, b=b, c=c)
     for dtype in [np.float16, np.float32, np.float64]:
+        tol = 1e-2 if dtype is np.float16 else 1e-5
         for ndim in range(1, 6):
             shape = rand_shape_nd(ndim, 5)
             data_np = np.random.randn(*shape).astype(dtype)
@@ -8123,21 +7981,92 @@ def f(x, a, b, c):
 
             # check imperative forward
             output = mx.nd.contrib.quadratic(mx.nd.array(data_np), a=a, b=b, c=c)
-            assert_almost_equal(output.asnumpy(),expected,
-                                rtol=1e-2 if dtype is np.float16 else 1e-5,
-                                atol=1e-2 if dtype is np.float16 else 1e-5)
+            assert_almost_equal(output, expected, rtol=tol, atol=tol)
             # check forward
-            check_symbolic_forward(quad_sym, [data_np], [expected],
-                                   rtol=1e-2 if dtype is np.float16 else 1e-5,
-                                   atol=1e-2 if dtype is np.float16 else 1e-5)
+            check_symbolic_forward(quad_sym, [data_np], [expected], rtol=tol, atol=tol)
             # check backward
             check_symbolic_backward(quad_sym, [data_np], [np.ones(expected.shape)],
-                                    [backward_expected],
-                                    rtol=1e-2 if dtype is np.float16 else 1e-5,
-                                    atol=1e-2 if dtype is np.float16 else 1e-5)
+                                    [backward_expected], rtol=tol, atol=tol)
             # check backward using finite difference
             check_numeric_gradient(quad_sym, [data_np], atol=0.001)
 
+def allclose_function(contexts):
+    def getRandom(base, percent = 1.):
+        return base * (1 + percent * (2 * np.random.random_sample() - 1.) / 100)
+
+    title = 'exp'
+    for ctx in contexts:
+        title += ' cpu' if ctx == mx.cpu() else ' gpu'
+
+    title += '        nElem     shape'
+    num_ctx = len(contexts)
+    result = [False, False]
+    for dtype in [np.float16, np.float32, np.float64]:
+        rtol = getRandom(1e-2 if dtype is np.float16 else 1e-5)
+        atol = getRandom(1e-4 if dtype is np.float16 else 1e-7)
+        print('\nnumpy.{}:  atol = {}  rtol = {}'.format(dtype.__name__, atol, rtol))
+        print(title)
+        for ndim in range(1, 10):
+            shape = rand_shape_nd(ndim, 8)
+            a_np = np.random.randn(*shape).astype(dtype)
+            b_np = (a_np + np.random.randn(*shape).astype(dtype) / 10000000).astype(dtype)
+            expected = np.allclose(a_np, b_np, rtol, atol)
+
+            for n, ctx in enumerate(contexts):
+                a_ctx = mx.nd.array(a_np, dtype = dtype, ctx=ctx)
+                b_ctx = mx.nd.array(b_np, dtype = dtype, ctx=ctx)
+                output = mx.nd.contrib.allclose(a_ctx, b_ctx, rtol=rtol, atol=atol)
+                result[n] = output.asnumpy() == 1
+                if expected != result[n]:
+                    # Preparing the output of elements of the array, which are considered as "not close" AND
+                    # corresponding elements of comparison CPU/GPU/Python vectors, which are considered as "close"
+                    v_ctx = 'CPU' if ctx == mx.cpu() else 'GPU'
+                    if expected:
+                        v_cmp = 'Python'
+                        a_b = a_ctx.asnumpy()
+                        b_b = b_ctx.asnumpy()
+                        a_g = np.asarray(a_np)
+                        b_g = np.asarray(b_np)
+
+                    else:
+                        v_cmp = v_ctx
+                        v_ctx = 'Python'
+                        a_b = np.asarray(a_np)
+                        b_b = np.asarray(b_np)
+                        a_g = a_ctx.asnumpy()
+                        b_g = b_ctx.asnumpy()
+
+                    print('\n *** Violations found on %s, but not on %s side  ***' % (v_ctx, v_cmp))
+                    frmt = "                 a[{0:d}]:                 b[{0:d}]:"  \
+                           "          abs(a[{0:d}]-b[{0:d}]) - atol + rtol*abs(b[{0:d}]):"
+
+                    # Define the indices of all violations and corresponding values of coordinates
+                    bad_indexes = np.abs(a_b - b_b) >= atol + rtol * abs(b_b)
+                    a_values = [a_b[bad_indexes], a_g[bad_indexes]]
+                    b_values = [b_b[bad_indexes], b_g[bad_indexes]]
+                    idx = np.asarray(np.where(bad_indexes == True))
+                    idx = idx.reshape(1, idx.size)
+                    idx_flat = np.asarray(np.where(bad_indexes.flatten() == True)).flatten()
+                    for i in range(len(a_values[0])):
+                        flat_idx = idx_flat[i]
+                        print('{}:  index = {}   flat_index = {}'.format('%4d'%i, idx[i], flat_idx))
+                        print(frmt.format(flat_idx))
+                        for j in range(2):
+                            diff = np.abs(a_values[j][i]-b_values[j][i]) - atol + rtol*abs(b_values[j][i])
+                            print('{}:  {}  {}              {}'.format('%6s'%v_ctx, a_values[j][i], b_values[j][i], diff))
+
+
+            if num_ctx == 1:
+                print(' {0:d}   {1:d}    {2:10d}   {3:}'.format(expected, result[0], np.prod(shape), shape))
+            else:
+                print(' {0:d}   {1:d}   {2:d}    {3:10d}   {4:}'.format(expected, result[0], result[1], np.prod(shape), shape))
+
+            if expected != result[0] or num_ctx > 1 and expected != result[1]:
+                assert False
+
+@with_seed()
+def test_allclose_function():
+    allclose_function([default_context()])
 
 @with_seed()
 def test_histogram():
@@ -8153,12 +8082,12 @@ def f(x, bins=10, range=None):
         bin_range = (-2.5, 2.5)
         mx_histo1, mx_bins1 = mx.nd.histogram(x, bins=bin_cnt, range=bin_range)
         np_histo1, np_bins1 = f(x.asnumpy(), bins=bin_cnt, range=bin_range)
-        assert_almost_equal(mx_bins1.asnumpy(), np_bins1)
-        assert_almost_equal(mx_histo1.asnumpy(), np_histo1, rtol=1e-3, atol=1e-5)
+        assert_almost_equal(mx_bins1, np_bins1)
+        assert_almost_equal(mx_histo1, np_histo1, rtol=1e-3, atol=1e-5)
         mx_histo2, mx_bins2 = mx.nd.histogram(x, bins=mx_bins)
         np_histo2, np_bins2 = f(x.asnumpy(), bins=np_bins)
-        assert_almost_equal(mx_histo2.asnumpy(), np_histo2, rtol=1e-3, atol=1e-5)
-        assert_almost_equal(mx_bins2.asnumpy(), np_bins2, rtol=1e-3, atol=1e-5)
+        assert_almost_equal(mx_histo2, np_histo2, rtol=1e-3, atol=1e-5)
+        assert_almost_equal(mx_bins2, np_bins2, rtol=1e-3, atol=1e-5)
 
         data = mx.sym.Variable("data")
 
@@ -8520,9 +8449,9 @@ def test_roi_align_value(sampling_ratio=0, position_sensitive=False):
                                                              spatial_scale, sampling_ratio,
                                                              position_sensitive, dy.asnumpy())
 
-        assert_almost_equal(output.asnumpy(), real_output, atol=1e-3)
-        assert_almost_equal(data.grad.asnumpy(), dx, atol=1e-3)
-        assert_almost_equal(rois.grad.asnumpy(), drois, atol=1e-3)
+        assert_almost_equal(output, real_output, atol=1e-3)
+        assert_almost_equal(data.grad, dx, atol=1e-3)
+        assert_almost_equal(rois.grad, drois, atol=1e-3)
 
     # modified from test_roipooling()
     def test_roi_align_autograd(sampling_ratio=0):
@@ -8695,24 +8624,8 @@ def test_diag():
     a_np = np.random.random((h, w)).astype(np.float32)
     a = mx.nd.array(a_np).astype('float32')
 
-    # k == 0
-    r = mx.nd.diag(a)
-    assert_almost_equal(r.asnumpy(), np.diag(a_np))
-
-    # k == 1
-    k = 1
-    r = mx.nd.diag(a, k=k)
-    assert_almost_equal(r.asnumpy(), np.diag(a_np, k=k))
-
-    # k == -1
-    k = -1
-    r = mx.nd.diag(a, k=k)
-    assert_almost_equal(r.asnumpy(), np.diag(a_np, k=k))
-
-    # random k
-    k = np.random.randint(-min(h,w) + 1, min(h,w))
-    r = mx.nd.diag(a, k=k)
-    assert_almost_equal(r.asnumpy(), np.diag(a_np, k=k))
+    for k in [0, 1, -1, np.random.randint(-min(h,w) + 1, min(h,w))]:
+        assert_almost_equal(mx.nd.diag(a, k=k), np.diag(a_np, k=k))
 
     # invalid k
     k = max(h,w) + 1
@@ -8740,9 +8653,7 @@ def test_diag():
 
     # k is random
     k = np.random.randint(-d,d)
-    r = mx.nd.diag(a, k=k)
-
-    assert_almost_equal(r.asnumpy(), np.diag(a_np, k=k))
+    assert_almost_equal(mx.nd.diag(a, k=k), np.diag(a_np, k=k))
 
     # Test 2d backward, k=0
     data = mx.sym.Variable('data')
@@ -8769,19 +8680,19 @@ def test_diag():
 
     # k = 0, axis1=0, axis2=1
     r = mx.nd.diag(data=a, k=0, axis1=0, axis2=1)
-    assert_almost_equal(r.asnumpy(), np.diagonal(a_np, offset=0, axis1=0, axis2=1))
+    assert_almost_equal(r, np.diagonal(a_np, offset=0, axis1=0, axis2=1))
 
     # k = 1, axis1=1, axis2=0
     r = mx.nd.diag(data=a, k=1, axis1=1, axis2=0)
-    assert_almost_equal(r.asnumpy(), np.diagonal(a_np, offset=1, axis1=1, axis2=0))
+    assert_almost_equal(r, np.diagonal(a_np, offset=1, axis1=1, axis2=0))
 
     # k = -1 axis1=1, axis3=3
     r = mx.nd.diag(data=a, k=-1, axis1=1, axis2=3)
-    assert_almost_equal(r.asnumpy(), np.diagonal(a_np, offset=-1, axis1=1, axis2=3))
+    assert_almost_equal(r, np.diagonal(a_np, offset=-1, axis1=1, axis2=3))
 
     # k = 2, axis1=-2, axis2=0
     r = mx.nd.diag(data=a, k=2, axis1=-2, axis2=0)
-    assert_almost_equal(r.asnumpy(), np.diagonal(a_np, offset=2, axis1=-2, axis2=0))
+    assert_almost_equal(r, np.diagonal(a_np, offset=2, axis1=-2, axis2=0))
 
     # Test 4d backward, k=0, axis1=3, axis2=0
     data = mx.sym.Variable('data')
@@ -8823,7 +8734,7 @@ def f(x, blocksize):
     data_np = data.asnumpy()
     expected = f(data_np, block)
     output = mx.nd.depth_to_space(data, block)
-    assert_almost_equal(output.asnumpy(), expected, atol=1e-3, rtol=1e-3)
+    assert_almost_equal(output, expected, atol=1e-3, rtol=1e-3)
 
     shape_out = (n, c // (block ** 2), h * block, w * block)
     data = mx.sym.Variable('data')
@@ -8874,7 +8785,7 @@ def f(x, blocksize):
     data_np = data.asnumpy()
     expected = f(data_np, block)
     output = mx.nd.space_to_depth(data, block)
-    assert_almost_equal(output.asnumpy(), expected, atol=1e-3, rtol=1e-3)
+    assert_almost_equal(output, expected, atol=1e-3, rtol=1e-3)
 
     shape_out = (n, c * (block ** 2), h // block, w // block)
     data = mx.sym.Variable('data')
diff --git a/tests/python/unittest/test_random.py b/tests/python/unittest/test_random.py
index fe276685bfe3..efcf16dc78da 100644
--- a/tests/python/unittest/test_random.py
+++ b/tests/python/unittest/test_random.py
@@ -19,7 +19,7 @@
 import math
 import itertools
 import mxnet as mx
-from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf, retry
+from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf, retry, assert_almost_equal
 import numpy as np
 import random as rnd
 from common import setup_module, with_seed, random_seed, teardown
@@ -583,14 +583,14 @@ def test_sample_multinomial():
                 prob = prob.reshape((1, prob.shape[0]))
             for i in range(x.shape[0]):
                 freq = np.bincount(y[i,:].astype('int32'), minlength=5)/np.float32(samples)*x[i,:].sum()
-                mx.test_utils.assert_almost_equal(freq, x[i], rtol=0.20, atol=1e-1)
+                assert_almost_equal(freq, x[i], rtol=0.20, atol=1e-1)
                 rprob = x[i][y[i].astype('int32')]/x[i].sum()
-                mx.test_utils.assert_almost_equal(np.log(rprob), prob.asnumpy()[i], atol=1e-5)
+                assert_almost_equal(np.log(rprob), prob.asnumpy()[i], atol=1e-5)
 
                 real_dx = np.zeros((5,))
                 for j in range(samples):
                     real_dx[int(y[i][j])] += 5.0 / rprob[j]
-                mx.test_utils.assert_almost_equal(real_dx, dx[i, :], rtol=1e-4, atol=1e-5)
+                assert_almost_equal(real_dx, dx[i, :], rtol=1e-4, atol=1e-5)
     for dtype in ['uint8', 'float16', 'float32']:
         # Bound check for the output data types. 'int32' and 'float64' require large memory so are skipped.
         x = mx.nd.zeros(2 ** 25)  # Larger than the max integer in float32 without precision loss.
@@ -883,8 +883,8 @@ def compute_expected_prob():
     # test ndarray
     true_classes = mx.nd.random.uniform(0, range_max, shape=(num_true,)).astype('int32')
     sampled_classes, exp_cnt_true, exp_cnt_sampled = mx.nd.contrib.rand_zipfian(true_classes, num_sampled, range_max)
-    mx.test_utils.assert_almost_equal(exp_cnt_sampled.asnumpy(), exp_cnt[sampled_classes].asnumpy(), rtol=1e-1, atol=1e-2)
-    mx.test_utils.assert_almost_equal(exp_cnt_true.asnumpy(), exp_cnt[true_classes].asnumpy(), rtol=1e-1, atol=1e-2)
+    assert_almost_equal(exp_cnt_sampled, exp_cnt[sampled_classes], rtol=1e-1, atol=1e-2)
+    assert_almost_equal(exp_cnt_true, exp_cnt[true_classes], rtol=1e-1, atol=1e-2)
 
     # test symbol
     true_classes_var = mx.sym.var('true_classes')
@@ -893,8 +893,8 @@ def compute_expected_prob():
     executor = outputs.bind(mx.context.current_context(), {'true_classes' : true_classes})
     executor.forward()
     sampled_classes, exp_cnt_true, exp_cnt_sampled = executor.outputs
-    mx.test_utils.assert_almost_equal(exp_cnt_sampled.asnumpy(), exp_cnt[sampled_classes].asnumpy(), rtol=1e-1, atol=1e-2)
-    mx.test_utils.assert_almost_equal(exp_cnt_true.asnumpy(), exp_cnt[true_classes].asnumpy(), rtol=1e-1, atol=1e-2)
+    assert_almost_equal(exp_cnt_sampled, exp_cnt[sampled_classes], rtol=1e-1, atol=1e-2)
+    assert_almost_equal(exp_cnt_true, exp_cnt[true_classes], rtol=1e-1, atol=1e-2)
 
 # Issue #10277 (https://github.com/apache/incubator-mxnet/issues/10277) discusses this test.
 @with_seed()
diff --git a/tests/python/unittest/test_sparse_operator.py b/tests/python/unittest/test_sparse_operator.py
index 91194c562a57..4c4e3dbdfc51 100644
--- a/tests/python/unittest/test_sparse_operator.py
+++ b/tests/python/unittest/test_sparse_operator.py
@@ -732,11 +732,9 @@ def check_sparse_mathematical_core(name, stype,
 
         assert arr_grad.stype == expected_grad_result_type
 
-        arr_grad = arr_grad.asnumpy()
-
         if verbose is True:
             print(name)
-            print("arr_grad", arr_grad)
+            print("arr_grad", arr_grad.asnumpy())
             print("input_grad", input_grad)
 
         assert_almost_equal(arr_grad, input_grad, equal_nan=True)
diff --git a/tests/python/unittest/test_subgraph.py b/tests/python/unittest/test_subgraph.py
index 4c13f9c70dfc..3da125a946bc 100644
--- a/tests/python/unittest/test_subgraph.py
+++ b/tests/python/unittest/test_subgraph.py
@@ -20,15 +20,8 @@
 import numpy as np
 import mxnet as mx
 import copy
-import math
-import ctypes
-import random
-import itertools
-from numpy.testing import assert_allclose, assert_array_equal
 from mxnet.test_utils import *
-from mxnet.base import py_str, MXNetError, _as_list, SymbolHandle, check_call, _LIB, c_handle_array, mx_uint
 from common import setup_module, with_seed, teardown
-import unittest
 from mxnet.gluon.model_zoo.vision import get_model
 
 def make_subgraph(subg, *args):

From 0c00a798197a502f3fa8cc4ae92b03b4acb28c59 Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Tue, 15 Oct 2019 17:14:46 -0700
Subject: [PATCH 453/813] Fix optimizer bug for np attribute (#16494)

---
 python/mxnet/optimizer/optimizer.py       | 2 +-
 tests/python/unittest/test_numpy_gluon.py | 9 +++++++++
 2 files changed, 10 insertions(+), 1 deletion(-)

diff --git a/python/mxnet/optimizer/optimizer.py b/python/mxnet/optimizer/optimizer.py
index c3a1f3374a94..bc87777e40fc 100644
--- a/python/mxnet/optimizer/optimizer.py
+++ b/python/mxnet/optimizer/optimizer.py
@@ -1942,7 +1942,7 @@ def __init__(self, optimizer):
 
     def __call__(self, index, grad, weight):
         """Updates weight given gradient and index."""
-        allow_np = self.optimizer.allow_np_array
+        allow_np = self.optimizer.allow_np_array if hasattr(self.optimizer, "allow_np_array") else is_np_array()
         if not isinstance(index, (list, tuple)):
             indices = [index]
             grads = [_as_classic(grad, allow_np)]
diff --git a/tests/python/unittest/test_numpy_gluon.py b/tests/python/unittest/test_numpy_gluon.py
index f24eb6a325bf..af5425336699 100644
--- a/tests/python/unittest/test_numpy_gluon.py
+++ b/tests/python/unittest/test_numpy_gluon.py
@@ -113,6 +113,15 @@ def hybrid_forward(self, F, pred, label):
         trainer.step(1)
 
 
+@with_seed()
+@use_np
+def test_optimizer_backward_compat():
+    optimizer = mx.optimizer.SGD()
+    delattr(optimizer, "allow_np_array")
+    updater = mx.optimizer.Updater(optimizer)
+    updater(0, np.ones((0, 0)), np.zeros((0, 0)))
+
+
 @with_seed()
 @use_np
 def test_np_loss_ndarray():

From c2bbde75c36be2fef4a5e6d941cc01f5aabd9fd2 Mon Sep 17 00:00:00 2001
From: vexilligera <vexilligera@gmail.com>
Date: Wed, 16 Oct 2019 06:00:03 +0000
Subject: [PATCH 454/813] Tests of NumPy interoperability (#16469)

* cumsum test

* ravel test

* test reciprocal remainder repeat reshape

* test rint roll sign

* print test

* print debug

* life is short use pytorch
---
 python/mxnet/numpy_dispatch_protocol.py       |   2 +
 .../unittest/test_numpy_interoperability.py   | 160 ++++++++++++++++--
 2 files changed, 151 insertions(+), 11 deletions(-)

diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index f483e299ffed..b02a7e53cfbf 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -98,8 +98,10 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'min',
     'ones_like',
     'prod',
+    'ravel',
     'repeat',
     'reshape',
+    'roll',
     'split',
     'squeeze',
     'stack',
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index f0c205f72dc4..af5991381bd4 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -25,6 +25,7 @@
 from mxnet.numpy_dispatch_protocol import with_array_function_protocol, with_array_ufunc_protocol
 from mxnet.numpy_dispatch_protocol import _NUMPY_ARRAY_FUNCTION_LIST, _NUMPY_ARRAY_UFUNC_LIST
 
+import itertools
 
 class OpArgMngr(object):
     """Operator argument manager for storing operator workloads."""
@@ -49,6 +50,10 @@ def _prepare_workloads():
         '1x1x0': np.array([[[]]])
     }
 
+    dt_int = [np.int8, np.int32, np.int64, np.uint8]
+    dt_float = [np.float16, np.float32, np.float64]
+    dt = dt_int + dt_float
+
     # workloads for array function protocol
     OpArgMngr.add_workload('argmax', array_pool['4x1'])
     OpArgMngr.add_workload('broadcast_arrays', array_pool['4x1'], array_pool['1x2'])
@@ -57,8 +62,14 @@ def _prepare_workloads():
     OpArgMngr.add_workload('concatenate', [array_pool['4x1'], array_pool['4x1']])
     OpArgMngr.add_workload('concatenate', [array_pool['4x1'], array_pool['4x1']], axis=1)
     OpArgMngr.add_workload('copy', array_pool['4x1'])
-    OpArgMngr.add_workload('cumsum', array_pool['4x1'])
-    OpArgMngr.add_workload('cumsum', array_pool['4x1'], axis=1)
+
+    for ctype in dt:
+        OpArgMngr.add_workload('cumsum', np.array([1, 2, 10, 11, 6, 5, 4], dtype=ctype))
+        OpArgMngr.add_workload('cumsum', np.array([[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]], dtype=ctype), axis=0)
+        OpArgMngr.add_workload('cumsum', np.array([[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]], dtype=ctype), axis=1)
+
+    OpArgMngr.add_workload('ravel', np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]]))
+
     OpArgMngr.add_workload('dot', array_pool['4x1'], array_pool['4x1'].T)
     OpArgMngr.add_workload('expand_dims', array_pool['4x1'], -1)
     OpArgMngr.add_workload('fix', array_pool['4x1'])
@@ -68,8 +79,72 @@ def _prepare_workloads():
     OpArgMngr.add_workload('mean', array_pool['4x1'], axis=0, keepdims=True)
     OpArgMngr.add_workload('ones_like', array_pool['4x1'])
     OpArgMngr.add_workload('prod', array_pool['4x1'])
+
     OpArgMngr.add_workload('repeat', array_pool['4x1'], 3)
-    OpArgMngr.add_workload('reshape', array_pool['4x1'], -1)
+    OpArgMngr.add_workload('repeat', np.array(_np.arange(12).reshape(4, 3)[:, 2]), 3)
+
+    m = _np.array([1, 2, 3, 4, 5, 6])
+    m_rect = m.reshape((2, 3))
+
+    # OpArgMngr.add_workload('repeat', np.array(m), [1, 3, 2, 1, 1, 2]) # Argument "repeats" only supports int
+    OpArgMngr.add_workload('repeat', np.array(m), 2)
+    B = np.array(m_rect)
+    # OpArgMngr.add_workload('repeat', B, [2, 1], axis=0)  # Argument "repeats" only supports int
+    # OpArgMngr.add_workload('repeat', B, [1, 3, 2], axis=1)  # Argument "repeats" only supports int
+    OpArgMngr.add_workload('repeat', B, 2, axis=0)
+    OpArgMngr.add_workload('repeat', B, 2, axis=1)
+
+    # test_repeat_broadcasting
+    a = _np.arange(60).reshape(3, 4, 5)
+    for axis in itertools.chain(range(-a.ndim, a.ndim), [None]):
+        OpArgMngr.add_workload('repeat', np.array(a), 2, axis=axis)
+    #    OpArgMngr.add_workload('repeat', np.array(a), [2], axis=axis)   # Argument "repeats" only supports int
+
+    arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])
+    OpArgMngr.add_workload('reshape', arr, (2, 6))
+    OpArgMngr.add_workload('reshape', arr, (3, 4))
+    # OpArgMngr.add_workload('reshape', arr, (3, 4), order='F') # Items are not equal with order='F'
+    OpArgMngr.add_workload('reshape', arr, (3, 4), order='C')
+    OpArgMngr.add_workload('reshape', np.array(_np.ones(100)), (100, 1, 1))
+    
+    # test_reshape_order
+    a = np.array(_np.arange(6))
+    # OpArgMngr.add_workload('reshape', a, (2, 3), order='F')  # Items are not equal with order='F'
+    a = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
+    b = a[:, 1]
+    # OpArgMngr.add_workload('reshape', b, (2, 2), order='F')  # Items are not equal with order='F'
+
+    a = np.array(_np.ones((0, 2)))
+    OpArgMngr.add_workload('reshape', a, -1, 2)
+
+    OpArgMngr.add_workload('rint', np.array(4607998452777363968))
+    OpArgMngr.add_workload('rint', array_pool['4x1'])
+
+    # test_roll1d(self)
+    OpArgMngr.add_workload('roll', np.array(_np.arange(10)), 2)
+
+    # test_roll2d(self)
+    x2 = np.array(_np.reshape(_np.arange(10), (2, 5)))
+    OpArgMngr.add_workload('roll', x2, 1)
+    OpArgMngr.add_workload('roll', x2, 1, axis=0)
+    OpArgMngr.add_workload('roll', x2, 1, axis=1)
+    # # Roll multiple axes at once.
+    OpArgMngr.add_workload('roll', x2, 1, axis=(0, 1))
+    OpArgMngr.add_workload('roll', x2, (1, 0), axis=(0, 1))
+    OpArgMngr.add_workload('roll', x2, (-1, 0), axis=(0, 1))
+    OpArgMngr.add_workload('roll', x2, (0, 1), axis=(0, 1))
+    OpArgMngr.add_workload('roll', x2, (0, -1), axis=(0, 1))
+    OpArgMngr.add_workload('roll', x2, (1, 1), axis=(0, 1))
+    OpArgMngr.add_workload('roll', x2, (-1, -1), axis=(0, 1))
+    # # Roll the same axis multiple times.
+    # OpArgMngr.add_workload('roll', x2, 1, axis=(0, 0)) # Check failed: axes[i - 1] < axes[i] (0 vs. 0) : axes have duplicates [0,0]
+    # OpArgMngr.add_workload('roll', x2, 1, axis=(1, 1)) # Check failed: axes[i - 1] < axes[i] (1 vs. 1) : axes have duplicates [1,1]
+    # # Roll more than one turn in either direction.
+    OpArgMngr.add_workload('roll', x2, 6, axis=1)
+    OpArgMngr.add_workload('roll', x2, -4, axis=1)
+    # # test_roll_empty
+    OpArgMngr.add_workload('roll', np.array([]), 1)
+    
     OpArgMngr.add_workload('split', array_pool['4x1'], 2)
     OpArgMngr.add_workload('squeeze', array_pool['4x1'])
     OpArgMngr.add_workload('stack', [array_pool['4x1']] * 2)
@@ -104,10 +179,64 @@ def _prepare_workloads():
     OpArgMngr.add_workload('mod', array_pool['4x1'], 2)
     OpArgMngr.add_workload('mod', 2, array_pool['4x1'])
     OpArgMngr.add_workload('mod', array_pool['4x1'], array_pool['1x1x0'])
-    OpArgMngr.add_workload('remainder', array_pool['4x1'], array_pool['1x2'])
-    OpArgMngr.add_workload('remainder', array_pool['4x1'], 2)
-    OpArgMngr.add_workload('remainder', 2, array_pool['4x1'])
-    OpArgMngr.add_workload('remainder', array_pool['4x1'], array_pool['1x1x0'])
+
+    # test remainder basic
+    OpArgMngr.add_workload('remainder', np.array([0, 1, 2, 4, 2], dtype=np.float16),
+                            np.array([-2, 5, 1, 4, 3], dtype=np.float16))
+
+    def _signs(dt):
+        if dt in [np.uint8]:
+            return (+1,)
+        else:
+            return (+1, -1)
+
+    for ct in dt:
+        for sg1, sg2 in itertools.product(_signs(ct), _signs(ct)):
+            a = np.array(sg1*71, dtype=ct)
+            b = np.array(sg2*19, dtype=ct)
+            OpArgMngr.add_workload('remainder', a, b)
+
+    # test remainder exact
+    nlst = list(range(-127, 0))
+    plst = list(range(1, 128))
+    dividend = nlst + [0] + plst
+    divisor = nlst + plst
+    arg = list(itertools.product(dividend, divisor))
+    tgt = list(divmod(*t) for t in arg)
+    a, b = np.array(arg, dtype=int).T
+    # convert exact integer results from Python to float so that
+    # signed zero can be used, it is checked.
+    for dt in [np.float16, np.float32, np.float64]:
+        fa = a.astype(dt)
+        fb = b.astype(dt)
+        OpArgMngr.add_workload('remainder', fa, fb)
+    
+    # test_float_remainder_roundoff
+    for ct in dt_float:
+        for sg1, sg2 in itertools.product((+1, -1), (+1, -1)):
+            a = np.array(sg1*78*6e-8, dtype=ct)
+            b = np.array(sg2*6e-8, dtype=ct)
+            OpArgMngr.add_workload('remainder', a, b)
+
+    # test_float_remainder_corner_cases
+    # Check remainder magnitude.
+    for ct in dt_float:
+        b = _np.array(1.0)
+        a = np.array(_np.nextafter(_np.array(0.0), -b), dtype=ct)
+        b = np.array(b, dtype=ct)
+        OpArgMngr.add_workload('remainder', a, b)
+        OpArgMngr.add_workload('remainder', -a, -b)
+
+        # Check nans, inf
+        for ct in [np.float16, np.float32, np.float64]:
+            fone = np.array(1.0, dtype=ct)
+            fzer = np.array(0.0, dtype=ct)
+            finf = np.array(np.inf, dtype=ct)
+            fnan = np.array(np.nan, dtype=ct)
+            # OpArgMngr.add_workload('remainder', fone, fzer) # failed
+            OpArgMngr.add_workload('remainder', fone, fnan)
+            OpArgMngr.add_workload('remainder', finf, fone)
+
     OpArgMngr.add_workload('maximum', array_pool['4x1'], array_pool['1x2'])
     OpArgMngr.add_workload('maximum', array_pool['4x1'], 2)
     OpArgMngr.add_workload('maximum', 2, array_pool['4x1'])
@@ -118,8 +247,12 @@ def _prepare_workloads():
     OpArgMngr.add_workload('minimum', array_pool['4x1'], array_pool['1x1x0'])
     OpArgMngr.add_workload('negative', array_pool['4x1'])
     OpArgMngr.add_workload('absolute', array_pool['4x1'])
-    OpArgMngr.add_workload('rint', array_pool['4x1'])
+    
     OpArgMngr.add_workload('sign', array_pool['4x1'])
+    OpArgMngr.add_workload('sign', np.array([-2, 5, 1, 4, 3], dtype=np.float16))
+    OpArgMngr.add_workload('sign', np.array([-.1, 0, .1]))
+    # OpArgMngr.add_workload('sign', np.array(_np.array([_np.nan]))) # failed
+
     OpArgMngr.add_workload('exp', array_pool['4x1'])
     OpArgMngr.add_workload('log', array_pool['4x1'])
     OpArgMngr.add_workload('log2', array_pool['4x1'])
@@ -128,7 +261,12 @@ def _prepare_workloads():
     OpArgMngr.add_workload('sqrt', array_pool['4x1'])
     OpArgMngr.add_workload('square', array_pool['4x1'])
     OpArgMngr.add_workload('cbrt', array_pool['4x1'])
-    OpArgMngr.add_workload('reciprocal', array_pool['4x1'])
+
+    for ctype in [np.float16, np.float32, np.float64]:
+        OpArgMngr.add_workload('reciprocal', np.array([-2, 5, 1, 4, 3], dtype=ctype))
+        OpArgMngr.add_workload('reciprocal', np.array([-2, 0, 1, 0, 3], dtype=ctype))
+        OpArgMngr.add_workload('reciprocal', np.array([0], dtype=ctype))
+
     OpArgMngr.add_workload('sin', array_pool['4x1'])
     OpArgMngr.add_workload('cos', array_pool['4x1'])
     OpArgMngr.add_workload('tan', array_pool['4x1'])
@@ -176,10 +314,10 @@ def _check_interoperability_helper(op_name, *args, **kwargs):
             assert isinstance(arr, np.ndarray)
         for arr, expected_arr in zip(out, expected_out):
             assert isinstance(arr, np.ndarray)
-            assert_almost_equal(arr.asnumpy(), expected_arr, rtol=1e-3, atol=1e-4, use_broadcast=False)
+            assert_almost_equal(arr.asnumpy(), expected_arr, rtol=1e-3, atol=1e-4, use_broadcast=False, equal_nan=True)
     else:
         assert isinstance(out, np.ndarray)
-        assert_almost_equal(out.asnumpy(), expected_out, rtol=1e-3, atol=1e-4, use_broadcast=False)
+        assert_almost_equal(out.asnumpy(), expected_out, rtol=1e-3, atol=1e-4, use_broadcast=False, equal_nan=True)
 
 
 def check_interoperability(op_list):

From bf57ff891c0e40d4b2aa773b0751a32111abb890 Mon Sep 17 00:00:00 2001
From: Rohit Kumar Srivastava <srivastava.141@osu.edu>
Date: Thu, 17 Oct 2019 11:04:14 -0700
Subject: [PATCH 455/813] added more tests to verify support for large vector
 (#16477)

* added more tests to verify support for large tensor

* code refactor and added comments
---
 tests/nightly/test_large_vector.py | 168 +++++++++++++++++++++++++++++
 1 file changed, 168 insertions(+)

diff --git a/tests/nightly/test_large_vector.py b/tests/nightly/test_large_vector.py
index aa6cb3d75b37..75a902ce221f 100644
--- a/tests/nightly/test_large_vector.py
+++ b/tests/nightly/test_large_vector.py
@@ -708,6 +708,174 @@ def test_full():
     assert a[-1] == 3
 
 
+def test_astype():
+    x = create_vector(size=LARGE_X//4)
+    x = nd.tile(x, 4)
+    y = x.astype('int32')
+    assert y.dtype == np.int32
+    assert y[-1] == LARGE_X//4-1
+
+
+def test_cast():
+    x = create_vector(size=LARGE_X//4)
+    x = nd.tile(x, 4)
+    y = nd.cast(x, np.int32)
+    assert y.dtype == np.int32
+    assert y[-1] == LARGE_X//4-1
+
+
+def test_repeat():
+    x = create_vector(size=LARGE_X//2)
+    y = nd.repeat(x, repeats=2, axis = 0)
+    assert y.shape[0] == LARGE_X
+    assert y[1] == 0
+    assert y[LARGE_X-1] == LARGE_X//2-1
+
+
+def create_input_for_rounding_ops():
+    # Creates an vector with values (-LARGE/2 .... -2, -1, 0, 1, 2, .... , LARGE/2-1)
+    # then divides each element by 2 i.e (-LARGE/4 .... -1, -0.5, 0, 0.5, 1, .... , LARGE/4-1)
+    inp = nd.arange(-LARGE_X//2, LARGE_X//2, dtype=np.float64)
+    inp = inp/2
+    return inp
+
+
+def assert_correctness_of_rounding_ops(output, mid, expected_vals):
+    # checks verifies 5 values at the middle positions of the input vector
+    # i.e mid-2, mid-1, mid, mid+1, mid+2
+    output_idx_to_inspect = [mid-2, mid-1, mid, mid+1, mid+2]
+    for i in range(len(output_idx_to_inspect)):
+        assert output[output_idx_to_inspect[i]] == expected_vals[i]
+
+
+def test_rounding_ops():
+    x = create_input_for_rounding_ops()
+    
+    def check_ceil():
+        y = nd.ceil(x)
+        # expected ouput for middle 5 values after applying ceil()
+        expected_output = [-1, 0, 0, 1, 1]
+        assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
+
+    def check_fix():
+        y = nd.fix(x)
+        # expected ouput for middle 5 values after applying fix()
+        expected_output = [-1, 0, 0, 0, 1]
+        assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
+
+    def check_floor():
+        y = nd.floor(x)
+        # expected ouput for middle 5 values after applying floor()
+        expected_output = [-1, -1, 0, 0, 1]
+        assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
+
+    def check_rint():
+        y = nd.rint(x)
+        # expected ouput for middle 5 values after applying rint()
+        expected_output = [-1, -1, 0, 0, 1]
+        assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
+
+    def check_round():
+        y = nd.round(x)
+        # expected ouput for middle 5 values after applying round()
+        expected_output = [-1, -1, 0, 1, 1]
+        assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
+
+    def check_trunc():
+        y = nd.trunc(x)
+        # expected ouput for middle 5 values after applying trunc()
+        expected_output = [-1, 0, 0, 0, 1]
+        assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
+
+    check_ceil()
+    check_fix()
+    check_floor()
+    check_rint()
+    check_round()
+    check_trunc()
+
+
+def create_input_for_trigonometric_ops(vals):
+    # Creates large vector input of size(LARGE_X) from vals using tile operator
+    inp = nd.array(vals)
+    inp = nd.tile(inp, LARGE_X//len(vals))
+    return inp
+
+
+def assert_correctness_of_trigonometric_ops(output, expected_vals):
+    # checks verifies 5 values at positions(0, 1, -3, -2, -1) of the input vector
+    output_idx_to_inspect = [0, 1, -3, -2, -1]
+    for i in range(len(output_idx_to_inspect)):
+        assert np.abs(output[output_idx_to_inspect[i]].asnumpy()-expected_vals[i]) <= 1e-3
+
+
+def test_trigonometric_ops():
+    def check_arcsin():
+        x = create_input_for_trigonometric_ops([-1, -.707, 0, .707, 1])
+        y = nd.arcsin(x)
+        # expected ouput for indices=(0, 1, -3, -2, -1) after applying arcsin()
+        expected_output = [-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2]
+        assert_correctness_of_trigonometric_ops(y, expected_output)
+
+    def check_arccos():
+        x = create_input_for_trigonometric_ops([-1, -.707, 0, .707, 1])
+        y = nd.arccos(x)
+        # expected ouput for indices=(0, 1, -3, -2, -1) after applying arccos()
+        expected_output = [np.pi, 3*np.pi/4, np.pi/2, np.pi/4, 0]
+        assert_correctness_of_trigonometric_ops(y, expected_output)
+
+    def check_arctan():
+        x = create_input_for_trigonometric_ops([-np.Inf, -1, 0, 1, np.Inf])
+        y = nd.arctan(x)
+        # expected ouput for indices=(0, 1, -3, -2, -1) after applying arctan()
+        expected_output = [-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2]
+        assert_correctness_of_trigonometric_ops(y, expected_output)
+
+    def check_sin():
+        x = create_input_for_trigonometric_ops([-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2])
+        y = nd.sin(x)
+        # expected ouput for indices=(0, 1, -3, -2, -1) after applying sin()
+        expected_output = [-1, -.707, 0, .707, 1]
+        assert_correctness_of_trigonometric_ops(y, expected_output)
+
+    def check_cos():
+        x = create_input_for_trigonometric_ops([0, np.pi/4, np.pi/2, 3*np.pi/4, np.pi])
+        y = nd.cos(x)
+        # expected ouput for indices=(0, 1, -3, -2, -1) after applying cos()
+        expected_output = [1, .707, 0, -.707, -1]
+        assert_correctness_of_trigonometric_ops(y, expected_output)
+
+    def check_tan():
+        x = create_input_for_trigonometric_ops([-np.pi/6, -np.pi/4, 0, np.pi/4, np.pi/6])
+        y = nd.tan(x)
+        # expected ouput for indices=(0, 1, -3, -2, -1) after applying tan()
+        expected_output = [-.577, -1, 0, 1, .577]
+        assert_correctness_of_trigonometric_ops(y, expected_output)
+
+    def check_radians():
+        x = create_input_for_trigonometric_ops([0, 90, 180, 270, 360])
+        y = nd.radians(x)
+        # expected ouput for indices=(0, 1, -3, -2, -1) after applying radians()
+        expected_output = [0, np.pi/2, np.pi, 3*np.pi/2, 2*np.pi]
+        assert_correctness_of_trigonometric_ops(y, expected_output)
+
+    def check_degrees():
+        x = create_input_for_trigonometric_ops([0, np.pi/2, np.pi, 3*np.pi/2, 2*np.pi])
+        y = nd.degrees(x)
+        # expected ouput for indices=(0, 1, -3, -2, -1) after applying degrees()
+        expected_output = [0, 90, 180, 270, 360]
+        assert_correctness_of_trigonometric_ops(y, expected_output)
+
+    check_arcsin()
+    check_arccos()
+    check_arctan()
+    check_sin()
+    check_cos()
+    check_tan()
+    check_radians()
+    check_degrees()
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From de524bbe2434842de19f0df24911ce6f2a39ed2f Mon Sep 17 00:00:00 2001
From: Aaron Markham <markhama@amazon.com>
Date: Thu, 17 Oct 2019 14:02:35 -0700
Subject: [PATCH 456/813] Fixing broken links (#16500)

* fixed broken link

fix broken links

fix more broken links

* Update docs/python_docs/python/tutorials/deploy/export/onnx.md

* Update docs/python_docs/python/tutorials/packages/onnx/super_resolution.md

* Apply suggestions from code review

* update cloud guide

* Apply suggestions from code review

Co-Authored-By: Talia <31782251+TEChopra1000@users.noreply.github.com>

* change file format

* change file format

* fix formatting
---
 .../python/tutorials/deploy/export/onnx.md    |   4 +-
 .../tutorials/deploy/run-on-aws/cloud.md      |  29 +++++
 .../tutorials/deploy/run-on-aws/cloud.rst     | 105 ------------------
 .../tutorials/deploy/run-on-aws/index.rst     |   2 +-
 .../gluon_from_experiment_to_deployment.md    |   4 +-
 .../getting-started/to-mxnet/index.rst        |   2 +-
 .../tutorials/packages/autograd/index.md      |  10 +-
 .../gluon/blocks/custom_layer_beginners.md    |  30 ++---
 .../packages/gluon/blocks/hybridize.md        |  12 +-
 .../tutorials/packages/gluon/blocks/init.md   |  12 +-
 .../tutorials/packages/gluon/blocks/nn.md     |  24 ++--
 .../packages/gluon/blocks/parameters.md       |  20 ++--
 .../gluon/image/image-augmentation.md         |  20 ++--
 .../packages/gluon/image/pretrained_models.md |  14 +--
 .../python/tutorials/packages/gluon/index.rst |  12 +-
 .../packages/gluon/loss/custom-loss.md        |  28 ++---
 .../tutorials/packages/gluon/loss/loss.md     |  52 ++++-----
 .../gluon/training/fit_api_tutorial.md        |  68 ++++++------
 .../learning_rates/learning_rate_finder.md    |  18 +--
 .../learning_rate_schedules_advanced.md       |  22 ++--
 .../packages/gluon/training/trainer.md        |  24 ++--
 .../tutorials/packages/kvstore/index.rst      |   7 +-
 .../tutorials/packages/kvstore/kvstore.md     |   4 +-
 .../packages/ndarray/01-ndarray-intro.md      |  14 +--
 .../packages/ndarray/02-ndarray-operations.md |  39 +++----
 .../packages/ndarray/03-ndarray-contexts.md   |   4 +-
 .../ndarray/gotchas_numpy_in_mxnet.md         |  38 +++----
 .../packages/ndarray/sparse/row_sparse.md     |  28 ++---
 .../packages/onnx/fine_tuning_gluon.md        |  12 +-
 .../packages/onnx/inference_on_onnx_model.md  |   2 +-
 .../packages/onnx/super_resolution.md         |   4 +-
 .../src/pages/api/faq/distributed_training.md |   4 +-
 .../tutorials/five_minutes_neural_network.md  |   2 +-
 33 files changed, 290 insertions(+), 380 deletions(-)
 create mode 100644 docs/python_docs/python/tutorials/deploy/run-on-aws/cloud.md
 delete mode 100644 docs/python_docs/python/tutorials/deploy/run-on-aws/cloud.rst

diff --git a/docs/python_docs/python/tutorials/deploy/export/onnx.md b/docs/python_docs/python/tutorials/deploy/export/onnx.md
index 0b325cb0ba9a..b292b359bd59 100644
--- a/docs/python_docs/python/tutorials/deploy/export/onnx.md
+++ b/docs/python_docs/python/tutorials/deploy/export/onnx.md
@@ -28,7 +28,7 @@ In this tutorial, we will learn how to use MXNet to ONNX exporter on pre-trained
 ## Prerequisites
 
 To run the tutorial you will need to have installed the following python modules:
-- [MXNet >= 1.3.0](http://mxnet.apache.org/install/index.html)
+- [MXNet >= 1.3.0](/get_started)
 - [onnx]( https://github.com/onnx/onnx#installation) v1.2.1 (follow the install guide)
 
 *Note:* MXNet-ONNX importer and exporter follows version 7 of ONNX operator set which comes with ONNX v1.2.1.
@@ -147,4 +147,4 @@ checker.check_graph(model_proto.graph)
 
 If the converted protobuf format doesn't qualify to ONNX proto specifications, the checker will throw errors, but in this case it successfully passes. 
 
-This method confirms exported model protobuf is valid. Now, the model is ready to be imported in other frameworks for inference!
\ No newline at end of file
+This method confirms exported model protobuf is valid. Now, the model is ready to be imported in other frameworks for inference!
diff --git a/docs/python_docs/python/tutorials/deploy/run-on-aws/cloud.md b/docs/python_docs/python/tutorials/deploy/run-on-aws/cloud.md
new file mode 100644
index 000000000000..1c2ff5448d2a
--- /dev/null
+++ b/docs/python_docs/python/tutorials/deploy/run-on-aws/cloud.md
@@ -0,0 +1,29 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+MXNet on the Cloud
+==================
+
+Deep learning can require extremely powerful hardware, often for
+unpredictable durations of time. Moreover, *MXNet* can benefit from both multiple GPUs and multiple machines. Accordingly, cloud computing, as offered by AWS and others, is especially well suited to training deep learning models. Using AWS, we can rapidly fire up multiple machines with multiple GPUs each at will and maintain the resources for precisely the amount of time needed.
+
+Here are some ways you can use MXNet on AWS:
+
+1. Use [Amazon SageMaker](https://aws.amazon.com/sagemaker/developer-resources/)
+1. Use the [AWS Deep Learning AMI with Conda](https://docs.aws.amazon.com/dlami/latest/devguide/overview-conda.html)
+1. Use an [AWS Deep Learning Container](https://docs.aws.amazon.com/dlami/latest/devguide/deep-learning-containers.html)
+1. Install MXNet on a [AWS Deep Learning Base AMI](https://docs.aws.amazon.com/dlami/latest/devguide/overview-base.html)
diff --git a/docs/python_docs/python/tutorials/deploy/run-on-aws/cloud.rst b/docs/python_docs/python/tutorials/deploy/run-on-aws/cloud.rst
deleted file mode 100644
index 20f69a80e118..000000000000
--- a/docs/python_docs/python/tutorials/deploy/run-on-aws/cloud.rst
+++ /dev/null
@@ -1,105 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
-   or more contributor license agreements.  See the NOTICE file
-   distributed with this work for additional information
-   regarding copyright ownership.  The ASF licenses this file
-   to you under the Apache License, Version 2.0 (the
-   "License"); you may not use this file except in compliance
-   with the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing,
-   software distributed under the License is distributed on an
-   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-   KIND, either express or implied.  See the License for the
-   specific language governing permissions and limitations
-   under the License.
-
-MXNet on the Cloud
-==================
-
-Deep learning can require extremely powerful hardware, often for
-unpredictable durations of time. Moreover, *MXNet* can benefit from both
-multiple GPUs and multiple machines. Accordingly, cloud computing, as
-offered by AWS and others, is especially well suited to training deep
-learning models. Using AWS, we can rapidly fire up multiple machines
-with multiple GPUs each at will and maintain the resources for precisely
-the amount of time needed.
-
-Set Up an AWS GPU Cluster from Scratch
---------------------------------------
-
-In this document, we provide a step-by-step guide that will teach you
-how to set up an AWS cluster with *MXNet*. We show how to:
-
--  ``Use Amazon S3 to host data``\ \_
--  ``Set up an EC2 GPU instance with all dependencies installed``\ \_
--  ``Build and run MXNet on a single computer``\ \_
--  ``Set up an EC2 GPU cluster for distributed training``\ \_
-
-Use Amazon S3 to Host Data
-:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`~`````````\ ~`\ ~~
-
-Amazon S3 provides distributed data storage which proves especially
-convenient for hosting large datasets. To use S3, you need
-``AWS credentials``\ \_, including an ``ACCESS_KEY_ID`` and a
-``SECRET_ACCESS_KEY``.
-
-To use *MXNet* with S3, set the environment variables
-``AWS_ACCESS_KEY_ID`` and ``AWS_SECRET_ACCESS_KEY`` by adding the
-following two lines in ``~/.bashrc`` (replacing the strings with the
-correct ones):
-
-.. code:: bash
-
-export AWS\_ACCESS\_KEY\_ID=AKIAIOSFODNN7EXAMPLE export
-AWS\_SECRET\_ACCESS\_KEY=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
-
-There are several ways to upload data to S3. One simple way is to use
-``s3cmd``\ \_. For example:
-
-.. code:: bash
-
-wget http://data.mxnet.io/mxnet/data/mnist.zip unzip mnist.zip && s3cmd
-put t\*-ubyte s3://dmlc/mnist/
-
-Use Pre-installed EC2 GPU Instance
-:sub:`:sub:`~`\ :sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`~`````````````\ ~~
-
-The ``Deep Learning AMI``\ \_ is an Amazon Linux image supported and
-maintained by Amazon Web Services for use on Amazon Elastic Compute
-Cloud (Amazon EC2). It contains ``MXNet-v0.9.3 tag``\ \_ and the
-necessary components to get going with deep learning, including Nvidia
-drivers, CUDA, cuDNN, Anaconda, Python2 and Python3. The AMI IDs are the
-following:
-
--  us-east-1: ami-e7c96af1
--  us-west-2: ami-dfb13ebf
--  eu-west-1: ami-6e5d6808
-
-Now you can launch *MXNet* directly on an EC2 GPU instance. You can also
-use ``Jupyter``\ \_ notebook on EC2 machine. Here is a
-``good tutorial``\ \_ on how to connect to a Jupyter notebook running on
-an EC2 instance.
-
-Set Up an EC2 GPU Instance from Scratch
-:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`~``````\ :sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`~```````\ :sub:`:sub:`~```
-
-*MXNet* requires the following libraries:
-
--  C++ compiler with C++11 support, such as ``gcc >= 4.8``
--  ``CUDA`` (``CUDNN`` in optional) for GPU linear algebra
--  ``BLAS`` (cblas, open-blas, atblas, mkl, or others)
-
-.. \_Use Amazon S3 to host data: #use-amazon-s3-to-host-data .. \_Set up
-an EC2 GPU instance with all dependencies installed:
-#set-up-an-ec2-gpu-instance .. \_Build and run MXNet on a single
-computer: #build-and-run-mxnet-on-a-gpu-instance .. \_Set up an EC2 GPU
-cluster for distributed training:
-#set-up-an-ec2-gpu-cluster-for-distributed-training .. \_AWS
-credentials:
-http://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSGettingStartedGuide/AWSCredentials.html
-.. \_s3cmd: http://s3tools.org/s3cmd .. *Deep Learning AMI:
-https://aws.amazon.com/marketplace/pp/B01M0AXXQB?qid=1475211685369&sr=0-1&ref*\ =srh\_res\_product\_title
-.. \_MXNet-v0.9.3 tag: https://github.com/apache/incubator-mxnet .. \_Jupyter:
-http://jupyter.org
diff --git a/docs/python_docs/python/tutorials/deploy/run-on-aws/index.rst b/docs/python_docs/python/tutorials/deploy/run-on-aws/index.rst
index 3627f03a830c..522c44ee4039 100644
--- a/docs/python_docs/python/tutorials/deploy/run-on-aws/index.rst
+++ b/docs/python_docs/python/tutorials/deploy/run-on-aws/index.rst
@@ -42,7 +42,7 @@ The following tutorials will help you learn how to deploy MXNet on various AWS p
 
    .. card::
       :title: Training with Data from S3
-      :link: https://mxnet.apache.org/versions/master/faq/s3_integration.html
+      :link: /api/faq/s3_integration
 
       How to train with data from Amazon S3 buckets.
 
diff --git a/docs/python_docs/python/tutorials/getting-started/gluon_from_experiment_to_deployment.md b/docs/python_docs/python/tutorials/getting-started/gluon_from_experiment_to_deployment.md
index 388f2440149c..8d2c4e100c76 100644
--- a/docs/python_docs/python/tutorials/getting-started/gluon_from_experiment_to_deployment.md
+++ b/docs/python_docs/python/tutorials/getting-started/gluon_from_experiment_to_deployment.md
@@ -77,7 +77,7 @@ from mxnet.gluon.data.vision import transforms
 from mxnet.gluon.model_zoo.vision import resnet50_v2
 ```
 
-Next, we define the hyper-parameters that we will use for fine-tuning. We will use the [MXNet learning rate scheduler](https://mxnet.apache.org/tutorials/gluon/learning_rate_schedules.html) to adjust learning rates during training.
+Next, we define the hyper-parameters that we will use for fine-tuning. We will use the [MXNet learning rate scheduler](../packages/gluon/training/learning_rates/learning_rate_schedules.html) to adjust learning rates during training.
 Here we set the `epochs` to 1 for quick demonstration, please change to 40 for actual training.
 
 ```python
@@ -324,4 +324,4 @@ You can also find more ways to run inference and deploy your models here:
 2. [Gluon book on fine-tuning](https://www.d2l.ai/chapter_computer-vision/fine-tuning.html)
 3. [Gluon CV transfer learning tutorial](https://gluon-cv.mxnet.io/build/examples_classification/transfer_learning_minc.html)
 4. [Gluon crash course](https://gluon-crash-course.mxnet.io/)
-5. [Gluon CPP inference example](https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/inference/)
\ No newline at end of file
+5. [Gluon CPP inference example](https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/inference/)
diff --git a/docs/python_docs/python/tutorials/getting-started/to-mxnet/index.rst b/docs/python_docs/python/tutorials/getting-started/to-mxnet/index.rst
index cf590d8aceb4..622a2891aa88 100644
--- a/docs/python_docs/python/tutorials/getting-started/to-mxnet/index.rst
+++ b/docs/python_docs/python/tutorials/getting-started/to-mxnet/index.rst
@@ -31,7 +31,7 @@ Comparison Guides
 
    .. card::
       :title: Caffe to MXNet
-      :link: https://mxnet.apache.org/versions/master/faq/caffe.html
+      :link: /api/faq/caffe.html
 
       How to convert Caffe models to MXNet and how to call Caffe operators from MXNet.
 
diff --git a/docs/python_docs/python/tutorials/packages/autograd/index.md b/docs/python_docs/python/tutorials/packages/autograd/index.md
index 84d1d1cece67..586794a696ba 100644
--- a/docs/python_docs/python/tutorials/packages/autograd/index.md
+++ b/docs/python_docs/python/tutorials/packages/autograd/index.md
@@ -29,15 +29,15 @@ Gradients are fundamental to the process of training neural networks, and tell u
 
 Under the hood, neural networks are composed of operators (e.g. sums, products, convolutions, etc) some of which use parameters (e.g. the weights in convolution kernels) for their computation, and it's our job to find the optimal values for these parameters. Gradients lead us to the solution!
 
-Gradients tell us how much a given variable increases or decreases when we change a variable it depends on. What we're interested in is the effect of changing a each parameter on the performance of the network. We usually define performance using a loss metric that we try to minimize, i.e. a metric that tells us how bad the predictions of a network are given ground truth. As an example, for regression we might try to minimize the [L2 loss](http://beta.mxnet.io/api/gluon/_autogen/mxnet.gluon.loss.L2Loss.html?highlight=l2#mxnet.gluon.loss.L2Loss) (also known as the Euclidean distance) between our predictions and true values, and for classification we minimize the [cross entropy loss](http://beta.mxnet.io/api/gluon/_autogen/mxnet.gluon.loss.SoftmaxCrossEntropyLoss.html).
+Gradients tell us how much a given variable increases or decreases when we change a variable it depends on. What we're interested in is the effect of changing a each parameter on the performance of the network. We usually define performance using a loss metric that we try to minimize, i.e. a metric that tells us how bad the predictions of a network are given ground truth. As an example, for regression we might try to minimize the [L2 loss](/api/python/docs/api/gluon/loss/index.html#mxnet.gluon.loss.L2Loss) (also known as the Euclidean distance) between our predictions and true values, and for classification we minimize the [cross entropy loss](/api/python/docs/api/gluon/loss/index.html#mxnet.gluon.loss.SoftmaxCrossEntropyLoss).
 
-Assuming we've calculated the gradient of each parameter with respect to the loss (details in next section), we can then use an optimizer such as [stochastic gradient descent](https://en.wikipedia.org/wiki/Stochastic_gradient_descent) to shift the parameters slightly in the *opposite direction* of the gradient. See [Optimizers](http://beta.mxnet.io/api/gluon-related/mxnet.optimizer.html) for more information on these methods. We repeat the process of calculating gradients and updating parameters over and over again, until the parameters of the network start to stabilize and converge to a good solution.
+Assuming we've calculated the gradient of each parameter with respect to the loss (details in next section), we can then use an optimizer such as [stochastic gradient descent](https://en.wikipedia.org/wiki/Stochastic_gradient_descent) to shift the parameters slightly in the *opposite direction* of the gradient. See [Optimizers](/api/python/docs/api/optimizer/index.html) for more information on these methods. We repeat the process of calculating gradients and updating parameters over and over again, until the parameters of the network start to stabilize and converge to a good solution.
 
 ## How do we calculate gradients?
 
 ### Short Answer:
 
-We differentiate. [MXNet Gluon](http://beta.mxnet.io/api/gluon/index.html) uses Reverse Mode Automatic Differentiation (`autograd`) to backprogate gradients from the loss metric to the network parameters.
+We differentiate. [MXNet Gluon](/api/python/docs/tutorials/packages/gluon/index.html) uses Reverse Mode Automatic Differentiation (`autograd`) to backprogate gradients from the loss metric to the network parameters.
 
 ![forward-backward](/_static/autograd/autograd_forward_backward.png)
 
@@ -159,7 +159,7 @@ print('is_training:', is_training, output)
 
 We called `dropout` while `autograd` was recording this time, so our network was in training mode and we see dropout of the input this time. Since the probability of dropout was 50%, the output is automatically scaled by 1/0.5=2 to preserve the average activation.
 
-We can force some operators to behave as they would during training, even in inference mode. One example is setting `mode='always'` on the [Dropout](https://mxnet.apache.org/api/python/ndarray/ndarray.html?highlight=dropout#mxnet.ndarray.Dropout) operator, but this usage is uncommon.
+We can force some operators to behave as they would during training, even in inference mode. One example is setting `mode='always'` on the [Dropout](/api/python/ndarray/ndarray.html#mxnet.ndarray.Dropout) operator, but this usage is uncommon.
 
 ## Advanced: Skipping the calculation of parameter gradients
 
@@ -196,7 +196,7 @@ print(x.grad)
 
 ## Advanced: Using Python control flow
 
-As mentioned before, one of the main advantages of `autograd` is the ability to automatically calculate gradients of dynamic graphs (i.e. graphs where the operators could be different on every forward pass). One example of this would be applying a tree structured recurrent network to parse a sentence using its parse tree. And we can use Python control flow operators to create a dynamic flow that depends on the data, rather than using [MXNet's control flow operators](https://mxnet.apache.org/versions/master/tutorials/control_flow/ControlFlowTutorial.html).
+As mentioned before, one of the main advantages of `autograd` is the ability to automatically calculate gradients of dynamic graphs (i.e. graphs where the operators could be different on every forward pass). One example of this would be applying a tree structured recurrent network to parse a sentence using its parse tree. And we can use Python control flow operators to create a dynamic flow that depends on the data, rather than using [MXNet's control flow operators](/api/python/docs/tutorials/packages/autograd/index.html#Advanced:-Using-Python-control-flow).
 
 We'll write a function as a toy example of a dynamic network. We'll add an `if` condition and a loop with a variable number of iterations, both of which will depend on the input data. Although these can now be used in static graphs (with conditional operators) it's still much more natural to use native control flow.
 
diff --git a/docs/python_docs/python/tutorials/packages/gluon/blocks/custom_layer_beginners.md b/docs/python_docs/python/tutorials/packages/gluon/blocks/custom_layer_beginners.md
index 01333a7f80a0..933a70bbdf2c 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/blocks/custom_layer_beginners.md
+++ b/docs/python_docs/python/tutorials/packages/gluon/blocks/custom_layer_beginners.md
@@ -30,7 +30,7 @@ The only instance method needed to be implemented is [forward(self, x)](https://
 In the example below, we define a new layer and implement `forward()`  method to normalize input data by fitting it into a range of [0, 1].
 
 ```python
-# Do some initial imports used throughout this tutorial 
+# Do some initial imports used throughout this tutorial
 from __future__ import print_function
 import mxnet as mx
 from mxnet import nd, gluon, autograd
@@ -53,7 +53,7 @@ The rest of methods of the `Block` class are already implemented, and majority o
 
 Looking into implementation of [existing layers](https://mxnet.apache.org/api/python/gluon/nn.html), one may find that more often a block inherits from a [HybridBlock](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/block.py#L428), instead of directly inheriting from `Block`.
 
-The reason for that is that `HybridBlock` allows to write custom layers that can be used in imperative programming as well as in symbolic programming. It is convenient to support both ways, because the imperative programming eases the debugging of the code and the symbolic one provides faster execution speed. You can learn more about the difference between symbolic vs. imperative programming from [this article](https://mxnet.apache.org/versions/master/architecture/program_model.html).
+The reason for that is that `HybridBlock` allows to write custom layers that can be used in imperative programming as well as in symbolic programming. It is convenient to support both ways, because the imperative programming eases the debugging of the code and the symbolic one provides faster execution speed. You can learn more about the difference between symbolic vs. imperative programming from this [deep learning programming paradigm](/api/architecture/program_model) article.
 
 Hybridization is a process that Apache MxNet uses to create a symbolic graph of a forward computation. This allows to increase computation performance by optimizing the computational symbolic graph. Once the symbolic graph is created, Apache MxNet caches and reuses it for subsequent computations.
 
@@ -143,7 +143,7 @@ class NormalizationHybridLayer(gluon.HybridBlock):
                                       shape=scales.shape,
                                       init=mx.init.Constant(scales.asnumpy()),
                                       differentiable=False)
-            
+
     def hybrid_forward(self, F, x, weights, scales):
         normalized_data = F.broadcast_div(F.broadcast_sub(x, F.min(x)), (F.broadcast_sub(F.max(x), F.min(x))))
         weighted_data = F.FullyConnected(normalized_data, weights, num_hidden=self.weights.shape[0], no_bias=True)
@@ -175,14 +175,14 @@ def print_params(title, net):
     """
     print(title)
     hybridlayer_params = {k: v for k, v in net.collect_params().items() if 'normalizationhybridlayer' in k }
-    
+
     for key, value in hybridlayer_params.items():
         print('{} = {}\n'.format(key, value.data()))
 
 net = gluon.nn.HybridSequential()                             # Define a Neural Network as a sequence of hybrid blocks
 with net.name_scope():                                        # Used to disambiguate saving and loading net parameters
     net.add(Dense(5))                                         # Add Dense layer with 5 neurons
-    net.add(NormalizationHybridLayer(hidden_units=5, 
+    net.add(NormalizationHybridLayer(hidden_units=5,
                                      scales = nd.array([2]))) # Add our custom layer
     net.add(Dense(1))                                         # Add Dense layer with 1 neurons
 
@@ -195,15 +195,15 @@ label = nd.random_uniform(low=-1, high=1, shape=(5, 1))
 
 mse_loss = gluon.loss.L2Loss()                                # Mean squared error between output and label
 trainer = gluon.Trainer(net.collect_params(),                 # Init trainer with Stochastic Gradient Descent (sgd) optimization method and parameters for it
-                        'sgd', 
+                        'sgd',
                         {'learning_rate': 0.1, 'momentum': 0.9 })
-                        
-with autograd.record():                                       # Autograd records computations done on NDArrays inside "with" block 
+
+with autograd.record():                                       # Autograd records computations done on NDArrays inside "with" block
     output = net(input)                                       # Run forward propogation
-    
-    print_params("=========== Parameters after forward pass ===========\n", net)    
+
+    print_params("=========== Parameters after forward pass ===========\n", net)
     loss = mse_loss(output, label)                            # Calculate MSE
-    
+
 loss.backward()                                               # Backward computes gradients and stores them as a separate array within each NDArray in .grad field
 trainer.step(input.shape[0])                                  # Trainer updates parameters of every block, using .grad field using oprimization method (sgd in this example)
                                                               # We provide batch size that is used as a divider in cost function formula
@@ -213,7 +213,7 @@ print_params("=========== Parameters after backward pass ===========\n", net)
 ```python
 =========== Parameters after forward pass ===========
 
-hybridsequential94_normalizationhybridlayer0_weights = 
+hybridsequential94_normalizationhybridlayer0_weights =
 [[-0.3983642  -0.505708   -0.02425683 -0.3133553  -0.35161012]
  [ 0.6467543   0.3918715  -0.6154656  -0.20702496 -0.4243446 ]
  [ 0.6077331   0.03922009  0.13425875  0.5729856  -0.14446527]
@@ -221,13 +221,13 @@ hybridsequential94_normalizationhybridlayer0_weights =
  [-0.39846328  0.22245121  0.13075739  0.33387476 -0.10088372]]
 <NDArray 5x5 @cpu(0)>
 
-hybridsequential94_normalizationhybridlayer0_scales = 
+hybridsequential94_normalizationhybridlayer0_scales =
 [2.]
 <NDArray 1 @cpu(0)>
 
 =========== Parameters after backward pass ===========
 
-hybridsequential94_normalizationhybridlayer0_weights = 
+hybridsequential94_normalizationhybridlayer0_weights =
 [[-0.29839832 -0.47213346  0.08348035 -0.2324698  -0.27368504]
  [ 0.76268613  0.43080837 -0.49052125 -0.11322092 -0.3339738 ]
  [ 0.48665082 -0.00144657  0.00376363  0.47501418 -0.23885089]
@@ -235,7 +235,7 @@ hybridsequential94_normalizationhybridlayer0_weights =
  [-0.44946212  0.20532274  0.07579394  0.29261002 -0.14063817]]
 <NDArray 5x5 @cpu(0)>
 
-hybridsequential94_normalizationhybridlayer0_scales = 
+hybridsequential94_normalizationhybridlayer0_scales =
 [2.]
 <NDArray 1 @cpu(0)>
 ```
diff --git a/docs/python_docs/python/tutorials/packages/gluon/blocks/hybridize.md b/docs/python_docs/python/tutorials/packages/gluon/blocks/hybridize.md
index 10d35ecde650..1dd778d74abd 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/blocks/hybridize.md
+++ b/docs/python_docs/python/tutorials/packages/gluon/blocks/hybridize.md
@@ -219,7 +219,7 @@ We can see that the three lines of print statements defined in the `hybrid_forwa
 
 ## Key differences and limitations of hybridization
 
-The difference between a purely imperative `Block` and hybridizable `HybridBlock` can superficially appear to be simply the injection of the `F` function space (resolving to [`mx.nd`](https://mxnet.apache.org/api/python/docs/api/ndarray/index.html) or [`mx.sym`](https://mxnet.apache.org/api/python/docs/api/symbol/index.html)) in the forward function that is renamed from `forward` to `hybrid_forward`. However there are some limitations that apply when using hybrid blocks. In the following section we will review the main differences, giving example of code snippets that generate errors when such blocks get hybridized.
+The difference between a purely imperative `Block` and hybridizable `HybridBlock` can superficially appear to be simply the injection of the `F` function space (resolving to [`mx.nd`](/api/python/docs/api/ndarray/index.html) or [`mx.sym`](/api/python/docs/api/symbol/index.html)) in the forward function that is renamed from `forward` to `hybrid_forward`. However there are some limitations that apply when using hybrid blocks. In the following section we will review the main differences, giving example of code snippets that generate errors when such blocks get hybridized.
 
 ### Indexing
 
@@ -234,7 +234,7 @@ Would generate the following error:
 
 `TypeError: Symbol only support integer index to fetch i-th output`
 
-There are however several operators that can help you with array manipulations like: [`F.split`](https://mxnet.apache.org/api/python/docs/api/ndarray/_autogen/mxnet.ndarray.split.html#mxnet.ndarray.split), [`F.slice`](https://mxnet.apache.org/api/python/docs/api/ndarray/_autogen/mxnet.ndarray.slice.html#mxnet.ndarray.slice), [`F.take`](https://mxnet.apache.org/api/python/docs/api/ndarray/_autogen/mxnet.ndarray.take.html),[`F.pick`](https://mxnet.apache.org/api/python/docs/api/ndarray/_autogen/mxnet.ndarray.pick.html), [`F.where`](https://mxnet.apache.org/api/python/docs/api/ndarray/_autogen/mxnet.ndarray.where.html), [`F.reshape`](https://mxnet.apache.org/api/python/docs/api/ndarray/_autogen/mxnet.ndarray.reshape.html) or [`F.reshape_like`](https://mxnet.apache.org/api/python/docs/api/ndarray/_autogen/mxnet.ndarray.reshape_like.html).
+There are however several operators that can help you with array manipulations like: [`F.split`](/api/python/docs/api/ndarray/ndarray.html#mxnet.ndarray.split), [`F.slice`](/api/python/docs/api/ndarray/ndarray.html#mxnet.ndarray.slice), [`F.take`](/api/python/docs/api/ndarray/ndarray.html#mxnet.ndarray.take),[`F.pick`](/api/python/docs/api/ndarray/ndarray.html#mxnet.ndarray.pick), [`F.where`](/api/python/docs/api/ndarray/ndarray.html#mxnet.ndarray.where), [`F.reshape`](/api/python/docs/api/ndarray/ndarray.html#mxnet.ndarray.reshape) or [`F.reshape_like`](/api/python/docs/api/ndarray/ndarray.html#mxnet.ndarray.reshape_like).
 
 ### Data Type
 
@@ -277,10 +277,10 @@ def hybrid_forward(self, F, x):
 
 Trying to access the shape of a tensor in a hybridized block would result in this error: `AttributeError: 'Symbol' object has no attribute 'shape'`.
 
-Again, you cannot use the shape of the symbol at runtime as symbols only describe operations and not the underlying data they operate on. 
-Note: This will change in the future as Apache MXNet will support [dynamic shape inference](https://cwiki.apache.org/confluence/display/MXNET/Dynamic+shape), and the shapes of symbols will be symbols themselves 
+Again, you cannot use the shape of the symbol at runtime as symbols only describe operations and not the underlying data they operate on.
+Note: This will change in the future as Apache MXNet will support [dynamic shape inference](https://cwiki.apache.org/confluence/display/MXNET/Dynamic+shape), and the shapes of symbols will be symbols themselves
 
-There are also a lot of operators that support special indices to help with most of the use-cases where you would want to access the shape information. For example, `F.reshape(x, (0,0,-1))` will keep the first two dimensions unchanged and collapse all further dimensions into the third dimension. See the documentation of the [`F.reshape`](https://mxnet.apache.org/api/python/docs/api/ndarray/_autogen/mxnet.ndarray.reshape.html) for more details.
+There are also a lot of operators that support special indices to help with most of the use-cases where you would want to access the shape information. For example, `F.reshape(x, (0,0,-1))` will keep the first two dimensions unchanged and collapse all further dimensions into the third dimension. See the documentation of the [F.reshape](/api/python/docs/api/ndarray/ndarray.html#mxnet.ndarray.reshape) for more details.
 
 ### Item assignment
 
@@ -294,7 +294,7 @@ def hybrid_forward(self, F, x):
 
 Would get you this error `TypeError: 'Symbol' object does not support item assignment`.
 
-Direct item assignment is not possible in symbolic graph since it needs to be part of a computational graph. One way is to use add more inputs to your graph and use masking or the [`F.where`](https://mxnet.apache.org/api/python/docs/api/ndarray/_autogen/mxnet.ndarray.where.html) operator.
+Direct item assignment is not possible in symbolic graph since it needs to be part of a computational graph. One way is to use add more inputs to your graph and use masking or the [F.where](/api/python/docs/api/ndarray/ndarray.html#mxnet.ndarray.where) operator.
 
 e.g to set the first element to 2 you can do:
 
diff --git a/docs/python_docs/python/tutorials/packages/gluon/blocks/init.md b/docs/python_docs/python/tutorials/packages/gluon/blocks/init.md
index 49df0a0795d6..ec9cd380acb0 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/blocks/init.md
+++ b/docs/python_docs/python/tutorials/packages/gluon/blocks/init.md
@@ -27,7 +27,7 @@ work:
   dimensionality.
 * We added layers without regard to the output dimension of the previous layer.
 * We even 'initialized' these parameters without knowing how many parameters
-  were were to initialize.
+  we were going to initialize.
 
 All of those things sound impossible and indeed, they are. After all, there's
 no way MXNet (or any other framework for that matter) could predict what the
@@ -36,7 +36,7 @@ convolutional networks and images this problem will become even more pertinent,
 since the input dimensionality (i.e. the resolution of an image) will affect
 the dimensionality of subsequent layers. The ability to
 determine parameter dimensionality during run-time rather than at coding time
-greatly simplifies the process of doing deep learning. 
+greatly simplifies the process of doing deep learning.
 
 ## Instantiating a Network
 
@@ -65,10 +65,10 @@ accessing the parameters, that's exactly what happens.
 print(net.collect_params())
 ```
 
-You'll notice `None` here in each `Dense` layer. This absense of value is how
+You'll notice `None` here in each `Dense` layer. This absence of value is how
 MXNet keeps track of unspecified dimensionality. In particular, trying to access
 `net[0].weight.data()` at this point would trigger a runtime error stating that
-the network needs initializing before it can do anything. 
+the network needs initializing before it can do anything.
 
 Note that if we did want to specify dimensionality, we could have done so by
 using the kwarg `in_units`, e.g. `Dense(256, activiation='relu', in_units=20)`.
@@ -318,5 +318,5 @@ cases you should now be aware of include custom initialization and tied paramete
 
 ## Recommended Next Steps
 
-* Check out the [API Docs](https://mxnet.apache.org/versions/master/api/python/optimization/optimization.html) on initialization for a list of available initialization methods.
-* See [this tutorial](https://mxnet.apache.org/versions/master/tutorials/gluon/naming.html) for more information on Gluon Parameters.
+* Check out the [API Docs](/api/python/docs/api/optimizer/index.html) on initialization for a list of available initialization methods.
+* See [this tutorial](/api/python/docs/tutorials/packages/gluon/blocks/naming.html) for more information on Gluon Parameters.
diff --git a/docs/python_docs/python/tutorials/packages/gluon/blocks/nn.md b/docs/python_docs/python/tutorials/packages/gluon/blocks/nn.md
index 9639c7c38847..a3324fea7318 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/blocks/nn.md
+++ b/docs/python_docs/python/tutorials/packages/gluon/blocks/nn.md
@@ -20,7 +20,7 @@
 <!-- adapted from diveintodeeplearning -->
 
 As network complexity increases, we move from designing single to entire layers
-of neurons. 
+of neurons.
 
 Neural network designs like
 [ResNet-152](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/He_Deep_Residual_Learning_CVPR_2016_paper.pdf)
@@ -52,7 +52,7 @@ net(x)
 
 This generates a network with a hidden layer of $256$ units, followed by a ReLU
 activation and another $10$ units governing the output. In particular, we used
-the [`nn.Sequential`](/api/gluon/_autogen/mxnet.gluon.nn.Sequential.html#mxnet.gluon.nn.Sequential)
+the [`nn.Sequential`](/api/python/docs/api/gluon/nn/index.html#mxnet.gluon.nn.Sequential)
 constructor to generate an empty network into which we then inserted both
 layers. What exactly happens inside `nn.Sequential`
 has remained rather mysterious so far. In the following we will see that this
@@ -81,11 +81,11 @@ layers to defining blocks (of one or more layers):
 
 ## A Sequential Block
 
-The [`Block`](/api/gluon/nn.html#blocks) class is a generic component
+The [`Block`](/api/python/docs/api/gluon/nn/index.html#mxnet.gluon.nn.Block) class is a generic component
 describing data flow. When the data flows through a sequence of blocks, each
 block applied to the output of the one before with the first block being
 applied on the input data itself, we have a special kind of block, namely the
-`Sequential` block. 
+`Sequential` block.
 
 `Sequential` has helper methods to manage the sequence, with `add` being the
 main one of interest allowing you to append blocks in sequence. Once the
@@ -172,8 +172,8 @@ initializes all of the Block-related parameters and then constructs the
 requisite layers. This attaches the coresponding layers and the required
 parameters to the class. Note that there is no need to define a backpropagation
 method in the class. The system automatically generates the `backward` method
-needed for back propagation by automatically finding the gradient (see the guide on [autograd](guide/packages/autograd.html)). The same
-applies to the [`initialize`](/api/gluon/_autogen/mxnet.gluon.nn.Block.initialize.html) method, which is generated automatically. Let's try
+needed for back propagation by automatically finding the gradient (see the tutorial on [autograd](/api/python/docs/tutorials/packages/autograd/index.html)). The same
+applies to the [`initialize`](/api/python/docs/api/gluon/nn/index.html#mxnet.gluon.nn.Block.initialize) method, which is generated automatically. Let's try
 this out:
 
 ```{.python .input  n=2}
@@ -193,12 +193,12 @@ great flexibility.
 ## Coding with `Blocks`
 
 ### Blocks
-The [`Sequential`](/api/gluon/_autogen/mxnet.gluon.nn.Sequential.html) class
+The [`Sequential`](/api/python/docs/api/gluon/nn/index.html#mxnet.gluon.nn.Sequential) class
 can make model construction easier and does not require you to define the
 `forward` method; however, directly inheriting from
-its parent class, [`Block`](/api/gluon/mxnet.gluon.nn.Block.html), can greatly
+its parent class, [`Block`](/api/python/docs/api/gluon/nn/index.html#mxnet.gluon.nn.Block), can greatly
 expand the flexibility of model construction. For example, implementing the
-`forward` method means you can introduce control flow in the network. 
+`forward` method means you can introduce control flow in the network.
 
 ### Constant parameters
 Now we'd like to introduce the notation of a *constant* parameter. These are
@@ -298,7 +298,7 @@ After all, we have lots of dictionary lookups, code execution, and lots of
 other Pythonic things going on in what is supposed to be a high performance
 deep learning library. The problems of Python's [Global Interpreter
 Lock](https://wiki.python.org/moin/GlobalInterpreterLock) are well
-known. 
+known.
 
 In the context of deep learning, we often have highly performant GPUs that
 depend on CPUs running Python to tell them what to do. This mismatch can
@@ -306,8 +306,8 @@ manifest in the form of GPU starvation when the CPUs can not provide
 instruction fast enough. We can improve this situation by deferring to a more
 performant language instead of Python when possible.
 
-Gluon does this by allowing for [Hybridization](hybridize.md). In it, the
+Gluon does this by allowing for [Hybridization](hybridize.html). In it, the
 Python interpreter executes the block the first time it's invoked. The Gluon
 runtime records what is happening and the next time around it short circuits
 any calls to Python. This can accelerate things considerably in some cases but
-care needs to be taken with [control flow](../../crash-course/3-autograd.md).
+care needs to be taken with [control flow](/api/python/docs/tutorials/packages/autograd/index.html#Advanced:-Using-Python-control-flow).
diff --git a/docs/python_docs/python/tutorials/packages/gluon/blocks/parameters.md b/docs/python_docs/python/tutorials/packages/gluon/blocks/parameters.md
index 99f3b4a5f1e5..05461e9929a6 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/blocks/parameters.md
+++ b/docs/python_docs/python/tutorials/packages/gluon/blocks/parameters.md
@@ -19,7 +19,7 @@
 
 <!-- adapted from diveintodeeplearning -->
 
-The ultimate goal of training deep neural networks is finding good parameter values for a given architecture. The [`nn.Sequential`](https://mxnet.apache.org/api/python/docs/api/gluon/_autogen/mxnet.gluon.nn.Sequential.html#mxnet.gluon.nn.Sequential) class is a perfect tool to work with standard models. However, very few models are entirely standard, and most scientists want to build novel things, which requires working with model parameters.
+The ultimate goal of training deep neural networks is finding good parameter values for a given architecture. The [nn.Sequential](/api/python/docs/api/gluon/nn/index.html#mxnet.gluon.nn.Sequential) class is a perfect tool to work with standard models. However, very few models are entirely standard, and most scientists want to build novel things, which requires working with model parameters.
 
 This section shows how to manipulate parameters. In particular we will cover the following aspects:
 
@@ -74,7 +74,7 @@ print(net[0].params['dense0_weight'].data())
 
 Note that the weights are nonzero as they were randomly initialized when we constructed the network.
 
-[`data`](https://mxnet.apache.org/api/python/docs/api/gluon/_autogen/mxnet.gluon.Parameter.data.html) is not the only method that we can invoke. For instance, we can compute the gradient with respect to the parameters. It has the same shape as the weight. However, since we did not invoke backpropagation yet, the values are all 0.
+[data](/api/python/docs/api/gluon/parameter.html#mxnet.gluon.Parameter.data) is not the only method that we can invoke. For instance, we can compute the gradient with respect to the parameters. It has the same shape as the weight. However, since we did not invoke backpropagation yet, the values are all 0.
 
 ```{.python .input  n=5}
 net[0].weight.grad()
@@ -82,7 +82,7 @@ net[0].weight.grad()
 
 ### All Parameters at Once
 
-Accessing parameters as described above can be a bit tedious, in particular if we have more complex blocks, or blocks of blocks (or even blocks of blocks of blocks), since we need to walk through the entire tree in reverse order to learn how the blocks were constructed. To avoid this, blocks come with a method [`collect_params`](https://mxnet.apache.org/api/python/docs/api/gluon/_autogen/mxnet.gluon.nn.Block.collect_params.html#mxnet.gluon.nn.Block.collect_params) which grabs all parameters of a network in one dictionary such that we can traverse it with ease. It does so by iterating over all constituents of a block and calls `collect_params` on sub-blocks as needed. To see the difference, consider the following:
+Accessing parameters as described above can be a bit tedious, in particular if we have more complex blocks, or blocks of blocks (or even blocks of blocks of blocks), since we need to walk through the entire tree in reverse order to learn how the blocks were constructed. To avoid this, blocks come with a method [collect_params](/api/python/docs/api/gluon/block.html#mxnet.gluon.Block.collect_params) which grabs all parameters of a network in one dictionary such that we can traverse it with ease. It does so by iterating over all constituents of a block and calls `collect_params` on sub-blocks as needed. To see the difference, consider the following:
 
 ```{.python .input  n=6}
 # Parameters only for the first layer
@@ -143,7 +143,7 @@ rgnet[0][1][0].bias.data()
 
 ### Saving and loading parameters
 
-In order to save parameters, we can use [`save_parameters`](https://mxnet.apache.org/api/python/docs/api/gluon/_autogen/mxnet.gluon.nn.Block.save_parameters.html#mxnet.gluon.nn.Block.save_parameters) method on the whole network or a particular subblock. The only parameter that is needed is the `file_name`. In a similar way, we can load parameters back from the file. We use [`load_parameters`](https://mxnet.apache.org/api/python/docs/api/gluon/_autogen/mxnet.gluon.nn.Block.load_parameters.html#mxnet.gluon.nn.Block.load_parameters) method for that:
+In order to save parameters, we can use [save_parameters](/api/python/docs/api/gluon/block.html#mxnet.gluon.Block.save_parameters) method on the whole network or a particular subblock. The only parameter that is needed is the `file_name`. In a similar way, we can load parameters back from the file. We use [load_parameters](/api/python/docs/api/gluon/block.html#mxnet.gluon.Block.load_parameters) method for that:
 
 ```{.python .input}
 rgnet.save_parameters('model.params')
@@ -152,7 +152,7 @@ rgnet.load_parameters('model.params')
 
 ## Parameter Initialization
 
-Now that we know how to access the parameters, let's look at how to initialize them properly. By default, MXNet initializes the weight matrices uniformly by drawing from $U[-0.07, 0.07]$ and the bias parameters are all set to $0$. However, we often need to use other methods to initialize the weights. MXNet's [`init`](https://mxnet.apache.org/api/python/docs/api/gluon-related/mxnet.initializer.html?#module-mxnet.initializer) module provides a variety of preset initialization methods, but if we want something unusual, we need to do a bit of extra work.
+Now that we know how to access the parameters, let's look at how to initialize them properly. By default, MXNet initializes the weight matrices uniformly by drawing from $U[-0.07, 0.07]$ and the bias parameters are all set to $0$. However, we often need to use other methods to initialize the weights. MXNet's [init](/api/python/docs/api/initializer/index.html#mxnet.initializer) module provides a variety of preset initialization methods, but if we want something unusual, we need to do a bit of extra work.
 
 ### Built-in Initialization
 
@@ -165,14 +165,14 @@ net.initialize(init=init.Normal(sigma=0.01), force_reinit=True)
 net[0].weight.data()[0]
 ```
 
-If we wanted to initialize all parameters to 1, we could do this simply by changing the initializer to [`Constant(1)`](https://mxnet.apache.org/api/python/docs/api/gluon-related/_autogen/mxnet.initializer.Constant.html#mxnet.initializer.Constant).
+If we wanted to initialize all parameters to 1, we could do this simply by changing the initializer to [Constant(1)](/api/python/docs/api/initializer/index.html#mxnet.initializer.Constant).
 
 ```{.python .input  n=10}
 net.initialize(init=init.Constant(1), force_reinit=True)
 net[0].weight.data()[0]
 ```
 
-If we want to initialize only a specific parameter in a different manner, we can simply set the initializer only for the appropriate subblock (or parameter) for that matter. For instance, below we initialize the second layer to a constant value of 42 and we use the [`Xavier`](https://mxnet.apache.org/api/python/docs/api/gluon-related/_autogen/mxnet.initializer.Xavier.html#mxnet.initializer.Xavier) initializer for the weights of the first layer.
+If we want to initialize only a specific parameter in a different manner, we can simply set the initializer only for the appropriate subblock (or parameter) for that matter. For instance, below we initialize the second layer to a constant value of 42 and we use the [Xavier](/api/python/docs/api/initializer/index.html#mxnet.initializer.Xavier) initializer for the weights of the first layer.
 
 ```{.python .input  n=11}
 net[1].initialize(init=init.Constant(42), force_reinit=True)
@@ -183,7 +183,7 @@ print(net[0].weight.data()[0])
 
 ### Custom Initialization
 
-Sometimes, the initialization methods we need are not provided in the `init` module. If this is the case, we can implement a subclass of the [`Initializer`](https://mxnet.apache.org/api/python/docs/api/gluon-related/_autogen/mxnet.initializer.Initializer.html#mxnet.initializer.Initializer) class so that we can use it like any other initialization method. Usually, we only need to implement the `_init_weight` method and modify the incoming NDArray according to the initial result. In the example below, we pick a nontrivial distribution, just to prove the point. We draw the coefficients from the following distribution:
+Sometimes, the initialization methods we need are not provided in the `init` module. If this is the case, we can implement a subclass of the [Initializer](/api/python/docs/api/initializer/index.html#mxnet.initializer.Initializer) class so that we can use it like any other initialization method. Usually, we only need to implement the `_init_weight` method and modify the incoming NDArray according to the initial result. In the example below, we pick a nontrivial distribution, just to prove the point. We draw the coefficients from the following distribution:
 
 $$
 \begin{aligned}
@@ -206,7 +206,7 @@ net.initialize(MyInit(), force_reinit=True)
 net[0].weight.data()[0]
 ```
 
-If even this functionality is insufficient, we can set parameters directly. Since `data()` returns an NDArray we can access it just like any other matrix. A note for advanced users - if you want to adjust parameters within an [`autograd`](https://mxnet.apache.org/api/python/docs/api/gluon-related/mxnet.autograd.html?#module-mxnet.autograd) scope you need to use [`set_data`](https://mxnet.apache.org/api/python/docs/api/gluon/_autogen/mxnet.gluon.Parameter.set_data.html#mxnet.gluon.Parameter.set_data) to avoid confusing the automatic differentiation mechanics.
+If even this functionality is insufficient, we can set parameters directly. Since `data()` returns an NDArray we can access it just like any other matrix. A note for advanced users - if you want to adjust parameters within an [autograd](/api/python/docs/api/autograd/index.html) scope you need to use [set_data](/api/python/docs/api/gluon/parameter.html#mxnet.gluon.Parameter.set_data) to avoid confusing the automatic differentiation mechanics.
 
 ```{.python .input  n=13}
 net[0].weight.data()[:] += 1
@@ -240,4 +240,4 @@ net[1].weight.data()[0,0] = 100
 print(net[1].weight.data()[0] == net[2].weight.data()[0])
 ```
 
-The above example shows that the parameters of the second and third layer are tied. They are identical rather than just being equal. That is, by changing one of the parameters the other one changes, too. What happens to the gradients is quite ingenious. Since the model parameters contain gradients, the gradients of the second hidden layer and the third hidden layer are accumulated in the [`shared.params.grad()`](https://mxnet.apache.org/api/python/docs/api/gluon/_autogen/mxnet.gluon.Parameter.grad.html) during backpropagation.
+The above example shows that the parameters of the second and third layer are tied. They are identical rather than just being equal. That is, by changing one of the parameters the other one changes, too. What happens to the gradients is quite ingenious. Since the model parameters contain gradients, the gradients of the second hidden layer and the third hidden layer are accumulated in the [shared.params.grad()](/api/python/docs/api/gluon/parameter.html#mxnet.gluon.Parameter.grad) during backpropagation.
diff --git a/docs/python_docs/python/tutorials/packages/gluon/image/image-augmentation.md b/docs/python_docs/python/tutorials/packages/gluon/image/image-augmentation.md
index a2cdbb7cc97a..70be781be3d6 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/image/image-augmentation.md
+++ b/docs/python_docs/python/tutorials/packages/gluon/image/image-augmentation.md
@@ -22,19 +22,19 @@ training data sets by making a series of random changes to the training images
 to produce similar, but different, training examples. Given its popularity in
 computer vision, the `mxnet.gluon.data.vision.transforms` model provides
 multiple pre-defined image augmentation methods. In this section we will briefly
-go through this module.  
+go through this module.
 
 First, import the module required for this section.
 
-```{.python .input  n=1}
+```python
 from matplotlib import pyplot as plt
 from mxnet import image
-from mxnet.gluon import data as gdata, utils 
+from mxnet.gluon import data as gdata, utils
 ```
 
 Then read the sample $400\times 500$ image.
 
-```{.python .input  n=2}
+```python
 utils.download('https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/cat.jpg')
 img = image.imread('cat.jpg')
 plt.imshow(img.asnumpy())
@@ -43,7 +43,7 @@ plt.show()
 
 In addition, we define a function to draw a list of images.
 
-```{.python .input  n=3}
+```python
 def show_images(imgs, num_rows, num_cols, scale=2):
     figsize = (num_cols * scale, num_rows * scale)
     _, axes = plt.subplots(num_rows, num_cols, figsize=figsize)
@@ -60,7 +60,7 @@ easier for us to observe the effect of image augmentation, we next define the
 auxiliary function `apply`. This function runs the image augmentation method
 `aug` multiple times on the input image `img` and shows all results.
 
-```{.python .input  n=4}
+```python
 def apply(img, aug, num_rows=2, num_cols=4, scale=3):
     Y = [aug(img) for _ in range(num_rows * num_cols)]
     show_images(Y, num_rows, num_cols, scale)
@@ -74,7 +74,7 @@ of image augmentation. Next, we use the `transforms` module to create the
 `RandomFlipLeftRight` instance, which introduces a 50% chance that the image is
 flipped left and right.
 
-```{.python .input  n=5}
+```python
 apply(img, gdata.vision.transforms.RandomFlipLeftRight())
 ```
 
@@ -83,14 +83,12 @@ However, at least for this example image, flipping up and down does not hinder
 recognition. Next, we create a `RandomFlipTopBottom` instance for a 50% chance
 of flipping the image up and down.
 
-```{.python .input  n=6}
+```python
 apply(img, gdata.vision.transforms.RandomFlipTopBottom())
 ```
 
 In the example image we used, the cat is in the middle of the image, but this
-may not be the case for all images. In the [“Pooling
-Layer”](../chapter_convolutional-neural-networks/pooling.md) section, we
-explained that the pooling layer can reduce the sensitivity of the convolutional
+may not be the case for all images. In the [Pooling Layer](https://d2l.ai/chapter_convolutional-neural-networks/pooling.html) section of the d2l.ai book, we explain that the pooling layer can reduce the sensitivity of the convolutional
 layer to the target location. In addition, we can make objects appear at
 different positions in the image in different proportions by randomly cropping
 the image. This can also reduce the sensitivity of the model to the target
diff --git a/docs/python_docs/python/tutorials/packages/gluon/image/pretrained_models.md b/docs/python_docs/python/tutorials/packages/gluon/image/pretrained_models.md
index a928f949260b..fca73ad46aff 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/image/pretrained_models.md
+++ b/docs/python_docs/python/tutorials/packages/gluon/image/pretrained_models.md
@@ -39,18 +39,18 @@ import numpy as np
 
 ## Loading the model
 
-The [Gluon Model Zoo](https://mxnet.apache.org/api/python/gluon/model_zoo.html) provides a collection of off-the-shelf models. You can get the ImageNet pre-trained model by using `pretrained=True`. 
+The [Gluon Model Zoo](https://mxnet.apache.org/api/python/gluon/model_zoo.html) provides a collection of off-the-shelf models. You can get the ImageNet pre-trained model by using `pretrained=True`.
 If you want to train on your own classification problem from scratch, you can get an untrained network with a specific number of classes using the `classes` parameter: for example `net = vision.resnet18_v1(classes=10)`. However note that you cannot use the `pretrained` and `classes` parameter at the same time. If you want to use pre-trained weights as initialization of your network except for the last layer, have a look at the last section of this tutorial.
 
 We can specify the *context* where we want to run the model: the default behavior is to use a CPU context. There are two reasons for this:
 * First, this will allow you to test the notebook even if your machine is not equipped with a GPU :)
 * Second, we're going to predict a single image and we don't have any specific performance requirements. For production applications where you'd want to predict large batches of images with the best possible throughput, a GPU could definitely be the way to go.
-* If you want to use a GPU, make sure you have pip installed the right version of mxnet, or you will get an error when using the `mx.gpu()` context. Refer to the [install instructions](https://mxnet.apache.org/install/index.html)
+* If you want to use a GPU, make sure you have pip installed the right version of mxnet, or you will get an error when using the `mx.gpu()` context. Refer to the [install instructions](/get_started)
 
 
 ```python
-# We set the context to CPU, you can switch to GPU if you have one and installed a compatible version of MXNet 
-ctx = mx.cpu() 
+# We set the context to CPU, you can switch to GPU if you have one and installed a compatible version of MXNet
+ctx = mx.cpu()
 ```
 
 
@@ -141,7 +141,7 @@ def transform(image):
     cropped, crop_info = mx.image.center_crop(resized, (224, 224))
     normalized = mx.image.color_normalize(cropped.astype(np.float32)/255,
                                       mean=mx.nd.array([0.485, 0.456, 0.406]),
-                                      std=mx.nd.array([0.229, 0.224, 0.225])) 
+                                      std=mx.nd.array([0.229, 0.224, 0.225]))
     # the network expect batches of the form (N,3,224,224)
     transposed = normalized.transpose((2,0,1))  # Transposing from (224, 224, 3) to (3, 224, 224)
     batchified = transposed.expand_dims(axis=0) # change the shape from (3, 224, 224) to (1, 3, 224, 224)
@@ -176,7 +176,7 @@ for index in top_pred:
 ```
 
 
-Let's turn this into a function. Our parameters are an image, a model, a list of categories and the number of top categories we'd like to print. 
+Let's turn this into a function. Our parameters are an image, a model, a list of categories and the number of top categories we'd like to print.
 
 
 ```python
@@ -237,7 +237,7 @@ print(resnet18.output)
 Now you can train your model on your new data using the pre-trained weights as initialization. This is called transfer learning and it has proved to be very useful especially in the cases where you only have access to a small dataset. Your network will have already learned how to perform general pattern detection and feature extraction on the larger dataset.
 You can learn more about transfer learning and fine-tuning with MXNet in these tutorials:
 - [Transferring knowledge through fine-tuning](http://gluon.mxnet.io/chapter08_computer-vision/fine-tuning.html)
-- [Fine Tuning an ONNX Model](https://mxnet.apache.org/tutorials/onnx/fine_tuning_gluon.html)
+- [Fine Tuning an ONNX Model](/api/python/docs/tutorials/packages/onnx/fine_tuning_gluon.html)
 
 
 That's it! Explore the model zoo, have fun with pre-trained models!
diff --git a/docs/python_docs/python/tutorials/packages/gluon/index.rst b/docs/python_docs/python/tutorials/packages/gluon/index.rst
index 2212b93af578..c41bf3d9d116 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/index.rst
+++ b/docs/python_docs/python/tutorials/packages/gluon/index.rst
@@ -178,31 +178,35 @@ Advanced Topics
 
    .. card::
       :title: Naming
-      :link: naming.html
+      :link: blocks/naming.html
 
       Best practices for the naming of things.
 
    .. card::
       :title: Custom Layers
-      :link: custom-layer.html
+      :link: blocks/custom-layer.html
 
       A guide to implementing custom layers.
 
    .. card::
       :title: Custom Operators
-      :link: https://mxnet.apache.org/versions/master/tutorials/gluon/customop.html
+      :link: ../../extend/customop.html
 
       Building custom operators with numpy.
 
+..
+<-- tutorial missing -->
    .. card::
       :title: Custom Loss
       :link: custom-loss/custom-loss.html
 
       A guide to implementing custom losses.
 
+..
+
    .. card::
       :title: Gotchas using NumPy in Apache MXNet
-      :link: https://mxnet.apache.org/versions/master/tutorials/gluon/gotchas_numpy_in_mxnet.html
+      :link: ../ndarray/gotchas_numpy_in_mxnet.html
 
       Common misconceptions when using NumPy in Apache MXNet.
 
diff --git a/docs/python_docs/python/tutorials/packages/gluon/loss/custom-loss.md b/docs/python_docs/python/tutorials/packages/gluon/loss/custom-loss.md
index 2357e80ab369..6c31ecbe9ee5 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/loss/custom-loss.md
+++ b/docs/python_docs/python/tutorials/packages/gluon/loss/custom-loss.md
@@ -17,11 +17,11 @@
 
 # Custom Loss Blocks
 
-All neural networks need a loss function for training. A loss function is a quantative measure of how bad the predictions of the network are when compared to ground truth labels. Given this score, a network can improve by iteratively updating its weights to minimise this loss. Some tasks use a combination of multiple loss functions, but often you'll just use one. MXNet Gluon provides a number of the most commonly used loss functions, and you'll choose certain functions depending on your network and task. Some common task and loss function pairs include:
+All neural networks need a loss function for training. A loss function is a quantitive measure of how bad the predictions of the network are when compared to ground truth labels. Given this score, a network can improve by iteratively updating its weights to minimise this loss. Some tasks use a combination of multiple loss functions, but often you'll just use one. MXNet Gluon provides a number of the most commonly used loss functions, and you'll choose certain functions depending on your network and task. Some common task and loss function pairs include:
 
-- Regression: [L1Loss](/api/python/docs/api/gluon/_autogen/mxnet.gluon.loss.L1Loss.html), [L2Loss](/api/python/docs/api/gluon/_autogen/mxnet.gluon.loss.L2Loss.html) 
-- Classification: [SigmoidBinaryCrossEntropyLoss](/api/python/docs/api/gluon/_autogen/mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss.html), [SoftmaxBinaryCrossEntropyLoss](/api/python/docs/api/gluon/_autogen/mxnet.gluon.loss.SoftmaxBinaryCrossEntropyLoss.html) 
-- Embeddings: [HingeLoss](/api/python/docs/api/gluon/_autogen/mxnet.gluon.loss.HingeLoss.html)
+- Regression: [L1Loss](/api/python/docs/api/gluon/loss/index.html#mxnet.gluon.loss.L1Loss), [L2Loss](/api/python/docs/api/gluon/loss/index.html#mxnet.gluon.loss.L2Loss)
+- Classification: [SigmoidBinaryCrossEntropyLoss](/api/python/docs/api/gluon/loss/index.html#mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss), [SoftmaxBinaryCrossEntropyLoss](/api/python/docs/api/gluon/loss/index.html#mxnet.gluon.loss.SoftmaxBinaryCrossEntropyLoss)
+- Embeddings: [HingeLoss](/api/python/docs/api/gluon/loss/index.html#mxnet.gluon.loss.HingeLoss)
 
 However, we may sometimes want to solve problems that require customized loss functions; this tutorial shows how we can do that in Gluon. We will implement contrastive loss which is typically used in Siamese networks.
 
@@ -35,17 +35,17 @@ import random
 
 ### What is Contrastive Loss
 
-[Contrastive loss](http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf) is a distance-based loss function. During training, pairs of images are fed into a model. If the images are similar, the loss function will return 0, otherwise 1. 
+[Contrastive loss](http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf) is a distance-based loss function. During training, pairs of images are fed into a model. If the images are similar, the loss function will return 0, otherwise 1.
 
 <img src="images/contrastive_loss.jpeg" width="400">
 
-*Y* is a binary label indicating similarity between training images. Contrastive loss uses the Euclidean distance *D* between images and is the sum of 2 terms: 
+*Y* is a binary label indicating similarity between training images. Contrastive loss uses the Euclidean distance *D* between images and is the sum of 2 terms:
  - the loss for a pair of similar points
  - the loss for a pair of dissimilar points
 
-The loss function uses a margin *m* which is has the effect that dissimlar pairs only contribute if their loss is within a certain margin. 
+The loss function uses a margin *m* which is has the effect that dissimlar pairs only contribute if their loss is within a certain margin.
 
-In order to implement such a customized loss function in Gluon, we only need to define a new class that is inheriting from the [`Loss`](https://mxnet.apache.org/api/python/gluon/loss.html#mxnet.gluon.loss.Loss) base class. We then define the contrastive loss logic in the [`hybrid_forward`](https://mxnet.apache.org/_modules/mxnet/gluon/block.html#HybridBlock.hybrid_forward) method. This method takes the images `image1`, `image2` and the label which defines whether  `image1` and `image2` are similar (=0) or  dissimilar (=1). The input F is an `mxnet.ndarry` or an `mxnet.symbol` if we hybridize the network. Gluon's `Loss` base class is in fact a [`HybridBlock`](https://mxnet.apache.org/api/python/gluon/gluon.html#mxnet.gluon.HybridBlock). This means we can either run  imperatively or symbolically. When we hybridize our custom loss function, we can get performance speedups.
+In order to implement such a customized loss function in Gluon, we only need to define a new class that is inheriting from the [`Loss`](/api/python/docs/api/gluon/loss/index.html#mxnet.gluon.loss.Loss) base class. We then define the contrastive loss logic in the [`hybrid_forward`](/api/python/docs/api/gluon/hybrid_block.html#mxnet.gluon.HybridBlock.hybrid_forward) method. This method takes the images `image1`, `image2` and the label which defines whether  `image1` and `image2` are similar (=0) or  dissimilar (=1). The input F is an `mxnet.ndarry` or an `mxnet.symbol` if we hybridize the network. Gluon's `Loss` base class is in fact a [`HybridBlock`](/api/python/docs/api/gluon/hybrid_block.html). This means we can either run  imperatively or symbolically. When we hybridize our custom loss function, we can get performance speedups.
 
 
 ```python
@@ -66,7 +66,7 @@ loss = ContrastiveLoss(margin=6.0)
 ```
 
 ### Define the Siamese network
-A [Siamese network](https://papers.nips.cc/paper/769-signature-verification-using-a-siamese-time-delay-neural-network.pdf) consists of 2 identical networks, that share the same weights. They are trained on pairs of images and each network processes one image. The label defines whether the pair of images is similar or not. The Siamese network learns to differentiate between two input images. 
+A [Siamese network](https://papers.nips.cc/paper/769-signature-verification-using-a-siamese-time-delay-neural-network.pdf) consists of 2 identical networks, that share the same weights. They are trained on pairs of images and each network processes one image. The label defines whether the pair of images is similar or not. The Siamese network learns to differentiate between two input images.
 
 Our network consists of 2 convolutional and max pooling layers that downsample the input image. The output is then fed through a fully connected layer with 256 hidden units and another fully connected layer with 2 hidden units.
 
@@ -84,7 +84,7 @@ class Siamese(gluon.HybridBlock):
                 self.cnn.add(gluon.nn.MaxPool2D(2, 2))
                 self.cnn.add(gluon.nn.Dense(256, activation='relu'))
                 self.cnn.add(gluon.nn.Dense(2, activation='softrelu'))
-                
+
     def hybrid_forward(self, F, input0, input1):
         out0 = self.cnn(input0)
         out1 = self.cnn(input1)
@@ -143,13 +143,13 @@ test_dataloader = gluon.data.DataLoader(test.transform(transform),
                                         shuffle=False, batch_size=1)
 ```
 
-Following code plots some examples from the test dataset. 
+Following code plots some examples from the test dataset.
 
 
 ```python
 img1, img2, label = test[0]
 print("Same: {}".format(int(label.asscalar()) == 0))
-fig, (ax0, ax1) = plt.subplots(ncols=2, figsize=(10, 5)) 
+fig, (ax0, ax1) = plt.subplots(ncols=2, figsize=(10, 5))
 ax0.imshow(img1.asnumpy()[:,:,0], cmap='gray')
 ax0.axis('off')
 ax1.imshow(img2.asnumpy()[:,:,0], cmap='gray')
@@ -191,7 +191,7 @@ for epoch in range(10):
 ```
 
 ### Test the trained Siamese network
-During inference we compute the Euclidean distance between the output vectors of the Siamese network. High distance indicates dissimilarity, low values indicate similarity.  
+During inference we compute the Euclidean distance between the output vectors of the Siamese network. High distance indicates dissimilarity, low values indicate similarity.
 
 
 ```python
@@ -224,7 +224,7 @@ Verify whether the last network layer uses the correct activation function: for
 In our example, we computed the square root of squared distances between 2 images: `F.sqrt(distances_squared)`. If images are very similar we take the sqare root of a value close to 0, which can lead to *NaN* values. Adding a small epsilon to `distances_squared` avoids this problem.
 
 #### Shape of intermediate loss vectors
-In most cases having the wrong tensor shape will lead to an error, as soon as we compare data with labels. But in some cases, we may be able to normally run the training, but it does not converge. For instance, if we don't set `keepdims=True` in our customized loss function, the shape of the tensor changes. The example still runs fine but does not converge. 
+In most cases having the wrong tensor shape will lead to an error, as soon as we compare data with labels. But in some cases, we may be able to normally run the training, but it does not converge. For instance, if we don't set `keepdims=True` in our customized loss function, the shape of the tensor changes. The example still runs fine but does not converge.
 
 If you encounter a similar problem, then it is useful to check the tensor shape after each computation step in the loss function.
 
diff --git a/docs/python_docs/python/tutorials/packages/gluon/loss/loss.md b/docs/python_docs/python/tutorials/packages/gluon/loss/loss.md
index 6ea44ef180f4..af5fe04ab2cb 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/loss/loss.md
+++ b/docs/python_docs/python/tutorials/packages/gluon/loss/loss.md
@@ -19,8 +19,8 @@
 
 Loss functions are used to train neural networks and to compute the difference between output and target variable. A critical component of training neural networks is the loss function. A loss function is a quantative measure of how bad the predictions of the network are when compared to ground truth labels. Given this score, a network can improve by iteratively updating its weights to minimise this loss. Some tasks use a combination of multiple loss functions, but often you'll just use one. MXNet Gluon provides a number of the most commonly used loss functions, and you'll choose certain loss functions depending on your network and task. Some common task and loss function pairs include:
 
-- regression: [L1Loss](/api/python/docs/api/gluon/_autogen/mxnet.gluon.loss.L1Loss.html), [L2Loss](/api/python/docs/api/gluon/_autogen/mxnet.gluon.loss.L2Loss.html) 
-- classification: [SigmoidBinaryCrossEntropyLoss](/api/python/docs/api/gluon/_autogen/mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss.html), [SoftmaxBinaryCrossEntropyLoss](/api/python/docs/api/gluon/_autogen/mxnet.gluon.loss.SoftmaxBinaryCrossEntropyLoss.html) 
+- regression: [L1Loss](/api/python/docs/api/gluon/loss/index.html#mxnet.gluon.loss.L1Loss), [L2Loss](/api/python/docs/api/gluon/loss/index.html#mxnet.gluon.loss.L2Loss) 
+- classification: [SigmoidBinaryCrossEntropyLoss](/api/python/docs/api/gluon/loss/index.html#mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss), [SoftmaxCrossEntropyLoss](/api/python/docs/api/gluon/loss/index.html#mxnet.gluon.loss.SoftmaxCrossEntropyLoss)
 - embeddings: [HingeLoss](/api/python/docs/api/gluon/_autogen/mxnet.gluon.loss.HingeLoss.html)
 
 We'll first import the modules, where the `mxnet.gluon.loss` module is imported as `gloss` to avoid the commonly used name `loss`.
@@ -30,7 +30,7 @@ from IPython import display
 from matplotlib import pyplot as plt
 import mxnet as mx
 from mxnet import nd, autograd
-from mxnet.gluon import nn, loss as gloss  
+from mxnet.gluon import nn, loss as gloss
 ```
 
 ## Basic Usages
@@ -58,7 +58,7 @@ These values should be equal to the math definition: $0.5\|x-y\|^2$.
 Next we show how to use a loss function to compute gradients.
 
 ```{.python .input}
-X = nd.random.uniform(shape=(2, 4)) 
+X = nd.random.uniform(shape=(2, 4))
 net = nn.Dense(1)
 net.initialize()
 with autograd.record():
@@ -86,11 +86,11 @@ def plot(x, y):
     plt.xlabel('x')
     plt.ylabel('loss')
     plt.show()
-    
+
 def show_regression_loss(loss):
     x = nd.arange(-5, 5, .1)
     y = loss(x, nd.zeros_like(x))
-    plot(x, y)  
+    plot(x, y)
 
 ```
 
@@ -100,7 +100,7 @@ Then plot the classification losses with label values fixed to be 1.
 def show_classification_loss(loss):
     x = nd.arange(-5, 5, .1)
     y = loss(x, nd.ones_like(x))
-    plot(x, y) 
+    plot(x, y)
 ```
 
 #### [L1 Loss](https://mxnet.apache.org/api/python/gluon/loss.html#mxnet.gluon.loss.L1Loss)
@@ -121,7 +121,7 @@ L2Loss, also called Mean Squared Error, is a regression loss function that compu
 
 $$ L = \frac{1}{2} \sum_i \vert {label}_i - {pred}_i \vert^2. $$
 
-Compared to L1, L2 loss it is a smooth function and it creates larger gradients for large loss values. However due to the squaring it puts high weight on outliers. 
+Compared to L1, L2 loss it is a smooth function and it creates larger gradients for large loss values. However due to the squaring it puts high weight on outliers.
 
 ```{.python .input}
 show_regression_loss(gloss.L2Loss())
@@ -130,7 +130,7 @@ show_regression_loss(gloss.L2Loss())
 #### [Huber Loss](https://mxnet.apache.org/api/python/gluon/loss.html#mxnet.gluon.loss.HuberLosss)
 
 HuberLoss  combines advantages of L1 and L2 loss. It calculates a smoothed L1 loss that is equal to L1 if the absolute error exceeds a threshold $$\rho$$, otherwise it is equal to L2. It is defined as:
-$$ 
+$$
 \begin{split}L = \sum_i \begin{cases} \frac{1}{2 {rho}} ({label}_i - {pred}_i)^2 &
                    \text{ if } |{label}_i - {pred}_i| < {rho} \\
                    |{label}_i - {pred}_i| - \frac{{rho}}{2} &
@@ -158,7 +158,7 @@ show_classification_loss(gloss.SigmoidBinaryCrossEntropyLoss())
 
 In classification, we often apply the
 softmax operator to the predicted outputs to obtain prediction probabilities,
-and then apply the cross entropy loss against the true labels: 
+and then apply the cross entropy loss against the true labels:
 
 $$ \begin{align}\begin{aligned}p = \softmax({pred})\\L = -\sum_i \sum_j {label}_j \log p_{ij}\end{aligned}\end{align}
 $$
@@ -184,9 +184,9 @@ $$
 show_classification_loss(gloss.HingeLoss())
 ```
 
-#### [Logistic Loss](https://mxnet.apache.org/versions/master/api/python/gluon/loss.html#mxnet.gluon.loss.LogisticLoss)
+#### [Logistic Loss](/api/python/docs/api/gluon/loss/index.html#mxnet.gluon.loss.LogisticLoss)
 
-The Logistic Loss function computes the performance of binary classification models. 
+The Logistic Loss function computes the performance of binary classification models.
 $$
 L = \sum_i \log(1 + \exp(- {pred}_i \cdot {label}_i))
 $$
@@ -196,7 +196,7 @@ The log loss decreases the closer the prediction is to the actual label. It is s
 show_classification_loss(gloss.LogisticLoss())
 ```
 
-#### [Kullback-Leibler Divergence Loss](https://mxnet.apache.org/versions/master/api/python/gluon/loss.html#mxnet.gluon.loss.KLDivLoss)
+#### [Kullback-Leibler Divergence Loss](/api/python/docs/api/gluon/loss/index.html#mxnet.gluon.loss.KLDivLoss)
 
 The Kullback-Leibler divergence loss measures the divergence between two probability distributions by calculating the difference between cross entropy and entropy. It takes as input the probability of predicted label and the probability of true label.
 
@@ -218,30 +218,30 @@ loss = loss_fn(output, target_dist)
 print('loss (kl divergence): {}'.format(loss.asnumpy().tolist()))
 ```
 
-#### [Triplet Loss](https://mxnet.apache.org/versions/master/api/python/gluon/loss.html#mxnet.gluon.loss.TripletLoss)
+#### [Triplet Loss](/api/python/docs/api/gluon/loss/index.html#mxnet.gluon.loss.TripletLoss)
 
-Triplet loss takes three input arrays and measures the relative similarity. It takes a positive and negative input and the anchor. 
+Triplet loss takes three input arrays and measures the relative similarity. It takes a positive and negative input and the anchor.
 
 $$
 L = \sum_i \max(\Vert {pos_i}_i - {pred} \Vert_2^2 -
     \Vert {neg_i}_i - {pred} \Vert_2^2 + {margin}, 0)
 $$
 
-The loss function minimizes the distance between similar inputs and maximizes the distance  between dissimilar ones.  
-In the case of learning embeddings for images of characters, the network may get as input the following 3 images: 
+The loss function minimizes the distance between similar inputs and maximizes the distance  between dissimilar ones.
+In the case of learning embeddings for images of characters, the network may get as input the following 3 images:
 
 ![triplet_loss](triplet_loss.png)
 
 The network would learn to minimize the distance between the two `A`'s and maximize the distance between `A` and `Z`.
 
-#### [CTC Loss](https://mxnet.apache.org/versions/master/api/python/gluon/loss.html#mxnet.gluon.loss.CTCLoss)
+#### [CTC Loss](/api/python/docs/api/gluon/loss/index.html#mxnet.gluon.loss.CTCLoss)
 
 CTC Loss is the [connectionist temporal classification loss](https://distill.pub/2017/ctc/) . It is used to train recurrent neural networks with variable time dimension. It learns the alignment and labelling of input sequences. It takes a sequence as input and gives probabilities for each timestep. For instance, in the following image the word is not well aligned with the 5 timesteps because of the different sizes of characters. CTC Loss finds for each timestep the highest probability e.g. `t1` presents with high probability a `C`. It combines the highest probapilities and returns the best path decoding. For an in-depth tutorial on how to use CTC-Loss in MXNet, check out this [example](https://github.com/apache/incubator-mxnet/tree/master/example/ctc).
 
 ![ctc_loss](ctc_loss.png)
 
-#### [Cosine Embedding Loss](https://mxnet.apache.org/versions/master/api/python/gluon/loss.html#mxnet.gluon.loss.CosineEmbeddingLoss)
-The cosine embedding loss computes the cosine distance between two input vectors. 
+#### [Cosine Embedding Loss](/api/python/docs/api/gluon/loss/index.html#mxnet.gluon.loss.CosineEmbeddingLoss)
+The cosine embedding loss computes the cosine distance between two input vectors.
 
 $$
 \begin{split}L = \sum_i \begin{cases} 1 - {cos\_sim({input1}_i, {input2}_i)} & \text{ if } {label}_i = 1\\
@@ -249,7 +249,7 @@ $$
 cos\_sim(input1, input2) = \frac{{input1}_i.{input2}_i}{||{input1}_i||.||{input2}_i||}\end{split}
 $$
 
-Cosine distance measures the similarity between two arrays given a label and is typically used for learning nonlinear embeddings.  
+Cosine distance measures the similarity between two arrays given a label and is typically used for learning nonlinear embeddings.
 For instance, in the following code example we measure the similarity between the input vectors `x` and `y`. Since they are the same the label equals `1`. The loss function returns $$ \sum_i 1 - {cos\_sim({input1}_i, {input2}_i)} $$ which is equal `0`.
 
 ```{.python .input}
@@ -270,7 +270,7 @@ loss = gloss.CosineEmbeddingLoss()
 print(loss(x,y,label))
 ```
 
-#### [PoissonNLLLoss](https://mxnet.apache.org/versions/master/api/python/gluon/loss.html#mxnet.gluon.loss.PoissonNLLLoss)
+#### [PoissonNLLLoss](/api/python/docs/api/gluon/loss/index.html#mxnet.gluon.loss.PoissonNLLLoss)
 Poisson distribution is widely used for modelling count data. It is defined as:
 
 $$
@@ -278,13 +278,13 @@ f(x) = \frac{\mu ^ {\kern 0.08 em x} e ^ {-\mu}} {x!} \qquad \qquad x = 0,1,2 ,
 $$
 
 
-For instance, the count of cars in road traffic approximately follows a Poisson distribution. Using an ordinary least squares model for Poisson distributed data would not work well because of two reasons: 
- - count data cannot be negative 
+For instance, the count of cars in road traffic approximately follows a Poisson distribution. Using an ordinary least squares model for Poisson distributed data would not work well because of two reasons:
+ - count data cannot be negative
  - variance may not be constant
 
-Instead we can use a Poisson regression model, also known as log-linear model. Thereby the Poisson incident rate $$\mu$$ is 
+Instead we can use a Poisson regression model, also known as log-linear model. Thereby the Poisson incident rate $$\mu$$ is
 modelled by a linear combination of unknown parameters.
-We can then use the PoissonNLLLoss which calculates the negative log likelihood for a target that follows a Poisson distribution. 
+We can then use the PoissonNLLLoss which calculates the negative log likelihood for a target that follows a Poisson distribution.
 
 $$ L = \text{pred} - \text{target} * \log(\text{pred}) +\log(\text{target!}) $$
 
diff --git a/docs/python_docs/python/tutorials/packages/gluon/training/fit_api_tutorial.md b/docs/python_docs/python/tutorials/packages/gluon/training/fit_api_tutorial.md
index 16f0ceac3a08..9e4cbe2f5114 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/training/fit_api_tutorial.md
+++ b/docs/python_docs/python/tutorials/packages/gluon/training/fit_api_tutorial.md
@@ -17,17 +17,17 @@
 
 # MXNet Gluon Fit API
 
-In this tutorial, you will learn how to use the [Gluon Fit API](https://cwiki.apache.org/confluence/display/MXNET/Gluon+Fit+API+-+Tech+Design) which is the easiest way to train deep learning models using the [Gluon API](https://mxnet.apache.org/versions/master/gluon/index.html) in Apache MXNet. 
+In this tutorial, you will learn how to use the [Gluon Fit API](https://cwiki.apache.org/confluence/display/MXNET/Gluon+Fit+API+-+Tech+Design) which is the easiest way to train deep learning models using the [Gluon API](/api/python/docs/tutorials/packages/gluon/index.html) in Apache MXNet.
 
 With the Fit API, you can train a deep learning model with a minimal amount of code. Just specify the network, loss function and the data you want to train on. You don't need to worry about the boiler plate code to loop through the dataset in batches (often called as 'training loop'). Advanced users can train with bespoke training loops, and many of these use cases will be covered by the Fit API.
 
-To demonstrate the Fit API, you will train an image classification model using the [ResNet-18](https://arxiv.org/abs/1512.03385) neural network architecture. The model will be trained using the [Fashion-MNIST dataset](https://research.zalando.com/welcome/mission/research-projects/fashion-mnist/). 
+To demonstrate the Fit API, you will train an image classification model using the [ResNet-18](https://arxiv.org/abs/1512.03385) neural network architecture. The model will be trained using the [Fashion-MNIST dataset](https://research.zalando.com/welcome/mission/research-projects/fashion-mnist/).
 
 ## Prerequisites
 
 To complete this tutorial, you will need:
 
-- [MXNet](https://mxnet.apache.org/install/#overview) (The version of MXNet will be >= 1.5.0, you can use `pip install mxnet` to get 1.5.0 release pip package or build from source with master, refer to [MXNet installation](https://mxnet.apache.org/versions/master/install/index.html?platform=Linux&language=Python&processor=CPU)
+- [MXNet](/get_started) (The version of MXNet will be >= 1.5.0, you can use `pip install mxnet` to get 1.5.0 release pip package or build from source with master, refer to [MXNet installation](/get_started?version=master&platform=linux&language=python&environ=pip&processor=cpu)
 - [Jupyter Notebook](https://jupyter.org/index.html) (For interactively running the provided .ipynb file)
 
 
@@ -44,16 +44,16 @@ ctx = [mx.gpu(i) for i in range(gpu_count)] if gpu_count > 0 else mx.cpu()
 
 ## Dataset
 
-[Fashion-MNIST](https://research.zalando.com/welcome/mission/research-projects/fashion-mnist/) dataset consists of fashion items divided into ten categories: t-shirt/top, trouser, pullover, dress, coat, sandal, shirt, sneaker, bag and ankle boot. 
+[Fashion-MNIST](https://research.zalando.com/welcome/mission/research-projects/fashion-mnist/) dataset consists of fashion items divided into ten categories: t-shirt/top, trouser, pullover, dress, coat, sandal, shirt, sneaker, bag and ankle boot.
 
-- It has 60,000 grayscale images of size 28 * 28 for training.  
-- It has 10,000 grayscale images of size 28 * 28 for testing/validation. 
+- It has 60,000 grayscale images of size 28 * 28 for training.
+- It has 10,000 grayscale images of size 28 * 28 for testing/validation.
 
 We will use the ```gluon.data.vision``` package to directly import the Fashion-MNIST dataset and perform pre-processing on it.
 
 
 ```python
-# Get the training data 
+# Get the training data
 fashion_mnist_train = gluon.data.vision.FashionMNIST(train=True)
 
 # Get the validation data
@@ -81,9 +81,9 @@ fashion_mnist_val = fashion_mnist_val.transform_first(transforms)
 batch_size = 256 # Batch size of the images
 num_workers = 4 # The number of parallel workers for loading the data using Data Loaders.
 
-train_data_loader = gluon.data.DataLoader(fashion_mnist_train, batch_size=batch_size, 
+train_data_loader = gluon.data.DataLoader(fashion_mnist_train, batch_size=batch_size,
                                           shuffle=True, num_workers=num_workers)
-val_data_loader = gluon.data.DataLoader(fashion_mnist_val, batch_size=batch_size, 
+val_data_loader = gluon.data.DataLoader(fashion_mnist_val, batch_size=batch_size,
                                         shuffle=False, num_workers=num_workers)
 ```
 
@@ -97,8 +97,8 @@ resnet_18_v1 = vision.resnet18_v1(pretrained=False, classes = 10)
 resnet_18_v1.initialize(init = mx.init.Xavier(), ctx=ctx)
 ```
 
-We will be using `SoftmaxCrossEntropyLoss` as the loss function since this is a multi-class classification problem. We will be using `sgd` (Stochastic Gradient Descent) as the optimizer. 
-You can experiment with a [different loss](https://mxnet.apache.org/versions/master/api/python/gluon/loss.html) or [optimizer](https://mxnet.apache.org/versions/master/api/python/optimization/optimization.html) as well. 
+We will be using `SoftmaxCrossEntropyLoss` as the loss function since this is a multi-class classification problem. We will be using `sgd` (Stochastic Gradient Descent) as the optimizer.
+You can experiment with a [different loss](/api/python/docs/api/gluon/loss/index.html) or [optimizer](/api/python/docs/api/optimizer/index.html) as well.
 
 
 ```python
@@ -111,7 +111,7 @@ Let's define the trainer object for training the model.
 ```python
 learning_rate = 0.04 # You can experiment with your own learning rate here
 num_epochs = 2 # You can run training for more epochs
-trainer = gluon.Trainer(resnet_18_v1.collect_params(), 
+trainer = gluon.Trainer(resnet_18_v1.collect_params(),
                         'sgd', {'learning_rate': learning_rate})
 ```
 
@@ -128,10 +128,10 @@ In the basic usage example, with just 2 lines of code, we will set up our model
 train_acc = mx.metric.Accuracy() # Metric to monitor
 
 # Define the estimator, by passing to it the model, loss function, metrics, trainer object and context
-est = estimator.Estimator(net=resnet_18_v1, 
-                          loss=loss_fn, 
-                          metrics=train_acc, 
-                          trainer=trainer, 
+est = estimator.Estimator(net=resnet_18_v1,
+                          loss=loss_fn,
+                          metrics=train_acc,
+                          trainer=trainer,
                           context=ctx)
 
 # ignore warnings for nightly test on CI only
@@ -146,9 +146,9 @@ with warnings.catch_warnings():
 ```text
     Training begin: using optimizer SGD with current learning rate 0.0400 <!--notebook-skip-line-->
     Train for 2 epochs. <!--notebook-skip-line-->
-    
+
     [Epoch 0] finished in 25.110s: train_accuracy : 0.7877 train_softmaxcrossentropyloss0 : 0.5905 <!--notebook-skip-line-->
-    
+
     [Epoch 1] finished in 23.595s: train_accuracy : 0.8823 train_softmaxcrossentropyloss0 : 0.3197 <!--notebook-skip-line-->
     Train finished using total 48s at epoch 1. train_accuracy : 0.8823 train_softmaxcrossentropyloss0 : 0.3197 <!--notebook-skip-line-->
 ```
@@ -157,19 +157,19 @@ with warnings.catch_warnings():
 
 The Fit API is also customizable with several `Event Handlers` which give a fine grained control over the steps in training and exposes callback methods that provide control over the stages involved in training. Available callback methods are: `train_begin`, `train_end`, `batch_begin`, `batch_end`, `epoch_begin` and `epoch_end`.
 
-You can use built-in event handlers such as `LoggingHandler`, `CheckpointHandler` or `EarlyStoppingHandler` to log and save the model at certain time-steps during training. You can also stop the training when the model's performance plateaus. 
-There are also some default utility handlers that will be added to your estimator by default. For example, `StoppingHandler` is used to control when the training ends, based on number of epochs or number of batches trained. 
-`MetricHandler` is used to calculate training metrics at end of each batch and epoch. 
+You can use built-in event handlers such as `LoggingHandler`, `CheckpointHandler` or `EarlyStoppingHandler` to log and save the model at certain time-steps during training. You can also stop the training when the model's performance plateaus.
+There are also some default utility handlers that will be added to your estimator by default. For example, `StoppingHandler` is used to control when the training ends, based on number of epochs or number of batches trained.
+`MetricHandler` is used to calculate training metrics at end of each batch and epoch.
 `ValidationHandler` is used to validate your model on test data at each epoch's end and then calculate validation metrics.
 You can create these utility handlers with different configurations and pass to estimator. This will override the default handler configuration.
-You can create a custom handler by inheriting one or multiple 
+You can create a custom handler by inheriting one or multiple
 [base event handlers](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/contrib/estimator/event_handler.py#L32)
  including: `TrainBegin`, `TrainEnd`, `EpochBegin`, `EpochEnd`, `BatchBegin`, `BatchEnd`.
 
 
 ### Custom Event Handler
 
-Here we will showcase an example custom event handler the inherits features from a few base handler classes. 
+Here we will showcase an example custom event handler the inherits features from a few base handler classes.
 Our custom event handler is a simple one: record the loss values at the end of every epoch in our training phase.
 
 Note: For each of the method, the `Estimator` object is passed along, so you can access training metrics.
@@ -205,7 +205,7 @@ class LossRecordHandler(TrainBegin, TrainEnd, EpochEnd):
 # Let's reset the model, trainer and accuracy objects from above
 
 resnet_18_v1.initialize(force_reinit=True, init = mx.init.Xavier(), ctx=ctx)
-trainer = gluon.Trainer(resnet_18_v1.collect_params(), 
+trainer = gluon.Trainer(resnet_18_v1.collect_params(),
                         'sgd', {'learning_rate': learning_rate})
 train_acc = mx.metric.Accuracy()
 ```
@@ -216,7 +216,7 @@ train_acc = mx.metric.Accuracy()
 est = estimator.Estimator(net=resnet_18_v1,
                           loss=loss_fn,
                           metrics=train_acc,
-                          trainer=trainer, 
+                          trainer=trainer,
                           context=ctx)
 
 # Define the handlers, let's say in built Checkpointhandler
@@ -224,7 +224,7 @@ checkpoint_handler = CheckpointHandler(model_dir='./',
                                        model_prefix='my_model',
                                        monitor=train_acc,  # Monitors a metric
                                        save_best=True)  # Save the best model in terms of
-# Let's instantiate another handler which we defined above 
+# Let's instantiate another handler which we defined above
 loss_record_handler = LossRecordHandler()
 # ignore warnings for nightly test on CI only
 import warnings
@@ -240,11 +240,11 @@ with warnings.catch_warnings():
 ```text
     Training begin: using optimizer SGD with current learning rate 0.0400 <!--notebook-skip-line-->
     Train for 2 epochs. <!--notebook-skip-line-->
-    
+
     [Epoch 0] finished in 25.236s: train_accuracy : 0.7917 train_softmaxcrossentropyloss0 : 0.5741 val_accuracy : 0.6612 val_softmaxcrossentropyloss0 : 0.8627 <!--notebook-skip-line-->
-    
+
     [Epoch 1] finished in 24.892s: train_accuracy : 0.8826 train_softmaxcrossentropyloss0 : 0.3229 val_accuracy : 0.8474 val_softmaxcrossentropyloss0 : 0.4262 <!--notebook-skip-line-->
-    
+
     Train finished using total 50s at epoch 1. train_accuracy : 0.8826 train_softmaxcrossentropyloss0 : 0.3229 val_accuracy : 0.8474 val_softmaxcrossentropyloss0 : 0.4262 <!--notebook-skip-line-->
 
     Training begin <!--notebook-skip-line-->
@@ -252,7 +252,7 @@ with warnings.catch_warnings():
     Epoch 2, loss 0.3229 <!--notebook-skip-line-->
 ```
 
-You can load the saved model, by using the `load_parameters` API in Gluon. For more details refer to the [Loading model parameters from file tutorial](save_load_params.html#saving-model-parameters-to-file)
+You can load the saved model, by using the `load_parameters` API in Gluon. For more details refer to the [Loading model parameters from file tutorial](../blocks/save_load_params.html#saving-model-parameters-to-file)
 
 
 ```python
@@ -260,10 +260,6 @@ resnet_18_v1 = vision.resnet18_v1(pretrained=False, classes=10)
 resnet_18_v1.load_parameters('./my_model-best.params', ctx=ctx)
 ```
 
-## Summary
-
-- To learn more about deep learning with MXNeT, see [Dive Into Deep Learning](https://gluon.io)
-
-## Next Steps 
+## Next Steps
 
-- For more hands on learning about deep learning, check out [Dive into Deep Learning](https://d2l.ai)
\ No newline at end of file
+- For more hands on learning about deep learning, check out [Dive into Deep Learning](https://d2l.ai)
diff --git a/docs/python_docs/python/tutorials/packages/gluon/training/learning_rates/learning_rate_finder.md b/docs/python_docs/python/tutorials/packages/gluon/training/learning_rates/learning_rate_finder.md
index 4fc8b780a2ef..a32c8a1e92cd 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/training/learning_rates/learning_rate_finder.md
+++ b/docs/python_docs/python/tutorials/packages/gluon/training/learning_rates/learning_rate_finder.md
@@ -20,7 +20,7 @@
 
 Setting the learning rate for stochastic gradient descent (SGD) is crucially important when training neural network because it controls both the speed of convergence and the ultimate performance of the network. Set the learning too low and you could be twiddling your thumbs for quite some time as the parameters update very slowly. Set it too high and the updates will skip over optimal solutions, or worse the optimizer might not converge at all!
 
-Leslie Smith from the U.S. Naval Research Laboratory presented a method for finding a good learning rate in a paper called ["Cyclical Learning Rates for Training Neural Networks"](https://arxiv.org/abs/1506.01186). We implement this method in MXNet (with the Gluon API) and create a 'Learning Rate Finder' which you can use while training your own networks. We take a look at the central idea of the paper, cyclical learning rate schedules, in the ['Advanced Learning Rate Schedules'](https://mxnet.apache.org/tutorials/gluon/learning_rate_schedules_advanced.html) tutorial.
+Leslie Smith from the U.S. Naval Research Laboratory presented a method for finding a good learning rate in a paper called ["Cyclical Learning Rates for Training Neural Networks"](https://arxiv.org/abs/1506.01186). We implement this method in MXNet (with the Gluon API) and create a 'Learning Rate Finder' which you can use while training your own networks. We take a look at the central idea of the paper, cyclical learning rate schedules, in the ['Advanced Learning Rate Schedules'](/api/python/docs/tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html) tutorial.
 
 ## Simple Idea
 
@@ -63,7 +63,7 @@ class Learner():
         self.net.initialize(mx.init.Xavier(), ctx=self.ctx)
         self.loss_fn = mx.gluon.loss.SoftmaxCrossEntropyLoss()
         self.trainer = mx.gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': .001})
-        
+
     def iteration(self, lr=None, take_step=True):
         """
         :param lr: learning rate to use for iteration (float)
@@ -81,9 +81,9 @@ class Learner():
         with mx.autograd.record():
             output = self.net(data)
             loss = self.loss_fn(output, label)
-        loss.backward()     
+        loss.backward()
         # Update parameters
-        if take_step: self.trainer.step(data.shape[0])  
+        if take_step: self.trainer.step(data.shape[0])
         # Set and return loss.
         self.iteration_loss = mx.nd.mean(loss).asscalar()
         return self.iteration_loss
@@ -102,7 +102,7 @@ from mxnet.gluon.data.vision import transforms
 transform = transforms.Compose([
     # Switches HWC to CHW, and converts to `float32`
     transforms.ToTensor(),
-    # Channel-wise, using pre-computed means and stds 
+    # Channel-wise, using pre-computed means and stds
     transforms.Normalize(mean=[0.4914, 0.4822, 0.4465],
                          std=[0.2023, 0.1994, 0.2010])
 ])
@@ -143,7 +143,7 @@ class LRFinder():
            and save and load parameters of the network (Learner)
         """
         self.learner = learner
-        
+
     def find(self, lr_start=1e-6, lr_multiplier=1.1, smoothing=0.3):
         """
         :param lr_start: learning rate to start search (float)
@@ -175,7 +175,7 @@ class LRFinder():
         self.learner.net.load_parameters("lr_finder.params", ctx=self.learner.ctx)
         self.learner.trainer.load_states("lr_finder.state")
         return self.results
-        
+
     def plot(self):
         lrs = [e[0] for e in self.results]
         losses = [e[1] for e in self.results]
@@ -209,7 +209,7 @@ class LRFinderStoppingCriteria():
         self.first_loss = None
         self.running_mean = None
         self.counter = 0
-        
+
     def __call__(self, loss):
         """
         :param loss: from single iteration (float)
@@ -327,6 +327,6 @@ Although we get quite similar results to when we set the learning rate at 0.05 (
 
 ## Wrap Up
 
-Give Learning Rate Finder a try on your current projects, and experiment with the different learning rate schedules found in the tutorials [here](https://mxnet.apache.org/tutorials/gluon/learning_rate_schedules.html) and [here](https://mxnet.apache.org/tutorials/gluon/learning_rate_schedules_advanced.html).
+Give Learning Rate Finder a try on your current projects, and experiment with the different learning rate schedules found in the [basic learning rate tutorial](/api/python/docs/tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html) and the [advanced learning rate tutorial](/api/python/docs/tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html).
 
 <!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/python_docs/python/tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.md b/docs/python_docs/python/tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.md
index 287f70d535e8..c59c9515f02e 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.md
+++ b/docs/python_docs/python/tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.md
@@ -16,7 +16,7 @@
 <!--- under the License. -->
 
 
- # Advanced Learning Rate Schedules
+# Advanced Learning Rate Schedules
 
 Given the importance of learning rate and the learning rate schedule for training neural networks, there have been a number of research papers published recently on the subject. Although many practitioners are using simple learning rate schedules such as stepwise decay, research has shown that there are other strategies that work better in most situations. We implement a number of different schedule shapes in this tutorial and introduce cyclical schedules.
 
@@ -56,7 +56,7 @@ One adjustment proposed by [Jeremy Howard, Sebastian Ruder (2018)](https://arxiv
 
 ```python
 class TriangularSchedule():
-    def __init__(self, min_lr, max_lr, cycle_length, inc_fraction=0.5):     
+    def __init__(self, min_lr, max_lr, cycle_length, inc_fraction=0.5):
         """
         min_lr: lower bound for learning rate (float)
         max_lr: upper bound for learning rate (float)
@@ -67,7 +67,7 @@ class TriangularSchedule():
         self.max_lr = max_lr
         self.cycle_length = cycle_length
         self.inc_fraction = inc_fraction
-        
+
     def __call__(self, iteration):
         if iteration <= self.cycle_length*self.inc_fraction:
             unit_cycle = iteration * 1 / (self.cycle_length * self.inc_fraction)
@@ -107,7 +107,7 @@ class CosineAnnealingSchedule():
         self.min_lr = min_lr
         self.max_lr = max_lr
         self.cycle_length = cycle_length
-        
+
     def __call__(self, iteration):
         if iteration <= self.cycle_length:
             unit_cycle = (1 + math.cos(iteration * math.pi / self.cycle_length)) / 2
@@ -153,7 +153,7 @@ class LinearWarmUp():
         # calling mx.lr_scheduler.LRScheduler effects state, so calling a copy
         self.finish_lr = copy.copy(schedule)(0)
         self.length = length
-    
+
     def __call__(self, iteration):
         if iteration <= self.length:
             return iteration * (self.finish_lr - self.start_lr)/(self.length) + self.start_lr
@@ -196,7 +196,7 @@ class LinearCoolDown():
         self.start_idx = start_idx
         self.finish_idx = start_idx + length
         self.length = length
-    
+
     def __call__(self, iteration):
         if iteration <= self.start_idx:
             return self.schedule(iteration)
@@ -239,11 +239,11 @@ class OneCycleSchedule():
             raise ValueError("Must specify finish_lr when using cooldown_length > 0.")
         if (cooldown_length == 0) and (finish_lr is not None):
             raise ValueError("Must specify cooldown_length > 0 when using finish_lr.")
-            
+
         finish_lr = finish_lr if (cooldown_length > 0) else start_lr
         schedule = TriangularSchedule(min_lr=start_lr, max_lr=max_lr, cycle_length=cycle_length)
         self.schedule = LinearCoolDown(schedule, finish_lr=finish_lr, start_idx=cycle_length, length=cooldown_length)
-        
+
     def __call__(self, iteration):
         return self.schedule(iteration)
 ```
@@ -280,7 +280,7 @@ class CyclicalSchedule():
         self.length_decay = cycle_length_decay
         self.magnitude_decay = cycle_magnitude_decay
         self.kwargs = kwargs
-    
+
     def __call__(self, iteration):
         cycle_idx = 0
         cycle_length = self.length
@@ -290,7 +290,7 @@ class CyclicalSchedule():
             cycle_idx += 1
             idx += cycle_length
         cycle_offset = iteration - idx + cycle_length
-        
+
         schedule = self.schedule_class(cycle_length=cycle_length, **self.kwargs)
         return schedule(cycle_offset) * self.magnitude_decay**cycle_idx
 ```
@@ -322,4 +322,4 @@ plot_schedule(schedule)
 
 **_Want to learn more?_** Checkout the "Learning Rate Schedules" tutorial for a more basic overview of learning rates found in `mx.lr_scheduler`, and an example of how to use them while training your own models.
 
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
\ No newline at end of file
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/python_docs/python/tutorials/packages/gluon/training/trainer.md b/docs/python_docs/python/tutorials/packages/gluon/training/trainer.md
index e475fda8bfc5..8f140894b2f3 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/training/trainer.md
+++ b/docs/python_docs/python/tutorials/packages/gluon/training/trainer.md
@@ -21,9 +21,9 @@ Training a neural network model consists of iteratively performing three simple
 
 The first step is the forward step which computes the loss.  In MXNet Gluon, this first step is achieved by doing a forward pass by calling `net.forward(X)` or simply `net(X)` and then calling the loss function with the result of the forward pass and the labels. For example `l = loss_fn(net(X), y)`.
 
-The second step is the backward step which computes the gradient of the loss with respect to the parameters. In Gluon, this step is  achieved by doing the first step in an [`autograd.record()`](https:///beta.mxnet.io/guide/packages/autograd/autograd.html) scope to record the computations needed to calculate the loss, and then calling `l.backward()` to compute the gradient of the loss with respect to the parameters.
+The second step is the backward step which computes the gradient of the loss with respect to the parameters. In Gluon, this step is  achieved by doing the first step in an [autograd.record()](/api/python/docs/api/autograd/index.html) scope to record the computations needed to calculate the loss, and then calling `l.backward()` to compute the gradient of the loss with respect to the parameters.
 
-The final step is to update the neural network model parameters using an optimization algorithm. In Gluon, this step is performed by the [`gluon.Trainer`](https:///beta.mxnet.io/api/gluon/mxnet.gluon.Trainer.html) and is the subject of this guide. When creating a  Gluon `Trainer` you must provide a collection of parameters that need to be learnt. You also provide an `Optimizer` that will be used to update the parameters every training iteration when `trainer.step` is called.
+The final step is to update the neural network model parameters using an optimization algorithm. In Gluon, this step is performed by the [gluon.Trainer](/api/python/docs/api/gluon/trainer.html) and is the subject of this guide. When creating a  Gluon `Trainer` you must provide a collection of parameters that need to be learnt. You also provide an `Optimizer` that will be used to update the parameters every training iteration when `trainer.step` is called.
 
 ## Basic Usage
 
@@ -97,7 +97,7 @@ print(curr_weight - net.weight.grad() * 1 / batch_size)
 
 In the previous example, we use the string argument `sgd` to select the optimization method, and `optimizer_params` to specify the optimization method arguments.
 
-All pre-defined optimization methods can be passed in this way and the complete list of implemented optimizers is provided in the [`mxnet.optimizer`](https:///beta.mxnet.io/api/gluon-related/mxnet.optimizer.html) module.
+All pre-defined optimization methods can be passed in this way and the complete list of implemented optimizers is provided in the [`mxnet.optimizer`](/api/python/docs/api/optimizer/index.html) module.
 
 However we can also pass an optimizer instance directly to the `Trainer` constructor.
 
@@ -114,14 +114,14 @@ trainer.step(batch_size)
 net.weight.data()
 ```
 
-For reference and implementation details about each optimizer, please refer to the [guide](https:///beta.mxnet.io/guide/packages/optimizer/optimizer.html) for the `optimizer` module.
+For reference and implementation details about each optimizer, please refer to the [guide](/api/python/docs/api/optimizer/index.html) for the `optimizer` module.
 
 ### KVStore Options
 
 The `Trainer` constructor also accepts the following keyword arguments for :
 
-- `kvstore` – how key value store  should be created for multi-gpu and distributed training. Check out  [`mxnet.kvstore.KVStore`](https:///beta.mxnet.io/api/gluon-related/mxnet.kvstore.KVStore.html#mxnet.kvstore.KVStore) for more information. String options are any of the following ['local', 'device', 'dist_device_sync', 'dist_device_async'].
-- `compression_params` – Specifies type of gradient compression and additional arguments depending on the type of compression being used. See [`mxnet.KVStore.set_gradient_compression_method`](https:///beta.mxnet.io/api/gluon-related/_autogen/mxnet.kvstore.KVStore.set_gradient_compression.html#mxnet.kvstore.KVStore.set_gradient_compression) for more details on gradient compression.
+- `kvstore` – how key value store  should be created for multi-gpu and distributed training. Check out  [`mxnet.kvstore.KVStore`](/api/python/docs/api/kvstore/index.html) for more information. String options are any of the following ['local', 'device', 'dist_device_sync', 'dist_device_async'].
+- `compression_params` – Specifies type of gradient compression and additional arguments depending on the type of compression being used. See [`mxnet.KVStore.set_gradient_compression_method`](/api/python/docs/api/kvstore/index.html#mxnet.kvstore.KVStore.set_gradient_compression) for more details on gradient compression.
 - `update_on_kvstore` – Whether to perform parameter updates on KVStore. If None, then the `Trainer` instance  will choose the more suitable option depending on the type of KVStore.
 
 ### Changing the Learning Rate
@@ -143,7 +143,7 @@ trainer.learning_rate
 
 
 
-In addition, there are multiple pre-defined learning rate scheduling methods that are already implemented in the [`mxnet.lr_scheduler`](https://mxnet.apache.org/api/python/docs/api/gluon-related/mxnet.lr_scheduler.html) module. The learning rate schedulers can be incorporated into your trainer by passing them in as an `optimizer_param` entry. Please refer to the [LR scheduler guide](https://mxnet.apache.org/versions/master/tutorials/gluon/learning_rate_schedules.html) to learn more.
+In addition, there are multiple pre-defined learning rate scheduling methods that are already implemented in the [mxnet.lr_scheduler](/api/python/docs/api/lr_scheduler/index.html) module. The learning rate schedulers can be incorporated into your trainer by passing them in as an `optimizer_param` entry. Please refer to the [LR scheduler guide](/api/python/docs/tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html) to learn more.
 
 
 
@@ -160,9 +160,9 @@ In addition, there are multiple pre-defined learning rate scheduling methods tha
 
 While optimization and optimizers play a significant role in deep learning model training, there are still other important components to model training. Here are a few suggestions about where to look next.
 
-* The [Optimizer API](https://mxnet.apache.org/api/python/docs/api/gluon-related/mxnet.optimizer.html) and [guide](https://mxnet.apache.org/api/python/docs/tutorials/packages/optimizer/optimizer.html) have information about all the different optimizers implemented in MXNet and their update steps. The [Dive into Deep Learning](https://en.diveintodeeplearning.org/chapter_optimization/index.html) book also has a chapter dedicated to optimization methods and explains various key optimizers in great detail.
+* The [Optimizer API](/api/python/docs/api/optimizer/index.html) and [optimizer guide](/api/python/docs/tutorials/packages/optimizer/index.html) have information about all the different optimizers implemented in MXNet and their update steps. The [Dive into Deep Learning](https://en.diveintodeeplearning.org/chapter_optimization/index.html) book also has a chapter dedicated to optimization methods and explains various key optimizers in great detail.
 
-- Take a look at the [guide to parameter initialization](https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/init.html) in MXNet to learn about what initialization schemes are already implemented, and how to implement your custom initialization schemes.
-- Also check out this  [guide on parameter management](https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/parameters.html) to learn about how to manage model parameters in gluon.
-- Make sure to take a look at the [guide to scheduling learning rates](https://mxnet.apache.org/versions/master/tutorials/gluon/learning_rate_schedules.html) to learn how to create learning rate schedules to make your training converge faster.
-- Finally take a look at the [KVStore API](https://mxnet.apache.org/api/python/docs/api/gluon-related/mxnet.kvstore.KVStore.html#mxnet.kvstore.KVStore) to learn how parameter values are synchronized over multiple devices.
+- Take a look at the [guide to parameter initialization](/api/python/docs/tutorials/packages/gluon/blocks/init.html) in MXNet to learn about what initialization schemes are already implemented, and how to implement your custom initialization schemes.
+- Also check out this  [guide on parameter management](/api/python/docs/tutorials/packages/gluon/blocks/parameters.html) to learn about how to manage model parameters in gluon.
+- Make sure to take a look at the [guide to scheduling learning rates](/api/python/docs/tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html) to learn how to create learning rate schedules to make your training converge faster.
+- Finally take a look at the [KVStore API](/api/python/docs/api/kvstore/index.html) to learn how parameter values are synchronized over multiple devices.
diff --git a/docs/python_docs/python/tutorials/packages/kvstore/index.rst b/docs/python_docs/python/tutorials/packages/kvstore/index.rst
index d3d762d91ea1..f03c308e6a37 100644
--- a/docs/python_docs/python/tutorials/packages/kvstore/index.rst
+++ b/docs/python_docs/python/tutorials/packages/kvstore/index.rst
@@ -26,16 +26,11 @@ KVStore
 
       How to use the KVStore API to use multiple GPUs when training a model.
 
-   .. card::
-      :title: Distributed Key-Value Store
-      :link: https://mxnet.apache.org/versions/master/tutorials/python/kvstore.html
-
-      How to use the KVStore API to share data across different devices.
 
 References
 -----------------
 
-- `KVStore API. <../api/gluon-related/mxnet.kvstore.html>`_
+- `KVStore API. </api/python/docs/api/kvstore/index.html>`_
 
 .. toctree::
    :hidden:
diff --git a/docs/python_docs/python/tutorials/packages/kvstore/kvstore.md b/docs/python_docs/python/tutorials/packages/kvstore/kvstore.md
index 5b5dc94a7c2f..c03a03d1080e 100644
--- a/docs/python_docs/python/tutorials/packages/kvstore/kvstore.md
+++ b/docs/python_docs/python/tutorials/packages/kvstore/kvstore.md
@@ -108,7 +108,7 @@ print(b[1].asnumpy())
 ## Handle a List of Key-Value Pairs
 
 All operations introduced so far involve a single key. KVStore also provides
-an interface for a list of key-value pairs. 
+an interface for a list of key-value pairs.
 
 For a single device:
 
@@ -166,6 +166,6 @@ When the distributed version is ready, we will update this section.
 <!-- flexibly as your choice.  -->
 
 ## Next Steps
-* [MXNet tutorials index](https://mxnet.io/api)
+* [MXNet tutorials index](/api/python/docs/tutorials/)
 
 <!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/python_docs/python/tutorials/packages/ndarray/01-ndarray-intro.md b/docs/python_docs/python/tutorials/packages/ndarray/01-ndarray-intro.md
index a8c8056614ab..6bc373e356e8 100644
--- a/docs/python_docs/python/tutorials/packages/ndarray/01-ndarray-intro.md
+++ b/docs/python_docs/python/tutorials/packages/ndarray/01-ndarray-intro.md
@@ -23,10 +23,10 @@ will introduce you to how data is handled with MXNet. You will learn the basics
 about MXNet's multi-dimensional array format, `ndarray`.
 
 This content was extracted and simplified from the gluon tutorials in
-[Dive Into Deep Learning](http://gluon.io/).
+[Dive Into Deep Learning](https://d2l.ai/).
 
 ## Prerequisites
-* [MXNet installed in a Python environment](../../../install/index.html?language=Python).
+* [MXNet installed in a Python environment](/get_started?version=master&platform=linux&language=python&environ=pip&processor=cpu).
 * Python 2.7.x or Python 3.x
 
 
@@ -78,9 +78,7 @@ print(x)
 values of any of its entries. This means that the entries can have any form of
 values, including very big ones! Typically, we'll want our matrices initialized
 and very often we want a matrix of all zeros, so we can use the `.zeros`
-function. If you're feeling experimental, try one of the several [array creation
-functions](https://mxnet.apache.org/api/{.python
-.input}/ndarray/ndarray.html#array-creation-routines).
+function.
 
 <!-- showing something
 different here (3,10) since the zeros may not produce anything different from
@@ -93,8 +91,7 @@ print(x)
 ```
 
 Similarly, `ndarray` has a function to create a matrix of all ones aptly named
-[ones](https://mxnet.apache.org/api/{.python
-.input}/ndarray.html?highlight=random_normal#mxnet.ndarray.ones).
+[ones](/api/python/docs/api/ndarray/ndarray.html#mxnet.ndarray.ones).
 
 ```python
 x = nd.ones((3, 4))
@@ -105,8 +102,7 @@ Often, we'll want to create arrays whose values are sampled randomly. This is
 especially common when we intend to use the array as a parameter in a neural
 network. In this snippet, we initialize with values drawn from a standard normal
 distribution with zero mean and unit variance using
-[random_normal](https://mxnet.apache.org/api/{.python
-.input}/ndarray.html?highlight=random_normal#mxnet.ndarray.random_normal).
+[random_normal](/api/python/docs/api/ndarray/ndarray.html#mxnet.ndarray.random_normal).
 
 <!--
 Is it that important to introduce zero mean and unit variance right now?
diff --git a/docs/python_docs/python/tutorials/packages/ndarray/02-ndarray-operations.md b/docs/python_docs/python/tutorials/packages/ndarray/02-ndarray-operations.md
index 0f2520f2a322..352f6b7a0f34 100644
--- a/docs/python_docs/python/tutorials/packages/ndarray/02-ndarray-operations.md
+++ b/docs/python_docs/python/tutorials/packages/ndarray/02-ndarray-operations.md
@@ -21,10 +21,10 @@
 This guide will introduce you to MXNet's array operations.
 
 This content was extracted and simplified from the gluon tutorials in
-[Dive Into Deep Learning](http://gluon.io/).
+[Dive Into Deep Learning](https://d2l.ai/).
 
 ## Prerequisites
-* [MXNet installed in a Python environment](../../../install/index.html?language=Python).
+* [MXNet installed in a Python environment](/get_started).
 * Python 2.7.x or Python 3.x
 
 
@@ -35,12 +35,12 @@ Such as element-wise addition:
 <!-- keeping it
 easy -->
 
-```{.python .input}
+```python
 import mxnet as mx
 from mxnet import nd
 ```
 
-```{.python .input}
+```python
 x = nd.ones((3, 4))
 y = nd.random_normal(0, 1, shape=(3, 4))
 print('x=', x)
@@ -51,7 +51,7 @@ print('x = x + y, x=', x)
 
 Multiplication:
 
-```{.python .input}
+```python
 x = nd.array([1, 2, 3])
 y = nd.array([2, 2, 2])
 x * y
@@ -61,7 +61,7 @@ And exponentiation:
 <!-- with these next ones we'll just have to take your word
 for it... -->
 
-```{.python .input}
+```python
 nd.exp(x)
 ```
 
@@ -69,13 +69,10 @@ We can also grab a matrix's transpose to compute a proper matrix-matrix product.
 <!-- because we need to do that before we have coffee every day... and you know
 how those dirty, improper matrixeses can be... -->
 
-```{.python .input}
+```python
 nd.dot(x, y.T)
 ```
 
-We'll explain these operations and present even more operators in the [linear
-algebra](P01-C03-linear-algebra.ipynb) chapter. But for now, we'll stick with
-the mechanics of working with NDArrays.
 
 ## In-place operations
 
@@ -96,7 +93,7 @@ detail, and quite possibily in its own notebook since I think it will help to
 show some gotchas like you mentioned verbally. I am still leaning toward
 delaying the introduction of this topic....-->
 
-```{.python .input}
+```python
 print('y=', y)
 print('id(y):', id(y))
 y = y + x
@@ -107,7 +104,7 @@ print('id(y):', id(y))
 We can assign the result to a previously allocated array with slice notation,
 e.g., `result[:] = ...`.
 
-```{.python .input}
+```python
 print('x=', x)
 z = nd.zeros_like(x)
 print('z is zeros_like x, z=', z)
@@ -123,7 +120,7 @@ before copying it to z. To make better use of memory, we can perform operations
 in place, avoiding temporary buffers. To do this we specify the `out` keyword
 argument every operator supports:
 
-```{.python .input}
+```python
 print('x=', x, 'is in id(x):', id(x))
 print('y=', y, 'is in id(y):', id(y))
 print('z=', z, 'is in id(z):', id(z))
@@ -139,7 +136,7 @@ itself. There are two ways to do this in MXNet.
 = x op y
 2. By using the op-equals operators like `+=`
 
-```{.python .input}
+```python
 print('x=', x, 'is in id(x):', id(x))
 x += y
 print('x=', x, 'is in id(x):', id(x))
@@ -158,7 +155,7 @@ the whole array: a[:]
 
 Here's an example of reading the second and third rows from `x`.
 
-```{.python .input}
+```python
 x = nd.array([1, 2, 3])
 print('1D complete array, x=', x)
 s = x[1:3]
@@ -171,7 +168,7 @@ print('slicing the 2nd and 3rd elements, s=', s)
 
 Now let's try writing to a specific element.
 
-```{.python .input}
+```python
 print('original x, x=', x)
 x[2] = 9.0
 print('replaced entire row with x[2] = 9.0, x=', x)
@@ -183,7 +180,7 @@ print('replaced range of elements with x[1:2,1:3] = 5.0, x=', x)
 
 Multi-dimensional slicing is also supported.
 
-```{.python .input}
+```python
 x = nd.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
 print('original x, x=', x)
 s = x[1:2,1:3]
@@ -217,7 +214,7 @@ a shape like (3,3) you lose some of the impact and miss some errors if people
 play with the values. Better to have a distinct shape so that it is more obvious
 what is happening and what can break.-->
 
-```{.python .input}
+```python
 x = nd.ones(shape=(3,6))
 print('x = ', x)
 y = nd.arange(6)
@@ -233,7 +230,7 @@ That's because broadcasting prefers to duplicate along the left most axis.
 We can alter this behavior by explicitly giving `y` a $2$D shape using `.reshape`.
 You can also chain `.arange` and `.reshape` to do this in one step.
 
-```{.python .input}
+```python
 y = y.reshape((3,1))
 print('y = ', y)
 print('x + y = ', x+y)
@@ -245,12 +242,12 @@ print('y = ', y)
 Converting MXNet NDArrays to and from
 NumPy is easy. The converted arrays do not share memory.
 
-```{.python .input}
+```python
 a = x.asnumpy()
 type(a)
 ```
 
-```{.python .input}
+```python
 y = nd.array(a)
 print('id(a)=', id(a), 'id(x)=', id(x), 'id(y)=', id(y))
 ```
diff --git a/docs/python_docs/python/tutorials/packages/ndarray/03-ndarray-contexts.md b/docs/python_docs/python/tutorials/packages/ndarray/03-ndarray-contexts.md
index f6e365974d64..006d77c03263 100644
--- a/docs/python_docs/python/tutorials/packages/ndarray/03-ndarray-contexts.md
+++ b/docs/python_docs/python/tutorials/packages/ndarray/03-ndarray-contexts.md
@@ -21,10 +21,10 @@
 This guide will introduce you to managing CPU versus GPU contexts for handling data.
 
 This content was extracted and simplified from the gluon tutorials in
-[Dive Into Deep Learning](http://gluon.io/).
+[Dive Into Deep Learning](https://d2l.ai/).
 
 ## Prerequisites
-* [MXNet installed (with GPU support) in a Python environment](../../../install/index.html?language=Python).
+* [MXNet installed (with GPU support) in a Python environment](/get_started).
 * Python 2.7.x or Python 3.x
 * **One or more GPUs**
 
diff --git a/docs/python_docs/python/tutorials/packages/ndarray/gotchas_numpy_in_mxnet.md b/docs/python_docs/python/tutorials/packages/ndarray/gotchas_numpy_in_mxnet.md
index 69ec2bf0afbf..1fe40bc1671f 100644
--- a/docs/python_docs/python/tutorials/packages/ndarray/gotchas_numpy_in_mxnet.md
+++ b/docs/python_docs/python/tutorials/packages/ndarray/gotchas_numpy_in_mxnet.md
@@ -22,29 +22,29 @@ The goal of this tutorial is to explain some common misconceptions about using [
 
 ## Asynchronous and non-blocking nature of Apache MXNet
 
-Instead of using NumPy arrays Apache MXNet offers its own array implementation named [NDArray](https://mxnet.apache.org/api/python/ndarray/ndarray.html). `NDArray API` was intentionally designed to be similar to `NumPy`, but there are differences.
+Instead of using NumPy arrays Apache MXNet offers its own array implementation named [NDArray](/api/python/docs/api/ndarray/index.html). `NDArray API` was intentionally designed to be similar to `NumPy`, but there are differences.
 
-One key difference is in the way calculations are executed. Every `NDArray` manipulation in Apache MXNet is done in asynchronous, non-blocking way. That means, that when we write code like `c = a * b`, where both `a` and `b` are `NDArrays`, the function is pushed to the [Execution Engine](https://mxnet.apache.org/architecture/overview.html#execution-engine), which starts the calculation. The function immediately returns back, and the  user thread can continue execution, despite the fact that the calculation may not have been completed yet. 
+One key difference is in the way calculations are executed. Every `NDArray` manipulation in Apache MXNet is done in asynchronous, non-blocking way. That means, that when we write code like `c = a * b`, where both `a` and `b` are `NDArrays`, the function is pushed to the [Execution Engine](/api/architecture/overview.html#execution-engine), which starts the calculation. The function immediately returns back, and the  user thread can continue execution, despite the fact that the calculation may not have been completed yet.
 
-`Execution Engine` builds the computation graph which may reorder or combine some calculations, but it honors dependency order: if there are other manipulation with `c` done later in the code, the `Execution Engine` will start doing them once the result of `c` is available. We don't need to write callbacks to start execution of subsequent code - the `Execution Engine` is going to do it for us. 
+`Execution Engine` builds the computation graph which may reorder or combine some calculations, but it honors dependency order: if there are other manipulation with `c` done later in the code, the `Execution Engine` will start doing them once the result of `c` is available. We don't need to write callbacks to start execution of subsequent code - the `Execution Engine` is going to do it for us.
 
-To get the result of the computation we only need to access the resulting variable, and the flow of the code will be blocked until the computation results are assigned to the resulting variable. This behavior allows to increase code performance while still supporting imperative programming mode. 
+To get the result of the computation we only need to access the resulting variable, and the flow of the code will be blocked until the computation results are assigned to the resulting variable. This behavior allows to increase code performance while still supporting imperative programming mode.
 
-Refer to the [intro tutorial to NDArray](https://mxnet.apache.org/tutorials/basic/ndarray.html), if you are new to Apache MXNet and would like to learn more how to manipulate NDArrays.
+Refer to the [intro tutorial to NDArray](/api/python/docs/tutorials/packages/ndarray/index.html), if you are new to Apache MXNet and would like to learn more how to manipulate NDArrays.
 
 ## Converting NDArray to NumPy Array blocks calculation
 
-Many people are familiar with NumPy and flexible doing tensor manipulations using it. `NDArray API` offers  a convinient [.asnumpy() method](https://mxnet.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.asnumpy) to cast `nd.array` to `np.array`. However, by doing this cast and using `np.array` for calculation, we cannot use all the goodness of `Execution Engine`. All manipulations done on `np.array` are blocking. Moreover, the cast to `np.array` itself is a blocking operation (same as [.asscalar()](https://mxnet.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.asscalar), [.wait_to_read()](https://mxnet.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.wait_to_read) and [.waitall()](https://mxnet.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.waitall)). 
+Many people are familiar with NumPy and flexible doing tensor manipulations using it. `NDArray API` offers  a convinient [.asnumpy() method](/api/python/docs/api/ndarray/ndarray.html#mxnet.ndarray.NDArray.asnumpy) to cast `nd.array` to `np.array`. However, by doing this cast and using `np.array` for calculation, we cannot use all the goodness of `Execution Engine`. All manipulations done on `np.array` are blocking. Moreover, the cast to `np.array` itself is a blocking operation (same as [.asscalar()](/api/python/docs/api/ndarray/ndarray.html#mxnet.ndarray.NDArray.asscalar), [.wait_to_read()](/api/python/docs/api/ndarray/ndarray.html#mxnet.ndarray.NDArray.wait_to_read) and [.waitall()](/api/python/docs/api/ndarray/ndarray.html#mxnet.ndarray.waitall)).
 
 That means that if we have a long computation graph and, at some point, we want to cast the result to `np.array`, it may feel like the casting takes a lot of time. But what really takes this time is `Execution Engine`, which finishes all the async calculations we have pushed into it to get the final result, which then will be converted to `np.array`.
 
-Because of the blocking nature of [.asnumpy() method](https://mxnet.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.asnumpy), using it reduces the execution performance, especially if the calculations are done on GPU: Apache MXNet has to copy data from GPU to CPU to return `np.array`. 
+Because of the blocking nature of [.asnumpy() method](/api/python/docs/api/ndarray/ndarray.html#mxnet.ndarray.NDArray.asnumpy), using it reduces the execution performance, especially if the calculations are done on GPU: Apache MXNet has to copy data from GPU to CPU to return `np.array`.
 
 The best solution is to **make manipulations directly on NDArrays by methods provided in [NDArray API](https://mxnet.apache.org/api/python/ndarray/ndarray.html)**.
 
 ## NumPy operators vs. NDArray operators
 
-Despite the fact that [NDArray API](https://mxnet.apache.org/api/python/ndarray/ndarray.html) was specifically designed to be similar to `NumPy`, sometimes it is not easy to replace existing `NumPy` computations. The main reason is that not all operators, that are available in `NumPy`, are available in `NDArray API`. The list of currently available operators is available on [NDArray class page](http://mxnet.apache.org/api/python/ndarray/ndarray.html#the-ndarray-class).
+Despite the fact that [NDArray API](/api/python/docs/api/ndarray/index.html) was specifically designed to be similar to `NumPy`, sometimes it is not easy to replace existing `NumPy` computations. The main reason is that not all operators, that are available in `NumPy`, are available in `NDArray API`. The list of currently available operators is available on [NDArray class page](/api/python/docs/api/ndarray/ndarray.html).
 
 If a required operator is missing from `NDArray API`, there are few things you can do.
 
@@ -57,7 +57,7 @@ There are a situation, when you can assemble a higher level operator using exist
 from mxnet import nd
 import numpy as np
 
-# NumPy has full_like() operator 
+# NumPy has full_like() operator
 np_y = np.full_like(a=np.arange(6, dtype=int), fill_value=10)
 
 # NDArray doesn't have it, but we can replace it with
@@ -73,29 +73,29 @@ np.array_equal(np_y, nd_y.asnumpy())
 
 ### Find similar operator with different name and/or signature
 
-Some operators may have slightly different name, but are similar in terms of functionality. For example [nd.ravel_multi_index()](https://mxnet.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.ravel_multi_index) is similar to [np.ravel()](https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.ma.ravel.html#numpy.ma.ravel). In other cases some operators may have similar names, but different signatures. For example [np.split()](https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.split.html#numpy.split) and [nd.split()](https://mxnet.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.split) are similar, but the former works with indices and the latter requires the number of splits to be provided.
+Some operators may have slightly different name, but are similar in terms of functionality. For example [nd.ravel_multi_index()](/api/python/docs/api/ndarray/ndarray.html#mxnet.ndarray.ravel_multi_index) is similar to [np.ravel()](https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.ma.ravel.html#numpy.ma.ravel). In other cases some operators may have similar names, but different signatures. For example [np.split()](https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.split.html#numpy.split) and [nd.split()](/api/python/docs/api/ndarray/ndarray.html#mxnet.ndarray.split) are similar, but the former works with indices and the latter requires the number of splits to be provided.
 
-One particular example of different input requirements is [nd.pad()](https://mxnet.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.pad). The trick is that it can only work with 4-dimensional tensors. If your input has less dimensions, then you need to expand its number before using `nd.pad()` as it is shown in the code block below: 
+One particular example of different input requirements is [nd.pad()](/api/python/docs/api/ndarray/ndarray.html#mxnet.ndarray.pad). The trick is that it can only work with 4-dimensional tensors. If your input has less dimensions, then you need to expand its number before using `nd.pad()` as it is shown in the code block below:
 
 
 ```python
 def pad_array(data, max_length):
     # expand dimensions to 4, because nd.pad can work only with 4 dims
     data_expanded = data.reshape(1, 1, 1, data.shape[0])
-    
+
     # pad all 4 dimensions with constant value of 0
     data_padded = nd.pad(data_expanded,
                              mode='constant',
                              pad_width=[0, 0, 0, 0, 0, 0, 0, max_length - data.shape[0]],
                              constant_value=0)
-    
-    # remove temporary dimensions 
+
+    # remove temporary dimensions
     data_reshaped_back = data_padded.reshape(max_length)
     return data_reshaped_back
 
 pad_array(nd.array([1, 2, 3]), max_length=10)
 ```
-    
+
 `[ 1.  2.  3.  0.  0.  0.  0.  0.  0.  0.]` <!--notebook-skip-line-->
 
 
@@ -104,7 +104,7 @@ pad_array(nd.array([1, 2, 3]), max_length=10)
 
 ### Search for an operator on [Github](https://github.com/apache/incubator-mxnet/labels/Operator)
 
-Apache MXNet community is responsive to requests, and everyone is welcomed to contribute new operators. Have in mind, that there is always a lag between new operators being merged into the codebase and release of a next stable version. For example, [nd.diag()](https://github.com/apache/incubator-mxnet/pull/11643) operator was recently introduced to Apache MXNet, but on the moment of writing this tutorial, it is not in any stable release. You can always get all latest implementations by installing the [master version](https://mxnet.apache.org/install/index.html?version=master#) of Apache MXNet.
+Apache MXNet community is responsive to requests, and everyone is welcomed to contribute new operators. Have in mind, that there is always a lag between new operators being merged into the codebase and release of a next stable version. For example, [nd.diag()](https://github.com/apache/incubator-mxnet/pull/11643) operator was recently introduced to Apache MXNet, but on the moment of writing this tutorial, it is not in any stable release. You can always get all latest implementations by installing the [master version](/get_started?version=master&platform=linux&language=python&environ=pip&processor=cpu#) of Apache MXNet.
 
 ## How to minimize the impact of blocking calls
 
@@ -156,10 +156,10 @@ for data, label in train_data:
         out = net(data)
         # This call saves new loss and returns previous loss
         prev_loss = loss_buffer.new_loss(ce(out, label))
-        
+
     loss_buffer.loss.backward()
     trainer.step(data.shape[0])
-    
+
     if prev_loss is not None:
         print("Loss: {}".format(np.mean(prev_loss.asnumpy())))
 ```
@@ -184,4 +184,4 @@ for data, label in train_data:
 
 For performance reasons, it is better to use native `NDArray API` methods and avoid using NumPy altogether. In case when you must use NumPy, you can use convenient method `.asnumpy()` on `NDArray` to get NumPy representation. By doing so, you block the whole computational process, and force data to be synced between CPU and GPU. If it is a necessary evil to do that, try to minimize the blocking time by calling `.asnumpy()` in time, when you expect the value to be already computed.
 
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
\ No newline at end of file
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/python_docs/python/tutorials/packages/ndarray/sparse/row_sparse.md b/docs/python_docs/python/tutorials/packages/ndarray/sparse/row_sparse.md
index 7218ef9beae1..1241182af85b 100644
--- a/docs/python_docs/python/tutorials/packages/ndarray/sparse/row_sparse.md
+++ b/docs/python_docs/python/tutorials/packages/ndarray/sparse/row_sparse.md
@@ -35,12 +35,12 @@ Y = mx.nd.dot(X, W)
 ```
 
 ```
-{'W': 
+{'W':
  [[ 3.  4.  5.]
   [ 6.  7.  8.]]
- <NDArray 2x3 @cpu(0)>, 'X': 
+ <NDArray 2x3 @cpu(0)>, 'X':
  [[ 1.  0.]]
- <NDArray 1x2 @cpu(0)>, 'Y': 
+ <NDArray 1x2 @cpu(0)>, 'Y':
  [[ 3.  4.  5.]]
  <NDArray 1x3 @cpu(0)>}
 ```
@@ -87,7 +87,7 @@ As you can see, row 0 of ``grad_W`` contains non-zero values while row 1 of ``gr
 If you look at how ``grad_W`` is calculated, notice that since column 1 of ``X`` is filled with zeros, row 1 of ``grad_W`` is filled with zeros too.
 
 In the real world, gradients for parameters that interact with sparse inputs ususally have gradients where many row slices are completely zeros. Storing and manipulating such sparse matrices with many row slices of all zeros in the default dense structure results in wasted memory and processing on the zeros. More importantly, many gradient based optimization methods such as SGD, [AdaGrad](https://stanford.edu/~jduchi/projects/DuchiHaSi10_colt.pdf) and [Adam](https://arxiv.org/pdf/1412.6980.pdf)
-take advantage of sparse gradients and prove to be efficient and effective. 
+take advantage of sparse gradients and prove to be efficient and effective.
 **In MXNet, the ``RowSparseNDArray`` stores the matrix in ``row sparse`` format, which is designed for arrays of which most row slices are all zeros.**
 In this tutorial, we will describe what the row sparse format is and how to use RowSparseNDArray for sparse gradient updates in MXNet.
 
@@ -95,7 +95,7 @@ In this tutorial, we will describe what the row sparse format is and how to use
 
 To complete this tutorial, we need:
 
-- MXNet. See the instructions for your operating system in [Setup and Installation](https://mxnet.apache.org/install/index.html)
+- MXNet. See the instructions for your operating system in [Setup and Installation](/get_started)
 - [Jupyter](http://jupyter.org/)
     ```
     pip install jupyter
@@ -267,10 +267,10 @@ indices = a.indices
 
 
 ```
-{'a.stype': 'row_sparse', 'data': 
+{'a.stype': 'row_sparse', 'data':
  [[ 1.  2.]
   [ 3.  4.]]
- <NDArray 2x2 @cpu(0)>, 'indices': 
+ <NDArray 2x2 @cpu(0)>, 'indices':
  [1 4]
  <NDArray 2 @cpu(0)>}
 ```
@@ -294,10 +294,10 @@ dense = rsp.tostype('default')
 
 
 ```
-{'dense': 
+{'dense':
  [[ 1.  1.]
   [ 1.  1.]]
- <NDArray 2x2 @cpu(0)>, 'rsp': 
+ <NDArray 2x2 @cpu(0)>, 'rsp':
  <RowSparseNDArray 2x2 @cpu(0)>}
 ```
 
@@ -318,10 +318,10 @@ dense = mx.nd.sparse.cast_storage(rsp, 'default')
 
 
 ```
-{'dense': 
+{'dense':
  [[ 1.  1.]
   [ 1.  1.]]
- <NDArray 2x2 @cpu(0)>, 'rsp': 
+ <NDArray 2x2 @cpu(0)>, 'rsp':
  <RowSparseNDArray 2x2 @cpu(0)>}
 ```
 
@@ -393,7 +393,7 @@ rsp_retained = mx.nd.sparse.retain(rsp, mx.nd.array([0, 1]))
         [ 0.,  0.],
         [ 3.,  4.],
         [ 5.,  6.],
-        [ 0.,  0.]], dtype=float32), 'rsp_retained': 
+        [ 0.,  0.]], dtype=float32), 'rsp_retained':
  <RowSparseNDArray 5x2 @cpu(0)>, 'rsp_retained.asnumpy()': array([[ 1.,  2.],
         [ 0.,  0.],
         [ 0.,  0.],
@@ -424,7 +424,7 @@ transpose_dot = mx.nd.sparse.dot(lhs, rhs, transpose_a=True)
 
 
 ```
-{'transpose_dot': 
+{'transpose_dot':
  <RowSparseNDArray 5x2 @cpu(0)>, 'transpose_dot.asnumpy()': array([[ 7.,  7.],
         [ 9.,  9.],
         [ 8.,  8.],
@@ -576,7 +576,7 @@ except mx.MXNetError as err:
     sys.stderr.write(str(err))
 ```
 
-## Next 
+## Next
 
 [Train a Linear Regression Model with Sparse Symbols](http://mxnet.apache.org/tutorials/sparse/train.html)
 
diff --git a/docs/python_docs/python/tutorials/packages/onnx/fine_tuning_gluon.md b/docs/python_docs/python/tutorials/packages/onnx/fine_tuning_gluon.md
index e8b0761dbc4d..f77731494215 100644
--- a/docs/python_docs/python/tutorials/packages/onnx/fine_tuning_gluon.md
+++ b/docs/python_docs/python/tutorials/packages/onnx/fine_tuning_gluon.md
@@ -31,7 +31,7 @@ In this tutorial we will:
 ## Pre-requisite
 
 To run the tutorial you will need to have installed the following python modules:
-- [MXNet > 1.1.0](http://mxnet.apache.org/install/index.html)
+- [MXNet > 1.1.0](/get_started)
 - [onnx](https://github.com/onnx/onnx)
 - matplotlib
 
@@ -333,7 +333,7 @@ The trainer will retrain and fine-tune the entire network. If we use `dense_laye
 
 
 ```python
-trainer = gluon.Trainer(net.collect_params(), 'sgd', 
+trainer = gluon.Trainer(net.collect_params(), 'sgd',
                         {'learning_rate': LEARNING_RATE,
                          'wd':WDECAY,
                          'momentum':MOMENTUM})
@@ -388,15 +388,15 @@ for epoch in range(5):
         loss.backward()
         trainer.step(data.shape[0])
 
-    nd.waitall() # wait at the end of the epoch    
-    new_val_accuracy = evaluate_accuracy_gluon(dataloader_test, net)    
+    nd.waitall() # wait at the end of the epoch
+    new_val_accuracy = evaluate_accuracy_gluon(dataloader_test, net)
     print("Epoch [{0}] Test Accuracy {1:.4f} ".format(epoch, new_val_accuracy))
 
     # We perform early-stopping regularization, to prevent the model from overfitting
     if val_accuracy > new_val_accuracy:
         print('Validation accuracy is decreasing, stopping training')
         break
-    val_accuracy = new_val_accuracy              
+    val_accuracy = new_val_accuracy
 ```
 
 `Epoch 4, Test Accuracy 0.8942307829856873`<!--notebook-skip-line-->
@@ -453,4 +453,4 @@ plot_predictions(caltech101_images_test, result, categories, TOP_P)
 ![png](https://github.com/dmlc/web-data/blob/master/mxnet/doc/tutorials/onnx/caltech101_correct.png?raw=true)<!--notebook-skip-line-->
 
 
-**Great!** The network classified these images correctly after being fine-tuned on a dataset that contains images of `wrench`, `dolphin` and `lotus`
\ No newline at end of file
+**Great!** The network classified these images correctly after being fine-tuned on a dataset that contains images of `wrench`, `dolphin` and `lotus`
diff --git a/docs/python_docs/python/tutorials/packages/onnx/inference_on_onnx_model.md b/docs/python_docs/python/tutorials/packages/onnx/inference_on_onnx_model.md
index 9b1e45cb40c7..faad53bc6793 100644
--- a/docs/python_docs/python/tutorials/packages/onnx/inference_on_onnx_model.md
+++ b/docs/python_docs/python/tutorials/packages/onnx/inference_on_onnx_model.md
@@ -29,7 +29,7 @@ In this tutorial we will:
 ## Pre-requisite
 
 To run the tutorial you will need to have installed the following python modules:
-- [MXNet > 1.1.0](http://mxnet.apache.org/install/index.html)
+- [MXNet > 1.1.0](/get_started)
 - [onnx](https://github.com/onnx/onnx) (follow the install guide)
 - matplotlib
 
diff --git a/docs/python_docs/python/tutorials/packages/onnx/super_resolution.md b/docs/python_docs/python/tutorials/packages/onnx/super_resolution.md
index 5d3ad021e25c..eec904d80a64 100644
--- a/docs/python_docs/python/tutorials/packages/onnx/super_resolution.md
+++ b/docs/python_docs/python/tutorials/packages/onnx/super_resolution.md
@@ -24,7 +24,7 @@ In this tutorial we will:
 
 ## Prerequisites
 This example assumes that the following python packages are installed:
-- [mxnet](http://mxnet.apache.org/install/index.html)
+- [mxnet](/get_started)
 - [onnx](https://github.com/onnx/onnx) (follow the install guide)
 - Pillow - A Python Image Processing package and is required for input pre-processing. It can be installed with ```pip install Pillow```.
 - matplotlib
@@ -137,4 +137,4 @@ You can now compare the input image and the resulting output image. As you will
 | ----------- | ------------ | <!--notebook-skip-line-->
 | ![input](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/onnx/images/super_res_input.jpg?raw=true) | ![output](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/onnx/images/super_res_output.jpg?raw=true) | <!--notebook-skip-line-->
 
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
\ No newline at end of file
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/static_site/src/pages/api/faq/distributed_training.md b/docs/static_site/src/pages/api/faq/distributed_training.md
index 169b52183d75..caf0123b7aea 100644
--- a/docs/static_site/src/pages/api/faq/distributed_training.md
+++ b/docs/static_site/src/pages/api/faq/distributed_training.md
@@ -69,7 +69,7 @@ The distributed mode of KVStore is enabled by calling `mxnet.kvstore.create` fun
 with a string argument which contains the word `dist` as follows:
 > kv = mxnet.kvstore.create('dist_sync')
 
-Refer [KVStore API]({{'/api/python/docs/api/gluon-related/mxnet.kvstore.KVStore.html#mxnet.kvstore.KVStore'|relative_url}}) for more information about KVStore.
+Refer [KVStore API]({{'/api/python/docs/api/kvstore/index.html#mxnet.kvstore.KVStore'|relative_url}}) for more information about KVStore.
 
 ### Distribution of Keys
 Each server doesn't necessarily store all the keys or parameter arrays.
@@ -91,7 +91,7 @@ In the case of distributed training though, we would need to divide the dataset
 
 Typically, this split of data for each worker happens through the data iterator,
 on passing the number of parts and the index of parts to iterate over.
-Some iterators in MXNet that support this feature are [mxnet.io.MNISTIterator]({{'/api/python/docs/api/gluon-related/_autogen/mxnet.io.MNISTIter.html#mxnet.io.MNISTIter'|relative_url}}) and [mxnet.io.ImageRecordIter]({{'/api/python/docs/api/gluon-related/_autogen/mxnet.io.ImageRecordIter.html#mxnet.io.ImageRecordIter'|relative_url}}).
+Some iterators in MXNet that support this feature are [mxnet.io.MNISTIterator]({{'//api/mxnet/io/index.html#mxnet.io.MNISTIter'|relative_url}}) and [mxnet.io.ImageRecordIter]({{'/api/mxnet/io/index.html#mxnet.io.ImageRecordIter'|relative_url}}).
 If you are using a different iterator, you can look at how the above iterators implement this.
 We can use the kvstore object to get the number of workers (`kv.num_workers`) and rank of the current worker (`kv.rank`).
 These can be passed as arguments to the iterator.
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/five_minutes_neural_network.md b/docs/static_site/src/pages/api/r/docs/tutorials/five_minutes_neural_network.md
index a5d57347e01e..f7121407892e 100644
--- a/docs/static_site/src/pages/api/r/docs/tutorials/five_minutes_neural_network.md
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/five_minutes_neural_network.md
@@ -25,7 +25,7 @@ permalink: /api/r/docs/tutorials/five_minutes_neural_network
 Develop a Neural Network with MXNet in Five Minutes
 =============================================
 
-This tutorial is designed for new users of the `mxnet` package for R. It shows how to construct a neural network to do regression in 5 minutes. It shows how to perform classification and regression tasks, respectively. The data we use is in the `mlbench` package. Instructions to install R and MXNet's R package in different environments can be found [here](https://mxnet.apache.org/install/index.html?platform=Linux&language=R&processor=CPU).
+This tutorial is designed for new users of the `mxnet` package for R. It shows how to construct a neural network to do regression in 5 minutes. It shows how to perform classification and regression tasks, respectively. The data we use is in the `mlbench` package. Instructions to install R and MXNet's R package in different environments can be found [here](/get_started?version=master&platform=linux&language=r&environ=pip&processor=cpu).
 
 ## Classification
 

From a4ea4a8330251dd244947074cf4ddb875e611dd5 Mon Sep 17 00:00:00 2001
From: Leonard Lausen <lausen@amazon.com>
Date: Thu, 17 Oct 2019 15:17:59 -0700
Subject: [PATCH 457/813] Load NDArray only to GPU if GPU is present (#16432)

* Load NDArray only to GPU if GPU is present

* Add test
---
 src/ndarray/ndarray.cc                |  8 +++++++-
 tests/python/unittest/test_ndarray.py | 16 ++++++++++++++++
 2 files changed, 23 insertions(+), 1 deletion(-)

diff --git a/src/ndarray/ndarray.cc b/src/ndarray/ndarray.cc
index d1c3132b79a7..e15f72fa6cfa 100644
--- a/src/ndarray/ndarray.cc
+++ b/src/ndarray/ndarray.cc
@@ -1819,7 +1819,13 @@ bool NDArray::Load(dmlc::Stream *strm) {
     *this = std::move(temp); return true;
   } else {
 #if MXNET_USE_CUDA
-    *this = temp.Copy(ctx); return true;
+    int device_count = -1;
+    cudaGetDeviceCount(&device_count);
+    if (device_count > 0) {
+      *this = temp.Copy(ctx); return true;
+    } else {
+      *this = std::move(temp); return true;
+    }
 #else
     *this = std::move(temp); return true;
 #endif
diff --git a/tests/python/unittest/test_ndarray.py b/tests/python/unittest/test_ndarray.py
index bee4bff0f7c0..6f3e8ba88b24 100644
--- a/tests/python/unittest/test_ndarray.py
+++ b/tests/python/unittest/test_ndarray.py
@@ -1985,6 +1985,22 @@ def test_large_int_rounding():
     assert np.all(a == large_integer)
 
 
+def test_load_saved_gpu_array_when_no_gpus_are_present():
+    # State obtained with mx.nd.arange(1, ctx=mx.gpu()).__getstate__()
+    # State needs to be exported manually, as running above command will only
+    # work if a gpu is present.
+    ndarray_state = {
+        'handle':
+        bytearray(
+            b'\xc9\xfa\x93\xf9\x00\x00\x00\x00\x01\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00\x02\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'
+        )
+    }
+    array = mx.nd.arange(1)
+    # Test that MXNDArrayLoadFromRawBytes works even if we have built with Cuda
+    # but there are no GPUs
+    array.__setstate__(ndarray_state)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From d1200c9ab827ca9347157e0e37a59d71f96c9296 Mon Sep 17 00:00:00 2001
From: Aaron Markham <markhama@amazon.com>
Date: Thu, 17 Oct 2019 17:43:07 -0700
Subject: [PATCH 458/813] add binary and docs build command options (#16514)

---
 dev_menu.py | 20 ++++++++++++++++++--
 1 file changed, 18 insertions(+), 2 deletions(-)

diff --git a/dev_menu.py b/dev_menu.py
index 0b5db2505671..2f8e19076aa9 100755
--- a/dev_menu.py
+++ b/dev_menu.py
@@ -132,8 +132,24 @@ def provision_virtualenv(venv_path=DEFAULT_PYENV):
     ('[Local] Python Unit tests',
         "./py3_venv/bin/nosetests -v tests/python/unittest/"
     ),
-    ('[Docker] Website and docs build outputs to "docs/_build/html/"',
-        "ci/build.py --platform ubuntu_cpu /work/runtime_functions.sh deploy_docs"),
+    ('[Docker] Build the MXNet binary - outputs to "lib/"',
+        "ci/build.py --platform ubuntu_cpu_lite /work/runtime_functions.sh build_ubuntu_cpu_docs"),
+    ('[Docker] Build the Jekyll website - outputs to "docs/static_site/build/html/"',
+        "ci/build.py --platform ubuntu_cpu_jekyll /work/runtime_functions.sh build_jekyll_docs"),
+    ('[Docker] Build the Python API docs - outputs to "docs/python_docs/python/build/_build/html/"',
+        "ci/build.py --platform ubuntu_cpu_python /work/runtime_functions.sh build_python_docs"),
+    ('[Docker] Build the C++ API docs - outputs to "docs/cpp_docs/build/html/html/"',
+        "ci/build.py --platform ubuntu_cpu_c /work/runtime_functions.sh build_c_docs"),
+    ('[Docker] Build the Clojure API docs - outputs to "contrib/clojure-package/target/doc"',
+        "ci/build.py --platform ubuntu_cpu_scala /work/runtime_functions.sh build_clojure_docs"),
+    ('[Docker] Build the Java API docs - outputs to "docs/scala-package/build/docs/java"',
+        "ci/build.py --platform ubuntu_cpu_scala /work/runtime_functions.sh build_java_docs"),
+    ('[Docker] Build the Julia API docs - outputs to "julia/docs/site/"',
+        "ci/build.py --platform ubuntu_cpu_julia /work/runtime_functions.sh build_julia_docs"),
+    ('[Docker] Build the R API docs - outputs to "R-package/build/mxnet-r-reference-manual.pdf"',
+        "ci/build.py --platform ubuntu_cpu_r /work/runtime_functions.sh build_r_docs"),
+    ('[Docker] Build the Scala API docs - outputs to "scala-package/docs/build/docs/scala"',
+        "ci/build.py --platform ubuntu_cpu_scala /work/runtime_functions.sh build_scala_docs"),
     ('[Docker] sanity_check. Check for linting and code formatting and licenses.',
     [
         "ci/build.py --platform ubuntu_cpu /work/runtime_functions.sh sanity_check",

From e4f8c505b4023420d86cff7fc16e273f83193f31 Mon Sep 17 00:00:00 2001
From: Zhennan Qin <zhennan.qin@intel.com>
Date: Thu, 17 Oct 2019 20:04:08 -0500
Subject: [PATCH 459/813] [MKLDNN] Fix uint quantized fc when not fusing with
 requantize (#16523)

Change-Id: I1a13e0d10a371aa631ab98c2f0499331048b7f6e
---
 .../mkldnn/mkldnn_quantized_fully_connected.cc       |  9 +++++++--
 src/operator/subgraph/mkldnn/mkldnn_fc.cc            | 12 +++++++++---
 2 files changed, 16 insertions(+), 5 deletions(-)

diff --git a/src/operator/quantization/mkldnn/mkldnn_quantized_fully_connected.cc b/src/operator/quantization/mkldnn/mkldnn_quantized_fully_connected.cc
index aca129a56f3e..f451ff3c977d 100644
--- a/src/operator/quantization/mkldnn/mkldnn_quantized_fully_connected.cc
+++ b/src/operator/quantization/mkldnn/mkldnn_quantized_fully_connected.cc
@@ -80,8 +80,13 @@ void MKLDNNQuantizedFullyConnectedForward(const nnvm::NodeAttrs &attrs,
   }
 
   Stream<cpu> *s = ctx.get_stream<cpu>();
-  mxnet_op::Kernel<QuantizationRangeForS8S8MultiplicationStruct, cpu>::Launch(s, 1,
-    min_output_ptr, max_output_ptr, &min_data, &max_data, &min_weight, &max_weight);
+  if (data.dtype() == mshadow::kInt8) {
+    mxnet_op::Kernel<QuantizationRangeForS8S8MultiplicationStruct, cpu>::Launch(
+        s, 1, min_output_ptr, max_output_ptr, &min_data, &max_data, &min_weight, &max_weight);
+  } else {
+    mxnet_op::Kernel<QuantizationRangeForS8U8MultiplicationStruct, cpu>::Launch(
+        s, 1, min_output_ptr, max_output_ptr, &min_data, &max_data, &min_weight, &max_weight);
+  }
 
   bool is_train = false;
   mkldnn::memory::desc out_md = GetMemDesc(out_data[fullc::kOut]);
diff --git a/src/operator/subgraph/mkldnn/mkldnn_fc.cc b/src/operator/subgraph/mkldnn/mkldnn_fc.cc
index a4f0bfa32ade..971dd0505a6d 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_fc.cc
+++ b/src/operator/subgraph/mkldnn/mkldnn_fc.cc
@@ -177,9 +177,15 @@ void SgMKLDNNFCOp::Forward(const OpContext &ctx,
           MaxAbs(cached_min_output_, cached_max_output_) / data_scale / weight_scale;
       } else {
         Stream<cpu> *s = ctx.get_stream<cpu>();
-        mxnet_op::Kernel<QuantizationRangeForS8S8MultiplicationStruct, cpu>::Launch(
-          s, 1, &cached_min_output_, &cached_max_output_,
-          &min_data, &max_data, &min_weight, &max_weight);
+        if (data.dtype() == mshadow::kInt8) {
+          mxnet_op::Kernel<QuantizationRangeForS8S8MultiplicationStruct, cpu>::Launch(
+              s, 1, &cached_min_output_, &cached_max_output_, &min_data, &max_data, &min_weight,
+              &max_weight);
+        } else {
+          mxnet_op::Kernel<QuantizationRangeForS8U8MultiplicationStruct, cpu>::Launch(
+              s, 1, &cached_min_output_, &cached_max_output_, &min_data, &max_data, &min_weight,
+              &max_weight);
+        }
       }
     }
 

From f6cfbdf92f30eb607b6d4e4a52376a7b0bc4e20e Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Thu, 17 Oct 2019 18:23:36 -0700
Subject: [PATCH 460/813] improve unary and binary operator handling and
 refactor tests (#16423)

---
 python/mxnet/ndarray/numpy/_op.py             | 358 ++++++++++---
 python/mxnet/numpy/multiarray.py              | 347 +++++++++---
 python/mxnet/symbol/numpy/_symbol.py          | 233 ++++++--
 python/mxnet/util.py                          | 106 ++++
 .../numpy/np_elemwise_broadcast_op.cc         |   2 +
 .../elemwise_binary_broadcast_op-inl.cuh      |  18 +-
 tests/python/unittest/test_numpy_op.py        | 506 +++++-------------
 7 files changed, 979 insertions(+), 591 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index fbdb8e98a15f..08bcdb7833fb 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -23,6 +23,7 @@
 import numpy as _np
 from ...base import numeric_types
 from ...util import _sanity_check_params, set_module
+from ...util import wrap_np_unary_func, wrap_np_binary_func
 from ...context import current_context
 from . import _internal as _npi
 from ..ndarray import NDArray
@@ -498,8 +499,10 @@ def unique(ar, return_index=False, return_inverse=False, return_counts=False, ax
 
 
 @set_module('mxnet.ndarray.numpy')
-def add(x1, x2, out=None):
-    """Add arguments element-wise.
+@wrap_np_binary_func
+def add(x1, x2, out=None, **kwargs):
+    """
+    Add arguments element-wise.
 
     Parameters
     ----------
@@ -521,8 +524,10 @@ def add(x1, x2, out=None):
 
 
 @set_module('mxnet.ndarray.numpy')
-def subtract(x1, x2, out=None):
-    """Subtract arguments element-wise.
+@wrap_np_binary_func
+def subtract(x1, x2, out=None, **kwargs):
+    """
+    Subtract arguments element-wise.
 
     Parameters
     ----------
@@ -546,8 +551,10 @@ def subtract(x1, x2, out=None):
 
 
 @set_module('mxnet.ndarray.numpy')
-def multiply(x1, x2, out=None):
-    """Multiply arguments element-wise.
+@wrap_np_binary_func
+def multiply(x1, x2, out=None, **kwargs):
+    """
+    Multiply arguments element-wise.
 
     Parameters
     ----------
@@ -570,8 +577,10 @@ def multiply(x1, x2, out=None):
 
 
 @set_module('mxnet.ndarray.numpy')
-def divide(x1, x2, out=None):
-    """Returns a true division of the inputs, element-wise.
+@wrap_np_binary_func
+def divide(x1, x2, out=None, **kwargs):
+    """
+    Returns a true division of the inputs, element-wise.
 
     Parameters
     ----------
@@ -596,8 +605,10 @@ def divide(x1, x2, out=None):
 
 
 @set_module('mxnet.ndarray.numpy')
-def mod(x1, x2, out=None):
-    """Return element-wise remainder of division.
+@wrap_np_binary_func
+def mod(x1, x2, out=None, **kwargs):
+    """
+    Return element-wise remainder of division.
 
     Parameters
     ----------
@@ -621,8 +632,10 @@ def mod(x1, x2, out=None):
 
 
 @set_module('mxnet.ndarray.numpy')
+@wrap_np_binary_func
 def remainder(x1, x2, out=None):
-    """Return element-wise remainder of division.
+    """
+    Return element-wise remainder of division.
 
     Parameters
     ----------
@@ -646,8 +659,10 @@ def remainder(x1, x2, out=None):
 
 
 @set_module('mxnet.ndarray.numpy')
-def power(x1, x2, out=None):
-    """First array elements raised to powers from second array, element-wise.
+@wrap_np_binary_func
+def power(x1, x2, out=None, **kwargs):
+    """
+    First array elements raised to powers from second array, element-wise.
 
     Parameters
     ----------
@@ -1023,7 +1038,8 @@ def expand_dims(a, axis):
 
 
 @set_module('mxnet.ndarray.numpy')
-def lcm(x1, x2, out=None):
+@wrap_np_binary_func
+def lcm(x1, x2, out=None, **kwargs):
     """
     Returns the lowest common multiple of ``|x1|`` and ``|x2|``
 
@@ -1123,8 +1139,11 @@ def _unary_func_helper(x, fn_array, fn_scalar, out=None, **kwargs):
 
 
 @set_module('mxnet.ndarray.numpy')
+@wrap_np_unary_func
 def sin(x, out=None, **kwargs):
-    r"""Trigonometric sine, element-wise.
+    r"""
+    Trigonometric sine, element-wise.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -1134,19 +1153,32 @@ def sin(x, out=None, **kwargs):
         must have a shape that the inputs broadcast to. If not provided
         or None, a freshly-allocated array is returned. The dtype of the
         output is the same as that of the input if the input is an ndarray.
+
     Returns
     -------
     y : ndarray or scalar
         The sine of each element of x. This is a scalar if `x` is a scalar.
+
     Notes
     ----
     This function only supports input type of float.
+
+    Examples
+    --------
+    >>> np.sin(np.pi/2.)
+    1.0
+    >>> np.sin(np.array((0., 30., 45., 60., 90.)) * np.pi / 180.)
+    array([0.        , 0.5       , 0.70710677, 0.86602545, 1.        ])
     """
     return _unary_func_helper(x, _npi.sin, _np.sin, out=out, **kwargs)
 
+
 @set_module('mxnet.ndarray.numpy')
+@wrap_np_unary_func
 def cos(x, out=None, **kwargs):
-    r"""Cosine, element-wise.
+    r"""
+    Cosine, element-wise.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -1156,21 +1188,36 @@ def cos(x, out=None, **kwargs):
         must have a shape that the inputs broadcast to. If not provided
         or None, a freshly-allocated array is returned. The dtype of the
         output is the same as that of the input if the input is an ndarray.
+
     Returns
     -------
     y : ndarray or scalar
         The corresponding cosine values. This is a scalar if x is a scalar.
+
     Notes
     ----
     This function only supports input type of float.
+
+    Examples
+    --------
+    >>> np.cos(np.array([0, np.pi/2, np.pi]))
+    array([ 1.000000e+00, -4.371139e-08, -1.000000e+00])
+    >>> # Example of providing the optional output parameter
+    >>> out1 = np.array([0], dtype='f')
+    >>> out2 = np.cos(np.array([0.1]), out1)
+    >>> out2 is out1
+    True
     """
     return _unary_func_helper(x, _npi.cos, _np.cos, out=out, **kwargs)
 
 
 @set_module('mxnet.ndarray.numpy')
+@wrap_np_unary_func
 def sinh(x, out=None, **kwargs):
-    """Hyperbolic sine, element-wise.
+    """
+    Hyperbolic sine, element-wise.
     Equivalent to ``1/2 * (np.exp(x) - np.exp(-x))`` or ``-1j * np.sin(1j*x)``.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -1180,21 +1227,36 @@ def sinh(x, out=None, **kwargs):
         must have a shape that the inputs broadcast to. If not provided
         or None, a freshly-allocated array is returned. The dtype of the
         output is the same as that of the input if the input is an ndarray.
+
     Returns
     -------
     y : ndarray or scalar
         The corresponding hyperbolic sine values. This is a scalar if `x` is a scalar.
+
     Notes
     ----
     This function only supports input type of float.
+
+    Examples
+    --------
+    >>> np.sinh(0)
+    0.0
+    >>> # Example of providing the optional output parameter
+    >>> out1 = np.array([0], dtype='f')
+    >>> out2 = np.sinh(np.array([0.1]), out1)
+    >>> out2 is out1
+    True
     """
     return _unary_func_helper(x, _npi.sinh, _np.sinh, out=out, **kwargs)
 
 
 @set_module('mxnet.ndarray.numpy')
+@wrap_np_unary_func
 def cosh(x, out=None, **kwargs):
-    """Hyperbolic cosine, element-wise.
+    """
+    Hyperbolic cosine, element-wise.
     Equivalent to ``1/2 * (np.exp(x) + np.exp(-x))`` and ``np.cos(1j*x)``.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -1204,22 +1266,31 @@ def cosh(x, out=None, **kwargs):
         must have a shape that the inputs broadcast to. If not provided
         or None, a freshly-allocated array is returned. The dtype of the
         output is the same as that of the input if the input is an ndarray.
+
     Returns
     -------
     y : ndarray or scalar
         The corresponding hyperbolic cosine values. This is a scalar if `x` is a scalar.
+
     Notes
     ----
     This function only supports input type of float.
+
+    Examples
+    --------
+    >>> np.cosh(0)
+    1.0
     """
     return _unary_func_helper(x, _npi.cosh, _np.cosh, out=out, **kwargs)
 
 
 @set_module('mxnet.ndarray.numpy')
+@wrap_np_unary_func
 def tanh(x, out=None, **kwargs):
     """
     Compute hyperbolic tangent element-wise.
     Equivalent to ``np.sinh(x)/np.cosh(x)``.
+
     Parameters
     ----------
     x : ndarray or scalar.
@@ -1229,10 +1300,12 @@ def tanh(x, out=None, **kwargs):
         must have a shape that the inputs fill into. If not provided
         or None, a freshly-allocated array is returned. The dtype of the
         output and input must be the same.
+
     Returns
     -------
     y : ndarray or scalar
        The corresponding hyperbolic tangent values.
+
     Notes
     -----
     If `out` is provided, the function writes the result into it,
@@ -1240,6 +1313,7 @@ def tanh(x, out=None, **kwargs):
     - input x does not support complex computation (like imaginary number)
     >>> np.tanh(np.pi*1j)
     TypeError: type <type 'complex'> not supported
+
     Examples
     --------
     >>> np.tanh(np.array[0, np.pi]))
@@ -1252,19 +1326,16 @@ def tanh(x, out=None, **kwargs):
     >>> out2 = np.tanh(np.array(0.1), out1)
     >>> out2 is out1
     True
-    >>> # Example of ValueError due to provision of shape mis-matched `out`
-    >>> np.tanh(np.zeros((3,3)),np.zeros((2,2)))
-    mxnet.base.MXNetError:
-    [07:17:36] ../src/ndarray/./../operator/tensor/../elemwise_op_common.h:135:
-    Check failed: assign(&dattr, vec.at(i)): Incompatible attr in node
-    at 0-th output: expected [3,3], got [2,2]
     """
     return _unary_func_helper(x, _npi.tanh, _np.tanh, out=out, **kwargs)
 
 
 @set_module('mxnet.ndarray.numpy')
+@wrap_np_unary_func
 def log10(x, out=None, **kwargs):
-    """Return the base 10 logarithm of the input array, element-wise.
+    """
+    Return the base 10 logarithm of the input array, element-wise.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -1274,22 +1345,31 @@ def log10(x, out=None, **kwargs):
         must have a shape that the inputs broadcast to. If not provided
         or None, a freshly-allocated array is returned. The dtype of the
         output is the same as that of the input if the input is an ndarray.
+
     Returns
     -------
     y : ndarray or scalar
         The logarithm to the base 10 of `x`, element-wise. NaNs are
         returned where x is negative. This is a scalar if `x` is a scalar.
+
     Notes
     ----
     This function only supports input type of float.
+
+    Examples
+    --------
+    >>> np.log10(np.array([1e-15, -3.]))
+    array([-15.,  nan])
     """
     return _unary_func_helper(x, _npi.log10, _np.log10, out=out, **kwargs)
 
 
 @set_module('mxnet.ndarray.numpy')
+@wrap_np_unary_func
 def sqrt(x, out=None, **kwargs):
     """
     Return the non-negative square-root of an array, element-wise.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -1298,22 +1378,33 @@ def sqrt(x, out=None, **kwargs):
         A location into which the result is stored. If provided, it must have
         a shape that the inputs broadcast to. If not provided or `None`,
         a freshly-allocated array is returned.
+
     Returns
     -------
     y : ndarray or scalar
         An array of the same shape as `x`, containing the positive
         square-root of each element in `x`. This is a scalar if `x` is a scalar.
+
     Notes
     ----
     This function only supports input type of float.
+
+    Examples
+    --------
+    >>> np.sqrt(np.array([1,4,9]))
+    array([1., 2., 3.])
+    >>> np.sqrt(np.array([4, -1, _np.inf]))
+    array([ 2., nan, inf])
     """
     return _unary_func_helper(x, _npi.sqrt, _np.sqrt, out=out, **kwargs)
 
 
 @set_module('mxnet.ndarray.numpy')
+@wrap_np_unary_func
 def cbrt(x, out=None, **kwargs):
     r"""
     Return the cube-root of an array, element-wise.
+
     Parameters
     ----------
     x : ndarray
@@ -1322,11 +1413,13 @@ def cbrt(x, out=None, **kwargs):
         A location into which the result is stored. If provided, it must have a shape that the
         inputs broadcast to. If not provided or None, a freshly-allocated array is returned.
         A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
+
     Returns
     ----------
     y : ndarray
         An array of the same shape as x, containing the cube cube-root of each element in x.
         If out was provided, y is a reference to it. This is a scalar if x is a scalar.
+
     Examples
     ----------
     >>> np.cbrt([1,8,27])
@@ -1336,9 +1429,11 @@ def cbrt(x, out=None, **kwargs):
 
 
 @set_module('mxnet.ndarray.numpy')
+@wrap_np_unary_func
 def abs(x, out=None, **kwargs):
-    r"""abs(x, out=None, **kwargs)
+    r"""
     Calculate the absolute value element-wise.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -1347,11 +1442,13 @@ def abs(x, out=None, **kwargs):
         A location into which the result is stored. If provided, it must have
         a shape that the inputs broadcast to. If not provided or `None`,
         a freshly-allocated array is returned.
+
     Returns
     -------
     absolute : ndarray
         An ndarray containing the absolute value of
         each element in `x`. This is a scalar if `x` is a scalar.
+
     Examples
     --------
     >>> x = np.array([-1.2, 1.2])
@@ -1362,10 +1459,12 @@ def abs(x, out=None, **kwargs):
 
 
 @set_module('mxnet.ndarray.numpy')
+@wrap_np_unary_func
 def absolute(x, out=None, **kwargs):
     r"""
     Calculate the absolute value element-wise.
     np.abs is a shorthand for this function.
+
     Parameters
     ----------
     x : ndarray
@@ -1374,10 +1473,12 @@ def absolute(x, out=None, **kwargs):
         A location into which the result is stored. If provided, it must have a shape
         that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned.
         A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
+
     Returns
     ----------
     absolute : ndarray
         An ndarray containing the absolute value of each element in x.
+
     Examples
     ----------
     >>> x = np.array([-1.2, 1.2])
@@ -1388,11 +1489,12 @@ def absolute(x, out=None, **kwargs):
 
 
 @set_module('mxnet.ndarray.numpy')
+@wrap_np_unary_func
 def sign(x, out=None, **kwargs):
     r"""
-    sign(x, out=None)
     Returns an element-wise indication of the sign of a number.
     The `sign` function returns ``-1 if x < 0, 0 if x==0, 1 if x > 0``. Only supports real number.
+
     Parameters
     ----------
     x : ndarray or a scalar
@@ -1401,11 +1503,13 @@ def sign(x, out=None, **kwargs):
         A location into which the result is stored.
         If provided, it must have the same shape and dtype as input ndarray.
         If not provided or `None`, a freshly-allocated array is returned.
+
     Returns
     -------
     y : ndarray
         The sign of `x`.
         This is a scalar if `x` is a scalar.
+
     Note
     -------
     - Only supports real number as input elements.
@@ -1413,17 +1517,18 @@ def sign(x, out=None, **kwargs):
     - ``out`` param: cannot perform auto broadcasting. ``out`` ndarray's shape must be the same as the expected output.
     - ``out`` param: cannot perform auto type cast. ``out`` ndarray's dtype must be the same as the expected output.
     - ``out`` param does not support scalar input case.
+
     Examples
     --------
     >>> a = np.array([-5., 4.5])
     >>> np.sign(a)
     array([-1.,  1.])
-    Scalars as input:
+    >>> # Use scalars as inputs:
     >>> np.sign(4.0)
     1.0
     >>> np.sign(0)
     0
-    Use ``out`` parameter:
+    >>> # Use ``out`` parameter:
     >>> b = np.zeros((2, ))
     >>> np.sign(a, out=b)
     array([-1.,  1.])
@@ -1434,9 +1539,11 @@ def sign(x, out=None, **kwargs):
 
 
 @set_module('mxnet.ndarray.numpy')
+@wrap_np_unary_func
 def exp(x, out=None, **kwargs):
-    r"""exp(x, out=None, **kwargs)
+    r"""
     Calculate the exponential of all elements in the input array.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -1445,11 +1552,13 @@ def exp(x, out=None, **kwargs):
         A location into which the result is stored. If provided, it must have
         a shape that the inputs broadcast to. If not provided or `None`,
         a freshly-allocated array is returned.
+
     Returns
     -------
     out : ndarray or scalar
         Output array, element-wise exponential of `x`.
         This is a scalar if `x` is a scalar.
+
     Examples
     --------
     >>> np.exp(1)
@@ -1462,9 +1571,11 @@ def exp(x, out=None, **kwargs):
 
 
 @set_module('mxnet.ndarray.numpy')
+@wrap_np_unary_func
 def expm1(x, out=None, **kwargs):
-    r"""expm1(x, out=None, **kwargs)
+    r"""
     Calculate `exp(x) - 1` of all elements in the input array.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -1473,11 +1584,13 @@ def expm1(x, out=None, **kwargs):
         A location into which the result is stored. If provided, it must have
         a shape that the inputs broadcast to. If not provided or `None`,
         a freshly-allocated array is returned.
+
     Returns
     -------
     out : ndarray or scalar
         Output array, element-wise exponential minus one: `out = exp(x) - 1`.
         This is a scalar if `x` is a scalar.
+
     Examples
     --------
     >>> np.expm1(1)
@@ -1490,10 +1603,11 @@ def expm1(x, out=None, **kwargs):
 
 
 @set_module('mxnet.ndarray.numpy')
+@wrap_np_unary_func
 def arcsin(x, out=None, **kwargs):
     r"""
-    arcsin(x, out=None)
     Inverse sine, element-wise.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -1502,12 +1616,14 @@ def arcsin(x, out=None, **kwargs):
         A location into which the result is stored.
         If provided, it must have the same shape as the input.
         If not provided or None, a freshly-allocated array is returned.
+
     Returns
     -------
     angle : ndarray or scalar
         Output array is same shape and type as x. This is a scalar if x is a scalar.
         The inverse sine of each element in `x`, in radians and in the
         closed interval ``[-pi/2, pi/2]``.
+
     Examples
     --------
     >>> np.arcsin(1)     # pi/2
@@ -1516,6 +1632,7 @@ def arcsin(x, out=None, **kwargs):
     -1.5707963267948966
     >>> np.arcsin(0)
     0.0
+
     Notes
     -----
     `arcsin` is a multivalued function: for each `x` there are infinitely
@@ -1532,6 +1649,7 @@ def arcsin(x, out=None, **kwargs):
     - Only support ndarray or scalar now.
     - `where` argument is not supported.
     - Complex input is not supported.
+
     References
     ----------
     Abramowitz, M. and Stegun, I. A., *Handbook of Mathematical Functions*,
@@ -1542,10 +1660,12 @@ def arcsin(x, out=None, **kwargs):
 
 
 @set_module('mxnet.ndarray.numpy')
+@wrap_np_unary_func
 def arccos(x, out=None, **kwargs):
     r"""
     Trigonometric inverse cosine, element-wise.
     The inverse of cos so that, if y = cos(x), then x = arccos(y).
+
     Parameters
     ----------
     x : ndarray
@@ -1554,14 +1674,17 @@ def arccos(x, out=None, **kwargs):
         A location into which the result is stored. If provided, it must have a shape that
         the inputs broadcast to. If not provided or None, a freshly-allocated array is returned.
         A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
+
     Returns
     ----------
     angle : ndarray
         The angle of the ray intersecting the unit circle at the given x-coordinate in radians [0, pi].
         This is a scalar if x is a scalar.
+
     See also
     ----------
     cos, arctan, arcsin
+
     Notes
     ----------
     arccos is a multivalued function: for each x there are infinitely many numbers z such that
@@ -1570,9 +1693,9 @@ def arccos(x, out=None, **kwargs):
     For each value that cannot be expressed as a real number or infinity, it yields nan and sets
     the invalid floating point error flag.
     The inverse cos is also known as acos or cos^-1.
+
     Examples
     ----------
-    We expect the arccos of 1 to be 0, and of -1 to be pi:
     >>> np.arccos([1, -1])
     array([ 0.        ,  3.14159265])
     """
@@ -1580,10 +1703,12 @@ def arccos(x, out=None, **kwargs):
 
 
 @set_module('mxnet.ndarray.numpy')
+@wrap_np_unary_func
 def arctan(x, out=None, **kwargs):
-    r"""arctan(x, out=None, **kwargs)
+    r"""
     Trigonometric inverse tangent, element-wise.
     The inverse of tan, so that if ``y = tan(x)`` then ``x = arctan(y)``.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -1592,12 +1717,14 @@ def arctan(x, out=None, **kwargs):
         A location into which the result is stored. If provided, it must have
         a shape that the inputs broadcast to. If not provided or `None`,
         a freshly-allocated array is returned.
+
     Returns
     -------
     out : ndarray or scalar
         Out has the same shape as `x`. It lies is in
         ``[-pi/2, pi/2]`` (``arctan(+/-inf)`` returns ``+/-pi/2``).
         This is a scalar if `x` is a scalar.
+
     Notes
     -----
     `arctan` is a multi-valued function: for each `x` there are infinitely
@@ -1608,9 +1735,9 @@ def arctan(x, out=None, **kwargs):
     it yields ``nan`` and sets the `invalid` floating point error flag.
     For complex-valued input, we do not have support for them yet.
     The inverse tangent is also known as `atan` or tan^{-1}.
+
     Examples
     --------
-    We expect the arctan of 0 to be 0, and of 1 to be pi/4:
     >>> x = np.array([0, 1])
     >>> np.arctan(x)
     array([0.       , 0.7853982])
@@ -1621,13 +1748,14 @@ def arctan(x, out=None, **kwargs):
 
 
 @set_module('mxnet.ndarray.numpy')
+@wrap_np_unary_func
 def log(x, out=None, **kwargs):
     """
-    log(x, out=None)
     Natural logarithm, element-wise.
     The natural logarithm `log` is the inverse of the exponential function,
     so that `log(exp(x)) = x`. The natural logarithm is logarithm in base
     `e`.
+
     Parameters
     ----------
     x : ndarray
@@ -1636,11 +1764,13 @@ def log(x, out=None, **kwargs):
         A location into which the result is stored.
         If provided, it must have the same shape and dtype as input ndarray.
         If not provided or `None`, a freshly-allocated array is returned.
+
     Returns
     -------
     y : ndarray
         The natural logarithm of `x`, element-wise.
         This is a scalar if `x` is a scalar.
+
     Notes
     -----
      Currently only supports data of real values and ``inf`` as input. Returns data of real value, ``inf``, ``-inf`` and
@@ -1653,16 +1783,16 @@ def log(x, out=None, **kwargs):
     - ``out`` param: cannot perform auto broadcasting. ``out`` ndarray's shape must be the same as the expected output.
     - ``out`` param: cannot perform auto type cast. ``out`` ndarray's dtype must be the same as the expected output.
     - ``out`` param does not support scalar input case.
+
     Examples
     --------
     >>> a = np.array([1, np.exp(1), np.exp(2), 0], dtype=np.float64)
     >>> np.log(a)
     array([  0.,   1.,   2., -inf], dtype=float64)
-    Due to internal calculation mechanism, using default float32 dtype may cause some special behavior:
+    >>> # Using default float32 dtype may lead to slightly different behavior:
     >>> a = np.array([1, np.exp(1), np.exp(2), 0], dtype=np.float32)
     >>> np.log(a)
     array([  0.,  0.99999994,   2., -inf])
-    Scalar calculation:
     >>> np.log(1)
     0.0
     """
@@ -1670,10 +1800,11 @@ def log(x, out=None, **kwargs):
 
 
 @set_module('mxnet.ndarray.numpy')
+@wrap_np_unary_func
 def degrees(x, out=None, **kwargs):
     """
-    degrees(x, out=None)
     Convert angles from radians to degrees.
+
     Parameters
     ----------
     x : ndarray
@@ -1682,12 +1813,14 @@ def degrees(x, out=None, **kwargs):
         A location into which the result is stored.
         If provided, it must have the same shape and dtype as input ndarray.
         If not provided or `None`, a freshly-allocated array is returned.
+
     Returns
     -------
     y : ndarray
         The corresponding degree values; if `out` was supplied this is a
         reference to it.
         This is a scalar if `x` is a scalar.
+
     Notes
     -------
     This function differs from the original `numpy.degrees
@@ -1697,13 +1830,13 @@ def degrees(x, out=None, **kwargs):
     - ``out`` param: cannot perform auto broadcasting. ``out`` ndarray's shape must be the same as the expected output.
     - ``out`` param: cannot perform auto type cast. ``out`` ndarray's dtype must be the same as the expected output.
     - ``out`` param does not support scalar input case.
+
     Examples
     --------
-    Convert a radian array to degrees
     >>> rad = np.arange(12.) * np.pi / 6
     >>> np.degrees(rad)
     array([  0.,  30.,  60.,  90., 120., 150., 180., 210., 240., 270., 300., 330.])
-    Use specified ``out`` ndarray:
+    >>> # Use specified ``out`` ndarray:
     >>> out = np.zeros((rad.shape))
     >>> np.degrees(rad, out)
     array([  0.,  30.,  60.,  90., 120., 150., 180., 210., 240., 270., 300., 330.])
@@ -1714,11 +1847,11 @@ def degrees(x, out=None, **kwargs):
 
 
 @set_module('mxnet.ndarray.numpy')
-def rad2deg(x, out=None):
+@wrap_np_unary_func
+def rad2deg(x, out=None, **kwargs):
     r"""
-    rad2deg(x, out=None)
-
     Convert angles from radians to degrees.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -1750,9 +1883,11 @@ def rad2deg(x, out=None):
 
 
 @set_module('mxnet.ndarray.numpy')
+@wrap_np_unary_func
 def rint(x, out=None, **kwargs):
     """
     Round elements of the array to the nearest integer.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -1761,10 +1896,12 @@ def rint(x, out=None, **kwargs):
         A location into which the result is stored.
         If provided, it must have the same shape and type as the input.
         If not provided or None, a freshly-allocated array is returned.
+
     Returns
     -------
     out : ndarray or scalar
         Output array is same shape and type as x. This is a scalar if x is a scalar.
+
     Notes
     -----
     This function differs from the original `numpy.rint
@@ -1773,6 +1910,7 @@ def rint(x, out=None, **kwargs):
     - only ndarray or scalar is accpted as valid input, tuple of ndarray is not supported
     - broadcasting to `out` of different shape is currently not supported
     - when input is plain python numerics, the result will not be stored in the `out` param
+
     Examples
     --------
     >>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
@@ -1783,9 +1921,11 @@ def rint(x, out=None, **kwargs):
 
 
 @set_module('mxnet.ndarray.numpy')
+@wrap_np_unary_func
 def log2(x, out=None, **kwargs):
     """
     Base-2 logarithm of x.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -1794,11 +1934,13 @@ def log2(x, out=None, **kwargs):
         A location into which the result is stored.
         If provided, it must have the same shape and type as the input.
         If not provided or None, a freshly-allocated array is returned.
+
     Returns
     -------
     y : ndarray
         The logarithm base two of `x`, element-wise.
         This is a scalar if `x` is a scalar.
+
     Notes
     -----
     This function differs from the original `numpy.log2
@@ -1807,6 +1949,7 @@ def log2(x, out=None, **kwargs):
     - only ndarray or scalar is accpted as valid input, tuple of ndarray is not supported
     - broadcasting to `out` of different shape is currently not supported
     - when input is plain python numerics, the result will not be stored in the `out` param
+
     Examples
     --------
     >>> x = np.array([0, 1, 2, 2**4])
@@ -1817,10 +1960,12 @@ def log2(x, out=None, **kwargs):
 
 
 @set_module('mxnet.ndarray.numpy')
+@wrap_np_unary_func
 def log1p(x, out=None, **kwargs):
     """
     Return the natural logarithm of one plus the input array, element-wise.
     Calculates ``log(1 + x)``.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -1830,11 +1975,13 @@ def log1p(x, out=None, **kwargs):
         must have a shape that the inputs fill into. If not provided
         or None, a freshly-allocated array is returned. The dtype of the
         output and input must be the same.
+
     Returns
     -------
     y : ndarray or scalar
         Natural logarithm of 1 + x, element-wise. This is a scalar
         if x is a scalar.
+
     Notes
     -----
     For real-valued input, `log1p` is accurate also for `x` so small
@@ -1846,6 +1993,7 @@ def log1p(x, out=None, **kwargs):
     For each value that cannot be expressed as a real number or infinity,
     it yields ``nan`` and sets the `invalid` floating point error flag.
     cannot support complex-valued input.
+
     Examples
     --------
     >>> np.log1p(1e-99)
@@ -1858,9 +2006,11 @@ def log1p(x, out=None, **kwargs):
 
 
 @set_module('mxnet.ndarray.numpy')
+@wrap_np_unary_func
 def radians(x, out=None, **kwargs):
     """
     Convert angles from degrees to radians.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -1869,10 +2019,12 @@ def radians(x, out=None, **kwargs):
         A location into which the result is stored.
         If provided, it must have the same shape and type as the input.
         If not provided or None, a freshly-allocated array is returned.
+
     Returns
     -------
     y : ndarray
         The corresponding radian values. This is a scalar if x is a scalar.
+
     Notes
     -----
     This function differs from the original `numpy.radians
@@ -1881,6 +2033,7 @@ def radians(x, out=None, **kwargs):
     - only ndarray or scalar is accpted as valid input, tuple of ndarray is not supported
     - broadcasting to `out` of different shape is currently not supported
     - when input is plain python numerics, the result will not be stored in the `out` param
+
     Examples
     --------
     >>> deg = np.arange(12.) * 30.
@@ -1893,11 +2046,11 @@ def radians(x, out=None, **kwargs):
 
 
 @set_module('mxnet.ndarray.numpy')
-def deg2rad(x, out=None):
+@wrap_np_unary_func
+def deg2rad(x, out=None, **kwargs):
     r"""
-    deg2rad(x, out=None)
-
     Convert angles from degrees to radians.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -1929,11 +2082,12 @@ def deg2rad(x, out=None):
 
 
 @set_module('mxnet.ndarray.numpy')
+@wrap_np_unary_func
 def reciprocal(x, out=None, **kwargs):
     r"""
-    reciprocal(x, out=None)
     Return the reciprocal of the argument, element-wise.
     Calculates ``1/x``.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -1942,10 +2096,12 @@ def reciprocal(x, out=None, **kwargs):
         A location into which the result is stored.
         If provided, it must have the same shape as the input.
         If not provided or None, a freshly-allocated array is returned.
+
     Returns
     -------
     y : ndarray or scalar
         Output array is same shape and type as x. This is a scalar if x is a scalar.
+
     Examples
     --------
     >>> np.reciprocal(2.)
@@ -1953,6 +2109,7 @@ def reciprocal(x, out=None, **kwargs):
     >>> x = np.array([1, 2., 3.33])
     >>> np.reciprocal(x)
     array([1.       , 0.5      , 0.3003003])
+
     Notes
     -----
     .. note::
@@ -1971,10 +2128,11 @@ def reciprocal(x, out=None, **kwargs):
 
 
 @set_module('mxnet.ndarray.numpy')
+@wrap_np_unary_func
 def square(x, out=None, **kwargs):
     r"""
-    square(x, out=None)
     Return the element-wise square of the input.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -1983,10 +2141,12 @@ def square(x, out=None, **kwargs):
         A location into which the result is stored.
         If provided, it must have the same shape as the input.
         If not provided or None, a freshly-allocated array is returned.
+
     Returns
     -------
     y : ndarray or scalar
         Output array is same shape and type as x. This is a scalar if x is a scalar.
+
     Examples
     --------
     >>> np.square(2.)
@@ -1994,6 +2154,7 @@ def square(x, out=None, **kwargs):
     >>> x = np.array([1, 2., -1])
     >>> np.square(x)
     array([1., 4., 1.])
+
     Notes
     -----
     The output `ndarray` has the same `ctx` as the input `ndarray`.
@@ -2008,23 +2169,23 @@ def square(x, out=None, **kwargs):
 
 
 @set_module('mxnet.ndarray.numpy')
-def negative(x, out=None, where=True, **kwargs):
+@wrap_np_unary_func
+def negative(x, out=None, **kwargs):
     r"""
-    negative(x, out=None, where=True)
     Numerical negative, element-wise.
+
     Parameters:
     ------------
     x : ndarray or scalar
         Input array.
     out : ndarray, None, or tuple of ndarray and None, optional
           A location into which the result is stored.
-    where : ndarray, optional
-            Values of True indicate to calculate the ufunc at that position,
-            values of False indicate to leave the value in the output alone.
+
     Returns:
     ---------
     y : ndarray or scalar
         Returned array or scalar: y = -x. This is a scalar if x is a scalar.
+
     Examples:
     ---------
     >>> np.negative(1)
@@ -2034,7 +2195,8 @@ def negative(x, out=None, where=True, **kwargs):
 
 
 @set_module('mxnet.ndarray.numpy')
-def fix(x, out=None):
+@wrap_np_unary_func
+def fix(x, out=None, **kwargs):
     r"""
     Round an array of floats element-wise to nearest integer towards zero.
     The rounded values are returned as floats.
@@ -2045,9 +2207,11 @@ def fix(x, out=None):
         An array of floats to be rounded
     out : ndarray, optional
         Output array
+
     Returns:
     -------
     y : ndarray of floats
+
     Examples
     ---------
     >>> np.fix(3.14)
@@ -2057,9 +2221,9 @@ def fix(x, out=None):
 
 
 @set_module('mxnet.ndarray.numpy')
-def tan(x, out=None, where=True, **kwargs):
+@wrap_np_unary_func
+def tan(x, out=None, **kwargs):
     r"""
-    tan(x, out=None, where=True)
     Compute tangent element-wise.
     Equivalent to np.sin(x)/np.cos(x) element-wise.
 
@@ -2075,11 +2239,14 @@ def tan(x, out=None, where=True, **kwargs):
     where : ndarray, optional
             Values of True indicate to calculate the ufunc at that position,
             values of False indicate to leave the value in the output alone.
+
     Returns:
     -------
     y : ndarray
     The corresponding tangent values. This is a scalar if x is a scalar.
+
     Examples:
+    ---------
     >>> np.tan(0.5)
     0.5463024898437905
     """
@@ -2088,11 +2255,13 @@ def tan(x, out=None, where=True, **kwargs):
 
 
 @set_module('mxnet.ndarray.numpy')
+@wrap_np_unary_func
 def ceil(x, out=None, **kwargs):
     r"""
     Return the ceiling of the input, element-wise.
     The ceil of the ndarray `x` is the smallest integer `i`, such that
     `i >= x`.  It is often denoted as :math:`\lceil x \rceil`.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -2102,17 +2271,19 @@ def ceil(x, out=None, **kwargs):
         must have a same shape that the inputs fill into. If not provided
         or None, a freshly-allocated array is returned. The dtype of the
         output and input must be the same.
+
     Returns
     -------
     y : ndarray or scalar
         The ceiling of each element in `x`, with `float` dtype.
         This is a scalar if `x` is a scalar.
+
     Examples
     --------
     >>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
     >>> np.ceil(a)
     array([-1., -1., -0.,  1.,  2.,  2.,  2.])
-    >>> #if you use parameter out, x and out must be ndarray. if not, you will get an error!
+    >>> #if you use parameter out, x and out must be ndarray.
     >>> a = np.array(1)
     >>> np.ceil(np.array(3.5), a)
     array(4.)
@@ -2123,11 +2294,13 @@ def ceil(x, out=None, **kwargs):
 
 
 @set_module('mxnet.ndarray.numpy')
+@wrap_np_unary_func
 def floor(x, out=None, **kwargs):
     r"""
     Return the floor of the input, element-wise.
     The floor of the ndarray `x` is the largest integer `i`, such that
     `i <= x`.  It is often denoted as :math:`\lfloor x \rfloor`.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -2137,17 +2310,19 @@ def floor(x, out=None, **kwargs):
         must have a same shape that the inputs fill into. If not provided
         or None, a freshly-allocated array is returned. The dtype of the
         output and input must be the same.
+
     Returns
     -------
     y : ndarray or scalar
         The floor of each element in `x`, with `float` dtype.
         This is a scalar if `x` is a scalar.
+
     Examples
     --------
     >>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
     >>> np.floor(a)
     array([-2., -2., -1.,  0.,  1.,  1.,  2.])
-    >>> #if you use parameter out, x and out must be ndarray. if not, you will get an error!
+    >>> #if you use parameter out, x and out must be ndarray.
     >>> a = np.array(1)
     >>> np.floor(np.array(3.5), a)
     array(3.)
@@ -2158,9 +2333,9 @@ def floor(x, out=None, **kwargs):
 
 
 @set_module('mxnet.ndarray.numpy')
+@wrap_np_unary_func
 def trunc(x, out=None, **kwargs):
     r"""
-    trunc(x, out=None)
     Return the truncated value of the input, element-wise.
     The truncated value of the scalar `x` is the nearest integer `i` which
     is closer to zero than `x` is. In short, the fractional part of the
@@ -2178,6 +2353,7 @@ def trunc(x, out=None, **kwargs):
     y : ndarray or scalar
         The truncated value of each element in `x`.
         This is a scalar if `x` is a scalar.
+
     Notes
     -----
     This function differs from the original numpy.trunc in the following aspects:
@@ -2196,10 +2372,11 @@ def trunc(x, out=None, **kwargs):
 
 
 @set_module('mxnet.ndarray.numpy')
+@wrap_np_unary_func
 def logical_not(x, out=None, **kwargs):
     r"""
-    logical_not(x, out=None)
     Compute the truth value of NOT x element-wise.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -2213,6 +2390,7 @@ def logical_not(x, out=None, **kwargs):
         Boolean result with the same shape as `x` of the NOT operation
         on elements of `x`.
         This is a scalar if `x` is a scalar.
+
     Notes
     -----
     This function differs from the original numpy.logical_not in the following aspects:
@@ -2220,6 +2398,7 @@ def logical_not(x, out=None, **kwargs):
         - Cannot cast type automatically. Dtype of `out` must be same as the expected one.
         - Cannot broadcast automatically. Shape of `out` must be same as the expected one.
         - If `x` is plain python numeric, the result won't be stored in out.
+
     Examples
     --------
     >>> x= np.array([True, False, 0, 1])
@@ -2234,10 +2413,11 @@ def logical_not(x, out=None, **kwargs):
 
 
 @set_module('mxnet.ndarray.numpy')
+@wrap_np_unary_func
 def arcsinh(x, out=None, **kwargs):
     r"""
-    arcsinh(x, out=None)
     Inverse hyperbolic sine, element-wise.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -2250,6 +2430,7 @@ def arcsinh(x, out=None, **kwargs):
     arcsinh : ndarray
         Array of the same shape as `x`.
         This is a scalar if `x` is a scalar.
+
     Notes
     -----
     `arcsinh` is a multivalued function: for each `x` there are infinitely
@@ -2265,6 +2446,7 @@ def arcsinh(x, out=None, **kwargs):
         - Cannot cast type automatically. DType of `out` must be same as the expected one.
         - Cannot broadcast automatically. Shape of `out` must be same as the expected one.
         - If `x` is plain python numeric, the result won't be stored in out.
+
     Examples
     --------
     >>> a = np.array([3.2, 5.0])
@@ -2277,10 +2459,11 @@ def arcsinh(x, out=None, **kwargs):
 
 
 @set_module('mxnet.ndarray.numpy')
+@wrap_np_unary_func
 def arccosh(x, out=None, **kwargs):
     r"""
-    arccosh(x, out=None)
     Inverse hyperbolic cosine, element-wise.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -2293,6 +2476,7 @@ def arccosh(x, out=None, **kwargs):
     arccosh : ndarray
         Array of the same shape as `x`.
         This is a scalar if `x` is a scalar.
+
     Notes
     -----
     `arccosh` is a multivalued function: for each `x` there are infinitely
@@ -2308,6 +2492,7 @@ def arccosh(x, out=None, **kwargs):
         - Cannot cast type automatically. Dtype of `out` must be same as the expected one.
         - Cannot broadcast automatically. Shape of `out` must be same as the expected one.
         - If `x` is plain python numeric, the result won't be stored in out.
+
     Examples
     --------
     >>> a = np.array([3.2, 5.0])
@@ -2320,10 +2505,11 @@ def arccosh(x, out=None, **kwargs):
 
 
 @set_module('mxnet.ndarray.numpy')
+@wrap_np_unary_func
 def arctanh(x, out=None, **kwargs):
     r"""
-    arctanh(x, out=None)
     Inverse hyperbolic tangent, element-wise.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -2336,6 +2522,7 @@ def arctanh(x, out=None, **kwargs):
     arctanh : ndarray
         Array of the same shape as `x`.
         This is a scalar if `x` is a scalar.
+
     Notes
     -----
     `arctanh` is a multivalued function: for each `x` there are infinitely
@@ -2351,6 +2538,7 @@ def arctanh(x, out=None, **kwargs):
         - Cannot cast type automatically. Dtype of `out` must be same as the expected one.
         - Cannot broadcast automatically. Shape of `out` must be same as the expected one.
         - If `x` is plain python numeric, the result won't be stored in out.
+
     Examples
     --------
     >>> a = np.array([0.0, -0.5])
@@ -2432,7 +2620,9 @@ def tile(A, reps):
 # pylint: disable=redefined-outer-name
 @set_module('mxnet.ndarray.numpy')
 def split(ary, indices_or_sections, axis=0):
-    """Split an array into multiple sub-arrays.
+    """
+    Split an array into multiple sub-arrays.
+
     Parameters
     ----------
     ary : ndarray
@@ -2451,10 +2641,12 @@ def split(ary, indices_or_sections, axis=0):
         an empty sub-array is returned correspondingly.
     axis : int, optional
         The axis along which to split, default is 0.
+
     Returns
     -------
     sub-arrays : list of ndarrays
         A list of sub-arrays.
+
     Raises
     ------
     ValueError
@@ -2674,7 +2866,9 @@ def vsplit(ary, indices_or_sections):
 
 @set_module('mxnet.ndarray.numpy')
 def concatenate(seq, axis=0, out=None):
-    """Join a sequence of arrays along an existing axis.
+    """
+    Join a sequence of arrays along an existing axis.
+
     Parameters
     ----------
     a1, a2, ... : sequence of ndarray
@@ -2687,6 +2881,7 @@ def concatenate(seq, axis=0, out=None):
         If provided, the destination to place the result. The shape must be
         correct, matching that of what concatenate would have returned if no
         out argument were specified.
+
     Returns
     -------
     res : ndarray
@@ -2700,6 +2895,7 @@ def stack(arrays, axis=0, out=None):
     """Join a sequence of arrays along a new axis.
         The axis parameter specifies the index of the new axis in the dimensions of the result.
         For example, if `axis=0` it will be the first dimension and if `axis=-1` it will be the last dimension.
+
     Parameters
     ----------
     arrays : sequence of ndarray
@@ -2709,6 +2905,7 @@ def stack(arrays, axis=0, out=None):
     out : ndarray, optional
         If provided, the destination to place the result. The shape must be correct,
         matching that of what stack would have returned if no out argument were specified.
+
     Returns
     -------
     stacked : ndarray
@@ -2816,8 +3013,10 @@ def dstack(arrays):
 
 
 @set_module('mxnet.ndarray.numpy')
-def maximum(x1, x2, out=None):
-    """Returns element-wise maximum of the input arrays with broadcasting.
+@wrap_np_binary_func
+def maximum(x1, x2, out=None, **kwargs):
+    """
+    Returns element-wise maximum of the input arrays with broadcasting.
 
     Parameters
     ----------
@@ -2833,8 +3032,10 @@ def maximum(x1, x2, out=None):
 
 
 @set_module('mxnet.ndarray.numpy')
-def minimum(x1, x2, out=None):
-    """Returns element-wise minimum of the input arrays with broadcasting.
+@wrap_np_binary_func
+def minimum(x1, x2, out=None, **kwargs):
+    """
+    Returns element-wise minimum of the input arrays with broadcasting.
 
     Parameters
     ----------
@@ -3261,9 +3462,9 @@ def indices(dimensions, dtype=_np.int32, ctx=None):
 
 
 @set_module('mxnet.ndarray.numpy')
-def copysign(x1, x2, out=None):
-    r"""copysign(x1, x2, out=None)
-
+@wrap_np_binary_func
+def copysign(x1, x2, out=None, **kwargs):
+    r"""
     Change the sign of x1 to that of x2, element-wise.
 
     If `x2` is a scalar, its sign will be copied to all elements of `x1`.
@@ -3730,10 +3931,9 @@ def around(x, decimals=0, out=None, **kwargs):
 
 
 @set_module('mxnet.ndarray.numpy')
-def arctan2(x1, x2, out=None):
+@wrap_np_binary_func
+def arctan2(x1, x2, out=None, **kwargs):
     r"""
-    arctan2(x1, x2, out=None)
-
     Element-wise arc tangent of ``x1/x2`` choosing the quadrant correctly.
 
     The quadrant (i.e., branch) is chosen so that ``arctan2(x1, x2)`` is
@@ -3816,7 +4016,8 @@ def arctan2(x1, x2, out=None):
 
 
 @set_module('mxnet.ndarray.numpy')
-def hypot(x1, x2, out=None):
+@wrap_np_binary_func
+def hypot(x1, x2, out=None, **kwargs):
     r"""
     Given the "legs" of a right triangle, return its hypotenuse.
 
@@ -3863,7 +4064,8 @@ def hypot(x1, x2, out=None):
 
 
 @set_module('mxnet.ndarray.numpy')
-def ldexp(x1, x2, out=None):
+@wrap_np_binary_func
+def ldexp(x1, x2, out=None, **kwargs):
     """
     Returns x1 * 2**x2, element-wise.
     The mantissas `x1` and twos exponents `x2` are used to construct
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 70977c0b7f2d..aec4f9ef4617 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -40,7 +40,7 @@
 from ..base import check_call, _LIB, NDArrayHandle, c_array
 from ..base import mx_real_t, c_array_buf, mx_uint, numeric_types, integer_types
 from ..context import Context
-from ..util import _sanity_check_params, set_module
+from ..util import _sanity_check_params, set_module, wrap_np_unary_func, wrap_np_binary_func
 from ..context import current_context
 from ..ndarray import numpy as _mx_nd_np
 from ..ndarray.numpy import _internal as _npi
@@ -2114,8 +2114,10 @@ def unique(ar, return_index=False, return_inverse=False, return_counts=False, ax
 
 
 @set_module('mxnet.numpy')
-def add(x1, x2, out=None):
-    """Add arguments element-wise.
+@wrap_np_binary_func
+def add(x1, x2, out=None, **kwargs):
+    """
+    Add arguments element-wise.
 
     Parameters
     ----------
@@ -2137,8 +2139,10 @@ def add(x1, x2, out=None):
 
 
 @set_module('mxnet.numpy')
-def subtract(x1, x2, out=None):
-    """Subtract arguments element-wise.
+@wrap_np_binary_func
+def subtract(x1, x2, out=None, **kwargs):
+    """
+    Subtract arguments element-wise.
 
     Parameters
     ----------
@@ -2161,8 +2165,10 @@ def subtract(x1, x2, out=None):
 
 
 @set_module('mxnet.numpy')
-def multiply(x1, x2, out=None):
-    """Multiply arguments element-wise.
+@wrap_np_binary_func
+def multiply(x1, x2, out=None, **kwargs):
+    """
+    Multiply arguments element-wise.
 
     Parameters
     ----------
@@ -2184,8 +2190,10 @@ def multiply(x1, x2, out=None):
 
 
 @set_module('mxnet.numpy')
-def divide(x1, x2, out=None):
-    """Returns a true division of the inputs, element-wise.
+@wrap_np_binary_func
+def divide(x1, x2, out=None, **kwargs):
+    """
+    Returns a true division of the inputs, element-wise.
 
     Parameters
     ----------
@@ -2209,8 +2217,10 @@ def divide(x1, x2, out=None):
 
 
 @set_module('mxnet.numpy')
-def mod(x1, x2, out=None):
-    """Return element-wise remainder of division.
+@wrap_np_binary_func
+def mod(x1, x2, out=None, **kwargs):
+    """
+    Return element-wise remainder of division.
 
     Parameters
     ----------
@@ -2234,8 +2244,10 @@ def mod(x1, x2, out=None):
 
 
 @set_module('mxnet.numpy')
-def remainder(x1, x2, out=None):
-    """Return element-wise remainder of division.
+@wrap_np_binary_func
+def remainder(x1, x2, out=None, **kwargs):
+    """
+    Return element-wise remainder of division.
 
     Parameters
     ----------
@@ -2259,8 +2271,10 @@ def remainder(x1, x2, out=None):
 
 
 @set_module('mxnet.numpy')
-def power(x1, x2, out=None):
-    """First array elements raised to powers from second array, element-wise.
+@wrap_np_binary_func
+def power(x1, x2, out=None, **kwargs):
+    """
+    First array elements raised to powers from second array, element-wise.
 
     Parameters
     ----------
@@ -2285,7 +2299,8 @@ def power(x1, x2, out=None):
 
 
 @set_module('mxnet.numpy')
-def lcm(x1, x2, out=None):
+@wrap_np_binary_func
+def lcm(x1, x2, out=None, **kwargs):
     """
     Returns the lowest common multiple of ``|x1|`` and ``|x2|``
 
@@ -2322,8 +2337,11 @@ def lcm(x1, x2, out=None):
 
 
 @set_module('mxnet.numpy')
+@wrap_np_unary_func
 def sin(x, out=None, **kwargs):
-    r"""Trigonometric sine, element-wise.
+    r"""
+    Trigonometric sine, element-wise.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -2333,20 +2351,32 @@ def sin(x, out=None, **kwargs):
         must have a shape that the inputs broadcast to. If not provided
         or None, a freshly-allocated array is returned. The dtype of the
         output is the same as that of the input if the input is an ndarray.
+
     Returns
     -------
     y : ndarray or scalar
         The sine of each element of x. This is a scalar if `x` is a scalar.
+
     Notes
     ----
     This function only supports input type of float.
+
+    Examples
+    --------
+    >>> np.sin(np.pi/2.)
+    1.0
+    >>> np.sin(np.array((0., 30., 45., 60., 90.)) * np.pi / 180.)
+    array([0.        , 0.5       , 0.70710677, 0.86602545, 1.        ])
     """
     return _mx_nd_np.sin(x, out=out, **kwargs)
 
 
 @set_module('mxnet.numpy')
+@wrap_np_unary_func
 def cos(x, out=None, **kwargs):
-    r"""Cosine, element-wise.
+    r"""
+    Cosine, element-wise.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -2356,20 +2386,36 @@ def cos(x, out=None, **kwargs):
         must have a shape that the inputs broadcast to. If not provided
         or None, a freshly-allocated array is returned. The dtype of the
         output is the same as that of the input if the input is an ndarray.
+
     Returns
     -------
     y : ndarray or scalar
         The corresponding cosine values. This is a scalar if x is a scalar.
+
     Notes
     ----
     This function only supports input type of float.
+
+    Examples
+    --------
+    >>> np.cos(np.array([0, np.pi/2, np.pi]))
+    array([ 1.000000e+00, -4.371139e-08, -1.000000e+00])
+    >>> # Example of providing the optional output parameter
+    >>> out1 = np.array([0], dtype='f')
+    >>> out2 = np.cos(np.array([0.1]), out1)
+    >>> out2 is out1
+    True
     """
     return _mx_nd_np.cos(x, out=out, **kwargs)
 
 
+@set_module('mxnet.numpy')
+@wrap_np_unary_func
 def sinh(x, out=None, **kwargs):
-    """Hyperbolic sine, element-wise.
+    """
+    Hyperbolic sine, element-wise.
     Equivalent to ``1/2 * (np.exp(x) - np.exp(-x))`` or ``-1j * np.sin(1j*x)``.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -2379,21 +2425,36 @@ def sinh(x, out=None, **kwargs):
         must have a shape that the inputs broadcast to. If not provided
         or None, a freshly-allocated array is returned. The dtype of the
         output is the same as that of the input if the input is an ndarray.
+
     Returns
     -------
     y : ndarray or scalar
         The corresponding hyperbolic sine values. This is a scalar if `x` is a scalar.
+
     Notes
     ----
     This function only supports input type of float.
+
+    Examples
+    --------
+    >>> np.sinh(0)
+    0.0
+    >>> # Example of providing the optional output parameter
+    >>> out1 = np.array([0], dtype='f')
+    >>> out2 = np.sinh(np.array([0.1]), out1)
+    >>> out2 is out1
+    True
     """
     return _mx_nd_np.sinh(x, out=out, **kwargs)
 
 
 @set_module('mxnet.numpy')
+@wrap_np_unary_func
 def cosh(x, out=None, **kwargs):
-    """Hyperbolic cosine, element-wise.
+    """
+    Hyperbolic cosine, element-wise.
     Equivalent to ``1/2 * (np.exp(x) + np.exp(-x))`` and ``np.cos(1j*x)``.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -2403,22 +2464,31 @@ def cosh(x, out=None, **kwargs):
         must have a shape that the inputs broadcast to. If not provided
         or None, a freshly-allocated array is returned. The dtype of the
         output is the same as that of the input if the input is an ndarray.
+
     Returns
     -------
     y : ndarray or scalar
         The corresponding hyperbolic cosine values. This is a scalar if `x` is a scalar.
+
     Notes
     ----
     This function only supports input type of float.
+
+    Examples
+    --------
+    >>> np.cosh(0)
+    1.0
     """
     return _mx_nd_np.cosh(x, out=out, **kwargs)
 
 
 @set_module('mxnet.numpy')
+@wrap_np_unary_func
 def tanh(x, out=None, **kwargs):
     """
     Compute hyperbolic tangent element-wise.
     Equivalent to ``np.sinh(x)/np.cosh(x)``.
+
     Parameters
     ----------
     x : ndarray or scalar.
@@ -2428,10 +2498,12 @@ def tanh(x, out=None, **kwargs):
         must have a shape that the inputs fill into. If not provided
         or None, a freshly-allocated array is returned. The dtype of the
         output and input must be the same.
+
     Returns
     ----------
     y : ndarray or scalar
        The corresponding hyperbolic tangent values.
+
     Notes
     -----
     If `out` is provided, the function writes the result into it,
@@ -2439,6 +2511,7 @@ def tanh(x, out=None, **kwargs):
     - input x does not support complex computation (like imaginary number)
     >>> np.tanh(np.pi*1j)
     TypeError: type <type 'complex'> not supported
+
     Examples
     --------
     >>> np.tanh(np.array[0, np.pi]))
@@ -2451,19 +2524,16 @@ def tanh(x, out=None, **kwargs):
     >>> out2 = np.tanh(np.array(0.1), out1)
     >>> out2 is out1
     True
-    >>> # Example of ValueError due to provision of shape mis-matched `out`
-    >>> np.tanh(np.zeros((3,3)),np.zeros((2,2)))
-    mxnet.base.MXNetError:
-    [07:17:36] ../src/ndarray/./../operator/tensor/../elemwise_op_common.h:135:
-    Check failed: assign(&dattr, vec.at(i)): Incompatible attr in node
-    at 0-th output: expected [3,3], got [2,2]
     """
     return _mx_nd_np.tanh(x, out=out, **kwargs)
 
 
 @set_module('mxnet.numpy')
+@wrap_np_unary_func
 def log10(x, out=None, **kwargs):
-    """Return the base 10 logarithm of the input array, element-wise.
+    """
+    Return the base 10 logarithm of the input array, element-wise.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -2473,22 +2543,31 @@ def log10(x, out=None, **kwargs):
         must have a shape that the inputs broadcast to. If not provided
         or None, a freshly-allocated array is returned. The dtype of the
         output is the same as that of the input if the input is an ndarray.
+
     Returns
     -------
     y : ndarray or scalar
         The logarithm to the base 10 of `x`, element-wise. NaNs are
         returned where x is negative. This is a scalar if `x` is a scalar.
+
     Notes
     ----
     This function only supports input type of float.
+
+    Examples
+    --------
+    >>> np.log10(np.array([1e-15, -3.]))
+    array([-15.,  nan])
     """
     return _mx_nd_np.log10(x, out=out, **kwargs)
 
 
 @set_module('mxnet.numpy')
+@wrap_np_unary_func
 def sqrt(x, out=None, **kwargs):
     """
     Return the non-negative square-root of an array, element-wise.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -2497,22 +2576,33 @@ def sqrt(x, out=None, **kwargs):
         A location into which the result is stored. If provided, it must have
         a shape that the inputs broadcast to. If not provided or `None`,
         a freshly-allocated array is returned.
+
     Returns
     -------
     y : ndarray or scalar
         An array of the same shape as `x`, containing the positive
         square-root of each element in `x`. This is a scalar if `x` is a scalar.
+
     Notes
     ----
     This function only supports input type of float.
+
+    Examples
+    --------
+    >>> np.sqrt(np.array([1,4,9]))
+    array([1., 2., 3.])
+    >>> np.sqrt(np.array([4, -1, _np.inf]))
+    array([ 2., nan, inf])
     """
     return _mx_nd_np.sqrt(x, out=out, **kwargs)
 
 
 @set_module('mxnet.numpy')
+@wrap_np_unary_func
 def cbrt(x, out=None, **kwargs):
     """
     Return the cube-root of an array, element-wise.
+
     Parameters
     ----------
     x : ndarray
@@ -2521,11 +2611,13 @@ def cbrt(x, out=None, **kwargs):
         A location into which the result is stored. If provided, it must have a shape that the
         inputs broadcast to. If not provided or None, a freshly-allocated array is returned.
         A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
+
     Returns
     ----------
     y : ndarray
         An array of the same shape as x, containing the cube cube-root of each element in x.
         If out was provided, y is a reference to it. This is a scalar if x is a scalar.
+
     Examples
     ----------
     >>> np.cbrt([1,8,27])
@@ -2535,9 +2627,11 @@ def cbrt(x, out=None, **kwargs):
 
 
 @set_module('mxnet.numpy')
+@wrap_np_unary_func
 def abs(x, out=None, **kwargs):
-    r"""abs(x, out=None, **kwargs)
+    r"""
     Calculate the absolute value element-wise.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -2546,11 +2640,13 @@ def abs(x, out=None, **kwargs):
         A location into which the result is stored. If provided, it must have
         a shape that the inputs broadcast to. If not provided or `None`,
         a freshly-allocated array is returned.
+
     Returns
     -------
     absolute : ndarray
         An ndarray containing the absolute value of
         each element in `x`. This is a scalar if `x` is a scalar.
+
     Examples
     --------
     >>> x = np.array([-1.2, 1.2])
@@ -2561,10 +2657,12 @@ def abs(x, out=None, **kwargs):
 
 
 @set_module('mxnet.numpy')
+@wrap_np_unary_func
 def absolute(x, out=None, **kwargs):
     """
     Calculate the absolute value element-wise.
     np.abs is a shorthand for this function.
+
     Parameters
     ----------
     x : ndarray
@@ -2573,10 +2671,12 @@ def absolute(x, out=None, **kwargs):
         A location into which the result is stored. If provided, it must have a shape
         that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned.
         A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
+
     Returns
     ----------
     absolute : ndarray
         An ndarray containing the absolute value of each element in x.
+
     Examples
     ----------
     >>> x = np.array([-1.2, 1.2])
@@ -2587,9 +2687,11 @@ def absolute(x, out=None, **kwargs):
 
 
 @set_module('mxnet.numpy')
+@wrap_np_unary_func
 def exp(x, out=None, **kwargs):
-    r"""exp(x, out=None, **kwargs)
+    r"""
     Calculate the exponential of all elements in the input array.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -2598,11 +2700,13 @@ def exp(x, out=None, **kwargs):
         A location into which the result is stored. If provided, it must have
         a shape that the inputs broadcast to. If not provided or `None`,
         a freshly-allocated array is returned.
+
     Returns
     -------
     out : ndarray or scalar
         Output array, element-wise exponential of `x`.
         This is a scalar if `x` is a scalar.
+
     Examples
     --------
     >>> np.exp(1)
@@ -2615,9 +2719,11 @@ def exp(x, out=None, **kwargs):
 
 
 @set_module('mxnet.numpy')
+@wrap_np_unary_func
 def expm1(x, out=None, **kwargs):
-    r"""expm1(x, out=None, **kwargs)
+    r"""
     Calculate `exp(x) - 1` for all elements in the array.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -2626,11 +2732,13 @@ def expm1(x, out=None, **kwargs):
         A location into which the result is stored. If provided, it must have
         a shape that the inputs broadcast to. If not provided or `None`,
         a freshly-allocated array is returned.
+
     Returns
     -------
     out : ndarray or scalar
         Output array, element-wise exponential minus one: `out = exp(x) - 1`.
         This is a scalar if `x` is a scalar.
+
     Examples
     --------
     >>> np.expm1(1)
@@ -2643,10 +2751,11 @@ def expm1(x, out=None, **kwargs):
 
 
 @set_module('mxnet.numpy')
+@wrap_np_unary_func
 def arcsin(x, out=None, **kwargs):
     r"""
-    arcsin(x, out=None)
     Inverse sine, element-wise.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -2655,12 +2764,14 @@ def arcsin(x, out=None, **kwargs):
         A location into which the result is stored.
         If provided, it must have the same shape as the input.
         If not provided or None, a freshly-allocated array is returned.
+
     Returns
     -------
     angle : ndarray or scalar
         Output array is same shape and type as x. This is a scalar if x is a scalar.
         The inverse sine of each element in `x`, in radians and in the
         closed interval ``[-pi/2, pi/2]``.
+
     Examples
     --------
     >>> np.arcsin(1)     # pi/2
@@ -2669,6 +2780,7 @@ def arcsin(x, out=None, **kwargs):
     -1.5707963267948966
     >>> np.arcsin(0)
     0.0
+
     Notes
     -----
     `arcsin` is a multivalued function: for each `x` there are infinitely
@@ -2685,6 +2797,7 @@ def arcsin(x, out=None, **kwargs):
     - Only support ndarray or scalar now.
     - `where` argument is not supported.
     - Complex input is not supported.
+
     References
     ----------
     Abramowitz, M. and Stegun, I. A., *Handbook of Mathematical Functions*,
@@ -2695,10 +2808,12 @@ def arcsin(x, out=None, **kwargs):
 
 
 @set_module('mxnet.numpy')
+@wrap_np_unary_func
 def arccos(x, out=None, **kwargs):
     """
     Trigonometric inverse cosine, element-wise.
     The inverse of cos so that, if y = cos(x), then x = arccos(y).
+
     Parameters
     ----------
     x : ndarray
@@ -2707,14 +2822,13 @@ def arccos(x, out=None, **kwargs):
         A location into which the result is stored. If provided, it must have a shape that
         the inputs broadcast to. If not provided or None, a freshly-allocated array is returned.
         A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
+
     Returns
     ----------
     angle : ndarray
         The angle of the ray intersecting the unit circle at the given x-coordinate in radians [0, pi].
         This is a scalar if x is a scalar.
-    See also
-    ----------
-    cos, arctan, arcsin
+
     Notes
     ----------
     arccos is a multivalued function: for each x there are infinitely many numbers z such that
@@ -2723,9 +2837,9 @@ def arccos(x, out=None, **kwargs):
     For each value that cannot be expressed as a real number or infinity, it yields nan and sets
     the invalid floating point error flag.
     The inverse cos is also known as acos or cos^-1.
+
     Examples
     ----------
-    We expect the arccos of 1 to be 0, and of -1 to be pi:
     >>> np.arccos([1, -1])
     array([ 0.        ,  3.14159265])
     """
@@ -2733,10 +2847,12 @@ def arccos(x, out=None, **kwargs):
 
 
 @set_module('mxnet.numpy')
+@wrap_np_unary_func
 def arctan(x, out=None, **kwargs):
-    r"""arctan(x, out=None, **kwargs)
+    r"""
     Trigonometric inverse tangent, element-wise.
     The inverse of tan, so that if ``y = tan(x)`` then ``x = arctan(y)``.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -2745,12 +2861,14 @@ def arctan(x, out=None, **kwargs):
         A location into which the result is stored. If provided, it must have
         a shape that the inputs broadcast to. If not provided or `None`,
         a freshly-allocated array is returned.
+
     Returns
     -------
     out : ndarray or scalar
         Out has the same shape as `x`. It lies is in
         ``[-pi/2, pi/2]`` (``arctan(+/-inf)`` returns ``+/-pi/2``).
         This is a scalar if `x` is a scalar.
+
     Notes
     -----
     `arctan` is a multi-valued function: for each `x` there are infinitely
@@ -2761,9 +2879,9 @@ def arctan(x, out=None, **kwargs):
     it yields ``nan`` and sets the `invalid` floating point error flag.
     For complex-valued input, we do not have support for them yet.
     The inverse tangent is also known as `atan` or tan^{-1}.
+
     Examples
     --------
-    We expect the arctan of 0 to be 0, and of 1 to be pi/4:
     >>> x = np.array([0, 1])
     >>> np.arctan(x)
     array([0.       , 0.7853982])
@@ -2774,11 +2892,12 @@ def arctan(x, out=None, **kwargs):
 
 
 @set_module('mxnet.numpy')
-def sign(x, out=None):
+@wrap_np_unary_func
+def sign(x, out=None, **kwargs):
     """
-    sign(x, out=None)
     Returns an element-wise indication of the sign of a number.
     The `sign` function returns ``-1 if x < 0, 0 if x==0, 1 if x > 0``. Only supports real number.
+
     Parameters
     ----------
     x : ndarray or a scalar
@@ -2787,11 +2906,13 @@ def sign(x, out=None):
         A location into which the result is stored.
         If provided, it must have the same shape and dtype as input ndarray.
         If not provided or `None`, a freshly-allocated array is returned.
+
     Returns
     -------
     y : ndarray
         The sign of `x`.
         This is a scalar if `x` is a scalar.
+
     Note
     -------
     - Only supports real number as input elements.
@@ -2799,6 +2920,7 @@ def sign(x, out=None):
     - ``out`` param: cannot perform auto broadcasting. ``out`` ndarray's shape must be the same as the expected output.
     - ``out`` param: cannot perform auto type cast. ``out`` ndarray's dtype must be the same as the expected output.
     - ``out`` param does not support scalar input case.
+
     Examples
     --------
     >>> a = np.array([-5., 4.5])
@@ -2820,13 +2942,14 @@ def sign(x, out=None):
 
 
 @set_module('mxnet.numpy')
+@wrap_np_unary_func
 def log(x, out=None, **kwargs):
     """
-    log(x, out=None)
     Natural logarithm, element-wise.
     The natural logarithm `log` is the inverse of the exponential function,
     so that `log(exp(x)) = x`. The natural logarithm is logarithm in base
     `e`.
+
     Parameters
     ----------
     x : ndarray
@@ -2835,11 +2958,13 @@ def log(x, out=None, **kwargs):
         A location into which the result is stored.
         If provided, it must have the same shape and dtype as input ndarray.
         If not provided or `None`, a freshly-allocated array is returned.
+
     Returns
     -------
     y : ndarray
         The natural logarithm of `x`, element-wise.
         This is a scalar if `x` is a scalar.
+
     Notes
     -----
     Currently only supports data of real values and ``inf`` as input. Returns data of real value, ``inf``, ``-inf`` and
@@ -2852,16 +2977,16 @@ def log(x, out=None, **kwargs):
     - ``out`` param: cannot perform auto broadcasting. ``out`` ndarray's shape must be the same as the expected output.
     - ``out`` param: cannot perform auto type cast. ``out`` ndarray's dtype must be the same as the expected output.
     - ``out`` param does not support scalar input case.
+
     Examples
     --------
     >>> a = np.array([1, np.exp(1), np.exp(2), 0], dtype=np.float64)
     >>> np.log(a)
     array([  0.,   1.,   2., -inf], dtype=float64)
-    Due to internal calculation mechanism, using default float32 dtype may cause some special behavior:
+    >>> # Using the default float32 dtype leads to slightly different behavior
     >>> a = np.array([1, np.exp(1), np.exp(2), 0])
     >>> np.log(a)
     array([  0.,  0.99999994,   2., -inf])
-    Scalar calculation:
     >>> np.log(1)
     0.0
     """
@@ -2869,9 +2994,11 @@ def log(x, out=None, **kwargs):
 
 
 @set_module('mxnet.numpy')
+@wrap_np_unary_func
 def rint(x, out=None, **kwargs):
     """
     Round elements of the array to the nearest integer.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -2880,10 +3007,12 @@ def rint(x, out=None, **kwargs):
         A location into which the result is stored.
         If provided, it must have the same shape and type as the input.
         If not provided or None, a freshly-allocated array is returned.
+
     Returns
     -------
     out : ndarray or scalar
         Output array is same shape and type as x. This is a scalar if x is a scalar.
+
     Notes
     -----
     This function differs from the original `numpy.rint
@@ -2892,6 +3021,7 @@ def rint(x, out=None, **kwargs):
     - only ndarray or scalar is accpted as valid input, tuple of ndarray is not supported
     - broadcasting to `out` of different shape is currently not supported
     - when input is plain python numerics, the result will not be stored in the `out` param
+
     Examples
     --------
     >>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
@@ -2902,9 +3032,11 @@ def rint(x, out=None, **kwargs):
 
 
 @set_module('mxnet.numpy')
+@wrap_np_unary_func
 def log2(x, out=None, **kwargs):
     """
     Base-2 logarithm of x.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -2913,11 +3045,13 @@ def log2(x, out=None, **kwargs):
         A location into which the result is stored.
         If provided, it must have the same shape and type as the input.
         If not provided or None, a freshly-allocated array is returned.
+
     Returns
     -------
     y : ndarray
         The logarithm base two of `x`, element-wise.
         This is a scalar if `x` is a scalar.
+
     Notes
     -----
     This function differs from the original `numpy.log2
@@ -2926,6 +3060,7 @@ def log2(x, out=None, **kwargs):
     - only ndarray or scalar is accpted as valid input, tuple of ndarray is not supported
     - broadcasting to `out` of different shape is currently not supported
     - when input is plain python numerics, the result will not be stored in the `out` param
+
     Examples
     --------
     >>> x = np.array([0, 1, 2, 2**4])
@@ -2936,10 +3071,12 @@ def log2(x, out=None, **kwargs):
 
 
 @set_module('mxnet.numpy')
+@wrap_np_unary_func
 def log1p(x, out=None, **kwargs):
     """
     Return the natural logarithm of one plus the input array, element-wise.
     Calculates ``log(1 + x)``.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -2949,11 +3086,13 @@ def log1p(x, out=None, **kwargs):
         must have a shape that the inputs fill into. If not provided
         or None, a freshly-allocated array is returned. The dtype of the
         output and input must be the same.
+
     Returns
     -------
     y : ndarray or scalar
         Natural logarithm of 1 + x, element-wise. This is a scalar
         if x is a scalar.
+
     Notes
     -----
     For real-valued input, `log1p` is accurate also for `x` so small
@@ -2965,6 +3104,7 @@ def log1p(x, out=None, **kwargs):
     For each value that cannot be expressed as a real number or infinity,
     it yields ``nan`` and sets the `invalid` floating point error flag.
     cannot support complex-valued input.
+
     Examples
     --------
     >>> np.log1p(1e-99)
@@ -2977,10 +3117,11 @@ def log1p(x, out=None, **kwargs):
 
 
 @set_module('mxnet.numpy')
+@wrap_np_unary_func
 def degrees(x, out=None, **kwargs):
     """
-    degrees(x, out=None)
     Convert angles from radians to degrees.
+
     Parameters
     ----------
     x : ndarray
@@ -2989,12 +3130,14 @@ def degrees(x, out=None, **kwargs):
         A location into which the result is stored.
         If provided, it must have the same shape and dtype as input ndarray.
         If not provided or `None`, a freshly-allocated array is returned.
+
     Returns
     -------
     y : ndarray
         The corresponding degree values; if `out` was supplied this is a
         reference to it.
         This is a scalar if `x` is a scalar.
+
     Notes
     -------
     This function differs from the original `numpy.degrees
@@ -3004,13 +3147,13 @@ def degrees(x, out=None, **kwargs):
     - ``out`` param: cannot perform auto broadcasting. ``out`` ndarray's shape must be the same as the expected output.
     - ``out`` param: cannot perform auto type cast. ``out`` ndarray's dtype must be the same as the expected output.
     - ``out`` param does not support scalar input case.
+
     Examples
     --------
-    Convert a radian array to degrees
     >>> rad = np.arange(12.) * np.pi / 6
     >>> np.degrees(rad)
     array([  0.,  30.,  60.,  90., 120., 150., 180., 210., 240., 270., 300., 330.])
-    Use specified ``out`` ndarray:
+    >>> # Use specified ``out`` ndarray:
     >>> out = np.zeros((rad.shape))
     >>> np.degrees(rad, out)
     array([  0.,  30.,  60.,  90., 120., 150., 180., 210., 240., 270., 300., 330.])
@@ -3021,10 +3164,9 @@ def degrees(x, out=None, **kwargs):
 
 
 @set_module('mxnet.numpy')
-def rad2deg(x, out=None):
+@wrap_np_unary_func
+def rad2deg(x, out=None, **kwargs):
     r"""
-    rad2deg(x, out=None)
-
     Convert angles from radians to degrees.
     Parameters
     ----------
@@ -3057,9 +3199,11 @@ def rad2deg(x, out=None):
 
 
 @set_module('mxnet.numpy')
+@wrap_np_unary_func
 def radians(x, out=None, **kwargs):
     """
     Convert angles from degrees to radians.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -3068,10 +3212,12 @@ def radians(x, out=None, **kwargs):
         A location into which the result is stored.
         If provided, it must have the same shape and type as the input.
         If not provided or None, a freshly-allocated array is returned.
+
     Returns
     -------
     y : ndarray
         The corresponding radian values. This is a scalar if x is a scalar.
+
     Notes
     -----
     This function differs from the original `numpy.radians
@@ -3080,6 +3226,7 @@ def radians(x, out=None, **kwargs):
     - only ndarray or scalar is accpted as valid input, tuple of ndarray is not supported
     - broadcasting to `out` of different shape is currently not supported
     - when input is plain python numerics, the result will not be stored in the `out` param
+
     Examples
     --------
     >>> deg = np.arange(12.) * 30.
@@ -3092,11 +3239,11 @@ def radians(x, out=None, **kwargs):
 
 
 @set_module('mxnet.numpy')
-def deg2rad(x, out=None):
+@wrap_np_unary_func
+def deg2rad(x, out=None, **kwargs):
     r"""
-    deg2rad(x, out=None)
-
     Convert angles from degrees to radians.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -3128,11 +3275,12 @@ def deg2rad(x, out=None):
 
 
 @set_module('mxnet.numpy')
+@wrap_np_unary_func
 def reciprocal(x, out=None, **kwargs):
     r"""
-    reciprocal(x, out=None)
     Return the reciprocal of the argument, element-wise.
     Calculates ``1/x``.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -3141,10 +3289,12 @@ def reciprocal(x, out=None, **kwargs):
         A location into which the result is stored.
         If provided, it must have the same shape as the input.
         If not provided or None, a freshly-allocated array is returned.
+
     Returns
     -------
     y : ndarray or scalar
         Output array is same shape and type as x. This is a scalar if x is a scalar.
+
     Examples
     --------
     >>> np.reciprocal(2.)
@@ -3152,6 +3302,7 @@ def reciprocal(x, out=None, **kwargs):
     >>> x = np.array([1, 2., 3.33])
     >>> np.reciprocal(x)
     array([1.       , 0.5      , 0.3003003])
+
     Notes
     -----
     .. note::
@@ -3170,10 +3321,11 @@ def reciprocal(x, out=None, **kwargs):
 
 
 @set_module('mxnet.numpy')
+@wrap_np_unary_func
 def square(x, out=None, **kwargs):
     r"""
-    square(x, out=None)
     Return the element-wise square of the input.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -3182,10 +3334,12 @@ def square(x, out=None, **kwargs):
         A location into which the result is stored.
         If provided, it must have the same shape as the input.
         If not provided or None, a freshly-allocated array is returned.
+
     Returns
     -------
     y : ndarray or scalar
         Output array is same shape and type as x. This is a scalar if x is a scalar.
+
     Examples
     --------
     >>> np.square(2.)
@@ -3193,6 +3347,7 @@ def square(x, out=None, **kwargs):
     >>> x = np.array([1, 2., -1])
     >>> np.square(x)
     array([1., 4., 1.])
+
     Notes
     -----
     The output `ndarray` has the same `ctx` as the input `ndarray`.
@@ -3207,10 +3362,11 @@ def square(x, out=None, **kwargs):
 
 
 @set_module('mxnet.numpy')
-def negative(x, out=None, where=True, **kwargs):
+@wrap_np_unary_func
+def negative(x, out=None, **kwargs):
     r"""
-    negative(x, out=None, where=True)
     Numerical negative, element-wise.
+
     Parameters:
     ------------
     x : ndarray or scalar
@@ -3221,13 +3377,12 @@ def negative(x, out=None, where=True, **kwargs):
           If not provided or None, a freshly-allocated array is returned.
           A tuple (possible only as a keyword argument) must have length
           equal to the number of outputs.
-    where : ndarray, optional
-            Values of True indicate to calculate the ufunc at that position,
-            values of False indicate to leave the value in the output alone.
+
     Returns:
     -------
     y : ndarray or scalar
         Returned array or scalar: y = -x. This is a scalar if x is a scalar.
+
     Examples:
     --------
     >>> np.negative(1)
@@ -3237,7 +3392,8 @@ def negative(x, out=None, where=True, **kwargs):
 
 
 @set_module('mxnet.numpy')
-def fix(x, out=None):
+@wrap_np_unary_func
+def fix(x, out=None, **kwargs):
     """
     Round an array of floats element-wise to nearest integer towards zero.
     The rounded values are returned as floats.
@@ -3248,10 +3404,12 @@ def fix(x, out=None):
         An array of floats to be rounded
     out : ndarray, optional
         Output array
+
     Returns:
     -------
     y : ndarray or scalar
     Returned array or scalar: y = -x. This is a scalar if x is a scalar.ndarray of floats
+
     Examples:
     ---------
     >>> np.fix(3.14)
@@ -3261,9 +3419,9 @@ def fix(x, out=None):
 
 
 @set_module('mxnet.numpy')
-def tan(x, out=None, where=True, **kwargs):
+@wrap_np_unary_func
+def tan(x, out=None, **kwargs):
     r"""
-    tan(x, out=None, where=True)
     Compute tangent element-wise.
     Equivalent to np.sin(x)/np.cos(x) element-wise.
 
@@ -3276,13 +3434,12 @@ def tan(x, out=None, where=True, **kwargs):
           it must have a shape that the inputs broadcast to. If not provided or None,
           a freshly-allocated array is returned. A tuple (possible only as a keyword argument)
           must have length equal to the number of outputs.
-    where : ndarray, optional
-            Values of True indicate to calculate the ufunc at that position,
-            values of False indicate to leave the value in the output alone.
+
     Returns:
     -------
     y : ndarray
     The corresponding tangent values. This is a scalar if x is a scalar.
+
     Examples:
     ---------
     >>> np.tan(0.5)
@@ -3293,11 +3450,13 @@ def tan(x, out=None, where=True, **kwargs):
 
 
 @set_module('mxnet.numpy')
+@wrap_np_unary_func
 def ceil(x, out=None, **kwargs):
     r"""
     Return the ceiling of the input, element-wise.
     The ceil of the ndarray `x` is the smallest integer `i`, such that
     `i >= x`.  It is often denoted as :math:`\lceil x \rceil`.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -3307,17 +3466,19 @@ def ceil(x, out=None, **kwargs):
         must have a shape that the inputs fill into. If not provided
         or None, a freshly-allocated array is returned. The dtype of the
         output and input must be the same.
+
     Returns
     -------
     y : ndarray or scalar
         The ceiling of each element in `x`, with `float` dtype.
         This is a scalar if `x` is a scalar.
+
     Examples
     --------
     >>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
     >>> np.ceil(a)
     array([-1., -1., -0.,  1.,  2.,  2.,  2.])
-    >>> #if you use parameter out, x and out must be ndarray. if not, you will get an error!
+    >>> # if you use parameter out, x and out must be ndarray.
     >>> a = np.array(1)
     >>> np.ceil(np.array(3.5), a)
     array(4.)
@@ -3328,11 +3489,13 @@ def ceil(x, out=None, **kwargs):
 
 
 @set_module('mxnet.numpy')
+@wrap_np_unary_func
 def floor(x, out=None, **kwargs):
     r"""
     Return the floor of the input, element-wise.
     The ceil of the ndarray `x` is the largest integer `i`, such that
     `i <= x`.  It is often denoted as :math:`\lfloor x \rfloor`.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -3342,17 +3505,19 @@ def floor(x, out=None, **kwargs):
         must have a shape that the inputs fill into. If not provided
         or None, a freshly-allocated array is returned. The dtype of the
         output and input must be the same.
+
     Returns
     -------
     y : ndarray or scalar
         The floor of each element in `x`, with `float` dtype.
         This is a scalar if `x` is a scalar.
+
     Examples
     --------
     >>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
     >>> np.floor(a)
     array([-2., -2., -1.,  0.,  1.,  1.,  2.])
-    >>> #if you use parameter out, x and out must be ndarray. if not, you will get an error!
+    >>> # if you use parameter out, x and out must be ndarray.
     >>> a = np.array(1)
     >>> np.floor(np.array(3.5), a)
     array(3.)
@@ -3363,9 +3528,9 @@ def floor(x, out=None, **kwargs):
 
 
 @set_module('mxnet.numpy')
+@wrap_np_unary_func
 def trunc(x, out=None, **kwargs):
     r"""
-    trunc(x, out=None)
     Return the truncated value of the input, element-wise.
     The truncated value of the scalar `x` is the nearest integer `i` which
     is closer to zero than `x` is. In short, the fractional part of the
@@ -3401,10 +3566,11 @@ def trunc(x, out=None, **kwargs):
 
 
 @set_module('mxnet.numpy')
+@wrap_np_unary_func
 def logical_not(x, out=None, **kwargs):
     r"""
-    logical_not(x, out=None)
     Compute the truth value of NOT x element-wise.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -3418,6 +3584,7 @@ def logical_not(x, out=None, **kwargs):
         Boolean result with the same shape as `x` of the NOT operation
         on elements of `x`.
         This is a scalar if `x` is a scalar.
+
     Notes
     -----
     This function differs from the original numpy.logical_not in the following aspects:
@@ -3440,10 +3607,11 @@ def logical_not(x, out=None, **kwargs):
 
 
 @set_module('mxnet.numpy')
+@wrap_np_unary_func
 def arcsinh(x, out=None, **kwargs):
     r"""
-    arcsinh(x, out=None)
     Inverse hyperbolic cosine, element-wise.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -3486,10 +3654,11 @@ def arcsinh(x, out=None, **kwargs):
 
 
 @set_module('mxnet.numpy')
+@wrap_np_unary_func
 def arccosh(x, out=None, **kwargs):
     r"""
-    arccosh(x, out=None)
     Inverse hyperbolic cosine, element-wise.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -3532,10 +3701,11 @@ def arccosh(x, out=None, **kwargs):
 
 
 @set_module('mxnet.numpy')
+@wrap_np_unary_func
 def arctanh(x, out=None, **kwargs):
     r"""
-    arctanh(x, out=None)
     Inverse hyperbolic tangent, element-wise.
+
     Parameters
     ----------
     x : ndarray or scalar
@@ -3590,6 +3760,7 @@ def tensordot(a, b, axes=2):
     integer_like scalar, ``N``; if it is such, then the last ``N``
     dimensions of `a` and the first ``N`` dimensions of `b` are summed
     over.
+
     Parameters
     ----------
     a, b : ndarray, len(shape) >= 1
@@ -3601,9 +3772,11 @@ def tensordot(a, b, axes=2):
         * (2,) ndarray
         Or, a list of axes to be summed over, first sequence applying to `a`,
         second to `b`. Both elements ndarray must be of the same length.
+
     See Also
     --------
     dot, einsum
+
     Notes
     -----
     Three common use cases are:
@@ -3617,6 +3790,7 @@ def tensordot(a, b, axes=2):
     (first) axes of `a` (`b`) - the argument `axes` should consist of
     two sequences of the same length, with the first axis to sum over given
     first in both sequences, the second axis second, and so forth.
+
     Examples
     --------
     >>> a = np.arange(60.).reshape(3,4,5)
@@ -4263,8 +4437,10 @@ def dstack(arrays):
 
 
 @set_module('mxnet.numpy')
-def maximum(x1, x2, out=None):
-    """Returns element-wise maximum of the input arrays with broadcasting.
+@wrap_np_binary_func
+def maximum(x1, x2, out=None, **kwargs):
+    """
+    Returns element-wise maximum of the input arrays with broadcasting.
 
     Parameters
     ----------
@@ -4280,8 +4456,10 @@ def maximum(x1, x2, out=None):
 
 
 @set_module('mxnet.numpy')
-def minimum(x1, x2, out=None):
-    """Returns element-wise minimum of the input arrays with broadcasting.
+@wrap_np_binary_func
+def minimum(x1, x2, out=None, **kwargs):
+    """
+    Returns element-wise minimum of the input arrays with broadcasting.
 
     Parameters
     ----------
@@ -4696,9 +4874,9 @@ def indices(dimensions, dtype=_np.int32, ctx=None):
 
 
 @set_module('mxnet.numpy')
-def copysign(x1, x2, out=None):
-    r"""copysign(x1, x2, out=None)
-
+@wrap_np_binary_func
+def copysign(x1, x2, out=None, **kwargs):
+    r"""
     Change the sign of x1 to that of x2, element-wise.
 
     If `x2` is a scalar, its sign will be copied to all elements of `x1`.
@@ -5141,10 +5319,9 @@ def around(x, decimals=0, out=None, **kwargs):
 
 
 @set_module('mxnet.numpy')
-def arctan2(x1, x2, out=None):
+@wrap_np_binary_func
+def arctan2(x1, x2, out=None, **kwargs):
     r"""
-    arctan2(x1, x2, out=None)
-
     Element-wise arc tangent of ``x1/x2`` choosing the quadrant correctly.
 
     The quadrant (i.e., branch) is chosen so that ``arctan2(x1, x2)`` is
@@ -5226,10 +5403,9 @@ def arctan2(x1, x2, out=None):
 
 
 @set_module('mxnet.numpy')
-def hypot(x1, x2, out=None):
+@wrap_np_binary_func
+def hypot(x1, x2, out=None, **kwargs):
     r"""
-    hypot(x1, x2, out=None)
-
     Given the "legs" of a right triangle, return its hypotenuse.
 
     Equivalent to ``sqrt(x1**2 + x2**2)``, element-wise.  If `x1` or
@@ -5275,7 +5451,8 @@ def hypot(x1, x2, out=None):
 
 
 @set_module('mxnet.numpy')
-def ldexp(x1, x2, out=None):
+@wrap_np_binary_func
+def ldexp(x1, x2, out=None, **kwargs):
     """
     Returns x1 * 2**x2, element-wise.
     The mantissas `x1` and twos exponents `x2` are used to construct
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 66b2aa2ac3fb..5e535ef3ef84 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -24,6 +24,7 @@
 from . import _op as _mx_np_op
 from ...base import _LIB, SymbolHandle, numeric_types, mx_uint
 from ...util import check_call, set_module, _sanity_check_params
+from ...util import wrap_np_unary_func, wrap_np_binary_func
 from ...context import current_context
 from ..symbol import Symbol
 from .._internal import _set_np_symbol_class
@@ -1138,44 +1139,52 @@ def _ufunc_helper(lhs, rhs, fn_array, fn_scalar, lfn_scalar, rfn_scalar=None, ou
 
 
 @set_module('mxnet.symbol.numpy')
-def add(x1, x2, out=None):
+@wrap_np_binary_func
+def add(x1, x2, out=None, **kwargs):
     return _ufunc_helper(x1, x2, _npi.add, _np.add, _npi.add_scalar, None, out)
 
 
 @set_module('mxnet.symbol.numpy')
-def subtract(x1, x2, out=None):
+@wrap_np_binary_func
+def subtract(x1, x2, out=None, **kwargs):
     return _ufunc_helper(x1, x2, _npi.subtract, _np.subtract, _npi.subtract_scalar,
                          _npi.rsubtract_scalar, out)
 
 
 @set_module('mxnet.symbol.numpy')
-def multiply(x1, x2, out=None):
+@wrap_np_binary_func
+def multiply(x1, x2, out=None, **kwargs):
     return _ufunc_helper(x1, x2, _npi.multiply, _np.multiply, _npi.multiply_scalar, None, out)
 
 
 @set_module('mxnet.symbol.numpy')
-def divide(x1, x2, out=None):
+@wrap_np_binary_func
+def divide(x1, x2, out=None, **kwargs):
     return _ufunc_helper(x1, x2, _npi.true_divide, _np.divide, _npi.true_divide_scalar,
                          _npi.rtrue_divide_scalar, out)
 
 
 @set_module('mxnet.symbol.numpy')
-def mod(x1, x2, out=None):
+@wrap_np_binary_func
+def mod(x1, x2, out=None, **kwargs):
     return _ufunc_helper(x1, x2, _npi.mod, _np.mod, _npi.mod_scalar, _npi.rmod_scalar, out)
 
 
 @set_module('mxnet.symbol.numpy')
-def remainder(x1, x2, out=None):
+@wrap_np_binary_func
+def remainder(x1, x2, out=None, **kwargs):
     return _ufunc_helper(x1, x2, _npi.mod, _np.mod, _npi.mod_scalar, _npi.rmod_scalar, out)
 
 
 @set_module('mxnet.symbol.numpy')
-def power(x1, x2, out=None):
+@wrap_np_binary_func
+def power(x1, x2, out=None, **kwargs):
     return _ufunc_helper(x1, x2, _npi.power, _np.power, _npi.power_scalar, _npi.rpower_scalar, out)
 
 
 @set_module('mxnet.symbol.numpy')
-def lcm(x1, x2, out=None):
+@wrap_np_binary_func
+def lcm(x1, x2, out=None, **kwargs):
     """
     Returns the lowest common multiple of ``|x1|`` and ``|x2|``
 
@@ -1570,19 +1579,24 @@ def _unary_func_helper(x, fn_array, fn_scalar, out=None, **kwargs):
 
 
 @set_module('mxnet.symbol.numpy')
+@wrap_np_unary_func
 def sin(x, out=None, **kwargs):
-    r"""Trigonometric sine, element-wise.
+    r"""
+    Trigonometric sine, element-wise.
+
     Parameters
     ----------
     x : _Symbol or scalar
         Angle, in radians (:math:`2 \pi` rad equals 360 degrees).
     out : _Symbol or None
         Dummy parameter to keep the consistency with the ndarray counterpart.
+
     Returns
     -------
     y : _Symbol
         The sine of each element of x.
         This is a scalar if `x` is a scalar.
+
     Notes
     ----
     This function only supports input type of float.
@@ -1591,18 +1605,23 @@ def sin(x, out=None, **kwargs):
 
 
 @set_module('mxnet.symbol.numpy')
+@wrap_np_unary_func
 def cos(x, out=None, **kwargs):
-    r"""Cosine, element-wise.
+    r"""
+    Cosine, element-wise.
+
     Parameters
     ----------
     x : _Symbol or scalar
         Angle, in radians (:math:`2 \pi` rad equals 360 degrees).
     out : _Symbol or None
         Dummy parameter to keep the consistency with the ndarray counterpart.
+
     Returns
     -------
     y : _Symbol
         The corresponding cosine values. This is a scalar if x is a scalar.
+
     Notes
     ----
     This function only supports input type of float.
@@ -1611,19 +1630,24 @@ def cos(x, out=None, **kwargs):
 
 
 @set_module('mxnet.symbol.numpy')
+@wrap_np_unary_func
 def sinh(x, out=None, **kwargs):
-    """Hyperbolic sine, element-wise.
+    """
+    Hyperbolic sine, element-wise.
     Equivalent to ``1/2 * (np.exp(x) - np.exp(-x))`` or ``-1j * np.sin(1j*x)``.
+
     Parameters
     ----------
     x : _Symbol or scalar
         Input array or scalar.
     out : _Symbol or None
         Dummy parameter to keep the consistency with the ndarray counterpart.
+
     Returns
     -------
     y : _Symbol or scalar
         The corresponding hyperbolic sine values. This is a scalar if `x` is a scalar.
+
     Notes
     ----
     This function only supports input type of float.
@@ -1632,19 +1656,24 @@ def sinh(x, out=None, **kwargs):
 
 
 @set_module('mxnet.symbol.numpy')
+@wrap_np_unary_func
 def cosh(x, out=None, **kwargs):
-    """Hyperbolic cosine, element-wise.
+    """
+    Hyperbolic cosine, element-wise.
     Equivalent to ``1/2 * (np.exp(x) + np.exp(-x))`` and ``np.cos(1j*x)``.
+
     Parameters
     ----------
     x : _Symbol or scalar
         Input array or scalar.
     out : ndarray or None
         Dummy parameter to keep the consistency with the ndarray counterpart.
+
     Returns
     -------
     y : _Symbol or scalar
         The corresponding hyperbolic cosine values. This is a scalar if `x` is a scalar.
+
     Notes
     ----
     This function only supports input type of float.
@@ -1653,20 +1682,24 @@ def cosh(x, out=None, **kwargs):
 
 
 @set_module('mxnet.symbol.numpy')
+@wrap_np_unary_func
 def tanh(x, out=None, **kwargs):
     """
     Compute hyperbolic tangent element-wise.
     Equivalent to ``np.sinh(x)/np.cosh(x)``.
+
     Parameters
     ----------
     x : _Symbol
         Input array.
     out : _Symbol or None
           Dummy parameter to keep the consistency with the ndarray counterpart.
+
     Returns
     -------
     y : _Symbol
         The corresponding hyperbolic tangent values.
+
     Notes
     -----
     If `out` is provided, the function writes the result into it,
@@ -1674,6 +1707,7 @@ def tanh(x, out=None, **kwargs):
     - input x does not support complex computation (like imaginary number)
     >>> np.tanh(np.pi*1j)
     TypeError: type <type 'complex'> not supported
+
     Examples
     --------
     >>> np.tanh(np.array[0, np.pi]))
@@ -1697,19 +1731,24 @@ def tanh(x, out=None, **kwargs):
 
 
 @set_module('mxnet.symbol.numpy')
+@wrap_np_unary_func
 def log10(x, out=None, **kwargs):
-    """Return the base 10 logarithm of the input array, element-wise.
+    """
+    Return the base 10 logarithm of the input array, element-wise.
+
     Parameters
     ----------
     x : _Symbol or scalar
         Input array or scalar.
     out : _Symbol or None
         Dummy parameter to keep the consistency with the ndarray counterpart.
+
     Returns
     -------
     y : _Symbol or scalar
         The logarithm to the base 10 of `x`, element-wise. NaNs are
         returned where x is negative. This is a scalar if `x` is a scalar.
+
     Notes
     ----
     This function only supports input type of float.
@@ -1718,20 +1757,24 @@ def log10(x, out=None, **kwargs):
 
 
 @set_module('mxnet.symbol.numpy')
+@wrap_np_unary_func
 def sqrt(x, out=None, **kwargs):
     """
     Return the non-negative square-root of an array, element-wise.
+
     Parameters
     ----------
     x : _Symbol or scalar
         The values whose square-roots are required.
     out : _Symbol, or None, optional
         Dummy parameter to keep the consistency with the ndarray counterpart.
+
     Returns
     -------
     y : _Symbol or scalar
         An array of the same shape as `x`, containing the positive
         square-root of each element in `x`. This is a scalar if `x` is a scalar.
+
     Notes
     ----
     This function only supports input type of float.
@@ -1740,15 +1783,18 @@ def sqrt(x, out=None, **kwargs):
 
 
 @set_module('mxnet.symbol.numpy')
+@wrap_np_unary_func
 def cbrt(x, out=None, **kwargs):
     r"""
     Return the cube-root of an array, element-wise.
+
     Parameters
     ----------
     x : _Symbol
         The values whose cube-roots are required.
     out : _Symbol or None
         Dummy parameter to keep the consistency with the ndarray counterpart.
+
     Returns
     ----------
     y : _Symbol
@@ -1759,15 +1805,18 @@ def cbrt(x, out=None, **kwargs):
 
 
 @set_module('mxnet.symbol.numpy')
+@wrap_np_unary_func
 def abs(x, out=None, **kwargs):
-    r"""abs(x, out=None, **kwargs)
+    r"""
     Calculate the absolute value element-wise.
+
     Parameters
     ----------
     x : _Symbol or scalar
         Input array.
     out : _Symbol or None
         Dummy parameter to keep the consistency with the ndarray counterpart.
+
     Returns
     -------
     absolute : _Symbol
@@ -1778,6 +1827,7 @@ def abs(x, out=None, **kwargs):
 
 
 @set_module('mxnet.symbol.numpy')
+@wrap_np_unary_func
 def absolute(x, out=None, **kwargs):
     r"""
     Calculate the absolute value element-wise.
@@ -1789,6 +1839,7 @@ def absolute(x, out=None, **kwargs):
         Input array.
     out : _Symbol or None
         Dummy parameter to keep the consistency with the ndarray counterpart.
+
     Returns
     ----------
     absolute : _Symbol
@@ -1798,22 +1849,25 @@ def absolute(x, out=None, **kwargs):
 
 
 @set_module('mxnet.symbol.numpy')
+@wrap_np_unary_func
 def sign(x, out=None, **kwargs):
     r"""
-    sign(x, out=None)
     Returns an element-wise indication of the sign of a number.
     The `sign` function returns ``-1 if x < 0, 0 if x==0, 1 if x > 0``. Only supports real number.
+
     Parameters
     ----------
     x : _Symbol or a scalar
         Input values.
     out : _Symbol or None, optional
         Dummy parameter to keep the consistency with the ndarray counterpart.
+
     Returns
     -------
     y : _Symbol
         The sign of `x`.
         This is a scalar if `x` is a scalar.
+
     Note
     -------
     - Only supports real number as input elements.
@@ -1826,15 +1880,18 @@ def sign(x, out=None, **kwargs):
 
 
 @set_module('mxnet.symbol.numpy')
+@wrap_np_unary_func
 def exp(x, out=None, **kwargs):
-    r"""exp(x, out=None, **kwargs)
+    r"""
     Calculate the exponential of all elements in the input array.
+
     Parameters
     ----------
     x : _Symbol or scalar
         Input values.
     out : _Symbol or None
         Dummy parameter to keep the consistency with the ndarray counterpart.
+
     Returns
     -------
     out : _Symbol
@@ -1845,15 +1902,18 @@ def exp(x, out=None, **kwargs):
 
 
 @set_module('mxnet.symbol.numpy')
+@wrap_np_unary_func
 def expm1(x, out=None, **kwargs):
-    r"""expm1(x, out=None, **kwargs)
+    r"""
     Calculate `exp(x) - 1` for all elements in the array.
+
     Parameters
     ----------
     x : _Symbol or scalar
         Input values.
     out : _Symbol or None
         Dummy parameter to keep the consistency with the ndarray counterpart.
+
     Returns
     -------
     out : _Symbol
@@ -1864,20 +1924,23 @@ def expm1(x, out=None, **kwargs):
 
 
 @set_module('mxnet.symbol.numpy')
+@wrap_np_unary_func
 def arcsin(x, out=None, **kwargs):
     r"""
-    arcsin(x, out=None)
     Inverse sine, element-wise.
+
     Parameters
     ----------
     x : _Symbol or scalar
         The values whose reciprocals are required.
     out : _Symbol, or None, optional
         Dummy parameter to keep the consistency with the ndarray counterpart.
+
     Returns
     -------
     angle : _Symbol or scalar
         Output array is same shape and type as x. This is a scalar if x is a scalar.
+
     Notes
     -----
     `arcsin` is a multivalued function: for each `x` there are infinitely
@@ -1894,6 +1957,7 @@ def arcsin(x, out=None, **kwargs):
     - Only support _Symbol or scalar now.
     - `where` argument is not supported.
     - Complex input is not supported.
+
     References
     ----------
     Abramowitz, M. and Stegun, I. A., *Handbook of Mathematical Functions*,
@@ -1904,24 +1968,29 @@ def arcsin(x, out=None, **kwargs):
 
 
 @set_module('mxnet.symbol.numpy')
+@wrap_np_unary_func
 def arccos(x, out=None, **kwargs):
     r"""
     Trigonometric inverse cosine, element-wise.
     The inverse of cos so that, if y = cos(x), then x = arccos(y).
+
     Parameters
     ----------
     x : _Symbol
         x-coordinate on the unit circle. For real arguments, the domain is [-1, 1].
     out : _Symbol or None
         Dummy parameter to keep the consistency with the ndarray counterpart.
+
     Returns
     ----------
     angle : _Symbol
         The angle of the ray intersecting the unit circle at the given x-coordinate in radians [0, pi].
         This is a scalar if x is a scalar.
+
     See also
     ----------
     cos, arctan, arcsin
+
     Notes
     ----------
     arccos is a multivalued function: for each x there are infinitely many numbers z such that
@@ -1935,22 +2004,26 @@ def arccos(x, out=None, **kwargs):
 
 
 @set_module('mxnet.symbol.numpy')
+@wrap_np_unary_func
 def arctan(x, out=None, **kwargs):
-    r"""arctan(x, out=None, **kwargs)
+    r"""
     Trigonometric inverse tangent, element-wise.
     The inverse of tan, so that if ``y = tan(x)`` then ``x = arctan(y)``.
+
     Parameters
     ----------
     x : _Symbol or scalar
         Input values.
     out : _Symbol or None
         Dummy parameter to keep the consistency with the ndarray counterpart.
+
     Returns
     -------
     out : _Symbol
         Out has the same shape as `x`. It lies is in
         ``[-pi/2, pi/2]`` (``arctan(+/-inf)`` returns ``+/-pi/2``).
         This is a scalar if `x` is a scalar.
+
     Notes
     -----
     `arctan` is a multi-valued function: for each `x` there are infinitely
@@ -1966,24 +2039,27 @@ def arctan(x, out=None, **kwargs):
 
 
 @set_module('mxnet.symbol.numpy')
+@wrap_np_unary_func
 def log(x, out=None, **kwargs):
     """
-    log(x, out=None)
     Natural logarithm, element-wise.
     The natural logarithm `log` is the inverse of the exponential function,
     so that `log(exp(x)) = x`. The natural logarithm is logarithm in base
     `e`.
+
     Parameters
     ----------
     x : _Symbol
         Input value. Elements must be of real value.
     out : _Symbol or None, optional
         Dummy parameter to keep the consistency with the ndarray counterpart.
+
     Returns
     -------
     y : _Symbol
         The natural logarithm of `x`, element-wise.
         This is a scalar if `x` is a scalar.
+
     Notes
     -----
      Currently only supports data of real values and ``inf`` as input. Returns data of real value, ``inf``, ``-inf`` and
@@ -2001,22 +2077,25 @@ def log(x, out=None, **kwargs):
 
 
 @set_module('mxnet.symbol.numpy')
+@wrap_np_unary_func
 def degrees(x, out=None, **kwargs):
     """
-    degrees(x, out=None)
     Convert angles from radians to degrees.
+
     Parameters
     ----------
     x : _Symbol
         Input value. Elements must be of real value.
     out : _Symbol or None, optional
         Dummy parameter to keep the consistency with the ndarray counterpart.
+
     Returns
     -------
     y : _Symbol of floats
         The corresponding degree values; if `out` was supplied this is a
         reference to it.
         This is a scalar if `x` is a scalar.
+
     Notes
     -------
     This function differs from the original `numpy.degrees
@@ -2031,11 +2110,11 @@ def degrees(x, out=None, **kwargs):
 
 
 @set_module('mxnet.symbol.numpy')
-def rad2deg(x, out=None):
+@wrap_np_unary_func
+def rad2deg(x, out=None, **kwargs):
     r"""
-    rad2deg(x, out=None)
-
     Convert angles from radians to degrees.
+
     Parameters
     ----------
     x : _Symbol or scalar
@@ -2060,19 +2139,24 @@ def rad2deg(x, out=None):
     return _unary_func_helper(x, _npi.rad2deg, _np.rad2deg, out=out)
 
 
+@set_module('mxnet.symbol.numpy')
+@wrap_np_unary_func
 def rint(x, out=None, **kwargs):
     """
     Round elements of the array to the nearest integer.
+
     Parameters
     ----------
     x : _Symbol or scalar
         Input array.
     out : _Symbol or None
         Dummy parameter to keep the consistency with the ndarray counterpart.
+
     Returns
     -------
     out : _Symbol or scalar
         Output array is same shape and type as x. This is a scalar if x is a scalar.
+
     Notes
     -----
     This function differs from the original `numpy.rint
@@ -2086,6 +2170,7 @@ def rint(x, out=None, **kwargs):
 
 
 @set_module('mxnet.symbol.numpy')
+@wrap_np_unary_func
 def log2(x, out=None, **kwargs):
     """
     Base-2 logarithm of x.
@@ -2115,6 +2200,7 @@ def log2(x, out=None, **kwargs):
 
 
 @set_module('mxnet.symbol.numpy')
+@wrap_np_unary_func
 def log1p(x, out=None, **kwargs):
     """
     Return the natural logarithm of one plus the input array, element-wise.
@@ -2153,6 +2239,7 @@ def log1p(x, out=None, **kwargs):
 
 
 @set_module('mxnet.symbol.numpy')
+@wrap_np_unary_func
 def radians(x, out=None, **kwargs):
     """
     Convert angles from degrees to radians.
@@ -2186,11 +2273,13 @@ def radians(x, out=None, **kwargs):
 
 
 @set_module('mxnet.symbol.numpy')
-def deg2rad(x, out=None):
+@wrap_np_unary_func
+def deg2rad(x, out=None, **kwargs):
     r"""
     deg2rad(x, out=None)
 
     Convert angles from degrees to radians.
+
     Parameters
     ----------
     x : _Symbol or scalar
@@ -2216,21 +2305,24 @@ def deg2rad(x, out=None):
 
 
 @set_module('mxnet.symbol.numpy')
+@wrap_np_unary_func
 def reciprocal(x, out=None, **kwargs):
     r"""
-    reciprocal(x, out=None)
     Return the reciprocal of the argument, element-wise.
     Calculates ``1/x``.
+
     Parameters
     ----------
     x : _Symbol or scalar
         The values whose reciprocals are required.
     out : _Symbol, or None, optional
         Dummy parameter to keep the consistency with the ndarray counterpart.
+
     Returns
     -------
     y : _Symbol or scalar
         Output array is same shape and type as x. This is a scalar if x is a scalar.
+
     Notes
     -----
     .. note::
@@ -2249,20 +2341,23 @@ def reciprocal(x, out=None, **kwargs):
 
 
 @set_module('mxnet.symbol.numpy')
+@wrap_np_unary_func
 def square(x, out=None, **kwargs):
     r"""
-    square(x, out=None)
     Return the element-wise square of the input.
+
     Parameters
     ----------
     x : _Symbol or scalar
         The values whose reciprocals are required.
     out : _Symbol, or None, optional
         Dummy parameter to keep the consistency with the ndarray counterpart.
+
     Returns
     -------
     y : _Symbol or scalar
         Output array is same shape and type as x. This is a scalar if x is a scalar.
+
     Notes
     -----
     The output `symbol` has the same `ctx` as the input `symbol`.
@@ -2276,10 +2371,11 @@ def square(x, out=None, **kwargs):
 
 
 @set_module('mxnet.symbol.numpy')
-def negative(x, out=None, where=True, **kwargs):
+@wrap_np_unary_func
+def negative(x, out=None, **kwargs):
     r"""
-    negative(x, out=None, where=True)
     Numerical negative, element-wise.
+
     Parameters:
     ------------
     x : _Symbol or scalar
@@ -2290,13 +2386,12 @@ def negative(x, out=None, where=True, **kwargs):
           If not provided or None, a freshly-allocated array is returned.
           A tuple (possible only as a keyword argument) must have length
           equal to the number of outputs.
-    where : _Symbol or scalar, optional
-            Values of True indicate to calculate the ufunc at that position,
-            values of False indicate to leave the value in the output alone.
+
     Returns:
     -------
     y : _Symbol or scalar
         Returned array or scalar: y = -x. This is a scalar if x is a scalar.
+
     Examples:
     ---------
     >>> np.negative(1)
@@ -2306,7 +2401,8 @@ def negative(x, out=None, where=True, **kwargs):
 
 
 @set_module('mxnet.symbol.numpy')
-def fix(x, out=None):
+@wrap_np_unary_func
+def fix(x, out=None, **kwargs):
     """
     Round to nearest integer towards zero.
 
@@ -2318,9 +2414,11 @@ def fix(x, out=None):
         An array of floats to be rounded
     out : _Symbol or scalar, optional
           Output array
+
     Returns:
     ---------
     y : _Symbol or scalar
+
     Examples:
     ----------
     >>> np.fix(3.14)
@@ -2330,9 +2428,9 @@ def fix(x, out=None):
 
 
 @set_module('mxnet.symbol.numpy')
-def tan(x, out=None, where=True, **kwargs):
+@wrap_np_unary_func
+def tan(x, out=None, **kwargs):
     r"""
-    tan(x, out=None, where=True)
     Compute tangent element-wise.
     Equivalent to np.sin(x)/np.cos(x) element-wise.
 
@@ -2345,9 +2443,7 @@ def tan(x, out=None, where=True, **kwargs):
         it must have a shape that the inputs broadcast to. If not provided or None,
         a freshly-allocated array is returned. A tuple (possible only as a keyword argument)
         must have length equal to the number of outputs.
-    where : array_like, optional
-            Values of True indicate to calculate the ufunc at that position,
-            values of False indicate to leave the value in the output alone.
+
     Returns:
     -------
     y : _Symbol or scalar
@@ -2358,23 +2454,26 @@ def tan(x, out=None, where=True, **kwargs):
 
 
 @set_module('mxnet.symbol.numpy')
+@wrap_np_unary_func
 def ceil(x, out=None, **kwargs):
     r"""
     Return the ceiling of the input, element-wise.
     The ceil of the ndarray `x` is the smallest integer `i`, such that
     `i >= x`.  It is often denoted as :math:`\lceil x \rceil`.
+
     Parameters
     ----------
     x : _Symbol or scalar
         Input array.
     out : _Symbol or None
           Dummy parameter to keep the consistency with the ndarray counterpart.
+
     Returns
     -------
-    y :
-        _Symbol or scalar
+    y : _Symbol or scalar
         The ceiling of each element in `x`, with `float` dtype.
         This is a scalar if `x` is a scalar.
+
     Examples
     --------
     >>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
@@ -2391,29 +2490,32 @@ def ceil(x, out=None, **kwargs):
 
 
 @set_module('mxnet.symbol.numpy')
+@wrap_np_unary_func
 def floor(x, out=None, **kwargs):
     r"""
     Return the floor of the input, element-wise.
     The floor of the ndarray `x` is the largest integer `i`, such that
     `i <= x`.  It is often denoted as :math:`\lfloor x \rfloor`.
+
     Parameters
     ----------
     x : _Symbol or scalar
         Input array.
     out : _Symbol or None
           Dummy parameter to keep the consistency with the ndarray counterpart.
+
     Returns
     -------
-    y :
-        _Symbol or scalar
+    y : _Symbol or scalar
         The floor of each element in `x`, with `float` dtype.
         This is a scalar if `x` is a scalar.
+
     Examples
     --------
     >>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
     >>> np.floor(a)
     array([-2., -2., -1.,  0.,  1.,  1.,  2.])
-    >>> #if you use parameter out, x and out must be ndarray. if not, you will get an error!
+    >>> # if you use parameter out, x and out must be ndarray. if not, you will get an error!
     >>> a = np.array(1)
     >>> np.floor(np.array(3.5), a)
     array(3.)
@@ -2424,9 +2526,9 @@ def floor(x, out=None, **kwargs):
 
 
 @set_module('mxnet.symbol.numpy')
+@wrap_np_unary_func
 def trunc(x, out=None, **kwargs):
     r"""
-    trunc(x, out=None)
     Return the truncated value of the input, element-wise.
     The truncated value of the scalar `x` is the nearest integer `i` which
     is closer to zero than `x` is. In short, the fractional part of the
@@ -2444,6 +2546,7 @@ def trunc(x, out=None, **kwargs):
     y : _Symbol or scalar
         The truncated value of each element in `x`.
         This is a scalar if `x` is a scalar.
+
     Notes
     -----
     This function differs from the original numpy.trunc in the following aspects:
@@ -2456,10 +2559,11 @@ def trunc(x, out=None, **kwargs):
 
 
 @set_module('mxnet.symbol.numpy')
+@wrap_np_unary_func
 def logical_not(x, out=None, **kwargs):
     r"""
-    logical_not(x, out=None)
     Compute the truth value of NOT x element-wise.
+
     Parameters
     ----------
     x : _Symbol or scalar
@@ -2473,6 +2577,7 @@ def logical_not(x, out=None, **kwargs):
         Boolean result with the same shape as `x` of the NOT operation
         on elements of `x`.
         This is a scalar if `x` is a scalar.
+
     Notes
     -----
     This function differs from the original numpy.logical_not in the following aspects:
@@ -2485,10 +2590,11 @@ def logical_not(x, out=None, **kwargs):
 
 
 @set_module('mxnet.symbol.numpy')
+@wrap_np_unary_func
 def arcsinh(x, out=None, **kwargs):
     r"""
-    arcsinh(x, out=None)
     Inverse hyperbolic sine, element-wise.
+
     Parameters
     ----------
     x : _Symbol or scalar
@@ -2501,6 +2607,7 @@ def arcsinh(x, out=None, **kwargs):
     arcsinh : _Symbol
         Array of the same shape as `x`.
         This is a scalar if `x` is a scalar.
+
     Notes
     -----
     `arcsinh` is a multivalued function: for each `x` there are infinitely
@@ -2521,10 +2628,11 @@ def arcsinh(x, out=None, **kwargs):
 
 
 @set_module('mxnet.symbol.numpy')
+@wrap_np_unary_func
 def arccosh(x, out=None, **kwargs):
     r"""
-    arccosh(x, out=None)
     Inverse hyperbolic cosine, element-wise.
+
     Parameters
     ----------
     x : _Symbol or scalar
@@ -2537,6 +2645,7 @@ def arccosh(x, out=None, **kwargs):
     arccosh : _Symbol
         Array of the same shape as `x`.
         This is a scalar if `x` is a scalar.
+
     Notes
     -----
     `arccosh` is a multivalued function: for each `x` there are infinitely
@@ -2557,10 +2666,11 @@ def arccosh(x, out=None, **kwargs):
 
 
 @set_module('mxnet.symbol.numpy')
+@wrap_np_unary_func
 def arctanh(x, out=None, **kwargs):
     r"""
-    arctanh(x, out=None)
     Inverse hyperbolic tangent, element-wise.
+
     Parameters
     ----------
     x : _Symbol or scalar
@@ -2573,6 +2683,7 @@ def arctanh(x, out=None, **kwargs):
     arctanh : _Symbol
         Array of the same shape as `x`.
         This is a scalar if `x` is a scalar.
+
     Notes
     -----
     `arctanh` is a multivalued function: for each `x` there are infinitely
@@ -2681,6 +2792,7 @@ def arange(start, stop=None, step=1, dtype=None, ctx=None):
 @set_module('mxnet.symbol.numpy')
 def split(ary, indices_or_sections, axis=0):
     """Split an array into multiple sub-arrays.
+
     Parameters
     ----------
     ary : ndarray
@@ -2699,10 +2811,12 @@ def split(ary, indices_or_sections, axis=0):
         an empty sub-array is returned correspondingly.
     axis : int, optional
         The axis along which to split, default is 0.
+
     Returns
     -------
     sub-arrays : list of ndarrays
         A list of sub-arrays.
+
     Raises
     ------
     ValueError
@@ -2991,12 +3105,14 @@ def dstack(arrays):
 
 
 @set_module('mxnet.symbol.numpy')
-def maximum(x1, x2, out=None):
+@wrap_np_binary_func
+def maximum(x1, x2, out=None, **kwargs):
     return _ufunc_helper(x1, x2, _npi.maximum, _np.maximum, _npi.maximum_scalar, None, out)
 
 
 @set_module('mxnet.symbol.numpy')
-def minimum(x1, x2, out=None):
+@wrap_np_binary_func
+def minimum(x1, x2, out=None, **kwargs):
     return _ufunc_helper(x1, x2, _npi.minimum, _np.minimum, _npi.minimum_scalar, None, out)
 
 
@@ -3340,9 +3456,9 @@ def indices(dimensions, dtype=_np.int32, ctx=None):
 
 
 @set_module('mxnet.symbol.numpy')
-def copysign(x1, x2, out=None):
-    r"""copysign(x1, x2, out=None)
-
+@wrap_np_binary_func
+def copysign(x1, x2, out=None, **kwargs):
+    r"""
     Change the sign of x1 to that of x2, element-wise.
 
     If `x2` is a scalar, its sign will be copied to all elements of `x1`.
@@ -3734,10 +3850,9 @@ def around(x, decimals=0, out=None, **kwargs):
 
 
 @set_module('mxnet.symbol.numpy')
-def arctan2(x1, x2, out=None):
+@wrap_np_binary_func
+def arctan2(x1, x2, out=None, **kwargs):
     r"""
-    arctan2(x1, x2, out=None)
-
     Element-wise arc tangent of ``x1/x2`` choosing the quadrant correctly.
 
     The quadrant (i.e., branch) is chosen so that ``arctan2(x1, x2)`` is
@@ -3802,7 +3917,8 @@ def arctan2(x1, x2, out=None):
 
 
 @set_module('mxnet.symbol.numpy')
-def hypot(x1, x2, out=None):
+@wrap_np_binary_func
+def hypot(x1, x2, out=None, **kwargs):
     r"""
     Given the "legs" of a right triangle, return its hypotenuse.
 
@@ -3895,7 +4011,8 @@ def unique(ar, return_index=False, return_inverse=False, return_counts=False, ax
 
 
 @set_module('mxnet.symbol.numpy')
-def ldexp(x1, x2, out=None):
+@wrap_np_binary_func
+def ldexp(x1, x2, out=None, **kwargs):
     """
     Returns x1 * 2**x2, element-wise.
     The mantissas `x1` and twos exponents `x2` are used to construct
diff --git a/python/mxnet/util.py b/python/mxnet/util.py
index 1050fb2a481d..c2dfd85dcfac 100644
--- a/python/mxnet/util.py
+++ b/python/mxnet/util.py
@@ -27,6 +27,15 @@
 from .base import _LIB, check_call
 
 
+_np_ufunc_default_kwargs = {
+    'where': True,
+    'casting': 'same_kind',
+    'order': 'K',
+    'dtype': None,
+    'subok': True,
+}
+
+
 def makedirs(d):
     """Create directories recursively if they don't exist. os.makedirs(exist_ok=True) is not
     available in Python2"""
@@ -558,6 +567,103 @@ def hybrid_forward(self, F, x, w):
     return use_np_shape(use_np_array(func))
 
 
+def np_ufunc_legal_option(key, value):
+    """
+    Checking if ufunc arguments are legal inputs
+
+    Parameters
+    ----------
+    key : string
+        the key of the ufunc argument.
+    value : string
+        the value of the ufunc argument.
+
+    Returns
+    -------
+    legal : boolean
+        Whether or not the argument is a legal one. True when the key is one of the ufunc
+        arguments and value is an allowed value. False when the key is not one of the ufunc
+        arugments or the value is not an allowed value even when the key is a legal one.
+    """
+    if key == 'where':
+        return True
+    elif key == 'casting':
+        return (value in set(['no', 'equiv', 'safe', 'same_kind', 'unsafe']))
+    elif key == 'order':
+        if isinstance(value, str):
+            return True
+    elif key == 'dtype':
+        import numpy as _np
+        return (value in set([_np.int8, _np.uint8, _np.int32, _np.int64,
+                              _np.float16, _np.float32, _np.float64,
+                              'int8', 'uint8', 'int32', 'int64',
+                              'float16', 'float32', 'float64']))
+    elif key == 'subok':
+        return isinstance(value, bool)
+    return False
+
+
+def wrap_np_unary_func(func):
+    """A convenience decorator for wrapping numpy-compatible unary ufuncs to provide uniform
+    error handling.
+
+    Parameters
+    ----------
+    func : a numpy-compatible unary function to be wrapped for better error handling.
+
+    Returns
+    -------
+    Function
+        A function wrapped with proper error handling.
+    """
+    @wraps_safely(func)
+    def _wrap_np_unary_func(x, out=None, **kwargs):
+        if len(kwargs) != 0:
+            for key, value in kwargs.items():
+                # if argument is not in the set of ufunc arguments
+                if key not in _np_ufunc_default_kwargs:
+                    raise TypeError("{} is an invalid keyword to function \'{}\'".format(key, func.__name__))
+                # if argument is one of the ufunc arguments, but not with the default value
+                if value != _np_ufunc_default_kwargs[key]:
+                    # if the provided value of the argument is a legal option, raise NotImplementedError
+                    if np_ufunc_legal_option(key, value):
+                        raise NotImplementedError("{}={} is not implemented yet".format(key, str(value)))
+                    # otherwise raise TypeError with not understood error message
+                    raise TypeError("{} {} not understood".format(key, value))
+        return func(x, out=out)
+    return _wrap_np_unary_func
+
+
+def wrap_np_binary_func(func):
+    """A convenience decorator for wrapping numpy-compatible binary ufuncs to provide uniform
+    error handling.
+
+    Parameters
+    ----------
+    func : a numpy-compatible binary function to be wrapped for better error handling.
+
+    Returns
+    -------
+    Function
+        A function wrapped with proper error handling.
+    """
+    @wraps_safely(func)
+    def _wrap_np_binary_func(x1, x2, out=None, **kwargs):
+        if len(kwargs) != 0:
+            for key, value in kwargs.items():
+                # if argument is not in the set of ufunc arguments
+                if key not in _np_ufunc_default_kwargs:
+                    raise TypeError("{} is an invalid keyword to function \'{}\'".format(key, func.__name__))
+                # if argument is one of the ufunc arguments, but not with the default value
+                if value != _np_ufunc_default_kwargs[key]:
+                    # if the provided value of the argument is a legal option, raise NotImplementedError
+                    if np_ufunc_legal_option(key, value):
+                        raise NotImplementedError("{}={} is not implemented yet".format(key, str(value)))
+                    # otherwise raise TypeError with not understood error message
+                    raise TypeError("{} {} not understood".format(key, value))
+        return func(x1, x2, out=out)
+    return _wrap_np_binary_func
+
 def _set_np_array(active):
     """Turns on/off NumPy array semantics for the current thread in which `mxnet.numpy.ndarray`
     is expected to be created, instead of the legacy `mx.nd.NDArray`.
diff --git a/src/operator/numpy/np_elemwise_broadcast_op.cc b/src/operator/numpy/np_elemwise_broadcast_op.cc
index 7e07e47b4a8f..c206ad453ba6 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op.cc
+++ b/src/operator/numpy/np_elemwise_broadcast_op.cc
@@ -109,6 +109,7 @@ NNVM_REGISTER_OP(_npi_lcm)
 [](const NodeAttrs& attrs){
      return std::vector<std::pair<int, int> >{{0, 0}, {1, 0}};
 })
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
 .set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, mshadow_op::lcm>)
 .add_argument("lhs", "NDArray-or-Symbol", "First input to the function")
 .add_argument("rhs", "NDArray-or-Symbol", "Second input to the function");
@@ -125,6 +126,7 @@ NNVM_REGISTER_OP(_npi_lcm_scalar)
   [](const NodeAttrs& attrs){
     return std::vector<std::pair<int, int> >{{0, 0}};
   })
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
 .add_argument("data", "NDArray-or-Symbol", "source input")
 .add_argument("scalar", "int", "scalar input")
 .set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::lcm>);
diff --git a/src/operator/tensor/elemwise_binary_broadcast_op-inl.cuh b/src/operator/tensor/elemwise_binary_broadcast_op-inl.cuh
index 8469f59e2e9c..d65e12aef86e 100644
--- a/src/operator/tensor/elemwise_binary_broadcast_op-inl.cuh
+++ b/src/operator/tensor/elemwise_binary_broadcast_op-inl.cuh
@@ -57,8 +57,22 @@ BinaryBroadcastBackwardUseNone(const nnvm::NodeAttrs& attrs,
         Tensor<gpu, 1, char> workspace =
             ctx.requested[0].get_space_typed<gpu, 1, char>(
                 Shape1(workspace_size * sizeof(index_t)), s);
-        Reduce<red::sum, NDim, DType, LOP>(s, lhs, req[0], workspace, out);
-        Reduce<red::sum, NDim, DType, ROP>(s, rhs, req[1], workspace, out);
+        if (out.shape_.Size() != 0) {
+          Reduce<red::sum, NDim, DType, LOP>(s, lhs, req[0], workspace, out);
+          Reduce<red::sum, NDim, DType, ROP>(s, rhs, req[1], workspace, out);
+        } else {
+          using namespace mxnet_op;
+          if (lhs.shape_.Size() != 0) {
+            MSHADOW_TYPE_SWITCH(lhs.type_flag_, LType, {
+              Kernel<set_zero, gpu>::Launch(s, lhs.shape_.Size(), lhs.dptr<LType>());
+            });
+          }
+          if (rhs.shape_.Size() != 0) {
+            MSHADOW_TYPE_SWITCH(rhs.type_flag_, RType, {
+              Kernel<set_zero, gpu>::Launch(s, rhs.shape_.Size(), rhs.dptr<RType>());
+            });
+          }
+        }
       });
     });
   }
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 28edc2e05306..2956f99f4eb7 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -219,67 +219,6 @@ def test_np_dot():
         assert False
 
 
-@with_seed()
-@use_np
-def test_np_ldexp():
-    class TestLdexp(HybridBlock):
-        def __init__(self):
-            super(TestLdexp, self).__init__()
-
-        def hybrid_forward(self, F, x1, x2):
-            return F.np.ldexp(x1, x2)
-
-    def _np_ldexp(x1, x2):
-        return x1 * _np.power(2.0, x2)
-
-    def dldx(x1, x2):
-        grad_a = _np.power(2.0, x2)
-        grad_b = _np_ldexp(x1, x2) * _np.log(2.0)
-        if len(x1) == 1:
-            grad_a = _np.sum(grad_a)
-        if len(x2) == 1:
-            grad_b = _np.sum(grad_b)
-        return [grad_a, grad_b]
-
-    shapes = [
-        ((3, 1), (3, 1)),
-        ((3, 1, 2), (3, 1, 2)),
-        ((1, ),(1, )),
-        ((1, ), (2, )),
-        ((3, ), (1, )),
-        ((3, 0), (3, 0)),  # zero-size shape
-        ((0, 1), (0, 1)),  # zero-size shape
-        ((2, 0, 2), (2, 0, 2)),  # zero-size shape
-        ]
-
-    for hybridize in [True, False]:
-        for shape1, shape2 in shapes:
-            for dtype in [_np.float16, _np.float32, _np.float64]:
-                test_ldexp = TestLdexp()
-                if hybridize:
-                    test_ldexp.hybridize()
-                x1 = rand_ndarray(shape=shape1, dtype=dtype).as_np_ndarray()
-                x1.attach_grad()
-                x2 = rand_ndarray(shape=shape2, dtype=dtype).as_np_ndarray()
-                x2.attach_grad()
-
-                np_out = _np_ldexp(x1.asnumpy(), x2.asnumpy())
-                with mx.autograd.record():
-                    mx_out = test_ldexp(x1, x2)
-                assert mx_out.shape == np_out.shape
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-1, atol=1e-1)
-
-                mx_out.backward()
-                np_backward = dldx(x1.asnumpy(), x2.asnumpy())
-                assert_almost_equal(x1.grad.asnumpy(), np_backward[0], atol=1e-1, rtol=1e-1)
-                assert_almost_equal(x2.grad.asnumpy(), np_backward[1], atol=1e-1, rtol=1e-1)
-
-                # Test imperative once again
-                mx_out = np.ldexp(x1, x2)
-                np_out = _np_ldexp(x1.asnumpy(), x2.asnumpy())
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-1, atol=1e-1)
-
-
 @with_seed()
 @use_np
 def test_np_vdot():
@@ -1405,6 +1344,21 @@ def hybrid_forward(self, F, a, *args, **kwargs):
                 y.backward()
                 assert_almost_equal(mx_test_data.grad.asnumpy(), ref_grad(np_test_data), rtol=1e-1, atol=1e-2, equal_nan=True)
 
+        np_out = getattr(_np, func)(np_test_data)
+        mx_out = getattr(mx.np, func)(mx_test_data)
+        assert mx_out.shape == np_out.shape
+        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+
+
+        assertRaises(NotImplementedError, getattr(np, func), mx_test_data, where=False)
+        assertRaises(NotImplementedError, getattr(np, func), mx_test_data,  subok=False)
+        assertRaises(NotImplementedError, getattr(np, func), mx_test_data,  dtype=_np.int8)
+        assertRaises(TypeError, getattr(np, func), mx_test_data,  dtype="abcdefg")
+        assertRaises(NotImplementedError, getattr(np, func), mx_test_data,  casting='safe')
+        assertRaises(TypeError, getattr(np, func), mx_test_data,  casting='mxnet')
+        assertRaises(NotImplementedError, getattr(np, func), mx_test_data,  order='C')
+        assertRaises(NotImplementedError, getattr(np, func), mx_test_data,  order='mxnet')
+
     funcs = {
         'absolute' : (lambda x: -1. * (x < 0) + (x > 0), -1.0, 1.0),
         'cbrt' : (lambda x: 1. / (3. * _np.cbrt(x) ** 2), -1.0, 1.0),
@@ -1451,6 +1405,129 @@ def hybrid_forward(self, F, a, *args, **kwargs):
             check_unary_func(func, ref_grad, shape, low, high)
 
 
+@with_seed()
+@use_np
+def test_np_binary_funcs():
+    def check_binary_func(func, lshape, rshape, low, high, lgrads, rgrads=None, alltypes=None):
+        class TestBinary(HybridBlock):
+            def __init__(self, func):
+                super(TestBinary, self).__init__()
+                self._func = func
+
+            def hybrid_forward(self, F, a, b, *args, **kwargs):
+                return getattr(F.np, self._func)(a, b)
+
+        np_func = getattr(_np, func)
+        mx_func = TestBinary(func)
+        alltypes = alltypes if alltypes else [[_np.float16, _np.float32, _np.float64]]
+        for dtypes, lgrad, rgrad in zip(alltypes, lgrads, rgrads if rgrads else lgrads):
+            for dtype in dtypes:
+                ldtype = rdtype = dtype
+                if isinstance(dtype, tuple):
+                    assert len(dtype) == 2
+                    ldtype, rdtype = dtype
+                np_test_x1 = _np.random.uniform(low, high, lshape).astype(ldtype)
+                np_test_x2 = _np.random.uniform(low, high, rshape).astype(rdtype)
+                mx_test_x1 = mx.numpy.array(np_test_x1, dtype=ldtype)
+                mx_test_x2 = mx.numpy.array(np_test_x2, dtype=rdtype)
+                for hybridize in [True, False]:
+                    if hybridize:
+                        mx_func.hybridize()
+                    if lgrad:
+                        mx_test_x1.attach_grad()
+                        mx_test_x2.attach_grad()
+                    np_out = np_func(np_test_x1, np_test_x2)
+                    with mx.autograd.record():
+                        y = mx_func(mx_test_x1, mx_test_x2)
+                    assert y.shape == np_out.shape
+                    assert_almost_equal(y.asnumpy(), np_out.astype(y.dtype), rtol=1e-3, atol=1e-5,
+                                        use_broadcast=False, equal_nan=True)
+
+                    if lgrad:
+                        y.backward()
+                        assert_almost_equal(mx_test_x1.grad.asnumpy(),
+                                            collapse_sum_like(lgrad(y.asnumpy(), np_test_x1, np_test_x2), mx_test_x1.shape),
+                                            rtol=1e-1, atol=1e-2, equal_nan=True, use_broadcast=False)
+                        if rgrads is None:
+                            assert_almost_equal(mx_test_x2.grad.asnumpy(),
+                                               collapse_sum_like(rgrad(y.asnumpy(), np_test_x2, np_test_x1), mx_test_x2.shape),
+                                               rtol=1e-1, atol=1e-2, equal_nan=True, use_broadcast=False)
+                        else:
+                            assert_almost_equal(mx_test_x2.grad.asnumpy(),
+                                                collapse_sum_like(rgrad(y.asnumpy(), np_test_x1, np_test_x2), mx_test_x2.shape),
+                                                rtol=1e-1, atol=1e-2, equal_nan=True, use_broadcast=False)
+
+                np_out = getattr(_np, func)(np_test_x1, np_test_x2)
+                mx_out = getattr(mx.np, func)(mx_test_x1, mx_test_x2)
+                assert mx_out.shape == np_out.shape
+                assert_almost_equal(mx_out.asnumpy(), np_out.astype(mx_out.dtype), rtol=1e-3, atol=1e-5,
+                                    use_broadcast=False, equal_nan=True)
+
+                assertRaises(NotImplementedError, getattr(np, func), mx_test_x1, mx_test_x2, where=False)
+                assertRaises(NotImplementedError, getattr(np, func), mx_test_x1, mx_test_x2,  subok=False)
+                assertRaises(NotImplementedError, getattr(np, func), mx_test_x1, mx_test_x2,  dtype=_np.int8)
+                assertRaises(TypeError, getattr(np, func), mx_test_x1, mx_test_x2,  dtype="abcdefg")
+                assertRaises(NotImplementedError, getattr(np, func), mx_test_x1, mx_test_x2,  casting='safe')
+                assertRaises(TypeError, getattr(np, func), mx_test_x1, mx_test_x2,  casting='mxnet')
+                assertRaises(NotImplementedError, getattr(np, func), mx_test_x1, mx_test_x2,  order='C')
+                assertRaises(NotImplementedError, getattr(np, func), mx_test_x1, mx_test_x2,  order='mxnet')
+
+
+    funcs = {
+        'add': (-1.0, 1.0, [lambda y, x1, x2: _np.ones(y.shape)], None),
+        'subtract':
+        (-1.0, 1.0, [lambda y, x1, x2: _np.ones(y.shape)],
+                    [lambda y, x1, x2: -_np.ones(y.shape)]),
+        'multiply': (-1.0, 1.0, [lambda y, x1, x2: _np.broadcast_to(x2, y.shape)],
+                                [lambda y, x1, x2: _np.broadcast_to(x1, y.shape)]),
+        'divide': (0.1, 1.0, [lambda y, x1, x2: _np.ones(y.shape) / x2],
+                               [lambda y, x1, x2: -x1 / (x2 * x2)]),
+        'mod': (1.0, 10.0,
+                [lambda y, x1, x2: _np.ones(y.shape),
+                 lambda y, x1, x2: _np.zeros(y.shape)],
+                [lambda y, x1, x2: -_np.floor(x1 / x2),
+                 lambda y, x1, x2: _np.zeros(y.shape)],
+                [[_np.float16, _np.float32, _np.float64], [_np.int32]]),
+        'remainder': (1.0, 10.0,
+                      [lambda y, x1, x2: _np.ones(y.shape),
+                       lambda y, x1, x2: _np.zeros(y.shape)],
+                      [lambda y, x1, x2: -_np.floor(x1 / x2),
+                       lambda y, x1, x2: _np.zeros(y.shape)],
+                      [[_np.float16, _np.float32, _np.float64], [_np.int32]]),
+        'power': (1.0, 2.0, [lambda y, x1, x2: _np.power(x1, x2 - 1.0) * x2],
+                             [lambda y, x1, x2: _np.power(x1, x2) * _np.log(x1)]),
+        'lcm': (-100, 100, [None], None, [[_np.int32]]),
+        'maximum': (-1, 1, [lambda y, x1, x2: _np.ones(y.shape) * (x1 >= x2)],
+                           [lambda y, x1, x2: _np.ones(y.shape) * (x1 < x2)]),
+        'minimum': (-1, 1, [lambda y, x1, x2: _np.ones(y.shape) * (x1 <= x2)],
+                           [lambda y, x1, x2: _np.ones(y.shape) * (x1 > x2)]),
+        'copysign': (-1, 1,
+                     [lambda y, x1, x2: _np.ones(y.shape) * (((x1 * x2) >= 0).astype(_np.float32) - ((x1 * x2) < 0).astype(_np.float32))],
+                     [lambda y, x1, x2: _np.zeros(y.shape)]),
+        'arctan2': (-1, 1, [lambda y, x1, x2: x2 / (_np.square(x1) + _np.square(x2))],
+                           [lambda y, x1, x2: -x1 / (_np.square(x1) + _np.square(x2))]),
+        'hypot': (-1, 1, [lambda y, x1, x2: x1 / y],
+                         [lambda y, x1, x2: x2 / y]),
+        'ldexp': (-3, 3, [None], None, [[_np.int32]]),
+    }
+    shape_pairs = [((3, 2), (3, 2)),
+                   ((3, 2), (3, 1)),
+                   ((3, 1), (3, 0)),
+                   ((0, 2), (1, 2)),
+                   ((2, 3, 4), (3, 1)),
+                   ((2, 3), ()),
+                   ((), (2, 3))]
+    for lshape, rshape in shape_pairs:
+        for func, func_data in funcs.items():
+            dtypes = None
+            assert (len(func_data) == 4 or len(func_data) == 5)
+            if len(func_data) is 4:
+                low, high, lgrads, rgrads = func_data
+            else:
+                low, high, lgrads, rgrads, dtypes = func_data
+            check_binary_func(func, lshape, rshape, low, high, lgrads, rgrads, dtypes)
+
+
 @with_seed()
 @use_np
 def test_npx_relu():
@@ -1938,32 +2015,6 @@ def test_np_randint():
             verify_generator(generator=generator_mx_same_seed, buckets=buckets, probs=probs, nrepeat=100)
 
 
-@with_seed()
-@use_np
-def test_np_minimum_maximum():
-    def check_symbol_output_type(op_name):
-        x1, x2 = mx.sym.var('x1').as_np_ndarray(), mx.sym.var('x2').as_np_ndarray()
-        ret = getattr(mx.sym.np, op_name)(x1, x2)
-        assert type(ret) == mx.sym.np._Symbol
-
-    def check_comp_op(op_name, x1, x2):
-        mx_out = getattr(np, op_name)(x1, x2)
-        if isinstance(x1, np.ndarray) or isinstance(x2, np.ndarray):
-            assert type(mx_out) == np.ndarray
-        np_out = getattr(_np, op_name)(x1.asnumpy() if isinstance(x1, np.ndarray) else x1,
-                                       x2.asnumpy() if isinstance(x2, np.ndarray) else x2)
-        assert same(mx_out.asnumpy() if isinstance(mx_out, np.ndarray) else mx_out, np_out)
-
-    op_names = ['minimum', 'maximum']
-    for op_name in op_names:
-        check_symbol_output_type(op_name)
-        check_comp_op(op_name, np.random.uniform(size=(2, 1)), np.random.uniform(size=(5, 1, 4)))
-        check_comp_op(op_name, np.random.uniform(size=(2, 0)), np.random.uniform(size=(5, 1, 1)))
-        check_comp_op(op_name, np.random.uniform(), np.random.uniform(size=(5, 1, 4)))
-        check_comp_op(op_name, _np.random.uniform(), np.random.uniform(size=(2, 3)))
-        check_comp_op(op_name, np.random.uniform(size=(2, 3)), _np.random.uniform())
-
-
 @with_seed()
 @use_np
 def test_np_swapaxes():
@@ -2507,111 +2558,6 @@ def hybrid_forward(self, F, x):
                     assert_almost_equal(mx_ret.asnumpy(), np_ret, atol=1e-5, rtol=1e-4)
 
 
-@with_seed()
-@use_np
-def test_np_copysign():
-    class TestCopysign(HybridBlock):
-        def __init__(self):
-            super(TestCopysign, self).__init__()
-
-        def hybrid_forward(self, F, a1, a2):
-            return F.np.copysign(a1, a2)
-
-    def get_grad(a1, a2):
-        sign = _np.logical_or(_np.logical_and(a1 < 0, a2 < 0),
-                              _np.logical_and(a1 >= 0, a2 >= 0))
-        sign = 2 * sign.astype(int) - 1
-        sign = sign.reshape(-1, *a1.shape)
-        sign = _np.sum(sign, axis=0)
-        return sign, _np.zeros_like(a2)
-
-    def get_grad_left(a1, a2):
-        sign = _np.logical_or(_np.logical_and(a1 < 0, a2 < 0),
-                              _np.logical_and(a1 >= 0, a2 >= 0))
-        sign = 2 * sign.astype(int) - 1
-        sign = sign.reshape(a1.shape)
-        return sign
-
-    def get_grad_right(a1, a2):
-        return _np.zeros_like(a2)
-
-    shapes = [
-        (),
-        (1),
-        (2, 1),
-        (3, 2, 1),
-        (4, 3, 2, 1),
-        (2, 4, 3, 2, 1)
-    ]
-    types = ['float16', 'float32', 'float64', 'int8', 'int32', 'int64']
-    for a1shape in shapes:
-        for a2shape in shapes:
-            for hybridize in [True, False]:
-                for dtype in types:
-                    test_copysign = TestCopysign()
-                    if hybridize:
-                        test_copysign.hybridize()
-                    rtol = 1e-3
-                    atol = 1e-5
-                    a1_np = _np.array(_np.random.uniform(-1.0, 1.0, a1shape), dtype=dtype)
-                    a2_np = _np.array(_np.random.uniform(-1.0, 1.0, a2shape), dtype=dtype)
-                    a1 = np.array(a1_np, dtype=dtype)
-                    a2 = np.array(a2_np, dtype=dtype)
-                    a1.attach_grad()
-                    a2.attach_grad()
-                    expected_np = _np.copysign(a1_np, a2_np)
-                    with mx.autograd.record():
-                        mx_out = test_copysign(a1, a2)
-                    assert mx_out.shape == expected_np.shape
-                    assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol)
-
-                    # Test gradient
-                    mx_out.backward()
-                    a1_grad, a2_grad = get_grad(a1_np, a2_np)
-                    assert_almost_equal(a1.grad.asnumpy(), a1_grad, rtol=rtol, atol=atol)
-                    assert_almost_equal(a2.grad.asnumpy(), a2_grad, rtol=rtol, atol=atol)
-
-                    # Test imperative once again
-                    mx_out = np.copysign(a1, a2)
-                    expected_np = _np.copysign(a1_np, a2_np)
-                    assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol)
-
-    types = ['float16', 'float32', 'float64']
-    for x_shape in shapes:
-        for dtype in types:
-            # Test left
-            x_np = _np.array(_np.random.uniform(-2.0, 2.0, x_shape), dtype=dtype)
-            scalar = _np.random.uniform(-2.0, 2.0)
-            x = np.array(x_np, dtype=dtype)
-            x.attach_grad()
-            expected_np = _np.copysign(x_np, scalar)
-            with mx.autograd.record():
-                mx_out = np.copysign(x, scalar)
-            assert mx_out.shape == expected_np.shape
-            assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol)
-
-            # Test gradient
-            mx_out.backward()
-            x_grad = get_grad_left(x_np, scalar)
-            assert_almost_equal(x.grad.asnumpy(), x_grad, rtol=rtol, atol=atol)
-
-            # Test right
-            x_np = _np.array(_np.random.uniform(-2.0, 2.0, x_shape), dtype=dtype)
-            scalar = _np.random.uniform(-2.0, 2.0)
-            x = np.array(x_np, dtype=dtype)
-            x.attach_grad()
-            expected_np = _np.copysign(scalar, x_np)
-            with mx.autograd.record():
-                mx_out = np.copysign(scalar, x)
-            assert mx_out.shape == expected_np.shape
-            assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol)
-
-            # Test gradient
-            mx_out.backward()
-            x_grad = get_grad_right(scalar, x_np)
-            assert_almost_equal(x.grad.asnumpy(), x_grad, rtol=rtol, atol=atol)
-
-
 @with_seed()
 @use_np
 def test_np_svd():
@@ -3011,73 +2957,6 @@ def hybrid_forward(self, F, x):
                     assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
 
 
-@with_seed()
-@use_np
-def test_np_arctan2():
-    class TestArctan2(HybridBlock):
-        def __init__(self):
-            super(TestArctan2, self).__init__()
-
-        def hybrid_forward(self, F, x1, x2):
-            return F.np.arctan2(x1, x2)
-
-    # Reduce dimension of src to dimension of des.
-    def dimReduce(src, des):
-        srcShape = src.shape
-        desShape = des.shape
-        if len(desShape) == 0:
-            return src.sum()
-        redu = []
-        for i, j in zip(range(len(srcShape)-1, -1, -1), range(len(desShape)-1, -1, -1)):
-            if srcShape[i] != desShape[j] and desShape[j] == 1:
-                redu.append(i)
-            if j == 0:
-                for k in range(0, i):
-                    redu.append(k)
-                break
-        if len(redu) > 0:
-            src = _np.reshape(src.sum(axis=tuple(redu)), desShape)
-        return src
-
-    types = ['float64', 'float32', 'float16']
-    for hybridize in [True, False]:
-        for shape1, shape2 in [[(3, 2), (3, 2)],  # tall matrices
-                               [(), ()],  # scalar only
-                               [(3, 0, 2), (3, 0, 2)],  # zero-dim
-                               [(3, 4, 5), (4, 1)],  # trailing dim broadcasting
-                               [(3, 4, 5), ()],  # scalar broadcasting
-                               [(), (1, 2, 3)],  # scalar broadcasting
-                               ]:
-            for oneType in types:
-                rtol = 1e-2 if oneType == 'float16' else 1e-3
-                atol = 1e-2 if oneType == 'float16' else 1e-5
-                test_arctan2 = TestArctan2()
-                if hybridize:
-                    test_arctan2.hybridize()
-                x1 = rand_ndarray(shape1, dtype=oneType).as_np_ndarray()
-                x2 = rand_ndarray(shape2, dtype=oneType).as_np_ndarray()
-                x11 = x1.asnumpy()
-                x21 = x2.asnumpy()
-                x1.attach_grad()
-                x2.attach_grad()
-                np_out = _np.arctan2(x1.asnumpy(), x2.asnumpy())
-                with mx.autograd.record():
-                    mx_out = test_arctan2(x1, x2)
-                assert mx_out.shape == np_out.shape
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
-                mx_out.backward()
-                np_backward_1 = x21 / (x11 * x11 + x21 * x21)
-                np_backward_2 = -1 * x11 / (x11 * x11 + x21 * x21)
-                np_backward_1 = dimReduce(np_backward_1, x11)
-                np_backward_2 = dimReduce(np_backward_2, x21)
-                assert_almost_equal(x1.grad.asnumpy(), np_backward_1, rtol=rtol, atol=atol)
-                assert_almost_equal(x2.grad.asnumpy(), np_backward_2, rtol=rtol, atol=atol)
-
-                mx_out = np.arctan2(x1, x2)
-                np_out = _np.arctan2(x1.asnumpy(), x2.asnumpy())
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
-
-
 @with_seed()
 @use_np
 def test_np_nonzero():
@@ -3110,72 +2989,6 @@ def hybrid_forward(self, F, x):
                 assert_almost_equal(mx_out.asnumpy(), np_out, rtol, atol)
 
 
-@with_seed()
-@use_np
-def test_np_hypot():
-    class TestHypot(HybridBlock):
-        def __init__(self):
-            super(TestHypot, self).__init__()
-
-        def hybrid_forward(self, F, x1, x2):
-            return F.np.hypot(x1, x2)
-
-    def dimReduce(src, des):
-        srcShape = src.shape
-        desShape = des.shape
-        if len(desShape) == 0:
-            return src.sum()
-        redu = []
-        for i, j in zip(range(len(srcShape)-1, -1, -1), range(len(desShape)-1, -1, -1)):
-            if srcShape[i] != desShape[j] and desShape[j] == 1:
-                redu.append(i)
-            if j == 0:
-                for k in range(0, i):
-                    redu.append(k)
-                break
-        if len(redu) > 0:
-            src = _np.reshape(src.sum(axis=tuple(redu)), desShape)
-        return src
-
-    types = ['float64', 'float32', 'float16']
-    for hybridize in [True, False]:
-        for shape1, shape2 in [[(3, 2), (3, 2)],  # tall matrices
-                               [(), ()],  # scalar only
-                               [(3, 0, 2), (3, 0, 2)],  # zero-dim
-                               [(3, 4, 5), (4, 1)],  # trailing dim broadcasting
-                               [(3, 4, 5), ()],  # scalar broadcasting
-                               [(), (1, 2, 3)],  # scalar broadcasting
-                               ]:
-            for oneType in types:
-                rtol = 1e-2 if oneType == 'float16' else 1e-3
-                atol = 1e-2 if oneType == 'float16' else 1e-5
-                test_hypot = TestHypot()
-                if hybridize:
-                    test_hypot.hybridize()
-                x1 = rand_ndarray(shape1, dtype=oneType).as_np_ndarray()
-                x2 = rand_ndarray(shape2, dtype=oneType).as_np_ndarray()
-                x11 = x1.asnumpy()
-                x21 = x2.asnumpy()
-                x1.attach_grad()
-                x2.attach_grad()
-                np_out = _np.hypot(x1.asnumpy(), x2.asnumpy())
-                with mx.autograd.record():
-                    mx_out = test_hypot(x1, x2)
-                assert mx_out.shape == np_out.shape
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
-                mx_out.backward()
-                np_backward_1 = x11 / np_out
-                np_backward_2 = x21 / np_out
-                np_backward_1 = dimReduce(np_backward_1, x11)
-                np_backward_2 = dimReduce(np_backward_2, x21)
-                assert_almost_equal(x1.grad.asnumpy(), np_backward_1, rtol=rtol, atol=atol)
-                assert_almost_equal(x2.grad.asnumpy(), np_backward_2, rtol=rtol, atol=atol)
-
-                mx_out = np.hypot(x1, x2)
-                np_out = _np.hypot(x1.asnumpy(), x2.asnumpy())
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
-
-
 @with_seed()
 @use_np
 def test_np_unique():
@@ -3224,49 +3037,6 @@ def hybrid_forward(self, F, a):
                     assert_almost_equal(mx_out[i].asnumpy(), np_out[i], rtol=1e-3, atol=1e-5)
 
 
-@with_seed()
-@use_np
-def test_np_lcm():
-    shapes = [
-        ((3, 1), (3,)),
-        ((3, 1), (3, 5)),
-        ((1, 4), (3, 1)),
-        ((), ()),
-        ((4, 0), ()),
-        ((3, 4, 5), ()),
-        ((), (3, 4, 5)),
-        ((3, 4, 5), (3, 1, 5)),
-        ((5, 1), (5, 2))
-    ]
-
-    class TestLcm(HybridBlock):
-        def __init__(self):
-            super(TestLcm, self).__init__()
-
-        def hybrid_forward(self, F, x1, x2):
-            return F.np.lcm(x1, x2)
-
-    for hybridize in [False]:
-        for shape in shapes:
-            test_lcm = TestLcm()
-            if hybridize:
-                test_lcm.hybridize()
-
-            x1 = rand_ndarray(shape[0]).astype(_np.int32).as_np_ndarray()
-            x2 = rand_ndarray(shape[1]).astype(_np.int32).as_np_ndarray()
-
-            np_out = _np.lcm(x1.asnumpy(), x2.asnumpy())
-            mx_out = test_lcm(x1, x2)
-
-            assert mx_out.shape == np_out.shape
-            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
-
-            # Test imperative once again
-            mx_out = np.lcm(x1, x2)
-            np_out = _np.lcm(x1.asnumpy(), x2.asnumpy())
-            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
-
-
 @with_seed()
 @use_np
 def test_np_take():

From 77e989838c8a72131d04f5b7f804ff4c4a1d5fbf Mon Sep 17 00:00:00 2001
From: Zhiqiang Xie <xiezhq@shanghaitech.edu.cn>
Date: Fri, 18 Oct 2019 09:24:04 +0800
Subject: [PATCH 461/813] Bug fix for the input of same axes of the swapaxes
 operator (#16513)

* bug fix for the input of same axes

* tests added

* numpy swapaxes tests added
---
 src/operator/swapaxis-inl.h            | 12 +++++++++++-
 tests/python/unittest/test_numpy_op.py |  8 +++++---
 tests/python/unittest/test_operator.py | 17 +++++++++++++++++
 3 files changed, 33 insertions(+), 4 deletions(-)

diff --git a/src/operator/swapaxis-inl.h b/src/operator/swapaxis-inl.h
index fd9872db6ec8..319fd508c380 100644
--- a/src/operator/swapaxis-inl.h
+++ b/src/operator/swapaxis-inl.h
@@ -35,6 +35,7 @@
 #include <string>
 #include <utility>
 #include "./operator_common.h"
+#include "./mshadow_op.h"
 
 namespace mxnet {
 namespace op {
@@ -63,7 +64,6 @@ template<typename xpu, typename DType>
 class SwapAxisOp : public Operator {
  public:
   explicit SwapAxisOp(SwapAxisParam p) {
-    CHECK_NE(p.dim1, p.dim2) << "dim1 can not be equal dim2.";
     this->param_ = p;
   }
 
@@ -131,6 +131,16 @@ class SwapAxisOp : public Operator {
 
     if (shape_in.Size() == 0U) return;
 
+    if (axis1 == axis2) {
+      if (out_req == kAddTo) {
+        mxnet_op::Kernel<mxnet_op::op_with_req<mshadow_op::identity, kAddTo>, xpu>::Launch(
+          s, data_out.Size(), data_out.dptr<DType>(), data_in.dptr<DType>());
+      } else {
+        mxnet_op::copy(s, data_out, data_in);
+      }
+      return;
+    }
+
     Shape<5> inter_shape;
 
     Reshape2Five(&inter_shape, shape_in, axis1, axis2);
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 2956f99f4eb7..2303c9cee29c 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -2018,9 +2018,11 @@ def test_np_randint():
 @with_seed()
 @use_np
 def test_np_swapaxes():
-    config = [((0, 1, 2), 0, 1),
-              ((0, 1, 2), -1, -2),
-              ((4, 5, 6, 7), 2, 3),
+    config = [((0, 1, 2), 0, 0),
+              ((0, 1, 2), 1, 2),
+              ((0, 1, 2), 1, -2),
+              ((4, 5, 6, 7), 1, 1),
+              ((4, 5, 6, 7), 2, -2),
               ((4, 5, 6, 7), -2, -3)]
 
     class TestSwapaxes(HybridBlock):
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 66bd9ec6b489..a16dc6c693ab 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -716,6 +716,23 @@ def test_swapaxes():
 
     assert_almost_equal(out, swap_)
 
+    config = [((1, 1, 2), 0, 1),
+              ((1, 1, 2), -1, -2),
+              ((4, 5, 6, 7), 1, 1),
+              ((4, 5, 6, 7), 2, 3),
+              ((4, 5, 6, 7), -2, 2),
+              ((4, 5, 6, 7), -2, -3)]
+
+    for shape, axis1, axis2 in config:
+        data_np = np.random.uniform(size=shape)
+        data_mx = mx.nd.array(data_np, dtype=data_np.dtype)
+        ret_np = np.swapaxes(data_np, axis1=axis1, axis2=axis2)
+        ret_mx = mx.symbol.SwapAxis(data, dim1=axis1, dim2=axis2)
+        exe_c = ret_mx.bind(default_context(), args=[data_mx])
+        exe_c.forward(is_train=True)
+        out = exe_c.outputs[0]
+        assert_almost_equal(out, ret_np)
+
 
 @with_seed()
 def test_scalarop():

From f2ed1d45b1d86bc9a06abbcdabb1a2ba0118378d Mon Sep 17 00:00:00 2001
From: Rohit Kumar Srivastava <srivastava.141@osu.edu>
Date: Thu, 17 Oct 2019 20:38:49 -0700
Subject: [PATCH 462/813] added support for large tensors for Dropout operator
 and tests to verify support for more operators (#16409)

* adding tests to very large tensor support for more operators

* adding large tensor support for dropout operator

* code refactor and added new comments
---
 src/operator/nn/dropout-inl.h                 |  12 +-
 .../tensor/elemwise_binary_broadcast_op.h     |   5 +-
 tests/nightly/test_large_array.py             | 240 ++++++++++++++++++
 3 files changed, 249 insertions(+), 8 deletions(-)

diff --git a/src/operator/nn/dropout-inl.h b/src/operator/nn/dropout-inl.h
index eda9051fd0a2..6387dff96eb7 100644
--- a/src/operator/nn/dropout-inl.h
+++ b/src/operator/nn/dropout-inl.h
@@ -182,10 +182,10 @@ class DropoutOp {
      * \param input_data Input data to perform the dropout on
      * \param pkeep Dropout rate (keep when the generated random number is less than this value)
      */
-    MSHADOW_XINLINE static void Map(int id,
+    MSHADOW_XINLINE static void Map(index_t id,
                                     RandGenerator<xpu, DType> gen,
-                                    const int N,
-                                    const int step,
+                                    const index_t N,
+                                    const index_t step,
                                     DType *dropout_out,
                                     DType *mask_out,
                                     const DType *input_data,
@@ -199,10 +199,10 @@ class DropoutOp {
   };
   struct BernoulliKernel {
     /*! \brief Bernoulli kernel for generating mask */
-    MSHADOW_XINLINE static void Map(int id,
+    MSHADOW_XINLINE static void Map(index_t id,
                                     RandGenerator<xpu, DType> gen,
-                                    const int N,
-                                    const int step,
+                                    const index_t N,
+                                    const index_t step,
                                     DType *mask_out,
                                     const real_t pkeep) {
       RNG_KERNEL_LOOP(xpu, DType, id, gen, N, step, {
diff --git a/src/operator/tensor/elemwise_binary_broadcast_op.h b/src/operator/tensor/elemwise_binary_broadcast_op.h
index 6a612e6f1cd5..3d3bcfacbd05 100644
--- a/src/operator/tensor/elemwise_binary_broadcast_op.h
+++ b/src/operator/tensor/elemwise_binary_broadcast_op.h
@@ -151,9 +151,10 @@ inline int BinaryBroadcastShapeCompact(const mxnet::TShape& lshape, const mxnet:
   *new_oshape = mxnet::TShape(odim, 1);
   int bl = oshape.ndim() - lshape.ndim();
   int br = oshape.ndim() - rshape.ndim();
-  int j = 0, lprod = 1, rprod = 1, oprod = 1;
+  int j = 0;
+  index_t lprod = 1, rprod = 1, oprod = 1;
   for (int i = 0; i < oshape.ndim(); ++i) {
-    int l = 1, r = 1, o = oshape[i];
+    index_t l = 1, r = 1, o = oshape[i];
     if (i >= bl) l = lshape[i-bl];
     if (i >= br) r = rshape[i-br];
     if ((lprod != rprod || l != r) &&
diff --git a/tests/nightly/test_large_array.py b/tests/nightly/test_large_array.py
index e51e220c232f..7ab1d025b75b 100644
--- a/tests/nightly/test_large_array.py
+++ b/tests/nightly/test_large_array.py
@@ -1199,6 +1199,246 @@ def test_full():
     assert a[-1][-1] == 3
 
 
+def test_astype():
+    x = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
+    y = x.astype('int32')
+    assert y.dtype == np.int32
+    assert y[-1][-1] == SMALL_Y-1
+
+
+def test_cast():
+    x = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
+    y = nd.cast(x, np.int32)
+    assert y.dtype == np.int32
+    assert y[-1][-1] == SMALL_Y-1
+
+
+def test_repeat():
+    x = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X//2)
+    y = nd.repeat(x, repeats=2, axis = 1)
+    assert y.shape == (SMALL_Y, LARGE_X)
+    assert y[0][1] == 0
+    assert y[-1][-1] == SMALL_Y-1
+    x = create_2d_tensor(rows=SMALL_Y//2, columns=LARGE_X)
+    y = nd.repeat(x, repeats=2, axis = 0)
+    assert y.shape == (SMALL_Y, LARGE_X)
+    assert y[0][1] == 0
+    assert y[-1][0] == SMALL_Y//2-1
+
+
+def create_input_for_rounding_ops():
+    # Creates an vector with values (-LARGE_X/2 .... -2, -1, 0, 1, 2, .... , LARGE_X/2-1)
+    # then divides each element by 2 i.e (-LARGE_X/4 .... -1, -0.5, 0, 0.5, 1, .... , LARGE_X/4-1)
+    # and finally broadcasts to 
+    inp = nd.arange(-LARGE_X//2, LARGE_X//2, dtype=np.float64).reshape(1, LARGE_X)
+    inp = inp/2
+    inp = nd.broadcast_to(inp, (SMALL_Y, LARGE_X))
+    return inp
+
+
+def assert_correctness_of_rounding_ops(output, mid, expected_vals):
+    # checks verifies 5 values at the middle positions of the input vector
+    # i.e mid-2, mid-1, mid, mid+1, mid+2
+    output_idx_to_inspect = [mid-2, mid-1, mid, mid+1, mid+2]
+    for i in range(len(output_idx_to_inspect)):
+        assert output[1][output_idx_to_inspect[i]] == expected_vals[i]
+
+
+# TODO(access2rohit): merge similar tests in large vector and array into one file. 
+def test_rounding_ops():
+    x = create_input_for_rounding_ops()
+
+    def check_ceil():
+        y = nd.ceil(x)
+        # expected ouput for middle 5 values after applying ceil()
+        expected_output = [-1, 0, 0, 1, 1]
+        assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
+
+    def check_fix():
+        y = nd.fix(x)
+        # expected ouput for middle 5 values after applying fix()
+        expected_output = [-1, 0, 0, 0, 1]
+        assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
+
+    def check_floor():
+        y = nd.floor(x)
+        # expected ouput for middle 5 values after applying floor()
+        expected_output = [-1, -1, 0, 0, 1]
+        assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
+
+    def check_rint():
+        y = nd.rint(x)
+        # expected ouput for middle 5 values after applying rint()
+        expected_output = [-1, -1, 0, 0, 1]
+        assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
+
+    def check_round():
+        y = nd.round(x)
+        # expected ouput for middle 5 values after applying round()
+        expected_output = [-1, -1, 0, 1, 1]
+        assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
+
+    def check_trunc():
+        y = nd.trunc(x)
+        # expected ouput for middle 5 values after applying trunc()
+        expected_output = [-1, 0, 0, 0, 1]
+        assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
+
+    check_ceil()
+    check_fix()
+    check_floor()
+    check_rint()
+    check_round()
+    check_trunc()
+
+
+def create_input_for_trigonometric_ops(vals):
+    # Creates large vector input of size(LARGE_X*10, SMALL_Y/10) from vals using tile operator
+    inp = nd.array(vals).reshape(1, 5)
+    inp = nd.broadcast_to(inp, (LARGE_X*10, SMALL_Y//10))
+    return inp
+
+
+def assert_correctness_of_trigonometric_ops(output, expected_vals):
+    # checks verifies 5 values at positions(0, 1, -3, -2, -1) of the input vector
+    output_idx_to_inspect = [0, 1, -3, -2, -1]
+    for i in range(len(output_idx_to_inspect)):
+        assert np.abs(output[1][output_idx_to_inspect[i]].asnumpy()-expected_vals[i]) <= 1e-3
+
+
+def test_trigonometric_ops():
+    def check_arcsin():
+        x = create_input_for_trigonometric_ops([-1, -.707, 0, .707, 1])
+        y = nd.arcsin(x)
+        # expected ouput for indices=(0, 1, -3, -2, -1) after applying arcsin()
+        expected_output = [-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2]
+        assert_correctness_of_trigonometric_ops(y, expected_output)
+
+    def check_arccos():
+        x = create_input_for_trigonometric_ops([-1, -.707, 0, .707, 1])
+        y = nd.arccos(x)
+        # expected ouput for indices=(0, 1, -3, -2, -1) after applying arccos()
+        expected_output = [np.pi, 3*np.pi/4, np.pi/2, np.pi/4, 0]
+        assert_correctness_of_trigonometric_ops(y, expected_output)
+
+    def check_arctan():
+        x = create_input_for_trigonometric_ops([-np.Inf, -1, 0, 1, np.Inf])
+        y = nd.arctan(x)
+        # expected ouput for indices=(0, 1, -3, -2, -1) after applying arctan()
+        expected_output = [-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2]
+        assert_correctness_of_trigonometric_ops(y, expected_output)
+
+    def check_sin():
+        x = create_input_for_trigonometric_ops([-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2])
+        y = nd.sin(x)
+        # expected ouput for indices=(0, 1, -3, -2, -1) after applying sin()
+        expected_output = [-1, -.707, 0, .707, 1]
+        assert_correctness_of_trigonometric_ops(y, expected_output)
+
+    def check_cos():
+        x = create_input_for_trigonometric_ops([0, np.pi/4, np.pi/2, 3*np.pi/4, np.pi])
+        y = nd.cos(x)
+        # expected ouput for indices=(0, 1, -3, -2, -1) after applying cos()
+        expected_output = [1, .707, 0, -.707, -1]
+        assert_correctness_of_trigonometric_ops(y, expected_output)
+
+    def check_tan():
+        x = create_input_for_trigonometric_ops([-np.pi/6, -np.pi/4, 0, np.pi/4, np.pi/6])
+        y = nd.tan(x)
+        # expected ouput for indices=(0, 1, -3, -2, -1) after applying tan()
+        expected_output = [-.577, -1, 0, 1, .577]
+        assert_correctness_of_trigonometric_ops(y, expected_output)
+
+    def check_radians():
+        x = create_input_for_trigonometric_ops([0, 90, 180, 270, 360])
+        y = nd.radians(x)
+        # expected ouput for indices=(0, 1, -3, -2, -1) after applying radians()
+        expected_output = [0, np.pi/2, np.pi, 3*np.pi/2, 2*np.pi]
+        assert_correctness_of_trigonometric_ops(y, expected_output)
+
+    def check_degrees():
+        x = create_input_for_trigonometric_ops([0, np.pi/2, np.pi, 3*np.pi/2, 2*np.pi])
+        y = nd.degrees(x)
+        # expected ouput for indices=(0, 1, -3, -2, -1) after applying degrees()
+        expected_output = [0, 90, 180, 270, 360]
+        assert_correctness_of_trigonometric_ops(y, expected_output)
+
+    check_arcsin()
+    check_arccos()
+    check_arctan()
+    check_sin()
+    check_cos()
+    check_tan()
+    check_radians()
+    check_degrees()
+
+
+def test_L2Normalization():
+    x = nd.ones((2, LARGE_X*2))
+    x[0] = 3
+    x[1] = 4
+    # Channel Mode
+    z = x.reshape(1, 2, LARGE_X*2)
+    y = nd.L2Normalization(z, mode='channel')
+    assert y[0][0][0] == 0.6
+    assert y[0][0][-1] == 0.6
+    assert y[0][1][0] == 0.8
+    assert y[0][1][-1] == 0.8
+    # Instance Mode
+    z = x.T
+    y = nd.L2Normalization(z, mode='instance')
+    assert y[0][0] == 0.6
+    assert y[0][1] == 0.8
+    assert y[-1][0] == 0.6
+    assert y[-1][1] == 0.8
+    # Spatial Mode
+    z = z.reshape(1, 200000000, 2)
+    y = nd.L2Normalization(z, mode='spatial')
+    assert y[0][0][0] == 0.6
+    assert y[0][0][1] == 0.8
+    assert y[0][-1][0] == 0.6
+    assert y[0][-1][1] == 0.8
+
+
+def test_instance_norm():
+    dtype = np.float32
+    forward_check_eps = 1E-3
+    axis = -1
+    eps = 1E-5
+    in_shape = (LARGE_X, 1, SMALL_Y)
+    ctx = mx.cpu()
+
+    # Implementation of instance normalization using numpy
+    def npy_instance_norm(data, gamma, beta, axis, eps=1E-5):
+        if axis < 0:
+            axis += data.ndim
+        broadcast_shape = [1 for _ in range(data.ndim)]
+        broadcast_shape[axis] = data.shape[axis]
+        mean = data.mean(axis=axis, keepdims=True).astype(dtype)
+        var = data.var(axis=axis, keepdims=True).astype(dtype)
+        std = np.sqrt(var + dtype(eps)).astype(dtype)
+        out = gamma * (data - mean) / std + \
+              beta
+        return out
+    data = np.random.normal(0, 1, in_shape).astype(dtype)
+    gamma = np.random.normal(0, 1, (1,)).astype(dtype)
+    beta = np.random.normal(0, 1, (1,)).astype(dtype)
+    data_s = mx.symbol.Variable('data')
+    gamma_s = mx.symbol.Variable('gamma')
+    beta_s = mx.symbol.Variable('beta')
+    out_s = mx.symbol.InstanceNorm(data=data_s, gamma=gamma_s, beta=beta_s,
+                                   eps=eps)
+    exe = out_s.simple_bind(ctx, data=in_shape)
+    exe.arg_dict['data'][:] = data
+    exe.arg_dict['gamma'][:] = gamma
+    exe.arg_dict['beta'][:] = beta
+    out_nd = exe.forward()[0]
+    # Calls implementation of instance norm in numpy and compares the output
+    out = npy_instance_norm(data, gamma, beta, axis, eps)
+    assert_almost_equal(out, out_nd.asnumpy(), forward_check_eps,
+                        forward_check_eps)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 63fbfb12296e41263a6f40df6e616601f6d24d8a Mon Sep 17 00:00:00 2001
From: Li Jun <875241499@qq.com>
Date: Fri, 18 Oct 2019 12:16:08 +0800
Subject: [PATCH 463/813] [DOC] Fix numpy op doc  (#16504)

* Fix np.random.shuffle doc

* Fix np.broadcast_to doc

* Formatting

* Fix typo

* Update python/mxnet/_numpy_op_doc.py

Co-Authored-By: John Andrilla <38335507+john-andrilla@users.noreply.github.com>
---
 python/mxnet/_numpy_op_doc.py | 69 +++++++++++++++++++++++++++++++++++
 1 file changed, 69 insertions(+)

diff --git a/python/mxnet/_numpy_op_doc.py b/python/mxnet/_numpy_op_doc.py
index 667c3bddb71f..f08d19a66549 100644
--- a/python/mxnet/_numpy_op_doc.py
+++ b/python/mxnet/_numpy_op_doc.py
@@ -748,3 +748,72 @@ def _np_moveaxis(a, source, destination):
     (5, 4, 3)
     """
     pass
+
+def _np__random_shuffle(x):
+    """
+    Modify a sequence in-place by shuffling its contents.
+
+    This function only shuffles the array along the first axis of a
+    multi-dimensional array. The order of sub-arrays is changed but
+    their contents remain the same.
+
+    Parameters
+    ----------
+    x: ndarray
+        The array or list to be shuffled.
+
+    Returns
+    -------
+    None
+
+    Examples
+    --------
+    >>> arr = np.arange(10)
+    >>> np.random.shuffle(arr)
+    >>> arr
+    array([5., 1., 0., 6., 7., 3., 9., 8., 4., 2.])  # random
+
+    Multi-dimensional arrays are only shuffled along the first axis:
+
+    >>> arr = np.arange(9).reshape((3, 3))
+    >>> np.random.shuffle(arr)
+    >>> arr
+    array([[6., 7., 8.], # random
+           [3., 4., 5.],
+           [0., 1., 2.]])
+    """
+    pass
+
+def _np_broadcast_to(array, shape, out=None):
+    """
+    Broadcast an array to a new shape.
+
+    Parameters
+    ----------
+    array : ndarray
+        The array to broadcast.
+    shape : tuple, optional, default=[]
+        The shape of the desired array.
+    out : ndarray, optional
+        The output ndarray to hold the result.
+
+    Returns
+    -------
+    out : ndarray or list of ndarrays
+
+    Raises
+    ------
+    MXNetError
+        - If the array is not compatible with the new shape according to NumPy's
+        broadcasting rules.
+        - If the shape of the output array is not consistent with the desired shape.
+
+    Examples
+    --------
+    >>> x = np.array([1, 2, 3])
+    >>> np.broadcast_to(x, (3, 3))
+    array([[1., 2., 3.],
+           [1., 2., 3.],
+           [1., 2., 3.]])
+    """
+    pass

From f01bcaa6d804292e025853cacfc88a41689155dc Mon Sep 17 00:00:00 2001
From: Xi Wang <xidulu@gmail.com>
Date: Fri, 18 Oct 2019 15:05:05 +0800
Subject: [PATCH 464/813] [Numpy] More numpy dispatch tests (#16426)

* tests added

* remove not equal

* fix tiny bug

* remove meshgrid test

* modify meshgrid return type, add test
---
 python/mxnet/numpy/stride_tricks.py           |  2 +-
 python/mxnet/numpy_dispatch_protocol.py       |  3 +++
 .../unittest/test_numpy_interoperability.py   | 21 +++++++++++++++++++
 3 files changed, 25 insertions(+), 1 deletion(-)

diff --git a/python/mxnet/numpy/stride_tricks.py b/python/mxnet/numpy/stride_tricks.py
index 1848a292e673..0b2fe523b0f3 100644
--- a/python/mxnet/numpy/stride_tricks.py
+++ b/python/mxnet/numpy/stride_tricks.py
@@ -51,6 +51,6 @@ def broadcast_arrays(*args):
 
     if all(array.shape == shape for array in args):
         # Common case where nothing needs to be broadcasted.
-        return args
+        return list(args)
 
     return [_mx_np_op.broadcast_to(array, shape) for array in args]
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index b02a7e53cfbf..1e1824fe43b4 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -113,6 +113,8 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'transpose',
     'var',
     'zeros_like',
+    'meshgrid',
+    'outer'
 ]
 
 
@@ -196,6 +198,7 @@ def _register_array_function():
     'ceil',
     'trunc',
     'floor',
+    'logical_not',
 ]
 
 
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index af5991381bd4..ac30f996ba18 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -77,6 +77,9 @@ def _prepare_workloads():
     OpArgMngr.add_workload('min', array_pool['4x1'])
     OpArgMngr.add_workload('mean', array_pool['4x1'])
     OpArgMngr.add_workload('mean', array_pool['4x1'], axis=0, keepdims=True)
+    OpArgMngr.add_workload('mean', np.array([[1, 2, 3], [4, 5, 6]]))
+    OpArgMngr.add_workload('mean', np.array([[1, 2, 3], [4, 5, 6]]), axis=0)
+    OpArgMngr.add_workload('mean', np.array([[1, 2, 3], [4, 5, 6]]), axis=1)
     OpArgMngr.add_workload('ones_like', array_pool['4x1'])
     OpArgMngr.add_workload('prod', array_pool['4x1'])
 
@@ -157,6 +160,10 @@ def _prepare_workloads():
     OpArgMngr.add_workload('transpose', array_pool['4x1'])
     OpArgMngr.add_workload('var', array_pool['4x1'])
     OpArgMngr.add_workload('zeros_like', array_pool['4x1'])
+    OpArgMngr.add_workload('outer', np.ones((5)), np.ones((2)))
+    OpArgMngr.add_workload('meshgrid', np.array([1, 2, 3]))
+    OpArgMngr.add_workload('meshgrid', np.array([1, 2, 3]), np.array([4, 5, 6, 7]))
+    OpArgMngr.add_workload('meshgrid', np.array([1, 2, 3]), np.array([4, 5, 6, 7]), indexing='ij')
 
     # workloads for array ufunc protocol
     OpArgMngr.add_workload('add', array_pool['4x1'], array_pool['1x2'])
@@ -175,6 +182,9 @@ def _prepare_workloads():
     OpArgMngr.add_workload('power', array_pool['4x1'], 2)
     OpArgMngr.add_workload('power', 2, array_pool['4x1'])
     OpArgMngr.add_workload('power', array_pool['4x1'], array_pool['1x1x0'])
+    OpArgMngr.add_workload('power', np.array([1, 2, 3], np.int32), 2.00001)
+    OpArgMngr.add_workload('power', np.array([15, 15], np.int64), np.array([15, 15], np.int64))
+    OpArgMngr.add_workload('power', 0, np.arange(1, 10))
     OpArgMngr.add_workload('mod', array_pool['4x1'], array_pool['1x2'])
     OpArgMngr.add_workload('mod', array_pool['4x1'], 2)
     OpArgMngr.add_workload('mod', 2, array_pool['4x1'])
@@ -256,6 +266,12 @@ def _signs(dt):
     OpArgMngr.add_workload('exp', array_pool['4x1'])
     OpArgMngr.add_workload('log', array_pool['4x1'])
     OpArgMngr.add_workload('log2', array_pool['4x1'])
+    OpArgMngr.add_workload('log2', np.array(2.**65))
+    OpArgMngr.add_workload('log2', np.array(np.inf))
+    OpArgMngr.add_workload('log2', np.array(1.))
+    OpArgMngr.add_workload('log1p', np.array(-1.))
+    OpArgMngr.add_workload('log1p', np.array(np.inf))
+    OpArgMngr.add_workload('log1p', np.array(1e-6))
     OpArgMngr.add_workload('log10', array_pool['4x1'])
     OpArgMngr.add_workload('expm1', array_pool['4x1'])
     OpArgMngr.add_workload('sqrt', array_pool['4x1'])
@@ -282,6 +298,11 @@ def _signs(dt):
     OpArgMngr.add_workload('ceil', array_pool['4x1'])
     OpArgMngr.add_workload('trunc', array_pool['4x1'])
     OpArgMngr.add_workload('floor', array_pool['4x1'])
+    OpArgMngr.add_workload('logical_not', np.ones(10, dtype=np.int32))
+    OpArgMngr.add_workload('logical_not', array_pool['4x1'])
+    OpArgMngr.add_workload('logical_not', np.array([True, False, True, False], dtype=np.bool))
+    
+
 
 
 _prepare_workloads()

From 27f708222828f1f8f62c24a33764ce75c3a7588e Mon Sep 17 00:00:00 2001
From: Tong He <hetong007@gmail.com>
Date: Fri, 18 Oct 2019 16:35:23 +0800
Subject: [PATCH 465/813] Fix learning rate scheduler being unexpectedly
 overwritten by optimizer's default value (#16487)

* fix overwritten lr

* new logic

* fix bug

* fix

* improve doc

* Trigger CI

* Trigger CI
---
 python/mxnet/optimizer/optimizer.py     | 20 ++++++++++++++------
 tests/python/unittest/test_optimizer.py |  4 ++++
 2 files changed, 18 insertions(+), 6 deletions(-)

diff --git a/python/mxnet/optimizer/optimizer.py b/python/mxnet/optimizer/optimizer.py
index bc87777e40fc..b7311b29fc9e 100644
--- a/python/mxnet/optimizer/optimizer.py
+++ b/python/mxnet/optimizer/optimizer.py
@@ -63,8 +63,11 @@ class Optimizer(object):
     clip_gradient : float, optional, default None
         Clip the gradient by projecting onto the box ``[-clip_gradient, clip_gradient]``.
 
-    learning_rate : float, optional, default 0.01
-        The initial learning rate.
+    learning_rate : float, optional, default None
+        The initial learning rate. If None, the optimization will use the
+        learning rate from ``lr_scheduler``. If not None, it will overwrite
+        the learning rate in ``lr_scheduler``. If None and ``lr_scheduler``
+        is also None, then it will be set to 0.01 by default.
 
     lr_scheduler : LRScheduler, optional, default None
         The learning rate scheduler.
@@ -97,14 +100,19 @@ class Optimizer(object):
         optimizer, its learning rate can be accessed as optimizer.learning_rate.
     """
     def __init__(self, rescale_grad=1., param_idx2name=None, wd=0.,
-                 clip_gradient=None, learning_rate=0.01,
+                 clip_gradient=None, learning_rate=None,
                  lr_scheduler=None, sym=None, begin_num_update=0,
                  multi_precision=False, param_dict=None):
         self.rescale_grad = rescale_grad
-        self.lr = learning_rate
         self.lr_scheduler = lr_scheduler
-        if lr_scheduler is not None:
-            self.lr_scheduler.base_lr = learning_rate
+        if self.lr_scheduler is None and learning_rate is None:
+            learning_rate = 0.01
+        self.lr = learning_rate
+        if self.lr_scheduler is not None and learning_rate is not None:
+            if self.lr_scheduler.base_lr != learning_rate:
+                print(UserWarning("learning rate from ``lr_scheduler`` has been "
+                                  "overwritten by ``learning_rate`` in optimizer."))
+                self.lr_scheduler.base_lr = learning_rate
 
         self.wd = wd
         self.lr_mult = {}
diff --git a/tests/python/unittest/test_optimizer.py b/tests/python/unittest/test_optimizer.py
index cea469960f64..f9adf63b24e2 100644
--- a/tests/python/unittest/test_optimizer.py
+++ b/tests/python/unittest/test_optimizer.py
@@ -39,6 +39,10 @@ def test_learning_rate():
     o2.lr_scheduler.base_lr = 0.4
     assert o2.learning_rate == 0.4
 
+    lr_s = lr_scheduler.FactorScheduler(step=1, base_lr=1024)
+    o3 = mx.optimizer.Optimizer(lr_scheduler=lr_s)
+    assert o3.learning_rate == 1024
+
 
 @raises(UserWarning)
 @with_seed()

From 73bff7dcee1ce4a81c66cad14dbe38c4c8dc2595 Mon Sep 17 00:00:00 2001
From: Rohit Kumar Srivastava <srivastava.141@osu.edu>
Date: Fri, 18 Oct 2019 08:29:46 -0700
Subject: [PATCH 466/813] adding large tensor support for add_n and tests for
 more ops (#16476)

---
 src/operator/tensor/elemwise_sum.h |  8 ++---
 tests/nightly/test_large_array.py  | 54 ++++++++++++++++++++++++++++++
 tests/nightly/test_large_vector.py | 54 ++++++++++++++++++++++++++++++
 3 files changed, 112 insertions(+), 4 deletions(-)

diff --git a/src/operator/tensor/elemwise_sum.h b/src/operator/tensor/elemwise_sum.h
index 08f57d15fbf8..e89e9d799903 100644
--- a/src/operator/tensor/elemwise_sum.h
+++ b/src/operator/tensor/elemwise_sum.h
@@ -39,15 +39,15 @@ namespace op {
 
 struct Sum {
   template<typename DType>
-  MSHADOW_XINLINE static DType sum(int i, const DType* a) {
+  MSHADOW_XINLINE static DType sum(index_t i, const DType* a) {
     return a[i];
   }
   template<typename DType, typename... DTypes>
-  MSHADOW_XINLINE static DType sum(int i, const DType* a, const DTypes... b) {
+  MSHADOW_XINLINE static DType sum(index_t i, const DType* a, const DTypes... b) {
     return a[i] + sum(i, b...);
   }
   template<typename DType, typename... DTypes>
-  MSHADOW_XINLINE static void Map(int i, DType* out, const OpReqType req, const DType* in0,
+  MSHADOW_XINLINE static void Map(index_t i, DType* out, const OpReqType req, const DType* in0,
     const DTypes... ins) {
     KERNEL_ASSIGN(out[i], req, sum(i, in0, ins...));
   }
@@ -64,7 +64,7 @@ void ElementWiseSumCompute_(const nnvm::NodeAttrs& attrs,
   size_t size = in_data.size();
   Stream<xpu> *s = ctx.get_stream<xpu>();
   DType* out_dptr = out_data[0].dptr<DType>();
-  int out_size = static_cast<int>((out_data[0].Size() + DataType<DType>::kLanes - 1)
+  index_t out_size = static_cast<index_t>((out_data[0].Size() + DataType<DType>::kLanes - 1)
                                   /DataType<DType>::kLanes);
   switch (size) {
     case 2: {
diff --git a/tests/nightly/test_large_array.py b/tests/nightly/test_large_array.py
index 7ab1d025b75b..14befb272486 100644
--- a/tests/nightly/test_large_array.py
+++ b/tests/nightly/test_large_array.py
@@ -15,6 +15,8 @@
 # specific language governing permissions and limitations
 # under the License.
 
+import os
+import tempfile
 import math
 import numpy as np
 import mxnet as mx
@@ -1439,6 +1441,58 @@ def npy_instance_norm(data, gamma, beta, axis, eps=1E-5):
                         forward_check_eps)
 
 
+def test_load_save():
+    x = create_2d_tensor(SMALL_Y, LARGE_X)
+    tmp = tempfile.mkdtemp()
+    tmpfile = os.path.join(tmp, 'large_tensor')
+    nd.save(tmpfile, [x])
+    y = nd.load(tmpfile)
+    y = y[0]
+    assert x[0][0] == y[0][0]
+    assert x[-1][-1]== y[-1][-1]
+
+
+def test_add_n():
+    x = [nd.ones(LARGE_X) for j in range(SMALL_Y)]
+    y = nd.add_n(*x)
+    assert y[0] == SMALL_Y
+    assert y[-1] == SMALL_Y
+
+
+def test_modulo():
+    x = mx.nd.ones((SMALL_Y, LARGE_X))*6
+    y = mx.nd.ones(LARGE_X)*4
+    z = (x%y)
+    assert z[0][0] == 2
+    assert z[-1][-1] == 2
+    x = mx.nd.ones((SMALL_Y, LARGE_X))*5
+    z = nd.modulo(x,y)
+    assert z[0][0] == 1
+    assert z[-1][-1] == 1
+
+
+def test_maximum():
+    x = mx.nd.ones((SMALL_Y, LARGE_X))*3
+    y = mx.nd.ones(LARGE_X)*4
+    z = nd.maximum(x, y)
+    assert z[0][0] == 4
+    assert z[-1][-1] == 4
+    z = nd.maximum(x, 5)
+    assert z[0][0] == 5
+    assert z[-1][-1] == 5
+
+
+def test_minimum():
+    x = mx.nd.ones((SMALL_Y, LARGE_X))*3
+    y = mx.nd.ones(LARGE_X)*2
+    z = nd.minimum(x, y)
+    assert z[0][0] == 2
+    assert z[-1][-1] == 2
+    z = nd.minimum(x, 5)
+    assert z[0][0] == 3
+    assert z[-1][-1] == 3
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()
diff --git a/tests/nightly/test_large_vector.py b/tests/nightly/test_large_vector.py
index 75a902ce221f..53a8bd07ff78 100644
--- a/tests/nightly/test_large_vector.py
+++ b/tests/nightly/test_large_vector.py
@@ -15,6 +15,8 @@
 # specific language governing permissions and limitations
 # under the License.
 
+import os
+import tempfile
 import math
 import numpy as np
 import mxnet as mx
@@ -876,6 +878,58 @@ def check_degrees():
     check_degrees()
 
 
+def test_load_save():
+    x = create_vector(size=LARGE_X)
+    tmp = tempfile.mkdtemp()
+    tmpfile = os.path.join(tmp, 'large_vector')
+    nd.save(tmpfile, [x])
+    y = nd.load(tmpfile)
+    y = y[0]
+    assert x[0] == y[0]
+    assert x[-1] == y[-1]
+
+
+def test_add_n():
+    x = [nd.ones(LARGE_X)]
+    y = nd.add_n(*x)
+    assert y[0] == 1
+    assert y[-1] == 1
+
+
+def test_modulo():
+    x = mx.nd.ones(LARGE_X)*6
+    y = mx.nd.ones(LARGE_X)*4
+    z = (x%y)
+    assert z[0] == 2
+    assert z[-1] == 2
+    x = mx.nd.ones(LARGE_X)*5
+    z = nd.modulo(x,y)
+    assert z[0] == 1
+    assert z[-1] == 1
+
+
+def test_maximum():
+    x = mx.nd.ones(LARGE_X)*3
+    y = mx.nd.ones(LARGE_X)*4
+    z = nd.maximum(x, y)
+    assert z[0] == 4
+    assert z[-1] == 4
+    z = nd.maximum(x, 5)
+    assert z[0] == 5
+    assert z[-1] == 5
+
+
+def test_minimum():
+    x = mx.nd.ones(LARGE_X)*3
+    y = mx.nd.ones(LARGE_X)*2
+    z = nd.minimum(x, y)
+    assert z[0] == 2
+    assert z[-1] == 2
+    z = nd.minimum(x, 5)
+    assert z[0] == 3
+    assert z[-1] == 3
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 4b8a95ffbb15af2f4258868a3fc9f2e2b4cd8a80 Mon Sep 17 00:00:00 2001
From: Aaron Markham <markhama@amazon.com>
Date: Fri, 18 Oct 2019 09:04:37 -0700
Subject: [PATCH 467/813] add option to remove indexes (#16525)

---
 docs/static_site/src/.htaccess | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/static_site/src/.htaccess b/docs/static_site/src/.htaccess
index 379e8210eca9..6273edb25795 100644
--- a/docs/static_site/src/.htaccess
+++ b/docs/static_site/src/.htaccess
@@ -1,3 +1,4 @@
+Options -Indexes
 DirectorySlash off
 RewriteEngine on
 RewriteOptions AllowNoSlash

From 32bb374e9f68186f3780441e48091814f8236ec9 Mon Sep 17 00:00:00 2001
From: Lai Wei <royweilai@gmail.com>
Date: Fri, 18 Oct 2019 14:24:20 -0700
Subject: [PATCH 468/813] disable tests (#16536)

* disable tests

* update
---
 .travis.yml                    | 4 +++-
 ci/docker/runtime_functions.sh | 4 ++--
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index 079293059842..b0aa26c1a3a1 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -31,9 +31,11 @@ install:
 # recommended.
 # https://docs.travis-ci.com/user/reference/overview/
 script:
+# Temporarily disable travis build due to travis constantly time out, tracked in
+# https://github:com/apache/incubator-mxnet/issues/16535:
   - export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
   - mv make/osx.mk config.mk
-  - make -j 2
+#  - make -j 2
 
   # Temporarily disabled due to https://github.com/apache/incubator-mxnet/issues/13136
   # We ignore several tests to avoid possible timeouts on large PRs.
diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index ef496dfe8168..f6696e3682a1 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -1311,8 +1311,8 @@ integrationtest_ubuntu_gpu_dist_kvstore() {
     export PYTHONPATH=./python/
     export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
     cd tests/nightly/
-    ../../tools/launch.py -n 7 --launcher local python dist_device_sync_kvstore.py
-    ../../tools/launch.py -n 7 --launcher local python dist_sync_kvstore.py --type=init_gpu
+    ../../tools/launch.py -n 4 --launcher local python dist_device_sync_kvstore.py
+    ../../tools/launch.py -n 4 --launcher local python dist_sync_kvstore.py --type=init_gpu
     popd
 }
 

From efa53693dd236579d960c8329c9b51a581650cff Mon Sep 17 00:00:00 2001
From: Rohit Kumar Srivastava <srivastava.141@osu.edu>
Date: Fri, 18 Oct 2019 14:34:12 -0700
Subject: [PATCH 469/813] adding large tensor support for pad operator (#15126)

---
 src/operator/pad.cc               | 326 +++++++++++++++---------------
 tests/nightly/test_large_array.py |  10 +
 2 files changed, 173 insertions(+), 163 deletions(-)

diff --git a/src/operator/pad.cc b/src/operator/pad.cc
index 9a5d7561ac01..b6dc0a7536be 100644
--- a/src/operator/pad.cc
+++ b/src/operator/pad.cc
@@ -37,23 +37,23 @@ template <typename DType>
 void single_image_edge(const Tensor<cpu, 3, DType> dst,
                        const Tensor<cpu, 3, DType> src, mxnet::TShape pad) {
   const int nslices = src.size(0);
-  const int iheight = src.size(1);
-  const int iwidth = src.size(2);
+  const index_t iheight = src.size(1);
+  const index_t iwidth = src.size(2);
 
-  const int oheight = dst.size(1);
-  const int owidth = dst.size(2);
+  const index_t oheight = dst.size(1);
+  const index_t owidth = dst.size(2);
 
-  const int pad_t = pad[4];
-  const int pad_l = pad[6];
-  int iStartX = std::max(0, -pad_l);
-  int iStartY = std::max(0, -pad_t);
-  int oStartX = std::max(0, pad_l);
-  int oStartY = std::max(0, pad_t);
+  const index_t pad_t = pad[4];
+  const index_t pad_l = pad[6];
+  index_t iStartX = std::max(index_t{0}, -pad_l);
+  index_t iStartY = std::max(index_t{0}, -pad_t);
+  index_t oStartX = std::max(index_t{0}, pad_l);
+  index_t oStartY = std::max(index_t{0}, pad_t);
 
-  int k, ip_x, ip_y;
+  index_t k, ip_x, ip_y;
 #pragma omp parallel for private(k, ip_x, ip_y)
   for (k = 0; k < nslices; k++) {
-    int i, j;
+    index_t i, j;
     for (i = 0; i < oheight; i++) {
       for (j = 0; j < owidth; j++) {
         if (j < pad_l) {
@@ -86,23 +86,23 @@ void single_image_edge_grad(const Tensor<cpu, 3, DType> &grad_in,
                             const Tensor<cpu, 3, DType> grad_out,
                             mxnet::TShape pad) {
   const int nslices = grad_in.size(0);
-  const int iheight = grad_in.size(1);
-  const int iwidth = grad_in.size(2);
+  const index_t iheight = grad_in.size(1);
+  const index_t iwidth = grad_in.size(2);
 
-  const int oheight = grad_out.size(1);
-  const int owidth = grad_out.size(2);
+  const index_t oheight = grad_out.size(1);
+  const index_t owidth = grad_out.size(2);
 
-  const int pad_t = pad[4];
-  const int pad_l = pad[6];
-  int iStartX = std::max(0, -pad_l);
-  int iStartY = std::max(0, -pad_t);
-  int oStartX = std::max(0, pad_l);
-  int oStartY = std::max(0, pad_t);
+  const index_t pad_t = pad[4];
+  const index_t pad_l = pad[6];
+  index_t iStartX = std::max(index_t{0}, -pad_l);
+  index_t iStartY = std::max(index_t{0}, -pad_t);
+  index_t oStartX = std::max(index_t{0}, pad_l);
+  index_t oStartY = std::max(index_t{0}, pad_t);
 
-  int k, ip_x, ip_y;
+  index_t k, ip_x, ip_y;
 #pragma omp parallel for private(k, ip_x, ip_y)
   for (k = 0; k < nslices; k++) {
-    int i, j;
+    index_t i, j;
     for (i = 0; i < oheight; i++) {
       for (j = 0; j < owidth; j++) {
         if (j < pad_l) {
@@ -137,15 +137,15 @@ template <typename DType>
 void single_image_constant(const Tensor<cpu, 3, DType> &dst,
                            const Tensor<cpu, 3, DType> src, mxnet::TShape pad,
                            DType constant_value) {
-  const int pad_t = pad[4];
-  const int pad_l = pad[6];
-  int c, w, h;
+  const index_t pad_t = pad[4];
+  const index_t pad_l = pad[6];
+  index_t c, w, h;
   // using these vars to avoid casting overhead each loop iteration
-  const int dst0 = dst.size(0);
-  const int dst1 = dst.size(1);
-  const int dst2 = dst.size(2);
-  const int src1 = src.size(1);
-  const int src2 = src.size(2);
+  const index_t dst0 = dst.size(0);
+  const index_t dst1 = dst.size(1);
+  const index_t dst2 = dst.size(2);
+  const index_t src1 = src.size(1);
+  const index_t src2 = src.size(2);
 #pragma omp parallel for private(c, w, h)
   for (c = 0; c < dst0; ++c) {
     for (h = 0; h < dst1; ++h) {
@@ -165,13 +165,13 @@ template <typename DType>
 void single_image_constant_grad(const Tensor<cpu, 3, DType> &in_grad,
                                 const Tensor<cpu, 3, DType> out_grad,
                                 mxnet::TShape pad) {
-  const int pad_t = pad[4];
-  const int pad_l = pad[6];
+  const index_t pad_t = pad[4];
+  const index_t pad_l = pad[6];
 
-  const int in_grad0 = in_grad.size(0);
-  const int in_grad1 = in_grad.size(1);
-  const int in_grad2 = in_grad.size(2);
-  int c, h, w;
+  const index_t in_grad0 = in_grad.size(0);
+  const index_t in_grad1 = in_grad.size(1);
+  const index_t in_grad2 = in_grad.size(2);
+  index_t c, h, w;
 #pragma omp parallel for private(c, w, h)
   for (c = 0; c < in_grad0; ++c) {
     for (h = 0; h < in_grad1; ++h) {
@@ -187,24 +187,24 @@ template <typename DType>
 void single_image_reflect(const Tensor<cpu, 3, DType> &dst,
                            const Tensor<cpu, 3, DType> src, mxnet::TShape pad) {
   const int nslices = src.size(0);
-  const int iheight = src.size(1);
-  const int iwidth = src.size(2);
+  const index_t iheight = src.size(1);
+  const index_t iwidth = src.size(2);
 
-  const int oheight = dst.size(1);
-  const int owidth = dst.size(2);
+  const index_t oheight = dst.size(1);
+  const index_t owidth = dst.size(2);
 
-  const int pad_t = pad[4];
-  const int pad_l = pad[6];
-  int iStartX = std::max(0, -pad_l);
-  int iStartY = std::max(0, -pad_t);
-  int oStartX = std::max(0, pad_l);
-  int oStartY = std::max(0, pad_t);
+  const index_t pad_t = pad[4];
+  const index_t pad_l = pad[6];
+  index_t iStartX = std::max(index_t{0}, -pad_l);
+  index_t iStartY = std::max(index_t{0}, -pad_t);
+  index_t oStartX = std::max(index_t{0}, pad_l);
+  index_t oStartY = std::max(index_t{0}, pad_t);
 
-  int k, ip_x, ip_y;
+  index_t k, ip_x, ip_y;
 #pragma omp parallel for private(k, ip_x, ip_y)
 
   for (k = 0; k < nslices; k++) {
-    int i, j;
+    index_t i, j;
     for (i = 0; i < oheight; i++) {
       for (j = 0; j < owidth; j++) {
         if (j < pad_l) {
@@ -238,24 +238,24 @@ void single_image_reflect_grad(const Tensor<cpu, 3, DType> &grad_in,
                             const Tensor<cpu, 3, DType> grad_out,
                             mxnet::TShape pad) {
   const int nslices = grad_in.size(0);
-  const int iheight = grad_in.size(1);
-  const int iwidth = grad_in.size(2);
+  const index_t iheight = grad_in.size(1);
+  const index_t iwidth = grad_in.size(2);
 
-  const int oheight = grad_out.size(1);
-  const int owidth = grad_out.size(2);
+  const index_t oheight = grad_out.size(1);
+  const index_t owidth = grad_out.size(2);
 
-  const int pad_t = pad[4];
-  const int pad_l = pad[6];
-  int iStartX = std::max(0, -pad_l);
-  int iStartY = std::max(0, -pad_t);
-  int oStartX = std::max(0, pad_l);
-  int oStartY = std::max(0, pad_t);
+  const index_t pad_t = pad[4];
+  const index_t pad_l = pad[6];
+  index_t iStartX = std::max(index_t{0}, -pad_l);
+  index_t iStartY = std::max(index_t{0}, -pad_t);
+  index_t oStartX = std::max(index_t{0}, pad_l);
+  index_t oStartY = std::max(index_t{0}, pad_t);
 
-  int k, ip_x, ip_y;
+  index_t k, ip_x, ip_y;
 #pragma omp parallel for private(k, ip_x, ip_y)
 
   for (k = 0; k < nslices; k++) {
-    int i, j;
+    index_t i, j;
     for (i = 0; i < oheight; i++) {
       for (j = 0; j < owidth; j++) {
         if (j < pad_l) {
@@ -294,28 +294,28 @@ template <typename DType>
 void single_image_edge(const Tensor<cpu, 4, DType> dst,
                        const Tensor<cpu, 4, DType> src, mxnet::TShape pad) {
   const int nslices = src.size(0);
-  const int idepth = src.size(1);
-  const int iheight = src.size(2);
-  const int iwidth = src.size(3);
-
-  const int odepth = dst.size(1);
-  const int oheight = dst.size(2);
-  const int owidth = dst.size(3);
-
-  const int pad_f = pad[4];
-  const int pad_t = pad[6];
-  const int pad_l = pad[8];
-  int iStartX = std::max(0, -pad_l);
-  int iStartY = std::max(0, -pad_t);
-  int iStartZ = std::max(0, -pad_f);
-  int oStartX = std::max(0, pad_l);
-  int oStartY = std::max(0, pad_t);
-  int oStartZ = std::max(0, pad_f);
-
-  int k, ip_x, ip_y, ip_z;
+  const index_t idepth = src.size(1);
+  const index_t iheight = src.size(2);
+  const index_t iwidth = src.size(3);
+
+  const index_t odepth = dst.size(1);
+  const index_t oheight = dst.size(2);
+  const index_t owidth = dst.size(3);
+
+  const index_t pad_f = pad[4];
+  const index_t pad_t = pad[6];
+  const index_t pad_l = pad[8];
+  index_t iStartX = std::max(index_t{0}, -pad_l);
+  index_t iStartY = std::max(index_t{0}, -pad_t);
+  index_t iStartZ = std::max(index_t{0}, -pad_f);
+  index_t oStartX = std::max(index_t{0}, pad_l);
+  index_t oStartY = std::max(index_t{0}, pad_t);
+  index_t oStartZ = std::max(index_t{0}, pad_f);
+
+  index_t k, ip_x, ip_y, ip_z;
 #pragma omp parallel for private(k, ip_x, ip_y, ip_z)
   for (k = 0; k < nslices; k++) {
-    int i, j, z;
+    index_t i, j, z;
     for (z = 0; z < odepth; z++) {
       for (i = 0; i < oheight; i++) {
         for (j = 0; j < owidth; j++) {
@@ -362,28 +362,28 @@ void single_image_edge_grad(const Tensor<cpu, 4, DType> &grad_in,
                             const Tensor<cpu, 4, DType> grad_out,
                             mxnet::TShape pad) {
   const int nslices = grad_in.size(0);
-  const int idepth = grad_in.size(1);
-  const int iheight = grad_in.size(2);
-  const int iwidth = grad_in.size(3);
-
-  const int odepth = grad_out.size(1);
-  const int oheight = grad_out.size(2);
-  const int owidth = grad_out.size(3);
-
-  const int pad_f = pad[4];
-  const int pad_t = pad[6];
-  const int pad_l = pad[8];
-  int iStartX = std::max(0, -pad_l);
-  int iStartY = std::max(0, -pad_t);
-  int iStartZ = std::max(0, -pad_f);
-  int oStartX = std::max(0, pad_l);
-  int oStartY = std::max(0, pad_t);
-  int oStartZ = std::max(0, pad_f);
-
-  int k, ip_x, ip_y, ip_z;
+  const index_t idepth = grad_in.size(1);
+  const index_t iheight = grad_in.size(2);
+  const index_t iwidth = grad_in.size(3);
+
+  const index_t odepth = grad_out.size(1);
+  const index_t oheight = grad_out.size(2);
+  const index_t owidth = grad_out.size(3);
+
+  const index_t pad_f = pad[4];
+  const index_t pad_t = pad[6];
+  const index_t pad_l = pad[8];
+  index_t iStartX = std::max(index_t{0}, -pad_l);
+  index_t iStartY = std::max(index_t{0}, -pad_t);
+  index_t iStartZ = std::max(index_t{0}, -pad_f);
+  index_t oStartX = std::max(index_t{0}, pad_l);
+  index_t oStartY = std::max(index_t{0}, pad_t);
+  index_t oStartZ = std::max(index_t{0}, pad_f);
+
+  index_t k, ip_x, ip_y, ip_z;
 #pragma omp parallel for private(k, ip_x, ip_y, ip_z)
   for (k = 0; k < nslices; k++) {
-    int i, j, z;
+    index_t i, j, z;
     for (z = 0; z < odepth; z++) {
       for (i = 0; i < oheight; i++) {
         for (j = 0; j < owidth; j++) {
@@ -430,19 +430,19 @@ template <typename DType>
 void single_image_constant(const Tensor<cpu, 4, DType> &dst,
                            const Tensor<cpu, 4, DType> src, mxnet::TShape pad,
                            DType constant_value) {
-  const int pad_f = pad[4];
-  const int pad_t = pad[6];
-  const int pad_l = pad[8];
-
-  const int dst0 = dst.size(0);
-  const int dst1 = dst.size(1);
-  const int dst2 = dst.size(2);
-  const int dst3 = dst.size(3);
-  const int src1 = src.size(1);
-  const int src2 = src.size(2);
-  const int src3 = src.size(3);
-
-  int c, d, w, h;
+  const index_t pad_f = pad[4];
+  const index_t pad_t = pad[6];
+  const index_t pad_l = pad[8];
+
+  const index_t dst0 = dst.size(0);
+  const index_t dst1 = dst.size(1);
+  const index_t dst2 = dst.size(2);
+  const index_t dst3 = dst.size(3);
+  const index_t src1 = src.size(1);
+  const index_t src2 = src.size(2);
+  const index_t src3 = src.size(3);
+
+  index_t c, d, w, h;
 #pragma omp parallel for private(c, d, w, h)
   for (c = 0; c < dst0; ++c) {
     for (d = 0; d < dst1; ++d) {
@@ -465,14 +465,14 @@ template <typename DType>
 void single_image_constant_grad(const Tensor<cpu, 4, DType> &in_grad,
                                 const Tensor<cpu, 4, DType> out_grad,
                                 mxnet::TShape pad) {
-  const int pad_f = pad[4];
-  const int pad_t = pad[6];
-  const int pad_l = pad[8];
-  const int in_grad0 = in_grad.size(0);
-  const int in_grad1 = in_grad.size(1);
-  const int in_grad2 = in_grad.size(2);
-  const int in_grad3 = in_grad.size(3);
-  int c, d, w, h;
+  const index_t pad_f = pad[4];
+  const index_t pad_t = pad[6];
+  const index_t pad_l = pad[8];
+  const index_t in_grad0 = in_grad.size(0);
+  const index_t in_grad1 = in_grad.size(1);
+  const index_t in_grad2 = in_grad.size(2);
+  const index_t in_grad3 = in_grad.size(3);
+  index_t c, d, w, h;
   #pragma omp parallel for private(c, d, w, h)
   for (c = 0; c < in_grad0; ++c) {
     for (d = 0; d < in_grad1; ++d) {
@@ -490,28 +490,28 @@ template <typename DType>
 void single_image_reflect(const Tensor<cpu, 4, DType> &dst,
                            const Tensor<cpu, 4, DType> src, mxnet::TShape pad) {
   const int nslices = src.size(0);
-  const int idepth = src.size(1);
-  const int iheight = src.size(2);
-  const int iwidth = src.size(3);
-
-  const int odepth = dst.size(1);
-  const int oheight = dst.size(2);
-  const int owidth = dst.size(3);
-
-  const int pad_f = pad[4];
-  const int pad_t = pad[6];
-  const int pad_l = pad[8];
-  int iStartX = std::max(0, -pad_l);
-  int iStartY = std::max(0, -pad_t);
-  int iStartZ = std::max(0, -pad_f);
-  int oStartX = std::max(0, pad_l);
-  int oStartY = std::max(0, pad_t);
-  int oStartZ = std::max(0, pad_f);
-
-  int l, ip_x, ip_y, ip_z;
+  const index_t idepth = src.size(1);
+  const index_t iheight = src.size(2);
+  const index_t iwidth = src.size(3);
+
+  const index_t odepth = dst.size(1);
+  const index_t oheight = dst.size(2);
+  const index_t owidth = dst.size(3);
+
+  const index_t pad_f = pad[4];
+  const index_t pad_t = pad[6];
+  const index_t pad_l = pad[8];
+  index_t iStartX = std::max(index_t{0}, -pad_l);
+  index_t iStartY = std::max(index_t{0}, -pad_t);
+  index_t iStartZ = std::max(index_t{0}, -pad_f);
+  index_t oStartX = std::max(index_t{0}, pad_l);
+  index_t oStartY = std::max(index_t{0}, pad_t);
+  index_t oStartZ = std::max(index_t{0}, pad_f);
+
+  index_t l, ip_x, ip_y, ip_z;
 #pragma omp parallel for private(l, ip_x, ip_y, ip_z)
   for (l = 0; l < nslices; l++) {
-    int i, j, k;
+    index_t i, j, k;
     for (k = 0; k < odepth; k++) {
       for (i = 0; i < oheight; i++) {
         for (j = 0; j < owidth; j++) {
@@ -558,28 +558,28 @@ void single_image_reflect_grad(const Tensor<cpu, 4, DType> &grad_in,
                                 const Tensor<cpu, 4, DType> grad_out,
                                 mxnet::TShape pad) {
   const int nslices = grad_in.size(0);
-  const int idepth = grad_in.size(1);
-  const int iheight = grad_in.size(2);
-  const int iwidth = grad_in.size(3);
-
-  const int odepth = grad_out.size(1);
-  const int oheight = grad_out.size(2);
-  const int owidth = grad_out.size(3);
-
-  const int pad_f = pad[4];
-  const int pad_t = pad[6];
-  const int pad_l = pad[8];
-  int iStartX = std::max(0, -pad_l);
-  int iStartY = std::max(0, -pad_t);
-  int iStartZ = std::max(0, -pad_f);
-  int oStartX = std::max(0, pad_l);
-  int oStartY = std::max(0, pad_t);
-  int oStartZ = std::max(0, pad_f);
-
-  int l, ip_x, ip_y, ip_z;
+  const index_t idepth = grad_in.size(1);
+  const index_t iheight = grad_in.size(2);
+  const index_t iwidth = grad_in.size(3);
+
+  const index_t odepth = grad_out.size(1);
+  const index_t oheight = grad_out.size(2);
+  const index_t owidth = grad_out.size(3);
+
+  const index_t pad_f = pad[4];
+  const index_t pad_t = pad[6];
+  const index_t pad_l = pad[8];
+  index_t iStartX = std::max(index_t{0}, -pad_l);
+  index_t iStartY = std::max(index_t{0}, -pad_t);
+  index_t iStartZ = std::max(index_t{0}, -pad_f);
+  index_t oStartX = std::max(index_t{0}, pad_l);
+  index_t oStartY = std::max(index_t{0}, pad_t);
+  index_t oStartZ = std::max(index_t{0}, pad_f);
+
+  index_t l, ip_x, ip_y, ip_z;
 /*#pragma omp parallel for private(l, ip_x, ip_y, ip_z)*/
   for (l = 0; l < nslices; l++) {
-    int i, j, k;
+    index_t i, j, k;
     for (k = 0; k < odepth; k++) {
       for (i = 0; i < oheight; i++) {
         for (j = 0; j < owidth; j++) {
diff --git a/tests/nightly/test_large_array.py b/tests/nightly/test_large_array.py
index 14befb272486..63dc2b71b9d4 100644
--- a/tests/nightly/test_large_array.py
+++ b/tests/nightly/test_large_array.py
@@ -1493,6 +1493,16 @@ def test_minimum():
     assert z[-1][-1] == 3
 
 
+def test_pad():
+    x = create_2d_tensor(rows=SMALL_Y-2, columns=LARGE_X//2-2, dtype=np.float32).reshape(1 , 1, SMALL_Y-2, LARGE_X//2-2)
+    y = nd.pad(x, mode="edge", pad_width=(0, 0, 0, 0, 1, 1, 1, 1))
+    assert y[0][0][1][0] == 0
+    assert y[0][0][1][-1] == 0
+    assert y[0][0][-1][0] == SMALL_Y-3
+    assert y[0][0][-1][-1] == SMALL_Y-3
+    assert y.shape == (1, 1, SMALL_Y, LARGE_X//2)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 2d4c3a466107e8685e7f825c7445b6f2157a1ecc Mon Sep 17 00:00:00 2001
From: Lai Wei <royweilai@gmail.com>
Date: Fri, 18 Oct 2019 20:10:24 -0700
Subject: [PATCH 470/813] fix pylint in CI (#16540)

* fix pylint

* revert centos pylint
---
 Makefile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 0a1e355ee5e8..e1eeefed4bb7 100644
--- a/Makefile
+++ b/Makefile
@@ -670,7 +670,7 @@ cpplint:
 	--exclude_path src/operator/contrib/ctc_include include/mkldnn
 
 pylint:
-	python3 -m pylint --rcfile=$(ROOTDIR)/ci/other/pylintrc --ignore-patterns=".*\.so$$,.*\.dll$$,.*\.dylib$$" python/mxnet tools/caffe_converter/*.py
+	python3 -m pylint --rcfile=$(ROOTDIR)/ci/other/pylintrc --ignore-patterns=".*\.so$$,.*\.dll$$,.*\.dylib$$" python/mxnet
 
 # Cython build
 cython:

From 27b3e52b467df9fd30858c6025e386bbaee9e4f8 Mon Sep 17 00:00:00 2001
From: Jake Lee <gstu1130@gmail.com>
Date: Fri, 18 Oct 2019 20:11:16 -0700
Subject: [PATCH 471/813] image crop gpu (#16464)

---
 src/operator/contrib/bilinear_resize-inl.cuh  |  8 +-
 src/operator/image/crop-inl.h                 | 22 ++++--
 src/operator/image/crop.cc                    |  4 +-
 src/operator/image/crop.cu                    | 34 +++++++++
 tests/python/gpu/test_gluon_transforms.py     | 74 +++----------------
 .../python/unittest/test_gluon_data_vision.py | 21 ++----
 6 files changed, 76 insertions(+), 87 deletions(-)
 create mode 100644 src/operator/image/crop.cu

diff --git a/src/operator/contrib/bilinear_resize-inl.cuh b/src/operator/contrib/bilinear_resize-inl.cuh
index b8dacb1c4f31..0f1605549d0b 100644
--- a/src/operator/contrib/bilinear_resize-inl.cuh
+++ b/src/operator/contrib/bilinear_resize-inl.cuh
@@ -62,7 +62,9 @@ static unsigned getNumThreads(int nElem, const bool smaller) {
 
 // caffe_gpu_interp2_kernel overloading with Tensor<xpu, 3, DType>
 template<typename xpu, typename Dtype, typename Acctype>
-__global__ void caffe_gpu_interp2_kernel(const int n,
+__global__ void
+__launch_bounds__(cuda::kMaxThreadsPerBlock, 1)
+caffe_gpu_interp2_kernel(const int n,
     const Acctype rheight, const Acctype rwidth,
     const Tensor<xpu, 3, Dtype> data1,
     Tensor<xpu, 3, Dtype> data2,
@@ -111,7 +113,9 @@ __global__ void caffe_gpu_interp2_kernel(const int n,
 
 // caffe_gpu_interp2_kernel overloading with Tensor<xpu, 4, DType>
 template<typename xpu, typename Dtype, typename Acctype>
-__global__ void caffe_gpu_interp2_kernel(const int n,
+__global__ void
+__launch_bounds__(cuda::kMaxThreadsPerBlock, 1)
+caffe_gpu_interp2_kernel(const int n,
     const Acctype rheight, const Acctype rwidth,
     const Tensor<xpu, 4, Dtype> data1,
     Tensor<xpu, 4, Dtype> data2,
diff --git a/src/operator/image/crop-inl.h b/src/operator/image/crop-inl.h
index a1a4b23f658e..c13049685dea 100644
--- a/src/operator/image/crop-inl.h
+++ b/src/operator/image/crop-inl.h
@@ -94,6 +94,7 @@ inline bool CropShape(const nnvm::NodeAttrs& attrs,
   return true;
 }
 
+template<typename xpu>
 inline void CropImpl(int x,
                       int y,
                       int width,
@@ -106,7 +107,7 @@ inline void CropImpl(int x,
   const TBlob& data = inputs[0];
   const TBlob& out = outputs[0];
   MXNET_NDIM_SWITCH(data.ndim(), ndim, {
-    Stream<cpu>* s = ctx.get_stream<cpu>();
+    Stream<xpu>* s = ctx.get_stream<xpu>();
     common::StaticArray<index_t, ndim> begin = {0}, step = {1};
     if (ndim == 3) {
       begin[0] = y;
@@ -118,7 +119,10 @@ inline void CropImpl(int x,
     MSHADOW_TYPE_SWITCH(out.type_flag_, DType, {
       MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
         size_t num_threads = out.shape_.FlatTo2D()[0];
-        mxnet_op::Kernel<slice_forward<ndim, Req, cpu>, cpu>::Launch(s, num_threads,
+        if (std::is_same<xpu, gpu>::value) {
+          num_threads *= out.shape_.get<ndim>()[ndim - 1];
+        }
+        mxnet_op::Kernel<slice_forward<ndim, Req, xpu>, xpu>::Launch(s, num_threads,
           out.dptr<DType>(), data.dptr<DType>(),
           data.shape_.get<ndim>(), out.shape_.get<ndim>(), begin, step);
       })
@@ -126,6 +130,7 @@ inline void CropImpl(int x,
   })
 }
 
+template<typename xpu>
 inline void CropBackwardImpl(int x,
                       int y,
                       int width,
@@ -138,7 +143,7 @@ inline void CropBackwardImpl(int x,
   if (req[0] == kNullOp) return;
   const TBlob& output_grad = inputs[0];
   const TBlob& input_grad = outputs[0];
-  Stream<cpu>* s = ctx.get_stream<cpu>();
+  Stream<xpu>* s = ctx.get_stream<xpu>();
   if (req[0] == kWriteTo) {
     Fill(s, input_grad, req[0], 0);
   } else if (req[0] == kWriteInplace) {
@@ -156,7 +161,10 @@ inline void CropBackwardImpl(int x,
     MSHADOW_TYPE_SWITCH(output_grad.type_flag_, DType, {
       MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
         size_t num_threads = output_grad.shape_.FlatTo2D()[0];
-        mxnet_op::Kernel<slice_assign<ndim, Req, cpu>, cpu>::Launch(s, num_threads,
+        if (std::is_same<xpu, gpu>::value) {
+          num_threads *= output_grad.shape_.get<ndim>()[ndim - 1];
+        }
+        mxnet_op::Kernel<slice_assign<ndim, Req, xpu>, xpu>::Launch(s, num_threads,
           input_grad.dptr<DType>(), output_grad.dptr<DType>(),
           input_grad.shape_.get<ndim>(), output_grad.shape_.get<ndim>(), begin, step);
       })
@@ -164,6 +172,7 @@ inline void CropBackwardImpl(int x,
   })
 }
 
+template<typename xpu>
 inline void CropOpForward(const nnvm::NodeAttrs &attrs,
                    const OpContext &ctx,
                    const std::vector<TBlob> &inputs,
@@ -171,9 +180,10 @@ inline void CropOpForward(const nnvm::NodeAttrs &attrs,
                    const std::vector<TBlob> &outputs) {
   CHECK_EQ(outputs.size(), 1U);
   const CropParam& param = nnvm::get<CropParam>(attrs.parsed);
-  CropImpl(param.x, param.y, param.width, param.height, inputs, outputs, ctx, req);
+  CropImpl<xpu>(param.x, param.y, param.width, param.height, inputs, outputs, ctx, req);
 }
 
+template<typename xpu>
 inline void CropOpBackward(const nnvm::NodeAttrs &attrs,
                    const OpContext &ctx,
                    const std::vector<TBlob> &inputs,
@@ -181,7 +191,7 @@ inline void CropOpBackward(const nnvm::NodeAttrs &attrs,
                    const std::vector<TBlob> &outputs) {
   CHECK_EQ(outputs.size(), 1U);
   const CropParam& param = nnvm::get<CropParam>(attrs.parsed);
-  CropBackwardImpl(param.x, param.y, param.width, param.height, inputs, outputs, ctx, req);
+  CropBackwardImpl<xpu>(param.x, param.y, param.width, param.height, inputs, outputs, ctx, req);
 }
 }  // namespace image
 }  // namespace op
diff --git a/src/operator/image/crop.cc b/src/operator/image/crop.cc
index 6067f89d7033..9a7aad38b486 100644
--- a/src/operator/image/crop.cc
+++ b/src/operator/image/crop.cc
@@ -69,7 +69,7 @@ to the given size.
 .set_attr_parser(ParamParser<CropParam>)
 .set_attr<mxnet::FInferShape>("FInferShape", CropShape)
 .set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
-.set_attr<FCompute>("FCompute<cpu>", CropOpForward)
+.set_attr<FCompute>("FCompute<cpu>", CropOpForward<cpu>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{ "_backward_image_crop" })
 .add_argument("data", "NDArray-or-Symbol", "The input.")
 .add_arguments(CropParam::__FIELDS__());
@@ -79,7 +79,7 @@ NNVM_REGISTER_OP(_backward_image_crop)
 .set_num_inputs(1)
 .set_num_outputs(1)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
-.set_attr<FCompute>("FCompute<cpu>", CropOpBackward);
+.set_attr<FCompute>("FCompute<cpu>", CropOpBackward<cpu>);
 
 }  // namespace image
 }  // namespace op
diff --git a/src/operator/image/crop.cu b/src/operator/image/crop.cu
new file mode 100644
index 000000000000..71fde06dacc0
--- /dev/null
+++ b/src/operator/image/crop.cu
@@ -0,0 +1,34 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one
+* or more contributor license agreements.  See the NOTICE file
+* distributed with this work for additional information
+* regarding copyright ownership.  The ASF licenses this file
+* to you under the Apache License, Version 2.0 (the
+* "License"); you may not use this file except in compliance
+* with the License.  You may obtain a copy of the License at
+*
+*   http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing,
+* software distributed under the License is distributed on an
+* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+* KIND, either express or implied.  See the License for the
+* specific language governing permissions and limitations
+* under the License.
+*/
+
+#include "crop-inl.h"
+
+namespace mxnet {
+namespace op {
+namespace image {
+
+NNVM_REGISTER_OP(_image_crop)
+.set_attr<FCompute>("FCompute<gpu>", CropOpForward<gpu>);
+
+NNVM_REGISTER_OP(_backward_image_crop)
+.set_attr<FCompute>("FCompute<gpu>", CropOpBackward<gpu>);
+
+}  // namespace image
+}  // namespace op
+}  // namespace mxnet
diff --git a/tests/python/gpu/test_gluon_transforms.py b/tests/python/gpu/test_gluon_transforms.py
index e303008dee9a..acdd22ac39ec 100644
--- a/tests/python/gpu/test_gluon_transforms.py
+++ b/tests/python/gpu/test_gluon_transforms.py
@@ -28,79 +28,22 @@
 curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
 sys.path.insert(0, os.path.join(curr_path, '../unittest'))
 from common import assertRaises, setup_module, with_seed, teardown
-
+from test_gluon_data_vision import test_to_tensor, test_normalize, test_crop_resize
 
 set_default_context(mx.gpu(0))
 
 @with_seed()
-def test_normalize():
-    # 3D Input
-    data_in_3d = nd.random.uniform(0, 1, (3, 300, 300))
-    out_nd_3d = transforms.Normalize(mean=(0, 1, 2), std=(3, 2, 1))(data_in_3d)
-    data_expected_3d = data_in_3d.asnumpy()
-    data_expected_3d[:][:][0] = data_expected_3d[:][:][0] / 3.0
-    data_expected_3d[:][:][1] = (data_expected_3d[:][:][1] - 1.0) / 2.0
-    data_expected_3d[:][:][2] = data_expected_3d[:][:][2] - 2.0
-    assert_almost_equal(data_expected_3d, out_nd_3d.asnumpy())
-
-    # 4D Input
-    data_in_4d = nd.random.uniform(0, 1, (2, 3, 300, 300))
-    out_nd_4d = transforms.Normalize(mean=(0, 1, 2), std=(3, 2, 1))(data_in_4d)
-    data_expected_4d = data_in_4d.asnumpy()
-    data_expected_4d[0][:][:][0] = data_expected_4d[0][:][:][0] / 3.0
-    data_expected_4d[0][:][:][1] = (data_expected_4d[0][:][:][1] - 1.0) / 2.0
-    data_expected_4d[0][:][:][2] = data_expected_4d[0][:][:][2] - 2.0
-    data_expected_4d[1][:][:][0] = data_expected_4d[1][:][:][0] / 3.0
-    data_expected_4d[1][:][:][1] = (data_expected_4d[1][:][:][1] - 1.0) / 2.0
-    data_expected_4d[1][:][:][2] = data_expected_4d[1][:][:][2] - 2.0
-    assert_almost_equal(data_expected_4d, out_nd_4d.asnumpy())
-
-    # Default normalize values i.e., mean=0, std=1
-    data_in_3d_def = nd.random.uniform(0, 1, (3, 300, 300))
-    out_nd_3d_def = transforms.Normalize()(data_in_3d_def)
-    data_expected_3d_def = data_in_3d_def.asnumpy()
-    assert_almost_equal(data_expected_3d_def, out_nd_3d_def.asnumpy())
+def test_normalize_gpu():
+    test_normalize()
 
-    # Invalid Input - Neither 3D or 4D input
-    invalid_data_in = nd.random.uniform(0, 1, (5, 5, 3, 300, 300))
-    normalize_transformer = transforms.Normalize(mean=(0, 1, 2), std=(3, 2, 1))
-    assertRaises(MXNetError, normalize_transformer, invalid_data_in)
-
-    # Invalid Input - Channel neither 1 or 3
-    invalid_data_in = nd.random.uniform(0, 1, (5, 4, 300, 300))
-    normalize_transformer = transforms.Normalize(mean=(0, 1, 2), std=(3, 2, 1))
-    assertRaises(MXNetError, normalize_transformer, invalid_data_in)
 
 @with_seed()
-def test_to_tensor():
-    # 3D Input
-    data_in = np.random.uniform(0, 255, (300, 300, 3)).astype(dtype=np.uint8)
-    out_nd = transforms.ToTensor()(nd.array(data_in, dtype='uint8'))
-    assert_almost_equal(out_nd.asnumpy(), np.transpose(
-        data_in.astype(dtype=np.float32) / 255.0, (2, 0, 1)))
-
-    # 4D Input
-    data_in = np.random.uniform(0, 255, (5, 300, 300, 3)).astype(dtype=np.uint8)
-    out_nd = transforms.ToTensor()(nd.array(data_in, dtype='uint8'))
-    assert_almost_equal(out_nd.asnumpy(), np.transpose(
-                        data_in.astype(dtype=np.float32) / 255.0, (0, 3, 1, 2)))
+def test_to_tensor_gpu():
+    test_to_tensor()
 
-    # Invalid Input
-    invalid_data_in = nd.random.uniform(0, 255, (5, 5, 300, 300, 3)).astype(dtype=np.uint8)
-    transformer = transforms.ToTensor()
-    assertRaises(MXNetError, transformer, invalid_data_in)
-
-    # Bounds (0->0, 255->1)
-    data_in = np.zeros((10, 20, 3)).astype(dtype=np.uint8)
-    out_nd = transforms.ToTensor()(nd.array(data_in, dtype='uint8'))
-    assert same(out_nd.asnumpy(), np.transpose(np.zeros(data_in.shape, dtype=np.float32), (2, 0, 1)))
-
-    data_in = np.full((10, 20, 3), 255).astype(dtype=np.uint8)
-    out_nd = transforms.ToTensor()(nd.array(data_in, dtype='uint8'))
-    assert same(out_nd.asnumpy(), np.transpose(np.ones(data_in.shape, dtype=np.float32), (2, 0, 1)))
 
 @with_seed()
-def test_resize():
+def test_resize_gpu():
     # Test with normal case 3D input float type
     data_in_3d = nd.random.uniform(0, 255, (300, 300, 3))
     out_nd_3d = transforms.Resize((100, 100))(data_in_3d)
@@ -155,3 +98,8 @@ def py_bilinear_resize_nhwc(x, outputHeight, outputWidth):
     data_in_4d = nd.random.uniform(0, 255, (2, 300, 300, 3)).astype('uint8')
     out_nd_4d = transforms.Resize((100, 100))(data_in_4d)
     assert_almost_equal(out_nd_4d.asnumpy(), py_bilinear_resize_nhwc(data_in_4d.asnumpy(), 100, 100), atol=1.0)
+
+
+@with_seed()
+def test_crop_resize_gpu():
+    test_crop_resize()
diff --git a/tests/python/unittest/test_gluon_data_vision.py b/tests/python/unittest/test_gluon_data_vision.py
index 627567ca4244..8bc0f8072260 100644
--- a/tests/python/unittest/test_gluon_data_vision.py
+++ b/tests/python/unittest/test_gluon_data_vision.py
@@ -146,17 +146,18 @@ def _test_crop_resize_with_diff_type(dtype):
         assert((out_batch_np[0:2,0:4,1,1].flatten() == [37,  52,  67,  82, 127, 142, 157, 172]).all())
         # test normal case with resize
         data_in = nd.random.uniform(0, 255, (300, 200, 3)).astype(dtype)
-        out_nd = transforms.CropResize(0, 0, 100, 50, (25, 25), 2)(data_in)
-        data_expected = image.imresize(nd.slice(data_in, (0, 0, 0), (50, 100 , 3)), 25, 25, 2)
+        out_nd = transforms.CropResize(0, 0, 100, 50, (25, 25), 1)(data_in)
+        data_expected = transforms.Resize(size=25, interpolation=1)(nd.slice(data_in, (0, 0, 0), (50, 100, 3)))
         assert_almost_equal(out_nd.asnumpy(), data_expected.asnumpy())
         # test 4D input with resize
         data_bath_in = nd.random.uniform(0, 255, (3, 300, 200, 3)).astype(dtype)
-        out_batch_nd = transforms.CropResize(0, 0, 100, 50, (25, 25), 2)(data_bath_in)
+        out_batch_nd = transforms.CropResize(0, 0, 100, 50, (25, 25), 1)(data_bath_in)
         for i in range(len(out_batch_nd)):
-            assert_almost_equal(image.imresize(nd.slice(data_bath_in[i], (0, 0, 0), (50, 100, 3)), 25, 25, 2).asnumpy(),
-                out_batch_nd[i].asnumpy())
+            actual = transforms.Resize(size=25, interpolation=1)(nd.slice(data_bath_in[i], (0, 0, 0), (50, 100, 3))).asnumpy()
+            expected = out_batch_nd[i].asnumpy()
+            assert_almost_equal(expected, actual)
         # test with resize height and width should be greater than 0
-        transformer = transforms.CropResize(0, 0, 100, 50, (-25, 25), 2)
+        transformer = transforms.CropResize(0, 0, 100, 50, (-25, 25), 1)
         assertRaises(MXNetError, transformer, data_in)
         # test height and width should be greater than 0 
         transformer = transforms.CropResize(0, 0, -100, -50)
@@ -188,14 +189,6 @@ def test_crop_backward(test_nd_arr, TestCase):
         data_in = nd.arange(60).reshape((5, 4, 3)).astype(dtype)
         for test_case in test_list:
             test_crop_backward(data_in, test_case)
-        
-
-
-    # check numeric gradient of nd.image.crop
-    # in_data = np.arange(36).reshape(3, 4, 3)
-    # data = mx.sym.Variable('data')
-    # image_crop_sym = mx.sym.image.crop(data, 0, 0, 2, 2)
-    # check_numeric_gradient(image_crop_sym, [in_data])
 
 
 @with_seed()

From a75ec06b27993df5e25496d356f41ca9ded9bb6e Mon Sep 17 00:00:00 2001
From: Haozheng Fan <fhztc1997618@gmail.com>
Date: Sat, 19 Oct 2019 14:13:06 +0800
Subject: [PATCH 472/813] [Numpy] einsum (#15911)

* einsum with optimization for imperative

* enable symbolic forward optimization with einsum_path

* fix tensordot temp space overwrite error

* enable symbolic backward optimization with einsum_path

* refactor tensordot

* rename MAXCHAR

* rename MAXCHAR to MAXAXIS

* add interoperability test

* fix broadcast shape

* fix forward broadcast bug

* fix backward broadcast bug

* remove dead code

* fix doc

* remove unicode

* Revert cpu resource number

* Fix hyperlink in doc

* Fix indent
---
 benchmark/python/einsum/benchmark_einsum.py   |   78 ++
 python/mxnet/ndarray/numpy/_op.py             |  241 +++-
 python/mxnet/numpy/multiarray.py              |  235 +++-
 python/mxnet/numpy_dispatch_protocol.py       |    3 +-
 python/mxnet/symbol/numpy/_symbol.py          |  129 +-
 src/operator/mxnet_op.h                       |   36 +
 src/operator/numpy/np_dot-inl.h               |   16 +-
 src/operator/numpy/np_dot.cc                  |    4 +-
 src/operator/numpy/np_einsum_op-inl.h         | 1092 +++++++++++++++++
 src/operator/numpy/np_einsum_op.cc            |  370 ++++++
 src/operator/numpy/np_einsum_op.cu            |   36 +
 src/operator/numpy/np_einsum_path_op-inl.h    |  964 +++++++++++++++
 src/operator/numpy/np_tensordot_op-inl.h      |  120 +-
 src/operator/numpy/np_tensordot_op.cc         |    8 +-
 .../unittest/test_numpy_interoperability.py   |   73 ++
 tests/python/unittest/test_numpy_op.py        |  131 ++
 16 files changed, 3507 insertions(+), 29 deletions(-)
 create mode 100644 benchmark/python/einsum/benchmark_einsum.py
 create mode 100644 src/operator/numpy/np_einsum_op-inl.h
 create mode 100644 src/operator/numpy/np_einsum_op.cc
 create mode 100644 src/operator/numpy/np_einsum_op.cu
 create mode 100644 src/operator/numpy/np_einsum_path_op-inl.h

diff --git a/benchmark/python/einsum/benchmark_einsum.py b/benchmark/python/einsum/benchmark_einsum.py
new file mode 100644
index 000000000000..3593de2db9e1
--- /dev/null
+++ b/benchmark/python/einsum/benchmark_einsum.py
@@ -0,0 +1,78 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import time
+import mxnet as mx
+from mxnet import np, npx
+
+def measure_cost(repeat, func_name, *args, **kwargs):
+    """Measure time cost of running a function
+    """
+    mx.nd.waitall()
+    start = time.time()
+    for _ in range(repeat):
+        func_name(*args, **kwargs)
+    mx.nd.waitall()
+    end = time.time()
+    diff = end - start
+    return diff / repeat
+
+
+def test_np_einsum():
+    print("Path optimization test:")
+    # Basic einsum
+    a = np.ones(64).reshape(2,4,8)
+    args = ['ijk,ilm,njm,nlk,abc->', a, a, a, a, a]
+    cost = measure_cost(500, np.einsum, *args)
+    print("Basic einsum: {} ms".format(cost * 1000))
+
+    # Sub-optimal einsum
+    # cost = measure_cost(500, np.einsum, *args, optimize='optimal')
+    # print("Optimal einsum: {} ms".format(cost * 1000))
+
+    # Greedy einsum
+    cost = measure_cost(500, np.einsum, *args, optimize=True)
+    print("Greedy einsum: {} ms".format(cost * 1000))
+
+    print('Inner Product:')
+    a = np.ones(6000000)
+    b = np.ones(6000000)
+    args = [a, b]
+    cost = measure_cost(50, np.tensordot, *args, axes=([0],[0]))
+    print('Tensordot: {} ms'.format(cost * 1000))
+    args = ['i, i', a, b]
+    cost = measure_cost(50, np.einsum, *args, optimize=True)
+    print('Greedy einsum: {} ms'.format(cost * 1000))
+    cost = measure_cost(50, np.einsum, *args)
+    print('Basic einsum: {} ms'.format(cost * 1000))
+
+    print('Matrix Product:')
+    a = np.ones(600000).reshape(200, 3000)
+    b = np.ones(600000).reshape(3000, 200)
+    args = [a, b]
+    cost = measure_cost(50, np.tensordot, *args, axes=([1],[0]))
+    print('Tensordot: {} ms'.format(cost * 1000))
+    args = ['ij, jk', a, b]
+    cost = measure_cost(50, np.einsum, *args, optimize=True)
+    print('Greedy einsum: {} ms'.format(cost * 1000))
+    cost = measure_cost(50, np.einsum, *args)
+    print('Basic einsum: {} ms'.format(cost * 1000))
+
+
+if __name__ == "__main__":
+    npx.set_np()
+    test_np_einsum()
diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index 08bcdb7833fb..ad76e43b2a90 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -38,7 +38,7 @@
            'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
            'around', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
            'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal',
-           'hsplit', 'rot90']
+           'hsplit', 'rot90', 'einsum']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -4488,3 +4488,242 @@ def rot90(m, k=1, axes=(0, 1)):
             [4., 6.]]])
     """
     return _npi.rot90(m, k=k, axes=axes)
+
+
+@set_module('mxnet.ndarray.numpy')
+def einsum(*operands, **kwargs):
+    r"""
+    einsum(subscripts, *operands, out=None, optimize=False)
+
+    Evaluates the Einstein summation convention on the operands.
+
+    Using the Einstein summation convention, many common multi-dimensional,
+    linear algebraic array operations can be represented in a simple fashion.
+    In *implicit* mode `einsum` computes these values.
+
+    In *explicit* mode, `einsum` provides further flexibility to compute
+    other array operations that might not be considered classical Einstein
+    summation operations, by disabling, or forcing summation over specified
+    subscript labels.
+
+    See the notes and examples for clarification.
+
+    Parameters
+    ----------
+    subscripts : str
+        Specifies the subscripts for summation as comma separated list of
+        subscript labels. An implicit (classical Einstein summation)
+        calculation is performed unless the explicit indicator '->' is
+        included as well as subscript labels of the precise output form.
+    operands : list of ndarray
+        These are the arrays for the operation.
+    out : ndarray, optional
+        If provided, the calculation is done into this array.
+    optimize : {False, True}, optional
+        Controls if intermediate optimization should occur. No optimization
+        will occur if False. Defaults to False.
+
+    Returns
+    -------
+    output : ndarray
+        The calculation based on the Einstein summation convention.
+
+    Notes
+    -----
+    The Einstein summation convention can be used to compute
+    many multi-dimensional, linear algebraic array operations. `einsum`
+    provides a succinct way of representing these.
+
+    A non-exhaustive list of these operations,
+    which can be computed by `einsum`, is shown below along with examples:
+
+    * Trace of an array, :py:func:`np.trace`.
+    * Return a diagonal, :py:func:`np.diag`.
+    * Array axis summations, :py:func:`np.sum`.
+    * Transpositions and permutations, :py:func:`np.transpose`.
+    * Matrix multiplication and dot product, :py:func:`np.matmul` :py:func:`np.dot`.
+    * Vector inner and outer products, :py:func:`np.inner` :py:func:`np.outer`.
+    * Broadcasting, element-wise and scalar multiplication, :py:func:`np.multiply`.
+    * Tensor contractions, :py:func:`np.tensordot`.
+
+    The subscripts string is a comma-separated list of subscript labels,
+    where each label refers to a dimension of the corresponding operand.
+    Whenever a label is repeated it is summed, so ``np.einsum('i,i', a, b)``
+    is equivalent to :py:func:`np.inner(a,b) <np.inner>`. If a label
+    appears only once, it is not summed, so ``np.einsum('i', a)`` produces a
+    view of ``a`` with no changes. A further example ``np.einsum('ij,jk', a, b)``
+    describes traditional matrix multiplication and is equivalent to
+    :py:func:`np.matmul(a,b) <np.matmul>`. Repeated subscript labels in one
+    operand take the diagonal. For example, ``np.einsum('ii', a)`` is equivalent
+    to :py:func:`np.trace(a) <np.trace>`.
+
+    In *implicit mode*, the chosen subscripts are important
+    since the axes of the output are reordered alphabetically.  This
+    means that ``np.einsum('ij', a)`` doesn't affect a 2D array, while
+    ``np.einsum('ji', a)`` takes its transpose. Additionally,
+    ``np.einsum('ij,jk', a, b)`` returns a matrix multiplication, while,
+    ``np.einsum('ij,jh', a, b)`` returns the transpose of the
+    multiplication since subscript 'h' precedes subscript 'i'.
+
+    In *explicit mode* the output can be directly controlled by
+    specifying output subscript labels.  This requires the
+    identifier '->' as well as the list of output subscript labels.
+    This feature increases the flexibility of the function since
+    summing can be disabled or forced when required. The call
+    ``np.einsum('i->', a)`` is like :py:func:`np.sum(a, axis=-1) <np.sum>`,
+    and ``np.einsum('ii->i', a)`` is like :py:func:`np.diag(a) <np.diag>`.
+    The difference is that `einsum` does not allow broadcasting by default.
+    Additionally ``np.einsum('ij,jh->ih', a, b)`` directly specifies the
+    order of the output subscript labels and therefore returns matrix
+    multiplication, unlike the example above in implicit mode.
+
+    To enable and control broadcasting, use an ellipsis.  Default
+    NumPy-style broadcasting is done by adding an ellipsis
+    to the left of each term, like ``np.einsum('...ii->...i', a)``.
+    To take the trace along the first and last axes,
+    you can do ``np.einsum('i...i', a)``, or to do a matrix-matrix
+    product with the left-most indices instead of rightmost, one can do
+    ``np.einsum('ij...,jk...->ik...', a, b)``.
+
+    When there is only one operand, no axes are summed, and no output
+    parameter is provided, a view into the operand is returned instead
+    of a new array.  Thus, taking the diagonal as ``np.einsum('ii->i', a)``
+    produces a view.
+
+    The ``optimize`` argument which will optimize the contraction order
+    of an einsum expression. For a contraction with three or more operands this
+    can greatly increase the computational efficiency at the cost of a larger
+    memory footprint during computation.
+
+    Typically a 'greedy' algorithm is applied which empirical tests have shown
+    returns the optimal path in the majority of cases. 'optimal' is not supported
+    for now.
+
+    This function differs from the original `numpy.einsum
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.einsum.html>`_ in
+    the following way(s):
+
+    - Does not support 'optimal' strategy
+    - Does not support the alternative subscript like
+        `einsum(op0, sublist0, op1, sublist1, ..., [sublistout])`
+    - Does not produce view in any cases
+
+    Examples
+    --------
+    >>> a = np.arange(25).reshape(5,5)
+    >>> b = np.arange(5)
+    >>> c = np.arange(6).reshape(2,3)
+
+    Trace of a matrix:
+
+    >>> np.einsum('ii', a)
+    array(60.)
+
+    Extract the diagonal (requires explicit form):
+
+    >>> np.einsum('ii->i', a)
+    array([ 0.,  6., 12., 18., 24.])
+
+    Sum over an axis (requires explicit form):
+
+    >>> np.einsum('ij->i', a)
+    array([ 10.,  35.,  60.,  85., 110.])
+    >>> np.sum(a, axis=1)
+    array([ 10.,  35.,  60.,  85., 110.])
+
+    For higher dimensional arrays summing a single axis can be done with ellipsis:
+
+    >>> np.einsum('...j->...', a)
+    array([ 10.,  35.,  60.,  85., 110.])
+
+    Compute a matrix transpose, or reorder any number of axes:
+
+    >>> np.einsum('ji', c)
+    array([[0., 3.],
+           [1., 4.],
+           [2., 5.]])
+    >>> np.einsum('ij->ji', c)
+    array([[0., 3.],
+           [1., 4.],
+           [2., 5.]])
+    >>> np.transpose(c)
+    array([[0., 3.],
+           [1., 4.],
+           [2., 5.]])
+
+    Vector inner products:
+
+    >>> np.einsum('i,i', b, b)
+    array(30.)
+
+    Matrix vector multiplication:
+
+    >>> np.einsum('ij,j', a, b)
+    array([ 30.,  80., 130., 180., 230.])
+    >>> np.dot(a, b)
+    array([ 30.,  80., 130., 180., 230.])
+    >>> np.einsum('...j,j', a, b)
+    array([ 30.,  80., 130., 180., 230.])
+
+    Broadcasting and scalar multiplication:
+
+    >>> np.einsum('..., ...', np.array(3), c)
+    array([[ 0.,  3.,  6.],
+           [ 9., 12., 15.]])
+    >>> np.einsum(',ij', np.array(3), c)
+    array([[ 0.,  3.,  6.],
+           [ 9., 12., 15.]])
+    >>> np.multiply(3, c)
+    array([[ 0.,  3.,  6.],
+           [ 9., 12., 15.]])
+
+    Vector outer product:
+
+    >>> np.einsum('i,j', np.arange(2)+1, b)
+    array([[0., 1., 2., 3., 4.],
+           [0., 2., 4., 6., 8.]])
+
+    Tensor contraction:
+
+    >>> a = np.arange(60.).reshape(3,4,5)
+    >>> b = np.arange(24.).reshape(4,3,2)
+    >>> np.einsum('ijk,jil->kl', a, b)
+    array([[4400., 4730.],
+           [4532., 4874.],
+           [4664., 5018.],
+           [4796., 5162.],
+           [4928., 5306.]])
+
+    Example of ellipsis use:
+
+    >>> a = np.arange(6).reshape((3,2))
+    >>> b = np.arange(12).reshape((4,3))
+    >>> np.einsum('ki,jk->ij', a, b)
+    array([[10., 28., 46., 64.],
+           [13., 40., 67., 94.]])
+    >>> np.einsum('ki,...k->i...', a, b)
+    array([[10., 28., 46., 64.],
+           [13., 40., 67., 94.]])
+    >>> np.einsum('k...,jk', a, b)
+    array([[10., 28., 46., 64.],
+           [13., 40., 67., 94.]])
+
+    Chained array operations. For more complicated contractions, speed ups
+    might be achieved by repeatedly computing a 'greedy' path. Performance
+    improvements can be particularly significant with larger arrays:
+
+    >>> a = np.ones(64).reshape(2,4,8)
+    # Basic `einsum`: ~42.22ms  (benchmarked on 3.4GHz Intel Xeon.)
+    >>> for iteration in range(500):
+    ...     np.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a)
+    # Greedy `einsum` (faster optimal path approximation): ~0.117ms
+    >>> for iteration in range(500):
+    ...     np.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize=True)
+    """
+    # Grab non-einsum kwargs; do not optimize by default.
+    optimize_arg = kwargs.pop('optimize', False)
+    out = kwargs.pop('out', None)
+
+    subscripts = operands[0]
+    operands = operands[1:]
+    return _npi.einsum(*operands, subscripts=subscripts, out=out, optimize=int(optimize_arg))
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index aec4f9ef4617..e507b17d68d8 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -55,7 +55,7 @@
            'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming',
            'blackman', 'flip', 'around', 'arctan2', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril',
            'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less',
-           'greater_equal', 'less_equal', 'hsplit', 'rot90']
+           'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -5959,3 +5959,236 @@ def hsplit(ary, indices_or_sections):
     [array([0., 1.]), array([], dtype=float32), array([2., 3.])]
     """
     return _mx_nd_np.hsplit(ary, indices_or_sections)
+
+
+@set_module('mxnet.numpy')
+def einsum(*operands, **kwargs):
+    r"""
+    einsum(subscripts, *operands, out=None, optimize=False)
+
+    Evaluates the Einstein summation convention on the operands.
+
+    Using the Einstein summation convention, many common multi-dimensional,
+    linear algebraic array operations can be represented in a simple fashion.
+    In *implicit* mode `einsum` computes these values.
+
+    In *explicit* mode, `einsum` provides further flexibility to compute
+    other array operations that might not be considered classical Einstein
+    summation operations, by disabling, or forcing summation over specified
+    subscript labels.
+
+    See the notes and examples for clarification.
+
+    Parameters
+    ----------
+    subscripts : str
+        Specifies the subscripts for summation as comma separated list of
+        subscript labels. An implicit (classical Einstein summation)
+        calculation is performed unless the explicit indicator '->' is
+        included as well as subscript labels of the precise output form.
+    operands : list of ndarray
+        These are the arrays for the operation.
+    out : ndarray, optional
+        If provided, the calculation is done into this array.
+    optimize : {False, True}, optional
+        Controls if intermediate optimization should occur. No optimization
+        will occur if False. Defaults to False.
+
+    Returns
+    -------
+    output : ndarray
+        The calculation based on the Einstein summation convention.
+
+    Notes
+    -----
+    The Einstein summation convention can be used to compute
+    many multi-dimensional, linear algebraic array operations. `einsum`
+    provides a succinct way of representing these.
+
+    A non-exhaustive list of these operations,
+    which can be computed by `einsum`, is shown below along with examples:
+
+    * Trace of an array, :py:func:`np.trace`.
+    * Return a diagonal, :py:func:`np.diag`.
+    * Array axis summations, :py:func:`np.sum`.
+    * Transpositions and permutations, :py:func:`np.transpose`.
+    * Matrix multiplication and dot product, :py:func:`np.matmul` :py:func:`np.dot`.
+    * Vector inner and outer products, :py:func:`np.inner` :py:func:`np.outer`.
+    * Broadcasting, element-wise and scalar multiplication, :py:func:`np.multiply`.
+    * Tensor contractions, :py:func:`np.tensordot`.
+
+    The subscripts string is a comma-separated list of subscript labels,
+    where each label refers to a dimension of the corresponding operand.
+    Whenever a label is repeated it is summed, so ``np.einsum('i,i', a, b)``
+    is equivalent to :py:func:`np.inner(a,b) <np.inner>`. If a label
+    appears only once, it is not summed, so ``np.einsum('i', a)`` produces a
+    view of ``a`` with no changes. A further example ``np.einsum('ij,jk', a, b)``
+    describes traditional matrix multiplication and is equivalent to
+    :py:func:`np.matmul(a,b) <np.matmul>`. Repeated subscript labels in one
+    operand take the diagonal. For example, ``np.einsum('ii', a)`` is equivalent
+    to :py:func:`np.trace(a) <np.trace>`.
+
+    In *implicit mode*, the chosen subscripts are important
+    since the axes of the output are reordered alphabetically.  This
+    means that ``np.einsum('ij', a)`` doesn't affect a 2D array, while
+    ``np.einsum('ji', a)`` takes its transpose. Additionally,
+    ``np.einsum('ij,jk', a, b)`` returns a matrix multiplication, while,
+    ``np.einsum('ij,jh', a, b)`` returns the transpose of the
+    multiplication since subscript 'h' precedes subscript 'i'.
+
+    In *explicit mode* the output can be directly controlled by
+    specifying output subscript labels.  This requires the
+    identifier '->' as well as the list of output subscript labels.
+    This feature increases the flexibility of the function since
+    summing can be disabled or forced when required. The call
+    ``np.einsum('i->', a)`` is like :py:func:`np.sum(a, axis=-1) <np.sum>`,
+    and ``np.einsum('ii->i', a)`` is like :py:func:`np.diag(a) <np.diag>`.
+    The difference is that `einsum` does not allow broadcasting by default.
+    Additionally ``np.einsum('ij,jh->ih', a, b)`` directly specifies the
+    order of the output subscript labels and therefore returns matrix
+    multiplication, unlike the example above in implicit mode.
+
+    To enable and control broadcasting, use an ellipsis.  Default
+    NumPy-style broadcasting is done by adding an ellipsis
+    to the left of each term, like ``np.einsum('...ii->...i', a)``.
+    To take the trace along the first and last axes,
+    you can do ``np.einsum('i...i', a)``, or to do a matrix-matrix
+    product with the left-most indices instead of rightmost, one can do
+    ``np.einsum('ij...,jk...->ik...', a, b)``.
+
+    When there is only one operand, no axes are summed, and no output
+    parameter is provided, a view into the operand is returned instead
+    of a new array.  Thus, taking the diagonal as ``np.einsum('ii->i', a)``
+    produces a view.
+
+    The ``optimize`` argument which will optimize the contraction order
+    of an einsum expression. For a contraction with three or more operands this
+    can greatly increase the computational efficiency at the cost of a larger
+    memory footprint during computation.
+
+    Typically a 'greedy' algorithm is applied which empirical tests have shown
+    returns the optimal path in the majority of cases. 'optimal' is not supported
+    for now.
+
+    This function differs from the original `numpy.einsum
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.einsum.html>`_ in
+    the following way(s):
+
+    - Does not support 'optimal' strategy
+    - Does not support the alternative subscript like
+        `einsum(op0, sublist0, op1, sublist1, ..., [sublistout])`
+    - Does not produce view in any cases
+
+    Examples
+    --------
+    >>> a = np.arange(25).reshape(5,5)
+    >>> b = np.arange(5)
+    >>> c = np.arange(6).reshape(2,3)
+
+    Trace of a matrix:
+
+    >>> np.einsum('ii', a)
+    array(60.)
+
+    Extract the diagonal (requires explicit form):
+
+    >>> np.einsum('ii->i', a)
+    array([ 0.,  6., 12., 18., 24.])
+
+    Sum over an axis (requires explicit form):
+
+    >>> np.einsum('ij->i', a)
+    array([ 10.,  35.,  60.,  85., 110.])
+    >>> np.sum(a, axis=1)
+    array([ 10.,  35.,  60.,  85., 110.])
+
+    For higher dimensional arrays summing a single axis can be done with ellipsis:
+
+    >>> np.einsum('...j->...', a)
+    array([ 10.,  35.,  60.,  85., 110.])
+
+    Compute a matrix transpose, or reorder any number of axes:
+
+    >>> np.einsum('ji', c)
+    array([[0., 3.],
+           [1., 4.],
+           [2., 5.]])
+    >>> np.einsum('ij->ji', c)
+    array([[0., 3.],
+           [1., 4.],
+           [2., 5.]])
+    >>> np.transpose(c)
+    array([[0., 3.],
+           [1., 4.],
+           [2., 5.]])
+
+    Vector inner products:
+
+    >>> np.einsum('i,i', b, b)
+    array(30.)
+
+    Matrix vector multiplication:
+
+    >>> np.einsum('ij,j', a, b)
+    array([ 30.,  80., 130., 180., 230.])
+    >>> np.dot(a, b)
+    array([ 30.,  80., 130., 180., 230.])
+    >>> np.einsum('...j,j', a, b)
+    array([ 30.,  80., 130., 180., 230.])
+
+    Broadcasting and scalar multiplication:
+
+    >>> np.einsum('..., ...', np.array(3), c)
+    array([[ 0.,  3.,  6.],
+           [ 9., 12., 15.]])
+    >>> np.einsum(',ij', np.array(3), c)
+    array([[ 0.,  3.,  6.],
+           [ 9., 12., 15.]])
+    >>> np.multiply(3, c)
+    array([[ 0.,  3.,  6.],
+           [ 9., 12., 15.]])
+
+    Vector outer product:
+
+    >>> np.einsum('i,j', np.arange(2)+1, b)
+    array([[0., 1., 2., 3., 4.],
+           [0., 2., 4., 6., 8.]])
+
+    Tensor contraction:
+
+    >>> a = np.arange(60.).reshape(3,4,5)
+    >>> b = np.arange(24.).reshape(4,3,2)
+    >>> np.einsum('ijk,jil->kl', a, b)
+    array([[4400., 4730.],
+           [4532., 4874.],
+           [4664., 5018.],
+           [4796., 5162.],
+           [4928., 5306.]])
+
+    Example of ellipsis use:
+
+    >>> a = np.arange(6).reshape((3,2))
+    >>> b = np.arange(12).reshape((4,3))
+    >>> np.einsum('ki,jk->ij', a, b)
+    array([[10., 28., 46., 64.],
+           [13., 40., 67., 94.]])
+    >>> np.einsum('ki,...k->i...', a, b)
+    array([[10., 28., 46., 64.],
+           [13., 40., 67., 94.]])
+    >>> np.einsum('k...,jk', a, b)
+    array([[10., 28., 46., 64.],
+           [13., 40., 67., 94.]])
+
+    Chained array operations. For more complicated contractions, speed ups
+    might be achieved by repeatedly computing a 'greedy' path. Performance
+    improvements can be particularly significant with larger arrays:
+
+    >>> a = np.ones(64).reshape(2,4,8)
+    # Basic `einsum`: ~42.22ms  (benchmarked on 3.4GHz Intel Xeon.)
+    >>> for iteration in range(500):
+    ...     np.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a)
+    # Greedy `einsum` (faster optimal path approximation): ~0.117ms
+    >>> for iteration in range(500):
+    ...     np.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize=True)
+    """
+    return _mx_nd_np.einsum(*operands, **kwargs)
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index 1e1824fe43b4..a241d2687ee4 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -114,7 +114,8 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'var',
     'zeros_like',
     'meshgrid',
-    'outer'
+    'outer',
+    'einsum'
 ]
 
 
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 5e535ef3ef84..1945c5b0e695 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -40,7 +40,7 @@
            'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
            'around', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
            'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal',
-           'less_equal', 'hsplit', 'rot90']
+           'less_equal', 'hsplit', 'rot90', 'einsum']
 
 
 def _num_outputs(sym):
@@ -4428,4 +4428,131 @@ def rot90(m, k=1, axes=(0, 1)):
     return _npi.rot90(m, k=k, axes=axes)
 
 
+@set_module('mxnet.symbol.numpy')
+def einsum(*operands, **kwargs):
+    r"""
+    einsum(subscripts, *operands, out=None, optimize=False)
+
+    Evaluates the Einstein summation convention on the operands.
+
+    Using the Einstein summation convention, many common multi-dimensional,
+    linear algebraic array operations can be represented in a simple fashion.
+    In *implicit* mode `einsum` computes these values.
+
+    In *explicit* mode, `einsum` provides further flexibility to compute
+    other array operations that might not be considered classical Einstein
+    summation operations, by disabling, or forcing summation over specified
+    subscript labels.
+
+    See the notes and examples for clarification.
+
+    Parameters
+    ----------
+    subscripts : str
+        Specifies the subscripts for summation as comma separated list of
+        subscript labels. An implicit (classical Einstein summation)
+        calculation is performed unless the explicit indicator '->' is
+        included as well as subscript labels of the precise output form.
+    operands : list of _Symbol
+        These are the arrays for the operation.
+    out : _Symbol, optional
+        If provided, the calculation is done into this array.
+    optimize : {False, True}, optional
+        Controls if intermediate optimization should occur. No optimization
+        will occur if False. Defaults to False.
+
+    Returns
+    -------
+    output : _Symbol
+        The calculation based on the Einstein summation convention.
+
+    Notes
+    -----
+    The Einstein summation convention can be used to compute
+    many multi-dimensional, linear algebraic array operations. `einsum`
+    provides a succinct way of representing these.
+
+    A non-exhaustive list of these operations,
+    which can be computed by `einsum`, is shown below along with examples:
+
+    * Trace of an array, :py:func:`np.trace`.
+    * Return a diagonal, :py:func:`np.diag`.
+    * Array axis summations, :py:func:`np.sum`.
+    * Transpositions and permutations, :py:func:`np.transpose`.
+    * Matrix multiplication and dot product, :py:func:`np.matmul` :py:func:`np.dot`.
+    * Vector inner and outer products, :py:func:`np.inner` :py:func:`np.outer`.
+    * Broadcasting, element-wise and scalar multiplication, :py:func:`np.multiply`.
+    * Tensor contractions, :py:func:`np.tensordot`.
+
+    The subscripts string is a comma-separated list of subscript labels,
+    where each label refers to a dimension of the corresponding operand.
+    Whenever a label is repeated it is summed, so ``np.einsum('i,i', a, b)``
+    is equivalent to :py:func:`np.inner(a,b) <np.inner>`. If a label
+    appears only once, it is not summed, so ``np.einsum('i', a)`` produces a
+    view of ``a`` with no changes. A further example ``np.einsum('ij,jk', a, b)``
+    describes traditional matrix multiplication and is equivalent to
+    :py:func:`np.matmul(a,b) <np.matmul>`. Repeated subscript labels in one
+    operand take the diagonal. For example, ``np.einsum('ii', a)`` is equivalent
+    to :py:func:`np.trace(a) <np.trace>`.
+
+    In *implicit mode*, the chosen subscripts are important
+    since the axes of the output are reordered alphabetically.  This
+    means that ``np.einsum('ij', a)`` doesn't affect a 2D array, while
+    ``np.einsum('ji', a)`` takes its transpose. Additionally,
+    ``np.einsum('ij,jk', a, b)`` returns a matrix multiplication, while,
+    ``np.einsum('ij,jh', a, b)`` returns the transpose of the
+    multiplication since subscript 'h' precedes subscript 'i'.
+
+    In *explicit mode* the output can be directly controlled by
+    specifying output subscript labels.  This requires the
+    identifier '->' as well as the list of output subscript labels.
+    This feature increases the flexibility of the function since
+    summing can be disabled or forced when required. The call
+    ``np.einsum('i->', a)`` is like :py:func:`np.sum(a, axis=-1) <np.sum>`,
+    and ``np.einsum('ii->i', a)`` is like :py:func:`np.diag(a) <np.diag>`.
+    The difference is that `einsum` does not allow broadcasting by default.
+    Additionally ``np.einsum('ij,jh->ih', a, b)`` directly specifies the
+    order of the output subscript labels and therefore returns matrix
+    multiplication, unlike the example above in implicit mode.
+
+    To enable and control broadcasting, use an ellipsis.  Default
+    NumPy-style broadcasting is done by adding an ellipsis
+    to the left of each term, like ``np.einsum('...ii->...i', a)``.
+    To take the trace along the first and last axes,
+    you can do ``np.einsum('i...i', a)``, or to do a matrix-matrix
+    product with the left-most indices instead of rightmost, one can do
+    ``np.einsum('ij...,jk...->ik...', a, b)``.
+
+    When there is only one operand, no axes are summed, and no output
+    parameter is provided, a view into the operand is returned instead
+    of a new array.  Thus, taking the diagonal as ``np.einsum('ii->i', a)``
+    produces a view.
+
+    The ``optimize`` argument which will optimize the contraction order
+    of an einsum expression. For a contraction with three or more operands this
+    can greatly increase the computational efficiency at the cost of a larger
+    memory footprint during computation.
+
+    Typically a 'greedy' algorithm is applied which empirical tests have shown
+    returns the optimal path in the majority of cases. 'optimal' is not supported
+    for now.
+
+    This function differs from the original `numpy.einsum
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.einsum.html>`_ in
+    the following way(s):
+
+    - Does not support 'optimal' strategy
+    - Does not support the alternative subscript like
+        `einsum(op0, sublist0, op1, sublist1, ..., [sublistout])`
+    - Does not produce view in any cases
+    """
+    # Grab non-einsum kwargs; do not optimize by default.
+    optimize_arg = kwargs.pop('optimize', False)
+    out = kwargs.pop('out', None)
+
+    subscripts = operands[0]
+    operands = operands[1:]
+    return _npi.einsum(*operands, subscripts=subscripts, out=out, optimize=int(optimize_arg))
+
+
 _set_np_symbol_class(_Symbol)
diff --git a/src/operator/mxnet_op.h b/src/operator/mxnet_op.h
index 950db174595e..18b39b532388 100644
--- a/src/operator/mxnet_op.h
+++ b/src/operator/mxnet_op.h
@@ -159,6 +159,42 @@ inline int get_num_threads<cpu>(const int N) {
     LOG(FATAL) << "ndim=" << NDim << "too large "; \
   }
 
+#define MXNET_NDIM_SWITCH_EX(NDim, ndim, ...)      \
+  if (NDim == 0) {                                 \
+  } else if (NDim == 1) {                          \
+    const int ndim = 1;                            \
+    {__VA_ARGS__}                                  \
+  } else if (NDim == 2) {                          \
+    const int ndim = 2;                            \
+    {__VA_ARGS__}                                  \
+  } else if (NDim == 3) {                          \
+    const int ndim = 3;                            \
+    {__VA_ARGS__}                                  \
+  } else if (NDim == 4) {                          \
+    const int ndim = 4;                            \
+    {__VA_ARGS__}                                  \
+  } else if (NDim == 5) {                          \
+    const int ndim = 5;                            \
+    {__VA_ARGS__}                                  \
+  } else if (NDim == 6) {                          \
+    const int ndim = 6;                            \
+    {__VA_ARGS__}                                  \
+  } else if (NDim == 7) {                          \
+    const int ndim = 7;                            \
+    {__VA_ARGS__}                                  \
+  } else if (NDim == 8) {                          \
+    const int ndim = 8;                            \
+    {__VA_ARGS__}                                  \
+  } else if (NDim == 9) {                          \
+    const int ndim = 9;                            \
+    {__VA_ARGS__}                                  \
+  } else if (NDim == 10) {                         \
+    const int ndim = 10;                           \
+    {__VA_ARGS__}                                  \
+  }  else {                                        \
+    LOG(FATAL) << "ndim=" << NDim << "too large "; \
+  }
+
 #define MXNET_NO_INT8_TYPE_SWITCH(type, DType, ...)        \
   switch (type) {                                          \
   case mshadow::kFloat32:                                  \
diff --git a/src/operator/numpy/np_dot-inl.h b/src/operator/numpy/np_dot-inl.h
index a854777c3109..fe63c0b0ec51 100644
--- a/src/operator/numpy/np_dot-inl.h
+++ b/src/operator/numpy/np_dot-inl.h
@@ -63,7 +63,13 @@ inline void NumpyDotForward(const nnvm::NodeAttrs& attrs,
       //         of a and the 2nd-to-last axis of b
       const Tuple<int> a_axes_summed({a_shape.ndim() - 1});
       const Tuple<int> b_axes_summed({b_shape.ndim() - 2});
-      TensordotImpl<xpu>(a_axes_summed, b_axes_summed, ctx, a, b, out, req);
+      size_t workspace_size = TensordotWorkspaceSize<xpu>(a_axes_summed,
+                                                          b_axes_summed,
+                                                          a, b, out,
+                                                          req);
+      Tensor<xpu, 1, char> workspace = ctx.requested[0].get_space_typed<xpu, 1, char>(
+        Shape1(workspace_size), ctx.get_stream<xpu>());
+      TensordotImpl<xpu>(a_axes_summed, b_axes_summed, ctx, a, b, out, req, workspace);
     }
   });
 }
@@ -98,8 +104,14 @@ inline void NumpyDotBackward(const nnvm::NodeAttrs& attrs,
       //         of a and the 2nd-to-last axis of b
       const Tuple<int> a_axes_summed({a_shape.ndim() - 1});
       const Tuple<int> b_axes_summed({b_shape.ndim() - 2});
+      size_t workspace_size = TensordotBackwardWorkspaceSize<xpu>(a_axes_summed, b_axes_summed,
+                                                                  ograd, a, b, grad_a,
+                                                                  grad_b, req);
+      Tensor<xpu, 1, char> workspace =
+        ctx.requested[0].get_space_typed<xpu, 1, char>(Shape1(workspace_size),
+                                                       ctx.get_stream<xpu>());
       TensordotBackwardImpl<xpu>(a_axes_summed, b_axes_summed, ctx, ograd, a, b, grad_a,
-          grad_b, req);
+                                 grad_b, req, workspace);
     }
   });
 }
diff --git a/src/operator/numpy/np_dot.cc b/src/operator/numpy/np_dot.cc
index 627e68877998..6afc896a7720 100644
--- a/src/operator/numpy/np_dot.cc
+++ b/src/operator/numpy/np_dot.cc
@@ -129,7 +129,7 @@ NNVM_REGISTER_OP(_np_dot)
 .set_attr<nnvm::FInferType>("FInferType", ElemwiseType<2, 1>)
 .set_attr<FResourceRequest>("FResourceRequest",
   [](const NodeAttrs& attrs) {
-    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+    return std::vector<ResourceRequest>(1, ResourceRequest::kTempSpace);
   })
 .set_attr<FCompute>("FCompute<cpu>", NumpyDotForward<cpu>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_np_dot"})
@@ -142,7 +142,7 @@ NNVM_REGISTER_OP(_backward_np_dot)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
 .set_attr<FResourceRequest>("FResourceRequest",
   [](const NodeAttrs& attrs) {
-    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+    return std::vector<ResourceRequest>(1, ResourceRequest::kTempSpace);
   })
 .set_attr<FCompute>("FCompute<cpu>", NumpyDotBackward<cpu>);
 
diff --git a/src/operator/numpy/np_einsum_op-inl.h b/src/operator/numpy/np_einsum_op-inl.h
new file mode 100644
index 000000000000..2145abec682b
--- /dev/null
+++ b/src/operator/numpy/np_einsum_op-inl.h
@@ -0,0 +1,1092 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*
+ * Copyright (c) 2005-2019, NumPy Developers.
+ * All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are
+ * met:
+ *
+ *  * Redistributions of source code must retain the above copyright
+ *     notice, this list of conditions and the following disclaimer.
+ *
+ *  * Redistributions in binary form must reproduce the above
+ *     copyright notice, this list of conditions and the following
+ *     disclaimer in the documentation and/or other materials provided
+ *     with the distribution.
+ *
+ *  * Neither the name of the NumPy Developers nor the names of any
+ *     contributors may be used to endorse or promote products derived
+ *     from this software without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+ * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+ * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+ * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+ * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ */
+
+/*!
+ * \file np_einsum_op-inl.h
+ * \brief Function definition of numpy-compatible einsum operator
+ */
+
+#ifndef MXNET_OPERATOR_NUMPY_NP_EINSUM_OP_INL_H_
+#define MXNET_OPERATOR_NUMPY_NP_EINSUM_OP_INL_H_
+
+#include <mxnet/operator_util.h>
+#include <string>
+#include <vector>
+#include <algorithm>
+#include "./np_tensordot_op-inl.h"
+#include "./np_einsum_path_op-inl.h"
+#include "../../common/static_array.h"
+#include "../mxnet_op.h"
+#include "../operator_common.h"
+#include "../mshadow_op.h"
+#include "../elemwise_op_common.h"
+
+namespace mxnet {
+namespace op {
+
+#define NPY_MAXDIMS 32
+#define NPY_MAXARGS 32
+
+inline TShape get_stride(const TShape& shape) {
+  int ndim = shape.ndim(), prod = 1;
+  TShape stride = TShape(ndim, -1);
+  for (int i = ndim - 1; i >= 0; i--) {
+    stride[i] = shape[i] > 1 ? prod : 0;
+    prod = prod * shape[i];
+  }
+  return stride;
+}
+
+inline TShape pad(const TShape& shape, int odim) {
+  int ndim = shape.ndim();
+  CHECK_GE(odim, ndim);
+  TShape ret(odim, 1);
+  for (int idim = 0; idim < ndim; ++idim) {
+    ret[idim] = shape[idim];
+  }
+  return ret;
+}
+
+/*
+ * Parses the subscripts for one operand into an output of 'ndim'
+ * labels. The resulting 'op_labels' array will have:
+ *  - the ASCII code of the label for the first occurrence of a label;
+ *  - the (negative) offset to the first occurrence of the label for
+ *    repeated labels;
+ *  - zero for broadcast dimensions, if subscripts has an ellipsis.
+ * For example:
+ *  - subscripts="abbcbc",  ndim=6 -> op_labels=[97, 98, -1, 99, -3, -2]
+ *  - subscripts="ab...bc", ndim=6 -> op_labels=[97, 98, 0, 0, -3, 99]
+ */
+inline int parse_operand_subscripts(const char *subscripts, int length,
+                                    int ndim, int iop, char *op_labels,
+                                    char *label_counts, int *min_label, int *max_label) {
+  using namespace mxnet_op;
+  int i;
+  int idim = 0;
+  int ellipsis = -1;
+
+  /* Process all labels for this operand */
+  for (i = 0; i < length; ++i) {
+    int label = subscripts[i];
+
+    /* A proper label for an axis. */
+    if (label > 0 && isalpha(label)) {
+      /* Check we don't exceed the operator dimensions. */
+      CHECK(idim < ndim)
+        << "einstein sum subscripts string contains "
+        << "too many subscripts for operand "
+        << iop;
+
+      op_labels[idim++] = label;
+      if (label < *min_label) {
+        *min_label = label;
+      }
+      if (label > *max_label) {
+        *max_label = label;
+      }
+      label_counts[label]++;
+    } else if (label == '.') {
+      /* The beginning of the ellipsis. */
+      /* Check it's a proper ellipsis. */
+      CHECK(!(ellipsis != -1 || i + 2 >= length
+              || subscripts[++i] != '.' || subscripts[++i] != '.'))
+        << "einstein sum subscripts string contains a "
+        << "'.' that is not part of an ellipsis ('...') "
+        << "in operand "
+        << iop;
+
+      ellipsis = idim;
+    } else {
+        CHECK(label == ' ')
+          << "invalid subscript '" << static_cast<char>(label)
+          << "' in einstein sum "
+          << "subscripts string, subscripts must "
+          << "be letters";
+    }
+  }
+
+  /* No ellipsis found, labels must match dimensions exactly. */
+  if (ellipsis == -1) {
+    CHECK(idim == ndim)
+      << "operand has more dimensions than subscripts "
+      << "given in einstein sum, but no '...' ellipsis "
+      << "provided to broadcast the extra dimensions.";
+  } else if (idim < ndim) {
+    /* Ellipsis found, may have to add broadcast dimensions. */
+    /* Move labels after ellipsis to the end. */
+    for (i = 0; i < idim - ellipsis; ++i) {
+      op_labels[ndim - i - 1] = op_labels[idim - i - 1];
+    }
+    /* Set all broadcast dimensions to zero. */
+    for (i = 0; i < ndim - idim; ++i) {
+      op_labels[ellipsis + i] = 0;
+    }
+  }
+
+  /*
+   * Find any labels duplicated for this operand, and turn them
+   * into negative offsets to the axis to merge with.
+   *
+   * In C, the char type may be signed or unsigned, but with
+   * twos complement arithmetic the char is ok either way here, and
+   * later where it matters the char is cast to a signed char.
+   */
+  for (idim = 0; idim < ndim - 1; ++idim) {
+    int label = op_labels[idim];
+    /* If it is a proper label, find any duplicates of it. */
+    if (label > 0) {
+      /* Search for the next matching label. */
+      char *next = reinterpret_cast<char*>(memchr(op_labels + idim + 1, label, ndim - idim - 1));
+
+      while (next != NULL) {
+        /* The offset from next to op_labels[idim] (negative). */
+        *next = static_cast<char>((op_labels + idim) - next);
+        /* Search for the next matching label. */
+        next = reinterpret_cast<char*>(memchr(next + 1, label, op_labels + ndim - 1 - next));
+      }
+    }
+  }
+  return 0;
+}
+
+/*
+ * Parses the subscripts for the output operand into an output that
+ * includes 'ndim_broadcast' unlabeled dimensions, and returns the total
+ * number of output dimensions, or -1 if there is an error. Similarly
+ * to parse_operand_subscripts, the 'out_labels' array will have, for
+ * each dimension:
+ *  - the ASCII code of the corresponding label;
+ *  - zero for broadcast dimensions, if subscripts has an ellipsis.
+ */
+inline int parse_output_subscripts(const char *subscripts, int length,
+                                   int ndim_broadcast,
+                                   const char *label_counts, char *out_labels) {
+  using namespace mxnet_op;
+  int i, bdim;
+  int ndim = 0;
+  int ellipsis = 0;
+
+  /* Process all the output labels. */
+  for (i = 0; i < length; ++i) {
+    int label = subscripts[i];
+
+    /* A proper label for an axis. */
+    if (label > 0 && isalpha(label)) {
+      /* Check that it doesn't occur again. */
+      CHECK(memchr(subscripts + i + 1, label, length - i - 1) == NULL)
+        << "einstein sum subscripts string includes "
+        << "output subscript '" << static_cast<char>(label)
+        << "' multiple times";
+
+      /* Check that it was used in the inputs. */
+      CHECK(label_counts[label] != 0)
+        << "einstein sum subscripts string included "
+        << "output subscript '" << static_cast<char>(label)
+        << "' which never appeared "
+        << "in an input";
+
+      /* Check that there is room in out_labels for this label. */
+      CHECK(ndim < NPY_MAXDIMS)
+        << "einstein sum subscripts string contains "
+        << "too many subscripts in the output";
+
+      out_labels[ndim++] = label;
+    } else if (label == '.') {
+      /* The beginning of the ellipsis. */
+      /* Check it is a proper ellipsis. */
+      CHECK(!(ellipsis || i + 2 >= length
+              || subscripts[++i] != '.' || subscripts[++i] != '.'))
+        << "einstein sum subscripts string "
+        << "contains a '.' that is not part of "
+        << "an ellipsis ('...') in the output";
+
+      /* Check there is room in out_labels for broadcast dims. */
+      CHECK(ndim + ndim_broadcast <= NPY_MAXDIMS)
+        << "einstein sum subscripts string contains "
+        << "too many subscripts in the output";
+
+      ellipsis = 1;
+      for (bdim = 0; bdim < ndim_broadcast; ++bdim) {
+        out_labels[ndim++] = 0;
+      }
+    } else {
+      CHECK(label == ' ')
+        << "invalid subscript '" << static_cast<char>(label)
+        << "' in einstein sum "
+        << "subscripts string, subscripts must "
+        << "be letters";
+    }
+  }
+
+  /* If no ellipsis was found there should be no broadcast dimensions. */
+  CHECK(!(!ellipsis && ndim_broadcast > 0))
+    << "output has more dimensions than subscripts "
+    << "given in einstein sum, but no '...' ellipsis "
+    << "provided to broadcast the extra dimensions.";
+
+  return ndim;
+}
+
+inline void get_combined_dims_view(const TBlob& op, int iop,
+                                   char *labels,
+                                   TShape* newshape,
+                                   TShape* newstride) {
+  using namespace mxnet_op;
+  int idim, ndim, icombine, combineoffset;
+  int icombinemap[NPY_MAXDIMS];
+  int newdim;
+
+  const TShape& shape = op.shape_;
+  TShape stride = get_stride(shape);
+  ndim = op.shape_.ndim();
+  newdim = newshape->ndim();
+
+  /* Initialize the dimensions and strides to zero */
+  for (idim = 0; idim < newdim; ++idim) {
+    (*newshape)[idim] = 0;
+    (*newstride)[idim] = 0;
+  }
+
+  /* Copy the dimensions and strides, except when collapsing */
+  icombine = 0;
+  for (idim = 0; idim < ndim; ++idim) {
+    /*
+     * The char type may be either signed or unsigned, we
+     * need it to be signed here.
+     */
+    int label = (signed char)labels[idim];
+    /* If this label says to merge axes, get the actual label */
+    if (label < 0) {
+      combineoffset = label;
+      label = labels[idim+label];
+    } else {
+      combineoffset = 0;
+      if (icombine != idim) {
+        labels[icombine] = labels[idim];
+      }
+      icombinemap[idim] = icombine;
+    }
+    /* If the label is 0, it's an unlabeled broadcast dimension */
+    if (label == 0) {
+      (*newshape)[icombine] = shape[idim];
+      (*newstride)[icombine] = stride[idim];
+    } else {
+      /* Update the combined axis dimensions and strides */
+      int i = icombinemap[idim + combineoffset];
+      CHECK(!(combineoffset < 0 && (*newshape)[i] != 0 &&
+              (*newshape)[i] != shape[idim]))
+        << "dimensions in operand " << iop
+        << " for collapsing index '" << label
+        << "' don't match (" << static_cast<int>((*newshape)[i])
+        << " != " << shape[idim] << ")";
+      (*newshape)[i] = shape[idim];
+      (*newstride)[i] += stride[idim];
+    }
+
+    /* If the label didn't say to combine axes, increment dest i */
+    if (combineoffset == 0) {
+      icombine++;
+    }
+  }
+}
+
+inline static int prepare_op_axes(int ndim, int iop, char *labels,
+                                  int *axes, int ndim_iter, char *iter_labels) {
+  using namespace mxnet_op;
+  int i, label, ibroadcast;
+
+  ibroadcast = ndim-1;
+  for (i = ndim_iter-1; i >= 0; --i) {
+    label = iter_labels[i];
+    /*
+     * If it's an unlabeled broadcast dimension, choose
+     * the next broadcast dimension from the operand.
+     */
+    if (label == 0) {
+      while (ibroadcast >= 0 && labels[ibroadcast] != 0) {
+        --ibroadcast;
+      }
+      /*
+       * If we used up all the operand broadcast dimensions,
+       * extend it with a "newaxis"
+       */
+      if (ibroadcast < 0) {
+        axes[i] = -1;
+      } else {
+        /* Otherwise map to the broadcast axis */
+        axes[i] = ibroadcast;
+        --ibroadcast;
+      }
+    } else {
+      /* It's a labeled dimension, find the matching one */
+      char *match = reinterpret_cast<char*>(memchr(labels, label, ndim));
+      /* If the op doesn't have the label, broadcast it */
+      if (match == NULL) {
+        axes[i] = -1;
+      } else {
+        /* Otherwise use it */
+        axes[i] = match - labels;
+      }
+    }
+  }
+  return 0;
+}
+
+struct NumpyEinsumParam: public dmlc::Parameter<NumpyEinsumParam> {
+  int num_args;
+  int optimize;
+  std::string  subscripts;
+  DMLC_DECLARE_PARAMETER(NumpyEinsumParam) {
+    DMLC_DECLARE_FIELD(num_args)
+      .set_lower_bound(1)
+      .describe("Number of input arrays.");
+    DMLC_DECLARE_FIELD(subscripts)
+      .set_default("")
+      .describe("Specifies the subscripts for summation as comma separated list"
+      " of subscript labels. An implicit (classical Einstein summation) calculation"
+      " is performed unless the explicit indicator ‘->’ is included as well as"
+      " subscript labels of the precise output form.");
+    DMLC_DECLARE_FIELD(optimize)
+      .set_default(0);
+  }
+};
+
+class EinsumOp {
+ public:
+  int num_args;
+  int optimize;
+  std::string subscripts;
+  std::shared_ptr<NDArray> tempspace;
+  std::vector<Step> paths;
+  explicit EinsumOp(int num_args, int optimize, std::string subscripts) {
+    this->num_args = num_args;
+    this->optimize = optimize;
+    this->subscripts = subscripts;
+  }
+};  // class EinsumOp
+
+template<int dimension, int req, bool back>
+struct numpy_einsum {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(index_t i, DType* out,
+                                  common::StaticArray<DType*, NPY_MAXARGS> op,
+                                  mshadow::Shape<dimension> oshape,
+                                  mshadow::Shape<dimension> ostride,
+                                  mshadow::Shape<dimension> reduceshape,
+                                  mshadow::Shape<dimension> reducestride,
+                                  mshadow::Shape<dimension> itershape,
+                                  common::StaticArray<mshadow::Shape<dimension>,
+                                                      NPY_MAXARGS> iterstride,
+                                  int nop,
+                                  int iop0,
+                                  const DType* out_grad) {
+    using namespace mxnet_op;
+    index_t oidx = back ? dot(unravel(dot(unravel(i, oshape), ostride), itershape),
+                              iterstride[iop0]) : i;
+    if (req == kWriteTo) {
+      out[oidx] = (DType)0;
+    }
+    for (int j = 0; j < reduceshape.Size(); j++) {
+      mshadow::Shape<dimension> idx = unravel(dot(unravel(j, reduceshape), reducestride) +
+                                              dot(unravel(i, oshape), ostride),
+                                              itershape);
+      DType tmp = back ? out_grad[dot(idx, iterstride[nop])] :  (DType)1;
+      for (int iop = 0; iop < nop; ++iop) {
+        if (iop != iop0) {
+          index_t k = dot(idx, iterstride[iop]);
+          tmp = tmp * op[iop][k];
+        }
+      }
+      out[oidx] = out[oidx] + tmp;
+    }
+  }
+};
+
+template<typename xpu, bool back>
+inline void NumpyEinsumProcess(const std::vector<TBlob>& inputs,
+                               const std::vector<OpReqType>& req,
+                               const std::vector<TBlob>& outputs,
+                               const char *subscripts, int nop,
+                               const OpContext& ctx) {
+  using namespace mxnet_op;
+
+  /* nop+1 (+1 is for the output) must fit in NPY_MAXARGS */
+  CHECK(nop < NPY_MAXARGS)
+    << "too many operands provided to einstein sum function";
+  CHECK(nop >= 1)
+    << "not enough operands provided to einstein sum function";
+
+  /* Step 1: Parse the subscripts string into label_counts and op_labels */
+  int iop, idim, min_label = 127, max_label = 0;
+  char label_counts[128], op_labels[NPY_MAXARGS][NPY_MAXDIMS];
+  memset(label_counts, 0, sizeof(label_counts));
+  for (iop = 0; iop < nop; ++iop) {
+    int length = static_cast<int>(strcspn(subscripts, ",-"));
+
+    CHECK(!(iop == nop - 1 && subscripts[length] == ','))
+      << "more operands provided to einstein sum function "
+      << "than specified in the subscripts string";
+    CHECK(!(iop < nop-1 && subscripts[length] != ','))
+      << "fewer operands provided to einstein sum function "
+      << "than specified in the subscripts string";
+    CHECK_GE(parse_operand_subscripts(subscripts, length,
+                                      inputs[iop + back].shape_.ndim(),
+                                      iop, op_labels[iop], label_counts,
+                                      &min_label, &max_label), 0);
+
+    /* Move subscripts to the start of the labels for the next op */
+    subscripts += length;
+    if (iop < nop - 1) {
+      subscripts++;
+    }
+  }
+
+  /*
+   * Find the number of broadcast dimensions, which is the maximum
+   * number of labels == 0 in an op_labels array.
+   */
+  int ndim_broadcast = 0;
+  for (iop = 0; iop < nop; ++iop) {
+    int count_zeros = 0;
+    int ndim;
+    char *labels = op_labels[iop];
+
+    ndim = inputs[iop + back].shape_.ndim();
+    for (idim = 0; idim < ndim; ++idim) {
+      if (labels[idim] == 0) {
+        ++count_zeros;
+      }
+    }
+
+    if (count_zeros > ndim_broadcast) {
+      ndim_broadcast = count_zeros;
+    }
+  }
+
+  /*
+   * If there is no output signature, fill output_labels and ndim_output
+   * using each label that appeared once, in alphabetical order.
+   */
+  int label, ndim_output;
+  char output_labels[NPY_MAXDIMS];
+  if (subscripts[0] == '\0') {
+    /* If no output was specified, always broadcast left, as usual. */
+    for (ndim_output = 0; ndim_output < ndim_broadcast; ++ndim_output) {
+      output_labels[ndim_output] = 0;
+    }
+    for (label = min_label; label <= max_label; ++label) {
+      if (label_counts[label] == 1) {
+        CHECK(ndim_output < NPY_MAXDIMS)
+          << "einstein sum subscript string has too many "
+          << "distinct labels";
+        output_labels[ndim_output++] = label;
+      }
+    }
+  } else {
+    CHECK(subscripts[0] == '-' && subscripts[1] == '>')
+      << "einstein sum subscript string does not "
+      << "contain proper '->' output specified";
+    subscripts += 2;
+
+    /* Parse the output subscript string. */
+    ndim_output = parse_output_subscripts(subscripts, strlen(subscripts),
+                                          ndim_broadcast, label_counts,
+                                          output_labels);
+    CHECK_GE(ndim_output, 0);
+  }
+
+  /*
+   * Step 2:
+   * Process all the input ops, combining dimensions into their
+   * diagonal where specified.
+   */
+  std::vector<TShape> opshape(nop), opstride_true(nop);
+  for (iop = 0; iop < nop; ++iop) {
+    char *labels = op_labels[iop];
+    int combine, ndim;
+
+    ndim = inputs[iop + back].shape_.ndim();
+
+    /*
+     * Check whether any dimensions need to be combined
+     *
+     * The char type may be either signed or unsigned, we
+     * need it to be signed here.
+     */
+    combine = 0;
+    for (idim = 0; idim < ndim; ++idim) {
+      if ((signed char)labels[idim] < 0) {
+        combine++;
+      }
+    }
+
+    /* If any dimensions are combined, create a view which combines them */
+    if (combine) {
+      TShape tshape(ndim - combine, -1);
+      TShape tstride(ndim - combine, -1);
+      get_combined_dims_view(inputs[iop + back], iop, labels,
+                             &tshape, &tstride);
+      opshape[iop] = tshape;
+      opstride_true[iop] = tstride;
+    } else {
+      /* No combining needed */
+      opshape[iop] = inputs[iop + back].shape_;
+      opstride_true[iop] = get_stride(opshape[iop]);
+    }
+  }
+
+  /*
+   * Step 3:
+   * Set up the labels for the iterator (output + combined labels).
+   * Can just share the output_labels memory, because iter_labels
+   * is output_labels with some more labels appended.
+   */
+  char *iter_labels = output_labels;
+  int ndim_iter = ndim_output;
+  for (label = min_label; label <= max_label; ++label) {
+    if (label_counts[label] > 0 &&
+        memchr(output_labels, label, ndim_output) == NULL) {
+      CHECK(ndim_iter < NPY_MAXDIMS)
+        << "too many subscripts in einsum";
+      iter_labels[ndim_iter++] = label;
+    }
+  }
+
+  /* Step 4: Set up the op_axes for the iterator */
+  TShape itershape(ndim_iter, -1), iterstride_true(ndim_iter, -1);
+  TShape oshape = back ? inputs[0].shape_ : outputs[0].shape_;
+  TShape ostride_true = get_stride(oshape);
+  TShape reduceshape, ostride, reducestride;
+  std::vector<TShape> iterstride(nop + 1, TShape(ndim_iter, 0));
+  std::vector<TShape> remainshape(nop), opstride(nop), remainstride(nop);
+  int op_axes_arrays[NPY_MAXARGS][NPY_MAXDIMS];
+  int *op_axes[NPY_MAXARGS];
+
+  for (iop = 0; iop < nop; ++iop) {
+    op_axes[iop] = op_axes_arrays[iop];
+    CHECK_GE(prepare_op_axes(opshape[iop].ndim(), iop, op_labels[iop],
+             op_axes[iop], ndim_iter, iter_labels), 0);
+    for (idim = 0; idim < ndim_iter; idim++) {
+      if (op_axes[iop][idim] != -1) {
+        iterstride[iop][idim] = opstride_true[iop][op_axes[iop][idim]];
+        if (itershape[idim] != -1) {
+          if (itershape[idim] == 1) {
+            itershape[idim] = opshape[iop][op_axes[iop][idim]];
+          }
+        } else {
+          itershape[idim] = opshape[iop][op_axes[iop][idim]];
+        }
+      }
+    }
+  }
+  for (idim = 0; idim < ndim_output; ++idim) {
+    iterstride[nop][idim] = ostride_true[idim];
+  }
+  iterstride_true = get_stride(itershape);
+  reduceshape = TShape(ndim_iter - ndim_output, 0);
+  for (idim = ndim_output; idim < ndim_iter; ++idim) {
+    reduceshape[idim - ndim_output] = itershape[idim];
+  }
+  for (iop = 0; iop < nop; iop++) {
+    std::vector<size_t> rsh;
+    for (idim = 0; idim < ndim_iter; idim++) {
+      if (op_axes_arrays[iop][idim] == -1 ||
+          itershape[idim] != opshape[iop][op_axes_arrays[iop][idim]]) {
+        rsh.push_back(itershape[idim]);
+      }
+    }
+    remainshape[iop] = TShape(rsh.begin(), rsh.end());
+  }
+
+  // calculate stride
+  ostride = TShape(ndim_output, 0);
+  for (idim = 0; idim < ndim_output; ++idim) {
+    ostride[idim] = iterstride_true[idim];
+  }
+  reducestride = TShape(ndim_iter - ndim_output, 0);
+  for (idim = ndim_output; idim < ndim_iter; ++idim) {
+    reducestride[idim - ndim_output] = iterstride_true[idim];
+  }
+  for (iop = 0; iop < nop; ++iop) {
+    opstride[iop] = TShape(opshape[iop].ndim(), 0);
+    remainstride[iop] = TShape(remainshape[iop].ndim(), 0);
+    int j = 0;
+    for (idim = 0; idim < ndim_iter; ++idim) {
+      if (op_axes_arrays[iop][idim] != -1 &&
+          itershape[idim] == opshape[iop][op_axes_arrays[iop][idim]]) {
+        opstride[iop][op_axes_arrays[iop][idim]] = iterstride_true[idim];
+      } else {
+        remainstride[iop][j++] = iterstride_true[idim];
+      }
+    }
+    CHECK_EQ(j, remainstride[iop].ndim());
+  }
+
+  // exclude the 0-dim case
+  if (ndim_iter == 0) {
+    ndim_iter = 1;
+  }
+  itershape = pad(itershape, ndim_iter);
+  for (iop = 0; iop <= nop; ++iop) {
+    iterstride[iop] = pad(iterstride[iop], ndim_iter);
+  }
+  oshape = pad(oshape, ndim_iter);
+  ostride = pad(ostride, ndim_iter);
+  reduceshape = pad(reduceshape, ndim_iter);
+  reducestride = pad(reducestride, ndim_iter);
+  for (iop = 0; iop < nop; ++iop) {
+    opshape[iop] = pad(opshape[iop], ndim_iter);
+    opstride[iop] = pad(opstride[iop], ndim_iter);
+    remainshape[iop] = pad(remainshape[iop], ndim_iter);
+    remainstride[iop] = pad(remainstride[iop], ndim_iter);
+  }
+
+  if (!back) {
+    if (oshape.Size() == 0) {
+      return;
+    }
+    const TBlob &out_data = outputs[0];
+    MSHADOW_TYPE_SWITCH(out_data.type_flag_, DType, {
+      mxnet::common::StaticArray<DType*, NPY_MAXARGS> op;
+      for (iop = 0; iop < nop; ++iop) {
+        op[iop] = inputs[iop].dptr<DType>();
+      }
+      MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
+        MXNET_NDIM_SWITCH_EX(ndim_iter, dimension, {
+          mxnet::common::StaticArray<mshadow::Shape<dimension>, NPY_MAXARGS> iterstride_arr;
+          for (iop = 0; iop <= nop; ++iop) {
+            iterstride_arr[iop] = iterstride[iop].get<dimension>();
+          }
+          Kernel<numpy_einsum<dimension, req_type, 0>,
+                 xpu>::Launch(ctx.get_stream<xpu>(),
+                              oshape.Size(),
+                              out_data.dptr<DType>(),
+                              op,
+                              oshape.get<dimension>(),
+                              ostride.get<dimension>(),
+                              reduceshape.get<dimension>(),
+                              reducestride.get<dimension>(),
+                              itershape.get<dimension>(),
+                              iterstride_arr,
+                              nop,
+                              -1,
+                              reinterpret_cast<DType*>(NULL));
+        })
+      })
+    })
+  } else {
+    if (oshape.Size() == 0) {
+      for (iop = 0; iop < nop; ++iop) {
+        const TBlob& out_data = outputs[iop];
+        if (opshape[iop].Size() > 0) {
+          MSHADOW_TYPE_SWITCH(out_data.type_flag_, DType, {
+            MXNET_ASSIGN_REQ_SWITCH(req[iop], req_type, {
+              if (req_type == kWriteTo) {
+                out_data.FlatTo1D<xpu, DType>(ctx.get_stream<xpu>()) = 0;
+              }
+            })
+          })
+        }
+      }
+      return;
+    }
+    for (int i = 0; i < nop; ++i) {
+      const TBlob &out_data = outputs[i];
+      const TBlob &out_grad = inputs[0];
+      MSHADOW_TYPE_SWITCH(out_data.type_flag_, DType, {
+        mxnet::common::StaticArray<DType*, NPY_MAXARGS> op;
+        for (iop = 0; iop < nop; ++iop) {
+          op[iop] = inputs[iop + back].dptr<DType>();
+        }
+        MXNET_ASSIGN_REQ_SWITCH(req[i], req_type, {
+          MXNET_NDIM_SWITCH_EX(ndim_iter, dimension, {
+            mxnet::common::StaticArray<mshadow::Shape<dimension>, NPY_MAXARGS> iterstride_arr;
+            for (iop = 0; iop <= nop; ++iop) {
+              iterstride_arr[iop] = iterstride[iop].get<dimension>();
+            }
+            Kernel<numpy_einsum<dimension, req_type, 1>,
+                  xpu>::Launch(ctx.get_stream<xpu>(),
+                              opshape[i].Size(),
+                              out_data.dptr<DType>(),
+                              op,
+                              opshape[i].get<dimension>(),
+                              opstride[i].get<dimension>(),
+                              remainshape[i].get<dimension>(),
+                              remainstride[i].get<dimension>(),
+                              itershape.get<dimension>(),
+                              iterstride_arr,
+                              nop,
+                              i,
+                              out_grad.dptr<DType>());
+          })
+        })
+      })
+    }
+  }
+}
+
+template<typename xpu>
+inline void NumpyEinsumForward(const OpStatePtr& state_ptr,
+                               const OpContext& ctx,
+                               const std::vector<TBlob>& inputs,
+                               const std::vector<OpReqType>& req,
+                               const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mxnet_op;
+  EinsumOp& state = state_ptr.get_state<EinsumOp>();
+  int num_args = state.num_args;
+  int optimize = state.optimize;
+  const char* subscripts = state.subscripts.c_str();
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  CHECK_EQ(inputs.size(), num_args);
+  CHECK_EQ(outputs.size(), 1U);
+  if (optimize == 0) {
+    NumpyEinsumProcess<xpu, 0>(inputs, req, outputs, subscripts, num_args, ctx);
+    return;
+  }
+  std::vector<Step>& paths = state.paths;
+  std::vector<std::vector<int> > pos;
+  std::string string_repr;
+  paths = einsum_path(state.subscripts, inputs, true, ctx.run_ctx, &pos, &string_repr);
+  int paths_len = paths.size(), temp_space_size = 0, max_temp_space_size = 0;
+  std::vector<TBlob> operands(inputs), tmp_operands, temp_space_vec(paths_len - 1);
+  for (int i = 0; i + 1 < paths_len; ++i) {
+    temp_space_size += paths[i].oshape.Size();
+  }
+  for (int i = 0; i < paths_len; ++i) {
+    max_temp_space_size = std::max(max_temp_space_size, static_cast<int>(paths[i].oshape.Size()));
+  }
+  temp_space_size += max_temp_space_size;
+  MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+    state.tempspace.reset<NDArray>(new NDArray(TShape(Shape1(temp_space_size)),
+                                               ctx.run_ctx.ctx,
+                                               false,
+                                               outputs[0].type_flag_));
+    Tensor<xpu, 1, DType> temp_space = state.tempspace->data().FlatTo1D<xpu, DType>();
+    int begin = max_temp_space_size;
+    for (int i = 0; i < paths_len - 1; ++i) {
+      TBlob tblob = TBlob(temp_space.Slice(begin, begin + paths[i].oshape.Size()));
+      temp_space_vec[i] = tblob.reshape(paths[i].oshape);
+      begin = begin + paths[i].oshape.Size();
+    }
+    for (int i = 0; i < paths_len; ++i) {
+      tmp_operands.clear();
+
+      // We remove inds from right to left
+      for (const int& p : paths[i].contract_inds) {
+        tmp_operands.push_back(operands[p]);
+        operands.erase(operands.begin() + p);
+      }
+      bool handle_out = (i == paths_len - 1);
+      // Call tensordot if still possible
+      if (paths[i].do_blas) {
+        // Contract!
+        if (paths[i].do_einsum || handle_out) {
+          TBlob max_temp_space = TBlob(temp_space.Slice(0, paths[i].tshape.Size()));
+          max_temp_space.FlatTo1D<xpu, DType>(s) = 0;
+          max_temp_space = max_temp_space.reshape(paths[i].tshape);
+          size_t tensordot_tempspace_size =
+            TensordotWorkspaceSize<xpu>(paths[i].left_pos,
+                                        paths[i].right_pos,
+                                        tmp_operands[0],
+                                        tmp_operands[1],
+                                        max_temp_space,
+                                        std::vector<OpReqType>{OpReqType::kWriteTo});
+          Tensor<xpu, 1, char> tensordot_tempspace =
+            ctx.requested[0].get_space_typed<xpu, 1, char>(Shape1(tensordot_tempspace_size), s);
+          TensordotImpl<xpu>(paths[i].left_pos,
+                             paths[i].right_pos,
+                             ctx,
+                             tmp_operands[0],
+                             tmp_operands[1],
+                             max_temp_space,
+                             std::vector<OpReqType>{OpReqType::kWriteTo},
+                             tensordot_tempspace);
+          NumpyEinsumProcess<xpu, 0>(std::vector<TBlob>{max_temp_space},
+            handle_out ? req : std::vector<OpReqType>{OpReqType::kWriteTo},
+            handle_out ? outputs : std::vector<TBlob>{temp_space_vec[i]},
+            paths[i].blas2einsum_str.c_str(),
+            1, ctx);
+        } else {
+          size_t tensordot_tempspace_size =
+            TensordotWorkspaceSize<xpu>(paths[i].left_pos,
+                                        paths[i].right_pos,
+                                        tmp_operands[0],
+                                        tmp_operands[1],
+                                        temp_space_vec[i],
+                                        std::vector<OpReqType>{OpReqType::kWriteTo});
+          Tensor<xpu, 1, char> tensordot_tempspace = ctx.requested[0].get_space_typed<xpu, 1, char>(
+            Shape1(tensordot_tempspace_size), s);
+          TensordotImpl<xpu>(paths[i].left_pos,
+                             paths[i].right_pos,
+                             ctx,
+                             tmp_operands[0],
+                             tmp_operands[1],
+                             temp_space_vec[i],
+                             std::vector<OpReqType>{OpReqType::kWriteTo},
+                             tensordot_tempspace);
+        }
+      } else {
+        NumpyEinsumProcess<xpu, 0>(tmp_operands,
+        handle_out ? req : std::vector<OpReqType>{OpReqType::kWriteTo},
+        handle_out ? outputs : std::vector<TBlob>{temp_space_vec[i]},
+        paths[i].einsum_str.c_str(), tmp_operands.size(), ctx);
+      }
+      if (!handle_out) {
+        operands.push_back(temp_space_vec[i]);
+      }
+    }
+  });
+}
+
+template<typename xpu>
+inline void NumpyEinsumBackward(const OpStatePtr& state_ptr,
+                                const OpContext& ctx,
+                                const std::vector<TBlob>& inputs,
+                                const std::vector<OpReqType>& req,
+                                const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mshadow_op;
+  const EinsumOp& state = state_ptr.get_state<EinsumOp>();
+  int num_args = state.num_args;
+  int optimize = state.optimize;
+  const char* subscripts = state.subscripts.c_str();
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  CHECK_EQ(inputs.size(), 1 + num_args);
+  CHECK_EQ(outputs.size(), num_args);
+  if (optimize == 0) {
+    NumpyEinsumProcess<xpu, 1>(inputs, req, outputs, subscripts, num_args, ctx);
+    return;
+  }
+  // calculate temporary space size for temp_grad
+  const std::vector<Step>& paths = state.paths;
+  int paths_len = paths.size(), temp_space_size = 0, max_temp_space_size = 0;
+  for (int i = 0; i < paths_len - 1; ++i) {
+    temp_space_size += paths[i].oshape.Size();
+  }
+  for (int i = 0; i < paths_len; ++i) {
+    max_temp_space_size = std::max(max_temp_space_size, static_cast<int>(paths[i].oshape.Size()));
+  }
+  temp_space_size += max_temp_space_size;
+  // replay the forward process
+  std::vector<std::vector<int> > op_idx(paths_len + 1);
+  for (int i = 0; i <= paths_len; ++i) {
+    if (i == 0) {
+      op_idx[i].reserve(num_args);
+      for (int j = 0; j < num_args; ++j) {
+        op_idx[i].push_back(j + 1);
+      }
+    } else {
+      op_idx[i] = op_idx[i - 1];
+      // We remove inds from right to left
+      for (const int& p : paths[i - 1].contract_inds) {
+        op_idx[i].erase(op_idx[i].begin() + p);
+      }
+      op_idx[i].push_back(-static_cast<int>(i - 1));
+    }
+  }
+  // calculate temporary space size for tensordot
+  int tensordot_max_tempspace_size = 0;
+  int begin_tensordot_tempspace = 0;
+  std::vector<TBlob> temp_inputs, temp_outputs;
+  std::vector<OpReqType> temp_req;
+  std::vector<size_t> tensordot_tempspace_size;
+  MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+    for (int i = 0; i < paths_len; ++i) {
+      temp_inputs.clear();
+      temp_outputs.clear();
+      temp_req.clear();
+      bool handle_out = (i == paths_len - 1);
+
+      if (handle_out) {
+        temp_inputs.push_back(inputs[0]);
+      } else {
+        temp_inputs.push_back(TBlob(reinterpret_cast<DType*>(NULL),
+                                    paths[i].oshape,
+                                    xpu::kDevMask));
+      }
+      for (auto p : paths[i].contract_inds) {
+        int idx = op_idx[i][p];
+        if (idx >= 1) {
+          temp_inputs.push_back(inputs[idx]);
+          temp_outputs.push_back(outputs[idx - 1]);
+          temp_req.push_back(req[idx - 1]);
+        } else {
+          temp_inputs.push_back(TBlob(reinterpret_cast<DType*>(NULL),
+                                      paths[-idx].oshape,
+                                      xpu::kDevMask));
+          temp_outputs.push_back(TBlob(reinterpret_cast<DType*>(NULL),
+                                      paths[-idx].oshape,
+                                      xpu::kDevMask));
+          temp_req.push_back(OpReqType::kWriteTo);
+        }
+      }
+      size_t cur_tensordot_tempspace_size = 0;
+      if (paths[i].do_blas) {
+        if (paths[i].do_einsum) {
+          cur_tensordot_tempspace_size =
+            TensordotBackwardWorkspaceSize<xpu>(paths[i].left_pos,
+                                                paths[i].right_pos,
+                                                TBlob(reinterpret_cast<DType*>(NULL),
+                                                      paths[i].tshape,
+                                                      xpu::kDevMask),
+                                                temp_inputs[1],
+                                                temp_inputs[2],
+                                                temp_outputs[0],
+                                                temp_outputs[1],
+                                                temp_req);
+        } else {
+          cur_tensordot_tempspace_size =
+            TensordotBackwardWorkspaceSize<xpu>(paths[i].left_pos,
+                                                paths[i].right_pos,
+                                                temp_inputs[0],
+                                                temp_inputs[1],
+                                                temp_inputs[2],
+                                                temp_outputs[0],
+                                                temp_outputs[1],
+                                                temp_req);
+        }
+      }
+      tensordot_tempspace_size.push_back(cur_tensordot_tempspace_size);
+      tensordot_max_tempspace_size = std::max(tensordot_max_tempspace_size,
+                                              static_cast<int>(cur_tensordot_tempspace_size));
+    }
+    begin_tensordot_tempspace = temp_space_size;
+    temp_space_size += (tensordot_max_tempspace_size + sizeof(DType) - 1) / sizeof(DType);
+  });
+  // allocate temporary space and propagate
+  std::vector<TBlob> temp_grad(paths_len - 1), temp_data(paths_len - 1);
+  MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+    // allocate temporary space for gradients of intermediate results
+    Tensor<xpu, 1, DType> temp_space = ctx.requested[0].get_space_typed<xpu, 1, DType>
+      (Shape1(temp_space_size), s);
+    int begin = max_temp_space_size;
+    for (int i = 0; i + 1 < paths_len; ++i) {
+      TBlob tblob = TBlob(temp_space.Slice(begin, begin + paths[i].oshape.Size()));
+      temp_grad[i] = tblob.reshape(paths[i].oshape);
+      begin = begin + paths[i].oshape.Size();
+    }
+
+    // reinterprete ndarray for intermediate results
+    Tensor<xpu, 1, DType> ndarray_space = state.tempspace->data().FlatTo1D<xpu, DType>();
+    begin = max_temp_space_size;
+    for (int i = 0; i + 1 < paths_len; ++i) {
+      TBlob tblob = TBlob(ndarray_space.Slice(begin, begin + paths[i].oshape.Size()));
+      temp_data[i] = tblob.reshape(paths[i].oshape);
+      begin = begin + paths[i].oshape.Size();
+    }
+
+    // go through the paths in the reversed order
+    for (int i = paths_len - 1; i >= 0; i--) {
+      temp_inputs.clear();
+      temp_outputs.clear();
+      temp_req.clear();
+      bool handle_out = (i == paths_len - 1);
+
+      if (handle_out) {
+        temp_inputs.push_back(inputs[0]);
+      } else {
+        temp_inputs.push_back(temp_grad[i]);
+      }
+      for (auto p : paths[i].contract_inds) {
+        int idx = op_idx[i][p];
+        if (idx >= 1) {
+          temp_inputs.push_back(inputs[idx]);
+          temp_outputs.push_back(outputs[idx - 1]);
+          temp_req.push_back(req[idx - 1]);
+        } else {
+          temp_inputs.push_back(temp_data[-idx]);
+          temp_outputs.push_back(temp_grad[-idx]);
+          temp_req.push_back(OpReqType::kWriteTo);
+        }
+      }
+      if (paths[i].do_blas) {
+        CHECK_EQ(temp_inputs.size(), 3U);
+        CHECK_EQ(temp_outputs.size(), 2U);
+        CHECK_EQ(temp_req.size(), 2U);
+        Tensor<xpu, 1, DType> tensordot_tempspace = temp_space.Slice(begin_tensordot_tempspace,
+                                                                     temp_space_size);
+        Tensor<xpu, 1, char> char_tempspace =
+          Tensor<xpu, 1, char>(reinterpret_cast<char*>(tensordot_tempspace.dptr_),
+                                                       Shape1(tensordot_tempspace_size[i]),
+                                                       tensordot_tempspace.stream_);
+        if (paths[i].do_einsum) {
+          TBlob max_temp_space = TBlob(temp_space.Slice(0, paths[i].tshape.Size()));
+          max_temp_space = max_temp_space.reshape(paths[i].tshape);
+          NumpyEinsumProcess<xpu, 0>(std::vector<TBlob>{temp_inputs[0]},
+                                     std::vector<OpReqType>{kWriteTo},
+                                     std::vector<TBlob>{max_temp_space},
+                                     paths[i].einsum2blas_str.c_str(),
+                                     1, ctx);
+          TensordotBackwardImpl<xpu>(paths[i].left_pos, paths[i].right_pos, ctx,
+                                     max_temp_space, temp_inputs[1], temp_inputs[2],
+                                     temp_outputs[0], temp_outputs[1], temp_req, char_tempspace);
+        } else {
+          TensordotBackwardImpl<xpu>(paths[i].left_pos, paths[i].right_pos, ctx,
+                                     temp_inputs[0], temp_inputs[1], temp_inputs[2],
+                                     temp_outputs[0], temp_outputs[1], temp_req, char_tempspace);
+        }
+      } else {
+        NumpyEinsumProcess<xpu, 1>(temp_inputs, temp_req, temp_outputs,
+                                   paths[i].einsum_str.c_str(),
+                                   temp_outputs.size(),
+                                   ctx);
+      }
+    }
+  });
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_NUMPY_NP_EINSUM_OP_INL_H_
diff --git a/src/operator/numpy/np_einsum_op.cc b/src/operator/numpy/np_einsum_op.cc
new file mode 100644
index 000000000000..4d232b9b7c04
--- /dev/null
+++ b/src/operator/numpy/np_einsum_op.cc
@@ -0,0 +1,370 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*
+ * Copyright (c) 2005-2019, NumPy Developers.
+ * All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are
+ * met:
+ *
+ *  * Redistributions of source code must retain the above copyright
+ *     notice, this list of conditions and the following disclaimer.
+ *
+ *  * Redistributions in binary form must reproduce the above
+ *     copyright notice, this list of conditions and the following
+ *     disclaimer in the documentation and/or other materials provided
+ *     with the distribution.
+ *
+ *  * Neither the name of the NumPy Developers nor the names of any
+ *     contributors may be used to endorse or promote products derived
+ *     from this software without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+ * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+ * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+ * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+ * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ */
+
+/*!
+ * \file np_einsum_op.cc
+ * \brief CPU Implementation of numpy-compatible einsum
+ */
+
+#include "./np_einsum_op-inl.h"
+#include <stdlib.h>
+#include <string.h>
+
+namespace mxnet {
+namespace op {
+
+inline std::vector<std::string> _parse_einsum_input(std::string subscripts,
+                                                    const mxnet::ShapeVector& shapes) {
+  const std::string einsum_symbols =
+    "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ";
+  std::bitset<MAXAXIS> einsum_symbols_set;
+  for (const char& c : einsum_symbols) {
+    einsum_symbols_set.set(c);
+  }
+
+  CHECK_NE(shapes.size(), 0U)
+    << "No input operands";
+
+  auto end_pos = std::remove(subscripts.begin(), subscripts.end(), ' ');
+  subscripts.erase(end_pos, subscripts.end());
+
+  // Ensure all characters are valid
+  for (const char& c : subscripts) {
+    if (c == '.' || c == ',' || c == '-' || c == '>') {
+      continue;
+    }
+    CHECK(einsum_symbols_set.test(c))
+      << "Character " << c
+      << " is not a valid symbol.";
+  }
+
+  // Check for proper "->"
+  if (subscripts.find('-') != std::string::npos ||
+      subscripts.find('>') != std::string::npos) {
+    bool invalid = (std::count(subscripts.begin(), subscripts.end(), '-') > 1 ||
+                    std::count(subscripts.begin(), subscripts.end(), '>') > 1);
+    CHECK(!invalid && _count_substring(subscripts, "->") == 1)
+      << "Subscripts can only contain one '->'.";
+  }
+
+  // Parse ellipses
+  if (subscripts.find('.') != std::string::npos) {
+    std::string used = subscripts;
+    used.erase(std::remove_if(used.begin(),
+                              used.end(),
+                              [](const char& c){return c == '.' ||
+                                                       c == ',' ||
+                                                       c == '-' ||
+                                                       c == '>';}),
+               used.end());
+
+    std::bitset<MAXAXIS> used_set = str2set(used);
+    std::string ellipse_inds = "";
+    for (const char& c : einsum_symbols) {
+      if (!used_set.test(static_cast<int>(c))) {
+        ellipse_inds.append(1, c);
+      }
+    }
+    int longest = 0;
+    std::string input_tmp, output_sub;
+    std::vector<std::string> split_subscripts;
+    bool out_sub;
+
+    if (subscripts.find("->") != std::string::npos) {
+      std::vector<std::string> tmp = split(subscripts, "->");
+      input_tmp = tmp[0];
+      output_sub = tmp[1];
+      split_subscripts = split(input_tmp, ",");
+      out_sub = true;
+    } else {
+      split_subscripts = split(subscripts, ",");
+      out_sub = false;
+    }
+
+    size_t size_split_subscripts = split_subscripts.size();
+    subscripts = "";
+    for (size_t i = 0; i < size_split_subscripts; ++i) {
+      const std::string& sub = split_subscripts[i];
+      if (sub.find('.') != std::string::npos) {
+        CHECK_EQ(std::count(sub.begin(), sub.end(), '.'), 3)
+          << "Invalid Ellipses";
+        CHECK_EQ(_count_substring(sub, "..."), 1)
+          << "Invalid Ellipses";
+
+        // Take into account numerical values
+        int ellipse_count = 0;
+        if (shapes[i].ndim() == 0) {
+          ellipse_count = 0;
+        } else {
+          ellipse_count = std::max(shapes[i].ndim(), 1);
+          ellipse_count -= sub.length() - 3;
+        }
+
+        if (ellipse_count > longest) {
+          longest = ellipse_count;
+        }
+
+        CHECK_GE(ellipse_count, 0)
+          << "Ellipses lengths do not match.";
+        if (ellipse_count == 0) {
+          split_subscripts[i].erase(sub.find("..."), 3);
+        } else {
+          std::string rep_inds = ellipse_inds.substr(ellipse_inds.length() - ellipse_count);
+          split_subscripts[i].replace(sub.find("..."), 3, rep_inds);
+        }
+      }
+      subscripts += split_subscripts[i];
+      if (i + 1 < size_split_subscripts) {
+        subscripts += ",";
+      }
+    }
+    std::string out_ellipse;
+    if (longest == 0) {
+      out_ellipse = "";
+    } else {
+      out_ellipse = ellipse_inds.substr(ellipse_inds.length() - longest);
+    }
+
+    if (out_sub) {
+      output_sub.replace(output_sub.find("..."), 3, out_ellipse);
+      subscripts += "->" + output_sub;
+    } else {
+      // Special care for outputless ellipses
+      std::bitset<MAXAXIS> out_ellipse_set = str2set(out_ellipse);
+      std::string tmp_subscripts = subscripts, output_subscript = "";
+      size_t len_tmp_subscripts = tmp_subscripts.length();
+      std::sort(tmp_subscripts.begin(), tmp_subscripts.end());
+      for (size_t i = 0; i < len_tmp_subscripts; ++i) {
+        const char& c = tmp_subscripts[i];
+        if (c == ',') {
+          continue;
+        }
+        CHECK(einsum_symbols_set.test(c))
+          << "Character " << c
+          << " is not a valid symbol.";
+        if ((i == 0 || tmp_subscripts[i - 1] != c) &&
+            (i == len_tmp_subscripts - 1 || tmp_subscripts[i + 1] != c) &&
+            !out_ellipse_set.test(c)) {
+          output_subscript.append(1, c);
+        }
+      }
+      subscripts += "->" + out_ellipse + output_subscript;
+    }
+  }
+
+  // Build output string if does not exist
+  std::vector<std::string> ret(2);
+  if (subscripts.find("->") != std::string::npos) {
+    ret = split(subscripts, "->");
+  } else {
+    ret[0] = subscripts;
+    ret[1] = "";
+    // Build output subscripts
+    std::string tmp_subscripts = subscripts;
+    size_t len_tmp_subscripts = tmp_subscripts.length();
+    std::sort(tmp_subscripts.begin(), tmp_subscripts.end());
+    for (size_t i = 0; i < len_tmp_subscripts; ++i) {
+      const char& c = tmp_subscripts[i];
+      if (c == ',') {
+        continue;
+      }
+      CHECK(einsum_symbols_set.test(c))
+        << "Character " << c
+        << " is not a valid symbol.";
+      if ((i == 0 || tmp_subscripts[i - 1] != c) &&
+          (i == len_tmp_subscripts - 1 || tmp_subscripts[i + 1] != c)) {
+        ret[1].append(1, c);
+      }
+    }
+  }
+
+  // Make sure output subscripts are in the input
+  std::bitset<MAXAXIS> input_subscripts_set = str2set(ret[0]);
+  for (const char& c : ret[1]) {
+    CHECK(input_subscripts_set.test(c))
+      << "Output character " << c
+      << " did not appear in the input";
+  }
+
+  // Make sure number operands is equivalent to the number of terms
+  CHECK_EQ(std::count(ret[0].begin(), ret[0].end(), ',') + 1, shapes.size())
+    << "Number of einsum subscripts must be equal to the "
+    << "number of operands.";
+
+  return ret;
+}
+
+
+bool NumpyEinsumShape(const nnvm::NodeAttrs& attrs,
+                      mxnet::ShapeVector *in_attrs,
+                      mxnet::ShapeVector *out_attrs) {
+  const NumpyEinsumParam &param = nnvm::get<NumpyEinsumParam>(attrs.parsed);
+  const std::string& subscripts = param.subscripts;
+  int num_args = param.num_args;
+  CHECK_EQ(in_attrs->size(), num_args);
+  CHECK_EQ(out_attrs->size(), 1U);
+  for (int i = 0; i < num_args; i++) {
+    if (!shape_is_known(in_attrs->at(i))) {
+      return false;
+    }
+  }
+
+  // Parsing
+  std::vector<std::string> parsed_subscripts = _parse_einsum_input(subscripts, *in_attrs);
+
+  // Build a few useful list and sets
+  std::vector<std::string> input_list = split(parsed_subscripts[0], ",");
+  size_t isize = input_list.size();
+
+  // Get length of each unique dimension and ensure all dimensions are correct
+  dim_t dimension_dict[MAXAXIS];
+  memset(dimension_dict, -1, sizeof(dimension_dict));
+  for (size_t i = 0; i < isize; ++i) {
+    const std::string& term = input_list[i];
+    const TShape& sh = in_attrs->at(i);
+    CHECK_EQ(sh.ndim(), term.length())
+      << "Einstein sum subscript " << input_list[i]
+      << " does not contain the "
+      << "correct number of indices for operand " << i << ".";
+    size_t len_term = term.length();
+    for (size_t j = 0; j < len_term; ++j) {
+      dim_t dim = sh[j];
+      const char& c = term[j];
+
+      if (dimension_dict[static_cast<int>(c)] != -1) {
+        // For broadcasting cases we always want the largest dim size
+        if (dimension_dict[static_cast<int>(c)] == 1) {
+          dimension_dict[static_cast<int>(c)] = dim;
+        }
+        CHECK(dim == 1 || dim == dimension_dict[static_cast<int>(c)])
+          << "Size of label '" << c
+          << "' for operand  " << i
+          << " (" << dimension_dict[static_cast<int>(c)]
+          << ") does not match previous terms ("
+          << dim << ").";
+      } else {
+        dimension_dict[static_cast<int>(c)] = dim;
+      }
+    }
+  }
+
+  // Get oshape
+  const std::string& output_str = parsed_subscripts[1];
+  size_t odim = output_str.size();
+  TShape oshape(odim, -1);
+  for (size_t i = 0; i < odim; ++i) {
+    oshape[i] = dimension_dict[static_cast<int>(output_str[i])];
+  }
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, oshape);
+  return shape_is_known(oshape);
+}
+
+OpStatePtr CreateEinsumState(const NodeAttrs& attrs,
+                             Context ctx,
+                             const mxnet::ShapeVector& in_shapes,
+                             const std::vector<int>& in_types) {
+  const NumpyEinsumParam& param = dmlc::get<NumpyEinsumParam>(attrs.parsed);
+  return OpStatePtr::Create<EinsumOp>(param.num_args, param.optimize, param.subscripts);
+}
+
+DMLC_REGISTER_PARAMETER(NumpyEinsumParam);
+
+NNVM_REGISTER_OP(_npi_einsum)
+.describe(R"doc()doc" ADD_FILELINE)
+.set_attr_parser(ParamParser<NumpyEinsumParam>)
+.set_num_inputs([](const nnvm::NodeAttrs& attrs) {
+  const NumpyEinsumParam& param = dmlc::get<NumpyEinsumParam>(attrs.parsed);
+  return static_cast<uint32_t>(param.num_args);
+})
+.set_num_outputs(1)
+.set_attr<std::string>("key_var_num_args", "num_args")
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const nnvm::NodeAttrs& attrs) {
+    int num_args = dmlc::get<NumpyEinsumParam>(attrs.parsed).num_args;
+    std::vector<std::string> ret;
+    for (int i = 0; i < num_args; i++) {
+      ret.push_back(std::string("arg") + std::to_string(i));
+    }
+    return ret;
+})
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyEinsumShape)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<-1, 1>)
+.set_attr<FCreateOpState>("FCreateOpState", CreateEinsumState)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>(1, ResourceRequest::kTempSpace);
+  })
+.set_attr<FStatefulCompute>("FStatefulCompute<cpu>", NumpyEinsumForward<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_einsum"})
+.add_argument("data", "NDArray-or-Symbol[]", "List of eimsum operands")
+.add_arguments(NumpyEinsumParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_backward_npi_einsum)
+.set_attr_parser(ParamParser<NumpyEinsumParam>)
+.set_num_inputs([](const nnvm::NodeAttrs& attrs) {
+  const NumpyEinsumParam& param = dmlc::get<NumpyEinsumParam>(attrs.parsed);
+  return static_cast<uint32_t>(param.num_args + 1);
+})
+.set_num_outputs([](const nnvm::NodeAttrs& attrs) {
+  const NumpyEinsumParam& param = dmlc::get<NumpyEinsumParam>(attrs.parsed);
+  return static_cast<uint32_t>(param.num_args);
+})
+.set_attr<bool>("TIsLayerOpBackward", true)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>(1, ResourceRequest::kTempSpace);
+  })
+.set_attr<FStatefulCompute>("FStatefulCompute<cpu>", NumpyEinsumBackward<cpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_einsum_op.cu b/src/operator/numpy/np_einsum_op.cu
new file mode 100644
index 000000000000..1f76f2436436
--- /dev/null
+++ b/src/operator/numpy/np_einsum_op.cu
@@ -0,0 +1,36 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file np_einsum_op.cu
+ * \brief GPU Implementation of numpy-compatible einsum
+ */
+
+#include "./np_einsum_op-inl.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(_npi_einsum)
+.set_attr<FStatefulCompute>("FStatefulCompute<gpu>", NumpyEinsumForward<gpu>);
+NNVM_REGISTER_OP(_backward_npi_einsum)
+.set_attr<FStatefulCompute>("FStatefulCompute<gpu>", NumpyEinsumBackward<gpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_einsum_path_op-inl.h b/src/operator/numpy/np_einsum_path_op-inl.h
new file mode 100644
index 000000000000..cebd4e8ce9af
--- /dev/null
+++ b/src/operator/numpy/np_einsum_path_op-inl.h
@@ -0,0 +1,964 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*
+ * Copyright (c) 2005-2019, NumPy Developers.
+ * All rights reserved.
+ * 
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are
+ * met:
+ *
+ *  * Redistributions of source code must retain the above copyright
+ *     notice, this list of conditions and the following disclaimer.
+ *
+ *  * Redistributions in binary form must reproduce the above
+ *     copyright notice, this list of conditions and the following
+ *     disclaimer in the documentation and/or other materials provided
+ *     with the distribution.
+ *
+ *  * Neither the name of the NumPy Developers nor the names of any
+ *     contributors may be used to endorse or promote products derived
+ *     from this software without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+ * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+ * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+ * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+ * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ */
+
+/*!
+ * \file np_einsum_path_op-inl.h
+ * \brief Function definition of numpy-compatible einsum_path operator
+ */
+
+#ifndef MXNET_OPERATOR_NUMPY_NP_EINSUM_PATH_OP_INL_H_
+#define MXNET_OPERATOR_NUMPY_NP_EINSUM_PATH_OP_INL_H_
+
+#include <mxnet/operator_util.h>
+#include <functional>
+#include <algorithm>
+#include <string>
+#include <vector>
+#include <bitset>
+
+namespace mxnet {
+namespace op {
+
+const int MAXAXIS = 128;
+
+typedef std::vector<std::bitset<MAXAXIS> > SetVector;
+
+struct Contraction {
+  std::bitset<MAXAXIS> new_result;
+  std::vector<std::bitset<MAXAXIS> > remaining;
+  std::bitset<MAXAXIS> idx_removed;
+  std::bitset<MAXAXIS> idx_contract;
+};
+
+struct Alternative {
+  int cost[2];
+  std::vector<int> positions;
+  SetVector new_input_sets;
+};
+
+struct Step {
+  std::vector<int> contract_inds;
+  std::bitset<MAXAXIS> idx_removed;
+  std::string einsum_str, blas2einsum_str, einsum2blas_str;
+  std::vector<std::string> input_list;
+  bool do_blas, do_einsum;
+  TShape oshape, tshape;
+  Tuple<int> left_pos, right_pos;
+};
+
+inline size_t _compute_size_by_dict(const std::string& indices,
+                                    const dim_t idx_dict[]) {
+  size_t ret = 1;
+  for (const char& c : indices) {
+    ret *= idx_dict[static_cast<int>(c)];
+  }
+  return ret;
+}
+
+inline size_t _compute_size_by_dict(const std::bitset<MAXAXIS>& indices,
+                                    const dim_t idx_dict[]) {
+  size_t ret = 1;
+  for (int i = 0; i < MAXAXIS; ++i) {
+    if (indices[i]) {
+      ret *= idx_dict[i];
+    }
+  }
+  return ret;
+}
+
+inline int _flop_count(const std::string& idx_contraction,
+                       bool inner,
+                       int num_terms,
+                       const dim_t size_dictionary[]) {
+  size_t overall_size = _compute_size_by_dict(idx_contraction, size_dictionary);
+  int op_factor = std::max(1, num_terms - 1);
+  if (inner) {
+    ++op_factor;
+  }
+  return overall_size * op_factor;
+}
+
+inline int _flop_count(const std::bitset<MAXAXIS>& idx_contraction,
+                       bool inner,
+                       int num_terms,
+                       const dim_t size_dictionary[]) {
+  size_t overall_size = _compute_size_by_dict(idx_contraction, size_dictionary);
+  int op_factor = std::max(1, num_terms - 1);
+  if (inner) {
+    ++op_factor;
+  }
+  return overall_size * op_factor;
+}
+
+inline Contraction _find_contraction(const std::vector<int>& positions,
+                                     const SetVector& input_sets,
+                                     const std::bitset<MAXAXIS>& output_set) {
+  Contraction ret;
+  std::bitset<MAXAXIS> idx_remain(output_set);
+  size_t size = input_sets.size();
+  for (size_t i = 0; i < size; ++i) {
+    if (std::find(positions.begin(), positions.end(), i) != positions.end()) {
+      ret.idx_contract |= input_sets[i];
+    } else {
+      ret.remaining.push_back(input_sets[i]);
+      idx_remain |= input_sets[i];
+    }
+  }
+  ret.new_result = idx_remain & ret.idx_contract;
+  ret.idx_removed = (ret.idx_contract & ~ret.new_result);
+  ret.remaining.push_back(ret.new_result);
+
+  return ret;
+}
+
+inline int _parse_possible_contraction(const std::vector<int>& positions,
+                                       const SetVector& input_sets,
+                                       const std::bitset<MAXAXIS>& output_set,
+                                       const dim_t idx_dict[],
+                                       int memory_limit,
+                                       int path_cost,
+                                       int naive_cost,
+                                       Alternative* ret) {
+  // Find the contraction
+  Contraction contract = _find_contraction(positions, input_sets, output_set);
+
+  // Sieve the results based on memory_limit
+  size_t new_size = _compute_size_by_dict(contract.new_result, idx_dict);
+  if (new_size > static_cast<size_t>(memory_limit)) {
+    return -1;
+  }
+
+  // Build sort tuple
+  size_t old_sizes = 0;
+  for (auto p : positions) {
+    old_sizes += _compute_size_by_dict(input_sets[p], idx_dict);
+  }
+  int remove_size = old_sizes - new_size;
+
+  int cost = _flop_count(contract.idx_contract, contract.idx_removed.any(),
+                            positions.size(), idx_dict);
+  ret->cost[0] = -remove_size;
+  ret->cost[1] = cost;
+
+  // Sieve based on total cost as well
+  if (path_cost + cost > naive_cost) {
+    return -1;
+  }
+
+  // Add contraction to possible choices
+  ret->positions = positions;
+  ret->new_input_sets = contract.remaining;
+  return 0;
+}
+
+inline void _update_other_results(std::vector<Alternative>* results,
+                                  const Alternative& best) {
+  const std::vector<int>& best_con = best.positions;
+  int bx = best_con[0], by = best_con[1];
+  size_t size = results->size();
+
+  for (int i = size - 1; i >= 0; --i) {
+    int x = results->at(i).positions[0], y = results->at(i).positions[1];
+
+    // Ignore results involving tensors just contracted
+    if (x == bx || x == by || y == bx || y == by) {
+      results->erase(results->begin() + i);
+      continue;
+    }
+
+    // Update the input_sets
+    CHECK_GT(by, bx)
+      << "by must be greater than bx";
+    results->at(i).new_input_sets.erase(results->at(i).new_input_sets.begin() +
+                                        by - static_cast<int>(by > x) - static_cast<int>(by > y));
+    results->at(i).new_input_sets.erase(results->at(i).new_input_sets.begin() +
+                                        bx - static_cast<int>(bx > x) - static_cast<int>(bx > y));
+    results->at(i).new_input_sets.push_back(best.new_input_sets.back());
+
+    // Update the position indices
+    results->at(i).positions[0] = x - static_cast<int>(x > bx) - static_cast<int>(x > by);
+    results->at(i).positions[1] = y - static_cast<int>(y > bx) - static_cast<int>(y > by);
+  }
+}
+
+inline std::vector<std::vector<int> > _greedy_path(const SetVector* input_sets,
+                                                   const std::bitset<MAXAXIS>& output_set,
+                                                   const dim_t idx_dict[],
+                                                   int memory_limit) {
+  size_t isize = input_sets->size();
+  size_t iteration_num = isize;
+  // Handle trivial cases that leaked through
+  if (isize == 1) {
+    return std::vector<std::vector<int> >{std::vector<int>{0}};
+  } else if (isize == 2) {
+    return std::vector<std::vector<int> >{std::vector<int>{0, 1}};
+  }
+
+  // Build up a naive cost
+  std::vector<int> range(isize);
+  for (size_t i = 0; i < isize; ++i) {
+    range[i] = i;
+  }
+  Contraction contract = _find_contraction(range, *input_sets, output_set);
+  int naive_cost = _flop_count(contract.idx_contract, contract.idx_removed.any(),
+                                  isize, idx_dict);
+
+  // Initially iterate over all pairs
+  std::vector<Alternative> known_contractions;
+  Alternative best;
+  int path_cost = 0;
+  std::vector<std::vector<int> > ret;
+
+  for (size_t iteration = 0; iteration + 1 < iteration_num; ++iteration) {
+    if (iteration == 0) {
+      for (int x = 0; x < static_cast<int>(isize); ++x) {
+        for (int y = x + 1; y < static_cast<int>(isize); ++y) {
+          if (!((input_sets->at(x) & input_sets->at(y)).any())) {
+            continue;
+          }
+          Alternative alternative;
+          int result = _parse_possible_contraction(std::vector<int>{x, y},
+                                                   *input_sets,
+                                                   output_set,
+                                                   idx_dict,
+                                                   memory_limit,
+                                                   path_cost,
+                                                   naive_cost,
+                                                   &alternative);
+          if (result != -1) {
+            known_contractions.push_back(alternative);
+          }
+        }
+      }
+    } else {
+      for (int x = 0; x < static_cast<int>(isize) - 1; ++x) {
+        int y = isize - 1;
+        if (!((input_sets->at(x) & input_sets->at(y)).any())) {
+            continue;
+          }
+          Alternative alternative;
+          int result = _parse_possible_contraction(std::vector<int>{x, y},
+                                                   *input_sets,
+                                                   output_set,
+                                                   idx_dict,
+                                                   memory_limit,
+                                                   path_cost,
+                                                   naive_cost,
+                                                   &alternative);
+          if (result != -1) {
+            known_contractions.push_back(alternative);
+          }
+      }
+    }
+
+    // If we do not have a inner contraction, rescan pairs including outer products
+    if (known_contractions.size() == 0) {
+      // Then check the outer productsj
+      for (int x = 0; x < static_cast<int>(isize); ++x) {
+        for (int y = x + 1; y < static_cast<int>(isize); ++y) {
+          Alternative alternative;
+          int result = _parse_possible_contraction(std::vector<int>{x, y},
+                                                   *input_sets,
+                                                   output_set,
+                                                   idx_dict,
+                                                   memory_limit,
+                                                   path_cost,
+                                                   naive_cost,
+                                                   &alternative);
+          if (result != -1) {
+            known_contractions.push_back(alternative);
+          }
+        }
+      }
+
+      // If we still did not find any remaining contractions, default back to einsum like behavior
+      if (known_contractions.size() == 0) {
+        std::vector<int> range(isize);
+        for (size_t i = 0; i < isize; ++i) {
+          range[i] = i;
+        }
+        ret.push_back(range);
+        break;
+      }
+    }
+
+    // Sort based on first index
+    int best_cost[2], idx = -1;
+    size_t size = known_contractions.size();
+    for (size_t i = 0; i < size; ++i) {
+      auto x = known_contractions[i];
+      if (idx == -1) {
+        best_cost[0] = x.cost[0];
+        best_cost[1] = x.cost[1];
+        idx = i;
+      } else if (x.cost[0] < best_cost[0] ||
+               (x.cost[0] == best_cost[0] &&
+               x.cost[1] < best_cost[1])) {
+        best_cost[0] = x.cost[0];
+        best_cost[1] = x.cost[1];
+        idx = i;
+      }
+    }
+    best = known_contractions[idx];
+
+    // Now propagate as many unused contractions as possible to next iteration
+    _update_other_results(&known_contractions, best);
+
+    // Next iteration only compute contractions with the new tensor
+    // All other contractions have been accounted for
+    input_sets = &best.new_input_sets;
+    isize = input_sets->size();
+
+    // Update path and total cost
+    ret.push_back(best.positions);
+    path_cost += best.cost[1];
+  }
+  return ret;
+}
+
+inline bool _can_dot(const std::vector<std::string>& inputs,
+                     const std::bitset<MAXAXIS>& result,
+                     const std::bitset<MAXAXIS>& idx_removed) {
+  // All `dot` calls remove indices
+  if (!idx_removed.any()) {
+    return false;
+  }
+
+  // BLAS can only handle two operands
+  if (inputs.size() != 2) {
+    return false;
+  }
+
+  const std::string& input_left = inputs[0];
+  const std::string& input_right = inputs[1];
+
+  if (input_left.size() == 0 || input_right.size() == 0) {
+    return false;
+  }
+
+  for (int i = 0; i < 2; ++i) {
+    for (const char& c : inputs[i]) {
+      // can't deal with repeated indices on same input or more than 2 total
+      size_t nl = std::count(input_left.begin(), input_left.end(), c);
+      size_t nr = std::count(input_right.begin(), input_right.end(), c);
+      if (nl > 1 || nr > 1 || nl + nr > 2) {
+        return false;
+      }
+
+      // can't do implicit summation or dimension collapse e.g.
+      // "ab,bc->c" (implicitly sum over 'a')
+      // "ab,ca->ca" (take diagonal of 'a')
+      if (nl + nr == static_cast<size_t>(result.test(c)) + 1) {
+        return false;
+      }
+    }
+  }
+
+  // Build a few temporaries
+  std::bitset<MAXAXIS> set_left;
+  std::bitset<MAXAXIS> set_right;
+  for (const char& c : input_left) {
+    set_left.set(c);
+  }
+  for (const char& c : input_right) {
+    set_right.set(c);
+  }
+  std::bitset<MAXAXIS> keep_left = set_left & ~idx_removed;
+  std::bitset<MAXAXIS> keep_right = set_right & ~idx_removed;
+  size_t rs = idx_removed.count();
+
+  // At this point we are a DOT, GEMV, or GEMM operation
+
+  // Handle inner products
+
+  // DDOT with aligned data
+  if (input_left == input_right)
+    return true;
+
+  // DDOT without aligned data (better to use einsum)
+  if (set_left == set_right)
+    return false;
+
+  // Handle the 4 possible (aligned) GEMV or GEMM cases
+
+  // GEMM or GEMV no transpose
+  if (std::equal(input_left.end() - rs,
+                 input_left.end(),
+                 input_right.begin())) {
+    return true;
+  }
+
+  // GEMM or GEMV transpose both
+  if (std::equal(input_left.begin(),
+                 input_left.begin() + rs,
+                 input_right.end() - rs)) {
+    return true;
+  }
+
+  // GEMM or GEMV transpose right
+  if (std::equal(input_left.end() - rs,
+                 input_left.end(),
+                 input_right.end() - rs)) {
+    return true;
+  }
+
+  // GEMM or GEMV transpose left
+  if (std::equal(input_left.begin(),
+                 input_left.begin() + rs,
+                 input_right.begin())) {
+    return true;
+  }
+
+  // Einsum is faster than GEMV if we have to copy data
+  if (!keep_left.any() || !keep_right.any()) {
+    return false;
+  }
+
+  // We are a matrix-matrix product, but we need to copy data
+  return true;
+}
+
+
+inline int _count_substring(const std::string& str,
+                            const std::string& sub) {
+  int count = 0;
+  std::string::size_type pos = 0;
+  while ((pos = str.find(sub, pos)) != std::string::npos) {
+    ++count;
+    pos += sub.length();
+  }
+  return count;
+}
+
+inline std::bitset<MAXAXIS> str2set(const std::string& str) {
+  std::bitset<MAXAXIS> ret;
+  for (const char& c : str) {
+    ret.set(static_cast<int>(c));
+  }
+  return ret;
+}
+
+inline std::string set2str(const std::bitset<MAXAXIS>& set) {
+  std::string ret;
+  for (int i = 0; i < MAXAXIS; ++i) {
+    if (set.test(i)) {
+      ret.append(1, static_cast<char>(i));
+    }
+  }
+  return ret;
+}
+
+inline std::vector<std::string> split(const std::string& str,
+                                      const std::string& sub) {
+  std::string::size_type pos = 0;
+  std::string::size_type start = 0;
+  std::vector<std::string> ret;
+  while ((pos = str.find(sub, start)) != std::string::npos) {
+    ret.push_back(str.substr(start, pos - start));
+    start = pos + sub.length();
+  }
+  ret.push_back(str.substr(start));
+  return ret;
+}
+
+inline std::vector<std::string> _parse_einsum_input(
+  std::string subscripts,
+  const std::vector<TBlob>& operands) {
+  const std::string einsum_symbols =
+    "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ";
+  std::bitset<MAXAXIS> einsum_symbols_set;
+  for (const char& c : einsum_symbols) {
+    einsum_symbols_set.set(c);
+  }
+
+  CHECK_NE(operands.size(), 0U)
+    << "No input operands";
+
+  auto end_pos = std::remove(subscripts.begin(), subscripts.end(), ' ');
+  subscripts.erase(end_pos, subscripts.end());
+
+  // Ensure all characters are valid
+  for (const char& c : subscripts) {
+    if (c == '.' || c == ',' || c == '-' || c == '>') {
+      continue;
+    }
+    CHECK(einsum_symbols_set.test(c))
+      << "Character " << c
+      << " is not a valid symbol.";
+  }
+
+  // Check for proper "->"
+  if (subscripts.find('-') != std::string::npos ||
+      subscripts.find('>') != std::string::npos) {
+    bool invalid = (std::count(subscripts.begin(), subscripts.end(), '-') > 1 ||
+                    std::count(subscripts.begin(), subscripts.end(), '>') > 1);
+    CHECK(!invalid && _count_substring(subscripts, "->") == 1)
+      << "Subscripts can only contain one '->'.";
+  }
+
+  // Parse ellipses
+  if (subscripts.find('.') != std::string::npos) {
+    std::string used = subscripts;
+    used.erase(std::remove_if(used.begin(),
+                              used.end(),
+                              [](const char& c){return c == '.' ||
+                                                       c == ',' ||
+                                                       c == '-' ||
+                                                       c == '>';}),
+               used.end());
+
+    std::bitset<MAXAXIS> used_set = str2set(used);
+    std::string ellipse_inds = "";
+    for (const char& c : einsum_symbols) {
+      if (!used_set.test(static_cast<int>(c))) {
+        ellipse_inds.append(1, c);
+      }
+    }
+    int longest = 0;
+    std::string input_tmp, output_sub;
+    std::vector<std::string> split_subscripts;
+    bool out_sub;
+
+    if (subscripts.find("->") != std::string::npos) {
+      std::vector<std::string> tmp = split(subscripts, "->");
+      input_tmp = tmp[0];
+      output_sub = tmp[1];
+      split_subscripts = split(input_tmp, ",");
+      out_sub = true;
+    } else {
+      split_subscripts = split(subscripts, ",");
+      out_sub = false;
+    }
+
+    size_t size_split_subscripts = split_subscripts.size();
+    subscripts = "";
+    for (size_t i = 0; i < size_split_subscripts; ++i) {
+      const std::string& sub = split_subscripts[i];
+      if (sub.find('.') != std::string::npos) {
+        CHECK_EQ(std::count(sub.begin(), sub.end(), '.'), 3)
+          << "Invalid Ellipses";
+        CHECK_EQ(_count_substring(sub, "..."), 1)
+          << "Invalid Ellipses";
+
+        // Take into account numerical values
+        int ellipse_count = 0;
+        if (operands[i].shape_.ndim() == 0) {
+          ellipse_count = 0;
+        } else {
+          ellipse_count = std::max(operands[i].shape_.ndim(), 1);
+          ellipse_count -= sub.length() - 3;
+        }
+
+        if (ellipse_count > longest) {
+          longest = ellipse_count;
+        }
+
+        CHECK_GE(ellipse_count, 0)
+          << "Ellipses lengths do not match.";
+        if (ellipse_count == 0) {
+          split_subscripts[i].erase(sub.find("..."), 3);
+        } else {
+          std::string rep_inds = ellipse_inds.substr(ellipse_inds.length() - ellipse_count);
+          split_subscripts[i].replace(sub.find("..."), 3, rep_inds);
+        }
+      }
+      subscripts += split_subscripts[i];
+      if (i + 1 < size_split_subscripts) {
+        subscripts += ",";
+      }
+    }
+    std::string out_ellipse;
+    if (longest == 0) {
+      out_ellipse = "";
+    } else {
+      out_ellipse = ellipse_inds.substr(ellipse_inds.length() - longest);
+    }
+
+    if (out_sub) {
+      output_sub.replace(output_sub.find("..."), 3, out_ellipse);
+      subscripts += "->" + output_sub;
+    } else {
+      // Special care for outputless ellipses
+      std::bitset<MAXAXIS> out_ellipse_set = str2set(out_ellipse);
+      std::string tmp_subscripts = subscripts, output_subscript = "";
+      size_t len_tmp_subscripts = tmp_subscripts.length();
+      std::sort(tmp_subscripts.begin(), tmp_subscripts.end());
+      for (size_t i = 0; i < len_tmp_subscripts; ++i) {
+        const char& c = tmp_subscripts[i];
+        if (c == ',') {
+          continue;
+        }
+        CHECK(einsum_symbols_set.test(c))
+          << "Character " << c
+          << " is not a valid symbol.";
+        if ((i == 0 || tmp_subscripts[i - 1] != c) &&
+            (i == len_tmp_subscripts - 1 || tmp_subscripts[i + 1] != c) &&
+            !out_ellipse_set.test(c)) {
+          output_subscript.append(1, c);
+        }
+      }
+      subscripts += "->" + out_ellipse + output_subscript;
+    }
+  }
+
+  // Build output string if does not exist
+  std::vector<std::string> ret(2);
+  if (subscripts.find("->") != std::string::npos) {
+    ret = split(subscripts, "->");
+  } else {
+    ret[0] = subscripts;
+    ret[1] = "";
+    // Build output subscripts
+    std::string tmp_subscripts = subscripts;
+    size_t len_tmp_subscripts = tmp_subscripts.length();
+    std::sort(tmp_subscripts.begin(), tmp_subscripts.end());
+    for (size_t i = 0; i < len_tmp_subscripts; ++i) {
+      const char& c = tmp_subscripts[i];
+      if (c == ',') {
+        continue;
+      }
+      CHECK(einsum_symbols_set.test(c))
+        << "Character " << c
+        << " is not a valid symbol.";
+      if ((i == 0 || tmp_subscripts[i - 1] != c) &&
+          (i == len_tmp_subscripts - 1 || tmp_subscripts[i + 1] != c)) {
+        ret[1].append(1, c);
+      }
+    }
+  }
+
+  // Make sure output subscripts are in the input
+  std::bitset<MAXAXIS> input_subscripts_set = str2set(ret[0]);
+  for (const char& c : ret[1]) {
+    CHECK(input_subscripts_set.test(c))
+      << "Output character " << c
+      << " did not appear in the input";
+  }
+
+  // Make sure number operands is equivalent to the number of terms
+  CHECK_EQ(std::count(ret[0].begin(), ret[0].end(), ',') + 1, operands.size())
+    << "Number of einsum subscripts must be equal to the "
+    << "number of operands.";
+
+  return ret;
+}
+
+inline bool _tensordot_type_check(int type_flag_, const RunContext& run_ctx) {
+  return type_flag_ == kFloat32 || type_flag_ == kFloat64 ||
+         (type_flag_ == kFloat16 && run_ctx.ctx.dev_mask() == mshadow::gpu::kDevMask);
+}
+
+inline std::vector<Step> einsum_path(const std::string& subscripts,
+                                     const std::vector<TBlob>& operands,
+                                     bool optimize,
+                                     const RunContext& run_ctx,
+                                     std::vector<std::vector<int> >* ret_path,
+                                     std::string* ret_string_repr) {
+  // Parsing
+  std::vector<std::string> parsed_subscripts = _parse_einsum_input(subscripts, operands);
+
+  // Build a few useful list and sets
+  std::vector<std::string> input_list = split(parsed_subscripts[0], ",");
+  size_t isize = input_list.size();
+  SetVector input_sets;
+  for (int i = 0; i < static_cast<int>(isize); ++i) {
+    input_sets.push_back(str2set(input_list[i]));
+  }
+  std::bitset<MAXAXIS> output_set = str2set(parsed_subscripts[1]);
+  std::bitset<MAXAXIS> indices = str2set(parsed_subscripts[0]);
+  indices.set(',', false);
+
+  // Get length of each unique dimension and ensure all dimensions are correct
+  dim_t dimension_dict[MAXAXIS];
+  SetVector broadcast_indices(isize);
+  memset(dimension_dict, -1, sizeof(dimension_dict));
+  for (size_t i = 0; i < isize; ++i) {
+    const std::string& term = input_list[i];
+    const TShape& sh = operands[i].shape_;
+    CHECK_EQ(sh.ndim(), term.length())
+      << "Einstein sum subscript " << input_list[i]
+      << " does not contain the "
+      << "correct number of indices for operand " << i << ".";
+    size_t len_term = term.length();
+    for (size_t j = 0; j < len_term; ++j) {
+      dim_t dim = sh[j];
+      const char& c = term[j];
+      // Build out broadcast indices
+      if (dim == 1) {
+        broadcast_indices[i].set(c);
+      }
+
+      if (dimension_dict[static_cast<int>(c)] != -1) {
+        // For broadcasting cases we always want the largest dim size
+        if (dimension_dict[static_cast<int>(c)] == 1) {
+          dimension_dict[static_cast<int>(c)] = dim;
+        }
+        CHECK(dim == 1 || dim == dimension_dict[static_cast<int>(c)])
+          << "Size of label '" << c
+          << "' for operand  " << i
+          << " (" << dimension_dict[static_cast<int>(c)]
+          << ") does not match previous terms ("
+          << dim << ").";
+      } else {
+        dimension_dict[static_cast<int>(c)] = dim;
+      }
+    }
+  }
+
+  // Compute size of each input array plus the output array
+  std::vector<size_t> size_list(isize + 1);
+  size_t max_size = -1, memory_arg;
+  for (size_t i = 0; i < isize; ++i) {
+    size_list[i] = _compute_size_by_dict(input_list[i], dimension_dict);
+    max_size = std::max(max_size, size_list[i]);
+  }
+  size_list[isize] = _compute_size_by_dict(parsed_subscripts[1], dimension_dict);
+  max_size = std::max(max_size, size_list[isize]);
+  memory_arg = max_size;
+
+  // Compute naive cost
+  // This isn't quite right, need to look into exactly how einsum does this
+  size_t sum_len_input_sets = 0;
+  for (auto x : input_sets) {
+    sum_len_input_sets += x.count();
+  }
+  bool inner_product = (sum_len_input_sets > indices.count());
+  int naive_cost = _flop_count(indices, inner_product, isize, dimension_dict);
+
+  // Compute the path
+  std::vector<std::vector<int> > path;
+  if (optimize == false) {
+    path.push_back(std::vector<int>());
+    for (size_t i = 0; i < isize; ++i) {
+      path[0].push_back(i);
+    }
+  } else {
+    path = _greedy_path(&input_sets, output_set, dimension_dict, memory_arg);
+  }
+
+  std::vector<int> cost_list;
+  std::vector<size_t> scale_list;
+  int opt_cost = 1;
+  size_t max_i = 0, max_scale = 0, size_path = path.size();
+  std::vector<Step> ret(size_path);
+  size_list.clear();
+
+  // Build contraction tuple (positions, gemm, einsum_str, remaining)
+  for (size_t i = 0; i < size_path; ++i) {
+    // Make sure we remove inds from right to left
+    std::vector<int> contract_inds = path[i];
+    std::sort(contract_inds.begin(), contract_inds.end(), std::greater<int>());
+
+    Contraction contract = _find_contraction(contract_inds, input_sets, output_set);
+    input_sets = contract.remaining;
+
+    int cost = _flop_count(contract.idx_contract,
+                           contract.idx_removed.any(),
+                           contract_inds.size(),
+                           dimension_dict);
+    opt_cost += cost;
+    cost_list.push_back(cost);
+    scale_list.push_back(contract.idx_contract.count());
+    size_list.push_back(_compute_size_by_dict(contract.new_result, dimension_dict));
+    max_i = std::max(max_i, size_list.back());
+    max_scale = std::max(max_scale, scale_list.back());
+
+    std::bitset<MAXAXIS> bcast;
+    std::vector<std::string> tmp_inputs;
+    for (const int& x : contract_inds) {
+      tmp_inputs.push_back(input_list[x]);
+      input_list.erase(input_list.begin() + x);
+      bcast |= broadcast_indices[x];
+      broadcast_indices.erase(broadcast_indices.begin() + x);
+    }
+
+    std::bitset<MAXAXIS> new_bcast_inds = bcast & ~contract.idx_removed;
+
+    // If we're broadcasting, nix blas
+    bool do_blas;
+    if ((contract.idx_removed & bcast).any() ||
+        !_tensordot_type_check(operands[0].type_flag_, run_ctx)) {
+      do_blas = false;
+    } else {
+      do_blas = _can_dot(tmp_inputs, contract.new_result, contract.idx_removed);
+    }
+
+    // Last contraction
+    std::string idx_result;
+    if (i + 1 == size_path) {
+      idx_result = parsed_subscripts[1];
+    } else {
+      idx_result = set2str(contract.new_result);
+      std::sort(idx_result.begin(), idx_result.end(),
+                [&dimension_dict](const char& a, const char& b) -> bool {
+                  return dimension_dict[static_cast<int>(a)] <
+                         dimension_dict[static_cast<int>(b)] ||
+                         (dimension_dict[static_cast<int>(a)] ==
+                         dimension_dict[static_cast<int>(b)] &&
+                         a < b);
+                });
+    }
+    size_t len_idx_result = idx_result.length();
+    ret[i].oshape = TShape(len_idx_result, -1);
+    for (size_t j = 0; j < len_idx_result; ++j) {
+      ret[i].oshape[j] = dimension_dict[static_cast<int>(idx_result[j])];
+    }
+
+    if (do_blas) {
+      CHECK_EQ(tmp_inputs.size(), 2U)
+        << "BLAS accepts exactly 2 inputs";
+      std::string tensor_result = tmp_inputs[0] + tmp_inputs[1];
+      tensor_result.erase(std::remove_if(tensor_result.begin(),
+                                         tensor_result.end(),
+                                         [&](const char& c) {
+                                           return contract.idx_removed.test(static_cast<int>(c));}),
+                          tensor_result.end());
+
+      // Find indices to contract over
+      std::vector<int> left_pos, right_pos;
+      left_pos.reserve(MAXAXIS);
+      right_pos.reserve(MAXAXIS);
+      size_t tmp[MAXAXIS] = {0};
+      size_t length_left_input = tmp_inputs[0].length();
+      size_t length_right_input = tmp_inputs[1].length();
+      for (size_t j = 0; j < length_right_input; ++j) {
+        if (contract.idx_removed.test(static_cast<int>(tmp_inputs[1][j]))) {
+          tmp[static_cast<int>(tmp_inputs[1][j])] = j;
+        }
+      }
+      for (size_t j = 0; j < length_left_input; ++j) {
+        if (contract.idx_removed.test(static_cast<int>(tmp_inputs[0][j]))) {
+          left_pos.push_back(static_cast<int>(j));
+          right_pos.push_back(static_cast<int>(tmp[static_cast<int>(tmp_inputs[0][j])]));
+        }
+      }
+      // Calculate left_pos and right_pos
+      ret[i].left_pos = Tuple<int>(left_pos);
+      ret[i].right_pos = Tuple<int>(right_pos);
+      // Calculate do_einsum
+      ret[i].do_einsum = (tensor_result != idx_result);
+      // Calculate tshape
+      CHECK_EQ(tensor_result.length(), len_idx_result)
+        << "tensordot produces dim " << tensor_result.length()
+        << ", while einsum produces dim " << len_idx_result << ".";
+      ret[i].tshape = TShape(len_idx_result, -1);
+      for (size_t j = 0; j < len_idx_result; ++j) {
+        ret[i].tshape[j] = dimension_dict[static_cast<int>(tensor_result[j])];
+      }
+      // Calculate blas2einsum_str
+      ret[i].blas2einsum_str = tensor_result + "->" + idx_result;
+      ret[i].einsum2blas_str = idx_result + "->" + tensor_result;
+    }
+    input_list.push_back(idx_result);
+    broadcast_indices.push_back(new_bcast_inds);
+    size_t len_tmp_inputs = tmp_inputs.size();
+    for (size_t j = 0; j < len_tmp_inputs; ++j) {
+      ret[i].einsum_str += tmp_inputs[j];
+      if (j + 1 < len_tmp_inputs) {
+        ret[i].einsum_str += ",";
+      }
+    }
+    ret[i].einsum_str += "->" + idx_result;
+    ret[i].contract_inds = contract_inds;
+    ret[i].idx_removed = contract.idx_removed;
+    ret[i].input_list = input_list;
+    ret[i].do_blas = do_blas;
+  }
+
+  if (ret_path == NULL || ret_string_repr == NULL) {
+    return ret;
+  }
+
+  // Return the path along with a nice string representation
+  std::string overall_contraction = parsed_subscripts[0] + "->" + parsed_subscripts[1];
+  std::string header[3] = {"scaling", "current", "remaining"};
+
+  double speedup = 1.0 * naive_cost / (1.0 * opt_cost);
+  std::ostringstream ss;
+  ss << "  Complete contraction:  " << overall_contraction << std::endl;
+  ss << "         Naive scaling:  " <<  indices.count() << std::endl;
+  ss << "     Optimized scaling:  " << max_scale << std::endl;
+  ss.precision(3);
+  ss << "      Naive FLOP count:  " << std::scientific << naive_cost << std::endl;
+  ss << "  Optimized FLOP count:  " << std::scientific << opt_cost << std::endl;
+  ss << "   Theoretical speedup:  " << std::scientific << speedup << std::endl;
+  ss << "  Largest intermediate:  " << std::scientific << max_i << "elements" << std::endl;
+  ss << std::string(74, '-') << std::endl;
+  ss << std::setw(6) << header[0] << " ";
+  ss << std::setw(24) << header[1] << " ";
+  ss << std::setw(40) << header[2] << std::endl;
+  ss << std::string(74, '-');
+
+  for (size_t i = 0; i < size_path; ++i) {
+    ss << std::endl;
+    ss << std::setw(4) << scale_list[i] << "    ";
+    ss << std::setw(24) << ret[i].einsum_str << " ";
+    std::string remaining_str;
+    size_t len_input_list = ret[i].input_list.size();
+    for (size_t j = 0; j < len_input_list; ++j) {
+      remaining_str += ret[i].input_list[j];
+      if (j + 1 < len_input_list) {
+        remaining_str += ",";
+      }
+    }
+    remaining_str += "->" + parsed_subscripts[1];
+    ss << std::setw(40) << remaining_str;
+  }
+  *ret_string_repr = ss.str();
+  *ret_path = path;
+  return ret;
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_NUMPY_NP_EINSUM_PATH_OP_INL_H_
diff --git a/src/operator/numpy/np_tensordot_op-inl.h b/src/operator/numpy/np_tensordot_op-inl.h
index da3891665c4b..1c62527db43f 100644
--- a/src/operator/numpy/np_tensordot_op-inl.h
+++ b/src/operator/numpy/np_tensordot_op-inl.h
@@ -140,7 +140,7 @@ inline mxnet::TShape GetReorderedShape(const mxnet::TShape& shape, const mxnet::
 }
 
 /**
- * gets matrix dot. Reshapes tensor a as ad1-by-ad2 matrix, tensor b as bd1-by-bd2 matrix, then 
+ * gets matrix dot. Reshapes tensor a as ad1-by-ad2 matrix, tensor b as bd1-by-bd2 matrix, then
  * calculates matrix dot a * b and stores in tensor out.
  */
 template<typename xpu>
@@ -205,7 +205,8 @@ void TensordotImpl(const Tuple<int>& a_axes_summed,
                    const TBlob& a,
                    const TBlob& b,
                    const TBlob& out,
-                   const std::vector<OpReqType>& req) {
+                   const std::vector<OpReqType>& req,
+                   const Tensor<xpu, 1, char>& workspace) {
   if (req[0] == kNullOp) {
     return;
   }
@@ -264,10 +265,8 @@ void TensordotImpl(const Tuple<int>& a_axes_summed,
       mxnet::TShape a_temp_shape = GetReorderedShape(a_shape, a_axes);
       mxnet::TShape b_temp_shape = GetReorderedShape(b_shape, b_axes);
 
-      Tensor<xpu, 1, DType> workspace = ctx.requested[0].get_space_typed<xpu, 1, DType>
-        (Shape1(a.Size() + b.Size()), s);
       DType* a_ptr = reinterpret_cast<DType*>(workspace.dptr_);
-      DType* b_ptr = reinterpret_cast<DType*>(workspace.dptr_ + a.Size());
+      DType* b_ptr = reinterpret_cast<DType*>(workspace.dptr_ + a.Size() * sizeof(DType));
       TBlob a_res = TBlob(a_ptr, a_temp_shape, xpu::kDevMask);
       TBlob b_res = TBlob(b_ptr, b_temp_shape, xpu::kDevMask);
 
@@ -281,6 +280,46 @@ void TensordotImpl(const Tuple<int>& a_axes_summed,
   });
 }
 
+/**
+ * Calculates workspace size of tensordot.
+ */
+template<typename xpu>
+size_t TensordotWorkspaceSize(const Tuple<int>& a_axes_summed,
+                              const Tuple<int>& b_axes_summed,
+                              const TBlob& a,
+                              const TBlob& b,
+                              const TBlob& out,
+                              const std::vector<OpReqType>& req) {
+  if (req[0] == kNullOp) {
+    return 0U;
+  }
+
+  if (out.shape_.Size() == 0U) {
+    return 0U;  // zero-size output, no need to launch kernel
+  }
+
+  const mxnet::TShape& a_shape = a.shape_;
+  const mxnet::TShape& b_shape = b.shape_;
+
+  MSHADOW_REAL_TYPE_SWITCH(out.type_flag_, DType, {
+    if (a_shape.Size() == 0U || b_shape.Size() == 0U) {
+      // 0-size input
+      return 0U;
+    } else if (a_shape.ndim() == 0 && b_shape.ndim() == 0) {
+      // Both 0-D scalars, equivalent to multiply
+      return 0U;
+    } else if (a_shape.ndim() == 0 || b_shape.ndim() == 0) {
+      // Either of them is a scalar, just scale by one of them
+      return 0U;
+    } else {
+      // Two tensors of at least 1 dimensions.
+      return (a.Size() + b.Size()) * sizeof(DType);
+    }
+  });
+  LOG(FATAL) << "InternalError: cannot reach here";
+  return 0U;
+}
+
 /**
  * forward function
  */
@@ -306,7 +345,10 @@ void TensordotOpForward(const nnvm::NodeAttrs& attrs,
   ShiftAxes(&a_axes_summed, a_shape.ndim());
   ShiftAxes(&b_axes_summed, b_shape.ndim());
 
-  TensordotImpl<xpu>(a_axes_summed, b_axes_summed, ctx, a, b, out, req);
+  size_t workspace_size = TensordotWorkspaceSize<xpu>(a_axes_summed, b_axes_summed, a, b, out, req);
+  Tensor<xpu, 1, char> workspace = ctx.requested[0].get_space_typed<xpu, 1, char>(
+    Shape1(workspace_size), ctx.get_stream<xpu>());
+  TensordotImpl<xpu>(a_axes_summed, b_axes_summed, ctx, a, b, out, req, workspace);
 }
 
 /**
@@ -332,7 +374,8 @@ void TensordotBackwardImpl(const Tuple<int>& a_axes_summed,
                            const TBlob& b,
                            const TBlob& grad_a,
                            const TBlob& grad_b,
-                           const std::vector<OpReqType>& req) {
+                           const std::vector<OpReqType>& req,
+                           const Tensor<xpu, 1, char>& workspace) {
   mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
 
   const mxnet::TShape& a_shape = a.shape_;
@@ -366,12 +409,15 @@ void TensordotBackwardImpl(const Tuple<int>& a_axes_summed,
       const OpReqType& scalar_req = (a_shape.ndim() == 0) ? req[0] : req[1];
       ASSIGN_DISPATCH(tensor_grad_, tensor_req,
                       broadcast_scalar(scalar_, tensor_grad_.shape_) * out_grad_);
-      Tensor<xpu, 1, DType> workspace =
-        ctx.requested[0].get_space_typed<xpu, 1, DType>(Shape1(out_grad.shape_.Size()), s);
-      ASSIGN_DISPATCH(workspace, kWriteTo, tensor_ * out_grad_);
+      Tensor<xpu, 1, DType> dtypespace =
+        Tensor<xpu, 1, DType>(reinterpret_cast<DType*>(workspace.dptr_),
+                              workspace.shape_,
+                              workspace.stride_,
+                              workspace.stream_);
+      ASSIGN_DISPATCH(dtypespace, kWriteTo, tensor_ * out_grad_);
 
       ReduceAxesComputeImpl<xpu, mshadow_op::sum, true>(
-        ctx, {TBlob(workspace)}, {scalar_req}, {TBlob(scalar_grad_)}, scalar_grad_.shape_);
+        ctx, {TBlob(dtypespace)}, {scalar_req}, {TBlob(scalar_grad_)}, scalar_grad_.shape_);
     } else {
       // Two tensors of at least 1 dimensions.
       Tuple<int> a_axes_remained;
@@ -405,12 +451,13 @@ void TensordotBackwardImpl(const Tuple<int>& a_axes_summed,
       mxnet::TShape b_temp_shape(GetReorderedShape(b_shape, b_axes));
       mxnet::TShape b_T_temp_shape(GetReorderedShape(b_shape, b_T_axes));
 
-      Tensor<xpu, 1, DType> workspace = ctx.requested[0].get_space_typed<xpu, 1, DType>
-        (Shape1((a.Size() + b.Size()) * 2), s);
       DType* a_ptr = reinterpret_cast<DType*>(workspace.dptr_);
-      DType* a_ptr2 = reinterpret_cast<DType*>(workspace.dptr_ + a.Size());
-      DType* b_ptr = reinterpret_cast<DType*>(workspace.dptr_ + 2 * a.Size());
-      DType* b_ptr2 = reinterpret_cast<DType*>(workspace.dptr_ + 2 * a.Size() + b.Size());
+      DType* a_ptr2 = reinterpret_cast<DType*>(workspace.dptr_ + a.Size() * sizeof(DType));
+      DType* b_ptr = reinterpret_cast<DType*>(workspace.dptr_ + 2 * a.Size() * sizeof(DType));
+      DType* b_ptr2 = reinterpret_cast<DType*>(workspace.dptr_ +
+                                               (2 * a.Size() +
+                                               b.Size()) *
+                                               sizeof(DType));
 
       TBlob a_res = TBlob(a_ptr, a_temp_shape, xpu::kDevMask);
       TBlob b_res = TBlob(b_ptr, b_temp_shape, xpu::kDevMask);
@@ -431,6 +478,39 @@ void TensordotBackwardImpl(const Tuple<int>& a_axes_summed,
   });
 }
 
+/**
+ * Calculates workspace size of tensordot backward.
+ */
+template<typename xpu>
+size_t TensordotBackwardWorkspaceSize(const Tuple<int>& a_axes_summed,
+                                      const Tuple<int>& b_axes_summed,
+                                      const TBlob& out_grad,
+                                      const TBlob& a,
+                                      const TBlob& b,
+                                      const TBlob& grad_a,
+                                      const TBlob& grad_b,
+                                      const std::vector<OpReqType>& req) {
+  const mxnet::TShape& a_shape = a.shape_;
+  const mxnet::TShape& b_shape = b.shape_;
+
+  if ((a_shape.Size() == 0U) || (b_shape.Size() == 0U)) {
+    return 0U;  // zero-size output, no need to launch kernel
+  }
+  MSHADOW_REAL_TYPE_SWITCH(out_grad.type_flag_, DType, {
+    if (a_shape.ndim() == 0 && b_shape.ndim() == 0) {
+      // Both 0-D scalars, equivalent to multiply
+      return 0U;
+    } else if (a_shape.ndim() == 0 || b_shape.ndim() == 0) {
+      // Either of them is a scalar, just scale by one of them
+      return out_grad.shape_.Size() * sizeof(DType);
+    } else {
+      return (a.Size() + b.Size()) * 2 * sizeof(DType);
+    }
+  });
+  LOG(FATAL) << "InternalError: cannot reach here";
+  return 0U;
+}
+
 /**
  * backward function.
  */
@@ -458,8 +538,14 @@ void TensordotOpBackward(const nnvm::NodeAttrs& attrs,
   ShiftAxes(&a_axes_summed, a_shape.ndim());
   ShiftAxes(&b_axes_summed, b_shape.ndim());
 
+  size_t workspace_size = TensordotBackwardWorkspaceSize<xpu>(a_axes_summed, b_axes_summed,
+                                                              out_grad, a, b, grad_a,
+                                                              grad_b, req);
+  Tensor<xpu, 1, char> workspace =
+    ctx.requested[0].get_space_typed<xpu, 1, char>(Shape1(workspace_size),
+                                                   ctx.get_stream<xpu>());
   TensordotBackwardImpl<xpu>(a_axes_summed, b_axes_summed, ctx, out_grad, a, b, grad_a,
-                             grad_b, req);
+                             grad_b, req, workspace);
 }
 
 struct TensordotIntAxesParam : public dmlc::Parameter<TensordotIntAxesParam> {
diff --git a/src/operator/numpy/np_tensordot_op.cc b/src/operator/numpy/np_tensordot_op.cc
index 50c1647e0264..aca45c1652ee 100644
--- a/src/operator/numpy/np_tensordot_op.cc
+++ b/src/operator/numpy/np_tensordot_op.cc
@@ -111,7 +111,7 @@ NNVM_REGISTER_OP(_npi_tensordot)
 .set_attr<nnvm::FInferType>("FInferType", mxnet::op::ElemwiseType<2, 1>)
 .set_attr<FResourceRequest>("FResourceRequest",
   [](const NodeAttrs& attrs) {
-    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+    return std::vector<ResourceRequest>(1, ResourceRequest::kTempSpace);
   })
 .set_attr<FCompute>("FCompute<cpu>", TensordotOpForward<cpu>)
 .set_attr<nnvm::FGradient>("FGradient", mxnet::op::ElemwiseGradUseIn{"_backward_npi_tensordot"})
@@ -126,7 +126,7 @@ NNVM_REGISTER_OP(_backward_npi_tensordot)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
 .set_attr<FResourceRequest>("FResourceRequest",
   [](const NodeAttrs& attrs) {
-    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+    return std::vector<ResourceRequest>(1, ResourceRequest::kTempSpace);
   })
 .set_attr<FCompute>("FCompute<cpu>", TensordotOpBackward<cpu>);
 
@@ -211,7 +211,7 @@ NNVM_REGISTER_OP(_npi_tensordot_int_axes)
 .set_attr<nnvm::FInferType>("FInferType", mxnet::op::ElemwiseType<2, 1>)
 .set_attr<FResourceRequest>("FResourceRequest",
   [](const NodeAttrs& attrs) {
-    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+    return std::vector<ResourceRequest>(1, ResourceRequest::kTempSpace);
   })
 .set_attr<FCompute>("FCompute<cpu>", TensordotIntAxesOpForward<cpu>)
 .set_attr<nnvm::FGradient>("FGradient",
@@ -227,7 +227,7 @@ NNVM_REGISTER_OP(_backward_npi_tensordot_int_axes)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
 .set_attr<FResourceRequest>("FResourceRequest",
   [](const NodeAttrs& attrs) {
-    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+    return std::vector<ResourceRequest>(1, ResourceRequest::kTempSpace);
   })
 .set_attr<FCompute>("FCompute<cpu>", TensordotIntAxesOpBackward<cpu>);
 
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index ac30f996ba18..f22d42bb678b 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -42,6 +42,78 @@ def get_workloads(name):
         return OpArgMngr._args.get(name, None)
 
 
+def _add_workload_einsum():
+    chars = 'abcdefghij'
+    sizes = [2, 3, 4, 5, 4, 3, 2, 6, 5, 4]
+    size_dict = dict(zip(chars, sizes))
+
+    configs = [
+        ('ij...,j...->ij...', [(2, 3, 4), (3,)]),
+        ('ij...,...j->ij...', [(2, 3, 4), (3,)]),
+        ('ij...,j->ij...', [(2, 3, 4), (3,)]),
+        ('cl, cpx->lpx', [(2, 3), (2, 3, 2731)]),
+        ('aabb->ab', [(5, 5, 5, 5)]),
+        ('mi,mi,mi->m', [(5, 5), (5, 5), (5, 5)]),
+        ('a,ab,abc->abc', None),
+        ('a,b,ab->ab', None),
+        ('ea,fb,gc,hd,abcd->efgh', None),
+        ('ea,fb,abcd,gc,hd->efgh', None),
+        ('abcd,ea,fb,gc,hd->efgh', None),
+        # test_complex
+        ('acdf,jbje,gihb,hfac,gfac,gifabc,hfac', None),
+        ('acdf,jbje,gihb,hfac,gfac,gifabc,hfac', None),
+        ('cd,bdhe,aidb,hgca,gc,hgibcd,hgac', None),
+        ('abhe,hidj,jgba,hiab,gab', None),
+        ('bde,cdh,agdb,hica,ibd,hgicd,hiac', None),
+        ('chd,bde,agbc,hiad,hgc,hgi,hiad', None),
+        ('chd,bde,agbc,hiad,bdi,cgh,agdb', None),
+        ('bdhe,acad,hiab,agac,hibd', None),
+        # test_collapse
+        ('ab,ab,c->', None),
+        ('ab,ab,c->c', None),
+        ('ab,ab,cd,cd->', None),
+        ('ab,ab,cd,cd->ac', None),
+        ('ab,ab,cd,cd->cd', None),
+        ('ab,ab,cd,cd,ef,ef->', None),
+        # test_inner_product
+        ('ab,ab', None),
+        ('ab,ba', None),
+        ('abc,abc', None),
+        ('abc,bac', None),
+        ('abc,cba', None),
+        # test_random_cases
+        ('aab,fa,df,ecc->bde', None),
+        ('ecb,fef,bad,ed->ac', None),
+        ('bcf,bbb,fbf,fc->', None),
+        ('bb,ff,be->e', None),
+        ('bcb,bb,fc,fff->', None),
+        ('fbb,dfd,fc,fc->', None),
+        ('afd,ba,cc,dc->bf', None),
+        ('adb,bc,fa,cfc->d', None),
+        ('bbd,bda,fc,db->acf', None),
+        ('dba,ead,cad->bce', None),
+        ('aef,fbc,dca->bde', None),
+        # test_broadcasting_dot_cases
+        ('ijk,kl,jl', [(1, 5, 4), (4, 6), (5, 6)]),
+        ('ijk,kl,jl,i->i', [(1, 5, 4), (4, 6), (5, 6), (10)]),
+        ('abjk,kl,jl', [(1, 1, 5, 4), (4, 6), (5, 6)]),
+        ('abjk,kl,jl,ab->ab', [(1, 1, 5, 4), (4, 6), (5, 6), (7, 7)]),
+        ('obk,ijk->ioj', [(2, 4, 8), (2, 4, 8)]),
+    ]
+    for optimize in [False, True]:
+        for config in configs:
+            subscripts, args = config
+            if args is None:
+                args = []
+                terms = subscripts.split('->')[0].split(',')
+                for term in terms:
+                    dims = [size_dict[x] for x in term]
+                    args.append(np.random.uniform(size=dims))
+            else:
+                args = [np.random.uniform(size=arg) for arg in args]
+            OpArgMngr.add_workload('einsum', subscripts, *args, optimize=optimize)
+
+
 @use_np
 def _prepare_workloads():
     array_pool = {
@@ -164,6 +236,7 @@ def _prepare_workloads():
     OpArgMngr.add_workload('meshgrid', np.array([1, 2, 3]))
     OpArgMngr.add_workload('meshgrid', np.array([1, 2, 3]), np.array([4, 5, 6, 7]))
     OpArgMngr.add_workload('meshgrid', np.array([1, 2, 3]), np.array([4, 5, 6, 7]), indexing='ij')
+    _add_workload_einsum()
 
     # workloads for array ufunc protocol
     OpArgMngr.add_workload('add', array_pool['4x1'], array_pool['1x2'])
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 2303c9cee29c..cc98bffe59ba 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -3305,6 +3305,137 @@ def hybrid_forward(self, F, a, *args, **kwargs):
                     assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
 
 
+@with_seed()
+@use_np
+def test_np_einsum():
+    class TestEinsum(HybridBlock):
+        def __init__(self, subscripts, optimize):
+            super(TestEinsum, self).__init__()
+            self.subscripts = subscripts
+            self.optimize = optimize
+
+        def hybrid_forward(self, F, *operands):
+            return F.np.einsum(self.subscripts, *operands, optimize=self.optimize)
+
+    def dbg(name, data):
+        print('type of {} = {}'.format(name, type(data)))
+        print('shape of {} = {}'.format(name, data.shape))
+        print('{} = {}'.format(name, data))
+
+    configs = [
+        ('ii', [(5, 5)], lambda *args: (_np.eye(5),)),
+        ('ii->i', [(5, 5)], lambda *args: (_np.eye(5),)),
+        ('ij->i', [(5, 5)], lambda *args: (_np.ones((5, 5)),)),
+        ('...j->...', [(5, 5)], lambda *args: (_np.ones((5, 5)),)),
+        ('ji', [(2, 3)], lambda *args: (_np.ones((2, 3)),)),
+        ('ij->ji', [(2, 3)], lambda *args: (_np.ones((2, 3)),)),
+        ('i, i', [(5,), (5,)], lambda *args: (args[1], args[0])),
+        ('ij, j', [(5, 5), (5,)], lambda *args: (_np.tile(args[1][None, :], [5, 1]),
+                                                 args[0].sum(axis=0))),
+        ('...j, j', [(5, 5), (5,)], lambda *args: (_np.tile(args[1][None, :], [5, 1]),
+                                                   _np.sum(args[0], axis=0))),
+        ('..., ...', [(), (2, 3)], lambda *args: (_np.sum(args[1], axis=None),
+                                                  args[0] * _np.ones((2, 3)))),
+        (', ij', [(), (2, 3)], lambda *args: (_np.sum(args[1], axis=None),
+                                              args[0] * _np.ones((2, 3)))),
+        ('i, j', [(2,), (5, )], lambda *args: (_np.sum(args[1], axis=None) * _np.ones(2),
+                                               _np.sum(args[0], axis=None) * _np.ones(5))),
+        ('ijk, jil->kl', [(3, 4, 5), (4, 3, 2)], lambda *args: (_np.tile(_np.transpose(_np.sum(args[1],
+                                                                                               axis=-1))[:, :, None],
+                                                                         [1, 1, 5]),
+                                                                _np.tile(_np.transpose(_np.sum(args[0],
+                                                                                               axis=-1))[:, :, None],
+                                                                         [1, 1, 2]))),
+        ('ii->i', [(3, 3)], lambda *args: (_np.eye(3),)),
+        ('ki, jk->ij', [(3, 2), (4, 3)], lambda *args: (_np.tile(args[1].sum(axis=0)[:, None], [1, 2]),
+                                                        _np.tile(args[0].sum(axis=1)[None, :], [4, 1]))),
+        ('ki, ...k->i...', [(3, 2), (4, 3)], lambda *args: (_np.tile(args[1].sum(axis=0)[:, None], [1, 2]),
+                                                            _np.tile(args[0].sum(axis=1)[None, :], [4, 1]))),
+        ('k..., jk', [(3, 2), (4, 3)], lambda *args: (_np.tile(args[1].sum(axis=0)[:, None], [1, 2]),
+                                                      _np.tile(args[0].sum(axis=1)[None, :], [4, 1]))),
+        ('ij, jk', [(5, 0), (0, 4)], lambda *args: (_np.empty((5, 0)), _np.empty((0, 4)))),
+        (('ij,jk,kl->il'), [(2, 2), (2, 5), (5, 2)], lambda *args: (_np.dot(_np.ones((2, 2)), _np.dot(args[1], args[2]).T),
+                                                                    _np.dot(args[0].T, _np.dot(_np.ones((2, 2)), args[2].T)),
+                                                                    _np.dot(_np.dot(args[0], args[1]).T, _np.ones((2, 2))))),
+        # broadcast bug
+        (('ij, ij -> i'), [(1, 4), (2, 4)], lambda *args: (_np.sum(args[1], axis=0)[None, :],
+                                                           _np.tile(args[0], [2, 1]))),
+    ]
+    dtypes = ['int32', 'float16', 'float32', 'float64']
+    for hybridize in [False, True]:
+        for dtype in dtypes:
+            for config in configs:
+                for optimize in [False, True]:
+                    rtol = 1e-0 if dtype == 'float16' else 1e-3
+                    atol = 1e-1 if dtype == 'float16' else 1e-5
+                    (subscripts, operands, get_grad) = config
+                    test_einsum = TestEinsum(subscripts, optimize)
+                    if hybridize:
+                        test_einsum.hybridize()
+                    x = []
+                    x_np = []
+                    for shape in operands:
+                        x_np.append(_np.array(_np.random.uniform(-10.0, 10.0, shape),
+                                            dtype=dtype))
+                        x.append(np.array(x_np[-1], dtype=dtype))
+                        x[-1].attach_grad()
+                    expected_np = _np.einsum(subscripts, *x_np, optimize=optimize)
+                    with mx.autograd.record():
+                        out_mx = test_einsum(*x)
+                    assert out_mx.shape == expected_np.shape
+                    assert_almost_equal(out_mx.asnumpy(), expected_np, rtol=rtol, atol=atol)
+                    out_mx.backward()
+                    for (iop, op) in enumerate(x):
+                        assert_almost_equal(op.grad.asnumpy(), get_grad(*x_np)[iop], rtol=rtol, atol=atol)
+
+                    # Test imperative once again
+                    for op in x:
+                        op.attach_grad()
+                    with mx.autograd.record():
+                        out_mx = np.einsum(subscripts, *x, optimize=optimize)
+                    out_mx.backward()
+                    expected_np = _np.einsum(subscripts, *x_np, optimize=optimize)
+                    assert_almost_equal(out_mx.asnumpy(), expected_np, rtol=rtol, atol=atol)
+                    for (iop, op) in enumerate(x):
+                        assert_almost_equal(op.grad.asnumpy(), get_grad(*x_np)[iop], rtol=rtol, atol=atol)
+    configs = [
+        (('ij,jk,kl->il'), [(2, 2), (2, 5), (5, 2)]),
+        (('ea,fb,abcd,gc,hd->efgh'), [(5, 5), (5, 5), (5, 5, 5, 5), (5, 5), (5, 5)]),
+    ]
+    dtypes = ['int32', 'float32', 'float64']
+    for hybridize in [False, True]:
+        for dtype in dtypes:
+            for config in configs:
+                (subscripts, operands) = config
+                rtol = 1e-0 if dtype == 'float16' else 1e-2
+                atol = 1e-1 if dtype == 'float16' else 1e-2
+                grad = []
+                x_np = []
+                for shape in operands:
+                    x_np.append(_np.array(_np.random.uniform(-2.0, 2.0, shape),
+                                          dtype=dtype))
+                for optimize in [False, True]:
+                    x = []
+                    for (iop, op) in enumerate(operands):
+                        x.append(np.array(x_np[iop], dtype=dtype))
+                        x[-1].attach_grad()
+                    test_einsum = TestEinsum(subscripts, optimize)
+                    if hybridize:
+                        test_einsum.hybridize()
+                    expected_np = _np.einsum(subscripts, *x_np, optimize=optimize)
+                    with mx.autograd.record():
+                        out_mx = test_einsum(*x)
+                    assert out_mx.shape == expected_np.shape
+                    assert_almost_equal(out_mx.asnumpy(), expected_np, rtol=rtol, atol=atol)
+                    out_mx.backward()
+                    cur_grad = []
+                    for (iop, op) in enumerate(x):
+                        cur_grad.append(op.grad.asnumpy())
+                    grad.append(cur_grad)
+                for (iop, op) in enumerate(grad[0]):
+                    assert_almost_equal(grad[0][iop], grad[1][iop], rtol=rtol, atol=atol)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 9fecfbb8c3f15255bec0e832336a6ea921af91ff Mon Sep 17 00:00:00 2001
From: reminisce <wujun.nju@gmail.com>
Date: Sat, 19 Oct 2019 08:19:36 -0700
Subject: [PATCH 473/813] Add test pipeline for USE_TVM_OP=OFF on Unix (#16450)

* Add no tvm op build and test pipelines

* Add steps

* Modify display

* Fix ci

* Try to fix one unit test

* Fix

* Address cr
---
 ci/docker/runtime_functions.sh  | 71 +++++++++++++++++++++++++++++++
 ci/jenkins/Jenkins_steps.groovy | 75 +++++++++++++++++++++++++++++++++
 ci/jenkins/Jenkinsfile_unix_cpu |  4 +-
 ci/jenkins/Jenkinsfile_unix_gpu |  9 ++--
 4 files changed, 155 insertions(+), 4 deletions(-)

diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index f6696e3682a1..ba69930e915c 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -522,6 +522,29 @@ build_ubuntu_cpu_cmake_debug() {
     popd
 }
 
+build_ubuntu_cpu_cmake_no_tvm_op() {
+    set -ex
+    pushd .
+    cd /work/build
+    build_ccache_wrappers
+    cmake \
+        -DCMAKE_CXX_COMPILER_LAUNCHER=ccache \
+        -DCMAKE_C_COMPILER_LAUNCHER=ccache \
+        -DUSE_CUDA=OFF \
+        -DUSE_TVM_OP=OFF \
+        -DPython3_EXECUTABLE=/usr/bin/python3 \
+        -DUSE_MKL_IF_AVAILABLE=OFF \
+        -DUSE_OPENMP=OFF \
+        -DUSE_OPENCV=ON \
+        -DUSE_SIGNAL_HANDLER=ON \
+        -DCMAKE_BUILD_TYPE=Release \
+        -G Ninja \
+        /work/mxnet
+
+    ninja -v
+    popd
+}
+
 build_ubuntu_cpu_cmake_asan() {
     set -ex
 
@@ -793,6 +816,27 @@ build_ubuntu_gpu_cuda101_cudnn7() {
     make cython PYTHON=python3
 }
 
+build_ubuntu_gpu_cuda101_cudnn7_no_tvm_op() {
+    set -ex
+    build_ccache_wrappers
+    make \
+        DEV=1                                     \
+        USE_BLAS=openblas                         \
+        USE_MKLDNN=0                              \
+        USE_CUDA=1                                \
+        USE_CUDA_PATH=/usr/local/cuda             \
+        USE_CUDNN=1                               \
+        USE_TVM_OP=0                              \
+        USE_CPP_PACKAGE=1                         \
+        USE_DIST_KVSTORE=1                        \
+        CUDA_ARCH="$CI_CUDA_COMPUTE_CAPABILITIES" \
+        USE_SIGNAL_HANDLER=1                      \
+        -j$(nproc)
+
+    make cython PYTHON=python2
+    make cython PYTHON=python3
+}
+
 build_ubuntu_amalgamation() {
     set -ex
     # Amalgamation can not be run with -j nproc
@@ -865,6 +909,33 @@ build_ubuntu_gpu_cmake() {
     ninja -v
 }
 
+build_ubuntu_gpu_cmake_no_tvm_op() {
+    set -ex
+    cd /work/build
+    build_ccache_wrappers
+    cmake \
+        -DCMAKE_CXX_COMPILER_LAUNCHER=ccache    \
+        -DCMAKE_C_COMPILER_LAUNCHER=ccache      \
+        -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache   \
+        -DUSE_SIGNAL_HANDLER=ON                 \
+        -DUSE_CUDA=ON                           \
+        -DUSE_CUDNN=ON                          \
+        -DUSE_TVM_OP=OFF                        \
+        -DPython3_EXECUTABLE=/usr/bin/python3   \
+        -DUSE_MKL_IF_AVAILABLE=OFF              \
+        -DUSE_MKLML_MKL=OFF                     \
+        -DUSE_MKLDNN=OFF                        \
+        -DUSE_DIST_KVSTORE=ON                   \
+        -DCMAKE_BUILD_TYPE=Release              \
+        -DCUDA_ARCH_NAME=Manual                 \
+        -DCUDA_ARCH_BIN=$CI_CMAKE_CUDA_ARCH_BIN \
+        -DBUILD_CYTHON_MODULES=1                \
+        -G Ninja                                \
+        /work/mxnet
+
+    ninja -v
+}
+
 build_ubuntu_cpu_large_tensor() {
     set -ex
     cd /work/build
diff --git a/ci/jenkins/Jenkins_steps.groovy b/ci/jenkins/Jenkins_steps.groovy
index 7473a08071bf..7f6200a28016 100644
--- a/ci/jenkins/Jenkins_steps.groovy
+++ b/ci/jenkins/Jenkins_steps.groovy
@@ -31,6 +31,7 @@ mx_pip = 'build/*.whl'
 
 // mxnet cmake libraries, in cmake builds we do not produce a libnvvm static library by default.
 mx_cmake_lib = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so'
+mx_cmake_lib_no_tvm_op = 'build/libmxnet.so, build/libmxnet.a, build/libsample_lib.so, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so'
 mx_cmake_lib_cython = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
 // mxnet cmake libraries, in cmake builds we do not produce a libnvvm static library by default.
 mx_cmake_lib_debug = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/libsample_lib.so, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests'
@@ -38,6 +39,7 @@ mx_cmake_mkldnn_lib = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/l
 mx_mkldnn_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, libsample_lib.so, lib/libiomp5.so, lib/libmkldnn.so.0, lib/libmklml_intel.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a'
 mx_tensorrt_lib = 'build/libmxnet.so, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, lib/libnvonnxparser_runtime.so.0, lib/libnvonnxparser.so.0, lib/libonnx_proto.so, lib/libonnx.so'
 mx_lib_cpp_examples = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, 3rdparty/ps-lite/build/libps.a, deps/lib/libprotobuf-lite.a, deps/lib/libzmq.a, build/cpp-package/example/*, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
+mx_lib_cpp_examples_no_tvm_op = 'lib/libmxnet.so, lib/libmxnet.a, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, 3rdparty/ps-lite/build/libps.a, deps/lib/libprotobuf-lite.a, deps/lib/libzmq.a, build/cpp-package/example/*, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
 mx_lib_cpp_examples_cpu = 'build/libmxnet.so, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/cpp-package/example/*'
 
 // Python unittest for CPU
@@ -133,6 +135,20 @@ def compile_unix_openblas_debug_cpu() {
     }]
 }
 
+def compile_unix_openblas_cpu_no_tvm_op() {
+    return ['CPU: Openblas, cmake, TVM_OP OFF': {
+      node(NODE_LINUX_CPU) {
+        ws('workspace/build-cpu-openblas-no-tvm-op') {
+          timeout(time: max_time, unit: 'MINUTES') {
+            utils.init_git()
+            utils.docker_run('ubuntu_cpu', 'build_ubuntu_cpu_cmake_no_tvm_op', false)
+            utils.pack_lib('cpu_openblas_no_tvm_op', mx_cmake_lib_no_tvm_op)
+          }
+        }
+      }
+    }]
+}
+
 def compile_unix_int64_cpu() {
     return ['CPU: USE_INT64_TENSOR_SIZE': {
       node(NODE_LINUX_CPU) {
@@ -245,6 +261,20 @@ def compile_unix_full_gpu() {
     }]
 }
 
+def compile_unix_full_gpu_no_tvm_op() {
+    return ['GPU: CUDA10.1+cuDNN7 TVM_OP OFF': {
+      node(NODE_LINUX_CPU) {
+        ws('workspace/build-gpu-no-tvm-op') {
+          timeout(time: max_time, unit: 'MINUTES') {
+            utils.init_git()
+            utils.docker_run('ubuntu_build_cuda', 'build_ubuntu_gpu_cuda101_cudnn7_no_tvm_op', false)
+            utils.pack_lib('gpu_no_tvm_op', mx_lib_cpp_examples_no_tvm_op)
+          }
+        }
+      }
+    }]
+}
+
 def compile_unix_cmake_mkldnn_gpu() {
     return ['GPU: CMake MKLDNN': {
       node(NODE_LINUX_CPU) {
@@ -273,6 +303,19 @@ def compile_unix_cmake_gpu() {
     }]
 }
 
+def compile_unix_cmake_gpu_no_tvm_op() {
+    return ['GPU: CMake TVM_OP OFF': {
+      node(NODE_LINUX_CPU) {
+        ws('workspace/build-cmake-gpu-no-tvm-op') {
+          timeout(time: max_time, unit: 'MINUTES') {
+            utils.init_git()
+            utils.docker_run('ubuntu_gpu_cu101', 'build_ubuntu_gpu_cmake_no_tvm_op', false)
+          }
+        }
+      }
+    }]
+}
+
 def compile_unix_tensorrt_gpu() {
     return ['TensorRT': {
       node(NODE_LINUX_CPU) {
@@ -756,6 +799,22 @@ def test_unix_python3_gpu() {
     }]
 }
 
+def test_unix_python3_gpu_no_tvm_op() {
+    return ['Python3: GPU TVM_OP OFF': {
+      node(NODE_LINUX_GPU) {
+        ws('workspace/ut-python3-gpu-no-tvm-op') {
+          try {
+            utils.unpack_and_init('gpu_no_tvm_op', mx_lib_cpp_examples_no_tvm_op)
+            python3_gpu_ut_cython('ubuntu_gpu_cu101')
+            utils.publish_test_coverage()
+          } finally {
+            utils.collect_test_results_unix('nosetests_gpu.xml', 'nosetests_python3_gpu.xml')
+          }
+        }
+      }
+    }]
+}
+
 def test_unix_python3_quantize_gpu() {
     return ['Python3: Quantize GPU': {
       node(NODE_LINUX_GPU_P3) {
@@ -790,6 +849,22 @@ def test_unix_python3_debug_cpu() {
     }]
 }
 
+def test_unix_python3_cpu_no_tvm_op() {
+    return ['Python3: CPU TVM_OP OFF': {
+      node(NODE_LINUX_CPU) {
+        ws('workspace/ut-python3-cpu-no-tvm-op') {
+          try {
+            utils.unpack_and_init('cpu_openblas_no_tvm_op', mx_cmake_lib_no_tvm_op)
+            python3_ut('ubuntu_cpu')
+          } finally {
+            utils.collect_test_results_unix('nosetests_unittest.xml', 'nosetests_python3_cpu_no_tvm_op_unittest.xml')
+            utils.collect_test_results_unix('nosetests_quantization.xml', 'nosetests_python3_cpu_no_tvm_op_quantization.xml')
+          }
+        }
+      }
+    }]
+}
+
 def test_unix_python2_mkldnn_cpu() {
     return ['Python2: MKLDNN-CPU': {
       node(NODE_LINUX_CPU) {
diff --git a/ci/jenkins/Jenkinsfile_unix_cpu b/ci/jenkins/Jenkinsfile_unix_cpu
index 3533a123ea86..931db9dce77f 100644
--- a/ci/jenkins/Jenkinsfile_unix_cpu
+++ b/ci/jenkins/Jenkinsfile_unix_cpu
@@ -39,7 +39,8 @@ core_logic: {
     custom_steps.compile_unix_mkl_cpu(),
     custom_steps.compile_unix_mkldnn_cpu(),
     custom_steps.compile_unix_mkldnn_mkl_cpu(),
-    custom_steps.compile_unix_int64_cpu()
+    custom_steps.compile_unix_int64_cpu(),
+    custom_steps.compile_unix_openblas_cpu_no_tvm_op(),
   ])
 
   utils.parallel_stage('Tests', [
@@ -68,6 +69,7 @@ core_logic: {
      *  https://github.com/apache/incubator-mxnet/issues/11801
     custom_steps.test_unix_distributed_kvstore_cpu()
     */
+    custom_steps.test_unix_python3_cpu_no_tvm_op(),
   ])
 }
 ,
diff --git a/ci/jenkins/Jenkinsfile_unix_gpu b/ci/jenkins/Jenkinsfile_unix_gpu
index e2a089b0469b..18e27198c330 100644
--- a/ci/jenkins/Jenkinsfile_unix_gpu
+++ b/ci/jenkins/Jenkinsfile_unix_gpu
@@ -40,8 +40,10 @@ core_logic: {
     custom_steps.compile_unix_cmake_mkldnn_gpu(),
     custom_steps.compile_unix_cmake_gpu(),
     custom_steps.compile_unix_tensorrt_gpu(),
-    custom_steps.compile_unix_int64_gpu()
-  ]) 
+    custom_steps.compile_unix_int64_gpu(),
+    custom_steps.compile_unix_full_gpu_no_tvm_op(),
+    custom_steps.compile_unix_cmake_gpu_no_tvm_op(),
+  ])
 
   utils.parallel_stage('Tests', [
     custom_steps.test_unix_python2_gpu(),
@@ -60,7 +62,8 @@ core_logic: {
     custom_steps.test_unix_cpp_package_gpu(),
     custom_steps.test_unix_scala_gpu(),
     custom_steps.test_unix_distributed_kvstore_gpu(),
-    custom_steps.test_static_python_gpu()
+    custom_steps.test_static_python_gpu(),
+    custom_steps.test_unix_python3_gpu_no_tvm_op(),
 
     // Disabled due to: https://github.com/apache/incubator-mxnet/issues/11407
     //custom_steps.test_unix_caffe_gpu()

From b58305955499d6dda4f21fcd08902827c6cef99c Mon Sep 17 00:00:00 2001
From: "Huang, Guangtai" <guangtai@amazon.com>
Date: Sat, 19 Oct 2019 23:20:54 +0800
Subject: [PATCH 474/813] Numpy dispatch test of ...... (#16422)

* abs

arctan2

argmax

around

broadcast_to

cbrt

handel NaN

clip

copysign

degrees

equal

disable 'equal' now

dot

`fix`

flip

hypot

lcm

ldexp

inner

sqrt

square

add true_divide and its test

remove 'equal'

slightly fix 'unique' and add its test

sum

fix bug of 'take' and add its test

support py2

fix lint

try to pass ci

change variable name

* Refactor interoperability test

* fix typo
---
 python/mxnet/ndarray/numpy/_op.py             |  44 +-
 python/mxnet/numpy/multiarray.py              |  31 +-
 python/mxnet/numpy/utils.py                   |   4 +-
 python/mxnet/numpy_dispatch_protocol.py       |  15 +-
 python/mxnet/symbol/numpy/_symbol.py          |  14 +-
 .../unittest/test_numpy_interoperability.py   | 577 ++++++++++++++++--
 6 files changed, 629 insertions(+), 56 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index ad76e43b2a90..cf66e29d6205 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -38,7 +38,7 @@
            'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
            'around', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
            'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal',
-           'hsplit', 'rot90', 'einsum']
+           'hsplit', 'rot90', 'einsum', 'true_divide']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -337,10 +337,10 @@ def take(a, indices, axis=None, mode='raise', out=None):
     if mode not in ('wrap', 'clip', 'raise'):
         raise NotImplementedError(
             "function take does not support mode '{}'".format(mode))
-    if axis:
-        return _npi.take(a, indices, axis, mode, out)
-    else:
+    if axis is None:
         return _npi.take(_npi.reshape(a, -1), indices, 0, mode, out)
+    else:
+        return _npi.take(a, indices, axis, mode, out)
 # pylint: enable=redefined-outer-name
 
 
@@ -495,7 +495,11 @@ def unique(ar, return_index=False, return_inverse=False, return_counts=False, ax
     >>> u[indices]
     array([1., 2., 6., 4., 2., 3., 2.])
     """
-    return _npi.unique(ar, return_index, return_inverse, return_counts, axis)
+    ret = _npi.unique(ar, return_index, return_inverse, return_counts, axis)
+    if isinstance(ret, list):
+        return tuple(ret)
+    else:
+        return ret
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -604,6 +608,36 @@ def divide(x1, x2, out=None, **kwargs):
                          _npi.rtrue_divide_scalar, out)
 
 
+@set_module('mxnet.ndarray.numpy')
+def true_divide(x1, x2, out=None):
+    """Returns a true division of the inputs, element-wise.
+
+    Instead of the Python traditional 'floor division', this returns a true
+    division.  True division adjusts the output type to present the best
+    answer, regardless of input types.
+
+    Parameters
+    ----------
+    x1 : ndarray or scalar
+        Dividend array.
+
+    x2 : ndarray or scalar
+        Divisor array.
+
+    out : ndarray
+        A location into which the result is stored. If provided, it must have a shape
+        that the inputs broadcast to. If not provided or None, a freshly-allocated array
+        is returned.
+
+    Returns
+    -------
+    out : ndarray or scalar
+        This is a scalar if both x1 and x2 are scalars.
+    """
+    return _ufunc_helper(x1, x2, _npi.true_divide, _np.divide, _npi.true_divide_scalar,
+                         _npi.rtrue_divide_scalar, out)
+
+
 @set_module('mxnet.ndarray.numpy')
 @wrap_np_binary_func
 def mod(x1, x2, out=None, **kwargs):
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index e507b17d68d8..8cca82bcf827 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -55,7 +55,7 @@
            'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming',
            'blackman', 'flip', 'around', 'arctan2', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril',
            'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less',
-           'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum']
+           'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -2216,6 +2216,35 @@ def divide(x1, x2, out=None, **kwargs):
     return _mx_nd_np.divide(x1, x2, out=out)
 
 
+@set_module('mxnet.numpy')
+def true_divide(x1, x2, out=None):
+    """Returns a true division of the inputs, element-wise.
+
+    Instead of the Python traditional 'floor division', this returns a true
+    division.  True division adjusts the output type to present the best
+    answer, regardless of input types.
+
+    Parameters
+    ----------
+    x1 : ndarray or scalar
+        Dividend array.
+
+    x2 : ndarray or scalar
+        Divisor array.
+
+    out : ndarray
+        A location into which the result is stored. If provided, it must have a shape
+        that the inputs broadcast to. If not provided or None, a freshly-allocated array
+        is returned.
+
+    Returns
+    -------
+    out : ndarray or scalar
+        This is a scalar if both x1 and x2 are scalars.
+    """
+    return _mx_nd_np.true_divide(x1, x2, out=out)
+
+
 @set_module('mxnet.numpy')
 @wrap_np_binary_func
 def mod(x1, x2, out=None, **kwargs):
diff --git a/python/mxnet/numpy/utils.py b/python/mxnet/numpy/utils.py
index b2d0dd96d324..b2335e29855d 100644
--- a/python/mxnet/numpy/utils.py
+++ b/python/mxnet/numpy/utils.py
@@ -23,7 +23,7 @@
 import numpy as onp
 
 __all__ = ['float16', 'float32', 'float64', 'uint8', 'int32', 'int8', 'int64',
-           'bool', 'bool_', 'pi', 'inf', 'nan']
+           'bool', 'bool_', 'pi', 'inf', 'nan', 'PZERO', 'NZERO']
 
 float16 = onp.float16
 float32 = onp.float32
@@ -38,3 +38,5 @@
 pi = onp.pi
 inf = onp.inf
 nan = onp.nan
+PZERO = onp.PZERO
+NZERO = onp.NZERO
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index a241d2687ee4..9fd3976dead4 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -84,6 +84,7 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
 
 _NUMPY_ARRAY_FUNCTION_LIST = [
     'argmax',
+    'around',
     'broadcast_arrays',
     'broadcast_to',
     'clip',
@@ -93,6 +94,8 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'dot',
     'expand_dims',
     'fix',
+    'flip',
+    'inner',
     'max',
     'mean',
     'min',
@@ -108,9 +111,11 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'std',
     'sum',
     'swapaxes',
+    'take',
     'tensordot',
     'tile',
     'transpose',
+    'unique',
     'var',
     'zeros_like',
     'meshgrid',
@@ -161,11 +166,17 @@ def _register_array_function():
 
 # https://docs.scipy.org/doc/numpy/reference/ufuncs.html#available-ufuncs
 _NUMPY_ARRAY_UFUNC_LIST = [
+    'abs',
     'add',
+    'arctan2',
+    'copysign',
+    'degrees',
+    'hypot',
+    'lcm',
+    # 'ldexp',
     'subtract',
     'multiply',
-    # Uncomment divide when mxnet.numpy.true_divide is added
-    # 'divide',
+    'true_divide',
     'negative',
     'power',
     'mod',
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 1945c5b0e695..4b4319f45373 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -40,7 +40,7 @@
            'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
            'around', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
            'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal',
-           'less_equal', 'hsplit', 'rot90', 'einsum']
+           'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide']
 
 
 def _num_outputs(sym):
@@ -1082,10 +1082,10 @@ def take(a, indices, axis=None, mode='raise', out=None):
     if mode not in ('wrap', 'clip', 'raise'):
         raise NotImplementedError(
             "function take does not support mode '{}'".format(mode))
-    if axis:
-        return _npi.take(a, indices, axis, mode, out)
-    else:
+    if axis is None:
         return _npi.take(_npi.reshape(a, -1), indices, 0, mode, out)
+    else:
+        return _npi.take(a, indices, axis, mode, out)
 # pylint: enable=redefined-outer-name
 
 
@@ -1164,6 +1164,12 @@ def divide(x1, x2, out=None, **kwargs):
                          _npi.rtrue_divide_scalar, out)
 
 
+@set_module('mxnet.ndarray.numpy')
+def true_divide(x1, x2, out=None):
+    return _ufunc_helper(x1, x2, _npi.true_divide, _np.divide, _npi.true_divide_scalar,
+                         _npi.rtrue_divide_scalar, out)
+
+
 @set_module('mxnet.symbol.numpy')
 @wrap_np_binary_func
 def mod(x1, x2, out=None, **kwargs):
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index f22d42bb678b..7495c9002f26 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -17,6 +17,8 @@
 
 # pylint: skip-file
 from __future__ import absolute_import
+from __future__ import division
+import itertools
 import numpy as _np
 from mxnet import np
 from mxnet.test_utils import assert_almost_equal
@@ -25,7 +27,11 @@
 from mxnet.numpy_dispatch_protocol import with_array_function_protocol, with_array_ufunc_protocol
 from mxnet.numpy_dispatch_protocol import _NUMPY_ARRAY_FUNCTION_LIST, _NUMPY_ARRAY_UFUNC_LIST
 
-import itertools
+
+_INT_DTYPES = [np.int8, np.int32, np.int64, np.uint8]
+_FLOAT_DTYPES = [np.float16, np.float32, np.float64]
+_DTYPES = _INT_DTYPES + _FLOAT_DTYPES
+
 
 class OpArgMngr(object):
     """Operator argument manager for storing operator workloads."""
@@ -114,50 +120,158 @@ def _add_workload_einsum():
             OpArgMngr.add_workload('einsum', subscripts, *args, optimize=optimize)
 
 
-@use_np
-def _prepare_workloads():
-    array_pool = {
-        '4x1': np.random.uniform(size=(4, 1)) + 2,
-        '1x2': np.random.uniform(size=(1, 2)) + 2,
-        '1x1x0': np.array([[[]]])
-    }
+def _add_workload_argmax():
+    OpArgMngr.add_workload('argmax', np.random.uniform(size=(4, 5, 6, 7, 8)), 0)
+    OpArgMngr.add_workload('argmax', np.random.uniform(size=(4, 5, 6, 7, 8)), 1)
+    OpArgMngr.add_workload('argmax', np.random.uniform(size=(4, 5, 6, 7, 8)), 2)
+    OpArgMngr.add_workload('argmax', np.random.uniform(size=(4, 5, 6, 7, 8)), 3)
+    OpArgMngr.add_workload('argmax', np.random.uniform(size=(4, 5, 6, 7, 8)), 4)
+    # OpArgMngr.add_workload('argmax', np.array([0, 1, 2, 3, np.nan]))
+    # OpArgMngr.add_workload('argmax', np.array([0, 1, 2, np.nan, 3]))
+    # OpArgMngr.add_workload('argmax', np.array([np.nan, 0, 1, 2, 3]))
+    # OpArgMngr.add_workload('argmax', np.array([np.nan, 0, np.nan, 2, 3]))
+    OpArgMngr.add_workload('argmax', np.array([False, False, False, False, True]))
+    OpArgMngr.add_workload('argmax', np.array([False, False, False, True, False]))
+    OpArgMngr.add_workload('argmax', np.array([True, False, False, False, False]))
+    OpArgMngr.add_workload('argmax', np.array([True, False, True, False, False]))
+
+
+def _add_workload_around():
+    OpArgMngr.add_workload('around', np.array([1.56, 72.54, 6.35, 3.25]), decimals=1)
 
-    dt_int = [np.int8, np.int32, np.int64, np.uint8]
-    dt_float = [np.float16, np.float32, np.float64]
-    dt = dt_int + dt_float
 
-    # workloads for array function protocol
-    OpArgMngr.add_workload('argmax', array_pool['4x1'])
+def _add_workload_broadcast_arrays(array_pool):
     OpArgMngr.add_workload('broadcast_arrays', array_pool['4x1'], array_pool['1x2'])
-    OpArgMngr.add_workload('broadcast_to', array_pool['4x1'], (4, 2))
-    OpArgMngr.add_workload('clip', array_pool['4x1'], 0.2, 0.8)
+
+
+def _add_workload_broadcast_to():
+    OpArgMngr.add_workload('broadcast_to', np.array(0), (0,))
+    OpArgMngr.add_workload('broadcast_to', np.array(0), (1,))
+    OpArgMngr.add_workload('broadcast_to', np.array(0), (3,))
+    OpArgMngr.add_workload('broadcast_to', np.ones(1), (1,))
+    OpArgMngr.add_workload('broadcast_to', np.ones(1), (2,))
+    OpArgMngr.add_workload('broadcast_to', np.ones(1), (1, 2, 3))
+    OpArgMngr.add_workload('broadcast_to', np.arange(3), (3,))
+    OpArgMngr.add_workload('broadcast_to', np.arange(3), (1, 3))
+    OpArgMngr.add_workload('broadcast_to', np.arange(3), (2, 3))
+    OpArgMngr.add_workload('broadcast_to', np.ones(0), 0)
+    OpArgMngr.add_workload('broadcast_to', np.ones(1), 1)
+    OpArgMngr.add_workload('broadcast_to', np.ones(1), 2)
+    OpArgMngr.add_workload('broadcast_to', np.ones(1), (0,))
+    OpArgMngr.add_workload('broadcast_to', np.ones((1, 2)), (0, 2))
+    OpArgMngr.add_workload('broadcast_to', np.ones((2, 1)), (2, 0))
+
+
+def _add_workload_clip():
+    OpArgMngr.add_workload('clip', (np.random.normal(size=(1000,)) * 1024).astype("float"), -12.8, 100.2)
+    OpArgMngr.add_workload('clip', (np.random.normal(size=(1000,)) * 1024).astype("float"), 0, 0)
+    OpArgMngr.add_workload('clip', (np.random.normal(size=(1000,)) * 1024).astype("int"), -120, 100)
+    OpArgMngr.add_workload('clip', (np.random.normal(size=(1000,)) * 1024).astype("int"), 0.0, 2.0)
+    OpArgMngr.add_workload('clip', (np.random.normal(size=(1000,)) * 1024).astype("int"), 0, 0)
+    OpArgMngr.add_workload('clip', (np.random.normal(size=(1000,)) * 1024).astype("uint8"), 0, 0)
+    OpArgMngr.add_workload('clip', (np.random.normal(size=(1000,)) * 1024).astype("uint8"), 0.0, 2.0)
+    OpArgMngr.add_workload('clip', (np.random.normal(size=(1000,)) * 1024).astype("uint8"), -120, 100)
+    # OpArgMngr.add_workload('clip', np.random.normal(size=(1000,)), np.zeros((1000,))+0.5, 1)
+    # OpArgMngr.add_workload('clip', np.random.normal(size=(1000,)), 0, np.zeros((1000,))+0.5)
+    # OpArgMngr.add_workload('clip', np.array([0, 1, 2, 3, 4, 5, 6, 7]), 3)
+    # OpArgMngr.add_workload('clip', np.array([0, 1, 2, 3, 4, 5, 6, 7]), a_min=3)
+    # OpArgMngr.add_workload('clip', np.array([0, 1, 2, 3, 4, 5, 6, 7]), a_max=4)
+    OpArgMngr.add_workload('clip', np.array([-2., np.nan, 0.5, 3., 0.25, np.nan]), -1, 1)
+
+
+def _add_workload_concatenate(array_pool):
     OpArgMngr.add_workload('concatenate', [array_pool['4x1'], array_pool['4x1']])
     OpArgMngr.add_workload('concatenate', [array_pool['4x1'], array_pool['4x1']], axis=1)
+
+
+def _add_workload_copy(array_pool):
     OpArgMngr.add_workload('copy', array_pool['4x1'])
 
-    for ctype in dt:
+
+def _add_workload_cumsum():
+    for ctype in _DTYPES:
         OpArgMngr.add_workload('cumsum', np.array([1, 2, 10, 11, 6, 5, 4], dtype=ctype))
         OpArgMngr.add_workload('cumsum', np.array([[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]], dtype=ctype), axis=0)
         OpArgMngr.add_workload('cumsum', np.array([[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]], dtype=ctype), axis=1)
 
+
+def _add_workload_ravel():
     OpArgMngr.add_workload('ravel', np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]]))
 
-    OpArgMngr.add_workload('dot', array_pool['4x1'], array_pool['4x1'].T)
+
+def _add_workload_dot():
+    OpArgMngr.add_workload('dot', np.random.normal(size=(2, 4)), np.random.normal(size=(4, 2)))
+    OpArgMngr.add_workload('dot', np.random.normal(size=(4, 2)), np.random.normal(size=(2, 1)))
+    OpArgMngr.add_workload('dot', np.random.normal(size=(4, 2)), np.random.normal(size=(2,)))
+    OpArgMngr.add_workload('dot', np.random.normal(size=(1, 2)), np.random.normal(size=(2, 4)))
+    OpArgMngr.add_workload('dot', np.random.normal(size=(2, 4)), np.random.normal(size=(4,)))
+    OpArgMngr.add_workload('dot', np.random.normal(size=(1, 2)), np.random.normal(size=(2, 1)))
+    OpArgMngr.add_workload('dot', np.ones((3, 1)), np.array([5.3]))
+    OpArgMngr.add_workload('dot', np.array([5.3]), np.ones((1, 3)))
+    OpArgMngr.add_workload('dot', np.random.normal(size=(1, 1)), np.random.normal(size=(1, 4)))
+    OpArgMngr.add_workload('dot', np.random.normal(size=(4, 1)), np.random.normal(size=(1, 1)))
+
+    dims = [(), (1,), (1, 1)]
+    for (dim1, dim2) in itertools.product(dims, dims):
+        b1 = np.zeros(dim1)
+        b2 = np.zeros(dim2)
+        OpArgMngr.add_workload('dot', b1, b2)
+    OpArgMngr.add_workload('dot', np.array([[1, 2], [3, 4]], dtype=float), np.array([[1, 0], [1, 1]], dtype=float))
+    OpArgMngr.add_workload('dot', np.random.normal(size=(1024, 16)), np.random.normal(size=(16, 32)))
+
+
+def _add_workload_expand_dims(array_pool):
     OpArgMngr.add_workload('expand_dims', array_pool['4x1'], -1)
-    OpArgMngr.add_workload('fix', array_pool['4x1'])
+
+
+def _add_workload_fix():
+    OpArgMngr.add_workload('fix', np.array([[1.0, 1.1, 1.5, 1.8], [-1.0, -1.1, -1.5, -1.8]]))
+    OpArgMngr.add_workload('fix', np.array([3.14]))
+
+
+def _add_workload_flip():
+    OpArgMngr.add_workload('flip', np.random.normal(size=(4, 4)), 1)
+    OpArgMngr.add_workload('flip', np.array([[0, 1, 2], [3, 4, 5]]), 1)
+    OpArgMngr.add_workload('flip', np.random.normal(size=(4, 4)), 0)
+    OpArgMngr.add_workload('flip', np.array([[0, 1, 2], [3, 4, 5]]), 0)
+    OpArgMngr.add_workload('flip', np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]), 0)
+    OpArgMngr.add_workload('flip', np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]), 1)
+    OpArgMngr.add_workload('flip', np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]), 2)
+    for i in range(4):
+        OpArgMngr.add_workload('flip', np.arange(2 * 3 * 4 * 5).reshape(2, 3, 4, 5), i)
+    OpArgMngr.add_workload('flip', np.array([[1, 2, 3], [4, 5, 6]]))
+    OpArgMngr.add_workload('flip', np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]), ())
+    OpArgMngr.add_workload('flip', np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]), (0, 2))
+    OpArgMngr.add_workload('flip', np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]), (1, 2))
+
+
+def _add_workload_max(array_pool):
     OpArgMngr.add_workload('max', array_pool['4x1'])
+
+
+def _add_workload_min(array_pool):
     OpArgMngr.add_workload('min', array_pool['4x1'])
+
+
+def _add_workload_mean(array_pool):
     OpArgMngr.add_workload('mean', array_pool['4x1'])
     OpArgMngr.add_workload('mean', array_pool['4x1'], axis=0, keepdims=True)
     OpArgMngr.add_workload('mean', np.array([[1, 2, 3], [4, 5, 6]]))
     OpArgMngr.add_workload('mean', np.array([[1, 2, 3], [4, 5, 6]]), axis=0)
     OpArgMngr.add_workload('mean', np.array([[1, 2, 3], [4, 5, 6]]), axis=1)
+
+
+def _add_workload_ones_like(array_pool):
     OpArgMngr.add_workload('ones_like', array_pool['4x1'])
+
+
+def _add_workload_prod(array_pool):
     OpArgMngr.add_workload('prod', array_pool['4x1'])
 
+
+def _add_workload_repeat(array_pool):
     OpArgMngr.add_workload('repeat', array_pool['4x1'], 3)
     OpArgMngr.add_workload('repeat', np.array(_np.arange(12).reshape(4, 3)[:, 2]), 3)
-
     m = _np.array([1, 2, 3, 4, 5, 6])
     m_rect = m.reshape((2, 3))
 
@@ -175,13 +289,15 @@ def _prepare_workloads():
         OpArgMngr.add_workload('repeat', np.array(a), 2, axis=axis)
     #    OpArgMngr.add_workload('repeat', np.array(a), [2], axis=axis)   # Argument "repeats" only supports int
 
+
+def _add_workload_reshape():
     arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])
     OpArgMngr.add_workload('reshape', arr, (2, 6))
     OpArgMngr.add_workload('reshape', arr, (3, 4))
     # OpArgMngr.add_workload('reshape', arr, (3, 4), order='F') # Items are not equal with order='F'
     OpArgMngr.add_workload('reshape', arr, (3, 4), order='C')
     OpArgMngr.add_workload('reshape', np.array(_np.ones(100)), (100, 1, 1))
-    
+
     # test_reshape_order
     a = np.array(_np.arange(6))
     # OpArgMngr.add_workload('reshape', a, (2, 3), order='F')  # Items are not equal with order='F'
@@ -192,9 +308,13 @@ def _prepare_workloads():
     a = np.array(_np.ones((0, 2)))
     OpArgMngr.add_workload('reshape', a, -1, 2)
 
+
+def _add_workload_rint(array_pool):
     OpArgMngr.add_workload('rint', np.array(4607998452777363968))
     OpArgMngr.add_workload('rint', array_pool['4x1'])
 
+
+def _add_workload_roll():
     # test_roll1d(self)
     OpArgMngr.add_workload('roll', np.array(_np.arange(10)), 2)
 
@@ -219,38 +339,221 @@ def _prepare_workloads():
     OpArgMngr.add_workload('roll', x2, -4, axis=1)
     # # test_roll_empty
     OpArgMngr.add_workload('roll', np.array([]), 1)
-    
+
+
+def _add_workload_split(array_pool):
     OpArgMngr.add_workload('split', array_pool['4x1'], 2)
+
+
+def _add_workload_squeeze(array_pool):
     OpArgMngr.add_workload('squeeze', array_pool['4x1'])
+
+
+def _add_workload_stack(array_pool):
     OpArgMngr.add_workload('stack', [array_pool['4x1']] * 2)
+
+
+def _add_workload_std(array_pool):
     OpArgMngr.add_workload('std', array_pool['4x1'])
-    OpArgMngr.add_workload('sum', array_pool['4x1'])
+
+
+def _add_workload_sum():
+    # OpArgMngr.add_workload('sum', np.ones(101, dtype=bool))
+    OpArgMngr.add_workload('sum', np.arange(1, 10).reshape((3, 3)), axis=1, keepdims=True)
+    OpArgMngr.add_workload('sum', np.ones(500, dtype=np.float32)/10.)
+    OpArgMngr.add_workload('sum', np.ones(500, dtype=np.float64)/10.)
+    for dt in (np.float16, np.float32, np.float64):
+        for v in (0, 1, 2, 7, 8, 9, 15, 16, 19, 127,
+                  128, 1024, 1235):
+            d = np.arange(1, v + 1, dtype=dt)
+            OpArgMngr.add_workload('sum', d)
+        d = np.ones(500, dtype=dt)
+        OpArgMngr.add_workload('sum', d[::2])
+        OpArgMngr.add_workload('sum', d[1::2])
+        OpArgMngr.add_workload('sum', d[::3])
+        OpArgMngr.add_workload('sum', d[1::3])
+        OpArgMngr.add_workload('sum', d[::-2])
+        OpArgMngr.add_workload('sum', d[-1::-2])
+        OpArgMngr.add_workload('sum', d[::-3])
+        OpArgMngr.add_workload('sum', d[-1::-3])
+        d = np.ones((1,), dtype=dt)
+        d += d
+        OpArgMngr.add_workload('sum', d)
+    # OpArgMngr.add_workload('sum', np.array([3]), initial=2)
+    # OpArgMngr.add_workload('sum', np.array([0.2]), initial=0.1)
+
+
+def _add_workload_swapaxes(array_pool):
     OpArgMngr.add_workload('swapaxes', array_pool['4x1'], 0, 1)
+
+
+def _add_workload_take():
+    OpArgMngr.add_workload('take', np.array([[1, 2], [3, 4]], dtype=int), np.array([], int))
+    for mode in ['wrap', 'clip']:
+        OpArgMngr.add_workload('take', np.array([[1, 2], [3, 4]], dtype=int), np.array(-1, int), mode=mode)
+        OpArgMngr.add_workload('take', np.array([[1, 2], [3, 4]], dtype=int), np.array(4, int), mode=mode)
+        OpArgMngr.add_workload('take', np.array([[1, 2], [3, 4]], dtype=int), np.array([-1], int), mode=mode)
+        OpArgMngr.add_workload('take', np.array([[1, 2], [3, 4]], dtype=int), np.array([4], int), mode=mode)
+    x = (np.random.normal(size=24)*100).reshape((2, 3, 4))
+    # OpArgMngr.add_workload('take', x, np.array([-1], int), axis=0)
+    OpArgMngr.add_workload('take', x, np.array([-1], int), axis=0, mode='clip')
+    OpArgMngr.add_workload('take', x, np.array([2], int), axis=0, mode='clip')
+    OpArgMngr.add_workload('take', x, np.array([-1], int), axis=0, mode='wrap')
+    OpArgMngr.add_workload('take', x, np.array([2], int), axis=0, mode='wrap')
+    OpArgMngr.add_workload('take', x, np.array([3], int), axis=0, mode='wrap')
+
+
+def _add_workload_tensordot(array_pool):
     OpArgMngr.add_workload('tensordot', array_pool['4x1'], array_pool['4x1'])
+
+
+def _add_workload_tile(array_pool):
     OpArgMngr.add_workload('tile', array_pool['4x1'], 2)
     OpArgMngr.add_workload('tile', np.array([[[]]]), (3, 2, 5))
+
+
+def _add_workload_transpose(array_pool):
     OpArgMngr.add_workload('transpose', array_pool['4x1'])
+
+
+def _add_workload_unique():
+    OpArgMngr.add_workload('unique', np.array([5, 7, 1, 2, 1, 5, 7]*10), True, True, True)
+    OpArgMngr.add_workload('unique', np.array([]), True, True, True)
+    OpArgMngr.add_workload('unique', np.array([[0, 1, 0], [0, 1, 0]]))
+    OpArgMngr.add_workload('unique', np.array([[0, 1, 0], [0, 1, 0]]), axis=0)
+    OpArgMngr.add_workload('unique', np.array([[0, 1, 0], [0, 1, 0]]), axis=1)
+    # OpArgMngr.add_workload('unique', np.arange(10, dtype=np.uint8).reshape(-1, 2).astype(bool), axis=1)
+
+
+def _add_workload_var(array_pool):
     OpArgMngr.add_workload('var', array_pool['4x1'])
+
+
+def _add_workload_zeros_like(array_pool):
     OpArgMngr.add_workload('zeros_like', array_pool['4x1'])
+
+
+def _add_workload_outer():
     OpArgMngr.add_workload('outer', np.ones((5)), np.ones((2)))
+
+
+def _add_workload_meshgrid():
     OpArgMngr.add_workload('meshgrid', np.array([1, 2, 3]))
     OpArgMngr.add_workload('meshgrid', np.array([1, 2, 3]), np.array([4, 5, 6, 7]))
     OpArgMngr.add_workload('meshgrid', np.array([1, 2, 3]), np.array([4, 5, 6, 7]), indexing='ij')
-    _add_workload_einsum()
 
-    # workloads for array ufunc protocol
+
+def _add_workload_abs():
+    OpArgMngr.add_workload('abs', np.random.uniform(size=(11,)).astype(np.float32))
+    OpArgMngr.add_workload('abs', np.random.uniform(size=(5,)).astype(np.float64))
+    OpArgMngr.add_workload('abs', np.array([np.inf, -np.inf, np.nan]))
+
+
+def _add_workload_add(array_pool):
     OpArgMngr.add_workload('add', array_pool['4x1'], array_pool['1x2'])
     OpArgMngr.add_workload('add', array_pool['4x1'], 2)
     OpArgMngr.add_workload('add', 2, array_pool['4x1'])
     OpArgMngr.add_workload('add', array_pool['4x1'], array_pool['1x1x0'])
+
+
+def _add_workload_arctan2():
+    OpArgMngr.add_workload('arctan2', np.array([1, -1, 1]), np.array([1, 1, -1]))
+    OpArgMngr.add_workload('arctan2', np.array([np.PZERO, np.NZERO]), np.array([np.NZERO, np.NZERO]))
+    OpArgMngr.add_workload('arctan2', np.array([np.PZERO, np.NZERO]), np.array([np.PZERO, np.PZERO]))
+    OpArgMngr.add_workload('arctan2', np.array([np.PZERO, np.NZERO]), np.array([-1, -1]))
+    OpArgMngr.add_workload('arctan2', np.array([np.PZERO, np.NZERO]), np.array([1, 1]))
+    OpArgMngr.add_workload('arctan2', np.array([-1, -1]), np.array([np.PZERO, np.NZERO]))
+    OpArgMngr.add_workload('arctan2', np.array([1, 1]), np.array([np.PZERO, np.NZERO]))
+    OpArgMngr.add_workload('arctan2', np.array([1, -1, 1, -1]), np.array([-np.inf, -np.inf, np.inf, np.inf]))
+    OpArgMngr.add_workload('arctan2', np.array([np.inf, -np.inf]), np.array([1, 1]))
+    OpArgMngr.add_workload('arctan2', np.array([np.inf, -np.inf]), np.array([-np.inf, -np.inf]))
+    OpArgMngr.add_workload('arctan2', np.array([np.inf, -np.inf]), np.array([np.inf, np.inf]))
+
+
+def _add_workload_copysign():
+    OpArgMngr.add_workload('copysign', np.array([1, 0, 0]), np.array([-1, -1, 1]))
+    OpArgMngr.add_workload('copysign', np.array([-2, 5, 1, 4, 3], dtype=np.float16), np.array([0, 1, 2, 4, 2], dtype=np.float16))
+
+
+def _add_workload_degrees():
+    OpArgMngr.add_workload('degrees', np.array(np.pi))
+    OpArgMngr.add_workload('degrees', np.array(-0.5*np.pi))
+
+
+def _add_workload_true_divide():
+    for dt in [np.float32, np.float64, np.float16]:
+        OpArgMngr.add_workload('true_divide', np.array([10, 10, -10, -10], dt), np.array([20, -20, 20, -20], dt))
+
+
+def _add_workload_inner():
+    OpArgMngr.add_workload('inner', np.zeros(shape=(1, 80), dtype=np.float64), np.zeros(shape=(1, 80), dtype=np.float64))
+    for dt in [np.float32, np.float64]:
+        # OpArgMngr.add_workload('inner', np.array(3, dtype=dt)[()], np.array([1, 2], dtype=dt))
+        # OpArgMngr.add_workload('inner', np.array([1, 2], dtype=dt), np.array(3, dtype=dt)[()])
+        A = np.array([[1, 2], [3, 4]], dtype=dt)
+        B = np.array([[1, 3], [2, 4]], dtype=dt)
+        C = np.array([1, 1], dtype=dt)
+        OpArgMngr.add_workload('inner', A.T, C)
+        OpArgMngr.add_workload('inner', C, A.T)
+        OpArgMngr.add_workload('inner', B, C)
+        OpArgMngr.add_workload('inner', C, B)
+        OpArgMngr.add_workload('inner', A, B)
+        OpArgMngr.add_workload('inner', A, A)
+        OpArgMngr.add_workload('inner', A, A.copy())
+        a = np.arange(5).astype(dt)
+        b = a[::-1]
+        OpArgMngr.add_workload('inner', b, a)
+        a = np.arange(24).reshape(2,3,4).astype(dt)
+        b = np.arange(24, 48).reshape(2,3,4).astype(dt)
+        OpArgMngr.add_workload('inner', a, b)
+        OpArgMngr.add_workload('inner', b, a)
+
+
+def _add_workload_hypot():
+    OpArgMngr.add_workload('hypot', np.array(1), np.array(1))
+    OpArgMngr.add_workload('hypot', np.array(0), np.array(0))
+    OpArgMngr.add_workload('hypot', np.array(np.nan), np.array(np.nan))
+    OpArgMngr.add_workload('hypot', np.array(np.nan), np.array(1))
+    OpArgMngr.add_workload('hypot', np.array(np.nan), np.array(np.inf))
+    OpArgMngr.add_workload('hypot', np.array(np.inf), np.array(np.nan))
+    OpArgMngr.add_workload('hypot', np.array(np.inf), np.array(0))
+    OpArgMngr.add_workload('hypot', np.array(0), np.array(np.inf))
+    OpArgMngr.add_workload('hypot', np.array(np.inf), np.array(np.inf))
+    OpArgMngr.add_workload('hypot', np.array(np.inf), np.array(23.0))
+
+
+def _add_workload_lcm():
+    OpArgMngr.add_workload('lcm', np.array([12, 120], dtype=np.int8), np.array([20, 200], dtype=np.int8))
+    OpArgMngr.add_workload('lcm', np.array([12, 120], dtype=np.uint8), np.array([20, 200], dtype=np.uint8))
+    OpArgMngr.add_workload('lcm', np.array(195225786*2, dtype=np.int32), np.array(195225786*5, dtype=np.int32))
+
+
+def _add_workload_ldexp():
+    OpArgMngr.add_workload('ldexp', np.array(2., np.float32), np.array(3, np.int8))
+    OpArgMngr.add_workload('ldexp', np.array(2., np.float64), np.array(3, np.int8))
+    OpArgMngr.add_workload('ldexp', np.array(2., np.float32), np.array(3, np.int32))
+    OpArgMngr.add_workload('ldexp', np.array(2., np.float64), np.array(3, np.int32))
+    OpArgMngr.add_workload('ldexp', np.array(2., np.float32), np.array(3, np.int64))
+    OpArgMngr.add_workload('ldexp', np.array(2., np.float64), np.array(3, np.int64))
+    OpArgMngr.add_workload('ldexp', np.array(2., np.float64), np.array(9223372036854775807, np.int64))
+    OpArgMngr.add_workload('ldexp', np.array(2., np.float64), np.array(-9223372036854775808, np.int64))
+
+
+def _add_workload_subtract(array_pool):
     OpArgMngr.add_workload('subtract', array_pool['4x1'], array_pool['1x2'])
     OpArgMngr.add_workload('subtract', array_pool['4x1'], 2)
     OpArgMngr.add_workload('subtract', 2, array_pool['4x1'])
     OpArgMngr.add_workload('subtract', array_pool['4x1'], array_pool['1x1x0'])
+
+
+def _add_workload_multiply(array_pool):
     OpArgMngr.add_workload('multiply', array_pool['4x1'], array_pool['1x2'])
     OpArgMngr.add_workload('multiply', array_pool['4x1'], 2)
     OpArgMngr.add_workload('multiply', 2, array_pool['4x1'])
     OpArgMngr.add_workload('multiply', array_pool['4x1'], array_pool['1x1x0'])
+
+
+def _add_workload_power(array_pool):
     OpArgMngr.add_workload('power', array_pool['4x1'], array_pool['1x2'])
     OpArgMngr.add_workload('power', array_pool['4x1'], 2)
     OpArgMngr.add_workload('power', 2, array_pool['4x1'])
@@ -258,14 +561,19 @@ def _prepare_workloads():
     OpArgMngr.add_workload('power', np.array([1, 2, 3], np.int32), 2.00001)
     OpArgMngr.add_workload('power', np.array([15, 15], np.int64), np.array([15, 15], np.int64))
     OpArgMngr.add_workload('power', 0, np.arange(1, 10))
+
+
+def _add_workload_mod(array_pool):
     OpArgMngr.add_workload('mod', array_pool['4x1'], array_pool['1x2'])
     OpArgMngr.add_workload('mod', array_pool['4x1'], 2)
     OpArgMngr.add_workload('mod', 2, array_pool['4x1'])
     OpArgMngr.add_workload('mod', array_pool['4x1'], array_pool['1x1x0'])
 
+
+def _add_workload_remainder():
     # test remainder basic
     OpArgMngr.add_workload('remainder', np.array([0, 1, 2, 4, 2], dtype=np.float16),
-                            np.array([-2, 5, 1, 4, 3], dtype=np.float16))
+                           np.array([-2, 5, 1, 4, 3], dtype=np.float16))
 
     def _signs(dt):
         if dt in [np.uint8]:
@@ -273,7 +581,7 @@ def _signs(dt):
         else:
             return (+1, -1)
 
-    for ct in dt:
+    for ct in _DTYPES:
         for sg1, sg2 in itertools.product(_signs(ct), _signs(ct)):
             a = np.array(sg1*71, dtype=ct)
             b = np.array(sg2*19, dtype=ct)
@@ -293,9 +601,9 @@ def _signs(dt):
         fa = a.astype(dt)
         fb = b.astype(dt)
         OpArgMngr.add_workload('remainder', fa, fb)
-    
+
     # test_float_remainder_roundoff
-    for ct in dt_float:
+    for ct in _FLOAT_DTYPES:
         for sg1, sg2 in itertools.product((+1, -1), (+1, -1)):
             a = np.array(sg1*78*6e-8, dtype=ct)
             b = np.array(sg2*6e-8, dtype=ct)
@@ -303,80 +611,262 @@ def _signs(dt):
 
     # test_float_remainder_corner_cases
     # Check remainder magnitude.
-    for ct in dt_float:
+    for ct in _FLOAT_DTYPES:
         b = _np.array(1.0)
         a = np.array(_np.nextafter(_np.array(0.0), -b), dtype=ct)
         b = np.array(b, dtype=ct)
         OpArgMngr.add_workload('remainder', a, b)
         OpArgMngr.add_workload('remainder', -a, -b)
 
-        # Check nans, inf
-        for ct in [np.float16, np.float32, np.float64]:
-            fone = np.array(1.0, dtype=ct)
-            fzer = np.array(0.0, dtype=ct)
-            finf = np.array(np.inf, dtype=ct)
-            fnan = np.array(np.nan, dtype=ct)
-            # OpArgMngr.add_workload('remainder', fone, fzer) # failed
-            OpArgMngr.add_workload('remainder', fone, fnan)
-            OpArgMngr.add_workload('remainder', finf, fone)
+    # Check nans, inf
+    for ct in [np.float16, np.float32, np.float64]:
+        fone = np.array(1.0, dtype=ct)
+        fzer = np.array(0.0, dtype=ct)
+        finf = np.array(np.inf, dtype=ct)
+        fnan = np.array(np.nan, dtype=ct)
+        # OpArgMngr.add_workload('remainder', fone, fzer)  # failed
+        OpArgMngr.add_workload('remainder', fone, fnan)
+        OpArgMngr.add_workload('remainder', finf, fone)
+
 
+def _add_workload_maximum(array_pool):
     OpArgMngr.add_workload('maximum', array_pool['4x1'], array_pool['1x2'])
     OpArgMngr.add_workload('maximum', array_pool['4x1'], 2)
     OpArgMngr.add_workload('maximum', 2, array_pool['4x1'])
     OpArgMngr.add_workload('maximum', array_pool['4x1'], array_pool['1x1x0'])
+
+
+def _add_workload_minimum(array_pool):
     OpArgMngr.add_workload('minimum', array_pool['4x1'], array_pool['1x2'])
     OpArgMngr.add_workload('minimum', array_pool['4x1'], 2)
     OpArgMngr.add_workload('minimum', 2, array_pool['4x1'])
     OpArgMngr.add_workload('minimum', array_pool['4x1'], array_pool['1x1x0'])
+
+
+def _add_workload_negative(array_pool):
     OpArgMngr.add_workload('negative', array_pool['4x1'])
+
+
+def _add_workload_absolute(array_pool):
     OpArgMngr.add_workload('absolute', array_pool['4x1'])
-    
+
+
+def _add_workload_sign(array_pool):
     OpArgMngr.add_workload('sign', array_pool['4x1'])
     OpArgMngr.add_workload('sign', np.array([-2, 5, 1, 4, 3], dtype=np.float16))
     OpArgMngr.add_workload('sign', np.array([-.1, 0, .1]))
     # OpArgMngr.add_workload('sign', np.array(_np.array([_np.nan]))) # failed
 
+
+def _add_workload_exp(array_pool):
     OpArgMngr.add_workload('exp', array_pool['4x1'])
+
+
+def _add_workload_log(array_pool):
     OpArgMngr.add_workload('log', array_pool['4x1'])
+
+
+def _add_workload_log2(array_pool):
     OpArgMngr.add_workload('log2', array_pool['4x1'])
     OpArgMngr.add_workload('log2', np.array(2.**65))
     OpArgMngr.add_workload('log2', np.array(np.inf))
     OpArgMngr.add_workload('log2', np.array(1.))
+
+
+def _add_workload_log1p():
     OpArgMngr.add_workload('log1p', np.array(-1.))
     OpArgMngr.add_workload('log1p', np.array(np.inf))
     OpArgMngr.add_workload('log1p', np.array(1e-6))
+
+
+def _add_workload_log10(array_pool):
     OpArgMngr.add_workload('log10', array_pool['4x1'])
+
+
+def _add_workload_expm1(array_pool):
     OpArgMngr.add_workload('expm1', array_pool['4x1'])
-    OpArgMngr.add_workload('sqrt', array_pool['4x1'])
-    OpArgMngr.add_workload('square', array_pool['4x1'])
-    OpArgMngr.add_workload('cbrt', array_pool['4x1'])
 
+
+def _add_workload_sqrt():
+    OpArgMngr.add_workload('sqrt', np.array([1, np.PZERO, np.NZERO, np.inf, np.nan]))
+
+
+def _add_workload_square():
+    OpArgMngr.add_workload('square', np.array([-2, 5, 1, 4, 3], dtype=np.float16))
+
+
+def _add_workload_cbrt():
+    OpArgMngr.add_workload('cbrt', np.array(-2.5**3, dtype=np.float32))
+    OpArgMngr.add_workload('cbrt', np.array([1., 2., -3., np.inf, -np.inf])**3)
+    OpArgMngr.add_workload('cbrt', np.array([np.inf, -np.inf, np.nan]))
+
+
+def _add_workload_reciprocal():
     for ctype in [np.float16, np.float32, np.float64]:
         OpArgMngr.add_workload('reciprocal', np.array([-2, 5, 1, 4, 3], dtype=ctype))
         OpArgMngr.add_workload('reciprocal', np.array([-2, 0, 1, 0, 3], dtype=ctype))
         OpArgMngr.add_workload('reciprocal', np.array([0], dtype=ctype))
 
+
+def _add_workload_sin(array_pool):
     OpArgMngr.add_workload('sin', array_pool['4x1'])
+
+
+def _add_workload_cos(array_pool):
     OpArgMngr.add_workload('cos', array_pool['4x1'])
+
+
+def _add_workload_tan(array_pool):
     OpArgMngr.add_workload('tan', array_pool['4x1'])
+
+
+def _add_workload_sinh(array_pool):
     OpArgMngr.add_workload('sinh', array_pool['4x1'])
+
+
+def _add_workload_cosh(array_pool):
     OpArgMngr.add_workload('cosh', array_pool['4x1'])
+
+
+def _add_workload_tanh(array_pool):
     OpArgMngr.add_workload('tanh', array_pool['4x1'])
+
+
+def _add_workload_arcsin(array_pool):
     OpArgMngr.add_workload('arcsin', array_pool['4x1'] - 2)
+
+
+def _add_workload_arccos(array_pool):
     OpArgMngr.add_workload('arccos', array_pool['4x1'] - 2)
+
+
+def _add_workload_arctan(array_pool):
     OpArgMngr.add_workload('arctan', array_pool['4x1'])
+
+
+def _add_workload_arcsinh(array_pool):
     OpArgMngr.add_workload('arcsinh', array_pool['4x1'])
+
+
+def _add_workload_arccosh(array_pool):
     OpArgMngr.add_workload('arccosh', array_pool['4x1'])
+
+
+def _add_workload_arctanh(array_pool):
     OpArgMngr.add_workload('arctanh', array_pool['4x1'] - 2)
+
+
+def _add_workload_ceil(array_pool):
     OpArgMngr.add_workload('ceil', array_pool['4x1'])
+
+
+def _add_workload_turnc(array_pool):
     OpArgMngr.add_workload('trunc', array_pool['4x1'])
+
+
+def _add_workload_floor(array_pool):
     OpArgMngr.add_workload('floor', array_pool['4x1'])
+
+
+def _add_workload_logical_not(array_pool):
     OpArgMngr.add_workload('logical_not', np.ones(10, dtype=np.int32))
     OpArgMngr.add_workload('logical_not', array_pool['4x1'])
     OpArgMngr.add_workload('logical_not', np.array([True, False, True, False], dtype=np.bool))
-    
 
 
+@use_np
+def _prepare_workloads():
+    array_pool = {
+        '4x1': np.random.uniform(size=(4, 1)) + 2,
+        '1x2': np.random.uniform(size=(1, 2)) + 2,
+        '1x1x0': np.array([[[]]])
+    }
+
+    _add_workload_argmax()
+    _add_workload_around()
+    _add_workload_broadcast_arrays(array_pool)
+    _add_workload_broadcast_to()
+    _add_workload_clip()
+    _add_workload_concatenate(array_pool)
+    _add_workload_copy(array_pool)
+    _add_workload_cumsum()
+    _add_workload_ravel()
+    _add_workload_dot()
+    _add_workload_expand_dims(array_pool)
+    _add_workload_fix()
+    _add_workload_flip()
+    _add_workload_max(array_pool)
+    _add_workload_min(array_pool)
+    _add_workload_mean(array_pool)
+    _add_workload_ones_like(array_pool)
+    _add_workload_prod(array_pool)
+    _add_workload_repeat(array_pool)
+    _add_workload_reshape()
+    _add_workload_rint(array_pool)
+    _add_workload_roll()
+    _add_workload_split(array_pool)
+    _add_workload_squeeze(array_pool)
+    _add_workload_stack(array_pool)
+    _add_workload_std(array_pool)
+    _add_workload_sum()
+    _add_workload_swapaxes(array_pool)
+    _add_workload_take()
+    _add_workload_tensordot(array_pool)
+    _add_workload_tile(array_pool)
+    _add_workload_transpose(array_pool)
+    _add_workload_unique()
+    _add_workload_var(array_pool)
+    _add_workload_zeros_like(array_pool)
+    _add_workload_outer()
+    _add_workload_meshgrid()
+    _add_workload_einsum()
+    _add_workload_abs()
+    _add_workload_add(array_pool)
+    _add_workload_arctan2()
+    _add_workload_copysign()
+    _add_workload_degrees()
+    _add_workload_true_divide()
+    _add_workload_inner()
+    _add_workload_hypot()
+    _add_workload_lcm()
+    _add_workload_ldexp()
+    _add_workload_subtract(array_pool)
+    _add_workload_multiply(array_pool)
+    _add_workload_power(array_pool)
+    _add_workload_mod(array_pool)
+    _add_workload_remainder()
+    _add_workload_maximum(array_pool)
+    _add_workload_minimum(array_pool)
+    _add_workload_negative(array_pool)
+    _add_workload_absolute(array_pool)
+    _add_workload_sign(array_pool)
+    _add_workload_exp(array_pool)
+    _add_workload_log(array_pool)
+    _add_workload_log2(array_pool)
+    _add_workload_log1p()
+    _add_workload_log10(array_pool)
+    _add_workload_expm1(array_pool)
+    _add_workload_sqrt()
+    _add_workload_square()
+    _add_workload_cbrt()
+    _add_workload_reciprocal()
+    _add_workload_sin(array_pool)
+    _add_workload_cos(array_pool)
+    _add_workload_tan(array_pool)
+    _add_workload_sinh(array_pool)
+    _add_workload_cosh(array_pool)
+    _add_workload_tanh(array_pool)
+    _add_workload_arcsin(array_pool)
+    _add_workload_arccos(array_pool)
+    _add_workload_arctan(array_pool)
+    _add_workload_arcsinh(array_pool)
+    _add_workload_arccosh(array_pool)
+    _add_workload_arctanh(array_pool)
+    _add_workload_ceil(array_pool)
+    _add_workload_turnc(array_pool)
+    _add_workload_floor(array_pool)
+    _add_workload_logical_not(array_pool)
+
 
 _prepare_workloads()
 
@@ -416,6 +906,7 @@ def _check_interoperability_helper(op_name, *args, **kwargs):
 
 def check_interoperability(op_list):
     for name in op_list:
+        print('Dispatch test:', name)
         workloads = OpArgMngr.get_workloads(name)
         assert workloads is not None, 'Workloads for operator `{}` has not been ' \
                                       'added for checking interoperability with ' \

From 149e034207bfe86118c7c02d8da8776a6a92ec17 Mon Sep 17 00:00:00 2001
From: Jagadeesh Kotra <jagadeesh.01101011@gmail.com>
Date: Sat, 19 Oct 2019 21:54:18 +0530
Subject: [PATCH 475/813] typo fix in r doc lstm tutorial (#16546)

---
 .../src/pages/api/r/docs/tutorials/multi_dim_lstm.md            | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/multi_dim_lstm.md b/docs/static_site/src/pages/api/r/docs/tutorials/multi_dim_lstm.md
index 0fd73b1460a4..e6e218f2ae13 100644
--- a/docs/static_site/src/pages/api/r/docs/tutorials/multi_dim_lstm.md
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/multi_dim_lstm.md
@@ -1,6 +1,6 @@
 ---
 layout: page_api
-title: LSTM Time Serie
+title: LSTM Time Series
 is_tutorial: true
 tag: r
 permalink: /api/r/docs/tutorials/multi_dim_lstm

From fc81c64f7f4d09d28978ab72d3cfa60ae6b831f0 Mon Sep 17 00:00:00 2001
From: Sheng Zha <szha@users.noreply.github.com>
Date: Sat, 19 Oct 2019 09:41:36 -0700
Subject: [PATCH 476/813] Correct Google Analytics Tracker (#16490)

---
 docs/python_docs/_static/google_analytics.js | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/python_docs/_static/google_analytics.js b/docs/python_docs/_static/google_analytics.js
index 5a8f6a243fb8..ce6987dbb26d 100644
--- a/docs/python_docs/_static/google_analytics.js
+++ b/docs/python_docs/_static/google_analytics.js
@@ -22,5 +22,5 @@
   m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
   })(window,document,'script','https://www.google-analytics.com/analytics.js','ga');
 
-  ga('create', 'UA-96378503-11', 'auto');
+  ga('create', 'UA-96378503-1', 'auto');
   ga('send', 'pageview');

From ffec31f6e95ba3427225f2ac59e2037318c14153 Mon Sep 17 00:00:00 2001
From: Andrei Ivanov <32910461+drivanov@users.noreply.github.com>
Date: Sat, 19 Oct 2019 16:51:23 -0700
Subject: [PATCH 477/813] Aggregated adamw update (#16398)

* Trigger CI

* MxNet operator for aggregated Adam update

* Fixing problem with getRescaleGrad(...) call in Python2
and some minor changes requested by Przemek

* Fix a problem appearing in Python2

* Minor cleanup

* Changing function name

* Trigger CI

* Eliminating "asnumpy()" conversion

* Trigger CI
---
 python/mxnet/ndarray/contrib.py               |  56 ++-
 src/operator/contrib/adamw-inl.h              | 368 ++++++++++++++++--
 src/operator/contrib/adamw.cc                 | 166 ++++++--
 src/operator/contrib/adamw.cu                 |  34 +-
 tests/python/gpu/test_operator_gpu.py         |   1 +
 .../python/unittest/test_contrib_optimizer.py | 236 ++++++-----
 6 files changed, 666 insertions(+), 195 deletions(-)

diff --git a/python/mxnet/ndarray/contrib.py b/python/mxnet/ndarray/contrib.py
index 601bc682db38..0297a1dbe575 100644
--- a/python/mxnet/ndarray/contrib.py
+++ b/python/mxnet/ndarray/contrib.py
@@ -21,6 +21,7 @@
 from __future__ import absolute_import
 import math
 import numpy as np
+import mxnet as mx
 from ..context import current_context
 from ..random import uniform
 from ..base import _as_list
@@ -32,6 +33,9 @@
 
 __all__ = ["rand_zipfian", "foreach", "while_loop", "cond", "isinf", "isfinite", "isnan"]
 
+def _flatten_list(nested_list):
+    return [item for sublist in nested_list for item in sublist]
+
 # pylint: disable=line-too-long
 def rand_zipfian(true_classes, num_sampled, range_max, ctx=None):
     """Draw random samples from an approximately log-uniform or Zipfian distribution.
@@ -514,7 +518,7 @@ def isfinite(data):
     [0. 0. 0. 1.]
     <NDArray 4 @cpu(0)>
     """
-    is_data_not_nan = data == data # pylint: disable=comparison-with-itself
+    is_data_not_nan = data == data  # pylint: disable=comparison-with-itself
     is_data_not_infinite = data.abs() != np.inf
     return ndarray.logical_and(is_data_not_infinite, is_data_not_nan)
 
@@ -542,14 +546,17 @@ def isnan(data):
     [1. 0.]
     <NDArray 2 @cpu(0)>
     """
-    return data != data # pylint: disable=comparison-with-itself
+    return data != data  # pylint: disable=comparison-with-itself
 
-def adamw_update(weight, grad, mean, var, rescale_grad, lr, eta, beta1=0.9, beta2=0.999,
-                 epsilon=1e-8, wd=0, clip_gradient=-1, out=None, name=None, **kwargs):
+def _get_rescale_grad(rescale_grad, ctx=mx.cpu()):
     if not isinstance(rescale_grad, ndarray.NDArray):
-        rescale_grad = ndarray.full(shape=(1,), val=rescale_grad, ctx=weight.context)
+        return ndarray.full(shape=(1,), val=rescale_grad, ctx=ctx)
     else:
-        rescale_grad = rescale_grad.as_in_context(weight.context)
+        return rescale_grad.as_in_context(ctx)
+
+def adamw_update(weight, grad, mean, var, rescale_grad, lr, eta, beta1=0.9, beta2=0.999,
+                 epsilon=1e-8, wd=0, clip_gradient=-1, out=None, name=None, **kwargs):
+    rescale_grad = _get_rescale_grad(rescale_grad, ctx=weight.context)
     return ndarray._internal._adamw_update(weight=weight, grad=grad, mean=mean, var=var,
                                            rescale_grad=rescale_grad, lr=lr, eta=eta,
                                            beta1=beta1, beta2=beta2, epsilon=epsilon,
@@ -559,13 +566,42 @@ def adamw_update(weight, grad, mean, var, rescale_grad, lr, eta, beta1=0.9, beta
 def mp_adamw_update(weight, grad, mean, var, weight32, rescale_grad, lr, eta, beta1=0.9,
                     beta2=0.999, epsilon=1e-8, wd=0, clip_gradient=-1, out=None,
                     name=None, **kwargs):
-    if not isinstance(rescale_grad, ndarray.NDArray):
-        rescale_grad = ndarray.full(shape=(1,), val=rescale_grad, ctx=weight.context)
-    else:
-        rescale_grad = rescale_grad.as_in_context(weight.context)
+    rescale_grad = _get_rescale_grad(rescale_grad, ctx=weight.context)
     return ndarray._internal._mp_adamw_update(weight=weight, grad=grad, mean=mean, var=var,
                                               weight32=weight32,
                                               rescale_grad=rescale_grad, lr=lr, eta=eta,
                                               beta1=beta1, beta2=beta2, epsilon=epsilon,
                                               wd=wd, clip_gradient=clip_gradient, out=out,
                                               name=name, **kwargs)
+
+def multi_adamw_update(weights, grads, mean, var, rescale_grad, lrs, wds, etas,
+                       out=None, name=None, size=0, **kwargs):
+    if not size:
+        size = len(weights)
+
+    rescale_grad = _get_rescale_grad(rescale_grad, ctx=weights[0].context)
+    temp_list = _flatten_list(zip(weights, grads, mean, var)) + [rescale_grad]
+    return ndarray._internal._multi_adamw_update(*temp_list,
+                                                 out=out,
+                                                 num_weights=size,
+                                                 lrs=lrs,
+                                                 wds=wds,
+                                                 etas=etas,
+                                                 name=name,
+                                                 **kwargs)
+
+def multi_mp_adamw_update(weights, grads, mean, var, weights32, rescale_grad, lrs, wds, etas,
+                          out=None, name=None, size=0, **kwargs):
+    if not size:
+        size = len(weights)
+
+    rescale_grad = _get_rescale_grad(rescale_grad, ctx=weights[0].context)
+    temp_list = _flatten_list(zip(weights, grads, mean, var, weights32)) + [rescale_grad]
+    return ndarray._internal._multi_mp_adamw_update(*temp_list,
+                                                    out=out,
+                                                    num_weights=size,
+                                                    lrs=lrs,
+                                                    wds=wds,
+                                                    etas=etas,
+                                                    name=name,
+                                                    **kwargs)
diff --git a/src/operator/contrib/adamw-inl.h b/src/operator/contrib/adamw-inl.h
index 6ae9e46b7def..fd139de3390f 100644
--- a/src/operator/contrib/adamw-inl.h
+++ b/src/operator/contrib/adamw-inl.h
@@ -18,26 +18,17 @@
  */
 
 /*!
- *  Copyright (c) 2016 by Contributors
- * \file optimizer_op-inl.h
+ *  Copyright (c) 2018 by Contributors
+ * \file adamw-inl.h
  * \brief Optimizer operators
- * \author Haibin Lin
+ * \author Haibin Lin, Moises Hernandez, Andrei Ivanov
  */
 #ifndef MXNET_OPERATOR_CONTRIB_ADAMW_INL_H_
 #define MXNET_OPERATOR_CONTRIB_ADAMW_INL_H_
-#include <dmlc/parameter.h>
 #include <mxnet/operator.h>
-#include <mxnet/operator_util.h>
-#include <mxnet/op_attr_types.h>
-#include <mshadow/base.h>
-#include <nnvm/op.h>
-#include <nnvm/op_attr_types.h>
 #include <vector>
-#include <cmath>
-#include "../operator_common.h"
 #include "../mshadow_op.h"
 #include "../elemwise_op_common.h"
-#include "../mxnet_op.h"
 
 namespace mxnet {
 namespace op {
@@ -87,17 +78,12 @@ inline bool MPUpdateInferShape(const nnvm::NodeAttrs& attrs,
                                mxnet::ShapeVector *out_attrs) {
   CHECK_EQ(in_attrs->size(), static_cast<size_t>(total_in)) << " in operator " << attrs.name;
   CHECK_EQ(out_attrs->size(), static_cast<size_t>(n_out)) << " in operator " << attrs.name;
-  // rescale_grad.shape = ()
   SHAPE_ASSIGN_CHECK(*in_attrs, total_in - 1, mxnet::TShape());
   // TODO(@reminisce): change "none" behavior in ElemwiseAttr
   return ElemwiseAttr<mxnet::TShape, shape_is_none, shape_assign, true, shape_string, n_in, n_out>(
       attrs, in_attrs, out_attrs, mxnet::TShape());
 }
 
-// rescale_grad is a reserved argument at position -1. Example:
-// n_in = 2: weight, grad (fp16)
-// n_out = 1: weight (fp16)
-// total_in = 6: weight, grad, mean, var, weight32, rescale_grad (fp32)
 template<int n_in, int n_out, int total_in>
 inline bool MPUpdateInferType(const nnvm::NodeAttrs& attrs,
                               std::vector<int> *in_attrs,
@@ -120,20 +106,14 @@ struct MPAdamWKernel {
     const float param_eta, const float param_lr, const float param_wd,
     const float param_rescale_grad, const float param_epsilon) {
     float w = weight32[i];
-    float mean = mean_data[i];
-    float var = var_data[i];
     float scaled_grad = param_rescale_grad*static_cast<float>(grad_data[i]);
-    if (param_clip_gradient >= 0.0f) {
-      mean = param_beta1 * mean +
-             (1 - param_beta1) * mshadow_op::clip::Map(scaled_grad, param_clip_gradient);
-      var = param_beta2 * var + (1 - param_beta2) *
-            mshadow_op::square::Map(mshadow_op::clip::Map(scaled_grad, param_clip_gradient));
-    } else {
-      mean = param_beta1 * mean + (1 - param_beta1) * scaled_grad;
-      var = param_beta2 * var + (1 - param_beta2) * mshadow_op::square::Map(scaled_grad);
-    }
-    mean_data[i] = mean;
-    var_data[i] = var;
+    if (param_clip_gradient >= 0.0f)
+      scaled_grad = mshadow_op::clip::Map(scaled_grad, param_clip_gradient);
+
+    float mean = mean_data[i] = param_beta1 * mean_data[i] + (1.0f - param_beta1) * scaled_grad;
+    float var = var_data[i] = param_beta2 * var_data[i] +
+                  (1.0f - param_beta2) * mshadow_op::square::Map(scaled_grad);
+
     w = w - param_eta * (param_lr * mean / (mshadow_op::square_root::Map(var) + param_epsilon)
                          + param_wd * w);
     weight32[i] = w;
@@ -141,7 +121,6 @@ struct MPAdamWKernel {
   }
 };
 
-
 template<typename xpu>
 struct MPAdamWUpdate {
   static inline void Forward(const nnvm::NodeAttrs& attrs,
@@ -151,7 +130,7 @@ struct MPAdamWUpdate {
                const std::vector<TBlob> &outputs,
                const float rescale_grad) {
     using namespace mxnet_op;
-    AdamWParam param = nnvm::get<AdamWParam>(attrs.parsed);
+    const auto& param = nnvm::get<AdamWParam>(attrs.parsed);
     Stream<xpu>* s = ctx.get_stream<xpu>();
     MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
       Tensor<xpu, 2, DType> weight = inputs[0].FlatTo2D<xpu, DType>(s);
@@ -183,25 +162,22 @@ struct AdamWUpdate {
     using namespace mshadow;
     using namespace mshadow::expr;
     using namespace mshadow_op;
-    const AdamWParam& param = nnvm::get<AdamWParam>(attrs.parsed);
+    const auto &param = nnvm::get<AdamWParam>(attrs.parsed);
     Stream<xpu>* s = ctx.get_stream<xpu>();
     MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
-      Tensor<xpu, 2, DType> weight = inputs[0].FlatTo2D<xpu, DType>(s);
+      const Tensor<xpu, 2, DType> &weight = inputs[0].FlatTo2D<xpu, DType>(s);
       Tensor<xpu, 2, DType> grad = inputs[1].FlatTo2D<xpu, DType>(s);
       Tensor<xpu, 2, DType> mean = inputs[2].FlatTo2D<xpu, DType>(s);
       Tensor<xpu, 2, DType> var = inputs[3].FlatTo2D<xpu, DType>(s);
       Tensor<xpu, 2, DType> out = outputs[0].FlatTo2D<xpu, DType>(s);
 
       grad = scalar<DType>(rescale_grad) * grad;
-      if (param.clip_gradient >= 0.0f) {
-        mean = scalar<DType>(param.beta1)*mean + scalar<DType>(1.f-param.beta1) *
-            F<clip>(grad, DType(param.clip_gradient));
-        var = scalar<DType>(param.beta2)*var + scalar<DType>(1.f-param.beta2)*F<square>(
-            F<clip>(grad, DType(param.clip_gradient)));
-      } else {
-        mean = scalar<DType>(param.beta1)*mean + scalar<DType>(1.f-param.beta1) * grad;
-        var = scalar<DType>(param.beta2)*var + scalar<DType>(1.f-param.beta2) * F<square>(grad);
-      }
+      if (param.clip_gradient >= 0.0f)
+        grad = F<clip>(grad, DType(param.clip_gradient));
+
+      mean = scalar<DType>(param.beta1) * mean + scalar<DType>(1.f-param.beta1) * grad;
+      var = scalar<DType>(param.beta2) * var + scalar<DType>(1.f-param.beta2) * F<square>(grad);
+
       Assign(out, req[0],
              weight -
              scalar<DType>(param.eta) * (scalar<DType>(param.lr) *
@@ -211,6 +187,312 @@ struct AdamWUpdate {
   }
 };
 
+////
+// Multiple gradients in single kernel
+////
+struct MultiAdamWParam : public dmlc::Parameter<MultiAdamWParam> {
+  mxnet::Tuple<float> lrs;
+  mxnet::Tuple<float> wds;
+  mxnet::Tuple<float> etas;
+  float beta1;
+  float beta2;
+  float epsilon;
+  float clip_gradient;
+  int num_weights;
+  DMLC_DECLARE_PARAMETER(MultiAdamWParam) {
+    DMLC_DECLARE_FIELD(lrs)
+    .describe("Learning rates");
+    DMLC_DECLARE_FIELD(beta1)
+    .set_default(0.9f)
+    .describe("The decay rate for the 1st moment estimates.");
+    DMLC_DECLARE_FIELD(beta2)
+    .set_default(0.999f)
+    .describe("The decay rate for the 2nd moment estimates.");
+    DMLC_DECLARE_FIELD(epsilon)
+    .set_default(1e-8f)
+    .describe("A small constant for numerical stability.");
+    DMLC_DECLARE_FIELD(wds)
+    .describe("Weight decay augments the objective function with a "
+              "regularization term that penalizes large weights. "
+              "The penalty scales with the square of the magnitude of each weight.");
+    DMLC_DECLARE_FIELD(etas)
+    .describe("Learning rates schedule multiplier");
+    DMLC_DECLARE_FIELD(clip_gradient)
+    .set_default(-1.0f)
+    .describe("Clip gradient to the range of [-clip_gradient, clip_gradient] "
+              "If clip_gradient <= 0, gradient clipping is turned off. "
+              "grad = max(min(grad, clip_gradient), -clip_gradient).");
+    DMLC_DECLARE_FIELD(num_weights)
+    .set_default(1)
+    .describe("Number of updated weights.");
+  }
+};
+
+
+template<typename ParamType, int input_stride>
+inline bool MP_MultiAdamW_InferShape(const nnvm::NodeAttrs& attrs,
+                                          mxnet::ShapeVector *in_attrs,
+                                          mxnet::ShapeVector *out_attrs) {
+  const ParamType& param = dmlc::get<ParamType>(attrs.parsed);
+  CHECK_EQ(in_attrs->size(), input_stride * param.num_weights +1);
+  CHECK_EQ(out_attrs->size(), param.num_weights);
+
+  bool all_inferred = true;
+  auto& input_shapes = *in_attrs;
+  auto& output_shapes = *out_attrs;
+
+  // Learning rates
+  CHECK_EQ(param.lrs.ndim(), param.num_weights)
+    << "Number of learning rates is inconsistent with num_weights "
+    << "parameter passed. Expected number of learning rates: "
+    << param.num_weights << ", and got " << param.lrs.ndim();
+  // Weight decays
+  CHECK_EQ(param.wds.ndim(), param.num_weights)
+    << "Number of weight decays is inconsistent with num_weights "
+    << "parameter passed. Expected number of weight decays: "
+    << param.num_weights << ", and got " << param.wds.ndim();
+  // Learning rates schedule multiplier
+  CHECK_EQ(param.etas.ndim(), param.num_weights)
+    << "Number of learning rates schedule multiplier is inconsistent with num_weights "
+    << "parameter passed. Expected number of learning rates schedule multiplier: "
+    << param.num_weights << ", and got " << param.lrs.ndim();
+
+  // Weights, gradients, mean and variance
+  for (int i = 0; i < param.num_weights; ++i) {
+    mxnet::ShapeVector input_vec;
+    mxnet::ShapeVector output_vec({output_shapes[i]});
+    for (int j = 0; j < input_stride; ++j) {
+      input_vec.push_back(input_shapes[i * input_stride + j]);
+    }
+    all_inferred = all_inferred && ElemwiseShape<input_stride, 1>(attrs, &input_vec, &output_vec);
+  }
+
+  SHAPE_ASSIGN_CHECK(*in_attrs, param.num_weights*input_stride, mxnet::TShape());
+  return all_inferred;
+}
+
+template <typename ParamType, int input_stride, int num_fp32_inputs>
+inline bool MP_MultiAdamW_InferType(const nnvm::NodeAttrs& attrs,
+                                    std::vector<int> *in_attrs,
+                                    std::vector<int> *out_attrs) {
+  const ParamType& param = dmlc::get<ParamType>(attrs.parsed);
+  CHECK_EQ(in_attrs->size(), input_stride * param.num_weights +1);
+  CHECK_EQ(out_attrs->size(), param.num_weights);
+
+  bool all_inferred = true;
+  auto& input_types = *in_attrs;
+  auto& output_types = *out_attrs;
+
+  // Weights, gradients,
+  for (int i = 0; i < param.num_weights; ++i) {
+    std::vector<int> input_vec;
+    std::vector<int> output_vec({output_types[i]});
+    for (int j = 0; j < input_stride - 2 - num_fp32_inputs; ++j) {
+      input_vec.push_back(input_types[i * input_stride + j]);
+    }
+    all_inferred = all_inferred &&
+            ElemwiseType<input_stride - 2 - num_fp32_inputs, 1>(attrs, &input_vec, &output_vec);
+  }
+  // mean, var
+  for (int i = 0; i < param.num_weights; ++i) {
+    TYPE_ASSIGN_CHECK(input_types, input_stride * i +2, mshadow::kFloat32);
+    TYPE_ASSIGN_CHECK(input_types, input_stride * i +3, mshadow::kFloat32);
+  }
+
+  // master copies of weights
+  for (int i = 0; i < param.num_weights; ++i) {
+    for (int j = 0; j < num_fp32_inputs; ++j) {
+      TYPE_ASSIGN_CHECK(input_types, input_stride * i + input_stride - 1 - j, mshadow::kFloat32);
+    }
+  }
+
+  TYPE_ASSIGN_CHECK(input_types, param.num_weights*input_stride, mshadow::kFloat32);
+  return all_inferred;
+}
+
+
+template<typename T>
+class Adam_type_identity {
+ public:
+  using type = T;
+};
+
+
+template<typename T>
+class Adam_single_precision {
+ public:
+  using type = float;
+};
+
+template<typename DType, typename MPDType>
+struct MultiAdamKernelParam {
+  static const int N = 50;
+  int count;
+  size_t max_size;
+  size_t sizes[N];
+  DType* weights[N];
+  DType* grad_data[N];
+  MPDType* mean_data[N];
+  MPDType* var_data[N];
+  MPDType* weights32[N];
+  DType* out_data[N];
+  MPDType clip_gradient;
+  MPDType beta1;
+  MPDType beta2;
+  MPDType etas[N];
+  MPDType lrs[N];
+  MPDType wds[N];
+  MPDType epsilon;
+};
+
+template<typename MPDType, bool has_mixed_precision>
+struct MultiMPAdamWKernel {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int i, const MultiAdamKernelParam<DType, MPDType>& param,
+                                  const OpReqType req, const float rescale_grad){
+    for (int index = 0; index < param.count; ++index) {
+      if ((size_t)i < param.sizes[index]) {
+        MPDType w = has_mixed_precision ? param.weights32[index][i]:
+                                          MPDType(param.weights[index][i]);
+        MPDType scaled_grad = static_cast<MPDType>(rescale_grad)*
+                              static_cast<MPDType>(param.grad_data[index][i]);
+
+        if (param.clip_gradient >= 0.0f)
+          scaled_grad = mshadow_op::clip::Map(scaled_grad, param.clip_gradient);
+
+        const auto mean = param.beta1 * (param.mean_data[index][i]- scaled_grad) + scaled_grad;
+        const auto adj = mshadow_op::square::Map(scaled_grad);
+        const auto var = param.beta2 * (param.var_data[index][i] - adj) + adj;
+
+        param.mean_data[index][i] = mean;
+        param.var_data[index][i] = var;
+        w = w - param.etas[index] * (param.lrs[index] *
+            mean / (mshadow_op::square_root::Map(var) + param.epsilon)
+            + param.wds[index] * w);
+        if (has_mixed_precision)
+          param.weights32[index][i] = w;
+
+        KERNEL_ASSIGN(param.out_data[index][i], req, w);
+      }
+    }
+  }
+};
+
+template<typename xpu,
+         typename DType,
+         typename MPDType,
+         typename ParamType = MultiAdamWParam,
+         int input_stride = 4>
+void FillMultiAdamKernelParam(const nnvm::NodeAttrs& attrs,
+                              const OpContext &ctx,
+                              const std::vector<TBlob> &inputs,
+                              const std::vector<TBlob> &outputs,
+                              MultiAdamKernelParam<DType, MPDType> *pParam) {
+  const ParamType& p = nnvm::get<ParamType>(attrs.parsed);
+  mxnet_op::Stream<xpu>* s = ctx.get_stream<xpu>();
+  pParam->clip_gradient = p.clip_gradient;
+  pParam->beta1 = p.beta1;
+  pParam->beta2 = p.beta2;
+
+  pParam->epsilon = p.epsilon;
+
+  pParam->count = p.num_weights;
+  pParam->max_size = 0;
+  constexpr bool isSame = std::is_same<DType, MPDType>::value;
+  for (int i = 0; i < pParam->count; ++i) {
+    const auto idx = i * input_stride;
+    pParam->sizes[i] = inputs[idx].shape_.Size();
+    if (pParam->max_size < pParam->sizes[i])
+      pParam->max_size = pParam->sizes[i];
+
+    pParam->weights[i] = inputs[idx].FlatTo2D<xpu, DType>(s).dptr_;
+    pParam->grad_data[i] = inputs[idx + 1].FlatTo2D<xpu, DType>(s).dptr_;
+    pParam->mean_data[i] = inputs[idx + 2].FlatTo2D<xpu, MPDType>(s).dptr_;
+    pParam->var_data[i]  = inputs[idx + 3].FlatTo2D<xpu, MPDType>(s).dptr_;
+    // if mixed precision, then the last input in a set
+    // is 32-bit master copy of the weights
+    if (!isSame)
+      pParam->weights32[i] = inputs[idx + input_stride - 1].FlatTo2D<xpu, MPDType>(s).dptr_;
+
+    pParam->out_data[i] = outputs[i].FlatTo2D<xpu, DType>(s).dptr_;
+  }
+  memcpy(pParam->etas, p.etas.begin(), pParam->count * sizeof(p.etas[0]));
+  memcpy(pParam->lrs, p.lrs.begin(), pParam->count * sizeof(p.lrs[0]));
+  memcpy(pParam->wds, p.wds.begin(), pParam->count * sizeof(p.wds[0]));
+}
+
+template<typename xpu, template<typename> class MPTypeChooser, int input_stride>
+static inline void MultiAdamWUpdate(const nnvm::NodeAttrs& attrs,
+                                    const OpContext &ctx,
+                                    const std::vector<TBlob> &inputs,
+                                    const std::vector<OpReqType> &req,
+                                    const std::vector<TBlob> &outputs,
+                                    const float rescale_grad) {
+  using namespace mxnet_op;
+  Stream<xpu>* s = ctx.get_stream<xpu>();
+  MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+    using MPDType = typename MPTypeChooser<DType>::type;
+    MultiAdamKernelParam<DType, MPDType> param;
+    FillMultiAdamKernelParam<xpu, DType, MPDType, MultiAdamWParam, input_stride>
+            (attrs, ctx, inputs, outputs, &param);
+
+    Kernel<MultiMPAdamWKernel<MPDType, !std::is_same<DType, MPDType>::value>, xpu>::
+                              Launch(s, param.max_size, param, req[0], rescale_grad);
+  });
+}
+
+template<typename xpu>
+void GetScaleFloat(const TBlob &scale_blob, float *pScalef);
+
+template<typename xpu>
+bool PrepareInputBlobs(const std::vector<TBlob> &inputs,
+                       std::vector<TBlob> *inputs_wo_scale,
+                       float *pScalef) {
+  const size_t num_in = inputs.size() - 1;
+  GetScaleFloat<xpu>(inputs[num_in], pScalef);
+  if (!std::isfinite(*pScalef) || *pScalef == 0)
+    return false;
+
+  inputs_wo_scale->reserve(num_in);
+  for (size_t i = 0; i < num_in; i++)
+    inputs_wo_scale->emplace_back(inputs[i]);
+
+  return true;
+}
+
+template<typename xpu, class F>
+inline void MPUpdate(const nnvm::NodeAttrs& attrs,
+                     const OpContext &ctx,
+                     const std::vector<TBlob> &inputs,
+                     const std::vector<OpReqType> &req,
+                     const std::vector<TBlob> &outputs) {
+  std::vector<TBlob> inputs_wo_scale;
+  float scalef;
+  if (!PrepareInputBlobs<xpu>(inputs, &inputs_wo_scale, &scalef))
+    return;
+
+  F::Forward(attrs, ctx, inputs_wo_scale, req, outputs, scalef);
+}
+
+template<typename xpu, bool MP>
+inline void multiMPUpdate(const nnvm::NodeAttrs& attrs,
+                          const OpContext &ctx,
+                          const std::vector<TBlob> &inputs,
+                          const std::vector<OpReqType> &req,
+                          const std::vector<TBlob> &outputs) {
+  std::vector<TBlob> inputs_wo_scale;
+  float scalef;
+  if (!PrepareInputBlobs<xpu>(inputs, &inputs_wo_scale, &scalef))
+    return;
+
+  if (!MP)
+    MultiAdamWUpdate<xpu, Adam_type_identity, 4>
+      (attrs, ctx, inputs_wo_scale, req, outputs, scalef);
+  else
+    MultiAdamWUpdate<xpu, Adam_single_precision, 5>
+      (attrs, ctx, inputs_wo_scale, req, outputs, scalef);
+}
+
 }  // namespace op
 }  // namespace mxnet
 
diff --git a/src/operator/contrib/adamw.cc b/src/operator/contrib/adamw.cc
index f0716c6020f9..2c730f0b3e7b 100644
--- a/src/operator/contrib/adamw.cc
+++ b/src/operator/contrib/adamw.cc
@@ -18,37 +18,18 @@
  */
 
 /*!
- *  Copyright (c) 2016 by Contributors
- * \file optimizer_op.cc
+ *  Copyright (c) 2018 by Contributors
+ * \file adamw.cc
  * \brief Optimizer operators
- * \author Haibin Lin
+ * \author Haibin Lin, Moises Hernandez, Andrei Ivanov
  */
 #include "./adamw-inl.h"
-#include "../optimizer_op-inl.h"
 
 namespace mxnet {
 namespace op {
 
 DMLC_REGISTER_PARAMETER(AdamWParam);
-
-template<template <typename xpu> class F>
-inline void MPUpdateCPU(const nnvm::NodeAttrs& attrs,
-                        const OpContext &ctx,
-                        const std::vector<TBlob> &inputs,
-                        const std::vector<OpReqType> &req,
-                        const std::vector<TBlob> &outputs) {
-  // copy to cpu and check NaN value
-  TBlob scale_blob = inputs[inputs.size() - 1];
-  MSHADOW_REAL_TYPE_SWITCH(scale_blob.type_flag_, DType, {
-    float scalef = static_cast<float>(*scale_blob.dptr<DType>());
-    if (!std::isfinite(scalef) || scalef == 0) return;
-    std::vector<TBlob> inputs_wo_scale;
-    size_t num_in = inputs.size();
-    inputs_wo_scale.reserve(num_in - 1);
-    for (size_t i = 0; i < num_in - 1; i++) inputs_wo_scale.emplace_back(inputs[i]);
-    F<cpu>::Forward(attrs, ctx, inputs_wo_scale, req, outputs, scalef);
-  });
-}
+DMLC_REGISTER_PARAMETER(MultiAdamWParam);
 
 NNVM_REGISTER_OP(_mp_adamw_update)
 .describe(R"code(Update function for multi-precision AdamW optimizer.
@@ -84,7 +65,7 @@ the update is skipped.
   [](const nnvm::NodeAttrs& attrs) {
     return std::vector<uint32_t>{2, 3, 4};
   })
-.set_attr<FCompute>("FCompute<cpu>", MPUpdateCPU<MPAdamWUpdate>)
+.set_attr<FCompute>("FCompute<cpu>", MPUpdate<cpu, MPAdamWUpdate<cpu>>)
 .add_argument("weight", "NDArray-or-Symbol", "Weight")
 .add_argument("grad", "NDArray-or-Symbol", "Gradient")
 .add_argument("mean", "NDArray-or-Symbol", "Moving mean")
@@ -127,7 +108,7 @@ the update is skipped.
   [](const nnvm::NodeAttrs& attrs) {
     return std::vector<uint32_t>{2, 3};
   })
-.set_attr<FCompute>("FCompute<cpu>", MPUpdateCPU<AdamWUpdate>)
+.set_attr<FCompute>("FCompute<cpu>", MPUpdate<cpu, AdamWUpdate<cpu>>)
 .add_argument("weight", "NDArray-or-Symbol", "Weight")
 .add_argument("grad", "NDArray-or-Symbol", "Gradient")
 .add_argument("mean", "NDArray-or-Symbol", "Moving mean")
@@ -137,5 +118,140 @@ the update is skipped.
               "the update is skipped.")
 .add_arguments(AdamWParam::__FIELDS__());
 
+template<>
+void GetScaleFloat<cpu>(const TBlob &scale_blob, float *pScalef) {
+  MSHADOW_REAL_TYPE_SWITCH(scale_blob.type_flag_, DType,
+    *pScalef = static_cast<float>(*scale_blob.dptr<DType>());
+  )
+}
+
+std::vector<std::string> ParamToVector(uint32_t num_args, const char *pName[], size_t nParams) {
+  std::vector<std::string> ret;
+  for (uint32_t i = 0; i < num_args; ++i) {
+    const auto idx = std::to_string(i);
+    for (size_t j = 0; j < nParams; ++j)
+      ret.push_back(std::string(pName[i]) + idx);
+  }
+
+  return ret;
+}
+
+inline uint32_t num_weights(const nnvm::NodeAttrs& attrs) {
+  return static_cast<uint32_t>(dmlc::get<MultiAdamWParam>(attrs.parsed).num_weights);
+}
+
+NNVM_REGISTER_OP(_multi_adamw_update)
+.describe(R"code(Update function for AdamW optimizer.
+
+AdamW is seen as a modification of Adam by decoupling the weight decay from the
+optimization steps taken w.r.t. the loss function.
+
+Adam update consists of the following steps, where g represents gradient and m, v
+are 1st and 2nd order moment estimates (mean and variance).
+
+.. math::
+
+ g_t = \nabla J(W_{t-1})\\
+ m_t = \beta_1 m_{t-1} + (1 - \beta_1) g_t\\
+ v_t = \beta_2 v_{t-1} + (1 - \beta_2) g_t^2\\
+ W_t = W_{t-1} - \eta_t (\alpha \frac{ m_t }{ \sqrt{ v_t } + \epsilon } + wd W_{t-1})
+
+It updates the weights using::
+
+ m = beta1*m + (1-beta1)*grad
+ v = beta2*v + (1-beta2)*(grad**2)
+ w -= eta * (learning_rate * m / (sqrt(v) + epsilon) + w * wd)
+
+Note that gradient is rescaled to grad = rescale_grad * grad. If rescale_grad is NaN, Inf, or 0,
+the update is skipped.
+)code" ADD_FILELINE)
+.set_num_inputs([](const nnvm::NodeAttrs& attrs) {
+    return num_weights(attrs) * 4 + 1;
+  })
+.set_num_outputs([](const nnvm::NodeAttrs& attrs) {
+    return num_weights(attrs);
+  })
+.set_attr_parser(ParamParser<MultiAdamWParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", MP_MultiAdamW_InferShape<MultiAdamWParam, 4>)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<-1, -1>)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    const char *paramName[] = {"weight_", "grad_", "mean_", "var_", "rescale_grad_"};
+    return ParamToVector(num_weights(attrs), paramName, sizeof(paramName)/sizeof(paramName[0]));
+  })
+// mutable: mean, var
+.set_attr<nnvm::FMutateInputs>("FMutateInputs",
+  [](const nnvm::NodeAttrs& attrs) {
+    std::vector<uint32_t> ret;
+    const auto iMax = num_weights(attrs);
+    for (size_t i = 0; i < iMax; ++i) {
+      ret.push_back(i * 4 + 2);
+      ret.push_back(i * 4 + 3);
+    }
+    return ret;
+  })
+
+.set_attr<FCompute>("FCompute<cpu>", multiMPUpdate<cpu, false>)
+.add_argument("data", "NDArray-or-Symbol[]", "data")
+.add_arguments(MultiAdamWParam::__FIELDS__());
+
+
+NNVM_REGISTER_OP(_multi_mp_adamw_update)
+.describe(R"code(Update function for multi-precision AdamW optimizer.
+
+AdamW is seen as a modification of Adam by decoupling the weight decay from the
+optimization steps taken w.r.t. the loss function.
+
+Adam update consists of the following steps, where g represents gradient and m, v
+are 1st and 2nd order moment estimates (mean and variance).
+
+.. math::
+
+ g_t = \nabla J(W_{t-1})\\
+ m_t = \beta_1 m_{t-1} + (1 - \beta_1) g_t\\
+ v_t = \beta_2 v_{t-1} + (1 - \beta_2) g_t^2\\
+ W_t = W_{t-1} - \eta_t (\alpha \frac{ m_t }{ \sqrt{ v_t } + \epsilon } + wd W_{t-1})
+
+It updates the weights using::
+
+ m = beta1*m + (1-beta1)*grad
+ v = beta2*v + (1-beta2)*(grad**2)
+ w -= eta * (learning_rate * m / (sqrt(v) + epsilon) + w * wd)
+
+Note that gradient is rescaled to grad = rescale_grad * grad. If rescale_grad is NaN, Inf, or 0,
+the update is skipped.
+)code" ADD_FILELINE)
+.set_num_inputs([](const nnvm::NodeAttrs& attrs) {
+    return num_weights(attrs) * 5 + 1;
+  })
+.set_num_outputs([](const nnvm::NodeAttrs& attrs) {
+    return num_weights(attrs);
+  })
+.set_attr_parser(ParamParser<MultiAdamWParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", MP_MultiAdamW_InferShape<MultiAdamWParam, 5>)
+.set_attr<nnvm::FInferType>("FInferType", MP_MultiAdamW_InferType<MultiAdamWParam, 5, 1>)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    const char *paramName[] = {"weight_", "grad_", "mean_", "var_", "weight32_", "rescale_grad_"};
+    return ParamToVector(num_weights(attrs), paramName, sizeof(paramName)/sizeof(paramName[0]));
+  })
+// mutable: mean, var, weights32
+.set_attr<nnvm::FMutateInputs>("FMutateInputs",
+  [](const nnvm::NodeAttrs& attrs) {
+    std::vector<uint32_t> ret;
+    const auto iMax = num_weights(attrs);
+    for (size_t i = 0; i < iMax; ++i) {
+      ret.push_back(i * 5 + 2);
+      ret.push_back(i * 5 + 3);
+      ret.push_back(i * 5 + 4);
+    }
+    return ret;
+  })
+
+.set_attr<FCompute>("FCompute<cpu>", multiMPUpdate<cpu, true>)
+.add_argument("data", "NDArray-or-Symbol[]", "data")
+.add_arguments(MultiAdamWParam::__FIELDS__());
+
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/contrib/adamw.cu b/src/operator/contrib/adamw.cu
index 1521749904b9..81b13c98ede4 100644
--- a/src/operator/contrib/adamw.cu
+++ b/src/operator/contrib/adamw.cu
@@ -21,40 +21,34 @@
  *  Copyright (c) 2018 by Contributors
  * \file adamw.cu
  * \brief Optimizer operators
- * \author Haibin Lin
+ * \author Haibin Lin, Moises Hernandez, Andrei Ivanov
  */
 #include "./adamw-inl.h"
 
 namespace mxnet {
 namespace op {
 
-template<template <typename xpu> class F>
-inline void MPUpdateGPU(const nnvm::NodeAttrs& attrs,
-                        const OpContext &ctx,
-                        const std::vector<TBlob> &inputs,
-                        const std::vector<OpReqType> &req,
-                        const std::vector<TBlob> &outputs) {
-  // copy to cpu and check NaN value
-  TBlob scale_blob = inputs[inputs.size() - 1];
+template<>
+void GetScaleFloat<gpu>(const TBlob &scale_blob, float *pScalef) {
   MSHADOW_REAL_TYPE_SWITCH(scale_blob.type_flag_, DType, {
     DType scale = 0;
     CUDA_CALL(cudaMemcpy(&scale, scale_blob.dptr<DType>(), sizeof(DType),
-       cudaMemcpyDeviceToHost));
-    float scalef = static_cast<float>(scale);
-    if (!std::isfinite(scalef) || scalef == 0) return;
-    std::vector<TBlob> inputs_wo_scale;
-    size_t num_in = inputs.size();
-    inputs_wo_scale.reserve(num_in - 1);
-    for (size_t i = 0; i < num_in - 1; i++) inputs_wo_scale.emplace_back(inputs[i]);
-    F<gpu>::Forward(attrs, ctx, inputs_wo_scale, req, outputs, scalef);
-  });
+                         cudaMemcpyDeviceToHost));
+    *pScalef = static_cast<float>(scale);
+  })
 }
 
 NNVM_REGISTER_OP(_adamw_update)
-.set_attr<FCompute>("FCompute<gpu>", MPUpdateGPU<AdamWUpdate>);
+.set_attr<FCompute>("FCompute<gpu>", MPUpdate<gpu, AdamWUpdate<gpu>>);
 
 NNVM_REGISTER_OP(_mp_adamw_update)
-.set_attr<FCompute>("FCompute<gpu>", MPUpdateGPU<MPAdamWUpdate>);
+.set_attr<FCompute>("FCompute<gpu>", MPUpdate<gpu, MPAdamWUpdate<gpu>>);
+
+NNVM_REGISTER_OP(_multi_adamw_update)
+.set_attr<FCompute>("FCompute<gpu>", multiMPUpdate<gpu, false>);
+
+NNVM_REGISTER_OP(_multi_mp_adamw_update)
+.set_attr<FCompute>("FCompute<gpu>", multiMPUpdate<gpu, true>);
 
 }  // namespace op
 }  // namespace mxnet
diff --git a/tests/python/gpu/test_operator_gpu.py b/tests/python/gpu/test_operator_gpu.py
index b79b08219221..06a16b1bb4f8 100644
--- a/tests/python/gpu/test_operator_gpu.py
+++ b/tests/python/gpu/test_operator_gpu.py
@@ -48,6 +48,7 @@
 from test_contrib_operator import test_multibox_target_op
 from test_tvm_op import *
 from test_library_loading import *
+from test_contrib_optimizer import test_adamw
 
 set_default_context(mx.gpu(0))
 del test_support_vector_machine_l1_svm  # noqa
diff --git a/tests/python/unittest/test_contrib_optimizer.py b/tests/python/unittest/test_contrib_optimizer.py
index 675cc94c64f1..7cfd0217aa31 100644
--- a/tests/python/unittest/test_contrib_optimizer.py
+++ b/tests/python/unittest/test_contrib_optimizer.py
@@ -16,12 +16,14 @@
 # under the License.
 
 import itertools
-
 import numpy as np
-
 import mxnet as mx
 from mxnet.test_utils import *
 
+curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
+sys.path.insert(0, os.path.join(curr_path, '../unittest'))
+from common import with_seed
+
 
 # * GroupAdaGrad
 class PyGroupAdaGrad(mx.optimizer.Optimizer):
@@ -94,102 +96,142 @@ def test_group_adagrad():
                 g_stype='row_sparse',
                 compare_states=False)
 
-def test_adamw():
-    shape = (3, 4)
-    weight = mx.nd.random.uniform(shape=shape)
-    weight_ref = weight.copy()
-    grad = mx.nd.random.uniform(shape=shape)
-    m = mx.nd.random.uniform(shape=shape)
-    v = mx.nd.random.uniform(shape=shape)
-    rescale_grad = mx.nd.array([10])
-    eta, lr, wd, epsilon = 1, 1, 0, 1e-8
-    beta1, beta2 = 0.9, 0.999
-    kwargs = {'eta': eta, 'lr': lr, 'wd': wd, 'epsilon': epsilon,
-              'beta1': beta1, 'beta2': beta2}
-
-    # update is skipped for rescale = nan scalar
-    mx.nd.contrib.adamw_update(weight, grad, m, v,
-                               np.nan, out=weight, **kwargs)
-    # weight remains unchanged
-    mx.test_utils.assert_almost_equal(weight_ref.asnumpy(), weight.asnumpy())
-
-    # update is skipped for rescale = 0
-    mx.nd.contrib.adamw_update(weight, grad, m, v,
-                               rescale_grad * 0, out=weight, **kwargs)
-    # weight remains unchanged
-    mx.test_utils.assert_almost_equal(weight_ref.asnumpy(), weight.asnumpy())
-
-    # update is skipped for rescale = nan
-    mx.nd.contrib.adamw_update(weight, grad, m, v,
-                               rescale_grad * np.nan, out=weight, **kwargs)
-    # weight remains unchanged
-    mx.test_utils.assert_almost_equal(weight_ref.asnumpy(), weight.asnumpy())
-
-    # update is skipped for rescale = inf
-    mx.nd.contrib.adamw_update(weight, grad, m, v,
-                               rescale_grad * np.inf, out=weight, **kwargs)
-    # weight remains unchanged
-    mx.test_utils.assert_almost_equal(weight_ref.asnumpy(), weight.asnumpy())
-
-    # multi-precision update is skipped for rescale = nan
-    weight_fp16 = weight.astype('float16')
-    grad_fp16 = grad.astype('float16')
-    weight_fp16_ref = weight_fp16.copy()
-    mx.nd.contrib.mp_adamw_update(weight_fp16, grad_fp16, m, v, weight,
-                                  rescale_grad * np.nan, out=weight_fp16, **kwargs)
-    mx.test_utils.assert_almost_equal(weight_ref.asnumpy(), weight.asnumpy())
-    mx.test_utils.assert_almost_equal(weight_fp16_ref.asnumpy(), weight_fp16.asnumpy())
-
-    # multi-precision update is skipped for rescale = nan scalar
-    mx.nd.contrib.mp_adamw_update(weight_fp16, grad_fp16, m, v, weight,
-                                  np.nan, out=weight_fp16, **kwargs)
-    mx.test_utils.assert_almost_equal(weight_ref.asnumpy(), weight.asnumpy())
-    mx.test_utils.assert_almost_equal(weight_fp16_ref.asnumpy(), weight_fp16.asnumpy())
-
-    # multi-precision update is skipped for rescale = inf
-    mx.nd.contrib.mp_adamw_update(weight_fp16, grad_fp16, m, v, weight,
-                                  rescale_grad * np.inf, out=weight_fp16, **kwargs)
-    mx.test_utils.assert_almost_equal(weight_ref.asnumpy(), weight.asnumpy())
-    mx.test_utils.assert_almost_equal(weight_fp16_ref.asnumpy(), weight_fp16.asnumpy())
-
-    # multi-precision update is skipped for rescale = 0
-    mx.nd.contrib.mp_adamw_update(weight_fp16, grad_fp16, m, v, weight,
-                                  rescale_grad * 0, out=weight_fp16, **kwargs)
-    mx.test_utils.assert_almost_equal(weight_ref.asnumpy(), weight.asnumpy())
-    mx.test_utils.assert_almost_equal(weight_fp16_ref.asnumpy(), weight_fp16.asnumpy())
-
-    # reference normal update
-    grad_rescale = rescale_grad * grad
-    m_ref = beta1*m + (1-beta1)*grad_rescale
-    v_ref = beta2*v + (1-beta2)*(grad_rescale**2)
-    weight_ref = weight - eta * (1 * m_ref / (v_ref.sqrt() + epsilon) + weight * wd)
-    m_test = m.copy()
-    v_test = v.copy()
-    weight_test = weight.copy()
-    # op normal update
-    mx.nd.contrib.adamw_update(weight_test, grad, m_test, v_test,
-                               rescale_grad, out=weight_test, **kwargs)
-    mx.test_utils.assert_almost_equal(weight_ref.asnumpy(), weight_test.asnumpy())
-    mx.test_utils.assert_almost_equal(m_ref.asnumpy(), m_test.asnumpy())
-    mx.test_utils.assert_almost_equal(v_ref.asnumpy(), v_test.asnumpy())
-
-    # reference normal multi-precision update
-    m_fp32 = m.copy()
-    v_fp32 = v.copy()
-    weight_fp32 = weight.copy()
-    grad_rescale = rescale_grad * grad_fp16.astype('float32')
-    m_ref = beta1*m_fp32 + (1-beta1)*grad_rescale
-    v_ref = beta2*v_fp32 + (1-beta2)*(grad_rescale**2)
-    weight_ref = weight - eta * (1 * m_ref / (v_ref.sqrt() + epsilon) + weight * wd)
-    weight_fp16_ref = weight_ref.astype('float16')
-    # op normal multi-precision update
-    mx.nd.contrib.mp_adamw_update(weight_fp16, grad_fp16, m_fp32, v_fp32, weight_fp32,
-                                  rescale_grad, out=weight_fp16, **kwargs)
-    mx.test_utils.assert_almost_equal(m_ref.asnumpy(), m_fp32.asnumpy())
-    mx.test_utils.assert_almost_equal(v_ref.asnumpy(), v_fp32.asnumpy())
-    mx.test_utils.assert_almost_equal(weight_ref.asnumpy(), weight_fp32.asnumpy())
-    mx.test_utils.assert_almost_equal(weight_fp16_ref.asnumpy(), weight_fp16.asnumpy())
 
+@with_seed()
+def test_adamw():
+    def get_refs(m, v, weight, grad_rescale, beta1, beta2, lr, eta, wd, epsilon, clip_grad=-1):
+        if clip_grad >= 0:
+            grad_rescale = mx.nd.clip(grad_rescale, -clip_grad, clip_grad)
+
+        mean_ref = beta1*m + (1-beta1)*grad_rescale
+        v_ref = beta2*v + (1-beta2)*(grad_rescale**2)
+        weight_ref = weight - eta * (lr * mean_ref / (v_ref.sqrt() + epsilon) + weight * wd)
+        return mean_ref, v_ref, weight_ref
+
+    def run_adamw_test(nElem=1, aggregate=False):
+        aggregate = aggregate or nElem > 1
+        rescale_factor = 10
+        eta, lr, wd, epsilon = 1, 1, 0.1, 1e-8
+        beta1, beta2 = 0.9, 0.999
+        clip_gradient = np.random.uniform(rescale_factor, rescale_factor)
+        weight, grad, m, v, etas, lrs, wds, weight_ref = [], [], [], [], [], [], [], []
+        for i in range(nElem):
+            shape = (np.random.randint(3, high=10), np.random.randint(3, high=10))
+            weight.append(mx.nd.random.uniform(shape=shape))
+            grad.append(mx.nd.random.uniform(-1.0, 1.0, shape=shape))
+            m.append(mx.nd.random.uniform(shape=shape))
+            v.append(mx.nd.random.uniform(shape=shape))
+            etas.append(eta - 1 / np.random.uniform(9, 10))
+            lrs.append(lr - 1 / np.random.uniform(9, 10))
+            wds.append(wd - 1 / np.random.uniform(95, 105))
+            weight_ref.append(weight[i].copy())
+
+        if aggregate:
+            kwargs = {'etas': etas, 'lrs': lrs, 'wds': wds}
+        else:
+            kwargs = {'eta': etas[0], 'lr': lrs[0], 'wd': wds[0]}
+
+        kwargs.update([('epsilon', epsilon), ('beta1', beta1), ('beta2', beta2), ('clip_gradient', clip_gradient)])
+
+        # Test 1: Update is skipped for rescale = nan scalar
+        rescale_grad = mx.nd.array([rescale_factor])
+        tested_grad = [rescale_grad * 0, rescale_grad * np.nan, rescale_grad * np.inf]
+        tested_rescaled_grad = [np.nan]
+        tested_rescaled_grad.extend(tested_grad)
+
+        for rescaled_grad in tested_rescaled_grad:
+            if aggregate:
+                mx.nd.contrib.multi_adamw_update(weight, grad, m, v,
+                                                 rescaled_grad, out=weight, **kwargs)
+            else:
+                mx.nd.contrib.adamw_update(weight[0], grad[0], m[0], v[0],
+                                           rescaled_grad, out=weight[0], **kwargs)
+
+            # weights should remain unchanged
+            for j in range(nElem):
+                assert_almost_equal(weight_ref[j], weight[j])
+
+
+        # Test 2: Same as Test 1 for multi-precision update
+        weight_fp16, grad_fp16, weight_fp16_refs = [], [], []
+        for i in range(nElem):
+            weight_fp16.append(weight[i].astype('float16'))
+            grad_fp16.append(grad[i].astype('float16'))
+            weight_fp16_refs.append(weight_fp16[i].copy())
+
+        for rescaled_grad in tested_grad:
+            if aggregate:
+                mx.nd.contrib.multi_mp_adamw_update(weight_fp16, grad_fp16, m, v, weight,
+                                                    rescaled_grad, out=weight_fp16, **kwargs)
+            else:
+                mx.nd.contrib.mp_adamw_update(weight_fp16[0], grad_fp16[0], m[0], v[0], weight[0],
+                                              rescaled_grad, out=weight_fp16[0], **kwargs)
+
+            # weights should remain unchanged
+            for i in range(nElem):
+                assert_almost_equal(weight_ref[i], weight[i])
+                assert_almost_equal(weight_fp16_refs[i], weight_fp16[i])
+
+
+        # Test 3: Reference normal update
+        grad_rescale, weight_test, m_refs, v_refs, weight_refs = [], [], [], [], []
+        for i in range(nElem):
+            grad_rescale.append(rescale_grad * grad[i])
+            m_ref, v_ref, weight_ref = get_refs(m[i], v[i], weight[i], grad_rescale[i], beta1, beta2, lrs[i], etas[i], wds[i], epsilon, clip_gradient)
+            m_refs.append(m_ref)
+            v_refs.append(v_ref)
+            weight_refs.append(weight_ref)
+            weight_test.append(weight[i].copy())
+
+        # op normal update
+        if aggregate:
+            mx.nd.contrib.multi_adamw_update(weight_test, grad, m, v,
+                                             rescale_grad, out=weight_test, **kwargs)
+        else:
+            mx.nd.contrib.adamw_update(weight_test[0], grad[0], m[0], v[0],
+                                       rescale_grad, out=weight_test[0], **kwargs)
+
+        # Compare results
+        atol = 1e-4 if aggregate else 1e-5
+        rtol = 1e-4 if aggregate else None
+        for i in range(nElem):
+            assert_almost_equal(weight_refs[i], weight_test[i], rtol=rtol, atol=atol)
+            assert_almost_equal(m_refs[i], m[i], rtol=rtol, atol=atol)
+            assert_almost_equal(v_refs[i], v[i], atol=atol)
+
+
+        # Test 4: Reference normal multi-precision update
+        grad_rescale, m_refs, v_refs, weight_refs, weight_fp16_refs = [], [], [], [], []
+        for i in range(nElem):
+            grad_rescale.append(rescale_grad * grad_fp16[i].astype('float32'))
+            m_ref, v_ref, weight_ref = get_refs(m[i], v[i], weight[i], grad_rescale[i], beta1, beta2, lrs[i], etas[i], wds[i], epsilon, clip_gradient)
+            m_refs.append(m_ref)
+            v_refs.append(v_ref)
+            weight_refs.append(weight_ref)
+            weight_fp16_refs.append(weight_ref.astype('float16'))
+
+        # op normal multi-precision update
+        if aggregate:
+            mx.nd.contrib.multi_mp_adamw_update(weight_fp16, grad_fp16, m, v, weight,
+                                                rescale_grad, out=weight_fp16, **kwargs)
+        else:
+            mx.nd.contrib.mp_adamw_update(weight_fp16[0], grad_fp16[0], m[0], v[0], weight[0],
+                                          rescale_grad, out=weight_fp16[0], **kwargs)
+
+        # Compare results
+        for i in range(nElem):
+            assert_almost_equal(m_refs[i], m[i], rtol=rtol, atol=atol)
+            assert_almost_equal(v_refs[i], v[i], atol=atol)
+            assert_almost_equal(weight_refs[i], weight[i], rtol=rtol, atol=atol)
+            assert_almost_equal(weight_fp16_refs[i], weight_fp16[i], rtol=1e-3, atol=atol)
+
+    # Testing aggregated Adam update for one element
+    run_adamw_test(1, aggregate=True)
+
+    # Testing Adam update, if nElem = 0, OR
+    #         aggregated Adam update, if nElem > 0
+    for nElem in range(6):
+        run_adamw_test(nElem+1)
 
 if __name__ == '__main__':
     import nose

From 5b67a69da83ddba7f331ac3ca7fd8416dad12f35 Mon Sep 17 00:00:00 2001
From: Xingjian Shi <xshiab@connect.ust.hk>
Date: Sat, 19 Oct 2019 18:01:23 -0700
Subject: [PATCH 478/813] try to fix block (#16465)

fix bug

fix

fix

fix lint

fix

backward-compatible for inferencing the ctx

fix lint

try to improve

try to fix

Update block.py

Revert "Update block.py"

This reverts commit bbcf41f838c88a91201fc6f129897e60b72ce3c2.

Revert "try to fix"

This reverts commit 7d7f35c9a0357a05ebe18b32ceae03421579cc86.

Revert "try to improve"

This reverts commit f510132ca96aa05d83b6eb4e432c9a6c76ec8e1f.

Revert "Revert "try to improve""

This reverts commit 872a7abe34c9afa97eb631fab8c3ce8558a22af8.

Revert "Revert "try to fix""

This reverts commit 48e235dd4c9ee6a88d1a2515a8a1f3e57319c217.

Revert "Revert "Update block.py""

This reverts commit e0d3949245050a4c60c4834db73c764d87fde6f7.

fix

fix lint
---
 python/mxnet/gluon/block.py         | 110 +++++++++++++++++++---------
 tests/python/gpu/test_gluon_gpu.py  |  15 ++++
 tests/python/unittest/test_gluon.py |  44 +++++++++++
 3 files changed, 133 insertions(+), 36 deletions(-)

diff --git a/python/mxnet/gluon/block.py b/python/mxnet/gluon/block.py
index b42fefd065e8..d474337772a0 100644
--- a/python/mxnet/gluon/block.py
+++ b/python/mxnet/gluon/block.py
@@ -93,6 +93,53 @@ def __exit__(self, ptype, value, trace):
         _BlockScope._current.value = self._old_scope
 
 
+def _gather_type_ctx_info(args):
+    """Analyze the elements inside the nested args object and find:
+        - If there exists ndarray
+        - If there exists symbol
+        - All contexts appearing in args
+
+    Parameters
+    ----------
+    args : list or NDArray or Symbol
+        Could be a nested architecture.
+
+    Returns
+    -------
+    has_symbol : bool
+        Whether the elements in args contains symbols
+    has_ndarray : bool
+        Whether the elements in args contains ndarrays
+    ctx_set : set of mxnet.context.Context
+        Contains all possible contexts of the inner ndarrays in args. Can be empty if there is no
+        ndarray inside args.
+    first_ctx : mxnet.context.Context or None
+        Context of the first appeared NDArray (for backward-compatibility)
+    """
+    if isinstance(args, NDArray):
+        return False, True, {args.context}, args.context
+    elif isinstance(args, Symbol):
+        return True, False, set(), None
+    elif isinstance(args, (list, tuple)):
+        has_symbol = False
+        has_ndarray = False
+        ctx_set = set()
+        first_ctx = None
+        for ele in args:
+            ele_has_sym, ele_has_nd, ele_ctx_set, ele_first_ctx =\
+                _gather_type_ctx_info(ele)
+            has_symbol = has_symbol or ele_has_sym
+            has_ndarray = has_ndarray or ele_has_nd
+            if first_ctx is None and ele_first_ctx is not None:
+                first_ctx = ele_first_ctx
+            ctx_set = ctx_set | ele_ctx_set
+            if has_symbol and has_ndarray:
+                break
+        return has_symbol, has_ndarray, ctx_set, first_ctx
+    else:
+        return False, False, set(), None
+
+
 def _flatten(args, inout_str):
     """Parse the arguments into a flattened list + an additional format array.
     The format array stores the structure of the original arguments to help reconstruct the inputs.
@@ -120,9 +167,11 @@ def _flatten(args, inout_str):
     if args is None:
         return [None], int(-1)
 
-    assert isinstance(args, (list, tuple)), \
-        "HybridBlock {} must be (nested) list of Symbol or NDArray, " \
-        "but got {} of type {}".format(inout_str, str(args), str(type(args)))
+    if not isinstance(args, (list, tuple)):
+        raise ValueError("When hybridized, the input of HybridBlock {}"
+                         " must be (nested) list of Symbol"
+                         " or NDArray, "
+                         "but got {} of type {}".format(inout_str, str(args), str(type(args))))
     flat = []
     fmts = []
     for i in args:
@@ -164,9 +213,10 @@ def _merger(args, fmt):
             else:
                 return args[:fmt], args[fmt:]
 
-        assert isinstance(args, (list, tuple)), \
-            "HybridBlock output must be (nested) list of Symbol or NDArray, " \
-            "but got {} of type {}".format(args, type(args))
+        if not isinstance(args, (list, tuple)):
+            raise ValueError("When hybridized, the output of HybridBlock must be (nested)"
+                             " list of Symbol or NDArray, "
+                             "but got {} of type {}".format(args, type(args)))
         ret = []
         for i in fmt:
             res, args = _merger(args, i)
@@ -1054,38 +1104,26 @@ def register_op_hook(self, callback, monitor_all=False):
     def forward(self, x, *args):
         """Defines the forward computation. Arguments can be either
         :py:class:`NDArray` or :py:class:`Symbol`."""
-        flatten_args = _flatten([x] + list(args), 'inputs')[0]
-        is_ndarray = None
-        ctx = None
-        exist_sym_nd = False
-        for ele in flatten_args:
-            if isinstance(ele, NDArray):
-                if is_ndarray is False:
-                    raise ValueError('In HybridBlock, we do not support mixed NDArrays and Symbols'
-                                     ' types for the input.\n'
-                                     'Received types are: {}.'
-                                     .format([type(ele) for ele in flatten_args]))
-                is_ndarray = True
-                exist_sym_nd = True
-                ctx = ele.context
-            elif isinstance(ele, Symbol):
-                if is_ndarray:
-                    raise ValueError('In HybridBlock, we do not support mixed NDArrays and Symbols'
-                                     ' types for the input.\n'
-                                     'Received types are: {}.'
-                                     .format([type(ele) for ele in flatten_args]))
-                is_ndarray = False
-                exist_sym_nd = True
-            else:
-                assert ele is None, 'Only support None, NDArray and Symbol as the input'
-        if not exist_sym_nd:
-            raise ValueError('There must at least one NDArray or Symbol in the input, received')
 
-        if is_ndarray:
-            with ctx:
-                if self._active:
+        has_symbol, has_ndarray, ctx_set, first_ctx = _gather_type_ctx_info([x] + list(args))
+        if has_symbol and has_ndarray:
+            raise ValueError('In HybridBlock, we do not support mixed NDArrays and Symbols'
+                             ' types for the input. Please check the type of the args.\n')
+        if not has_symbol and not has_ndarray:
+            raise ValueError('In HybridBlock, there must be one NDArray or one Symbol in the input.'
+                             ' Please check the type of the args.\n')
+        if has_ndarray:
+            ctx = first_ctx
+            if self._active:
+                if len(ctx_set) > 1:
+                    raise ValueError('Find multiple contexts in the input, '
+                                     'After hybridized, the HybridBlock only supports one input '
+                                     'context. You can print the ele.context in the '
+                                     'input arguments to inspect their contexts. '
+                                     'Find all contexts = {}'.format(ctx_set))
+                with ctx:
                     return self._call_cached_op(x, *args)
-
+            with ctx:
                 try:
                     params = {k: v.data(ctx) for k, v in self._reg_params.items()}
                 except DeferredInitializationError:
diff --git a/tests/python/gpu/test_gluon_gpu.py b/tests/python/gpu/test_gluon_gpu.py
index 48fff7810c2e..b938b5783415 100644
--- a/tests/python/gpu/test_gluon_gpu.py
+++ b/tests/python/gpu/test_gluon_gpu.py
@@ -580,6 +580,21 @@ def test_bulking_gluon_gpu():
     _test_bulking(_test_bulking_in_process)
 
 
+@with_seed()
+def test_hybridblock_mix_ctx_raise():
+    class FooHybrid(gluon.HybridBlock):
+        def hybrid_forward(self, F, a, b):
+            if isinstance(a, (list, tuple)):
+                a = sum(a)
+            if isinstance(b, (list, tuple)):
+                b = sum(b)
+            return a + b
+    foo_hybrid = FooHybrid()
+    foo_hybrid.hybridize()
+    assert_raises(ValueError, lambda: foo_hybrid(mx.nd.ones((10,), ctx=mx.gpu()),
+                                                 mx.nd.ones((10,), ctx=mx.cpu())))
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()
diff --git a/tests/python/unittest/test_gluon.py b/tests/python/unittest/test_gluon.py
index c6da0f4d9d96..f1d0cc7ac274 100644
--- a/tests/python/unittest/test_gluon.py
+++ b/tests/python/unittest/test_gluon.py
@@ -529,6 +529,50 @@ def hybrid_forward(self, F, a, b):
     assert_raises(ValueError, lambda: foo1(mx.nd.ones((10,)), mx.nd.ones((10,))))
 
 
+@with_seed()
+def test_hybrid_block_hybrid_no_hybrid():
+    class FooHybrid(gluon.HybridBlock):
+        def hybrid_forward(self, F, a, b):
+            if isinstance(a, (list, tuple)):
+                a = sum(a)
+            if isinstance(b, (list, tuple)):
+                b = sum(b)
+            return a + b
+
+    class Foo(gluon.Block):
+        def forward(self, a, b):
+            if isinstance(a, (list, tuple)):
+                a = sum(a)
+            if isinstance(b, (list, tuple)):
+                b = sum(b)
+            return a + b
+    # When hybridize is not called, HybridBlock acts the same as Block
+    foo_hybrid = FooHybrid()
+    foo = Foo()
+    for a, b in [(mx.nd.ones((10,)), 1),
+                 (mx.nd.ones((20,)), 2),
+                 ([mx.nd.ones((10,)), mx.nd.ones((10,))],
+                  [mx.nd.ones((10)), mx.nd.ones((10,)), mx.nd.ones((10,))]),
+                 ([mx.nd.ones((10,)), mx.nd.ones((10,))], 3)]:
+        hybrid_block_out = foo_hybrid(a, b)
+        block_out = foo(a, b)
+        assert_almost_equal(hybrid_block_out.asnumpy(), block_out.asnumpy())
+    # When hybridize is called, we need to make sure that the model raises for the unsupported cases
+    # 1. Scalar values in the input
+    # 2. No mixing of sym/ndarray
+    # 3. No mixing of cpu ndarray and gpu ndarray  (Tested in gpu/test_gluon_gpu.py)
+    # 4. Allow mixing of cpu_pinned and cpu
+    foo_hybrid = FooHybrid()
+    foo_hybrid.hybridize()
+    assert_raises(ValueError, lambda: foo_hybrid(mx.nd.ones((10,)), 1))
+    foo_hybrid = FooHybrid()
+    foo_hybrid.hybridize()
+    assert_raises(ValueError, lambda: foo_hybrid(mx.nd.ones((10,)), mx.sym.var('a')))
+    foo_hybrid = FooHybrid()
+    foo_hybrid.hybridize()
+    assert_raises(ValueError, lambda: foo_hybrid(mx.nd.ones((10,), ctx=mx.cpu(1)),
+                                                 mx.nd.ones((10,), ctx=mx.cpu(2))))
+
 
 @with_seed()
 def check_layer_forward(layer, dshape):

From c1d02ce5e01ad7601aec3ad63d92f5a181b159f5 Mon Sep 17 00:00:00 2001
From: Zhiqiang Xie <xiezhq@shanghaitech.edu.cn>
Date: Sun, 20 Oct 2019 09:35:38 +0800
Subject: [PATCH 479/813] setup and concatenate, copy, expand_dims, expm1
 (#16493)

norm, svd, split, squeeze, std, swapaxes, tensorfot, tile, trace, transpose, tril

fix swapaxes, sanity check issues

current svd not compatible

inputs of indentical axes supported for swapaxes, skip int tests of linalg.norm

re-factor the code structure

delete an extra blank line
---
 python/mxnet/numpy_dispatch_protocol.py       |   3 +
 .../unittest/test_numpy_interoperability.py   | 276 ++++++++++++++----
 2 files changed, 223 insertions(+), 56 deletions(-)

diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index 9fd3976dead4..3682334ebbea 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -118,6 +118,9 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'unique',
     'var',
     'zeros_like',
+    'linalg.norm',
+    'trace',
+    'tril',
     'meshgrid',
     'outer',
     'einsum'
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index 7495c9002f26..e16be469aa3b 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -23,6 +23,7 @@
 from mxnet import np
 from mxnet.test_utils import assert_almost_equal
 from mxnet.test_utils import use_np
+from mxnet.test_utils import is_op_runnable
 from common import assertRaises, with_seed
 from mxnet.numpy_dispatch_protocol import with_array_function_protocol, with_array_ufunc_protocol
 from mxnet.numpy_dispatch_protocol import _NUMPY_ARRAY_FUNCTION_LIST, _NUMPY_ARRAY_UFUNC_LIST
@@ -48,6 +49,201 @@ def get_workloads(name):
         return OpArgMngr._args.get(name, None)
 
 
+def _add_workload_concatenate(array_pool):
+    OpArgMngr.add_workload('concatenate', [array_pool['4x1'], array_pool['4x1']])
+    OpArgMngr.add_workload('concatenate', [array_pool['4x1'], array_pool['4x1']], axis=1)
+    OpArgMngr.add_workload('concatenate', [np.random.uniform(size=(3, 3))])
+    OpArgMngr.add_workload('concatenate', (np.arange(4).reshape((2, 2)), np.arange(4).reshape((2, 2))))
+    OpArgMngr.add_workload('concatenate', (np.arange(4),))
+    OpArgMngr.add_workload('concatenate', (np.array(np.arange(4)),))
+    OpArgMngr.add_workload('concatenate', (np.arange(4), np.arange(3)))
+    OpArgMngr.add_workload('concatenate', (np.array(np.arange(4)), np.arange(3)))
+    OpArgMngr.add_workload('concatenate', (np.arange(4), np.arange(3)), axis=0)
+    OpArgMngr.add_workload('concatenate', (np.arange(4), np.arange(3)), axis=-1)
+    a23 = np.random.uniform(size=(2, 3))
+    a13 = np.random.uniform(size=(1, 3))
+    OpArgMngr.add_workload('concatenate', (a23, a13))
+    OpArgMngr.add_workload('concatenate', (a23, a13), axis=0)
+    OpArgMngr.add_workload('concatenate', (a23.T, a13.T), axis=1)
+    OpArgMngr.add_workload('concatenate', (a23.T, a13.T), axis=-1)
+    res = np.arange(2*3*7).reshape((2, 3, 7))
+    a0 = res[..., :4]
+    a1 = res[..., 4:6]
+    a2 = res[..., 6:]
+    OpArgMngr.add_workload('concatenate', (a0, a1, a2), axis=2)
+    OpArgMngr.add_workload('concatenate', (a0, a1, a2), axis=-1)
+    OpArgMngr.add_workload('concatenate', (a0.T, a1.T, a2.T), axis=0)
+    out = np.empty(4, np.float32)
+    OpArgMngr.add_workload('concatenate', (np.array([1, 2]), np.array([3, 4])), out=out)
+
+
+def _add_workload_copy():
+    OpArgMngr.add_workload('copy', np.random.uniform(size=(4, 1)))
+    OpArgMngr.add_workload('copy', np.random.uniform(size=(2, 2)))
+    OpArgMngr.add_workload('copy', np.random.uniform(size=(2,2)))
+
+
+def _add_workload_expand_dims():
+    OpArgMngr.add_workload('expand_dims', np.random.uniform(size=(4, 1)), -1)
+    for axis in range(-5, 4):
+        OpArgMngr.add_workload('expand_dims', np.empty((2, 3, 4, 5)), axis)
+
+
+def _add_workload_split():
+    OpArgMngr.add_workload('split', np.random.uniform(size=(4, 1)), 2)
+    OpArgMngr.add_workload('split', np.arange(10), 2)
+    assertRaises(ValueError, np.split, np.arange(10), 3)
+
+
+def _add_workload_squeeze():
+    OpArgMngr.add_workload('squeeze', np.random.uniform(size=(4, 1)))
+    OpArgMngr.add_workload('squeeze', np.random.uniform(size=(20, 10, 10, 1, 1)))
+    OpArgMngr.add_workload('squeeze', np.random.uniform(size=(20, 1, 10, 1, 20)))
+    OpArgMngr.add_workload('squeeze', np.random.uniform(size=(1, 1, 20, 10)))
+    OpArgMngr.add_workload('squeeze', np.array([[[1.5]]]))
+
+
+def _add_workload_std():
+    OpArgMngr.add_workload('std', np.random.uniform(size=(4, 1)))
+    A = np.array([[1, 2, 3], [4, 5, 6]])
+    OpArgMngr.add_workload('std', A)
+    OpArgMngr.add_workload('std', A, 0)
+    OpArgMngr.add_workload('std', A, 1)
+    OpArgMngr.add_workload('std', np.array([1, -1, 1, -1]))
+    OpArgMngr.add_workload('std', np.array([1, -1, 1, -1]), ddof=1)
+    OpArgMngr.add_workload('std', np.array([1, -1, 1, -1]), ddof=2)
+    OpArgMngr.add_workload('std', np.arange(10), out=np.array(0.))
+
+
+def _add_workload_swapaxes():
+    OpArgMngr.add_workload('swapaxes', np.random.uniform(size=(4, 1)), 0, 1)
+    OpArgMngr.add_workload('swapaxes', np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]), 0, 2)
+    a = np.arange(1*2*3*4).reshape(1, 2, 3, 4).copy()
+    b = a.copy()
+    # no AxisError defined in mxnet numpy
+    # assertRaises(np.AxisError, np.swapaxes, -5, 0)
+    for i in range(-4, 4):
+        for j in range(-4, 4):
+            for k, src in enumerate((a, b)):
+                OpArgMngr.add_workload('swapaxes', src, i, j)
+
+
+def _add_workload_tensordot():
+    OpArgMngr.add_workload('tensordot', np.random.uniform(size=(4, 1)), np.random.uniform(size=(4, 1)))
+    OpArgMngr.add_workload('tensordot', np.random.uniform(size=(3, 0)), np.random.uniform(size=(0, 4)), (1, 0))
+    OpArgMngr.add_workload('tensordot', np.array(1), np.array(1), ([], []))
+
+
+def _add_workload_tile():
+    OpArgMngr.add_workload('tile', np.random.uniform(size=(4, 1)), 2)
+    a = np.array([0, 1, 2])
+    b = np.array([[1, 2], [3, 4]])
+    OpArgMngr.add_workload('tile', a, 2)
+    OpArgMngr.add_workload('tile', a, (2, 2))
+    OpArgMngr.add_workload('tile', a, (1, 2))
+    OpArgMngr.add_workload('tile', b, 2)
+    OpArgMngr.add_workload('tile', b, (2, 1))
+    OpArgMngr.add_workload('tile', b, (2, 2))
+    OpArgMngr.add_workload('tile', np.arange(5), 1)
+    OpArgMngr.add_workload('tile', np.array([[], []]), 2)
+    OpArgMngr.add_workload('tile', np.array([[[]]]), (3, 2, 5))
+    reps = [(2,), (1, 2), (2, 1), (2, 2), (2, 3, 2), (3, 2)]
+    shape = [(3,), (2, 3), (3, 4, 3), (3, 2, 3), (4, 3, 2, 4), (2, 2)]
+    for s in shape:
+        b = np.random.randint(0, 10, size=s)
+        for r in reps:
+            # RuntimeError to be tracked
+            # where s = (3, 4, 3), r = (2, 3, 2)
+            # OpArgMngr.add_workload('tile', b, r)
+            pass
+
+
+def _add_workload_transpose():
+    OpArgMngr.add_workload('transpose', np.random.uniform(size=(4, 1)))
+    OpArgMngr.add_workload('transpose', np.array([[]]))
+    OpArgMngr.add_workload('transpose', np.array([[1, 2]]))
+    OpArgMngr.add_workload('transpose', np.array([[1, 2, 3], [4, 5, 6]]))
+    OpArgMngr.add_workload('transpose', np.array([[1, 2], [3, 4], [5, 6]]), (1, 0))
+    OpArgMngr.add_workload('transpose', np.array([[1, 2], [3, 4]]))
+
+
+def _add_workload_linalg_norm():
+    OpArgMngr.add_workload('linalg.norm', np.random.uniform(size=(4, 1)))
+    for dt in ["double", "float32", "int64"]:
+        OpArgMngr.add_workload('linalg.norm', np.array([], dtype=dt))
+        OpArgMngr.add_workload('linalg.norm', np.array([np.array([]), np.array([])], dtype=dt))
+        # numerical error exceed the tolerance
+        if dt == "int64":
+            continue
+        for v in ([1, 2, 3, 4], [-1, -2, -3, -4], [-1, 2, -3, 4]):
+            OpArgMngr.add_workload('linalg.norm', np.array(v, dtype=dt))
+        A = np.array([[1, 2, 3], [4, 5, 6]], dtype=dt)
+        [OpArgMngr.add_workload('linalg.norm', A[:, k]) for k in range(A.shape[1])]
+        OpArgMngr.add_workload('linalg.norm', A, axis=0)
+        [OpArgMngr.add_workload('linalg.norm', A[k, :]) for k in range(A.shape[0])]
+        OpArgMngr.add_workload('linalg.norm', A, axis=1)
+        B = np.arange(1, 25).reshape(2, 3, 4).astype(dt)
+        for axis in itertools.combinations(range(-B.ndim, B.ndim), 2):
+            row_axis, col_axis = axis
+            if row_axis < 0:
+                row_axis += B.ndim
+            if col_axis < 0:
+                col_axis += B.ndim
+            if row_axis == col_axis:
+                # improper assertion behavior
+                # assertRaises(ValueError, np.linalg.norm, B, axis=axis)
+                pass
+            else:
+                OpArgMngr.add_workload('linalg.norm', B, axis=axis)
+                k_index = B.ndim - row_axis - col_axis
+                for k in range(B.shape[k_index]):
+                    if row_axis < col_axis:
+                        OpArgMngr.add_workload('linalg.norm', np.take(B[:], np.array(k), axis=k_index))
+                    else:
+                        OpArgMngr.add_workload('linalg.norm', np.take(B[:], np.array(k), axis=k_index).T)
+        A = np.arange(1, 25, dtype=dt).reshape(2, 3, 4)
+        OpArgMngr.add_workload('linalg.norm', A, ord=None, axis=None)
+        OpArgMngr.add_workload('linalg.norm', A, ord=None,axis=None, keepdims=True)
+        for k in range(A.ndim):
+            OpArgMngr.add_workload('linalg.norm', A, axis=k)
+            OpArgMngr.add_workload('linalg.norm', A, axis=k, keepdims=True)
+        for k in itertools.permutations(range(A.ndim), 2):
+            OpArgMngr.add_workload('linalg.norm', A, axis=k)
+            OpArgMngr.add_workload('linalg.norm', A, axis=k, keepdims=True)
+        OpArgMngr.add_workload('linalg.norm', np.array([[]], dtype=dt))
+        A = np.array([[1, 3], [5, 7]], dtype=dt)
+        OpArgMngr.add_workload('linalg.norm', A)
+        OpArgMngr.add_workload('linalg.norm', A, 'fro')
+        A = (1 / 10) * np.array([[1, 2, 3], [6, 0, 5], [3, 2, 1]], dtype=dt)
+        OpArgMngr.add_workload('linalg.norm', A)
+        OpArgMngr.add_workload('linalg.norm', A, 'fro')
+    for dt in [np.float16, np.float32, np.float64]:
+        OpArgMngr.add_workload('linalg.norm', np.array([[1, 0, 1], [0, 1, 1]], dtype=dt))
+        OpArgMngr.add_workload('linalg.norm', np.array([[1, 0, 1], [0, 1, 1]], dtype=dt), 'fro')
+
+
+def _add_workload_trace():
+    OpArgMngr.add_workload('trace', np.random.uniform(size=(4, 1)))
+    OpArgMngr.add_workload('trace', np.random.uniform(size=(3, 2)))
+
+
+def _add_workload_tril():
+    OpArgMngr.add_workload('tril', np.random.uniform(size=(4, 1)))
+    for dt in ['float16', 'float32', 'float64', 'int32', 'int64', 'int8', 'uint8']:
+        OpArgMngr.add_workload('tril', np.ones((2, 2), dtype=dt))
+        a = np.array([
+            [[1, 1], [1, 1]],
+            [[1, 1], [1, 0]],
+            [[1, 1], [0, 0]],
+        ], dtype=dt)
+        OpArgMngr.add_workload('tril', a)
+        arr = np.array([[1, 1, np.inf],
+                        [1, 1, 1],
+                        [np.inf, 1, 1]])
+        OpArgMngr.add_workload('tril', arr)
+        OpArgMngr.add_workload('tril', np.zeros((3, 3), dtype=dt))
+
+
 def _add_workload_einsum():
     chars = 'abcdefghij'
     sizes = [2, 3, 4, 5, 4, 3, 2, 6, 5, 4]
@@ -120,6 +316,15 @@ def _add_workload_einsum():
             OpArgMngr.add_workload('einsum', subscripts, *args, optimize=optimize)
 
 
+def _add_workload_expm1():
+    OpArgMngr.add_workload('expm1', np.random.uniform(size=(4, 1)))
+    OpArgMngr.add_workload('expm1', np.random.uniform(size=(1, 1)))
+    OpArgMngr.add_workload('expm1', np.array([np.inf]))
+    OpArgMngr.add_workload('expm1', np.array([-np.inf]))
+    OpArgMngr.add_workload('expm1', np.array([0.]))
+    OpArgMngr.add_workload('expm1', np.array([-0.]))
+
+
 def _add_workload_argmax():
     OpArgMngr.add_workload('argmax', np.random.uniform(size=(4, 5, 6, 7, 8)), 0)
     OpArgMngr.add_workload('argmax', np.random.uniform(size=(4, 5, 6, 7, 8)), 1)
@@ -179,15 +384,6 @@ def _add_workload_clip():
     OpArgMngr.add_workload('clip', np.array([-2., np.nan, 0.5, 3., 0.25, np.nan]), -1, 1)
 
 
-def _add_workload_concatenate(array_pool):
-    OpArgMngr.add_workload('concatenate', [array_pool['4x1'], array_pool['4x1']])
-    OpArgMngr.add_workload('concatenate', [array_pool['4x1'], array_pool['4x1']], axis=1)
-
-
-def _add_workload_copy(array_pool):
-    OpArgMngr.add_workload('copy', array_pool['4x1'])
-
-
 def _add_workload_cumsum():
     for ctype in _DTYPES:
         OpArgMngr.add_workload('cumsum', np.array([1, 2, 10, 11, 6, 5, 4], dtype=ctype))
@@ -220,10 +416,6 @@ def _add_workload_dot():
     OpArgMngr.add_workload('dot', np.random.normal(size=(1024, 16)), np.random.normal(size=(16, 32)))
 
 
-def _add_workload_expand_dims(array_pool):
-    OpArgMngr.add_workload('expand_dims', array_pool['4x1'], -1)
-
-
 def _add_workload_fix():
     OpArgMngr.add_workload('fix', np.array([[1.0, 1.1, 1.5, 1.8], [-1.0, -1.1, -1.5, -1.8]]))
     OpArgMngr.add_workload('fix', np.array([3.14]))
@@ -341,22 +533,10 @@ def _add_workload_roll():
     OpArgMngr.add_workload('roll', np.array([]), 1)
 
 
-def _add_workload_split(array_pool):
-    OpArgMngr.add_workload('split', array_pool['4x1'], 2)
-
-
-def _add_workload_squeeze(array_pool):
-    OpArgMngr.add_workload('squeeze', array_pool['4x1'])
-
-
 def _add_workload_stack(array_pool):
     OpArgMngr.add_workload('stack', [array_pool['4x1']] * 2)
 
 
-def _add_workload_std(array_pool):
-    OpArgMngr.add_workload('std', array_pool['4x1'])
-
-
 def _add_workload_sum():
     # OpArgMngr.add_workload('sum', np.ones(101, dtype=bool))
     OpArgMngr.add_workload('sum', np.arange(1, 10).reshape((3, 3)), axis=1, keepdims=True)
@@ -383,10 +563,6 @@ def _add_workload_sum():
     # OpArgMngr.add_workload('sum', np.array([0.2]), initial=0.1)
 
 
-def _add_workload_swapaxes(array_pool):
-    OpArgMngr.add_workload('swapaxes', array_pool['4x1'], 0, 1)
-
-
 def _add_workload_take():
     OpArgMngr.add_workload('take', np.array([[1, 2], [3, 4]], dtype=int), np.array([], int))
     for mode in ['wrap', 'clip']:
@@ -403,19 +579,6 @@ def _add_workload_take():
     OpArgMngr.add_workload('take', x, np.array([3], int), axis=0, mode='wrap')
 
 
-def _add_workload_tensordot(array_pool):
-    OpArgMngr.add_workload('tensordot', array_pool['4x1'], array_pool['4x1'])
-
-
-def _add_workload_tile(array_pool):
-    OpArgMngr.add_workload('tile', array_pool['4x1'], 2)
-    OpArgMngr.add_workload('tile', np.array([[[]]]), (3, 2, 5))
-
-
-def _add_workload_transpose(array_pool):
-    OpArgMngr.add_workload('transpose', array_pool['4x1'])
-
-
 def _add_workload_unique():
     OpArgMngr.add_workload('unique', np.array([5, 7, 1, 2, 1, 5, 7]*10), True, True, True)
     OpArgMngr.add_workload('unique', np.array([]), True, True, True)
@@ -683,10 +846,6 @@ def _add_workload_log10(array_pool):
     OpArgMngr.add_workload('log10', array_pool['4x1'])
 
 
-def _add_workload_expm1(array_pool):
-    OpArgMngr.add_workload('expm1', array_pool['4x1'])
-
-
 def _add_workload_sqrt():
     OpArgMngr.add_workload('sqrt', np.array([1, np.PZERO, np.NZERO, np.inf, np.nan]))
 
@@ -788,11 +947,11 @@ def _prepare_workloads():
     _add_workload_broadcast_to()
     _add_workload_clip()
     _add_workload_concatenate(array_pool)
-    _add_workload_copy(array_pool)
+    _add_workload_copy()
     _add_workload_cumsum()
     _add_workload_ravel()
     _add_workload_dot()
-    _add_workload_expand_dims(array_pool)
+    _add_workload_expand_dims()
     _add_workload_fix()
     _add_workload_flip()
     _add_workload_max(array_pool)
@@ -804,19 +963,22 @@ def _prepare_workloads():
     _add_workload_reshape()
     _add_workload_rint(array_pool)
     _add_workload_roll()
-    _add_workload_split(array_pool)
-    _add_workload_squeeze(array_pool)
+    _add_workload_split()
+    _add_workload_squeeze()
     _add_workload_stack(array_pool)
-    _add_workload_std(array_pool)
+    _add_workload_std()
     _add_workload_sum()
-    _add_workload_swapaxes(array_pool)
+    _add_workload_swapaxes()
     _add_workload_take()
-    _add_workload_tensordot(array_pool)
-    _add_workload_tile(array_pool)
-    _add_workload_transpose(array_pool)
+    _add_workload_tensordot()
+    _add_workload_tile()
+    _add_workload_transpose()
     _add_workload_unique()
     _add_workload_var(array_pool)
     _add_workload_zeros_like(array_pool)
+    _add_workload_linalg_norm()
+    _add_workload_trace()
+    _add_workload_tril()
     _add_workload_outer()
     _add_workload_meshgrid()
     _add_workload_einsum()
@@ -845,7 +1007,7 @@ def _prepare_workloads():
     _add_workload_log2(array_pool)
     _add_workload_log1p()
     _add_workload_log10(array_pool)
-    _add_workload_expm1(array_pool)
+    _add_workload_expm1()
     _add_workload_sqrt()
     _add_workload_square()
     _add_workload_cbrt()
@@ -890,6 +1052,8 @@ def _check_interoperability_helper(op_name, *args, **kwargs):
         onp_op = getattr(getattr(_np, strs[0]), strs[1])
     else:
         assert False
+    if not is_op_runnable():
+        return
     out = onp_op(*args, **kwargs)
     expected_out = _get_numpy_op_output(onp_op, *args, **kwargs)
     if isinstance(out, (tuple, list)):

From cdfaf397f73c3f2323c8c36475b30b2d683af697 Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Sat, 19 Oct 2019 21:03:56 -0700
Subject: [PATCH 480/813] add sum for boolean type in mainline (#16436)

---
 3rdparty/mshadow/mshadow/base.h             | 10 ++++++++++
 src/operator/mxnet_op.h                     | 22 +++++++++++++++------
 src/operator/numpy/np_broadcast_reduce_op.h |  4 +++-
 src/operator/tensor/broadcast_reduce_op.h   |  2 +-
 tests/python/unittest/test_numpy_op.py      | 13 +++++++-----
 5 files changed, 38 insertions(+), 13 deletions(-)

diff --git a/3rdparty/mshadow/mshadow/base.h b/3rdparty/mshadow/mshadow/base.h
index e0e9602c00db..ec798337e7d0 100755
--- a/3rdparty/mshadow/mshadow/base.h
+++ b/3rdparty/mshadow/mshadow/base.h
@@ -650,6 +650,11 @@ template<>
 MSHADOW_XINLINE int64_t MinValue<int64_t>(void) {
   return LLONG_MIN;
 }
+/*! \brief minimum value of bool */
+template<>
+MSHADOW_XINLINE bool MinValue<bool>(void) {
+  return false;
+}
 
 /*!
  * \brief negative infinity of certain types
@@ -711,6 +716,11 @@ template<>
 MSHADOW_XINLINE int64_t MaxValue<int64_t>(void) {
   return LLONG_MAX;
 }
+/*! \brief maximum value of bool */
+template<>
+MSHADOW_XINLINE bool MaxValue<bool>(void) {
+  return true;
+}
 
 /*!
  * \brief positive infinity of certain types
diff --git a/src/operator/mxnet_op.h b/src/operator/mxnet_op.h
index 18b39b532388..6c8f55a8b0a6 100644
--- a/src/operator/mxnet_op.h
+++ b/src/operator/mxnet_op.h
@@ -350,6 +350,14 @@ struct AccType<mshadow::half::half_t> {
                     "floating point types, not int64";     \
     }                                                      \
     break;                                                 \
+  case mshadow::kBool:                                     \
+    {                                                      \
+      typedef bool DType;                                  \
+      typedef int64_t AType;                               \
+      LOG(FATAL) << "This operation only support "         \
+                    "floating point types, not bool";      \
+    }                                                      \
+    break;                                                 \
   default:                                                 \
     LOG(FATAL) << "Unknown type enum " << type;            \
   }
@@ -405,6 +413,13 @@ struct AccType<mshadow::half::half_t> {
       {__VA_ARGS__}                                        \
     }                                                      \
     break;                                                 \
+  case mshadow::kBool:                                     \
+    {                                                      \
+      typedef bool DType;                                  \
+      typedef int64_t AType;                               \
+      {__VA_ARGS__}                                        \
+    }                                                      \
+    break;                                                 \
   default:                                                 \
     LOG(FATAL) << "Unknown type enum " << type;            \
   }
@@ -644,16 +659,11 @@ template <typename xpu>
 MSHADOW_CINLINE void copy(mshadow::Stream<xpu> *s, const TBlob& to, const TBlob& from) {
   CHECK_EQ(from.Size(), to.Size());
   CHECK_EQ(from.dev_mask(), to.dev_mask());
-  if (from.type_flag_ == mshadow::kBool || to.type_flag_ == mshadow::kBool) {
-    CHECK_EQ(from.type_flag_, to.type_flag_) << "Only supports copying between boolean ndarrays.";
-    mshadow::Copy(to.FlatTo1D<xpu, bool>(s), from.FlatTo1D<xpu, bool>(s), s);
-    return;
-  }
   MSHADOW_TYPE_SWITCH(to.type_flag_, DType, {
     if (to.type_flag_ == from.type_flag_) {
       mshadow::Copy(to.FlatTo1D<xpu, DType>(s), from.FlatTo1D<xpu, DType>(s), s);
     } else {
-      MSHADOW_TYPE_SWITCH(from.type_flag_, SrcDType, {
+      MSHADOW_TYPE_SWITCH_WITH_BOOL(from.type_flag_, SrcDType, {
         to.FlatTo1D<xpu, DType>(s) = mshadow::expr::tcast<DType>(from.FlatTo1D<xpu, SrcDType>(s));
       })
     }
diff --git a/src/operator/numpy/np_broadcast_reduce_op.h b/src/operator/numpy/np_broadcast_reduce_op.h
index 816091a48b48..4951f62457a7 100644
--- a/src/operator/numpy/np_broadcast_reduce_op.h
+++ b/src/operator/numpy/np_broadcast_reduce_op.h
@@ -226,7 +226,7 @@ void NumpyReduceAxesCompute(const nnvm::NodeAttrs& attrs,
   if (param.initial.has_value()) {
     LOG(FATAL) << "initial is not supported yet";
   }
-  if (inputs[0].shape_.Size() == 0) {
+  if (inputs[0].shape_.Size() == 0 && outputs[0].shape_.Size() != 0) {
     using namespace mxnet_op;
     using namespace mshadow;
     Stream<xpu>* s = ctx.get_stream<xpu>();
@@ -236,6 +236,7 @@ void NumpyReduceAxesCompute(const nnvm::NodeAttrs& attrs,
     return;
   }
   CHECK_NE(req[0], kWriteInplace) << "Reduce does not support write in-place";
+#if MXNET_USE_TVM_OP
   // If boolean ndarray, use the kernel generated by TVM
   if (inputs[0].type_flag_ == mshadow::kBool) {
     std::string reducer_name;
@@ -247,6 +248,7 @@ void NumpyReduceAxesCompute(const nnvm::NodeAttrs& attrs,
     TVMOpReduce(ctx, inputs[0], param.axis, outputs[0], req[0], reducer_name);
     return;
   }
+#endif
   if (param.axis.has_value() && param.axis.value().ndim() == 0) {
     UnaryOp::IdentityCompute<xpu>(attrs, ctx, inputs, req, outputs);
   }
diff --git a/src/operator/tensor/broadcast_reduce_op.h b/src/operator/tensor/broadcast_reduce_op.h
index 32a4278ec626..414b606bbe0a 100644
--- a/src/operator/tensor/broadcast_reduce_op.h
+++ b/src/operator/tensor/broadcast_reduce_op.h
@@ -616,7 +616,7 @@ void ReduceAxesComputeImpl(const OpContext& ctx,
   mxnet::TShape src_shape, dst_shape;
   BroadcastReduceShapeCompact(inputs[0].shape_, small, &src_shape, &dst_shape);
   Stream<xpu> *s = ctx.get_stream<xpu>();
-  MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+  MSHADOW_TYPE_SWITCH_WITH_BOOL(inputs[0].type_flag_, DType, {
     MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, OType, {
       const TBlob in_data = inputs[0].reshape(src_shape);
       const TBlob out_data = outputs[0].reshape(dst_shape);
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index cc98bffe59ba..5676ff8ac919 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -32,7 +32,8 @@
 from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf, retry
 from mxnet.runtime import Features
 from mxnet.numpy_op_signature import _get_builtin_op
-from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf, has_tvm_ops
+from mxnet.test_utils import current_context, verify_generator, gen_buckets_probs_with_ppf
+from mxnet.test_utils import is_op_runnable, has_tvm_ops
 import platform
 
 
@@ -434,13 +435,15 @@ def is_int(dtype):
     shape = rand_shape_nd(in_data_dim, dim=3)
     acc_type = {'float16': 'float32', 'float32': 'float64', 'float64': 'float64',
                 'int8': 'int32', 'int32': 'int64', 'int64': 'int64', 'bool': 'int64'}
+    is_windows = sys.platform.startswith('win')
     for hybridize in [False, True]:
         for keepdims in [True, False]:
             for axis in ([i for i in range(in_data_dim)] + [(), None]):
                 for itype in ['float16', 'float32', 'float64', 'int8', 'int32', 'int64', 'bool']:
                     for dtype in ['float16', 'float32', 'float64', 'int8', 'int32', 'int64']:
                         if (is_int(dtype) and not is_int(itype))\
-                                or (itype == 'bool' and dtype not in ('float32', 'float64', 'int32', 'int64')):
+                                or (itype == 'bool' and\
+                                    (dtype not in ('float32', 'float64', 'int32', 'int64') or is_windows)):
                             continue
                         # test gluon
                         test_sum = TestSum(axis=axis, dtype=dtype, keepdims=keepdims)
@@ -456,8 +459,8 @@ def is_int(dtype):
                             x = np.random.uniform(-1.0, 1.0, size=shape, dtype=itype)
                         expected_ret = _np.sum(x.asnumpy(), axis=axis, dtype=acc_type[itype], keepdims=keepdims)
                         expected_ret = expected_ret.astype(dtype)
-                        if itype == 'bool':  # special handling of boolean ndarray
-                            if has_tvm_ops():
+                        if itype == 'bool':
+                            if is_op_runnable() and (not is_windows):  # special handling of boolean ndarray
                                 y = test_sum(x)
                                 assert y.dtype == expected_ret.dtype
                                 assert_almost_equal(y.asnumpy(), expected_ret, rtol=1e-4, atol=1e-5,
@@ -479,7 +482,7 @@ def is_int(dtype):
                             x_sym = mx.sym.Variable("x").as_np_ndarray()
                             mx_sym = mx.sym.np.sum(x_sym, axis=axis, dtype=dtype, keepdims=keepdims).as_nd_ndarray()
                             check_numeric_gradient(mx_sym, [x.as_nd_ndarray()],
-                                                   numeric_eps=1e-3, rtol=1e-3, atol=1e-4, dtype=_np.float32)
+                                                   numeric_eps=1e-3, rtol=1e-2, atol=1e-3, dtype=_np.float32)
 
                         # test imperative
                         mx_out = np.sum(x, axis=axis, dtype=dtype, keepdims=keepdims)

From 1648f4c3a5b9d1ec86ef3757eee2376afc36e5f4 Mon Sep 17 00:00:00 2001
From: Haozheng Fan <fhztc1997618@gmail.com>
Date: Sun, 20 Oct 2019 14:30:25 +0800
Subject: [PATCH 481/813] [Numpy] SVD outputs tuple (#16530)

* Convert output to tuple

* Fix hyper link in doc
---
 python/mxnet/_numpy_op_doc.py          | 65 ----------------------
 python/mxnet/ndarray/numpy/linalg.py   | 77 +++++++++++++++++++++++++-
 python/mxnet/numpy/linalg.py           | 76 ++++++++++++++++++++++++-
 python/mxnet/symbol/numpy/linalg.py    | 64 ++++++++++++++++++++-
 src/operator/numpy/linalg/np_gesvd.cc  |  6 +-
 src/operator/numpy/linalg/np_gesvd.cu  |  4 +-
 tests/python/unittest/test_numpy_op.py | 51 ++++++++++-------
 7 files changed, 250 insertions(+), 93 deletions(-)

diff --git a/python/mxnet/_numpy_op_doc.py b/python/mxnet/_numpy_op_doc.py
index f08d19a66549..6cad854d21b4 100644
--- a/python/mxnet/_numpy_op_doc.py
+++ b/python/mxnet/_numpy_op_doc.py
@@ -480,71 +480,6 @@ def _np_reshape(a, newshape, order='C', out=None):
     """
 
 
-def _np__linalg_svd(a):
-    r"""
-    Singular Value Decomposition.
-
-    When `a` is a 2D array, it is factorized as ``ut @ np.diag(s) @ v``,
-    where `ut` and `v` are 2D orthonormal arrays and `s` is a 1D
-    array of `a`'s singular values. When `a` is higher-dimensional, SVD is
-    applied in stacked mode as explained below.
-
-    Parameters
-    ----------
-    a : (..., M, N) ndarray 
-        A real or complex array with ``a.ndim >= 2`` and ``M <= N``.
-
-    Returns
-    -------
-    ut: (..., M, M) ndarray
-        Orthonormal array(s). The first ``a.ndim - 2`` dimensions have the same
-        size as those of the input `a`.
-    s : (..., M) ndarray
-        Vector(s) with the singular values, within each vector sorted in
-        descending order. The first ``a.ndim - 2`` dimensions have the same
-        size as those of the input `a`.
-    v : (..., M, N) ndarray
-        Orthonormal array(s). The first ``a.ndim - 2`` dimensions have the same
-        size as those of the input `a`.
-
-    Notes
-    -----
-
-    The decomposition is performed using LAPACK routine ``_gesvd``.
-
-    SVD is usually described for the factorization of a 2D matrix :math:`A`.
-    The higher-dimensional case will be discussed below. In the 2D case, SVD is
-    written as :math:`A = U^T S V`, where :math:`A = a`, :math:`U^T = ut`,
-    :math:`S= \mathtt{np.diag}(s)` and :math:`V = v`. The 1D array `s`
-    contains the singular values of `a` and `ut` and `v` are orthonormal. The rows
-    of `v` are the eigenvectors of :math:`A^T A` and the columns of `ut` are
-    the eigenvectors of :math:`A A^T`. In both cases the corresponding
-    (possibly non-zero) eigenvalues are given by ``s**2``.
-
-    If `a` has more than two dimensions, then broadcasting rules apply.
-    This means that SVD is working in "stacked" mode: it iterates over 
-    all indices of the first ``a.ndim - 2`` dimensions and for each
-    combination SVD is applied to the last two indices. The matrix `a` 
-    can be reconstructed from the decomposition with either 
-    ``(ut * s[..., None, :]) @ v`` or
-    ``ut @ (s[..., None] * v)``. (The ``@`` operator denotes batch matrix multiplication)
-
-    Examples
-    --------
-    >>> a = np.arange(54).reshape(6, 9)
-    >>> ut, s, v = np.linalg.svd(a)
-    >>> ut.shape, s.shape, v.shape
-    ((6, 6), (6,), (6, 9))
-    >>> s = s.reshape(6, 1)
-    >>> ret = np.dot(ut, s * v)
-    >>> (ret - a > 1e-3).sum()
-    array(0.)
-    >>> (ret - a < -1e-3).sum()
-    array(0.)
-    """
-    pass
-
-
 def _np_roll(a, shift, axis=None):
     """
     Roll array elements along a given axis.
diff --git a/python/mxnet/ndarray/numpy/linalg.py b/python/mxnet/ndarray/numpy/linalg.py
index 36f3f21a7588..ea9e6a91c2c1 100644
--- a/python/mxnet/ndarray/numpy/linalg.py
+++ b/python/mxnet/ndarray/numpy/linalg.py
@@ -19,8 +19,9 @@
 
 from __future__ import absolute_import
 from . import _op as _mx_nd_np
+from . import _internal as _npi
 
-__all__ = ['norm']
+__all__ = ['norm', 'svd']
 
 
 def norm(x, ord=None, axis=None, keepdims=False):
@@ -66,3 +67,77 @@ def norm(x, ord=None, axis=None, keepdims=False):
     if ord == 'fro' and x.ndim > 2 and axis is None:
         raise ValueError('Improper number of dimensions to norm')
     return _mx_nd_np.sqrt(_mx_nd_np.sum(x * x, axis=axis, keepdims=keepdims))
+
+
+def svd(a):
+    r"""
+    Singular Value Decomposition.
+
+    When `a` is a 2D array, it is factorized as ``ut @ np.diag(s) @ v``,
+    where `ut` and `v` are 2D orthonormal arrays and `s` is a 1D
+    array of `a`'s singular values. When `a` is higher-dimensional, SVD is
+    applied in stacked mode as explained below.
+
+    Parameters
+    ----------
+    a : (..., M, N) ndarray
+        A real array with ``a.ndim >= 2`` and ``M <= N``.
+
+    Returns
+    -------
+    ut: (..., M, M) ndarray
+        Orthonormal array(s). The first ``a.ndim - 2`` dimensions have the same
+        size as those of the input `a`.
+    s : (..., M) ndarray
+        Vector(s) with the singular values, within each vector sorted in
+        descending order. The first ``a.ndim - 2`` dimensions have the same
+        size as those of the input `a`.
+    v : (..., M, N) ndarray
+        Orthonormal array(s). The first ``a.ndim - 2`` dimensions have the same
+        size as those of the input `a`.
+
+    Notes
+    -----
+
+    The decomposition is performed using LAPACK routine ``_gesvd``.
+
+    SVD is usually described for the factorization of a 2D matrix :math:`A`.
+    The higher-dimensional case will be discussed below. In the 2D case, SVD is
+    written as :math:`A = U^T S V`, where :math:`A = a`, :math:`U^T = ut`,
+    :math:`S= \mathtt{np.diag}(s)` and :math:`V = v`. The 1D array `s`
+    contains the singular values of `a` and `ut` and `v` are orthonormal. The rows
+    of `v` are the eigenvectors of :math:`A^T A` and the columns of `ut` are
+    the eigenvectors of :math:`A A^T`. In both cases the corresponding
+    (possibly non-zero) eigenvalues are given by ``s**2``.
+
+    The sign of rows of `u` and `v` are determined as described in
+    `Auto-Differentiating Linear Algebra <https://arxiv.org/pdf/1710.08717.pdf>`_.
+
+    If `a` has more than two dimensions, then broadcasting rules apply.
+    This means that SVD is working in "stacked" mode: it iterates over
+    all indices of the first ``a.ndim - 2`` dimensions and for each
+    combination SVD is applied to the last two indices. The matrix `a`
+    can be reconstructed from the decomposition with either
+    ``(ut * s[..., None, :]) @ v`` or
+    ``ut @ (s[..., None] * v)``. (The ``@`` operator denotes batch matrix multiplication)
+
+    This function differs from the original `numpy.linalg.svd
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.svd.html>`_ in
+    the following way(s):
+     - The sign of rows of `u` and `v` may differ.
+     - Does not support complex input.
+
+    Examples
+    --------
+    >>> a = np.arange(54).reshape(6, 9)
+    >>> ut, s, v = np.linalg.svd(a)
+    >>> ut.shape, s.shape, v.shape
+    ((6, 6), (6,), (6, 9))
+    >>> s = s.reshape(6, 1)
+    >>> ret = np.dot(ut, s * v)
+    >>> (ret - a > 1e-3).sum()
+    array(0.)
+    >>> (ret - a < -1e-3).sum()
+    array(0.)
+    """
+    return tuple(_npi.svd(a))
diff --git a/python/mxnet/numpy/linalg.py b/python/mxnet/numpy/linalg.py
index 9758af47233d..1ca34716d7d4 100644
--- a/python/mxnet/numpy/linalg.py
+++ b/python/mxnet/numpy/linalg.py
@@ -20,7 +20,7 @@
 from __future__ import absolute_import
 from ..ndarray import numpy as _mx_nd_np
 
-__all__ = ['norm']
+__all__ = ['norm', 'svd']
 
 
 def norm(x, ord=None, axis=None, keepdims=False):
@@ -60,3 +60,77 @@ def norm(x, ord=None, axis=None, keepdims=False):
            Baltimore, MD, Johns Hopkins University Press, 1985, pg. 15
     """
     return _mx_nd_np.linalg.norm(x, ord, axis, keepdims)
+
+
+def svd(a):
+    r"""
+    Singular Value Decomposition.
+
+    When `a` is a 2D array, it is factorized as ``ut @ np.diag(s) @ v``,
+    where `ut` and `v` are 2D orthonormal arrays and `s` is a 1D
+    array of `a`'s singular values. When `a` is higher-dimensional, SVD is
+    applied in stacked mode as explained below.
+
+    Parameters
+    ----------
+    a : (..., M, N) ndarray
+        A real array with ``a.ndim >= 2`` and ``M <= N``.
+
+    Returns
+    -------
+    ut: (..., M, M) ndarray
+        Orthonormal array(s). The first ``a.ndim - 2`` dimensions have the same
+        size as those of the input `a`.
+    s : (..., M) ndarray
+        Vector(s) with the singular values, within each vector sorted in
+        descending order. The first ``a.ndim - 2`` dimensions have the same
+        size as those of the input `a`.
+    v : (..., M, N) ndarray
+        Orthonormal array(s). The first ``a.ndim - 2`` dimensions have the same
+        size as those of the input `a`.
+
+    Notes
+    -----
+
+    The decomposition is performed using LAPACK routine ``_gesvd``.
+
+    SVD is usually described for the factorization of a 2D matrix :math:`A`.
+    The higher-dimensional case will be discussed below. In the 2D case, SVD is
+    written as :math:`A = U^T S V`, where :math:`A = a`, :math:`U^T = ut`,
+    :math:`S= \mathtt{np.diag}(s)` and :math:`V = v`. The 1D array `s`
+    contains the singular values of `a` and `ut` and `v` are orthonormal. The rows
+    of `v` are the eigenvectors of :math:`A^T A` and the columns of `ut` are
+    the eigenvectors of :math:`A A^T`. In both cases the corresponding
+    (possibly non-zero) eigenvalues are given by ``s**2``.
+
+    The sign of rows of `u` and `v` are determined as described in
+    `Auto-Differentiating Linear Algebra <https://arxiv.org/pdf/1710.08717.pdf>`_.
+
+    If `a` has more than two dimensions, then broadcasting rules apply.
+    This means that SVD is working in "stacked" mode: it iterates over
+    all indices of the first ``a.ndim - 2`` dimensions and for each
+    combination SVD is applied to the last two indices. The matrix `a`
+    can be reconstructed from the decomposition with either
+    ``(ut * s[..., None, :]) @ v`` or
+    ``ut @ (s[..., None] * v)``. (The ``@`` operator denotes batch matrix multiplication)
+
+    This function differs from the original `numpy.linalg.svd
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.svd.html>`_ in
+    the following way(s):
+     - The sign of rows of `u` and `v` may differ.
+     - Does not support complex input.
+
+    Examples
+    --------
+    >>> a = np.arange(54).reshape(6, 9)
+    >>> ut, s, v = np.linalg.svd(a)
+    >>> ut.shape, s.shape, v.shape
+    ((6, 6), (6,), (6, 9))
+    >>> s = s.reshape(6, 1)
+    >>> ret = np.dot(ut, s * v)
+    >>> (ret - a > 1e-3).sum()
+    array(0.)
+    >>> (ret - a < -1e-3).sum()
+    array(0.)
+    """
+    return _mx_nd_np.linalg.svd(a)
diff --git a/python/mxnet/symbol/numpy/linalg.py b/python/mxnet/symbol/numpy/linalg.py
index d1918ef8b903..354562160f0d 100644
--- a/python/mxnet/symbol/numpy/linalg.py
+++ b/python/mxnet/symbol/numpy/linalg.py
@@ -20,8 +20,9 @@
 from __future__ import absolute_import
 from . import _symbol
 from . import _op as _mx_sym_np
+from . import _internal as _npi
 
-__all__ = ['norm']
+__all__ = ['norm', 'svd']
 
 
 def norm(x, ord=None, axis=None, keepdims=False):
@@ -66,3 +67,64 @@ def norm(x, ord=None, axis=None, keepdims=False):
         raise ValueError('Improper number of dimensions to norm')
     # TODO(junwu): When ord = 'fro', axis = None, and x.ndim > 2, raise exception
     return _symbol.sqrt(_mx_sym_np.sum(x * x, axis=axis, keepdims=keepdims))
+
+
+def svd(a):
+    r"""
+    Singular Value Decomposition.
+
+    When `a` is a 2D array, it is factorized as ``ut @ np.diag(s) @ v``,
+    where `ut` and `v` are 2D orthonormal arrays and `s` is a 1D
+    array of `a`'s singular values. When `a` is higher-dimensional, SVD is
+    applied in stacked mode as explained below.
+
+    Parameters
+    ----------
+    a : (..., M, N) _Symbol
+        A real array with ``a.ndim >= 2`` and ``M <= N``.
+
+    Returns
+    -------
+    ut: (..., M, M) _Symbol
+        Orthonormal array(s). The first ``a.ndim - 2`` dimensions have the same
+        size as those of the input `a`.
+    s : (..., M) _Symbol
+        Vector(s) with the singular values, within each vector sorted in
+        descending order. The first ``a.ndim - 2`` dimensions have the same
+        size as those of the input `a`.
+    v : (..., M, N) _Symbol
+        Orthonormal array(s). The first ``a.ndim - 2`` dimensions have the same
+        size as those of the input `a`.
+
+    Notes
+    -----
+
+    The decomposition is performed using LAPACK routine ``_gesvd``.
+
+    SVD is usually described for the factorization of a 2D matrix :math:`A`.
+    The higher-dimensional case will be discussed below. In the 2D case, SVD is
+    written as :math:`A = U^T S V`, where :math:`A = a`, :math:`U^T = ut`,
+    :math:`S= \mathtt{np.diag}(s)` and :math:`V = v`. The 1D array `s`
+    contains the singular values of `a` and `ut` and `v` are orthonormal. The rows
+    of `v` are the eigenvectors of :math:`A^T A` and the columns of `ut` are
+    the eigenvectors of :math:`A A^T`. In both cases the corresponding
+    (possibly non-zero) eigenvalues are given by ``s**2``.
+
+    The sign of rows of `u` and `v` are determined as described in
+    `Auto-Differentiating Linear Algebra <https://arxiv.org/pdf/1710.08717.pdf>`_.
+
+    If `a` has more than two dimensions, then broadcasting rules apply.
+    This means that SVD is working in "stacked" mode: it iterates over
+    all indices of the first ``a.ndim - 2`` dimensions and for each
+    combination SVD is applied to the last two indices. The matrix `a`
+    can be reconstructed from the decomposition with either
+    ``(ut * s[..., None, :]) @ v`` or
+    ``ut @ (s[..., None] * v)``. (The ``@`` operator denotes batch matrix multiplication)
+
+    This function differs from the original `numpy.linalg.svd
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.svd.html>`_ in
+    the following way(s):
+     - The sign of rows of `u` and `v` may differ.
+     - Does not support complex input.
+    """
+    return _npi.svd(a)
diff --git a/src/operator/numpy/linalg/np_gesvd.cc b/src/operator/numpy/linalg/np_gesvd.cc
index 2be9c778decf..8c8c9ec8680b 100644
--- a/src/operator/numpy/linalg/np_gesvd.cc
+++ b/src/operator/numpy/linalg/np_gesvd.cc
@@ -89,7 +89,7 @@ inline bool NumpyLaGesvdShape(const nnvm::NodeAttrs& attrs,
   return false;
 }
 
-NNVM_REGISTER_OP(_np__linalg_svd)
+NNVM_REGISTER_OP(_npi_svd)
 .describe(R"code()code" ADD_FILELINE)
 .set_num_inputs(1)
 .set_num_outputs(3)
@@ -102,10 +102,10 @@ NNVM_REGISTER_OP(_np__linalg_svd)
 .set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& attrs) {
   return std::vector<ResourceRequest>{ResourceRequest::kTempSpace}; })
 .set_attr<FCompute>("FCompute<cpu>", NumpyLaGesvdForward<cpu, gesvd>)
-.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{"_backward_np_linalg_svd"})
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{"_backward_npi_svd"})
 .add_argument("A", "NDArray-or-Symbol", "Input matrices to be factorized");
 
-NNVM_REGISTER_OP(_backward_np_linalg_svd)
+NNVM_REGISTER_OP(_backward_npi_svd)
 .set_num_inputs(6)
 .set_num_outputs(1)
 .set_attr<nnvm::FInplaceOption>("FInplaceOption", [](const NodeAttrs& attrs) {
diff --git a/src/operator/numpy/linalg/np_gesvd.cu b/src/operator/numpy/linalg/np_gesvd.cu
index ee1546360447..b2358fbdd4b7 100644
--- a/src/operator/numpy/linalg/np_gesvd.cu
+++ b/src/operator/numpy/linalg/np_gesvd.cu
@@ -31,10 +31,10 @@ namespace op {
 
 #if MXNET_USE_CUSOLVER == 1
 
-NNVM_REGISTER_OP(_np__linalg_svd)
+NNVM_REGISTER_OP(_npi_svd)
 .set_attr<FCompute>("FCompute<gpu>", NumpyLaGesvdForward<gpu, gesvd>);
 
-NNVM_REGISTER_OP(_backward_np_linalg_svd)
+NNVM_REGISTER_OP(_backward_npi_svd)
 .set_attr<FCompute>("FCompute<gpu>", NumpyLaGesvdBackward<gpu, gesvd_backward>);
 
 #endif
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 5676ff8ac919..501b7eba746e 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -2565,7 +2565,7 @@ def hybrid_forward(self, F, x):
 
 @with_seed()
 @use_np
-def test_np_svd():
+def test_np_linalg_svd():
     class TestSVD(HybridBlock):
         def __init__(self):
             super(TestSVD, self).__init__()
@@ -2593,6 +2593,28 @@ def get_grad(UT, L, V):
         dA = _np.matmul(UT, _np.matmul(G2, V) + 1.0 / L[..., None] * dV)
         return dA
 
+    def check_svd(UT, L, V, data_np):
+        shape = data_np.shape
+        # check UT @ L @ V == A
+        t = _np.matmul(UT * L[..., None, :], V)
+        assert t.shape == data_np.shape
+        assert_almost_equal(t, data_np, rtol=rtol, atol=atol)
+        # check UT @ U == I
+        I = _np.matmul(UT, _np.swapaxes(UT, -2, -1))
+        I_np = _np.ones_like(UT) * _np.eye(shape[-2])
+        assert I.shape == I_np.shape
+        assert_almost_equal(I, I_np, rtol=rtol, atol=atol)
+        # check U @ UT == I
+        I = _np.matmul(_np.swapaxes(UT, -2, -1), UT)
+        I_np = _np.ones_like(UT) * _np.eye(shape[-2])
+        assert I.shape == I_np.shape
+        assert_almost_equal(I, I_np, rtol=rtol, atol=atol)
+        # check V @ VT == I
+        I = _np.matmul(V, _np.swapaxes(V, -2, -1))
+        I_np = _np.ones_like(UT) * _np.eye(shape[-2])
+        assert I.shape == I_np.shape
+        assert_almost_equal(I, I_np, rtol=rtol, atol=atol)
+
     shapes = [
         (3, 3),
         (3, 5),
@@ -2626,25 +2648,8 @@ def get_grad(UT, L, V):
                 UT = ret[0].asnumpy()
                 L = ret[1].asnumpy()
                 V = ret[2].asnumpy()
-                # check UT @ L @ V == A
-                t = _np.matmul(UT * L[..., None, :], V)
-                assert t.shape == data_np.shape
-                assert_almost_equal(t, data_np, rtol=rtol, atol=atol)
-                # check UT @ U == I
-                I = _np.matmul(UT, _np.swapaxes(UT, -2, -1))
-                I_np = _np.ones_like(UT) * _np.eye(shape[-2])
-                assert I.shape == I_np.shape
-                assert_almost_equal(I, I_np, rtol=rtol, atol=atol)
-                # check U @ UT == I
-                I = _np.matmul(_np.swapaxes(UT, -2, -1), UT)
-                I_np = _np.ones_like(UT) * _np.eye(shape[-2])
-                assert I.shape == I_np.shape
-                assert_almost_equal(I, I_np, rtol=rtol, atol=atol)
-                # check V @ VT == I
-                I = _np.matmul(V, _np.swapaxes(V, -2, -1))
-                I_np = _np.ones_like(UT) * _np.eye(shape[-2])
-                assert I.shape == I_np.shape
-                assert_almost_equal(I, I_np, rtol=rtol, atol=atol)
+                # check svd validity
+                check_svd(UT, L, V, data_np)
                 # check descending singular values
                 s = [L[..., i] - L[..., i + 1] for i in range(L.shape[-1] - 1)]
                 s = _np.array(s)
@@ -2656,6 +2661,12 @@ def get_grad(UT, L, V):
                 if ((s > 1e-5).all() and (L.size == 0 or (L > 1e-5).all())):
                     backward_expected = get_grad(ret[0].asnumpy(), ret[1].asnumpy(), ret[2].asnumpy())
                     assert_almost_equal(data.grad.asnumpy(), backward_expected, rtol=rtol, atol=atol)
+                # Test imperative once again
+                ret = np.linalg.svd(data)
+                UT = ret[0].asnumpy()
+                L = ret[1].asnumpy()
+                V = ret[2].asnumpy()
+                check_svd(UT, L, V, data_np)
 
 
 @with_seed()

From 5accae0278e11ec6f036dd206be4af7c9c1326e4 Mon Sep 17 00:00:00 2001
From: alicia <32725332+Alicia1529@users.noreply.github.com>
Date: Sat, 19 Oct 2019 13:16:12 +0800
Subject: [PATCH 482/813] numpy op doc: max, min, prod (#16506)

---
 python/mxnet/_numpy_op_doc.py | 213 +++++++++++++++++++++++++++++++++-
 1 file changed, 211 insertions(+), 2 deletions(-)

diff --git a/python/mxnet/_numpy_op_doc.py b/python/mxnet/_numpy_op_doc.py
index 6cad854d21b4..d9bb378d3049 100644
--- a/python/mxnet/_numpy_op_doc.py
+++ b/python/mxnet/_numpy_op_doc.py
@@ -130,8 +130,8 @@ def _npx_nonzero(a):
 
     Notes
     -----
-    This function differs from the original numpy.prod in the following aspects:
-        - Do not support python numeric.
+    This function differs from the original numpy.nonzero in the following aspects:
+        - Does not support python numeric.
         - The return value is same as numpy.transpose(numpy.nonzero(a)).
 
     Examples
@@ -641,6 +641,214 @@ def _np_squeeze(a, axis=None, out=None):
     pass
 
 
+def _np_max(a, axis=None, out=None, keepdims=False):
+    """
+    Return the maximum of an array or maximum along an axis.
+    
+    Parameters
+    ----------
+    a : ndarray
+        Input data.
+    axis : int, optional
+        Axis along which to operate.  By default, flattened input is used.
+    out : ndarray, optional
+        Alternative output array in which to place the result.  Must
+        be of the same shape and buffer length as the expected output.
+        See `doc.ufuncs` (Section "Output arguments") for more details.
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left
+        in the result as dimensions with size one. With this option,
+        the result will broadcast correctly against the original `arr`.
+    
+    Returns
+    -------
+    max : ndarray 
+        Maximum of `a`. If `axis` is None, the result is an array of dimension 1.
+        If `axis` is given, the result is an array of dimension
+        ``a.ndim - 1``.
+    
+    See Also
+    --------
+    min :
+        The minimum value of an array along a given axis, ignoring any nan.
+    maximum :
+        Element-wise maximum of two arrays, ignoring any nan.
+    argmax :
+        Return the indices of the maximum values.
+    
+    Notes
+    -----
+    NaN in the orginal `numpy` is denoted as nan and will be ignored.
+    
+    Don't use `max` for element-wise comparison of 2 arrays; when
+    ``a.shape[0]`` is 2, ``maximum(a[0], a[1])`` is faster than
+    ``max(a, axis=0)``.
+    
+    Examples
+    --------
+    >>> a = np.arange(4).reshape((2,2))
+    >>> a
+    array([[0., 1.],
+        [2., 3.]]) 
+    >>> np.max(a)            # Maximum of the flattened array
+    array(3.)
+    >>> np.max(a, axis=0)    # Maxima along the first axis
+    array([2., 3.])
+    >>> np.max(a, axis=1)    # Maxima along the second axis
+    array([1., 3.])
+    
+    >>> b = np.arange(5, dtype=np.float32)
+    >>> b[2] = np.nan
+    >>> np.max(b)
+    array(4.)
+    """
+    pass
+
+
+def _np_min(a, axis=None, out=None, keepdims=False):
+    """
+    Return the minimum of an array or minimum along an axis.
+    
+    Parameters
+    ----------
+    a : ndarray
+        Input data.
+    axis : int, optional
+        Axis along which to operate.  By default, flattened input is used.
+    out : ndarray, optional
+        Alternative output array in which to place the result.  Must
+        be of the same shape and buffer length as the expected output.
+        See `doc.ufuncs` (Section "Output arguments") for more details.
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left
+        in the result as dimensions with size one. With this option,
+        the result will broadcast correctly against the original `arr`.
+    
+    Returns
+    -------
+    min : ndarray
+        Minimum of `a`. If `axis` is None, the result is an array of dimension 1.
+        If `axis` is given, the result is an array of dimension
+        ``a.ndim - 1``.
+    
+    See Also
+    --------
+    max :
+        The maximum value of an array along a given axis, ignoring any nan.
+    minimum :
+        Element-wise minimum of two arrays, ignoring any nan.
+    
+    Notes
+    -----
+    NaN in the orginal `numpy` is denoted as nan and will be ignored.
+    
+    Don't use `min` for element-wise comparison of 2 arrays; when
+    ``a.shape[0]`` is 2, ``minimum(a[0], a[1])`` is faster than
+    ``min(a, axis=0)``.
+
+    Examples
+    --------
+    >>> a = np.arange(4).reshape((2,2))
+    >>> a
+    array([[0., 1.],
+        [2., 3.]])
+    >>> np.min(a)           # Minimum of the flattened array
+    array(0.)
+    >>> np.min(a, axis=0)   # Minima along the first axis
+    array([0., 1.])
+    >>> np.min(a, axis=1)   # Minima along the second axis
+    array([0., 2.])
+    >>> b = np.arange(5, dtype=np.float32)
+    >>> b[2] = np.nan
+    >>> np.min(b)
+    array(0.) # nan will be ignored
+    """    
+    pass
+
+
+def _np_prod(a, axis=None, dtype=None, out=None, keepdims=False):
+    """
+    Return the product of array elements over a given axis.
+    
+    Parameters
+    ----------
+    a : ndarray
+        Input data.
+    axis : None or int or tuple of ints, optional
+        Axis or axes along which a product is performed.
+        The default (`axis` = `None`) is perform a product over all
+        the dimensions of the input array. `axis` may be negative, in
+        which case it counts from the last to the first axis.
+        If this is a tuple of ints, a product is performed on multiple
+        axes, instead of a single axis or all the axes as before.
+    dtype : data-type, optional
+        The data-type of the returned array, as well as of the accumulator
+        in which the elements are multiplied.  By default, if `a` is of
+        integer type, `dtype` is the default platform integer. (Note: if
+        the type of `a` is unsigned, then so is `dtype`.)  Otherwise,
+        the dtype is the same as that of `a`.
+    out : ndarray, optional
+        Alternative output array in which to place the result. It must have
+        the same shape as the expected output, but the type of the
+        output values will be cast if necessary.
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left
+        in the result as dimensions with size one. With this option,
+        the result will broadcast correctly against the original `arr`.
+    
+    Returns
+    -------
+    product_along_axis : ndarray, see `dtype` parameter above.
+        An array shaped as `a` but with the specified axis removed.
+        Returns a reference to `out` if specified.
+    
+    See Also
+    --------
+    ndarray.prod : equivalent method
+    
+    Notes
+    -----
+    Arithmetic is modular when using integer types, and no error is
+    raised on overflow.  That means that, on a 32-bit platform:
+    
+    >>> x = np.array([536870910, 536870910, 536870910, 536870910])
+    >>> np.prod(x) #random
+    array(8.307675e+34)
+    
+    Examples
+    --------
+    By default, calculate the product of all elements:
+    
+    >>> np.prod(np.array([1.,2.]))
+    array(2.)
+    
+    Even when the input array is two-dimensional:
+    
+    >>> np.prod(np.array([1.,2.,3.,4.]).reshape((2,2)))
+    array(24.)
+    
+    But we can also specify the axis over which to multiply:
+    
+    >>> np.prod(np.array([1.,2.,3.,4.]).reshape((2,2)), axis=1)
+    array([  2.,  12.])
+
+    If the type of `x` is unsigned, then the output type is
+    the unsigned platform integer:
+    
+    >>> x = np.array([1, 2, 3], dtype=np.uint8)
+    >>> np.prod(x).dtype == np.uint8
+    True
+
+    If `x` is of a signed integer type, then the output type
+    is the default platform integer:
+    
+    >>> x = np.array([1, 2, 3], dtype=np.int8)
+    >>> np.prod(x).dtype == np.int8
+    True
+    """
+    pass
+
+
 def _np_moveaxis(a, source, destination):
     """Move axes of an array to new positions.
     Other axes remain in their original order.
@@ -719,6 +927,7 @@ def _np__random_shuffle(x):
     """
     pass
 
+
 def _np_broadcast_to(array, shape, out=None):
     """
     Broadcast an array to a new shape.

From b94971683d991923a2c90536ffb478e9e925263c Mon Sep 17 00:00:00 2001
From: kshitij12345 <kshitijkalambarkar@gmail.com>
Date: Thu, 15 Aug 2019 17:27:08 +0530
Subject: [PATCH 483/813] add interface for rand

add relevant tests

address comments.

* fix document string -> Returns description.

Fix
---
 python/mxnet/ndarray/numpy/random.py   | 29 ++++++++++++-
 python/mxnet/numpy/random.py           | 30 ++++++++++++-
 python/mxnet/symbol/numpy/random.py    | 30 ++++++++++++-
 tests/python/unittest/test_numpy_op.py | 58 +++++++++++++++++++++++---
 4 files changed, 137 insertions(+), 10 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/random.py b/python/mxnet/ndarray/numpy/random.py
index 9e401695709d..583f56e046f3 100644
--- a/python/mxnet/ndarray/numpy/random.py
+++ b/python/mxnet/ndarray/numpy/random.py
@@ -23,7 +23,7 @@
 from ..ndarray import NDArray
 
 
-__all__ = ['randint', 'uniform', 'normal', "choice"]
+__all__ = ['randint', 'uniform', 'normal', "choice", "rand"]
 
 
 def randint(low, high=None, size=None, dtype=None, ctx=None, out=None):
@@ -317,3 +317,30 @@ def choice(a, size=None, replace=True, p=None, ctx=None, out=None):
             return _npi.choice(a=a, size=size, replace=replace, ctx=ctx, weighted=False, out=out)
         else:
             return _npi.choice(p, a=a, size=size, replace=replace, ctx=ctx, weighted=True, out=out)
+
+
+def rand(*size, **kwargs):
+    r"""Random values in a given shape.
+
+    Create an array of the given shape and populate it with random
+    samples from a uniform distribution over [0, 1).
+    Parameters
+    ----------
+    d0, d1, ..., dn : int, optional
+        The dimensions of the returned array, should be all positive.
+        If no argument is given a single Python float is returned.
+    Returns
+    -------
+    out : ndarray
+       Random values.
+    Examples
+    --------
+    >>> np.random.rand(3,2)
+    array([[ 0.14022471,  0.96360618],  #random
+           [ 0.37601032,  0.25528411],  #random
+           [ 0.49313049,  0.94909878]]) #random
+    """
+    output_shape = ()
+    for s in size:
+        output_shape += (s,)
+    return uniform(0, 1, size=output_shape, **kwargs)
diff --git a/python/mxnet/numpy/random.py b/python/mxnet/numpy/random.py
index 746ce99c6d9c..d0ae237a5b92 100644
--- a/python/mxnet/numpy/random.py
+++ b/python/mxnet/numpy/random.py
@@ -20,8 +20,7 @@
 from __future__ import absolute_import
 from ..ndarray import numpy as _mx_nd_np
 
-
-__all__ = ["randint", "uniform", "normal", "choice"]
+__all__ = ["randint", "uniform", "normal", "choice", "rand"]
 
 
 def randint(low, high=None, size=None, dtype=None, ctx=None, out=None):
@@ -231,3 +230,30 @@ def choice(a, size=None, replace=True, p=None, ctx=None, out=None):
     array([2, 3, 0])
     """
     return _mx_nd_np.random.choice(a, size, replace, p, ctx, out)
+
+
+def rand(*size, **kwargs):
+    r"""Random values in a given shape.
+
+    Create an array of the given shape and populate it with random
+    samples from a uniform distribution over [0, 1).
+    Parameters
+    ----------
+    d0, d1, ..., dn : int, optional
+        The dimensions of the returned array, should be all positive.
+        If no argument is given a single Python float is returned.
+    Returns
+    -------
+    out : ndarray
+       Random values.
+    Examples
+    --------
+    >>> np.random.rand(3,2)
+    array([[ 0.14022471,  0.96360618],  #random
+           [ 0.37601032,  0.25528411],  #random
+           [ 0.49313049,  0.94909878]]) #random
+    """
+    output_shape = ()
+    for s in size:
+        output_shape += (s,)
+    return _mx_nd_np.random.uniform(0, 1, size=output_shape, **kwargs)
diff --git a/python/mxnet/symbol/numpy/random.py b/python/mxnet/symbol/numpy/random.py
index 84cc5704b138..d891ea0c21a0 100644
--- a/python/mxnet/symbol/numpy/random.py
+++ b/python/mxnet/symbol/numpy/random.py
@@ -21,8 +21,7 @@
 from ...context import current_context
 from . import _internal as _npi
 
-
-__all__ = ['randint', 'uniform', 'normal']
+__all__ = ['randint', 'uniform', 'normal', 'rand']
 
 
 def randint(low, high=None, size=None, dtype=None, ctx=None, out=None):
@@ -86,6 +85,33 @@ def randint(low, high=None, size=None, dtype=None, ctx=None, out=None):
     return _npi.random_randint(low, high, shape=size, dtype=dtype, ctx=ctx, out=out)
 
 
+def rand(*size, **kwargs):
+    r"""Random values in a given shape.
+
+    Create an array of the given shape and populate it with random
+    samples from a uniform distribution over [0, 1).
+    Parameters
+    ----------
+    d0, d1, ..., dn : int, optional
+        The dimensions of the returned array, should be all positive.
+        If no argument is given a single Python float is returned.
+    Returns
+    -------
+    out : ndarray
+       Random values.
+    Examples
+    --------
+    >>> np.random.rand(3,2)
+    array([[ 0.14022471,  0.96360618],  #random
+           [ 0.37601032,  0.25528411],  #random
+           [ 0.49313049,  0.94909878]]) #random
+    """
+    output_shape = ()
+    for s in size:
+        output_shape += (s,)
+    return uniform(0, 1, size=output_shape, **kwargs)
+
+
 def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
     """Draw samples from a uniform distribution.
 
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 501b7eba746e..32e1b0cf4fe1 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -20,7 +20,9 @@
 import sys
 import unittest
 import numpy as _np
+import platform
 import mxnet as mx
+import scipy.stats as ss
 from mxnet import np, npx
 from mxnet.gluon import HybridBlock
 from mxnet.base import MXNetError
@@ -28,13 +30,9 @@
 from mxnet.test_utils import check_numeric_gradient, use_np, collapse_sum_like
 from common import assertRaises, with_seed
 import random
-import scipy.stats as ss
-from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf, retry
-from mxnet.runtime import Features
+from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf
 from mxnet.numpy_op_signature import _get_builtin_op
-from mxnet.test_utils import current_context, verify_generator, gen_buckets_probs_with_ppf
 from mxnet.test_utils import is_op_runnable, has_tvm_ops
-import platform
 
 
 @with_seed()
@@ -3450,6 +3448,56 @@ def dbg(name, data):
                     assert_almost_equal(grad[0][iop], grad[1][iop], rtol=rtol, atol=atol)
 
 
+@with_seed()
+@use_np
+def test_np_rand():
+    # Test shapes.
+    shapes = [
+        (3, 3),
+        (3, 4),
+        (0, 0),
+        (3, 3, 3),
+        (0, 0, 0),
+        (2, 2, 4, 3),
+        (2, 2, 4, 3),
+        (2, 0, 3, 0),
+        (2, 0, 2, 3)
+    ]
+    dtypes = ['float16', 'float32', 'float64']
+    for dtype in dtypes:
+        for shape in shapes:
+            data_mx = np.random.rand(*shape, dtype=dtype)
+            assert data_mx.shape == shape
+
+    # Test random generator.
+    ctx = mx.context.current_context()
+    samples = 1000000
+    trials = 8
+    num_buckets = 10
+    lower = 0.0
+    upper = 1.0
+    for dtype in ['float16', 'float32', 'float64']:
+        buckets, probs = gen_buckets_probs_with_ppf(
+            lambda x: ss.uniform.ppf(x, lower, upper), num_buckets)
+        # Quantize bucket boundaries to reflect the actual dtype
+        # and adjust probs accordingly
+        buckets = np.array(buckets, dtype=dtype).tolist()
+        probs = [(ss.uniform.cdf(buckets[i][1], lower, upper) -
+                  ss.uniform.cdf(buckets[i][0], lower, upper))
+                 for i in range(num_buckets)]
+
+        def generator_mx(x): return np.random.rand(
+            samples, ctx=ctx, dtype=dtype).asnumpy()
+        verify_generator(generator=generator_mx, buckets=buckets,
+                         probs=probs, nsamples=samples, nrepeat=trials)
+        generator_mx_same_seed =\
+            lambda x: _np.concatenate(
+                [np.random.rand(x // 10, ctx=ctx, dtype=dtype).asnumpy()
+                    for _ in range(10)])
+        verify_generator(generator=generator_mx_same_seed, buckets=buckets,
+                         probs=probs, nsamples=samples, nrepeat=trials)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 217ae02bbd4eafd5b803cae2a10fa1ab9e194201 Mon Sep 17 00:00:00 2001
From: reminisce <wujun.nju@gmail.com>
Date: Sun, 20 Oct 2019 12:51:50 -0700
Subject: [PATCH 484/813] Fix numpy bugs (#16537)

* legacy ndarray setitem supports value as expanded arrays

* Doc for ndarray

* Add doc for ndarray

* Fix infer shape error while loading legacy models in numpy mode

* Add true_divide to support float

* Add true divide scalar

* Add true_divide test

* Fix bug

* Add for loading symbols

* Fix

* Fix sanity

* Fix windows

* Fix python2

* Skip saving is_np_shape attr in json in legacy mode

* Fix compile

* Fix test
---
 python/mxnet/ndarray/ndarray.py         |  13 ++
 python/mxnet/numpy/multiarray.py        | 242 +++++++++++++++++++++---
 python/mxnet/symbol/numpy/_symbol.py    |  14 +-
 python/mxnet/util.py                    |   7 +-
 src/c_api/c_api_symbolic.cc             |  38 +++-
 src/common/utils.h                      |   4 +
 src/operator/mshadow_op.h               |  28 +++
 src/operator/mxnet_op.h                 |  15 ++
 src/operator/numpy/np_true_divide-inl.h | 146 ++++++++++++++
 src/operator/numpy/np_true_divide.cc    |  40 +---
 src/operator/numpy/np_true_divide.cu    |  10 +-
 src/operator/operator_tune.cc           |   2 +
 tests/python/unittest/test_ndarray.py   |  24 ++-
 tests/python/unittest/test_numpy_op.py  |  44 +++++
 tests/python/unittest/test_symbol.py    |  47 ++++-
 15 files changed, 597 insertions(+), 77 deletions(-)
 create mode 100644 src/operator/numpy/np_true_divide-inl.h

diff --git a/python/mxnet/ndarray/ndarray.py b/python/mxnet/ndarray/ndarray.py
index 4e3c7efa7be3..8b826d2cc0a4 100644
--- a/python/mxnet/ndarray/ndarray.py
+++ b/python/mxnet/ndarray/ndarray.py
@@ -737,6 +737,19 @@ def _prepare_value_nd(self, value, bcast_shape, squeeze_axes=None):
             squeeze_axes = tuple([ax for ax in squeeze_axes if ax < len(value_nd.shape)])
             value_nd = value_nd.squeeze(axis=tuple(squeeze_axes))
 
+        # handle the cases like the following
+        # a = nd.zeros((3, 3)), b = nd.ones((1, 1, 1, 1, 3)), a[0] = b
+        # b cannot broadcast directly to a[0].shape unless its leading 1-size axes are trimmed
+        if value_nd.ndim > len(bcast_shape):
+            squeeze_axes = []
+            for i in range(value_nd.ndim - len(bcast_shape)):
+                if value_nd.shape[i] == 1:
+                    squeeze_axes.append(i)
+                else:
+                    break
+            if squeeze_axes:
+                value_nd = value_nd.squeeze(squeeze_axes)
+
         if value_nd.shape != bcast_shape:
             if value_nd.size == 0:
                 value_nd = value_nd.reshape(bcast_shape)
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 8cca82bcf827..623b5fc482d7 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -134,11 +134,47 @@ def _np_ndarray_cls(handle, writable=True, stype=0):
 @set_module('mxnet.numpy')  # pylint: disable=invalid-name
 class ndarray(NDArray):
     """
+    ndarray(handle, writable=True):
+
     An array object represents a multidimensional, homogeneous array of fixed-size items.
     An associated data-type object describes the format of each element in the array
     (its byte-order, how many bytes it occupies in memory, whether it is an integer, a
     floating point number, or something else, etc.). Arrays should be constructed using
     `array`, `zeros` or `empty`. Currently, only c-contiguous arrays are supported.
+
+    Arrays should be constructed using `array`, `zeros` or `empty` (refer
+    to the See Also section below).  The parameters given here refer to
+    a low-level method (`ndarray(...)`) for instantiating an array.
+
+    For more information, refer to the `mxnet.numpy` module and examine the
+    methods and attributes of an array.
+
+    Parameters
+    ----------
+    handle: int
+        The ndarray handle in backend (C++).
+    writable: bool
+        Indicates whether inplace-assignment is allowed for the array.
+
+    Attributes
+    ----------
+    T : ndarray
+        Transpose of the array.
+    dtype : dtype object
+        Describes the format of the elements in the array.
+    size : int
+        Number of elements in the array.
+    ndim : int
+        The array's number of dimensions.
+    shape : tuple of ints
+        Shape of the array.
+
+    See Also
+    --------
+    array : Construct an array.
+    zeros : Create an array, each element of which is zero.
+    empty : Create an array, but leave its allocated memory unchanged (i.e.,
+            it contains "garbage").
     """
 
     @staticmethod
@@ -286,9 +322,139 @@ def _set_np_advanced_indexing(self, key, value):
 
     # pylint: disable=too-many-return-statements
     def __getitem__(self, key):
-        """
-        Overriding the method in NDArray class in a numpy fashion.
-        Calling numpy ndarray's _get_np_basic_indexing(key) and _get_np_advanced_indexing(key).
+        """Return self[key].
+
+        Returns a sliced view of this array if the elements fetched are contiguous in memory;
+        otherwise, returns a newly created NDArray.
+        This functions supports advanced indexing defined in the following reference with
+        some restrictions. Boolean indexing is supported only for a single boolean ndarray
+        as a key. Mixing boolean ndarray with other index types is not supported in ``advanced``
+        indexing.
+
+        For basic indexing, i.e., if ``key`` consists only of integers,
+        ``slice``, ``Ellipsis`` (``...``) and ``None``, a mutable view is
+        returned that shares memory with this array if the accessed portion is
+        contiguous in memory.
+        Otherwise, a newly created ``ndarray`` is returned.
+
+        This functions supports advanced indexing as defined in `the NumPy
+        advanced indexing documentation
+        <https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#advanced-indexing>`_.
+
+        Parameters
+        ----------
+        key : int, slice, list, np.ndarray, mx.np.ndarray, or tuple of all previous types
+            Indexing key.
+
+        Examples
+        --------
+        The default is to give explicit indices for all axes:
+
+        >>> x = np.arange(6).reshape(2, 3)
+        >>> x
+        array([[0., 1., 2.],
+               [3., 4., 5.]])
+        >>> x[0, :2]
+        array([0., 1.])
+        >>> x[:, :-1]
+        array([[0., 1.],
+               [3., 4.]])
+
+        If fewer indices are given, they are automatically supplemented by an
+        appropriate number of ``slice(None)`` ("``:``") to the right. For
+        instance, a single integer indexes along the first axis:
+
+        >>> x[0]
+        array([0., 1., 2.])
+        >>> x[1:]
+        array([[3., 4., 5.]])
+
+        To omit a range of axes that should be kept as-is, an `Ellipsis`
+        ("``...``") can be used:
+
+        >>> x = np.arange(16).reshape(2, 2, 2, 2)
+        >>> x[0, ..., 1]
+        array([[1., 3.],
+               [5., 7.]])
+        >>> x[0, :, :, 1]  # equivalent
+        array([[1., 3.],
+               [5., 7.]])
+
+        New axes of length 1 can be created by inserting ``None``
+        (`numpy.newaxis`) in the index:
+
+        >>> x = np.arange(6).reshape(2, 3)
+        >>> x[None, :, :]
+        array([[[0., 1., 2.],
+                [3., 4., 5.]]])
+        >>> x[None, :, :].shape
+        (1, 2, 3)
+
+        If the indexed portion of the array is contiguous in memory, no data
+        is copied. Instead, a shared-memory view of the original array is
+        returned, and changes to that view affect the original array:
+
+        >>> x = np.arange(8).reshape(2, 2, 2)
+        >>> y = x[0]  # contiguous
+        >>> y
+        array([[0., 1.],
+               [2., 3.]])
+        >>> y[:] = -1
+        >>> x
+        array([[[-1., -1.],
+                [-1., -1.]],
+               [[ 4.,  5.],
+                [ 6.,  7.]]])
+        >>> x = np.arange(8).reshape(2, 2, 2)
+        >>> y = x[1, :1, :]  # contiguous
+        >>> y
+        array([[4., 5.]])
+        >>> y[:] = -1
+        >>> x
+        array([[[ 0.,  1.],
+                [ 2.,  3.]],
+               [[-1., -1.],
+                [ 6.,  7.]]])
+        >>> x = np.arange(0, 8).reshape(2, 2, 2)
+        >>> y = x[:, :, 1]  # not contiguous
+        >>> y
+        array([[1., 3.],
+               [5., 7.]])
+        >>> y[:] = -1
+        >>> x
+        array([[[0., 1.],
+                [2., 3.]],
+               [[4., 5.],
+                [6., 7.]]])
+
+        If the indexing key contains `list`, `numpy.ndarray` or `NDArray`
+        objects, advanced indexing is triggered, which always returns a
+        copy:
+
+        >>> x = np.arange(8).reshape(2, 2, 2)
+        >>> x[[0, 1]]
+        array([[[0., 1.],
+                [2., 3.]],
+               [[4., 5.],
+                [6., 7.]]])
+        >>> x[[0, 1], :]  # equivalent
+        array([[[0., 1.],
+                [2., 3.]],
+               [[4., 5.],
+                [6., 7.]]])
+        >>> y = np.array([0, 1], dtype='int32')
+        >>> x[1:, y]
+        array([[[4., 5.],
+                [6., 7.]]])
+        >>> y = np.array([0, 1], dtype='int32')
+        >>> x[1:, y]
+        array([[[4., 5.],
+                [6., 7.]]])
+
+        Get negative elements in an ndarray through boolean array indexing
+        >>> x = np.array([1., -1., -2., 3])
+        >>> x[x < 0]
+        array([-1., -2.])
         """
         # handling possible boolean indexing first
         ndim = self.ndim
@@ -356,11 +522,51 @@ def __getitem__(self, key):
             raise RuntimeError
 
     def __setitem__(self, key, value):
-        """
-        x.__setitem__(i, y) <=> x[i]=y
-        Sets ``self[key]`` to ``value``.
+        """Sets ``self[key]`` to ``value``.
 
-        Overriding the method in NDArray class in a numpy fashion.
+        This functions supports advanced indexing as defined in `the NumPy
+        advanced indexing documentation
+        <https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#advanced-indexing>`_,
+        with the restriction that boolean array indexing is not supported.
+
+        Parameters
+        ----------
+        key : int, slice, list, np.ndarray, mx.np.ndarray, or tuple of all previous types
+            The indexing key.
+        value : scalar or array-like object that can be broadcast to the shape of self[key]
+            The value to set.
+
+        Examples
+        --------
+        >>> x = np.zeros((2, 3))
+        >>> x[:] = 1
+        >>> x
+        array([[ 1.,  1.,  1.],
+               [ 1.,  1.,  1.]])
+        >>> x[:, 1:2] = 2
+        >>> x
+        array([[ 1.,  2.,  1.],
+               [ 1.,  2.,  1.]])
+        >>> x[1:2, 1:] = 3
+        >>> x
+        array([[ 1.,  2.,  1.],
+               [ 1.,  3.,  3.]])
+        >>> x[1:, 0:2] = np.zeros((1, 2))
+        >>> x
+        array([[ 1.,  2.,  1.],
+               [ 0.,  0.,  3.]])
+        >>> x[1, 2] = 4
+        >>> x
+        array([[ 1.,  2.,  1.],
+               [ 0.,  0.,  4.]])
+        >>> x[[0], [1, 2]] = 5
+        >>> x
+        array([[ 1.,  5.,  5.],
+               [ 0.,  0.,  4.]])
+        >>> x[::-1, 0:2:2] = [6]
+        >>> x
+        array([[ 6.,  5.,  5.],
+               [ 6.,  0.,  4.]])
         """
         if isinstance(value, NDArray) and not isinstance(value, ndarray):
             raise TypeError('Cannot assign mx.nd.NDArray to mxnet.numpy.ndarray')
@@ -496,25 +702,16 @@ def __rmul__(self, other):
         return self.__mul__(other)
 
     def __div__(self, other):
-        raise AttributeError('ndarray.__div__ is replaced by __truediv__. If you are using'
-                             ' Python2, please use the statement from __future__ import division'
-                             ' to change the / operator to mean true division throughout the'
-                             ' module. If you are using Python3, this error should not have'
-                             ' been encountered.')
+        """x.__div__(y) <=> x / y"""
+        return divide(self, other)
 
     def __rdiv__(self, other):
-        raise AttributeError('ndarray.__rdiv__ is replaced by __rtruediv__. If you are using'
-                             ' Python2, please use the statement from __future__ import division'
-                             ' to change the / operator to mean true division throughout the'
-                             ' module. If you are using Python3, this error should not have'
-                             ' been encountered.')
+        """x.__rdiv__(y) <=> y / x"""
+        return divide(other, self)
 
     def __idiv__(self, other):
-        raise AttributeError('ndarray.__idiv__ is replaced by __irtruediv__. If you are using'
-                             ' Python2, please use the statement from __future__ import division'
-                             ' to change the / operator to mean true division throughout the'
-                             ' module. If you are using Python3, this error should not have'
-                             ' been encountered.')
+        """x.__idiv__(y) <=> x /= y"""
+        return divide(self, other, out=self)
 
     def __truediv__(self, other):
         """x.__truediv__(y) <=> x / y"""
@@ -525,6 +722,7 @@ def __rtruediv__(self, other):
         return divide(other, self)
 
     def __itruediv__(self, other):
+        """x.__itruediv__(y) <=> x /= y"""
         return divide(self, other, out=self)
 
     def __mod__(self, other):
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 4b4319f45373..aa456c8e5166 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -90,18 +90,12 @@ def __rmul__(self, other):
         return multiply(other, self)
 
     def __div__(self, other):
-        raise AttributeError('_Symbol.__div__ is replaced by __truediv__. If you are using'
-                             ' Python2, please use the statement from __future__ import division'
-                             ' to change the / operator to mean true division throughout the'
-                             ' module. If you are using Python3, this error should not have'
-                             ' been encountered.')
+        """x.__truediv__(y) <=> x / y"""
+        return divide(self, other)
 
     def __rdiv__(self, other):
-        raise AttributeError('_Symbol.__rdiv__ is replaced by __rtruediv__. If you are using'
-                             ' Python2, please use the statement from __future__ import division'
-                             ' to change the / operator to mean true division throughout the'
-                             ' module. If you are using Python3, this error should not have'
-                             ' been encountered.')
+        """x.__rdiv__(y) <=> y / x"""
+        return divide(other, self)
 
     def __mod__(self, other):
         """x.__mod__(y) <=> x % y"""
diff --git a/python/mxnet/util.py b/python/mxnet/util.py
index c2dfd85dcfac..cef034fd0caa 100644
--- a/python/mxnet/util.py
+++ b/python/mxnet/util.py
@@ -627,9 +627,11 @@ def _wrap_np_unary_func(x, out=None, **kwargs):
                 if value != _np_ufunc_default_kwargs[key]:
                     # if the provided value of the argument is a legal option, raise NotImplementedError
                     if np_ufunc_legal_option(key, value):
-                        raise NotImplementedError("{}={} is not implemented yet".format(key, str(value)))
+                        raise NotImplementedError("{}={} is not implemented yet for operator {}"
+                                                  .format(key, str(value), func.__name__))
                     # otherwise raise TypeError with not understood error message
-                    raise TypeError("{} {} not understood".format(key, value))
+                    raise TypeError("{}={} not understood for operator {}"
+                                    .format(key, value, func.__name__))
         return func(x, out=out)
     return _wrap_np_unary_func
 
@@ -664,6 +666,7 @@ def _wrap_np_binary_func(x1, x2, out=None, **kwargs):
         return func(x1, x2, out=out)
     return _wrap_np_binary_func
 
+
 def _set_np_array(active):
     """Turns on/off NumPy array semantics for the current thread in which `mxnet.numpy.ndarray`
     is expected to be created, instead of the legacy `mx.nd.NDArray`.
diff --git a/src/c_api/c_api_symbolic.cc b/src/c_api/c_api_symbolic.cc
index fa7736659fee..06d4b3e93ba0 100644
--- a/src/c_api/c_api_symbolic.cc
+++ b/src/c_api/c_api_symbolic.cc
@@ -56,6 +56,10 @@ nnvm::Graph Symbol2Graph(const nnvm::Symbol &s) {
   nnvm::Graph g;
   g.outputs = s.outputs;
   g.attrs["mxnet_version"] = std::make_shared<nnvm::any>(static_cast<int>(MXNET_VERSION));
+  if (Imperative::Get()->is_np_shape()) {
+    g.attrs["is_np_shape"] = std::make_shared<nnvm::any>(
+        static_cast<int>(Imperative::Get()->is_np_shape()));
+  }
   return g;
 }
 
@@ -418,6 +422,32 @@ int MXSymbolCutSubgraph(SymbolHandle sym, SymbolHandle **input_symbols,
   API_END_HANDLE_ERROR();
 }
 
+
+/*!
+ * \brief Convert shape attr in graph nodes to comply with NumPy semantics for
+ * legacy models (before 1.6.0) if global flag is_np_shape has been turned on,
+ * i.e., use -1 to indicate unknown number of dimensions and unknown dimension sizes.
+ */
+void ConvertShapeAttrToNumPyCompatible(nnvm::Graph* g) {
+  if (Imperative::Get()->is_np_shape()
+    && (!g->HasAttr("is_np_shape") || !g->GetAttr<int>("is_np_shape"))) {
+    DFSVisit(g->outputs, [](nnvm::NodePtr n) {
+      if (n->is_variable()) {
+        auto it = n->attrs.dict.find("__shape__");
+        if (it != n->attrs.dict.end()) {
+          mxnet::TShape shape;
+          std::istringstream is(it->second);
+          is >> shape;
+          common::ConvertToNumpyShape(&shape);
+          std::ostringstream os;
+          os << shape;
+          it->second = os.str();
+        }
+      }
+    });
+  }
+}
+
 int MXSymbolCreateFromFile(const char *fname, SymbolHandle *out) {
   nnvm::Symbol *s = new nnvm::Symbol();
   API_BEGIN();
@@ -426,7 +456,9 @@ int MXSymbolCreateFromFile(const char *fname, SymbolHandle *out) {
   nnvm::Graph g;
   g.attrs["json"] = std::make_shared<nnvm::any>(
     std::string(std::istreambuf_iterator<char>(is), std::istreambuf_iterator<char>()));
-  s->outputs = nnvm::ApplyPass(g, "LoadLegacyJSON").outputs;
+  g = nnvm::ApplyPass(g, "LoadLegacyJSON");
+  ConvertShapeAttrToNumPyCompatible(&g);
+  s->outputs = g.outputs;
   *out = s;
   is.set_stream(nullptr);
   API_END_HANDLE_ERROR(delete s);
@@ -437,7 +469,9 @@ int MXSymbolCreateFromJSON(const char *json, SymbolHandle *out) {
   API_BEGIN();
   nnvm::Graph g;
   g.attrs["json"] = std::make_shared<nnvm::any>(std::string(json));
-  s->outputs = nnvm::ApplyPass(g, "LoadLegacyJSON").outputs;
+  g = nnvm::ApplyPass(g, "LoadLegacyJSON");
+  ConvertShapeAttrToNumPyCompatible(&g);
+  s->outputs = g.outputs;
   *out = s;
   API_END_HANDLE_ERROR(delete s);
 }
diff --git a/src/common/utils.h b/src/common/utils.h
index fbecc8b4e955..2b4b821a1835 100644
--- a/src/common/utils.h
+++ b/src/common/utils.h
@@ -838,6 +838,10 @@ inline mxnet::TShape CanonicalizeAxes(const mxnet::TShape& src) {
   return axes;
 }
 
+inline bool is_float(const int dtype) {
+  return dtype == mshadow::kFloat32 || dtype == mshadow::kFloat64 || dtype == mshadow::kFloat16;
+}
+
 }  // namespace common
 }  // namespace mxnet
 #endif  // MXNET_COMMON_UTILS_H_
diff --git a/src/operator/mshadow_op.h b/src/operator/mshadow_op.h
index e14c8dbb0b78..31470a297e25 100644
--- a/src/operator/mshadow_op.h
+++ b/src/operator/mshadow_op.h
@@ -120,6 +120,34 @@ struct identity_with_cast {
   }
 };
 
+struct true_divide : public mxnet_op::tunable  {
+  template<typename DType,
+           typename std::enable_if<!std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static DType Map(DType a, DType b) {
+    return a / b;
+  }
+
+  template<typename DType,
+           typename std::enable_if<std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static float Map(DType a, DType b) {
+    return static_cast<float>(a) / static_cast<float>(b);
+  }
+};
+
+struct rtrue_divide : public mxnet_op::tunable  {
+  template<typename DType,
+           typename std::enable_if<!std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static DType Map(DType a, DType b) {
+    return b / a;
+  }
+
+  template<typename DType,
+           typename std::enable_if<std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static float Map(DType a, DType b) {
+    return static_cast<float>(b) / static_cast<float>(a);
+  }
+};
+
 MXNET_BINARY_MATH_OP_NC(left, a);
 
 MXNET_BINARY_MATH_OP_NC(right, b);
diff --git a/src/operator/mxnet_op.h b/src/operator/mxnet_op.h
index 6c8f55a8b0a6..8ccc34247b6f 100644
--- a/src/operator/mxnet_op.h
+++ b/src/operator/mxnet_op.h
@@ -750,6 +750,7 @@ struct op_with_req {
     KERNEL_ASSIGN(out[i], req, OP::Map(input_1[i], input_2[i], input_3[i]));
   }
 
+  /*! \brief input is a tensor and the output is a boolean tensor */
   template<typename DType,
            typename std::enable_if<!std::is_same<DType, bool>::value, int>::type = 0>
   MSHADOW_XINLINE static void Map(index_t i, bool *out, const DType *in) {
@@ -769,6 +770,20 @@ struct op_with_req {
   MSHADOW_XINLINE static void Map(index_t i, bool *out, const DType *in, const DType value) {
     KERNEL_ASSIGN(out[i], req, OP::Map(in[i], value));
   }
+
+  /*! \brief inputs are two tensors with a float output tensor */
+  template<typename DType,
+           typename std::enable_if<std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static void Map(index_t i, float *out, const DType *lhs, const DType *rhs) {
+    KERNEL_ASSIGN(out[i], req, OP::Map(lhs[i], rhs[i]));
+  }
+
+  /*! \brief input is a tensor and a scalar value with a float output tensor */
+  template<typename DType,
+           typename std::enable_if<std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static void Map(index_t i, float *out, const DType *in, const DType value) {
+    KERNEL_ASSIGN(out[i], req, OP::Map(in[i], value));
+  }
 };
 
 template<typename OP, typename xpu>
diff --git a/src/operator/numpy/np_true_divide-inl.h b/src/operator/numpy/np_true_divide-inl.h
new file mode 100644
index 000000000000..cc74e19aef8f
--- /dev/null
+++ b/src/operator/numpy/np_true_divide-inl.h
@@ -0,0 +1,146 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file np_true_divide-inl.h
+ * \brief Function definitions of true_divide operator
+ */
+
+#ifndef MXNET_OPERATOR_NUMPY_NP_TRUE_DIVIDE_INL_H_
+#define MXNET_OPERATOR_NUMPY_NP_TRUE_DIVIDE_INL_H_
+
+#include <vector>
+#include "../../common/utils.h"
+#include "../tensor/elemwise_binary_broadcast_op.h"
+
+namespace mxnet {
+namespace op {
+
+template<typename xpu, typename OP>
+void TrueDivideScalarCompute(const nnvm::NodeAttrs &attrs,
+                             const OpContext &ctx,
+                             const std::vector<TBlob> &inputs,
+                             const std::vector<OpReqType> &req,
+                             const std::vector<TBlob> &outputs) {
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 1U);
+  if (req[0] == kNullOp || outputs[0].Size() == 0U) return;
+  using namespace mshadow;
+  using namespace mshadow::expr;
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  const double alpha = nnvm::get<double>(attrs.parsed);
+  if (common::is_float(inputs[0].type_flag_)) {
+    MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+      MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
+        mxnet_op::Kernel<mxnet_op::op_with_req<OP, Req>, xpu>::Launch(
+            s, inputs[0].Size(), outputs[0].dptr<DType>(), inputs[0].dptr<DType>(), DType(alpha));
+      });
+    });
+  } else {
+    CHECK_EQ(outputs[0].type_flag_, kFloat32) << "true_divide only supports float32 output "
+                                                 "when input's dtype is "
+                                              << type_string(inputs[0].type_flag_);
+    MXNET_INT_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+      MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
+        mxnet_op::Kernel<mxnet_op::op_with_req<OP, Req>, xpu>::Launch(
+            s, inputs[0].Size(), outputs[0].dptr<float>(), inputs[0].dptr<DType>(), DType(alpha));
+      });
+    });
+  }
+}
+
+template<typename xpu, typename OP>
+void TrueDivideElemwiseCompute(const nnvm::NodeAttrs &attrs,
+                               const OpContext &ctx,
+                               const std::vector<TBlob> &inputs,
+                               const std::vector<OpReqType> &req,
+                               const std::vector<TBlob> &outputs) {
+  using namespace mxnet_op;
+  if (req[0] == kNullOp || outputs[0].Size() == 0U) return;
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  CHECK_EQ(inputs.size(), 2U);
+  CHECK_EQ(outputs.size(), 1U);
+  MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
+    if (common::is_float(inputs[0].type_flag_)) {
+      MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+        Kernel<mxnet_op::op_with_req<OP, Req>, xpu>::Launch(s, outputs[0].Size(),
+                                                            outputs[0].dptr<DType>(),
+                                                            inputs[0].dptr<DType>(),
+                                                            inputs[1].dptr<DType>());
+      });
+    } else {
+      CHECK_EQ(outputs[0].type_flag_, kFloat32) << "true_divide only supports float32 output "
+                                                   "when input's dtype is "
+                                                << type_string(inputs[0].type_flag_);
+      MXNET_INT_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+        Kernel<mxnet_op::op_with_req<OP, Req>, xpu>::Launch(s, outputs[0].Size(),
+                                                            outputs[0].dptr<float>(),
+                                                            inputs[0].dptr<DType>(),
+                                                            inputs[1].dptr<DType>());
+      });
+    }
+  });
+}
+
+template<typename xpu, typename OP>
+void TrueDivideBroadcastCompute(const nnvm::NodeAttrs& attrs,
+                                const OpContext& ctx,
+                                const std::vector<TBlob>& inputs,
+                                const std::vector<OpReqType>& req,
+                                const std::vector<TBlob>& outputs) {
+  if (outputs[0].shape_.Size() == 0U) return;
+  mxnet::TShape new_lshape, new_rshape, new_oshape;
+  int ndim = BinaryBroadcastShapeCompact(inputs[0].shape_, inputs[1].shape_, outputs[0].shape_,
+                                         &new_lshape, &new_rshape, &new_oshape);
+  if (!ndim) {
+    TrueDivideElemwiseCompute<xpu, OP>(attrs, ctx, inputs, req, outputs);
+  } else {
+    if (req[0] == kNullOp) return;
+    mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+    BROADCAST_NDIM_SWITCH(ndim, NDim, {
+      mshadow::Shape<NDim> oshape = new_oshape.get<NDim>();
+      mshadow::Shape<NDim> lstride = mxnet_op::calc_stride(new_lshape.get<NDim>());
+      mshadow::Shape<NDim> rstride = mxnet_op::calc_stride(new_rshape.get<NDim>());
+      if (common::is_float(inputs[0].type_flag_)) {
+        MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+          mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, DType, DType, OP>, xpu>::
+            template LaunchEx(s, new_oshape.Size(), req[0], lstride, rstride, oshape,
+                              inputs[0].dptr<DType>(), inputs[1].dptr<DType>(),
+                              outputs[0].dptr<DType>());
+        });
+      } else {
+        CHECK_EQ(outputs[0].type_flag_, mshadow::kFloat32)
+            << "true_divide only supports float32 output when input's dtype is "
+            << type_string(inputs[0].type_flag_);
+        MXNET_INT_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+          mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, DType, float, OP>, xpu>::
+            template LaunchEx(s, new_oshape.Size(), req[0], lstride, rstride, oshape,
+                              inputs[0].dptr<DType>(), inputs[1].dptr<DType>(),
+                              outputs[0].dptr<float>());
+        });
+      }
+    });
+  }
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_NUMPY_NP_TRUE_DIVIDE_INL_H_
diff --git a/src/operator/numpy/np_true_divide.cc b/src/operator/numpy/np_true_divide.cc
index 429762778700..5a4634c3ff8c 100644
--- a/src/operator/numpy/np_true_divide.cc
+++ b/src/operator/numpy/np_true_divide.cc
@@ -22,8 +22,8 @@
  * \file np_true_divide.cc
  * \brief CPU Implementation of true_divide operator.
  */
-#include "../tensor/elemwise_binary_broadcast_op.h"
-#include "../tensor/elemwise_binary_scalar_op.h"
+
+#include "./np_true_divide-inl.h"
 
 namespace mxnet {
 namespace op {
@@ -41,37 +41,17 @@ bool TrueDivideType(const nnvm::NodeAttrs& attrs,
     const int lhs_dtype = in_attrs->at(0);
     const int rhs_dtype = in_attrs->at(1);
     CHECK_EQ(lhs_dtype, rhs_dtype)
-        << "_true_divide currently only supports same dtype for dividend and divisor";
+        << "true_divide currently only supports same dtype for dividend and divisor";
   }
-  auto is_float = [](const int dtype) {
-    return dtype == mshadow::kFloat32 || dtype == mshadow::kFloat64 || dtype == mshadow::kFloat16;
-  };
-
-  for (const int dtype : *in_attrs) {
-    CHECK(is_float(dtype)) << "_true_divide currently only supports float dtype";
+  if (common::is_float(in_attrs->at(0))) {
+    TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
+  } else {
+    TYPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::kFloat32);
   }
-  TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
   return true;
 }
 
 NNVM_REGISTER_OP(_npi_true_divide)
-.describe(R"code(
-Returns a true division of the inputs, element-wise.
-
-It currently only supports dtype float16, float32, and float64.
-
-Example::
-
-   x = [[ 6.,  6.,  6.],
-        [ 6.,  6.,  6.]]
-
-   y = [[ 2.],
-        [ 3.]]
-
-   _true_divide(x, y) = [[ 3.,  3.,  3.],
-                         [ 2.,  2.,  2.]]
-
-)code" ADD_FILELINE)
 .set_num_inputs(2)
 .set_num_outputs(1)
 .set_attr<nnvm::FListInputNames>("FListInputNames",
@@ -84,7 +64,7 @@ Example::
   [](const NodeAttrs& attrs){
     return std::vector<std::pair<int, int> >{{0, 0}, {1, 0}};
   })
-.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, op::mshadow_op::div>)
+.set_attr<FCompute>("FCompute<cpu>", TrueDivideBroadcastCompute<cpu, op::mshadow_op::true_divide>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_broadcast_div"})
 .add_argument("lhs", "NDArray-or-Symbol", "Dividend array")
 .add_argument("rhs", "NDArray-or-Symbol", "Divisor array");
@@ -101,7 +81,7 @@ NNVM_REGISTER_OP(_npi_true_divide_scalar)
   [](const NodeAttrs& attrs) {
     return std::vector<std::pair<int, int> >{{0, 0}};
   })
-.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, op::mshadow_op::div>)
+.set_attr<FCompute>("FCompute<cpu>", TrueDivideScalarCompute<cpu, op::mshadow_op::true_divide>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_div_scalar"})
 .add_argument("data", "NDArray-or-Symbol", "source input")
 .add_argument("scalar", "float", "scalar input");
@@ -118,7 +98,7 @@ NNVM_REGISTER_OP(_npi_rtrue_divide_scalar)
   [](const NodeAttrs& attrs) {
     return std::vector<std::pair<int, int> >{{0, 0}};
   })
-.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::rdiv>)
+.set_attr<FCompute>("FCompute<cpu>", TrueDivideScalarCompute<cpu, mshadow_op::rtrue_divide>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_rdiv_scalar"})
 .add_argument("data", "NDArray-or-Symbol", "source input")
 .add_argument("scalar", "float", "scalar input");
diff --git a/src/operator/numpy/np_true_divide.cu b/src/operator/numpy/np_true_divide.cu
index be10c44f92a1..c026d689233d 100644
--- a/src/operator/numpy/np_true_divide.cu
+++ b/src/operator/numpy/np_true_divide.cu
@@ -22,20 +22,20 @@
  * \file np_true_divide.cu
  * \brief GPU Implementation of true_divide operator.
  */
-#include "../tensor/elemwise_binary_broadcast_op.h"
-#include "../tensor/elemwise_binary_scalar_op.h"
+
+#include "./np_true_divide-inl.h"
 
 namespace mxnet {
 namespace op {
 
 NNVM_REGISTER_OP(_npi_true_divide)
-.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, mshadow_op::div>);
+.set_attr<FCompute>("FCompute<gpu>", TrueDivideBroadcastCompute<gpu, mshadow_op::true_divide>);
 
 NNVM_REGISTER_OP(_npi_true_divide_scalar)
-.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::div>);
+.set_attr<FCompute>("FCompute<gpu>", TrueDivideScalarCompute<gpu, mshadow_op::true_divide>);
 
 NNVM_REGISTER_OP(_npi_rtrue_divide_scalar)
-.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::rdiv>);
+.set_attr<FCompute>("FCompute<gpu>", TrueDivideScalarCompute<gpu, mshadow_op::rtrue_divide>);
 
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/operator_tune.cc b/src/operator/operator_tune.cc
index 50a6b41701cf..d0642eedfdf3 100644
--- a/src/operator/operator_tune.cc
+++ b/src/operator/operator_tune.cc
@@ -318,6 +318,7 @@ IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::plus);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::minus);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::mul);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::div);  // NOLINT()
+IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::true_divide);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::minus_sign);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::rminus);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::rdiv);  // NOLINT()
@@ -328,6 +329,7 @@ IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::div);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::minus_sign);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::rminus);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::rdiv);  // NOLINT()
+IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::rtrue_divide);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::div_grad);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::div_grad);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::div_rgrad);  // NOLINT()
diff --git a/tests/python/unittest/test_ndarray.py b/tests/python/unittest/test_ndarray.py
index 6f3e8ba88b24..9b3ce3192b9e 100644
--- a/tests/python/unittest/test_ndarray.py
+++ b/tests/python/unittest/test_ndarray.py
@@ -33,6 +33,8 @@
 from mxnet import runtime
 from numpy.testing import assert_allclose
 import mxnet.autograd
+from mxnet.base import integer_types
+from mxnet.ndarray.ndarray import py_slice
 
 
 def check_with_uniform(uf, arg_shapes, dim=None, npuf=None, rmin=-10, type_list=[np.float32]):
@@ -1385,6 +1387,13 @@ def assert_same(np_array, np_index, mx_array, mx_index, mx_value, np_value=None)
                 raise e
             assert same(np_array, mx_array.asnumpy())
 
+        def _is_basic_index(index):
+            if isinstance(index, (integer_types, py_slice)):
+                return True
+            if isinstance(index, tuple) and all(isinstance(i, (integer_types, py_slice)) for i in index):
+                return True
+            return False
+
         np_index = index
         if isinstance(index, mx.nd.NDArray):
             np_index = index.asnumpy()
@@ -1412,12 +1421,19 @@ def assert_same(np_array, np_index, mx_array, mx_index, mx_value, np_value=None)
             # test value is an numeric_type
             assert_same(np_array, np_index, mx_array, index, np.random.randint(low=-10000, high=0))
             if len(indexed_array_shape) > 1:
+                np_value = np.random.randint(low=-10000, high=0, size=(indexed_array_shape[-1],))
                 # test NDArray with broadcast
-                assert_same(np_array, np_index, mx_array, index,
-                            mx.nd.random.uniform(low=-10000, high=0, shape=(indexed_array_shape[-1],)))
+                assert_same(np_array, np_index, mx_array, index, mx.nd.array(np_value))
                 # test numpy array with broadcast
-                assert_same(np_array, np_index, mx_array, index,
-                            np.random.randint(low=-10000, high=0, size=(indexed_array_shape[-1],)))
+                assert_same(np_array, np_index, mx_array, index, np_value)
+
+                # test value shape are expanded to be longer than index array's shape
+                # this is currently only supported in basic indexing
+                if _is_basic_index(index):
+                    expanded_value_shape = (1, 1, 1) + np_value.shape
+                    assert_same(np_array, np_index, mx_array, index, np.array(np_value.reshape(expanded_value_shape)))
+                    assert_same(np_array, np_index, mx_array, index, np_value.reshape(expanded_value_shape))
+
                 # test list with broadcast
                 assert_same(np_array, np_index, mx_array, index,
                             [np.random.randint(low=-10000, high=0)] * indexed_array_shape[-1])
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 32e1b0cf4fe1..4f01fa9ed4fa 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -19,6 +19,7 @@
 from __future__ import absolute_import
 import sys
 import unittest
+import itertools
 import numpy as _np
 import platform
 import mxnet as mx
@@ -3498,6 +3499,49 @@ def generator_mx(x): return np.random.rand(
                          probs=probs, nsamples=samples, nrepeat=trials)
 
 
+@with_seed()
+@use_np
+def test_np_true_divide():
+    shapes = [
+        [()],
+        [(0,)],
+        [(2, 0, 3)],
+        [(0, 0, 0)],
+        [(10,)],
+        [(3, 4)],
+        [(2, 3, 4)],
+        [(2, 3, 4, 5)],
+        [(2, 3, 4, 5, 6)],
+        [(0,), (0,)],
+        [(0,), (1,)],
+        [(2, 0, 3), (1, 1)],
+        [(), (2, 3)],
+        [(2, 3), ()],
+        [(2, 3, 1), (1, 4)],
+        [(2, 1, 4, 1), (3, 1, 5)],
+    ]
+    dtypes = [np.int8, np.uint8, np.int32, np.int64, np.float16, np.float32, np.float64]
+    for shape_pair, dtype in itertools.product(shapes, dtypes):
+        a = np.random.uniform(3, 50, size=shape_pair[0]).astype(dtype)
+        b = np.random.uniform(3, 50, size=shape_pair[-1]).astype(dtype)
+        out_mx = a / b
+        if _np.issubdtype(dtype, _np.integer):
+            assert out_mx.dtype == np.float32
+        else:
+            assert out_mx.dtype == dtype
+        out_np = _np.true_divide(a.asnumpy(), b.asnumpy())
+        assert_almost_equal(out_mx.asnumpy(), out_np, rtol=1e-3, atol=1e-3, use_broadcast=False)
+
+        val = _np.random.randint(3, 50)
+        out_mx = a / val
+        out_np = _np.true_divide(a.asnumpy(), val)
+        assert_almost_equal(out_mx.asnumpy(), out_np, rtol=1e-3, atol=1e-3, use_broadcast=False)
+
+        out_mx = val / a
+        out_np = _np.true_divide(val, a.asnumpy())
+        assert_almost_equal(out_mx.asnumpy(), out_np, rtol=1e-3, atol=1e-3, use_broadcast=False)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()
diff --git a/tests/python/unittest/test_symbol.py b/tests/python/unittest/test_symbol.py
index ae770ed0644f..48c4f1664226 100644
--- a/tests/python/unittest/test_symbol.py
+++ b/tests/python/unittest/test_symbol.py
@@ -18,11 +18,13 @@
 import copy
 import os
 import re
+import json
 import mxnet as mx
 import numpy as np
-from common import assertRaises, models
+from common import assertRaises, models, TemporaryDirectory
 from mxnet.base import NotImplementedForSymbol
-from mxnet.test_utils import discard_stderr, rand_shape_nd
+from mxnet.test_utils import discard_stderr, rand_shape_nd, use_np
+from mxnet.util import np_shape
 import pickle as pkl
 
 def test_symbol_basic():
@@ -414,6 +416,47 @@ def test_gen_atomic_symbol_multiple_outputs():
                    bidirectional=True, state_outputs=True, mode='lstm')
     atomic_sym = s._gen_atomic_symbol()
 
+
+def test_load_save_symbol():
+    batch_size = 10
+    num_hdidden = 128
+    num_features = 784
+
+    def get_net():
+        data = mx.sym.var('data')
+        weight = mx.sym.var('weight', shape=(num_hdidden, 0))
+        return mx.sym.FullyConnected(data, weight, num_hidden=num_hdidden)
+
+    for flag1 in [False, True]:
+        with np_shape(flag1):
+            net_json_str = get_net().tojson()
+            net_data = json.loads(net_json_str)
+            assert "attrs" in net_data
+            if flag1:
+                assert "is_np_shape" in net_data["attrs"]
+            else:
+                assert "is_np_shape" not in net_data["attrs"]
+
+        with TemporaryDirectory() as work_dir:
+            fname = os.path.join(work_dir, 'test_sym.json')
+            with open(fname, 'w') as fp:
+                json.dump(net_data, fp)
+
+            # test loading 1.5.0 symbol file since 1.6.0
+            # w/ or w/o np_shape semantics
+            for flag2 in [False, True]:
+                if flag1:  # Do not need to test this case since 0 indicates zero-size dim
+                    continue
+                with np_shape(flag2):
+                    net = mx.sym.load(fname)
+                    arg_shapes, out_shapes, aux_shapes = net.infer_shape(data=(batch_size, num_features))
+                    assert arg_shapes[0] == (batch_size, num_features)  # data
+                    assert arg_shapes[1] == (num_hdidden, num_features)  # weight
+                    assert arg_shapes[2] == (num_hdidden,)  # bias
+                    assert out_shapes[0] == (batch_size, num_hdidden)  # output
+                    assert len(aux_shapes) == 0
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 746cbc55fd666bb4529e88d247fed8e0907270f9 Mon Sep 17 00:00:00 2001
From: Caenorst <cfujitsang@nvidia.com>
Date: Sun, 20 Oct 2019 19:23:09 -0400
Subject: [PATCH 485/813] Add unit tests for TensorRT integration and fix some
 bugs (#15399)

* add unit tests and fix a bunch of bugs

* avoid init_tensorrt_params to modify inputs arg/aux_params

* fix include in tensorrt-inl.h, fix bug following modification of buildCudaEngine, changer assert_allclose to assert_almost_equal

* remove line for lint

* update onnx-tensorrt

* remove commented out, planned but not implemented work

* restrict compatibility of deconvolution for tensorrt and make proper unit test, remove test_tensorrt_deconvolution.py

* update test_tensorrt.py adding with_seed and fixing fp16 weights loading

* update libnvinfer to 5.1.5

* fix typo

* get assert_allclose from numpy.testing

* using NVIDIA machine learning repo instead of just libnvinfer

* dereference trt_network for createParser

* fix for python2

* fix further for python2 syntax

* move test_tensorrt.py to tests/python/tensorrt/

* update unit tests

* update lenet5_train.py

* remove redundant fix to batchnorm gamma correction

* update test_resnet18.py
---
 3rdparty/onnx-tensorrt                        |   2 +-
 ci/docker/install/tensorrt.sh                 |   7 +-
 python/mxnet/contrib/tensorrt.py              |   2 +
 .../subgraph/tensorrt/nnvm_to_onnx-inl.h      |  15 +-
 .../subgraph/tensorrt/nnvm_to_onnx.cc         |  43 +-
 .../subgraph/tensorrt/onnx_to_tensorrt.cc     |   2 +-
 src/operator/subgraph/tensorrt/tensorrt-inl.h | 119 +++-
 src/operator/subgraph/tensorrt/tensorrt.cc    |   2 +-
 tests/python/tensorrt/common.py               |  30 -
 tests/python/tensorrt/lenet5_common.py        |  31 --
 tests/python/tensorrt/lenet5_train.py         |  12 +-
 tests/python/tensorrt/test_cvnets.py          |   7 +-
 tests/python/tensorrt/test_ops.py             | 527 ++++++++++++++++--
 tests/python/tensorrt/test_resnet18.py        |   2 +-
 .../tensorrt/test_tensorrt_batchnorm.py       |  65 ---
 tests/python/tensorrt/test_tensorrt_deconv.py |  63 ---
 tests/python/tensorrt/test_tensorrt_lenet5.py |  28 +-
 17 files changed, 687 insertions(+), 270 deletions(-)
 delete mode 100644 tests/python/tensorrt/common.py
 delete mode 100644 tests/python/tensorrt/lenet5_common.py
 delete mode 100644 tests/python/tensorrt/test_tensorrt_batchnorm.py
 delete mode 100644 tests/python/tensorrt/test_tensorrt_deconv.py

diff --git a/3rdparty/onnx-tensorrt b/3rdparty/onnx-tensorrt
index 1e209e546061..f4745fcaff86 160000
--- a/3rdparty/onnx-tensorrt
+++ b/3rdparty/onnx-tensorrt
@@ -1 +1 @@
-Subproject commit 1e209e546061173ccc37b25bbca69a795c6c86e4
+Subproject commit f4745fcaff868a519834917c657f105a8eef2f53
diff --git a/ci/docker/install/tensorrt.sh b/ci/docker/install/tensorrt.sh
index 21d9b4d40b36..674a3315eb09 100755
--- a/ci/docker/install/tensorrt.sh
+++ b/ci/docker/install/tensorrt.sh
@@ -41,9 +41,10 @@ popd
 
 # Install TensorRT
 echo "TensorRT build enabled. Installing TensorRT."
-wget -qO tensorrt.deb https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/nvinfer-runtime-trt-repo-ubuntu1604-5.0.2-ga-cuda10.0_1-1_amd64.deb
+wget -qO tensorrt.deb https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/nvidia-machine-learning-repo-ubuntu1604_1.0.0-1_amd64.deb
 dpkg -i tensorrt.deb
 apt-get update
-apt-get install -y --allow-downgrades libnvinfer5=5.1.2-1+cuda10.0
-apt-get install -y --allow-downgrades libnvinfer-dev=5.1.2-1+cuda10.0
+apt-get install -y --allow-downgrades libnvinfer5=5.1.5-1+cuda10.0
+apt-get install -y --allow-downgrades libnvinfer-dev=5.1.5-1+cuda10.0
+apt-mark hold libnvinfer5 libnvinfer-dev
 rm tensorrt.deb
diff --git a/python/mxnet/contrib/tensorrt.py b/python/mxnet/contrib/tensorrt.py
index d94600e1cee3..2676f40a35e2 100644
--- a/python/mxnet/contrib/tensorrt.py
+++ b/python/mxnet/contrib/tensorrt.py
@@ -42,6 +42,8 @@ def init_tensorrt_params(sym, arg_params, aux_params):
     :param aux_params: aux_params
     :return arg_params, aux_params: remaining params that are not in TensorRT nodes
     """
+    arg_params = arg_params.copy()
+    aux_params = aux_params.copy()
     for s in sym.get_internals():
         new_params_names = ""
         tensorrt_params = {}
diff --git a/src/operator/subgraph/tensorrt/nnvm_to_onnx-inl.h b/src/operator/subgraph/tensorrt/nnvm_to_onnx-inl.h
index 5a433f1d9820..d444e7a6239a 100644
--- a/src/operator/subgraph/tensorrt/nnvm_to_onnx-inl.h
+++ b/src/operator/subgraph/tensorrt/nnvm_to_onnx-inl.h
@@ -84,7 +84,13 @@ void ConvDeconvConvertHelper(NodeProto *node_proto,
                              ConvDeconvType type);
 
 // Forward declarations
-void ConvertConvolution(NodeProto *node_proto,
+void ConvertIdentity(NodeProto* node_proto,
+                     const NodeAttrs &attrs,
+                     const nnvm::IndexedGraph& ig,
+                     const array_view<IndexedGraph::NodeEntry> &inputs);
+
+void ConvertConvolution(
+                        NodeProto *node_proto,
                         const NodeAttrs &attrs,
                         const nnvm::IndexedGraph &ig,
                         const array_view<IndexedGraph::NodeEntry> &inputs);
@@ -139,12 +145,12 @@ void ConvertElementwiseAdd(NodeProto *node_proto,
                     const nnvm::IndexedGraph &ig,
                     const array_view<IndexedGraph::NodeEntry> &inputs);
 
-void ConvertElementwiseSub(NodeProto *node_proto,
+void ConvertElementwiseMul(NodeProto *node_proto,
                     const NodeAttrs &attrs,
                     const nnvm::IndexedGraph &ig,
                     const array_view<IndexedGraph::NodeEntry> &inputs);
 
-void ConvertElementwiseMul(NodeProto *node_proto,
+void ConvertElementwiseSub(NodeProto *node_proto,
                     const NodeAttrs &attrs,
                     const nnvm::IndexedGraph &ig,
                     const array_view<IndexedGraph::NodeEntry> &inputs);
@@ -168,6 +174,7 @@ std::string ConvertNnvmGraphToOnnx(const nnvm::Graph &g,
     std::unordered_map<std::string, NDArray>* params_map);
 
 static const std::unordered_map<std::string, ConverterFunction> converter_map = {
+  {"_copy", ConvertIdentity},
   {"Activation", ConvertActivation},
   {"BatchNorm", ConvertBatchNorm},
   {"clip", ConvertClip},
@@ -176,8 +183,8 @@ static const std::unordered_map<std::string, ConverterFunction> converter_map =
   {"Concat", ConvertConcatenate},
   {"Dropout", ConvertDropout},
   {"elemwise_add", ConvertElementwiseAdd},
-  {"elemwise_sub", ConvertElementwiseSub},
   {"elemwise_mul", ConvertElementwiseMul},
+  {"elemwise_sub", ConvertElementwiseSub},
   {"Flatten", ConvertFlatten},
   {"FullyConnected", ConvertFullyConnected},
   {"Pad", ConvertPad},
diff --git a/src/operator/subgraph/tensorrt/nnvm_to_onnx.cc b/src/operator/subgraph/tensorrt/nnvm_to_onnx.cc
index 84580d0b05d0..4107c58df65f 100644
--- a/src/operator/subgraph/tensorrt/nnvm_to_onnx.cc
+++ b/src/operator/subgraph/tensorrt/nnvm_to_onnx.cc
@@ -171,6 +171,12 @@ std::string ConvertNnvmGraphToOnnx(
   return serialized_onnx_graph;
 }
 
+void ConvertIdentity(NodeProto* node_proto, const NodeAttrs& attrs,
+                     const nnvm::IndexedGraph& /*ig*/,
+                     const array_view<IndexedGraph::NodeEntry>& /*inputs*/) {
+  node_proto->set_op_type("Identity");
+}
+
 template <class ConvDeconvParam>
 void ConvDeconvConvertHelper(NodeProto* node_proto, const NodeAttrs& attrs,
                              const nnvm::IndexedGraph& /*ig*/,
@@ -262,10 +268,12 @@ void ConvertPooling(NodeProto* node_proto, const NodeAttrs& attrs,
   const bool global_pool = pooling_param.global_pool;
 
   if (global_pool) {
-    if (pool_type == 0) {
+    if (pool_type == pool_enum::kMaxPooling) {
       node_proto->set_op_type("GlobalMaxPool");
-    } else {
+    } else if (pool_type == pool_enum::kAvgPooling) {
       node_proto->set_op_type("GlobalAveragePool");
+    } else {
+      LOG(FATAL) << "Pool type of node '" << attrs.name << "' unsupported: " << attrs.name;
     }
     return;
   }
@@ -298,12 +306,29 @@ void ConvertPooling(NodeProto* node_proto, const NodeAttrs& attrs,
     strides->add_ints(static_cast<int64>(kval));
   }
 
-  if (pool_type == 0) {
+  // ceil_mode
+  AttributeProto* const ceil_mode = node_proto->add_attribute();
+  ceil_mode->set_name("ceil_mode");
+  ceil_mode->set_type(AttributeProto::INT);
+  ceil_mode->set_i(static_cast<int64>(pooling_param.pooling_convention == pool_enum::kFull));
+
+  if (pool_type == pool_enum::kMaxPooling) {
     node_proto->set_op_type("MaxPool");
-  } else {
+  } else if (pool_type == pool_enum::kAvgPooling) {
     node_proto->set_op_type("AveragePool");
-  }  // average pooling
-  // not global pooling
+  } else {
+    LOG(FATAL) << "Pool type of node '" << attrs.name << "' unsupported: " << attrs.name;
+  }
+
+  // count_include_pad
+  AttributeProto* const count_include_pad = node_proto->add_attribute();
+  count_include_pad->set_name("count_include_pad");
+  count_include_pad->set_type(AttributeProto::INT);
+  if (pooling_param.count_include_pad.has_value()) {
+    count_include_pad->set_i(pooling_param.count_include_pad.value());
+  } else {
+    count_include_pad->set_i(1);
+  }
 }  // end ConvertPooling
 
 void ConvertRelu(NodeProto* node_proto, const NodeAttrs& /*attrs*/,
@@ -608,7 +633,7 @@ void ConvertOutput(
 void ConvertClip(NodeProto* node_proto, const NodeAttrs& attrs,
                  const nnvm::IndexedGraph& /*ig*/,
                  const array_view<IndexedGraph::NodeEntry>& /*inputs*/) {
-  const auto param = nnvm::get<ClipParam>(attrs.parsed);
+  const auto& param = nnvm::get<ClipParam>(attrs.parsed);
 
   node_proto->set_op_type("Clip");
 
@@ -628,7 +653,7 @@ void ConvertClip(NodeProto* node_proto, const NodeAttrs& attrs,
 void ConvertPad(NodeProto* node_proto, const NodeAttrs& attrs,
                 const nnvm::IndexedGraph& /*ig*/,
                 const array_view<IndexedGraph::NodeEntry>& /*inputs*/) {
-  const auto param = nnvm::get<PadParam>(attrs.parsed);
+  const auto& param = nnvm::get<PadParam>(attrs.parsed);
 
   node_proto->set_op_type("Pad");
 
@@ -647,7 +672,7 @@ void ConvertPad(NodeProto* node_proto, const NodeAttrs& attrs,
       mode->set_s("reflect");
       break;
     default:
-      throw dmlc::Error("Such mode of padding doesn't exist doesn't exist");
+      throw dmlc::Error("Such mode of padding doesn't exist");
   }
 
   // pads
diff --git a/src/operator/subgraph/tensorrt/onnx_to_tensorrt.cc b/src/operator/subgraph/tensorrt/onnx_to_tensorrt.cc
index 27f6da436a8f..4e7ff660cca0 100644
--- a/src/operator/subgraph/tensorrt/onnx_to_tensorrt.cc
+++ b/src/operator/subgraph/tensorrt/onnx_to_tensorrt.cc
@@ -79,7 +79,7 @@ std::tuple<unique_ptr<nvinfer1::ICudaEngine>,
   auto trt_logger = std::unique_ptr<TRT_Logger>(new TRT_Logger(verbosity));
   auto trt_builder = nvinfer1::createInferBuilder(*trt_logger);
   auto trt_network = trt_builder->createNetwork();
-  auto trt_parser  = InferObject(nvonnxparser::createParser(trt_network, *trt_logger));
+  auto trt_parser  = InferObject(nvonnxparser::createParser(*trt_network, *trt_logger));
   ::ONNX_NAMESPACE::ModelProto parsed_model;
   // We check for a valid parse, but the main effect is the side effect
   // of populating parsed_model
diff --git a/src/operator/subgraph/tensorrt/tensorrt-inl.h b/src/operator/subgraph/tensorrt/tensorrt-inl.h
index 7b0dcc1bd4a5..fac927e037b2 100644
--- a/src/operator/subgraph/tensorrt/tensorrt-inl.h
+++ b/src/operator/subgraph/tensorrt/tensorrt-inl.h
@@ -34,6 +34,14 @@
 #include <string>
 #include <vector>
 
+#include "../../nn/activation-inl.h"
+#include "../../nn/batch_norm-inl.h"
+#include "../../nn/concat-inl.h"
+#include "../../nn/convolution-inl.h"
+#include "../../nn/deconvolution-inl.h"
+#include "../../nn/dropout-inl.h"
+#include "../../nn/fully_connected-inl.h"
+#include "../../nn/pooling-inl.h"
 #include "../common.h"
 #include "../subgraph_property.h"
 #include "nnvm_to_onnx-inl.h"
@@ -85,18 +93,12 @@ struct TRTEngineParam {
 class TensorrtSelector : public SubgraphSelector {
  public:
   const std::unordered_set<std::string> unconditionalTRTops = {
-    "BatchNorm",
+    "_copy",
     "clip",
-    "Concat",
-    "Convolution",
-    "Deconvolution",
-    "Dropout",
     "elemwise_add",
     "elemwise_sub",
     "elemwise_mul",
     "Flatten",
-    "FullyConnected",
-    "mean",
     "Pad",
     "relu",
     "rsqrt",
@@ -112,13 +114,94 @@ class TensorrtSelector : public SubgraphSelector {
 
   bool isTRTCompatible(const nnvm::Node &n) {
     const std::string op_name = n.op()->name;
+    if (op_name == "FullyConnected") {
+      const auto& param = nnvm::get<FullyConnectedParam>(n.attrs.parsed);
+      return !param.no_bias;
+    }
+
     if (op_name == "Pooling") {
-      return (n.attrs.dict.at("pool_type") == "avg" ||
-          n.attrs.dict.at("pool_type") == "max");
+      const auto& param = nnvm::get<PoolingParam>(n.attrs.parsed);
+      if (param.layout.has_value()) {
+        if (param.layout.value() == mshadow::kNHWC) {
+          LOG(INFO) << "Warning: NHWC layout (node: " << n.attrs.name
+                    << ") is not supported by TensorRT";
+          return false;
+        } else if (param.layout.value() == mshadow::kNDHWC) {
+          LOG(INFO) << "Warning: NDHWC layout (node: " << n.attrs.name
+                    << ") is not supported by TensorRT";
+          return false;
+        }
+      }
+      if (param.pooling_convention != pool_enum::kValid && !param.global_pool)
+        return false;
+      if (param.pool_type == pool_enum::kAvgPooling) {
+        if ((!param.global_pool) &&
+            (!param.count_include_pad.has_value() || param.count_include_pad.value()))
+          return false;
+        return true;
+      } else if (param.pool_type == pool_enum::kMaxPooling) {
+        return true;
+      } else {
+        return false;
+      }
     }
 
-    if (unconditionalTRTops.count(op_name)) {
-      return true;
+    if (op_name == "Convolution") {
+      const auto& param = nnvm::get<ConvolutionParam>(n.attrs.parsed);
+      if (!param.layout.has_value())
+        return true;
+      switch (param.layout.value()) {
+        case mshadow::kNCHW:
+        case mshadow::kNCW:
+        case mshadow::kNCDHW:
+          return true;
+        case mshadow::kNHWC:
+          LOG(INFO) << "Warning: NHWC layout (node: " << n.attrs.name
+                    << ") is not supported by TensorRT";
+          return false;
+        case mshadow::kNDHWC:
+          LOG(INFO) << "Warning: NDHWC layout (node: " << n.attrs.name
+                    << ") is not supported by TensorRT";
+          return false;
+        default:
+          LOG(INFO) << "Warning: Layout (node: " << n.attrs.name
+                    << ") is unknown (so unsupported by TensorRT)";
+          return false;
+      }
+    }
+
+    if (op_name == "Deconvolution") {
+      const auto& param = nnvm::get<DeconvolutionParam>(n.attrs.parsed);
+      if (!param.layout.has_value())
+        return true;
+      switch (param.layout.value()) {
+        case mshadow::kNCHW:
+        case mshadow::kNCW:
+        case mshadow::kNCDHW:
+          return true;
+        case mshadow::kNHWC:
+          LOG(INFO) << "Warning: NHWC layout (node: " << n.attrs.name
+                    << ") is no tsupported by TensorRT";
+          return false;
+        case mshadow::kNDHWC:
+          LOG(INFO) << "Warning: NDHWC layout (node: " << n.attrs.name
+                    << ") is not supported by TensorRT";
+          return false;
+        default:
+          LOG(INFO) << "Warning: Layout (node: " << n.attrs.name
+                    << ") is unknown (so unsupported by TensorRT)";
+          return false;
+      }
+    }
+
+    if (op_name == "Concat") {
+      const auto& param = nnvm::get<ConcatParam>(n.attrs.parsed);
+      return (param.dim != 0);
+    }
+
+    if (op_name == "Dropout") {
+      const auto& param = nnvm::get<DropoutParam>(n.attrs.parsed);
+      return param.mode == dropout::kTraining && param.axes.ndim() == 0;
     }
 
     if (op_name == "Activation") {
@@ -127,6 +210,20 @@ class TensorrtSelector : public SubgraphSelector {
         n.attrs.dict.at("act_type") == "sigmoid";
     }
 
+    if (op_name == "BatchNorm") {
+      const auto& param = nnvm::get<BatchNormParam>(n.attrs.parsed);
+      if (param.axis != 1) {
+        LOG(INFO) << "Warning: Only Layout NC(D)(H)W are supported by TensorRT "
+                  << "(node " << n.attrs.name << ")";
+        return false;
+      }
+      return true;
+    }
+
+    if (unconditionalTRTops.count(op_name)) {
+      return true;
+    }
+
     return false;
   }
 
diff --git a/src/operator/subgraph/tensorrt/tensorrt.cc b/src/operator/subgraph/tensorrt/tensorrt.cc
index 4ef1d7dece9d..e872e9274f21 100644
--- a/src/operator/subgraph/tensorrt/tensorrt.cc
+++ b/src/operator/subgraph/tensorrt/tensorrt.cc
@@ -131,7 +131,7 @@ inline bool TRTInferType(const nnvm::NodeAttrs& attrs,
     auto it_params = params_map.find(node->attrs.name);
     auto it_inputs = inputs_to_idx.find(node->attrs.name);
     if (it_params != params_map.end()) {
-      types[eid] = it_params->second.dtype();
+      types[eid] = -1;
     } else if (it_inputs != inputs_to_idx.end()) {
       types[eid] = in_types->at(it_inputs->second);
     } else {
diff --git a/tests/python/tensorrt/common.py b/tests/python/tensorrt/common.py
deleted file mode 100644
index b37f8f3ff809..000000000000
--- a/tests/python/tensorrt/common.py
+++ /dev/null
@@ -1,30 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-from ctypes.util import find_library
-
-
-def check_tensorrt_installation():
-    assert find_library('nvinfer') is not None, "Can't find the TensorRT shared library"
-
-
-def merge_dicts(*dict_args):
-    """Merge arg_params and aux_params to populate shared_buffer"""
-    result = {}
-    for dictionary in dict_args:
-        result.update(dictionary)
-    return result
diff --git a/tests/python/tensorrt/lenet5_common.py b/tests/python/tensorrt/lenet5_common.py
deleted file mode 100644
index 347d6f3c11ba..000000000000
--- a/tests/python/tensorrt/lenet5_common.py
+++ /dev/null
@@ -1,31 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-import numpy as np
-import mxnet as mx
-from common import *
-
-def get_iters(mnist, batch_size):
-    """Get MNIST iterators."""
-    train_iter = mx.io.NDArrayIter(mnist['train_data'],
-                                   mnist['train_label'],
-                                   batch_size,
-                                   shuffle=True)
-    val_iter = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], batch_size)
-    test_iter = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], batch_size)
-    all_test_labels = np.array(mnist['test_label'])
-    return train_iter, val_iter, test_iter, all_test_labels
diff --git a/tests/python/tensorrt/lenet5_train.py b/tests/python/tensorrt/lenet5_train.py
index e679c05894a3..9e3f0a785516 100644
--- a/tests/python/tensorrt/lenet5_train.py
+++ b/tests/python/tensorrt/lenet5_train.py
@@ -17,8 +17,18 @@
 
 import os
 import mxnet as mx
-from lenet5_common import get_iters
+import numpy as np
 
+def get_iters(mnist, batch_size):
+    """Get MNIST iterators."""
+    train_iter = mx.io.NDArrayIter(mnist['train_data'],
+                                   mnist['train_label'],
+                                   batch_size,
+                                   shuffle=True)
+    val_iter = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], batch_size)
+    test_iter = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], batch_size)
+    all_test_labels = np.array(mnist['test_label'])
+    return train_iter, val_iter, test_iter, all_test_labels
 
 def lenet5():
     """LeNet-5 Symbol"""
diff --git a/tests/python/tensorrt/test_cvnets.py b/tests/python/tensorrt/test_cvnets.py
index 9282bc6f0de6..4b8eb48e926c 100644
--- a/tests/python/tensorrt/test_cvnets.py
+++ b/tests/python/tensorrt/test_cvnets.py
@@ -35,13 +35,14 @@ def get_classif_model(model_name, use_tensorrt, ctx=mx.gpu(0), batch_size=128):
     net.export(model_name)
     _sym, arg_params, aux_params = mx.model.load_checkpoint(model_name, 0)
     if use_tensorrt:
-        sym = _sym.get_backend_symbol('TensorRT') 
-        mx.contrib.tensorrt.init_tensorrt_params(sym, arg_params, aux_params)
+        sym = _sym.get_backend_symbol('TensorRT')
+        arg_params, aux_params = mx.contrib.tensorrt.init_tensorrt_params(sym, arg_params,
+                                                                          aux_params)
     else:
         sym = _sym
     executor = sym.simple_bind(ctx=ctx, data=(batch_size, 3, h, w),
                                softmax_label=(batch_size,),
-			       grad_req='null', force_rebind=True)
+                               grad_req='null', force_rebind=True)
     executor.copy_params_from(arg_params, aux_params)
     return executor
 
diff --git a/tests/python/tensorrt/test_ops.py b/tests/python/tensorrt/test_ops.py
index 2df9104aa06c..af1c453111d9 100644
--- a/tests/python/tensorrt/test_ops.py
+++ b/tests/python/tensorrt/test_ops.py
@@ -15,54 +15,503 @@
 # specific language governing permissions and limitations
 # under the License.
 
-from mxnet.test_utils import assert_almost_equal
 import mxnet as mx
 import numpy as np
+from itertools import product
+import copy
+
+from numpy.testing import assert_allclose
+
+import sys
 import os
+curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
+sys.path.insert(0, os.path.join(curr_path, '../unittest'))
+from common import setup_module, with_seed
+
+def check_unsupported_single_sym(sym):
+    wrapped_sym = mx.sym.Group([mx.sym.identity(s) for s in sym])
+    trt_sym = wrapped_sym.get_backend_symbol('TensorRT')
+    assert len(wrapped_sym.get_internals()) == len(trt_sym.get_internals())
 
-def check_elementwise_random(op='sum', shape=(1, 3, 224, 224)):
-    """
-    Check elementwise operators with vanilla/TensorRT executors with uniform random tensors
-    """
-    a = mx.sym.Variable('a')
-    b = mx.sym.Variable('b')
-    if op == 'sum':
-        sym = a + b
-    elif op == 'sub':
-        sym = a - b
-    elif op == 'mul':
-        sym = a * b
-
-    a_data = mx.ndarray.random.uniform(shape=shape, ctx=mx.gpu())
-    b_data = mx.ndarray.random.uniform(shape=shape, ctx=mx.gpu())
-
-    executor = sym.simple_bind(ctx=mx.gpu(), a=shape, b=shape,
-                               grad_req='null', force_rebind=True)
-    y = executor.forward(is_train=False, a=a_data, b=b_data)
-    trt_sym = sym.get_backend_symbol('TensorRT')
-    original_precision_value = mx.contrib.tensorrt.get_use_fp16()
-    try:
+def check_single_sym(sym, data_shapes, arg_params_shapes=None, aux_params_shapes=None,
+                     rtol_fp32=1e-5, atol_fp32=0., rtol_fp16=1e-3, atol_fp16=0.):
+    if arg_params_shapes is None:
+        arg_params_shapes = {}
+    if aux_params_shapes is None:
+        aux_params_shapes = {}
+    for i in range(3):
+        data = {k: mx.nd.array(np.random.rand(*v) + 0.01, dtype='float32', ctx=mx.cpu())
+                for k, v in data_shapes.items()}
+        arg_params = {k: mx.nd.array(np.random.rand(*v) + 0.01, dtype='float32', ctx=mx.cpu())
+                      for k, v in arg_params_shapes.items()}
+        aux_params = {k: mx.nd.array(np.random.rand(*v) + 0.01, dtype='float32', ctx=mx.cpu())
+                      for k, v in aux_params_shapes.items()}
+        wrapped_sym = mx.sym.Group([mx.sym.identity(s) for s in sym])
+
+        # Test FP32 MXNet Native
+        shapes = {}
+        shapes.update(data_shapes)
+        shapes.update(arg_params_shapes)
+        shapes.update(aux_params_shapes)
+        orig_executor = wrapped_sym.simple_bind(ctx=mx.gpu(0), grad_req='null',
+                                                force_rebind=True, **shapes)
+        orig_executor.copy_params_from(arg_params, aux_params)
+        orig_executor.forward(is_train=False, **data)
+        orig_outputs = [arr.asnumpy() for arr in orig_executor.outputs]
+
+        # Test FP32 MXNet-TRT
+        mx.contrib.tensorrt.set_use_fp16(False)
+        trt_sym = wrapped_sym.get_backend_symbol('TensorRT')
+        assert len(trt_sym.get_internals()) < len(wrapped_sym.get_internals())
+        remaining_arg_params, remaining_aux_params = \
+            mx.contrib.tensorrt.init_tensorrt_params(trt_sym, arg_params, aux_params)
+        shapes = {}
+        shapes.update(data_shapes)
+        shapes.update({k: v.shape for k, v in remaining_arg_params.items()})
+        shapes.update({k: v.shape for k, v in remaining_aux_params.items()})
+        trt_fp32_executor = trt_sym.simple_bind(ctx=mx.gpu(0), grad_req='null',
+                                                force_rebind=True, **shapes)
+        trt_fp32_executor.copy_params_from(remaining_arg_params, remaining_aux_params)
+        trt_fp32_executor.forward(is_train=False, **data)
+        trt_fp32_outputs = [arr.asnumpy() for arr in trt_fp32_executor.outputs]
+
+        # Test FP16 MXNet-TRT
         mx.contrib.tensorrt.set_use_fp16(True)
-        executor = trt_sym.simple_bind(ctx=mx.gpu(), a=shape, b=shape,
-                                       grad_req='null', force_rebind=True)
-        y_trt = executor.forward(is_train=False, a=a_data, b=b_data)
+        data = {k: v.astype('float16') for k, v in data.items()}
+        arg_params = {k: v.astype('float16') for k, v in arg_params.items()}
+        aux_params = {k: v.astype('float16') for k, v in aux_params.items()}
+        trt_sym = wrapped_sym.get_backend_symbol('TensorRT')
+        assert len(trt_sym.get_internals()) < len(wrapped_sym.get_internals())
+        remaining_arg_params, remaining_aux_params = \
+            mx.contrib.tensorrt.init_tensorrt_params(trt_sym, arg_params, aux_params)
+        shapes = {}
+        shapes.update(data_shapes)
+        shapes.update({k: v.shape for k, v in remaining_arg_params.items()})
+        shapes.update({k: v.shape for k, v in remaining_aux_params.items()})
+
+        trt_fp16_executor = trt_sym.simple_bind(ctx=mx.gpu(0),
+                                                type_dict={k: 'float16' for k in shapes.keys()},
+                                                grad_req='null', force_rebind=True, **shapes)
+        trt_fp16_executor.copy_params_from(remaining_arg_params, remaining_aux_params)
+        trt_fp16_executor.forward(is_train=False, **data)
+        trt_fp16_outputs = [arr.asnumpy() for arr in trt_fp16_executor.outputs]
+
+        for j, (orig, fp16, fp32) in enumerate(zip(orig_outputs, trt_fp16_outputs, trt_fp32_outputs)):
+            abs_orig = abs(orig)
+            diff32 = abs(fp32 - orig)
+            diff16 = abs(fp16.astype('float32') - orig)
+            _atol32 = diff32 - rtol_fp32 * abs_orig
+            _atol16 = diff16 - rtol_fp16 * abs_orig
+            print("{}: diff32({:.2E}) | diff16({:.2E}) | atol32({:.2E}) | atol16({:.2E}) | orig.min({:.2E})".format(
+                  j, diff32.max(), diff16.max(), _atol32.max(), _atol16.max(), abs_orig.min()))
+            assert_allclose(fp32, orig, rtol=rtol_fp32, atol=atol_fp32)
+            assert_allclose(fp16, orig, rtol=rtol_fp16, atol=atol_fp16)
+
+@with_seed()
+def test_noop():
+    data = mx.sym.Variable('data')
+    check_unsupported_single_sym(data)
+
+
+@with_seed()
+def test_identity():
+    data = mx.sym.Variable('data')
+    sym = mx.sym.identity(data)
+    check_single_sym(sym, data_shapes={'data': (8,3,32,32)},
+                     rtol_fp32=0., atol_fp32=0., rtol_fp16=1e-3, atol_fp16=1e-7)
+
+
+@with_seed()
+def test_convolution2d():
+    data = mx.sym.Variable('data')
+    weight = mx.sym.Variable('weight')
+    bias = mx.sym.Variable('bias')
+    data_shape = (8,3,16,16)
+    num_filter = 7
+    for kernel in [(3, 3), (1, 1), (3, 1)]:
+        for stride in [(1, 1), (2, 2), (2, 1)]:
+            if stride[0] > kernel[0] or stride[1] > kernel[1]: # doesn't make any sense
+                continue
+            if kernel == (3, 3) and stride == (1, 1):
+                atol_fp32 = 0.
+                rtol_fp32 = 1e-5
+                atol_fp16 = 0.
+                rtol_fp16 = 1e-2
+            else:
+                atol_fp32 = 0.
+                rtol_fp32 = 0.
+                atol_fp16 = 0.
+                rtol_fp16 = 1e-2
+            for pad in [(1, 1), (0, 0), (1, 0)]:
+                for group in [1, 2]:
+                    for layout in ['NCHW', 'NHWC']:
+                        weight_shape = (num_filter, data_shape[1]) + kernel
+                        bias_shape = (num_filter,)
+                        sym = mx.sym.Convolution(data, weight=weight, bias=bias, kernel=kernel,
+                                                 stride=stride, pad=pad, num_filter=num_filter,
+                                                 no_bias=False, layout=layout)
+                        if layout == 'NCHW':
+                            print("kernel: {} | stride: {} | pad: {} | group: {} | layout: {} | with_bias".format(
+                                  kernel, stride, pad, group, layout))
+                            check_single_sym(sym, {'data': data_shape},
+                                             {'weight': weight_shape, 'bias': bias_shape},
+                                             rtol_fp32=rtol_fp32, atol_fp32=atol_fp32,
+                                             rtol_fp16=rtol_fp16, atol_fp16=atol_fp16)
+                        else:
+                            check_unsupported_single_sym(sym)
+                        sym = mx.sym.Convolution(data, weight=weight, kernel=kernel, stride=stride,
+                                                 pad=pad, num_filter=num_filter, no_bias=True,
+                                                 layout=layout)
+                        if layout == 'NCHW':
+                            print("kernel: {} | stride: {} | pad: {} | group: {} | layout: {} | without_bias".format(
+                                  kernel, stride, pad, group, layout))
+                            check_single_sym(sym, {'data': data_shape},
+                                             {'weight': weight_shape},
+                                             rtol_fp32=rtol_fp32, atol_fp32=atol_fp32,
+                                             rtol_fp16=rtol_fp16, atol_fp16=atol_fp16)
+                        else:
+                            check_unsupported_single_sym(sym)
+
+@with_seed()
+def test_deconvolution2d():
+    data = mx.sym.Variable('data')
+    weight = mx.sym.Variable('weight')
+    bias = mx.sym.Variable('bias')
+    data_shape = (8,3,16,16)
+    num_filter = 7
+    for kernel in [(3, 3), (1, 1), (3, 1)]:
+        for stride in [(1, 1), (2, 2), (2, 1)]:
+            if stride[0] > kernel[0] or stride[1] > kernel[1]: # doesn't make any sense
+                continue
+            if kernel == (3, 3) and stride == (1, 1):
+                atol_fp32 = 0.
+                rtol_fp32 = 5e-5
+                atol_fp16 = 0.
+                rtol_fp16 = 1e-2
+            else:
+                atol_fp32 = 0.
+                rtol_fp32 = 1e-6
+                atol_fp16 = 0.
+                rtol_fp16 = 1e-2
+            for pad in [(1, 1), (0, 0), (1, 0)]:
+                for group in [1, 2]:
+                    for layout in ['NCHW', 'NHWC']:
+                        weight_shape = (data_shape[1], num_filter) + kernel
+                        bias_shape = (num_filter,)
+                        sym = mx.sym.Deconvolution(data, weight=weight, bias=bias, kernel=kernel,
+                                                 stride=stride, pad=pad, num_filter=num_filter,
+                                                 no_bias=False, layout=layout)
+                        if layout == 'NCHW':
+                            print("kernel: {} | stride: {} | pad: {} | group: {} | layout: {} | with_bias".format(
+                                  kernel, stride, pad, group, layout))
+                            check_single_sym(sym, {'data': data_shape},
+                                             {'weight': weight_shape, 'bias': bias_shape},
+                                             rtol_fp32=rtol_fp32, atol_fp32=atol_fp32,
+                                             rtol_fp16=rtol_fp16, atol_fp16=atol_fp16)
+                        else:
+                            check_unsupported_single_sym(sym)
+                        sym = mx.sym.Deconvolution(data, weight=weight, kernel=kernel, stride=stride,
+                                                 pad=pad, num_filter=num_filter, no_bias=True,
+                                                 layout=layout)
+                        if layout == 'NCHW':
+                            print("kernel: {} | stride: {} | pad: {} | group: {} | layout: {} | without_bias".format(
+                                  kernel, stride, pad, group, layout))
+                            check_single_sym(sym, {'data': data_shape},
+                                             {'weight': weight_shape},
+                                             rtol_fp32=rtol_fp32, atol_fp32=atol_fp32,
+                                             rtol_fp16=rtol_fp16, atol_fp16=atol_fp16)
+                        else:
+                            check_unsupported_single_sym(sym)
+
+@with_seed()
+def test_fully_connected(): # TODO(cfujitsang): take care of flatten option
+    data = mx.sym.Variable('data')
+    weight = mx.sym.Variable('weight')
+    bias = mx.sym.Variable('bias')
+    data_shape = (8,64)
+    num_hidden = 7
+    weight_shape = (num_hidden, data_shape[1])
+    bias_shape = (num_hidden,)
+    sym = mx.sym.FullyConnected(data, weight=weight, bias=bias, no_bias=False,
+                                num_hidden=num_hidden)
+    check_single_sym(sym, {'data': data_shape}, {'weight': weight_shape, 'bias': bias_shape},
+                     rtol_fp16=5e-3, atol_fp16=0.)
+    sym = mx.sym.FullyConnected(data, weight=weight, no_bias=True, num_hidden=num_hidden)
+    check_unsupported_single_sym(sym)
+
+
+@with_seed()
+def test_relu():
+    data = mx.sym.Variable('data')
+    sym = mx.sym.relu(data)
+    for data_shape in [(10, 32), (10, 3, 32), (10, 3, 32, 32), (10, 3, 7, 32, 32)]:
+        check_single_sym(sym, {'data': data_shape}, rtol_fp32=0., atol_fp32=0.,
+                         rtol_fp16=1e-3, atol_fp16=1e-7)
+
+
+@with_seed()
+def test_activation():
+    data = mx.sym.Variable('data')
+    for act_type in ['relu', 'sigmoid', 'tanh']:
+        sym = mx.sym.Activation(data, act_type=act_type)
+        for data_shape in [(10, 32), (10, 3, 32), (10, 3, 32, 32), (10,3,7,32,32)]:
+            check_single_sym(sym, {'data': data_shape}, rtol_fp32=0., atol_fp32=0.,
+                             rtol_fp16=1e-3, atol_fp16=1e-7)
+    for act_type in ['softrelu', 'softsign']:
+        sym = mx.sym.Activation(data, act_type=act_type)
+        check_unsupported_single_sym(sym)
+
+
+@with_seed()
+def test_pooling2d():
+    data = mx.sym.Variable('data')
+    data_shape = (4, 3, 32,32)
+    for pool_type in ['max', 'avg', 'lp', 'sum']:
+        if pool_type == 'max':
+            rtol_fp32 = 1e-6
+            atol_fp32 = 0.
+            rtol_fp16 = 1e-3
+            atol_fp16 = 0.
+        else:
+            rtol_fp32 = 5e-6
+            atol_fp32 = 0.
+            rtol_fp16 = 1e-3
+            atol_fp16 = 0.
+        for layout in ['NHWC', 'NCHW']:
+            for (stride, pad, kernel, count_include_pad, pooling_convention) \
+                 in product([(2,2), (2,1)], [(0,0), (1,1)], [(2,2), (3,2)],
+                            [True, False], ['valid', 'full']):
+                print("pool_type: {} | layout: {} | stride: {} | pad: {} | ".format(
+                      pool_type, layout, stride, pad) +
+                      "kernel: {} | count_include_pad: {} | pooling_convention: {}".format(
+                      kernel, count_include_pad, pooling_convention))
+                sym = mx.sym.Pooling(data, kernel=kernel, pool_type=pool_type, stride=stride,
+                                     pad=pad, layout=layout, count_include_pad=count_include_pad,
+                                     pooling_convention=pooling_convention)
+                if (layout == 'NHWC') or \
+                    pool_type not in ('max', 'avg') or \
+                    pooling_convention != 'valid' or \
+                    (pool_type == 'avg' and count_include_pad):
+                    check_unsupported_single_sym(sym)
+                else:
+                    check_single_sym(sym, {'data': data_shape},
+                                     rtol_fp32=rtol_fp32, atol_fp32=atol_fp32,
+                                     rtol_fp16=rtol_fp16, atol_fp16=atol_fp16)
+            print("pool_type: {} | layout: {} | global_pool".format(pool_type, layout))
+            sym = mx.sym.Pooling(data, global_pool=True, pool_type=pool_type, layout=layout)
+            if layout == 'NHWC' or pool_type not in ('max', 'avg'):
+                check_unsupported_single_sym(sym)
+            else:
+                if pool_type == 'max':
+                    rtol_fp32 = 0.
+                    atol_fp32 = 0.
+                    rtol_fp16 = 1e-3
+                    atol_fp16 = 0.
+                else:
+                    rtol_fp32 = 1e-5
+                    atol_fp32 = 0.
+                    rtol_fp16 = 1e-3
+                    atol_fp16 = 0.
+                check_single_sym(sym, {'data': data_shape}, rtol_fp32=rtol_fp32,
+                                 atol_fp32=atol_fp32, rtol_fp16=rtol_fp16, atol_fp16=atol_fp16)
+
+
+@with_seed()
+def test_softmax_output():
+    data = mx.sym.Variable('data')
+    label = mx.sym.Variable('label')
+    data_shape = (8, 100)
+    label_shape = (8, 100)
+    sym = mx.sym.SoftmaxOutput(data, label)
+    check_single_sym(sym, {'data': data_shape, 'label': label_shape},
+                     rtol_fp32=1e-6, atol_fp32=0., rtol_fp16=5e-3, atol_fp16=0.)
+    sym = mx.sym.SoftmaxOutput(data)
+    check_single_sym(sym, {'data': data_shape},
+                     rtol_fp32=1e-6, atol_fp32=0., rtol_fp16=5e-3, atol_fp16=0.)
+
+
+
+def check_batch_norm(sym, data_shapes, arg_params_shapes=None, aux_params_shapes=None,
+                     rtol_fp32=1e-5, atol_fp32=1e-7, rtol_fp16=1e-2, atol_fp16=1e-3):
+    if arg_params_shapes is None:
+        arg_params_shapes = {}
+    if aux_params_shapes is None:
+        aux_params_shapes = {}
+    for i in range(3):
+        data = {
+            'data': mx.nd.array(np.random.rand(*data_shapes['data']) + 0.01,
+                                dtype='float32', ctx=mx.cpu())
+        }
+        arg_params = {
+            'gamma': mx.nd.array(np.random.rand(*arg_params_shapes['gamma']) * 0.1 + 1.,
+                                 dtype='float32', ctx=mx.cpu()),
+            'beta': mx.nd.array(np.random.rand(*arg_params_shapes['beta']),
+                                dtype='float32', ctx=mx.cpu())
+        }
+        aux_params = {
+            'moving_mean': mx.nd.array(
+                0.45 + np.random.rand(*aux_params_shapes['moving_mean']) * 0.1 + 0.01,
+                                      dtype='float32', ctx=mx.cpu()),
+            'moving_var': mx.nd.array(
+                0.95 + np.random.rand(*aux_params_shapes['moving_var']) * 0.1,
+                                      dtype='float32', ctx=mx.cpu())
+        }
+        wrapped_sym = mx.sym.Group([mx.sym.identity(s) for s in sym])
+
+        # Test FP32 MXNet Native
+        shapes = {}
+        shapes.update(data_shapes)
+        shapes.update(arg_params_shapes)
+        shapes.update(aux_params_shapes)
+        orig_executor = wrapped_sym.simple_bind(ctx=mx.gpu(0), grad_req='null',
+                                                force_rebind=True, **shapes)
+        orig_executor.copy_params_from(arg_params, aux_params)
+        orig_executor.forward(is_train=False, **data)
+        orig_outputs = [arr.asnumpy() for arr in orig_executor.outputs]
+
+        # Test FP32 MXNet-TRT
         mx.contrib.tensorrt.set_use_fp16(False)
-        executor = trt_sym.simple_bind(ctx=mx.gpu(), a=shape, b=shape,
-                                       grad_req='null', force_rebind=True)
-        y_trt_fp32 = executor.forward(is_train=False, a=a_data, b=b_data)
-        assert_almost_equal(y[0].asnumpy(), y_trt[0].asnumpy(), 1e-1, 1e-2)
-        assert_almost_equal(y[0].asnumpy(), y_trt_fp32[0].asnumpy(), 1e-4, 1e-4)
-    finally:
-        mx.contrib.tensorrt.set_use_fp16(original_precision_value)
+        trt_sym = wrapped_sym.get_backend_symbol('TensorRT')
+        assert len(trt_sym.get_internals()) < len(wrapped_sym.get_internals())
+        remaining_arg_params, remaining_aux_params = \
+            mx.contrib.tensorrt.init_tensorrt_params(trt_sym, arg_params, aux_params)
+        shapes = {}
+        shapes.update(data_shapes)
+        shapes.update({k: v.shape for k, v in remaining_arg_params.items()})
+        shapes.update({k: v.shape for k, v in remaining_aux_params.items()})
+        trt_fp32_executor = trt_sym.simple_bind(ctx=mx.gpu(0), grad_req='null',
+                                                force_rebind=True, **shapes)
+        trt_fp32_executor.copy_params_from(remaining_arg_params, remaining_aux_params)
+        trt_fp32_executor.forward(is_train=False, **data)
+        trt_fp32_outputs = [arr.asnumpy() for arr in trt_fp32_executor.outputs]
+
+        # Test FP16 MXNet-TRT
+        mx.contrib.tensorrt.set_use_fp16(True)
+        data = {k: v.astype('float16') for k, v in data.items()}
+        arg_params = {k: v.astype('float32') for k, v in arg_params.items()}
+        aux_params = {k: v.astype('float32') for k, v in aux_params.items()}
+        trt_sym = wrapped_sym.get_backend_symbol('TensorRT')
+        remaining_arg_params, remaining_aux_params = \
+            mx.contrib.tensorrt.init_tensorrt_params(trt_sym, arg_params, aux_params)
+        shapes = {}
+        shapes.update(data_shapes)
+        shapes.update({k: v.shape for k, v in remaining_arg_params.items()})
+        shapes.update({k: v.shape for k, v in remaining_aux_params.items()})
+
+        trt_fp16_executor = trt_sym.simple_bind(ctx=mx.gpu(0),
+                                                type_dict={k: 'float16' for k in shapes.keys()},
+                                                grad_req='null', force_rebind=True, **shapes)
+        trt_fp16_executor.copy_params_from(remaining_arg_params, remaining_aux_params)
+        trt_fp16_executor.forward(is_train=False, **data)
+        trt_fp16_outputs = [arr.asnumpy() for arr in trt_fp16_executor.outputs]
+
+
+        for j, (orig, fp16, fp32) in enumerate(zip(orig_outputs,
+                                                   trt_fp16_outputs,
+                                                   trt_fp32_outputs)):
+            abs_orig = abs(orig)
+            diff32 = abs(fp32 - orig)
+            diff16 = abs(fp16.astype('float32') - orig)
+            _atol32 = diff32 - rtol_fp32 * abs_orig
+            _atol16 = diff16 - rtol_fp16 * abs_orig
+            print("{}: diff32({:.2E}) | diff16({:.2E}) | atol32({:.2E}) | atol16({:.2E}) | orig.min({:.2E})".format(
+                  j, diff32.max(), diff16.max(), _atol32.max(), _atol16.max(), abs_orig.min()))
+            assert_allclose(fp32, orig, rtol=rtol_fp32, atol=atol_fp32)
+            assert_allclose(fp16.astype('float32'), orig, rtol=rtol_fp16, atol=atol_fp16)
+
+@with_seed()
+def test_batch_norm():
+    data = mx.sym.Variable('data')
+    gamma = mx.sym.Variable('gamma')
+    beta = mx.sym.Variable('beta')
+    moving_mean = mx.sym.Variable('moving_mean')
+    moving_var = mx.sym.Variable('moving_var')
+    data_shape = (4,3,32,32)
+    gamma_shape = (3,)
+    beta_shape = (3,)
+    moving_mean_shape = (3,)
+    moving_var_shape = (3,)
+    for fix_gamma in [True, False]:
+        for use_global_stats in [True, False]:
+            for axis in [0, 1, 2, 3]:
+                sym = mx.sym.BatchNorm(data, gamma=gamma, beta=beta, moving_mean=moving_mean,
+                                       fix_gamma=fix_gamma, moving_var=moving_var, momentum=0.9,
+                                       axis=axis, use_global_stats=use_global_stats, eps=1e-5)
+                if axis == 1:
+                    check_batch_norm(sym,
+                        {'data': data_shape}, {'gamma': gamma_shape, 'beta': beta_shape},
+                        {'moving_mean': moving_mean_shape, 'moving_var': moving_var_shape},
+                        atol_fp32=2e-7)
+                else:
+                    check_unsupported_single_sym(sym)
+
+
+@with_seed()
+def test_clip():
+    data = mx.sym.Variable('data')
+    sym = mx.sym.clip(data, 0.25, 0.75)
+    for data_shape in [(10, 32), (10, 3, 32), (10, 3, 32, 32), (10,3,7,32,32)]:
+        check_single_sym(sym, {'data': data_shape},
+                         rtol_fp32=0., atol_fp32=0.,
+                         rtol_fp16=1e-3, atol_fp16=0.)
+
+
+@with_seed()
+def test_concat():
+    lhs = mx.sym.Variable('lhs')
+    rhs = mx.sym.Variable('rhs')
+    shape = [3, 5, 7, 9]
+    lhs_shape = tuple(shape)
+    for axis in range(1, 4):
+        sym = mx.sym.concat(lhs, rhs, dim=axis)
+        rhs_shape = copy.copy(shape)
+        rhs_shape[axis] = 1
+        rhs_shape = tuple(rhs_shape)
+        check_single_sym(sym, {'lhs': lhs_shape, 'rhs': rhs_shape},
+                         rtol_fp32=0., atol_fp32=0., rtol_fp16=1e-3, atol_fp16=1e-7)
+
+
+@with_seed()
+def test_elemwise_ops():
+    lhs = mx.sym.Variable('lhs')
+    rhs = mx.sym.Variable('rhs')
+    shape = (3, 5, 7, 9)
+    lhs_shape = tuple(shape)
+    sym = mx.sym.elemwise_add(lhs, rhs)
+    check_single_sym(sym, {'lhs': shape, 'rhs': shape},
+                     rtol_fp32=0., atol_fp32=0.)
+
+    sym = mx.sym.elemwise_sub(lhs, rhs)
+    # TODO(cfujitsang): is atol_fp16 ok ?
+    check_single_sym(sym, {'lhs': shape, 'rhs': shape},
+                     rtol_fp32=0., atol_fp32=0., rtol_fp16=1e-3, atol_fp16=1e-3)
 
+    sym = mx.sym.elemwise_mul(lhs, rhs)
+    check_single_sym(sym, {'lhs': shape, 'rhs': shape},
+                     rtol_fp32=0., atol_fp32=0., rtol_fp16=5e-3, atol_fp16=1e-7)
 
-def test_elementwise():
-    for op in ['sum', 'sub', 'mul']:
-        for shape in [(20, 25), (3, 4, 20), (1, 3, 20, 25), (10, 10, 100, 100)]:
-            for itry in range(10):
-                check_elementwise_random(op, shape)
+@with_seed()
+def test_flatten():
+    data = mx.sym.Variable('data')
+    sym = mx.sym.flatten(data)
+    for data_shape in [(3, 5, 7), (3, 5, 7, 9), (3, 5, 7, 9, 11)]:
+        check_single_sym(sym, {'data': data_shape},
+                         rtol_fp32=0., atol_fp32=0., atol_fp16=1e-7)
 
+@with_seed()
+def test_dropout():
+    data = mx.sym.Variable('data')
+    for data_shape in [(3, 5), (3, 5, 7), (3, 5, 7, 9)]:
+        for mode in ['training', 'always']:
+            sym = mx.sym.Dropout(data, p=0.7, mode=mode)
+            if mode == 'training':
+                check_single_sym(sym, {'data': data_shape},
+                                 rtol_fp32=0., atol_fp32=0., atol_fp16=1e-7)
+            else:
+                check_unsupported_single_sym(sym)
+            sym = mx.sym.Dropout(data, p=0.7, mode=mode, axes=(0,))
+            check_unsupported_single_sym(sym)
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     import nose
     nose.runmodule()
diff --git a/tests/python/tensorrt/test_resnet18.py b/tests/python/tensorrt/test_resnet18.py
index 36a7f33fe6a0..9fd99abb121b 100644
--- a/tests/python/tensorrt/test_resnet18.py
+++ b/tests/python/tensorrt/test_resnet18.py
@@ -48,7 +48,7 @@ def test_tensorrt_resnet18_feature_vect():
     executor.copy_params_from(arg_params, aux_params)
     y = executor.forward(is_train=False, data=input_data)
     trt_sym = sym.get_backend_symbol('TensorRT')
-    mx.contrib.tensorrt.init_tensorrt_params(trt_sym, arg_params, aux_params)
+    arg_params, aux_params = mx.contrib.tensorrt.init_tensorrt_params(trt_sym, arg_params, aux_params)
     original_precision_value = mx.contrib.tensorrt.get_use_fp16()
     try:
         mx.contrib.tensorrt.set_use_fp16(True)
diff --git a/tests/python/tensorrt/test_tensorrt_batchnorm.py b/tests/python/tensorrt/test_tensorrt_batchnorm.py
deleted file mode 100644
index 62af3bbf329b..000000000000
--- a/tests/python/tensorrt/test_tensorrt_batchnorm.py
+++ /dev/null
@@ -1,65 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-import mxnet as mx
-from mxnet.test_utils import assert_almost_equal
-
-def get_params():
-    arg_params = {}
-    aux_params = {}
-    arg_params["trt_bn_test_conv_weight"] = mx.nd.ones((1, 1, 3, 3))
-    arg_params["trt_bn_test_bn_gamma"] = mx.nd.zeros((1,))
-    arg_params["trt_bn_test_bn_beta"] = mx.nd.zeros((1,))
-    aux_params["trt_bn_test_bn_moving_mean"] = mx.nd.ones(1)
-    aux_params["trt_bn_test_bn_moving_var"] = mx.nd.ones(1)
-    return arg_params, aux_params
-
-def get_symbol():
-    data = mx.sym.Variable("data")
-    conv = mx.sym.Convolution(data=data, kernel=(3,3), no_bias=True, num_filter=1, num_group=1,
-                              name="trt_bn_test_conv")
-    bn = mx.sym.BatchNorm(data=conv, fix_gamma=True, use_global_stats=False, name="trt_bn_test_bn")
-    return bn
-
-def test_batch_norm_runs_correctly_with_fix_gamma():
-    arg_params, aux_params = get_params()
-    arg_params_trt, aux_params_trt = get_params()
-
-    sym = get_symbol()
-    sym_trt = get_symbol().get_backend_symbol("TensorRT")
-
-    mx.contrib.tensorrt.init_tensorrt_params(sym_trt, arg_params_trt, aux_params_trt)
-
-    executor = sym.simple_bind(ctx=mx.gpu(), data=(1, 1, 3, 3), grad_req='null', force_rebind=True)
-    executor.copy_params_from(arg_params, aux_params)
-
-    executor_trt = sym_trt.simple_bind(ctx=mx.gpu(), data=(1, 1, 3, 3), grad_req='null',
-                                  force_rebind=True)
-    executor_trt.copy_params_from(arg_params_trt, aux_params_trt)
-
-    input_data = mx.nd.random.uniform(low=0, high=1, shape=(1, 1, 3, 3))
-
-    y = executor.forward(is_train=False, data=input_data)
-    y_trt = executor_trt.forward(is_train=False, data=input_data)
-
-    print(y[0].asnumpy())
-    print(y_trt[0].asnumpy())
-    assert_almost_equal(y[0].asnumpy(), y_trt[0].asnumpy(), 1e-4, 1e-4)
-
-if __name__ == '__main__':
-    import nose
-    nose.runmodule()
diff --git a/tests/python/tensorrt/test_tensorrt_deconv.py b/tests/python/tensorrt/test_tensorrt_deconv.py
deleted file mode 100644
index ef567d1dae3c..000000000000
--- a/tests/python/tensorrt/test_tensorrt_deconv.py
+++ /dev/null
@@ -1,63 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-import mxnet as mx
-from mxnet.test_utils import assert_almost_equal
-
-def get_params():
-    arg_params = {}
-    aux_params = {}
-    arg_params["trt_bn_test_conv_weight"] = mx.nd.ones((1, 1, 3, 3))
-    arg_params["trt_bn_test_deconv_weight"] = mx.nd.ones((1, 1, 3, 3))
-    return arg_params, aux_params
-
-def get_symbol():
-    data = mx.sym.Variable("data")
-    conv = mx.sym.Convolution(data=data, kernel=(3,3), no_bias=True, num_filter=1, num_group=1,
-                              name="trt_bn_test_conv")
-    deconv = mx.sym.Deconvolution(data=conv, kernel=(3, 3), no_bias=True, num_filter=1,
-                                  num_group=1, name="trt_bn_test_deconv")
-    return deconv
-
-def test_deconvolution_produce_same_output_as_tensorrt():
-    arg_params, aux_params = get_params()
-    arg_params_trt, aux_params_trt = get_params()
-
-    sym = get_symbol()
-    sym_trt = get_symbol().get_backend_symbol("TensorRT")
-
-    mx.contrib.tensorrt.init_tensorrt_params(sym_trt, arg_params_trt, aux_params_trt)
-
-    executor = sym.simple_bind(ctx=mx.gpu(), data=(1, 1, 3, 3), grad_req='null', force_rebind=True)
-    executor.copy_params_from(arg_params, aux_params)
-
-    executor_trt = sym_trt.simple_bind(ctx=mx.gpu(), data=(1, 1, 3, 3), grad_req='null',
-                                  force_rebind=True)
-    executor_trt.copy_params_from(arg_params_trt, aux_params_trt)
-
-    input_data = mx.nd.random.uniform(low=0, high=1, shape=(1, 1, 3, 3))
-
-    y = executor.forward(is_train=False, data=input_data)
-    y_trt = executor_trt.forward(is_train=False, data=input_data)
-
-    print(y[0].asnumpy())
-    print(y_trt[0].asnumpy())
-    assert_almost_equal(y[0].asnumpy(), y_trt[0].asnumpy(), 1e-4, 1e-4)
-
-if __name__ == '__main__':
-    import nose
-    nose.runmodule()
diff --git a/tests/python/tensorrt/test_tensorrt_lenet5.py b/tests/python/tensorrt/test_tensorrt_lenet5.py
index d105d6517887..78f41ca53909 100644
--- a/tests/python/tensorrt/test_tensorrt_lenet5.py
+++ b/tests/python/tensorrt/test_tensorrt_lenet5.py
@@ -18,18 +18,32 @@
 import os
 import numpy as np
 import mxnet as mx
-from common import *
-from lenet5_common import get_iters
-
+from ctypes.util import find_library
+
+def check_tensorrt_installation():
+    assert find_library('nvinfer') is not None, "Can't find the TensorRT shared library"
+
+def get_iters(mnist, batch_size):
+    """Get MNIST iterators."""
+    train_iter = mx.io.NDArrayIter(mnist['train_data'],
+                                   mnist['train_label'],
+                                   batch_size,
+                                   shuffle=True)
+    val_iter = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], batch_size)
+    test_iter = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], batch_size)
+    all_test_labels = np.array(mnist['test_label'])
+    return train_iter, val_iter, test_iter, all_test_labels
 
 def run_inference(sym, arg_params, aux_params, mnist, all_test_labels, batch_size, use_tensorrt):
     """Run inference with either MXNet or TensorRT"""
 
     data_size = (batch_size,) + mnist['test_data'].shape[1:]
     type_dict = {'data': 'float32', 'softmax_label': 'float32'}
+
     if use_tensorrt:
         _sym = sym.get_backend_symbol('TensorRT')
-        mx.contrib.tensorrt.init_tensorrt_params(_sym, arg_params, aux_params)
+        arg_params, aux_params = mx.contrib.tensorrt.init_tensorrt_params(_sym, arg_params,
+                                                                          aux_params)
     else:
         _sym = sym
     for k, v in arg_params.items():
@@ -43,10 +57,11 @@ def run_inference(sym, arg_params, aux_params, mnist, all_test_labels, batch_siz
                                 grad_req='null',
                                 force_rebind=True)
     executor.copy_params_from(arg_params, aux_params)
+
     # Get this value from all_test_labels
     # Also get classes from the dataset
     num_ex = 10000
-    all_preds = np.zeros([num_ex, 10], dtype=np.float32)
+    all_preds = np.zeros([num_ex, 10])
     test_iter = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], batch_size)
 
     example_ct = 0
@@ -96,12 +111,11 @@ def test_tensorrt_inference():
     print("MXNet-TensorRT accuracy: %f" % trt_pct)
 
     absolute_accuracy_diff = abs(mx_pct - trt_pct)
-    epsilon = 1.01e-2
+    epsilon = 3e-2
     assert absolute_accuracy_diff < epsilon, \
         """Absolute diff. between MXNet & TensorRT accuracy (%f) exceeds threshold (%f):
            MXNet = %f, TensorRT = %f""" % (absolute_accuracy_diff, epsilon, mx_pct, trt_pct)
 
-
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 93ec1f2e9173906d8509f3b8d92a2bdcf2cf0fe3 Mon Sep 17 00:00:00 2001
From: Tao Lv <tao.a.lv@intel.com>
Date: Tue, 22 Oct 2019 00:03:04 +0800
Subject: [PATCH 486/813] [Doc] Use mirror link in the download page (#16501)

* use mirror link in the download page

* use archive for the version before 1.2.1

* trigger ci
---
 .../src/pages/get_started/download.md          | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/docs/static_site/src/pages/get_started/download.md b/docs/static_site/src/pages/get_started/download.md
index b8f2e00024d4..cf3f8c1ddf48 100644
--- a/docs/static_site/src/pages/get_started/download.md
+++ b/docs/static_site/src/pages/get_started/download.md
@@ -29,24 +29,24 @@ These source archives are generated from tagged releases. Updates and patches wi
 
 | Version | Source                                                                                                      | PGP                                                                                                             | SHA                                                                                                                |
 |---------|-------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------|
-| 1.5.1   | [Download](https://apache.org/dist/incubator/mxnet/1.5.1/apache-mxnet-src-1.5.1-incubating.tar.gz)                 | [Download](https://apache.org/dist/incubator/mxnet/1.5.1/apache-mxnet-src-1.5.1-incubating.tar.gz.asc)    |  [Download](https://apache.org/dist/incubator/mxnet/1.5.1/apache-mxnet-src-1.5.1-incubating.tar.gz.sha512)     |
-| 1.5.0   | [Download](https://apache.org/dist/incubator/mxnet/1.5.0/apache-mxnet-src-1.5.0-incubating.tar.gz)                 | [Download](https://apache.org/dist/incubator/mxnet/1.5.0/apache-mxnet-src-1.5.0-incubating.tar.gz.asc)    |  [Download](https://apache.org/dist/incubator/mxnet/1.5.0/apache-mxnet-src-1.5.0-incubating.tar.gz.sha512)     |
+| 1.5.1   | [Download](https://www.apache.org/dyn/closer.cgi/incubator/mxnet/1.5.1/apache-mxnet-src-1.5.1-incubating.tar.gz)   | [Download](https://apache.org/dist/incubator/mxnet/1.5.1/apache-mxnet-src-1.5.1-incubating.tar.gz.asc)    |  [Download](https://apache.org/dist/incubator/mxnet/1.5.1/apache-mxnet-src-1.5.1-incubating.tar.gz.sha512)     |
+| 1.5.0   | [Download](https://www.apache.org/dyn/closer.cgi/incubator/mxnet/1.5.0/apache-mxnet-src-1.5.0-incubating.tar.gz)   | [Download](https://apache.org/dist/incubator/mxnet/1.5.0/apache-mxnet-src-1.5.0-incubating.tar.gz.asc)    |  [Download](https://apache.org/dist/incubator/mxnet/1.5.0/apache-mxnet-src-1.5.0-incubating.tar.gz.sha512)     |
 | 1.4.1   | [Download](https://www.apache.org/dyn/closer.cgi/incubator/mxnet/1.4.1/apache-mxnet-src-1.4.1-incubating.tar.gz)   | [Download](https://apache.org/dist/incubator/mxnet/1.4.1/apache-mxnet-src-1.4.1-incubating.tar.gz.asc)    | [Download](https://apache.org/dist/incubator/mxnet/1.4.1/apache-mxnet-src-1.4.1-incubating.tar.gz.sha512)      |
 | 1.4.0   | [Download](https://www.apache.org/dyn/closer.cgi/incubator/mxnet/1.4.0/apache-mxnet-src-1.4.0-incubating.tar.gz)   | [Download](https://apache.org/dist/incubator/mxnet/1.4.0/apache-mxnet-src-1.4.0-incubating.tar.gz.asc)    | [Download](https://apache.org/dist/incubator/mxnet/1.4.0/apache-mxnet-src-1.4.0-incubating.tar.gz.sha512)      |
 | 1.3.1   | [Download](https://www.apache.org/dyn/closer.cgi/incubator/mxnet/1.3.1/apache-mxnet-src-1.3.1-incubating.tar.gz)   | [Download](https://apache.org/dist/incubator/mxnet/1.3.1/apache-mxnet-src-1.3.1-incubating.tar.gz.asc)    | [Download](https://apache.org/dist/incubator/mxnet/1.3.1/apache-mxnet-src-1.3.1-incubating.tar.gz.sha512)      |
-| 1.3.0   | [Download](https://archive.apache.org/dist/incubator/mxnet/1.3.0/apache-mxnet-src-1.3.0-incubating.tar.gz)   | [Download](https://archive.apache.org/dist/incubator/mxnet/1.3.0/apache-mxnet-src-1.3.0-incubating.tar.gz.asc)    | [Download](https://archive.apache.org/dist/incubator/mxnet/1.3.0/apache-mxnet-src-1.3.0-incubating.tar.gz.sha512)      |
+| 1.3.0   | [Download](https://www.apache.org/dyn/closer.cgi/incubator/mxnet/1.3.0/apache-mxnet-src-1.3.0-incubating.tar.gz)   | [Download](https://apache.org/dist/incubator/mxnet/1.3.0/apache-mxnet-src-1.3.0-incubating.tar.gz.asc)    | [Download](https://apache.org/dist/incubator/mxnet/1.3.0/apache-mxnet-src-1.3.0-incubating.tar.gz.sha512)      |
 | 1.2.1   | [Download](https://archive.apache.org/dist/incubator/mxnet/1.2.1/apache-mxnet-src-1.2.1-incubating.tar.gz)   | [Download](https://archive.apache.org/dist/incubator/mxnet/1.2.1/apache-mxnet-src-1.2.1-incubating.tar.gz.asc)    | [Download](https://archive.apache.org/dist/incubator/mxnet/1.2.1/apache-mxnet-src-1.2.1-incubating.tar.gz.sha512)      |
 | 1.2.0   | [Download](https://archive.apache.org/dist/incubator/mxnet/1.2.0/apache-mxnet-src-1.2.0-incubating.tar.gz)   | [Download](https://archive.apache.org/dist/incubator/mxnet/1.2.0/apache-mxnet-src-1.2.0-incubating.tar.gz.asc)    | [Download](https://archive.apache.org/dist/incubator/mxnet/1.2.0/apache-mxnet-src-1.2.0-incubating.tar.gz.sha512)      |
-| 1.1.0   | [Download](https://archive.apache.org/dist/incubator/mxnet/1.1.0/apache-mxnet-src-1.1.0-incubating.tar.gz)      | [Download](https://archive.apache.org/dist/incubator/mxnet/1.1.0/apache-mxnet-src-1.1.0-incubating.tar.gz.asc)      | [Download](https://archive.apache.org/dist/incubator/mxnet/1.1.0/apache-mxnet-src-1.1.0-incubating.tar.gz.sha512)     |
-| 1.0.0   | [Download](https://archive.apache.org/dist/incubator/mxnet/1.0.0/apache-mxnet-src-1.0.0-incubating.tar.gz)   | [Download](https://archive.apache.org/dist/incubator/mxnet/1.0.0/apache-mxnet-src-1.0.0-incubating.tar.gz.asc)   | [Download](https://archive.apache.org/dist/incubator/mxnet/1.0.0/apache-mxnet-src-1.0.0-incubating.tar.gz.sha512)   |
-| 0.12.1  | [Download](https://archive.apache.org/dist/incubator/mxnet/0.12.1/apache-mxnet-src-0.12.1-incubating.tar.gz) | [Download](https://archive.apache.org/dist/incubator/mxnet/0.12.1/apache-mxnet-src-0.12.1-incubating.tar.gz.asc) | [Download](https://archive.apache.org/dist/incubator/mxnet/0.12.1/apache-mxnet-src-0.12.1-incubating.tar.gz.sha512) |
-| 0.12.0  | [Download](https://archive.apache.org/dist/incubator/mxnet/0.12.0/apache-mxnet-src-0.12.0-incubating.tar.gz) | [Download](https://archive.apache.org/dist/incubator/mxnet/0.12.0/apache-mxnet-src-0.12.0-incubating.tar.gz.asc) | [Download](https://archive.apache.org/dist/incubator/mxnet/0.12.0/apache-mxnet-src-0.12.0-incubating.tar.gz.sha512) |
-| 0.11.0  | [Download](https://archive.apache.org/dist/incubator/mxnet/0.11.0/apache-mxnet-src-0.11.0-incubating.tar.gz) | [Download](https://archive.apache.org/dist/incubator/mxnet/0.11.0/apache-mxnet-src-0.11.0-incubating.tar.gz.asc) | [Download](https://archive.apache.org/dist/incubator/mxnet/0.11.0/apache-mxnet-src-0.11.0-incubating.tar.gz.sha512) |
+| 1.1.0   | [Download](https://archive.apache.org/dist/incubator/mxnet/1.1.0/apache-mxnet-src-1.1.0-incubating.tar.gz)   | [Download](https://archive.apache.org/dist/incubator/mxnet/1.1.0/apache-mxnet-src-1.1.0-incubating.tar.gz.asc)    | [Download](https://archive.apache.org/dist/incubator/mxnet/1.1.0/apache-mxnet-src-1.1.0-incubating.tar.gz.sha512)     |
+| 1.0.0   | [Download](https://archive.apache.org/dist/incubator/mxnet/1.0.0/apache-mxnet-src-1.0.0-incubating.tar.gz)   | [Download](https://archive.apache.org/dist/incubator/mxnet/1.0.0/apache-mxnet-src-1.0.0-incubating.tar.gz.asc)    | [Download](https://archive.apache.org/dist/incubator/mxnet/1.0.0/apache-mxnet-src-1.0.0-incubating.tar.gz.sha512)   |
+| 0.12.1  | [Download](https://archive.apache.org/dist/incubator/mxnet/0.12.1/apache-mxnet-src-0.12.1-incubating.tar.gz) | [Download](https://archive.apache.org/dist/incubator/mxnet/0.12.1/apache-mxnet-src-0.12.1-incubating.tar.gz.asc)  | [Download](https://archive.apache.org/dist/incubator/mxnet/0.12.1/apache-mxnet-src-0.12.1-incubating.tar.gz.sha512) |
+| 0.12.0  | [Download](https://archive.apache.org/dist/incubator/mxnet/0.12.0/apache-mxnet-src-0.12.0-incubating.tar.gz) | [Download](https://archive.apache.org/dist/incubator/mxnet/0.12.0/apache-mxnet-src-0.12.0-incubating.tar.gz.asc)  | [Download](https://archive.apache.org/dist/incubator/mxnet/0.12.0/apache-mxnet-src-0.12.0-incubating.tar.gz.sha512) |
+| 0.11.0  | [Download](https://archive.apache.org/dist/incubator/mxnet/0.11.0/apache-mxnet-src-0.11.0-incubating.tar.gz) | [Download](https://archive.apache.org/dist/incubator/mxnet/0.11.0/apache-mxnet-src-0.11.0-incubating.tar.gz.asc)  | [Download](https://archive.apache.org/dist/incubator/mxnet/0.11.0/apache-mxnet-src-0.11.0-incubating.tar.gz.sha512) |
 
 ## Verify the Integrity of the Files
 It is essential that you verify the integrity of the downloaded file using the PGP signature (.asc file) or a hash (.md5 or .sha* file). Please read [Verifying Apache Software Foundation Releases](https://www.apache.org/info/verification.html) for more information on why you should verify our releases.
 
-The PGP signature can be verified using PGP or GPG. First download the KEYS as well as the .asc signature file for the relevant distribution. Make sure you get these files from the main distribution site, rather than from a mirror. Then verify the signatures using one of the following alternatives:
+The PGP signature can be verified using PGP or GPG. First download the [KEYS](https://apache.org/dist/incubator/mxnet/KEYS) as well as the .asc signature file for the relevant distribution. Make sure you get these files from the main distribution site, rather than from a mirror. Then verify the signatures using one of the following alternatives:
 
 ```bash
 % gpg --import KEYS

From 06ce371b54a51c58e2cf1bfb93f1f9b2c568c364 Mon Sep 17 00:00:00 2001
From: Talia <31782251+TEChopra1000@users.noreply.github.com>
Date: Mon, 21 Oct 2019 09:08:01 -0700
Subject: [PATCH 487/813] checking broken link fixes work (#16538)

* checking broken link fixes work

* Update docs/python_docs/themes/mx-theme/mxtheme/footer.html

Co-Authored-By: Aaron Markham <markhama@amazon.com>
---
 docs/python_docs/python/tutorials/deploy/export/onnx.md       | 2 +-
 .../tutorials/performance/backend/mkldnn/mkldnn_readme.md     | 2 +-
 docs/python_docs/themes/mx-theme/mxtheme/footer.html          | 4 ++--
 docs/static_site/src/_includes/get_started/linux/cpp/cpp.md   | 2 +-
 docs/static_site/src/_includes/get_started/macos/cpp/cpp.md   | 2 +-
 docs/static_site/src/_includes/get_started/macos/scala/cpu.md | 2 +-
 docs/static_site/src/_includes/get_started/windows/cpp/cpp.md | 4 ++--
 docs/static_site/src/pages/api/faq/env_var.md                 | 2 +-
 docs/static_site/src/pages/features.html                      | 2 +-
 docs/static_site/src/pages/get_started/windows_setup.md       | 4 ++--
 example/gluon/word_language_model/README.md                   | 2 +-
 python/mxnet/gluon/block.py                                   | 4 ++--
 python/mxnet/gluon/contrib/data/text.py                       | 4 ++--
 python/mxnet/gluon/trainer.py                                 | 2 +-
 14 files changed, 19 insertions(+), 19 deletions(-)

diff --git a/docs/python_docs/python/tutorials/deploy/export/onnx.md b/docs/python_docs/python/tutorials/deploy/export/onnx.md
index b292b359bd59..f44d476312aa 100644
--- a/docs/python_docs/python/tutorials/deploy/export/onnx.md
+++ b/docs/python_docs/python/tutorials/deploy/export/onnx.md
@@ -44,7 +44,7 @@ logging.basicConfig(level=logging.INFO)
 
 ## Downloading a model from the MXNet model zoo
 
-We download the pre-trained ResNet-18 [ImageNet](http://www.image-net.org/) model from the [MXNet Model Zoo](http://data.mxnet.io/models/imagenet/).
+We download the pre-trained ResNet-18 [ImageNet](http://www.image-net.org/) model from the [MXNet Model Zoo](/api/python/docs/api/gluon/model_zoo/index.html).
 We will also download synset file to match labels.
 
 ```python
diff --git a/docs/python_docs/python/tutorials/performance/backend/mkldnn/mkldnn_readme.md b/docs/python_docs/python/tutorials/performance/backend/mkldnn/mkldnn_readme.md
index 65973fc0db43..7c19fbb97931 100644
--- a/docs/python_docs/python/tutorials/performance/backend/mkldnn/mkldnn_readme.md
+++ b/docs/python_docs/python/tutorials/performance/backend/mkldnn/mkldnn_readme.md
@@ -20,7 +20,7 @@
 A better training and inference performance is expected to be achieved on Intel-Architecture CPUs with MXNet built with [Intel MKL-DNN](https://github.com/intel/mkl-dnn) on multiple operating system, including Linux, Windows and MacOS.
 In the following sections, you will find build instructions for MXNet with Intel MKL-DNN on Linux, MacOS and Windows.
 
-Please find MKL-DNN optimized operators and other features in the [MKL-DNN operator list](../mkldnn/operator_list.md).
+Please find MKL-DNN optimized operators and other features in the [MKL-DNN operator list](https://github.com/apache/incubator-mxnet/blob/v1.5.x/docs/tutorials/mkldnn/operator_list.md).
 
 The detailed performance data collected on Intel Xeon CPU with MXNet built with Intel MKL-DNN can be found [here](https://mxnet.apache.org/api/faq/perf#intel-cpu).
 
diff --git a/docs/python_docs/themes/mx-theme/mxtheme/footer.html b/docs/python_docs/themes/mx-theme/mxtheme/footer.html
index 4926f4fcb5d3..55a5fb2312d9 100644
--- a/docs/python_docs/themes/mx-theme/mxtheme/footer.html
+++ b/docs/python_docs/themes/mx-theme/mxtheme/footer.html
@@ -10,7 +10,7 @@ <h4 class="footer-category-title">Resources</h4>
                     <li><a href="https://issues.apache.org/jira/projects/MXNET/issues">Jira Tracker</a></li>
                     <li><a href="https://github.com/apache/incubator-mxnet/labels/Roadmap">Github Roadmap</a></li>
                     <li><a href="https://discuss.mxnet.io">MXNet Discuss forum</a></li>
-                    <li><a href="/mxnet.io-v2/community/contribute">Contribute To MXNet</a></li>
+                    <li><a href="/community/contribute">Contribute To MXNet</a></li>
 
                 </ul>
             </div>
@@ -43,4 +43,4 @@ <h4 class="footer-category-title">Resources</h4>
             </div>
         </div>
     </div>
-</footer>
\ No newline at end of file
+</footer>
diff --git a/docs/static_site/src/_includes/get_started/linux/cpp/cpp.md b/docs/static_site/src/_includes/get_started/linux/cpp/cpp.md
index 376dd13424aa..dc492d801262 100644
--- a/docs/static_site/src/_includes/get_started/linux/cpp/cpp.md
+++ b/docs/static_site/src/_includes/get_started/linux/cpp/cpp.md
@@ -1,4 +1,4 @@
 To enable the C++ package, build from source using `make USE_CPP_PACKAGE=1`.
-Refer to the [MXNet C++ setup guide](get_started/c_plus_plus)
+Refer to the [MXNet C++ setup guide](/get_started/cpp_setup.html)
 for full instructions.
                 
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/macos/cpp/cpp.md b/docs/static_site/src/_includes/get_started/macos/cpp/cpp.md
index bb06cb617773..cb9e613928d9 100644
--- a/docs/static_site/src/_includes/get_started/macos/cpp/cpp.md
+++ b/docs/static_site/src/_includes/get_started/macos/cpp/cpp.md
@@ -1,3 +1,3 @@
 To enable the C++ package, build from source using `make USE_CPP_PACKAGE=1`.
-Refer to the [MXNet C++ setup guide](get_started/c_plus_plus) for full instructions.
+Refer to the [MXNet C++ setup guide](/get_started/cpp_setup.html) for full instructions.
                 
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/macos/scala/cpu.md b/docs/static_site/src/_includes/get_started/macos/scala/cpu.md
index 623a8a56fb4d..0bc69eb2e63e 100644
--- a/docs/static_site/src/_includes/get_started/macos/scala/cpu.md
+++ b/docs/static_site/src/_includes/get_started/macos/scala/cpu.md
@@ -1,5 +1,5 @@
 You can use the Maven packages defined in the following dependency to include MXNet in your Scala
-project. Please refer to the [MXNet-Scala setup guide](scala_setup.html) for a detailed set
+project. Please refer to the [MXNet-Scala setup guide](/get_started/scala_setup.html) for a detailed set
 of instructions to help you with the setup process.
 
 <a href="https://mvnrepository.com/artifact/org.apache.mxnet/mxnet-full_2.11-osx-x86_64-cpu"><img
diff --git a/docs/static_site/src/_includes/get_started/windows/cpp/cpp.md b/docs/static_site/src/_includes/get_started/windows/cpp/cpp.md
index bb06cb617773..023735b6c3b1 100644
--- a/docs/static_site/src/_includes/get_started/windows/cpp/cpp.md
+++ b/docs/static_site/src/_includes/get_started/windows/cpp/cpp.md
@@ -1,3 +1,3 @@
 To enable the C++ package, build from source using `make USE_CPP_PACKAGE=1`.
-Refer to the [MXNet C++ setup guide](get_started/c_plus_plus) for full instructions.
-                
\ No newline at end of file
+Refer to the [MXNet C++ setup guide](/get_started/cpp_setup.html) for full instructions.
+                
diff --git a/docs/static_site/src/pages/api/faq/env_var.md b/docs/static_site/src/pages/api/faq/env_var.md
index e4c4ac20fc02..5cc6571fe0df 100644
--- a/docs/static_site/src/pages/api/faq/env_var.md
+++ b/docs/static_site/src/pages/api/faq/env_var.md
@@ -317,7 +317,7 @@ If ctypes is used, it must be `mxnet._ctypes.ndarray.NDArrayBase`.
 * MXNET_SUBGRAPH_BACKEND
   - Values: String ```(default="MKLDNN")``` if MKLDNN is avaliable, otherwise ```(default="")```
   - This variable controls the subgraph partitioning in MXNet.
-  - This variable is used to perform MKL-DNN FP32 operator fusion and quantization. Please refer to the [MKL-DNN operator list](../tutorials/mkldnn/operator_list.md) for how this variable is used and the list of fusion passes.
+  - This variable is used to perform MKL-DNN FP32 operator fusion and quantization. Please refer to the [MKL-DNN operator list](https://github.com/apache/incubator-mxnet/blob/v1.5.x/docs/tutorials/mkldnn/operator_list.md) for how this variable is used and the list of fusion passes.
   - Set ```MXNET_SUBGRAPH_BACKEND=NONE``` to disable subgraph backend.
 
 * MXNET_SAFE_ACCUMULATION
diff --git a/docs/static_site/src/pages/features.html b/docs/static_site/src/pages/features.html
index 6865bc90e4c5..bed6ab82ea96 100644
--- a/docs/static_site/src/pages/features.html
+++ b/docs/static_site/src/pages/features.html
@@ -16,7 +16,7 @@ <h3 class="feature-title">Hybrid Front-End</h3>
         <p class="feature-paragraph">The Gluon Python API lets you use MXNet in a fully imperative manner. It also
             allows you to simply switch to
             symbolic mode by calling the <a
-                    href="{{'/api/python/docs/tutorials/packages/gluon/hybridize' | relative_url}}">hybridize</a>
+                    href="/api/python/docs/tutorials/packages/gluon/blocks/hybridize.html">hybridize</a>
             functionality. The symbolic execution provides faster and more optimized
             execution as well as the ability to export the network for inference in different language bindings like
             java or C++.
diff --git a/docs/static_site/src/pages/get_started/windows_setup.md b/docs/static_site/src/pages/get_started/windows_setup.md
index 2fe2123d2e1c..559c7e4fa989 100644
--- a/docs/static_site/src/pages/get_started/windows_setup.md
+++ b/docs/static_site/src/pages/get_started/windows_setup.md
@@ -74,7 +74,7 @@ Install MXNet with CPU support with Python:
 pip install mxnet
 ```
 
-Now [validate your MXNet installation with Python](get_started/validate_mxnet).
+Now [validate your MXNet installation with Python](/get_started/validate_mxnet).
 
 ### Install with Intel CPUs
 
@@ -88,7 +88,7 @@ The following steps will setup MXNet with MKL. MKL-DNN can be enabled only when
 pip install mxnet-mkl
 ```
 
-Now [validate your MXNet installation with Python](validate_mxnet).
+Now [validate your MXNet installation with Python](/get_started/validate_mxnet).
 
 ### Install with NVIDIA GPUs
 
diff --git a/example/gluon/word_language_model/README.md b/example/gluon/word_language_model/README.md
index 04a4a4ac70b8..b2516a46b39a 100644
--- a/example/gluon/word_language_model/README.md
+++ b/example/gluon/word_language_model/README.md
@@ -29,7 +29,7 @@ The following techniques have been adopted for SOTA results:
 
 ### Wiki Text
 
-The wikitext-2 data is from [(The wikitext long term dependency language modeling dataset)](https://www.salesforce.com/products/einstein/ai-research/the-wikitext-dependency-language-modeling-dataset/). The training script automatically loads the dataset into `$PWD/data`.
+The wikitext-2 data is from [(The wikitext long term dependency language modeling dataset)](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/). The training script automatically loads the dataset into `$PWD/data`.
 
 
 ## Usage
diff --git a/python/mxnet/gluon/block.py b/python/mxnet/gluon/block.py
index d474337772a0..eff7dd754572 100644
--- a/python/mxnet/gluon/block.py
+++ b/python/mxnet/gluon/block.py
@@ -428,7 +428,7 @@ def save_parameters(self, filename):
         References
         ----------
         `Saving and Loading Gluon Models \
-        <https://mxnet.incubator.apache.org/tutorials/gluon/save_load_params.html>`_
+        <https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html>`_
         """
         params = self._collect_params_with_prefix()
         arg_dict = {key : val._reduce() for key, val in params.items()}
@@ -480,7 +480,7 @@ def load_parameters(self, filename, ctx=None, allow_missing=False,
         References
         ----------
         `Saving and Loading Gluon Models \
-        <https://mxnet.incubator.apache.org/tutorials/gluon/save_load_params.html>`_
+        <https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html>`_
         """
         if is_np_array():
             # failure may happen when loading parameters saved as NDArrays within
diff --git a/python/mxnet/gluon/contrib/data/text.py b/python/mxnet/gluon/contrib/data/text.py
index 9e78e3c2e23c..c0f4a3b924bf 100644
--- a/python/mxnet/gluon/contrib/data/text.py
+++ b/python/mxnet/gluon/contrib/data/text.py
@@ -106,7 +106,7 @@ class WikiText2(_WikiText):
     """WikiText-2 word-level dataset for language modeling, from Salesforce research.
 
     From
-    https://einstein.ai/research/the-wikitext-long-term-dependency-language-modeling-dataset
+    https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/
 
     License: Creative Commons Attribution-ShareAlike
 
@@ -144,7 +144,7 @@ class WikiText103(_WikiText):
     """WikiText-103 word-level dataset for language modeling, from Salesforce research.
 
     From
-    https://einstein.ai/research/the-wikitext-long-term-dependency-language-modeling-dataset
+    https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/
 
     License: Creative Commons Attribution-ShareAlike
 
diff --git a/python/mxnet/gluon/trainer.py b/python/mxnet/gluon/trainer.py
index 8a863fc2c6eb..01f76d637a97 100644
--- a/python/mxnet/gluon/trainer.py
+++ b/python/mxnet/gluon/trainer.py
@@ -43,7 +43,7 @@ class Trainer(object):
         The set of parameters to optimize.
     optimizer : str or Optimizer
         The optimizer to use. See
-        `help <https://mxnet.incubator.apache.org/api/python/tutorials/packages/optimizer/optimizer.html>`_
+        `help <https://mxnet.apache.org/api/python/docs/api/optimizer/index.html#mxnet.optimizer.Optimizer>`_
         on Optimizer for a list of available optimizers.
     optimizer_params : dict
         Key-word arguments to be passed to optimizer constructor. For example,

From 91bb39898e6b12fb45451852019939f694e54f71 Mon Sep 17 00:00:00 2001
From: perdasilva <perdasilva@gmail.com>
Date: Mon, 21 Oct 2019 18:38:48 +0200
Subject: [PATCH 488/813] [CD] Adds python docker pipeline (#16547)

* Adds python docker pipeline

* Refactors docker login functionality out of docker_cache script

tweak

* Refactors docker run functionality out of build.py

Test safe docker run tweaks

Fix up

* Adds CD docker utils

* Updates logging configuration to file config

* Adds restore static and dynamic methods, instead of making the library type a parameter

* Removes CUDA 9.1 from base images script

* Adds explanatory comment to signal masking in safe_docker_run

* Adds push success message with pull command and link to repository

* Fixes logging
---
 cd/Jenkinsfile_cd_pipeline               |  14 +-
 cd/Jenkinsfile_release_job               |   3 +-
 cd/Jenkinsfile_utils.groovy              |  12 +
 cd/python/docker/Dockerfile              |  40 +++
 cd/python/docker/Dockerfile.test         |  39 +++
 cd/python/docker/Jenkins_pipeline.groovy |  74 ++++
 cd/python/docker/python_images.sh        | 128 +++++++
 cd/python/docker/test_python_image.sh    |  47 +++
 cd/python/pypi/Jenkins_pipeline.groovy   |   2 +-
 cd/utils/docker_tag.sh                   |  59 ++++
 cd/utils/mxnet_base_image.sh             |  49 +++
 ci/build.py                              | 179 ++--------
 ci/docker_cache.py                       |  87 +----
 ci/docker_login.py                       | 137 ++++++++
 ci/logging.conf                          |  41 +++
 ci/safe_docker_run.py                    | 247 +++++++++++++
 ci/test_docker_cache.py                  |  40 ++-
 ci/test_docker_login.py                  | 234 +++++++++++++
 ci/test_safe_docker_run.py               | 427 +++++++++++++++++++++++
 ci/util.py                               |  17 +
 20 files changed, 1631 insertions(+), 245 deletions(-)
 create mode 100644 cd/python/docker/Dockerfile
 create mode 100644 cd/python/docker/Dockerfile.test
 create mode 100644 cd/python/docker/Jenkins_pipeline.groovy
 create mode 100755 cd/python/docker/python_images.sh
 create mode 100755 cd/python/docker/test_python_image.sh
 create mode 100755 cd/utils/docker_tag.sh
 create mode 100755 cd/utils/mxnet_base_image.sh
 create mode 100755 ci/docker_login.py
 create mode 100644 ci/logging.conf
 create mode 100755 ci/safe_docker_run.py
 create mode 100644 ci/test_docker_login.py
 create mode 100644 ci/test_safe_docker_run.py

diff --git a/cd/Jenkinsfile_cd_pipeline b/cd/Jenkinsfile_cd_pipeline
index e0e94770b682..afb7b9b6d27f 100644
--- a/cd/Jenkinsfile_cd_pipeline
+++ b/cd/Jenkinsfile_cd_pipeline
@@ -61,9 +61,17 @@ pipeline {
               stage("Build") {
                 cd_utils.trigger_release_job("Build static libmxnet", "mxnet_lib/static", params.MXNET_VARIANTS)    
               }
-              stage("PyPI Release") {
-                echo "Building PyPI Release"
-                cd_utils.trigger_release_job("Release PyPI Packages", "python/pypi", params.MXNET_VARIANTS)
+              stage("Releases") {
+                cd_utils.error_checked_parallel([
+                  "PyPI Release": {
+                    echo "Building PyPI Release"
+                    cd_utils.trigger_release_job("Release PyPI Packages", "python/pypi", params.MXNET_VARIANTS)
+                  },
+                  "Python Docker Release": {
+                    echo "Building Python Docker Release"
+                    cd_utils.trigger_release_job("Release Python Docker Images", "python/docker", params.MXNET_VARIANTS)
+                  }
+                ])
               }
             },
 
diff --git a/cd/Jenkinsfile_release_job b/cd/Jenkinsfile_release_job
index c2be26124029..4d6f3b5e9012 100644
--- a/cd/Jenkinsfile_release_job
+++ b/cd/Jenkinsfile_release_job
@@ -92,7 +92,8 @@ pipeline {
           def valid_job_types = [
             "mxnet_lib/static",
             "mxnet_lib/dynamic", 
-            "python/pypi"
+            "python/pypi",
+            "python/docker"
           ]
           
           // Convert mxnet variants to a list
diff --git a/cd/Jenkinsfile_utils.groovy b/cd/Jenkinsfile_utils.groovy
index 5182b04a3b5b..966f0a218057 100644
--- a/cd/Jenkinsfile_utils.groovy
+++ b/cd/Jenkinsfile_utils.groovy
@@ -160,6 +160,18 @@ def restore_artifact(variant, libtype) {
   }
 }
 
+
+// Restores the statically linked libmxnet for the given variant
+def restore_static_libmxnet(variant) {
+  restore_artifact(variant, 'static')
+}
+
+
+// Restores the dynamically linked libmxnet for the given variant
+def restore_dynamic_libmxnet(variant) {
+  restore_artifact(variant, 'dynamic')
+}
+
 // A generic pipeline that can be used by *most* CD jobs
 // It can be used when implementing the pipeline steps in the Jenkins_steps.groovy
 // script for a particular delivery channel. However, it should also implement the
diff --git a/cd/python/docker/Dockerfile b/cd/python/docker/Dockerfile
new file mode 100644
index 000000000000..dc70da188793
--- /dev/null
+++ b/cd/python/docker/Dockerfile
@@ -0,0 +1,40 @@
+# -*- mode: dockerfile -*-
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+# Python MXNet Dockerfile
+
+# NOTE: Assumes wheel_build directory is the context root when building
+
+ARG BASE_IMAGE
+FROM ${BASE_IMAGE}
+
+ARG PYTHON_CMD=python
+RUN apt-get update && \
+    apt-get install -y wget ${PYTHON_CMD}-dev gcc && \
+    wget https://bootstrap.pypa.io/get-pip.py && \
+    ${PYTHON_CMD} get-pip.py
+
+ARG MXNET_COMMIT_ID
+ENV MXNET_COMMIT_ID=${MXNET_COMMIT_ID}
+
+RUN mkdir -p /mxnet
+COPY dist/*.whl /mxnet/.
+
+WORKDIR /mxnet
+RUN WHEEL_FILE=$(ls -t /mxnet | head -n 1) && pip install ${WHEEL_FILE} && rm -f ${WHEEL_FILE}
+
diff --git a/cd/python/docker/Dockerfile.test b/cd/python/docker/Dockerfile.test
new file mode 100644
index 000000000000..bed059d0fc73
--- /dev/null
+++ b/cd/python/docker/Dockerfile.test
@@ -0,0 +1,39 @@
+# -*- mode: dockerfile -*-
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+# Python MXNet Dockerfile
+
+# NOTE: Assumes 'ci' directory is root of the context when building
+
+ARG BASE_IMAGE
+FROM ${BASE_IMAGE}
+
+# Install test dependencies
+RUN pip install nose
+
+ARG USER_ID=1001
+ARG GROUP_ID=1001
+
+COPY ./docker/install/ubuntu_adduser.sh /work/ubuntu_adduser.sh
+COPY ./docker/install/requirements /work/requirements
+
+RUN mkdir -p /work
+RUN /work/ubuntu_adduser.sh
+RUN pip install -r /work/requirements
+
+WORKDIR /work/mxnet
diff --git a/cd/python/docker/Jenkins_pipeline.groovy b/cd/python/docker/Jenkins_pipeline.groovy
new file mode 100644
index 000000000000..0d4925e00576
--- /dev/null
+++ b/cd/python/docker/Jenkins_pipeline.groovy
@@ -0,0 +1,74 @@
+// -*- mode: groovy -*-
+
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+//
+// Jenkins pipeline
+// See documents at https://jenkins.io/doc/book/pipeline/jenkinsfile/
+
+// NOTE: 
+// ci_utils and cd_utils are loaded by the originating Jenkins job, e.g. jenkins/Jenkinsfile_release_job
+
+def get_pipeline(mxnet_variant) {
+  def node_type = mxnet_variant.startsWith('cu') ? NODE_LINUX_GPU : NODE_LINUX_CPU
+  return cd_utils.generic_pipeline(mxnet_variant, this, node_type)
+}
+
+// Returns the (Docker) environment for the given variant
+// The environment corresponds to the docker files in the 'docker' directory
+def get_environment(mxnet_variant) {
+  if (mxnet_variant.startsWith("cu")) {
+    // Remove 'mkl' suffix from variant to properly format test environment
+    return "ubuntu_gpu_${mxnet_variant.replace('mkl', '')}"
+  }
+  return "ubuntu_cpu"
+}
+
+
+def build(mxnet_variant) {
+  ws("workspace/python_docker/${mxnet_variant}/${env.BUILD_NUMBER}") {
+    ci_utils.init_git()
+    cd_utils.restore_static_libmxnet(mxnet_variant)
+
+    // package wheel file
+    def nvidia_docker = mxnet_variant.startsWith('cu')
+    def environment = get_environment(mxnet_variant)
+    ci_utils.docker_run(environment, "cd_package_pypi ${mxnet_variant}", nvidia_docker)
+
+    // build python docker images
+    sh "./cd/python/docker/python_images.sh build ${mxnet_variant} py3"
+    sh "./cd/python/docker/python_images.sh build ${mxnet_variant} py2"
+  }
+}
+
+def test(mxnet_variant) {
+  ws("workspace/python_docker/${mxnet_variant}/${env.BUILD_NUMBER}") {
+    // test python docker images
+    sh "./cd/python/docker/python_images.sh test ${mxnet_variant} py3"
+    sh "./cd/python/docker/python_images.sh test ${mxnet_variant} py2"
+  }
+}
+
+def push(mxnet_variant) {
+  ws("workspace/python_docker/${mxnet_variant}/${env.BUILD_NUMBER}") {
+    // push python docker images
+    sh "./cd/python/docker/python_images.sh push ${mxnet_variant} py3"
+    sh "./cd/python/docker/python_images.sh push ${mxnet_variant} py2"
+  }
+}
+
+return this
diff --git a/cd/python/docker/python_images.sh b/cd/python/docker/python_images.sh
new file mode 100755
index 000000000000..305676d2c40a
--- /dev/null
+++ b/cd/python/docker/python_images.sh
@@ -0,0 +1,128 @@
+#!/usr/bin/env bash
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# Executes mxnet python images pipeline functions: build, test, publish
+# Assumes script is run from the root of the mxnet repository
+# Assumes script is being run within MXNet CD infrastructure
+
+set -xe
+
+usage="Usage: python_images.sh <build|test|publish> MXNET-VARIANT <py2|py3>"
+
+command=${1:?$usage}
+mxnet_variant=${2:?$usage}
+python_version=${3:?usage}
+
+cd_utils='cd/utils'
+ci_utils='ci/'
+
+case ${python_version} in
+    py3)
+        python_cmd="python3"
+        ;;
+    py2)
+        python_cmd="python"
+        ;;
+    *)
+        echo "Error: specify python version with either 'py2' or 'py3'"
+        exit 1
+        ;;
+esac
+
+docker_tags=($(./${cd_utils}/docker_tag.sh ${mxnet_variant}))
+main_tag="${docker_tags[0]}_${python_version}"
+base_image=$(./${cd_utils}/mxnet_base_image.sh ${mxnet_variant})
+repository="python"
+image_name="${repository}:${main_tag}"
+
+resources_path='cd/python/docker'
+
+if [ ! -z "${RELEASE_DOCKERHUB_REPOSITORY}" ]; then
+    image_name="${RELEASE_DOCKERHUB_REPOSITORY}/${image_name}"
+fi
+
+build() {
+    # NOTE: Ensure the correct context root is passed in when building - Dockerfile expects ./wheel_build
+    docker build -t "${image_name}" --build-arg PYTHON_CMD=${python_cmd} --build-arg BASE_IMAGE="${base_image}" --build-arg MXNET_COMMIT_ID=${GIT_COMMIT} -f ${resources_path}/Dockerfile ./wheel_build
+}
+
+test() {
+    local runtime_param=""
+    if [[ ${mxnet_variant} == cu* ]]; then
+        runtime_param="--runtime=nvidia"
+    fi
+    local test_image_name="${image_name}_test"
+    
+    # Ensure the correct context root is passed in when building - Dockerfile.test expects ci directory
+    docker build -t "${test_image_name}" --build-arg USER_ID=`id -u` --build-arg GROUP_ID=`id -g` --build-arg BASE_IMAGE="${image_name}" -f ${resources_path}/Dockerfile.test ./ci
+    ./ci/safe_docker_run.py ${runtime_param} --cap-add "SYS_PTRACE" -u `id -u`:`id -g` -v `pwd`:/work/mxnet "${test_image_name}" ${resources_path}/test_python_image.sh "${mxnet_variant}" "${python_cmd}"
+}
+
+push() {
+    if [ -z "${RELEASE_DOCKERHUB_REPOSITORY}" ]; then
+        echo "Cannot publish image without RELEASE_DOCKERHUB_REPOSITORY environment variable being set."
+        exit 1
+    fi
+
+    # The secret name env var is set in the Jenkins configuration
+    # Manage Jenkins -> Configure System
+    ./${ci_utils}/docker_login.py --secret-name "${RELEASE_DOCKERHUB_SECRET_NAME}"
+
+    # Push image
+    docker push "${image_name}"
+
+    # Iterate over remaining tags, if any
+    for ((i=1;i<${#docker_tags[@]};i++)); do
+        local docker_tag="${docker_tags[${i}]}"
+        local latest_image_name="${RELEASE_DOCKERHUB_REPOSITORY}/${repository}:${docker_tag}"
+
+        # latest and latest gpu should only be pushed for py3
+        if [[ ${docker_tag} == "latest" || ${docker_tag} == "latest_gpu" ]]; then
+            if [[ ${python_version} == "py2" ]]; then
+                continue
+            fi
+        else
+            latest_image_name="${latest_image_name}_${python_version}"
+        fi
+
+        docker tag "${image_name}" "${latest_image_name}"
+        docker push "${latest_image_name}"
+        echo "Successfully pushed ${latest_image_name}. Pull it with:"
+        echo "docker pull ${latest_image_name}"
+        echo "For a complete list of tags see https://hub.docker.com/u/${RELEASE_DOCKERHUB_REPOSITORY}/${repository}"
+    done    
+}
+
+case ${command} in
+    "build")
+        build
+        ;;
+
+    "test")
+        test
+        ;;
+
+    "push")
+        push
+        ;;
+
+    *)
+        echo $usage
+        exit 1
+esac
diff --git a/cd/python/docker/test_python_image.sh b/cd/python/docker/test_python_image.sh
new file mode 100755
index 000000000000..88e03ea84a12
--- /dev/null
+++ b/cd/python/docker/test_python_image.sh
@@ -0,0 +1,47 @@
+#!/usr/bin/env bash
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# To be run _within_ a runtime image
+# Tests the Runtime docker image
+# Assumes the mxnet source directory is mounted on /mxnet and cwd is /mxnet
+
+set -ex
+
+# Variant parameter should be passed in
+mxnet_variant=${1:?"Missing mxnet variant"}
+python_cmd=${2:?"Missing python version (python or python3)"}
+
+if [ -z "${MXNET_COMMIT_ID}" ]; then
+    echo "MXNET_COMMIT_ID environment variable is empty. Please rebuild the image with MXNET_COMMIT_ID build-arg specified."
+    exit 1
+fi
+
+# Execute tests
+if [[ $mxnet_variant == cu* ]]; then
+    mnist_params="--gpu 0"
+    test_conv_params="--gpu"
+fi
+
+if [[ $mxnet_variant == *mkl ]]; then
+    ${python_cmd} tests/python/mkl/test_mkldnn.py
+fi
+
+${python_cmd} tests/python/train/test_conv.py ${test_conv_params}
+${python_cmd} example/image-classification/train_mnist.py ${mnist_params}
+
diff --git a/cd/python/pypi/Jenkins_pipeline.groovy b/cd/python/pypi/Jenkins_pipeline.groovy
index bf8103270146..e9f172a570fe 100644
--- a/cd/python/pypi/Jenkins_pipeline.groovy
+++ b/cd/python/pypi/Jenkins_pipeline.groovy
@@ -45,7 +45,7 @@ def get_environment(mxnet_variant) {
 def build(mxnet_variant) {
   ws("workspace/python_pypi/${mxnet_variant}/${env.BUILD_NUMBER}") {
     ci_utils.init_git()
-    cd_utils.restore_artifact(mxnet_variant, 'static')
+    cd_utils.restore_static_libmxnet(mxnet_variant)
     
     // create wheel file
     def environment = get_environment(mxnet_variant)
diff --git a/cd/utils/docker_tag.sh b/cd/utils/docker_tag.sh
new file mode 100755
index 000000000000..d16da4930774
--- /dev/null
+++ b/cd/utils/docker_tag.sh
@@ -0,0 +1,59 @@
+#!/usr/bin/env bash
+# -*- coding: utf-8 -*-
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+
+mxnet_variant=${1:?"Please specify the mxnet variant as the first parameter"}
+is_release=${RELEASE_BUILD:-false}
+version=${VERSION:-nightly}
+
+# The docker tags will be in the form <version>_<hardware>(_mkl)
+# Eg. nightly_cpu, 1.4.0_cpu_mkl, nightly_gpu_cu80_mkl, etc.
+
+if [[ ${mxnet_variant} == "cpu" ]]; then
+    tag_suffix="cpu"
+elif [[ ${mxnet_variant} == "mkl" ]]; then
+    tag_suffix="cpu_mkl"
+elif [[ ${mxnet_variant} == cu* ]]; then
+    tag_suffix="gpu_${mxnet_variant}"
+
+    # *mkl => *_mkl
+    if [[ $tag_suffix == *mkl ]]; then
+        tag_suffix="${tag_suffix:0:${#tag_suffix}-3}_mkl"
+    fi
+else
+    echo "Error: Unrecognized mxnet variant: '${mxnet_variant}'."
+    exit 1
+fi
+
+echo "${version}_${tag_suffix}"
+
+# Print out latest tags as well
+if [[ ${is_release} == "true" ]]; then
+    if [[ ${mxnet_variant} == "cpu" ]]; then
+        echo "latest"
+        echo "latest_cpu"
+    elif [[ ${mxnet_variant} == "mkl" ]]; then
+        echo "latest_cpu_mkl"
+    elif [[ ${mxnet_variant} == "cu90" ]]; then
+        echo "latest_gpu"
+    elif [[ ${mxnet_variant} == "cu90mkl" ]]; then
+        echo "latest_gpu_mkl"
+    fi
+fi
diff --git a/cd/utils/mxnet_base_image.sh b/cd/utils/mxnet_base_image.sh
new file mode 100755
index 000000000000..dcfe7216dcb4
--- /dev/null
+++ b/cd/utils/mxnet_base_image.sh
@@ -0,0 +1,49 @@
+#!/usr/bin/env bash
+# -*- coding: utf-8 -*-
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+mxnet_variant=${1:?"Please specify the mxnet variant as the first parameter"}
+
+case ${mxnet_variant} in 
+    cu80*)
+    echo "nvidia/cuda:8.0-cudnn7-runtime-ubuntu16.04"
+    ;;
+    cu90*)
+    echo "nvidia/cuda:9.0-cudnn7-runtime-ubuntu16.04"
+    ;;
+    cu92*)
+    echo "nvidia/cuda:9.2-cudnn7-runtime-ubuntu16.04"
+    ;;
+    cu100*)
+    echo "nvidia/cuda:10.0-cudnn7-runtime-ubuntu16.04"
+    ;;
+    cu101*)
+    echo "nvidia/cuda:10.1-cudnn7-runtime-ubuntu16.04"
+    ;;
+    cpu)
+    echo "ubuntu:16.04"
+    ;;
+    mkl)
+    echo "ubuntu:16.04"
+    ;;
+    *)
+    echo "Error: Unrecognized mxnet-variant: '${mxnet_variant}'"
+    exit 1
+    ;;
+esac
diff --git a/ci/build.py b/ci/build.py
index e6a183fefa34..8798c7ed2ef2 100755
--- a/ci/build.py
+++ b/ci/build.py
@@ -27,63 +27,17 @@
 
 import argparse
 import glob
-import logging
-import os
+import pprint
 import re
 import shutil
+import signal
 import subprocess
-import sys
-import tempfile
 from itertools import chain
 from subprocess import check_call, check_output
 from typing import *
-from util import *
-import docker
-import docker.models
-import docker.errors
-import signal
-import atexit
-import pprint
-
-
-class Cleanup:
-    """A class to cleanup containers"""
-    def __init__(self):
-        self.containers = set()
-        self.docker_stop_timeout = 3
 
-    def add_container(self, container: docker.models.containers.Container):
-        assert isinstance(container, docker.models.containers.Container)
-        self.containers.add(container)
-
-    def remove_container(self, container: docker.models.containers.Container):
-        assert isinstance(container, docker.models.containers.Container)
-        self.containers.remove(container)
-
-    def _cleanup_containers(self):
-        if self.containers:
-            logging.warning("Cleaning up containers")
-        else:
-            return
-        # noinspection PyBroadException
-        try:
-            stop_timeout = int(os.environ.get("DOCKER_STOP_TIMEOUT", self.docker_stop_timeout))
-        except Exception:
-            stop_timeout = 3
-        for container in self.containers:
-            try:
-                container.stop(timeout=stop_timeout)
-                logging.info("☠: stopped container %s", trim_container_id(container.id))
-                container.remove()
-                logging.info("🚽: removed container %s", trim_container_id(container.id))
-            except Exception as e:
-                logging.exception(e)
-        self.containers.clear()
-        logging.info("Cleaning up containers finished.")
-
-    def __call__(self):
-        """Perform cleanup"""
-        self._cleanup_containers()
+from safe_docker_run import SafeDockerClient
+from util import *
 
 
 def get_dockerfiles_path():
@@ -205,18 +159,13 @@ def default_ccache_dir() -> str:
     return os.path.join(os.path.expanduser("~"), ".ccache")
 
 
-def trim_container_id(cid):
-    """:return: trimmed container id"""
-    return cid[:12]
-
-
-def container_run(platform: str,
+def container_run(docker_client: SafeDockerClient,
+                  platform: str,
                   nvidia_runtime: bool,
                   docker_registry: str,
                   shared_memory_size: str,
                   local_ccache_dir: str,
                   command: List[str],
-                  cleanup: Cleanup,
                   environment: Dict[str, str],
                   dry_run: bool = False) -> int:
     """Run command in a container"""
@@ -232,13 +181,6 @@ def container_run(platform: str,
         'CCACHE_LOGFILE': '/tmp/ccache.log',  # a container-scoped log, useful for ccache
                                               # verification.
     })
-    # These variables are passed to the container to the process tree killer can find runaway
-    # process inside the container
-    # https://wiki.jenkins.io/display/JENKINS/ProcessTreeKiller
-    # https://github.com/jenkinsci/jenkins/blob/578d6bacb33a5e99f149de504c80275796f0b231/core/src/main/java/hudson/model/Run.java#L2393
-    #
-    jenkins_env_vars = ['BUILD_NUMBER', 'BUILD_ID', 'BUILD_TAG']
-    environment.update({k: os.environ[k] for k in jenkins_env_vars if k in os.environ})
     environment.update({k: os.environ[k] for k in ['CCACHE_MAXSIZE'] if k in os.environ})
 
     tag = get_docker_tag(platform=platform, registry=docker_registry)
@@ -248,7 +190,7 @@ def container_run(platform: str,
     os.makedirs(local_build_folder, exist_ok=True)
     os.makedirs(local_ccache_dir, exist_ok=True)
     logging.info("Using ccache directory: %s", local_ccache_dir)
-    docker_client = docker.from_env()
+
     # Equivalent command
     docker_cmd_list = [
         get_docker_binary(nvidia_runtime),
@@ -276,8 +218,7 @@ def container_run(platform: str,
     docker_cmd = ' \\\n\t'.join(docker_cmd_list)
     logging.info("Running %s in container %s", command, tag)
     logging.info("Executing the equivalent of:\n%s\n", docker_cmd)
-    # return code of the command inside docker
-    ret = 0
+
     if not dry_run:
         #############################
         #
@@ -288,10 +229,10 @@ def container_run(platform: str,
             # noinspection PyShadowingNames
             # runc is default (docker info | grep -i runtime)
             runtime = 'nvidia'
-        container = docker_client.containers.run(
+
+        return docker_client.run(
             tag,
             runtime=runtime,
-            detach=True,
             command=command,
             shm_size=shared_memory_size,
             user='{}:{}'.format(os.getuid(), os.getgid()),
@@ -305,61 +246,7 @@ def container_run(platform: str,
                     {'bind': '/work/ccache', 'mode': 'rw'},
             },
             environment=environment)
-        try:
-            logging.info("Started container: %s", trim_container_id(container.id))
-            # Race condition:
-            # If the previous call is interrupted then it's possible that the container is not cleaned up
-            # We avoid by masking the signals temporarily
-            cleanup.add_container(container)
-            signal.pthread_sigmask(signal.SIG_UNBLOCK, {signal.SIGINT, signal.SIGTERM})
-            #
-            #############################
-
-            stream = container.logs(stream=True, stdout=True, stderr=True)
-            sys.stdout.flush()
-            for chunk in stream:
-                sys.stdout.buffer.write(chunk)
-                sys.stdout.buffer.flush()
-            sys.stdout.flush()
-            stream.close()
-            try:
-                logging.info("Waiting for status of container %s for %d s.",
-                            trim_container_id(container.id),
-                            container_wait_s)
-                wait_result = container.wait(timeout=container_wait_s)
-                logging.info("Container exit status: %s", wait_result)
-                ret = wait_result.get('StatusCode', 200)
-                if ret != 0:
-                    logging.error("Container exited with an error 😞")
-                    logging.info("Executed command for reproduction:\n\n%s\n", " ".join(sys.argv))
-                else:
-                    logging.info("Container exited with success 👍")
-            except Exception as e:
-                logging.exception(e)
-                ret = 150
-
-            # Stop
-            try:
-                logging.info("Stopping container: %s", trim_container_id(container.id))
-                container.stop()
-            except Exception as e:
-                logging.exception(e)
-                ret = 151
-
-            # Remove
-            try:
-                logging.info("Removing container: %s", trim_container_id(container.id))
-                container.remove()
-            except Exception as e:
-                logging.exception(e)
-                ret = 152
-            cleanup.remove_container(container)
-            containers = docker_client.containers.list()
-            if containers:
-                logging.info("Other running containers: %s", [trim_container_id(x.id) for x in containers])
-        except docker.errors.NotFound as e:
-            logging.info("Container was stopped before cleanup started: %s", e)
-    return ret
+    return 0
 
 
 def list_platforms() -> str:
@@ -388,17 +275,6 @@ def log_environment():
     logging.debug("Build environment: %s", pp.pformat(dict(os.environ)))
 
 
-def script_name() -> str:
-    """:returns: script name with leading paths removed"""
-    return os.path.split(sys.argv[0])[1]
-
-def config_logging():
-    import time
-    logging.getLogger().setLevel(logging.INFO)
-    logging.getLogger("requests").setLevel(logging.WARNING)
-    logging.basicConfig(format='{}: %(asctime)sZ %(levelname)s %(message)s'.format(script_name()))
-    logging.Formatter.converter = time.gmtime
-
 def main() -> int:
     config_logging()
 
@@ -471,20 +347,7 @@ def main() -> int:
 
     command = list(chain(*args.command))
     docker_binary = get_docker_binary(args.nvidiadocker)
-
-    # Cleanup on signals and exit
-    cleanup = Cleanup()
-
-    def signal_handler(signum, _):
-        signal.pthread_sigmask(signal.SIG_BLOCK, {signum})
-        logging.warning("Signal %d received, cleaning up...", signum)
-        cleanup()
-        logging.warning("done. Exiting with error.")
-        sys.exit(1)
-
-    atexit.register(cleanup)
-    signal.signal(signal.SIGTERM, signal_handler)
-    signal.signal(signal.SIGINT, signal_handler)
+    docker_client = SafeDockerClient()
 
     environment = dict([(e.split('=')[:2] if '=' in e else (e, os.environ[e]))
                         for e in args.environment])
@@ -498,7 +361,7 @@ def signal_handler(signum, _):
             load_docker_cache(tag=tag, docker_registry=args.docker_registry)
         if not args.run_only:
             build_docker(platform=platform, docker_binary=docker_binary, registry=args.docker_registry,
-                     num_retries=args.docker_build_retries, no_cache=args.no_cache)
+                         num_retries=args.docker_build_retries, no_cache=args.no_cache)
         else:
             logging.info("Skipping docker build step.")
 
@@ -510,23 +373,23 @@ def signal_handler(signum, _):
         ret = 0
         if command:
             ret = container_run(
-                platform=platform, nvidia_runtime=args.nvidiadocker,
+                docker_client=docker_client, platform=platform, nvidia_runtime=args.nvidiadocker,
                 shared_memory_size=args.shared_memory_size, command=command, docker_registry=args.docker_registry,
-                local_ccache_dir=args.ccache_dir, cleanup=cleanup, environment=environment)
+                local_ccache_dir=args.ccache_dir, environment=environment)
         elif args.print_docker_run:
             command = []
             ret = container_run(
-                platform=platform, nvidia_runtime=args.nvidiadocker,
+                docker_client=docker_client, platform=platform, nvidia_runtime=args.nvidiadocker,
                 shared_memory_size=args.shared_memory_size, command=command, docker_registry=args.docker_registry,
-                local_ccache_dir=args.ccache_dir, dry_run=True, cleanup=cleanup, environment=environment)
+                local_ccache_dir=args.ccache_dir, dry_run=True, environment=environment)
         else:
             # With no commands, execute a build function for the target platform
             command = ["/work/mxnet/ci/docker/runtime_functions.sh", "build_{}".format(platform)]
             logging.info("No command specified, trying default build: %s", ' '.join(command))
             ret = container_run(
-                platform=platform, nvidia_runtime=args.nvidiadocker,
+                docker_client=docker_client, platform=platform, nvidia_runtime=args.nvidiadocker,
                 shared_memory_size=args.shared_memory_size, command=command, docker_registry=args.docker_registry,
-                local_ccache_dir=args.ccache_dir, cleanup=cleanup, environment=environment)
+                local_ccache_dir=args.ccache_dir, environment=environment)
 
         if ret != 0:
             logging.critical("Execution of %s failed with status: %d", command, ret)
@@ -553,9 +416,9 @@ def signal_handler(signum, _):
                 continue
             command = ["/work/mxnet/ci/docker/runtime_functions.sh", build_platform]
             container_run(
-                platform=platform, nvidia_runtime=args.nvidiadocker,
+                docker_client=docker_client, platform=platform, nvidia_runtime=args.nvidiadocker,
                 shared_memory_size=args.shared_memory_size, command=command, docker_registry=args.docker_registry,
-                local_ccache_dir=args.ccache_dir, cleanup=cleanup, environment=environment)
+                local_ccache_dir=args.ccache_dir, environment=environment)
             shutil.move(buildir(), plat_buildir)
             logging.info("Built files left in: %s", plat_buildir)
 
diff --git a/ci/docker_cache.py b/ci/docker_cache.py
index 3a2a1fb415ee..254d6237d6e2 100755
--- a/ci/docker_cache.py
+++ b/ci/docker_cache.py
@@ -24,21 +24,21 @@
 state as if the container would have been built locally already.
 """
 
-import os
-import logging
 import argparse
-import sys
+import logging
+import os
 import subprocess
-import json
+import sys
 from typing import *
+
 import build as build_util
+from docker_login import login_dockerhub, logout_dockerhub
 from util import retry
 
-DOCKERHUB_LOGIN_NUM_RETRIES = 5
-DOCKERHUB_RETRY_SECONDS = 5
 DOCKER_CACHE_NUM_RETRIES = 3
 DOCKER_CACHE_TIMEOUT_MINS = 45
 PARALLEL_BUILDS = 10
+DOCKER_CACHE_RETRY_SECONDS = 5
 
 
 def build_save_containers(platforms, registry, load_cache) -> int:
@@ -111,41 +111,8 @@ def _upload_image(registry, docker_tag, image_id) -> None:
     subprocess.check_call(push_cmd)
 
 
-@retry(target_exception=subprocess.CalledProcessError, tries=DOCKERHUB_LOGIN_NUM_RETRIES,
-       delay_s=DOCKERHUB_RETRY_SECONDS)
-def _login_dockerhub():
-    """
-    Login to the Docker Hub account
-    :return: None
-    """
-    dockerhub_credentials = _get_dockerhub_credentials()
-
-    logging.info('Logging in to DockerHub')
-    # We use password-stdin instead of --password to avoid leaking passwords in case of an error.
-    # This method will produce the following output:
-    # > WARNING! Your password will be stored unencrypted in /home/jenkins_slave/.docker/config.json.
-    # > Configure a credential helper to remove this warning. See
-    # > https://docs.docker.com/engine/reference/commandline/login/#credentials-store
-    # Since we consider the restricted slaves a secure environment, that's fine. Also, using this will require
-    # third party applications which would need a review first as well.
-    p = subprocess.run(['docker', 'login', '--username', dockerhub_credentials['username'], '--password-stdin'],
-                       stdout=subprocess.PIPE, input=str.encode(dockerhub_credentials['password']))
-    logging.info(p.stdout)
-    logging.info('Successfully logged in to DockerHub')
-
-
-def _logout_dockerhub():
-    """
-    Log out of DockerHub to delete local credentials
-    :return: None
-    """
-    logging.info('Logging out of DockerHub')
-    subprocess.call(['docker', 'logout'])
-    logging.info('Successfully logged out of DockerHub')
-
-
 @retry(target_exception=subprocess.TimeoutExpired, tries=DOCKER_CACHE_NUM_RETRIES,
-       delay_s=DOCKERHUB_RETRY_SECONDS)
+       delay_s=DOCKER_CACHE_RETRY_SECONDS)
 def load_docker_cache(registry, docker_tag) -> None:
     """
     Load the precompiled docker cache from the registry
@@ -187,37 +154,6 @@ def delete_local_docker_cache(docker_tag):
         logging.debug('Error during local cache deletion %s', error)
 
 
-def _get_dockerhub_credentials():  # pragma: no cover
-    import boto3
-    import botocore
-    secret_name = os.environ['DOCKERHUB_SECRET_NAME']
-    endpoint_url = os.environ['DOCKERHUB_SECRET_ENDPOINT_URL']
-    region_name = os.environ['DOCKERHUB_SECRET_ENDPOINT_REGION']
-
-    session = boto3.Session()
-    client = session.client(
-        service_name='secretsmanager',
-        region_name=region_name,
-        endpoint_url=endpoint_url
-    )
-    try:
-        get_secret_value_response = client.get_secret_value(
-            SecretId=secret_name
-        )
-    except botocore.exceptions.ClientError as client_error:
-        if client_error.response['Error']['Code'] == 'ResourceNotFoundException':
-            logging.exception("The requested secret %s was not found", secret_name)
-        elif client_error.response['Error']['Code'] == 'InvalidRequestException':
-            logging.exception("The request was invalid due to:")
-        elif client_error.response['Error']['Code'] == 'InvalidParameterException':
-            logging.exception("The request had invalid params:")
-        raise
-    else:
-        secret = get_secret_value_response['SecretString']
-        secret_dict = json.loads(secret)
-        return secret_dict
-
-
 def main() -> int:
     """
     Utility to create and publish the Docker cache to Docker Hub
@@ -248,11 +184,16 @@ def script_name() -> str:
     args = parser.parse_args()
 
     platforms = build_util.get_platforms()
+
+    secret_name = os.environ['DOCKERHUB_SECRET_NAME']
+    endpoint_url = os.environ['DOCKERHUB_SECRET_ENDPOINT_URL']
+    region_name = os.environ['DOCKERHUB_SECRET_ENDPOINT_REGION']
+
     try:
-        _login_dockerhub()
+        login_dockerhub(secret_name, endpoint_url, region_name)
         return build_save_containers(platforms=platforms, registry=args.docker_registry, load_cache=True)
     finally:
-        _logout_dockerhub()
+        logout_dockerhub()
 
 
 if __name__ == '__main__':
diff --git a/ci/docker_login.py b/ci/docker_login.py
new file mode 100755
index 000000000000..b3b4d46e17ce
--- /dev/null
+++ b/ci/docker_login.py
@@ -0,0 +1,137 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import argparse
+import json
+import logging
+import os
+import subprocess
+import sys
+
+from util import retry, config_logging
+
+DOCKERHUB_LOGIN_NUM_RETRIES = 5
+DOCKERHUB_RETRY_SECONDS = 5
+
+
+def _get_dockerhub_credentials(secret_name: str, secret_endpoint_url: str, secret_endpoint_region_name: str):
+    import boto3
+    import botocore
+
+    session = boto3.Session()
+    client = session.client(
+        service_name='secretsmanager',
+        region_name=secret_endpoint_region_name,
+        endpoint_url=secret_endpoint_url
+    )
+    try:
+        get_secret_value_response = client.get_secret_value(
+            SecretId=secret_name
+        )
+    except botocore.exceptions.ClientError as client_error:
+        if client_error.response['Error']['Code'] == 'ResourceNotFoundException':
+            logging.exception("The requested secret %s was not found", secret_name)
+        elif client_error.response['Error']['Code'] == 'InvalidRequestException':
+            logging.exception("The request was invalid due to:")
+        elif client_error.response['Error']['Code'] == 'InvalidParameterException':
+            logging.exception("The request had invalid params:")
+        raise
+    else:
+        secret = get_secret_value_response['SecretString']
+        secret_dict = json.loads(secret)
+        return secret_dict
+
+
+@retry(target_exception=subprocess.CalledProcessError, tries=DOCKERHUB_LOGIN_NUM_RETRIES,
+       delay_s=DOCKERHUB_RETRY_SECONDS)
+def login_dockerhub(secret_name: str, secret_endpoint_url: str, secret_endpoint_region_name: str):
+    """
+    Login to the Docker Hub account
+    :return: None
+    """
+    dockerhub_credentials = _get_dockerhub_credentials(secret_name, secret_endpoint_url, secret_endpoint_region_name)
+
+    logging.info('Logging in to DockerHub')
+    # We use password-stdin instead of --password to avoid leaking passwords in case of an error.
+    # This method will produce the following output:
+    # > WARNING! Your password will be stored unencrypted in /home/jenkins_slave/.docker/config.json.
+    # > Configure a credential helper to remove this warning. See
+    # > https://docs.docker.com/engine/reference/commandline/login/#credentials-store
+    # Since we consider the restricted slaves a secure environment, that's fine. Also, using this will require
+    # third party applications which would need a review first as well.
+    p = subprocess.run(['docker', 'login', '--username', dockerhub_credentials['username'], '--password-stdin'],
+                       stdout=subprocess.PIPE, input=str.encode(dockerhub_credentials['password']))
+    logging.info(p.stdout)
+    if p.returncode == 0:
+        logging.info('Successfully logged in to DockerHub')
+        return
+
+    raise RuntimeError("Failed to login to DockerHub")
+
+
+def logout_dockerhub():
+    """
+    Log out of DockerHub to delete local credentials
+    :return: None
+    """
+    logging.info('Logging out of DockerHub')
+    subprocess.call(['docker', 'logout'])
+    logging.info('Successfully logged out of DockerHub')
+
+
+def main(command_line_arguments):
+    config_logging()
+
+    parser = argparse.ArgumentParser(
+        description="Safe docker login utility to avoid leaking passwords",
+        epilog=""
+    )
+    parser.add_argument("--secret-name",
+                        help="Secret name",
+                        type=str,
+                        required=True)
+
+    parser.add_argument("--secret-endpoint-url",
+                        help="Endpoint Url",
+                        type=str,
+                        default=os.environ.get("DOCKERHUB_SECRET_ENDPOINT_URL", None))
+
+    parser.add_argument("--secret-endpoint-region",
+                        help="AWS Region",
+                        type=str,
+                        default=os.environ.get("DOCKERHUB_SECRET_ENDPOINT_REGION", None))
+
+    args = parser.parse_args(args=command_line_arguments)
+
+    if args.secret_endpoint_url is None:
+        raise RuntimeError("Could not determine secret-endpoint-url, please specify with --secret-endpoint-url")
+
+    if args.secret_endpoint_region is None:
+        raise RuntimeError("Could not determine secret-endpoint-region, please specify with --secret-endpoint-region")
+
+    try:
+        login_dockerhub(args.secret_name, args.secret_endpoint_url, args.secret_endpoint_region)
+    except Exception as err:
+        logging.exception(err)
+        exit(1)
+
+
+if __name__ == '__main__':
+    main(sys.argv[1:])
diff --git a/ci/logging.conf b/ci/logging.conf
new file mode 100644
index 000000000000..d80365e27bf1
--- /dev/null
+++ b/ci/logging.conf
@@ -0,0 +1,41 @@
+# -*- coding: utf-8 -*-
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+[loggers]
+keys=root
+
+[handlers]
+keys=consoleHandler
+
+[formatters]
+keys=simpleFormatter
+
+[logger_root]
+level=DEBUG
+handlers=consoleHandler
+
+[handler_consoleHandler]
+class=StreamHandler
+level=DEBUG
+formatter=simpleFormatter
+args=(sys.stdout,)
+
+[formatter_simpleFormatter]
+format=%(asctime)s - %(name)s - %(levelname)s - %(message)s
+datefmt=
\ No newline at end of file
diff --git a/ci/safe_docker_run.py b/ci/safe_docker_run.py
new file mode 100755
index 000000000000..e3b55bccdff8
--- /dev/null
+++ b/ci/safe_docker_run.py
@@ -0,0 +1,247 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""
+Docker command wrapper to guard against Zombie containers
+"""
+
+import argparse
+import atexit
+import logging
+import os
+import signal
+import sys
+from functools import reduce
+from itertools import chain
+from typing import Dict, Any
+
+import docker
+from docker.errors import NotFound
+from docker.models.containers import Container
+
+from util import config_logging
+
+DOCKER_STOP_TIMEOUT_SECONDS = 3
+CONTAINER_WAIT_SECONDS = 600
+
+
+class SafeDockerClient:
+    """
+    A wrapper around the docker client to ensure that no zombie containers are left hanging around
+    in case the script is not allowed to finish normally
+    """
+
+    @staticmethod
+    def _trim_container_id(cid):
+        """:return: trimmed container id"""
+        return cid[:12]
+
+    def __init__(self):
+        self._docker_client = docker.from_env()
+        self._containers = set()
+        self._docker_stop_timeout = DOCKER_STOP_TIMEOUT_SECONDS
+        self._container_wait_seconds = CONTAINER_WAIT_SECONDS
+
+        def signal_handler(signum, _):
+            signal.pthread_sigmask(signal.SIG_BLOCK, {signum})
+            logging.warning("Signal %d received, cleaning up...", signum)
+            self._clean_up()
+            logging.warning("done. Exiting with error.")
+            sys.exit(1)
+
+        atexit.register(self._clean_up)
+        signal.signal(signal.SIGTERM, signal_handler)
+        signal.signal(signal.SIGINT, signal_handler)
+
+    def _clean_up(self):
+        if self._containers:
+            logging.warning("Cleaning up containers")
+        else:
+            return
+        # noinspection PyBroadException
+        try:
+            stop_timeout = int(os.environ.get("DOCKER_STOP_TIMEOUT", self._docker_stop_timeout))
+        except Exception:
+            stop_timeout = 3
+        for container in self._containers:
+            try:
+                container.stop(timeout=stop_timeout)
+                logging.info("☠: stopped container %s", self._trim_container_id(container.id))
+                container.remove()
+                logging.info("🚽: removed container %s", self._trim_container_id(container.id))
+            except Exception as e:
+                logging.exception(e)
+        self._containers.clear()
+        logging.info("Cleaning up containers finished.")
+
+    def _add_container(self, container: Container) -> Container:
+        self._containers.add(container)
+        return container
+
+    def _remove_container(self, container: Container):
+        self._containers.remove(container)
+
+    def run(self, *args, **kwargs) -> int:
+        if "detach" in kwargs and kwargs.get("detach") is False:
+            raise ValueError("Can only safe run with 'detach' set to True")
+        else:
+            kwargs["detach"] = True
+
+        # These variables are passed to the container so the process tree killer can find runaway
+        # process inside the container
+        # https://wiki.jenkins.io/display/JENKINS/ProcessTreeKiller
+        # https://github.com/jenkinsci/jenkins/blob/578d6bacb33a5e99f149de504c80275796f0b231/core/src/main/java/hudson/model/Run.java#L2393
+        if "environment" not in kwargs:
+            kwargs["environment"] = {}
+
+        jenkins_env_vars = ["BUILD_NUMBER", "BUILD_ID", "BUILD_TAG"]
+        kwargs["environment"].update({k: os.environ[k] for k in jenkins_env_vars if k in os.environ})
+
+        ret = 0
+        try:
+            # Race condition:
+            # If the call to docker_client.containers.run is interrupted, it is possible that
+            # the container won't be cleaned up. We avoid this by temporarily masking the signals.
+            signal.pthread_sigmask(signal.SIG_BLOCK, {signal.SIGINT, signal.SIGTERM})
+            container = self._add_container(self._docker_client.containers.run(*args, **kwargs))
+            signal.pthread_sigmask(signal.SIG_UNBLOCK, {signal.SIGINT, signal.SIGTERM})
+            logging.info("Started container: %s", self._trim_container_id(container.id))
+            stream = container.logs(stream=True, stdout=True, stderr=True)
+            sys.stdout.flush()
+            for chunk in stream:
+                sys.stdout.buffer.write(chunk)
+                sys.stdout.buffer.flush()
+            sys.stdout.flush()
+            stream.close()
+
+            try:
+                logging.info("Waiting for status of container %s for %d s.",
+                             self._trim_container_id(container.id),
+                             self._container_wait_seconds)
+                wait_result = container.wait(timeout=self._container_wait_seconds)
+                logging.info("Container exit status: %s", wait_result)
+                ret = wait_result.get('StatusCode', 200)
+                if ret != 0:
+                    logging.error("Container exited with an error 😞")
+                    logging.info("Executed command for reproduction:\n\n%s\n", " ".join(sys.argv))
+                else:
+                    logging.info("Container exited with success 👍")
+            except Exception as err:
+                logging.exception(err)
+                return 150
+
+            try:
+                logging.info("Stopping container: %s", self._trim_container_id(container.id))
+                container.stop()
+            except Exception as e:
+                logging.exception(e)
+                ret = 151
+
+            try:
+                logging.info("Removing container: %s", self._trim_container_id(container.id))
+                container.remove()
+            except Exception as e:
+                logging.exception(e)
+                ret = 152
+            self._remove_container(container)
+            containers = self._docker_client.containers.list()
+            if containers:
+                logging.info("Other running containers: %s", [self._trim_container_id(x.id) for x in containers])
+        except NotFound as e:
+            logging.info("Container was stopped before cleanup started: %s", e)
+
+        return ret
+
+
+def _volume_mount(volume_dfn: str) -> Dict[str, Any]:
+    """
+    Converts docker volume mount format, e.g. docker run --volume /local/path:/container/path:ro
+    to an object understood by the python docker library, e.g. {"local/path": {"bind": "/container/path", "mode": "ro"}}
+    This is used by the argparser for automatic conversion and input validation.
+    If the mode is not specified, 'rw' is assumed.
+    :param volume_dfn: A string to convert to a volume mount object in the format <local path>:<container path>[:ro|rw]
+    :return: An object in the form {"<local path>" : {"bind": "<container path>", "mode": "rw|ro"}}
+    """
+    if volume_dfn is None:
+        raise argparse.ArgumentTypeError("Missing value for volume definition")
+
+    parts = volume_dfn.split(":")
+
+    if len(parts) < 2 or len(parts) > 3:
+        raise argparse.ArgumentTypeError("Invalid volume definition {}".format(volume_dfn))
+
+    mode = "rw"
+    if len(parts) == 3:
+        mode = parts[2]
+
+    if mode not in ["rw", "ro"]:
+        raise argparse.ArgumentTypeError("Invalid volume mount mode {} in volume definition {}".format(mode, volume_dfn))
+
+    return {parts[0]: {"bind": parts[1], "mode": mode}}
+
+
+def main(command_line_arguments):
+    config_logging()
+
+    parser = argparse.ArgumentParser(
+        description="""Wrapper around docker run that protects against Zombie containers""", epilog="")
+
+    parser.add_argument("-u", "--user",
+                        help="Username or UID (format: <name|uid>[:<group|gid>])",
+                        default=None)
+
+    parser.add_argument("-v", "--volume",
+                        action='append',
+                        type=_volume_mount,
+                        help="Bind mount a volume",
+                        default=[])
+
+    parser.add_argument("--cap-add",
+                        help="Add Linux capabilities",
+                        action="append",
+                        type=str,
+                        default=[])
+
+    parser.add_argument("--runtime",
+                        help="Runtime to use for this container",
+                        default=None)
+
+    parser.add_argument("--name",
+                        help="Assign a name to the container",
+                        default=None)
+
+    parser.add_argument("image", metavar="IMAGE")
+    parser.add_argument("command", metavar="COMMAND")
+    parser.add_argument("args", nargs='*', metavar="ARG")
+
+    args = parser.parse_args(args=command_line_arguments)
+    docker_client = SafeDockerClient()
+    return docker_client.run(args.image, **{
+        "command": " ".join(list(chain([args.command] + args.args))),
+        "user": args.user,
+        "runtime": args.runtime,
+        "name": args.name,
+        "volumes": reduce(lambda dct, v: {**dct, **v}, args.volume, {}),
+        "cap_add": args.cap_add
+    })
+
+
+if __name__ == "__main__":
+    exit(main(sys.argv[1:]))
diff --git a/ci/test_docker_cache.py b/ci/test_docker_cache.py
index 0a3bc4640c05..aeb399ff6b45 100644
--- a/ci/test_docker_cache.py
+++ b/ci/test_docker_cache.py
@@ -88,7 +88,7 @@ def setUp(self):
         base = os.path.split(os.path.realpath(__file__))[0]
         os.chdir(base)
 
-        docker_cache._login_dockerhub = MagicMock()  # Override login
+        docker_cache.login_dockerhub = MagicMock()  # Override login
 
         # Stop in case previous execution was dirty
         try:
@@ -135,7 +135,7 @@ def test_full_cache(self):
                 """
         platform = 'test_full_cache'
         docker_tag = build_util.get_docker_tag(platform=platform, registry=DOCKER_REGISTRY_PATH)
-        dockerfile_path = os.path.join(DOCKERFILE_DIR, 'Dockerfile.' + platform)
+        dockerfile_path = os.path.join(DOCKERFILE_DIR, 'Dockerfile.build.' + platform)
         try:
             with open(dockerfile_path, 'w') as dockerfile_handle:
                 dockerfile_handle.write(dockerfile_content)
@@ -144,13 +144,25 @@ def test_full_cache(self):
             docker_cache.delete_local_docker_cache(docker_tag=docker_tag)
 
             def warm_up_lambda_func():
-                build_util.build_docker(docker_binary='docker', platform=platform, registry=DOCKER_REGISTRY_PATH)
+                build_util.build_docker(
+                    docker_binary='docker',
+                    platform=platform,
+                    registry=DOCKER_REGISTRY_PATH,
+                    num_retries=3,
+                    no_cache=False
+                )
             _assert_docker_build(lambda_func=warm_up_lambda_func, expected_cache_hit_count=0,
                                  expected_cache_miss_count=4)
 
             # Assert local cache is properly primed
             def primed_cache_lambda_func():
-                build_util.build_docker(docker_binary='docker', platform=platform, registry=DOCKER_REGISTRY_PATH)
+                build_util.build_docker(
+                    docker_binary='docker',
+                    platform=platform,
+                    registry=DOCKER_REGISTRY_PATH,
+                    num_retries=3,
+                    no_cache=False
+                )
             _assert_docker_build(lambda_func=primed_cache_lambda_func, expected_cache_hit_count=4,
                                  expected_cache_miss_count=0)
 
@@ -169,8 +181,6 @@ def clean_cache_lambda_func():
             os.remove(dockerfile_path)
             docker_cache.delete_local_docker_cache(docker_tag=docker_tag)
 
-
-
     def test_partial_cache(self):
         """
         Test whether it's possible to restore cache and then pit it up partially by using a Dockerfile which shares
@@ -196,7 +206,7 @@ def test_partial_cache(self):
                 """
         platform = 'test_partial_cache'
         docker_tag = build_util.get_docker_tag(platform=platform, registry=DOCKER_REGISTRY_PATH)
-        dockerfile_path = os.path.join(DOCKERFILE_DIR, 'Dockerfile.' + platform)
+        dockerfile_path = os.path.join(DOCKERFILE_DIR, 'Dockerfile.build.' + platform)
         try:
             # Write initial Dockerfile
             with open(dockerfile_path, 'w') as dockerfile_handle:
@@ -206,13 +216,25 @@ def test_partial_cache(self):
             docker_cache.delete_local_docker_cache(docker_tag=docker_tag)
 
             def warm_up_lambda_func():
-                build_util.build_docker(docker_binary='docker', platform=platform, registry=DOCKER_REGISTRY_PATH)
+                build_util.build_docker(
+                    docker_binary='docker',
+                    platform=platform,
+                    registry=DOCKER_REGISTRY_PATH,
+                    num_retries=3,
+                    no_cache=False
+                )
             _assert_docker_build(lambda_func=warm_up_lambda_func, expected_cache_hit_count=0,
                                  expected_cache_miss_count=4)
 
             # Assert local cache is properly primed
             def primed_cache_lambda_func():
-                build_util.build_docker(docker_binary='docker', platform=platform, registry=DOCKER_REGISTRY_PATH)
+                build_util.build_docker(
+                    docker_binary='docker',
+                    platform=platform,
+                    registry=DOCKER_REGISTRY_PATH,
+                    num_retries=3,
+                    no_cache=False
+                )
             _assert_docker_build(lambda_func=primed_cache_lambda_func, expected_cache_hit_count=4,
                                  expected_cache_miss_count=0)
 
diff --git a/ci/test_docker_login.py b/ci/test_docker_login.py
new file mode 100644
index 000000000000..6c989ade92ff
--- /dev/null
+++ b/ci/test_docker_login.py
@@ -0,0 +1,234 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""
+Docker login tests
+"""
+import os
+import subprocess
+import unittest
+from unittest.mock import create_autospec, patch, call, MagicMock
+
+import boto3
+from boto3 import client
+from botocore.stub import Stubber
+
+from docker_login import login_dockerhub, logout_dockerhub, main, DOCKERHUB_RETRY_SECONDS, DOCKERHUB_LOGIN_NUM_RETRIES
+
+
+SECRET_NAME = "secret_name"
+SECRET_ENDPOINT_URL = "https://endpoint.url"
+SECRET_ENDPOINT_REGION = "us-east-2"
+
+
+def mock_boto(num_calls: int = 1):
+    mock_client = client("secretsmanager", region_name="us-east-1")
+    mock_session = create_autospec(boto3.Session)
+    mock_session.client.return_value = mock_client
+
+    # Stub get_secret_value response
+    stub = Stubber(mock_client)
+    for i in range(num_calls):
+        stub.add_response(
+            method="get_secret_value",
+            expected_params={
+                "SecretId": "secret_name"  # Matches os.environ['SECRET_NAME']
+            }, service_response={
+                "SecretString": """{"username": "myuser", "password": "mypass"}"""
+            })
+    return mock_session, stub
+
+
+class TestDockerLogin(unittest.TestCase):
+
+    @patch("subprocess.run", name="mock_subprocess_run")
+    def test_docker_login_success(self, mock_run):
+        """
+        Tests successful docker login returns True and calls docker appropriately
+        """
+        mock_session, stub = mock_boto()
+        stub.activate()
+        with patch("boto3.Session", return_value=mock_session):
+            mock_process = MagicMock(auto_spec=subprocess.Popen, name="mock_process")
+
+            # Simulate successful login
+            mock_process.returncode = 0
+            mock_run.return_value = mock_process
+
+            login_dockerhub(SECRET_NAME, SECRET_ENDPOINT_URL, SECRET_ENDPOINT_REGION)
+
+            # Check boto client is properly created
+            print(mock_session.client.call_args_list)
+            assert mock_session.client.call_args_list == [
+                call(service_name="secretsmanager", region_name="us-east-2", endpoint_url="https://endpoint.url")
+            ]
+
+            # Check that login call passes in the password in the correct way
+            assert mock_run.call_args_list == [
+                call(
+                    ["docker", "login", "--username", "myuser", "--password-stdin"],
+                    stdout=subprocess.PIPE,
+                    input=str.encode("mypass")
+                )
+            ]
+        stub.deactivate()
+
+    @patch("subprocess.run", name="mock_subprocess_run")
+    @patch("time.sleep")
+    def test_docker_login_retry(self, mock_sleep, mock_run):
+        """
+        Tests retry mechanism
+        """
+        num_tries = 3
+        mock_session, stub = mock_boto(num_calls=num_tries)
+        stub.activate()
+        with patch("boto3.Session", return_value=mock_session):
+            mock_process = MagicMock(auto_spec=subprocess.Popen, name="mock_process")
+
+            # Simulate successful login
+            mock_process.returncode = 0
+
+            # Simulate (num_tries - 1) errors + 1 success
+            mock_run.side_effect = \
+                [subprocess.CalledProcessError(1, "cmd", "some error")] * (num_tries - 1) + [mock_process]
+
+            login_dockerhub(SECRET_NAME, SECRET_ENDPOINT_URL, SECRET_ENDPOINT_REGION)
+
+            # Check boto client is properly created
+            print(mock_session.client.call_args_list)
+            assert mock_session.client.call_args_list == [
+                call(service_name="secretsmanager", region_name="us-east-2", endpoint_url="https://endpoint.url")
+            ] * num_tries
+
+            # Check that login call passes in the password in the correct way
+            cmd = ["docker", "login", "--username", "myuser", "--password-stdin"]
+            assert mock_run.call_args_list == [
+                call(cmd, stdout=subprocess.PIPE, input=str.encode("mypass"))
+            ] * num_tries
+
+            # Assert sleep was called appropriately
+            assert mock_sleep.call_args_list == [
+                call(2 ** retry_num * DOCKERHUB_RETRY_SECONDS) for retry_num in range(0, num_tries - 1)
+            ]
+        stub.deactivate()
+
+    @patch("subprocess.run", name="mock_subprocess_run")
+    @patch("time.sleep")
+    def test_docker_login_retry_exhausted(self, mock_sleep, mock_run):
+        """
+        Tests retry mechanism
+        """
+        num_tries = DOCKERHUB_LOGIN_NUM_RETRIES
+        mock_session, stub = mock_boto(num_calls=num_tries)
+        stub.activate()
+        with patch("boto3.Session", return_value=mock_session):
+            # Simulate num_tries errors
+            mock_run.side_effect = [subprocess.CalledProcessError(1, "cmd", "some error")] * num_tries
+
+            with self.assertRaises(subprocess.CalledProcessError):
+                login_dockerhub(SECRET_NAME, SECRET_ENDPOINT_URL, SECRET_ENDPOINT_REGION)
+
+            # Check boto client is properly created
+            assert mock_session.client.call_args_list == [
+                call(service_name="secretsmanager", region_name="us-east-2", endpoint_url="https://endpoint.url")
+            ] * num_tries
+
+            # Check that login call passes in the password in the correct way
+            cmd = ["docker", "login", "--username", "myuser", "--password-stdin"]
+            assert mock_run.call_args_list == [
+                call(cmd, stdout=subprocess.PIPE, input=str.encode("mypass"))
+            ] * num_tries
+
+            # Assert sleep was called appropriately
+            assert mock_sleep.call_args_list == [
+                call(2 ** retry_num * DOCKERHUB_RETRY_SECONDS) for retry_num in range(0, num_tries-1)
+            ]
+        stub.deactivate()
+
+    @patch("subprocess.run", name="mock_subprocess_run")
+    def test_docker_login_failed(self, mock_run):
+        """
+        Tests failed docker login return false
+        """
+        mock_session, stub = mock_boto()
+        stub.activate()
+        with patch("boto3.Session", return_value=mock_session):
+
+            mock_process = MagicMock(auto_spec=subprocess.Popen, name="mock_process")
+
+            # Simulate failed login
+            mock_process.returncode = 1
+            mock_run.return_value = mock_process
+
+            with self.assertRaises(RuntimeError):
+                login_dockerhub(SECRET_NAME, SECRET_ENDPOINT_URL, SECRET_ENDPOINT_REGION)
+        stub.deactivate()
+
+    @patch("subprocess.call", name="mock_subprocess_call")
+    def test_logout(self, mock_call):
+        """
+        Tests logout calls docker command appropriately
+        """
+        logout_dockerhub()
+        assert mock_call.call_args_list == [
+            call(["docker", "logout"])
+        ]
+
+    @patch("docker_login.login_dockerhub")
+    def test_main_exit(self, mock_login):
+        """
+        Tests main exits with error on failed docker login
+        """
+        mock_login.side_effect = RuntimeError("Didn't work")
+        with self.assertRaises(SystemExit):
+            main(["--secret-name", "name", "--secret-endpoint-url", "url", "--secret-endpoint-region", "r"])
+
+    @patch("docker_login.login_dockerhub")
+    def test_main_default_argument_values(self, mock_login):
+        """
+        Tests default arguments
+        """
+
+        # Good env
+        env = {
+            "DOCKERHUB_SECRET_ENDPOINT_URL": "url",
+            "DOCKERHUB_SECRET_ENDPOINT_REGION": "region"
+        }
+        with patch.dict(os.environ, env):
+            main(["--secret-name", "name"])
+            assert mock_login.call_args_list == [
+                call("name", "url", "region")
+            ]
+
+        # Bad envs - none or not all required vars defined
+        tests = [
+            {},
+            {"DOCKERHUB_SECRET_ENDPOINT_URL": "url"},
+            {"DOCKERHUB_SECRET_ENDPOINT_REGION": "region"}
+        ]
+        for bad_env in tests:
+            with patch.dict(os.environ, bad_env):
+                with self.assertRaises(RuntimeError):
+                    main(["--secret-name", "name"])
+
+
+if __name__ == '__main__':
+    import nose
+    nose.main()
diff --git a/ci/test_safe_docker_run.py b/ci/test_safe_docker_run.py
new file mode 100644
index 000000000000..433d42e8b2ea
--- /dev/null
+++ b/ci/test_safe_docker_run.py
@@ -0,0 +1,427 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""
+Safe docker run tests
+"""
+import itertools
+import os
+import signal
+import unittest
+from typing import Optional
+from unittest.mock import create_autospec, patch, call
+
+from docker import DockerClient
+from docker.models.containers import Container, ContainerCollection
+
+from safe_docker_run import SafeDockerClient, main
+
+
+def create_mock_container(status_code: int = 0):
+    """
+    Creates a mock docker container that exits with the specified status code
+    """
+    mock_container = create_autospec(Container, name="mock_container")
+    mock_container.wait.return_value = {
+        "StatusCode": status_code
+    }
+    return mock_container
+
+
+def create_mock_container_collection(container: Container):
+    """
+    Creates a mock ContainerCollection that return the supplied container when the 'run' method is called
+    """
+    mock_container_collection = create_autospec(ContainerCollection, name="mock_collection")
+    mock_container_collection.run.return_value = container
+    return mock_container_collection
+
+
+class MockDockerClient:
+    """
+    A mock DockerClient when docker.from_env is called
+    The supplied container will be returned when the client.containers.run method is called
+    """
+    def __init__(self, container: Container):
+        self._mock_client = create_autospec(DockerClient, name="mock_client")
+        self._mock_client.containers = create_mock_container_collection(container)
+        self._patch = patch("docker.from_env", return_value=self._mock_client)
+
+    def __enter__(self):
+        self._patch.start()
+        return self._mock_client
+
+    def __exit__(self, _, __, ___):
+        self._patch.stop()
+
+
+class TestSafeDockerRun(unittest.TestCase):
+
+    @patch("safe_docker_run.signal.pthread_sigmask")
+    @patch.dict(os.environ, {
+        "BUILD_NUMBER": "BUILD_NUMBER_5",
+        "BUILD_ID": "BUILD_ID_1",
+        "BUILD_TAG": "BUILD_TAG_7"
+    })
+    def test_run_successful(self, mock_pthread_sigmask):
+        """
+        Tests successful run
+        """
+        mock_container = create_mock_container()
+
+        with MockDockerClient(mock_container) as mock_client:
+            safe_docker = SafeDockerClient()
+
+            # Check return code is 0
+            assert safe_docker.run("image", "command") == 0
+
+            # Check call to container is correct
+            assert mock_client.containers.run.call_args_list == [
+                call("image", "command", detach=True, environment={
+                    "BUILD_NUMBER": "BUILD_NUMBER_5",
+                    "BUILD_ID": "BUILD_ID_1",
+                    "BUILD_TAG": "BUILD_TAG_7"
+                })
+            ]
+
+            # Check correct signals are blocked then unblocked
+            assert mock_pthread_sigmask.call_args_list == [
+                call(signal.SIG_BLOCK, {signal.SIGINT, signal.SIGTERM}),
+                call(signal.SIG_UNBLOCK, {signal.SIGINT, signal.SIGTERM})
+            ]
+
+            # Assert container is stopped and removed
+            assert mock_container.stop.call_count == 1
+            assert mock_container.remove.call_count == 1
+            assert len(safe_docker._containers) == 0
+
+    def test_run_detach(self):
+        """
+        Tests detach=True is passed to the underlying call by default
+        """
+        mock_container = create_mock_container()
+
+        # Test detach=True is passed in even if not specified
+        with MockDockerClient(mock_container) as mock_client:
+            safe_docker = SafeDockerClient()
+            assert safe_docker.run("image", "command") == 0
+            assert mock_client.containers.run.call_count == 1
+            _, kwargs = mock_client.containers.run.call_args
+            assert kwargs["detach"] is True
+
+        # Test passing in detach=True does not cause any issues
+        with MockDockerClient(mock_container) as mock_client:
+            safe_docker = SafeDockerClient()
+            assert safe_docker.run("image", "command", detach=True) == 0
+            assert mock_client.containers.run.call_count == 1
+            _, kwargs = mock_client.containers.run.call_args
+            assert kwargs["detach"] is True
+
+        # Test detach=False fails
+        with MockDockerClient(mock_container) as mock_client:
+            safe_docker = SafeDockerClient()
+            with self.assertRaises(ValueError):
+                safe_docker.run("image", "command", detach=False)
+                assert mock_client.containers.run.call_args_list == []
+
+    def test_jenkins_vars(self):
+        """
+        Tests jenkins environment variables are appropriately passed to the underlying docker run call
+        """
+        # NOTE: It's important that these variables are passed to the underlying docker container
+        # These variables are passed to the container so the process tree killer can find runaway
+        # process inside the container
+        # https://wiki.jenkins.io/display/JENKINS/ProcessTreeKiller
+        # https://github.com/jenkinsci/jenkins/blob/578d6bacb33a5e99f149de504c80275796f0b231/core/src/main/java/hudson/model/Run.java#L2393
+
+        jenkins_vars = {
+            "BUILD_NUMBER": "BUILD_NUMBER_5",
+            "BUILD_ID": "BUILD_ID_1",
+            "BUILD_TAG": "BUILD_TAG_7"
+        }
+        mock_container = create_mock_container()
+
+        # Test environment is empty if the jenkins vars are not present
+        with MockDockerClient(mock_container) as mock_client:
+            safe_docker = SafeDockerClient()
+            assert safe_docker.run("image", "command") == 0
+            assert mock_client.containers.run.call_count == 1
+            _, kwargs = mock_client.containers.run.call_args
+            assert kwargs["environment"] == {}
+
+        # Test environment contains jenkins env vars if they are present
+        with MockDockerClient(mock_container) as mock_client:
+            with patch.dict(os.environ, jenkins_vars):
+                safe_docker = SafeDockerClient()
+                assert safe_docker.run("image", "command") == 0
+                assert mock_client.containers.run.call_count == 1
+                _, kwargs = mock_client.containers.run.call_args
+                assert kwargs["environment"] == jenkins_vars
+
+        # Test jenkins env vars are added to callers env vars
+        user_env = {"key1": "value1", "key2": "value2"}
+        with MockDockerClient(mock_container) as mock_client:
+            with patch.dict(os.environ, jenkins_vars):
+                safe_docker = SafeDockerClient()
+                assert safe_docker.run("image", "command", environment=user_env) == 0
+                assert mock_client.containers.run.call_count == 1
+                _, kwargs = mock_client.containers.run.call_args
+                assert kwargs["environment"] == {**jenkins_vars, **user_env}
+
+    def test_run_args_kwargs_passed(self):
+        """
+        Tests args and kwargs are passed to the container run call
+        """
+        mock_container = create_mock_container()
+
+        # Test detach=True is passed in even if not specified
+        with MockDockerClient(mock_container) as mock_client:
+            safe_docker = SafeDockerClient()
+            assert safe_docker.run(
+                "image",
+                "command",
+                "another_arg",
+                str_param="value",
+                bool_param=True,
+                none_param=None,
+                int_param=5,
+                float_param=5.2,
+                list_param=["this", "is", "a", "list"],
+                map_param={
+                    "a": "5",
+                    "b": True,
+                    "c": 2
+                }) == 0
+            assert mock_client.containers.run.call_args_list == [
+                call(
+                    "image",
+                    "command",
+                    "another_arg",
+                    detach=True,
+                    environment={},
+                    str_param="value",
+                    bool_param=True,
+                    none_param=None,
+                    int_param=5,
+                    float_param=5.2,
+                    list_param=["this", "is", "a", "list"],
+                    map_param={
+                        "a": "5",
+                        "b": True,
+                        "c": 2
+                    }
+                )
+            ]
+
+    def test_container_returns_non_zero_status_code(self):
+        """
+        Tests non-zero code from container is returned and the container
+        is cleaned up
+        """
+        mock_container = create_mock_container(status_code=10)
+        with MockDockerClient(mock_container):
+            safe_docker = SafeDockerClient()
+            # check return code and that container gets cleaned up
+            assert safe_docker.run("image", "command") == 10
+            assert mock_container.stop.call_count == 1
+            assert mock_container.remove.call_count == 1
+            assert len(safe_docker._containers) == 0
+
+    def test_container_wait_raises_returns_150(self):
+        """
+        Tests 150 is returned if an error is raised when calling container.wait
+        """
+        mock_container = create_mock_container()
+        mock_container.wait.side_effect = RuntimeError("Something bad happened")
+        with MockDockerClient(mock_container):
+            safe_docker = SafeDockerClient()
+            assert safe_docker.run("image", "command") == 150
+
+    def test_container_stop_raises_returns_151(self):
+        """
+        Tests 151 is returned if an error is raised when calling container.stop
+        """
+        mock_container = create_mock_container()
+        mock_container.stop.side_effect = RuntimeError("Something bad happened")
+        with MockDockerClient(mock_container):
+            safe_docker = SafeDockerClient()
+            assert safe_docker.run("image", "command") == 151
+
+    def test_container_remove_raises_returns_152(self):
+        """
+        Tests 152 is returned if an error is raised when calling container.remove
+        """
+        mock_container = create_mock_container()
+        mock_container.remove.side_effect = RuntimeError("Something bad happened")
+        with MockDockerClient(mock_container):
+            safe_docker = SafeDockerClient()
+            assert safe_docker.run("image", "command") == 152
+
+    def test_main(self):
+        """
+        Tests main function against different command line arguments
+        """
+        tests = [
+            # ( supplied command line arguments, expected call )
+            (
+                ["image", "command"],
+                call("image", command="command", runtime=None, user=None, name=None, volumes={}, cap_add=[])
+            ),
+            (
+                ["image", "command", "arg1", "arg2"],
+                call("image", command="command arg1 arg2", runtime=None, user=None, name=None, volumes={}, cap_add=[])
+            ),
+            (
+                ["--runtime", "nvidia", "image", "command"],
+                call("image", command="command", runtime="nvidia", user=None, name=None, volumes={}, cap_add=[])
+            ),
+            (
+                ["--user", "1001:1001", "image", "command"],
+                call("image", command="command", runtime=None, user="1001:1001", name=None, volumes={}, cap_add=[])
+            ),
+            ([
+                "--volume", "/local/path1:/container/path1",
+                "--volume", "/local/path2:/container/path2:ro",
+                "image",
+                "command"
+            ], call("image", command="command", runtime=None, user=None, name=None, volumes={
+                "/local/path1": {
+                    "bind": "/container/path1",
+                    "mode": "rw"
+                },
+                "/local/path2": {
+                    "bind": "/container/path2",
+                    "mode": "ro"
+                }
+            }, cap_add=[])),
+            ([
+                "--runtime", "nvidia",
+                "-u", "1001:1001",
+                "-v", "/local/path1:/container/path1",
+                "-v", "/local/path2:/container/path2:ro",
+                "--cap-add", "bob",
+                "--cap-add", "jimmy",
+                "--name",
+                "container_name",
+                "image",
+                "command",
+                "arg1",
+                "arg2"
+            ], call(
+                "image",
+                command="command arg1 arg2",
+                runtime="nvidia",
+                user="1001:1001",
+                name="container_name",
+                volumes={
+                    "/local/path1": {
+                        "bind": "/container/path1",
+                        "mode": "rw"
+                    },
+                    "/local/path2": {
+                        "bind": "/container/path2",
+                        "mode": "ro"
+                    }
+                }, cap_add=["bob", "jimmy"])
+            )
+        ]
+
+        # Tests valid arguments
+        mock_docker = create_autospec(SafeDockerClient)
+        mock_docker.run.return_value = 0
+        with patch("safe_docker_run.SafeDockerClient", return_value=mock_docker):
+            for test in tests:
+                arguments, expected_call = test
+                main(arguments)
+                assert mock_docker.run.call_args == expected_call
+
+        # Tests invalid arguments
+        tests = [
+            [],
+            None,
+            ["image"],
+            # Test some bad volume mounts
+            ["-v", "bob", "image", "args"],
+            ["-v", "/local/path", "image", "args"],
+            ["-v", "/local/path:/container/path:blah", "image", "args"],
+            ["-v", "", "image", "args"],
+            ["-v", "a:b:c:d", "image", "args"]
+        ]
+
+        mock_docker = create_autospec(SafeDockerClient)
+        with patch("safe_docker_run.SafeDockerClient", return_value=mock_docker):
+            with self.assertRaises(SystemExit):
+                for test in tests:
+                    main(test)
+
+    def test_clean_up(self):
+        """
+        Tests container clean up in case of SIGTERM and SIGINT
+        """
+        import subprocess
+        import time
+        import docker.errors
+
+        docker_client = docker.from_env()
+        container_name = "safedockertestcontainer1234"
+
+        def get_container(name: str) -> Optional[Container]:
+            try:
+                return docker_client.containers.get(name)
+            except docker.errors.NotFound:
+                return None
+
+        def remove_container_if_exists(name: str):
+            container = get_container(name)
+            if container:
+                container.stop()
+                container.remove()
+
+        def wait_for_container(name: str) -> bool:
+            for _ in itertools.count(5):
+                if get_container(name):
+                    return True
+                time.sleep(1)
+            return False
+
+        # Clear any containers with container name
+        remove_container_if_exists(container_name)
+
+        # None => not signal is emitted - we should still finish with no containers at the end due
+        # to the atexit
+        for sig in [None, signal.SIGTERM, signal.SIGINT]:
+            # Execute the safe docker run script in a different process
+            proc = subprocess.Popen(['./safe_docker_run.py', "--name", container_name, "ubuntu:18.04", "sleep 10"])
+            # NOTE: we need to wait for the container to come up as not all operating systems support blocking signals
+            if wait_for_container(container_name) is False:
+                raise RuntimeError("Test container did not come up")
+
+            # Issue the signal and wait for the process to finish
+            if sig:
+                proc.send_signal(sig)
+            proc.wait()
+
+            # The container should no longer exist
+            assert get_container(container_name) is None
+
+
+if __name__ == '__main__':
+    import nose
+    nose.main()
diff --git a/ci/util.py b/ci/util.py
index 9a8d52eb1716..4b3a399184f9 100644
--- a/ci/util.py
+++ b/ci/util.py
@@ -18,6 +18,9 @@
 import os
 import contextlib
 import logging
+import logging.config
+import sys
+
 
 def get_mxnet_root() -> str:
     curpath = os.path.abspath(os.path.dirname(__file__))
@@ -32,6 +35,7 @@ def is_mxnet_root(path: str) -> bool:
         curpath = parent
     return curpath
 
+
 @contextlib.contextmanager
 def remember_cwd():
     '''
@@ -113,3 +117,16 @@ def chdir_to_script_directory():
     os.chdir(base)
 
 
+def script_name() -> str:
+    """:returns: script name with leading paths removed"""
+    return os.path.split(sys.argv[0])[1]
+
+
+def config_logging():
+    conf_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "logging.conf")
+    logging.config.fileConfig(os.getenv('LOGGING_CONF', conf_path))
+
+    # Force botocore and requests are set to WARNING to avoid leaking any credentials
+    # or sensitive information
+    logging.getLogger("botocore").setLevel(logging.WARNING)
+    logging.getLogger("requests").setLevel(logging.WARNING)

From 1fb6f004e17588811dd1d4577c5284413ea1d7dc Mon Sep 17 00:00:00 2001
From: Anirudh Subramanian <anirudh2290@ufl.edu>
Date: Mon, 21 Oct 2019 11:37:29 -0700
Subject: [PATCH 489/813] Build dmlc-core with old thread_local implementation
 (#16526)

---
 3rdparty/dmlc-core                    |  2 +-
 CMakeLists.txt                        |  3 +
 Makefile                              |  2 +
 tests/cpp/engine/thread_local_test.cc | 80 +++++++++++++++++++++++++++
 4 files changed, 86 insertions(+), 1 deletion(-)
 create mode 100644 tests/cpp/engine/thread_local_test.cc

diff --git a/3rdparty/dmlc-core b/3rdparty/dmlc-core
index 9088d2ee02cd..0e13243556d7 160000
--- a/3rdparty/dmlc-core
+++ b/3rdparty/dmlc-core
@@ -1 +1 @@
-Subproject commit 9088d2ee02cdfe393fd0569af4aefebe94f8b105
+Subproject commit 0e13243556d7de754a923c8f97998721d694f29b
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 70b09917de13..99ad34bed3dc 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -101,6 +101,9 @@ if("$ENV{VERBOSE}" STREQUAL "1")
   set(CMAKE_VERBOSE_MAKEFILE ON)
 endif()
 
+#Switch off modern thread local for dmlc-core, please see: https://github.com/dmlc/dmlc-core/issues/571#issuecomment-543467484
+add_definitions(-DDMLC_MODERN_THREAD_LOCAL=0)
+
 
 if(MSVC)
   add_definitions(-DWIN32_LEAN_AND_MEAN)
diff --git a/Makefile b/Makefile
index e1eeefed4bb7..be0d34051873 100644
--- a/Makefile
+++ b/Makefile
@@ -94,6 +94,8 @@ include $(DMLC_CORE)/make/dmlc.mk
 # all tge possible warning tread
 WARNFLAGS= -Wall -Wsign-compare
 CFLAGS = -DMSHADOW_FORCE_STREAM $(WARNFLAGS)
+# use old thread local implementation in DMLC-CORE
+CFLAGS += -DDMLC_MODERN_THREAD_LOCAL=0
 
 ifeq ($(DEV), 1)
 	CFLAGS += -g -Werror
diff --git a/tests/cpp/engine/thread_local_test.cc b/tests/cpp/engine/thread_local_test.cc
new file mode 100644
index 000000000000..e074e18af2e9
--- /dev/null
+++ b/tests/cpp/engine/thread_local_test.cc
@@ -0,0 +1,80 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file engine_thread_local_test.cc
+ * \brief Tests thread safety and lifetime of thread local store
+*/
+#include <gtest/gtest.h>
+#include <time.h>
+#include <dmlc/logging.h>
+#include <dmlc/thread_group.h>
+#include <dmlc/omp.h>
+#include <mxnet/c_api.h>
+#include <mxnet/engine.h>
+#include <mxnet/ndarray.h>
+#include <dmlc/timer.h>
+#include <cstdio>
+#include <thread>
+#include <chrono>
+#include <vector>
+
+struct A {
+    std::vector<int> a;
+};
+int num_threads = 10;
+int num_elements = num_threads * 10;
+
+static int ThreadSafetyTest(int num, std::vector<int>* tmp_inputs, std::vector<int*>* res) {
+    A *ret = dmlc::ThreadLocalStore<A>::Get();
+    for (size_t i = num * 10; i < num * 10 + 10; ++i) {
+        (*tmp_inputs)[i] = i;
+    }
+    ret->a.clear();
+    ret->a.reserve(10);
+    for (size_t i = num * 10; i < num * 10 + 10; ++i) {
+        ret->a.push_back((*tmp_inputs)[i]);
+    }
+    (*res)[num] = dmlc::BeginPtr(ret->a);
+    return 0;
+}
+
+TEST(ThreadLocal, verify_thread_safety) {
+    std::vector<int> tmp_inputs;
+    tmp_inputs.resize(num_elements);
+    std::vector<int*> outputs;
+    outputs.resize(num_threads);
+    auto func = [&](int num) {
+        ThreadSafetyTest(num, &tmp_inputs, &outputs);
+    };
+    std::vector<std::thread> worker_threads(num_threads);
+    int count = 0;
+    for (auto&& i : worker_threads) {
+        i = std::thread(func, count);
+        count++;
+    }
+    for (auto&& i : worker_threads) {
+        i.join();
+    }
+
+    for (size_t i = 0; i < num_elements; i++) {
+        CHECK(outputs[i/10][i%10] == i);
+    }
+}

From 261d09d9acba5c5f712ef83cd24aa7d26cdea4f5 Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Mon, 21 Oct 2019 13:56:27 -0700
Subject: [PATCH 490/813] pickler override for np ndarrays (#16561)

---
 python/mxnet/gluon/data/dataloader.py       | 32 +++++++++++++++++++++
 tests/python/unittest/test_numpy_ndarray.py | 12 ++++++++
 2 files changed, 44 insertions(+)

diff --git a/python/mxnet/gluon/data/dataloader.py b/python/mxnet/gluon/data/dataloader.py
index 68412267da6b..cb6ae5a164b9 100644
--- a/python/mxnet/gluon/data/dataloader.py
+++ b/python/mxnet/gluon/data/dataloader.py
@@ -74,6 +74,38 @@ def reduce_ndarray(data):
 
 ForkingPickler.register(nd.NDArray, reduce_ndarray)
 
+if sys.platform == 'darwin' or sys.platform == 'win32':
+    def rebuild_np_ndarray(*args):
+        """Rebuild ndarray from pickled shared memory"""
+        # pylint: disable=no-value-for-parameter
+        return _mx_np.ndarray(nd.ndarray._new_from_shared_mem(*args))
+
+    def reduce_np_ndarray(data):
+        """Reduce ndarray to shared memory handle"""
+        return rebuild_np_ndarray, data._to_shared_mem()
+else:
+    def rebuild_np_ndarray(pid, fd, shape, dtype):
+        """Rebuild ndarray from pickled shared memory"""
+        # pylint: disable=no-value-for-parameter
+        if sys.version_info[0] == 2:
+            fd = multiprocessing.reduction.rebuild_handle(fd)
+        else:
+            fd = fd.detach()
+        return _mx_np.ndarray(nd.ndarray._new_from_shared_mem(pid, fd, shape, dtype))
+
+    def reduce_np_ndarray(data):
+        """Reduce ndarray to shared memory handle"""
+        # keep a local ref before duplicating fd
+        data = data.as_in_context(context.Context('cpu_shared', 0))
+        pid, fd, shape, dtype = data._to_shared_mem()
+        if sys.version_info[0] == 2:
+            fd = multiprocessing.reduction.reduce_handle(fd)
+        else:
+            fd = multiprocessing.reduction.DupFd(fd)
+        return rebuild_np_ndarray, (pid, fd, shape, dtype)
+
+ForkingPickler.register(_mx_np.ndarray, reduce_np_ndarray)
+
 
 class ConnectionWrapper(object):
     """Connection wrapper for multiprocessing that supports sending
diff --git a/tests/python/unittest/test_numpy_ndarray.py b/tests/python/unittest/test_numpy_ndarray.py
index b6692c009c18..6077f4df13ae 100644
--- a/tests/python/unittest/test_numpy_ndarray.py
+++ b/tests/python/unittest/test_numpy_ndarray.py
@@ -1098,6 +1098,18 @@ def test_np_get_dtype():
             assert type(mx_arr.dtype) == type(np_arr.dtype)
 
 
+@use_np
+def test_np_ndarray_pickle():
+    a = np.random.uniform(size=(4, 5))
+    a_copy = a.copy()
+    import pickle
+    with open("np_ndarray_pickle_test_file", 'wb') as f:
+        pickle.dump(a_copy, f)
+    with open("np_ndarray_pickle_test_file", 'rb') as f:
+        a_load = pickle.load(f)
+    same(a.asnumpy(), a_load.asnumpy())
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 62b063802634048fe9da0a736dd6ee429e410f27 Mon Sep 17 00:00:00 2001
From: Rohit Kumar Srivastava <srivastava.141@osu.edu>
Date: Mon, 21 Oct 2019 16:28:21 -0700
Subject: [PATCH 491/813] Added large tensor support and test for gather_nd
 (#16371)

* adding large tensor support and test for gather_nd

* added gather_test to large_vector and added comments in large array and vector tests
---
 python/mxnet/ndarray/ndarray.py    |  5 ++++-
 src/operator/tensor/indexing_op.h  | 10 +++++-----
 tests/nightly/test_large_array.py  | 11 +++++++++++
 tests/nightly/test_large_vector.py | 13 +++++++++++++
 4 files changed, 33 insertions(+), 6 deletions(-)

diff --git a/python/mxnet/ndarray/ndarray.py b/python/mxnet/ndarray/ndarray.py
index 8b826d2cc0a4..8325dafb7891 100644
--- a/python/mxnet/ndarray/ndarray.py
+++ b/python/mxnet/ndarray/ndarray.py
@@ -1063,7 +1063,10 @@ def _advanced_index_to_array(idx, ax_len, ctx):
 
         The ``ax_len`` is used to convert `slice` objects to integer arrays.
         """
-        idx_dtype = 'int32'
+        if sys.version_info[0] > 2 and _int64_enabled():
+            idx_dtype = 'int64'
+        else:
+            idx_dtype = 'int32'
         if isinstance(idx, NDArray):
             if idx.dtype != idx_dtype:
                 idx = idx.astype(idx_dtype)
diff --git a/src/operator/tensor/indexing_op.h b/src/operator/tensor/indexing_op.h
index 16520ddbb242..bb524dd0f5e9 100644
--- a/src/operator/tensor/indexing_op.h
+++ b/src/operator/tensor/indexing_op.h
@@ -1311,15 +1311,15 @@ inline bool GatherNDType(const nnvm::NodeAttrs& attrs,
 
 struct gather_nd {
   template<typename DType, typename IType>
-  MSHADOW_XINLINE static void Map(int i, OpReqType req, int N, int M, int K,
+  MSHADOW_XINLINE static void Map(index_t i, OpReqType req, index_t N, index_t M, index_t K,
                                   const mshadow::Shape<10> strides,
                                   DType* out, const DType* data,
                                   const IType* indices) {
-    int offset = 0;
-    for (int j = 0; j < M; ++j) {
-      offset += strides[j] * static_cast<int>(indices[j*N + i]);
+    index_t offset = 0;
+    for (index_t j = 0; j < M; ++j) {
+      offset += strides[j] * static_cast<index_t>(indices[j*N + i]);
     }
-    for (int j = 0; j < K; ++j) {
+    for (index_t j = 0; j < K; ++j) {
       KERNEL_ASSIGN(out[i*K + j], req, data[offset+j]);
     }
   }
diff --git a/tests/nightly/test_large_array.py b/tests/nightly/test_large_array.py
index 63dc2b71b9d4..9c2fbd61a9ac 100644
--- a/tests/nightly/test_large_array.py
+++ b/tests/nightly/test_large_array.py
@@ -1503,6 +1503,17 @@ def test_pad():
     assert y.shape == (1, 1, SMALL_Y, LARGE_X//2)
 
 
+def test_gather():
+    arr = mx.nd.ones((LARGE_X, SMALL_Y))
+    idx = mx.nd.random.randint(0, LARGE_X, SMALL_X)
+    # Calls gather_nd internally
+    tmp = arr[idx]
+    assert np.sum(tmp[0] == 1) == SMALL_Y
+    # Calls gather_nd internally
+    arr[idx] += 1
+    assert np.sum(arr[idx[0]] == 2) == SMALL_Y
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()
diff --git a/tests/nightly/test_large_vector.py b/tests/nightly/test_large_vector.py
index 53a8bd07ff78..23f4b8e4f310 100644
--- a/tests/nightly/test_large_vector.py
+++ b/tests/nightly/test_large_vector.py
@@ -930,6 +930,19 @@ def test_minimum():
     assert z[-1] == 3
 
 
+def test_gather():
+    arr = mx.nd.ones(LARGE_X)
+    # Passing dtype=np.int64 since randomly generated indices are 
+    # very large that exceeds int32 limits.
+    idx = mx.nd.random.randint(0, LARGE_X, 10, dtype=np.int64)
+    # Calls gather_nd internally
+    tmp = arr[idx]
+    assert np.sum(tmp == 1) == 10
+    # Calls gather_nd internally
+    arr[idx] += 1
+    assert np.sum(arr[idx] == 2) == 10
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From dcf5fc8ca59dd12bb1eafbba65450b6f5268bd95 Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Mon, 21 Oct 2019 16:52:42 -0700
Subject: [PATCH 492/813] fix doc for topk (#16571)

---
 src/operator/tensor/ordering_op.cc | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/src/operator/tensor/ordering_op.cc b/src/operator/tensor/ordering_op.cc
index 58c98f3a380c..e36416114e31 100644
--- a/src/operator/tensor/ordering_op.cc
+++ b/src/operator/tensor/ordering_op.cc
@@ -35,7 +35,10 @@ DMLC_REGISTER_PARAMETER(ArgSortParam);
 
 NNVM_REGISTER_OP(topk)
 .add_alias("_npx_topk")
-.describe(R"code(Returns the top *k* elements in an input array along the given axis.
+.describe(R"code(Returns the indices of the top *k* elements in an input array along the given
+ axis (by default).
+ If ret_type is set to 'value' returns the value of top *k* elements (instead of indices).
+ In case of ret_type = 'both', both value and index would be returned.
  The returned elements will be sorted.
 
 Examples::

From 0ba1ce207ae57a86ba596251c6f4581c9e356180 Mon Sep 17 00:00:00 2001
From: JiangZhaoh <54654391+JiangZhaoh@users.noreply.github.com>
Date: Tue, 22 Oct 2019 11:36:15 +0800
Subject: [PATCH 493/813] [numpy]op test in new pattern (#16556)

* all changes

fix sanity problem

change is_op_runnable's location

Fix

* Disable fp16 test since TVM op is not ready for this dtype

* Assert equal for boolean ndarrays

* Fix test util
---
 python/mxnet/numpy_dispatch_protocol.py       |   8 ++
 python/mxnet/test_utils.py                    |   3 +
 .../unittest/test_numpy_interoperability.py   | 102 ++++++++++++++++++
 3 files changed, 113 insertions(+)

diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index 3682334ebbea..6db44fad7780 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -117,6 +117,8 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'transpose',
     'unique',
     'var',
+    'vdot',
+    'vstack',
     'zeros_like',
     'linalg.norm',
     'trace',
@@ -214,6 +216,12 @@ def _register_array_function():
     'trunc',
     'floor',
     'logical_not',
+    'equal',
+    'not_equal',
+    'less',
+    'less_equal',
+    'greater',
+    'greater_equal'
 ]
 
 
diff --git a/python/mxnet/test_utils.py b/python/mxnet/test_utils.py
index 6ee37982a124..4862aee8570d 100644
--- a/python/mxnet/test_utils.py
+++ b/python/mxnet/test_utils.py
@@ -565,6 +565,9 @@ def assert_almost_equal(a, b, rtol=None, atol=None, names=('a', 'b'), equal_nan=
                 b = b.asnumpy()
 
     if use_np_allclose:
+        if hasattr(a, 'dtype') and a.dtype == np.bool_ and hasattr(b, 'dtype') and b.dtype == np.bool_:
+            np.testing.assert_equal(a, b)
+            return
         if almost_equal(a, b, rtol, atol, equal_nan=equal_nan):
             return
     else:
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index e16be469aa3b..9e8156f3239c 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -32,6 +32,14 @@
 _INT_DTYPES = [np.int8, np.int32, np.int64, np.uint8]
 _FLOAT_DTYPES = [np.float16, np.float32, np.float64]
 _DTYPES = _INT_DTYPES + _FLOAT_DTYPES
+_TVM_OPS = [
+    'equal',
+    'not_equal',
+    'less',
+    'less_equal',
+    'greater',
+    'greater_equal'
+]
 
 
 class OpArgMngr(object):
@@ -535,6 +543,13 @@ def _add_workload_roll():
 
 def _add_workload_stack(array_pool):
     OpArgMngr.add_workload('stack', [array_pool['4x1']] * 2)
+    OpArgMngr.add_workload('stack', [array_pool['4x1']] * 2, 1)
+    OpArgMngr.add_workload('stack', [array_pool['4x1']] * 2, -1)
+    OpArgMngr.add_workload('stack', [array_pool['4x1']] * 2, -2)
+    OpArgMngr.add_workload('stack', np.random.normal(size=(2, 4, 3)), 2)
+    OpArgMngr.add_workload('stack', np.random.normal(size=(2, 4, 3)), -3)
+    OpArgMngr.add_workload('stack', np.array([[], [], []]), 1)
+    OpArgMngr.add_workload('stack', np.array([[], [], []]))
 
 
 def _add_workload_sum():
@@ -590,10 +605,22 @@ def _add_workload_unique():
 
 def _add_workload_var(array_pool):
     OpArgMngr.add_workload('var', array_pool['4x1'])
+    OpArgMngr.add_workload('var', np.array([np.float16(1.)]))
+    OpArgMngr.add_workload('var', np.array([1]))
+    OpArgMngr.add_workload('var', np.array([1.]))
+    OpArgMngr.add_workload('var', np.array([[1, 2, 3], [4, 5, 6]]))
+    OpArgMngr.add_workload('var', np.array([[1, 2, 3], [4, 5, 6]]), 0)
+    OpArgMngr.add_workload('var', np.array([[1, 2, 3], [4, 5, 6]]), 1)
+    OpArgMngr.add_workload('var', np.array([np.nan]))
+    OpArgMngr.add_workload('var', np.array([1, -1, 1, -1]))
+    OpArgMngr.add_workload('var', np.array([1,2,3,4], dtype='f8'))
 
 
 def _add_workload_zeros_like(array_pool):
     OpArgMngr.add_workload('zeros_like', array_pool['4x1'])
+    OpArgMngr.add_workload('zeros_like', np.random.uniform(size=(3, 3)).astype(np.float64))
+    OpArgMngr.add_workload('zeros_like', np.random.uniform(size=(3, 3)).astype(np.float32))
+    OpArgMngr.add_workload('zeros_like', np.random.randint(2, size = (3, 3)))
 
 
 def _add_workload_outer():
@@ -933,6 +960,71 @@ def _add_workload_logical_not(array_pool):
     OpArgMngr.add_workload('logical_not', np.array([True, False, True, False], dtype=np.bool))
 
 
+def _add_workload_vdot():
+    OpArgMngr.add_workload('vdot', np.random.normal(size=(2, 4)), np.random.normal(size=(4, 2)))
+    OpArgMngr.add_workload('vdot', np.random.normal(size=(2, 4)).astype(np.float64), np.random.normal(size=(2, 4)).astype(np.float64))
+
+
+def _add_workload_vstack(array_pool):
+    OpArgMngr.add_workload('vstack', (array_pool['4x1'], np.random.uniform(size=(5, 1))))
+    OpArgMngr.add_workload('vstack', array_pool['4x1'])
+    OpArgMngr.add_workload('vstack', array_pool['1x1x0'])
+
+
+def _add_workload_equal(array_pool):
+    # TODO(junwu): fp16 does not work yet with TVM generated ops
+    # OpArgMngr.add_workload('equal', np.array([0, 1, 2, 4, 2], dtype=np.float16), np.array([-2, 5, 1, 4, 3], dtype=np.float16))
+    OpArgMngr.add_workload('equal', np.array([0, 1, 2, 4, 2], dtype=np.float32), np.array([-2, 5, 1, 4, 3], dtype=np.float32))
+    # TODO(junwu): mxnet currently does not have a consistent behavior as NumPy in dealing with np.nan
+    # OpArgMngr.add_workload('equal', np.array([np.nan]), np.array([np.nan]))
+    OpArgMngr.add_workload('equal', array_pool['4x1'], array_pool['1x2'])
+
+
+def _add_workload_not_equal(array_pool):
+    # TODO(junwu): fp16 does not work yet with TVM generated ops
+    # OpArgMngr.add_workload('not_equal', np.array([0, 1, 2, 4, 2], dtype=np.float16), np.array([-2, 5, 1, 4, 3], dtype=np.float16))
+    OpArgMngr.add_workload('not_equal', np.array([0, 1, 2, 4, 2], dtype=np.float32), np.array([-2, 5, 1, 4, 3], dtype=np.float32))
+    # TODO(junwu): mxnet currently does not have a consistent behavior as NumPy in dealing with np.nan
+    # OpArgMngr.add_workload('not_equal', np.array([np.nan]), np.array([np.nan]))
+    OpArgMngr.add_workload('not_equal', array_pool['4x1'], array_pool['1x2'])
+
+
+def _add_workload_greater(array_pool):
+    # TODO(junwu): fp16 does not work yet with TVM generated ops
+    # OpArgMngr.add_workload('greater', np.array([0, 1, 2, 4, 2], dtype=np.float16), np.array([-2, 5, 1, 4, 3], dtype=np.float16))
+    OpArgMngr.add_workload('greater', np.array([0, 1, 2, 4, 2], dtype=np.float32), np.array([-2, 5, 1, 4, 3], dtype=np.float32))
+    OpArgMngr.add_workload('greater', array_pool['4x1'], array_pool['1x2'])
+    # TODO(junwu): mxnet currently does not have a consistent behavior as NumPy in dealing with np.nan
+    # OpArgMngr.add_workload('greater', np.array([np.nan]), np.array([np.nan]))
+
+
+def _add_workload_greater_equal(array_pool):
+    # TODO(junwu): fp16 does not work yet with TVM generated ops
+    # OpArgMngr.add_workload('greater_equal', np.array([0, 1, 2, 4, 2], dtype=np.float16), np.array([-2, 5, 1, 4, 3], dtype=np.float16))
+    OpArgMngr.add_workload('greater_equal', np.array([0, 1, 2, 4, 2], dtype=np.float32), np.array([-2, 5, 1, 4, 3], dtype=np.float32))
+    OpArgMngr.add_workload('greater_equal', array_pool['4x1'], array_pool['1x2'])
+    # TODO(junwu): mxnet currently does not have a consistent behavior as NumPy in dealing with np.nan
+    # OpArgMngr.add_workload('greater_equal', np.array([np.nan]), np.array([np.nan]))
+
+
+def _add_workload_less(array_pool):
+    # TODO(junwu): fp16 does not work yet with TVM generated ops
+    # OpArgMngr.add_workload('less', np.array([0, 1, 2, 4, 2], dtype=np.float16), np.array([-2, 5, 1, 4, 3], dtype=np.float16))
+    OpArgMngr.add_workload('less', np.array([0, 1, 2, 4, 2], dtype=np.float32), np.array([-2, 5, 1, 4, 3], dtype=np.float32))
+    OpArgMngr.add_workload('less', array_pool['4x1'], array_pool['1x2'])
+    # TODO(junwu): mxnet currently does not have a consistent behavior as NumPy in dealing with np.nan
+    # OpArgMngr.add_workload('less', np.array([np.nan]), np.array([np.nan]))
+
+
+def _add_workload_less_equal(array_pool):
+    # TODO(junwu): fp16 does not work yet with TVM generated ops
+    # OpArgMngr.add_workload('less_equal', np.array([0, 1, 2, 4, 2], dtype=np.float16), np.array([-2, 5, 1, 4, 3], dtype=np.float16))
+    OpArgMngr.add_workload('less_equal', np.array([0, 1, 2, 4, 2], dtype=np.float32), np.array([-2, 5, 1, 4, 3], dtype=np.float32))
+    OpArgMngr.add_workload('less_equal', array_pool['4x1'], array_pool['1x2'])
+    # TODO(junwu): mxnet currently does not have a consistent behavior as NumPy in dealing with np.nan
+    # OpArgMngr.add_workload('less_equal', np.array([np.nan]), np.array([np.nan]))
+
+
 @use_np
 def _prepare_workloads():
     array_pool = {
@@ -1028,6 +1120,14 @@ def _prepare_workloads():
     _add_workload_turnc(array_pool)
     _add_workload_floor(array_pool)
     _add_workload_logical_not(array_pool)
+    _add_workload_vdot()
+    _add_workload_vstack(array_pool)
+    _add_workload_equal(array_pool)
+    _add_workload_not_equal(array_pool)
+    _add_workload_greater(array_pool)
+    _add_workload_greater_equal(array_pool)
+    _add_workload_less(array_pool)
+    _add_workload_less_equal(array_pool)
 
 
 _prepare_workloads()
@@ -1070,6 +1170,8 @@ def _check_interoperability_helper(op_name, *args, **kwargs):
 
 def check_interoperability(op_list):
     for name in op_list:
+        if name in _TVM_OPS and not is_op_runnable():
+            continue
         print('Dispatch test:', name)
         workloads = OpArgMngr.get_workloads(name)
         assert workloads is not None, 'Workloads for operator `{}` has not been ' \

From ca4af0e0cb7baa2032d282bc5bf8d999366bdc09 Mon Sep 17 00:00:00 2001
From: reminisce <wujun.nju@gmail.com>
Date: Mon, 21 Oct 2019 23:15:50 -0700
Subject: [PATCH 494/813] Enforce adding documentation for builtin numpy
 operators (#16575)

* Add test

* Delete incomplete code
---
 src/operator/numpy/np_elemwise_broadcast_op.cu |  3 ---
 tests/python/unittest/test_numpy_op.py         | 12 +++++++++---
 2 files changed, 9 insertions(+), 6 deletions(-)

diff --git a/src/operator/numpy/np_elemwise_broadcast_op.cu b/src/operator/numpy/np_elemwise_broadcast_op.cu
index 3b780325227a..a682ec989ea8 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op.cu
+++ b/src/operator/numpy/np_elemwise_broadcast_op.cu
@@ -138,8 +138,5 @@ NNVM_REGISTER_OP(_backward_npi_ldexp_scalar)
 NNVM_REGISTER_OP(_backward_npi_rldexp_scalar)
 .set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Backward<gpu, mshadow_op::rldexp_grad>);
 
-NNVM_REGISTER_OP(_np_bitwise_xor)
-.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, mshadow_op::bitwise_xor>);
-
 }  // namespace op
 }  // namespace mxnet
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 4f01fa9ed4fa..b764ac73d30c 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -34,6 +34,7 @@
 from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf
 from mxnet.numpy_op_signature import _get_builtin_op
 from mxnet.test_utils import is_op_runnable, has_tvm_ops
+from mxnet.operator import get_all_registered_operators
 
 
 @with_seed()
@@ -3164,10 +3165,15 @@ def check_output_n_grad(data_shape, idx_shape, axis, mode):
 def test_np_builtin_op_signature():
     import inspect
     from mxnet import _numpy_op_doc
-    for op_name in dir(_numpy_op_doc):
+    builtin_np_op_names = [name for name in get_all_registered_operators() if name.startswith('_np_')]
+    for op_name in builtin_np_op_names:
+        _op_from_doc = getattr(_numpy_op_doc, op_name, None)
+        assert _op_from_doc is not None, "Failed to find documentation for operator {}. " \
+                                         "Please add the documentation in _numpy_op_doc.py for this operator."\
+            .format(op_name)
         op = _get_builtin_op(op_name)
-        if op is not None:
-            assert str(op.__signature__) == str(inspect.signature(getattr(_numpy_op_doc, op_name)))
+        assert op is not None
+        assert str(op.__signature__) == str(inspect.signature(_op_from_doc))
 
 
 @with_seed()

From 34e4f71704b98b14477aca4944e50725e780d96d Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Tue, 22 Oct 2019 04:47:17 -0700
Subject: [PATCH 495/813] Move imagenet inference to nightly (#16577)

* move to nightly

* bracket fix

* test imgnet comment rest

* br

* change dir

* change lib

* uncomment other tests

* change lib for build

* remove extraneous libs

* add cd to access shell script

* ready to merge

* fix dir structure
---
 ci/docker/runtime_functions.sh       |  8 ++++++++
 cpp-package/tests/ci_test.sh         |  2 --
 tests/nightly/JenkinsfileForBinaries | 11 ++++++++++-
 3 files changed, 18 insertions(+), 3 deletions(-)

diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index ba69930e915c..935406bb16a8 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -1462,6 +1462,14 @@ nightly_test_installation() {
     source ./tests/jenkins/run_test_installation_docs.sh docs/install/index.md 1 1686; ${1}
 }
 
+# Runs Imagenet inference
+nightly_test_imagenet_inference() {
+    set -ex
+    echo $PWD
+    cp /work/mxnet/build/cpp-package/example/imagenet_inference .
+    /work/mxnet/cpp-package/example/inference/unit_test_imagenet_inference.sh
+}
+
 #Runs a simple MNIST training example
 nightly_test_image_classification() {
     set -ex
diff --git a/cpp-package/tests/ci_test.sh b/cpp-package/tests/ci_test.sh
index 2ac9d6fcd741..39f9e06861b3 100755
--- a/cpp-package/tests/ci_test.sh
+++ b/cpp-package/tests/ci_test.sh
@@ -66,8 +66,6 @@ cp ../../build/cpp-package/example/test_regress_label .
 sh unittests/unit_test_mlp_csv.sh
 
 cd inference
-cp ../../../build/cpp-package/example/imagenet_inference .
-./unit_test_imagenet_inference.sh
 
 cp ../../../build/cpp-package/example/sentiment_analysis_rnn .
 ./unit_test_sentiment_analysis_rnn.sh
diff --git a/tests/nightly/JenkinsfileForBinaries b/tests/nightly/JenkinsfileForBinaries
index 5158274010b3..a66159d0075b 100755
--- a/tests/nightly/JenkinsfileForBinaries
+++ b/tests/nightly/JenkinsfileForBinaries
@@ -20,6 +20,7 @@
 
 mx_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a'
 mx_cmake_lib = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so'
+mx_lib_cpp_example = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, build/cpp-package/example/imagenet_inference'
 
 node('utility') {
   // Loading the utilities requires a node context unfortunately
@@ -36,7 +37,7 @@ core_logic: {
         ws('workspace/build-gpu') {
           utils.init_git()
           utils.docker_run('ubuntu_build_cuda', 'build_ubuntu_gpu_cuda101_cudnn7', false)
-          utils.pack_lib('gpu', mx_lib)
+          utils.pack_lib('gpu', mx_lib_cpp_example)
         }
       }
     }/*,
@@ -69,6 +70,14 @@ core_logic: {
         }
       }
     },
+    'ImageNet Inference: GPU': {
+      node(NODE_LINUX_GPU) {
+        ws('workspace/nt-ImageInferenceTest') {
+          utils.unpack_and_init('gpu', mx_lib_cpp_example)
+          utils.docker_run('ubuntu_nightly_gpu', 'nightly_test_imagenet_inference', true)
+        }
+      }
+    },
     'KVStore_SingleNode: GPU': {
       node('mxnetlinux-gpu-p3-8xlarge') {
         ws('workspace/nt-KVStoreTest') {

From 06b86da6c3cd2003bb21c5d922bb9ad2872582d1 Mon Sep 17 00:00:00 2001
From: Aaron Markham <markhama@amazon.com>
Date: Tue, 22 Oct 2019 09:39:17 -0700
Subject: [PATCH 496/813] detect number of procs during sphinx build (#16512)

* detect number of procs during sphinx build

* improve variable naming
---
 docs/python_docs/python/Makefile_sphinx | 20 +++++++++++++++++++-
 1 file changed, 19 insertions(+), 1 deletion(-)

diff --git a/docs/python_docs/python/Makefile_sphinx b/docs/python_docs/python/Makefile_sphinx
index 12fb7fcb8982..3b45f53cd620 100644
--- a/docs/python_docs/python/Makefile_sphinx
+++ b/docs/python_docs/python/Makefile_sphinx
@@ -18,8 +18,26 @@
 # Makefile for Sphinx documentation
 #
 
+# Begin number of processors detection
+NPROCS := 1
+OS := $(shell uname)
+export NPROCS
+
+ifeq ($(NUMJOBS),)
+
+ifeq ($(OS),Linux)
+  NPROCS := $(shell grep -c ^processor /proc/cpuinfo)
+else ifeq ($(OS),Darwin)
+  NPROCS := $(shell system_profiler | awk '/Number of CPUs/ {print $$4}{next;}')
+endif # $(OS)
+
+else
+  NPROCS := $(NUMJOBS)
+endif # $(NUMJOBS)
+# End number of processors detection
+
 # You can set these variables from the command line.
-SPHINXOPTS    = -j 32 -c ../scripts
+SPHINXOPTS    = -j$(NPROCS) -c ../scripts
 SPHINXBUILD   = sphinx-build
 PAPER         =
 BUILDDIR      = _build

From 1ffdd47fea27c9da4dbf1096592cf87738eda442 Mon Sep 17 00:00:00 2001
From: Serge Panev <spanev@nvidia.com>
Date: Wed, 23 Oct 2019 00:49:50 +0200
Subject: [PATCH 497/813] Make mrcnn_mask_target arg mask_size a 2d tuple
 (#16567)

Signed-off-by: Serge Panev <spanev@nvidia.com>
---
 src/operator/contrib/mrcnn_mask_target-inl.h   |  8 +++++---
 src/operator/contrib/mrcnn_mask_target.cu      | 10 ++++++----
 tests/python/unittest/test_contrib_operator.py |  2 +-
 3 files changed, 12 insertions(+), 8 deletions(-)

diff --git a/src/operator/contrib/mrcnn_mask_target-inl.h b/src/operator/contrib/mrcnn_mask_target-inl.h
index 891453369089..af297e745487 100644
--- a/src/operator/contrib/mrcnn_mask_target-inl.h
+++ b/src/operator/contrib/mrcnn_mask_target-inl.h
@@ -45,8 +45,8 @@ namespace mrcnn_index {
 struct MRCNNMaskTargetParam : public dmlc::Parameter<MRCNNMaskTargetParam> {
   int num_rois;
   int num_classes;
-  int mask_size;
   int sample_ratio;
+  mxnet::TShape mask_size;
 
   DMLC_DECLARE_PARAMETER(MRCNNMaskTargetParam) {
     DMLC_DECLARE_FIELD(num_rois)
@@ -54,7 +54,8 @@ struct MRCNNMaskTargetParam : public dmlc::Parameter<MRCNNMaskTargetParam> {
     DMLC_DECLARE_FIELD(num_classes)
     .describe("Number of classes.");
     DMLC_DECLARE_FIELD(mask_size)
-    .describe("Size of the pooled masks.");
+    .set_expect_ndim(2).enforce_nonzero()
+    .describe("Size of the pooled masks height and width: (h, w).");
     DMLC_DECLARE_FIELD(sample_ratio).set_default(2)
     .describe("Sampling ratio of ROI align. Set to -1 to use adaptative size.");
   }
@@ -91,7 +92,8 @@ inline bool MRCNNMaskTargetShape(const NodeAttrs& attrs,
   CHECK_EQ(tshape[0], batch_size) << " batch size should be the same for all the inputs.";
 
   // out: 2 * (B, N, C, MS, MS)
-  auto oshape = Shape5(batch_size, num_rois, param.num_classes, param.mask_size, param.mask_size);
+  auto oshape = Shape5(batch_size, num_rois, param.num_classes,
+                       param.mask_size[0], param.mask_size[1]);
   out_shape->clear();
   out_shape->push_back(oshape);
   out_shape->push_back(oshape);
diff --git a/src/operator/contrib/mrcnn_mask_target.cu b/src/operator/contrib/mrcnn_mask_target.cu
index 0170e739d437..779fb0141e64 100644
--- a/src/operator/contrib/mrcnn_mask_target.cu
+++ b/src/operator/contrib/mrcnn_mask_target.cu
@@ -196,15 +196,16 @@ __global__ void MRCNNMaskTargetKernel(const DType *rois,
                                       int num_gtmasks,
                                       int gt_height,
                                       int gt_width,
-                                      int mask_size,
+                                      int mask_size_h,
+                                      int mask_size_w,
                                       int sample_ratio) {
   // computing sampled_masks
   RoIAlignForward(gt_masks, rois, matches, total_out_el,
-                  num_classes, gt_height, gt_width, mask_size, mask_size,
+                  num_classes, gt_height, gt_width, mask_size_h, mask_size_w,
                   sample_ratio, num_rois, num_gtmasks, sampled_masks);
   // computing mask_cls
   int num_masks = batch_size * num_rois * num_classes;
-  int mask_vol = mask_size * mask_size;
+  int mask_vol = mask_size_h * mask_size_w;
   for (int mask_idx = blockIdx.x; mask_idx < num_masks; mask_idx += gridDim.x) {
     int cls_idx = mask_idx % num_classes;
     int roi_idx = (mask_idx / num_classes) % num_rois;
@@ -252,7 +253,8 @@ void MRCNNMaskTargetRun<gpu>(const MRCNNMaskTargetParam& param, const std::vecto
     (rois.dptr_, gt_masks.dptr_, matches.dptr_, cls_targets.dptr_,
     out_masks.dptr_, out_mask_cls.dptr_,
     num_el, batch_size, param.num_classes, param.num_rois,
-    num_gtmasks, gt_height, gt_width, param.mask_size, param.sample_ratio);
+    num_gtmasks, gt_height, gt_width,
+    param.mask_size[0], param.mask_size[1], param.sample_ratio);
     MSHADOW_CUDA_POST_KERNEL_CHECK(MRCNNMaskTargetKernel);
   });
 }
diff --git a/tests/python/unittest/test_contrib_operator.py b/tests/python/unittest/test_contrib_operator.py
index c23be6c47783..e78884f74c71 100644
--- a/tests/python/unittest/test_contrib_operator.py
+++ b/tests/python/unittest/test_contrib_operator.py
@@ -359,7 +359,7 @@ def test_op_mrcnn_mask_target():
 
     num_rois = 2
     num_classes = 4
-    mask_size = 3
+    mask_size = (3, 3)
     ctx = mx.gpu(0)
     # (B, N, 4)
     rois = mx.nd.array([[[2.3, 4.3, 2.2, 3.3],

From 20aa10cb696940b4bb01d58528fb800c6ec9fc9b Mon Sep 17 00:00:00 2001
From: Sheng Zha <szha@users.noreply.github.com>
Date: Tue, 22 Oct 2019 16:03:57 -0700
Subject: [PATCH 498/813] split issue templates (#16558)

---
 .github/ISSUE_TEMPLATE.md                 | 52 -----------------------
 .github/ISSUE_TEMPLATE/bug_report.md      | 36 ++++++++++++++++
 .github/ISSUE_TEMPLATE/feature_request.md | 17 ++++++++
 .github/ISSUE_TEMPLATE/flaky_test.md      | 18 ++++++++
 4 files changed, 71 insertions(+), 52 deletions(-)
 delete mode 100644 .github/ISSUE_TEMPLATE.md
 create mode 100644 .github/ISSUE_TEMPLATE/bug_report.md
 create mode 100644 .github/ISSUE_TEMPLATE/feature_request.md
 create mode 100644 .github/ISSUE_TEMPLATE/flaky_test.md

diff --git a/.github/ISSUE_TEMPLATE.md b/.github/ISSUE_TEMPLATE.md
deleted file mode 100644
index 5279dd24a716..000000000000
--- a/.github/ISSUE_TEMPLATE.md
+++ /dev/null
@@ -1,52 +0,0 @@
-Note: Providing complete information in the most concise form is the best way to get help. This issue template serves as the checklist for essential information to most of the technical issues and bug reports. For non-technical issues and feature requests, feel free to present the information in what you believe is the best form.
-
-For Q & A and discussion, please start a discussion thread at https://discuss.mxnet.io 
-
-## Description
-(Brief description of the problem in no more than 2 sentences.)
-
-## Environment info (Required)
-
-```
-What to do:
-1. Download the diagnosis script from https://raw.githubusercontent.com/apache/incubator-mxnet/master/tools/diagnose.py
-2. Run the script using `python diagnose.py` and paste its output here.
-
-```
-
-Package used (Python/R/Scala/Julia):
-(I'm using ...)
-
-For Scala user, please provide:
-1. Java version: (`java -version`)
-2. Maven version: (`mvn -version`)
-3. Scala runtime if applicable: (`scala -version`)
-
-For R user, please provide R `sessionInfo()`:
-
-## Build info (Required if built from source)
-
-Compiler (gcc/clang/mingw/visual studio):
-
-MXNet commit hash:
-(Paste the output of `git rev-parse HEAD` here.)
-
-Build config:
-(Paste the content of config.mk, or the build command.)
-
-## Error Message:
-(Paste the complete error message, including stack trace.)
-
-## Minimum reproducible example
-(If you are using your own code, please provide a short script that reproduces the error. Otherwise, please provide link to the existing example.)
-
-## Steps to reproduce
-(Paste the commands you ran that produced the error.)
-
-1.
-2.
-
-## What have you tried to solve it?
-
-1.
-2.
diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
new file mode 100644
index 000000000000..4540f8f5f7af
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -0,0 +1,36 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: ''
+labels: 'Bug'
+assignees: ''
+
+---
+## Description
+(A clear and concise description of what the bug is.)
+
+### Error Message
+(Paste the complete error message, including stack trace.)
+
+## To Reproduce
+(If you developed your own code, please provide a short script that reproduces the error. For existing examples, please provide link.)
+
+### Steps to reproduce
+(Paste the commands you ran that produced the error.)
+
+1.
+2.
+
+## What have you tried to solve it?
+
+1.
+2.
+
+## Environment
+
+We recommend using our script for collecting the diagnositc information. Run the following command and paste the outputs below:
+```
+curl --retry 10 -s https://raw.githubusercontent.com/dmlc/gluon-nlp/master/tools/diagnose.py | python
+
+# paste outputs here
+```
diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md
new file mode 100644
index 000000000000..777f7b7ad400
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/feature_request.md
@@ -0,0 +1,17 @@
+---
+name: Feature request
+about: Suggest an idea for this project
+title: ''
+labels: 'Feature request'
+assignees: ''
+
+---
+
+## Description
+(A clear and concise description of what the feature is.)
+- If the proposal is about a new model, provide description of what the model is.
+- If the proposal is about an API, provide mock examples if possible.
+
+## References
+- list reference and related literature
+- list known implementations
diff --git a/.github/ISSUE_TEMPLATE/flaky_test.md b/.github/ISSUE_TEMPLATE/flaky_test.md
new file mode 100644
index 000000000000..9e01ea1af564
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/flaky_test.md
@@ -0,0 +1,18 @@
+---
+name: Flaky test
+about: Report a flaky test
+title: ''
+labels: 'Flaky'
+assignees: ''
+
+---
+## Description
+(The location and name of the flaky test.)
+
+## Occurrences
+(Links to the known occurrences.)
+
+## What have you tried to solve it?
+
+1.
+2.

From 10941abc26a6853cf94c2616ac78f840afd79348 Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Tue, 22 Oct 2019 17:03:08 -0700
Subject: [PATCH 499/813] Dgl ops 2 (#16416)

* add hyperbolic

* add logical ops, batch_dot, sign and divide

* fix divide

* add linear regression, logistic regression

* make arithmetic op readable

* remove hyperbolic

* remove batch dot due to seg fault

* fix numpy ands

* nit space
---
 tests/nightly/test_large_array.py | 168 ++++++++++++++++++++++++------
 1 file changed, 136 insertions(+), 32 deletions(-)

diff --git a/tests/nightly/test_large_array.py b/tests/nightly/test_large_array.py
index 9c2fbd61a9ac..0cb21cedee35 100644
--- a/tests/nightly/test_large_array.py
+++ b/tests/nightly/test_large_array.py
@@ -800,8 +800,7 @@ def test_batchnorm():
 def test_add():
     a = nd.ones(shape=(LARGE_X, SMALL_Y))
     b = nd.ones(shape=(LARGE_X, SMALL_Y))
-    c = b
-    c = c.__add__(a)
+    c = b.__add__(a)
     assert c[0][-1] == 2
     assert c.shape == a.shape
 
@@ -809,8 +808,7 @@ def test_add():
 def test_sub():
     a = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
     b = nd.ones(shape=(LARGE_X, SMALL_Y))
-    c = b
-    c = c.__sub__(a)
+    c = b.__sub__(a)
     assert c[0][-1] == -2
     assert c.shape == a.shape
 
@@ -818,16 +816,14 @@ def test_sub():
 def test_rsub():
     a = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
     b = nd.ones(shape=(LARGE_X, SMALL_Y))
-    c = b
-    c = c.__rsub__(a)
+    c = b.__rsub__(a)
     assert c[0][-1] == 2
     assert c.shape == a.shape
 
 
 def test_neg():
     a = nd.ones(shape=(LARGE_X, SMALL_Y))
-    c = a
-    c = c.__neg__()
+    c = a.__neg__()
     assert c[0][-1] == -1
     assert c.shape == a.shape
 
@@ -835,8 +831,7 @@ def test_neg():
 def test_mul():
     a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
     b = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
-    c = b
-    c = c.__mul__(a)
+    c = b.__mul__(a)
     assert c[0][-1] == 6
     assert c.shape == a.shape
 
@@ -844,17 +839,17 @@ def test_mul():
 def test_div():
     a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
     b = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
-    c = b
-    c = c.__div__(a)
+    c = b.__div__(a)
+    mx_divide = nd.divide(b, a)
     assert c[0][-1] == 3/2
+    assert mx_divide[0][-1] == c[0][-1]
     assert c.shape == a.shape
 
 
 def test_rdiv():
     a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
     b = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
-    c = b
-    c = c.__rdiv__(a)
+    c = b.__rdiv__(a)
     assert c[0][-1] == 2/3
     assert c.shape == a.shape
 
@@ -862,8 +857,7 @@ def test_rdiv():
 def test_mod():
     a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
     b = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
-    c = b
-    c = c.__mod__(a)
+    c = b.__mod__(a)
     assert c[0][-1] == 1
     assert c.shape == a.shape
 
@@ -871,8 +865,7 @@ def test_mod():
 def test_rmod():
     a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
     b = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
-    c = b
-    c = c.__rmod__(a)
+    c = b.__rmod__(a)
     assert c[0][-1] == 2
     assert c.shape == a.shape
 
@@ -880,8 +873,7 @@ def test_rmod():
 def test_imod():
     a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
     b = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
-    c = b
-    c = c.__imod__(a)
+    c = b.__imod__(a)
     assert c[0][-1] == 1
     assert c.shape == a.shape
 
@@ -889,8 +881,7 @@ def test_imod():
 def test_pow():
     a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
     b = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
-    c = b
-    c = c.__pow__(a)
+    c = b.__pow__(a)
     assert c[0][-1] == 9
     assert c.shape == a.shape
 
@@ -898,8 +889,7 @@ def test_pow():
 def test_rpow():
     a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
     b = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
-    c = b
-    c = c.__rpow__(a)
+    c = b.__rpow__(a)
     assert c[0][-1] == 8
     assert c.shape == a.shape
 
@@ -1081,8 +1071,7 @@ def test_log_softmax():
 def test_iadd():
     a = nd.array(np.ones((SMALL_Y, LARGE_X)))
     b = nd.array(np.ones((SMALL_Y, LARGE_X)))
-    c = b
-    c += a
+    c = b + a
     assert c.shape == a.shape
     assert c[0][-1] == 2
 
@@ -1090,8 +1079,7 @@ def test_iadd():
 def test_isub():
     a = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
     b = nd.array(np.ones((SMALL_Y, LARGE_X)))
-    c = a
-    c -= b
+    c = a - b
     assert c.shape == a.shape
     assert c[0][-1] == 2
 
@@ -1099,8 +1087,7 @@ def test_isub():
 def test_imul():
     a = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
     b = nd.array(np.ones((SMALL_Y, LARGE_X)))
-    c = b
-    c *= a
+    c = b * a
     assert c.shape == a.shape
     assert c[0][-1] == 3
 
@@ -1108,8 +1095,7 @@ def test_imul():
 def test_idiv():
     a = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 4)))
     b = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 2)))
-    c = a
-    c /= b
+    c = a / b
     assert c.shape == a.shape
     assert c[0][-1] == 2
 
@@ -1201,6 +1187,76 @@ def test_full():
     assert a[-1][-1] == 3
 
 
+def test_sign():
+    a = mx.nd.random.normal(-1,1, shape=(LARGE_X, SMALL_Y))
+    mx_res = mx.nd.sign(a)
+    assert_almost_equal(mx_res[-1][-1].asnumpy(), np.sign(a[-1][-1].asnumpy()))
+
+
+def test_logical():
+    def test_logical_and(a, b):
+        mx_res = mx.nd.logical_and(a, b)
+        assert_almost_equal(mx_res[-1][-1].asnumpy(), np.logical_and(a[-1][-1].asnumpy(), b[-1][-1].asnumpy()))
+
+    def test_logical_or(a, b):
+        mx_res = mx.nd.logical_or(a, b)
+        assert_almost_equal(mx_res[-1][-1].asnumpy(), np.logical_or(a[-1][-1].asnumpy(), b[-1][-1].asnumpy()))
+
+    def test_logical_not(a, b):
+        mx_res = mx.nd.logical_not(a, b)
+        assert_almost_equal(mx_res[-1][-1].asnumpy(), np.logical_not(a[-1][-1].asnumpy(), b[-1][-1].asnumpy()))
+
+    def test_logical_xor(a, b):
+        mx_res = mx.nd.logical_xor(a, b)
+        assert_almost_equal(mx_res[-1][-1].asnumpy(), np.logical_xor(a[-1][-1].asnumpy(), b[-1][-1].asnumpy()))
+
+    a = mx.nd.ones((LARGE_X, SMALL_Y))
+    b = mx.nd.zeros((LARGE_X, SMALL_Y))
+    test_logical_and(a, b)
+    test_logical_or(a, b)
+    test_logical_not(a, b)
+    test_logical_xor(a, b)
+
+
+def test_regression():
+    shape = (LARGE_X, SMALL_Y)
+
+    def check_regression(symbol, forward, backward, shape):
+        # init executor
+        data_s = mx.symbol.Variable('data')
+        label_s = mx.symbol.Variable('label')
+        out_s = symbol(data=data_s, label=label_s)
+        grad_req = {'data': 'write', 'label': 'null'}
+        exe = out_s.simple_bind(ctx=default_context(), data=shape, label=shape, grad_req=grad_req)
+
+        arg_map = dict(zip(out_s.list_arguments(), exe.arg_arrays))
+        grad_map = dict(zip(out_s.list_arguments(), exe.grad_arrays))
+
+        # init data
+        data = mx.random.uniform(-1, -1, shape)
+        arg_map["data"][:] = data
+        atol = 1e-5
+        density = 0.5
+        stype = 'default'
+        label = arg_map["label"]
+        label[:] = rand_ndarray(shape, stype, density=density)
+        exe.forward(is_train=True)
+        exe.backward()
+        np_out = forward(data.asnumpy())
+        out_grad = backward(np_out, label.asnumpy().reshape(np_out.shape)) / shape[1]
+        assert_almost_equal(exe.outputs[0].asnumpy(), np_out, atol=atol)
+        assert_almost_equal(grad_map["data"].asnumpy(), out_grad, atol=atol)
+
+    check_regression(mx.symbol.LogisticRegressionOutput,
+                     lambda x: 1.0 / (1.0 + np.exp(-x)),
+                     lambda x, y: x - y,
+                     shape)
+    check_regression(mx.symbol.LinearRegressionOutput,
+                     lambda x: x,
+                     lambda x, y: x - y,
+                     shape)
+
+
 def test_astype():
     x = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
     y = x.astype('int32')
@@ -1351,6 +1407,48 @@ def check_tan():
         expected_output = [-.577, -1, 0, 1, .577]
         assert_correctness_of_trigonometric_ops(y, expected_output)
 
+    def check_arcsinh():
+        x = create_input_for_trigonometric_ops([-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2])
+        y = nd.arcsinh(x)
+        # expected ouput for indices=(0, 1, -3, -2, -1) after applying arcsinh()
+        expected_output = [np.arcsinh(-np.pi/2), np.arcsinh(-np.pi/4), 0, np.arcsinh(np.pi/4), np.arcsinh(np.pi/2)]
+        assert_correctness_of_trigonometric_ops(y, expected_output)
+
+    def check_arccosh():
+        x = create_input_for_trigonometric_ops([1, np.pi/2, 3*np.pi/4, np.pi])
+        y = nd.arccosh(x)
+        # expected ouput for indices=(0, 1, -3, -2, -1) after applying arccosh()
+        expected_output = [0, np.arccosh(np.pi/2), np.arccosh(3*np.pi/4), np.arccosh(np.pi)]
+        assert_correctness_of_trigonometric_ops(y, expected_output)
+
+    def check_arctanh():
+        x = create_input_for_trigonometric_ops([-1/4, -1/2, 0, 1/4, 1/2])
+        y = nd.arctanh(x)
+        # expected ouput for indices=(0, 1, -3, -2, -1) after applying arctanh()
+        expected_output = [np.arctanh(-1/4), np.arctanh(-1/2), 0, np.arctanh(1/4), np.arctanh(1/2)]
+        assert_correctness_of_trigonometric_ops(y, expected_output)
+
+    def check_sinh():
+        x = create_input_for_trigonometric_ops([-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2])
+        y = nd.sinh(x)
+        # expected ouput for indices=(0, 1, -3, -2, -1) after applying sinh()
+        expected_output = [np.sinh(-np.pi/2), np.sinh(-np.pi/4), 0, np.sinh(np.pi/4), np.sinh(np.pi/2)]
+        assert_correctness_of_trigonometric_ops(y, expected_output)
+
+    def check_cosh():
+        x = create_input_for_trigonometric_ops([0, 1, np.pi/2, 3*np.pi/4, np.pi])
+        y = nd.cosh(x)
+        # expected ouput for indices=(0, 1, -3, -2, -1) after applying cosh()
+        expected_output = [1, np.cosh(1), np.cosh(np.pi/2), np.cosh(3*np.pi/4), np.cosh(np.pi)]
+        assert_correctness_of_trigonometric_ops(y, expected_output)
+
+    def check_tanh():
+        x = create_input_for_trigonometric_ops([-1/4, -1/2, 0, 1/4, 1/2])
+        y = nd.tanh(x)
+        # expected ouput for indices=(0, 1, -3, -2, -1) after applying tanh()
+        expected_output = [np.tanh(-1/4), np.tanh(-1/2), 0, np.tanh(1/4), np.tanh(1/2)]
+        assert_correctness_of_trigonometric_ops(y, expected_output)
+
     def check_radians():
         x = create_input_for_trigonometric_ops([0, 90, 180, 270, 360])
         y = nd.radians(x)
@@ -1371,6 +1469,12 @@ def check_degrees():
     check_sin()
     check_cos()
     check_tan()
+    check_arcsinh()
+    check_arccosh()
+    check_arctanh()
+    check_sinh()
+    check_cosh()
+    check_tanh()
     check_radians()
     check_degrees()
 

From b05d72ac5697904e8d00954402174a7891c65ece Mon Sep 17 00:00:00 2001
From: Dick Carter <dcarter@nvidia.com>
Date: Tue, 22 Oct 2019 21:35:31 -0700
Subject: [PATCH 500/813] RNNOp to call cudaEventCreate lazily (#16584)

* RNNOp to call cudaEventCreate lazily

* Saw unrelated failure.  Trigger CI.
---
 src/operator/rnn-inl.h | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/src/operator/rnn-inl.h b/src/operator/rnn-inl.h
index cf6fe10fd328..ead7501a48b0 100644
--- a/src/operator/rnn-inl.h
+++ b/src/operator/rnn-inl.h
@@ -493,7 +493,6 @@ class RNNOp {
 
     CUDNN_CALL(cudnnCreateRNNDescriptor(&rnn_desc_));
     CUDNN_CALL(cudnnCreateDropoutDescriptor(&dropout_desc_));
-    CUDA_CALL(cudaEventCreateWithFlags(&dgrad_sync_event_, cudaEventDisableTiming));
 
 #if MXNET_USE_CUDNN_GE_7200
     CUDNN_CALL(cudnnCreateRNNDataDescriptor(&x_data_desc_));
@@ -538,7 +537,8 @@ class RNNOp {
     CUDNN_CALL(cudnnDestroyFilterDescriptor(dw_desc_));
     CUDNN_CALL(cudnnDestroyRNNDescriptor(rnn_desc_));
     CUDNN_CALL(cudnnDestroyDropoutDescriptor(dropout_desc_));
-    CUDA_CALL(cudaEventDestroy(dgrad_sync_event_));
+    if (dgrad_sync_event_created_)
+      CUDA_CALL(cudaEventDestroy(dgrad_sync_event_));
 
     if (init_cudnn_) {
       for (size_t i = 0; i < x_desc_vec_.size(); ++i) {
@@ -1502,6 +1502,10 @@ class RNNOp {
     if (CUDNN_VERSION <= 7604 && dgrad_sync_needed_) {
       // Without blocking the CPU, create a synchronization point of all current GPU activity.  No
       // need to call cudaStreamWaitEvent- cudaEventRecord on the legacy default stream suffices.
+      if (!dgrad_sync_event_created_) {
+        CUDA_CALL(cudaEventCreateWithFlags(&dgrad_sync_event_, cudaEventDisableTiming));
+        dgrad_sync_event_created_ = true;
+      }
       CUDA_CALL(cudaEventRecord(dgrad_sync_event_, cudaStreamLegacy));
     }
   }
@@ -1535,6 +1539,7 @@ class RNNOp {
 
   cudnnTensorFormat_t format_;
   cudaEvent_t dgrad_sync_event_;
+  bool dgrad_sync_event_created_ = false;
   bool dgrad_sync_needed_ = false;
 #endif  // MXNET_USE_CUDNN
   bool init_space_, temp_init_space_;

From 5296ddc9eb34788f28c4a92da51e1228bf35c8e0 Mon Sep 17 00:00:00 2001
From: Sheng Zha <szha@users.noreply.github.com>
Date: Tue, 22 Oct 2019 21:57:19 -0700
Subject: [PATCH 501/813] add encoding to the stub files for potential utf8
 char in doc strings (#16580)

---
 python/mxnet/base.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/python/mxnet/base.py b/python/mxnet/base.py
index dd5fcf0f6db9..cbd9abe9d754 100644
--- a/python/mxnet/base.py
+++ b/python/mxnet/base.py
@@ -676,6 +676,7 @@ def get_module_file(module_name):
                                    'from ..base import _Null'],
                         'ndarray': ['from ._internal import NDArrayBase',
                                     'from ..base import _Null']}
+        module_file.write('# coding: utf-8')
         module_file.write('# File content is auto-generated. Do not modify.' + os.linesep)
         module_file.write('# pylint: skip-file' + os.linesep)
         module_file.write(os.linesep.join(dependencies[module_name.split('.')[1]]))

From 91ad2664e1c83c80ff318b29fcd32608bee90d04 Mon Sep 17 00:00:00 2001
From: Talia <31782251+TEChopra1000@users.noreply.github.com>
Date: Wed, 23 Oct 2019 09:32:18 -0700
Subject: [PATCH 502/813] fixed broken links across multiple files (#16581)

---
 .../getting-started/crash-course/5-predict.md        |  2 +-
 .../getting-started/crash-course/6-use_gpus.md       |  2 +-
 .../gluon_from_experiment_to_deployment.md           | 10 +++++-----
 .../tutorials/getting-started/to-mxnet/pytorch.md    | 12 ++++++------
 .../python/tutorials/packages/gluon/image/mnist.md   |  2 +-
 .../src/pages/api/architecture/note_data_loading.md  |  2 +-
 .../docs/tutorials/mxnet_cpp_inference_tutorial.md   | 10 +++++-----
 .../src/pages/api/faq/distributed_training.md        |  6 +++---
 docs/static_site/src/pages/api/faq/float16.md        |  8 ++++----
 .../src/pages/api/faq/gradient_compression.md        |  2 +-
 .../src/pages/api/faq/model_parallel_lstm.md         |  2 +-
 docs/static_site/src/pages/api/faq/recordio.md       |  1 -
 .../src/pages/get_started/build_from_source.md       |  2 +-
 julia/docs/src/tutorial/char-lstm.md                 |  6 +++---
 julia/docs/src/tutorial/mnist.md                     |  2 +-
 julia/docs/src/user-guide/overview.md                |  2 --
 julia/examples/char-lstm/README.md                   |  2 +-
 17 files changed, 35 insertions(+), 38 deletions(-)

diff --git a/docs/python_docs/python/tutorials/getting-started/crash-course/5-predict.md b/docs/python_docs/python/tutorials/getting-started/crash-course/5-predict.md
index 7a7738d8df1b..9afe95b58403 100644
--- a/docs/python_docs/python/tutorials/getting-started/crash-course/5-predict.md
+++ b/docs/python_docs/python/tutorials/getting-started/crash-course/5-predict.md
@@ -21,7 +21,7 @@ A saved model can be used in multiple places, such as to continue training, to f
 
 ## Prerequisites
 
-Please run the [previous tutorial](train.md) to train the network and save its parameters to file. You will need this file to run the following steps.
+Please run the [previous tutorial](4-train.html) to train the network and save its parameters to file. You will need this file to run the following steps.
 
 ```{.python .input  n=1}
 from mxnet import nd
diff --git a/docs/python_docs/python/tutorials/getting-started/crash-course/6-use_gpus.md b/docs/python_docs/python/tutorials/getting-started/crash-course/6-use_gpus.md
index b78c38ab7077..a0788ba7df2d 100644
--- a/docs/python_docs/python/tutorials/getting-started/crash-course/6-use_gpus.md
+++ b/docs/python_docs/python/tutorials/getting-started/crash-course/6-use_gpus.md
@@ -99,7 +99,7 @@ net(x)
 
 Finally, we show how to use multiple GPUs to jointly train a neural network through data parallelism. Let's assume there are *n* GPUs. We split each data batch into *n* parts, and then each GPU will run the forward and backward passes using one part of the data.
 
-Let's first copy the data definitions and the transform function from the [previous tutorial](predict.md).
+Let's first copy the data definitions and the transform function from the [previous tutorial](5-predict.html).
 
 ```{.python .input}
 batch_size = 256
diff --git a/docs/python_docs/python/tutorials/getting-started/gluon_from_experiment_to_deployment.md b/docs/python_docs/python/tutorials/getting-started/gluon_from_experiment_to_deployment.md
index 8d2c4e100c76..b1f65e682263 100644
--- a/docs/python_docs/python/tutorials/getting-started/gluon_from_experiment_to_deployment.md
+++ b/docs/python_docs/python/tutorials/getting-started/gluon_from_experiment_to_deployment.md
@@ -20,7 +20,7 @@
 
 ## Overview
 MXNet Gluon API comes with a lot of great features, and it can provide you everything you need: from experimentation to deploying the model. In this tutorial, we will walk you through a common use case on how to build a model using gluon, train it on your data, and deploy it for inference.
-This tutorial covers training and inference in Python, please continue to [C++ inference part](https://mxnet.apache.org/versions/master/tutorials/c++/mxnet_cpp_inference_tutorial.html) after you finish.
+This tutorial covers training and inference in Python, please continue to [C++ inference part](/api/cpp/docs/tutorials/cpp_inference) after you finish.
 
 Let's say you need to build a service that provides flower species recognition. A common problem is that you don't have enough data to train a good model. In such cases, a technique called Transfer Learning can be used to make a more robust model.
 In Transfer Learning we make use of a pre-trained model that solves a related task, and was trained on a very large standard dataset, such as ImageNet. ImageNet is from a different domain, but we can utilize the knowledge in this pre-trained model to perform the new task at hand.
@@ -77,7 +77,7 @@ from mxnet.gluon.data.vision import transforms
 from mxnet.gluon.model_zoo.vision import resnet50_v2
 ```
 
-Next, we define the hyper-parameters that we will use for fine-tuning. We will use the [MXNet learning rate scheduler](../packages/gluon/training/learning_rates/learning_rate_schedules.html) to adjust learning rates during training.
+Next, we define the hyper-parameters that we will use for fine-tuning. We will use the [MXNet learning rate scheduler](/api/python/docs/tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html) to adjust learning rates during training.
 Here we set the `epochs` to 1 for quick demonstration, please change to 40 for actual training.
 
 ```python
@@ -161,7 +161,7 @@ test_data = gluon.data.DataLoader(
 
 We will use pre-trained ResNet50_v2 model which was pre-trained on the [ImageNet Dataset](http://www.image-net.org/) with 1000 classes. To match the classes in the Flower dataset, we must redefine the last softmax (output) layer to be 102, then initialize the parameters.
 
-Before we go to training, one unique Gluon feature you should be aware of is hybridization. It allows you to convert your imperative code to a static symbolic graph, which is much more efficient to execute. There are two main benefits of hybridizing your model: better performance and easier serialization for deployment. The best part is that it's as simple as just calling `net.hybridize()`. To know more about Gluon hybridization, please follow the [hybridization tutorial](https://mxnet.apache.org/tutorials/gluon/hybrid.html).
+Before we go to training, one unique Gluon feature you should be aware of is hybridization. It allows you to convert your imperative code to a static symbolic graph, which is much more efficient to execute. There are two main benefits of hybridizing your model: better performance and easier serialization for deployment. The best part is that it's as simple as just calling `net.hybridize()`. To know more about Gluon hybridization, please follow the [hybridization tutorial](/api/python/docs/tutorials/packages/gluon/blocks/hybridize.html).
 
 
 
@@ -265,7 +265,7 @@ finetune_net.export("flower-recognition", epoch=epochs)
 ## Load the model and run inference using the MXNet Module API
 
 MXNet provides various useful tools and interfaces for deploying your model for inference. For example, you can use [MXNet Model Server](https://github.com/awslabs/mxnet-model-server) to start a service and host your trained model easily.
-Besides that, you can also use MXNet's different language APIs to integrate your model with your existing service. We provide [Python](https://mxnet.apache.org/api/python/module/module.html),    [Java](https://mxnet.apache.org/api/java/index.html), [Scala](https://mxnet.apache.org/api/scala/index.html), and [C++](https://mxnet.apache.org/api/c++/index.html) APIs.
+Besides that, you can also use MXNet's different language APIs to integrate your model with your existing service. We provide [Python](/api/python.html),    [Java](/api/java.html), [Scala](/api/scala.html), and [C++](/api/cpp) APIs.
 
 Here we will briefly introduce how to run inference using Module API in Python. There is more detailed explanation available in the [Predict Image Tutorial](https://mxnet.apache.org/tutorials/python/predict_image.html).
 In general, prediction consists of the following steps:
@@ -315,7 +315,7 @@ You can continue to the [next tutorial](https://mxnet.apache.org/versions/master
 
 You can also find more ways to run inference and deploy your models here:
 1. [Java Inference examples](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer)
-2. [Scala Inference examples](https://mxnet.apache.org/tutorials/scala/)
+2. [Scala Inference examples](/api/scala/docs/tutorials/infer)
 4. [MXNet Model Server Examples](https://github.com/awslabs/mxnet-model-server/tree/master/examples)
 
 ## References
diff --git a/docs/python_docs/python/tutorials/getting-started/to-mxnet/pytorch.md b/docs/python_docs/python/tutorials/getting-started/to-mxnet/pytorch.md
index d7720bac4348..1ab490fbaa42 100644
--- a/docs/python_docs/python/tutorials/getting-started/to-mxnet/pytorch.md
+++ b/docs/python_docs/python/tutorials/getting-started/to-mxnet/pytorch.md
@@ -164,7 +164,7 @@ mx_trainer = gluon.Trainer(mx_net.collect_params(),
                            'sgd', {'learning_rate': 0.1})
 ```
 
-The code difference between frameworks is small. The main difference is that in Apache MXNet we use [Trainer](https://mxnet.apache.org/api/python/docs/api/gluon/mxnet.gluon.Trainer.html) class, which accepts optimization algorithm as an argument. We also use [.collect_params()](/api/python/docs/api/gluon/_autogen/mxnet.gluon.nn.Block.collect_params.html) method to get parameters of the network.
+The code difference between frameworks is small. The main difference is that in Apache MXNet we use [Trainer](/api/python/docs/api/gluon/trainer.html) class, which accepts optimization algorithm as an argument. We also use [.collect_params()](/api/python/docs/api/gluon/block.html#mxnet.gluon.Block.collect_params) method to get parameters of the network.
 
 ### 4. Training
 
@@ -212,13 +212,13 @@ Some of the differences in Apache MXNet when compared to PyTorch are as follows:
 
 * In Apache MXNet, you don't need to flatten the 4-D input into 2-D when feeding the data into forward pass.
 
-* In Apache MXNet, you need to perform the calculation within the [autograd.record()](/api/python/docs/api/gluon-related/_autogen/mxnet.autograd.record.html) scope so that it can be automatically differentiated in the backward pass.
+* In Apache MXNet, you need to perform the calculation within the [autograd.record()](/api/python/docs/api/autograd/index.html?autograd%20record#mxnet.autograd.record) scope so that it can be automatically differentiated in the backward pass.
 
 * It is not necessary to clear the gradient every time as with PyTorch's `trainer.zero_grad()` because by default the new gradient is written in, not accumulated.
 
-* You need to specify the update step size (usually batch size) when performing [step()](/api/python/docs/api/gluon/_autogen/mxnet.gluon.Trainer.step.html) on the trainer.
+* You need to specify the update step size (usually batch size) when performing [step()](/api/python/docs/api/gluon/trainer.html?#mxnet.gluon.Trainer.step) on the trainer.
 
-* You need to call [.asscalar()](/api/python/docs/api/ndarray/_autogen/mxnet.ndarray.NDArray.asscalar.html) to turn a multidimensional array into a scalar.
+* You need to call [.asscalar()](/api/python/docs/api/ndarray/ndarray.html?#mxnet.ndarray.NDArray.asscalar) to turn a multidimensional array into a scalar.
 
 * In this sample, Apache MXNet is twice as fast as PyTorch. Though you need to be cautious with such toy comparisons.
 
@@ -230,9 +230,9 @@ As we saw above, Apache MXNet Gluon API and PyTorch have many similarities. The
 
 While Apache MXNet Gluon API is very similar to PyTorch, there are some extra functionality that can make your code even faster.
 
-* Check out [Hybridize tutorial](/api/python/docs/guide/packages/gluon/hybridize.html) to learn how to write imperative code which can be converted to symbolic one.
+* Check out [Hybridize tutorial](/api/python/docs/tutorials/packages/gluon/blocks/hybridize.html) to learn how to write imperative code which can be converted to symbolic one.
 
-* Also, check out how to extend Apache MXNet with your own [custom layers](/api/python/docs/guide/extend/custom_layer.html).
+* Also, check out how to extend Apache MXNet with your own [custom layers](/api/python/docs/tutorials/packages/gluon/blocks/custom-layer.html?custom_layers).
 
 ## Appendix
 
diff --git a/docs/python_docs/python/tutorials/packages/gluon/image/mnist.md b/docs/python_docs/python/tutorials/packages/gluon/image/mnist.md
index 8a3d8229413b..a6898278edf6 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/image/mnist.md
+++ b/docs/python_docs/python/tutorials/packages/gluon/image/mnist.md
@@ -112,7 +112,7 @@ to train the MLP network we defined above.
 
 For our training, we will make use of the stochastic gradient descent (SGD) optimizer. In particular, we'll be using mini-batch SGD. Standard SGD processes train data one example at a time. In practice, this is very slow and one can speed up the process by processing examples in small batches. In this case, our batch size will be 100, which is a reasonable choice. Another parameter we select here is the learning rate, which controls the step size the optimizer takes in search of a solution. We'll pick a learning rate of 0.02, again a reasonable choice. Settings such as batch size and learning rate are what are usually referred to as hyper-parameters. What values we give them can have a great impact on training performance.
 
-We will use [Trainer](https://mxnet.io/api/python/docs/api/gluon/mxnet.gluon.Trainer.html) class to apply the
+We will use [Trainer](/api/python/docs/api/gluon/trainer.html) class to apply the
 [SGD optimizer](https://mxnet.io/api/python/docs/api/gluon-related/_autogen/mxnet.optimizer.SGD.html) on the
 initialized parameters.
 
diff --git a/docs/static_site/src/pages/api/architecture/note_data_loading.md b/docs/static_site/src/pages/api/architecture/note_data_loading.md
index 1279d0361e5f..01bf1f23f600 100644
--- a/docs/static_site/src/pages/api/architecture/note_data_loading.md
+++ b/docs/static_site/src/pages/api/architecture/note_data_loading.md
@@ -125,7 +125,7 @@ then compress into JPEG format.
 After that, we save a header that indicates the index and label
 for that image to be used when constructing the *Data* field for that record.
 We then pack several images together into a file.
-You may want to also review the [example using im2rec.py to create a RecordIO dataset](https://mxnet.apache.org/tutorials/basic/data.html#loading-data-using-image-iterators).
+You may want to also review the [example using im2rec.py to create a RecordIO dataset](https://mxnet.apache.org/api/faq/recordio).
 
 ### Access Arbitrary Parts Of Data
 
diff --git a/docs/static_site/src/pages/api/cpp/docs/tutorials/mxnet_cpp_inference_tutorial.md b/docs/static_site/src/pages/api/cpp/docs/tutorials/mxnet_cpp_inference_tutorial.md
index 9392eca2977f..0d96817560d0 100644
--- a/docs/static_site/src/pages/api/cpp/docs/tutorials/mxnet_cpp_inference_tutorial.md
+++ b/docs/static_site/src/pages/api/cpp/docs/tutorials/mxnet_cpp_inference_tutorial.md
@@ -29,7 +29,7 @@ tag: cpp
 ## Overview
 MXNet provides various useful tools and interfaces for deploying your model for inference. For example, you can use [MXNet Model Server](https://github.com/awslabs/mxnet-model-server) to start a service and host your trained model easily.
 Besides that, you can also use MXNet's different language APIs to integrate your model with your existing service. We provide [Python]({{'/api/python/docs/api/symbol-related/mxnet.module'|relative_url}}),    [Java]({{'/api/java/docs/api'|relative_url}}), [Scala]({{'/api/scala/docs/api'|relative_url}}), and [C++]({{'/api/cpp/docs/api'|relative_url}}) APIs.
-We will focus on the MXNet C++ API. We have slightly modified the code in [C++ Inference Example](https://github.com/apache/incubator-mxnet/tree/master/example/inference) for our use case.
+We will focus on the MXNet C++ API. We have slightly modified the code in [C++ Inference Example](https://github.com/apache/incubator-mxnet/tree/master/cpp-package/example/inference) for our use case.
 
 ## Prerequisites
 
@@ -105,7 +105,7 @@ class Predictor {
 
 ### Load the model, synset file, and normalization values
 
-In the Predictor constructor, you need to provide paths to saved json and param files. After that, add the following methods `LoadModel` and `LoadParameters` to load the network and its parameters. This part is the same as [the example](https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/inference/inception_inference.cpp).
+In the Predictor constructor, you need to provide paths to saved json and param files. After that, add the following methods `LoadModel` and `LoadParameters` to load the network and its parameters. This part is the same as [the example](https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/inference/imagenet_inference.cpp).
 
 Next, we need to load synset file, and normalization values. We have made the following change since our synset file contains flower names and we used both mean and standard deviation for image normalization.
 
@@ -280,12 +280,12 @@ Then it will predict your image:
 
 Now you can explore more ways to run inference and deploy your models:
 1. [Java Inference examples](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer)
-2. [Scala Inference examples]({{'/api/scala/docs/tutorials'|relative_url}})
-3. [ONNX model inference examples]({{'/api/python/docs/tutorials/deploy/index.html'|relative_url}})
+2. [Scala Inference examples](/api/scala/docs/tutorials)
+3. [ONNX model inference examples](/api/python/docs/tutorials/deploy/index.html)
 4. [MXNet Model Server Examples](https://github.com/awslabs/mxnet-model-server/tree/master/examples)
 
 ## References
 
-1. [Gluon end to end tutorial]({{'/api/python/docs/tutorials/packages/gluon/gluon_from_experiment_to_deployment.html'|relative_url}})
+1. [Gluon end to end tutorial](/api/python/docs/tutorials/getting-started/gluon_from_experiment_to_deployment.html)
 2. [Gluon C++ inference example](https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/inference/)
 3. [Gluon C++ package](https://github.com/apache/incubator-mxnet/tree/master/cpp-package)
diff --git a/docs/static_site/src/pages/api/faq/distributed_training.md b/docs/static_site/src/pages/api/faq/distributed_training.md
index caf0123b7aea..622ace60f780 100644
--- a/docs/static_site/src/pages/api/faq/distributed_training.md
+++ b/docs/static_site/src/pages/api/faq/distributed_training.md
@@ -91,7 +91,7 @@ In the case of distributed training though, we would need to divide the dataset
 
 Typically, this split of data for each worker happens through the data iterator,
 on passing the number of parts and the index of parts to iterate over.
-Some iterators in MXNet that support this feature are [mxnet.io.MNISTIterator]({{'//api/mxnet/io/index.html#mxnet.io.MNISTIter'|relative_url}}) and [mxnet.io.ImageRecordIter]({{'/api/mxnet/io/index.html#mxnet.io.ImageRecordIter'|relative_url}}).
+Some iterators in MXNet that support this feature are [mxnet.io.MNISTIterator](/api/python/docs/api/mxnet/io/index.html?MNISTIter#mxnet.io.MNISTIter) and [mxnet.io.ImageRecordIter](api/python/docs/api/mxnet/io/index.html?imagerecorditer#mxnet.io.ImageRecordIter).
 If you are using a different iterator, you can look at how the above iterators implement this.
 We can use the kvstore object to get the number of workers (`kv.num_workers`) and rank of the current worker (`kv.rank`).
 These can be passed as arguments to the iterator.
@@ -101,7 +101,7 @@ to see an example usage.
 ### Updating weights
 KVStore server supports two modes, one which aggregates the gradients and updates the weights using those gradients, and second where the server only aggregates gradients. In the latter case, when a worker process pulls from kvstore, it gets the aggregated gradients. The worker then uses these gradients and applies the weights locally.
 
-When using Gluon there is an option to choose between these modes by passing `update_on_kvstore` variable when you create the [Trainer]({{'/api/python/docs/api/gluon/mxnet.gluon.Trainer.html'|relative_url}}) object like this:
+When using Gluon there is an option to choose between these modes by passing `update_on_kvstore` variable when you create the [Trainer](/api/python/docs/api/gluon/trainer.html) object like this:
 
 ```
 trainer = gluon.Trainer(net.collect_params(), optimizer='sgd',
@@ -190,7 +190,7 @@ git clone --recursive https://github.com/apache/incubator-mxnet
 ```
 
 #### Example
-Let us consider training a VGG11 model on the CIFAR10 dataset using [example/gluon/image_classification.py](https://github.com/apache/incubator-mxnet/blob/master/example/gluon/image_classification.py).
+Let us consider training a VGG11 model on the CIFAR10 dataset using [example/gluon/image_classification.py](https://github.com/apache/incubator-mxnet/blob/master/tools/launch.py).
 ```
 cd example/gluon/
 ```
diff --git a/docs/static_site/src/pages/api/faq/float16.md b/docs/static_site/src/pages/api/faq/float16.md
index d824acb3ce6d..e63bf87ac68f 100644
--- a/docs/static_site/src/pages/api/faq/float16.md
+++ b/docs/static_site/src/pages/api/faq/float16.md
@@ -39,7 +39,7 @@ The float16 data type is a 16 bit floating point representation according to the
 - CUDA 9 or higher
 - cuDNN v7 or higher
 
-This tutorial also assumes understanding of how to train a network with float32 (the default). Please refer to [logistic regression tutorial](https://mxnet.apache.org/versions/master/tutorials/gluon/logistic_regression_explained.html) to get started with Apache MXNet and Gluon API. This tutorial focuses on the changes needed to switch from float32 to mixed precision and tips on achieving the best performance with mixed precision.
+This tutorial also assumes understanding of how to train a network with float32 (the default). Please refer to [logistic regression tutorial](/api/python/docs/tutorials/getting-started/logistic_regression_explained.html) to get started with Apache MXNet and Gluon API. This tutorial focuses on the changes needed to switch from float32 to mixed precision and tips on achieving the best performance with mixed precision.
 
 ## Using the Gluon API
 
@@ -47,13 +47,13 @@ This tutorial also assumes understanding of how to train a network with float32
 
 With Gluon API, you need to take care of three things to convert a model to support computation with float16.
 
-1. Cast Gluon `Block`'s parameters and expected input type to float16 by calling the [cast]({{'/api/python/docs/api/gluon/mxnet.gluon.nn.Block.html#mxnet.gluon.nn.Block.cast'|relative_url}}) method of the `Block` representing the network.
+1. Cast Gluon `Block`'s parameters and expected input type to float16 by calling the [cast](/api/python/docs/api/gluon/block.html?cast#mxnet.gluon.Block.cast) method of the `Block` representing the network.
 
 ```python
 net.cast('float16')
 ```
 
-2. Ensure the data input to the network is of float16 type. If your `DataLoader` or `Iterator` produces output in another datatype, then you would have to cast your data. There are different ways you can do this. The easiest would be to use the [astype]({{'/api/python/docs/api/ndarray/_autogen/mxnet.ndarray.NDArray.astype.html#mxnet.ndarray.NDArray.astype'|relative_url}}) method of NDArrays.
+2. Ensure the data input to the network is of float16 type. If your `DataLoader` or `Iterator` produces output in another datatype, then you would have to cast your data. There are different ways you can do this. The easiest would be to use the [astype](/api/python/docs/api/ndarray/ndarray.html?astype#mxnet.ndarray.NDArray.astype) method of NDArrays.
 
 ```python
 data = data.astype('float16', copy=False)
@@ -98,7 +98,7 @@ net.features = pretrained_net.features
 net.cast('float16')
 ```
 
-You can check the parameters of the model by calling [summary]({{'/api/python/docs/api/gluon/mxnet.gluon.nn.Block.html#mxnet.gluon.nn.Block.summary'|relative_url}}) with some fake data. Notice the provided `dtype=np.float16` in the line below. As it was mentioned earlier, we have to provide data as float16 as well.
+You can check the parameters of the model by calling [summary](/api/python/docs/api/gluon/block.html?block%20summary#mxnet.gluon.Block.summary) with some fake data. Notice the provided `dtype=np.float16` in the line below. As it was mentioned earlier, we have to provide data as float16 as well.
 
 ```python
 net.summary(mx.nd.uniform(shape=(1, 3, 224, 224), dtype=np.float16))
diff --git a/docs/static_site/src/pages/api/faq/gradient_compression.md b/docs/static_site/src/pages/api/faq/gradient_compression.md
index 1f4c5fb21903..e2b47c646ada 100644
--- a/docs/static_site/src/pages/api/faq/gradient_compression.md
+++ b/docs/static_site/src/pages/api/faq/gradient_compression.md
@@ -110,7 +110,7 @@ A reference `gluon` implementation with a gradient compression option can be fou
 mod = mx.mod.Module(..., compression_params={'type’:'2bit', 'threshold':0.5})
 ```
 
-A `module` example is provided with [this guide for setting up MXNet with distributed training](https://mxnet.apache.org/versions/master/faq/multi_devices.html#distributed-training-with-multiple-machines). It comes with the option of turning on gradient compression as an argument to the [train_mnist.py script](https://github.com/apache/incubator-mxnet/blob/master/example/image-classification/train_mnist.py).
+A `module` example is provided with [this guide for setting up MXNet with distributed training](/api/faq/distributed_training). It comes with the option of turning on gradient compression as an argument to the [train_mnist.py script](https://github.com/apache/incubator-mxnet/blob/master/example/image-classification/train_mnist.py).
 
 ### Configuration Details
 
diff --git a/docs/static_site/src/pages/api/faq/model_parallel_lstm.md b/docs/static_site/src/pages/api/faq/model_parallel_lstm.md
index 60df280b38fe..08cf6be76a90 100644
--- a/docs/static_site/src/pages/api/faq/model_parallel_lstm.md
+++ b/docs/static_site/src/pages/api/faq/model_parallel_lstm.md
@@ -37,7 +37,7 @@ One key strength of _MXNet_ is its ability to leverage
 powerful heterogeneous hardware environments to achieve significant speedups.
 
 There are two primary ways that we can spread a workload across multiple devices.
-In a previous document, [we addressed data parallelism](multi_devices),
+In a previous document, [we addressed data parallelism](/api/faq/distributed_training),
 an approach in which samples within a batch are divided among the available devices.
 With data parallelism, each device stores a complete copy of the model.
 Here, we explore _model parallelism_, a different approach.
diff --git a/docs/static_site/src/pages/api/faq/recordio.md b/docs/static_site/src/pages/api/faq/recordio.md
index 75407cb3da5f..2e8fcdd647f3 100644
--- a/docs/static_site/src/pages/api/faq/recordio.md
+++ b/docs/static_site/src/pages/api/faq/recordio.md
@@ -38,7 +38,6 @@ We provide two tools for creating a RecordIO dataset.
 * [im2rec.py](https://github.com/apache/incubator-mxnet/blob/master/tools/im2rec.py) - implements the tool using the Python API.
 
 Both provide the same output: a RecordIO dataset.
-You may want to also review the [example using real-world data with im2rec.py.](https://mxnet.apache.org/tutorials/basic/data.html#loading-data-using-image-iterators)
 
 ### Prerequisites
 
diff --git a/docs/static_site/src/pages/get_started/build_from_source.md b/docs/static_site/src/pages/get_started/build_from_source.md
index e8f7d468b399..20a4542461c4 100644
--- a/docs/static_site/src/pages/get_started/build_from_source.md
+++ b/docs/static_site/src/pages/get_started/build_from_source.md
@@ -98,7 +98,7 @@ Those can be extended with [LAPACK (Linear Algebra Package)](https://github.com/
 
 MXNet supports multiple mathematical backends for computations on the CPU:
 * [Apple Accelerate](https://developer.apple.com/documentation/accelerate)
-* [ATLAS](https://math-atlas.sourceforge.net/)
+* [ATLAS](http://math-atlas.sourceforge.net/)
 * [MKL](https://software.intel.com/en-us/intel-mkl) (MKL, MKLML)
 * [MKL-DNN](https://github.com/intel/mkl-dnn)
 * [OpenBLAS](https://www.openblas.net/)
diff --git a/julia/docs/src/tutorial/char-lstm.md b/julia/docs/src/tutorial/char-lstm.md
index bc7f7b471d94..ab7e9352b5ab 100644
--- a/julia/docs/src/tutorial/char-lstm.md
+++ b/julia/docs/src/tutorial/char-lstm.md
@@ -31,7 +31,7 @@ networks yet, the example shown here is an implementation of LSTM by
 using the default FeedForward model via explicitly unfolding over time.
 We will be using fixed-length input sequence for training. The code is
 adapted from the [char-rnn example for MXNet's Python
-binding](https://github.com/dmlc/mxnet/blob/master/example/rnn/char_lstm.ipynb),
+binding](https://github.com/dmlc/mxnet-notebooks/blob/master/python/tutorials/char_lstm.ipynb),
 which demonstrates how to use low-level
 [Symbolic API](@ref) to build customized neural
 network models directly.
@@ -165,7 +165,7 @@ char-lstm. To train the model, we just follow the standard high-level
 API. Firstly, we construct a LSTM symbolic architecture:
 
 Note all the parameters are defined in
-[examples/char-lstm/config.jl](https://github.com/dmlc/MXNet.jl/blob/master/examples/char-lstm/config.jl).
+[examples/char-lstm/config.jl](https://github.com/apache/incubator-mxnet/blob/master/julia/examples/char-lstm/config.jl).
 Now we load the text file and define the data provider. The data
 `input.txt` we used in this example is [a tiny Shakespeare
 dataset](https://github.com/dmlc/web-data/tree/master/mxnet/tinyshakespeare).
@@ -318,6 +318,6 @@ illustrations](http://colah.github.io/posts/2015-08-Understanding-LSTMs/),
 but could otherwise be very useful for debugging. As we can see, the
 LSTM unfolded over time is just a (very) deep neural network. The
 complete code for producing this visualization can be found in
-[examples/char-lstm/visualize.jl](https://github.com/apache/incubator-mxnet/tree/master/julia/examples/char-lstmvisualize.jl).
+[examples/char-lstm/visualize.jl](https://github.com/apache/incubator-mxnet/blob/master/julia/examples/char-lstm/visualize.jl).
 
 ![image](images/char-lstm-vis.svg)
diff --git a/julia/docs/src/tutorial/mnist.md b/julia/docs/src/tutorial/mnist.md
index cc5267071f11..edc1a67d2485 100644
--- a/julia/docs/src/tutorial/mnist.md
+++ b/julia/docs/src/tutorial/mnist.md
@@ -23,7 +23,7 @@ multi-layer perceptron and then a convolutional neural network (the
 LeNet architecture) on the [MNIST handwritten digit
 dataset](http://yann.lecun.com/exdb/mnist/). The code for this tutorial
 could be found in
-[examples/mnist](https://github.com/dmlc/MXNet.jl/tree/master/examples/mnist).  There are also two Jupyter notebooks that expand a little more on the [MLP](https://github.com/ultradian/julia_notebooks/blob/master/mxnet/mnistMLP.ipynb) and the [LeNet](https://github.com/ultradian/julia_notebooks/blob/master/mxnet/mnistLenet.ipynb), using the more general `ArrayDataProvider`. 
+[examples/mnist](https://github.com/apache/incubator-mxnet/blob/master/julia/docs/src/tutorial/mnist.md).  There are also two Jupyter notebooks that expand a little more on the [MLP](https://github.com/ultradian/julia_notebooks/blob/master/mxnet/mnistMLP.ipynb) and the [LeNet](https://github.com/ultradian/julia_notebooks/blob/master/mxnet/mnistLenet.ipynb), using the more general `ArrayDataProvider`. 
 
 Simple 3-layer MLP
 ------------------
diff --git a/julia/docs/src/user-guide/overview.md b/julia/docs/src/user-guide/overview.md
index 974cc7dee974..342448a15bed 100644
--- a/julia/docs/src/user-guide/overview.md
+++ b/julia/docs/src/user-guide/overview.md
@@ -269,8 +269,6 @@ symbolic composition system. It is like
 [Theano](http://deeplearning.net/software/theano/), except that we
 avoided long expression compilation time by providing *larger* neural
 network related building blocks to guarantee computation performance.
-See also [this note](https://mxnet.readthedocs.org/en/latest/program_model.html)
-for the design and trade-off of the MXNet symbolic composition system.
 
 The basic type is `mx.SymbolicNode`. The following is a trivial example of
 composing two symbols with the `+` operation.
diff --git a/julia/examples/char-lstm/README.md b/julia/examples/char-lstm/README.md
index ac745dd4cc41..155f29603623 100644
--- a/julia/examples/char-lstm/README.md
+++ b/julia/examples/char-lstm/README.md
@@ -29,7 +29,7 @@ and `StatsBase.jl`.
 ## Training
 
 This example is adapted from the
-[example in Python binding](https://github.com/dmlc/mxnet/blob/master/example/rnn/char_lstm.ipynb) of
+[example in Python binding](https://github.com/dmlc/mxnet-notebooks/blob/master/python/tutorials/char_lstm.ipynb) of
 MXNet. The data `input.txt` can be downloaded [here](https://github.com/dmlc/web-data/tree/master/mxnet/tinyshakespeare).
 
 Modify parameters in [config.jl](config.jl) and then run [train.jl](train.jl). An example output

From e22e93f50f9e09d758a283733c901f4917b58321 Mon Sep 17 00:00:00 2001
From: Soji Adeshina <adesojia@amazon.com>
Date: Wed, 23 Oct 2019 14:44:57 -0700
Subject: [PATCH 503/813] fix missing docs due to git add issues (#16496)

---
 .../python/api/gluon/data/index.rst           | 63 +++++++++++++++++++
 .../api/gluon/data/vision/datasets/index.rst  | 26 ++++++++
 .../python/api/gluon/data/vision/index.rst    | 53 ++++++++++++++++
 .../gluon/data/vision/transforms/index.rst    | 48 ++++++++++++++
 .../python/api/mxnet/log/index.rst            | 23 +++++++
 .../python/api/mxnet/model/index.rst          | 23 +++++++
 6 files changed, 236 insertions(+)
 create mode 100644 docs/python_docs/python/api/gluon/data/index.rst
 create mode 100644 docs/python_docs/python/api/gluon/data/vision/datasets/index.rst
 create mode 100644 docs/python_docs/python/api/gluon/data/vision/index.rst
 create mode 100644 docs/python_docs/python/api/gluon/data/vision/transforms/index.rst
 create mode 100644 docs/python_docs/python/api/mxnet/log/index.rst
 create mode 100644 docs/python_docs/python/api/mxnet/model/index.rst

diff --git a/docs/python_docs/python/api/gluon/data/index.rst b/docs/python_docs/python/api/gluon/data/index.rst
new file mode 100644
index 000000000000..f9e8a21e69d2
--- /dev/null
+++ b/docs/python_docs/python/api/gluon/data/index.rst
@@ -0,0 +1,63 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+gluon.data
+==========
+
+.. automodule:: mxnet.gluon.data
+
+Datasets
+--------
+
+.. autosummary::
+
+   Dataset
+   ArrayDataset
+   RecordFileDataset
+   SimpleDataset
+
+Sampling
+--------
+
+.. autosummary::
+
+   Sampler
+   SequentialSampler
+   RandomSampler
+   BatchSampler
+
+DataLoader
+----------
+
+.. autosummary::
+
+   DataLoader
+
+
+API Reference
+-------------
+.. automodule:: mxnet.gluon.data
+    :members:
+    :imported-members:
+    :autosummary:
+
+.. toctree::
+   :hidden:
+   :maxdepth: 2
+   :glob:
+
+   */index
\ No newline at end of file
diff --git a/docs/python_docs/python/api/gluon/data/vision/datasets/index.rst b/docs/python_docs/python/api/gluon/data/vision/datasets/index.rst
new file mode 100644
index 000000000000..6b007526607a
--- /dev/null
+++ b/docs/python_docs/python/api/gluon/data/vision/datasets/index.rst
@@ -0,0 +1,26 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+vision.datasets
+===============
+
+Gluon provides pre-defined vision datasets functions in the :py:mod:`mxnet.gluon.data.vision.datasets`
+module.
+
+.. automodule:: mxnet.gluon.data.vision.datasets
+    :members:
+    :autosummary:
diff --git a/docs/python_docs/python/api/gluon/data/vision/index.rst b/docs/python_docs/python/api/gluon/data/vision/index.rst
new file mode 100644
index 000000000000..2731b5f4245a
--- /dev/null
+++ b/docs/python_docs/python/api/gluon/data/vision/index.rst
@@ -0,0 +1,53 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+data.vision
+============
+
+.. automodule:: mxnet.gluon.data.vision
+
+Datasets
+^^^^^^^^
+
+.. autosummary::
+    :nosignatures:
+
+    mxnet.gluon.data.vision.datasets
+
+
+Data transformations
+^^^^^^^^^^^^^^^^^^^^
+
+
+.. autosummary::
+    :nosignatures:
+
+    mxnet.gluon.data.vision.transforms
+
+
+API Reference
+-------------
+.. automodule:: mxnet.gluon.data.vision
+    :members:
+    :autosummary:
+
+.. toctree::
+   :hidden:
+   :maxdepth: 2
+   :glob:
+
+   */index
\ No newline at end of file
diff --git a/docs/python_docs/python/api/gluon/data/vision/transforms/index.rst b/docs/python_docs/python/api/gluon/data/vision/transforms/index.rst
new file mode 100644
index 000000000000..60d975d87aff
--- /dev/null
+++ b/docs/python_docs/python/api/gluon/data/vision/transforms/index.rst
@@ -0,0 +1,48 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+vision.transforms
+=================
+
+Gluon provides pre-defined vision transformation and data augmentation functions in the :py:mod:`mxnet.gluon.data.vision.transforms`
+module.
+
+.. currentmodule:: mxnet.gluon.data.vision
+
+.. autosummary::
+   :nosignatures:
+
+   transforms.Compose
+   transforms.Cast
+   transforms.ToTensor
+   transforms.Normalize
+   transforms.RandomResizedCrop
+   transforms.CenterCrop
+   transforms.Resize
+   transforms.RandomFlipLeftRight
+   transforms.RandomFlipTopBottom
+   transforms.RandomBrightness
+   transforms.RandomContrast
+   transforms.RandomSaturation
+   transforms.RandomHue
+   transforms.RandomColorJitter
+   transforms.RandomLighting
+
+API Reference
+-------------
+.. automodule:: mxnet.gluon.data.vision.transforms
+    :members:
diff --git a/docs/python_docs/python/api/mxnet/log/index.rst b/docs/python_docs/python/api/mxnet/log/index.rst
new file mode 100644
index 000000000000..fd4d8788c28a
--- /dev/null
+++ b/docs/python_docs/python/api/mxnet/log/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.log
+=========
+
+.. automodule:: mxnet.log
+    :members:
+    :autosummary:
\ No newline at end of file
diff --git a/docs/python_docs/python/api/mxnet/model/index.rst b/docs/python_docs/python/api/mxnet/model/index.rst
new file mode 100644
index 000000000000..69bcddce6bc1
--- /dev/null
+++ b/docs/python_docs/python/api/mxnet/model/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.model
+===========
+
+.. automodule:: mxnet.model
+    :members:
+    :autosummary:

From 05a4c4f88b27d031f997ff76bdf8a7f7f3b47768 Mon Sep 17 00:00:00 2001
From: Marco de Abreu <marcoabreu@users.noreply.github.com>
Date: Thu, 24 Oct 2019 01:20:45 +0200
Subject: [PATCH 504/813] Create SECURITY.md (#16573)

* Create SECURITY.md

* Update SECURITY.md
---
 SECURITY.md | 25 +++++++++++++++++++++++++
 1 file changed, 25 insertions(+)
 create mode 100644 SECURITY.md

diff --git a/SECURITY.md b/SECURITY.md
new file mode 100644
index 000000000000..bbb4505499c1
--- /dev/null
+++ b/SECURITY.md
@@ -0,0 +1,25 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Security Policy
+
+## Reporting a Vulnerability
+The Apache Software Foundation takes a very active stance in eliminating security problems and denial of service attacks against its products.
+
+We strongly encourage folks to report such problems to our private security mailing list first, before disclosing them in a public forum.
+
+For instructions how to report a security vulnerability, please consult our [security guide](https://mxnet.apache.org/api/faq/security).

From c3395ca60b20f4388dd76746696497148b82fc80 Mon Sep 17 00:00:00 2001
From: Xingjian Shi <xshiab@connect.ust.hk>
Date: Wed, 23 Oct 2019 18:17:51 -0700
Subject: [PATCH 505/813] [Numpy] Support N_D(N>=3) batch_dot (#16586)

* Support N_D(N>=3) batch_dot

* use 1E-4

* fix lint

* remove unnecessary comment

* Update test_numpy_op.py
---
 src/operator/tensor/dot-inl.h          | 177 +++++++++----------------
 src/operator/tensor/dot.cc             |  84 +++++++++---
 src/operator/tensor/dot.cu             |   3 -
 tests/python/unittest/test_numpy_op.py | 119 +++++++++++++++++
 tests/python/unittest/test_operator.py |   4 +-
 5 files changed, 249 insertions(+), 138 deletions(-)

diff --git a/src/operator/tensor/dot-inl.h b/src/operator/tensor/dot-inl.h
index 96c869f40d40..8405404dc627 100644
--- a/src/operator/tensor/dot-inl.h
+++ b/src/operator/tensor/dot-inl.h
@@ -30,6 +30,7 @@
 #include <algorithm>
 #include <utility>
 #include <type_traits>
+
 #include "./util/tensor_util-inl.h"
 #include "../mshadow_op.h"
 #include "../elemwise_op_common.h"
@@ -1353,6 +1354,7 @@ void BatchDotForward_(const nnvm::NodeAttrs& attrs,
   using namespace mshadow;
   using namespace mshadow::expr;
   mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+  if (req[0] == kNullOp) return;
   const DotParam& param = nnvm::get<DotParam>(attrs.parsed);
   CHECK_EQ(outputs[0].type_flag_, inputs[0].type_flag_)
       << "Binary function only support input/output with the same type";
@@ -1362,115 +1364,46 @@ void BatchDotForward_(const nnvm::NodeAttrs& attrs,
     (outputs[0].type_flag_ == kFloat16 && ctx.run_ctx.ctx.dev_mask() == mshadow::gpu::kDevMask))
     << "dot only supports float32/float64 for CPU, and float16/float32/float64 for GPU";
   MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, DType, {
-    mshadow::Tensor<xpu, 3, DType> out = outputs[0].get<xpu, 3, DType>(s);
-    mshadow::Tensor<xpu, 3, DType> mlhs = inputs[0].get<xpu, 3, DType>(s);
-    mshadow::Tensor<xpu, 3, DType> mrhs = inputs[1].get<xpu, 3, DType>(s);
-    mshadow::Tensor<xpu, 1, DType*> workspace =
-      ctx.requested[0].get_space_typed<xpu, 1, DType*>(mshadow::Shape1(3 * out.size(0)), s);
-    if (kNullOp != req[0]) {
-      if (param.transpose_a && param.transpose_b) {
-        mshadow::BatchGEMM<true, true>(out, mlhs, mrhs, (DType)1.0f,
-                                       (kAddTo == req[0]) ? (DType)1.0f : (DType)0.0f,
-                                       workspace);
-      } else if (!param.transpose_a && param.transpose_b) {
-        mshadow::BatchGEMM<false, true>(out, mlhs, mrhs, (DType)1.0f,
-                                       (kAddTo == req[0]) ? (DType)1.0f : (DType)0.0f,
-                                       workspace);
-      } else if (param.transpose_a && !param.transpose_b) {
-        mshadow::BatchGEMM<true, false>(out, mlhs, mrhs, (DType)1.0f,
-                                       (kAddTo == req[0]) ? (DType)1.0f : (DType)0.0f,
-                                       workspace);
-      } else {
-        mshadow::BatchGEMM<false, false>(out, mlhs, mrhs, (DType)1.0f,
-                                       (kAddTo == req[0]) ? (DType)1.0f : (DType)0.0f,
-                                       workspace);
+    int ndim = outputs[0].ndim();
+    if (outputs[0].shape_.Size() == 0 || inputs[0].shape_.Size() == 0
+                                      || inputs[1].shape_.Size() == 0) {
+      if (outputs[0].shape_.Size() != 0 && req[0] != kAddTo) {
+        mxnet_op::Kernel<mxnet_op::set_zero, xpu>::Launch(s, outputs[0].shape_.Size(),
+                                                          outputs[0].dptr<DType>());
       }
+      return;
     }
-  });
-}
-
-template<typename xpu>
-void BatchDotBackward_(const nnvm::NodeAttrs& attrs,
-                       const OpContext& ctx,
-                       const std::vector<TBlob>& inputs,
-                       const std::vector<OpReqType>& req,
-                       const std::vector<TBlob>& outputs) {
-  using namespace mshadow;
-  using namespace mshadow::expr;
-  mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
-  const DotParam& param = nnvm::get<DotParam>(attrs.parsed);
-  CHECK_NE(req[1], kWriteInplace);
-  CHECK_NE(req[0], kWriteInplace);
-  CHECK(outputs[0].type_flag_ == kFloat32 || outputs[0].type_flag_ == kFloat64 ||
-    (outputs[0].type_flag_ == kFloat16 && ctx.run_ctx.ctx.dev_mask() == mshadow::gpu::kDevMask))
-    << "dot only supports float32/float64 for CPU, and float16/float32/float64 for GPU";
-  MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, DType, {
-    mshadow::Tensor<xpu, 3, DType> mout_grad = inputs[0].get<xpu, 3, DType>(s);
-    mshadow::Tensor<xpu, 3, DType> mlhs_data = inputs[1].get<xpu, 3, DType>(s);
-    mshadow::Tensor<xpu, 3, DType> mrhs_data = inputs[2].get<xpu, 3, DType>(s);
-    mshadow::Tensor<xpu, 3, DType> mlhs_grad = outputs[0].get<xpu, 3, DType>(s);
-    mshadow::Tensor<xpu, 3, DType> mrhs_grad = outputs[1].get<xpu, 3, DType>(s);
-    mshadow::Tensor<xpu, 2, DType*> workspace =
-      ctx.requested[0].get_space_typed<xpu, 2, DType*>(
-        mshadow::Shape2(2, 3 * mout_grad.size(0)), s);
-    mshadow::Tensor<xpu, 1, DType*> rhs_workspace = workspace[0];
-    mshadow::Tensor<xpu, 1, DType*> lhs_workspace = workspace[1];
+    size_t batch_size = outputs[0].shape_.ProdShape(0, ndim - 2);
+    mshadow::Tensor<xpu, 3, DType> out =
+        outputs[0].get_with_shape<xpu, 3, DType>(Shape3(batch_size,
+                                                        outputs[0].shape_[ndim - 2],
+                                                        outputs[0].shape_[ndim - 1]), s);
+    mshadow::Tensor<xpu, 3, DType> mlhs =
+        inputs[0].get_with_shape<xpu, 3, DType>(Shape3(batch_size,
+                                                       inputs[0].shape_[ndim - 2],
+                                                       inputs[0].shape_[ndim - 1]), s);
+    mshadow::Tensor<xpu, 3, DType> mrhs =
+        inputs[1].get_with_shape<xpu, 3, DType>(Shape3(batch_size,
+                                                       inputs[1].shape_[ndim - 2],
+                                                       inputs[1].shape_[ndim - 1]), s);
+    mshadow::Tensor<xpu, 1, DType*> workspace =
+        ctx.requested[0].get_space_typed<xpu, 1, DType*>(mshadow::Shape1(3 * out.size(0)), s);
     if (param.transpose_a && param.transpose_b) {
-      // Gradient of z = dot(x.T, y.T)
-      // dy = dot(x, dz).T = dot(dz.T, x.T)
-      // dx = dot(dz, y).T = dot(y.T, dz.T)
-      if (kNullOp != req[1]) {
-        mshadow::BatchGEMM<true, true>(mrhs_grad, mout_grad, mlhs_data, (DType)1.0f,
-                                        (kAddTo == req[1]) ? (DType)1.0f :  (DType)0.0f,
-                                        rhs_workspace);
-      }
-      if (kNullOp != req[0]) {
-        mshadow::BatchGEMM<true, true>(mlhs_grad, mrhs_data, mout_grad, (DType)1.0f,
-                                        (kAddTo == req[0]) ? (DType)1.0f : (DType)0.0f,
-                                        lhs_workspace);
-      }
+      mshadow::BatchGEMM<true, true>(out, mlhs, mrhs, (DType)1.0f,
+                                     (kAddTo == req[0]) ? (DType)1.0f : (DType)0.0f,
+                                     workspace);
     } else if (!param.transpose_a && param.transpose_b) {
-      // Gradient of z = dot(x, y.T)
-      // dy = dot(x.T, dz).T = dot(dz.T, x)
-      // dx = dot(dz, y)
-      if (kNullOp != req[1]) {
-        mshadow::BatchGEMM<true, false>(mrhs_grad, mout_grad, mlhs_data, (DType)1.0f,
-                                        (kAddTo == req[1]) ? (DType)1.0f : (DType)0.0f,
-                                        rhs_workspace);
-      }
-      if (kNullOp != req[0]) {
-        mshadow::BatchGEMM<false, false>(mlhs_grad, mout_grad, mrhs_data, (DType)1.0f,
-                                        (kAddTo == req[0]) ? (DType)1.0f : (DType)0.0f,
-                                        lhs_workspace);
-      }
+      mshadow::BatchGEMM<false, true>(out, mlhs, mrhs, (DType)1.0f,
+                                     (kAddTo == req[0]) ? (DType)1.0f : (DType)0.0f,
+                                     workspace);
     } else if (param.transpose_a && !param.transpose_b) {
-      // Gradient of z = dot(x.T, y)
-      // dy = dot(x, dz)
-      // dx = dot(dz, y.T).T = dot(y, dz.T)
-      if (kNullOp != req[1]) {
-        mshadow::BatchGEMM<false, false>(mrhs_grad, mlhs_data, mout_grad, (DType)1.0f,
-                                        (kAddTo == req[1]) ? (DType)1.0f : (DType)0.0f,
-                                        rhs_workspace);
-      }
-      if (kNullOp != req[0]) {
-        mshadow::BatchGEMM<false, true>(mlhs_grad, mrhs_data, mout_grad, (DType)1.0f,
-                                        (kAddTo == req[0]) ? (DType)1.0f : (DType)0.0f,
-                                        lhs_workspace);
-      }
+      mshadow::BatchGEMM<true, false>(out, mlhs, mrhs, (DType)1.0f,
+                                     (kAddTo == req[0]) ? (DType)1.0f : (DType)0.0f,
+                                     workspace);
     } else {
-      // Gradient of z = dot(x, y)
-      // dy = dot(x.T, dz)
-      // dx = dot(dz, y.T)
-      if (kNullOp != req[1]) {
-        mshadow::BatchGEMM<true, false>(mrhs_grad, mlhs_data, mout_grad, (DType)1.0f,
-                                        (kAddTo == req[1]) ? (DType)1.0f : (DType)0.0f,
-                                        rhs_workspace);
-      }
-      if (kNullOp != req[0]) {
-        mshadow::BatchGEMM<false, true>(mlhs_grad, mout_grad, mrhs_data, (DType)1.0f,
-                                        (kAddTo == req[0]) ? (DType)1.0f : (DType)0.0f,
-                                        lhs_workspace);
-      }
+      mshadow::BatchGEMM<false, false>(out, mlhs, mrhs, (DType)1.0f,
+                                     (kAddTo == req[0]) ? (DType)1.0f : (DType)0.0f,
+                                     workspace);
     }
   });
 }
@@ -1485,24 +1418,34 @@ inline bool BatchDotShape(const nnvm::NodeAttrs& attrs,
   mxnet::TShape& rshape = (*in_attrs)[1];
   // return false if lhs and rhs both have fully unknown shape
   if (!ndim_is_known(lshape) || !ndim_is_known(rshape)) return false;
-  if (lshape.ndim() == 3 && rshape.ndim() == 3) {
+  if (lshape.ndim() >= 3 && rshape.ndim() >= 3 && lshape.ndim() == rshape.ndim()) {
+    int ndim = lshape.ndim();
     // only partially infer shape if last dim of lhs and second dim of rhs is known
-    bool last_dim_known = dim_size_is_known(lshape, 2);
-    bool second_dim_known = dim_size_is_known(rshape, 1);
+    bool last_dim_known = dim_size_is_known(lshape, ndim - 1);
+    bool second_dim_known = dim_size_is_known(rshape, ndim - 2);
     if ( !last_dim_known || !second_dim_known) return false;
-    CHECK(lshape[0] == rshape[0])
-      << "batch_dot shape error(batch_size must be equal): " << lshape << " X " << rshape
-      << " trans_a=" << param.transpose_a << " trans_b=" << param.transpose_b;
-    index_t out_m = param.transpose_a ? lshape[2] : lshape[1];
-    index_t lshape_k = param.transpose_a ? lshape[1] : lshape[2];
-    index_t out_n = param.transpose_b ? rshape[1] : rshape[2];
-    index_t rshape_k = param.transpose_b ? rshape[2] : rshape[1];
-    CHECK(lshape_k == rshape_k)
-      << "batch_dot shape error(shape mismatch): " << lshape << " X " << rshape
+    for (int i = 0; i < ndim - 2; i++) {
+      CHECK_EQ(lshape[i], rshape[i])
+        << "batch_dot shape error (the leading batch dimensions must be equal): "
+        << lshape << " X " << rshape
+        << " trans_a=" << param.transpose_a << " trans_b=" << param.transpose_b;
+    }
+    dim_t out_m = param.transpose_a ? lshape[ndim - 1] : lshape[ndim - 2];
+    dim_t lshape_k = param.transpose_a ? lshape[ndim - 2] : lshape[ndim - 1];
+    dim_t out_n = param.transpose_b ? rshape[ndim - 2] : rshape[ndim - 1];
+    dim_t rshape_k = param.transpose_b ? rshape[ndim - 1] : rshape[ndim - 2];
+    CHECK_EQ(lshape_k, rshape_k)
+      << "batch_dot shape error (shape mismatch): " << lshape << " X " << rshape
       << " trans_a=" << param.transpose_a << " trans_b=" << param.transpose_b;
-    SHAPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::Shape3(lshape[0], out_m, out_n));
+    std::vector<dim_t> out_shape_vec;
+    for (int i = 0; i < ndim - 2; i++) {
+      out_shape_vec.push_back(lshape[i]);
+    }
+    out_shape_vec.push_back(out_m);
+    out_shape_vec.push_back(out_n);
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, mxnet::TShape(out_shape_vec));
   } else {
-    LOG(FATAL) << "batch_dot currently only support 3D*3D array"
+    LOG(FATAL) << "batch_dot currently only support N-D*N-D array (N >= 3)"
                << lshape << " v.s. " << rshape;
   }
   // return true if output shape is fully inferred
diff --git a/src/operator/tensor/dot.cc b/src/operator/tensor/dot.cc
index 11a056146e1d..556260ed9600 100644
--- a/src/operator/tensor/dot.cc
+++ b/src/operator/tensor/dot.cc
@@ -115,13 +115,13 @@ NNVM_REGISTER_OP(batch_dot)
 .describe(R"doc(Batchwise dot product.
 
 ``batch_dot`` is used to compute dot product of ``x`` and ``y`` when ``x`` and
-``y`` are data in batch, namely 3D arrays in shape of `(batch_size, :, :)`.
+``y`` are data in batch, namely N-D (N >= 3) arrays in shape of `(B0, ..., B_i, :, :)`.
 
-For example, given ``x`` with shape `(batch_size, n, m)` and ``y`` with shape
-`(batch_size, m, k)`, the result array will have shape `(batch_size, n, k)`,
+For example, given ``x`` with shape `(B_0, ..., B_i, N, M)` and ``y`` with shape
+`(B_0, ..., B_i, M, K)`, the result array will have shape `(B_0, ..., B_i, N, K)`,
 which is computed by::
 
-   batch_dot(x,y)[i,:,:] = dot(x[i,:,:], y[i,:,:])
+   batch_dot(x,y)[b_0, ..., b_i, :, :] = dot(x[b_0, ..., b_i, :, :], y[b_0, ..., b_i, :, :])
 
 )doc" ADD_FILELINE)
 .set_num_inputs(2)
@@ -138,21 +138,73 @@ which is computed by::
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
   })
 .set_attr<FCompute>("FCompute<cpu>", BatchDotForward_<cpu>)
-.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_batch_dot"})
+.set_attr<nnvm::FGradient>("FGradient",
+    [](const nnvm::NodePtr& n,
+       const std::vector<nnvm::NodeEntry>& ograds) {
+  const DotParam& param = nnvm::get<DotParam>(n->attrs.parsed);
+  nnvm::NodePtr lhs_grad;
+  nnvm::NodePtr rhs_grad;
+  std::string lhs_gnode_name = n->attrs.name + "_backward_lhs";
+  std::string rhs_gnode_name = n->attrs.name + "_backward_rhs";
+  if (param.transpose_a && param.transpose_b) {
+    // Gradient of z = dot(x.T, y.T)
+    // dx = dot(dz, y).T = dot(y.T, dz.T)
+    // dy = dot(x, dz).T = dot(dz.T, x.T)
+    lhs_grad = MakeNode("batch_dot", lhs_gnode_name,
+                        {n->inputs[1], ograds[0]}, &(n->attrs.dict), &n);
+    rhs_grad = MakeNode("batch_dot", rhs_gnode_name,
+                        {ograds[0], n->inputs[0]}, &(n->attrs.dict), &n);
+  } else if (!param.transpose_a && param.transpose_b) {
+    // Gradient of z = dot(x, y.T)
+    // dx = dot(dz, y)
+    // dy = dot(x.T, dz).T = dot(dz.T, x)
+    auto lhs_attrs_dict = n->attrs.dict;
+    auto rhs_attrs_dict = n->attrs.dict;
+    lhs_attrs_dict["transpose_a"] = "false";
+    lhs_attrs_dict["transpose_b"] = "false";
+    rhs_attrs_dict["transpose_a"] = "true";
+    rhs_attrs_dict["transpose_b"] = "false";
+    lhs_grad = MakeNode("batch_dot", lhs_gnode_name,
+                        {ograds[0], n->inputs[1]}, &lhs_attrs_dict, &n);
+    rhs_grad = MakeNode("batch_dot", rhs_gnode_name,
+                        {ograds[0], n->inputs[0]}, &rhs_attrs_dict, &n);
+  } else if (param.transpose_a && !param.transpose_b) {
+    // Gradient of z = dot(x.T, y)
+    // dx = dot(dz, y.T).T = dot(y, dz.T)
+    // dy = dot(x, dz)
+    auto lhs_attrs_dict = n->attrs.dict;
+    auto rhs_attrs_dict = n->attrs.dict;
+    lhs_attrs_dict["transpose_a"] = "false";
+    lhs_attrs_dict["transpose_b"] = "true";
+    rhs_attrs_dict["transpose_a"] = "false";
+    rhs_attrs_dict["transpose_b"] = "false";
+    lhs_grad = MakeNode("batch_dot", lhs_gnode_name,
+                        {n->inputs[1], ograds[0]}, &lhs_attrs_dict, &n);
+    rhs_grad = MakeNode("batch_dot", rhs_gnode_name,
+                        {n->inputs[0], ograds[0]}, &rhs_attrs_dict, &n);
+  } else {
+    // Gradient of z = dot(x, y)
+    // dx = dot(dz, y.T)
+    // dy = dot(x.T, dz)
+    auto lhs_attrs_dict = n->attrs.dict;
+    auto rhs_attrs_dict = n->attrs.dict;
+    lhs_attrs_dict["transpose_a"] = "false";
+    lhs_attrs_dict["transpose_b"] = "true";
+    rhs_attrs_dict["transpose_a"] = "true";
+    rhs_attrs_dict["transpose_b"] = "false";
+    lhs_grad = MakeNode("batch_dot", lhs_gnode_name,
+                        {ograds[0], n->inputs[1]}, &lhs_attrs_dict, &n);
+    rhs_grad = MakeNode("batch_dot", rhs_gnode_name,
+                        {n->inputs[0], ograds[0]}, &rhs_attrs_dict, &n);
+  }
+  std::vector<nnvm::NodeEntry> ret;
+  ret.emplace_back(nnvm::NodeEntry{lhs_grad, 0, 0});
+  ret.emplace_back(nnvm::NodeEntry{rhs_grad, 0, 0});
+  return ret;
+})
 .add_argument("lhs", "NDArray-or-Symbol", "The first input")
 .add_argument("rhs", "NDArray-or-Symbol", "The second input")
 .add_arguments(DotParam::__FIELDS__());
 
-NNVM_REGISTER_OP(_backward_batch_dot)
-.set_num_inputs(3)
-.set_num_outputs(2)
-.set_attr_parser(ParamParser<DotParam>)
-.set_attr<FResourceRequest>("FResourceRequest",
-  [](const NodeAttrs& attrs) {
-    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
-  })
-.set_attr<nnvm::TIsBackward>("TIsBackward", true)
-.set_attr<FCompute>("FCompute<cpu>", BatchDotBackward_<cpu>);
-
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/tensor/dot.cu b/src/operator/tensor/dot.cu
index 8ee2e2832fbb..b245b1c9e5ed 100644
--- a/src/operator/tensor/dot.cu
+++ b/src/operator/tensor/dot.cu
@@ -38,8 +38,5 @@ NNVM_REGISTER_OP(_backward_dot)
 NNVM_REGISTER_OP(batch_dot)
 .set_attr<FCompute>("FCompute<gpu>", BatchDotForward_<gpu>);
 
-NNVM_REGISTER_OP(_backward_batch_dot)
-.set_attr<FCompute>("FCompute<gpu>", BatchDotBackward_<gpu>);
-
 }  // namespace op
 }  // namespace mxnet
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index b764ac73d30c..ae8ad621df75 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -24,6 +24,7 @@
 import platform
 import mxnet as mx
 import scipy.stats as ss
+from nose.tools import assert_raises
 from mxnet import np, npx
 from mxnet.gluon import HybridBlock
 from mxnet.base import MXNetError
@@ -901,6 +902,124 @@ def hybrid_forward(self, F, a):
             expected_grad[basic_index] = 1
             assert same(a.grad.asnumpy(), expected_grad)
 
+@with_seed()
+@use_np
+def test_npx_batch_dot():
+    ctx = mx.context.current_context()
+    dtypes = ['float32', 'float64']
+    if ctx.device_type == 'gpu':
+        dtypes += ['float16']
+    eps_dict = {'float32': 1E-4, 'float64': 1E-4, 'float16': 1E-3}
+    class TestBatchDot(HybridBlock):
+        def __init__(self, transpose_a, transpose_b):
+            super(TestBatchDot, self).__init__()
+            self._transpose_a = transpose_a
+            self._transpose_b = transpose_b
+
+        def hybrid_forward(self, F, lhs, rhs):
+            return F.npx.batch_dot(lhs, rhs,
+                                   transpose_a=self._transpose_a,
+                                   transpose_b=self._transpose_b)
+
+    def batch_dot_numpy(lhs, rhs, transpose_a, transpose_b):
+        assert lhs.ndim == rhs.ndim >= 3
+        if transpose_a:
+            lhs = lhs.swapaxes(-1, -2)
+        if transpose_b:
+            rhs = rhs.swapaxes(-1, -2)
+        return _np.matmul(lhs, rhs)
+
+    def gt_grad_batch_dot_numpy(lhs, rhs, ograd, transpose_a, transpose_b, lhs_req, rhs_req,
+                                init_lhs_grad, init_rhs_grad):
+
+        if transpose_a and transpose_b:
+            # Gradient of z = dot(x.T, y.T)
+            # dx = dot(dz, y).T = dot(y.T, dz.T)
+            # dy = dot(x, dz).T = dot(dz.T, x.T)
+            lhs_grad = batch_dot_numpy(rhs, ograd, transpose_a=True, transpose_b=True)
+            rhs_grad = batch_dot_numpy(ograd, lhs, transpose_a=True, transpose_b=True)
+        elif not transpose_a and transpose_b:
+            # Gradient of z = dot(x, y.T)
+            # dx = dot(dz, y)
+            # dy = dot(x.T, dz).T = dot(dz.T, x)
+            lhs_grad = batch_dot_numpy(ograd, rhs, transpose_a=False, transpose_b=False)
+            rhs_grad = batch_dot_numpy(ograd, lhs, transpose_a=True, transpose_b=False)
+        elif transpose_a and not transpose_b:
+            # Gradient of z = dot(x.T, y)
+            # dx = dot(dz, y.T).T = dot(y, dz.T)
+            # dy = dot(x, dz)
+            lhs_grad = batch_dot_numpy(rhs, ograd, transpose_a=False, transpose_b=True)
+            rhs_grad = batch_dot_numpy(lhs, ograd, transpose_a=False, transpose_b=False)
+        else:
+            # Gradient of z = dot(x, y)
+            # dx = dot(dz, y.T)
+            # dy = dot(x.T, dz)
+            lhs_grad = batch_dot_numpy(ograd, rhs, transpose_a=False, transpose_b=True)
+            rhs_grad = batch_dot_numpy(lhs, ograd, transpose_a=True, transpose_b=False)
+        if lhs_req == 'add':
+            lhs_grad += init_lhs_grad
+        if rhs_req == 'add':
+            rhs_grad += init_rhs_grad
+        return lhs_grad, rhs_grad
+
+
+    configs = [
+        ((2, 3, 0), (2, 4, 0), False, True),
+        ((2, 4, 3), (2, 4, 3), True, False),
+        ((0, 3, 0), (0, 0, 2), False, False),
+        ((3, 2, 3, 2), (3, 2, 2, 3), True, True),
+        ((3, 1, 5, 2), (3, 1, 2, 1), False, False)
+    ]
+    bad_configs = [
+        ((5, 3, 2), (5, 1, 3), False, False),
+        ((2, 5, 3, 1), (2, 4, 3, 1), True, False)
+    ]
+    for hybridize in [True, False]:
+        for lhs_shape, rhs_shape, transpose_a, transpose_b in configs:
+            for dtype in dtypes:
+                eps = eps_dict[dtype]
+                for lhs_grad_req in ['write', 'add']:
+                    for rhs_grad_req in ['write', 'add']:
+                        f_batch_dot = TestBatchDot(transpose_a=transpose_a,
+                                                   transpose_b=transpose_b)
+                        if hybridize:
+                            f_batch_dot.hybridize()
+                        lhs_val = mx.np.array(_np.random.uniform(-1.0, 1.0, lhs_shape), dtype=dtype)
+                        rhs_val = mx.np.array(_np.random.uniform(-1.0, 1.0, rhs_shape), dtype=dtype)
+                        lhs_val.attach_grad(grad_req=lhs_grad_req)
+                        rhs_val.attach_grad(grad_req=rhs_grad_req)
+                        gt_out = batch_dot_numpy(lhs_val.asnumpy(), rhs_val.asnumpy(),
+                                                 transpose_a, transpose_b)
+                        init_lhs_grad = mx.np.random.uniform(-1.0, 1.0, lhs_shape, dtype=dtype)
+                        init_rhs_grad = mx.np.random.uniform(-1.0, 1.0, rhs_shape, dtype=dtype)
+                        o_grad = mx.np.random.uniform(-1.0, 1.0, gt_out.shape, dtype=dtype)
+                        if lhs_grad_req == 'add':
+                            lhs_val.grad[:] = init_lhs_grad
+                        if rhs_grad_req == 'add':
+                            rhs_val.grad[:] = init_rhs_grad
+                        with mx.autograd.record():
+                            out = f_batch_dot(lhs_val, rhs_val)
+                        out.backward(o_grad)
+                        assert_almost_equal(out.asnumpy(), gt_out, rtol=eps, atol=eps)
+                        gt_lhs_grad, gt_rhs_grad = gt_grad_batch_dot_numpy(lhs_val.asnumpy(),
+                                                              rhs_val.asnumpy(),
+                                                              o_grad.asnumpy(),
+                                                              transpose_a=transpose_a,
+                                                              transpose_b=transpose_b,
+                                                              lhs_req=lhs_grad_req,
+                                                              rhs_req=rhs_grad_req,
+                                                              init_lhs_grad=init_lhs_grad.asnumpy(),
+                                                              init_rhs_grad=init_rhs_grad.asnumpy())
+                        assert_almost_equal(lhs_val.grad.asnumpy(), gt_lhs_grad, rtol=eps, atol=eps)
+                        assert_almost_equal(rhs_val.grad.asnumpy(), gt_rhs_grad, rtol=eps, atol=eps)
+    for lhs_shape, rhs_shape, transpose_a, transpose_b in bad_configs:
+        for dtype in dtypes:
+            lhs_val = mx.np.array(_np.random.uniform(-1.0, 1.0, lhs_shape), dtype=dtype)
+            rhs_val = mx.np.array(_np.random.uniform(-1.0, 1.0, rhs_shape), dtype=dtype)
+            assert_raises(MXNetError, lambda: mx.npx.batch_dot(lhs_val, rhs_val,
+                                                               transpose_a=transpose_a,
+                                                               transpose_b=transpose_b))
+
 
 @with_seed()
 @use_np
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index a16dc6c693ab..c87fa6148d3b 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -3291,9 +3291,9 @@ def test_batch_dot():
                         agrad_npy = np.empty((batch_size, m, k), dtype=data_type)
                         bgrad_npy = np.empty((batch_size, k, n), dtype=data_type)
                         a_init_grad_npy = np.random.normal(size=(batch_size, m, k))
-                        a_init_grad_npy = a_npy.astype(data_type)
+                        a_init_grad_npy = a_init_grad_npy.astype(data_type)
                         b_init_grad_npy = np.random.normal(size=(batch_size, k, n))
-                        b_init_grad_npy = b_npy.astype(data_type)
+                        b_init_grad_npy = b_init_grad_npy.astype(data_type)
                         for i in range(batch_size):
                             c_npy[i, :, :] = np.dot(a_npy[i, :, :], b_npy[i, :, :])
                             bgrad_npy[i, :, :] = np.dot(a_npy[i, :, :].T, ograd_npy[i, :, :])

From 0742a9b9c4d1c15a5141869cecedec139f325dea Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Wed, 23 Oct 2019 19:12:14 -0700
Subject: [PATCH 506/813] Large Vector tests for DGL Ops Part 2 (#16497)

* add hyperbolic, logical, sign and regression tests for large vector

* changed hyperbolic functions into existing trignometric functions

* fix trigo and simple bind needs shape as tuple

* fix logical ops, add with_seed

* fix arcosh in largearray, remove regression from largevector
---
 tests/nightly/test_large_array.py      |  4 +-
 tests/nightly/test_large_vector.py     | 85 +++++++++++++++++++++++++-
 tests/python/unittest/test_operator.py |  1 +
 3 files changed, 85 insertions(+), 5 deletions(-)

diff --git a/tests/nightly/test_large_array.py b/tests/nightly/test_large_array.py
index 0cb21cedee35..c18a95400f22 100644
--- a/tests/nightly/test_large_array.py
+++ b/tests/nightly/test_large_array.py
@@ -1415,10 +1415,10 @@ def check_arcsinh():
         assert_correctness_of_trigonometric_ops(y, expected_output)
 
     def check_arccosh():
-        x = create_input_for_trigonometric_ops([1, np.pi/2, 3*np.pi/4, np.pi])
+        x = create_input_for_trigonometric_ops([1, np.pi/2, 3*np.pi/4, np.pi, 5*np.pi/4])
         y = nd.arccosh(x)
         # expected ouput for indices=(0, 1, -3, -2, -1) after applying arccosh()
-        expected_output = [0, np.arccosh(np.pi/2), np.arccosh(3*np.pi/4), np.arccosh(np.pi)]
+        expected_output = [0, np.arccosh(np.pi/2), np.arccosh(3*np.pi/4), np.arccosh(np.pi), np.arccosh(5*np.pi/4)]
         assert_correctness_of_trigonometric_ops(y, expected_output)
 
     def check_arctanh():
diff --git a/tests/nightly/test_large_vector.py b/tests/nightly/test_large_vector.py
index 23f4b8e4f310..b8edc83220bd 100644
--- a/tests/nightly/test_large_vector.py
+++ b/tests/nightly/test_large_vector.py
@@ -556,8 +556,8 @@ def test_concat():
     a = nd.ones(LARGE_X)
     b = nd.zeros(LARGE_X)
     c = nd.concat(a, b, dim=0)
-    assert c[0][0] == 1
-    assert c[-1][-1] == 0
+    assert c[0] == 1
+    assert c[-1] == 0
     assert c.shape[0] == (2 * LARGE_X)
 
 
@@ -710,6 +710,37 @@ def test_full():
     assert a[-1] == 3
 
 
+def test_sign():
+    a = mx.nd.random.normal(-1, 1, shape=LARGE_X)
+    mx_res = mx.nd.sign(a)
+    assert_almost_equal(mx_res[-1].asnumpy(), np.sign(a[-1].asnumpy()))
+
+
+def test_logical():
+    def check_logical_and(a, b):
+        mx_res = mx.nd.logical_and(a, b)
+        assert_almost_equal(mx_res[-1].asnumpy(), np.logical_and(a[-1].asnumpy(), b[-1].asnumpy()))
+
+    def check_logical_or(a, b):
+        mx_res = mx.nd.logical_or(a, b)
+        assert_almost_equal(mx_res[-1].asnumpy(), np.logical_or(a[-1].asnumpy(), b[-1].asnumpy()))
+
+    def check_logical_not(a, b):
+        mx_res = mx.nd.logical_not(a, b)
+        assert_almost_equal(mx_res[-1].asnumpy(), np.logical_not(a[-1].asnumpy(), b[-1].asnumpy()))
+
+    def check_logical_xor(a, b):
+        mx_res = mx.nd.logical_xor(a, b)
+        assert_almost_equal(mx_res[-1].asnumpy(), np.logical_xor(a[-1].asnumpy(), b[-1].asnumpy()))
+
+    a = mx.nd.ones(LARGE_X)
+    b = mx.nd.zeros(LARGE_X)
+    check_logical_and(a, b)
+    check_logical_or(a, b)
+    check_logical_not(a, b)
+    check_logical_xor(a, b)
+
+
 def test_astype():
     x = create_vector(size=LARGE_X//4)
     x = nd.tile(x, 4)
@@ -752,7 +783,7 @@ def assert_correctness_of_rounding_ops(output, mid, expected_vals):
 
 def test_rounding_ops():
     x = create_input_for_rounding_ops()
-    
+
     def check_ceil():
         y = nd.ceil(x)
         # expected ouput for middle 5 values after applying ceil()
@@ -854,6 +885,48 @@ def check_tan():
         expected_output = [-.577, -1, 0, 1, .577]
         assert_correctness_of_trigonometric_ops(y, expected_output)
 
+    def check_arcsinh():
+        x = create_input_for_trigonometric_ops([-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2])
+        y = nd.arcsinh(x)
+        # expected ouput for indices=(0, 1, -3, -2, -1) after applying arcsinh()
+        expected_output = [np.arcsinh(-np.pi/2), np.arcsinh(-np.pi/4), 0, np.arcsinh(np.pi/4), np.arcsinh(np.pi/2)]
+        assert_correctness_of_trigonometric_ops(y, expected_output)
+
+    def check_arccosh():
+        x = create_input_for_trigonometric_ops([1, np.pi/2, 3*np.pi/4, np.pi, 5*np.pi/4])
+        y = nd.arccosh(x)
+        # expected ouput for indices=(0, 1, -3, -2, -1) after applying arccosh()
+        expected_output = [0, np.arccosh(np.pi/2), np.arccosh(3*np.pi/4), np.arccosh(np.pi), np.arccosh(5*np.pi/4)]
+        assert_correctness_of_trigonometric_ops(y, expected_output)
+
+    def check_arctanh():
+        x = create_input_for_trigonometric_ops([-1/4, -1/2, 0, 1/4, 1/2])
+        y = nd.arctanh(x)
+        # expected ouput for indices=(0, 1, -3, -2, -1) after applying arctanh()
+        expected_output = [np.arctanh(-1/4), np.arctanh(-1/2), 0, np.arctanh(1/4), np.arctanh(1/2)]
+        assert_correctness_of_trigonometric_ops(y, expected_output)
+
+    def check_sinh():
+        x = create_input_for_trigonometric_ops([-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2])
+        y = nd.sinh(x)
+        # expected ouput for indices=(0, 1, -3, -2, -1) after applying sinh()
+        expected_output = [np.sinh(-np.pi/2), np.sinh(-np.pi/4), 0, np.sinh(np.pi/4), np.sinh(np.pi/2)]
+        assert_correctness_of_trigonometric_ops(y, expected_output)
+
+    def check_cosh():
+        x = create_input_for_trigonometric_ops([0, 1, np.pi/2, 3*np.pi/4, np.pi])
+        y = nd.cosh(x)
+        # expected ouput for indices=(0, 1, -3, -2, -1) after applying cosh()
+        expected_output = [1, np.cosh(1), np.cosh(np.pi/2), np.cosh(3*np.pi/4), np.cosh(np.pi)]
+        assert_correctness_of_trigonometric_ops(y, expected_output)
+
+    def check_tanh():
+        x = create_input_for_trigonometric_ops([-1/4, -1/2, 0, 1/4, 1/2])
+        y = nd.tanh(x)
+        # expected ouput for indices=(0, 1, -3, -2, -1) after applying tanh()
+        expected_output = [np.tanh(-1/4), np.tanh(-1/2), 0, np.tanh(1/4), np.tanh(1/2)]
+        assert_correctness_of_trigonometric_ops(y, expected_output)
+
     def check_radians():
         x = create_input_for_trigonometric_ops([0, 90, 180, 270, 360])
         y = nd.radians(x)
@@ -874,6 +947,12 @@ def check_degrees():
     check_sin()
     check_cos()
     check_tan()
+    check_arcsinh()
+    check_arccosh()
+    check_arctanh()
+    check_sinh()
+    check_cosh()
+    check_tanh()
     check_radians()
     check_degrees()
 
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index c87fa6148d3b..075816fdc6de 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -2931,6 +2931,7 @@ def test_big_transpose():
     assert_allclose(x_np, z.asnumpy().astype('uint8'))
 
 
+@with_seed()
 def test_larger_transpose():
     x = mx.nd.random.normal(shape=(50,51))
     y = mx.nd.transpose(x)

From ca5a2a0b1dface8b7849b4674694e7458a234ade Mon Sep 17 00:00:00 2001
From: Jake Lee <gstu1130@gmail.com>
Date: Wed, 23 Oct 2019 23:31:17 -0700
Subject: [PATCH 507/813] [Numpy] Loading numpy-incompatible NDArray in
 numpy-compatible mode (#16597)

* Make MXIsNumpyShape return enum

* address the comment
---
 include/mxnet/c_api.h                                  |  2 +-
 include/mxnet/imperative.h                             | 10 ++++++----
 .../src/main/native/org_apache_mxnet_native_c_api.cc   |  4 ++--
 src/c_api/c_api_ndarray.cc                             |  2 +-
 src/ndarray/ndarray.cc                                 |  3 ++-
 5 files changed, 12 insertions(+), 9 deletions(-)

diff --git a/include/mxnet/c_api.h b/include/mxnet/c_api.h
index 177ec5d40146..ac0c6726f2c7 100644
--- a/include/mxnet/c_api.h
+++ b/include/mxnet/c_api.h
@@ -1170,7 +1170,7 @@ MXNET_DLL int MXAutogradIsTraining(bool* curr);
  * \param curr returns the current status
  * \return 0 when success, -1 when failure happens
  */
-MXNET_DLL int MXIsNumpyShape(bool* curr);
+MXNET_DLL int MXIsNumpyShape(int* curr);
 /*!
  * \brief set numpy compatibility switch
  * \param is_np_shape 1 when numpy shape semantics is thread local on,
diff --git a/include/mxnet/imperative.h b/include/mxnet/imperative.h
index 18f6424e54f7..dbd81e575872 100644
--- a/include/mxnet/imperative.h
+++ b/include/mxnet/imperative.h
@@ -108,12 +108,14 @@ class Imperative {
       is_recording_ = is_recording;
       return old;
   }
-  /*! \brief whether numpy compatibility is on. */
-  bool is_np_shape() const {
+  /*! \brief return current numpy compatibility status,
+   *  GlobalOn(2), ThreadLocalOn(1), Off(0).
+   * */
+  int is_np_shape() const {
     if (is_np_shape_global_) {
-      return true;
+      return 2;
     }
-    return is_np_shape_thread_local_;
+    return is_np_shape_thread_local_ ? 1 : 0;
   }
   /*! \brief specify numpy compatibility off, thread local on or global on. */
   bool set_is_np_shape(int is_np_shape) {
diff --git a/scala-package/native/src/main/native/org_apache_mxnet_native_c_api.cc b/scala-package/native/src/main/native/org_apache_mxnet_native_c_api.cc
index 5c704c9646a2..26eea3dd062b 100644
--- a/scala-package/native/src/main/native/org_apache_mxnet_native_c_api.cc
+++ b/scala-package/native/src/main/native/org_apache_mxnet_native_c_api.cc
@@ -2777,9 +2777,9 @@ JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxDumpProfile
 // Numpy
 JNIEXPORT jint JNICALL Java_org_apache_mxnet_LibInfo_mxIsNumpyShape
   (JNIEnv *env, jobject obj, jobject compatibleRef) {
-  bool isNumpyShape;
+  int isNumpyShape;
   int ret = MXIsNumpyShape(&isNumpyShape);
-  SetIntField(env, compatibleRef, static_cast<int>(isNumpyShape));
+  SetIntField(env, compatibleRef, isNumpyShape);
   return ret;
 }
 
diff --git a/src/c_api/c_api_ndarray.cc b/src/c_api/c_api_ndarray.cc
index b80e17c18071..de208c0fed99 100644
--- a/src/c_api/c_api_ndarray.cc
+++ b/src/c_api/c_api_ndarray.cc
@@ -276,7 +276,7 @@ int MXAutogradSetIsRecording(int is_recording, int* prev) {
   API_END();
 }
 
-int MXIsNumpyShape(bool* curr) {
+int MXIsNumpyShape(int* curr) {
   API_BEGIN();
   *curr = Imperative::Get()->is_np_shape();
   API_END();
diff --git a/src/ndarray/ndarray.cc b/src/ndarray/ndarray.cc
index e15f72fa6cfa..44da670b800d 100644
--- a/src/ndarray/ndarray.cc
+++ b/src/ndarray/ndarray.cc
@@ -1732,7 +1732,8 @@ bool NDArray::Load(dmlc::Stream *strm) {
            " Please turn on np shape semantics in Python using `with np_shape(True)`"
            " or decorator `use_np_shape` to scope the code of loading the ndarray.";
   } else {
-    CHECK(!Imperative::Get()->is_np_shape())
+    // when the flag is global on, skip the check since it would be always global on.
+    CHECK(Imperative::Get()->is_np_shape() == GlobalOn || !Imperative::Get()->is_np_shape())
         << "ndarray was not saved in np shape semantics, but being loaded in np shape semantics."
            " Please turn off np shape semantics in Python using `with np_shape(False)`"
            " to scope the code of loading the ndarray.";

From 8270672d249aa634ad161b9014cd0020b0bb553f Mon Sep 17 00:00:00 2001
From: Zhennan Qin <zhennan.qin@intel.com>
Date: Thu, 24 Oct 2019 04:47:01 -0500
Subject: [PATCH 508/813] Surpress subgraph log in CI (#16607)

Change-Id: Ia2ed6fdbb1d2cb5cc607a8856ca13ee338e27eac
---
 .travis.yml                                   |  1 +
 ci/docker/runtime_functions.sh                | 19 +++++++++++++++-
 ci/windows/test_py2_cpu.ps1                   |  1 +
 ci/windows/test_py2_gpu.ps1                   |  1 +
 ci/windows/test_py3_cpu.ps1                   |  1 +
 ci/windows/test_py3_gpu.ps1                   |  1 +
 perl-package/AI-MXNet/t/test_autograd.t       |  1 +
 perl-package/AI-MXNet/t/test_gluon_trainer.t  |  2 +-
 perl-package/AI-MXNet/t/test_module.t         |  1 +
 perl-package/AI-MXNet/t/test_sparse_ndarray.t |  1 +
 src/executor/graph_executor.cc                | 22 +++++++++----------
 src/operator/subgraph/build_subgraph.cc       |  8 +++----
 12 files changed, 42 insertions(+), 17 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index b0aa26c1a3a1..485faadee277 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -34,6 +34,7 @@ script:
 # Temporarily disable travis build due to travis constantly time out, tracked in
 # https://github:com/apache/incubator-mxnet/issues/16535:
   - export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
+  - export MXNET_SUBGRAPH_VERBOSE=0
   - mv make/osx.mk config.mk
 #  - make -j 2
 
diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index 935406bb16a8..aab49f28a427 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -1006,6 +1006,7 @@ cd_unittest_ubuntu() {
     export PYTHONPATH=./python/
     export MXNET_MKLDNN_DEBUG=1  # Ignored if not present
     export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
+    export MXNET_SUBGRAPH_VERBOSE=0
     export MXNET_ENABLE_CYTHON=0
     export CD_JOB=1 # signal this is a CD run so any unecessary tests can be skipped
 
@@ -1048,6 +1049,7 @@ unittest_ubuntu_python2_cpu_cython() {
     export PYTHONPATH=./python/
     export MXNET_MKLDNN_DEBUG=1
     export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
+    export MXNET_SUBGRAPH_VERBOSE=0
     export MXNET_ENABLE_CYTHON=1
     export MXNET_ENFORCE_CYTHON=1
     check_cython 2
@@ -1061,6 +1063,7 @@ unittest_ubuntu_python2_cpu() {
     export PYTHONPATH=./python/
     export MXNET_MKLDNN_DEBUG=0
     export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
+    export MXNET_SUBGRAPH_VERBOSE=0
     export MXNET_ENABLE_CYTHON=0
     nosetests-2.7 $NOSE_COVERAGE_ARGUMENTS $NOSE_TIMER_ARGUMENTS --with-xunit --xunit-file nosetests_unittest.xml --verbose tests/python/unittest
     nosetests-2.7 $NOSE_COVERAGE_ARGUMENTS $NOSE_TIMER_ARGUMENTS --with-xunit --xunit-file nosetests_train.xml --verbose tests/python/train
@@ -1072,6 +1075,7 @@ unittest_ubuntu_python3_cpu() {
     export PYTHONPATH=./python/
     export MXNET_MKLDNN_DEBUG=0  # Ignored if not present
     export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
+    export MXNET_SUBGRAPH_VERBOSE=0
     export MXNET_ENABLE_CYTHON=0
     nosetests-3.4 $NOSE_COVERAGE_ARGUMENTS $NOSE_TIMER_ARGUMENTS --with-xunit --xunit-file nosetests_unittest.xml --verbose tests/python/unittest
     nosetests-3.4 $NOSE_COVERAGE_ARGUMENTS $NOSE_TIMER_ARGUMENTS --with-xunit --xunit-file nosetests_quantization.xml --verbose tests/python/quantization
@@ -1082,6 +1086,7 @@ unittest_ubuntu_python3_cpu_mkldnn() {
     export PYTHONPATH=./python/
     export MXNET_MKLDNN_DEBUG=0  # Ignored if not present
     export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
+    export MXNET_SUBGRAPH_VERBOSE=0
     export MXNET_ENABLE_CYTHON=0
     nosetests-3.4 $NOSE_COVERAGE_ARGUMENTS $NOSE_TIMER_ARGUMENTS --with-xunit --xunit-file nosetests_unittest.xml --verbose tests/python/unittest
     nosetests-3.4 $NOSE_COVERAGE_ARGUMENTS $NOSE_TIMER_ARGUMENTS --with-xunit --xunit-file nosetests_mkl.xml --verbose tests/python/mkl
@@ -1092,6 +1097,7 @@ unittest_ubuntu_python2_gpu() {
     export PYTHONPATH=./python/
     export MXNET_MKLDNN_DEBUG=0  # Ignored if not present
     export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
+    export MXNET_SUBGRAPH_VERBOSE=0
     export CUDNN_VERSION=${CUDNN_VERSION:-7.0.3}
     nosetests-2.7 $NOSE_COVERAGE_ARGUMENTS $NOSE_TIMER_ARGUMENTS --with-xunit --xunit-file nosetests_gpu.xml --verbose tests/python/gpu
 }
@@ -1101,6 +1107,7 @@ unittest_ubuntu_python3_gpu() {
     export PYTHONPATH=./python/
     export MXNET_MKLDNN_DEBUG=0 # Ignored if not present
     export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
+    export MXNET_SUBGRAPH_VERBOSE=0
     export CUDNN_VERSION=${CUDNN_VERSION:-7.0.3}
     export MXNET_ENABLE_CYTHON=0
     nosetests-3.4 $NOSE_COVERAGE_ARGUMENTS $NOSE_TIMER_ARGUMENTS --with-xunit --xunit-file nosetests_gpu.xml --verbose tests/python/gpu
@@ -1111,6 +1118,7 @@ unittest_ubuntu_python3_gpu_cython() {
     export PYTHONPATH=./python/
     export MXNET_MKLDNN_DEBUG=1 # Ignored if not present
     export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
+    export MXNET_SUBGRAPH_VERBOSE=0
     export CUDNN_VERSION=${CUDNN_VERSION:-7.0.3}
     export MXNET_ENABLE_CYTHON=1
     export MXNET_ENFORCE_CYTHON=1
@@ -1122,6 +1130,7 @@ unittest_ubuntu_python3_gpu_nocudnn() {
     set -ex
     export PYTHONPATH=./python/
     export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
+    export MXNET_SUBGRAPH_VERBOSE=0
     export CUDNN_OFF_TEST_ONLY=true
     export MXNET_ENABLE_CYTHON=0
     nosetests-3.4 $NOSE_COVERAGE_ARGUMENTS $NOSE_TIMER_ARGUMENTS --with-xunit --xunit-file nosetests_gpu.xml --verbose tests/python/gpu
@@ -1131,6 +1140,7 @@ unittest_ubuntu_tensorrt_gpu() {
     set -ex
     export PYTHONPATH=./python/
     export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
+    export MXNET_SUBGRAPH_VERBOSE=0
     export LD_LIBRARY_PATH=/work/mxnet/lib:$LD_LIBRARY_PATH
     export CUDNN_VERSION=${CUDNN_VERSION:-7.0.3}
     export MXNET_ENABLE_CYTHON=0
@@ -1145,6 +1155,7 @@ unittest_ubuntu_python2_quantization_gpu() {
     export PYTHONPATH=./python/
     export MXNET_MKLDNN_DEBUG=0  # Ignored if not present
     export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
+    export MXNET_SUBGRAPH_VERBOSE=0
     export CUDNN_VERSION=${CUDNN_VERSION:-7.0.3}
     export MXNET_ENABLE_CYTHON=0
     nosetests-2.7 $NOSE_COVERAGE_ARGUMENTS $NOSE_TIMER_ARGUMENTS --with-xunit --xunit-file nosetests_quantization_gpu.xml --verbose tests/python/quantization_gpu
@@ -1157,6 +1168,7 @@ unittest_ubuntu_python3_quantization_gpu() {
     export PYTHONPATH=./python/
     export MXNET_MKLDNN_DEBUG=0 # Ignored if not present
     export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
+    export MXNET_SUBGRAPH_VERBOSE=0
     export CUDNN_VERSION=${CUDNN_VERSION:-7.0.3}
     export MXNET_ENABLE_CYTHON=0
     nosetests-3.4 $NOSE_COVERAGE_ARGUMENTS $NOSE_TIMER_ARGUMENTS --with-xunit --xunit-file nosetests_quantization_gpu.xml --verbose tests/python/quantization_gpu
@@ -1319,6 +1331,7 @@ integrationtest_ubuntu_gpu_python() {
     set -ex
     export PYTHONPATH=./python/
     export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
+    export MXNET_SUBGRAPH_VERBOSE=0
     python example/image-classification/test_score.py
 }
 
@@ -1347,6 +1360,7 @@ integrationtest_ubuntu_cpu_dist_kvstore() {
     pushd .
     export PYTHONPATH=./python/
     export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
+    export MXNET_SUBGRAPH_VERBOSE=0
     export MXNET_USE_OPERATOR_TUNING=0
     cd tests/nightly/
     ../../tools/launch.py -n 7 --launcher local python dist_sync_kvstore.py --type=gluon_step_cpu
@@ -1381,6 +1395,7 @@ integrationtest_ubuntu_gpu_dist_kvstore() {
     pushd .
     export PYTHONPATH=./python/
     export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
+    export MXNET_SUBGRAPH_VERBOSE=0
     cd tests/nightly/
     ../../tools/launch.py -n 4 --launcher local python dist_device_sync_kvstore.py
     ../../tools/launch.py -n 4 --launcher local python dist_sync_kvstore.py --type=init_gpu
@@ -1568,6 +1583,7 @@ nightly_tutorial_test_ubuntu_python3_gpu() {
     export MXNET_DOCS_BUILD_MXNET=0
     make html
     export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
+    export MXNET_SUBGRAPH_VERBOSE=0
     export PYTHONPATH=/work/mxnet/python/
     export MXNET_TUTORIAL_TEST_KERNEL=python3
     cd /work/mxnet/tests/tutorials
@@ -1581,6 +1597,7 @@ nightly_tutorial_test_ubuntu_python2_gpu() {
     export MXNET_DOCS_BUILD_MXNET=0
     make html
     export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
+    export MXNET_SUBGRAPH_VERBOSE=0
     export PYTHONPATH=/work/mxnet/python/
     export MXNET_TUTORIAL_TEST_KERNEL=python2
     cd /work/mxnet/tests/tutorials
@@ -1974,7 +1991,7 @@ cd_package_pypi() {
     popd
 }
 
-# Sanity checks wheel file 
+# Sanity checks wheel file
 cd_integration_test_pypi() {
     set -ex
     local python_cmd=${1:?"This function requires a python command as the first argument"}
diff --git a/ci/windows/test_py2_cpu.ps1 b/ci/windows/test_py2_cpu.ps1
index df9b15ba1ec3..c39d1fa45328 100644
--- a/ci/windows/test_py2_cpu.ps1
+++ b/ci/windows/test_py2_cpu.ps1
@@ -20,6 +20,7 @@
 $env:MXNET_LIBRARY_PATH=join-path $pwd.Path windows_package\lib\libmxnet.dll
 $env:PYTHONPATH=join-path $pwd.Path windows_package\python
 $env:MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
+$env:MXNET_SUBGRAPH_VERBOSE=0
 $env:MXNET_HOME=[io.path]::combine($PSScriptRoot, 'mxnet_home')
 
 C:\Python27\Scripts\pip install -r tests\requirements.txt
diff --git a/ci/windows/test_py2_gpu.ps1 b/ci/windows/test_py2_gpu.ps1
index f2974ff6f7b6..b2ea62fc7cd4 100644
--- a/ci/windows/test_py2_gpu.ps1
+++ b/ci/windows/test_py2_gpu.ps1
@@ -20,6 +20,7 @@
 $env:MXNET_LIBRARY_PATH=join-path $pwd.Path windows_package\lib\libmxnet.dll
 $env:PYTHONPATH=join-path $pwd.Path windows_package\python
 $env:MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
+$env:MXNET_SUBGRAPH_VERBOSE=0
 $env:MXNET_HOME=[io.path]::combine($PSScriptRoot, 'mxnet_home')
 
 C:\Python27\Scripts\pip install -r tests\requirements.txt
diff --git a/ci/windows/test_py3_cpu.ps1 b/ci/windows/test_py3_cpu.ps1
index 900bfd161cd0..1e09b5c98ce1 100644
--- a/ci/windows/test_py3_cpu.ps1
+++ b/ci/windows/test_py3_cpu.ps1
@@ -20,6 +20,7 @@
 $env:MXNET_LIBRARY_PATH=join-path $pwd.Path windows_package\lib\libmxnet.dll
 $env:PYTHONPATH=join-path $pwd.Path windows_package\python
 $env:MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
+$env:MXNET_SUBGRAPH_VERBOSE=0
 $env:MXNET_HOME=[io.path]::combine($PSScriptRoot, 'mxnet_home')
 
 C:\Python37\Scripts\pip install -r tests\requirements.txt
diff --git a/ci/windows/test_py3_gpu.ps1 b/ci/windows/test_py3_gpu.ps1
index b6e951b291fb..9bf7d04d8a88 100644
--- a/ci/windows/test_py3_gpu.ps1
+++ b/ci/windows/test_py3_gpu.ps1
@@ -20,6 +20,7 @@
 $env:MXNET_LIBRARY_PATH=join-path $pwd.Path windows_package\lib\libmxnet.dll
 $env:PYTHONPATH=join-path $pwd.Path windows_package\python
 $env:MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
+$env:MXNET_SUBGRAPH_VERBOSE=0
 $env:MXNET_HOME=[io.path]::combine($PSScriptRoot, 'mxnet_home')
 
 C:\Python37\Scripts\pip install -r tests\requirements.txt
diff --git a/perl-package/AI-MXNet/t/test_autograd.t b/perl-package/AI-MXNet/t/test_autograd.t
index 931c6d59333b..2ddad60df989 100644
--- a/perl-package/AI-MXNet/t/test_autograd.t
+++ b/perl-package/AI-MXNet/t/test_autograd.t
@@ -23,6 +23,7 @@ use AI::MXNet::TestUtils qw(same almost_equal rand_ndarray);
 use AI::MXNet::Base qw(:DEFAULT pones);
 use Test::More tests => 246;
 $ENV{MXNET_STORAGE_FALLBACK_LOG_VERBOSE} = 0;
+$ENV{MXNET_SUBGRAPH_VERBOSE} = 0;
 
 sub autograd_assert
 {
diff --git a/perl-package/AI-MXNet/t/test_gluon_trainer.t b/perl-package/AI-MXNet/t/test_gluon_trainer.t
index 81113af28c20..3b1130af4ecf 100644
--- a/perl-package/AI-MXNet/t/test_gluon_trainer.t
+++ b/perl-package/AI-MXNet/t/test_gluon_trainer.t
@@ -25,6 +25,7 @@ use AI::MXNet::TestUtils qw(almost_equal dies_ok);
 use Scalar::Util qw(refaddr);
 use AI::MXNet::Base;
 $ENV{MXNET_STORAGE_FALLBACK_LOG_VERBOSE} = 0;
+$ENV{MXNET_SUBGRAPH_VERBOSE} = 0;
 
 sub test_multi_trainer
 {
@@ -252,4 +253,3 @@ sub test_trainer_reset_kv
 }
 
 test_trainer_reset_kv();
-
diff --git a/perl-package/AI-MXNet/t/test_module.t b/perl-package/AI-MXNet/t/test_module.t
index 3bbd8fdc4ea4..55e098683399 100644
--- a/perl-package/AI-MXNet/t/test_module.t
+++ b/perl-package/AI-MXNet/t/test_module.t
@@ -22,6 +22,7 @@ use AI::MXNet qw(mx);
 use AI::MXNet::Base;
 use AI::MXNet::TestUtils qw(almost_equal enumerate same_array dies_like rand_ndarray);
 $ENV{MXNET_STORAGE_FALLBACK_LOG_VERBOSE} = 0;
+$ENV{MXNET_SUBGRAPH_VERBOSE} = 0;
 
 sub test_module_layout
 {
diff --git a/perl-package/AI-MXNet/t/test_sparse_ndarray.t b/perl-package/AI-MXNet/t/test_sparse_ndarray.t
index f143346b4890..afb0b25aa816 100644
--- a/perl-package/AI-MXNet/t/test_sparse_ndarray.t
+++ b/perl-package/AI-MXNet/t/test_sparse_ndarray.t
@@ -24,6 +24,7 @@ use AI::MXNet::TestUtils qw(zip assert enumerate same rand_shape_2d rand_shape_3
     rand_sparse_ndarray random_arrays almost_equal rand_ndarray randint allclose dies_ok);
 use AI::MXNet::Base qw(pones pzeros pdl product rand_sparse);
 $ENV{MXNET_STORAGE_FALLBACK_LOG_VERBOSE} = 0;
+$ENV{MXNET_SUBGRAPH_VERBOSE} = 0;
 
 
 sub sparse_nd_ones
diff --git a/src/executor/graph_executor.cc b/src/executor/graph_executor.cc
index d92253266f35..882105da1321 100644
--- a/src/executor/graph_executor.cc
+++ b/src/executor/graph_executor.cc
@@ -1627,16 +1627,16 @@ static nnvm::Graph InferForwardAttrs(nnvm::Graph g,
 
 static bool SubgraphBackendCheck(const op::SubgraphBackendPtr& backend,
                                  const Context& default_ctx,
-                                 bool verbose = false) {
+                                 int verbose = 1) {
   if (backend->HasAttr("enable") && (backend->GetAttr<bool>("enable") != true)) {
-    if (verbose) {
+    if (verbose > 1) {
       LOG(INFO) << "Subgraph backend " << backend->GetName()
                 << " isn't activated.";
     }
     return false;
   }
   if (backend->HasAttr("context") && backend->GetAttr<Context>("context") != default_ctx) {
-    if (verbose) {
+    if (verbose > 1) {
       LOG(INFO) << "Subgraph backend " << backend->GetName()
                 << " isn't activated as context mismatch.";
     }
@@ -1647,7 +1647,7 @@ static bool SubgraphBackendCheck(const op::SubgraphBackendPtr& backend,
 
 static bool SubgraphPropertyCheck(const std::string& backend_name,
                                   const op::SubgraphPropertyPtr& prop, bool need_grad,
-                                  bool verbose = false) {
+                                  int verbose = 1) {
   auto full_name =
       prop->HasAttr("property_name") ? prop->GetAttr<std::string>("property_name") : std::string();
   if (prop->HasAttr("disable") && prop->GetAttr<bool>("disable") == true) {
@@ -1657,7 +1657,7 @@ static bool SubgraphPropertyCheck(const std::string& backend_name,
   }
   if (prop->HasAttr("inference_only") && prop->GetAttr<bool>("inference_only") == true) {
     if (need_grad) {
-      if (verbose) {
+      if (verbose > 1) {
         LOG(INFO) << "skip partitioning graph with subgraph property " << full_name
                   << " from backend " << backend_name << " as it requires `grad_req=null`.";
       }
@@ -1699,7 +1699,7 @@ static nnvm::Symbol BuildSubgraph(
     const std::unordered_map<std::string, int>& arg_stype_map, const Context& default_ctx,
     const std::map<std::string, Context>& ctx_map, std::vector<Context>* in_arg_ctxes,
     std::vector<Context>* arg_grad_ctxes, std::vector<OpReqType>* grad_req_types,
-    std::vector<Context>* aux_state_ctxes, bool verbose = false) {
+    std::vector<Context>* aux_state_ctxes, int verbose = 1) {
   // setup map for in_arg_ctxes, arg_grad_ctxes, aux_state_ctxes and grad_req_types
   std::unordered_map<std::string, Context> in_arg_ctx_map;
   std::unordered_map<std::string, Context> arg_grad_ctx_map;
@@ -1794,7 +1794,7 @@ static nnvm::Symbol BuildSubgraph(const nnvm::Symbol& src, const op::SubgraphBac
                                   std::vector<NDArray>* in_args,
                                   std::vector<NDArray>* arg_grad_store,
                                   std::vector<OpReqType>* grad_req_type,
-                                  std::vector<NDArray>* aux_states, bool verbose = false) {
+                                  std::vector<NDArray>* aux_states, int verbose = 1) {
   // setup map for in_args, arg_grad_store, grad_req_type and aux_states
   std::unordered_map<std::string, NDArray> in_args_map;
   std::unordered_map<std::string, NDArray> arg_grad_store_map;
@@ -1929,11 +1929,11 @@ Executor *Executor::SimpleBind(nnvm::Symbol symbol,
   auto exec = new exec::GraphExecutor();
   bool init = false;
   if (!exec->subgraph_property().empty()) {
-    static bool verbose = dmlc::GetEnv("MXNET_SUBGRAPH_VERBOSE", false);
+    static int verbose = dmlc::GetEnv("MXNET_SUBGRAPH_VERBOSE", 1);
     const auto& backend_name = exec->subgraph_property();
     const auto& backend = op::SubgraphBackendRegistry::Get()->GetSubgraphBackend(backend_name);
     if (exec::SubgraphBackendCheck(backend, default_ctx, verbose)) {
-      LOG(INFO) << "Subgraph backend " << backend_name << " is activated.";
+      if (verbose) LOG(INFO) << "Subgraph backend " << backend_name << " is activated.";
       std::vector<Context> tmp_in_arg_ctxes = in_arg_ctxes;
       std::vector<Context> tmp_arg_grad_ctxes = arg_grad_ctxes;
       std::vector<Context> tmp_aux_state_ctxes = aux_state_ctxes;
@@ -2001,7 +2001,7 @@ Executor *Executor::Bind(nnvm::Symbol symbol,
                          const std::vector<NDArray> &aux_states,
                          Executor* shared_exec) {
   auto exec = new exec::GraphExecutor();
-  static bool verbose = dmlc::GetEnv("MXNET_SUBGRAPH_VERBOSE", false);
+  static int verbose = dmlc::GetEnv("MXNET_SUBGRAPH_VERBOSE", 1);
   std::vector<NDArray> tmp_in_args = in_args;
   std::vector<NDArray> tmp_arg_grad_store = arg_grad_store;
   std::vector<OpReqType> tmp_grad_req_type = grad_req_type;
@@ -2011,7 +2011,7 @@ Executor *Executor::Bind(nnvm::Symbol symbol,
     const auto& backend_name = exec->subgraph_property();
     const auto& backend = op::SubgraphBackendRegistry::Get()->GetSubgraphBackend(backend_name);
     if (exec::SubgraphBackendCheck(backend, default_ctx, verbose)) {
-      LOG(INFO) << "Subgraph backend " << backend_name << " is activated.";
+      if (verbose) LOG(INFO) << "Subgraph backend " << backend_name << " is activated.";
       symbol = exec::BuildSubgraph(symbol, backend, default_ctx, group2ctx, &tmp_in_args,
                                    &tmp_arg_grad_store, &tmp_grad_req_type, &tmp_aux_states,
                                    verbose);
diff --git a/src/operator/subgraph/build_subgraph.cc b/src/operator/subgraph/build_subgraph.cc
index d43647ac83b9..0f4c570331a2 100644
--- a/src/operator/subgraph/build_subgraph.cc
+++ b/src/operator/subgraph/build_subgraph.cc
@@ -318,8 +318,8 @@ void PreSelectSubgraphNodes(const nnvm::Graph& g, SubgraphSelectorV2Ptr subgraph
       for (auto node : excluded_nodes) {
         excluded_node_names += node->node->attrs.name + ", ";
       }
-      static bool verbose = dmlc::GetEnv("MXNET_SUBGRAPH_VERBOSE", false);
-      if (verbose) {
+      static int verbose = dmlc::GetEnv("MXNET_SUBGRAPH_VERBOSE", 1);
+      if (verbose > 1) {
         LOG(INFO) << "Found a cycle when BFS from node " << simple_nodes[snid]->node->attrs.name
                   << ". Excluding nodes " << excluded_node_names << "and retrying";
       }
@@ -706,9 +706,9 @@ void TopSortEntries(const nnvm::Graph& g,
 }
 
 nnvm::Graph BuildSubgraph(nnvm::Graph&& g) {
-    static bool verbose = dmlc::GetEnv("MXNET_SUBGRAPH_VERBOSE", false);
+    static int verbose = dmlc::GetEnv("MXNET_SUBGRAPH_VERBOSE", 1);
   if (!g.HasAttr("subgraph_property")) {  // treat the whole graph as a subgraph
-    if (verbose) {
+    if (verbose > 1) {
       LOG(INFO) << "The graph has no attribute of subgraph_property attached. "
                    "The original graph is returned.";
     }

From bde443e2399a8ce9ffe5957e5c3021b79c8055d2 Mon Sep 17 00:00:00 2001
From: Zhennan Qin <zhennan.qin@intel.com>
Date: Thu, 24 Oct 2019 05:04:22 -0500
Subject: [PATCH 509/813] Fix dequantize memory corruption (#16606)

Change-Id: I51b62a32987bdbcf96f04b1bc6617e66796f648b
---
 src/operator/quantization/dequantize.cc | 2 --
 1 file changed, 2 deletions(-)

diff --git a/src/operator/quantization/dequantize.cc b/src/operator/quantization/dequantize.cc
index e8e2cd90b86c..9ce135040fb4 100644
--- a/src/operator/quantization/dequantize.cc
+++ b/src/operator/quantization/dequantize.cc
@@ -43,8 +43,6 @@ bool DequantizeStorageType(const nnvm::NodeAttrs& attrs,
   }
 #endif
   (*out_attrs)[0] = kDefaultStorage;
-  (*out_attrs)[1] = kDefaultStorage;
-  (*out_attrs)[2] = kDefaultStorage;
   return true;
 }
 

From dd4eaf5c23046d07a4578a219e2dd3622e5620fa Mon Sep 17 00:00:00 2001
From: Zhennan Qin <zhennan.qin@intel.com>
Date: Thu, 24 Oct 2019 05:05:06 -0500
Subject: [PATCH 510/813] [MKLDNN]Fix reorder2default (#16602)

* Fix reorder2default

Change-Id: I74c87af9535f6264e6d1ea7eaed089a6480a3358

* fix

Change-Id: I6d07b43b520a47e7c78bd4b4b6390f5fb95e6957

* Fix

Change-Id: Id72f25c34291be4711f55569c6d61467edd6113d

* Fix CI

Change-Id: I8c33a82555d5ace2d0b682c1e3eefa13f3a44768

* Run CI

Change-Id: Ie8a6dab80ef91c0337cafbae4e3db277e0c7ebf7
---
 src/ndarray/ndarray.cc | 25 +++++++++++++++----------
 1 file changed, 15 insertions(+), 10 deletions(-)

diff --git a/src/ndarray/ndarray.cc b/src/ndarray/ndarray.cc
index 44da670b800d..aaa7aedf8bcd 100644
--- a/src/ndarray/ndarray.cc
+++ b/src/ndarray/ndarray.cc
@@ -1635,11 +1635,13 @@ void NDArray::Save(dmlc::Stream *strm) const {
     nd_cpu.WaitToRead();
     save_data = nd_cpu.data();
   } else {
+#if MXNET_USE_MKLDNN == 1
+    // For mkldnn, a copy of *this can ensure no write access pending on *this.
+    nd_cpu = this->Copy(Context::CPU());
+    nd_cpu.WaitToRead();
+#else
     this->WaitToRead();
     nd_cpu = *this;
-#if MXNET_USE_MKLDNN == 1
-    if (nd_cpu.IsMKLDNNData())
-      nd_cpu = nd_cpu.Reorder2Default();
 #endif
     save_data = nd_cpu.data();
   }
@@ -2024,15 +2026,18 @@ void NDArray::SyncCopyToCPU(void *data, size_t size) const {
   TBlob dst(data, dshape, cpu::kDevMask, this->dtype_, 0); // NOLINT(*)
 
   if (this->ctx().dev_mask() == cpu::kDevMask) {
-    this->WaitToRead();
-    RunContext rctx{this->ctx(), nullptr, nullptr, false};
-    NDArray src = *this;
+    Engine::Get()->PushAsync(
+        [&](RunContext rctx, Engine::CallbackOnComplete on_complete) {
+          RunContext ctx{this->ctx(), nullptr, nullptr, false};
+          NDArray src = *this;
 #if MXNET_USE_MKLDNN == 1
-    if (src.IsMKLDNNData())
-      src = this->Reorder2Default();
+          src = this->Reorder2Default();
 #endif
-    ndarray::Copy<cpu, cpu>(src.data(), &dst,
-                            Context::CPU(), Context::CPU(), rctx);
+          ndarray::Copy<cpu, cpu>(src.data(), &dst, Context::CPU(), Context::CPU(), ctx);
+          on_complete();
+        },
+        this->ctx(), {this->var()}, {}, FnProperty::kNormal, 0, "SyncCopyCPU2CPU");
+    this->WaitToWrite();
   } else {
 #if MXNET_USE_CUDA
     Engine::Get()->PushAsync(

From e10e94e64cde6655aa3e7952981661fb49785f5b Mon Sep 17 00:00:00 2001
From: Talia <31782251+TEChopra1000@users.noreply.github.com>
Date: Thu, 24 Oct 2019 09:09:24 -0700
Subject: [PATCH 511/813] second round of fixing broken links in multiple files
 (#16598)

---
 .../python/tutorials/packages/gluon/image/mnist.md   |  2 +-
 .../python/tutorials/packages/ndarray/sparse/csr.md  |  2 +-
 .../tutorials/packages/ndarray/sparse/row_sparse.md  |  2 +-
 .../tutorials/packages/ndarray/sparse/train.md       | 10 +++++-----
 .../_includes/get_started/devices/nvidia-jetson.md   |  2 +-
 .../src/_includes/get_started/get_started.html       |  8 ++++----
 .../src/_includes/get_started/linux/java/cpu.md      |  2 +-
 .../src/_includes/get_started/linux/java/gpu.md      |  2 +-
 .../get_started/linux/julia/build-from-source.md     |  2 +-
 .../src/_includes/get_started/linux/r/cpu.md         |  2 +-
 .../src/_includes/get_started/linux/r/gpu.md         |  2 +-
 .../src/_includes/get_started/macos/java/cpu.md      |  2 +-
 .../src/_includes/get_started/pip_snippet.md         |  2 +-
 .../get_started/windows/julia/build-from-source.md   |  2 +-
 .../src/_includes/get_started/windows/perl/perl.md   |  2 +-
 .../windows/python/cpu/build-from-source.md          |  2 +-
 .../windows/python/gpu/build-from-source.md          |  2 +-
 .../src/_includes/get_started/windows/r/cpu.md       |  2 +-
 .../src/_includes/get_started/windows/r/gpu.md       |  2 +-
 .../pages/api/r/docs/tutorials/callback_function.md  |  8 ++++----
 .../api/r/docs/tutorials/custom_loss_function.md     |  8 ++++----
 .../src/pages/api/r/docs/tutorials/multi_dim_lstm.md |  4 ++--
 .../src/pages/api/r/docs/tutorials/ndarray.md        | 12 ++++++------
 .../src/pages/api/r/docs/tutorials/symbol.md         | 12 ++++++------
 docs/static_site/src/pages/get_started/index.html    |  2 +-
 julia/docs/src/tutorial/mnist.md                     |  2 +-
 26 files changed, 50 insertions(+), 50 deletions(-)

diff --git a/docs/python_docs/python/tutorials/packages/gluon/image/mnist.md b/docs/python_docs/python/tutorials/packages/gluon/image/mnist.md
index a6898278edf6..39726a3a511c 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/image/mnist.md
+++ b/docs/python_docs/python/tutorials/packages/gluon/image/mnist.md
@@ -113,7 +113,7 @@ to train the MLP network we defined above.
 For our training, we will make use of the stochastic gradient descent (SGD) optimizer. In particular, we'll be using mini-batch SGD. Standard SGD processes train data one example at a time. In practice, this is very slow and one can speed up the process by processing examples in small batches. In this case, our batch size will be 100, which is a reasonable choice. Another parameter we select here is the learning rate, which controls the step size the optimizer takes in search of a solution. We'll pick a learning rate of 0.02, again a reasonable choice. Settings such as batch size and learning rate are what are usually referred to as hyper-parameters. What values we give them can have a great impact on training performance.
 
 We will use [Trainer](/api/python/docs/api/gluon/trainer.html) class to apply the
-[SGD optimizer](https://mxnet.io/api/python/docs/api/gluon-related/_autogen/mxnet.optimizer.SGD.html) on the
+[SGD optimizer](/api/python/docs/api/optimizer/index.html#mxnet.optimizer.SGD) on the
 initialized parameters.
 
 ```python
diff --git a/docs/python_docs/python/tutorials/packages/ndarray/sparse/csr.md b/docs/python_docs/python/tutorials/packages/ndarray/sparse/csr.md
index 0b362513c0ae..b91279cff4d4 100644
--- a/docs/python_docs/python/tutorials/packages/ndarray/sparse/csr.md
+++ b/docs/python_docs/python/tutorials/packages/ndarray/sparse/csr.md
@@ -556,7 +556,7 @@ except mx.MXNetError as err:
 
 ## Next 
 
-[Train a Linear Regression Model with Sparse Symbols](http://mxnet.apache.org/tutorials/sparse/train.html)
+[Train a Linear Regression Model with Sparse Symbols](/api/python/docs/tutorials/packages/ndarray/sparse/train.html)
 
 
 <!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/python_docs/python/tutorials/packages/ndarray/sparse/row_sparse.md b/docs/python_docs/python/tutorials/packages/ndarray/sparse/row_sparse.md
index 1241182af85b..7500e82cf9e6 100644
--- a/docs/python_docs/python/tutorials/packages/ndarray/sparse/row_sparse.md
+++ b/docs/python_docs/python/tutorials/packages/ndarray/sparse/row_sparse.md
@@ -578,7 +578,7 @@ except mx.MXNetError as err:
 
 ## Next
 
-[Train a Linear Regression Model with Sparse Symbols](http://mxnet.apache.org/tutorials/sparse/train.html)
+[Train a Linear Regression Model with Sparse Symbols](/api/python/docs/tutorials/packages/ndarray/sparse/train.html)
 
 
 <!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/python_docs/python/tutorials/packages/ndarray/sparse/train.md b/docs/python_docs/python/tutorials/packages/ndarray/sparse/train.md
index 71669e142a4b..336185cf7583 100644
--- a/docs/python_docs/python/tutorials/packages/ndarray/sparse/train.md
+++ b/docs/python_docs/python/tutorials/packages/ndarray/sparse/train.md
@@ -27,18 +27,18 @@ then train a linear regression model using sparse symbols with the Module API.
 
 To complete this tutorial, we need:
 
-- MXNet. See the instructions for your operating system in [Setup and Installation](https://mxnet.io/get_started).  
+- MXNet. See the instructions for your operating system in [Setup and Installation](/get_started).  
 
-- [Jupyter Notebook](https://jupyter.org/index.html) and [Python Requests](http://docs.python-requests.org/en/master/) packages.
+- [Jupyter Notebook](https://jupyter.org/index.html) and [Python Requests](https://3.python-requests.org/) packages.
 ```
 pip install jupyter requests
 ```
 
 - Basic knowledge of Symbol in MXNet. See the detailed tutorial for Symbol in [Symbol - Neural Network Graphs and Auto-differentiation](https://mxnet.apache.org/tutorials/basic/symbol.html).
 
-- Basic knowledge of CSRNDArray in MXNet. See the detailed tutorial for CSRNDArray in [CSRNDArray - NDArray in Compressed Sparse Row Storage Format](https://mxnet.apache.org/versions/master/tutorials/sparse/csr.html).
+- Basic knowledge of CSRNDArray in MXNet. See the detailed tutorial for CSRNDArray in [CSRNDArray - NDArray in Compressed Sparse Row Storage Format](/api/python/docs/tutorials/packages/ndarray/sparse/csr.html).
 
-- Basic knowledge of RowSparseNDArray in MXNet. See the detailed tutorial for RowSparseNDArray in [RowSparseNDArray - NDArray for Sparse Gradient Updates](https://mxnet.apache.org/versions/master/tutorials/sparse/row_sparse.html).
+- Basic knowledge of RowSparseNDArray in MXNet. See the detailed tutorial for RowSparseNDArray in [RowSparseNDArray - NDArray for Sparse Gradient Updates](/api/python/docs/tutorials/packages/ndarray/sparse/row_sparse.html).
 
 ## Variables
 
@@ -155,7 +155,7 @@ f = mx.sym.sparse.elemwise_add(c, c)
 ### Storage Type Inference
 
 What will be the output storage types of sparse symbols? In MXNet, for any sparse symbol, the result storage types are inferred based on storage types of inputs.
-You can read the [Sparse Symbol API](https://mxnet.apache.org/versions/master/api/python/symbol/sparse.html) documentation to find what output storage types are. In the example below we will try out the storage types introduced in the Row Sparse and Compressed Sparse Row tutorials: `default` (dense), `csr`, and `row_sparse`.
+You can read the [Sparse Symbol API](/api/python/docs/api/symbol/sparse/index.html) documentation to find what output storage types are. In the example below we will try out the storage types introduced in the Row Sparse and Compressed Sparse Row tutorials: `default` (dense), `csr`, and `row_sparse`.
 
 
 ```python
diff --git a/docs/static_site/src/_includes/get_started/devices/nvidia-jetson.md b/docs/static_site/src/_includes/get_started/devices/nvidia-jetson.md
index fe515f3392d7..40fb1d2e82f5 100644
--- a/docs/static_site/src/_includes/get_started/devices/nvidia-jetson.md
+++ b/docs/static_site/src/_includes/get_started/devices/nvidia-jetson.md
@@ -1,4 +1,4 @@
 # NVIDIA Jetson Devices
 
 To install MXNet on a Jetson TX or Nano, please refer to the [Jetson installation
-guide](get_started/jetson_setup).
\ No newline at end of file
+guide](/get_started/jetson_setup).
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/get_started.html b/docs/static_site/src/_includes/get_started/get_started.html
index 4905d28ce2d3..77367c7ed337 100644
--- a/docs/static_site/src/_includes/get_started/get_started.html
+++ b/docs/static_site/src/_includes/get_started/get_started.html
@@ -256,8 +256,8 @@ <h2>Installing MXNet</h2>
             </div> <!-- END - C++-->
 
             <br>
-            For more installation options, refer to the <a href="get_started/ubuntu_setup.html">Ubuntu installation guide</a> and
-            <a href="get_started/centos_setup.html">CentOS installation guide</a>.
+            For more installation options, refer to the <a href="/get_started/ubuntu_setup.html">Ubuntu installation guide</a> and
+            <a href="/get_started/centos_setup.html">CentOS installation guide</a>.
         </div> <!-- END - Linux -->
 
 
@@ -354,7 +354,7 @@ <h2>Installing MXNet</h2>
                 </div> <!-- End of cpu gpu -->
             </div>
             <br>
-            For more installation options, refer to the <a href="get_started/osx_setup.html">MXNet macOS installation guide</a>.
+            For more installation options, refer to the <a href="/get_started/osx_setup.html">MXNet macOS installation guide</a>.
         </div> <!-- END - Mac OS -->
 
 
@@ -440,7 +440,7 @@ <h2>Installing MXNet</h2>
                 </div> <!-- End of cpu gpu -->
             </div> <!-- End of C++ -->
 
-            For more installation options, refer to the <a href="get_started/windows_setup.html">MXNet Windows installation guide</a>.
+            For more installation options, refer to the <a href="/get_started/windows_setup.html">MXNet Windows installation guide</a>.
         </div> <!-- End of Windows -->
 
 
diff --git a/docs/static_site/src/_includes/get_started/linux/java/cpu.md b/docs/static_site/src/_includes/get_started/linux/java/cpu.md
index 5345a2d754b2..fc6f598fa5ee 100644
--- a/docs/static_site/src/_includes/get_started/linux/java/cpu.md
+++ b/docs/static_site/src/_includes/get_started/linux/java/cpu.md
@@ -1,6 +1,6 @@
 You can use the Maven packages defined in the following dependency to include MXNet in your Java
 project. The Java API is provided as a subset of the Scala API and is intended for inference only.
-Please refer to the <a href="get_started/java_setup.html">MXNet-Java setup guide</a> for a detailed set of
+Please refer to the <a href="/get_started/java_setup.html">MXNet-Java setup guide</a> for a detailed set of
 instructions to help you with the setup process.
 
 <a href="https://repository.apache.org/#nexus-search;gav~org.apache.mxnet~~1.5.0~~">
diff --git a/docs/static_site/src/_includes/get_started/linux/java/gpu.md b/docs/static_site/src/_includes/get_started/linux/java/gpu.md
index 5e687a353fe4..6f6757f6e2ea 100644
--- a/docs/static_site/src/_includes/get_started/linux/java/gpu.md
+++ b/docs/static_site/src/_includes/get_started/linux/java/gpu.md
@@ -1,6 +1,6 @@
 You can use the Maven packages defined in the following dependency to include MXNet in your Java
 project. The Java API is provided as a subset of the Scala API and is intended for inference only.
-Please refer to the <a href="get_started/java_setup.html">MXNet-Java setup guide</a> for a detailed set of
+Please refer to the <a href="/get_started/java_setup.html">MXNet-Java setup guide</a> for a detailed set of
 instructions to help you with the setup process.
 
 <a href="https://repository.apache.org/#nexus-search;gav~org.apache.mxnet~~1.5.0~~">
diff --git a/docs/static_site/src/_includes/get_started/linux/julia/build-from-source.md b/docs/static_site/src/_includes/get_started/linux/julia/build-from-source.md
index fbbc0bd248a9..018aca9d7387 100644
--- a/docs/static_site/src/_includes/get_started/linux/julia/build-from-source.md
+++ b/docs/static_site/src/_includes/get_started/linux/julia/build-from-source.md
@@ -1,2 +1,2 @@
-Refer to the [Julia section of the MXNet Ubuntu installation guide](get_started/ubuntu_setup#install-the-mxnet-package-for-julia).
+Refer to the [Julia section of the MXNet Ubuntu installation guide](/get_started/ubuntu_setup#install-the-mxnet-package-for-julia).
 
diff --git a/docs/static_site/src/_includes/get_started/linux/r/cpu.md b/docs/static_site/src/_includes/get_started/linux/r/cpu.md
index c0a4e015b61d..88ca5dd39933 100644
--- a/docs/static_site/src/_includes/get_started/linux/r/cpu.md
+++ b/docs/static_site/src/_includes/get_started/linux/r/cpu.md
@@ -1,5 +1,5 @@
 The default version of R that is installed with `apt-get` is insufficient. You will need
-to first [install R v3.4.4+ and build MXNet from source](get_started/ubuntu_setup.html#install-the-mxnet-package-for-r).
+to first [install R v3.4.4+ and build MXNet from source](/get_started/ubuntu_setup.html#install-the-mxnet-package-for-r).
 
 After you have setup R v3.4.4+ and MXNet, you can build and install the MXNet R bindings with the following, assuming that `incubator-mxnet` is the source directory you used to build MXNet as follows:
 
diff --git a/docs/static_site/src/_includes/get_started/linux/r/gpu.md b/docs/static_site/src/_includes/get_started/linux/r/gpu.md
index 57afe7a8d65e..16fbfd09d4d4 100644
--- a/docs/static_site/src/_includes/get_started/linux/r/gpu.md
+++ b/docs/static_site/src/_includes/get_started/linux/r/gpu.md
@@ -1,7 +1,7 @@
 The default version of R that is installed with `apt-get` is insufficient. You will need
 to first
 [install R v3.4.4+ and build MXNet from
-source](get_started/ubuntu_setup.html#install-the-mxnet-package-for-r).
+source](/get_started/ubuntu_setup.html#install-the-mxnet-package-for-r).
 
 After you have setup R v3.4.4+ and MXNet, you can build and install the MXNet R bindings
 with the
diff --git a/docs/static_site/src/_includes/get_started/macos/java/cpu.md b/docs/static_site/src/_includes/get_started/macos/java/cpu.md
index 2050149fd33d..002037a15771 100644
--- a/docs/static_site/src/_includes/get_started/macos/java/cpu.md
+++ b/docs/static_site/src/_includes/get_started/macos/java/cpu.md
@@ -1,7 +1,7 @@
 You can use the Maven packages defined in the following dependency to include MXNet in
 your Java project. The Java API is provided as a subset of the Scala API and is intended for
 inference only.
-Please refer to the [MXNet-Java setup guide](get_started/java_setup.html) for a detailed set of instructions to help you with the setup process.
+Please refer to the [MXNet-Java setup guide](/get_started/java_setup.html) for a detailed set of instructions to help you with the setup process.
 
 <a href="https://repository.apache.org/#nexus-search;gav~org.apache.mxnet~~1.5.0~~"><img
 src="https://img.shields.io/badge/org.apache.mxnet-mac cpu-green.svg"
diff --git a/docs/static_site/src/_includes/get_started/pip_snippet.md b/docs/static_site/src/_includes/get_started/pip_snippet.md
index f5cc4ea12803..2c4d932fc816 100644
--- a/docs/static_site/src/_includes/get_started/pip_snippet.md
+++ b/docs/static_site/src/_includes/get_started/pip_snippet.md
@@ -1,6 +1,6 @@
 MXNet offers MKL pip packages that will be much faster when running on Intel hardware.
 Check the chart below for other options, refer to <a href="https://pypi.org/project/mxnet/">PyPI for
-other MXNet pip packages</a>, or <a href="get_started/validate_mxnet.html">validate your MXNet installation</a>.
+other MXNet pip packages</a>, or <a href="/get_started/validate_mxnet.html">validate your MXNet installation</a>.
 
 <div style="text-align: center">
     <img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/install/pip-packages-1.5.1.png"
diff --git a/docs/static_site/src/_includes/get_started/windows/julia/build-from-source.md b/docs/static_site/src/_includes/get_started/windows/julia/build-from-source.md
index f9e61cb1c64e..4fc600468ad1 100644
--- a/docs/static_site/src/_includes/get_started/windows/julia/build-from-source.md
+++ b/docs/static_site/src/_includes/get_started/windows/julia/build-from-source.md
@@ -1 +1 @@
-Refer to the [Julia section of the MXNet Windows installation guide](get_started/windows_setup.html#install-the-mxnet-package-for-julia).
+Refer to the [Julia section of the MXNet Windows installation guide](/get_started/windows_setup.html#install-the-mxnet-package-for-julia).
diff --git a/docs/static_site/src/_includes/get_started/windows/perl/perl.md b/docs/static_site/src/_includes/get_started/windows/perl/perl.md
index a24ae0aa13f0..1a8eea5261ba 100644
--- a/docs/static_site/src/_includes/get_started/windows/perl/perl.md
+++ b/docs/static_site/src/_includes/get_started/windows/perl/perl.md
@@ -1 +1 @@
-Refer to the [Perl section of the MXNet Windows installation guide](get_started/windows_setup.html#install-the-mxnet-package-for-perl).
\ No newline at end of file
+Refer to the [Perl section of the MXNet Windows installation guide](/get_started/windows_setup.html#install-the-mxnet-package-for-perl).
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/windows/python/cpu/build-from-source.md b/docs/static_site/src/_includes/get_started/windows/python/cpu/build-from-source.md
index fe0ac055c914..af36205337d2 100644
--- a/docs/static_site/src/_includes/get_started/windows/python/cpu/build-from-source.md
+++ b/docs/static_site/src/_includes/get_started/windows/python/cpu/build-from-source.md
@@ -1 +1 @@
-Refer to the [MXNet Windows installation guide](get_started/windows_setup.html)
\ No newline at end of file
+Refer to the [MXNet Windows installation guide](/get_started/windows_setup.html)
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/windows/python/gpu/build-from-source.md b/docs/static_site/src/_includes/get_started/windows/python/gpu/build-from-source.md
index 762f720b5403..55bca3a129d8 100644
--- a/docs/static_site/src/_includes/get_started/windows/python/gpu/build-from-source.md
+++ b/docs/static_site/src/_includes/get_started/windows/python/gpu/build-from-source.md
@@ -1 +1 @@
-To build from source, refer to the [MXNet Windows installation guide](get_started/windows_setup.html).
+To build from source, refer to the [MXNet Windows installation guide](/get_started/windows_setup.html).
diff --git a/docs/static_site/src/_includes/get_started/windows/r/cpu.md b/docs/static_site/src/_includes/get_started/windows/r/cpu.md
index 3110475a2abd..926b8355c984 100644
--- a/docs/static_site/src/_includes/get_started/windows/r/cpu.md
+++ b/docs/static_site/src/_includes/get_started/windows/r/cpu.md
@@ -1,7 +1,7 @@
 Note: packages for 3.6.x are not yet available.
 Install 3.5.x of R from [CRAN](https://cran.r-project.org/bin/windows/base/old/).
 
-You can [build MXNet-R from source](get_started/windows_setup.html#install-mxnet-package-for-r), or
+You can [build MXNet-R from source](/get_started/windows_setup.html#install-mxnet-package-for-r), or
 you can use a
 pre-built binary:
 
diff --git a/docs/static_site/src/_includes/get_started/windows/r/gpu.md b/docs/static_site/src/_includes/get_started/windows/r/gpu.md
index 0840d2d2acbc..084f1a5a4012 100644
--- a/docs/static_site/src/_includes/get_started/windows/r/gpu.md
+++ b/docs/static_site/src/_includes/get_started/windows/r/gpu.md
@@ -1,4 +1,4 @@
-You can [build MXNet-R from source](get_started/windows_setup.html#install-mxnet-package-for-r), or
+You can [build MXNet-R from source](/get_started/windows_setup.html#install-mxnet-package-for-r), or
 you can use a
 pre-built binary:
 
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/callback_function.md b/docs/static_site/src/pages/api/r/docs/tutorials/callback_function.md
index 52e4db92f84b..d74112db98b5 100644
--- a/docs/static_site/src/pages/api/r/docs/tutorials/callback_function.md
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/callback_function.md
@@ -272,7 +272,7 @@ Yes! You can stop the training early with `return(FALSE)`. See the following exa
 When the validation metric dips below the threshold we set, the training process stops.
 
 ## Next Steps
-* [Neural Networks with MXNet in Five Minutes](https://mxnet.io/tutorials/r/fiveMinutesNeuralNetwork.html)
-* [Classify Real-World Images with a Pretrained Model](https://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
-* [Handwritten Digits Classification Competition](https://mxnet.io/tutorials/r/mnistCompetition.html)
-* [Character Language Model Using RNN](https://mxnet.io/tutorials/r/charRnnModel.html)
+* [Neural Networks with MXNet in Five Minutes](/api/r/docs/tutorials/five_minutes_neural_network)
+* [Classify Real-World Images with a Pretrained Model](/api/r/docs/tutorials/classify_real_image_with_pretrained_model)
+* [Handwritten Digits Classification Competition](/api/r/docs/tutorials/mnist_competition)
+* [Character Language Model Using RNN](/api/r/docs/tutorials/char_rnn_model)
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/custom_loss_function.md b/docs/static_site/src/pages/api/r/docs/tutorials/custom_loss_function.md
index 0f37123f23da..a4ca967d8e2c 100644
--- a/docs/static_site/src/pages/api/r/docs/tutorials/custom_loss_function.md
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/custom_loss_function.md
@@ -225,7 +225,7 @@ sum(abs(test.y - pred6[1,])) / length(test.y)
 
 
 ## Next Steps
-* [Neural Networks with MXNet in Five Minutes](https://mxnet.io/tutorials/r/fiveMinutesNeuralNetwork.html)
-* [Classify Real-World Images with a PreTrained Model](https://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
-* [Handwritten Digits Classification Competition](https://mxnet.io/tutorials/r/mnistCompetition.html)
-* [Character Language Model Using RNN](https://mxnet.io/tutorials/r/charRnnModel.html)
+* [Neural Networks with MXNet in Five Minutes](/api/r/docs/tutorials/five_minutes_neural_network)
+* [Classify Real-World Images with a PreTrained Model](/api/r/docs/tutorials/classify_real_image_with_pretrained_model)
+* [Handwritten Digits Classification Competition](/api/r/docs/tutorials/mnist_competition)
+* [Character Language Model Using RNN](/api/r/docs/tutorials/char_rnn_model)
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/multi_dim_lstm.md b/docs/static_site/src/pages/api/r/docs/tutorials/multi_dim_lstm.md
index e6e218f2ae13..2c24cdf92fc3 100644
--- a/docs/static_site/src/pages/api/r/docs/tutorials/multi_dim_lstm.md
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/multi_dim_lstm.md
@@ -55,7 +55,7 @@ PM2.5 concentration levels.
 
 Load and pre-process the data
 ---------
-The first step is to load in the data and preprocess it. It is assumed that the data has been downloaded in a .csv file: data.csv from the [pollution dataset](https://archive.ics.uci.edu/ml/datasets/Beijing+PM2.5+Data)
+The first step is to load in the data and preprocess it. It is assumed that the data has been downloaded in a .csv file: data.csv from the [pollution dataset](https://archive.ics.uci.edu/ml/datasets/Beijing+PM2.5+Data).
 
  ```r
 ## Loading required packages
@@ -324,4 +324,4 @@ We also repeated the above experiments to generate the next 100 samples to 301st
 
 The above tutorial is just for demonstration purposes and has not been tuned extensively for accuracy.
 
-For more tutorials on MXNet-R, head on to [MXNet-R tutorials](https://mxnet.apache.org/tutorials/r/index.html)
+For more tutorials on MXNet-R, head on to [MXNet-R tutorials](/api/r/docs/tutorials)
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/ndarray.md b/docs/static_site/src/pages/api/r/docs/tutorials/ndarray.md
index 9113b0d313d5..dc3d1c5a028e 100644
--- a/docs/static_site/src/pages/api/r/docs/tutorials/ndarray.md
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/ndarray.md
@@ -223,9 +223,9 @@ The actual computations are finished, allowing us to copy the results someplace
 the results.
 
 ## Next Steps
-* [Symbol](https://mxnet.io/tutorials/r/symbol.html)
-* [Write and use callback functions](https://mxnet.io/tutorials/r/CallbackFunction.html)
-* [Neural Networks with MXNet in Five Minutes](https://mxnet.io/tutorials/r/fiveMinutesNeuralNetwork.html)
-* [Classify Real-World Images with Pre-trained Model](https://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
-* [Handwritten Digits Classification Competition](https://mxnet.io/tutorials/r/mnistCompetition.html)
-* [Character Language Model using RNN](https://mxnet.io/tutorials/r/charRnnModel.html)
+* [Symbol](/api/r/docs/tutorials/symbol)
+* [Write and use callback functions](/api/r/docs/tutorials/callback_function)
+* [Neural Networks with MXNet in Five Minutes](/api/r/docs/tutorials/five_minutes_neural_network)
+* [Classify Real-World Images with Pre-trained Model](/api/r/docs/tutorials/classify_real_image_with_pretrained_model)
+* [Handwritten Digits Classification Competition](/api/r/docs/tutorials/mnist_competition)
+* [Character Language Model using RNN](/api/r/docs/tutorials/char_rnn_model)
diff --git a/docs/static_site/src/pages/api/r/docs/tutorials/symbol.md b/docs/static_site/src/pages/api/r/docs/tutorials/symbol.md
index 9c3150f97157..b5d6b8fd32a7 100644
--- a/docs/static_site/src/pages/api/r/docs/tutorials/symbol.md
+++ b/docs/static_site/src/pages/api/r/docs/tutorials/symbol.md
@@ -130,7 +130,7 @@ In the example, *net* is used as a function to apply to an existing symbol
 
 The [model API](https://github.com/apache/incubator-mxnet/blob/master/R-package/R/model.R) is a thin wrapper around the symbolic executors to support neural net training.
 
-We encourage you to read [Symbolic Configuration and Execution in Pictures for python package](../../api/python/symbol_in_pictures/symbol_in_pictures.md)for a detailed explanation of concepts in pictures.
+We encourage you to read [Symbolic Configuration and Execution in Pictures for python package](/api/python/symbol_in_pictures/symbol_in_pictures.md)for a detailed explanation of concepts in pictures.
 
 ## How Efficient Is the Symbolic API?
 
@@ -147,8 +147,8 @@ be more memory efficient than CXXNet and gets to the same runtime with
 greater flexibility.
 
 ## Next Steps
-* [Write and use callback functions](https://mxnet.io/tutorials/r/CallbackFunction.html)
-* [Neural Networks with MXNet in Five Minutes](https://mxnet.io/tutorials/r/fiveMinutesNeuralNetwork.html)
-* [Classify Real-World Images with Pre-trained Model](https://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
-* [Handwritten Digits Classification Competition](https://mxnet.io/tutorials/r/mnistCompetition.html)
-* [Character Language Model using RNN](https://mxnet.io/tutorials/r/charRnnModel.html)
+* [Write and use callback functions](/api/r/docs/tutorials/callback_function)
+* [Neural Networks with MXNet in Five Minutes](/api/r/docs/tutorials/five_minutes_neural_network)
+* [Classify Real-World Images with Pre-trained Model](/api/r/docs/tutorials/classify_real_image_with_pretrained_model)
+* [Handwritten Digits Classification Competition](/api/r/docs/tutorials/mnist_competition)
+* [Character Language Model using RNN](/api/r/docs/tutorials/char_rnn_model)
diff --git a/docs/static_site/src/pages/get_started/index.html b/docs/static_site/src/pages/get_started/index.html
index e89b5e3b36e8..02e7cf1b8641 100644
--- a/docs/static_site/src/pages/get_started/index.html
+++ b/docs/static_site/src/pages/get_started/index.html
@@ -28,6 +28,6 @@
 <div class="get-started-from-source">
 <div class="wrapper">
     <h2>Download from source</h2>
-    <p>The signed source code for Apache MXNet (incubating) is available for download <a href="get_started/download">here</a></p>
+    <p>The signed source code for Apache MXNet (incubating) is available for download <a href="/get_started/download">here</a></p>
 </div>
 </div>
diff --git a/julia/docs/src/tutorial/mnist.md b/julia/docs/src/tutorial/mnist.md
index edc1a67d2485..a404f75efe12 100644
--- a/julia/docs/src/tutorial/mnist.md
+++ b/julia/docs/src/tutorial/mnist.md
@@ -23,7 +23,7 @@ multi-layer perceptron and then a convolutional neural network (the
 LeNet architecture) on the [MNIST handwritten digit
 dataset](http://yann.lecun.com/exdb/mnist/). The code for this tutorial
 could be found in
-[examples/mnist](https://github.com/apache/incubator-mxnet/blob/master/julia/docs/src/tutorial/mnist.md).  There are also two Jupyter notebooks that expand a little more on the [MLP](https://github.com/ultradian/julia_notebooks/blob/master/mxnet/mnistMLP.ipynb) and the [LeNet](https://github.com/ultradian/julia_notebooks/blob/master/mxnet/mnistLenet.ipynb), using the more general `ArrayDataProvider`. 
+[examples/mnist](/api/julia/docs/api/tutorial/mnist/).  There are also two Jupyter notebooks that expand a little more on the [MLP](https://github.com/ultradian/julia_notebooks/blob/master/mxnet/mnistMLP.ipynb) and the [LeNet](https://github.com/ultradian/julia_notebooks/blob/master/mxnet/mnistLenet.ipynb), using the more general `ArrayDataProvider`. 
 
 Simple 3-layer MLP
 ------------------

From 82ddc93e215e45419d3dc3db0ec30f24f40de851 Mon Sep 17 00:00:00 2001
From: Jacob Kim <me@thejacobkim.com>
Date: Fri, 25 Oct 2019 01:10:07 +0900
Subject: [PATCH 512/813] Python Docstring Convetion (#16550)

* Docstring convetnion for

* Docstring convention for

* Docstring convention for

* Docstring convention for

* Docstring convention for

* Docstring convention for

* Docstring convention

* Revert removing new line

* Remove white space
---
 python/mxnet/kvstore.py    |  7 ++-----
 python/mxnet/metric.py     | 20 ++++++--------------
 python/mxnet/profiler.py   |  3 +--
 python/mxnet/rtc.py        |  3 ++-
 python/mxnet/runtime.py    | 22 ++++++----------------
 python/mxnet/test_utils.py | 12 +++++++-----
 python/mxnet/util.py       |  6 ++----
 7 files changed, 26 insertions(+), 47 deletions(-)

diff --git a/python/mxnet/kvstore.py b/python/mxnet/kvstore.py
index 5d332ff45ecb..61c64ec0984f 100644
--- a/python/mxnet/kvstore.py
+++ b/python/mxnet/kvstore.py
@@ -31,8 +31,7 @@
 from .profiler import set_kvstore_handle
 
 def _ctype_key_value(keys, vals):
-    """
-    Returns ctype arrays for the key-value args, and the whether string keys are used.
+    """Returns ctype arrays for the key-value args, and the whether string keys are used.
     For internal use only.
     """
     if isinstance(keys, (tuple, list)):
@@ -66,9 +65,7 @@ def _ctype_key_value(keys, vals):
         return (c_keys, c_handle_array(vals), use_str_keys)
 
 def _ctype_dict(param_dict):
-    """
-    Returns ctype arrays for keys and values(converted to strings) in a dictionary
-    """
+    """Returns ctype arrays for keys and values(converted to strings) in a dictionary"""
     assert(isinstance(param_dict, dict)), \
         "unexpected type for param_dict: " + str(type(param_dict))
     c_keys = c_array(ctypes.c_char_p, [c_str(k) for k in param_dict.keys()])
diff --git a/python/mxnet/metric.py b/python/mxnet/metric.py
index 07ec2ef4d61d..6e2d66cb9d15 100644
--- a/python/mxnet/metric.py
+++ b/python/mxnet/metric.py
@@ -153,8 +153,7 @@ def reset(self):
         self.global_sum_metric = 0.0
 
     def reset_local(self):
-        """Resets the local portion of the internal evaluation results
-        to initial state."""
+        """Resets the local portion of the internal evaluation results to initial state."""
         self.num_inst = 0
         self.sum_metric = 0.0
 
@@ -372,8 +371,7 @@ def reset(self):
             pass
 
     def reset_local(self):
-        """Resets the local portion of the internal evaluation results
-        to initial state."""
+        """Resets the local portion of the internal evaluation results to initial state."""
         try:
             for metric in self.metrics:
                 metric.reset_local()
@@ -592,8 +590,7 @@ def update(self, labels, preds):
 
 
 class _BinaryClassificationMetrics(object):
-    """
-    Private container class for classification metric statistics. True/false positive and
+    """Private container class for classification metric statistics. True/false positive and
      true/false negative counts are sufficient statistics for various classification metrics.
     This class provides the machinery to track those statistics across mini-batches of
     (label, prediction) pairs.
@@ -610,9 +607,7 @@ def __init__(self):
         self.global_true_negatives = 0
 
     def update_binary_stats(self, label, pred):
-        """
-        Update various binary classification counts for a single (label, pred)
-        pair.
+        """Update various binary classification counts for a single (label, pred) pair.
 
         Parameters
         ----------
@@ -691,9 +686,7 @@ def global_fscore(self):
             return 0.
 
     def matthewscc(self, use_global=False):
-        """
-        Calculate the Matthew's Correlation Coefficent
-        """
+        """Calculate the Matthew's Correlation Coefficent"""
         if use_global:
             if not self.global_total_examples:
                 return 0.
@@ -1604,8 +1597,7 @@ def reset(self):
         self.reset_local()
 
     def reset_local(self):
-        """Resets the local portion of the internal evaluation results
-        to initial state."""
+        """Resets the local portion of the internal evaluation results to initial state."""
         self.num_inst = 0.
         self.lcm = numpy.zeros((self.k, self.k))
 
diff --git a/python/mxnet/profiler.py b/python/mxnet/profiler.py
index 7dbc060ed60f..8e8ac87c9e06 100644
--- a/python/mxnet/profiler.py
+++ b/python/mxnet/profiler.py
@@ -207,8 +207,7 @@ def pause(profile_process='worker'):
 
 
 def resume(profile_process='worker'):
-    """
-    Resume paused profiling.
+    """Resume paused profiling.
 
     Parameters
     ----------
diff --git a/python/mxnet/rtc.py b/python/mxnet/rtc.py
index 4dea0e656b7e..5dfc5ea6dfe2 100644
--- a/python/mxnet/rtc.py
+++ b/python/mxnet/rtc.py
@@ -172,7 +172,8 @@ def get_kernel(self, name, signature):
 
 class CudaKernel(object):
     """Constructs CUDA kernel. Should be created by `CudaModule.get_kernel`,
-    not intended to be used by users."""
+    not intended to be used by users.
+    """
     def __init__(self, handle, name, is_ndarray, dtypes):
         self.handle = handle
         self._name = name
diff --git a/python/mxnet/runtime.py b/python/mxnet/runtime.py
index 0f7de76937c0..f2e98fe674fa 100644
--- a/python/mxnet/runtime.py
+++ b/python/mxnet/runtime.py
@@ -26,9 +26,7 @@
 from .base import _LIB, check_call
 
 class Feature(ctypes.Structure):
-    """
-    Compile time feature description, member fields: `name` and `enabled`.
-    """
+    """Compile time feature description, member fields: `name` and `enabled`."""
     _fields_ = [
         ("_name", ctypes.c_char_p),
         ("_enabled", ctypes.c_bool)
@@ -36,16 +34,12 @@ class Feature(ctypes.Structure):
 
     @property
     def name(self):
-        """
-        Feature name.
-        """
+        """Feature name."""
         return self._name.decode()
 
     @property
     def enabled(self):
-        """
-        True if MXNet was compiled with the given compile-time feature.
-        """
+        """True if MXNet was compiled with the given compile-time feature."""
         return self._enabled
 
     def __repr__(self):
@@ -55,8 +49,7 @@ def __repr__(self):
             return "✖ {}".format(self.name)
 
 def feature_list():
-    """
-    Check the library for compile-time features. The list of features are maintained in libinfo.h and libinfo.cc
+    """Check the library for compile-time features. The list of features are maintained in libinfo.h and libinfo.cc
 
     Returns
     -------
@@ -70,9 +63,7 @@ def feature_list():
     return features
 
 class Features(collections.OrderedDict):
-    """
-    OrderedDict of name to Feature
-    """
+    """OrderedDict of name to Feature"""
     instance = None
     def __new__(cls):
         if cls.instance is None:
@@ -84,8 +75,7 @@ def __repr__(self):
         return str(list(self.values()))
 
     def is_enabled(self, feature_name):
-        """
-        Check for a particular feature by name
+        """Check for a particular feature by name
 
         Parameters
         ----------
diff --git a/python/mxnet/test_utils.py b/python/mxnet/test_utils.py
index 4862aee8570d..6c8fefca4490 100644
--- a/python/mxnet/test_utils.py
+++ b/python/mxnet/test_utils.py
@@ -1935,8 +1935,7 @@ def same_array(array1, array2):
 
 @contextmanager
 def discard_stderr():
-    """
-    Discards error output of a routine if invoked as:
+    """Discards error output of a routine if invoked as:
 
     with discard_stderr():
         ...
@@ -2324,7 +2323,8 @@ def __exit__(self, ptype, value, trace):
 
 def collapse_sum_like(a, shape):
     """Given `a` as a numpy ndarray, perform reduce_sum on `a` over the axes that do not
-    exist in `shape`. Note that an ndarray with `shape` must be broadcastable to `a`."""
+    exist in `shape`. Note that an ndarray with `shape` must be broadcastable to `a`.
+    """
     assert len(a.shape) >= len(shape)
     if np.prod(shape) == 0 or a.size == 0:
         return np.zeros(shape, dtype=a.dtype)
@@ -2349,7 +2349,8 @@ def is_cd_run():
 
 def has_tvm_ops():
     """Returns True if MXNet is compiled with TVM generated operators. If current ctx
-    is GPU, it only returns True for CUDA compute capability > 52 where FP16 is supported."""
+    is GPU, it only returns True for CUDA compute capability > 52 where FP16 is supported.
+    """
     built_with_tvm_op = _features.is_enabled("TVM_OP")
     ctx = current_context()
     if ctx.device_type == 'gpu':
@@ -2367,7 +2368,8 @@ def has_tvm_ops():
 def is_op_runnable():
     """Returns True for all CPU tests. Returns True for GPU tests that are either of the following.
     1. Built with USE_TVM_OP=0.
-    2. Built with USE_TVM_OP=1, but with compute capability >= 53."""
+    2. Built with USE_TVM_OP=1, but with compute capability >= 53.
+    """
     ctx = current_context()
     if ctx.device_type == 'gpu':
         if not _features.is_enabled("TVM_OP"):
diff --git a/python/mxnet/util.py b/python/mxnet/util.py
index cef034fd0caa..9e15caae9698 100644
--- a/python/mxnet/util.py
+++ b/python/mxnet/util.py
@@ -60,8 +60,7 @@ def get_gpu_memory(gpu_dev_id):
 
 
 def set_np_shape(active):
-    """
-    Turns on/off NumPy shape semantics, in which `()` represents the shape of scalar tensors,
+    """Turns on/off NumPy shape semantics, in which `()` represents the shape of scalar tensors,
     and tuples with `0` elements, for example, `(0,)`, `(1, 0, 2)`, represent the shapes
     of zero-size tensors. This is turned off by default for keeping backward compatibility.
 
@@ -568,8 +567,7 @@ def hybrid_forward(self, F, x, w):
 
 
 def np_ufunc_legal_option(key, value):
-    """
-    Checking if ufunc arguments are legal inputs
+    """Checking if ufunc arguments are legal inputs
 
     Parameters
     ----------

From 487d69a0efd5611b1fa887268f40aed6b5a2e3b2 Mon Sep 17 00:00:00 2001
From: Itsuki Toyota <titsuki@cpan.org>
Date: Fri, 25 Oct 2019 01:10:31 +0900
Subject: [PATCH 513/813] [MXNET-1434] Fix a broken link for basic C++ tutorial
 (#16461)

---
 cpp-package/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cpp-package/README.md b/cpp-package/README.md
index 05fb506db42b..77ff0ee36e80 100644
--- a/cpp-package/README.md
+++ b/cpp-package/README.md
@@ -55,7 +55,7 @@ In order to consume the C++ API please follow the steps below.
 
 ## Tutorial
 
-A basic tutorial can be found at <https://mxnet.apache.org/tutorials/c++/basics.html>.
+A basic tutorial can be found at <https://mxnet.apache.org/api/cpp/docs/tutorials/basics>.
 
 ## Examples
 

From 9c99bf2d3e94ed9d1c9ea28e34d36a8e15bdfdb6 Mon Sep 17 00:00:00 2001
From: Przemyslaw Tredak <ptredak@nvidia.com>
Date: Thu, 24 Oct 2019 09:10:48 -0700
Subject: [PATCH 514/813] Fix for wrong reqs set after switching from training
 to inference (#16553)

* Debugging reqs

* Move literal strings to const static members

* Fix lint
---
 src/imperative/cached_op.cc       | 76 +++++++++++++++++++------------
 src/imperative/cached_op.h        |  7 +++
 src/imperative/imperative_utils.h |  2 +
 3 files changed, 57 insertions(+), 28 deletions(-)

diff --git a/src/imperative/cached_op.cc b/src/imperative/cached_op.cc
index 6818d757ab79..39c2880d627b 100644
--- a/src/imperative/cached_op.cc
+++ b/src/imperative/cached_op.cc
@@ -32,6 +32,22 @@ DMLC_REGISTER_PARAMETER(CachedOpConfig);
 
 constexpr uint32_t kEidNotExist = std::numeric_limits<uint32_t>::max();
 
+const char CachedOp::FULL[] = "full";
+const char CachedOp::FORWARD[] = "forward";
+const char CachedOp::BACKWARD[] = "backward";
+const char CachedOp::REF_COUNT[] = "ref_count";
+const char CachedOp::MEM_PLAN[] = "mem_plan";
+const char CachedOp::STORAGE_PLAN[] = "storage_plan";
+
+namespace {
+
+std::string AddPrefix(const std::string& prefix,
+                      const std::string& s) {
+  return prefix + "_" + s;
+}
+
+}  // namespace
+
 struct CachedOp::GraphInfo {
   nnvm::Graph fwd_graph;
   nnvm::Graph full_graph;
@@ -136,7 +152,7 @@ CachedOp::CachedOp(
       for (const auto& j : idx[i].inputs) ++ref_count[idx.entry_id(j)];
     }
 
-    fwd_graph_.attrs["forward_ref_count"] =
+    fwd_graph_.attrs[AddPrefix(FORWARD, REF_COUNT)] =
         std::make_shared<dmlc::any>(std::move(ref_count));
 
     inlining_ = !config_.static_alloc &&
@@ -201,9 +217,9 @@ CachedOp::CachedOp(
       }
     }
 
-    auto full_ref_count = fwd_graph_.GetAttr<std::vector<uint32_t> >("forward_ref_count");
+    auto full_ref_count = fwd_graph_.GetAttr<std::vector<uint32_t> >(AddPrefix(FORWARD, REF_COUNT));
     for (size_t i = 0; i < num_forward_entries; ++i) full_ref_count.at(i) += ref_count[i];
-    fwd_graph_.attrs["full_ref_count"] =
+    fwd_graph_.attrs[AddPrefix(FULL, REF_COUNT)] =
         std::make_shared<dmlc::any>(std::move(full_ref_count));
 
     size_t num_forward_inputs = num_inputs();
@@ -336,14 +352,15 @@ bool CachedOp::SetForwardGraph(
 
   // When dynmaic shape exists, it is not feasible to plan memory ahead of time
   if (contain_dynamic_shape) {
-    g.attrs.erase("forward_mem_plan");
-    g.attrs.erase("full_mem_plan");
+    g.attrs.erase(AddPrefix(FORWARD, MEM_PLAN));
+    g.attrs.erase(AddPrefix(FULL, MEM_PLAN));
     return false;
   }
+  const std::string& prefix = recording ? FULL : FORWARD;
   if (!match) {
-    g.attrs.erase("forward_mem_plan");
-    g.attrs.erase("full_mem_plan");
-  } else if (g.attrs.count(recording ? "full_mem_plan" : "forward_mem_plan")) {
+    g.attrs.erase(AddPrefix(FORWARD, MEM_PLAN));
+    g.attrs.erase(AddPrefix(FULL, MEM_PLAN));
+  } else if (g.attrs.count(AddPrefix(prefix, MEM_PLAN))) {
     return true;
   }
 
@@ -363,9 +380,9 @@ bool CachedOp::SetForwardGraph(
   }
 
   auto mem_plan = PlanMemory(
-      &g, std::move(storage), g.GetAttr<std::vector<uint32_t> >(
-          recording ? "full_ref_count" : "forward_ref_count"));
-  g.attrs[recording ? "full_mem_plan" : "forward_mem_plan"] =
+      &g, std::move(storage), g.GetAttr<std::vector<uint32_t> >(AddPrefix(prefix, REF_COUNT)),
+      AddPrefix(prefix, STORAGE_PLAN));
+  g.attrs[AddPrefix(prefix, MEM_PLAN)] =
       std::make_shared<dmlc::any>(std::move(mem_plan));
 
   return false;
@@ -432,7 +449,7 @@ bool CachedOp::SetBackwardGraph(
   size_t num_forward_nodes = fwd_graph_.indexed_graph().num_nodes();
   size_t num_forward_entries = fwd_graph_.indexed_graph().num_node_entries();
 
-  if (!g.attrs.count("backward_ref_count")) {
+  if (!g.attrs.count(AddPrefix(BACKWARD, REF_COUNT))) {
     std::vector<uint32_t> ref_count(idx.num_node_entries(), 0);
     for (size_t i = num_forward_nodes; i < idx.num_nodes(); ++i) {
       for (const auto& j : idx[i].inputs) ++ref_count[idx.entry_id(j)];
@@ -443,7 +460,7 @@ bool CachedOp::SetBackwardGraph(
       }
     }
     for (const auto& i : idx.outputs()) ++ref_count[idx.entry_id(i)];
-    g.attrs["backward_ref_count"] = std::make_shared<dmlc::any>(std::move(ref_count));
+    g.attrs[AddPrefix(BACKWARD, REF_COUNT)] = std::make_shared<dmlc::any>(std::move(ref_count));
   }
 
   auto shapes = info->fwd_graph.GetAttr<mxnet::ShapeVector>("shape");
@@ -476,8 +493,8 @@ bool CachedOp::SetBackwardGraph(
                                     false, node_range, entry_range);
 
   if (!match) {
-    g.attrs.erase("backward_mem_plan");
-  } else if (g.attrs.count("backward_mem_plan")) {
+    g.attrs.erase(AddPrefix(BACKWARD, MEM_PLAN));
+  } else if (g.attrs.count(AddPrefix(BACKWARD, MEM_PLAN))) {
     return true;
   }
 
@@ -491,11 +508,13 @@ bool CachedOp::SetBackwardGraph(
   for (const auto i : idx.outputs()) storage[idx.entry_id(i)] = exec::kExternalStorageID;
 
   auto mem_plan = PlanMemory(
-      &g, std::move(storage), g.GetAttr<std::vector<uint32_t> >("backward_ref_count"),
+      &g, std::move(storage),
+      g.GetAttr<std::vector<uint32_t> >(AddPrefix(BACKWARD, REF_COUNT)),
+      AddPrefix(BACKWARD, STORAGE_PLAN),
       {num_forward_nodes, idx.num_nodes()},
       {num_forward_entries, idx.num_node_entries()},
       detect_inplace_addto);
-  g.attrs["backward_mem_plan"] = std::make_shared<dmlc::any>(std::move(mem_plan));
+  g.attrs[AddPrefix(BACKWARD, MEM_PLAN)] = std::make_shared<dmlc::any>(std::move(mem_plan));
 
   return false;
 }
@@ -526,9 +545,10 @@ void CachedOp::StaticAllocMemory(
   const auto& default_ctx = state.context;
   nnvm::Graph& g = keep_fwd ? state.info.full_graph : state.info.fwd_graph;
   const auto& idx = g.indexed_graph();
-  const auto& vstorage_inplace = g.GetAttr<std::vector<int> >("storage_inplace_index");
-  const auto& mem_plan = g.GetAttr<MemoryPlanVector>(
-      keep_fwd ? "backward_mem_plan" : (recording ? "full_mem_plan" : "forward_mem_plan"));
+  const std::string& graph_type = keep_fwd ? BACKWARD : (recording ? FULL : FORWARD);
+  const auto& storage_plan_attr = AddPrefix(graph_type, STORAGE_PLAN);
+  const auto& storage_plan = g.GetAttr<std::vector<int> >(storage_plan_attr);
+  const auto& mem_plan = g.GetAttr<MemoryPlanVector>(AddPrefix(graph_type, MEM_PLAN));
   std::vector<int> addto_entry;
   if (g.attrs.count("addto_entry")) {
     addto_entry = g.GetAttr<std::vector<int> >("addto_entry");
@@ -558,9 +578,9 @@ void CachedOp::StaticAllocMemory(
   for (size_t i = start_eid; i < end_eid; ++i) {
     if (addto_entry.size() && addto_entry[i]) {
       state.array_reqs[i] = kAddTo;
-    } else if (vstorage_inplace[i] >= 0) {
+    } else if (storage_plan[i] >= 0) {
       state.array_reqs[i] = kWriteInplace;
-    } else if (vstorage_inplace[i] == -2) {
+    } else if (storage_plan[i] == -2) {
       // -2 indicate that the entry is never referenced.
       state.array_reqs[i] = kNullOp;
     } else {
@@ -862,8 +882,9 @@ OpStatePtr CachedOp::DynamicForward(
   }
 
   // Allocate NDArrays
-  std::vector<uint32_t> ref_count = g.GetAttr<std::vector<uint32_t> >(
-      recording ? "full_ref_count" : "forward_ref_count");
+  const std::string& graph_type = recording ? FULL : FORWARD;
+  std::vector<uint32_t> ref_count =
+    g.GetAttr<std::vector<uint32_t> >(AddPrefix(graph_type, REF_COUNT));
 
   std::vector<OpReqType> array_reqs(arrays.size(), kWriteTo);
   for (size_t i = 0; i < idx.num_node_entries(); ++i) {
@@ -871,8 +892,7 @@ OpStatePtr CachedOp::DynamicForward(
   }
   const auto& dispatch_modes = g.GetAttr<DispatchModeVector>("dispatch_mode");
   if (!use_naive_run) {
-    const auto& mem_plan = g.GetAttr<MemoryPlanVector >(
-        recording ? "full_mem_plan" : "forward_mem_plan");
+    const auto& mem_plan = g.GetAttr<MemoryPlanVector >(AddPrefix(graph_type, MEM_PLAN));
     AllocateMemory(g, idx, default_ctx, 0, idx.num_node_entries(),
                   mem_plan, arrays, &array_reqs);
     const auto& dtypes = g.GetAttr<DTypeVector>("dtype");
@@ -1011,7 +1031,7 @@ void CachedOp::DynamicBackward(
   }
 
   // Allocate NDArrays
-  auto ref_count = g.GetAttr<std::vector<uint32_t> >("backward_ref_count");
+  auto ref_count = g.GetAttr<std::vector<uint32_t> >(AddPrefix(BACKWARD, REF_COUNT));
   if (retain_graph) {
     for (size_t i = 0; i < num_forward_entries; ++i) ++ref_count[i];
   }
@@ -1027,7 +1047,7 @@ void CachedOp::DynamicBackward(
     if (ref_count[i] == 0) array_reqs[i] = kNullOp;
   }
 
-  const auto& mem_plan = g.GetAttr<MemoryPlanVector >("backward_mem_plan");
+  const auto& mem_plan = g.GetAttr<MemoryPlanVector >(AddPrefix(BACKWARD, MEM_PLAN));
   AllocateMemory(g, idx, default_ctx, num_forward_entries, idx.num_node_entries(),
                  mem_plan, arrays, &array_reqs);
 
diff --git a/src/imperative/cached_op.h b/src/imperative/cached_op.h
index db049d59ed80..84f96300c27b 100644
--- a/src/imperative/cached_op.h
+++ b/src/imperative/cached_op.h
@@ -140,6 +140,13 @@ class CachedOp {
   void RegisterOpHook(const CachedOp::CachedOpMonCallback& callback,
                       bool monitor_all = false);
 
+  static const char FULL[];
+  static const char FORWARD[];
+  static const char BACKWARD[];
+  static const char REF_COUNT[];
+  static const char MEM_PLAN[];
+  static const char STORAGE_PLAN[];
+
  private:
   struct GraphInfo;
   struct DynamicRuntime;
diff --git a/src/imperative/imperative_utils.h b/src/imperative/imperative_utils.h
index c5932bb3bbfe..8317c6073a24 100644
--- a/src/imperative/imperative_utils.h
+++ b/src/imperative/imperative_utils.h
@@ -814,6 +814,7 @@ inline MemoryPlanVector PlanMemory(
     nnvm::Graph* p_g,
     nnvm::StorageVector&& storage,
     const std::vector<uint32_t>& ref_count,
+    const std::string& storage_plan,
     const std::pair<uint32_t, uint32_t>& node_range = {0, 0},
     const std::pair<uint32_t, uint32_t>& entry_range = {0, 0},
     bool detect_inplace_addto = false) {
@@ -831,6 +832,7 @@ inline MemoryPlanVector PlanMemory(
   const auto& dtypes = g.GetAttr<DTypeVector>("dtype");
   const auto& shapes = g.GetAttr<mxnet::ShapeVector>("shape");
   const auto& storage_inplace = g.GetAttr<std::vector<int> >("storage_inplace_index");
+  g.attrs[storage_plan] = std::make_shared<any>(storage_inplace);
   const auto& storage_ids = g.GetAttr<StorageVector>("storage_id");
   uint32_t entry_start = entry_range.first;
   uint32_t entry_end =

From ef5633448517e8e67f3985fe8984efa8612c32dd Mon Sep 17 00:00:00 2001
From: Iblis Lin <iblis@hs.ntnu.edu.tw>
Date: Fri, 25 Oct 2019 00:12:15 +0800
Subject: [PATCH 515/813] julia/docs: more DRY on page rendering (#16396)

---
 julia/docs/Project.toml             |  2 +-
 julia/docs/make.jl                  | 33 +++++++++++++++++++++++++++++
 julia/docs/mkdocs.yml               |  1 +
 julia/docs/src/api.md               | 15 +------------
 julia/docs/src/api/ndarray.md       | 20 ++---------------
 julia/docs/src/api/symbolic-node.md | 11 +---------
 julia/docs/src/index.md             | 16 ++------------
 julia/src/executor.jl               |  2 +-
 julia/src/symbolic-node/show.jl     |  2 +-
 9 files changed, 43 insertions(+), 59 deletions(-)

diff --git a/julia/docs/Project.toml b/julia/docs/Project.toml
index a4b243b0ffea..023a222beba6 100644
--- a/julia/docs/Project.toml
+++ b/julia/docs/Project.toml
@@ -4,4 +4,4 @@ DocumenterMarkdown = "997ab1e6-3595-5248-9280-8efb232c3433"
 MXNet = "a7949054-b901-59c6-b8e3-7238c29bf7f0"
 
 [compat]
-Documenter = "~0.21"
+Documenter = "~0.23"
diff --git a/julia/docs/make.jl b/julia/docs/make.jl
index 3e541c636888..3ea9b07d1056 100644
--- a/julia/docs/make.jl
+++ b/julia/docs/make.jl
@@ -19,6 +19,39 @@ using Documenter
 using DocumenterMarkdown
 using MXNet
 
+"""
+Return all files of a submodule
+
+julia> listpages("ndarray")
+15-element Array{String,1}:
+ "ndarray.jl"
+ "ndarray/activation.jl"
+ "ndarray/arithmetic.jl"
+ "ndarray/array.jl"
+ ...
+ "ndarray/statistic.jl"
+ "ndarray/trig.jl"
+ "ndarray/type.jl"
+"""
+listpages(x) =
+  ["$x.jl"; joinpath.(x, readdir(joinpath(@__DIR__, "..", "src", x)))]
+
+const api_pages = [
+  "api/context.md",
+  "api/ndarray.md",
+  "api/symbolic-node.md",
+  "api/model.md",
+  "api/initializers.md",
+  "api/optimizers.md",
+  "api/callbacks.md",
+  "api/metric.md",
+  "api/io.md",
+  "api/nn-factory.md",
+  "api/executor.md",
+  "api/kvstore.md",
+  "api/visualize.md",
+]
+
 makedocs(
   sitename = "MXNet.jl",
   modules  = MXNet,
diff --git a/julia/docs/mkdocs.yml b/julia/docs/mkdocs.yml
index 22cb71869673..383505621540 100644
--- a/julia/docs/mkdocs.yml
+++ b/julia/docs/mkdocs.yml
@@ -62,4 +62,5 @@ nav:
     - Symbolic API: api/symbolic-node.md
     - Neural Networks Factory: api/nn-factory.md
     - Executor: api/executor.md
+    - Key-Value Store: api/kvstore.md
     - Network Visualization: api/visualize.md
diff --git a/julia/docs/src/api.md b/julia/docs/src/api.md
index 60cb0831d1bf..04cfadd6d698 100644
--- a/julia/docs/src/api.md
+++ b/julia/docs/src/api.md
@@ -18,18 +18,5 @@
 # API Documentation
 
 ```@contents
-Pages = [
-  "api/symbolic-node.md",
-  "api/ndarray.md",
-  "api/context.md",
-  "api/model.md",
-  "api/initializers.md",
-  "api/optimizers.md",
-  "api/callbacks.md",
-  "api/metric.md",
-  "api/io.md",
-  "api/nn-factory.md",
-  "api/executor.md",
-  "api/visualize.md",
-]
+Pages = api_pages
 ```
diff --git a/julia/docs/src/api/ndarray.md b/julia/docs/src/api/ndarray.md
index 64f59dc5393e..640e8b3ec372 100644
--- a/julia/docs/src/api/ndarray.md
+++ b/julia/docs/src/api/ndarray.md
@@ -19,7 +19,7 @@
 
 ## Arithmetic Operations
 
-In the following example `y` can be a `Real` value or another `NDArray`
+In the following example `y` can be a `Real` value or another `NDArray`.
 
 | API | Example  |                            |
 |-----|----------|----------------------------|
@@ -70,21 +70,5 @@ In the following example `y` can be a `Real` value or another `NDArray`
 
 ```@autodocs
 Modules = [MXNet.mx]
-Pages = [
-  "ndarray.jl",
-  "ndarray/activation.jl",
-  "ndarray/arithmetic.jl",
-  "ndarray/array.jl",
-  "ndarray/autoimport.jl",
-  "ndarray/comparison.jl",
-  "ndarray/context.jl",
-  "ndarray/io.jl",
-  "ndarray/linalg.jl",
-  "ndarray/reduction.jl",
-  "ndarray/remap.jl",
-  "ndarray/show.jl",
-  "ndarray/statistic.jl",
-  "ndarray/trig.jl",
-  "ndarray/type.jl",
-]
+Pages = listpages("ndarray")
 ```
diff --git a/julia/docs/src/api/symbolic-node.md b/julia/docs/src/api/symbolic-node.md
index 0efe4605c414..785dda87fbde 100644
--- a/julia/docs/src/api/symbolic-node.md
+++ b/julia/docs/src/api/symbolic-node.md
@@ -19,14 +19,5 @@
 
 ```@autodocs
 Modules = [MXNet.mx]
-Pages = [
-  "symbolic-node.jl",
-  "symbolic-node/arithmetic.jl",
-  "symbolic-node/array.jl",
-  "symbolic-node/autodiff.jl",
-  "symbolic-node/io.jl",
-  "symbolic-node/op.jl",
-  "symbolic-node/show.jl",
-  "symbolic-node/type.jl",
-]
+Pages = listpages("symbolic-node")
 ```
diff --git a/julia/docs/src/index.md b/julia/docs/src/index.md
index aacd844cc38e..4213265b4bd4 100644
--- a/julia/docs/src/index.md
+++ b/julia/docs/src/index.md
@@ -55,18 +55,6 @@ Depth = 2
 ## API Documentation
 
 ```@contents
-Pages = [
-  "api/context.md",
-  "api/ndarray.md",
-  "api/symbolic-node.md",
-  "api/model.md",
-  "api/initializers.md",
-  "api/optimizers.md",
-  "api/callbacks.md",
-  "api/metric.md",
-  "api/io.md",
-  "api/nn-factory.md",
-  "api/executor.md",
-  "api/visualize.md",
-]
+Pages = api_pages
+Depth = 2
 ```
diff --git a/julia/src/executor.jl b/julia/src/executor.jl
index 37f2dde615b8..7f6c2bb5aa58 100644
--- a/julia/src/executor.jl
+++ b/julia/src/executor.jl
@@ -245,7 +245,7 @@ Total 11 TempSpace resource requested
 ```
 """
 Base.print(io::IO, x::Executor) = print(io, debug_str(x))
-Base.print(x::Executor)         = print(STDOUT, x)
+Base.print(x::Executor)         = print(stdout, x)
 
 function debug_str(x::Executor)
   s_ref = Ref{Cstring}(C_NULL)
diff --git a/julia/src/symbolic-node/show.jl b/julia/src/symbolic-node/show.jl
index f07c6b4655ee..9d40ea124505 100644
--- a/julia/src/symbolic-node/show.jl
+++ b/julia/src/symbolic-node/show.jl
@@ -57,6 +57,6 @@ function Base.print(io::IO, sym::SymbolicNode)
   print(io, unsafe_string(out[]))
 end
 
-Base.print(sym::SymbolicNode) = print(STDOUT, sym)
+Base.print(sym::SymbolicNode) = print(stdout, sym)
 
 

From 4e03e6ac20dcf885b39792ebf4fd9e695e8f3eac Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Thu, 24 Oct 2019 20:30:52 -0700
Subject: [PATCH 516/813] Disables test_bulking_operator_gpu due to flakiness
 (#16611)

---
 tests/python/gpu/test_operator_gpu.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tests/python/gpu/test_operator_gpu.py b/tests/python/gpu/test_operator_gpu.py
index 06a16b1bb4f8..8b6928a2aa39 100644
--- a/tests/python/gpu/test_operator_gpu.py
+++ b/tests/python/gpu/test_operator_gpu.py
@@ -2395,6 +2395,7 @@ def _test_bulking_in_process(seed, time_per_iteration):
 
 
 @with_seed()
+@unittest.skip('skippping temporarily, tracked by https://github.com/apache/incubator-mxnet/issues/16517')
 def test_bulking_operator_gpu():
     _test_bulking(_test_bulking_in_process)
 

From c0e616f8f41b1fe8aa5edb92766a80c3e2b54775 Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Fri, 25 Oct 2019 09:54:41 -0700
Subject: [PATCH 517/813] C Api for simplebind, fix comment for trigoops, add
 atol to assert (#16585)

* C Api for simplebind, fix comment for trigoops, add atol to assert

* fix build issues

* fix lint and add regression test

* fix indent

* api doc and function name change

* fix lint and add infer shape test
---
 include/mxnet/c_api.h              |  38 +++++
 python/mxnet/symbol/symbol.py      | 110 +++++++++-----
 src/c_api/c_api_executor.cc        | 231 ++++++++++++++++++++++++-----
 tests/nightly/test_large_array.py  |   6 +-
 tests/nightly/test_large_vector.py |  49 +++++-
 5 files changed, 353 insertions(+), 81 deletions(-)

diff --git a/include/mxnet/c_api.h b/include/mxnet/c_api.h
index ac0c6726f2c7..2463a5b75cfd 100644
--- a/include/mxnet/c_api.h
+++ b/include/mxnet/c_api.h
@@ -2255,6 +2255,44 @@ MXNET_DLL int MXExecutorSimpleBindEx(SymbolHandle symbol_handle,
                                      NDArrayHandle** aux_states,
                                      ExecutorHandle shared_exec_handle,
                                      ExecutorHandle* out);
+
+
+MXNET_DLL int MXExecutorSimpleBindEx64(SymbolHandle symbol_handle,
+                                     int dev_type,
+                                     int dev_id,
+                                     const uint32_t num_g2c_keys,
+                                     const char** g2c_keys,
+                                     const int* g2c_dev_types,
+                                     const int* g2c_dev_ids,
+                                     const uint32_t provided_grad_req_list_len,
+                                     const char** provided_grad_req_names,
+                                     const char** provided_grad_req_types,
+                                     const uint32_t num_provided_arg_shapes,
+                                     const char** provided_arg_shape_names,
+                                     const int64_t* provided_arg_shape_data,
+                                     const uint32_t* provided_arg_shape_idx,
+                                     const uint32_t num_provided_arg_dtypes,
+                                     const char** provided_arg_dtype_names,
+                                     const int* provided_arg_dtypes,
+                                     const uint32_t num_provided_arg_stypes,
+                                     const char** provided_arg_stype_names,
+                                     const int* provided_arg_stypes,
+                                     const uint32_t num_shared_arg_names,
+                                     const char** shared_arg_name_list,
+                                     int* shared_buffer_len,
+                                     const char** shared_buffer_name_list,
+                                     NDArrayHandle* shared_buffer_handle_list,
+                                     const char*** updated_shared_buffer_name_list,
+                                     NDArrayHandle** updated_shared_buffer_handle_list,
+                                     uint32_t* num_in_args,
+                                     NDArrayHandle** in_args,
+                                     NDArrayHandle** arg_grads,
+                                     uint32_t* num_aux_states,
+                                     NDArrayHandle** aux_states,
+                                     ExecutorHandle shared_exec_handle,
+                                     ExecutorHandle* out);
+
+
 /*!
  * \brief DEPRECATED. Use MXExecutorReshapeEx instead.
  * Return a new executor with the same symbol and shared memory,
diff --git a/python/mxnet/symbol/symbol.py b/python/mxnet/symbol/symbol.py
index b8e8db57188c..6146ab9dc50e 100644
--- a/python/mxnet/symbol/symbol.py
+++ b/python/mxnet/symbol/symbol.py
@@ -1695,42 +1695,80 @@ def simple_bind(self, ctx, grad_req='write', type_dict=None, stype_dict=None,
         aux_state_handles = ctypes.POINTER(NDArrayHandle)()
 
         try:
-            check_call(_LIB.MXExecutorSimpleBindEx(self.handle,
-                                                   ctypes.c_int(ctx.device_typeid),
-                                                   ctypes.c_int(ctx.device_id),
-                                                   num_ctx_map_keys,
-                                                   ctx_map_keys,
-                                                   ctx_map_dev_types,
-                                                   ctx_map_dev_ids,
-                                                   mx_uint(provided_req_type_list_len),
-                                                   provided_grad_req_names,
-                                                   provided_grad_req_types,
-                                                   mx_uint(len(provided_arg_shape_names)),
-                                                   c_str_array(provided_arg_shape_names),
-                                                   c_array_buf(mx_int,
-                                                               array('I', provided_arg_shape_data)),
-                                                   c_array_buf(mx_uint,
-                                                               array('i', provided_arg_shape_idx)),
-                                                   num_provided_arg_types,
-                                                   provided_arg_type_names,
-                                                   provided_arg_type_data,
-                                                   num_provided_arg_stypes,
-                                                   provided_arg_stype_names,
-                                                   provided_arg_stype_data,
-                                                   mx_uint(len(shared_arg_name_list)),
-                                                   c_str_array(shared_arg_name_list),
-                                                   ctypes.byref(shared_buffer_len),
-                                                   shared_buffer_names,
-                                                   shared_buffer_handles,
-                                                   ctypes.byref(updated_shared_buffer_names),
-                                                   ctypes.byref(updated_shared_buffer_handles),
-                                                   ctypes.byref(num_in_args),
-                                                   ctypes.byref(in_arg_handles),
-                                                   ctypes.byref(arg_grad_handles),
-                                                   ctypes.byref(num_aux_states),
-                                                   ctypes.byref(aux_state_handles),
-                                                   shared_exec_handle,
-                                                   ctypes.byref(exe_handle)))
+            if sys.version_info[0] > 2 and _int64_enabled():
+                check_call(_LIB.MXExecutorSimpleBindEx64(self.handle,
+                                                         ctypes.c_int(ctx.device_typeid),
+                                                         ctypes.c_int(ctx.device_id),
+                                                         num_ctx_map_keys,
+                                                         ctx_map_keys,
+                                                         ctx_map_dev_types,
+                                                         ctx_map_dev_ids,
+                                                         mx_uint(provided_req_type_list_len),
+                                                         provided_grad_req_names,
+                                                         provided_grad_req_types,
+                                                         mx_uint(len(provided_arg_shape_names)),
+                                                         c_str_array(provided_arg_shape_names),
+                                                         c_array_buf(mx_int64,
+                                                                     array('q', provided_arg_shape_data)),
+                                                         c_array_buf(mx_uint,
+                                                                     array('i', provided_arg_shape_idx)),
+                                                         num_provided_arg_types,
+                                                         provided_arg_type_names,
+                                                         provided_arg_type_data,
+                                                         num_provided_arg_stypes,
+                                                         provided_arg_stype_names,
+                                                         provided_arg_stype_data,
+                                                         mx_uint(len(shared_arg_name_list)),
+                                                         c_str_array(shared_arg_name_list),
+                                                         ctypes.byref(shared_buffer_len),
+                                                         shared_buffer_names,
+                                                         shared_buffer_handles,
+                                                         ctypes.byref(updated_shared_buffer_names),
+                                                         ctypes.byref(updated_shared_buffer_handles),
+                                                         ctypes.byref(num_in_args),
+                                                         ctypes.byref(in_arg_handles),
+                                                         ctypes.byref(arg_grad_handles),
+                                                         ctypes.byref(num_aux_states),
+                                                         ctypes.byref(aux_state_handles),
+                                                         shared_exec_handle,
+                                                         ctypes.byref(exe_handle)))
+            else:
+                check_call(_LIB.MXExecutorSimpleBindEx(self.handle,
+                                                       ctypes.c_int(ctx.device_typeid),
+                                                       ctypes.c_int(ctx.device_id),
+                                                       num_ctx_map_keys,
+                                                       ctx_map_keys,
+                                                       ctx_map_dev_types,
+                                                       ctx_map_dev_ids,
+                                                       mx_uint(provided_req_type_list_len),
+                                                       provided_grad_req_names,
+                                                       provided_grad_req_types,
+                                                       mx_uint(len(provided_arg_shape_names)),
+                                                       c_str_array(provided_arg_shape_names),
+                                                       c_array_buf(mx_int,
+                                                                   array('I', provided_arg_shape_data)),
+                                                       c_array_buf(mx_uint,
+                                                                   array('i', provided_arg_shape_idx)),
+                                                       num_provided_arg_types,
+                                                       provided_arg_type_names,
+                                                       provided_arg_type_data,
+                                                       num_provided_arg_stypes,
+                                                       provided_arg_stype_names,
+                                                       provided_arg_stype_data,
+                                                       mx_uint(len(shared_arg_name_list)),
+                                                       c_str_array(shared_arg_name_list),
+                                                       ctypes.byref(shared_buffer_len),
+                                                       shared_buffer_names,
+                                                       shared_buffer_handles,
+                                                       ctypes.byref(updated_shared_buffer_names),
+                                                       ctypes.byref(updated_shared_buffer_handles),
+                                                       ctypes.byref(num_in_args),
+                                                       ctypes.byref(in_arg_handles),
+                                                       ctypes.byref(arg_grad_handles),
+                                                       ctypes.byref(num_aux_states),
+                                                       ctypes.byref(aux_state_handles),
+                                                       shared_exec_handle,
+                                                       ctypes.byref(exe_handle)))
         except MXNetError as e:
             error_msg = "simple_bind error. Arguments:\n"
             for k, v in kwargs.items():
diff --git a/src/c_api/c_api_executor.cc b/src/c_api/c_api_executor.cc
index ff85b4fd62fa..afc64f73de7c 100644
--- a/src/c_api/c_api_executor.cc
+++ b/src/c_api/c_api_executor.cc
@@ -515,44 +515,11 @@ int MXExecutorSimpleBind(SymbolHandle symbol_handle,
   API_END();
 }
 
-/*!
- * \brief
- * \param symbol_handle symbol handle
- * \param dev_type default device type
- * \param dev_id default device id
- * \param num_g2c_keys number of group2ctx keys
- * \param g2c_keys key list of group2ctx
- * \param g2c_dev_types device type list of group2ctx
- * \param g2c_dev_ids id list of group2ctx
- * \param provided_grad_req_list_len grad_req length provided by users in front-end
- * \param provided_grad_req_names grad_req names provided by users in front-end
- * \param provided_grad_req_types req types provided by users in front-end
- * \param num_provided_arg_shapes number of user provided in_arg and aux_state shapes
- * \param provided_arg_shape_names name list of provided shapes
- * \param provided_arg_shape_data provided shape data
- * \param provided_arg_shape_idx provided shape data index
- * \param num_provided_arg_dtypes number of user provided in_arg and axu_state dtypes
- * \param provided_arg_dtype_names argument name list of provided dtypes
- * \param provided_arg_dtypes data of provided dtypes
- * \param num_provided_arg_stypes number of user provided in_arg and axu_state storage types
- * \param provided_arg_stype_names argument name list of provided storage types
- * \param provided_arg_stypes data of provided storage types
- * \param num_shared_arg_names number of parameter names passed from _bind_ith_exec
- * \param shared_arg_name_list parameter name list passed from _bind_ith_exec
- * \param shared_buffer_len number of shared data arrays passed from _bind_ith_exec
- * \param shared_buffer_name_list shared data array names passed from _bind_ith_exec
- * \param shared_buffer_handle_list shared data array handles passed from _bind_ith_exec
- * \param updated_shared_buffer_name_list updated shared data array names after binding
- * \param updated_shared_buffer_handle_list updated shared data arrays after binding
- * \param num_in_args number of input arguments of this sym
- * \param in_args list_arguments associated with the current executor
- * \param arg_grads list of gradients of in_args associated with the current executor
- * \param num_aux_states number of aux states of this sym
- * \param aux_states list_auxiliary_states associated with the current executor
- * \param shared_exec_handle shared excutor handle passed from _bind_ith_exec
- * \param out the handle of the executor to be created
- */
-int MXExecutorSimpleBindEx(SymbolHandle symbol_handle,
+
+namespace mxnet {
+
+template<typename DType>
+int _SimpleBindImpl(SymbolHandle symbol_handle,
                            int dev_type,
                            int dev_id,
                            const uint32_t num_g2c_keys,
@@ -564,7 +531,7 @@ int MXExecutorSimpleBindEx(SymbolHandle symbol_handle,
                            const char** provided_grad_req_types,
                            const uint32_t num_provided_arg_shapes,
                            const char** provided_arg_shape_names,
-                           const int* provided_arg_shape_data,
+                           const DType* provided_arg_shape_data,
                            const uint32_t* provided_arg_shape_idx,
                            const uint32_t num_provided_arg_dtypes,
                            const char** provided_arg_dtype_names,
@@ -849,6 +816,192 @@ int MXExecutorSimpleBindEx(SymbolHandle symbol_handle,
   API_END();
 }
 
+}  // namespace mxnet
+
+
+/*!
+ * \brief Executor for simple_bind
+ * when INT64_TENSOR_SIZE = OFF
+ * \param symbol_handle symbol handle
+ * \param dev_type default device type
+ * \param dev_id default device id
+ * \param num_g2c_keys number of group2ctx keys
+ * \param g2c_keys key list of group2ctx
+ * \param g2c_dev_types device type list of group2ctx
+ * \param g2c_dev_ids id list of group2ctx
+ * \param provided_grad_req_list_len grad_req length provided by users in front-end
+ * \param provided_grad_req_names grad_req names provided by users in front-end
+ * \param provided_grad_req_types req types provided by users in front-end
+ * \param num_provided_arg_shapes number of user provided in_arg and aux_state shapes
+ * \param provided_arg_shape_names name list of provided shapes
+ * \param provided_arg_shape_data provided shape data
+ * \param provided_arg_shape_idx provided shape data index
+ * \param num_provided_arg_dtypes number of user provided in_arg and axu_state dtypes
+ * \param provided_arg_dtype_names argument name list of provided dtypes
+ * \param provided_arg_dtypes data of provided dtypes
+ * \param num_provided_arg_stypes number of user provided in_arg and axu_state storage types
+ * \param provided_arg_stype_names argument name list of provided storage types
+ * \param provided_arg_stypes data of provided storage types
+ * \param num_shared_arg_names number of parameter names passed from _bind_ith_exec
+ * \param shared_arg_name_list parameter name list passed from _bind_ith_exec
+ * \param shared_buffer_len number of shared data arrays passed from _bind_ith_exec
+ * \param shared_buffer_name_list shared data array names passed from _bind_ith_exec
+ * \param shared_buffer_handle_list shared data array handles passed from _bind_ith_exec
+ * \param updated_shared_buffer_name_list updated shared data array names after binding
+ * \param updated_shared_buffer_handle_list updated shared data arrays after binding
+ * \param num_in_args number of input arguments of this sym
+ * \param in_args list_arguments associated with the current executor
+ * \param arg_grads list of gradients of in_args associated with the current executor
+ * \param num_aux_states number of aux states of this sym
+ * \param aux_states list_auxiliary_states associated with the current executor
+ * \param shared_exec_handle shared excutor handle passed from _bind_ith_exec
+ * \param out the handle of the executor to be created
+ */
+int MXExecutorSimpleBindEx(SymbolHandle symbol_handle,
+                           int dev_type,
+                           int dev_id,
+                           const uint32_t num_g2c_keys,
+                           const char** g2c_keys,
+                           const int* g2c_dev_types,
+                           const int* g2c_dev_ids,
+                           const uint32_t provided_grad_req_list_len,
+                           const char** provided_grad_req_names,
+                           const char** provided_grad_req_types,
+                           const uint32_t num_provided_arg_shapes,
+                           const char** provided_arg_shape_names,
+                           const int* provided_arg_shape_data,
+                           const uint32_t* provided_arg_shape_idx,
+                           const uint32_t num_provided_arg_dtypes,
+                           const char** provided_arg_dtype_names,
+                           const int* provided_arg_dtypes,
+                           const uint32_t num_provided_arg_stypes,
+                           const char** provided_arg_stype_names,
+                           const int* provided_arg_stypes,
+                           const uint32_t num_shared_arg_names,
+                           const char** shared_arg_name_list,
+                           int* shared_buffer_len,
+                           const char** shared_buffer_name_list,
+                           NDArrayHandle* shared_buffer_handle_list,
+                           const char*** updated_shared_buffer_name_list,
+                           NDArrayHandle** updated_shared_buffer_handle_list,
+                           uint32_t* num_in_args,
+                           NDArrayHandle** in_args,
+                           NDArrayHandle** arg_grads,
+                           uint32_t* num_aux_states,
+                           NDArrayHandle** aux_states,
+                           ExecutorHandle shared_exec_handle,
+                           ExecutorHandle* out) {
+  return mxnet::_SimpleBindImpl(symbol_handle,
+                            dev_type, dev_id,
+                            num_g2c_keys, g2c_keys, g2c_dev_types, g2c_dev_ids,
+                            provided_grad_req_list_len, provided_grad_req_names,
+                            provided_grad_req_types,
+                            num_provided_arg_shapes, provided_arg_shape_names,
+                            provided_arg_shape_data, provided_arg_shape_idx,
+                            num_provided_arg_dtypes, provided_arg_dtype_names, provided_arg_dtypes,
+                            num_provided_arg_stypes, provided_arg_stype_names, provided_arg_stypes,
+                            num_shared_arg_names, shared_arg_name_list,
+                            shared_buffer_len, shared_buffer_name_list,
+                            shared_buffer_handle_list, updated_shared_buffer_name_list,
+                            updated_shared_buffer_handle_list,
+                            num_in_args, in_args, arg_grads,
+                            num_aux_states, aux_states,
+                            shared_exec_handle, out);
+}
+
+
+// TODO(ChaiBapchya): add API doc for rest of C APIs for int64
+/*!
+ * \brief Large tensor specific implementation for simple_bind executor
+ * when USE_INT64_TENSOR_SIZE = ON
+ * \param symbol_handle symbol handle
+ * \param dev_type default device type
+ * \param dev_id default device id
+ * \param num_g2c_keys number of group2ctx keys
+ * \param g2c_keys key list of group2ctx
+ * \param g2c_dev_types device type list of group2ctx
+ * \param g2c_dev_ids id list of group2ctx
+ * \param provided_grad_req_list_len grad_req length provided by users in front-end
+ * \param provided_grad_req_names grad_req names provided by users in front-end
+ * \param provided_grad_req_types req types provided by users in front-end
+ * \param num_provided_arg_shapes number of user provided in_arg and aux_state shapes
+ * \param provided_arg_shape_names name list of provided shapes
+ * \param provided_arg_shape_data provided shape data
+ * \param provided_arg_shape_idx provided shape data index
+ * \param num_provided_arg_dtypes number of user provided in_arg and axu_state dtypes
+ * \param provided_arg_dtype_names argument name list of provided dtypes
+ * \param provided_arg_dtypes data of provided dtypes
+ * \param num_provided_arg_stypes number of user provided in_arg and axu_state storage types
+ * \param provided_arg_stype_names argument name list of provided storage types
+ * \param provided_arg_stypes data of provided storage types
+ * \param num_shared_arg_names number of parameter names passed from _bind_ith_exec
+ * \param shared_arg_name_list parameter name list passed from _bind_ith_exec
+ * \param shared_buffer_len number of shared data arrays passed from _bind_ith_exec
+ * \param shared_buffer_name_list shared data array names passed from _bind_ith_exec
+ * \param shared_buffer_handle_list shared data array handles passed from _bind_ith_exec
+ * \param updated_shared_buffer_name_list updated shared data array names after binding
+ * \param updated_shared_buffer_handle_list updated shared data arrays after binding
+ * \param num_in_args number of input arguments of this sym
+ * \param in_args list_arguments associated with the current executor
+ * \param arg_grads list of gradients of in_args associated with the current executor
+ * \param num_aux_states number of aux states of this sym
+ * \param aux_states list_auxiliary_states associated with the current executor
+ * \param shared_exec_handle shared excutor handle passed from _bind_ith_exec
+ * \param out the handle of the executor to be created
+ */
+int MXExecutorSimpleBindEx64(SymbolHandle symbol_handle,
+                           int dev_type,
+                           int dev_id,
+                           const uint32_t num_g2c_keys,
+                           const char** g2c_keys,
+                           const int* g2c_dev_types,
+                           const int* g2c_dev_ids,
+                           const uint32_t provided_grad_req_list_len,
+                           const char** provided_grad_req_names,
+                           const char** provided_grad_req_types,
+                           const uint32_t num_provided_arg_shapes,
+                           const char** provided_arg_shape_names,
+                           const int64_t* provided_arg_shape_data,
+                           const uint32_t* provided_arg_shape_idx,
+                           const uint32_t num_provided_arg_dtypes,
+                           const char** provided_arg_dtype_names,
+                           const int* provided_arg_dtypes,
+                           const uint32_t num_provided_arg_stypes,
+                           const char** provided_arg_stype_names,
+                           const int* provided_arg_stypes,
+                           const uint32_t num_shared_arg_names,
+                           const char** shared_arg_name_list,
+                           int* shared_buffer_len,
+                           const char** shared_buffer_name_list,
+                           NDArrayHandle* shared_buffer_handle_list,
+                           const char*** updated_shared_buffer_name_list,
+                           NDArrayHandle** updated_shared_buffer_handle_list,
+                           uint32_t* num_in_args,
+                           NDArrayHandle** in_args,
+                           NDArrayHandle** arg_grads,
+                           uint32_t* num_aux_states,
+                           NDArrayHandle** aux_states,
+                           ExecutorHandle shared_exec_handle,
+                           ExecutorHandle* out) {
+  return mxnet::_SimpleBindImpl(symbol_handle,
+                            dev_type, dev_id,
+                            num_g2c_keys, g2c_keys, g2c_dev_types, g2c_dev_ids,
+                            provided_grad_req_list_len, provided_grad_req_names,
+                            provided_grad_req_types,
+                            num_provided_arg_shapes, provided_arg_shape_names,
+                            provided_arg_shape_data, provided_arg_shape_idx,
+                            num_provided_arg_dtypes, provided_arg_dtype_names, provided_arg_dtypes,
+                            num_provided_arg_stypes, provided_arg_stype_names, provided_arg_stypes,
+                            num_shared_arg_names, shared_arg_name_list,
+                            shared_buffer_len, shared_buffer_name_list,
+                            shared_buffer_handle_list, updated_shared_buffer_name_list,
+                            updated_shared_buffer_handle_list,
+                            num_in_args, in_args, arg_grads,
+                            num_aux_states, aux_states,
+                            shared_exec_handle, out);
+}
+
+
 int MXExecutorReshape(int partial_shaping,
                       int allow_up_sizing,
                       int dev_type,
diff --git a/tests/nightly/test_large_array.py b/tests/nightly/test_large_array.py
index c18a95400f22..74ac179a7e60 100644
--- a/tests/nightly/test_large_array.py
+++ b/tests/nightly/test_large_array.py
@@ -1351,17 +1351,17 @@ def check_trunc():
 
 
 def create_input_for_trigonometric_ops(vals):
-    # Creates large vector input of size(LARGE_X*10, SMALL_Y/10) from vals using tile operator
+    # Creates large vector input of size(LARGE_X*10, SMALL_Y/10) from vals using broadcast_to operator
     inp = nd.array(vals).reshape(1, 5)
     inp = nd.broadcast_to(inp, (LARGE_X*10, SMALL_Y//10))
     return inp
 
 
-def assert_correctness_of_trigonometric_ops(output, expected_vals):
+def assert_correctness_of_trigonometric_ops(output, expected_vals, atol=1e-3):
     # checks verifies 5 values at positions(0, 1, -3, -2, -1) of the input vector
     output_idx_to_inspect = [0, 1, -3, -2, -1]
     for i in range(len(output_idx_to_inspect)):
-        assert np.abs(output[1][output_idx_to_inspect[i]].asnumpy()-expected_vals[i]) <= 1e-3
+        assert np.abs(output[1][output_idx_to_inspect[i]].asnumpy()-expected_vals[i]) <= atol
 
 
 def test_trigonometric_ops():
diff --git a/tests/nightly/test_large_vector.py b/tests/nightly/test_large_vector.py
index b8edc83220bd..c6a99a5d0826 100644
--- a/tests/nightly/test_large_vector.py
+++ b/tests/nightly/test_large_vector.py
@@ -64,7 +64,7 @@ def test_ndarray_random_randint():
     high = 2**34
     a = nd.random.randint(low, high, dtype=np.int64, shape=LARGE_X).asnumpy()
     assert a.shape == (LARGE_X,)
-    assert (a >= low).all()  and (a < high).all()
+    assert (a >= low).all() and (a < high).all()
 
 
 def test_ndarray_empty():
@@ -710,6 +710,39 @@ def test_full():
     assert a[-1] == 3
 
 
+def test_regression():
+    shape = (LARGE_X, )
+
+    def check_regression(symbol, forward, shape):
+        # init executor
+        data_s = mx.symbol.Variable('data')
+        label_s = mx.symbol.Variable('label')
+        out_s = symbol(data=data_s, label=label_s)
+        exe = out_s.simple_bind(ctx=mx.cpu(0), data=shape, label=shape)
+
+        arg_map = dict(zip(out_s.list_arguments(), exe.arg_arrays))
+
+        # init data
+        data = mx.random.uniform(-1, -1, shape)
+        arg_map["data"][:] = data
+        atol = 1e-5
+        density = 0.5
+        stype = 'default'
+        label = arg_map["label"]
+        label[:] = rand_ndarray(shape, stype, density=density)
+        exe.forward(is_train=True)
+        exe.backward()
+        np_out = forward(data.asnumpy())
+        assert_almost_equal(exe.outputs[0].asnumpy(), np_out, atol=atol)
+
+    check_regression(mx.symbol.LogisticRegressionOutput,
+                     lambda x: 1.0 / (1.0 + np.exp(-x)),
+                     shape)
+    check_regression(mx.symbol.LinearRegressionOutput,
+                     lambda x: x,
+                     shape)
+
+
 def test_sign():
     a = mx.nd.random.normal(-1, 1, shape=LARGE_X)
     mx_res = mx.nd.sign(a)
@@ -978,11 +1011,11 @@ def test_add_n():
 def test_modulo():
     x = mx.nd.ones(LARGE_X)*6
     y = mx.nd.ones(LARGE_X)*4
-    z = (x%y)
+    z = (x % y)
     assert z[0] == 2
     assert z[-1] == 2
     x = mx.nd.ones(LARGE_X)*5
-    z = nd.modulo(x,y)
+    z = nd.modulo(x, y)
     assert z[0] == 1
     assert z[-1] == 1
 
@@ -1022,6 +1055,16 @@ def test_gather():
     assert np.sum(arr[idx] == 2) == 10
 
 
+def test_infer_shape():
+    data_1 = mx.symbol.Variable('data_1')
+    data_2 = mx.symbol.Variable('data_2')
+    add = data_1+data_2
+    # > add.infer_shape(data_1=(LARGE_X,), data_2=(LARGE_X,))
+    # OUTPUT - arg_shapes, out_shapes, aux_shapes
+    _, out_shapes, _ = add.infer_shape(data_1=(LARGE_X,), data_2=(LARGE_X,))
+    assert out_shapes == [(LARGE_X,)]
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From c574067d75d612b5e2a95eaed7618cc9c1e3b540 Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Fri, 25 Oct 2019 12:36:52 -0700
Subject: [PATCH 518/813] Imagenet inference to nightly fix (#16599)

* split to cd and shell

* comment

* lots of prints

* copy binary at correct location

* remove comments

* add mkl lib

* update docker run build function

* set nvidia docker true to run imagenet inference on GPU

* Revert "set nvidia docker true to run imagenet inference on GPU"

This reverts commit 98f8eef2057351d7964f1e9326ea6772c216f0af.
As we don't need GPU for compilation.
---
 ci/docker/runtime_functions.sh       |  5 +++--
 tests/nightly/JenkinsfileForBinaries | 10 +++++-----
 2 files changed, 8 insertions(+), 7 deletions(-)

diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index aab49f28a427..b53db3f980f1 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -1481,8 +1481,9 @@ nightly_test_installation() {
 nightly_test_imagenet_inference() {
     set -ex
     echo $PWD
-    cp /work/mxnet/build/cpp-package/example/imagenet_inference .
-    /work/mxnet/cpp-package/example/inference/unit_test_imagenet_inference.sh
+    cp /work/mxnet/build/cpp-package/example/imagenet_inference /work/mxnet/cpp-package/example/inference/
+    cd /work/mxnet/cpp-package/example/inference/
+    ./unit_test_imagenet_inference.sh
 }
 
 #Runs a simple MNIST training example
diff --git a/tests/nightly/JenkinsfileForBinaries b/tests/nightly/JenkinsfileForBinaries
index a66159d0075b..af87b2c35658 100755
--- a/tests/nightly/JenkinsfileForBinaries
+++ b/tests/nightly/JenkinsfileForBinaries
@@ -20,7 +20,7 @@
 
 mx_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a'
 mx_cmake_lib = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so'
-mx_lib_cpp_example = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, build/cpp-package/example/imagenet_inference'
+mx_lib_cpp_example_mkl = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, build/cpp-package/example/imagenet_inference, lib/libmkldnn.so.0, lib/libmklml_intel.so'
 
 node('utility') {
   // Loading the utilities requires a node context unfortunately
@@ -34,10 +34,10 @@ core_logic: {
   stage('Build') {
     parallel 'GPU: CUDA10.1+cuDNN7': {
       node(NODE_LINUX_CPU) {
-        ws('workspace/build-gpu') {
+        ws('workspace/build-mkldnn-gpu') {
           utils.init_git()
-          utils.docker_run('ubuntu_build_cuda', 'build_ubuntu_gpu_cuda101_cudnn7', false)
-          utils.pack_lib('gpu', mx_lib_cpp_example)
+          utils.docker_run('ubuntu_build_cuda', 'build_ubuntu_gpu_mkldnn', false)
+          utils.pack_lib('gpu', mx_lib_cpp_example_mkl)
         }
       }
     }/*,
@@ -73,7 +73,7 @@ core_logic: {
     'ImageNet Inference: GPU': {
       node(NODE_LINUX_GPU) {
         ws('workspace/nt-ImageInferenceTest') {
-          utils.unpack_and_init('gpu', mx_lib_cpp_example)
+          utils.unpack_and_init('gpu', mx_lib_cpp_example_mkl)
           utils.docker_run('ubuntu_nightly_gpu', 'nightly_test_imagenet_inference', true)
         }
       }

From 78627387d41f284067bec164063ae53dbc32adf3 Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Fri, 25 Oct 2019 15:55:50 -0700
Subject: [PATCH 519/813] Fix python doc build issue (#16630)

* pin the pip versions

* remove nbconvert comment
---
 docs/python_docs/environment.yml | 19 +++++++++----------
 1 file changed, 9 insertions(+), 10 deletions(-)

diff --git a/docs/python_docs/environment.yml b/docs/python_docs/environment.yml
index 11e43a1733f3..5f66d7715af9 100644
--- a/docs/python_docs/environment.yml
+++ b/docs/python_docs/environment.yml
@@ -27,13 +27,12 @@ dependencies:
 - matplotlib
 - notebook
 - pip:
-  # using nbconvert master until v5.5 comes out
-  - git+https://github.com/jupyter/nbconvert@master
-  - nbsphinx>=0.4.2
-  - recommonmark
-  - notedown
-  - pypandoc
-  - breathe
-  - mock
-  - awscli
-  - autodocsumm
+  - nbconvert==5.6.1
+  - nbsphinx==0.4.3
+  - recommonmark==0.6.0
+  - notedown==1.5.1
+  - pypandoc==1.4
+  - breathe==4.13.1
+  - mock==3.0.5
+  - awscli==1.16.266
+  - autodocsumm==0.1.11

From 0712f00a2a1fd0d33db97678a176064017a0e75d Mon Sep 17 00:00:00 2001
From: Brenton Chu <brentonlongchu@gmail.com>
Date: Fri, 25 Oct 2019 20:27:10 -0700
Subject: [PATCH 520/813] Faster general take (#16615)

* Sped up perf of take op when axis != 0

* Formatting and syntax fixes

* Rename Take to specify axis

* Fix line length lint errors
---
 src/operator/tensor/indexing_op.cc | 59 ++++++++++++++++-------------
 src/operator/tensor/indexing_op.cu | 61 ++++++++++++++++--------------
 src/operator/tensor/indexing_op.h  | 28 +++++++-------
 3 files changed, 79 insertions(+), 69 deletions(-)

diff --git a/src/operator/tensor/indexing_op.cc b/src/operator/tensor/indexing_op.cc
index 9961218b5482..470abee71a59 100644
--- a/src/operator/tensor/indexing_op.cc
+++ b/src/operator/tensor/indexing_op.cc
@@ -29,7 +29,7 @@ namespace mxnet {
 namespace op {
 
 template<bool clip = true>
-struct TakeCPU {
+struct TakeZeroAxisCPU {
   // assume that idx have been flattened to a 1-D tensor (N,)
   // assume that out_data and in_data have been flattened to 2-D tensors, (N, M) and (K, M)
   // M is the number of columns of in_data and out_data
@@ -88,8 +88,9 @@ void EmbeddingOpForwardDnsImpl<cpu>(mshadow::Stream<cpu>* s,
       Tensor<cpu, 2, DType> wmat = weight.get<cpu, 2, DType>(s);
       Tensor<cpu, 2, DType> out = output.get_with_shape<cpu, 2, DType>(
         Shape2(oshape.ProdShape(0, oshape.ndim()-1), oshape[oshape.ndim()-1]), s);
-      Kernel<TakeCPU<true>, cpu>::Launch(s, oshape.Size() / wmat.shape_[1], out.dptr_, wmat.dptr_,
-                                         idx.dptr_, wmat.shape_[1], wmat.shape_[0]);
+      Kernel<TakeZeroAxisCPU<true>, cpu>::Launch(s, oshape.Size() / wmat.shape_[1], out.dptr_,
+                                                 wmat.dptr_, idx.dptr_,
+                                                 wmat.shape_[1], wmat.shape_[0]);
     });
   });
 }
@@ -308,17 +309,17 @@ void TakeOpForward<cpu>(const nnvm::NodeAttrs& attrs,
       }
       if (actual_axis == 0) {
         if (param.mode == take_::kClip) {
-          Kernel<TakeCPU<true>, cpu>::Launch(s, idxshape.Size(),
-                                             outputs[take_::kOut].dptr<DType>(),
-                                             inputs[take_::kArr].dptr<DType>(),
-                                             inputs[take_::kIdx].dptr<IType>(),
-                                             oshape.Size()/idxshape.Size(), arrshape[0]);
+          Kernel<TakeZeroAxisCPU<true>, cpu>::Launch(s, idxshape.Size(),
+                                                     outputs[take_::kOut].dptr<DType>(),
+                                                     inputs[take_::kArr].dptr<DType>(),
+                                                     inputs[take_::kIdx].dptr<IType>(),
+                                                     oshape.Size()/idxshape.Size(), arrshape[0]);
         } else {
-          Kernel<TakeCPU<false>, cpu>::Launch(s, idxshape.Size(),
-                                              outputs[take_::kOut].dptr<DType>(),
-                                              inputs[take_::kArr].dptr<DType>(),
-                                              inputs[take_::kIdx].dptr<IType>(),
-                                              oshape.Size()/idxshape.Size(), arrshape[0]);
+          Kernel<TakeZeroAxisCPU<false>, cpu>::Launch(s, idxshape.Size(),
+                                                      outputs[take_::kOut].dptr<DType>(),
+                                                      inputs[take_::kArr].dptr<DType>(),
+                                                      inputs[take_::kIdx].dptr<IType>(),
+                                                      oshape.Size()/idxshape.Size(), arrshape[0]);
         }
       } else {
         mshadow::Shape<10> in_strides;
@@ -332,21 +333,25 @@ void TakeOpForward<cpu>(const nnvm::NodeAttrs& attrs,
           out_strides[i] = stride;
         }
         if (param.mode == take_::kClip) {
-          Kernel<Take<true>, cpu>::Launch(s, oshape.Size(),
-                                          outputs[take_::kOut].dptr<DType>(),
-                                          inputs[take_::kArr].dptr<DType>(),
-                                          inputs[take_::kIdx].dptr<IType>(),
-                                          in_strides, out_strides, arrshape.ndim(),
-                                          oshape.ndim(), idxshape.ndim(),
-                                          arrshape[actual_axis], actual_axis);
+          Kernel<TakeNonzeroAxis<true>, cpu>::Launch(s, oshape.Size(),
+                                                     outputs[take_::kOut].dptr<DType>(),
+                                                     inputs[take_::kArr].dptr<DType>(),
+                                                     inputs[take_::kIdx].dptr<IType>(),
+                                                     out_strides[actual_axis-1],
+                                                     in_strides[actual_axis-1],
+                                                     in_strides[actual_axis], arrshape.ndim(),
+                                                     oshape.ndim(), idxshape.ndim(),
+                                                     arrshape[actual_axis], actual_axis);
         } else {
-          Kernel<Take<false>, cpu>::Launch(s, oshape.Size(),
-                                           outputs[take_::kOut].dptr<DType>(),
-                                           inputs[take_::kArr].dptr<DType>(),
-                                           inputs[take_::kIdx].dptr<IType>(),
-                                           in_strides, out_strides, arrshape.ndim(),
-                                           oshape.ndim(), idxshape.ndim(),
-                                           arrshape[actual_axis], actual_axis);
+          Kernel<TakeNonzeroAxis<false>, cpu>::Launch(s, oshape.Size(),
+                                                      outputs[take_::kOut].dptr<DType>(),
+                                                      inputs[take_::kArr].dptr<DType>(),
+                                                      inputs[take_::kIdx].dptr<IType>(),
+                                                      out_strides[actual_axis-1],
+                                                      in_strides[actual_axis-1],
+                                                      in_strides[actual_axis], arrshape.ndim(),
+                                                      oshape.ndim(), idxshape.ndim(),
+                                                      arrshape[actual_axis], actual_axis);
         }
       }
     });
diff --git a/src/operator/tensor/indexing_op.cu b/src/operator/tensor/indexing_op.cu
index 0b4c20bf2bb5..3ccf1f39d4f7 100644
--- a/src/operator/tensor/indexing_op.cu
+++ b/src/operator/tensor/indexing_op.cu
@@ -116,11 +116,8 @@ struct AddTakeGradRspDeterministicKernel {
   }
 };
 
-/*! \brief name the struct Take instead of take
- * to avoid conflict with the take function in mshadow
- */
 template<bool clip = true>
-struct TakeGPU {
+struct TakeZeroAxisGPU {
   // assume that idx have been flattened to a 1-D tensor (N,)
   // assume that out_data and in_data have been flattened to 2-D tensors, (N, M) and (K, M)
   // M is the number of columns of in_data and out_data
@@ -180,8 +177,8 @@ void EmbeddingOpForwardDnsImpl<gpu>(mshadow::Stream<gpu>* s,
       Tensor<gpu, 2, DType> wmat = weight.get<gpu, 2, DType>(s);
       Tensor<gpu, 2, DType> out = output.get_with_shape<gpu, 2, DType>(
         Shape2(oshape.ProdShape(0, oshape.ndim()-1), oshape[oshape.ndim()-1]), s);
-      Kernel<TakeGPU<true>, gpu>::Launch(s, oshape.Size(), out.dptr_, wmat.dptr_,
-                                         idx.dptr_, wmat.shape_[1], wmat.shape_[0]);
+      Kernel<TakeZeroAxisGPU<true>, gpu>::Launch(s, oshape.Size(), out.dptr_, wmat.dptr_,
+                                                 idx.dptr_, wmat.shape_[1], wmat.shape_[0]);
     });
   });
 }
@@ -502,17 +499,17 @@ void TakeOpForward<gpu>(const nnvm::NodeAttrs& attrs,
       }
       if (actual_axis == 0) {
         if (param.mode == take_::kClip) {
-          Kernel<TakeGPU<true>, gpu>::Launch(s, oshape.Size(),
-                                             outputs[take_::kOut].dptr<DType>(),
-                                             inputs[take_::kArr].dptr<DType>(),
-                                             inputs[take_::kIdx].dptr<IType>(),
-                                             oshape.Size()/idxshape.Size(), arrshape[0]);
+          Kernel<TakeZeroAxisGPU<true>, gpu>::Launch(s, oshape.Size(),
+                                                     outputs[take_::kOut].dptr<DType>(),
+                                                     inputs[take_::kArr].dptr<DType>(),
+                                                     inputs[take_::kIdx].dptr<IType>(),
+                                                     oshape.Size()/idxshape.Size(), arrshape[0]);
         } else {
-          Kernel<TakeGPU<false>, gpu>::Launch(s, oshape.Size(),
-                                              outputs[take_::kOut].dptr<DType>(),
-                                              inputs[take_::kArr].dptr<DType>(),
-                                              inputs[take_::kIdx].dptr<IType>(),
-                                              oshape.Size()/idxshape.Size(), arrshape[0]);
+          Kernel<TakeZeroAxisGPU<false>, gpu>::Launch(s, oshape.Size(),
+                                                      outputs[take_::kOut].dptr<DType>(),
+                                                      inputs[take_::kArr].dptr<DType>(),
+                                                      inputs[take_::kIdx].dptr<IType>(),
+                                                      oshape.Size()/idxshape.Size(), arrshape[0]);
         }
       } else {
         mshadow::Shape<10> in_strides;
@@ -526,19 +523,27 @@ void TakeOpForward<gpu>(const nnvm::NodeAttrs& attrs,
           out_strides[i] = stride;
         }
         if (param.mode == take_::kClip) {
-          Kernel<Take<true>, gpu>::Launch(s, oshape.Size(),
-                                          outputs[take_::kOut].dptr<DType>(),
-                                          inputs[take_::kArr].dptr<DType>(),
-                                          inputs[take_::kIdx].dptr<IType>(),
-                                          in_strides, out_strides, arrshape.ndim(), oshape.ndim(),
-                                          idxshape.ndim(), arrshape[actual_axis], actual_axis);
+          Kernel<TakeNonzeroAxis<true>, gpu>::Launch(s, oshape.Size(),
+                                                     outputs[take_::kOut].dptr<DType>(),
+                                                     inputs[take_::kArr].dptr<DType>(),
+                                                     inputs[take_::kIdx].dptr<IType>(),
+                                                     out_strides[actual_axis-1],
+                                                     in_strides[actual_axis-1],
+                                                     in_strides[actual_axis],
+                                                     arrshape.ndim(), oshape.ndim(),
+                                                     idxshape.ndim(), arrshape[actual_axis],
+                                                     actual_axis);
         } else {
-          Kernel<Take<false>, gpu>::Launch(s, oshape.Size(),
-                                           outputs[take_::kOut].dptr<DType>(),
-                                           inputs[take_::kArr].dptr<DType>(),
-                                           inputs[take_::kIdx].dptr<IType>(),
-                                           in_strides, out_strides, arrshape.ndim(), oshape.ndim(),
-                                           idxshape.ndim(), arrshape[actual_axis], actual_axis);
+          Kernel<TakeNonzeroAxis<false>, gpu>::Launch(s, oshape.Size(),
+                                                      outputs[take_::kOut].dptr<DType>(),
+                                                      inputs[take_::kArr].dptr<DType>(),
+                                                      inputs[take_::kIdx].dptr<IType>(),
+                                                      out_strides[actual_axis-1],
+                                                      in_strides[actual_axis-1],
+                                                      in_strides[actual_axis],
+                                                      arrshape.ndim(), oshape.ndim(),
+                                                      idxshape.ndim(), arrshape[actual_axis],
+                                                      actual_axis);
         }
       }
     });
diff --git a/src/operator/tensor/indexing_op.h b/src/operator/tensor/indexing_op.h
index bb524dd0f5e9..828d761fefd4 100644
--- a/src/operator/tensor/indexing_op.h
+++ b/src/operator/tensor/indexing_op.h
@@ -296,11 +296,11 @@ inline bool SparseEmbeddingOpBackwardStorageType(const nnvm::NodeAttrs& attrs,
   return dispatched;
 }
 
-/*! \brief name the struct Take instead of take
- * to avoid conflict with the take function in mshadow
+/*! \brief name the struct TakeNonzeroAxis for general take when
+ * axis is not zero, use TakeZeroAxisGPU or TakeZeroAxisCPU for axis zero
  */
 template<bool clip = true>
-struct Take {
+struct TakeNonzeroAxis {
   /*!
    * \brief Map function for take operator
    * \param i           global thread id
@@ -315,28 +315,28 @@ struct Take {
    */
   template<typename DType, typename IType>
   MSHADOW_XINLINE static void Map(index_t i, DType* out_data, const DType* in_data,
-                                  const IType* idx,
-                                  const mshadow::Shape<10> in_stride,
-                                  const mshadow::Shape<10> out_stride,
+                                  const IType* idx, const int out_prev_stride,
+                                  const int in_prev_stride, const int in_stride,
                                   const int in_ndims, const int out_ndims, const int idx_ndims,
                                   const int axis_dim, const int axis) {
     // i is the global flattened index in the output
-    const int64_t out_head_index = (axis == 0) ? 0 : (i / out_stride[axis - 1]);
-    const int64_t out_rest_index = (axis == 0) ? i : (i % out_stride[axis - 1]);
-    const int64_t out_mid_index = out_rest_index / in_stride[axis];
+    const int64_t out_head_index = i / out_prev_stride;
+    const int64_t out_rest_index = i % out_prev_stride;
+    const int64_t out_mid_index = out_rest_index / in_stride;
     const int64_t out_tail_index = (axis == in_ndims - 1) ?
-                                   0 : (out_rest_index % in_stride[axis]);
+                                   0 : (out_rest_index % in_stride);
     int64_t idx_index = static_cast<int64_t>(idx[out_mid_index]);
     if (clip) {
       idx_index = (idx_index < 0) ? 0 : idx_index;
       idx_index = (idx_index > axis_dim - 1) ? (axis_dim - 1) : idx_index;
+    } else {
+      idx_index %= axis_dim;
+      idx_index += (idx_index < 0) ? axis_dim : 0;
     }
-    idx_index %= axis_dim;
-    idx_index += (idx_index < 0) ? axis_dim : 0;
     const int64_t in_tail_index = out_tail_index;
     const int64_t in_head_index = out_head_index;
-    int64_t in_src_index = in_tail_index + idx_index * in_stride[axis];
-    in_src_index += (axis == 0) ? 0 : in_head_index * in_stride[axis - 1];
+    int64_t in_src_index = in_tail_index + idx_index * in_stride;
+    in_src_index += in_head_index * in_prev_stride;
     out_data[i] = in_data[in_src_index];
   }
 };

From 8c44af4eba798b379c374c15582f9aea7dd7d8fd Mon Sep 17 00:00:00 2001
From: Leonard Lausen <lausen@amazon.com>
Date: Sat, 26 Oct 2019 00:13:42 -0700
Subject: [PATCH 521/813] [Gluon] Don't serialize shared parameters twice
 (#16582)

Add deduplicate argument (default of False) to save_parameters.
---
 python/mxnet/gluon/block.py         | 35 ++++++++++++++++++++-----
 tests/python/unittest/test_gluon.py | 40 +++++++++++++++++++++++++++++
 2 files changed, 69 insertions(+), 6 deletions(-)

diff --git a/python/mxnet/gluon/block.py b/python/mxnet/gluon/block.py
index eff7dd754572..629ff22ec4e0 100644
--- a/python/mxnet/gluon/block.py
+++ b/python/mxnet/gluon/block.py
@@ -24,7 +24,7 @@
 import copy
 import warnings
 import re
-from collections import OrderedDict
+from collections import OrderedDict, defaultdict
 
 from ..base import mx_real_t, MXNetError
 from .. import symbol, ndarray, initializer
@@ -413,7 +413,7 @@ def _collect_params_with_prefix(self, prefix=''):
             ret.update(child._collect_params_with_prefix(prefix + name))
         return ret
 
-    def save_parameters(self, filename):
+    def save_parameters(self, filename, deduplicate=False):
         """Save parameters to file.
 
         Saved parameters can only be loaded with `load_parameters`. Note that this
@@ -424,6 +424,10 @@ def save_parameters(self, filename):
         ----------
         filename : str
             Path to file.
+        deduplicate : bool, default False
+            If True, save shared parameters only once. Otherwise, if a Block
+            contains multiple sub-blocks that share parameters, each of the
+            shared parameters will be separately saved for every sub-block.
 
         References
         ----------
@@ -431,7 +435,17 @@ def save_parameters(self, filename):
         <https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html>`_
         """
         params = self._collect_params_with_prefix()
-        arg_dict = {key : val._reduce() for key, val in params.items()}
+
+        if deduplicate:
+            # Shared parameters are stored only a single time as of MXNet 1.6.
+            # Shared parameters are registered under multiple prefixes returned by
+            # _collect_params_with_prefix. We select a single one and only store
+            # it. In load_parameters it is sufficient for a shared parameter to
+            # only set it for a single prefix.
+            reverse_params = {v: k for k, v in params.items()}
+            params = {v: k for k, v in reverse_params.items()}
+
+        arg_dict = {key: val._reduce() for key, val in params.items()}
         save_fn = _mx_npx.save if is_np_array() else ndarray.save
         save_fn(filename, arg_dict)
 
@@ -510,15 +524,24 @@ def load_parameters(self, filename, ctx=None, allow_missing=False,
 
         if not any('.' in i for i in loaded.keys()):
             # legacy loading
-            del loaded
+            loaded = None  # This should be changed to `del loaded` when dropping Python 2
             self.collect_params().load(
                 filename, ctx, allow_missing, ignore_extra, self.prefix,
                 cast_dtype=cast_dtype, dtype_source=dtype_source)
             return
 
         if not allow_missing:
-            for name in params.keys():
-                assert name in loaded, \
+            # Shared parameters are stored only a single time as of MXNet 1.6.
+            # We thus retrieve all prefixes (through _collect_params_with_prefix)
+            # that a shared parameter is used with. Check that there are no
+            # missing parameters that were not yet already loaded from the
+            # shared version.
+            params_inv = defaultdict(list)
+            for k, v in params.items():
+                params_inv[v].append(k)
+
+            for name, param in params.items():
+                assert any(p in loaded for p in params_inv[param]), \
                     "Parameter '%s' is missing in file '%s', which contains parameters: %s. " \
                     "Set allow_missing=True to ignore missing parameters."%(
                         name, filename, _brief_print_list(loaded.keys()))
diff --git a/tests/python/unittest/test_gluon.py b/tests/python/unittest/test_gluon.py
index f1d0cc7ac274..f1413e2b99c2 100644
--- a/tests/python/unittest/test_gluon.py
+++ b/tests/python/unittest/test_gluon.py
@@ -1511,6 +1511,46 @@ def forward(self, x):
     net2 = Network()
     net2.load_parameters('tmp.params')
 
+@with_seed()
+def test_save_load_deduplicate_with_shared_params():
+    class B(mx.gluon.Block):
+        def __init__(self, params=None):
+            super(B, self).__init__(params=params)
+
+            with self.name_scope():
+                self.weight = self.params.get('weight', shape=(10, 10))
+
+    class C(mx.gluon.Block):
+        def __init__(self, b1, b2):
+            super(C, self).__init__()
+            self.b1 = b1
+            self.b2 = b2
+
+    b1 = B()
+    b2 = B(b1.collect_params())
+    c = C(b1, b2)
+    c.initialize()
+    c.save_parameters('tmp.params', deduplicate=True)
+
+    params = mx.nd.load('tmp.params')
+    assert len(params) == 1  # Only a single copy of the shared parameter is saved
+
+    b1 = B()
+    b2 = B(b1.collect_params())
+    c = C(b1, b2)
+    c.load_parameters('tmp.params')
+
+    # Test default behavior
+    c.save_parameters('tmp2.params', deduplicate=False)
+
+    params = mx.nd.load('tmp2.params')
+    assert len(params) == 2  # Only a single copy of the shared parameter is saved
+
+    b1 = B()
+    b2 = B(b1.collect_params())
+    c = C(b1, b2)
+    c.load_parameters('tmp2.params')
+
 @with_seed()
 def test_symbol_block_save_load():
     class Net(gluon.HybridBlock):

From e2624551616e930f4106e7366568572208994740 Mon Sep 17 00:00:00 2001
From: Haozheng Fan <fhztc1997618@gmail.com>
Date: Sat, 26 Oct 2019 15:43:08 +0800
Subject: [PATCH 522/813] Fix index overflow bug in einsum (#16589)

* fix index overflow

* check index overflow

* fix index overflow in einsum path

* fix indent

* reduce NPY_MAXARGS

* safe accumulate
---
 benchmark/python/einsum/benchmark_einsum.py   |   9 +
 src/operator/mxnet_op.h                       |  12 ++
 src/operator/numpy/np_einsum_op-inl.h         | 170 +++++++++---------
 src/operator/numpy/np_einsum_op.cc            |  11 ++
 src/operator/numpy/np_einsum_path_op-inl.h    | 114 ++++++------
 .../unittest/test_numpy_interoperability.py   |  34 ++++
 tests/python/unittest/test_numpy_op.py        |  33 ++--
 7 files changed, 226 insertions(+), 157 deletions(-)

diff --git a/benchmark/python/einsum/benchmark_einsum.py b/benchmark/python/einsum/benchmark_einsum.py
index 3593de2db9e1..6de8223287da 100644
--- a/benchmark/python/einsum/benchmark_einsum.py
+++ b/benchmark/python/einsum/benchmark_einsum.py
@@ -48,6 +48,15 @@ def test_np_einsum():
     cost = measure_cost(500, np.einsum, *args, optimize=True)
     print("Greedy einsum: {} ms".format(cost * 1000))
 
+    print("RNN Use Case:")
+    a = np.random.uniform(0, 1, size=(64, 128, 512))
+    b = np.random.uniform(0, 1, size=(128, 512, 2, 2))
+    args = ['bij, ijkl->bkl', a, b]
+    cost = measure_cost(2, np.einsum, *args, optimize=True)
+    print('Greedy einsum: {} ms'.format(cost * 1000))
+    cost = measure_cost(2, np.einsum, *args)
+    print('Basic einsum: {} ms'.format(cost * 1000))
+
     print('Inner Product:')
     a = np.ones(6000000)
     b = np.ones(6000000)
diff --git a/src/operator/mxnet_op.h b/src/operator/mxnet_op.h
index 8ccc34247b6f..463c71b5b0eb 100644
--- a/src/operator/mxnet_op.h
+++ b/src/operator/mxnet_op.h
@@ -619,6 +619,18 @@ MSHADOW_XINLINE Shape<ndim> calc_stride(const Shape<ndim>& shape) {
   return stride;
 }
 
+/* Increment coordinates */
+template<int ndim>
+MSHADOW_XINLINE bool inc(Shape<ndim>* coord, const Shape<ndim>& shape) {
+  ++(*coord)[ndim-1];
+  #pragma unroll
+  for (int i = ndim - 1; i > 0 && (*coord)[i] >= shape[i]; --i) {
+    (*coord)[i] -= shape[i];
+    ++(*coord)[i-1];
+  }
+  return (*coord)[0] < shape[0];
+}
+
 /* Increment coordinates and modify index */
 template<int ndim>
 MSHADOW_XINLINE void inc(Shape<ndim>* coord, const Shape<ndim>& shape,
diff --git a/src/operator/numpy/np_einsum_op-inl.h b/src/operator/numpy/np_einsum_op-inl.h
index 2145abec682b..051280763331 100644
--- a/src/operator/numpy/np_einsum_op-inl.h
+++ b/src/operator/numpy/np_einsum_op-inl.h
@@ -73,8 +73,8 @@
 namespace mxnet {
 namespace op {
 
-#define NPY_MAXDIMS 32
-#define NPY_MAXARGS 32
+#define NPY_MAXDIMS 16
+#define NPY_MAXARGS 16
 
 inline TShape get_stride(const TShape& shape) {
   int ndim = shape.ndim(), prod = 1;
@@ -415,40 +415,45 @@ class EinsumOp {
   }
 };  // class EinsumOp
 
-template<int dimension, int req, bool back>
-struct numpy_einsum {
+template<int dimension, int req, bool back, typename AType>
+struct numpy_einsum{
   template<typename DType>
   MSHADOW_XINLINE static void Map(index_t i, DType* out,
                                   common::StaticArray<DType*, NPY_MAXARGS> op,
                                   mshadow::Shape<dimension> oshape,
-                                  mshadow::Shape<dimension> ostride,
+                                  common::StaticArray<mshadow::Shape<dimension>,
+                                                      NPY_MAXARGS> ostride,
                                   mshadow::Shape<dimension> reduceshape,
-                                  mshadow::Shape<dimension> reducestride,
-                                  mshadow::Shape<dimension> itershape,
                                   common::StaticArray<mshadow::Shape<dimension>,
-                                                      NPY_MAXARGS> iterstride,
+                                                      NPY_MAXARGS> rstride,
                                   int nop,
                                   int iop0,
                                   const DType* out_grad) {
     using namespace mxnet_op;
-    index_t oidx = back ? dot(unravel(dot(unravel(i, oshape), ostride), itershape),
-                              iterstride[iop0]) : i;
+    mshadow::Shape<dimension> oidx = unravel(i, oshape);
+    i = back ? dot(oidx, ostride[iop0]) : i;
     if (req == kWriteTo) {
-      out[oidx] = (DType)0;
+      out[i] = (DType)0;
+    }
+    for (int rdim = 0; rdim < dimension; ++rdim) {
+      if (reduceshape[rdim] == 0) {
+        return;
+      }
     }
-    for (int j = 0; j < reduceshape.Size(); j++) {
-      mshadow::Shape<dimension> idx = unravel(dot(unravel(j, reduceshape), reducestride) +
-                                              dot(unravel(i, oshape), ostride),
-                                              itershape);
-      DType tmp = back ? out_grad[dot(idx, iterstride[nop])] :  (DType)1;
+    mshadow::Shape<dimension> ridx = unravel(0, reduceshape);
+    AType sum = 0;
+    do {
+      AType tmp = back ? static_cast<AType>(out_grad[dot(oidx, ostride[nop]) +
+                                                     dot(ridx, rstride[nop])]): (AType)1;
       for (int iop = 0; iop < nop; ++iop) {
         if (iop != iop0) {
-          index_t k = dot(idx, iterstride[iop]);
-          tmp = tmp * op[iop][k];
+          index_t k = dot(oidx, ostride[iop]) + dot(ridx, rstride[iop]);
+          tmp = tmp * static_cast<AType>(op[iop][k]);
         }
       }
-      out[oidx] = out[oidx] + tmp;
-    }
+      sum = sum + tmp;
+    }while (inc(&ridx, reduceshape));
+    out[i] = out[i] + static_cast<DType>(sum);
   }
 };
 
@@ -603,12 +608,12 @@ inline void NumpyEinsumProcess(const std::vector<TBlob>& inputs,
   }
 
   /* Step 4: Set up the op_axes for the iterator */
-  TShape itershape(ndim_iter, -1), iterstride_true(ndim_iter, -1);
+  TShape itershape(ndim_iter, -1);
+  std::vector<TShape> iterstride(nop + 1, TShape(ndim_iter, 0));
   TShape oshape = back ? inputs[0].shape_ : outputs[0].shape_;
   TShape ostride_true = get_stride(oshape);
-  TShape reduceshape, ostride, reducestride;
-  std::vector<TShape> iterstride(nop + 1, TShape(ndim_iter, 0));
-  std::vector<TShape> remainshape(nop), opstride(nop), remainstride(nop);
+  TShape reduceshape;
+  std::vector<TShape> remainshape(nop);
   int op_axes_arrays[NPY_MAXARGS][NPY_MAXDIMS];
   int *op_axes[NPY_MAXARGS];
 
@@ -632,7 +637,6 @@ inline void NumpyEinsumProcess(const std::vector<TBlob>& inputs,
   for (idim = 0; idim < ndim_output; ++idim) {
     iterstride[nop][idim] = ostride_true[idim];
   }
-  iterstride_true = get_stride(itershape);
   reduceshape = TShape(ndim_iter - ndim_output, 0);
   for (idim = ndim_output; idim < ndim_iter; ++idim) {
     reduceshape[idim - ndim_output] = itershape[idim];
@@ -648,30 +652,6 @@ inline void NumpyEinsumProcess(const std::vector<TBlob>& inputs,
     remainshape[iop] = TShape(rsh.begin(), rsh.end());
   }
 
-  // calculate stride
-  ostride = TShape(ndim_output, 0);
-  for (idim = 0; idim < ndim_output; ++idim) {
-    ostride[idim] = iterstride_true[idim];
-  }
-  reducestride = TShape(ndim_iter - ndim_output, 0);
-  for (idim = ndim_output; idim < ndim_iter; ++idim) {
-    reducestride[idim - ndim_output] = iterstride_true[idim];
-  }
-  for (iop = 0; iop < nop; ++iop) {
-    opstride[iop] = TShape(opshape[iop].ndim(), 0);
-    remainstride[iop] = TShape(remainshape[iop].ndim(), 0);
-    int j = 0;
-    for (idim = 0; idim < ndim_iter; ++idim) {
-      if (op_axes_arrays[iop][idim] != -1 &&
-          itershape[idim] == opshape[iop][op_axes_arrays[iop][idim]]) {
-        opstride[iop][op_axes_arrays[iop][idim]] = iterstride_true[idim];
-      } else {
-        remainstride[iop][j++] = iterstride_true[idim];
-      }
-    }
-    CHECK_EQ(j, remainstride[iop].ndim());
-  }
-
   // exclude the 0-dim case
   if (ndim_iter == 0) {
     ndim_iter = 1;
@@ -681,14 +661,10 @@ inline void NumpyEinsumProcess(const std::vector<TBlob>& inputs,
     iterstride[iop] = pad(iterstride[iop], ndim_iter);
   }
   oshape = pad(oshape, ndim_iter);
-  ostride = pad(ostride, ndim_iter);
   reduceshape = pad(reduceshape, ndim_iter);
-  reducestride = pad(reducestride, ndim_iter);
   for (iop = 0; iop < nop; ++iop) {
     opshape[iop] = pad(opshape[iop], ndim_iter);
-    opstride[iop] = pad(opstride[iop], ndim_iter);
     remainshape[iop] = pad(remainshape[iop], ndim_iter);
-    remainstride[iop] = pad(remainstride[iop], ndim_iter);
   }
 
   if (!back) {
@@ -696,28 +672,33 @@ inline void NumpyEinsumProcess(const std::vector<TBlob>& inputs,
       return;
     }
     const TBlob &out_data = outputs[0];
-    MSHADOW_TYPE_SWITCH(out_data.type_flag_, DType, {
+    MXNET_ACC_TYPE_SWITCH(out_data.type_flag_, DType, AType, {
       mxnet::common::StaticArray<DType*, NPY_MAXARGS> op;
       for (iop = 0; iop < nop; ++iop) {
         op[iop] = inputs[iop].dptr<DType>();
       }
       MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
         MXNET_NDIM_SWITCH_EX(ndim_iter, dimension, {
-          mxnet::common::StaticArray<mshadow::Shape<dimension>, NPY_MAXARGS> iterstride_arr;
-          for (iop = 0; iop <= nop; ++iop) {
-            iterstride_arr[iop] = iterstride[iop].get<dimension>();
+          mxnet::common::StaticArray<mshadow::Shape<dimension>, NPY_MAXARGS> ostride_arr;
+          mxnet::common::StaticArray<mshadow::Shape<dimension>, NPY_MAXARGS> rstride_arr;
+          for (iop = 0; iop < nop; ++iop) {
+            mshadow::Shape<dimension> otmp, rtmp;
+            for (idim = 0; idim < dimension; ++idim) {
+              otmp[idim] = idim < ndim_output ? iterstride[iop][idim] : 1;
+              rtmp[idim] = idim < dimension - ndim_output ? iterstride[iop][idim + ndim_output] : 1;
+            }
+            ostride_arr[iop] = otmp;
+            rstride_arr[iop] = rtmp;
           }
-          Kernel<numpy_einsum<dimension, req_type, 0>,
+          Kernel<numpy_einsum<dimension, req_type, 0, AType>,
                  xpu>::Launch(ctx.get_stream<xpu>(),
                               oshape.Size(),
                               out_data.dptr<DType>(),
                               op,
                               oshape.get<dimension>(),
-                              ostride.get<dimension>(),
+                              ostride_arr,
                               reduceshape.get<dimension>(),
-                              reducestride.get<dimension>(),
-                              itershape.get<dimension>(),
-                              iterstride_arr,
+                              rstride_arr,
                               nop,
                               -1,
                               reinterpret_cast<DType*>(NULL));
@@ -743,31 +724,44 @@ inline void NumpyEinsumProcess(const std::vector<TBlob>& inputs,
     for (int i = 0; i < nop; ++i) {
       const TBlob &out_data = outputs[i];
       const TBlob &out_grad = inputs[0];
-      MSHADOW_TYPE_SWITCH(out_data.type_flag_, DType, {
+      std::vector<TShape> opstride(nop + 1, TShape(ndim_iter, 0));
+      std::vector<TShape> remainstride(nop + 1, TShape(ndim_iter, 0));
+      for (iop = 0; iop <= nop; ++iop) {
+        int j = 0;
+        for (idim = 0; idim < ndim_iter; ++idim) {
+          if (op_axes_arrays[i][idim] == -1 ||
+              opshape[i][op_axes_arrays[i][idim]] == 1) {
+            remainstride[iop][j++] = iterstride[iop][idim];
+          } else {
+            opstride[iop][op_axes_arrays[i][idim]] = iterstride[iop][idim];
+          }
+        }
+      }
+      MXNET_ACC_TYPE_SWITCH(out_data.type_flag_, DType, AType, {
         mxnet::common::StaticArray<DType*, NPY_MAXARGS> op;
         for (iop = 0; iop < nop; ++iop) {
           op[iop] = inputs[iop + back].dptr<DType>();
         }
         MXNET_ASSIGN_REQ_SWITCH(req[i], req_type, {
           MXNET_NDIM_SWITCH_EX(ndim_iter, dimension, {
-            mxnet::common::StaticArray<mshadow::Shape<dimension>, NPY_MAXARGS> iterstride_arr;
+            mxnet::common::StaticArray<mshadow::Shape<dimension>, NPY_MAXARGS> opstride_arr;
+            mxnet::common::StaticArray<mshadow::Shape<dimension>, NPY_MAXARGS> remainstride_arr;
             for (iop = 0; iop <= nop; ++iop) {
-              iterstride_arr[iop] = iterstride[iop].get<dimension>();
+              opstride_arr[iop] = opstride[iop].get<dimension>();
+              remainstride_arr[iop] = remainstride[iop].get<dimension>();
             }
-            Kernel<numpy_einsum<dimension, req_type, 1>,
+            Kernel<numpy_einsum<dimension, req_type, 1, AType>,
                   xpu>::Launch(ctx.get_stream<xpu>(),
-                              opshape[i].Size(),
-                              out_data.dptr<DType>(),
-                              op,
-                              opshape[i].get<dimension>(),
-                              opstride[i].get<dimension>(),
-                              remainshape[i].get<dimension>(),
-                              remainstride[i].get<dimension>(),
-                              itershape.get<dimension>(),
-                              iterstride_arr,
-                              nop,
-                              i,
-                              out_grad.dptr<DType>());
+                               opshape[i].Size(),
+                               out_data.dptr<DType>(),
+                               op,
+                               opshape[i].get<dimension>(),
+                               opstride_arr,
+                               remainshape[i].get<dimension>(),
+                               remainstride_arr,
+                               nop,
+                               i,
+                               out_grad.dptr<DType>());
           })
         })
       })
@@ -798,13 +792,14 @@ inline void NumpyEinsumForward(const OpStatePtr& state_ptr,
   std::vector<std::vector<int> > pos;
   std::string string_repr;
   paths = einsum_path(state.subscripts, inputs, true, ctx.run_ctx, &pos, &string_repr);
-  int paths_len = paths.size(), temp_space_size = 0, max_temp_space_size = 0;
+  int paths_len = paths.size();
+  size_t temp_space_size = 0, max_temp_space_size = 0;
   std::vector<TBlob> operands(inputs), tmp_operands, temp_space_vec(paths_len - 1);
   for (int i = 0; i + 1 < paths_len; ++i) {
     temp_space_size += paths[i].oshape.Size();
   }
   for (int i = 0; i < paths_len; ++i) {
-    max_temp_space_size = std::max(max_temp_space_size, static_cast<int>(paths[i].oshape.Size()));
+    max_temp_space_size = std::max(max_temp_space_size, paths[i].oshape.Size());
   }
   temp_space_size += max_temp_space_size;
   MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
@@ -813,7 +808,7 @@ inline void NumpyEinsumForward(const OpStatePtr& state_ptr,
                                                false,
                                                outputs[0].type_flag_));
     Tensor<xpu, 1, DType> temp_space = state.tempspace->data().FlatTo1D<xpu, DType>();
-    int begin = max_temp_space_size;
+    size_t begin = max_temp_space_size;
     for (int i = 0; i < paths_len - 1; ++i) {
       TBlob tblob = TBlob(temp_space.Slice(begin, begin + paths[i].oshape.Size()));
       temp_space_vec[i] = tblob.reshape(paths[i].oshape);
@@ -910,12 +905,13 @@ inline void NumpyEinsumBackward(const OpStatePtr& state_ptr,
   }
   // calculate temporary space size for temp_grad
   const std::vector<Step>& paths = state.paths;
-  int paths_len = paths.size(), temp_space_size = 0, max_temp_space_size = 0;
+  int paths_len = paths.size();
+  size_t temp_space_size = 0, max_temp_space_size = 0;
   for (int i = 0; i < paths_len - 1; ++i) {
     temp_space_size += paths[i].oshape.Size();
   }
   for (int i = 0; i < paths_len; ++i) {
-    max_temp_space_size = std::max(max_temp_space_size, static_cast<int>(paths[i].oshape.Size()));
+    max_temp_space_size = std::max(max_temp_space_size, paths[i].oshape.Size());
   }
   temp_space_size += max_temp_space_size;
   // replay the forward process
@@ -936,8 +932,8 @@ inline void NumpyEinsumBackward(const OpStatePtr& state_ptr,
     }
   }
   // calculate temporary space size for tensordot
-  int tensordot_max_tempspace_size = 0;
-  int begin_tensordot_tempspace = 0;
+  size_t tensordot_max_tempspace_size = 0;
+  size_t begin_tensordot_tempspace = 0;
   std::vector<TBlob> temp_inputs, temp_outputs;
   std::vector<OpReqType> temp_req;
   std::vector<size_t> tensordot_tempspace_size;
@@ -999,7 +995,7 @@ inline void NumpyEinsumBackward(const OpStatePtr& state_ptr,
       }
       tensordot_tempspace_size.push_back(cur_tensordot_tempspace_size);
       tensordot_max_tempspace_size = std::max(tensordot_max_tempspace_size,
-                                              static_cast<int>(cur_tensordot_tempspace_size));
+                                              cur_tensordot_tempspace_size);
     }
     begin_tensordot_tempspace = temp_space_size;
     temp_space_size += (tensordot_max_tempspace_size + sizeof(DType) - 1) / sizeof(DType);
@@ -1010,7 +1006,7 @@ inline void NumpyEinsumBackward(const OpStatePtr& state_ptr,
     // allocate temporary space for gradients of intermediate results
     Tensor<xpu, 1, DType> temp_space = ctx.requested[0].get_space_typed<xpu, 1, DType>
       (Shape1(temp_space_size), s);
-    int begin = max_temp_space_size;
+    size_t begin = max_temp_space_size;
     for (int i = 0; i + 1 < paths_len; ++i) {
       TBlob tblob = TBlob(temp_space.Slice(begin, begin + paths[i].oshape.Size()));
       temp_grad[i] = tblob.reshape(paths[i].oshape);
diff --git a/src/operator/numpy/np_einsum_op.cc b/src/operator/numpy/np_einsum_op.cc
index 4d232b9b7c04..522780f5f3ad 100644
--- a/src/operator/numpy/np_einsum_op.cc
+++ b/src/operator/numpy/np_einsum_op.cc
@@ -305,6 +305,17 @@ bool NumpyEinsumShape(const nnvm::NodeAttrs& attrs,
     oshape[i] = dimension_dict[static_cast<int>(output_str[i])];
   }
   SHAPE_ASSIGN_CHECK(*out_attrs, 0, oshape);
+  size_t lim = static_cast<size_t>(std::numeric_limits<index_t>::max());
+  for (int i = 0; i < num_args; ++i) {
+    CHECK_LE(in_attrs->at(i).Size(), lim)
+      << "Size of operand " << i
+      << " exceeds the maximum index."
+      << " Try setting `USE_INT64_TENSOR_SIZE`.";
+  }
+  CHECK_LE(oshape.Size(), lim)
+    << "Size of output"
+    << " exceeds the maximum index."
+    << " Try setting `USE_INT64_TENSOR_SIZE`.";
   return shape_is_known(oshape);
 }
 
diff --git a/src/operator/numpy/np_einsum_path_op-inl.h b/src/operator/numpy/np_einsum_path_op-inl.h
index cebd4e8ce9af..968d52106da7 100644
--- a/src/operator/numpy/np_einsum_path_op-inl.h
+++ b/src/operator/numpy/np_einsum_path_op-inl.h
@@ -80,7 +80,7 @@ struct Contraction {
 };
 
 struct Alternative {
-  int cost[2];
+  int64_t cost[2];
   std::vector<int> positions;
   SetVector new_input_sets;
 };
@@ -115,28 +115,28 @@ inline size_t _compute_size_by_dict(const std::bitset<MAXAXIS>& indices,
   return ret;
 }
 
-inline int _flop_count(const std::string& idx_contraction,
-                       bool inner,
-                       int num_terms,
-                       const dim_t size_dictionary[]) {
+inline int64_t _flop_count(const std::string& idx_contraction,
+                           bool inner,
+                           int num_terms,
+                           const dim_t size_dictionary[]) {
   size_t overall_size = _compute_size_by_dict(idx_contraction, size_dictionary);
   int op_factor = std::max(1, num_terms - 1);
   if (inner) {
     ++op_factor;
   }
-  return overall_size * op_factor;
+  return static_cast<int64_t>(overall_size) * op_factor;
 }
 
-inline int _flop_count(const std::bitset<MAXAXIS>& idx_contraction,
-                       bool inner,
-                       int num_terms,
-                       const dim_t size_dictionary[]) {
+inline int64_t _flop_count(const std::bitset<MAXAXIS>& idx_contraction,
+                           bool inner,
+                           int num_terms,
+                           const dim_t size_dictionary[]) {
   size_t overall_size = _compute_size_by_dict(idx_contraction, size_dictionary);
   int op_factor = std::max(1, num_terms - 1);
   if (inner) {
     ++op_factor;
   }
-  return overall_size * op_factor;
+  return static_cast<int64_t>(overall_size) * op_factor;
 }
 
 inline Contraction _find_contraction(const std::vector<int>& positions,
@@ -164,16 +164,16 @@ inline int _parse_possible_contraction(const std::vector<int>& positions,
                                        const SetVector& input_sets,
                                        const std::bitset<MAXAXIS>& output_set,
                                        const dim_t idx_dict[],
-                                       int memory_limit,
-                                       int path_cost,
-                                       int naive_cost,
+                                       size_t memory_limit,
+                                       int64_t path_cost,
+                                       int64_t naive_cost,
                                        Alternative* ret) {
   // Find the contraction
   Contraction contract = _find_contraction(positions, input_sets, output_set);
 
   // Sieve the results based on memory_limit
   size_t new_size = _compute_size_by_dict(contract.new_result, idx_dict);
-  if (new_size > static_cast<size_t>(memory_limit)) {
+  if (new_size > memory_limit) {
     return -1;
   }
 
@@ -182,10 +182,10 @@ inline int _parse_possible_contraction(const std::vector<int>& positions,
   for (auto p : positions) {
     old_sizes += _compute_size_by_dict(input_sets[p], idx_dict);
   }
-  int remove_size = old_sizes - new_size;
+  int64_t remove_size = static_cast<int64_t>(old_sizes) - static_cast<int64_t>(new_size);
 
-  int cost = _flop_count(contract.idx_contract, contract.idx_removed.any(),
-                            positions.size(), idx_dict);
+  int64_t cost = _flop_count(contract.idx_contract, contract.idx_removed.any(),
+                             positions.size(), idx_dict);
   ret->cost[0] = -remove_size;
   ret->cost[1] = cost;
 
@@ -206,7 +206,7 @@ inline void _update_other_results(std::vector<Alternative>* results,
   int bx = best_con[0], by = best_con[1];
   size_t size = results->size();
 
-  for (int i = size - 1; i >= 0; --i) {
+  for (int i = static_cast<int>(size) - 1; i >= 0; --i) {
     int x = results->at(i).positions[0], y = results->at(i).positions[1];
 
     // Ignore results involving tensors just contracted
@@ -233,9 +233,9 @@ inline void _update_other_results(std::vector<Alternative>* results,
 inline std::vector<std::vector<int> > _greedy_path(const SetVector* input_sets,
                                                    const std::bitset<MAXAXIS>& output_set,
                                                    const dim_t idx_dict[],
-                                                   int memory_limit) {
-  size_t isize = input_sets->size();
-  size_t iteration_num = isize;
+                                                   size_t memory_limit) {
+  int isize = static_cast<int>(input_sets->size());
+  int iteration_num = isize;
   // Handle trivial cases that leaked through
   if (isize == 1) {
     return std::vector<std::vector<int> >{std::vector<int>{0}};
@@ -245,23 +245,23 @@ inline std::vector<std::vector<int> > _greedy_path(const SetVector* input_sets,
 
   // Build up a naive cost
   std::vector<int> range(isize);
-  for (size_t i = 0; i < isize; ++i) {
+  for (int i = 0; i < isize; ++i) {
     range[i] = i;
   }
   Contraction contract = _find_contraction(range, *input_sets, output_set);
-  int naive_cost = _flop_count(contract.idx_contract, contract.idx_removed.any(),
-                                  isize, idx_dict);
+  int64_t naive_cost = _flop_count(contract.idx_contract, contract.idx_removed.any(),
+                                   isize, idx_dict);
 
   // Initially iterate over all pairs
   std::vector<Alternative> known_contractions;
   Alternative best;
-  int path_cost = 0;
+  int64_t path_cost = 0;
   std::vector<std::vector<int> > ret;
 
-  for (size_t iteration = 0; iteration + 1 < iteration_num; ++iteration) {
+  for (int iteration = 0; iteration + 1 < iteration_num; ++iteration) {
     if (iteration == 0) {
-      for (int x = 0; x < static_cast<int>(isize); ++x) {
-        for (int y = x + 1; y < static_cast<int>(isize); ++y) {
+      for (int x = 0; x < isize; ++x) {
+        for (int y = x + 1; y < isize; ++y) {
           if (!((input_sets->at(x) & input_sets->at(y)).any())) {
             continue;
           }
@@ -280,7 +280,7 @@ inline std::vector<std::vector<int> > _greedy_path(const SetVector* input_sets,
         }
       }
     } else {
-      for (int x = 0; x < static_cast<int>(isize) - 1; ++x) {
+      for (int x = 0; x < isize - 1; ++x) {
         int y = isize - 1;
         if (!((input_sets->at(x) & input_sets->at(y)).any())) {
             continue;
@@ -303,8 +303,8 @@ inline std::vector<std::vector<int> > _greedy_path(const SetVector* input_sets,
     // If we do not have a inner contraction, rescan pairs including outer products
     if (known_contractions.size() == 0) {
       // Then check the outer productsj
-      for (int x = 0; x < static_cast<int>(isize); ++x) {
-        for (int y = x + 1; y < static_cast<int>(isize); ++y) {
+      for (int x = 0; x < isize; ++x) {
+        for (int y = x + 1; y < isize; ++y) {
           Alternative alternative;
           int result = _parse_possible_contraction(std::vector<int>{x, y},
                                                    *input_sets,
@@ -323,7 +323,7 @@ inline std::vector<std::vector<int> > _greedy_path(const SetVector* input_sets,
       // If we still did not find any remaining contractions, default back to einsum like behavior
       if (known_contractions.size() == 0) {
         std::vector<int> range(isize);
-        for (size_t i = 0; i < isize; ++i) {
+        for (int i = 0; i < isize; ++i) {
           range[i] = i;
         }
         ret.push_back(range);
@@ -332,17 +332,17 @@ inline std::vector<std::vector<int> > _greedy_path(const SetVector* input_sets,
     }
 
     // Sort based on first index
-    int best_cost[2], idx = -1;
-    size_t size = known_contractions.size();
-    for (size_t i = 0; i < size; ++i) {
+    int64_t best_cost[2];
+    int idx = -1, size = static_cast<int>(known_contractions.size());
+    for (int i = 0; i < size; ++i) {
       auto x = known_contractions[i];
       if (idx == -1) {
         best_cost[0] = x.cost[0];
         best_cost[1] = x.cost[1];
         idx = i;
       } else if (x.cost[0] < best_cost[0] ||
-               (x.cost[0] == best_cost[0] &&
-               x.cost[1] < best_cost[1])) {
+                 (x.cost[0] == best_cost[0] &&
+                  x.cost[1] < best_cost[1])) {
         best_cost[0] = x.cost[0];
         best_cost[1] = x.cost[1];
         idx = i;
@@ -356,7 +356,7 @@ inline std::vector<std::vector<int> > _greedy_path(const SetVector* input_sets,
     // Next iteration only compute contractions with the new tensor
     // All other contractions have been accounted for
     input_sets = &best.new_input_sets;
-    isize = input_sets->size();
+    isize = static_cast<int>(input_sets->size());
 
     // Update path and total cost
     ret.push_back(best.positions);
@@ -708,9 +708,9 @@ inline std::vector<Step> einsum_path(const std::string& subscripts,
 
   // Build a few useful list and sets
   std::vector<std::string> input_list = split(parsed_subscripts[0], ",");
-  size_t isize = input_list.size();
+  int isize = static_cast<int>(input_list.size());
   SetVector input_sets;
-  for (int i = 0; i < static_cast<int>(isize); ++i) {
+  for (int i = 0; i < isize; ++i) {
     input_sets.push_back(str2set(input_list[i]));
   }
   std::bitset<MAXAXIS> output_set = str2set(parsed_subscripts[1]);
@@ -721,7 +721,7 @@ inline std::vector<Step> einsum_path(const std::string& subscripts,
   dim_t dimension_dict[MAXAXIS];
   SetVector broadcast_indices(isize);
   memset(dimension_dict, -1, sizeof(dimension_dict));
-  for (size_t i = 0; i < isize; ++i) {
+  for (int i = 0; i < isize; ++i) {
     const std::string& term = input_list[i];
     const TShape& sh = operands[i].shape_;
     CHECK_EQ(sh.ndim(), term.length())
@@ -756,8 +756,8 @@ inline std::vector<Step> einsum_path(const std::string& subscripts,
 
   // Compute size of each input array plus the output array
   std::vector<size_t> size_list(isize + 1);
-  size_t max_size = -1, memory_arg;
-  for (size_t i = 0; i < isize; ++i) {
+  size_t max_size = 0, memory_arg;
+  for (int i = 0; i < isize; ++i) {
     size_list[i] = _compute_size_by_dict(input_list[i], dimension_dict);
     max_size = std::max(max_size, size_list[i]);
   }
@@ -778,7 +778,7 @@ inline std::vector<Step> einsum_path(const std::string& subscripts,
   std::vector<std::vector<int> > path;
   if (optimize == false) {
     path.push_back(std::vector<int>());
-    for (size_t i = 0; i < isize; ++i) {
+    for (int i = 0; i < isize; ++i) {
       path[0].push_back(i);
     }
   } else {
@@ -801,7 +801,7 @@ inline std::vector<Step> einsum_path(const std::string& subscripts,
     Contraction contract = _find_contraction(contract_inds, input_sets, output_set);
     input_sets = contract.remaining;
 
-    int cost = _flop_count(contract.idx_contract,
+    int64_t cost = _flop_count(contract.idx_contract,
                            contract.idx_removed.any(),
                            contract_inds.size(),
                            dimension_dict);
@@ -847,9 +847,9 @@ inline std::vector<Step> einsum_path(const std::string& subscripts,
                          a < b);
                 });
     }
-    size_t len_idx_result = idx_result.length();
+    int len_idx_result = static_cast<int>(idx_result.length());
     ret[i].oshape = TShape(len_idx_result, -1);
-    for (size_t j = 0; j < len_idx_result; ++j) {
+    for (int j = 0; j < len_idx_result; ++j) {
       ret[i].oshape[j] = dimension_dict[static_cast<int>(idx_result[j])];
     }
 
@@ -867,18 +867,18 @@ inline std::vector<Step> einsum_path(const std::string& subscripts,
       std::vector<int> left_pos, right_pos;
       left_pos.reserve(MAXAXIS);
       right_pos.reserve(MAXAXIS);
-      size_t tmp[MAXAXIS] = {0};
-      size_t length_left_input = tmp_inputs[0].length();
-      size_t length_right_input = tmp_inputs[1].length();
-      for (size_t j = 0; j < length_right_input; ++j) {
+      int tmp[MAXAXIS] = {0};
+      int length_left_input = static_cast<int>(tmp_inputs[0].length());
+      int length_right_input = static_cast<int>(tmp_inputs[1].length());
+      for (int j = 0; j < length_right_input; ++j) {
         if (contract.idx_removed.test(static_cast<int>(tmp_inputs[1][j]))) {
           tmp[static_cast<int>(tmp_inputs[1][j])] = j;
         }
       }
-      for (size_t j = 0; j < length_left_input; ++j) {
+      for (int j = 0; j < length_left_input; ++j) {
         if (contract.idx_removed.test(static_cast<int>(tmp_inputs[0][j]))) {
-          left_pos.push_back(static_cast<int>(j));
-          right_pos.push_back(static_cast<int>(tmp[static_cast<int>(tmp_inputs[0][j])]));
+          left_pos.push_back(j);
+          right_pos.push_back(tmp[static_cast<int>(tmp_inputs[0][j])]);
         }
       }
       // Calculate left_pos and right_pos
@@ -887,11 +887,11 @@ inline std::vector<Step> einsum_path(const std::string& subscripts,
       // Calculate do_einsum
       ret[i].do_einsum = (tensor_result != idx_result);
       // Calculate tshape
-      CHECK_EQ(tensor_result.length(), len_idx_result)
+      CHECK_EQ(static_cast<int>(tensor_result.length()), len_idx_result)
         << "tensordot produces dim " << tensor_result.length()
         << ", while einsum produces dim " << len_idx_result << ".";
       ret[i].tshape = TShape(len_idx_result, -1);
-      for (size_t j = 0; j < len_idx_result; ++j) {
+      for (int j = 0; j < len_idx_result; ++j) {
         ret[i].tshape[j] = dimension_dict[static_cast<int>(tensor_result[j])];
       }
       // Calculate blas2einsum_str
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index 9e8156f3239c..62004ac6d263 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -258,6 +258,7 @@ def _add_workload_einsum():
     size_dict = dict(zip(chars, sizes))
 
     configs = [
+        # test_einsum_broadcast
         ('ij...,j...->ij...', [(2, 3, 4), (3,)]),
         ('ij...,...j->ij...', [(2, 3, 4), (3,)]),
         ('ij...,j->ij...', [(2, 3, 4), (3,)]),
@@ -310,6 +311,39 @@ def _add_workload_einsum():
         ('abjk,kl,jl,ab->ab', [(1, 1, 5, 4), (4, 6), (5, 6), (7, 7)]),
         ('obk,ijk->ioj', [(2, 4, 8), (2, 4, 8)]),
     ]
+    # check_einsum_sums
+    configs.extend([('i->', [(i,)]) for i in range(1, 17)])
+    configs.extend([('...i->...', [(2, 3, i,)]) for i in range(1, 17)])
+    configs.extend([('i...->...', [(2, i,)]) for i in range(1, 17)])
+    configs.extend([('i...->...', [(2, 3, i,)]) for i in range(1, 17)])
+    configs.extend([('ii', [(i, i,)]) for i in range(1, 17)])
+    configs.extend([('..., ...', [(3, i,), (2, 3, i,)]) for i in range(1, 17)])
+    configs.extend([('...i, ...i', [(2, 3, i,), (i,)]) for i in range(1, 17)])
+    configs.extend([('i..., i...', [(i, 3, 2,), (i,)]) for i in range(1, 11)])
+    configs.extend([('i, j', [(3,), (i,)]) for i in range(1, 17)])
+    configs.extend([('ij, j', [(4, i), (i,)]) for i in range(1, 17)])
+    configs.extend([('ji, j', [(i, 4), (i,)]) for i in range(1, 17)])
+    configs.extend([('ij, jk', [(4, i), (i, 6)]) for i in range(1, 8)])
+    configs.extend([
+        ('ij,jk,kl', [(3, 4), (4, 5), (5, 6)]),
+        ('ijk, jil -> kl', [(3, 4, 5), (4, 3, 2)]),
+        ('i, i, i -> i', [(8,), (8,), (8,)]),
+        (',i->', [(), (9,)]),
+        ('i,->', [(9,), ()]),
+    ])
+    configs.extend([('...,...', [(n,), (n,)]) for n in range(1, 25)])
+    configs.extend([('i,i', [(n,), (n,)]) for n in range(1, 25)])
+    configs.extend([('i,->i', [(n,), ()]) for n in range(1, 25)])
+    configs.extend([(',i->i', [(), (n,)]) for n in range(1, 25)])
+    configs.extend([('i,->', [(n,), ()]) for n in range(1, 25)])
+    configs.extend([(',i->', [(), (n,)]) for n in range(1, 25)])
+    configs.extend([('...,...', [(n - 1,), (n - 1,)]) for n in range(1, 25)])
+    configs.extend([('i,i', [(n - 1,), (n - 1,)]) for n in range(1, 25)])
+    configs.extend([('i,->i', [(n - 1,), ()]) for n in range(1, 25)])
+    configs.extend([(',i->i', [(), (n - 1,)]) for n in range(1, 25)])
+    configs.extend([('i,->', [(n - 1,), ()]) for n in range(1, 25)])
+    configs.extend([(',i->', [(), (n - 1,)]) for n in range(1, 25)])
+
     for optimize in [False, True]:
         for config in configs:
             subscripts, args = config
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index ae8ad621df75..5476fbee8be4 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -3496,16 +3496,22 @@ def dbg(name, data):
                                                                     _np.dot(args[0].T, _np.dot(_np.ones((2, 2)), args[2].T)),
                                                                     _np.dot(_np.dot(args[0], args[1]).T, _np.ones((2, 2))))),
         # broadcast bug
-        (('ij, ij -> i'), [(1, 4), (2, 4)], lambda *args: (_np.sum(args[1], axis=0)[None, :],
-                                                           _np.tile(args[0], [2, 1]))),
+        ('ij, ij -> i', [(1, 4), (2, 4)], lambda *args: (_np.sum(args[1], axis=0)[None, :],
+                                                         _np.tile(args[0], [2, 1]))),
+        # issue #16576
+        # commented due to long running time
+        # ('abiz,abjz->abij', [(64, 8, 128, 512), (64, 8, 128, 512)], lambda *args: (_np.matmul(_np.ones((64, 8, 128, 128)), args[1]),
+        #                                                                            _np.matmul(_np.ones((64, 8, 128, 128)), args[0]))),
     ]
-    dtypes = ['int32', 'float16', 'float32', 'float64']
+    dtypes = ['float16', 'float32', 'float64', 'int32']
+    acc_type = {'float16': 'float32', 'float32': 'float64', 'float64': 'float64',
+                'int32': 'int64'}
     for hybridize in [False, True]:
         for dtype in dtypes:
             for config in configs:
                 for optimize in [False, True]:
-                    rtol = 1e-0 if dtype == 'float16' else 1e-3
-                    atol = 1e-1 if dtype == 'float16' else 1e-5
+                    rtol = 1e-2 if dtype == 'float16' else 1e-3
+                    atol = 1e-4 if dtype == 'float16' else 1e-5 
                     (subscripts, operands, get_grad) = config
                     test_einsum = TestEinsum(subscripts, optimize)
                     if hybridize:
@@ -3513,11 +3519,11 @@ def dbg(name, data):
                     x = []
                     x_np = []
                     for shape in operands:
-                        x_np.append(_np.array(_np.random.uniform(-10.0, 10.0, shape),
-                                            dtype=dtype))
-                        x.append(np.array(x_np[-1], dtype=dtype))
+                        tmp = _np.array(_np.random.uniform(-1.0, 1.0, shape), dtype=dtype)
+                        x_np.append(tmp.astype(acc_type[dtype]))
+                        x.append(np.array(tmp, dtype=dtype))
                         x[-1].attach_grad()
-                    expected_np = _np.einsum(subscripts, *x_np, optimize=optimize)
+                    expected_np = _np.einsum(subscripts, *x_np, optimize=optimize).astype(dtype)
                     with mx.autograd.record():
                         out_mx = test_einsum(*x)
                     assert out_mx.shape == expected_np.shape
@@ -3535,7 +3541,7 @@ def dbg(name, data):
                     expected_np = _np.einsum(subscripts, *x_np, optimize=optimize)
                     assert_almost_equal(out_mx.asnumpy(), expected_np, rtol=rtol, atol=atol)
                     for (iop, op) in enumerate(x):
-                        assert_almost_equal(op.grad.asnumpy(), get_grad(*x_np)[iop], rtol=rtol, atol=atol)
+                        assert_almost_equal(op.grad.asnumpy(), get_grad(*x_np)[iop].astype(dtype), rtol=rtol, atol=atol)
     configs = [
         (('ij,jk,kl->il'), [(2, 2), (2, 5), (5, 2)]),
         (('ea,fb,abcd,gc,hd->efgh'), [(5, 5), (5, 5), (5, 5, 5, 5), (5, 5), (5, 5)]),
@@ -3545,8 +3551,8 @@ def dbg(name, data):
         for dtype in dtypes:
             for config in configs:
                 (subscripts, operands) = config
-                rtol = 1e-0 if dtype == 'float16' else 1e-2
-                atol = 1e-1 if dtype == 'float16' else 1e-2
+                rtol = 1e-2 if dtype == 'float16' else 1e-3
+                atol = 1e-4 if dtype == 'float16' else 1e-5 
                 grad = []
                 x_np = []
                 for shape in operands:
@@ -3560,7 +3566,8 @@ def dbg(name, data):
                     test_einsum = TestEinsum(subscripts, optimize)
                     if hybridize:
                         test_einsum.hybridize()
-                    expected_np = _np.einsum(subscripts, *x_np, optimize=optimize)
+                    expected_np = _np.einsum(subscripts, *[op.astype(acc_type[dtype]) for op in x_np],
+                                             optimize=optimize).astype(dtype)
                     with mx.autograd.record():
                         out_mx = test_einsum(*x)
                     assert out_mx.shape == expected_np.shape

From 29e467be25d6b70d9e188cfb49ed2389f396ab39 Mon Sep 17 00:00:00 2001
From: Zhennan Qin <zhennan.qin@intel.com>
Date: Sat, 26 Oct 2019 15:56:35 +0800
Subject: [PATCH 523/813] Move some subgraph verbose to
 MXNET_SUBGRAPH_VERBOSE=2 (#16622)

* Move subgraph pass log to verbose=2

* Run CI
---
 src/operator/subgraph/build_subgraph.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/operator/subgraph/build_subgraph.cc b/src/operator/subgraph/build_subgraph.cc
index 0f4c570331a2..8e7617d57c44 100644
--- a/src/operator/subgraph/build_subgraph.cc
+++ b/src/operator/subgraph/build_subgraph.cc
@@ -717,7 +717,7 @@ nnvm::Graph BuildSubgraph(nnvm::Graph&& g) {
   using namespace sg;
 
   const SubgraphPropertyPtr& subg_prop = g.GetAttr<SubgraphPropertyPtr>("subgraph_property");
-  if (verbose) {
+  if (verbose > 1) {
     const std::string& prop_name = subg_prop->HasAttr("property_name")
                                        ? subg_prop->GetAttr<std::string>("property_name")
                                        : "partition graph";

From c130cc9ef94776fb7ef5fb9ef1fb6559522dcacf Mon Sep 17 00:00:00 2001
From: Xingjian Shi <xshiab@connect.ust.hk>
Date: Sat, 26 Oct 2019 18:47:09 -0700
Subject: [PATCH 524/813] add npx reshape (#16640)

---
 python/mxnet/_numpy_op_doc.py          |  66 ++++++++
 src/operator/numpy/np_matrix_op-inl.h  |  54 ++++++-
 src/operator/numpy/np_matrix_op.cc     | 206 ++++++++++++++++++++++---
 src/operator/numpy/np_matrix_op.cu     |   3 +
 tests/python/unittest/test_numpy_op.py |  63 ++++++++
 5 files changed, 371 insertions(+), 21 deletions(-)

diff --git a/python/mxnet/_numpy_op_doc.py b/python/mxnet/_numpy_op_doc.py
index d9bb378d3049..bcbef9d047d1 100644
--- a/python/mxnet/_numpy_op_doc.py
+++ b/python/mxnet/_numpy_op_doc.py
@@ -961,3 +961,69 @@ def _np_broadcast_to(array, shape, out=None):
            [1., 2., 3.]])
     """
     pass
+
+
+def _npx_reshape(a, newshape, reverse=False, order='C'):
+    """
+    Gives a new shape to an array without changing its data.
+    This function always returns a copy of the input array if
+    ``out`` is not provided.
+
+    Parameters
+    ----------
+    a : ndarray
+        Array to be reshaped.
+    newshape : int or tuple of ints
+        The new shape should be compatible with the original shape.
+        If an integer, then the result will be a 1-D array of that length.
+        One shape dimension can be -1. In this case, the value is inferred
+        from the length of the array and remaining dimensions.
+        -2 to -6 are used for data manipulation.
+
+        - -2 copy this dimension from the input to the output shape.
+        - -3 will skip current dimension if and only if the current dim size is one.
+        - -4 copy all remain of the input dimensions to the output shape.
+        - -5 use the product of two consecutive dimensions of the input
+          shape as the output.
+        - -6 split one dimension of the input into two dimensions passed
+          subsequent to -6 in the new shape.
+
+    reverse : bool, optional
+        If set to true, the special values will be inferred from right to left.
+    order : {'C'}, optional
+        Read the elements of `a` using this index order, and place the
+        elements into the reshaped array using this index order.  'C'
+        means to read / write the elements using C-like index order,
+        with the last axis index changing fastest, back to the first
+        axis index changing slowest. Other order types such as 'F'/'A'
+        may be added in the future.
+
+    Returns
+    -------
+    reshaped_array : ndarray
+        It will be always a copy of the original array. This behavior is different
+        from the official NumPy ``reshape`` operator where views of the original array may be
+        generated.
+
+    Examples
+    --------
+    >>> x = np.ones((2, 3, 8))
+    >>> npx.reshape(x, (-2, -2, 2, -1)).shape
+    (2, 3, 2, 4)
+    >>> x = np.ones((8, 3, 3, 3, 4, 4))
+    >>> npx.reshape(x, (-6, 2, -1, -4)).shape
+    (2, 4, 3, 3, 3, 4, 4)
+    >>> x = np.ones((8, 3, 3, 3, 4, 4))
+    >>> npx.reshape(x, (-5, -4)).shape
+    (24, 3, 3, 4, 4)
+    >>> x = np.ones((8, 1, 1, 1, 3))
+    >>> npx.reshape(x, (-2, -3, -3, -3, -2)).shape
+    (8, 3)
+    >>> x = np.ones((8, 3, 3, 3, 3, 8))
+    >>> npx.reshape(x, (-4, -5), reverse=True).shape
+    (8, 3, 3, 3, 24)
+    >>> x = np.ones((8, 3, 2, 4, 8))
+    >>> npx.reshape(x, (-4, -1, 2, -6), reverse=True).shape
+    (8, 3, 2, 4, 4, 2)
+    """
+    pass
diff --git a/src/operator/numpy/np_matrix_op-inl.h b/src/operator/numpy/np_matrix_op-inl.h
index b3206bf4aa75..9ce84835f1a8 100644
--- a/src/operator/numpy/np_matrix_op-inl.h
+++ b/src/operator/numpy/np_matrix_op-inl.h
@@ -27,6 +27,7 @@
 
 #include <vector>
 #include <algorithm>
+#include <string>
 #include "../tensor/matrix_op-inl.h"
 #include "../nn/concat-inl.h"
 #include "../../common/utils.h"
@@ -51,6 +52,58 @@ struct NumpyVstackParam : public dmlc::Parameter<NumpyVstackParam> {
   }
 };
 
+struct NumpyReshapeParam : public dmlc::Parameter<NumpyReshapeParam> {
+  mxnet::TShape newshape;
+  std::string order;
+  DMLC_DECLARE_PARAMETER(NumpyReshapeParam) {
+    DMLC_DECLARE_FIELD(newshape)
+        .describe("The new shape should be compatible with the original shape."
+                  " If an integer, then the result will be a 1-D array of that length."
+                  " One shape dimension can be -1. In this case, the value is inferred"
+                  " from the length of the array and remaining dimensions.");
+    DMLC_DECLARE_FIELD(order)
+        .set_default("C")
+        .describe("Read the elements of a using this index order, and place the elements into"
+                  " the reshaped array using this index order. 'C' means to read/write the elements"
+                  " using C-like index order, with the last axis index changing fastest,"
+                  " back to the first axis index changing slowest."
+                  " Note that currently only C-like order is"
+                  " supported");
+  }
+};
+
+struct NumpyXReshapeParam : public dmlc::Parameter<NumpyXReshapeParam> {
+  mxnet::TShape newshape;
+  bool reverse;
+  std::string order;
+  DMLC_DECLARE_PARAMETER(NumpyXReshapeParam) {
+    DMLC_DECLARE_FIELD(newshape)
+        .describe("The new shape should be compatible with the original shape."
+                  " If an integer, then the result will be a 1-D array of that length."
+                  " One shape dimension can be -1. In this case, the value is inferred"
+                  " from the length of the array and remaining dimensions."
+                  " -2 to -6 are used for data manipulation."
+                  " -2 copy this dimension from the input to the output shape."
+                  " -3 will skip current dimension if and only if the current dim size is one."
+                  " -4 copy all remain of the input dimensions to the output shape."
+                  " -5 use the product of two consecutive dimensions of the input"
+                  " shape as the output."
+                  " -6 split one dimension of the input into two dimensions passed"
+                  " subsequent to -6 in the new shape.");
+    DMLC_DECLARE_FIELD(reverse)
+        .set_default(false)
+        .describe("If true then the special values are inferred from right to left");
+    DMLC_DECLARE_FIELD(order)
+        .set_default("C")
+        .describe("Read the elements of a using this index order, and place the elements into"
+                  " the reshaped array using this index order. 'C' means to read/write the elements"
+                  " using C-like index order, with the last axis index changing fastest,"
+                  " back to the first axis index changing slowest."
+                  " Note that currently only C-like order is"
+                  " supported");
+  }
+};
+
 template<typename xpu>
 void NumpyTranspose(const nnvm::NodeAttrs& attrs,
                     const OpContext& ctx,
@@ -731,7 +784,6 @@ inline void HSplitOpBackward(const nnvm::NodeAttrs &attrs,
   }
   SplitOpBackwardImpl<xpu>(attrs, ctx, inputs, req, outputs, real_axis);
 }
-
 }  // namespace op
 }  // namespace mxnet
 
diff --git a/src/operator/numpy/np_matrix_op.cc b/src/operator/numpy/np_matrix_op.cc
index 7bcd6ad27b52..0a6f9a150d8b 100644
--- a/src/operator/numpy/np_matrix_op.cc
+++ b/src/operator/numpy/np_matrix_op.cc
@@ -34,6 +34,9 @@ DMLC_REGISTER_PARAMETER(NumpyTransposeParam);
 DMLC_REGISTER_PARAMETER(NumpyRollParam);
 DMLC_REGISTER_PARAMETER(NumpyMoveaxisParam);
 DMLC_REGISTER_PARAMETER(NumpyRot90Param);
+DMLC_REGISTER_PARAMETER(NumpyReshapeParam);
+DMLC_REGISTER_PARAMETER(NumpyXReshapeParam);
+
 
 bool NumpyTransposeShape(const nnvm::NodeAttrs& attrs,
                          mxnet::ShapeVector *in_attrs,
@@ -126,26 +129,6 @@ NNVM_REGISTER_OP(_np_transpose)
 .add_argument("a", "NDArray-or-Symbol", "Source input")
 .add_arguments(NumpyTransposeParam::__FIELDS__());
 
-struct NumpyReshapeParam : public dmlc::Parameter<NumpyReshapeParam> {
-  mxnet::TShape newshape;
-  std::string order;
-  DMLC_DECLARE_PARAMETER(NumpyReshapeParam) {
-      DMLC_DECLARE_FIELD(newshape)
-          .describe("The new shape should be compatible with the original shape."
-                    " If an integer, then the result will be a 1-D array of that length."
-                    " One shape dimension can be -1. In this case, the value is inferred"
-                    " from the length of the array and remaining dimensions.");
-      DMLC_DECLARE_FIELD(order)
-      .set_default("C")
-      .describe("Read the elements of a using this index order, and place the elements into"
-                " the reshaped array using this index order. 'C' means to read/write the elements"
-                " using C-like index order, with the last axis index changing fastest, back to the"
-                " first axis index changing slowest. Note that currently only C-like order is"
-                " supported");
-  }
-};
-
-DMLC_REGISTER_PARAMETER(NumpyReshapeParam);
 
 bool NumpyReshapeInferShape(const mxnet::TShape& src, mxnet::TShape* dst) {
   if (shape_is_known(src) && shape_is_known(*dst)) {
@@ -202,6 +185,164 @@ bool NumpyReshapeShape(const nnvm::NodeAttrs& attrs,
   return success;
 }
 
+bool NumpyXReshapeInferShape(const mxnet::TShape& src,
+                             const mxnet::TShape& target,
+                             mxnet::TShape* output,
+                             const std::string &default_error_msg) {
+  bool target_shape_is_known = true;
+  dim_t target_size = 1;
+  for (int i = 0; i < target.ndim(); ++i) {
+    if (target[i] < 0) {
+      target_shape_is_known = false;
+      target_size  = -1;
+      break;
+    } else {
+      target_size *= target[i];
+    }
+  }
+  if (shape_is_known(src) && target_shape_is_known) {
+    CHECK_EQ(src.Size(), target_size) << default_error_msg;
+    *output = TShape(target.begin(), target.end());
+    return true;
+  } else if (!shape_is_known(src) || target.ndim() == -1) {
+    return false;
+  } else {
+    int unknown_axis = -1;
+    dim_t known_dim_size_prod = 1;
+    std::vector<dim_t> output_shape_vector;
+    int src_inx = 0;
+    for (int i = 0; i < target.ndim(); ++i) {
+      dim_t proposed_dim = target[i];
+      CHECK(proposed_dim >= -6)
+        << "Dimension size must be greater than -6, received " << proposed_dim;
+      if (proposed_dim == -1) {
+        // infer the known dimension
+        CHECK_LT(unknown_axis, 0)
+          << "One and only one dim can be inferred";
+        unknown_axis = output_shape_vector.size();
+        output_shape_vector.push_back(-1);
+        src_inx++;
+      } else if (proposed_dim == -2) {
+        // copy the dimension from src to output
+        CHECK_LT(src_inx, src.ndim())
+          << "Unmatching dimension of proposed new shape";
+        known_dim_size_prod *= src[src_inx];
+        output_shape_vector.push_back(src[src_inx++]);
+      } else if (proposed_dim == -3) {
+        // skip the source dimension if and only if it is one
+        CHECK_EQ(src[src_inx], 1)
+          <<"-3 index should only be used to skip dimension size 1";
+        src_inx++;
+      } else if (proposed_dim == -4) {
+        // copy all remaining dims from source
+        while (src_inx < src.ndim()) {
+          known_dim_size_prod *= src[src_inx];
+          const dim_t dn = src[src_inx++];
+          output_shape_vector.push_back(dn);
+        }
+      } else if (proposed_dim == -5) {
+        // merge two dims from source
+        CHECK_LT(src_inx, src.ndim()-1)
+          <<"Not enough dimensions left for the product";
+        const dim_t d1 = src[src_inx++];
+        const dim_t d2 = src[src_inx++];
+        if (!mxnet::dim_size_is_known(d1) || !mxnet::dim_size_is_known(d2)) {
+          CHECK_LT(unknown_axis, 0)
+            << "One and only one dim can be inferred";
+          unknown_axis = output_shape_vector.size();
+          output_shape_vector.push_back(-1);
+        } else {
+          known_dim_size_prod *= d1*d2;
+          output_shape_vector.push_back(d1 * d2);
+        }
+      } else if (proposed_dim == -6) {
+        // split the source dim s into two dims
+        // read the left dim and then the right dim (either can be -1)
+        CHECK_LT(i + 2, target.ndim());
+        CHECK_LT(src_inx, src.ndim());
+        const dim_t d0 = src[src_inx++];
+        dim_t d1 = target[++i];
+        dim_t d2 = target[++i];
+        CHECK(d1 != -1 || d2 != -1) << "Split dims cannot both be -1.";
+        if (d1 == -1 && d0 >= 0) d1 = d0 / d2;  // d0 must be known to do this
+        if (d2 == -1 && d0 >= 0) d2 = d0 / d1;  // d0 must be known to do this
+        CHECK(d1 * d2 == static_cast<dim_t>(d0) || static_cast<dim_t>(d0) == dim_t(-1))
+          <<"Split dims " << d1 << ", " << d2 << " do not divide original dim " << d0;
+        if (d1 == -1) {
+          CHECK_LT(unknown_axis, 0)
+            << "One and only one dim can be inferred";
+          unknown_axis = output_shape_vector.size();
+        } else if (d2 == -1) {
+          CHECK_LT(unknown_axis, 0)
+            << "One and only one dim can be inferred";
+          unknown_axis = output_shape_vector.size() + 1;
+        }
+        known_dim_size_prod *= d0 == -1 ? 1 : d0;
+        output_shape_vector.push_back(d1);
+        output_shape_vector.push_back(d2);
+      } else {
+        // greater than 0, new shape
+        known_dim_size_prod *= proposed_dim;
+        output_shape_vector.push_back(proposed_dim);
+        src_inx++;
+      }
+    }
+
+    if (unknown_axis > -1) {
+      // if the input in zero size tensor, the output must be of known shape of zero size
+      CHECK_NE(known_dim_size_prod, 0) << default_error_msg;
+      CHECK(src.Size() % known_dim_size_prod == 0) << default_error_msg;
+      output_shape_vector[unknown_axis] = src.Size() / known_dim_size_prod;
+    }
+
+    *output = mxnet::TShape(output_shape_vector.begin(), output_shape_vector.end());
+    CHECK_EQ((*output).Size(), src.Size()) << default_error_msg;
+    return true;
+  }
+}
+
+bool NumpyXReshapeShape(const nnvm::NodeAttrs& attrs,
+                       mxnet::ShapeVector* in_attrs,
+                       mxnet::ShapeVector* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U) << "Input: [data]";
+  CHECK_EQ(out_attrs->size(), 1U);
+  const NumpyXReshapeParam& param = nnvm::get<NumpyXReshapeParam>(attrs.parsed);
+  // sanity check
+  bool has_unknown_dim_size = false;
+  for (int i = 0; i < param.newshape.ndim(); ++i) {
+    if (param.newshape[i] < 0) {
+      CHECK_GE(param.newshape[i], -6)
+        << "Dimension size must be greater than or equal to -6";
+      if (param.newshape[i] == -1) {
+        CHECK(!has_unknown_dim_size) << "Can only specify one unknown dimension";
+        has_unknown_dim_size = true;
+      }
+    }
+  }
+
+  mxnet::TShape output_shape;
+  bool success;
+  std::stringstream ss;
+  ss << "Cannot reshape array of shape " << in_attrs->at(0)
+     << " into shape " << param.newshape
+     << " , reverse = " << param.reverse;
+  std::string err_msg = ss.str();
+  if (!param.reverse) {
+    success = NumpyXReshapeInferShape(in_attrs->at(0),
+                                      param.newshape, &output_shape, err_msg);
+  } else {
+    mxnet::TShape rev_in_shape = in_attrs->at(0);
+    mxnet::TShape rev_newshape = param.newshape;
+    std::reverse(rev_in_shape.begin(), rev_in_shape.end());
+    std::reverse(rev_newshape.begin(), rev_newshape.end());
+    success = NumpyXReshapeInferShape(rev_in_shape,
+                                      rev_newshape, &output_shape, err_msg);
+    std::reverse(output_shape.begin(), output_shape.end());
+  }
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, output_shape);
+  return success;
+}
+
 NNVM_REGISTER_OP(_np_reshape)
 .describe(R"code()code" ADD_FILELINE)
 .add_alias("_npi_reshape")
@@ -227,6 +368,31 @@ NNVM_REGISTER_OP(_np_reshape)
 .add_argument("a", "NDArray-or-Symbol", "Array to be reshaped.")
 .add_arguments(NumpyReshapeParam::__FIELDS__());
 
+
+NNVM_REGISTER_OP(_npx_reshape)
+.describe(R"code()code" ADD_FILELINE)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<NumpyXReshapeParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyXReshapeShape)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_reshape"})
+.set_attr<FCompute>("FCompute<cpu>", UnaryOp::IdentityCompute<cpu>)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::pair<int, int> >{{0, 0}};
+  })
+.set_attr<nnvm::FInplaceIdentity>("FInplaceIdentity",
+  [](const NodeAttrs& attrs){
+    return std::vector<bool>{true};
+  })
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"a"};
+  })
+.add_argument("a", "NDArray-or-Symbol", "Array to be reshaped.")
+.add_arguments(NumpyXReshapeParam::__FIELDS__());
+
 bool NumpySqueezeShape(const nnvm::NodeAttrs& attrs,
                        mxnet::ShapeVector *in_attrs,
                        mxnet::ShapeVector *out_attrs) {
diff --git a/src/operator/numpy/np_matrix_op.cu b/src/operator/numpy/np_matrix_op.cu
index 8c8301bb3bbf..6b4f7a11a9a2 100644
--- a/src/operator/numpy/np_matrix_op.cu
+++ b/src/operator/numpy/np_matrix_op.cu
@@ -109,5 +109,8 @@ NNVM_REGISTER_OP(_npi_hsplit)
 NNVM_REGISTER_OP(_npi_hsplit_backward)
 .set_attr<FCompute>("FCompute<gpu>", HSplitOpBackward<gpu>);
 
+NNVM_REGISTER_OP(_npx_reshape)
+.set_attr<FCompute>("FCompute<gpu>", UnaryOp::IdentityCompute<gpu>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 5476fbee8be4..98a7b05dca9f 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -3674,6 +3674,69 @@ def test_np_true_divide():
         assert_almost_equal(out_mx.asnumpy(), out_np, rtol=1e-3, atol=1e-3, use_broadcast=False)
 
 
+@with_seed()
+@use_np
+def test_npx_reshape():
+    class TestNumpyXReshape(HybridBlock):
+        def __init__(self, newshape, reverse):
+            super(TestNumpyXReshape, self).__init__()
+            self._newshape = newshape
+            self._reverse = reverse
+
+        def hybrid_forward(self, F, a, *args, **kwargs):
+            return F.npx.reshape(a, self._newshape, reverse=self._reverse)
+
+    test_cases = [
+        [(2, 3, 5, 5),  (-2, -1),         False, (2, 75)],
+        [(2, 3, 5, 5),  (-2, -2, -1),     False, (2, 3, 25)],
+        [(5, 3, 4, 5),  (-2, -1, -2),     False, (5, 15, 4)],
+        [(2, 3, 5, 4),  (-1, -2, -2),     False, (8, 3, 5)],
+        [(2, 3, 5, 5),  (-2, -2, -2, -2), False, (2, 3, 5, 5)],
+        [(2, 1, 4, 5),  (-2, -3, -2, -2), False, (2, 4, 5)],
+        [(1, 1, 4, 1),  (-3, -3, -2, -2), False, (4, 1)],
+        [(1, 1, 1, 1),  (-3, -3, -3, -3), False, ()],
+        [(2, 4, 5, 3),  (-1, 2, 2, 1),    False, (30, 2, 2, 1)],
+        [(2, 3, 5, 6),  (-4,),            False, (2, 3, 5, 6)],
+        [(2, 3, 5, 6),  (6, 1, -4),       False, (6, 1, 5, 6)],
+        [(2, 3, 5, 6),  (-5, -5),         False, (6, 30)],
+        [(2, 3, 5, 6),  (-5, -1),         False, (6, 30)],
+        [(64,),         (-6, 16, 4),      False, (16, 4)],
+        [(64,),         (-6, 16, -1),     False, (16, 4)],
+        [(64, 1, 2, 3), (-6, 16, -1, -4), False, (16, 4, 1, 2, 3)],
+        [(8, 5, 4, 6),  (-4, -1, 3, -6),  True,  (8, 5, 4, 2, 3)]
+    ]
+    for hybridize in [True, False]:
+        for shape, newshape, reverse, expected_ret_shape in test_cases:
+            for grad_req in ['write', 'add']:
+                # test gluon
+                test_reshape = TestNumpyXReshape(newshape=newshape, reverse=reverse)
+                if hybridize:
+                    test_reshape.hybridize()
+
+                a = mx.np.random.uniform(-1, 1, shape).astype(np.float32)
+                init_a_grad = mx.np.random.uniform(-1, 1, shape).astype(np.float32)
+                a.attach_grad(grad_req=grad_req)
+                if grad_req == 'add':
+                    a.grad[:] = init_a_grad
+                with mx.autograd.record():
+                    y = test_reshape(a)
+                assert y.shape == expected_ret_shape,\
+                    'y.shape={}, expected_ret_shape={}'.format(y.shape, expected_ret_shape)
+                assert_almost_equal(y.asnumpy(), a.asnumpy().reshape(expected_ret_shape), rtol=1e-3, atol=1e-5)
+
+                # test backward
+                mx.autograd.backward(y)
+                expected_grad = _np.ones(shape)
+                if grad_req == 'add':
+                    expected_grad += init_a_grad.asnumpy()
+                assert_almost_equal(a.grad.asnumpy(), expected_grad, rtol=1e-3, atol=1e-5)
+
+                # test imperative
+                npx_out = npx.reshape(a, newshape, reverse=reverse)
+                expected_out = _np.reshape(a.asnumpy(), expected_ret_shape)
+                assert_almost_equal(npx_out.asnumpy(), expected_out, rtol=1e-3, atol=1e-5)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 9f21cddb3f6cc81e67a192f313066f7e9edd7fa8 Mon Sep 17 00:00:00 2001
From: Leonard Lausen <lausen@amazon.com>
Date: Sun, 27 Oct 2019 10:21:14 -0700
Subject: [PATCH 525/813] RNNOp only call cuda/cudnn if GPU ctx is requested
 (#16632)

---
 src/operator/rnn-inl.h | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/src/operator/rnn-inl.h b/src/operator/rnn-inl.h
index ead7501a48b0..b448261f215d 100644
--- a/src/operator/rnn-inl.h
+++ b/src/operator/rnn-inl.h
@@ -422,6 +422,8 @@ class RNNOp {
     init_mem_ = false;
     reserve_mem_size_ = 0;
 #endif
+
+    if (ctx_.dev_type == kGPU) {
 #if MXNET_USE_CUDNN == 1
     init_cudnn_ = false;
     dtype_ = mshadow::DataType<DType>::kCudnnFlag;
@@ -505,6 +507,7 @@ class RNNOp {
       LOG(FATAL) << "RNN on GPU is only available for cuDNN at the moment.";
     }
 #endif  // MXNET_USE_CUDNN == 1
+    }
 
     if (ctx_.dev_type == kCPU) {
       this->init_space_ = false;
@@ -523,6 +526,7 @@ class RNNOp {
   }
 
   ~RNNOp() {
+    if (ctx_.dev_type == kGPU) {
 #if MXNET_USE_CUDNN == 1
     CUDNN_CALL(cudnnDestroyTensorDescriptor(hx_desc_));
     CUDNN_CALL(cudnnDestroyTensorDescriptor(cx_desc_));
@@ -557,6 +561,7 @@ class RNNOp {
     CUDNN_CALL(cudnnDestroyRNNDataDescriptor(dy_data_desc_));
 #endif  // MXNET_USE_CUDNN_GE_7200
 #endif  // MXNET_USE_CUDNN
+    }
   }
 
   void Forward(const OpContext &ctx, const std::vector<TBlob> &in_data,

From 73c6b4a78f2a1f843dd136ad4a7eea7fd4323ea6 Mon Sep 17 00:00:00 2001
From: Hu Shiwen <yajiedesign@gmail.com>
Date: Mon, 28 Oct 2019 03:31:07 +0800
Subject: [PATCH 526/813] fix bad encode (#16641)

---
 src/operator/contrib/allclose_op-inl.h | 4 ++--
 src/operator/numpy/np_einsum_op-inl.h  | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/operator/contrib/allclose_op-inl.h b/src/operator/contrib/allclose_op-inl.h
index a858450f0007..a10c7795e568 100644
--- a/src/operator/contrib/allclose_op-inl.h
+++ b/src/operator/contrib/allclose_op-inl.h
@@ -58,8 +58,8 @@ struct AllCloseParam : public dmlc::Parameter<AllCloseParam> {
       .describe("Absolute tolerance.");
     DMLC_DECLARE_FIELD(equal_nan)
       .set_default(true)
-      .describe("Whether to compare NaN’s as equal. If True, NaN’s in A will be considered equal "
-                "to NaN’s in B in the output array.");
+      .describe("Whether to compare NaN's as equal. If True, NaN's in A will be considered equal "
+                "to NaN's in B in the output array.");
   }
 };
 
diff --git a/src/operator/numpy/np_einsum_op-inl.h b/src/operator/numpy/np_einsum_op-inl.h
index 051280763331..d2f399b2533d 100644
--- a/src/operator/numpy/np_einsum_op-inl.h
+++ b/src/operator/numpy/np_einsum_op-inl.h
@@ -394,7 +394,7 @@ struct NumpyEinsumParam: public dmlc::Parameter<NumpyEinsumParam> {
       .set_default("")
       .describe("Specifies the subscripts for summation as comma separated list"
       " of subscript labels. An implicit (classical Einstein summation) calculation"
-      " is performed unless the explicit indicator ‘->’ is included as well as"
+      " is performed unless the explicit indicator '->' is included as well as"
       " subscript labels of the precise output form.");
     DMLC_DECLARE_FIELD(optimize)
       .set_default(0);

From 84d61a1df3eca95be68c15d39fe057064a4da018 Mon Sep 17 00:00:00 2001
From: Robert Stone <talby@trap.mtview.ca.us>
Date: Sun, 27 Oct 2019 12:31:50 -0700
Subject: [PATCH 527/813] [Perl] - ndarray to native array conversion fix
 (#16635)

---
 perl-package/AI-MXNet/lib/AI/MXNet/NDArray.pm |  6 +++++-
 perl-package/AI-MXNet/t/test_ndarray.t        | 19 ++++++++++++++++++-
 2 files changed, 23 insertions(+), 2 deletions(-)

diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/NDArray.pm b/perl-package/AI-MXNet/lib/AI/MXNet/NDArray.pm
index f75cc84b2a8f..1d968c14a487 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/NDArray.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/NDArray.pm
@@ -116,7 +116,11 @@ method STORABLE_thaw($cloning, $buf, $writable)
 
 method split_array(@args)
 {
-     $self->shape->[0] > 1 ? $self->split(num_outputs => $self->shape->[0], squeeze_axis => @{ $self->shape } > 1 ? 1 : 0, axis => 0) : [$self];
+    my $shape = $self->shape;
+    return [] if $shape->[0] == 0;
+    my $list = $self->split(num_outputs=>$shape->[0],
+        squeeze_axis=>int(@$shape > 1), axis=>0);
+    $shape->[0] == 1 ? [ $list ] : $list;
 }
 
 method at(Index @indices)
diff --git a/perl-package/AI-MXNet/t/test_ndarray.t b/perl-package/AI-MXNet/t/test_ndarray.t
index a6cd113c3f89..1e290b4bc715 100644
--- a/perl-package/AI-MXNet/t/test_ndarray.t
+++ b/perl-package/AI-MXNet/t/test_ndarray.t
@@ -19,7 +19,7 @@ use strict;
 use warnings;
 use AI::MXNet qw(mx);
 use AI::MXNet::TestUtils qw(almost_equal same rand_ndarray randint zip);
-use Test::More tests => 251;
+use Test::More tests => 261;
 use PDL;
 use File::Temp qw(tempdir);
 use IO::File;
@@ -217,6 +217,22 @@ sub test_histogram
     ok(same($bins->aspdl, pdl([10, 20, 30, 60])));
 }
 
+sub test_array_overload
+{
+    # array conversions are largely calls to mx->nd->split(), but have
+    # special cases around dimensions of length 0 and 1.
+    is_deeply([ @{ mx->nd->array(zeros(7, 0)) } ], []);
+    is_deeply(mx->nd->zeros([3, 7])->[0]->shape, [ 7 ]);
+    is_deeply(mx->nd->zeros([2, 7])->[0]->shape, [ 7 ]);
+    is_deeply(mx->nd->zeros([1, 7])->[0]->shape, [ 7 ]);
+    is_deeply(mx->nd->zeros([3, 7, 11])->[0]->shape, [7, 11]);
+    is_deeply(mx->nd->zeros([2, 7, 11])->[0]->shape, [7, 11]);
+    is_deeply(mx->nd->zeros([1, 7, 11])->[0]->shape, [7, 11]);
+    is_deeply(mx->nd->zeros([3, 7, 11, 13])->[0]->shape, [7, 11, 13]);
+    is_deeply(mx->nd->zeros([2, 7, 11, 13])->[0]->shape, [7, 11, 13]);
+    is_deeply(mx->nd->zeros([1, 7, 11, 13])->[0]->shape, [7, 11, 13]);
+}
+
 test_ndarray_slice();
 test_ndarray_reshape();
 test_moveaxis();
@@ -226,3 +242,4 @@ test_linalg_gemm2();
 test_image_to_tensor();
 test_buffer_load();
 test_histogram();
+test_array_overload();

From d12e674e58214fb73e467d16f7362320f3ad8c28 Mon Sep 17 00:00:00 2001
From: Talia <31782251+TEChopra1000@users.noreply.github.com>
Date: Sun, 27 Oct 2019 12:33:31 -0700
Subject: [PATCH 528/813] fixing broken links in multiple files - round 3
 (#16634)

---
 .../python/tutorials/extend/custom_layer.md   |  2 +-
 .../gluon_from_experiment_to_deployment.md    |  4 +--
 .../gluon/training/fit_api_tutorial.md        |  2 +-
 .../packages/ndarray/sparse/train.md          |  4 +--
 .../packages/ndarray/sparse/train_gluon.md    | 35 +++++++++----------
 .../packages/onnx/fine_tuning_gluon.md        |  2 +-
 .../python/tutorials/packages/viz/index.rst   |  2 +-
 .../backend/mkldnn/mkldnn_quantization.md     |  4 +--
 .../tutorials/performance/backend/profiler.md |  2 +-
 .../performance/backend/tensorrt/tensorrt.md  |  4 +--
 docs/static_site/src/pages/api/api.html       |  2 +-
 .../tutorials/mxnet_cpp_inference_tutorial.md | 16 ++++-----
 docs/static_site/src/pages/api/faq/float16.md |  2 +-
 docs/static_site/src/pages/api/faq/perf.md    |  6 ++--
 .../pages/get_started/build_from_source.md    |  2 +-
 julia/docs/src/api/io.md                      |  2 +-
 julia/docs/src/tutorial/char-lstm.md          |  2 +-
 julia/docs/src/tutorial/mnist.md              |  4 +--
 18 files changed, 48 insertions(+), 49 deletions(-)

diff --git a/docs/python_docs/python/tutorials/extend/custom_layer.md b/docs/python_docs/python/tutorials/extend/custom_layer.md
index 6002a7812ec7..2fe795ba5439 100644
--- a/docs/python_docs/python/tutorials/extend/custom_layer.md
+++ b/docs/python_docs/python/tutorials/extend/custom_layer.md
@@ -57,7 +57,7 @@ The rest of methods of the `Block` class are already implemented, and majority o
 
 Looking into implementation of [existing layers](https://mxnet.apache.org/api/python/gluon/nn.html), one may find that more often a block inherits from a [HybridBlock](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/block.py#L428), instead of directly inheriting from `Block`.
 
-The reason for that is that `HybridBlock` allows to write custom layers that can be used in imperative programming as well as in symbolic programming. It is convinient to support both ways, because the imperative programming eases the debugging of the code and the symbolic one provides faster execution speed. You can learn more about the difference between symbolic vs. imperative programming from [this article](https://mxnet.apache.org/architecture/program_model.html).
+The reason for that is that `HybridBlock` allows to write custom layers that can be used in imperative programming as well as in symbolic programming. It is convinient to support both ways, because the imperative programming eases the debugging of the code and the symbolic one provides faster execution speed. You can learn more about the difference between symbolic vs. imperative programming from [this article](/api/architecture/program_model).
 
 Hybridization is a process that Apache MxNet uses to create a symbolic graph of a forward computation. This allows to increase computation performance by optimizing the computational symbolic graph. Once the symbolic graph is created, Apache MxNet caches and reuses it for subsequent computations.
 
diff --git a/docs/python_docs/python/tutorials/getting-started/gluon_from_experiment_to_deployment.md b/docs/python_docs/python/tutorials/getting-started/gluon_from_experiment_to_deployment.md
index b1f65e682263..47b629991650 100644
--- a/docs/python_docs/python/tutorials/getting-started/gluon_from_experiment_to_deployment.md
+++ b/docs/python_docs/python/tutorials/getting-started/gluon_from_experiment_to_deployment.md
@@ -99,14 +99,14 @@ ctx = [mx.gpu(i) for i in range(num_gpus)] if num_gpus > 0 else [mx.cpu()]
 batch_size = per_device_batch_size * max(num_gpus, 1)
 ```
 
-Now we will apply data augmentations on training images. This makes minor alterations on the training images, and our model will consider them as distinct images. This can be very useful for fine-tuning on a relatively small dataset, and it will help improve the model. We can use the Gluon [DataSet API](https://mxnet.apache.org/tutorials/gluon/datasets.html), [DataLoader API](https://mxnet.apache.org/tutorials/gluon/datasets.html), and [Transform API](https://mxnet.apache.org/tutorials/gluon/data_augmentation.html) to load the images and apply the following data augmentations:
+Now we will apply data augmentations on training images. This makes minor alterations on the training images, and our model will consider them as distinct images. This can be very useful for fine-tuning on a relatively small dataset, and it will help improve the model. We can use the Gluon [DataSet API](/api/python/docs/api/gluon/data/index.html#mxnet.gluon.data.Dataset), [DataLoader API](/api/python/docs/api/gluon/data/index.html#mxnet.gluon.data.DataLoader), and [Transform API](/api/python/docs/api/gluon/data/index.html#mxnet.gluon.data.Dataset.transform) to load the images and apply the following data augmentations:
 1. Randomly crop the image and resize it to 224x224
 2. Randomly flip the image horizontally
 3. Randomly jitter color and add noise
 4. Transpose the data from `[height, width, num_channels]` to `[num_channels, height, width]`, and map values from [0, 255] to [0, 1]
 5. Normalize with the mean and standard deviation from the ImageNet dataset.
 
-For validation and inference, we only need to apply step 1, 4, and 5. We also need to save the mean and standard deviation values for [inference using C++](https://mxnet.apache.org/versions/master/tutorials/c++/mxnet_cpp_inference_tutorial.html).
+For validation and inference, we only need to apply step 1, 4, and 5. We also need to save the mean and standard deviation values for [inference using C++](/api/cpp/docs/tutorials/cpp_inference).
 
 ```python
 jitter_param = 0.4
diff --git a/docs/python_docs/python/tutorials/packages/gluon/training/fit_api_tutorial.md b/docs/python_docs/python/tutorials/packages/gluon/training/fit_api_tutorial.md
index 9e4cbe2f5114..896e5f217aa3 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/training/fit_api_tutorial.md
+++ b/docs/python_docs/python/tutorials/packages/gluon/training/fit_api_tutorial.md
@@ -252,7 +252,7 @@ with warnings.catch_warnings():
     Epoch 2, loss 0.3229 <!--notebook-skip-line-->
 ```
 
-You can load the saved model, by using the `load_parameters` API in Gluon. For more details refer to the [Loading model parameters from file tutorial](../blocks/save_load_params.html#saving-model-parameters-to-file)
+You can load the saved model, by using the `load_parameters` API in Gluon. For more details refer to the [Loading model parameters from file tutorial](/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html#saving-model-parameters-to-file)
 
 
 ```python
diff --git a/docs/python_docs/python/tutorials/packages/ndarray/sparse/train.md b/docs/python_docs/python/tutorials/packages/ndarray/sparse/train.md
index 336185cf7583..23654fc6a33a 100644
--- a/docs/python_docs/python/tutorials/packages/ndarray/sparse/train.md
+++ b/docs/python_docs/python/tutorials/packages/ndarray/sparse/train.md
@@ -240,8 +240,8 @@ The function you will explore is: *y = x<sub>1</sub>  +  2x<sub>2</sub> + ... 10
 
 ### Preparing the Data
 
-In MXNet, both [mx.io.LibSVMIter](https://mxnet.apache.org/versions/master/api/python/io/io.html#mxnet.io.LibSVMIter)
-and [mx.io.NDArrayIter](https://mxnet.apache.org/versions/master/api/python/io/io.html#mxnet.io.NDArrayIter)
+In MXNet, both [mx.io.LibSVMIter](/api/python/docs/api/mxnet/io/index.html#mxnet.io.LibSVMIter)
+and [mx.io.NDArrayIter](/api/python/docs/api/mxnet/io/index.html#mxnet.io.NDArrayIter)
 support loading sparse data in CSR format. In this example, we'll use the `NDArrayIter`.
 
 You may see some warnings from SciPy. You don't need to worry about those for this example.
diff --git a/docs/python_docs/python/tutorials/packages/ndarray/sparse/train_gluon.md b/docs/python_docs/python/tutorials/packages/ndarray/sparse/train_gluon.md
index 402cc2aeb739..688071062e20 100644
--- a/docs/python_docs/python/tutorials/packages/ndarray/sparse/train_gluon.md
+++ b/docs/python_docs/python/tutorials/packages/ndarray/sparse/train_gluon.md
@@ -20,7 +20,7 @@
 
 When working on machine learning problems, you may encounter situations where the input data is sparse (i.e. the majority of values are zero). One example of this is in recommendation systems. You could have millions of user and product features, but only a few of these features are present for each sample. Without special treatment, the sheer magnitude of the feature space can lead to out-of-memory situations and cause significant slowdowns when training and making predictions.
 
-MXNet supports a number of sparse storage types (often called 'stype' for short) for these situations. In this tutorial, we'll start by generating some sparse data, write it to disk in the LibSVM format and then read back using the [`LibSVMIter`](https://mxnet.apache.org/api/python/io/io.html) for training. We use the Gluon API to train the model and leverage sparse storage types such as [`CSRNDArray`](https://mxnet.apache.org/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray) and [`RowSparseNDArray`](https://mxnet.apache.org/api/python/ndarray/sparse.html?highlight=rowsparsendarray#mxnet.ndarray.sparse.RowSparseNDArray) to maximise performance and memory efficiency.
+MXNet supports a number of sparse storage types (often called 'stype' for short) for these situations. In this tutorial, we'll start by generating some sparse data, write it to disk in the LibSVM format and then read back using the [`LibSVMIter`](/api/python/docs/api/mxnet/io/index.html#mxnet.io.LibSVMIter) for training. We use the Gluon API to train the model and leverage sparse storage types such as [`CSRNDArray`](/api/python/docs/api/ndarray/sparse/index.html#mxnet.ndarray.sparse.CSRNDArray) and [`RowSparseNDArray`](/api/python/docs/api/ndarray/sparse/index.html#mxnet.ndarray.sparse.RowSparseNDArray) to maximise performance and memory efficiency.
 
 
 ```python
@@ -63,7 +63,7 @@ print('{:,.0f} non-zero elements'.format(data.data.size))
 10,000 non-zero elements
 ```
 
-Our storage type is CSR (Compressed Sparse Row) which is the ideal type for sparse data along multiple axes. See [this in-depth tutorial](https://mxnet.apache.org/versions/master/tutorials/sparse/csr.html) for more information. Just to confirm the generation process ran correctly, we can see that the vast majority of values are indeed zero. One of the first questions to ask would be how much memory is saved by storing this data in a [`CSRNDArray`](https://mxnet.apache.org/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray) versus a standard [`NDArray`](https://mxnet.apache.org/versions/master/api/python/ndarray/sparse.html?highlight=ndarray#module-mxnet.ndarray). Since sparse arrays are constructed from many components (e.g. `data`, `indices` and `indptr`) we define a function called `get_nbytes` to calculate the number of bytes taken in memory to store an array. We compare the same data stored in a standard [`NDArray`](https://mxnet.apache.org/versions/master/api/python/ndarray/sparse.html?highlight=ndarray#module-mxnet.ndarray) (with `data.tostype('default')`) to the [`CSRNDArray`](https://mxnet.apache.org/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray).
+Our storage type is CSR (Compressed Sparse Row) which is the ideal type for sparse data along multiple axes. See [this in-depth tutorial](https://mxnet.apache.org/versions/master/tutorials/sparse/csr.html) for more information. Just to confirm the generation process ran correctly, we can see that the vast majority of values are indeed zero. One of the first questions to ask would be how much memory is saved by storing this data in a [`CSRNDArray`](/api/python/docs/api/ndarray/sparse/index.html#mxnet.ndarray.sparse.CSRNDArray) versus a standard [`NDArray`](/api/python/docs/api/ndarray/ndarray.html#module-mxnet.ndarray). Since sparse arrays are constructed from many components (e.g. `data`, `indices` and `indptr`) we define a function called `get_nbytes` to calculate the number of bytes taken in memory to store an array. We compare the same data stored in a standard [`NDArray`](/api/python/docs/api/ndarray/ndarray.html#module-mxnet.ndarray) (with `data.tostype('default')`) to the [`CSRNDArray`](/api/python/docs/api/ndarray/sparse/index.html#mxnet.ndarray.sparse.CSRNDArray).
 
 
 ```python
@@ -94,9 +94,9 @@ Given the extremely high sparsity of the data, we observe a huge memory saving h
 
 ### Writing Sparse Data
 
-Since there is such a large size difference between dense and sparse storage formats here, we ideally want to store the data on disk in a sparse storage format too. MXNet supports a format called LibSVM and has a data iterator called [`LibSVMIter`](https://mxnet.apache.org/api/python/io/io.html?highlight=libsvmiter) specifically for data formatted this way.
+Since there is such a large size difference between dense and sparse storage formats here, we ideally want to store the data on disk in a sparse storage format too. MXNet supports a format called LibSVM and has a data iterator called [`LibSVMIter`](/api/python/docs/api/mxnet/io/index.html#mxnet.io.LibSVMIter) specifically for data formatted this way.
 
-A LibSVM file has a row for each sample, and each row starts with the label: in this case `0.0` or `1.0` since we have a classification task. After this we have a variable number of `key:value` pairs separated by spaces, where the key is column/feature index and the value is the value of that feature. When working with your own sparse data in a custom format you should try to convert your data into this format. We define a `save_as_libsvm` function to save the `data` ([`CSRNDArray`](https://mxnet.apache.org/versions/master/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray)) and `label` (`NDArray`) to disk in LibSVM format.
+A LibSVM file has a row for each sample, and each row starts with the label: in this case `0.0` or `1.0` since we have a classification task. After this we have a variable number of `key:value` pairs separated by spaces, where the key is column/feature index and the value is the value of that feature. When working with your own sparse data in a custom format you should try to convert your data into this format. We define a `save_as_libsvm` function to save the `data` ([`CSRNDArray`](/api/python/docs/api/ndarray/sparse/index.html#mxnet.ndarray.sparse.CSRNDArray)) and `label` (`NDArray`) to disk in LibSVM format.
 
 
 ```python
@@ -148,10 +148,9 @@ Some storage overhead is introduced by serializing the data as characters (with
 
 ### Reading Sparse Data
 
-Using [`LibSVMIter`](https://mxnet.apache.org/api/python/io/io.html?highlight=libsvmiter), we can quickly and easily load data into batches ready for training. Although Gluon [`Dataset`](https://mxnet.apache.org/versions/master/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset)s can be written to return sparse arrays, Gluon [`DataLoader`](https://mxnet.apache.org/versions/master/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader)s currently convert each sample to dense before stacking up to create the batch. As a result, [`LibSVMIter`](https://mxnet.apache.org/api/python/io/io.html?highlight=libsvmiter) is the recommended method of loading sparse data in batches.
-
-Similar to using a [`DataLoader`](https://mxnet.apache.org/versions/master/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader), you must specify the required `batch_size`. Since we're dealing with sparse data and the column shape isn't explicitly stored in the LibSVM file, we additionally need to provide the shape of the data and label. Our [`LibSVMIter`](https://mxnet.apache.org/api/python/io/io.html?highlight=libsvmiter) returns batches in a slightly different form to a [`DataLoader`](https://mxnet.apache.org/versions/master/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader). We get `DataBatch` objects instead of `tuple`. See the [appendix of this tutorial](https://mxnet.apache.org/versions/master/tutorials/gluon/datasets.html) for more information.
+Using [`LibSVMIter`](/api/python/docs/api/mxnet/io/index.html#mxnet.io.LibSVMIter), we can quickly and easily load data into batches ready for training. Although Gluon [`Dataset`](/api/python/docs/api/gluon/data/index.html#mxnet.gluon.data.Dataset)s can be written to return sparse arrays, Gluon [`DataLoader`](/api/python/docs/api/gluon/data/index.html#mxnet.gluon.data.DataLoader)s currently convert each sample to dense before stacking up to create the batch. As a result, [`LibSVMIter`](/api/python/docs/api/mxnet/io/index.html#mxnet.io.LibSVMIter) is the recommended method of loading sparse data in batches.
 
+Similar to using a [`DataLoader`](/api/python/docs/api/gluon/data/index.html#mxnet.gluon.data.DataLoader), you must specify the required `batch_size`. Since we're dealing with sparse data and the column shape isn't explicitly stored in the LibSVM file, we additionally need to provide the shape of the data and label. Our [`LibSVMIter`](/api/python/docs/api/mxnet/io/index.html#mxnet.io.LibSVMIter) returns batches in a slightly different form to a [`DataLoader`](/api/python/docs/api/gluon/data/index.html#mxnet.gluon.data.DataLoader). We get `DataBatch` objects instead of `tuple`. 
 
 ```python
 data_iter = mx.io.LibSVMIter(data_libsvm=filepath, data_shape=(num_features,), label_shape=(1,), batch_size=10)
@@ -215,7 +214,7 @@ Although results will change depending on system specifications and degree of sp
 
 Our next step is to define a network. We have an input of 1,000,000 features and we want to make a binary prediction. We don't have any spatial or temporal relationships between features, so we'll use a 3 layer fully-connected network where the last layer has 1 output unit (with sigmoid activation). Since we're working with sparse data, we'd ideally like to use network operators that can exploit this sparsity for improved performance and memory efficiency.
 
-Gluon's [`nn.Dense`](https://mxnet.apache.org/versions/master/api/python/gluon/nn.html?highlight=dense#mxnet.gluon.nn.Dense) block can used with [`CSRNDArray`](https://mxnet.apache.org/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray) input arrays but it doesn't exploit the sparsity. Under the hood, [`Dense`](https://mxnet.apache.org/versions/master/api/python/gluon/nn.html?highlight=dense#mxnet.gluon.nn.Dense) uses the [`FullyConnected`](https://mxnet.apache.org/versions/master/api/python/ndarray/ndarray.html?highlight=fullyconnected#mxnet.ndarray.FullyConnected) operator which isn't optimized for [`CSRNDArray`](https://mxnet.apache.org/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray) arrays. We'll implement a `Block` that does exploit this sparsity, *but first*, let's just remind ourselves of the [`Dense`](https://mxnet.apache.org/versions/master/api/python/gluon/nn.html?highlight=dense#mxnet.gluon.nn.Dense) implementation by creating an equivalent `Block` called `FullyConnected`.
+Gluon's [`nn.Dense`](/api/python/docs/api/gluon/nn/index.html#mxnet.gluon.nn.Dense) block can used with [`CSRNDArray`](/api/python/docs/api/ndarray/sparse/index.html#mxnet.ndarray.sparse.CSRNDArray) input arrays but it doesn't exploit the sparsity. Under the hood, [`Dense`](/api/python/docs/api/gluon/nn/index.html#mxnet.gluon.nn.Dense) uses the [`FullyConnected`](/api/python/docs/api/ndarray/ndarray.html#mxnet.ndarray.FullyConnected) operator which isn't optimized for [`CSRNDArray`](/api/python/docs/api/ndarray/sparse/index.html#mxnet.ndarray.sparse.CSRNDArray) arrays. We'll implement a `Block` that does exploit this sparsity, *but first*, let's just remind ourselves of the [`Dense`](/api/python/docs/api/gluon/nn/index.html#mxnet.gluon.nn.Dense) implementation by creating an equivalent `Block` called `FullyConnected`.
 
 
 ```python
@@ -235,11 +234,11 @@ class FullyConnected(mx.gluon.HybridBlock):
         return F.FullyConnected(x, weight, bias, num_hidden=self._units)
 ```
 
-Our `weight` and `bias` parameters are dense (see `stype='default'`) and so are their gradients (see `grad_stype='default'`). Our `weight` parameter has shape `(units, in_units)` because the [`FullyConnected`](https://mxnet.apache.org/versions/master/api/python/ndarray/ndarray.html?highlight=fullyconnected#mxnet.ndarray.FullyConnected) operator performs the following calculation:
+Our `weight` and `bias` parameters are dense (see `stype='default'`) and so are their gradients (see `grad_stype='default'`). Our `weight` parameter has shape `(units, in_units)` because the [`FullyConnected`](/api/python/docs/api/ndarray/ndarray.html#mxnet.ndarray.FullyConnected) operator performs the following calculation:
 
 $$Y = XW^T + b$$
 
-We could instead have created our parameter with shape `(in_units, units)` and avoid the transpose of the weight matrix. We'll see why this is so important later on. And instead of [`FullyConnected`](https://mxnet.apache.org/versions/master/api/python/ndarray/ndarray.html?highlight=fullyconnected#mxnet.ndarray.FullyConnected) we could have used [`mx.sparse.dot`](https://mxnet.apache.org/versions/master/api/python/ndarray/sparse.html?highlight=sparse.dot#mxnet.ndarray.sparse.dot) to fully exploit the sparsity of the [`CSRNDArray`](https://mxnet.apache.org/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray) input arrays. We'll now implement an alternative `Block` called `FullyConnectedSparse` using these ideas. We take `grad_stype` of the `weight` as an argument (called `weight_grad_stype`), since we're going to change this later on.
+We could instead have created our parameter with shape `(in_units, units)` and avoid the transpose of the weight matrix. We'll see why this is so important later on. And instead of [`FullyConnected`](/api/python/docs/api/ndarray/ndarray.html#mxnet.ndarray.FullyConnected) we could have used [`mx.sparse.dot`](/api/python/docs/api/ndarray/sparse/index.html?#mxnet.ndarray.sparse.dot) to fully exploit the sparsity of the [`CSRNDArray`](/api/python/docs/api/ndarray/sparse/index.html#mxnet.ndarray.sparse.CSRNDArray) input arrays. We'll now implement an alternative `Block` called `FullyConnectedSparse` using these ideas. We take `grad_stype` of the `weight` as an argument (called `weight_grad_stype`), since we're going to change this later on.
 
 
 ```python
@@ -261,7 +260,7 @@ class FullyConnectedSparse(mx.gluon.HybridBlock):
 
 Once again, we're using a dense `weight`, so both `FullyConnected` and `FullyConnectedSparse` will return dense array outputs. When constructing a multi-layer network therefore, only the first layer needs to be optimized for sparse inputs. Our first layer is often responsible for reducing the feature dimension dramatically (e.g. 1,000,000 features down to 128 features). We'll set the number of units in our 3 layers to be 128, 8 and 1.
 
-We will use [`timeit`](https://docs.python.org/2/library/timeit.html) to check the performance of these two variants, and analyse some [MXNet Profiler](https://mxnet.apache.org/versions/master/tutorials/python/profiler.html) traces that have been created from these benchmarks. Additionally, we will inspect the memory usage of the weights (and gradients) using the `print_memory_allocation` function defined below:
+We will use [`timeit`](https://docs.python.org/2/library/timeit.html) to check the performance of these two variants, and analyse some [MXNet Profiler](/api/python/docs/tutorials/performance/backend/profiler.html) traces that have been created from these benchmarks. Additionally, we will inspect the memory usage of the weights (and gradients) using the `print_memory_allocation` function defined below:
 
 
 ```python
@@ -324,7 +323,7 @@ for batch in data_iter:
 
 ![fully connected](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/ndarray/sparse/fully_connected.png)
 
-We can see the first [`FullyConnected`](https://mxnet.apache.org/versions/master/api/python/ndarray/ndarray.html?highlight=fullyconnected#mxnet.ndarray.FullyConnected) operator takes a significant proportion of time to execute (~25% of the iteration) because there are 1,000,000 input features (to 128). After this, the other [`FullyConnected`](https://mxnet.apache.org/versions/master/api/python/ndarray/ndarray.html?highlight=fullyconnected#mxnet.ndarray.FullyConnected) operators are much faster because they have input features of 128 (to 8) and 8 (to 1). On the backward pass, we see the same pattern (but in reverse). And finally, the parameter update step takes a large amount of time on the weight matrix of the first `FullyConnected` `Block`. When checking the memory allocations below, we can see the weight matrix of the first `FullyConnected` `Block` is responsible for 99.999% of the memory compared to other [`FullyConnected`](https://mxnet.apache.org/versions/master/api/python/ndarray/ndarray.html?highlight=fullyconnected#mxnet.ndarray.FullyConnected) weight matrices.
+We can see the first [`FullyConnected`](/api/python/docs/api/ndarray/ndarray.html#mxnet.ndarray.FullyConnected) operator takes a significant proportion of time to execute (~25% of the iteration) because there are 1,000,000 input features (to 128). After this, the other [`FullyConnected`](/api/python/docs/api/ndarray/ndarray.html#mxnet.ndarray.FullyConnected) operators are much faster because they have input features of 128 (to 8) and 8 (to 1). On the backward pass, we see the same pattern (but in reverse). And finally, the parameter update step takes a large amount of time on the weight matrix of the first `FullyConnected` `Block`. When checking the memory allocations below, we can see the weight matrix of the first `FullyConnected` `Block` is responsible for 99.999% of the memory compared to other [`FullyConnected`](/api/python/docs/api/ndarray/ndarray.html#mxnet.ndarray.FullyConnected) weight matrices.
 
 
 ```python
@@ -384,7 +383,7 @@ for batch in data_iter:
 
 ![fully connected sparse](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/ndarray/sparse/fully_connected_sparse.png)
 
-We see the forward pass of `dot` and `add` (equivalent to [`FullyConnected`](https://mxnet.apache.org/versions/master/api/python/ndarray/ndarray.html?highlight=fullyconnected#mxnet.ndarray.FullyConnected) operator) is much faster now: 1.54ms vs 0.26ms. And this explains the reduction in overall time for the epoch. We didn't gain any benefit on the backward pass or parameter updates though.
+We see the forward pass of `dot` and `add` (equivalent to [`FullyConnected`](/api/python/docs/api/ndarray/ndarray.html#mxnet.ndarray.FullyConnected) operator) is much faster now: 1.54ms vs 0.26ms. And this explains the reduction in overall time for the epoch. We didn't gain any benefit on the backward pass or parameter updates though.
 
 ![fully connected sparse backward](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/ndarray/sparse/fully_connected_sparse_backward.png)
 
@@ -408,7 +407,7 @@ Memory Allocation for Weight Gradient:
 
 ### Benchmark: `FullyConnectedSparse` with `grad_stype=row_sparse` 
 
-One useful outcome of sparsity in our [`CSRNDArray`](https://mxnet.apache.org/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray) input is that our gradients will be row sparse. We can exploit this fact to give us potentially huge memory savings and speed improvements. Creating our `weight` parameter with shape `(units, in_units)` and not transposing in the forward pass are important pre-requisite for obtaining row sparse gradients. Using [`nn.Dense`](https://mxnet.apache.org/versions/master/api/python/gluon/nn.html?highlight=dense#mxnet.gluon.nn.Dense) would have led to column sparse gradients which are not supported in MXNet. We previously had `grad_stype` of the `weight` parameter in the first layer set to `'default'` so we were handling the gradient as a dense array. Switching this to `'row_sparse'` can give us these potential improvements.
+One useful outcome of sparsity in our [`CSRNDArray`](/api/python/docs/api/ndarray/sparse/index.html#mxnet.ndarray.sparse.CSRNDArray) input is that our gradients will be row sparse. We can exploit this fact to give us potentially huge memory savings and speed improvements. Creating our `weight` parameter with shape `(units, in_units)` and not transposing in the forward pass are important pre-requisite for obtaining row sparse gradients. Using [`nn.Dense`](/api/python/docs/api/gluon/nn/index.html#mxnet.gluon.nn.Dense) would have led to column sparse gradients which are not supported in MXNet. We previously had `grad_stype` of the `weight` parameter in the first layer set to `'default'` so we were handling the gradient as a dense array. Switching this to `'row_sparse'` can give us these potential improvements.
 
 
 ```python
@@ -472,12 +471,12 @@ You can optimize this example further by setting the weight's `stype` to `'row_s
 
 ## Conclusion
 
-As part of this tutorial, we learned how to write sparse data to disk in LibSVM format and load it back in sparse batches with the [`LibSVMIter`](https://mxnet.apache.org/api/python/io/io.html?highlight=libsvmiter). We learned how to improve the performance of Gluon's [`nn.Dense`](https://mxnet.apache.org/versions/master/api/python/gluon/nn.html?highlight=dense#mxnet.gluon.nn.Dense) on sparse arrays using `mx.nd.sparse`. And lastly, we set `grad_stype` to `'row_sparse'` to reduce the size of the gradient and speed up the parameter update step.
+As part of this tutorial, we learned how to write sparse data to disk in LibSVM format and load it back in sparse batches with the [`LibSVMIter`](/api/python/docs/api/mxnet/io/index.html#mxnet.io.LibSVMIter). We learned how to improve the performance of Gluon's [`nn.Dense`](/api/python/docs/api/gluon/nn/index.html#mxnet.gluon.nn.Dense) on sparse arrays using `mx.nd.sparse`. And lastly, we set `grad_stype` to `'row_sparse'` to reduce the size of the gradient and speed up the parameter update step.
 
 ## Recommended Next Steps
 
-* More detail on the [`CSRNDArray`](https://mxnet.apache.org/api/python/ndarray/sparse.html?highlight=csrndarray#mxnet.ndarray.sparse.CSRNDArray) sparse array format can be found in [this tutorial](https://mxnet.apache.org/versions/master/tutorials/sparse/csr.html).
-* More detail on the [`RowSparseNDArray`](https://mxnet.apache.org/api/python/ndarray/sparse.html?highlight=rowsparsendarray#mxnet.ndarray.sparse.RowSparseNDArray) sparse array format can be found in [this tutorial](https://mxnet.apache.org/versions/master/tutorials/sparse/row_sparse.html).
-* Users of the Module API can see a symbolic only example in [this tutorial](https://mxnet.apache.org/versions/master/tutorials/sparse/train.html).
+* More detail on the [`CSRNDArray`](/api/python/docs/api/ndarray/sparse/index.html#mxnet.ndarray.sparse.CSRNDArray) sparse array format can be found in [this tutorial](/api/python/docs/tutorials/packages/ndarray/sparse/csr.html).
+* More detail on the [`RowSparseNDArray`](/api/python/docs/api/ndarray/sparse/index.html#mxnet.ndarray.sparse.RowSparseNDArray) sparse array format can be found in [this tutorial](/api/python/docs/tutorials/packages/ndarray/sparse/row_sparse.html).
+* Users of the Module API can see a symbolic only example in [this tutorial](/api/python/docs/tutorials/packages/ndarray/sparse/train.html).
 
 <!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/python_docs/python/tutorials/packages/onnx/fine_tuning_gluon.md b/docs/python_docs/python/tutorials/packages/onnx/fine_tuning_gluon.md
index f77731494215..e1eb3044a9fa 100644
--- a/docs/python_docs/python/tutorials/packages/onnx/fine_tuning_gluon.md
+++ b/docs/python_docs/python/tutorials/packages/onnx/fine_tuning_gluon.md
@@ -36,7 +36,7 @@ To run the tutorial you will need to have installed the following python modules
 - matplotlib
 
 We recommend that you have first followed this tutorial:
-- [Inference using an ONNX model on MXNet Gluon](https://mxnet.apache.org/tutorials/onnx/inference_on_onnx_model.html)
+- [Inference using an ONNX model on MXNet Gluon](/api/python/docs/tutorials/packages/onnx/inference_on_onnx_model.html)
 
 
 ```python
diff --git a/docs/python_docs/python/tutorials/packages/viz/index.rst b/docs/python_docs/python/tutorials/packages/viz/index.rst
index c9254a983824..367c8ecc67fb 100644
--- a/docs/python_docs/python/tutorials/packages/viz/index.rst
+++ b/docs/python_docs/python/tutorials/packages/viz/index.rst
@@ -29,7 +29,7 @@ Visualization
 References
 ----------
 
-- `mxnet.viz <../api/symbol-related/mxnet.visualization.html>`_
+- `mxnet.viz </api/python/docs/api/mxnet/visualization/index.html>`_
 
 .. toctree::
    :hidden:
diff --git a/docs/python_docs/python/tutorials/performance/backend/mkldnn/mkldnn_quantization.md b/docs/python_docs/python/tutorials/performance/backend/mkldnn/mkldnn_quantization.md
index da442e9fb42f..8c15af267cd4 100644
--- a/docs/python_docs/python/tutorials/performance/backend/mkldnn/mkldnn_quantization.md
+++ b/docs/python_docs/python/tutorials/performance/backend/mkldnn/mkldnn_quantization.md
@@ -23,7 +23,7 @@ If you are not familiar with Apache/MXNet quantization flow, please reference [q
 
 ## Installation and Prerequisites
 
-Installing MXNet with MKLDNN backend is an easy and essential process. You can follow [How to build and install MXNet with MKL-DNN backend](https://mxnet.apache.org/tutorials/mkldnn/MKLDNN_README.html) to build and install MXNet from source. Also, you can install the release or nightly version via PyPi and pip directly by running:
+Installing MXNet with MKLDNN backend is an easy and essential process. You can follow [How to build and install MXNet with MKL-DNN backend](/api/python/docs/tutorials/performance/backend/mkldnn/mkldnn_readme.html) to build and install MXNet from source. Also, you can install the release or nightly version via PyPi and pip directly by running:
 
 ```
 # release version
@@ -38,7 +38,7 @@ A quantization script [imagenet_gen_qsym_mkldnn.py](https://github.com/apache/in
 
 ## Integrate Quantization Flow to Your Project
 
-Quantization flow works for both symbolic and Gluon models. If you're using Gluon, you can first refer [Saving and Loading Gluon Models](https://mxnet.apache.org/versions/master/tutorials/gluon/save_load_params.html) to hybridize your computation graph and export it as a symbol before running quantization.
+Quantization flow works for both symbolic and Gluon models. If you're using Gluon, you can first refer [Saving and Loading Gluon Models](/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html) to hybridize your computation graph and export it as a symbol before running quantization.
 
 In general, the quantization flow includes 4 steps. The user can get the acceptable accuracy from step 1 to 3 with minimum effort. Most of thing in this stage is out-of-box and the data scientists and researchers only need to focus on how to represent data and layers in their model. After a quantized model is generated, you may want to deploy it online and the performance will be the next key point. Thus, step 4, calibration, can improve the performance a lot by reducing lots of runtime calculation.
 
diff --git a/docs/python_docs/python/tutorials/performance/backend/profiler.md b/docs/python_docs/python/tutorials/performance/backend/profiler.md
index 6969517cbd58..f90d5ba9559e 100644
--- a/docs/python_docs/python/tutorials/performance/backend/profiler.md
+++ b/docs/python_docs/python/tutorials/performance/backend/profiler.md
@@ -212,7 +212,7 @@ Let's zoom in to check the time taken by operators
 The above picture visualizes the sequence in which the operators were executed and the time taken by each operator.
 
 ### Profiling Custom Operators
-Should the existing NDArray operators fail to meet all your model's needs, MXNet supports [Custom Operators](https://mxnet.apache.org/versions/master/tutorials/gluon/customop.html) that you can define in Python. In `forward()` and `backward()` of a custom operator, there are two kinds of code: "pure Python" code (NumPy operators included) and "sub-operators" (NDArray operators called within `forward()` and `backward()`). With that said, MXNet can profile the execution time of both kinds without additional setup. Specifically, the MXNet profiler will break a single custom operator call into a pure Python event and several sub-operator events if there are any. Furthermore, all of those events will have a prefix in their names, which is, conveniently, the name of the custom operator you called.
+Should the existing NDArray operators fail to meet all your model's needs, MXNet supports [Custom Operators](/api/python/docs/tutorials/extend/customop.html) that you can define in Python. In `forward()` and `backward()` of a custom operator, there are two kinds of code: "pure Python" code (NumPy operators included) and "sub-operators" (NDArray operators called within `forward()` and `backward()`). With that said, MXNet can profile the execution time of both kinds without additional setup. Specifically, the MXNet profiler will break a single custom operator call into a pure Python event and several sub-operator events if there are any. Furthermore, all of those events will have a prefix in their names, which is, conveniently, the name of the custom operator you called.
 
 Let's try profiling custom operators with the following code example:
 
diff --git a/docs/python_docs/python/tutorials/performance/backend/tensorrt/tensorrt.md b/docs/python_docs/python/tutorials/performance/backend/tensorrt/tensorrt.md
index 8dc19f183729..63dd678f3f5f 100644
--- a/docs/python_docs/python/tutorials/performance/backend/tensorrt/tensorrt.md
+++ b/docs/python_docs/python/tutorials/performance/backend/tensorrt/tensorrt.md
@@ -39,7 +39,7 @@ nvidia-docker run -ti mxnet/tensorrt python
 
 ## Sample Models
 ### Resnet 18
-TensorRT is an inference only library, so for the purposes of this blog post we will be using a pre-trained network, in this case a Resnet 18.  Resnets are a computationally intensive model architecture that are often used as a backbone for various computer vision tasks. Resnets are also commonly used as a reference for benchmarking deep learning library performance.  In this section we'll use a pretrained Resnet 18 from the [Gluon Model Zoo](https://mxnet.apache.org/versions/master/api/python/gluon/model_zoo.html) and compare its inference speed with TensorRT using MXNet with TensorRT integration turned off as a baseline.
+TensorRT is an inference only library, so for the purposes of this blog post we will be using a pre-trained network, in this case a Resnet 18.  Resnets are a computationally intensive model architecture that are often used as a backbone for various computer vision tasks. Resnets are also commonly used as a reference for benchmarking deep learning library performance.  In this section we'll use a pretrained Resnet 18 from the [Gluon Model Zoo](/api/python/docs/api/gluon/model_zoo/index.html) and compare its inference speed with TensorRT using MXNet with TensorRT integration turned off as a baseline.
 
 ## Model Initialization
 ```python
@@ -128,7 +128,7 @@ This means that when an MXNet computation graph is constructed, it will be parse
 
 During this process MXNet will take care of passing along the input to the node and fetching the results.  MXNet will also attempt to remove any duplicated weights (parameters) during the graph initialization to keep memory usage low.  That is, if there are graph weights that are used only in the TensorRT sections of the graph, they will be removed from the MXNet set of parameters, and their memory will be freed.
 
-The examples below shows a Gluon implementation of a Wavenet before and after a TensorRT graph pass. You can see that for this network TensorRT supports a subset of the operators involved. This makes it an interesting example to visualize, as several subgraphs are extracted and replaced with special TensorRT nodes. The Resnet used as an example above would be less interesting to visualization. The entire Resnet graph is supported by TensorRT, and hence the optimized graph would be a single TensorRT node.  If your browser is unable to render svg files you can view the graphs in png format: [unoptimized](_static/tutorials/tensorrt/wavenet_unoptimized.png) and [optimized](_static/tutorials/tensorrt/wavenet_optimized.png).
+The examples below shows a Gluon implementation of a Wavenet before and after a TensorRT graph pass. You can see that for this network TensorRT supports a subset of the operators involved. This makes it an interesting example to visualize, as several subgraphs are extracted and replaced with special TensorRT nodes. The Resnet used as an example above would be less interesting to visualization. The entire Resnet graph is supported by TensorRT, and hence the optimized graph would be a single TensorRT node.  If your browser is unable to render svg files you can view the graphs in png format: [unoptimized](wavenet_unoptimized.svg) and [optimized](wavenet_optimized.svg).
 
 ## Before
 ![before](wavenet_unoptimized.svg)
diff --git a/docs/static_site/src/pages/api/api.html b/docs/static_site/src/pages/api/api.html
index a1f4ae140701..824756898606 100644
--- a/docs/static_site/src/pages/api/api.html
+++ b/docs/static_site/src/pages/api/api.html
@@ -52,7 +52,7 @@
 - title: Julia
   guide_link: /api/julia
   api_link: /api/julia/docs/api
-  tutorial_link: https://github.com/apache/incubator-mxnet/tree/master/julia/examples
+  tutorial_link: https://mxnet.incubator.apache.org/api/julia/docs/api/#tutorials
   description:
   icon: /assets/img/julia_logo.svg
   tag: julia
diff --git a/docs/static_site/src/pages/api/cpp/docs/tutorials/mxnet_cpp_inference_tutorial.md b/docs/static_site/src/pages/api/cpp/docs/tutorials/mxnet_cpp_inference_tutorial.md
index 0d96817560d0..6d9998d7a7a9 100644
--- a/docs/static_site/src/pages/api/cpp/docs/tutorials/mxnet_cpp_inference_tutorial.md
+++ b/docs/static_site/src/pages/api/cpp/docs/tutorials/mxnet_cpp_inference_tutorial.md
@@ -28,23 +28,23 @@ tag: cpp
 
 ## Overview
 MXNet provides various useful tools and interfaces for deploying your model for inference. For example, you can use [MXNet Model Server](https://github.com/awslabs/mxnet-model-server) to start a service and host your trained model easily.
-Besides that, you can also use MXNet's different language APIs to integrate your model with your existing service. We provide [Python]({{'/api/python/docs/api/symbol-related/mxnet.module'|relative_url}}),    [Java]({{'/api/java/docs/api'|relative_url}}), [Scala]({{'/api/scala/docs/api'|relative_url}}), and [C++]({{'/api/cpp/docs/api'|relative_url}}) APIs.
+Besides that, you can also use MXNet's different language APIs to integrate your model with your existing service. We provide [Python](/api/python/docs/api/), [Java](/api/java/docs/api/#package), [Scala](/api/scala/docs/api), and [C++](/api/cpp/docs/api/) APIs.
 We will focus on the MXNet C++ API. We have slightly modified the code in [C++ Inference Example](https://github.com/apache/incubator-mxnet/tree/master/cpp-package/example/inference) for our use case.
 
 ## Prerequisites
 
-To complete this tutorial, you need:
-- Complete the training part of [Gluon end to end tutorial]({{'api/python/docs/tutorials/packages/gluon/image-augmentation.html'|relative_url}})
-- Learn the basics about [MXNet C++ API]({{'/api/cpp'|relative_url}})
+To complete this tutorial, you need to:
+- Complete the training part of [Gluon end to end tutorial](/api/python/docs/tutorials/getting-started/gluon_from_experiment_to_deployment.html)
+- Learn the basics about [MXNet C++ API](/api/cpp)
 
 
 ## Setup the MXNet C++ API
-To use the C++ API in MXNet, you need to build MXNet from source with C++ package. Please follow the [built from source guide]({{'/get_started/ubuntu_setup.html'|relative_url}}), and [C++ Package documentation]({{'/api/cpp'|relative_url}})
+To use the C++ API in MXNet, you need to build MXNet from source with C++ package. Please follow the [built from source guide](/get_started/ubuntu_setup.html), and [C++ Package documentation](/api/cpp)
 The summary of those two documents is that you need to build MXNet from source with `USE_CPP_PACKAGE` flag set to 1. For example: `make -j USE_CPP_PACKAGE=1`.
 
 ## Load the model and run inference
 
-After you complete [the previous tutorial]({{'/api/python/docs/tutorials/packages/gluon/gluon_from_experiment_to_deployment.html'|relative_url}}), you will get the following output files:
+After you complete [the previous tutorial](/api/python/docs/tutorials/getting-started/gluon_from_experiment_to_deployment.html), you will get the following output files:
 1. Model Architecture stored in `flower-recognition-symbol.json`
 2. Model parameter values stored in `flower-recognition-0040.params` (`0040` is for 40 epochs we ran)
 3. Label names stored in `synset.txt`
@@ -280,8 +280,8 @@ Then it will predict your image:
 
 Now you can explore more ways to run inference and deploy your models:
 1. [Java Inference examples](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer)
-2. [Scala Inference examples](/api/scala/docs/tutorials)
-3. [ONNX model inference examples](/api/python/docs/tutorials/deploy/index.html)
+2. [Scala Inference examples](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer)
+3. [ONNX model inference examples](/api/python/docs/tutorials/packages/onnx/inference_on_onnx_model.html)
 4. [MXNet Model Server Examples](https://github.com/awslabs/mxnet-model-server/tree/master/examples)
 
 ## References
diff --git a/docs/static_site/src/pages/api/faq/float16.md b/docs/static_site/src/pages/api/faq/float16.md
index e63bf87ac68f..6ffb04054554 100644
--- a/docs/static_site/src/pages/api/faq/float16.md
+++ b/docs/static_site/src/pages/api/faq/float16.md
@@ -133,7 +133,7 @@ if dtype == 'float16':
 output = mx.sym.SoftmaxOutput(data=net_out, name='softmax')
 ```
 
-If you would like to train ResNet50 model on ImageNet using float16 precision, you can find the full script [here](https://github.com/apache/incubator-mxnet/tree/master/example/image-classificatiIfon/train_imagenet.py)
+If you would like to train ResNet50 model on ImageNet using float16 precision, you can find the full script [here](https://github.com/apache/incubator-mxnet/blob/master/docs/static_site/src/pages/api/faq/float16.md)
 
 If you don't have ImageNet dataset at your disposal, you can still run the script above using synthetic float16 data by providing the following command:
 
diff --git a/docs/static_site/src/pages/api/faq/perf.md b/docs/static_site/src/pages/api/faq/perf.md
index 675304f01241..202a099b324f 100644
--- a/docs/static_site/src/pages/api/faq/perf.md
+++ b/docs/static_site/src/pages/api/faq/perf.md
@@ -64,7 +64,7 @@ Note that _MXNet_ treats all CPUs on a single machine as a single device.
 So whether you specify `cpu(0)` or `cpu()`, _MXNet_ will use all CPU cores on the machine.
 
 ### Scoring results
-The following table shows performance of [MXNet-1.2.0.rc1](https://github.com/apache/incubator-mxnet/releases/download/1.2.0.rc1/apache-mxnet-src-1.2.0.rc1-incubating.tar.gz),
+The following table shows performance of MXNet-1.2.0.rc1,
 namely number of images that can be predicted per second.
 We used [example/image-classification/benchmark_score.py](https://github.com/dmlc/mxnet/blob/master/example/image-classification/benchmark_score.py)
 to measure the performance on different AWS EC2 machines.
@@ -151,7 +151,7 @@ and V100 (EC2 p3.2xlarge).
 
 Based on
 [example/image-classification/benchmark_score.py](https://github.com/dmlc/mxnet/blob/master/example/image-classification/benchmark_score.py)
-and  [MXNet-1.2.0.rc1](https://github.com/apache/incubator-mxnet/releases/download/1.2.0.rc1/apache-mxnet-src-1.2.0.rc1-incubating.tar.gz), with cuDNN 7.0.5
+and  MXNet-1.2.0.rc1, with cuDNN 7.0.5
 
 - K80 (single GPU)
 
@@ -214,7 +214,7 @@ Below is the performance result on V100 using float 16.
 
 Based on
 [example/image-classification/train_imagenet.py](https://github.com/dmlc/mxnet/blob/master/example/image-classification/train_imagenet.py)
-and  [MXNet-1.2.0.rc1](https://github.com/apache/incubator-mxnet/releases/download/1.2.0.rc1/apache-mxnet-src-1.2.0.rc1-incubating.tar.gz), with CUDNN 7.0.5. The benchmark script is available at
+and  MXNet-1.2.0.rc1, with CUDNN 7.0.5. The benchmark script is available at
 [here](https://github.com/mli/mxnet-benchmark/blob/master/run_vary_batch.sh),
 where the batch size for Alexnet is increased by 16x.
 
diff --git a/docs/static_site/src/pages/get_started/build_from_source.md b/docs/static_site/src/pages/get_started/build_from_source.md
index 20a4542461c4..1dfa95a82ade 100644
--- a/docs/static_site/src/pages/get_started/build_from_source.md
+++ b/docs/static_site/src/pages/get_started/build_from_source.md
@@ -50,7 +50,7 @@ Building from source follows this general two-step flow of building the shared l
             * [non-Intel CPUs](#recommended-for-Systems-with-non-Intel-CPUs)
 2. [Install the language API binding(s)](#installing-mxnet-language-bindings) you would like to use for MXNet.
 MXNet's newest and most popular API is Gluon. Gluon is built into the Python binding. If Python isn't your preference, you still have more options. MXNet supports several other language APIs:
-    - [Python (includes Gluon)]({{'/api/python/index'|relative_url}})
+    - [Python (includes Gluon)]({{'/api/python/docs/api/index.html'|relative_url}})
     - [C++]({{'/api/cpp'|relative_url}})
     - [Clojure]({{'/api/clojure'|relative_url}})
     - [Java]({{'/api/java'|relative_url}})
diff --git a/julia/docs/src/api/io.md b/julia/docs/src/api/io.md
index 34ad3c42bce7..52d172010af4 100644
--- a/julia/docs/src/api/io.md
+++ b/julia/docs/src/api/io.md
@@ -54,7 +54,7 @@ end
 By default, `eachbatch` simply returns the provider itself, so the iterator interface
 is implemented on the provider type itself. But the extra layer of abstraction allows us to
 implement a data provider easily via a Julia `Task` coroutine. See the
-data provider defined in [the char-lstm example](tutorial/char-lstm) for an example of using coroutine to define data
+data provider defined in [the char-lstm example](/api/julia/docs/api/tutorial/char-lstm/) for an example of using coroutine to define data
 providers.
 
 The detailed interface functions for the iterator API is listed below:
diff --git a/julia/docs/src/tutorial/char-lstm.md b/julia/docs/src/tutorial/char-lstm.md
index ab7e9352b5ab..1109f3554c17 100644
--- a/julia/docs/src/tutorial/char-lstm.md
+++ b/julia/docs/src/tutorial/char-lstm.md
@@ -38,7 +38,7 @@ network models directly.
 
 The most important code snippets of this example is shown and explained
 here. To see and run the complete code, please refer to the
-[examples/char-lstm](https://github.com/dmlc/MXNet.jl/tree/master/examples/char-lstm)
+[examples/char-lstm](https://github.com/apache/incubator-mxnet/blob/master/julia/docs/src/tutorial/char-lstm.md)
 directory. You will need to install
 [Iterators.jl](https://github.com/JuliaLang/Iterators.jl) and
 [StatsBase.jl](https://github.com/JuliaStats/StatsBase.jl) to run this
diff --git a/julia/docs/src/tutorial/mnist.md b/julia/docs/src/tutorial/mnist.md
index a404f75efe12..942752364526 100644
--- a/julia/docs/src/tutorial/mnist.md
+++ b/julia/docs/src/tutorial/mnist.md
@@ -23,7 +23,7 @@ multi-layer perceptron and then a convolutional neural network (the
 LeNet architecture) on the [MNIST handwritten digit
 dataset](http://yann.lecun.com/exdb/mnist/). The code for this tutorial
 could be found in
-[examples/mnist](/api/julia/docs/api/tutorial/mnist/).  There are also two Jupyter notebooks that expand a little more on the [MLP](https://github.com/ultradian/julia_notebooks/blob/master/mxnet/mnistMLP.ipynb) and the [LeNet](https://github.com/ultradian/julia_notebooks/blob/master/mxnet/mnistLenet.ipynb), using the more general `ArrayDataProvider`. 
+[examples/mnist](https://github.com/apache/incubator-mxnet/tree/master/julia/examples/mnist).  There are also two Jupyter notebooks that expand a little more on the [MLP](https://github.com/ultradian/julia_notebooks/blob/master/mxnet/mnistMLP.ipynb) and the [LeNet](https://github.com/ultradian/julia_notebooks/blob/master/mxnet/mnistLenet.ipynb), using the more general `ArrayDataProvider`. 
 
 Simple 3-layer MLP
 ------------------
@@ -36,7 +36,7 @@ using MXNet
 ```
 
 to load the `MXNet` module. Then we are ready to define the network
-architecture via the [symbolic API](../user-guide/overview.md). We start
+architecture via the [symbolic API](/api/julia/docs/api/user-guide/overview/). We start
 with a placeholder `data` symbol,
 
 ```julia

From 22e5ae39d0be39b9f280e89baeaf002c3572bd83 Mon Sep 17 00:00:00 2001
From: Xi Wang <xidulu@gmail.com>
Date: Mon, 28 Oct 2019 03:37:55 +0800
Subject: [PATCH 529/813] add type switch to weight tensor (#16543)

---
 src/operator/numpy/random/np_choice_op.h | 20 +++++++++++++-------
 tests/python/unittest/test_numpy_op.py   | 21 +++++++++++----------
 2 files changed, 24 insertions(+), 17 deletions(-)

diff --git a/src/operator/numpy/random/np_choice_op.h b/src/operator/numpy/random/np_choice_op.h
index 335cc2741759..a6a7cecfefd5 100644
--- a/src/operator/numpy/random/np_choice_op.h
+++ b/src/operator/numpy/random/np_choice_op.h
@@ -118,15 +118,17 @@ struct random_indices {
 
 // Weighted sample without replacement.
 // Use perturbed Gumbel variates as keys.
+template <typename IType>
 struct generate_keys {
-  MSHADOW_XINLINE static void Map(index_t i, float *uniforms, float *weights) {
+  MSHADOW_XINLINE static void Map(index_t i, float *uniforms, IType *weights) {
     uniforms[i] = -logf(-logf(uniforms[i])) + logf(weights[i]);
   }
 };
 
 // Weighted sample with replacement.
+template <typename IType>
 struct categorical_sampling {
-  MSHADOW_XINLINE static void Map(index_t i, float *weights, size_t length,
+  MSHADOW_XINLINE static void Map(index_t i, IType *weights, size_t length,
                                   float *uniforms, int64_t *outs) {
     outs[i] = 0;
     float acc = 0.0;
@@ -179,15 +181,19 @@ void NumpyChoiceForward(const nnvm::NodeAttrs &attrs, const OpContext &ctx,
     prnd->SampleUniform(&random_numbers, 0, 1);
     workspace_ptr += ((random_tensor_size * sizeof(float) / 7 + 1) * 8);
     if (replace) {
-      Kernel<categorical_sampling, xpu>::Launch(
-          s, output_size, inputs[weight_index].dptr<float>(), input_size,
-          random_numbers.dptr_, outputs[0].dptr<int64_t>());
+      MSHADOW_REAL_TYPE_SWITCH(inputs[weight_index].type_flag_, IType, {
+        Kernel<categorical_sampling<IType>, xpu>::Launch(
+            s, output_size, inputs[weight_index].dptr<IType>(), input_size,
+            random_numbers.dptr_, outputs[0].dptr<int64_t>());
+      });
     } else {
       Tensor<xpu, 1, int64_t> indices = Tensor<xpu, 1, int64_t>(
           reinterpret_cast<int64_t *>(workspace_ptr), Shape1(indices_size), s);
       indices = expr::range((int64_t)0, input_size);
-      Kernel<generate_keys, xpu>::Launch(s, input_size, random_numbers.dptr_,
-                                         inputs[weight_index].dptr<float>());
+      MSHADOW_REAL_TYPE_SWITCH(inputs[weight_index].type_flag_, IType, {
+        Kernel<generate_keys<IType>, xpu>::Launch(s, input_size, random_numbers.dptr_,
+                                           inputs[weight_index].dptr<IType>());
+      });
       _sort<xpu>(random_numbers.dptr_, indices.dptr_, input_size);
       Copy(outputs[0].FlatTo1D<xpu, int64_t>(s), indices.Slice(0, output_size), s);
     }
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 98a7b05dca9f..01778099412a 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -2490,16 +2490,17 @@ def test_indexing_mode(sampler, set_size, samples_size, replace, weight=None):
     #     test_sample_without_replacement(np.random.choice, num_classes, shape, 10 ** 5, weight)
 
     # Test hypridize mode:
-    for hybridize in [True, False]:
-        for replace in [True, False]:
-            test_choice = TestUniformChoice(num_classes // 2, replace)
-            test_choice_weighted = TestWeightedChoice(num_classes // 2, replace)
-            if hybridize:
-                test_choice.hybridize()
-                test_choice_weighted.hybridize()
-            weight = np.array(_np.random.dirichlet([1.0] * num_classes))
-            test_indexing_mode(test_choice, num_classes, num_classes // 2, replace, None)
-            test_indexing_mode(test_choice_weighted, num_classes, num_classes // 2, replace, weight)
+    for wtype in ['float16', 'float32', 'float64']:
+        for hybridize in [True, False]:
+            for replace in [True, False]:
+                test_choice = TestUniformChoice(num_classes // 2, replace)
+                test_choice_weighted = TestWeightedChoice(num_classes // 2, replace)
+                if hybridize:
+                    test_choice.hybridize()
+                    test_choice_weighted.hybridize()
+                weight = np.array(_np.random.dirichlet([1.0] * num_classes)).astype(wtype)
+                test_indexing_mode(test_choice, num_classes, num_classes // 2, replace, None)
+                test_indexing_mode(test_choice_weighted, num_classes, num_classes // 2, replace, weight)
 
 
 @with_seed()

From 6ab4220e8d50fe3874c5f5280b40b7de1ca444ae Mon Sep 17 00:00:00 2001
From: reminisce <wujun.nju@gmail.com>
Date: Sun, 27 Oct 2019 16:31:39 -0700
Subject: [PATCH 530/813] numpy doc enhancement (#16637)

* Change NDArray to ndarray for npx ops

Add nonzero

boolean mask supports boolean ndarray

Add argmin op and interoperability test for nonzero

Fix vdot, inner, outter docs

Add nonzero to mx.nd.np

Add docs

Fix

* Fix lint

* Fix

* Fix

* Fix get_constant
---
 python/mxnet/_numpy_op_doc.py                 |  60 ++
 python/mxnet/base.py                          |   3 +
 python/mxnet/gluon/parameter.py               |   3 +-
 python/mxnet/ndarray/numpy/_op.py             | 156 ++++-
 python/mxnet/ndarray/numpy/random.py          |  12 +-
 python/mxnet/numpy/linalg.py                  |  25 +
 python/mxnet/numpy/multiarray.py              | 545 +++++++++++++++++-
 python/mxnet/numpy/random.py                  |  80 ++-
 python/mxnet/numpy/stride_tricks.py           |   9 +
 python/mxnet/numpy/utils.py                   |   4 +-
 python/mxnet/numpy_dispatch_protocol.py       |   2 +
 python/mxnet/numpy_extension/random.py        |   2 +-
 python/mxnet/symbol/numpy/_symbol.py          |  55 +-
 python/mxnet/symbol/numpy/random.py           |   8 +-
 python/mxnet/util.py                          |  63 +-
 src/operator/contrib/boolean_mask.cc          |   2 +-
 src/operator/contrib/boolean_mask.cu          |   2 +-
 .../numpy/np_broadcast_reduce_op_index.cc     |  11 +
 .../numpy/np_broadcast_reduce_op_index.cu     |   3 +
 src/operator/numpy/np_nonzero_op.cc           |   3 +-
 src/operator/numpy/np_nonzero_op.cu           |   2 +-
 tests/python/unittest/test_numpy_gluon.py     |  23 +
 .../unittest/test_numpy_interoperability.py   |  28 +
 tests/python/unittest/test_numpy_op.py        |  63 +-
 24 files changed, 1062 insertions(+), 102 deletions(-)

diff --git a/python/mxnet/_numpy_op_doc.py b/python/mxnet/_numpy_op_doc.py
index bcbef9d047d1..33158baf10a5 100644
--- a/python/mxnet/_numpy_op_doc.py
+++ b/python/mxnet/_numpy_op_doc.py
@@ -34,6 +34,24 @@ def _np_ones_like(a):
     -------
     out : ndarray
         Array of ones with the same shape and type as `a`.
+
+    Examples
+    --------
+    >>> x = np.arange(6)
+    >>> x = x.reshape((2, 3))
+    >>> x
+    array([[0., 1., 2.],
+           [3., 4., 5.]])
+    >>> np.ones_like(x)
+    array([[1., 1., 1.],
+           [1., 1., 1.]])
+
+    >>> y = np.arange(3, dtype=float)
+    >>> y
+    array([0., 1., 2.], dtype=float64)
+    >>>
+    >>> np.ones_like(y)
+    array([1., 1., 1.], dtype=float64)
     """
     pass
 
@@ -52,6 +70,23 @@ def _np_zeros_like(a):
     -------
     out : ndarray
         Array of zeros with the same shape and type as `a`.
+
+    Examples
+    --------
+    >>> x = np.arange(6)
+    >>> x = x.reshape((2, 3))
+    >>> x
+    array([[0., 1., 2.],
+           [3., 4., 5.]])
+    >>> np.zeros_like(x)
+    array([[0., 0., 0.],
+           [0., 0., 0.]])
+    >>> y = np.arange(3, dtype=float)
+    >>> y
+    array([0., 1., 2.], dtype=float64)
+    >>>
+    >>> np.zeros_like(y)
+    array([0., 0., 0.], dtype=float64)
     """
     pass
 
@@ -477,6 +512,31 @@ def _np_reshape(a, newshape, order='C', out=None):
     See Also
     --------
     ndarray.reshape : Equivalent method.
+
+    Examples
+    --------
+    >>> a = np.arange(6).reshape((3, 2))
+    >>> a
+    array([[0., 1.],
+           [2., 3.],
+           [4., 5.]])
+
+    >>> np.reshape(a, (2, 3)) # C-like index ordering
+    array([[0., 1., 2.],
+           [3., 4., 5.]])
+
+    >>> np.reshape(np.ravel(a), (2, 3)) # equivalent to C ravel then C reshape
+    array([[0., 1., 2.],
+           [3., 4., 5.]])
+
+    >>> a = np.array([[1,2,3], [4,5,6]])
+    >>> np.reshape(a, 6)
+    array([1., 2., 3., 4., 5., 6.])
+
+    >>> np.reshape(a, (3,-1))       # the unspecified value is inferred to be 2
+    array([[1., 2.],
+           [3., 4.],
+           [5., 6.]])
     """
 
 
diff --git a/python/mxnet/base.py b/python/mxnet/base.py
index cbd9abe9d754..db1fa29ab9b4 100644
--- a/python/mxnet/base.py
+++ b/python/mxnet/base.py
@@ -20,6 +20,7 @@
 """ctypes library of mxnet and helper functions."""
 from __future__ import absolute_import
 
+import re
 import atexit
 import ctypes
 import os
@@ -853,3 +854,5 @@ def _init_np_op_module(root_module_name, np_module_name, mx_module_name, make_op
 
         if hasattr(_np_op_doc, name):
             function.__doc__ = getattr(_np_op_doc, name).__doc__
+        else:
+            function.__doc__ = re.sub('NDArray', 'ndarray', function.__doc__)
diff --git a/python/mxnet/gluon/parameter.py b/python/mxnet/gluon/parameter.py
index 8800684ad0b4..957dc2cd69b7 100644
--- a/python/mxnet/gluon/parameter.py
+++ b/python/mxnet/gluon/parameter.py
@@ -674,7 +674,8 @@ def __init__(self, **kwargs):
     """
     def __init__(self, name, value):
         if not isinstance(value, ndarray.NDArray):
-            value = ndarray.array(value)
+            array_fn = _mx_np.array if is_np_array() else ndarray.array
+            value = array_fn(value)
         self.value = value
 
         class Init(initializer.Initializer):
diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index cf66e29d6205..fdb9694146b5 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -34,11 +34,11 @@
            'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot', 'histogram', 'eye',
            'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate',
-           'stack', 'vstack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax',
+           'stack', 'vstack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'argmin',
            'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
            'around', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
            'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal',
-           'hsplit', 'rot90', 'einsum', 'true_divide']
+           'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -3165,8 +3165,6 @@ def clip(a, a_min, a_max, out=None):
 @set_module('mxnet.ndarray.numpy')
 def argmax(a, axis=None, out=None):
     r"""
-    argmax(a, axis=None, out=None)
-
     Returns the indices of the maximum values along an axis.
 
     Parameters
@@ -3234,6 +3232,75 @@ def argmax(a, axis=None, out=None):
     return _npi.argmax(a, axis=axis, keepdims=False, out=out)
 
 
+@set_module('mxnet.ndarray.numpy')
+def argmin(a, axis=None, out=None):
+    r"""
+    Returns the indices of the maximum values along an axis.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input array. Only support ndarrays of dtype `float16`, `float32`, and `float64`.
+    axis : int, optional
+        By default, the index is into the flattened array, otherwise
+        along the specified axis.
+    out : ndarray or None, optional
+        If provided, the result will be inserted into this array. It should
+        be of the appropriate shape and dtype.
+
+    Returns
+    -------
+    index_array : ndarray of indices whose dtype is same as the input ndarray.
+        Array of indices into the array. It has the same shape as `a.shape`
+        with the dimension along `axis` removed.
+
+    Notes
+    -----
+    In case of multiple occurrences of the maximum values, the indices
+    corresponding to the first occurrence are returned.
+
+    This function differs from the original `numpy.argmax
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmax.html>`_ in
+    the following aspects:
+
+    - Input type does not support Python native iterables(list, tuple, ...).
+    - Output has dtype that is same as the input ndarray.
+    - ``out`` param: cannot perform auto broadcasting. ``out`` ndarray's shape must be the same as the expected output.
+    - ``out`` param: cannot perform auto type cast. ``out`` ndarray's dtype must be the same as the expected output.
+    - ``out`` param does not support scalar input case.
+
+    Examples
+    --------
+    >>> a = np.arange(6).reshape(2,3) + 10
+    >>> a
+    array([[10., 11., 12.],
+           [13., 14., 15.]])
+    >>> np.argmin(a)
+    array(0.)
+    >>> np.argmin(a, axis=0)
+    array([0., 0., 0.])
+    >>> np.argmin(a, axis=1)
+    array([0., 0.])
+
+    >>> b = np.arange(6)
+    >>> b[2] = 0
+    >>> b
+    array([0., 1., 0., 3., 4., 5.])
+    >>> np.argmax(b)  # Only the first occurrence is returned.
+    array(0.)
+
+    Specify ``out`` ndarray:
+
+    >>> a = np.arange(6).reshape(2,3) + 10
+    >>> b = np.zeros((2,))
+    >>> np.argmin(a, axis=1, out=b)
+    array([0., 0.])
+    >>> b
+    array([0., 0.])
+    """
+    return _npi.argmin(a, axis=axis, keepdims=False, out=out)
+
+
 @set_module('mxnet.ndarray.numpy')
 def mean(a, axis=None, dtype=None, out=None, keepdims=False):  # pylint: disable=arguments-differ
     """
@@ -4761,3 +4828,84 @@ def einsum(*operands, **kwargs):
     subscripts = operands[0]
     operands = operands[1:]
     return _npi.einsum(*operands, subscripts=subscripts, out=out, optimize=int(optimize_arg))
+
+
+@set_module('mxnet.ndarray.numpy')
+def nonzero(a):
+    """
+    Return the indices of the elements that are non-zero.
+
+    Returns a tuple of arrays, one for each dimension of `a`,
+    containing the indices of the non-zero elements in that
+    dimension. The values in `a` are always returned in
+    row-major, C-style order.
+
+    To group the indices by element, rather than dimension, use `argwhere`,
+    which returns a row for each non-zero element.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input array.
+
+    Returns
+    -------
+    tuple_of_arrays : tuple
+        Indices of elements that are non-zero.
+
+    See Also
+    --------
+    ndarray.nonzero :
+        Equivalent ndarray method.
+
+    Notes
+    -----
+    While the nonzero values can be obtained with ``a[nonzero(a)]``, it is
+    recommended to use ``x[x.astype(bool)]`` or ``x[x != 0]`` instead, which
+    will correctly handle 0-d arrays.
+
+    Examples
+    --------
+    >>> x = np.array([[3, 0, 0], [0, 4, 0], [5, 6, 0]])
+    >>> x
+    array([[3, 0, 0],
+           [0, 4, 0],
+           [5, 6, 0]], dtype=int32)
+    >>> np.nonzero(x)
+    (array([0, 1, 2, 2], dtype=int64), array([0, 1, 0, 1], dtype=int64))
+
+    >>> x[np.nonzero(x)]
+    array([3, 4, 5, 6])
+    >>> np.transpose(np.stack(np.nonzero(x)))
+    array([[0, 0],
+           [1, 1],
+           [2, 0],
+           [2, 1]], dtype=int64)
+
+    A common use for ``nonzero`` is to find the indices of an array, where
+    a condition is True.  Given an array `a`, the condition `a` > 3 is a
+    boolean array and since False is interpreted as 0, np.nonzero(a > 3)
+    yields the indices of the `a` where the condition is true.
+
+    >>> a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.int32)
+    >>> a > 3
+    array([[False, False, False],
+           [ True,  True,  True],
+           [ True,  True,  True]])
+    >>> np.nonzero(a > 3)
+    (array([1, 1, 1, 2, 2, 2], dtype=int64), array([0, 1, 2, 0, 1, 2], dtype=int64))
+
+    Using this result to index `a` is equivalent to using the mask directly:
+
+    >>> a[np.nonzero(a > 3)]
+    array([4, 5, 6, 7, 8, 9], dtype=int32)
+    >>> a[a > 3]
+    array([4, 5, 6, 7, 8, 9], dtype=int32)
+
+    ``nonzero`` can also be called as a method of the array.
+
+    >>> (a > 3).nonzero()
+    (array([1, 1, 1, 2, 2, 2], dtype=int64), array([0, 1, 2, 0, 1, 2], dtype=int64))
+    """
+    out = _npi.nonzero(a).transpose()
+    return tuple([out[i] for i in range(len(out))])
diff --git a/python/mxnet/ndarray/numpy/random.py b/python/mxnet/ndarray/numpy/random.py
index 583f56e046f3..9d1a6f9119ee 100644
--- a/python/mxnet/ndarray/numpy/random.py
+++ b/python/mxnet/ndarray/numpy/random.py
@@ -23,11 +23,11 @@
 from ..ndarray import NDArray
 
 
-__all__ = ['randint', 'uniform', 'normal', "choice", "rand"]
+__all__ = ['randint', 'uniform', 'normal', "choice", "rand", "multinomial"]
 
 
 def randint(low, high=None, size=None, dtype=None, ctx=None, out=None):
-    """Return random integers from `low` (inclusive) to `high` (exclusive).
+    r"""Return random integers from `low` (inclusive) to `high` (exclusive).
 
     Return random integers from the "discrete uniform" distribution of
     the specified dtype in the "half-open" interval [`low`, `high`). If
@@ -88,7 +88,7 @@ def randint(low, high=None, size=None, dtype=None, ctx=None, out=None):
 
 
 def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
-    """Draw samples from a uniform distribution.
+    r"""Draw samples from a uniform distribution.
 
     Samples are uniformly distributed over the half-open interval
     ``[low, high)`` (includes low, but excludes high).  In other words,
@@ -143,7 +143,7 @@ def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
 
 
 def normal(loc=0.0, scale=1.0, size=None, dtype=None, ctx=None, out=None):
-    """Draw random samples from a normal (Gaussian) distribution.
+    r"""Draw random samples from a normal (Gaussian) distribution.
 
     Samples are distributed according to a normal distribution parametrized
     by *loc* (mean) and *scale* (standard deviation).
@@ -194,7 +194,7 @@ def normal(loc=0.0, scale=1.0, size=None, dtype=None, ctx=None, out=None):
 
 
 def multinomial(n, pvals, size=None):
-    """multinomial(n, pvals, size=None)
+    r"""multinomial(n, pvals, size=None)
 
     Draw samples from a multinomial distribution.
 
@@ -246,7 +246,7 @@ def multinomial(n, pvals, size=None):
 
 
 def choice(a, size=None, replace=True, p=None, ctx=None, out=None):
-    """Generates a random sample from a given 1-D array
+    r"""Generates a random sample from a given 1-D array
 
     Parameters
     -----------
diff --git a/python/mxnet/numpy/linalg.py b/python/mxnet/numpy/linalg.py
index 1ca34716d7d4..9ee5156c3bb1 100644
--- a/python/mxnet/numpy/linalg.py
+++ b/python/mxnet/numpy/linalg.py
@@ -54,10 +54,35 @@ def norm(x, ord=None, axis=None, keepdims=False):
     n : float or ndarray
         Norm of the matrix or vector(s).
 
+    Notes
+    -----
+    This operator differs from NumPy in the aspect that it always returns a
+    zero-dim tensor for the cases where Python float values are expected
+    in NumPy.
+
     References
     ----------
     .. [1] G. H. Golub and C. F. Van Loan, *Matrix Computations*,
            Baltimore, MD, Johns Hopkins University Press, 1985, pg. 15
+
+    Examples
+    --------
+    >>> from numpy import linalg as LA
+    >>> a = np.arange(9) - 4
+    >>> a
+    array([-4., -3., -2., -1.,  0.,  1.,  2.,  3.,  4.])
+    >>> b = a.reshape((3, 3))
+    >>> b
+    array([[-4., -3., -2.],
+           [-1.,  0.,  1.],
+           [ 2.,  3.,  4.]])
+    >>> LA.norm(a)
+    array(7.745967)
+    >>>
+    >>> LA.norm(b)
+    array(7.745967)
+    >>> LA.norm(b, 'fro')
+    array(7.745967)
     """
     return _mx_nd_np.linalg.norm(x, ord, axis, keepdims)
 
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 623b5fc482d7..5c9de8194a74 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -52,10 +52,10 @@
            'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh',
            'tensordot', 'histogram', 'eye', 'linspace', 'logspace', 'expand_dims', 'tile', 'arange',
            'split', 'vsplit', 'concatenate', 'stack', 'vstack', 'dstack', 'mean', 'maximum', 'minimum',
-           'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming',
+           'swapaxes', 'clip', 'argmax', 'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming',
            'blackman', 'flip', 'around', 'arctan2', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril',
            'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less',
-           'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide']
+           'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -478,7 +478,7 @@ def __getitem__(self, key):
             for i in range(key_ndim):
                 if key_shape[i] != shape[i]:
                     raise IndexError('boolean index did not match indexed array along dimension {};'
-                                     'dimension is {} but corresponding boolean dimension is {}'
+                                     ' dimension is {} but corresponding boolean dimension is {}'
                                      .format(i, shape[i], key_shape[i]))
             remaining_dims = shape[key_ndim:]
             data = _reshape_view(self, -1, *remaining_dims)
@@ -831,6 +831,17 @@ def item(self, *args):
         # TODO(junwu): no need to call asnumpy() on the whole array.
         return self.asnumpy().item(*args)
 
+    def nonzero(self):
+        """Return the indices of the elements that are non-zero.
+
+        Refer to `numpy.nonzero` for full documentation.
+
+        See Also
+        --------
+        numpy.nonzero : equivalent function
+        """
+        return nonzero(self)
+
     @property
     # pylint: disable= invalid-name, undefined-variable
     def T(self):
@@ -1369,13 +1380,10 @@ def argmax_channel(self, *args, **kwargs):
         """
         raise AttributeError('mxnet.numpy.ndarray object has no attribute argmax_channel')
 
-    def argmin(self, *args, **kwargs):
-        """Convenience fluent method for :py:func:`argmin`.
-
-        The arguments are the same as for :py:func:`argmin`, with
-        this array as data.
-        """
-        raise NotImplementedError
+    def argmin(self, axis=None, out=None):  # pylint: disable=arguments-differ
+        """Return indices of the minium values along the given axis.
+        Refer to `mxnet.numpy.argmin` for full documentation."""
+        return argmin(self, axis, out)
 
     def clip(self, min=None, max=None, out=None):  # pylint: disable=arguments-differ
         """Return an array whose values are limited to [min, max].
@@ -1925,6 +1933,16 @@ def empty(shape, dtype=_np.float32, order='C', ctx=None):
     -------
     out : ndarray
         Array of uninitialized (arbitrary) data of the given shape, dtype, and order.
+
+    Examples
+    --------
+    >>> np.empty([2, 2])
+    array([[ 0.000000e+00, -2.524355e-29],
+           [          nan, -8.592023e+09]])  # uninitialized
+
+    >>> np.empty([2, 2], dtype=int)
+    array([[8751743591039004782, 3196766424264760104],
+           [7583328881310196768,     562950123910254]], dtype=int64)  # uninitialized
     """
     if order != 'C':
         raise NotImplementedError('`empty` only supports order equal to `C`, while received {}'
@@ -1958,6 +1976,19 @@ def array(object, dtype=None, ctx=None):
     -------
     out : ndarray
         An array object satisfying the specified requirements.
+
+    Examples
+    --------
+    >>> np.array([1, 2, 3])
+    array([1., 2., 3.])
+
+    >>> np.array([[1, 2], [3, 4]])
+    array([[1., 2.],
+           [3., 4.]])
+
+    >>> np.array([[1, 0], [0, 1]], dtype=bool)
+    array([[ True, False],
+           [False,  True]])
     """
     if ctx is None:
         ctx = current_context()
@@ -2003,6 +2034,18 @@ def zeros(shape, dtype=_np.float32, order='C', ctx=None):
     -------
     out : ndarray
         Array of zeros with the given shape, dtype, and ctx.
+
+    Examples
+    --------
+    >>> np.zeros(5)
+    array([0., 0., 0., 0., 0.])
+
+    >>> np.zeros((5,), dtype=int)
+    array([0, 0, 0, 0, 0], dtype=int64)
+
+    >>> np.zeros((2, 1))
+    array([[0.],
+           [0.]])
     """
     return _mx_nd_np.zeros(shape, dtype, order, ctx)
 
@@ -2032,6 +2075,23 @@ def ones(shape, dtype=_np.float32, order='C', ctx=None):
     -------
     out : ndarray
         Array of ones with the given shape, dtype, and ctx.
+
+    Examples
+    --------
+    >>> np.ones(5)
+    array([1., 1., 1., 1., 1.])
+
+    >>> np.ones((5,), dtype=int)
+    array([1, 1, 1, 1, 1], dtype=int64)
+
+    >>> np.ones((2, 1))
+    array([[1.],
+           [1.]])
+
+    >>> s = (2,2)
+    >>> np.ones(s)
+    array([[1., 1.],
+           [1., 1.]])
     """
     return _mx_nd_np.ones(shape, dtype, order, ctx)
 
@@ -2332,6 +2392,18 @@ def add(x1, x2, out=None, **kwargs):
     -------
     add : ndarray or scalar
         The sum of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars.
+
+    Examples
+    --------
+    >>> np.add(1.0, 4.0)
+    5.0
+    >>>
+    >>> x1 = np.arange(9.0).reshape((3, 3))
+    >>> x2 = np.arange(3.0)
+    >>> np.add(x1, x2)
+    array([[ 0.,  2.,  4.],
+           [ 3.,  5.,  7.],
+           [ 6.,  8., 10.]])
     """
     return _mx_nd_np.add(x1, x2, out)
 
@@ -2358,6 +2430,17 @@ def subtract(x1, x2, out=None, **kwargs):
     -------
     subtract : ndarray or scalar
         The difference of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars.
+
+    Examples
+    --------
+    >>> np.subtract(1.0, 4.0)
+    -3.0
+    >>> x1 = np.arange(9.0).reshape((3, 3))
+    >>> x2 = np.arange(3.0)
+    >>> np.subtract(x1, x2)
+    array([[0., 0., 0.],
+           [3., 3., 3.],
+           [6., 6., 6.]])
     """
     return _mx_nd_np.subtract(x1, x2, out)
 
@@ -2383,6 +2466,17 @@ def multiply(x1, x2, out=None, **kwargs):
     -------
     out : ndarray or scalar
         The difference of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars.
+
+    Examples
+    --------
+    >>> np.multiply(2.0, 4.0)
+    8.0
+    >>> x1 = np.arange(9.0).reshape((3, 3))
+    >>> x2 = np.arange(3.0)
+    >>> np.multiply(x1, x2)
+    array([[ 0.,  1.,  4.],
+           [ 0.,  4., 10.],
+           [ 0.,  7., 16.]])
     """
     return _mx_nd_np.multiply(x1, x2, out)
 
@@ -2410,6 +2504,11 @@ def divide(x1, x2, out=None, **kwargs):
     -------
     out : ndarray or scalar
         This is a scalar if both x1 and x2 are scalars.
+
+    Examples
+    --------
+    >>> np.true_divide(x, 4)
+    array([0.  , 0.25, 0.5 , 0.75, 1.  ])
     """
     return _mx_nd_np.divide(x1, x2, out=out)
 
@@ -2439,6 +2538,12 @@ def true_divide(x1, x2, out=None):
     -------
     out : ndarray or scalar
         This is a scalar if both x1 and x2 are scalars.
+
+    Examples
+    --------
+    >>> x = np.arange(5)
+    >>> np.true_divide(x, 4)
+    array([0.  , 0.25, 0.5 , 0.75, 1.  ])
     """
     return _mx_nd_np.true_divide(x1, x2, out=out)
 
@@ -2466,6 +2571,11 @@ def mod(x1, x2, out=None, **kwargs):
     -------
     out : ndarray or scalar
         This is a scalar if both x1 and x2 are scalars.
+
+    Examples
+    --------
+    >>> np.mod(np.arange(7), 5)
+    array([0., 1., 2., 3., 4., 0., 1.])
     """
     return _mx_nd_np.mod(x1, x2, out=out)
 
@@ -2493,6 +2603,11 @@ def remainder(x1, x2, out=None, **kwargs):
     -------
     out : ndarray or scalar
         This is a scalar if both x1 and x2 are scalars.
+
+    Examples
+    --------
+    >>> np.remainder(np.arange(7), 5)
+    array([0., 1., 2., 3., 4., 0., 1.])
     """
     return _mx_nd_np.remainder(x1, x2, out=out)
 
@@ -2521,6 +2636,29 @@ def power(x1, x2, out=None, **kwargs):
     out : ndarray or scalar
         The bases in x1 raised to the exponents in x2.
         This is a scalar if both x1 and x2 are scalars.
+
+    Examples
+    --------
+    >>> x1 = np.arange(6)
+    >>> np.power(x1, 3)
+    array([  0.,   1.,   8.,  27.,  64., 125.])
+
+    Raise the bases to different exponents.
+
+    >>> x2 = np.array([1.0, 2.0, 3.0, 3.0, 2.0, 1.0])
+    >>> np.power(x1, x2)
+    array([ 0.,  1.,  8., 27., 16.,  5.])
+
+    The effect of broadcasting.
+
+    >>> x2 = np.array([[1, 2, 3, 3, 2, 1], [1, 2, 3, 3, 2, 1]])
+    >>> x2
+    array([[1., 2., 3., 3., 2., 1.],
+           [1., 2., 3., 3., 2., 1.]])
+
+    >>> np.power(x1, x2)
+    array([[ 0.,  1.,  8., 27., 16.,  5.],
+           [ 0.,  1.,  8., 27., 16.,  5.]])
     """
     return _mx_nd_np.power(x1, x2, out=out)
 
@@ -3610,7 +3748,7 @@ def negative(x, out=None, **kwargs):
     y : ndarray or scalar
         Returned array or scalar: y = -x. This is a scalar if x is a scalar.
 
-    Examples:
+    Examples
     --------
     >>> np.negative(1)
     -1
@@ -3637,7 +3775,7 @@ def fix(x, out=None, **kwargs):
     y : ndarray or scalar
     Returned array or scalar: y = -x. This is a scalar if x is a scalar.ndarray of floats
 
-    Examples:
+    Examples
     ---------
     >>> np.fix(3.14)
     3
@@ -3667,10 +3805,10 @@ def tan(x, out=None, **kwargs):
     y : ndarray
     The corresponding tangent values. This is a scalar if x is a scalar.
 
-    Examples:
+    Examples
     ---------
-    >>> np.tan(0.5)
-    0.5463024898437905
+    >>> np.tan(np.array([-np.pi, np.pi/2, np.pi]))
+    array([-8.7422777e-08, -2.2877332e+07,  8.7422777e-08])
     """
 
     return _mx_nd_np.tan(x, out=out, **kwargs)
@@ -4044,7 +4182,7 @@ def histogram(a, bins=10, range=None, normed=None, weights=None, density=None):
     ----------
     a : ndarray
         Input data. The histogram is computed over the flattened array.
-    bins : int or NDArray
+    bins : int or ndarray
         If `bins` is an int, it defines the number of equal-width
         bins in the given range (10, by default). If `bins` is a
         sequence, it defines a monotonically increasing array of bin edges,
@@ -4062,6 +4200,11 @@ def histogram(a, bins=10, range=None, normed=None, weights=None, density=None):
         Not supported yet, coming soon.
     density : bool, optional
         Not supported yet, coming soon.
+
+    Examples
+    --------
+    >>> np.histogram(np.arange(4), bins=np.arange(5))
+    [array([1, 1, 1, 1], dtype=int64), array([0., 1., 2., 3., 4.])]
     """
     return _mx_nd_np.histogram(a, bins=bins, range=range, normed=normed, weights=weights, density=density)
 
@@ -4089,6 +4232,16 @@ def eye(N, M=None, k=0, dtype=_np.float32, **kwargs):
     I : ndarray of shape (N,M)
         An array where all elements are equal to zero,
         except for the k-th diagonal, whose values are equal to one.
+
+    Examples
+    --------
+    >>> np.eye(2, dtype=int)
+    array([[1, 0],
+           [0, 1]], dtype=int64)
+    >>> np.eye(3, k=1)
+    array([[0., 1., 0.],
+           [0., 0., 1.],
+           [0., 0., 0.]])
     """
     return _mx_nd_np.eye(N, M, k, dtype, **kwargs)
 
@@ -4274,6 +4427,37 @@ def expand_dims(a, axis):
     res : ndarray
         Output array. The number of dimensions is one greater than that of
         the input array.
+
+    See Also
+    --------
+    squeeze : The inverse operation, removing singleton dimensions
+    reshape : Insert, remove, and combine dimensions, and resize existing ones
+
+    Examples
+    --------
+    >>> x = np.array([1,2])
+    >>> x.shape
+    (2,)
+
+    >>> y = np.expand_dims(x, axis=0)
+    >>> y
+    array([[1., 2.]])
+
+    >>> y.shape
+    (1, 2)
+
+    >>> y = np.expand_dims(x, axis=1)  # Equivalent to x[:,np.newaxis]
+    >>> y
+    array([[1.],
+           [2.]])
+
+    >>> y.shape
+    (2, 1)
+
+    Note that some examples may use None instead of np.newaxis. These are the same objects:
+
+    >>> np.newaxis is None
+    True
     """
     return _npi.expand_dims(a, axis)
 
@@ -4417,6 +4601,20 @@ def arange(start, stop=None, step=1, dtype=None, ctx=None):
         ``ceil((stop - start)/step)``.  Because of floating point overflow,
         this rule may result in the last element of `out` being greater
         than `stop`.
+
+    Examples
+    --------
+    >>> np.arange(3)
+    array([0., 1., 2.])
+
+    >>> np.arange(3.0)
+    array([0., 1., 2.])
+
+    >>> np.arange(3,7)
+    array([3., 4., 5., 6.])
+
+    >>> np.arange(3,7,2)
+    array([3., 5.])
     """
     return _mx_nd_np.arange(start, stop, step, dtype, ctx)
 
@@ -4424,6 +4622,7 @@ def arange(start, stop=None, step=1, dtype=None, ctx=None):
 @set_module('mxnet.numpy')
 def split(ary, indices_or_sections, axis=0):
     """Split an array into multiple sub-arrays.
+
     Parameters
     ----------
     ary : ndarray
@@ -4442,15 +4641,38 @@ def split(ary, indices_or_sections, axis=0):
         an empty sub-array is returned correspondingly.
     axis : int, optional
         The axis along which to split, default is 0.
+
     Returns
     -------
     sub-arrays : list of ndarrays
         A list of sub-arrays.
+
     Raises
     ------
     ValueError
         If `indices_or_sections` is given as an integer, but
-        a split does not result in equal division."""
+        a split does not result in equal division.
+
+    See Also
+    --------
+    hsplit : Split array into multiple sub-arrays horizontally (column-wise).
+    vsplit : Split array into multiple sub-arrays vertically (row wise).
+    dsplit : Split array into multiple sub-arrays along the 3rd axis (depth).
+    concatenate : Join a sequence of arrays along an existing axis.
+    stack : Join a sequence of arrays along a new axis.
+    hstack : Stack arrays in sequence horizontally (column wise).
+    vstack : Stack arrays in sequence vertically (row wise).
+    dstack : Stack arrays in sequence depth wise (along third dimension).
+
+    Examples
+    --------
+    >>> x = np.arange(9.0)
+    >>> np.split(x, 3)
+    [array([0., 1., 2.]), array([3., 4., 5.]), array([6., 7., 8.])]
+
+    >>> np.split(x, [3, 5, 6, 8])
+    [array([0., 1., 2.]), array([3., 4.]), array([5.]), array([6., 7.]), array([])]
+    """
     return _mx_nd_np.split(ary, indices_or_sections, axis=axis)
 
 
@@ -4533,6 +4755,7 @@ def vsplit(ary, indices_or_sections):
 @set_module('mxnet.numpy')
 def concatenate(seq, axis=0, out=None):
     """Join a sequence of arrays along an existing axis.
+
     Parameters
     ----------
     a1, a2, ... : sequence of array_like
@@ -4545,10 +4768,35 @@ def concatenate(seq, axis=0, out=None):
         If provided, the destination to place the result. The shape must be
         correct, matching that of what concatenate would have returned if no
         out argument were specified.
+
     Returns
     -------
     res : ndarray
         The concatenated array.
+
+    See Also
+    --------
+    split : Split array into a list of multiple sub-arrays of equal size.
+    hsplit : Split array into multiple sub-arrays horizontally (column wise)
+    vsplit : Split array into multiple sub-arrays vertically (row wise)
+    dsplit : Split array into multiple sub-arrays along the 3rd axis (depth).
+    stack : Stack a sequence of arrays along a new axis.
+    hstack : Stack arrays in sequence horizontally (column wise)
+    vstack : Stack arrays in sequence vertically (row wise)
+    dstack : Stack arrays in sequence depth wise (along third dimension)
+
+    Examples
+    --------
+    >>> a = np.array([[1, 2], [3, 4]])
+    >>> b = np.array([[5, 6]])
+    >>> np.concatenate((a, b), axis=0)
+    array([[1., 2.],
+           [3., 4.],
+           [5., 6.]])
+
+    >>> np.concatenate((a, b.T), axis=1)
+    array([[1., 2., 5.],
+           [3., 4., 6.]])
     """
     return _mx_nd_np.concatenate(seq, axis=axis, out=out)
 
@@ -4558,6 +4806,7 @@ def stack(arrays, axis=0, out=None):
     """Join a sequence of arrays along a new axis.
         The axis parameter specifies the index of the new axis in the dimensions of the result.
         For example, if `axis=0` it will be the first dimension and if `axis=-1` it will be the last dimension.
+
     Parameters
     ----------
     arrays : sequence of array_like
@@ -4567,10 +4816,40 @@ def stack(arrays, axis=0, out=None):
     out : ndarray, optional
         If provided, the destination to place the result. The shape must be correct,
         matching that of what stack would have returned if no out argument were specified.
+
     Returns
     -------
     stacked : ndarray
-        The stacked array has one more dimension than the input arrays."""
+        The stacked array has one more dimension than the input arrays.
+
+    See Also
+    --------
+    concatenate : Join a sequence of arrays along an existing axis.
+    split : Split array into a list of multiple sub-arrays of equal size.
+
+    Examples
+    --------
+    >>> arrays = [np.random.rand(3, 4) for _ in range(10)]
+    >>> np.stack(arrays, axis=0).shape
+    (10, 3, 4)
+
+    >>> np.stack(arrays, axis=1).shape
+    (3, 10, 4)
+
+    >>> np.stack(arrays, axis=2).shape
+    (3, 4, 10)
+
+    >>> a = np.array([1, 2, 3])
+    >>> b = np.array([2, 3, 4])
+    >>> np.stack((a, b))
+    array([[1., 2., 3.],
+           [2., 3., 4.]])
+
+    >>> np.stack((a, b), axis=-1)
+    array([[1., 2.],
+           [2., 3.],
+           [3., 4.]])
+    """
     return _mx_nd_np.stack(arrays, axis=axis, out=out)
 
 
@@ -4678,7 +4957,17 @@ def maximum(x1, x2, out=None, **kwargs):
     Returns
     -------
     out : mxnet.numpy.ndarray or scalar
-        The maximum of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars."""
+        The maximum of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars.
+
+    Examples
+    --------
+    >>> np.maximum(np.array([2, 3, 4]), np.array([1, 5, 2]))
+    array([2., 5., 4.])
+
+    >>> np.maximum(np.eye(2), np.array([0.5, 2])) # broadcasting
+    array([[1. , 2. ],
+           [0.5, 2. ]])
+    """
     return _mx_nd_np.maximum(x1, x2, out=out)
 
 
@@ -4697,7 +4986,17 @@ def minimum(x1, x2, out=None, **kwargs):
     Returns
     -------
     out : mxnet.numpy.ndarray or scalar
-        The minimum of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars."""
+        The minimum of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars.
+
+    Examples
+    --------
+    >>> np.minimum(np.array([2, 3, 4]), np.array([1, 5, 2]))
+    array([1., 3., 2.])
+
+    >>> np.minimum(np.eye(2), np.array([0.5, 2])) # broadcasting
+    array([[0.5, 0. ],
+           [0. , 1. ]])
+    """
     return _mx_nd_np.minimum(x1, x2, out=out)
 
 
@@ -4718,6 +5017,29 @@ def swapaxes(a, axis1, axis2):
     -------
     a_swapped : ndarray
         Swapped array. This is always a copy of the input array.
+
+    Examples
+    --------
+    >>> x = np.array([[1,2,3]])
+    >>> np.swapaxes(x,0,1)
+    array([[1.],
+           [2.],
+           [3.]])
+
+    >>> x = np.array([[[0,1],[2,3]],[[4,5],[6,7]]])
+    >>> x
+    array([[[0., 1.],
+            [2., 3.]],
+
+           [[4., 5.],
+            [6., 7.]]])
+
+    >>> np.swapaxes(x,0,2)
+    array([[[0., 4.],
+            [2., 6.]],
+
+           [[1., 5.],
+            [3., 7.]]])
     """
     return _npi.swapaxes(a, dim1=axis1, dim2=axis2)
 
@@ -4776,8 +5098,6 @@ def clip(a, a_min, a_max, out=None):
 @set_module('mxnet.numpy')
 def argmax(a, axis=None, out=None):
     r"""
-    argmax(a, axis=None, out=None)
-
     Returns the indices of the maximum values along an axis.
 
     Parameters
@@ -4844,13 +5164,82 @@ def argmax(a, axis=None, out=None):
     return _mx_nd_np.argmax(a, axis, out)
 
 
+@set_module('mxnet.numpy')
+def argmin(a, axis=None, out=None):
+    r"""
+    Returns the indices of the minimum values along an axis.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input array. Only support ndarrays of dtype `float16`, `float32`, and `float64`.
+    axis : int, optional
+        By default, the index is into the flattened array, otherwise
+        along the specified axis.
+    out : ndarray or None, optional
+        If provided, the result will be inserted into this array. It should
+        be of the appropriate shape and dtype.
+
+    Returns
+    -------
+    index_array : ndarray of indices whose dtype is same as the input ndarray.
+        Array of indices into the array. It has the same shape as `a.shape`
+        with the dimension along `axis` removed.
+
+    Notes
+    -----
+    In case of multiple occurrences of the minimum values, the indices
+    corresponding to the first occurrence are returned.
+
+    This function differs from the original `numpy.argmin
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmin.html>`_ in
+    the following aspects:
+
+    - Input type does not support Python native iterables(list, tuple, ...).
+    - Output has dtype that is same as the input ndarray.
+    - ``out`` param: cannot perform auto broadcasting. ``out`` ndarray's shape must be the same as the expected output.
+    - ``out`` param: cannot perform auto type cast. ``out`` ndarray's dtype must be the same as the expected output.
+    - ``out`` param does not support scalar input case.
+
+    Examples
+    --------
+    >>> a = np.arange(6).reshape(2,3) + 10
+    >>> a
+    array([[10., 11., 12.],
+           [13., 14., 15.]])
+    >>> np.argmin(a)
+    array(0.)
+    >>> np.argmin(a, axis=0)
+    array([0., 0., 0.])
+    >>> np.argmin(a, axis=1)
+    array([0., 0.])
+
+    >>> b = np.arange(6)
+    >>> b[2] = 0
+    >>> b
+    array([0., 1., 0., 3., 4., 5.])
+    >>> np.argmax(b)  # Only the first occurrence is returned.
+    array(0.)
+
+    Specify ``out`` ndarray:
+
+    >>> a = np.arange(6).reshape(2,3) + 10
+    >>> b = np.zeros((2,))
+    >>> np.argmin(a, axis=1, out=b)
+    array([0., 0.])
+    >>> b
+    array([0., 0.])
+    """
+    return _mx_nd_np.argmin(a, axis, out)
+
+
 @set_module('mxnet.numpy')
 def mean(a, axis=None, dtype=None, out=None, keepdims=False):  # pylint: disable=arguments-differ
     """
-    mean(a, axis=None, dtype=None, out=None, keepdims=None)
     Compute the arithmetic mean along the specified axis.
     Returns the average of the array elements.
     The average is taken over the flattened array by default, otherwise over the specified axis.
+
     Parameters
     ----------
     a : ndarray
@@ -4872,11 +5261,13 @@ def mean(a, axis=None, dtype=None, out=None, keepdims=False):  # pylint: disable
         If the default value is passed, then keepdims will not be passed through to the mean
         method of sub-classes of ndarray, however any non-default value will be. If the sub-class
         method does not implement keepdims any exceptions will be raised.
+
     Returns
     -------
     m : ndarray, see dtype parameter above
         If out=None, returns a new array containing the mean values,
         otherwise a reference to the output array is returned.
+
     Notes
     -----
     This function differs from the original `numpy.mean
@@ -4884,6 +5275,7 @@ def mean(a, axis=None, dtype=None, out=None, keepdims=False):  # pylint: disable
     the following way(s):
     - only ndarray is accepted as valid input, python iterables or scalar is not supported
     - default data type for integer input is float32
+
     Examples
     --------
     >>> a = np.array([[1, 2], [3, 4]])
@@ -5758,16 +6150,19 @@ def inner(a, b):
     Examples
     --------
     Ordinary inner product for vectors:
+
     >>> a = np.array([1,2,3])
     >>> b = np.array([0,1,0])
     >>> np.inner(a, b)
-    2
+    array(2.)
+
     A multidimensional example:
+
     >>> a = np.arange(24).reshape((2,3,4))
     >>> b = np.arange(4)
     >>> np.inner(a, b)
-    array([[ 14,  38,  62],
-           [ 86, 110, 134]])
+    array([[ 14.,  38.,  62.],
+           [ 86., 110., 134.]])
     """
     return tensordot(a, b, [-1, -1])
 
@@ -5796,6 +6191,7 @@ def outer(a, b):
     -------
     out : (M, N) ndarray
         ``out[i, j] = a[i] * b[j]``
+
     See also
     --------
     inner
@@ -5812,13 +6208,14 @@ def outer(a, b):
     Examples
     --------
     Make a (*very* coarse) grid for computing a Mandelbrot set:
+
     >>> rl = np.outer(np.ones((5,)), np.linspace(-2, 2, 5))
     >>> rl
     array([[-2., -1.,  0.,  1.,  2.],
-        [-2., -1.,  0.,  1.,  2.],
-        [-2., -1.,  0.,  1.,  2.],
-        [-2., -1.,  0.,  1.,  2.],
-        [-2., -1.,  0.,  1.,  2.]])
+           [-2., -1.,  0.,  1.,  2.],
+           [-2., -1.,  0.,  1.,  2.],
+           [-2., -1.,  0.,  1.,  2.],
+           [-2., -1.,  0.,  1.,  2.]])
     """
     return tensordot(a.flatten(), b.flatten(), 0)
 
@@ -5851,12 +6248,13 @@ def vdot(a, b):
     Examples
     --------
     Note that higher-dimensional arrays are flattened!
+
     >>> a = np.array([[1, 4], [5, 6]])
     >>> b = np.array([[4, 1], [2, 2]])
     >>> np.vdot(a, b)
-    30
+    array(30.)
     >>> np.vdot(b, a)
-    30
+    array(30.)
     >>> 1*4 + 4*1 + 5*2 + 6*2
     30
     """
@@ -6060,6 +6458,7 @@ def rot90(m, k=1, axes=(0, 1)):
     """
     Rotate an array by 90 degrees in the plane specified by axes.
     Rotation direction is from the first towards the second axis.
+
     Parameters
     ----------
     m : ndarray
@@ -6075,9 +6474,11 @@ def rot90(m, k=1, axes=(0, 1)):
     y : ndarray
         A rotated view of `m`.
 
+    Notes
     -----
     rot90(m, k=1, axes=(1,0)) is the reverse of rot90(m, k=1, axes=(0,1))
     rot90(m, k=1, axes=(1,0)) is equivalent to rot90(m, k=-1, axes=(0,1))
+
     Examples
     --------
     >>> m = np.array([[1,2],[3,4]], 'int')
@@ -6419,3 +6820,83 @@ def einsum(*operands, **kwargs):
     ...     np.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize=True)
     """
     return _mx_nd_np.einsum(*operands, **kwargs)
+
+
+@set_module('mxnet.numpy')
+def nonzero(a):
+    """
+    Return the indices of the elements that are non-zero.
+
+    Returns a tuple of arrays, one for each dimension of `a`,
+    containing the indices of the non-zero elements in that
+    dimension. The values in `a` are always returned in
+    row-major, C-style order.
+
+    To group the indices by element, rather than dimension, use `argwhere`,
+    which returns a row for each non-zero element.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input array.
+
+    Returns
+    -------
+    tuple_of_arrays : tuple
+        Indices of elements that are non-zero.
+
+    See Also
+    --------
+    ndarray.nonzero :
+        Equivalent ndarray method.
+
+    Notes
+    -----
+    While the nonzero values can be obtained with ``a[nonzero(a)]``, it is
+    recommended to use ``x[x.astype(bool)]`` or ``x[x != 0]`` instead, which
+    will correctly handle 0-d arrays.
+
+    Examples
+    --------
+    >>> x = np.array([[3, 0, 0], [0, 4, 0], [5, 6, 0]])
+    >>> x
+    array([[3, 0, 0],
+           [0, 4, 0],
+           [5, 6, 0]], dtype=int32)
+    >>> np.nonzero(x)
+    (array([0, 1, 2, 2], dtype=int64), array([0, 1, 0, 1], dtype=int64))
+
+    >>> x[np.nonzero(x)]
+    array([3, 4, 5, 6])
+    >>> np.transpose(np.stack(np.nonzero(x)))
+    array([[0, 0],
+           [1, 1],
+           [2, 0],
+           [2, 1]], dtype=int64)
+
+    A common use for ``nonzero`` is to find the indices of an array, where
+    a condition is True.  Given an array `a`, the condition `a` > 3 is a
+    boolean array and since False is interpreted as 0, np.nonzero(a > 3)
+    yields the indices of the `a` where the condition is true.
+
+    >>> a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.int32)
+    >>> a > 3
+    array([[False, False, False],
+           [ True,  True,  True],
+           [ True,  True,  True]])
+    >>> np.nonzero(a > 3)
+    (array([1, 1, 1, 2, 2, 2], dtype=int64), array([0, 1, 2, 0, 1, 2], dtype=int64))
+
+    Using this result to index `a` is equivalent to using the mask directly:
+
+    >>> a[np.nonzero(a > 3)]
+    array([4, 5, 6, 7, 8, 9], dtype=int32)
+    >>> a[a > 3]
+    array([4, 5, 6, 7, 8, 9], dtype=int32)
+
+    ``nonzero`` can also be called as a method of the array.
+
+    >>> (a > 3).nonzero()
+    (array([1, 1, 1, 2, 2, 2], dtype=int64), array([0, 1, 2, 0, 1, 2], dtype=int64))
+    """
+    return _mx_nd_np.nonzero(a)
diff --git a/python/mxnet/numpy/random.py b/python/mxnet/numpy/random.py
index d0ae237a5b92..1cad4a55c466 100644
--- a/python/mxnet/numpy/random.py
+++ b/python/mxnet/numpy/random.py
@@ -20,11 +20,11 @@
 from __future__ import absolute_import
 from ..ndarray import numpy as _mx_nd_np
 
-__all__ = ["randint", "uniform", "normal", "choice", "rand"]
+__all__ = ["randint", "uniform", "normal", "choice", "rand", "multinomial"]
 
 
 def randint(low, high=None, size=None, dtype=None, ctx=None, out=None):
-    """Return random integers from `low` (inclusive) to `high` (exclusive).
+    r"""Return random integers from `low` (inclusive) to `high` (exclusive).
 
     Return random integers from the "discrete uniform" distribution of
     the specified dtype in the "half-open" interval [`low`, `high`). If
@@ -76,7 +76,7 @@ def randint(low, high=None, size=None, dtype=None, ctx=None, out=None):
 
 
 def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
-    """Draw samples from a uniform distribution.
+    r"""Draw samples from a uniform distribution.
 
     Samples are uniformly distributed over the half-open interval
     ``[low, high)`` (includes low, but excludes high).  In other words,
@@ -95,7 +95,8 @@ def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
         Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
         ``m * n * k`` samples are drawn.  If size is ``None`` (default),
         a scalar tensor containing a single value is returned if
-        ``low`` and ``high`` are both scalars.
+        ``low`` and ``high`` are both scalars. Otherwise,
+        ``np.broadcast(low, high).size`` samples are drawn.
     dtype : {'float16', 'float32', 'float64'}, optional
         Data type of output samples. Default is 'float32'
     ctx : Context, optional
@@ -105,12 +106,33 @@ def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
     -------
     out : ndarray
         Drawn samples from the parameterized uniform distribution.
+
+    See Also
+    --------
+    randint : Discrete uniform distribution, yielding integers.
+    rand : Convenience function that accepts dimensions as input, e.g.,
+           ``rand(2,2)`` would generate a 2-by-2 array of floats,
+           uniformly distributed over ``[0, 1)``.
+
+    Notes
+    -----
+    The probability density function of the uniform distribution is
+
+    .. math:: p(x) = \frac{1}{b - a}
+
+    anywhere within the interval ``[a, b)``, and zero elsewhere.
+
+    When ``high`` == ``low``, values of ``low`` will be returned.
+    If ``high`` < ``low``, the results are officially undefined
+    and may eventually raise an error, i.e. do not rely on this
+    function to behave when passed arguments satisfying that
+    inequality condition.
     """
     return _mx_nd_np.random.uniform(low, high, size=size, ctx=ctx, dtype=dtype, out=out)
 
 
 def normal(loc=0.0, scale=1.0, size=None, dtype=None, ctx=None, out=None):
-    """Draw random samples from a normal (Gaussian) distribution.
+    r"""Draw random samples from a normal (Gaussian) distribution.
 
     Samples are distributed according to a normal distribution parametrized
     by *loc* (mean) and *scale* (standard deviation).
@@ -125,7 +147,8 @@ def normal(loc=0.0, scale=1.0, size=None, dtype=None, ctx=None, out=None):
     size : int or tuple of ints, optional
         Output shape. If the given shape is, e.g., `(m, n, k)`, then `m * n * k`
         samples are drawn. If size is `None` (default), a scalar tensor containing
-        a single value is returned if loc and scale are both scalars.
+        a single value is returned if loc and scale are both scalars. Otherwise,
+        ``np.broadcast(low, high).size`` samples are drawn.
     dtype : {'float16', 'float32', 'float64'}, optional
         Data type of output samples. Default is 'float32'
     ctx : Context, optional
@@ -137,17 +160,53 @@ def normal(loc=0.0, scale=1.0, size=None, dtype=None, ctx=None, out=None):
     -------
     out : ndarray
         Drawn samples from the parameterized normal distribution.
+
+    Notes
+    -----
+    The probability density for the Gaussian distribution is
+
+    .. math:: p(x) = \frac{1}{\sqrt{ 2 \pi \sigma^2 }}
+                     e^{ - \frac{ (x - \mu)^2 } {2 \sigma^2} },
+
+    where :math:`\mu` is the mean and :math:`\sigma` the standard
+    deviation. The square of the standard deviation, :math:`\sigma^2`,
+    is called the variance.
+
+    The function has its peak at the mean, and its "spread" increases with
+    the standard deviation (the function reaches 0.607 times its maximum at
+    :math:`x + \sigma` and :math:`x - \sigma` [2]_).  This implies that
+    `numpy.random.normal` is more likely to return samples lying close to
+    the mean, rather than those far away.
+
+    References
+    ----------
+    .. [1] Wikipedia, "Normal distribution",
+           https://en.wikipedia.org/wiki/Normal_distribution
+    .. [2] P. R. Peebles Jr., "Central Limit Theorem" in "Probability,
+           Random Variables and Random Signal Principles", 4th ed., 2001,
+           pp. 51, 51, 125.
+
+    Examples
+    --------
+    >>> mu, sigma = 0, 0.1 # mean and standard deviation
+    >>> s = np.random.normal(mu, sigma, 1000)
+
+    Verify the mean and the variance:
+
+    >>> np.abs(mu - np.mean(s)) < 0.01
+    array(True)
     """
     return _mx_nd_np.random.normal(loc, scale, size, dtype, ctx, out)
 
 
 def multinomial(n, pvals, size=None, **kwargs):
-    """multinomial(n, pvals, size=None)
+    r"""
     Draw samples from a multinomial distribution.
     The multinomial distribution is a multivariate generalisation of the binomial distribution.
     Take an experiment with one of ``p`` possible outcomes. An example of such an experiment is throwing a dice,
     where the outcome can be 1 through 6. Each sample drawn from the distribution represents n such experiments.
     Its values, ``X_i = [X_0, X_1, ..., X_p]``, represent the number of times the outcome was ``i``.
+
     Parameters
     ----------
     n : int
@@ -157,18 +216,23 @@ def multinomial(n, pvals, size=None, **kwargs):
     size : int or tuple of ints, optional
         Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples
         are drawn. Default is None, in which case a single value is returned.
+
     Returns
     -------
     out : ndarray
         The drawn samples, of shape size, if that was provided. If not, the shape is ``(N,)``.
         In other words, each entry ``out[i,j,...,:]`` is an N-dimensional value drawn from the distribution.
+
     Examples
     --------
     Throw a dice 1000 times, and 1000 times again:
+
     >>> np.random.multinomial(1000, [1/6.]*6, size=2)
     array([[164, 161, 179, 158, 150, 188],
            [178, 162, 177, 143, 163, 177]])
+
     A loaded die is more likely to land on number 6:
+
     >>> np.random.multinomial(100, [1/7.]*5 + [2/7.])
     array([19, 14, 12, 11, 21, 23])
     >>> np.random.multinomial(100, [1.0 / 3, 2.0 / 3])
@@ -178,7 +242,7 @@ def multinomial(n, pvals, size=None, **kwargs):
 
 
 def choice(a, size=None, replace=True, p=None, ctx=None, out=None):
-    """Generates a random sample from a given 1-D array
+    r"""Generates a random sample from a given 1-D array
 
     Parameters
     -----------
diff --git a/python/mxnet/numpy/stride_tricks.py b/python/mxnet/numpy/stride_tricks.py
index 0b2fe523b0f3..b4a4d0a7b44a 100644
--- a/python/mxnet/numpy/stride_tricks.py
+++ b/python/mxnet/numpy/stride_tricks.py
@@ -46,6 +46,15 @@ def broadcast_arrays(*args):
         These arrays are copies of the original arrays unless that all the input
         arrays have the same shape, the input list of arrays are returned
         instead of a list of copies.
+
+    Examples
+    --------
+    >>> x = np.array([[1,2,3]])
+    >>> y = np.array([[4],[5]])
+    >>> np.broadcast_arrays(x, y)
+    [array([[1., 2., 3.],
+           [1., 2., 3.]]), array([[4., 4., 4.],
+           [5., 5., 5.]])]
     """
     shape = _broadcast_shape(*args)
 
diff --git a/python/mxnet/numpy/utils.py b/python/mxnet/numpy/utils.py
index b2335e29855d..c34650a61f31 100644
--- a/python/mxnet/numpy/utils.py
+++ b/python/mxnet/numpy/utils.py
@@ -23,7 +23,7 @@
 import numpy as onp
 
 __all__ = ['float16', 'float32', 'float64', 'uint8', 'int32', 'int8', 'int64',
-           'bool', 'bool_', 'pi', 'inf', 'nan', 'PZERO', 'NZERO']
+           'bool', 'bool_', 'pi', 'inf', 'nan', 'PZERO', 'NZERO', 'newaxis']
 
 float16 = onp.float16
 float32 = onp.float32
@@ -40,3 +40,5 @@
 nan = onp.nan
 PZERO = onp.PZERO
 NZERO = onp.NZERO
+
+newaxis = None
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index 6db44fad7780..cec2f245a5e1 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -83,6 +83,7 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
 
 
 _NUMPY_ARRAY_FUNCTION_LIST = [
+    'argmin',
     'argmax',
     'around',
     'broadcast_arrays',
@@ -99,6 +100,7 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'max',
     'mean',
     'min',
+    'nonzero',
     'ones_like',
     'prod',
     'ravel',
diff --git a/python/mxnet/numpy_extension/random.py b/python/mxnet/numpy_extension/random.py
index 5aa58a0cc69d..9c059ca9ade4 100644
--- a/python/mxnet/numpy_extension/random.py
+++ b/python/mxnet/numpy_extension/random.py
@@ -25,7 +25,7 @@
 
 
 def seed(seed, ctx='all'):  # pylint: disable=redefined-outer-name
-    """Seeds the random number generators in MXNet.
+    r"""Seeds the random number generators in MXNet.
 
     This affects the behavior of modules in MXNet that uses random number generators,
     like the dropout operator and `ndarray`'s random sampling operators.
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index aa456c8e5166..ddf2feb30b18 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -36,7 +36,7 @@
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot', 'histogram', 'eye',
            'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate',
-           'stack', 'vstack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax',
+           'stack', 'vstack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'argmin',
            'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
            'around', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
            'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal',
@@ -385,13 +385,10 @@ def argmax_channel(self, *args, **kwargs):
         """
         raise AttributeError('_Symbol object has no attribute argmax_channel')
 
-    def argmin(self, *args, **kwargs):
-        """Convenience fluent method for :py:func:`argmin`.
-
-        The arguments are the same as for :py:func:`argmin`, with
-        this array as data.
-        """
-        raise NotImplementedError
+    def argmin(self, axis=None, out=None):  # pylint: disable=arguments-differ
+        """Return indices of the minimum values along the given axis.
+        Refer to `mxnet.numpy.argmax` for full documentation."""
+        return argmin(self, axis, out)
 
     def clip(self, min=None, max=None, out=None):  # pylint: disable=arguments-differ
         """Return an array whose values are limited to [min, max].
@@ -3187,8 +3184,6 @@ def swapaxes(a, axis1, axis2):
 @set_module('mxnet.symbol.numpy')
 def argmax(a, axis=None, out=None):
     r"""
-    argmax(a, axis=None, out=None)
-
     Returns the indices of the maximum values along an axis.
 
     Parameters
@@ -3226,6 +3221,46 @@ def argmax(a, axis=None, out=None):
     return _npi.argmax(a, axis=axis, keepdims=False, out=out)
 
 
+@set_module('mxnet.symbol.numpy')
+def argmin(a, axis=None, out=None):
+    r"""
+    Returns the indices of the minimum values along an axis.
+
+    Parameters
+    ----------
+    a : _Symbol
+        Input array. Only support dtype `float16`, `float32`, and `float64`.
+    axis : int, optional
+        By default, the index is into the flattened array, otherwise
+        along the specified axis.
+    out : _Symbol or None, optional
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+
+    Returns
+    -------
+    index_array : _Symbol of indices whose dtype is same as the input ndarray.
+        Array of indices into the array. It has the same shape as `a.shape`
+        with the dimension along `axis` removed.
+
+    Notes
+    -----
+    In case of multiple occurrences of the minimum values, the indices
+    corresponding to the first occurrence are returned.
+
+    This function differs from the original `numpy.argmin
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmin.html>`_ in
+    the following aspects:
+
+    - Input type does not support Python native iterables(list, tuple, ...).
+    - Output has dtype that is same as the input ndarray.
+    - ``out`` param: cannot perform auto broadcasting. ``out`` symbol's shape must be the same as the expected output.
+    - ``out`` param: cannot perform auto type cast. ``out`` symnbol's dtype must be the same as the expected output.
+    - ``out`` param does not support scalar input case.
+
+    """
+    return _npi.argmin(a, axis=axis, keepdims=False, out=out)
+
+
 @set_module('mxnet.symbol.numpy')
 def mean(a, axis=None, dtype=None, out=None, keepdims=False):  # pylint: disable=arguments-differ
     """
diff --git a/python/mxnet/symbol/numpy/random.py b/python/mxnet/symbol/numpy/random.py
index d891ea0c21a0..48bccb64a2b4 100644
--- a/python/mxnet/symbol/numpy/random.py
+++ b/python/mxnet/symbol/numpy/random.py
@@ -25,7 +25,7 @@
 
 
 def randint(low, high=None, size=None, dtype=None, ctx=None, out=None):
-    """Return random integers from `low` (inclusive) to `high` (exclusive).
+    r"""Return random integers from `low` (inclusive) to `high` (exclusive).
 
     Return random integers from the "discrete uniform" distribution of
     the specified dtype in the "half-open" interval [`low`, `high`). If
@@ -113,7 +113,7 @@ def rand(*size, **kwargs):
 
 
 def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
-    """Draw samples from a uniform distribution.
+    r"""Draw samples from a uniform distribution.
 
     Samples are uniformly distributed over the half-open interval
     ``[low, high)`` (includes low, but excludes high).  In other words,
@@ -168,7 +168,7 @@ def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
 
 
 def normal(loc=0.0, scale=1.0, size=None, dtype=None, ctx=None, out=None):
-    """Draw random samples from a normal (Gaussian) distribution.
+    r"""Draw random samples from a normal (Gaussian) distribution.
 
     Samples are distributed according to a normal distribution parametrized
     by *loc* (mean) and *scale* (standard deviation).
@@ -217,7 +217,7 @@ def normal(loc=0.0, scale=1.0, size=None, dtype=None, ctx=None, out=None):
 
 
 def choice(a, size=None, replace=True, p=None, ctx=None, out=None):
-    """Generates a random sample from a given 1-D array
+    r"""Generates a random sample from a given 1-D array
 
     Parameters
     -----------
diff --git a/python/mxnet/util.py b/python/mxnet/util.py
index 9e15caae9698..3a85e31e7e43 100644
--- a/python/mxnet/util.py
+++ b/python/mxnet/util.py
@@ -690,15 +690,76 @@ def _set_np_array(active):
 
 def set_np(shape=True, array=True):
     """Setting NumPy shape and array semantics at the same time.
-    It is required to keep NumPy shape semantics active when activating NumPy array semantics.
+    It is required to keep NumPy shape semantics active while activating NumPy array semantics.
     Deactivating NumPy shape semantics while NumPy array semantics is still active is not allowed.
+    It is highly recommended to set these two flags to `True` at the same time to fully enable
+    NumPy-like behaviors. Please refer to the Examples section for a better understanding.
 
     Parameters
     ----------
     shape : bool
         A boolean value indicating whether the NumPy-shape semantics should be turned on or off.
+        When this flag is set to `True`, zero-size and zero-dim shapes are all valid shapes in
+        shape inference process, instead of treated as unknown shapes in legacy mode.
     array : bool
         A boolean value indicating whether the NumPy-array semantics should be turned on or off.
+        When this flag is set to `True`, it enables Gluon code flow to use or generate `mxnet.numpy.ndarray`s
+        instead of `mxnet.ndarray.NDArray`. For example, a `Block` would create parameters of type
+        `mxnet.numpy.ndarray`.
+
+    Examples
+    --------
+    >>> import mxnet as mx
+
+    Creating zero-dim ndarray in legacy mode would fail at shape inference.
+
+    >>> mx.nd.ones(shape=())
+    mxnet.base.MXNetError: Operator _ones inferring shapes failed.
+
+    >>> mx.nd.ones(shape=(2, 0, 3))
+    mxnet.base.MXNetError: Operator _ones inferring shapes failed.
+
+    In legacy mode, Gluon layers would create parameters and outputs of type `mx.nd.NDArray`.
+
+    >>> from mxnet.gluon import nn
+    >>> dense = nn.Dense(2)
+    >>> dense.initialize()
+    >>> dense(mx.nd.ones(shape=(3, 2)))
+    [[0.01983214 0.07832371]
+     [0.01983214 0.07832371]
+     [0.01983214 0.07832371]]
+    <NDArray 3x2 @cpu(0)>
+
+    >>> [p.data() for p in dense.collect_params().values()]
+    [
+    [[0.0068339  0.01299825]
+     [0.0301265  0.04819721]]
+    <NDArray 2x2 @cpu(0)>,
+    [0. 0.]
+    <NDArray 2 @cpu(0)>]
+
+    When the `shape` flag is `True`, both shape inferences are successful.
+
+    >>> from mxnet import np, npx
+    >>> npx.set_np()  # this is required to activate NumPy-like behaviors
+
+    >>> np.ones(shape=())
+    array(1.)
+    >>> np.ones(shape=(2, 0, 3))
+    array([], shape=(2, 0, 3))
+
+    When the `array` flag is `True`, Gluon layers would create parameters and outputs of type `mx.np.ndarray`.
+
+    >>> dense = nn.Dense(2)
+    >>> dense.initialize()
+    >>> dense(np.ones(shape=(3, 2)))
+    array([[0.01983214, 0.07832371],
+           [0.01983214, 0.07832371],
+           [0.01983214, 0.07832371]])
+
+    >>> [p.data() for p in dense.collect_params().values()]
+    [array([[0.0068339 , 0.01299825],
+           [0.0301265 , 0.04819721]]), array([0., 0.])]
     """
     if not shape and array:
         raise ValueError('NumPy Shape semantics is required in using NumPy array semantics.')
diff --git a/src/operator/contrib/boolean_mask.cc b/src/operator/contrib/boolean_mask.cc
index a54cc917776d..cd2fd8e42f8e 100644
--- a/src/operator/contrib/boolean_mask.cc
+++ b/src/operator/contrib/boolean_mask.cc
@@ -129,7 +129,7 @@ inline void BooleanMaskForward<cpu>(const nnvm::NodeAttrs& attrs,
 
   const_cast<NDArray &>(out).Init(s);
   // do the copy
-  MSHADOW_TYPE_SWITCH(data.dtype(), DType, {
+  MSHADOW_TYPE_SWITCH_WITH_BOOL(data.dtype(), DType, {
     size_t input_size = data.shape().Size();
     size_t col_size = input_size / idx_size;
     mshadow::Stream<cpu> *stream = ctx.get_stream<cpu>();
diff --git a/src/operator/contrib/boolean_mask.cu b/src/operator/contrib/boolean_mask.cu
index 71d91c63f64e..f6c1df0c62a8 100644
--- a/src/operator/contrib/boolean_mask.cu
+++ b/src/operator/contrib/boolean_mask.cu
@@ -86,7 +86,7 @@ inline void BooleanMaskForward<gpu>(const nnvm::NodeAttrs& attrs,
   size_t input_size = data.shape().Size();
   size_t col_size = input_size / idx.shape()[0];
   // Do the copy
-  MSHADOW_TYPE_SWITCH(out.dtype(), DType, {
+  MSHADOW_TYPE_SWITCH_WITH_BOOL(out.dtype(), DType, {
     if (valid_num > 0) {
       mxnet_op::Kernel<BooleanMaskForwardKernel, gpu>::Launch(
         s, input_size, out.data().dptr<DType>(), data.data().dptr<DType>(), prefix_sum, col_size);
diff --git a/src/operator/numpy/np_broadcast_reduce_op_index.cc b/src/operator/numpy/np_broadcast_reduce_op_index.cc
index bd6915cc9b27..15831c7e79ba 100644
--- a/src/operator/numpy/np_broadcast_reduce_op_index.cc
+++ b/src/operator/numpy/np_broadcast_reduce_op_index.cc
@@ -57,5 +57,16 @@ NNVM_REGISTER_OP(_npi_argmax)
 .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
 .add_arguments(ReduceAxisParam::__FIELDS__());
 
+NNVM_REGISTER_OP(_npi_argmin)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<ReduceAxisParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyReduceAxisShape)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
+.add_argument("data", "NDArray-or-Symbol", "The input")
+.set_attr<FCompute>("FCompute<cpu>", SearchAxisCompute<cpu, mshadow::red::minimum>)
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.add_arguments(ReduceAxisParam::__FIELDS__());
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/np_broadcast_reduce_op_index.cu b/src/operator/numpy/np_broadcast_reduce_op_index.cu
index a07baa9c070c..0420133ee7c0 100644
--- a/src/operator/numpy/np_broadcast_reduce_op_index.cu
+++ b/src/operator/numpy/np_broadcast_reduce_op_index.cu
@@ -30,5 +30,8 @@ namespace op {
 NNVM_REGISTER_OP(_npi_argmax)
 .set_attr<FCompute>("FCompute<gpu>", SearchAxisCompute<gpu, mshadow::red::maximum>);
 
+NNVM_REGISTER_OP(_npi_argmin)
+.set_attr<FCompute>("FCompute<gpu>", SearchAxisCompute<gpu, mshadow::red::minimum>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/np_nonzero_op.cc b/src/operator/numpy/np_nonzero_op.cc
index 00f9081ba984..0eaf0878a24a 100644
--- a/src/operator/numpy/np_nonzero_op.cc
+++ b/src/operator/numpy/np_nonzero_op.cc
@@ -91,7 +91,7 @@ void NonzeroForwardCPU(const nnvm::NodeAttrs& attrs,
   std::vector<int32_t> prefix_sum(in_size, 0);
   size_t valid_num = 0;
   // Calculate prefix sum
-  MSHADOW_TYPE_SWITCH(in.dtype(), DType, {
+  MSHADOW_TYPE_SWITCH_WITH_BOOL(in.dtype(), DType, {
     DType* in_dptr = in.data().dptr<DType>();
     for (size_t i = 0; i < in_size; i++) {
       prefix_sum[i] = (i == 0) ? 0 : prefix_sum[i - 1];
@@ -113,6 +113,7 @@ void NonzeroForwardCPU(const nnvm::NodeAttrs& attrs,
 }
 
 NNVM_REGISTER_OP(_npx_nonzero)
+.add_alias("_npi_nonzero")
 .set_num_inputs(1)
 .set_num_outputs(1)
 .set_attr<nnvm::FListInputNames>("FListInputNames",
diff --git a/src/operator/numpy/np_nonzero_op.cu b/src/operator/numpy/np_nonzero_op.cu
index 33925ea2e156..c732d2c78493 100644
--- a/src/operator/numpy/np_nonzero_op.cu
+++ b/src/operator/numpy/np_nonzero_op.cu
@@ -80,7 +80,7 @@ void NonzeroForwardGPU(const nnvm::NodeAttrs& attrs,
     ctx.requested[0].get_space_typed<gpu, 1, char>(Shape1(temp_storage_bytes), stream);
   prefix_sum = reinterpret_cast<int32_t*>(workspace.dptr_);
   d_temp_storage = workspace.dptr_ + buffer_size;
-  MSHADOW_TYPE_SWITCH(in.dtype(), DType, {
+  MSHADOW_TYPE_SWITCH_WITH_BOOL(in.dtype(), DType, {
     mxnet_op::Kernel<PrefixSumInit, gpu>::Launch(
       stream, in_size, prefix_sum, in.data().dptr<DType>());
   });
diff --git a/tests/python/unittest/test_numpy_gluon.py b/tests/python/unittest/test_numpy_gluon.py
index af5425336699..12e89a2d9b39 100644
--- a/tests/python/unittest/test_numpy_gluon.py
+++ b/tests/python/unittest/test_numpy_gluon.py
@@ -156,6 +156,29 @@ def test_np_loss_ndarray():
     assert_almost_equal(L, _np.array([1.06346405,  0.04858733]), use_broadcast=False)
 
 
+@with_seed()
+@use_np
+def test_np_get_constant():
+    const_arr = _np.random.uniform(0, 100, size=(10, 10)).astype(_np.float32)
+
+    class Foo(gluon.HybridBlock):
+        def __init__(self, prefix=None, params=None):
+            super(Foo, self).__init__(prefix=prefix, params=params)
+            self.weight = self.params.get_constant('const', const_arr)
+
+        def hybrid_forward(self, F, x, weight):
+            return x + weight.astype(np.float32)
+
+    x = np.random.uniform(size=const_arr.shape, dtype=const_arr.dtype)
+    for hybridize in [False, True]:
+        foo = Foo()
+        if hybridize:
+            foo.hybridize()
+        foo.initialize()
+        out = foo(x)
+        assert_almost_equal(out.asnumpy(), (x.asnumpy() + const_arr), atol=1e-5, rtol=1e-4, use_broadcast=False)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index 62004ac6d263..860fecc5cda0 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -383,6 +383,22 @@ def _add_workload_argmax():
     OpArgMngr.add_workload('argmax', np.array([True, False, True, False, False]))
 
 
+def _add_workload_argmin():
+    OpArgMngr.add_workload('argmin', np.random.uniform(size=(4, 5, 6, 7, 8)), 0)
+    OpArgMngr.add_workload('argmin', np.random.uniform(size=(4, 5, 6, 7, 8)), 1)
+    OpArgMngr.add_workload('argmin', np.random.uniform(size=(4, 5, 6, 7, 8)), 2)
+    OpArgMngr.add_workload('argmin', np.random.uniform(size=(4, 5, 6, 7, 8)), 3)
+    OpArgMngr.add_workload('argmin', np.random.uniform(size=(4, 5, 6, 7, 8)), 4)
+    # OpArgMngr.add_workload('argmin', np.array([0, 1, 2, 3, np.nan]))
+    # OpArgMngr.add_workload('argmin', np.array([0, 1, 2, np.nan, 3]))
+    # OpArgMngr.add_workload('argmin', np.array([np.nan, 0, 1, 2, 3]))
+    # OpArgMngr.add_workload('argmin', np.array([np.nan, 0, np.nan, 2, 3]))
+    OpArgMngr.add_workload('argmin', np.array([False, False, False, False, True]))
+    OpArgMngr.add_workload('argmin', np.array([False, False, False, True, False]))
+    OpArgMngr.add_workload('argmin', np.array([True, False, False, False, False]))
+    OpArgMngr.add_workload('argmin', np.array([True, False, True, False, False]))
+
+
 def _add_workload_around():
     OpArgMngr.add_workload('around', np.array([1.56, 72.54, 6.35, 3.25]), decimals=1)
 
@@ -1059,6 +1075,16 @@ def _add_workload_less_equal(array_pool):
     # OpArgMngr.add_workload('less_equal', np.array([np.nan]), np.array([np.nan]))
 
 
+def _add_workload_nonzero():
+    OpArgMngr.add_workload('nonzero', np.random.randint(0, 2))
+    OpArgMngr.add_workload('nonzero', np.random.randint(0, 2, size=()))
+    OpArgMngr.add_workload('nonzero', np.random.randint(0, 2, size=(0, 1, 2)))
+    OpArgMngr.add_workload('nonzero', np.random.randint(0, 2, size=(0, 1, 0)))
+    OpArgMngr.add_workload('nonzero', np.random.randint(0, 2, size=(2, 3, 4)))
+    OpArgMngr.add_workload('nonzero', np.array([False, False, False], dtype=np.bool_))
+    OpArgMngr.add_workload('nonzero', np.array([True, False, False], dtype=np.bool_))
+
+
 @use_np
 def _prepare_workloads():
     array_pool = {
@@ -1067,6 +1093,7 @@ def _prepare_workloads():
         '1x1x0': np.array([[[]]])
     }
 
+    _add_workload_argmin()
     _add_workload_argmax()
     _add_workload_around()
     _add_workload_broadcast_arrays(array_pool)
@@ -1083,6 +1110,7 @@ def _prepare_workloads():
     _add_workload_max(array_pool)
     _add_workload_min(array_pool)
     _add_workload_mean(array_pool)
+    _add_workload_nonzero()
     _add_workload_ones_like(array_pool)
     _add_workload_prod(array_pool)
     _add_workload_repeat(array_pool)
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 01778099412a..c4f756d56a33 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -2173,7 +2173,7 @@ def hybrid_forward(self, F, x):
 
 @with_seed()
 @use_np
-def test_np_argmax():
+def test_np_argmin_argmax():
     workloads = [
         ((), 0, False),
         ((), -1, False),
@@ -2188,49 +2188,52 @@ def test_np_argmax():
         ((5, 0, 3), 1, True),
     ]
     dtypes = ['float16', 'float32', 'float64']
+    ops = ['argmin', 'argmax']
 
-    class TestArgMax(HybridBlock):
-        def __init__(self, axis=None):
-            super(TestArgMax, self).__init__()
+    class TestArgExtreme(HybridBlock):
+        def __init__(self, op_name, axis=None):
+            super(TestArgExtreme, self).__init__()
+            self._op_name = op_name
             self._axis = axis
 
         def hybrid_forward(self, F, x):
-            return F.np.argmax(x, self._axis)
-
-    for shape, axis, throw_exception in workloads:
-        for dtype in dtypes:
-            a = np.random.uniform(size=shape, dtype=dtype)
-            if throw_exception:
-                # Cannot use assert_exception because sometimes the main thread
-                # proceeds to `assert False` before the exception is thrown
-                # in the worker thread. Have to use mx.nd.waitall() here
-                # to block the main thread.
-                try:
-                    np.argmax(a, axis)
-                    mx.nd.waitall()
-                    assert False
-                except mx.MXNetError:
-                    pass
-            else:
-                mx_ret = np.argmax(a, axis=axis)
-                np_ret = _np.argmax(a.asnumpy(), axis=axis)
-                assert same(mx_ret.asnumpy(), np_ret)
+            return getattr(x, self._op_name)(self._axis)
 
-            for hybridize in [False, True]:
-                net = TestArgMax(axis)
-                if hybridize:
-                    net.hybridize()
+    for op_name in ops:
+        for shape, axis, throw_exception in workloads:
+            for dtype in dtypes:
+                a = np.random.uniform(size=shape, dtype=dtype)
                 if throw_exception:
+                    # Cannot use assert_exception because sometimes the main thread
+                    # proceeds to `assert False` before the exception is thrown
+                    # in the worker thread. Have to use mx.nd.waitall() here
+                    # to block the main thread.
                     try:
-                        net(a)
+                        getattr(np, op_name)(a, axis)
                         mx.nd.waitall()
                         assert False
                     except mx.MXNetError:
                         pass
                 else:
-                    mx_ret = net(a)
+                    mx_ret = getattr(np, op_name)(a, axis=axis)
+                    np_ret = getattr(_np, op_name)(a.asnumpy(), axis=axis)
                     assert same(mx_ret.asnumpy(), np_ret)
 
+                for hybridize in [False, True]:
+                    net = TestArgExtreme(op_name, axis)
+                    if hybridize:
+                        net.hybridize()
+                    if throw_exception:
+                        try:
+                            net(a)
+                            mx.nd.waitall()
+                            assert False
+                        except mx.MXNetError:
+                            pass
+                    else:
+                        mx_ret = net(a)
+                        assert same(mx_ret.asnumpy(), np_ret)
+
 
 @with_seed()
 @use_np

From ffc5392d6c1328258cbdb8ceae724f055a8cae76 Mon Sep 17 00:00:00 2001
From: Haozheng Fan <fhztc1997618@gmail.com>
Date: Mon, 28 Oct 2019 07:36:26 +0800
Subject: [PATCH 531/813] Disable float16 test (#16643)

---
 tests/python/unittest/test_numpy_op.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index c4f756d56a33..391a07411b15 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -3507,7 +3507,7 @@ def dbg(name, data):
         # ('abiz,abjz->abij', [(64, 8, 128, 512), (64, 8, 128, 512)], lambda *args: (_np.matmul(_np.ones((64, 8, 128, 128)), args[1]),
         #                                                                            _np.matmul(_np.ones((64, 8, 128, 128)), args[0]))),
     ]
-    dtypes = ['float16', 'float32', 'float64', 'int32']
+    dtypes = ['float32', 'float64', 'int32']
     acc_type = {'float16': 'float32', 'float32': 'float64', 'float64': 'float64',
                 'int32': 'int64'}
     for hybridize in [False, True]:

From 11dff51f71c4de0f564cca3f1c22ae2bbeeba43a Mon Sep 17 00:00:00 2001
From: Zhennan Qin <zhennan.qin@intel.com>
Date: Mon, 28 Oct 2019 10:11:28 +0800
Subject: [PATCH 532/813] Fix GetMKLDNNData for delay alloc (#16618)

* Fix GetMKLDNNData for delay alloc

* Run CI

* Run CI

* Run CI

* Run CI

* Run CI

Change-Id: I7ac2796e0ee8439c92fd2bd7a70a23a359b76b12
---
 src/ndarray/ndarray.cc | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/ndarray/ndarray.cc b/src/ndarray/ndarray.cc
index aaa7aedf8bcd..78a6cfb15fd2 100644
--- a/src/ndarray/ndarray.cc
+++ b/src/ndarray/ndarray.cc
@@ -647,6 +647,7 @@ const mkldnn::memory *NDArray::GetMKLDNNData() const {
     // If this is a view, we can't create a MKLDNN memory for the chunk
     // because we don't have the complete data type and shape information for
     // the chunk.
+    CheckAndAlloc();
     void *off_addr = static_cast<char *>(ptr_->shandle.dptr) + byte_offset_;
     // Create the primitive desc for the new mkldnn memory.
     mkldnn::memory::dims dims(shape().ndim());
@@ -665,6 +666,7 @@ const mkldnn::memory *NDArray::GetMKLDNNData() const {
   } else {
     // If this isn't a view, we can create a MKLDNN memory and store it in the
     // chunk.
+    CheckAndAlloc();
     ptr_->SetMKLMem(shape_, dtype_);
     MKLDNNStream::Get()->RegisterMem(ptr_->mkl_mem_->GetMem());
     return ptr_->mkl_mem_->GetRaw();

From 93228649340bcacb8056d47d8f6f8a78a9805ae4 Mon Sep 17 00:00:00 2001
From: Haozheng Fan <fhztc1997618@gmail.com>
Date: Mon, 28 Oct 2019 14:02:49 +0800
Subject: [PATCH 533/813] Infra for tvm op runtime dispatch (#16100)

* infra for dispatch tvm op

* fix ci and sanity error

* disable shape with hint and fix coding style

* rename to avoid conflict with original dot

* update tvm and use soft link

* config file moves to lib/ when using Makefile

* add tvmop.conf to ci

* fix rebase

* fix rebase

* use inspect to detect dispatchable func
---
 CMakeLists.txt                            |   2 +-
 Makefile                                  |   4 +-
 benchmark/python/tvmop/benchmark_tvmop.py |  57 ++++++
 ci/jenkins/Jenkins_steps.groovy           |  20 +-
 contrib/tvmop/compile.py                  |  13 ++
 contrib/tvmop/core/__init__.py            |   2 +-
 contrib/tvmop/core/multiarray.py          |  53 ++++++
 contrib/tvmop/opdef.py                    |  42 ++++-
 contrib/tvmop/space.py                    | 212 ++++++++++++++++++++++
 include/mxnet/c_api.h                     |  26 +++
 python/mxnet/__init__.py                  |   2 +
 python/mxnet/_ctypes/space.py             |  96 ++++++++++
 python/mxnet/base.py                      |   6 -
 python/mxnet/libinfo.py                   |  31 ++++
 python/mxnet/space.py                     |   1 +
 python/mxnet/tvmop.py                     |  37 ++++
 src/c_api/c_api.cc                        |  23 +++
 src/operator/contrib/tvmop/dot.cc         | 153 ++++++++++++++++
 src/operator/tvmop/op_module.cc           |  12 +-
 src/operator/tvmop/op_module.h            |  74 ++++++++
 20 files changed, 841 insertions(+), 25 deletions(-)
 create mode 100644 benchmark/python/tvmop/benchmark_tvmop.py
 create mode 100644 contrib/tvmop/core/multiarray.py
 create mode 100644 contrib/tvmop/space.py
 create mode 100644 python/mxnet/_ctypes/space.py
 create mode 120000 python/mxnet/space.py
 create mode 100644 python/mxnet/tvmop.py
 create mode 100644 src/operator/contrib/tvmop/dot.cc

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 99ad34bed3dc..a06aa9dba485 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -763,7 +763,7 @@ if(USE_TVM_OP)
     endif()
   endif()
 
-  set(TVM_OP_COMPILE_OPTIONS "-o${CMAKE_CURRENT_BINARY_DIR}/libtvmop.so")
+  set(TVM_OP_COMPILE_OPTIONS "-o${CMAKE_CURRENT_BINARY_DIR}/libtvmop.so" "--config" "${CMAKE_CURRENT_BINARY_DIR}/tvmop.conf")
   if(CUDA_ARCH_BIN)
     set(TVM_OP_COMPILE_OPTIONS "${TVM_OP_COMPILE_OPTIONS}" "--cuda-arch" "${CUDA_ARCH_BIN}")
   endif()
diff --git a/Makefile b/Makefile
index be0d34051873..63a978d01d8a 100644
--- a/Makefile
+++ b/Makefile
@@ -622,6 +622,7 @@ DMLCCORE:
 
 lib/libtvm_runtime.so:
 	echo "Compile TVM"
+	@mkdir -p $(@D)
 	[ -e $(LLVM_PATH)/bin/llvm-config ] || sh $(ROOTDIR)/contrib/tvmop/prepare_tvm.sh; \
 	cd $(TVM_PATH)/build; \
 	cmake -DUSE_LLVM="$(LLVM_PATH)/bin/llvm-config" \
@@ -632,12 +633,13 @@ lib/libtvm_runtime.so:
 	ls $(ROOTDIR)/lib; \
 	cd $(ROOTDIR)
 
-TVM_OP_COMPILE_OPTIONS = -o $(ROOTDIR)/lib/libtvmop.so
+TVM_OP_COMPILE_OPTIONS = -o $(ROOTDIR)/lib/libtvmop.so --config $(ROOTDIR)/lib/tvmop.conf
 ifneq ($(CUDA_ARCH),)
 	TVM_OP_COMPILE_OPTIONS += --cuda-arch "$(CUDA_ARCH)"
 endif
 lib/libtvmop.so: lib/libtvm_runtime.so $(wildcard contrib/tvmop/*/*.py contrib/tvmop/*.py)
 	echo "Compile TVM operators"
+	@mkdir -p $(@D)
 	PYTHONPATH=$(TVM_PATH)/python:$(TVM_PATH)/topi/python:$(ROOTDIR)/contrib \
 		LD_LIBRARY_PATH=$(ROOTDIR)/lib \
 	    python3 $(ROOTDIR)/contrib/tvmop/compile.py $(TVM_OP_COMPILE_OPTIONS)
diff --git a/benchmark/python/tvmop/benchmark_tvmop.py b/benchmark/python/tvmop/benchmark_tvmop.py
new file mode 100644
index 000000000000..14ad49bb09ef
--- /dev/null
+++ b/benchmark/python/tvmop/benchmark_tvmop.py
@@ -0,0 +1,57 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import time
+import mxnet as mx
+import numpy as _np
+from mxnet import np, npx
+
+def measure_cost(repeat, func_name, *args, **kwargs):
+    """Measure time cost of running a function
+    """
+    mx.nd.waitall()
+    start = time.time()
+    for _ in range(repeat):
+        func_name(*args, **kwargs)
+    mx.nd.waitall()
+    end = time.time()
+    diff = end - start
+    return diff / repeat
+
+
+def test_tvm_dot():
+    # benchmark
+    for i in list(range(1000, 1100, 4)):
+        m = i
+        k = i
+        n = i
+        print("{} * {} X {} * {}".format(m, k, k, n))
+        a = mx.nd.random.uniform(shape=(m, k), dtype='float32')
+        b = mx.nd.random.uniform(shape=(k, n), dtype='float32')
+        cost = measure_cost(2, mx.nd.contrib.tvm_dot, a, b)
+        print("dispatch cost: {} ms".format(cost * 1000))
+        a = mx.nd.random.uniform(shape=(m, k), dtype='float32')
+        b = mx.nd.random.uniform(shape=(k, n), dtype='float32')
+        cost = measure_cost(2, mx.nd.contrib.tvm_dot_fallback, a, b)
+        print("fallback cost: {} ms".format(cost * 1000))
+        a = mx.nd.random.uniform(shape=(m, k), dtype='float32')
+        b = mx.nd.random.uniform(shape=(k, n), dtype='float32')
+        cost = measure_cost(2, mx.nd.dot, a, b)
+        print("dot cost: {} ms".format(cost * 1000))
+
+if __name__ == "__main__":
+    test_tvm_dot()
diff --git a/ci/jenkins/Jenkins_steps.groovy b/ci/jenkins/Jenkins_steps.groovy
index 7f6200a28016..d7c2b9679ca3 100644
--- a/ci/jenkins/Jenkins_steps.groovy
+++ b/ci/jenkins/Jenkins_steps.groovy
@@ -23,24 +23,24 @@
 utils = load('ci/Jenkinsfile_utils.groovy')
 
 // mxnet libraries
-mx_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a'
-mx_lib_cython = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
+mx_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, lib/tvmop.conf, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a'
+mx_lib_cython = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, lib/tvmop.conf, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
 
 // Python wheels
 mx_pip = 'build/*.whl'
 
 // mxnet cmake libraries, in cmake builds we do not produce a libnvvm static library by default.
-mx_cmake_lib = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so'
+mx_cmake_lib = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/tvmop.conf, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so'
 mx_cmake_lib_no_tvm_op = 'build/libmxnet.so, build/libmxnet.a, build/libsample_lib.so, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so'
-mx_cmake_lib_cython = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
+mx_cmake_lib_cython = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/tvmop.conf, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
 // mxnet cmake libraries, in cmake builds we do not produce a libnvvm static library by default.
-mx_cmake_lib_debug = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/libsample_lib.so, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests'
-mx_cmake_mkldnn_lib = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so, build/3rdparty/mkldnn/src/libmkldnn.so.0'
-mx_mkldnn_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, libsample_lib.so, lib/libiomp5.so, lib/libmkldnn.so.0, lib/libmklml_intel.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a'
-mx_tensorrt_lib = 'build/libmxnet.so, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, lib/libnvonnxparser_runtime.so.0, lib/libnvonnxparser.so.0, lib/libonnx_proto.so, lib/libonnx.so'
-mx_lib_cpp_examples = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, 3rdparty/ps-lite/build/libps.a, deps/lib/libprotobuf-lite.a, deps/lib/libzmq.a, build/cpp-package/example/*, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
+mx_cmake_lib_debug = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/tvmop.conf, build/libsample_lib.so, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests'
+mx_cmake_mkldnn_lib = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/tvmop.conf, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so, build/3rdparty/mkldnn/src/libmkldnn.so.0'
+mx_mkldnn_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, lib/tvmop.conf, libsample_lib.so, lib/libiomp5.so, lib/libmkldnn.so.0, lib/libmklml_intel.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a'
+mx_tensorrt_lib = 'build/libmxnet.so, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/tvmop.conf, lib/libnvonnxparser_runtime.so.0, lib/libnvonnxparser.so.0, lib/libonnx_proto.so, lib/libonnx.so'
+mx_lib_cpp_examples = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, lib/tvmop.conf, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, 3rdparty/ps-lite/build/libps.a, deps/lib/libprotobuf-lite.a, deps/lib/libzmq.a, build/cpp-package/example/*, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
 mx_lib_cpp_examples_no_tvm_op = 'lib/libmxnet.so, lib/libmxnet.a, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, 3rdparty/ps-lite/build/libps.a, deps/lib/libprotobuf-lite.a, deps/lib/libzmq.a, build/cpp-package/example/*, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
-mx_lib_cpp_examples_cpu = 'build/libmxnet.so, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/cpp-package/example/*'
+mx_lib_cpp_examples_cpu = 'build/libmxnet.so, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/tvmop.conf, build/cpp-package/example/*'
 
 // Python unittest for CPU
 // Python 2
diff --git a/contrib/tvmop/compile.py b/contrib/tvmop/compile.py
index 3c0efdd6b806..b0254218077a 100644
--- a/contrib/tvmop/compile.py
+++ b/contrib/tvmop/compile.py
@@ -18,12 +18,15 @@
 # coding: utf-8
 """TVM Operator compile entry point"""
 import tvm
+from tvm import autotvm
 
 import os
 import argparse
 import re
+import json
 import logging
 from tvmop.opdef import __OP_DEF__
+from tvmop.space import ConfigSpaces, ConfigSpace
 from tvm.autotvm.measure.measure_methods import set_cuda_target_arch
 
 logging.basicConfig(level=logging.INFO)
@@ -70,6 +73,8 @@ def get_cuda_arch(arch):
                         help="Target path which stores compiled library")
     parser.add_argument('--cuda-arch', type=str, default=None, dest='cuda_arch',
                         help='The cuda arch for compiling kernels for')
+    parser.add_argument("--config", action="store", required=True, dest="config_path",
+                        help="Path which stores the config file")
     arguments = parser.parse_args()
 
     func_list_llvm = []
@@ -78,6 +83,7 @@ def get_cuda_arch(arch):
     # TODO: attach instruction features to the library, e.g., avx-512, etc.
     for operator_def in __OP_DEF__:
         for sch, args, name in operator_def.invoke_all():
+            name = operator_def.get_op_name(name, args)
             if tvm.module.enabled(get_target(operator_def.target)):
                 func_list = func_list_llvm if operator_def.target == "cpu" else func_list_cuda
                 func_lower = tvm.lower(sch, args,
@@ -96,3 +102,10 @@ def get_cuda_arch(arch):
             set_cuda_target_arch(cuda_arch)
     func_binary = tvm.build(lowered_funcs, name="tvmop")
     func_binary.export_library(arguments.target_path)
+
+    config_spaces = ConfigSpaces()
+    for operator_def in __OP_DEF__:
+        for config_space, name in operator_def.get_config_spaces():
+            config_spaces[name] = ConfigSpace.from_tvm(config_space)
+    with open(arguments.config_path, "w") as f:
+        json.dump(config_spaces.to_json_dict(), f)
diff --git a/contrib/tvmop/core/__init__.py b/contrib/tvmop/core/__init__.py
index 841d4ad9db27..e309f237df05 100644
--- a/contrib/tvmop/core/__init__.py
+++ b/contrib/tvmop/core/__init__.py
@@ -15,4 +15,4 @@
 # specific language governing permissions and limitations
 # under the License.
 
-from . import umath, fromnumeric
+from . import umath, fromnumeric, multiarray
diff --git a/contrib/tvmop/core/multiarray.py b/contrib/tvmop/core/multiarray.py
new file mode 100644
index 000000000000..c8eed5b45368
--- /dev/null
+++ b/contrib/tvmop/core/multiarray.py
@@ -0,0 +1,53 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# coding: utf-8
+import tvm
+from tvm import autotvm
+from .. import defop, AllTypes
+from .. import assign_by_req, reduce_axes
+
+def compute_dot(A, B):
+    M = A.shape[0]
+    K = A.shape[1]
+    N = B.shape[1]
+    k = tvm.reduce_axis((0, K), 'k')
+    C = tvm.compute((M, N),
+                    lambda x, y: tvm.sum(A[x, k] * B[k, y], axis=k),
+                    name='C')
+    return C
+
+
+@defop(name="dot", target="cpu", dtype=AllTypes)
+def dot(dtype, fallback):
+    cfg = autotvm.get_config()
+    cfg.define_knob("bn", [64] if fallback else [64, 32])
+    cfg.define_knob("factor", [4] if fallback else [4])
+    M = tvm.var("M")
+    K = tvm.var("K")
+    N = tvm.var("N")
+    A = tvm.placeholder((M, K), name='A', dtype=dtype)
+    B = tvm.placeholder((K, N), name='B', dtype=dtype)
+    C = compute_dot(A, B)
+    s = tvm.create_schedule(C.op)
+    # Blocking by loop tiling
+    xo, yo, xi, yi = s[C].tile(C.op.axis[0], C.op.axis[1], cfg["bn"].val, cfg["bn"].val)
+    k, = s[C].op.reduce_axis
+    ko, ki = s[C].split(k, factor=cfg["factor"].val)
+    # Hoist reduction domain outside the blocking loop
+    s[C].reorder(xo, yo, ko, ki, xi, yi)
+    return s, [A, B, C]
diff --git a/contrib/tvmop/opdef.py b/contrib/tvmop/opdef.py
index 39c42f4dd465..1e0f34669b10 100644
--- a/contrib/tvmop/opdef.py
+++ b/contrib/tvmop/opdef.py
@@ -17,6 +17,8 @@
 
 # coding: utf-8
 import tvm
+import inspect
+from tvm import autotvm
 from itertools import product
 
 __OP_DEF__ = []
@@ -68,6 +70,7 @@ def __init__(self, func, name, target, auto_broadcast, **kwargs):
         self.name = name
         self.target = target
         self.auto_broadcast = auto_broadcast
+        self.dispatchable = 'fallback' in inspect.signature(self.func).parameters
 
     def __call__(self, *args, **kwargs):
         return self.func(*args, **kwargs)
@@ -75,12 +78,41 @@ def __call__(self, *args, **kwargs):
     def invoke_all(self):
         for each_kwargs in self.arg_combination:
             if self.attrs_valid(**each_kwargs):
-                sch, args = self.func(**each_kwargs)
                 name = self.name \
-                    + ''.join(["{}_{}".format(key, each_kwargs[key]) for key in self.attrs]) \
-                    + ''.join(["%s_%d" % (arg.dtype, len(arg.shape))
-                               for arg in args if hasattr(arg, 'shape')])
-                yield sch, args, name
+                    + ''.join(["{}_{}".format(key, each_kwargs[key]) for key in self.attrs])
+                if self.dispatchable is False:
+                    sch, args = self.func(**each_kwargs)
+                    yield sch, args, name
+                else:
+                    # register dispatch schedules
+                    config_space = autotvm.ConfigSpace()
+                    with autotvm.task.ApplyConfig(config_space):
+                            sch, args = self.func(fallback=False, **each_kwargs)
+                    for i in range(len(config_space)):
+                        config_entity = config_space.get(i)
+                        with autotvm.task.ApplyConfig(config_entity):
+                            sch, args = self.func(fallback=False, **each_kwargs)
+                        subname = name + "index_" + str(i)
+                        yield sch, args, subname
+                    # register fallback schedule
+                    config_space = autotvm.ConfigSpace()
+                    with autotvm.task.ApplyConfig(config_space):
+                            sch, args = self.func(fallback=True, **each_kwargs)
+                    subname = name + "fallback"
+                    yield sch, args, subname
+
+    def get_op_name(self, name, args):
+        return name + ''.join(["%s_%d" % (arg.dtype, len(arg.shape)) for arg in args if hasattr(arg, 'shape')])
+
+    def get_config_spaces(self):
+        for each_kwargs in self.arg_combination:
+            if self.attrs_valid(**each_kwargs) and self.dispatchable is True:
+                name = self.name \
+                    + ''.join(["{}_{}".format(key, each_kwargs[key]) for key in self.attrs])
+                config_space = autotvm.ConfigSpace()
+                with autotvm.task.ApplyConfig(config_space):
+                    self.func(fallback=False, **each_kwargs)
+                yield config_space, name
 
     def get_binds(self, args):
         if self.auto_broadcast:
diff --git a/contrib/tvmop/space.py b/contrib/tvmop/space.py
new file mode 100644
index 000000000000..589b931bc37d
--- /dev/null
+++ b/contrib/tvmop/space.py
@@ -0,0 +1,212 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# coding: utf-8
+"""ConfigSpace API."""
+from collections import OrderedDict
+import numpy as _np
+
+class OtherOptionSpace(object):
+    """The parameter space for general option"""
+    def __init__(self, entities):
+        self.entities = [OtherOptionEntity(e) for e in entities]
+
+    @classmethod
+    def from_tvm(cls, x):
+        return cls([e.val for e in x.entities])
+
+    def __len__(self):
+        return len(self.entities)
+
+    def __repr__(self):
+        return "OtherOption(%s) len=%d" % (self.entities, len(self))
+
+
+class OtherOptionEntity(object):
+    """The parameter entity for general option, with a detailed value"""
+    def __init__(self, val):
+        self.val = val
+
+    @classmethod
+    def from_tvm(cls, x):
+        """Build a OtherOptionEntity from autotvm.OtherOptionEntity
+
+        Parameters
+        ----------
+        cls: class
+            Calling class
+        x: autotvm.OtherOptionEntity
+            The source object
+
+        Returns
+        -------
+        ret: OtherOptionEntity
+            The corresponding OtherOptionEntity object
+        """
+        return cls(x.val)
+
+    def __repr__(self):
+        return str(self.val)
+
+
+class ConfigSpace(object):
+    """The configuration space of a schedule."""
+    def __init__(self, space_map, _entity_map):
+        self.space_map = space_map
+        self._entity_map = _entity_map
+        self._length = None
+
+    @classmethod
+    def from_tvm(cls, x):
+        """Build a ConfigSpace from autotvm.ConfigSpace
+
+        Parameters
+        ----------
+        cls: class
+            Calling class
+        x: autotvm.ConfigSpace
+            The source object
+
+        Returns
+        -------
+        ret: ConfigSpace
+            The corresponding ConfigSpace object
+        """
+        space_map = OrderedDict([(k, OtherOptionSpace.from_tvm(v)) for k, v in x.space_map.items()])
+        _entity_map = OrderedDict([(k, OtherOptionEntity.from_tvm(v)) for k, v in x._entity_map.items()])
+        return cls(space_map, _entity_map)
+
+    def __len__(self):
+
+        if self._length is None:
+            self._length = int(_np.prod([len(x) for x in self.space_map.values()]))
+        return self._length
+
+    def __repr__(self):
+        res = "ConfigSpace (len=%d, space_map=\n" % len(self)
+        for i, (name, space) in enumerate(self.space_map.items()):
+            res += "  %2d %s: %s\n" % (i, name, space)
+        return res + ")"
+
+    def to_json_dict(self):
+        """convert to a json serializable dictionary
+
+        Return
+        ------
+        ret: dict
+            a json serializable dictionary
+        """
+        ret = {}
+        entity_map = []
+        for k, v in self._entity_map.items():
+            if isinstance(v, OtherOptionEntity):
+                entity_map.append((k, 'ot', v.val))
+            else:
+                raise RuntimeError("Invalid entity instance: " + v)
+        ret['e'] = entity_map
+        space_map = []
+        for k, v in self.space_map.items():
+            entities = [e.val for e in v.entities]
+            space_map.append((k, 'ot', entities))
+        ret['s'] = space_map
+        return ret
+
+    @classmethod
+    def from_json_dict(cls, json_dict):
+        """Build a ConfigSpace from json serializable dictionary
+
+        Parameters
+        ----------
+        cls: class
+            The calling class
+        json_dict: dict
+            Json serializable dictionary.
+
+        Returns
+        -------
+        ret: ConfigSpace
+            The corresponding ConfigSpace object
+        """
+        entity_map = OrderedDict()
+        for item in json_dict["e"]:
+            key, knob_type, knob_args = item
+            if knob_type == 'ot':
+                entity = OtherOptionEntity(knob_args)
+            else:
+                raise RuntimeError("Invalid config knob type: " + knob_type)
+            entity_map[str(key)] = entity
+        space_map = OrderedDict()
+        for item in json_dict["s"]:
+            key, knob_type, knob_args = item
+            if knob_type == 'ot':
+                space = OtherOptionSpace(knob_args)
+            else:
+                raise RuntimeError("Invalid config knob type: " + knob_type)
+            space_map[str(key)] = space
+        return cls(space_map, entity_map)
+
+
+class ConfigSpaces(object):
+    """The configuration spaces of all ops."""
+    def __init__(self):
+        self.spaces = {}
+
+    def __setitem__(self, name, space):
+        self.spaces[name] = space
+
+    def __len__(self):
+        return len(self.spaces)
+
+    def __repr__(self):
+        res = "ConfigSpaces (len=%d, config_space=\n" % len(self)
+        for i, (key, val) in enumerate(self.spaces.items()):
+            res += "  %2d %s:\n %s\n" % (i, key, val)
+        return res + ")"
+
+    def to_json_dict(self):
+        """convert to a json serializable dictionary
+
+        Return
+        ------
+        ret: dict
+            a json serializable dictionary
+        """
+        ret = []
+        for k, v in self.spaces.items():
+            ret.append((k, v.to_json_dict()))
+        return ret
+
+    @classmethod
+    def from_json_dict(cls, json_dict):
+        """Build a ConfigSpaces from json serializable dictionary
+
+        Parameters
+        ----------
+        cls: class
+            The calling class
+        json_dict: dict
+            Json serializable dictionary.
+
+        Returns
+        -------
+        ret: ConfigSpaces
+            The corresponding ConfigSpaces object
+        """
+        ret = cls()
+        for key, val in json_dict:
+            ret.spaces[key] = ConfigSpace.from_json_dict(val)
+        return ret
diff --git a/include/mxnet/c_api.h b/include/mxnet/c_api.h
index 2463a5b75cfd..bbd67f059fdf 100644
--- a/include/mxnet/c_api.h
+++ b/include/mxnet/c_api.h
@@ -525,6 +525,32 @@ MXNET_DLL int MXGetVersion(int *out);
  */
 #if MXNET_USE_TVM_OP
 MXNET_DLL int MXLoadTVMOp(const char *libpath);
+
+struct OtherOptionEntity {
+  int val;
+};
+
+struct OtherOptionSpace {
+  OtherOptionEntity* entities;
+  int entities_size;
+};
+
+struct ConfigSpace {
+  int entity_map_size;
+  char** entity_map_key;
+  OtherOptionEntity* entity_map_val;
+  int space_map_size;
+  char** space_map_key;
+  OtherOptionSpace* space_map_val;
+};
+
+typedef struct ConfigSpaces {
+  int spaces_size;
+  char** spaces_key;
+  ConfigSpace* spaces_val;
+} ConfigSpaces;
+
+MXNET_DLL int MXLoadTVMConfig(ConfigSpaces config);
 #endif  // MXNET_USE_TVM_OP
 
 
diff --git a/python/mxnet/__init__.py b/python/mxnet/__init__.py
index 3dc80cd5a0b4..a4ba4f3b6093 100644
--- a/python/mxnet/__init__.py
+++ b/python/mxnet/__init__.py
@@ -87,6 +87,8 @@
 
 from . import gluon
 
+from . import tvmop
+
 __version__ = base.__version__
 
 # Dist kvstore module which launches a separate process when role is set to "server".
diff --git a/python/mxnet/_ctypes/space.py b/python/mxnet/_ctypes/space.py
new file mode 100644
index 000000000000..d1ee6e555e25
--- /dev/null
+++ b/python/mxnet/_ctypes/space.py
@@ -0,0 +1,96 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# coding: utf-8
+"""ConfigSpace ctypes API."""
+import ctypes
+
+from ..base import _LIB
+from ..base import c_str_array, c_array
+from ..base import check_call
+
+class COtherOptionEntity(ctypes.Structure):
+    """ctypes data structure for OtherOptionEntity"""
+    _fields_ = [("val", ctypes.c_int)]
+
+
+class COtherOptionSpace(ctypes.Structure):
+    """ctypes data structure for OtherOptionSpace"""
+    _fields_ = [("entities", ctypes.POINTER(COtherOptionEntity)),
+                ("entities_size", ctypes.c_int)]
+
+
+class CConfigSpace(ctypes.Structure):
+    """ctypes data structure for ConfigSpace"""
+    _fields_ = [("entity_map_size", ctypes.c_int),
+                ("entity_map_key", ctypes.POINTER(ctypes.c_char_p)),
+                ("entity_map_val", ctypes.POINTER(COtherOptionEntity)),
+                ("space_map_size", ctypes.c_int),
+                ("space_map_key", ctypes.POINTER(ctypes.c_char_p)),
+                ("space_map_val", ctypes.POINTER(COtherOptionSpace))]
+
+
+class CConfigSpaces(ctypes.Structure):
+    """ctypes data structure for ConfigSpaces"""
+    _fields_ = [("spaces_size", ctypes.c_int),
+                ("spaces_key", ctypes.POINTER(ctypes.c_char_p)),
+                ("spaces_val", ctypes.POINTER(CConfigSpace))]
+
+
+def c_other_option_entity(x):
+    """constructor for OtherOptionEntity"""
+    ret = COtherOptionEntity()
+    ret.val = x.val
+    return ret
+
+
+def c_other_option_space(x):
+    """constructor for OtherOptionSpace"""
+    ret = COtherOptionSpace()
+    ret.entities = c_array(COtherOptionEntity,
+                           [c_other_option_entity(e) for e in x.entities])
+    ret.entities_size = len(x.entities)
+    return ret
+
+
+def c_config_space(x):
+    """constructor for ConfigSpace"""
+    ret = CConfigSpace()
+    ret.entity_map_key = c_str_array(x._entity_map.keys())
+    ret.entity_map_val = c_array(COtherOptionEntity,
+                                 [c_other_option_entity(e) for e in x._entity_map.values()])
+    ret.entity_map_size = len(x._entity_map)
+    ret.space_map_key = c_str_array(x.space_map.keys())
+    ret.space_map_val = c_array(COtherOptionSpace,
+                                [c_other_option_space(v) for v in x.space_map.values()])
+    ret.space_map_size = len(x.space_map)
+    return ret
+
+
+def c_config_spaces(x):
+    """constructor for ConfigSpaces"""
+    ret = CConfigSpaces()
+    ret.spaces_size = len(x.spaces)
+    ret.spaces_key = c_str_array(x.spaces.keys())
+    ret.spaces_val = c_array(CConfigSpace, [c_config_space(c) for c in x.spaces.values()])
+    return ret
+
+
+def _set_tvm_op_config(x):
+    """ctypes implementation of populating the config singleton"""
+    check_call(_LIB.MXLoadTVMConfig(c_config_spaces(x)))
+    return x
diff --git a/python/mxnet/base.py b/python/mxnet/base.py
index db1fa29ab9b4..35acba3dbe53 100644
--- a/python/mxnet/base.py
+++ b/python/mxnet/base.py
@@ -743,12 +743,6 @@ def write_all_str(module_file, module_all_list):
 ctypes.pythonapi.PyCapsule_GetPointer.restype = ctypes.c_void_p
 
 
-from .runtime import Features
-if Features().is_enabled("TVM_OP"):
-    _LIB_TVM_OP = libinfo.find_lib_path("libtvmop")
-    check_call(_LIB.MXLoadTVMOp(c_str(_LIB_TVM_OP[0])))
-
-
 _NP_OP_PREFIX = '_np_'
 _NP_OP_SUBMODULE_LIST = ['_random_', '_linalg_']
 
diff --git a/python/mxnet/libinfo.py b/python/mxnet/libinfo.py
index 8e0ae05f3378..589a69ce60cc 100644
--- a/python/mxnet/libinfo.py
+++ b/python/mxnet/libinfo.py
@@ -110,5 +110,36 @@ def find_include_path():
                                ' or ' + src_incl_path + '\n')
 
 
+def find_conf_path(prefix='tvmop'):
+    """Find TVM op config files.
+
+    Returns
+    -------
+    conf_path : string
+        Path to the config files.
+    """
+    conf_from_env = os.environ.get('MXNET_CONF_PATH')
+    if conf_from_env:
+        if os.path.isfile(conf_from_env):
+            if not os.path.isabs(conf_from_env):
+                logging.warning("MXNET_CONF_PATH should be an absolute path, instead of: %s",
+                                conf_from_env)
+            else:
+                return conf_from_env
+        else:
+            logging.warning("MXNET_CONF_PATH '%s' doesn't exist", conf_from_env)
+
+    curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
+    makefile_path = os.path.join(curr_path, '../../lib/')
+    cmake_build_path = os.path.join(curr_path, '../../build/')
+    candidates_path = [makefile_path, cmake_build_path]
+    candidates_path = [p + prefix + '.conf' for p in candidates_path]
+    conf_path = [p for p in candidates_path if os.path.exists(p) and os.path.isfile(p)]
+    if len(conf_path) == 0:
+        raise RuntimeError('Cannot find the TVM op config.\n' +
+                           'List of candidates:\n' + str('\n'.join(candidates_path)))
+    return conf_path
+
+
 # current version
 __version__ = "1.6.0"
diff --git a/python/mxnet/space.py b/python/mxnet/space.py
new file mode 120000
index 000000000000..18f6a91e6055
--- /dev/null
+++ b/python/mxnet/space.py
@@ -0,0 +1 @@
+../../contrib/tvmop/space.py
\ No newline at end of file
diff --git a/python/mxnet/tvmop.py b/python/mxnet/tvmop.py
new file mode 100644
index 000000000000..9ec278afb7a0
--- /dev/null
+++ b/python/mxnet/tvmop.py
@@ -0,0 +1,37 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# coding: utf-8
+"""Init tvm ops."""
+from .runtime import Features
+
+if Features().is_enabled("TVM_OP"):
+    import json
+
+    from ._ctypes.space import _set_tvm_op_config
+    from .base import check_call, _LIB, c_str
+    from .space import ConfigSpaces
+    from .libinfo import find_lib_path, find_conf_path
+
+    _LIB_TVM_OP = find_lib_path("libtvmop")
+    check_call(_LIB.MXLoadTVMOp(c_str(_LIB_TVM_OP[0])))
+
+    # op sch config
+    _CONF_TVM_OP = find_conf_path("tvmop")
+    with open(_CONF_TVM_OP[0], "r") as f:
+        ret = ConfigSpaces.from_json_dict(json.load(f))
+    _set_tvm_op_config(ret)
diff --git a/src/c_api/c_api.cc b/src/c_api/c_api.cc
index 1696bf572e4c..fb88cae2cbba 100644
--- a/src/c_api/c_api.cc
+++ b/src/c_api/c_api.cc
@@ -181,6 +181,29 @@ int MXLoadTVMOp(const char *libpath) {
   tvm::runtime::TVMOpModule::Get()->Load(libpath);
   API_END();
 }
+
+int MXLoadTVMConfig(ConfigSpaces config) {
+  API_BEGIN();
+  for (int k = 0; k < config.spaces_size; ++k) {
+    tvm::runtime::TVMOpConfig& entry = ::dmlc::Registry<tvm::runtime::TVMOpConfig>::Get()
+      ->__REGISTER_OR_GET__(std::string(config.spaces_key[k]));
+    const ConfigSpace& c = config.spaces_val[k];
+    for (int i = 0; i < c.entity_map_size; ++i) {
+      entry.add_entity(std::string(c.entity_map_key[i]), c.entity_map_val[i].val);
+    }
+    for (int i = 0; i < c.space_map_size; ++i) {
+      std::string name = std::string(c.space_map_key[i]);
+      std::vector<int> entities;
+      for (int j = 0; j < c.space_map_val[i].entities_size; ++j) {
+        int val = c.space_map_val[i].entities[j].val;
+        entities.push_back(val);
+      }
+      entry.add_space(name, entities);
+    }
+  }
+  API_END();
+}
+
 #endif  // MXNET_USE_TVM_OP
 
 int MXNDArrayCreateNone(NDArrayHandle *out) {
diff --git a/src/operator/contrib/tvmop/dot.cc b/src/operator/contrib/tvmop/dot.cc
new file mode 100644
index 000000000000..c6bec570a7e8
--- /dev/null
+++ b/src/operator/contrib/tvmop/dot.cc
@@ -0,0 +1,153 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file dot.cc
+ * \brief
+ * \author Haozheng Fan
+ */
+#ifdef MXNET_USE_TVM_OP
+#include <tvm/runtime/packed_func.h>
+#include <tvm/runtime/registry.h>
+#include <tvm/runtime/c_runtime_api.h>
+#include <mxnet/base.h>
+#include <string>
+#include "../../tensor/elemwise_binary_broadcast_op.h"
+#include "../../tvmop/op_module.h"
+#include "../../tensor/elemwise_binary_op.h"
+
+namespace mxnet {
+namespace op {
+
+int SplitSch(const ::tvm::runtime::TVMOpConfig& config,
+             const ::std::string& name,
+             const std::vector<int>& size) {
+  const ::tvm::runtime::OtherOptionSpace& space = config.get_space(name);
+  int weight = config.get_weight(name);
+  int num_space = space.size(), num_size = size.size();
+  for (int i = 0; i < num_space; ++i) {
+    bool flag = true;
+    for (int j = 0; j < num_size; ++j) {
+      if (size[j] % space[i].get_val() != 0) {
+        flag = false;
+        break;
+      }
+    }
+    if (flag) {
+      return i * weight;
+    }
+  }
+  return -1;
+}
+
+std::string DotSch(const std::string name,
+                   const nnvm::NodeAttrs& attrs,
+                   const mxnet::ShapeVector& in_attrs,
+                   const mxnet::ShapeVector& out_attrs) {
+  const ::tvm::runtime::TVMOpConfig& config = tvm::runtime::GetOpConfig(name);
+  int m = in_attrs[0][0];
+  int k = in_attrs[0][1];
+  int n = in_attrs[1][1];
+  int idx_bn = SplitSch(config, "bn", {m, n});
+  int idx_factor = SplitSch(config, "factor", {k});
+  int idx = idx_bn + idx_factor;
+  if (idx_bn == -1 || idx_factor == -1) {
+    return "fallback";
+  }
+  return "index_" + std::to_string(idx);
+}
+
+void TVMDotForward(const nnvm::NodeAttrs& attrs,
+                const mxnet::OpContext& ctx,
+                const std::vector<TBlob>& inputs,
+                const std::vector<OpReqType>& req,
+                const std::vector<TBlob>& outputs) {
+  CHECK_EQ(inputs.size(), 2U);
+  CHECK_EQ(outputs.size(), 1U);
+  std::string funcname = "dot";
+  std::string sch = DotSch(funcname, attrs, {inputs[0].shape_, inputs[1].shape_},
+                           {outputs[0].shape_});
+  tvm::runtime::TVMOpModule::Get()->Call(funcname + sch, ctx, {inputs[0], inputs[1], outputs[0]});
+}
+
+void TVMDotFallbackForward(const nnvm::NodeAttrs& attrs,
+                        const mxnet::OpContext& ctx,
+                        const std::vector<TBlob>& inputs,
+                        const std::vector<OpReqType>& req,
+                        const std::vector<TBlob>& outputs) {
+  CHECK_EQ(inputs.size(), 2U);
+  CHECK_EQ(outputs.size(), 1U);
+  std::string funcname = "dot";
+  std::string sch = "fallback";
+  tvm::runtime::TVMOpModule::Get()->Call(funcname + sch, ctx, {inputs[0], inputs[1], outputs[0]});
+}
+
+bool TVMDotShape(const nnvm::NodeAttrs& attrs,
+              mxnet::ShapeVector *in_attrs,
+              mxnet::ShapeVector *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 2U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  const mxnet::TShape& a_shape = in_attrs->at(0);
+  const mxnet::TShape& b_shape = in_attrs->at(1);
+  CHECK_EQ(a_shape.ndim(), 2U);
+  CHECK_EQ(b_shape.ndim(), 2U);
+  mxnet::TShape tmp_shape(2, -1);
+  tmp_shape[1] = b_shape[0];
+  SHAPE_ASSIGN_CHECK(*in_attrs, 0, tmp_shape);
+
+  tmp_shape[0] = a_shape[1];
+  tmp_shape[1] = -1;
+  SHAPE_ASSIGN_CHECK(*in_attrs, 1, tmp_shape);
+
+  tmp_shape[0] = a_shape[0];
+  tmp_shape[1] = b_shape[1];
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, tmp_shape);
+  return shape_is_known(*in_attrs) && shape_is_known(*out_attrs);
+}
+
+NNVM_REGISTER_OP(_contrib_tvm_dot)
+    .set_num_inputs(2)
+    .set_num_outputs(1)
+    .add_argument("a", "NDArray-or-Symbol", "first input")
+    .add_argument("b", "NDArray-or-Symbol", "second input")
+    .set_attr<nnvm::FListInputNames>("FListInputNames",
+      [](const NodeAttrs& attrs) {
+        return std::vector<std::string>{"a", "b"};
+      })
+    .set_attr<mxnet::FInferShape>("FInferShape", TVMDotShape)
+    .set_attr<nnvm::FInferType>("FInferType", mxnet::op::ElemwiseType<2, 1>)
+    .set_attr<mxnet::FCompute>("FCompute<cpu>", TVMDotForward);
+
+NNVM_REGISTER_OP(_contrib_tvm_dot_fallback)
+    .set_num_inputs(2)
+    .set_num_outputs(1)
+    .add_argument("a", "NDArray-or-Symbol", "first input")
+    .add_argument("b", "NDArray-or-Symbol", "second input")
+    .set_attr<nnvm::FListInputNames>("FListInputNames",
+      [](const NodeAttrs& attrs) {
+        return std::vector<std::string>{"a", "b"};
+      })
+    .set_attr<mxnet::FInferShape>("FInferShape", TVMDotShape)
+    .set_attr<nnvm::FInferType>("FInferType", mxnet::op::ElemwiseType<2, 1>)
+    .set_attr<mxnet::FCompute>("FCompute<cpu>", TVMDotFallbackForward);
+
+}  // namespace op
+}  // namespace mxnet
+#endif  // MXNET_USE_TVM_OP
diff --git a/src/operator/tvmop/op_module.cc b/src/operator/tvmop/op_module.cc
index ea7f73069698..b45df5dbdd4a 100644
--- a/src/operator/tvmop/op_module.cc
+++ b/src/operator/tvmop/op_module.cc
@@ -31,7 +31,9 @@
 #include <vector>
 #include "op_module.h"
 
-using namespace tvm::runtime;
+namespace dmlc {
+  DMLC_REGISTRY_ENABLE(::tvm::runtime::TVMOpConfig);
+}  // namespace dmlc
 
 namespace tvm {
 namespace runtime {
@@ -137,6 +139,14 @@ void TVMOpModule::CallEx(const std::string &func_name,
 #endif
 }
 
+const TVMOpConfig& GetOpConfig(const std::string& name) {
+  const TVMOpConfig* ret = ::dmlc::Registry<TVMOpConfig>::Get()->Find(name);
+  CHECK(ret != NULL)
+    << "op " << name << "does not exist.";
+  return *ret;
+}
+
 }  // namespace runtime
 }  // namespace tvm
+
 #endif  // MXNET_USE_TVM_OP
diff --git a/src/operator/tvmop/op_module.h b/src/operator/tvmop/op_module.h
index d28dd1b5b0c2..269a0aa50c11 100644
--- a/src/operator/tvmop/op_module.h
+++ b/src/operator/tvmop/op_module.h
@@ -32,6 +32,7 @@
 #include <mutex>
 #include <string>
 #include <vector>
+#include <map>
 
 namespace tvm {
 namespace runtime {
@@ -72,6 +73,79 @@ class TVMOpModule {
   std::shared_ptr<Module> module_ptr_;
 };
 
+class OtherOptionEntity {
+ public:
+  explicit OtherOptionEntity(int val): val_(val) {}
+  OtherOptionEntity(): val_(0) {}
+  inline int get_val() const {
+    return val_;
+  }
+ private:
+  int val_;
+};
+
+class OtherOptionSpace {
+ public:
+  explicit OtherOptionSpace(const std::vector<int>& entities) {
+    int size = entities.size();
+    for (int i = 0; i < size; ++i) {
+      this->entities_.push_back(OtherOptionEntity(entities[i]));
+    }
+  }
+
+  OtherOptionSpace() {}
+
+  inline OtherOptionEntity &operator[] (int idx) {
+    return entities_[idx];
+  }
+
+  inline const OtherOptionEntity &operator[] (int idx) const {
+    return entities_[idx];
+  }
+
+  inline int size() const {
+    return entities_.size();
+  }
+
+ private:
+  std::vector<OtherOptionEntity> entities_;
+};
+
+class TVMOpConfig {
+ public:
+  std::string name;
+
+  inline TVMOpConfig& add_space(const std::string& name, const std::vector<int>& val) {
+    int size = val.size();
+    space_map_[name] = OtherOptionSpace(val);
+    weight_map_[name] = weight_acc_;
+    weight_acc_ *= size;
+    return *this;
+  }
+  inline TVMOpConfig& add_entity(const std::string& name, const int val) {
+    entity_map_[name] = OtherOptionEntity(val);
+    return *this;
+  }
+
+  TVMOpConfig(): weight_acc_(1) {}
+
+  inline const OtherOptionSpace& get_space(const std::string& name) const {
+    return space_map_.at(name);
+  }
+
+  inline int get_weight(const std::string& name) const {
+    return weight_map_.at(name);
+  }
+
+ private:
+  std::map<std::string, OtherOptionEntity> entity_map_;
+  std::map<std::string, OtherOptionSpace> space_map_;
+  std::map<std::string, int> weight_map_;
+  int weight_acc_;
+};
+
+const TVMOpConfig& GetOpConfig(const std::string& name);
+
 }  // namespace runtime
 }  // namespace tvm
 

From 86ed5f5c0a72d1e34b29b0396432d04888274ce2 Mon Sep 17 00:00:00 2001
From: "Huang, Guangtai" <guangtai@amazon.com>
Date: Mon, 28 Oct 2019 16:24:05 +0800
Subject: [PATCH 534/813] [NumPy][Operator] NumPy operator `may_share_memory`
 and `shares_memory` (#16533)

* init

* finish & fix bug of 'take'

* fix bug

* add dispatch
---
 python/mxnet/ndarray/numpy/_op.py             | 76 +++++++++++++++++-
 python/mxnet/numpy/multiarray.py              | 79 ++++++++++++++++++-
 python/mxnet/numpy_dispatch_protocol.py       |  4 +-
 python/mxnet/symbol/numpy/_symbol.py          | 41 +++++++++-
 src/operator/numpy/np_memory_op.cc            | 62 +++++++++++++++
 src/operator/numpy/np_memory_op.cu            | 34 ++++++++
 src/operator/numpy/np_memory_op.h             | 75 ++++++++++++++++++
 .../unittest/test_numpy_interoperability.py   | 15 ++++
 tests/python/unittest/test_numpy_op.py        | 27 ++++++-
 9 files changed, 406 insertions(+), 7 deletions(-)
 create mode 100644 src/operator/numpy/np_memory_op.cc
 create mode 100644 src/operator/numpy/np_memory_op.cu
 create mode 100644 src/operator/numpy/np_memory_op.h

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index fdb9694146b5..84aa4a1572d9 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -38,7 +38,7 @@
            'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
            'around', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
            'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal',
-           'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero']
+           'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory', 'may_share_memory']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -4909,3 +4909,77 @@ def nonzero(a):
     """
     out = _npi.nonzero(a).transpose()
     return tuple([out[i] for i in range(len(out))])
+
+
+@set_module('mxnet.ndarray.numpy')
+def shares_memory(a, b, max_work=None):
+    """
+    Determine if two arrays share memory
+
+    Parameters
+    ----------
+    a, b : ndarray
+        Input arrays
+
+    Returns
+    -------
+    out : bool
+
+    See Also
+    --------
+    may_share_memory
+
+    Examples
+    --------
+    >>> np.may_share_memory(np.array([1,2]), np.array([5,8,9]))
+    False
+
+    This function differs from the original `numpy.shares_memory
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.shares_memory.html>`_ in
+    the following way(s):
+
+    - Does not support `max_work`, it is a dummy argument
+    - Actually it is same as `may_share_memory` in MXNet DeepNumPy
+    """
+    return _npi.share_memory(a, b).item()
+
+
+@set_module('mxnet.ndarray.numpy')
+def may_share_memory(a, b, max_work=None):
+    """
+    Determine if two arrays might share memory
+
+    A return of True does not necessarily mean that the two arrays
+    share any element.  It just means that they *might*.
+
+    Only the memory bounds of a and b are checked by default.
+
+    Parameters
+    ----------
+    a, b : ndarray
+        Input arrays
+
+    Returns
+    -------
+    out : bool
+
+    See Also
+    --------
+    shares_memory
+
+    Examples
+    --------
+    >>> np.may_share_memory(np.array([1,2]), np.array([5,8,9]))
+    False
+    >>> x = np.zeros([3, 4])
+    >>> np.may_share_memory(x[:,0], x[:,1])
+    True
+
+    This function differs from the original `numpy.may_share_memory
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.may_share_memory.html>`_ in
+    the following way(s):
+
+    - Does not support `max_work`, it is a dummy argument
+    - Actually it is same as `shares_memory` in MXNet DeepNumPy
+    """
+    return _npi.share_memory(a, b).item()
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 5c9de8194a74..ef88638c857e 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -55,7 +55,8 @@
            'swapaxes', 'clip', 'argmax', 'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming',
            'blackman', 'flip', 'around', 'arctan2', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril',
            'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less',
-           'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero']
+           'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory',
+           'may_share_memory']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -1330,7 +1331,7 @@ def take(self, indices, axis=None, mode='raise'):  # pylint: disable=arguments-d
         The arguments are the same as for :py:func:`take`, with
         this array as data.
         """
-        take(self, indices, axis, mode=mode)
+        return take(self, indices, axis, mode=mode)
 
     def one_hot(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`one_hot`.
@@ -6900,3 +6901,77 @@ def nonzero(a):
     (array([1, 1, 1, 2, 2, 2], dtype=int64), array([0, 1, 2, 0, 1, 2], dtype=int64))
     """
     return _mx_nd_np.nonzero(a)
+
+
+@set_module('mxnet.numpy')
+def shares_memory(a, b, max_work=None):
+    """
+    Determine if two arrays share memory
+
+    Parameters
+    ----------
+    a, b : ndarray
+        Input arrays
+
+    Returns
+    -------
+    out : bool
+
+    See Also
+    --------
+    may_share_memory
+
+    Examples
+    --------
+    >>> np.may_share_memory(np.array([1,2]), np.array([5,8,9]))
+    False
+
+    This function differs from the original `numpy.shares_memory
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.shares_memory.html>`_ in
+    the following way(s):
+
+    - Does not support `max_work`, it is a dummy argument
+    - Actually it is same as `may_share_memory` in MXNet DeepNumPy
+    """
+    return _mx_nd_np.shares_memory(a, b, max_work)
+
+
+@set_module('mxnet.numpy')
+def may_share_memory(a, b, max_work=None):
+    """
+    Determine if two arrays might share memory
+
+    A return of True does not necessarily mean that the two arrays
+    share any element.  It just means that they *might*.
+
+    Only the memory bounds of a and b are checked by default.
+
+    Parameters
+    ----------
+    a, b : ndarray
+        Input arrays
+
+    Returns
+    -------
+    out : bool
+
+    See Also
+    --------
+    shares_memory
+
+    Examples
+    --------
+    >>> np.may_share_memory(np.array([1,2]), np.array([5,8,9]))
+    False
+    >>> x = np.zeros([3, 4])
+    >>> np.may_share_memory(x[:,0], x[:,1])
+    True
+
+    This function differs from the original `numpy.may_share_memory
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.may_share_memory.html>`_ in
+    the following way(s):
+
+    - Does not support `max_work`, it is a dummy argument
+    - Actually it is same as `shares_memory` in MXNet DeepNumPy
+    """
+    return _mx_nd_np.may_share_memory(a, b, max_work)
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index cec2f245a5e1..6a5f166a70eb 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -127,7 +127,9 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'tril',
     'meshgrid',
     'outer',
-    'einsum'
+    'einsum',
+    'shares_memory',
+    'may_share_memory',
 ]
 
 
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index ddf2feb30b18..2e6d41446930 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -40,7 +40,7 @@
            'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
            'around', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
            'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal',
-           'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide']
+           'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'shares_memory', 'may_share_memory']
 
 
 def _num_outputs(sym):
@@ -4590,4 +4590,43 @@ def einsum(*operands, **kwargs):
     return _npi.einsum(*operands, subscripts=subscripts, out=out, optimize=int(optimize_arg))
 
 
+@set_module('mxnet.symbol.numpy')
+def shares_memory(a, b, max_work=None):
+    """
+    Determine if two arrays share memory
+
+    Parameters
+    ----------
+    a, b : _Symbol
+        Input arrays
+
+    Returns
+    -------
+    out : _Symbol
+    """
+    return _npi.share_memory(a, b)
+
+
+@set_module('mxnet.symbol.numpy')
+def may_share_memory(a, b, max_work=None):
+    """
+    Determine if two arrays might share memory
+
+    A return of True does not necessarily mean that the two arrays
+    share any element.  It just means that they *might*.
+
+    Only the memory bounds of a and b are checked by default.
+
+    Parameters
+    ----------
+    a, b : _Symbol
+        Input arrays
+
+    Returns
+    -------
+    out : _Symbol
+    """
+    return _npi.share_memory(a, b)
+
+
 _set_np_symbol_class(_Symbol)
diff --git a/src/operator/numpy/np_memory_op.cc b/src/operator/numpy/np_memory_op.cc
new file mode 100644
index 000000000000..522998e9c45d
--- /dev/null
+++ b/src/operator/numpy/np_memory_op.cc
@@ -0,0 +1,62 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_memory_op.cc
+ */
+
+#include "./np_memory_op.h"
+
+namespace mxnet {
+namespace op {
+
+inline bool NumpyShareMemoryType(const nnvm::NodeAttrs& attrs,
+                                 std::vector<int> *in_attrs,
+                                 std::vector<int> *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 2U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::kBool);
+  return out_attrs->at(0) != -1;
+}
+
+inline bool NumpyShareMemoryShape(const nnvm::NodeAttrs& attrs,
+                                  mxnet::ShapeVector *in_attrs,
+                                  mxnet::ShapeVector *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 2U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, mxnet::TShape(0, -1));
+  return true;
+}
+
+NNVM_REGISTER_OP(_npi_share_memory)
+.set_num_inputs(2)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"a", "b"};
+  })
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyShareMemoryShape)
+.set_attr<nnvm::FInferType>("FInferType", NumpyShareMemoryType)
+.set_attr<FCompute>("FCompute<cpu>", NumpyShareMemoryCompute<cpu>)
+.add_argument("a", "NDArray-or-Symbol", "First input")
+.add_argument("b", "NDArray-or-Symbol", "Second input");
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_memory_op.cu b/src/operator/numpy/np_memory_op.cu
new file mode 100644
index 000000000000..61bf70d26b8e
--- /dev/null
+++ b/src/operator/numpy/np_memory_op.cu
@@ -0,0 +1,34 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_memory_op.cu
+ */
+
+#include "./np_memory_op.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(_npi_share_memory)
+.set_attr<FCompute>("FCompute<gpu>", NumpyShareMemoryCompute<gpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_memory_op.h b/src/operator/numpy/np_memory_op.h
new file mode 100644
index 000000000000..2c0f3f063f65
--- /dev/null
+++ b/src/operator/numpy/np_memory_op.h
@@ -0,0 +1,75 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file np_memory_op.h
+ * \brief Function definition of numpy memory op
+ */
+
+#ifndef MXNET_OPERATOR_NUMPY_NP_MEMORY_OP_H_
+#define MXNET_OPERATOR_NUMPY_NP_MEMORY_OP_H_
+
+#include <mxnet/operator_util.h>
+#include <vector>
+#include <string>
+#include "../operator_common.h"
+
+namespace mxnet {
+namespace op {
+
+template<typename xpu>
+void NumpyShareMemoryCompute(const nnvm::NodeAttrs& attrs,
+                             const OpContext& ctx,
+                             const std::vector<TBlob>& inputs,
+                             const std::vector<OpReqType>& req,
+                             const std::vector<TBlob>& outputs) {
+  using namespace mxnet_op;
+  using namespace mshadow;
+  CHECK_EQ(inputs.size(), 2U);
+  CHECK_EQ(outputs.size(), 1U);
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  const TBlob& a = inputs[0];
+  const TBlob& b = inputs[1];
+  Tensor<xpu, 1, bool> outdata = outputs[0].FlatTo1D<xpu, bool>(s);
+
+  if (a.Size() == 0 || b.Size() == 0) {
+    ASSIGN_DISPATCH(outdata, OpReqType::kWriteTo, false);
+    return;
+  }
+  MSHADOW_TYPE_SWITCH_WITH_BOOL(a.type_flag_, AType, {
+    MSHADOW_TYPE_SWITCH_WITH_BOOL(b.type_flag_, BType, {
+      uint64_t start1 = reinterpret_cast<uint64_t>(a.dptr_);
+      uint64_t end1 = start1 + a.Size() * sizeof(AType);
+      uint64_t start2 = reinterpret_cast<uint64_t>(b.dptr_);
+      uint64_t end2 = start2 + b.Size() * sizeof(BType);
+      if (!(start1 < end2 && start2 < end1 && start1 < end1 && start2 < end2)) {
+        ASSIGN_DISPATCH(outdata, OpReqType::kWriteTo, false);
+      } else {
+        ASSIGN_DISPATCH(outdata, OpReqType::kWriteTo, true);
+      }
+    });
+  });
+  return;
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_NUMPY_NP_MEMORY_OP_H_
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index 860fecc5cda0..624fc0a107b0 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -1234,6 +1234,8 @@ def check_interoperability(op_list):
     for name in op_list:
         if name in _TVM_OPS and not is_op_runnable():
             continue
+        if name in ['shares_memory', 'may_share_memory']:  # skip list
+            continue
         print('Dispatch test:', name)
         workloads = OpArgMngr.get_workloads(name)
         assert workloads is not None, 'Workloads for operator `{}` has not been ' \
@@ -1243,6 +1245,19 @@ def check_interoperability(op_list):
             _check_interoperability_helper(name, *workload['args'], **workload['kwargs'])
 
 
+@with_seed()
+@use_np
+@with_array_function_protocol
+def test_np_memory_array_function():
+    ops = [_np.shares_memory, _np.may_share_memory]
+    for op in ops:
+        data_mx = np.zeros([13, 21, 23, 22], dtype=np.float32)
+        data_np = _np.zeros([13, 21, 23, 22], dtype=np.float32)
+        assert op(data_mx[0,:,:,:], data_mx[1,:,:,:]) == op(data_np[0,:,:,:], data_np[1,:,:,:])
+        assert op(data_mx[0,0,0,2:5], data_mx[0,0,0,4:7]) == op(data_np[0,0,0,2:5], data_np[0,0,0,4:7])
+        assert op(data_mx, np.ones((5, 0))) == op(data_np, _np.ones((5, 0)))
+
+
 @with_seed()
 @use_np
 @with_array_function_protocol
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 391a07411b15..bfe6c3d43b50 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -3515,7 +3515,7 @@ def dbg(name, data):
             for config in configs:
                 for optimize in [False, True]:
                     rtol = 1e-2 if dtype == 'float16' else 1e-3
-                    atol = 1e-4 if dtype == 'float16' else 1e-5 
+                    atol = 1e-4 if dtype == 'float16' else 1e-5
                     (subscripts, operands, get_grad) = config
                     test_einsum = TestEinsum(subscripts, optimize)
                     if hybridize:
@@ -3556,7 +3556,7 @@ def dbg(name, data):
             for config in configs:
                 (subscripts, operands) = config
                 rtol = 1e-2 if dtype == 'float16' else 1e-3
-                atol = 1e-4 if dtype == 'float16' else 1e-5 
+                atol = 1e-4 if dtype == 'float16' else 1e-5
                 grad = []
                 x_np = []
                 for shape in operands:
@@ -3741,6 +3741,29 @@ def hybrid_forward(self, F, a, *args, **kwargs):
                 assert_almost_equal(npx_out.asnumpy(), expected_out, rtol=1e-3, atol=1e-5)
 
 
+@with_seed()
+@use_np
+def test_np_share_memory():
+    ops = [np.shares_memory, np.may_share_memory]
+    # reshape not support boolean types
+    dtypes = [np.int8, np.uint8, np.int32, np.int64, np.float16, np.float32, np.float64]
+    for op in ops:
+        for dt in dtypes:
+            x = np.zeros([13, 21, 23, 22], dtype=dt)
+            assert not op(x[0,:,:,:], x[1,:,:,:])
+            assert not op(x[2,:,:,:], x[3,:,:,:])
+            assert not op(x[2:5,0,0,0], x[3:4,0,0,0])
+            assert not op(x[2:5,0,0,0], x[4:7,0,0,0])
+            assert op(x[0,0,0,2:5], x[0,0,0,3:4])
+            assert op(x[0,6,0,2:5], x[0,6,0,4:7])
+            assert not op(x[0,5,0,2:5], x[0,6,0,4:7])
+
+            for adt in dtypes:
+                assert not op(x, np.ones((5, 0), dtype=adt))
+                assert not op(np.ones((5, 0), dtype=adt), x)
+                assert not op(np.ones((5, 0), dtype=dt), np.ones((0, 3, 0), dtype=adt))
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 60d74bc948869588c2f143fd3d55231859dc979f Mon Sep 17 00:00:00 2001
From: Rohit Kumar Srivastava <srivastava.141@osu.edu>
Date: Mon, 28 Oct 2019 21:59:58 -0700
Subject: [PATCH 535/813] Showing proper error message when an attempt is made
 to create large tensor but MXNet is not built with it (#16570)

* added error message when creating NDarray and syncing copies to and from CPU

* bug fix for 1>>31 -> uint32_t{1}<<31 and showing error message when user attempts to pass large ndarrays as inputs to operators or large shape of inputs for memory allocation.

* adding error messages to other operators where user can create NDArray indirectly

* bug fix

* removing additional checks

* Revert "removing additional checks"

This reverts commit d0355591e4f1940ccea95e6444744f2dc72d5d64.

* adding tests and comments. Removed int64 check from linspace
---
 include/mxnet/c_api.h                  | 12 +++---
 python/mxnet/ndarray/ndarray.py        | 10 +++++
 python/mxnet/symbol/symbol.py          |  7 +++-
 src/c_api/c_api.cc                     | 13 +++++-
 src/c_api/c_api_ndarray.cc             |  8 +++-
 src/ndarray/ndarray.cc                 | 15 +++++++
 src/ndarray/ndarray_function.cc        |  5 +++
 src/operator/tensor/init_op.h          | 30 ++++++++++++--
 tests/python/unittest/test_operator.py | 56 ++++++++++++++++++++++++++
 9 files changed, 144 insertions(+), 12 deletions(-)

diff --git a/include/mxnet/c_api.h b/include/mxnet/c_api.h
index bbd67f059fdf..fcd5f3edeabe 100644
--- a/include/mxnet/c_api.h
+++ b/include/mxnet/c_api.h
@@ -596,12 +596,12 @@ MXNET_DLL int MXNDArrayCreate(const uint32_t *shape,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXNDArrayCreateEx(const uint32_t *shape,
-                              uint32_t ndim,
-                              int dev_type,
-                              int dev_id,
-                              int delay_alloc,
-                              int dtype,
-                              NDArrayHandle *out);
+                                uint32_t ndim,
+                                int dev_type,
+                                int dev_id,
+                                int delay_alloc,
+                                int dtype,
+                                NDArrayHandle *out);
 
 MXNET_DLL int MXNDArrayCreateEx64(const int64_t *shape,
                                   int ndim,
diff --git a/python/mxnet/ndarray/ndarray.py b/python/mxnet/ndarray/ndarray.py
index 8325dafb7891..f656daea3016 100644
--- a/python/mxnet/ndarray/ndarray.py
+++ b/python/mxnet/ndarray/ndarray.py
@@ -58,6 +58,7 @@
 _STORAGE_TYPE_DEFAULT = 0
 _STORAGE_TYPE_ROW_SPARSE = 1
 _STORAGE_TYPE_CSR = 2
+_SIGNED_INT32_UPPER_LIMIT = (2**31 - 1)
 
 # pylint: disable= no-member
 _DTYPE_NP_TO_MX = {
@@ -155,6 +156,15 @@ def _new_alloc_handle(shape, ctx, delay_alloc, dtype=mx_real_t):
             ctypes.c_int(int(_DTYPE_NP_TO_MX[np.dtype(dtype).type])),
             ctypes.byref(hdl)))
     else:
+        # When shape is larger than unit32 then there is an overflow error at python end itself.
+        # It needs to be caught here since the call doesn't even reach backend.
+        size = 1
+        for idx in shape:
+            size = size * idx
+        if size > _SIGNED_INT32_UPPER_LIMIT:
+            raise Exception("[_new_alloc_handle] Size of tensor you are trying to allocate is " +
+                            "larger than 2^31 elements. Please build with flag " +
+                            "USE_INT64_TENSOR_SIZE=1")
         check_call(_LIB.MXNDArrayCreateEx(
             c_array_buf(mx_uint, native_array('I', shape)),
             mx_uint(len(shape)),
diff --git a/python/mxnet/symbol/symbol.py b/python/mxnet/symbol/symbol.py
index 6146ab9dc50e..27d79fc03c31 100644
--- a/python/mxnet/symbol/symbol.py
+++ b/python/mxnet/symbol/symbol.py
@@ -39,7 +39,7 @@
 from ..base import check_call, MXNetError, NotImplementedForSymbol
 from ..context import Context, current_context
 from ..ndarray import NDArray, _DTYPE_NP_TO_MX, _DTYPE_MX_TO_NP, _GRAD_REQ_MAP
-from ..ndarray.ndarray import _STORAGE_TYPE_STR_TO_ID, _int64_enabled
+from ..ndarray.ndarray import _STORAGE_TYPE_STR_TO_ID, _int64_enabled, _SIGNED_INT32_UPPER_LIMIT
 from ..ndarray import _ndarray_cls
 from ..executor import Executor
 from . import _internal
@@ -1237,6 +1237,11 @@ def _infer_shape_impl(self, partial, *args, **kwargs):
                 ctypes.byref(aux_shape_data),
                 ctypes.byref(complete)))
         else:
+            for size in sdata:
+                if size > _SIGNED_INT32_UPPER_LIMIT:
+                    raise Exception("[_infer_shape_impl] Size of tensor you are trying to " +
+                                    "allocate is larger than 2^31 elements. Please build " +
+                                    "with flag USE_INT64_TENSOR_SIZE=1")
             arg_shape_data = ctypes.POINTER(ctypes.POINTER(mx_int))()
             out_shape_data = ctypes.POINTER(ctypes.POINTER(mx_int))()
             aux_shape_data = ctypes.POINTER(ctypes.POINTER(mx_int))()
diff --git a/src/c_api/c_api.cc b/src/c_api/c_api.cc
index fb88cae2cbba..ba33084a026d 100644
--- a/src/c_api/c_api.cc
+++ b/src/c_api/c_api.cc
@@ -220,7 +220,13 @@ void CreateNDArray(const DataType* shape,
                    int delay_alloc,
                    int dtype,
                    NDArrayHandle* out) {
-  *out = new NDArray(mxnet::TShape(shape, shape + ndim),
+  mxnet::TShape requested_shape = mxnet::TShape(shape, shape + ndim);
+  if (!features::is_enabled(features::INT64_TENSOR_SIZE)) {
+    CHECK_LT(requested_shape.Size(), (int64_t{1} << 31) - 1) <<
+              "[CreateNDArray] Size of tensor you are trying to allocate is larger than "
+              "2^31 elements. Please build with flag USE_INT64_TENSOR_SIZE=1";
+  }
+  *out = new NDArray(requested_shape,
                      Context::Create(static_cast<Context::DeviceType>(dev_type), dev_id),
                      delay_alloc != 0, dtype);
 }
@@ -608,6 +614,11 @@ inline void GetShape(NDArrayHandle handle, const dtype** out_pdata, int* out_dim
                      MXAPIThreadLocalEntry<dtype>* ret) {
   NDArray* arr = static_cast<NDArray*>(handle);
   if (!arr->is_none()) {
+    if (!features::is_enabled(features::INT64_TENSOR_SIZE)) {
+      CHECK_LT(arr->shape().Size(), (int64_t{1} << 31) - 1) <<
+                      "[Get Shape] Size of tensor you are trying to allocate is larger than "
+                      "2^31 elements. Please build with flag USE_INT64_TENSOR_SIZE=1";
+    }
     mxnet::TShape s = arr->shape();
     if (!Imperative::Get()->is_np_shape()) {
       common::ConvertToLegacyShape(&s);
diff --git a/src/c_api/c_api_ndarray.cc b/src/c_api/c_api_ndarray.cc
index de208c0fed99..6bfb3b35743d 100644
--- a/src/c_api/c_api_ndarray.cc
+++ b/src/c_api/c_api_ndarray.cc
@@ -54,7 +54,13 @@ void SetNDInputsOutputs(const nnvm::Op* op,
   ndinputs->clear();
   ndinputs->reserve(num_inputs);
   for (int i = 0; i < num_inputs; ++i) {
-    ndinputs->emplace_back(reinterpret_cast<NDArray*>(inputs[i]));
+    NDArray* inp = reinterpret_cast<NDArray*>(inputs[i]);
+    if (!features::is_enabled(features::INT64_TENSOR_SIZE)) {
+      CHECK_LT(inp->shape().Size(), (int64_t{1} << 31) - 1) <<
+                "[SetNDInputsOutputs] Size of tensor you are trying to allocate is larger than "
+                "2^31 elements. Please build with flag USE_INT64_TENSOR_SIZE=1";
+    }
+    ndinputs->emplace_back(inp);
   }
 
   ndoutputs->clear();
diff --git a/src/ndarray/ndarray.cc b/src/ndarray/ndarray.cc
index 78a6cfb15fd2..e1075c9c15da 100644
--- a/src/ndarray/ndarray.cc
+++ b/src/ndarray/ndarray.cc
@@ -142,6 +142,11 @@ void NDArray::Chunk::CheckAndAllocData(const mxnet::TShape &shape, int dtype) {
   CHECK_NE(aux_shapes.size(), 0)
       << "data is expected to be allocated after aux_data";
   auto dbytes = shape.Size() * mshadow::mshadow_sizeof(dtype);
+  if (!features::is_enabled(features::INT64_TENSOR_SIZE)) {
+    CHECK_LT(shape.Size(), (int64_t{1} << 31) - 1) <<
+              "[CheckAndAllocData] Size of tensor you are trying to allocate is larger than "
+              "2^31 elements. Please build with flag USE_INT64_TENSOR_SIZE=1";
+  }
   if (shandle.size < dbytes) {
     // free storage
     Storage::Get()->Free(shandle);
@@ -1884,6 +1889,11 @@ NDArray NDArray::Copy(Context ctx) const {
 
 void NDArray::SyncCopyFromCPU(const void *data, size_t size) const {
   mxnet::TShape dshape = this->shape();
+  if (!features::is_enabled(features::INT64_TENSOR_SIZE)) {
+    CHECK_LT(size, (int64_t{1} << 31) - 1) <<
+              "[SyncCopyFromCPU] Size of tensor you are trying to allocate is larger than "
+              "2^31 elements. Please build with flag USE_INT64_TENSOR_SIZE=1";
+  }
   CHECK_EQ(dshape.Size(), size)
       << "Memory size do not match";
   // zero-size array, no need to copy
@@ -2019,6 +2029,11 @@ void NDArray::SyncCopyFromNDArray(const NDArray& src, int i, int j) {
 
 void NDArray::SyncCopyToCPU(void *data, size_t size) const {
   mxnet::TShape dshape = this->shape();
+  if (!features::is_enabled(features::INT64_TENSOR_SIZE)) {
+    CHECK_LT(size, (int64_t{1} << 31) - 1) <<
+              "[SyncCopyToCPU] Size of tensor you are trying to allocate is larger than "
+              "2^31 elements. Please build with flag USE_INT64_TENSOR_SIZE=1";
+  }
   CHECK_EQ(dshape.Size(), size)
       << "Memory size do not match";
   // zero-size array, no need to copy
diff --git a/src/ndarray/ndarray_function.cc b/src/ndarray/ndarray_function.cc
index 34429446bd62..ed121899436a 100644
--- a/src/ndarray/ndarray_function.cc
+++ b/src/ndarray/ndarray_function.cc
@@ -38,6 +38,11 @@ void Copy<cpu, cpu>(const TBlob &from, TBlob *to,
                     RunContext ctx) {
   MSHADOW_TYPE_SWITCH_WITH_BOOL(to->type_flag_, DType, {
     if (to->type_flag_ == from.type_flag_) {
+      if (!features::is_enabled(features::INT64_TENSOR_SIZE)) {
+        CHECK_LT(from.Size(), (int64_t{1} << 31) - 1) <<
+                  "Size of tensor you are trying to allocate is larger than "
+                  "2^31 elements. Please build with flag USE_INT64_TENSOR_SIZE=1";
+      }
       const index_t size = static_cast<index_t>(from.Size());
       CHECK_EQ(size, to->Size()) << "copying size mismatch, from: " << size * sizeof(DType)
                << " bytes, to: " << to->Size() * sizeof(DType) << " bytes.";
diff --git a/src/operator/tensor/init_op.h b/src/operator/tensor/init_op.h
index d2107a1406e2..a0139f7fde2d 100644
--- a/src/operator/tensor/init_op.h
+++ b/src/operator/tensor/init_op.h
@@ -272,10 +272,22 @@ inline bool InitShape(const nnvm::NodeAttrs& attrs,
   CHECK_EQ(in_attrs->size(), 0U);
   CHECK_EQ(out_attrs->size(), 1U);
   mxnet::TShape param_shape = param.shape;
+  if (shape_is_known(param_shape) && !features::is_enabled(features::INT64_TENSOR_SIZE)) {
+    CHECK_LT(param_shape.Size(), (int64_t{1} << 31) - 1) <<
+              "[InitShape-input] Size of tensor you are trying to allocate is larger than "
+              "2^31 elements. Please build with flag USE_INT64_TENSOR_SIZE=1";
+  }
   if (!Imperative::Get()->is_np_shape()) {
     common::ConvertToNumpyShape(&param_shape);
   }
-  if (shape_is_known((*out_attrs)[0]) && !shape_is_known(param_shape)) return true;
+  if (shape_is_known((*out_attrs)[0]) && !shape_is_known(param_shape)) {
+    if (!features::is_enabled(features::INT64_TENSOR_SIZE)) {
+      CHECK_LT(out_attrs->at(0).Size() , (int64_t{1} << 31) - 1) <<
+                "[InitShape-output] Size of tensor you are trying to allocate is larger than "
+                "2^31 elements. Please build with flag USE_INT64_TENSOR_SIZE=1";
+    }
+    return true;
+  }
   SHAPE_ASSIGN_CHECK(*out_attrs, 0, param_shape);
   return shape_is_known(out_attrs->at(0));
 }
@@ -336,6 +348,11 @@ inline bool InitStorageType(const nnvm::NodeAttrs& attrs,
 template <bool is_integer = false, typename ValueType, typename xpu>
 void Fill(mshadow::Stream<xpu> *s, const TBlob& b, const OpReqType req, ValueType val) {
   // If b is a zero-size tensor, do nothing.
+  if (!features::is_enabled(features::INT64_TENSOR_SIZE)) {
+    CHECK_LT(b.Size(), (int64_t{1} << 31) - 1) <<
+              "[Fill] Size of tensor you are trying to allocate is larger than "
+              "2^31 elements. Please build with flag USE_INT64_TENSOR_SIZE=1";
+  }
   if (b.Size() == 0) return;
   if (req != kNullOp) {
     const size_t size = b.Size();
@@ -580,7 +597,13 @@ inline bool RangeShape(const nnvm::NodeAttrs& attrs,
   }
   const double out_size = std::ceil((param.stop.value() - param.start) / param.step)
                           * param.repeat;
-  SHAPE_ASSIGN_CHECK(*out_attrs, 0, mxnet::TShape({static_cast<nnvm::dim_t>(out_size)}));
+  mxnet::TShape output_shape = mxnet::TShape({static_cast<nnvm::dim_t>(out_size)});
+  if (!features::is_enabled(features::INT64_TENSOR_SIZE)) {
+    CHECK_LT(output_shape.Size(), (int64_t{1} << 31) - 1) <<
+              "[RangeShape] Size of tensor you are trying to allocate is larger than "
+              "2^31 elements. Please build with flag USE_INT64_TENSOR_SIZE=1";
+  }
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, output_shape);
   return true;
 }
 
@@ -622,7 +645,8 @@ inline bool LinspaceShape(const nnvm::NodeAttrs& attrs,
   CHECK_EQ(out_attrs->size(), 1U);
   CHECK_GE(param.num, 0)
     << "Number of sequence should be non-negative, received " << param.num;
-  SHAPE_ASSIGN_CHECK(*out_attrs, 0, mxnet::TShape({static_cast<nnvm::dim_t>(param.num)}));
+  mxnet::TShape shape = mxnet::TShape({static_cast<nnvm::dim_t>(param.num)});
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, shape);
   return true;
 }
 
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 075816fdc6de..33f739bd10fc 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -9279,6 +9279,62 @@ def test_min_max_inf():
                 assert_array_equal(max_data_np, max_data_mx.asnumpy())
 
 
+def test_large_tensor_disabled_err_msg():
+    LARGE_X = 4300000000
+    MEDIUM_X = 1000000000
+    SMALL_Y = 1
+    shape = (2, LARGE_X)
+
+    def check_nd_array():
+        x = np.arange(0, LARGE_X)
+        assertRaises(MXNetError, mx.nd.array, x)
+
+    def check_nd_ones():
+        assertRaises(MXNetError, mx.nd.ones, shape)
+
+    def check_nd_zeros():
+        assertRaises(MXNetError, mx.nd.zeros, shape)
+
+    def check_nd_full():
+        val = 1
+        assertRaises(Exception, mx.nd.full, shape, val)
+
+    def check_nd_arange():
+        start = 0
+        stop = LARGE_X
+        assertRaises(Exception, mx.nd.arange, start, stop)
+
+    def check_nd_random():
+        shape = (2, LARGE_X)
+        def check_random_exp():
+            lam = 4
+            assertRaises(MXNetError, mx.nd.random_exponential, lam, shape)
+
+        def check_random_gamma():
+            alpha = 9
+            beta = 0.5
+            assertRaises(MXNetError, mx.nd.random_gamma, alpha, beta, shape)
+
+        def check_random_normal():
+            loc = 0
+            scale = 1
+            assertRaises(MXNetError, mx.nd.random_normal, loc, scale, shape)
+
+        def check_random_poisson():
+            lam = 4
+            assertRaises(MXNetError, mx.nd.random_poisson, alpha, lam, shape)
+
+        def check_random_randint():
+            low = 0
+            high = 1000000
+            assertRaises(MXNetError, mx.nd.random_randint, low, high, shape)
+
+        def check_random_uniform():
+            low = 0
+            hight = 1
+            assertRaises(MXNetError, mx.nd.random_uniform, alpha, beta, shape)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 5aa74e066277c2b55a8307f32973425bf85de4c0 Mon Sep 17 00:00:00 2001
From: Anirudh Subramanian <anirudh2290@ufl.edu>
Date: Tue, 29 Oct 2019 13:30:57 -0700
Subject: [PATCH 536/813] Move ops which don't support FP16 dtype to FP32 list
 (#16668)

---
 python/mxnet/contrib/amp/lists/symbol.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/python/mxnet/contrib/amp/lists/symbol.py b/python/mxnet/contrib/amp/lists/symbol.py
index 397f4775f8cd..2146853a6866 100644
--- a/python/mxnet/contrib/amp/lists/symbol.py
+++ b/python/mxnet/contrib/amp/lists/symbol.py
@@ -368,6 +368,9 @@
     'arctanh',
     '_sparse_arcsin',
     '_sparse_arctanh',
+    '_contrib_MultiBoxDetection',
+    '_contrib_MultiBoxPrior',
+    '_contrib_MultiBoxTarget',
 
     # Exponents
     'exp',
@@ -575,9 +578,6 @@
     'stack',
     '_Maximum',
     '_Minimum',
-    '_contrib_MultiBoxDetection',
-    '_contrib_MultiBoxPrior',
-    '_contrib_MultiBoxTarget',
     '_contrib_MultiProposal',
     '_contrib_PSROIPooling',
     '_contrib_Proposal',

From 8e50fd9d862a2917e0727b8cd17f1e5cf0eba081 Mon Sep 17 00:00:00 2001
From: phinzphinz <phinzphinz@users.noreply.github.com>
Date: Wed, 30 Oct 2019 01:55:02 +0100
Subject: [PATCH 537/813] no such method => modified function args (#16610)

* no such method => modified function args

ERROR: MethodError: no method matching mapreduce(::getfield(MXNet.mx, Symbol("##8072#8073")), ::typeof(+), ::Float64, ::Array{NDArray{Float32,1},1})

* julia could not build package before

* Update julia/src/metric.jl

Co-Authored-By: Iblis Lin <iblis@hs.ntnu.edu.tw>
---
 julia/deps/build.jl | 2 +-
 julia/src/metric.jl | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/julia/deps/build.jl b/julia/deps/build.jl
index a87343d9dab5..a79d2a062c18 100644
--- a/julia/deps/build.jl
+++ b/julia/deps/build.jl
@@ -54,7 +54,7 @@ if Sys.isunix()
   nvcc_path = Sys.which("nvcc")
   if nvcc_path ≢ nothing
     @info "Found nvcc: $nvcc_path"
-    push!(CUDAPATHS, replace(nvcc_path, "bin/nvcc", "lib64"))
+    push!(CUDAPATHS, replace(nvcc_path, "bin/nvcc" => "lib64"))
   end
 end
 
diff --git a/julia/src/metric.jl b/julia/src/metric.jl
index f1cdc68d947f..2ae7fc85144b 100644
--- a/julia/src/metric.jl
+++ b/julia/src/metric.jl
@@ -260,7 +260,7 @@ end
 
 function get(metric::MSE)
   # Delay copy until last possible moment
-  mse_sum = mapreduce(nda->copy(nda)[1], +, 0.0, metric.mse_sum)
+  mse_sum = mapreduce(nda->copy(nda)[1], +, metric.mse_sum; init = zero(MX_float))
   [(:MSE, mse_sum / metric.n_sample)]
 end
 

From 27bddf8f1667cd7161f0c41ecabe6123736e403d Mon Sep 17 00:00:00 2001
From: Zhiqiang Xie <xiezhq@shanghaitech.edu.cn>
Date: Wed, 30 Oct 2019 17:34:50 +0800
Subject: [PATCH 538/813] [Numpy] Numpy operator diff (#15906)

* numpy diff operator implemented

append and prepend not supported yet

remove the prepend and append checking interface from the backend

refine the code, enrich the test set and all tests passed

registered the diff operator into npi scope

all tests passed

comments and minor modification

format codes and fix warning for sanity check

minor modification for sanity check

fix sanity

fix the tolerance bound of testing np.diff

resolve minor coding style issue

replace the given tests by random picking

minor fix

* interoperability test added
---
 python/mxnet/ndarray/numpy/_op.py             |  50 +++-
 python/mxnet/numpy/multiarray.py              |  51 +++-
 python/mxnet/numpy_dispatch_protocol.py       |   1 +
 python/mxnet/symbol/numpy/_symbol.py          |  51 +++-
 src/operator/numpy/np_diff-inl.h              | 220 ++++++++++++++++++
 src/operator/numpy/np_diff.cc                 | 109 +++++++++
 src/operator/numpy/np_diff.cu                 |  37 +++
 .../unittest/test_numpy_interoperability.py   |  24 ++
 tests/python/unittest/test_numpy_op.py        |  52 +++++
 9 files changed, 592 insertions(+), 3 deletions(-)
 create mode 100644 src/operator/numpy/np_diff-inl.h
 create mode 100644 src/operator/numpy/np_diff.cc
 create mode 100644 src/operator/numpy/np_diff.cu

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index 84aa4a1572d9..297c40b1431e 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -38,7 +38,7 @@
            'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
            'around', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
            'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal',
-           'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory', 'may_share_memory']
+           'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory', 'may_share_memory', 'diff']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -4983,3 +4983,51 @@ def may_share_memory(a, b, max_work=None):
     - Actually it is same as `shares_memory` in MXNet DeepNumPy
     """
     return _npi.share_memory(a, b).item()
+
+
+def diff(a, n=1, axis=-1, prepend=None, append=None):
+    r"""
+    numpy.diff(a, n=1, axis=-1, prepend=<no value>, append=<no value>)
+
+    Calculate the n-th discrete difference along the given axis.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input array
+    n : int, optional
+        The number of times values are differenced. If zero, the input is returned as-is.
+    axis : int, optional
+        The axis along which the difference is taken, default is the last axis.
+    prepend, append : ndarray, optional
+        Not supported yet
+
+    Returns
+    -------
+    diff : ndarray
+        The n-th differences.
+        The shape of the output is the same as a except along axis where the dimension is smaller by n.
+        The type of the output is the same as the type of the difference between any two elements of a.
+
+    Examples
+    --------
+    >>> x = np.array([1, 2, 4, 7, 0])
+    >>> np.diff(x)
+    array([ 1,  2,  3, -7])
+    >>> np.diff(x, n=2)
+    array([  1,   1, -10])
+
+    >>> x = np.array([[1, 3, 6, 10], [0, 5, 6, 8]])
+    >>> np.diff(x)
+    array([[2, 3, 4],
+        [5, 1, 2]])
+    >>> np.diff(x, axis=0)
+    array([[-1,  2,  0, -2]])
+
+    Notes
+    -----
+    Optional inputs `prepend` and `append` are not supported yet
+    """
+    if (prepend or append):
+        raise NotImplementedError('prepend and append options are not supported yet')
+    return _npi.diff(a, n=n, axis=axis)
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index ef88638c857e..a6d90881da4f 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -56,7 +56,7 @@
            'blackman', 'flip', 'around', 'arctan2', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril',
            'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less',
            'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory',
-           'may_share_memory']
+           'may_share_memory', 'diff']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -6975,3 +6975,52 @@ def may_share_memory(a, b, max_work=None):
     - Actually it is same as `shares_memory` in MXNet DeepNumPy
     """
     return _mx_nd_np.may_share_memory(a, b, max_work)
+
+
+def diff(a, n=1, axis=-1, prepend=None, append=None):
+    r"""
+    numpy.diff(a, n=1, axis=-1, prepend=<no value>, append=<no value>)
+
+    Calculate the n-th discrete difference along the given axis.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input array
+    n : int, optional
+        The number of times values are differenced. If zero, the input is returned as-is.
+    axis : int, optional
+        The axis along which the difference is taken, default is the last axis.
+    prepend, append : ndarray, optional
+        Not supported yet
+
+    Returns
+    -------
+    diff : ndarray
+        The n-th differences.
+        The shape of the output is the same as a except along axis where the dimension is smaller by n.
+        The type of the output is the same as the type of the difference between any two elements of a.
+        This is the same as the type of a in most cases.
+
+    Examples
+    --------
+    >>> x = np.array([1, 2, 4, 7, 0])
+    >>> np.diff(x)
+    array([ 1,  2,  3, -7])
+    >>> np.diff(x, n=2)
+    array([  1,   1, -10])
+
+    >>> x = np.array([[1, 3, 6, 10], [0, 5, 6, 8]])
+    >>> np.diff(x)
+    array([[2, 3, 4],
+        [5, 1, 2]])
+    >>> np.diff(x, axis=0)
+    array([[-1,  2,  0, -2]])
+
+    Notes
+    -----
+    Optional inputs `prepend` and `append` are not supported yet
+    """
+    if (prepend or append):
+        raise NotImplementedError('prepend and append options are not supported yet')
+    return _mx_nd_np.diff(a, n=n, axis=axis)
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index 6a5f166a70eb..2411f51b7aa6 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -130,6 +130,7 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'einsum',
     'shares_memory',
     'may_share_memory',
+    'diff',
 ]
 
 
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 2e6d41446930..6f7f912d6e36 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -40,7 +40,7 @@
            'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
            'around', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
            'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal',
-           'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'shares_memory', 'may_share_memory']
+           'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'shares_memory', 'may_share_memory', 'diff']
 
 
 def _num_outputs(sym):
@@ -4629,4 +4629,53 @@ def may_share_memory(a, b, max_work=None):
     return _npi.share_memory(a, b)
 
 
+def diff(a, n=1, axis=-1, prepend=None, append=None):
+    r"""
+    numpy.diff(a, n=1, axis=-1, prepend=<no value>, append=<no value>)
+
+    Calculate the n-th discrete difference along the given axis.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input array
+    n : int, optional
+        The number of times values are differenced. If zero, the input is returned as-is.
+    axis : int, optional
+        The axis along which the difference is taken, default is the last axis.
+    prepend, append : ndarray, optional
+        Not supported yet
+
+    Returns
+    -------
+    diff : ndarray
+        The n-th differences.
+        The shape of the output is the same as a except along axis where the dimension is smaller by n.
+        The type of the output is the same as the type of the difference between any two elements of a.
+        This is the same as the type of a in most cases.
+
+    Examples
+    --------
+    >>> x = np.array([1, 2, 4, 7, 0])
+    >>> np.diff(x)
+    array([ 1,  2,  3, -7])
+    >>> np.diff(x, n=2)
+    array([  1,   1, -10])
+
+    >>> x = np.array([[1, 3, 6, 10], [0, 5, 6, 8]])
+    >>> np.diff(x)
+    array([[2, 3, 4],
+        [5, 1, 2]])
+    >>> np.diff(x, axis=0)
+    array([[-1,  2,  0, -2]])
+
+    Notes
+    -----
+    Optional inputs `prepend` and `append` are not supported yet
+    """
+    if (prepend or append):
+        raise NotImplementedError('prepend and append options are not supported yet')
+    return _npi.diff(a, n=n, axis=axis)
+
+
 _set_np_symbol_class(_Symbol)
diff --git a/src/operator/numpy/np_diff-inl.h b/src/operator/numpy/np_diff-inl.h
new file mode 100644
index 000000000000..69f175e802dd
--- /dev/null
+++ b/src/operator/numpy/np_diff-inl.h
@@ -0,0 +1,220 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file np_diff-inl.h
+ * \brief Function definition of numpy-compatible diff operator
+ */
+
+#ifndef MXNET_OPERATOR_NUMPY_NP_DIFF_INL_H_
+#define MXNET_OPERATOR_NUMPY_NP_DIFF_INL_H_
+
+#include <mxnet/base.h>
+#include <mxnet/operator_util.h>
+#include <vector>
+#include "../mxnet_op.h"
+#include "../operator_common.h"
+#include "../tensor/broadcast_reduce_op.h"
+
+namespace mxnet {
+namespace op {
+
+struct DiffParam : public dmlc::Parameter<DiffParam> {
+  int n, axis;
+  dmlc::optional<mxnet::TShape> prepend;
+  dmlc::optional<mxnet::TShape> append;
+  DMLC_DECLARE_PARAMETER(DiffParam) {
+    DMLC_DECLARE_FIELD(n).set_default(1).describe(
+        "The number of times values are differenced."
+        " If zero, the input is returned as-is.");
+    DMLC_DECLARE_FIELD(axis).set_default(-1).describe(
+        "Axis along which the cumulative sum is computed."
+        " The default (None) is to compute the diff over the flattened array.");
+  }
+};
+
+inline void YanghuiTri(std::vector<int>* buffer, int n) {
+  // apply basic yanghui's triangular to calculate the factors
+  (*buffer)[0] = 1;
+  for (int i = 1; i <= n; ++i) {
+    (*buffer)[i] = 1;
+    for (int j = i - 1; j > 0; --j) {
+      (*buffer)[j] += (*buffer)[j - 1];
+    }
+  }
+}
+
+struct diff_forward {
+  template <typename IType, typename OType, int ndim>
+  MSHADOW_XINLINE static void Map(int i, int* diffFactor, OType* out,
+                                  const IType* in, const int n,
+                                  const int stride,
+                                  const mshadow::Shape<ndim> oshape,
+                                  const mshadow::Shape<ndim> ishape) {
+    using namespace broadcast;
+
+    // j represent the memory index of the corresponding input entry
+    int j = ravel(unravel(i, oshape), ishape);
+    int indicator = 1;
+    out[i] = 0;
+    for (int k = n; k >= 0; --k) {
+      out[i] += in[j + stride * k] * indicator * diffFactor[k];
+      indicator *= -1;
+    }
+  }
+};
+
+template <typename xpu>
+void DiffForwardImpl(const OpContext& ctx, const TBlob& in, const TBlob& out,
+                     const int n, const int axis) {
+  using namespace mshadow;
+  using namespace mxnet_op;
+
+  // undefined behavior for n < 0
+  CHECK_GE(n, 0);
+  int axis_checked = CheckAxis(axis, in.ndim());
+  // nothing in the output
+  if (n >= in.shape_[axis_checked]) return;
+  // stride for elements on the given axis, same in input and output
+  int stride = 1;
+  for (int i = in.ndim() - 1; i > axis_checked; --i) {
+    stride *= in.shape_[i];
+  }
+
+  Stream<xpu>* s = ctx.get_stream<xpu>();
+  std::vector<int> buffer(n+1, 0);
+  YanghuiTri(&buffer, n);
+  Tensor<xpu, 1, int> diffFactor =
+      ctx.requested[0].get_space_typed<xpu, 1, int>(Shape1(n + 1), s);
+  Copy(diffFactor, Tensor<cpu, 1, int>(&buffer[0], Shape1(n + 1), 0), s);
+
+  MSHADOW_TYPE_SWITCH(in.type_flag_, IType, {
+    MSHADOW_TYPE_SWITCH(out.type_flag_, OType, {
+      MXNET_NDIM_SWITCH(in.ndim(), ndim, {
+        Kernel<diff_forward, xpu>::Launch(
+          s, out.Size(), diffFactor.dptr_,
+          out.dptr<OType>(), in.dptr<IType>(),
+          n, stride, out.shape_.get<ndim>(),
+          in.shape_.get<ndim>());
+      });
+    });
+  });
+}
+
+template <typename xpu>
+void DiffForward(const nnvm::NodeAttrs& attrs, const OpContext& ctx,
+                 const std::vector<TBlob>& inputs,
+                 const std::vector<OpReqType>& req,
+                 const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mxnet_op;
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(req.size(), 1U);
+  CHECK_EQ(outputs.size(), 1U);
+  const DiffParam& param = nnvm::get<DiffParam>(attrs.parsed);
+
+  DiffForwardImpl<xpu>(ctx, inputs[0], outputs[0], param.n, param.axis);
+}
+
+struct diff_backward {
+  template <typename IType, typename OType, int ndim>
+  MSHADOW_XINLINE static void Map(int i, int* diffFactor, OType* igrad,
+                                  const IType* ograd, const int n,
+                                  const int stride, const int axis,
+                                  const mshadow::Shape<ndim> oshape,
+                                  const mshadow::Shape<ndim> ishape) {
+    using namespace broadcast;
+    if (n == 0) {
+      igrad[i] = ograd[i];
+      return;
+    }
+
+    Shape<ndim> coor = unravel(i, oshape);
+    // one head thread for a whole sequence along the axis
+    if (coor[axis] != 0) return;
+    int j = ravel(coor, ishape);
+    // initialize the elements of output array
+    for (int k = 0; k < oshape[axis]; ++k) igrad[i + k * stride] = 0;
+    for (int k = 0; k < ishape[axis]; ++k) {
+      int indicator = 1;
+      for (int m = n; m >= 0; --m) {
+        igrad[i + (m + k) * stride] +=
+            ograd[j + k * stride] * indicator * diffFactor[m];
+        indicator *= -1;
+      }
+    }
+  }
+};
+
+template <typename xpu>
+void DiffBackwardImpl(const OpContext& ctx, const TBlob& ograd,
+                      const TBlob& igrad, const int n, const int axis) {
+  using namespace mshadow;
+  using namespace mxnet_op;
+
+  // undefined behavior for n < 0
+  CHECK_GE(n, 0);
+  int axis_checked = CheckAxis(axis, igrad.ndim());
+  // nothing in the ograd and igrad
+  if (n >= igrad.shape_[axis_checked]) return;
+  // stride for elements on the given axis, same in input and output
+  int stride = 1;
+  for (int i = igrad.ndim() - 1; i > axis_checked; --i) {
+    stride *= igrad.shape_[i];
+  }
+
+  Stream<xpu>* s = ctx.get_stream<xpu>();
+  std::vector<int> buffer(n+1, 0);
+  YanghuiTri(&buffer, n);
+  Tensor<xpu, 1, int> diffFactor =
+      ctx.requested[0].get_space_typed<xpu, 1, int>(Shape1(n + 1), s);
+  Copy(diffFactor, Tensor<cpu, 1, int>(&buffer[0], Shape1(n + 1), 0), s);
+
+  MSHADOW_TYPE_SWITCH(ograd.type_flag_, IType, {
+    MSHADOW_TYPE_SWITCH(igrad.type_flag_, OType, {
+      MXNET_NDIM_SWITCH(igrad.ndim(), ndim, {
+        Kernel<diff_backward, xpu>::Launch(
+          s, igrad.Size(), diffFactor.dptr_,
+          igrad.dptr<OType>(), ograd.dptr<IType>(),
+          n, stride, axis_checked,
+          igrad.shape_.get<ndim>(), ograd.shape_.get<ndim>());
+      });
+    });
+  });
+}
+
+template <typename xpu>
+void DiffBackward(const nnvm::NodeAttrs& attrs, const OpContext& ctx,
+                  const std::vector<TBlob>& inputs,
+                  const std::vector<OpReqType>& req,
+                  const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mxnet_op;
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(req.size(), 1U);
+  CHECK_EQ(outputs.size(), 1U);
+  const DiffParam& param = nnvm::get<DiffParam>(attrs.parsed);
+
+  DiffBackwardImpl<xpu>(ctx, inputs[0], outputs[0], param.n, param.axis);
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_NUMPY_NP_DIFF_INL_H_
diff --git a/src/operator/numpy/np_diff.cc b/src/operator/numpy/np_diff.cc
new file mode 100644
index 000000000000..a3dae332d842
--- /dev/null
+++ b/src/operator/numpy/np_diff.cc
@@ -0,0 +1,109 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file np_diff.cc
+ * \brief CPU implementation of numpy-compatible diff operator
+ */
+
+#include "./np_diff-inl.h"
+
+namespace mxnet {
+namespace op {
+
+inline TShape NumpyDiffShapeImpl(const TShape& ishape,
+                                 const int n,
+                                 const int axis) {
+  CHECK_GE(n, 0);
+  int axis_checked = CheckAxis(axis, ishape.ndim());
+
+  TShape oshape = ishape;
+  if (n >= ishape[axis_checked]) {
+    oshape[axis_checked] = 0;
+  } else {
+    oshape[axis_checked] -= n;
+  }
+  return oshape;
+}
+
+inline bool DiffShape(const nnvm::NodeAttrs& attrs,
+                        std::vector<TShape>* in_attrs,
+                        std::vector<TShape>* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  if (!shape_is_known(in_attrs->at(0))) {
+    return false;
+  }
+  const DiffParam& param = nnvm::get<DiffParam>(attrs.parsed);
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0,
+                     NumpyDiffShapeImpl((*in_attrs)[0], param.n, param.axis));
+  return shape_is_known(out_attrs->at(0));
+}
+
+inline bool DiffType(const nnvm::NodeAttrs& attrs,
+                       std::vector<int>* in_attrs,
+                       std::vector<int>* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
+  TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
+
+  return out_attrs->at(0) != -1 && in_attrs->at(0) != -1;
+}
+
+DMLC_REGISTER_PARAMETER(DiffParam);
+
+NNVM_REGISTER_OP(_npi_diff)
+.set_attr_parser(ParamParser<DiffParam>)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"a"};
+  })
+.set_attr<mxnet::FInferShape>("FInferShape", DiffShape)
+.set_attr<nnvm::FInferType>("FInferType", DiffType)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", DiffForward<cpu>)
+.set_attr<nnvm::FGradient>("FGradient",
+                            ElemwiseGradUseNone{"_backward_npi_diff"})
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::pair<int, int> >{{0, 0}};
+  })
+.add_argument("a", "NDArray-or-Symbol", "Input ndarray")
+.add_arguments(DiffParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_backward_npi_diff)
+.set_attr_parser(ParamParser<DiffParam>)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", DiffBackward<cpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_diff.cu b/src/operator/numpy/np_diff.cu
new file mode 100644
index 000000000000..daea6e368e05
--- /dev/null
+++ b/src/operator/numpy/np_diff.cu
@@ -0,0 +1,37 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file np_diff.cu
+ * \brief GPU implementation of numpy-compatible diff operator
+ */
+
+#include "./np_diff-inl.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(_npi_diff)
+.set_attr<FCompute>("FCompute<gpu>", DiffForward<gpu>);
+
+NNVM_REGISTER_OP(_backward_npi_diff)
+.set_attr<FCompute>("FCompute<gpu>", DiffBackward<gpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index 624fc0a107b0..103f2c117ea6 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -1085,6 +1085,29 @@ def _add_workload_nonzero():
     OpArgMngr.add_workload('nonzero', np.array([True, False, False], dtype=np.bool_))
 
 
+def _add_workload_diff():
+    x = np.array([1, 4, 6, 7, 12])
+    OpArgMngr.add_workload('diff', x)
+    OpArgMngr.add_workload('diff', x, 2)
+    OpArgMngr.add_workload('diff', x, 3)
+    OpArgMngr.add_workload('diff', np.array([1.1, 2.2, 3.0, -0.2, -0.1]))
+    x = np.zeros((10, 20, 30))
+    x[:, 1::2, :] = 1
+    OpArgMngr.add_workload('diff', x)
+    OpArgMngr.add_workload('diff', x, axis=-1)
+    OpArgMngr.add_workload('diff', x, axis=0)
+    OpArgMngr.add_workload('diff', x, axis=1)
+    OpArgMngr.add_workload('diff', x, axis=-2)
+    x = 20 * np.random.uniform(size=(10,20,30))
+    OpArgMngr.add_workload('diff', x)
+    OpArgMngr.add_workload('diff', x, n=2)
+    OpArgMngr.add_workload('diff', x, axis=0)
+    OpArgMngr.add_workload('diff', x, n=2, axis=0)
+    x = np.array([list(range(3))])
+    for n in range(1, 5):
+        OpArgMngr.add_workload('diff', x, n=n)
+
+
 @use_np
 def _prepare_workloads():
     array_pool = {
@@ -1190,6 +1213,7 @@ def _prepare_workloads():
     _add_workload_greater_equal(array_pool)
     _add_workload_less(array_pool)
     _add_workload_less_equal(array_pool)
+    _add_workload_diff()
 
 
 _prepare_workloads()
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index bfe6c3d43b50..67c1ede6cc1a 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -3764,6 +3764,58 @@ def test_np_share_memory():
                 assert not op(np.ones((5, 0), dtype=dt), np.ones((0, 3, 0), dtype=adt))
 
 
+@with_seed()
+@use_np
+def test_np_diff():
+    def np_diff_backward(ograd, n, axis):
+        res = ograd
+        for i in range(n):
+            res = _np.negative(_np.diff(res, n=1, axis=axis, prepend=0, append=0))
+        return res
+
+    class TestDiff(HybridBlock):
+        def __init__(self, n=1, axis=-1):
+            super(TestDiff, self).__init__()
+            self._n = n
+            self._axis = axis
+
+        def hybrid_forward(self, F, a):
+            return F.np.diff(a, n=self._n, axis=self._axis)
+
+    shapes = [tuple(random.randrange(10) for i in range(random.randrange(6))) for j in range(5)]
+    for hybridize in [True, False]:
+        for shape in shapes:
+            for axis in [i for i in range(-len(shape), len(shape))]:
+                for n in [i for i in range(0, shape[axis]+1)]:
+                    test_np_diff = TestDiff(n=n, axis=axis)
+                    if hybridize:
+                        test_np_diff.hybridize()
+                    for itype in [_np.float16, _np.float32, _np.float64]:
+                        # note the tolerance shall be scaled by the input n
+                        if itype == _np.float16:
+                            rtol = atol = 1e-2*len(shape)*n
+                        else:
+                            rtol = atol = 1e-5*len(shape)*n
+                        x = rand_ndarray(shape).astype(itype).as_np_ndarray()
+                        x.attach_grad()
+                        np_out = _np.diff(x.asnumpy(), n=n, axis=axis)
+                        with mx.autograd.record():
+                            mx_out = test_np_diff(x)
+                        assert mx_out.shape == np_out.shape
+                        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
+                        mx_out.backward()
+                        if (np_out.size == 0):
+                            np_backward = _np.zeros(shape)
+                        else:                    
+                            np_backward = np_diff_backward(_np.ones(np_out.shape, dtype=itype), n=n, axis=axis)
+                        assert x.grad.shape == np_backward.shape
+                        assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=rtol, atol=atol)
+
+                        mx_out = np.diff(x, n=n, axis=axis)
+                        np_out = _np.diff(x.asnumpy(), n=n, axis=axis)
+                        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 77e8f516e7f3d7d2a6c40387028a82ffd761909c Mon Sep 17 00:00:00 2001
From: Jake Lee <gstu1130@gmail.com>
Date: Wed, 30 Oct 2019 16:33:15 -0700
Subject: [PATCH 539/813] fix cuDNN RNN dtype_with_fallback_ bug (#16671)

---
 src/operator/rnn-inl.h | 17 ++++-------------
 1 file changed, 4 insertions(+), 13 deletions(-)

diff --git a/src/operator/rnn-inl.h b/src/operator/rnn-inl.h
index b448261f215d..d5fd351986e3 100644
--- a/src/operator/rnn-inl.h
+++ b/src/operator/rnn-inl.h
@@ -1376,21 +1376,12 @@ class RNNOp {
                                            seed_));
 
       // RNN descriptors
-      cudnnDataType_t dtype_with_fallback_;
+      // adopt pseudo-fp16 for all architectures
+      cudnnDataType_t dtype_with_fallback_ =
+        (cudnnGetVersion() >= 7500 && dtype_ == CUDNN_DATA_HALF) ? CUDNN_DATA_FLOAT
+                                                             : dtype_;
       cudnnRNNAlgo_t rnn_algo = CUDNN_RNN_ALGO_STANDARD;
       dgrad_sync_needed_ = (rnn_algo == CUDNN_RNN_ALGO_STANDARD) && param_.bidirectional;
-      // On arch's 50 and 52(Maxwell), the gpu doesn't support native fp16 compute.
-      // Before cuDNN 7.5.0, when running fp16, cuDNN fallback to fp32 under the hood on Maxwell.
-      // That's not the case begining from 7.5.0. Thereby adding fallback explicitly here.
-#if __CUDA_ARCH__ < 530 && CUDNN_VERSION >= 7500
-      if (dtype_ == CUDNN_DATA_HALF) {
-        dtype_with_fallback_ = CUDNN_DATA_FLOAT;
-      } else {
-        dtype_with_fallback_ = dtype_;
-      }
-#else
-        dtype_with_fallback_ = dtype_;
-#endif
       CUDNN_CALL(cudnnSetRNNDescriptor_v6(s->dnn_handle_,
                                           rnn_desc_,
                                           param_.state_size,

From a6a9706bf962c756e2c934a2e86377c009935e9c Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Wed, 30 Oct 2019 21:02:06 -0700
Subject: [PATCH 540/813] Miscellaneous fix for several numpy issues (#16664)

* fix behavior of np.array when given official numpy ndarray

* bool for expand_dims and cast

* recover original Makefile

* address comments

* add boolean support for cumsum

* add gpu cast boolean support

* add error message
---
 python/mxnet/numpy/multiarray.py              | 21 ++++++++------
 src/ndarray/ndarray_function.cu               |  2 +-
 src/operator/mxnet_op.h                       |  2 +-
 src/operator/numpy/np_cumsum-inl.h            |  4 +--
 src/operator/numpy/np_cumsum.cc               |  3 ++
 src/operator/numpy/np_init_op.h               |  2 +-
 src/operator/tensor/elemwise_unary_op.h       |  2 +-
 .../unittest/test_numpy_interoperability.py   |  5 ++--
 tests/python/unittest/test_numpy_ndarray.py   | 28 +++++++++++--------
 tests/python/unittest/test_numpy_op.py        | 12 +++++++-
 10 files changed, 53 insertions(+), 28 deletions(-)

diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index a6d90881da4f..bc4b409d5be7 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -577,7 +577,7 @@ def __setitem__(self, key, value):
             if not isinstance(key, tuple) or len(key) != 0:
                 raise IndexError('scalar tensor can only accept `()` as index')
             if isinstance(value, numeric_types):
-                self.full(value)
+                self._full(value)
             elif isinstance(value, ndarray) and value.size == 1:
                 if value.shape != self.shape:
                     value = value.reshape(self.shape)
@@ -1993,15 +1993,20 @@ def array(object, dtype=None, ctx=None):
     """
     if ctx is None:
         ctx = current_context()
-    if isinstance(object, ndarray):
+    if isinstance(object, (ndarray, _np.ndarray)):
         dtype = object.dtype if dtype is None else dtype
+    elif isinstance(object, NDArray):
+        raise ValueError("If you're trying to create a mxnet.numpy.ndarray "
+                         "from mx.nd.NDArray, please use the zero-copy as_np_ndarray function.")
     else:
-        dtype = _np.float32 if dtype is None else dtype
-        if not isinstance(object, (ndarray, _np.ndarray)):
-            try:
-                object = _np.array(object, dtype=dtype)
-            except Exception as e:
-                raise TypeError('{}'.format(str(e)))
+        if dtype is None:
+            dtype = object.dtype if hasattr(object, "dtype") else _np.float32
+        try:
+            object = _np.array(object, dtype=dtype)
+        except Exception as e:
+            # printing out the error raised by official NumPy's array function
+            # for transparency on users' side
+            raise TypeError('{}'.format(str(e)))
     ret = empty(object.shape, dtype=dtype, ctx=ctx)
     if len(object.shape) == 0:
         ret[()] = object
diff --git a/src/ndarray/ndarray_function.cu b/src/ndarray/ndarray_function.cu
index 2a1461cc8c48..da7b60db7f13 100644
--- a/src/ndarray/ndarray_function.cu
+++ b/src/ndarray/ndarray_function.cu
@@ -76,7 +76,7 @@ void Copy<gpu, gpu>(const TBlob &from, TBlob *to,
                       from.FlatTo1D<gpu, DType>(s),
                       s);
       } else {
-        MSHADOW_TYPE_SWITCH(from.type_flag_, SrcDType, {
+        MSHADOW_TYPE_SWITCH_WITH_BOOL(from.type_flag_, SrcDType, {
           to->FlatTo1D<gpu, DType>(s) =
             mshadow::expr::tcast<DType>(from.FlatTo1D<gpu, SrcDType>(s));
         })
diff --git a/src/operator/mxnet_op.h b/src/operator/mxnet_op.h
index 463c71b5b0eb..91478660a123 100644
--- a/src/operator/mxnet_op.h
+++ b/src/operator/mxnet_op.h
@@ -671,7 +671,7 @@ template <typename xpu>
 MSHADOW_CINLINE void copy(mshadow::Stream<xpu> *s, const TBlob& to, const TBlob& from) {
   CHECK_EQ(from.Size(), to.Size());
   CHECK_EQ(from.dev_mask(), to.dev_mask());
-  MSHADOW_TYPE_SWITCH(to.type_flag_, DType, {
+  MSHADOW_TYPE_SWITCH_WITH_BOOL(to.type_flag_, DType, {
     if (to.type_flag_ == from.type_flag_) {
       mshadow::Copy(to.FlatTo1D<xpu, DType>(s), from.FlatTo1D<xpu, DType>(s), s);
     } else {
diff --git a/src/operator/numpy/np_cumsum-inl.h b/src/operator/numpy/np_cumsum-inl.h
index 6c6b56d46e76..375d83b2240f 100644
--- a/src/operator/numpy/np_cumsum-inl.h
+++ b/src/operator/numpy/np_cumsum-inl.h
@@ -98,7 +98,7 @@ void CumsumForwardImpl(const OpContext& ctx,
   }
 
   Stream<xpu> *s = ctx.get_stream<xpu>();
-  MSHADOW_TYPE_SWITCH(in.type_flag_, IType, {
+  MSHADOW_TYPE_SWITCH_WITH_BOOL(in.type_flag_, IType, {
     MSHADOW_TYPE_SWITCH(out.type_flag_, OType, {
       Kernel<cumsum_forward, xpu>::Launch(
         s, out.Size() / middle, out.dptr<OType>(),
@@ -157,7 +157,7 @@ void CumsumBackwardImpl(const OpContext& ctx,
     }
   }
   Stream<xpu> *s = ctx.get_stream<xpu>();
-  MSHADOW_TYPE_SWITCH(igrad.type_flag_, IType, {
+  MSHADOW_TYPE_SWITCH_WITH_BOOL(igrad.type_flag_, IType, {
     MSHADOW_TYPE_SWITCH(ograd.type_flag_, OType, {
       Kernel<cumsum_backward, xpu>::Launch(
         s, igrad.Size() / middle, igrad.dptr<IType>(),
diff --git a/src/operator/numpy/np_cumsum.cc b/src/operator/numpy/np_cumsum.cc
index 0ddbf521186c..2d5dbb99f90a 100644
--- a/src/operator/numpy/np_cumsum.cc
+++ b/src/operator/numpy/np_cumsum.cc
@@ -55,6 +55,9 @@ inline bool CumsumType(const nnvm::NodeAttrs& attrs,
   } else {
     TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
     TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
+    if (out_attrs->at(0) == mshadow::kBool) {
+      (*out_attrs)[0] = mshadow::kInt64;
+    }
   }
 
   return out_attrs->at(0) != -1 && in_attrs->at(0) != -1;
diff --git a/src/operator/numpy/np_init_op.h b/src/operator/numpy/np_init_op.h
index 69999ae8710e..df30d611aa02 100644
--- a/src/operator/numpy/np_init_op.h
+++ b/src/operator/numpy/np_init_op.h
@@ -205,7 +205,7 @@ void IdentityCompute(const nnvm::NodeAttrs& attrs,
   Stream<xpu> *s = ctx.get_stream<xpu>();
   const TBlob& out_data = outputs[0];
   int n = out_data.shape_[0];
-  MSHADOW_TYPE_SWITCH(out_data.type_flag_, DType, {
+  MSHADOW_TYPE_SWITCH_WITH_BOOL(out_data.type_flag_, DType, {
     MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
       Kernel<identity<req_type>, xpu>::Launch(
           s, out_data.Size(), out_data.dptr<DType>(), n);
diff --git a/src/operator/tensor/elemwise_unary_op.h b/src/operator/tensor/elemwise_unary_op.h
index b7625fccf258..188ccd68a340 100644
--- a/src/operator/tensor/elemwise_unary_op.h
+++ b/src/operator/tensor/elemwise_unary_op.h
@@ -451,7 +451,7 @@ void CastCompute(const nnvm::NodeAttrs& attrs,
   Stream<xpu> *s = ctx.get_stream<xpu>();
   MSHADOW_TYPE_SWITCH_WITH_BOOL(outputs[0].type_flag_, DstDType, {
     Tensor<xpu, 1, DstDType> out = outputs[0].FlatTo1D<xpu, DstDType>(s);
-    MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, SrcDType, {
+    MSHADOW_TYPE_SWITCH_WITH_BOOL(inputs[0].type_flag_, SrcDType, {
       Tensor<xpu, 1, SrcDType> data = inputs[0].FlatTo1D<xpu, SrcDType>(s);
       if (outputs[0].type_flag_ != inputs[0].type_flag_ ||
           req[0] != kWriteInplace) {
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index 103f2c117ea6..5d6e8af7fa47 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -93,6 +93,7 @@ def _add_workload_copy():
 
 def _add_workload_expand_dims():
     OpArgMngr.add_workload('expand_dims', np.random.uniform(size=(4, 1)), -1)
+    OpArgMngr.add_workload('expand_dims', np.random.uniform(size=(4, 1)) > 0.5, -1)
     for axis in range(-5, 4):
         OpArgMngr.add_workload('expand_dims', np.empty((2, 3, 4, 5)), axis)
 
@@ -852,8 +853,8 @@ def _signs(dt):
     # test_float_remainder_corner_cases
     # Check remainder magnitude.
     for ct in _FLOAT_DTYPES:
-        b = _np.array(1.0)
-        a = np.array(_np.nextafter(_np.array(0.0), -b), dtype=ct)
+        b = _np.array(1.0, dtype=ct)
+        a = np.array(_np.nextafter(_np.array(0.0, dtype=ct), -b), dtype=ct)
         b = np.array(b, dtype=ct)
         OpArgMngr.add_workload('remainder', a, b)
         OpArgMngr.add_workload('remainder', -a, -b)
diff --git a/tests/python/unittest/test_numpy_ndarray.py b/tests/python/unittest/test_numpy_ndarray.py
index 6077f4df13ae..239f300e028e 100644
--- a/tests/python/unittest/test_numpy_ndarray.py
+++ b/tests/python/unittest/test_numpy_ndarray.py
@@ -18,6 +18,7 @@
 # pylint: skip-file
 from __future__ import absolute_import
 from __future__ import division
+import itertools
 import os
 import unittest
 import numpy as _np
@@ -87,6 +88,7 @@ def test_np_array_creation():
         [],
         (),
         [[1, 2], [3, 4]],
+        _np.random.randint(-10, 10, size=rand_shape_nd(3)),
         _np.random.uniform(size=rand_shape_nd(3)),
         _np.random.uniform(size=(3, 0, 4))
     ]
@@ -94,10 +96,12 @@ def test_np_array_creation():
         for src in objects:
             mx_arr = np.array(src, dtype=dtype)
             assert mx_arr.ctx == mx.current_context()
+            if dtype is None:
+                dtype = src.dtype if isinstance(src, _np.ndarray) else _np.float32
             if isinstance(src, mx.nd.NDArray):
-                np_arr = _np.array(src.asnumpy(), dtype=dtype if dtype is not None else _np.float32)
+                np_arr = _np.array(src.asnumpy(), dtype=dtype)
             else:
-                np_arr = _np.array(src, dtype=dtype if dtype is not None else _np.float32)
+                np_arr = _np.array(src, dtype=dtype)
             assert mx_arr.dtype == np_arr.dtype
             assert same(mx_arr.asnumpy(), np_arr)
 
@@ -471,9 +475,6 @@ def test_np_grad_ndarray_type():
 @with_seed()
 @use_np
 def test_np_ndarray_astype():
-    mx_data = np.array([2, 3, 4, 5], dtype=_np.int32)
-    np_data = mx_data.asnumpy()
-
     class TestAstype(HybridBlock):
         def __init__(self, dtype, copy):
             super(TestAstype, self).__init__()
@@ -483,24 +484,29 @@ def __init__(self, dtype, copy):
         def hybrid_forward(self, F, x):
             return x.astype(dtype=self._dtype, copy=self._copy)
 
-    def check_astype_equal(dtype, copy, expect_zero_copy=False, hybridize=False):
-        test_astype = TestAstype(dtype, copy)
+    def check_astype_equal(itype, otype, copy, expect_zero_copy=False, hybridize=False):
+        expect_zero_copy = copy is False and itype == otype
+        mx_data = np.array([2, 3, 4, 5], dtype=itype)
+        np_data = mx_data.asnumpy()
+        test_astype = TestAstype(otype, copy)
         if hybridize:
             test_astype.hybridize()
         mx_ret = test_astype(mx_data)
         assert type(mx_ret) is np.ndarray
-        np_ret = np_data.astype(dtype=dtype, copy=copy)
+        np_ret = np_data.astype(dtype=otype, copy=copy)
         assert mx_ret.dtype == np_ret.dtype
         assert same(mx_ret.asnumpy(), np_ret)
         if expect_zero_copy and not hybridize:
             assert id(mx_ret) == id(mx_data)
             assert id(np_ret) == id(np_data)
 
-    for dtype in [np.int8, np.uint8, np.int32, np.float16, np.float32, np.float64, np.bool, np.bool_,
-                  'int8', 'uint8', 'int32', 'float16', 'float32', 'float64', 'bool']:
+    dtypes = [np.int8, np.uint8, np.int32, np.float16, np.float32, np.float64, np.bool, np.bool_,
+              'int8', 'uint8', 'int32', 'float16', 'float32', 'float64', 'bool']
+
+    for itype, otype in itertools.product(dtypes, dtypes):
         for copy in [True, False]:
             for hybridize in [True, False]:
-                check_astype_equal(dtype, copy, copy is False and mx_data.dtype == dtype, hybridize)
+                check_astype_equal(itype, otype, copy, hybridize)
 
 
 @with_seed()
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 67c1ede6cc1a..0b15c7ea0d2d 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -442,7 +442,7 @@ def is_int(dtype):
             for axis in ([i for i in range(in_data_dim)] + [(), None]):
                 for itype in ['float16', 'float32', 'float64', 'int8', 'int32', 'int64', 'bool']:
                     for dtype in ['float16', 'float32', 'float64', 'int8', 'int32', 'int64']:
-                        if (is_int(dtype) and not is_int(itype))\
+                        if (is_int(dtype) and not is_int(itype)) or (is_windows and is_int(itype))\
                                 or (itype == 'bool' and\
                                     (dtype not in ('float32', 'float64', 'int32', 'int64') or is_windows)):
                             continue
@@ -2390,6 +2390,16 @@ def hybrid_forward(self, F, a):
                         np_out = _np.cumsum(x.asnumpy(), axis=axis, dtype=otype)
                         assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
 
+    for shape in shapes:
+        for axis in [None] + [i for i in range(0, len(shape))]:
+            for otype in [None, _np.int32, _np.int64]:
+                for itype in [_np.bool, _np.int8, _np.int32, _np.int64]:
+                    x = rand_ndarray(shape).astype(itype).as_np_ndarray()
+                    np_out = _np.cumsum(x.asnumpy(), axis=axis, dtype=otype)
+                    mx_out = np.cumsum(x, axis=axis, dtype=otype)
+                    assert mx_out.shape == np_out.shape
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+
 
 @with_seed()
 @use_np

From f9baec9a020d125d601790659e15fd7f9c2978c1 Mon Sep 17 00:00:00 2001
From: vexilligera <vexilligera@gmail.com>
Date: Thu, 31 Oct 2019 04:59:27 +0000
Subject: [PATCH 541/813] [Numpy] implement np.column_stack (#16594)

* implement np.column_stack

* cpplint

* remove column_stack from numpy interoperability test temporarily

* style and test fix

* fix pylint and add interoperability test

* fix doc string, add comment, remove dead code

* pylint

* ci

* ci

* ci

* [Numpy] Numpy operator diff (#15906)

* numpy diff operator implemented

append and prepend not supported yet

remove the prepend and append checking interface from the backend

refine the code, enrich the test set and all tests passed

registered the diff operator into npi scope

all tests passed

comments and minor modification

format codes and fix warning for sanity check

minor modification for sanity check

fix sanity

fix the tolerance bound of testing np.diff

resolve minor coding style issue

replace the given tests by random picking

minor fix

* interoperability test added

* implement np.column_stack

* cpplint

* remove column_stack from numpy interoperability test temporarily

* style and test fix

* fix pylint and add interoperability test

* fix doc string, add comment, remove dead code

* pylint

* ci

* ci

* ci

* rebase resolve conflicts

* pylint
---
 python/mxnet/ndarray/numpy/_op.py             |  35 ++++-
 python/mxnet/numpy/multiarray.py              |  38 ++++-
 python/mxnet/numpy_dispatch_protocol.py       |   1 +
 python/mxnet/symbol/numpy/_symbol.py          |  40 ++++-
 src/operator/numpy/np_matrix_op-inl.h         |  80 ++++++++++
 src/operator/numpy/np_matrix_op.cc            | 148 +++++++++++++++++-
 src/operator/numpy/np_matrix_op.cu            |   6 +
 .../unittest/test_numpy_interoperability.py   |   6 +
 tests/python/unittest/test_numpy_op.py        | 106 +++++++++++++
 9 files changed, 452 insertions(+), 8 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index 297c40b1431e..256cfb7d5708 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -34,13 +34,12 @@
            'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot', 'histogram', 'eye',
            'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate',
-           'stack', 'vstack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'argmin',
-           'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
+           'stack', 'vstack', 'column_stack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax',
+           'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
            'around', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
            'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal',
            'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory', 'may_share_memory', 'diff']
 
-
 @set_module('mxnet.ndarray.numpy')
 def zeros(shape, dtype=_np.float32, order='C', ctx=None):
     """Return a new array of given shape and type, filled with zeros.
@@ -3004,6 +3003,36 @@ def get_list(arrays):
     return _npi.vstack(*arrays)
 
 
+@set_module('mxnet.ndarray.numpy')
+def column_stack(tup):
+    """
+    Stack 1-D arrays as columns into a 2-D array.
+    Take a sequence of 1-D arrays and stack them as columns
+    to make a single 2-D array. 2-D arrays are stacked as-is,
+    just like with `hstack`.  1-D arrays are turned into 2-D columns
+    first.
+
+    Returns
+    --------
+    stacked : 2-D array
+        The array formed by stacking the given arrays.
+
+    See Also
+    --------
+    stack, hstack, vstack, concatenate
+
+    Examples
+    --------
+    >>> a = np.array((1,2,3))
+    >>> b = np.array((2,3,4))
+    >>> np.column_stack((a,b))
+    array([[1., 2.],
+           [2., 3.],
+           [3., 4.]])
+    """
+    return _npi.column_stack(*tup)
+
+
 @set_module('mxnet.ndarray.numpy')
 def dstack(arrays):
     """
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index bc4b409d5be7..8e0d5b209a8d 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -51,7 +51,7 @@
            'degrees', 'log2', 'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative',
            'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh',
            'tensordot', 'histogram', 'eye', 'linspace', 'logspace', 'expand_dims', 'tile', 'arange',
-           'split', 'vsplit', 'concatenate', 'stack', 'vstack', 'dstack', 'mean', 'maximum', 'minimum',
+           'split', 'vsplit', 'concatenate', 'stack', 'vstack', 'column_stack', 'dstack', 'mean', 'maximum', 'minimum',
            'swapaxes', 'clip', 'argmax', 'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming',
            'blackman', 'flip', 'around', 'arctan2', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril',
            'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less',
@@ -4904,6 +4904,42 @@ def vstack(arrays, out=None):
     return _mx_nd_np.vstack(arrays)
 
 
+@set_module('mxnet.numpy')
+def column_stack(tup):
+    """
+    Stack 1-D arrays as columns into a 2-D array.
+
+    Take a sequence of 1-D arrays and stack them as columns
+    to make a single 2-D array. 2-D arrays are stacked as-is,
+    just like with `hstack`.  1-D arrays are turned into 2-D columns
+    first.
+
+    Parameters
+    ----------
+    tup : sequence of 1-D or 2-D arrays.
+        Arrays to stack. All of them must have the same first dimension.
+
+    Returns
+    --------
+    stacked : 2-D array
+        The array formed by stacking the given arrays.
+
+    See Also
+    --------
+    stack, hstack, vstack, concatenate
+
+    Examples
+    --------
+    >>> a = np.array((1,2,3))
+    >>> b = np.array((2,3,4))
+    >>> np.column_stack((a,b))
+    array([[1., 2.],
+           [2., 3.],
+           [3., 4.]])
+    """
+    return _mx_nd_np.column_stack(tup)
+
+
 @set_module('mxnet.numpy')
 def dstack(arrays):
     """
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index 2411f51b7aa6..cfab2a49699d 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -121,6 +121,7 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'var',
     'vdot',
     'vstack',
+    'column_stack',
     'zeros_like',
     'linalg.norm',
     'trace',
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 6f7f912d6e36..7469875f267a 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -36,8 +36,8 @@
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot', 'histogram', 'eye',
            'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate',
-           'stack', 'vstack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'argmin',
-           'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
+           'stack', 'vstack', 'column_stack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax',
+           'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
            'around', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
            'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal',
            'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'shares_memory', 'may_share_memory', 'diff']
@@ -3072,6 +3072,42 @@ def get_list(arrays):
     return _npi.vstack(*arrays)
 
 
+@set_module('mxnet.symbol.numpy')
+def column_stack(tup):
+    """
+    Stack 1-D arrays as columns into a 2-D array.
+
+    Take a sequence of 1-D arrays and stack them as columns
+    to make a single 2-D array. 2-D arrays are stacked as-is,
+    just like with `hstack`.  1-D arrays are turned into 2-D columns
+    first.
+
+    Parameters
+    ----------
+    tup : sequence of 1-D or 2-D arrays.
+        Arrays to stack. All of them must have the same first dimension.
+
+    Returns
+    -------
+    stacked : 2-D array
+        The array formed by stacking the given arrays.
+
+    See Also
+    --------
+    stack, hstack, vstack, concatenate
+
+    Examples
+    --------
+    >>> a = np.array((1,2,3))
+    >>> b = np.array((2,3,4))
+    >>> np.column_stack((a,b))
+    array([[1., 2.],
+           [2., 3.],
+           [3., 4.]])
+    """
+    return _npi.column_stack(*tup)
+
+
 @set_module('mxnet.symbol.numpy')
 def dstack(arrays):
     """
diff --git a/src/operator/numpy/np_matrix_op-inl.h b/src/operator/numpy/np_matrix_op-inl.h
index 9ce84835f1a8..2545adcb3555 100644
--- a/src/operator/numpy/np_matrix_op-inl.h
+++ b/src/operator/numpy/np_matrix_op-inl.h
@@ -52,6 +52,14 @@ struct NumpyVstackParam : public dmlc::Parameter<NumpyVstackParam> {
   }
 };
 
+struct NumpyColumnStackParam : public dmlc::Parameter<NumpyColumnStackParam> {
+  int num_args;
+  DMLC_DECLARE_PARAMETER(NumpyColumnStackParam) {
+    DMLC_DECLARE_FIELD(num_args).set_lower_bound(1)
+    .describe("Number of inputs to be column stacked");
+  }
+};
+
 struct NumpyReshapeParam : public dmlc::Parameter<NumpyReshapeParam> {
   mxnet::TShape newshape;
   std::string order;
@@ -124,6 +132,78 @@ void NumpyTranspose(const nnvm::NodeAttrs& attrs,
   }
 }
 
+template<typename xpu>
+void NumpyColumnStackForward(const nnvm::NodeAttrs& attrs,
+                             const OpContext& ctx,
+                             const std::vector<TBlob>& inputs,
+                             const std::vector<OpReqType>& req,
+                             const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mshadow_op;
+
+  const NumpyColumnStackParam& param = nnvm::get<NumpyColumnStackParam>(attrs.parsed);
+  CHECK_EQ(inputs.size(), param.num_args);
+  CHECK_EQ(outputs.size(), 1);
+  CHECK_EQ(req.size(), 1);
+
+  // reshape if necessary
+  std::vector<TBlob> data(param.num_args);
+  for (int i = 0; i < param.num_args; i++) {
+    if (inputs[i].shape_.ndim() == 0 || inputs[i].shape_.ndim() == 1) {
+      TShape shape = Shape2(inputs[i].shape_.Size(), 1);
+      data[i] = inputs[i].reshape(shape);
+    } else {
+      data[i] = inputs[i];
+    }
+  }
+
+  // initialize ConcatOp
+  ConcatParam cparam;
+  cparam.num_args = param.num_args;
+  cparam.dim = 1;
+  MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    ConcatOp<xpu, DType> op;
+    op.Init(cparam);
+    op.Forward(ctx, data, req, outputs);
+  });
+}
+
+template<typename xpu>
+void NumpyColumnStackBackward(const nnvm::NodeAttrs& attrs,
+                              const OpContext& ctx,
+                              const std::vector<TBlob>& inputs,
+                              const std::vector<OpReqType>& req,
+                              const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mshadow_op;
+
+  const NumpyColumnStackParam& param = nnvm::get<NumpyColumnStackParam>(attrs.parsed);
+  CHECK_EQ(inputs.size(), 1);
+  CHECK_EQ(outputs.size(), param.num_args);
+  CHECK_EQ(req.size(), param.num_args);
+
+  // reshape if necessary
+  std::vector<TBlob> data(param.num_args);
+  for (int i = 0; i < param.num_args; i++) {
+    if (outputs[i].shape_.ndim() == 0 || outputs[i].shape_.ndim() == 1) {
+      TShape shape = Shape2(outputs[i].shape_.Size(), 1);
+      data[i] = outputs[i].reshape(shape);
+    } else {
+      data[i] = outputs[i];
+    }
+  }
+
+  // initialize ConcatOp
+  ConcatParam cparam;
+  cparam.num_args = param.num_args;
+  cparam.dim = 1;
+  MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    ConcatOp<xpu, DType> op;
+    op.Init(cparam);
+    op.Backward(ctx, inputs[0], req, data);
+  });
+}
+
 template<typename xpu>
 void NumpyVstackForward(const nnvm::NodeAttrs& attrs,
                         const OpContext& ctx,
diff --git a/src/operator/numpy/np_matrix_op.cc b/src/operator/numpy/np_matrix_op.cc
index 0a6f9a150d8b..18594cd9cff1 100644
--- a/src/operator/numpy/np_matrix_op.cc
+++ b/src/operator/numpy/np_matrix_op.cc
@@ -613,6 +613,152 @@ Examples::
 .add_argument("data", "NDArray-or-Symbol[]", "List of arrays to stack")
 .add_arguments(StackParam::__FIELDS__());
 
+bool NumpyColumnStackType(const nnvm::NodeAttrs& attrs,
+                          std::vector<int> *in_type,
+                          std::vector<int> *out_type) {
+  const NumpyColumnStackParam& param = nnvm::get<NumpyColumnStackParam>(attrs.parsed);
+  CHECK_EQ(in_type->size(), param.num_args);
+  CHECK_EQ(out_type->size(), 1);
+  int dtype = -1;
+  for (int i = 0; i < param.num_args; i++) {
+    if (dtype == -1) {
+      dtype = in_type->at(i);
+    }
+  }
+  if (dtype == -1) {
+    dtype = out_type->at(0);
+  }
+  for (int i = 0; i < param.num_args; i++) {
+    TYPE_ASSIGN_CHECK(*in_type, i, dtype);
+  }
+  TYPE_ASSIGN_CHECK(*out_type, 0, dtype);
+  return dtype != -1;
+}
+
+bool NumpyColumnStackShape(const nnvm::NodeAttrs& attrs,
+                           mxnet::ShapeVector* in_attrs,
+                           mxnet::ShapeVector* out_attrs) {
+  CHECK_EQ(out_attrs->size(), 1U);
+  const NumpyColumnStackParam& param = nnvm::get<NumpyColumnStackParam>(attrs.parsed);
+  CHECK_EQ(in_attrs->size(), param.num_args);
+  std::vector<mxnet::TShape> in_attrs_tmp(param.num_args);
+  TShape dshape;
+  // For each array in the input, reshape to 2D if ndim < 2.
+  for (int i = 0; i < param.num_args; i++) {
+    if ((*in_attrs)[i].ndim() == 0) {
+      in_attrs_tmp[i] = TShape(2, 1);
+    } else if ((*in_attrs)[i].ndim() == 1) {
+      // Transpose 1D row into a column.
+      in_attrs_tmp[i] = TShape(2, 1);
+      in_attrs_tmp[i][0] = (*in_attrs)[i][0];
+    } else {
+      in_attrs_tmp[i] = (*in_attrs)[i];
+    }
+    TShape tmp(in_attrs_tmp[i].ndim(), -1);
+    shape_assign(&dshape, tmp);
+  }
+  TShape tmp((*out_attrs)[0].ndim(), -1);
+  shape_assign(&dshape, tmp);
+  for (int i = 0; i < param.num_args; i++) {
+    SHAPE_ASSIGN_CHECK(in_attrs_tmp, i, dshape)
+  }
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, dshape)
+  if (dshape.ndim() == -1) {
+    return false;
+  }
+  // Accumulate along column axis.
+  int cnt = 0, sum = 0, pos = -1;
+  for (int i = 0; i < param.num_args; i++) {
+    TShape tmp = in_attrs_tmp[i];
+    if (!dim_size_is_known(tmp, 1)) {
+      cnt++;
+      pos = i;
+    } else {
+      sum += tmp[1];
+    }
+    tmp[1] = -1;
+    shape_assign(&dshape, tmp);
+  }
+  tmp = out_attrs->at(0);
+  if (!dim_size_is_known(tmp, 1)) {
+    cnt++;
+    pos = -1;
+  } else {
+    sum += tmp[1];
+  }
+  tmp[1] = -1;
+  shape_assign(&dshape, tmp);
+  for (int i = 0; i < param.num_args; i++) {
+    SHAPE_ASSIGN_CHECK(in_attrs_tmp, i, dshape)
+  }
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, dshape)\
+  dshape[1] = 0;
+  if (!shape_is_known(dshape)) {
+    return false;
+  }
+  dshape[1] = sum;
+  if (cnt == 0) {
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, dshape);
+  } else if (cnt == 1) {
+    // Infer missing dimension if only one column dimension of the input is missing
+    if (pos >= 0) {
+      in_attrs_tmp[pos][1] = out_attrs->at(0)[1] - sum;
+    } else {
+      out_attrs->at(0)[1] = sum;
+    }
+  } else {
+    return false;
+  }
+  for (int i = 0; i < param.num_args; i++) {
+    if (in_attrs->at(i).ndim() == 1) {
+      in_attrs->at(i)[0] = in_attrs_tmp[i][1];
+    } else if (in_attrs->at(i).ndim() >= 2) {
+      in_attrs->at(i) = in_attrs_tmp[i];
+    }
+  }
+
+  return true;
+}
+
+DMLC_REGISTER_PARAMETER(NumpyColumnStackParam);
+
+NNVM_REGISTER_OP(_npi_column_stack)
+.describe(R"code()code" ADD_FILELINE)
+.set_attr_parser(ParamParser<NumpyColumnStackParam>)
+.set_num_inputs([](const nnvm::NodeAttrs& attrs) {
+  const NumpyColumnStackParam& param = dmlc::get<NumpyColumnStackParam>(attrs.parsed);
+  return static_cast<uint32_t>(param.num_args);
+})
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const nnvm::NodeAttrs& attrs) {
+    int num_args = dmlc::get<NumpyColumnStackParam>(attrs.parsed).num_args;
+    std::vector<std::string> ret;
+    for (int i = 0; i < num_args; ++i) {
+      ret.push_back(std::string("arg") + std::to_string(i));
+    }
+    return ret;
+  })
+.set_attr<std::string>("key_var_num_args", "num_args")
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyColumnStackShape)
+.set_attr<nnvm::FInferType>("FInferType", NumpyColumnStackType)
+.set_attr<FCompute>("FCompute<cpu>", NumpyColumnStackForward<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_np_column_stack"})
+.add_argument("data", "NDArray-or-Symbol[]", "List of arrays to column_stack")
+.add_arguments(NumpyColumnStackParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_backward_np_column_stack)
+.set_attr_parser(ParamParser<NumpyColumnStackParam>)
+.set_num_inputs(1)
+.set_num_outputs([](const nnvm::NodeAttrs& attrs) {
+  const NumpyColumnStackParam& param = dmlc::get<NumpyColumnStackParam>(attrs.parsed);
+  return static_cast<uint32_t>(param.num_args);
+})
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FCompute>("FCompute<cpu>", NumpyColumnStackBackward<cpu>);
+
+DMLC_REGISTER_PARAMETER(NumpyVstackParam);
+
 bool NumpyVstackType(const nnvm::NodeAttrs& attrs,
                      std::vector<int> *in_type,
                      std::vector<int> *out_type) {
@@ -718,8 +864,6 @@ bool NumpyVstackShape(const nnvm::NodeAttrs& attrs,
   return true;
 }
 
-DMLC_REGISTER_PARAMETER(NumpyVstackParam);
-
 NNVM_REGISTER_OP(_npi_vstack)
 .describe(R"code()code" ADD_FILELINE)
 .set_attr_parser(ParamParser<NumpyVstackParam>)
diff --git a/src/operator/numpy/np_matrix_op.cu b/src/operator/numpy/np_matrix_op.cu
index 6b4f7a11a9a2..fccc8f257e64 100644
--- a/src/operator/numpy/np_matrix_op.cu
+++ b/src/operator/numpy/np_matrix_op.cu
@@ -59,6 +59,12 @@ NNVM_REGISTER_OP(_npi_dstack)
 NNVM_REGISTER_OP(_backward_np_dstack)
 .set_attr<FCompute>("FCompute<gpu>", DStackGradCompute<gpu>);
 
+NNVM_REGISTER_OP(_npi_column_stack)
+.set_attr<FCompute>("FCompute<gpu>", NumpyColumnStackForward<gpu>);
+
+NNVM_REGISTER_OP(_backward_np_column_stack)
+.set_attr<FCompute>("FCompute<gpu>", NumpyColumnStackBackward<gpu>);
+
 NNVM_REGISTER_OP(_np_roll)
 .set_attr<FCompute>("FCompute<gpu>", NumpyRollCompute<gpu>);
 
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index 5d6e8af7fa47..15912dc47ad3 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -1021,6 +1021,11 @@ def _add_workload_vstack(array_pool):
     OpArgMngr.add_workload('vstack', array_pool['4x1'])
     OpArgMngr.add_workload('vstack', array_pool['1x1x0'])
 
+def _add_workload_column_stack():
+    OpArgMngr.add_workload('column_stack', (np.array([1, 2, 3]), np.array([2, 3, 4])))
+    OpArgMngr.add_workload('column_stack', (np.array([[1], [2], [3]]), np.array([[2], [3], [4]])))
+    OpArgMngr.add_workload('column_stack', [np.array(_np.arange(3)) for _ in range(2)])
+
 
 def _add_workload_equal(array_pool):
     # TODO(junwu): fp16 does not work yet with TVM generated ops
@@ -1208,6 +1213,7 @@ def _prepare_workloads():
     _add_workload_logical_not(array_pool)
     _add_workload_vdot()
     _add_workload_vstack(array_pool)
+    _add_workload_column_stack()
     _add_workload_equal(array_pool)
     _add_workload_not_equal(array_pool)
     _add_workload_greater(array_pool)
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 0b15c7ea0d2d..605fa85e1f77 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -3690,6 +3690,58 @@ def test_np_true_divide():
 
 @with_seed()
 @use_np
+def test_np_column_stack():
+    class TestColumnStack(HybridBlock):
+        def __init__(self):
+            super(TestColumnStack, self).__init__()
+
+        def hybrid_forward(self, F, a, *args):
+            return F.np.column_stack([a] + list(args))
+
+    def g(data):
+        return _np.ones_like(data)
+
+    configs = [
+        ((), (), ()),
+        ((2), (2), (2)),
+        ((0), (0), (0)),
+        ((0, 3, 0), (0, 0, 0), (0, 1, 0)),
+        ((2, 2), (2, 1), (2, 3)),
+        ((4, 3), (4, 0), (4, 1)),
+        ((2, 2, 2), (2, 4, 2), (2, 2, 2)),
+        ((0, 1, 1), (0, 1, 1), (0, 1, 1))
+    ]
+    types = ['float16', 'float32', 'float64', 'int8', 'int32', 'int64']
+    for config, hybridize, dtype in itertools.product(configs, [True, False], types):
+        test_column_stack = TestColumnStack()
+        if hybridize:
+            test_column_stack.hybridize()
+        rtol = 1e-3
+        atol = 1e-5
+        v = []
+        v_np = []
+        for i in range(3):
+            v_np.append(_np.array(_np.random.uniform(-10.0, 10.0, config[i]), dtype=dtype))
+            v.append(mx.nd.array(v_np[i]).as_np_ndarray())
+            v[i].attach_grad()
+        expected_np = _np.column_stack(v_np)
+        with mx.autograd.record():
+            mx_out = test_column_stack(*v)
+        assert mx_out.shape == expected_np.shape
+        assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol)
+
+        # Test gradient
+        mx_out.backward()
+        for i in range(3):
+            expected_grad = g(v_np[i])
+            assert_almost_equal(v[i].grad.asnumpy(), expected_grad, rtol=rtol, atol=atol)
+
+        # Test imperative once again
+        mx_out = np.column_stack(v)
+        expected_np = _np.column_stack(v_np)
+        assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol)
+
+
 def test_npx_reshape():
     class TestNumpyXReshape(HybridBlock):
         def __init__(self, newshape, reverse):
@@ -3826,6 +3878,60 @@ def hybrid_forward(self, F, a):
                         assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
 
 
+@with_seed()
+@use_np
+def test_np_column_stack():
+    class TestColumnStack(HybridBlock):
+        def __init__(self):
+            super(TestColumnStack, self).__init__()
+
+        def hybrid_forward(self, F, a, *args):
+            return F.np.column_stack([a] + list(args))
+
+    def g(data):
+        return _np.ones_like(data)
+
+    configs = [
+        ((), (), ()),
+        ((2), (2), (2)),
+        ((0), (0), (0)),
+        ((0, 3, 0), (0, 0, 0), (0, 1, 0)),
+        ((2, 2), (2, 1), (2, 3)),
+        ((4, 3), (4, 0), (4, 1)),
+        ((2, 2, 2), (2, 4, 2), (2, 2, 2)),
+        ((0, 1, 1), (0, 1, 1), (0, 1, 1))
+    ]
+    types = ['float16', 'float32', 'float64', 'int8', 'int32', 'int64']
+    for config, hybridize, dtype in itertools.product(configs, [True, False], types):
+        test_column_stack = TestColumnStack()
+        if hybridize:
+            test_column_stack.hybridize()
+        rtol = 1e-3
+        atol = 1e-5
+        v = []
+        v_np = []
+        for i in range(3):
+            v_np.append(_np.array(_np.random.uniform(-10.0, 10.0, config[i]), dtype=dtype))
+            v.append(mx.nd.array(v_np[i]).as_np_ndarray())
+            v[i].attach_grad()
+        expected_np = _np.column_stack(v_np)
+        with mx.autograd.record():
+            mx_out = test_column_stack(*v)
+        assert mx_out.shape == expected_np.shape
+        assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol)
+
+        # Test gradient
+        mx_out.backward()
+        for i in range(3):
+            expected_grad = g(v_np[i])
+            assert_almost_equal(v[i].grad.asnumpy(), expected_grad, rtol=rtol, atol=atol)
+
+        # Test imperative once again
+        mx_out = np.column_stack(v)
+        expected_np = _np.column_stack(v_np)
+        assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From b5d07e30321da47d604b99048c1b57c03ec819b0 Mon Sep 17 00:00:00 2001
From: Leonard Lausen <lausen@amazon.com>
Date: Thu, 31 Oct 2019 13:56:43 +0800
Subject: [PATCH 542/813] Add check if scipy is imported in sparse.py (#16574)

---
 python/mxnet/ndarray/sparse.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/python/mxnet/ndarray/sparse.py b/python/mxnet/ndarray/sparse.py
index 52c5de78ddd4..69c8383df989 100644
--- a/python/mxnet/ndarray/sparse.py
+++ b/python/mxnet/ndarray/sparse.py
@@ -553,8 +553,8 @@ def asscipy(self):
         indices = self.indices.asnumpy()
         indptr = self.indptr.asnumpy()
         if not spsp:
-            raise ImportError("scipy is not available. \
-                               Please check if the scipy python bindings are installed.")
+            raise ImportError("scipy could not be imported. "
+                              "Please make sure that the scipy is installed.")
         return spsp.csr_matrix((data, indices, indptr), shape=self.shape, dtype=self.dtype)
 
 # pylint: disable=abstract-method
@@ -940,6 +940,9 @@ def csr_matrix(arg1, shape=None, ctx=None, dtype=None):
                     row = row.asnumpy()
                 if isinstance(col, NDArray):
                     col = col.asnumpy()
+                if not spsp:
+                    raise ImportError("scipy could not be imported. "
+                                      "Please make sure that the scipy is installed.")
                 coo = spsp.coo_matrix((data, (row, col)), shape=shape)
                 _check_shape(coo.shape, shape)
                 csr = coo.tocsr()

From aa1074dc1704d3732ab205c43d48083ef8c69680 Mon Sep 17 00:00:00 2001
From: Tao Lv <tao.a.lv@intel.com>
Date: Thu, 31 Oct 2019 22:55:13 +0800
Subject: [PATCH 543/813] Upgrade MKL-DNN dependency to v1.0 (#16555)

* [mkldnn-v1.0] Initiate the transition to MKL-DNN v1.0 (#15706)

* update mkldnn to 1.0.1 release

* change makefile

* change cmake

* update ci build and pip package build

* fix typo in mkldnn.mk

* fix build for USE_BLAS=mkl & bump MKL version

* skip mkldnn unit tests

* remove iomp5 from mx_mkldnn_lib

* ci: skip test_mkldnn_install

* retrigger ci

* retrigger ci

* retrigger ci

* [mkldnn-v1.0] Update MKL-DNN to v1.0.2 (#16012)

* bump mkldnn to v1.0.2

* skip quantization unit test

* add useless build flag

* Fixes openblas installation for static build

* empty commit

* [mkldnn-v1.0] Enable base code with new APIs. (#16064)

* fix comments (#8)

* add base code for mkldnn 1.0

* fix comments

* Update mkldnn.mk

* add base code for mkldnn 1.0

* fix build

* fix lint

* fix lint

* [mkldnn-v1.0] Add MKL-DNN Convolution (#16141)

* add mkldnn conv

* revert unnecessary change

* fix testcase fail for cpu: test_convolution_independent_gradients

* fix failed testcase: test_reshape_transpose_6d&&test_weight_async_reorder

* fix comments

* change variable name from weights to weight in mkldnn_conv

* [mkldnn-v1.0] Add MKL-DNN activation (#16195)

* add mkldnn act; pass lint; pass mnist training

* make bwd as private member

* [mkldnn-v1.0] Add MKL-DNN BN (#16199)

* add mkldnn bn

* add static_cast to transform data type

* change mkldnn_args_map_t

* retrigger CI

* add mkldnn lrn (#16223)

* [mkldnn-v1.0] Add MKL-DNN Transpose (#16250)

* add mkldnn transpose

* using mkldnn_args_map_t instead of std::unordered_map<int, mkldnn::memory>

* [mkldnn-v1.0] Add MKL-DNN softmax (#16246)

* add mkldnn softmax

* trigger CI

* [mkldnn-v1.0] Add MKL-DNN FC (#16221)

* add mkldnn fc; pass lint; pass mnist training

* add TODO info for future debug

* [mkldnn-v1.0] Add MKL-DNN  deconv (#16259)

* add mkldnn deconv

* coding style

* trigger CI

* add mkldnn softmax_output (#16222)

* [mkldnn-v1.0] Add MKL-DNN Pooling (#16272)

* add mkldnn pooling

* add workaround for mkldnn v1.0 pooling fwd && bwd workspace mismatch

* code clean

* fix lint error

* trigger CI

* trigger CI

* add extra work_space check and fix some typo

* trigger CI

* [mkldnn-v1.0] Add MKL-DNN reshape&flatten&expand_dims (#16258)

* Add mkldnn 1.0 support for reshape/flatten/expanddims ops

* improve log & modify definition location of args_map_

* fix comments

* rebase code

* trigger CI

* trigger CI

* trigger CI

* trigger CI

* [mkldnn-v1.0] Add MKL-DNN int8 activation&pooling&flatten (#16425)

* Add mkldnn quantized activation/pooling/flatten

* int8 flatten

* [mkldnn-1.0] int8 conv quantize dequantize requantize (#16283)

* int8 conv quantize dequantize requantize

Change-Id: Ibd9df97288a95c61d6d85ec3831fd18b626ca283

* Fix lint

* Fix clang build

Change-Id: I9468774d014c852901e4cc3bffabd8a3d8004519

* add mkldnn sum concat (#16263)

* [mkldnn-1.0] mkldnn int8 elemwise_add (#16454)

* add mkldnn int8 elemwise_add

* add workaround to fix format any issue

* code clean

* upgrade int8 bn to MKLDNN1.0 (#16458)

* [mkldnn-v1.0] Fused RNN Op (#16420)

* [mkldnn-v1.0] Add MKL-DNN int8 fc (#16457)

* Add mkldnn_v1.0 int8 fc

* trigger CI

* trigger CI

* [mkldnn-v1.0] Update enabling flag for MKL dropout (#16433)

* use MSHADOW_USE_MKL to determine whther to use mkl optimized dropout

* rebase code

* [mkldnn-1.0] upgrade int8 concat to MKLDNN1.0 (#16466)

* [mkldnn-1.0] upgrade int8 concat to MKLDNN1.0

* fix lint

* use mkldnn_args_map_t

* update dict usage style

* retrigger CI

* retrigger CI again

* retrigger CI again 2

* [mkldnn-v1.0] Add MKL-DNN slice (#16484)

* change slice to mkldnn v1.0

* fix lint

* [mkldnn-1.0] add mkldnn subgraph fc (#16468)

* add mkldnn subgraph fc

* code clean

* trigger CI

* [mkldnn-v1.0]enable mkldnn concat (#16507)

* enable mkldnn concat

* trigger CI

* trigger CI

* [mkldnn-v1.0] Enable mkldnn cpp-test, copy op, concat op (#16503)

* [mkldnn-v1.0] Enable mkldnn test, copy op, concat op

Exclude gpu topology via MXNET_USE_CUDA

nit

default format

Remove whitespace

* Unix-GPU Tensor-RT build timeout, re-trigger CI

* [mkldnn-1.0] add skipped case for mkldnn_v1.0 (#16470)

* add skipped case for mkldnn_v1.0

* enable mkl quantized testcase

* enable skipped testcase

* trigger CI

* trigger CI

* trigger CI

* trigger CI

* [mkldnn-1.0]enable mkldnn elemwise_sum (#16521)

* enable mkldnn elemwise_sum

* trigger CI

* trigger CI

* trigger CI

* [mkldnn-v1.0] Enable more checks for MXNET_USE_MKLDNN (#16520)

* open USE_MKLDNN check

* trigger ci

* ci

* [mkldnn-v1.0]Minor fix for leakyrelu compile flag (#16519)

* change to MXNET_USE_MKLDNN == 100

* trigger

* remove MKL license (#16534)

* change MXNET_USE_MKLDNN from 100 to 1 (#16551)

* re-enable unit tests (#16565)

* [mkldnn-v1.0] Skip flaky test for unidirectional rnn_relu (#16545)

Skip `test_rnnrelu_sym`, and add some issue tracking message

Add return

Revert test_rnnrelu_sym to origin

* Add some annotations and log strings, rename mem_desc variables (#16609)

* [mkldnn-v1.0]set fc weight layout as mkldnn v0.2x did (#16593)

* set fc weight layout as mkldnn v0.2x did

* fix lint

* [mkldnn-v1.0] Upgrade to MKL-DNN v1.0.4 patch release (#16592)

* upgrade to mkldnn v1.0.3 patch release

* retrigger ci

* mkldnn v1.0.4 patch release

* [mkldnn-1.0]Rebase to master (#16648)

* fixed broken links across multiple files (#16581)

* fix missing docs due to git add issues (#16496)

* Create SECURITY.md (#16573)

* Create SECURITY.md

* Update SECURITY.md

* [Numpy] Support N_D(N>=3) batch_dot (#16586)

* Support N_D(N>=3) batch_dot

* use 1E-4

* fix lint

* remove unnecessary comment

* Update test_numpy_op.py

* Large Vector tests for DGL Ops Part 2 (#16497)

* add hyperbolic, logical, sign and regression tests for large vector

* changed hyperbolic functions into existing trignometric functions

* fix trigo and simple bind needs shape as tuple

* fix logical ops, add with_seed

* fix arcosh in largearray, remove regression from largevector

* [Numpy] Loading numpy-incompatible NDArray in numpy-compatible mode (#16597)

* Make MXIsNumpyShape return enum

* address the comment

* Surpress subgraph log in CI (#16607)

Change-Id: Ia2ed6fdbb1d2cb5cc607a8856ca13ee338e27eac

* Fix dequantize memory corruption (#16606)

Change-Id: I51b62a32987bdbcf96f04b1bc6617e66796f648b

* [MKLDNN]Fix reorder2default (#16602)

* Fix reorder2default

Change-Id: I74c87af9535f6264e6d1ea7eaed089a6480a3358

* fix

Change-Id: I6d07b43b520a47e7c78bd4b4b6390f5fb95e6957

* Fix

Change-Id: Id72f25c34291be4711f55569c6d61467edd6113d

* Fix CI

Change-Id: I8c33a82555d5ace2d0b682c1e3eefa13f3a44768

* Run CI

Change-Id: Ie8a6dab80ef91c0337cafbae4e3db277e0c7ebf7

* second round of fixing broken links in multiple files (#16598)

* Python Docstring Convetion (#16550)

* Docstring convetnion for

* Docstring convention for

* Docstring convention for

* Docstring convention for

* Docstring convention for

* Docstring convention for

* Docstring convention

* Revert removing new line

* Remove white space

* [MXNET-1434] Fix a broken link for basic C++ tutorial (#16461)

* Fix for wrong reqs set after switching from training to inference (#16553)

* Debugging reqs

* Move literal strings to const static members

* Fix lint

* julia/docs: more DRY on page rendering (#16396)

* [mkldnn-v1.0]rebase with master (#16649)

* fixed broken links across multiple files (#16581)

* fix missing docs due to git add issues (#16496)

* Create SECURITY.md (#16573)

* Create SECURITY.md

* Update SECURITY.md

* [Numpy] Support N_D(N>=3) batch_dot (#16586)

* Support N_D(N>=3) batch_dot

* use 1E-4

* fix lint

* remove unnecessary comment

* Update test_numpy_op.py

* Large Vector tests for DGL Ops Part 2 (#16497)

* add hyperbolic, logical, sign and regression tests for large vector

* changed hyperbolic functions into existing trignometric functions

* fix trigo and simple bind needs shape as tuple

* fix logical ops, add with_seed

* fix arcosh in largearray, remove regression from largevector

* [Numpy] Loading numpy-incompatible NDArray in numpy-compatible mode (#16597)

* Make MXIsNumpyShape return enum

* address the comment

* Surpress subgraph log in CI (#16607)

Change-Id: Ia2ed6fdbb1d2cb5cc607a8856ca13ee338e27eac

* Fix dequantize memory corruption (#16606)

Change-Id: I51b62a32987bdbcf96f04b1bc6617e66796f648b

* [MKLDNN]Fix reorder2default (#16602)

* Fix reorder2default

Change-Id: I74c87af9535f6264e6d1ea7eaed089a6480a3358

* fix

Change-Id: I6d07b43b520a47e7c78bd4b4b6390f5fb95e6957

* Fix

Change-Id: Id72f25c34291be4711f55569c6d61467edd6113d

* Fix CI

Change-Id: I8c33a82555d5ace2d0b682c1e3eefa13f3a44768

* Run CI

Change-Id: Ie8a6dab80ef91c0337cafbae4e3db277e0c7ebf7

* second round of fixing broken links in multiple files (#16598)

* Python Docstring Convetion (#16550)

* Docstring convetnion for

* Docstring convention for

* Docstring convention for

* Docstring convention for

* Docstring convention for

* Docstring convention for

* Docstring convention

* Revert removing new line

* Remove white space

* [MXNET-1434] Fix a broken link for basic C++ tutorial (#16461)

* Fix for wrong reqs set after switching from training to inference (#16553)

* Debugging reqs

* Move literal strings to const static members

* Fix lint

* julia/docs: more DRY on page rendering (#16396)

* Disables test_bulking_operator_gpu due to flakiness (#16611)

* C Api for simplebind, fix comment for trigoops, add atol to assert (#16585)

* C Api for simplebind, fix comment for trigoops, add atol to assert

* fix build issues

* fix lint and add regression test

* fix indent

* api doc and function name change

* fix lint and add infer shape test

* Imagenet inference to nightly fix (#16599)

* split to cd and shell

* comment

* lots of prints

* copy binary at correct location

* remove comments

* add mkl lib

* update docker run build function

* set nvidia docker true to run imagenet inference on GPU

* Revert "set nvidia docker true to run imagenet inference on GPU"

This reverts commit 98f8eef2057351d7964f1e9326ea6772c216f0af.
As we don't need GPU for compilation.

* Fix python doc build issue (#16630)

* pin the pip versions

* remove nbconvert comment

* Faster general take (#16615)

* Sped up perf of take op when axis != 0

* Formatting and syntax fixes

* Rename Take to specify axis

* Fix line length lint errors

* [Gluon] Don't serialize shared parameters twice (#16582)

Add deduplicate argument (default of False) to save_parameters.

* Fix index overflow bug in einsum (#16589)

* fix index overflow

* check index overflow

* fix index overflow in einsum path

* fix indent

* reduce NPY_MAXARGS

* safe accumulate

* Move some subgraph verbose to MXNET_SUBGRAPH_VERBOSE=2 (#16622)

* Move subgraph pass log to verbose=2

* Run CI

* add npx reshape (#16640)

* RNNOp only call cuda/cudnn if GPU ctx is requested (#16632)

* fix bad encode (#16641)

* [Perl] - ndarray to native array conversion fix (#16635)

* fixing broken links in multiple files - round 3 (#16634)

* add type switch to weight tensor (#16543)

* numpy doc enhancement (#16637)

* Change NDArray to ndarray for npx ops

Add nonzero

boolean mask supports boolean ndarray

Add argmin op and interoperability test for nonzero

Fix vdot, inner, outter docs

Add nonzero to mx.nd.np

Add docs

Fix

* Fix lint

* Fix

* Fix

* Fix get_constant

* Disable float16 test (#16643)

* Fix GetMKLDNNData for delay alloc (#16618)

* Fix GetMKLDNNData for delay alloc

* Run CI

* Run CI

* Run CI

* Run CI

* Run CI

Change-Id: I7ac2796e0ee8439c92fd2bd7a70a23a359b76b12

* Revert "[mkldnn-1.0]Rebase to master (#16648)"

This reverts commit dea3dd23d1982c913b3af6cfc7f4115c2cfa7244.

* [mkldnn-v1.0] Minor fix of mkldnn-v1.0 transition (#16644)

mk and rm directory in mkldnn.mk

ndarray.cc redundant whitespace

mkldnn_act rename variables of bwd primitives

mkldnn_rnn.cc iterator -> const_iterator

Use != instead of < for iterator in for-loop

Code comment for explaining the reason why excludes the last layer

* [mkldnn-v1.0]rm int8 sum workaround (#16623)

* rm int8 sum workaround due to mkldnn lib update

* simple dims asignments in mkldnn_quantized_elemwise_add.cc

* make MKLDNN macro simple for imperative_utils.h (#16652)

* fix ci jenkins step groovy (#16659)

* Adopt autograd.record() context to RNNOp (#16657)

* Use memcopy instead of set_handle when num_layer=0, direction=1 (#16663)

* fallback mkldnn fc bwd in imperative mode (#16672)

* disable MKLDNN FC backward

* [mkldnn-v1.0] Must reorder and emplace weights for inference primitives (#16682)

* add default parameter for mkldnn rnn
---
 3rdparty/mkldnn                               |    2 +-
 CMakeLists.txt                                |   18 +-
 LICENSE                                       |   51 +-
 Makefile                                      |   19 +-
 ci/docker/Dockerfile.build.centos7_cpu        |    2 -
 ci/docker/Dockerfile.build.ubuntu_build_cuda  |    2 -
 ci/docker/Dockerfile.build.ubuntu_cpu         |    3 -
 ci/docker/Dockerfile.build.ubuntu_cpu_julia   |    5 +-
 ci/docker/Dockerfile.build.ubuntu_gpu_cu100   |    3 -
 ci/docker/Dockerfile.build.ubuntu_gpu_cu101   |    3 -
 ci/docker/Dockerfile.build.ubuntu_gpu_cu80    |    3 -
 ci/docker/Dockerfile.build.ubuntu_gpu_cu90    |    3 -
 ci/docker/Dockerfile.build.ubuntu_gpu_cu92    |    3 -
 ci/docker/Dockerfile.build.ubuntu_nightly_cpu |    3 -
 ci/docker/Dockerfile.build.ubuntu_nightly_gpu |    3 -
 ci/docker/install/ubuntu_mklml.sh             |   25 -
 ci/docker/runtime_functions.sh                |    7 +-
 ci/jenkins/Jenkins_steps.groovy               |    4 +-
 cmake/DownloadMKLML.cmake                     |   78 --
 include/mxnet/ndarray.h                       |   18 +-
 mkldnn.mk                                     |   29 +-
 .../assembly/src/main/assembly/assembly.xml   |    8 +-
 .../mxnet/util/NativeLibraryLoader.scala      |    8 +-
 src/imperative/imperative_utils.h             |   24 +-
 src/ndarray/ndarray.cc                        |  251 ++--
 src/operator/nn/concat.cc                     |   26 +-
 src/operator/nn/convolution.cc                |    4 +-
 src/operator/nn/deconvolution.cc              |  139 +-
 src/operator/nn/dropout-inl.h                 |    2 +-
 src/operator/nn/fully_connected.cc            |    5 +-
 src/operator/nn/mkldnn/mkldnn_act-inl.h       |   40 +-
 src/operator/nn/mkldnn/mkldnn_act.cc          |  145 +--
 src/operator/nn/mkldnn/mkldnn_base-inl.h      |  164 ++-
 src/operator/nn/mkldnn/mkldnn_base.cc         |  375 +++---
 .../nn/mkldnn/mkldnn_batch_norm-inl.h         |  230 ++--
 src/operator/nn/mkldnn/mkldnn_concat-inl.h    |   17 +-
 src/operator/nn/mkldnn/mkldnn_concat.cc       |   82 +-
 .../nn/mkldnn/mkldnn_convolution-inl.h        |   65 +-
 src/operator/nn/mkldnn/mkldnn_convolution.cc  |  535 +++-----
 src/operator/nn/mkldnn/mkldnn_copy.cc         |    9 +-
 .../nn/mkldnn/mkldnn_deconvolution.cc         |  460 +++----
 .../nn/mkldnn/mkldnn_fully_connected-inl.h    |   13 +-
 .../nn/mkldnn/mkldnn_fully_connected.cc       |  129 +-
 src/operator/nn/mkldnn/mkldnn_lrn-inl.h       |  188 +--
 src/operator/nn/mkldnn/mkldnn_ops-inl.h       |   35 +-
 src/operator/nn/mkldnn/mkldnn_pooling-inl.h   |   36 +-
 src/operator/nn/mkldnn/mkldnn_pooling.cc      |  176 ++-
 src/operator/nn/mkldnn/mkldnn_reshape-inl.h   |  120 +-
 src/operator/nn/mkldnn/mkldnn_reshape.cc      |   61 +-
 src/operator/nn/mkldnn/mkldnn_rnn-inl.h       |  444 +++++++
 src/operator/nn/mkldnn/mkldnn_rnn.cc          | 1118 +++++++++++++++++
 src/operator/nn/mkldnn/mkldnn_rnn_impl.h      |  740 -----------
 src/operator/nn/mkldnn/mkldnn_slice-inl.h     |    2 +-
 src/operator/nn/mkldnn/mkldnn_slice.cc        |   27 +-
 src/operator/nn/mkldnn/mkldnn_softmax.cc      |   40 +-
 .../nn/mkldnn/mkldnn_softmax_output.cc        |   37 +-
 src/operator/nn/mkldnn/mkldnn_sum.cc          |   89 +-
 src/operator/nn/mkldnn/mkldnn_transpose.cc    |   54 +-
 src/operator/operator_common.h                |   35 +-
 .../mkldnn/mkldnn_dequantize-inl.h            |   43 +-
 .../quantization/mkldnn/mkldnn_quantize-inl.h |   42 +-
 .../mkldnn/mkldnn_quantize_v2-inl.h           |   42 +-
 .../mkldnn/mkldnn_quantized_batch_norm.cc     |   46 +-
 .../mkldnn/mkldnn_quantized_concat.cc         |   41 +-
 .../mkldnn/mkldnn_quantized_conv.cc           |   34 +-
 .../mkldnn/mkldnn_quantized_elemwise_add.cc   |   60 +-
 .../mkldnn_quantized_fully_connected.cc       |   27 +-
 .../mkldnn/mkldnn_quantized_pooling.cc        |    4 +-
 .../mkldnn/mkldnn_requantize-inl.h            |   24 +-
 .../quantization/quantized_pooling.cc         |    2 +-
 src/operator/rnn-inl.h                        |  521 ++++----
 src/operator/rnn.cc                           |  485 ++-----
 src/operator/rnn_impl.h                       |    7 -
 src/operator/softmax_output.cc                |    1 +
 .../subgraph/mkldnn/mkldnn_conv-inl.h         |    4 +-
 src/operator/subgraph/mkldnn/mkldnn_conv.cc   |  101 +-
 src/operator/subgraph/mkldnn/mkldnn_fc-inl.h  |    2 +-
 .../mkldnn/mkldnn_subgraph_property.cc        |   18 +-
 .../tensor/elemwise_unary_op_basic.cc         |    8 +-
 src/operator/tensor/matrix_op-inl.h           |   14 +
 src/operator/tensor/matrix_op.cc              |  185 +--
 tests/cpp/include/test_core_op.h              |    2 +-
 tests/cpp/include/test_mkldnn.h               |  236 ++--
 tests/cpp/include/test_op.h                   |    4 +-
 tests/cpp/kvstore/gpu_topology_test.cc        |    4 +
 tests/cpp/operator/batchnorm_test.cc          |   16 +-
 tests/cpp/operator/mkldnn_operator_test.cc    |   54 +-
 tests/cpp/operator/mkldnn_test.cc             |  116 +-
 tests/python/mkl/test_mkldnn.py               |   68 +
 .../python/quantization/test_quantization.py  |   13 +-
 tests/python/unittest/test_operator.py        |  243 ++--
 tools/pip/setup.py                            |   16 +-
 tools/staticbuild/build_lib.sh                |   10 +-
 93 files changed, 4205 insertions(+), 4505 deletions(-)
 delete mode 100755 ci/docker/install/ubuntu_mklml.sh
 delete mode 100644 cmake/DownloadMKLML.cmake
 create mode 100644 src/operator/nn/mkldnn/mkldnn_rnn-inl.h
 create mode 100644 src/operator/nn/mkldnn/mkldnn_rnn.cc
 delete mode 100644 src/operator/nn/mkldnn/mkldnn_rnn_impl.h

diff --git a/3rdparty/mkldnn b/3rdparty/mkldnn
index 9008d8ab096a..a0a87d662ede 160000
--- a/3rdparty/mkldnn
+++ b/3rdparty/mkldnn
@@ -1 +1 @@
-Subproject commit 9008d8ab096ae29f158840231ff431aea8bf3467
+Subproject commit a0a87d662edeef38d01db4ac5dd25f59a1f0881f
diff --git a/CMakeLists.txt b/CMakeLists.txt
index a06aa9dba485..0eba24f61d14 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -29,8 +29,7 @@ mxnet_option(USE_SSE              "Build with x86 SSE instruction support" ON IF
 mxnet_option(USE_F16C             "Build with x86 F16C instruction support" ON) # autodetects support if ON
 mxnet_option(USE_LAPACK           "Build with lapack support" ON)
 mxnet_option(USE_MKL_IF_AVAILABLE "Use MKL if found" ON)
-mxnet_option(USE_MKLML_MKL        "Use MKLDNN variant of MKL (if MKL found)" ON IF USE_MKL_IF_AVAILABLE AND (NOT APPLE) AND (NOT MSVC) )
-mxnet_option(USE_MKLDNN           "Use MKLDNN variant of MKL (if MKL found)" ON IF USE_MKL_IF_AVAILABLE AND (NOT APPLE) AND (NOT MSVC) AND (CMAKE_HOST_SYSTEM_PROCESSOR STREQUAL "x86_64") AND (NOT CMAKE_CROSSCOMPILING))
+mxnet_option(USE_MKLDNN           "Build with MKL-DNN support" ON IF USE_MKL_IF_AVAILABLE AND (NOT APPLE) AND (NOT MSVC) AND (CMAKE_HOST_SYSTEM_PROCESSOR STREQUAL "x86_64") AND (NOT CMAKE_CROSSCOMPILING))
 mxnet_option(USE_OPERATOR_TUNING  "Enable auto-tuning of operators" ON IF NOT MSVC)
 mxnet_option(USE_GPERFTOOLS       "Build with GPerfTools support" OFF)
 mxnet_option(USE_JEMALLOC         "Build with Jemalloc support"   ON)
@@ -257,25 +256,22 @@ if(ENABLE_TESTCOVERAGE)
 endif()
 
 if(USE_MKLDNN)
-  include(cmake/DownloadMKLML.cmake)
   # CPU architecture (e.g., C5) can't run on another architecture (e.g., g3).
-  if(NOT MSVC)
-    set(ARCH_OPT_FLAGS "-mtune=generic")
-  else()
+  if(MSVC)
     set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} /EHsc")
     set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} /EHsc /Gy")
   endif()
 
-  set(WITH_TEST OFF CACHE INTERNAL "" FORCE)
-  set(WITH_EXAMPLE OFF CACHE INTERNAL "" FORCE)
-  set(ARCH_OPT_FLAGS "" CACHE INTERNAL "" FORCE)
+  set(MKLDNN_BUILD_TESTS OFF CACHE INTERNAL "" FORCE)
+  set(MKLDNN_BUILD_EXAMPLES OFF CACHE INTERNAL "" FORCE)
+  set(MKLDNN_ARCH_OPT_FLAGS "" CACHE INTERNAL "" FORCE)
+  set(MKLDNN_USE_MKL NONE CACHE INTERNAL "" FORCE)
+  set(MKLDNN_ENABLE_JIT_PROFILING OFF CACHE INTERNAL "" FORCE)
 
   add_subdirectory(3rdparty/mkldnn)
 
   include_directories(3rdparty/mkldnn/include)
   include_directories(${PROJECT_BINARY_DIR}/3rdparty/mkldnn/include)
-  add_definitions(-DUSE_MKL=1)
-  add_definitions(-DCUB_MKL=1)
   add_definitions(-DMXNET_USE_MKLDNN=1)
   list(APPEND mxnet_LINKER_LIBS mkldnn)
 endif()
diff --git a/LICENSE b/LICENSE
index 4532449470e8..877e458ef7dd 100644
--- a/LICENSE
+++ b/LICENSE
@@ -651,43 +651,8 @@
     <none yet>
 
     =======================================================================================
-
-    13. MKL BLAS
-    For details, see, [Intel® Simplified license](https://software.intel.com/en-us/license/intel-simplified-software-license) and MKLDNN_README.md
-
-    Copyright (c) 2018 Intel Corporation.
-
-    Use and Redistribution.  You may use and redistribute the software (the “Software”), without modification, provided the following conditions are met:
-
-    * Redistributions must reproduce the above copyright notice and the following terms of use in the Software and in the documentation and/or other materials provided with the distribution.
-
-    * Neither the name of Intel nor the names of its suppliers may be used to endorse or promote products derived from this Software without specific prior written permission.
-
-    * No reverse engineering, decompilation, or disassembly of this Software is permitted.
-
-    Limited patent license.  Intel grants you a world-wide, royalty-free, non-exclusive license under patents it now or hereafter owns or controls to make, have made, use, import, offer to sell and sell (“Utilize”) this Software, but solely to the extent that any such patent is necessary to Utilize the Software alone. The patent license shall not apply to any combinations which include this software.  No hardware per se is licensed hereunder.
-
-    Third party and other Intel programs.  “Third Party Programs” are the files listed in the “third-party-programs.txt” text file that is included with the Software and may include Intel programs under separate license terms. Third Party Programs, even if included with the distribution of the Materials, are governed by separate license terms and those license terms solely govern your use of those programs. 
-
-    DISCLAIMER.  THIS SOFTWARE IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT ARE DISCLAIMED. THIS SOFTWARE IS NOT INTENDED FOR USE IN SYSTEMS OR APPLICATIONS WHERE FAILURE OF THE SOFTWARE MAY CAUSE PERSONAL INJURY OR DEATH AND YOU AGREE THAT YOU ARE FULLY RESPONSIBLE FOR ANY CLAIMS, COSTS, DAMAGES, EXPENSES, AND ATTORNEYS’ FEES ARISING OUT OF ANY SUCH USE, EVEN IF ANY CLAIM ALLEGES THAT INTEL WAS NEGLIGENT REGARDING THE DESIGN OR MANUFACTURE OF THE MATERIALS.
-
-    LIMITATION OF LIABILITY. IN NO EVENT WILL INTEL BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. YOU AGREE TO INDEMNIFY AND HOLD INTEL HARMLESS AGAINST ANY CLAIMS AND EXPENSES RESULTING FROM YOUR USE OR UNAUTHORIZED USE OF THE SOFTWARE.
-
-    No support.  Intel may make changes to the Software, at any time without notice, and is not obligated to support, update or provide training for the Software.
-
-    Termination. Intel may terminate your right to use the Software in the event of your breach of this Agreement and you fail to cure the breach within a reasonable period of time.
-
-    Feedback.  Should you provide Intel with comments, modifications, corrections, enhancements or other input (“Feedback”) related to the Software Intel will be free to use, disclose, reproduce, license or otherwise distribute or exploit the Feedback in its sole discretion without any obligations or restrictions of any kind, including without limitation, intellectual property rights or licensing obligations.
-
-    Compliance with laws.  You agree to comply with all relevant laws and regulations governing your use, transfer, import or export (or prohibition thereof) of the Software.
-
-    Governing law.  All disputes will be governed by the laws of the United States of America and the State of Delaware without reference to conflict of law principles and subject to the exclusive jurisdiction of the state or federal courts sitting in the State of Delaware, and each party agrees that it submits to the personal jurisdiction and venue of those courts and waives any objections. The United Nations Convention on Contracts for the International Sale of Goods (1980) is specifically excluded and will not apply to the Software.
-
-    *Other names and brands may be claimed as the property of others.
-
-    =======================================================================================
-
-    14. FindJeMalloc.cmake
+    
+    13. FindJeMalloc.cmake
     For details, see cmake/Modules/FindJeMalloc.cmake
 
     This file is based on https://github.com/STEllAR-GROUP/hpx/blob/master/cmake/FindJemalloc.cmake
@@ -778,7 +743,7 @@
 
     =======================================================================================
 
-    15. FindPythonLibsNew.cmake
+    14. FindPythonLibsNew.cmake
 
     For details, see 3rdparty/onnx-tensorrt/third_party/onnx/third_party/pybind11/tools/FindPythonLibsNew.cmake
 
@@ -817,7 +782,7 @@
 
     =======================================================================================
 
-    16. erfinv-inl.h
+    15. erfinv-inl.h
 
     For details, see /src/operator/contrib/erfinv-inl.h
 
@@ -860,7 +825,7 @@
 
     =======================================================================================
 
-    17. mersenne.h
+    16. mersenne.h
 
     For details, see /3rdparty/nvidia_cub/test/mersenne.h
 
@@ -909,7 +874,7 @@
 
     =======================================================================================
 
-    18. FindEigen3.cmake
+    17. FindEigen3.cmake
 
     For details, see /3rdparty/onnx-tensorrt/third_party/onnx/third_party/pybind11/tools/FindEigen3.cmake
 
@@ -920,7 +885,7 @@
 
     =======================================================================================
 
-    19. protoc-gen-mypy.py
+    18. protoc-gen-mypy.py
 
     For details, see /3rdparty/onnx-tensorrt/third_party/onnx/tools/protoc-gen-mypy.py
 
@@ -936,7 +901,7 @@
 
     =======================================================================================
 
-    20. rang
+    19. rang
 
     For details, see /3rdparty/tvm/3rdparty/rang/LICENSE
 
diff --git a/Makefile b/Makefile
index 63a978d01d8a..4746cc434de2 100644
--- a/Makefile
+++ b/Makefile
@@ -84,8 +84,6 @@ endif
 
 ifeq ($(USE_MKLDNN), 1)
 	MKLDNNROOT = $(ROOTDIR)/3rdparty/mkldnn/build/install
-	MKLROOT = $(ROOTDIR)/3rdparty/mkldnn/build/install
-	export USE_MKLML = 1
 endif
 
 include $(TPARTYDIR)/mshadow/make/mshadow.mk
@@ -151,14 +149,9 @@ endif
 
 ifeq ($(USE_MKLDNN), 1)
 	CFLAGS += -DMXNET_USE_MKLDNN=1
-	CFLAGS += -DUSE_MKL=1
 	CFLAGS += -I$(ROOTDIR)/src/operator/nn/mkldnn/
-	ifneq ($(MKLDNNROOT), $(MKLROOT))
-		CFLAGS += -I$(MKLROOT)/include
-		LDFLAGS += -L$(MKLROOT)/lib
-	endif
 	CFLAGS += -I$(MKLDNNROOT)/include
-	LDFLAGS += -L$(MKLDNNROOT)/lib -lmkldnn -Wl,-rpath,'$${ORIGIN}'
+	LDFLAGS += -L$(MKLDNNROOT)/lib -L$(MKLDNNROOT)/lib64 -lmkldnn -Wl,-rpath,'$${ORIGIN}'
 endif
 
 # setup opencv
@@ -604,9 +597,7 @@ lib/libmxnet.so: $(ALLX_DEP)
 	-Wl,${WHOLE_ARCH} $(filter %libnnvm.a, $^) -Wl,${NO_WHOLE_ARCH}
 ifeq ($(USE_MKLDNN), 1)
 ifeq ($(UNAME_S), Darwin)
-	install_name_tool -change '@rpath/libmklml.dylib' '@loader_path/libmklml.dylib' $@
-	install_name_tool -change '@rpath/libiomp5.dylib' '@loader_path/libiomp5.dylib' $@
-	install_name_tool -change '@rpath/libmkldnn.0.dylib' '@loader_path/libmkldnn.0.dylib' $@
+	install_name_tool -change '@rpath/libmkldnn.1.dylib' '@loader_path/libmkldnn.1.dylib' $@
 endif
 endif
 
@@ -698,10 +689,8 @@ rpkg:
 	cp src/io/image_recordio.h R-package/src
 	cp -rf lib/libmxnet.so R-package/inst/libs
 
-	if [ -e "lib/libmkldnn.so.0" ]; then \
-		cp -rf lib/libmkldnn.so.0 R-package/inst/libs; \
-		cp -rf lib/libiomp5.so R-package/inst/libs; \
-		cp -rf lib/libmklml_intel.so R-package/inst/libs; \
+	if [ -e "lib/libmkldnn.so.1" ]; then \
+		cp -rf lib/libmkldnn.so.1 R-package/inst/libs; \
 	fi
 
 	if [ -e "lib/libtvm_runtime.so" ]; then \
diff --git a/ci/docker/Dockerfile.build.centos7_cpu b/ci/docker/Dockerfile.build.centos7_cpu
index e2802aa2fb2b..0cfa5a9f6e47 100644
--- a/ci/docker/Dockerfile.build.centos7_cpu
+++ b/ci/docker/Dockerfile.build.centos7_cpu
@@ -30,8 +30,6 @@ COPY install/centos7_python.sh /work/
 RUN /work/centos7_python.sh
 COPY install/centos7_scala.sh /work/
 RUN /work/centos7_scala.sh
-COPY install/ubuntu_mklml.sh /work/
-RUN /work/ubuntu_mklml.sh
 
 ARG USER_ID=0
 COPY install/centos7_adduser.sh /work/
diff --git a/ci/docker/Dockerfile.build.ubuntu_build_cuda b/ci/docker/Dockerfile.build.ubuntu_build_cuda
index e085c2dc09a0..ce6d0007875e 100644
--- a/ci/docker/Dockerfile.build.ubuntu_build_cuda
+++ b/ci/docker/Dockerfile.build.ubuntu_build_cuda
@@ -42,8 +42,6 @@ COPY install/ubuntu_perl.sh /work/
 RUN /work/ubuntu_perl.sh
 COPY install/ubuntu_clang.sh /work/
 RUN /work/ubuntu_clang.sh
-COPY install/ubuntu_mklml.sh /work/
-RUN /work/ubuntu_mklml.sh
 COPY install/ubuntu_ar.sh /work/
 RUN /work/ubuntu_ar.sh
 
diff --git a/ci/docker/Dockerfile.build.ubuntu_cpu b/ci/docker/Dockerfile.build.ubuntu_cpu
index f41d629289a7..b1eb89bb3f36 100644
--- a/ci/docker/Dockerfile.build.ubuntu_cpu
+++ b/ci/docker/Dockerfile.build.ubuntu_cpu
@@ -58,9 +58,6 @@ RUN /work/ubuntu_gcc8.sh
 COPY install/ubuntu_mkl.sh /work/
 RUN /work/ubuntu_mkl.sh
 
-COPY install/ubuntu_mklml.sh /work/
-RUN /work/ubuntu_mklml.sh
-
 COPY install/ubuntu_caffe.sh /work/
 RUN /work/ubuntu_caffe.sh
 
diff --git a/ci/docker/Dockerfile.build.ubuntu_cpu_julia b/ci/docker/Dockerfile.build.ubuntu_cpu_julia
index 108869b680cd..b1eb89bb3f36 100644
--- a/ci/docker/Dockerfile.build.ubuntu_cpu_julia
+++ b/ci/docker/Dockerfile.build.ubuntu_cpu_julia
@@ -58,9 +58,6 @@ RUN /work/ubuntu_gcc8.sh
 COPY install/ubuntu_mkl.sh /work/
 RUN /work/ubuntu_mkl.sh
 
-COPY install/ubuntu_mklml.sh /work/
-RUN /work/ubuntu_mklml.sh
-
 COPY install/ubuntu_caffe.sh /work/
 RUN /work/ubuntu_caffe.sh
 
@@ -78,4 +75,4 @@ RUN /work/ubuntu_adduser.sh
 
 COPY runtime_functions.sh /work/
 
-WORKDIR /work/mxnet
\ No newline at end of file
+WORKDIR /work/mxnet
diff --git a/ci/docker/Dockerfile.build.ubuntu_gpu_cu100 b/ci/docker/Dockerfile.build.ubuntu_gpu_cu100
index 894930bc6303..514f6bb1495b 100644
--- a/ci/docker/Dockerfile.build.ubuntu_gpu_cu100
+++ b/ci/docker/Dockerfile.build.ubuntu_gpu_cu100
@@ -46,9 +46,6 @@ RUN /work/ubuntu_perl.sh
 COPY install/ubuntu_clang.sh /work/
 RUN /work/ubuntu_clang.sh
 
-COPY install/ubuntu_mklml.sh /work/
-RUN /work/ubuntu_mklml.sh
-
 COPY install/ubuntu_tvm.sh /work/
 RUN /work/ubuntu_tvm.sh
 
diff --git a/ci/docker/Dockerfile.build.ubuntu_gpu_cu101 b/ci/docker/Dockerfile.build.ubuntu_gpu_cu101
index 9699b37aa45f..7e0f8d93ed37 100644
--- a/ci/docker/Dockerfile.build.ubuntu_gpu_cu101
+++ b/ci/docker/Dockerfile.build.ubuntu_gpu_cu101
@@ -46,9 +46,6 @@ RUN /work/ubuntu_perl.sh
 COPY install/ubuntu_clang.sh /work/
 RUN /work/ubuntu_clang.sh
 
-COPY install/ubuntu_mklml.sh /work/
-RUN /work/ubuntu_mklml.sh
-
 COPY install/ubuntu_tvm.sh /work/
 RUN /work/ubuntu_tvm.sh
 
diff --git a/ci/docker/Dockerfile.build.ubuntu_gpu_cu80 b/ci/docker/Dockerfile.build.ubuntu_gpu_cu80
index a1031af811cf..83f05fddf261 100644
--- a/ci/docker/Dockerfile.build.ubuntu_gpu_cu80
+++ b/ci/docker/Dockerfile.build.ubuntu_gpu_cu80
@@ -46,9 +46,6 @@ RUN /work/ubuntu_perl.sh
 COPY install/ubuntu_clang.sh /work/
 RUN /work/ubuntu_clang.sh
 
-COPY install/ubuntu_mklml.sh /work/
-RUN /work/ubuntu_mklml.sh
-
 COPY install/ubuntu_tvm.sh /work/
 RUN /work/ubuntu_tvm.sh
 
diff --git a/ci/docker/Dockerfile.build.ubuntu_gpu_cu90 b/ci/docker/Dockerfile.build.ubuntu_gpu_cu90
index 56ebd55c94e0..579ad7bffddf 100644
--- a/ci/docker/Dockerfile.build.ubuntu_gpu_cu90
+++ b/ci/docker/Dockerfile.build.ubuntu_gpu_cu90
@@ -46,9 +46,6 @@ RUN /work/ubuntu_perl.sh
 COPY install/ubuntu_clang.sh /work/
 RUN /work/ubuntu_clang.sh
 
-COPY install/ubuntu_mklml.sh /work/
-RUN /work/ubuntu_mklml.sh
-
 COPY install/ubuntu_tvm.sh /work/
 RUN /work/ubuntu_tvm.sh
 
diff --git a/ci/docker/Dockerfile.build.ubuntu_gpu_cu92 b/ci/docker/Dockerfile.build.ubuntu_gpu_cu92
index f6008a5c09ca..dc125aee371d 100644
--- a/ci/docker/Dockerfile.build.ubuntu_gpu_cu92
+++ b/ci/docker/Dockerfile.build.ubuntu_gpu_cu92
@@ -46,9 +46,6 @@ RUN /work/ubuntu_perl.sh
 COPY install/ubuntu_clang.sh /work/
 RUN /work/ubuntu_clang.sh
 
-COPY install/ubuntu_mklml.sh /work/
-RUN /work/ubuntu_mklml.sh
-
 COPY install/ubuntu_tvm.sh /work/
 RUN /work/ubuntu_tvm.sh
 
diff --git a/ci/docker/Dockerfile.build.ubuntu_nightly_cpu b/ci/docker/Dockerfile.build.ubuntu_nightly_cpu
index 8e36a74cbde7..5717df1b9130 100644
--- a/ci/docker/Dockerfile.build.ubuntu_nightly_cpu
+++ b/ci/docker/Dockerfile.build.ubuntu_nightly_cpu
@@ -46,9 +46,6 @@ RUN /work/ubuntu_perl.sh
 COPY install/ubuntu_clang.sh /work/
 RUN /work/ubuntu_clang.sh
 
-COPY install/ubuntu_mklml.sh /work/
-RUN /work/ubuntu_mklml.sh
-
 COPY install/ubuntu_caffe.sh /work/
 RUN /work/ubuntu_caffe.sh
 
diff --git a/ci/docker/Dockerfile.build.ubuntu_nightly_gpu b/ci/docker/Dockerfile.build.ubuntu_nightly_gpu
index 7d051b1bd04a..5e812c433b43 100644
--- a/ci/docker/Dockerfile.build.ubuntu_nightly_gpu
+++ b/ci/docker/Dockerfile.build.ubuntu_nightly_gpu
@@ -46,9 +46,6 @@ RUN /work/ubuntu_perl.sh
 COPY install/ubuntu_clang.sh /work/
 RUN /work/ubuntu_clang.sh
 
-COPY install/ubuntu_mklml.sh /work/
-RUN /work/ubuntu_mklml.sh
-
 COPY install/ubuntu_tvm.sh /work/
 RUN /work/ubuntu_tvm.sh
 
diff --git a/ci/docker/install/ubuntu_mklml.sh b/ci/docker/install/ubuntu_mklml.sh
deleted file mode 100755
index 99fd0b9e01f2..000000000000
--- a/ci/docker/install/ubuntu_mklml.sh
+++ /dev/null
@@ -1,25 +0,0 @@
-#!/usr/bin/env bash
-
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-# build and install are separated so changes to build don't invalidate
-# the whole docker cache for the image
-
-set -ex
-wget -q --no-check-certificate -O /tmp/mklml.tgz https://github.com/intel/mkl-dnn/releases/download/v0.21/mklml_lnx_2019.0.5.20190502.tgz
-tar -zxf /tmp/mklml.tgz && cp -rf mklml_*/* /usr/local/ && rm -rf mklml_*
diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index b53db3f980f1..581bb2fd5280 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -692,6 +692,7 @@ build_ubuntu_cpu_mkldnn_mkl() {
         USE_TVM_OP=1                  \
         USE_BLAS=mkl                  \
         USE_SIGNAL_HANDLER=1          \
+        USE_INTEL_PATH=/opt/intel/    \
         -j$(nproc)
 }
 
@@ -877,9 +878,9 @@ build_ubuntu_gpu_cmake_mkldnn() {
         /work/mxnet
 
     ninja -v
-    # libmkldnn.so.0 is a link file. We need an actual binary file named libmkldnn.so.0.
-    cp 3rdparty/mkldnn/src/libmkldnn.so.0 3rdparty/mkldnn/src/libmkldnn.so.0.tmp
-    mv 3rdparty/mkldnn/src/libmkldnn.so.0.tmp 3rdparty/mkldnn/src/libmkldnn.so.0
+    # libmkldnn.so.1 is a link file. We need an actual binary file named libmkldnn.so.1.
+    cp 3rdparty/mkldnn/src/libmkldnn.so.1 3rdparty/mkldnn/src/libmkldnn.so.1.tmp
+    mv 3rdparty/mkldnn/src/libmkldnn.so.1.tmp 3rdparty/mkldnn/src/libmkldnn.so.1
 }
 
 build_ubuntu_gpu_cmake() {
diff --git a/ci/jenkins/Jenkins_steps.groovy b/ci/jenkins/Jenkins_steps.groovy
index d7c2b9679ca3..0770320f1407 100644
--- a/ci/jenkins/Jenkins_steps.groovy
+++ b/ci/jenkins/Jenkins_steps.groovy
@@ -35,8 +35,8 @@ mx_cmake_lib_no_tvm_op = 'build/libmxnet.so, build/libmxnet.a, build/libsample_l
 mx_cmake_lib_cython = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/tvmop.conf, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
 // mxnet cmake libraries, in cmake builds we do not produce a libnvvm static library by default.
 mx_cmake_lib_debug = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/tvmop.conf, build/libsample_lib.so, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests'
-mx_cmake_mkldnn_lib = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/tvmop.conf, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so, build/3rdparty/mkldnn/src/libmkldnn.so.0'
-mx_mkldnn_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, lib/tvmop.conf, libsample_lib.so, lib/libiomp5.so, lib/libmkldnn.so.0, lib/libmklml_intel.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a'
+mx_cmake_mkldnn_lib = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/tvmop.conf, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so, build/3rdparty/mkldnn/src/libmkldnn.so.1'
+mx_mkldnn_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, lib/tvmop.conf, libsample_lib.so, lib/libmkldnn.so.1, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a'
 mx_tensorrt_lib = 'build/libmxnet.so, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/tvmop.conf, lib/libnvonnxparser_runtime.so.0, lib/libnvonnxparser.so.0, lib/libonnx_proto.so, lib/libonnx.so'
 mx_lib_cpp_examples = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, lib/tvmop.conf, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, 3rdparty/ps-lite/build/libps.a, deps/lib/libprotobuf-lite.a, deps/lib/libzmq.a, build/cpp-package/example/*, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
 mx_lib_cpp_examples_no_tvm_op = 'lib/libmxnet.so, lib/libmxnet.a, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, 3rdparty/ps-lite/build/libps.a, deps/lib/libprotobuf-lite.a, deps/lib/libzmq.a, build/cpp-package/example/*, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
diff --git a/cmake/DownloadMKLML.cmake b/cmake/DownloadMKLML.cmake
deleted file mode 100644
index bcb2fb408fd4..000000000000
--- a/cmake/DownloadMKLML.cmake
+++ /dev/null
@@ -1,78 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-# This script will download MKLML
-
-message(STATUS "Downloading MKLML...")
-
-set(MKLDNN_RELEASE v0.21)
-set(MKLML_RELEASE_FILE_SUFFIX 2019.0.5.20190502)
-
-set(MKLML_LNX_MD5 dfcea335652dbf3518e1d02cab2cea97)
-set(MKLML_WIN_MD5 ff8c5237570f03eea37377ccfc95a08a)
-set(MKLML_MAC_MD5 0a3d83ec1fed9ea318e8573bb5e14c24)
-
-if(MSVC)
-  set(MKL_NAME "mklml_win_${MKLML_RELEASE_FILE_SUFFIX}")
-
-  file(DOWNLOAD "https://github.com/intel/mkl-dnn/releases/download/${MKLDNN_RELEASE}/${MKL_NAME}.zip"
-       "${CMAKE_CURRENT_BINARY_DIR}/mklml/${MKL_NAME}.zip"
-       EXPECTED_MD5 "${MKLML_WIN_MD5}" SHOW_PROGRESS)
-  file(DOWNLOAD "https://github.com/apache/incubator-mxnet/releases/download/utils/7z.exe"
-       "${CMAKE_CURRENT_BINARY_DIR}/mklml/7z2.exe"
-       EXPECTED_MD5 "E1CF766CF358F368EC97662D06EA5A4C" SHOW_PROGRESS)
-
-  execute_process(COMMAND "${CMAKE_CURRENT_BINARY_DIR}/mklml/7z2.exe" "-o${CMAKE_CURRENT_BINARY_DIR}/mklml/" "-y")
-  execute_process(COMMAND "${CMAKE_CURRENT_BINARY_DIR}/mklml/7z.exe"
-                  "x" "${CMAKE_CURRENT_BINARY_DIR}/mklml/${MKL_NAME}.zip" "-o${CMAKE_CURRENT_BINARY_DIR}/mklml/" "-y")
-
-  set(MKLROOT "${CMAKE_CURRENT_BINARY_DIR}/mklml/${MKL_NAME}")
-
-  message(STATUS "Setting MKLROOT path to ${MKLROOT}")
-
-  include_directories(${MKLROOT}/include)
-
-elseif(APPLE)
-  set(MKL_NAME "mklml_mac_${MKLML_RELEASE_FILE_SUFFIX}")
-
-  file(DOWNLOAD "https://github.com/intel/mkl-dnn/releases/download/${MKLDNN_RELEASE}/${MKL_NAME}.tgz"
-       "${CMAKE_CURRENT_BINARY_DIR}/mklml/${MKL_NAME}.tgz"
-       EXPECTED_MD5 "${MKLML_MAC_MD5}" SHOW_PROGRESS)
-  execute_process(COMMAND "tar" "-xzf" "${CMAKE_CURRENT_BINARY_DIR}/mklml/${MKL_NAME}.tgz"
-                  "-C" "${CMAKE_CURRENT_BINARY_DIR}/mklml/")
-
-  set(MKLROOT "${CMAKE_CURRENT_BINARY_DIR}/mklml/${MKL_NAME}")
-
-  message(STATUS "Setting MKLROOT path to ${MKLROOT}")
-  include_directories(${MKLROOT}/include)
-
-elseif(UNIX)
-  set(MKL_NAME "mklml_lnx_${MKLML_RELEASE_FILE_SUFFIX}")
-
-  file(DOWNLOAD "https://github.com/intel/mkl-dnn/releases/download/${MKLDNN_RELEASE}/${MKL_NAME}.tgz"
-       "${CMAKE_CURRENT_BINARY_DIR}/mklml/${MKL_NAME}.tgz"
-       EXPECTED_MD5 "${MKLML_LNX_MD5}" SHOW_PROGRESS)
-  execute_process(COMMAND "tar" "-xzf" "${CMAKE_CURRENT_BINARY_DIR}/mklml/${MKL_NAME}.tgz"
-                  "-C" "${CMAKE_CURRENT_BINARY_DIR}/mklml/")
-
-  set(MKLROOT "${CMAKE_CURRENT_BINARY_DIR}/mklml/${MKL_NAME}")
-  message(STATUS "Setting MKLROOT path to ${MKLROOT}")
-  include_directories(${MKLROOT}/include)
-
-else()
-  message(FATAL_ERROR "Wrong platform")
-endif()
diff --git a/include/mxnet/ndarray.h b/include/mxnet/ndarray.h
index 176aa0aaa197..1b0b119a02ac 100644
--- a/include/mxnet/ndarray.h
+++ b/include/mxnet/ndarray.h
@@ -709,7 +709,7 @@ class NDArray {
    * Create NDArray from mkldnn memory descriptor.
    * mem_pd The mkldnn memory descriptor to be created.
    */
-  explicit NDArray(mkldnn::memory::primitive_desc mem_pd);
+  explicit NDArray(const mkldnn::memory::desc &md);
   /*
    * Test if the data is stored in one of special MKLDNN format.
    */
@@ -737,15 +737,14 @@ class NDArray {
    * This function returns mkldnn::memory with the given primitive_desc
    * as long as the array size meets the required size in the given primitive_desc.
    */
-  const mkldnn::memory *GetMKLDNNData(
-      const mkldnn::memory::primitive_desc &desc) const;
+  const mkldnn::memory *GetMKLDNNData(const mkldnn::memory::desc &md) const;
   /*
    * This function returns mkldnn::memory with the given primitive_desc.
    * The returned mkldnn::memory will have the same physical layout as
    * the given primitive_desc.
    */
   const mkldnn::memory *GetMKLDNNDataReorder(
-      const mkldnn::memory::primitive_desc &desc) const;
+      const mkldnn::memory::desc &md) const;
 
   /*
    * This function copies data from mkldnn memory.
@@ -755,16 +754,15 @@ class NDArray {
    * This function allocates memory for array and creates mkldnn memory
    * with the specified format.
    */
-  mkldnn::memory *CreateMKLDNNData(
-      const mkldnn::memory::primitive_desc &desc);
+  mkldnn::memory *CreateMKLDNNData(const mkldnn::memory::desc &md);
 
   /*
    * These are the async version of the methods above.
    * It changes the layout of this NDArray, but it happens after all accesses to
    * the array are complete.
    */
-  void Reorder2DefaultAsync();
-  void MKLDNNDataReorderAsync(const mkldnn::memory::primitive_desc &desc);
+  void Reorder2DefaultAsync() const;
+  void MKLDNNDataReorderAsync(const mkldnn::memory::desc &md) const;
 
   /*
    * This creates a new NDArray with the reordered data.
@@ -789,7 +787,7 @@ class NDArray {
    /*!
    * \ Fix mkldnn memory descriptor mismatch from NDArray.
    */
-  void UpdateMKLDNNMemDesc(mkldnn::memory::format format);
+  void UpdateMKLDNNMemDesc(const mkldnn::memory::desc &desc);
 #endif
 
   /*!
@@ -1054,7 +1052,7 @@ class NDArray {
     // save the result in shandle.
     void Reorder2Default();
     // Reroder data to a specified layout.
-    void MKLDNNDataReorder(const mkldnn::memory::primitive_desc &desc);
+    void MKLDNNDataReorder(const mkldnn::memory::desc &md);
     bool IsMKLDNN() const;
     bool IsDefault() const;
 #endif
diff --git a/mkldnn.mk b/mkldnn.mk
index 802f3dc747c2..bc2190018bdf 100644
--- a/mkldnn.mk
+++ b/mkldnn.mk
@@ -21,15 +21,11 @@ ifeq ($(USE_MKLDNN), 1)
 	MXNET_LIBDIR = $(ROOTDIR)/lib
 	MXNET_INCLDIR = $(ROOTDIR)/include
 ifeq ($(UNAME_S), Darwin)
-	OMP_LIBFILE = $(MKLDNNROOT)/lib/libiomp5.dylib
-	MKLML_LIBFILE = $(MKLDNNROOT)/lib/libmklml.dylib
-	MKLDNN_LIBFILE = $(MKLDNNROOT)/lib/libmkldnn.0.dylib
-	MKLDNN_LIB64FILE = $(MKLDNNROOT)/lib64/libmkldnn.0.dylib
+	MKLDNN_LIBFILE = $(MKLDNNROOT)/lib/libmkldnn.1.dylib
+	MKLDNN_LIB64FILE = $(MKLDNNROOT)/lib64/libmkldnn.1.dylib
 else
-	OMP_LIBFILE = $(MKLDNNROOT)/lib/libiomp5.so
-	MKLML_LIBFILE = $(MKLDNNROOT)/lib/libmklml_intel.so
-	MKLDNN_LIBFILE = $(MKLDNNROOT)/lib/libmkldnn.so.0
-	MKLDNN_LIB64FILE = $(MKLDNNROOT)/lib64/libmkldnn.so.0
+	MKLDNN_LIBFILE = $(MKLDNNROOT)/lib/libmkldnn.so.1
+	MKLDNN_LIB64FILE = $(MKLDNNROOT)/lib64/libmkldnn.so.1
 endif
 endif
 
@@ -38,22 +34,21 @@ endif
 mkldnn_build: $(MKLDNN_LIBFILE)
 
 $(MKLDNN_LIBFILE):
-	mkdir -p $(MKLDNNROOT)
-	cd $(MKLDNN_SUBMODDIR) && rm -rf external && cd scripts && ./prepare_mkl.sh && cd .. && cp -a external/*/* $(MKLDNNROOT)/.
-	cmake $(MKLDNN_SUBMODDIR) -DCMAKE_INSTALL_PREFIX=$(MKLDNNROOT) -B$(MKLDNN_BUILDDIR) -DARCH_OPT_FLAGS="-mtune=generic" -DWITH_TEST=OFF -DWITH_EXAMPLE=OFF
+	mkdir -p $(MKLDNNROOT)/lib
+	cmake $(MKLDNN_SUBMODDIR) -DCMAKE_INSTALL_PREFIX=$(MKLDNNROOT) -B$(MKLDNN_BUILDDIR) -DMKLDNN_ARCH_OPT_FLAGS="" -DMKLDNN_BUILD_TESTS=OFF -DMKLDNN_BUILD_EXAMPLES=OFF -DMKLDNN_ENABLE_JIT_PROFILING=OFF
 	$(MAKE) -C $(MKLDNN_BUILDDIR) VERBOSE=1
 	$(MAKE) -C $(MKLDNN_BUILDDIR) install
+	mkdir -p $(MXNET_LIBDIR)
 	if [ -f "$(MKLDNN_LIB64FILE)" ]; then \
-		mv $(MKLDNNROOT)/lib64/libmkldnn* $(MKLDNNROOT)/lib/; \
+		cp $(MKLDNNROOT)/lib64/libmkldnn* $(MXNET_LIBDIR); \
+		cp $(MKLDNNROOT)/lib64/libmkldnn* $(MKLDNNROOT)/lib/; \
+	else \
+		cp $(MKLDNNROOT)/lib/libmkldnn* $(MXNET_LIBDIR); \
 	fi
-	mkdir -p $(MXNET_LIBDIR)
-	cp $(OMP_LIBFILE) $(MXNET_LIBDIR)
-	cp $(MKLML_LIBFILE) $(MXNET_LIBDIR)
-	cp $(MKLDNN_LIBFILE) $(MXNET_LIBDIR)
 	cp $(MKLDNN_BUILDDIR)/include/mkldnn_version.h $(MXNET_INCLDIR)/mkldnn/.
+
 mkldnn_clean:
 	$(RM) -r 3rdparty/mkldnn/build
-	$(RM) -r $(MKLDNNROOT)
 
 ifeq ($(USE_MKLDNN), 1)
 mkldnn: mkldnn_build
diff --git a/scala-package/assembly/src/main/assembly/assembly.xml b/scala-package/assembly/src/main/assembly/assembly.xml
index bcc5408cd3db..060a97b82064 100644
--- a/scala-package/assembly/src/main/assembly/assembly.xml
+++ b/scala-package/assembly/src/main/assembly/assembly.xml
@@ -57,12 +57,8 @@
         <include>libtvm_runtime.so</include>
         <include>libgfortran.so.3</include>
         <include>libquadmath.so.0</include>
-        <include>libiomp5.so</include>
-        <include>libiomp5.dylib</include>
-        <include>libmklml_intel.so</include>
-        <include>libmklml.dylib</include>
-        <include>libmkldnn.so.0</include>
-        <include>libmkldnn.0.dylib</include>
+        <include>libmkldnn.so.1</include>
+        <include>libmkldnn.1.dylib</include>
       </includes>
       <outputDirectory>lib/native</outputDirectory>
     </fileSet>
diff --git a/scala-package/core/src/main/scala/org/apache/mxnet/util/NativeLibraryLoader.scala b/scala-package/core/src/main/scala/org/apache/mxnet/util/NativeLibraryLoader.scala
index 9609ba25da40..49e5d685adfe 100644
--- a/scala-package/core/src/main/scala/org/apache/mxnet/util/NativeLibraryLoader.scala
+++ b/scala-package/core/src/main/scala/org/apache/mxnet/util/NativeLibraryLoader.scala
@@ -89,12 +89,8 @@ private[mxnet] object NativeLibraryLoader {
     saveLibraryToTemp("libtvm_runtime.so", "/lib/native/libtvm_runtime.so", false)
     saveLibraryToTemp("libgfortran.so.3", "/lib/native/libgfortran.so.3", false)
     saveLibraryToTemp("libquadmath.so.0", "/lib/native/libquadmath.so.0", false)
-    saveLibraryToTemp("libiomp5.so", "/lib/native/libiomp5.so", false)
-    saveLibraryToTemp("libiomp5.dylib", "/lib/native/libiomp5.dylib", false)
-    saveLibraryToTemp("libmklml_intel.so", "/lib/native/libmklml_intel.so", false)
-    saveLibraryToTemp("libmklml.dylib", "/lib/native/libmklml.dylib", false)
-    saveLibraryToTemp("libmkldnn.so.0", "/lib/native/libmkldnn.so.0", false)
-    saveLibraryToTemp("libmkldnn.0.dylib", "/lib/native/libmkldnn.0.dylib", false)
+    saveLibraryToTemp("libmkldnn.so.1", "/lib/native/libmkldnn.so.1", false)
+    saveLibraryToTemp("libmkldnn.1.dylib", "/lib/native/libmkldnn.1.dylib", false)
     val tempfile: File = saveLibraryToTemp(libname, libFileInJar, true)
 
     loadLibraryFromFile(libname, tempfile)
diff --git a/src/imperative/imperative_utils.h b/src/imperative/imperative_utils.h
index 8317c6073a24..b609c54b50f3 100644
--- a/src/imperative/imperative_utils.h
+++ b/src/imperative/imperative_utils.h
@@ -477,8 +477,18 @@ inline void PushFComputeEx(const FComputeEx& fn,
         // copying A to B may not happen, and will corrupt A's memory.
         InvalidateOutputs(outputs, req);
       }
-#endif
+      // add for mkldnn OP + no mkldnn OP
+      const auto is_mkldnn = Op::GetAttr<bool>("TIsMKLDNN");
+      if (!is_mkldnn.get(attrs.op, false)) {
+        std::vector<NDArray> inputs_fallback;
+        CreateDefaultInputs(inputs, &inputs_fallback);
+        fn(attrs, opctx, inputs_fallback, req, outputs);
+      } else {
+        fn(attrs, opctx, inputs, req, outputs);
+      }
+#else
       fn(attrs, opctx, inputs, req, outputs);
+#endif
       if (ctx.dev_mask() == gpu::kDevMask && exec_type == ExecType::kSync && !rctx.is_bulk) {
         rctx.get_stream<gpu>()->Wait();
       }
@@ -531,8 +541,18 @@ inline void PushOperator(const OpStatePtr& state,
         // copying A to B may not happen, and will corrupt A's memory.
         InvalidateOutputs(outputs, req);
       }
-#endif
+      // add for mkldnn OP + no mkldnn OP
+      const auto is_mkldnn = Op::GetAttr<bool>("TIsMKLDNN");
+      if (!is_mkldnn.get(attrs.op, false)) {
+        std::vector<NDArray> inputs_fallback;
+        CreateDefaultInputs(inputs, &inputs_fallback);
+        fcompute_ex(state, opctx, inputs_fallback, req, outputs);
+      } else {
+        fcompute_ex(state, opctx, inputs, req, outputs);
+      }
+#else
       fcompute_ex(state, opctx, inputs, req, outputs);
+#endif
       if (ctx.dev_mask() == gpu::kDevMask && exec_type == ExecType::kSync
           && rctx.get_stream<gpu>() && !rctx.is_bulk) {
         rctx.get_stream<gpu>()->Wait();
diff --git a/src/ndarray/ndarray.cc b/src/ndarray/ndarray.cc
index e1075c9c15da..f0dca2ea2aee 100644
--- a/src/ndarray/ndarray.cc
+++ b/src/ndarray/ndarray.cc
@@ -31,9 +31,6 @@
 #include <mxnet/resource.h>
 #include <mxnet/imperative.h>
 #include <mshadow/tensor.h>
-#if MXNET_USE_MKLDNN == 1
-#include <mkldnn.hpp>
-#endif
 #include "./ndarray_function.h"
 #include "../common/utils.h"
 #include "../operator/tensor/matrix_op-inl.h"
@@ -182,25 +179,23 @@ nnvm::Symbol NDArray::get_autograd_symbol() const {
 
 #if MXNET_USE_MKLDNN == 1
 
-NDArray::NDArray(mkldnn::memory::primitive_desc mem_pd)
+NDArray::NDArray(const mkldnn::memory::desc &md)
     : storage_type_(kDefaultStorage), entry_(nullptr) {
-  auto mem_desc = mem_pd.desc();
-  shape_ = mxnet::TShape(mem_desc.data.dims, mem_desc.data.dims + mem_desc.data.ndims);
-  dtype_ = get_mxnet_type(mem_desc.data.data_type);
+  shape_ = mxnet::TShape(md.data.dims, md.data.dims + md.data.ndims);
+  dtype_ = get_mxnet_type(md.data.data_type);
   ptr_ = std::make_shared<Chunk>(shape_, Context::CPU(), true, dtype_);
-  ptr_->CheckAndAlloc(mem_pd.get_size());
-  ptr_->mkl_mem_ = std::make_shared<MKLDNNMemory>(mem_pd, ptr_->shandle.dptr);
+  ptr_->CheckAndAlloc(md.get_size());
+  ptr_->mkl_mem_ = std::make_shared<MKLDNNMemory>(md, ptr_->shandle.dptr);
 }
 
 NDArray::NDArray(const std::shared_ptr<mkldnn::memory> &mkldnn_mem)
     : storage_type_(kDefaultStorage), entry_(nullptr) {
-  auto mem_pd = mkldnn_mem->get_primitive_desc();
-  auto mem_desc = mem_pd.desc();
+  auto mem_desc = mkldnn_mem->get_desc();
   shape_ = mxnet::TShape(mem_desc.data.dims, mem_desc.data.dims + mem_desc.data.ndims);
   dtype_ = get_mxnet_type(mem_desc.data.data_type);
   ptr_ = std::make_shared<Chunk>(shape_, Context::CPU(), true, dtype_);
   ptr_->shandle.dptr = mkldnn_mem->get_data_handle();
-  ptr_->shandle.size = mem_pd.get_size();
+  ptr_->shandle.size = mem_desc.get_size();
   ptr_->delay_alloc = false;
   ptr_->mkl_mem_ = std::make_shared<MKLDNNMemory>(mkldnn_mem);
   ptr_->static_data = true;
@@ -219,22 +214,24 @@ NDArray NDArray::MKLDNNDataReshape(const mxnet::TShape &shape) const {
     NDArray ret(shape, ctx(), true, dtype());
     // We shouldn't submit the reorder primitive here because submit will
     // be called in operators.
-    mkldnn_memory_format_t format = ptr_->mkl_mem_->GetDefaultFormat();
-    CHECK_NE(format, ptr_->mkl_mem_->GetFormat());
-    mkldnn::memory::primitive_desc def_pd = ptr_->mkl_mem_->GetPrimitiveDesc(format);
-    mkldnn::memory *def_mem = TmpMemMgr::Get()->Alloc(def_pd);
+    mkldnn_format_tag_t format = ptr_->mkl_mem_->GetDefaultFormat();
+    CHECK(ptr_->IsMKLDNN());
+    mkldnn::memory::desc def_desc = ptr_->mkl_mem_->GetDesc(format);
+    mkldnn::memory *def_mem = TmpMemMgr::Get()->Alloc(def_desc);
     MKLDNNStream *stream = MKLDNNStream::Get();
     std::shared_ptr<mkldnn::memory> curr_mem = ptr_->mkl_mem_->GetMem();
     stream->RegisterMem(curr_mem);
-    stream->RegisterPrim(mkldnn::reorder(*curr_mem, *def_mem));
+    std::unordered_map<int, mkldnn::memory> args({{MKLDNN_ARG_FROM, *curr_mem},
+                                                 {MKLDNN_ARG_TO, *def_mem}});
+    stream->RegisterPrimArgs(mkldnn::reorder(*curr_mem, *def_mem), args);
     // def_mem points to a memory region in the temp space. It's only valid
     // inside an operator. As such, the returned NDArray can only be valid
     // inside an operator and the shared point doesn't need to do anything
     // when it's destroyed.
-    auto tmp = std::shared_ptr<mkldnn::memory>(def_mem, [](mkldnn::memory *mem){});
+    auto tmp = std::shared_ptr<mkldnn::memory>(def_mem, [](mkldnn::memory *mem) {});
     ret.ptr_->mkl_mem_.reset(new MKLDNNMemory(tmp));
     ret.ptr_->shandle.dptr = def_mem->get_data_handle();
-    ret.ptr_->shandle.size = def_mem->get_primitive_desc().get_size();
+    ret.ptr_->shandle.size = def_mem->get_desc().get_size();
     ret.ptr_->delay_alloc = false;
     ret.ptr_->static_data = true;
     ret.byte_offset_ = byte_offset_;
@@ -242,7 +239,6 @@ NDArray NDArray::MKLDNNDataReshape(const mxnet::TShape &shape) const {
     return ret;
   }
 }
-
 #endif
 
 NDArray NDArray::Reshape(const mxnet::TShape &shape) const {
@@ -420,57 +416,56 @@ void NDArray::Chunk::Reorder2Default() {
   if (mkl_mem_ == nullptr)
     return;
 
-  mkldnn_memory_format_t format = mkl_mem_->GetDefaultFormat();
-  if (format ==  mkl_mem_->GetFormat())
+  if (IsDefault())
     return;
 
-  mkldnn::memory::primitive_desc def_pd = mkl_mem_->GetPrimitiveDesc(format);
-  mkldnn_mem_ptr def_mem(new mkldnn::memory(def_pd));
+  mkldnn_format_tag_t format = mkl_mem_->GetDefaultFormat();
+  mkldnn::memory::desc def_desc = mkl_mem_->GetDesc(format);
+  mkldnn_mem_ptr def_mem(new mkldnn::memory(def_desc, CpuEngine::Get()->get_engine()));
   mkl_mem_->ReorderTo(def_mem.get());
 
-  CHECK(shandle.size >= def_pd.get_size());
-  CheckAndAlloc(def_pd.get_size());
+  CHECK(shandle.size >= def_desc.get_size());
+  CheckAndAlloc(def_desc.get_size());
   // TODO(zhengda) We need to avoid memory copy here.
-  memcpy(shandle.dptr, def_mem->get_data_handle(), def_pd.get_size());
+  memcpy(shandle.dptr, def_mem->get_data_handle(), def_desc.get_size());
   mkl_mem_ = nullptr;
 }
 
-void NDArray::Chunk::MKLDNNDataReorder(const mkldnn::memory::primitive_desc &pd) {
+void NDArray::Chunk::MKLDNNDataReorder(const mkldnn::memory::desc &md) {
   // If the memory already uses the specified layout, don't do anything.
-  if (mkl_mem_ != nullptr && mkl_mem_->SameFormat(pd))
+  if (mkl_mem_ != nullptr && mkl_mem_->SameFormat(md))
     return;
-  mkldnn::memory::primitive_desc _pd = pd;
-  mkldnn::memory::desc _desc = _pd.desc();
-  mkldnn_memory_format_t def_format = GetDefaultFormat(_desc);
+
   // If the memory is default, don't do anything.
-  if (def_format == _desc.data.format && IsDefault())
+  if (!mxnet::IsMKLDNN(md) && IsDefault())
     return;
-  // If the specified layout is default, we should use Reorder2Default.
-  if (def_format == _desc.data.format) {
+  if (!mxnet::IsMKLDNN(md)) {
+    // If the specified layout is default, we should use Reorder2Default.
     Reorder2Default();
     return;
   }
+  auto engine = CpuEngine::Get()->get_engine();
+  mkldnn::stream s(engine);
 
-  std::shared_ptr<mkldnn::memory> new_mem(new mkldnn::memory(pd));
+  std::shared_ptr<mkldnn::memory> new_mem(new mkldnn::memory(md, engine));
   std::shared_ptr<mkldnn::memory> old_mem;
   if (IsDefault()) {
-    mkldnn::memory::primitive_desc def_pd = GetPrimitiveDesc(pd, def_format);
-    old_mem.reset(new mkldnn::memory(def_pd, shandle.dptr));
+    mkldnn_format_tag_t def_format = GetDefaultFormat(md);
+    mkldnn::memory::desc def_desc = GetDesc(md, def_format);
+    old_mem.reset(new mkldnn::memory(def_desc, engine, shandle.dptr));
   } else {
     old_mem = this->mkl_mem_->GetMem();
   }
-  CHECK(old_mem->get_primitive_desc().desc().data.ndims == _desc.data.ndims);
+  CHECK(old_mem->get_desc().data.ndims == md.data.ndims);
 
   // This may be called in MKLDNN operators. We can't use MKLDNNStream here.
-  std::vector<mkldnn::primitive> net;
-  net.push_back(mkldnn::reorder(*old_mem, *new_mem));
-  mkldnn::stream(mkldnn::stream::kind::eager).submit(net).wait();
+  mkldnn::reorder(*old_mem, *new_mem).execute(s, *old_mem, *new_mem);
 
-  CHECK(shandle.size >= pd.get_size());
-  CheckAndAlloc(pd.get_size());
+  CHECK(shandle.size >= md.get_size());
+  CheckAndAlloc(md.get_size());
   // TODO(zhengda) We need to avoid memory copy here.
-  memcpy(shandle.dptr, new_mem->get_data_handle(), pd.get_size());
-  mkl_mem_.reset(new MKLDNNMemory(pd, shandle.dptr));
+  memcpy(shandle.dptr, new_mem->get_data_handle(), md.get_size());
+  mkl_mem_.reset(new MKLDNNMemory(md, shandle.dptr));
 }
 
 void NDArray::Chunk::SetMKLMem(const mxnet::TShape &shape, int dtype) {
@@ -484,50 +479,43 @@ void NDArray::Chunk::SetMKLMem(const mxnet::TShape &shape, int dtype) {
 
   mkldnn::memory::dims dims;
   // These are shapes supprted by MKLDNN.
-  if (shape.ndim() >= 1 && shape.ndim() <= 5) {
+  if (shape.ndim() >= 1 && shape.ndim() <= 6) {
     dims.resize(shape.ndim());
     for (size_t i = 0; i < dims.size(); i++)
       dims[i] = shape[i];
   } else {
     LOG(FATAL) << "MKLDNN doesn't support " << shape.ndim() << " dimensions";
   }
-  mkldnn::memory::format layout = mkldnn::memory::format::format_undef;
+  mkldnn::memory::format_tag layout = mkldnn::memory::format_tag::undef;
   switch (dims.size()) {
-    case 1: layout = mkldnn::memory::format::x; break;
-    case 2: layout = mkldnn::memory::format::nc; break;
-    case 3: layout = mkldnn::memory::format::ncw; break;
-    case 4: layout = mkldnn::memory::format::nchw; break;
-    // This isn't the right layout when the data has 5 dimensions in MXNet.
-    // MXNet interprets 5 dimensions as ncdhw, but MKLDNN doesn't have
-    // a corresponding format.
-    case 5: layout = mkldnn::memory::format::goihw; break;
+    case 1: layout = mkldnn::memory::format_tag::a; break;
+    case 2: layout = mkldnn::memory::format_tag::ab; break;
+    case 3: layout = mkldnn::memory::format_tag::abc; break;
+    case 4: layout = mkldnn::memory::format_tag::abcd; break;
+    case 5: layout = mkldnn::memory::format_tag::abcde; break;
+    case 6: layout = mkldnn::memory::format_tag::abcdef; break;
+    default:
+      LOG(FATAL) << "Not implemented dimension (" << dims.size() << ") for MKLDNN";
   }
   mkldnn::memory::desc data_md{dims, get_mkldnn_type(dtype), layout};
-  auto cpu_engine = CpuEngine::Get()->get_engine();
   if (shandle.dptr == nullptr) {
     CHECK(delay_alloc);
     CheckAndAlloc();
   }
-  mkldnn::memory::primitive_desc pd(data_md, cpu_engine);
-  CHECK(shandle.size >= pd.get_size());
-  mkl_mem_.reset(new MKLDNNMemory(pd, shandle.dptr));
+  CHECK(shandle.size >= data_md.get_size());
+  mkl_mem_.reset(new MKLDNNMemory(data_md, shandle.dptr));
 }
 
-const mkldnn::memory *NDArray::GetMKLDNNData(
-    const mkldnn::memory::primitive_desc &desc) const {
+const mkldnn::memory *NDArray::GetMKLDNNData(const mkldnn::memory::desc &desc) const {
   if (desc.get_size() != shape().Size() * GetTypeSize(dtype_)) {
     LOG(FATAL) << "The size of NDArray doesn't match the requested MKLDNN memory desc";
     return nullptr;
   }
   const mkldnn::memory *mem = GetMKLDNNData();
-  mkldnn::memory::primitive_desc _desc = desc;
-  mkldnn::memory::desc desc1 = mem->get_primitive_desc().desc();
-  mkldnn::memory::desc desc2 = _desc.desc();
+  mkldnn::memory::desc desc1 = mem->get_desc();
   // The MKL memory has the same format and shape as required,
   // or both use the default format, we can return the MKL memory.
-  if (mem->get_primitive_desc() == desc
-      || (desc1.data.format == GetDefaultFormat(desc1)
-        && desc2.data.format == GetDefaultFormat(desc2))) {
+  if (desc1 == desc || ((!mxnet::IsMKLDNN(desc1)) && (!mxnet::IsMKLDNN(desc)))) {
     return GetMKLDNNExact(mem, desc);
   } else {
     return nullptr;
@@ -535,8 +523,8 @@ const mkldnn::memory *NDArray::GetMKLDNNData(
 }
 
 const mkldnn::memory *NDArray::GetMKLDNNDataReorder(
-    const mkldnn::memory::primitive_desc &new_pd) const {
-  if (new_pd.get_size() != shape().Size() * GetTypeSize(dtype_)) {
+    const mkldnn::memory::desc &new_desc) const {
+  if (new_desc.get_size() != shape().Size() * GetTypeSize(dtype_)) {
     LOG(FATAL) << "The size of NDArray doesn't match the requested MKLDNN memory desc";
     return nullptr;
   }
@@ -545,39 +533,40 @@ const mkldnn::memory *NDArray::GetMKLDNNDataReorder(
   const mkldnn::memory *mem = GetMKLDNNData();
   // If the memory descriptor matches, it's easy.
   MKLDNNStream *stream = MKLDNNStream::Get();
-  if (mem->get_primitive_desc() == new_pd) {
-    return GetMKLDNNExact(mem, new_pd);
+  if (mem->get_desc() == new_desc) {
+    return GetMKLDNNExact(mem, new_desc);
   }
 
-  mkldnn::memory::primitive_desc _pd = new_pd;
-  mkldnn::memory::desc desc1 = mem->get_primitive_desc().desc();
-  mkldnn::memory::desc desc2 = _pd.desc();
+  mkldnn::memory::desc old_desc = mem->get_desc();
   // Now we need to determine if we should reorder the memory.
   // If both use the default formats, we think we don't need to reorder.
-  if (desc1.data.format == GetDefaultFormat(desc1) &&
-      desc2.data.format == GetDefaultFormat(desc2)) {
-    mkldnn_mem_ptr ret(new mkldnn::memory(new_pd, mem->get_data_handle()));
+  if ((!mxnet::IsMKLDNN(old_desc)) && (!mxnet::IsMKLDNN(new_desc))) {
+    mkldnn_mem_ptr ret(new mkldnn::memory(new_desc,
+        CpuEngine::Get()->get_engine(), mem->get_data_handle()));
     stream->RegisterMem(ret);
     return ret.get();
-  } else if (same_shape(desc1, desc2)) {
+  } else if (same_shape(old_desc, new_desc)) {
     // If they have the same shape, we can reorder data directly.
-    mkldnn::memory *ret = TmpMemMgr::Get()->Alloc(new_pd);
-    stream->RegisterPrim(mkldnn::reorder(*mem, *ret));
+    mkldnn::memory *ret = TmpMemMgr::Get()->Alloc(new_desc);
+    std::unordered_map<int, mkldnn::memory> args({{MKLDNN_ARG_FROM, *mem}, {MKLDNN_ARG_TO, *ret}});
+    stream->RegisterPrimArgs(mkldnn::reorder(*mem, *ret), args);
     return ret;
   } else {
     // If they have different shapes, we need to reshape the array first.
     // Since this method will only be used inside an operator, we can call
     // MKLDNNDataReshape to reshape an array.
-    mxnet::TShape required_shape(desc2.data.ndims, -1);
-    for (int i = 0; i < desc2.data.ndims; i++)
-      required_shape[i] = desc2.data.dims[i];
+    mxnet::TShape required_shape(new_desc.data.ndims, -1);
+    for (int i = 0; i < new_desc.data.ndims; i++)
+      required_shape[i] = new_desc.data.dims[i];
     NDArray reshaped = MKLDNNDataReshape(required_shape);
     const mkldnn::memory *ret = reshaped.GetMKLDNNData();
-    if (ret->get_primitive_desc() == new_pd) {
-      return GetMKLDNNExact(ret, new_pd);
+    if (ret->get_desc() == new_desc) {
+      return GetMKLDNNExact(ret, new_desc);
     } else {
-      mkldnn::memory *ret2 = TmpMemMgr::Get()->Alloc(new_pd);
-      stream->RegisterPrim(mkldnn::reorder(*ret, *ret2));
+      mkldnn::memory *ret2 = TmpMemMgr::Get()->Alloc(new_desc);
+      std::unordered_map<int, mkldnn::memory> args({{MKLDNN_ARG_FROM, *ret},
+                                                   {MKLDNN_ARG_TO, *ret2}});
+      stream->RegisterPrimArgs(mkldnn::reorder(*ret, *ret2), args);
       return ret2;
     }
   }
@@ -588,18 +577,18 @@ NDArray NDArray::Reorder2Default() const {
 
   if (ptr_->mkl_mem_ == nullptr)
     return *this;
-  mkldnn_memory_format_t format = ptr_->mkl_mem_->GetDefaultFormat();
-  if (format == ptr_->mkl_mem_->GetFormat())
+  if (!ptr_->mkl_mem_->IsMKLDNN())
     return *this;
 
   // create new ndarray from  mkldnn layout
-  mkldnn::memory::desc from_desc = ptr_->mkl_mem_->GetPrimitiveDesc().desc();
+  mkldnn::memory::desc from_desc = ptr_->mkl_mem_->GetDesc();
   mxnet::TShape tshape(from_desc.data.ndims, -1);
   for (int i = 0; i < from_desc.data.ndims; i++) tshape[i] = from_desc.data.dims[i];
   NDArray ret(tshape, ctx(), false, dtype());
-  mkldnn::memory::primitive_desc def_pd = ptr_->mkl_mem_->GetPrimitiveDesc(format);
-  CHECK(ret.ptr_->shandle.size >= def_pd.get_size());
-  mkldnn::memory def_mem(def_pd, ret.ptr_->shandle.dptr);
+  mkldnn_format_tag_t format = ptr_->mkl_mem_->GetDefaultFormat();
+  mkldnn::memory::desc def_desc = ptr_->mkl_mem_->GetDesc(format);
+  CHECK(ret.ptr_->shandle.size >= def_desc.get_size());
+  mkldnn::memory def_mem(def_desc, CpuEngine::Get()->get_engine(), ret.ptr_->shandle.dptr);
   ptr_->mkl_mem_->ReorderTo(&def_mem);
   // reshape as needed
   ret.shape_ = shape_;
@@ -608,7 +597,7 @@ NDArray NDArray::Reorder2Default() const {
   return ret;
 }
 
-void NDArray::Reorder2DefaultAsync() {
+void NDArray::Reorder2DefaultAsync() const {
   std::vector<Engine::VarHandle> const_vars;
   std::vector<Engine::VarHandle> mutable_vars(1, this->var());
   NDArray tmp = *this;
@@ -620,21 +609,21 @@ void NDArray::Reorder2DefaultAsync() {
     FnProperty::kNormal, 0, "Reorder2Default");
 }
 
-void NDArray::MKLDNNDataReorderAsync(const mkldnn::memory::primitive_desc &desc) {
+void NDArray::MKLDNNDataReorderAsync(const mkldnn::memory::desc &desc) const {
   std::vector<Engine::VarHandle> const_vars;
   std::vector<Engine::VarHandle> mutable_vars(1, this->var());
   NDArray tmp = *this;
   const auto version = this->version();
   Engine::Get()->PushAsync(
-      [tmp, version, desc](RunContext ctx, Engine::CallbackOnComplete on_complete) {
-        // MXNet will try to reuse NDArray from memory planning, so we need to ensure
-        // the NDArray is still holding the original trunk data.
-        if (tmp.version() == version) {
-          tmp.ptr_->MKLDNNDataReorder(desc);
-        }
-        on_complete();
-      },
-      ctx(), const_vars, mutable_vars, FnProperty::kNormal, 0, "Reorder");
+    [tmp, version, desc](RunContext ctx, Engine::CallbackOnComplete on_complete) {
+      // MXNet will try to reuse NDArray from memory planning, so we need to ensure
+      // the NDArray is still holding the original trunk data.
+      if (tmp.version() == version) {
+        tmp.ptr_->MKLDNNDataReorder(desc);
+      }
+      on_complete();
+    }, ctx(), const_vars, mutable_vars,
+    FnProperty::kNormal, 0, "Reorder");
 }
 
 const mkldnn::memory *NDArray::GetMKLDNNData() const {
@@ -658,14 +647,12 @@ const mkldnn::memory *NDArray::GetMKLDNNData() const {
     mkldnn::memory::dims dims(shape().ndim());
     for (size_t i = 0; i < dims.size(); i++)
       dims[i] = shape()[i];
-    mkldnn::memory::format cpp_format = static_cast<mkldnn::memory::format>(
+    mkldnn::memory::format_tag cpp_format = static_cast<mkldnn::memory::format_tag>(
         GetDefaultFormat(shape().ndim()));
     mkldnn::memory::data_type cpp_type = get_mkldnn_type(dtype_);
     mkldnn::memory::desc data_md(dims, cpp_type, cpp_format);
-    mkldnn::memory::primitive_desc new_pd(data_md,
-                                          CpuEngine::Get()->get_engine());
-
-    std::shared_ptr<mkldnn::memory> ret(new mkldnn::memory(new_pd, off_addr));
+    std::shared_ptr<mkldnn::memory> ret(
+        new mkldnn::memory(data_md, CpuEngine::Get()->get_engine(), off_addr));
     MKLDNNStream::Get()->RegisterMem(ret);
     return ret.get();
   } else {
@@ -689,7 +676,7 @@ void NDArray::CopyFrom(const mkldnn::memory &mem) {
   if (ptr_->mkl_mem_ && ptr_->mkl_mem_->GetRaw() == &mem)
     return;
 
-  CHECK(mem.get_primitive_desc().get_size() == shape().Size() * GetTypeSize(dtype_))
+  CHECK(mem.get_desc().get_size() == shape().Size() * GetTypeSize(dtype_))
       << "The size of NDArray doesn't match the requested MKLDNN memory desc";
   // If this array uses MKLDNN layout, we have to make sure it's not a view.
   // Otherwise, we'll have to change the layout inside the array.
@@ -701,28 +688,25 @@ void NDArray::CopyFrom(const mkldnn::memory &mem) {
   MKLDNNCopy(mem, this_mem);
 }
 
-mkldnn::memory *NDArray::CreateMKLDNNData(const mkldnn::memory::primitive_desc &desc) {
+mkldnn::memory *NDArray::CreateMKLDNNData(const mkldnn::memory::desc &desc) {
   if (desc.get_size() != shape().Size() * GetTypeSize(dtype_)) {
-    LOG(FATAL) << "The size of NDArray doesn't match the requested MKLDNN memory desc";
+    LOG(FATAL) << "The size of NDArray doesn't match the requested MKLDNN memory desc. "
+        << "MKLDNN memory requests for " << desc.get_size() << " bytes, but got "
+        << shape().Size() * GetTypeSize(dtype_) << " bytes from NDArray";
     return nullptr;
   }
-
-  mkldnn::memory::primitive_desc _desc = desc;
-  mkldnn_memory_format_t required_format = _desc.desc().data.format;
-  mkldnn_memory_format_t def_format = GetDefaultFormat(_desc.desc());
-  // If the required format is a default format, we don't need to worry about the shape.
-  // If the shape isn't the same, it actually implicitly reshapes data.
-  if (required_format == def_format && !IsView()) {
+  bool isDefaultFormat = IsDefaultFormat(desc);
+  if (isDefaultFormat && !IsView()) {
     ptr_->SetMKLMem(shape_, dtype_);
     MKLDNNStream::Get()->RegisterMem(ptr_->mkl_mem_->GetMem());
     return GetMKLDNNExact(ptr_->mkl_mem_->GetRaw(), desc);
-  } else if (required_format == def_format) {
+  } else if (isDefaultFormat) {
     ptr_->CheckAndAlloc();
     CHECK(ptr_->shandle.dptr);
     // When this is a view and a user wants the default layout, we can simply
     // create a new mkldnn memory that points to the right memory.
-    std::shared_ptr<mkldnn::memory> mem(new mkldnn::memory(
-            desc, static_cast<char *>(ptr_->shandle.dptr) + byte_offset_));
+    std::shared_ptr<mkldnn::memory> mem(new mkldnn::memory(desc,
+        CpuEngine::Get()->get_engine(), static_cast<char *>(ptr_->shandle.dptr) + byte_offset_));
     MKLDNNStream::Get()->RegisterMem(mem);
     return mem.get();
   } else if (IsView()) {
@@ -736,7 +720,7 @@ mkldnn::memory *NDArray::CreateMKLDNNData(const mkldnn::memory::primitive_desc &
 
   if (ptr_->mkl_mem_)
     CHECK(ptr_->mkl_mem_->GetDataHandle() == ptr_->shandle.dptr);
-  if (ptr_->mkl_mem_ && ptr_->mkl_mem_->GetPrimitiveDesc() == desc) {
+  if (ptr_->mkl_mem_ && ptr_->mkl_mem_->GetDesc() == desc) {
     MKLDNNStream::Get()->RegisterMem(ptr_->mkl_mem_->GetMem());
     return GetMKLDNNExact(ptr_->mkl_mem_->GetRaw(), desc);
   }
@@ -748,17 +732,14 @@ mkldnn::memory *NDArray::CreateMKLDNNData(const mkldnn::memory::primitive_desc &
   return ptr_->mkl_mem_->GetRaw();
 }
 
-void NDArray::UpdateMKLDNNMemDesc(mkldnn::memory::format format) {
-  const mkldnn::memory *mem = GetMKLDNNData();
-  auto mem_desc = mem->get_primitive_desc().desc();
+void NDArray::UpdateMKLDNNMemDesc(const mkldnn::memory::desc &desc) {
+  auto new_desc = desc;
   auto this_dtype = get_mkldnn_type(dtype());
-  mkldnn::memory::desc data_md(
-      mkldnn::memory::dims(mem_desc.data.dims, mem_desc.data.dims + mem_desc.data.ndims),
-      this_dtype, format);
-  mkldnn::memory::primitive_desc pd(data_md, CpuEngine::Get()->get_engine());
-  ptr_->mkl_mem_.reset(new MKLDNNMemory(pd, ptr_->shandle.dptr));
+  new_desc.data.data_type = static_cast<mkldnn_data_type_t>(this_dtype);
+  ptr_->mkl_mem_.reset(new MKLDNNMemory(new_desc, ptr_->shandle.dptr));
   MKLDNNStream::Get()->RegisterMem(ptr_->mkl_mem_->GetMem());
 }
+
 #endif
 
 void NDArray::SetTBlob() const {
@@ -1127,9 +1108,9 @@ inline void CopyFromToDnsImpl(const NDArray& from, const NDArray& to, RunContext
     // by MKLDNN.
     auto from_mem = from.GetMKLDNNData();
     auto to_mem = to.GetMKLDNNData();
-    if (from_mem->get_primitive_desc() == to_mem->get_primitive_desc()) {
-      size_t size = std::min(from_mem->get_primitive_desc().get_size(),
-                             to_mem->get_primitive_desc().get_size());
+    if (from_mem->get_desc() == to_mem->get_desc()) {
+      size_t size = std::min(from_mem->get_desc().get_size(),
+                             to_mem->get_desc().get_size());
       memcpy(to_mem->get_data_handle(), from_mem->get_data_handle(), size);
     } else {
       const_cast<NDArray &>(to).CopyFrom(*from_mem);
diff --git a/src/operator/nn/concat.cc b/src/operator/nn/concat.cc
index 9e016bf884f2..fa62b0044a53 100644
--- a/src/operator/nn/concat.cc
+++ b/src/operator/nn/concat.cc
@@ -203,7 +203,7 @@ inline static bool ConcatForwardInferStorageType(const nnvm::NodeAttrs& attrs,
     dispatched = storage_type_assign(&out_stype, kDefaultStorage,
                                      dispatch_mode, DispatchMode::kFComputeEx);
   }
-#endif
+#endif  // MXNET_USE_MKLDNN == 1
   if (!dispatched && common::ContainsOnlyStorage(*in_attrs, kDefaultStorage)) {
     dispatched = storage_type_assign(&out_stype, kDefaultStorage,
                                      dispatch_mode, DispatchMode::kFCompute);
@@ -214,7 +214,7 @@ inline static bool ConcatForwardInferStorageType(const nnvm::NodeAttrs& attrs,
 #if MXNET_USE_MKLDNN == 1
   if (!MKLDNNEnvSet())
     *dispatch_mode = DispatchMode::kFComputeFallback;
-#endif
+#endif  // MXNET_USE_MKLDNN == 1
   return dispatched;
 }
 
@@ -232,12 +232,12 @@ inline static bool BackwardConcatStorageType(const nnvm::NodeAttrs& attrs,
       && param.dim > 0)
     wanted_mode = DispatchMode::kFComputeEx;
   else
-#endif
+#endif  // MXNET_USE_MKLDNN == 1
     wanted_mode = DispatchMode::kFCompute;
 #if MXNET_USE_MKLDNN == 1
   if (!MKLDNNEnvSet())
     wanted_mode = DispatchMode::kFComputeFallback;
-#endif
+#endif  // MXNET_USE_MKLDNN == 1
   return storage_type_assign(out_attrs, mxnet::kDefaultStorage,
                              dispatch_mode, wanted_mode);
 }
@@ -249,12 +249,12 @@ bool SupportMKLDNNConcat(const std::vector<NDArray> &arrs) {
     // DO not support zero-size tensors.
     if (arr.shape().Size() == 0) return false;
     int ndim = arr.shape().ndim();
-    const int mkldnn_ndims = arr.GetMKLDNNData()->get_primitive_desc().desc().data.ndims;
+    const int mkldnn_ndims = arr.GetMKLDNNData()->get_desc().data.ndims;
     if (!(ndim == 2 || ndim == 4) || ndim != mkldnn_ndims) return false;
   }
   return true;
 }
-#endif
+#endif  // MXNET_USE_MKLDNN == 1
 static void ConcatComputeExCPU(const nnvm::NodeAttrs& attrs,
                                const OpContext& op_ctx,
                                const std::vector<NDArray>& inputs,
@@ -274,7 +274,7 @@ static void ConcatComputeExCPU(const nnvm::NodeAttrs& attrs,
     MKLDNN_OPCHECK_RUN(ConcatCompute<cpu>, attrs, op_ctx, inputs, req, outputs);
   } else if (common::ContainsOnlyStorage(inputs, kDefaultStorage)) {
     FallBackCompute(ConcatCompute<cpu>, attrs, op_ctx, inputs, req, outputs);
-#endif
+#endif  // MXNET_USE_MKLDNN == 1
   } else {
     LogUnimplementedOp(attrs, op_ctx, inputs, req, outputs);
   }
@@ -294,7 +294,7 @@ static void ConcatGradComputeExCPU(const nnvm::NodeAttrs& attrs,
   }
   FallBackCompute(ConcatGradCompute<cpu>, attrs, ctx, inputs, req, outputs);
 }
-#endif
+#endif  // MXNET_USE_MKLDNN == 1
 
 struct ConcatGrad {
   const char *op_name;
@@ -306,7 +306,7 @@ struct ConcatGrad {
     for (size_t i = 0; i < n->inputs.size(); i++) {
       heads.push_back(n->inputs[i]);
     }
-#endif
+#endif  // MXNET_USE_MKLDNN == 1
     return MakeGradNode(op_name, n, heads, n->attrs.dict);
   }
 };
@@ -386,7 +386,7 @@ Example::
   return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
 })
 .set_attr<bool>("TIsMKLDNN", true)
-#endif
+#endif  // MXNET_USE_MKLDNN == 1
 CONCAT_FORWARD_ATTRS
 .set_attr<mxnet::FInferShape>("FInferShape", ConcatShape)
 .add_argument("data", "NDArray-or-Symbol[]", "List of arrays to concatenate")
@@ -402,13 +402,13 @@ NNVM_REGISTER_OP(_backward_Concat)
 .set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& n) {
   return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
 })
-#endif
+#endif  // MXNET_USE_MKLDNN == 1
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
 .set_attr<FInferStorageType>("FInferStorageType", BackwardConcatStorageType)
 #if MXNET_USE_MKLDNN == 1
 .set_attr<bool>("TIsMKLDNN", true)
 .set_attr<FComputeEx>("FComputeEx<cpu>", ConcatGradComputeExCPU)
-#endif
+#endif  // MXNET_USE_MKLDNN == 1
 .set_attr<FCompute>("FCompute<cpu>", ConcatGradCompute<cpu>);
 
 // _rnn_param_concat is a custom concat op with specialized infer_shape,
@@ -420,7 +420,7 @@ NNVM_REGISTER_OP(_rnn_param_concat)
 .set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& n) {
   return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
 })
-#endif
+#endif  // MXNET_USE_MKLDNN == 1
 CONCAT_FORWARD_ATTRS
 .set_attr<mxnet::FInferShape>("FInferShape", RNNParamConcatShape)
 .add_argument("data", "NDArray-or-Symbol[]", "List of arrays to concatenate")
diff --git a/src/operator/nn/convolution.cc b/src/operator/nn/convolution.cc
index 32ed93e4a463..e31073034594 100644
--- a/src/operator/nn/convolution.cc
+++ b/src/operator/nn/convolution.cc
@@ -60,7 +60,7 @@ static void ConvolutionComputeExCPU(const nnvm::NodeAttrs& attrs,
   const ConvolutionParam& params = nnvm::get<ConvolutionParam>(attrs.parsed);
   if (SupportMKLDNNConv(params, inputs[0])) {
     MKLDNN_OPCHECK_INIT(false, outputs.size(), inputs, outputs);
-    MKLDNNConvolutionForward(attrs, ctx, inputs, req, outputs);
+    MKLDNNRun(MKLDNNConvolutionForward, attrs, ctx, inputs, req, outputs);
     MKLDNN_OPCHECK_RUN(ConvolutionCompute<cpu>, attrs, ctx, inputs, req, outputs);
     return;
   }
@@ -75,7 +75,7 @@ static void ConvolutionGradComputeExCPU(const nnvm::NodeAttrs& attrs,
   const ConvolutionParam& params = nnvm::get<ConvolutionParam>(attrs.parsed);
   if (SupportMKLDNNConv(params, inputs[0])) {
     MKLDNN_OPCHECK_INIT(true, outputs.size(), inputs, outputs);
-    MKLDNNConvolutionBackward(attrs, ctx, inputs, req, outputs);
+    MKLDNNRun(MKLDNNConvolutionBackward, attrs, ctx, inputs, req, outputs);
     MKLDNN_OPCHECK_RUN(ConvolutionGradCompute<cpu>, attrs, ctx, inputs, req, outputs);
     return;
   }
diff --git a/src/operator/nn/deconvolution.cc b/src/operator/nn/deconvolution.cc
index 9f461f4e9de3..b61f9ff37002 100644
--- a/src/operator/nn/deconvolution.cc
+++ b/src/operator/nn/deconvolution.cc
@@ -28,13 +28,74 @@
 #include "../operator_common.h"
 #include "../../common/utils.h"
 #if MXNET_USE_MKLDNN == 1
-#include "./mkldnn/mkldnn_ops-inl.h"
 #include "./mkldnn/mkldnn_base-inl.h"
-#endif
+#include "./mkldnn/mkldnn_ops-inl.h"
+#endif  // MXNET_USE_MKLDNN
 
 namespace mxnet {
 namespace op {
 
+#if MXNET_USE_MKLDNN == 1
+static void DeconvolutionComputeExCPU(const nnvm::NodeAttrs& attrs,
+                                      const OpContext& ctx,
+                                      const std::vector<NDArray>& inputs,
+                                      const std::vector<OpReqType>& req,
+                                      const std::vector<NDArray>& outputs) {
+  const DeconvolutionParam& params = nnvm::get<DeconvolutionParam>(attrs.parsed);
+  if (SupportMKLDNNDeconv(params, inputs[0])) {
+    MKLDNN_OPCHECK_INIT(false, outputs.size(), inputs, outputs);
+    MKLDNNRun(MKLDNNDeconvolutionForward, attrs, ctx, inputs, req, outputs);
+    MKLDNN_OPCHECK_RUN(DeconvolutionCompute<cpu>, attrs, ctx, inputs, req, outputs);
+    return;
+  }
+  FallBackCompute(DeconvolutionCompute<cpu>, attrs, ctx, inputs, req, outputs);
+}
+
+static void DeconvolutionGradComputeExCPU(const nnvm::NodeAttrs& attrs,
+                                          const OpContext& ctx,
+                                          const std::vector<NDArray>& inputs,
+                                          const std::vector<OpReqType>& req,
+                                          const std::vector<NDArray>& outputs) {
+  const DeconvolutionParam& params = nnvm::get<DeconvolutionParam>(attrs.parsed);
+  if (SupportMKLDNNDeconv(params, inputs[0])) {
+    MKLDNN_OPCHECK_INIT(true, outputs.size(), inputs, outputs);
+    MKLDNNRun(MKLDNNDeconvolutionBackward, attrs, ctx, inputs, req, outputs);
+    MKLDNN_OPCHECK_RUN(DeconvolutionGradCompute<cpu>, attrs, ctx, inputs, req, outputs);
+    return;
+  }
+  FallBackCompute(DeconvolutionGradCompute<cpu>, attrs, ctx, inputs, req, outputs);
+}
+
+inline static bool DeconvStorageType(const nnvm::NodeAttrs& attrs,
+                                     const int dev_mask,
+                                     DispatchMode* dispatch_mode,
+                                     std::vector<int>* in_attrs,
+                                     std::vector<int>* out_attrs) {
+  const DeconvolutionParam& param = nnvm::get<DeconvolutionParam>(attrs.parsed);
+  uint32_t in_expected = param.no_bias ? 2 : 3;
+  CHECK_EQ(in_attrs->size(), in_expected);
+  CHECK_EQ(out_attrs->size(), 1);
+
+  return MKLDNNStorageType(attrs, dev_mask, true, dispatch_mode, in_attrs,
+                           out_attrs);
+}
+
+inline static bool BackwardDeconvStorageType(const nnvm::NodeAttrs& attrs,
+                                             const int dev_mask,
+                                             DispatchMode* dispatch_mode,
+                                             std::vector<int> *in_attrs,
+                                             std::vector<int> *out_attrs) {
+  const DeconvolutionParam& param = nnvm::get<DeconvolutionParam>(attrs.parsed);
+  uint32_t in_expected = param.no_bias ? 3 : 4;
+  uint32_t out_expected = param.no_bias ? 2 : 3;
+  CHECK_EQ(in_attrs->size(), in_expected);
+  CHECK_EQ(out_attrs->size(), out_expected);
+
+  return MKLDNNStorageType(attrs, dev_mask, true, dispatch_mode, in_attrs,
+                           out_attrs);
+}
+#endif
+
 static bool DeconvolutionShape(const nnvm::NodeAttrs& attrs,
                                mxnet::ShapeVector *in_shape,
                                mxnet::ShapeVector *out_shape) {
@@ -284,70 +345,6 @@ static bool DeconvolutionType(const nnvm::NodeAttrs& attrs,
   return true;
 }
 
-#if MXNET_USE_MKLDNN == 1
-inline static bool DeconvStorageType(const nnvm::NodeAttrs& attrs,
-                                     const int dev_mask,
-                                     DispatchMode* dispatch_mode,
-                                     std::vector<int> *in_attrs,
-                                     std::vector<int> *out_attrs) {
-  const DeconvolutionParam& param = nnvm::get<DeconvolutionParam>(attrs.parsed);
-  uint32_t in_expected = param.no_bias ? 2 : 3;
-  CHECK_EQ(in_attrs->size(), in_expected);
-  CHECK_EQ(out_attrs->size(), 1);
-
-  return MKLDNNStorageType(attrs, dev_mask, true, dispatch_mode, in_attrs,
-                           out_attrs);
-}
-
-inline static bool BackwardDeconvStorageType(const nnvm::NodeAttrs& attrs,
-                                             const int dev_mask,
-                                             DispatchMode* dispatch_mode,
-                                             std::vector<int> *in_attrs,
-                                             std::vector<int> *out_attrs) {
-  const DeconvolutionParam& param = nnvm::get<DeconvolutionParam>(attrs.parsed);
-  uint32_t out_expected = param.no_bias ? 2 : 3;
-  CHECK_EQ(in_attrs->size(), param.no_bias ? 3U : 4U);
-  CHECK_EQ(out_attrs->size(), out_expected);
-
-  return MKLDNNStorageType(attrs, dev_mask, true, dispatch_mode, in_attrs,
-                           out_attrs);
-}
-
-static void DeconvolutionComputeExCPU(const nnvm::NodeAttrs& attrs,
-                                      const OpContext& ctx,
-                                      const std::vector<NDArray>& inputs,
-                                      const std::vector<OpReqType>& req,
-                                      const std::vector<NDArray>& outputs) {
-  const DeconvolutionParam& param = nnvm::get<DeconvolutionParam>(attrs.parsed);
-  if (SupportMKLDNNDeconv(param, inputs[0])) {
-    MKLDNN_OPCHECK_INIT(false, outputs.size(), inputs, outputs);
-    MKLDNNDeconvolutionForward(attrs, ctx, inputs, req, outputs);
-    MKLDNN_OPCHECK_RUN(DeconvolutionCompute<cpu>, attrs, ctx, inputs, req,
-                       outputs);
-    return;
-  }
-  FallBackCompute(DeconvolutionCompute<cpu>, attrs, ctx, inputs, req,
-                  outputs);
-}
-
-static void DeconvolutionGradComputeExCPU(const nnvm::NodeAttrs& attrs,
-                                          const OpContext& ctx,
-                                          const std::vector<NDArray>& inputs,
-                                          const std::vector<OpReqType>& req,
-                                          const std::vector<NDArray>& outputs) {
-  const DeconvolutionParam& param = nnvm::get<DeconvolutionParam>(attrs.parsed);
-  if (SupportMKLDNNDeconv(param, inputs[0])) {
-    MKLDNN_OPCHECK_INIT(true, outputs.size(), inputs, outputs);
-    MKLDNNDeconvolutionBackward(attrs, ctx, inputs, req, outputs);
-    MKLDNN_OPCHECK_RUN(DeconvolutionGradCompute<cpu>, attrs, ctx, inputs, req,
-                       outputs);
-    return;
-  }
-  FallBackCompute(DeconvolutionGradCompute<cpu>, attrs, ctx, inputs, req,
-                  outputs);
-}
-#endif
-
 static void DeconvolutionParamParser(nnvm::NodeAttrs* attrs) {
   using namespace mshadow;
   DeconvolutionParam param_;
@@ -430,18 +427,16 @@ NNVM_REGISTER_OP(Deconvolution)
 })
 .set_attr<mxnet::FInferShape>("FInferShape", DeconvolutionShape)
 .set_attr<nnvm::FInferType>("FInferType", DeconvolutionType)
-#if MXNET_USE_MKLDNN == 1
-.set_attr<FInferStorageType>("FInferStorageType", DeconvStorageType)
-#endif
 .set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& n) {
   return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
 })
 .set_attr<FCompute>("FCompute<cpu>", DeconvolutionCompute<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", DeconvolutionGrad{"_backward_Deconvolution"})
 #if MXNET_USE_MKLDNN == 1
 .set_attr<bool>("TIsMKLDNN", true)
+.set_attr<FInferStorageType>("FInferStorageType", DeconvStorageType)
 .set_attr<FComputeEx>("FComputeEx<cpu>", DeconvolutionComputeExCPU)
 #endif
-.set_attr<nnvm::FGradient>("FGradient", DeconvolutionGrad{"_backward_Deconvolution"})
 .add_argument("data", "NDArray-or-Symbol", "Input tensor to the deconvolution operation.")
 .add_argument("weight", "NDArray-or-Symbol", "Weights representing the kernel.")
 .add_argument("bias", "NDArray-or-Symbol", "Bias added to the result after the deconvolution "
@@ -454,15 +449,13 @@ NNVM_REGISTER_OP(_backward_Deconvolution)
   return params.no_bias ? 2 : 3;
 })
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
-#if MXNET_USE_MKLDNN == 1
-.set_attr<FInferStorageType>("FInferStorageType", BackwardDeconvStorageType)
-#endif
 .set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& n) {
   return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
 })
 .set_attr_parser(DeconvolutionParamParser)
 #if MXNET_USE_MKLDNN == 1
 .set_attr<bool>("TIsMKLDNN", true)
+.set_attr<FInferStorageType>("FInferStorageType", BackwardDeconvStorageType)
 .set_attr<FComputeEx>("FComputeEx<cpu>", DeconvolutionGradComputeExCPU)
 #endif
 .set_attr<FCompute>("FCompute<cpu>", DeconvolutionGradCompute<cpu>);
diff --git a/src/operator/nn/dropout-inl.h b/src/operator/nn/dropout-inl.h
index 6387dff96eb7..61239d33800c 100644
--- a/src/operator/nn/dropout-inl.h
+++ b/src/operator/nn/dropout-inl.h
@@ -39,7 +39,7 @@
 #include "../random/sampler.h"
 #include "../tensor/elemwise_binary_broadcast_op.h"
 
-#if defined(USE_MKL) && defined(_OPENMP) && !defined(__CUDACC__)
+#if (MSHADOW_USE_MKL == 1) && defined(_OPENMP) && !defined(__CUDACC__)
 #define MXNET_USE_MKL_DROPOUT 1
 #endif
 
diff --git a/src/operator/nn/fully_connected.cc b/src/operator/nn/fully_connected.cc
index fba13f3ece88..1f6d9e313202 100644
--- a/src/operator/nn/fully_connected.cc
+++ b/src/operator/nn/fully_connected.cc
@@ -147,7 +147,10 @@ void FullyConnectedGradComputeExCPU(const nnvm::NodeAttrs& attrs,
                                     const std::vector<NDArray> &inputs,
                                     const std::vector<OpReqType> &req,
                                     const std::vector<NDArray> &outputs) {
-  if (SupportMKLDNNFC(inputs[0])) {
+  // TODO(rongzha1): disable due to flakiness in cpp test IMPERATIVE.FullyConnectedOp
+  // Will be fixed when we decide to enable the backward of FC.
+  bool mkldnn_fc_backward_enable = false;
+  if (mkldnn_fc_backward_enable && SupportMKLDNNFC(inputs[0])) {
     MKLDNN_OPCHECK_INIT(true, outputs.size(), inputs, outputs);
     MKLDNNFCBackward(attrs, ctx, inputs, req, outputs);
     MKLDNN_OPCHECK_RUN(FullyConnectedGradCompute<cpu>, attrs, ctx, inputs, req,
diff --git a/src/operator/nn/mkldnn/mkldnn_act-inl.h b/src/operator/nn/mkldnn/mkldnn_act-inl.h
index 9c21b7f70f52..cf3e4f47d1ff 100644
--- a/src/operator/nn/mkldnn/mkldnn_act-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_act-inl.h
@@ -20,7 +20,7 @@
 /*!
  * Copyright (c) 2019 by Contributors
  * \file mkldnn_act-inl.h
- * \brief MKLDNN(Quantized) Activation operator based on subgraph
+ * \brief MKLDNN Activation operator
  * /author Zhiyuan Huang
 */
 
@@ -33,8 +33,6 @@
 #include <utility>
 #include "../activation-inl.h"
 #include "../../leaky_relu-inl.h"
-#include "./mkldnn_ops-inl.h"
-#include "./mkldnn_base-inl.h"
 
 namespace mxnet {
 namespace op {
@@ -51,9 +49,10 @@ struct MKLDNNActParam {
 
 mkldnn::algorithm GetMKLDNNActAlgo(const ActivationParam& param);
 mkldnn::algorithm GetMKLDNNActAlgo(const LeakyReLUParam& param);
+
 mkldnn::eltwise_forward::primitive_desc GetActFwdDescImpl(
     const MKLDNNActParam& param, bool is_train,
-    const mkldnn::memory &input_mem, int dtype);
+    const mkldnn::memory &input_mem);
 
 class MKLDNNActForward {
  public:
@@ -61,14 +60,13 @@ class MKLDNNActForward {
 
   MKLDNNActForward(const MKLDNNActParam& param, bool is_train,
                    const NDArray &data, const mkldnn::memory &mem): fwd_pd(
-                       GetActFwdDescImpl(param, is_train, mem, data.dtype())) {}
-  void SetNewMem(const mkldnn::memory &data, const mkldnn::memory &output);
-  const mkldnn::eltwise_forward &GetFwd() const;
+                       GetActFwdDescImpl(param, is_train, mem)) {
+    fwd_ = std::make_shared<mkldnn::eltwise_forward>(fwd_pd);
+  }
+  const inline mkldnn::eltwise_forward &GetFwd() const;
 
  private:
   std::shared_ptr<mkldnn::eltwise_forward> fwd_;
-  std::shared_ptr<mkldnn::memory> data_;
-  std::shared_ptr<mkldnn::memory> out_;
 };
 
 typedef ParamOpSign<MKLDNNActParam> MKLDNNActSignature;
@@ -80,8 +78,28 @@ void MKLDNNActivationForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
                              const NDArray &in_data, const OpReqType &req,
                              const NDArray &out_data);
 void MKLDNNLeakyReluForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
-                             const NDArray &in_data, const OpReqType &req,
-                             const NDArray &out_data);
+                            const NDArray &in_data, const OpReqType &req,
+                            const NDArray &out_data);
+
+mkldnn::eltwise_backward::primitive_desc GetActBwdDescImpl(
+    const MKLDNNActParam &param, const mkldnn::memory &input_mem,
+    const mkldnn::memory &diff_dst_memory);
+
+class MKLDNNActBackward {
+ public:
+  const mkldnn::eltwise_backward::primitive_desc bwd_pd;
+
+  explicit MKLDNNActBackward(const MKLDNNActParam &param, const NDArray &data,
+                             const mkldnn::memory &mem,
+                             const mkldnn::memory &diff_dst_memory): bwd_pd(
+                                 GetActBwdDescImpl(param, mem, diff_dst_memory)) {
+    bwd_prim_ = std::make_shared<mkldnn::eltwise_backward>(bwd_pd);
+  }
+  const inline mkldnn::eltwise_backward &GetBwd() const;
+
+ private:
+  std::shared_ptr<mkldnn::eltwise_backward> bwd_prim_;
+};
 }  // namespace op
 }  // namespace mxnet
 
diff --git a/src/operator/nn/mkldnn/mkldnn_act.cc b/src/operator/nn/mkldnn/mkldnn_act.cc
index f221ddf5e345..f3966e6566ce 100644
--- a/src/operator/nn/mkldnn/mkldnn_act.cc
+++ b/src/operator/nn/mkldnn/mkldnn_act.cc
@@ -23,6 +23,8 @@
  * \author Da Zheng
 */
 
+#if MXNET_USE_MKLDNN == 1
+
 #include <dmlc/logging.h>
 #include <dmlc/parameter.h>
 #include <mxnet/operator.h>
@@ -33,10 +35,7 @@
 #include <utility>
 #include "../../operator_common.h"
 #include "mkldnn_act-inl.h"
-
-#if MXNET_USE_MKLDNN == 1
-
-#include <mkldnn.hpp>
+#include "./mkldnn_base-inl.h"
 
 namespace mxnet {
 namespace op {
@@ -107,11 +106,9 @@ mkldnn::algorithm GetMKLDNNActAlgo(const LeakyReLUParam& param) {
 
 mkldnn::eltwise_forward::primitive_desc GetActFwdDescImpl(
     const MKLDNNActParam& param, bool is_train,
-    const mkldnn::memory &input_mem, int dtype) {
-  mkldnn::memory::primitive_desc data_mpd = input_mem.get_primitive_desc();
-  mkldnn::memory::desc data_md = data_mpd.desc();
-  auto cpu_engine = data_mpd.get_engine();
-
+    const mkldnn::memory &input_mem) {
+  mkldnn::memory::desc data_md = input_mem.get_desc();
+  auto cpu_engine = CpuEngine::Get()->get_engine();
   auto alg = param.alg;
 
   auto prop = is_train ? mkldnn::prop_kind::forward_training :
@@ -120,28 +117,7 @@ mkldnn::eltwise_forward::primitive_desc GetActFwdDescImpl(
   return mkldnn::eltwise_forward::primitive_desc(desc, cpu_engine);
 }
 
-void MKLDNNActForward::SetNewMem(const mkldnn::memory &data, const mkldnn::memory &output) {
-  if (this->data_ == nullptr)
-    this->data_ = std::make_shared<mkldnn::memory>(data.get_primitive_desc(),
-            data.get_data_handle());
-  else
-    this->data_->set_data_handle(data.get_data_handle());
-
-  CHECK(fwd_pd.dst_primitive_desc() == output.get_primitive_desc());
-  if (this->out_ == nullptr)
-    this->out_ = std::make_shared<mkldnn::memory>(fwd_pd.dst_primitive_desc(),
-            output.get_data_handle());
-  else
-    this->out_->set_data_handle(output.get_data_handle());
-
-  if (this->fwd_ == nullptr) {
-    this->fwd_ = std::shared_ptr<mkldnn::eltwise_forward>(
-        new mkldnn::eltwise_forward(fwd_pd, mkldnn::primitive::at(*this->data_),
-                                    *this->out_));
-  }
-}
-
-const mkldnn::eltwise_forward &MKLDNNActForward::GetFwd() const {
+const inline mkldnn::eltwise_forward &MKLDNNActForward::GetFwd() const {
   return *fwd_;
 }
 
@@ -155,10 +131,9 @@ MKLDNNActForward &GetActForward(const MKLDNNActParam& param,
 #endif
   MKLDNNActSignature key(param);
   key.AddSign(ctx.is_train);
-  key.AddSign(param.alg);
+  key.AddSign(static_cast<int>(param.alg));
   key.AddSign(param.slope);
   key.AddSign(in_data);
-
   auto it = fwds.find(key);
   if (it == fwds.end()) {
     MKLDNNActForward fwd(param, ctx.is_train, in_data, in_mem);
@@ -182,9 +157,9 @@ void MKLDNNActivationForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
 
   auto input_mem = in_buffer.GetMKLDNNData();
   MKLDNNActForward &fwd = GetActForward(param_, ctx, in_buffer, *input_mem);
-  auto out_mem_t = CreateMKLDNNMem(out_data, fwd.fwd_pd.dst_primitive_desc(), req, &in_buffer);
-  fwd.SetNewMem(*input_mem, *out_mem_t.second);
-  stream->RegisterPrim(fwd.GetFwd());
+  auto out_mem_t = CreateMKLDNNMem(out_data, fwd.fwd_pd.dst_desc(), req, &in_buffer);
+  stream->RegisterPrimArgs(fwd.GetFwd(),
+                           {{ MKLDNN_ARG_SRC, *input_mem}, { MKLDNN_ARG_DST, *out_mem_t.second}});
   CommitOutput(out_data, out_mem_t);
   stream->Submit();
 }
@@ -205,32 +180,21 @@ void MKLDNNLeakyReluForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
 
   auto input_mem = in_buffer.GetMKLDNNData();
   MKLDNNActForward &fwd = GetActForward(param_, ctx, in_buffer, *input_mem);
-  auto out_mem_t = CreateMKLDNNMem(out_data, fwd.fwd_pd.dst_primitive_desc(), req, &in_buffer);
-  fwd.SetNewMem(*input_mem, *out_mem_t.second);
-  stream->RegisterPrim(fwd.GetFwd());
+  auto out_mem_t = CreateMKLDNNMem(out_data, fwd.fwd_pd.dst_desc(), req, &in_buffer);
+  stream->RegisterPrimArgs(fwd.GetFwd(),
+                           {{ MKLDNN_ARG_SRC, *input_mem}, { MKLDNN_ARG_DST, *out_mem_t.second}});
   CommitOutput(out_data, out_mem_t);
   stream->Submit();
 }
 
-static mkldnn::eltwise_backward::primitive_desc GetActBwdDescImpl(
+mkldnn::eltwise_backward::primitive_desc GetActBwdDescImpl(
     const MKLDNNActParam &param, const mkldnn::memory &input_mem,
-    const mkldnn::memory &diff_dst_memory, int dtype) {
-  mkldnn::memory::primitive_desc data_mpd = input_mem.get_primitive_desc();
-  mkldnn::memory::desc data_md = data_mpd.desc();
-  mkldnn::memory::desc diff_md = diff_dst_memory.get_primitive_desc().desc();
-  auto cpu_engine = data_mpd.get_engine();
+    const mkldnn::memory &diff_dst_memory) {
+  mkldnn::memory::desc data_md = input_mem.get_desc();
+  mkldnn::memory::desc diff_md = diff_dst_memory.get_desc();
+  auto cpu_engine = CpuEngine::Get()->get_engine();
   auto alg = param.alg;
 
-  MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
-    mkldnn::eltwise_forward::desc fw_desc(mkldnn::prop_kind::forward_training,
-                                          alg, data_md, param.slope);
-    mkldnn::eltwise_forward::primitive_desc fw_pdesc(fw_desc, cpu_engine);
-    mkldnn::eltwise_backward::desc bw_desc(alg, diff_md, data_md, param.slope);
-    mkldnn::eltwise_backward::primitive_desc bw_pdesc(bw_desc, cpu_engine,
-                                                      fw_pdesc);
-    return bw_pdesc;
-  });
-  LOG(FATAL) << "Unsupported data type for MKLDNN activation";
   mkldnn::eltwise_forward::desc fw_desc(mkldnn::prop_kind::forward_training,
                                         alg, data_md, param.slope);
   mkldnn::eltwise_forward::primitive_desc fw_pdesc(fw_desc, cpu_engine);
@@ -240,45 +204,9 @@ static mkldnn::eltwise_backward::primitive_desc GetActBwdDescImpl(
   return bw_pdesc;
 }
 
-class MKLDNNActBackward {
-  std::shared_ptr<mkldnn::eltwise_backward> bwd;
-  std::shared_ptr<mkldnn::memory> data;
-  std::shared_ptr<mkldnn::memory> diff_dst_memory;
-  std::shared_ptr<mkldnn::memory> diff_src_memory;
-
- public:
-  const mkldnn::eltwise_backward::primitive_desc pd;
-
-  explicit MKLDNNActBackward(const MKLDNNActParam &param, const NDArray &data,
-                             const mkldnn::memory &mem,
-                             const mkldnn::memory &diff_dst_memory)
-      : pd(GetActBwdDescImpl(param, mem, diff_dst_memory, data.dtype())) {}
-
-  void SetNewMem(const mkldnn::memory &data,
-                 const mkldnn::memory &diff_dst_memory,
-                 const mkldnn::memory &diff_src_memory) {
-    if (this->bwd != nullptr) {
-      this->data->set_data_handle(data.get_data_handle());
-      this->diff_dst_memory->set_data_handle(diff_dst_memory.get_data_handle());
-      this->diff_src_memory->set_data_handle(diff_src_memory.get_data_handle());
-    } else {
-      this->data = std::shared_ptr<mkldnn::memory>(new mkldnn::memory(
-          data.get_primitive_desc(), data.get_data_handle()));
-      this->diff_dst_memory = std::shared_ptr<mkldnn::memory>(
-          new mkldnn::memory(diff_dst_memory.get_primitive_desc(),
-                             diff_dst_memory.get_data_handle()));
-      this->diff_src_memory = std::shared_ptr<mkldnn::memory>(
-          new mkldnn::memory(diff_src_memory.get_primitive_desc(),
-                             diff_src_memory.get_data_handle()));
-      this->bwd = std::shared_ptr<mkldnn::eltwise_backward>(
-          new mkldnn::eltwise_backward(
-              this->pd, mkldnn::primitive::at(*this->data),
-              *this->diff_dst_memory, *this->diff_src_memory));
-    }
-  }
-
-  const inline mkldnn::eltwise_backward &GetBwd() const { return *bwd; }
-};
+const inline mkldnn::eltwise_backward &MKLDNNActBackward::GetBwd() const {
+  return *bwd_prim_;
+}
 
 static inline MKLDNNActBackward &GetActBackward(const MKLDNNActParam &param,
                                                 const OpContext &ctx,
@@ -327,15 +255,19 @@ void MKLDNNActivationBackward(const nnvm::NodeAttrs& attrs, const OpContext &ctx
   auto input_mem = in_buffer.GetMKLDNNData();
   // We need to make sure the two inputs to eltwise_backward has the same memory
   // descriptor. Otherwise, the perf will suffer.
-  if (input_mem->get_primitive_desc() != diff_dst_memory->get_primitive_desc())
-    input_mem = in_buffer.GetMKLDNNDataReorder(diff_dst_memory->get_primitive_desc());
+  if (input_mem->get_desc() != diff_dst_memory->get_desc())
+    input_mem = in_buffer.GetMKLDNNDataReorder(diff_dst_memory->get_desc());
   MKLDNNActBackward &bwd =
       GetActBackward(param_, ctx, in_buffer, out_buffer, *input_mem);
   MKLDNNStream *stream = MKLDNNStream::Get();
   mkldnn_output_t diff_src_memory =
-      CreateMKLDNNMem(in_grad, bwd.pd.diff_src_primitive_desc(), req);
-  bwd.SetNewMem(*input_mem, *diff_dst_memory, *diff_src_memory.second);
-  stream->RegisterPrim(bwd.GetBwd());
+      CreateMKLDNNMem(in_grad, bwd.bwd_pd.diff_src_desc(), req);
+  mkldnn_args_map_t args = {
+    { MKLDNN_ARG_SRC, *input_mem },
+    { MKLDNN_ARG_DIFF_DST, *diff_dst_memory },
+    { MKLDNN_ARG_DIFF_SRC, *diff_src_memory.second },
+  };
+  stream->RegisterPrimArgs(bwd.GetBwd(), args);
   CommitOutput(in_grad, diff_src_memory);
   stream->Submit();
 }
@@ -367,20 +299,23 @@ void MKLDNNLeakyReluBackward(const nnvm::NodeAttrs& attrs,
   auto input_mem = in_buffer.GetMKLDNNData();
   // We need to make sure the two inputs to eltwise_backward has the same memory
   // descriptor. Otherwise, the perf will suffer.
-  if (input_mem->get_primitive_desc() != diff_dst_memory->get_primitive_desc())
-    input_mem = in_buffer.GetMKLDNNDataReorder(diff_dst_memory->get_primitive_desc());
+  if (input_mem->get_desc() != diff_dst_memory->get_desc())
+    input_mem = in_buffer.GetMKLDNNDataReorder(diff_dst_memory->get_desc());
   MKLDNNActBackward &bwd =
       GetActBackward(param_, ctx, in_buffer, out_buffer, *input_mem);
   MKLDNNStream *stream = MKLDNNStream::Get();
   mkldnn_output_t diff_src_memory =
-      CreateMKLDNNMem(output, bwd.pd.diff_src_primitive_desc(), req);
-  bwd.SetNewMem(*input_mem, *diff_dst_memory, *diff_src_memory.second);
-  stream->RegisterPrim(bwd.GetBwd());
+      CreateMKLDNNMem(output, bwd.bwd_pd.diff_src_desc(), req);
+  mkldnn_args_map_t args = {
+    { MKLDNN_ARG_SRC, *input_mem },
+    { MKLDNN_ARG_DIFF_DST, *diff_dst_memory },
+    { MKLDNN_ARG_DIFF_SRC, *diff_src_memory.second },
+  };
+  stream->RegisterPrimArgs(bwd.GetBwd(), args);
   CommitOutput(output, diff_src_memory);
   stream->Submit();
 }
 
 }  // namespace op
 }  // namespace mxnet
-
 #endif
diff --git a/src/operator/nn/mkldnn/mkldnn_base-inl.h b/src/operator/nn/mkldnn/mkldnn_base-inl.h
index 961aa8b05a84..0f371d174e40 100644
--- a/src/operator/nn/mkldnn/mkldnn_base-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_base-inl.h
@@ -59,7 +59,7 @@
 #include "mxnet/ndarray.h"
 #include "mxnet/op_attr_types.h"
 #include "mxnet/resource.h"
-using namespace mkldnn;
+
 namespace mxnet {
 
 // =====  CpuEngine =======================================
@@ -80,7 +80,7 @@ class CpuEngine {
   mkldnn::engine &get_engine() { return _cpu_engine; }
 
  protected:
-  CpuEngine() : _cpu_engine(mkldnn::engine::cpu, 0) {}
+  CpuEngine() : _cpu_engine(mkldnn::engine::kind::cpu, 0) {}
   ~CpuEngine() {}
 
  private:
@@ -93,27 +93,22 @@ struct data_type_enum {};
 
 template <>
 struct data_type_enum<float> {
-  enum { type = mkldnn::memory::data_type::f32 };
+  enum { type = static_cast<unsigned int>(mkldnn::memory::data_type::f32) };
 };
 
 template <>
 struct data_type_enum<int32_t> {
-  enum { type = mkldnn::memory::data_type::s32 };
-};
-
-template <>
-struct data_type_enum<int16_t> {
-  enum { type = mkldnn::memory::data_type::s16 };
+  enum { type = static_cast<unsigned int>(mkldnn::memory::data_type::s32) };
 };
 
 template <>
 struct data_type_enum<int8_t> {
-  enum { type = mkldnn::memory::data_type::s8 };
+  enum { type = static_cast<unsigned int>(mkldnn::memory::data_type::s8) };
 };
 
 template <>
 struct data_type_enum<uint8_t> {
-  enum { type = mkldnn::memory::data_type::u8 };
+  enum { type = static_cast<unsigned int>(mkldnn::memory::data_type::u8) };
 };
 
 static inline bool SupportMKLDNNArray(int dtype, const mxnet::TShape &shape) {
@@ -206,7 +201,7 @@ static int GetTypeSize(int dtype) {
 
 static inline size_t GetArraySize(const NDArray &arr) {
   if (arr.IsMKLDNNData()) {
-    return arr.GetMKLDNNData()->get_primitive_desc().get_size();
+    return arr.GetMKLDNNData()->get_desc().get_size();
   }
   return arr.shape().Size() * GetTypeSize(arr.dtype());
 }
@@ -223,10 +218,25 @@ static inline mkldnn::memory::data_type get_mkldnn_type(int dtype) {
       return mkldnn::memory::data_type::u8;
     default:
       LOG(FATAL) << "unknown type for MKLDNN";
-      return mkldnn::memory::data_type::data_undef;
+      return mkldnn::memory::data_type::undef;
   }
 }
 
+template<typename T>
+static inline mkldnn::memory::data_type get_mkldnn_type() {
+  return static_cast<mkldnn::memory::data_type>(data_type_enum<T>::type);
+}
+
+static inline mkldnn_data_type_t get_mkldnn_type_t(int dtype) {
+  return static_cast<mkldnn_data_type_t>(get_mkldnn_type(dtype));
+}
+
+template<typename T>
+static inline mkldnn_data_type_t get_mkldnn_type_t() {
+  return static_cast<mkldnn_data_type_t>(data_type_enum<T>::type);
+}
+
+
 static inline int get_mxnet_type(mkldnn_data_type_t dtype) {
   auto mkldnn_dtype = static_cast<mkldnn::memory::data_type>(dtype);
   switch (mkldnn_dtype) {
@@ -261,7 +271,21 @@ inline static mkldnn::memory::desc GetMemDesc(const NDArray &arr, int dtype = -1
   mkldnn::memory::dims dims(ndim);
   dtype = (dtype == -1) ? arr.dtype() : dtype;
   for (size_t i = 0; i < dims.size(); i++) dims[i] = arr.shape()[i];
-  return mkldnn::memory::desc{dims, get_mkldnn_type(dtype), mkldnn::memory::format::any};
+  return mkldnn::memory::desc{dims, get_mkldnn_type(dtype), mkldnn::memory::format_tag::any};
+}
+
+inline static mkldnn::memory::desc GetFCWeightDesc(const NDArray &arr, int dtype = -1) {
+  int ndim = arr.shape().ndim();
+  mkldnn::memory::dims dims(ndim);
+  dtype = (dtype == -1) ? arr.dtype() : dtype;
+  for (size_t i = 0; i < dims.size(); i++) dims[i] = arr.shape()[i];
+  auto format = mkldnn::memory::format_tag::any;
+  // for batch 256 alexnet benchmark test
+  if (dims.size() == 2) {
+    format = mkldnn::memory::format_tag::ab;
+  }
+
+  return mkldnn::memory::desc{dims, get_mkldnn_type(dtype), format};
 }
 
 inline static mkldnn::memory::desc GetWeightDesc(const NDArray &arr,
@@ -286,8 +310,17 @@ inline static mkldnn::memory::desc GetWeightDesc(const NDArray &arr,
           static_cast<int>(arr.shape()[C]), static_cast<int>(arr.shape()[H]),
           static_cast<int>(arr.shape()[W])};
     }
-    return mkldnn::memory::desc{tz, get_mkldnn_type(dtype), mkldnn::memory::format::any};
+    return mkldnn::memory::desc{tz, get_mkldnn_type(dtype), mkldnn::memory::format_tag::any};
+  }
+}
+
+inline static bool CheckMKLDNNInputArrayIsView(const std::vector<NDArray> &inputs) {
+  for (const auto &in : inputs) {
+    if (in.IsView() && in.IsMKLDNNData()) {
+      return true;
+    }
   }
+  return false;
 }
 
 typedef std::shared_ptr<mkldnn::memory> mkldnn_mem_ptr;
@@ -352,19 +385,24 @@ class TmpMemMgr {
     this->est_size = 0;
   }
 
-  mkldnn::memory *Alloc(const mkldnn::memory::primitive_desc &pd);
+  mkldnn::memory *Alloc(const mkldnn::memory::desc &md);
 };
 
+typedef std::unordered_map<int, mkldnn::memory> mkldnn_args_map_t;
 class MKLDNNStream {
-  std::vector<mkldnn::primitive> net;
+  std::vector<std::pair<mkldnn::primitive, mkldnn_args_map_t> > net_prim_args;
   // Here we hold all memory related to the operators in the stream.
   std::vector<std::shared_ptr<const mkldnn::memory> > mem_holder;
+  mkldnn::stream s;
 
  public:
   static MKLDNNStream *Get();
 
-  void RegisterPrim(const mkldnn::primitive &prim) {
-    net.push_back(prim);
+  MKLDNNStream(): s(CpuEngine::Get()->get_engine()) {}
+
+  void RegisterPrimArgs(const mkldnn::primitive &prim,
+                        const mkldnn_args_map_t &args) {
+    net_prim_args.emplace_back(prim, args);
   }
 
   void RegisterMem(std::shared_ptr<const mkldnn::memory> mem) {
@@ -372,7 +410,7 @@ class MKLDNNStream {
   }
 
   bool HasOps() const {
-    return !net.empty();
+    return !net_prim_args.empty();
   }
 
   /*
@@ -381,9 +419,11 @@ class MKLDNNStream {
    * might want to separate mkldnn execution and memory cleanup.
    */
   void Submit(bool cleanup = true) {
-    if (!net.empty()) {
-      mkldnn::stream(mkldnn::stream::kind::eager).submit(net).wait();
-      net.clear();
+    if (!net_prim_args.empty()) {
+      for (auto &v : net_prim_args) {
+        v.first.execute(s, v.second);
+      }
+      net_prim_args.clear();
     }
     if (cleanup)
       Cleanup();
@@ -405,18 +445,18 @@ typedef std::pair<OutDataOp, mkldnn::memory *> mkldnn_output_t;
 void MKLDNNCopy(const mkldnn::memory &mem, const mkldnn::memory* this_mem);
 
 /*
- * Here we want to get MKLDNN memory whose primitive desc is exactly the same as
+ * Here we want to get MKLDNN memory whose desc is exactly the same as
  * the given one. operator== can't guarantee that. == can return true even if
  * the formats are different. I need to double check its format.
  */
 static inline mkldnn::memory *GetMKLDNNExact(
-    const mkldnn::memory *mem, mkldnn::memory::primitive_desc desc) {
-  mkldnn::memory::primitive_desc src_desc = mem->get_primitive_desc();
-  if (desc == src_desc && desc.desc().data.format == src_desc.desc().data.format) {
+    const mkldnn::memory *mem, const mkldnn::memory::desc &desc) {
+  mkldnn::memory::desc src_desc = mem->get_desc();
+  if (desc == src_desc) {
     return const_cast<mkldnn::memory *>(mem);
   } else {
     std::shared_ptr<mkldnn::memory> ret(new mkldnn::memory(
-            desc, mem->get_data_handle()));
+            desc, CpuEngine::Get()->get_engine(), mem->get_data_handle()));
     MKLDNNStream::Get()->RegisterMem(ret);
     return ret.get();
   }
@@ -434,10 +474,10 @@ static inline mkldnn::memory *GetMKLDNNExact(
  * the output back to the output NDArray.
  */
 mkldnn_output_t CreateMKLDNNMem(const NDArray &out_arr,
-                                const mkldnn::memory::primitive_desc &desc,
+                                const mkldnn::memory::desc &desc,
                                 OpReqType req, const NDArray* in_arr = nullptr);
 mkldnn_output_t CreateMKLDNNWeightGrad(const NDArray &out_arr,
-                                       const mkldnn::memory::primitive_desc &desc,
+                                       const mkldnn::memory::desc &desc,
                                        OpReqType req);
 /* This function has to be used with one of the functions above. */
 void CommitOutput(const NDArray &arr, const mkldnn_output_t &res);
@@ -466,13 +506,15 @@ static inline void CreateDefaultInputs(const std::vector<NDArray> &arrs,
 const mkldnn::memory *GetWeights(const NDArray &arr, int num_groups);
 
 const mkldnn::memory *GetWeights(const NDArray &arr,
-                                 const mkldnn::memory::primitive_desc &target_pd,
+                                 const mkldnn::memory::desc &target_md,
                                  int num_groups);
 
-mkldnn_memory_format_t GetDefaultFormat(const mkldnn::memory::desc &desc);
-mkldnn_memory_format_t GetDefaultFormat(int num_dims);
-mkldnn::memory::primitive_desc GetPrimitiveDesc(mkldnn::memory::primitive_desc pd,
-                                                mkldnn_memory_format_t format);
+bool IsDefaultFormat(const mkldnn::memory::desc &desc);
+bool IsMKLDNN(const mkldnn::memory::desc &desc);
+
+mkldnn_format_tag_t GetDefaultFormat(const mkldnn::memory::desc &md);
+mkldnn_format_tag_t GetDefaultFormat(int num_dims);
+mkldnn::memory::desc GetDesc(const mkldnn::memory::desc &md, const mkldnn_format_tag_t &format);
 
 inline bool same_shape(const mxnet::TShape &shape, const mkldnn_dims_t dims, int ndims) {
   if (shape.ndim() != ndims)
@@ -500,7 +542,7 @@ inline bool same_shape(const mxnet::TShape &shape, int dtype,
 }
 
 /*
- * There is a large overhead of getting mkldnn::memory::primitive_desc from
+ * There is a large overhead of getting mkldnn::memory::desc from
  * mkldnn::memory. This class is created to cache the metadata of mkldnn memory
  * to provide a much more lightweight method to access them.
  */
@@ -510,16 +552,15 @@ class MKLDNNMemory {
   size_t size;      // The number of bytes.
 
  public:
-  MKLDNNMemory(mkldnn::memory::primitive_desc pd, void *addr): desc(pd.desc()) {
-    mem.reset(new mkldnn::memory(pd, addr));
-    size = pd.get_size();
+  MKLDNNMemory(mkldnn::memory::desc md, void *addr): desc(md) {
+    mem.reset(new mkldnn::memory(md, CpuEngine::Get()->get_engine(), addr));
+    size = desc.get_size();
   }
 
   explicit MKLDNNMemory(std::shared_ptr<mkldnn::memory> mem): desc(
-      mem->get_primitive_desc().desc()) {
+      mem->get_desc()) {
     this->mem = mem;
-    mkldnn::memory::primitive_desc pd = mem->get_primitive_desc();
-    size = pd.get_size();
+    size = desc.get_size();
   }
 
   void SetDataHandle(void *handle) {
@@ -542,28 +583,29 @@ class MKLDNNMemory {
     return size;
   }
 
-  mkldnn::memory::primitive_desc GetPrimitiveDesc() const {
-    return mem->get_primitive_desc();
+  mkldnn::memory::desc GetDesc() const {
+    return mem->get_desc();
   }
 
-  mkldnn::memory::primitive_desc GetPrimitiveDesc(mkldnn_memory_format_t format) const {
-    return mxnet::GetPrimitiveDesc(mem->get_primitive_desc(), format);
+  mkldnn::memory::desc GetDesc(mkldnn_format_tag_t format) const {
+    mkldnn::memory::dims dims(desc.data.dims, desc.data.dims + desc.data.ndims);
+    mkldnn::memory::data_type cpp_type =
+        static_cast<mkldnn::memory::data_type>(desc.data.data_type);
+    mkldnn::memory::desc data_md(dims, cpp_type,
+        static_cast<mkldnn::memory::format_tag>(format));
+    return data_md;
   }
 
-  mkldnn_memory_format_t GetDefaultFormat() const {
+  mkldnn_format_tag_t GetDefaultFormat() const {
     return mxnet::GetDefaultFormat(desc);
   }
 
-  mkldnn_memory_format_t GetFormat() const {
-    return desc.data.format;
-  }
-
   bool IsMKLDNN() const {
-    return GetFormat() != GetDefaultFormat();
+    return mxnet::IsMKLDNN(desc);
   }
 
-  bool SameFormat(mkldnn::memory::primitive_desc pd) const {
-    return mem->get_primitive_desc() == pd;
+  bool SameFormat(mkldnn::memory::desc md) const {
+    return mem->get_desc() == md;
   }
 
   bool SameFormat(const mxnet::TShape &shape, int dtype) const {
@@ -571,9 +613,8 @@ class MKLDNNMemory {
   }
 
   void ReorderTo(mkldnn::memory *other) const {
-    std::vector<mkldnn::primitive> net;
-    net.push_back(mkldnn::reorder(*mem, *other));
-    mkldnn::stream(mkldnn::stream::kind::eager).submit(net).wait();
+    mkldnn::stream s(CpuEngine::Get()->get_engine());
+    mkldnn::reorder(*mem, *other).execute(s, *mem, *other);
   }
 };
 
@@ -630,12 +671,19 @@ bool MKLDNNStorageType(const nnvm::NodeAttrs &attrs,
     if (debug) check.CopyResult(outputs, indice);
 
 struct MKLDNNPostEltwiseParam {
-  mkldnn::algorithm alg = mkldnn::algorithm::algorithm_undef;
+  mkldnn::algorithm alg = mkldnn::algorithm::undef;
   float scale = 1.f;
   float alpha = 0.f;
   float beta = 1.f;
 };
 
+void MKLDNNRun(mxnet::FComputeEx fn,
+               const nnvm::NodeAttrs &attrs,
+               const mxnet::OpContext &ctx,
+               const std::vector<mxnet::NDArray> &inputs_,
+               const std::vector<mxnet::OpReqType> &req,
+               const std::vector<mxnet::NDArray> &outputs_);
+
 }  // namespace mxnet
 #endif
 #endif  // MXNET_OPERATOR_NN_MKLDNN_MKLDNN_BASE_INL_H_
diff --git a/src/operator/nn/mkldnn/mkldnn_base.cc b/src/operator/nn/mkldnn/mkldnn_base.cc
index fca908fc8e39..1b147c69ba62 100644
--- a/src/operator/nn/mkldnn/mkldnn_base.cc
+++ b/src/operator/nn/mkldnn/mkldnn_base.cc
@@ -54,27 +54,32 @@ void *AlignMem(void *mem, size_t size, size_t alignment, size_t *space) {
   return reinterpret_cast<void *>(addr);
 }
 
-mkldnn::memory *TmpMemMgr::Alloc(const mkldnn::memory::primitive_desc &pd) {
+mkldnn::memory *TmpMemMgr::Alloc(const mkldnn::memory::desc &md) {
   // We need to include the size of the memory used for alignment.
-  this->est_size += pd.get_size() + alignment;
-  void *mem = AlignMem(this->curr_mem, pd.get_size(), alignment, &this->curr_size);
+  this->est_size += md.get_size() + alignment;
+  void *mem = AlignMem(this->curr_mem, md.get_size(), alignment, &this->curr_size);
   if (mem) {
     // The memory is allocated from the temporary memory space in the
     // operator. It'll only become invalid after we exit from the operator.
-    mkldnn_mem_ptr ret(new mkldnn::memory(pd, mem));
+    mkldnn_mem_ptr ret(new mkldnn::memory(md, CpuEngine::Get()->get_engine(), mem));
     MKLDNNStream::Get()->RegisterMem(ret);
     CHECK_EQ(mem, mem);
-    this->curr_size -= pd.get_size();
-    this->curr_mem = static_cast<char *>(mem) + pd.get_size();
+    this->curr_size -= md.get_size();
+    this->curr_mem = static_cast<char *>(mem) + md.get_size();
     return ret.get();
   } else {
-    // If curr_mem has been initialized and we still reach here. It means
-    // the current allocated memory isn't enough.
+    // If curr_mem has been initialized and we still reach here, it means the current
+    // allocated memory isn't enough. But it doesn't matter for multiple invokes of a
+    // operator, as the TmpMemMgr could estimate the space at the first iteration and
+    // then re-requests abundant space from MXNet resource. MKL-DNN could allocate
+    // the space by itself. Thus, we just let it continue for estimating the maximum
+    // required space size. It will be allocated at next call.
     if (this->curr_mem && dmlc::GetEnv("MXNET_MKLDNN_DEBUG", false)) {
-      LOG(WARNING) << "Allocate " << pd.get_size()
-          << " bytes with malloc directly";
+      LOG(WARNING) << "mkl-dnn debug message: The rest of the temporary space is not "
+          << "adequate for allocating " << md.get_size() << " bytes. Thus, mkl-dnn "
+          << "allocate the space by itself.";
     }
-    mkldnn_mem_ptr ret(new mkldnn::memory(pd));
+    mkldnn_mem_ptr ret(new mkldnn::memory(md, CpuEngine::Get()->get_engine()));
     MKLDNNStream::Get()->RegisterMem(ret);
     return ret.get();
   }
@@ -82,85 +87,88 @@ mkldnn::memory *TmpMemMgr::Alloc(const mkldnn::memory::primitive_desc &pd) {
 
 void MKLDNNCopy(const mkldnn::memory &mem, const mkldnn::memory* this_mem) {
   MKLDNNStream *stream = MKLDNNStream::Get();
+  mkldnn::memory::desc from_desc = mem.get_desc();
+  mkldnn::memory::desc this_desc = this_mem->get_desc();
+  mkldnn_format_tag_t from_def_format = GetDefaultFormat(from_desc);
+  mkldnn_format_tag_t this_def_format = GetDefaultFormat(this_desc);
 
-  mkldnn::memory::primitive_desc from_pd = mem.get_primitive_desc();
-  mkldnn::memory::desc from_desc = from_pd.desc();
-  mkldnn::memory::primitive_desc this_pd = this_mem->get_primitive_desc();
-  mkldnn::memory::desc this_desc = this_pd.desc();
-  mkldnn_memory_format_t from_def_format = GetDefaultFormat(from_desc);
-  mkldnn_memory_format_t this_def_format = GetDefaultFormat(this_desc);
-  // It's possible that the memory and the NDArray don't have the same shape.
-  if (!same_shape(this_desc, from_desc)
-      // If the source memory uses the default layout, we can reshape directly.
-      && from_def_format == from_desc.data.format) {
+  if (!same_shape(this_desc, from_desc) && IsDefaultFormat(from_desc)) {
     // In this case, we can simply create a new MKLDNN memory for the required
     // shape.
     mkldnn::memory::dims dims(this_desc.data.dims,
                               this_desc.data.dims + this_desc.data.ndims);
     auto this_dtype = static_cast<mkldnn::memory::data_type>(this_desc.data.data_type);
-    auto this_format = static_cast<mkldnn::memory::format>(GetDefaultFormat(this_desc));
-    mkldnn::memory::desc data_md(dims, this_dtype, this_format);
-    mkldnn::memory::primitive_desc pd(data_md, from_pd.get_engine());
-    mkldnn_mem_ptr tmp_mem(new mkldnn::memory(pd, mem.get_data_handle()));
+    mkldnn::memory::desc data_md(dims, this_dtype,
+                                 static_cast<mkldnn::memory::format_tag>(this_def_format));
+
+    mkldnn_mem_ptr tmp_mem(new mkldnn::memory(data_md, mem.get_engine(), mem.get_data_handle()));
     stream->RegisterMem(tmp_mem);
-    stream->RegisterPrim(mkldnn::reorder(*tmp_mem, *this_mem));
+    std::unordered_map<int, mkldnn::memory> args({{MKLDNN_ARG_FROM, *tmp_mem},
+                                                 {MKLDNN_ARG_TO, *this_mem}});
+    stream->RegisterPrimArgs(mkldnn::reorder(*tmp_mem, *this_mem), args);
   } else if (!same_shape(this_desc, from_desc)) {
     // In this case, the source memory stores data in a customized layout. We
     // need to reorganize the data in memory before we can reshape.
-    mkldnn::memory::primitive_desc def_pd = GetPrimitiveDesc(from_pd, from_def_format);
-    mkldnn::memory *def_mem = TmpMemMgr::Get()->Alloc(def_pd);
-    stream->RegisterPrim(mkldnn::reorder(mem, *def_mem));
+    mkldnn::memory::desc def_desc = GetDesc(from_desc, from_def_format);
+    mkldnn::memory *def_mem = TmpMemMgr::Get()->Alloc(def_desc);
+    std::unordered_map<int, mkldnn::memory> args({{MKLDNN_ARG_FROM, mem},
+                                                 {MKLDNN_ARG_TO, *def_mem}});
+    stream->RegisterPrimArgs(mkldnn::reorder(mem, *def_mem), args);
+
     // Now we can reshape it
-    mkldnn::memory::dims dims(this_desc.data.dims,
-                              this_desc.data.dims + this_desc.data.ndims);
-    auto this_dtype = static_cast<mkldnn::memory::data_type>(this_desc.data.data_type);
-    auto this_format = static_cast<mkldnn::memory::format>(GetDefaultFormat(this_desc));
-    mkldnn::memory::desc data_md(dims, this_dtype, this_format);
-    mkldnn::memory::primitive_desc pd(data_md, from_pd.get_engine());
-    mkldnn_mem_ptr tmp_mem(new mkldnn::memory(pd, def_mem->get_data_handle()));
+    mkldnn_mem_ptr tmp_mem(new mkldnn::memory(this_desc,
+        mem.get_engine(), def_mem->get_data_handle()));
     stream->RegisterMem(tmp_mem);
-    stream->RegisterPrim(mkldnn::reorder(*tmp_mem, *this_mem));
-  } else if (from_pd == this_pd) {
+    args = {{MKLDNN_ARG_FROM, *tmp_mem}, {MKLDNN_ARG_TO, *this_mem}};
+    stream->RegisterPrimArgs(mkldnn::reorder(*tmp_mem, *this_mem), args);
+} else if (this_desc == from_desc) {
+    std::unordered_map<int, mkldnn::memory> args({{MKLDNN_ARG_FROM, mem},
+                                                 {MKLDNN_ARG_TO, *this_mem}});
     // If the layout is the same, we can just copy data.
-    stream->RegisterPrim(mkldnn::reorder(mem, *this_mem));
-  } else {
+    stream->RegisterPrimArgs(mkldnn::reorder(mem, *this_mem), args);
+} else {
     // If both are not using the default layouts. There isn't much we can do,
     // other than reorder data layout directly.
-    if (this_def_format != this_desc.data.format
-        && from_def_format != from_desc.data.format) {
-      stream->RegisterPrim(mkldnn::reorder(mem, *this_mem));
-    } else if (this_def_format == this_desc.data.format) {
+    if (!IsDefaultFormat(this_desc) && !IsDefaultFormat(from_desc)) {
+      std::unordered_map<int, mkldnn::memory> args({{MKLDNN_ARG_FROM, mem},
+                                                   {MKLDNN_ARG_TO, *this_mem}});
+      stream->RegisterPrimArgs(mkldnn::reorder(mem, *this_mem), args);
+    } else if (IsDefaultFormat(this_desc)) {
       // If the dest mem uses the default memory layout, we can simply use
       // the default format of the source memory to improve perf of reorder.
-      mkldnn::memory::primitive_desc pd = GetPrimitiveDesc(from_pd,
-                                                           from_def_format);
-      mkldnn_mem_ptr tmp_mem(new mkldnn::memory(pd, this_mem->get_data_handle()));
+      mkldnn::memory::desc desc = GetDesc(from_desc, from_def_format);
+      mkldnn_mem_ptr tmp_mem(new mkldnn::memory(desc,
+          mem.get_engine(), this_mem->get_data_handle()));
       stream->RegisterMem(tmp_mem);
-      stream->RegisterPrim(mkldnn::reorder(mem, *tmp_mem));
+      std::unordered_map<int, mkldnn::memory> args({{MKLDNN_ARG_FROM, mem},
+                                                   {MKLDNN_ARG_TO, *tmp_mem}});
+      stream->RegisterPrimArgs(mkldnn::reorder(mem, *tmp_mem), args);
     } else {
       // If the src mem uses the default memory layout, we can use
       // the default format of the source memory to improve perf.
-      mkldnn::memory::primitive_desc pd = GetPrimitiveDesc(this_pd,
-                                                           this_def_format);
-      mkldnn_mem_ptr tmp_mem(new mkldnn::memory(pd, mem.get_data_handle()));
+      mkldnn::memory::desc desc = GetDesc(this_desc, this_def_format);
+      mkldnn_mem_ptr tmp_mem(new mkldnn::memory(desc,
+          this_mem->get_engine(), mem.get_data_handle()));
       stream->RegisterMem(tmp_mem);
-      stream->RegisterPrim(mkldnn::reorder(*tmp_mem, *this_mem));
+      std::unordered_map<int, mkldnn::memory> args({{MKLDNN_ARG_FROM, *tmp_mem},
+                                                   {MKLDNN_ARG_TO, *this_mem}});
+      stream->RegisterPrimArgs(mkldnn::reorder(*tmp_mem, *this_mem), args);
     }
   }
 }
 
 bool CanWriteTo(const NDArray &out_arr,
                 const NDArray &in_arr,
-                const mkldnn::memory::primitive_desc &desc) {
+                const mkldnn::memory::desc &desc) {
   auto in_mem = in_arr.GetMKLDNNData();
   bool add_same = in_mem->get_data_handle() == out_arr.GetMKLDNNData()->get_data_handle();
-  bool pdesc_same = out_arr.GetMKLDNNData()->get_primitive_desc() == desc &&
-      in_mem->get_primitive_desc() == desc;
+  bool pdesc_same = out_arr.GetMKLDNNData()->get_desc() == desc &&
+      in_mem->get_desc() == desc;
   return add_same && pdesc_same;
 }
 
 mkldnn_output_t CreateMKLDNNMem(const NDArray &out_arr,
-                                const mkldnn::memory::primitive_desc &desc,
+                                const mkldnn::memory::desc &desc,
                                 OpReqType req,
                                 const NDArray* in_arr) {
   if (kAddTo == req) {
@@ -188,7 +196,7 @@ mkldnn_output_t CreateMKLDNNMem(const NDArray &out_arr,
 }
 
 mkldnn_output_t CreateMKLDNNWeightGrad(const NDArray &out_arr,
-                                       const mkldnn::memory::primitive_desc &desc,
+                                       const mkldnn::memory::desc &desc,
                                        OpReqType req) {
   if (kAddTo == req) {
     auto tmp = TmpMemMgr::Get()->Alloc(desc);
@@ -197,10 +205,8 @@ mkldnn_output_t CreateMKLDNNWeightGrad(const NDArray &out_arr,
     auto tmp = TmpMemMgr::Get()->Alloc(desc);
     return mkldnn_output_t(OutDataOp::CopyBack, tmp);
   } else {
-    auto _desc = desc;
-    auto def_format = GetDefaultFormat(_desc.desc());
     mkldnn::memory *mem = nullptr;
-    if (def_format == _desc.desc().data.format) {
+    if (IsDefaultFormat(desc)) {
       mem = const_cast<NDArray &>(out_arr).CreateMKLDNNData(desc);
     }
     if (mem == nullptr) {
@@ -217,8 +223,8 @@ void CommitOutput(const NDArray &arr, const mkldnn_output_t &res) {
     const_cast<NDArray &>(arr).CopyFrom(*res.second);
   } else if (res.first == AddBack) {
     auto res_memory = res.second;
-    auto target_pd = arr.GetMKLDNNData()->get_primitive_desc();
-    auto mem = arr.GetMKLDNNData(res.second->get_primitive_desc());
+    auto target_pd = arr.GetMKLDNNData()->get_desc();
+    auto mem = arr.GetMKLDNNData(res.second->get_desc());
     if (mem == nullptr) {
       auto tmp_memory = TmpMemMgr::Get()->Alloc(target_pd);
       MKLDNNCopy(*res_memory, tmp_memory);
@@ -232,12 +238,12 @@ void CommitOutput(const NDArray &arr, const mkldnn_output_t &res) {
 const mkldnn::memory *GetWeights(const NDArray &arr, int num_groups) {
   const auto type = get_mkldnn_type(arr.dtype());
   auto tz = mkldnn::memory::dims{0};
-  auto format = mkldnn::memory::format::format_undef;
+  auto format_tag = mkldnn::memory::format_tag::undef;
   auto engine = CpuEngine::Get()->get_engine();
   const int O = 0, I = 1, H = 2, W = 3;
   if (arr.shape().ndim() == 2) {
     tz = mkldnn::memory::dims{static_cast<int>(arr.shape()[O]), static_cast<int>(arr.shape()[I])};
-    format = mkldnn::memory::format::oi;
+    format_tag = mkldnn::memory::format_tag::oi;
   } else if (arr.shape().ndim() == 3) {
     tz = num_groups > 1
              ? mkldnn::memory::dims{num_groups, static_cast<int>(arr.shape()[O] / num_groups),
@@ -246,7 +252,8 @@ const mkldnn::memory *GetWeights(const NDArray &arr, int num_groups) {
              : mkldnn::memory::dims{static_cast<int>(arr.shape()[O]),
                                     static_cast<int>(arr.shape()[I]),
                                     static_cast<int>(arr.shape()[H])};
-    format = num_groups > 1 ? mkldnn::memory::format::goiw : mkldnn::memory::format::oiw;
+    format_tag = num_groups > 1 ? mkldnn::memory::format_tag::goiw
+                                : mkldnn::memory::format_tag::oiw;
   } else if (arr.shape().ndim() == 4) {
     tz = num_groups > 1
              ? mkldnn::memory::dims{num_groups, static_cast<int>(arr.shape()[O] / num_groups),
@@ -256,168 +263,100 @@ const mkldnn::memory *GetWeights(const NDArray &arr, int num_groups) {
              : mkldnn::memory::dims{
                    static_cast<int>(arr.shape()[O]), static_cast<int>(arr.shape()[I]),
                    static_cast<int>(arr.shape()[H]), static_cast<int>(arr.shape()[W])};
-    format = num_groups > 1 ? mkldnn::memory::format::goihw : mkldnn::memory::format::oihw;
+    format_tag = num_groups > 1 ? mkldnn::memory::format_tag::goihw
+                                : mkldnn::memory::format_tag::oihw;
   } else {
     LOG(FATAL) << "The weight array has an unsupported number of dimensions";
   }
-  const auto md = mkldnn::memory::desc{tz, type, format};
-  const auto pd = mkldnn::memory::primitive_desc{md, engine};
-  return arr.GetMKLDNNData(pd);
+  const auto md = mkldnn::memory::desc{tz, type, format_tag};
+  return arr.GetMKLDNNData(md);
 }
 
 const mkldnn::memory *GetWeights(const NDArray &arr,
-                                 const mkldnn::memory::primitive_desc &target_pd, int num_groups) {
-  const mkldnn::memory *mem = arr.GetMKLDNNData(target_pd);
+                                 const mkldnn::memory::desc &target_desc, int num_groups) {
+  const mkldnn::memory *mem = arr.GetMKLDNNData(target_desc);
   // If the weight array already uses the target layout, simply return it directly.
   if (mem) return mem;
   mem = GetWeights(arr, num_groups);
-  if (mem == nullptr) mem = arr.GetMKLDNNDataReorder(target_pd);
-  if (mem->get_primitive_desc() == target_pd) return mem;
+  if (mem == nullptr) mem = arr.GetMKLDNNDataReorder(target_desc);
+  if (mem->get_desc() == target_desc) return mem;
 
-  auto ret = TmpMemMgr::Get()->Alloc(target_pd);
-  MKLDNNStream::Get()->RegisterPrim(mkldnn::reorder(*mem, *ret));
+  auto ret = TmpMemMgr::Get()->Alloc(target_desc);
+  std::unordered_map<int, mkldnn::memory> args({{MKLDNN_ARG_FROM, *mem},
+                                               {MKLDNN_ARG_TO, *ret}});
+  MKLDNNStream::Get()->RegisterPrimArgs(mkldnn::reorder(*mem, *ret), args);
   return ret;
 }
 
-mkldnn_memory_format_t GetDefaultFormat(int num_dims) {
+
+// default: block and dims' stride increase monotonically
+// mkldnn: 1.winograd 2.rnn packed 3. block and dims'stride is not increase monotonically
+bool IsMKLDNN(const mkldnn::memory::desc &desc) {
+  bool rslt = true;
+  if (desc.data.format_kind == mkldnn_blocked) {
+    if (desc.data.format_desc.blocking.inner_nblks == 0) {
+      int i = 0;
+      for (i = 0; i < desc.data.ndims-1; i++) {
+        if (desc.data.format_desc.blocking.strides[i]
+            < desc.data.format_desc.blocking.strides[i + 1]) {
+          break;
+        }
+      }
+      if (i == desc.data.ndims-1) {
+        rslt = false;
+      }
+    }
+  }
+  return rslt;
+}
+
+mkldnn_format_tag_t GetDefaultFormat(int num_dims) {
   switch (num_dims) {
-    case 1: return mkldnn_x;
-    case 2: return mkldnn_nc;
-    case 3: return mkldnn_ncw;
-    case 4: return mkldnn_nchw;
-    case 5: return mkldnn_goihw;
+    case 1: return mkldnn_a;
+    case 2: return mkldnn_ab;
+    case 3: return mkldnn_abc;
+    case 4: return mkldnn_abcd;
+    case 5: return mkldnn_abcde;
+    case 6: return mkldnn_abcdef;
     default:
-      LOG(FATAL) << "Unsupported MKLDNN dimensions: " << num_dims;
-      return mkldnn_format_undef;
+      LOG(FATAL) << "Not implemented dimension (" << num_dims << ") for MKLDNN";
+      return mkldnn_format_tag_undef;
   }
 }
 
-mkldnn_memory_format_t GetDefaultFormat(const mkldnn::memory::desc &desc) {
-  if (desc.data.ndims == 1) {
-    return desc.data.format;
-  } else if (desc.data.ndims == 2) {
-    if (desc.data.format == mkldnn_io)
-      return mkldnn_oi;
-    else
-      return desc.data.format;
-  } else if (desc.data.ndims == 3) {
-    switch (desc.data.format) {
-      case mkldnn_ncw:
-      case mkldnn_nwc:
-      case mkldnn_nCw8c:
-      case mkldnn_nCw16c:
-        return mkldnn_ncw;
-      case mkldnn_oiw:
-      case mkldnn_wio:
-      case mkldnn_Owi8o:
-      case mkldnn_OIw8i8o:
-      case mkldnn_OIw8o8i:
-      case mkldnn_OIw16i16o:
-      case mkldnn_OIw16o16i:
-      case mkldnn_Oiw16o:
-      case mkldnn_Owi16o:
-      case mkldnn_OIw8i16o2i:
-      case mkldnn_OIw8o16i2o:
-      case mkldnn_IOw16o16i:
-        return mkldnn_oiw;
-      default:
-        LOG(FATAL) << "Unknown MKLDNN format for 3 dimensions: " << desc.data.format;
-        return mkldnn_format_undef;
-    }
-  } else if (desc.data.ndims == 4) {
-    switch (desc.data.format) {
-      case mkldnn_nchw:
-      case mkldnn_nhwc:
-      case mkldnn_chwn:
-      case mkldnn_nChw4c:
-      case mkldnn_nChw8c:
-      case mkldnn_nChw16c:
-        return mkldnn_nchw;
-      case mkldnn_oihw:
-      case mkldnn_ihwo:
-      case mkldnn_hwio:
-      case mkldnn_iohw:
-      case mkldnn_oIhw8i:
-      case mkldnn_oIhw16i:
-      case mkldnn_OIhw4i4o:
-      case mkldnn_OIhw8i8o:
-      case mkldnn_hwio_s8s8:
-      case mkldnn_OIhw16i16o:
-      case mkldnn_OIhw4i16o4i:
-      case mkldnn_OIhw4i16o4i_s8s8:
-      case mkldnn_OIhw8i16o2i:
-      case mkldnn_OIhw8o16i2o:
-      case mkldnn_OIhw8o8i:
-      case mkldnn_OIhw16o16i:
-      case mkldnn_IOhw16o16i:
-      case mkldnn_Oihw8o:
-      case mkldnn_Oihw16o:
-      case mkldnn_Ohwi8o:
-      case mkldnn_Ohwi16o:
-      case mkldnn_OhIw16o4i:
-        return mkldnn_oihw;
-      case mkldnn_goiw:
-      case mkldnn_gOwi8o:
-      case mkldnn_gOIw8o8i:
-      case mkldnn_gOIw8i8o:
-      case mkldnn_gOIw16i16o:
-      case mkldnn_gOIw16o16i:
-      case mkldnn_gOiw16o:
-      case mkldnn_gOwi16o:
-      case mkldnn_gOIw8i16o2i:
-      case mkldnn_gOIw8o16i2o:
-      case mkldnn_gIOw16o16i:
-        return mkldnn_goiw;
-      default:
-        LOG(FATAL) << "Unknown MKLDNN format for 4 dimensions: " << desc.data.format;
-        return mkldnn_format_undef;
-    }
-  } else if (desc.data.ndims == 5) {
-    switch (desc.data.format) {
-      case mkldnn_goihw:
-      case mkldnn_giohw:
-      case mkldnn_hwigo:
-      case mkldnn_hwigo_s8s8:
-      case mkldnn_gOIhw4i4o:
-      case mkldnn_gOIhw8i8o:
-      case mkldnn_gOIhw16i16o:
-      case mkldnn_gOIhw4i16o4i:
-      case mkldnn_gOIhw4i16o4i_s8s8:
-      case mkldnn_gOIhw8i16o2i:
-      case mkldnn_gOIhw8o16i2o:
-      case mkldnn_gOIhw8o8i:
-      case mkldnn_gOIhw4o4i:
-      case mkldnn_gOIhw16o16i:
-      case mkldnn_gIOhw16o16i:
-      case mkldnn_gOihw8o:
-      case mkldnn_Goihw8g:
-      case mkldnn_gOihw16o:
-      case mkldnn_Goihw16g:
-      case mkldnn_gOhwi8o:
-      case mkldnn_gOhwi16o:
-      case mkldnn_gOhIw16o4i:
-      case mkldnn_Goihw16g_s8s8:
-        return mkldnn_goihw;
-      default:
-        LOG(FATAL) << "Unknown MKLDNN format for 5 dimensions: " << desc.data.format;
-        return mkldnn_format_undef;
+mkldnn_format_tag_t GetDefaultFormat(const mkldnn::memory::desc &desc) {
+  return GetDefaultFormat(desc.data.ndims);
+}
+
+bool IsDefaultFormat(const mkldnn::memory::desc &desc) {
+  bool rslt = false;
+  if (desc.data.format_kind == mkldnn_blocked) {
+    if (desc.data.format_desc.blocking.inner_nblks == 0) {
+      int i = 0;
+      for (i = 0; i < desc.data.ndims-1; i++) {
+        if (desc.data.format_desc.blocking.strides[i]
+            < desc.data.format_desc.blocking.strides[i + 1]) {
+          break;
+        }
+      }
+      if (i == desc.data.ndims-1) {
+        rslt = true;
+      }
     }
-  } else {
-    LOG(FATAL) << "Unsupported dimensions: " << desc.data.ndims;
-    return mkldnn_format_undef;
   }
+  return rslt;
 }
 
-mkldnn::memory::primitive_desc GetPrimitiveDesc(mkldnn::memory::primitive_desc pd,
-                                                mkldnn_memory_format_t format) {
-  mkldnn::memory::dims dims(pd.desc().data.ndims);
+mkldnn::memory::desc GetDesc(const mkldnn::memory::desc &desc,
+                             const mkldnn_format_tag_t &format) {
+  mkldnn::memory::dims dims(desc.data.ndims);
   for (size_t i = 0; i < dims.size(); i++)
-    dims[i] = pd.desc().data.dims[i];
-  mkldnn::memory::format cpp_format = static_cast<mkldnn::memory::format>(format);
+    dims[i] = desc.data.dims[i];
+  mkldnn::memory::format_tag cpp_format = static_cast<mkldnn::memory::format_tag>(format);
   mkldnn::memory::data_type cpp_type = static_cast<mkldnn::memory::data_type>(
-      pd.desc().data.data_type);
+      desc.data.data_type);
   mkldnn::memory::desc data_md(dims, cpp_type, cpp_format);
-  return mkldnn::memory::primitive_desc(data_md, pd.get_engine());
+  return mkldnn::memory::desc(dims, cpp_type, cpp_format);
 }
 
 template <typename Compute, typename AttrState>
@@ -513,10 +452,11 @@ static bool SimilarArray(const mxnet::NDArray &arr1, const mxnet::NDArray &arr2,
   std::atomic<bool> success(true);
 #pragma omp parallel for
 #ifdef _MSC_VER
-  for (int64_t i = 0; i < arr1.shape().Size(); i++) {
+  for (int64_t i = 0; i < arr1.shape().Size(); i++)
 #else
-  for (size_t i = 0; i < arr1.shape().Size(); i++) {
+  for (size_t i = 0; i < arr1.shape().Size(); i++)
 #endif
+  {
     if (std::abs(data1[i] - data2[i]) > atol + rtol * std::abs(data2[i]))
       success.store(false);
   }
@@ -639,6 +579,33 @@ bool MKLDNNStorageType(const nnvm::NodeAttrs &attrs,
   return dispatched;
 }
 
+inline static const std::vector<NDArray> GetMKLDNNInputArray(const std::vector<NDArray> &inputs) {
+  std::vector<NDArray> ret;
+  ret.reserve(inputs.size());
+  for (const auto &in : inputs) {
+    if (in.IsView() && in.IsMKLDNNData()) {
+      ret.push_back(in.Reorder2Default());
+    } else {
+      ret.push_back(in);
+    }
+  }
+  return ret;
+}
+
+void MKLDNNRun(mxnet::FComputeEx fn,
+               const nnvm::NodeAttrs &attrs,
+               const mxnet::OpContext &ctx,
+               const std::vector<mxnet::NDArray> &inputs,
+               const std::vector<mxnet::OpReqType> &req,
+               const std::vector<mxnet::NDArray> &outputs) {
+  if (CheckMKLDNNInputArrayIsView(inputs)) {
+    const auto mkldnn_inputs = GetMKLDNNInputArray(inputs);
+    fn(attrs, ctx, mkldnn_inputs, req, outputs);
+  } else {
+    fn(attrs, ctx, inputs, req, outputs);
+  }
+}
+
 }  // namespace mxnet
 
 #endif
diff --git a/src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h b/src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h
index 61de08fdde23..26637c7c0b65 100644
--- a/src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h
@@ -44,54 +44,44 @@ typedef mkldnn::batch_normalization_forward::desc               t_bn_f_desc;
 typedef mkldnn::batch_normalization_backward::primitive_desc    t_bn_b_pdesc;
 typedef mkldnn::batch_normalization_backward::desc              t_bn_b_desc;
 
-using mkldnn::use_global_stats;
-using mkldnn::use_scale_shift;
-using mkldnn::forward_training;
-using mkldnn::forward_inference;
-
-inline static unsigned _GetFlags(const std::vector<NDArray> &in_data,
+inline static mkldnn::normalization_flags _GetFlags(const std::vector<NDArray> &in_data,
                                  const std::vector<NDArray> &aux_states,
                                  const BatchNormParam &param, bool is_train_and_not_global_stats) {
-  unsigned flags = 0U;
+  mkldnn::normalization_flags flags = static_cast<mkldnn::normalization_flags>(0U);
   if (in_data.size() == 3U) {
-    flags |= use_scale_shift;
+    flags |=  mkldnn::normalization_flags::use_scale_shift;
   }
 
   // aux_states[0]: inMean
   // aux_states[1]: inVariance
   if (aux_states.size() == 2U && !is_train_and_not_global_stats) {
-    flags |= use_global_stats;
+    flags |=  mkldnn::normalization_flags::use_global_stats;
   }
   return flags;
 }
 
-template <typename DType>
 inline static t_bn_f_pdesc _GetFwd(const mkldnn::memory &data_mem,
                                    bool is_train,
-                                   DType eps,
-                                   unsigned flags) {
-  auto data_mpd   = data_mem.get_primitive_desc();
-  auto data_md    = data_mpd.desc();
-  auto engine     = CpuEngine::Get()->get_engine();
+                                   float eps,
+                                   mkldnn::normalization_flags flags) {
+  auto data_md   = data_mem.get_desc();
+  auto engine    = CpuEngine::Get()->get_engine();
 
   if (is_train) {
-    t_bn_f_desc bnFwd_desc(forward_training, data_md, eps, flags);
+    t_bn_f_desc bnFwd_desc(mkldnn::prop_kind::forward_training, data_md, eps, flags);
     return t_bn_f_pdesc(bnFwd_desc, engine);
   } else {
-    t_bn_f_desc bnFwd_desc(forward_inference, data_md, eps, flags);
+    t_bn_f_desc bnFwd_desc(mkldnn::prop_kind::forward_inference, data_md, eps, flags);
     return t_bn_f_pdesc(bnFwd_desc, engine);
   }
 }
 
-template <typename DType>
 inline static t_bn_b_pdesc _GetBwd(const mkldnn::memory &data_mem,
                                    const mkldnn::memory &diff_mem,
-                                   DType eps,
-                                   unsigned flags) {
-  auto data_mpd   = data_mem.get_primitive_desc();
-  auto data_md    = data_mpd.desc();
-  auto diff_mpd   = diff_mem.get_primitive_desc();
-  auto diff_md    = diff_mpd.desc();
+                                   float eps,
+                                   mkldnn::normalization_flags flags) {
+  auto data_md    = data_mem.get_desc();
+  auto diff_md    = diff_mem.get_desc();
   auto engine     = CpuEngine::Get()->get_engine();
 
   t_bn_b_desc  bnBwd_desc(mkldnn::prop_kind::backward, diff_md, data_md, eps, flags);
@@ -101,18 +91,15 @@ inline static t_bn_b_pdesc _GetBwd(const mkldnn::memory &data_mem,
 typedef ParamOpSign<BatchNormParam> MKLDNNBNSignature;
 
 class MKLDNNBNForward {
-  std::shared_ptr<const mkldnn::memory> data_m;
   std::shared_ptr<const mkldnn::memory> weight_m;
-  std::shared_ptr<const mkldnn::memory> out_m;
-  std::shared_ptr<const mkldnn::memory> mean_m;
-  std::shared_ptr<const mkldnn::memory> var_m;
   std::shared_ptr<mkldnn::batch_normalization_forward> fwd;
   bool is_train_and_not_global_stats;
   t_bn_f_pdesc pd;
 
  public:
   MKLDNNBNForward(const t_bn_f_pdesc &_pd, bool is_train_and_not_global_stats): pd(_pd) {
-    weight_m.reset(new mkldnn::memory(pd.weights_primitive_desc()));
+    weight_m.reset(new mkldnn::memory(pd.weights_desc(), CpuEngine::Get()->get_engine()));
+    fwd.reset(new mkldnn::batch_normalization_forward(pd));
     this->is_train_and_not_global_stats = is_train_and_not_global_stats;
   }
 
@@ -124,59 +111,6 @@ class MKLDNNBNForward {
     return pd;
   }
 
-  const mkldnn::memory &GetMean() const {
-    return *mean_m;
-  }
-
-  const mkldnn::memory &GetVar() const {
-    return *var_m;
-  }
-
-  void SetDataHandle(const mkldnn::memory *data, const mkldnn::memory *mean,
-                     const mkldnn::memory *var, const mkldnn::memory *out) {
-    if (data_m) {
-      data_m->set_data_handle(data->get_data_handle());
-    } else {
-      data_m.reset(new mkldnn::memory(data->get_primitive_desc(),
-                                      data->get_data_handle()));
-    }
-    if (out_m) {
-      out_m->set_data_handle(out->get_data_handle());
-    } else {
-      out_m.reset(new mkldnn::memory(out->get_primitive_desc(),
-                                     out->get_data_handle()));
-    }
-    if (mean_m) {
-      mean_m->set_data_handle(mean->get_data_handle());
-    } else {
-      mean_m.reset(new mkldnn::memory(mean->get_primitive_desc(),
-                                      mean->get_data_handle()));
-    }
-    if (var_m) {
-      var_m->set_data_handle(var->get_data_handle());
-    } else {
-      var_m.reset(new mkldnn::memory(var->get_primitive_desc(),
-                                     var->get_data_handle()));
-    }
-
-    if (fwd == nullptr) {
-      if (!is_train_and_not_global_stats)
-        fwd.reset(new mkldnn::batch_normalization_forward(
-                pd, *data_m, mkldnn::primitive::at(*mean_m),
-                mkldnn::primitive::at(*var_m), *weight_m, *out_m));
-      else
-        fwd.reset(new mkldnn::batch_normalization_forward(
-                pd, mkldnn::primitive::at(*data_m),
-                mkldnn::primitive::at(*weight_m), *out_m,
-                *mean_m, *var_m));
-    }
-  }
-
-  void SetDataHandle(const NDArray &data, const NDArray &mean,
-                     const NDArray &var, const mkldnn::memory &out) {
-    SetDataHandle(data.GetMKLDNNData(), mean.GetMKLDNNData(), var.GetMKLDNNData(), &out);
-  }
-
   const mkldnn::batch_normalization_forward &GetFwd() const {
     return *fwd;
   }
@@ -185,7 +119,7 @@ class MKLDNNBNForward {
 template<typename DType>
 static MKLDNNBNForward &GetBNForward(const BatchNormParam& param,
                                      const OpContext &ctx, const mkldnn::memory *data_mem,
-                                     unsigned flags) {
+                                     mkldnn::normalization_flags flags) {
 #if DMLC_CXX11_THREAD_LOCAL
   static thread_local std::unordered_map<MKLDNNBNSignature, MKLDNNBNForward, OpHash> fwds;
 #else
@@ -193,13 +127,12 @@ static MKLDNNBNForward &GetBNForward(const BatchNormParam& param,
 #endif
   MKLDNNBNSignature key(param);
   key.AddSign(ctx.is_train);
-  key.AddSign(param.use_global_stats);
   key.AddSign(*data_mem);
 
   auto it = fwds.find(key);
   if (it == fwds.end()) {
     auto fwd_pd = _GetFwd(*data_mem, ctx.is_train,
-                          (DType) param.eps, flags);
+                          param.eps, flags);
     MKLDNNBNForward fwd(fwd_pd, ctx.is_train && !param.use_global_stats);
     it = AddToCache(&fwds, key, fwd);
   }
@@ -209,7 +142,7 @@ static MKLDNNBNForward &GetBNForward(const BatchNormParam& param,
 template<typename DType>
 static MKLDNNBNForward &GetBNForward(const BatchNormParam& param,
                                      const OpContext &ctx, const NDArray &in_data,
-                                     unsigned flags) {
+                                     mkldnn::normalization_flags flags) {
   return GetBNForward<DType>(param, ctx, in_data.GetMKLDNNData(), flags);
 }
 
@@ -220,18 +153,20 @@ void MKLDNNBatchNormForward(const OpContext &ctx, const BatchNormParam &param,
                             const std::vector<NDArray>   &out_data,
                             const std::vector<NDArray>   &aux_states) {
   TmpMemMgr::Get()->Init(ctx.requested[batchnorm::kTempSpace]);
-  unsigned flags = _GetFlags(in_data, aux_states, param, ctx.is_train && !param.use_global_stats);
+  mkldnn::normalization_flags flags = _GetFlags(in_data,
+                                                aux_states,
+                                                param,
+                                                ctx.is_train && !param.use_global_stats);
   const NDArray &data = in_data[batchnorm::kData];
-
   auto &fwd = GetBNForward<DType>(param, ctx, data, flags);
-  const NDArray &out  = out_data[batchnorm::kOut];
+  const NDArray &out = out_data[batchnorm::kOut];
 
   // for output memory
-  auto out_mem = const_cast<NDArray &>(out).CreateMKLDNNData(fwd.GetPd().dst_primitive_desc());
+  auto out_mem = const_cast<NDArray &>(out).CreateMKLDNNData(fwd.GetPd().dst_desc());
 
   // mxnet will always use scale shift.
   // But if fix_gamma is true, then all scale elements will be set to 1.0f
-  if (flags & use_scale_shift) {
+  if (static_cast<int>(flags) & static_cast<int>(mkldnn::normalization_flags::use_scale_shift)) {
     const NDArray &gamma    = in_data[batchnorm::kGamma];
     const NDArray &beta     = in_data[batchnorm::kBeta];
     CHECK_EQ(gamma.storage_type(), mxnet::kDefaultStorage);
@@ -241,7 +176,7 @@ void MKLDNNBatchNormForward(const OpContext &ctx, const BatchNormParam &param,
     DType* weight_buf = reinterpret_cast<DType *>(weight_mem.get_data_handle());
 
     nnvm::dim_t channels_ = data.shape()[1];
-    CHECK(weight_mem.get_primitive_desc().get_size() == channels_ * sizeof(DType) * 2);
+    CHECK(weight_mem.get_desc().get_size() == channels_ * sizeof(DType) * 2);
     DType* weight_ptr = gamma.data().dptr<DType>();
     DType* bias_ptr = beta.data().dptr<DType>();
     if (!param.fix_gamma) {
@@ -249,17 +184,22 @@ void MKLDNNBatchNormForward(const OpContext &ctx, const BatchNormParam &param,
       memcpy(&weight_buf[channels_], bias_ptr, sizeof(weight_buf[0]) * channels_);
     } else if (IsBNWriting(req[batchnorm::kGamma])) {
       for (int i = 0; i < channels_; i++) {
-        weight_buf[i] = (DType)1.0f;
-        weight_ptr[i] = (DType)1.0f;
+        weight_buf[i] = static_cast<DType>(1.0f);
+        weight_ptr[i] = static_cast<DType>(1.0f);
         weight_buf[channels_ + i] = bias_ptr[i];  // bias
       }
     } else {
       for (int i = 0; i < channels_; i++) {
-        weight_buf[i] = (DType)1.0f;
+        weight_buf[i] = static_cast<DType>(1.0f);
         weight_buf[channels_ + i] = bias_ptr[i];  // bias
       }
     }
 
+    mkldnn_args_map_t net_args;
+    net_args[MKLDNN_ARG_SRC] = *data.GetMKLDNNData();
+    net_args[MKLDNN_ARG_SCALE_SHIFT] = weight_mem;
+    net_args[MKLDNN_ARG_DST] = *out_mem;
+
     if (!ctx.is_train || param.use_global_stats) {
       DType* omean    = out_data[batchnorm::kMean].data().dptr<DType>();
       DType* ovar     = out_data[batchnorm::kVar].data().dptr<DType>();
@@ -270,26 +210,21 @@ void MKLDNNBatchNormForward(const OpContext &ctx, const BatchNormParam &param,
         omean[i] = inmean[i];
         ovar[i] = VARIANCE_TO_INVSTD(invar[i], param.eps);
       }
-
-      fwd.SetDataHandle(data, aux_states[batchnorm::kMovingMean],
-                        aux_states[batchnorm::kMovingVar],
-                        *out_mem);
-      MKLDNNStream::Get()->RegisterPrim(fwd.GetFwd());
+      net_args[MKLDNN_ARG_MEAN] = *(aux_states[batchnorm::kMovingMean].GetMKLDNNData());
+      net_args[MKLDNN_ARG_VARIANCE] = *(aux_states[batchnorm::kMovingVar].GetMKLDNNData());
+      MKLDNNStream::Get()->RegisterPrimArgs(fwd.GetFwd(), net_args);
       MKLDNNStream::Get()->Submit();
     } else {  // training
       const NDArray &outMean  = out_data[batchnorm::kMean];
       const NDArray &outVar   = out_data[batchnorm::kVar];
-      DType* omean    = outMean.data().dptr<DType>();
-      DType* ovar     = outVar.data().dptr<DType>();
-
-      fwd.SetDataHandle(data, outMean, outVar, *out_mem);
-      MKLDNNStream::Get()->RegisterPrim(fwd.GetFwd());
+      net_args[MKLDNN_ARG_MEAN] = *(outMean.GetMKLDNNData());
+      net_args[MKLDNN_ARG_VARIANCE] = *(outVar.GetMKLDNNData());
+      MKLDNNStream::Get()->RegisterPrimArgs(fwd.GetFwd(), net_args);
       MKLDNNStream::Get()->Submit();
-      DType* mean_mem_ptr = reinterpret_cast<DType*>(fwd.GetMean().get_data_handle());
-      DType* var_mem_ptr  = reinterpret_cast<DType*>(fwd.GetVar().get_data_handle());
+
+      DType* ovar = outVar.data().dptr<DType>();
       for (int i = 0; i < channels_; i++) {
-        omean[i] = mean_mem_ptr[i];
-        ovar[i]  = VARIANCE_TO_INVSTD(var_mem_ptr[i], param.eps);
+        ovar[i] = VARIANCE_TO_INVSTD(ovar[i], param.eps);
       }
     }
   } else {  // no input gamma and beta
@@ -299,11 +234,6 @@ void MKLDNNBatchNormForward(const OpContext &ctx, const BatchNormParam &param,
 
 class MKLDNNBNBackward {
   std::shared_ptr<mkldnn::batch_normalization_backward> bwd;
-  std::shared_ptr<mkldnn::memory> data_m;
-  std::shared_ptr<mkldnn::memory> diff_m;
-  std::shared_ptr<mkldnn::memory> gradi_m;
-  std::shared_ptr<mkldnn::memory> mean_m;
-  std::shared_ptr<mkldnn::memory> var_m;
   const std::shared_ptr<mkldnn::memory> weight_m;
   const std::shared_ptr<mkldnn::memory> gradw_m;
 
@@ -311,41 +241,16 @@ class MKLDNNBNBackward {
   const t_bn_b_pdesc pd;
 
   explicit MKLDNNBNBackward(const t_bn_b_pdesc &_pd)
-      : weight_m(new mkldnn::memory(_pd.weights_primitive_desc())),
-        gradw_m(new mkldnn::memory(_pd.diff_weights_primitive_desc())),
-        pd(_pd) {}
+      : weight_m(new mkldnn::memory(_pd.weights_desc(), CpuEngine::Get()->get_engine())),
+        gradw_m(new mkldnn::memory(_pd.diff_weights_desc(), CpuEngine::Get()->get_engine())),
+        pd(_pd) {
+    bwd.reset(new mkldnn::batch_normalization_backward(pd));
+  }
 
   const mkldnn::memory &GetWeight() const { return *weight_m; }
 
   const mkldnn::memory &GetGradw() const { return *gradw_m; }
 
-  void SetDataHandle(const mkldnn::memory &data, const mkldnn::memory &diff,
-                     const NDArray &mean, const mkldnn::memory &var,
-                     const mkldnn::memory &gradi) {
-    auto mean_ptr = mean.data().dptr_;
-    if (bwd == nullptr) {
-      data_m.reset(new mkldnn::memory(data.get_primitive_desc(),
-                                      data.get_data_handle()));
-      diff_m.reset(new mkldnn::memory(diff.get_primitive_desc(),
-                                      diff.get_data_handle()));
-      gradi_m.reset(new mkldnn::memory(gradi.get_primitive_desc(),
-                                       gradi.get_data_handle()));
-      mean_m.reset(new mkldnn::memory(pd.mean_primitive_desc(), mean_ptr));
-      var_m.reset(new mkldnn::memory(pd.variance_primitive_desc(),
-                                     var.get_data_handle()));
-      bwd.reset(new mkldnn::batch_normalization_backward(
-          pd, *data_m, mkldnn::primitive::at(*mean_m),
-          mkldnn::primitive::at(*var_m), *diff_m, *weight_m, *gradi_m,
-          *gradw_m));
-    } else {
-      data_m->set_data_handle(data.get_data_handle());
-      diff_m->set_data_handle(diff.get_data_handle());
-      gradi_m->set_data_handle(gradi.get_data_handle());
-      mean_m->set_data_handle(mean_ptr);
-      var_m->set_data_handle(var.get_data_handle());
-    }
-  }
-
   const mkldnn::batch_normalization_backward &GetBwd() const { return *bwd; }
 };
 
@@ -353,7 +258,7 @@ template <typename DType>
 static MKLDNNBNBackward &GetBNBackward(
     const BatchNormParam &param, const OpContext &ctx, const NDArray &in_data,
     const mkldnn::memory &in_mem, const NDArray &diff_data,
-    const mkldnn::memory &diff_mem, unsigned flags) {
+    const mkldnn::memory &diff_mem, mkldnn::normalization_flags flags) {
 #if DMLC_CXX11_THREAD_LOCAL
   static thread_local std::unordered_map<MKLDNNBNSignature, MKLDNNBNBackward, OpHash> bwds;
 #else
@@ -385,7 +290,10 @@ void MKLDNNBatchNormBackward(const OpContext &ctx, const BatchNormParam &param,
   CHECK_EQ(in_data.size(), 3U);
   CHECK_EQ(out_data.size(), 3U);
   CHECK_EQ(in_grad.size(), 3U);
-  unsigned flags = _GetFlags(in_data, aux_states, param, ctx.is_train && !param.use_global_stats);
+  mkldnn::normalization_flags flags = _GetFlags(in_data,
+                                                aux_states,
+                                                param,
+                                                ctx.is_train && !param.use_global_stats);
 
   const NDArray &data         = in_data[batchnorm::kData];
   const NDArray &diff         = out_grad[batchnorm::kOut];
@@ -405,13 +313,13 @@ void MKLDNNBatchNormBackward(const OpContext &ctx, const BatchNormParam &param,
   // MKLDNN batchnorm should run on special layouts. If one of them isn't, we
   // should reorder them.
   if (data.IsDefaultData())
-    data_mem = data.GetMKLDNNDataReorder(diff_mem->get_primitive_desc());
+    data_mem = data.GetMKLDNNDataReorder(diff_mem->get_desc());
   else if (diff.IsDefaultData())
-    diff_mem = diff.GetMKLDNNDataReorder(data_mem->get_primitive_desc());
+    diff_mem = diff.GetMKLDNNDataReorder(data_mem->get_desc());
   auto &bwd = GetBNBackward<DType>(param, ctx, data, *data_mem, diff, *diff_mem, flags);
-  auto gradi_mem = const_cast<NDArray &>(gradIn).CreateMKLDNNData(data_mem->get_primitive_desc());
+  auto gradi_mem = const_cast<NDArray &>(gradIn).CreateMKLDNNData(data_mem->get_desc());
 
-  if (flags & use_scale_shift) {
+  if (static_cast<int>(flags) & static_cast<int>(mkldnn::normalization_flags::use_scale_shift)) {
     const NDArray &gamma    = in_data[batchnorm::kGamma];
     const NDArray &beta     = in_data[batchnorm::kBeta];
     DType *weight_buf = reinterpret_cast<DType *>(bwd.GetWeight().get_data_handle());
@@ -420,20 +328,27 @@ void MKLDNNBatchNormBackward(const OpContext &ctx, const BatchNormParam &param,
       if (!param.fix_gamma)
         weight_buf[i] = (gamma.data().dptr<DType>())[i];   // weight
       else
-        weight_buf[i] = (DType)1.0f;
+        weight_buf[i] = static_cast<DType>(1.0f);
     }
 
     for (int i = 0; i < channels_; i++) {
       weight_buf[channels_ + i] = (beta.data().dptr<DType>())[i];  // bias
     }
 
+    mkldnn_args_map_t net_args;
+    net_args[MKLDNN_ARG_SRC] = *data_mem;
+    net_args[MKLDNN_ARG_DIFF_SRC] = *gradi_mem;
+    net_args[MKLDNN_ARG_SCALE_SHIFT] = bwd.GetWeight();
+    net_args[MKLDNN_ARG_DIFF_SCALE_SHIFT] = bwd.GetGradw();
+    net_args[MKLDNN_ARG_DIFF_DST] = *diff_mem;
+
     // training but no input mean and variance
     if (ctx.is_train && !param.use_global_stats) {
       DType* moving_mean_ptr  = reinterpret_cast<DType *>(moving_mean.data().dptr<DType>());
       DType* moving_var_ptr   = reinterpret_cast<DType *>(moving_var.data().dptr<DType>());
       DType* out_mean_ptr     = reinterpret_cast<DType *>(out_mean.data().dptr<DType>());
       DType* out_var_ptr      = reinterpret_cast<DType *>(out_var.data().dptr<DType>());
-      mkldnn::memory var_mem(bwd.pd.variance_primitive_desc());
+      mkldnn::memory var_mem(bwd.pd.variance_desc(), CpuEngine::Get()->get_engine());
       DType *tmp_var_ptr = reinterpret_cast<DType *>(var_mem.get_data_handle());
 
       DType minus_mom = (1.0f - param.momentum);
@@ -445,13 +360,14 @@ void MKLDNNBatchNormBackward(const OpContext &ctx, const BatchNormParam &param,
         moving_var_ptr[i] = moving_var_ptr[i] * param.momentum +
                             variance * minus_mom;
       }
-      bwd.SetDataHandle(*data_mem, *diff_mem, out_mean, var_mem, *gradi_mem);
-      MKLDNNStream::Get()->RegisterPrim(bwd.GetBwd());
+      net_args[MKLDNN_ARG_MEAN] = *(out_mean.GetMKLDNNData());
+      net_args[MKLDNN_ARG_VARIANCE] = var_mem;
+      MKLDNNStream::Get()->RegisterPrimArgs(bwd.GetBwd(), net_args);
       MKLDNNStream::Get()->Submit();
     } else {
-      bwd.SetDataHandle(*data_mem, *diff_mem, moving_mean,
-                        *moving_var.GetMKLDNNData(), *gradi_mem);
-      MKLDNNStream::Get()->RegisterPrim(bwd.GetBwd());
+      net_args[MKLDNN_ARG_MEAN] =  *(moving_mean.GetMKLDNNData());
+      net_args[MKLDNN_ARG_VARIANCE] = *(moving_var.GetMKLDNNData());
+      MKLDNNStream::Get()->RegisterPrimArgs(bwd.GetBwd(), net_args);
       MKLDNNStream::Get()->Submit();
     }
 
diff --git a/src/operator/nn/mkldnn/mkldnn_concat-inl.h b/src/operator/nn/mkldnn/mkldnn_concat-inl.h
index d3866cc3d23d..ff47ef35f98f 100644
--- a/src/operator/nn/mkldnn/mkldnn_concat-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_concat-inl.h
@@ -20,7 +20,7 @@
 /*!
  * \file mkldnn_concat-inl.h
  * \brief
- * \author Wenting Jiang
+ * \author
 */
 #ifndef MXNET_OPERATOR_NN_MKLDNN_MKLDNN_CONCAT_INL_H_
 #define MXNET_OPERATOR_NN_MKLDNN_MKLDNN_CONCAT_INL_H_
@@ -40,25 +40,20 @@ class MKLDNNConcatFwd {
  public:
   mkldnn::concat::primitive_desc fwd_pd;
 
-  MKLDNNConcatFwd(int concat_dim, const std::vector<mkldnn::memory::primitive_desc> &data_md)
-      : fwd_pd(concat_dim, data_md) {
-    data.resize(data_md.size());
+  MKLDNNConcatFwd(int concat_dim, const std::vector<mkldnn::memory::desc> &data_md)
+      : fwd_pd(concat_dim, data_md, CpuEngine::Get()->get_engine()) {
+      fwd_ = std::make_shared<mkldnn::concat>(fwd_pd);
   }
 
-  void SetNewMem(const std::vector<const mkldnn::memory *> &in_data, const mkldnn::memory &output);
-
   const mkldnn::concat &GetFwd() const;
 
  private:
-  std::shared_ptr<mkldnn::concat> fwd;
-  std::vector<std::shared_ptr<mkldnn::memory>> data;
-  std::vector<mkldnn::primitive::at> data_mem;
-  std::shared_ptr<mkldnn::memory> out;
+  std::shared_ptr<mkldnn::concat> fwd_;
 };
 
 static MKLDNNConcatFwd &GetConcatForward(
     int concat_dim, const std::vector<NDArray> &in_data,
-    const std::vector<mkldnn::memory::primitive_desc> &data_md) {
+    const std::vector<mkldnn::memory::desc> &data_md) {
 #if DMLC_CXX11_THREAD_LOCAL
   static thread_local std::unordered_map<OpSignature, MKLDNNConcatFwd, OpHash> fwds;
 #else
diff --git a/src/operator/nn/mkldnn/mkldnn_concat.cc b/src/operator/nn/mkldnn/mkldnn_concat.cc
index 7b266efc2a14..aa30ffc557a1 100644
--- a/src/operator/nn/mkldnn/mkldnn_concat.cc
+++ b/src/operator/nn/mkldnn/mkldnn_concat.cc
@@ -20,7 +20,7 @@
 /*!
  * \file mkldnn_concat.cc
  * \brief
- * \author Wenting Jiang
+ * \author
 */
 
 #if MXNET_USE_MKLDNN == 1
@@ -29,28 +29,7 @@
 namespace mxnet {
 namespace op {
 
-void MKLDNNConcatFwd::SetNewMem(const std::vector<const mkldnn::memory *> &in_data,
-                                const mkldnn::memory &output) {
-  CHECK_EQ(in_data.size(), data.size());
-  for (size_t i = 0; i < data.size(); i++) {
-    if (this->data[i] == nullptr) {
-      this->data[i] = std::shared_ptr<mkldnn::memory>(
-          new mkldnn::memory(in_data[i]->get_primitive_desc(), in_data[i]->get_data_handle()));
-      this->data_mem.push_back(*this->data[i]);
-    } else {
-      this->data[i]->set_data_handle(in_data[i]->get_data_handle());
-    }
-  }
-  if (this->out == nullptr)
-    this->out = std::shared_ptr<mkldnn::memory>(
-        new mkldnn::memory(fwd_pd.dst_primitive_desc(), output.get_data_handle()));
-  else
-    this->out->set_data_handle(output.get_data_handle());
-
-  if (this->fwd == nullptr) fwd.reset(new mkldnn::concat(fwd_pd, data_mem, *out));
-}
-
-const mkldnn::concat &MKLDNNConcatFwd::GetFwd() const { return *fwd; }
+const mkldnn::concat &MKLDNNConcatFwd::GetFwd() const { return *fwd_; }
 
 void MKLDNNConcatForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
                          const std::vector<NDArray> &in_data,
@@ -58,24 +37,28 @@ void MKLDNNConcatForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
                          const std::vector<NDArray> &out_data) {
   TmpMemMgr::Get()->Init(ctx.requested[concat_enum::kTempSpace]);
   const ConcatParam& param = nnvm::get<ConcatParam>(attrs.parsed);
-  int num_in_data = param.num_args;
-  int concat_dim = param.dim;
-  std::vector<mkldnn::memory::primitive_desc> data_md;
+  const int num_in_data = param.num_args;
+  const int concat_dim = param.dim;
+  std::vector<mkldnn::memory::desc> data_md;
   std::vector<const mkldnn::memory *> data_mem;
   data_md.reserve(num_in_data);
   data_mem.reserve(num_in_data);
   for (int i = 0; i < num_in_data; i++) {
     const mkldnn::memory *tmp_mem = in_data[i].GetMKLDNNData();
-    mkldnn::memory::primitive_desc tmp_pd = tmp_mem->get_primitive_desc();
-    data_md.push_back(tmp_pd);
+    mkldnn::memory::desc tmp_md = tmp_mem->get_desc();
+    data_md.push_back(tmp_md);
     data_mem.push_back(tmp_mem);
   }
   MKLDNNConcatFwd &fwd = GetConcatForward(concat_dim, in_data, data_md);
   mxnet::mkldnn_output_t out_mem = CreateMKLDNNMem(out_data[concat_enum::kOut],
-                                                   fwd.fwd_pd.dst_primitive_desc(),
+                                                   fwd.fwd_pd.dst_desc(),
                                                    req[concat_enum::kOut]);
-  fwd.SetNewMem(data_mem, *out_mem.second);
-  MKLDNNStream::Get()->RegisterPrim(fwd.GetFwd());
+  std::unordered_map<int, mkldnn::memory> net_args;
+  net_args.insert({MKLDNN_ARG_DST, *out_mem.second});
+  for (int i = 0; i < num_in_data; i++) {
+    net_args.insert({MKLDNN_ARG_MULTIPLE_SRC + i, *data_mem[i]});
+  }
+  MKLDNNStream::Get()->RegisterPrimArgs(fwd.GetFwd(), net_args);
   CommitOutput(out_data[concat_enum::kOut], out_mem);
   MKLDNNStream::Get()->Submit();
 }
@@ -86,11 +69,9 @@ void MKLDNNConcatBackward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
                           const std::vector<NDArray>& outputs) {
   TmpMemMgr::Get()->Init(ctx.requested[concat_enum::kTempSpace]);
   const ConcatParam& param = nnvm::get<ConcatParam>(attrs.parsed);
-  int num_in_data = param.num_args;
-  int axis_ = param.dim;
-  auto engine = CpuEngine::Get()->get_engine();
-  auto gz_mem = inputs[0].GetMKLDNNData();
-  mkldnn::memory::primitive_desc gz_pd = gz_mem->get_primitive_desc();
+  const int num_in_data = param.num_args;
+  const int axis = param.dim;
+  const auto gradz_mem = inputs[0].GetMKLDNNData();
   /* init the offset */
   mkldnn::memory::dims offsets(outputs[0].shape().ndim());
   for (auto &v : offsets) {
@@ -99,22 +80,25 @@ void MKLDNNConcatBackward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
 
   for (int i = 0; i < num_in_data; i++) {
     mkldnn::memory::dims diff_src_tz(outputs[i].shape().begin(), outputs[i].shape().end());
-    auto diff_src_mpd = outputs[i].GetMKLDNNData()->get_primitive_desc();
-    auto gradi_mem_ = CreateMKLDNNMem(outputs[i], diff_src_mpd, req[i]);
-    // create view from gy to gxs[i]
-    std::shared_ptr<mkldnn::view::primitive_desc> view_pd;
-    view_pd.reset(new mkldnn::view::primitive_desc(gz_pd, diff_src_tz, offsets));
-    // create reorder primitive from gy to gxs[i]
-    mkldnn::reorder::primitive_desc reorder_pd(
-        view_pd.get()->dst_primitive_desc(), diff_src_mpd);
-    offsets[axis_] += diff_src_tz[axis_];
-    MKLDNNStream::Get()->RegisterPrim(mkldnn::reorder(
-            reorder_pd, *gz_mem, *gradi_mem_.second));
-    CommitOutput(outputs[i], gradi_mem_);
+    auto diff_src_md = outputs[i].GetMKLDNNData()->get_desc();
+    auto gradi_mem = CreateMKLDNNMem(outputs[i], diff_src_md, req[i]);
+
+    auto from_md = gradz_mem->get_desc().submemory_desc(diff_src_tz, offsets);
+    auto from_mem = new mkldnn::memory(from_md, gradz_mem->get_engine(),
+                                       gradz_mem->get_data_handle());
+    offsets[axis] += diff_src_tz[axis];
+
+    std::unordered_map<int, mkldnn::memory> net_args({
+        {MKLDNN_ARG_FROM, *gradz_mem},
+        {MKLDNN_ARG_TO, *gradi_mem.second}
+    });
+    MKLDNNStream::Get()->RegisterPrimArgs(mkldnn::reorder(*from_mem, *gradi_mem.second), net_args);
+    CommitOutput(outputs[i], gradi_mem);
   }
+
   MKLDNNStream::Get()->Submit();
 }
 
 }  // namespace op
 }  // namespace mxnet
-#endif
+#endif  // MXNET_USE_MKLDNN == 1
diff --git a/src/operator/nn/mkldnn/mkldnn_convolution-inl.h b/src/operator/nn/mkldnn/mkldnn_convolution-inl.h
index 880b9d19cd81..ac2d3169340e 100644
--- a/src/operator/nn/mkldnn/mkldnn_convolution-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_convolution-inl.h
@@ -79,45 +79,28 @@ struct MKLDNNConvFullParam {
   MKLDNNPostEltwiseParam postsum_act_param;
 };
 
-mkldnn::convolution_forward::primitive_desc GetConvFwdImpl(const MKLDNNConvFullParam &param,
-                                                           const bool is_train,
-                                                           const NDArray &data,
-                                                           const NDArray &weights,
-                                                           const NDArray *bias,
-                                                           const NDArray &output);
+std::shared_ptr<mkldnn::convolution_forward::primitive_desc> GetConvFwdImpl(
+    const ConvolutionParam &param, const bool is_train, const NDArray &data, const NDArray &weight,
+    const NDArray *bias, const NDArray &output);
 
 class MKLDNNConvForward {
  public:
-  mkldnn::convolution_forward::primitive_desc fwd_pd;
-
   MKLDNNConvForward(const MKLDNNConvFullParam &param, const bool is_train, const NDArray &data,
-                    const NDArray &weights, const NDArray *bias, const NDArray &output);
-
-  void SetNewMem(const mkldnn::memory &data, const mkldnn::memory &weight,
-                 const mkldnn::memory *bias, const mkldnn::memory &output);
+                    const NDArray &weight, const NDArray *bias, const NDArray &output);
 
-  void SetNewMem(const mkldnn::memory &data, const mkldnn::memory &output) {
-    this->data_->set_data_handle(data.get_data_handle());
-    this->out_->set_data_handle(output.get_data_handle());
-  }
+  const mkldnn::convolution_forward &GetFwd() const { return *fwd_; }
 
-  const mkldnn::convolution_forward &GetFwd() const {
-    return *fwd_;
-  }
+  const mkldnn::convolution_forward::primitive_desc &GetPd() const { return *pd_; }
 
  private:
   std::shared_ptr<mkldnn::convolution_forward> fwd_;
-  std::shared_ptr<mkldnn::memory> data_;
-  std::shared_ptr<mkldnn::memory> weight_;
-  std::shared_ptr<mkldnn::memory> bias_;
-  std::shared_ptr<mkldnn::memory> out_;
+  std::shared_ptr<mkldnn::convolution_forward::primitive_desc> pd_;
 };
 
 typedef ParamOpSign<ConvolutionParam> MKLDNNConvSignature;
 
-MKLDNNConvForward &GetConvFwd(const ConvolutionParam &param,
-                              const bool is_train, const NDArray &data,
-                              const NDArray &weights, const NDArray *bias,
+MKLDNNConvForward &GetConvFwd(const MKLDNNConvFullParam &param, const bool is_train,
+                              const NDArray &data, const NDArray &weight, const NDArray *bias,
                               const NDArray &output);
 
 void MKLDNNConvolutionForwardFullFeature(const MKLDNNConvFullParam &param,
@@ -127,6 +110,36 @@ void MKLDNNConvolutionForwardFullFeature(const MKLDNNConvFullParam &param,
                                          const std::vector<OpReqType> &req,
                                          const std::vector<NDArray> &out_data);
 
+void MKLDNNConvolutionForward(const nnvm::NodeAttrs &attrs,
+                              const OpContext &ctx,
+                              const std::vector<NDArray> &in_data,
+                              const std::vector<OpReqType> &req,
+                              const std::vector<NDArray> &out_data);
+
+class MKLDNNConvBackward {
+ public:
+  MKLDNNConvBackward(const MKLDNNConvFullParam &param, const NDArray &data, const NDArray &weight,
+                     const NDArray *bias, const NDArray &output);
+
+  const mkldnn::convolution_backward_data &GetBwdData() const { return *bwd_data_; }
+
+  const mkldnn::convolution_backward_weights &GetBwdWeights() const { return *bwd_weight_; }
+
+  const mkldnn::convolution_backward_data::primitive_desc &GetDataPd() const {
+    return *bwd_data_pd_;
+  }
+
+  const mkldnn::convolution_backward_weights::primitive_desc &GetWeightsPd() const {
+    return *bwd_weight_pd_;
+  }
+
+ private:
+  std::shared_ptr<mkldnn::convolution_backward_data::primitive_desc> bwd_data_pd_;
+  std::shared_ptr<mkldnn::convolution_backward_weights::primitive_desc> bwd_weight_pd_;
+  std::shared_ptr<mkldnn::convolution_backward_data> bwd_data_;
+  std::shared_ptr<mkldnn::convolution_backward_weights> bwd_weight_;
+};
+
 }  // namespace op
 }  // namespace mxnet
 
diff --git a/src/operator/nn/mkldnn/mkldnn_convolution.cc b/src/operator/nn/mkldnn/mkldnn_convolution.cc
index 9cab2dd0e2b3..ada42a22cc8c 100644
--- a/src/operator/nn/mkldnn/mkldnn_convolution.cc
+++ b/src/operator/nn/mkldnn/mkldnn_convolution.cc
@@ -21,8 +21,7 @@
  * \file mkldnn_convolution.cc
  * \brief
  * \author Da Zheng
-*/
-
+ */
 
 #if MXNET_USE_MKLDNN == 1
 
@@ -45,8 +44,10 @@ bool SupportMKLDNNConv(const ConvolutionParam& params, const NDArray &input) {
           (input.shape().ndim() == 4));
 }
 
-mkldnn::convolution_forward::primitive_desc GetConvFwdImpl(const MKLDNNConvFullParam &param,
-                                                           const bool is_train, const NDArray &data,
+std::shared_ptr<mkldnn::convolution_forward::primitive_desc> GetConvFwdImpl(
+                                                           const MKLDNNConvFullParam &param,
+                                                           const bool is_train,
+                                                           const NDArray &data,
                                                            const NDArray &weights,
                                                            const NDArray *bias,
                                                            const NDArray &output) {
@@ -57,7 +58,7 @@ mkldnn::convolution_forward::primitive_desc GetConvFwdImpl(const MKLDNNConvFullP
   auto bias_md =
       bias ? (param.mkldnn_param.quantized ? GetMemDesc(*bias, mshadow::kInt32) : GetMemDesc(*bias))
            : mkldnn::memory::desc{
-             {}, mkldnn::memory::data_type::data_undef, mkldnn::memory::format::any};
+             {}, mkldnn::memory::data_type::undef, mkldnn::memory::format_tag::any};
   auto bias_md_ptr = bias ? &bias_md : nullptr;
 
   mkldnn::memory::dims strides(param.conv_param.kernel.ndim());
@@ -98,19 +99,19 @@ mkldnn::convolution_forward::primitive_desc GetConvFwdImpl(const MKLDNNConvFullP
   if (param.mkldnn_param.quantized && param.requantize_scales.size()) {
     int mask = (param.requantize_scales.size() > 1) ? 2 : 0;
     attr.set_output_scales(mask, param.requantize_scales);
-    attr.set_int_output_round_mode(round_nearest);
   }
   auto GetConvFwdPd = [&param, &data, &weights, &output,
                        &attr](const mkldnn::convolution_forward::desc &desc) {
     auto engine = CpuEngine::Get()->get_engine();
     try {
-      auto conv_pd = mkldnn::convolution_forward::primitive_desc(desc, attr, engine);
-      while (conv_pd.dst_primitive_desc().get_size() != GetArraySize(output) ||
-             conv_pd.src_primitive_desc().get_size() != GetArraySize(data) ||
+      auto conv_pd =
+          std::make_shared<mkldnn::convolution_forward::primitive_desc>(desc, attr, engine);
+      while (conv_pd->dst_desc().get_size() != GetArraySize(output) ||
+             conv_pd->src_desc().get_size() != GetArraySize(data) ||
              (!param.mkldnn_param.quantized &&
-              conv_pd.weights_primitive_desc().get_size() != GetArraySize(weights))) {
+              conv_pd->weights_desc().get_size() != GetArraySize(weights))) {
         // next_impl() will visit desc and engine, please make sure they are still alive here.
-        CHECK(conv_pd.next_impl()) << "No convolution implementation for this request.";
+        CHECK(conv_pd->next_impl()) << "No convolution implementation for this request.";
       }
       return conv_pd;
     } catch (mkldnn::error &e) {
@@ -126,13 +127,12 @@ mkldnn::convolution_forward::primitive_desc GetConvFwdImpl(const MKLDNNConvFullP
 
   if (param.conv_param.dilate.ndim() == 0 && bias_md_ptr == nullptr) {
     mkldnn::convolution_forward::desc desc(prop, mkldnn::algorithm::convolution_direct, data_md,
-                                           weight_md, out_md, strides, padding, padding,
-                                           mkldnn::padding_kind::zero);
+                                           weight_md, out_md, strides, padding, padding);
     return GetConvFwdPd(desc);
   } else if (param.conv_param.dilate.ndim() == 0) {
     mkldnn::convolution_forward::desc desc(prop, mkldnn::algorithm::convolution_direct, data_md,
                                            weight_md, *bias_md_ptr, out_md, strides, padding,
-                                           padding, mkldnn::padding_kind::zero);
+                                           padding);
     return GetConvFwdPd(desc);
   } else {
     mkldnn::memory::dims dilates(param.conv_param.kernel.ndim());
@@ -147,23 +147,22 @@ mkldnn::convolution_forward::primitive_desc GetConvFwdImpl(const MKLDNNConvFullP
     }
     if (bias_md_ptr == nullptr) {
       mkldnn::convolution_forward::desc desc(prop, mkldnn::algorithm::convolution_direct, data_md,
-                                             weight_md, out_md, strides, dilates, padding, padding,
-                                             mkldnn::padding_kind::zero);
+                                             weight_md, out_md, strides, dilates, padding, padding);
       return GetConvFwdPd(desc);
     } else {
       mkldnn::convolution_forward::desc desc(prop, mkldnn::algorithm::convolution_direct, data_md,
                                              weight_md, *bias_md_ptr, out_md, strides, dilates,
-                                             padding, padding, mkldnn::padding_kind::zero);
+                                             padding, padding);
       return GetConvFwdPd(desc);
     }
   }
 }
 
-static mkldnn::convolution_backward_data::primitive_desc GetConvBwdData(
-    const ConvolutionParam& param, const NDArray &data, const NDArray &weights,
+static std::shared_ptr<mkldnn::convolution_backward_data::primitive_desc> GetConvBwdData(
+    const ConvolutionParam &param, const NDArray &data, const NDArray &weight,
     const NDArray &output, const mkldnn::convolution_forward::primitive_desc &fwd_pd) {
   auto data_md = GetMemDesc(data);
-  auto weight_md = GetWeightDesc(weights, param.num_group);
+  auto weight_md = GetWeightDesc(weight, param.num_group);
   auto out_md = GetMemDesc(output);
   auto engine = CpuEngine::Get()->get_engine();
   mkldnn::memory::dims strides(param.kernel.ndim());
@@ -187,21 +186,29 @@ static mkldnn::convolution_backward_data::primitive_desc GetConvBwdData(
                << ", supporting only 1 or 2.";
   }
 
-  // MKL-DNN introduced padded formats since 0.15 which require more memory
-  // for computation compared with the actual tensor size. Currently, MKL-DNN
-  // operators are still reusing those memory from memory planning and the
-  // memory size may smaller than what MKL-DNN kernels require. So here we need
-  // select suboptimal kernel for computation according to tensor sizes.
-  if (param.dilate.ndim() == 0) {
-    mkldnn::convolution_backward_data::desc desc(mkldnn::algorithm::convolution_direct,
-        data_md, weight_md, out_md, strides, padding, padding, mkldnn::padding_kind::zero);
-    auto conv_pd = mkldnn::convolution_backward_data::primitive_desc(desc, engine, fwd_pd);
-    while (conv_pd.diff_dst_primitive_desc().get_size() != GetArraySize(output) ||
-           conv_pd.diff_src_primitive_desc().get_size() != GetArraySize(data) ||
-           conv_pd.weights_primitive_desc().get_size() != GetArraySize(weights)) {
-      CHECK(conv_pd.next_impl()) << "No implementation";
+  auto GetConvBwdDataPd = [&data, &weight, &output,
+                           &fwd_pd](const mkldnn::convolution_backward_data::desc &desc) {
+    auto engine = CpuEngine::Get()->get_engine();
+    try {
+      auto conv_pd =
+          std::make_shared<mkldnn::convolution_backward_data::primitive_desc>(desc, engine, fwd_pd);
+      while (conv_pd->diff_dst_desc().get_size() != GetArraySize(output) ||
+             conv_pd->diff_src_desc().get_size() != GetArraySize(data) ||
+             conv_pd->weights_desc().get_size() != GetArraySize(weight)) {
+        // next_impl() will visit desc and engine, please make sure they are still alive here.
+        CHECK(conv_pd->next_impl()) << "No convolution backward implementation for this request.";
+      }
+      return conv_pd;
+    } catch (mkldnn::error &e) {
+      LOG(ERROR) << e.message;
+      throw;
     }
-    return conv_pd;
+  };
+
+  if (param.dilate.ndim() == 0) {
+    mkldnn::convolution_backward_data::desc desc(mkldnn::algorithm::convolution_direct, data_md,
+                                                 weight_md, out_md, strides, padding, padding);
+    return GetConvBwdDataPd(desc);
   } else {
     mkldnn::memory::dims dilates(param.kernel.ndim());
     if (param.dilate.ndim() == 1) {
@@ -213,25 +220,18 @@ static mkldnn::convolution_backward_data::primitive_desc GetConvBwdData(
       LOG(FATAL) << "Unexpected MKL-DNN Conv dilate size "
                  << param.dilate.ndim() << ", supporting only 1 or 2.";
     }
-    mkldnn::convolution_backward_data::desc desc(mkldnn::algorithm::convolution_direct,
-        data_md, weight_md, out_md, strides, dilates, padding, padding,
-        mkldnn::padding_kind::zero);
-    auto conv_pd = mkldnn::convolution_backward_data::primitive_desc(desc, engine, fwd_pd);
-    while (conv_pd.diff_dst_primitive_desc().get_size() != GetArraySize(output) ||
-           conv_pd.diff_src_primitive_desc().get_size() != GetArraySize(data) ||
-           conv_pd.weights_primitive_desc().get_size() != GetArraySize(weights)) {
-      CHECK(conv_pd.next_impl()) << "No implementation";
-    }
-    return conv_pd;
+    mkldnn::convolution_backward_data::desc desc(mkldnn::algorithm::convolution_direct, data_md,
+                                                 weight_md, out_md, strides, dilates, padding,
+                                                 padding);
+    return GetConvBwdDataPd(desc);
   }
 }
 
-static mkldnn::convolution_backward_weights::primitive_desc GetConvBwdWeights(
-    const ConvolutionParam& param, const NDArray &data,
-    const NDArray &weights, const NDArray *bias, const NDArray &output,
-    const mkldnn::convolution_forward::primitive_desc &fwd_pd) {
+static std::shared_ptr<mkldnn::convolution_backward_weights::primitive_desc> GetConvBwdWeights(
+    const ConvolutionParam &param, const NDArray &data, const NDArray &weight, const NDArray *bias,
+    const NDArray &output, const mkldnn::convolution_forward::primitive_desc &fwd_pd) {
   auto data_md = GetMemDesc(data);
-  auto weight_md = GetWeightDesc(weights, param.num_group);
+  auto weight_md = GetWeightDesc(weight, param.num_group);
   auto out_md = GetMemDesc(output);
   auto engine = CpuEngine::Get()->get_engine();
   mkldnn::memory::dims strides(param.kernel.ndim());
@@ -255,33 +255,35 @@ static mkldnn::convolution_backward_weights::primitive_desc GetConvBwdWeights(
                << ", supporting only 1 or 2.";
   }
 
-  // MKL-DNN introduced padded formats since 0.15 which require more memory
-  // for computation compared with the actual tensor size. Currently, MKL-DNN
-  // operators are still reusing those memory from memory planning and the
-  // memory size may smaller than what MKL-DNN kernels require. So here we need
-  // select suboptimal kernel for computation according to tensor sizes.
-  if (param.dilate.ndim() == 0 && bias == nullptr) {
-    mkldnn::convolution_backward_weights::desc desc(mkldnn::algorithm::convolution_direct,
-        data_md, weight_md, out_md, strides, padding, padding, mkldnn::padding_kind::zero);
-    auto conv_pd = mkldnn::convolution_backward_weights::primitive_desc(desc, engine, fwd_pd);
-    while (conv_pd.diff_dst_primitive_desc().get_size() != GetArraySize(output) ||
-           conv_pd.src_primitive_desc().get_size() != GetArraySize(data) ||
-           conv_pd.diff_weights_primitive_desc().get_size() != GetArraySize(weights)) {
-      CHECK(conv_pd.next_impl()) << "No implementation";
+  auto GetConvBwdWeightsPd = [&data, &weight, &output,
+                              &fwd_pd](const mkldnn::convolution_backward_weights::desc &desc) {
+    auto engine = CpuEngine::Get()->get_engine();
+    try {
+      auto conv_pd = std::make_shared<mkldnn::convolution_backward_weights::primitive_desc>(
+          desc, engine, fwd_pd);
+      while (conv_pd->diff_dst_desc().get_size() != GetArraySize(output) ||
+             conv_pd->src_desc().get_size() != GetArraySize(data) ||
+             conv_pd->diff_weights_desc().get_size() != GetArraySize(weight)) {
+        // next_impl() will visit desc and engine, please make sure they are still alive here.
+        CHECK(conv_pd->next_impl()) << "No convolution backward implementation for this request.";
+      }
+      return conv_pd;
+    } catch (mkldnn::error &e) {
+      LOG(ERROR) << e.message;
+      throw;
     }
-    return conv_pd;
+  };
+
+  if (param.dilate.ndim() == 0 && bias == nullptr) {
+    mkldnn::convolution_backward_weights::desc desc(mkldnn::algorithm::convolution_direct, data_md,
+                                                    weight_md, out_md, strides, padding, padding);
+    return GetConvBwdWeightsPd(desc);
   } else if (param.dilate.ndim() == 0) {
     auto bias_md = GetMemDesc(*bias);
-    mkldnn::convolution_backward_weights::desc desc(mkldnn::algorithm::convolution_direct,
-        data_md, weight_md, bias_md, out_md, strides, padding, padding,
-        mkldnn::padding_kind::zero);
-    auto conv_pd = mkldnn::convolution_backward_weights::primitive_desc(desc, engine, fwd_pd);
-    while (conv_pd.diff_dst_primitive_desc().get_size() != GetArraySize(output) ||
-           conv_pd.src_primitive_desc().get_size() != GetArraySize(data) ||
-           conv_pd.diff_weights_primitive_desc().get_size() != GetArraySize(weights)) {
-      CHECK(conv_pd.next_impl()) << "No implementation";
-    }
-    return conv_pd;
+    mkldnn::convolution_backward_weights::desc desc(mkldnn::algorithm::convolution_direct, data_md,
+                                                    weight_md, bias_md, out_md, strides, padding,
+                                                    padding);
+    return GetConvBwdWeightsPd(desc);
   } else {
     mkldnn::memory::dims dilates(param.kernel.ndim());
     if (param.dilate.ndim() == 1) {
@@ -295,313 +297,154 @@ static mkldnn::convolution_backward_weights::primitive_desc GetConvBwdWeights(
     }
     if (bias == nullptr) {
       mkldnn::convolution_backward_weights::desc desc(mkldnn::algorithm::convolution_direct,
-          data_md, weight_md, out_md, strides, dilates, padding, padding,
-          mkldnn::padding_kind::zero);
-      auto conv_pd = mkldnn::convolution_backward_weights::primitive_desc(desc, engine, fwd_pd);
-      while (conv_pd.diff_dst_primitive_desc().get_size() != GetArraySize(output) ||
-             conv_pd.src_primitive_desc().get_size() != GetArraySize(data) ||
-             conv_pd.diff_weights_primitive_desc().get_size() != GetArraySize(weights)) {
-        CHECK(conv_pd.next_impl()) << "No implementation";
-      }
-      return conv_pd;
+                                                      data_md, weight_md, out_md, strides, dilates,
+                                                      padding, padding);
+      return GetConvBwdWeightsPd(desc);
     } else {
       auto bias_md = GetMemDesc(*bias);
       mkldnn::convolution_backward_weights::desc desc(mkldnn::algorithm::convolution_direct,
-                                                      data_md, weight_md, bias_md, out_md,
-                                                      strides, dilates, padding, padding,
-                                                      mkldnn::padding_kind::zero);
-      auto conv_pd = mkldnn::convolution_backward_weights::primitive_desc(desc, engine, fwd_pd);
-      while (conv_pd.diff_dst_primitive_desc().get_size() != GetArraySize(output) ||
-             conv_pd.src_primitive_desc().get_size() != GetArraySize(data) ||
-             conv_pd.diff_weights_primitive_desc().get_size() != GetArraySize(weights)) {
-        CHECK(conv_pd.next_impl()) << "No implementation";
-      }
-      return conv_pd;
+                                                      data_md, weight_md, bias_md, out_md, strides,
+                                                      dilates, padding, padding);
+      return GetConvBwdWeightsPd(desc);
     }
   }
 }
 
 MKLDNNConvForward::MKLDNNConvForward(const MKLDNNConvFullParam &param, const bool is_train,
-                                     const NDArray &data, const NDArray &weights,
+                                     const NDArray &data, const NDArray &weight,
                                      const NDArray *bias, const NDArray &output)
-    : fwd_pd(GetConvFwdImpl(param, is_train, data, weights, bias, output)) {
-  data_ = std::make_shared<mkldnn::memory>(fwd_pd.src_primitive_desc(), nullptr);
-  weight_ = std::make_shared<mkldnn::memory>(fwd_pd.weights_primitive_desc(), nullptr);
-  out_ = std::make_shared<mkldnn::memory>(fwd_pd.dst_primitive_desc(), nullptr);
-  if (bias) {
-    bias_ = std::make_shared<mkldnn::memory>(fwd_pd.bias_primitive_desc(), nullptr);
-    fwd_ = std::make_shared<mkldnn::convolution_forward>(fwd_pd, *this->data_, *this->weight_,
-                                                         *this->bias_, *this->out_);
-  } else {
-    fwd_ = std::make_shared<mkldnn::convolution_forward>(fwd_pd, *this->data_, *this->weight_,
-                                                         *this->out_);
-  }
+    : pd_(GetConvFwdImpl(param, is_train, data, weight, bias, output)) {
+  fwd_ = std::make_shared<mkldnn::convolution_forward>(GetPd());
 }
 
-void MKLDNNConvForward::SetNewMem(const mkldnn::memory &data, const mkldnn::memory &weight,
-                                  const mkldnn::memory *bias, const mkldnn::memory &output) {
-  data_->set_data_handle(data.get_data_handle());
-  weight_->set_data_handle(weight.get_data_handle());
-  out_->set_data_handle(output.get_data_handle());
-  if (bias != nullptr) bias_->set_data_handle(bias->get_data_handle());
-}
-
-MKLDNNConvForward &GetConvFwd(const ConvolutionParam &param,
-                              const bool is_train, const NDArray &data,
-                              const NDArray &weights, const NDArray *bias,
+MKLDNNConvForward &GetConvFwd(const MKLDNNConvFullParam &param, const bool is_train,
+                              const NDArray &data, const NDArray &weight, const NDArray *bias,
                               const NDArray &output) {
+  using conv_fwd_map = std::unordered_map<MKLDNNConvSignature, MKLDNNConvForward, OpHash>;
 #if DMLC_CXX11_THREAD_LOCAL
-  static thread_local std::unordered_map<MKLDNNConvSignature, MKLDNNConvForward, OpHash> fwds;
+  static thread_local conv_fwd_map fwds;
 #else
-  static MX_THREAD_LOCAL std::unordered_map<MKLDNNConvSignature, MKLDNNConvForward, OpHash> fwds;
+  static MX_THREAD_LOCAL conv_fwd_map fwds;
 #endif
-  MKLDNNConvSignature key(param);
+  // TODO(zhennan): Hash conv_param for now, need to hash full param if we want to enable cache for
+  // fused conv
+  MKLDNNConvSignature key(param.conv_param);
   key.AddSign(is_train);
-  // Here we can sign the conv op with NDArray because conv primitive will
-  // decide the right layout for the, so we only need to get the shape and the
-  // data type of the arrays.
+  // Here we can sign the conv op with NDArray because conv primitive will decide the right layout
+  // for the, so we only need to get the shape and the data type of the arrays.
   key.AddSign(data);
-  key.AddSign(weights);
+  key.AddSign(weight);
   key.AddSign(output);
-  if (bias)
-    key.AddSign(*bias);
+  if (bias) key.AddSign(*bias);
 
   auto it = fwds.find(key);
   if (it == fwds.end()) {
-    MKLDNNConvFullParam full_param;
-    full_param.conv_param = param;
-    full_param.mkldnn_param.Init(std::unordered_map<std::string, std::string>());
-    MKLDNNConvForward fwd(full_param, is_train, data, weights, bias, output);
+    auto fwd = MKLDNNConvForward(param, is_train, data, weight, bias, output);
     it = AddToCache(&fwds, key, fwd);
   }
   return it->second;
 }
 
-void MKLDNNConvolutionForwardFullFeature(const MKLDNNConvFullParam &param,
-                                         const OpContext &ctx,
+void MKLDNNConvolutionForwardFullFeature(const MKLDNNConvFullParam &param, const OpContext &ctx,
                                          MKLDNNConvForward *fwd,
                                          const std::vector<NDArray> &in_data,
                                          const std::vector<OpReqType> &req,
                                          const std::vector<NDArray> &out_data) {
   TmpMemMgr::Get()->Init(ctx.requested[conv::kTempSpace]);
 
-  auto data = in_data[conv::kData];
-  if (data.IsView() && data.IsMKLDNNData())
-    data = data.Reorder2Default();
-
-  auto weight = in_data[conv::kWeight];
-  if (weight.IsView() && weight.IsMKLDNNData())
-    weight = weight.Reorder2Default();
-
+  auto &data = in_data[conv::kData];
+  auto &weight = in_data[conv::kWeight];
   bool no_bias = param.conv_param.no_bias && !param.mkldnn_param.with_bn;
 
-  auto data_mem = data.GetMKLDNNDataReorder(
-      fwd->fwd_pd.src_primitive_desc());
+  auto data_mem = data.GetMKLDNNDataReorder(fwd->GetPd().src_desc());
   const mkldnn::memory *weight_mem;
   if (ctx.is_train) {
-    // TODO(zhengda) kvstore doesn't handle MKLDNN correctly. Let's reorder it
-    // to the default format for now.
+    // TODO(zhengda) kvstore doesn't handle MKLDNN correctly. Let's reorder it to the default format
+    // for now.
     if (weight.IsMKLDNNData())
-      // This asks the engine to change the layout of the weight array after
-      // it's used.
+      // This asks the engine to change the layout of the weight array after it's used.
       weight.Reorder2DefaultAsync();
-    weight_mem = GetWeights(weight, fwd->fwd_pd.weights_primitive_desc(),
-                            param.conv_param.num_group);
+    weight_mem = GetWeights(weight, fwd->GetPd().weights_desc(), param.conv_param.num_group);
   } else {
-    // For inference, we want to reorder the weight array so we don't need to
-    // reorder data every time.
+    // For inference, we want to reorder the weight array so we don't need to reorder data every
+    // time.
     if (weight.IsDefaultData()) {
-      // We also need to modify the layout on the original weight array. The
-      // data conversion happens after the weight array is used.
-      weight.MKLDNNDataReorderAsync(fwd->fwd_pd.weights_primitive_desc());
-      weight_mem = GetWeights(weight, fwd->fwd_pd.weights_primitive_desc(),
-                              param.conv_param.num_group);
-
+      // We also need to modify the layout on the original weight array. The data conversion happens
+      // after the weight array is used.
+      weight.MKLDNNDataReorderAsync(fwd->GetPd().weights_desc());
+      weight_mem = GetWeights(weight, fwd->GetPd().weights_desc(), param.conv_param.num_group);
     } else {
       weight_mem = weight.GetMKLDNNData();
-      CHECK(weight_mem->get_primitive_desc() == fwd->fwd_pd.weights_primitive_desc());
+      CHECK(weight_mem->get_desc() == fwd->GetPd().weights_desc());
     }
   }
   mkldnn_output_t out_mem;
   if (param.mkldnn_param.with_sum) {
-    out_mem = mkldnn_output_t(
-        OutDataOp::Noop,
-        const_cast<mkldnn::memory *>(out_data[conv::kOut].GetMKLDNNData()));
+    out_mem = mkldnn_output_t(OutDataOp::Noop,
+                              const_cast<mkldnn::memory *>(out_data[conv::kOut].GetMKLDNNData()));
   } else {
-    out_mem = CreateMKLDNNMem(out_data[conv::kOut],
-                              fwd->fwd_pd.dst_primitive_desc(), req[conv::kOut]);
+    out_mem = CreateMKLDNNMem(out_data[conv::kOut], fwd->GetPd().dst_desc(), req[conv::kOut]);
   }
 
-  const mkldnn::memory *bias_mem = nullptr;
+  mkldnn_args_map_t net_args;
   if (!no_bias) {
-    bias_mem = in_data[conv::kBias].GetMKLDNNData();
+    const mkldnn::memory *bias_mem = in_data[conv::kBias].GetMKLDNNData();
+    net_args.insert({MKLDNN_ARG_BIAS, *bias_mem});
   }
-  fwd->SetNewMem(*data_mem, *weight_mem, bias_mem, *out_mem.second);
-  MKLDNNStream::Get()->RegisterPrim(fwd->GetFwd());
 
+  net_args.insert({MKLDNN_ARG_SRC, *data_mem});
+  net_args.insert({MKLDNN_ARG_WEIGHTS, *weight_mem});
+  net_args.insert({MKLDNN_ARG_DST, *out_mem.second});
+  MKLDNNStream::Get()->RegisterPrimArgs(fwd->GetFwd(), net_args);
   CommitOutput(out_data[conv::kOut], out_mem);
   MKLDNNStream::Get()->Submit();
 }
 
-void MKLDNNConvolutionForward(const nnvm::NodeAttrs &attrs,
-                              const OpContext &ctx,
+void MKLDNNConvolutionForward(const nnvm::NodeAttrs &attrs, const OpContext &ctx,
                               const std::vector<NDArray> &in_data,
                               const std::vector<OpReqType> &req,
                               const std::vector<NDArray> &out_data) {
   MKLDNNConvFullParam param;
   param.conv_param = nnvm::get<ConvolutionParam>(attrs.parsed);
   param.mkldnn_param.Init(std::unordered_map<std::string, std::string>());
-  auto &fwd = GetConvFwd(
-      param.conv_param, ctx.is_train, in_data[conv::kData], in_data[conv::kWeight],
-      param.conv_param.no_bias ? nullptr : &in_data[conv::kBias],
-      out_data[conv::kOut]);
+  auto &fwd =
+      GetConvFwd(param, ctx.is_train, in_data[conv::kData], in_data[conv::kWeight],
+                 param.conv_param.no_bias ? nullptr : &in_data[conv::kBias], out_data[conv::kOut]);
   MKLDNNConvolutionForwardFullFeature(param, ctx, &fwd, in_data, req, out_data);
 }
 
-class MKLDNNConvBackward {
-  std::shared_ptr<mkldnn::convolution_backward_data> bwd_data;
-  std::shared_ptr<mkldnn::convolution_backward_weights> bwd_weight;
-  // conv::kData
-  std::shared_ptr<mkldnn::memory> out_grad;
-  std::shared_ptr<mkldnn::memory> in_grad;
-  std::shared_ptr<mkldnn::memory> weight;
-  // conv::kWeight
-  std::shared_ptr<mkldnn::memory> data;
-  std::shared_ptr<mkldnn::memory> output;
-  std::shared_ptr<mkldnn::memory> in_grad_weight;
-  std::shared_ptr<mkldnn::memory> in_grad_bias;
-
- public:
-  mkldnn::convolution_backward_data::primitive_desc bwdData_pd;
-  mkldnn::convolution_backward_weights::primitive_desc bwdWeights_pd;
-
-  MKLDNNConvBackward(
-      const ConvolutionParam &param, const NDArray &data,
-      const NDArray &weights, const NDArray *bias, const NDArray &output,
-      const mkldnn::convolution_forward::primitive_desc &fwd_pd):
-      bwdData_pd(GetConvBwdData(param, data, weights, output, fwd_pd)),
-      bwdWeights_pd(GetConvBwdWeights(param, data, weights, bias, output, fwd_pd)) {
-  }
-
-  void SetDataNewMem(const mkldnn::memory &out_grad, const mkldnn::memory &weight,
-                     const mkldnn::memory &in_grad) {
-    if (this->out_grad == nullptr)
-      this->out_grad = std::shared_ptr<mkldnn::memory>(new mkldnn::memory(
-        bwdData_pd.diff_dst_primitive_desc(), out_grad.get_data_handle()));
-    else
-      this->out_grad->set_data_handle(out_grad.get_data_handle());
-    if (this->in_grad == nullptr)
-      this->in_grad = std::shared_ptr<mkldnn::memory>(new mkldnn::memory(
-        bwdData_pd.diff_src_primitive_desc(), in_grad.get_data_handle()));
-    else
-      this->in_grad->set_data_handle(in_grad.get_data_handle());
-    if (this->weight == nullptr)
-      this->weight = std::shared_ptr<mkldnn::memory>(new mkldnn::memory(
-         bwdData_pd.weights_primitive_desc(), weight.get_data_handle()));
-    else
-      this->weight->set_data_handle(weight.get_data_handle());
-    if (this->bwd_data == nullptr)
-      this->bwd_data = std::shared_ptr<mkldnn::convolution_backward_data>(
-        new mkldnn::convolution_backward_data(
-          this->bwdData_pd, mkldnn::primitive::at(*this->out_grad),
-          mkldnn::primitive::at(*this->weight), *this->in_grad));
-  }
-
-  void SetWeightNewMem(const mkldnn::memory &data,
-                       const mkldnn::memory &out_grad,
-                       const mkldnn::memory &in_grad_weight) {
-    if (this->data == nullptr)
-      this->data = std::shared_ptr<mkldnn::memory>(new mkldnn::memory(
-          bwdWeights_pd.src_primitive_desc(), data.get_data_handle()));
-    else
-      this->data->set_data_handle(data.get_data_handle());
-    if (this->output == nullptr)
-      this->output = std::shared_ptr<mkldnn::memory>(new mkldnn::memory(
-          bwdWeights_pd.diff_dst_primitive_desc(), out_grad.get_data_handle()));
-    else
-      this->output->set_data_handle(out_grad.get_data_handle());
-    if (this->in_grad_weight == nullptr)
-      this->in_grad_weight = std::shared_ptr<mkldnn::memory>(
-          new mkldnn::memory(bwdWeights_pd.diff_weights_primitive_desc(),
-                             in_grad_weight.get_data_handle()));
-    else
-      this->in_grad_weight->set_data_handle(in_grad_weight.get_data_handle());
-
-    if (this->bwd_weight == nullptr)
-      this->bwd_weight = std::shared_ptr<mkldnn::convolution_backward_weights>(
-          new mkldnn::convolution_backward_weights(
-              this->bwdWeights_pd, mkldnn::primitive::at(*this->data),
-              mkldnn::primitive::at(*this->output), *this->in_grad_weight));
-  }
-
-  void SetWeightNewMem(const mkldnn::memory &data,
-                       const mkldnn::memory &out_grad,
-                       const mkldnn::memory &in_grad_weight,
-                       const mkldnn::memory &in_grad_bias) {
-    if (this->data == nullptr)
-      this->data = std::shared_ptr<mkldnn::memory>(new mkldnn::memory(
-          bwdWeights_pd.src_primitive_desc(), data.get_data_handle()));
-    else
-      this->data->set_data_handle(data.get_data_handle());
-    if (this->output == nullptr)
-      this->output = std::shared_ptr<mkldnn::memory>(new mkldnn::memory(
-          bwdWeights_pd.diff_dst_primitive_desc(), out_grad.get_data_handle()));
-    else
-      this->output->set_data_handle(out_grad.get_data_handle());
-    if (this->in_grad_weight == nullptr)
-      this->in_grad_weight = std::shared_ptr<mkldnn::memory>(
-          new mkldnn::memory(bwdWeights_pd.diff_weights_primitive_desc(),
-                             in_grad_weight.get_data_handle()));
-    else
-      this->in_grad_weight->set_data_handle(in_grad_weight.get_data_handle());
-
-    if (this->in_grad_bias == nullptr)
-      this->in_grad_bias = std::shared_ptr<mkldnn::memory>(
-          new mkldnn::memory(bwdWeights_pd.diff_bias_primitive_desc(),
-                             in_grad_bias.get_data_handle()));
-    else
-      this->in_grad_bias->set_data_handle(in_grad_bias.get_data_handle());
-    if (this->bwd_weight == nullptr)
-      this->bwd_weight = std::shared_ptr<mkldnn::convolution_backward_weights>(
-          new mkldnn::convolution_backward_weights(
-              this->bwdWeights_pd, mkldnn::primitive::at(*this->data),
-              mkldnn::primitive::at(*this->output), *this->in_grad_weight,
-              *this->in_grad_bias));
-  }
-
-  const mkldnn::convolution_backward_data &GetBwdData() const {
-    return *bwd_data;
-  }
-
-  const mkldnn::convolution_backward_weights &GetBwdWeights() const {
-    return *bwd_weight;
-  }
-};
+MKLDNNConvBackward::MKLDNNConvBackward(const MKLDNNConvFullParam &param, const NDArray &data,
+                                       const NDArray &weight, const NDArray *bias,
+                                       const NDArray &output) {
+  const auto fwd_pd = GetConvFwdImpl(param, true, data, weight, bias, output);
+  bwd_data_pd_ = GetConvBwdData(param.conv_param, data, weight, output, *fwd_pd);
+  bwd_weight_pd_ = GetConvBwdWeights(param.conv_param, data, weight, bias, output, *fwd_pd);
+  bwd_data_ = std::make_shared<mkldnn::convolution_backward_data>(GetDataPd());
+  bwd_weight_ = std::make_shared<mkldnn::convolution_backward_weights>(GetWeightsPd());
+}
 
-static inline MKLDNNConvBackward &GetConvBwd(
-    const nnvm::NodeAttrs &attrs, const NDArray &data, const NDArray &weights,
-    const NDArray *bias, const NDArray &output,
-    const mkldnn::convolution_forward::primitive_desc &fwd_pd) {
+static inline MKLDNNConvBackward &GetConvBwd(const MKLDNNConvFullParam &param, const NDArray &data,
+                                             const NDArray &weight, const NDArray *bias,
+                                             const NDArray &output) {
+  using mkldnn_conv_bwd_map = std::unordered_map<MKLDNNConvSignature, MKLDNNConvBackward, OpHash>;
 #if DMLC_CXX11_THREAD_LOCAL
-  static thread_local std::unordered_map<MKLDNNConvSignature, MKLDNNConvBackward, OpHash> bwds;
+  static thread_local mkldnn_conv_bwd_map bwds;
 #else
-  static MX_THREAD_LOCAL std::unordered_map<MKLDNNConvSignature, MKLDNNConvBackward, OpHash> bwds;
+  static MX_THREAD_LOCAL mkldnn_conv_bwd_map bwds;
 #endif
-  const ConvolutionParam& param = nnvm::get<ConvolutionParam>(attrs.parsed);
-  MKLDNNConvSignature key(param);
-  // Here we can sign the conv op with NDArray because conv primitive will
-  // decide the right layout for the, so we only need to get the shape and the
-  // data type of the arrays.
+  // TODO(zhennan): Hash conv_param for now, need to hash full param if we want to enable cache for
+  // fused conv
+  MKLDNNConvSignature key(param.conv_param);
+  // Here we can sign the conv op with NDArray because conv primitive will decide the right layout
+  // for the, so we only need to get the shape and the data type of the arrays.
   key.AddSign(data);
-  key.AddSign(weights);
+  key.AddSign(weight);
   key.AddSign(output);
-  if (bias)
-    key.AddSign(*bias);
-
+  if (bias) key.AddSign(*bias);
 
   auto it = bwds.find(key);
   if (it == bwds.end()) {
-    MKLDNNConvBackward bwd(param, data, weights, bias, output, fwd_pd);
+    auto bwd = MKLDNNConvBackward(param, data, weight, bias, output);
     it = AddToCache(&bwds, key, bwd);
   }
   return it->second;
@@ -617,65 +460,49 @@ void MKLDNNConvolutionBackward(const nnvm::NodeAttrs& attrs, const OpContext &ct
   full_param.conv_param = nnvm::get<ConvolutionParam>(attrs.parsed);
   full_param.mkldnn_param.Init(std::unordered_map<std::string, std::string>());
 
-  auto data = inputs[conv::kData + 1];
-  if (data.IsView() && data.IsMKLDNNData())
-    data = data.Reorder2Default();
+  auto &data = inputs[conv::kData + 1];
+  auto &weight = inputs[conv::kWeight + 1];
+  const auto *bias = full_param.conv_param.no_bias ? nullptr : &inputs[conv::kBias + 1];
+  auto &out_grad = inputs[conv::kOut];
 
-  auto weight = inputs[conv::kWeight + 1];
-  if (weight.IsView() && weight.IsMKLDNNData())
-    weight = weight.Reorder2Default();
-
-  const NDArray* bias = full_param.conv_param.no_bias ? nullptr : &inputs[conv::kBias + 1];
-
-  auto out_grad = inputs[conv::kOut];
-  if (out_grad.IsView() && out_grad.IsMKLDNNData())
-    out_grad = out_grad.Reorder2Default();
-
-  mkldnn::convolution_forward::primitive_desc fwd_pd = GetConvFwdImpl(
-      full_param, ctx.is_train, data, weight, bias, out_grad);
   const ConvolutionParam &param = full_param.conv_param;
 
   CHECK_NE(req[conv::kWeight], kWriteInplace) << "cannot write weight inplace";
-  MKLDNNConvBackward &convBwd = GetConvBwd(attrs, data,
-      weight, bias, out_grad, fwd_pd);
-  auto out_grad_mem = out_grad.GetMKLDNNDataReorder(
-      convBwd.bwdData_pd.diff_dst_primitive_desc());
+  MKLDNNConvBackward &convBwd = GetConvBwd(full_param, data, weight, bias, out_grad);
+  auto out_grad_mem = out_grad.GetMKLDNNDataReorder(convBwd.GetDataPd().diff_dst_desc());
   if (req[conv::kData]) {
-    auto weight_mem = GetWeights(weight,
-        convBwd.bwdData_pd.weights_primitive_desc(), param.num_group);
-    auto in_grad_mem = CreateMKLDNNMem(in_grad[conv::kData],
-        convBwd.bwdData_pd.diff_src_primitive_desc(), req[conv::kData]);
-    convBwd.SetDataNewMem(*out_grad_mem, *weight_mem, *in_grad_mem.second);
-    MKLDNNStream::Get()->RegisterPrim(convBwd.GetBwdData());
+    auto weight_mem = GetWeights(weight, convBwd.GetDataPd().weights_desc(), param.num_group);
+    auto in_grad_mem = CreateMKLDNNMem(in_grad[conv::kData], convBwd.GetDataPd().diff_src_desc(),
+                                       req[conv::kData]);
+    MKLDNNStream::Get()->RegisterPrimArgs(convBwd.GetBwdData(),
+                                          {{MKLDNN_ARG_DIFF_DST, *out_grad_mem},
+                                           {MKLDNN_ARG_WEIGHTS, *weight_mem},
+                                           {MKLDNN_ARG_DIFF_SRC, *in_grad_mem.second}});
     CommitOutput(in_grad[conv::kData], in_grad_mem);
   }
   if (req[conv::kWeight] || req[conv::kBias]) {
-    MKLDNNConvBackward &convBwdWeight = GetConvBwd(attrs, data,
-        weight, bias, out_grad, fwd_pd);
-    if (convBwdWeight.bwdData_pd.diff_dst_primitive_desc() !=
-        convBwdWeight.bwdWeights_pd.diff_dst_primitive_desc())
-      out_grad_mem = out_grad.GetMKLDNNDataReorder(
-          convBwdWeight.bwdWeights_pd.diff_dst_primitive_desc());
-    auto data_mem = data.GetMKLDNNDataReorder(
-        convBwdWeight.bwdWeights_pd.src_primitive_desc());
+    if (convBwd.GetDataPd().diff_dst_desc() != convBwd.GetWeightsPd().diff_dst_desc())
+      out_grad_mem = out_grad.GetMKLDNNDataReorder(convBwd.GetWeightsPd().diff_dst_desc());
+    auto data_mem = data.GetMKLDNNDataReorder(convBwd.GetWeightsPd().src_desc());
     auto in_grad_weight = CreateMKLDNNWeightGrad(
-        in_grad[conv::kWeight],
-        convBwdWeight.bwdWeights_pd.diff_weights_primitive_desc(),
-        req[conv::kWeight]);
-    if (param.no_bias) {
-      convBwdWeight.SetWeightNewMem(*data_mem, *out_grad_mem,
-          *in_grad_weight.second);
-      MKLDNNStream::Get()->RegisterPrim(convBwdWeight.GetBwdWeights());
-    } else {
-      auto in_grad_bias = CreateMKLDNNMem(
-          in_grad[conv::kBias],
-          convBwdWeight.bwdWeights_pd.diff_bias_primitive_desc(), req[conv::kBias]);
-      convBwdWeight.SetWeightNewMem(*data_mem, *out_grad_mem,
-          *in_grad_weight.second, *in_grad_bias.second);
-      MKLDNNStream::Get()->RegisterPrim(convBwdWeight.GetBwdWeights());
-      CommitOutput(in_grad[conv::kBias], in_grad_bias);
+        in_grad[conv::kWeight], convBwd.GetWeightsPd().diff_weights_desc(), req[conv::kWeight]);
+
+    mkldnn_args_map_t net_args = {{MKLDNN_ARG_DIFF_DST, *out_grad_mem},
+                                  {MKLDNN_ARG_SRC, *data_mem},
+                                  {MKLDNN_ARG_DIFF_WEIGHTS, *in_grad_weight.second}};
+    mkldnn_output_t in_grad_bias;
+    if (!param.no_bias) {
+      in_grad_bias = CreateMKLDNNMem(in_grad[conv::kBias],
+                                          convBwd.GetWeightsPd().diff_bias_desc(),
+                                          req[conv::kBias]);
+      net_args.insert({MKLDNN_ARG_DIFF_BIAS, *in_grad_bias.second});
     }
+    MKLDNNStream::Get()->RegisterPrimArgs(convBwd.GetBwdWeights(), net_args);
     CommitOutput(in_grad[conv::kWeight], in_grad_weight);
+    // CommitOutput Should run after RegisterPrimArgs for memory dependency
+    if (!param.no_bias) {
+      CommitOutput(in_grad[conv::kBias], in_grad_bias);
+    }
   }
   MKLDNNStream::Get()->Submit();
 }
diff --git a/src/operator/nn/mkldnn/mkldnn_copy.cc b/src/operator/nn/mkldnn/mkldnn_copy.cc
index a7c280e1e713..cf8daa4b45df 100644
--- a/src/operator/nn/mkldnn/mkldnn_copy.cc
+++ b/src/operator/nn/mkldnn/mkldnn_copy.cc
@@ -18,12 +18,11 @@
  */
 
 /*!
- * \file mkldnn_softmax.cc
+ * \file mkldnn_copy.cc
  * \brief
- * \author Da Zheng
+ * \author
 */
 
-#include "../softmax-inl.h"
 #include "./mkldnn_ops-inl.h"
 #include "./mkldnn_base-inl.h"
 
@@ -47,9 +46,9 @@ void MKLDNNCopy(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
     // We should try and force the input memory has the same format
     // as the input output. If not, we'll have to reorder memory.
     auto out_mem = out_data.GetMKLDNNData();
-    in_mem = data.GetMKLDNNData(out_mem ->get_primitive_desc());
+    in_mem = data.GetMKLDNNData(out_mem ->get_desc());
     if (in_mem == nullptr)
-      in_mem = data.GetMKLDNNDataReorder(out_mem->get_primitive_desc());
+      in_mem = data.GetMKLDNNDataReorder(out_mem->get_desc());
     MKLDNNSum(*out_mem, *in_mem, *out_mem);
   } else {
     const_cast<NDArray &>(out_data).CopyFrom(*in_mem);
diff --git a/src/operator/nn/mkldnn/mkldnn_deconvolution.cc b/src/operator/nn/mkldnn/mkldnn_deconvolution.cc
index 02a7368cce97..6537540fa209 100644
--- a/src/operator/nn/mkldnn/mkldnn_deconvolution.cc
+++ b/src/operator/nn/mkldnn/mkldnn_deconvolution.cc
@@ -20,34 +20,33 @@
 /*!
  * \file mkldnn_deconvolution.cc
  * \brief
- * \author Da Zheng, Rong Zhang (rong.a.zhang@intel.com)
-*/
+ */
 
 #if MXNET_USE_MKLDNN == 1
 
 #include "../deconvolution-inl.h"
-#include "./mkldnn_ops-inl.h"
 #include "./mkldnn_base-inl.h"
+#include "./mkldnn_ops-inl.h"
 
 namespace mxnet {
 namespace op {
 
-bool SupportMKLDNNDeconv(const DeconvolutionParam& params, const NDArray &input) {
-  if (params.kernel.ndim() != 2)
-    return false;
+bool SupportMKLDNNDeconv(const DeconvolutionParam &params,
+                         const NDArray &input) {
+  if (params.kernel.ndim() != 2) return false;
   return input.dtype() == mshadow::kFloat32 && input.shape().ndim() == 4;
 }
 
 static inline mkldnn::memory::desc GetBiasDesc(mkldnn::memory::desc md) {
   mkldnn::memory::dims dims(1);
-  // This is convolution on 4D data. The second dimension is the channel.
+  // This is deconvolution on 4D data. The second dimension is the channel.
   dims[0] = md.data.dims[1];
-  return mkldnn::memory::desc(dims,
-      static_cast<mkldnn::memory::data_type>(md.data.data_type),
-      mkldnn::memory::format::any);
+  return mkldnn::memory::desc(
+      dims, static_cast<mkldnn::memory::data_type>(md.data.data_type),
+      mkldnn::memory::format_tag::any);
 }
 
-static mkldnn::convolution_forward::primitive_desc GetDeconvBwd_(
+std::shared_ptr<mkldnn::convolution_forward::primitive_desc> GetDeconvBwd_(
     const mkldnn::memory::desc &data_md, const mkldnn::memory::desc &weights_md,
     bool has_bias, const mkldnn::memory::desc &out_md,
     const mkldnn::engine &engine, const mkldnn::memory::dims &strides,
@@ -58,34 +57,40 @@ static mkldnn::convolution_forward::primitive_desc GetDeconvBwd_(
   // memory size may smaller than what MKL-DNN kernels require. So here we need
   // select suboptimal kernel for computation according to tensor sizes.
   if (!has_bias) {
-    mkldnn::convolution_forward::desc desc(mkldnn::prop_kind::forward_training,
-        mkldnn::algorithm::convolution_direct, out_md, weights_md, data_md, strides,
-        dilates, padding, padding, mkldnn::padding_kind::zero);
-    auto deconv_pd =  mkldnn::convolution_forward::primitive_desc(desc, engine);
-    while (deconv_pd.dst_primitive_desc().get_size() != GetMemDescSize(data_md) ||
-           deconv_pd.src_primitive_desc().get_size() != GetMemDescSize(out_md) ||
-           deconv_pd.weights_primitive_desc().get_size() != GetMemDescSize(weights_md)) {
-      CHECK(deconv_pd.next_impl()) << "No implementation";
+    mkldnn::convolution_forward::desc desc(
+        mkldnn::prop_kind::forward_training,
+        mkldnn::algorithm::convolution_direct, out_md, weights_md, data_md,
+        strides, dilates, padding, padding);
+    auto deconv_pd =
+        std::make_shared<mkldnn::convolution_forward::primitive_desc>(desc,
+                                                                      engine);
+    while (deconv_pd->dst_desc().get_size() != GetMemDescSize(data_md) ||
+           deconv_pd->src_desc().get_size() != GetMemDescSize(out_md) ||
+           deconv_pd->weights_desc().get_size() != GetMemDescSize(weights_md)) {
+      CHECK(deconv_pd->next_impl()) << "No implementation";
     }
     return deconv_pd;
   } else {
     auto bias_md = GetBiasDesc(data_md);
-    mkldnn::convolution_forward::desc desc(mkldnn::prop_kind::forward_training,
+    mkldnn::convolution_forward::desc desc(
+        mkldnn::prop_kind::forward_training,
         mkldnn::algorithm::convolution_direct, out_md, weights_md, bias_md,
-        data_md, strides, dilates, padding, padding, mkldnn::padding_kind::zero);
-    auto deconv_pd =  mkldnn::convolution_forward::primitive_desc(desc, engine);
-    while (deconv_pd.dst_primitive_desc().get_size() != GetMemDescSize(data_md) ||
-           deconv_pd.src_primitive_desc().get_size() != GetMemDescSize(out_md) ||
-           deconv_pd.weights_primitive_desc().get_size() != GetMemDescSize(weights_md)) {
-      CHECK(deconv_pd.next_impl()) << "No implementation";
+        data_md, strides, dilates, padding, padding);
+    auto deconv_pd =
+        std::make_shared<mkldnn::convolution_forward::primitive_desc>(desc,
+                                                                      engine);
+    while (deconv_pd->dst_desc().get_size() != GetMemDescSize(data_md) ||
+           deconv_pd->src_desc().get_size() != GetMemDescSize(out_md) ||
+           deconv_pd->weights_desc().get_size() != GetMemDescSize(weights_md)) {
+      CHECK(deconv_pd->next_impl()) << "No implementation";
     }
     return deconv_pd;
   }
 }
 
-static mkldnn::convolution_backward_data::primitive_desc GetDeconvFwdImpl(
-    const DeconvolutionParam& param, const NDArray &data, const NDArray &weights,
-    bool has_bias, const NDArray &output) {
+std::shared_ptr<mkldnn::convolution_backward_data::primitive_desc>
+GetDeconvFwdImpl(const DeconvolutionParam &param, const NDArray &data,
+                 const NDArray &weights, bool has_bias, const NDArray &output) {
   auto data_md = GetMemDesc(data);
   auto weight_md = GetWeightDesc(weights, param.num_group);
   auto out_md = GetMemDesc(output);
@@ -103,27 +108,30 @@ static mkldnn::convolution_backward_data::primitive_desc GetDeconvFwdImpl(
   dilate[0] = param.dilate[0] - 1;
   dilate[1] = param.dilate[1] - 1;
   auto bwd_pd = GetDeconvBwd_(data_md, weight_md, has_bias, out_md, engine,
-      strides, padding, dilate);
-  mkldnn::convolution_backward_data::desc desc(mkldnn::algorithm::convolution_direct,
-      out_md, weight_md, data_md, strides, dilate, padding, padding,
-      mkldnn::padding_kind::zero);
-  auto deconv_pd = mkldnn::convolution_backward_data::primitive_desc(desc, engine, bwd_pd);
+                              strides, padding, dilate);
+  mkldnn::convolution_backward_data::desc desc(
+      mkldnn::algorithm::convolution_direct, out_md, weight_md, data_md,
+      strides, dilate, padding, padding);
+  auto deconv_pd =
+      std::make_shared<mkldnn::convolution_backward_data::primitive_desc>(
+          desc, engine, *bwd_pd);
   // MKL-DNN introduced padded formats since 0.15 which require more memory
   // for computation compared with the actual tensor size. Currently, MKL-DNN
   // operators are still reusing those memory from memory planning and the
   // memory size may smaller than what MKL-DNN kernels require. So here we need
   // select suboptimal kernel for computation according to tensor sizes.
-  while (deconv_pd.diff_dst_primitive_desc().get_size() != GetMemDescSize(data_md) ||
-         deconv_pd.diff_src_primitive_desc().get_size() != GetMemDescSize(out_md) ||
-         deconv_pd.weights_primitive_desc().get_size() != GetMemDescSize(weight_md)) {
-    CHECK(deconv_pd.next_impl()) << "No implementation";
+  while (deconv_pd->diff_dst_desc().get_size() != GetMemDescSize(data_md) ||
+         deconv_pd->diff_src_desc().get_size() != GetMemDescSize(out_md) ||
+         deconv_pd->weights_desc().get_size() != GetMemDescSize(weight_md)) {
+    CHECK(deconv_pd->next_impl()) << "No implementation";
   }
   return deconv_pd;
 }
 
-static mkldnn::convolution_forward::primitive_desc GetDeconvBwdDataImpl(
-    const DeconvolutionParam &param, const NDArray &data,
-    const NDArray &weights, bool has_bias, const NDArray &output) {
+std::shared_ptr<mkldnn::convolution_forward::primitive_desc>
+GetDeconvBwdDataImpl(const DeconvolutionParam &param, const NDArray &data,
+                     const NDArray &weights, bool has_bias,
+                     const NDArray &output) {
   auto data_md = GetMemDesc(data);
   auto weight_md = GetWeightDesc(weights, param.num_group);
   auto out_md = GetMemDesc(output);
@@ -140,11 +148,11 @@ static mkldnn::convolution_forward::primitive_desc GetDeconvBwdDataImpl(
   mkldnn::memory::dims dilate{0, 0};
   dilate[0] = param.dilate[0] - 1;
   dilate[1] = param.dilate[1] - 1;
-  return GetDeconvBwd_(data_md, weight_md, has_bias, out_md, engine,
-      strides, padding, dilate);
+  return GetDeconvBwd_(data_md, weight_md, has_bias, out_md, engine, strides,
+                       padding, dilate);
 }
 
-static mkldnn::convolution_backward_weights::primitive_desc
+std::shared_ptr<mkldnn::convolution_backward_weights::primitive_desc>
 GetDeconvBwdWeightsImpl(
     const DeconvolutionParam &param, const NDArray &data,
     const NDArray &weights, bool has_bias, const NDArray &output,
@@ -172,125 +180,64 @@ GetDeconvBwdWeightsImpl(
   // memory size may smaller than what MKL-DNN kernels require. So here we need
   // select suboptimal kernel for computation according to tensor sizes.
   if (!has_bias) {
-    mkldnn::convolution_backward_weights::desc desc(mkldnn::algorithm::convolution_direct,
-        out_md, weight_md, data_md, strides, dilate, padding, padding, mkldnn::padding_kind::zero);
-    auto deconv_pd = mkldnn::convolution_backward_weights::primitive_desc(desc, engine, fwd_pd);
-    while (deconv_pd.diff_dst_primitive_desc().get_size() != GetMemDescSize(data_md) ||
-           deconv_pd.src_primitive_desc().get_size() != GetMemDescSize(out_md) ||
-           deconv_pd.diff_weights_primitive_desc().get_size() != GetMemDescSize(weight_md)) {
-      CHECK(deconv_pd.next_impl()) << "No implementation";
+    mkldnn::convolution_backward_weights::desc desc(
+        mkldnn::algorithm::convolution_direct, out_md, weight_md, data_md,
+        strides, dilate, padding, padding);
+    auto deconv_pd =
+        std::make_shared<mkldnn::convolution_backward_weights::primitive_desc>(
+            desc, engine, fwd_pd);
+    while (deconv_pd->diff_dst_desc().get_size() != GetMemDescSize(data_md) ||
+           deconv_pd->src_desc().get_size() != GetMemDescSize(out_md) ||
+           deconv_pd->diff_weights_desc().get_size() !=
+               GetMemDescSize(weight_md)) {
+      CHECK(deconv_pd->next_impl()) << "No implementation";
     }
     return deconv_pd;
   } else {
     auto bias_md = GetBiasDesc(data_md);
-    mkldnn::convolution_backward_weights::desc desc(mkldnn::algorithm::convolution_direct,
-        out_md, weight_md, bias_md, data_md, strides, dilate, padding, padding,
-        mkldnn::padding_kind::zero);
-    auto deconv_pd = mkldnn::convolution_backward_weights::primitive_desc(desc, engine, fwd_pd);
-    while (deconv_pd.diff_dst_primitive_desc().get_size() != GetMemDescSize(data_md) ||
-           deconv_pd.src_primitive_desc().get_size() != GetMemDescSize(out_md) ||
-           deconv_pd.diff_weights_primitive_desc().get_size() != GetMemDescSize(weight_md)) {
-      CHECK(deconv_pd.next_impl()) << "No implementation";
+    mkldnn::convolution_backward_weights::desc desc(
+        mkldnn::algorithm::convolution_direct, out_md, weight_md, bias_md,
+        data_md, strides, dilate, padding, padding);
+    auto deconv_pd =
+        std::make_shared<mkldnn::convolution_backward_weights::primitive_desc>(
+            desc, engine, fwd_pd);
+    while (deconv_pd->diff_dst_desc().get_size() != GetMemDescSize(data_md) ||
+           deconv_pd->src_desc().get_size() != GetMemDescSize(out_md) ||
+           deconv_pd->diff_weights_desc().get_size() !=
+               GetMemDescSize(weight_md)) {
+      CHECK(deconv_pd->next_impl()) << "No implementation";
     }
     return deconv_pd;
   }
 }
 
 class MKLDNNDeconvForward {
-  std::shared_ptr<mkldnn::convolution_backward_data> fwd;
-  std::shared_ptr<mkldnn::memory> data;
-  std::shared_ptr<mkldnn::memory> weight;
-  std::shared_ptr<mkldnn::memory> bias;
-  std::shared_ptr<mkldnn::memory> out;
-  OutDataOp data_op;
-
  public:
-  MKLDNNDeconvForward(const DeconvolutionParam& param,
-                      const NDArray &data,
-                      const NDArray &weights,
-                      bool has_bias,
+  MKLDNNDeconvForward(const DeconvolutionParam &param, const NDArray &data,
+                      const NDArray &weights, bool has_bias,
                       const NDArray &output);
-  void SetDataHandle(const DeconvolutionParam& param,
-                     const OpContext &ctx,
-                     const NDArray &in_data,
-                     const NDArray &weight,
-                     const std::vector<OpReqType> &req,
-                     const std::vector<NDArray> &out_data);
+  const mkldnn::convolution_backward_data &GetFwd() const { return *fwd; }
 
-  void Execute(const std::vector<NDArray> &out_data);
+  const mkldnn::convolution_backward_data::primitive_desc &GetPd() const {
+    return *fwd_pd;
+  }
 
  private:
-  mkldnn::convolution_backward_data::primitive_desc fwd_pd;
+  std::shared_ptr<mkldnn::convolution_backward_data> fwd;
+  std::shared_ptr<mkldnn::convolution_backward_data::primitive_desc> fwd_pd;
 };  // class MKLDNNDeconvForward
 
-MKLDNNDeconvForward::MKLDNNDeconvForward(const DeconvolutionParam& param,
-                                const NDArray &data,
-                                const NDArray &weights,
-                                bool has_bias,
-                                const NDArray &output)
-                                :fwd_pd(GetDeconvFwdImpl(param, data, weights, has_bias, output)) {
-  this->data = std::shared_ptr<mkldnn::memory>(new mkldnn::memory(
-          fwd_pd.diff_dst_primitive_desc()));
-  this->weight = std::shared_ptr<mkldnn::memory>(new mkldnn::memory(
-          fwd_pd.weights_primitive_desc()));
-  this->out = std::shared_ptr<mkldnn::memory>(new mkldnn::memory(
-          fwd_pd.diff_src_primitive_desc()));
-  this->fwd = std::shared_ptr<mkldnn::convolution_backward_data>(
-    new mkldnn::convolution_backward_data(fwd_pd,
-                                          mkldnn::primitive::at(*this->data),
-                                          mkldnn::primitive::at(*this->weight),
-                                          *this->out));
+MKLDNNDeconvForward::MKLDNNDeconvForward(const DeconvolutionParam &param,
+                                         const NDArray &data,
+                                         const NDArray &weights, bool has_bias,
+                                         const NDArray &output)
+    : fwd_pd(GetDeconvFwdImpl(param, data, weights, has_bias, output)) {
+  fwd = std::make_shared<mkldnn::convolution_backward_data>(GetPd());
 }
 
-void MKLDNNDeconvForward::SetDataHandle(const DeconvolutionParam& param,
-                                        const OpContext &ctx,
-                                        const NDArray &in_data,
-                                        const NDArray &weight,
-                                        const std::vector<OpReqType> &req,
-                                        const std::vector<NDArray> &out_data) {
-  auto data_mem = in_data.GetMKLDNNDataReorder(
-      fwd_pd.diff_dst_primitive_desc());
-  const mkldnn::memory *weight_mem;
-  if (ctx.is_train) {
-    // TODO(zhengda) kvstore doesn't handle MKLDNN correctly. Let's reorder it
-    // to the default format for now.
-    if (weight.IsMKLDNNData())
-      // This asks the engine to reorder data after the weight array is used.
-      const_cast<NDArray&>(weight).Reorder2DefaultAsync();
-    weight_mem = GetWeights(weight, fwd_pd.weights_primitive_desc(), param.num_group);
-  } else {
-    // For inference, we want to reorder the weight array so we don't need to
-    // reorder data every time.
-    if (weight.IsDefaultData()) {
-      // We also need to modify the layout on the original weight array.
-      // Don't switch below sequence because naive engine will executes
-      // pushAsync synchronously.
-      const_cast<NDArray&>(weight).MKLDNNDataReorderAsync(fwd_pd.weights_primitive_desc());
-      weight_mem = GetWeights(weight, fwd_pd.weights_primitive_desc(), param.num_group);
-    } else {
-      weight_mem = weight.GetMKLDNNData();
-      CHECK(weight_mem->get_primitive_desc() == fwd_pd.weights_primitive_desc());
-    }
-  }
-  auto out_mem = CreateMKLDNNMem(out_data[deconv::kOut],
-      fwd_pd.diff_src_primitive_desc(), req[deconv::kOut]);
-  auto output = out_mem.second;
-  this->data->set_data_handle(data_mem->get_data_handle());
-  this->weight->set_data_handle(weight_mem->get_data_handle());
-  this->out->set_data_handle(output->get_data_handle());
-  this->data_op = out_mem.first;
-}
-
-void MKLDNNDeconvForward::Execute(const std::vector<NDArray> &out_data) {
-  MKLDNNStream::Get()->RegisterPrim(*fwd);
-  CommitOutput(out_data[deconv::kOut], mkldnn_output_t(this->data_op, this->out.get()));
-  MKLDNNStream::Get()->Submit();
-}
-
-static void MKLDNNDeconvFwdBiasPostProcess(const DeconvolutionParam& param,
-                                           const OpContext &ctx,
-                                           const NDArray &bias,
-                                           const std::vector<NDArray> &out_data) {
+static void MKLDNNDeconvFwdBiasPostProcess(
+    const DeconvolutionParam &param, const OpContext &ctx, const NDArray &bias,
+    const std::vector<NDArray> &out_data) {
   // add bias, broadcast bias to dim 1: channel
   if (!param.no_bias) {
     // MKLDNN only supports float right now.
@@ -306,18 +253,19 @@ static void MKLDNNDeconvFwdBiasPostProcess(const DeconvolutionParam& param,
   }
 }
 
-static inline MKLDNNDeconvForward &GetDeconvFwd(
-    const nnvm::NodeAttrs& attrs, const NDArray &data,
-    const NDArray &weights, const NDArray *bias,
-    const NDArray &output) {
+MKLDNNDeconvForward &GetDeconvFwd(const nnvm::NodeAttrs &attrs,
+                                  const NDArray &data, const NDArray &weights,
+                                  const NDArray *bias, const NDArray &output) {
 #if DMLC_CXX11_THREAD_LOCAL
-  static thread_local
-        std::unordered_map<DeconvSignature, MKLDNNDeconvForward, OpHash> fwds;
+  static thread_local std::unordered_map<DeconvSignature, MKLDNNDeconvForward,
+                                         OpHash>
+      fwds;
 #else
   static MX_THREAD_LOCAL
-        std::unordered_map<DeconvSignature, MKLDNNDeconvForward, OpHash> fwds;
+      std::unordered_map<DeconvSignature, MKLDNNDeconvForward, OpHash>
+          fwds;
 #endif
-  const DeconvolutionParam& param = nnvm::get<DeconvolutionParam>(attrs.parsed);
+  const DeconvolutionParam &param = nnvm::get<DeconvolutionParam>(attrs.parsed);
   DeconvSignature key(param);
   // Here we can sign the conv op with NDArray because conv primitive will
   // decide the right layout for the, so we only need to get the shape and the
@@ -325,82 +273,95 @@ static inline MKLDNNDeconvForward &GetDeconvFwd(
   key.AddSign(data);
   key.AddSign(weights);
   key.AddSign(output);
-  if (bias)
-    key.AddSign(*bias);
+  if (bias) key.AddSign(*bias);
 
   auto it = fwds.find(key);
   if (it == fwds.end()) {
     bool has_bias = (bias != nullptr);
-    MKLDNNDeconvForward fwd(param, data, weights, has_bias, output);
+    auto fwd = MKLDNNDeconvForward(param, data, weights, has_bias, output);
     it = AddToCache(&fwds, key, fwd);
   }
   return it->second;
 }
 
-void MKLDNNDeconvolutionForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
+void MKLDNNDeconvolutionForward(const nnvm::NodeAttrs &attrs,
+                                const OpContext &ctx,
                                 const std::vector<NDArray> &in_data,
                                 const std::vector<OpReqType> &req,
                                 const std::vector<NDArray> &out_data) {
   TmpMemMgr::Get()->Init(ctx.requested[deconv::kTempSpace]);
-  const DeconvolutionParam& param = nnvm::get<DeconvolutionParam>(attrs.parsed);
+  const DeconvolutionParam &param = nnvm::get<DeconvolutionParam>(attrs.parsed);
 
-  auto data = in_data[deconv::kData];
-  if (data.IsView() && data.IsMKLDNNData())
-    data = data.Reorder2Default();
+  auto &data = in_data[deconv::kData];
+  auto &weight = in_data[deconv::kWeight];
+  const NDArray *bias = param.no_bias ? nullptr : &in_data[deconv::kBias];
 
-  auto weight = in_data[deconv::kWeight];
-  if (weight.IsView() && weight.IsMKLDNNData())
-    weight = weight.Reorder2Default();
+  MKLDNNDeconvForward &fwd =
+      GetDeconvFwd(attrs, data, weight, bias, out_data[deconv::kOut]);
 
-  const NDArray* bias = param.no_bias ? nullptr : &in_data[deconv::kBias];
+  auto data_mem = data.GetMKLDNNDataReorder(fwd.GetPd().diff_dst_desc());
+  const mkldnn::memory *weight_mem;
+  if (ctx.is_train) {
+    // TODO(zhengda) kvstore doesn't handle MKLDNN correctly. Let's reorder it
+    // to the default format for now.
+    if (weight.IsMKLDNNData())
+      // This asks the engine to change the layout of the weight array after
+      // it's used.
+      weight.Reorder2DefaultAsync();
+    weight_mem =
+        GetWeights(weight, fwd.GetPd().weights_desc(), param.num_group);
+  } else {
+    // For inference, we want to reorder the weight array so we don't need to
+    // reorder data every time.
+    if (weight.IsDefaultData()) {
+      // We also need to modify the layout on the original weight array. The
+      // data conversion happens after the weight array is used.
+      weight.MKLDNNDataReorderAsync(fwd.GetPd().weights_desc());
+      weight_mem =
+          GetWeights(weight, fwd.GetPd().weights_desc(), param.num_group);
 
-  MKLDNNDeconvForward &deconvFwd = GetDeconvFwd(
-      attrs, data, weight, bias, out_data[deconv::kOut]);
+    } else {
+      weight_mem = weight.GetMKLDNNData();
+      CHECK(weight_mem->get_desc() == fwd.GetPd().weights_desc());
+    }
+  }
+  mkldnn_output_t out_mem;
+  out_mem = CreateMKLDNNMem(out_data[deconv::kOut], fwd.GetPd().diff_src_desc(),
+                            req[deconv::kOut]);
 
-  deconvFwd.SetDataHandle(param, ctx, data, weight, req, out_data);
+  mkldnn_args_map_t net_args;
 
-  deconvFwd.Execute(out_data);
+  net_args.insert({MKLDNN_ARG_DIFF_DST, *data_mem});
+  net_args.insert({MKLDNN_ARG_WEIGHTS, *weight_mem});
+  net_args.insert({MKLDNN_ARG_DIFF_SRC, *out_mem.second});
+  MKLDNNStream::Get()->RegisterPrimArgs(fwd.GetFwd(), net_args);
+  CommitOutput(out_data[deconv::kOut], out_mem);
+  MKLDNNStream::Get()->Submit();
 
   MKLDNNDeconvFwdBiasPostProcess(param, ctx, *bias, out_data);
 }
 
 class MKLDNNDeconvBackwardData {
   std::shared_ptr<mkldnn::convolution_forward> bwd;
-  std::shared_ptr<mkldnn::memory> data;
-  std::shared_ptr<mkldnn::memory> weight;
-  std::shared_ptr<mkldnn::memory> out;
 
  public:
-  const mkldnn::convolution_forward::primitive_desc pd;
-
+  std::shared_ptr<mkldnn::convolution_forward::primitive_desc> bwd_pd;
   MKLDNNDeconvBackwardData(const DeconvolutionParam &param, const NDArray &data,
-                           const NDArray &weights, const NDArray &output)
-      : pd(GetDeconvBwdDataImpl(param, data, weights, false, output)) {
-  }
-
-  void SetNewMem(const mkldnn::memory &data, const mkldnn::memory &weight,
-                 const mkldnn::memory &output) {
-    if (bwd == nullptr) {
-    this->data = std::shared_ptr<mkldnn::memory>(
-        new mkldnn::memory(pd.src_primitive_desc(), data.get_data_handle()));
-    this->weight = std::shared_ptr<mkldnn::memory>(
-        new mkldnn::memory(pd.weights_primitive_desc(), weight.get_data_handle()));
-    this->out = std::shared_ptr<mkldnn::memory>(
-        new mkldnn::memory(pd.dst_primitive_desc(), output.get_data_handle()));
-    bwd = std::shared_ptr<mkldnn::convolution_forward>(
-        new mkldnn::convolution_forward(pd, mkldnn::primitive::at(*this->data),
-                                        mkldnn::primitive::at(*this->weight),
-                                        *this->out));
-    } else {
-      this->data->set_data_handle(data.get_data_handle());
-      this->weight->set_data_handle(weight.get_data_handle());
-      this->out->set_data_handle(output.get_data_handle());
-    }
-  }
+                           const NDArray &weights, const NDArray &output);
 
   const mkldnn::convolution_forward &GetBwd() const { return *bwd; }
+  const mkldnn::convolution_forward::primitive_desc &GetDataPd() const {
+    return *bwd_pd;
+  }
 };
 
+MKLDNNDeconvBackwardData::MKLDNNDeconvBackwardData(
+    const DeconvolutionParam &param, const NDArray &data,
+    const NDArray &weights, const NDArray &output)
+    : bwd_pd(GetDeconvBwdDataImpl(param, data, weights, false, output)) {
+  bwd = std::make_shared<mkldnn::convolution_forward>(GetDataPd());
+}
+
 typedef ParamOpSign<DeconvolutionParam> MKLDNNDeconvSignature;
 
 static inline MKLDNNDeconvBackwardData &GetDeconvBwdData(
@@ -425,7 +386,7 @@ static inline MKLDNNDeconvBackwardData &GetDeconvBwdData(
 
   auto it = bwds.find(key);
   if (it == bwds.end()) {
-    MKLDNNDeconvBackwardData bwd(param, data, weights, output);
+    auto bwd = MKLDNNDeconvBackwardData(param, data, weights, output);
     it = AddToCache(&bwds, key, bwd);
   }
   return it->second;
@@ -433,44 +394,30 @@ static inline MKLDNNDeconvBackwardData &GetDeconvBwdData(
 
 class MKLDNNDeconvBackwardWeights {
   std::shared_ptr<mkldnn::convolution_backward_weights> bwd;
-  std::shared_ptr<mkldnn::memory> data;
-  std::shared_ptr<mkldnn::memory> weight;
-  std::shared_ptr<mkldnn::memory> out;
 
  public:
-  const mkldnn::convolution_backward_weights::primitive_desc pd;
-
+  std::shared_ptr<mkldnn::convolution_backward_weights::primitive_desc>
+      bwd_data_pd;
   MKLDNNDeconvBackwardWeights(
       const DeconvolutionParam &param, const NDArray &data,
       const NDArray &weights, const NDArray &output,
-      const mkldnn::convolution_forward::primitive_desc &bwd_data_pd)
-      : pd(GetDeconvBwdWeightsImpl(param, data, weights, false, output,
-                                   bwd_data_pd)) {}
-
-  void SetNewMem(
-      const mkldnn::memory &data, const mkldnn::memory &weight,
-      const mkldnn::memory &output,
-      const mkldnn::convolution_forward::primitive_desc &bwd_data_pd) {
-    if (bwd == nullptr) {
-      this->data = std::shared_ptr<mkldnn::memory>(new mkldnn::memory(
-          bwd_data_pd.src_primitive_desc(), data.get_data_handle()));
-      this->weight = std::shared_ptr<mkldnn::memory>(new mkldnn::memory(
-          bwd_data_pd.weights_primitive_desc(), weight.get_data_handle()));
-      this->out = std::shared_ptr<mkldnn::memory>(new mkldnn::memory(
-          bwd_data_pd.dst_primitive_desc(), output.get_data_handle()));
-      bwd = std::shared_ptr<mkldnn::convolution_backward_weights>(
-          new mkldnn::convolution_backward_weights(pd, *this->data,
-                                                   *this->weight, *this->out));
-    } else {
-      this->data->set_data_handle(data.get_data_handle());
-      this->weight->set_data_handle(weight.get_data_handle());
-      this->out->set_data_handle(output.get_data_handle());
-    }
-  }
-
+      const mkldnn::convolution_forward::primitive_desc &bwd_data_pd);
   const mkldnn::convolution_backward_weights &GetBwd() const { return *bwd; }
+  const mkldnn::convolution_backward_weights::primitive_desc &GetWeightsPd()
+      const {
+    return *bwd_data_pd;
+  }
 };
 
+MKLDNNDeconvBackwardWeights::MKLDNNDeconvBackwardWeights(
+    const DeconvolutionParam &param, const NDArray &data,
+    const NDArray &weights, const NDArray &output,
+    const mkldnn::convolution_forward::primitive_desc &bwd_data_pd)
+    : bwd_data_pd(GetDeconvBwdWeightsImpl(param, data, weights, false, output,
+                                          bwd_data_pd)) {
+  bwd = std::make_shared<mkldnn::convolution_backward_weights>(GetWeightsPd());
+}
+
 static inline MKLDNNDeconvBackwardWeights &GetDeconvBwdWeights(
     const DeconvolutionParam &param, const NDArray &data,
     const NDArray &weights, const NDArray &output,
@@ -494,7 +441,8 @@ static inline MKLDNNDeconvBackwardWeights &GetDeconvBwdWeights(
 
   auto it = bwds.find(key);
   if (it == bwds.end()) {
-    MKLDNNDeconvBackwardWeights bwd(param, data, weights, output, bwd_data_pd);
+    auto bwd =
+        MKLDNNDeconvBackwardWeights(param, data, weights, output, bwd_data_pd);
     auto ins_ret = bwds.insert(
         std::pair<MKLDNNDeconvSignature, MKLDNNDeconvBackwardWeights>(key,
                                                                       bwd));
@@ -513,47 +461,50 @@ void MKLDNNDeconvolutionBackward(const nnvm::NodeAttrs &attrs,
   const std::vector<NDArray> &in_grad = outputs;
   const DeconvolutionParam &param = nnvm::get<DeconvolutionParam>(attrs.parsed);
 
-  auto data = inputs[deconv::kData + 1];
-  if (data.IsView() && data.IsMKLDNNData())
-    data = data.Reorder2Default();
-
-  auto weight = inputs[deconv::kWeight + 1];
-  if (weight.IsView() && weight.IsMKLDNNData())
-    weight = weight.Reorder2Default();
+  auto &data = inputs[deconv::kData + 1];
+  auto &weight = inputs[deconv::kWeight + 1];
+  auto &out_grad = inputs[deconv::kOut];
 
   CHECK_NE(req[deconv::kWeight], kWriteInplace)
       << "cannot write weight inplace";
   MKLDNNDeconvBackwardData &bwd_data =
       GetDeconvBwdData(param, data, weight, inputs[deconv::kOut]);
-  auto out_grad_mem = inputs[deconv::kOut].GetMKLDNNDataReorder(
-      bwd_data.pd.src_primitive_desc());
+  auto out_grad_mem =
+      out_grad.GetMKLDNNDataReorder(bwd_data.GetDataPd().src_desc());
   if (req[deconv::kData]) {
-    auto weight_mem =
-        GetWeights(weight, bwd_data.pd.weights_primitive_desc(), param.num_group);
+    auto weight_mem = GetWeights(weight, bwd_data.GetDataPd().weights_desc(),
+                                 param.num_group);
     auto in_grad_mem =
-        CreateMKLDNNMem(in_grad[deconv::kData],
-                        bwd_data.pd.dst_primitive_desc(), req[deconv::kData]);
-    bwd_data.SetNewMem(*out_grad_mem, *weight_mem, *in_grad_mem.second);
-    MKLDNNStream::Get()->RegisterPrim(bwd_data.GetBwd());
+        CreateMKLDNNMem(in_grad[deconv::kData], bwd_data.GetDataPd().dst_desc(),
+                        req[deconv::kData]);
+    mkldnn_args_map_t net_args = {{MKLDNN_ARG_SRC, *out_grad_mem},
+                                  {MKLDNN_ARG_WEIGHTS, *weight_mem},
+                                  {MKLDNN_ARG_DST, *in_grad_mem.second}};
+    MKLDNNStream::Get()->RegisterPrimArgs(bwd_data.GetBwd(), net_args);
     CommitOutput(in_grad[deconv::kData], in_grad_mem);
   }
   if (req[deconv::kWeight]) {
     MKLDNNDeconvBackwardWeights &bwd_weights = GetDeconvBwdWeights(
-        param, data, weight,
-        inputs[deconv::kOut], bwd_data.pd);
-    if (bwd_data.pd.src_primitive_desc() != bwd_weights.pd.src_primitive_desc())
-      out_grad_mem = inputs[deconv::kOut].GetMKLDNNDataReorder(
-          bwd_weights.pd.src_primitive_desc());
-    auto data_mem = data.GetMKLDNNDataReorder(
-        bwd_weights.pd.diff_dst_primitive_desc());
+        param, data, weight, inputs[deconv::kOut], bwd_data.GetDataPd());
+    if (bwd_data.GetDataPd().src_desc() !=
+        bwd_weights.GetWeightsPd().src_desc())
+      out_grad_mem =
+          out_grad.GetMKLDNNDataReorder(bwd_weights.GetWeightsPd().src_desc());
+    auto data_mem =
+        data.GetMKLDNNDataReorder(bwd_weights.GetWeightsPd().diff_dst_desc());
     auto in_grad_weight = CreateMKLDNNWeightGrad(
-        in_grad[deconv::kWeight], bwd_weights.pd.diff_weights_primitive_desc(),
-        req[deconv::kWeight]);
-    bwd_weights.SetNewMem(*out_grad_mem, *data_mem, *in_grad_weight.second, bwd_data.pd);
-    MKLDNNStream::Get()->RegisterPrim(bwd_weights.GetBwd());
+        in_grad[deconv::kWeight],
+        bwd_weights.GetWeightsPd().diff_weights_desc(), req[deconv::kWeight]);
+
+    mkldnn_args_map_t net_args = {
+        {MKLDNN_ARG_SRC, *out_grad_mem},
+        {MKLDNN_ARG_DIFF_DST, *data_mem},
+        {MKLDNN_ARG_DIFF_WEIGHTS, *in_grad_weight.second}};
+    MKLDNNStream::Get()->RegisterPrimArgs(bwd_weights.GetBwd(), net_args);
     CommitOutput(in_grad[deconv::kWeight], in_grad_weight);
   }
   MKLDNNStream::Get()->Submit();
+
   if (!param.no_bias) {
     typedef float DType;
     Stream<cpu> *s = ctx.get_stream<cpu>();
@@ -573,5 +524,4 @@ void MKLDNNDeconvolutionBackward(const nnvm::NodeAttrs &attrs,
 
 }  // namespace op
 }  // namespace mxnet
-
 #endif  // MXNET_USE_MKLDNN == 1
diff --git a/src/operator/nn/mkldnn/mkldnn_fully_connected-inl.h b/src/operator/nn/mkldnn/mkldnn_fully_connected-inl.h
index fddaedc2459d..7d64cf5a92a7 100644
--- a/src/operator/nn/mkldnn/mkldnn_fully_connected-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_fully_connected-inl.h
@@ -50,7 +50,7 @@ struct MKLDNNFCParam: public dmlc::Parameter<MKLDNNFCParam> {
     DMLC_DECLARE_FIELD(enable_float_output).set_default(false)
     .describe("Whether to enable float32 output");
     DMLC_DECLARE_FIELD(with_eltwise).set_default(false)
-    .describe("Whether there's a post elemwise after FullyConnected operator");
+    .describe("Whether there's a post with_eltwise after FullyConnected operator");
     DMLC_DECLARE_FIELD(min_calib_range)
     .set_default(dmlc::optional<float>())
     .describe("The minimum scalar value in the form of float32 obtained "
@@ -85,10 +85,9 @@ class MKLDNNFullyConnectedForward {
                               const NDArray &data, const NDArray &weight,
                               const NDArray *bias,
                               const mkldnn::memory::desc &out_md)
-      : fwd_pd(GetFCFwdImpl(full_param, is_train, data, weight, bias, out_md)) {}
-
-  void SetNewMem(const mkldnn::memory &data, const mkldnn::memory &weight,
-                 const mkldnn::memory *bias, const mkldnn::memory &output);
+      : fwd_pd(GetFCFwdImpl(full_param, is_train, data, weight, bias, out_md)) {
+          fwd_ = std::make_shared<mkldnn::inner_product_forward>(fwd_pd);
+      }
 
   const mkldnn::inner_product_forward &GetFwd() const {
     return *fwd_;
@@ -96,10 +95,6 @@ class MKLDNNFullyConnectedForward {
 
  private:
   std::shared_ptr<mkldnn::inner_product_forward> fwd_;
-  std::shared_ptr<mkldnn::memory> data_;
-  std::shared_ptr<mkldnn::memory> weight_;
-  std::shared_ptr<mkldnn::memory> bias_;
-  std::shared_ptr<mkldnn::memory> out_;
 };
 
 typedef ParamOpSign<FullyConnectedParam> MKLDNNFullyconSignature;
diff --git a/src/operator/nn/mkldnn/mkldnn_fully_connected.cc b/src/operator/nn/mkldnn/mkldnn_fully_connected.cc
index fbe37e227cd1..1e7f879c5322 100644
--- a/src/operator/nn/mkldnn/mkldnn_fully_connected.cc
+++ b/src/operator/nn/mkldnn/mkldnn_fully_connected.cc
@@ -37,7 +37,7 @@ mkldnn::inner_product_forward::primitive_desc GetFCFwdImpl(
     const NDArray &data, const NDArray &weight, const NDArray *bias,
     const mkldnn::memory::desc &out_md) {
   auto data_md = GetMemDesc(data);
-  auto weight_md = GetMemDesc(weight);
+  auto weight_md = GetFCWeightDesc(weight);
   auto engine = CpuEngine::Get()->get_engine();
   auto propagation =
     is_train ? mkldnn::prop_kind::forward_training : mkldnn::prop_kind::forward_scoring;
@@ -67,7 +67,6 @@ mkldnn::inner_product_forward::primitive_desc GetFCFwdImpl(
       }
 
       attr.set_output_scales(mask, scales);
-      attr.set_int_output_round_mode(round_nearest);
     }
   }
 
@@ -102,7 +101,7 @@ inline static mkldnn::inner_product_backward_data::primitive_desc GetFCBwdData(
     const NDArray &data, const NDArray &weight, const NDArray &output,
     mkldnn::inner_product_forward::primitive_desc fwd_pd) {
   auto data_md = GetMemDesc(data);
-  auto weight_md = GetMemDesc(weight);
+  auto weight_md = GetFCWeightDesc(weight);
   auto out_md = GetMemDesc(output);
   auto engine = CpuEngine::Get()->get_engine();
   mkldnn::inner_product_backward_data::desc desc(data_md, weight_md, out_md);
@@ -113,7 +112,7 @@ inline static mkldnn::inner_product_backward_weights::primitive_desc GetFCBwdWei
     const NDArray &data, const NDArray &weight, const NDArray *bias,
     const NDArray &output, mkldnn::inner_product_forward::primitive_desc fwd_pd) {
   auto data_md = GetMemDesc(data);
-  auto weight_md = GetMemDesc(weight);
+  auto weight_md = GetFCWeightDesc(weight);
   auto out_md = GetMemDesc(output);
   auto engine = CpuEngine::Get()->get_engine();
   if (bias) {
@@ -130,51 +129,6 @@ inline static mkldnn::inner_product_backward_weights::primitive_desc GetFCBwdWei
   }
 }
 
-void MKLDNNFullyConnectedForward::SetNewMem(const mkldnn::memory &data,
-                                            const mkldnn::memory &weight,
-                                            const mkldnn::memory *bias,
-                                            const mkldnn::memory &output) {
-  if (this->data_ == nullptr)
-    this->data_ = std::shared_ptr<mkldnn::memory>(new mkldnn::memory(
-            fwd_pd.src_primitive_desc(), data.get_data_handle()));
-  else
-    this->data_->set_data_handle(data.get_data_handle());
-
-  if (this->weight_ == nullptr)
-    this->weight_ = std::shared_ptr<mkldnn::memory>(new mkldnn::memory(
-            fwd_pd.weights_primitive_desc(), weight.get_data_handle()));
-  else
-    this->weight_->set_data_handle(weight.get_data_handle());
-
-  if (this->out_ == nullptr)
-    this->out_ = std::shared_ptr<mkldnn::memory>(new mkldnn::memory(
-            fwd_pd.dst_primitive_desc(), output.get_data_handle()));
-  else
-    this->out_->set_data_handle(output.get_data_handle());
-
-  if (bias != nullptr) {
-    if (this->bias_ == nullptr)
-      this->bias_ = std::shared_ptr<mkldnn::memory>(new mkldnn::memory(
-      fwd_pd.bias_primitive_desc(), bias->get_data_handle()));
-    else
-      this->bias_->set_data_handle(bias->get_data_handle());
-
-    if (this->fwd_ == nullptr)
-      this->fwd_ = std::shared_ptr<mkldnn::inner_product_forward>(
-          new mkldnn::inner_product_forward(
-              fwd_pd, mkldnn::primitive::at(*this->data_),
-              mkldnn::primitive::at(*this->weight_),
-              mkldnn::primitive::at(*this->bias_), *this->out_));
-  } else {
-     if (this->fwd_ == nullptr) {
-      this->fwd_ = std::shared_ptr<mkldnn::inner_product_forward>(
-          new mkldnn::inner_product_forward(
-              fwd_pd, mkldnn::primitive::at(*this->data_),
-              mkldnn::primitive::at(*this->weight_), *this->out_));
-    }
-  }
-}
-
 MKLDNNFullyConnectedForward &GetFCFwd(
     const FullyConnectedParam &param, const bool is_train,
     const NDArray &data, const NDArray &weight,
@@ -223,13 +177,13 @@ void MKLDNNFCFlattenData(const FullyConnectedParam &param,
       mkldnn::memory::dims out_dims{static_cast<int>(oshape.ProdShape(0, oshape.ndim()-1)),
         static_cast<int>(oshape[ishape.ndim()-1])};
       *out_md = mkldnn::memory::desc(out_dims, get_mkldnn_type(out_data.dtype()),
-        mkldnn::memory::format::any);
+                                     mkldnn::memory::format_tag::any);
     } else {
       *in_data = in_data->MKLDNNDataReshape(Shape2(ishape[0], ishape.ProdShape(1, ishape.ndim())));
       mkldnn::memory::dims out_dims{static_cast<int>(oshape[0]),
         static_cast<int>(oshape.ProdShape(1, oshape.ndim()))};
       *out_md = mkldnn::memory::desc(out_dims, get_mkldnn_type(out_data.dtype()),
-        mkldnn::memory::format::any);
+                                     mkldnn::memory::format_tag::any);
     }
   }
 }
@@ -244,35 +198,34 @@ void MKLDNNFCForwardFullFeature(const MKLDNNFCFullParam &full_param,
   NDArray weight = in_data[fullc::kWeight];
   NDArray data = in_data[fullc::kData];
 
-  auto data_mem = data.GetMKLDNNDataReorder(fwd->fwd_pd.src_primitive_desc());
+  auto data_mem = data.GetMKLDNNDataReorder(fwd->fwd_pd.src_desc());
   const mkldnn::memory *weight_mem;
   if (ctx.is_train) {
     if (weight.IsMKLDNNData()) {
       weight.Reorder2DefaultAsync();
     }
-    weight_mem = GetWeights(weight, fwd->fwd_pd.weights_primitive_desc(), 1);
+    weight_mem = GetWeights(weight, fwd->fwd_pd.weights_desc(), 1);
   } else {
-    if (weight.IsDefaultData()) {
-      // We also need to modify the layout on the original weight array.
-      // Don't switch below sequence because naive engine will executes
-      // pushAsync synchronously.
-      weight.MKLDNNDataReorderAsync(fwd->fwd_pd.weights_primitive_desc());
-      weight_mem = GetWeights(weight, fwd->fwd_pd.weights_primitive_desc(), 1);
-    } else {
-      weight_mem = weight.GetMKLDNNData();
-      CHECK(weight_mem->get_primitive_desc() == fwd->fwd_pd.weights_primitive_desc());
+    weight_mem = weight.GetMKLDNNData();
+    if (weight_mem->get_desc() != fwd->fwd_pd.weights_desc()) {
+      weight.MKLDNNDataReorderAsync(fwd->fwd_pd.weights_desc());
+      weight_mem = GetWeights(weight, fwd->fwd_pd.weights_desc(), 1);
     }
   }
   auto out_mem = CreateMKLDNNMem(out_data[fullc::kOut],
-      fwd->fwd_pd.dst_primitive_desc(), req[fullc::kOut], &data);
+                                 fwd->fwd_pd.dst_desc(), req[fullc::kOut], &data);
+
+  mkldnn_args_map_t args = {
+      {MKLDNN_ARG_SRC, *data_mem},
+      {MKLDNN_ARG_WEIGHTS, *weight_mem},
+      {MKLDNN_ARG_DST, *out_mem.second},
+  };
   if (!full_param.default_param.no_bias) {
     auto bias_mem = in_data[fullc::kBias].GetMKLDNNDataReorder(
-        fwd->fwd_pd.bias_primitive_desc());
-    fwd->SetNewMem(*data_mem, *weight_mem, bias_mem, *out_mem.second);
-  } else {
-    fwd->SetNewMem(*data_mem, *weight_mem, nullptr, *out_mem.second);
+        fwd->fwd_pd.bias_desc());
+    args[MKLDNN_ARG_BIAS] = *bias_mem;
   }
-  MKLDNNStream::Get()->RegisterPrim(fwd->GetFwd());
+  MKLDNNStream::Get()->RegisterPrimArgs(fwd->GetFwd(), args);
   CommitOutput(out_data[fullc::kOut], out_mem);
   MKLDNNStream::Get()->Submit();
 }
@@ -339,13 +292,18 @@ void MKLDNNFCBackward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
     mkldnn::inner_product_backward_data::primitive_desc ipBwdData_pd = GetFCBwdData(
         data, weight, out_grad, fwd_pd);
     auto out_grad_mem = out_grad.GetMKLDNNDataReorder(
-        ipBwdData_pd.diff_dst_primitive_desc());
-    auto weight_mem = weight.GetMKLDNNDataReorder(ipBwdData_pd.weights_primitive_desc());
+        ipBwdData_pd.diff_dst_desc());
+    auto weight_mem = weight.GetMKLDNNDataReorder(ipBwdData_pd.weights_desc());
     auto in_grad_mem = CreateMKLDNNMem(in_grad[fullc::kData],
-                                       ipBwdData_pd.diff_src_primitive_desc(),
+                                       ipBwdData_pd.diff_src_desc(),
                                        req[fullc::kData]);
-    MKLDNNStream::Get()->RegisterPrim(mkldnn::inner_product_backward_data(
-          ipBwdData_pd, *out_grad_mem, *weight_mem, *in_grad_mem.second));
+    mkldnn_args_map_t args = {
+      {MKLDNN_ARG_DIFF_DST, *out_grad_mem},
+      {MKLDNN_ARG_WEIGHTS, *weight_mem},
+      {MKLDNN_ARG_DIFF_SRC, *in_grad_mem.second}
+    };
+
+    MKLDNNStream::Get()->RegisterPrimArgs(mkldnn::inner_product_backward_data(ipBwdData_pd), args);
     CommitOutput(in_grad[fullc::kData], in_grad_mem);
   }
   if (req[fullc::kWeight]) {
@@ -353,23 +311,26 @@ void MKLDNNFCBackward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
       = GetFCBwdWeights(data, weight, param.no_bias ? nullptr : &in_grad[fullc::kBias],
           out_grad, fwd_pd);
     auto out_grad_mem = out_grad.GetMKLDNNDataReorder(
-        ipBwdWeights_pd.diff_dst_primitive_desc());
-    auto data_mem = data.GetMKLDNNDataReorder(ipBwdWeights_pd.src_primitive_desc());
+        ipBwdWeights_pd.diff_dst_desc());
+    auto data_mem = data.GetMKLDNNDataReorder(ipBwdWeights_pd.src_desc());
     auto in_grad_weight = CreateMKLDNNWeightGrad(in_grad[fullc::kWeight],
-                                                 ipBwdWeights_pd.diff_weights_primitive_desc(),
+                                                 ipBwdWeights_pd.diff_weights_desc(),
                                                  req[fullc::kWeight]);
+    mkldnn_args_map_t args = {
+      {MKLDNN_ARG_DIFF_DST, *out_grad_mem},
+      {MKLDNN_ARG_SRC, *data_mem},
+      {MKLDNN_ARG_DIFF_WEIGHTS, *in_grad_weight.second},
+    };
+
     mkldnn_output_t in_grad_bias;
-    if (param.no_bias) {
-      MKLDNNStream::Get()->RegisterPrim(mkldnn::inner_product_backward_weights(
-            ipBwdWeights_pd, *data_mem, *out_grad_mem, *in_grad_weight.second));
-    } else {
+    if (!param.no_bias) {
       in_grad_bias = CreateMKLDNNMem(in_grad[fullc::kBias],
-                                     ipBwdWeights_pd.diff_bias_primitive_desc(),
+                                     ipBwdWeights_pd.diff_bias_desc(),
                                      req[fullc::kBias]);
-      MKLDNNStream::Get()->RegisterPrim(mkldnn::inner_product_backward_weights(
-            ipBwdWeights_pd, *data_mem, *out_grad_mem, *in_grad_weight.second,
-            *in_grad_bias.second));
+      args[MKLDNN_ARG_DIFF_BIAS] = *in_grad_bias.second;
     }
+    MKLDNNStream::Get()->RegisterPrimArgs(
+        mkldnn::inner_product_backward_weights(ipBwdWeights_pd), args);
     CommitOutput(in_grad[fullc::kWeight], in_grad_weight);
     CommitOutput(in_grad[fullc::kBias], in_grad_bias);
   }
diff --git a/src/operator/nn/mkldnn/mkldnn_lrn-inl.h b/src/operator/nn/mkldnn/mkldnn_lrn-inl.h
index 31b293a14c2c..ca7095fd3f02 100644
--- a/src/operator/nn/mkldnn/mkldnn_lrn-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_lrn-inl.h
@@ -34,27 +34,27 @@
 namespace mxnet {
 namespace op {
 
-inline algorithm GetMKLDNNLRNAlgo(const LRNParam &param) {
+inline mkldnn::algorithm GetMKLDNNLRNAlgo(const LRNParam &param) {
   // TODO(Patric): lrn_within_channel will cause core dump in MKLDNN backward
   //               Need to confirm with MKLDNN team and fix later
-  return algorithm::lrn_across_channels;
+  return mkldnn::algorithm::lrn_across_channels;
 }
 
 inline mkldnn::lrn_forward::primitive_desc GetLRNFwdDesc(
-    const LRNParam &param, const bool is_train, const memory::desc &src_md) {
+    const LRNParam &param, const bool is_train, const mkldnn::memory::desc &src_md) {
   mkldnn::engine &engine = CpuEngine::Get()->get_engine();
-  const algorithm  alg = GetMKLDNNLRNAlgo(param);
+  const mkldnn::algorithm  alg = GetMKLDNNLRNAlgo(param);
   const float alpha = param.alpha;
   const float beta = param.beta;
   const int   nsize = param.nsize;
   const float k = param.knorm;
-  auto kind = prop_kind::forward_training;
+  auto kind = mkldnn::prop_kind::forward_training;
   if (is_train) {
-    kind = prop_kind::forward_training;
+    kind = mkldnn::prop_kind::forward_training;
   } else {
-    kind = prop_kind::forward_scoring;
+    kind = mkldnn::prop_kind::forward_scoring;
   }
-  lrn_forward::desc fwd_desc(kind, alg, src_md, nsize, alpha, beta, k);
+  mkldnn::lrn_forward::desc fwd_desc(kind, alg, src_md, nsize, alpha, beta, k);
   return mkldnn::lrn_forward::primitive_desc(fwd_desc, engine);
 }
 
@@ -63,13 +63,13 @@ inline mkldnn::lrn_backward::primitive_desc GetLRNBwdDesc(
     const mkldnn::memory::desc &diff_md,
     const mkldnn::lrn_forward::primitive_desc &lrnFwd_desc) {
   mkldnn::engine &engine = CpuEngine::Get()->get_engine();
-  const algorithm alg = GetMKLDNNLRNAlgo(param);
+  const mkldnn::algorithm alg = GetMKLDNNLRNAlgo(param);
   const float alpha = param.alpha;
   const float beta = param.beta;
   const int nsize = param.nsize;
   const float k = param.knorm;
 
-  lrn_backward::desc lrnBwd_desc(alg, data_in_md,
+  mkldnn::lrn_backward::desc lrnBwd_desc(alg, data_in_md,
                 diff_md, nsize, alpha, beta, k);
   return mkldnn::lrn_backward::primitive_desc(lrnBwd_desc,
                                engine, lrnFwd_desc);
@@ -83,33 +83,24 @@ class MKLDNNLRNFwd {
  public:
   MKLDNNLRNFwd(const LRNParam& param,
                bool  is_train,
-               const NDArray &in_data):
-               is_train(is_train) {
+               const NDArray &in_data) {
     _Init(param, is_train, in_data);
   }
 
   ~MKLDNNLRNFwd() {}
 
-  void SetNewMem(const NDArray &data,
-                 const NDArray &output,
-                 const OpReqType req);
-
-  void SetNewMem(const NDArray &in_data,
-                 const mkldnn::memory *out_mem);
-
-  void Execute(const NDArray &out_data);
+  void Execute(const OpContext &ctx,
+               const NDArray &in_data,
+               const OpReqType req,
+               const NDArray &out_data);
 
   mkldnn::lrn_forward &GetFwd();
-
   const mkldnn::memory *GetWs();
+  mkldnn::lrn_forward::primitive_desc &GetFwdPd();
 
  private:
   std::shared_ptr<mkldnn::lrn_forward> fwd;
-  std::shared_ptr<mkldnn::memory> in_mem;
-  std::shared_ptr<mkldnn::memory> out_mem;
-  std::shared_ptr<mkldnn::memory> ws_mem;
-  mkldnn_output_t output_mem_t;
-  bool is_train;
+  mkldnn::lrn_forward::primitive_desc fwd_pd;
 
  private:
   void _Init(const LRNParam &param, bool is_train, const NDArray &in_data);
@@ -119,52 +110,37 @@ void MKLDNNLRNFwd::_Init(const LRNParam &param,
                          bool is_train,
                          const NDArray &in_data) {
   mkldnn::memory::desc in_data_md =
-      in_data.GetMKLDNNData()->get_primitive_desc().desc();
-  mkldnn::lrn_forward::primitive_desc fwd_pd =
+      in_data.GetMKLDNNData()->get_desc();
+  this->fwd_pd =
       GetLRNFwdDesc(param, is_train, in_data_md);
 
-  this->in_mem.reset(new mkldnn::memory(in_data.GetMKLDNNData()
-                     ->get_primitive_desc()));
-  this->out_mem.reset(new mkldnn::memory(fwd_pd.dst_primitive_desc()));
-  if (is_train) {
-    // If it's training, we have to create a workspace memory. Otherwise,
-    // MKLDNN will have segmentation fault.
-    ws_mem.reset(new mkldnn::memory(fwd_pd.workspace_primitive_desc()));
-    this->fwd = std::shared_ptr<mkldnn::lrn_forward>(
-        new mkldnn::lrn_forward(fwd_pd, mkldnn::primitive::at(*this->in_mem),
-                                *this->ws_mem, *this->out_mem));
-  } else {
-    this->fwd = std::shared_ptr<mkldnn::lrn_forward>(
-        new mkldnn::lrn_forward(fwd_pd, mkldnn::primitive::at(*(this->in_mem)),
-                                *(this->out_mem)));
-  }
-}
-
-void MKLDNNLRNFwd::SetNewMem(const NDArray &in_data,
-                             const NDArray &out_data,
-                             const OpReqType req) {
-  const mkldnn::memory *in_data_mem = in_data.GetMKLDNNData();
-  output_mem_t = CreateMKLDNNMem(out_data, this->out_mem->get_primitive_desc(), req);
-  this->in_mem->set_data_handle(in_data_mem->get_data_handle());
-  this->out_mem->set_data_handle(output_mem_t.second->get_data_handle());
+  this->fwd = std::shared_ptr<mkldnn::lrn_forward>(new mkldnn::lrn_forward(this->fwd_pd));
 }
 
-void MKLDNNLRNFwd::SetNewMem(const NDArray &in_data,
-                             const mkldnn::memory *out_mem) {
-  const mkldnn::memory *in_data_mem = in_data.GetMKLDNNData();
-  this->in_mem->set_data_handle(in_data_mem->get_data_handle());
-  this->out_mem->set_data_handle(out_mem->get_data_handle());
-}
-
-void MKLDNNLRNFwd::Execute(const NDArray &out_data) {
-  MKLDNNStream::Get()->RegisterPrim(*(this->fwd));
+void MKLDNNLRNFwd::Execute(const OpContext &ctx,
+                            const NDArray &in_data,
+                            const OpReqType req,
+                            const NDArray &out_data) {
+  auto output_mem_t = CreateMKLDNNMem(out_data, (this->fwd_pd).dst_desc(), req);
+
+  mkldnn_args_map_t args = {
+    { MKLDNN_ARG_SRC, *in_data.GetMKLDNNData()},
+    { MKLDNN_ARG_DST, *output_mem_t.second },
+  };
+  std::shared_ptr<mkldnn::memory> workspace;
+  if (ctx.is_train) {
+    auto engine = CpuEngine::Get()->get_engine();
+    workspace = std::make_shared<mkldnn::memory>((this->fwd_pd).workspace_desc(), engine);
+    args[MKLDNN_ARG_WORKSPACE] = *(workspace);
+  }
+  MKLDNNStream::Get()->RegisterPrimArgs(*(this->fwd), args);
   CommitOutput(out_data, output_mem_t);
   MKLDNNStream::Get()->Submit();
 }
 
 mkldnn::lrn_forward &MKLDNNLRNFwd::GetFwd() { return *this->fwd; }
+mkldnn::lrn_forward::primitive_desc &MKLDNNLRNFwd::GetFwdPd() { return this->fwd_pd; }
 
-const mkldnn::memory *MKLDNNLRNFwd::GetWs() { return this->ws_mem.get(); }
 // End of LRN Class and its functions
 
 static MKLDNNLRNFwd &GetLRNFwd(const LRNParam& param,
@@ -180,10 +156,11 @@ static MKLDNNLRNFwd &GetLRNFwd(const LRNParam& param,
                                             OpHash> lrn_fwds;
 #endif
   auto kind_ =
-      ctx.is_train ? prop_kind::forward_training : prop_kind::forward_scoring;
+      ctx.is_train ? mkldnn::prop_kind::forward_training
+                   : mkldnn::prop_kind::forward_scoring;
 
   MKLDNNLRNSignature key(param);
-  key.AddSign(kind_);
+  key.AddSign(static_cast<int>(kind_));
   key.AddSign(in_data);
 
   auto it = lrn_fwds.find(key);
@@ -201,17 +178,12 @@ void MKLDNNLRNForward(const OpContext &ctx, const LRNParam &param,
   if (in_buffer.IsView() && in_buffer.IsMKLDNNData())
     in_buffer = in_buffer.Reorder2Default();
   MKLDNNLRNFwd fwd = GetLRNFwd(param, ctx, in_buffer);
-  fwd.SetNewMem(in_buffer, out_data, req);
-  fwd.Execute(out_data);
+  fwd.Execute(ctx, in_buffer, req, out_data);
 }
 
 // LRN Backward Class
 class MKLDNNLRNBwd {
   std::shared_ptr<mkldnn::lrn_backward> bwd;
-  std::shared_ptr<mkldnn::memory> in_data_mem;
-  std::shared_ptr<mkldnn::memory> diff_dst_mem;
-  std::shared_ptr<mkldnn::memory> ws_mem;
-  std::shared_ptr<mkldnn::memory> diff_src_mem;
 
  public:
   const mkldnn::lrn_forward::primitive_desc fwd_pd;
@@ -222,40 +194,26 @@ class MKLDNNLRNBwd {
   MKLDNNLRNBwd(const LRNParam &param, const mkldnn::memory::desc in_data_md,
                const mkldnn::memory::desc diff_md)
       : fwd_pd(GetLRNFwdDesc(param, true, in_data_md)),
-        bwd_pd(GetLRNBwdDesc(param, in_data_md, diff_md, this->fwd_pd)) {}
-
-  void SetNewMem(const NDArray &in_data, const NDArray &out_grad,
-                 const mkldnn::memory *ws, const mkldnn::memory *diff_src_mem) {
-    if (bwd == nullptr) {
-      this->in_data_mem.reset(
-          new mkldnn::memory(this->fwd_pd.src_primitive_desc(),
-                             in_data.GetMKLDNNData()->get_data_handle()));
-      this->diff_dst_mem.reset(
-          new mkldnn::memory(this->fwd_pd.dst_primitive_desc(),
-                             out_grad.GetMKLDNNData()->get_data_handle()));
-      this->ws_mem.reset(
-          new mkldnn::memory(this->fwd_pd.workspace_primitive_desc(),
-                             ws->get_data_handle()));
-      this->diff_src_mem.reset(
-          new mkldnn::memory(this->bwd_pd.diff_src_primitive_desc(),
-                             diff_src_mem->get_data_handle()));
-      this->bwd.reset(new mkldnn::lrn_backward(
-          this->bwd_pd, mkldnn::primitive::at(*this->in_data_mem),
-          mkldnn::primitive::at(*this->diff_dst_mem), *this->ws_mem,
-          *this->diff_src_mem));
-    } else {
-      this->in_data_mem->set_data_handle(
-          in_data.GetMKLDNNData()->get_data_handle());
-      this->diff_dst_mem->set_data_handle(
-          out_grad.GetMKLDNNData()->get_data_handle());
-      this->ws_mem->set_data_handle(ws->get_data_handle());
-      this->diff_src_mem->set_data_handle(diff_src_mem->get_data_handle());
-    }
-  }
-
-  void Execute(const NDArray &in_grad, const mkldnn_output_t &diff_src_mem_) {
-    MKLDNNStream::Get()->RegisterPrim(*(this->bwd));
-    CommitOutput(in_grad, diff_src_mem_);
+        bwd_pd(GetLRNBwdDesc(param, in_data_md, diff_md, this->fwd_pd)) {
+          bwd = std::make_shared<mkldnn::lrn_backward>(bwd_pd);
+        }
+
+  const mkldnn::lrn_backward &GetBwd() const { return *bwd; }
+
+  void Execute(const NDArray &out_grad,
+                  const NDArray &in_data,
+                  const NDArray &in_grad,
+                  const mkldnn_output_t &diff_src_mem) {
+    auto engine = CpuEngine::Get()->get_engine();
+    auto workspace = std::make_shared<mkldnn::memory>((this->fwd_pd).workspace_desc(), engine);
+    mkldnn_args_map_t args = {
+      { MKLDNN_ARG_SRC, *in_data.GetMKLDNNData() },
+      { MKLDNN_ARG_DIFF_DST, *out_grad.GetMKLDNNData()},
+      { MKLDNN_ARG_WORKSPACE, *workspace },
+      { MKLDNN_ARG_DIFF_SRC, *diff_src_mem.second }
+    };
+    MKLDNNStream::Get()->RegisterPrimArgs(*(this->bwd), args);
+    CommitOutput(in_grad, diff_src_mem);
     MKLDNNStream::Get()->Submit();
   }
 };  // End of LRN Class
@@ -277,9 +235,9 @@ static MKLDNNLRNBwd &GetLRNBwd(const LRNParam &param, const NDArray &in_data,
   auto it = lrn_bwds.find(key);
   if (it == lrn_bwds.end()) {
     const mkldnn::memory::desc in_data_md =
-        in_data.GetMKLDNNData()->get_primitive_desc().desc();
+        in_data.GetMKLDNNData()->get_desc();
     const mkldnn::memory::desc diff_md =
-        out_grad.GetMKLDNNData()->get_primitive_desc().desc();
+        out_grad.GetMKLDNNData()->get_desc();
     MKLDNNLRNBwd bwd(param, in_data_md, diff_md);
     it = AddToCache(&lrn_bwds, key, bwd);
   }
@@ -300,23 +258,13 @@ void MKLDNNLRNBackward(const OpContext &ctx, const LRNParam &param,
     in_buffer = in_data.Reorder2Default();
   }
   MKLDNNLRNBwd &bwd = GetLRNBwd(param, in_buffer, in_grad, out_grad);
-  // Repeat FW for getting workspace
-  // TODO(Patric): To keep the function stateless, we can't pass workspace
-  //               from LRN forward to backward. We have to re-compute
-  //               LRN forward to get the workspace.
-  //               Will refine this code later.
-  MKLDNNLRNFwd fwd = GetLRNFwd(param, ctx, in_buffer);
-  std::shared_ptr<const mkldnn::memory> dst_temp(
-      new mkldnn::memory(bwd.fwd_pd.dst_primitive_desc()));
-  fwd.SetNewMem(in_buffer, dst_temp.get());
-  MKLDNNStream::Get()->RegisterPrim(fwd.GetFwd());
-
   mkldnn_output_t diff_src_mem =
-      CreateMKLDNNMem(in_grad, bwd.bwd_pd.diff_src_primitive_desc(), req);
-  bwd.SetNewMem(in_buffer, out_grad, fwd.GetWs(), diff_src_mem.second);
-  bwd.Execute(in_grad, diff_src_mem);
+      CreateMKLDNNMem(in_grad, bwd.bwd_pd.diff_src_desc(), req);
+
+  bwd.Execute(out_grad, in_buffer, in_grad, diff_src_mem);
 }
 }  // namespace op
 }  // namespace mxnet
 #endif  // MXNET_USE_MKLDNN == 1
 #endif  // MXNET_OPERATOR_NN_MKLDNN_MKLDNN_LRN_INL_H__
+
diff --git a/src/operator/nn/mkldnn/mkldnn_ops-inl.h b/src/operator/nn/mkldnn/mkldnn_ops-inl.h
index c0218f4100b5..71f3eafa8ee9 100644
--- a/src/operator/nn/mkldnn/mkldnn_ops-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_ops-inl.h
@@ -26,7 +26,6 @@
 #ifndef MXNET_OPERATOR_NN_MKLDNN_MKLDNN_OPS_INL_H_
 #define MXNET_OPERATOR_NN_MKLDNN_MKLDNN_OPS_INL_H_
 
-#if MXNET_USE_MKLDNN == 1
 
 #include <mxnet/io.h>
 #include <mxnet/base.h>
@@ -36,6 +35,8 @@
 #include <dmlc/logging.h>
 #include <dmlc/optional.h>
 #include <vector>
+
+#if MXNET_USE_MKLDNN == 1
 #include <mkldnn.hpp>
 
 namespace mxnet {
@@ -71,6 +72,21 @@ void MKLDNNDeconvolutionBackward(const nnvm::NodeAttrs& attrs, const OpContext &
                                  const std::vector<OpReqType>& req,
                                  const std::vector<NDArray>& outputs);
 
+/* For activation */
+void MKLDNNActivationForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
+                             const NDArray &in_data, const OpReqType &req,
+                             const NDArray &out_data);
+void MKLDNNActivationBackward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
+                              const NDArray &out_grad, const NDArray &in_data,
+                              const OpReqType &req, const NDArray &in_grad);
+
+void MKLDNNLeakyReluForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
+                            const NDArray &in_data, const OpReqType &req,
+                            const NDArray &out_data);
+void MKLDNNLeakyReluBackward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
+                             const std::vector<NDArray>& inputs, const OpReqType &req,
+                             const NDArray &output);
+
 /* For softmax */
 void MKLDNNSoftmaxForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
                           const NDArray &in_data, const OpReqType &req,
@@ -102,20 +118,6 @@ void MKLDNNConcatBackward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
                           const std::vector<OpReqType>& req,
                           const std::vector<NDArray>& outputs);
 
-/* For activation */
-void MKLDNNActivationForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
-                             const NDArray &in_data, const OpReqType &req,
-                             const NDArray &out_data);
-void MKLDNNActivationBackward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
-                              const NDArray &out_grad, const NDArray &in_data,
-                              const OpReqType &req, const NDArray &in_grad);
-void MKLDNNLeakyReluForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
-                            const NDArray &in_data, const OpReqType &req,
-                            const NDArray &out_data);
-void MKLDNNLeakyReluBackward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
-                             const std::vector<NDArray>& inputs, const OpReqType &req,
-                             const NDArray &output);
-
 void MKLDNNSum(const mkldnn::memory &arr1, const mkldnn::memory &arr2,
                const mkldnn::memory &out);
 
@@ -130,9 +132,8 @@ void MKLDNNReshapeForward(const nnvm::NodeAttrs& attrs,
                           const NDArray &input,
                           const OpReqType &req,
                           const NDArray &output);
-
 }  // namespace op
 }  // namespace mxnet
-#endif  // MXNET_USE_MKLDNN == 1
 
+#endif  // MXNET_USE_MKLDNN == 1
 #endif  // MXNET_OPERATOR_NN_MKLDNN_MKLDNN_OPS_INL_H_
diff --git a/src/operator/nn/mkldnn/mkldnn_pooling-inl.h b/src/operator/nn/mkldnn/mkldnn_pooling-inl.h
index 9b9f0193979b..22e9abd156a3 100644
--- a/src/operator/nn/mkldnn/mkldnn_pooling-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_pooling-inl.h
@@ -43,33 +43,26 @@ class MKLDNNPoolingFwd {
                    const int padding_t, const int padding_b,
                    const int padding_l, const int padding_r,
                    const mkldnn::algorithm alg_kind,
-                   const bool with_workspace, const bool is_train) :
-                   is_train_(is_train),
+                   const bool with_workspace, const bool is_train):
                    with_workspace_(with_workspace),
-                   alg_kind_(alg_kind),
-                   fwd_(nullptr), data_(nullptr), out_(nullptr), workspace_(nullptr) {
+                   fwd_(nullptr) {
     Init(input, output,
          kernel_h, kernel_w, stride_h, stride_w,
-         padding_t, padding_b, padding_l, padding_r);
+         padding_t, padding_b, padding_l, padding_r,
+         is_train, alg_kind);
   }
 
   ~MKLDNNPoolingFwd() {}
-  void SetNewMem(const NDArray& in_data,
-                 const NDArray& out_data,
-                 const OpReqType& req,
-                 const mxnet::NDArray *workspace = nullptr);
-  void Execute(const NDArray& out_data);
+  void Execute(const NDArray &in_data,
+               const OpReqType req,
+               const NDArray& out_data,
+               const NDArray *workspace);
 
  private:
-  bool is_train_;
   bool with_workspace_;
-  mkldnn::algorithm alg_kind_;
+
   std::shared_ptr<mkldnn::pooling_forward::primitive_desc> fwd_pd_;
   std::shared_ptr<mkldnn::pooling_forward> fwd_;
-  std::shared_ptr<mkldnn::memory> data_;
-  std::shared_ptr<mkldnn::memory> out_;
-  std::shared_ptr<mkldnn::memory> workspace_;
-  mkldnn_output_t output_mem_t_;
 
  private:
   void Init(const mxnet::NDArray &input,
@@ -77,26 +70,21 @@ class MKLDNNPoolingFwd {
             const int kernel_h, const int kernel_w,
             const int stride_h, const int stride_w,
             const int padding_t, const int padding_b,
-            const int padding_l, const int padding_r);
+            const int padding_l, const int padding_r,
+            const bool is_train, const mkldnn::algorithm alg_kind);
 };
 
 class MKLDNNPoolingBwd {
   std::shared_ptr<const mkldnn::pooling_backward> bwd;
-  std::shared_ptr<mkldnn::memory> diff_dst;
-  std::shared_ptr<mkldnn::memory> diff_src;
-  std::shared_ptr<mkldnn::memory> ws;
   bool with_workspace;
 
  public:
   const mkldnn::pooling_backward::primitive_desc pd;
 
-  MKLDNNPoolingBwd(const pooling_backward::primitive_desc &pdesc,
+  MKLDNNPoolingBwd(const mkldnn::pooling_backward::primitive_desc &pdesc,
                    bool with_ws);
 
   ~MKLDNNPoolingBwd() {}
-  void SetNewMem(const mxnet::NDArray *workspace,
-                 const mxnet::NDArray &out_grad,
-                 const mkldnn::memory *diff_src_mem);
   const mkldnn::pooling_backward &GetBwd();
   const mkldnn::pooling_backward::primitive_desc &GetPd();
 };
diff --git a/src/operator/nn/mkldnn/mkldnn_pooling.cc b/src/operator/nn/mkldnn/mkldnn_pooling.cc
index f4d681ded78d..f9dbe5bbfd8f 100644
--- a/src/operator/nn/mkldnn/mkldnn_pooling.cc
+++ b/src/operator/nn/mkldnn/mkldnn_pooling.cc
@@ -34,18 +34,17 @@ void MKLDNNPoolingFwd::Init(const mxnet::NDArray &input, const mxnet::NDArray &o
                             const int kernel_h,  const int kernel_w,
                             const int stride_h,  const int stride_w,
                             const int padding_t, const int padding_b,
-                            const int padding_l, const int padding_r) {
-  // mkldnn::memory::desc
-  auto src_md = input.GetMKLDNNData()->get_primitive_desc().desc();
+                            const int padding_l, const int padding_r,
+                            const bool is_train, const mkldnn::algorithm alg_kind) {
+  auto src_md = input.GetMKLDNNData()->get_desc();
   mkldnn::memory::dims dims = {src_md.data.dims[0],
                                src_md.data.dims[1],
                                static_cast<int>(output.shape()[2]),
                                static_cast<int>(output.shape()[3])};
   auto dst_md = mkldnn::memory::desc({dims},
                                      static_cast<mkldnn::memory::data_type>(src_md.data.data_type),
-                                     static_cast<mkldnn::memory::format>(src_md.data.format));
+                                     mkldnn::memory::format_tag::any);
   const mkldnn::engine engine = CpuEngine::Get()->get_engine();
-  const mkldnn::algorithm alg_kind = this->alg_kind_;
   if (alg_kind != mkldnn::algorithm::pooling_max &&
       alg_kind != mkldnn::algorithm::pooling_avg &&
       alg_kind != mkldnn::algorithm::pooling_avg_include_padding &&
@@ -54,10 +53,10 @@ void MKLDNNPoolingFwd::Init(const mxnet::NDArray &input, const mxnet::NDArray &o
   }
 
   mkldnn::prop_kind prop = mkldnn::prop_kind::forward_scoring;
-  if (this->is_train_ && alg_kind != mkldnn::algorithm::pooling_avg) {
+  if (is_train && alg_kind != mkldnn::algorithm::pooling_avg) {
     prop = mkldnn::prop_kind::forward_training;
   }
-  if (this->is_train_ && prop == mkldnn::prop_kind::forward_scoring) {
+  if (is_train && prop == mkldnn::prop_kind::forward_scoring) {
     LOG(INFO) << "MKLDNN Pooling: training with prop_kind is forward_scoring";
   }
 
@@ -67,49 +66,43 @@ void MKLDNNPoolingFwd::Init(const mxnet::NDArray &input, const mxnet::NDArray &o
   const mkldnn::memory::dims kernel  = {kernel_h,  kernel_w  };
   // mkldnn::pooling_forward::desc
   const auto fwd_desc = mkldnn::pooling_forward::desc(prop, alg_kind, src_md, dst_md,
-                                                      strides, kernel, pad_l, pad_r,
-                                                      mkldnn::padding_kind::zero);
+                                                      strides, kernel, pad_l, pad_r);
   this->fwd_pd_.reset(new mkldnn::pooling_forward::primitive_desc(fwd_desc, engine));
-  this->data_.reset(new mkldnn::memory(input.GetMKLDNNData()->get_primitive_desc()));
-  this->out_.reset(new mkldnn::memory(this->fwd_pd_->dst_primitive_desc()));
-  if (this->with_workspace_) {
-    this->workspace_.reset(new mkldnn::memory(this->fwd_pd_->workspace_primitive_desc()));
-    this->fwd_.reset(new mkldnn::pooling_forward(*(this->fwd_pd_),
-                                                 mkldnn::primitive::at(*(this->data_)),
-                                                 *(this->out_),
-                                                 *(this->workspace_)));
-  } else {
-    this->fwd_.reset(new mkldnn::pooling_forward(*(this->fwd_pd_),
-                                                 mkldnn::primitive::at(*(this->data_)),
-                                                 *(this->out_)));
-  }
+  this->fwd_.reset(new mkldnn::pooling_forward(*(this->fwd_pd_)));
+
   return;
 }
 
-void MKLDNNPoolingFwd::SetNewMem(const NDArray& in_data,
-                                 const NDArray& out_data,
-                                 const OpReqType& req,
-                                 const mxnet::NDArray *workspace) {
-  auto input_mem = in_data.GetMKLDNNData();
-  output_mem_t_ = CreateMKLDNNMem(out_data, fwd_pd_->dst_primitive_desc(), req);
-  // mkldnn::memory
-  this->data_->set_data_handle(input_mem->get_data_handle());
-  this->out_->set_data_handle(output_mem_t_.second->get_data_handle());
-  if (this->with_workspace_ && workspace == nullptr) {
-    LOG(FATAL) << "MKLDNN Pooling: incorrect workspace input";
-  }
+void MKLDNNPoolingFwd::Execute(const NDArray &in_data,
+                               const OpReqType req,
+                               const NDArray& out_data,
+                               const NDArray *workspace) {
+  NDArray in_buffer = in_data;
+  if (in_data.IsView() && in_data.IsMKLDNNData())
+    in_buffer = in_data.Reorder2Default();
+
+  auto input_mem = in_buffer.GetMKLDNNData();
+  auto output_mem_t_ = CreateMKLDNNMem(out_data, this->fwd_pd_->dst_desc(), req);
+
+  mkldnn_args_map_t args = {
+    {MKLDNN_ARG_SRC, *input_mem },
+    {MKLDNN_ARG_DST, *(output_mem_t_.second) },
+  };
 
   if (this->with_workspace_) {
-    // mkldnn::memory
-    auto ws_mem = workspace->GetMKLDNNData();
-    this->workspace_->set_data_handle(ws_mem->get_data_handle());
-  }
-}
+    auto engine = CpuEngine::Get()->get_engine();
+
+    if (workspace == nullptr) {
+        LOG(FATAL) << "MKLDNN Pooling: incorrect workspace input";
+    }
 
-void MKLDNNPoolingFwd::Execute(const NDArray& out_data) {
+    auto ws = std::make_shared<mkldnn::memory>((*(this->fwd_pd_)).workspace_desc(),
+                      engine, workspace->GetMKLDNNData()->get_data_handle());
+    args[MKLDNN_ARG_WORKSPACE] = *ws;
+  }
   if (this->fwd_) {
-    MKLDNNStream::Get()->RegisterPrim(*(this->fwd_));
-    CommitOutput(out_data, this->output_mem_t_);
+    MKLDNNStream::Get()->RegisterPrimArgs(*(this->fwd_), args);
+    CommitOutput(out_data, output_mem_t_);
     MKLDNNStream::Get()->Submit();
   } else {
     LOG(FATAL) << "MKLDNN Pooling: forward primitive is nullptr";
@@ -143,8 +136,8 @@ static inline int GetPaddingSizeFull(int x, int padl, int padr, int k, int s) {
 }
 
 mkldnn::pooling_forward::primitive_desc GetPoolingFwdPdesc(
-    const PoolingParam &param, const bool is_train, const memory::desc &data_md,
-    const memory::desc &out_md) {
+    const PoolingParam &param, const bool is_train, const mkldnn::memory::desc &data_md,
+    const mkldnn::memory::desc &out_md) {
   CHECK_EQ(param.kernel.ndim(), 2) << "Not Implemented";
   int kernel_h_, kernel_w_;
   if (param.global_pool) {
@@ -183,19 +176,18 @@ mkldnn::pooling_forward::primitive_desc GetPoolingFwdPdesc(
 
   const mkldnn::algorithm alg = GetMKLDNNPoolAlgo(param);
   mkldnn::prop_kind kind = mkldnn::prop_kind::forward_scoring;
-  if (is_train && alg != algorithm::pooling_avg) {
+  if (is_train && alg != mkldnn::algorithm::pooling_avg) {
     kind = mkldnn::prop_kind::forward_training;
   }
 
-  const pooling_forward::desc poolingFwd_desc(kind, alg, data_md, out_md,
+  const mkldnn::pooling_forward::desc poolingFwd_desc(kind, alg, data_md, out_md,
                                               {static_cast<int>(stride_h_),
                                                static_cast<int>(stride_w_)},
                                               {kernel_h_, kernel_w_},
                                               {static_cast<int>(pad_t_),
                                                static_cast<int>(pad_l_)},
                                               {static_cast<int>(pad_b_),
-                                               static_cast<int>(pad_r_)},
-                                              padding_kind::zero);
+                                               static_cast<int>(pad_r_)});
   return mkldnn::pooling_forward::primitive_desc(poolingFwd_desc, engine);
 }
 
@@ -223,7 +215,7 @@ MKLDNNPoolingFwd &GetPoolingFwd(const PoolingParam &param,
   auto it = pooling_fwds.find(key);
   if (it == pooling_fwds.end()) {
     CHECK_EQ(param.kernel.ndim(), 2) << "Not Implemented";
-    auto data_md = data.GetMKLDNNData()->get_primitive_desc().desc();
+    auto data_md = data.GetMKLDNNData()->get_desc();
     int kernel_h_, kernel_w_;
     if (param.global_pool) {
       kernel_h_ = data_md.data.dims[2];
@@ -270,42 +262,14 @@ void MKLDNNPoolingCompute(const OpContext &ctx, const PoolingParam &param,
                           const NDArray &in_data, const OpReqType req,
                           const NDArray &out_data, const NDArray *workspace) {
   auto &fwd = GetPoolingFwd(param, ctx.is_train, in_data, out_data);
-  fwd.SetNewMem(in_data, out_data, req, workspace);
-  fwd.Execute(out_data);
+  fwd.Execute(in_data, req, out_data, workspace);
 }
 
 MKLDNNPoolingBwd::MKLDNNPoolingBwd(
-    const pooling_backward::primitive_desc &pdesc, bool with_ws)
-    : with_workspace(with_ws), pd(pdesc) {}
-
-void MKLDNNPoolingBwd::SetNewMem(const mxnet::NDArray *workspace,
-                                 const mxnet::NDArray &out_grad,
-                                 const mkldnn::memory *diff_src_mem) {
-  if (bwd == nullptr) {
-    diff_dst.reset(
-        new mkldnn::memory(out_grad.GetMKLDNNData()->get_primitive_desc(),
-                           out_grad.GetMKLDNNData()->get_data_handle()));
-    diff_src.reset(new mkldnn::memory(pd.diff_src_primitive_desc(),
-                                      diff_src_mem->get_data_handle()));
-    if (with_workspace) {
-      CHECK(workspace != nullptr);
-      ws.reset(
-          new mkldnn::memory(workspace->GetMKLDNNData()->get_primitive_desc(),
-                             workspace->GetMKLDNNData()->get_data_handle()));
-      bwd.reset(
-          new pooling_backward(pd, *diff_dst, primitive::at(*ws), *diff_src));
-    } else {
-      bwd.reset(new pooling_backward(pd, *diff_dst, *diff_src));
-    }
-  } else {
-    diff_dst->set_data_handle(out_grad.GetMKLDNNData()->get_data_handle());
-    diff_src->set_data_handle(diff_src_mem->get_data_handle());
-    if (with_workspace) {
-      CHECK(workspace != nullptr);
-      ws->set_data_handle(workspace->GetMKLDNNData()->get_data_handle());
+    const mkldnn::pooling_backward::primitive_desc &pdesc, bool with_ws)
+    : with_workspace(with_ws), pd(pdesc) {
+      bwd = std::make_shared<mkldnn::pooling_backward>(pd);
     }
-  }
-}
 
 const mkldnn::pooling_backward &MKLDNNPoolingBwd::GetBwd() {
   return *this->bwd;
@@ -333,27 +297,29 @@ MKLDNNPoolingBwd &GetPoolingBwd(const PoolingParam &param,
 
   auto it = pooling_bwds.find(key);
   if (it == pooling_bwds.end()) {
-    auto diff_dst_mem = out_grad.GetMKLDNNData();
+    NDArray diff_dst_buff = out_grad;
+    if (in_data.IsMKLDNNData() == false && diff_dst_buff.IsMKLDNNData() == true) {
+      diff_dst_buff = out_grad.Reorder2Default();
+    }
+    auto diff_dst_mem = diff_dst_buff.GetMKLDNNData();
     auto input_mem = in_data.GetMKLDNNData();
-    mkldnn::memory::primitive_desc data_mpd = input_mem->get_primitive_desc();
-    const mkldnn::memory::desc data_md = data_mpd.desc();
-    const memory::dims dims = {data_md.data.dims[0], data_md.data.dims[1],
+    const mkldnn::memory::desc data_md = input_mem->get_desc();
+    const mkldnn::memory::dims dims = {data_md.data.dims[0], data_md.data.dims[1],
                                static_cast<int>(out_grad.shape()[2]),
                                static_cast<int>(out_grad.shape()[3])};
-    const memory::desc out_md(
-        {dims}, static_cast<memory::data_type>(data_md.data.data_type),
-        static_cast<memory::format>(data_md.data.format));
+    const mkldnn::memory::desc out_md(
+        {dims}, static_cast<mkldnn::memory::data_type>(data_md.data.data_type),
+        mkldnn::memory::format_tag::any);
     auto fwd_pd = GetPoolingFwdPdesc(param, true, data_md, out_md);
-
     const mkldnn::memory::desc diff_md =
-        diff_dst_mem->get_primitive_desc().desc();
-    const memory::dims dims1 = {diff_md.data.dims[0], diff_md.data.dims[1],
+        diff_dst_mem->get_desc();
+    const mkldnn::memory::dims dims1 = {diff_md.data.dims[0], diff_md.data.dims[1],
                                 static_cast<int>(in_grad.shape()[2]),
                                 static_cast<int>(in_grad.shape()[3])};
-    const memory::desc diff_in_md(
-        {dims1}, static_cast<memory::data_type>(diff_md.data.data_type),
-        static_cast<memory::format>(diff_md.data.format));
-    const mkldnn::engine cpu_engine = data_mpd.get_engine();
+    const mkldnn::memory::desc diff_in_md(
+        {dims1}, static_cast<mkldnn::memory::data_type>(diff_md.data.data_type),
+        mkldnn::memory::format_tag::any);
+    const mkldnn::engine cpu_engine = CpuEngine::Get()->get_engine();;
     const mkldnn::algorithm alg = GetMKLDNNPoolAlgo(param);
 
     int kernel_h_, kernel_w_;
@@ -379,11 +345,10 @@ MKLDNNPoolingBwd &GetPoolingBwd(const PoolingParam &param,
       stride_h_ = stride_w_ = 1;
     }
 
-    const pooling_backward::desc desc(
+    const mkldnn::pooling_backward::desc desc(
         alg, diff_in_md, diff_md, {stride_h_, stride_w_},
-        {kernel_h_, kernel_w_}, {pad_t_, pad_l_}, {pad_b_, pad_r_},
-        mkldnn::padding_kind::zero);
-    const auto pdesc = pooling_backward::primitive_desc(desc, cpu_engine, fwd_pd);
+        {kernel_h_, kernel_w_}, {pad_t_, pad_l_}, {pad_b_, pad_r_});
+    const auto pdesc = mkldnn::pooling_backward::primitive_desc(desc, cpu_engine, fwd_pd);
     MKLDNNPoolingBwd bwd(pdesc, with_workspace);
     it = AddToCache(&pooling_bwds, key, bwd);
   }
@@ -401,10 +366,17 @@ void MKLDNNPoolingGradCompute(const OpContext &ctx, const PoolingParam &param,
 
   auto &bwd = GetPoolingBwd(param, in_data, in_grad, out_grad);
   auto diff_src_mem =
-      CreateMKLDNNMem(in_grad, bwd.pd.diff_src_primitive_desc(), req);
+      CreateMKLDNNMem(in_grad, bwd.pd.diff_src_desc(), req);
+
+  mkldnn_args_map_t args = {
+    {MKLDNN_ARG_DIFF_DST, *(out_grad.GetMKLDNNData())},
+    {MKLDNN_ARG_DIFF_SRC, *diff_src_mem.second },
+  };
+  if (MKLDNNRequireWorkspace(param) && workspace != nullptr) {
+    args[MKLDNN_ARG_WORKSPACE] = *(workspace->GetMKLDNNData());
+  }
 
-  bwd.SetNewMem(workspace, out_grad, diff_src_mem.second);
-  MKLDNNStream::Get()->RegisterPrim(bwd.GetBwd());
+  MKLDNNStream::Get()->RegisterPrimArgs(bwd.GetBwd(), args);
   CommitOutput(in_grad, diff_src_mem);
   MKLDNNStream::Get()->Submit();
 }
diff --git a/src/operator/nn/mkldnn/mkldnn_reshape-inl.h b/src/operator/nn/mkldnn/mkldnn_reshape-inl.h
index 726d72156718..c89e4585e85d 100644
--- a/src/operator/nn/mkldnn/mkldnn_reshape-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_reshape-inl.h
@@ -1,59 +1,61 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- */
-
-/*!
- *  Copyright (c) 2019 by Contributors
- * \file mkldnn_reshape-inl.h
- * \brief Function definition of mkldnn reshape operator
- */
-
-#ifndef MXNET_OPERATOR_NN_MKLDNN_MKLDNN_RESHAPE_INL_H_
-#define MXNET_OPERATOR_NN_MKLDNN_MKLDNN_RESHAPE_INL_H_
-
-#if MXNET_USE_MKLDNN == 1
-#include <vector>
-#include "mkldnn_base-inl.h"
-#include "../../tensor/matrix_op-inl.h"
-
-namespace mxnet {
-namespace op {
-
-class MKLDNNReshapeFwd {
- public:
-  MKLDNNReshapeFwd(const OpReqType &req, const NDArray &input, const NDArray &output);
-  int GetWorkspaceSize();
-  void SetNewMem(const NDArray &input, const NDArray &output, void *workspace = nullptr);
-  void Execute(const NDArray &input, const NDArray &output, void *workspace = nullptr);
-
- private:
-  std::shared_ptr<mkldnn::memory> data_;
-  std::shared_ptr<mkldnn::memory> out_;
-  std::shared_ptr<mkldnn::memory> temp_;
-  std::vector<mkldnn::primitive> prims_;
-};
-
-typedef OpSignature MKLDNNReshapeSignature;
-MKLDNNReshapeFwd &GetReshapeForward(const OpReqType &req, const NDArray &input,
-                                    const NDArray &output);
-
-}  // namespace op
-}  // namespace mxnet
-
-#endif  // MXNET_USE_MKLDNN == 1
-#endif  // MXNET_OPERATOR_NN_MKLDNN_MKLDNN_RESHAPE_INL_H_
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file mkldnn_reshape-inl.h
+ * \brief Function definition of mkldnn reshape operator
+ */
+
+#ifndef MXNET_OPERATOR_NN_MKLDNN_MKLDNN_RESHAPE_INL_H_
+#define MXNET_OPERATOR_NN_MKLDNN_MKLDNN_RESHAPE_INL_H_
+
+#if MXNET_USE_MKLDNN == 1
+#include <vector>
+#include "mkldnn_base-inl.h"
+#include "../../tensor/matrix_op-inl.h"
+
+namespace mxnet {
+namespace op {
+
+class MKLDNNReshapeFwd {
+ protected:
+  std::shared_ptr<mkldnn::memory> out_;
+  std::shared_ptr<mkldnn::memory> temp_;
+  std::vector<mkldnn::primitive> prims_;
+
+ public:
+  MKLDNNReshapeFwd(const OpReqType &req,
+                   const NDArray &input,
+                   const NDArray &output);
+  int GetWorkspaceSize();
+  void Execute(const NDArray &input,
+               const NDArray &output,
+               const OpReqType &req,
+               void* workspace = nullptr);
+};
+
+typedef OpSignature MKLDNNReshapeSignature;
+MKLDNNReshapeFwd &GetReshapeForward(const OpReqType &req, const NDArray &input,
+                                    const NDArray &output);
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_USE_MKLDNN == 1
+#endif  // MXNET_OPERATOR_NN_MKLDNN_MKLDNN_RESHAPE_INL_H_
diff --git a/src/operator/nn/mkldnn/mkldnn_reshape.cc b/src/operator/nn/mkldnn/mkldnn_reshape.cc
index 9c226a052b0b..0fc9f20703af 100644
--- a/src/operator/nn/mkldnn/mkldnn_reshape.cc
+++ b/src/operator/nn/mkldnn/mkldnn_reshape.cc
@@ -35,58 +35,65 @@ namespace op {
 MKLDNNReshapeFwd::MKLDNNReshapeFwd(const OpReqType &req, const NDArray &input,
                                    const NDArray &output) {
   const auto engine = CpuEngine::Get()->get_engine();
-  data_ = std::make_shared<mkldnn::memory>(input.GetMKLDNNData()->get_primitive_desc(), nullptr);
+  auto in_mem = input.GetMKLDNNData();
+
   // Create temp memory
   auto temp_dims = mkldnn::memory::dims(input.shape().begin(), input.shape().end());
   auto temp_type = static_cast<mkldnn::memory::data_type>(get_mkldnn_type(input.dtype()));
-  auto temp_fmt = static_cast<mkldnn::memory::format>(GetDefaultFormat(input.shape().ndim()));
+  auto temp_fmt = static_cast<mkldnn::memory::format_tag>(GetDefaultFormat(input.shape().ndim()));
   auto temp_desc = mkldnn::memory::desc(temp_dims, temp_type, temp_fmt);
-  auto temp_pd = mkldnn::memory::primitive_desc(temp_desc, engine);
-  out_ = std::make_shared<mkldnn::memory>(temp_pd, nullptr);
+
+  out_ = std::make_shared<mkldnn::memory>(temp_desc, engine, nullptr);
   if (req == kWriteInplace) {
     // If the input has MKL-DNN internal layout, we need reorder it to a temporal buffer with
     // default layout and copy from the temporal buffer back to output buffer which has the same
     // address with input buffer.
     // If the input has default layout, then nothing need to do.
     if (input.IsMKLDNNData()) {
-      temp_ = std::make_shared<mkldnn::memory>(temp_pd, nullptr);
-      prims_.push_back(mkldnn::reorder(*data_, *temp_));  // reorder to default
+      temp_ = std::make_shared<mkldnn::memory>(temp_desc, engine, nullptr);
+      prims_.push_back(mkldnn::reorder(*in_mem, *temp_));  // reorder to default
       prims_.push_back(mkldnn::reorder(*temp_, *out_));   // copy back
     }
   } else if (req == kWriteTo) {
-    prims_.push_back(mkldnn::reorder(*data_, *out_));
+    prims_.push_back(mkldnn::reorder(*in_mem, *out_));
   } else {
     LOG(FATAL) << "not supported req type: " << req;
   }
 }
 
 int MKLDNNReshapeFwd::GetWorkspaceSize() {
-  return temp_ ? temp_->get_primitive_desc().get_size() : 0;
-}
-
-void MKLDNNReshapeFwd::SetNewMem(const NDArray &input,
-                                 const NDArray &output,
-                                 void* workspace) {
-  this->data_->set_data_handle(input.GetMKLDNNData()->get_data_handle());
-  this->out_->set_data_handle(output.GetMKLDNNData()->get_data_handle());
-  if (workspace) {
-    this->temp_->set_data_handle(workspace);
-  }
+  return temp_ ? temp_->get_desc().get_size() : 0;
 }
 
 void MKLDNNReshapeFwd::Execute(const NDArray &input,
                                const NDArray &output,
+                               const OpReqType &req,
                                void* workspace) {
-  if (this->prims_.size()) {
-    // set memory handles
-    SetNewMem(input, output, workspace);
-    // register primitives
-    auto stream = MKLDNNStream::Get();
-    for (auto &v : this->prims_) {
-      stream->RegisterPrim(v);
+  auto stream = MKLDNNStream::Get();
+  auto in_mem = input.GetMKLDNNData();
+  // register primitives and arguments
+  std::vector<mkldnn_args_map_t> args_map;
+  size_t prims_size = prims_.size();
+  if (prims_size == 1) {
+    args_map.push_back({{MKLDNN_ARG_FROM, *in_mem},
+                        {MKLDNN_ARG_TO, *output.GetMKLDNNData()}});
+  } else if (prims_size == 2) {
+    if (workspace) {
+      temp_->set_data_handle(workspace);
     }
-    stream->Submit();
+    args_map.push_back({{MKLDNN_ARG_FROM, *in_mem},
+                        {MKLDNN_ARG_TO, *temp_}});
+    args_map.push_back({{MKLDNN_ARG_FROM, *temp_},
+                        {MKLDNN_ARG_TO, *output.GetMKLDNNData()}});
+  } else {
+    CHECK(prims_size == 0 && req != kWriteTo)
+          << "kWriteTo should never reach here.";
+  }
+
+  for (size_t i = 0; i < prims_size; i++) {
+    stream->RegisterPrimArgs(prims_[i], args_map[i]);
   }
+  stream->Submit();
   // invalidate mkldnn memory in output
   const_cast<NDArray &>(output).InvalidateMKLDNNData();
 }
@@ -137,7 +144,7 @@ void MKLDNNReshapeForward(const nnvm::NodeAttrs& attrs,
       .get_space_typed<cpu, 1, char>(mshadow::Shape1(ws_size), s);
     ws_ptr = static_cast<void*>(ws.dptr_);
   }
-  fwd.Execute(input, output, ws_ptr);
+  fwd.Execute(input, output, req, ws_ptr);
 }
 
 }  // namespace op
diff --git a/src/operator/nn/mkldnn/mkldnn_rnn-inl.h b/src/operator/nn/mkldnn/mkldnn_rnn-inl.h
new file mode 100644
index 000000000000..ad3f7332a8f4
--- /dev/null
+++ b/src/operator/nn/mkldnn/mkldnn_rnn-inl.h
@@ -0,0 +1,444 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file mkldnn_rnn-inl.h
+ * \brief Common functions used by MKLDNN RNN operator
+ * \author Zixuan Wei
+*/
+
+#ifndef MXNET_OPERATOR_NN_MKLDNN_MKLDNN_RNN_INL_H_
+#define MXNET_OPERATOR_NN_MKLDNN_MKLDNN_RNN_INL_H_
+
+#if MXNET_USE_MKLDNN == 1
+
+#include <vector>
+#include "../../rnn-inl.h"
+#include "./mkldnn_base-inl.h"
+
+namespace mxnet {
+namespace op {
+
+struct MKLDNNRnnLayerParam {
+  using memory = mkldnn::memory;
+  using dims = mkldnn::memory::dims;
+
+  int mode;
+  bool bidirectional;
+  bool state_outputs;
+  int num_layer;
+  int batch_size;
+  int input_size;
+  int state_size;
+  int seq_len;
+
+  dims src_dims;           // Dimensions of source input in format_tag::tnc
+  dims weight_layer_dims;  // Dimensions of layer weights in format_tag::ldigo
+  dims weight_iter_dims;   // Dimensions of iter weights in format_tag::ldigo
+  dims bias_dims;          // Dimensions of bias in format_tag::ldgo
+  dims dst_dims;           // Dimensions of output in format_tag::tnc
+  dims state_dims;         // Dimensions of the state cell in format_tag::ldnc
+
+  size_t workspace_size;  // used for the cached mkl-dnn memory in Forward inference
+  size_t reserve_size;    // used for the reserved cached memory in Backward
+  size_t single_w_size;   // weights size of a single cell
+  size_t single_b_size;   // bias size of a single cell from mkl-dnn
+  size_t naive_single_b_size;  // bias size of a single cell from framework
+  size_t single_state_size;    // state size of a single cell, hy, cy
+
+  MKLDNNRnnLayerParam(int num_layer, int batch_size, int seq_len,
+                      int input_size, int state_size,
+                      int mode, bool bidirectional = true)
+      : mode(mode), bidirectional(bidirectional), state_outputs(true),
+        num_layer(num_layer), batch_size(batch_size), input_size(input_size),
+        state_size(state_size), seq_len(seq_len) { }
+
+  void SetDims();
+};
+
+typedef std::vector<MKLDNNRnnLayerParam> LayerParamVector;
+struct MKLDNNRnnFullParam {
+  RNNParam default_param;
+  LayerParamVector layer_params;
+};
+
+MKLDNNRnnFullParam MKLDNNRnnFullParamParser(const RNNParam& rnn_param, const int seq_len,
+                                            const int batch_size, const int input_size);
+
+/*
+ * Use this to allocate memory from MKLDNNRnnOp temporary space.
+ */
+class MKLDNNRnnMemMgr {
+  // The memory buffer in NDArray life-cycle
+  NDArray workspace_;
+  // This points to the memory buffer from a NDArray
+  char *curr_mem;
+  // The total bytes of the workspace of a MKLDNNRnnOp
+  size_t mem_size = 0;
+  // The current available memory bytes
+  size_t curr_size = 0;
+  const size_t alignment = kMKLDNNAlign;
+  const mkldnn::engine& cpu_engine = CpuEngine::Get()->get_engine();
+  // Here we hold all memory related to the stateful RNN operators
+  std::vector<std::shared_ptr<const mkldnn::memory> > mem_holder;
+
+ public:
+  void Init(dim_t size, const Context& ctx, int dtype = mshadow::kFloat32);
+
+  void RegisterMem(std::shared_ptr<const mkldnn::memory> mem) {
+    mem_holder.push_back(mem);
+  }
+
+  mkldnn::memory *Alloc(const mkldnn::memory::desc &md);
+};
+
+/*
+ * Rnn Primitive.
+ */
+class RnnPrimitive {
+ public:
+  /* Create a RnnPrimitive with rnn type:
+   * lstm_forward, lbr_gru_forward, vanilla_rnn_forward
+   */
+  template<typename rnn_fwd, typename... Args>
+  static RnnPrimitive Create(Args&&... args) {
+    RnnPrimitive rnn_fwd_prim;
+    rnn_fwd_prim.pd_.reset(
+      new typename rnn_fwd::desc(std::forward<Args>(args)...),
+      [](typename rnn_fwd::desc* pd) {
+        delete reinterpret_cast<typename rnn_fwd::desc*>(pd);
+      });
+    const typename rnn_fwd::desc& fwd_desc =
+        *(reinterpret_cast<typename rnn_fwd::desc*>(rnn_fwd_prim.pd_.get()));
+    typename rnn_fwd::primitive_desc fwd_pd(fwd_desc, CpuEngine::Get()->get_engine());
+    rnn_fwd_prim.weights_layer_desc_ = fwd_pd.weights_layer_desc();
+    rnn_fwd_prim.weights_iter_desc_  = fwd_pd.weights_iter_desc();
+    rnn_fwd_prim.workspace_desc_ = fwd_pd.workspace_desc();
+
+    rnn_fwd_prim.primitive_ = std::shared_ptr<mkldnn::primitive>(new rnn_fwd(fwd_pd));
+
+    return rnn_fwd_prim;
+  }
+
+  RnnPrimitive() {
+    this->pd_ = nullptr;
+    this->primitive_ = nullptr;
+    this->weights_layer_desc_ = mkldnn::memory::desc();
+    this->weights_iter_desc_ = mkldnn::memory::desc();
+    this->workspace_desc_ = mkldnn::memory::desc();
+  }
+
+  RnnPrimitive(const RnnPrimitive& rnn_fwd_prim) {
+    this->pd_ = rnn_fwd_prim.pd_;
+    this->primitive_ = rnn_fwd_prim.primitive_;
+    this->weights_layer_desc_ = rnn_fwd_prim.weights_layer_desc_;
+    this->weights_iter_desc_ = rnn_fwd_prim.weights_iter_desc_;
+    this->workspace_desc_ = rnn_fwd_prim.workspace_desc_;
+  }
+
+  RnnPrimitive& operator=(const RnnPrimitive& rnn_fwd_prim) {
+    if (this != &rnn_fwd_prim) {
+      this->pd_ = rnn_fwd_prim.pd_;
+      this->primitive_ = rnn_fwd_prim.primitive_;
+      this->weights_layer_desc_ = rnn_fwd_prim.weights_layer_desc_;
+      this->weights_iter_desc_ = rnn_fwd_prim.weights_iter_desc_;
+      this->workspace_desc_ = rnn_fwd_prim.workspace_desc_;
+    }
+
+    return *this;
+  }
+
+  const void* GetPrimDesc() const { return pd_.get(); }
+  const mkldnn::primitive& GetPrim() const { return *primitive_; }
+
+  const mkldnn::memory::desc& GetLayerDesc() const {
+    return weights_layer_desc_;
+  }
+
+  const mkldnn::memory::desc& GetIterDesc() const {
+    return weights_iter_desc_;
+  }
+
+  const mkldnn::memory::desc& GetWorkspaceDesc() const {
+    return workspace_desc_;
+  }
+
+ private:
+  std::shared_ptr<void> pd_;
+  std::shared_ptr<mkldnn::primitive> primitive_;
+  mkldnn::memory::desc weights_layer_desc_;
+  mkldnn::memory::desc weights_iter_desc_;
+  mkldnn::memory::desc workspace_desc_;
+};
+
+RnnPrimitive GetRnnFwdPrim(const MKLDNNRnnLayerParam &layer_param, const bool is_train,
+                           const NDArray &data, const NDArray &params);
+
+/*
+ * Use this to manage memory and primitive of MKL-DNN RNN forward inference. 
+ */
+class MKLDNNRnnForward {
+ public:
+  MKLDNNRnnForward(const MKLDNNRnnLayerParam &layer_param, const bool is_train,
+                   const NDArray &data, const NDArray &params)
+      : initialized_(false), param_(layer_param),
+        fwd_inf_(GetRnnFwdPrim(layer_param, false, data, params)) { }
+
+  void SetNewDataMem(void* x, void* hx, void* cx,
+                     void* y, void* hy, void* cy,
+                     const int dtype = mshadow::kFloat32);
+  void SetWeightsMem(MKLDNNRnnMemMgr* mgr, void* w_ptr, void* b_ptr,
+                     const bool is_train = false,
+                     const int dtype = mshadow::kFloat32);
+  void ReorderWeights();
+
+  const mkldnn::primitive& GetFwd() const { return fwd_inf_.GetPrim(); }
+
+  const size_t GetSize(int dtype) const {
+    size_t bytes = mshadow::mshadow_sizeof(dtype);
+    size_t size = 0;
+    size += fwd_inf_.GetLayerDesc().get_size();
+    size += fwd_inf_.GetIterDesc().get_size();
+    return size / bytes + 1;
+  }
+
+  const MKLDNNRnnLayerParam &GetParam() const { return param_; }
+
+  const mkldnn_args_map_t &GetArgsMap() const { return net_args_; }
+
+  const bool IsInitialized() const { return initialized_; }
+  void Reset() { initialized_ = false; }
+
+ private:
+  bool initialized_;
+  MKLDNNRnnLayerParam param_;
+  RnnPrimitive fwd_inf_;    // forward inference primitive
+
+  mkldnn::memory *weights_layer_ = nullptr;
+  mkldnn::memory *weights_iter_ = nullptr;
+  mkldnn::memory *bias_ = nullptr;
+
+  mkldnn::memory *weights_layer_r_ = nullptr;
+  mkldnn::memory *weights_iter_r_ = nullptr;
+
+  /*
+   * net_args must contain some keys as below:
+   *   MKLDNN_ARG_SRC
+   *   MKLDNN_ARG_SRC_ITER
+   *   MKLDNN_WEIGHTS_LAYER
+   *   MKLDNN_WEIGHTS_ITER
+   *   MKLDNN_BIAS
+   *   MKLDNN_ARG_DST
+   *   MKLDNN_ARG_DST_ITER
+   * if mode == Lstm, it also needs two additional key:
+   *   MKLDNN_ARG_SRC_ITER_C
+   *   MKLDNN_ARG_DST_ITER_C
+   */
+  mkldnn_args_map_t net_args_;
+
+  friend class MKLDNNRnnForwardTraining;
+};
+
+typedef std::shared_ptr<mkldnn::memory> mkldnn_shared_mem_t;
+/*
+ * Use this to manage memory and primitive of MKL-DNN RNN forward training.
+ */
+class MKLDNNRnnForwardTraining {
+ public:
+  MKLDNNRnnForwardTraining(const MKLDNNRnnLayerParam &layer_param, const bool is_train,
+                           const NDArray &data, const NDArray &params)
+    : fwd_trn_(GetRnnFwdPrim(layer_param, is_train, data, params)),
+      param_(&layer_param) { }
+
+  void SetTrnMem(const MKLDNNRnnForward& fwd);
+  void FetchData(const MKLDNNRnnForward& fwd);
+
+  const MKLDNNRnnLayerParam& GetParam() const { return *param_; }
+  const void* GetPrimDesc() const { return fwd_trn_.GetPrimDesc(); }
+  const mkldnn::primitive& GetFwd() const { return fwd_trn_.GetPrim(); }
+  const mkldnn_args_map_t& GetArgsMap() const { return net_args_; }
+
+ private:
+  RnnPrimitive fwd_trn_;
+  const MKLDNNRnnLayerParam* param_;
+
+  mkldnn_shared_mem_t weights_layer_ = nullptr;
+  mkldnn_shared_mem_t weights_iter_ = nullptr;
+  mkldnn::memory* bias_ = nullptr;
+
+  mkldnn_shared_mem_t workspace_ = nullptr;
+
+  // Key MKLDNN_ARGS_WORKSPACE must be included in forward training
+  mkldnn_args_map_t net_args_;
+
+  friend class MKLDNNRnnBackward;
+};
+
+/*
+ * Rnn Backward primitive
+ */
+class RnnBwdPrimitive {
+ public:
+  template <typename rnn_fwd, typename rnn_bwd, typename... Args>
+  static RnnBwdPrimitive Create(typename rnn_fwd::primitive_desc const & fwd_pd, Args&&... args) {
+    RnnBwdPrimitive bwd_prim;
+    typename rnn_bwd::desc bwd_desc(std::forward<Args>(args)...);
+    typename rnn_bwd::primitive_desc bwd_pd(bwd_desc, CpuEngine::Get()->get_engine(), fwd_pd);
+    bwd_prim.weights_layer_desc_ = bwd_pd.weights_layer_desc();
+    bwd_prim.weights_iter_desc_ = bwd_pd.weights_iter_desc();
+    bwd_prim.diff_weights_layer_desc_ = bwd_pd.diff_weights_layer_desc();
+    bwd_prim.diff_weights_iter_desc_ = bwd_pd.diff_weights_iter_desc();
+    bwd_prim.diff_bias_desc_ = bwd_pd.diff_bias_desc();
+
+    bwd_prim.primitive_ = std::shared_ptr<mkldnn::primitive>(new rnn_bwd(bwd_pd));
+
+    return bwd_prim;
+  }
+
+  RnnBwdPrimitive() {
+    this->primitive_ = nullptr;
+    this->weights_layer_desc_ = mkldnn::memory::desc();
+    this->weights_iter_desc_ = mkldnn::memory::desc();
+    this->diff_weights_layer_desc_ = mkldnn::memory::desc();
+    this->diff_weights_iter_desc_ = mkldnn::memory::desc();
+    this->diff_bias_desc_ = mkldnn::memory::desc();
+  }
+
+  RnnBwdPrimitive(const RnnBwdPrimitive& bwd) {
+    this->primitive_ = bwd.primitive_;
+    this->weights_layer_desc_ = bwd.weights_layer_desc_;
+    this->weights_iter_desc_ = bwd.weights_iter_desc_;
+    this->diff_weights_layer_desc_ = bwd.diff_weights_layer_desc_;
+    this->diff_weights_iter_desc_ = bwd.diff_weights_iter_desc_;
+    this->diff_bias_desc_ = bwd.diff_bias_desc_;
+  }
+
+  RnnBwdPrimitive& operator=(const RnnBwdPrimitive& bwd) {
+    if (this != &bwd) {
+      this->primitive_ = bwd.primitive_;
+      this->weights_layer_desc_ = bwd.weights_layer_desc_;
+      this->weights_iter_desc_ = bwd.weights_iter_desc_;
+      this->diff_weights_layer_desc_ = bwd.diff_weights_layer_desc_;
+      this->diff_weights_iter_desc_ = bwd.diff_weights_iter_desc_;
+      this->diff_bias_desc_ = bwd.diff_bias_desc_;
+    }
+
+    return *this;
+  }
+
+ private:
+  std::shared_ptr<mkldnn::primitive> primitive_;
+  mkldnn::memory::desc weights_layer_desc_;
+  mkldnn::memory::desc weights_iter_desc_;
+  mkldnn::memory::desc diff_weights_layer_desc_;
+  mkldnn::memory::desc diff_weights_iter_desc_;
+  mkldnn::memory::desc diff_bias_desc_;
+  friend class MKLDNNRnnBackward;
+};
+RnnBwdPrimitive GetRnnBwdPrim(const MKLDNNRnnForwardTraining& fwd,
+                              const NDArray& data, const NDArray& params);
+
+/*
+ * Use this to manage memory and primitive of MKL-DNN RNN backward.
+ */
+class MKLDNNRnnBackward {
+ public:
+  MKLDNNRnnBackward(const MKLDNNRnnForwardTraining& fwd,
+                    const NDArray& data, const NDArray& params)
+      : bwd_(GetRnnBwdPrim(fwd, data, params)),
+        fwd_ptr_(&fwd) { }
+
+  void FetchDataWeightsMem(const MKLDNNRnnForwardTraining& fwd);
+  void SetWeightsGradsMem();
+  void SetDataGradsMem(void* diff_src, void* diff_state, void* diff_statecell,
+                       void* diff_out, void* diff_state_out, void* diff_statecell_out,
+                       const int dtype = mshadow::kFloat32);
+  void CommitWeightsDiff(void* diff_weights, void* diff_bias, const int dtype = mshadow::kFloat32);
+
+  const mkldnn::primitive& GetBwd() const { return *bwd_.primitive_; }
+  const mkldnn_args_map_t& GetArgsMap() const { return net_args_; }
+
+ private:
+  bool initialized_;
+  RnnBwdPrimitive bwd_;
+  const MKLDNNRnnForwardTraining* fwd_ptr_;
+
+  mkldnn_shared_mem_t weights_layer_;
+  mkldnn_shared_mem_t weights_iter_;
+
+  mkldnn_shared_mem_t diff_weights_layer_;
+  mkldnn_shared_mem_t diff_weights_iter_;
+  mkldnn_shared_mem_t diff_bias_;
+
+  mkldnn_args_map_t net_args_;
+};
+
+/*
+ * Use MKLDNNRnnOp to manage fused or unfused RNN layers. A MKLDNNRnnOp contains
+ * the parameter(s) and primitive(s) of RNN layer(s). According to the direction,
+ * input size, and state size, multple layers could be inplemented by unfused and
+ * fused layers - MKLDNNRnnForward, which holds the memory and forward primitive
+ * of MKL-DNN.
+ */
+class MKLDNNRnnOp {
+ public:
+  explicit MKLDNNRnnOp(const RNNParam &param, const int seq_len,
+                       const int batch_size, const int input_size)
+      : initialized_(false), weights_version_(0),
+        full_param_(MKLDNNRnnFullParamParser(param, seq_len, batch_size, input_size)) { }
+
+  void Forward(const OpContext &ctx,
+               const std::vector<NDArray> &inputs,
+               const std::vector<OpReqType> &req,
+               const std::vector<NDArray> &outputs);
+
+  void Backward(const OpContext &ctx,
+                const std::vector<NDArray> &inputs,
+                const std::vector<OpReqType> &req,
+                const std::vector<NDArray> &outputs);
+
+  const RNNParam& GetParam() const { return full_param_.default_param; }
+
+ private:
+  bool initialized_;
+  size_t weights_version_;
+  MKLDNNRnnFullParam full_param_;
+  MKLDNNRnnMemMgr mgr_;
+  std::vector<MKLDNNRnnForward> fwd_inf_vec_;              // forward inference layers
+  std::vector<MKLDNNRnnForwardTraining> fwd_trn_vec_;      // forward training layers
+  std::vector<MKLDNNRnnBackward> bwd_vec_;                 // backward layers
+
+  // Used to store the intermediate results of multi-layer
+  std::vector<mkldnn::memory *> dst_;
+
+  // Used to store the intermediate diff_src of multi_layer
+  mkldnn_shared_mem_t diff_src;
+
+  void Init(const OpContext &ctx,
+            const std::vector<NDArray> &inputs,
+            const std::vector<OpReqType> &req,
+            const std::vector<NDArray> &outputs);
+};
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_USE_MKLDNN == 1
+#endif  // MXNET_OPERATOR_NN_MKLDNN_MKLDNN_RNN_INL_H_
diff --git a/src/operator/nn/mkldnn/mkldnn_rnn.cc b/src/operator/nn/mkldnn/mkldnn_rnn.cc
new file mode 100644
index 000000000000..e797b649d295
--- /dev/null
+++ b/src/operator/nn/mkldnn/mkldnn_rnn.cc
@@ -0,0 +1,1118 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file mkldnn_rnn.cc
+ * \brief Common functions used by MKLDNN RNN operator
+ * \author Zixuan Wei
+*/
+
+#if MXNET_USE_MKLDNN == 1
+
+#include <numeric>
+#include "./mkldnn_rnn-inl.h"
+
+namespace mxnet {
+namespace op {
+
+inline int GetRnnGatesNum(int mode) {
+  switch (mode) {
+    case rnn_enum::kLstm:
+      return 4;
+    case rnn_enum::kGru:
+      return 3;
+    case rnn_enum::kRnnRelu:
+    case rnn_enum::kRnnTanh:
+      return 1;
+    default:
+      LOG(FATAL) << "unsupported RNN mode:" << mode;
+      return -1;
+  }
+}
+
+void MKLDNNRnnLayerParam::SetDims() {
+  const int ngates = GetRnnGatesNum(mode);
+  //* NOTES: LBR-GRU's new gate formula needs two bias. So it has one more bias with LBR-GRU
+  const int nbias = mode == rnn_enum::kGru ? (ngates + 1) : ngates;
+  const int num_direction = bidirectional ? 2 : 1;
+
+  src_dims.assign({seq_len, batch_size, input_size});
+  weight_layer_dims.assign({num_layer, num_direction, input_size, ngates, state_size});
+  weight_iter_dims.assign({num_layer, num_direction, state_size, ngates, state_size});
+  bias_dims.assign({num_layer, num_direction, nbias, state_size});
+  dst_dims.assign({seq_len, batch_size, state_size * num_direction});
+  state_dims.assign({num_layer, num_direction, batch_size, state_size});
+
+  // unidirectional size of a single cell
+  single_w_size = (input_size + state_size) * ngates * state_size;
+  single_b_size = nbias * state_size;
+  naive_single_b_size = ngates * state_size * 2;  // naive RNN variants have double bias
+  single_state_size = batch_size * state_size;
+
+  // Get workspace size for cached weights memory
+  // multiplication of tensor dimensions
+  static auto tz_volume = [](const memory::dims& tz_dims) {
+    return std::accumulate(tz_dims.begin(), tz_dims.end(), static_cast<memory::dim>(1),
+        std::multiplies<memory::dim>());
+  };
+
+  workspace_size = tz_volume(weight_layer_dims) + tz_volume(weight_iter_dims) +
+      tz_volume(bias_dims);
+  reserve_size = 0;
+}
+
+MKLDNNRnnFullParam MKLDNNRnnFullParamParser(const RNNParam& rnn_param, const int seq_len,
+                                            const int batch_size, const int input_size) {
+  MKLDNNRnnFullParam full_param;
+  full_param.default_param = rnn_param;
+  size_t state_size = rnn_param.state_size;
+  LayerParamVector &layer_params = full_param.layer_params;
+
+  full_param.default_param.seq_length_ = seq_len;
+  full_param.default_param.batch_size_ = batch_size;
+  full_param.default_param.input_size_ = input_size;
+  // Set basic size by constructing MKLDNNRnnLayerParam instance(s)
+  if (rnn_param.bidirectional) {  // unfused bidirectional multi-layer RNN
+    layer_params.emplace_back(1, batch_size, seq_len, input_size, state_size, rnn_param.mode);
+    for (size_t layer = 1; layer < rnn_param.num_layers; ++layer) {
+      layer_params.emplace_back(1, batch_size, seq_len, state_size * 2, state_size,
+          rnn_param.mode);
+    }
+  } else if (input_size == static_cast<int>(state_size)) {  // fused multi-layer RNN
+    layer_params.emplace_back(rnn_param.num_layers, batch_size, seq_len, input_size,
+        state_size, rnn_param.mode, false);
+  } else {  // unfused 1st layer, plus fused 2-end layers
+    layer_params.emplace_back(1, batch_size, seq_len, input_size, state_size, rnn_param.mode,
+        false);
+    if (rnn_param.num_layers > 1)
+      layer_params.emplace_back(rnn_param.num_layers - 1, batch_size, seq_len, state_size,
+          state_size, rnn_param.mode, false);
+  }
+
+  // Set dims, workspace size, and state_outputs flag
+  for (auto& layer_param : layer_params) {
+    layer_param.SetDims();
+    layer_param.state_outputs = rnn_param.state_outputs;
+  }
+  return full_param;
+}
+
+void MKLDNNRnnMemMgr::Init(dim_t size, const Context& ctx, int dtype) {
+  workspace_ = NDArray(TShape({size}), ctx, false, dtype);
+  curr_mem = static_cast<char *>(workspace_.data().dptr_);
+  mem_size = size * mshadow::mshadow_sizeof(dtype);
+  curr_size = size * mshadow::mshadow_sizeof(dtype);
+}
+
+mkldnn::memory *MKLDNNRnnMemMgr::Alloc(const mkldnn::memory::desc &md) {
+  if (curr_mem == nullptr) {
+    curr_mem = static_cast<char *>(workspace_.data().dptr_);
+  }
+
+  mkldnn_mem_ptr ret(new mkldnn::memory());
+  size_t addr = reinterpret_cast<size_t>(curr_mem);
+  size_t last_chunk = addr % alignment;
+  size_t padding = alignment - last_chunk;
+  addr += padding;
+  CHECK_EQ(addr % alignment, 0);
+
+  curr_size -= (md.get_size() + padding);
+  if (curr_size < 0) {
+    ret.reset(new mkldnn::memory(md, cpu_engine));
+  } else {
+    curr_mem += (md.get_size() + padding);
+    ret.reset(new mkldnn::memory(md, cpu_engine, reinterpret_cast<void *>(addr)));
+  }
+  RegisterMem(ret);
+  return ret.get();
+}
+
+RnnPrimitive GetRnnFwdPrim(
+    const MKLDNNRnnLayerParam &layer_param, const bool is_train,
+    const NDArray &data, const NDArray &params) {
+  using namespace mkldnn;
+  using tag = mkldnn::memory::format_tag;
+  const int mode = layer_param.mode;
+  memory::data_type data_type = get_mkldnn_type(data.dtype());
+  memory::data_type weight_type = get_mkldnn_type(params.dtype());
+  const prop_kind prop = is_train ? prop_kind::forward_training : prop_kind::forward_inference;
+  const rnn_direction mkldnn_rnn_direction = layer_param.bidirectional ?
+      rnn_direction::bidirectional_concat : rnn_direction::unidirectional;
+
+  auto src_layer_desc    = memory::desc(layer_param.src_dims, data_type, tag::tnc);
+  auto weight_layer_desc = memory::desc(layer_param.weight_layer_dims, weight_type, tag::any);
+  auto weight_iter_desc  = memory::desc(layer_param.weight_iter_dims, weight_type, tag::any);
+  auto bias_desc         = memory::desc(layer_param.bias_dims, data_type, tag::ldgo);
+  auto dst_layer_desc    = memory::desc(layer_param.dst_dims, data_type, tag::tnc);
+  auto src_state_desc    = memory::desc(layer_param.state_dims, data_type, tag::ldnc);
+  auto dst_state_desc = layer_param.state_outputs ? memory::desc(
+      layer_param.state_dims, data_type, tag::ldnc) : memory::desc();
+
+  auto fwd = RnnPrimitive();
+  switch (mode) {
+    case rnn_enum::kLstm:
+      fwd = RnnPrimitive::Create<lstm_forward>(prop, mkldnn_rnn_direction,
+          src_layer_desc, src_state_desc, src_state_desc, weight_layer_desc,
+          weight_iter_desc, bias_desc, dst_layer_desc, dst_state_desc,
+          dst_state_desc);
+      break;
+    case rnn_enum::kGru:
+      fwd = RnnPrimitive::Create<lbr_gru_forward>(prop, mkldnn_rnn_direction,
+          src_layer_desc, src_state_desc, weight_layer_desc,
+          weight_iter_desc, bias_desc, dst_layer_desc, dst_state_desc);
+      break;
+    case rnn_enum::kRnnRelu:
+    case rnn_enum::kRnnTanh:
+      fwd = RnnPrimitive::Create<vanilla_rnn_forward>(prop,
+          mode == rnn_enum::kRnnTanh ? algorithm::eltwise_tanh : algorithm::eltwise_relu,
+          mkldnn_rnn_direction, src_layer_desc, src_state_desc, weight_layer_desc,
+          weight_iter_desc, bias_desc, dst_layer_desc, dst_state_desc);
+      break;
+    default:
+      LOG(FATAL) << "unsupported RNN mode:" << mode;
+      break;
+  }
+  return fwd;
+}
+
+RnnBwdPrimitive GetRnnBwdPrim(const MKLDNNRnnForwardTraining &fwd,
+                              const NDArray &data, const NDArray &params) {
+  using namespace mkldnn;
+  using tag = mkldnn::memory::format_tag;
+  const MKLDNNRnnLayerParam& layer_param = fwd.GetParam();
+  const int mode = layer_param.mode;
+  memory::data_type data_type = get_mkldnn_type(data.dtype());
+  memory::data_type weight_type = get_mkldnn_type(params.dtype());
+  const prop_kind prop = prop_kind::backward;
+  rnn_direction mkldnn_rnn_direction = layer_param.bidirectional ?
+      rnn_direction::bidirectional_concat : rnn_direction::unidirectional;
+
+  auto src_layer_desc    = memory::desc(layer_param.src_dims, data_type, tag::tnc);
+  auto weight_layer_desc = memory::desc(layer_param.weight_layer_dims, weight_type, tag::any);
+  auto weight_iter_desc  = memory::desc(layer_param.weight_iter_dims, weight_type, tag::any);
+  auto bias_desc         = memory::desc(layer_param.bias_dims, data_type, tag::ldgo);
+  auto dst_layer_desc    = memory::desc(layer_param.dst_dims, data_type, tag::tnc);
+  auto src_state_desc    = memory::desc(layer_param.state_dims, data_type, tag::ldnc);
+  auto dst_state_desc = layer_param.state_outputs ? memory::desc(
+      layer_param.state_dims, data_type, tag::ldnc) : memory::desc();
+
+  const void* fwd_desc = fwd.GetPrimDesc();
+  auto bwd = RnnBwdPrimitive();
+  switch (mode) {
+    case rnn_enum::kLstm: {
+      const lstm_forward::primitive_desc* desc =
+          reinterpret_cast<const lstm_forward::primitive_desc*>(fwd_desc);
+      bwd = RnnBwdPrimitive::Create<lstm_forward, lstm_backward>(*desc,
+          prop, mkldnn_rnn_direction,
+          // data desc
+          src_layer_desc, src_state_desc, src_state_desc, weight_layer_desc,
+          weight_iter_desc, bias_desc, dst_layer_desc, dst_state_desc,
+          dst_state_desc,
+          // diff desc
+          src_layer_desc, src_state_desc, src_state_desc, weight_layer_desc,
+          weight_iter_desc, bias_desc, dst_layer_desc, dst_state_desc,
+          dst_state_desc);
+    } break;
+    case rnn_enum::kGru: {
+      const lbr_gru_forward::primitive_desc* desc =
+          reinterpret_cast<const lbr_gru_forward::primitive_desc*>(fwd_desc);
+      bwd = RnnBwdPrimitive::Create<lbr_gru_forward, lbr_gru_backward>(*desc,
+          prop, mkldnn_rnn_direction,
+          // data desc
+          src_layer_desc, src_state_desc, weight_layer_desc,
+          weight_iter_desc, bias_desc, dst_layer_desc, dst_state_desc,
+          // diff desc
+          src_layer_desc, src_state_desc, weight_layer_desc,
+          weight_iter_desc, bias_desc, dst_layer_desc, dst_state_desc);
+    } break;
+    case rnn_enum::kRnnRelu:
+    case rnn_enum::kRnnTanh: {
+      const vanilla_rnn_forward::primitive_desc* desc =
+          reinterpret_cast<const vanilla_rnn_forward::primitive_desc*>(fwd_desc);
+      bwd = RnnBwdPrimitive::Create<vanilla_rnn_forward, vanilla_rnn_backward>(
+          *desc, prop,
+          mode == rnn_enum::kRnnTanh ? algorithm::eltwise_tanh : algorithm::eltwise_relu,
+          mkldnn_rnn_direction,
+          // data desc
+          src_layer_desc, src_state_desc, weight_layer_desc,
+          weight_iter_desc, bias_desc, dst_layer_desc, dst_state_desc,
+          // diff desc
+          src_layer_desc, src_state_desc, weight_layer_desc,
+          weight_iter_desc, bias_desc, dst_layer_desc, dst_state_desc);
+    } break;
+    default:
+      LOG(FATAL) << "unsupported RNN mode:" << mode;
+      break;
+  }
+  return bwd;
+}
+
+/*
+ * Naive weights layout is:
+ *         | l0_l2r_wx | l0_l2r_wh | l0_r2l_wx | l0_r2l_wh |
+ *         | l1_l2r_wx | l1_l2r_wh | l1_r2l_wx | l1_r2l_wh |
+ *         ...
+ *
+ * We need concat them to be:
+ *         | l0_l2r_wx | l0_r2l_wx | l1_l2r_wx | l1_r2l_wx |
+ *         | l0_l2r_wh | l0_r2l_wh | l1_l2r_wh | l1_r2l_wh |
+ *         ...
+ *
+ * All the memory blocks are in goi format.
+ */
+static void ConcatWeights(const mkldnn::memory &dst,
+                          const int concat_dimension,
+                          const std::vector<void*> &src_ptrs,
+                          const mkldnn::memory::format_tag src_format) {
+  using memory = mkldnn::memory;
+  auto cpu_engine = dst.get_engine();
+  mkldnn::stream s(cpu_engine);
+  const memory::desc& dst_desc = dst.get_desc();
+  // Use dst memory dims to initialize src memory dims, then set the concat
+  // dim to 1. And Rnn weights are 5-dimension tensor.
+  memory::dims src_dims(dst_desc.data.dims, dst_desc.data.dims + 5);
+  src_dims.at(concat_dimension) = 1;
+  std::vector<memory::desc> src_descs;
+  std::unordered_map<int, memory> concat_args;
+
+  for (size_t i = 0; i < src_ptrs.size(); ++i) {
+    src_descs.emplace_back(src_dims,
+        static_cast<memory::data_type>(dst_desc.data.data_type), src_format);
+    concat_args.emplace(MKLDNN_ARG_MULTIPLE_SRC + i,
+        memory(src_descs.back(), cpu_engine, src_ptrs.at(i)));
+  }
+  concat_args.emplace(MKLDNN_ARG_DST, dst);
+
+  auto concat_pd = mkldnn::concat::primitive_desc(dst.get_desc(),
+      concat_dimension, src_descs, cpu_engine);
+  mkldnn::concat(concat_pd).execute(s, concat_args);
+}
+
+#define RNN_HANDLE_FUNC_NAME set_handle
+#define RNN_HANDLE_FUNC(RNN_FUNC_NAME)                                         \
+auto RNN_FUNC_NAME = [&cpu_engine, &args](int arg_name, const desc& md,        \
+    void* handle) {                                                            \
+  if (args.find(arg_name) != args.end()) {                                     \
+    if (handle != nullptr) args.at(arg_name).set_data_handle(handle);          \
+  } else {                                                                     \
+    args[arg_name] = handle ? mkldnn::memory(md, cpu_engine, handle)           \
+        : mkldnn::memory(md, cpu_engine);                                      \
+  }                                                                            \
+}
+
+#define RNN_FWD_SET(NAME, DIMS, TAG, HANDLE, DTYPE) \
+RNN_FWD_SET_(RNN_HANDLE_FUNC_NAME, NAME, DIMS, TAG, HANDLE, DTYPE)
+
+#define RNN_FWD_SET_(FUNC, NAME, DIMS, TAG, HANDLE, DTYPE) \
+FUNC(MKLDNN_ARG_##NAME, {DIMS, get_mkldnn_type(DTYPE), TAG}, HANDLE)
+
+#define RNN_BWD_SET(NAME, ARGS, HANDLE) \
+RNN_BWD_SET_(RNN_HANDLE_FUNC_NAME, NAME, ARGS, HANDLE)
+
+#define RNN_BWD_SET_(FUNC, NAME, ARGS, HANDLE) \
+FUNC(MKLDNN_ARG_DIFF_##NAME, ARGS.at(MKLDNN_ARG_##NAME).get_desc(), HANDLE)
+
+/*
+ * Set new src data handler to Forward memory. The memory primitives are
+ * not initialized until SetNewDataMem is first invoked. Src data handler
+ * must not be nullptr, except for cx with LSTM mode. If either hy, cy is
+ * nullptr, it may run with non-state_ouput or non-LSTM mode. Thus, the
+ * corresponding memory should be a empty mkldnn::memory().
+ */
+void MKLDNNRnnForward::SetNewDataMem(void* x, void* hx, void* cx,
+                                     void* y, void* hy, void* cy,
+                                     const int dtype) {
+  using dims = mkldnn::memory::dims;
+  using desc = mkldnn::memory::desc;
+  using format_tag = mkldnn::memory::format_tag;
+  auto& cpu_engine = CpuEngine::Get()->get_engine();
+  mkldnn_args_map_t& args = net_args_;
+
+  RNN_HANDLE_FUNC(RNN_HANDLE_FUNC_NAME);
+
+  // Set various data memory
+  RNN_FWD_SET(SRC,      param_.src_dims,   format_tag::tnc,  x,  dtype);
+  RNN_FWD_SET(DST,      param_.dst_dims,   format_tag::tnc,  y,  dtype);
+  RNN_FWD_SET(SRC_ITER, param_.state_dims, format_tag::ldnc, hx, dtype);
+
+  if (param_.state_outputs) {
+    RNN_FWD_SET(DST_ITER, param_.state_dims, format_tag::ldnc, hy, dtype);
+  }
+
+  if (param_.mode == rnn_enum::kLstm) {
+    RNN_FWD_SET(SRC_ITER_C, param_.state_dims, format_tag::ldnc, cx, dtype);
+    if (param_.state_outputs) {
+      RNN_FWD_SET(DST_ITER_C, param_.state_dims, format_tag::ldnc, cy, dtype);
+    }
+  }
+}
+
+/*
+ * Reorder the concatenated weights memory to a efficient memory block
+ * with primitive-prefered format.
+ */
+void MKLDNNRnnForward::ReorderWeights() {
+  auto& cpu_engine = CpuEngine::Get()->get_engine();
+  mkldnn::stream s(cpu_engine);
+  mkldnn::reorder(*weights_layer_r_, *weights_layer_)
+      .execute(s, *weights_layer_r_, *weights_layer_);
+  mkldnn::reorder(*weights_iter_r_, *weights_iter_)
+      .execute(s, *weights_iter_r_, *weights_iter_);
+  s.wait();
+}
+
+void AdjustGruGateOrder(char* weight,
+                        const size_t input_size,
+                        const size_t hidden_size,
+                        const int dtype) {
+  // mxnet gru gate order is reset, update and new gates
+  // mkldnn gru gate order is update, reset and new gates
+  size_t single_weight_bytes = input_size * hidden_size * mshadow::mshadow_sizeof(dtype);
+  char* weight_reset = weight;
+  char* weight_update = weight + single_weight_bytes;
+  std::swap_ranges(weight_reset, weight_update, weight_update);
+}
+
+/*
+ * Fuse uni-directional bias among single layer.
+ */
+template <typename DType>
+void FuseBias(DType* fuse_bias, DType* naive_bias,
+              const int mode, const size_t state_size) {
+  const size_t ngates = GetRnnGatesNum(mode);
+  const int omp_threads = mxnet::engine::OpenMP::Get()->GetRecommendedOMPThreadCount();
+  const size_t nbias = mode == rnn_enum::kGru ? ngates + 1 : ngates;
+  // MSVC-14.0 (OpenMP 2.0 compatible) doesn't support unsigned integral type in
+  // OpenMP 'for' statement.
+  const int state_size_ = static_cast<int>(state_size);
+  const int single_b_sz = static_cast<int>(nbias * state_size);
+  DType* bx = naive_bias;
+  DType* bh = naive_bias + state_size * ngates;
+  if (mode == rnn_enum::kGru) {
+    // While mxnet gru gate order is reset, update and new gates,
+    // mkldnn gru gate order is update, reset and new gates. So
+    // we need to swap the order of reset and update from mxnet.
+    #pragma omp parallel for num_threads(omp_threads)
+    for (int j = 0; j < state_size_; j++) {
+      // Swap summed reset, update bias
+      fuse_bias[j + state_size] = bx[j] + bh[j];
+      fuse_bias[j] = bx[j + state_size] + bh[j + state_size];
+
+      // Memcpy two new gates
+      fuse_bias[j + 2 * state_size] = bx[j + 2 * state_size];
+      fuse_bias[j + 3 * state_size] = bh[j + 2 * state_size];
+    }
+  } else {
+    #pragma omp parallel for num_threads(omp_threads)
+    for (int j = 0; j < single_b_sz; ++j) {
+      // Sum two bias
+      fuse_bias[j] = bx[j] + bh[j];
+    }
+  }
+}
+
+inline void EmplaceNetArgs(mkldnn_args_map_t* net_args, const int arg_name,
+                           const mkldnn::memory* mem) {
+  if (net_args->find(arg_name) != net_args->end()) {
+    if (net_args->at(arg_name).get_data_handle() == mem->get_data_handle()) {
+      return;
+    } else {
+      net_args->at(arg_name).set_data_handle(mem->get_data_handle());
+    }
+  } else {
+    net_args->emplace(arg_name, *mem);
+  }
+}
+
+/*
+ * Copy naive memory to mkldnn-format memory. It will initialize the memory
+ * when first invoked. Then, the naive weight_layer and weight_iter are
+ * concatenated to xxx_xx_r memory. Per the different gates order of GRU,
+ * it will swap the memory blocks of gates among concatenated memory
+ * inplace. From then on, the xxx_xx_r memory is reordered to target
+ * memory with preferred format_tag. Finally, naive bias is fused to MKLDNN
+ * bias memory.
+ */
+void MKLDNNRnnForward::SetWeightsMem(MKLDNNRnnMemMgr* mgr, void *w_ptr, void *b_ptr,
+                                     const bool is_train, const int dtype) {
+  using format_tag = mkldnn::memory::format_tag;
+  auto mkldnn_dtype = get_mkldnn_type(dtype);
+  // Get the weights' memory for RNN forward primitive
+  if (weights_layer_ == nullptr) {
+    weights_layer_ = mgr->Alloc(fwd_inf_.GetLayerDesc());
+  }
+  if (weights_iter_ == nullptr) {
+    weights_iter_ = mgr->Alloc(fwd_inf_.GetIterDesc());
+  }
+  if (bias_ == nullptr) {
+    bias_ = mgr->Alloc(
+        {param_.bias_dims, mkldnn_dtype, format_tag::ldgo});
+  }
+
+  // Get the intermediate memory for weights concat & reorder
+  if (weights_layer_r_ == nullptr) {
+    weights_layer_r_ = mgr->Alloc(
+        {param_.weight_layer_dims, mkldnn_dtype, format_tag::ldgoi});
+  }
+  if (weights_iter_r_ == nullptr) {
+    weights_iter_r_ = mgr->Alloc(
+        {param_.weight_iter_dims, mkldnn_dtype, format_tag::ldgoi});
+  }
+
+  // Get the bytes of a real type
+  size_t dtype_bytes = mshadow::mshadow_sizeof(dtype);
+
+  // convert void* to char* for arithmetic operations
+  char *weights_ptr = static_cast<char *>(w_ptr);
+  size_t wx_bytes = GetRnnGatesNum(param_.mode) * param_.state_size *
+        param_.input_size * dtype_bytes;  //* DIMS: ngates x state_size x input_size
+  size_t wh_bytes = GetRnnGatesNum(param_.mode) * param_.state_size *
+        param_.state_size * dtype_bytes;  //* DIMS: ngates x state_size x state_size
+  char *l2r_wx = weights_ptr;
+  char *l2r_wh = l2r_wx + wx_bytes;       //* DIMS: ngates x state_size * state_size
+
+  if (param_.num_layer == 1 && param_.bidirectional) {
+    //* single bidirectinal layer, concat weights on direction axis
+    char *r2l_wx = weights_ptr + param_.single_w_size * dtype_bytes;
+    char *r2l_wh = r2l_wx + wx_bytes;  //* DIMS: ngates x state_size * state_size
+    ConcatWeights(*weights_layer_r_, 1, {l2r_wx, r2l_wx}, format_tag::ldgoi);
+    ConcatWeights(*weights_iter_r_, 1, {l2r_wh, r2l_wh}, format_tag::ldgoi);
+  } else if (param_.num_layer == 1 && !param_.bidirectional) {
+    //* single uni-directional layer, no concatenate operator needed
+    std::memcpy(weights_layer_r_->get_data_handle(), l2r_wx, wx_bytes);
+    std::memcpy(weights_iter_r_->get_data_handle(), l2r_wh, wh_bytes);
+  } else if (param_.num_layer > 1 && !param_.bidirectional) {
+    //* concat fused multi-layer weights on layer axis
+    std::vector<void *> l2r_wx_ptrs;
+    std::vector<void *> l2r_wh_ptrs;
+    for (int lyr = 0; lyr < param_.num_layer; ++lyr) {
+      char *lth_wx = l2r_wx + lyr * param_.single_w_size * dtype_bytes;
+      char *lth_wh = lth_wx + wx_bytes;
+      l2r_wx_ptrs.push_back(lth_wx);
+      l2r_wh_ptrs.push_back(lth_wh);
+    }
+    ConcatWeights(*weights_layer_r_, 0, l2r_wx_ptrs, format_tag::ldgoi);
+    ConcatWeights(*weights_iter_r_, 0, l2r_wh_ptrs, format_tag::ldgoi);
+  } else {
+    LOG(FATAL) << "Undifined RNN fusion workflow for num_layer = " << param_.num_layer
+               << ", and bidirectional is " << param_.bidirectional;
+  }
+
+  // Adjust gates order of LBR-GRU among concatenated memory inplace.
+  char* fused_wx = static_cast<char*>(weights_layer_r_->get_data_handle());
+  char* fused_wh = static_cast<char*>(weights_iter_r_->get_data_handle());
+  if (param_.mode == rnn_enum::kGru) {
+    for (size_t lyr = 0; lyr < static_cast<size_t>(param_.num_layer); ++lyr) {
+      for (size_t d = 0; d < param_.bidirectional + 1U; ++d) {
+        AdjustGruGateOrder(fused_wx, param_.input_size, param_.state_size, dtype);
+        AdjustGruGateOrder(fused_wh, param_.state_size, param_.state_size, dtype);
+        fused_wx += wx_bytes;
+        fused_wh += wh_bytes;
+      }
+    }
+  }
+  // Reorder after adjustment only when is_train == false. When is_train == true, i.e.
+  // in forward training path, we use plain memory (ldxxx) as the space for weights and
+  // their gradients. Then, forward training primitives could fetch them from the scope
+  // of forward inference. And from there, we don't need to reorder the plain memory to
+  // the optimal rnn-packed memory for forward inference.
+  ReorderWeights();
+
+  // Process bias
+  MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
+    DType* naive_b_ptr = static_cast<DType*>(b_ptr);
+    DType* fused_bias = static_cast<DType*>(bias_->get_data_handle());
+    for (int lyr = 0; lyr < param_.num_layer; ++lyr) {
+      for (int d = 0; d < param_.bidirectional + 1; ++d) {
+        FuseBias<DType>(fused_bias, naive_b_ptr, param_.mode, param_.state_size);
+        fused_bias += param_.single_b_size;
+        naive_b_ptr += param_.naive_single_b_size;
+      }
+    }
+  });
+
+  // insert weights into net_args
+  EmplaceNetArgs(&this->net_args_, MKLDNN_ARG_WEIGHTS_LAYER, this->weights_layer_);
+  EmplaceNetArgs(&this->net_args_, MKLDNN_ARG_WEIGHTS_ITER,  this->weights_iter_);
+  EmplaceNetArgs(&this->net_args_, MKLDNN_ARG_BIAS,          this->bias_);
+
+  initialized_ = true;
+}
+
+void MKLDNNRnnForwardTraining::SetTrnMem(const MKLDNNRnnForward& fwd) {
+  using memory = mkldnn::memory;
+  const auto& cpu_engine = CpuEngine::Get()->get_engine();
+  auto s = mkldnn::stream(cpu_engine);
+  // Prepare mkldnn::memorys for weights_layer, weight_iter, and workspace
+  if (workspace_ == nullptr)
+    workspace_ = mkldnn_shared_mem_t(new memory(fwd_trn_.GetWorkspaceDesc(), cpu_engine));
+  if (weights_layer_ == nullptr)
+    weights_layer_ = mkldnn_shared_mem_t(new memory(fwd_trn_.GetLayerDesc(), cpu_engine));
+  if (weights_iter_ == nullptr)
+    weights_iter_ = mkldnn_shared_mem_t(new memory(fwd_trn_.GetIterDesc(), cpu_engine));
+
+  // fill weights memory using the reordered weights of fwd_inference primitive
+  if (fwd.weights_layer_r_->get_desc() == fwd_trn_.GetLayerDesc()) {
+    weights_layer_->set_data_handle(fwd.weights_layer_r_->get_data_handle());
+  } else {
+    mkldnn::reorder(*fwd.weights_layer_r_, *weights_layer_)
+        .execute(s, *fwd.weights_layer_r_, *weights_layer_);
+  }
+
+  if (fwd.weights_iter_r_->get_desc() == fwd_trn_.GetIterDesc()) {
+    weights_iter_->set_data_handle(fwd.weights_iter_r_->get_data_handle());
+  } else {
+    mkldnn::reorder(*fwd.weights_iter_r_, *weights_iter_)
+        .execute(s, *fwd.weights_iter_r_, *weights_iter_);
+  }
+  s.wait();
+
+  // bias are always in format_tag::ldgo
+  this->bias_ = fwd.bias_;
+
+  // insert weights into net_args
+  EmplaceNetArgs(&this->net_args_, MKLDNN_ARG_WEIGHTS_LAYER, this->weights_layer_.get());
+  EmplaceNetArgs(&this->net_args_, MKLDNN_ARG_WEIGHTS_ITER,  this->weights_iter_.get());
+  EmplaceNetArgs(&this->net_args_, MKLDNN_ARG_BIAS,          this->bias_);
+  EmplaceNetArgs(&this->net_args_, MKLDNN_ARG_WORKSPACE,     this->workspace_.get());
+}
+
+void MKLDNNRnnForwardTraining::FetchData(const MKLDNNRnnForward& fwd) {
+  for (auto& kv : fwd.net_args_) {
+    switch (kv.first) {
+      case MKLDNN_ARG_WEIGHTS_LAYER:
+      case MKLDNN_ARG_WEIGHTS_ITER:
+      case MKLDNN_ARG_BIAS:
+      case MKLDNN_ARG_WORKSPACE:
+        continue;
+
+      default:
+        EmplaceNetArgs(&this->net_args_, kv.first, &kv.second);
+    }
+  }
+}
+
+void MKLDNNRnnOp::Init(const OpContext &ctx,
+                       const std::vector<NDArray> &inputs,
+                       const std::vector<OpReqType> &req,
+                       const std::vector<NDArray> &outputs) {
+  using memory = mkldnn::memory;
+  using format_tag = mkldnn::memory::format_tag;
+
+  // In the `autograd.record()` context, RNNOp is required to run into
+  // `forward_training` mode.
+  const bool is_training = (ctx.is_train || ctx.need_grad);
+  const size_t num_fusion = full_param_.layer_params.size();
+  if (fwd_inf_vec_.size() < num_fusion) {
+    size_t buffer_size = 0;  // Element number, instead of bytes, in the buffer
+    for (auto& layer_param : full_param_.layer_params) {
+      buffer_size += layer_param.workspace_size + layer_param.reserve_size;
+    }
+    buffer_size += outputs[rnn_enum::kOut].data().Size() * (num_fusion - 1);
+    buffer_size += kMKLDNNAlign * num_fusion * 5;  // Add margin for alignment
+
+    for (auto& layer_param : full_param_.layer_params) {
+      fwd_inf_vec_.emplace_back(layer_param,
+          ctx.is_train, inputs[rnn_enum::kData], inputs[rnn_enum::kParams]);
+      buffer_size += fwd_inf_vec_.back().GetSize(inputs[rnn_enum::kParams].dtype());
+    }
+    mgr_.Init(buffer_size, ctx.run_ctx.ctx, inputs[rnn_enum::kParams].dtype());
+  }
+
+  if (is_training && fwd_trn_vec_.size() < num_fusion) {
+    for (auto& layer_param : full_param_.layer_params) {
+      fwd_trn_vec_.emplace_back(layer_param,
+          true, inputs[rnn_enum::kData], inputs[rnn_enum::kParams]);
+    }
+  }
+
+  // Get the bytes of a real type
+  const NDArray &weights = inputs[rnn_enum::kParams];
+  int dtype = weights.dtype();
+  size_t dtype_bytes = mshadow::mshadow_sizeof(dtype);
+
+  const RNNParam &default_param = full_param_.default_param;
+  char *weights_ptr = static_cast<char *>(weights.data().dptr_);
+  char *bias_ptr = weights_ptr + (weights.data().Size() -
+      GetRnnBiasSize(default_param.num_layers, default_param.state_size,
+        default_param.bidirectional + 1, default_param.mode)) * dtype_bytes;
+  for (auto& fwd_layer : fwd_inf_vec_) {
+    size_t single_w_bytes = fwd_layer.GetParam().single_w_size * dtype_bytes;
+    size_t single_b_bytes = fwd_layer.GetParam().naive_single_b_size * dtype_bytes;
+    size_t directions = fwd_layer.GetParam().bidirectional ? 2 : 1;
+    size_t layer_weights_bytes = single_w_bytes * directions;
+    size_t layer_bias_bytes = single_b_bytes * directions;  // Naive MXNet has double bias
+
+    if (!fwd_layer.IsInitialized() || is_training)
+      fwd_layer.SetWeightsMem(&(this->mgr_), weights_ptr, bias_ptr, is_training, dtype);
+    weights_ptr += layer_weights_bytes;
+    bias_ptr += layer_bias_bytes;
+  }
+
+  if (is_training) {
+    CHECK_EQ(fwd_trn_vec_.size(), fwd_inf_vec_.size()) <<
+      "Layers' configurations of forward inference and forward training are disparate.";
+    for (size_t lyr = 0; lyr < fwd_inf_vec_.size(); ++lyr)
+      fwd_trn_vec_.at(lyr).SetTrnMem(fwd_inf_vec_.at(lyr));
+  }
+
+  CHECK_EQ(num_fusion, fwd_inf_vec_.size()) <<
+      "Layer vector's size has a different value than the number of fusion.";
+  if (dst_.size() < num_fusion - 1) {
+    int data_dtype = outputs[rnn_enum::kOut].dtype();
+    // Here we need `fwd_inf_vec_.size() - 1` spaces for the intermediate results of the multiple
+    // fused layers. And for the result of the last fused layer, `outputs[rnn_enum::kOut]` could
+    // provide the space. Hence, `forward_inf_vec_.back()` is excluded when allocates the spaces
+    // for intermediate results.
+    for (std::vector<MKLDNNRnnForward>::const_iterator fwd = fwd_inf_vec_.begin();
+        fwd != fwd_inf_vec_.end() - 1; ++fwd)
+      dst_.push_back(mgr_.Alloc(
+        {fwd->GetParam().dst_dims, get_mkldnn_type(data_dtype), format_tag::tnc}));
+  }
+
+  initialized_ = true;
+}
+
+void MKLDNNRnnBackward::FetchDataWeightsMem(const MKLDNNRnnForwardTraining& fwd) {
+  using memory = mkldnn::memory;
+  auto& cpu_engine = CpuEngine::Get()->get_engine();
+  auto s = mkldnn::stream(cpu_engine);
+
+  if (this->weights_layer_ == nullptr)
+    this->weights_layer_ = mkldnn_shared_mem_t(new memory(bwd_.weights_layer_desc_, cpu_engine));
+  if (this->weights_iter_ == nullptr)
+    this->weights_iter_ = mkldnn_shared_mem_t(new memory(bwd_.weights_iter_desc_, cpu_engine));
+
+  for (auto& kv : fwd.net_args_) {
+    const mkldnn::memory* valid_mem;
+    switch (kv.first) {
+      case MKLDNN_ARG_WEIGHTS_LAYER: {
+        if (bwd_.weights_layer_desc_ == fwd.fwd_trn_.GetLayerDesc()) {
+          this->weights_layer_->set_data_handle(kv.second.get_data_handle());
+        } else {
+          mkldnn::reorder(*fwd.weights_layer_, *this->weights_layer_)
+              .execute(s, *fwd.weights_layer_, *this->weights_layer_);
+        }
+        valid_mem = this->weights_layer_.get();
+      } break;
+      case MKLDNN_ARG_WEIGHTS_ITER: {
+        if (bwd_.weights_iter_desc_ == fwd.fwd_trn_.GetLayerDesc()) {
+          this->weights_iter_->set_data_handle(kv.second.get_data_handle());
+        } else {
+          mkldnn::reorder(*fwd.weights_iter_, *this->weights_iter_)
+              .execute(s, *fwd.weights_iter_, *this->weights_iter_);
+        }
+        valid_mem = this->weights_iter_.get();
+      } break;
+
+      default:
+        valid_mem = &kv.second;
+    }
+    EmplaceNetArgs(&this->net_args_, kv.first, valid_mem);
+  }
+  s.wait();
+}
+
+void MKLDNNRnnBackward::SetWeightsGradsMem() {
+  auto& cpu_engine = CpuEngine::Get()->get_engine();
+  if (this->diff_weights_layer_ == nullptr)
+    this->diff_weights_layer_ = std::make_shared<mkldnn::memory>(
+        bwd_.diff_weights_layer_desc_, cpu_engine);
+  if (this->diff_weights_iter_ == nullptr)
+    this->diff_weights_iter_ = std::make_shared<mkldnn::memory>(
+        bwd_.diff_weights_iter_desc_, cpu_engine);
+  if (this->diff_bias_ == nullptr)
+    this->diff_bias_ = std::make_shared<mkldnn::memory>(
+        bwd_.diff_bias_desc_, cpu_engine);
+  EmplaceNetArgs(&this->net_args_, MKLDNN_ARG_DIFF_WEIGHTS_LAYER,
+      this->diff_weights_layer_.get());
+  EmplaceNetArgs(&this->net_args_, MKLDNN_ARG_DIFF_WEIGHTS_ITER,
+      this->diff_weights_iter_.get());
+  EmplaceNetArgs(&this->net_args_, MKLDNN_ARG_DIFF_BIAS,
+      this->diff_bias_.get());
+}
+
+void MKLDNNRnnBackward::SetDataGradsMem(
+    void* diff_src, void* diff_state, void* diff_statecell,
+    void* diff_dst, void* diff_state_out, void* diff_statecell_out,
+    const int dtype) {
+  using desc = mkldnn::memory::desc;
+  auto& cpu_engine = CpuEngine::Get()->get_engine();
+  mkldnn_args_map_t& args = this->net_args_;
+
+  RNN_HANDLE_FUNC(RNN_HANDLE_FUNC_NAME);
+
+  // Set various diff memory
+  auto& fwd_args = fwd_ptr_->GetArgsMap();
+  RNN_BWD_SET(SRC,      fwd_args, diff_src);
+  RNN_BWD_SET(SRC_ITER, fwd_args, diff_state);
+  RNN_BWD_SET(DST,      fwd_args, diff_dst);
+
+  if (fwd_ptr_->GetParam().state_outputs)
+    RNN_BWD_SET(DST_ITER, fwd_args, diff_state_out);
+
+  if (fwd_ptr_->GetParam().mode == rnn_enum::kLstm) {
+    RNN_BWD_SET(SRC_ITER_C, fwd_args, diff_statecell);
+    if (fwd_ptr_->GetParam().state_outputs) {
+      RNN_BWD_SET(DST_ITER_C, fwd_args, diff_statecell_out);
+    }
+  }
+}
+
+template <typename DType>
+void HalveWeightsDiff(DType* w, const size_t size) {
+  const int omp_threads = mxnet::engine::OpenMP::Get()->GetRecommendedOMPThreadCount();
+  #pragma omp parallel for num_threads(omp_threads)
+  for (int i = 0; i < static_cast<int>(size); ++i) {
+    w[i] *= 0.5;
+  }
+}
+
+void MKLDNNRnnBackward::CommitWeightsDiff(void* diff_weights, void* diff_bias, int dtype) {
+  using tag = mkldnn::memory::format_tag;
+  auto& cpu_engine = CpuEngine::Get()->get_engine();
+  auto s = mkldnn::stream(cpu_engine);
+
+  const MKLDNNRnnLayerParam& param = fwd_ptr_->GetParam();
+  const int num_layer = param.num_layer;
+  const int direction = param.bidirectional ? 2 : 1;
+  const int ngates = GetRnnGatesNum(param.mode);
+  const size_t dtype_bytes = mshadow::mshadow_sizeof(dtype);
+  const size_t wxh_bytes = param.single_w_size * dtype_bytes;
+  const size_t wx_bytes = param.input_size * param.state_size * ngates * dtype_bytes;
+  const size_t wh_bytes = param.state_size * param.state_size * ngates * dtype_bytes;
+  char* diff_wx_ptr = static_cast<char *>(diff_weights_layer_->get_data_handle());
+  char* diff_wh_ptr = static_cast<char *>(diff_weights_iter_->get_data_handle());
+
+  /* naive weights layout is:
+          1st-layer: | wx_lr  | wh_lr  | wx_rl | wh_rl |
+          2st-layer: | wx_lr  | wh_lr  | wx_rl | wh_rl |
+  size:              |    wxh_bytes    |
+                     |wx_bytes|wh_bytes|      
+  */
+  char* naive_weights = static_cast<char *>(diff_weights);
+  if (param.mode != rnn_enum::kGru) {
+    for (int shift = 0; shift < num_layer * direction; ++shift) {
+      std::memcpy(naive_weights + shift * wxh_bytes,
+          diff_wx_ptr + shift * wx_bytes, wx_bytes);
+    }
+    // align naive_weights to weights_iter memory
+    naive_weights += wx_bytes;
+    for (int shift = 0; shift < num_layer * direction; ++shift) {
+      std::memcpy(naive_weights + shift * wxh_bytes,
+          diff_wh_ptr + shift * wh_bytes, wh_bytes);
+    }
+  } else {
+    const size_t wx_bytes_per_gate = param.input_size * param.state_size * dtype_bytes;
+    const size_t wh_bytes_per_gate = param.state_size * param.state_size * dtype_bytes;
+    for (int shift = 0; shift < num_layer * direction; ++shift) {
+      std::memcpy(naive_weights + shift * wxh_bytes + wx_bytes_per_gate,
+          diff_wx_ptr + shift * wx_bytes, wx_bytes_per_gate);
+      std::memcpy(naive_weights + shift * wxh_bytes,
+          diff_wx_ptr + shift * wx_bytes + wx_bytes_per_gate, wx_bytes_per_gate);
+      std::memcpy(naive_weights + shift * wxh_bytes + 2 * wx_bytes_per_gate,
+          diff_wx_ptr + shift * wx_bytes + 2 * wx_bytes_per_gate, wx_bytes_per_gate);
+    }
+    // align naive_weights to weights_iter memory
+    naive_weights += wx_bytes;
+    for (int shift = 0; shift < num_layer * direction; ++shift) {
+      std::memcpy(naive_weights + shift * wxh_bytes + wh_bytes_per_gate,
+          diff_wh_ptr + shift * wh_bytes, wh_bytes_per_gate);
+      std::memcpy(naive_weights + shift * wxh_bytes,
+          diff_wh_ptr + shift * wh_bytes + wh_bytes_per_gate, wh_bytes_per_gate);
+      std::memcpy(naive_weights + shift * wxh_bytes + 2 * wh_bytes_per_gate,
+          diff_wh_ptr + shift * wh_bytes + 2 * wh_bytes_per_gate, wh_bytes_per_gate);
+    }
+  }
+
+  char* naive_bias = static_cast<char *>(diff_bias);
+  char* diff_bias_ptr = static_cast<char *>(this->diff_bias_->get_data_handle());
+  const size_t bias_bytes = param.single_b_size * dtype_bytes;
+  const size_t naive_bias_bytes = param.naive_single_b_size * dtype_bytes;
+  if (param.mode != rnn_enum::kGru) {
+    MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
+      DType* typed_bias = reinterpret_cast<DType *>(diff_bias_ptr);
+      HalveWeightsDiff(typed_bias, num_layer * direction * param.single_b_size);
+    });
+    for (int shift = 0; shift < num_layer * direction; ++shift) {
+      std::memcpy(naive_bias + shift * naive_bias_bytes,
+          diff_bias_ptr + shift * bias_bytes, bias_bytes);
+      std::memcpy(naive_bias + shift * naive_bias_bytes + bias_bytes,
+          diff_bias_ptr + shift * bias_bytes, bias_bytes);
+    }
+  } else {
+    const size_t bias_bytes_per_gate = param.state_size * dtype_bytes;
+    MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
+      for (int shift = 0; shift < num_layer * direction; ++shift) {
+        char* naive_reset = naive_bias + shift * naive_bias_bytes;
+        char* naive_update = naive_reset + bias_bytes_per_gate;
+        char* update = diff_bias_ptr + shift * bias_bytes;
+        char* reset = update + bias_bytes_per_gate;
+
+        DType* typed_update = reinterpret_cast<DType *>(update);
+        HalveWeightsDiff(typed_update, param.state_size * 2);
+
+        std::memcpy(naive_update, update, bias_bytes_per_gate);
+        std::memcpy(naive_reset, reset, bias_bytes_per_gate);
+        std::memcpy(naive_update + naive_bias_bytes / 2, update, bias_bytes_per_gate);
+        std::memcpy(naive_reset + naive_bias_bytes / 2, reset, bias_bytes_per_gate);
+
+        char* naive_new_bx = naive_update + bias_bytes_per_gate;
+        char* naive_new_bh = naive_new_bx + naive_bias_bytes / 2;
+        char* new_bx = reset + bias_bytes_per_gate;
+        char* new_bh = new_bx + bias_bytes_per_gate;
+        std::memcpy(naive_new_bx, new_bx, bias_bytes_per_gate);
+        std::memcpy(naive_new_bh, new_bh, bias_bytes_per_gate);
+      }
+    });
+  }
+}
+
+template <typename MKLDNNRnnX>
+inline void RegisterMKLDNNRnn(MKLDNNRnnX const& rnn) {
+  MKLDNNStream::Get()->RegisterPrimArgs(rnn.GetFwd(), rnn.GetArgsMap());
+}
+
+template <>
+inline void RegisterMKLDNNRnn(MKLDNNRnnBackward const& rnn) {
+  MKLDNNStream::Get()->RegisterPrimArgs(rnn.GetBwd(), rnn.GetArgsMap());
+}
+
+void MKLDNNRnnOp::Forward(const OpContext &ctx,
+                          const std::vector<NDArray> &inputs,
+                          const std::vector<OpReqType> &req,
+                          const std::vector<NDArray> &outputs) {
+  // In the `autograd.record()` context, RNNOp is required to run into
+  // forward_training mode.
+  const bool is_training = (ctx.is_train || ctx.need_grad);
+  // check output requests
+  if (kAddTo == req[rnn_enum::kOut])
+    LOG(FATAL) << "Currently, `add` operation is not supported by RNNs.";
+  const RNNParam& default_param = full_param_.default_param;
+  if (default_param.state_outputs) {
+    if (kAddTo == req[rnn_enum::kStateOut])
+      LOG(FATAL) << "Currently, `add` operation is not supported by RNNs.";
+    if (default_param.mode == rnn_enum::kLstm && kAddTo == req[rnn_enum::kStateCellOut])
+      LOG(FATAL) << "Currently, `add` operation against lstm-cell output is not supported.";
+  }
+
+  // Initialize weights version
+  if (!initialized_ && weights_version_ == 0) {
+    weights_version_ = inputs[rnn_enum::kParams].version();
+  }
+
+  // Check if weights NDArray was changed. If so, reset initialized_
+  if (weights_version_ != inputs[rnn_enum::kParams].version() &&
+      fwd_inf_vec_.size() > 0) {
+    initialized_ = false;
+    for (auto& fwd : fwd_inf_vec_) fwd.Reset();
+    weights_version_ = inputs[rnn_enum::kParams].version();
+  }
+
+  if (!initialized_ || is_training || fwd_inf_vec_.size() == 0) {
+    Init(ctx, inputs, req, outputs);
+  }
+
+  // Get data type
+  int data_dtype = inputs[rnn_enum::kData].dtype();
+
+  // Get input & output NDArray
+  char *src = static_cast<char *>(inputs[rnn_enum::kData].data().dptr_);
+  char *src_state = static_cast<char *>(inputs[rnn_enum::kState].data().dptr_);
+  char *dst = req[rnn_enum::kOut] == kNullOp ? nullptr
+      : static_cast<char *>(outputs[rnn_enum::kOut].data().dptr_);
+  char *dst_state = nullptr;          // Output state
+  char *src_state_cell = nullptr;     // Used in LSTM for cell state
+  char *dst_state_cell = nullptr;     // Used in LSTM for cell state
+
+  if (default_param.state_outputs && req[rnn_enum::kStateOut] != kNullOp) {
+    dst_state = static_cast<char *>(outputs[rnn_enum::kStateOut].data().dptr_);
+  }
+
+  if (default_param.mode == rnn_enum::kLstm) {
+    src_state_cell = static_cast<char *>(inputs[rnn_enum::kStateCell].data().dptr_);
+    if (default_param.state_outputs && req[rnn_enum::kStateCellOut] != kNullOp) {
+      dst_state_cell = static_cast<char *>(outputs[rnn_enum::kStateCellOut].data().dptr_);
+    }
+  }
+
+  if (fwd_inf_vec_.size() == 1) {
+    fwd_inf_vec_.front().SetNewDataMem(src, src_state, src_state_cell,
+        dst, dst_state, dst_state_cell, data_dtype);
+    if (is_training) {
+      fwd_trn_vec_.front().FetchData(fwd_inf_vec_.front());
+    }
+  } else {
+    CHECK_EQ(fwd_inf_vec_.size(), dst_.size() + 1) << "Output memory error.";
+    size_t cell_bytes = (default_param.bidirectional + 1) * default_param.batch_size_ *
+        default_param.state_size * mshadow::mshadow_sizeof(data_dtype);
+
+    // Set input data memory for the first layer. This stores intermediate output
+    // results in this->xxx, used as the source input of the next layer.
+    fwd_inf_vec_.front().SetNewDataMem(src, src_state, src_state_cell,
+        this->dst_.front()->get_data_handle(), dst_state, dst_state_cell, data_dtype);
+    if (is_training) {
+      fwd_trn_vec_.front().FetchData(fwd_inf_vec_.front());
+    }
+    // 1st_lyr -> dst_handle -> next_lyr -> dst_handle -> next_lyr -> ...
+    for (size_t lyr = 1; lyr < fwd_inf_vec_.size() - 1; ++lyr) {
+      src_state += cell_bytes;
+      if (src_state_cell) src_state_cell += cell_bytes;
+      if (dst_state) dst_state += cell_bytes;
+      if (dst_state_cell) dst_state_cell += cell_bytes;
+      fwd_inf_vec_.at(lyr).SetNewDataMem(this->dst_.at(lyr - 1)->get_data_handle(),
+          src_state, src_state_cell,
+          this->dst_.at(lyr)->get_data_handle(), dst_state, dst_state_cell, data_dtype);
+      if (is_training) {
+        fwd_trn_vec_.at(lyr).FetchData(fwd_inf_vec_.at(lyr));
+      }
+    }
+    // Set output data memory for the last layer.
+    src_state += cell_bytes;
+    if (src_state_cell) src_state_cell += cell_bytes;
+    if (dst_state) dst_state += cell_bytes;
+    if (dst_state_cell) dst_state_cell += cell_bytes;
+    fwd_inf_vec_.back().SetNewDataMem(this->dst_.back()->get_data_handle(),
+        src_state, src_state_cell, dst, dst_state, dst_state_cell, data_dtype);
+    if (is_training) {
+      fwd_trn_vec_.back().FetchData(fwd_inf_vec_.back());
+    }
+  }
+  if (is_training) {
+    for (auto& trn_lyr : fwd_trn_vec_) RegisterMKLDNNRnn(trn_lyr);
+  } else {
+    for (auto& inf_lyr : fwd_inf_vec_) RegisterMKLDNNRnn(inf_lyr);
+  }
+  MKLDNNStream::Get()->Submit();
+}
+
+void MKLDNNRnnOp::Backward(const OpContext& ctx,
+                           const std::vector<NDArray>& inputs,
+                           const std::vector<OpReqType>& req,
+                           const std::vector<NDArray>& outputs) {
+  using tag = mkldnn::memory::format_tag;
+  const RNNParam& default_param = full_param_.default_param;
+  if (kAddTo == req[rnn_enum::kData] || kAddTo == req[rnn_enum::kParams])
+    LOG(FATAL) << "Currently, `add` operations against gradients of input and weights"
+               << " are not supported by RNNs.";
+  if (default_param.state_outputs) {
+    if (kAddTo == req[rnn_enum::kStateOut])
+      LOG(FATAL) << "Currently, `add` operation against gradients of begining state"
+                 << " is not supported by RNNs.";
+    if (default_param.mode == rnn_enum::kLstm && req[rnn_enum::kStateCell])
+      LOG(FATAL) << "Currently, `add` operation against gradients of begining cell-state"
+                 << " is not supported by LSTM.";
+  }
+  // Initialize the bwd_vec_
+  if (bwd_vec_.size() != fwd_inf_vec_.size()) {
+    bwd_vec_.clear();
+    for (size_t lyr = 0; lyr < fwd_inf_vec_.size(); ++lyr)
+      bwd_vec_.emplace_back(fwd_trn_vec_.at(lyr), inputs[rnn_enum::kData],
+          inputs[rnn_enum::kParams]);
+  }
+  // Fetch weights, src and dst from Forward layer
+  if (bwd_vec_.size() != fwd_trn_vec_.size())
+    LOG(FATAL) << "MKL-DNN RNN fusion error.";
+  for (size_t lyr = 0; lyr < bwd_vec_.size(); ++lyr) {
+    bwd_vec_.at(lyr).FetchDataWeightsMem(fwd_trn_vec_.at(lyr));
+    bwd_vec_.at(lyr).SetWeightsGradsMem();
+  }
+
+  const int data_dtype = inputs[rnn_enum::kData].dtype();
+  const int w_dtype = inputs[rnn_enum::kParams].dtype();
+  const size_t w_bytes = mshadow::mshadow_sizeof(w_dtype);
+  // index description of outputs NDArray
+  //   0    1    2     3
+  // | dx | dw | dhx | dcx|
+  char* dx = req[rnn_enum::kData] == kNullOp ? nullptr
+      : static_cast<char *>(outputs[rnn_enum::kData].data().dptr_);
+  char* dw = static_cast<char *>(outputs[rnn_enum::kParams].data().dptr_);
+  char* db = dw + (inputs[rnn_enum::kParams].data().Size() -
+      GetRnnBiasSize(default_param.num_layers, default_param.state_size,
+        default_param.bidirectional + 1, default_param.mode)) * w_bytes;
+  char* dhx = req[rnn_enum::kState] == kNullOp ? nullptr
+      : static_cast<char *>(outputs[rnn_enum::kState].data().dptr_);
+  char* dcx = nullptr;
+  if (full_param_.default_param.mode == rnn_enum::kLstm
+      && req[rnn_enum::kStateCell] != kNullOp)
+    dcx = static_cast<char *>(outputs[rnn_enum::kStateCell].data().dptr_);
+
+  // index description of inputs NDArray
+  //   0   1   2    3   4    5     6    7    8     9
+  // | x | w | hx | y | dy | hy | dhy | cx | cy | dcy |
+  char* dy = static_cast<char *>(inputs[4].data().dptr_);
+  char* dhy = nullptr;
+  if (default_param.state_outputs)
+    dhy = static_cast<char *>(inputs[6].data().dptr_);
+
+  char* dcy = nullptr;
+  if ((default_param.mode == rnn_enum::kLstm) && default_param.state_outputs)
+    dcy = static_cast<char *>(inputs[9].data().dptr_);
+
+  if (bwd_vec_.size() == 1) {
+    bwd_vec_.back().SetDataGradsMem(dx, dhx, dcx, dy, dhy, dcy, data_dtype);
+    RegisterMKLDNNRnn(bwd_vec_.back());
+  } else {
+    const size_t cell_bytes = (default_param.bidirectional + 1) * default_param.batch_size_ *
+        default_param.state_size * mshadow::mshadow_sizeof(data_dtype);
+    if (diff_src == nullptr) {
+      auto desc = mkldnn::memory::desc(full_param_.layer_params.back().src_dims,
+          get_mkldnn_type(data_dtype), tag::tnc);
+      diff_src = std::make_shared<mkldnn::memory>(desc, CpuEngine::Get()->get_engine());
+    }
+    // Sets primitives from bottom to top, then submits them in reversed order.
+    bwd_vec_.front().SetDataGradsMem(dx, dhx, dcx,
+        diff_src->get_data_handle(), dhy, dcy, data_dtype);
+    for (size_t lyr = 1; lyr < bwd_vec_.size() - 1; ++lyr) {
+      if (dhx) dhx += cell_bytes;
+      if (dcx) dcx += cell_bytes;
+      if (dhy) dhy += cell_bytes;
+      if (dcy) dcy += cell_bytes;
+      bwd_vec_.at(lyr).SetDataGradsMem(diff_src->get_data_handle(), dhx, dcx,
+          diff_src->get_data_handle(), dhy, dcy, data_dtype);
+    }
+    if (dhx) dhx += cell_bytes;
+    if (dcx) dcx += cell_bytes;
+    if (dhy) dhy += cell_bytes;
+    if (dcy) dcy += cell_bytes;
+    bwd_vec_.back().SetDataGradsMem(diff_src->get_data_handle(), dhx, dcx,
+        dy, dhy, dcy, data_dtype);
+
+    for (std::vector<MKLDNNRnnBackward>::const_reverse_iterator bwd = bwd_vec_.rbegin();
+        bwd != bwd_vec_.rend(); ++bwd) {
+      RegisterMKLDNNRnn(*bwd);
+    }
+  }
+  MKLDNNStream::Get()->Submit();
+
+  // Commit weights diff
+  if (req[rnn_enum::kParams] != kNullOp) {
+    for (size_t lyr = 0; lyr < bwd_vec_.size(); ++lyr) {
+      bwd_vec_.at(lyr).CommitWeightsDiff(dw, db, w_dtype);
+      dw += full_param_.layer_params.at(lyr).single_w_size * w_bytes;
+      db += full_param_.layer_params.at(lyr).single_b_size * w_bytes;
+    }
+  }
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_USE_MKLDNN == 1
diff --git a/src/operator/nn/mkldnn/mkldnn_rnn_impl.h b/src/operator/nn/mkldnn/mkldnn_rnn_impl.h
deleted file mode 100644
index ea8e07ea617c..000000000000
--- a/src/operator/nn/mkldnn/mkldnn_rnn_impl.h
+++ /dev/null
@@ -1,740 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- */
-
-#ifndef MXNET_OPERATOR_NN_MKLDNN_MKLDNN_RNN_IMPL_H_
-#define MXNET_OPERATOR_NN_MKLDNN_MKLDNN_RNN_IMPL_H_
-#if MXNET_USE_MKLDNN == 1
-#include <dmlc/logging.h>
-#include <dmlc/parameter.h>
-#include <mxnet/operator.h>
-#include <mxnet/storage.h>
-#include <algorithm>
-#include <map>
-#include <vector>
-#include <utility>
-#include <string>
-#include "../../math_functions-inl.h"
-#include "../../operator_common.h"
-#include "../../rnn_impl.h"
-#include "../../rnn-inl.h"
-#include "mkldnn.hpp"
-#include "./mkldnn_base-inl.h"
-
-namespace mxnet {
-namespace op {
-
-static algorithm GetMKLDNNRNNAlgo(int mode,
-                                  int* ngates,
-                                  int* nstates) {
-  algorithm algo = algorithm::vanilla_rnn;
-  switch (mode) {
-    case rnn_enum::kLstm:
-      *ngates = 4;
-      *nstates = 2;
-      algo = algorithm::vanilla_lstm;
-      break;
-    case rnn_enum::kGru:
-      *ngates = 3;
-      *nstates = 1;
-      algo = algorithm::vanilla_gru;
-      break;
-    case rnn_enum::kRnnRelu:
-    case rnn_enum::kRnnTanh:
-      *ngates = 1;
-      *nstates = 1;
-      algo = algorithm::vanilla_rnn;
-      break;
-    default:
-      LOG(FATAL) << "unsupported RNN mode:" << mode;
-      break;
-  }
-  return algo;
-}
-
-static void ConcatData(mkldnn::memory::format src_format,
-                       mkldnn::memory::format dst_format,
-                       std::vector<mkldnn::memory::dims> srcs_cds,
-                       mkldnn::memory::dims dst_cds,
-                       mkldnn::memory::data_type mkldnn_dtype,
-                       int concat_dimension,
-                       std::vector<void*> srcs_data,
-                       const mkldnn::memory &dst) {
-  auto cpu_engine = CpuEngine::Get()->get_engine();
-  std::vector<mkldnn::memory::primitive_desc> srcs_pd;
-  std::vector<mkldnn::memory> srcs;
-  for (size_t i = 0; i < srcs_cds.size(); i++) {
-    auto desc = mkldnn::memory::desc(srcs_cds[i], mkldnn_dtype, src_format);
-    auto mpd = mkldnn::memory::primitive_desc(desc, cpu_engine);
-    auto src_memory = mkldnn::memory(mpd, srcs_data[i]);
-    srcs_pd.push_back(mpd);
-    srcs.push_back(src_memory);
-  }
-  std::vector<primitive::at> inputs;
-  for (size_t i = 0; i < srcs_cds.size(); i++) {
-    inputs.push_back(srcs[i]);
-  }
-  auto dst_desc = mkldnn::memory::desc(dst_cds, mkldnn_dtype, dst_format);
-  auto concat_pd = concat::primitive_desc(dst_desc, concat_dimension, srcs_pd);
-  MKLDNNStream::Get()->RegisterPrim(concat(concat_pd, inputs, dst));
-  MKLDNNStream::Get()->Submit();
-}
-
-//  cached mkldnn memory
-//  first layer wx, wh with next L - 1 layers wx and wh
-//  with L layers hx and cx, src and dst data/iter etc.
-//  it will prepare memory on before and after reorder and concat.
-//  for unidirectional, it will fused as dim like 1  + (L - 1) when I != H.
-//  for bidirectional, it will fused as data + back_data (weight, bias, iter etc),
-//  also need to identify first layer and next layers
-static size_t GetMKLDNNRNNCacheMemorySize(int L,
-                                          int D,
-                                          int T,
-                                          int N,
-                                          int I,
-                                          int H,
-                                          int mode) {
-  size_t size = 0;
-  switch (mode) {
-    case rnn_enum::kLstm:
-      size = 2 * (D * (I + H) * 4 * H + (L - 1) * D * (D * H + H) * 4 * H +
-             L * D * 2 * N * H) + T * N * D * H + L * 2 * D * 4 * H + (L + 2) * D * 2 * N * H +
-             6 * D * (I + H + 2) * 4 * H + T * N * I * 2;
-      break;
-    case rnn_enum::kGru:
-      size = 2 * (D * (I + H) * 3 * H + (L - 1) * D * (D * H + H) * 3 * H +
-             L * D * 2 * N * H) + T * N * D * H + L * 2 * D * 3 * H + (L + 2) * D * 2 * N * H +
-             6 * D * (I + H + 2) * 3 * H + T * N * I * 2;
-      break;
-    case rnn_enum::kRnnRelu:
-    case rnn_enum::kRnnTanh:
-      size = 2 * (D * (I + H) * 1 * H + (L - 1) * D * (D * H + H) * 1 * H +
-             L * D * 2 * N * H) + T * N * D * H + L * 2 * D * 1 * H + (L + 2) * D * 2 * N * H +
-             6 * D * (I + H + 2) * 1 * H + T * N * I * 2;
-      break;
-    default:
-      LOG(FATAL) << "unknown RNN mode " << mode;
-      break;
-  }
-  return size;
-}
-
-template <typename DType>
-static void AdjustGruWeightGateOrder(DType* weight,
-                                     const int I,
-                                     const int H) {
-  // mxnet gru gate order is reset, update and new gates
-  // mkldnn gru gate order is update, reset and new gates
-  const int omp_threads = mxnet::engine::OpenMP::Get()->GetRecommendedOMPThreadCount();
-  DType* weight_reset = weight;
-  DType* weight_update = weight + I * H;
-  #pragma omp parallel for num_threads(omp_threads)
-  for (int i = 0; i < I * H; i++) {
-    DType tmp = weight_update[i];
-    weight_update[i] = weight_reset[i];
-    weight_reset[i] = tmp;
-  }
-}
-
-template <typename DType>
-static void AdjustGruBiasGateOrder(DType* bias,
-                                   const int H) {
-  // mxnet gru gate order is reset, update and new gates
-  // mkldnn gru gate order is update, reset and new gates
-  const int omp_threads = mxnet::engine::OpenMP::Get()->GetRecommendedOMPThreadCount();
-  DType* bias_reset = bias;
-  DType* bias_update = bias + H;
-  #pragma omp parallel for num_threads(omp_threads)
-  for (int i = 0; i < H; i++) {
-    DType tmp = bias_update[i];
-    bias_update[i] = bias_reset[i];
-    bias_reset[i] = tmp;
-  }
-}
-// since there is different sematics of MKLDNN's Fused RNN and MXNet FusedRNN,
-// bidirectional will be fused layer by layer,
-// unidirectional will be done by fused 1 + fused (L - 1) layers or fused L layers(when I = H)
-
-template <typename DType>
-static void MKLDNNRNNForwardSingleLayerBi(bool state_outputs,
-                                          const int T,
-                                          const int N,
-                                          const int I,
-                                          const int H,
-                                          DType* x_ptr,
-                                          mkldnn::memory *user_src_layer_memory,
-                                          DType* hx_ptr,
-                                          DType* cx_ptr,
-                                          DType* w_ptr,
-                                          DType* b_ptr,
-                                          DType* y_ptr,
-                                          DType* hy_ptr,
-                                          DType* cy_ptr,
-                                          std::vector<mkldnn::memory> *concat_weight_memory,
-                                          std::vector<mkldnn::memory> *concat_iter_memory,
-                                          std::vector<mkldnn::memory> *x_memory,
-                                          std::vector<mkldnn::memory> *hcx_memory,
-                                          std::vector<mkldnn::memory> *wx_memory,
-                                          std::vector<mkldnn::memory> *wh_memory,
-                                          std::vector<mkldnn::memory> *bias_memory,
-                                          std::vector<mkldnn::memory> *y_memory,
-                                          std::vector<mkldnn::memory> *hcy_memory,
-                                          std::vector<primitive> *rnn_forward_prim,
-                                          int layer_index,
-                                          bool *has_cache,
-                                          int lvalue,
-                                          int dtype,
-                                          bool is_train,
-                                          int mode) {
-  int ngates = 0, nstates = 0;
-  algorithm nalgorithm = GetMKLDNNRNNAlgo(mode, &ngates, &nstates);
-  mkldnn::memory::data_type mkldnn_dtype = get_mkldnn_type(dtype);
-  const int single_cell_size = N * H;
-  const int single_b_size = ngates * H;
-  DType* wx = w_ptr;  //  ngates * H, I
-  DType* wh = w_ptr + I * H * ngates;  //  ngates * H, H
-  DType* back_wx = w_ptr + ngates * H * (I + H);
-  DType* back_wh = back_wx + I * H * ngates;
-  DType* bx = b_ptr;
-  DType* bh = b_ptr + H * ngates;
-  DType* back_bx = b_ptr + single_b_size * 2;
-  DType* back_bh = back_bx + H * ngates;
-  const int omp_threads = mxnet::engine::OpenMP::Get()->GetRecommendedOMPThreadCount();
-  auto cpu_engine = CpuEngine::Get()->get_engine();
-  auto null_memory_ = null_memory(cpu_engine);
-  int offset1 = 0, offset2 = 0;
-  bool initialized = *has_cache;
-  mkldnn::memory::dims src_layer_tz = {T, N, I};
-  mkldnn::memory::dims dst_layer_tz = {T, N, 2 * H};
-  mkldnn::memory::dims weights_layer_tz = {1, 2, I, ngates, H};  //  ldigo
-  mkldnn::memory::dims weights_layer_r_tz = {1, 1, I, ngates, H};  //  ldigo for reorder
-  mkldnn::memory::dims weights_iter_tz = {1, 2, H, ngates, H};  //  ldigo
-  mkldnn::memory::dims weights_iter_r_tz = {1, 1, H, ngates, H};  //  ldigo for reorder
-  mkldnn::memory::dims bias_tz = {1, 2, ngates, H};
-  mkldnn::memory::dims src_iter_tz = {1, 2, nstates, N, H};  //  ldsnc
-  mkldnn::memory::dims dst_iter_tz = {1, 2, nstates, N, H};  //  ldsnc
-
-  if (!initialized) {
-    if (mode == rnn_enum::kGru) {
-      AdjustGruWeightGateOrder(wx, I, H);
-      AdjustGruWeightGateOrder(back_wx, I, H);
-      AdjustGruWeightGateOrder(wh, H, H);
-      AdjustGruWeightGateOrder(back_wh, H, H);
-      AdjustGruBiasGateOrder(bx, H);
-      AdjustGruBiasGateOrder(back_bx, H);
-      AdjustGruBiasGateOrder(bh, H);
-      AdjustGruBiasGateOrder(back_bh, H);
-    }
-    auto src_wx = (*concat_weight_memory)[2 * layer_index];
-    auto src_wh = (*concat_weight_memory)[2 * layer_index + 1];
-    std::vector<void*> srcs_data1;
-    srcs_data1.push_back(wx);
-    srcs_data1.push_back(back_wx);
-    ConcatData(mkldnn::memory::format::ldgoi, mkldnn::memory::format::ldgoi,
-        {weights_layer_r_tz, weights_layer_r_tz}, weights_layer_tz,
-        mkldnn_dtype, 1, srcs_data1, src_wx);
-    srcs_data1.clear();
-    srcs_data1.push_back(wh);
-    srcs_data1.push_back(back_wh);
-    ConcatData(mkldnn::memory::format::ldgoi, mkldnn::memory::format::ldgoi,
-        {weights_iter_r_tz, weights_iter_r_tz}, weights_iter_tz,
-         mkldnn_dtype, 1, srcs_data1, src_wh);
-    int tmpvalue = 0;
-    if (lvalue > 0) {
-      tmpvalue = lvalue + 1;
-    }
-    MKLDNNStream::Get()->RegisterPrim(reorder(src_wx, (*wx_memory)[tmpvalue]));
-    MKLDNNStream::Get()->RegisterPrim(reorder(src_wh, (*wh_memory)[tmpvalue]));
-
-    DType* user_bias = reinterpret_cast<DType *>
-        ((*bias_memory)[tmpvalue].get_data_handle());
-    #pragma omp parallel for num_threads(omp_threads)
-    for (int j = 0; j < single_b_size; j++) {
-      user_bias[j] = bx[j] + bh[j];
-      user_bias[single_b_size + j] = back_bx[j] + back_bh[j];
-    }
-  }
-  if (lvalue > 0) {
-    (*wx_memory)[layer_index].set_data_handle((*wx_memory)[lvalue + 1].get_data_handle());
-    (*wh_memory)[layer_index].set_data_handle((*wh_memory)[lvalue + 1].get_data_handle());
-    (*bias_memory)[layer_index].set_data_handle((*bias_memory)[lvalue + 1].get_data_handle());
-  }
-
-  auto src_layer_md = mkldnn::memory::desc(
-      { src_layer_tz }, mkldnn_dtype, mkldnn::memory::format::tnc);
-  auto weight_layer_md = mkldnn::memory::desc(
-      { weights_layer_tz }, mkldnn_dtype, mkldnn::memory::format::ldigo);
-  auto weight_iter_md = mkldnn::memory::desc(
-      { weights_iter_tz }, mkldnn_dtype, mkldnn::memory::format::ldigo);
-  auto dst_layer_md = mkldnn::memory::desc(
-      { dst_layer_tz }, mkldnn_dtype, mkldnn::memory::format::tnc);
-  auto dst_iter_md = mkldnn::memory::desc(
-      { dst_iter_tz }, mkldnn_dtype, mkldnn::memory::format::ldsnc);
-  auto src_iter_md = mkldnn::memory::desc(
-      {src_iter_tz}, mkldnn_dtype, mkldnn::memory::format::ldsnc);
-  auto bias_md = mkldnn::memory::desc({bias_tz},
-      mkldnn_dtype, mkldnn::memory::format::ldgo);
-
-  auto user_src_iter_memory = (*concat_iter_memory)[2];
-  if (mode == rnn_enum::kLstm) {
-    std::vector<void*> srcs_data1;
-    srcs_data1.push_back(hx_ptr);
-    srcs_data1.push_back(cx_ptr);
-    auto tmp1_src_iter_memory = (*concat_iter_memory)[0];
-    ConcatData(mkldnn::memory::format::ldsnc, mkldnn::memory::format::ldsnc,
-        {{1, 1, 1, N, H}, {1, 1, 1, N, H}}, {1, 1, nstates, N, H}, mkldnn_dtype, 2,
-        srcs_data1, tmp1_src_iter_memory);
-    std::vector<void*> srcs_data2;
-    srcs_data2.push_back(hx_ptr + single_cell_size);
-    srcs_data2.push_back(cx_ptr + single_cell_size);
-    auto tmp2_src_iter_memory = (*concat_iter_memory)[1];
-    ConcatData(mkldnn::memory::format::ldsnc, mkldnn::memory::format::ldsnc,
-        {{1, 1, 1, N, H}, {1, 1, 1, N, H}}, {1, 1, nstates, N, H}, mkldnn_dtype, 2,
-        srcs_data2, tmp2_src_iter_memory);
-    std::vector<void*> srcs_data3;
-    srcs_data3.push_back(reinterpret_cast<DType *>(tmp1_src_iter_memory.get_data_handle()));
-    srcs_data3.push_back(reinterpret_cast<DType *>(tmp2_src_iter_memory.get_data_handle()));
-    ConcatData(mkldnn::memory::format::ldsnc, mkldnn::memory::format::ldsnc,
-        {{1, 1, nstates, N, H}, {1, 1, nstates, N, H}}, {1, 2, nstates, N, H},
-        mkldnn_dtype, 1, srcs_data3, user_src_iter_memory);
-  } else {
-    user_src_iter_memory.set_data_handle(hx_ptr);
-  }
-  (*hcx_memory)[layer_index].set_data_handle(user_src_iter_memory.get_data_handle());
-
-  rnn_cell::desc rnn_cell(nalgorithm,
-      mode == rnn_enum::kRnnRelu ? algorithm::eltwise_relu : algorithm::eltwise_tanh);
-
-  rnn_forward::desc layer_desc(prop_kind::forward_inference, rnn_cell,
-      rnn_direction::bidirectional_concat, src_layer_md,
-      src_iter_md, weight_layer_md, weight_iter_md,
-      bias_md, dst_layer_md, dst_iter_md);
-
-  auto prim_desc
-       = rnn_forward::primitive_desc(layer_desc, cpu_engine);
-
-  if (x_ptr && layer_index == 0) {
-    (*x_memory)[layer_index].set_data_handle(x_ptr);
-  } else {
-    (*x_memory)[layer_index].set_data_handle((*user_src_layer_memory).get_data_handle());
-  }
-  (*y_memory)[layer_index].set_data_handle(y_ptr);
-
-  if (rnn_forward_prim->size() <= (size_t)layer_index) {
-    primitive rnn_prim = rnn_forward(prim_desc, (*x_memory)[layer_index],
-          (*hcx_memory)[layer_index], (*wx_memory)[layer_index],
-          (*wh_memory)[layer_index], (*bias_memory)[layer_index],
-          (*y_memory)[layer_index],
-         (*hcy_memory)[layer_index], null_memory_);
-    rnn_forward_prim->push_back(rnn_prim);
-  }
-  MKLDNNStream::Get()->RegisterPrim((*rnn_forward_prim)[layer_index]);
-  MKLDNNStream::Get()->Submit();
-
-  if (state_outputs) {
-    DType* dst_hcy = reinterpret_cast<DType *> ((*hcy_memory)[layer_index].get_data_handle());
-    if (mode == rnn_enum::kLstm) {
-      offset1 = nstates * single_cell_size;
-      offset2 = (nstates + 1) * single_cell_size;
-      #pragma omp parallel for num_threads(omp_threads)
-      for (int n = 0; n < single_cell_size; n++) {
-        hy_ptr[n] = dst_hcy[n];
-        hy_ptr[n + single_cell_size] = dst_hcy[n + offset1];
-        cy_ptr[n] = dst_hcy[n + single_cell_size];
-        cy_ptr[n + single_cell_size] = dst_hcy[n + offset2];
-      }
-    } else {
-      #pragma omp parallel for num_threads(omp_threads)
-      for (int n = 0; n < 2 * single_cell_size; n++) {
-        hy_ptr[n] = dst_hcy[n];
-      }
-    }
-  }
-}
-
-
-template <typename DType>
-static void MKLDNNRNNForwardUnidi(bool state_outputs,
-                                  const int L,
-                                  const int T,
-                                  const int N,
-                                  const int I,
-                                  const int H,
-                                  DType* x_ptr,
-                                  mkldnn::memory *user_src_layer_memory,
-                                  DType* hx_ptr,
-                                  DType* cx_ptr,
-                                  DType* w_ptr,
-                                  DType* b_ptr,
-                                  DType* y_ptr,
-                                  DType* hy_ptr,
-                                  DType* cy_ptr,
-                                  std::vector<mkldnn::memory> *concat_weight_memory,
-                                  std::vector<mkldnn::memory> *concat_iter_memory,
-                                  std::vector<mkldnn::memory> *x_memory,
-                                  std::vector<mkldnn::memory> *hcx_memory,
-                                  std::vector<mkldnn::memory> *wx_memory,
-                                  std::vector<mkldnn::memory> *wh_memory,
-                                  std::vector<mkldnn::memory> *bias_memory,
-                                  std::vector<mkldnn::memory> *y_memory,
-                                  std::vector<mkldnn::memory> *hcy_memory,
-                                  std::vector<primitive> *rnn_forward_prim,
-                                  int layer_index,
-                                  bool *has_cache,
-                                  int dtype,
-                                  bool is_train,
-                                  int mode) {
-  int ngates = 0, nstates = 0;
-  algorithm nalgorithm = GetMKLDNNRNNAlgo(mode, &ngates, &nstates);
-  mkldnn::memory::data_type mkldnn_dtype = get_mkldnn_type(dtype);
-  const int cell_size = N * H;
-  const int single_cell_size = N * H;
-  const int single_b_size = ngates * H;
-  int w_size = (I + H) * H * ngates;
-  const int omp_threads = mxnet::engine::OpenMP::Get()->GetRecommendedOMPThreadCount();
-  auto cpu_engine = CpuEngine::Get()->get_engine();
-  auto null_memory_ = null_memory(cpu_engine);
-  int offset1 = 0, offset2 = 0;
-  bool initialized = *has_cache;
-
-  mkldnn::memory::dims src_layer_tz = {T, N, I};
-  mkldnn::memory::dims dst_layer_tz = {T, N, H};
-  mkldnn::memory::dims weights_layer_tz = {L, 1, I, ngates, H};  //  ldigo
-  mkldnn::memory::dims weights_iter_tz = {L, 1, H, ngates, H};  //  ldigo
-  mkldnn::memory::dims bias_tz = {L, 1, ngates, H};
-  mkldnn::memory::dims src_iter_tz = {L, 1, nstates, N, H};  //  ldsnc
-  mkldnn::memory::dims dst_iter_tz = {L, 1, nstates, N, H};  //  ldsnc
-  mkldnn::memory::dims weights_layer_r_tz = {1, 1, I, ngates, H};  //  ldigo for reorder
-  mkldnn::memory::dims weights_iter_r_tz = {1, 1, H, ngates, H};  //  ldigo for reorder
-
-  auto weight_layer_md = mkldnn::memory::desc(
-      { weights_layer_tz }, mkldnn_dtype, mkldnn::memory::format::ldigo);
-  auto weight_iter_md = mkldnn::memory::desc(
-      { weights_iter_tz }, mkldnn_dtype, mkldnn::memory::format::ldigo);
-  auto src_layer_md = mkldnn::memory::desc(
-      { src_layer_tz }, mkldnn_dtype, mkldnn::memory::format::tnc);
-  auto dst_layer_md = mkldnn::memory::desc(
-      {dst_layer_tz}, mkldnn_dtype, mkldnn::memory::format::tnc);
-  auto src_iter_md = mkldnn::memory::desc(
-      {src_iter_tz}, mkldnn_dtype, mkldnn::memory::format::ldsnc);
-  auto bias_md = mkldnn::memory::desc({bias_tz},
-      mkldnn_dtype, mkldnn::memory::format::ldgo);
-  auto dst_iter_md = mkldnn::memory::desc(
-      {dst_iter_tz}, mkldnn_dtype, mkldnn::memory::format::ldsnc);
-
-  for (int l = 0; l < L; l++) {
-    if (mode == rnn_enum::kLstm) {
-      std::vector<void*> srcs_data;
-      srcs_data.push_back(hx_ptr);
-      srcs_data.push_back(cx_ptr);
-      auto tmp_src_iter_memory = (*concat_iter_memory)[l + layer_index];
-      ConcatData(mkldnn::memory::format::ldsnc, mkldnn::memory::format::ldsnc,
-          {{1, 1, 1, N, H}, {1, 1, 1, N, H}}, {1, 1, nstates, N, H}, mkldnn_dtype,
-          2, srcs_data, tmp_src_iter_memory);
-    } else {
-      (*concat_iter_memory)[l + layer_index].set_data_handle(hx_ptr);
-    }
-    hx_ptr += cell_size;
-    if (mode == rnn_enum::kLstm) {
-      cx_ptr += cell_size;
-    }
-  }
-
-  auto user_src_iter_memory = null_memory_;
-  if (L == 1) {
-    user_src_iter_memory = (*concat_iter_memory)[layer_index];
-  } else {
-    user_src_iter_memory = (*concat_iter_memory)[L + layer_index];
-    std::vector<void*> src_l_data;
-    std::vector<mkldnn::memory::dims> src_l_dim;
-    for (int l = 0; l < L; l++) {
-      src_l_data.push_back(reinterpret_cast<DType *>
-          ((*concat_iter_memory)[l + layer_index].get_data_handle()));
-      src_l_dim.push_back({1, 1, nstates, N, H});
-    }
-    ConcatData(mkldnn::memory::format::ldsnc, mkldnn::memory::format::ldsnc, src_l_dim,
-        {L, 1, nstates, N, H}, mkldnn_dtype, 0, src_l_data, user_src_iter_memory);
-  }
-  (*hcx_memory)[layer_index].set_data_handle(user_src_iter_memory.get_data_handle());
-
-  auto src_wx_f = (*concat_weight_memory)[2 * layer_index];
-  auto src_wh_f = (*concat_weight_memory)[2 * layer_index + 1];
-
-  std::vector<void*> srcs_data_x;
-  std::vector<void*> srcs_data_h;
-  std::vector<mkldnn::memory::dims> src_l_dim_x;
-  std::vector<mkldnn::memory::dims> src_l_dim_h;
-  if (!initialized) {
-    if (L == 1) {
-      DType* wx = w_ptr;
-      DType* wh = w_ptr + I * H * ngates;
-      if (mode == rnn_enum::kGru) {
-        AdjustGruWeightGateOrder(wx, I, H);
-        AdjustGruWeightGateOrder(wh, H, H);
-        AdjustGruBiasGateOrder(b_ptr, H);
-        AdjustGruBiasGateOrder(b_ptr + H * ngates, H);
-      }
-      src_wx_f.set_data_handle(wx);
-      src_wh_f.set_data_handle(wh);
-    } else {
-      for (int l = 0; l < L; l++) {
-        DType* wx = w_ptr;
-        DType* wh = w_ptr + I * H * ngates;
-        DType* bx = b_ptr + l * ngates * H * 2;
-        DType* bh = b_ptr + l * ngates * H * 2 + H * ngates;
-        if (mode == rnn_enum::kGru) {
-          AdjustGruWeightGateOrder(wx, I, H);
-          AdjustGruWeightGateOrder(wh, H, H);
-          AdjustGruBiasGateOrder(bx, H);
-          AdjustGruBiasGateOrder(bh, H);
-        }
-        srcs_data_x.push_back(wx);
-        srcs_data_h.push_back(wh);
-        src_l_dim_x.push_back(weights_layer_r_tz);
-        src_l_dim_h.push_back(weights_iter_r_tz);
-        w_ptr = w_ptr + w_size;
-      }
-      ConcatData(mkldnn::memory::format::ldgoi, mkldnn::memory::format::ldgoi,
-          src_l_dim_x, weights_layer_tz, mkldnn_dtype, 0, srcs_data_x, src_wx_f);
-      ConcatData(mkldnn::memory::format::ldgoi, mkldnn::memory::format::ldgoi,
-          src_l_dim_h, weights_iter_tz, mkldnn_dtype, 0, srcs_data_h, src_wh_f);
-    }
-    MKLDNNStream::Get()->RegisterPrim(reorder(src_wx_f, (*wx_memory)[layer_index]));
-    MKLDNNStream::Get()->RegisterPrim(reorder(src_wh_f, (*wh_memory)[layer_index]));
-
-    DType* user_bias_f = reinterpret_cast<DType *> ((*bias_memory)[layer_index].get_data_handle());
-    #pragma omp parallel for num_threads(omp_threads)
-    for (int j = 0; j < L * single_b_size; j++) {
-      int k = j / single_b_size;
-      user_bias_f[j] = b_ptr[j + k * single_b_size] + b_ptr[j + k * single_b_size + single_b_size];
-    }
-  }
-
-  rnn_cell::desc rnn_cell(nalgorithm,
-      mode == rnn_enum::kRnnRelu ? algorithm::eltwise_relu : algorithm::eltwise_tanh);
-
-  rnn_forward::desc layer_desc(prop_kind::forward_inference, rnn_cell,
-      rnn_direction::unidirectional, src_layer_md,
-      src_iter_md, weight_layer_md, weight_iter_md,
-      bias_md, dst_layer_md, dst_iter_md);
-
-  auto prim_desc
-       = rnn_forward::primitive_desc(layer_desc, cpu_engine);
-
-  if (x_ptr && layer_index == 0) {
-    (*x_memory)[layer_index].set_data_handle(x_ptr);
-  } else {
-    (*x_memory)[layer_index].set_data_handle((*user_src_layer_memory).get_data_handle());
-  }
-  (*y_memory)[layer_index].set_data_handle(y_ptr);
-
-  if (rnn_forward_prim->size() <= (size_t)layer_index) {
-    primitive rnn_prim = rnn_forward(prim_desc, (*x_memory)[layer_index],
-          (*hcx_memory)[layer_index], (*wx_memory)[layer_index],
-          (*wh_memory)[layer_index], (*bias_memory)[layer_index],
-          (*y_memory)[layer_index],
-         (*hcy_memory)[layer_index], null_memory_);
-    rnn_forward_prim->push_back(rnn_prim);
-  }
-  MKLDNNStream::Get()->RegisterPrim((*rnn_forward_prim)[layer_index]);
-  MKLDNNStream::Get()->Submit();
-
-  if (state_outputs) {
-    DType* dst_hcy = reinterpret_cast<DType *> ((*hcy_memory)[layer_index].get_data_handle());
-    if (mode == rnn_enum::kLstm) {
-      for (int l = 0; l < L; l++) {
-        offset1 = l * single_cell_size;
-        offset2 = l * nstates * single_cell_size;
-        #pragma omp parallel for num_threads(omp_threads)
-        for (int n = 0; n < single_cell_size; n++) {
-          hy_ptr[offset1 + n] = dst_hcy[offset2 + n];
-          cy_ptr[offset1 + n] = dst_hcy[offset2 + n + single_cell_size];
-        }
-      }
-    } else {
-      #pragma omp parallel for num_threads(omp_threads)
-      for (int n = 0; n < L * single_cell_size; n++) {
-        hy_ptr[n] = dst_hcy[n];
-      }
-    }
-  }
-}
-
-template <typename DType>
-static void MKLDNNRNNForward(bool state_outputs,
-                             const int L,
-                             const int D,
-                             const int T,
-                             const int N,
-                             const int I,
-                             const int H,
-                             DType* x_ptr,
-                             DType* hx_ptr,
-                             DType* cx_ptr,
-                             DType* w_ptr,
-                             DType* b_ptr,
-                             DType* y_ptr,
-                             DType* hy_ptr,
-                             DType* cy_ptr,
-                             std::vector<mkldnn::memory> *concat_weight_memory,
-                             std::vector<mkldnn::memory> *concat_iter_memory,
-                             std::vector<mkldnn::memory> *x_memory,
-                             std::vector<mkldnn::memory> *hcx_memory,
-                             std::vector<mkldnn::memory> *wx_memory,
-                             std::vector<mkldnn::memory> *wh_memory,
-                             std::vector<mkldnn::memory> *bias_memory,
-                             std::vector<mkldnn::memory> *y_memory,
-                             std::vector<mkldnn::memory> *hcy_memory,
-                             std::vector<primitive> *rnn_forward_prim,
-                             bool *has_cache,
-                             int dtype,
-                             bool is_train,
-                             int mode) {
-  int ngates = 0, nstates = 0;
-  GetMKLDNNRNNAlgo(mode, &ngates, &nstates);
-  const int b_size = 2 * H * ngates * D;
-  const int cell_size = N * H * D;
-  //  First layer
-  int w_size = (I + H) * H * ngates * D;
-  auto cpu_engine = CpuEngine::Get()->get_engine();
-  auto null_memory_ = null_memory(cpu_engine);
-  DType* tmpNull = NULL;
-  // when D = 1 and I == H, L layers can be fused together
-  if (D == 1 && I == H) {
-    MKLDNNRNNForwardUnidi(state_outputs, L, T, N, I, H, x_ptr, &null_memory_,
-        hx_ptr, cx_ptr, w_ptr, b_ptr, y_ptr, hy_ptr, cy_ptr, concat_weight_memory,
-        concat_iter_memory, x_memory, hcx_memory, wx_memory, wh_memory,
-        bias_memory, y_memory, hcy_memory, rnn_forward_prim,
-        0, has_cache, dtype, is_train, mode);
-  } else {
-    auto user_src_layer_memory_l = null_memory_;
-    if (D == 2) {
-      MKLDNNRNNForwardSingleLayerBi(state_outputs, T, N, I, H, x_ptr, &user_src_layer_memory_l,
-          hx_ptr, cx_ptr, w_ptr, b_ptr, y_ptr, hy_ptr, cy_ptr, concat_weight_memory,
-          concat_iter_memory, x_memory, hcx_memory, wx_memory, wh_memory,
-          bias_memory, y_memory, hcy_memory, rnn_forward_prim,
-          0, has_cache, 0, dtype, is_train, mode);
-    } else {
-      MKLDNNRNNForwardUnidi(state_outputs, 1, T, N, I, H, x_ptr, &user_src_layer_memory_l,
-          hx_ptr, cx_ptr, w_ptr, b_ptr, y_ptr, hy_ptr, cy_ptr, concat_weight_memory,
-          concat_iter_memory, x_memory, hcx_memory, wx_memory, wh_memory,
-          bias_memory, y_memory, hcy_memory, rnn_forward_prim,
-          0, has_cache, dtype, is_train, mode);
-    }
-    if (L > 1) {
-      user_src_layer_memory_l = (*y_memory)[0];
-      //  go to next L - 1 layers.
-      //  If D = 2, do it layer by layer. If D = 1, fused L - 1 layers
-      w_ptr += w_size;
-      b_ptr += b_size;
-      if (D == 2) {
-        w_size = (H * D + H) * H * ngates * D;
-        for (int l = 0; l < L - 1; l++) {
-          if (state_outputs) {
-            hy_ptr += cell_size;
-            if (mode == rnn_enum::kLstm) {
-              cy_ptr += cell_size;
-            }
-          }
-          hx_ptr += cell_size;
-          if (mode == rnn_enum::kLstm) {
-            cx_ptr += cell_size;
-          }
-          MKLDNNRNNForwardSingleLayerBi(state_outputs, T, N, D * H, H, tmpNull,
-              &user_src_layer_memory_l, hx_ptr, cx_ptr, w_ptr, b_ptr, y_ptr, hy_ptr,
-              cy_ptr, concat_weight_memory, concat_iter_memory, x_memory,
-              hcx_memory, wx_memory, wh_memory, bias_memory,
-              y_memory, hcy_memory, rnn_forward_prim,
-              1, has_cache, l + 1, dtype, is_train, mode);
-          user_src_layer_memory_l = (*y_memory)[1];
-          w_ptr += w_size;
-          b_ptr += b_size;
-        }
-      }
-      if (D == 1) {
-        if (state_outputs) {
-          hy_ptr += cell_size;
-          if (mode == rnn_enum::kLstm) {
-            cy_ptr += cell_size;
-          }
-        }
-        w_size = (H + H) * H * ngates;
-        MKLDNNRNNForwardUnidi(state_outputs, L - 1, T, N, H, H, tmpNull, &user_src_layer_memory_l,
-            hx_ptr, cx_ptr, w_ptr, b_ptr, y_ptr, hy_ptr, cy_ptr, concat_weight_memory,
-            concat_iter_memory, x_memory, hcx_memory, wx_memory,
-            wh_memory, bias_memory, y_memory, hcy_memory,
-            rnn_forward_prim, 1, has_cache, dtype, is_train, mode);
-      }
-    }
-  }
-  *has_cache = true;
-}
-
-template <typename DType>
-static void MKLDNNRNNForwardInference(bool state_outputs,
-                                      const int num_layers,
-                                      const int direction,
-                                      const int seq_length,
-                                      const int batch_size,
-                                      const int input_size,
-                                      const int state_size,
-                                      DType* x_ptr,
-                                      DType* hx_ptr,
-                                      DType* cx_ptr,
-                                      DType* w_ptr,
-                                      DType* b_ptr,
-                                      DType* y_ptr,
-                                      DType* hy_ptr,
-                                      DType* cy_ptr,
-                                      std::vector<mkldnn::memory>* concat_weight_memory,
-                                      std::vector<mkldnn::memory>* concat_iter_memory,
-                                      std::vector<mkldnn::memory> *x_memory,
-                                      std::vector<mkldnn::memory> *hcx_memory,
-                                      std::vector<mkldnn::memory> *wx_memory,
-                                      std::vector<mkldnn::memory> *wh_memory,
-                                      std::vector<mkldnn::memory> *bias_memory,
-                                      std::vector<mkldnn::memory> *y_memory,
-                                      std::vector<mkldnn::memory> *hcy_memory,
-                                      std::vector<primitive> *rnn_forward_prim,
-                                      bool *has_cache,
-                                      int dtype,
-                                      bool is_train,
-                                      int mode) {
-  switch (mode) {
-    case rnn_enum::kLstm:
-    case rnn_enum::kGru:
-    case rnn_enum::kRnnTanh:
-    case rnn_enum::kRnnRelu:
-      MKLDNNRNNForward<DType>(state_outputs, num_layers, direction, seq_length,
-                              batch_size, input_size, state_size, x_ptr, hx_ptr,
-                              cx_ptr, w_ptr, b_ptr, y_ptr, hy_ptr, cy_ptr,
-                              concat_weight_memory, concat_iter_memory, x_memory,
-                              hcx_memory, wx_memory, wh_memory,
-                              bias_memory, y_memory, hcy_memory, rnn_forward_prim,
-                              has_cache, dtype, is_train, mode);
-      break;
-    default:
-      LOG(FATAL) << "unknown RNN mode" << mode;
-      break;
-  }
-}
-
-}  // namespace op
-}  // namespace mxnet
-#endif  // MXNET_USE_MKLDNN == 1
-#endif  // MXNET_OPERATOR_NN_MKLDNN_MKLDNN_RNN_IMPL_H_
diff --git a/src/operator/nn/mkldnn/mkldnn_slice-inl.h b/src/operator/nn/mkldnn/mkldnn_slice-inl.h
index f41db01a9837..e6258c8c3f43 100644
--- a/src/operator/nn/mkldnn/mkldnn_slice-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_slice-inl.h
@@ -45,7 +45,7 @@ class MKLDNNSliceFwd {
                  const NDArray &in,
                  const NDArray &out);
   void SetNewMem(const mkldnn::memory &input, const mkldnn::memory &output);
-  const mkldnn::reorder &GetPd() const;
+  void Register();
 
  private:
   std::shared_ptr<mkldnn::memory> data_;
diff --git a/src/operator/nn/mkldnn/mkldnn_slice.cc b/src/operator/nn/mkldnn/mkldnn_slice.cc
index 2a817a25a5b8..dba10f8b6cd5 100644
--- a/src/operator/nn/mkldnn/mkldnn_slice.cc
+++ b/src/operator/nn/mkldnn/mkldnn_slice.cc
@@ -49,13 +49,15 @@ MKLDNNSliceFwd::MKLDNNSliceFwd(const SliceParam &param,
     dims[i] = oshape[i];
     offsets[i] = s;
   }
-  auto in_mem_pd = in.GetMKLDNNData()->get_primitive_desc();
-  auto out_mem_pd = out.GetMKLDNNData()->get_primitive_desc();
-  auto view_pd = mkldnn::view::primitive_desc(in_mem_pd, dims, offsets);
-  auto reorder_pd = reorder::primitive_desc(view_pd.dst_primitive_desc(), out_mem_pd);
-  this->data_ = std::make_shared<mkldnn::memory>(view_pd.dst_primitive_desc(), nullptr);
-  this->out_ = std::make_shared<mkldnn::memory>(view_pd.dst_primitive_desc(), nullptr);
-  this->fwd_ = std::make_shared<mkldnn::reorder>(reorder_pd, *this->data_, *this->out_);
+
+  auto in_md = in.GetMKLDNNData()->get_desc();
+  auto out_md = out.GetMKLDNNData()->get_desc();
+  auto sub_md = in_md.submemory_desc(dims, offsets);
+
+  auto engine = CpuEngine::Get()->get_engine();
+  this->data_ = std::make_shared<mkldnn::memory>(sub_md, engine, nullptr);
+  this->out_ = std::make_shared<mkldnn::memory>(out_md, engine, nullptr);
+  this->fwd_ = std::make_shared<mkldnn::reorder>(*this->data_, *this->out_);
 }
 
 void MKLDNNSliceFwd::SetNewMem(const mkldnn::memory &input, const mkldnn::memory &output) {
@@ -63,8 +65,9 @@ void MKLDNNSliceFwd::SetNewMem(const mkldnn::memory &input, const mkldnn::memory
   this->out_->set_data_handle(output.get_data_handle());
 }
 
-const mkldnn::reorder &MKLDNNSliceFwd::GetPd() const {
-  return *fwd_;
+void MKLDNNSliceFwd::Register() {
+  MKLDNNStream::Get()->RegisterPrimArgs(*fwd_,
+      {{MKLDNN_ARG_FROM, *(this->data_)}, {MKLDNN_ARG_TO, *(this->out_)}});
 }
 
 MKLDNNSliceFwd &GetSliceForward(const SliceParam &param, const bool is_train,
@@ -91,10 +94,10 @@ void MKLDNNSlice(const SliceParam &param, const OpContext& ctx,
                  const NDArray &in, OpReqType req, const NDArray &out) {
   MKLDNNSliceFwd &fwd = GetSliceForward(param, ctx.is_train, in, out);
   auto in_mem = in.GetMKLDNNData();
-  auto out_mem_pd = out.GetMKLDNNData()->get_primitive_desc();
-  auto out_mem = CreateMKLDNNMem(out, out_mem_pd, req);
+  auto out_md = out.GetMKLDNNData()->get_desc();
+  auto out_mem = CreateMKLDNNMem(out, out_md, req);
   fwd.SetNewMem(*in_mem, *out_mem.second);
-  MKLDNNStream::Get()->RegisterPrim(fwd.GetPd());
+  fwd.Register();
   CommitOutput(out, out_mem);
   MKLDNNStream::Get()->Submit();
 }
diff --git a/src/operator/nn/mkldnn/mkldnn_softmax.cc b/src/operator/nn/mkldnn/mkldnn_softmax.cc
index 77ab43b63fd5..5b43cb0b0864 100644
--- a/src/operator/nn/mkldnn/mkldnn_softmax.cc
+++ b/src/operator/nn/mkldnn/mkldnn_softmax.cc
@@ -31,13 +31,26 @@
 namespace mxnet {
 namespace op {
 
+static mkldnn::softmax_forward::primitive_desc GetSoftmaxFwdPd(
+                                bool is_train, const int axis,
+                                const mkldnn::memory &input_mem) {
+  mkldnn::memory::desc data_md = input_mem.get_desc();
+  auto cpu_engine = CpuEngine::Get()->get_engine();
+  auto prop = is_train ? mkldnn::prop_kind::forward_training
+                       : mkldnn::prop_kind::forward_scoring;
+  auto desc = mkldnn::softmax_forward::desc(prop, data_md, axis);
+  return mkldnn::softmax_forward::primitive_desc(desc, cpu_engine);
+}
+
+
 bool SupportMKLDNNSoftmax(const SoftmaxParam &param,
                           const NDArray &data,
                           const NDArray &output) {
+  // MKLDNN does not support temperature argument in their softmax function
+  // now. Need update this once they start to support it.
   const int ndim = data.shape().ndim();
   const int in_dtype = data.dtype();
   const int out_dtype = output.dtype();
-
   const int axis = CheckAxis(param.axis, ndim);
   // MKLDNN does not support temperature argument in their softmax function
   // now. Need update this once they start to support it.
@@ -48,21 +61,12 @@ bool SupportMKLDNNSoftmax(const SoftmaxParam &param,
       axis != (ndim - 1)) {
     return false;
   }
+
   // only supports ndim = 1, 2, 3, 4 for now
   return (ndim >= 1 && ndim <= 4);
 }
 
-static mkldnn::softmax_forward::primitive_desc GetSoftmaxFwdPd(const int axis,
-                                                               const bool is_train,
-                                                               const mkldnn::memory &input) {
-  auto data_md = input.get_primitive_desc().desc();
-  auto prop = is_train ? mkldnn::prop_kind::forward_training : mkldnn::prop_kind::forward_scoring;
-  auto desc = mkldnn::softmax_forward::desc(prop, data_md, axis);
-  auto pd = mkldnn::softmax_forward::primitive_desc(desc, CpuEngine::Get()->get_engine());
-  return pd;
-}
-
-void MKLDNNSoftmaxForward(const nnvm::NodeAttrs &attrs,
+void MKLDNNSoftmaxForward(const nnvm::NodeAttrs& attrs,
                           const OpContext &ctx,
                           const NDArray &in_data,
                           const OpReqType &req,
@@ -71,21 +75,23 @@ void MKLDNNSoftmaxForward(const nnvm::NodeAttrs &attrs,
   // same as the FCompute path, softmax only supports kWriteTo and kWriteInplace for now.
   CHECK_NE(req, kAddTo);
   const SoftmaxParam& param = nnvm::get<SoftmaxParam>(attrs.parsed);
-  const int axis = CheckAxis(param.axis, in_data.shape().ndim());
-
+  int axis = CheckAxis(param.axis, in_data.shape().ndim());
   NDArray data = in_data;
   if (in_data.IsView() && in_data.IsMKLDNNData()) {
     data = in_data.Reorder2Default();
   }
 
   auto data_mem = data.GetMKLDNNData();
-  auto pd = GetSoftmaxFwdPd(axis, ctx.is_train, *data_mem);
-  auto out_mem = CreateMKLDNNMem(out_data, pd.dst_primitive_desc(), req);
+  auto pd = GetSoftmaxFwdPd(ctx.is_train, axis, *data_mem);
+  auto out_mem = CreateMKLDNNMem(out_data, pd.dst_desc(), req);
   MKLDNNStream *stream = MKLDNNStream::Get();
-  stream->RegisterPrim(mkldnn::softmax_forward(pd, *data_mem, *out_mem.second));
+  stream->RegisterPrimArgs(pd,
+                           {{MKLDNN_ARG_SRC, *data_mem}, {MKLDNN_ARG_DST, *out_mem.second}});
   CommitOutput(out_data, out_mem);
   stream->Submit();
 }
+
 }   // namespace op
 }   // namespace mxnet
 #endif
+
diff --git a/src/operator/nn/mkldnn/mkldnn_softmax_output.cc b/src/operator/nn/mkldnn/mkldnn_softmax_output.cc
index ae34fe633d6f..dbd3abf2276d 100644
--- a/src/operator/nn/mkldnn/mkldnn_softmax_output.cc
+++ b/src/operator/nn/mkldnn/mkldnn_softmax_output.cc
@@ -27,15 +27,13 @@
 #include "../../softmax_output-inl.h"
 #include "./mkldnn_ops-inl.h"
 #include "./mkldnn_base-inl.h"
-
 namespace mxnet {
 namespace op {
 
 static mkldnn::softmax_forward::primitive_desc GetSoftmaxOutputFwdDescImpl(
                const SoftmaxOutputParam& param, bool is_train,
                const int axis, const mkldnn::memory &input_mem) {
-  mkldnn::memory::primitive_desc data_mpd = input_mem.get_primitive_desc();
-  mkldnn::memory::desc data_md = data_mpd.desc();
+  mkldnn::memory::desc data_md = input_mem.get_desc();
   auto cpu_engine = CpuEngine::Get()->get_engine();
   auto prop = is_train ? mkldnn::prop_kind::forward_training
                        : mkldnn::prop_kind::forward_scoring;
@@ -47,8 +45,6 @@ typedef ParamOpSign<SoftmaxOutputParam> MKLDNNSoftmaxOuputSignature;
 
 class MKLDNNSoftmaxOutputFwd {
   std::shared_ptr<mkldnn::softmax_forward> fwd_;
-  std::shared_ptr<mkldnn::memory> data_;
-  std::shared_ptr<mkldnn::memory> out_;
 
  public:
   const mkldnn::softmax_forward::primitive_desc fwd_pd;
@@ -56,29 +52,10 @@ class MKLDNNSoftmaxOutputFwd {
   MKLDNNSoftmaxOutputFwd(const SoftmaxOutputParam& param, bool is_train,
                          const int axis, const mkldnn::memory &mem): fwd_pd(
                          GetSoftmaxOutputFwdDescImpl(param, is_train, axis, mem)) {
+    fwd_ = std::make_shared<mkldnn::softmax_forward>(fwd_pd);
   }
 
-  void SetNewMem(const mkldnn::memory &data, const mkldnn::memory &output) {
-    if (this->data_ == nullptr)
-      this->data_ = std::shared_ptr<mkldnn::memory>(new mkldnn::memory(
-        data.get_primitive_desc(), data.get_data_handle()));
-    else
-      this->data_->set_data_handle(data.get_data_handle());
-
-    if (this->out_ == nullptr)
-      this->out_ = std::shared_ptr<mkldnn::memory>(new mkldnn::memory(
-        output.get_primitive_desc(), output.get_data_handle()));
-    else
-      this->out_->set_data_handle(output.get_data_handle());
-
-    if (this->fwd_ == nullptr) {
-      this->fwd_ = std::shared_ptr<mkldnn::softmax_forward>(
-        new mkldnn::softmax_forward(fwd_pd, mkldnn::primitive::at(*this->data_),
-        *this->out_));
-    }
-  }
-
-  const mkldnn::softmax_forward &GetFwd() const {
+  const inline mkldnn::softmax_forward &GetFwd() const {
     return *fwd_;
   }
 };
@@ -129,17 +106,17 @@ void MKLDNNSoftmaxOutputForward(const nnvm::NodeAttrs& attrs,
 
   auto input_mem = idata.GetMKLDNNData();
   auto out_mem = CreateMKLDNNMem(out_data[softmaxout_enum::kOut],
-                                 input_mem->get_primitive_desc(), req[softmaxout_enum::kOut]);
+                                 input_mem->get_desc(), req[softmaxout_enum::kOut]);
 
   MKLDNNSoftmaxOutputFwd &fwd = GetSoftmaxOutputForward(param, ctx, idata);
-  fwd.SetNewMem(*input_mem, *out_mem.second);
 
   MKLDNNStream *stream = MKLDNNStream::Get();
-  stream->RegisterPrim(fwd.GetFwd());
-
+  stream->RegisterPrimArgs(fwd.GetFwd(),
+                           {{MKLDNN_ARG_SRC, *input_mem}, {MKLDNN_ARG_DST, *out_mem.second}});
   CommitOutput(out_data[softmaxout_enum::kOut], out_mem);
   stream->Submit();
 }
 }   // namespace op
 }   // namespace mxnet
 #endif
+
diff --git a/src/operator/nn/mkldnn/mkldnn_sum.cc b/src/operator/nn/mkldnn/mkldnn_sum.cc
index 724b8a2613d6..5027bcbaabb1 100644
--- a/src/operator/nn/mkldnn/mkldnn_sum.cc
+++ b/src/operator/nn/mkldnn/mkldnn_sum.cc
@@ -28,35 +28,38 @@
 #include "./mkldnn_ops-inl.h"
 #include "./mkldnn_base-inl.h"
 
-#if MXNET_USE_MKLDNN == 1
 namespace mxnet {
 namespace op {
 
-void MKLDNNSum(const mkldnn::memory &arr1, const mkldnn::memory &arr2,
-         const mkldnn::memory &out) {
-  std::vector<mkldnn::memory::primitive_desc> input_pds(2);
+#if MXNET_USE_MKLDNN == 1
+void MKLDNNSum(const mkldnn::memory &arr1,
+               const mkldnn::memory &arr2,
+               const mkldnn::memory &out) {
+  std::vector<mkldnn::memory::desc> input_pds(2);
   std::vector<float> scales(2, 1);
-  std::vector<mkldnn::primitive::at> inputs;
-  input_pds[0] = arr1.get_primitive_desc();
-  input_pds[1] = arr2.get_primitive_desc();
+  input_pds[0] = arr1.get_desc();
+  input_pds[1] = arr2.get_desc();
   CHECK(input_pds[0] == input_pds[0]);
   const mkldnn::memory *in_mem1 = &arr1;
   const mkldnn::memory *in_mem2 = &arr2;
-  auto output_pd = out.get_primitive_desc();
+  auto output_pd = out.get_desc();
   if (input_pds[0] != output_pd) {
     auto tmp_memory1 = TmpMemMgr::Get()->Alloc(output_pd);
     auto tmp_memory2 = TmpMemMgr::Get()->Alloc(output_pd);
     mxnet::MKLDNNCopy(arr1, tmp_memory1);
     mxnet::MKLDNNCopy(arr2, tmp_memory2);
-    input_pds[0] = tmp_memory1->get_primitive_desc();
-    input_pds[1] = tmp_memory2->get_primitive_desc();
+    input_pds[0] = tmp_memory1->get_desc();
+    input_pds[1] = tmp_memory2->get_desc();
     in_mem1 = tmp_memory1;
     in_mem2 = tmp_memory2;
   }
-  inputs.push_back(*in_mem1);
-  inputs.push_back(*in_mem2);
-  mkldnn::sum::primitive_desc sum_pd(scales, input_pds);
-  MKLDNNStream::Get()->RegisterPrim(mkldnn::sum(sum_pd, inputs, out));
+  mkldnn::sum::primitive_desc sum_pd(output_pd, scales, input_pds, CpuEngine::Get()->get_engine());
+  mkldnn_args_map_t args = {
+    { MKLDNN_ARG_MULTIPLE_SRC, *in_mem1 },
+    { MKLDNN_ARG_MULTIPLE_SRC + 1, *in_mem2 },
+    { MKLDNN_ARG_DST, out },
+  };
+  MKLDNNStream::Get()->RegisterPrimArgs(mkldnn::sum(sum_pd), args);
 }
 
 class MKLDNNSumFwd {
@@ -64,25 +67,20 @@ class MKLDNNSumFwd {
   mkldnn::sum::primitive_desc fwd_pd;
 
   MKLDNNSumFwd(const std::vector<float> &scales,
-               const std::vector<mkldnn::memory::primitive_desc> &data_md)
-      : fwd_pd(scales, data_md) {
-    data_.resize(data_md.size());
+               const std::vector<mkldnn::memory::desc> &data_md)
+      : fwd_pd(scales, data_md, CpuEngine::Get()->get_engine()) {
+    fwd_ = std::make_shared<mkldnn::sum>(fwd_pd);
   }
 
-  void SetNewMem(const std::vector<const mkldnn::memory *> &in_data, const mkldnn::memory &output);
-
   const mkldnn::sum &GetFwd() const { return *fwd_; }
 
  private:
   std::shared_ptr<mkldnn::sum> fwd_;
-  std::vector<std::shared_ptr<mkldnn::memory>> data_;
-  std::vector<mkldnn::primitive::at> data_mem_;
-  std::shared_ptr<mkldnn::memory> out_;
 };
 
 static MKLDNNSumFwd &GetSumForward(
     const std::vector<float> &scales, const std::vector<NDArray> &in_data,
-    const std::vector<mkldnn::memory::primitive_desc> &data_md) {
+    const std::vector<mkldnn::memory::desc> &data_md) {
 #if DMLC_CXX11_THREAD_LOCAL
   static thread_local std::unordered_map<OpSignature, MKLDNNSumFwd, OpHash> fwds;
 #else
@@ -99,35 +97,12 @@ static MKLDNNSumFwd &GetSumForward(
   return it->second;
 }
 
-void MKLDNNSumFwd::SetNewMem(const std::vector<const mkldnn::memory *> &in_data,
-                             const mkldnn::memory &output) {
-  auto num_inputs = data_.size();
-  CHECK_EQ(in_data.size(), num_inputs);
-  for (index_t i = 0; i < static_cast<index_t>(num_inputs); ++i) {
-    if (this->data_[i] == nullptr) {
-      this->data_[i] = std::shared_ptr<mkldnn::memory>(
-          new mkldnn::memory(in_data[i]->get_primitive_desc(), in_data[i]->get_data_handle()));
-      this->data_mem_.push_back(*this->data_[i]);
-    } else {
-      this->data_[i]->set_data_handle(in_data[i]->get_data_handle());
-    }
-  }
-  if (this->out_ == nullptr)
-    this->out_ = std::shared_ptr<mkldnn::memory>(
-        new mkldnn::memory(fwd_pd.dst_primitive_desc(), output.get_data_handle()));
-  else
-    this->out_->set_data_handle(output.get_data_handle());
-
-  if (this->fwd_ == nullptr)
-    this->fwd_.reset(new mkldnn::sum(fwd_pd, this->data_mem_, *this->out_));
-}
-
 void MKLDNNSumForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
                       const std::vector<NDArray> &inputs, const OpReqType &req,
                       const NDArray &out_data) {
   TmpMemMgr::Get()->Init(ctx.requested[0]);
-  auto num_inputs = inputs.size();
-  std::vector<mkldnn::memory::primitive_desc> data_md;
+  const int num_inputs = inputs.size();
+  std::vector<mkldnn::memory::desc> data_md;
   std::vector<const mkldnn::memory *> data_mem;
   std::vector<float> scales(num_inputs, 1);
   std::vector<NDArray> in_bufs(num_inputs);
@@ -135,7 +110,7 @@ void MKLDNNSumForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
   data_md.reserve(num_inputs);
   data_mem.reserve(num_inputs);
 
-  for (index_t i = 0; i < static_cast<index_t>(num_inputs); ++i) {
+  for (int i = 0; i < num_inputs; ++i) {
     const mkldnn::memory *in_mem;
     if (inputs[i].IsMKLDNNData() && inputs[i].IsView()) {
       in_bufs[i] = inputs[i].Reorder2Default();
@@ -144,22 +119,26 @@ void MKLDNNSumForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
       in_bufs[i] = inputs[i];
       in_mem = inputs[i].GetMKLDNNData();
     }
-    mkldnn::memory::primitive_desc tmp_pd = in_mem->get_primitive_desc();
-    data_md.push_back(tmp_pd);
+    mkldnn::memory::desc tmp_md = in_mem->get_desc();
+    data_md.push_back(tmp_md);
     data_mem.push_back(in_mem);
   }
 
   MKLDNNSumFwd &fwd = GetSumForward(scales, in_bufs, data_md);
   mxnet::mkldnn_output_t out_mem = CreateMKLDNNMem(out_data,
-                                                   fwd.fwd_pd.dst_primitive_desc(),
+                                                   fwd.fwd_pd.dst_desc(),
                                                    req,
                                                    &in_bufs[0]);
-  fwd.SetNewMem(data_mem, *out_mem.second);
-  MKLDNNStream::Get()->RegisterPrim(fwd.GetFwd());
+  mkldnn_args_map_t net_args;
+  net_args.insert({MKLDNN_ARG_DST, *out_mem.second});
+  for (int i = 0; i < num_inputs; ++i) {
+    net_args.insert({MKLDNN_ARG_MULTIPLE_SRC + i, *data_mem[i]});
+  }
+  MKLDNNStream::Get()->RegisterPrimArgs(fwd.GetFwd(), net_args);
   CommitOutput(out_data, out_mem);
   MKLDNNStream::Get()->Submit();
 }
+#endif
 
 }  // namespace op
 }  // namespace mxnet
-#endif
diff --git a/src/operator/nn/mkldnn/mkldnn_transpose.cc b/src/operator/nn/mkldnn/mkldnn_transpose.cc
index 48444feedcec..2ec38d586552 100644
--- a/src/operator/nn/mkldnn/mkldnn_transpose.cc
+++ b/src/operator/nn/mkldnn/mkldnn_transpose.cc
@@ -45,9 +45,10 @@ bool SupportMKLDNNTranspose(const TransposeParam& param,
 typedef ParamOpSign<TransposeParam> MKLDNNTransposeSignature;
 
 class MKLDNNTransposeForward {
+ public:
   std::shared_ptr<mkldnn::memory> data_;
   std::shared_ptr<mkldnn::memory> out_;
-  std::shared_ptr<mkldnn::memory::primitive_desc> dst_pd_;
+  std::shared_ptr<mkldnn::memory::desc> dst_md_;
   std::shared_ptr<mkldnn::reorder> transpose_;
 
  public:
@@ -67,38 +68,23 @@ class MKLDNNTransposeForward {
 
     auto engine = CpuEngine::Get()->get_engine();
     auto in_mem = data.GetMKLDNNData();
-    auto src_pd = in_mem->get_primitive_desc();
-    data_ = std::make_shared<mkldnn::memory>(src_pd, nullptr);
-
-    // destination
-    // Not all formats are well defined with a certain name in MKL-DNN.
-    // For example, transpose(NCHW, (0, 2, 1, 3)) -> NHCW, which is not explicitly defined in
-    // MKL-DNN. To support general transposing, we need create destination format from scratch.
-    mkldnn_memory_desc_t dst_fmt;
-    dst_fmt.primitive_kind = mkldnn_memory;
-    dst_fmt.ndims = data_ndim;
-    dst_fmt.data_type = mkldnn_f32;
-    dst_fmt.format = mkldnn_blocked;
-
-    for (int i = 0; i < data_ndim; i++)
-      dst_fmt.dims[i] = shape[i];
+    auto src_md = in_mem->get_desc();
+    data_ = std::make_shared<mkldnn::memory>(src_md, engine, nullptr);
 
+    mkldnn_dims_t strides;
+    mkldnn_dims_t sh;
     unsigned int total_stride = 1;
     for (int i = data_ndim - 1; i >= 0; i--) {
-      dst_fmt.layout_desc.blocking.padding_dims[i] = shape[i];
-      dst_fmt.layout_desc.blocking.block_dims[i] = 1;
-      dst_fmt.layout_desc.blocking.offset_padding_to_data[i]= 0;
-      // strides[0]: stride between the first elements of adjacent blocks.
-      dst_fmt.layout_desc.blocking.strides[0][axes[i]] = total_stride;
-      // strides[1]: strides between elements in the same block.
-      dst_fmt.layout_desc.blocking.strides[1][axes[i]] = 1;
-
+      sh[i] = shape[i];
+      strides[axes[i]] = total_stride;
       total_stride *= shape[axes[i]];
     }
 
-    dst_fmt.layout_desc.blocking.offset_padding = 0;
-    dst_pd_ = std::make_shared<mkldnn::memory::primitive_desc>(dst_fmt, engine);
-    out_ = std::make_shared<mkldnn::memory>(*dst_pd_, nullptr);
+    mkldnn_memory_desc_t dst_fmt;
+    mkldnn_memory_desc_init_by_strides(&dst_fmt, data_ndim, sh, mkldnn_f32, strides);
+
+    dst_md_ = std::make_shared<mkldnn::memory::desc>(dst_fmt);
+    out_ = std::make_shared<mkldnn::memory>(*dst_md_, engine, nullptr);
 
     transpose_ = std::make_shared<mkldnn::reorder>(*data_, *out_);
   }
@@ -121,6 +107,14 @@ class MKLDNNTransposeForward {
   const mkldnn::reorder &GetFwd() const {
     return *transpose_;
   }
+
+  void Execute() const {
+    auto stream = MKLDNNStream::Get();
+    mkldnn_args_map_t net_args;
+    net_args.insert({{MKLDNN_ARG_FROM, *(data_)}, {MKLDNN_ARG_TO, *(out_)}});
+    stream->RegisterPrimArgs(*transpose_, net_args);
+    stream->Submit();
+  }
 };
 
 static MKLDNNTransposeForward &GetTransposeForward(const TransposeParam& param,
@@ -150,13 +144,11 @@ void MKLDNNTransposeForward(const nnvm::NodeAttrs& attrs,
                             const NDArray &output) {
   const TransposeParam& param = nnvm::get<TransposeParam>(attrs.parsed);
 
-  auto stream = MKLDNNStream::Get();
   auto fwd = GetTransposeForward(param, data);
-
   fwd.SetNewMem(data, output);
-  stream->RegisterPrim(fwd.GetFwd());
-  stream->Submit();
+  fwd.Execute();
 }
 }  // namespace op
 }  // namespace mxnet
 #endif
+
diff --git a/src/operator/operator_common.h b/src/operator/operator_common.h
index 5290c09ec00d..c23a5a852dcb 100644
--- a/src/operator/operator_common.h
+++ b/src/operator/operator_common.h
@@ -528,15 +528,44 @@ class OpSignature {
 
 #if MXNET_USE_MKLDNN == 1
   void AddSign(const mkldnn::memory &mem) {
-    auto desc = mem.get_primitive_desc().desc();
-    hash = hash * 2 + desc.data.format;
-    eles.push_back(desc.data.format);
+    auto desc = mem.get_desc();
+    hash = hash * 2 + desc.data.format_kind;
+    eles.push_back(desc.data.format_kind);
     hash = hash * 2 + desc.data.data_type;
     eles.push_back(desc.data.data_type);
     for (int i = 0; i < desc.data.ndims; i++) {
       hash = hash * 2 + desc.data.dims[i];
       eles.push_back(desc.data.dims[i]);
     }
+    switch (desc.data.format_kind) {
+      case mkldnn_blocked:
+        hash = hash * 2 + desc.data.ndims;
+        eles.push_back(desc.data.ndims);
+        for (int i = 0; i < desc.data.ndims; i++) {
+          hash = hash * 2 + desc.data.format_desc.blocking.strides[i];
+          eles.push_back(desc.data.format_desc.blocking.strides[i]);
+        }
+        hash = hash * 2 + desc.data.format_desc.blocking.inner_nblks;
+        eles.push_back(desc.data.format_desc.blocking.inner_nblks);
+        for (int i = 0; i < desc.data.format_desc.blocking.inner_nblks; i++) {
+          hash = hash * 2 + desc.data.format_desc.blocking.inner_blks[i];
+          hash = hash * 2 + desc.data.format_desc.blocking.inner_idxs[i];
+          eles.push_back(desc.data.format_desc.blocking.inner_blks[i]);
+          eles.push_back(desc.data.format_desc.blocking.inner_idxs[i]);
+        }
+        break;
+      case mkldnn_format_kind_wino:
+        hash = hash * 2 + desc.data.format_desc.wino_desc.wino_format;
+        eles.push_back(desc.data.format_desc.wino_desc.wino_format);
+        break;
+      case mkldnn_format_kind_rnn_packed:
+        hash = hash * 2 + desc.data.format_desc.rnn_packed_desc.format;
+        eles.push_back(desc.data.format_desc.rnn_packed_desc.format);
+        break;
+      default:
+      // nothing need to add
+        break;
+    }
   }
 #endif
 
diff --git a/src/operator/quantization/mkldnn/mkldnn_dequantize-inl.h b/src/operator/quantization/mkldnn/mkldnn_dequantize-inl.h
index 27fa070afbe0..7ad7aeb7f757 100644
--- a/src/operator/quantization/mkldnn/mkldnn_dequantize-inl.h
+++ b/src/operator/quantization/mkldnn/mkldnn_dequantize-inl.h
@@ -48,8 +48,8 @@ class SgMKLDNNDequantizeOperator {
   DequantizeParam param_;
   float cached_data_min_{0.f};
   float cached_data_max_{0.f};
-  std::shared_ptr<mkldnn::memory> i_mem_;
-  std::shared_ptr<mkldnn::memory> o_mem_;
+  mkldnn::memory::desc o_desc_;
+  mkldnn_args_map_t args_;
   std::shared_ptr<mkldnn::reorder> fwd_pd_;
 };
 
@@ -79,37 +79,30 @@ void SgMKLDNNDequantizeOperator::Forward(const OpContext &ctx, const std::vector
       LOG(FATAL) << "mkldnn dequantize op only supports int8 and uint8 as output type";
     }
     float scale = real_range / quantized_range;
-    primitive_attr attr;
+    mkldnn::primitive_attr attr;
     const int mask = 0;
     std::vector<float> scales = {scale};
     attr.set_output_scales(mask, scales);
-    attr.set_int_output_round_mode(round_nearest);
     mkldnn::engine cpu_engine = mxnet::CpuEngine::Get()->get_engine();
-    auto i_mpd = i_mem->get_primitive_desc();
-    auto i_desc = i_mpd.desc();
+    auto i_desc = i_mem->get_desc();
     size_t i_ndim = in_buffer.shape().ndim();
-    mkldnn::memory::dims i_dims = mkldnn::memory::dims(i_ndim);
-    for (size_t i = 0; i < i_ndim; i++) {
-      i_dims[i] = static_cast<int>(in_buffer.shape()[i]);
-    }
-    mkldnn::memory::format o_fmt = static_cast<mkldnn::memory::format>(i_desc.data.format);
-    if (o_fmt == mkldnn::memory::format::nhwc) {
-      // For 4d tensor, nchw is the default format
-      o_fmt = mkldnn::memory::format::nchw;
+    if (i_ndim == 4) {
+      mkldnn::memory::format_tag o_fmt = mkldnn::memory::format_tag::nchw;
+      mkldnn::memory::dims o_dims(i_desc.data.dims, i_desc.data.dims + i_desc.data.ndims);
+      o_desc_ = mkldnn::memory::desc(o_dims, get_mkldnn_type<float>(), o_fmt);
+    } else {
+      o_desc_ = i_desc;
+      o_desc_.data.data_type = get_mkldnn_type_t<float>();
     }
-    auto o_desc =
-        mkldnn::memory::desc(i_dims, (mkldnn::memory::data_type)data_type_enum<float>::type, o_fmt);
-    auto o_mpd = memory::primitive_desc(o_desc, cpu_engine);
-    auto reorder_pd = reorder::primitive_desc(i_mpd, o_mpd, attr);
-    i_mem_ = std::make_shared<mkldnn::memory>(i_mpd, nullptr);
-    o_mem_ = std::make_shared<mkldnn::memory>(o_mpd, nullptr);
-    fwd_pd_ = std::make_shared<mkldnn::reorder>(reorder_pd, *i_mem_, *o_mem_);
+    auto reorder_pd =
+        mkldnn::reorder::primitive_desc(cpu_engine, i_desc, cpu_engine, o_desc_, attr);
+    fwd_pd_ = std::make_shared<mkldnn::reorder>(reorder_pd);
     initialized_ = true;
   }
-  auto o_mem = CreateMKLDNNMem(outputs[0], o_mem_->get_primitive_desc(), req[0]);
-  i_mem_->set_data_handle(i_mem->get_data_handle());
-  o_mem_->set_data_handle(o_mem.second->get_data_handle());
-  MKLDNNStream::Get()->RegisterPrim(*fwd_pd_);
+  auto o_mem = CreateMKLDNNMem(outputs[0], o_desc_, req[0]);
+  args_[MKLDNN_ARG_FROM] = *i_mem;
+  args_[MKLDNN_ARG_TO] = *o_mem.second;
+  MKLDNNStream::Get()->RegisterPrimArgs(*fwd_pd_, args_);
   CommitOutput(outputs[0], o_mem);
   MKLDNNStream::Get()->Submit();
 }
diff --git a/src/operator/quantization/mkldnn/mkldnn_quantize-inl.h b/src/operator/quantization/mkldnn/mkldnn_quantize-inl.h
index 7a00f621d452..07e2820f5c84 100644
--- a/src/operator/quantization/mkldnn/mkldnn_quantize-inl.h
+++ b/src/operator/quantization/mkldnn/mkldnn_quantize-inl.h
@@ -35,11 +35,11 @@
 namespace mxnet {
 namespace op {
 
-template<typename SrcType, typename DstType>
+template <typename SrcType, typename DstType>
 static void MKLDNNQuantizeComputeKer(const std::vector<NDArray>& inputs,
                                      const std::vector<NDArray>& outputs,
                                      const QuantizeParam& param,
-                                     const std::vector<OpReqType> &req) {
+                                     const std::vector<OpReqType>& req) {
   using namespace mshadow;
   using namespace mxnet_op;
   using red::limits::MaxValue;
@@ -60,38 +60,30 @@ static void MKLDNNQuantizeComputeKer(const std::vector<NDArray>& inputs,
     LOG(FATAL) << "mkldnn quantize op only supports int8 and uint8 as output type";
   }
   float scale = quantized_range / real_range;
-  primitive_attr attr;
+  mkldnn::primitive_attr attr;
   const int mask = 0;
   std::vector<float> scales = {scale};
   attr.set_output_scales(mask, scales);
-  attr.set_int_output_round_mode(round_nearest);
   mkldnn::engine cpu_engine = mxnet::CpuEngine::Get()->get_engine();
-
   NDArray in_buffer = inputs[0];
-  if (inputs[0].IsView() && inputs[0].IsMKLDNNData())
-    in_buffer = inputs[0].Reorder2Default();
+  if (inputs[0].IsView() && inputs[0].IsMKLDNNData()) in_buffer = inputs[0].Reorder2Default();
 
   auto i_mem = in_buffer.GetMKLDNNData();
-  auto i_mpd = i_mem->get_primitive_desc();
-  auto i_desc = i_mpd.desc();
-  mkldnn::memory::format i_fmt = static_cast<mkldnn::memory::format>(i_desc.data.format);
-  if (i_fmt == mkldnn::memory::format::nchw ||
-      i_fmt == mkldnn::memory::format::nChw8c ||
-      i_fmt == mkldnn_nChw16c) {
-    i_fmt = mkldnn::memory::format::nhwc;
-  }
+  auto i_desc = i_mem->get_desc();
   size_t i_ndim = in_buffer.shape().ndim();
-  mkldnn::memory::dims i_dims = mkldnn::memory::dims(i_ndim);
-  for (size_t i = 0; i < i_ndim; i++) {
-    i_dims[i] = static_cast<int>(in_buffer.shape()[i]);
+  mkldnn::memory::desc o_desc;
+  if (i_ndim == 4) {
+    mkldnn::memory::format_tag o_fmt = mkldnn::memory::format_tag::nhwc;
+    mkldnn::memory::dims o_dims(i_desc.data.dims, i_desc.data.dims + i_desc.data.ndims);
+    o_desc = mkldnn::memory::desc(o_dims, get_mkldnn_type<DstType>(), o_fmt);
+  } else {
+    o_desc = i_desc;
+    o_desc.data.data_type = get_mkldnn_type_t<DstType>();
   }
-  auto o_desc = mkldnn::memory::desc(i_dims,
-                                    (mkldnn::memory::data_type)data_type_enum<DstType>::type,
-                                    i_fmt);
-  auto o_mpd = memory::primitive_desc(o_desc, cpu_engine);
-  auto reorder_pd  = reorder::primitive_desc(i_mpd, o_mpd, attr);
-  auto o_mem = CreateMKLDNNMem(outputs[0], o_mpd, req[0]);
-  MKLDNNStream::Get()->RegisterPrim(mkldnn::reorder(reorder_pd, *i_mem, *o_mem.second));
+  auto reorder_pd = mkldnn::reorder::primitive_desc(cpu_engine, i_desc, cpu_engine, o_desc, attr);
+  auto o_mem = CreateMKLDNNMem(outputs[0], o_desc, req[0]);
+  MKLDNNStream::Get()->RegisterPrimArgs(
+      mkldnn::reorder(reorder_pd), {{MKLDNN_ARG_FROM, *i_mem}, {MKLDNN_ARG_TO, *o_mem.second}});
   CommitOutput(outputs[0], o_mem);
   MKLDNNStream::Get()->Submit();
 }
diff --git a/src/operator/quantization/mkldnn/mkldnn_quantize_v2-inl.h b/src/operator/quantization/mkldnn/mkldnn_quantize_v2-inl.h
index 7cdce8e32bc8..6e10efa99f32 100644
--- a/src/operator/quantization/mkldnn/mkldnn_quantize_v2-inl.h
+++ b/src/operator/quantization/mkldnn/mkldnn_quantize_v2-inl.h
@@ -47,8 +47,8 @@ class SgMKLDNNQuantizeOperator {
   QuantizeV2Param param_;
   float cached_data_min_{0.f};
   float cached_data_max_{0.f};
-  std::shared_ptr<mkldnn::memory> i_mem_;
-  std::shared_ptr<mkldnn::memory> o_mem_;
+  mkldnn::memory::desc o_desc_;
+  mkldnn_args_map_t args_;
   std::shared_ptr<mkldnn::reorder> fwd_pd_;
 };
 
@@ -127,36 +127,30 @@ void SgMKLDNNQuantizeOperator::Forward(const OpContext &ctx, const std::vector<N
       cached_data_max_ = data_max;
       float real_range = MaxAbs(data_min, data_max);
       float scale = quantized_range / real_range;
-      primitive_attr attr;
+      mkldnn::primitive_attr attr;
       const int mask = 0;
       std::vector<float> scales = {scale};
       attr.set_output_scales(mask, scales);
-      attr.set_int_output_round_mode(round_nearest);
       mkldnn::engine cpu_engine = mxnet::CpuEngine::Get()->get_engine();
-      auto i_mpd = i_mem->get_primitive_desc();
-      auto i_desc = i_mpd.desc();
-      mkldnn::memory::format i_fmt = static_cast<mkldnn::memory::format>(i_desc.data.format);
-      if (i_fmt == mkldnn::memory::format::nchw || i_fmt == mkldnn::memory::format::nChw8c ||
-          i_fmt == mkldnn_nChw16c) {
-        i_fmt = mkldnn::memory::format::nhwc;
-      }
+      auto i_desc = i_mem->get_desc();
       size_t i_ndim = in_buffer.shape().ndim();
-      mkldnn::memory::dims i_dims = mkldnn::memory::dims(i_ndim);
-      for (size_t i = 0; i < i_ndim; i++) {
-        i_dims[i] = static_cast<int>(in_buffer.shape()[i]);
+      if (i_ndim == 4) {
+        mkldnn::memory::format_tag o_fmt = mkldnn::memory::format_tag::nhwc;
+        mkldnn::memory::dims o_dims(i_desc.data.dims, i_desc.data.dims + i_desc.data.ndims);
+        o_desc_ = mkldnn::memory::desc(o_dims, get_mkldnn_type(out_type), o_fmt);
+      } else {
+        o_desc_ = i_desc;
+        o_desc_.data.data_type = get_mkldnn_type_t(out_type);
       }
-      auto o_desc = mkldnn::memory::desc(i_dims, get_mkldnn_type(out_type), i_fmt);
-      auto o_mpd = memory::primitive_desc(o_desc, cpu_engine);
-      auto reorder_pd = reorder::primitive_desc(i_mpd, o_mpd, attr);
-      i_mem_ = std::make_shared<mkldnn::memory>(i_mpd, nullptr);
-      o_mem_ = std::make_shared<mkldnn::memory>(o_mpd, nullptr);
-      fwd_pd_ = std::make_shared<mkldnn::reorder>(reorder_pd, *i_mem_, *o_mem_);
+      auto reorder_pd =
+          mkldnn::reorder::primitive_desc(cpu_engine, i_desc, cpu_engine, o_desc_, attr);
+      fwd_pd_ = std::make_shared<mkldnn::reorder>(reorder_pd);
       initalized_ = true;
     }
-    auto o_mem = CreateMKLDNNMem(outputs[0], o_mem_->get_primitive_desc(), req[0]);
-    i_mem_->set_data_handle(i_mem->get_data_handle());
-    o_mem_->set_data_handle(o_mem.second->get_data_handle());
-    MKLDNNStream::Get()->RegisterPrim(*fwd_pd_);
+    auto o_mem = CreateMKLDNNMem(outputs[0], o_desc_, req[0]);
+    args_[MKLDNN_ARG_FROM] = *i_mem;
+    args_[MKLDNN_ARG_TO] = *o_mem.second;
+    MKLDNNStream::Get()->RegisterPrimArgs(*fwd_pd_, args_);
     CommitOutput(outputs[0], o_mem);
     MKLDNNStream::Get()->Submit();
   }
diff --git a/src/operator/quantization/mkldnn/mkldnn_quantized_batch_norm.cc b/src/operator/quantization/mkldnn/mkldnn_quantized_batch_norm.cc
index 9dd86bd4dd4c..4723ea41bf2f 100644
--- a/src/operator/quantization/mkldnn/mkldnn_quantized_batch_norm.cc
+++ b/src/operator/quantization/mkldnn/mkldnn_quantized_batch_norm.cc
@@ -44,21 +44,22 @@ static void MKLDNNQuantizedBatchNormForward(const nnvm::NodeAttrs &attrs, const
 
   // reorder if data type = uint8
   if (in_data[quantized_batchnorm::kData].dtype() == mshadow::kUint8) {
-    auto u8_pd = data_mem->get_primitive_desc();
-    auto u8_md = u8_pd.desc();
-    mkldnn::memory::desc s8_md(
-        mkldnn::memory::dims(u8_md.data.dims, u8_md.data.dims + u8_md.data.ndims),
-        mkldnn::memory::data_type::s8, static_cast<mkldnn::memory::format>(u8_md.data.format));
-    auto s8_pd = mkldnn::memory::primitive_desc(s8_md, CpuEngine::Get()->get_engine());
-    auto data_reorder_mem = TmpMemMgr::Get()->Alloc(s8_pd);
+    auto u8_md = data_mem->get_desc();
+    auto s8_md = u8_md;
+    s8_md.data.data_type = static_cast<mkldnn_data_type_t>(mkldnn::memory::data_type::s8);
+    auto data_reorder_mem = TmpMemMgr::Get()->Alloc(s8_md);
 
     std::vector<float> reorder_scale;
     reorder_scale = {static_cast<float>(kInt8Range) / kUint8Range};
-    primitive_attr reorder_attr;
-    reorder_attr.set_int_output_round_mode(round_mode::round_nearest);
+    mkldnn::primitive_attr reorder_attr;
     reorder_attr.set_output_scales(0, reorder_scale);
-    const auto reorder_pd = mkldnn::reorder::primitive_desc(u8_pd, s8_pd, reorder_attr);
-    MKLDNNStream::Get()->RegisterPrim(mkldnn::reorder(reorder_pd, *data_mem, *data_reorder_mem));
+    mkldnn::engine cpu_engine = CpuEngine::Get()->get_engine();
+    const auto reorder_pd =
+        mkldnn::reorder::primitive_desc(cpu_engine, u8_md, cpu_engine, s8_md, reorder_attr);
+    mkldnn_args_map_t reorder_args;
+    reorder_args[MKLDNN_ARG_SRC] = *data_mem;
+    reorder_args[MKLDNN_ARG_DST] = *data_reorder_mem;
+    MKLDNNStream::Get()->RegisterPrimArgs(mkldnn::reorder(reorder_pd), reorder_args);
     data_mem = data_reorder_mem;
   }
   const size_t channelAxis = static_cast<size_t>(
@@ -79,10 +80,11 @@ static void MKLDNNQuantizedBatchNormForward(const nnvm::NodeAttrs &attrs, const
   }
   const float max_abs_output = std::max(std::abs(*min_output_ptr), std::abs(*max_output_ptr));
 
-  unsigned flags = mkldnn::use_global_stats | mkldnn::use_scale_shift;
+  mkldnn::normalization_flags flags =
+      mkldnn::normalization_flags::use_global_stats | mkldnn::normalization_flags::use_scale_shift;
   auto &fwd = GetBNForward<float>(param, ctx, data_mem, flags);
   const mkldnn::memory &weight_mem = fwd.GetWeight();
-  CHECK_EQ(weight_mem.get_primitive_desc().get_size(), channel_count * sizeof(float) * 2);
+  CHECK_EQ(weight_mem.get_desc().get_size(), channel_count * sizeof(float) * 2);
   float *weight_buf = reinterpret_cast<float *>(weight_mem.get_data_handle());
 
   float *gamma_ptr = in_data[quantized_batchnorm::kGamma].data().dptr<float>();
@@ -94,9 +96,8 @@ static void MKLDNNQuantizedBatchNormForward(const nnvm::NodeAttrs &attrs, const
   float *moving_var_ptr = moving_var.data().dptr<float>();
 
   // rescale gamma and beta, to make mean=0 and var=1
-  auto rescaled_mean_mem =
-      TmpMemMgr::Get()->Alloc(moving_mean.GetMKLDNNData()->get_primitive_desc());
-  auto rescaled_var_mem = TmpMemMgr::Get()->Alloc(moving_var.GetMKLDNNData()->get_primitive_desc());
+  auto rescaled_mean_mem = TmpMemMgr::Get()->Alloc(moving_mean.GetMKLDNNData()->get_desc());
+  auto rescaled_var_mem = TmpMemMgr::Get()->Alloc(moving_var.GetMKLDNNData()->get_desc());
   float *rescaled_mean_ptr = reinterpret_cast<float *>(rescaled_mean_mem->get_data_handle());
   float *rescaled_var_ptr = reinterpret_cast<float *>(rescaled_var_mem->get_data_handle());
 
@@ -111,11 +112,16 @@ static void MKLDNNQuantizedBatchNormForward(const nnvm::NodeAttrs &attrs, const
     rescaled_var_ptr[channel] = 1.0f;
   }
 
-  auto out_mem = CreateMKLDNNMem(outputs[batchnorm::kOut],
-      fwd.GetPd().dst_primitive_desc(), req[batchnorm::kOut], &data);
-  fwd.SetDataHandle(data_mem, rescaled_mean_mem, rescaled_var_mem, out_mem.second);
+  const NDArray &out = outputs[batchnorm::kOut];
+  auto out_mem = const_cast<NDArray &>(out).CreateMKLDNNData(fwd.GetPd().dst_desc());
+  mkldnn_args_map_t net_args;
+  net_args[MKLDNN_ARG_SRC] = *data_mem;
+  net_args[MKLDNN_ARG_SCALE_SHIFT] = weight_mem;
+  net_args[MKLDNN_ARG_DST] = *out_mem;
+  net_args[MKLDNN_ARG_MEAN] = *rescaled_mean_mem;
+  net_args[MKLDNN_ARG_VARIANCE] = *rescaled_var_mem;
 
-  MKLDNNStream::Get()->RegisterPrim(fwd.GetFwd());
+  MKLDNNStream::Get()->RegisterPrimArgs(fwd.GetFwd(), net_args);
   MKLDNNStream::Get()->Submit();
 }
 
diff --git a/src/operator/quantization/mkldnn/mkldnn_quantized_concat.cc b/src/operator/quantization/mkldnn/mkldnn_quantized_concat.cc
index 2a4c6d612e65..619e8bf1e1fb 100644
--- a/src/operator/quantization/mkldnn/mkldnn_quantized_concat.cc
+++ b/src/operator/quantization/mkldnn/mkldnn_quantized_concat.cc
@@ -60,7 +60,7 @@ static void MKLDNNQuantizedConcatForward(const nnvm::NodeAttrs& attrs, const OpC
   out_data[quantized_concat_enum::kMin].data().dptr<float>()[0] = output_neg_min;
   out_data[quantized_concat_enum::kMax].data().dptr<float>()[0] = output_pos_max;
   auto out_scale = GetScale(out_data[quantized_concat_enum::kOut], output_neg_min, output_pos_max);
-  std::vector<mkldnn::memory::primitive_desc> data_md;
+  std::vector<mkldnn::memory::desc> data_md;
   std::vector<const mkldnn::memory*> data_mem;
   // new_data_mem is for auto-free new created mkldnn memory
   std::vector<std::shared_ptr<mkldnn::memory>> new_data_mem;
@@ -71,36 +71,37 @@ static void MKLDNNQuantizedConcatForward(const nnvm::NodeAttrs& attrs, const OpC
       CHECK(in_data[i].dtype() == out_dtype);
       auto mem = in_data[i].GetMKLDNNData();
       data_mem.push_back(mem);
-      data_md.push_back(mem->get_primitive_desc());
+      data_md.push_back(mem->get_desc());
     } else {
       auto mem = in_data[i].GetMKLDNNData();
-      auto pd = mem->get_primitive_desc();
+      auto mem_desc = mem->get_desc();
       if (in_data[i].dtype() != out_dtype) {
-        auto mem_desc = pd.desc();
-        mkldnn::memory::desc new_md(
-            mkldnn::memory::dims(mem_desc.data.dims, mem_desc.data.dims + mem_desc.data.ndims),
-            get_mkldnn_type(out_dtype), static_cast<mkldnn::memory::format>(mem_desc.data.format));
-        pd = mkldnn::memory::primitive_desc(new_md, CpuEngine::Get()->get_engine());
+        mem_desc.data.data_type = static_cast<mkldnn_data_type_t>(get_mkldnn_type(out_dtype));
       }
-      const auto rescaled_mem = std::make_shared<mkldnn::memory>(pd);
+      const auto rescaled_mem =
+          std::make_shared<mkldnn::memory>(mem_desc, CpuEngine::Get()->get_engine());
       new_data_mem.push_back(rescaled_mem);
       std::vector<float> reorder_scale = {out_scale / i_scale};
-      primitive_attr reorder_attr;
-      reorder_attr.set_int_output_round_mode(round_mode::round_nearest);
+      mkldnn::primitive_attr reorder_attr;
       reorder_attr.set_output_scales(0, reorder_scale);
-      const auto reorder_pd =
-          mkldnn::reorder::primitive_desc(mem->get_primitive_desc(), pd, reorder_attr);
-      MKLDNNStream::Get()->RegisterPrim(mkldnn::reorder(reorder_pd, *mem, *rescaled_mem));
+      const auto reorder_pd = mkldnn::reorder::primitive_desc(*mem, *rescaled_mem, reorder_attr);
+      mkldnn_args_map_t reorder_args;
+      reorder_args[MKLDNN_ARG_SRC] = *mem;
+      reorder_args[MKLDNN_ARG_DST] = *rescaled_mem;
+      MKLDNNStream::Get()->RegisterPrimArgs(mkldnn::reorder(reorder_pd), reorder_args);
       data_mem.push_back(rescaled_mem.get());
-      data_md.push_back(pd);
+      data_md.push_back(mem_desc);
     }
   }
   MKLDNNConcatFwd& fwd = GetConcatForward(param_.dim, in_data, data_md);
-  mxnet::mkldnn_output_t out_mem =
-      CreateMKLDNNMem(out_data[quantized_concat_enum::kOut], fwd.fwd_pd.dst_primitive_desc(),
-                      req[concat_enum::kOut]);
-  fwd.SetNewMem(data_mem, *out_mem.second);
-  MKLDNNStream::Get()->RegisterPrim(fwd.GetFwd());
+  mxnet::mkldnn_output_t out_mem = CreateMKLDNNMem(out_data[quantized_concat_enum::kOut],
+                                                   fwd.fwd_pd.dst_desc(), req[concat_enum::kOut]);
+  mkldnn_args_map_t  net_args;
+  net_args[MKLDNN_ARG_DST] = *out_mem.second;
+  for (int i = 0; i < param_.num_args; i++) {
+    net_args[MKLDNN_ARG_MULTIPLE_SRC + i] = *data_mem[i];
+  }
+  MKLDNNStream::Get()->RegisterPrimArgs(fwd.GetFwd(), net_args);
   CommitOutput(out_data[concat_enum::kOut], out_mem);
   MKLDNNStream::Get()->Submit();
 }
diff --git a/src/operator/quantization/mkldnn/mkldnn_quantized_conv.cc b/src/operator/quantization/mkldnn/mkldnn_quantized_conv.cc
index f81071704762..6ac2250281d3 100644
--- a/src/operator/quantization/mkldnn/mkldnn_quantized_conv.cc
+++ b/src/operator/quantization/mkldnn/mkldnn_quantized_conv.cc
@@ -43,11 +43,12 @@ static void MKLDNNQuantizedConvForward(const nnvm::NodeAttrs& attrs,
   TmpMemMgr::Get()->Init(ctx.requested[conv::kTempSpace]);
   NDArray weight = in_data[conv::kWeight];
   ConvolutionParam param = nnvm::get<ConvolutionParam>(attrs.parsed);
-  auto &fwd = GetConvFwd(
-      param, ctx.is_train, in_data[conv::kData], in_data[conv::kWeight],
-      param.no_bias ? nullptr : &in_data[conv::kBias],
-      out_data[conv::kOut]);
-  auto data_mem = in_data[conv::kData].GetMKLDNNDataReorder(fwd.fwd_pd.src_primitive_desc());
+  MKLDNNConvFullParam full_param;
+  full_param.conv_param = param;
+  full_param.mkldnn_param.Init(std::unordered_map<std::string, std::string>());
+  auto &fwd = GetConvFwd(full_param, ctx.is_train, in_data[conv::kData], in_data[conv::kWeight],
+                         param.no_bias ? nullptr : &in_data[conv::kBias], out_data[conv::kOut]);
+  auto data_mem = in_data[conv::kData].GetMKLDNNDataReorder(fwd.GetPd().src_desc());
   const mkldnn::memory *weight_mem;
   // For inference, we want to reorder the weight array so we don't need to
   // reorder data every time.
@@ -55,20 +56,23 @@ static void MKLDNNQuantizedConvForward(const nnvm::NodeAttrs& attrs,
     // We also need to modify the layout on the original weight array.
     // Don't switch below sequence because naive engine will executes
     // pushAsync synchronously.
-    weight.MKLDNNDataReorderAsync(fwd.fwd_pd.weights_primitive_desc());
-    weight_mem = GetWeights(weight, fwd.fwd_pd.weights_primitive_desc(), param.num_group);
+    weight.MKLDNNDataReorderAsync(fwd.GetPd().weights_desc());
+    weight_mem = GetWeights(weight, fwd.GetPd().weights_desc(), param.num_group);
   } else {
     weight_mem = weight.GetMKLDNNData();
-    CHECK(weight_mem->get_primitive_desc() == fwd.fwd_pd.weights_primitive_desc());
   }
-  auto out_mem = CreateMKLDNNMem(out_data[conv::kOut], fwd.fwd_pd.dst_primitive_desc(),
+  auto out_mem = CreateMKLDNNMem(out_data[conv::kOut], fwd.GetPd().dst_desc(),
                                  req[conv::kOut]);
-  const mkldnn::memory *bias_mem = nullptr;
-  if (!param.no_bias)
-    bias_mem = in_data[conv::kBias].GetMKLDNNDataReorder(fwd.fwd_pd.bias_primitive_desc());
-  fwd.SetNewMem(*data_mem, *weight_mem, bias_mem, *out_mem.second);
-  MKLDNNStream::Get()->RegisterPrim(fwd.GetFwd());
-
+  mkldnn_args_map_t net_args;
+  if (!param.no_bias) {
+    const mkldnn::memory *bias_mem =
+        in_data[conv::kBias].GetMKLDNNDataReorder(fwd.GetPd().bias_desc());
+    net_args.insert({MKLDNN_ARG_BIAS, *bias_mem});
+  }
+  net_args.insert({MKLDNN_ARG_SRC, *data_mem});
+  net_args.insert({MKLDNN_ARG_WEIGHTS, *weight_mem});
+  net_args.insert({MKLDNN_ARG_DST, *out_mem.second});
+  MKLDNNStream::Get()->RegisterPrimArgs(fwd.GetFwd(), net_args);
   CommitOutput(out_data[conv::kOut], out_mem);
   MKLDNNStream::Get()->Submit();
   Stream<cpu> *s = ctx.get_stream<cpu>();
diff --git a/src/operator/quantization/mkldnn/mkldnn_quantized_elemwise_add.cc b/src/operator/quantization/mkldnn/mkldnn_quantized_elemwise_add.cc
index 2be6b2baca63..2078ac4fead8 100644
--- a/src/operator/quantization/mkldnn/mkldnn_quantized_elemwise_add.cc
+++ b/src/operator/quantization/mkldnn/mkldnn_quantized_elemwise_add.cc
@@ -73,17 +73,17 @@ static void MKLDNNQuantizedElemwiseAddForward(const nnvm::NodeAttrs& attrs, cons
 
   // output default set as int32
   float output_data_range = kInt32Range;
-  auto output_data_type = mkldnn::memory::s32;
+  auto output_data_type = mkldnn::memory::data_type::s32;
   // dataA && dataB are uint8
   if (out_data[quantized_elemwise_add_enum::kOut].dtype() == mshadow::kInt8) {
     output_data_range = kInt8Range;
-    output_data_type = mkldnn::memory::s8;
+    output_data_type = mkldnn::memory::data_type::s8;
   } else if (out_data[quantized_elemwise_add_enum::kOut].dtype() == mshadow::kUint8) {
     output_data_range = kUint8Range;
-    output_data_type = mkldnn::memory::u8;
+    output_data_type = mkldnn::memory::data_type::u8;
   } else {
     output_data_range = kInt32Range;
-    output_data_type = mkldnn::memory::s32;
+    output_data_type = mkldnn::memory::data_type::s32;
   }
 
   float output_min = 0;
@@ -100,12 +100,13 @@ static void MKLDNNQuantizedElemwiseAddForward(const nnvm::NodeAttrs& attrs, cons
   // 2: scale 0 for dataA, scale 1 for data B
   const int scales_num = 2;
   std::vector<float> scales(scales_num, 1);
+  auto engine = CpuEngine::Get()->get_engine();
   if (in_data[quantized_elemwise_add_enum::kDataA].dtype()
       != in_data[quantized_elemwise_add_enum::kDataB].dtype()) {
-    auto s8_pd = (is_dataA_int8 == true)
-                 ? dataA_mem->get_primitive_desc()
-                 : dataB_mem->get_primitive_desc();
-    rescaled_mem = TmpMemMgr::Get()->Alloc(s8_pd);
+    auto s8_desc = (is_dataA_int8 == true)
+                 ? dataA_mem->get_desc()
+                 : dataB_mem->get_desc();
+    rescaled_mem = TmpMemMgr::Get()->Alloc(s8_desc);
     float u8_reorder_scale = 0;
     if (params.max_calib_range.has_value() && params.min_calib_range.has_value()) {
       if (is_dataA_int8 == true) {
@@ -130,14 +131,16 @@ static void MKLDNNQuantizedElemwiseAddForward(const nnvm::NodeAttrs& attrs, cons
       }
     }
     std::vector<float> reorder_scale = {u8_reorder_scale};
-    primitive_attr reorder_attr;
-    reorder_attr.set_int_output_round_mode(round_mode::round_nearest);
+    mkldnn::primitive_attr reorder_attr;
     reorder_attr.set_output_scales(0, reorder_scale);
     auto u8_mem = (is_dataA_int8 == true) ? dataB_mem : dataA_mem;
-    const auto reorder_pd = mkldnn::reorder::primitive_desc(u8_mem->get_primitive_desc(),
-                                                            s8_pd,
+    const auto reorder_pd = mkldnn::reorder::primitive_desc(engine,
+                                                            u8_mem->get_desc(),
+                                                            engine,
+                                                            s8_desc,
                                                             reorder_attr);
-    MKLDNNStream::Get()->RegisterPrim(mkldnn::reorder(reorder_pd, *u8_mem, *rescaled_mem));
+    mkldnn_args_map_t args({{MKLDNN_ARG_FROM, *u8_mem }, {MKLDNN_ARG_TO, *rescaled_mem}});
+    MKLDNNStream::Get()->RegisterPrimArgs(mkldnn::reorder(reorder_pd), args);
 
     if (is_dataA_int8 == true) {
       dataB_mem = rescaled_mem;
@@ -155,27 +158,24 @@ static void MKLDNNQuantizedElemwiseAddForward(const nnvm::NodeAttrs& attrs, cons
     }
   }
 
-  std::vector<mkldnn::primitive::at> in_prims;
-  std::vector<mkldnn::memory::primitive_desc> in_pds;
-  in_prims.push_back(*dataA_mem);
-  in_prims.push_back(*dataB_mem);
-  in_pds.push_back(dataA_mem->get_primitive_desc());
-  in_pds.push_back(dataB_mem->get_primitive_desc());
-  size_t i_ndim = in_data[quantized_elemwise_add_enum::kDataA].shape().ndim();
-  mkldnn::memory::dims i_dims = mkldnn::memory::dims(i_ndim);
-  for (size_t i = 0; i < i_ndim; i++) {
-    i_dims[i] = static_cast<int>(in_data[quantized_elemwise_add_enum::kDataA].shape()[i]);
-  }
-  mkldnn::memory::format i_fmt = static_cast<mkldnn::memory::format>(
-                                   in_pds[quantized_elemwise_add_enum::kDataA].desc().data.format);
-  auto output_desc = mkldnn::memory::desc(i_dims, output_data_type, i_fmt);
-  mkldnn::sum::primitive_desc pdesc(output_desc, scales, in_pds);
+  std::vector<mkldnn::memory::desc> in_desc;
+  in_desc.push_back(dataA_mem->get_desc());
+  in_desc.push_back(dataB_mem->get_desc());
+  const auto in_shape = in_data[quantized_elemwise_add_enum::kDataA].shape();
+  mkldnn::memory::dims i_dims(in_shape.begin(), in_shape.end());
+  auto output_desc = mkldnn::memory::desc(i_dims,
+                                          output_data_type,
+                                          mkldnn::memory::format_tag::any);
+  mkldnn::sum::primitive_desc pdesc(output_desc, scales, in_desc, engine);
   auto mem = CreateMKLDNNMem(out_data[quantized_elemwise_add_enum::kOut],
-                             pdesc.dst_primitive_desc(),
+                             pdesc.dst_desc(),
                              req[0],
                              &in_data[0]);
+  mkldnn_args_map_t args({{MKLDNN_ARG_MULTIPLE_SRC,  *dataA_mem},
+                          {MKLDNN_ARG_MULTIPLE_SRC + 1,  *dataB_mem},
+                          {MKLDNN_ARG_DST, *mem.second}});
   MKLDNNStream *stream = MKLDNNStream::Get();
-  stream->RegisterPrim(mkldnn::sum(pdesc, in_prims, *mem.second));
+  stream->RegisterPrimArgs(mkldnn::sum(pdesc), args);
   CommitOutput(out_data[quantized_elemwise_add_enum::kOut], mem);
   stream->Submit();
 
diff --git a/src/operator/quantization/mkldnn/mkldnn_quantized_fully_connected.cc b/src/operator/quantization/mkldnn/mkldnn_quantized_fully_connected.cc
index f451ff3c977d..3e21564b3b04 100644
--- a/src/operator/quantization/mkldnn/mkldnn_quantized_fully_connected.cc
+++ b/src/operator/quantization/mkldnn/mkldnn_quantized_fully_connected.cc
@@ -94,28 +94,35 @@ void MKLDNNQuantizedFullyConnectedForward(const nnvm::NodeAttrs &attrs,
   auto &fwd = GetFCFwd(param, is_train, data, weight,
       param.no_bias ? nullptr : &quantized_bias, out_md);
 
-  auto data_mem = in_data[fullc::kData].GetMKLDNNDataReorder(fwd.fwd_pd.src_primitive_desc());
+  auto data_mem = in_data[fullc::kData].GetMKLDNNDataReorder(fwd.fwd_pd.src_desc());
   const mkldnn::memory *weight_mem = nullptr;
 
   if (weight.IsDefaultData()) {
     // We also need to modify the layout on the original weight array.
     // Don't switch below sequence because naive engine will executes
     // pushAsync synchronously.
-    weight.MKLDNNDataReorderAsync(fwd.fwd_pd.weights_primitive_desc());
-    weight_mem = GetWeights(weight, fwd.fwd_pd.weights_primitive_desc(), 1);
+    weight.MKLDNNDataReorderAsync(fwd.fwd_pd.weights_desc());
+    weight_mem = GetWeights(weight, fwd.fwd_pd.weights_desc(), 1);
   } else {
     weight_mem = weight.GetMKLDNNData();
-    CHECK(weight_mem->get_primitive_desc() == fwd.fwd_pd.weights_primitive_desc());
+    CHECK(weight_mem->get_desc() == fwd.fwd_pd.weights_desc());
   }
-  auto out_mem = CreateMKLDNNMem(out_data[fullc::kOut], fwd.fwd_pd.dst_primitive_desc(),
+  auto out_mem = CreateMKLDNNMem(out_data[fullc::kOut], fwd.fwd_pd.dst_desc(),
                                  req[fullc::kOut]);
-  const mkldnn::memory *bias_mem = nullptr;
-  if (!param.no_bias)
-    bias_mem = quantized_bias.GetMKLDNNDataReorder(fwd.fwd_pd.bias_primitive_desc());
 
-  fwd.SetNewMem(*data_mem, *weight_mem, bias_mem, *out_mem.second);
-  MKLDNNStream::Get()->RegisterPrim(fwd.GetFwd());
+  mkldnn_args_map_t args = {
+      {MKLDNN_ARG_SRC, *data_mem},
+      {MKLDNN_ARG_WEIGHTS, *weight_mem},
+      {MKLDNN_ARG_DST, *out_mem.second},
+  };
+
+  const mkldnn::memory *bias_mem = nullptr;
+  if (!param.no_bias) {
+    bias_mem = quantized_bias.GetMKLDNNDataReorder(fwd.fwd_pd.bias_desc());
+    args[MKLDNN_ARG_BIAS] = *bias_mem;
+  }
 
+  MKLDNNStream::Get()->RegisterPrimArgs(fwd.GetFwd(), args);
   CommitOutput(out_data[fullc::kOut], out_mem);
   MKLDNNStream::Get()->Submit();
 }
diff --git a/src/operator/quantization/mkldnn/mkldnn_quantized_pooling.cc b/src/operator/quantization/mkldnn/mkldnn_quantized_pooling.cc
index 07e14412618d..190dfed23197 100644
--- a/src/operator/quantization/mkldnn/mkldnn_quantized_pooling.cc
+++ b/src/operator/quantization/mkldnn/mkldnn_quantized_pooling.cc
@@ -38,9 +38,7 @@ static void MKLDNNQuantizedPoolingForward(const nnvm::NodeAttrs& attrs, const Op
     || in_data[0].dtype() == mshadow::kInt8)
     << "mkldnn_quantized_pooling op only supports uint8 and int8 as input type";
   const PoolingParam& param = nnvm::get<PoolingParam>(attrs.parsed);
-  auto fwd = GetPoolingFwd(param, ctx.is_train, in_data[0], out_data[0]);
-  fwd.SetNewMem(in_data[0], out_data[0], req[0]);
-  fwd.Execute(out_data[0]);
+  MKLDNNPoolingCompute(ctx, param, in_data[0], req[0], out_data[0], nullptr);
   out_data[1].data().dptr<float>()[0] = in_data[1].data().dptr<float>()[0];
   out_data[2].data().dptr<float>()[0] = in_data[2].data().dptr<float>()[0];
 }
diff --git a/src/operator/quantization/mkldnn/mkldnn_requantize-inl.h b/src/operator/quantization/mkldnn/mkldnn_requantize-inl.h
index 03d9b9067b57..a80b855dceb1 100644
--- a/src/operator/quantization/mkldnn/mkldnn_requantize-inl.h
+++ b/src/operator/quantization/mkldnn/mkldnn_requantize-inl.h
@@ -71,11 +71,10 @@ static void MKLDNNRequantizeForwardKer(const nnvm::NodeAttrs& attrs,
   float second_scale = second_quantized_range / second_real_range;
   float scale = first_scale * second_scale;
 
-  primitive_attr attr;
+  mkldnn::primitive_attr attr;
   const int mask = 0;
   std::vector<float> scales = {scale};
   attr.set_output_scales(mask, scales);
-  attr.set_int_output_round_mode(round_nearest);
   mkldnn::engine cpu_engine = mxnet::CpuEngine::Get()->get_engine();
 
   NDArray in_buffer = inputs[0];
@@ -83,20 +82,13 @@ static void MKLDNNRequantizeForwardKer(const nnvm::NodeAttrs& attrs,
     in_buffer = inputs[0].Reorder2Default();
 
   auto i_mem = in_buffer.GetMKLDNNData();
-  auto i_mpd = i_mem->get_primitive_desc();
-  auto i_desc = i_mpd.desc();
-  mkldnn::memory::format i_fmt = static_cast<mkldnn::memory::format>(i_desc.data.format);
-  mkldnn::memory::dims i_dims = mkldnn::memory::dims(i_dim);
-  for (size_t i = 0; i < i_dim; i++) {
-    i_dims[i] = static_cast<int>(in_buffer.shape()[i]);
-  }
-  auto o_desc = mkldnn::memory::desc(i_dims,
-                                    (mkldnn::memory::data_type)data_type_enum<DstType>::type,
-                                    i_fmt);
-  auto o_mpd = memory::primitive_desc(o_desc, cpu_engine);
-  auto reorder_pd  = reorder::primitive_desc(i_mpd, o_mpd, attr);
-  auto o_mem = CreateMKLDNNMem(outputs[0], o_mpd, req[0]);
-  MKLDNNStream::Get()->RegisterPrim(mkldnn::reorder(reorder_pd, *i_mem, *o_mem.second));
+  auto i_desc = i_mem->get_desc();
+  auto o_desc = i_desc;
+  o_desc.data.data_type = get_mkldnn_type_t<DstType>();
+  auto reorder_pd  = mkldnn::reorder::primitive_desc(cpu_engine, i_desc, cpu_engine, o_desc, attr);
+  auto o_mem = CreateMKLDNNMem(outputs[0], o_desc, req[0]);
+  MKLDNNStream::Get()->RegisterPrimArgs(
+      mkldnn::reorder(reorder_pd), {{MKLDNN_ARG_FROM, *i_mem}, {MKLDNN_ARG_TO, *o_mem.second}});
   CommitOutput(outputs[0], o_mem);
   MKLDNNStream::Get()->Submit();
 }
diff --git a/src/operator/quantization/quantized_pooling.cc b/src/operator/quantization/quantized_pooling.cc
index 1839e2a29d77..eeb2ac4de26c 100644
--- a/src/operator/quantization/quantized_pooling.cc
+++ b/src/operator/quantization/quantized_pooling.cc
@@ -98,7 +98,7 @@ bool QuantizedPoolingType(const nnvm::NodeAttrs& attrs,
   CHECK_EQ(in_type->size(), 3U);
   CHECK_EQ(out_type->size(), 3U);
   if (param.pool_type == pool_enum::kMaxPooling || param.pool_type == pool_enum::kAvgPooling) {
-#if MXNET_USE_MKLDNN  == 1
+#if MXNET_USE_MKLDNN == 1
     TYPE_ASSIGN_CHECK(*out_type, 0, (*in_type)[0]);
 #else
     TYPE_ASSIGN_CHECK(*in_type, 0, mshadow::kInt8);
diff --git a/src/operator/rnn-inl.h b/src/operator/rnn-inl.h
index d5fd351986e3..db2360313aef 100644
--- a/src/operator/rnn-inl.h
+++ b/src/operator/rnn-inl.h
@@ -26,11 +26,6 @@
 #ifndef MXNET_OPERATOR_RNN_INL_H_
 #define MXNET_OPERATOR_RNN_INL_H_
 
-#if MXNET_USE_CUDNN == 1
-STATIC_ASSERT_CUDNN_VERSION_GE(7000);
-#endif
-#define MXNET_USE_CUDNN_GE_7200 MXNET_USE_CUDNN == 1 && CUDNN_VERSION >= 7200
-
 #include <dmlc/logging.h>
 #include <dmlc/parameter.h>
 #include <mxnet/operator.h>
@@ -46,13 +41,87 @@ STATIC_ASSERT_CUDNN_VERSION_GE(7000);
 #include "./math_functions-inl.h"
 #include "./operator_common.h"
 #include "./rnn_impl.h"
-#if MXNET_USE_MKLDNN == 1
-#include "./nn/mkldnn/mkldnn_rnn_impl.h"
+
+#if MXNET_USE_CUDNN == 1
+STATIC_ASSERT_CUDNN_VERSION_GE(7000);
 #endif
+#define MXNET_USE_CUDNN_GE_7200 MXNET_USE_CUDNN == 1 && CUDNN_VERSION >= 7200
 
 namespace mxnet {
 namespace op {
 
+namespace rnn_enum {
+  enum RNNOpInputs {kData, kParams, kState, kStateCell, kSequenceLength};
+  enum RNNOpOutputs {kOut, kStateOut, kStateCellOut};
+  enum RNNModeType {kRnnRelu, kRnnTanh, kLstm, kGru};
+  enum RNNOpResource {kTempSpace, kCuDNNDropoutDescSpace};
+}
+
+struct RNNParam : public dmlc::Parameter<RNNParam> {
+  uint32_t state_size;
+  uint32_t num_layers;
+  bool bidirectional, state_outputs;
+  int mode;
+  float p;
+  int seq_length_, batch_size_, input_size_;
+
+  bool use_sequence_length;
+  dmlc::optional<int> projection_size;
+  dmlc::optional<double> lstm_state_clip_min, lstm_state_clip_max;
+  bool lstm_state_clip_nan;
+
+  DMLC_DECLARE_PARAMETER(RNNParam) {
+    DMLC_DECLARE_FIELD(state_size)
+    .describe("size of the state for each layer");
+
+    DMLC_DECLARE_FIELD(num_layers)
+    .describe("number of stacked layers");
+
+    DMLC_DECLARE_FIELD(bidirectional).set_default(false)
+    .describe("whether to use bidirectional recurrent layers");
+
+    DMLC_DECLARE_FIELD(mode)
+    .add_enum("rnn_relu", rnn_enum::kRnnRelu)
+    .add_enum("rnn_tanh", rnn_enum::kRnnTanh)
+    .add_enum("lstm", rnn_enum::kLstm)
+    .add_enum("gru", rnn_enum::kGru)
+    .describe("the type of RNN to compute");
+
+    DMLC_DECLARE_FIELD(p).set_default(0.)
+    .set_range(0, 1)
+    .describe("drop rate of the dropout on the outputs of each RNN layer, except the last layer.");
+
+    DMLC_DECLARE_FIELD(state_outputs).set_default(false)
+    .describe("Whether to have the states as symbol outputs.");
+
+    DMLC_DECLARE_FIELD(projection_size)
+    .set_default(dmlc::optional<int>())
+    .describe("size of project size");
+
+    DMLC_DECLARE_FIELD(lstm_state_clip_min)
+    .set_default(dmlc::optional<double>())
+    .describe("Minimum clip value of LSTM states. This option must be used together with "
+              "lstm_state_clip_max.");
+
+    DMLC_DECLARE_FIELD(lstm_state_clip_max)
+    .set_default(dmlc::optional<double>())
+    .describe("Maximum clip value of LSTM states. This option must be used together with "
+              "lstm_state_clip_min.");
+
+    DMLC_DECLARE_FIELD(lstm_state_clip_nan)
+    .set_default(false)
+    .describe("Whether to stop NaN from propagating in state by clipping it to min/max. "
+              "If clipping range is not specified, this option is ignored.");
+
+    DMLC_DECLARE_FIELD(use_sequence_length)
+    .set_default(false)
+    .describe(
+        "If set to true, this layer takes in an extra input parameter "
+        "`sequence_length` "
+        "to specify variable length sequence");
+  }
+};
+
 inline int GetRnnParamSize(int num_layer,
                            int input_size,
                            int state_size,
@@ -86,9 +155,9 @@ inline int GetRnnParamSize(int num_layer,
 }
 
 inline int GetRnnBiasSize(int num_layer,
-                           int state_size,
-                           int direction,
-                           int mode) {
+                          int state_size,
+                          int direction,
+                          int mode) {
   int size = 2 * state_size * direction * num_layer;
   switch (mode) {
     case rnn_enum::kRnnRelu:
@@ -104,6 +173,15 @@ inline int GetRnnBiasSize(int num_layer,
   return size;
 }
 
+/*
+ * Calculate the space size of the intermediate results for RNN inference.
+ * The inference procedure of a fusion RNN operator calculates the outputs
+ * layer by layer. In one layer calculation, the steps are:
+ *  - wx[1...Ngates] * x[1...T] among all time stamp(sz: TxNxHxNgates)
+ *  - wh[1...Ngates] * h[t] time by time(sz: NxHxNgates)
+ *  - output -> h[t](, c[t] additionally with Lstm) time by time(sz: NxH(x2))
+ *  - intermediate y[1...T] as next layer's inputs(sz: TxNxHxD)
+ */
 inline size_t GetRNNWorkspaceSize(int seq_length,
                                   int batch_size,
                                   int hidden_size,
@@ -112,15 +190,19 @@ inline size_t GetRNNWorkspaceSize(int seq_length,
   size_t size = 0;
   switch (mode) {
     case rnn_enum::kLstm:
-      size = (seq_length + 1) * batch_size * hidden_size * 4 + batch_size * hidden_size * 2
-             + seq_length * batch_size * hidden_size * direction + hidden_size * seq_length * 8;
+      size = seq_length * batch_size * hidden_size * (4 + direction) +  // wx*x + inter-y
+          batch_size * hidden_size * 6 +                                // wh*h + h + c
+          seq_length * hidden_size * 8;                    // Used in Backward, Δbx, Δbh
       break;
     case rnn_enum::kGru:
-      size = seq_length * batch_size * hidden_size * direction * 4 + batch_size * hidden_size * 8;
+      // Differs with Lstm, the outputs of three gates are also held in memory
+      size = seq_length * batch_size * hidden_size * direction * (3 + 1) +  // wx*x + inter-y
+          batch_size * hidden_size * (6 + direction);                       // wh*h + h + Ngates
       break;
     case rnn_enum::kRnnRelu:
     case rnn_enum::kRnnTanh:
-      size = seq_length * batch_size * hidden_size * direction * 2 + batch_size * hidden_size * 4;
+      size = seq_length * batch_size * hidden_size * direction * 2 +  // wx*x + inter-y
+          batch_size * hidden_size * (1 + direction);                 // h + Ngates
       break;
     default:
       LOG(FATAL) << "unknown RNN mode " << mode;
@@ -158,71 +240,6 @@ inline size_t GetRNNReserveSpaceSize(int num_layer,
   return size;
 }
 
-struct RNNParam : public dmlc::Parameter<RNNParam> {
-  uint32_t state_size;
-  uint32_t num_layers;
-  bool bidirectional, state_outputs;
-  int mode;
-  float p;
-  int seq_length_, batch_size_, input_size_;
-
-  bool use_sequence_length;
-  dmlc::optional<int> projection_size;
-  dmlc::optional<double> lstm_state_clip_min, lstm_state_clip_max;
-  bool lstm_state_clip_nan;
-
-  DMLC_DECLARE_PARAMETER(RNNParam) {
-    DMLC_DECLARE_FIELD(state_size)
-    .describe("size of the state for each layer");
-
-    DMLC_DECLARE_FIELD(num_layers)
-    .describe("number of stacked layers");
-
-    DMLC_DECLARE_FIELD(bidirectional).set_default(false)
-    .describe("whether to use bidirectional recurrent layers");
-
-    DMLC_DECLARE_FIELD(mode)
-    .add_enum("rnn_relu", rnn_enum::kRnnRelu)
-    .add_enum("rnn_tanh", rnn_enum::kRnnTanh)
-    .add_enum("lstm", rnn_enum::kLstm)
-    .add_enum("gru", rnn_enum::kGru)
-    .describe("the type of RNN to compute");
-
-    DMLC_DECLARE_FIELD(p).set_default(0.)
-    .set_range(0, 1)
-    .describe("drop rate of the dropout on the outputs of each RNN layer, except the last layer.");
-
-    DMLC_DECLARE_FIELD(state_outputs).set_default(false)
-    .describe("Whether to have the states as symbol outputs.");
-
-    DMLC_DECLARE_FIELD(projection_size)
-    .set_default(dmlc::optional<int>())
-    .describe("size of project size");
-
-    DMLC_DECLARE_FIELD(lstm_state_clip_min)
-    .set_default(dmlc::optional<double>())
-    .describe("Minimum clip value of LSTM states. This option must be used together with "
-              "lstm_state_clip_max.");
-
-    DMLC_DECLARE_FIELD(lstm_state_clip_max)
-    .set_default(dmlc::optional<double>())
-    .describe("Maximum clip value of LSTM states. This option must be used together with "
-              "lstm_state_clip_min.");
-
-    DMLC_DECLARE_FIELD(lstm_state_clip_nan)
-    .set_default(false)
-    .describe("Whether to stop NaN from propagating in state by clipping it to min/max. "
-              "If clipping range is not specified, this option is ignored.");
-
-    DMLC_DECLARE_FIELD(use_sequence_length)
-        .set_default(false)
-        .describe(
-            "If set to true, this layer takes in an extra input parameter "
-            "`sequence_length` "
-            "to specify variable length sequence");
-  }
-};
-
 inline size_t GetNumInputArguments(RNNParam param_) {
   size_t num_inputs = (param_.mode == rnn_enum::kLstm) ? 4U : 3U;
   if (param_.use_sequence_length) num_inputs += 1U;
@@ -398,114 +415,94 @@ class RNNOp {
  public:
   RNNParam param_;
   Context ctx_;
-#if MXNET_USE_MKLDNN == 1
-  std::vector<mkldnn::memory> concat_weight_memory;
-  std::vector<mkldnn::memory> concat_iter_memory;
-  std::vector<primitive> rnn_forward_prim;
-  std::vector<mkldnn::memory> x_memory;
-  std::vector<mkldnn::memory> hcx_memory;
-  std::vector<mkldnn::memory> wx_memory;
-  std::vector<mkldnn::memory> wh_memory;
-  std::vector<mkldnn::memory> bias_memory;
-  std::vector<mkldnn::memory> y_memory;
-  std::vector<mkldnn::memory> hcy_memory;
-  size_t weights_version;
-  bool has_cache;
-  bool init_mem_;
-  size_t reserve_mem_size_;
-  NDArray mem_space_;
-#endif
+
   explicit RNNOp(RNNParam param, Context ctx) {
     this->param_ = param;
     this->ctx_ = ctx;
-#if MXNET_USE_MKLDNN == 1
-    init_mem_ = false;
-    reserve_mem_size_ = 0;
-#endif
 
     if (ctx_.dev_type == kGPU) {
 #if MXNET_USE_CUDNN == 1
-    init_cudnn_ = false;
-    dtype_ = mshadow::DataType<DType>::kCudnnFlag;
-    // TensorCore algos only allowed on fp16-I/O convolutions if permitted by the global policy.
-    // No tests in place for fp16 RNNs, so leave TensorCore disabled for now.
-    cudnn_tensor_core_ = false;
-    // When fp16 RNN tests are introduced, we can enable TensorCore as follows:
-    // cudnn_tensor_core =
-    //     mshadow::DataType<DType>::kFlag == mshadow::kFloat16 && GetEnvAllowTensorCore();
-    // Defaults
-    input_mode_ = CUDNN_LINEAR_INPUT;  // Don't support this yet
-    // RNN Mode
-    switch (param_.mode) {
-      case rnn_enum::kRnnRelu:
-        mode_ = CUDNN_RNN_RELU;
-        break;
-      case rnn_enum::kRnnTanh:
-        mode_ = CUDNN_RNN_TANH;
-        break;
-      case rnn_enum::kLstm:
-        mode_ = CUDNN_LSTM;
-        break;
-      case rnn_enum::kGru:
-        mode_ = CUDNN_GRU;
-        break;
-      default:
-        LOG(FATAL) << "Not implmented";
-    }
+      init_cudnn_ = false;
+      dtype_ = mshadow::DataType<DType>::kCudnnFlag;
+      // TensorCore algos only allowed on fp16-I/O convolutions if permitted by the global policy.
+      // No tests in place for fp16 RNNs, so leave TensorCore disabled for now.
+      cudnn_tensor_core_ = false;
+      // When fp16 RNN tests are introduced, we can enable TensorCore as follows:
+      // cudnn_tensor_core =
+      //     mshadow::DataType<DType>::kFlag == mshadow::kFloat16 && GetEnvAllowTensorCore();
+      // Defaults
+      input_mode_ = CUDNN_LINEAR_INPUT;  // Don't support this yet
+      // RNN Mode
+      switch (param_.mode) {
+        case rnn_enum::kRnnRelu:
+          mode_ = CUDNN_RNN_RELU;
+          break;
+        case rnn_enum::kRnnTanh:
+          mode_ = CUDNN_RNN_TANH;
+          break;
+        case rnn_enum::kLstm:
+          mode_ = CUDNN_LSTM;
+          break;
+        case rnn_enum::kGru:
+          mode_ = CUDNN_GRU;
+          break;
+        default:
+          LOG(FATAL) << "Not implmented";
+      }
 #if MXNET_USE_CUDNN_GE_7200
-    if (param_.projection_size.has_value()) {
-      CHECK_EQ(param_.mode, rnn_enum::kLstm)
-        << "Projection is only supported for LSTM.";
-      CHECK_GE(param_.state_size, param_.projection_size.value())
-        << "State size must be larger than projection size.";
-    }
+      if (param_.projection_size.has_value()) {
+        CHECK_EQ(param_.mode, rnn_enum::kLstm)
+          << "Projection is only supported for LSTM.";
+        CHECK_GE(param_.state_size, param_.projection_size.value())
+          << "State size must be larger than projection size.";
+      }
 #else
-    CHECK(!param_.projection_size.has_value())
-      << "Projection is only supported for LSTM with CuDNN version later than 7.1.1.";
+      CHECK(!param_.projection_size.has_value())
+        << "Projection is only supported for LSTM with CuDNN version later than 7.1.1.";
 #endif  // MXNET_USE_CUDNN_GE_7200
 #if MXNET_USE_CUDNN_GE_7200
-    if (param_.lstm_state_clip_min.has_value()
-        || param_.lstm_state_clip_max.has_value()) {
-      CHECK_EQ(param_.mode, rnn_enum::kLstm)
-        << "State clipping is only supported for LSTM.";
-      CHECK(param_.lstm_state_clip_min.has_value() && param_.lstm_state_clip_max.has_value())
-        << "lstm_state_clip_min and lstm_state_clip_max must be specified together.";
-      CHECK_GE(param_.lstm_state_clip_max.value(), param_.lstm_state_clip_min.value())
-        << "lstm_state_clip_max must be greater or equal to lstm_state_clip_min";
-    }
+      if (param_.lstm_state_clip_min.has_value()
+          || param_.lstm_state_clip_max.has_value()) {
+        CHECK_EQ(param_.mode, rnn_enum::kLstm)
+          << "State clipping is only supported for LSTM.";
+        CHECK(param_.lstm_state_clip_min.has_value() && param_.lstm_state_clip_max.has_value())
+          << "lstm_state_clip_min and lstm_state_clip_max must be specified together.";
+        CHECK_GE(param_.lstm_state_clip_max.value(), param_.lstm_state_clip_min.value())
+          << "lstm_state_clip_max must be greater or equal to lstm_state_clip_min";
+      }
 #else
-    CHECK(!param_.lstm_state_clip_min.has_value()
-          && !param_.lstm_state_clip_max.has_value())
-      << "State clipping is only supported for LSTM with CuDNN version later than 7.2.1.";
+      CHECK(!param_.lstm_state_clip_min.has_value()
+            && !param_.lstm_state_clip_max.has_value())
+        << "State clipping is only supported for LSTM with CuDNN version later than 7.2.1.";
 #endif  // MXNET_USE_CUDNN_GE_7200
-    // RNN Direction
-    direction_ = param_.bidirectional ? CUDNN_BIDIRECTIONAL : CUDNN_UNIDIRECTIONAL;
-    // Create descriptors
-    CUDNN_CALL(cudnnCreateTensorDescriptor(&hx_desc_));
-    CUDNN_CALL(cudnnCreateTensorDescriptor(&cx_desc_));
-    CUDNN_CALL(cudnnCreateTensorDescriptor(&hy_desc_));
-    CUDNN_CALL(cudnnCreateTensorDescriptor(&cy_desc_));
-    CUDNN_CALL(cudnnCreateTensorDescriptor(&dhx_desc_));
-    CUDNN_CALL(cudnnCreateTensorDescriptor(&dcx_desc_));
-    CUDNN_CALL(cudnnCreateTensorDescriptor(&dhy_desc_));
-    CUDNN_CALL(cudnnCreateTensorDescriptor(&dcy_desc_));
-
-    CUDNN_CALL(cudnnCreateFilterDescriptor(&w_desc_));
-    CUDNN_CALL(cudnnCreateFilterDescriptor(&dw_desc_));
-
-    CUDNN_CALL(cudnnCreateRNNDescriptor(&rnn_desc_));
-    CUDNN_CALL(cudnnCreateDropoutDescriptor(&dropout_desc_));
+      // RNN Direction
+      direction_ = param_.bidirectional ? CUDNN_BIDIRECTIONAL : CUDNN_UNIDIRECTIONAL;
+      // Create descriptors
+      CUDNN_CALL(cudnnCreateTensorDescriptor(&hx_desc_));
+      CUDNN_CALL(cudnnCreateTensorDescriptor(&cx_desc_));
+      CUDNN_CALL(cudnnCreateTensorDescriptor(&hy_desc_));
+      CUDNN_CALL(cudnnCreateTensorDescriptor(&cy_desc_));
+      CUDNN_CALL(cudnnCreateTensorDescriptor(&dhx_desc_));
+      CUDNN_CALL(cudnnCreateTensorDescriptor(&dcx_desc_));
+      CUDNN_CALL(cudnnCreateTensorDescriptor(&dhy_desc_));
+      CUDNN_CALL(cudnnCreateTensorDescriptor(&dcy_desc_));
+
+      CUDNN_CALL(cudnnCreateFilterDescriptor(&w_desc_));
+      CUDNN_CALL(cudnnCreateFilterDescriptor(&dw_desc_));
+
+      CUDNN_CALL(cudnnCreateRNNDescriptor(&rnn_desc_));
+      CUDNN_CALL(cudnnCreateDropoutDescriptor(&dropout_desc_));
 
 #if MXNET_USE_CUDNN_GE_7200
-    CUDNN_CALL(cudnnCreateRNNDataDescriptor(&x_data_desc_));
-    CUDNN_CALL(cudnnCreateRNNDataDescriptor(&y_data_desc_));
-    CUDNN_CALL(cudnnCreateRNNDataDescriptor(&dx_data_desc_));
-    CUDNN_CALL(cudnnCreateRNNDataDescriptor(&dy_data_desc_));
-#endif
+      CUDNN_CALL(cudnnCreateRNNDataDescriptor(&x_data_desc_));
+      CUDNN_CALL(cudnnCreateRNNDataDescriptor(&y_data_desc_));
+      CUDNN_CALL(cudnnCreateRNNDataDescriptor(&dx_data_desc_));
+      CUDNN_CALL(cudnnCreateRNNDataDescriptor(&dy_data_desc_));
+#endif  // MXNET_USE_CUDNN_GE_7200
 #else
-    if (ctx_.dev_type == kGPU) {
-      LOG(FATAL) << "RNN on GPU is only available for cuDNN at the moment.";
-    }
+      if (ctx_.dev_type == kGPU) {
+        LOG(FATAL) << "RNN on GPU is only available for cuDNN at the moment.";
+      }
 #endif  // MXNET_USE_CUDNN == 1
     }
 
@@ -857,7 +854,7 @@ class RNNOp {
       }
       DType* work_cpu_space = static_cast<DType*>(temp_cpu_space_.data().dptr_);
 
-      if (ctx.is_train) {
+      if (ctx.is_train || ctx.need_grad) {
         // allocate reserve space
 
         const size_t r_size = GetRNNReserveSpaceSize(param_.num_layers, direction,
@@ -891,64 +888,23 @@ class RNNOp {
                                   param_.p,
                                   param_.mode);
       } else {
-#if MXNET_USE_MKLDNN == 1
-        if (dmlc::GetEnv("MXNET_USE_MKLDNN_RNN", 1) && param_.mode != rnn_enum::kGru) {
-          // TODO(zixuanweeei): MKLDNN GRU has precision issue. A stable one
-          //   will be added to MXNet when we figure out the issue.
-          int dtype = in_data[rnn_enum::kData].type_flag_;
-          MKLDNNRNNForwardInference<DType>(param_.state_outputs,
-                                           param_.num_layers,
-                                           direction,
-                                           param_.seq_length_,
-                                           param_.batch_size_,
-                                           param_.input_size_,
-                                           param_.state_size,
-                                           x.dptr_,
-                                           hx.dptr_,
-                                           cx_ptr,
-                                           w.dptr_,
-                                           b_ptr,
-                                           y.dptr_,
-                                           hy_ptr,
-                                           cy_ptr,
-                                           &concat_weight_memory,
-                                           &concat_iter_memory,
-                                           &x_memory,
-                                           &hcx_memory,
-                                           &wx_memory,
-                                           &wh_memory,
-                                           &bias_memory,
-                                           &y_memory,
-                                           &hcy_memory,
-                                           &rnn_forward_prim,
-                                           &has_cache,
-                                           dtype,
-                                           ctx.is_train,
-                                           param_.mode);
-        } else {
-#endif  // MXNET_USE_MKLDNN == 1
-          //  Before integrating MKLDNN GRU fp32 inference
-          //  using below code for keep func being OK
-          RNNForwardInference<DType>(work_cpu_space,
-                                     param_.state_outputs,
-                                     param_.num_layers,
-                                     direction,
-                                     param_.seq_length_,
-                                     param_.batch_size_,
-                                     param_.input_size_,
-                                     param_.state_size,
-                                     x.dptr_,
-                                     hx.dptr_,
-                                     cx_ptr,
-                                     w.dptr_,
-                                     b_ptr,
-                                     y.dptr_,
-                                     hy_ptr,
-                                     cy_ptr,
-                                     param_.mode);
-#if MXNET_USE_MKLDNN == 1
-        }
-#endif
+        RNNForwardInference<DType>(work_cpu_space,
+                                   param_.state_outputs,
+                                   param_.num_layers,
+                                   direction,
+                                   param_.seq_length_,
+                                   param_.batch_size_,
+                                   param_.input_size_,
+                                   param_.state_size,
+                                   x.dptr_,
+                                   hx.dptr_,
+                                   cx_ptr,
+                                   w.dptr_,
+                                   b_ptr,
+                                   y.dptr_,
+                                   hy_ptr,
+                                   cy_ptr,
+                                   param_.mode);
       }
     }
   }
@@ -1489,6 +1445,10 @@ class RNNOp {
     }
 #endif  // MXNET_USE_CUDNN == 1 && defined(__CUDACC__)
   }
+  // naive private variables used in CPU Context
+  bool init_space_, temp_init_space_;
+  size_t reserve_cpu_space_size_, temp_cpu_space_size_;
+  NDArray reserve_cpu_space_, temp_cpu_space_;
 
 #if MXNET_USE_CUDNN == 1 && defined(__CUDACC__)
   // cuDNN versions up to and including v7.6.4 did not sync a last dgrad kernel back to the main
@@ -1538,39 +1498,8 @@ class RNNOp {
   bool dgrad_sync_event_created_ = false;
   bool dgrad_sync_needed_ = false;
 #endif  // MXNET_USE_CUDNN
-  bool init_space_, temp_init_space_;
-  size_t reserve_cpu_space_size_, temp_cpu_space_size_;
-  NDArray reserve_cpu_space_, temp_cpu_space_;
 };  //  class RNNOp
 
-static OpStatePtr CreateRNNState(const nnvm::NodeAttrs &attrs,
-                                 const Context ctx,
-                                 const mxnet::ShapeVector &in_shapes,
-                                 const std::vector<int> &in_types) {
-  const RNNParam& param = nnvm::get<RNNParam>(attrs.parsed);
-  OpStatePtr state = OpStatePtr();
-  int dtype = in_types[rnn_enum::kData];
-  int itype = dtype;
-  if (param.use_sequence_length) {
-      size_t seq_len_input_idx = rnn_enum::kSequenceLength;
-      if  (param.mode != rnn_enum::kLstm) {
-        seq_len_input_idx -= 1;
-      }
-    itype = in_types[seq_len_input_idx];
-  }
-
-  MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
-      MSHADOW_TYPE_SWITCH(itype, IType, {
-    if (ctx.dev_type == kGPU) {
-      state = OpStatePtr::Create<RNNOp<gpu, DType, IType>>(param, ctx);
-    } else {
-      state = OpStatePtr::Create<RNNOp<cpu, DType, IType>>(param, ctx);
-    }
-  });
-    });
-  return state;
-}
-
 template<typename xpu>
 void RNNStatefulCompute(const OpStatePtr& state,
                         const OpContext& ctx,
@@ -1582,14 +1511,14 @@ void RNNStatefulCompute(const OpStatePtr& state,
   // Hacky. This relies on fact that seq-len type is either the last input,
   // or we aren't using seq-len input and this type should be same as dtype.
   // Would prefer direct access to RNNParam object here but not sure how to get.
-  int itype = inputs[inputs.size()-1].type_flag_;
+  int itype = inputs[inputs.size() - 1].type_flag_;
 
   MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
-      MSHADOW_TYPE_SWITCH(itype, IType, {
-          RNNOp<xpu, DType, IType>& op = state.get_state<RNNOp<xpu, DType, IType>>();
-          op.Forward(ctx, inputs, req, outputs);
-        });
+    MSHADOW_TYPE_SWITCH(itype, IType, {
+      RNNOp<xpu, DType, IType>& op = state.get_state<RNNOp<xpu, DType, IType>>();
+      op.Forward(ctx, inputs, req, outputs);
     });
+  });
 }
 
 /*
@@ -1621,38 +1550,38 @@ void RNNStatefulGradCompute(const OpStatePtr& state,
   // Hacky. This relies on fact that seq-len type is either the last input,
   // or we aren't using seq-len input and this type should be same as dtype.
   // Would prefer direct access to RNNParam object here but not sure how to get.
-  int itype = outputs[outputs.size()-1].type_flag_;
+  int itype = outputs[outputs.size() - 1].type_flag_;
 
   MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
-      MSHADOW_TYPE_SWITCH(itype, IType, {
-          RNNOp<xpu, DType, IType>& op = state.get_state<RNNOp<xpu, DType, IType>>();
-          const RNNParam& param = op.param_;
-          int index = 5;
-          if (param.state_outputs) {
-            out_data.push_back(inputs[index++]);
-            out_grad.push_back(inputs[index++]);
-          }
-
-          if (param.mode == rnn_enum::kLstm) {
-            in_data.push_back(inputs[index++]);
-            if (param.state_outputs) {
-              out_data.push_back(inputs[index++]);
-              out_grad.push_back(inputs[index]);
-            }
-          }
-
-
-          if (param.use_sequence_length) {
-            size_t seq_len_input_idx = rnn_enum::kSequenceLength;
-            if  (param.mode != rnn_enum::kLstm) {
-              seq_len_input_idx -= 1;
-            }
-            in_data.push_back(outputs[seq_len_input_idx]);
-          }
-
-          op.Backward(ctx, out_grad, in_data, out_data, req, in_grad);
-        });
+    MSHADOW_TYPE_SWITCH(itype, IType, {
+      RNNOp<xpu, DType, IType>& op = state.get_state<RNNOp<xpu, DType, IType>>();
+      const RNNParam& param = op.param_;
+      int index = 5;
+      if (param.state_outputs) {
+        out_data.push_back(inputs[index++]);
+        out_grad.push_back(inputs[index++]);
+      }
+
+      if (param.mode == rnn_enum::kLstm) {
+        in_data.push_back(inputs[index++]);
+        if (param.state_outputs) {
+          out_data.push_back(inputs[index++]);
+          out_grad.push_back(inputs[index]);
+        }
+      }
+
+
+      if (param.use_sequence_length) {
+        size_t seq_len_input_idx = rnn_enum::kSequenceLength;
+        if  (param.mode != rnn_enum::kLstm) {
+          seq_len_input_idx -= 1;
+        }
+        in_data.push_back(outputs[seq_len_input_idx]);
+      }
+
+      op.Backward(ctx, out_grad, in_data, out_data, req, in_grad);
     });
+  });
 }
 
 }  // namespace op
diff --git a/src/operator/rnn.cc b/src/operator/rnn.cc
index b2ac2f0cb615..6d568c81bc1c 100644
--- a/src/operator/rnn.cc
+++ b/src/operator/rnn.cc
@@ -27,6 +27,9 @@
 #include <iterator>
 
 #include "./rnn-inl.h"
+#if MXNET_USE_MKLDNN == 1
+#include "./nn/mkldnn/mkldnn_rnn-inl.h"
+#endif  // MXNET_USE_MKLDNN == 1
 
 namespace mxnet {
 namespace op {
@@ -190,9 +193,9 @@ inline static bool RNNStorageType(const nnvm::NodeAttrs& attrs,
                                   std::vector<int> *out_attrs) {
   DispatchMode wanted_mode = DispatchMode::kFCompute;
 
-  #if MXNET_USE_MKLDNN == 1
-    wanted_mode = DispatchMode::kFComputeEx;
-  #endif
+#if MXNET_USE_MKLDNN == 1
+  wanted_mode = DispatchMode::kFComputeEx;
+#endif  // MXNET_USE_MKLDNN == 1
 
   return storage_type_assign(out_attrs, mxnet::kDefaultStorage,
                              dispatch_mode, wanted_mode);
@@ -222,432 +225,73 @@ struct RNNGrad {
   }
 };
 
-#if MXNET_USE_MKLDNN == 1
-static void RNNStatefulComputeCPU(const OpStatePtr& state_ptr,
-                                  const OpContext& ctx,
-                                  const std::vector<NDArray>& inputs,
-                                  const std::vector<OpReqType>& req,
-                                  const std::vector<NDArray>& outputs) {
-  std::vector<TBlob> in_blobs;
-  std::vector<TBlob> out_blobs;
-  std::vector<NDArray> temp_ndarrays_i;
-  std::vector<NDArray> temp_ndarrays_o;
-  for (const NDArray& in : inputs) {
-    if (in.storage_type() == kDefaultStorage) {
-      temp_ndarrays_i.push_back(in.Reorder2Default());
-      in_blobs.emplace_back(temp_ndarrays_i.back().data());
-    } else {
-      in_blobs.emplace_back(in.data());
+static OpStatePtr CreateRNNState(const nnvm::NodeAttrs &attrs,
+                                 const Context ctx,
+                                 const mxnet::ShapeVector &in_shapes,
+                                 const std::vector<int> &in_types) {
+  const RNNParam& param = nnvm::get<RNNParam>(attrs.parsed);
+  OpStatePtr state = OpStatePtr();
+  int dtype = in_types[rnn_enum::kData];
+  int itype = dtype;
+  if (param.use_sequence_length) {
+    size_t seq_len_input_idx = rnn_enum::kSequenceLength;
+    if (param.mode != rnn_enum::kLstm) {
+      seq_len_input_idx -= 1;
     }
+    itype = in_types[seq_len_input_idx];
   }
 
-  for (const NDArray& out : outputs) {
-    if (out.storage_type() == kDefaultStorage) {
-      temp_ndarrays_o.push_back(out.Reorder2Default());
-      out_blobs.emplace_back(temp_ndarrays_o.back().data());
-    } else {
-      out_blobs.emplace_back(out.data());
-    }
+#if MXNET_USE_MKLDNN == 1
+  if ((in_types[0] == mshadow::kFloat32 || in_types[0] == mshadow::kFloat16)
+      && in_shapes[0].ndim() == 3 && ctx.dev_type == kCPU) {
+    const mxnet::TShape& data_shape = in_shapes[rnn_enum::kData];
+    state = OpStatePtr::Create<MKLDNNRnnOp>(param, data_shape[0],
+        data_shape[1], data_shape[2]);
+    return state;
   }
-  int dtype = in_blobs[rnn_enum::kData].type_flag_;
-  int itype = in_blobs[inputs.size()-1].type_flag_;
-  mkldnn::memory::data_type mkldnn_dtype = get_mkldnn_type(dtype);
-  Stream<cpu> *s = ctx.get_stream<cpu>();
-  auto cpu_engine = CpuEngine::Get()->get_engine();
+#endif  // MXNET_USE_MKLDNN == 1
+
   MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
     MSHADOW_TYPE_SWITCH(itype, IType, {
-      RNNOp<cpu, DType, IType>& op = state_ptr.get_state<RNNOp<cpu, DType, IType>>();
-      const RNNParam& param = op.param_;
-      int ngates = 0, nstates = 0;
-      GetMKLDNNRNNAlgo(param.mode, &ngates, &nstates);
-      int D = param.bidirectional ? 2 : 1;
-      Tensor<cpu, 3, DType> x = in_blobs[rnn_enum::kData].get<cpu, 3, DType>(s);
-      int T = x.shape_[0];
-      int N = x.shape_[1];
-      int I = x.shape_[2];
-      int H = param.state_size;
-      int L = param.num_layers;
-
-      const size_t r_size = GetMKLDNNRNNCacheMemorySize(L, D, T, N, I, H, param.mode);
-      if (op.init_mem_ && op.reserve_mem_size_ < r_size) {
-        op.init_mem_ = false;
-      }
-      const size_t weights_version = inputs[rnn_enum::kParams].version();
-      if (!op.init_mem_) {
-        op.mem_space_ = NDArray(TShape({static_cast<dim_t>(r_size)}), op.ctx_, false, dtype);
-        op.reserve_mem_size_ = r_size;
-        op.init_mem_ = true;
-        op.has_cache = false;
-        // Assign weights_version
-        op.weights_version = weights_version;
-      }
-      // Check if NDArray was changed.
-      if (op.weights_version != weights_version) {
-        op.has_cache = false;
-        op.weights_version = weights_version;
+      if (ctx.dev_type == kGPU) {
+        state = OpStatePtr::Create<RNNOp<gpu, DType, IType>>(param, ctx);
+      } else {
+        state = OpStatePtr::Create<RNNOp<cpu, DType, IType>>(param, ctx);
       }
-
-      DType* workptr = static_cast<DType*>(op.mem_space_.data().dptr_);
-      mkldnn::memory::dims src_layer_tz_0 = {T, N, I};
-      mkldnn::memory::dims src_layer_tz = {T, N, D * H};
-      mkldnn::memory::dims dst_layer_tz = {T, N, D * H};
-      auto dst_layer_md = mkldnn::memory::desc(
-        { dst_layer_tz }, mkldnn_dtype, mkldnn::memory::format::tnc);
-      if (op.x_memory.size() == 0) {
-        if (D == 1 && I == H) {
-          auto user_src_layer_md = mkldnn::memory::desc(
-              { src_layer_tz }, mkldnn_dtype, mkldnn::memory::format::tnc);
-          auto user_src_layer_memory_n = mkldnn::memory({ user_src_layer_md, cpu_engine });
-          op.x_memory.push_back(user_src_layer_memory_n);
-
-          mkldnn::memory::dims weights_layer_tz = {L, 1, I, ngates, H};  //  ldigo
-          mkldnn::memory::dims weights_iter_tz = {L, 1, H, ngates, H};  //  ldigo
-          mkldnn::memory::dims bias_tz = {L, 1, ngates, H};
-          auto user_weight_layer_md = mkldnn::memory::desc(
-              { weights_layer_tz }, mkldnn_dtype, mkldnn::memory::format::ldigo);
-          auto user_weight_iter_md = mkldnn::memory::desc(
-              { weights_iter_tz }, mkldnn_dtype, mkldnn::memory::format::ldigo);
-          auto user_bias_md = mkldnn::memory::desc({ bias_tz },
-              mkldnn_dtype, mkldnn::memory::format::ldgo);
-          DType* weight_layer_n = workptr;  //  L * I * ngates * H
-          auto user_weight_layer_memory_n
-              = mkldnn::memory({ user_weight_layer_md, cpu_engine }, weight_layer_n);
-          op.wx_memory.push_back(user_weight_layer_memory_n);
-
-          DType* weight_iter_n = weight_layer_n + L * I * ngates * H;  //  L * H * ngates * H
-          auto user_weight_iter_memory_n
-              = mkldnn::memory({ user_weight_iter_md, cpu_engine }, weight_iter_n);
-          op.wh_memory.push_back(user_weight_iter_memory_n);
-
-          DType* bias_n = weight_iter_n + L * H * ngates * H;  //  L * ngates * H
-          auto user_bias_memory_n =
-              mkldnn::memory({ user_bias_md, cpu_engine }, bias_n);
-          op.bias_memory.push_back(user_bias_memory_n);
-
-          auto wx_md_n = mkldnn::memory::desc(
-              { weights_layer_tz }, mkldnn_dtype, mkldnn::memory::format::ldgoi);
-          DType* wx_n = bias_n + L * ngates * H;  //   L * ngates * I * H
-          auto wx_memory_n =
-              mkldnn::memory({ wx_md_n, cpu_engine }, wx_n);
-          DType* wh_n = wx_n + L * ngates * I * H;  //  L * ngates * H * H
-          auto wh_md_n = mkldnn::memory::desc(
-              { weights_iter_tz }, mkldnn_dtype, mkldnn::memory::format::ldgoi);
-          auto wh_memory_n =
-              mkldnn::memory({ wh_md_n, cpu_engine }, wh_n);
-
-          op.concat_weight_memory.push_back(wx_memory_n);
-          op.concat_weight_memory.push_back(wh_memory_n);
-          workptr = wh_n + L * ngates * H * H;
-
-          mkldnn::memory::dims src_iter_tz_n1 = {1, 1, nstates, N, H};  //  ldsnc
-          auto src_iter_md_n1 = mkldnn::memory::desc(
-              { src_iter_tz_n1 }, mkldnn_dtype, mkldnn::memory::format::ldsnc);
-          for (int l = 0; l < L; l++) {
-            DType* src_iter_n1 = workptr;  //  nstates * N * H
-            auto src_iter_memory_n1 =
-                mkldnn::memory({ src_iter_md_n1, cpu_engine }, src_iter_n1);
-            op.concat_iter_memory.push_back(src_iter_memory_n1);
-            workptr = src_iter_n1 + nstates * N * H;
-          }
-          mkldnn::memory::dims src_iter_tz_n = {L, 1, nstates, N, H};  //  ldsnc
-          auto src_iter_md_n = mkldnn::memory::desc(
-              { src_iter_tz_n }, mkldnn_dtype, mkldnn::memory::format::ldsnc);
-          DType* src_iter_n = workptr;  //  L * nstates * N * H
-          auto src_iter_memory_n =
-              mkldnn::memory({ src_iter_md_n, cpu_engine }, src_iter_n);
-          op.concat_iter_memory.push_back(src_iter_memory_n);
-          op.hcx_memory.push_back(src_iter_memory_n);
-          DType* dst_layer_n = src_iter_n + L * nstates * N * H;  //  T * N * D * H
-          auto dst_layer_memory_n
-              = mkldnn::memory({ dst_layer_md, cpu_engine }, dst_layer_n);
-          op.y_memory.push_back(dst_layer_memory_n);
-
-          mkldnn::memory::dims dst_iter_tz_n = {L, 1, nstates, N, H};  //  ldsnc
-          auto dst_iter_md_n = mkldnn::memory::desc(
-              { dst_iter_tz_n }, mkldnn_dtype, mkldnn::memory::format::ldsnc);
-          DType* dst_iter_n = dst_layer_n + T * N * D * H;  //  L * nstates * N * H
-          auto dst_iter_memory_n =
-              mkldnn::memory({ dst_iter_md_n, cpu_engine }, dst_iter_n);
-          op.hcy_memory.push_back(dst_iter_memory_n);
-          workptr = dst_iter_n + L * nstates * N * H;
-
-        } else {
-          auto user_src_layer_md_0 = mkldnn::memory::desc(
-              { src_layer_tz_0 }, mkldnn_dtype, mkldnn::memory::format::tnc);
-          auto user_src_layer_memory_0 = mkldnn::memory({ user_src_layer_md_0, cpu_engine });
-          op.x_memory.push_back(user_src_layer_memory_0);
-
-          mkldnn::memory::dims weights_layer_tz_0 = {1, D, I, ngates, H};  //  ldigo
-          mkldnn::memory::dims weights_iter_tz_0 = {1, D, H, ngates, H};  //  ldigo
-          mkldnn::memory::dims bias_tz_0 = {1, D, ngates, H};
-          auto user_weight_layer_md_0 = mkldnn::memory::desc(
-              { weights_layer_tz_0 }, mkldnn_dtype, mkldnn::memory::format::ldigo);
-          auto user_weight_iter_md_0 = mkldnn::memory::desc(
-              { weights_iter_tz_0 }, mkldnn_dtype, mkldnn::memory::format::ldigo);
-          auto user_bias_md_0 = mkldnn::memory::desc({ bias_tz_0 },
-              mkldnn_dtype, mkldnn::memory::format::ldgo);
-
-          DType* weight_layer_0 = workptr;  //  D * I * ngates * H
-          auto user_weight_layer_memory_0
-              = mkldnn::memory({ user_weight_layer_md_0, cpu_engine }, weight_layer_0);
-          op.wx_memory.push_back(user_weight_layer_memory_0);
-
-          DType* weight_iter_0 = weight_layer_0 + D * I * ngates * H;  //  D * H * ngates * H
-          auto user_weight_iter_memory_0
-              = mkldnn::memory({ user_weight_iter_md_0, cpu_engine }, weight_iter_0);
-          op.wh_memory.push_back(user_weight_iter_memory_0);
-
-          DType* bias_0 = weight_iter_0 + D * H * ngates * H;  //  D * ngates * H
-          auto user_bias_memory_0 =
-              mkldnn::memory({ user_bias_md_0, cpu_engine }, bias_0);
-          op.bias_memory.push_back(user_bias_memory_0);
-          workptr = bias_0 + D * ngates * H;
-
-          auto wx_md_0 = mkldnn::memory::desc(
-              { weights_layer_tz_0 }, mkldnn_dtype, mkldnn::memory::format::ldgoi);
-          auto wx_memory_0 =
-              mkldnn::memory({ wx_md_0, cpu_engine });
-          auto wh_md_0 = mkldnn::memory::desc(
-              { weights_iter_tz_0 }, mkldnn_dtype, mkldnn::memory::format::ldgoi);
-          auto wh_memory_0 =
-              mkldnn::memory({ wh_md_0, cpu_engine });
-          if (D == 2) {
-            DType* wx_0 = workptr;  //  D * ngates * I * H
-            wx_memory_0.set_data_handle(wx_0);
-            DType* wh_0 = wx_0 + D * ngates * I * H;  //  D * ngates * H * H
-            wh_memory_0.set_data_handle(wh_0);
-            workptr = wh_0 + D * ngates * H * H;
-          }
-          op.concat_weight_memory.push_back(wx_memory_0);
-          op.concat_weight_memory.push_back(wh_memory_0);
-
-          mkldnn::memory::dims src_iter_undi_tz_0 = {1, 1, nstates, N, H};  //  ldsnc
-          auto src_iter_undi_md_0 = mkldnn::memory::desc(
-              { src_iter_undi_tz_0 }, mkldnn_dtype, mkldnn::memory::format::ldsnc);
-          DType* src_iter_undi_0 = workptr;  //  nstates * N * H
-          auto src_iter_undi_memory_0 =
-              mkldnn::memory({ src_iter_undi_md_0, cpu_engine }, src_iter_undi_0);
-          op.concat_iter_memory.push_back(src_iter_undi_memory_0);
-          workptr = src_iter_undi_0 + nstates * N * H;
-          if (D == 1) {
-            op.hcx_memory.push_back(src_iter_undi_memory_0);
-          } else {
-            DType* src_iter_undi2_0 = workptr;  //  nstates * N * H
-            auto src_iter_undi2_memory_0 =
-                mkldnn::memory({ src_iter_undi_md_0, cpu_engine }, src_iter_undi2_0);
-            op.concat_iter_memory.push_back(src_iter_undi2_memory_0);
-
-            mkldnn::memory::dims src_iter_tz_0 = {1, D, nstates, N, H};  //  ldsnc
-            auto src_iter_md_0 = mkldnn::memory::desc(
-                { src_iter_tz_0 }, mkldnn_dtype, mkldnn::memory::format::ldsnc);
-            DType* src_iter_0 = src_iter_undi2_0 + nstates * N * H;  //  D * nstates * N * H
-            auto src_iter_memory_0 =
-                mkldnn::memory({ src_iter_md_0, cpu_engine }, src_iter_0);
-            op.concat_iter_memory.push_back(src_iter_memory_0);
-            op.hcx_memory.push_back(src_iter_memory_0);
-            workptr = src_iter_0 + D * nstates * N * H;
-          }
-
-          DType* dst_layer_0 = workptr;  //  T * N * D * H
-          auto dst_layer_memory_0
-              = mkldnn::memory({ dst_layer_md, cpu_engine }, dst_layer_0);
-          op.y_memory.push_back(dst_layer_memory_0);
-
-          mkldnn::memory::dims dst_iter_tz_0 = {1, D, nstates, N, H};  //  ldsnc
-          auto dst_iter_md_0 = mkldnn::memory::desc(
-              { dst_iter_tz_0 }, mkldnn_dtype, mkldnn::memory::format::ldsnc);
-          DType* dst_iter_0 = dst_layer_0 + T * N * D * H;  //  D * nstates * N * H
-          auto dst_iter_memory_0 =
-              mkldnn::memory({ dst_iter_md_0, cpu_engine }, dst_iter_0);
-          op.hcy_memory.push_back(dst_iter_memory_0);
-          workptr = dst_iter_0 + D * nstates * N * H;
-
-          //  next L - 1 layers
-          if (L > 1 && D == 1) {
-            auto user_src_layer_md = mkldnn::memory::desc(
-                { src_layer_tz }, mkldnn_dtype, mkldnn::memory::format::tnc);
-            auto user_src_layer_memory = mkldnn::memory({ user_src_layer_md, cpu_engine });
-            op.x_memory.push_back(user_src_layer_memory);
-
-            mkldnn::memory::dims weights_layer_tz = {L - 1, 1, H, ngates, H};  //  ldigo
-            mkldnn::memory::dims weights_iter_tz = {L - 1, 1, H, ngates, H};  //  ldigo
-            mkldnn::memory::dims bias_tz = {L - 1, 1, ngates, H};
-            auto user_weight_layer_md = mkldnn::memory::desc(
-                { weights_layer_tz }, mkldnn_dtype, mkldnn::memory::format::ldigo);
-            auto user_weight_iter_md = mkldnn::memory::desc(
-                { weights_iter_tz }, mkldnn_dtype, mkldnn::memory::format::ldigo);
-            auto user_bias_md = mkldnn::memory::desc({ bias_tz },
-                mkldnn_dtype, mkldnn::memory::format::ldgo);
-
-            DType* weight_layer_n = workptr;  //  (L - 1) * H * ngates * H
-            auto user_weight_layer_memory_n
-                = mkldnn::memory({ user_weight_layer_md, cpu_engine }, weight_layer_n);
-            op.wx_memory.push_back(user_weight_layer_memory_n);
-
-            DType* weight_iter_n = weight_layer_n +
-                (L - 1) * H * ngates * H;  //  (L - 1) * H * ngates * H
-            auto user_weight_iter_memory_n
-                = mkldnn::memory({ user_weight_iter_md, cpu_engine }, weight_iter_n);
-            op.wh_memory.push_back(user_weight_iter_memory_n);
-
-            DType* bias_n = weight_iter_n + (L - 1) * H * ngates * H;  //  (L - 1) * ngates * H
-            auto user_bias_memory_n =
-                mkldnn::memory({ user_bias_md, cpu_engine }, bias_n);
-            op.bias_memory.push_back(user_bias_memory_n);
-
-            auto wx_md_n = mkldnn::memory::desc(
-                { weights_layer_tz }, mkldnn_dtype, mkldnn::memory::format::ldgoi);
-            DType* wx_n = bias_n + (L - 1) * ngates * H;  //  (L - 1) * ngates * H * H
-            auto wx_memory_n =
-                mkldnn::memory({ wx_md_n, cpu_engine }, wx_n);
-            DType* wh_n = wx_n + (L - 1) * ngates * H * H;  //  (L - 1) * ngates * H * H
-            auto wh_md_n = mkldnn::memory::desc(
-                { weights_iter_tz }, mkldnn_dtype, mkldnn::memory::format::ldgoi);
-            auto wh_memory_n =
-                mkldnn::memory({ wh_md_n, cpu_engine }, wh_n);
-
-            op.concat_weight_memory.push_back(wx_memory_n);
-            op.concat_weight_memory.push_back(wh_memory_n);
-            workptr = wh_n + (L - 1) * ngates * H * H;
-
-            mkldnn::memory::dims src_iter_tz_n1 = {1, 1, nstates, N, H};  //  ldsnc
-            auto src_iter_md_n1 = mkldnn::memory::desc(
-                { src_iter_tz_n1 }, mkldnn_dtype, mkldnn::memory::format::ldsnc);
-            for (int l = 0; l < L - 1; l++) {
-              DType* src_iter_n1 = workptr;  //  nstates * N * H
-              auto src_iter_memory_n1 =
-                  mkldnn::memory({ src_iter_md_n1, cpu_engine }, src_iter_n1);
-              op.concat_iter_memory.push_back(src_iter_memory_n1);
-              workptr = src_iter_n1 + nstates * N * H;
-            }
-            mkldnn::memory::dims src_iter_tz_n = {L - 1, 1, nstates, N, H};  //  ldsnc
-            auto src_iter_md_n = mkldnn::memory::desc(
-                { src_iter_tz_n }, mkldnn_dtype, mkldnn::memory::format::ldsnc);
-            DType* src_iter_n = workptr;  //  (L - 1) * nstates * N * H
-            auto src_iter_memory_n =
-                mkldnn::memory({ src_iter_md_n, cpu_engine }, src_iter_n);
-            op.concat_iter_memory.push_back(src_iter_memory_n);
-            op.hcx_memory.push_back(src_iter_memory_n);
-
-            DType* dst_layer_n = src_iter_n + (L - 1) * nstates * N * H;  //  T * N * D * H
-            auto dst_layer_memory_n
-                = mkldnn::memory({ dst_layer_md, cpu_engine }, dst_layer_n);
-            op.y_memory.push_back(dst_layer_memory_n);
-
-            mkldnn::memory::dims dst_iter_tz_n = {L - 1, 1, nstates, N, H};  //  ldsnc
-            auto dst_iter_md_n = mkldnn::memory::desc(
-                { dst_iter_tz_n }, mkldnn_dtype, mkldnn::memory::format::ldsnc);
-            DType* dst_iter_n = dst_layer_n + T * N * D * H;  //  (L - 1) * nstates * N * H
-            auto dst_iter_memory_n =
-                mkldnn::memory({ dst_iter_md_n, cpu_engine }, dst_iter_n);
-            op.hcy_memory.push_back(dst_iter_memory_n);
-          }
-
-          if (L > 1 && D == 2) {
-            mkldnn::memory::dims weights_layer_tz = {1, D, H * D, ngates, H};  //  ldigo
-            mkldnn::memory::dims weights_iter_tz = {1, D, H, ngates, H};  //  ldigo
-            mkldnn::memory::dims bias_tz = {1, D, ngates, H};
-            auto user_weight_layer_md = mkldnn::memory::desc(
-                { weights_layer_tz }, mkldnn_dtype, mkldnn::memory::format::ldigo);
-            auto user_weight_iter_md = mkldnn::memory::desc(
-                { weights_iter_tz }, mkldnn_dtype, mkldnn::memory::format::ldigo);
-            auto user_bias_md = mkldnn::memory::desc({ bias_tz },
-                mkldnn_dtype, mkldnn::memory::format::ldgo);
-
-            auto user_src_layer_md = mkldnn::memory::desc(
-                { src_layer_tz }, mkldnn_dtype, mkldnn::memory::format::tnc);
-            auto user_src_layer_memory = mkldnn::memory({ user_src_layer_md, cpu_engine });
-            op.x_memory.push_back(user_src_layer_memory);
-
-            auto wx_md_n = mkldnn::memory::desc(
-                { weights_layer_tz }, mkldnn_dtype, mkldnn::memory::format::ldgoi);
-            auto wh_md_n = mkldnn::memory::desc(
-                { weights_iter_tz }, mkldnn_dtype, mkldnn::memory::format::ldgoi);
-
-            for (int l = 0; l < L; l++) {
-              DType* weight_layer_n = workptr;  //  D * (H * D) * ngates * H
-              auto user_weight_layer_memory_n
-                  = mkldnn::memory({ user_weight_layer_md, cpu_engine }, weight_layer_n);
-              op.wx_memory.push_back(user_weight_layer_memory_n);
-
-              DType* weight_iter_n = weight_layer_n +
-                  D * (H * D) * ngates * H;  //  D * H * ngates * H
-              auto user_weight_iter_memory_n
-                  = mkldnn::memory({ user_weight_iter_md, cpu_engine }, weight_iter_n);
-              op.wh_memory.push_back(user_weight_iter_memory_n);
-
-              DType* bias_n = weight_iter_n + D * H * ngates * H;  //  D * ngates * H
-              auto user_bias_memory_n =
-                  mkldnn::memory({ user_bias_md, cpu_engine }, bias_n);
-              op.bias_memory.push_back(user_bias_memory_n);
-              workptr = bias_n + D * ngates * H;
-            }
-
-            DType* wx_n = workptr;  //  D * ngates * (D * H) * H
-            DType* wh_n = wx_n + D * ngates * (D * H) * H;  //  D * ngates * H * H
-            auto wx_memory_n =
-                mkldnn::memory({ wx_md_n, cpu_engine }, wx_n);
-            auto wh_memory_n =
-                mkldnn::memory({ wh_md_n, cpu_engine }, wh_n);
-            op.concat_weight_memory.push_back(wx_memory_n);
-            op.concat_weight_memory.push_back(wh_memory_n);
-
-            mkldnn::memory::dims src_iter_undi_tz = {1, 1, nstates, N, H};  //  ldsnc
-            auto src_iter_undi_md = mkldnn::memory::desc(
-                { src_iter_undi_tz }, mkldnn_dtype, mkldnn::memory::format::ldsnc);
-            DType* src_iter_undi = wh_n + D * ngates * H * H;  //  nstates * N * H
-            auto src_iter_undi_memory =
-                mkldnn::memory({ src_iter_undi_md, cpu_engine }, src_iter_undi);
-            op.concat_iter_memory.push_back(src_iter_undi_memory_0);
-
-            DType* src_iter_undi2 = src_iter_undi + nstates * N * H;  //  nstates * N * H
-            auto src_iter_undi2_memory =
-                mkldnn::memory({ src_iter_undi_md, cpu_engine }, src_iter_undi2);
-            op.concat_iter_memory.push_back(src_iter_undi2_memory);
-
-            mkldnn::memory::dims src_iter_tz = {1, D, nstates, N, H};  //  ldsnc
-            auto src_iter_md = mkldnn::memory::desc(
-                { src_iter_tz }, mkldnn_dtype, mkldnn::memory::format::ldsnc);
-            DType* src_iter = src_iter_undi2 + nstates * N * H;  //  D * nstates * N * H
-            auto src_iter_memory =
-                mkldnn::memory({ src_iter_md, cpu_engine }, src_iter);
-            op.concat_iter_memory.push_back(src_iter_memory);
-            op.hcx_memory.push_back(src_iter_memory);
-
-            DType* dst_layer_n = src_iter + D * nstates * N * H;  //  T * N * D * H
-            auto dst_layer_memory_n
-                = mkldnn::memory({ dst_layer_md, cpu_engine }, dst_layer_n);
-            op.y_memory.push_back(dst_layer_memory_n);
-
-            mkldnn::memory::dims dst_iter_tz_n = {1, D, nstates, N, H};  //  ldsnc
-            auto dst_iter_md_n = mkldnn::memory::desc(
-                { dst_iter_tz_n }, mkldnn_dtype, mkldnn::memory::format::ldsnc);
-            DType* dst_iter_n = dst_layer_n + T * N * D * H;  //  D * nstates * N * H
-            auto dst_iter_memory_n =
-                mkldnn::memory({ dst_iter_md_n, cpu_engine }, dst_iter_n);
-            op.hcy_memory.push_back(dst_iter_memory_n);
-          }
-        }
-      }
-      op.Forward(ctx, in_blobs, req, out_blobs);
     });
   });
+  return state;
 }
 
-static void RNNStatefulComputeExCPU(const OpStatePtr& state_ptr, const OpContext& ctx,
+#if MXNET_USE_MKLDNN == 1
+static void RNNStatefulComputeExCPU(const OpStatePtr& state_ptr,
+                                    const OpContext& ctx,
                                     const std::vector<NDArray>& inputs,
                                     const std::vector<OpReqType>& req,
                                     const std::vector<NDArray>& outputs) {
-  if (SupportMKLDNNRNN(inputs[0])) {
-    RNNStatefulComputeCPU(state_ptr, ctx, inputs, req, outputs);
-    return;
+  if ((inputs[0].dtype() == mshadow::kFloat32 || inputs[0].dtype() == mshadow::kFloat16) &&
+      inputs[0].shape().ndim() == 3) {
+    MKLDNNRnnOp& op = state_ptr.get_state<MKLDNNRnnOp>();
+    op.Forward(ctx, inputs, req, outputs);
+  } else {
+    FallBackCompute(RNNStatefulCompute<cpu>, state_ptr, ctx, inputs, req, outputs);
   }
-  int use_mkldnn_rnn = dmlc::GetEnv("MXNET_USE_MKLDNN_RNN", 1);
-  dmlc::SetEnv("MXNET_USE_MKLDNN_RNN", 0);
-  FallBackCompute(RNNStatefulCompute<cpu>, state_ptr, ctx, inputs, req, outputs);
-  dmlc::SetEnv("MXNET_USE_MKLDNN_RNN", use_mkldnn_rnn);
 }
-#endif
+
+static void RNNStatefulGradComputeExCPU(const OpStatePtr& state_ptr,
+                                        const OpContext& ctx,
+                                        const std::vector<NDArray>& inputs,
+                                        const std::vector<OpReqType>& req,
+                                        const std::vector<NDArray>& outputs) {
+  if ((inputs[0].dtype() == mshadow::kFloat32 || inputs[0].dtype() == mshadow::kFloat16) &&
+      inputs[0].shape().ndim() == 3) {
+    MKLDNNRnnOp& op = state_ptr.get_state<MKLDNNRnnOp>();
+    op.Backward(ctx, inputs, req, outputs);
+  } else {
+    FallBackCompute(RNNStatefulGradCompute<cpu>, state_ptr, ctx, inputs, req, outputs);
+  }
+}
+#endif  // MXNET_USE_MKLDNN == 1
 
 NNVM_REGISTER_OP(RNN)
 .add_alias("_npx_rnn")
@@ -726,6 +370,16 @@ The definition of GRU here is slightly different from paper but compatible with
   const RNNParam& params = nnvm::get<RNNParam>(attrs.parsed);
   return ListArguments(params);
 })
+.set_attr<nnvm::FListOutputNames>("FListOutputNames", [](const NodeAttrs& attrs) {
+  const RNNParam& params = nnvm::get<RNNParam>(attrs.parsed);
+  std::vector<std::string> names{"output"};
+  if (params.state_outputs) {
+    names.emplace_back("state_output");
+    if (params.mode == rnn_enum::kLstm)
+      names.emplace_back("statecell_output");
+  }
+  return names;
+})
 .set_attr<mxnet::FInferShape>("FInferShape", RNNShape)
 .set_attr<nnvm::FInferType>("FInferType", RNNType)
 .set_attr<FInferStorageType>("FInferStorageType", RNNStorageType)
@@ -756,7 +410,12 @@ NNVM_REGISTER_OP(_backward_RNN)
 .set_attr_parser(ParamParser<RNNParam>)
 .set_attr<bool>("TIsLayerOpBackward", true)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FInferStorageType>("FInferStorageType", RNNStorageType)
 .set_attr<FStatefulCompute>("FStatefulCompute<cpu>", RNNStatefulGradCompute<cpu>)
+#if MXNET_USE_MKLDNN == 1
+.set_attr<bool>("TIsMKLDNN", true)
+.set_attr<FStatefulComputeEx>("FStatefulComputeEx<cpu>", RNNStatefulGradComputeExCPU)
+#endif
 .set_attr<FResourceRequestEx>("FResourceRequestEx", RNNResourceEx);
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/rnn_impl.h b/src/operator/rnn_impl.h
index 425ea4a3c6ab..e1b4a2b79c0a 100644
--- a/src/operator/rnn_impl.h
+++ b/src/operator/rnn_impl.h
@@ -44,13 +44,6 @@
 namespace mxnet {
 namespace op {
 
-namespace rnn_enum {
-  enum RNNOpInputs {kData, kParams, kState, kStateCell, kSequenceLength};
-  enum RNNOpOutputs {kOut, kStateOut, kStateCellOut};
-  enum RNNModeType {kRnnRelu, kRnnTanh, kLstm, kGru};
-  enum RNNOpResource {kTempSpace, kCuDNNDropoutDescSpace};
-}
-
 template<typename DType>
 inline DType sigmoid(DType x) {
   return 1.0f / (1.0f + exp(-x));
diff --git a/src/operator/softmax_output.cc b/src/operator/softmax_output.cc
index 548225f0496b..ec2670974f49 100644
--- a/src/operator/softmax_output.cc
+++ b/src/operator/softmax_output.cc
@@ -26,6 +26,7 @@
 #include "./softmax_output-inl.h"
 #if MXNET_USE_MKLDNN == 1
 #include "./nn/mkldnn/mkldnn_ops-inl.h"
+#include "./nn/mkldnn/mkldnn_base-inl.h"
 #endif
 namespace mxnet {
 namespace op {
diff --git a/src/operator/subgraph/mkldnn/mkldnn_conv-inl.h b/src/operator/subgraph/mkldnn/mkldnn_conv-inl.h
index 002b012bc35e..509d25037ad7 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_conv-inl.h
+++ b/src/operator/subgraph/mkldnn/mkldnn_conv-inl.h
@@ -47,10 +47,10 @@ static inline bool IsOutputUInt8(const MKLDNNConvFusionParam& param) {
             param.alg == mkldnn::algorithm::eltwise_bounded_relu);
   };
   if ((!mkldnn_param.with_sum) && mkldnn_param.with_act) {
-    CHECK(param.full_conv_param.act_param.alg != mkldnn::algorithm::algorithm_undef);
+    CHECK(param.full_conv_param.act_param.alg != mkldnn::algorithm::undef);
     result = IsOutputUInt8Helper(param.full_conv_param.act_param);
   } else if (mkldnn_param.with_postsum_act) {
-    CHECK(param.full_conv_param.postsum_act_param.alg != mkldnn::algorithm::algorithm_undef);
+    CHECK(param.full_conv_param.postsum_act_param.alg != mkldnn::algorithm::undef);
     result = IsOutputUInt8Helper(param.full_conv_param.postsum_act_param);
   }
   return result;
diff --git a/src/operator/subgraph/mkldnn/mkldnn_conv.cc b/src/operator/subgraph/mkldnn/mkldnn_conv.cc
index 886a21b44fc9..e1f9174898c7 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_conv.cc
+++ b/src/operator/subgraph/mkldnn/mkldnn_conv.cc
@@ -128,40 +128,37 @@ static void ConvertWeightBias2MKLDNN(const MKLDNNConvFullParam &param,
                                      NDArray *weight, NDArray *bias, bool has_bias,
                                      float data_scale, const std::vector<float> &weight_scales) {
   MKLDNNStream *stream = MKLDNNStream::Get();
-  const auto new_weight = NDArray(fwd_pd.weights_primitive_desc());
+  const auto new_weight = NDArray(fwd_pd.weights_desc());
   const auto conv_weights_memory = new_weight.GetMKLDNNData();
-  primitive_attr weight_attr;
+  mkldnn::primitive_attr weight_attr;
   if (weight_scales.size()) {
     const int weight_mask = (weight_scales.size()) == 1 ? 0 : 1;
-    weight_attr.set_int_output_round_mode(round_mode::round_nearest);
     weight_attr.set_output_scales(weight_mask, weight_scales);
   }
   auto default_weights_memory = GetWeights(*weight, param.conv_param.num_group);
   if (default_weights_memory == nullptr) default_weights_memory = weight->GetMKLDNNData();
   const auto weight_reorder_pd =
-      mkldnn::reorder::primitive_desc(default_weights_memory->get_primitive_desc(),
-                                      conv_weights_memory->get_primitive_desc(), weight_attr);
-  stream->RegisterPrim(
-      mkldnn::reorder(weight_reorder_pd, *default_weights_memory, *conv_weights_memory));
-
+      mkldnn::reorder::primitive_desc(*default_weights_memory, *conv_weights_memory, weight_attr);
+  MKLDNNStream::Get()->RegisterPrimArgs(
+      mkldnn::reorder(weight_reorder_pd),
+      {{MKLDNN_ARG_FROM, *default_weights_memory}, {MKLDNN_ARG_TO, *conv_weights_memory}});
   NDArray new_bias;
   if (has_bias && data_scale) {
     std::vector<float> bias_scales(weight_scales.size());
     for (size_t c = 0; c < weight_scales.size(); ++c) {
       bias_scales[c] = weight_scales[c] * data_scale;
     }
-    new_bias = NDArray(fwd_pd.bias_primitive_desc());
+    new_bias = NDArray(fwd_pd.bias_desc());
     const auto conv_bias_memory = new_bias.GetMKLDNNData();
     const int bias_mask = (bias_scales.size()) == 1 ? 0 : 1;
-    primitive_attr bias_attr;
-    bias_attr.set_int_output_round_mode(round_mode::round_nearest);
+    mkldnn::primitive_attr bias_attr;
     bias_attr.set_output_scales(bias_mask, bias_scales);
     auto bias_weights_memory = bias->GetMKLDNNData();
-    auto bias_reorder_pd =
-        mkldnn::reorder::primitive_desc(bias_weights_memory->get_primitive_desc(),
-                                        conv_bias_memory->get_primitive_desc(), bias_attr);
-    stream->RegisterPrim(
-        mkldnn::reorder(bias_reorder_pd, *bias_weights_memory, *conv_bias_memory));
+    const auto bias_reorder_pd =
+        mkldnn::reorder::primitive_desc(*bias_weights_memory, *conv_bias_memory, bias_attr);
+    MKLDNNStream::Get()->RegisterPrimArgs(
+        mkldnn::reorder(bias_reorder_pd),
+        {{MKLDNN_ARG_FROM, *bias_weights_memory}, {MKLDNN_ARG_TO, *conv_bias_memory}});
   }
   stream->Submit();
   *weight = new_weight;
@@ -186,6 +183,7 @@ class SgMKLDNNConvOperator {
   nnvm::Symbol subgraph_sym_;
   MKLDNNConvFusionParam param_;
   std::shared_ptr<MKLDNNConvForward> fwd_;
+  mkldnn_args_map_t args_;
   NDArray cached_weight_;
   NDArray cached_bias_;
   float cached_data_min_;
@@ -253,22 +251,24 @@ void SgMKLDNNConvOperator::Forward(const OpContext &ctx,
       auto in_mkl_mem = inputs[in_sum].GetMKLDNNData();
       auto out_mkl_mem = outputs[kOut].GetMKLDNNData();
       if (outputs[kOut].dtype() == mshadow::kInt32) {
-        auto mem_desc = in_mkl_mem->get_primitive_desc().desc();
-        auto this_dtype = get_mkldnn_type(mshadow::kInt32);
-        mkldnn::memory::desc omd(
-            mkldnn::memory::dims(mem_desc.data.dims, mem_desc.data.dims + mem_desc.data.ndims),
-            this_dtype, static_cast<mkldnn::memory::format>(mem_desc.data.format));
-        mkldnn::memory::primitive_desc opd(omd, CpuEngine::Get()->get_engine());
-        mkldnn_mem_ptr tmp_mem(new mkldnn::memory(opd, out_mkl_mem->get_data_handle()));
+        const auto& mem_desc = in_mkl_mem->get_desc();
+        const auto this_dtype = get_mkldnn_type(mshadow::kInt32);
+        auto omd = mem_desc;
+        omd.data.data_type = static_cast<mkldnn_data_type_t>(this_dtype);
+        mkldnn_mem_ptr tmp_mem(new mkldnn::memory(omd, CpuEngine::Get()->get_engine(),
+                                                  out_mkl_mem->get_data_handle()));
         MKLDNNStream::Get()->RegisterMem(tmp_mem);
-        MKLDNNStream::Get()->RegisterPrim(mkldnn::reorder(*in_mkl_mem, *tmp_mem));
+        MKLDNNStream::Get()->RegisterPrimArgs(
+            mkldnn::reorder(*in_mkl_mem, *tmp_mem),
+            {{MKLDNN_ARG_FROM, *in_mkl_mem}, {MKLDNN_ARG_TO, *tmp_mem}});
         output = NDArray(tmp_mem);
       } else {
-      mkldnn_mem_ptr tmp_mem(
-          new mkldnn::memory(in_mkl_mem->get_primitive_desc(), out_mkl_mem->get_data_handle()));
-      MKLDNNStream::Get()->RegisterMem(tmp_mem);
-      mxnet::MKLDNNCopy(*in_mkl_mem, tmp_mem.get());
-      output = NDArray(tmp_mem);
+        mkldnn_mem_ptr tmp_mem(new mkldnn::memory(in_mkl_mem->get_desc(),
+                                                  CpuEngine::Get()->get_engine(),
+                                                  out_mkl_mem->get_data_handle()));
+        MKLDNNStream::Get()->RegisterMem(tmp_mem);
+        mxnet::MKLDNNCopy(*in_mkl_mem, tmp_mem.get());
+        output = NDArray(tmp_mem);
       }
     }
   }
@@ -391,27 +391,25 @@ void SgMKLDNNConvOperator::Forward(const OpContext &ctx,
     fwd_.reset(new MKLDNNConvForward(
         full_conv_param, ctx.is_train, data, cached_weight_,
         has_bias ? &cached_bias_ : nullptr, output));
-    ConvertWeightBias2MKLDNN(full_conv_param, fwd_->fwd_pd, &cached_weight_, &cached_bias_,
+    ConvertWeightBias2MKLDNN(full_conv_param, fwd_->GetPd(), &cached_weight_, &cached_bias_,
                              has_bias, data_scale_, weight_scales_);
-    fwd_->SetNewMem(*data.GetMKLDNNData(), *cached_weight_.GetMKLDNNData(),
-                    has_bias ? cached_bias_.GetMKLDNNData() : nullptr,
-                    *output.GetMKLDNNData());
+    args_[MKLDNN_ARG_SRC] = *data.GetMKLDNNData();
+    args_[MKLDNN_ARG_WEIGHTS] = *cached_weight_.GetMKLDNNData();
+    if (has_bias) args_[MKLDNN_ARG_BIAS] = *cached_bias_.GetMKLDNNData();
+    args_[MKLDNN_ARG_DST] = *output.GetMKLDNNData();
     initialized_ = true;
   }
 
   if (mkldnn_param.with_sum) {
-    const auto output_mem = output.GetMKLDNNData();
-    const auto out_mem_desc = output_mem->get_primitive_desc().desc();
-    const auto dst_format = fwd_->fwd_pd.dst_primitive_desc().desc().data.format;
-    if (out_mem_desc.data.format != dst_format) {
-      auto tmp_out_mem = output.GetMKLDNNDataReorder(fwd_->fwd_pd.dst_primitive_desc());
-      mkldnn::memory::desc data_md(
-          mkldnn::memory::dims(out_mem_desc.data.dims,
-                               out_mem_desc.data.dims + out_mem_desc.data.ndims),
-          static_cast<mkldnn::memory::data_type>(out_mem_desc.data.data_type),
-          static_cast<memory::format>(dst_format));
-      mkldnn::memory::primitive_desc pd(data_md, CpuEngine::Get()->get_engine());
-      mkldnn_mem_ptr new_out_mem(new mkldnn::memory(pd, output_mem->get_data_handle()));
+    const auto& output_mem = output.GetMKLDNNData();
+    const auto& out_mem_desc = output_mem->get_desc();
+    const auto& dst_mem_desc = fwd_->GetPd().dst_desc();
+    if (out_mem_desc != dst_mem_desc) {
+      auto tmp_out_mem = output.GetMKLDNNDataReorder(fwd_->GetPd().dst_desc());
+      auto data_md = dst_mem_desc;
+      data_md.data.data_type = static_cast<mkldnn_data_type_t>(out_mem_desc.data.data_type);
+      mkldnn_mem_ptr new_out_mem(new mkldnn::memory(data_md, CpuEngine::Get()->get_engine(),
+                                                    output_mem->get_data_handle()));
       MKLDNNStream::Get()->RegisterMem(new_out_mem);
       mxnet::MKLDNNCopy(*tmp_out_mem, new_out_mem.get());
       output = NDArray(new_out_mem);
@@ -419,10 +417,11 @@ void SgMKLDNNConvOperator::Forward(const OpContext &ctx,
   }
 
   if (mkldnn_param.quantized) {
-    auto data_mem = data.GetMKLDNNDataReorder(fwd_->fwd_pd.src_primitive_desc());
-    mkldnn::memory *mem = output.CreateMKLDNNData(fwd_->fwd_pd.dst_primitive_desc());
-    fwd_->SetNewMem(*data_mem, *mem);
-    MKLDNNStream::Get()->RegisterPrim(fwd_->GetFwd());
+    auto data_mem = data.GetMKLDNNDataReorder(fwd_->GetPd().src_desc());
+    mkldnn::memory *mem = output.CreateMKLDNNData(fwd_->GetPd().dst_desc());
+    args_[MKLDNN_ARG_SRC] = *data_mem;
+    args_[MKLDNN_ARG_DST] = *mem;
+    MKLDNNStream::Get()->RegisterPrimArgs(fwd_->GetFwd(), args_);
     MKLDNNStream::Get()->Submit();
   } else {
     std::vector<NDArray> new_inputs;
@@ -441,9 +440,7 @@ void SgMKLDNNConvOperator::Forward(const OpContext &ctx,
   }
   if (mkldnn_param.with_sum) {
     auto out = const_cast<NDArray &>(outputs[kOut]);
-    auto format = static_cast<mkldnn::memory::format>(
-        fwd_->fwd_pd.dst_primitive_desc().desc().data.format);
-    out.UpdateMKLDNNMemDesc(format);
+    out.UpdateMKLDNNMemDesc(fwd_->GetPd().dst_desc());
   }
 }
 
diff --git a/src/operator/subgraph/mkldnn/mkldnn_fc-inl.h b/src/operator/subgraph/mkldnn/mkldnn_fc-inl.h
index cb355dab9abe..9a09d91ae5d0 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_fc-inl.h
+++ b/src/operator/subgraph/mkldnn/mkldnn_fc-inl.h
@@ -55,7 +55,7 @@ static inline mkldnn::algorithm GetMKLDNNEltwiseAlgo(const std::string op_name)
   else
     LOG(FATAL) << "Unsupported eltwise fusion op: " << op_name;
 
-  return mkldnn::algorithm::algorithm_undef;
+  return mkldnn::algorithm::undef;
 }
 
 static inline bool IsOutputUint8(const MKLDNNFCFullParam& full_param) {
diff --git a/src/operator/subgraph/mkldnn/mkldnn_subgraph_property.cc b/src/operator/subgraph/mkldnn/mkldnn_subgraph_property.cc
index d0d2b51918b1..269017ea6a03 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_subgraph_property.cc
+++ b/src/operator/subgraph/mkldnn/mkldnn_subgraph_property.cc
@@ -34,25 +34,33 @@ MXNET_REGISTER_SUBGRAPH_BACKEND(MKLDNN)
 
 MXNET_REGISTER_SUBGRAPH_PROPERTY(MKLDNN, SgMKLDNNConvProperty);
 
+#endif  // MXNET_USE_MKLDNN == 1
+#if MXNET_USE_MKLDNN == 1
 MXNET_REGISTER_SUBGRAPH_PROPERTY(MKLDNN, SgMKLDNNFCProperty);
-
-
+#endif  // MXNET_USE_MKLDNN == 1
+#if MXNET_USE_MKLDNN == 1
 MXNET_REGISTER_SUBGRAPH_BACKEND(MKLDNN_QUANTIZE)
 .set_attr("context", Context::CPU());
 
 MXNET_REGISTER_SUBGRAPH_PROPERTY(MKLDNN_QUANTIZE, SgMKLDNNConvProperty)
 .set_attr("quantize", true);
 
+#endif  // MXNET_USE_MKLDNN == 1
+#if MXNET_USE_MKLDNN == 1
+
 MXNET_REGISTER_SUBGRAPH_PROPERTY(MKLDNN_QUANTIZE, SgMKLDNNFCProperty)
 .set_attr("quantize", true);
-
+#endif  // MXNET_USE_MKLDNN == 1
+#if MXNET_USE_MKLDNN == 1
 MXNET_REGISTER_SUBGRAPH_PROPERTY(MKLDNN_QUANTIZE, SgMKLDNNPostQuantizeProperty);
+#endif  // MXNET_USE_MKLDNN == 1
 
+#if MXNET_USE_MKLDNN == 1
 MXNET_REGISTER_SUBGRAPH_PROPERTY(MKLDNN_QUANTIZE, SgMKLDNNFCPostQuantizeProperty);
 
 MXNET_REGISTER_SUBGRAPH_PROPERTY(MKLDNN_QUANTIZE, SgMKLDNNPostQuantizeAlignScaleProperty);
-
+#endif  // MXNET_USE_MKLDNN == 1
+#if MXNET_USE_MKLDNN == 1
 }  // namespace op
 }  // namespace mxnet
-
 #endif  // MXNET_USE_MKLDNN == 1
diff --git a/src/operator/tensor/elemwise_unary_op_basic.cc b/src/operator/tensor/elemwise_unary_op_basic.cc
index 7c35c44305a8..2a834bb9dc55 100644
--- a/src/operator/tensor/elemwise_unary_op_basic.cc
+++ b/src/operator/tensor/elemwise_unary_op_basic.cc
@@ -217,7 +217,7 @@ static void CopyEx(const nnvm::NodeAttrs& attrs,
     FallBackCompute(UnaryOp::IdentityCompute<cpu>, attrs, ctx, inputs, req, outputs);
     return;
   }
-#endif
+#endif  // MXNET_USE_MKLDNN == 1
   UnaryOp::IdentityComputeEx<cpu>(attrs, ctx, inputs, req, outputs);
 }
 
@@ -238,7 +238,7 @@ static inline bool CopyStorageType(const nnvm::NodeAttrs& attrs,
       && out_attrs->at(0) == kDefaultStorage) {
     *dispatch_mode = DispatchMode::kFComputeEx;
   }
-#endif
+#endif  // MXNET_USE_MKLDNN == 1
   return ret;
 }
 
@@ -253,7 +253,7 @@ MXNET_OPERATOR_REGISTER_UNARY(_copy)
   return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
 })
 .set_attr<bool>("TIsMKLDNN", true)
-#endif
+#endif  // MXNET_USE_MKLDNN == 1
 .set_attr<nnvm::FInplaceIdentity>("FInplaceIdentity",
   [](const NodeAttrs& attrs){
     return std::vector<bool>{true};
@@ -275,7 +275,7 @@ NNVM_REGISTER_OP(_backward_copy)
 .set_attr<bool>("TIsMKLDNN", true)
 .set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& n) {
   return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
-})
+})  // MXNET_USE_MKLDNN == 1
 #endif
 .set_attr<nnvm::FInplaceIdentity>("FInplaceIdentity",
   [](const NodeAttrs& attrs){
diff --git a/src/operator/tensor/matrix_op-inl.h b/src/operator/tensor/matrix_op-inl.h
index 7c3005e583b6..801e4e7126b4 100644
--- a/src/operator/tensor/matrix_op-inl.h
+++ b/src/operator/tensor/matrix_op-inl.h
@@ -459,6 +459,10 @@ struct ExpandDimParam : public dmlc::Parameter<ExpandDimParam> {
               "the input `NDArray`'s dimension is `ndim`, the range of "
               "the inserted axis is `[-ndim, ndim]`");
   }
+
+  bool operator==(const ExpandDimParam &other) const {
+    return this->axis == other.axis;
+  }
 };
 
 
@@ -3040,6 +3044,16 @@ struct hash<mxnet::op::ReshapeParam> {
     return ret;
   }
 };
+
+template<>
+struct hash<mxnet::op::ExpandDimParam> {
+  size_t operator()(const mxnet::op::ExpandDimParam& val) {
+    size_t ret = 0;
+    ret = dmlc::HashCombine(ret, val.axis);
+    return ret;
+  }
+};
+
 }  // namespace std
 
 #endif  // MXNET_OPERATOR_TENSOR_MATRIX_OP_INL_H_
diff --git a/src/operator/tensor/matrix_op.cc b/src/operator/tensor/matrix_op.cc
index 99fba15d47ba..0f63061d7c09 100644
--- a/src/operator/tensor/matrix_op.cc
+++ b/src/operator/tensor/matrix_op.cc
@@ -25,9 +25,11 @@
 // this will be invoked by gcc and compile CPU version
 #include "./matrix_op-inl.h"
 #include "./elemwise_unary_op.h"
+#if MXNET_USE_MKLDNN == 1
 #include "../nn/mkldnn/mkldnn_ops-inl.h"
 #include "../nn/mkldnn/mkldnn_base-inl.h"
 #include "../nn/mkldnn/mkldnn_slice-inl.h"
+#endif
 
 namespace mxnet {
 namespace op {
@@ -114,7 +116,7 @@ static void ReshapeComputeExCPU(const nnvm::NodeAttrs& attrs,
   CHECK_EQ(inputs.size(), 1U);
   CHECK_EQ(outputs.size(), 1U);
   // If inputs are supposed to be in MKLDNN format and
-  // MKLDNNsupport the data type or the shape. Then convert
+  // MKLDNN support the data type or the shape. Then convert
   // it to the output format and shape
   MKLDNNReshapeForward(attrs, ctx, inputs[0], req[0], outputs[0]);
 }
@@ -134,66 +136,42 @@ inline static bool ReshapeStorageType(const nnvm::NodeAttrs& attrs,
 NNVM_REGISTER_OP(Reshape)
 .add_alias("reshape")
 .describe(R"code(Reshapes the input array.
-
 .. note:: ``Reshape`` is deprecated, use ``reshape``
-
 Given an array and a shape, this function returns a copy of the array in the new shape.
 The shape is a tuple of integers such as (2,3,4). The size of the new shape should be same as the size of the input array.
-
 Example::
-
   reshape([1,2,3,4], shape=(2,2)) = [[1,2], [3,4]]
-
 Some dimensions of the shape can take special values from the set {0, -1, -2, -3, -4}. The significance of each is explained below:
-
 - ``0``  copy this dimension from the input to the output shape.
-
   Example::
-
   - input shape = (2,3,4), shape = (4,0,2), output shape = (4,3,2)
   - input shape = (2,3,4), shape = (2,0,0), output shape = (2,3,4)
-
 - ``-1`` infers the dimension of the output shape by using the remainder of the input dimensions
   keeping the size of the new array same as that of the input array.
   At most one dimension of shape can be -1.
-
   Example::
-
   - input shape = (2,3,4), shape = (6,1,-1), output shape = (6,1,4)
   - input shape = (2,3,4), shape = (3,-1,8), output shape = (3,1,8)
   - input shape = (2,3,4), shape=(-1,), output shape = (24,)
-
 - ``-2`` copy all/remainder of the input dimensions to the output shape.
-
   Example::
-
   - input shape = (2,3,4), shape = (-2,), output shape = (2,3,4)
   - input shape = (2,3,4), shape = (2,-2), output shape = (2,3,4)
   - input shape = (2,3,4), shape = (-2,1,1), output shape = (2,3,4,1,1)
-
 - ``-3`` use the product of two consecutive dimensions of the input shape as the output dimension.
-
   Example::
-
   - input shape = (2,3,4), shape = (-3,4), output shape = (6,4)
   - input shape = (2,3,4,5), shape = (-3,-3), output shape = (6,20)
   - input shape = (2,3,4), shape = (0,-3), output shape = (2,12)
   - input shape = (2,3,4), shape = (-3,-2), output shape = (6,4)
-
 - ``-4`` split one dimension of the input into two dimensions passed subsequent to -4 in shape (can contain -1).
-
   Example::
-
   - input shape = (2,3,4), shape = (-4,1,2,-2), output shape =(1,2,3,4)
   - input shape = (2,3,4), shape = (2,-4,-1,3,-2), output shape = (2,1,3,4)
-
 If the argument `reverse` is set to 1, then the special values are inferred from right to left.
-
   Example::
-
   - without reverse=1, for input shape = (10,5,4), shape = (-1,0), output shape would be (40,5)
   - with reverse=1, output shape will be (50,4).
-
 )code" ADD_FILELINE)
 .set_num_inputs(1)
 .set_num_outputs(1)
@@ -230,7 +208,7 @@ static void FlattenEx(const nnvm::NodeAttrs& attrs,
   CHECK_EQ(inputs.size(), 1U);
   CHECK_EQ(outputs.size(), 1U);
   // If inputs are supposed to be in MKLDNN format and
-  // MKLDNNsupport the data type or the shape. Then convert
+  // MKLDNN support the data type or the shape. Then convert
   // it to the output format and shape
   MKLDNNReshapeForward(attrs, ctx, inputs[0], req[0], outputs[0]);
 }
@@ -251,17 +229,12 @@ NNVM_REGISTER_OP(Flatten)
 .add_alias("flatten")
 .add_alias("_npx_batch_flatten")
 .describe(R"code(Flattens the input array into a 2-D array by collapsing the higher dimensions.
-
 .. note:: `Flatten` is deprecated. Use `flatten` instead.
-
 For an input array with shape ``(d1, d2, ..., dk)``, `flatten` operation reshapes
 the input array into an output array of shape ``(d1, d2*...*dk)``.
-
 Note that the behavior of this function is different from numpy.ndarray.flatten,
 which behaves similar to mxnet.ndarray.reshape((-1,)).
-
 Example::
-
     x = [[
         [1,2,3],
         [4,5,6],
@@ -271,10 +244,8 @@ Example::
         [4,5,6],
         [7,8,9]
     ]],
-
     flatten(x) = [[ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.],
        [ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.]]
-
 )code" ADD_FILELINE)
 .set_num_inputs(1)
 .set_num_outputs(1)
@@ -334,30 +305,21 @@ inline static bool TransposeStorageType(const nnvm::NodeAttrs& attrs,
 
 NNVM_REGISTER_OP(transpose)
 .describe(R"code(Permutes the dimensions of an array.
-
 Examples::
-
   x = [[ 1, 2],
        [ 3, 4]]
-
   transpose(x) = [[ 1.,  3.],
                   [ 2.,  4.]]
-
   x = [[[ 1.,  2.],
         [ 3.,  4.]],
-
        [[ 5.,  6.],
         [ 7.,  8.]]]
-
   transpose(x) = [[[ 1.,  5.],
                    [ 3.,  7.]],
-
                   [[ 2.,  6.],
                    [ 4.,  8.]]]
-
   transpose(x, axes=(1,0,2)) = [[[ 1.,  2.],
                                  [ 5.,  6.]],
-
                                 [[ 3.,  4.],
                                  [ 7.,  8.]]]
 )code" ADD_FILELINE)
@@ -395,13 +357,36 @@ Examples::
 .add_arguments(TransposeParam::__FIELDS__());
 
 
+#if MXNET_USE_MKLDNN == 1
+static void ExpandDimEx(const nnvm::NodeAttrs& attrs,
+                        const OpContext& ctx,
+                        const std::vector<NDArray>& inputs,
+                        const std::vector<OpReqType>& req,
+                        const std::vector<NDArray>& outputs) {
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 1U);
+  // If inputs are supposed to be in MKLDNN format and
+  // MKLDNN support the data type or the shape. Then convert
+  // it to the output format and shape
+  MKLDNNReshapeForward(attrs, ctx, inputs[0], req[0], outputs[0]);
+}
+
+inline static bool ExpandDimStorageType(const nnvm::NodeAttrs& attrs,
+                                        const int dev_mask,
+                                        DispatchMode* dispatch_mode,
+                                        std::vector<int>* in_attrs,
+                                        std::vector<int>* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  return MKLDNNStorageType(attrs, dev_mask, true, dispatch_mode, in_attrs, out_attrs);
+}
+#endif
+
 NNVM_REGISTER_OP(expand_dims)
 .add_alias("_npi_expand_dims")
 .describe(R"code(Inserts a new axis of size 1 into the array shape
-
 For example, given ``x`` with shape ``(2,3,4)``, then ``expand_dims(x, axis=1)``
 will return a new array with shape ``(2,1,3,4)``.
-
 )code" ADD_FILELINE)
 .set_num_inputs(1)
 .set_num_outputs(1)
@@ -418,6 +403,14 @@ will return a new array with shape ``(2,1,3,4)``.
   })
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_reshape"})
 .set_attr<FCompute>("FCompute<cpu>", UnaryOp::IdentityCompute<cpu>)
+#if MXNET_USE_MKLDNN == 1
+.set_attr<bool>("TIsMKLDNN", true)
+.set_attr<FComputeEx>("FComputeEx<cpu>", ExpandDimEx)
+.set_attr<FInferStorageType>("FInferStorageType", ExpandDimStorageType)
+.set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& n) {
+  return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+})
+#endif
 .add_argument("data", "NDArray-or-Symbol", "Source input")
 .add_arguments(ExpandDimParam::__FIELDS__());
 
@@ -449,44 +442,33 @@ NNVM_REGISTER_OP(slice)
 MXNET_ADD_SPARSE_OP_ALIAS(slice)
 .add_alias("crop")
 .describe(R"code(Slices a region of the array.
-
 .. note:: ``crop`` is deprecated. Use ``slice`` instead.
-
 This function returns a sliced array between the indices given
 by `begin` and `end` with the corresponding `step`.
-
 For an input array of ``shape=(d_0, d_1, ..., d_n-1)``,
 slice operation with ``begin=(b_0, b_1...b_m-1)``,
 ``end=(e_0, e_1, ..., e_m-1)``, and ``step=(s_0, s_1, ..., s_m-1)``,
 where m <= n, results in an array with the shape
 ``(|e_0-b_0|/|s_0|, ..., |e_m-1-b_m-1|/|s_m-1|, d_m, ..., d_n-1)``.
-
 The resulting array's *k*-th dimension contains elements
 from the *k*-th dimension of the input array starting
 from index ``b_k`` (inclusive) with step ``s_k``
 until reaching ``e_k`` (exclusive).
-
 If the *k*-th elements are `None` in the sequence of `begin`, `end`,
 and `step`, the following rule will be used to set default values.
 If `s_k` is `None`, set `s_k=1`. If `s_k > 0`, set `b_k=0`, `e_k=d_k`;
 else, set `b_k=d_k-1`, `e_k=-1`.
-
 The storage type of ``slice`` output depends on storage types of inputs
-
 - slice(csr) = csr
 - otherwise, ``slice`` generates output with default storage
-
 .. note:: When input data storage type is csr, it only supports
    step=(), or step=(None,), or step=(1,) to generate a csr output.
    For other step parameter values, it falls back to slicing
    a dense tensor.
-
 Example::
-
   x = [[  1.,   2.,   3.,   4.],
        [  5.,   6.,   7.,   8.],
        [  9.,  10.,  11.,  12.]]
-
   slice(x, begin=(0,1), end=(2,4)) = [[ 2.,  3.,  4.],
                                      [ 6.,  7.,  8.]]
   slice(x, begin=(None, 0), end=(None, 3), step=(-1, 2)) = [[9., 11.],
@@ -566,23 +548,17 @@ NNVM_REGISTER_OP(_slice_assign_scalar)
 
 NNVM_REGISTER_OP(slice_axis)
 .describe(R"code(Slices along a given axis.
-
 Returns an array slice along a given `axis` starting from the `begin` index
 to the `end` index.
-
 Examples::
-
   x = [[  1.,   2.,   3.,   4.],
        [  5.,   6.,   7.,   8.],
        [  9.,  10.,  11.,  12.]]
-
   slice_axis(x, axis=0, begin=1, end=3) = [[  5.,   6.,   7.,   8.],
                                            [  9.,  10.,  11.,  12.]]
-
   slice_axis(x, axis=1, begin=0, end=2) = [[  1.,   2.],
                                            [  5.,   6.],
                                            [  9.,  10.]]
-
   slice_axis(x, axis=1, begin=-3, end=-1) = [[  2.,   3.],
                                              [  6.,   7.],
                                              [ 10.,  11.]]
@@ -606,46 +582,31 @@ NNVM_REGISTER_OP(_backward_slice_axis)
 
 NNVM_REGISTER_OP(slice_like)
 .describe(R"code(Slices a region of the array like the shape of another array.
-
 This function is similar to ``slice``, however, the `begin` are always `0`s
 and `end` of specific axes are inferred from the second input `shape_like`.
-
 Given the second `shape_like` input of ``shape=(d_0, d_1, ..., d_n-1)``,
 a ``slice_like`` operator with default empty `axes`, it performs the
 following operation:
-
 `` out = slice(input, begin=(0, 0, ..., 0), end=(d_0, d_1, ..., d_n-1))``.
-
 When `axes` is not empty, it is used to speficy which axes are being sliced.
-
 Given a 4-d input data, ``slice_like`` operator with ``axes=(0, 2, -1)``
 will perform the following operation:
-
 `` out = slice(input, begin=(0, 0, 0, 0), end=(d_0, None, d_2, d_3))``.
-
 Note that it is allowed to have first and second input with different dimensions,
 however, you have to make sure the `axes` are specified and not exceeding the
 dimension limits.
-
 For example, given `input_1` with ``shape=(2,3,4,5)`` and `input_2` with
 ``shape=(1,2,3)``, it is not allowed to use:
-
 `` out = slice_like(a, b)`` because ndim of `input_1` is 4, and ndim of `input_2`
 is 3.
-
 The following is allowed in this situation:
-
 `` out = slice_like(a, b, axes=(0, 2))``
-
 Example::
-
   x = [[  1.,   2.,   3.,   4.],
        [  5.,   6.,   7.,   8.],
        [  9.,  10.,  11.,  12.]]
-
   y = [[  0.,   0.,   0.],
        [  0.,   0.,   0.]]
-
   slice_like(x, y) = [[ 1.,  2.,  3.]
                       [ 5.,  6.,  7.]]
   slice_like(x, y, axes=(0, 1)) = [[ 1.,  2.,  3.]
@@ -691,23 +652,15 @@ NNVM_REGISTER_OP(clip)
 MXNET_ADD_SPARSE_OP_ALIAS(clip)
 .add_alias("_npi_clip")
 .describe(R"code(Clips (limits) the values in an array.
-
 Given an interval, values outside the interval are clipped to the interval edges.
 Clipping ``x`` between `a_min` and `a_max` would be::
-
 .. math::
-
    clip(x, a_min, a_max) = \max(\min(x, a_max), a_min))
-
 Example::
-
     x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
-
     clip(x,1,8) = [ 1.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  8.]
-
 The storage type of ``clip`` output depends on storage types of inputs and the a_min, a_max \
 parameter values:
-
    - clip(default) = default
    - clip(row_sparse, a_min <= 0, a_max >= 0) = row_sparse
    - clip(csr, a_min <= 0, a_max >= 0) = csr
@@ -715,7 +668,6 @@ parameter values:
    - clip(row_sparse, a_min > 0, a_max > 0) = default
    - clip(csr, a_min < 0, a_max < 0) = csr
    - clip(csr, a_min > 0, a_max > 0) = csr
-
 )code" ADD_FILELINE)
 .set_num_inputs(1)
 .set_num_outputs(1)
@@ -769,28 +721,20 @@ NNVM_REGISTER_OP(_backward_clip)
 NNVM_REGISTER_OP(repeat)
 .add_alias("_np_repeat")
 .describe(R"code(Repeats elements of an array.
-
 By default, ``repeat`` flattens the input array into 1-D and then repeats the
 elements::
-
   x = [[ 1, 2],
        [ 3, 4]]
-
   repeat(x, repeats=2) = [ 1.,  1.,  2.,  2.,  3.,  3.,  4.,  4.]
-
 The parameter ``axis`` specifies the axis along which to perform repeat::
-
   repeat(x, repeats=2, axis=1) = [[ 1.,  1.,  2.,  2.],
                                   [ 3.,  3.,  4.,  4.]]
-
   repeat(x, repeats=2, axis=0) = [[ 1.,  2.],
                                   [ 1.,  2.],
                                   [ 3.,  4.],
                                   [ 3.,  4.]]
-
   repeat(x, repeats=2, axis=-1) = [[ 1.,  1.,  2.,  2.],
                                    [ 3.,  3.,  4.,  4.]]
-
 )code" ADD_FILELINE)
 .set_num_outputs(1)
 .set_num_inputs(1)
@@ -820,35 +764,25 @@ NNVM_REGISTER_OP(_backward_repeat)
 NNVM_REGISTER_OP(tile)
 .add_alias("_npi_tile")
 .describe(R"code(Repeats the whole array multiple times.
-
 If ``reps`` has length *d*, and input array has dimension of *n*. There are
 three cases:
-
 - **n=d**. Repeat *i*-th dimension of the input by ``reps[i]`` times::
-
     x = [[1, 2],
          [3, 4]]
-
     tile(x, reps=(2,3)) = [[ 1.,  2.,  1.,  2.,  1.,  2.],
                            [ 3.,  4.,  3.,  4.,  3.,  4.],
                            [ 1.,  2.,  1.,  2.,  1.,  2.],
                            [ 3.,  4.,  3.,  4.,  3.,  4.]]
-
 - **n>d**. ``reps`` is promoted to length *n* by pre-pending 1's to it. Thus for
   an input shape ``(2,3)``, ``repos=(2,)`` is treated as ``(1,2)``::
-
-
     tile(x, reps=(2,)) = [[ 1.,  2.,  1.,  2.],
                           [ 3.,  4.,  3.,  4.]]
-
 - **n<d**. The input is promoted to be d-dimensional by prepending new axes. So a
   shape ``(2,2)`` array is promoted to ``(1,2,2)`` for 3-D replication::
-
     tile(x, reps=(2,2,3)) = [[[ 1.,  2.,  1.,  2.,  1.,  2.],
                               [ 3.,  4.,  3.,  4.,  3.,  4.],
                               [ 1.,  2.,  1.,  2.,  1.,  2.],
                               [ 3.,  4.,  3.,  4.,  3.,  4.]],
-
                              [[ 1.,  2.,  1.,  2.,  1.,  2.],
                               [ 3.,  4.,  3.,  4.,  3.,  4.],
                               [ 1.,  2.,  1.,  2.,  1.,  2.],
@@ -881,17 +815,12 @@ NNVM_REGISTER_OP(_backward_tile)
 
 NNVM_REGISTER_OP(reverse)
 .describe(R"code(Reverses the order of elements along given axis while preserving array shape.
-
 Note: reverse and flip are equivalent. We use reverse in the following examples.
-
 Examples::
-
   x = [[ 0.,  1.,  2.,  3.,  4.],
        [ 5.,  6.,  7.,  8.,  9.]]
-
   reverse(x, axis=0) = [[ 5.,  6.,  7.,  8.,  9.],
                         [ 0.,  1.,  2.,  3.,  4.]]
-
   reverse(x, axis=1) = [[ 4.,  3.,  2.,  1.,  0.],
                         [ 9.,  8.,  7.,  6.,  5.]]
 )code" ADD_FILELINE)
@@ -927,16 +856,12 @@ NNVM_REGISTER_OP(_backward_reverse)
 
 NNVM_REGISTER_OP(stack)
 .describe(R"code(Join a sequence of arrays along a new axis.
-
 The axis parameter specifies the index of the new axis in the dimensions of the
 result. For example, if axis=0 it will be the first dimension and if axis=-1 it
 will be the last dimension.
-
 Examples::
-
   x = [1, 2]
   y = [3, 4]
-
   stack(x, y) = [[1, 2],
                  [3, 4]]
   stack(x, y, axis=1) = [[1, 3],
@@ -979,15 +904,12 @@ NNVM_REGISTER_OP(squeeze)
 .describe(R"code(Remove single-dimensional entries from the shape of an array.
 Same behavior of defining the output tensor shape as numpy.squeeze for the most of cases.
 See the following note for exception.
-
 Examples::
-
   data = [[[0], [1], [2]]]
   squeeze(data) = [0, 1, 2]
   squeeze(data, axis=0) = [[0], [1], [2]]
   squeeze(data, axis=2) = [[0, 1, 2]]
   squeeze(data, axis=(0, 2)) = [0, 1, 2]
-
 .. Note::
   The output of this operator will keep at least one dimension not removed. For example,
   squeeze([[[4]]]) = [4], while in numpy.squeeze, the output will become a scalar.
@@ -1019,20 +941,15 @@ Similar to ONNX DepthToSpace operator:
 https://github.com/onnx/onnx/blob/master/docs/Operators.md#DepthToSpace.
 The output is a new tensor where the values from depth dimension are moved in spatial blocks
 to height and width dimension. The reverse of this operation is ``space_to_depth``.
-
 .. math::
-
     \begin{gather*}
     x \prime = reshape(x, [N, block\_size, block\_size, C / (block\_size ^ 2), H * block\_size, W * block\_size]) \\
     x \prime \prime = transpose(x \prime, [0, 3, 4, 1, 5, 2]) \\
     y = reshape(x \prime \prime, [N, C / (block\_size ^ 2), H * block\_size, W * block\_size])
     \end{gather*}
-
 where :math:`x` is an input tensor with default layout as :math:`[N, C, H, W]`: [batch, channels, height, width]
 and :math:`y` is the output tensor of layout :math:`[N, C / (block\_size ^ 2), H * block\_size, W * block\_size]`
-
 Example::
-
   x = [[[[0, 1, 2],
          [3, 4, 5]],
         [[6, 7, 8],
@@ -1041,7 +958,6 @@ Example::
          [15, 16, 17]],
         [[18, 19, 20],
          [21, 22, 23]]]]
-
   depth_to_space(x, 2) = [[[[0, 6, 1, 7, 2, 8],
                             [12, 18, 13, 19, 14, 20],
                             [3, 9, 4, 10, 5, 11],
@@ -1069,29 +985,21 @@ NNVM_REGISTER_OP(space_to_depth)
 .describe(R"code(Rearranges(permutes) blocks of spatial data into depth.
 Similar to ONNX SpaceToDepth operator:
 https://github.com/onnx/onnx/blob/master/docs/Operators.md#SpaceToDepth
-
 The output is a new tensor where the values from height and width dimension are
 moved to the depth dimension. The reverse of this operation is ``depth_to_space``.
-
 .. math::
-
     \begin{gather*}
     x \prime = reshape(x, [N, C, H / block\_size, block\_size, W / block\_size, block\_size]) \\
     x \prime \prime = transpose(x \prime, [0, 3, 5, 1, 2, 4]) \\
     y = reshape(x \prime \prime, [N, C * (block\_size ^ 2), H / block\_size, W / block\_size])
     \end{gather*}
-
 where :math:`x` is an input tensor with default layout as :math:`[N, C, H, W]`: [batch, channels, height, width]
 and :math:`y` is the output tensor of layout :math:`[N, C * (block\_size ^ 2), H / block\_size, W / block\_size]`
-
 Example::
-
   x = [[[[0, 6, 1, 7, 2, 8],
          [12, 18, 13, 19, 14, 20],
          [3, 9, 4, 10, 5, 11],
          [15, 21, 16, 22, 17, 23]]]]
-
-
   space_to_depth(x, 2) = [[[[0, 1, 2],
                             [3, 4, 5]],
                            [[6, 7, 8],
@@ -1122,9 +1030,7 @@ Example::
 NNVM_REGISTER_OP(_split_v2)
 .add_alias("_npi_split")
 .describe(R"code(Splits an array along a particular axis into multiple sub-arrays.
-
 Example::
-
    x  = [[[ 1.]
           [ 2.]]
          [[ 3.]
@@ -1132,61 +1038,44 @@ Example::
          [[ 5.]
           [ 6.]]]
    x.shape = (3, 2, 1)
-
    y = split_v2(x, axis=1, indices_or_sections=2) // a list of 2 arrays with shape (3, 1, 1)
    y = [[[ 1.]]
         [[ 3.]]
         [[ 5.]]]
-
        [[[ 2.]]
         [[ 4.]]
         [[ 6.]]]
-
    y[0].shape = (3, 1, 1)
-
    z = split_v2(x, axis=0, indices_or_sections=3) // a list of 3 arrays with shape (1, 2, 1)
    z = [[[ 1.]
          [ 2.]]]
-
        [[[ 3.]
          [ 4.]]]
-
        [[[ 5.]
          [ 6.]]]
-
    z[0].shape = (1, 2, 1)
-
    w = split_v2(x, axis=0, indices_or_sections=(1,)) // a list of 2 arrays with shape [(1, 2, 1), (2, 2, 1)]
    w = [[[ 1.]
          [ 2.]]]
-
        [[[3.]
          [4.]]
-
         [[5.]
          [6.]]]
-
   w[0].shape = (1, 2, 1)
   w[1].shape = (2, 2, 1)
-
 `squeeze_axis=True` removes the axis with length 1 from the shapes of the output arrays.
 **Note** that setting `squeeze_axis` to ``1`` removes axis with length 1 only
 along the `axis` which it is split.
 Also `squeeze_axis` can be set to true only if ``input.shape[axis] == indices_or_sections``.
-
 Example::
-
    z = split_v2(x, axis=0, indices_or_sections=3, squeeze_axis=1) // a list of 3 arrays with shape (2, 1)
    z = [[ 1.]
         [ 2.]]
-
        [[ 3.]
         [ 4.]]
-
        [[ 5.]
         [ 6.]]
    z[0].shape = (2, 1)
-
 )code" ADD_FILELINE)
 .set_attr_parser(ParamParser<SplitParam>)
 .set_num_inputs(1)
diff --git a/tests/cpp/include/test_core_op.h b/tests/cpp/include/test_core_op.h
index 87df39a2754d..286496108128 100644
--- a/tests/cpp/include/test_core_op.h
+++ b/tests/cpp/include/test_core_op.h
@@ -79,7 +79,7 @@ class CoreOpExecutor : public test::op::OperatorDataInitializer<DType>
       keys.emplace_back(i_iter->first.c_str());
       values.emplace_back(i_iter->second.c_str());
     }
-    return imperative::ParseAttrs(op, op->num_inputs, count, &keys[0], &values[0]);
+    return imperative::ParseAttrs(op, op->num_inputs, count, keys.data(), values.data());
   }
 
   /*!
diff --git a/tests/cpp/include/test_mkldnn.h b/tests/cpp/include/test_mkldnn.h
index f1682772a14a..1466c992fca9 100644
--- a/tests/cpp/include/test_mkldnn.h
+++ b/tests/cpp/include/test_mkldnn.h
@@ -37,29 +37,29 @@
 
 using namespace mxnet;
 
-inline static mkldnn::memory::primitive_desc GetMemPD(const mxnet::TShape s, int dtype,
-                                               mkldnn::memory::format format) {
+inline static mkldnn::memory::desc GetMemDesc(const mxnet::TShape s, const int dtype,
+                                              const mkldnn::memory::format_tag format_tag) {
   mkldnn::memory::dims dims(s.ndim());
   for (size_t i = 0; i < dims.size(); i++)
     dims[i] = s[i];
-  mkldnn::memory::desc desc{dims, get_mkldnn_type(dtype), format};
-  return mkldnn::memory::primitive_desc(desc, CpuEngine::Get()->get_engine());
+  mkldnn::memory::desc desc{dims, get_mkldnn_type(dtype), format_tag};
+  return desc;
 }
 
-inline static mkldnn::memory::primitive_desc GetExpandedMemPD(
-    mkldnn::memory::primitive_desc pd, float scale, int dim = 0) {
-  CHECK(dim < pd.desc().data.ndims) << "dimension cannot be larger than total dimensions of input";
-  mxnet::TShape s(pd.desc().data.ndims, -1);
-  for (size_t i = 0; i < pd.desc().data.ndims; i++)
-    s[i] = pd.desc().data.dims[i];
-  s[dim] = static_cast<int>(s[dim] * scale);
-  return GetMemPD(s, mshadow::DataType<mshadow::default_real_t>::kFlag,
-                  static_cast<mkldnn::memory::format>(pd.desc().data.format));
+inline static mkldnn::memory::desc GetExpandedMemDesc(
+    mkldnn::memory::desc md, const float scale, const int dim = 0) {
+  CHECK(dim < md.data.ndims) << "dimension cannot be larger than total dimensions of input";
+  mxnet::TShape s(md.data.ndims, -1);
+  for (size_t i = 0; i < md.data.ndims; i++)
+    s[i] = md.data.dims[i];
+  s[dim] = static_cast<int64_t>(s[dim] * scale);
+  return GetMemDesc(s, mshadow::DataType<mshadow::default_real_t>::kFlag,
+                    static_cast<mkldnn::memory::format_tag>(GetDefaultFormat(md)));
 }
 
 struct TestArrayShapes {
   std::vector<mxnet::TShape> shapes;
-  std::vector<mkldnn::memory::primitive_desc> pds;
+  std::vector<mkldnn::memory::desc> mds;
 };
 
 // Init arrays with the default layout.
@@ -78,17 +78,17 @@ inline static void InitDefaultArray(NDArray *arr, bool is_rand = false) {
 
 
 // Init arrays with the specified layout.
-inline static void InitMKLDNNArray(NDArray *arr, const mkldnn::memory::primitive_desc &pd,
-                            bool is_rand = false) {
+inline static void InitMKLDNNArray(NDArray *arr, const mkldnn::memory::desc &desc,
+                                   bool is_rand = false) {
   InitDefaultArray(arr, is_rand);
-  arr->MKLDNNDataReorderAsync(pd);
+  arr->MKLDNNDataReorderAsync(desc);
   arr->WaitToRead();
 }
 
-inline static bool IsSameShape(mkldnn::memory::primitive_desc pd, mxnet::TShape shape) {
-  if (pd.desc().data.ndims != shape.ndim()) return false;
+inline static bool IsSameShape(const mkldnn::memory::desc &desc, const mxnet::TShape &shape) {
+  if (desc.data.ndims != shape.ndim()) return false;
   for (size_t i = 0; i < shape.ndim(); i++)
-    if (pd.desc().data.dims[i] != shape[i]) return false;
+    if (desc.data.dims[i] != shape[i]) return false;
   return true;
 }
 
@@ -97,81 +97,81 @@ inline static bool IsSameShape(mkldnn::memory::primitive_desc pd, mxnet::TShape
 // it's specific for certain array shapes. It covers at least one special format
 // for each of the formats: nchw, oihw, goihw.
 // To test the logic of the code in NDArray, these formats should be enough.
-inline static std::vector<mkldnn::memory::format> GetMKLDNNFormat(size_t num_dims, int dtype) {
+inline static std::vector<mkldnn::memory::format_tag> GetMKLDNNFormat(size_t num_dims, int dtype) {
   if (num_dims == 4) {
     mkldnn::memory::dims data_dims{1, 3, 224, 224};
     mkldnn::memory::desc data_md{data_dims, get_mkldnn_type(dtype),
-                                 mkldnn::memory::format::any};
+                                 mkldnn::memory::format_tag::any};
     mkldnn::memory::dims weight_dims{96, 3, 11, 11};
     mkldnn::memory::desc weight_md{weight_dims, get_mkldnn_type(dtype),
-                                   mkldnn::memory::format::any};
+                                   mkldnn::memory::format_tag::any};
     mkldnn::memory::dims output_dims{1, 96, 54, 54};
     mkldnn::memory::desc out_md{output_dims, get_mkldnn_type(dtype),
-                                mkldnn::memory::format::any};
+                                mkldnn::memory::format_tag::any};
     mkldnn::memory::dims strides{4, 4};
     mkldnn::memory::dims padding{0, 0};
 
     mkldnn::convolution_forward::desc desc(mkldnn::prop_kind::forward_training,
                                            mkldnn::algorithm::convolution_direct,
                                            data_md, weight_md, out_md, strides,
-                                           padding, padding, mkldnn::padding_kind::zero);
+                                           padding, padding);
     mkldnn::convolution_forward::primitive_desc pd(desc, CpuEngine::Get()->get_engine());
-    while (pd.dst_primitive_desc().get_size() != GetMemDescSize(out_md) ||
-           pd.src_primitive_desc().get_size() != GetMemDescSize(data_md) ||
-           pd.weights_primitive_desc().get_size() != GetMemDescSize(weight_md)) {
+    while (pd.dst_desc().get_size() != GetMemDescSize(out_md) ||
+           pd.src_desc().get_size() != GetMemDescSize(data_md) ||
+           pd.weights_desc().get_size() != GetMemDescSize(weight_md)) {
       CHECK(pd.next_impl()) << "No implementation";
     }
 
-    std::vector<mkldnn::memory::format> ret(1);
-    ret[0] = static_cast<mkldnn::memory::format>(pd.dst_primitive_desc().desc().data.format);
-    printf("format: %d \n", ret[0]);
+    std::vector<mkldnn::memory::format_tag> ret(1);
+    ret[0] = static_cast<mkldnn::memory::format_tag>(GetDefaultFormat(pd.dst_desc()));
+    printf("format: %d \n", static_cast<int>(ret[0]));
     return ret;
   } else if (num_dims == 5) {
     mkldnn::memory::dims data_dims{1, 32, 112, 112};
     mkldnn::memory::desc data_md{data_dims, get_mkldnn_type(dtype),
-                                 mkldnn::memory::format::any};
+                                 mkldnn::memory::format_tag::any};
     mkldnn::memory::dims weight_dims{32, 1, 1, 3, 3};
     mkldnn::memory::desc weight_md{weight_dims, get_mkldnn_type(dtype),
-                                   mkldnn::memory::format::any};
+                                   mkldnn::memory::format_tag::any};
     mkldnn::memory::dims output_dims{1, 32, 112, 112};
     mkldnn::memory::desc out_md{output_dims, get_mkldnn_type(dtype),
-                                mkldnn::memory::format::any};
+                                mkldnn::memory::format_tag::any};
     mkldnn::memory::dims strides{1, 1};
     mkldnn::memory::dims padding{1, 1};
 
     mkldnn::convolution_forward::desc desc(mkldnn::prop_kind::forward_training,
                                            mkldnn::algorithm::convolution_direct,
                                            data_md, weight_md, out_md, strides,
-                                           padding, padding, mkldnn::padding_kind::zero);
+                                           padding, padding);
     mkldnn::convolution_forward::primitive_desc pd(desc, CpuEngine::Get()->get_engine());
-    while (pd.dst_primitive_desc().get_size() != GetMemDescSize(out_md) ||
-           pd.src_primitive_desc().get_size() != GetMemDescSize(data_md) ||
-           pd.weights_primitive_desc().get_size() != GetMemDescSize(weight_md)) {
+    while (pd.dst_desc().get_size() != GetMemDescSize(out_md) ||
+           pd.src_desc().get_size() != GetMemDescSize(data_md) ||
+           pd.weights_desc().get_size() != GetMemDescSize(weight_md)) {
       CHECK(pd.next_impl()) << "No implementation";
     }
 
-    std::vector<mkldnn::memory::format> ret(1);
-    ret[0] = static_cast<mkldnn::memory::format>(pd.weights_primitive_desc().desc().data.format);
-    printf("format: %d\n", ret[0]);
+    std::vector<mkldnn::memory::format_tag> ret(1);
+    ret[0] = static_cast<mkldnn::memory::format_tag>(GetDefaultFormat(pd.weights_desc()));
+    printf("format: %d\n", static_cast<int>(ret[0]));
     return ret;
   } else {
-    return std::vector<mkldnn::memory::format>();
+    return std::vector<mkldnn::memory::format_tag>();
   }
 }
 
 inline static TestArrayShapes GetTestArrayShapes(bool spatial_data_format = false) {
   int dtype = mshadow::DataType<mshadow::default_real_t>::kFlag;
   mxnet::ShapeVector shapes;
-  std::vector<mkldnn::memory::primitive_desc> pds;
+  std::vector<mkldnn::memory::desc> mds;
   {
     // 1D
     mxnet::TShape s(1, -1);
     s[0] = 279936;
     shapes.push_back(s);
-    pds.push_back(GetMemPD(s, dtype, mkldnn::memory::format::x));
+    mds.push_back(GetMemDesc(s, dtype, mkldnn::memory::format_tag::x));
     s[0] = 34848;
     shapes.push_back(s);
-    pds.push_back(GetMemPD(s, dtype, mkldnn::memory::format::x));
+    mds.push_back(GetMemDesc(s, dtype, mkldnn::memory::format_tag::x));
   }
   {
     // 2D
@@ -179,27 +179,27 @@ inline static TestArrayShapes GetTestArrayShapes(bool spatial_data_format = fals
     s[0] = 96;
     s[1] = 2916;
     shapes.push_back(s);
-    pds.push_back(GetMemPD(s, dtype, mkldnn::memory::format::nc));
+    mds.push_back(GetMemDesc(s, dtype, mkldnn::memory::format_tag::nc));
     s[0] = 96;
     s[1] = 363;
     shapes.push_back(s);
-    pds.push_back(GetMemPD(s, dtype, mkldnn::memory::format::nc));
+    mds.push_back(GetMemDesc(s, dtype, mkldnn::memory::format_tag::nc));
   }
   {
     // 4D
     mxnet::TShape s1(4, -1);
     s1[0] = 10; s1[1] = 96; s1[2] = 54; s1[3] = 54;
     shapes.push_back(s1);
-    pds.push_back(GetMemPD(s1, dtype, mkldnn::memory::format::nchw));
+    mds.push_back(GetMemDesc(s1, dtype, mkldnn::memory::format_tag::nchw));
 
     mxnet::TShape s2(4, -1);
     s2[0] = 96; s2[1] = 3; s2[2] = 11; s2[3] = 11;
     shapes.push_back(s2);
-    pds.push_back(GetMemPD(s2, dtype, mkldnn::memory::format::oihw));
+    mds.push_back(GetMemDesc(s2, dtype, mkldnn::memory::format_tag::oihw));
 
-    std::vector<mkldnn::memory::format> formats = GetMKLDNNFormat(4, dtype);
+    std::vector<mkldnn::memory::format_tag> formats = GetMKLDNNFormat(4, dtype);
     if (!spatial_data_format) {
-      pds.push_back(GetMemPD(s1, dtype, formats[0]));
+      mds.push_back(GetMemDesc(s1, dtype, formats[0]));
     }
   }
   {
@@ -207,17 +207,17 @@ inline static TestArrayShapes GetTestArrayShapes(bool spatial_data_format = fals
     mxnet::TShape s(5, -1);
     s[0] = 96; s[1] = 1; s[2] = 3; s[3] = 11; s[4] = 11;
     shapes.push_back(s);
-    pds.push_back(GetMemPD(s, dtype, mkldnn::memory::format::goihw));
+    mds.push_back(GetMemDesc(s, dtype, mkldnn::memory::format_tag::goihw));
 
-    std::vector<mkldnn::memory::format> formats = GetMKLDNNFormat(5, dtype);
+    std::vector<mkldnn::memory::format_tag> formats = GetMKLDNNFormat(5, dtype);
     if (!spatial_data_format) {
-      pds.push_back(GetMemPD(s, dtype, formats[0]));
+      mds.push_back(GetMemDesc(s, dtype, formats[0]));
     }
   }
 
   TestArrayShapes ret;
   ret.shapes = shapes;
-  ret.pds = pds;
+  ret.mds = mds;
   return ret;
 }
 
@@ -266,7 +266,7 @@ inline NDArray CreateKernelNDArray(mxnet::TShape kernel, int num_filters, mxnet:
   target_shape[3] = kernel[1];
   int dtype = mshadow::DataType<mshadow::default_real_t>::kFlag;
   NDArray arr(target_shape, Context());
-  auto pd = GetMemPD(target_shape, dtype, mkldnn::memory::format::nchw);
+  auto pd = GetMemDesc(target_shape, dtype, mkldnn::memory::format_tag::nchw);
   InitMKLDNNArray(&arr, pd);
   return arr;
 }
@@ -274,7 +274,7 @@ inline NDArray CreateKernelNDArray(mxnet::TShape kernel, int num_filters, mxnet:
 inline NDArray CreateBiasNDArray(mxnet::TShape target_shape) {
   int dtype = mshadow::DataType<mshadow::default_real_t>::kFlag;
   NDArray arr(target_shape, Context());
-  auto pd = GetMemPD(target_shape, dtype, mkldnn::memory::format::x);
+  auto pd = GetMemDesc(target_shape, dtype, mkldnn::memory::format_tag::x);
   InitMKLDNNArray(&arr, pd);
   return arr;
 }
@@ -333,10 +333,10 @@ inline std::vector<NDArrayAttrs> GetTestInputArrays(
     std::vector<float> scale = {1}, bool spatial_data_format = false) {
   TestArrayShapes tas = GetTestArrayShapes(spatial_data_format);
   std::vector<mxnet::TShape> shapes = tas.shapes;
-  std::vector<mkldnn::memory::primitive_desc> pds = tas.pds;
+  std::vector<mkldnn::memory::desc> mds = tas.mds;
 
   std::vector<NDArrayAttrs> in_arrs;
-  std::string desc;
+  std::string desc_str;
 
   int slice_amount = scale[0];
   for (auto shape : shapes) {
@@ -362,60 +362,60 @@ inline std::vector<NDArrayAttrs> GetTestInputArrays(
     }
 
 
-    for (auto pd : pds) {
+    for (auto md : mds) {
       for (size_t dim = 0; dim < scale.size(); ++dim) {
         // preserve if matching layout else just expand on 0 dim
-        if (shape.ndim() == pd.desc().data.ndims)
-          pd = GetExpandedMemPD(pd, scale[dim], dim);
+        if (shape.ndim() == md.data.ndims)
+          md = GetExpandedMemDesc(md, scale[dim], dim);
         else
-          pd = GetExpandedMemPD(pd, scale[dim]);
+          md = GetExpandedMemDesc(md, scale[dim]);
       }
 
-      if (shape.Size() != pd.get_size() / sizeof(mshadow::default_real_t))
+      if (shape.Size() != md.get_size() / sizeof(mshadow::default_real_t))
         continue;
 
       // Type 2, 3.
       arr = NDArray(shape, Context());
-      if (shape.ndim() == pd.desc().data.ndims && IsSameShape(pd, shape)
+      if (shape.ndim() == md.data.ndims && IsSameShape(md, shape)
           && types & ArrayTypes::MKLDNN) {
-        desc = "MKLDNN NDArray";
-        InitMKLDNNArray(&arr, pd, rand);
-        in_arrs.emplace_back(arr, desc);
-      } else if (shape.ndim() == pd.desc().data.ndims && !IsSameShape(pd, shape)
+        desc_str = "MKLDNN NDArray";
+        InitMKLDNNArray(&arr, md, rand);
+        in_arrs.emplace_back(arr, desc_str);
+      } else if (shape.ndim() == md.data.ndims && !IsSameShape(md, shape)
           && types & ArrayTypes::MKLDNNDiffShape) {
-        desc = "MKLDNN NDArray with different shape";
-        InitMKLDNNArray(&arr, pd, rand);
-        in_arrs.emplace_back(arr, desc);
-      } else if (shape.ndim() != pd.desc().data.ndims && types & ArrayTypes::MKLDNNDiffDim) {
+        desc_str = "MKLDNN NDArray with different shape";
+        InitMKLDNNArray(&arr, md, rand);
+        in_arrs.emplace_back(arr, desc_str);
+      } else if (shape.ndim() != md.data.ndims && types & ArrayTypes::MKLDNNDiffDim) {
         std::stringstream ss;
         ss << "MKLDNN NDArray with different dim " <<
-           shape.ndim() << "/" << pd.desc().data.ndims;
-        desc = ss.str();
-        InitMKLDNNArray(&arr, pd, rand);
-        in_arrs.emplace_back(arr, desc);
+           shape.ndim() << "/" << md.data.ndims;
+        desc_str = ss.str();
+        InitMKLDNNArray(&arr, md, rand);
+        in_arrs.emplace_back(arr, desc_str);
       }
 
 
       // Type 5, 6.
       arr = NDArray(shape, Context());
-      if (shape.ndim() == pd.desc().data.ndims && IsSameShape(pd, shape)
+      if (shape.ndim() == md.data.ndims && IsSameShape(md, shape)
           && types & ArrayTypes::MKLDNNReshaped) {
-        desc = "Reshaped MKLDNN NDArray";
-        InitMKLDNNArray(&arr, pd, rand);
-        in_arrs.emplace_back(arr.Slice(slice_amount, arr.shape()[0] - slice_amount), desc);
-      } else if (shape.ndim() == pd.desc().data.ndims && !IsSameShape(pd, shape)
+        desc_str = "Reshaped MKLDNN NDArray";
+        InitMKLDNNArray(&arr, md, rand);
+        in_arrs.emplace_back(arr.Slice(slice_amount, arr.shape()[0] - slice_amount), desc_str);
+      } else if (shape.ndim() == md.data.ndims && !IsSameShape(md, shape)
           && types & ArrayTypes::MKLDNNReshapedDiffShape) {
-        desc = "Reshaped MKLDNN NDArray with different shape";
-        InitMKLDNNArray(&arr, pd, rand);
-        in_arrs.emplace_back(arr.Slice(slice_amount, arr.shape()[0] - slice_amount), desc);
-      } else if (shape.ndim() != pd.desc().data.ndims
+        desc_str = "Reshaped MKLDNN NDArray with different shape";
+        InitMKLDNNArray(&arr, md, rand);
+        in_arrs.emplace_back(arr.Slice(slice_amount, arr.shape()[0] - slice_amount), desc_str);
+      } else if (shape.ndim() != md.data.ndims
           && types & ArrayTypes::MKLDNNReshapedDiffDim) {
         std::stringstream ss;
         ss << "MKLDNN NDArray with different dim " <<
-           shape.ndim() << "/" << pd.desc().data.ndims;
-        desc = ss.str();
-        InitMKLDNNArray(&arr, pd, rand);
-        in_arrs.emplace_back(arr.Slice(slice_amount, arr.shape()[0] - slice_amount), desc);
+           shape.ndim() << "/" << md.data.ndims;
+        desc_str = ss.str();
+        InitMKLDNNArray(&arr, md, rand);
+        in_arrs.emplace_back(arr.Slice(slice_amount, arr.shape()[0] - slice_amount), desc_str);
       }
     }
   }
@@ -444,7 +444,7 @@ inline std::vector<NDArrayAttrs> GetTestInputArrays(
  */
 inline std::vector<NDArrayAttrs> GetTestOutputArrays(
     const mxnet::TShape &shp,
-    const std::vector<mkldnn::memory::primitive_desc> &pds,
+    const std::vector<mkldnn::memory::desc> &mds,
     std::vector<float>scale = {1}, bool rand = true, int types = ArrayTypes::All) {
   mxnet::TShape shape = shp;
 
@@ -452,7 +452,7 @@ inline std::vector<NDArrayAttrs> GetTestOutputArrays(
     shape[dim] = static_cast<int>(shape[dim] * scale[dim]);
 
   std::vector<NDArrayAttrs> in_arrs;
-  std::string desc;
+  std::string desc_str;
   // Type 1.
   NDArray arr(shape, Context());
 
@@ -500,30 +500,30 @@ inline std::vector<NDArrayAttrs> GetTestOutputArrays(
     in_arrs.emplace_back(arr3.Slice(1, shape[0] + 1), "Reused+Reshaped NDArray");
   }
 
-  for (auto pd : pds) {
-    if (shape.Size() != pd.get_size() / sizeof(mshadow::default_real_t))
+  for (auto md : mds) {
+    if (shape.Size() != md.get_size() / sizeof(mshadow::default_real_t))
       continue;
 
-    if (scale.size() > pd.desc().data.ndims)
+    if (scale.size() > md.data.ndims)
       continue;
 
     for (int dim = 0; dim < scale.size(); dim++)
-      pd = GetExpandedMemPD(pd, scale[dim]);
+      md = GetExpandedMemDesc(md, scale[dim]);
 
     // Type 2, 3.
     arr = NDArray(shape, Context());
-    desc = "MKLDNN NDArray";
-    if (shape.ndim() != pd.desc().data.ndims) {
+    desc_str = "MKLDNN NDArray";
+    if (shape.ndim() != md.data.ndims) {
       std::stringstream ss;
       ss << "MKLDNN NDArray with different memory layout "
-         << shape.ndim() << "/" << pd.desc().data.ndims;
-      desc = ss.str();
+         << shape.ndim() << "/" << md.data.ndims;
+      desc_str = ss.str();
     }
 
-    if ((types & ArrayTypes::MKLDNN && shape.ndim() == pd.desc().data.ndims) ||
-        (types & ArrayTypes::MKLDNNDiffDim && shape.ndim() != pd.desc().data.ndims)) {
-      in_arrs.emplace_back(arr, desc);
-      InitMKLDNNArray(&in_arrs.back().arr, pd, rand);
+    if ((types & ArrayTypes::MKLDNN && shape.ndim() == md.data.ndims) ||
+        (types & ArrayTypes::MKLDNNDiffDim && shape.ndim() != md.data.ndims)) {
+      in_arrs.emplace_back(arr, desc_str);
+      InitMKLDNNArray(&in_arrs.back().arr, md, rand);
     }
 
     // Type 8, 9.
@@ -532,18 +532,18 @@ inline std::vector<NDArrayAttrs> GetTestOutputArrays(
     s[0] = shape.Size();
     NDArray arr = NDArray(s, Context());
     arr = arr.AsArray(shape, arr.dtype());
-    InitMKLDNNArray(&arr, pd, rand);
-    desc = "Reused MKLDNN NDArray";
-    if (shape.ndim() != pd.desc().data.ndims) {
+    InitMKLDNNArray(&arr, md, rand);
+    desc_str = "Reused MKLDNN NDArray";
+    if (shape.ndim() != md.data.ndims) {
       std::stringstream ss;
       ss << "Reused MKLDNN NDArray with different memory layout "
-         << shape.ndim() << "/" << pd.desc().data.ndims;
-      desc = ss.str();
+         << shape.ndim() << "/" << md.data.ndims;
+      desc_str = ss.str();
     }
 
-    if ((types & ArrayTypes::MKLDNNReused && shape.ndim() == pd.desc().data.ndims) ||
-        (types & ArrayTypes::MKLDNNReusedDiffDim && shape.ndim() != pd.desc().data.ndims)) {
-      in_arrs.emplace_back(arr, desc);
+    if ((types & ArrayTypes::MKLDNNReused && shape.ndim() == md.data.ndims) ||
+        (types & ArrayTypes::MKLDNNReusedDiffDim && shape.ndim() != md.data.ndims)) {
+      in_arrs.emplace_back(arr, desc_str);
     }
   }
   return in_arrs;
@@ -581,9 +581,9 @@ using VerifyFunc = std::function<void (const std::vector<NDArray *> &in_arrs,
                                        const std::vector<NDArray *> &out_arrs)>;
 
 inline void VerifyAddRequest(const std::vector<NDArray*> &in_arrs,
-                      const std::vector<NDArray*> &original_outputs,
-                      const std::vector<NDArray*> &new_outputs,
-                      VerifyFunc verify_fn) {
+                             const std::vector<NDArray*> &original_outputs,
+                             const std::vector<NDArray*> &new_outputs,
+                             VerifyFunc verify_fn) {
   CHECK(original_outputs.size() == new_outputs.size());
   std::vector<NDArray*> tmp_outputs;
   NDArray tmp;
@@ -596,7 +596,7 @@ inline void VerifyAddRequest(const std::vector<NDArray*> &in_arrs,
 }
 
 inline void VerifyCopyResult(const std::vector<NDArray *> &in_arrs,
-                      const std::vector<NDArray *> &out_arrs) {
+                             const std::vector<NDArray *> &out_arrs) {
   NDArray tmp1 = in_arrs[0]->Reorder2Default();
   NDArray tmp2 = out_arrs[0]->Reorder2Default();
   EXPECT_EQ(tmp1.shape().Size(), tmp2.shape().Size());
@@ -607,7 +607,7 @@ inline void VerifyCopyResult(const std::vector<NDArray *> &in_arrs,
 }
 
 inline void VerifySumResult(const std::vector<NDArray *> &in_arrs,
-                     const std::vector<NDArray *> &out_arrs) {
+                            const std::vector<NDArray *> &out_arrs) {
   NDArray in1 = in_arrs[0]->Reorder2Default();
   NDArray in2 = in_arrs[1]->Reorder2Default();
   NDArray out = out_arrs[0]->Reorder2Default();
@@ -621,5 +621,5 @@ inline void VerifySumResult(const std::vector<NDArray *> &in_arrs,
     ASSERT_EQ(d1[i] + d2[i], o[i]);
 }
 
-#endif  // MXNET_USE_MKLDNN
+#endif  // MXNET_USE_MKLDNN == 1
 #endif  // TEST_MKLDNN_H_
diff --git a/tests/cpp/include/test_op.h b/tests/cpp/include/test_op.h
index 67d98c4457e1..172c162e6f15 100644
--- a/tests/cpp/include/test_op.h
+++ b/tests/cpp/include/test_op.h
@@ -153,9 +153,9 @@ struct OpInfo {
   /*! \brief The operator data */
   std::shared_ptr< OperatorExecutor > executor_;
   /*! \brief The operator prop class */
-  std::shared_ptr<OperatorProp>                         prop_;
+  std::shared_ptr<OperatorProp> prop_;
   /*! \brief The input type(s) */
-  std::vector<int>                                      in_type_;
+  std::vector<int> in_type_;
 };
 
 /*! \brief Pair of op info objects, generally for validating ops against each other */
diff --git a/tests/cpp/kvstore/gpu_topology_test.cc b/tests/cpp/kvstore/gpu_topology_test.cc
index 29afb16bdc5b..d26894c21ea7 100644
--- a/tests/cpp/kvstore/gpu_topology_test.cc
+++ b/tests/cpp/kvstore/gpu_topology_test.cc
@@ -23,6 +23,8 @@
  * \brief gpu topology tests
 */
 
+#if MXNET_USE_CUDA
+
 #include <gtest/gtest.h>
 #include <mxnet/base.h>
 #include <mxnet/kvstore.h>
@@ -670,3 +672,5 @@ TEST(GpuTopology, TestKernighanLin2) {
            << " not equal neither: " << 0
            << " nor: "               << P.size() << ".";
 }
+
+#endif  // MXNET_USE_CUDA
diff --git a/tests/cpp/operator/batchnorm_test.cc b/tests/cpp/operator/batchnorm_test.cc
index ed0e70b831f1..74c2b546f161 100644
--- a/tests/cpp/operator/batchnorm_test.cc
+++ b/tests/cpp/operator/batchnorm_test.cc
@@ -710,12 +710,12 @@ static constexpr size_t CYCLE_COUNT = 3;
 
 template<typename OperatorProp1, typename OperatorProp2, typename OperatorExecutor>
 static test::op::OpInfoPair<OperatorProp1, OperatorProp2, OperatorExecutor> testForwardAndBackward(
-  const bool isGPU1,
-  const bool isGPU2,
-  const mxnet::TShape &inputShape,
-  const test::op::kwargs_t& kwargs,
-  const size_t count = 1,
-  const size_t cycleCount = CYCLE_COUNT) {
+    const bool isGPU1,
+    const bool isGPU2,
+    const mxnet::TShape &inputShape,
+    const test::op::kwargs_t& kwargs,
+    const size_t count = 1,
+    const size_t cycleCount = CYCLE_COUNT) {
   test::op::OpInfo<OperatorProp1, OperatorExecutor> info_1 =
     TestBatchNormOperatorForward<OperatorProp1, OperatorExecutor>(isGPU1, inputShape,
                                                                   kwargs, count);
@@ -1014,14 +1014,14 @@ TEST(BATCH_NORM, TestTiming_2D) {
   }
 MSHADOW_REAL_TYPE_SWITCH_EX(
   mshadow::kFloat32, DType, AccReal, {
-#if MXNET_USE_MKLDNN
+#if MXNET_USE_MKLDNN == 1
   // MKL
   timingTest<BatchNormCoreOpProp, BNOperatorExecutor<DType, AccReal>>(
     "MKL BatchNormProp<cpu> 2D",
     false, false,
     blank_kwargs_nocudnn,
     2, THISCOUNT);
-#endif
+#endif  // MXNET_USE_MKLDNN == 1
   // CPU
   test::ScopeSet<volatile bool> disableMKL(&mxnet::op::batchnorm::disable_mkl, true);
   timingTest<BatchNormCoreOpProp, BNOperatorExecutor<DType, AccReal>>(
diff --git a/tests/cpp/operator/mkldnn_operator_test.cc b/tests/cpp/operator/mkldnn_operator_test.cc
index 961785dcfc87..8ae1db6c7712 100644
--- a/tests/cpp/operator/mkldnn_operator_test.cc
+++ b/tests/cpp/operator/mkldnn_operator_test.cc
@@ -458,7 +458,7 @@ void VerifyConcatResult(const std::vector<NDArray *> &in_arrs,
 }
 
 void VerifyConcatBackwardsResult(const std::vector<NDArray *> &in_arrs,
-                        const std::vector<NDArray *> &out_arrs) {
+                                 const std::vector<NDArray *> &out_arrs) {
   // in_arrs is larger array, out_arr is ammler
   int num_inputs = out_arrs.size();
   int input_size = out_arrs[0]->shape().Size();
@@ -491,7 +491,7 @@ void TestOp(const OpAttrs &attrs, VerifyFunc verify_fn) {
   std::vector<DispatchMode> dispatches = attrs.dispatches;
 
   TestArrayShapes tas = GetTestArrayShapes();
-  std::vector<mkldnn::memory::primitive_desc> pds = tas.pds;
+  std::vector<mkldnn::memory::desc> mds = tas.mds;
 
   if (attrs.requests.find(OpReqType::kWriteTo) != attrs.requests.end()) {
     std::vector<NDArrayAttrs> in_arrs = GetTestInputArrays();
@@ -499,7 +499,7 @@ void TestOp(const OpAttrs &attrs, VerifyFunc verify_fn) {
       for (auto &dispatch : dispatches) {
         std::vector<std::vector<NDArrayAttrs>> out_arrs(attrs.num_outputs);
         for (int i = 0; i < attrs.num_outputs; i++)
-          out_arrs[i] = GetTestOutputArrays(in_arr.arr.shape(), pds);
+          out_arrs[i] = GetTestOutputArrays(in_arr.arr.shape(), mds);
         for (int i = 0; i < attrs.num_inputs; i++)
           inputs[i] = &in_arr.arr;
         for (size_t output_i = 0; output_i < out_arrs[0].size(); output_i++) {
@@ -549,7 +549,7 @@ void TestOp(const OpAttrs &attrs, VerifyFunc verify_fn) {
     for (auto &in_arr : in_arrs) {
       for (auto &dispatch : dispatches) {
         for (int i = 0; i < attrs.num_outputs; i++)
-          out_arrs[i] = GetTestOutputArrays(in_arr.arr.shape(), pds);
+          out_arrs[i] = GetTestOutputArrays(in_arr.arr.shape(), mds);
         for (size_t i = 0; i < attrs.num_inputs; i++)
           inputs[i] = &in_arr.arr;
         for (size_t output_i = 0; output_i < out_arrs[0].size(); output_i++) {
@@ -573,14 +573,14 @@ void TestOp(const OpAttrs &attrs, VerifyFunc verify_fn) {
 }
 
 void TestConcatOp(const OpAttrs &attrs, VerifyFunc verify_fn,
-            bool backwards = false) {
+                  bool backwards = false) {
   std::vector<NDArray*> inputs(attrs.num_inputs);
   std::vector<NDArray*> outputs(attrs.num_outputs);
   std::vector<OpReqType> req(attrs.num_outputs);
   std::vector<DispatchMode> dispatches = attrs.dispatches;
 
   TestArrayShapes tas = GetTestArrayShapes();
-  std::vector<mkldnn::memory::primitive_desc> pds = tas.pds;
+  std::vector<mkldnn::memory::desc> mds = tas.mds;
 
   std::vector<NDArrayAttrs> in_arrs = GetTestInputArrays();
 
@@ -611,7 +611,7 @@ void TestConcatOp(const OpAttrs &attrs, VerifyFunc verify_fn,
         scale_vector[i] = 1;
       scale_vector[dim] = scale;
       for (int i = 0; i < attrs.num_outputs; i++)
-        out_arrs[i] = GetTestOutputArrays(in_arr.arr.shape(), pds, scale_vector);
+        out_arrs[i] = GetTestOutputArrays(in_arr.arr.shape(), mds, scale_vector);
 
       for (int i = 0; i < attrs.num_inputs; i++)
         inputs[i] = &in_arr.arr;
@@ -678,7 +678,7 @@ void TestOpEx(const OpAttrs &forward_attrs, const OpAttrs &backwards_attrs) {
   std::vector<OpReqType> req(forward_attrs.num_outputs);
 
   TestArrayShapes tas = GetTestArrayShapes();
-  std::vector<mkldnn::memory::primitive_desc> pds = tas.pds;
+  std::vector<mkldnn::memory::desc> mds = tas.mds;
 
   std::vector<NDArrayAttrs> in_arrs = GetTestInputArrays(forward_attrs.input_types, true);
   std::vector<std::vector<NDArrayAttrs>> out_arrs(forward_attrs.num_outputs);
@@ -695,9 +695,9 @@ void TestOpEx(const OpAttrs &forward_attrs, const OpAttrs &backwards_attrs) {
 
       for (int i = 0; i < forward_attrs.num_outputs; i++) {
         out_arrs[i] =
-            GetTestOutputArrays(in_arr.arr.shape(), pds, {1}, forward_attrs.output_types);
+            GetTestOutputArrays(in_arr.arr.shape(), mds, {1}, forward_attrs.output_types);
         ex_out_arrs[i] =
-            GetTestOutputArrays(in_arr.arr.shape(), pds, {1}, forward_attrs.output_types);
+            GetTestOutputArrays(in_arr.arr.shape(), mds, {1}, forward_attrs.output_types);
       }
 
       for (int i = 0; i < forward_attrs.num_inputs; i++)
@@ -806,7 +806,7 @@ void TestOpExBNBackward(const OpAttrs &forward_attrs,
         Context(), backwards_attrs.attrs, backwards_input, backwards_ex_outputs,
         backwards_req, DispatchMode::kFComputeEx, mxnet::OpStatePtr());
     Engine::Get()->WaitForAll();
-    AssertEqual(backwards_outputs, backwards_ex_outputs);
+    AssertEqual(backwards_outputs, backwards_ex_outputs, 1e-4, 1e-2);
   }
 }
 
@@ -821,7 +821,7 @@ void TestOpExBN(const OpAttrs &forward_attrs, const OpAttrs &backwards_attrs) {
   std::vector<OpReqType> req(forward_attrs.num_outputs);
 
   TestArrayShapes tas = GetTestArrayShapes();
-  std::vector<mkldnn::memory::primitive_desc> pds = tas.pds;
+  std::vector<mkldnn::memory::desc> mds = tas.mds;
 
   std::vector<NDArrayAttrs> in_arrs = GetTestInputArrays(forward_attrs.input_types, false);
   std::vector<std::vector<NDArrayAttrs>> out_arrs(forward_attrs.num_outputs);
@@ -837,9 +837,9 @@ void TestOpExBN(const OpAttrs &forward_attrs, const OpAttrs &backwards_attrs) {
         continue;
       for (int i = 0; i < forward_attrs.num_outputs; i++) {
         out_arrs[i] =
-            GetTestOutputArrays(in_arr.arr.shape(), pds, {1}, true, forward_attrs.output_types);
+            GetTestOutputArrays(in_arr.arr.shape(), mds, {1}, true, forward_attrs.output_types);
         ex_out_arrs[i] =
-            GetTestOutputArrays(in_arr.arr.shape(), pds, {1}, true, forward_attrs.output_types);
+            GetTestOutputArrays(in_arr.arr.shape(), mds, {1}, true, forward_attrs.output_types);
       }
       for (size_t output_i = 0; output_i < out_arrs[0].size(); output_i++) {
         inputs_buffer.clear();
@@ -867,11 +867,11 @@ void TestOpExBN(const OpAttrs &forward_attrs, const OpAttrs &backwards_attrs) {
             Context(), forward_attrs.attrs, inputs2, ex_outputs, req,
             DispatchMode::kFComputeEx, mxnet::OpStatePtr());
         Engine::Get()->WaitForAll();
-        AssertEqual(outputs, ex_outputs);
+        AssertEqual(outputs, ex_outputs, 1e-04, 1e-02);
 
         if (!backwards_attrs.requests.empty()) {
           TestOpExBNBackward(forward_attrs, backwards_attrs, OpReqType::kWriteTo,
-                           inputs, outputs, in_arr, &out_arrs[0][output_i]);
+                             inputs, outputs, in_arr, &out_arrs[0][output_i]);
         }
       }
     }
@@ -900,7 +900,7 @@ void TestFullyConnectedOp(const OpAttrs &forward_attrs, const OpAttrs &backwards
   std::vector<OpReqType> back_req(backwards_attrs.num_outputs);
 
   TestArrayShapes tas = GetTestArrayShapes();
-  std::vector<mkldnn::memory::primitive_desc> pds = tas.pds;
+  std::vector<mkldnn::memory::desc> mds = tas.mds;
 
   std::vector<NDArrayAttrs> in_arrs = GetTestInputArrays(forward_attrs.input_types, true);
   std::vector<std::vector<NDArrayAttrs>> out_arrs(forward_attrs.num_outputs);
@@ -937,9 +937,9 @@ void TestFullyConnectedOp(const OpAttrs &forward_attrs, const OpAttrs &backwards
 
       for (int i = 0; i < forward_attrs.num_outputs; i++) {
         out_arrs[i] =
-            GetTestOutputArrays(out_shape, pds, {1}, forward_attrs.output_types);
+            GetTestOutputArrays(out_shape, mds, {1}, forward_attrs.output_types);
         ex_out_arrs[i] =
-            GetTestOutputArrays(out_shape, pds, {1}, forward_attrs.output_types);
+            GetTestOutputArrays(out_shape, mds, {1}, forward_attrs.output_types);
       }
 
       for (size_t output_i = 0; output_i < out_arrs[0].size(); output_i++) {
@@ -1014,7 +1014,7 @@ void TestConvOp(const OpAttrs &forward_attrs, const OpAttrs &backwards_attrs,
   std::vector<DispatchMode> dispatches = forward_attrs.dispatches;
 
   TestArrayShapes tas = GetTestArrayShapes();
-  std::vector<mkldnn::memory::primitive_desc> pds = tas.pds;
+  std::vector<mkldnn::memory::desc> mds = tas.mds;
 
   P param;
   param.Init(forward_attrs.attrs.dict);
@@ -1050,9 +1050,9 @@ void TestConvOp(const OpAttrs &forward_attrs, const OpAttrs &backwards_attrs,
     scale_vector[3] = scale;
 
     for (size_t i = 0; i < forward_attrs.num_outputs; ++i) {
-      out_arrs[i] = GetTestOutputArrays(in_arr.arr.shape(), pds,
+      out_arrs[i] = GetTestOutputArrays(in_arr.arr.shape(), mds,
                                         scale_vector, true, forward_attrs.output_types);
-      ex_out_arrs[i] = GetTestOutputArrays(in_arr.arr.shape(), pds,
+      ex_out_arrs[i] = GetTestOutputArrays(in_arr.arr.shape(), mds,
                                            scale_vector, true, forward_attrs.output_types);
     }
     NDArray ndkernel = CreateKernelNDArray(kernel, num_filter, in_arr.arr.shape(), is_deconv);
@@ -1140,7 +1140,7 @@ void TestPoolingOp(const OpAttrs &forward_attrs, const OpAttrs &backwards_attrs)
   std::vector<DispatchMode> dispatches = forward_attrs.dispatches;
 
   TestArrayShapes tas = GetTestArrayShapes();
-  std::vector<mkldnn::memory::primitive_desc> pds = tas.pds;
+  std::vector<mkldnn::memory::desc> mds = tas.mds;
 
   mxnet::op::PoolingParam param;
   param.Init(forward_attrs.attrs.dict);
@@ -1160,7 +1160,7 @@ void TestPoolingOp(const OpAttrs &forward_attrs, const OpAttrs &backwards_attrs)
     if (input_shape.ndim() != kernel.ndim() + 2)
       continue;
     // cannot pool if ndarray and mkldnn memory have different ndim
-    if (in_arr.arr.IsView() || in_arr.arr.GetMKLDNNData()->get_primitive_desc().desc().data.ndims
+    if (in_arr.arr.IsView() || in_arr.arr.GetMKLDNNData()->get_desc().data.ndims
         != in_arr.arr.shape().ndim())
       continue;
     std::vector<float> scale_vector(in_arr.arr.shape().ndim());
@@ -1173,8 +1173,8 @@ void TestPoolingOp(const OpAttrs &forward_attrs, const OpAttrs &backwards_attrs)
             static_cast<float>(input_shape[i]);
     }
     for (int i = 0; i < forward_attrs.num_outputs; i++) {
-      out_arrs[i] = GetTestOutputArrays(in_arr.arr.shape(), pds, scale_vector);
-      ex_out_arrs[i] = GetTestOutputArrays(in_arr.arr.shape(), pds, scale_vector);
+      out_arrs[i] = GetTestOutputArrays(in_arr.arr.shape(), mds, scale_vector);
+      ex_out_arrs[i] = GetTestOutputArrays(in_arr.arr.shape(), mds, scale_vector);
     }
 
     for (int i = 0; i < forward_attrs.num_inputs; i++)
@@ -1353,4 +1353,4 @@ TEST(IMPERATIVE, BNOp) {
   TestOpExBN(forward_attrs, backwards_attrs);
 }
 
-#endif
+#endif  // MXNET_USE_MKLDNN == 1
diff --git a/tests/cpp/operator/mkldnn_test.cc b/tests/cpp/operator/mkldnn_test.cc
index ab624e3a3c44..bcdb38ac4aa8 100644
--- a/tests/cpp/operator/mkldnn_test.cc
+++ b/tests/cpp/operator/mkldnn_test.cc
@@ -88,10 +88,10 @@ TEST(MKLDNN_UTIL_FUNC, AlignMem) {
 }
 
 static void VerifyDefMem(const mkldnn::memory &mem) {
-  mkldnn::memory::primitive_desc pd = mem.get_primitive_desc();
+  mkldnn::memory::desc desc = mem.get_desc();
   mshadow::default_real_t *data
       = static_cast<mshadow::default_real_t *>(mem.get_data_handle());
-  size_t size = pd.get_size() / sizeof(mshadow::default_real_t);
+  size_t size = desc.get_size() / sizeof(mshadow::default_real_t);
   size_t num_same = 0;
   for (int i = 0; i < size; i++)
     num_same += data[i] == static_cast<mshadow::default_real_t>(i % 100 - 50);
@@ -100,29 +100,30 @@ static void VerifyDefMem(const mkldnn::memory &mem) {
 
 TEST(MKLDNN_UTIL_FUNC, MemFormat) {
   // Check whether the number of format is correct.
-  CHECK_EQ(mkldnn_format_last, 158);
-  CHECK_EQ(mkldnn_nchw, 7);
-  CHECK_EQ(mkldnn_oihw, 17);
+  CHECK_EQ(mkldnn_format_tag_last, 131);
+  CHECK_EQ(mkldnn_nchw, 5);
+  CHECK_EQ(mkldnn_oihw, 5);
 }
 
 static void VerifyMem(const mkldnn::memory &mem) {
-  mkldnn::memory::primitive_desc pd = mem.get_primitive_desc();
+  mkldnn::memory::desc desc = mem.get_desc();
+  mkldnn::memory::dims dims(desc.data.ndims);
+  for (size_t i = 0; i < dims.size(); i++)
+    dims[i] = desc.data.dims[i];
+  mkldnn::memory::desc new_desc{dims,
+      static_cast<mkldnn::memory::data_type>(desc.data.data_type),
+      static_cast<mkldnn::memory::format_tag>(GetDefaultFormat(desc))};
 
-  if (pd.desc().data.format == GetDefaultFormat(pd.desc())) {
+  if (desc == new_desc) {
     VerifyDefMem(mem);
   } else {
-    mkldnn::memory::dims dims(pd.desc().data.ndims);
-    for (size_t i = 0; i < dims.size(); i++)
-      dims[i] = pd.desc().data.dims[i];
-    mkldnn::memory::desc desc{dims,
-                              static_cast<mkldnn::memory::data_type>(pd.desc().data.data_type),
-                              static_cast<mkldnn::memory::format>(GetDefaultFormat(pd.desc()))};
-    mkldnn::memory::primitive_desc new_pd(desc, CpuEngine::Get()->get_engine());
-    mkldnn::memory new_mem(new_pd);
-
-    std::vector<mkldnn::primitive> net;
-    net.push_back(mkldnn::reorder(mem, new_mem));
-    mkldnn::stream(mkldnn::stream::kind::eager).submit(net).wait();
+    mkldnn::memory* src_mem = const_cast<mkldnn::memory*>(&mem);
+    mkldnn::memory new_mem(new_desc, CpuEngine::Get()->get_engine());
+
+    mkldnn::stream s(CpuEngine::Get()->get_engine());
+    mkldnn::reorder(*src_mem, new_mem)
+        .execute(s, *src_mem, new_mem);
+
     VerifyDefMem(new_mem);
   }
 }
@@ -130,23 +131,23 @@ static void VerifyMem(const mkldnn::memory &mem) {
 TEST(MKLDNN_NDArray, GetDataReorder) {
   TestArrayShapes tas = GetTestArrayShapes();
   mxnet::ShapeVector shapes = tas.shapes;
-  std::vector<mkldnn::memory::primitive_desc> pds = tas.pds;
+  std::vector<mkldnn::memory::desc> mds = tas.mds;
 
 
   // Reorder from the default to any other layout.
   for (auto s : shapes) {
     NDArray arr(s, Context());
     InitDefaultArray(&arr);
-    for (auto pd : pds) {
-      if (s.Size() == pd.get_size() / sizeof(mshadow::default_real_t)) {
-        const mkldnn::memory *mem = arr.GetMKLDNNDataReorder(pd);
+    for (auto md : mds) {
+      if (s.Size() == md.get_size() / sizeof(mshadow::default_real_t)) {
+        const mkldnn::memory *mem = arr.GetMKLDNNDataReorder(md);
         printf("reorder from (");
         for (size_t i = 0; i < s.ndim(); i++)
           printf("%ld, ", s[i]);
         printf(") to (");
-        for (int i = 0; i < pd.desc().data.ndims; i++)
-          printf("%d, ", pd.desc().data.dims[i]);
-        printf("), format: %d\n", pd.desc().data.format);
+        for (int i = 0; i < md.data.ndims; i++)
+          printf("%ld, ", md.data.dims[i]);
+        printf("), format: %d\n", static_cast<int>(GetDefaultFormat(md)));
         MKLDNNStream::Get()->Submit(false);
         VerifyMem(*mem);
         MKLDNNStream::Get()->Cleanup();
@@ -156,8 +157,8 @@ TEST(MKLDNN_NDArray, GetDataReorder) {
 
   // Reorder from a special layout to another layout.
   for (auto s : shapes) {
-    for (auto from_pd : pds) {
-      if (from_pd.get_size() / sizeof(mshadow::default_real_t) == s.Size()) {
+    for (auto md : mds) {
+      if (md.get_size() / sizeof(mshadow::default_real_t) == s.Size()) {
         NDArray arr(s, Context());
         // There is possibility that the dimensions of an NDArray doesn't match
         // with the MKLDNN memory inside.
@@ -165,21 +166,20 @@ TEST(MKLDNN_NDArray, GetDataReorder) {
         for (size_t i = 0; i < s.ndim(); i++)
           printf("%ld, ", s[i]);
         printf(") with MKLDNN memory (");
-        for (int i = 0; i < from_pd.desc().data.ndims; i++)
-          printf("%d, ", from_pd.desc().data.dims[i]);
-        printf("), format: %d\n", from_pd.desc().data.format);
-        InitMKLDNNArray(&arr, from_pd);
-        for (auto to_pd : pds) {
-          if (to_pd.get_size() / sizeof(mshadow::default_real_t) == s.Size()) {
-            const mkldnn::memory *mem = arr.GetMKLDNNDataReorder(to_pd);
+        for (int i = 0; i < md.data.ndims; i++)
+          printf("%ld, ", md.data.dims[i]);
+        printf("), format: %d\n", static_cast<int>(GetDefaultFormat(md)));
+        InitMKLDNNArray(&arr, md);
+        for (auto to_md : mds) {
+          if (to_md.get_size() / sizeof(mshadow::default_real_t) == s.Size()) {
+            const mkldnn::memory *mem = arr.GetMKLDNNDataReorder(to_md);
             printf("reorder from (");
             for (size_t i = 0; i < s.ndim(); i++)
               printf("%ld, ", s[i]);
-            printf("), format: %d to (",
-                   arr.GetMKLDNNData()->get_primitive_desc().desc().data.format);
-            for (int i = 0; i < to_pd.desc().data.ndims; i++)
-              printf("%d, ", to_pd.desc().data.dims[i]);
-            printf("), format: %d\n", to_pd.desc().data.format);
+            printf("), format: %d to (", static_cast<int>(GetDefaultFormat(to_md)));
+            for (int i = 0; i < to_md.data.ndims; i++)
+              printf("%ld, ", to_md.data.dims[i]);
+            printf("), format: %d\n", static_cast<int>(GetDefaultFormat(to_md)));
             MKLDNNStream::Get()->Submit(false);
             VerifyMem(*mem);
             MKLDNNStream::Get()->Cleanup();
@@ -194,7 +194,7 @@ TEST(MKLDNN_BASE, MKLDNNSum) {
   std::vector<NDArrayAttrs> in_arrs = GetTestInputArrays();
   std::vector<NDArrayAttrs> in_arrs2 = GetTestInputArrays(ArrayTypes::All, true);
   TestArrayShapes tas = GetTestArrayShapes();
-  std::vector<mkldnn::memory::primitive_desc> pds = tas.pds;
+  std::vector<mkldnn::memory::desc> mds = tas.mds;
 
   for (int i = 0; i < in_arrs.size(); i++) {
     auto in_arr = in_arrs[i];
@@ -204,7 +204,7 @@ TEST(MKLDNN_BASE, MKLDNNSum) {
     if (in_arr.arr.IsMKLDNNData() && in_arr.arr.IsView()) {
       continue;
     }
-    std::vector<NDArrayAttrs> out_arrs = GetTestOutputArrays(in_arr.arr.shape(), pds);
+    std::vector<NDArrayAttrs> out_arrs = GetTestOutputArrays(in_arr.arr.shape(), mds);
     for (auto &out_arr : out_arrs) {
       auto in_mem1 = in_arr.arr.GetMKLDNNData();
       auto in_mem2 = in_arr2.arr.GetMKLDNNData();
@@ -232,7 +232,7 @@ TEST(MKLDNN_BASE, MKLDNNSum) {
     NDArrayAttrs orig_arr(in_arr.arr.Copy(in_arr.arr.ctx()), "In Place Copy");
     orig_arr.arr.WaitToRead();
     PrintVerifyMsg(orig_arr, in_arr);
-    InitMKLDNNArray(&orig_arr.arr, input_mem->get_primitive_desc());
+    InitMKLDNNArray(&orig_arr.arr, input_mem->get_desc());
     orig_arr.arr.CopyFrom(*input_mem);
     op::MKLDNNSum(*input_mem, *input_mem2, *input_mem);
     MKLDNNStream::Get()->Submit();
@@ -244,7 +244,7 @@ TEST(MKLDNN_BASE, CreateMKLDNNMem) {
   std::vector<NDArrayAttrs> in_arrs = GetTestInputArrays();
   std::vector<NDArrayAttrs> in_arrs2 = GetTestInputArrays(ArrayTypes::All, true);
   TestArrayShapes tas = GetTestArrayShapes();
-  std::vector<mkldnn::memory::primitive_desc> pds = tas.pds;
+  std::vector<mkldnn::memory::desc> mds = tas.mds;
   MKLDNNStream *stream = MKLDNNStream::Get();
 
   // kWriteTo
@@ -256,7 +256,7 @@ TEST(MKLDNN_BASE, CreateMKLDNNMem) {
     if (in_arr.arr.IsMKLDNNData() && in_arr.arr.IsView()) {
       continue;
     }
-    std::vector<NDArrayAttrs> out_arrs = GetTestOutputArrays(in_arr.arr.shape(), pds);
+    std::vector<NDArrayAttrs> out_arrs = GetTestOutputArrays(in_arr.arr.shape(), mds);
     for (auto &out_arr : out_arrs) {
       auto in_mem = in_arr.arr.GetMKLDNNData();
       auto in_mem2 = in_arr2.arr.GetMKLDNNData();
@@ -264,7 +264,7 @@ TEST(MKLDNN_BASE, CreateMKLDNNMem) {
       orig_output.WaitToRead();
       PrintVerifyMsg(in_arr, out_arr);
       auto out_mem = out_arr.arr.GetMKLDNNData();
-      auto output_mem_t = CreateMKLDNNMem(out_arr.arr, out_mem->get_primitive_desc(), kWriteTo);
+      auto output_mem_t = CreateMKLDNNMem(out_arr.arr, out_mem->get_desc(), kWriteTo);
       op::MKLDNNSum(*in_mem, *in_mem2, *output_mem_t.second);
       CommitOutput(out_arr.arr, output_mem_t);
       stream->Submit();
@@ -286,10 +286,10 @@ TEST(MKLDNN_BASE, CreateMKLDNNMem) {
     NDArrayAttrs orig_arr(in_arr.arr.Copy(in_arr.arr.ctx()), "In Place Copy");
     orig_arr.arr.WaitToRead();
     PrintVerifyMsg(orig_arr, in_arr);
-    InitMKLDNNArray(&orig_arr.arr, input_mem->get_primitive_desc());
+    InitMKLDNNArray(&orig_arr.arr, input_mem->get_desc());
     orig_arr.arr.CopyFrom(*input_mem);
     auto output_mem_t = CreateMKLDNNMem(in_arr.arr,
-        input_mem->get_primitive_desc(), kWriteInplace, &in_arr.arr);
+        input_mem->get_desc(), kWriteInplace, &in_arr.arr);
     op::MKLDNNSum(*input_mem, *input_mem2, *output_mem_t.second);
     CommitOutput(in_arr.arr, output_mem_t);
     stream->Submit();
@@ -305,7 +305,7 @@ TEST(MKLDNN_BASE, CreateMKLDNNMem) {
     if (in_arr.arr.IsMKLDNNData() && in_arr.arr.IsView()) {
       continue;
     }
-    std::vector<NDArrayAttrs> out_arrs = GetTestOutputArrays(in_arr.arr.shape(), pds);
+    std::vector<NDArrayAttrs> out_arrs = GetTestOutputArrays(in_arr.arr.shape(), mds);
     for (auto &out_arr : out_arrs) {
       auto in_mem = in_arr.arr.GetMKLDNNData();
       auto in_mem2 = in_arr2.arr.GetMKLDNNData();
@@ -313,7 +313,7 @@ TEST(MKLDNN_BASE, CreateMKLDNNMem) {
       orig_output.WaitToRead();
       PrintVerifyMsg(in_arr, out_arr);
       auto out_mem = out_arr.arr.GetMKLDNNData();
-      auto output_mem_t = CreateMKLDNNMem(out_arr.arr, out_mem->get_primitive_desc(), kAddTo);
+      auto output_mem_t = CreateMKLDNNMem(out_arr.arr, out_mem->get_desc(), kAddTo);
       op::MKLDNNSum(*in_mem, *in_mem2, *output_mem_t.second);
       CommitOutput(out_arr.arr, output_mem_t);
       stream->Submit();
@@ -336,9 +336,9 @@ TEST(MKLDNN_BASE, CreateMKLDNNMem) {
     NDArrayAttrs orig_arr(in_arr.arr.Copy(in_arr.arr.ctx()), "In Place Copy");
     orig_arr.arr.WaitToRead();
     PrintVerifyMsg(orig_arr, in_arr);
-    InitMKLDNNArray(&orig_arr.arr, input_mem->get_primitive_desc());
+    InitMKLDNNArray(&orig_arr.arr, input_mem->get_desc());
     orig_arr.arr.CopyFrom(*input_mem);
-    auto output_mem_t = CreateMKLDNNMem(in_arr.arr, input_mem->get_primitive_desc(), kNullOp);
+    auto output_mem_t = CreateMKLDNNMem(in_arr.arr, input_mem->get_desc(), kNullOp);
     op::MKLDNNSum(*input_mem, *input_mem2, *output_mem_t.second);
     CommitOutput(in_arr.arr, output_mem_t);
     stream->Submit();
@@ -373,8 +373,8 @@ TEST(MKLDNN_NDArray, GetTestInputArraysConcat) {
 
 TEST(MKLDNN_NDArray, GetTestOutputArraysConcat) {
   auto shapes_pds = GetTestArrayShapes();
-  std::vector<mxnet::TShape> shapes; shapes = shapes_pds.shapes;
-  std::vector<mkldnn::memory::primitive_desc> pds = shapes_pds.pds;
+  std::vector<mxnet::TShape> shapes = shapes_pds.shapes;
+  std::vector<mkldnn::memory::desc> mds = shapes_pds.mds;
   for (auto &shape : shapes) {
     for (int dim = 0; dim < 5; dim++) {
       for (int num_inputs = 2; num_inputs < 5; num_inputs++) {
@@ -386,7 +386,7 @@ TEST(MKLDNN_NDArray, GetTestOutputArraysConcat) {
         for (int i = 0; i < shape.ndim(); i++)
           scale_vector[i] = 1;
         scale_vector[dim] = num_inputs;
-        auto output_arrs = GetTestOutputArrays(shape, pds, scale_vector);
+        auto output_arrs = GetTestOutputArrays(shape, mds, scale_vector);
         for (auto &out_arr : output_arrs) {
           auto out_shape = out_arr.arr.shape();
           EXPECT_EQ(shape.Size() * num_inputs, out_arr.arr.shape().Size());
@@ -399,13 +399,13 @@ TEST(MKLDNN_NDArray, GetTestOutputArraysConcat) {
 
 TEST(MKLDNN_NDArray, CopyFrom) {
   TestArrayShapes tas = GetTestArrayShapes();
-  std::vector<mkldnn::memory::primitive_desc> pds = tas.pds;
+  std::vector<mkldnn::memory::desc> mds = tas.mds;
 
   std::vector<NDArrayAttrs> in_arrs = GetTestInputArrays();
   for (auto &in_arr : in_arrs) {
     if (in_arr.arr.IsMKLDNNData() && in_arr.arr.IsView())
       continue;
-    std::vector<NDArrayAttrs> out_arrs = GetTestOutputArrays(in_arr.arr.shape(), pds);
+    std::vector<NDArrayAttrs> out_arrs = GetTestOutputArrays(in_arr.arr.shape(), mds);
     for (auto &out_arr : out_arrs) {
       const mkldnn::memory *mem = in_arr.arr.GetMKLDNNData();
       out_arr.arr.CopyFrom(*mem);
@@ -417,4 +417,4 @@ TEST(MKLDNN_NDArray, CopyFrom) {
   }
 }
 
-#endif
+#endif  // MXNET_USE_MKLDNN  == 1
diff --git a/tests/python/mkl/test_mkldnn.py b/tests/python/mkl/test_mkldnn.py
index f88c0a888320..e43daf12c464 100644
--- a/tests/python/mkl/test_mkldnn.py
+++ b/tests/python/mkl/test_mkldnn.py
@@ -568,5 +568,73 @@ def test_weight_async_reorder():
         for output in mod.get_outputs():
             output.wait_to_read()
 
+@with_seed()
+def test_concat():
+    def ref_concat(a, b, axis):
+      return np.concatenate((a, b), axis=axis)
+
+    a_sym = mx.sym.Variable("a")
+    b_sym = mx.sym.Variable("b")
+    dshape = rand_shape_nd(4)
+    a_shape = tuple(dshape)
+    b_shape = tuple(dshape)
+
+    for axis in range(0, 4):
+      z = mx.sym.concat(a_sym, b_sym, dim=axis)
+      a = np.random.uniform(-1, 1, a_shape)
+      b = np.random.uniform(-1, 1, b_shape)
+      exe = z.simple_bind(ctx=mx.cpu(), a=a_shape, b=b_shape)
+      out = exe.forward(is_train=False, a=a, b=b)
+      ref_out = ref_concat(a, b, axis=axis)
+      out = out[0].asnumpy()
+      assert_almost_equal(out, ref_out)
+
+    def check_concat_training(stype):
+        data_shape = rand_shape_nd(4)
+        for density in [1.0, 0.5, 0.0]:
+            a_sym = mx.sym.Variable('a')
+            b_sym = mx.sym.Variable('b')
+            sym = mx.sym.concat(a_sym, b_sym, dim=1)
+            a = rand_ndarray(shape=data_shape, stype=stype, density=density)
+            b = rand_ndarray(shape=data_shape, stype=stype, density=density)
+            in_location = [a, b]
+            check_numeric_gradient(sym, in_location, numeric_eps=1e-3, rtol=1e-3, atol=5e-3)
+    stypes = ['row_sparse', 'default']
+    for stype in stypes:
+        check_concat_training(stype)
+
+@with_seed()
+def test_elemwise_add():
+    def ref_add(a, b):
+      return np.add(a, b)
+
+    a_sym = mx.sym.Variable("a")
+    b_sym = mx.sym.Variable("b")
+    dshape = rand_shape_nd(4)
+    a_shape = tuple(dshape)
+    b_shape = tuple(dshape)
+    z = mx.sym.elemwise_add(a_sym, b_sym)
+    a = np.random.uniform(-1, 1, a_shape)
+    b = np.random.uniform(-1, 1, b_shape)
+    exe = z.simple_bind(ctx=mx.cpu(), a=a_shape, b=b_shape)
+    out = exe.forward(is_train=False, a=a, b=b)
+    ref_out = ref_add(a, b)
+    out = out[0].asnumpy()
+    assert_almost_equal(out, ref_out, rtol=1e-6, atol=1e-6)
+
+    def check_elemwise_add_training(stype):
+        data_shape = rand_shape_nd(4)
+        for density in [1.0, 0.5, 0.0]:
+            a_sym = mx.sym.Variable('a')
+            b_sym = mx.sym.Variable('b')
+            sym = mx.sym.elemwise_add(a_sym, b_sym)
+            a = rand_ndarray(shape=data_shape, stype=stype, density=density)
+            b = rand_ndarray(shape=data_shape, stype=stype, density=density)
+            in_location = [a, b]
+            check_numeric_gradient(sym, in_location, numeric_eps=1e-3, rtol=1e-3, atol=5e-3)
+    stypes = ['row_sparse', 'default']
+    for stype in stypes:
+        check_elemwise_add_training(stype)
+
 if __name__ == '__main__':
     install.test_mkldnn_install()
diff --git a/tests/python/quantization/test_quantization.py b/tests/python/quantization/test_quantization.py
index 0a9fcda56a64..6d6ba41ab7fb 100644
--- a/tests/python/quantization/test_quantization.py
+++ b/tests/python/quantization/test_quantization.py
@@ -407,7 +407,7 @@ def check_quantized_pooling(data_shape, kernel, pool_type, pad, stride, global_p
 def test_quantized_fc():
     def check_quantized_fc(data_shape, num_hidden, no_bias, qdtype, flatten=True):
         if is_test_for_native_cpu():
-            hasMKL = False;
+            hasMKL = False
             for key in os.environ.keys():
                 if operator.eq(key, "BUILD_TAG"):
                     if os.environ['BUILD_TAG'].find("MKL") != -1:
@@ -617,12 +617,11 @@ def check_quantized_bn(data_shape, qdtype):
         # qdtype = uint8
         if qdtype == 'uint8':
             data_low = 0.0
-            data_high = 127.0
+            data_high = 255.0
         else:
             data_low = -127.0
             data_high = 127.0
-        # output type = int8
-        quantized_range = 127.0
+
         # run fp32 bn
         data_sym = mx.sym.Variable(name='data', shape=data_shape, dtype='float32')
         bn_fp32 = mx.sym.BatchNorm(data=data_sym, name='bn', use_global_stats=True, fix_gamma=False)
@@ -653,12 +652,12 @@ def check_quantized_bn(data_shape, qdtype):
 
         calib_data = NDArrayIter(data=data, batch_size=data_shape[0])
         calib_data = DummyIter(calib_data)
-        # quantize bn with quantized_type = int8: MKLDNN BN only support int8 output
         qsym, qarg_params, qaux_params = mx.contrib.quant.quantize_model(sym=bn_fp32,
                                                                              arg_params=arg_params,
                                                                              aux_params=bn_fp32_exe.aux_dict,
                                                                              ctx=mx.current_context(),
-                                                                             quantized_dtype='int8',
+                                                                             quantized_dtype=qdtype,
+                                                                             quantize_mode='full',
                                                                              calib_mode='naive',
                                                                              calib_data=calib_data,
                                                                              num_calib_examples=20)
@@ -670,7 +669,7 @@ def check_quantized_bn(data_shape, qdtype):
         mod.forward(batch, is_train=False)
         output_int8_to_fp32 = mod.get_outputs()[0]
 
-        assert_almost_equal(output.asnumpy(), output_int8_to_fp32.asnumpy(), rtol=1e-1, atol=4)
+        assert_almost_equal(output.asnumpy(), output_int8_to_fp32.asnumpy(), rtol=1e-1, atol=8)
 
     for qdtype in ['int8', 'uint8']:
       check_quantized_bn((32, 512, 4, 4), qdtype)
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 33f739bd10fc..376e177d0659 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -36,6 +36,15 @@
 import os
 
 def check_rnn_consistency(cell1, cell2, T, N, I, H, grad_req, rtol=1e-2, atol=1e-4):
+    if default_context().device_type == 'cpu':
+    # NOTE(zixuanweeei): Currently, we don't add `add` requests support on fused mkl-dnn rnn operator.
+    # We tracked this issue by https://github.com/apache/incubator-mxnet/issues/16578
+        if isinstance(grad_req, dict) and 'add' in grad_req.values():
+            print("Skip the test when requiring `add` operation against gradients on CPU context.")
+            return
+        if isinstance(grad_req, str) and grad_req == 'add':
+            print("Skip the test when requiring `add` operation against gradients on CPU context.")
+            return
     dshape = (N, T, I)
     data = mx.sym.Variable('data')
 
@@ -86,7 +95,7 @@ def test_rnn_with_new_param():
         for mode, ngates in zip(rnn_modes, ngates_):
             first_layer_size = (input_size * state_size + state_size * state_size + state_size * 2) * ngates
             rest_layer_size = (state_size * directions * state_size + state_size * state_size + state_size * 2) \
-                 * ngates * (num_layers - 1)
+                * ngates * (num_layers - 1)
             param_size = (first_layer_size + rest_layer_size) * directions
             sym = mx.sym.RNN(mode=mode, num_layers=num_layers, bidirectional=bidirectional,
                 state_outputs=False, state_size=state_size, name='rnn')
@@ -118,112 +127,133 @@ def test_rnn_with_new_param():
 @with_seed()
 @assert_raises_cudnn_not_satisfied(min_version='5.1.10')
 def test_lstm_sym():
-    T, N, I, H = 5, 32, 800, 800
-    fused = mx.rnn.FusedRNNCell(H, num_layers=3, mode='lstm', get_next_state=True, prefix='')
-    stack = mx.rnn.SequentialRNNCell()
-    stack.add(mx.rnn.LSTMCell(H, prefix='l0_'))
-    stack.add(mx.rnn.LSTMCell(H, prefix='l1_'))
-    stack.add(mx.rnn.LSTMCell(H, prefix='l2_'))
-
-    check_rnn_consistency(fused, stack, T, N, I, H, 'write')
-    check_rnn_consistency(fused, stack, T, N, I, H, 'add')
-    check_rnn_consistency(fused, stack, T, N, I, H, 'null')
+    Ts = [1, 5]
+    Ns = [1, 32]
+    Is = [32, 128, 512]
+    Hs = [32, 128, 512]
+    for T, N, I, H in itertools.product(Ts, Ns, Is, Hs):
+        fused = mx.rnn.FusedRNNCell(H, num_layers=3, mode='lstm', get_next_state=True, prefix='')
+        stack = mx.rnn.SequentialRNNCell()
+        stack.add(mx.rnn.LSTMCell(H, prefix='l0_'))
+        stack.add(mx.rnn.LSTMCell(H, prefix='l1_'))
+        stack.add(mx.rnn.LSTMCell(H, prefix='l2_'))
+
+        check_rnn_consistency(fused, stack, T, N, I, H, 'write')
+        check_rnn_consistency(fused, stack, T, N, I, H, 'add')
+        check_rnn_consistency(fused, stack, T, N, I, H, 'null')
 
 @with_seed()
 @assert_raises_cudnn_not_satisfied(min_version='5.1.10')
 def test_lstm_bidirectional():
-    T, N, I, H = 5, 20, 800, 800
-    fused = mx.rnn.FusedRNNCell(H, num_layers=2, mode='lstm',
-                                bidirectional=True, get_next_state=True, prefix='')
-
-    stack = mx.rnn.SequentialRNNCell()
-    stack.add(mx.rnn.BidirectionalCell(
-                mx.rnn.LSTMCell(H, prefix='l0_'),
-                mx.rnn.LSTMCell(H, prefix='r0_'),
-                output_prefix='bi_lstm_0_'))
-    stack.add(mx.rnn.BidirectionalCell(
-                mx.rnn.LSTMCell(H, prefix='l1_'),
-                mx.rnn.LSTMCell(H, prefix='r1_'),
-                output_prefix='bi_lstm_1_'))
-
-    check_rnn_consistency(fused, stack, T, N, I, H, 'write')
-    check_rnn_consistency(fused, stack, T, N, I, H, 'add')
-    check_rnn_consistency(fused, stack, T, N, I, H, 'null')
-    check_rnn_consistency(fused, stack, T, N, I, H, {'data': 'add', 'parameters': 'null'})
+    Ts = [1, 5]
+    Ns = [1, 32]
+    Is = [32, 128, 512]
+    Hs = [32, 128, 512]
+    for T, N, I, H in itertools.product(Ts, Ns, Is, Hs):
+        fused = mx.rnn.FusedRNNCell(H, num_layers=2, mode='lstm',
+                                    bidirectional=True, get_next_state=True, prefix='')
+
+        stack = mx.rnn.SequentialRNNCell()
+        stack.add(mx.rnn.BidirectionalCell(
+                    mx.rnn.LSTMCell(H, prefix='l0_'),
+                    mx.rnn.LSTMCell(H, prefix='r0_'),
+                    output_prefix='bi_lstm_0_'))
+        stack.add(mx.rnn.BidirectionalCell(
+                    mx.rnn.LSTMCell(H, prefix='l1_'),
+                    mx.rnn.LSTMCell(H, prefix='r1_'),
+                    output_prefix='bi_lstm_1_'))
+
+        check_rnn_consistency(fused, stack, T, N, I, H, 'write')
+        check_rnn_consistency(fused, stack, T, N, I, H, 'add')
+        check_rnn_consistency(fused, stack, T, N, I, H, 'null')
+        check_rnn_consistency(fused, stack, T, N, I, H, {'data': 'add', 'parameters': 'null'})
 
 @with_seed()
 @assert_raises_cudnn_not_satisfied(min_version='5.1.10')
 def test_gru_sym():
-    T, N, I, H = 5, 32, 800, 800
-    fused = mx.rnn.FusedRNNCell(H, num_layers=3, mode='gru', get_next_state=True, prefix='')
-    stack = mx.rnn.SequentialRNNCell()
-    stack.add(mx.rnn.GRUCell(H, prefix='l0_'))
-    stack.add(mx.rnn.GRUCell(H, prefix='l1_'))
-    stack.add(mx.rnn.GRUCell(H, prefix='l2_'))
-
-    check_rnn_consistency(fused, stack, T, N, I, H, 'write')
-    check_rnn_consistency(fused, stack, T, N, I, H, 'add')
-    check_rnn_consistency(fused, stack, T, N, I, H, 'null')
+    Ts = [1, 5]
+    Ns = [1, 32]
+    Is = [32, 128, 512]
+    Hs = [32, 128, 512]
+    for T, N, I, H in itertools.product(Ts, Ns, Is, Hs):
+        fused = mx.rnn.FusedRNNCell(H, num_layers=3, mode='gru', get_next_state=True, prefix='')
+        stack = mx.rnn.SequentialRNNCell()
+        stack.add(mx.rnn.GRUCell(H, prefix='l0_'))
+        stack.add(mx.rnn.GRUCell(H, prefix='l1_'))
+        stack.add(mx.rnn.GRUCell(H, prefix='l2_'))
+
+        check_rnn_consistency(fused, stack, T, N, I, H, 'write', atol=2e-4)
+        check_rnn_consistency(fused, stack, T, N, I, H, 'add', atol=2e-4)
+        check_rnn_consistency(fused, stack, T, N, I, H, 'null', atol=2e-4)
 
 @with_seed()
 @assert_raises_cudnn_not_satisfied(min_version='5.1.10')
 def test_gru_bidirectional():
-    T, N, I, H = 5, 20, 800, 800
-
-    fused = mx.rnn.FusedRNNCell(H, num_layers=2, mode='gru',
-                                bidirectional=True, get_next_state=True, prefix='')
-
-    stack = mx.rnn.SequentialRNNCell()
-    stack.add(mx.rnn.BidirectionalCell(
-                mx.rnn.GRUCell(H, prefix='l0_'),
-                mx.rnn.GRUCell(H, prefix='r0_'),
-                output_prefix='bi_gru_0_'))
-
-    stack.add(mx.rnn.BidirectionalCell(
-                mx.rnn.GRUCell(H, prefix='l1_'),
-                mx.rnn.GRUCell(H, prefix='r1_'),
-                output_prefix='bi_gru_1_'))
-
-    check_rnn_consistency(fused, stack, T, N, I, H, 'write')
-    check_rnn_consistency(fused, stack, T, N, I, H, 'add')
-    check_rnn_consistency(fused, stack, T, N, I, H, 'null')
+    Ts = [1, 5]
+    Ns = [1, 32]
+    Is = [32, 128, 512]
+    Hs = [32, 128, 512]
+    for T, N, I, H in itertools.product(Ts, Ns, Is, Hs):
+        fused = mx.rnn.FusedRNNCell(H, num_layers=2, mode='gru',
+                                    bidirectional=True, get_next_state=True, prefix='')
+
+        stack = mx.rnn.SequentialRNNCell()
+        stack.add(mx.rnn.BidirectionalCell(
+                    mx.rnn.GRUCell(H, prefix='l0_'),
+                    mx.rnn.GRUCell(H, prefix='r0_'),
+                    output_prefix='bi_gru_0_'))
+
+        stack.add(mx.rnn.BidirectionalCell(
+                    mx.rnn.GRUCell(H, prefix='l1_'),
+                    mx.rnn.GRUCell(H, prefix='r1_'),
+                    output_prefix='bi_gru_1_'))
+
+        check_rnn_consistency(fused, stack, T, N, I, H, 'write', atol=2e-4)
+        check_rnn_consistency(fused, stack, T, N, I, H, 'add', atol=2e-4)
+        check_rnn_consistency(fused, stack, T, N, I, H, 'null', atol=2e-4)
 
 @with_seed()
 @assert_raises_cudnn_not_satisfied(min_version='5.1.10')
 def test_rnntanh_sym():
-    T, N, I, H = 5, 32, 800, 800
-
-    fused = mx.rnn.FusedRNNCell(H, num_layers=3, mode='rnn_tanh', get_next_state=True, prefix='')
-    stack = mx.rnn.SequentialRNNCell()
-    stack.add(mx.rnn.RNNCell(H, activation='tanh', prefix='l0_'))
-    stack.add(mx.rnn.RNNCell(H, activation='tanh', prefix='l1_'))
-    stack.add(mx.rnn.RNNCell(H, activation='tanh', prefix='l2_'))
-
-    check_rnn_consistency(fused, stack, T, N, I, H, 'write')
-    check_rnn_consistency(fused, stack, T, N, I, H, 'add')
-    check_rnn_consistency(fused, stack, T, N, I, H, 'null')
+    Ts = [1, 5]
+    Ns = [1, 32]
+    Is = [32, 128, 512]
+    Hs = [32, 128, 512]
+    for T, N, I, H in itertools.product(Ts, Ns, Is, Hs):
+        fused = mx.rnn.FusedRNNCell(H, num_layers=3, mode='rnn_tanh', get_next_state=True, prefix='')
+        stack = mx.rnn.SequentialRNNCell()
+        stack.add(mx.rnn.RNNCell(H, activation='tanh', prefix='l0_'))
+        stack.add(mx.rnn.RNNCell(H, activation='tanh', prefix='l1_'))
+        stack.add(mx.rnn.RNNCell(H, activation='tanh', prefix='l2_'))
+
+        check_rnn_consistency(fused, stack, T, N, I, H, 'write')
+        check_rnn_consistency(fused, stack, T, N, I, H, 'add')
+        check_rnn_consistency(fused, stack, T, N, I, H, 'null')
 
 @with_seed()
 @assert_raises_cudnn_not_satisfied(min_version='5.1.10')
 def test_rnntanh_bidirectional():
-    T, N, I, H = 5, 20, 800, 800
-
-    fused = mx.rnn.FusedRNNCell(H, num_layers=2, mode='rnn_tanh',
-                                bidirectional=True, get_next_state=True, prefix='')
-
-    stack = mx.rnn.SequentialRNNCell()
-    stack.add(mx.rnn.BidirectionalCell(
-                mx.rnn.RNNCell(H, activation='tanh', prefix='l0_'),
-                mx.rnn.RNNCell(H, activation='tanh', prefix='r0_'),
-                output_prefix='bi_rnntanh_0_'))
-    stack.add(mx.rnn.BidirectionalCell(
-                mx.rnn.RNNCell(H, activation='tanh', prefix='l1_'),
-                mx.rnn.RNNCell(H, activation='tanh', prefix='r1_'),
-                output_prefix='bi_rnntanh_1_'))
-
-    check_rnn_consistency(fused, stack, T, N, I, H, 'write')
-    check_rnn_consistency(fused, stack, T, N, I, H, 'add')
-    check_rnn_consistency(fused, stack, T, N, I, H, 'null')
+    Ts = [1, 5]
+    Ns = [1, 32]
+    Is = [32, 128, 512]
+    Hs = [32, 128, 512]
+    for T, N, I, H in itertools.product(Ts, Ns, Is, Hs):
+        fused = mx.rnn.FusedRNNCell(H, num_layers=2, mode='rnn_tanh',
+                                    bidirectional=True, get_next_state=True, prefix='')
+
+        stack = mx.rnn.SequentialRNNCell()
+        stack.add(mx.rnn.BidirectionalCell(
+                    mx.rnn.RNNCell(H, activation='tanh', prefix='l0_'),
+                    mx.rnn.RNNCell(H, activation='tanh', prefix='r0_'),
+                    output_prefix='bi_rnntanh_0_'))
+        stack.add(mx.rnn.BidirectionalCell(
+                    mx.rnn.RNNCell(H, activation='tanh', prefix='l1_'),
+                    mx.rnn.RNNCell(H, activation='tanh', prefix='r1_'),
+                    output_prefix='bi_rnntanh_1_'))
+
+        check_rnn_consistency(fused, stack, T, N, I, H, 'write')
+        check_rnn_consistency(fused, stack, T, N, I, H, 'add')
+        check_rnn_consistency(fused, stack, T, N, I, H, 'null')
 
 @with_seed()
 @assert_raises_cudnn_not_satisfied(min_version='5.1.10')
@@ -243,24 +273,27 @@ def test_rnnrelu_sym():
 @with_seed()
 @assert_raises_cudnn_not_satisfied(min_version='5.1.10')
 def test_rnnrelu_bidirectional():
-    T, N, I, H = 5, 20, 200, 200
-
-    fused = mx.rnn.FusedRNNCell(H, num_layers=2, mode='rnn_relu',
-                                bidirectional=True, get_next_state=True, prefix='')
-
-    stack = mx.rnn.SequentialRNNCell()
-    stack.add(mx.rnn.BidirectionalCell(
-                mx.rnn.RNNCell(H, activation='relu', prefix='l0_'),
-                mx.rnn.RNNCell(H, activation='relu', prefix='r0_'),
-                output_prefix='bi_rnnrelu_0_'))
-    stack.add(mx.rnn.BidirectionalCell(
-                mx.rnn.RNNCell(H, activation='relu', prefix='l1_'),
-                mx.rnn.RNNCell(H, activation='relu', prefix='r1_'),
-                output_prefix='bi_rnnrelu_1_'))
-
-    check_rnn_consistency(fused, stack, T, N, I, H, 'write', rtol=1e-2, atol=1e-2)
-    check_rnn_consistency(fused, stack, T, N, I, H, 'add', rtol=1e-2, atol=1e-2)
-    check_rnn_consistency(fused, stack, T, N, I, H, 'null', rtol=1e-2, atol=1e-2)
+    Ts = [1, 5]
+    Ns = [1, 32]
+    Is = [32, 128, 512]
+    Hs = [32, 128, 512]
+    for T, N, I, H in itertools.product(Ts, Ns, Is, Hs):
+        fused = mx.rnn.FusedRNNCell(H, num_layers=2, mode='rnn_relu',
+                                    bidirectional=True, get_next_state=True, prefix='')
+
+        stack = mx.rnn.SequentialRNNCell()
+        stack.add(mx.rnn.BidirectionalCell(
+                    mx.rnn.RNNCell(H, activation='relu', prefix='l0_'),
+                    mx.rnn.RNNCell(H, activation='relu', prefix='r0_'),
+                    output_prefix='bi_rnnrelu_0_'))
+        stack.add(mx.rnn.BidirectionalCell(
+                    mx.rnn.RNNCell(H, activation='relu', prefix='l1_'),
+                    mx.rnn.RNNCell(H, activation='relu', prefix='r1_'),
+                    output_prefix='bi_rnnrelu_1_'))
+
+        check_rnn_consistency(fused, stack, T, N, I, H, 'write', rtol=1e-2, atol=1e-2)
+        check_rnn_consistency(fused, stack, T, N, I, H, 'add', rtol=1e-2, atol=1e-2)
+        check_rnn_consistency(fused, stack, T, N, I, H, 'null', rtol=1e-2, atol=1e-2)
 
 @with_seed()
 def test_lstm_dropout():
diff --git a/tools/pip/setup.py b/tools/pip/setup.py
index 82aa632d28ad..dd430f5a6f87 100644
--- a/tools/pip/setup.py
+++ b/tools/pip/setup.py
@@ -147,19 +147,11 @@ def has_ext_modules(self):
                 'dmlc_tracker': []}
 if variant.endswith('MKL'):
     if platform.system() == 'Darwin':
-        shutil.copy(os.path.join(os.path.dirname(LIB_PATH[0]), 'libmklml.dylib'), os.path.join(CURRENT_DIR, 'mxnet'))
-        shutil.copy(os.path.join(os.path.dirname(LIB_PATH[0]), 'libiomp5.dylib'), os.path.join(CURRENT_DIR, 'mxnet'))
-        shutil.copy(os.path.join(os.path.dirname(LIB_PATH[0]), 'libmkldnn.0.dylib'), os.path.join(CURRENT_DIR, 'mxnet'))
-        package_data['mxnet'].append('mxnet/libmklml.dylib')
-        package_data['mxnet'].append('mxnet/libiomp5.dylib')
-        package_data['mxnet'].append('mxnet/libmkldnn.0.dylib')
+        shutil.copy(os.path.join(os.path.dirname(LIB_PATH[0]), 'libmkldnn.1.dylib'), os.path.join(CURRENT_DIR, 'mxnet'))
+        package_data['mxnet'].append('mxnet/libmkldnn.1.dylib')
     else:
-        shutil.copy(os.path.join(os.path.dirname(LIB_PATH[0]), 'libmklml_intel.so'), os.path.join(CURRENT_DIR, 'mxnet'))
-        shutil.copy(os.path.join(os.path.dirname(LIB_PATH[0]), 'libiomp5.so'), os.path.join(CURRENT_DIR, 'mxnet'))
-        shutil.copy(os.path.join(os.path.dirname(LIB_PATH[0]), 'libmkldnn.so.0'), os.path.join(CURRENT_DIR, 'mxnet'))
-        package_data['mxnet'].append('mxnet/libmklml_intel.so')
-        package_data['mxnet'].append('mxnet/libiomp5.so')
-        package_data['mxnet'].append('mxnet/libmkldnn.so.0')
+        shutil.copy(os.path.join(os.path.dirname(LIB_PATH[0]), 'libmkldnn.so.1'), os.path.join(CURRENT_DIR, 'mxnet'))
+        package_data['mxnet'].append('mxnet/libmkldnn.so.1')
     shutil.copytree(os.path.join(CURRENT_DIR, 'mxnet-build/3rdparty/mkldnn/build/install/include'),
                     os.path.join(CURRENT_DIR, 'mxnet/include/mkldnn'))
 if platform.system() == 'Linux':
diff --git a/tools/staticbuild/build_lib.sh b/tools/staticbuild/build_lib.sh
index 927c15d1dabc..4a82b80d00ba 100755
--- a/tools/staticbuild/build_lib.sh
+++ b/tools/staticbuild/build_lib.sh
@@ -35,20 +35,14 @@ $MAKE DEPS_PATH=$DEPS_PATH PSLITE
 
 if [[ $VARIANT == *mkl ]]; then
     if [[ $PLATFORM == 'linux' ]]; then
-        IOMP_LIBFILE='libiomp5.so'
-        MKLML_LIBFILE='libmklml_intel.so'
-        MKLDNN_LIBFILE='libmkldnn.so.0'
+        MKLDNN_LIBFILE='libmkldnn.so.1'
     else
-        IOMP_LIBFILE='libiomp5.dylib'
-        MKLML_LIBFILE='libmklml.dylib'
-        MKLDNN_LIBFILE='libmkldnn.0.dylib'
+        MKLDNN_LIBFILE='libmkldnn.1.dylib'
     fi
     $MAKE DEPS_PATH=$DEPS_PATH mkldnn
     if [ ! -d lib ]; then
         mkdir lib
     fi
-    cp 3rdparty/mkldnn/build/install/lib/$IOMP_LIBFILE lib
-    cp 3rdparty/mkldnn/build/install/lib/$MKLML_LIBFILE lib
     cp 3rdparty/mkldnn/build/install/lib/$MKLDNN_LIBFILE lib
 fi
 

From 979e610281312da99173a494057ed68fff7ee4b2 Mon Sep 17 00:00:00 2001
From: Przemyslaw Tredak <ptredak@nvidia.com>
Date: Thu, 31 Oct 2019 11:10:06 -0700
Subject: [PATCH 544/813] Added launch bounds to the reduce kernels (#16397)

* Added launch bounds to the reduce_kernel_M1

* Trigger CI

* Reretrigger the CI
---
 src/operator/tensor/broadcast_reduce-inl.cuh | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/operator/tensor/broadcast_reduce-inl.cuh b/src/operator/tensor/broadcast_reduce-inl.cuh
index 1b0127a8f322..fc863a972670 100644
--- a/src/operator/tensor/broadcast_reduce-inl.cuh
+++ b/src/operator/tensor/broadcast_reduce-inl.cuh
@@ -262,6 +262,7 @@ __global__ void reduce_lines_kernel(const int N, const int M, const bool addto,
 }
 
 template<typename Reducer, int ndim, typename AType, typename DType, typename OType, typename OP>
+__launch_bounds__(kMaxThreadsPerBlock)
 __global__ void reduce_kernel_M1(const int N, const bool addto,
                                 const DType* __restrict big, OType *small, const Shape<ndim> bshape,
                                 const Shape<ndim> sshape) {
@@ -277,6 +278,7 @@ __global__ void reduce_kernel_M1(const int N, const bool addto,
 }
 
 template<typename Reducer, int ndim, typename DType, typename OP1, typename OP2>
+__launch_bounds__(kMaxThreadsPerBlock)
 __global__ void reduce_kernel_M1(const int N, const bool addto,
                                  const DType* __restrict big,
                                  const DType* __restrict lhs,

From 9cb896014e6d04376fbe4b539208118ca9bf22e6 Mon Sep 17 00:00:00 2001
From: Sheng Zha <szha@users.noreply.github.com>
Date: Thu, 31 Oct 2019 15:26:00 -0700
Subject: [PATCH 545/813] [Estimator] handle composite metrics in estimator
 (#16676)

* handle composite metrics in estimator

* fix composite metric case in handlers

* remove unused import
---
 .../gluon/contrib/estimator/estimator.py      | 15 ++------
 .../gluon/contrib/estimator/event_handler.py  | 14 +++++---
 python/mxnet/gluon/contrib/estimator/utils.py | 34 +++++++++++++++++++
 tests/nightly/estimator/test_sentiment_rnn.py |  6 +++-
 4 files changed, 51 insertions(+), 18 deletions(-)
 create mode 100644 python/mxnet/gluon/contrib/estimator/utils.py

diff --git a/python/mxnet/gluon/contrib/estimator/estimator.py b/python/mxnet/gluon/contrib/estimator/estimator.py
index 5c6855612828..17e543f0e744 100644
--- a/python/mxnet/gluon/contrib/estimator/estimator.py
+++ b/python/mxnet/gluon/contrib/estimator/estimator.py
@@ -24,6 +24,7 @@
 
 from .event_handler import MetricHandler, ValidationHandler, LoggingHandler, StoppingHandler
 from .event_handler import TrainBegin, EpochBegin, BatchBegin, BatchEnd, EpochEnd, TrainEnd
+from .utils import _check_metrics
 from ...data import DataLoader
 from ...loss import SoftmaxCrossEntropyLoss
 from ...loss import Loss as gluon_loss
@@ -31,7 +32,7 @@
 from ...utils import split_and_load
 from .... import autograd
 from ....context import Context, cpu, gpu, num_gpus
-from ....metric import EvalMetric, Accuracy
+from ....metric import Accuracy
 from ....metric import Loss as metric_loss
 
 __all__ = ['Estimator']
@@ -68,7 +69,7 @@ def __init__(self, net,
 
         self.net = net
         self.loss = self._check_loss(loss)
-        self.train_metrics = self._check_metrics(metrics)
+        self.train_metrics = _check_metrics(metrics)
 
         self.context = self._check_context(context)
         self._initialize(initializer)
@@ -84,16 +85,6 @@ def _check_loss(self, loss):
                              "refer to gluon.loss.Loss:{}".format(loss))
         return loss
 
-    def _check_metrics(self, metrics):
-        if isinstance(metrics, EvalMetric):
-            metrics = [metrics]
-        else:
-            metrics = metrics or []
-            if not all([isinstance(metric, EvalMetric) for metric in metrics]):
-                raise ValueError("metrics must be a Metric or a list of Metric, "
-                                 "refer to mxnet.metric.EvalMetric:{}".format(metrics))
-        return metrics
-
     def _check_context(self, context):
         # infer available context
         gpus = num_gpus()
diff --git a/python/mxnet/gluon/contrib/estimator/event_handler.py b/python/mxnet/gluon/contrib/estimator/event_handler.py
index da2c84455e35..c5a4f1a3f836 100644
--- a/python/mxnet/gluon/contrib/estimator/event_handler.py
+++ b/python/mxnet/gluon/contrib/estimator/event_handler.py
@@ -26,8 +26,9 @@
 
 import numpy as np
 
-from ....metric import EvalMetric
+from ....metric import EvalMetric, CompositeEvalMetric
 from ....metric import Loss as metric_loss
+from .utils import _check_metrics
 
 __all__ = ['TrainBegin', 'TrainEnd', 'EpochBegin', 'EpochEnd', 'BatchBegin', 'BatchEnd',
            'StoppingHandler', 'MetricHandler', 'ValidationHandler',
@@ -118,7 +119,7 @@ class MetricHandler(EpochBegin, BatchEnd):
     """
 
     def __init__(self, train_metrics):
-        self.train_metrics = train_metrics or []
+        self.train_metrics = _check_metrics(train_metrics)
         # order to be called among all callbacks
         # metrics need to be calculated before other callbacks can access them
         self.priority = -np.Inf
@@ -173,7 +174,7 @@ def __init__(self,
         self.eval_fn = eval_fn
         self.epoch_period = epoch_period
         self.batch_period = batch_period
-        self.val_metrics = val_metrics
+        self.val_metrics = _check_metrics(val_metrics)
         self.current_batch = 0
         self.current_epoch = 0
         # order to be called among all callbacks
@@ -255,8 +256,8 @@ def __init__(self, file_name=None,
                              "E.g: LoggingHandler(verbose=LoggingHandler.LOG_PER_EPOCH)"
                              % verbose)
         self.verbose = verbose
-        self.train_metrics = train_metrics or []
-        self.val_metrics = val_metrics or []
+        self.train_metrics = _check_metrics(train_metrics)
+        self.val_metrics = _check_metrics(val_metrics)
         self.batch_index = 0
         self.current_epoch = 0
         self.processed_samples = 0
@@ -637,6 +638,9 @@ def __init__(self,
         if not isinstance(monitor, EvalMetric):
             raise ValueError("Please provide one of the metric objects as monitor, "
                              "You can create these objects using estimator.prepare_loss_and_metric()")
+        if isinstance(monitor, CompositeEvalMetric):
+            raise ValueError("CompositeEvalMetric is not supported for EarlyStoppingHandler, "
+                             "please specify a simple metric instead.")
         self.monitor = monitor
         self.baseline = baseline
         self.patience = patience
diff --git a/python/mxnet/gluon/contrib/estimator/utils.py b/python/mxnet/gluon/contrib/estimator/utils.py
new file mode 100644
index 000000000000..f5be0878e0d9
--- /dev/null
+++ b/python/mxnet/gluon/contrib/estimator/utils.py
@@ -0,0 +1,34 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# coding: utf-8
+# pylint: disable=wildcard-import, unused-variable
+"""Gluon Estimator Utility Functions"""
+
+from ....metric import EvalMetric, CompositeEvalMetric
+
+def _check_metrics(metrics):
+    if isinstance(metrics, CompositeEvalMetric):
+        metrics = [m for metric in metrics.metrics for m in _check_metrics(metric)]
+    elif isinstance(metrics, EvalMetric):
+        metrics = [metrics]
+    else:
+        metrics = metrics or []
+        if not all([isinstance(metric, EvalMetric) for metric in metrics]):
+            raise ValueError("metrics must be a Metric or a list of Metric, "
+                             "refer to mxnet.metric.EvalMetric:{}".format(metrics))
+    return metrics
diff --git a/tests/nightly/estimator/test_sentiment_rnn.py b/tests/nightly/estimator/test_sentiment_rnn.py
index d2eef9b6733c..233355b7ebfd 100644
--- a/tests/nightly/estimator/test_sentiment_rnn.py
+++ b/tests/nightly/estimator/test_sentiment_rnn.py
@@ -190,10 +190,14 @@ def run(net, train_dataloader, test_dataloader, num_epochs, ctx, lr):
     trainer = mx.gluon.Trainer(net.collect_params(), 'adam', {'learning_rate': lr})
     # Define loss and evaluation metrics
     loss = gluon.loss.SoftmaxCrossEntropyLoss()
+    metrics = mx.metric.CompositeEvalMetric()
     acc = mx.metric.Accuracy()
+    nested_metrics = mx.metric.CompositeEvalMetric()
+    metrics.add([acc, mx.metric.Loss()])
+    nested_metrics.add([metrics, mx.metric.Accuracy()])
 
     # Define estimator
-    est = estimator.Estimator(net=net, loss=loss, metrics=acc,
+    est = estimator.Estimator(net=net, loss=loss, metrics=nested_metrics,
                               trainer=trainer, context=ctx)
     # Begin training
     est.fit(train_data=train_dataloader, val_data=test_dataloader,

From ef19b09c297f5dbc100b1283c401074231f883d1 Mon Sep 17 00:00:00 2001
From: Sheng Zha <szha@users.noreply.github.com>
Date: Thu, 31 Oct 2019 15:45:39 -0700
Subject: [PATCH 546/813] [Estimator] refactor estimator to allow overriding
 evaluate/fit of a batch (#16678)

* refactor estimator to allow overriding evaluate/fit of a batch

* add doc to explain call structure and how to override

* fix and doc
---
 .../gluon/contrib/estimator/estimator.py      | 114 +++++++++++++-----
 1 file changed, 84 insertions(+), 30 deletions(-)

diff --git a/python/mxnet/gluon/contrib/estimator/estimator.py b/python/mxnet/gluon/contrib/estimator/estimator.py
index 17e543f0e744..d3eded0cc8cd 100644
--- a/python/mxnet/gluon/contrib/estimator/estimator.py
+++ b/python/mxnet/gluon/contrib/estimator/estimator.py
@@ -171,8 +171,10 @@ def prepare_loss_and_metrics(self):
         Based on loss functions and training metrics in estimator
         Create metric wrappers to record loss values,
         Create copies of train loss/metric objects to record validation values
-        Returns train_metrics and val_metrics
 
+        Returns
+        -------
+        train_metrics, val_metrics
         """
         if any(not hasattr(self, attribute) for attribute in
                ['train_metrics', 'val_metrics']):
@@ -190,21 +192,50 @@ def prepare_loss_and_metrics(self):
                 self.val_metrics.append(val_metric)
         return self.train_metrics, self.val_metrics
 
+    def evaluate_batch(self,
+                       val_batch,
+                       val_metrics,
+                       batch_axis=0):
+        """Evaluate model on a batch of validation data.
+
+        Parameters
+        ----------
+        val_batch : tuple
+            Data and label of a batch from the validation data loader.
+        val_metrics : EvalMetric or list of EvalMetrics
+            Metrics to update validation result.
+        batch_axis : int, default 0
+            Batch axis to split the validation data into devices.
+        """
+        data, label = self._get_data_and_label(val_batch, self.context, batch_axis)
+        pred = [self.net(x) for x in data]
+        loss = [self.loss[0](y_hat, y) for y_hat, y in zip(pred, label)]
+        # update metrics
+        for metric in val_metrics:
+            if isinstance(metric, metric_loss):
+                metric.update(0, loss)
+            else:
+                metric.update(label, pred)
+
     def evaluate(self,
                  val_data,
                  val_metrics,
                  batch_axis=0):
-        """Evaluate model on validation data
-
-         Parameters
-         ----------
-         val_data : DataLoader
-             Validation data loader with data and labels.
-         val_metrics : EvalMetric or list of EvalMetrics
-             Metrics to update validation result.
-         batch_axis : int, default 0
-             Batch axis to split the validation data into devices.
-         """
+        """Evaluate model on validation data.
+
+        This function calls :py:func:`evaluate_batch` on each of the batches from the
+        validation data loader. Thus, for custom use cases, it's possible to inherit the
+        estimator class and override :py:func:`evaluate_batch`.
+
+        Parameters
+        ----------
+        val_data : DataLoader
+            Validation data loader with data and labels.
+        val_metrics : EvalMetric or list of EvalMetrics
+            Metrics to update validation result.
+        batch_axis : int, default 0
+            Batch axis to split the validation data into devices.
+        """
         if not isinstance(val_data, DataLoader):
             raise ValueError("Estimator only support input as Gluon DataLoader. Alternatively, you "
                              "can transform your DataIter or any NDArray into Gluon DataLoader. "
@@ -214,15 +245,44 @@ def evaluate(self,
             metric.reset()
 
         for _, batch in enumerate(val_data):
-            data, label = self._get_data_and_label(batch, self.context, batch_axis)
+            self.evaluate_batch(batch, val_metrics, batch_axis)
+
+    def fit_batch(self, train_batch,
+                  batch_axis=0):
+        """Trains the model on a batch of training data.
+
+        Parameters
+        ----------
+        train_batch : tuple
+            Data and label of a batch from the training data loader.
+        batch_axis : int, default 0
+            Batch axis to split the training data into devices.
+
+        Returns
+        -------
+        data: List of NDArray
+            Sharded data from the batch.
+        label: List of NDArray
+            Sharded label from the batch.
+        pred: List of NDArray
+            Prediction of each of the shareded batch.
+        loss: List of NDArray
+            Loss of each of the shareded batch.
+        """
+        data, label = self._get_data_and_label(train_batch, self.context, batch_axis)
+
+        batch_size = train_batch[0].shape[batch_axis]
+
+        with autograd.record():
             pred = [self.net(x) for x in data]
             loss = [self.loss[0](y_hat, y) for y_hat, y in zip(pred, label)]
-            # update metrics
-            for metric in val_metrics:
-                if isinstance(metric, metric_loss):
-                    metric.update(0, loss)
-                else:
-                    metric.update(label, pred)
+
+        for l in loss:
+            l.backward()
+
+        self.trainer.step(batch_size)
+
+        return data, label, pred, loss
 
     def fit(self, train_data,
             val_data=None,
@@ -234,6 +294,10 @@ def fit(self, train_data,
         number of epochs or batches. The batch size is inferred from the
         data loader's batch_size.
 
+        This function calls :py:func:`fit_batch` on each of the batches from the
+        training data loader. Thus, for custom use cases, it's possible to inherit the
+        estimator class and override :py:func:`fit_batch`.
+
         Parameters
         ----------
         train_data : DataLoader
@@ -284,22 +348,12 @@ def fit(self, train_data,
                 handler.epoch_begin(estimator_ref)
 
             for i, batch in enumerate(train_data):
-                data, label = self._get_data_and_label(batch, self.context, batch_axis)
-
-                batch_size = batch[0].shape[0]
-
                 # batch begin
                 for handler in batch_begin:
                     handler.batch_begin(estimator_ref, batch=batch)
 
-                with autograd.record():
-                    pred = [self.net(x) for x in data]
-                    loss = [self.loss[0](y_hat, y) for y_hat, y in zip(pred, label)]
-
-                for l in loss:
-                    l.backward()
+                _, label, pred, loss = self.fit_batch(batch, batch_axis)
 
-                self.trainer.step(batch_size)
                 # batch end
 
                 batch_end_result = []

From 4149f8b8752989fce5d80cc13f92d99774988b4f Mon Sep 17 00:00:00 2001
From: Przemyslaw Tredak <ptredak@nvidia.com>
Date: Thu, 31 Oct 2019 21:07:24 -0700
Subject: [PATCH 547/813] Pointwise fusion for GPU (#15167)

* Beginning of RTC of pointwise ops

* Code generation from the given JSON

* add initial simple_partition_pass and use it for pointwise fusion

* fix the fusion, use a symbol.Copy() at the beginning of binding function, use the name of input nodes in the cuda code

* Fixes

* Adding support for attribute inference for backward nodes when fusing

* keep proper input ordering for fused Op

* instantiate the indexed_graph before starting the subgraph replacement, return a new graph to reset the indexed_graph

* Fuse backward

* fix ordering of subgraph node inputs using subgraph topological ordering instead of main graph topological ordering, add tvm.patch

* excluse forward node fusion during the fusion of the nodes in the backward graph

* Dealing with fused backward nodes inferattr

* use subgraph.indexed_graph() instead of main for _FusedOpHelper nodes node_id, invert control_deps loop to modify topology of subgraph before calling its indexed_graph(), check that all node of the first DFSVisit are actually in the subgraph

* Adding support for other reqs in codegen

* Fix

* Cleaning

* Change the TVM submodule

* More cleaning

* Making linter happy

* Do fusion only if default context is GPU

* Fixes for tests
Add powerscalar and rpowerscalar, fix return type of zero and one
Cleaning, fixing lint
Go back to proper TVM submodule

* Fix the TVM commit

* Fix lint

* Guard fusion with MXNET_USE_CUDA

* Fix

* Fix clang-tidy

* Add erf and erfinv backward

* Gluon support for fusion

* Cleaning

* Cleaning and allow shape/type change in FusedOp

* Fixing Gluon bugs

* Fixing after rebase

* Fixing race condition and guarding against races when using NVRTC

* Cleaning and renaming FusedOp to _FusedOp

* Going easy on Windows compiler

* Disable fusion on Windows for now

* Refactor InferAttr and InferShapeAttr

* Added slice and half2 support to FusedOp

* Fix lint errors

* Added multiple types support for vector loading/storing

* add slice fusion when it's at the beginning of subgraphs

* Removed constant ndim assumption in fused op

* Fix memory alignment issue in slice for FusedOp

* Fixes

* Fix lint errors

* Do not include cuda_fp16.h

* Refactor fused op op lists

* Make linter happy

* Changes from review

* Fixes after rebase

* Expand FusedOp support for slice

* Fix for fp16 _zeros and _ones

* Fix

* Moving aux functions to unnamed namespace and detail namespace -> fusion
namespace

* Disabling fusion if it alters topological order of inputs

* Print code only when env variable is set

* Fix

* Fix lint and 2 tests that specify the same names for multiple inputs

* Fixes from review and disabling fusion of slice with non-default step

* Add amp_cast to fusion, fixes

* Add amp_multicast and its backward to the list of support ops

* Apply wording suggestions from code review

Co-Authored-By: Aaron Markham <markhama@amazon.com>

* Apply wording suggestions from code review

Co-Authored-By: Aaron Markham <markhama@amazon.com>

* Make clearer comment

* Adding punctuation and capitalization to \brief descriptions

* Fix

* Fix

* Add backward_cast to fusion

* Adding unittests for fusion. Fix for erfinv_grad

* Adding slice ops and add_n to tests

* Fixes from review

* Setting inplace option

* Fix lint

* Storing double in half

* Retrigger CI

* Slight relaxing of the relative tolerance in the test

* Move the env variable check to the end

* Fix a race condition between InferShape and scheduled Forward

* Fix flakey test_fusion test involving fp32 erfinv op.

* Fix from review

* Added broadcast_like and slice_like to fused op

* Minor fix and cleanup

* Added negative axis support in slice_axis, temporarily disabled fusion of slice_like and broadcast_like

* Added axes support to slice_like

* Added axis support to broadcast_like

* Add fast_load_slice function to fused op code

* Added runtime switch for choosing fast and slow slice kernel

* Fix lint and warning

* Going easy on Windows compiler (again)

* Fix slice_like

* Debug broadcast_like fusion

* Fix lint

* Fix lint

* Trigger CI

* Get rid of the initializer list

* Fix backward calls with different gradient type

* avoid cycle when adding node specific for inputs of subgraph for pointwise fusion

* Fix lint

* Add namespace to the fusion implementations

* Set launch bounds on the fused kernel

* Fix NumPy tests

* Test showcasing an issue fixed in PR #16553

* Cast scalarts to FP32 and perform (a*1.0/b) instead of (a/b)

Fix lint errors

Fix lint

* Fix a bug in cycle detection for inputs only op in pointwise fusion

* Add comments to simple_partition_pass.h file
---
 docs/static_site/src/pages/api/faq/env_var.md |  25 +-
 src/common/exec_utils.cc                      |  79 ++
 src/common/exec_utils.h                       |  19 +
 src/executor/exec_pass.h                      |  42 +
 src/executor/graph_executor.cc                |  48 +-
 src/executor/infer_graph_attr_pass.cc         | 287 +++--
 src/executor/pointwise_fusion_pass.cc         | 308 ++++++
 src/executor/simple_partition_pass.h          | 445 ++++++++
 src/imperative/cached_op.cc                   | 292 +++--
 src/imperative/cached_op.h                    |   2 -
 src/imperative/imperative.cc                  |   4 +-
 src/operator/fusion/fused_op-inl.h            | 999 ++++++++++++++++++
 src/operator/fusion/fused_op.cc               | 307 ++++++
 src/operator/fusion/fused_op.cu               | 746 +++++++++++++
 src/operator/fusion/fused_op.h                | 203 ++++
 src/operator/mshadow_op.h                     |   2 +-
 .../tensor/elemwise_unary_op_basic.cc         |   2 +-
 src/storage/pooled_storage_manager.h          |   4 +-
 tests/python/gpu/test_fusion.py               | 223 ++++
 tests/python/unittest/test_gluon.py           |  41 +
 20 files changed, 3862 insertions(+), 216 deletions(-)
 create mode 100644 src/common/exec_utils.cc
 create mode 100644 src/executor/pointwise_fusion_pass.cc
 create mode 100644 src/executor/simple_partition_pass.h
 create mode 100644 src/operator/fusion/fused_op-inl.h
 create mode 100644 src/operator/fusion/fused_op.cc
 create mode 100644 src/operator/fusion/fused_op.cu
 create mode 100644 src/operator/fusion/fused_op.h
 create mode 100644 tests/python/gpu/test_fusion.py

diff --git a/docs/static_site/src/pages/api/faq/env_var.md b/docs/static_site/src/pages/api/faq/env_var.md
index 5cc6571fe0df..04678d9962b2 100644
--- a/docs/static_site/src/pages/api/faq/env_var.md
+++ b/docs/static_site/src/pages/api/faq/env_var.md
@@ -200,12 +200,12 @@ The following environments can be used to profile the application without changi
 
 * MXNET_PROFILER_AUTOSTART
   - Values: 0(false) or 1(true) ```(default=0)```
-	- Set to 1, MXNet starts the profiler automatically. The profiling result is stored into profile.json in the working directory.
+  - Set to 1, MXNet starts the profiler automatically. The profiling result is stored into profile.json in the working directory.
 
 * MXNET_PROFILER_MODE
   - Values: 0(false) or 1(true) ```(default=0)```
-	- If set to '0', profiler records the events of the symbolic operators.
-	- If set to '1', profiler records the events of all operators.
+  - If set to '0', profiler records the events of the symbolic operators.
+  - If set to '1', profiler records the events of all operators.
 
 ## Interface between Python and the C API
 
@@ -241,14 +241,14 @@ If ctypes is used, it must be `mxnet._ctypes.ndarray.NDArrayBase`.
 
 * MXNET_CUDA_ALLOW_TENSOR_CORE
   - 0(false) or 1(true) ```(default=1)```
-	- If set to '0', disallows Tensor Core use in CUDA ops.
-	- If set to '1', allows Tensor Core use in CUDA ops.
+  - If set to '0', disallows Tensor Core use in CUDA ops.
+  - If set to '1', allows Tensor Core use in CUDA ops.
   - This variable can only be set once in a session.
 
 * MXNET_CUDA_TENSOR_OP_MATH_ALLOW_CONVERSION
   - 0(false) or 1(true) ```(default=0)```
-	- If set to '0', disallows implicit type conversions to Float16 to use Tensor Cores
-	- If set to '1', allows CUDA ops like RNN and Convolution to use TensorCores even with Float32 input data by using implicit type casting to Float16. Only has an effect if `MXNET_CUDA_ALLOW_TENSOR_CORE` is `1`.
+  - If set to '0', disallows implicit type conversions to Float16 to use Tensor Cores
+  - If set to '1', allows CUDA ops like RNN and Convolution to use TensorCores even with Float32 input data by using implicit type casting to Float16. Only has an effect if `MXNET_CUDA_ALLOW_TENSOR_CORE` is `1`.
 
 * MXNET_CUDA_LIB_CHECKING
   - 0(false) or 1(true) ```(default=1)```
@@ -328,6 +328,17 @@ If ctypes is used, it must be `mxnet._ctypes.ndarray.NDArrayBase`.
     with float32.
   - Model accuracies do not necessarily improve with this environment variable turned on.
 
+* MXNET_USE_FUSION
+  - Values: 0(false) or 1(true) ```(default=1)```
+  - If this variable is set, MXNet will try fusing some of the operations (pointwise operations only for now).
+  - It works in Symbolic execution as well as in Gluon models hybridized with ```static_alloc=True``` option.
+  - Only applies to MXNet that has been compiled with CUDA (```pip install mxnet-cuXX``` or built from source with ```USE_CUDA=1```) and running on GPU.
+
+* MXNET_FUSION_VERBOSE
+  - Values: 0(false) or 1(true) ```(default=0)```
+  - Only applies to MXNet that has been compiled with CUDA and when ```MXNET_USE_FUSION``` option is enabled.
+  - If this variable is set, MXNet will print the code for fused operators that it generated.
+
 Settings for Minimum Memory Usage
 ---------------------------------
 - Make sure ```min(MXNET_EXEC_NUM_TEMP, MXNET_GPU_WORKER_NTHREADS) = 1```
diff --git a/src/common/exec_utils.cc b/src/common/exec_utils.cc
new file mode 100644
index 000000000000..6782abd8b21f
--- /dev/null
+++ b/src/common/exec_utils.cc
@@ -0,0 +1,79 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file exec_utils.cc
+ * \brief Implementation of executor util functions.
+ */
+
+#include "exec_utils.h"
+#include <unordered_set>
+#include <unordered_map>
+#include <string>
+
+namespace mxnet {
+namespace common {
+
+void CopyGraph(nnvm::Graph *dst, const nnvm::Graph &src, bool copy_variables) {
+  using nnvm::Node;
+  using nnvm::NodePtr;
+  using nnvm::NodeEntry;
+  std::unordered_map<Node*, NodePtr> old_new;
+  // use DFSVisit to copy all the nodes
+  DFSVisit(src.outputs, [&old_new, copy_variables](const NodePtr& node) {
+      NodePtr np;
+      if (copy_variables || !node->is_variable()) {
+        np = Node::Create();
+        np->attrs = node->attrs;
+      } else {
+        np = node;
+      }
+      old_new[node.get()] = std::move(np);
+    });
+  // connect nodes of new graph
+  for (const auto &kv : old_new) {
+    for (const NodeEntry& e : kv.first->inputs) {
+      Node *ptr = e.node.get();
+      kv.second->inputs.emplace_back(NodeEntry{old_new[ptr], e.index, e.version});
+    }
+    for (const NodePtr& p : kv.first->control_deps) {
+      kv.second->control_deps.emplace_back(old_new[p.get()]);
+    }
+  }
+  // set the head
+  for (const NodeEntry &e : src.outputs) {
+    (*dst).outputs.emplace_back(NodeEntry{old_new[e.node.get()], e.index, e.version});
+  }
+}
+
+bool CheckForInputNameDuplicates(const nnvm::IndexedGraph &idx) {
+  std::unordered_set<std::string> names;
+  for (const auto& nid : idx.input_nodes()) {
+    const std::string &name = idx[nid].source->attrs.name;
+    if (names.count(name)) {
+      LOG(WARNING) << "Variable name " << name << " is used more than once!";
+      return false;
+    }
+    names.insert(name);
+  }
+  return true;
+}
+
+}  // namespace common
+}  // namespace mxnet
diff --git a/src/common/exec_utils.h b/src/common/exec_utils.h
index d8b7a33bf22b..3bd2ef3597a9 100644
--- a/src/common/exec_utils.h
+++ b/src/common/exec_utils.h
@@ -621,6 +621,25 @@ inline nnvm::Graph AssignContext(nnvm::Graph g,
   return g;
 }
 
+/*!
+ * \brief Copy the graph, optionally leaving original Variable nodes.
+ *
+ * \param dst destination graph
+ * \param src source graph being copied
+ * \param copy_variable whether to copy or reuse Variable nodes from the
+ *                      source graph
+ */
+void CopyGraph(nnvm::Graph *dst, const nnvm::Graph &src, bool copy_variables);
+
+/*!
+ * \brief Check whether graph contains any duplicated names in its inputs.
+ *
+ * \param idx Indexed graph being checked
+ *
+ * \return true if there are no duplicates, false otherwise
+ */
+bool CheckForInputNameDuplicates(const nnvm::IndexedGraph &idx);
+
 }  // namespace common
 }  // namespace mxnet
 #endif  // MXNET_COMMON_EXEC_UTILS_H_
diff --git a/src/executor/exec_pass.h b/src/executor/exec_pass.h
index f544d6ba3392..25a326171510 100644
--- a/src/executor/exec_pass.h
+++ b/src/executor/exec_pass.h
@@ -34,10 +34,34 @@
 #include <vector>
 #include <memory>
 #include <string>
+#include <utility>
+#include <tuple>
 
 namespace mxnet {
 namespace exec {
 
+template <typename Attr>
+using FAccessSubgraphAttr = std::function<std::tuple<const nnvm::NodePtr,
+                                          std::vector<Attr>,
+                                          std::vector<Attr>>
+                              (const NodeAttrs& attrs)>;
+
+using FAccessSubgraphShape = FAccessSubgraphAttr<mxnet::TShape>;
+using FAccessSubgraphType = FAccessSubgraphAttr<int>;
+using FAccessSubgraphStorageType = FAccessSubgraphAttr<int>;
+
+template <typename Attr>
+using FProvideSubgraphAttr = std::function<void (const NodeAttrs& attrs,
+                                                 const std::vector<nnvm::NodePtr> &nodes,
+                                                 const std::vector<std::vector<Attr>> &in_attrs,
+                                                 const std::vector<std::vector<Attr>> &out_attrs)>;
+using FProvideSubgraphShape = FProvideSubgraphAttr<mxnet::TShape>;
+using FProvideSubgraphType = FProvideSubgraphAttr<int>;
+using FProvideSubgraphStorageType = FProvideSubgraphAttr<int>;
+
+using TIsFusion = bool;
+using TIsFusionHelper = bool;
+
 /*! \brief reuse graph definition */
 using nnvm::Graph;
 
@@ -170,6 +194,24 @@ void AttachOpResources(const Graph& g,
  */
 Graph DetectInplaceAddTo(Graph g);
 
+/*!
+ * \brief Fuse pointwise operations in the forward pass.
+ *
+ * \param g input graph (needs to be entire graph, not just forward part)
+ *
+ * \return graph with fused pointwise operations in the forward pass
+ */
+Graph FusePointwiseForward(Graph&& g);
+
+/*!
+ * \brief Fuse pointwise operations in the backward pass.
+ *
+ * \param g input graph (needs to be entire graph, not just forward part)
+ *
+ * \return graph with fused pointwise operations in the backward pass
+ */
+Graph FusePointwiseBackward(Graph&& g);
+
 /*!
  * \brief Infer shapes in the graph given the information.
  * \param graph The input graph.
diff --git a/src/executor/graph_executor.cc b/src/executor/graph_executor.cc
index 882105da1321..4f1553bc19d5 100644
--- a/src/executor/graph_executor.cc
+++ b/src/executor/graph_executor.cc
@@ -26,6 +26,7 @@
 #include <nnvm/graph.h>
 #include <nnvm/pass_functions.h>
 #include <vector>
+#include <set>
 #include <algorithm>
 
 #include "./exec_pass.h"
@@ -337,6 +338,7 @@ nnvm::Graph GraphExecutor::InitFullGraph(nnvm::Symbol symbol,
   if (!need_grad_) return g;
   for (size_t i = 0; i < g.outputs.size(); ++i) {
     NodeEntry ngrad(nnvm::Node::Create(), 0, 0);
+    ngrad.node->attrs.name = "_head_grad_" + std::to_string(i);
     head_grad_entry_.emplace_back(AttrHint(ngrad, g.outputs[i]));
     head_grad_map_[ngrad.node.get()] = i;
   }
@@ -377,6 +379,7 @@ nnvm::Graph GraphExecutor::InitFullGraph(nnvm::Symbol symbol,
   for (const auto &e : g_grad.outputs) {
     g.outputs.push_back(e);
   }
+
   return g;
 }
 
@@ -796,6 +799,7 @@ void GraphExecutor::Init(nnvm::Symbol symbol,
                          const nnvm::NodeEntryMap<NDArray>& feed_dict) {
   nnvm::Graph g = InitGraph(symbol, default_ctx, ctx_map, in_arg_ctxes, arg_grad_ctxes,
                             aux_state_ctxes, grad_req_types);
+
   // The following code of shape and dtype inferences and argument
   // initialization is for simple_bind only. Regular bind operation
   // should do this differently.
@@ -976,6 +980,7 @@ Executor* GraphExecutor::Reshape(const bool partial_shaping,
              this);
   return exec;
 }
+
 /*!
  * \brief This function is triggered by both simple_bind
  * and bind flows.
@@ -993,6 +998,41 @@ Graph GraphExecutor::InitGraph(nnvm::Symbol symbol,
   // setup gradient
   nnvm::Graph g = InitFullGraph(symbol, grad_req_types);
 
+#if MXNET_USE_CUDA && !defined(_WIN32)
+  if (default_ctx.dev_mask() == Context::kGPU && dmlc::GetEnv("MXNET_USE_FUSION", true)) {
+    nnvm::Graph unoptimized_graph;
+    common::CopyGraph(&unoptimized_graph, g, false);
+
+    if (common::CheckForInputNameDuplicates(unoptimized_graph.indexed_graph())) {
+      g.attrs["num_forward_outputs"] = std::make_shared<nnvm::any>(num_forward_outputs_);
+      g = FusePointwiseForward(std::move(g));
+      g.attrs["num_forward_outputs"] = std::make_shared<nnvm::any>(num_forward_outputs_);
+      g = FusePointwiseBackward(std::move(g));
+      // Check the topological order of inputs
+      const auto &original_inputs = unoptimized_graph.indexed_graph().input_nodes();
+      const auto &new_inputs = g.indexed_graph().input_nodes();
+      if (original_inputs.size() != new_inputs.size()) {
+        LOG(WARNING)
+          << "Number of inputs after fusion does not match original number of inputs. "
+          << "This is most probably a bug. Disabling fusion for this run.";
+        g = unoptimized_graph;
+      } else {
+        for (size_t i = 0; i < new_inputs.size(); ++i) {
+          if (unoptimized_graph.indexed_graph()[original_inputs[i]].source->attrs.name !=
+              g.indexed_graph()[new_inputs[i]].source->attrs.name) {
+            LOG(WARNING) << "Disabling fusion due to altered topological order of inputs.";
+            g = unoptimized_graph;
+            break;
+          }
+        }
+      }
+    } else {
+      LOG(WARNING)
+        << "Graph contains duplicate names for some of its inputs - fusion is NOT enabled!";
+     }
+  }
+#endif  // MXNET_USE_CUDA
+
   // create "device" and "context" attrs for the graph
   g = AssignContext(g, default_ctx, ctx_map,
                     in_arg_ctxes,
@@ -1946,7 +1986,7 @@ Executor *Executor::SimpleBind(nnvm::Symbol symbol,
       symbol = exec::BuildSubgraph(symbol, backend, arg_shape_map, arg_dtype_map, arg_stype_map,
                                    default_ctx, group2ctx, &tmp_in_arg_ctxes, &tmp_arg_grad_ctxes,
                                    &tmp_grad_req_types, &tmp_aux_state_ctxes, verbose);
-      exec->Init(symbol, default_ctx, group2ctx, tmp_in_arg_ctxes, tmp_arg_grad_ctxes,
+      exec->Init(symbol.Copy(), default_ctx, group2ctx, tmp_in_arg_ctxes, tmp_arg_grad_ctxes,
                  tmp_aux_state_ctxes, arg_shape_map, arg_dtype_map, arg_stype_map,
                  tmp_grad_req_types, shared_arg_names, &tmp_in_args, &tmp_arg_grads,
                  &tmp_aux_states, shared_buffer, shared_exec);
@@ -1985,7 +2025,7 @@ Executor *Executor::SimpleBind(nnvm::Symbol symbol,
   }
   if (!init) {
     // init without subgraph
-    exec->Init(symbol, default_ctx, group2ctx, in_arg_ctxes, arg_grad_ctxes, aux_state_ctxes,
+    exec->Init(symbol.Copy(), default_ctx, group2ctx, in_arg_ctxes, arg_grad_ctxes, aux_state_ctxes,
                arg_shape_map, arg_dtype_map, arg_stype_map, grad_req_types, shared_arg_names,
                in_args, arg_grads, aux_states, shared_buffer, shared_exec);
   }
@@ -2017,8 +2057,8 @@ Executor *Executor::Bind(nnvm::Symbol symbol,
                                    verbose);
     }
   }
-  exec->Init(symbol, default_ctx, group2ctx, tmp_in_args, tmp_arg_grad_store, tmp_grad_req_type,
-             tmp_aux_states, reinterpret_cast<Executor*>(shared_exec));
+  exec->Init(symbol.Copy(), default_ctx, group2ctx, tmp_in_args, tmp_arg_grad_store,
+             tmp_grad_req_type, tmp_aux_states, reinterpret_cast<Executor*>(shared_exec));
   return exec;
 }
 }  // namespace mxnet
diff --git a/src/executor/infer_graph_attr_pass.cc b/src/executor/infer_graph_attr_pass.cc
index d72325392604..80e4084c478e 100644
--- a/src/executor/infer_graph_attr_pass.cc
+++ b/src/executor/infer_graph_attr_pass.cc
@@ -63,6 +63,156 @@ bool ApplyOpInferAttr<int, FInferStorageType>(const nnvm::Graph& g,
   return true;
 }
 
+template<typename AttrType, typename IsNone>
+inline void GetAttrFromForwardNode(const uint32_t nid,
+                                   const nnvm::IndexedGraph &idx,
+                                   std::vector<AttrType>* rshape_ptr,
+                                   IsNone fis_none) {
+  std::vector<AttrType>& rshape = *rshape_ptr;
+  const nnvm::IndexedGraph::Node& inode = idx[nid];
+  // gradient function, used to get node correspondence.
+  static auto& fgrad =
+      Op::GetAttr<nnvm::FGradient>("FGradient");
+  nnvm::NodePtr fwd_ptr = inode.source->control_deps[0];
+  const nnvm::IndexedGraph::Node& fnode = idx[inode.control_deps[0]];
+  // use gradient function to find out the correspondence.
+  std::vector<nnvm::NodeEntry> ograd(fwd_ptr->num_outputs());
+  for (size_t i = 0; i < ograd.size(); ++i) {
+    ograd[i].index = static_cast<uint32_t>(i);
+  }
+  // input gradient list
+  const std::vector<nnvm::NodeEntry>& igrad = fgrad[fwd_ptr->op()](fwd_ptr, ograd);
+  const nnvm::Node* igrad_node = nullptr;
+  // Input gradient assignement
+  for (size_t i = 0; i < igrad.size(); ++i) {
+    if (igrad[i].node->op() == inode.source->op()) {
+      uint32_t eid = idx.entry_id(nid, igrad[i].index);
+      if (fis_none(rshape[eid])) {
+        rshape[eid] = rshape[idx.entry_id(fnode.inputs[i])];
+      } else if (!fis_none(rshape[idx.entry_id(fnode.inputs[i])])) {
+        // Need to skip empty forward shape, because it may not be
+        // available now and it is possible to infer the forward
+        // shape in one of the next a few passes
+        CHECK_EQ(rshape[eid], rshape[idx.entry_id(fnode.inputs[i])])
+            << "Backward shape inconsistent with the forward shape";
+      }
+      if (igrad_node == nullptr) {
+        igrad_node = igrad[i].node.get();
+      } else {
+        CHECK(igrad_node == igrad[i].node.get());
+      }
+    }
+  }
+  // out grad entries
+  CHECK(igrad_node != nullptr)
+    << "Cannot find matching backward op for " << inode.source->attrs.name;
+  for (size_t i = 0; i < igrad_node->inputs.size(); ++i) {
+    const nnvm::NodeEntry& e = igrad_node->inputs[i];
+    if (e.node == nullptr) {
+      uint32_t eid = idx.entry_id(inode.inputs[i]);
+      if (fis_none(rshape[eid])) {
+        rshape[eid] = rshape[idx.entry_id(inode.control_deps[0], e.index)];
+      }
+    }
+  }
+}
+
+template<typename FAccessSubgraphType, typename AttrType, typename IsNone>
+void GetAttrFromFusedNode(uint32_t nid,
+                          const nnvm::IndexedGraph& idx,
+                          std::vector<AttrType>* rshape_ptr,
+                          IsNone fis_none,
+                          const std::string& infer_fusion_name) {
+  std::vector<AttrType>& rshape = *rshape_ptr;
+  const auto& inode = idx[nid];
+  // gradient function, used to get node correspondence.
+  static auto& fgrad =
+      Op::GetAttr<nnvm::FGradient>("FGradient");
+  nnvm::NodePtr fused_fwd_ptr = inode.source->control_deps[0];
+  static auto& finfer_fused_shape =
+    Op::GetAttr<FAccessSubgraphType>(infer_fusion_name);
+  auto finfer = finfer_fused_shape.get(fused_fwd_ptr->op(), nullptr);
+  CHECK(finfer != nullptr) << "Operator " << fused_fwd_ptr->attrs.name <<
+    " is marked as Fusion but does not allow accessing attributes";
+  const auto& inferred_attrs = finfer(fused_fwd_ptr->attrs);
+  const auto& fwd_ptr = std::get<0>(inferred_attrs);
+  const auto& input_attrs = std::get<1>(inferred_attrs);
+  const auto& output_attrs = std::get<2>(inferred_attrs);
+
+  // use gradient function to find out the correspondence.
+  std::vector<nnvm::NodeEntry> ograd(fwd_ptr->num_outputs());
+  for (size_t i = 0; i < ograd.size(); ++i) {
+    ograd[i].index = static_cast<uint32_t>(i);
+  }
+  // input gradient list
+  const std::vector<nnvm::NodeEntry>& igrad = fgrad[fwd_ptr->op()](fwd_ptr, ograd);
+  const nnvm::Node* igrad_node = nullptr;
+  // Set the attributes of output gradients
+  // using attributes of forward node inputs
+  for (size_t i = 0; i < igrad.size(); ++i) {
+    if (igrad[i].node->op() == inode.source->op()) {
+      uint32_t eid = idx.entry_id(nid, igrad[i].index);
+      if (fis_none(rshape[eid])) {
+        rshape[eid] = input_attrs[i];
+      } else if (!fis_none(input_attrs[i])) {
+        // Need to skip empty forward shape, because it may not be
+        // available now and it is possible to infer the forward
+        // shape in one of the next a few passes
+        CHECK_EQ(rshape[eid], input_attrs[i])
+            << "Backward shape inconsistent with the forward shape";
+      }
+      if (igrad_node == nullptr) {
+        igrad_node = igrad[i].node.get();
+      } else {
+        CHECK(igrad_node == igrad[i].node.get());
+      }
+    }
+  }
+
+  // Set the attributes of input gradients
+  // using attributes of forward node outputs
+  CHECK(igrad_node != nullptr)
+    << "Cannot find matching backward op for " << inode.source->attrs.name;
+  for (size_t i = 0; i < igrad_node->inputs.size(); ++i) {
+    const nnvm::NodeEntry& e = igrad_node->inputs[i];
+    if (e.node == nullptr) {
+      uint32_t eid = idx.entry_id(inode.inputs[i]);
+      if (fis_none(rshape[eid])) {
+        rshape[eid] = output_attrs[e.index];
+      }
+    }
+  }
+}
+
+template <typename FProvideSubgraphType, typename AttrType>
+void ProvideAttrToFusion(const uint32_t nid,
+                         const nnvm::IndexedGraph& idx,
+                         const std::vector<AttrType>& rshape,
+                         const std::string& provide_fusion_name) {
+  const auto& inode = idx[nid];
+  std::vector<std::vector<AttrType>> in_attrs;
+  std::vector<std::vector<AttrType>> out_attrs;
+  for (const auto& dep_node : inode.source->control_deps) {
+    in_attrs.push_back({});
+    out_attrs.push_back({});
+    auto &current_in_attrs = in_attrs.back();
+    auto &current_out_attrs = out_attrs.back();
+    uint32_t dep_node_id = idx.node_id(dep_node.get());
+    for (const auto& e : idx[dep_node_id].inputs) {
+      current_in_attrs.push_back(rshape[idx.entry_id(e)]);
+    }
+    for (size_t i = 0; i < dep_node->num_outputs(); ++i) {
+      current_out_attrs.push_back(rshape[idx.entry_id(dep_node_id, i)]);
+    }
+  }
+  auto provide =
+    Op::GetAttr<FProvideSubgraphType>(provide_fusion_name).get(inode.source->op(), nullptr);
+  CHECK(provide != nullptr) <<
+    "Encountered Fusion operator that does not implement providing subgraph attr " <<
+    provide_fusion_name << ".";
+  provide(inode.source->attrs, inode.source->control_deps, in_attrs, out_attrs);
+}
+
 /*!\brief
  * This is a duplicate of the InferAttr function in nnvm with minor modification
  * to support inferring storage type whose function signature is different from
@@ -73,6 +223,7 @@ bool ApplyOpInferAttr<int, FInferStorageType>(const nnvm::Graph& g,
  * \param ret graph used for attribute inference
  * \param emmpty_val empty value of the attribute
  * \param infer_name name of the function used for attribute inference
+ * \param infer_fusion_name name of the function used for accessing attributes in fused nodes
  * \param input_name name of the attribute in the graph used to store the
  *                   input data for attribute inference
  * \param attr_key_name name of the attribute used for inference for variable nodes
@@ -90,10 +241,13 @@ bool ApplyOpInferAttr<int, FInferStorageType>(const nnvm::Graph& g,
  * \param default_mode_val default value of the dispatch mode attribute on the node. Used
  *                         for storage type inference
  */
-template<typename AttrType, typename FInferType, typename IsNone, typename FDefault>
+template<typename AttrType, typename FInferType, typename FAccessSubgraphType,
+         typename FProvideSubgraphType, typename IsNone, typename FDefault>
 nnvm::Graph InferAttr(nnvm::Graph &&ret,
                       const AttrType empty_val,
                       const char* infer_name,
+                      const char* infer_fusion_name,
+                      const char* provide_fusion_name,
                       const char* input_name,
                       const char* attr_key_name,
                       const char* attr_name,
@@ -114,9 +268,6 @@ nnvm::Graph InferAttr(nnvm::Graph &&ret,
       Op::GetAttr<FInferType>(infer_name);
   static auto& is_backward =
       Op::GetAttr<nnvm::TIsBackward>("TIsBackward");
-  // gradient function, used to get node correspondence.
-  static auto& fgrad =
-      Op::GetAttr<nnvm::FGradient>("FGradient");
   // reshape shape vector
   AttrVector rshape;
   // dispatch mode vector
@@ -209,53 +360,19 @@ nnvm::Graph InferAttr(nnvm::Graph &&ret,
         op::dispatch_mode_assign(&dispatch_modes[nid], default_mode_val);
       }
     } else if (is_backward.get(inode.source->op(), false) &&
-               inode.control_deps.size() && bwd_identity_assign) {
+               inode.source->control_deps.size() && bwd_identity_assign) {
       CHECK(dispatch_mode_name == nullptr)
         << "Backward inference for node attributes is not available";
-      CHECK_GE(inode.control_deps.size(), 1U)
+      CHECK_GE(inode.source->control_deps.size(), 1U)
         << "BackwardOp need to have control_deps to its forward op";
-      const IndexedGraph::Node& fnode = idx[inode.control_deps[0]];
       nnvm::NodePtr fwd_ptr = inode.source->control_deps[0];
       CHECK(fwd_ptr->op() != nullptr) << "Forward op cannot be a variable";
-      // use gradient function to find out the correspondence.
-      std::vector<nnvm::NodeEntry> ograd(fwd_ptr->num_outputs());
-      for (size_t i = 0; i < ograd.size(); ++i) {
-        ograd[i].index = static_cast<uint32_t>(i);
-      }
-      // input gradient list
-      auto igrad = fgrad[fwd_ptr->op()](fwd_ptr, ograd);
-      const nnvm::Node* igrad_node = nullptr;
-      // Input gradient assignement
-      for (size_t i = 0; i < igrad.size(); ++i) {
-        if (igrad[i].node->op() == inode.source->op()) {
-          uint32_t eid = idx.entry_id(nid, igrad[i].index);
-          if (fis_none(rshape[eid])) {
-            rshape[eid] = rshape[idx.entry_id(fnode.inputs[i])];
-          } else if (!fis_none(rshape[idx.entry_id(fnode.inputs[i])])) {
-            // Need to skip empty forward shape, because it may not be
-            // available now and it is possible to infer the forward
-            // shape in one of the next a few passes
-            CHECK_EQ(rshape[eid], rshape[idx.entry_id(fnode.inputs[i])])
-                << "Backward shape inconsistent with the forward shape";
-          }
-          if (igrad_node == nullptr) {
-            igrad_node = igrad[i].node.get();
-          } else {
-            CHECK(igrad_node == igrad[i].node.get());
-          }
-        }
-      }
-      // out grad entries
-      CHECK(igrad_node != nullptr)
-        << "Cannot find matching backward op for " << inode.source->attrs.name;
-      for (size_t i = 0; i < igrad_node->inputs.size(); ++i) {
-        const nnvm::NodeEntry& e = igrad_node->inputs[i];
-        if (e.node == nullptr) {
-          uint32_t eid = idx.entry_id(inode.inputs[i]);
-          if (fis_none(rshape[eid])) {
-            rshape[eid] = rshape[idx.entry_id(inode.control_deps[0], e.index)];
-          }
-        }
+
+      static auto& is_fusion_helper = Op::GetAttr<exec::TIsFusionHelper>("TIsFusionHelper");
+      if (!is_fusion_helper.get(fwd_ptr->op(), false)) {
+        GetAttrFromForwardNode(nid, idx, &rshape, fis_none);
+      } else {
+        GetAttrFromFusedNode<FAccessSubgraphType>(nid, idx, &rshape, fis_none, infer_fusion_name);
       }
     } else {
       DispatchMode* dispatch_mode = nullptr;
@@ -280,6 +397,10 @@ nnvm::Graph InferAttr(nnvm::Graph &&ret,
         if (finfer != nullptr) {
           // Call inference function of the operator.
           try {
+            static auto& is_fusion = Op::GetAttr<exec::TIsFusion>("TIsFusion");
+            if (is_fusion.get(inode.source->op(), false)) {
+              ProvideAttrToFusion<FProvideSubgraphType>(nid, idx, rshape, provide_fusion_name);
+            }
             forward_known = ApplyOpInferAttr(ret, finfer, inode.source->attrs,
                                              nid, &ishape, &oshape, dispatch_mode);
           } catch (const std::exception& e) {
@@ -394,9 +515,6 @@ nnvm::Graph InferShapeAttr(nnvm::Graph &&ret,
       Op::GetAttr<FInferType>(infer_name);
   static auto& is_backward =
       Op::GetAttr<nnvm::TIsBackward>("TIsBackward");
-  // gradient function, used to get node correspondence.
-  static auto& fgrad =
-      Op::GetAttr<nnvm::FGradient>("FGradient");
   // reshape shape vector
   AttrVector rshape;
   // dispatch mode vector
@@ -500,53 +618,20 @@ nnvm::Graph InferShapeAttr(nnvm::Graph &&ret,
         op::dispatch_mode_assign(&dispatch_modes[nid], default_mode_val);
       }
     } else if (is_backward.get(inode.source->op(), false) &&
-               inode.control_deps.size() && bwd_identity_assign) {
+               inode.source->control_deps.size() && bwd_identity_assign) {
       CHECK(dispatch_mode_name == nullptr)
         << "Backward inference for node attributes is not available";
-      CHECK_GE(inode.control_deps.size(), 1U)
+      CHECK_GE(inode.source->control_deps.size(), 1U)
         << "BackwardOp need to have control_deps to its forward op";
-      const IndexedGraph::Node& fnode = idx[inode.control_deps[0]];
       nnvm::NodePtr fwd_ptr = inode.source->control_deps[0];
       CHECK(fwd_ptr->op() != nullptr) << "Forward op cannot be a variable";
-      // use gradient function to find out the correspondence.
-      std::vector<nnvm::NodeEntry> ograd(fwd_ptr->num_outputs());
-      for (size_t i = 0; i < ograd.size(); ++i) {
-        ograd[i].index = static_cast<uint32_t>(i);
-      }
-      // input gradient list
-      auto igrad = fgrad[fwd_ptr->op()](fwd_ptr, ograd);
-      const nnvm::Node* igrad_node = nullptr;
-      // Input gradient assignement
-      for (size_t i = 0; i < igrad.size(); ++i) {
-        if (igrad[i].node->op() == inode.source->op()) {
-          uint32_t eid = idx.entry_id(nid, igrad[i].index);
-          if (fis_none(rshape[eid])) {
-            rshape[eid] = rshape[idx.entry_id(fnode.inputs[i])];
-          } else if (!fis_none(rshape[idx.entry_id(fnode.inputs[i])])) {
-            // Need to skip empty forward shape, because it may not be
-            // available now and it is possible to infer the forward
-            // shape in one of the next a few passes
-            CHECK_EQ(rshape[eid], rshape[idx.entry_id(fnode.inputs[i])])
-                << "Backward shape inconsistent with the forward shape";
-          }
-          if (igrad_node == nullptr) {
-            igrad_node = igrad[i].node.get();
-          } else {
-            CHECK(igrad_node == igrad[i].node.get());
-          }
-        }
-      }
-      // out grad entries
-      CHECK(igrad_node != nullptr)
-        << "Cannot find matching backward op for " << inode.source->attrs.name;
-      for (size_t i = 0; i < igrad_node->inputs.size(); ++i) {
-        const nnvm::NodeEntry& e = igrad_node->inputs[i];
-        if (e.node == nullptr) {
-          uint32_t eid = idx.entry_id(inode.inputs[i]);
-          if (fis_none(rshape[eid])) {
-            rshape[eid] = rshape[idx.entry_id(inode.control_deps[0], e.index)];
-          }
-        }
+
+      static auto& is_fusion_helper = Op::GetAttr<exec::TIsFusionHelper>("TIsFusionHelper");
+      if (!is_fusion_helper.get(fwd_ptr->op(), false)) {
+        GetAttrFromForwardNode(nid, idx, &rshape, fis_none);
+      } else {
+        GetAttrFromFusedNode<exec::FAccessSubgraphShape>(nid, idx, &rshape, fis_none,
+                                                         "FAccessSubgraphShape");
       }
     } else {
       DispatchMode* dispatch_mode = nullptr;
@@ -581,6 +666,11 @@ nnvm::Graph InferShapeAttr(nnvm::Graph &&ret,
         if (finfer != nullptr) {
           // Call inference function of the operator.
           try {
+            static auto& is_fusion = Op::GetAttr<exec::TIsFusion>("TIsFusion");
+            if (is_fusion.get(inode.source->op(), false)) {
+              ProvideAttrToFusion<exec::FProvideSubgraphShape>(nid, idx, rshape,
+                                                               "FProvideSubgraphShape");
+            }
             forward_known = ApplyOpInferAttr(ret, finfer, inode.source->attrs,
                                              nid, &ishape, &oshape, dispatch_mode);
           } catch (const std::exception& e) {
@@ -686,10 +776,11 @@ nnvm::Graph InferType(nnvm::Graph&& graph,
   if (dtype_attr_key.length() != 0) {
     graph.attrs["dtype_attr_key"] = std::make_shared<any>(dtype_attr_key);
   }
-  return InferAttr<int, nnvm::FInferType>(
+  return InferAttr<int, nnvm::FInferType, exec::FAccessSubgraphType,
+                   exec::FProvideSubgraphType>(
       std::move(graph), -1,
-      "FInferType", "dtype_inputs", "dtype_attr_key",
-      "dtype", "dtype_num_unknown_nodes",
+      "FInferType", "FAccessSubgraphType", "FProvideSubgraphType",
+      "dtype_inputs", "dtype_attr_key", "dtype", "dtype_num_unknown_nodes",
       [](const int t) { return t == -1; },
       common::SameType, true, nullptr);
 }
@@ -719,10 +810,12 @@ nnvm::Graph InferStorageType(nnvm::Graph&& graph,
   }
 
   // for storage type, the backward attr is not necessarily the same as it's correspondence
-  nnvm::Graph ret = InferAttr<int, FInferStorageType>(
+  nnvm::Graph ret = InferAttr<int, FInferStorageType, exec::FAccessSubgraphStorageType,
+                              exec::FProvideSubgraphStorageType>(
       std::move(graph), -1,
-      "FInferStorageType", "storage_type_inputs", "storage_type_attr_key",
-      "storage_type", "storage_type_num_unknown_nodes",
+      "FInferStorageType", "FAccessSubgraphStorageType", "FProvideSubgraphStorageType",
+      "storage_type_inputs", "storage_type_attr_key", "storage_type",
+      "storage_type_num_unknown_nodes",
       [](const int t) { return t == -1; },
       common::DefaultStorageType, false, "dispatch_mode", DispatchMode::kVariable);
 
diff --git a/src/executor/pointwise_fusion_pass.cc b/src/executor/pointwise_fusion_pass.cc
new file mode 100644
index 000000000000..c6e2405cb2a4
--- /dev/null
+++ b/src/executor/pointwise_fusion_pass.cc
@@ -0,0 +1,308 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file pointwise_fusion_pass.cc
+ * \brief Pass applying pointwise fusion.
+ * \author Clement Fuji Tsang
+ */
+
+#include <mxnet/base.h>
+#include <mxnet/operator.h>
+#include <mxnet/op_attr_types.h>
+#include <nnvm/graph_attr_types.h>
+#include <nnvm/pass_functions.h>
+#include <algorithm>
+#include <queue>
+#include "./simple_partition_pass.h"
+#include "../operator/fusion/fused_op-inl.h"
+#include "../operator/fusion/fused_op.h"
+#include "../operator/operator_common.h"
+
+#if MXNET_USE_CUDA
+
+namespace mxnet {
+namespace exec {
+namespace {
+  bool IsFusionCompatible(nnvm::Node* n) {
+    using namespace mxnet::fusion;
+    if (n->op() == nullptr)
+      return false;
+    std::string op_name = n->op()->name;
+    if (ops_desc.count(op_name))
+      return true;
+    if (slice_ops.count(op_name))
+      return false;
+    if (std::find(variable_io_ops.begin(),
+                  variable_io_ops.end(),
+                  op_name) !=
+        variable_io_ops.end())
+      return true;
+    return false;
+  }
+
+  bool IsInputsOnlyCompatible(nnvm::Node* n) {
+    using namespace mxnet::fusion;
+    if (n->op() == nullptr)
+      return false;
+    std::string op_name = n->op()->name;
+    if (slice_ops.count(op_name)) {
+      if (op_name == "slice") {
+        // slice with non-default step attribute is not supported
+        // currently
+        if (n->attrs.dict.count("step") &&
+            !(n->attrs.dict.at("step") == "()" ||
+              n->attrs.dict.at("step") == "[]")) {
+          return false;
+        }
+      }
+      return true;
+    }
+    return false;
+  }
+
+  nnvm::NodePtr CreateSubgraphNode(const Graph& subgraph, size_t inputs_size) {
+    nnvm::Symbol subgraph_sym;
+    auto node = nnvm::Node::Create();
+    subgraph_sym.outputs = subgraph.outputs;
+    node->attrs.subgraphs.emplace_back(std::make_shared<nnvm::Symbol>(subgraph_sym));
+    std::ostringstream name_oss;
+    // the name of the new node will be the concatenation of all the node names in the subgraph
+    DFSVisit(subgraph.outputs, [&name_oss](const nnvm::NodePtr n) {
+      if (n->op() != nullptr)
+        name_oss << n->op()->name << "_";
+    });
+    auto subgraph_name = name_oss.str();
+    subgraph_name.pop_back();
+    node->attrs.name = subgraph_name;
+    node->attrs.dict["num_inputs"] = std::to_string(inputs_size);
+    node->attrs.dict["num_outputs"] = std::to_string(subgraph.outputs.size());
+    node->attrs.op = Op::Get("_FusedOp");
+    node->op()->attr_parser(&(node->attrs));
+    return node;
+  }
+}  // namespace
+
+/*!
+ * \brief Replace a set of nodes by a subgraph node.
+ *        This function is used specifically in pointwise fusion.
+ */
+template<typename FCreateNode>
+Graph ReplaceSubgraphsPointwise(Graph&& g, const std::vector<NodeRawPtrSet>& subgraph_sets,
+                                FCreateNode create_subgraph_node) {
+  for (auto subgraph_set : subgraph_sets) {
+    // Create MXNet subgraph
+    Graph subgraph;
+    const auto sub_outputs_in_main = GetSubgraphOutputs(g, subgraph_set);
+    subgraph.outputs.resize(sub_outputs_in_main.size());
+    for (auto p : sub_outputs_in_main) {
+      subgraph.outputs[p.second] = p.first;
+    }
+    // To generate a subgraph an input has to be replaced by data node (no op)
+    // and it has to be agnostic to the node from which it's an output
+    // (For example, even if two inputs are two different outputs from the same node,
+    // they need to be replaced by two completely separate data nodes)
+    auto inputs = GetSubgraphInputs(subgraph, subgraph_set);
+    auto subgraph_node = create_subgraph_node(subgraph, inputs.size());
+    subgraph_node->inputs = inputs;
+    // replug inputs of node out of subgraph to be output of the subgraph node
+    // if it was a node in the subgraph
+    DFSVisit(g.outputs,
+        [&subgraph_node, &subgraph_set, &sub_outputs_in_main](const nnvm::NodePtr node) {
+      if (!subgraph_set.count(node.get())) {
+        for (auto &e : node->inputs) {
+          auto it = sub_outputs_in_main.find(e);
+          if (it != sub_outputs_in_main.end()) {
+            e.node = subgraph_node;
+            e.index = it->second;
+          }
+        }
+      }
+    });
+    // replug outputs of the graph to be output of the subgraph node
+    // if it was a node in the subgraph
+    for (auto &e : g.outputs) {
+      auto it = sub_outputs_in_main.find(e);
+      if (it != sub_outputs_in_main.end()) {
+        e.node = subgraph_node;
+        e.index = it->second;
+      }
+    }
+    // move control dependencies between nodes of the subgraph and out of the subgraph
+    // to a dependencies between the subgraph node and the nodes out of the subgraph
+    DFSVisit(subgraph.outputs, [&subgraph_node, &subgraph_set](const nnvm::NodePtr& node) {
+      if (subgraph_set.count(node.get())) {
+        auto it = node->control_deps.begin();
+        static auto& is_fusion = Op::GetAttr<exec::TIsFusionHelper>("TIsFusionHelper");
+        std::vector<nnvm::NodePtr> new_control_deps;
+        while (it != node->control_deps.end()) {
+          if (subgraph_set.count(it->get())) {
+            new_control_deps.push_back(*it);
+          } else {
+            if ((*it)->is_variable() || !is_fusion.get((*it)->op(), false)) {
+              uint32_t node_id = subgraph_node->control_deps.size();
+              subgraph_node->control_deps.push_back(*it);
+              auto helper_node = op::MakeNode("_FusedOpOutHelper",
+                                              subgraph_node->attrs.name + "_"
+                                              + node->attrs.name + "_outhelper",
+                                              nullptr,
+                                              nullptr,
+                                              nullptr);
+              helper_node->attrs.parsed =
+                FusedOpHelperParamPtr(new FusedOpHelperParam(
+                      nnvm::get<FusedOpPtr>(subgraph_node->attrs.parsed),
+                      node_id));
+              new_control_deps.push_back(helper_node);
+            } else {
+              new_control_deps.push_back(*it);
+            }
+          }
+          ++it;
+        }
+        node->control_deps = new_control_deps;
+      }
+    });
+
+    const auto& index = subgraph.indexed_graph();
+    DFSVisit(g.outputs, [&subgraph_node, &subgraph_set, &index](const nnvm::NodePtr& node) {
+      for (auto &e : node->control_deps) {
+        if (subgraph_set.count(e.get())) {
+          uint32_t node_id = index.node_id(e.get());
+          auto helper_node = op::MakeNode("_FusedOpHelper",
+                                          subgraph_node->attrs.name + "_"
+                                          + node->attrs.name + "_helper",
+                                          nullptr,
+                                          nullptr,
+                                          nullptr);
+          helper_node->attrs.parsed =
+            FusedOpHelperParamPtr(new FusedOpHelperParam(
+                  nnvm::get<FusedOpPtr>(subgraph_node->attrs.parsed),
+                  node_id));
+          e = helper_node;
+        }
+      }
+    });
+  }
+  Graph new_graph;
+  new_graph.outputs = g.outputs;
+  return new_graph;
+}
+
+/* \brief Add nodes as inputs to the subgraph. This is used for operations
+ *        which are only compatible when they are the first nodes in the
+ *        subgraph.
+ */
+template <typename IsCompatible>
+void AddInputsOnlyCompatible(const Graph &g,
+                             std::vector<std::unordered_set<nnvm::Node*> >* subsets,
+                             IsCompatible is_compatible) {
+  std::unordered_map<nnvm::Node*, uint32_t> node2setidx;
+  size_t subgraphs_fullsize = 0;
+  for (auto& s : *subsets) {
+    subgraphs_fullsize += s.size();
+  }
+  node2setidx.reserve(subgraphs_fullsize);
+  for (size_t i = 0; i < subsets->size(); ++i) {
+    for (auto& n : (*subsets)[i]) {
+      node2setidx.insert({n, i});
+    }
+  }
+  std::vector<std::vector<nnvm::Node*> > to_add(subsets->size());
+  DFSVisit(g.outputs, [&is_compatible, &node2setidx, &to_add](const nnvm::NodePtr& n) {
+    const auto& it = node2setidx.find(n.get());
+    if (it != node2setidx.end()) {
+      for (auto& e : n->inputs) {
+        if (is_compatible(e.node.get()))
+          to_add[it->second].push_back(e.node.get());
+      }
+    }
+  });
+
+  // Avoid duplicating the node that is input of two subsets
+  std::unordered_set<nnvm::Node*> added;
+  for (size_t i = 0; i < subsets->size(); ++i) {
+    std::vector<nnvm::NodeEntry> heads;
+    for (auto n : subsets->at(i)) {
+      for (auto e : n->inputs) {
+        if (!subsets->at(i).count(e.node.get()))
+          heads.push_back(e);
+      }
+    }
+    for (size_t j = 0; j < to_add[i].size(); ++j) {
+      if (!added.count(to_add[i][j])) {
+        bool make_cycle = false;
+        const auto& node = to_add[i][j];
+        std::vector<nnvm::NodeEntry> _heads;
+        std::copy_if(heads.begin(), heads.end(), std::back_inserter(_heads),
+                     [&node](const nnvm::NodeEntry& n) {
+                       return n.node.get() != node;
+                     });
+        DFSVisit(_heads, [&make_cycle, &node](const nnvm::NodePtr& n) {
+          if (n.get() == node)
+            make_cycle = true;
+        });
+        if (!make_cycle) {
+          (*subsets)[i].insert(to_add[i][j]);
+          added.insert(to_add[i][j]);
+        }
+      }
+    }
+  }
+}
+
+Graph FusePointwiseForward(Graph &&g) {
+  Graph ret;
+  g.indexed_graph();
+  const auto& num_forward_outputs = g.GetAttr<size_t>("num_forward_outputs");
+  Graph fg;
+  fg.outputs.insert(fg.outputs.begin(), g.outputs.begin(),
+                    g.outputs.begin() + num_forward_outputs);
+  auto subsets = GetCompatibleSubsets(fg, IsFusionCompatible);
+  AddInputsOnlyCompatible(fg, &subsets, IsInputsOnlyCompatible);
+  g = ReplaceSubgraphsPointwise(std::move(g), subsets, CreateSubgraphNode);
+  ret.outputs = g.outputs;
+  return ret;
+}
+
+Graph FusePointwiseBackward(Graph &&g) {
+  Graph ret;
+  g.indexed_graph();
+  const auto& num_forward_outputs = g.GetAttr<size_t>("num_forward_outputs");
+  Graph fg;
+  fg.outputs.insert(fg.outputs.begin(), g.outputs.begin(),
+                    g.outputs.begin() + num_forward_outputs);
+  std::unordered_set<nnvm::Node*> exclusion_set;
+  DFSVisit(fg.outputs, [&exclusion_set](const nnvm::NodePtr& n) {
+    exclusion_set.insert(n.get());
+  });
+  auto subsets = GetCompatibleSubsets(g, [&exclusion_set](nnvm::Node* n) {
+    if (exclusion_set.count(n))
+      return false;
+    return IsFusionCompatible(n);
+  });
+  g = ReplaceSubgraphsPointwise(std::move(g), subsets, CreateSubgraphNode);
+  ret.outputs = g.outputs;
+  return ret;
+}
+
+}  // namespace exec
+}  // namespace mxnet
+
+#endif  // MXNET_USE_CUDA
diff --git a/src/executor/simple_partition_pass.h b/src/executor/simple_partition_pass.h
new file mode 100644
index 000000000000..5b26a4523c13
--- /dev/null
+++ b/src/executor/simple_partition_pass.h
@@ -0,0 +1,445 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file simple_partition_pass.h
+ * \brief Simple pass for partitioning a graph.
+ * \author Clement Fuji Tsang
+ */
+#ifndef MXNET_EXECUTOR_SIMPLE_PARTITION_PASS_H_
+#define MXNET_EXECUTOR_SIMPLE_PARTITION_PASS_H_
+
+#include <mxnet/base.h>
+#include <mxnet/op_attr_types.h>
+#include <mxnet/operator.h>
+#include <nnvm/graph_attr_types.h>
+#include <utility>
+#include <deque>
+#include <algorithm>
+#include <vector>
+
+#include "exec_pass.h"
+
+namespace mxnet {
+namespace exec {
+
+
+/*!
+ * \brief Custom graph class, which contains bi-directional nodes
+ * required for traversing in both directions (from outputs to inputs
+ * and vice versa). It is a non-owning layer on top of NNVM graph, since
+ * NNVM graph enables traversing only in 1 direction (from outputs to inputs).
+ */
+class BidirectionalGraph {
+ public:
+  struct Node {
+    nnvm::Node* nnvmptr;
+    std::vector<Node*> inputs;
+    std::vector<Node*> outputs;
+  };
+
+  explicit BidirectionalGraph(const Graph &g) {
+    auto& idx = g.indexed_graph();
+    auto num_nodes = idx.num_nodes();
+    nodes.reserve(num_nodes);
+    nnvm2nid.reserve(num_nodes);
+    outputs.reserve(idx.outputs().size());
+    // Create all the nodes in a new graph from
+    // nodes in the NNVM graph and store them
+    // in nodes array
+    DFSVisit(g.outputs, [this](const nnvm::NodePtr& n) {
+      Node new_node;
+      new_node.nnvmptr = n.get();
+      nnvm2nid[n.get()] = static_cast<uint32_t>(nodes.size());
+      nodes.emplace_back(std::move(new_node));
+    });
+    // Create all connections between nodes in
+    // the graph (both directions)
+    for (const auto& it : nnvm2nid) {
+      nnvm::Node* nnvmnode = it.first;
+      uint32_t nid = it.second;
+      for (auto& n : nnvmnode->inputs) {
+        uint32_t input_nid = nnvm2nid[n.node.get()];
+        nodes[input_nid].outputs.emplace_back(&nodes[nid]);
+        nodes[nid].inputs.emplace_back(&nodes[input_nid]);
+      }
+    }
+    // Create output connections from the graph
+    for (auto& e : g.outputs) {
+      uint32_t nid = nnvm2nid[e.node.get()];
+      outputs.emplace_back(&nodes[nid]);
+    }
+  }
+
+  /* \brief Get all subsets of nodes, where:
+   *  - graph constructed from nodes in each subset is a connected graph
+   *  - every node fulfills a predicate is_compatible
+   *  - if nodes u and v are part of a subset, then for each path between
+   *    u and v in the original directed graph, all nodes on those paths
+   *    are also part of the subset
+   * \param is_compatible A function taking nnvm::Node* and returning bool
+   *                      which identifies which nodes should be included in
+   *                      subsets.
+   */
+  template<typename FCompatible>
+  std::vector<std::unordered_set<Node*>> get_subsets(FCompatible is_compatible) {
+    std::vector<std::unordered_set<Node*>> subgraphs;
+    std::unordered_set<Node*> incomp_set;
+    std::unordered_set<Node*> all_set(nodes.size());
+    std::vector<PairSet> separation_sets;
+    // Check each node for compatibility
+    // and, if it is incompatible, mark nodes
+    // on each side of it as not possible to be
+    // in the same subset
+    for (Node& node : nodes) {
+      if (!is_compatible(node.nnvmptr)) {
+        incomp_set.insert(&node);
+        std::unordered_set<Node*> in_graph;
+        std::unordered_set<Node*> out_graph;
+        std::vector<Node*> dummy_head;
+        dummy_head.emplace_back(&node);
+        DFS(dummy_head, false, [&out_graph, &is_compatible](Node* node) {
+          if (is_compatible(node->nnvmptr))
+            out_graph.insert(node);
+        });
+        DFS(dummy_head, true, [&in_graph, is_compatible](Node* node) {
+          if (is_compatible(node->nnvmptr))
+            in_graph.insert(node);
+        });
+        if (!(in_graph.empty() || out_graph.empty()))
+          separation_sets.push_back(std::make_pair(in_graph, out_graph));
+      }
+      all_set.emplace(&node);
+    }
+    IncompMap incomp_map;
+    std::unordered_set<Node*> comp_set;
+    comp_set.insert(all_set.begin(), all_set.end());
+    for (Node* n : incomp_set) {
+      comp_set.erase(n);
+    }
+    // For each node construct the map of nodes that cannot be in
+    // the same subset
+    for (Node* n : comp_set) {
+      for (PairSet p : separation_sets) {
+        if (p.first.count(n)) {
+          incomp_map[n].insert(p.second.begin(), p.second.end());
+        } else if (p.second.count(n)) {
+          incomp_map[n].insert(p.first.begin(), p.first.end());
+        }
+      }
+      for (Node* incomp_n : incomp_set) {
+        incomp_map[n].erase(incomp_n);
+      }
+    }
+    std::unordered_set<Node*> unused_set;
+    unused_set.reserve(comp_set.size());
+
+    for (auto& n : comp_set) {
+      unused_set.insert(n);
+    }
+    std::unordered_set<Node*> visited;
+    std::deque<Node*> stack(outputs.begin(), outputs.end());
+    // Create subsets
+    while (!stack.empty()) {
+      Node* vertex = stack.front();
+      stack.pop_front();
+      if (!visited.count(vertex)) {
+        visited.insert(vertex);
+        if (unused_set.count(vertex)) {
+          subgraphs.emplace_back(naive_grow_subgraph(vertex, &unused_set, &incomp_map));
+        }
+        for (Node* input : vertex->inputs) {
+          stack.emplace_back(input);
+        }
+      }
+    }
+    return subgraphs;
+  }
+
+ private:
+  using PairSet = std::pair<std::unordered_set<Node*>, std::unordered_set<Node*>>;
+  using PairVec = std::pair<std::vector<Node*>, std::vector<Node*>>;
+  using IncompMap = std::unordered_map<Node*, std::unordered_set<Node*>>;
+
+  /* \brief Traverse the graph using DFS in either direction.
+   * \param heads Starting nodes for the DFS algorithm.
+   * \param reverse If true, DFS will traverse the graph from
+   *                outputs to inputs. Otherwise, it will
+   *                traverse the graph from inputs to outputs.
+   * \param fvisit Function to call on each visisted node.
+   */
+  template <typename FVisit>
+  void DFS(const std::vector<Node*>& heads, bool reverse, FVisit fvisit) {
+    std::unordered_set<Node*> visited;
+    std::vector<Node*> vec(heads.begin(), heads.end());
+    visited.reserve(heads.size());
+    while (!vec.empty()) {
+      Node* vertex = vec.back();
+      vec.pop_back();
+      if (visited.count(vertex) == 0) {
+        visited.insert(vertex);
+        fvisit(vertex);
+        std::vector<Node*> nexts = reverse ? vertex->inputs : vertex->outputs;
+        for (Node* node : nexts) {
+          if (visited.count(node) == 0) {
+            vec.emplace_back(node);
+          }
+        }
+      }
+    }
+  }
+
+  /* \brief Get the connected subgraph that contains the head node,
+   *        only previously unused nodes, according to the rules
+   *        from incompatibility map.
+   * \param head Node which needs to be part of the returned subgraph.
+   * \param unused_set Only nodes from this set will be considered when
+   *                   adding to the growing subgraph.
+   * \param incomp_map Map containing data on which nodes are incompatible
+   *                   to be in the same subgraph.
+   */
+  std::unordered_set<Node*> naive_grow_subgraph(Node* head,
+                                                std::unordered_set<Node*>* unused_set,
+                                                IncompMap* incomp_map) {
+    std::unordered_set<Node*> subgraph;
+    std::unordered_set<Node*> incomp_set;
+    std::deque<Node*> stack;
+    stack.emplace_back(head);
+    while (!stack.empty()) {
+      Node* vertex = stack.back();
+      stack.pop_back();
+      if (unused_set->count(vertex) && !incomp_set.count(vertex)) {
+        unused_set->erase(vertex);
+        subgraph.insert(vertex);
+        incomp_set.insert((*incomp_map)[vertex].begin(), (*incomp_map)[vertex].end());
+        // Traverse the grpah in both directions
+        for (Node* input : vertex->inputs) {
+          if (unused_set->count(input) && !incomp_set.count(input)) {
+            stack.emplace_back(input);
+          }
+        }
+        for (Node* output : vertex->outputs) {
+          if (unused_set->count(output) && !incomp_set.count(output)) {
+            stack.emplace_back(output);
+          }
+        }
+      }
+    }
+    return subgraph;
+  }
+
+  friend class Graph;
+
+  std::vector<Node> nodes;
+  std::unordered_map<nnvm::Node*, uint32_t> nnvm2nid;
+  std::vector<Node*> outputs;
+};  // class BidirectionalGraph
+
+using NodeEntrySet = std::unordered_set<nnvm::NodeEntry, nnvm::NodeEntryHash,
+                                        nnvm::NodeEntryEqual>;
+using NodeRawPtrSet = std::unordered_set<nnvm::Node*>;
+
+/*!
+ * \brief Get the output nodes of the subgraph in the main graph.
+ * \return a map between the node in the main graph and the output index of the subgraph node
+*/
+nnvm::NodeEntryMap<uint32_t> GetSubgraphOutputs(Graph g, NodeRawPtrSet subgraph_set) {
+  nnvm::NodeEntryMap<uint32_t> outputs;
+  uint32_t count = 0;
+  for (auto& e : g.outputs) {
+    if (subgraph_set.count(e.node.get()) && !outputs.count(e)) {
+      outputs.insert({e, count++});
+    }
+  }
+  DFSVisit(g.outputs, [&subgraph_set, &outputs, &count](const nnvm::NodePtr &node){
+    if (!subgraph_set.count(node.get())) {
+      for (auto& e : node->inputs) {
+        if (subgraph_set.count(e.node.get()) && !outputs.count(e)) {
+          outputs.insert({e, count++});
+        }
+      }
+    }
+  });
+  return outputs;
+}
+
+/*!
+ * \brief Create new input nodes of the subgraph and plug them.
+ * \return the inputs of the subgraph node in the main graph
+*/
+std::vector<nnvm::NodeEntry> GetSubgraphInputs(Graph g, NodeRawPtrSet subgraph_set) {
+  std::vector<nnvm::NodeEntry> inputs;
+  nnvm::NodeEntryMap<nnvm::NodeEntry> entry_map;
+  DFSVisit(g.outputs, [&subgraph_set, &inputs, &entry_map](const nnvm::NodePtr &node){
+    if (subgraph_set.count(node.get())) {
+      for (auto &e : node->inputs) {
+        if (!subgraph_set.count(e.node.get())) {
+          if (entry_map.count(e)) {
+            e = entry_map[e];
+          } else {
+            auto new_node = nnvm::Node::Create();
+            new_node->attrs.name = "input_" + std::to_string(inputs.size());
+            entry_map.insert({e, nnvm::NodeEntry{new_node, 0, 0}});
+            inputs.push_back(e);
+            e.node = new_node;
+            e.index = 0;
+          }
+        }
+      }
+    }
+  });
+  // Fix ordering of w.r.t to topology
+  Graph _g;
+  _g.outputs = g.outputs;
+  const auto &idx = _g.indexed_graph();
+  std::sort(inputs.begin(), inputs.end(),
+      [&idx, &entry_map](const nnvm::NodeEntry lhs, const nnvm::NodeEntry rhs) {
+        return idx.entry_id(entry_map.at(lhs)) < idx.entry_id(entry_map.at(rhs));
+      });
+  return inputs;
+}
+
+std::unordered_map<uint32_t, uint32_t> GetGraphInputsMap(const Graph& g) {
+  std::unordered_map<uint32_t, uint32_t> outputs;
+  auto& idx = g.indexed_graph();
+  outputs.reserve(idx.num_nodes());
+  std::vector<uint32_t> input_nodes = idx.input_nodes();
+  for (size_t i = 0; i < input_nodes.size(); ++i) {
+    outputs[input_nodes[i]] = static_cast<uint32_t>(i);
+  }
+  return outputs;
+}
+
+/*!
+ * \brief Helper function to display what nodes are in a specific subset.
+ */
+void dispNodesSet(Graph g, NodeRawPtrSet s) {
+  DFSVisit(g.outputs, [&s](const nnvm::NodePtr n){
+    if (s.count(n.get())) {
+      std::cout << "  Y " << n->attrs.name << std::endl;
+    } else {
+      std::cout << "  N " << n->attrs.name << std::endl;
+    }
+  });
+}
+
+/*!
+ * \brief Replace a set of nodes by a subgraph node.
+ */
+template<typename FCreateNode>
+Graph ReplaceSubgraphs(Graph&& g, const std::vector<NodeRawPtrSet>& subgraph_sets,
+                       FCreateNode create_subgraph_node) {
+  for (auto subgraph_set : subgraph_sets) {
+    // Create MXNet subgraph
+    Graph subgraph;
+    const auto sub_outputs_in_main = GetSubgraphOutputs(g, subgraph_set);
+    subgraph.outputs.resize(sub_outputs_in_main.size());
+    for (auto p : sub_outputs_in_main) {
+      subgraph.outputs[p.second] = p.first;
+    }
+    // To generate a subgraph an input has to be replaced by data node (no op)
+    // and it has to be agnostic to the node from which it's an output
+    // (For example, even if two inputs are two different outputs from the same node,
+    // they need to be replaced by two completely separate data nodes)
+    auto inputs = GetSubgraphInputs(subgraph, subgraph_set);
+    auto subgraph_node = create_subgraph_node(subgraph);
+    subgraph_node->inputs = inputs;
+    // replug inputs of node out of subgraph to be output of the subgraph node
+    // if it was a node in the subgraph
+    DFSVisit(g.outputs,
+        [&subgraph_node, &subgraph_set, &sub_outputs_in_main](const nnvm::NodePtr node) {
+      if (!subgraph_set.count(node.get())) {
+        for (auto &e : node->inputs) {
+          auto it = sub_outputs_in_main.find(e);
+          if (it != sub_outputs_in_main.end()) {
+            e.node = subgraph_node;
+            e.index = it->second;
+          }
+        }
+      }
+    });
+    // replug outputs of the graph to be output of the subgraph node
+    // if it was a node in the subgraph
+    for (auto &e : g.outputs) {
+      auto it = sub_outputs_in_main.find(e);
+      if (it != sub_outputs_in_main.end()) {
+        e.node = subgraph_node;
+        e.index = it->second;
+      }
+    }
+    // move control dependencies between nodes of the subgraph and out of the subgraph
+    // to a dependencies between the subgraph node and the nodes out of the subgraph
+    DFSVisit(g.outputs, [&subgraph_node, &subgraph_set](const nnvm::NodePtr& node) {
+      for (auto &e : node->control_deps) {
+        if (subgraph_set.count(e.get()))
+          e = subgraph_node;
+      }
+    });
+    DFSVisit(subgraph.outputs, [&subgraph_node, &subgraph_set](const nnvm::NodePtr& node) {
+      auto it = node->control_deps.begin();
+      while (it != node->control_deps.end()) {
+        if (subgraph_set.count(it->get())) {
+          ++it;
+        } else {
+          subgraph_node->control_deps.push_back(*it);
+          it = node->control_deps.erase(it);
+        }
+      }
+    });
+  }
+  Graph new_graph;
+  new_graph.outputs = g.outputs;
+  return new_graph;
+}
+
+/* \brief Get all subsets of nodes, where:
+ *  - graph constructed from nodes in each subset is a connected graph
+ *  - every node fulfills a predicate is_compatible
+ *  - if nodes u and v are part of a subset, then for each path between
+ *    u and v in the original directed graph, all nodes on those paths
+ *    are also part of the subset
+ * \param g NNVM graph
+ * \param is_compatible A function taking nnvm::Node* and returning bool
+ *                      which identifies which nodes should be included in
+ *                      subsets.
+ */
+template<typename FCompatible>
+std::vector<NodeRawPtrSet> GetCompatibleSubsets(const Graph& g, FCompatible is_compatible) {
+  BidirectionalGraph biG = BidirectionalGraph(g);
+  std::vector<std::unordered_set<BidirectionalGraph::Node*>> subsets =
+    biG.get_subsets(is_compatible);
+  std::vector<NodeRawPtrSet> nnvm_subsets;
+  nnvm_subsets.reserve(subsets.size());
+  for (auto& subset : subsets) {
+    if (subset.size() > 1) {
+      NodeRawPtrSet node_set;
+      node_set.reserve(subset.size());
+      for (auto& n : subset) {
+        node_set.insert(n->nnvmptr);
+      }
+      nnvm_subsets.push_back(node_set);
+    }
+  }
+  return nnvm_subsets;
+}
+
+}  // namespace exec
+}  // namespace mxnet
+#endif  // MXNET_EXECUTOR_SIMPLE_PARTITION_PASS_H_
diff --git a/src/imperative/cached_op.cc b/src/imperative/cached_op.cc
index 39c2880d627b..dd392d3e0401 100644
--- a/src/imperative/cached_op.cc
+++ b/src/imperative/cached_op.cc
@@ -50,7 +50,10 @@ std::string AddPrefix(const std::string& prefix,
 
 struct CachedOp::GraphInfo {
   nnvm::Graph fwd_graph;
+  nnvm::Graph grad_graph;
   nnvm::Graph full_graph;
+  std::vector<nnvm::NodeEntry> ograd_entries;
+  std::unordered_map<uint32_t, uint32_t> fwd_input_to_grad_output;
   std::vector<OpReqType> bwd_output_reqs;
   std::vector<uint32_t> bwd_input_eid;
 };
@@ -61,13 +64,167 @@ struct CachedOp::DynamicRuntime {
   std::vector<OpStatePtr> op_states;
 };
 
+void CreateFullGraph(const nnvm::Symbol& sym,
+                     nnvm::Graph* fwd_graph,
+                     nnvm::Graph* grad_graph,
+                     nnvm::Graph* full_graph,
+                     std::vector<nnvm::NodeEntry>* ograd_entries,
+                     std::unordered_map<uint32_t, uint32_t>* fwd_input_to_grad_output) {
+  using namespace nnvm;
+  static const std::vector<const Op*> zero_ops{Op::Get("zeros_like"), Op::Get("_zeros")};
+  static const auto _copy_op = Op::Get("_copy");
+  {
+    NodeEntryMap<size_t> dedup_out;
+    for (const NodeEntry& nodeEntry : sym.outputs) {
+      if (dedup_out.find(nodeEntry) != dedup_out.end()) {
+        NodePtr copy_node = Node::Create();
+        copy_node->attrs.op = _copy_op;
+        copy_node->attrs.name =
+            nodeEntry.node->attrs.name + "_copy" + std::to_string(dedup_out[nodeEntry]++);
+        copy_node->inputs.emplace_back(nodeEntry);
+        if (_copy_op->attr_parser != nullptr) {
+          _copy_op->attr_parser(&(copy_node->attrs));
+        }
+        fwd_graph->outputs.emplace_back(std::move(copy_node));
+      } else {
+        dedup_out.emplace(nodeEntry, 0);
+        fwd_graph->outputs.push_back(nodeEntry);
+      }
+    }
+  }
+
+  // construct backward graph
+  {
+    ograd_entries->reserve(fwd_graph->outputs.size());
+    for (size_t i = 0; i < fwd_graph->outputs.size(); ++i) {
+      nnvm::NodePtr np = Node::Create();
+      np->attrs.name = "_head_grad_" + std::to_string(i);
+      ograd_entries->emplace_back(np);
+    }
+
+    std::vector<NodeEntry> xs;
+    const IndexedGraph& indexed_graph = fwd_graph->indexed_graph();
+    for (size_t i = 0; i < indexed_graph.input_nodes().size(); ++i) {
+      const uint32_t node_id = indexed_graph.input_nodes()[i];
+      if (indexed_graph.mutable_input_nodes().count(node_id))
+        continue;
+      (*fwd_input_to_grad_output)[i] = xs.size();
+      xs.emplace_back(indexed_graph[node_id].weak_ref.lock());
+    }
+
+    CHECK(!xs.empty())
+        << "There are no inputs in computation graph that require gradients.";
+
+    *grad_graph = pass::MXGradient(
+        *fwd_graph, fwd_graph->outputs, xs, *ograd_entries,
+        exec::AggregateGradient, nullptr, nullptr,
+        zero_ops, "_copy");
+  }
+
+  // construct full graph
+  {
+    full_graph->outputs = fwd_graph->outputs;
+    for (const auto& i : grad_graph->outputs) full_graph->outputs.emplace_back(i);
+  }
+}
+
+void SetRefCounts(nnvm::Graph* fwd_graph, const nnvm::Graph& full_graph) {
+  const auto& idx = fwd_graph->indexed_graph();
+  CHECK_GE(idx.input_nodes().size(), 1) << "CachedOp requires at least 1 input";
+
+  std::vector<uint32_t> ref_count(idx.num_node_entries(), 0);
+  for (const auto& i : idx.input_nodes()) ++ref_count[idx.entry_id(i, 0)];
+  for (const auto& i : idx.outputs()) ++ref_count[idx.entry_id(i)];
+  for (size_t i = 0; i < idx.num_nodes(); ++i) {
+    for (const auto& j : idx[i].inputs) ++ref_count[idx.entry_id(j)];
+  }
+
+  fwd_graph->attrs[AddPrefix(CachedOp::FORWARD, CachedOp::REF_COUNT)] =
+      std::make_shared<dmlc::any>(std::move(ref_count));
+
+  size_t num_forward_nodes = idx.num_nodes();
+  size_t num_forward_entries = idx.num_node_entries();
+
+  const auto& full_idx = full_graph.indexed_graph();
+
+  std::vector<uint32_t> temp_ref_count(full_idx.num_node_entries(), 0);
+  for (size_t i = num_forward_nodes; i < full_idx.num_nodes(); ++i) {
+    for (const auto& j : full_idx[i].inputs) {
+       ++temp_ref_count[full_idx.entry_id(j)];
+    }
+  }
+
+  auto full_ref_count = fwd_graph->GetAttr<std::vector<uint32_t> >(AddPrefix(CachedOp::FORWARD,
+                                                                             CachedOp::REF_COUNT));
+  for (size_t i = 0; i < num_forward_entries; ++i) full_ref_count.at(i) += temp_ref_count[i];
+  fwd_graph->attrs[AddPrefix(CachedOp::FULL, CachedOp::REF_COUNT)] =
+      std::make_shared<dmlc::any>(std::move(full_ref_count));
+}
+
+void OptimizeGraph(nnvm::Graph * full_graph, nnvm::Graph * fwd_graph, nnvm::Graph * grad_graph,
+                   const Context& context, size_t num_forward_outputs, const bool inlining) {
+#if MXNET_USE_CUDA && !defined(_WIN32)
+  if (context.dev_mask() == kGPU &&
+      !inlining &&
+      dmlc::GetEnv("MXNET_USE_FUSION", true)) {
+    nnvm::Graph unoptimized_graph;
+    common::CopyGraph(&unoptimized_graph, *full_graph, false);
+
+    if (common::CheckForInputNameDuplicates(unoptimized_graph.indexed_graph())) {
+      full_graph->attrs["num_forward_outputs"] = std::make_shared<nnvm::any>(num_forward_outputs);
+      *full_graph = exec::FusePointwiseForward(std::move(*full_graph));
+      full_graph->attrs["num_forward_outputs"] = std::make_shared<nnvm::any>(num_forward_outputs);
+      *full_graph = exec::FusePointwiseBackward(std::move(*full_graph));
+      // Check the topological order of inputs
+      const auto &original_inputs = unoptimized_graph.indexed_graph().input_nodes();
+      const auto &new_inputs = full_graph->indexed_graph().input_nodes();
+      if (original_inputs.size() != new_inputs.size()) {
+        LOG(WARNING)
+          << "Number of inputs after fusion does not match original number of inputs. "
+          << "This is most probably a bug. Disabling fusion for this run.";
+        *full_graph = unoptimized_graph;
+      } else {
+        for (size_t i = 0; i < new_inputs.size(); ++i) {
+          if (unoptimized_graph.indexed_graph()[original_inputs[i]].source->attrs.name !=
+              full_graph->indexed_graph()[new_inputs[i]].source->attrs.name) {
+            LOG(WARNING) << "Disabling fusion due to altered topological order of inputs.";
+            *full_graph = unoptimized_graph;
+            break;
+          }
+        }
+      }
+    } else {
+      LOG(WARNING)
+        << "Graph contains duplicate names for some of its inputs - fusion is NOT enabled!";
+     }
+  }
+#endif  // MXNET_USE_CUDA
+
+  *fwd_graph = nnvm::Graph();
+  fwd_graph->outputs = std::vector<nnvm::NodeEntry>(full_graph->outputs.begin(),
+                                                    full_graph->outputs.begin() +
+                                                    num_forward_outputs);
+  *grad_graph = nnvm::Graph();
+  grad_graph->outputs = std::vector<nnvm::NodeEntry>(full_graph->outputs.begin() +
+                                                     num_forward_outputs,
+                                                     full_graph->outputs.end());
+  SetRefCounts(fwd_graph, *full_graph);
+}
+
 struct CachedOp::CachedOpState {
   CachedOpState(const Context& context_,
                 const nnvm::Graph& fwd_graph_,
-                const nnvm::Graph& full_graph_) {
+                const nnvm::Graph& full_graph_,
+                const bool inlining_) {
     context = context_;
-    info.fwd_graph = fwd_graph_;
-    info.full_graph = full_graph_;
+    nnvm::Symbol sym;
+    sym.outputs = fwd_graph_.outputs;
+    CreateFullGraph(sym.Copy(), &info.fwd_graph, &info.grad_graph,
+                    &info.full_graph, &info.ograd_entries,
+                    &info.fwd_input_to_grad_output);
+
+    OptimizeGraph(&info.full_graph, &info.fwd_graph, &info.grad_graph,
+                  context_, fwd_graph_.outputs.size(), inlining_);
 
     size_t max_nodes = info.full_graph.indexed_graph().num_nodes();
     size_t max_entries = info.full_graph.indexed_graph().num_node_entries();
@@ -112,10 +269,6 @@ struct CachedOp::CachedOpState {
 CachedOp::CachedOp(
     const nnvm::Symbol& sym,
     const std::vector<std::pair<std::string, std::string> >& flags) {
-  using namespace nnvm;
-  using namespace imperative;
-  static const std::vector<const Op*> zero_ops{Op::Get("zeros_like"), Op::Get("_zeros")};
-  static const auto _copy_op = Op::Get("_copy");
   config_.Init(flags);
   this->dynamic_shape_checked_ = false;
 
@@ -123,38 +276,14 @@ CachedOp::CachedOp(
     CHECK(config_.static_alloc) << "static_alloc must be True when static_shape is True";
   }
 
-  // construct forward graph
+  auto grad_graph = nnvm::Graph();
+  std::unordered_map<uint32_t, uint32_t> fwd_input_to_grad_output;
+  CreateFullGraph(sym, &fwd_graph_, &grad_graph, &full_graph_,
+                  &ograd_entries_, &fwd_input_to_grad_output);
+
   {
-    NodeEntryMap<size_t> dedup_out;
-    for (const NodeEntry& nodeEntry : sym.outputs) {
-      if (dedup_out.find(nodeEntry) != dedup_out.end()) {
-        NodePtr copy_node = Node::Create();
-        copy_node->attrs.op = _copy_op;
-        copy_node->attrs.name =
-            nodeEntry.node->attrs.name + "_copy" + std::to_string(dedup_out[nodeEntry]++);
-        copy_node->inputs.emplace_back(nodeEntry);
-        if (_copy_op->attr_parser != nullptr) {
-          _copy_op->attr_parser(&(copy_node->attrs));
-        }
-        fwd_graph_.outputs.emplace_back(std::move(copy_node));
-      } else {
-        dedup_out.emplace(nodeEntry, 0);
-        fwd_graph_.outputs.push_back(nodeEntry);
-      }
-    }
     const auto& idx = fwd_graph_.indexed_graph();
-    CHECK_GE(idx.input_nodes().size(), 1) << "CachedOp requires at least 1 input";
-
-    std::vector<uint32_t> ref_count(idx.num_node_entries(), 0);
-    for (const auto& i : idx.input_nodes()) ++ref_count[idx.entry_id(i, 0)];
-    for (const auto& i : idx.outputs()) ++ref_count[idx.entry_id(i)];
-    for (size_t i = 0; i < idx.num_nodes(); ++i) {
-      for (const auto& j : idx[i].inputs) ++ref_count[idx.entry_id(j)];
-    }
-
-    fwd_graph_.attrs[AddPrefix(FORWARD, REF_COUNT)] =
-        std::make_shared<dmlc::any>(std::move(ref_count));
-
+    bwd_output_reqs_ = std::vector<OpReqType>(grad_graph.outputs.size(), kWriteTo);
     inlining_ = !config_.static_alloc &&
         (idx.num_nodes() - idx.input_nodes().size()) <= config_.inline_limit;
   }
@@ -175,53 +304,9 @@ CachedOp::CachedOp(
     }
   }
 
-  // construct backward graph
-  {
-    ograd_entries_.reserve(fwd_graph_.outputs.size());
-    for (size_t i = 0; i < fwd_graph_.outputs.size(); ++i)
-      ograd_entries_.emplace_back(Node::Create());
-
-    std::vector<NodeEntry> xs;
-    const IndexedGraph& indexed_graph = fwd_graph_.indexed_graph();
-    for (size_t i = 0; i < indexed_graph.input_nodes().size(); ++i) {
-      const uint32_t node_id = indexed_graph.input_nodes()[i];
-      if (indexed_graph.mutable_input_nodes().count(node_id))
-        continue;
-      fwd_input_to_grad_output_[i] = xs.size();
-      xs.emplace_back(indexed_graph[node_id].weak_ref.lock());
-    }
-
-    CHECK(!xs.empty())
-        << "There are no inputs in computation graph that require gradients.";
-
-    grad_graph_ = pass::MXGradient(
-        fwd_graph_, fwd_graph_.outputs, xs, ograd_entries_,
-        exec::AggregateGradient, nullptr, nullptr,
-        zero_ops, "_copy");
-  }
-
-  // construct full graph
+  // Set the backward dependency vectors
   {
-    size_t num_forward_nodes = fwd_graph_.indexed_graph().num_nodes();
-    size_t num_forward_entries = fwd_graph_.indexed_graph().num_node_entries();
-
-    full_graph_.outputs = fwd_graph_.outputs;
-    bwd_output_reqs_ = std::vector<OpReqType>(grad_graph_.outputs.size(), kWriteTo);
-    for (const auto& i : grad_graph_.outputs) full_graph_.outputs.emplace_back(i);
     const auto& idx = full_graph_.indexed_graph();
-
-    std::vector<uint32_t> ref_count(idx.num_node_entries(), 0);
-    for (size_t i = num_forward_nodes; i < idx.num_nodes(); ++i) {
-      for (const auto& j : idx[i].inputs) {
-         ++ref_count[idx.entry_id(j)];
-      }
-    }
-
-    auto full_ref_count = fwd_graph_.GetAttr<std::vector<uint32_t> >(AddPrefix(FORWARD, REF_COUNT));
-    for (size_t i = 0; i < num_forward_entries; ++i) full_ref_count.at(i) += ref_count[i];
-    fwd_graph_.attrs[AddPrefix(FULL, REF_COUNT)] =
-        std::make_shared<dmlc::any>(std::move(full_ref_count));
-
     size_t num_forward_inputs = num_inputs();
     size_t num_forward_outputs = num_outputs();
     for (uint32_t i = 0; i < ograd_entries_.size(); ++i) {
@@ -239,6 +324,8 @@ CachedOp::CachedOp(
       bwd_out_dep_.push_back(i);
     }
   }
+
+  SetRefCounts(&fwd_graph_, full_graph_);
 }
 
 CachedOp::~CachedOp() {
@@ -428,10 +515,10 @@ bool CachedOp::SetBackwardGraph(
     info->bwd_output_reqs = reqs;
     info->bwd_input_eid.clear();
     g = nnvm::Graph();
-    g.outputs = fwd_graph_.outputs;
-    for (size_t i = 0; i < grad_graph_.outputs.size(); ++i) {
+    g.outputs = info->fwd_graph.outputs;
+    for (size_t i = 0; i < info->grad_graph.outputs.size(); ++i) {
       if (info->bwd_output_reqs[i] == kNullOp) continue;
-      g.outputs.emplace_back(grad_graph_.outputs[i]);
+      g.outputs.emplace_back(info->grad_graph.outputs[i]);
     }
     g.attrs["context"] = std::make_shared<dmlc::any>(
         std::vector<Context>(g.indexed_graph().num_nodes(), default_ctx));
@@ -442,12 +529,12 @@ bool CachedOp::SetBackwardGraph(
   if (info->bwd_input_eid.size() != inputs.size()) {
     info->bwd_input_eid.clear();
     SetBackwardInputEid(bwd_in_dep_, bwd_out_dep_, bwd_ograd_dep_,
-                        ograd_entries_, idx, &info->bwd_input_eid);
+                        info->ograd_entries, idx, &info->bwd_input_eid);
     CHECK_EQ(inputs.size(), info->bwd_input_eid.size());
   }
 
-  size_t num_forward_nodes = fwd_graph_.indexed_graph().num_nodes();
-  size_t num_forward_entries = fwd_graph_.indexed_graph().num_node_entries();
+  size_t num_forward_nodes = info->fwd_graph.indexed_graph().num_nodes();
+  size_t num_forward_entries = info->fwd_graph.indexed_graph().num_node_entries();
 
   if (!g.attrs.count(AddPrefix(BACKWARD, REF_COUNT))) {
     std::vector<uint32_t> ref_count(idx.num_node_entries(), 0);
@@ -528,7 +615,8 @@ OpStatePtr CachedOp::GetCachedOpState(
       return i;
     }
   }
-  auto state_ptr = OpStatePtr::Create<CachedOpState>(ctx, fwd_graph_, full_graph_);
+  auto state_ptr = OpStatePtr::Create<CachedOpState>(ctx, fwd_graph_, full_graph_,
+                                                     inlining_);
 
   cached_op_states_[ctx].push_back(state_ptr);
   return state_ptr;
@@ -937,8 +1025,10 @@ OpStatePtr CachedOp::Forward(
   CHECK_EQ(inputs.size(), num_inputs());
 
   Context default_ctx = inputs[0]->ctx();
+  auto state_ptr = GetCachedOpState(default_ctx);
+  auto& state = state_ptr.get_state<CachedOpState>();
 
-  const auto& idx = fwd_graph_.indexed_graph();
+  const auto& idx = state.info.fwd_graph.indexed_graph();
   for (size_t i = 0; i < inputs.size(); ++i) {
     CHECK_EQ(inputs[i]->ctx(), default_ctx)
         << "CachedOp requires all inputs to live on the same context. But "
@@ -1006,9 +1096,9 @@ void CachedOp::DynamicBackward(
   auto& buff = runtime.buff;
   auto& states = runtime.op_states;
 
-  size_t num_forward_outputs = fwd_graph_.outputs.size();
-  size_t num_forward_nodes = fwd_graph_.indexed_graph().num_nodes();
-  size_t num_forward_entries = fwd_graph_.indexed_graph().num_node_entries();
+  size_t num_forward_outputs = runtime.info.fwd_graph.outputs.size();
+  size_t num_forward_nodes = runtime.info.fwd_graph.indexed_graph().num_nodes();
+  size_t num_forward_entries = runtime.info.fwd_graph.indexed_graph().num_node_entries();
   buff.resize(idx.num_node_entries());
   std::vector<NDArray*> arrays;
   arrays.reserve(buff.size());
@@ -1104,9 +1194,9 @@ void CachedOp::StaticBackward(
 
   if (config_.static_shape) {
     for (auto i : config_.param_indices) {
-      const auto iter = fwd_input_to_grad_output_.find(i);
-      if (iter == fwd_input_to_grad_output_.end()) continue;
-      auto entry = grad_graph_.outputs[iter->second];
+      const auto iter = state.info.fwd_input_to_grad_output.find(i);
+      if (iter == state.info.fwd_input_to_grad_output.end()) continue;
+      auto entry = state.info.grad_graph.outputs[iter->second];
       if (!idx.exist(entry.node.get())) continue;
       auto eid = idx.entry_id(entry);
       if (!arrays[eid]->IsSame(*outputs[iter->second]) ||
@@ -1121,9 +1211,9 @@ void CachedOp::StaticBackward(
       }
     }
     for (auto i : config_.data_indices) {
-      const auto iter = fwd_input_to_grad_output_.find(i);
-      if (iter == fwd_input_to_grad_output_.end()) continue;
-      auto entry = grad_graph_.outputs[iter->second];
+      const auto iter = state.info.fwd_input_to_grad_output.find(i);
+      if (iter == state.info.fwd_input_to_grad_output.end()) continue;
+      auto entry = state.info.grad_graph.outputs[iter->second];
       if (!idx.exist(entry.node.get())) continue;
       auto eid = idx.entry_id(entry);
       state.array_reqs[eid] = reqs[iter->second];
@@ -1133,8 +1223,8 @@ void CachedOp::StaticBackward(
       arrays[eid] = outputs[iter->second];
     }
   } else {
-    for (size_t i = 0; i < grad_graph_.outputs.size(); ++i) {
-      auto entry = grad_graph_.outputs[i];
+    for (size_t i = 0; i < state.info.grad_graph.outputs.size(); ++i) {
+      auto entry = state.info.grad_graph.outputs[i];
       if (!idx.exist(entry.node.get())) continue;
       auto eid = idx.entry_id(entry);
       state.array_reqs[eid] = reqs[i];
diff --git a/src/imperative/cached_op.h b/src/imperative/cached_op.h
index 84f96300c27b..01347153cafe 100644
--- a/src/imperative/cached_op.h
+++ b/src/imperative/cached_op.h
@@ -205,13 +205,11 @@ class CachedOp {
 
   CachedOpConfig config_;
   nnvm::Graph fwd_graph_;
-  nnvm::Graph grad_graph_;
   nnvm::Graph full_graph_;
   bool inlining_;
   bool dynamic_shape_checked_;
   std::vector<nnvm::NodeEntry> ograd_entries_;
   std::vector<uint32_t> bwd_in_dep_, bwd_out_dep_, bwd_ograd_dep_;
-  std::unordered_map<uint32_t, uint32_t> fwd_input_to_grad_output_;
   std::vector<bool> save_inputs_, save_outputs_;
   std::vector<OpReqType> bwd_output_reqs_;
 
diff --git a/src/imperative/imperative.cc b/src/imperative/imperative.cc
index b3924cc4d79e..6f70d6a78421 100644
--- a/src/imperative/imperative.cc
+++ b/src/imperative/imperative.cc
@@ -305,7 +305,9 @@ std::vector<NDArray*> Imperative::Backward(
   std::vector<NodeEntry> ograd_entries;
   ograd_entries.reserve(ograds.size());
   for (size_t i = 0; i < outputs.size(); ++i) {
-    ograd_entries.emplace_back(NodeEntry{Node::Create(), 0, 0});
+    nnvm::NodePtr np = Node::Create();
+    np->attrs.name = "_head_grad_" + std::to_string(i);
+    ograd_entries.emplace_back(NodeEntry{np, 0, 0});
     AGInfo& info = AGInfo::Create(ograd_entries.back().node);
     info.ctx = outputs[i]->ctx();
     if (ograds[i] != nullptr) {
diff --git a/src/operator/fusion/fused_op-inl.h b/src/operator/fusion/fused_op-inl.h
new file mode 100644
index 000000000000..3085bfd1dc07
--- /dev/null
+++ b/src/operator/fusion/fused_op-inl.h
@@ -0,0 +1,999 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+#ifndef MXNET_OPERATOR_FUSION_FUSED_OP_INL_H_
+#define MXNET_OPERATOR_FUSION_FUSED_OP_INL_H_
+
+#include <string>
+#include <map>
+#include <vector>
+
+#if MXNET_USE_CUDA
+
+namespace mxnet {
+
+namespace fusion {
+
+const char fp16_support_string[] = R"code(
+struct __align__(2) __half {
+  __host__ __device__ __half() { }
+  unsigned short __x;
+};
+/* Definitions of intrinsics */
+__device__ inline __half __float2half(const float f) {
+  __half val;
+ asm("{  cvt.rn.f16.f32 %0, %1;}\n" : "=h"(val.__x) : "f"(f));
+  return val;
+}
+__device__ inline float __half2float(const __half h) {
+  float val;
+ asm("{  cvt.f32.f16 %0, %1;}\n" : "=f"(val) : "h"(h.__x));
+  return val;
+}
+
+typedef __half half;
+)code";
+
+const char type_support_string[] = R"code(
+using float32 = float;
+using float64 = double;
+using float16 = half;
+using uint8 = unsigned char;
+using int8 = char;
+using int32 = int;
+using int64 = long long;
+)code";
+
+const std::map<std::string, std::vector<std::vector<std::string>>> ops_desc = {
+  {"elemwise_add"                      , {{"op::add(%, %)", "_0", "_1"}}},
+  {"_plus"                             , {{"op::add(%, %)", "_0", "_1"}}},
+  {"_Plus"                             , {{"op::add(%, %)", "_0", "_1"}}},
+  {"_add"                              , {{"op::add(%, %)", "_0", "_1"}}},
+  {"elemwise_sub"                      , {{"op::sub(%, %)", "_0", "_1"}}},
+  {"_minus"                            , {{"op::sub(%, %)", "_0", "_1"}}},
+  {"_Minus"                            , {{"op::sub(%, %)", "_0", "_1"}}},
+  {"_sub"                              , {{"op::sub(%, %)", "_0", "_1"}}},
+  {"elemwise_mul"                      , {{"op::mul(%, %)", "_0", "_1"}}},
+  {"_mul"                              , {{"op::mul(%, %)", "_0", "_1"}}},
+  {"_Mul"                              , {{"op::mul(%, %)", "_0", "_1"}}},
+  {"elemwise_div"                      , {{"op::div(%, %)", "_0", "_1"}}},
+  {"_div"                              , {{"op::div(%, %)", "_0", "_1"}}},
+  {"_Div"                              , {{"op::div(%, %)", "_0", "_1"}}},
+  {"_Power"                            , {{"op::power(%, %)", "_0", "_1"}}},
+  {"_power"                            , {{"op::power(%, %)", "_0", "_1"}}},
+  {"_Maximum"                          , {{"op::max(%, %)", "_0", "_1"}}},
+  {"_maximum"                          , {{"op::max(%, %)", "_0", "_1"}}},
+  {"_Minimum"                          , {{"op::min(%, %)", "_0", "_1"}}},
+  {"_minimum"                          , {{"op::min(%, %)", "_0", "_1"}}},
+  {"amp_cast"                          , {{"op::identity(%)", "_0"}}},
+  {"_backward_amp_cast"                , {{"op::identity(%)", "_0"}}},
+  {"relu"                              , {{"op::relu(%)", "_0"}}},
+  {"sigmoid"                           , {{"op::sigmoid(%)", "_0"}}},
+  {"softsign"                          , {{"op::softsign(%)", "_0"}}},
+  {"exp"                               , {{"op::exp(%)", "_0"}}},
+  {"expm1"                             , {{"op::expm1(%)", "_0"}}},
+  {"log"                               , {{"op::log(%)", "_0"}}},
+  {"log10"                             , {{"op::log10(%)", "_0"}}},
+  {"log2"                              , {{"op::log2(%)", "_0"}}},
+  {"log1p"                             , {{"op::log1p(%)", "_0"}}},
+  {"degrees"                           , {{"op::degrees(%)", "_0"}}},
+  {"radians"                           , {{"op::radians(%)", "_0"}}},
+  {"sin"                               , {{"op::sin(%)", "_0"}}},
+  {"cos"                               , {{"op::cos(%)", "_0"}}},
+  {"tan"                               , {{"op::tan(%)", "_0"}}},
+  {"arcsin"                            , {{"op::arcsin(%)", "_0"}}},
+  {"arccos"                            , {{"op::arccos(%)", "_0"}}},
+  {"arctan"                            , {{"op::arctan(%)", "_0"}}},
+  {"sinh"                              , {{"op::sinh(%)", "_0"}}},
+  {"cosh"                              , {{"op::cosh(%)", "_0"}}},
+  {"tanh"                              , {{"op::tanh(%)", "_0"}}},
+  {"arcsinh"                           , {{"op::arcsinh(%)", "_0"}}},
+  {"arccosh"                           , {{"op::arccosh(%)", "_0"}}},
+  {"arctanh"                           , {{"op::arctanh(%)", "_0"}}},
+  {"sqrt"                              , {{"op::sqrt(%)", "_0"}}},
+  {"rsqrt"                             , {{"op::rsqrt(%)", "_0"}}},
+  {"cbrt"                              , {{"op::cbrt(%)", "_0"}}},
+  {"rcbrt"                             , {{"op::rcbrt(%)", "_0"}}},
+  {"square"                            , {{"op::square(%)", "_0"}}},
+  {"squeeze"                           , {{"op::identity(%)", "_0"}}},
+  {"zeros_like"                        , {{"op::zero(%)", "_0"}}},
+  {"ones_like"                         , {{"op::one(%)", "_0"}}},
+  {"flatten"                           , {{"op::identity(%)", "_0"}}},
+  {"Reshape"                           , {{"op::identity(%)", "_0"}}},
+  {"reshape"                           , {{"op::identity(%)", "_0"}}},
+  {"_backward_reshape"                 , {{"op::identity(%)", "_0"}}},
+  {"expand_dims"                       , {{"op::identity(%)", "_0"}}},
+  {"round"                             , {{"op::round(%)", "_0"}}},
+  {"rint"                              , {{"op::rint(%)", "_0"}}},
+  {"fix"                               , {{"op::fix(%)", "_0"}}},
+  {"floor"                             , {{"op::floor(%)", "_0"}}},
+  {"ceil"                              , {{"op::ceil(%)", "_0"}}},
+  {"trunc"                             , {{"op::trunc(%)", "_0"}}},
+  {"sign"                              , {{"op::sign(%)", "_0"}}},
+  {"reciprocal"                        , {{"op::reciprocal(%)", "_0"}}},
+  {"abs"                               , {{"op::abs(%)", "_0"}}},
+  {"gamma"                             , {{"op::gamma(%)", "_0"}}},
+  {"gammaln"                           , {{"op::gammaln(%)", "_0"}}},
+  {"erf"                               , {{"op::erf(%)", "_0"}}},
+  {"erfinv"                            , {{"op::erfinv(%)", "_0"}}},
+  {"_copy"                             , {{"op::identity(%)", "_0"}}},
+  {"_identity_with_attr_like_rhs"      , {{"op::identity(%)", "_0"}}},
+  {"_plus_scalar"                      , {{"op::add(%, float(%))", "_0", "scalar"}}},
+  {"_PlusScalar"                       , {{"op::add(%, float(%))", "_0", "scalar"}}},
+  {"_minus_scalar"                     , {{"op::sub(%, float(%))", "_0", "scalar"}}},
+  {"_MinusScalar"                      , {{"op::sub(%, float(%))", "_0", "scalar"}}},
+  {"_rminus_scalar"                    , {{"(-op::sub(%, float(%)))", "_0", "scalar"}}},
+  {"_RMinusScalar"                     , {{"(-op::sub(%, float(%)))", "_0", "scalar"}}},
+  {"_mul_scalar"                       , {{"op::mul(%, float(%))", "_0", "scalar"}}},
+  {"_MulScalar"                        , {{"op::mul(%, float(%))", "_0", "scalar"}}},
+  {"_div_scalar"                       , {{"op::mul(%, 1.0f/float(%))", "_0", "scalar"}}},
+  {"_DivScalar"                        , {{"op::mul(%, 1.0f/float(%))", "_0", "scalar"}}},
+  {"_rdiv_scalar"                      , {{"op::rdiv(%, float(%))", "_0", "scalar"}}},
+  {"_power_scalar"                     , {{"op::power(%, float(%))", "_0", "scalar"}}},
+  {"_PowerScalar"                      , {{"op::power(%, float(%))", "_0", "scalar"}}},
+  {"_rpower_scalar"                    , {{"op::rpow(%, float(%))", "_0", "scalar"}}},
+  {"_RPowerScalar"                     , {{"op::rpow(%, float(%))", "_0", "scalar"}}},
+  {"_RDivScalar"                       , {{"op::rdiv(%, float(%))", "_0", "scalar"}}},
+  {"Cast"                              , {{"op::cast<%>(%)", "dtype", "_0"}}},
+  {"cast"                              , {{"op::cast<%>(%)", "dtype", "_0"}}},
+  {"Activation"                        , {{"op::%(%)", "act_type", "_0"}}},
+  {"clip"                              , {{"op::clip(%, %, %)", "_0", "a_min", "a_max"}}},
+  {"_zeros"                            , {{"op::zero<%>()", "dtype"}}},
+  {"_ones"                             , {{"op::one<%>()", "dtype"}}},
+  {"negative"                          , {{"(-%)", "_0"}}},
+  {"_hypot"                            , {{"op::hypot(%, %)", "_0", "_1"}}},
+  {"_hypot_scalar"                     , {{"op::hypot(%, float(%))", "_0", "scalar"}}},
+  {"_backward_relu"                    , {{"op::backward_relu(%, %)", "_1", "_0"}}},
+  {"_backward_sigmoid"                 , {{"op::backward_sigmoid(%, %)", "_1", "_0"}}},
+  {"_backward_expm1"                   , {{"op::backward_expm1(%, %)", "_1", "_0"}}},
+  {"_backward_log"                     , {{"op::backward_log(%, %)", "_1", "_0"}}},
+  {"_backward_log10"                   , {{"op::backward_log10(%, %)", "_1", "_0"}}},
+  {"_backward_log2"                    , {{"op::backward_log2(%, %)", "_1", "_0"}}},
+  {"_backward_log1p"                   , {{"op::backward_log1p(%, %)", "_1", "_0"}}},
+  {"_backward_sin"                     , {{"op::backward_sin(%, %)", "_1", "_0"}}},
+  {"_backward_cos"                     , {{"op::backward_cos(%, %)", "_1", "_0"}}},
+  {"_backward_tan"                     , {{"op::backward_tan(%, %)", "_1", "_0"}}},
+  {"_backward_arcsin"                  , {{"op::backward_arcsin(%, %)", "_1", "_0"}}},
+  {"_backward_arccos"                  , {{"op::backward_arccos(%, %)", "_1", "_0"}}},
+  {"_backward_arctan"                  , {{"op::backward_arctan(%, %)", "_1", "_0"}}},
+  {"_backward_sinh"                    , {{"op::backward_sinh(%, %)", "_1", "_0"}}},
+  {"_backward_cosh"                    , {{"op::backward_cosh(%, %)", "_1", "_0"}}},
+  {"_backward_tanh"                    , {{"op::backward_tanh(%, %)", "_1", "_0"}}},
+  {"_backward_arcsinh"                 , {{"op::backward_arcsinh(%, %)", "_1", "_0"}}},
+  {"_backward_arccosh"                 , {{"op::backward_arccosh(%, %)", "_1", "_0"}}},
+  {"_backward_arctanh"                 , {{"op::backward_arctanh(%, %)", "_1", "_0"}}},
+  {"_backward_sqrt"                    , {{"op::backward_sqrt(%, %)", "_1", "_0"}}},
+  {"_backward_rsqrt"                   , {{"op::backward_rsqrt(%, %)", "_1", "_0"}}},
+  {"_backward_cbrt"                    , {{"op::backward_cbrt(%, %)", "_1", "_0"}}},
+  {"_backward_rcbrt"                   , {{"op::backward_rcbrt(%, %)", "_1", "_0"}}},
+  {"_backward_square"                  , {{"op::backward_square(%, %)", "_1", "_0"}}},
+  {"_backward_div_scalar"              , {{"(% * 1.0f/float(%))", "_0", "scalar"}}},
+  {"_backward_div_scalar"              , {{"(% * 1.0f/float(%))", "_0", "scalar"}}},
+  {"_backward_rdiv_scalar"             , {{"(-% * float(%) / (% * %))", "_0",
+                                           "scalar", "_1", "_1"}}},
+  {"_backward_hypot_scalar"            , {{"(% * % / op::hypot(%, float(%)))",
+                                           "_0", "_1", "_1", "scalar"}}},
+  {"_backward_radians"                 , {{"op::radians(%)", "_0"}}},
+  {"_backward_erf"                     , {{"op::backward_erf(%, %)", "_1", "_0"}}},
+  {"_backward_erfinv"                  , {{"op::backward_erfinv(%, %)", "_1", "_0"}}},
+  {"_backward_reciprocal"              , {{"op::backward_reciprocal(%, %)", "_1", "_0"}}},
+  {"_backward_abs"                     , {{"(% * op::sign(%))", "_0", "_1"}}},
+  {"_backward_degrees"                 , {{"op::degrees(%)", "_0"}}},
+  {"_backward_sign"                    , {{"op::zero(%)", "_0"}}},
+  {"_backward_clip"                    , {{"op::backward_clip(%, %, %, %)", "_1", "_0",
+                                                                            "a_min", "a_max"}}},
+  {"smooth_l1"                         , {{"op::smooth_l1(%, float(%))", "_0", "scalar"}}},
+  {"_backward_smooth_l1"               , {{"op::backward_smooth_l1(%, float(%), %)",
+                                           "_1", "scalar", "_0"}}},
+  // TODO(ptredak): arange
+  // TODO(ptredak): LeakyRelu
+  // TODO(ptredak): mod and rmod
+  {"_backward_sub"                     , {{"(%)", "_0"},
+                                          {"(-(%))", "_0"}}},
+  {"_backward_mul"                     , {{"(% * %)", "_0", "_2"},
+                                          {"(% * %)", "_0", "_1"}}},
+  {"_backward_mul_scalar"              , {{"(% * float(%))", "_0", "scalar"}}},
+  {"_backward_div"                     , {{"(% / %)", "_0", "_2"},
+                                          {"(-% * % / (% * %))", "_0", "_1", "_2", "_2"}}},
+  {"_backward_power"                   , {{"(% * % * powf(%, % - 1))", "_0", "_2", "_1", "_2"},
+                                          {"(% * powf(%, %) * logf(%))", "_0", "_1", "_2", "_1"}}},
+  {"_backward_power_scalar"            , {{"(% * float(%) * powf(%, float(%) - 1))",
+                                           "_0", "scalar", "_1", "scalar"}}},
+  {"_backward_rpower_scalar"           , {{"(% * % * logf(float(%)))", "_0", "_1", "scalar"}}},
+  {"_backward_maximum"                 , {{"((% >= %) ? % : 0)", "_1", "_2", "_0"},
+                                          {"((% >= %) ? 0 : %)", "_1", "_2", "_0"}}},
+  {"_backward_minimum"                 , {{"((% <= %) ? % : 0)", "_1", "_2", "_0"},
+                                          {"((% <= %) ? 0 : %)", "_1", "_2", "_0"}}},
+  {"_backward_hypot"                   , {{"(% * % / op::hypot(%, %))", "_0", "_1", "_1", "_2"},
+                                          {"(% * % / op::hypot(%, %))", "_0", "_2", "_1", "_2"}}}
+};
+
+const std::map<std::string, std::string> slice_ops = {
+  {"slice_axis"   , ""},
+  {"slice"   , ""},
+  {"slice_like"   , ""},
+  {"broadcast_like"   , ""},
+};
+
+const std::vector<std::string> variable_io_ops = {
+  "add_n",
+  "_backward_Activation",
+  "amp_multicast",
+  "_backward_amp_multicast",
+  "_backward_cast"
+};
+
+const char function_definitions[] = R"code(
+
+#define INT_MAX (2147483647)
+
+namespace op {
+
+template <typename DType>
+struct LoadType {
+  using Type = DType;
+};
+
+template <>
+struct LoadType<half> {
+  using Type = float;
+};
+
+template <typename DType>
+inline typename LoadType<DType>::Type load(const DType input) {
+  return input;
+}
+
+template <>
+inline float load(const half input) {
+  return __half2float(input);
+}
+
+template <typename DType1, typename DType2>
+inline DType1 store(const DType2 input, DType1* ref) {
+  return input;
+}
+
+template <typename DType>
+inline half store(const DType input, half* ref) {
+  return __float2half(input);
+}
+
+template <int size>
+struct VectorConfig {
+    static_assert(size >= 4, "VectorConfig needs to have size of at least 4B");
+    using IndexType = float;
+};
+
+template <>
+struct VectorConfig<8> {
+    using IndexType = double;
+};
+
+template <>
+struct VectorConfig<16> {
+    using IndexType = double2;
+};
+
+template <>
+struct VectorConfig<32> {
+    using IndexType = double4;
+};
+
+template <typename DType>
+inline DType add_elem(const DType& x, const DType& y) {
+  return x + y;
+}
+
+template <>
+inline half add_elem(const half& x, const half& y) {
+  return __float2half(__half2float(x) + __half2float(y));
+}
+
+template <typename DType, int nvec>
+union VectorType {
+    typename VectorConfig<sizeof(DType)*nvec>::IndexType y;
+    DType x[nvec];
+    VectorType () {};
+    VectorType (const VectorType<DType, nvec>& y2) {
+        y = y2.y;
+    }
+    VectorType (const decltype(y) &y2) {
+        y = y2;
+    }
+    inline VectorType<DType, nvec>& operator+=(const VectorType<DType, nvec>& rhs) {
+      #pragma unroll
+      for (int i = 0; i < nvec; ++i) {
+        x[i] = add_elem(x[i], rhs.x[i]);
+      }
+      return *this;
+    }
+};
+
+template <int ndim>
+struct Shape {
+   int x[ndim];
+   size_t size;
+   inline const int& operator [](const int i) const {
+       return x[i];
+   }
+   inline int& operator [](const int i) {
+       return x[i];
+   }
+   inline void set(const int def) {
+       #pragma unroll
+       for (int i = 0; i < ndim; i++) {
+           x[i] = def;
+       }
+   }
+};
+
+template <>
+struct Shape<0> {
+   size_t size;
+};
+
+template <int nvec, typename DType, int ndim>
+inline VectorType<DType, nvec> load_index(const DType * input, int i, const Shape<ndim> &shape) {
+  if (i < shape.size) {
+    const auto* vector_input = reinterpret_cast<
+                                const typename VectorConfig<sizeof(DType)*nvec>::IndexType *>(
+                                    input + i);
+    VectorType<DType, nvec> ret = {*vector_input};
+    return ret;
+  } else {
+    VectorType<DType, nvec> ret({0});
+    return ret;
+  }
+}
+
+template <int nvec, typename DType, int ndim>
+inline VectorType<DType, nvec> global_load_index(const DType * input, int i, const Shape<ndim> &shape) {
+  if (i < shape.size) {
+    const auto* vector_input = reinterpret_cast<
+                                const typename VectorConfig<sizeof(DType)*nvec>::IndexType *>(
+                                    input + i);
+    VectorType<DType, nvec> ret = {__ldg(vector_input)};
+    return ret;
+  } else {
+    VectorType<DType, nvec> ret({0});
+    return ret;
+  }
+}
+
+template <int nvec, typename DType, int ndim>
+inline VectorType<DType, nvec> load_slice(const DType * input, const Shape<ndim>& shape, Shape<ndim> begin, Shape<ndim> end, int offset) {
+  int idx[nvec];
+
+  Shape<ndim> ref_strides;
+  Shape<ndim> strides;
+  ref_strides[ndim-1] = 1;
+  strides[ndim-1] = 1;
+  #pragma unroll
+  for (int dim = ndim-1; dim >=0; dim--) {
+    if (begin[dim] < 0) begin[dim] = shape[dim] - begin[dim];
+    if (end[dim] < 0) end[dim] = shape[dim] - end[dim];
+    if (end[dim] == INT_MAX) end[dim] = shape[dim];
+    if (dim > 0) {
+      ref_strides[dim-1] = ref_strides[dim] * (end[dim] - begin[dim]);
+      strides[dim-1] = strides[dim] * shape[dim];
+    }
+  }
+  #pragma unroll
+  for (int j = 0; j < nvec; j++) {
+    idx[j] = 0;
+    int ref_idx = offset + j;
+    #pragma unroll
+    for (int dim = 0; dim < ndim; dim++) {
+       int stride = ref_strides[dim];
+       if (shape[dim] > 1) {
+         idx[j] += (ref_idx / stride + begin[dim]) * strides[dim];
+       }
+       ref_idx = ref_idx % stride;
+    }
+  }
+  VectorType<DType, nvec> ret;
+  #pragma unroll
+  for (int j = 0; j < nvec; j++) {
+      ret.x[j] = *(input + idx[j]);
+  }
+  return ret;
+}
+
+template <int nvec, typename DType, int ndim>
+inline VectorType<DType, nvec> fast_load_slice(const DType * input, const Shape<ndim>& shape, Shape<ndim> begin, Shape<ndim> end, int offset) {
+  int idx = 0;
+
+  Shape<ndim> ref_strides;
+  Shape<ndim> strides;
+  ref_strides[ndim-1] = 1;
+  strides[ndim-1] = 1;
+  #pragma unroll
+  for (int dim = ndim-1; dim >=0; dim--) {
+    if (begin[dim] < 0) begin[dim] = shape[dim] - begin[dim];
+    if (end[dim] < 0) end[dim] = shape[dim] - end[dim];
+    if (end[dim] == INT_MAX) end[dim] = shape[dim];
+    if (dim > 0) {
+      ref_strides[dim-1] = ref_strides[dim] * (end[dim] - begin[dim]);
+      strides[dim-1] = strides[dim] * shape[dim];
+    }
+  }
+  int ref_idx = offset;
+  #pragma unroll
+  for (int dim = 0; dim < ndim; dim++) {
+     int stride = ref_strides[dim];
+     if (shape[dim] > 1) {
+       idx += (ref_idx / stride + begin[dim]) * strides[dim];
+     }
+     ref_idx = ref_idx % stride;
+  }
+  return global_load_index<nvec>(input, idx, shape);
+}
+
+template <int nvec, typename DType, int ndim>
+inline void store_index(const VectorType<DType, nvec> value, int i,
+                        DType * output, const Shape<ndim>& shape) {
+  if (i < (shape.size + nvec - 1) / nvec) {
+    auto vector_output = reinterpret_cast<
+                          typename VectorConfig<sizeof(DType)*nvec>::IndexType *>(output);
+    vector_output[i] = value.y;
+  }
+}
+
+template <int nvec, typename DType, int ndim>
+inline void store_add_index(const VectorType<DType, nvec> value, int i,
+                            DType * output, const Shape<ndim>& shape) {
+  if (i < (shape.size + nvec - 1) / nvec) {
+    auto vector_output = reinterpret_cast<
+                          typename VectorConfig<sizeof(DType)*nvec>::IndexType *>(output);
+    VectorType<DType, nvec> ret(vector_output[i]);
+    ret += value;
+    vector_output[i] = ret.y;
+  }
+}
+
+template <typename DType>
+inline DType identity(const DType val) {
+  return val;
+}
+
+template <typename DType, typename DType2>
+inline DType add(const DType a, const DType2 b) {
+  return a + b;
+}
+
+template <typename DType, typename DType2>
+inline DType sub(const DType a, const DType2 b) {
+  return a - b;
+}
+
+template <typename DType, typename DType2>
+inline DType mul(const DType a, const DType2 b) {
+  return a * b;
+}
+
+template <typename DType, typename DType2>
+inline DType div(const DType a, const DType2 b) {
+  return a / b;
+}
+
+template <typename DType, typename DType2>
+inline DType rdiv(const DType a, const DType2 b) {
+  return b / a;
+}
+
+template <typename DType, typename DType2>
+inline DType power(const DType a, const DType2 b) {
+  return powf(a, b);
+}
+
+template <typename DType, typename DType2>
+inline DType rpow(const DType a, const DType2 b) {
+  return powf(b, a);
+}
+
+template <typename DType, typename DType2>
+inline DType max(const DType a, const DType2 b) {
+  return a > b ? a : b;
+}
+
+template <typename DType, typename DType2>
+inline DType min(const DType a, const DType2 b) {
+  return a < b ? a : b;
+}
+
+template <typename DType, typename DType2>
+inline DType hypot(const DType a, const DType2 b) {
+  return hypotf(a, b);
+}
+
+template <typename OutType, typename DType>
+inline typename LoadType<OutType>::Type cast(const DType val) {
+  return static_cast<typename LoadType<OutType>::Type>(val);
+}
+
+// activations
+
+template <typename DType>
+inline DType relu(const DType val) {
+  return val > 0 ? val : 0;
+}
+
+template <typename DType>
+inline DType sigmoid(const DType val) {
+  return 1.f/(1 + expf(-val));
+}
+
+template <typename DType>
+inline DType softrelu(const DType val) {
+  return logf(1 + expf(val));
+}
+
+template <typename DType>
+inline DType softsign(const DType val) {
+  return val / (1 + fabsf(val));
+}
+
+// exp and log
+
+template <typename DType>
+inline DType exp(const DType val) {
+  return expf(val);
+}
+
+template <typename DType>
+inline DType expm1(const DType val) {
+  return expm1f(val);
+}
+
+template <typename DType>
+inline DType log(const DType val) {
+  return logf(val);
+}
+
+template <typename DType>
+inline DType log10(const DType val) {
+  return log10f(val);
+}
+
+template <typename DType>
+inline DType log2(const DType val) {
+  return log2f(val);
+}
+
+template <typename DType>
+inline DType log1p(const DType val) {
+  return log1pf(val);
+}
+
+// trigonometric
+
+constexpr double pi = 3.14159265358979323846;
+
+template <typename DType>
+inline DType degrees(const DType val) {
+  return (val / pi) * 180;
+}
+
+template <typename DType>
+inline DType radians(const DType val) {
+  return (val / 180.0) * pi;
+}
+
+template <typename DType>
+inline DType sin(const DType val) {
+  return sinf(val);
+}
+
+template <typename DType>
+inline DType cos(const DType val) {
+  return cosf(val);
+}
+
+template <typename DType>
+inline DType tan(const DType val) {
+  return tanf(val);
+}
+
+template <typename DType>
+inline DType arcsin(const DType val) {
+  return asinf(val);
+}
+
+template <typename DType>
+inline DType arccos(const DType val) {
+  return acosf(val);
+}
+
+template <typename DType>
+inline DType arctan(const DType val) {
+  return atanf(val);
+}
+
+template <typename DType>
+inline DType sinh(const DType val) {
+  return sinhf(val);
+}
+
+template <typename DType>
+inline DType cosh(const DType val) {
+  return coshf(val);
+}
+
+template <typename DType>
+inline DType tanh(const DType val) {
+  return tanhf(val);
+}
+
+template <typename DType>
+inline DType arcsinh(const DType val) {
+  return asinhf(val);
+}
+
+template <typename DType>
+inline DType arccosh(const DType val) {
+  return acoshf(val);
+}
+
+template <typename DType>
+inline DType arctanh(const DType val) {
+  return atanhf(val);
+}
+
+// sqrt
+
+template <typename DType>
+inline DType sqrt(const DType val) {
+  return sqrtf(val);
+}
+
+template <typename DType>
+inline DType rsqrt(const DType val) {
+  return rsqrtf(val);
+}
+
+template <typename DType>
+inline DType cbrt(const DType val) {
+  return cbrtf(val);
+}
+
+template <typename DType>
+inline DType rcbrt(const DType val) {
+  return rcbrtf(val);
+}
+
+template <typename DType>
+inline DType square(const DType val) {
+  return val * val;
+}
+
+template <typename DType>
+inline typename LoadType<DType>::Type zero(const DType val) {
+  return 0;
+}
+
+template <typename DType>
+inline typename LoadType<DType>::Type zero() {
+  return 0;
+}
+
+template <typename DType>
+inline typename LoadType<DType>::Type one(const DType val) {
+  return 1;
+}
+
+template <typename DType>
+inline typename LoadType<DType>::Type one() {
+  return 1;
+}
+
+template <typename DType>
+inline DType round(const DType val) {
+  return roundf(val);
+}
+
+template <typename DType>
+inline DType rint(const DType val) {
+  return rintf(val);
+}
+
+template <typename DType>
+inline DType fix(const DType val) {
+    const auto floor = floorf(val);
+    const auto ceil = ceilf(val);
+    return (floor > 0 ? floor : -floor) < (ceil > 0 ? ceil : -ceil) ? floor : ceil;
+}
+
+template <typename DType>
+inline DType floor(const DType val) {
+    return floorf(val);
+}
+
+template <typename DType>
+inline DType ceil(const DType val) {
+    return ceilf(val);
+}
+
+template <typename DType>
+inline DType trunc(const DType val) {
+    return truncf(val);
+}
+
+template <typename DType>
+inline DType clip(const DType val, const float a_min, const float a_max) {
+  return max(min(val, a_max), a_min);
+}
+
+template <typename DType>
+inline DType sign(const DType val) {
+  if (val < 0) return -1;
+  return val > 0 ? 1 : 0;
+}
+
+template <typename DType>
+inline DType reciprocal(const DType val) {
+  return 1.0f / val;
+}
+
+template <typename DType>
+inline DType abs(const DType val) {
+  return fabsf(val);
+}
+
+template <typename DType>
+inline DType gamma(const DType val) {
+  return tgammaf(val);
+}
+
+template <typename DType>
+inline DType gammaln(const DType val) {
+  return lgammaf(val);
+}
+
+template <typename DType>
+inline DType erf(const DType val) {
+  return erff(val);
+}
+
+template <typename DType>
+inline DType erfinv(const DType val) {
+  return erfinvf(val);
+}
+
+template <typename DType1, typename DType2>
+inline DType1 smooth_l1(const DType1 val, const DType2 scalar) {
+  const auto bsq = scalar * scalar;
+  const auto ibsq = 1.0f / bsq;
+  if (val > ibsq) {
+    return val - 0.5f * ibsq;
+  } else if (val < -ibsq) {
+    return -val - 0.5f * ibsq;
+  } else {
+    return 0.5f * val * val * bsq;
+  }
+}
+
+}  // namespace op
+
+)code";
+
+const char backward_function_definitions[] = R"code(
+
+namespace op {
+
+template <typename DType, typename DTypeGrad>
+inline DTypeGrad backward_relu(const DType val, const DTypeGrad grad) {
+  return val > 0 ? grad : 0;
+}
+
+template <typename DType, typename DTypeGrad>
+inline DTypeGrad backward_sigmoid(const DType out, const DTypeGrad grad) {
+  return grad * out * (1 - out);
+}
+
+template <typename DType, typename DTypeGrad>
+inline DTypeGrad backward_softrelu(const DType val, const DTypeGrad grad) {
+  return grad * sigmoid(val);
+}
+
+template <typename DType, typename DTypeGrad>
+inline DTypeGrad backward_softsign(const DType val, const DTypeGrad grad) {
+  const DType ap1 = 1 + fabsf(val);
+  return grad / (ap1 * ap1);
+}
+
+template <typename DType, typename DTypeGrad>
+inline DTypeGrad backward_exp(const DType val, const DTypeGrad grad) {
+  return grad * expf(val);
+}
+
+template <typename DType, typename DTypeGrad>
+inline DTypeGrad backward_expm1(const DType val, const DTypeGrad grad) {
+  return grad * expf(val);
+}
+
+template <typename DType, typename DTypeGrad>
+inline DTypeGrad backward_log(const DType val, const DTypeGrad grad) {
+  return grad / val;
+}
+
+template <typename DType, typename DTypeGrad>
+inline DTypeGrad backward_log10(const DType val, const DTypeGrad grad) {
+  return grad / (val * logf(10));
+}
+
+template <typename DType, typename DTypeGrad>
+inline DTypeGrad backward_log2(const DType val, const DTypeGrad grad) {
+  return grad / (val * logf(2));
+}
+
+template <typename DType, typename DTypeGrad>
+inline DTypeGrad backward_log1p(const DType val, const DTypeGrad grad) {
+  return grad / (1 + val);
+}
+
+template <typename DType, typename DTypeGrad>
+inline DTypeGrad backward_sin(const DType val, const DTypeGrad grad) {
+  return grad * cosf(val);
+}
+
+template <typename DType, typename DTypeGrad>
+inline DTypeGrad backward_cos(const DType val, const DTypeGrad grad) {
+  return -grad * sinf(val);
+}
+
+// Uses output from tan
+template <typename DType, typename DTypeGrad>
+inline DTypeGrad backward_tan(const DType out, const DTypeGrad grad) {
+  return grad * (out * out + 1);
+}
+
+template <typename DType, typename DTypeGrad>
+inline DTypeGrad backward_arcsin(const DType val, const DTypeGrad grad) {
+  return grad / sqrtf(1 - val*val);
+}
+
+template <typename DType, typename DTypeGrad>
+inline DTypeGrad backward_arccos(const DType val, const DTypeGrad grad) {
+  return -grad / sqrtf(1 - val*val);
+}
+
+template <typename DType, typename DTypeGrad>
+inline DTypeGrad backward_arctan(const DType val, const DTypeGrad grad) {
+  return grad / (1 + val*val);
+}
+
+template <typename DType, typename DTypeGrad>
+inline DTypeGrad backward_sinh(const DType val, const DTypeGrad grad) {
+  return grad * coshf(val);
+}
+
+template <typename DType, typename DTypeGrad>
+inline DTypeGrad backward_cosh(const DType val, const DTypeGrad grad) {
+  return grad * sinhf(val);
+}
+
+// Uses tanh output
+template <typename DType, typename DTypeGrad>
+inline DTypeGrad backward_tanh(const DType out, const DTypeGrad grad) {
+  return grad * (1 - out * out);
+}
+
+template <typename DType, typename DTypeGrad>
+inline DTypeGrad backward_arcsinh(const DType val, const DTypeGrad grad) {
+  return grad / sqrtf(val * val + 1);
+}
+
+template <typename DType, typename DTypeGrad>
+inline DTypeGrad backward_arccosh(const DType val, const DTypeGrad grad) {
+  return grad / sqrtf(val * val - 1);
+}
+
+template <typename DType, typename DTypeGrad>
+inline DTypeGrad backward_arctanh(const DType val, const DTypeGrad grad) {
+  return grad / (1 - val * val);
+}
+
+template <typename DType, typename DTypeGrad>
+inline DTypeGrad backward_sqrt(const DType out, const DTypeGrad grad) {
+  return 0.5 * grad / out;
+}
+
+template <typename DType, typename DTypeGrad>
+inline DTypeGrad backward_rsqrt(const DType val, const DTypeGrad grad) {
+  const DType inv = 1 / val;
+  return -0.5 * grad * sqrtf(inv) * inv;
+}
+
+template <typename DType, typename DTypeGrad>
+inline DTypeGrad backward_cbrt(const DType out, const DTypeGrad grad) {
+  return grad / (3.0f * out * out);
+}
+
+template <typename DType, typename DTypeGrad>
+inline DTypeGrad backward_rcbrt(const DType val, const DTypeGrad grad) {
+  const DType inv = 1 / val;
+  return -1.f/3.f * grad * cbrtf(inv) * inv;
+}
+
+template <typename DType, typename DTypeGrad>
+inline DTypeGrad backward_square(const DType val, const DTypeGrad grad) {
+  return 2 * val * grad;
+}
+
+template <typename DType, typename DTypeGrad>
+inline DTypeGrad backward_clip(const DType val, const DTypeGrad grad, const float a_min, const float a_max) {
+  if (val > a_max || val < a_min) {
+    return 0;
+  } else {
+    return grad;
+  }
+}
+
+template <typename DType, typename DTypeGrad>
+inline DTypeGrad backward_reciprocal(const DType val, const DTypeGrad grad) {
+  return -grad / (val * val);
+}
+
+template <typename DType, typename DTypeGrad>
+inline DTypeGrad backward_erf(const DType val, const DTypeGrad grad) {
+  return 2.0f / sqrt(pi) * exp(-(val*val)) * grad;
+}
+
+template <typename DType, typename DTypeGrad>
+inline DTypeGrad backward_erfinv(const DType val, const DTypeGrad grad) {
+  return 0.5f * sqrt(pi) * exp(val * val) * grad;
+}
+
+template <typename DType, typename DType2, typename DTypeGrad>
+inline DTypeGrad backward_smooth_l1(const DType val, const DType2 scalar, const DTypeGrad grad) {
+  auto bsq = scalar * scalar;
+  auto ibsq = 1.0f / bsq;
+  if (val > ibsq) {
+    return grad;
+  } else if (val < -ibsq) {
+    return -grad;
+  } else {
+    return bsq * val * grad;
+  }
+}
+
+}  // namespace op
+
+)code";
+
+const char kernel_begin[] = R"code(
+const int tid = threadIdx.x + blockIdx.x * blockDim.x;
+for (int i = tid; i < N; i+= gridDim.x * blockDim.x) {
+    int offset = i*nvec;
+
+)code";
+
+const char kernel_end[] = R"code(
+}
+}
+)code";
+
+}  // namespace fusion
+
+}  // namespace mxnet
+
+#endif  // MXNET_USE_CUDA
+
+#endif  // MXNET_OPERATOR_FUSION_FUSED_OP_INL_H_
diff --git a/src/operator/fusion/fused_op.cc b/src/operator/fusion/fused_op.cc
new file mode 100644
index 000000000000..071215b840a5
--- /dev/null
+++ b/src/operator/fusion/fused_op.cc
@@ -0,0 +1,307 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+#include <tuple>
+
+#include "./fused_op.h"
+#include "../operator_common.h"
+#include "../../executor/exec_pass.h"
+
+#if MXNET_USE_CUDA
+
+namespace mxnet {
+
+DMLC_REGISTER_PARAMETER(FusedOpConfig);
+
+std::mutex FusedOp::mutex_;
+
+void FusedOpParamParser(nnvm::NodeAttrs* attrs) {
+  FusedOpConfig param;
+  try {
+    param.Init(attrs->dict);
+  } catch (const dmlc::ParamError& e) {
+    std::ostringstream os;
+    os << e.what();
+    os << ", in operator " << attrs->op->name << "("
+       << "name=\"" << attrs->name << "\"";
+    for (const auto& k : attrs->dict) {
+      os << ", " << k.first << "=\"" << k.second << "\"";
+    }
+    os << ")";
+    throw dmlc::ParamError(os.str());
+  }
+  attrs->parsed = FusedOpPtr(new FusedOp(attrs, param));
+}
+
+FusedOp::FusedOp(const nnvm::NodeAttrs* attrs, const FusedOpConfig& config) {
+  this->inputs_ = std::vector<FusedOpEntry>(config.num_inputs);
+  this->outputs_ = std::vector<FusedOpEntry>(config.num_outputs);
+  this->subgraph_ = nnvm::Graph();
+  this->subgraph_.outputs = attrs->subgraphs[0]->outputs;
+  this->initialized_ = false;
+  this->cc_major_ = -1;
+  this->cc_minor_ = -1;
+}
+
+bool FusedOp::InferShape(const nnvm::NodeAttrs &attrs,
+                         std::vector<mxnet::TShape> *in_attrs,
+                         std::vector<mxnet::TShape> *out_attrs) {
+  this->subgraph_.attrs.erase("shape");
+  this->subgraph_.attrs.erase("shape_inputs");
+  std::vector<mxnet::TShape> input_shapes(*in_attrs);
+  this->subgraph_ = mxnet::exec::InferShape(std::move(this->subgraph_),
+                                          std::move(input_shapes),
+                                          "__shape__");
+
+  const auto& g = this->subgraph_.indexed_graph();
+  const auto& input_nids = g.input_nodes();
+
+  std::vector<mxnet::TShape> out_shapes;
+  const std::vector<mxnet::TShape> shapes = this->subgraph_.GetAttr<mxnet::ShapeVector>("shape");
+  for (auto& e : g.outputs()) {
+    out_shapes.push_back(shapes[g.entry_id(e)]);
+  }
+  CHECK_EQ(out_shapes.size(), out_attrs->size());
+  for (size_t i = 0; i < out_attrs->size(); ++i) {
+    op::shape_assign(&(out_attrs->at(i)), out_shapes[i]);
+  }
+
+  // assign to in_attrs
+  for (size_t i = 0; i < in_attrs->size(); ++i) {
+    const auto eid = g.entry_id(input_nids[i], 0);
+    SHAPE_ASSIGN_CHECK(*in_attrs, i, shapes[eid]);
+  }
+
+  bool inferred = true;
+  for (const auto& attr : *in_attrs) {
+    inferred = inferred && !op::shape_is_none(attr);
+  }
+  for (const auto& attr : *out_attrs) {
+    inferred = inferred && !op::shape_is_none(attr);
+  }
+  if (inferred) {
+    std::lock_guard<std::mutex> lock(my_mutex_);
+    intermediate_shapes_.push_back({*in_attrs, *out_attrs, shapes});
+  }
+  return inferred;
+}
+
+bool FusedOp::InferType(const nnvm::NodeAttrs &attrs,
+                        std::vector<int> *in_attrs,
+                        std::vector<int> *out_attrs) {
+  this->subgraph_.attrs.erase("dtype");
+  this->subgraph_.attrs.erase("dtype_inputs");
+  std::vector<int> input_types(*in_attrs);
+  this->subgraph_ = mxnet::exec::InferType(std::move(this->subgraph_),
+                                         std::move(input_types),
+                                         "__dtype__");
+
+  const auto& g = this->subgraph_.indexed_graph();
+  const auto& input_nids = g.input_nodes();
+
+  std::vector<int> out_types;
+  const std::vector<int> types = this->subgraph_.GetAttr<nnvm::DTypeVector>("dtype");
+  for (auto& e : g.outputs()) {
+    out_types.push_back(types[g.entry_id(e)]);
+  }
+  CHECK_EQ(out_types.size(), out_attrs->size());
+  for (size_t i = 0; i < out_attrs->size(); ++i) {
+    op::type_assign(&(out_attrs->at(i)), out_types[i]);
+  }
+
+  // assign to in_attrs
+  for (size_t i = 0; i < in_attrs->size(); ++i) {
+    const auto eid = g.entry_id(input_nids[i], 0);
+    TYPE_ASSIGN_CHECK(*in_attrs, i, types[eid]);
+  }
+
+  bool inferred = true;
+  for (const auto& attr : *in_attrs) {
+    inferred = inferred && !op::type_is_none(attr);
+  }
+  for (const auto& attr : *out_attrs) {
+    inferred = inferred && !op::type_is_none(attr);
+  }
+  if (inferred) {
+    std::lock_guard<std::mutex> lock(my_mutex_);
+    intermediate_dtypes_.push_back({*in_attrs, *out_attrs, types});
+  }
+  return inferred;
+}
+
+template <typename Attr>
+std::tuple<const nnvm::NodePtr,
+           std::vector<Attr>,
+           std::vector<Attr>>
+  FusedOp::GetAttrs(const std::string& attr_name,
+                                                                  const uint32_t node_id) {
+  const auto& g = this->subgraph_.indexed_graph();
+  const std::vector<Attr> attrs = this->subgraph_.GetAttr<std::vector<Attr>>(attr_name);
+  const auto& node = g[node_id];
+  std::vector<Attr> inputs, outputs;
+  for (const auto& e : node.inputs) {
+    inputs.emplace_back(attrs[g.entry_id(e)]);
+  }
+  outputs.resize(node.source->num_outputs());
+  for (size_t i = 0; i < g.num_nodes(); ++i) {
+    if (i == node_id) continue;
+    const auto& other_node = g[i];
+    for (const auto& e : other_node.inputs) {
+      if (e.node_id == node_id) {
+        outputs[e.index] = attrs[g.entry_id(e)];
+      }
+    }
+  }
+  for (const auto& e : g.outputs()) {
+    if (e.node_id == node_id) {
+      outputs[e.index] = attrs[g.entry_id(e)];
+    }
+  }
+
+  return std::make_tuple(node.weak_ref.lock(),
+                         inputs,
+                         outputs);
+}
+
+bool FusedOpInferShape(const nnvm::NodeAttrs& attrs,
+                       std::vector<mxnet::TShape> *in_attrs,
+                       std::vector<mxnet::TShape> *out_attrs) {
+  const FusedOpPtr& op = nnvm::get<FusedOpPtr>(attrs.parsed);
+  return op->InferShape(attrs, in_attrs, out_attrs);
+}
+
+bool FusedOpInferType(const nnvm::NodeAttrs& attrs,
+                      std::vector<int> *in_attrs,
+                      std::vector<int> *out_attrs) {
+  const FusedOpPtr& op = nnvm::get<FusedOpPtr>(attrs.parsed);
+  return op->InferType(attrs, in_attrs, out_attrs);
+}
+
+void FusedOpProvideShape(const nnvm::NodeAttrs& attrs,
+                         const std::vector<nnvm::NodePtr>& nodes,
+                         const std::vector<std::vector<mxnet::TShape>> &in_attrs,
+                         const std::vector<std::vector<mxnet::TShape>> &out_attrs) {
+  const FusedOpPtr& op = nnvm::get<FusedOpPtr>(attrs.parsed);
+  op->ProvideShape(nodes, in_attrs, out_attrs);
+}
+
+void FusedOpProvideType(const nnvm::NodeAttrs& attrs,
+                        const std::vector<nnvm::NodePtr>& nodes,
+                        const std::vector<std::vector<int>> &in_attrs,
+                        const std::vector<std::vector<int>> &out_attrs) {
+  const FusedOpPtr& op = nnvm::get<FusedOpPtr>(attrs.parsed);
+  op->ProvideType(nodes, in_attrs, out_attrs);
+}
+
+void FusedOpProvideStorageType(const nnvm::NodeAttrs& attrs,
+                               const std::vector<nnvm::NodePtr>& nodes,
+                               const std::vector<std::vector<int>> &in_attrs,
+                               const std::vector<std::vector<int>> &out_attrs) {}
+
+NNVM_REGISTER_OP(_FusedOp)
+.set_attr<exec::TIsFusion>("TIsFusion", true)
+.set_num_inputs([](const NodeAttrs& attrs) {
+    const FusedOpPtr& op = nnvm::get<FusedOpPtr>(attrs.parsed);
+    return op->num_inputs();
+  })
+.set_num_outputs([](const NodeAttrs& attrs) {
+    const FusedOpPtr& op = nnvm::get<FusedOpPtr>(attrs.parsed);
+    return op->num_outputs();
+  })
+.set_attr<nnvm::FInplaceOption>("FInplaceOption", [](const NodeAttrs& attrs) {
+    const FusedOpPtr& op = nnvm::get<FusedOpPtr>(attrs.parsed);
+    const auto num_inputs = op->num_inputs();
+    const auto num_outputs = op->num_outputs();
+    std::vector<std::pair<int, int> > ret;
+    for (unsigned int i = 0; i < num_inputs; ++i) {
+      for (unsigned int j = 0; j < num_outputs; ++j) {
+        ret.emplace_back(i, j);
+      }
+    }
+    return ret;
+    })
+.set_attr<exec::FProvideSubgraphShape>("FProvideSubgraphShape", FusedOpProvideShape)
+.set_attr<exec::FProvideSubgraphType>("FProvideSubgraphType", FusedOpProvideType)
+.set_attr<exec::FProvideSubgraphStorageType>("FProvideSubgraphStorageType",
+                                             FusedOpProvideStorageType)
+.set_attr<mxnet::FInferShape>("FInferShape", FusedOpInferShape)
+.set_attr<nnvm::FInferType>("FInferType", FusedOpInferType)
+.set_attr_parser(FusedOpParamParser)
+.add_argument("data", "NDArray-or-Symbol[]", "Data");
+
+std::tuple<const nnvm::NodePtr,
+           std::vector<mxnet::TShape>,
+           std::vector<mxnet::TShape>>
+FusedOpHelperShape(const NodeAttrs& attrs) {
+  const auto& p = nnvm::get<FusedOpHelperParamPtr>(attrs.parsed);
+  const auto& op = p->op;
+  const auto& node_id = p->node_id;
+  return op->GetAttrs<mxnet::TShape>("shape", node_id);
+}
+
+std::tuple<const nnvm::NodePtr,
+           std::vector<int>,
+           std::vector<int>>
+FusedOpHelperType(const NodeAttrs& attrs) {
+  const auto& p = nnvm::get<FusedOpHelperParamPtr>(attrs.parsed);
+  const auto& op = p->op;
+  const auto& node_id = p->node_id;
+  return op->GetAttrs<int>("dtype", node_id);
+}
+
+NNVM_REGISTER_OP(_FusedOpHelper)
+.set_num_inputs(0)
+.set_num_outputs(0)
+.set_attr<nnvm::TIsGhost>("TIsGhost", true)
+.set_attr<exec::TIsFusionHelper>("TIsFusionHelper", true)
+.set_attr<exec::FAccessSubgraphShape>("FAccessSubgraphShape", FusedOpHelperShape)
+.set_attr<exec::FAccessSubgraphType>("FAccessSubgraphType", FusedOpHelperType);
+
+
+std::tuple<const nnvm::NodePtr,
+           std::vector<mxnet::TShape>,
+           std::vector<mxnet::TShape>>
+FusedOpOutHelperShape(const NodeAttrs& attrs) {
+  const auto& p = nnvm::get<FusedOpHelperParamPtr>(attrs.parsed);
+  const auto& op = p->op;
+  const auto& node_id = p->node_id;
+  return op->GetAuxShape(node_id);
+}
+
+std::tuple<const nnvm::NodePtr,
+           std::vector<int>,
+           std::vector<int>>
+FusedOpOutHelperType(const NodeAttrs& attrs) {
+  const auto& p = nnvm::get<FusedOpHelperParamPtr>(attrs.parsed);
+  const auto& op = p->op;
+  const auto& node_id = p->node_id;
+  return op->GetAuxType(node_id);
+}
+
+NNVM_REGISTER_OP(_FusedOpOutHelper)
+.set_num_inputs(0)
+.set_num_outputs(0)
+.set_attr<nnvm::TIsGhost>("TIsGhost", true)
+.set_attr<exec::TIsFusionHelper>("TIsFusionHelper", true)
+.set_attr<exec::FAccessSubgraphShape>("FAccessSubgraphShape", FusedOpOutHelperShape)
+.set_attr<exec::FAccessSubgraphType>("FAccessSubgraphType", FusedOpOutHelperType);
+
+}  // namespace mxnet
+
+#endif  // MXNET_USE_CUDA
diff --git a/src/operator/fusion/fused_op.cu b/src/operator/fusion/fused_op.cu
new file mode 100644
index 000000000000..f6df38bac247
--- /dev/null
+++ b/src/operator/fusion/fused_op.cu
@@ -0,0 +1,746 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+#include <sys/stat.h>
+#include <nvrtc.h>
+#include <cuda.h>
+#include <nnvm/pass_functions.h>
+#include <algorithm>
+#include <mutex>
+#include "./fused_op.h"
+#include "./fused_op-inl.h"
+#include "../operator_common.h"
+#include "../elemwise_op_common.h"
+#include "../../executor/exec_pass.h"
+#include "../../common/cuda_utils.h"
+
+namespace mxnet {
+
+namespace {
+
+inline std::string mshadowTypeToString(int type) {
+  switch (type) {
+    case mshadow::kFloat32:
+      return "float";
+    case mshadow::kFloat64:
+      return "double";
+    case mshadow::kFloat16:
+      return "half";
+    case mshadow::kUint8:
+      return "unsigned char";
+    case mshadow::kInt8:
+      return "char";
+    case mshadow::kInt32:
+      return "int";
+    case mshadow::kInt64:
+      return "long long";
+    default:
+      LOG(FATAL) << "Unknown type enum " << type;
+  }
+  return "";
+}
+
+inline int mshadowTypeToVectorLength(int type) {
+  switch (type) {
+    case mshadow::kFloat32:
+      return 1;
+    case mshadow::kFloat64:
+      return 1;
+    case mshadow::kFloat16:
+      return 2;
+    case mshadow::kUint8:
+      return 4;
+    case mshadow::kInt8:
+      return 4;
+    case mshadow::kInt32:
+      return 1;
+    case mshadow::kInt64:
+      return 1;
+    default:
+      LOG(FATAL) << "Unknown type enum " << type;
+  }
+  return 0;
+}
+
+inline void replaceString(std::string *input, const std::string old, const std::string repl) {
+    size_t pos = 0;
+    while ((pos = input->find(old, pos)) != std::string::npos) {
+        input->replace(pos, old.size(), repl);
+        pos += repl.size();
+    }
+}
+
+inline std::vector<int> splitStringToVector(const std::string& input, const std::string def) {
+    size_t pos_start = 0, pos_end;
+    const std::string& s = input.substr(1, input.length()-2);
+    std::vector<int> res;
+
+    auto convert_token = [def](std::string token){
+        if (token == def) {
+            return 0;
+        }
+        return std::stoi(token);
+    };
+
+    while ((pos_end = s.find(",", pos_start)) != std::string::npos) {
+        std::string token = s.substr(pos_start, pos_end - pos_start);
+        pos_start = pos_end + 1;
+        if (token.length() > 0) {
+            res.push_back(convert_token(token));
+        }
+    }
+
+    if (pos_start < s.length()) {
+        res.push_back(convert_token(s.substr(pos_start)));
+    }
+    return res;
+}
+
+std::string ParseOpDescription(const std::vector<std::string>& op_desc,
+                               const std::map<std::pair<int, int>, std::string>& variables,
+                               const nnvm::IndexedGraph::Node& node) {
+  const auto* source = node.source;
+  std::string fmt = op_desc[0];
+  for (size_t j = 1; j < op_desc.size(); ++j) {
+    const std::string& desc = op_desc[j];
+    std::string sub;
+    if (desc[0] == '_') {
+      // Argument
+      const int arg_id = std::stoi(desc.substr(1));
+      sub = variables.at({node.inputs[arg_id].node_id, node.inputs[arg_id].index});
+    } else {
+      sub = source->attrs.dict.at(desc);
+    }
+    size_t pos = fmt.find("%");
+    CHECK_NE(pos, std::string::npos);
+    fmt.replace(pos, 1, sub);
+  }
+  return fmt;
+}
+
+void AddShape(const mxnet::TShape& shape,
+              std::vector<std::vector<int>>* shapes) {
+  // We need alignment to 8 bytes for size_t in the Shape struct
+  // so if ndim is odd, there will be 4B of padding
+  int ndim = shape.ndim();
+  const int offset = ndim % 2 == 0 ? 2 : 3;
+  shapes->push_back(std::vector<int>(ndim + offset));
+  std::vector<int>& tensor_shapes = shapes->back();
+  size_t total_size = 1;
+  for (int i = ndim-1; i >= 0; i--) {
+    tensor_shapes[i] = shape[i];
+    total_size *= shape[i];
+  }
+  size_t * shape_size_ptr = reinterpret_cast<size_t*>(&tensor_shapes[ndim + offset - 2]);
+  *shape_size_ptr = total_size;
+}
+
+void AddPointerAndShape(const TBlob& data,
+                        std::vector<void*> *ptrs,
+                        std::vector<std::vector<int>>* shapes,
+                        mshadow::Stream<gpu> * s) {
+  using namespace mshadow;
+  MSHADOW_TYPE_SWITCH(data.type_flag_, DType, {
+    Tensor<gpu, 1, DType> tensor = data.FlatTo1D<gpu, DType>(s);
+    ptrs->push_back(tensor.dptr_);
+    AddShape(data.shape_, shapes);
+  });
+}
+
+}  // namespace
+
+void FusedOp::GenerateCode(int kernel_index, const std::vector<OpReqType> &req,
+                           const std::vector<int> &in_dtypes,
+                           const std::vector<int> &out_dtypes,
+                           const std::vector<int> &in_ndims,
+                           const std::vector<int> &out_ndims,
+                           const mxnet::ShapeVector &node_shapes,
+                           const std::vector<int> &node_dtypes,
+                           const int nvec,
+                           const std::string &kernel_name,
+                           std::vector<uint32_t>* check_shapes) {
+  const auto& g = this->subgraph_.indexed_graph();
+  std::string code = "";
+  int temp_name_counter = 0;
+  using NodeEntry = nnvm::IndexedGraph::NodeEntry;
+  std::map<std::pair<int, int>, std::string> variables;
+  std::map<int, int> load_index;
+  bool check_shapes_compile = true;
+
+  std::vector<uint32_t> outputs(g.num_nodes());
+
+  for (size_t i = 0; i < g.num_nodes(); ++i) {
+    const auto& node = g[i];
+    if (node.source != nullptr) {
+      outputs[i] = node.source->num_outputs();
+    } else {
+      outputs[i] = 0;
+    }
+  }
+
+  for (size_t i = 0; i < g.num_nodes(); ++i) {
+    const auto& node = g[i];
+    const auto* source = node.source;
+    if (source != nullptr) {
+        if (source->is_variable()) {
+            load_index[i] = 1;
+        } else {
+            std::string op_name = source->op()->name;
+            if (fusion::slice_ops.find(op_name) != fusion::slice_ops.end()) {
+                load_index[node.inputs[0].node_id] = 0;
+            }
+        }
+    }
+  }
+  for (size_t i = 0; i < g.num_nodes(); ++i) {
+    const auto& node = g[i];
+    const auto* source = node.source;
+    if (source != nullptr) {
+      if (source->is_variable()) {
+        if (load_index[i]) {
+          const auto& var_name = source->attrs.name;
+          code += "const auto vec_" + var_name + " = op::load_index<nvec>(" +
+                   var_name + ", offset, " + var_name + "_shape);\n";
+          variables[{i, 0}] = var_name;
+        }
+        CHECK_EQ(outputs[i], 1);
+      } else {
+        std::string op_name = source->op()->name;
+        if (fusion::slice_ops.find(op_name) != fusion::slice_ops.end()) {
+          int node_id = node.inputs[0].node_id;
+          const uint32_t input_entry_id = g.entry_id(node.inputs[0]);
+          const auto& shape = node_shapes[input_entry_id];
+          const int ndim = shape.ndim();
+          const auto& var_name = g[node_id].source->attrs.name;
+          const auto vec_name = "vec_" + var_name + "_" + std::to_string(i);
+          load_index[node_id] = 0;
+          auto parse_tuple = [](const std::string& input, const std::string def) {
+            std::string out = input;
+            replaceString(&out, "(", "{");
+            replaceString(&out, ")", "}");
+            replaceString(&out, "None", def);
+            replaceString(&out, " ", "");
+            return out;
+          };
+          auto build_tuple = [ndim](int axis, const std::string str, const std::string def) {
+            std::string tuple = "{";
+            for (int i = 0; i < axis; i++) {
+                tuple = tuple + def + ",";
+            }
+            tuple += str;
+            for (int i = axis + 1; i < ndim; i++) {
+                tuple = tuple + "," + def;
+            }
+            tuple += "}";
+            return tuple;
+          };
+          auto check_tuple = [ndim, nvec](const std::string str) {
+            std::vector<int> tuple = splitStringToVector(str, "INT_MAX");
+            if (tuple[ndim-1] % nvec == 0) {
+              return true;
+            }
+            return false;
+          };
+          auto build_string_axis = [ndim](int axis) {
+            if (axis < 0) {
+                axis = ndim + axis;
+            }
+            return std::to_string(axis);
+          };
+          auto build_string_end = [i, ndim, var_name](std::string* code) {
+            std::string end_var_name = var_name + "_" + std::to_string(i) + "_end";
+            *code += "op::Shape<" + std::to_string(ndim) + "> "+ end_var_name + ";\n";
+            *code += end_var_name + ".set(INT_MAX);\n";
+            return end_var_name;
+          };
+          std::string begin;
+          std::string end;
+          if (op_name == "broadcast_like" || op_name == "slice_like") {
+            uint32_t like_id = g.entry_id(i, 0);
+            begin = build_tuple(0, "0", "0");
+            std::string extra_var_name = "extra_" + std::to_string(like_id) + "_shape";
+            if (std::find(extra_shape_args_.begin(), extra_shape_args_.end(), like_id) ==
+                extra_shape_args_.end()) {
+                extra_shape_args_.push_back(like_id);
+            }
+            if (check_shapes) {
+              check_shapes->push_back(like_id);
+              check_shapes->push_back(input_entry_id);
+            }
+            end = extra_var_name;
+          } else {
+            begin = parse_tuple(source->attrs.dict.at("begin"), "0");
+            end = parse_tuple(source->attrs.dict.at("end"), "INT_MAX");
+            if (op_name == "slice_axis") {
+              int axis = std::stoi(source->attrs.dict.at("axis"));
+              begin = build_tuple(axis, begin, "0");
+              end = build_tuple(axis, end, "INT_MAX");
+            }
+            if (check_shapes) {
+              if (check_tuple(begin) && check_tuple(end)) {
+                check_shapes->push_back(input_entry_id);
+              } else {
+                check_shapes_compile = false;
+              }
+            }
+          }
+          std::string slice_func = "load_slice";
+          if (!check_shapes) {
+            slice_func = "fast_" + slice_func;
+          }
+          code += "const auto " + vec_name + " = op::" + slice_func + "<nvec>(" +
+                  var_name + ", " + var_name + "_shape," + begin +
+                  "," + end + ", offset);\n";
+          CHECK_EQ(outputs[i], 1);
+          variables[{i, 0}] = vec_name;
+          continue;
+        }
+      }
+    }
+  }
+
+  if (!check_shapes_compile) {
+      check_shapes->clear();
+  }
+
+  size_t counter = 0;
+  for (const auto& entry : g.outputs()) {
+    std::string var_name = "output" + std::to_string(counter);
+    code += "op::VectorType<DType_" + var_name + \
+            ", nvec> vec_" + var_name + ";\n";
+    ++counter;
+  }
+
+  code += "for (int j = 0; j < nvec; j++ ) {\n";
+
+
+  for (size_t i = 0; i < g.num_nodes(); ++i) {
+    const auto& node = g[i];
+    const auto* source = node.source;
+    if (source != nullptr) {
+      std::string var_name = "temp" + std::to_string(temp_name_counter++);
+      if (source->is_variable()) {
+        if (load_index[i]) {
+            code += "const auto " + var_name + " = op::load(vec_" +
+                    variables[{i, 0}] + ".x[j]);\n";
+            CHECK_EQ(outputs[i], 1);
+            variables[{i, 0}] = var_name;
+        }
+      } else {
+        std::string op_name = source->op()->name;
+        if (fusion::ops_desc.find(op_name) != fusion::ops_desc.end()) {
+          const std::vector<std::vector<std::string>>& op_descs =
+            fusion::ops_desc.at(op_name);
+          CHECK_EQ(outputs[i], op_descs.size());
+          size_t count = 0;
+          for (const auto& op_desc : op_descs) {
+            var_name = "temp" + std::to_string(temp_name_counter++);
+            const std::string& fmt = ParseOpDescription(op_desc, variables, node);
+            code += "const auto " + var_name + " = " + fmt + ";\n";
+            variables[{i, count}] = var_name;
+            ++count;
+          }
+          continue;
+        }
+
+        if (fusion::slice_ops.find(op_name) != fusion::slice_ops.end()) {
+          code += "const auto " + var_name + " = op::load(" + variables[{i, 0}] + ".x[j]);\n";
+          variables[{i, 0}] = var_name;
+          continue;
+        }
+
+
+        // Special cases with variable number
+        // of inputs/outputs, listed in
+        // fusion::variable_io_ops
+        if (op_name == "add_n") {
+          CHECK_EQ(outputs[i], 1);
+          const auto& arg = variables[{node.inputs[0].node_id, node.inputs[0].index}];
+          code += "auto " + var_name + " = " + arg + ";\n";
+          for (size_t inp = 1; inp < node.inputs.size(); ++inp) {
+            const auto& temp_arg = variables[{node.inputs[inp].node_id, node.inputs[inp].index}];
+            code += var_name + " = op::add(" + var_name + ", " + temp_arg + ");\n";
+          }
+          variables[{i, 0}] = var_name;
+          continue;
+        }
+
+        if (op_name == "_backward_Activation") {
+          CHECK_EQ(outputs[i], 1);
+          std::string act_type = node.source->attrs.dict.at("act_type");
+          std::string rhs, lhs;
+          rhs = variables[{node.inputs[0].node_id, node.inputs[0].index}];
+          if (act_type == "relu" ||
+              act_type == "sigmoid" ||
+              act_type == "tanh") {
+            lhs = variables[{node.inputs[1].node_id, node.inputs[1].index}];
+          } else {
+            lhs = variables[{node.inputs[2].node_id, node.inputs[2].index}];
+          }
+          code += "const auto " + var_name + " = op::backward_" + act_type +
+                  "(" + lhs + ", " + rhs + ");\n";
+
+          variables[{i, 0}] = var_name;
+          continue;
+        }
+
+        if (op_name == "amp_multicast" || op_name == "_backward_amp_multicast") {
+          CHECK_EQ(outputs[i], node.inputs.size());
+          for (size_t counter = 0; counter < outputs[i]; ++counter) {
+            const auto& input = node.inputs[counter];
+            var_name = "temp" + std::to_string(temp_name_counter++);
+            const auto& arg = variables[{input.node_id, input.index}];
+            code += "const auto " + var_name + " = " + arg + ";\n";
+            variables[{i, counter}] = var_name;
+          }
+          continue;
+        }
+
+        if (op_name == "_backward_cast") {
+          CHECK_EQ(outputs[i], 1);
+          const int output_type = node_dtypes[g.entry_id(i, 0)];
+          const auto& arg = variables[{node.inputs[0].node_id, node.inputs[0].index}];
+          code += "const auto " + var_name + " = op::cast<" + mshadowTypeToString(output_type) +
+                  ">(" + arg + ");\n";
+          variables[{i, 0}] = var_name;
+          continue;
+        }
+
+        LOG(FATAL) << "Unrecognized op " + op_name;
+      }
+    } else {
+      LOG(FATAL) << "Encountered node with NULL source.";
+    }
+  }
+
+  counter = 0;
+  for (const auto& entry : g.outputs()) {
+    const std::string& var = variables[{entry.node_id, entry.index}];
+    const auto var_name = "output" + std::to_string(counter);
+    code += "vec_" + var_name + ".x[j] = op::store("+ var +", " + var_name + ");\n";
+    ++counter;
+  }
+
+  code += "}\n";
+
+  counter = 0;
+
+  for (const auto& entry : g.outputs()) {
+    const std::string& var = variables[{entry.node_id, entry.index}];
+    if (req[counter] == kWriteTo || req[counter] == kWriteInplace) {
+      const auto var_name = "output" + std::to_string(counter);
+      code += "op::store_index(vec_" + var_name + ", i, " + var_name + ", " +
+              var_name + "_shape);\n";
+    } else if (req[counter] == kAddTo) {
+      const auto var_name = "output" + std::to_string(counter);
+      code += "op::store_add_index(vec_" + var_name + ", i, " + var_name + ", " +
+              var_name + "_shape);\n";
+    } else if (req[counter] == kNullOp) {
+      // NULL req, do not do anything
+    } else {
+      LOG(FATAL) << "Encountered unexpected req.";
+    }
+    ++counter;
+  }
+
+  this->code_[kernel_index] = code;
+
+  // Add boilerplate and type information
+  if (dmlc::GetEnv("MXNET_FUSION_VERBOSE", false)) {
+    LOG(INFO) << code_[kernel_index];
+  }
+  std::string kernel_params = "";
+  std::string tensor_params = "";
+  nnvm::Symbol sym;
+  sym.outputs = this->subgraph_.outputs;
+  const std::vector<std::string> input_names = sym.ListInputNames(nnvm::Symbol::kAll);
+  size_t num_params = in_dtypes.size() + out_dtypes.size();
+  size_t i = 0;
+  std::string aux_code = "static const int nvec = " + std::to_string(nvec) + ";\n";
+
+  for (const auto &shape_id : extra_shape_args_) {
+      std::string shape_name = "extra_" + std::to_string(shape_id) + "_shape";
+      int ndim = node_shapes[shape_id].ndim();
+      kernel_params += " const op::Shape<" + std::to_string(ndim) + "> " + shape_name;
+      kernel_params += ", ";
+  }
+  for (const auto &type : in_dtypes) {
+    std::string type_name = mshadowTypeToString(type);
+    std::string dtype_var = "DType_" + input_names[i];
+    std::string dim_var = "ndim_" + input_names[i];
+    std::string dim_val = std::to_string(in_ndims[i]);
+    aux_code = "using " + dtype_var + " = " + type_name + ";\n" + aux_code;
+    aux_code = "static const int " + dim_var + " = " + dim_val + ";\n" + aux_code;
+    tensor_params += dtype_var + "* " +input_names[i];
+    kernel_params += " const op::Shape<" + dim_val + "> " + input_names[i]+"_shape";
+    ++i;
+    if (i < num_params) {
+      tensor_params += ", ";
+    }
+    kernel_params += ", ";
+  }
+  for (const auto &type : out_dtypes) {
+    std::string type_name = mshadowTypeToString(type);
+    std::string out_name = "output" + std::to_string(i - in_dtypes.size());
+    std::string dtype_var = "DType_" + out_name;
+    std::string dim_var = "ndim_" + out_name;
+    std::string dim_val = std::to_string(out_ndims[i - in_dtypes.size()]);
+    aux_code = "static const int " + dim_var + " = " + dim_val + ";\n" + aux_code;
+    aux_code = "using " + dtype_var + " = " + type_name + ";\n" + aux_code;
+    tensor_params += dtype_var + "* " + out_name;
+    kernel_params += " const op::Shape<" + dim_val + "> " + out_name+"_shape";
+    ++i;
+    if (i < num_params) {
+      tensor_params += ", ";
+    }
+    kernel_params += ", ";
+  }
+  kernel_params += tensor_params;
+
+  code_[kernel_index] = std::string(fusion::fp16_support_string) + "\n" +
+          fusion::type_support_string + "\n" +
+          fusion::function_definitions + "\n" +
+          fusion::backward_function_definitions + "\n" +
+          aux_code + "\n" +
+          "__launch_bounds__(" + std::to_string(FusedOp::NTHREADS) + ")\n" +
+          "__global__ void FusedKernel_" + kernel_name +
+          "(size_t N, " + kernel_params + ") {\n" +
+          fusion::kernel_begin + "\n" +
+          code_[kernel_index] + "\n" +
+          fusion::kernel_end;
+}
+
+void FusedOp::CompileCode(int kernel_index, const std::string &kernel_name) {
+  // Guard NVRTC calls
+  std::lock_guard<std::mutex> lock_nvrtc(mutex_);
+  nvrtcProgram program;
+  NVRTC_CALL(
+      nvrtcCreateProgram(&program,                                  // prog
+                         &code_[kernel_index][0],                                 // buffer
+                         (kernel_name + "_kernel.cu").c_str(),      // name
+                         0,                                         // num headers
+                         NULL,                                      // headers
+                         NULL));                                    // include names
+  std::string gpu_arch = "--gpu-architecture=compute_" +
+                         std::to_string(this->cc_major_) +
+                         std::to_string(this->cc_minor_);
+
+  const char *opts[] = {gpu_arch.c_str(),
+                        "--std=c++11",
+                        "-default-device"};
+  const std::string kernel_name_demangled = "FusedKernel_" + kernel_name;
+  NVRTC_CALL(nvrtcAddNameExpression(program, (kernel_name_demangled).c_str()));
+
+  nvrtcResult compileResult = nvrtcCompileProgram(program,  // prog
+                                                  3,        // num options
+                                                  opts);    // options
+  // Obtain compilation log from the program.
+  size_t log_size;
+  NVRTC_CALL(nvrtcGetProgramLogSize(program, &log_size));
+  std::string log(log_size, '\0');
+  NVRTC_CALL(nvrtcGetProgramLog(program, &log[0]));
+  CHECK_EQ(compileResult, NVRTC_SUCCESS)
+    << "NVRTC Compilation failed. Please set environment variable MXNET_USE_FUSION to 0.\n" << log;
+  // Obtain PTX from the program.
+  size_t ptx_size;
+  NVRTC_CALL(nvrtcGetPTXSize(program, &ptx_size));
+  ptx_[kernel_index].reserve(ptx_size);
+  NVRTC_CALL(nvrtcGetPTX(program, &ptx_[kernel_index][0]));
+  const char *name;
+  NVRTC_CALL(nvrtcGetLoweredName(program,
+                                 kernel_name_demangled.c_str(),
+                                 &name));
+  kernel_name_[kernel_index] = name;
+  // Destroy the program.
+  NVRTC_CALL(nvrtcDestroyProgram(&program));
+  int device;
+  CUdevice cu_device;
+  CUcontext context;
+  CUmodule module;
+  CUDA_CALL(cudaGetDevice(&device));
+  CUDA_DRIVER_CALL(cuDeviceGet(&cu_device, device));
+  CUDA_DRIVER_CALL(cuDevicePrimaryCtxRetain(&context, cu_device));
+  CUDA_DRIVER_CALL(cuModuleLoadData(&module, &ptx_[kernel_index][0]));
+  CUDA_DRIVER_CALL(cuModuleGetFunction(&kernel_[kernel_index],
+                                       module,
+                                       kernel_name_[kernel_index].c_str()));
+}
+
+bool FusedOp::CheckComputeCapability(const OpContext &ctx) {
+  const int dev_id = ctx.run_ctx.ctx.dev_id;
+  const int cc_major = ComputeCapabilityMajor(dev_id);
+  const int cc_minor = ComputeCapabilityMinor(dev_id);
+
+  const bool ret = cc_major == this->cc_major_ && cc_minor == this->cc_minor_;
+  this->cc_major_ = cc_major;
+  this->cc_minor_ = cc_minor;
+  return ret;
+}
+
+void FusedOp::CheckShapesAndTypes(const std::vector<TBlob> &inputs,
+                                  const std::vector<TBlob> &outputs,
+                                  std::vector<int> *in_dtypes,
+                                  std::vector<int> *in_ndims,
+                                  std::vector<int> *out_dtypes,
+                                  std::vector<int> *out_ndims,
+                                  int *nvec) {
+  std::vector<mxnet::TShape> in_shapes;
+  std::vector<mxnet::TShape> out_shapes;
+  CHECK_EQ(inputs.size(), inputs_.size());
+  CHECK_EQ(outputs.size(), outputs_.size());
+
+  for (size_t counter = 0; counter < inputs.size(); ++counter) {
+    const auto& blob = inputs[counter];
+    in_dtypes->push_back(blob.type_flag_);
+    in_ndims->push_back(blob.ndim());
+    in_shapes.push_back(blob.shape_);
+    initialized_ = initialized_ && blob.type_flag_ == inputs_[counter].dtype;
+    inputs_[counter].dtype = blob.type_flag_;
+    *nvec = max(*nvec, mshadowTypeToVectorLength(blob.type_flag_));
+  }
+
+  for (size_t counter = 0; counter < outputs.size(); ++counter) {
+    const auto& blob = outputs[counter];
+    out_dtypes->push_back(blob.type_flag_);
+    out_ndims->push_back(blob.ndim());
+    out_shapes.push_back(blob.shape_);
+    initialized_ = initialized_ && blob.type_flag_ == outputs_[counter].dtype;
+    outputs_[counter].dtype = blob.type_flag_;
+    *nvec = max(*nvec, mshadowTypeToVectorLength(blob.type_flag_));
+  }
+
+  for (auto it = intermediate_shapes_.begin();
+       it != intermediate_shapes_.end();
+       ++it) {
+    if (it->input_attr == in_shapes && it->output_attr == out_shapes) {
+      intermediate_shapes_.erase(intermediate_shapes_.begin(), it);
+      break;
+    }
+  }
+  for (auto it = intermediate_dtypes_.begin();
+       it != intermediate_dtypes_.end();
+       ++it) {
+    if (it->input_attr == *in_dtypes && it->output_attr == *out_dtypes) {
+      intermediate_dtypes_.erase(intermediate_dtypes_.begin(), it);
+      break;
+    }
+  }
+}
+
+template <>
+void FusedOp::Forward<gpu>(const nnvm::NodeAttrs& attrs,
+                           const OpContext &ctx,
+                           const std::vector<TBlob> &inputs,
+                           const std::vector<OpReqType> &req,
+                           const std::vector<TBlob> &outputs) {
+  using namespace mshadow;
+  std::lock_guard<std::mutex> lock(my_mutex_);
+  CHECK_GE(outputs.size(), 1) << "There needs to be at least 1 output.";
+
+  std::vector<int> in_dtypes;
+  std::vector<int> in_ndims;
+  std::vector<int> out_dtypes;
+  std::vector<int> out_ndims;
+  int nvec = 1;
+
+  CheckShapesAndTypes(inputs, outputs, &in_dtypes, &in_ndims,
+                      &out_dtypes, &out_ndims, &nvec);
+
+  const auto& node_shapes = intermediate_shapes_[0].internal_attr;
+  const auto& node_dtypes = intermediate_dtypes_[0].internal_attr;
+
+  // Check and save compute capability of the current GPU
+  if (!CheckComputeCapability(ctx)) initialized_ = false;
+
+  initialized_ = initialized_ && (req == saved_reqs_);
+  saved_reqs_ = req;
+
+  if (!initialized_) {
+    this->GenerateCode(0, req, in_dtypes, out_dtypes, in_ndims, out_ndims,
+                       node_shapes, node_dtypes, nvec, attrs.name, &check_shape_args_);
+    this->CompileCode(0, attrs.name);
+    if (check_shape_args_.size() > 0) {
+        this->GenerateCode(1, req, in_dtypes, out_dtypes, in_ndims, out_ndims,
+                           node_shapes, node_dtypes, nvec, attrs.name, NULL);
+        this->CompileCode(1, attrs.name);
+    }
+    initialized_ = true;
+  }
+  Stream<gpu>* s = ctx.get_stream<gpu>();
+  auto stream = Stream<gpu>::GetStream(s);
+  std::vector<void*> args;
+  size_t N = 0;
+  for (const auto& output : outputs) {
+    N = std::max(N, output.shape_.Size());
+  }
+  N = (N + nvec - 1)/nvec;
+  args.push_back(&N);
+
+  unsigned int num_blocks = (N + FusedOp::NTHREADS - 1) / FusedOp::NTHREADS;
+
+  std::vector<void*> ptrs;
+  std::vector<std::vector<int>> shapes;
+
+  for (const auto &shape_id : extra_shape_args_) {
+    AddShape(node_shapes[shape_id], &shapes);
+  }
+  for (const auto &data : inputs) {
+    AddPointerAndShape(data, &ptrs, &shapes, s);
+  }
+  for (const auto &data : outputs) {
+    AddPointerAndShape(data, &ptrs, &shapes, s);
+  }
+
+  for (auto &tensor_shapes : shapes) {
+    args.push_back(tensor_shapes.data());
+  }
+  for (auto &ptr : ptrs) {
+    args.push_back(reinterpret_cast<void *>(&ptr));
+  }
+  int kernel_index = 0;
+  if (check_shape_args_.size() > 0) {
+      kernel_index = 1;
+      for (const auto &shape_id : check_shape_args_) {
+          const auto& shape = node_shapes[shape_id];
+          if (shape[shape.ndim()-1] % nvec != 0) {
+              kernel_index = 0;
+          }
+      }
+  }
+  CUDA_DRIVER_CALL(
+      cuLaunchKernel(kernel_[kernel_index],
+        num_blocks, 1, 1,          // grid dim
+        FusedOp::NTHREADS, 1, 1,   // block dim
+        0, stream,                 // shared mem and stream
+        &(args[0]), 0));           // arguments
+}
+
+void FusedOpForwardGPU(const nnvm::NodeAttrs& attrs,
+                    const OpContext &ctx,
+                    const std::vector<TBlob> &inputs,
+                    const std::vector<OpReqType> &req,
+                    const std::vector<TBlob> &outputs) {
+  const FusedOpPtr& op = nnvm::get<FusedOpPtr>(attrs.parsed);
+  op->Forward<gpu>(attrs, ctx, inputs, req, outputs);
+}
+
+NNVM_REGISTER_OP(_FusedOp)
+.set_attr<FCompute>("FCompute<gpu>", FusedOpForwardGPU);
+
+}  // namespace mxnet
diff --git a/src/operator/fusion/fused_op.h b/src/operator/fusion/fused_op.h
new file mode 100644
index 000000000000..035e5432fca4
--- /dev/null
+++ b/src/operator/fusion/fused_op.h
@@ -0,0 +1,203 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+#ifndef MXNET_OPERATOR_FUSION_FUSED_OP_H_
+#define MXNET_OPERATOR_FUSION_FUSED_OP_H_
+
+
+#include <mxnet/operator.h>
+#include <nnvm/graph.h>
+#include <vector>
+#include <string>
+#include <utility>
+#include <mutex>
+#include <tuple>
+
+#if MXNET_USE_CUDA
+
+
+namespace mxnet {
+
+struct FusedOpConfig : public dmlc::Parameter<FusedOpConfig> {
+  int num_inputs;
+  int num_outputs;
+  DMLC_DECLARE_PARAMETER(FusedOpConfig) {
+    DMLC_DECLARE_FIELD(num_inputs)
+    .describe("Number of inputs.");
+    DMLC_DECLARE_FIELD(num_outputs)
+    .describe("Number of outputs.");
+  }
+};
+
+struct FusedOpEntry {
+  FusedOpEntry() : dtype(-1) {}
+  int dtype;
+};
+
+class FusedOp {
+ public:
+  static const int NTHREADS = 512;
+
+  explicit FusedOp(const nnvm::NodeAttrs* attrs, const FusedOpConfig& config);
+  ~FusedOp() {}
+  uint32_t num_inputs() const {
+    return inputs_.size();
+  }
+  uint32_t num_outputs() const {
+    return outputs_.size();
+  }
+
+  template <typename xpu>
+  void Forward(const nnvm::NodeAttrs& attrs,
+               const OpContext &ctx,
+               const std::vector<TBlob> &inputs,
+               const std::vector<OpReqType> &req,
+               const std::vector<TBlob> &outputs);
+
+  bool InferShape(const nnvm::NodeAttrs &attrs,
+                  std::vector<mxnet::TShape> *in_attrs,
+                  std::vector<mxnet::TShape> *out_attrs);
+
+  bool InferType(const nnvm::NodeAttrs &attrs,
+                 std::vector<int> *in_attrs,
+                 std::vector<int> *out_attrs);
+
+  template <typename Attr>
+  std::tuple<const nnvm::NodePtr,
+             std::vector<Attr>,
+             std::vector<Attr>>
+    GetAttrs(const std::string& attr_name,
+             const uint32_t node_id);
+
+  void ProvideShape(const std::vector<nnvm::NodePtr>& nodes,
+                    const std::vector<std::vector<mxnet::TShape>> &in_attrs,
+                    const std::vector<std::vector<mxnet::TShape>> &out_attrs) {
+    aux_nodes_ = nodes;
+    aux_in_shapes_ = in_attrs;
+    aux_out_shapes_ = out_attrs;
+  }
+
+  void ProvideType(const std::vector<nnvm::NodePtr>& nodes,
+                   const std::vector<std::vector<int>> &in_attrs,
+                   const std::vector<std::vector<int>> &out_attrs) {
+    aux_nodes_ = nodes;
+    aux_in_types_ = in_attrs;
+    aux_out_types_ = out_attrs;
+  }
+
+  std::tuple<const nnvm::NodePtr,
+             std::vector<mxnet::TShape>,
+             std::vector<mxnet::TShape>>
+    GetAuxShape(const int node_id) const {
+    return std::make_tuple(aux_nodes_[node_id],
+                           aux_in_shapes_[node_id],
+                           aux_out_shapes_[node_id]);
+  }
+
+  std::tuple<const nnvm::NodePtr,
+             std::vector<int>,
+             std::vector<int>>
+    GetAuxType(const int node_id) const {
+    return std::make_tuple(aux_nodes_[node_id],
+                           aux_in_types_[node_id],
+                           aux_out_types_[node_id]);
+  }
+
+ private:
+  void GenerateCode(int kernel_index,
+                    const std::vector<OpReqType> &req,
+                    const std::vector<int> &in_dtypes,
+                    const std::vector<int> &out_dtypes,
+                    const std::vector<int> &in_ndims,
+                    const std::vector<int> &out_ndims,
+                    const mxnet::ShapeVector &node_shapes,
+                    const std::vector<int> &node_dtypes,
+                    const int nvec,
+                    const std::string& kernel_name,
+                    std::vector<uint32_t> *check_shapes);
+  void CompileCode(int kernel_index,
+                   const std::string &kernel_name);
+  bool CheckComputeCapability(const OpContext &ctx);
+  void CheckShapesAndTypes(const std::vector<TBlob> &inputs,
+                           const std::vector<TBlob> &outputs,
+                           std::vector<int> *in_dtypes,
+                           std::vector<int> *in_ndims,
+                           std::vector<int> *out_dtypes,
+                           std::vector<int> *out_ndims,
+                           int *nvec);
+
+  std::vector<FusedOpEntry> inputs_;
+  std::vector<FusedOpEntry> outputs_;
+
+  std::string code_[2];
+  nnvm::Graph subgraph_;
+
+  template <typename T>
+  struct IntermediateAttr {
+    std::vector<T> input_attr;
+    std::vector<T> output_attr;
+    std::vector<T> internal_attr;
+  };
+
+  // Shapes and types inside the subgraph
+  // copied here, because a subsequent call
+  // to InferShape/InferType can overwrite the
+  // original information stored in subgraph_
+  // attributes while the previous iterations
+  // still need them.
+  std::vector<IntermediateAttr<mxnet::TShape> > intermediate_shapes_;
+  std::vector<IntermediateAttr<int> > intermediate_dtypes_;
+
+  std::vector<nnvm::NodePtr> aux_nodes_;
+  std::vector<std::vector<mxnet::TShape>> aux_in_shapes_;
+  std::vector<std::vector<mxnet::TShape>> aux_out_shapes_;
+  std::vector<std::vector<int>> aux_in_types_;
+  std::vector<std::vector<int>> aux_out_types_;
+  std::vector<OpReqType> saved_reqs_;
+  std::vector<uint32_t> extra_shape_args_;
+  std::vector<uint32_t> check_shape_args_;
+
+  std::string ptx_[2];
+  std::string kernel_name_[2];
+  CUfunction kernel_[2];
+  bool initialized_;
+  int cc_major_;
+  int cc_minor_;
+
+  static std::mutex mutex_;
+  std::mutex my_mutex_;
+};
+
+using FusedOpPtr = std::shared_ptr<FusedOp>;
+
+struct FusedOpHelperParam {
+  FusedOpPtr op;
+  uint32_t node_id;
+
+  FusedOpHelperParam(FusedOpPtr op, uint32_t node_id) :
+    op(op),
+    node_id(node_id) {}
+};
+
+using FusedOpHelperParamPtr = std::shared_ptr<FusedOpHelperParam>;
+
+}  // namespace mxnet
+
+#endif  // MXNET_USE_CUDA
+#endif  // MXNET_OPERATOR_FUSION_FUSED_OP_H_
diff --git a/src/operator/mshadow_op.h b/src/operator/mshadow_op.h
index 31470a297e25..c5a2b1308c73 100644
--- a/src/operator/mshadow_op.h
+++ b/src/operator/mshadow_op.h
@@ -213,7 +213,7 @@ struct softrelu : public mxnet_op::tunable {
 
 MXNET_UNARY_MATH_OP(softrelu_grad, -math::expm1(-a));
 
-MXNET_UNARY_MATH_OP(erfinv_grad, 0.5 * math::sqrt(PI) * math::exp(math::sqr(erfinv::Map(a))));
+MXNET_UNARY_MATH_OP(erfinv_grad, 0.5 * math::sqrt(PI) * math::exp(math::sqr(a)));
 
 MXNET_UNARY_MATH_OP(erf_grad, 2.0 / math::sqrt(PI) * math::exp(-(a * a)));
 
diff --git a/src/operator/tensor/elemwise_unary_op_basic.cc b/src/operator/tensor/elemwise_unary_op_basic.cc
index 2a834bb9dc55..56674409601c 100644
--- a/src/operator/tensor/elemwise_unary_op_basic.cc
+++ b/src/operator/tensor/elemwise_unary_op_basic.cc
@@ -906,7 +906,7 @@ Example::
 
 )code" ADD_FILELINE)
 .set_attr<FCompute>("FCompute<cpu>", UnaryOp::Compute<cpu, mshadow_op::erfinv>)
-.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_erfinv"});
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{"_backward_erfinv"});
 
 MXNET_OPERATOR_REGISTER_BINARY(_backward_erfinv)
 .set_attr<FCompute>("FCompute<cpu>",
diff --git a/src/storage/pooled_storage_manager.h b/src/storage/pooled_storage_manager.h
index 6e54ddd7e52a..d9d727786613 100644
--- a/src/storage/pooled_storage_manager.h
+++ b/src/storage/pooled_storage_manager.h
@@ -106,8 +106,8 @@ class GPUPooledStorageManager final : public StorageManager {
   }
 
   size_t RoundAllocSize(size_t size) {
-    // Round up small allocs to the page_size_ to consolidate the pool lookups
-    size = std::max(size, page_size_);
+    // Round up small allocs to multiple of page_size_ to consolidate the pool lookups
+    size = RoundToMultiple(size, page_size_);
     // To ensure proper freeing under some driver variants, make sure
     // large allocs entirely occupy their slabs, which cannot then be
     // locked by smaller permanent allocations sharing the slab.
diff --git a/tests/python/gpu/test_fusion.py b/tests/python/gpu/test_fusion.py
new file mode 100644
index 000000000000..6adf935fb29c
--- /dev/null
+++ b/tests/python/gpu/test_fusion.py
@@ -0,0 +1,223 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import os
+import random
+import mxnet as mx
+import numpy as np
+from mxnet.test_utils import *
+
+curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
+sys.path.insert(0, os.path.join(curr_path, '../unittest'))
+from common import with_seed
+
+def check_fused_symbol(sym, **kwargs):
+    inputs = sym.list_inputs()
+    shapes = {inp : kwargs[inp].shape for inp in inputs}
+    # Double identity so that there is always something to fuse
+    test_sym = mx.sym.Group([mx.sym.identity(mx.sym.identity(s)) for s in sym])
+    rtol = {'float16' : 1e-2,
+            'float32' : 1.5e-6,
+            'float64' : 1.5e-6,
+            }
+    atol = {'float16' : 1e-3,
+            'float32' : 1e-7,
+            'float64' : 1e-7,
+            }
+    for dtype in ['float16', 'float32', 'float64']:
+        data = {inp : kwargs[inp].astype(dtype) for inp in inputs}
+        for grad_req in ['write', 'add']:
+            type_dict = {inp : dtype for inp in inputs}
+            os.environ["MXNET_USE_FUSION"] = "0"
+            orig_exec = test_sym.simple_bind(ctx=mx.gpu(0), grad_req=grad_req, type_dict=type_dict, **shapes)
+            os.environ["MXNET_USE_FUSION"] = "1"
+            fused_exec = test_sym.simple_bind(ctx=mx.gpu(0), grad_req=grad_req, type_dict=type_dict, **shapes)
+            fwd_orig = orig_exec.forward(is_train=True, **data)
+            out_grads = [mx.nd.ones_like(arr) for arr in fwd_orig]
+            orig_exec.backward(out_grads=out_grads)
+            fwd_fused = fused_exec.forward(is_train=True, **data)
+            fused_exec.backward(out_grads=out_grads)
+            for orig, fused in zip(fwd_orig, fwd_fused):
+                np.testing.assert_allclose(orig.asnumpy(), fused.asnumpy(), rtol=rtol[dtype], atol=atol[dtype])
+            for orig, fused in zip(orig_exec.grad_arrays, fused_exec.grad_arrays):
+                if orig is None and fused is None:
+                    continue
+                assert orig is not None
+                assert fused is not None
+                np.testing.assert_allclose(orig.asnumpy(), fused.asnumpy(), rtol=rtol[dtype], atol=atol[dtype])
+
+def check_unary_ops():
+    unary_ops = [
+            'relu',
+            'sigmoid',
+            'softsign',
+            'exp',
+            'expm1',
+            'log',
+            'log10',
+            'log2',
+            'log1p',
+            'degrees',
+            'radians',
+            'sin',
+            'cos',
+            'tan',
+            'arcsin',
+            'arccos',
+            'arctan',
+            'sinh',
+            'cosh',
+            'tanh',
+            'arcsinh',
+            'arctanh',
+            'sqrt',
+            'rsqrt',
+            'cbrt',
+            'rcbrt',
+            'square',
+            'squeeze',
+            'zeros_like',
+            'ones_like',
+            'flatten',
+            'round',
+            'rint',
+            'fix',
+            'floor',
+            'ceil',
+            'trunc',
+            'sign',
+            'reciprocal',
+            'abs',
+            'gamma',
+            'gammaln',
+            'erf',
+            'negative',
+            ]
+
+    def announce_check(op_name):
+        print("Checking fusion of " + op_name)
+
+    arr = mx.random.uniform(shape=rand_shape_2d())
+    a = mx.sym.Variable('a')
+    for op_name in unary_ops:
+        announce_check(op_name)
+        op = getattr(mx.sym, op_name)
+        sym = op(a)
+        check_fused_symbol(sym, a=arr)
+
+    # unary ops requiring special treatment
+
+    # arccosh needs input to be >= 1
+    arr2 = arr + 1
+    announce_check('arccosh')
+    check_fused_symbol(mx.sym.arccosh(a), a=arr2)
+
+    # erfinv needs -1 < input < 1, but we avoid the limits of this range where the slope nears +inf.
+    arr2 = (arr - 0.5) * 1.99
+    announce_check('erfinv')
+    check_fused_symbol(mx.sym.erfinv(a), a=arr2)
+
+    # Activation requires act_type attribute
+    for act_type in ['relu', 'sigmoid', 'tanh', 'softrelu', 'softsign']:
+        announce_check("Activation(act_type='{}')".format(act_type))
+        check_fused_symbol(mx.sym.Activation(a, act_type=act_type), a=arr)
+
+    # Cast requires dtype
+    for dtype in ['float16', 'float32', 'float64', 'int32']:
+        announce_check("Cast(dtype='{}')".format(dtype))
+        check_fused_symbol(mx.sym.Cast(a, dtype=dtype), a=arr)
+
+    # reshape requires shape
+    announce_check('reshape')
+    check_fused_symbol(mx.sym.reshape(a, shape=(-1,)), a=arr)
+
+    # expand_dims requires axis
+    announce_check('expand_dims')
+    check_fused_symbol(mx.sym.expand_dims(a, axis=1), a=arr)
+
+    # clip requires a_min, a_max
+    announce_check('clip')
+    check_fused_symbol(mx.sym.clip(a, a_min=0.3, a_max=0.7), a=arr)
+
+    # smooth_l1 requires a scalar
+    announce_check('smooth_l1')
+    check_fused_symbol(mx.sym.smooth_l1(a, scalar=0.3), a=arr)
+
+def check_binary_ops():
+    a = mx.sym.Variable('a')
+    b = mx.sym.Variable('b')
+    shape = rand_shape_2d()
+    arr1 = mx.random.uniform(shape=shape)
+    arr2 = mx.random.uniform(shape=shape)
+
+    check_fused_symbol(a+b, a=arr1, b=arr2)
+    check_fused_symbol(a+3, a=arr1)
+    check_fused_symbol(a-b, a=arr1, b=arr2)
+    check_fused_symbol(a-3, a=arr1)
+    check_fused_symbol(3-a, a=arr1)
+    check_fused_symbol(a*b, a=arr1, b=arr2)
+    check_fused_symbol(a*3, a=arr1)
+    check_fused_symbol(a/b, a=arr1, b=arr2)
+    check_fused_symbol(a/3, a=arr1)
+    check_fused_symbol(3/a, a=arr1)
+    check_fused_symbol(a**b, a=arr1, b=arr2)
+    check_fused_symbol(a**3, a=arr1)
+    check_fused_symbol(mx.sym.pow(3,a), a=arr1)
+    check_fused_symbol(mx.sym.maximum(a,b), a=arr1, b=arr2)
+    check_fused_symbol(mx.sym.minimum(a,b), a=arr1, b=arr2)
+    check_fused_symbol(mx.sym.hypot(a,b), a=arr1, b=arr2)
+    check_fused_symbol(mx.sym.hypot(a,3), a=arr1)
+
+def check_other_ops():
+    a = mx.sym.Variable('a')
+    b = mx.sym.Variable('b')
+    c = mx.sym.Variable('c')
+    shape = rand_shape_2d()
+    shape = (5,) + shape
+    arr1 = mx.random.uniform(shape=shape)
+    arr2 = mx.random.uniform(shape=shape)
+    arr3 = mx.random.uniform(shape=shape)
+
+    check_fused_symbol(mx.sym.add_n(a,b,c), a=arr1, b=arr2, c=arr3)
+
+    check_fused_symbol(mx.sym.slice_axis(a, axis=0, begin=1, end=4), a=arr1)
+
+    begin = (random.randint(0, shape[0]-1),
+             random.randint(0, shape[1]-1),
+             random.randint(0, shape[2]-1))
+    end = (random.randint(begin[0]+1, shape[0]),
+           random.randint(begin[1]+1, shape[1]),
+           random.randint(begin[2]+1, shape[2]))
+    check_fused_symbol(mx.sym.slice(a, begin=begin, end=end), a=arr1)
+
+    arr1 = mx.random.uniform(shape=(2,3,4,5))
+    arr2 = mx.random.uniform(shape=(1,2,3))
+    check_fused_symbol(mx.sym.slice_like(a,b, axes=[-2, 0]), a=arr1, b=arr2)
+
+    arr1 = mx.random.uniform(shape=(1,1,2,3))
+    arr2 = mx.random.uniform(shape=(2,2,2,3))
+    check_fused_symbol(mx.sym.broadcast_like(a, b, lhs_axes=[0], rhs_axes=[0]), a=arr1, b=arr2)
+
+@with_seed()
+def test_fusion():
+    check_unary_ops()
+    check_binary_ops()
+    check_other_ops()
+
+if __name__ == '__main__':
+    import nose
+    nose.runmodule()
diff --git a/tests/python/unittest/test_gluon.py b/tests/python/unittest/test_gluon.py
index f1413e2b99c2..da8dba7ce476 100644
--- a/tests/python/unittest/test_gluon.py
+++ b/tests/python/unittest/test_gluon.py
@@ -3119,6 +3119,47 @@ def forward(self, x):
         shape = (np.random.randint(1, 10), np.random.randint(1, 10), 1)
         block(mx.nd.ones(shape))
 
+@with_seed()
+def test_reqs_switching_training_inference():
+    class Foo(gluon.HybridBlock):
+        def __init__(self, **kwargs):
+            super(Foo, self).__init__(**kwargs)
+
+        def hybrid_forward(self, F, x):
+            y = 2 * x
+            return F.sqrt(x) + F.sqrt(y)
+
+    f = Foo()
+    f.hybridize(static_alloc=True)
+    x = mx.nd.ones(shape=(10,10))
+    x.attach_grad()
+    x2 = mx.nd.ones(shape=x.shape) * 2
+    x2.attach_grad()
+
+    # Call first in training mode
+    with mx.autograd.record():
+        y = f(x)
+    y.backward()
+
+    grad1 = x.grad.asnumpy()
+
+    # Compute the gradient with some other input
+    with mx.autograd.record():
+        y = f(x2)
+    y.backward()
+
+    # Call inference mode
+    y = f(x)
+
+    # Call training mode again
+    with mx.autograd.record():
+        y = f(x)
+    y.backward()
+
+    grad2 = x.grad.asnumpy()
+
+    mx.test_utils.assert_almost_equal(grad1, grad2)
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 9f6e819d38edb34bea125faeec508a6da7a8bb1c Mon Sep 17 00:00:00 2001
From: Tao Lv <tao.a.lv@intel.com>
Date: Fri, 1 Nov 2019 14:36:13 +0800
Subject: [PATCH 548/813] fix install dir (#16690)

---
 mkldnn.mk | 11 ++---------
 1 file changed, 2 insertions(+), 9 deletions(-)

diff --git a/mkldnn.mk b/mkldnn.mk
index bc2190018bdf..aa92108e33b0 100644
--- a/mkldnn.mk
+++ b/mkldnn.mk
@@ -22,10 +22,8 @@ ifeq ($(USE_MKLDNN), 1)
 	MXNET_INCLDIR = $(ROOTDIR)/include
 ifeq ($(UNAME_S), Darwin)
 	MKLDNN_LIBFILE = $(MKLDNNROOT)/lib/libmkldnn.1.dylib
-	MKLDNN_LIB64FILE = $(MKLDNNROOT)/lib64/libmkldnn.1.dylib
 else
 	MKLDNN_LIBFILE = $(MKLDNNROOT)/lib/libmkldnn.so.1
-	MKLDNN_LIB64FILE = $(MKLDNNROOT)/lib64/libmkldnn.so.1
 endif
 endif
 
@@ -35,16 +33,11 @@ mkldnn_build: $(MKLDNN_LIBFILE)
 
 $(MKLDNN_LIBFILE):
 	mkdir -p $(MKLDNNROOT)/lib
-	cmake $(MKLDNN_SUBMODDIR) -DCMAKE_INSTALL_PREFIX=$(MKLDNNROOT) -B$(MKLDNN_BUILDDIR) -DMKLDNN_ARCH_OPT_FLAGS="" -DMKLDNN_BUILD_TESTS=OFF -DMKLDNN_BUILD_EXAMPLES=OFF -DMKLDNN_ENABLE_JIT_PROFILING=OFF
+	cmake $(MKLDNN_SUBMODDIR) -DCMAKE_INSTALL_PREFIX=$(MKLDNNROOT) -DCMAKE_INSTALL_LIBDIR=lib -B$(MKLDNN_BUILDDIR) -DMKLDNN_ARCH_OPT_FLAGS="" -DMKLDNN_BUILD_TESTS=OFF -DMKLDNN_BUILD_EXAMPLES=OFF -DMKLDNN_ENABLE_JIT_PROFILING=OFF
 	$(MAKE) -C $(MKLDNN_BUILDDIR) VERBOSE=1
 	$(MAKE) -C $(MKLDNN_BUILDDIR) install
 	mkdir -p $(MXNET_LIBDIR)
-	if [ -f "$(MKLDNN_LIB64FILE)" ]; then \
-		cp $(MKLDNNROOT)/lib64/libmkldnn* $(MXNET_LIBDIR); \
-		cp $(MKLDNNROOT)/lib64/libmkldnn* $(MKLDNNROOT)/lib/; \
-	else \
-		cp $(MKLDNNROOT)/lib/libmkldnn* $(MXNET_LIBDIR); \
-	fi
+	cp $(MKLDNN_LIBFILE) $(MXNET_LIBDIR)
 	cp $(MKLDNN_BUILDDIR)/include/mkldnn_version.h $(MXNET_INCLDIR)/mkldnn/.
 
 mkldnn_clean:

From 6c42992f2a12c253ab92fa7a85556de5da357f89 Mon Sep 17 00:00:00 2001
From: JiangZhaoh <54654391+JiangZhaoh@users.noreply.github.com>
Date: Fri, 1 Nov 2019 16:20:06 +0800
Subject: [PATCH 549/813] [numpy] add numpy operator : append (#16564)

* add operator : append ; fix op concatenate when axis = None

* pylint disable

remove mistake

disable pylint
---
 python/mxnet/ndarray/numpy/_op.py             |  62 ++++++++-
 python/mxnet/numpy/multiarray.py              |  47 ++++++-
 python/mxnet/numpy_dispatch_protocol.py       |   1 +
 python/mxnet/symbol/numpy/_symbol.py          |  64 ++++++++-
 src/operator/numpy/np_matrix_op-inl.h         |  81 +++++++++++
 src/operator/numpy/np_matrix_op.cc            | 105 +++++++++++++--
 src/operator/numpy/np_matrix_op.cu            |   4 +-
 .../unittest/test_numpy_interoperability.py   |  20 +++
 tests/python/unittest/test_numpy_op.py        | 127 ++++++++++++++----
 9 files changed, 458 insertions(+), 53 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index 256cfb7d5708..c215159edb5e 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -33,7 +33,7 @@
            'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2',
            'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot', 'histogram', 'eye',
-           'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate',
+           'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate', 'append',
            'stack', 'vstack', 'column_stack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax',
            'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
            'around', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
@@ -2919,8 +2919,64 @@ def concatenate(seq, axis=0, out=None):
     -------
     res : ndarray
         The concatenated array.
+
+    Examples
+    --------
+    >>> a = np.array([[1, 2], [3, 4]])
+    >>> b = np.array([[5, 6]])
+    >>> np.concatenate((a, b), axis=0)
+    array([[1., 2.],
+           [3., 4.],
+           [5., 6.]])
+
+    >>> np.concatenate((a, b), axis=None)
+    array([1., 2., 3., 4., 5., 6.])
+
+    >>> np.concatenate((a, b.T), axis=1)
+    array([[1., 2., 5.],
+           [3., 4., 6.]])
+    """
+    return _npi.concatenate(*seq, axis=axis, out=out)
+
+
+@set_module('mxnet.ndarray.numpy')
+def append(arr, values, axis=None):  # pylint: disable=redefined-outer-name
+    """
+    Append values to the end of an array.
+
+    Parameters
+    ----------
+    arr : ndarray
+        Values are appended to a copy of this array.
+    values : ndarray
+        These values are appended to a copy of `arr`.  It must be of the
+        correct shape (the same shape as `arr`, excluding `axis`).  If
+        `axis` is not specified, `values` can be any shape and will be
+        flattened before use.
+    axis : int, optional
+        The axis along which `values` are appended.  If `axis` is not
+        given, both `arr` and `values` are flattened before use.
+
+    Returns
+    -------
+    append : ndarray
+        A copy of `arr` with `values` appended to `axis`.  Note that
+        `append` does not occur in-place: a new array is allocated and
+        filled.  If `axis` is None, `out` is a flattened array.
+
+    Examples
+    --------
+    >>> np.append(np.array([1, 2, 3]), np.array([[4, 5, 6],[7, 8, 9]]))
+    array([1., 2., 3., 4., 5., 6., 7., 8., 9.])
+
+    When `axis` is specified, `values` must have the correct shape.
+
+    >>> np.append(np.array([[1, 2, 3], [4, 5, 6]]), np.array([[7, 8, 9]]), axis=0)
+    array([[1., 2., 3.],
+           [4., 5., 6.],
+           [7., 8., 9.]])
     """
-    return _npi.concatenate(*seq, dim=axis, out=out)
+    return _npi.concatenate(arr, values, axis=axis, out=None)
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -5014,7 +5070,7 @@ def may_share_memory(a, b, max_work=None):
     return _npi.share_memory(a, b).item()
 
 
-def diff(a, n=1, axis=-1, prepend=None, append=None):
+def diff(a, n=1, axis=-1, prepend=None, append=None):  # pylint: disable=redefined-outer-name
     r"""
     numpy.diff(a, n=1, axis=-1, prepend=<no value>, append=<no value>)
 
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 8e0d5b209a8d..85bd2ac0e2b6 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -49,7 +49,7 @@
            'mod', 'remainder', 'power', 'arctan2', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10',
            'sqrt', 'cbrt', 'abs', 'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log',
            'degrees', 'log2', 'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative',
-           'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh',
+           'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'append',
            'tensordot', 'histogram', 'eye', 'linspace', 'logspace', 'expand_dims', 'tile', 'arange',
            'split', 'vsplit', 'concatenate', 'stack', 'vstack', 'column_stack', 'dstack', 'mean', 'maximum', 'minimum',
            'swapaxes', 'clip', 'argmax', 'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming',
@@ -4803,10 +4803,53 @@ def concatenate(seq, axis=0, out=None):
     >>> np.concatenate((a, b.T), axis=1)
     array([[1., 2., 5.],
            [3., 4., 6.]])
+
+    >>> np.concatenate((a, b), axis=None)
+    array([1., 2., 3., 4., 5., 6.])
     """
     return _mx_nd_np.concatenate(seq, axis=axis, out=out)
 
 
+@set_module('mxnet.numpy')
+def append(arr, values, axis=None):  # pylint: disable=redefined-outer-name
+    """
+    Append values to the end of an array.
+
+    Parameters
+    ----------
+    arr : ndarray
+        Values are appended to a copy of this array.
+    values : ndarray
+        These values are appended to a copy of `arr`.  It must be of the
+        correct shape (the same shape as `arr`, excluding `axis`).  If
+        `axis` is not specified, `values` can be any shape and will be
+        flattened before use.
+    axis : int, optional
+        The axis along which `values` are appended.  If `axis` is not
+        given, both `arr` and `values` are flattened before use.
+
+    Returns
+    -------
+    append : ndarray
+        A copy of `arr` with `values` appended to `axis`.  Note that
+        `append` does not occur in-place: a new array is allocated and
+        filled.  If `axis` is None, `out` is a flattened array.
+
+    Examples
+    --------
+    >>> np.append(np.array([1, 2, 3]), np.array([[4, 5, 6],[7, 8, 9]]))
+    array([1., 2., 3., 4., 5., 6., 7., 8., 9.])
+
+    When `axis` is specified, `values` must have the correct shape.
+
+    >>> np.append(np.array([[1, 2, 3], [4, 5, 6]]), np.array([[7, 8, 9]]), axis=0)
+    array([[1., 2., 3.],
+           [4., 5., 6.],
+           [7., 8., 9.]])
+    """
+    return _mx_nd_np.append(arr, values, axis=axis)
+
+
 @set_module('mxnet.numpy')
 def stack(arrays, axis=0, out=None):
     """Join a sequence of arrays along a new axis.
@@ -7018,7 +7061,7 @@ def may_share_memory(a, b, max_work=None):
     return _mx_nd_np.may_share_memory(a, b, max_work)
 
 
-def diff(a, n=1, axis=-1, prepend=None, append=None):
+def diff(a, n=1, axis=-1, prepend=None, append=None):  # pylint: disable=redefined-outer-name
     r"""
     numpy.diff(a, n=1, axis=-1, prepend=<no value>, append=<no value>)
 
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index cfab2a49699d..cdd21af829de 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -86,6 +86,7 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'argmin',
     'argmax',
     'around',
+    'append',
     'broadcast_arrays',
     'broadcast_to',
     'clip',
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 7469875f267a..d3837d2bd1dd 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -35,7 +35,7 @@
            'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2', 'log1p',
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot', 'histogram', 'eye',
-           'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate',
+           'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate', 'append',
            'stack', 'vstack', 'column_stack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax',
            'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
            'around', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
@@ -2992,6 +2992,7 @@ def vsplit(ary, indices_or_sections):
 @set_module('mxnet.symbol.numpy')
 def concatenate(seq, axis=0, out=None):
     """Join a sequence of arrays along an existing axis.
+
     Parameters
     ----------
     a1, a2, ... : sequence of array_like
@@ -3004,12 +3005,69 @@ def concatenate(seq, axis=0, out=None):
         If provided, the destination to place the result. The shape must be
         correct, matching that of what concatenate would have returned if no
         out argument were specified.
+
     Returns
     -------
     res : ndarray
         The concatenated array.
+
+    Examples
+    --------
+    >>> a = np.array([[1, 2], [3, 4]])
+    >>> b = np.array([[5, 6]])
+    >>> np.concatenate((a, b), axis=0)
+    array([[1., 2.],
+           [3., 4.],
+           [5., 6.]])
+
+    >>> np.concatenate((a, b), axis=None)
+    array([1., 2., 3., 4., 5., 6.])
+
+    >>> np.concatenate((a, b.T), axis=1)
+    array([[1., 2., 5.],
+           [3., 4., 6.]])
+    """
+    return _npi.concatenate(*seq, axis=axis, out=out)
+
+
+@set_module('mxnet.symbol.numpy')
+def append(arr, values, axis=None):  # pylint: disable=redefined-outer-name
+    """
+    Append values to the end of an array.
+
+    Parameters
+    ----------
+    arr : ndarray
+        Values are appended to a copy of this array.
+    values : ndarray
+        These values are appended to a copy of `arr`.  It must be of the
+        correct shape (the same shape as `arr`, excluding `axis`).  If
+        `axis` is not specified, `values` can be any shape and will be
+        flattened before use.
+    axis : int, optional
+        The axis along which `values` are appended.  If `axis` is not
+        given, both `arr` and `values` are flattened before use.
+
+    Returns
+    -------
+    append : ndarray
+        A copy of `arr` with `values` appended to `axis`.  Note that
+        `append` does not occur in-place: a new array is allocated and
+        filled.  If `axis` is None, `out` is a flattened array.
+
+    Examples
+    --------
+    >>> np.append(np.array([1, 2, 3]), np.array([[4, 5, 6],[7, 8, 9]]))
+    array([1., 2., 3., 4., 5., 6., 7., 8., 9.])
+
+    When `axis` is specified, `values` must have the correct shape.
+
+    >>> np.append(np.array([[1, 2, 3], [4, 5, 6]]), np.array([[7, 8, 9]]), axis=0)
+    array([[1., 2., 3.],
+           [4., 5., 6.],
+           [7., 8., 9.]])
     """
-    return _npi.concatenate(*seq, dim=axis, out=out)
+    return _npi.concatenate(arr, values, axis=axis, out=None)
 
 
 @set_module('mxnet.symbol.numpy')
@@ -4665,7 +4723,7 @@ def may_share_memory(a, b, max_work=None):
     return _npi.share_memory(a, b)
 
 
-def diff(a, n=1, axis=-1, prepend=None, append=None):
+def diff(a, n=1, axis=-1, prepend=None, append=None):  # pylint: disable=redefined-outer-name
     r"""
     numpy.diff(a, n=1, axis=-1, prepend=<no value>, append=<no value>)
 
diff --git a/src/operator/numpy/np_matrix_op-inl.h b/src/operator/numpy/np_matrix_op-inl.h
index 2545adcb3555..a9828f40436d 100644
--- a/src/operator/numpy/np_matrix_op-inl.h
+++ b/src/operator/numpy/np_matrix_op-inl.h
@@ -864,6 +864,87 @@ inline void HSplitOpBackward(const nnvm::NodeAttrs &attrs,
   }
   SplitOpBackwardImpl<xpu>(attrs, ctx, inputs, req, outputs, real_axis);
 }
+
+struct NumpyConcatenateParam : public dmlc::Parameter<NumpyConcatenateParam> {
+  int num_args;
+  dmlc::optional<int> axis;
+  DMLC_DECLARE_PARAMETER(NumpyConcatenateParam) {
+    DMLC_DECLARE_FIELD(num_args)
+    .set_lower_bound(1)
+    .describe("Number of inputs to be concated.");
+    DMLC_DECLARE_FIELD(axis)
+    .set_default(dmlc::optional<int>(0))
+    .describe("The axis along which `values` are appended.  If `axis` is not"
+              "given, both `arr` and `values` are flattened before use.");
+  }
+};
+
+template<typename xpu>
+void NumpyConcatenateForward(const nnvm::NodeAttrs& attrs,
+                             const OpContext& ctx,
+                             const std::vector<TBlob>& inputs,
+                             const std::vector<OpReqType>& req,
+                             const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mshadow_op;
+
+  const NumpyConcatenateParam& param = nnvm::get<NumpyConcatenateParam>(attrs.parsed);
+  CHECK_EQ(inputs.size(), param.num_args);
+  CHECK_EQ(outputs.size(), 1U);
+  CHECK_EQ(req.size(), 1U);
+
+  std::vector<TBlob> data(param.num_args);
+  for (int i = 0; i < param.num_args; i++) {
+    if (!param.axis.has_value()) {
+      data[i] = inputs[i].reshape(Shape1(inputs[i].shape_.Size()));
+    } else {
+      data[i] = inputs[i];
+    }
+  }
+
+  ConcatParam cparam;
+  cparam.num_args = param.num_args;
+  cparam.dim = param.axis.has_value() ? param.axis.value() : 0;
+  MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    ConcatOp<xpu, DType> op;
+    op.Init(cparam);
+    op.Forward(ctx, data, req, outputs);
+  });
+}
+
+template<typename xpu>
+void NumpyConcatenateBackward(const nnvm::NodeAttrs& attrs,
+                              const OpContext& ctx,
+                              const std::vector<TBlob>& inputs,
+                              const std::vector<OpReqType>& req,
+                              const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mshadow_op;
+
+  const NumpyConcatenateParam& param = nnvm::get<NumpyConcatenateParam>(attrs.parsed);
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), param.num_args);
+  CHECK_EQ(req.size(), param.num_args);
+
+  std::vector<TBlob> data(param.num_args);
+  for (int i = 0; i < param.num_args; i++) {
+    if (!param.axis.has_value()) {
+      data[i] = outputs[i].reshape(Shape1(outputs[i].shape_.Size()));
+    } else {
+      data[i] = outputs[i];
+    }
+  }
+
+  ConcatParam cparam;
+  cparam.num_args = param.num_args;
+  cparam.dim = param.axis.has_value() ? param.axis.value() : 0;
+  MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    ConcatOp<xpu, DType> op;
+    op.Init(cparam);
+    op.Backward(ctx, inputs[0], req, data);
+  });
+}
+
 }  // namespace op
 }  // namespace mxnet
 
diff --git a/src/operator/numpy/np_matrix_op.cc b/src/operator/numpy/np_matrix_op.cc
index 18594cd9cff1..3967cde91d2a 100644
--- a/src/operator/numpy/np_matrix_op.cc
+++ b/src/operator/numpy/np_matrix_op.cc
@@ -456,10 +456,6 @@ NNVM_REGISTER_OP(_np_squeeze)
 .add_argument("a", "NDArray-or-Symbol", "data to squeeze")
 .add_arguments(SqueezeParam::__FIELDS__());
 
-bool ConcatShape(const nnvm::NodeAttrs& attrs,
-                 mxnet::ShapeVector *in_shape,
-                 mxnet::ShapeVector *out_shape);
-
 bool DStackShape(const nnvm::NodeAttrs& attrs,
                  mxnet::ShapeVector *in_shape,
                  mxnet::ShapeVector *out_shape) {
@@ -525,6 +521,84 @@ bool ConcatType(const nnvm::NodeAttrs& attrs,
                 std::vector<int> *in_type,
                 std::vector<int> *out_type);
 
+bool NumpyConcatenateType(const nnvm::NodeAttrs& attrs,
+                          std::vector<int> *in_type,
+                          std::vector<int> *out_type) {
+  const NumpyConcatenateParam& param = nnvm::get<NumpyConcatenateParam>(attrs.parsed);
+  const int num_args = param.num_args;
+  CHECK_EQ(in_type->size(), num_args);
+  CHECK_EQ(out_type->size(), 1);
+  int dtype = -1;
+  for (int i = 0; i < num_args; i++) {
+    if (dtype == -1) {
+      dtype = in_type->at(i);
+    }
+  }
+  if (dtype == -1) {
+    dtype = out_type->at(0);
+  }
+  for (int i = 0; i < num_args; i++) {
+    TYPE_ASSIGN_CHECK(*in_type, i, dtype);
+  }
+  TYPE_ASSIGN_CHECK(*out_type, 0, dtype);
+  return dtype != -1;
+}
+
+bool NumpyConcatenateShape(const nnvm::NodeAttrs& attrs,
+                           mxnet::ShapeVector *in_shape,
+                           mxnet::ShapeVector *out_shape) {
+  using namespace mshadow;
+  const NumpyConcatenateParam& param_ = nnvm::get<NumpyConcatenateParam>(attrs.parsed);
+  const int num_args = param_.num_args;
+  CHECK_EQ(in_shape->size(), num_args);
+
+  int param_axis;
+  if (!(param_.axis.has_value())) {
+    for (int i = 0 ; i < num_args ; ++i) {
+      (*in_shape)[i] = Shape1((*in_shape)[i].Size());
+    }
+    param_axis = 0;
+  } else {
+    param_axis = param_.axis.value();
+  }
+
+  mxnet::TShape dshape;
+  dim_t size = 0;
+  bool has_unknown_dim_size = false;
+  int axis = -1;
+  for (int i = 0; i < num_args; ++i) {
+    mxnet::TShape tmp = (*in_shape)[i];
+    if (tmp.ndim() > 0) {
+      axis = CheckAxis(param_axis, tmp.ndim());
+      has_unknown_dim_size = !mxnet::dim_size_is_known(tmp, axis) || has_unknown_dim_size;
+      size += tmp[axis];
+      tmp[axis] = -1;
+      shape_assign(&dshape, tmp);
+    }
+  }
+
+  mxnet::TShape tmp = (*out_shape)[0];
+  if (tmp.ndim() > 0) {
+    axis = CheckAxis(param_axis, tmp.ndim());
+    tmp[axis] = -1;
+    shape_assign(&dshape, tmp);
+  }
+
+  if (dshape.ndim() == -1) return false;
+  CHECK_NE(dshape.ndim(), 0) << "zero-dimensional arrays cannot be concatenated";
+
+  for (int i = 0; i < num_args; ++i) {
+    CHECK(shape_assign(&(*in_shape)[i], dshape))
+        << "Incompatible input shape: expected " << dshape << ", got " << (*in_shape)[i];
+  }
+
+  if (!has_unknown_dim_size) dshape[axis] = size;
+  CHECK(shape_assign(&(*out_shape)[0], dshape))
+      << "Incompatible output shape: expected " << dshape << ", got " << (*out_shape)[0];
+
+  return shape_is_known(dshape);
+}
+
 struct NumpyConcatGrad {
   const char *op_name;
   std::vector<nnvm::NodeEntry> operator()(const nnvm::NodePtr& n,
@@ -535,17 +609,19 @@ struct NumpyConcatGrad {
   }
 };
 
+DMLC_REGISTER_PARAMETER(NumpyConcatenateParam);
+
 NNVM_REGISTER_OP(_npi_concatenate)
 .describe(R"code(Join a sequence of arrays along an existing axis.)code" ADD_FILELINE)
 .set_num_inputs([](const NodeAttrs& attrs) {
-  const ConcatParam& params = nnvm::get<ConcatParam>(attrs.parsed);
+  const NumpyConcatenateParam& params = nnvm::get<NumpyConcatenateParam>(attrs.parsed);
   return params.num_args;
 })
 .set_num_outputs(1)
-.set_attr_parser(ParamParser<ConcatParam>)
+.set_attr_parser(ParamParser<NumpyConcatenateParam>)
 .set_attr<nnvm::FListInputNames>("FListInputNames",
   [](const NodeAttrs& attrs) {
-    const ConcatParam& params = nnvm::get<ConcatParam>(attrs.parsed);
+    const NumpyConcatenateParam& params = nnvm::get<NumpyConcatenateParam>(attrs.parsed);
     std::vector<std::string> ret;
     for (int i = 0; i < params.num_args; ++i) {
       ret.push_back(std::string("data") + std::to_string(i));
@@ -557,21 +633,22 @@ NNVM_REGISTER_OP(_npi_concatenate)
     return std::vector<std::string>{"out"};
 })
 .set_attr<std::string>("key_var_num_args", "num_args")
-.set_attr<nnvm::FInferType>("FInferType", ConcatType)
-.set_attr<mxnet::FInferShape>("FInferShape", ConcatShape)
-.set_attr<FCompute>("FCompute<cpu>", ConcatCompute<cpu>)
-.set_attr<nnvm::FGradient>("FGradient", NumpyConcatGrad{"_backward_np_concat"})
+.set_attr<nnvm::FInferType>("FInferType", NumpyConcatenateType)
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyConcatenateShape)
+.set_attr<FCompute>("FCompute<cpu>", NumpyConcatenateForward<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_np_concat"})
 .add_argument("data", "NDArray-or-Symbol[]", "List of arrays to concatenate")
 .add_arguments(ConcatParam::__FIELDS__());
 
 NNVM_REGISTER_OP(_backward_np_concat)
+.set_num_inputs(1)
 .set_num_outputs([](const NodeAttrs& attrs) {
-  const ConcatParam& params = nnvm::get<ConcatParam>(attrs.parsed);
+  const NumpyConcatenateParam& params = nnvm::get<NumpyConcatenateParam>(attrs.parsed);
   return params.num_args;
 })
-.set_attr_parser(ParamParser<ConcatParam>)
+.set_attr_parser(ParamParser<NumpyConcatenateParam>)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
-.set_attr<FCompute>("FCompute<cpu>", ConcatGradCompute<cpu>);
+.set_attr<FCompute>("FCompute<cpu>", NumpyConcatenateBackward<cpu>);
 
 NNVM_REGISTER_OP(_npi_stack)
 .describe(R"code(Join a sequence of arrays along a new axis.
diff --git a/src/operator/numpy/np_matrix_op.cu b/src/operator/numpy/np_matrix_op.cu
index fccc8f257e64..7ca205565413 100644
--- a/src/operator/numpy/np_matrix_op.cu
+++ b/src/operator/numpy/np_matrix_op.cu
@@ -39,10 +39,10 @@ NNVM_REGISTER_OP(_np_squeeze)
 .set_attr<FCompute>("FCompute<gpu>", UnaryOp::IdentityCompute<gpu>);
 
 NNVM_REGISTER_OP(_npi_concatenate)
-.set_attr<FCompute>("FCompute<gpu>", ConcatCompute<gpu>);
+.set_attr<FCompute>("FCompute<gpu>", NumpyConcatenateForward<gpu>);
 
 NNVM_REGISTER_OP(_backward_np_concat)
-.set_attr<FCompute>("FCompute<gpu>", ConcatGradCompute<gpu>);
+.set_attr<FCompute>("FCompute<gpu>", NumpyConcatenateBackward<gpu>);
 
 NNVM_REGISTER_OP(_npi_stack)
 .set_attr<FCompute>("FCompute<gpu>", StackOpForward<gpu>);
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index 15912dc47ad3..8416b1a9099f 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -83,6 +83,25 @@ def _add_workload_concatenate(array_pool):
     OpArgMngr.add_workload('concatenate', (a0.T, a1.T, a2.T), axis=0)
     out = np.empty(4, np.float32)
     OpArgMngr.add_workload('concatenate', (np.array([1, 2]), np.array([3, 4])), out=out)
+    OpArgMngr.add_workload('concatenate', [array_pool['4x1'], array_pool['4x1']], axis=None)
+    OpArgMngr.add_workload('concatenate', (np.arange(4).reshape((2, 2)), np.arange(4).reshape((2, 2))), axis=None)
+    OpArgMngr.add_workload('concatenate', (a23, a13), axis=None)
+
+
+def _add_workload_append():
+    def get_new_shape(shape, axis):
+        shape_lst = list(shape)
+        if axis is not None:
+            shape_lst[axis] = _np.random.randint(0, 3)
+        return tuple(shape_lst)
+
+    for shape in [(0, 0), (2, 3), (2, 1, 3)]:
+        for axis in [0, 1, None]:
+            a = np.random.uniform(-1.0, 1.0, size=get_new_shape(shape, axis))
+            b = np.random.uniform(-1.0, 1.0, size=get_new_shape(shape, axis))
+            OpArgMngr.add_workload('append', a, b, axis=axis)
+
+    OpArgMngr.add_workload('append', np.array([]), np.array([]))
 
 
 def _add_workload_copy():
@@ -1125,6 +1144,7 @@ def _prepare_workloads():
     _add_workload_argmin()
     _add_workload_argmax()
     _add_workload_around()
+    _add_workload_append()
     _add_workload_broadcast_arrays(array_pool)
     _add_workload_broadcast_to()
     _add_workload_clip()
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 605fa85e1f77..a2716fb5363f 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -1908,43 +1908,112 @@ def hybrid_forward(self, F, a, *args):
 
     def get_new_shape(shape, axis):
         shape_lst = list(shape)
-        shape_lst[axis] = random.randint(0, 3)
+        if axis is not None:
+            shape_lst[axis] = random.randint(0, 3)
         return tuple(shape_lst)
 
-    for shape in [(0, 0), (2, 3)]:
+    for shape in [(0, 0), (2, 3), (2, 1, 3)]:
         for hybridize in [True, False]:
-            for axis in range(2):
-                # test gluon
-                test_concat = TestConcat(axis=axis)
-                if hybridize:
-                    test_concat.hybridize()
+            for axis in [0, 1, None]:
+                for grad_req in ['write', 'add', 'null']:
+                    # test gluon
+                    test_concat = TestConcat(axis=axis)
+                    if hybridize:
+                        test_concat.hybridize()
+
+                    grad_req_c = grad_req
+                    grad_req_d = grad_req
+                    if grad_req == 'null':
+                        ide = random.randint(0, 2)
+                        grad_req_c = 'write' if ide == 0 else 'add'
+                        grad_req_c = 'write' if ide == 1 else 'add'
+
+                    a = mx.nd.random.uniform(-1.0, 1.0, shape=get_new_shape(shape, axis)).as_np_ndarray()
+                    a.attach_grad(grad_req)
+                    b = mx.nd.random.uniform(-1.0, 1.0, shape=get_new_shape(shape, axis)).as_np_ndarray()
+                    b.attach_grad(grad_req)
+                    c = mx.nd.random.uniform(-1.0, 1.0, shape=get_new_shape(shape, axis)).as_np_ndarray()
+                    c.attach_grad(grad_req_c)
+                    d = mx.nd.random.uniform(-1.0, 1.0, shape=get_new_shape(shape, axis)).as_np_ndarray()
+                    d.attach_grad(grad_req_d)
+                    expected_ret = _np.concatenate([a.asnumpy(), b.asnumpy(), c.asnumpy(), d.asnumpy()], axis=axis)
 
-                a = mx.nd.random.uniform(-1.0, 1.0, shape=get_new_shape(shape, axis)).as_np_ndarray()
-                a.attach_grad()
-                b = mx.nd.random.uniform(-1.0, 1.0, shape=get_new_shape(shape, axis)).as_np_ndarray()
-                b.attach_grad()
-                c = mx.nd.random.uniform(-1.0, 1.0, shape=get_new_shape(shape, axis)).as_np_ndarray()
-                c.attach_grad()
-                d = mx.nd.random.uniform(-1.0, 1.0, shape=get_new_shape(shape, axis)).as_np_ndarray()
-                d.attach_grad()
-                expected_ret = _np.concatenate([a.asnumpy(), b.asnumpy(), c.asnumpy(), d.asnumpy()], axis=axis)
-                with mx.autograd.record():
-                    y = test_concat(a, b, c, d)
+                    with mx.autograd.record():
+                        y = test_concat(a, b, c, d)
+                    
+                    assert y.shape == expected_ret.shape
+                    assert_almost_equal(y.asnumpy(), expected_ret, rtol=1e-3, atol=1e-5)
+
+                    y.backward()
+                    if grad_req != 'null':
+                        assert_almost_equal(a.grad.asnumpy(), _np.ones(a.shape), rtol=1e-3, atol=1e-5)
+                    if grad_req != 'null':
+                        assert_almost_equal(b.grad.asnumpy(), _np.ones(b.shape), rtol=1e-3, atol=1e-5)
+                    if grad_req_c != 'null':
+                        assert_almost_equal(c.grad.asnumpy(), _np.ones(c.shape), rtol=1e-3, atol=1e-5)
+                    if grad_req_d != 'null':
+                        assert_almost_equal(d.grad.asnumpy(), _np.ones(d.shape), rtol=1e-3, atol=1e-5)
 
-                assert y.shape == expected_ret.shape
-                assert_almost_equal(y.asnumpy(), expected_ret, rtol=1e-3, atol=1e-5)
+                    # test imperative
+                    mx_out = np.concatenate([a, b, c, d], axis=axis)
+                    np_out = _np.concatenate([a.asnumpy(), b.asnumpy(), c.asnumpy(), d.asnumpy()], axis=axis)
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
 
-                y.backward()
 
-                assert_almost_equal(a.grad.asnumpy(), _np.ones(a.shape), rtol=1e-3, atol=1e-5)
-                assert_almost_equal(b.grad.asnumpy(), _np.ones(b.shape), rtol=1e-3, atol=1e-5)
-                assert_almost_equal(c.grad.asnumpy(), _np.ones(c.shape), rtol=1e-3, atol=1e-5)
-                assert_almost_equal(d.grad.asnumpy(), _np.ones(d.shape), rtol=1e-3, atol=1e-5)
+@with_seed()
+@use_np
+def test_np_append():
+    class TestAppend(HybridBlock):
+        def __init__(self, axis=None):
+            super(TestAppend, self).__init__()
+            self._axis = axis
 
-                # test imperative
-                mx_out = np.concatenate([a, b, c, d], axis=axis)
-                np_out = _np.concatenate([a.asnumpy(), b.asnumpy(), c.asnumpy(), d.asnumpy()], axis=axis)
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+        def hybrid_forward(self, F, a, b):
+            return F.np.append(a, b, axis=self._axis)
+
+    def get_new_shape(shape, axis):
+        shape_lst = list(shape)
+        if axis is not None:
+            shape_lst[axis] = random.randint(0, 3)
+        return tuple(shape_lst)
+
+    for shape in [(0, 0), (2, 3), (2, 1, 3)]:
+        for hybridize in [True, False]:
+            for axis in [0, 1, None]:
+                for grad_req_a in ['write', 'add', 'null']:
+                    if grad_req_a == 'null':
+                        continue
+                    #set grad_req
+                    grad_req_b = grad_req_a
+                    if grad_req_a == 'null':
+                        ide = random.randint(0, 2)
+                        grad_req_b = 'write' if ide == 0 else 'add'
+
+                    #test gluon
+                    test_append = TestAppend(axis=axis)
+                    if hybridize:
+                        test_append.hybridize()
+
+                    a = mx.nd.random.uniform(-1.0, 1.0, shape=get_new_shape(shape, axis)).as_np_ndarray()
+                    a.attach_grad(grad_req=grad_req_a)
+                    b = mx.nd.random.uniform(-1.0, 1.0, shape=get_new_shape(shape, axis)).as_np_ndarray()
+                    b.attach_grad(grad_req=grad_req_b)
+                    expected_ret = _np.append(a.asnumpy(), b.asnumpy(), axis=axis)
+
+                    with mx.autograd.record():
+                        y = test_append(a, b)
+
+                    assert y.shape == expected_ret.shape
+                    assert_almost_equal(y.asnumpy(), expected_ret, rtol=1e-3, atol=1e-5)
+                    y.backward()
+
+                    if grad_req_a != 'null':
+                        assert_almost_equal(a.grad.asnumpy(), _np.ones(a.shape), rtol=1e-3, atol=1e-5)
+                    assert_almost_equal(b.grad.asnumpy(), _np.ones(b.shape), rtol=1e-3, atol=1e-5)
+                    #test imperative
+                    mx_out = np.append(a, b, axis=axis)
+                    np_out = _np.append(a.asnumpy(), b.asnumpy(), axis=axis)
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
 
 
 @with_seed()

From 33d108ba0290c3587d784fd6fe110c70cb275792 Mon Sep 17 00:00:00 2001
From: Leonard Lausen <lausen@amazon.com>
Date: Fri, 1 Nov 2019 02:37:10 -0700
Subject: [PATCH 550/813] Initializer.__eq__ (#16680)

---
 python/mxnet/initializer.py         |  5 +++++
 tests/python/unittest/test_gluon.py | 15 +++++++++++++++
 2 files changed, 20 insertions(+)

diff --git a/python/mxnet/initializer.py b/python/mxnet/initializer.py
index eafe73651dbc..5910bf91578e 100755
--- a/python/mxnet/initializer.py
+++ b/python/mxnet/initializer.py
@@ -268,6 +268,11 @@ def _init_default(self, name, _):
             '"weight", "bias", "gamma" (1.0), and "beta" (0.0).' \
             'Please use mx.sym.Variable(init=mx.init.*) to set initialization pattern' % name)
 
+    def __eq__(self, other):
+        if not isinstance(other, Initializer):
+            return NotImplemented
+        # pylint: disable=unidiomatic-typecheck
+        return type(self) is type(other) and self._kwargs == other._kwargs
 
 # pylint: disable=invalid-name
 _register = registry.get_register_func(Initializer, 'initializer')
diff --git a/tests/python/unittest/test_gluon.py b/tests/python/unittest/test_gluon.py
index da8dba7ce476..5d15b27fa7ea 100644
--- a/tests/python/unittest/test_gluon.py
+++ b/tests/python/unittest/test_gluon.py
@@ -3119,6 +3119,21 @@ def forward(self, x):
         shape = (np.random.randint(1, 10), np.random.randint(1, 10), 1)
         block(mx.nd.ones(shape))
 
+def test_shared_parameters_with_non_default_initializer():
+    class MyBlock(gluon.HybridBlock):
+        def __init__(self, **kwargs):
+            super(MyBlock, self).__init__(**kwargs)
+
+            with self.name_scope():
+                self.param = self.params.get("param", shape=(1, ), init=mx.init.Constant(-10.0))
+
+    bl = MyBlock()
+    bl2 = MyBlock(params=bl.collect_params())
+    assert bl.param is bl2.param
+    bl3 = MyBlock()
+    assert bl.param is not bl3.param
+    assert bl.param.init == bl3.param.init
+
 @with_seed()
 def test_reqs_switching_training_inference():
     class Foo(gluon.HybridBlock):

From b5c2b82c178b473250d3b0f9270e6d964b637d4b Mon Sep 17 00:00:00 2001
From: Tao Lv <tao.a.lv@intel.com>
Date: Fri, 1 Nov 2019 20:54:51 +0800
Subject: [PATCH 551/813] fix binary dependencies in CD and nightly (#16693)

---
 cd/mxnet_lib/dynamic/Jenkins_pipeline.groovy | 2 +-
 cd/mxnet_lib/static/Jenkins_pipeline.groovy  | 2 +-
 tests/nightly/JenkinsfileForBinaries         | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/cd/mxnet_lib/dynamic/Jenkins_pipeline.groovy b/cd/mxnet_lib/dynamic/Jenkins_pipeline.groovy
index 57812d2c3690..af68314cacf5 100644
--- a/cd/mxnet_lib/dynamic/Jenkins_pipeline.groovy
+++ b/cd/mxnet_lib/dynamic/Jenkins_pipeline.groovy
@@ -30,7 +30,7 @@ licenses = 'licenses/*'
 
 // libmxnet dependencies
 mx_deps = ''
-mx_mkldnn_deps = 'lib/libiomp5.so, lib/libmkldnn.so.0, lib/libmklml_intel.so'
+mx_mkldnn_deps = 'lib/libmkldnn.so.1'
 
 // library type
 // either static or dynamic - depending on how it links to its dependencies
diff --git a/cd/mxnet_lib/static/Jenkins_pipeline.groovy b/cd/mxnet_lib/static/Jenkins_pipeline.groovy
index 39d0bb1c6c70..ac2e45071d15 100644
--- a/cd/mxnet_lib/static/Jenkins_pipeline.groovy
+++ b/cd/mxnet_lib/static/Jenkins_pipeline.groovy
@@ -31,7 +31,7 @@ licenses = 'licenses/*'
 
 // libmxnet dependencies
 mx_deps = 'lib/libgfortran.so.3, lib/libquadmath.so.0'
-mx_mkldnn_deps = 'lib/libgfortran.so.3, lib/libquadmath.so.0, lib/libiomp5.so, lib/libmkldnn.so.0, lib/libmklml_intel.so, 3rdparty/mkldnn/build/install/include/mkldnn_version.h'
+mx_mkldnn_deps = 'lib/libgfortran.so.3, lib/libquadmath.so.0, lib/libmkldnn.so.1, 3rdparty/mkldnn/build/install/include/mkldnn_version.h'
 
 // library type
 // either static or dynamic - depending on how it links to its dependencies
diff --git a/tests/nightly/JenkinsfileForBinaries b/tests/nightly/JenkinsfileForBinaries
index af87b2c35658..48db4457c1ac 100755
--- a/tests/nightly/JenkinsfileForBinaries
+++ b/tests/nightly/JenkinsfileForBinaries
@@ -20,7 +20,7 @@
 
 mx_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a'
 mx_cmake_lib = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so'
-mx_lib_cpp_example_mkl = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, build/cpp-package/example/imagenet_inference, lib/libmkldnn.so.0, lib/libmklml_intel.so'
+mx_lib_cpp_example_mkl = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, build/cpp-package/example/imagenet_inference, lib/libmkldnn.so.1'
 
 node('utility') {
   // Loading the utilities requires a node context unfortunately

From c01375741f0b0e065eb94688316bb40237434f49 Mon Sep 17 00:00:00 2001
From: Xinyu Chen <xinyu1.chen@intel.com>
Date: Fri, 1 Nov 2019 20:56:36 +0800
Subject: [PATCH 552/813] [MKL-DNN] Add mxnet mkldnn cmake tutorial (#16688)

* add mxnet mkldnn cmake instruction

* imporve doc

* OMP->OpenMP
---
 .../performance/backend/mkldnn/mkldnn_readme.md   | 15 ++++++++++++++-
 1 file changed, 14 insertions(+), 1 deletion(-)

diff --git a/docs/python_docs/python/tutorials/performance/backend/mkldnn/mkldnn_readme.md b/docs/python_docs/python/tutorials/performance/backend/mkldnn/mkldnn_readme.md
index 7c19fbb97931..c66fd2e8e642 100644
--- a/docs/python_docs/python/tutorials/performance/backend/mkldnn/mkldnn_readme.md
+++ b/docs/python_docs/python/tutorials/performance/backend/mkldnn/mkldnn_readme.md
@@ -57,11 +57,24 @@ cd incubator-mxnet
 
 ### Build MXNet with MKL-DNN
 
+To achieve better performance, the Intel OpenMP and llvm OpenMP are recommended as below instruction. Otherwise, default GNU OpenMP will be used and you may get the sub-optimal performance. If you don't have the full [MKL](https://software.intel.com/en-us/intel-mkl) library installation, you might use OpenBLAS as the blas library, by setting USE_BLAS=openblas.
+
+```
+# build with llvm OpenMP and Intel MKL/openblas
+mkdir build && cd build
+cmake -DUSE_CUDA=OFF -DUSE_MKL_IF_AVAILABLE=ON -DUSE_MKLDNN=ON -DUSE_OPENMP=ON -DUSE_OPENCV=ON ..
+make -j $(nproc)
+```
+
 ```
+# build with Intel MKL and Intel OpenMP
 make -j $(nproc) USE_OPENCV=1 USE_MKLDNN=1 USE_BLAS=mkl USE_INTEL_PATH=/opt/intel
 ```
 
-If you don't have the full [MKL](https://software.intel.com/en-us/intel-mkl) library installation, you might use OpenBLAS as the blas library, by setting USE_BLAS=openblas.
+```
+# build with openblas and GNU OpenMP(sub-optimal performance)
+make -j $(nproc) USE_OPENCV=1 USE_MKLDNN=1 USE_BLAS=openblas
+```
 
 <h2 id="2">MacOS</h2>
 

From f3c6be594e509f51d02bc5f570246111a90be958 Mon Sep 17 00:00:00 2001
From: Zhennan Qin <zhennan.qin@intel.com>
Date: Fri, 1 Nov 2019 20:58:02 +0800
Subject: [PATCH 553/813] Revert "[MKLDNN]Fix reorder2default (#16602)"
 (#16697)

This reverts commit dd4eaf5c23046d07a4578a219e2dd3622e5620fa.
---
 src/ndarray/ndarray.cc | 25 ++++++++++---------------
 1 file changed, 10 insertions(+), 15 deletions(-)

diff --git a/src/ndarray/ndarray.cc b/src/ndarray/ndarray.cc
index f0dca2ea2aee..6dc6bafa7288 100644
--- a/src/ndarray/ndarray.cc
+++ b/src/ndarray/ndarray.cc
@@ -1623,13 +1623,11 @@ void NDArray::Save(dmlc::Stream *strm) const {
     nd_cpu.WaitToRead();
     save_data = nd_cpu.data();
   } else {
-#if MXNET_USE_MKLDNN == 1
-    // For mkldnn, a copy of *this can ensure no write access pending on *this.
-    nd_cpu = this->Copy(Context::CPU());
-    nd_cpu.WaitToRead();
-#else
     this->WaitToRead();
     nd_cpu = *this;
+#if MXNET_USE_MKLDNN == 1
+    if (nd_cpu.IsMKLDNNData())
+      nd_cpu = nd_cpu.Reorder2Default();
 #endif
     save_data = nd_cpu.data();
   }
@@ -2024,18 +2022,15 @@ void NDArray::SyncCopyToCPU(void *data, size_t size) const {
   TBlob dst(data, dshape, cpu::kDevMask, this->dtype_, 0); // NOLINT(*)
 
   if (this->ctx().dev_mask() == cpu::kDevMask) {
-    Engine::Get()->PushAsync(
-        [&](RunContext rctx, Engine::CallbackOnComplete on_complete) {
-          RunContext ctx{this->ctx(), nullptr, nullptr, false};
-          NDArray src = *this;
+    this->WaitToRead();
+    RunContext rctx{this->ctx(), nullptr, nullptr, false};
+    NDArray src = *this;
 #if MXNET_USE_MKLDNN == 1
-          src = this->Reorder2Default();
+    if (src.IsMKLDNNData())
+      src = this->Reorder2Default();
 #endif
-          ndarray::Copy<cpu, cpu>(src.data(), &dst, Context::CPU(), Context::CPU(), ctx);
-          on_complete();
-        },
-        this->ctx(), {this->var()}, {}, FnProperty::kNormal, 0, "SyncCopyCPU2CPU");
-    this->WaitToWrite();
+    ndarray::Copy<cpu, cpu>(src.data(), &dst,
+                            Context::CPU(), Context::CPU(), rctx);
   } else {
 #if MXNET_USE_CUDA
     Engine::Get()->PushAsync(

From 9f6070f560bbf38b433e3cac0a47d180da672c80 Mon Sep 17 00:00:00 2001
From: Sheng Zha <szha@users.noreply.github.com>
Date: Fri, 1 Nov 2019 08:57:32 -0700
Subject: [PATCH 554/813] [Estimator] refactor estimator and clarify docs
 (#16694)

* refactor estimator and clarify docs

* fix info message and test

* clean up after releasing logging handler
---
 .../gluon/contrib/estimator/estimator.py      | 134 ++++++++----------
 .../gluon/contrib/estimator/event_handler.py  |  67 ++++++---
 python/mxnet/gluon/contrib/estimator/utils.py |  31 +++-
 tests/python/unittest/test_gluon_estimator.py |  11 +-
 .../unittest/test_gluon_event_handler.py      |   3 +-
 5 files changed, 139 insertions(+), 107 deletions(-)

diff --git a/python/mxnet/gluon/contrib/estimator/estimator.py b/python/mxnet/gluon/contrib/estimator/estimator.py
index d3eded0cc8cd..4f2b8fd99cac 100644
--- a/python/mxnet/gluon/contrib/estimator/estimator.py
+++ b/python/mxnet/gluon/contrib/estimator/estimator.py
@@ -24,15 +24,14 @@
 
 from .event_handler import MetricHandler, ValidationHandler, LoggingHandler, StoppingHandler
 from .event_handler import TrainBegin, EpochBegin, BatchBegin, BatchEnd, EpochEnd, TrainEnd
-from .utils import _check_metrics
+from .event_handler import _check_event_handlers
+from .utils import _check_metrics, _suggest_metric_for_loss, _check_handler_metric_ref
 from ...data import DataLoader
-from ...loss import SoftmaxCrossEntropyLoss
 from ...loss import Loss as gluon_loss
 from ...trainer import Trainer
 from ...utils import split_and_load
 from .... import autograd
 from ....context import Context, cpu, gpu, num_gpus
-from ....metric import Accuracy
 from ....metric import Loss as metric_loss
 
 __all__ = ['Estimator']
@@ -48,8 +47,8 @@ class Estimator(object):
     ----------
     net : gluon.Block
         The model used for training.
-    loss : gluon.loss.Loss or list of gluon.loss.Loss
-        Loss(objective functions) to calculate during training.
+    loss : gluon.loss.Loss
+        Loss (objective) function to calculate during training.
     metrics : EvalMetric or list of EvalMetric
         Metrics for evaluating models.
     initializer : Initializer
@@ -69,19 +68,17 @@ def __init__(self, net,
 
         self.net = net
         self.loss = self._check_loss(loss)
-        self.train_metrics = _check_metrics(metrics)
+        self._train_metrics = _check_metrics(metrics)
+        self._add_default_training_metrics()
+        self._add_validation_metrics()
 
         self.context = self._check_context(context)
         self._initialize(initializer)
         self.trainer = self._check_trainer(trainer)
 
     def _check_loss(self, loss):
-        if isinstance(loss, gluon_loss):
-            loss = [loss]
-        elif isinstance(loss, list) and all([isinstance(l, gluon_loss) for l in loss]):
-            loss = loss
-        else:
-            raise ValueError("loss must be a Loss or a list of Loss, "
+        if not isinstance(loss, gluon_loss):
+            raise ValueError("loss must be a Loss, "
                              "refer to gluon.loss.Loss:{}".format(loss))
         return loss
 
@@ -166,31 +163,30 @@ def _get_data_and_label(self, batch, ctx, batch_axis=0):
         label = split_and_load(label, ctx_list=ctx, batch_axis=batch_axis)
         return data, label
 
-    def prepare_loss_and_metrics(self):
-        """
-        Based on loss functions and training metrics in estimator
-        Create metric wrappers to record loss values,
-        Create copies of train loss/metric objects to record validation values
+    def _add_default_training_metrics(self):
+        if not self._train_metrics:
+            suggested_metric = _suggest_metric_for_loss(self.loss)
+            if suggested_metric:
+                self._train_metrics = [suggested_metric]
+            loss_name = self.loss.name.rstrip('1234567890')
+            self._train_metrics.append(metric_loss(loss_name))
 
-        Returns
-        -------
-        train_metrics, val_metrics
-        """
-        if any(not hasattr(self, attribute) for attribute in
-               ['train_metrics', 'val_metrics']):
-            # Use default mx.metric.Accuracy() for SoftmaxCrossEntropyLoss()
-            if not self.train_metrics and any([isinstance(l, SoftmaxCrossEntropyLoss) for l in self.loss]):
-                self.train_metrics = [Accuracy()]
-            self.val_metrics = []
-            for loss in self.loss:
-                # remove trailing numbers from loss name to avoid confusion
-                self.train_metrics.append(metric_loss(loss.name.rstrip('1234567890')))
-            for metric in self.train_metrics:
-                val_metric = copy.deepcopy(metric)
-                metric.name = "train " + metric.name
-                val_metric.name = "validation " + val_metric.name
-                self.val_metrics.append(val_metric)
-        return self.train_metrics, self.val_metrics
+        for metric in self._train_metrics:
+            metric.name = "training " + metric.name
+
+    def _add_validation_metrics(self):
+        self._val_metrics = [copy.deepcopy(metric) for metric in self._train_metrics]
+
+        for metric in self._val_metrics:
+            metric.name = "validation " + metric.name
+
+    @property
+    def train_metrics(self):
+        return self._train_metrics
+
+    @property
+    def val_metrics(self):
+        return self._val_metrics
 
     def evaluate_batch(self,
                        val_batch,
@@ -209,7 +205,7 @@ def evaluate_batch(self,
         """
         data, label = self._get_data_and_label(val_batch, self.context, batch_axis)
         pred = [self.net(x) for x in data]
-        loss = [self.loss[0](y_hat, y) for y_hat, y in zip(pred, label)]
+        loss = [self.loss(y_hat, y) for y_hat, y in zip(pred, label)]
         # update metrics
         for metric in val_metrics:
             if isinstance(metric, metric_loss):
@@ -275,7 +271,7 @@ def fit_batch(self, train_batch,
 
         with autograd.record():
             pred = [self.net(x) for x in data]
-            loss = [self.loss[0](y_hat, y) for y_hat, y in zip(pred, label)]
+            loss = [self.loss(y_hat, y) for y_hat, y in zip(pred, label)]
 
         for l in loss:
             l.backward()
@@ -377,63 +373,47 @@ def fit(self, train_data,
             handler.train_end(estimator_ref)
 
     def _prepare_default_handlers(self, val_data, event_handlers):
-        event_handlers = event_handlers or []
-        default_handlers = []
-        self.prepare_loss_and_metrics()
+        event_handlers = _check_event_handlers(event_handlers)
+        added_default_handlers = []
 
         # no need to add to default handler check as StoppingHandler does not use metrics
-        event_handlers.append(StoppingHandler(self.max_epoch, self.max_batch))
-        default_handlers.append("StoppingHandler")
+        added_default_handlers.append(StoppingHandler(self.max_epoch, self.max_batch))
 
         if not any(isinstance(handler, MetricHandler) for handler in event_handlers):
-            event_handlers.append(MetricHandler(train_metrics=self.train_metrics))
-            default_handlers.append("MetricHandler")
+            added_default_handlers.append(MetricHandler(train_metrics=self.train_metrics))
 
         if not any(isinstance(handler, ValidationHandler) for handler in event_handlers):
             # no validation handler
             if val_data:
-                # add default validation handler if validation data found
-                event_handlers.append(ValidationHandler(val_data=val_data, eval_fn=self.evaluate,
-                                                        val_metrics=self.val_metrics))
-                default_handlers.append("ValidationHandler")
                 val_metrics = self.val_metrics
+                # add default validation handler if validation data found
+                added_default_handlers.append(ValidationHandler(val_data=val_data,
+                                                                eval_fn=self.evaluate,
+                                                                val_metrics=val_metrics))
             else:
                 # set validation metrics to None if no validation data and no validation handler
                 val_metrics = []
 
         if not any(isinstance(handler, LoggingHandler) for handler in event_handlers):
-            event_handlers.append(LoggingHandler(train_metrics=self.train_metrics,
-                                                 val_metrics=val_metrics))
-            default_handlers.append("LoggingHandler")
+            added_default_handlers.append(LoggingHandler(train_metrics=self.train_metrics,
+                                                         val_metrics=val_metrics))
 
         # if there is a mix of user defined event handlers and default event handlers
-        # they should have the same set of loss and metrics
-        if default_handlers and len(event_handlers) != len(default_handlers):
-            msg = "You are training with the following default event handlers: %s. " \
-                  "They use loss and metrics from estimator.prepare_loss_and_metrics(). " \
-                  "Please use the same set of metrics for all your other handlers." % \
-                  ", ".join(default_handlers)
+        # they should have the same set of metrics
+        mixing_handlers = event_handlers and added_default_handlers
+
+        event_handlers.extend(added_default_handlers)
+
+        if mixing_handlers:
+            msg = "The following default event handlers are added: {}.".format(
+                ", ".join([type(h).__name__ for h in added_default_handlers]))
             warnings.warn(msg)
-            # check if all handlers has the same set of references to loss and metrics
-            references = []
+
+
+            # check if all handlers have the same set of references to metrics
+            known_metrics = set(self.train_metrics + self.val_metrics)
             for handler in event_handlers:
-                for attribute in dir(handler):
-                    if any(keyword in attribute for keyword in ['metric' or 'monitor']):
-                        reference = getattr(handler, attribute)
-                        if isinstance(reference, list):
-                            references += reference
-                        else:
-                            references.append(reference)
-            # remove None metric references
-            references = set([ref for ref in references if ref])
-            for metric in references:
-                if metric not in self.train_metrics + self.val_metrics:
-                    msg = "We have added following default handlers for you: %s and used " \
-                          "estimator.prepare_loss_and_metrics() to pass metrics to " \
-                          "those handlers. Please use the same set of metrics " \
-                          "for all your handlers." % \
-                          ", ".join(default_handlers)
-                    raise ValueError(msg)
+                _check_handler_metric_ref(handler, known_metrics)
 
         event_handlers.sort(key=lambda handler: getattr(handler, 'priority', 0))
         return event_handlers
diff --git a/python/mxnet/gluon/contrib/estimator/event_handler.py b/python/mxnet/gluon/contrib/estimator/event_handler.py
index c5a4f1a3f836..7e143d6f19aa 100644
--- a/python/mxnet/gluon/contrib/estimator/event_handler.py
+++ b/python/mxnet/gluon/contrib/estimator/event_handler.py
@@ -16,7 +16,7 @@
 # under the License.
 
 # coding: utf-8
-# pylint: disable=wildcard-import, unused-argument
+# pylint: disable=wildcard-import, unused-argument, too-many-ancestors
 """Gluon EventHandlers for Estimators"""
 
 import logging
@@ -34,33 +34,47 @@
            'StoppingHandler', 'MetricHandler', 'ValidationHandler',
            'LoggingHandler', 'CheckpointHandler', 'EarlyStoppingHandler']
 
+class EventHandler(object):
+    pass
 
-class TrainBegin(object):
+
+def _check_event_handlers(handlers):
+    if isinstance(handlers, EventHandler):
+        handlers = [handlers]
+    else:
+        handlers = handlers or []
+        if not all([isinstance(handler, EventHandler) for handler in handlers]):
+            raise ValueError("handlers must be an EventHandler or a list of EventHandler, "
+                             "got: {}".format(handlers))
+    return handlers
+
+
+class TrainBegin(EventHandler):
     def train_begin(self, estimator, *args, **kwargs):
         pass
 
 
-class TrainEnd(object):
+class TrainEnd(EventHandler):
     def train_end(self, estimator, *args, **kwargs):
         pass
 
 
-class EpochBegin(object):
+class EpochBegin(EventHandler):
     def epoch_begin(self, estimator, *args, **kwargs):
         pass
 
 
-class EpochEnd(object):
+class EpochEnd(EventHandler):
     def epoch_end(self, estimator, *args, **kwargs):
         return False
 
 
-class BatchBegin(object):
+class BatchBegin(EventHandler):
     def batch_begin(self, estimator, *args, **kwargs):
         pass
 
 
-class BatchEnd(object):
+class BatchEnd(EventHandler):
     def batch_end(self, estimator, *args, **kwargs):
         return False
 
@@ -242,14 +256,16 @@ def __init__(self, file_name=None,
         super(LoggingHandler, self).__init__()
         self.logger = logging.getLogger(__name__)
         self.logger.setLevel(logging.INFO)
-        stream_handler = logging.StreamHandler()
-        self.logger.addHandler(stream_handler)
+        self._added_logging_handlers = [logging.StreamHandler()]
         # save logger to file only if file name or location is specified
         if file_name or file_location:
             file_name = file_name or 'estimator_log'
             file_location = file_location or './'
             file_handler = logging.FileHandler(os.path.join(file_location, file_name), mode=filemode)
-            self.logger.addHandler(file_handler)
+            self._added_logging_handlers.append(file_handler)
+        for handler in self._added_logging_handlers:
+            self.logger.addHandler(handler)
+
         if verbose not in [self.LOG_PER_EPOCH, self.LOG_PER_BATCH]:
             raise ValueError("verbose level must be either LOG_PER_EPOCH or "
                              "LOG_PER_BATCH, received %s. "
@@ -265,6 +281,12 @@ def __init__(self, file_name=None,
         # it will also shut down logging at train end
         self.priority = np.Inf
 
+    def __del__(self):
+        for handler in self._added_logging_handlers:
+            handler.flush()
+            self.logger.removeHandler(handler)
+            handler.close()
+
     def train_begin(self, estimator, *args, **kwargs):
         self.train_start = time.time()
         trainer = estimator.trainer
@@ -393,8 +415,8 @@ def __init__(self,
         self.model_prefix = model_prefix
         self.save_best = save_best
         if self.save_best and not isinstance(self.monitor, EvalMetric):
-            raise ValueError("To save best model only, please provide one of the metric objects as monitor, "
-                             "You can get these objects using estimator.prepare_loss_and_metric()")
+            raise ValueError("To save best model only, please provide one of the metric objects "
+                             "from estimator.train_metrics and estimator.val_metrics as monitor.")
         self.epoch_period = epoch_period
         self.batch_period = batch_period
         self.current_batch = 0
@@ -487,10 +509,10 @@ def _save_checkpoint(self, estimator):
             monitor_name, monitor_value = self.monitor.get()
             # check if monitor exists in train stats
             if np.isnan(monitor_value):
-                warnings.warn(RuntimeWarning('Skipping save best because %s is not updated, make sure you '
-                                             'pass one of the metric objects as monitor, '
-                                             'you can use estimator.prepare_loss_and_metrics to'
-                                             'create all metric objects', monitor_name))
+                warnings.warn(RuntimeWarning(
+                    'Skipping save best because %s is not updated, make sure you pass one of the '
+                    'metric objects estimator.train_metrics and estimator.val_metrics as monitor',
+                    monitor_name))
             else:
                 if self.monitor_op(monitor_value, self.best):
                     prefix = self.model_prefix + '-best'
@@ -517,7 +539,7 @@ def _save_symbol(self, estimator):
             sym.save(symbol_file)
         else:
             self.logger.info("Model architecture(symbol file) is not saved, please use HybridBlock "
-                             "to construct your model, can call net.hybridize() before passing to "
+                             "to construct your model, and call net.hybridize() before passing to "
                              "Estimator in order to save model architecture as %s.", symbol_file)
 
     def _save_params_and_trainer(self, estimator, file_prefix):
@@ -636,8 +658,9 @@ def __init__(self,
         super(EarlyStoppingHandler, self).__init__()
 
         if not isinstance(monitor, EvalMetric):
-            raise ValueError("Please provide one of the metric objects as monitor, "
-                             "You can create these objects using estimator.prepare_loss_and_metric()")
+            raise ValueError(
+                "Please provide one of the metric objects from estimator.train_metrics and "
+                "estimator.val_metrics as monitor.")
         if isinstance(monitor, CompositeEvalMetric):
             raise ValueError("CompositeEvalMetric is not supported for EarlyStoppingHandler, "
                              "please specify a simple metric instead.")
@@ -693,9 +716,9 @@ def train_begin(self, estimator, *args, **kwargs):
     def epoch_end(self, estimator, *args, **kwargs):
         monitor_name, monitor_value = self.monitor.get()
         if np.isnan(monitor_value):
-            warnings.warn(RuntimeWarning('%s is not updated, make sure you pass one of the metric objects'
-                                         'as monitor, you can use estimator.prepare_loss_and_metrics to'
-                                         'create all metric objects', monitor_name))
+            warnings.warn(RuntimeWarning(
+                '%s is not updated, make sure you pass one of the metric objects from'
+                'estimator.train_metrics and estimator.val_metrics as monitor.', monitor_name))
         else:
             if self.monitor_op(monitor_value - self.min_delta, self.best):
                 self.best = monitor_value
diff --git a/python/mxnet/gluon/contrib/estimator/utils.py b/python/mxnet/gluon/contrib/estimator/utils.py
index f5be0878e0d9..d9126a2f6763 100644
--- a/python/mxnet/gluon/contrib/estimator/utils.py
+++ b/python/mxnet/gluon/contrib/estimator/utils.py
@@ -19,7 +19,8 @@
 # pylint: disable=wildcard-import, unused-variable
 """Gluon Estimator Utility Functions"""
 
-from ....metric import EvalMetric, CompositeEvalMetric
+from ...loss import SoftmaxCrossEntropyLoss
+from ....metric import Accuracy, EvalMetric, CompositeEvalMetric
 
 def _check_metrics(metrics):
     if isinstance(metrics, CompositeEvalMetric):
@@ -30,5 +31,31 @@ def _check_metrics(metrics):
         metrics = metrics or []
         if not all([isinstance(metric, EvalMetric) for metric in metrics]):
             raise ValueError("metrics must be a Metric or a list of Metric, "
-                             "refer to mxnet.metric.EvalMetric:{}".format(metrics))
+                             "refer to mxnet.metric.EvalMetric: {}".format(metrics))
     return metrics
+
+def _check_handler_metric_ref(handler, known_metrics):
+    for attribute in dir(handler):
+        if any(keyword in attribute for keyword in ['metric' or 'monitor']):
+            reference = getattr(handler, attribute)
+            if not reference:
+                continue
+            elif isinstance(reference, list):
+                for metric in reference:
+                    _check_metric_known(handler, metric, known_metrics)
+            else:
+                _check_metric_known(handler, reference, known_metrics)
+
+def _check_metric_known(handler, metric, known_metrics):
+    if metric not in known_metrics:
+        raise ValueError(
+            'Event handler {} refers to a metric instance {} outside of '
+            'the known training and validation metrics. Please use the metrics from '
+            'estimator.train_metrics and estimator.val_metrics '
+            'instead.'.format(type(handler).__name__,
+                              metric))
+
+def _suggest_metric_for_loss(loss):
+    if isinstance(loss, SoftmaxCrossEntropyLoss):
+        return Accuracy()
+    return None
diff --git a/tests/python/unittest/test_gluon_estimator.py b/tests/python/unittest/test_gluon_estimator.py
index ae47d925670f..bae576734a3e 100644
--- a/tests/python/unittest/test_gluon_estimator.py
+++ b/tests/python/unittest/test_gluon_estimator.py
@@ -96,7 +96,8 @@ def test_validation():
             epochs=num_epochs)
 
     # using validation handler
-    train_metrics, val_metrics = est.prepare_loss_and_metrics()
+    train_metrics = est.train_metrics
+    val_metrics = est.val_metrics
     validation_handler = ValidationHandler(val_data=dataloader, eval_fn=est.evaluate,
                                            val_metrics=val_metrics)
 
@@ -222,7 +223,6 @@ def test_metric():
                     loss=loss,
                     trainer=trainer,
                     context=ctx)
-    est.prepare_loss_and_metrics()
     assert isinstance(est.train_metrics[0], mx.metric.Accuracy)
 
 
@@ -343,11 +343,11 @@ def test_default_handlers():
 
     # handler with prepared loss and metrics
     # use mix of default and user defined handlers
-    train_metrics, val_metrics = est.prepare_loss_and_metrics()
+    train_metrics = est.train_metrics
+    val_metrics = est.val_metrics
     logging = LoggingHandler(train_metrics=train_metrics, val_metrics=val_metrics)
     with warnings.catch_warnings(record=True) as w:
         est.fit(train_data=train_data, epochs=num_epochs, event_handlers=[logging])
-        assert 'You are training with the' in str(w[-1].message)
         # provide metric handler by default
         assert 'MetricHandler' in str(w[-1].message)
 
@@ -364,7 +364,8 @@ def test_default_handlers():
         est.fit(train_data=train_data, epochs=num_epochs, event_handlers=[logging])
 
     # test handler order
-    train_metrics, val_metrics = est.prepare_loss_and_metrics()
+    train_metrics = est.train_metrics
+    val_metrics = est.val_metrics
     early_stopping = EarlyStoppingHandler(monitor=val_metrics[0])
     handlers = est._prepare_default_handlers(val_data=None, event_handlers=[early_stopping])
     assert len(handlers) == 4
diff --git a/tests/python/unittest/test_gluon_event_handler.py b/tests/python/unittest/test_gluon_event_handler.py
index 7ea5ff3f4b62..b29c72a0f908 100644
--- a/tests/python/unittest/test_gluon_event_handler.py
+++ b/tests/python/unittest/test_gluon_event_handler.py
@@ -143,7 +143,8 @@ def test_logging():
         ce_loss = loss.SoftmaxCrossEntropyLoss()
         acc = mx.metric.Accuracy()
         est = estimator.Estimator(net, loss=ce_loss, metrics=acc)
-        train_metrics, val_metrics = est.prepare_loss_and_metrics()
+        train_metrics = est.train_metrics
+        val_metrics = est.val_metrics
         logging_handler = event_handler.LoggingHandler(file_name=file_name,
                                                        file_location=tmpdir,
                                                        train_metrics=train_metrics,

From 1aa1b5a9ab53bb57a3c653793fb824d01f2d5e81 Mon Sep 17 00:00:00 2001
From: Przemyslaw Tredak <ptredak@nvidia.com>
Date: Fri, 1 Nov 2019 09:51:19 -0700
Subject: [PATCH 555/813] Eliminate common expressions (#15657)

* Eliminate common expressions from a graph

* Guarding against optimizing out stateful ops and ops that require
resource

* Fix lint

* Added THasDeterministicOutput to multiple ops

* DDebug eliminate common expr

* Added test

* Expose get_optimized_symbol

* Fix

* Fix 2

* Add doc to the Python call

* Add env var MXNET_ELIMINATE_COMMON_EXPR, default true

* Add comments, improve readability of eliminate_common_expr_pass.cc

* Expand testing

* Lower priority of THasDeterministicOutput attr for equal Node test

* Change mx.gpu() to mx.cpu() in tests

* Skip CSE test on Windows (as env variable setting during test does not work there)

* Add missing import sys

* Add missing import logging
---
 docs/static_site/src/pages/api/faq/env_var.md |   4 +
 include/mxnet/op_attr_types.h                 |  11 +
 python/mxnet/executor.py                      |  16 +-
 src/executor/eliminate_common_expr_pass.cc    | 224 ++++++++++++++++++
 src/executor/exec_pass.h                      |   9 +
 src/executor/graph_executor.cc                |   3 +
 src/imperative/cached_op.cc                   |   6 +-
 src/operator/contrib/boolean_mask.cu          |   1 +
 src/operator/contrib/bounding_box.cc          |   2 +
 src/operator/contrib/hawkes_ll.cc             |   1 +
 src/operator/contrib/index_array.cc           |   1 +
 src/operator/loss_binary_op.cc                |   1 +
 src/operator/nn/concat.cc                     |   2 +
 src/operator/nn/convolution.cc                |   1 +
 src/operator/nn/ctc_loss.cc                   |   1 +
 src/operator/nn/deconvolution.cc              |   1 +
 src/operator/nn/fully_connected.cc            |   1 +
 src/operator/nn/group_norm.cc                 |   1 +
 src/operator/nn/layer_norm.cc                 |   1 +
 src/operator/nn/moments.cc                    |   1 +
 src/operator/nn/softmax_activation.cc         |   1 +
 src/operator/nn/upsampling.cc                 |   1 +
 .../numpy/np_broadcast_reduce_op_value.cc     |   7 +
 src/operator/numpy/np_dot.cc                  |   1 +
 src/operator/numpy/np_tensordot_op.cc         |   2 +
 .../tensor/broadcast_reduce_minmax_value.cc   |   2 +
 .../tensor/broadcast_reduce_norm_value.cc     |   1 +
 .../tensor/broadcast_reduce_prod_value.cc     |   2 +
 .../tensor/broadcast_reduce_sum_value.cc      |   3 +
 src/operator/tensor/cast_storage.cc           |   1 +
 src/operator/tensor/dot.cc                    |   2 +
 .../tensor/elemwise_binary_op_basic.cc        |   2 +
 src/operator/tensor/elemwise_scatter_op.cc    |   1 +
 src/operator/tensor/elemwise_sum.cc           |   1 +
 src/operator/tensor/histogram.cc              |   1 +
 src/operator/tensor/indexing_op.cc            |   3 +
 src/operator/tensor/la_op.cc                  |   4 +
 src/operator/tensor/matrix_op.cc              |   7 +
 src/operator/tensor/ordering_op.cc            |   3 +
 src/operator/tensor/ravel.cc                  |   2 +
 src/operator/tensor/square_sum.cc             |   1 +
 tests/python/unittest/test_symbol.py          |  88 ++++++-
 42 files changed, 421 insertions(+), 3 deletions(-)
 create mode 100644 src/executor/eliminate_common_expr_pass.cc

diff --git a/docs/static_site/src/pages/api/faq/env_var.md b/docs/static_site/src/pages/api/faq/env_var.md
index 04678d9962b2..e4fe58a116c5 100644
--- a/docs/static_site/src/pages/api/faq/env_var.md
+++ b/docs/static_site/src/pages/api/faq/env_var.md
@@ -339,6 +339,10 @@ If ctypes is used, it must be `mxnet._ctypes.ndarray.NDArrayBase`.
   - Only applies to MXNet that has been compiled with CUDA and when ```MXNET_USE_FUSION``` option is enabled.
   - If this variable is set, MXNet will print the code for fused operators that it generated.
 
+* MXNET_ELIMINATE_COMMON_EXPR
+  - Values: 0(false) or 1(true) ```(default=1)```
+  - If this variable is set, MXNet will simplify the computation graph, eliminating duplicated operations on the same inputs.
+
 Settings for Minimum Memory Usage
 ---------------------------------
 - Make sure ```min(MXNET_EXEC_NUM_TEMP, MXNET_GPU_WORKER_NTHREADS) = 1```
diff --git a/include/mxnet/op_attr_types.h b/include/mxnet/op_attr_types.h
index 75d843c98bd2..7c0ea77dc986 100644
--- a/include/mxnet/op_attr_types.h
+++ b/include/mxnet/op_attr_types.h
@@ -218,6 +218,17 @@ using FCreateOpState = std::function<OpStatePtr (const NodeAttrs& attrs,
                                                  Context ctx,
                                                  const mxnet::ShapeVector& in_shape,
                                                  const std::vector<int>& in_type)>;
+
+/*!
+ * \brief Whether the operator always produces the same
+ *        output given the same input.
+ *        This enables certain optimizations
+ *        like common expression elimination.
+ *
+ * \note Register under "THasDeterministicOutput"
+ */
+using THasDeterministicOutput = bool;
+
 /*!
  * \brief Execution mode of this operator.
  */
diff --git a/python/mxnet/executor.py b/python/mxnet/executor.py
index edc10dff18c2..3b79f0c8d1b4 100644
--- a/python/mxnet/executor.py
+++ b/python/mxnet/executor.py
@@ -25,7 +25,7 @@
 import copy
 import numpy as np
 from .base import _LIB
-from .base import mx_uint, NDArrayHandle, ExecutorHandle, py_str, mx_int
+from .base import mx_uint, NDArrayHandle, SymbolHandle, ExecutorHandle, py_str, mx_int
 from .base import check_call, c_handle_array, c_array_buf, c_str_array
 from .ndarray import NDArray
 from .ndarray import _ndarray_cls
@@ -511,3 +511,17 @@ def debug_str(self):
         check_call(_LIB.MXExecutorPrint(
             self.handle, ctypes.byref(debug_str)))
         return py_str(debug_str.value)
+
+    def get_optimized_symbol(self):
+        """Get an optimized version of the symbol from the executor.
+
+        Returns
+        -------
+        symbol : Symbol
+            Optimized symbol from the executor.
+        """
+        from .symbol import Symbol
+        sym_handle = SymbolHandle()
+        check_call(_LIB.MXExecutorGetOptimizedSymbol(self.handle, ctypes.byref(sym_handle)))
+        ret = Symbol(sym_handle)
+        return ret
diff --git a/src/executor/eliminate_common_expr_pass.cc b/src/executor/eliminate_common_expr_pass.cc
new file mode 100644
index 000000000000..5c77ec25b325
--- /dev/null
+++ b/src/executor/eliminate_common_expr_pass.cc
@@ -0,0 +1,224 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file eliminate_common_expr.cc
+ * \brief Eliminate common expressions in the graph
+ * \author Przemyslaw Tredak
+ */
+
+#include <mxnet/base.h>
+#include <mxnet/op_attr_types.h>
+
+#include <vector>
+#include <map>
+#include <utility>
+#include <sstream>
+
+namespace mxnet {
+namespace exec {
+
+namespace {
+
+using nnvm::Node;
+using nnvm::NodePtr;
+using nnvm::Graph;
+using nnvm::IndexedGraph;
+
+// NodeInput holds the sufficient subset of NodeEntry fields for Node-input equality tests
+using NodeInput = std::pair<const Node*, uint32_t>;
+
+/*!
+ * \brief Convert a Node's input vector of `NodeEntry` to a vector of the simpler `NodeInput`
+ */
+std::vector<NodeInput> ConvertInputs(const std::vector<nnvm::NodeEntry>& inputs) {
+  std::vector<NodeInput> ret;
+  for (const auto& entry : inputs) {
+    ret.emplace_back(entry.node.get(), entry.index);
+  }
+  return ret;
+}
+
+/*!
+ * \brief Determine if two Nodes have equal function such that one Node can be eliminated.
+ */
+bool NodeEqual(const Node* n, const Node* m) {
+  if (n->is_variable() || m->is_variable()) return false;
+  if (n->op() != m->op()) return false;
+  // Nodes with different attributes are considered not identical,
+  // though this may reject Node pairs that are in fact functionally the same.
+  if (n->attrs.dict != m->attrs.dict) return false;
+
+  // Ops that mutate inputs cannot be optimized out
+  static auto& fmutate_inputs = Op::GetAttr<nnvm::FMutateInputs>("FMutateInputs");
+  if (fmutate_inputs.get(n->op(), nullptr) != nullptr) return false;
+
+  // Stateful ops cannot be be equal to each other
+  static auto& fstateful = Op::GetAttr<FCreateOpState>("FCreateOpState");
+  if (fstateful.get(n->op(), nullptr) != nullptr)
+    return false;
+
+  // Check to see if the user has explicitly set THasDeterministicOutput to override the
+  // subsequent determination of Node equality based on resource use.
+  static auto& deterministic_output =
+      Op::GetAttr<THasDeterministicOutput>("THasDeterministicOutput");
+  if (deterministic_output.contains(n->op()))
+    return deterministic_output[n->op()];
+
+  // Ops that require resource could ask for
+  // random resource, so need to be explicitly marked
+  // to be eligible
+  static auto& resource_request = Op::GetAttr<FResourceRequest>("FResourceRequest");
+  static auto& resource_request_ex = Op::GetAttr<FResourceRequestEx>("FResourceRequestEx");
+  if (resource_request.get(n->op(), nullptr) != nullptr) return false;
+  if (resource_request_ex.get(n->op(), nullptr) != nullptr) return false;
+
+  return true;
+}
+
+// Graph traversal to create a list of pairs of identical-function nodes that can be combined.
+std::vector<std::pair<NodePtr, NodePtr> > GetCommonNodes(const Graph& g) {
+  std::vector<std::pair<NodePtr, NodePtr> > ret;
+  // A map between a vector of inputs and those nodes that have those inputs
+  std::map<std::vector<NodeInput>, std::vector<const NodePtr*> > grouped_nodes;
+  // Traverse the graph and group the nodes by their vector of inputs
+  nnvm::DFSVisit(g.outputs, [&grouped_nodes](const NodePtr& n) {
+    if (n->inputs.size() != 0) {
+      grouped_nodes[ConvertInputs(n->inputs)].push_back(&n);
+    }
+  });
+  // Now check for identical node ops within the node groups (having identical inputs)
+  for (const auto& pair : grouped_nodes) {
+    auto &node_group = pair.second;  // Group of nodes that share the same vector of inputs
+    if (node_group.size() > 1) {
+      std::unordered_set<size_t> visited;
+      for (size_t i = 0; i < node_group.size(); ++i) {
+        if (visited.count(i)) continue;
+        for (size_t j = i + 1; j < node_group.size(); ++j) {
+          // If the two Nodes have equal function, then one Node (called the 'replaced') can
+          // be eliminated in favor of the other Node (the 'src').
+          if (NodeEqual(node_group[i]->get(), node_group[j]->get())) {
+            visited.insert(j);
+            NodePtr src = *node_group[i];
+            NodePtr replaced = *node_group[j];
+            ret.emplace_back(src, replaced);
+          }
+        }
+      }
+    }
+  }
+  return ret;
+}
+
+/*!
+ * \brief Do a single pass of Node elimination given pairs of identical Nodes.
+ */
+void EliminateCommonNodes(Graph* g,
+                          const std::vector<std::pair<NodePtr, NodePtr> >& common_nodes) {
+  for (const auto &p : common_nodes) {
+    std::vector <NodePtr> nodes_to_change;
+    const NodePtr &src = p.first;
+    const NodePtr &replaced = p.second;
+    // Create a `nodes_to_change` list containing the Nodes that refer to the `replaced` Node
+    // that is targeted for elimination.
+    DFSVisit(g->outputs, [replaced, &nodes_to_change](const NodePtr &n) {
+      for (const auto &dep : n->control_deps) {
+        if (dep == replaced) {
+          nodes_to_change.push_back(n);
+          return;
+        }
+      }
+      for (const auto &inp : n->inputs) {
+        if (inp.node == replaced) {
+          nodes_to_change.push_back(n);
+          return;
+        }
+      }
+    });
+
+    // Change references to the `replaced` Node within the `nodes_to_change` list to be
+    // references to the equivalent `src` Node.
+    for (auto &n : nodes_to_change) {
+      for (auto &dep : n->control_deps) {
+        if (dep == replaced) {
+          dep = src;
+        }
+      }
+      for (auto &inp : n->inputs) {
+        if (inp.node == replaced) {
+          inp.node = src;
+        }
+      }
+    }
+
+    // Add `replaced` Node control dependencies to those of the `src` Node.
+    for (const auto &n : replaced->control_deps) {
+      src->control_deps.push_back(n);
+    }
+
+    // Change graph outputs driven by the `replaced` Node to now point to the `src` Node.
+    for (auto& out : g->outputs) {
+      if (out.node == replaced) {
+        out.node = src;
+      }
+    }
+  }
+  // Check for duplicates in outputs and
+  // insert Copy nodes as appropriate
+  const Op* copy_op = Op::Get("_copy");
+  nnvm::NodeEntryMap<size_t> unique_outputs;
+  for (size_t i = 0; i < g->outputs.size(); ++i) {
+    auto kv = unique_outputs.find(g->outputs[i]);
+    if (kv == unique_outputs.end()) {
+      unique_outputs.emplace(g->outputs[i], 0);
+    } else {
+      NodePtr copy_node = Node::Create();
+      std::ostringstream os;
+      os << kv->first.node->attrs.name << "_" << kv->second << "_copy";
+      kv->second++;
+      copy_node->attrs.op = copy_op;
+      copy_node->attrs.name = os.str();
+      copy_node->inputs.emplace_back(kv->first);
+      g->outputs[i] = nnvm::NodeEntry{copy_node, 0, 0};
+    }
+  }
+}
+
+}  // namespace
+
+/*!
+ * \brief Simplify a graph by iteratively eliminating Nodes with identical inputs and function.
+ */
+nnvm::Graph EliminateCommonExpr(nnvm::Graph&& g) {
+  using nnvm::NodePtr;
+  bool keep_running = true;
+  while (keep_running) {
+    const auto& common_nodes = GetCommonNodes(g);
+    if (common_nodes.empty()) {
+      keep_running = false;
+    } else {
+      EliminateCommonNodes(&g, common_nodes);
+    }
+  }
+  return g;
+}
+
+}  // namespace exec
+}  // namespace mxnet
diff --git a/src/executor/exec_pass.h b/src/executor/exec_pass.h
index 25a326171510..a5f125affcb0 100644
--- a/src/executor/exec_pass.h
+++ b/src/executor/exec_pass.h
@@ -194,6 +194,15 @@ void AttachOpResources(const Graph& g,
  */
 Graph DetectInplaceAddTo(Graph g);
 
+/*!
+ * \brief Eliminate common expressions in the graph.
+ *
+ * \param g input forward graph
+ *
+ * \return graph with common expressions eliminated
+ */
+Graph EliminateCommonExpr(Graph && g);
+
 /*!
  * \brief Fuse pointwise operations in the forward pass.
  *
diff --git a/src/executor/graph_executor.cc b/src/executor/graph_executor.cc
index 4f1553bc19d5..7fa1de373d07 100644
--- a/src/executor/graph_executor.cc
+++ b/src/executor/graph_executor.cc
@@ -331,6 +331,9 @@ nnvm::Graph GraphExecutor::InitFullGraph(nnvm::Symbol symbol,
 
   nnvm::Graph g;
   g.outputs = symbol.outputs;
+  bool do_elim_common_expr = dmlc::GetEnv("MXNET_ELIMINATE_COMMON_EXPR", true);
+  if (do_elim_common_expr)
+    g = exec::EliminateCommonExpr(std::move(g));
   need_grad_ = false;
   for (OpReqType req : grad_req_types) {
     if (req != kNullOp) need_grad_ = true;
diff --git a/src/imperative/cached_op.cc b/src/imperative/cached_op.cc
index dd392d3e0401..269729c18f58 100644
--- a/src/imperative/cached_op.cc
+++ b/src/imperative/cached_op.cc
@@ -93,6 +93,10 @@ void CreateFullGraph(const nnvm::Symbol& sym,
     }
   }
 
+  bool do_elim_common_expr = dmlc::GetEnv("MXNET_ELIMINATE_COMMON_EXPR", true);
+  if (do_elim_common_expr)
+    *fwd_graph = exec::EliminateCommonExpr(std::move(*fwd_graph));
+
   // construct backward graph
   {
     ograd_entries->reserve(fwd_graph->outputs.size());
@@ -278,7 +282,7 @@ CachedOp::CachedOp(
 
   auto grad_graph = nnvm::Graph();
   std::unordered_map<uint32_t, uint32_t> fwd_input_to_grad_output;
-  CreateFullGraph(sym, &fwd_graph_, &grad_graph, &full_graph_,
+  CreateFullGraph(sym.Copy(), &fwd_graph_, &grad_graph, &full_graph_,
                   &ograd_entries_, &fwd_input_to_grad_output);
 
   {
diff --git a/src/operator/contrib/boolean_mask.cu b/src/operator/contrib/boolean_mask.cu
index f6c1df0c62a8..a5ef4a70d99b 100644
--- a/src/operator/contrib/boolean_mask.cu
+++ b/src/operator/contrib/boolean_mask.cu
@@ -157,6 +157,7 @@ NNVM_REGISTER_OP(_contrib_boolean_mask)
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
   })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<FComputeEx>("FComputeEx<gpu>", BooleanMaskForward<gpu>);
 
 NNVM_REGISTER_OP(_backward_contrib_boolean_mask)
diff --git a/src/operator/contrib/bounding_box.cc b/src/operator/contrib/bounding_box.cc
index 62b7c2e0bf4b..3ab11bb2d6f9 100644
--- a/src/operator/contrib/bounding_box.cc
+++ b/src/operator/contrib/bounding_box.cc
@@ -102,6 +102,7 @@ Examples::
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
   })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<FCompute>("FCompute<cpu>", BoxNMSForward<cpu>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{"_backward_contrib_box_nms"})
 .add_argument("data", "NDArray-or-Symbol", "The input")
@@ -186,6 +187,7 @@ NNVM_REGISTER_OP(_contrib_bipartite_matching)
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
   })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<mxnet::FInferShape>("FInferShape", MatchingShape)
 .set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 2>)
 .set_attr<FCompute>("FCompute<cpu>", BipartiteMatchingForward<cpu>)
diff --git a/src/operator/contrib/hawkes_ll.cc b/src/operator/contrib/hawkes_ll.cc
index 758ab2012580..1e2fff5c9871 100644
--- a/src/operator/contrib/hawkes_ll.cc
+++ b/src/operator/contrib/hawkes_ll.cc
@@ -104,6 +104,7 @@ Example::
     .set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& n) {
         return std::vector<ResourceRequest>{ResourceRequest::Type::kTempSpace};
     })
+    .set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
     .add_argument(
       "lda", "NDArray-or-Symbol",
       "Shape (N, K) The intensity for each of the K processes, for each sample"
diff --git a/src/operator/contrib/index_array.cc b/src/operator/contrib/index_array.cc
index a70dee106314..ef4f030863f2 100644
--- a/src/operator/contrib/index_array.cc
+++ b/src/operator/contrib/index_array.cc
@@ -163,6 +163,7 @@ Examples::
 .set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& n) {
   return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
 })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .add_argument("data", "NDArray-or-Symbol", "Input data")
 .add_arguments(IndexArrayParam::__FIELDS__());
 
diff --git a/src/operator/loss_binary_op.cc b/src/operator/loss_binary_op.cc
index 696c8589a0dc..5bf49669db89 100644
--- a/src/operator/loss_binary_op.cc
+++ b/src/operator/loss_binary_op.cc
@@ -65,6 +65,7 @@ Example::
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
   })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<FCompute>("FCompute<cpu>", SoftmaxCrossEntropyForward<cpu>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_softmax_cross_entropy"})
 .set_attr<nnvm::FListInputNames>("FListInputNames",
diff --git a/src/operator/nn/concat.cc b/src/operator/nn/concat.cc
index fa62b0044a53..4d90810915a2 100644
--- a/src/operator/nn/concat.cc
+++ b/src/operator/nn/concat.cc
@@ -385,6 +385,7 @@ Example::
 .set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& n) {
   return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
 })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<bool>("TIsMKLDNN", true)
 #endif  // MXNET_USE_MKLDNN == 1
 CONCAT_FORWARD_ATTRS
@@ -422,6 +423,7 @@ NNVM_REGISTER_OP(_rnn_param_concat)
 })
 #endif  // MXNET_USE_MKLDNN == 1
 CONCAT_FORWARD_ATTRS
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<mxnet::FInferShape>("FInferShape", RNNParamConcatShape)
 .add_argument("data", "NDArray-or-Symbol[]", "List of arrays to concatenate")
 .add_arguments(ConcatParam::__FIELDS__());
diff --git a/src/operator/nn/convolution.cc b/src/operator/nn/convolution.cc
index e31073034594..6d9f84ffc510 100644
--- a/src/operator/nn/convolution.cc
+++ b/src/operator/nn/convolution.cc
@@ -503,6 +503,7 @@ There are other options to tune the performance.
 .set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& n) {
   return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
 })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .add_argument("data", "NDArray-or-Symbol", "Input data to the ConvolutionOp.")
 .add_argument("weight", "NDArray-or-Symbol", "Weight matrix.")
 .add_argument("bias", "NDArray-or-Symbol", "Bias parameter.")
diff --git a/src/operator/nn/ctc_loss.cc b/src/operator/nn/ctc_loss.cc
index f718b42bfaa4..aba76fb0c452 100644
--- a/src/operator/nn/ctc_loss.cc
+++ b/src/operator/nn/ctc_loss.cc
@@ -115,6 +115,7 @@ information on the definition and the algorithm.
 .set_attr<FInferStorageType>("FInferStorageType", CTCLossOpStorageType)
 .set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& attrs)
   { return std::vector<ResourceRequest>{ResourceRequest::kTempSpace}; })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<FCompute>("FCompute<cpu>", CTCLossOpForward<cpu>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{"_backward_ctc_loss"})
 .add_argument("data", "NDArray-or-Symbol", "Input ndarray")
diff --git a/src/operator/nn/deconvolution.cc b/src/operator/nn/deconvolution.cc
index b61f9ff37002..bbcec53e933d 100644
--- a/src/operator/nn/deconvolution.cc
+++ b/src/operator/nn/deconvolution.cc
@@ -430,6 +430,7 @@ NNVM_REGISTER_OP(Deconvolution)
 .set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& n) {
   return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
 })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<FCompute>("FCompute<cpu>", DeconvolutionCompute<cpu>)
 .set_attr<nnvm::FGradient>("FGradient", DeconvolutionGrad{"_backward_Deconvolution"})
 #if MXNET_USE_MKLDNN == 1
diff --git a/src/operator/nn/fully_connected.cc b/src/operator/nn/fully_connected.cc
index 1f6d9e313202..5d722581257f 100644
--- a/src/operator/nn/fully_connected.cc
+++ b/src/operator/nn/fully_connected.cc
@@ -314,6 +314,7 @@ If ``no_bias`` is set to be true, then the ``bias`` term is ignored.
   return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
 })
 #endif
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<mxnet::FInferShape>("FInferShape", FullyConnectedShape)
 .set_attr<nnvm::FInferType>("FInferType", FullyConnectedType)
 .set_attr<FCompute>("FCompute<cpu>", FullyConnectedCompute<cpu>)
diff --git a/src/operator/nn/group_norm.cc b/src/operator/nn/group_norm.cc
index b4698abeff83..06430c281920 100644
--- a/src/operator/nn/group_norm.cc
+++ b/src/operator/nn/group_norm.cc
@@ -111,6 +111,7 @@ Both ``gamma`` and ``beta`` are learnable parameters.
 .set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& n) {
   return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
 })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .add_argument("data", "NDArray-or-Symbol", "Input data")
 .add_argument("gamma", "NDArray-or-Symbol", "gamma array")
 .add_argument("beta", "NDArray-or-Symbol", "beta array")
diff --git a/src/operator/nn/layer_norm.cc b/src/operator/nn/layer_norm.cc
index 0b53d5091194..1b2a43b2501c 100644
--- a/src/operator/nn/layer_norm.cc
+++ b/src/operator/nn/layer_norm.cc
@@ -194,6 +194,7 @@ axis to be the last item in the input shape.
 .set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& n) {
   return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
 })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .add_argument("data", "NDArray-or-Symbol", "Input data to layer normalization")
 .add_argument("gamma", "NDArray-or-Symbol", "gamma array")
 .add_argument("beta", "NDArray-or-Symbol", "beta array")
diff --git a/src/operator/nn/moments.cc b/src/operator/nn/moments.cc
index 37b8cdf18750..180615e53d61 100644
--- a/src/operator/nn/moments.cc
+++ b/src/operator/nn/moments.cc
@@ -66,6 +66,7 @@ If x is 1-D and axes = [0] this is just the mean and variance of a vector.
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
   })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseInOut{"_backward_moments"})
 .set_attr<nnvm::FInplaceOption>("FInplaceOption",
   [](const NodeAttrs& attrs) {
diff --git a/src/operator/nn/softmax_activation.cc b/src/operator/nn/softmax_activation.cc
index 8a28243dfced..9e5a3ab8f6a2 100644
--- a/src/operator/nn/softmax_activation.cc
+++ b/src/operator/nn/softmax_activation.cc
@@ -75,6 +75,7 @@ NNVM_REGISTER_OP(_backward_SoftmaxActivation)
 .set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& n) {
   return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
 })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr_parser(ParamParser<SoftmaxActivationParam>)
 .set_attr<FCompute>("FCompute<cpu>", SoftmaxActivationGradCompute<cpu>);
 
diff --git a/src/operator/nn/upsampling.cc b/src/operator/nn/upsampling.cc
index 971ff6ad560b..d36b2598ce82 100644
--- a/src/operator/nn/upsampling.cc
+++ b/src/operator/nn/upsampling.cc
@@ -195,6 +195,7 @@ Example::
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
   }
 })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<FCompute>("FCompute<cpu>", UpSamplingCompute<cpu>)
 .set_attr<nnvm::FGradient>("FGradient", UpSamplingGrad{"_backward_UpSampling"})
 .set_attr<std::string>("key_var_num_args", "num_args")
diff --git a/src/operator/numpy/np_broadcast_reduce_op_value.cc b/src/operator/numpy/np_broadcast_reduce_op_value.cc
index fdda792a9ed8..435fe1df1134 100644
--- a/src/operator/numpy/np_broadcast_reduce_op_value.cc
+++ b/src/operator/numpy/np_broadcast_reduce_op_value.cc
@@ -138,6 +138,7 @@ NNVM_REGISTER_OP(_np_sum)
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
   })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_np_sum"});
 
 NNVM_REGISTER_OP(_backward_np_sum)
@@ -176,6 +177,7 @@ NNVM_REGISTER_OP(_np_max)
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
   })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<nnvm::FGradient>("FGradient", ReduceGrad{"_backward_np_max"});
 
 NNVM_REGISTER_OP(_backward_np_max)
@@ -203,6 +205,7 @@ return std::vector<std::string>{"a"};
 [](const NodeAttrs& attrs) {
 return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
 })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<nnvm::FGradient>("FGradient", ReduceGrad{"_backward_np_min"});
 
 NNVM_REGISTER_OP(_backward_np_min)
@@ -229,6 +232,7 @@ NNVM_REGISTER_OP(_np_prod)
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
   })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<nnvm::FGradient>("FGradient", ReduceGrad{"_backward_np_prod"});
 
 NNVM_REGISTER_OP(_backward_np_prod)
@@ -282,6 +286,7 @@ NNVM_REGISTER_OP(_npi_mean)
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
   })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_np_mean"});
 
 NNVM_REGISTER_OP(_backward_np_mean)
@@ -350,6 +355,7 @@ NNVM_REGISTER_OP(_npi_std)
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
   })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes);
 
 NNVM_REGISTER_OP(_npi_var)
@@ -377,6 +383,7 @@ NNVM_REGISTER_OP(_npi_var)
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
   })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes);
 
 bool NumpyBroadcastToShape(const nnvm::NodeAttrs& attrs,
diff --git a/src/operator/numpy/np_dot.cc b/src/operator/numpy/np_dot.cc
index 6afc896a7720..feb032ae07ea 100644
--- a/src/operator/numpy/np_dot.cc
+++ b/src/operator/numpy/np_dot.cc
@@ -131,6 +131,7 @@ NNVM_REGISTER_OP(_np_dot)
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>(1, ResourceRequest::kTempSpace);
   })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<FCompute>("FCompute<cpu>", NumpyDotForward<cpu>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_np_dot"})
 .add_argument("a", "NDArray-or-Symbol", "First input")
diff --git a/src/operator/numpy/np_tensordot_op.cc b/src/operator/numpy/np_tensordot_op.cc
index aca45c1652ee..96de0decf73a 100644
--- a/src/operator/numpy/np_tensordot_op.cc
+++ b/src/operator/numpy/np_tensordot_op.cc
@@ -113,6 +113,7 @@ NNVM_REGISTER_OP(_npi_tensordot)
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>(1, ResourceRequest::kTempSpace);
   })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<FCompute>("FCompute<cpu>", TensordotOpForward<cpu>)
 .set_attr<nnvm::FGradient>("FGradient", mxnet::op::ElemwiseGradUseIn{"_backward_npi_tensordot"})
 .add_argument("a", "NDArray-or-Symbol", "First input")
@@ -213,6 +214,7 @@ NNVM_REGISTER_OP(_npi_tensordot_int_axes)
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>(1, ResourceRequest::kTempSpace);
   })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<FCompute>("FCompute<cpu>", TensordotIntAxesOpForward<cpu>)
 .set_attr<nnvm::FGradient>("FGradient",
   mxnet::op::ElemwiseGradUseIn{"_backward_npi_tensordot_int_axes"})
diff --git a/src/operator/tensor/broadcast_reduce_minmax_value.cc b/src/operator/tensor/broadcast_reduce_minmax_value.cc
index f8bc33ba375d..e77d42b042ae 100644
--- a/src/operator/tensor/broadcast_reduce_minmax_value.cc
+++ b/src/operator/tensor/broadcast_reduce_minmax_value.cc
@@ -35,6 +35,7 @@ MXNET_OPERATOR_REGISTER_MINMAX_REDUCE(max)
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
   })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<nnvm::FGradient>("FGradient", ReduceGrad{"_backward_max"});
 
 MXNET_OPERATOR_REGISTER_REDUCE_BACKWARD(_backward_max)
@@ -49,6 +50,7 @@ MXNET_OPERATOR_REGISTER_MINMAX_REDUCE(min)
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
   })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<nnvm::FGradient>("FGradient", ReduceGrad{"_backward_min"});
 
 MXNET_OPERATOR_REGISTER_REDUCE_BACKWARD(_backward_min)
diff --git a/src/operator/tensor/broadcast_reduce_norm_value.cc b/src/operator/tensor/broadcast_reduce_norm_value.cc
index 63a05b4980fc..4cd92d44997e 100644
--- a/src/operator/tensor/broadcast_reduce_norm_value.cc
+++ b/src/operator/tensor/broadcast_reduce_norm_value.cc
@@ -98,6 +98,7 @@ Examples::
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
   })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<FCompute>("FCompute<cpu>", LpNormCompute<cpu>)
 .set_attr<FComputeEx>("FComputeEx<cpu>", L2NormComputeEx<cpu>)
 .add_argument("data", "NDArray-or-Symbol", "The input")
diff --git a/src/operator/tensor/broadcast_reduce_prod_value.cc b/src/operator/tensor/broadcast_reduce_prod_value.cc
index 4778865bf11d..a38f37a3e55c 100644
--- a/src/operator/tensor/broadcast_reduce_prod_value.cc
+++ b/src/operator/tensor/broadcast_reduce_prod_value.cc
@@ -34,6 +34,7 @@ MXNET_OPERATOR_REGISTER_REDUCE(prod)
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
   })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<nnvm::FGradient>("FGradient", ReduceGrad{ "_backward_prod" });
 
 MXNET_OPERATOR_REGISTER_REDUCE_BACKWARD(_backward_prod)
@@ -49,6 +50,7 @@ MXNET_OPERATOR_REGISTER_REDUCE(nanprod)
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
   })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<nnvm::FGradient>("FGradient", ReduceGrad{ "_backward_nanprod" });
 
 MXNET_OPERATOR_REGISTER_REDUCE_BACKWARD(_backward_nanprod)
diff --git a/src/operator/tensor/broadcast_reduce_sum_value.cc b/src/operator/tensor/broadcast_reduce_sum_value.cc
index c5c9f5cb48e4..53e37e437f96 100644
--- a/src/operator/tensor/broadcast_reduce_sum_value.cc
+++ b/src/operator/tensor/broadcast_reduce_sum_value.cc
@@ -72,6 +72,7 @@ Example::
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
   })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_sum"});
 
 MXNET_OPERATOR_REGISTER_REDUCE_BACKWARD(_backward_sum)
@@ -88,6 +89,7 @@ MXNET_ADD_SPARSE_OP_ALIAS(mean)
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
   })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_mean"});
 
 MXNET_OPERATOR_REGISTER_REDUCE_BACKWARD(_backward_mean)
@@ -103,6 +105,7 @@ MXNET_OPERATOR_REGISTER_REDUCE(nansum)
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
   })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<nnvm::FGradient>("FGradient", ReduceGrad{ "_backward_nansum" });
 
 MXNET_OPERATOR_REGISTER_REDUCE_BACKWARD(_backward_nansum)
diff --git a/src/operator/tensor/cast_storage.cc b/src/operator/tensor/cast_storage.cc
index 5d93979a5bb7..ce5025696619 100644
--- a/src/operator/tensor/cast_storage.cc
+++ b/src/operator/tensor/cast_storage.cc
@@ -79,6 +79,7 @@ Example::
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
   })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<FCompute>("FCompute<cpu>", UnaryOp::IdentityCompute<cpu>)
 .set_attr<FComputeEx>("FComputeEx<cpu>", CastStorageComputeEx<cpu>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_copy"})
diff --git a/src/operator/tensor/dot.cc b/src/operator/tensor/dot.cc
index 556260ed9600..32d1c81ed40b 100644
--- a/src/operator/tensor/dot.cc
+++ b/src/operator/tensor/dot.cc
@@ -89,6 +89,7 @@ above patterns, ``dot`` will fallback and generate output with default storage.
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
   })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<FCompute>("FCompute<cpu>", DotForward_<cpu>)
 .set_attr<FComputeEx>("FComputeEx<cpu>", DotForwardEx<cpu>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_dot"})
@@ -137,6 +138,7 @@ which is computed by::
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
   })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<FCompute>("FCompute<cpu>", BatchDotForward_<cpu>)
 .set_attr<nnvm::FGradient>("FGradient",
     [](const nnvm::NodePtr& n,
diff --git a/src/operator/tensor/elemwise_binary_op_basic.cc b/src/operator/tensor/elemwise_binary_op_basic.cc
index c5e30c68de7e..50772bc075d4 100644
--- a/src/operator/tensor/elemwise_binary_op_basic.cc
+++ b/src/operator/tensor/elemwise_binary_op_basic.cc
@@ -86,6 +86,7 @@ MXNET_OPERATOR_REGISTER_BINARY(elemwise_add)
 .set_attr<bool>("TIsMKLDNN", true)
 #endif
 .set_attr<FComputeEx>("FComputeEx<cpu>", ElemwiseAddEx)
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<FResourceRequest>("FResourceRequest",  /* For Sparse CSR */
                             [](const NodeAttrs& attrs) {
                             return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};})
@@ -232,6 +233,7 @@ The storage type of ``elemwise_mul`` output depends on storage types of inputs
                               [](const NodeAttrs& attrs) {
                                 return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
                               })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .add_alias("_mul").add_alias("_Mul")
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_mul"});
 
diff --git a/src/operator/tensor/elemwise_scatter_op.cc b/src/operator/tensor/elemwise_scatter_op.cc
index dd6da0ce41aa..41f22b057a53 100644
--- a/src/operator/tensor/elemwise_scatter_op.cc
+++ b/src/operator/tensor/elemwise_scatter_op.cc
@@ -93,6 +93,7 @@ with default storage
                             [](const NodeAttrs& attrs) {
                               return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
                             })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_div"});
 
 /*! \brief _scatter_plus_scalar */
diff --git a/src/operator/tensor/elemwise_sum.cc b/src/operator/tensor/elemwise_sum.cc
index 75553ef2c2a5..d1b86d161e89 100644
--- a/src/operator/tensor/elemwise_sum.cc
+++ b/src/operator/tensor/elemwise_sum.cc
@@ -178,6 +178,7 @@ The storage type of ``add_n`` output depends on storage types of inputs
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
   })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 #if MXNET_USE_MKLDNN == 1
 .set_attr<bool>("TIsMKLDNN", true)
 #endif
diff --git a/src/operator/tensor/histogram.cc b/src/operator/tensor/histogram.cc
index b7896e9e0016..78234873772d 100644
--- a/src/operator/tensor/histogram.cc
+++ b/src/operator/tensor/histogram.cc
@@ -152,6 +152,7 @@ Example::
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
 })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<mxnet::FInferShape>("FInferShape", HistogramOpShape)
 .set_attr<nnvm::FInferType>("FInferType", HistogramOpType)
 .set_attr<FCompute>("FCompute<cpu>", HistogramOpForward<cpu>)
diff --git a/src/operator/tensor/indexing_op.cc b/src/operator/tensor/indexing_op.cc
index 470abee71a59..4bba683f0f28 100644
--- a/src/operator/tensor/indexing_op.cc
+++ b/src/operator/tensor/indexing_op.cc
@@ -551,6 +551,7 @@ The storage type of weight can be either row_sparse or default.
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
   })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<FCompute>("FCompute<cpu>", EmbeddingOpForward<cpu>)
 .set_attr<FComputeEx>("FComputeEx<cpu>", SparseEmbeddingOpForwardEx<cpu>)
 .set_attr<nnvm::FGradient>("FGradient",
@@ -624,6 +625,7 @@ Examples::
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
   })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<mxnet::FInferShape>("FInferShape", EmbeddingOpShape<SparseEmbeddingParam>)
 .set_attr<nnvm::FInferType>("FInferType", EmbeddingOpType<SparseEmbeddingParam>)
 .set_attr<FInferStorageType>("FInferStorageType", SparseEmbeddingOpForwardStorageType)
@@ -728,6 +730,7 @@ The storage type of ``take`` output depends upon the input storage type:
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
   })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<FCompute>("FCompute<cpu>", TakeOpForward<cpu>)
 .set_attr<FComputeEx>("FComputeEx<cpu>", TakeOpForwardEx<cpu>)
 .set_attr<nnvm::FGradient>("FGradient",
diff --git a/src/operator/tensor/la_op.cc b/src/operator/tensor/la_op.cc
index ce7d1d5de692..3d0e43251e03 100644
--- a/src/operator/tensor/la_op.cc
+++ b/src/operator/tensor/la_op.cc
@@ -806,6 +806,7 @@ Examples::
   { return std::vector<std::pair<int, int>>{{0, 0}}; })
 .set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& attrs)
   { return std::vector<ResourceRequest>{ResourceRequest::kTempSpace}; })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<FCompute>("FCompute<cpu>", LaOpForward<cpu, 2, 2, 1, 2, gelqf>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{"_backward_linalg_gelqf"})
 .add_argument("A", "NDArray-or-Symbol", "Tensor of input matrices to be factorized");
@@ -875,6 +876,7 @@ Examples::
   { return std::vector<std::pair<int, int>>{{0, 0}}; })
 .set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& attrs)
   { return std::vector<ResourceRequest>{ResourceRequest::kTempSpace}; })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<FCompute>("FCompute<cpu>", LaOpForwSyevd<cpu, syevd>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{"_backward_linalg_syevd"})
 .add_argument("A", "NDArray-or-Symbol", "Tensor of input matrices to be factorized");
@@ -925,6 +927,7 @@ Examples::
   { return std::vector<std::pair<int, int>>{{0, 0}}; })
 .set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& attrs)
   { return std::vector<ResourceRequest>{ResourceRequest::kTempSpace}; })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<FCompute>("FCompute<cpu>", LaOpForward<cpu, 2, 2, 1, 1, inverse>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{"_backward_linalg_inverse"})
 .add_argument("A", "NDArray-or-Symbol", "Tensor of square matrix");
@@ -978,6 +981,7 @@ Examples::
 .set_attr<nnvm::FInferType>("FInferType", DetType<1>)
 .set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& attrs)
   { return std::vector<ResourceRequest>{ResourceRequest::kTempSpace}; })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<FCompute>("FCompute<cpu>", LaOpDetForward<cpu, 1, det>)
 .set_attr<nnvm::FGradient>("FGradient", ReduceDetGrad<1>{"_backward_linalg_det"})
 .add_argument("A", "NDArray-or-Symbol", "Tensor of square matrix");
diff --git a/src/operator/tensor/matrix_op.cc b/src/operator/tensor/matrix_op.cc
index 0f63061d7c09..eee5ea67f6e1 100644
--- a/src/operator/tensor/matrix_op.cc
+++ b/src/operator/tensor/matrix_op.cc
@@ -196,6 +196,7 @@ If the argument `reverse` is set to 1, then the special values are inferred from
   [](const NodeAttrs& attrs){
     return std::vector<bool>{true};
   })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .add_argument("data", "NDArray-or-Symbol", "Input data to reshape.")
 .add_arguments(ReshapeParam::__FIELDS__());
 
@@ -269,6 +270,7 @@ Example::
   [](const NodeAttrs& attrs){
     return std::vector<bool>{true};
   })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .add_argument("data", "NDArray-or-Symbol", "Input array.");
 
 #if MXNET_USE_MKLDNN == 1
@@ -484,6 +486,7 @@ Example::
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
 })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<FInferStorageType>("FInferStorageType", SliceForwardInferStorageType)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_slice"})
 .set_attr<FCompute>("FCompute<cpu>", SliceOpForward<cpu>)
@@ -836,6 +839,7 @@ Examples::
 [](const NodeAttrs& attrs) {
   return std::vector<ResourceRequest> {ResourceRequest::kTempSpace};
 })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
 .set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
 .set_attr<FCompute>("FCompute<cpu>", ReverseOpForward<cpu>)
@@ -977,6 +981,7 @@ Example::
   [](const NodeAttrs& n) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
 })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"space_to_depth"})
 .add_argument("data", "NDArray-or-Symbol", "Input ndarray")
 .add_arguments(DepthToSpaceParam::__FIELDS__());
@@ -1023,6 +1028,7 @@ Example::
   [](const NodeAttrs& n) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
 })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"depth_to_space"})
 .add_argument("data", "NDArray-or-Symbol", "Input ndarray")
 .add_arguments(DepthToSpaceParam::__FIELDS__());
@@ -1091,6 +1097,7 @@ Example::
   [](const NodeAttrs& n) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
 })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_split_v2_backward"})
 .add_argument("data", "NDArray-or-Symbol", "The input")
 .add_arguments(SplitParam::__FIELDS__());
diff --git a/src/operator/tensor/ordering_op.cc b/src/operator/tensor/ordering_op.cc
index e36416114e31..6c375ce8e3c2 100644
--- a/src/operator/tensor/ordering_op.cc
+++ b/src/operator/tensor/ordering_op.cc
@@ -91,6 +91,7 @@ Examples::
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
   })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .add_argument("data", "NDArray-or-Symbol", "The input array")
 .add_arguments(TopKParam::__FIELDS__());
 
@@ -154,6 +155,7 @@ Examples::
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
   })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .add_argument("data", "NDArray-or-Symbol", "The input array")
 .add_arguments(SortParam::__FIELDS__());
 
@@ -190,6 +192,7 @@ Examples::
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
   })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .add_argument("data", "NDArray-or-Symbol", "The input array")
 .add_arguments(ArgSortParam::__FIELDS__());
 }  // namespace op
diff --git a/src/operator/tensor/ravel.cc b/src/operator/tensor/ravel.cc
index 94d79c7d07a6..e04628efab92 100644
--- a/src/operator/tensor/ravel.cc
+++ b/src/operator/tensor/ravel.cc
@@ -45,6 +45,7 @@ Examples::
 .set_attr_parser(ParamParser<RavelParam>)
 .set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& attrs)
   { return std::vector<ResourceRequest>{ResourceRequest::kTempSpace}; })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<nnvm::FListInputNames>("FListInputNames", [](const NodeAttrs& attrs)
   { return std::vector<std::string>{"data"}; } )
 .set_attr<mxnet::FInferShape>("FInferShape", RavelOpShape)
@@ -70,6 +71,7 @@ Examples::
 .set_attr_parser(ParamParser<RavelParam>)
 .set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& attrs)
   { return std::vector<ResourceRequest>{ResourceRequest::kTempSpace}; })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<nnvm::FListInputNames>("FListInputNames", [](const NodeAttrs& attrs)
   { return std::vector<std::string>{"data"}; } )
 .set_attr<mxnet::FInferShape>("FInferShape", UnravelOpShape)
diff --git a/src/operator/tensor/square_sum.cc b/src/operator/tensor/square_sum.cc
index af365bae05dc..255ec5bb8032 100644
--- a/src/operator/tensor/square_sum.cc
+++ b/src/operator/tensor/square_sum.cc
@@ -71,6 +71,7 @@ MXNET_OPERATOR_REGISTER_REDUCE_BACKWARD(_backward_square_sum)
   [](const NodeAttrs& attrs) {
     return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
   })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
 .set_attr<FInferStorageType>("FInferStorageType", SquareSumBackwardInferStorageType)
 .set_attr<FComputeEx>("FComputeEx<cpu>", SquareSumOpBackwardEx<cpu>);
 
diff --git a/tests/python/unittest/test_symbol.py b/tests/python/unittest/test_symbol.py
index 48c4f1664226..a2aad2c079fc 100644
--- a/tests/python/unittest/test_symbol.py
+++ b/tests/python/unittest/test_symbol.py
@@ -16,7 +16,9 @@
 # under the License.
 
 import copy
+import sys
 import os
+import logging
 import re
 import json
 import mxnet as mx
@@ -391,7 +393,6 @@ def test_children_same_name():
     for c in b.get_children():
         pass
 
-
 def test_transpose_nullop():
     for dim in range(1, 7):
         a = mx.sym.Variable('a')
@@ -417,6 +418,91 @@ def test_gen_atomic_symbol_multiple_outputs():
     atomic_sym = s._gen_atomic_symbol()
 
 
+def test_eliminate_common_expr():
+    if not sys.platform.startswith('linux'):
+        logging.info("Bypass the CSE test on non-Linux OS as setting env variables during test does not work on Windows")
+        return
+    def set_back_env_var(var_name, old_env_var):
+        if old_env_var is None:
+            os.environ.pop(var_name)
+        else:
+            os.environ[var_name] = old_env_var
+
+    # helper function to test a single model
+    def check_cse_on_symbol(sym, expected_savings, check_data, **kwargs):
+        inputs = sym.list_inputs()
+        shapes = {inp : kwargs[inp].shape for inp in inputs}
+        rtol = {'float16' : 1e-2,
+                'float32' : 1.5e-6,
+                'float64' : 1.5e-6,
+                }
+        atol = {'float16' : 1e-3,
+                'float32' : 1e-7,
+                'float64' : 1e-7,
+                }
+        env_var_name = 'MXNET_ELIMINATE_COMMON_EXPR'
+        old_env_var = os.environ.get(env_var_name, None)
+        try:
+            for dtype in ['float16', 'float32', 'float64']:
+                data = {inp : kwargs[inp].astype(dtype) for inp in inputs}
+                for grad_req in ['write', 'add']:
+                    type_dict = {inp : dtype for inp in inputs}
+                    os.environ[env_var_name] = '0'
+                    orig_exec = sym.simple_bind(ctx=mx.cpu(0), grad_req=grad_req,
+                                                type_dict=type_dict, **shapes)
+                    os.environ[env_var_name] = '1'
+                    cse_exec = sym.simple_bind(ctx=mx.cpu(0), grad_req=grad_req,
+                                               type_dict=type_dict, **shapes)
+                    fwd_orig = orig_exec.forward(is_train=True, **data)
+                    out_grads = [mx.nd.ones_like(arr) for arr in fwd_orig]
+                    orig_exec.backward(out_grads=out_grads)
+                    fwd_cse = cse_exec.forward(is_train=True, **data)
+                    cse_exec.backward(out_grads=out_grads)
+                    if check_data:
+                        for orig, cse in zip(fwd_orig, fwd_cse):
+                            np.testing.assert_allclose(orig.asnumpy(), cse.asnumpy(),
+                                                       rtol=rtol[dtype], atol=atol[dtype])
+                        for orig, cse in zip(orig_exec.grad_arrays, cse_exec.grad_arrays):
+                            if orig is None and cse is None:
+                                continue
+                            assert orig is not None
+                            assert cse is not None
+                            np.testing.assert_allclose(orig.asnumpy(), cse.asnumpy(),
+                                                       rtol=rtol[dtype], atol=atol[dtype])
+                    orig_sym_internals = orig_exec.get_optimized_symbol().get_internals()
+                    cse_sym_internals = cse_exec.get_optimized_symbol().get_internals()
+                    # test that the graph has been simplified as expected
+                    assert (len(cse_sym_internals) + expected_savings) == len(orig_sym_internals)
+        finally:
+            set_back_env_var(env_var_name, old_env_var)
+
+    a = mx.sym.Variable('a')
+    b = mx.sym.Variable('b')
+    c = mx.sym.Variable('c')
+    shape = rand_shape_nd(2)
+    arr1 = mx.random.uniform(shape=shape)
+    arr2 = mx.random.uniform(shape=shape)
+    arr3 = mx.random.uniform(shape=shape)
+
+    check_cse_on_symbol((a+5) + (a+5), expected_savings=1, check_data=True, a=arr1, b=arr2)
+    check_cse_on_symbol((a+1) + (a+2), expected_savings=0, check_data=True, a=arr1, b=arr2)
+    check_cse_on_symbol((1+a) + (a+1), expected_savings=1, check_data=True, a=arr1, b=arr2)
+    check_cse_on_symbol((a+b) + (a+b), expected_savings=1, check_data=True, a=arr1, b=arr2)
+    check_cse_on_symbol(((a+b)+c) +((a+b)+c), expected_savings=2, check_data=True,
+                                                                  a=arr1, b=arr2, c=arr3)
+    d = a + 1
+
+    # a*d node gets eliminated, but then a copy is inserted to isolate the outputs, so no net gain.
+    check_cse_on_symbol(mx.sym.Group([a*d, a*d]), expected_savings=0, check_data=True, a=arr1)
+
+    # a*d node gets eliminated, then the duplicated add-of-b, but then a copy is added for net of 1.
+    check_cse_on_symbol(mx.sym.Group([a*d+b, a*d+b]), expected_savings=1, check_data=True,
+                                                                          a=arr1, b=arr2)
+
+    # dropout uses a resource that precludes any optimization
+    check_cse_on_symbol(mx.sym.Dropout(a) +
+                        mx.sym.Dropout(a), expected_savings=0, check_data=False, a=arr1)
+
 def test_load_save_symbol():
     batch_size = 10
     num_hdidden = 128

From aa491f2f9f3db8802bc4c0d193bdbabd69bcb714 Mon Sep 17 00:00:00 2001
From: vexilligera <vexilligera@gmail.com>
Date: Fri, 1 Nov 2019 17:43:36 +0000
Subject: [PATCH 556/813] [NumPy] Add NumPy support for Cholesky decomposition
 (#16667)

* cholesky test

* cholesky docs and tests

* remove undefined GPU case

* add support for empty array for consistent behaviour as numpy on all devices

* Fix empty array support

* prevent numpy from going crazy

* numpy exception handling

* add shape inference for compatibility with numpy

* fix cpp comments

* fix logic
---
 python/mxnet/ndarray/numpy/linalg.py          |  61 +++++++++-
 python/mxnet/numpy/linalg.py                  |  61 +++++++++-
 python/mxnet/numpy_dispatch_protocol.py       |   1 +
 python/mxnet/symbol/numpy/linalg.py           |  61 +++++++++-
 src/operator/numpy/linalg/np_potrf-inl.h      |  33 ++++++
 src/operator/numpy/linalg/np_potrf.cc         |  62 ++++++++++
 src/operator/numpy/linalg/np_potrf.cu         |  40 +++++++
 src/operator/tensor/la_op-inl.h               |   8 +-
 .../unittest/test_numpy_interoperability.py   |  24 ++++
 tests/python/unittest/test_numpy_op.py        | 112 ++++++++++++++++++
 10 files changed, 459 insertions(+), 4 deletions(-)
 create mode 100644 src/operator/numpy/linalg/np_potrf-inl.h
 create mode 100644 src/operator/numpy/linalg/np_potrf.cc
 create mode 100644 src/operator/numpy/linalg/np_potrf.cu

diff --git a/python/mxnet/ndarray/numpy/linalg.py b/python/mxnet/ndarray/numpy/linalg.py
index ea9e6a91c2c1..b7766ad4700e 100644
--- a/python/mxnet/ndarray/numpy/linalg.py
+++ b/python/mxnet/ndarray/numpy/linalg.py
@@ -21,7 +21,7 @@
 from . import _op as _mx_nd_np
 from . import _internal as _npi
 
-__all__ = ['norm', 'svd']
+__all__ = ['norm', 'svd', 'cholesky']
 
 
 def norm(x, ord=None, axis=None, keepdims=False):
@@ -141,3 +141,62 @@ def svd(a):
     array(0.)
     """
     return tuple(_npi.svd(a))
+
+
+def cholesky(a):
+    r"""
+    Cholesky decomposition.
+
+    Return the Cholesky decomposition, `L * L.T`, of the square matrix `a`,
+    where `L` is lower-triangular and .T is the transpose operator. `a` must be
+    symmetric and positive-definite. Only `L` is actually returned. Complex-valued
+    input is currently not supported.
+
+    Parameters
+    ----------
+    a : (..., M, M) ndarray
+        Symmetric, positive-definite input matrix.
+
+    Returns
+    -------
+    L : (..., M, M) ndarray
+        Lower-triangular Cholesky factor of `a`.
+
+    Raises
+    ------
+    MXNetError
+        If the decomposition fails, for example, if `a` is not positive-definite.
+
+    Notes
+    -----
+    Broadcasting rules apply.
+
+    The Cholesky decomposition is often used as a fast way of solving
+
+    .. math:: A \mathbf{x} = \mathbf{b}
+
+    (when `A` is both symmetric and positive-definite).
+
+    First, we solve for :math:`\mathbf{y}` in
+
+    .. math:: L \mathbf{y} = \mathbf{b},
+
+    and then for :math:`\mathbf{x}` in
+
+    .. math:: L.T \mathbf{x} = \mathbf{y}.
+
+    Examples
+    --------
+    >>> A = np.array([[16, 4], [4, 10]])
+    >>> A
+    array([[16.,  4.],
+           [ 4., 10.]])
+    >>> L = np.linalg.cholesky(A)
+    >>> L
+    array([[4., 0.],
+           [1., 3.]])
+    >>> np.dot(L, L.T)
+    array([[16.,  4.],
+           [ 4., 10.]])
+    """
+    return _npi.cholesky(a)
diff --git a/python/mxnet/numpy/linalg.py b/python/mxnet/numpy/linalg.py
index 9ee5156c3bb1..402171e24aed 100644
--- a/python/mxnet/numpy/linalg.py
+++ b/python/mxnet/numpy/linalg.py
@@ -20,7 +20,7 @@
 from __future__ import absolute_import
 from ..ndarray import numpy as _mx_nd_np
 
-__all__ = ['norm', 'svd']
+__all__ = ['norm', 'svd', 'cholesky']
 
 
 def norm(x, ord=None, axis=None, keepdims=False):
@@ -159,3 +159,62 @@ def svd(a):
     array(0.)
     """
     return _mx_nd_np.linalg.svd(a)
+
+
+def cholesky(a):
+    r"""
+    Cholesky decomposition.
+
+    Return the Cholesky decomposition, `L * L.T`, of the square matrix `a`,
+    where `L` is lower-triangular and .T is the transpose operator. `a` must be
+    symmetric and positive-definite. Only `L` is actually returned. Complex-valued
+    input is currently not supported.
+
+    Parameters
+    ----------
+    a : (..., M, M) ndarray
+        Symmetric, positive-definite input matrix.
+
+    Returns
+    -------
+    L : (..., M, M) ndarray
+        Lower-triangular Cholesky factor of `a`.
+
+    Raises
+    ------
+    MXNetError
+        If the decomposition fails, for example, if `a` is not positive-definite.
+
+    Notes
+    -----
+    Broadcasting rules apply.
+
+    The Cholesky decomposition is often used as a fast way of solving
+
+    .. math:: A \mathbf{x} = \mathbf{b}
+
+    (when `A` is both symmetric and positive-definite).
+
+    First, we solve for :math:`\mathbf{y}` in
+
+    .. math:: L \mathbf{y} = \mathbf{b},
+
+    and then for :math:`\mathbf{x}` in
+
+    .. math:: L.T \mathbf{x} = \mathbf{y}.
+
+    Examples
+    --------
+    >>> A = np.array([[16, 4], [4, 10]])
+    >>> A
+    array([[16.,  4.],
+           [ 4., 10.]])
+    >>> L = np.linalg.cholesky(A)
+    >>> L
+    array([[4., 0.],
+           [1., 3.]])
+    >>> np.dot(L, L.T)
+    array([[16.,  4.],
+           [ 4., 10.]])
+    """
+    return _mx_nd_np.linalg.cholesky(a)
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index cdd21af829de..702ab38fa388 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -125,6 +125,7 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'column_stack',
     'zeros_like',
     'linalg.norm',
+    'linalg.cholesky',
     'trace',
     'tril',
     'meshgrid',
diff --git a/python/mxnet/symbol/numpy/linalg.py b/python/mxnet/symbol/numpy/linalg.py
index 354562160f0d..d06e2d9d4aac 100644
--- a/python/mxnet/symbol/numpy/linalg.py
+++ b/python/mxnet/symbol/numpy/linalg.py
@@ -22,7 +22,7 @@
 from . import _op as _mx_sym_np
 from . import _internal as _npi
 
-__all__ = ['norm', 'svd']
+__all__ = ['norm', 'svd', 'cholesky']
 
 
 def norm(x, ord=None, axis=None, keepdims=False):
@@ -128,3 +128,62 @@ def svd(a):
      - Does not support complex input.
     """
     return _npi.svd(a)
+
+
+def cholesky(a):
+    r"""
+    Cholesky decomposition.
+
+    Return the Cholesky decomposition, `L * L.T`, of the square matrix `a`,
+    where `L` is lower-triangular and .T is the transpose operator. `a` must be
+    symmetric and positive-definite. Only `L` is actually returned. Complex-valued
+    input is currently not supported.
+
+    Parameters
+    ----------
+    a : (..., M, M) ndarray
+        Symmetric, positive-definite input matrix.
+
+    Returns
+    -------
+    L : (..., M, M) ndarray
+        Lower-triangular Cholesky factor of `a`.
+
+    Raises
+    ------
+    MXNetError
+        If the decomposition fails, for example, if `a` is not positive-definite.
+
+    Notes
+    -----
+    Broadcasting rules apply.
+
+    The Cholesky decomposition is often used as a fast way of solving
+
+    .. math:: A \mathbf{x} = \mathbf{b}
+
+    (when `A` is both symmetric and positive-definite).
+
+    First, we solve for :math:`\mathbf{y}` in
+
+    .. math:: L \mathbf{y} = \mathbf{b},
+
+    and then for :math:`\mathbf{x}` in
+
+    .. math:: L.T \mathbf{x} = \mathbf{y}.
+
+    Examples
+    --------
+    >>> A = np.array([[16, 4], [4, 10]])
+    >>> A
+    array([[16.,  4.],
+           [ 4., 10.]])
+    >>> L = np.linalg.cholesky(A)
+    >>> L
+    array([[4., 0.],
+           [1., 3.]])
+    >>> np.dot(L, L.T)
+    array([[16.,  4.],
+           [ 4., 10.]])
+    """
+    return _npi.cholesky(a)
diff --git a/src/operator/numpy/linalg/np_potrf-inl.h b/src/operator/numpy/linalg/np_potrf-inl.h
new file mode 100644
index 000000000000..6317fc46a371
--- /dev/null
+++ b/src/operator/numpy/linalg/np_potrf-inl.h
@@ -0,0 +1,33 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_potrf-inl.h
+ * \brief Placeholder for Cholesky implementation in la_op
+ */
+#ifndef MXNET_OPERATOR_NUMPY_LINALG_NP_POTRF_INL_H_
+#define MXNET_OPERATOR_NUMPY_LINALG_NP_POTRF_INL_H_
+
+#include <mxnet/operator_util.h>
+#include <vector>
+#include "../../tensor/la_op.h"
+#include "../../tensor/la_op-inl.h"
+
+#endif  // MXNET_OPERATOR_NUMPY_LINALG_NP_POTRF_INL_H_
diff --git a/src/operator/numpy/linalg/np_potrf.cc b/src/operator/numpy/linalg/np_potrf.cc
new file mode 100644
index 000000000000..cad2b3084c21
--- /dev/null
+++ b/src/operator/numpy/linalg/np_potrf.cc
@@ -0,0 +1,62 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_potrf.cc
+ * \brief CPU implementation placeholder of Cholesky Operator
+ */
+#include <mxnet/operator_util.h>
+#include <vector>
+#include "./np_potrf-inl.h"
+#include "../../mshadow_op.h"
+#include "../../mxnet_op.h"
+#include "../../operator_common.h"
+#include "../../elemwise_op_common.h"
+
+namespace mxnet {
+namespace op {
+
+inline bool NumpyLaCholeskyShape(const nnvm::NodeAttrs& attrs,
+                                 mxnet::ShapeVector* in_attrs,
+                                 mxnet::ShapeVector* out_attrs) {
+  const mxnet::TShape& in_shape = (*in_attrs)[0];
+  CHECK_GE(in_shape.ndim(), 2)
+    << "Array must be at least two-dimensional";
+  return ElemwiseShape<1, 1>(attrs, in_attrs, out_attrs);
+}
+
+// calls forward and backward implemented in la_op
+NNVM_REGISTER_OP(_npi_cholesky)
+.describe(R"code()code" ADD_FILELINE)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<LaCholeskyParam>)
+.set_attr<nnvm::FListInputNames>("FListInputNames", [](const NodeAttrs& attrs)
+  { return std::vector<std::string>{"A"}; } )
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyLaCholeskyShape)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption", [](const NodeAttrs& attrs)
+  { return std::vector<std::pair<int, int>>{{0, 0}}; })
+.set_attr<FCompute>("FCompute<cpu>", LaOpForward<cpu, 2, 2, 1, 1, potrf>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{"_backward_linalg_potrf"})
+.add_argument("A", "NDArray-or-Symbol", "Tensor of input matrices to be decomposed");
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/linalg/np_potrf.cu b/src/operator/numpy/linalg/np_potrf.cu
new file mode 100644
index 000000000000..b2d3db3c040c
--- /dev/null
+++ b/src/operator/numpy/linalg/np_potrf.cu
@@ -0,0 +1,40 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file np_potrf.cu
+ * \brief GPU implementation placeholder of Cholesky Operator
+ */
+
+#include <mxnet/operator_util.h>
+#include <vector>
+#include "./np_potrf-inl.h"
+
+namespace mxnet {
+namespace op {
+
+#if MXNET_USE_CUSOLVER == 1
+
+NNVM_REGISTER_OP(_npi_cholesky)
+.set_attr<FCompute>("FCompute<gpu>", LaOpForward<gpu, 2, 2, 1, 1, potrf>);
+
+#endif
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/tensor/la_op-inl.h b/src/operator/tensor/la_op-inl.h
index 42d1f4527575..c3ed4fef5cd3 100644
--- a/src/operator/tensor/la_op-inl.h
+++ b/src/operator/tensor/la_op-inl.h
@@ -124,6 +124,9 @@ struct potrf {
   static void op(const Tensor<xpu, 3, DType>& A, const Tensor<xpu, 3, DType>& B,
                  Stream<xpu> *s, const nnvm::NodeAttrs& attrs) {
     const LaCholeskyParam& param = nnvm::get<LaCholeskyParam>(attrs.parsed);
+    if (A.shape_.Size() == 0U) {
+      return;
+    }
     if ( A.dptr_ != B.dptr_ ) Copy(B, A, s);
     linalg_batch_potrf(B, param.lower, s);
     using namespace mxnet_op;
@@ -587,13 +590,16 @@ struct potrf_backward {
                  const Tensor<xpu, 3, DType>& dA,
                  Stream<xpu>* s, const nnvm::NodeAttrs& attrs) {
     // Backward of B = potrf(A).
-    //   dA = 0.5 * B**T * copyLTU(B**T * dB) * B**(-1)
+    //   dA = 0.5 * B**(-T) * copyLTU(B**T * dB) * B**(-1)
     // Here, copyLTU(M) creates a symmetric matrix from the square matrix M
     // by setting the upper triangle to be equal to the lower triangle, leaving
     // lower triangle and diagonal unchanged.
     // The function also handles the case when B is upper triangular by appropriate
     // transpositions.
     const LaCholeskyParam& param = nnvm::get<LaCholeskyParam>(attrs.parsed);
+    if (dA.shape_.Size() == 0U) {
+      return;
+    }
     if ( dB.dptr_ != dA.dptr_ ) {
       Copy(dA, dB, s);
     }
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index 8416b1a9099f..5a5f0ca3ee68 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -250,6 +250,29 @@ def _add_workload_linalg_norm():
         OpArgMngr.add_workload('linalg.norm', np.array([[1, 0, 1], [0, 1, 1]], dtype=dt), 'fro')
 
 
+def _add_workload_linalg_cholesky():
+    shapes = [(1, 1), (2, 2), (3, 3), (50, 50), (3, 10, 10)]
+    dtypes = (np.float32, np.float64)
+
+    for shape, dtype in itertools.product(shapes, dtypes):
+        _np.random.seed(1)
+        a = _np.random.randn(*shape)
+
+        t = list(range(len(shape)))
+        t[-2:] = -1, -2
+
+        a = _np.matmul(a.transpose(t).conj(), a)
+
+        OpArgMngr.add_workload('linalg.cholesky', np.array(a, dtype=dtype))
+    
+    # test_0_size
+    for dtype in dtypes:
+        a = np.zeros((0, 1, 1))
+        OpArgMngr.add_workload('linalg.cholesky', np.array(a, dtype=dtype))
+        a = np.zeros((1, 0, 0))
+        OpArgMngr.add_workload('linalg.cholesky', np.array(a, dtype=dtype))
+
+
 def _add_workload_trace():
     OpArgMngr.add_workload('trace', np.random.uniform(size=(4, 1)))
     OpArgMngr.add_workload('trace', np.random.uniform(size=(3, 2)))
@@ -1180,6 +1203,7 @@ def _prepare_workloads():
     _add_workload_var(array_pool)
     _add_workload_zeros_like(array_pool)
     _add_workload_linalg_norm()
+    _add_workload_linalg_cholesky()
     _add_workload_trace()
     _add_workload_tril()
     _add_workload_outer()
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index a2716fb5363f..9c3988fad386 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -2871,6 +2871,118 @@ def check_svd(UT, L, V, data_np):
                 check_svd(UT, L, V, data_np)
 
 
+@with_seed()
+@use_np
+def test_np_linalg_cholesky():
+    class TestCholesky(HybridBlock):
+        def __init__(self):
+            super(TestCholesky, self).__init__()
+
+        def hybrid_forward(self, F, data):
+            return F.np.linalg.cholesky(data)
+
+    def get_grad(L):
+        # shape of m is [batch, n, n]
+        if 0 in L.shape:
+            return L
+
+        def copyltu(m):
+            eye = _np.array([_np.eye(m.shape[-1]) for i in range(m.shape[0])])
+            lower = _np.tril(m) - eye * m
+            lower_mask = _np.tril(_np.ones_like(m))
+            ret = lower_mask * m + lower.swapaxes(-1, -2)
+            return ret
+
+        shape = L.shape
+        L = L.reshape(-1, shape[-2], shape[-1])
+        dL = _np.ones_like(L)
+        L_inv = _np.linalg.inv(L)
+        L_inv_T = L_inv.swapaxes(-1, -2)
+        L_T = L.swapaxes(-1, -2)
+        sym_L_inv = 0.5 * (L_inv + L_inv_T)
+        dA = 0.5 * _np.matmul(_np.matmul(L_inv_T, copyltu(_np.matmul(L_T, dL))), L_inv)
+        return dA.reshape(shape)
+
+    def check_cholesky(L, data_np):
+        assert L.shape == data_np.shape
+        # catch error if numpy throws rank < 2
+        try:
+            L_expected = _np.linalg.cholesky(data_np)
+        except Exception as e:
+            print(data_np)
+            print(data_np.shape)
+            print(e)
+        else:
+            assert L.shape == L_expected.shape
+            assert_almost_equal(L.asnumpy(), L_expected, rtol=rtol, atol=atol)
+
+    shapes = [
+        (0, 0),
+        (1, 1),
+        (5, 5),
+        (6, 6),
+        (6, 6, 6),
+        (1, 0, 0),
+        (0, 1, 1),
+        (2, 3, 4, 4),
+    ]
+    dtypes = ['float32', 'float64']
+    for hybridize, dtype, shape in itertools.product([True, False], dtypes, shapes):
+        atol = rtol = 1e-2
+
+        test_cholesky = TestCholesky()
+        if hybridize:
+            test_cholesky.hybridize()
+        
+        # Numerical issue:
+        # When backpropagating through Cholesky decomposition, we need to compute the inverse
+        # of L according to dA = 0.5 * L**(-T) * copyLTU(L**T * dL) * L**(-1) where A = LL^T.
+        # The inverse is calculated by "trsm" method in CBLAS. When the data type is float32,
+        # this causes numerical instability. It happens when the matrix is ill-conditioned.
+        # In this example, the issue occurs frequently if the symmetric positive definite input
+        # matrix A is constructed by A = LL^T + \epsilon * I. A proper way of testing such
+        # operators involving numerically unstable operations is to use well-conditioned random
+        # matrices as input. Here we test Cholesky decomposition for FP32 and FP64 separately.
+        # See rocBLAS:
+        # https://github.com/ROCmSoftwarePlatform/rocBLAS/wiki/9.Numerical-Stability-in-TRSM
+
+        # generate symmetric PD matrices
+        if 0 in shape:
+            data_np = np.ones(shape)
+        else:
+            data_np_l = _np.random.uniform(-10., 10., shape)
+            if dtype == 'float32':
+                data_np_l_flat = data_np_l.reshape((-1, shape[-2], shape[-1]))
+            else:
+                data_np_l_flat = _np.tril(data_np_l.reshape((-1, shape[-2], shape[-1])))
+            for i in range(data_np_l_flat.shape[0]):
+                for j in range(data_np_l_flat.shape[-1]):
+                    if data_np_l_flat[i, j, j] < 0:
+                        data_np_l_flat[i, j, j] = -data_np_l_flat[i, j, j]
+                    elif data_np_l_flat[i, j, j] == 0:
+                        data_np_l_flat[i, j, j] = 2
+            data_np = _np.matmul(data_np_l_flat, data_np_l_flat.swapaxes(-1, -2))
+            data_np = data_np.reshape(shape)
+
+        # When dtype is np.FP32, truncation from FP64 to FP32 could also be a source of
+        # instability since the ground-truth gradient is computed using FP64 data.
+        data = np.array(data_np, dtype=dtype)
+        data.attach_grad()
+        with mx.autograd.record():
+            L = test_cholesky(data)
+
+        # check cholesky validity
+        check_cholesky(L, data_np)
+        # check backward. backward does not support empty input
+        if 0 not in L.shape:
+            mx.autograd.backward(L)
+            backward_expected = get_grad(L.asnumpy())
+            assert_almost_equal(data.grad.asnumpy(), backward_expected, rtol=rtol, atol=atol)
+        # check imperative once again
+        L = np.linalg.cholesky(data)
+        check_cholesky(L, data_np)
+
+
 @with_seed()
 @use_np
 def test_np_vstack():

From 1210c7dd83c949f62672940fd1490dd1b02cf0bb Mon Sep 17 00:00:00 2001
From: reminisce <wujun.nju@gmail.com>
Date: Fri, 1 Nov 2019 15:25:21 -0700
Subject: [PATCH 557/813] Fallback to official NumPy operators (#16698)

* Initial commit

Fix ndarray coversion in np mode

Add resize op

Delete

Add test

* Rebase

* Fix doc

* Fix lint

* Fix
---
 python/mxnet/__init__.py                      |  1 +
 python/mxnet/ndarray/numpy/_op.py             | 58 ++++++++++++-
 python/mxnet/numpy/multiarray.py              | 60 +++++++++++++-
 python/mxnet/numpy_dispatch_protocol.py       |  1 +
 python/mxnet/numpy_op_fallback.py             | 82 +++++++++++++++++++
 python/mxnet/operator.py                      | 56 +++++++++----
 python/mxnet/symbol/numpy/_symbol.py          | 58 ++++++++++++-
 src/operator/custom/custom.cc                 |  1 +
 .../unittest/test_numpy_interoperability.py   | 13 +++
 tests/python/unittest/test_numpy_op.py        | 35 ++++++++
 10 files changed, 338 insertions(+), 27 deletions(-)
 create mode 100644 python/mxnet/numpy_op_fallback.py

diff --git a/python/mxnet/__init__.py b/python/mxnet/__init__.py
index a4ba4f3b6093..088ffc1ea9d0 100644
--- a/python/mxnet/__init__.py
+++ b/python/mxnet/__init__.py
@@ -101,3 +101,4 @@
 
 from . import numpy_op_signature
 from . import numpy_dispatch_protocol
+from . import numpy_op_fallback
diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index c215159edb5e..9a106083a10e 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -38,7 +38,8 @@
            'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
            'around', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
            'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal',
-           'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory', 'may_share_memory', 'diff']
+           'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory', 'may_share_memory', 'diff', 'resize']
+
 
 @set_module('mxnet.ndarray.numpy')
 def zeros(shape, dtype=_np.float32, order='C', ctx=None):
@@ -5070,10 +5071,9 @@ def may_share_memory(a, b, max_work=None):
     return _npi.share_memory(a, b).item()
 
 
+@set_module('mxnet.ndarray.numpy')
 def diff(a, n=1, axis=-1, prepend=None, append=None):  # pylint: disable=redefined-outer-name
     r"""
-    numpy.diff(a, n=1, axis=-1, prepend=<no value>, append=<no value>)
-
     Calculate the n-th discrete difference along the given axis.
 
     Parameters
@@ -5116,3 +5116,55 @@ def diff(a, n=1, axis=-1, prepend=None, append=None):  # pylint: disable=redefin
     if (prepend or append):
         raise NotImplementedError('prepend and append options are not supported yet')
     return _npi.diff(a, n=n, axis=axis)
+
+
+@set_module('mxnet.ndarray.numpy')
+def resize(a, new_shape):
+    """
+    Return a new array with the specified shape.
+    If the new array is larger than the original array, then the new
+    array is filled with repeated copies of `a`.  Note that this behavior
+    is different from a.resize(new_shape) which fills with zeros instead
+    of repeated copies of `a`.
+
+    Parameters
+    ----------
+    a : ndarray
+        Array to be resized.
+    new_shape : int or tuple of int
+        Shape of resized array.
+
+    Returns
+    -------
+    reshaped_array : ndarray
+        The new array is formed from the data in the old array, repeated
+        if necessary to fill out the required number of elements.  The
+        data are repeated in the order that they are stored in memory.
+
+    See Also
+    --------
+    ndarray.resize : resize an array in-place.
+
+    Notes
+    -----
+    Warning: This functionality does **not** consider axes separately,
+    i.e. it does not apply interpolation/extrapolation.
+    It fills the return array with the required number of elements, taken
+    from `a` as they are laid out in memory, disregarding strides and axes.
+    (This is in case the new shape is smaller. For larger, see above.)
+    This functionality is therefore not suitable to resize images,
+    or data where each axis represents a separate and distinct entity.
+
+    Examples
+    --------
+    >>> a = np.array([[0, 1], [2, 3]])
+    >>> np.resize(a, (2, 3))
+    array([[0., 1., 2.],
+           [3., 0., 1.]])
+    >>> np.resize(a, (1, 4))
+    array([[0., 1., 2., 3.]])
+    >>> np.resize(a,(2, 4))
+    array([[0., 1., 2., 3.],
+           [0., 1., 2., 3.]])
+    """
+    return _npi.resize_fallback(a, new_shape=new_shape)
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 85bd2ac0e2b6..0580e139df31 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -44,6 +44,7 @@
 from ..context import current_context
 from ..ndarray import numpy as _mx_nd_np
 from ..ndarray.numpy import _internal as _npi
+from ..ndarray.ndarray import _storage_type
 
 __all__ = ['ndarray', 'empty', 'array', 'zeros', 'ones', 'full', 'add', 'subtract', 'multiply', 'divide',
            'mod', 'remainder', 'power', 'arctan2', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10',
@@ -56,7 +57,7 @@
            'blackman', 'flip', 'around', 'arctan2', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril',
            'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less',
            'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory',
-           'may_share_memory', 'diff']
+           'may_share_memory', 'diff', 'resize']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -120,6 +121,8 @@ def _reshape_view(a, *shape):
 # Have to use 0 as default value for stype since pylint does not allow
 # importing _STORAGE_TYPE_DEFAULT from ndarray.py.
 def _np_ndarray_cls(handle, writable=True, stype=0):
+    if stype == -1:
+        stype = _storage_type(handle)
     if stype != 0:
         raise ValueError('_np_ndarray_cls currently only supports default storage '
                          'type, while received stype = {}'.format(stype))
@@ -7061,10 +7064,9 @@ def may_share_memory(a, b, max_work=None):
     return _mx_nd_np.may_share_memory(a, b, max_work)
 
 
+@set_module('mxnet.numpy')
 def diff(a, n=1, axis=-1, prepend=None, append=None):  # pylint: disable=redefined-outer-name
     r"""
-    numpy.diff(a, n=1, axis=-1, prepend=<no value>, append=<no value>)
-
     Calculate the n-th discrete difference along the given axis.
 
     Parameters
@@ -7108,3 +7110,55 @@ def diff(a, n=1, axis=-1, prepend=None, append=None):  # pylint: disable=redefin
     if (prepend or append):
         raise NotImplementedError('prepend and append options are not supported yet')
     return _mx_nd_np.diff(a, n=n, axis=axis)
+
+
+@set_module('mxnet.numpy')
+def resize(a, new_shape):
+    """
+    Return a new array with the specified shape.
+    If the new array is larger than the original array, then the new
+    array is filled with repeated copies of `a`.  Note that this behavior
+    is different from a.resize(new_shape) which fills with zeros instead
+    of repeated copies of `a`.
+
+    Parameters
+    ----------
+    a : ndarray
+        Array to be resized.
+    new_shape : int or tuple of int
+        Shape of resized array.
+
+    Returns
+    -------
+    reshaped_array : ndarray
+        The new array is formed from the data in the old array, repeated
+        if necessary to fill out the required number of elements.  The
+        data are repeated in the order that they are stored in memory.
+
+    See Also
+    --------
+    ndarray.resize : resize an array in-place.
+
+    Notes
+    -----
+    Warning: This functionality does **not** consider axes separately,
+    i.e. it does not apply interpolation/extrapolation.
+    It fills the return array with the required number of elements, taken
+    from `a` as they are laid out in memory, disregarding strides and axes.
+    (This is in case the new shape is smaller. For larger, see above.)
+    This functionality is therefore not suitable to resize images,
+    or data where each axis represents a separate and distinct entity.
+
+    Examples
+    --------
+    >>> a = np.array([[0, 1], [2, 3]])
+    >>> np.resize(a, (2, 3))
+    array([[0., 1., 2.],
+           [3., 0., 1.]])
+    >>> np.resize(a, (1, 4))
+    array([[0., 1., 2., 3.]])
+    >>> np.resize(a,(2, 4))
+    array([[0., 1., 2., 3.],
+           [0., 1., 2., 3.]])
+    """
+    return _mx_nd_np.resize(a, new_shape)
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index 702ab38fa388..ae92b9885217 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -134,6 +134,7 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'shares_memory',
     'may_share_memory',
     'diff',
+    'resize',
 ]
 
 
diff --git a/python/mxnet/numpy_op_fallback.py b/python/mxnet/numpy_op_fallback.py
new file mode 100644
index 000000000000..b98a211c7169
--- /dev/null
+++ b/python/mxnet/numpy_op_fallback.py
@@ -0,0 +1,82 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Fallback-to-NumPy operator implementation."""
+
+from __future__ import absolute_import
+import functools
+import ast
+import numpy as np
+from . import operator
+from . import numpy as _mx_np  # pylint: disable=reimported
+from .util import np_array, use_np
+from .ndarray.numpy import _internal as _nd_npi
+from .symbol.numpy import _internal as _sym_npi
+
+
+def register(op_name, imperative=True, symbolic=True):
+    """Register operators that fallback to NumPy in modules
+    ``mxnet.ndarray.numpy._internal`` and ``mxnet.symbol.numpy._internal``."""
+    def _save_op(mod):
+        if hasattr(mod, op_name):
+            raise ValueError('Duplicate name {} found in module {}'.format(op_name, str(mod)))
+        op = functools.partial(mod.Custom, op_type=op_name)
+        setattr(mod, op_name, op)
+
+    def _register_helper(prop_cls):
+        with np_array():
+            prop_cls = operator.register(op_name)(prop_cls)
+        if imperative:
+            _save_op(_nd_npi)
+        if symbolic:
+            _save_op(_sym_npi)
+        return prop_cls
+
+    return _register_helper
+
+
+@use_np  # enforce np shape and array semantics for all the methods in this class
+class Resize(operator.CustomOp):
+    """Fallback to NumPy resize operator."""
+    def __init__(self, new_shape):
+        super(Resize, self).__init__()
+        self._new_shape = new_shape
+
+    def forward(self, is_train, req, in_data, out_data, aux):
+        out = np.resize(in_data[0].asnumpy(), self._new_shape)
+        self.assign(out_data[0], req[0], _mx_np.array(out, dtype=out.dtype, ctx=out_data[0].ctx))
+
+    def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
+        raise NotImplementedError('Operator resize does not support gradient computation')
+
+
+@register('resize_fallback')
+class ResizeProp(operator.CustomOpProp):
+    """Fallback resize operator properties."""
+    def __init__(self, new_shape):
+        super(ResizeProp, self).__init__(need_top_grad=True)
+        self._new_shape = ast.literal_eval(new_shape)
+
+    def list_arguments(self):
+        return ['a']
+
+    def infer_shape(self, in_shape):
+        out_shape = (self._new_shape,) if np.isscalar(self._new_shape) else self._new_shape
+        return (in_shape[0],), (out_shape,), ()
+
+    def create_operator(self, ctx, in_shapes, in_dtypes):
+        return Resize(self._new_shape)
diff --git a/python/mxnet/operator.py b/python/mxnet/operator.py
index 8d14a805ef56..3affa212bfd4 100644
--- a/python/mxnet/operator.py
+++ b/python/mxnet/operator.py
@@ -38,9 +38,12 @@
 from .ndarray.ndarray import _STORAGE_TYPE_UNDEFINED, _STORAGE_TYPE_DEFAULT
 from .ndarray.ndarray import _STORAGE_TYPE_CSR, _STORAGE_TYPE_ROW_SPARSE
 from .ndarray import _ndarray_cls
+from .numpy.multiarray import _np_ndarray_cls
+from .util import is_np_array
 
 c_int_p = POINTER(c_int)
 
+
 class PythonOp(object):
     """Base class for operators implemented in Python.
 
@@ -149,6 +152,7 @@ def need_top_grad(self):
         """
         return self.need_top_grad_
 
+
 class NumpyOp(PythonOp):
     """Base class for numpy operators. numpy operators allow parts
     of computation in symbolic graph to be writen in numpy. This feature
@@ -168,6 +172,7 @@ def get_symbol(self, *args, **kwargs):
         infer_functype = CFUNCTYPE(None, c_int, POINTER(c_int),
                                    POINTER(POINTER(mx_int)), c_void_p)
         list_functype = CFUNCTYPE(None, POINTER(POINTER(POINTER(c_char))), c_void_p)
+
         class NumpyOpInfo(Structure):
             """Structure that holds Callback information. Passed to NumpyOpProp"""
             _fields_ = [
@@ -182,6 +187,7 @@ class NumpyOpInfo(Structure):
                 ('p_list_outputs', c_void_p),
                 ('p_list_arguments', c_void_p),
                 ]
+
         def forward_entry(num_tensor, tensor_ptrs, tensor_dims,
                           tensor_shapes, tensor_tags, _):
             """C Callback for NumpyOp::Forward"""
@@ -235,7 +241,6 @@ def list_arguments_entry(out, _):
             ret = c_array(c_char_p, ret)
             out[0] = cast(ret, POINTER(POINTER(c_char)))
 
-
         self.info_ = NumpyOpInfo(fb_functype(forward_entry),
                                  fb_functype(backward_entry),
                                  infer_functype(infer_shape_entry),
@@ -252,6 +257,7 @@ def list_arguments_entry(out, _):
         PythonOp._ref_holder.append(self)
         return sym
 
+
 class NDArrayOp(PythonOp):
     """Base class for numpy operators. numpy operators allow parts
     of computation in symbolic graph to be writen in numpy. This feature
@@ -425,6 +431,7 @@ def declare_backward_dependency(self, out_grad, in_data, out_data):
         deps.extend(out_data)
         return deps
 
+
 class CustomOp(object):
     """Base class for operators implemented in python"""
     def __init__(self):
@@ -467,9 +474,16 @@ def assign(self, dst, req, src):
         if req == 'null':
             return
         elif req in ('write', 'inplace'):
-            dst[:] = src
+            if is_np_array():
+                dst[()] = src
+            else:
+                dst[:] = src
         elif req == 'add':
-            dst[:] += src
+            if is_np_array():
+                dst[()] += src
+            else:
+                dst[:] += src
+
 
 class CustomOpProp(object):
     """Base class for operator property class implemented in python.
@@ -673,6 +687,7 @@ def create_operator(self, ctx, in_shapes, in_dtypes):
         # pylint: disable=W0613
         return CustomOp()
 
+
 class _Registry(object):
     """CustomOp registry."""
     def __init__(self):
@@ -689,8 +704,10 @@ def inc(self):
         self.lock.release()
         return cur
 
+
 _registry = _Registry()
 
+
 def register(reg_name):
     """Register a subclass of CustomOpProp to the registry with name reg_name."""
     def do_register(prop_cls):
@@ -712,6 +729,7 @@ def do_register(prop_cls):
                                       POINTER(c_int), POINTER(c_int),
                                       POINTER(MXCallbackList), c_void_p)
         req_enum = ('null', 'write', 'inplace', 'add')
+        create_ndarray_fn = _np_ndarray_cls if is_np_array() else _ndarray_cls
 
         def creator(op_type, argc, keys, vals, ret):
             """internal function"""
@@ -760,7 +778,6 @@ def infer_shape_entry(num_tensor, tensor_dims,
                     return False
                 return True
 
-
             def infer_storage_type_backward_entry(num_tensor, tensor_stypes, tags, _):
                 # pylint: disable=C0301
                 """C Callback for CustomOpProp::InferStorageTypeBackward"""
@@ -975,13 +992,13 @@ def forward_entry(num_ndarray, ndarraies, tags, reqs, is_train, _):
                             tensors = [[] for i in range(5)]
                             for i in range(num_ndarray):
                                 if tags[i] == 1 or tags[i] == 4:
-                                    tensors[tags[i]].append(_ndarray_cls(cast(ndarraies[i],
-                                                                              NDArrayHandle),
-                                                                         writable=True))
+                                    tensors[tags[i]].append(
+                                        create_ndarray_fn(cast(ndarraies[i], NDArrayHandle), writable=True)
+                                    )
                                 else:
-                                    tensors[tags[i]].append(_ndarray_cls(cast(ndarraies[i],
-                                                                              NDArrayHandle),
-                                                                         writable=False))
+                                    tensors[tags[i]].append(
+                                        create_ndarray_fn(cast(ndarraies[i], NDArrayHandle), writable=False)
+                                    )
                             reqs = [req_enum[reqs[i]] for i in range(len(tensors[1]))]
                             with ctx:
                                 op.forward(is_train=is_train, req=reqs,
@@ -1011,15 +1028,15 @@ def backward_entry(num_ndarray, ndarraies, tags, reqs, is_train, _):
                                     # be set to default
                                     stype = _STORAGE_TYPE_DEFAULT
                                 if tags[i] == 2 or tags[i] == 4:
-                                    tensors[tags[i]].append(_ndarray_cls(cast(ndarraies[i],
-                                                                              NDArrayHandle),
-                                                                         writable=True,
-                                                                         stype=stype))
+                                    tensors[tags[i]].append(
+                                        create_ndarray_fn(cast(ndarraies[i], NDArrayHandle),
+                                                          writable=True, stype=stype)
+                                    )
                                 else:
-                                    tensors[tags[i]].append(_ndarray_cls(cast(ndarraies[i],
-                                                                              NDArrayHandle),
-                                                                         writable=False,
-                                                                         stype=stype))
+                                    tensors[tags[i]].append(
+                                        create_ndarray_fn(cast(ndarraies[i], NDArrayHandle),
+                                                          writable=False, stype=stype)
+                                    )
                             reqs = [req_enum[reqs[i]] for i in range(len(tensors[2]))]
                             with ctx:
                                 op.backward(req=reqs,
@@ -1100,6 +1117,7 @@ def delete_entry(_):
         return prop_cls
     return do_register
 
+
 register("custom_op")(CustomOpProp)
 
 
@@ -1119,8 +1137,10 @@ def get_all_registered_operators():
     mx_registered_operator_names = [py_str(plist[i]) for i in range(size.value)]
     return mx_registered_operator_names
 
+
 OperatorArguments = collections.namedtuple('OperatorArguments', ['narg', 'names', 'types'])
 
+
 def get_operator_arguments(op_name):
     """Given operator name, fetch operator arguments - number of arguments,
     argument names, argument types.
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index d3837d2bd1dd..1eab6ad0342a 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -40,7 +40,8 @@
            'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
            'around', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
            'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal',
-           'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'shares_memory', 'may_share_memory', 'diff']
+           'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'shares_memory', 'may_share_memory', 'diff',
+           'resize']
 
 
 def _num_outputs(sym):
@@ -4723,10 +4724,9 @@ def may_share_memory(a, b, max_work=None):
     return _npi.share_memory(a, b)
 
 
+@set_module('mxnet.symbol.numpy')
 def diff(a, n=1, axis=-1, prepend=None, append=None):  # pylint: disable=redefined-outer-name
     r"""
-    numpy.diff(a, n=1, axis=-1, prepend=<no value>, append=<no value>)
-
     Calculate the n-th discrete difference along the given axis.
 
     Parameters
@@ -4772,4 +4772,56 @@ def diff(a, n=1, axis=-1, prepend=None, append=None):  # pylint: disable=redefin
     return _npi.diff(a, n=n, axis=axis)
 
 
+@set_module('mxnet.symbol.numpy')
+def resize(a, new_shape):
+    """
+    Return a new array with the specified shape.
+    If the new array is larger than the original array, then the new
+    array is filled with repeated copies of `a`.  Note that this behavior
+    is different from a.resize(new_shape) which fills with zeros instead
+    of repeated copies of `a`.
+
+    Parameters
+    ----------
+    a : _Symbol
+        Array to be resized.
+    new_shape : int or tuple of int
+        Shape of resized array.
+
+    Returns
+    -------
+    reshaped_array : _Symbol
+        The new array is formed from the data in the old array, repeated
+        if necessary to fill out the required number of elements.  The
+        data are repeated in the order that they are stored in memory.
+
+    See Also
+    --------
+    ndarray.resize : resize an array in-place.
+
+    Notes
+    -----
+    Warning: This functionality does **not** consider axes separately,
+    i.e. it does not apply interpolation/extrapolation.
+    It fills the return array with the required number of elements, taken
+    from `a` as they are laid out in memory, disregarding strides and axes.
+    (This is in case the new shape is smaller. For larger, see above.)
+    This functionality is therefore not suitable to resize images,
+    or data where each axis represents a separate and distinct entity.
+
+    Examples
+    --------
+    >>> a = np.array([[0, 1], [2, 3]])
+    >>> np.resize(a, (2, 3))
+    array([[0., 1., 2.],
+           [3., 0., 1.]])
+    >>> np.resize(a, (1, 4))
+    array([[0., 1., 2., 3.]])
+    >>> np.resize(a,(2, 4))
+    array([[0., 1., 2., 3.],
+           [0., 1., 2., 3.]])
+    """
+    return _npi.resize_fallback(a, new_shape=new_shape)
+
+
 _set_np_symbol_class(_Symbol)
diff --git a/src/operator/custom/custom.cc b/src/operator/custom/custom.cc
index 441405f64a3a..9130830b900c 100644
--- a/src/operator/custom/custom.cc
+++ b/src/operator/custom/custom.cc
@@ -537,6 +537,7 @@ inline bool InferStorageType(const nnvm::NodeAttrs& attrs, const int dev_mask,
 }
 
 NNVM_REGISTER_OP(Custom)
+.add_alias("_npi_Custom")
 .describe(R"code(Apply a custom operator implemented in a frontend language (like Python).
 
 Custom operators should override required methods like `forward` and `backward`.
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index 5a5f0ca3ee68..d2620cf8551e 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -1156,6 +1156,18 @@ def _add_workload_diff():
         OpArgMngr.add_workload('diff', x, n=n)
 
 
+def _add_workload_resize():
+    OpArgMngr.add_workload('resize', np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.int32), (5, 1))
+    OpArgMngr.add_workload('resize', np.eye(3), 3)
+    OpArgMngr.add_workload('resize', np.ones(1), ())
+    OpArgMngr.add_workload('resize', np.ones(()), (1,))
+    OpArgMngr.add_workload('resize', np.eye(3), (3, 2, 1))
+    OpArgMngr.add_workload('resize', np.eye(3), (2, 3, 3))
+    OpArgMngr.add_workload('resize', np.ones(10), 15)
+    OpArgMngr.add_workload('resize', np.zeros((10, 0)), (0, 10))
+    OpArgMngr.add_workload('resize', np.zeros((10, 0)), (0, 100))
+
+
 @use_np
 def _prepare_workloads():
     array_pool = {
@@ -1265,6 +1277,7 @@ def _prepare_workloads():
     _add_workload_less(array_pool)
     _add_workload_less_equal(array_pool)
     _add_workload_diff()
+    _add_workload_resize()
 
 
 _prepare_workloads()
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 9c3988fad386..3b4b4b6491fc 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -4113,6 +4113,41 @@ def g(data):
         assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol)
 
 
+@with_seed()
+@use_np
+def test_np_resize():
+    class TestResize(HybridBlock):
+        def __init__(self, new_shape):
+            super(TestResize, self).__init__()
+            self._new_shape = new_shape
+
+        def hybrid_forward(self, F, x, *args, **kwargs):
+            return F.np.resize(x, self._new_shape)
+
+    dtypes = [np.int8, np.uint8, np.int32, np.int64, np.float16, np.float32, np.float64, np.bool_]
+    shape_config = [
+        [(), (2, 3)],
+        [(2, 3), (2,)],
+        [(2, 3), 2],
+        [(2, 0, 1), (2, 2)],
+        [(2, 0, 1), (3, 4, 5)],
+        [((1,)), ()],
+    ]
+    flags = [True, False]
+    for dtype, shape_pair, hybridize in itertools.product(dtypes, shape_config, flags):
+        a = np.random.uniform(low=0, high=100, size=shape_pair[0], dtype='float64').astype(dtype)
+        test = TestResize(shape_pair[1])
+        if hybridize:
+            test.hybridize()
+        ret = test(a)
+        expected_ret = _np.resize(a.asnumpy(), shape_pair[1])
+        assert_almost_equal(ret.asnumpy(), expected_ret, atol=1e-5, rtol=1e-5, use_broadcast=False)
+
+        # check imperative again
+        ret = np.resize(a, shape_pair[1])
+        assert_almost_equal(ret.asnumpy(), expected_ret, atol=1e-5, rtol=1e-5, use_broadcast=False)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 904ba00b4896c70065b7e66f684f1f1a22689058 Mon Sep 17 00:00:00 2001
From: Carin Meier <cmeier@gigasquidsoftware.com>
Date: Sat, 2 Nov 2019 08:39:42 -0400
Subject: [PATCH 558/813] improve example docs with installation instructions
 (#16625)

---
 contrib/clojure-package/examples/bert/README.md       |  7 +++++++
 .../examples/cnn-text-classification/README.md        |  7 +++++++
 contrib/clojure-package/examples/gan/README.md        |  7 +++++++
 .../examples/imclassification/README.md               | 11 +++++++++++
 .../clojure-package/examples/multi-label/README.md    |  9 +++++++++
 .../clojure-package/examples/neural-style/README.md   |  7 +++++++
 .../examples/pre-trained-models/README.md             |  7 +++++++
 contrib/clojure-package/examples/profiler/README.md   | 11 ++++++++++-
 contrib/clojure-package/examples/rnn/README.md        |  7 +++++++
 contrib/clojure-package/examples/tutorial/README.md   |  9 +++++++++
 .../clojure-package/examples/visualization/README.md  | 10 ++++++++++
 11 files changed, 91 insertions(+), 1 deletion(-)

diff --git a/contrib/clojure-package/examples/bert/README.md b/contrib/clojure-package/examples/bert/README.md
index 7285773e9706..0681a0694fc8 100644
--- a/contrib/clojure-package/examples/bert/README.md
+++ b/contrib/clojure-package/examples/bert/README.md
@@ -105,6 +105,13 @@ It will fine tune the base bert model for use in a classification task for 3 epo
 
 ### Setup Guide
 
+
+## Installation
+
+Before you run this example, make sure that you have the clojure package installed.
+In the main clojure package directory, do `lein install`. Then you can run
+`lein install` in this directory.
+
 #### Step 1: Download the model
 
 For this tutorial, you can get the model and vocabulary by running following bash file. This script will use `wget` to download these artifacts from AWS S3.
diff --git a/contrib/clojure-package/examples/cnn-text-classification/README.md b/contrib/clojure-package/examples/cnn-text-classification/README.md
index 8f8e6200ec7c..152ee4f10189 100644
--- a/contrib/clojure-package/examples/cnn-text-classification/README.md
+++ b/contrib/clojure-package/examples/cnn-text-classification/README.md
@@ -26,6 +26,13 @@ and `rt-polarity.pos` must be put in a directory. For example, `data/mr-data/rt-
 You also must download the glove word embeddings. The suggested one to use is the smaller 50 dimension one
 `glove.6B.50d.txt` which is contained in the download file here: [GloVe](https://nlp.stanford.edu/projects/glove/)
 
+
+## Installation
+
+Before you run this example, make sure that you have the clojure package installed.
+In the main clojure package directory, do `lein install`. Then you can run
+`lein install` in this directory.
+
 ## Usage
 
 You can run through the repl with
diff --git a/contrib/clojure-package/examples/gan/README.md b/contrib/clojure-package/examples/gan/README.md
index 9c4bea8ed586..30ecafb266db 100644
--- a/contrib/clojure-package/examples/gan/README.md
+++ b/contrib/clojure-package/examples/gan/README.md
@@ -19,6 +19,13 @@
 
 This is an example of how to do a GAN with the MNIST data
 
+## Installation
+
+Before you run this example, make sure that you have the clojure package installed.
+In the main clojure package directory, do `lein install`. Then you can run
+`lein install` in this directory.
+
+
 ## Usage
 
 Do `lein run` and the images generated will be in the `results` directory. The gout* images are the ones generated, the diff* images are the visualization of the input gradient different fed to the generator
diff --git a/contrib/clojure-package/examples/imclassification/README.md b/contrib/clojure-package/examples/imclassification/README.md
index 06ae9461816e..d5ab99ced90d 100644
--- a/contrib/clojure-package/examples/imclassification/README.md
+++ b/contrib/clojure-package/examples/imclassification/README.md
@@ -23,6 +23,17 @@ There is an example of the high level training api fit and also how to use multi
 
 To see more examples of how to use different parts of the module api look at the module example
 
+An example of using the profiler.
+
+## Installation
+
+Before you run this example, make sure that you have the clojure package installed.
+In the main clojure package directory, do `lein install`. Then you can run
+`lein install` in this directory.
+
+
+## Usage
+
 To run the example you must do
 
 * `lein install` in the root of the main project directory
diff --git a/contrib/clojure-package/examples/multi-label/README.md b/contrib/clojure-package/examples/multi-label/README.md
index aebb83be985d..a2ea7e01e686 100644
--- a/contrib/clojure-package/examples/multi-label/README.md
+++ b/contrib/clojure-package/examples/multi-label/README.md
@@ -21,6 +21,15 @@ This is a quick example of doing multi-label classification.
 It involves using a proxy to implement the DataIter to make a custom
 data iterator for MNIST
 
+
+## Installation
+
+Before you run this example, make sure that you have the clojure package installed.
+In the main clojure package directory, do `lein install`. Then you can run
+`lein install` in this directory.
+
+## Usage
+
 To run
 `lein run`. This will execute the cpu version.
 
diff --git a/contrib/clojure-package/examples/neural-style/README.md b/contrib/clojure-package/examples/neural-style/README.md
index 209e56ae20d2..bbf1c737be84 100644
--- a/contrib/clojure-package/examples/neural-style/README.md
+++ b/contrib/clojure-package/examples/neural-style/README.md
@@ -19,6 +19,13 @@
 
 An example of neural style transfer
 
+
+## Installation
+
+Before you run this example, make sure that you have the clojure package installed.
+In the main clojure package directory, do `lein install`. Then you can run
+`lein install` in this directory.
+
 ## Usage
 
 use the `download.sh` script to get the params file and the input and output file
diff --git a/contrib/clojure-package/examples/pre-trained-models/README.md b/contrib/clojure-package/examples/pre-trained-models/README.md
index c6201adb2bbe..6a3b25e3388d 100644
--- a/contrib/clojure-package/examples/pre-trained-models/README.md
+++ b/contrib/clojure-package/examples/pre-trained-models/README.md
@@ -21,6 +21,13 @@ This shows examples of how to use the pretrained models. MXNet comes with a numb
 https://mxnet.apache.org/model_zoo/index.html
 
 
+## Installation
+
+Before you run this example, make sure that you have the clojure package installed.
+In the main clojure package directory, do `lein install`. Then you can run
+`lein install` in this directory.
+
+
 ## Predict Image from pretrained models
 
 From the example on https://mxnet.apache.org/tutorials/python/predict_image.html
diff --git a/contrib/clojure-package/examples/profiler/README.md b/contrib/clojure-package/examples/profiler/README.md
index 8ab37d713c09..162d012cf5dc 100644
--- a/contrib/clojure-package/examples/profiler/README.md
+++ b/contrib/clojure-package/examples/profiler/README.md
@@ -17,7 +17,16 @@
 
 # profiler
 
-An example of using the profiler. 
+An example of using the profiler.
+
+
+## Installation
+
+Before you run this example, make sure that you have the clojure package installed.
+In the main clojure package directory, do `lein install`. Then you can run
+`lein install` in this directory.
+
+## Usage
 
 To run use `lein run`
 A file will be generated in the directory afterwards `profile-matmul-20iter.json`
diff --git a/contrib/clojure-package/examples/rnn/README.md b/contrib/clojure-package/examples/rnn/README.md
index 640b63d43f10..1cedc179612a 100644
--- a/contrib/clojure-package/examples/rnn/README.md
+++ b/contrib/clojure-package/examples/rnn/README.md
@@ -20,6 +20,13 @@
 
 Demonstration of LSTM RNN trainined using Obamas text
 
+## Installation
+
+Before you run this example, make sure that you have the clojure package installed.
+In the main clojure package directory, do `lein install`. Then you can run
+`lein install` in this directory.
+
+
 ## Usage
 
 
diff --git a/contrib/clojure-package/examples/tutorial/README.md b/contrib/clojure-package/examples/tutorial/README.md
index cd33529b49c5..27a9f54155ae 100644
--- a/contrib/clojure-package/examples/tutorial/README.md
+++ b/contrib/clojure-package/examples/tutorial/README.md
@@ -17,6 +17,15 @@
 
 # tutorial
 
+
+## Installation
+
+Before you run this example, make sure that you have the clojure package installed.
+In the main clojure package directory, do `lein install`. Then you can run
+`lein install` in this directory.
+
+## Usage
+
 Tutorials are based on the Scala api examples here https://mxnet.apache.org/api/scala/ndarray.html
 
 Start with ndarray then move onto symbol and module
diff --git a/contrib/clojure-package/examples/visualization/README.md b/contrib/clojure-package/examples/visualization/README.md
index 0638d9c49c5d..c9eb75f6d496 100644
--- a/contrib/clojure-package/examples/visualization/README.md
+++ b/contrib/clojure-package/examples/visualization/README.md
@@ -17,5 +17,15 @@
 
 # visualization
 
+
+
+## Installation
+
+Before you run this example, make sure that you have the clojure package installed.
+In the main clojure package directory, do `lein install`. Then you can run
+`lein install` in this directory.
+
+## Usage
+
 Run `lein run` to have a sample network visualization printed for you
 "testviz.pdf"

From 7fde59a02933c11bcd43e3646bf08988272f470a Mon Sep 17 00:00:00 2001
From: Kellen Sunderland <kellen.sunderland@gmail.com>
Date: Sat, 2 Nov 2019 07:17:26 -0700
Subject: [PATCH 559/813] Fix formatting and typos in CD README.md (#16703)

---
 cd/README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/cd/README.md b/cd/README.md
index a8e3fb794daf..a3dc0387a4b3 100644
--- a/cd/README.md
+++ b/cd/README.md
@@ -46,12 +46,12 @@ Currently, 10 variants are supported:
 
 ### CD Pipeline Job
 
-The [CD pipeline job](Jenkinsfile_cd_pipeline) take three parameters:
+The [CD pipeline job](Jenkinsfile_cd_pipeline) take two parameters:
 
  * **RELEASE_BUILD**: Flags the run as a *release build*. The underlying jobs can then use this environment variable to disambiguate between nightly and release builds. Defaults to *false*.
  * **MXNET_VARIANTS**: A comma separated list of variants to build. Defaults to *all* variants.
 
-This job defines and executes the CD pipeline. For example, first publish the MXNet library, then, in parallel, execute the python and maven releases. Every step of the pipeline executes a trigger for a release job](Jenkinsfile_release_job).
+This job defines and executes the CD pipeline. For example, first publish the MXNet library, then, in parallel, execute the python and maven releases. Every step of the pipeline executes a trigger for a [release job](Jenkinsfile_release_job).
 
 ### Release Job
 
@@ -195,4 +195,4 @@ def test(mxnet_variant) {
 
 Examples:
 
-Both the [statically linked libmxnet](mxnet_lib/static/Jenkins_pipeline.groovy) and [dynamically linked libmxnet](mxnet_lib/dynamic/Jenkins_pipeline.groovy) pipelines have long running compilation and testing stages that **do not** require specialized/expensive hardware (e.g. GPUs). Therefore, as mush as possible, it is important to run each stage in on its own node, and design the pipeline to spend the least amount of time possible on expensive hardware. E.g. for GPU builds, only run GPU tests on GPU instances, all other stages can be executed on CPU nodes.
\ No newline at end of file
+Both the [statically linked libmxnet](mxnet_lib/static/Jenkins_pipeline.groovy) and [dynamically linked libmxnet](mxnet_lib/dynamic/Jenkins_pipeline.groovy) pipelines have long running compilation and testing stages that **do not** require specialized/expensive hardware (e.g. GPUs). Therefore, as much as possible, it is important to run each stage in on its own node, and design the pipeline to spend the least amount of time possible on expensive hardware. E.g. for GPU builds, only run GPU tests on GPU instances, all other stages can be executed on CPU nodes.

From be92cc63ce20f354ff9f6aa6f7af0ad558cc5fd3 Mon Sep 17 00:00:00 2001
From: Carin Meier <cmeier@gigasquidsoftware.com>
Date: Sat, 2 Nov 2019 14:29:06 -0400
Subject: [PATCH 560/813] change the wording and log level to be more in line
 with the general use (#16626)

case of loading from the jar
---
 .../core/src/main/scala/org/apache/mxnet/Base.scala | 13 ++++++-------
 1 file changed, 6 insertions(+), 7 deletions(-)

diff --git a/scala-package/core/src/main/scala/org/apache/mxnet/Base.scala b/scala-package/core/src/main/scala/org/apache/mxnet/Base.scala
index 001bd04d2c95..c3378ec4074c 100644
--- a/scala-package/core/src/main/scala/org/apache/mxnet/Base.scala
+++ b/scala-package/core/src/main/scala/org/apache/mxnet/Base.scala
@@ -62,18 +62,17 @@ private[mxnet] object Base {
   // The primitives currently supported for NDArray operations
   val MX_PRIMITIVES = new Group ((Double, Float))
 
+
+  /* Find the native libray either on the path or copy it from
+   * the jar in the dependency
+   * jar into a temp directory and load it
+   */
   try {
     try {
       tryLoadLibraryOS("mxnet-scala")
     } catch {
       case e: UnsatisfiedLinkError =>
-        logger.warn("MXNet Scala native library not found in path. " +
-          "Copying native library from the archive. " +
-          "Consider installing the library somewhere in the path " +
-          "(for Windows: PATH, for Linux: LD_LIBRARY_PATH), " +
-          "or specifying by Java cmd option -Djava.library.path=[lib path].")
-        logger.warn("LD_LIBRARY_PATH=" + System.getenv("LD_LIBRARY_PATH"))
-        logger.warn("java.library.path=" + System.getProperty("java.library.path"))
+        logger.info("Copying and loading native library from the jar archive")
         NativeLibraryLoader.loadLibrary("mxnet-scala")
     }
   } catch {

From 27a8fd5ab88ff9ed53536623ffd3e34ad16c5417 Mon Sep 17 00:00:00 2001
From: rongzha1 <rong.a.zhang@intel.com>
Date: Sun, 3 Nov 2019 08:43:07 +0800
Subject: [PATCH 561/813] MKLDNN FC : Add error info when mkldnn fc bias
 dimension is wrong (#16692)

* add fail info for mkldnn fc

* fix lint
---
 src/operator/nn/mkldnn/mkldnn_fully_connected.cc | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/operator/nn/mkldnn/mkldnn_fully_connected.cc b/src/operator/nn/mkldnn/mkldnn_fully_connected.cc
index 1e7f879c5322..1403cd114201 100644
--- a/src/operator/nn/mkldnn/mkldnn_fully_connected.cc
+++ b/src/operator/nn/mkldnn/mkldnn_fully_connected.cc
@@ -86,6 +86,8 @@ mkldnn::inner_product_forward::primitive_desc GetFCFwdImpl(
   };
 
   if (bias) {
+    if ((*bias).shape().ndim() != 1)
+      LOG(FATAL) << "Unexpected shape for bias " << (*bias).shape();
     auto bias_md = GetMemDesc(*bias);
     mkldnn::inner_product_forward::desc desc(propagation,
         data_md, weight_md, bias_md, out_md);

From e139442461b4f82dc149fdc7dc6e009a348f4d9c Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Sat, 2 Nov 2019 22:15:55 -0700
Subject: [PATCH 562/813] support mixed-precision true_divide (#16711)

---
 src/common/utils.h                            |  36 +++
 src/operator/leaky_relu-inl.h                 |   3 +-
 src/operator/mshadow_op.h                     |  40 +++
 src/operator/mxnet_op.h                       | 113 +++++++
 src/operator/nn/dropout-inl.h                 |   6 +-
 src/operator/nn/softmax-inl.h                 |   6 +-
 src/operator/numpy/np_true_divide-inl.h       | 284 +++++++++++++++---
 src/operator/numpy/np_true_divide.cc          |  51 +++-
 src/operator/numpy/np_true_divide.cu          |   2 +-
 .../tensor/elemwise_binary_broadcast_op.h     |  57 +++-
 .../tensor/elemwise_binary_scalar_op.h        |   2 +-
 tests/python/unittest/test_numpy_op.py        |  24 +-
 12 files changed, 549 insertions(+), 75 deletions(-)

diff --git a/src/common/utils.h b/src/common/utils.h
index 2b4b821a1835..b919cb301dff 100644
--- a/src/common/utils.h
+++ b/src/common/utils.h
@@ -842,6 +842,42 @@ inline bool is_float(const int dtype) {
   return dtype == mshadow::kFloat32 || dtype == mshadow::kFloat64 || dtype == mshadow::kFloat16;
 }
 
+inline int more_precise_type(const int type1, const int type2) {
+  if (type1 == type2) return type1;
+  if (is_float(type1) && is_float(type2)) {
+    if (type1 == mshadow::kFloat64 || type2 == mshadow::kFloat64) {
+      return mshadow::kFloat64;
+    }
+    if (type1 == mshadow::kFloat32 || type2 == mshadow::kFloat32) {
+      return mshadow::kFloat32;
+    }
+    return mshadow::kFloat16;
+  } else if (is_float(type1) || is_float(type2)) {
+    return is_float(type1) ? type1 : type2;
+  }
+  if (type1 == mshadow::kInt64 || type2 == mshadow::kInt64) {
+    return mshadow::kInt64;
+  }
+  if (type1 == mshadow::kInt32 || type2 == mshadow::kInt32) {
+    return mshadow::kInt32;
+  }
+  CHECK(!((type1 == mshadow::kUint8 && type2 == mshadow::kInt8) ||
+          (type1 == mshadow::kInt8 && type2 == mshadow::kUint8)))
+    << "1 is UInt8 and 1 is Int8 should not get here";
+  if (type1 == mshadow::kUint8 || type2 == mshadow::kUint8) {
+    return mshadow::kUint8;
+  }
+  return mshadow::kInt8;
+}
+
+inline int np_binary_out_type(const int type1, const int type2) {
+  if ((type1 == mshadow::kUint8 && type2 == mshadow::kInt8) ||
+      (type1 == mshadow::kInt8 && type2 == mshadow::kUint8)) {
+    return mshadow::kInt32;
+  }
+  return more_precise_type(type1, type2);
+}
+
 }  // namespace common
 }  // namespace mxnet
 #endif  // MXNET_COMMON_UTILS_H_
diff --git a/src/operator/leaky_relu-inl.h b/src/operator/leaky_relu-inl.h
index d73fa1be54a4..3d81cfc0d967 100644
--- a/src/operator/leaky_relu-inl.h
+++ b/src/operator/leaky_relu-inl.h
@@ -134,8 +134,7 @@ class LeakyReLUOp : public Operator {
             mshadow::Shape<NDim> oshape = new_oshape.get<NDim>();
             mshadow::Shape<NDim> lstride = mxnet_op::calc_stride(new_lshape.get<NDim>());
             mshadow::Shape<NDim> rstride = mxnet_op::calc_stride(new_rshape.get<NDim>());
-            mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, DType, DType,
-                                                               mshadow_op::xelu>, xpu>::
+            mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, mshadow_op::xelu>, xpu>::
             template LaunchEx(s, new_oshape.Size(), req[leakyrelu::kOut], lstride, rstride, oshape,
             in_data[leakyrelu::kData].dptr<DType>(), in_data[leakyrelu::kGamma].dptr<DType>(),
             out_data[leakyrelu::kOut].dptr<DType>());
diff --git a/src/operator/mshadow_op.h b/src/operator/mshadow_op.h
index c5a2b1308c73..1ece97b0efd8 100644
--- a/src/operator/mshadow_op.h
+++ b/src/operator/mshadow_op.h
@@ -132,6 +132,26 @@ struct true_divide : public mxnet_op::tunable  {
   MSHADOW_XINLINE static float Map(DType a, DType b) {
     return static_cast<float>(a) / static_cast<float>(b);
   }
+
+#ifndef _WIN32
+  template<typename DType,
+           typename std::enable_if<std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static mshadow::half::half_t Map(DType a, mshadow::half::half_t b) {
+    return static_cast<mshadow::half::half_t>(a) / b;
+  }
+
+  template<typename DType,
+           typename std::enable_if<std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static float Map(DType a, float b) {
+    return static_cast<float>(a) / b;
+  }
+
+  template<typename DType,
+           typename std::enable_if<std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static double Map(DType a, double b) {
+    return static_cast<double>(a) / b;
+  }
+#endif
 };
 
 struct rtrue_divide : public mxnet_op::tunable  {
@@ -146,6 +166,26 @@ struct rtrue_divide : public mxnet_op::tunable  {
   MSHADOW_XINLINE static float Map(DType a, DType b) {
     return static_cast<float>(b) / static_cast<float>(a);
   }
+
+#ifndef _WIN32
+  template<typename DType,
+           typename std::enable_if<std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static mshadow::half::half_t Map(DType a, mshadow::half::half_t b) {
+    return b / static_cast<mshadow::half::half_t>(a);
+  }
+
+  template<typename DType,
+           typename std::enable_if<std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static float Map(DType a, float b) {
+    return b / static_cast<float>(a);
+  }
+
+  template<typename DType,
+           typename std::enable_if<std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static double Map(DType a, double b) {
+    return b / static_cast<double>(a);
+  }
+#endif
 };
 
 MXNET_BINARY_MATH_OP_NC(left, a);
diff --git a/src/operator/mxnet_op.h b/src/operator/mxnet_op.h
index 91478660a123..5d297a547c8f 100644
--- a/src/operator/mxnet_op.h
+++ b/src/operator/mxnet_op.h
@@ -471,6 +471,69 @@ struct AccType<mshadow::half::half_t> {
       {__VA_ARGS__}                                        \
     }                                                      \
     break;                                                 \
+  case mshadow::kBool:                                     \
+    {                                                      \
+      typedef bool DType;                                  \
+      {__VA_ARGS__}                                        \
+    }                                                      \
+    break;                                                 \
+  default:                                                 \
+    LOG(FATAL) << "Unknown type enum " << type;            \
+  }
+
+#define MXNET_INT32_INT64_TYPE_SWITCH(type, DType, ...)\
+  switch (type) {                                          \
+  case mshadow::kFloat32:                                  \
+    {                                                      \
+      typedef float DType;                                 \
+      LOG(FATAL) << "This operation only support "         \
+                    "integer types, not float32";          \
+    }                                                      \
+    break;                                                 \
+  case mshadow::kFloat64:                                  \
+    {                                                      \
+      typedef double DType;                                \
+      LOG(FATAL) << "This operation only support "         \
+                    "integer types, not float64";          \
+    }                                                      \
+    break;                                                 \
+  case mshadow::kFloat16:                                  \
+    {                                                      \
+      typedef mshadow::half::half_t DType;                 \
+      LOG(FATAL) << "This operation only support "         \
+                    "integer types, not float16";          \
+    }                                                      \
+    break;                                                 \
+  case mshadow::kUint8:                                    \
+    {                                                      \
+      LOG(FATAL) << "This operation only support "         \
+                    "integer types, not uint8";            \
+    }                                                      \
+    break;                                                 \
+  case mshadow::kInt8:                                     \
+    {                                                      \
+      LOG(FATAL) << "This operation only support "         \
+                    "integer types, not int8";             \
+    }                                                      \
+    break;                                                 \
+  case mshadow::kInt32:                                    \
+    {                                                      \
+      typedef int32_t DType;                               \
+      {__VA_ARGS__}                                        \
+    }                                                      \
+    break;                                                 \
+  case mshadow::kInt64:                                    \
+    {                                                      \
+      typedef int64_t DType;                               \
+      {__VA_ARGS__}                                        \
+    }                                                      \
+    break;                                                 \
+  case mshadow::kBool:                                     \
+    {                                                      \
+      LOG(FATAL) << "This operation only support "         \
+                    "integer types, not bool";             \
+    }                                                      \
+    break;                                                 \
   default:                                                 \
     LOG(FATAL) << "Unknown type enum " << type;            \
   }
@@ -783,6 +846,56 @@ struct op_with_req {
     KERNEL_ASSIGN(out[i], req, OP::Map(in[i], value));
   }
 
+#ifndef _WIN32
+  /*! \brief inputs are two tensors with a half_t output tensor */
+  template<typename DType,
+           typename std::enable_if<std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static void Map(index_t i,
+                                  mshadow::half::half_t *out,
+                                  const DType *lhs,
+                                  const mshadow::half::half_t *rhs) {
+    KERNEL_ASSIGN(out[i], req, OP::Map(lhs[i], rhs[i]));
+  }
+
+  /*! \brief inputs are two tensors with a float output tensor */
+  template<typename DType,
+           typename std::enable_if<std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static void Map(index_t i, float *out, const DType *lhs, const float *rhs) {
+    KERNEL_ASSIGN(out[i], req, OP::Map(lhs[i], rhs[i]));
+  }
+
+  /*! \brief inputs are two tensors with a double output tensor */
+  template<typename DType,
+           typename std::enable_if<std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static void Map(index_t i, double *out, const DType *lhs, const double *rhs) {
+    KERNEL_ASSIGN(out[i], req, OP::Map(lhs[i], rhs[i]));
+  }
+
+  /*! \brief inputs are two tensors with a half_t output tensor */
+  template<typename DType,
+           typename std::enable_if<std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static void Map(index_t i,
+                                  mshadow::half::half_t *out,
+                                  const DType *lhs,
+                                  const mshadow::half::half_t value) {
+    KERNEL_ASSIGN(out[i], req, OP::Map(lhs[i], value));
+  }
+
+  /*! \brief inputs are two tensors with a float output tensor */
+  template<typename DType,
+           typename std::enable_if<std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static void Map(index_t i, float *out, const DType *lhs, const float value) {
+    KERNEL_ASSIGN(out[i], req, OP::Map(lhs[i], value));
+  }
+
+  /*! \brief inputs are two tensors with a double output tensor */
+  template<typename DType,
+           typename std::enable_if<std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static void Map(index_t i, double *out, const DType *lhs, const double value) {
+    KERNEL_ASSIGN(out[i], req, OP::Map(lhs[i], value));
+  }
+#endif
+
   /*! \brief inputs are two tensors with a float output tensor */
   template<typename DType,
            typename std::enable_if<std::is_integral<DType>::value, int>::type = 0>
diff --git a/src/operator/nn/dropout-inl.h b/src/operator/nn/dropout-inl.h
index 61239d33800c..1eff5cd8591d 100644
--- a/src/operator/nn/dropout-inl.h
+++ b/src/operator/nn/dropout-inl.h
@@ -394,8 +394,7 @@ class DropoutOp {
               mshadow::Shape<NDim> oshape = new_oshape.get<NDim>();
               mshadow::Shape<NDim> lstride = mxnet_op::calc_stride(new_lshape.get<NDim>());
               mshadow::Shape<NDim> rstride = mxnet_op::calc_stride(new_rshape.get<NDim>());
-              mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, DType, DType,
-                               mshadow_op::mul>, xpu>::
+              mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, mshadow_op::mul>, xpu>::
               template LaunchEx(s, new_oshape.Size(), req[dropout::kOut],
               lstride, rstride, oshape,
               in.dptr<DType>(),
@@ -463,8 +462,7 @@ class DropoutOp {
             mshadow::Shape<NDim> oshape = new_oshape.get<NDim>();
             mshadow::Shape<NDim> lstride = mxnet_op::calc_stride(new_lshape.get<NDim>());
             mshadow::Shape<NDim> rstride = mxnet_op::calc_stride(new_rshape.get<NDim>());
-            mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, DType, DType,
-                             mshadow_op::mul>, xpu>::
+            mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, mshadow_op::mul>, xpu>::
             template LaunchEx(s, new_oshape.Size(), req[0], lstride, rstride, oshape,
             grad.dptr<DType>(), mask.dptr<DType>(), gdata.dptr<DType>());
           });
diff --git a/src/operator/nn/softmax-inl.h b/src/operator/nn/softmax-inl.h
index 601a0526650c..89da570c133b 100644
--- a/src/operator/nn/softmax-inl.h
+++ b/src/operator/nn/softmax-inl.h
@@ -790,7 +790,7 @@ void SoftmaxCompute(const nnvm::NodeAttrs& attrs,
           << "Mask needs to be provided when using softmax with use_length=True.";
         type = inputs[1].type_flag_;
       }
-      MXNET_INT_TYPE_SWITCH(type, IType, {
+      MXNET_INT32_INT64_TYPE_SWITCH(type, IType, {
           IType* mask_ptr = nullptr;
           if (param.use_length.value()) {
             mask_ptr = inputs[1].dptr<IType>();
@@ -834,7 +834,7 @@ void SoftmaxGradCompute(const nnvm::NodeAttrs& attrs,
                         const std::vector<TBlob>& outputs) {
   using namespace mxnet_op;
   if (softmax_use_length(attrs)) {
-    MXNET_INT_TYPE_SWITCH(inputs[2].type_flag_, IType, {
+    MXNET_INT32_INT64_TYPE_SWITCH(inputs[2].type_flag_, IType, {
       if (req[1] != kNullOp) {
         mxnet_op::Kernel<mxnet_op::set_zero, xpu>::Launch(
           ctx.get_stream<xpu>(), outputs[1].Size(), outputs[1].dptr<IType>());
@@ -856,7 +856,7 @@ void SoftmaxGradCompute(const nnvm::NodeAttrs& attrs,
   MXNET_REAL_ACC_TYPE_SWITCH(inputs[0].type_flag_, OType, AType, {
     MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, DType, {
       MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
-        MXNET_INT_TYPE_SWITCH(itype, IType, {
+        MXNET_INT32_INT64_TYPE_SWITCH(itype, IType, {
           IType * length_ptr = nullptr;
           if (softmax_use_length(attrs)) {
             length_ptr = inputs[2].dptr<IType>();
diff --git a/src/operator/numpy/np_true_divide-inl.h b/src/operator/numpy/np_true_divide-inl.h
index cc74e19aef8f..0bc60a08803e 100644
--- a/src/operator/numpy/np_true_divide-inl.h
+++ b/src/operator/numpy/np_true_divide-inl.h
@@ -43,30 +43,42 @@ void TrueDivideScalarCompute(const nnvm::NodeAttrs &attrs,
   CHECK_EQ(outputs.size(), 1U);
   if (req[0] == kNullOp || outputs[0].Size() == 0U) return;
   using namespace mshadow;
+  using namespace mxnet_op;
   using namespace mshadow::expr;
   Stream<xpu> *s = ctx.get_stream<xpu>();
   const double alpha = nnvm::get<double>(attrs.parsed);
-  if (common::is_float(inputs[0].type_flag_)) {
+  const TBlob& data = inputs[0];
+  const TBlob& out = outputs[0];
+  if (out.type_flag_ == data.type_flag_) {
     MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, DType, {
       MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
-        mxnet_op::Kernel<mxnet_op::op_with_req<OP, Req>, xpu>::Launch(
-            s, inputs[0].Size(), outputs[0].dptr<DType>(), inputs[0].dptr<DType>(), DType(alpha));
+        Kernel<op_with_req<OP, Req>, xpu>::Launch(
+          s, data.Size(), out.dptr<DType>(), data.dptr<DType>(), DType(alpha));
       });
     });
   } else {
+#ifndef _WIN32
     CHECK_EQ(outputs[0].type_flag_, kFloat32) << "true_divide only supports float32 output "
                                                  "when input's dtype is "
                                               << type_string(inputs[0].type_flag_);
     MXNET_INT_TYPE_SWITCH(inputs[0].type_flag_, DType, {
       MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
-        mxnet_op::Kernel<mxnet_op::op_with_req<OP, Req>, xpu>::Launch(
-            s, inputs[0].Size(), outputs[0].dptr<float>(), inputs[0].dptr<DType>(), DType(alpha));
+        Kernel<op_with_req<OP, Req>, xpu>::Launch(
+          s, data.Size(), out.dptr<float>(), data.dptr<DType>(),
+          static_cast<float>(alpha));
       });
     });
+#else
+    Tensor<xpu, 1, float> temp_tensor =
+      ctx.requested[0].get_space_typed<xpu, 1, float>(mshadow::Shape1(data.Size()), s);
+    TBlob temp_tblob(temp_tensor);
+    CastCompute<xpu>(attrs, ctx, {data}, {kWriteTo}, {temp_tblob});
+    TrueDivideScalarCompute<xpu, OP>(attrs, ctx, {temp_tblob}, req, outputs);
+#endif
   }
 }
 
-template<typename xpu, typename OP>
+template<typename xpu>
 void TrueDivideElemwiseCompute(const nnvm::NodeAttrs &attrs,
                                const OpContext &ctx,
                                const std::vector<TBlob> &inputs,
@@ -77,66 +89,254 @@ void TrueDivideElemwiseCompute(const nnvm::NodeAttrs &attrs,
   Stream<xpu> *s = ctx.get_stream<xpu>();
   CHECK_EQ(inputs.size(), 2U);
   CHECK_EQ(outputs.size(), 1U);
-  MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
-    if (common::is_float(inputs[0].type_flag_)) {
-      MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
-        Kernel<mxnet_op::op_with_req<OP, Req>, xpu>::Launch(s, outputs[0].Size(),
-                                                            outputs[0].dptr<DType>(),
-                                                            inputs[0].dptr<DType>(),
-                                                            inputs[1].dptr<DType>());
+
+  const TBlob& lhs = inputs[0];
+  const TBlob& rhs = inputs[1];
+  const TBlob& out = outputs[0];
+  if (lhs.type_flag_ == rhs.type_flag_) {
+    // Case when types of the 2 input tensors are the same
+    if (common::is_float(lhs.type_flag_)) {
+      // If both are the same floats, normal launch
+      MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
+        MSHADOW_REAL_TYPE_SWITCH(lhs.type_flag_, DType, {
+          Kernel<op_with_req<mshadow_op::true_divide, Req>, xpu>::Launch(
+            s, out.Size(), out.dptr<DType>(), lhs.dptr<DType>(), rhs.dptr<DType>());
+        });
       });
     } else {
-      CHECK_EQ(outputs[0].type_flag_, kFloat32) << "true_divide only supports float32 output "
-                                                   "when input's dtype is "
-                                                << type_string(inputs[0].type_flag_);
-      MXNET_INT_TYPE_SWITCH(inputs[0].type_flag_, DType, {
-        Kernel<mxnet_op::op_with_req<OP, Req>, xpu>::Launch(s, outputs[0].Size(),
-                                                            outputs[0].dptr<float>(),
-                                                            inputs[0].dptr<DType>(),
-                                                            inputs[1].dptr<DType>());
+      // If both are the same integers, output is float32
+      CHECK_EQ(out.type_flag_, kFloat32) << "true_divide only supports float32 output "
+                                            "when input's dtype is "
+                                         << type_string(lhs.type_flag_);
+      MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
+        MXNET_INT_TYPE_SWITCH(lhs.type_flag_, DType, {
+          Kernel<op_with_req<mshadow_op::true_divide, Req>, xpu>::Launch(
+            s, out.Size(), out.dptr<float>(), lhs.dptr<DType>(), rhs.dptr<DType>());
+        });
       });
     }
-  });
+  } else {
+#ifndef _WIN32
+    // Non-windows case: no usage of temporary space
+    // Case when types of the 2 input tensors are different
+    if (common::is_float(lhs.type_flag_) && common::is_float(rhs.type_flag_)) {
+      // both lhs and rhs are float types, output type is the more precise one
+      LOG(ERROR) << "not implemented yet...";
+    } else if (common::is_float(lhs.type_flag_) || common::is_float(rhs.type_flag_)) {
+      // one is float type, the other is integer type, the output type should be the same as float
+      CHECK_EQ(out.type_flag_,
+               common::is_float(lhs.type_flag_) ? lhs.type_flag_ : rhs.type_flag_)
+        << "This case out type should be same as the float type";
+      if (common::is_float(lhs.type_flag_)) {
+        // lhs is the float one
+        MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
+          MSHADOW_REAL_TYPE_SWITCH(lhs.type_flag_, LType, {
+            MXNET_INT_TYPE_SWITCH(rhs.type_flag_, RType, {
+              Kernel<op_with_req<mshadow_op::rtrue_divide, Req>, xpu>::Launch(
+                s, out.Size(), out.dptr<LType>(), rhs.dptr<RType>(), lhs.dptr<LType>());
+            });
+          });
+        });
+      } else {
+        // rhs is the float one
+        MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
+          MXNET_INT_TYPE_SWITCH(lhs.type_flag_, LType, {
+            MSHADOW_REAL_TYPE_SWITCH(rhs.type_flag_, RType, {
+              Kernel<op_with_req<mshadow_op::true_divide, Req>, xpu>::Launch(
+                s, out.Size(), out.dptr<RType>(), lhs.dptr<LType>(), rhs.dptr<RType>());
+            });
+          });
+        });
+      }
+    } else {
+      // lhs is integer type, rhs is integer type, output type should be float
+      LOG(ERROR) << "not implemented yet...";
+    }
+#else
+    // Windows case: using temp space for casting the type
+    // Case when types of the 2 input tensors are different
+    if (common::is_float(lhs.type_flag_) && common::is_float(rhs.type_flag_)) {
+      // both lhs and rhs are float types, output type is the more precise one
+      LOG(ERROR) << "not implemented yet...";
+    } else if (common::is_float(lhs.type_flag_) || common::is_float(rhs.type_flag_)) {
+      // lhs is float type, rhs is integer type, the output type should be the same as lhs
+      CHECK_EQ(out.type_flag_,
+               common::is_float(lhs.type_flag_) ? lhs.type_flag_ : rhs.type_flag_)
+        << "This case out type should be same as the float type";
+      TBlob temp_tblob;
+      if (common::is_float(lhs.type_flag_)) {
+        // lhs is the float one
+        MSHADOW_REAL_TYPE_SWITCH(lhs.type_flag_, LType, {
+          Tensor<xpu, 1, LType> temp_tensor =
+            ctx.requested[0].get_space_typed<xpu, 1, LType>(mshadow::Shape1(rhs.Size()), s);
+          temp_tblob = TBlob(temp_tensor);
+        });
+        CastCompute<xpu>(attrs, ctx, {rhs}, {kWriteTo}, {temp_tblob});
+        TrueDivideElemwiseCompute<xpu>(
+          attrs, ctx, {lhs, temp_tblob.reshape(rhs.shape_)}, req, outputs);
+      } else {
+        // rhs is the float one
+        MSHADOW_REAL_TYPE_SWITCH(rhs.type_flag_, RType, {
+          Tensor<xpu, 1, RType> temp_tensor =
+            ctx.requested[0].get_space_typed<xpu, 1, RType>(mshadow::Shape1(lhs.Size()), s);
+          temp_tblob = TBlob(temp_tensor);
+        });
+        CastCompute<xpu>(attrs, ctx, {lhs}, {kWriteTo}, {temp_tblob});
+        TrueDivideElemwiseCompute<xpu>(
+          attrs, ctx, {temp_tblob.reshape(lhs.shape_), rhs}, req, outputs);
+      }
+    } else {
+      // lhs is integer type, rhs is integer type, output type should be float
+      LOG(ERROR) << "not implemented yet...";
+    }
+#endif
+  }
 }
 
-template<typename xpu, typename OP>
+template<typename xpu>
 void TrueDivideBroadcastCompute(const nnvm::NodeAttrs& attrs,
                                 const OpContext& ctx,
                                 const std::vector<TBlob>& inputs,
                                 const std::vector<OpReqType>& req,
                                 const std::vector<TBlob>& outputs) {
+  using namespace mxnet_op;
   if (outputs[0].shape_.Size() == 0U) return;
+  CHECK_EQ(inputs.size(), 2U);
   mxnet::TShape new_lshape, new_rshape, new_oshape;
   int ndim = BinaryBroadcastShapeCompact(inputs[0].shape_, inputs[1].shape_, outputs[0].shape_,
                                          &new_lshape, &new_rshape, &new_oshape);
   if (!ndim) {
-    TrueDivideElemwiseCompute<xpu, OP>(attrs, ctx, inputs, req, outputs);
+    TrueDivideElemwiseCompute<xpu>(attrs, ctx, inputs, req, outputs);
   } else {
     if (req[0] == kNullOp) return;
     mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+    const TBlob& lhs = inputs[0];
+    const TBlob& rhs = inputs[1];
+    const TBlob& out = outputs[0];
+#ifndef _WIN32
     BROADCAST_NDIM_SWITCH(ndim, NDim, {
       mshadow::Shape<NDim> oshape = new_oshape.get<NDim>();
-      mshadow::Shape<NDim> lstride = mxnet_op::calc_stride(new_lshape.get<NDim>());
-      mshadow::Shape<NDim> rstride = mxnet_op::calc_stride(new_rshape.get<NDim>());
-      if (common::is_float(inputs[0].type_flag_)) {
-        MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
-          mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, DType, DType, OP>, xpu>::
-            template LaunchEx(s, new_oshape.Size(), req[0], lstride, rstride, oshape,
-                              inputs[0].dptr<DType>(), inputs[1].dptr<DType>(),
-                              outputs[0].dptr<DType>());
-        });
-      } else {
-        CHECK_EQ(outputs[0].type_flag_, mshadow::kFloat32)
+      mshadow::Shape<NDim> lstride = calc_stride(new_lshape.get<NDim>());
+      mshadow::Shape<NDim> rstride = calc_stride(new_rshape.get<NDim>());
+      if (lhs.type_flag_ == rhs.type_flag_) {
+        // When the both inputs have the same data types
+        if (common::is_float(lhs.type_flag_)) {
+          // If both inputs are the same float types, output is the same float type
+          MSHADOW_REAL_TYPE_SWITCH(lhs.type_flag_, DType, {
+            Kernel<binary_broadcast_kernel<NDim, mshadow_op::true_divide>, xpu>::
+              template LaunchEx(s, new_oshape.Size(), req[0], lstride, rstride, oshape,
+                                lhs.dptr<DType>(), rhs.dptr<DType>(), out.dptr<DType>());
+          });
+        } else {
+          CHECK_EQ(out.type_flag_, mshadow::kFloat32)
             << "true_divide only supports float32 output when input's dtype is "
-            << type_string(inputs[0].type_flag_);
-        MXNET_INT_TYPE_SWITCH(inputs[0].type_flag_, DType, {
-          mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, DType, float, OP>, xpu>::
-            template LaunchEx(s, new_oshape.Size(), req[0], lstride, rstride, oshape,
-                              inputs[0].dptr<DType>(), inputs[1].dptr<DType>(),
-                              outputs[0].dptr<float>());
-        });
+            << type_string(lhs.type_flag_);
+          MXNET_INT_TYPE_SWITCH(lhs.type_flag_, DType, {
+            // If both inputs are the same integer types, output is float type
+            Kernel<binary_broadcast_kernel<NDim, mshadow_op::true_divide>, xpu>::
+              template LaunchEx(s, new_oshape.Size(), req[0], lstride, rstride, oshape,
+                                lhs.dptr<DType>(), rhs.dptr<DType>(), out.dptr<float>());
+          });
+        }
+      } else {
+        if (common::is_float(lhs.type_flag_) && common::is_float(rhs.type_flag_)) {
+          // lhs and rhs have different float types, the output is the more precise one
+          LOG(ERROR) << "not implemented yet...";
+        } else if (common::is_float(lhs.type_flag_) || common::is_float(rhs.type_flag_)) {
+          // one of lhs and rhs is float, the output is the same type as the float one
+          if (common::is_float(lhs.type_flag_)) {
+            // lhs is float type, output will be the same float type
+            CHECK_EQ(lhs.type_flag_, out.type_flag_)
+              << "lhs should have the same type as out, infer type broken?";
+            MSHADOW_REAL_TYPE_SWITCH(lhs.type_flag_, LType, {
+              MXNET_INT_TYPE_SWITCH(rhs.type_flag_, RType, {
+                Kernel<binary_broadcast_kernel<NDim, mshadow_op::rtrue_divide>, xpu>::
+                  template LaunchEx(s, new_oshape.Size(), req[0], rstride, lstride, oshape,
+                                    rhs.dptr<RType>(), lhs.dptr<LType>(), out.dptr<LType>());
+              });
+            });
+          } else {
+            // rhs is float type, output will be the same float type
+            CHECK_EQ(rhs.type_flag_, out.type_flag_)
+              << "rhs should have the same type as out, infer type broken?";
+            MXNET_INT_TYPE_SWITCH(lhs.type_flag_, LType, {
+              MSHADOW_REAL_TYPE_SWITCH(rhs.type_flag_, RType, {
+                Kernel<binary_broadcast_kernel<NDim, mshadow_op::true_divide>, xpu>::
+                  template LaunchEx(s, new_oshape.Size(), req[0], lstride, rstride, oshape,
+                                    lhs.dptr<LType>(), rhs.dptr<RType>(), out.dptr<RType>());
+              });
+            });
+          }
+        } else {
+          // lhs and rhs have different integer types, the output is float type
+          LOG(ERROR) << "not implemented yet...";
+        }
       }
     });
+#else
+    if (lhs.type_flag_ == rhs.type_flag_) {
+      BROADCAST_NDIM_SWITCH(ndim, NDim, {
+        mshadow::Shape<NDim> oshape = new_oshape.get<NDim>();
+        mshadow::Shape<NDim> lstride = calc_stride(new_lshape.get<NDim>());
+        mshadow::Shape<NDim> rstride = calc_stride(new_rshape.get<NDim>());
+        // When the both inputs have the same data types
+        if (common::is_float(lhs.type_flag_)) {
+          // If both inputs are the same float types, output is the same float type
+          MSHADOW_REAL_TYPE_SWITCH(lhs.type_flag_, DType, {
+            Kernel<binary_broadcast_kernel<NDim, mshadow_op::true_divide>, xpu>::
+              template LaunchEx(s, new_oshape.Size(), req[0], lstride, rstride, oshape,
+                                lhs.dptr<DType>(), rhs.dptr<DType>(), out.dptr<DType>());
+          });
+        } else {
+          CHECK_EQ(out.type_flag_, mshadow::kFloat32)
+            << "true_divide only supports float32 output when input's dtype is "
+            << type_string(lhs.type_flag_);
+          MXNET_INT_TYPE_SWITCH(lhs.type_flag_, DType, {
+            // If both inputs are the same integer types, output is float type
+            Kernel<binary_broadcast_kernel<NDim, mshadow_op::true_divide>, xpu>::
+              template LaunchEx(s, new_oshape.Size(), req[0], lstride, rstride, oshape,
+                                lhs.dptr<DType>(), rhs.dptr<DType>(), out.dptr<float>());
+          });
+        }
+      });
+    } else {
+      if (common::is_float(lhs.type_flag_) && common::is_float(rhs.type_flag_)) {
+        // lhs and rhs have different float types, the output is the more precise one
+        LOG(ERROR) << "not implemented yet...";
+      } else if (common::is_float(lhs.type_flag_) || common::is_float(rhs.type_flag_)) {
+        // one of lhs and rhs is float, the output is the same type as the float one
+        TBlob temp_tblob;
+        if (common::is_float(lhs.type_flag_)) {
+          // lhs is float type, output will be the same float type
+          CHECK_EQ(lhs.type_flag_, out.type_flag_)
+            << "lhs should have the same type as out, infer type broken?";
+          MSHADOW_REAL_TYPE_SWITCH(lhs.type_flag_, LType, {
+            Tensor<xpu, 1, LType> temp_tensor =
+              ctx.requested[0].get_space_typed<xpu, 1, LType>(mshadow::Shape1(rhs.Size()), s);
+            temp_tblob = TBlob(temp_tensor);
+          });
+          CastCompute<xpu>(attrs, ctx, {rhs}, {kWriteTo}, {temp_tblob});
+          TrueDivideBroadcastCompute<xpu>(
+            attrs, ctx, {lhs, temp_tblob.reshape(rhs.shape_)}, req, outputs);
+        } else {
+          // rhs is float type, output will be the same float type
+          CHECK_EQ(rhs.type_flag_, out.type_flag_)
+            << "rhs should have the same type as out, infer type broken?";
+          MSHADOW_REAL_TYPE_SWITCH(rhs.type_flag_, RType, {
+            Tensor<xpu, 1, RType> temp_tensor =
+              ctx.requested[0].get_space_typed<xpu, 1, RType>(mshadow::Shape1(lhs.Size()), s);
+            temp_tblob = TBlob(temp_tensor);
+          });
+          CastCompute<xpu>(attrs, ctx, {lhs}, {kWriteTo}, {temp_tblob});
+          TrueDivideBroadcastCompute<xpu>(
+            attrs, ctx, {temp_tblob.reshape(lhs.shape_), rhs}, req, outputs);
+        }
+      } else {
+        // lhs and rhs have different integer types, the output is float type
+        LOG(ERROR) << "not implemented yet...";
+      }
+    }
+#endif
   }
 }
 
diff --git a/src/operator/numpy/np_true_divide.cc b/src/operator/numpy/np_true_divide.cc
index 5a4634c3ff8c..d2135befef42 100644
--- a/src/operator/numpy/np_true_divide.cc
+++ b/src/operator/numpy/np_true_divide.cc
@@ -28,26 +28,35 @@
 namespace mxnet {
 namespace op {
 
+int TrueDivideOutType(int ltype, int rtype) {
+  if (common::is_float(ltype) && common::is_float(rtype)) {
+    // If both inputs are float, return the one with the higher precision
+    return common::more_precise_type(ltype, rtype);
+  } else if (common::is_float(ltype) || common::is_float(rtype)) {
+    // If only one of the inputs is float, return that float type
+    return (common::is_float(ltype)) ? ltype : rtype;
+  }
+  // If neither of the inputs is float, return the default float32 type
+  return mshadow::kFloat32;
+}
+
 template <int num_inputs>
 bool TrueDivideType(const nnvm::NodeAttrs& attrs,
                     std::vector<int>* in_attrs,
                     std::vector<int>* out_attrs) {
   CHECK_EQ(in_attrs->size(), static_cast<size_t>(num_inputs));
+  CHECK_GT(in_attrs->size(), 0U);
   CHECK_EQ(out_attrs->size(), 1U);
+
   for (const int dtype : *in_attrs) {
     if (dtype == -1) return false;
   }
-  if (num_inputs == 2) {
-    const int lhs_dtype = in_attrs->at(0);
-    const int rhs_dtype = in_attrs->at(1);
-    CHECK_EQ(lhs_dtype, rhs_dtype)
-        << "true_divide currently only supports same dtype for dividend and divisor";
-  }
-  if (common::is_float(in_attrs->at(0))) {
-    TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
-  } else {
-    TYPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::kFloat32);
-  }
+
+  const int lhs_dtype = in_attrs->at(0);
+  const int rhs_dtype = (num_inputs == 2) ?
+                        in_attrs->at(1) :
+                        (common::is_float(lhs_dtype) ? lhs_dtype : mshadow::kFloat32);
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, TrueDivideOutType(lhs_dtype, rhs_dtype));
   return true;
 }
 
@@ -64,7 +73,13 @@ NNVM_REGISTER_OP(_npi_true_divide)
   [](const NodeAttrs& attrs){
     return std::vector<std::pair<int, int> >{{0, 0}, {1, 0}};
   })
-.set_attr<FCompute>("FCompute<cpu>", TrueDivideBroadcastCompute<cpu, op::mshadow_op::true_divide>)
+#ifdef _WIN32
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+#endif
+.set_attr<FCompute>("FCompute<cpu>", TrueDivideBroadcastCompute<cpu>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_broadcast_div"})
 .add_argument("lhs", "NDArray-or-Symbol", "Dividend array")
 .add_argument("rhs", "NDArray-or-Symbol", "Divisor array");
@@ -81,6 +96,12 @@ NNVM_REGISTER_OP(_npi_true_divide_scalar)
   [](const NodeAttrs& attrs) {
     return std::vector<std::pair<int, int> >{{0, 0}};
   })
+#ifdef _WIN32
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+#endif
 .set_attr<FCompute>("FCompute<cpu>", TrueDivideScalarCompute<cpu, op::mshadow_op::true_divide>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_div_scalar"})
 .add_argument("data", "NDArray-or-Symbol", "source input")
@@ -98,6 +119,12 @@ NNVM_REGISTER_OP(_npi_rtrue_divide_scalar)
   [](const NodeAttrs& attrs) {
     return std::vector<std::pair<int, int> >{{0, 0}};
   })
+#ifdef _WIN32
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+#endif
 .set_attr<FCompute>("FCompute<cpu>", TrueDivideScalarCompute<cpu, mshadow_op::rtrue_divide>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_rdiv_scalar"})
 .add_argument("data", "NDArray-or-Symbol", "source input")
diff --git a/src/operator/numpy/np_true_divide.cu b/src/operator/numpy/np_true_divide.cu
index c026d689233d..7211f4a0a006 100644
--- a/src/operator/numpy/np_true_divide.cu
+++ b/src/operator/numpy/np_true_divide.cu
@@ -29,7 +29,7 @@ namespace mxnet {
 namespace op {
 
 NNVM_REGISTER_OP(_npi_true_divide)
-.set_attr<FCompute>("FCompute<gpu>", TrueDivideBroadcastCompute<gpu, mshadow_op::true_divide>);
+.set_attr<FCompute>("FCompute<gpu>", TrueDivideBroadcastCompute<gpu>);
 
 NNVM_REGISTER_OP(_npi_true_divide_scalar)
 .set_attr<FCompute>("FCompute<gpu>", TrueDivideScalarCompute<gpu, mshadow_op::true_divide>);
diff --git a/src/operator/tensor/elemwise_binary_broadcast_op.h b/src/operator/tensor/elemwise_binary_broadcast_op.h
index 3d3bcfacbd05..ad06df8d92be 100644
--- a/src/operator/tensor/elemwise_binary_broadcast_op.h
+++ b/src/operator/tensor/elemwise_binary_broadcast_op.h
@@ -187,9 +187,10 @@ inline int BinaryBroadcastShapeCompact(const mxnet::TShape& lshape, const mxnet:
 }
 
 namespace mxnet_op {
-template<int ndim, typename IType, typename DType, typename OP>
+template<int ndim, typename OP>
 struct binary_broadcast_kernel {
   /*! \brief Map function for binary_broadcast_kernel */
+  template<typename IType, typename DType>
   MSHADOW_XINLINE static void Map(index_t base, index_t length, OpReqType req,
                                   const Shape <ndim> &lstride, const Shape <ndim> &rstride,
                                   const Shape <ndim> &oshape, IType *lhs, IType *rhs,
@@ -208,6 +209,7 @@ struct binary_broadcast_kernel {
   }
 
   /*! \brief Map function for binary_broadcast_kernel */
+  template<typename IType, typename DType>
   MSHADOW_XINLINE static void Map(index_t base, index_t length, OpReqType req,
                                   const Shape <ndim> &lstride, const Shape <ndim> &rstride,
                                   const Shape <ndim> &oshape, IType lhs, IType *rhs,
@@ -224,6 +226,49 @@ struct binary_broadcast_kernel {
       KERNEL_ASSIGN(out[base + i], req, OP::Map(lhs, rhs[ridx]));
     }
   }
+
+#ifndef _WIN32
+  /*! \brief Map function for binary_broadcast_kernel */
+  template<typename IType, typename DType,
+           typename std::enable_if<!std::is_same<IType, DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static void Map(index_t base, index_t length, OpReqType req,
+                                  const Shape <ndim> &lstride, const Shape <ndim> &rstride,
+                                  const Shape <ndim> &oshape, IType *lhs, DType *rhs,
+                                  DType *out) {
+    Shape <ndim> coord = unravel(base, oshape);
+    auto lidx = static_cast<index_t>(dot(coord, lstride));
+    auto ridx = static_cast<index_t>(dot(coord, rstride));
+    KERNEL_ASSIGN(out[base], req, OP::Map(lhs[lidx], rhs[ridx]));
+    // starts from 1 to avoid extra inc at end of loop
+    for (index_t i = 1; i < length; ++i) {
+      inc(&coord, oshape, &lidx, lstride, &ridx, rstride);
+      // When tuning, don't actually run the op, since it's not going to be tuned against
+      // the actual op we'll eventually be using
+      KERNEL_ASSIGN(out[base + i], req, OP::Map(lhs[lidx], rhs[ridx]));
+    }
+  }
+
+  /*! \brief Map function for binary_broadcast_kernel */
+  template<typename IType, typename DType,
+           typename std::enable_if<!std::is_same<IType, DType>::value &&
+                                   !std::is_pointer<IType>::value, int>::type = 0>
+  MSHADOW_XINLINE static void Map(index_t base, index_t length, OpReqType req,
+                                  const Shape <ndim> &lstride, const Shape <ndim> &rstride,
+                                  const Shape <ndim> &oshape, IType lhs, DType *rhs,
+                                  DType *out) {
+    Shape <ndim> coord = unravel(base, oshape);
+    auto lidx = static_cast<index_t>(dot(coord, lstride));
+    auto ridx = static_cast<index_t>(dot(coord, rstride));
+    KERNEL_ASSIGN(out[base], req, OP::Map(lhs, rhs[ridx]));
+    // starts from 1 to avoid extra inc at end of loop
+    for (index_t i = 1; i < length; ++i) {
+      inc(&coord, oshape, &lidx, lstride, &ridx, rstride);
+      // When tuning, don't actually run the op, since it's not going to be tuned against
+      // the actual op we'll eventually be using
+      KERNEL_ASSIGN(out[base + i], req, OP::Map(lhs, rhs[ridx]));
+    }
+  }
+#endif
 };
 
 template<int req, typename OP, bool col_vec>
@@ -307,7 +352,7 @@ void BinaryBroadcastCompute(const nnvm::NodeAttrs& attrs,
           mshadow::Shape<NDim> oshape = new_oshape.get<NDim>();
           mshadow::Shape<NDim> lstride = mxnet_op::calc_stride(new_lshape.get<NDim>());
           mshadow::Shape<NDim> rstride = mxnet_op::calc_stride(new_rshape.get<NDim>());
-          mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, DType, DType, OP>, xpu>::
+          mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, OP>, xpu>::
           template LaunchEx(s, new_oshape.Size(), req[0], lstride, rstride, oshape,
           inputs[0].dptr<DType>(), inputs[1].dptr<DType>(), outputs[0].dptr<DType>());
         });
@@ -336,7 +381,7 @@ void BinaryBroadcastComputeLogic(const nnvm::NodeAttrs& attrs,
             mshadow::Shape<NDim> oshape = new_oshape.get<NDim>();
             mshadow::Shape<NDim> lstride = mxnet_op::calc_stride(new_lshape.get<NDim>());
             mshadow::Shape<NDim> rstride = mxnet_op::calc_stride(new_rshape.get<NDim>());
-            mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, DType, bool, OP>, xpu>::
+            mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, OP>, xpu>::
             template LaunchEx(s, new_oshape.Size(), req[0], lstride, rstride, oshape,
                               inputs[0].dptr<DType>(), inputs[1].dptr<DType>(),
                               outputs[0].dptr<bool>());
@@ -444,11 +489,11 @@ void BinaryBroadcastCsrDnsDnsImpl(const OpContext& ctx,
       Shape<NDim> lstride = calc_stride(new_csrshape.get<NDim>());
       Shape<NDim> rstride = calc_stride(new_dnsshape.get<NDim>());
       if (reverse && std::is_same<OP, mshadow_op::minus>::value) {
-        Kernel<binary_broadcast_kernel<NDim, DType, DType, mshadow_op::plus>, xpu>::
+        Kernel<binary_broadcast_kernel<NDim, mshadow_op::plus>, xpu>::
         template LaunchEx(s, new_oshape.Size(), req, lstride, rstride, oshape,
         DType(0), dns_data.dptr<DType>(), out_data.dptr<DType>());
       } else {
-        Kernel<binary_broadcast_kernel<NDim, DType, DType, OP>, xpu>::
+        Kernel<binary_broadcast_kernel<NDim, OP>, xpu>::
         template LaunchEx(s, new_oshape.Size(), req, lstride, rstride, oshape,
         DType(0), dns_data.dptr<DType>(), out_data.dptr<DType>());
       }
@@ -658,7 +703,7 @@ void BinaryBroadcastBackwardUseIn(const nnvm::NodeAttrs& attrs,
     [](const NodeAttrs& attrs) {                                      \
       return std::vector<std::string>{"lhs", "rhs"};                  \
     })                                                                \
-  .set_attr<mxnet::FInferShape>("FInferShape", BinaryBroadcastShape)   \
+  .set_attr<mxnet::FInferShape>("FInferShape", BinaryBroadcastShape)  \
   .set_attr<nnvm::FInferType>("FInferType", ElemwiseType<2, 1>)       \
   .set_attr<nnvm::FInplaceOption>("FInplaceOption",                   \
     [](const NodeAttrs& attrs){                                       \
diff --git a/src/operator/tensor/elemwise_binary_scalar_op.h b/src/operator/tensor/elemwise_binary_scalar_op.h
index 02b005eed995..834bbdbfc3d1 100644
--- a/src/operator/tensor/elemwise_binary_scalar_op.h
+++ b/src/operator/tensor/elemwise_binary_scalar_op.h
@@ -256,7 +256,7 @@ class BinaryScalarOp : public UnaryOp {
     using namespace mshadow::expr;
     Stream<xpu> *s = ctx.get_stream<xpu>();
     const double alpha = nnvm::get<double>(attrs.parsed);
-    MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    MSHADOW_TYPE_SWITCH_WITH_BOOL(inputs[0].type_flag_, DType, {
         MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
           mxnet_op::Kernel<mxnet_op::op_with_req<OP, Req>, xpu>::Launch(
               s, inputs[0].Size(), outputs[0].dptr<bool>(), inputs[0].dptr<DType>(), DType(alpha));
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 3b4b4b6491fc..e6d12da23582 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -1940,7 +1940,7 @@ def get_new_shape(shape, axis):
 
                     with mx.autograd.record():
                         y = test_concat(a, b, c, d)
-                    
+
                     assert y.shape == expected_ret.shape
                     assert_almost_equal(y.asnumpy(), expected_ret, rtol=1e-3, atol=1e-5)
 
@@ -2933,7 +2933,7 @@ def check_cholesky(L, data_np):
         test_cholesky = TestCholesky()
         if hybridize:
             test_cholesky.hybridize()
-        
+
         # Numerical issue:
         # When backpropagating through Cholesky decomposition, we need to compute the inverse
         # of L according to dA = 0.5 * L**(-T) * copyLTU(L**T * dL) * L**(-1) where A = LL^T.
@@ -3847,12 +3847,14 @@ def test_np_true_divide():
         [(2, 3, 1), (1, 4)],
         [(2, 1, 4, 1), (3, 1, 5)],
     ]
-    dtypes = [np.int8, np.uint8, np.int32, np.int64, np.float16, np.float32, np.float64]
+    dtypes = [np.bool, np.int8, np.uint8, np.int32, np.int64, np.float16, np.float32, np.float64]
+    itypes = [np.bool, np.int8, np.uint8, np.int32, np.int64]
+    ftypes = [np.float16, np.float32, np.float64]
     for shape_pair, dtype in itertools.product(shapes, dtypes):
         a = np.random.uniform(3, 50, size=shape_pair[0]).astype(dtype)
         b = np.random.uniform(3, 50, size=shape_pair[-1]).astype(dtype)
         out_mx = a / b
-        if _np.issubdtype(dtype, _np.integer):
+        if _np.issubdtype(dtype, _np.integer) or (dtype is np.bool):
             assert out_mx.dtype == np.float32
         else:
             assert out_mx.dtype == dtype
@@ -3868,6 +3870,20 @@ def test_np_true_divide():
         out_np = _np.true_divide(val, a.asnumpy())
         assert_almost_equal(out_mx.asnumpy(), out_np, rtol=1e-3, atol=1e-3, use_broadcast=False)
 
+    for shape_pair, itype, ftype in itertools.product(shapes, itypes, ftypes):
+        i_ = np.random.uniform(3, 50, size=shape_pair[0]).astype(itype)
+        f_ = np.random.uniform(3, 50, size=shape_pair[-1]).astype(ftype)
+
+        out_mx = i_ / f_
+        assert out_mx.dtype == ftype
+        out_np = _np.true_divide(i_.asnumpy(), f_.asnumpy())
+        assert_almost_equal(out_mx.asnumpy(), out_np, rtol=1e-3, atol=1e-3, use_broadcast=False)
+
+        out_mx = f_ / i_
+        assert out_mx.dtype == ftype
+        out_np = _np.true_divide(f_.asnumpy(), i_.asnumpy())
+        assert_almost_equal(out_mx.asnumpy(), out_np, rtol=1e-3, atol=1e-3, use_broadcast=False)
+
 
 @with_seed()
 @use_np

From 94aab3948e734d3d7aae797d91d5a621976c0782 Mon Sep 17 00:00:00 2001
From: Wuxun Zhang <wuxun.zhang@intel.com>
Date: Mon, 4 Nov 2019 13:31:49 +0800
Subject: [PATCH 563/813] [Quantization] Enhance gluon quantization API
 (#16695)

* enhance gluon quantization API

* add quantize_mode into quantize_model_mkldnn function

* fix lint

* rebase code
---
 python/mxnet/contrib/quantization.py | 31 +++++++++++++++++++++-------
 1 file changed, 24 insertions(+), 7 deletions(-)

diff --git a/python/mxnet/contrib/quantization.py b/python/mxnet/contrib/quantization.py
index ef24bddf9bd1..b0714037bb42 100644
--- a/python/mxnet/contrib/quantization.py
+++ b/python/mxnet/contrib/quantization.py
@@ -419,6 +419,9 @@ def __init__(self, calib_data):
         # suppose there must be one label in data_example
         num_data = len(data_example)
         assert num_data > 0
+        # here reshape is to handle the 5D/6D input data
+        if len(data_example[0].shape) > 4:
+            data_example[0] = data_example[0].reshape((-1,) + data_example[0].shape[2:])
         self.provide_data = [DataDesc(name='data', shape=(data_example[0].shape))]
         self.provide_data += [DataDesc(name='data{}'.format(i), shape=x.shape) for i, x in enumerate(data_example[1:])]
         self.batch_size = data_example[0].shape[0]
@@ -428,7 +431,11 @@ def reset(self):
         self._iter = iter(self._data)
 
     def next(self):
-        return DataBatch(data=next(self._iter))
+        next_data = next(self._iter)
+        # here reshape is to handle the 5D/6D input data
+        if len(next_data[0].shape) > 4:
+            next_data[0] = next_data[0].reshape((-1,) + next_data[0].shape[2:])
+        return DataBatch(data=next_data)
 # pylint: enable=super-init-not-called
 
 def _as_data_iter(calib_data):
@@ -577,7 +584,7 @@ def quantize_model_mkldnn(sym, arg_params, aux_params,
                           data_names=('data',), label_names=('softmax_label',),
                           ctx=cpu(), excluded_sym_names=None, excluded_op_names=None,
                           calib_mode='entropy', calib_data=None, num_calib_examples=None,
-                          quantized_dtype='int8', logger=logging):
+                          quantized_dtype='int8', quantize_mode='smart', logger=logging):
     """User-level API for generating a fusion + quantized model from a FP32 model
     w/ or w/o calibration with Intel MKL-DNN.
     The backend quantized operators are only enabled for Linux systems. Please do not run
@@ -605,7 +612,8 @@ def quantize_model_mkldnn(sym, arg_params, aux_params,
                                                    excluded_op_names=excluded_op_names,
                                                    calib_mode=calib_mode, calib_data=calib_data,
                                                    num_calib_examples=num_calib_examples,
-                                                   quantized_dtype=quantized_dtype, logger=logger)
+                                                   quantized_dtype=quantized_dtype, quantize_mode=quantize_mode,
+                                                   logger=logger)
 
     qsym = qsym.get_backend_symbol('MKLDNN_QUANTIZE')
 
@@ -613,7 +621,7 @@ def quantize_model_mkldnn(sym, arg_params, aux_params,
 
 def quantize_graph(sym, arg_params, aux_params, ctx=cpu(),
                    excluded_sym_names=None, excluded_op_names=None, calib_mode='entropy',
-                   quantized_dtype='int8', logger=logging):
+                   quantized_dtype='int8', quantize_mode='full', logger=logging):
     """User-level API for generating a quantized model from a FP32 model w/o calibration
     and a collector for naive or entropy calibration.
     The backend quantized operators are only enabled for Linux systems. Please do not run
@@ -648,6 +656,10 @@ def quantize_graph(sym, arg_params, aux_params, ctx=cpu(),
         The quantized destination type for input data. Currently support 'int8'
         , 'uint8' and 'auto'. 'auto' means automatically select output type according to calibration result.
         Default value is 'int8'.
+    quantize_mode : str
+        The mode that quantization pass to apply. Support 'full' and 'smart'.
+        'full' means quantize all operator if possible.
+        'smart' means quantization pass will smartly choice which operator should be quantized.
     logger : Object
         A logging object for printing information during the process of quantization.
     Returns
@@ -672,7 +684,8 @@ def quantize_graph(sym, arg_params, aux_params, ctx=cpu(),
                                          excluded_operators=excluded_op_names,
                                          offline_params=list(
                                              arg_params.keys()),
-                                         quantized_dtype=quantized_dtype)
+                                         quantized_dtype=quantized_dtype,
+                                         quantize_mode=quantize_mode)
 
     th_dict = {}
     collector = None
@@ -755,7 +768,7 @@ def calib_graph(qsym, arg_params, aux_params, collector,
 
     return qsym, qarg_params, aux_params
 
-def quantize_net(network, quantized_dtype='auto',
+def quantize_net(network, quantized_dtype='auto', quantize_mode='full',
                  exclude_layers=None, exclude_layers_match=None, exclude_operators=None,
                  calib_data=None, data_shapes=None, calib_mode='none',
                  num_calib_examples=None, ctx=cpu(), logger=logging):
@@ -771,6 +784,10 @@ def quantize_net(network, quantized_dtype='auto',
         The quantized destination type for input data. Currently support 'int8'
         , 'uint8' and 'auto'. 'auto' means automatically select output type according to calibration result.
         Default value is 'int8'.
+    quantize_mode : str
+        The mode that quantization pass to apply. Support 'full' and 'smart'.
+        'full' means quantize all operator if possible.
+        'smart' means quantization pass will smartly choice which operator should be quantized.
     exclude_layers : list of strings
         A list of strings representing the names of the symbols that users want to excluding
     exclude_layers_match : list of strings
@@ -872,7 +889,7 @@ def __exit__(self, exc_type, exc_value, traceback):
     qsym, qarg_params, aux_params, collector = quantize_graph(
         sym=symnet, arg_params=args, aux_params=auxs, ctx=ctx,
         excluded_sym_names=exclude_layers, excluded_op_names=exclude_operators,
-        calib_mode=calib_mode, quantized_dtype=quantized_dtype, logger=logger)
+        calib_mode=calib_mode, quantized_dtype=quantized_dtype, quantize_mode=quantize_mode, logger=logger)
 
     if calib_mode is not None and calib_mode != 'none':
         if not isinstance(ctx, Context):

From bb6305d11d4383af2022e53ad94d6a1d5d93cb00 Mon Sep 17 00:00:00 2001
From: Xinyu Chen <xinyu1.chen@intel.com>
Date: Mon, 4 Nov 2019 18:11:09 +0800
Subject: [PATCH 564/813] [MKLDNN] support mkldnn gelu (#16710)

* support mkldnn gelu and convgelu fusion

* remove slope for gelu
---
 src/operator/nn/mkldnn/mkldnn_act.cc            |  5 ++++-
 .../subgraph/mkldnn/mkldnn_conv_property.h      |  3 ++-
 tests/python/mkl/test_subgraph.py               | 17 ++++++++++++++---
 3 files changed, 20 insertions(+), 5 deletions(-)

diff --git a/src/operator/nn/mkldnn/mkldnn_act.cc b/src/operator/nn/mkldnn/mkldnn_act.cc
index f3966e6566ce..6ea7ac4a30b3 100644
--- a/src/operator/nn/mkldnn/mkldnn_act.cc
+++ b/src/operator/nn/mkldnn/mkldnn_act.cc
@@ -58,7 +58,8 @@ bool SupportMKLDNNAct(const ActivationParam& param, const NDArray &input) {
 
 bool SupportMKLDNNLeakyRelu(const LeakyReLUParam& param) {
   return param.act_type == leakyrelu::kLeakyReLU
-      || param.act_type == leakyrelu::kELU;
+      || param.act_type == leakyrelu::kELU
+      || param.act_type == leakyrelu::kGELU;
 }
 
 bool SupportMKLDNNLeakyRelu(const LeakyReLUParam& param, const NDArray &input) {
@@ -98,6 +99,8 @@ mkldnn::algorithm GetMKLDNNActAlgo(const LeakyReLUParam& param) {
       return mkldnn::algorithm::eltwise_relu;
     case leakyrelu::kELU:
       return mkldnn::algorithm::eltwise_elu;
+    case leakyrelu::kGELU:
+      return mkldnn::algorithm::eltwise_gelu;
     default:
       LOG(FATAL) << "unknown activation type for LeakyReLU: " << param.act_type;
       return mkldnn::algorithm::eltwise_relu;
diff --git a/src/operator/subgraph/mkldnn/mkldnn_conv_property.h b/src/operator/subgraph/mkldnn/mkldnn_conv_property.h
index ff6589e6fb0a..44069138fcc5 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_conv_property.h
+++ b/src/operator/subgraph/mkldnn/mkldnn_conv_property.h
@@ -125,7 +125,8 @@ class SgMKLDNNConvSelector : public SubgraphSelector {
         } else if ((!disable_conv_act_) && new_node.op()->name == "LeakyReLU") {
           const LeakyReLUParam &param =
               nnvm::get<LeakyReLUParam>(new_node.attrs.parsed);
-          if (param.act_type == leakyrelu::kLeakyReLU) {
+          if (param.act_type == leakyrelu::kLeakyReLU ||
+              param.act_type == leakyrelu::kGELU) {
             matched_list_.push_back(&new_node);
             // not support conv+relu+sum yet.
             status_ = kSuccess;
diff --git a/tests/python/mkl/test_subgraph.py b/tests/python/mkl/test_subgraph.py
index dc494b25b549..2fb7d4939135 100644
--- a/tests/python/mkl/test_subgraph.py
+++ b/tests/python/mkl/test_subgraph.py
@@ -329,6 +329,8 @@ def conv_act(no_bias, data_shape, alg):
     relu = mx.symbol.clip(data=conv, name='relu6', a_min=0, a_max=6)
   elif alg == "leakyrelu":
     relu = mx.symbol.LeakyReLU(data=conv, slope=0.25, act_type='leaky')
+  elif alg == "gelu":
+    relu = mx.symbol.LeakyReLU(data=conv, act_type='gelu')
   else:
     relu = mx.symbol.Activation(data=conv, name=alg, act_type=alg)
   return relu, attr
@@ -343,6 +345,8 @@ def conv_act_sum(no_bias, data_shape, alg):
     relu = mx.symbol.clip(data=conv, name='relu6', a_min=0, a_max=6)
   elif alg == "leakyrelu":
     relu = mx.symbol.LeakyReLU(data=conv, slope=0.25, act_type='leaky')
+  elif alg == "gelu":
+    relu = mx.symbol.LeakyReLU(data=conv, act_type='gelu')
   else:
     relu = mx.symbol.Activation(data=conv, name=alg, act_type=alg)
   conv1 = mx.symbol.Convolution(data=data, weight=weight, name='conv1', num_filter=64,
@@ -385,6 +389,8 @@ def conv_bn_act(no_bias, data_shape, alg):
     relu = mx.symbol.clip(data=bn1, name='relu6', a_min=0, a_max=6)
   elif alg == "leakyrelu":
     relu = mx.symbol.LeakyReLU(data=bn1, slope=0.25, act_type='leaky')
+  elif alg == "gelu":
+    relu = mx.symbol.LeakyReLU(data=bn1, act_type='gelu')
   else:
     relu = mx.symbol.Activation(data=bn1, name=alg, act_type=alg)
   return relu, attr
@@ -403,6 +409,8 @@ def conv_bn_sum_act(no_bias, data_shape, alg):
     relu = mx.symbol.clip(data=sum1, name='relu6', a_min=0, a_max=6)
   elif alg == "leakyrelu":
     relu = mx.symbol.LeakyReLU(data=sum1, slope=0.25, act_type='leaky')
+  elif alg == "gelu":
+    relu = mx.symbol.LeakyReLU(data=sum1, act_type='gelu')
   else:
     relu = mx.symbol.Activation(data=sum1, name=alg, act_type=alg)
   return relu, attr
@@ -702,7 +710,8 @@ def test_pos_conv_act():
               "tanh": True,
               "softrelu": True,
               "relu6": True,
-              "leakyrelu": True}
+              "leakyrelu": True,
+              "gelu": True}
   for data_shape in DATA_SHAPE:
     for (alg, quantize) in act_list.items():
       net, attrs = conv_act(False, data_shape, alg)
@@ -741,7 +750,8 @@ def test_pos_conv_bn_act():
               "tanh": True,
               "softrelu": True,
               "relu6": True,
-              "leakyrelu": True}
+              "leakyrelu": True,
+              "gelu": True}
   for data_shape in DATA_SHAPE:
     for (alg, quantize) in act_list.items():
       net, attrs = conv_bn_act(False, data_shape, alg)
@@ -756,7 +766,8 @@ def test_pos_conv_bn_sum_act():
               "tanh": True,
               "softrelu": True,
               "relu6": False,
-              "leakyrelu": True}
+              "leakyrelu": True,
+              "gelu": True}
   for data_shape in DATA_SHAPE:
     for (alg, quantize) in act_list.items():
       net, attrs = conv_bn_sum_act(False, data_shape, alg)

From 5a2fce5dff52de0c20914a31783ef9824df63f9e Mon Sep 17 00:00:00 2001
From: "Joshua Z. Zhang" <cheungchih@gmail.com>
Date: Mon, 4 Nov 2019 14:33:39 -0800
Subject: [PATCH 565/813] [WIP][New Op] Add deformable conv v2 (#16341)

* add deformable conv v2

* fix lint and compiler warning

* fix lint

* fix pylint

* fix clang and lint

* fix base class, add test case

* fix gluon impl, add cpu forward

* address comment

* fix duplicate kernel name

* fix cpplint

* con't fix cpplint

* address comments

* fix mask scale

* make initial mask centered at 1 rather than 0.5

* fix submodule
---
 python/mxnet/gluon/contrib/cnn/conv_layers.py | 180 +++++-
 .../modulated_deformable_convolution-inl.h    | 576 ++++++++++++++++++
 .../modulated_deformable_convolution.cc       | 111 ++++
 .../modulated_deformable_convolution.cu       |  47 ++
 .../nn/modulated_deformable_im2col.cuh        | 541 ++++++++++++++++
 .../contrib/nn/modulated_deformable_im2col.h  | 291 +++++++++
 tests/python/gpu/test_gluon_contrib_gpu.py    |  27 +
 .../python/unittest/test_contrib_operator.py  |  38 +-
 tests/python/unittest/test_gluon_contrib.py   |  30 +
 9 files changed, 1839 insertions(+), 2 deletions(-)
 create mode 100644 src/operator/contrib/modulated_deformable_convolution-inl.h
 create mode 100644 src/operator/contrib/modulated_deformable_convolution.cc
 create mode 100644 src/operator/contrib/modulated_deformable_convolution.cu
 create mode 100644 src/operator/contrib/nn/modulated_deformable_im2col.cuh
 create mode 100644 src/operator/contrib/nn/modulated_deformable_im2col.h

diff --git a/python/mxnet/gluon/contrib/cnn/conv_layers.py b/python/mxnet/gluon/contrib/cnn/conv_layers.py
index 9dd208702932..098463eca968 100644
--- a/python/mxnet/gluon/contrib/cnn/conv_layers.py
+++ b/python/mxnet/gluon/contrib/cnn/conv_layers.py
@@ -19,7 +19,7 @@
 # pylint: disable= arguments-differ
 """Custom convolutional neural network layers in model_zoo."""
 
-__all__ = ['DeformableConvolution']
+__all__ = ['DeformableConvolution', 'ModulatedDeformableConvolution']
 
 from .... import symbol
 from ...block import HybridBlock
@@ -219,3 +219,181 @@ def __repr__(self):
         return s.format(name=self.__class__.__name__,
                         mapping='{0} -> {1}'.format(shape[1] if shape[1] else None, shape[0]),
                         **self._kwargs_deformable_conv)
+
+
+class ModulatedDeformableConvolution(HybridBlock):
+    """2-D Deformable Convolution v2 (Dai, 2018).
+
+    The modulated deformable convolution operation is described in https://arxiv.org/abs/1811.11168
+
+    Parameters
+    ----------
+    channels : int,
+        The dimensionality of the output space
+        i.e. the number of output channels in the convolution.
+    kernel_size : int or tuple/list of 2 ints, (Default value = (1,1))
+        Specifies the dimensions of the convolution window.
+    strides : int or tuple/list of 2 ints, (Default value = (1,1))
+        Specifies the strides of the convolution.
+    padding : int or tuple/list of 2 ints, (Default value = (0,0))
+        If padding is non-zero, then the input is implicitly zero-padded
+        on both sides for padding number of points.
+    dilation : int or tuple/list of 2 ints, (Default value = (1,1))
+        Specifies the dilation rate to use for dilated convolution.
+    groups : int, (Default value = 1)
+        Controls the connections between inputs and outputs.
+        At groups=1, all inputs are convolved to all outputs.
+        At groups=2, the operation becomes equivalent to having two convolution
+        layers side by side, each seeing half the input channels, and producing
+        half the output channels, and both subsequently concatenated.
+    num_deformable_group : int, (Default value = 1)
+        Number of deformable group partitions.
+    layout : str, (Default value = NCHW)
+        Dimension ordering of data and weight. Can be 'NCW', 'NWC', 'NCHW',
+        'NHWC', 'NCDHW', 'NDHWC', etc. 'N', 'C', 'H', 'W', 'D' stands for
+        batch, channel, height, width and depth dimensions respectively.
+        Convolution is performed over 'D', 'H', and 'W' dimensions.
+    use_bias : bool, (Default value = True)
+        Whether the layer for generating the output features uses a bias vector.
+    in_channels : int, (Default value = 0)
+        The number of input channels to this layer. If not specified,
+        initialization will be deferred to the first time `forward` is called
+        and input channels will be inferred from the shape of input data.
+    activation : str, (Default value = None)
+        Activation function to use. See :func:`~mxnet.ndarray.Activation`.
+        If you don't specify anything, no activation is applied
+        (ie. "linear" activation: `a(x) = x`).
+    weight_initializer : str or `Initializer`, (Default value = None)
+        Initializer for the `weight` weights matrix for the convolution layer
+        for generating the output features.
+    bias_initializer : str or `Initializer`, (Default value = zeros)
+        Initializer for the bias vector for the convolution layer
+        for generating the output features.
+    offset_weight_initializer : str or `Initializer`, (Default value = zeros)
+        Initializer for the `weight` weights matrix for the convolution layer
+        for generating the offset.
+    offset_bias_initializer : str or `Initializer`, (Default value = zeros),
+        Initializer for the bias vector for the convolution layer
+        for generating the offset.
+    offset_use_bias: bool, (Default value = True)
+        Whether the layer for generating the offset uses a bias vector.
+
+    Inputs:
+        - **data**: 4D input tensor with shape
+          `(batch_size, in_channels, height, width)` when `layout` is `NCHW`.
+          For other layouts shape is permuted accordingly.
+
+    Outputs:
+        - **out**: 4D output tensor with shape
+          `(batch_size, channels, out_height, out_width)` when `layout` is `NCHW`.
+          out_height and out_width are calculated as::
+
+              out_height = floor((height+2*padding[0]-dilation[0]*(kernel_size[0]-1)-1)/stride[0])+1
+              out_width = floor((width+2*padding[1]-dilation[1]*(kernel_size[1]-1)-1)/stride[1])+1
+    """
+
+    def __init__(self, channels, kernel_size=(1, 1), strides=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1,
+                 num_deformable_group=1, layout='NCHW', use_bias=True, in_channels=0, activation=None,
+                 weight_initializer=None, bias_initializer='zeros',
+                 offset_weight_initializer='zeros', offset_bias_initializer='zeros', offset_use_bias=True,
+                 op_name='ModulatedDeformableConvolution', adj=None, prefix=None, params=None):
+        super(ModulatedDeformableConvolution, self).__init__(prefix=prefix, params=params)
+        with self.name_scope():
+            self._channels = channels
+            self._in_channels = in_channels
+
+            assert layout in ('NCHW', 'NHWC'), "Only supports 'NCHW' and 'NHWC' layout for now"
+            if isinstance(kernel_size, numeric_types):
+                kernel_size = (kernel_size,) * 2
+            if isinstance(strides, numeric_types):
+                strides = (strides,) * len(kernel_size)
+            if isinstance(padding, numeric_types):
+                padding = (padding,) * len(kernel_size)
+            if isinstance(dilation, numeric_types):
+                dilation = (dilation,) * len(kernel_size)
+            self._op_name = op_name
+
+            offset_channels = 27
+            self._kwargs_offset = {
+                'kernel': kernel_size, 'stride': strides, 'dilate': dilation,
+                'pad': padding, 'num_filter': offset_channels, 'num_group': groups,
+                'no_bias': not offset_use_bias, 'layout': layout}
+
+            self._kwargs_deformable_conv = {
+                'kernel': kernel_size, 'stride': strides, 'dilate': dilation,
+                'pad': padding, 'num_filter': channels, 'num_group': groups,
+                'num_deformable_group': num_deformable_group,
+                'no_bias': not use_bias, 'layout': layout}
+
+            if adj:
+                self._kwargs_offset['adj'] = adj
+                self._kwargs_deformable_conv['adj'] = adj
+
+            deformable_conv_weight_shape = [0] * (len(kernel_size) + 2)
+            deformable_conv_weight_shape[0] = channels
+            deformable_conv_weight_shape[2] = kernel_size[0]
+            deformable_conv_weight_shape[3] = kernel_size[1]
+
+            self.deformable_conv_weight = self.params.get('deformable_conv_weight',
+                                                          shape=deformable_conv_weight_shape,
+                                                          init=weight_initializer,
+                                                          allow_deferred_init=True)
+
+            if use_bias:
+                self.deformable_conv_bias = self.params.get('deformable_conv_bias', shape=(channels,),
+                                                            init=bias_initializer,
+                                                            allow_deferred_init=True)
+            else:
+                self.deformable_conv_bias = None
+
+            dshape = [0] * (len(kernel_size) + 2)
+            dshape[layout.find('N')] = 1
+            dshape[layout.find('C')] = in_channels
+
+            op = getattr(symbol, 'Convolution')
+            offset = op(symbol.var('data', shape=dshape), **self._kwargs_offset)
+
+            offsetshapes = offset.infer_shape_partial()[0]
+
+            self.offset_weight = self.params.get('offset_weight', shape=offsetshapes[1],
+                                                 init=offset_weight_initializer,
+                                                 allow_deferred_init=True)
+
+            if offset_use_bias:
+                self.offset_bias = self.params.get('offset_bias', shape=offsetshapes[2],
+                                                   init=offset_bias_initializer,
+                                                   allow_deferred_init=True)
+            else:
+                self.offset_bias = None
+
+            if activation:
+                self.act = Activation(activation, prefix=activation + '_')
+            else:
+                self.act = None
+
+    def hybrid_forward(self, F, x, offset_weight, deformable_conv_weight, offset_bias=None, deformable_conv_bias=None):
+        if offset_bias is None:
+            offset = F.Convolution(x, offset_weight, cudnn_off=True, **self._kwargs_offset)
+        else:
+            offset = F.Convolution(x, offset_weight, offset_bias, cudnn_off=True, **self._kwargs_offset)
+
+        offset_t = F.slice_axis(offset, axis=1, begin=0, end=18)
+        mask = F.slice_axis(offset, axis=1, begin=18, end=None)
+        mask = F.sigmoid(mask) * 2
+
+        if deformable_conv_bias is None:
+            act = F.contrib.ModulatedDeformableConvolution(data=x, offset=offset_t, mask=mask,
+                                                           weight=deformable_conv_weight,
+                                                           name='fwd', **self._kwargs_deformable_conv)
+        else:
+            act = F.contrib.ModulatedDeformableConvolution(data=x, offset=offset_t, mask=mask,
+                                                           weight=deformable_conv_weight,
+                                                           bias=deformable_conv_bias, name='fwd',
+                                                           **self._kwargs_deformable_conv)
+
+        if self.act:
+            act = self.act(act)
+        return act
+
+    def _alias(self):
+        return 'modulated_deformable_conv'
diff --git a/src/operator/contrib/modulated_deformable_convolution-inl.h b/src/operator/contrib/modulated_deformable_convolution-inl.h
new file mode 100644
index 000000000000..07e8e29fe443
--- /dev/null
+++ b/src/operator/contrib/modulated_deformable_convolution-inl.h
@@ -0,0 +1,576 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2018 Microsoft
+ * Licensed under The MIT License [see LICENSE for details]
+ * \file modulated_deformable_convolution-inl.h
+ * \brief
+ * \ref: https://github.com/Yangqing/caffe/wiki/Convolution-in-Caffe:-a-memo
+ * \ref: https://arxiv.org/abs/1811.11168
+ * \author Yuwen Xiong, Haozhi Qi, Jifeng Dai, Xizhou Zhu, Han Hu
+*/
+#ifndef MXNET_OPERATOR_CONTRIB_MODULATED_DEFORMABLE_CONVOLUTION_INL_H_
+#define MXNET_OPERATOR_CONTRIB_MODULATED_DEFORMABLE_CONVOLUTION_INL_H_
+
+#include <mxnet/io.h>
+#include <mxnet/base.h>
+#include <mxnet/ndarray.h>
+#include <mxnet/operator.h>
+#include <mxnet/operator_util.h>
+#include <dmlc/logging.h>
+#include <dmlc/optional.h>
+#include <algorithm>
+#include <map>
+#include <vector>
+#include <string>
+#include <utility>
+#include "../operator_common.h"
+#include "../nn/im2col.h"
+#include "./nn/modulated_deformable_im2col.h"
+#include "../linalg.h"
+
+
+namespace mxnet {
+namespace op {
+
+namespace dmconv {
+  enum ModulatedDeformableConvolutionOpInputs { kData, kOffset, kMask, kWeight, kBias };
+  enum ModulatedDeformableConvolutionOpOutputs { kOut };
+  enum ModulatedDeformableConvolutionOpResource { kTempSpace };
+}
+
+struct ModulatedDeformableConvolutionParam
+  : public dmlc::Parameter<ModulatedDeformableConvolutionParam> {
+  mxnet::TShape kernel;
+  mxnet::TShape stride;
+  mxnet::TShape dilate;
+  mxnet::TShape pad;
+  uint32_t num_filter;
+  uint32_t num_group;
+  uint32_t num_deformable_group;
+  uint64_t workspace;
+  bool no_bias;
+  uint32_t im2col_step;
+  dmlc::optional<int> layout;
+  DMLC_DECLARE_PARAMETER(ModulatedDeformableConvolutionParam) {
+    DMLC_DECLARE_FIELD(kernel).describe("Convolution kernel size: (h, w) or (d, h, w)");
+    DMLC_DECLARE_FIELD(stride).set_default(mxnet::TShape(0, -1))
+      .describe("Convolution stride: (h, w) or (d, h, w). Defaults to 1 for each dimension.");
+    DMLC_DECLARE_FIELD(dilate).set_default(mxnet::TShape(0, -1))
+      .describe("Convolution dilate: (h, w) or (d, h, w). Defaults to 1 for each dimension.");
+    DMLC_DECLARE_FIELD(pad).set_default(mxnet::TShape(0, -1))
+      .describe("Zero pad for convolution: (h, w) or (d, h, w). Defaults to no padding.");
+    DMLC_DECLARE_FIELD(num_filter).set_range(1, 100000)
+      .describe("Convolution filter(channel) number");
+    DMLC_DECLARE_FIELD(num_group).set_default(1)
+      .describe("Number of group partitions.");
+    DMLC_DECLARE_FIELD(num_deformable_group).set_default(1)
+      .describe("Number of deformable group partitions.");
+    DMLC_DECLARE_FIELD(workspace).set_default(1024).set_range(0, 8192)
+      .describe("Maximum temperal workspace allowed for convolution (MB).");
+    DMLC_DECLARE_FIELD(no_bias).set_default(false)
+      .describe("Whether to disable bias parameter.");
+    DMLC_DECLARE_FIELD(im2col_step).set_default(64)
+      .describe("Maximum number of images per im2col computation; "
+                "The total batch size should be divisable by this value or "
+                "smaller than this value; if you face out of memory problem, "
+                "you can try to use a smaller value here.");
+    DMLC_DECLARE_FIELD(layout)
+      .add_enum("NCW", mshadow::kNCW)
+      .add_enum("NCHW", mshadow::kNCHW)
+      .add_enum("NCDHW", mshadow::kNCDHW)
+      .set_default(dmlc::optional<int>())
+      .describe("Set layout for input, output and weight. Empty for\n    "
+        "default layout: NCW for 1d, NCHW for 2d and NCDHW for 3d.");
+  }
+};
+
+template<typename xpu, typename DType>
+class ModulatedDeformableConvolutionOp : public Operator {
+ public:
+  explicit ModulatedDeformableConvolutionOp(ModulatedDeformableConvolutionParam p) {
+    this->param_ = p;
+    // convert MBytes first to Bytes and then to elements.
+    param_.workspace = (param_.workspace << 20) / sizeof(DType);
+    CHECK(param_.layout.value() == mshadow::kNCW ||
+      param_.layout.value() == mshadow::kNCHW ||
+      param_.layout.value() == mshadow::kNCDHW)
+      << "Only support NCW, NCHW and NCDHW layout";
+  }
+
+  virtual void Forward(const OpContext &ctx,
+    const std::vector<TBlob> &in_data,
+    const std::vector<OpReqType> &req,
+    const std::vector<TBlob> &out_data,
+    const std::vector<TBlob> &aux_args) {
+    using namespace mshadow;
+    using namespace mshadow::expr;
+    CHECK_EQ(req[dmconv::kOut], kWriteTo);
+    size_t expected = param_.no_bias ? 4 : 5;
+    CHECK_EQ(in_data.size(), expected);
+    CHECK_EQ(out_data.size(), 1U);
+    LayerSetUp(in_data[dmconv::kData].shape_,
+               in_data[dmconv::kOffset].shape_,
+               in_data[dmconv::kMask].shape_,
+               out_data[dmconv::kOut].shape_);
+    Stream<xpu>* s = ctx.get_stream<xpu>();
+    // allocate workspace for col_buffer
+    Tensor<xpu, 1, DType> workspace = ctx.requested[dmconv::kTempSpace]
+      .get_space_typed<xpu, 1, DType>(Shape1(col_buffer_size_ + num_*output_dim_), s);
+    // calculate the shape of col_buffer
+    mxnet::TShape col_buffer_shape(num_spatial_axes_ + 2, -1);
+    col_buffer_shape[0] = conv_in_channels_ * param_.kernel.Size();
+    //  for (index_t i = 1; i < col_buffer_shape.ndim(); ++i) {
+    //  col_buffer_shape[i] = out_data[0].shape_[i + 1];
+    col_buffer_shape[1] = im2col_step_;
+    for (index_t i = 2; i < col_buffer_shape.ndim(); ++i) {
+      col_buffer_shape[i] = out_data[0].shape_[i];
+    }
+    // create a column buffer using workspace and col_buffer_shape
+    TBlob col_buffer(workspace.dptr_, col_buffer_shape, xpu::kDevMask, DataType<DType>::kFlag);
+    mxnet::TShape output_buffer_shape(1, -1);
+    output_buffer_shape[0] = num_*output_dim_;
+    TBlob output_buffer(workspace.dptr_ + col_buffer_size_, output_buffer_shape,
+                        xpu::kDevMask, DataType<DType>::kFlag);
+
+    // initialize weight and col_buffer 3D tensors for using gemm
+    index_t M = conv_out_channels_ / group_;
+    index_t N = im2col_step_ * conv_out_spatial_dim_;
+    index_t K = kernel_dim_;
+    Tensor<xpu, 3, DType> weight_3d = in_data[dmconv::kWeight].get_with_shape<xpu, 3, DType>(
+      Shape3(group_, M, K), s);
+    Tensor<xpu, 3, DType> col_buffer_3d = col_buffer.get_with_shape<xpu, 3, DType>(
+      Shape3(group_, K, N), s);
+    Tensor<xpu, 4, DType> output_4d = output_buffer.get_with_shape<xpu, 4, DType>(
+                Shape4(num_ / im2col_step_, group_, M, N), s);
+    for (index_t n = 0; n < num_ / im2col_step_; ++n) {
+      // transform image to col_buffer in order to use gemm
+      modulated_deformable_im2col(s,
+              in_data[dmconv::kData].dptr<DType>() + n*im2col_step_*input_dim_,
+              in_data[dmconv::kOffset].dptr<DType>() + n*im2col_step_*input_offset_dim_,
+              in_data[dmconv::kMask].dptr<DType>() + n*im2col_step_ * input_mask_dim_,
+              in_data[dmconv::kData].shape_,
+              col_buffer.shape_, param_.kernel, param_.pad, param_.stride, param_.dilate,
+              param_.num_deformable_group, col_buffer.dptr<DType>());
+      Tensor<xpu, 3, DType> output_3d = output_4d[n];
+      for (index_t g = 0; g < group_; ++g) {
+        // Legacy approach shown here for comparison:
+        //   Assign(output_3d[g], req[dmconv::kOut], dot(weight_3d[g], col_buffer_3d[g]));
+        linalg_gemm(weight_3d[g], col_buffer_3d[g], output_3d[g], false, false, s, kWriteTo);
+      }
+    }
+    Tensor<xpu, 4, DType> trans_output_4d = output_buffer.get_with_shape<xpu, 4, DType>(
+        Shape4(num_ / im2col_step_, conv_out_channels_, im2col_step_, conv_out_spatial_dim_), s);
+    Tensor<xpu, 4, DType> original_output_4d = out_data[dmconv::kOut].get_with_shape<xpu, 4, DType>(
+        Shape4(num_ / im2col_step_, im2col_step_, conv_out_channels_, conv_out_spatial_dim_), s);
+    original_output_4d = swapaxis<2, 1>(trans_output_4d);
+
+    if (bias_term_) {
+      Tensor<xpu, 1, DType> bias = in_data[dmconv::kBias].get<xpu, 1, DType>(s);
+      Tensor<xpu, 3, DType> output_3d = out_data[dmconv::kOut].get_with_shape<xpu, 3, DType>(
+        Shape3(num_, conv_out_channels_, conv_out_spatial_dim_), s);
+      // has bias term, broadcast it to the same shape of output_3d in channel dim
+      output_3d += mshadow::expr::broadcast<1>(bias, output_3d.shape_);
+    }
+  }
+
+  virtual void Backward(const OpContext &ctx,
+    const std::vector<TBlob>& out_grad,
+    const std::vector<TBlob>& in_data,
+    const std::vector<TBlob>& out_data,
+    const std::vector<OpReqType>& req,
+    const std::vector<TBlob>& in_grad,
+    const std::vector<TBlob>& aux_args) {
+    using namespace mshadow;
+    using namespace mshadow::expr;
+    CHECK_EQ(out_grad.size(), 1U);
+    size_t expected = param_.no_bias == 0 ? 5 : 4;
+    CHECK(in_data.size() == expected && in_grad.size() == expected);
+    CHECK_EQ(req.size(), expected);
+    CHECK_EQ(in_data[dmconv::kWeight].CheckContiguous(), true);
+    LayerSetUp(in_grad[dmconv::kData].shape_,
+               in_grad[dmconv::kOffset].shape_,
+               in_grad[dmconv::kMask].shape_,
+               out_grad[dmconv::kOut].shape_);
+    Stream<xpu> *s = ctx.get_stream<xpu>();
+    // allocate workspace for col_buffer
+    Tensor<xpu, 1, DType> workspace = ctx.requested[dmconv::kTempSpace]
+      .get_space_typed<xpu, 1, DType>(Shape1(col_buffer_size_ + num_*output_dim_), s);
+    // calculate the shape of col_buffer
+    mxnet::TShape col_buffer_shape(num_spatial_axes_ + 2, -1);
+    col_buffer_shape[0] = conv_in_channels_ * param_.kernel.Size();
+    col_buffer_shape[1] = im2col_step_;
+    for (index_t i = 2; i < col_buffer_shape.ndim(); ++i) {
+      col_buffer_shape[i] = out_grad[dmconv::kData].shape_[i];
+    }
+    // create a column buffer using workspace and col_buffer_shape
+    TBlob col_buffer(workspace.dptr_, col_buffer_shape, xpu::kDevMask, DataType<DType>::kFlag);
+    mxnet::TShape output_buffer_shape(1, -1);
+    output_buffer_shape[0] = num_*output_dim_;
+    TBlob output_buffer(workspace.dptr_ + col_buffer_size_,
+      output_buffer_shape, xpu::kDevMask, DataType<DType>::kFlag);
+
+    Tensor<xpu, 4, DType> trans_output_4d = output_buffer.get_with_shape<xpu, 4, DType>(
+        Shape4(num_ / im2col_step_, conv_out_channels_, im2col_step_, conv_out_spatial_dim_), s);
+    Tensor<xpu, 4, DType> original_output_4d = out_grad[dmconv::kOut].get_with_shape<xpu, 4, DType>(
+        Shape4(num_ / im2col_step_, im2col_step_, conv_out_channels_, conv_out_spatial_dim_), s);
+    trans_output_4d = swapaxis<2, 1>(original_output_4d);
+
+    // initialize weight and col_buffer 3D tensors for using gemm
+    // For computing dLoss/d(in_data[kData])
+    index_t M = kernel_dim_;
+    index_t N = im2col_step_ * conv_out_spatial_dim_;
+    index_t K = conv_out_channels_ / group_;
+    Tensor<xpu, 3, DType> weight_3d = in_data[dmconv::kWeight].get_with_shape<xpu, 3, DType>(
+      Shape3(group_, K, M), s);
+    Tensor<xpu, 4, DType> out_grad_4d = output_buffer.get_with_shape<xpu, 4, DType>(
+          Shape4(num_ / im2col_step_, group_, K, N), s);
+    Tensor<xpu, 3, DType> col_buffer_3d = col_buffer.get_with_shape<xpu, 3, DType>(
+      Shape3(group_, M, N), s);
+    // For computing dLoss/dWeight
+    Tensor<xpu, 3, DType> dweight_3d = in_grad[dmconv::kWeight].get_with_shape<xpu, 3, DType>(
+      Shape3(group_, K, M), s);
+
+    Tensor<xpu, 1, DType> data_grad = in_grad[dmconv::kData].FlatTo1D<xpu, DType>(s);
+    if (req[dmconv::kData] == kWriteTo)
+        data_grad = 0;
+
+
+    for (index_t n = 0; n < num_ / im2col_step_; ++n) {
+      Tensor<xpu, 3, DType> out_grad_3d = out_grad_4d[n];
+      for (index_t g = 0; g < group_; ++g) {
+        // Legacy approach shown here for comparison:
+        //   col_buffer_3d[g] = dot(weight_3d[g].T(), out_grad_3d[g]);
+        linalg_gemm(weight_3d[g], out_grad_3d[g], col_buffer_3d[g], true, false, s);
+      }
+
+      // gradient w.r.t. input coordinate data
+      modulated_deformable_col2im_coord(s, col_buffer.dptr<DType>(),
+        in_data[dmconv::kData].dptr<DType>() + n*im2col_step_*input_dim_,
+        in_data[dmconv::kOffset].dptr<DType>() + n*im2col_step_*input_offset_dim_,
+        in_data[dmconv::kMask].dptr<DType>() + n*im2col_step_*input_mask_dim_,
+        in_grad[dmconv::kData].shape_, col_buffer.shape_,
+        param_.kernel, param_.pad, param_.stride, param_.dilate, param_.num_deformable_group,
+        in_grad[dmconv::kOffset].dptr<DType>() + n*im2col_step_*input_offset_dim_,
+        in_grad[dmconv::kMask].dptr<DType>() + n*im2col_step_*input_mask_dim_,
+        req[dmconv::kOffset], req[dmconv::kMask]);
+
+      // gradient w.r.t. input data
+      modulated_deformable_col2im(s, col_buffer.dptr<DType>(),
+        in_data[dmconv::kOffset].dptr<DType>() + n*im2col_step_*input_offset_dim_,
+        in_data[dmconv::kMask].dptr<DType>() + n*im2col_step_*input_mask_dim_,
+        in_grad[dmconv::kData].shape_, col_buffer.shape_,
+        param_.kernel, param_.pad, param_.stride, param_.dilate, param_.num_deformable_group,
+        in_grad[dmconv::kData].dptr<DType>() + n*im2col_step_*input_dim_,
+        req[dmconv::kData]);
+
+      // gradient w.r.t. weight, dWeight should accumulate across the batch and group
+      modulated_deformable_im2col(s,
+        in_data[dmconv::kData].dptr<DType>() + n*im2col_step_*input_dim_,
+        in_data[dmconv::kOffset].dptr<DType>() + n*im2col_step_*input_offset_dim_,
+        in_data[dmconv::kMask].dptr<DType>() + n*im2col_step_*input_mask_dim_,
+        in_data[dmconv::kData].shape_,
+        col_buffer.shape_, param_.kernel, param_.pad, param_.stride, param_.dilate,
+        param_.num_deformable_group, col_buffer.dptr<DType>());
+
+      for (index_t g = 0; g < group_; ++g) {
+        auto request = (n == 0) ? req[dmconv::kWeight] : kAddTo;
+        // Legacy approach shown here for comparison:
+        //   Assign(dweight_3d[g], request, dot(out_grad_3d[g], col_buffer_3d[g].T()));
+        linalg_gemm(out_grad_3d[g], col_buffer_3d[g], dweight_3d[g], false, true, s, request);
+      }
+    }
+
+    // gradient w.r.t bias
+    if (bias_term_) {
+      Tensor<xpu, 1, DType> dbias = in_grad[dmconv::kBias].get<xpu, 1, DType>(s);
+      Tensor<xpu, 3, DType> dout = out_grad[dmconv::kOut].get_with_shape<xpu, 3, DType>(
+        Shape3(num_, conv_out_channels_, conv_out_spatial_dim_), s);
+      ASSIGN_DISPATCH(dbias, req[dmconv::kBias], sumall_except_dim<1>(dout));
+    }
+  }
+
+ private:
+  void LayerSetUp(const mxnet::TShape& ishape, const mxnet::TShape& offset_shape,
+                  const mxnet::TShape& mask_shape, const mxnet::TShape& oshape) {
+    channel_axis_ = 1;  // hard code channel axis
+    const index_t first_spatial_axis = channel_axis_ + 1;
+    const index_t num_axes = param_.kernel.ndim() + 2;
+    num_spatial_axes_ = num_axes - first_spatial_axis;
+    is_1x1_ = true;
+    for (index_t i = 0; i < param_.kernel.ndim(); ++i) {
+      is_1x1_ &= param_.kernel[i] == 1 && param_.stride[i] == 1 && param_.pad[i] == 0;
+      if (!is_1x1_) break;
+    }
+
+    // batch size
+    num_ = ishape[0];
+    // number of input channels
+    channels_ = ishape[1];
+    group_ = param_.num_group;
+    conv_out_channels_ = param_.num_filter;
+    conv_in_channels_ = channels_;
+    bias_term_ = !param_.no_bias;
+    kernel_dim_ = conv_in_channels_ / group_ * param_.kernel.Size();
+    weight_offset_ = conv_out_channels_ * kernel_dim_ / group_;
+    conv_out_spatial_dim_ = oshape.ProdShape(2, oshape.ndim());
+    col_offset_ = kernel_dim_ * conv_out_spatial_dim_;
+    output_offset_ = conv_out_channels_ * conv_out_spatial_dim_ / group_;
+    // size of the column buffer used for storing im2col-ed pixels
+    im2col_step_ = std::min(param_.im2col_step, static_cast<uint32_t>(num_));
+    col_buffer_size_ = kernel_dim_ * group_ * im2col_step_ * conv_out_spatial_dim_;
+    // input/output image size (#channels * height * width)
+    input_dim_ = ishape.ProdShape(1, ishape.ndim());
+    input_offset_dim_ = offset_shape.ProdShape(1, offset_shape.ndim());
+    input_mask_dim_ = mask_shape.ProdShape(1, mask_shape.ndim());
+    output_dim_ = oshape.ProdShape(1, oshape.ndim());
+    num_kernels_im2col_ = conv_in_channels_ * conv_out_spatial_dim_;
+    num_kernels_col2im_ = input_dim_;
+  }
+
+ private:
+  ModulatedDeformableConvolutionParam param_;
+  index_t channel_axis_;  // channel axis of the input
+  index_t channels_;  // number of channels of input image
+  index_t num_spatial_axes_;  // number of spatial axes
+  index_t num_;  // batch size
+  index_t group_;  // number of groups
+  index_t conv_out_channels_;  // number of output channels (num_filter)
+  index_t conv_out_spatial_dim_;  // number of pixels of output images per channel
+  index_t conv_in_channels_;  // number of input channels
+  index_t kernel_dim_;  // number of input channels per group * kernel size
+  index_t weight_offset_;  // number of output channels per group * kernel_dim_
+  index_t col_offset_;
+  index_t output_offset_;
+  index_t col_buffer_size_;
+  index_t input_dim_;
+  index_t input_offset_dim_;
+  index_t input_mask_dim_;
+  index_t output_dim_;
+  index_t num_kernels_im2col_;
+  index_t num_kernels_col2im_;
+  index_t im2col_step_;
+  bool bias_term_;  // has bias term?
+  bool is_1x1_;
+};  // class ConvolutionOp
+
+template<typename xpu>
+Operator* CreateOp(ModulatedDeformableConvolutionParam param, int dtype,
+  std::vector<mxnet::TShape> *in_shape,
+  std::vector<mxnet::TShape> *out_shape,
+  Context ctx);
+
+#if DMLC_USE_CXX11
+class ModulatedDeformableConvolutionProp : public OperatorProperty {
+ public:
+  std::vector<std::string> ListArguments() const override {
+    if (!param_.no_bias) {
+      return{ "data", "offset", "mask", "weight", "bias" };
+    } else {
+      return{ "data", "offset", "mask", "weight" };
+    }
+  }
+
+  void Init(const std::vector<std::pair<std::string, std::string> >& kwargs) override {
+    using namespace mshadow;
+    param_.Init(kwargs);
+    if (param_.kernel.ndim() == 2) {
+      param_.layout = param_.layout ? param_.layout.value() : mshadow::kNCHW;
+      if (param_.stride.ndim() == 0) param_.stride = Shape2(1, 1);
+      if (param_.dilate.ndim() == 0) param_.dilate = Shape2(1, 1);
+      if (param_.pad.ndim() == 0) param_.pad = Shape2(0, 0);
+    } else {
+      LOG(FATAL) << "not implemented";
+    }
+  }
+
+  std::map<std::string, std::string> GetParams() const override {
+    return param_.__DICT__();
+  }
+
+  bool InferShape(std::vector<mxnet::TShape> *in_shape,
+    std::vector<mxnet::TShape> *out_shape,
+    std::vector<mxnet::TShape> *aux_shape) const override {
+    using namespace mshadow;
+    if (!param_.no_bias) {
+      CHECK_EQ(in_shape->size(), 5U) << "Input:[data, offset, mask, weight, bias]";
+    } else {
+      CHECK_EQ(in_shape->size(), 4U) << "Input:[data, offset, mask, weight]";
+    }
+    out_shape->resize(1, mxnet::TShape());
+    const mxnet::TShape &dshp = (*in_shape)[dmconv::kData];
+    const mxnet::TShape &oshp = (*in_shape)[dmconv::kOffset];
+    const mxnet::TShape &mshp = (*in_shape)[dmconv::kMask];
+    if (dshp.ndim() == 0) return false;
+    if (param_.kernel.ndim() == 2) {
+      // 2d dmconv
+      CHECK_EQ(dshp.ndim(), 4U) \
+        << "Input data should be 4D in batch-num_filter-y-x";
+      CHECK_EQ(oshp.ndim(), 4U) \
+        << "Input offset should be 4D in batch-num_filter-y-x";
+      CHECK_EQ(mshp.ndim(), 4U) \
+        << "Input offset should be 4D in batch-num_filter-y-x";
+      Shape<4> dshape = ConvertLayout(dshp.get<4>(), param_.layout.value(), kNCHW);
+      Shape<4> offsetshape = ConvertLayout(oshp.get<4>(), param_.layout.value(), kNCHW);
+      Shape<4> maskshape = ConvertLayout(mshp.get<4>(), param_.layout.value(), kNCHW);
+      Shape<4> wshape = Shape4(param_.num_filter / param_.num_group, dshape[1] / param_.num_group,
+        param_.kernel[0], param_.kernel[1]);
+      wshape = ConvertLayout(wshape, kNCHW, param_.layout.value());
+      wshape[0] *= param_.num_group;
+      SHAPE_ASSIGN_CHECK(*in_shape, dmconv::kWeight, wshape);
+      if (!param_.no_bias) {
+        SHAPE_ASSIGN_CHECK(*in_shape, dmconv::kBias, Shape1(param_.num_filter));
+      }
+
+      const index_t ksize_y = static_cast<index_t>(param_.kernel[0]);
+      const index_t ksize_x = static_cast<index_t>(param_.kernel[1]);
+      if (dshape[0] > static_cast<index_t>(param_.im2col_step)) {
+           CHECK_EQ(dshape[0] % param_.im2col_step, 0U) \
+           << "input batchsize must be smaller than or divide im2col_step";
+      }
+      CHECK_EQ(dshape[1] % param_.num_group, 0U) \
+        << "input num_filter must divide group size";
+      CHECK_EQ(dshape[1] % param_.num_deformable_group, 0U) \
+        << "input num_filter must divide deformable group size";
+      CHECK_EQ(param_.num_filter % param_.num_group, 0U) \
+        << "output num_filter must divide group size";
+      CHECK_GT(param_.kernel.Size(), 0U) \
+        << "incorrect kernel size: " << param_.kernel;
+      CHECK_GT(param_.stride.Size(), 0U) \
+        << "incorrect stride size: " << param_.stride;
+      CHECK_GT(param_.dilate.Size(), 0U) \
+        << "incorrect dilate size: " << param_.dilate;
+      Shape<4> oshape;
+      oshape[0] = dshape[0];
+      oshape[1] = param_.num_filter;
+      oshape[2] = (dshape[2] + 2 * param_.pad[0] -
+        (param_.dilate[0] * (ksize_y - 1) + 1)) / param_.stride[0] + 1;
+      oshape[3] = (dshape[3] + 2 * param_.pad[1] -
+        (param_.dilate[1] * (ksize_x - 1) + 1)) / param_.stride[1] + 1;
+      SHAPE_ASSIGN_CHECK(*out_shape, 0, ConvertLayout(oshape, kNCHW, param_.layout.value()));
+      CHECK_EQ(oshape[1] % param_.num_deformable_group, 0U) \
+        << "output num_filter must divide deformable group size";
+      CHECK_EQ(oshape[2], offsetshape[2]) \
+        << "output height must equal to offset map height";
+      CHECK_EQ(oshape[3], offsetshape[3]) \
+        << "output width must equal to offset map width";
+      CHECK_EQ(offsetshape[1] % (param_.kernel[0] * param_.kernel[1]), 0U) \
+        << "offset filter must divide deformable group size";
+      CHECK_EQ(offsetshape[1] / (2 * param_.kernel[0] * param_.kernel[1]), \
+               param_.num_deformable_group) \
+        << "offset filter must divide deformable group size";
+      CHECK_EQ(oshape[2], maskshape[2]) \
+        << "output height must equal to mask map height";
+      CHECK_EQ(oshape[3], maskshape[3]) \
+        << "output width must equal to mask map width";
+      CHECK_EQ(maskshape[1] % (param_.kernel[0] * param_.kernel[1]), 0U) \
+        << "offset filter must divide deformable group size";
+      CHECK_EQ(maskshape[1] / (param_.kernel[0] * param_.kernel[1]), \
+               param_.num_deformable_group) \
+        << "offset filter must divide deformable group size";
+      // Perform incomplete shape inference. Fill in the missing values in data shape.
+      // 1) We can always fill in the batch_size.
+      // 2) We can back-calculate the input height/width if the corresponding stride is 1.
+      oshape = ConvertLayout((*out_shape)[0].get<4>(), param_.layout.value(), kNCHW);
+      dshape[0] = oshape[0];
+      if (param_.stride[0] == 1) {
+        dshape[2] = oshape[2] + param_.dilate[0] * (ksize_y - 1) - 2 * param_.pad[0];
+      }
+      if (param_.stride[1] == 1) {
+        dshape[3] = oshape[3] + param_.dilate[1] * (ksize_x - 1) - 2 * param_.pad[1];
+      }
+      SHAPE_ASSIGN_CHECK(*in_shape, dmconv::kData,
+        ConvertLayout(dshape, kNCHW, param_.layout.value()));
+      // Check whether the kernel sizes are valid
+      if (dshape[2] != 0) {
+        CHECK_LE(ksize_y, dshape[2] + 2 * param_.pad[0]) << "kernel size exceed input";
+      }
+      if (dshape[3] != 0) {
+        CHECK_LE(ksize_x, dshape[3] + 2 * param_.pad[1]) << "kernel size exceed input";
+      }
+      return true;
+    } else {
+      LOG(FATAL) << "not implemented";
+      return false;
+    }
+  }
+
+  bool InferType(std::vector<int> *in_type,
+    std::vector<int> *out_type,
+    std::vector<int> *aux_type) const override {
+    CHECK_GE(in_type->size(), 1U);
+    int dtype = (*in_type)[0];
+    CHECK_NE(dtype, -1) << "First input must have specified type";
+    for (std::size_t i = 0; i < in_type->size(); ++i) {
+      if ((*in_type)[i] == -1) {
+        (*in_type)[i] = dtype;
+      } else {
+        UNIFORM_TYPE_CHECK((*in_type)[i], dtype, ListArguments()[i]);
+      }
+    }
+    out_type->clear();
+    out_type->push_back(dtype);
+    return true;
+  }
+
+  OperatorProperty* Copy() const override {
+    auto ptr = new ModulatedDeformableConvolutionProp();
+    ptr->param_ = param_;
+    return ptr;
+  }
+
+  std::string TypeString() const override {
+    return "_contrib_ModulatedDeformableConvolution";
+  }
+
+  std::vector<int> DeclareBackwardDependency(
+    const std::vector<int> &out_grad,
+    const std::vector<int> &in_data,
+    const std::vector<int> &out_data) const override {
+    return{ out_grad[dmconv::kOut], in_data[dmconv::kData],
+            in_data[dmconv::kOffset], in_data[dmconv::kMask],
+            in_data[dmconv::kWeight] };
+  }
+
+  std::vector<ResourceRequest> ForwardResource(
+    const std::vector<mxnet::TShape> &in_shape) const override {
+    return{ ResourceRequest::kTempSpace };
+  }
+
+  std::vector<ResourceRequest> BackwardResource(
+    const std::vector<mxnet::TShape> &in_shape) const override {
+    return{ ResourceRequest::kTempSpace };
+  }
+
+  Operator* CreateOperator(Context ctx) const override {
+    LOG(FATAL) << "Not Implemented.";
+    return NULL;
+  }
+
+  Operator* CreateOperatorEx(Context ctx, std::vector<mxnet::TShape> *in_shape,
+    std::vector<int> *in_type) const override;
+
+ private:
+  ModulatedDeformableConvolutionParam param_;
+};  // class ConvolutionProp
+#endif  // DMLC_USE_CXX11
+}  // namespace op
+}  // namespace mxnet
+#endif  // MXNET_OPERATOR_CONTRIB_MODULATED_DEFORMABLE_CONVOLUTION_INL_H_
diff --git a/src/operator/contrib/modulated_deformable_convolution.cc b/src/operator/contrib/modulated_deformable_convolution.cc
new file mode 100644
index 000000000000..5fa25f797a7a
--- /dev/null
+++ b/src/operator/contrib/modulated_deformable_convolution.cc
@@ -0,0 +1,111 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2018 Microsoft
+ * Licensed under The MIT License [see LICENSE for details]
+ * \file modulated_deformable_convolution.cc
+ * \brief
+ * \author Yuwen Xiong, Haozhi Qi, Jifeng Dai, Xizhou Zhu, Han Hu
+*/
+
+#include "./modulated_deformable_convolution-inl.h"
+
+namespace mxnet {
+namespace op {
+DMLC_REGISTER_PARAMETER(ModulatedDeformableConvolutionParam);
+
+template<>
+Operator* CreateOp<cpu>(ModulatedDeformableConvolutionParam param, int dtype,
+                        std::vector<TShape> *in_shape,
+                        std::vector<TShape> *out_shape,
+                        Context ctx) {
+  Operator *op = nullptr;
+  MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
+    op = new ModulatedDeformableConvolutionOp<cpu, DType>(param);
+  })
+  return op;
+}
+
+// DO_BIND_DISPATCH comes from operator_common.h
+Operator *ModulatedDeformableConvolutionProp::CreateOperatorEx(Context ctx,
+                                            std::vector<TShape> *in_shape,
+                                            std::vector<int> *in_type) const {
+  std::vector<TShape> out_shape, aux_shape;
+  std::vector<int> out_type, aux_type;
+  CHECK(InferType(in_type, &out_type, &aux_type));
+  CHECK(InferShape(in_shape, &out_shape, &aux_shape));
+  DO_BIND_DISPATCH(CreateOp, param_, (*in_type)[0], in_shape, &out_shape, ctx);
+}
+
+MXNET_REGISTER_OP_PROPERTY(
+  _contrib_ModulatedDeformableConvolution, ModulatedDeformableConvolutionProp)
+.describe(R"code(Compute 2-D modulated deformable convolution on 4-D input.
+
+The modulated deformable convolution operation is described in https://arxiv.org/abs/1811.11168
+
+For 2-D modulated deformable convolution, the shapes are
+
+- **data**: *(batch_size, channel, height, width)*
+- **offset**: *(batch_size, num_deformable_group * kernel[0] * kernel[1] * 2, height, width)*
+- **mask**: *(batch_size, num_deformable_group * kernel[0] * kernel[1], height, width)*
+- **weight**: *(num_filter, channel, kernel[0], kernel[1])*
+- **bias**: *(num_filter,)*
+- **out**: *(batch_size, num_filter, out_height, out_width)*.
+
+Define::
+
+  f(x,k,p,s,d) = floor((x+2*p-d*(k-1)-1)/s)+1
+
+then we have::
+
+  out_height=f(height, kernel[0], pad[0], stride[0], dilate[0])
+  out_width=f(width, kernel[1], pad[1], stride[1], dilate[1])
+
+If ``no_bias`` is set to be true, then the ``bias`` term is ignored.
+
+The default data ``layout`` is *NCHW*, namely *(batch_size, channle, height,
+width)*.
+
+If ``num_group`` is larger than 1, denoted by *g*, then split the input ``data``
+evenly into *g* parts along the channel axis, and also evenly split ``weight``
+along the first dimension. Next compute the convolution on the *i*-th part of
+the data with the *i*-th weight part. The output is obtained by concating all
+the *g* results.
+
+If ``num_deformable_group`` is larger than 1, denoted by *dg*, then split the
+input ``offset`` evenly into *dg* parts along the channel axis, and also evenly
+split ``out`` evenly into *dg* parts along the channel axis. Next compute the
+deformable convolution, apply the *i*-th part of the offset part on the *i*-th
+out.
+
+
+Both ``weight`` and ``bias`` are learnable parameters.
+
+
+)code" ADD_FILELINE)
+.add_argument("data", "NDArray-or-Symbol", "Input data to the ModulatedDeformableConvolutionOp.")
+.add_argument("offset", "NDArray-or-Symbol", "Input offset to ModulatedDeformableConvolutionOp.")
+.add_argument("mask", "NDArray-or-Symbol", "Input mask to the ModulatedDeformableConvolutionOp.")
+.add_argument("weight", "NDArray-or-Symbol", "Weight matrix.")
+.add_argument("bias", "NDArray-or-Symbol", "Bias parameter.")
+.add_arguments(ModulatedDeformableConvolutionParam::__FIELDS__());
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/contrib/modulated_deformable_convolution.cu b/src/operator/contrib/modulated_deformable_convolution.cu
new file mode 100644
index 000000000000..fce73dd49b1f
--- /dev/null
+++ b/src/operator/contrib/modulated_deformable_convolution.cu
@@ -0,0 +1,47 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2018 Microsoft
+ * Licensed under The MIT License [see LICENSE for details]
+ * \file modulated_deformable_convolution.cu
+ * \brief
+ * \author Yuwen Xiong, Haozhi Qi, Jifeng Dai, Xizhou Zhu, Han Hu
+*/
+
+#include "./modulated_deformable_convolution-inl.h"
+#include <vector>
+
+namespace mxnet {
+namespace op {
+
+  template<>
+  Operator* CreateOp<gpu>(ModulatedDeformableConvolutionParam param, int dtype,
+    std::vector<TShape> *in_shape,
+    std::vector<TShape> *out_shape,
+    Context ctx) {
+    Operator *op = NULL;
+    MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
+      op = new ModulatedDeformableConvolutionOp<gpu, DType>(param);
+    })
+      return op;
+  }
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/contrib/nn/modulated_deformable_im2col.cuh b/src/operator/contrib/nn/modulated_deformable_im2col.cuh
new file mode 100644
index 000000000000..16d9cef46d4e
--- /dev/null
+++ b/src/operator/contrib/nn/modulated_deformable_im2col.cuh
@@ -0,0 +1,541 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ ******************* BEGIN Caffe Copyright Notice and Disclaimer ****************
+ *
+ * COPYRIGHT
+ *
+ * All contributions by the University of California:
+ * Copyright (c) 2014-2017 The Regents of the University of California (Regents)
+ * All rights reserved.
+ *
+ * All other contributions:
+ * Copyright (c) 2014-2017, the respective contributors
+ * All rights reserved.
+ *
+ * Caffe uses a shared copyright model: each contributor holds copyright over
+ * their contributions to Caffe. The project versioning records all such
+ * contribution and copyright details. If a contributor wants to further mark
+ * their specific copyright on a particular contribution, they should indicate
+ * their copyright solely in the commit message of the change when it is
+ * committed.
+ *
+ * LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright notice, this
+ * list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright notice,
+ * this list of conditions and the following disclaimer in the documentation
+ * and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+ * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ * CONTRIBUTION AGREEMENT
+ *
+ * By contributing to the BVLC/caffe repository through pull-request, comment,
+ * or otherwise, the contributor releases their content to the
+ * license and copyright terms herein.
+ *
+ ***************** END Caffe Copyright Notice and Disclaimer ********************
+ *
+ * Copyright (c) 2018 Microsoft
+ * Licensed under The MIT License [see LICENSE for details]
+ * \file modulated_deformable_im2col.cuh
+ * \brief Function definitions of converting an image to
+ * column matrix based on kernel, padding, dilation, and offset.
+ * These functions are mainly used in modulated deformable convolution operators.
+ * \ref: https://arxiv.org/abs/1811.11168
+ * \author Yuwen Xiong, Haozhi Qi, Jifeng Dai, Xizhou Zhu, Han Hu
+ */
+
+#ifndef MXNET_OPERATOR_CONTRIB_NN_MODULATED_DEFORMABLE_IM2COL_CUH_
+#define MXNET_OPERATOR_CONTRIB_NN_MODULATED_DEFORMABLE_IM2COL_CUH_
+
+#include <mxnet/base.h>
+#include <mxnet/operator.h>
+#include <algorithm>
+#include <cstring>
+#include <vector>
+#include "../../mxnet_op.h"
+#include "../../../common/cuda_utils.h"
+
+
+
+namespace mxnet {
+namespace op {
+
+template <typename DType>
+__device__ DType dmcn_im2col_bilinear(const DType* bottom_data, const int data_width,
+  const int height, const int width, DType h, DType w) {
+
+  int h_low = floor(h);
+  int w_low = floor(w);
+  int h_high = h_low + 1;
+  int w_high = w_low + 1;
+
+  DType lh = h - h_low;
+  DType lw = w - w_low;
+  DType hh = 1 - lh, hw = 1 - lw;
+
+  DType v1 = 0;
+  if (h_low >= 0 && w_low >= 0)
+    v1 = bottom_data[h_low * data_width + w_low];
+  DType v2 = 0;
+  if (h_low >=0 && w_high <= width - 1)
+    v2 = bottom_data[h_low * data_width + w_high];
+  DType v3 = 0;
+  if (h_high <= height - 1 && w_low >= 0)
+    v3 = bottom_data[h_high * data_width + w_low];
+  DType v4 = 0;
+  if (h_high <= height - 1 && w_high <= width - 1)
+    v4 = bottom_data[h_high * data_width + w_high];
+
+  DType w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;
+
+  DType val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
+  return val;
+}
+
+
+template <typename DType>
+__device__ DType dmcn_get_gradient_weight(DType argmax_h, DType argmax_w,
+  const int h, const int w, const int height, const int width) {
+
+  if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) {
+    //empty
+    return 0;
+  }
+
+  int argmax_h_low = floor(argmax_h);
+  int argmax_w_low = floor(argmax_w);
+  int argmax_h_high = argmax_h_low + 1;
+  int argmax_w_high = argmax_w_low + 1;
+
+  DType weight = 0;
+  if (h == argmax_h_low && w == argmax_w_low)
+      weight = (h + 1 - argmax_h) * (w + 1 - argmax_w);
+  if (h == argmax_h_low && w == argmax_w_high)
+      weight = (h + 1 - argmax_h) * (argmax_w + 1 - w);
+  if (h == argmax_h_high && w == argmax_w_low)
+      weight = (argmax_h + 1 - h) * (w + 1 - argmax_w);
+  if (h == argmax_h_high && w == argmax_w_high)
+      weight = (argmax_h + 1 - h) * (argmax_w + 1 - w);
+  return weight;
+}
+
+
+template <typename DType>
+__device__ DType dmcn_get_coordinate_weight(DType argmax_h, DType argmax_w,
+  const int height, const int width, const DType* im_data,
+  const int data_width, const int bp_dir) {
+
+  if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width)
+  {
+    //empty
+    return 0;
+  }
+
+  int argmax_h_low = floor(argmax_h);
+  int argmax_w_low = floor(argmax_w);
+  int argmax_h_high = argmax_h_low + 1;
+  int argmax_w_high = argmax_w_low + 1;
+
+  DType weight = 0;
+
+  if (bp_dir == 0) {
+    if (argmax_h_low >= 0 && argmax_w_low >= 0)
+        weight += -1 * (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_low * data_width + argmax_w_low];
+    if (argmax_h_low >= 0 && argmax_w_high <= width - 1)
+        weight += -1 * (argmax_w - argmax_w_low) * im_data[argmax_h_low * data_width + argmax_w_high];
+    if (argmax_h_high <= height - 1 && argmax_w_low >= 0)
+        weight += (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_high * data_width + argmax_w_low];
+    if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1)
+        weight += (argmax_w - argmax_w_low) * im_data[argmax_h_high * data_width + argmax_w_high];
+  } else if (bp_dir == 1) {
+    if (argmax_h_low >= 0 && argmax_w_low >= 0)
+        weight += -1 * (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_low];
+    if (argmax_h_low >= 0 && argmax_w_high <= width - 1)
+        weight += (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_high];
+    if (argmax_h_high <= height - 1 && argmax_w_low >= 0)
+        weight += -1 * (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_low];
+    if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1)
+        weight += (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_high];
+  }
+
+  return weight;
+}
+
+
+/*!
+ * \brief deformable_im2col gpu kernel.
+ * DO NOT call this directly. Use wrapper function im2col() instead;
+ */
+template <typename DType>
+__global__ void modulated_deformable_im2col_gpu_kernel(const int n,
+  const DType* data_im, const DType* data_offset, const DType* data_mask,
+  const int height, const int width, const int kernel_h, const int kernel_w,
+  const int pad_h, const int pad_w,
+  const int stride_h, const int stride_w,
+  const int dilation_h, const int dilation_w,
+  const int channel_per_deformable_group,
+  const int batch_size, const int num_channels, const int deformable_group,
+  const int height_col, const int width_col,
+  DType* data_col) {
+  CUDA_KERNEL_LOOP(index, n) {
+    // index index of output matrix
+    const int w_col = index % width_col;
+    const int h_col = (index / width_col) % height_col;
+    const int b_col = (index / width_col / height_col) % batch_size;
+    const int c_im = (index / width_col / height_col) / batch_size;
+    const int c_col = c_im * kernel_h * kernel_w;
+
+    // compute deformable group index
+    const int deformable_group_index = c_im / channel_per_deformable_group;
+
+    const int h_in = h_col * stride_h - pad_h;
+    const int w_in = w_col * stride_w - pad_w;
+
+    DType* data_col_ptr = data_col + ((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col;
+    //const DType* data_im_ptr = data_im + ((b_col * num_channels + c_im) * height + h_in) * width + w_in;
+    const DType* data_im_ptr = data_im + (b_col * num_channels + c_im) * height * width;
+    const DType* data_offset_ptr = data_offset + (b_col * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col;
+
+    const DType* data_mask_ptr = data_mask + (b_col *  deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col;
+
+    for (int i = 0; i < kernel_h; ++i) {
+      for (int j = 0; j < kernel_w; ++j) {
+        const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_col) * width_col + w_col;
+        const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col + w_col;
+        const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_col) * width_col + w_col;
+        const DType offset_h = data_offset_ptr[data_offset_h_ptr];
+        const DType offset_w = data_offset_ptr[data_offset_w_ptr];
+        const DType mask = data_mask_ptr[data_mask_hw_ptr];
+        DType val = static_cast<DType>(0);
+        const DType h_im = h_in + i * dilation_h + offset_h;
+        const DType w_im = w_in + j * dilation_w + offset_w;
+        //if (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) {
+        if (h_im > -1 && w_im > -1 && h_im < height && w_im < width) {
+          //const DType map_h = i * dilation_h + offset_h;
+          //const DType map_w = j * dilation_w + offset_w;
+          //const int cur_height = height - h_in;
+          //const int cur_width = width - w_in;
+          //val = dmcn_im2col_bilinear(data_im_ptr, width, cur_height, cur_width, map_h, map_w);
+          val = dmcn_im2col_bilinear(data_im_ptr, width, height, width, h_im, w_im);
+        }
+        *data_col_ptr = val * mask;
+        data_col_ptr += batch_size * height_col * width_col;
+        //data_col_ptr += height_col * width_col;
+      }
+    }
+  }
+}
+
+
+
+/*!\brief
+ * cpu function of deformable_im2col algorithm
+ * \param s device stream
+ * \param data_im pointer of an image (N, C, H, W, ...) in the image batch
+ * \param data_offset pointer of offset (N, deformable_group*kernel_h*kernel_w*2, H, W, ...) in the offset batch
+ * \param im_shape input image shape in dimensions (N, C, H, W,)
+ * \param col_shape column buffer shape (#channels, N, output_im_height, output_im_width, ...)
+ * \param kernel_shape kernel filter shape
+ * \param pad pad shape
+ * \param stride stride shape
+ * \param dilation dilation shape
+ * \param deformable_group #offset group that deformable convolution use
+ * \param data_col column buffer pointer
+ */
+template <typename DType>
+inline void modulated_deformable_im2col(mshadow::Stream<gpu>* s,
+  const DType* data_im, const DType* data_offset, const DType* data_mask,
+  const TShape& im_shape, const TShape& col_shape, const TShape& kernel_shape,
+  const TShape& pad, const TShape& stride, const TShape& dilation,
+  const uint32_t deformable_group, DType* data_col) {
+  // num_axes should be smaller than block size
+  index_t num_spatial_axes = kernel_shape.ndim();
+  CHECK_LT(num_spatial_axes, mshadow::cuda::kBaseThreadNum);
+  index_t channel_per_deformable_group = im_shape[1] / deformable_group;
+  index_t num_kernels = im_shape[1] * col_shape.ProdShape(1, col_shape.ndim());
+  using namespace mxnet_op;
+  switch (num_spatial_axes) {
+  case 2:
+    modulated_deformable_im2col_gpu_kernel<DType> // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<cuda_get_num_blocks(num_kernels), mshadow::cuda::kBaseThreadNum,
+           0, mshadow::Stream<gpu>::GetStream(s)>>>(
+        num_kernels, data_im, data_offset, data_mask, im_shape[2], im_shape[3], kernel_shape[0], kernel_shape[1],
+        pad[0], pad[1], stride[0], stride[1], dilation[0], dilation[1], channel_per_deformable_group,
+        col_shape[1], im_shape[1], deformable_group, col_shape[2], col_shape[3], data_col);
+    MSHADOW_CUDA_POST_KERNEL_CHECK(modulated_deformable_im2col_gpu_kernel);
+    break;
+  default:
+    LOG(FATAL) << "im2col_nd_gpu does not support computation with "
+               << num_spatial_axes << " spatial axes";
+  }
+}
+
+
+/*!
+* \brief deformable_col2im gpu kernel.
+* \brief DO NOT call this directly. Use wrapper function deformable_col2im() instead;
+*/
+template <typename DType>
+__global__ void modulated_deformable_col2im_gpu_kernel(const int n,
+  const DType* data_col, const DType* data_offset, const DType* data_mask,
+  const int channels, const int height, const int width,
+  const int kernel_h, const int kernel_w,
+  const int pad_h, const int pad_w,
+  const int stride_h, const int stride_w,
+  const int dilation_h, const int dilation_w,
+  const int channel_per_deformable_group,
+  const int batch_size, const int deformable_group,
+  const int height_col, const int width_col,
+  DType* grad_im, OpReqType req) {
+  CUDA_KERNEL_LOOP(index, n) {
+    const int j = (index / width_col / height_col / batch_size) % kernel_w;
+    const int i = (index / width_col / height_col / batch_size / kernel_w) % kernel_h;
+    const int c = index / width_col / height_col / batch_size / kernel_w / kernel_h;
+    // compute the start and end of the output
+
+    const int deformable_group_index = c / channel_per_deformable_group;
+
+    int w_out = index % width_col;
+    int h_out = (index / width_col) % height_col;
+    int b = (index / width_col / height_col) % batch_size;
+    int w_in = w_out * stride_w - pad_w;
+    int h_in = h_out * stride_h - pad_h;
+
+    const DType* data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col;
+    const DType* data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col;
+    const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out;
+    const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out;
+    const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_out) * width_col + w_out;
+    const DType offset_h = data_offset_ptr[data_offset_h_ptr];
+    const DType offset_w = data_offset_ptr[data_offset_w_ptr];
+    const DType mask = data_mask_ptr[data_mask_hw_ptr];
+    const DType cur_inv_h_data = h_in + i * dilation_h + offset_h;
+    const DType cur_inv_w_data = w_in + j * dilation_w + offset_w;
+
+    const DType cur_top_grad = data_col[index] * mask;
+    const int cur_h = (int)cur_inv_h_data;
+    const int cur_w = (int)cur_inv_w_data;
+    for (int dy = -2; dy <= 2; dy++) {
+      for (int dx = -2; dx <= 2; dx++) {
+        if (cur_h + dy >= 0 && cur_h + dy < height &&
+          cur_w + dx >= 0 && cur_w + dx < width &&
+          abs(cur_inv_h_data - (cur_h + dy)) < 1 &&
+          abs(cur_inv_w_data - (cur_w + dx)) < 1
+          ) {
+          int cur_bottom_grad_pos = ((b * channels + c) * height + cur_h + dy) * width + cur_w + dx;
+          DType weight = dmcn_get_gradient_weight(cur_inv_h_data, cur_inv_w_data, cur_h + dy, cur_w + dx, height, width);
+          atomicAdd(grad_im + cur_bottom_grad_pos, weight * cur_top_grad);
+        }
+      }
+    }
+  }
+}
+
+
+/*!\brief
+ * gpu function of deformable_col2im algorithm
+ * \param s device stream
+ * \param data_col start pointer of the column buffer to be filled
+ * \param data_offset pointer of offset (N, deformable_group*kernel_h*kernel_w*2, H, W, ...) in the offset batch
+ * \param im_shape input image shape in dimensions (N, C, H, W,)
+ * \param col_shape column buffer shape
+ * \param kernel_shape kernel filter shape
+ * \param pad pad shape
+ * \param stride stride shape
+ * \param dilation dilation shape
+ * \param deformable_group #offset group that deformable convolution use
+ * \param grad_im pointer of images (N, C, H, W,...) in the image batch
+ */
+template <typename DType>
+inline void modulated_deformable_col2im(mshadow::Stream<gpu>* s,
+  const DType* data_col, const DType* data_offset, const DType* data_mask,
+  const TShape& im_shape, const TShape& col_shape, const TShape& kernel_shape,
+  const TShape& pad, const TShape& stride,
+  const TShape& dilation, const uint32_t deformable_group,
+  DType* grad_im, OpReqType req) {
+  index_t num_spatial_axes = kernel_shape.ndim();
+  index_t im_size = im_shape.ProdShape(1, im_shape.ndim());
+  index_t channel_per_deformable_group = im_shape[1] / deformable_group;
+  index_t num_kernels = col_shape.ProdShape(0, col_shape.ndim());
+  // num_axes should be smaller than block size
+  CHECK_LT(num_spatial_axes, mshadow::cuda::kBaseThreadNum);
+  using namespace mxnet_op;
+  switch (num_spatial_axes) {
+  case 2:
+    // To avoid involving atomic operations, we will launch one kernel per
+    // bottom dimension, and then in the kernel add up the top dimensions.
+    // NOLINT_NEXT_LINE(whitespace/operators)
+    modulated_deformable_col2im_gpu_kernel<DType><<<cuda_get_num_blocks(num_kernels), mshadow::cuda::kBaseThreadNum,
+                               0, mshadow::Stream<gpu>::GetStream(s)>>>(
+        num_kernels, data_col, data_offset, data_mask, im_shape[1], im_shape[2], im_shape[3],
+        kernel_shape[0], kernel_shape[1], pad[0], pad[1], stride[0], stride[1],
+        dilation[0], dilation[1], channel_per_deformable_group,
+        col_shape[1], deformable_group, col_shape[2], col_shape[3], grad_im, req);
+    MSHADOW_CUDA_POST_KERNEL_CHECK(modulated_deformable_col2im_gpu_kernel);
+    break;
+  default:
+    LOG(FATAL) << "col2im_nd_gpu does not support computation with "
+               << num_spatial_axes << " spatial axes";
+  }
+}
+
+
+/*!
+ * \brief deformable_col2im_coord gpu kernel.
+ * \brief DO NOT call this directly. Use wrapper function deformable_col2im_coord() instead;
+ */
+template <typename DType>
+__global__ void modulated_deformable_col2im_coord_gpu_kernel(const int n,
+  const DType* data_col, const DType* data_im,
+  const DType* data_offset, const DType* data_mask,
+  const int channels, const int height, const int width,
+  const int kernel_h, const int kernel_w,
+  const int pad_h, const int pad_w,
+  const int stride_h, const int stride_w,
+  const int dilation_h, const int dilation_w,
+  const int channel_per_deformable_group,
+  const int batch_size, const int offset_channels, const int deformable_group,
+  const int height_col, const int width_col,
+  DType* grad_offset, DType* grad_mask, OpReqType offset_req, OpReqType mask_req) {
+  CUDA_KERNEL_LOOP(index, n) {
+    DType val = 0, mval = 0;
+    int w = index % width_col;
+    int h = (index / width_col) % height_col;
+    int c = (index / width_col / height_col) % offset_channels;
+    int b = (index / width_col / height_col) / offset_channels;
+    // compute the start and end of the output
+
+    const int deformable_group_index = c / (2 * kernel_h * kernel_w);
+    const int col_step = kernel_h * kernel_w;
+    int cnt = 0;
+    const DType* data_col_ptr = data_col + deformable_group_index * channel_per_deformable_group * batch_size * width_col * height_col;
+    const DType* data_im_ptr = data_im + (b * deformable_group + deformable_group_index) * channel_per_deformable_group / kernel_h / kernel_w * height * width;
+    const DType* data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col;
+    const DType* data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col;
+
+    const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w;
+
+    for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group; col_c += col_step) {
+      const int col_pos = (((col_c * batch_size + b) * height_col) + h) * width_col + w;
+      const int bp_dir = offset_c % 2;
+
+      int j = (col_pos / width_col / height_col / batch_size) % kernel_w;
+      int i = (col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h;
+      int w_out = col_pos % width_col;
+      int h_out = (col_pos / width_col) % height_col;
+      int w_in = w_out * stride_w - pad_w;
+      int h_in = h_out * stride_h - pad_h;
+      const int data_offset_h_ptr = (((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out);
+      const int data_offset_w_ptr = (((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out);
+      const int data_mask_hw_ptr = (((i * kernel_w + j) * height_col + h_out) * width_col + w_out);
+      const DType offset_h = data_offset_ptr[data_offset_h_ptr];
+      const DType offset_w = data_offset_ptr[data_offset_w_ptr];
+      const DType mask = data_mask_ptr[data_mask_hw_ptr];
+      DType inv_h = h_in + i * dilation_h + offset_h;
+      DType inv_w = w_in + j * dilation_w + offset_w;
+      if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width) {
+        inv_h = inv_w = -2;
+      } else {
+        mval += data_col_ptr[col_pos] * dmcn_im2col_bilinear(data_im_ptr + cnt * height * width, width, height, width, inv_h, inv_w);
+      }
+      const DType weight = dmcn_get_coordinate_weight(
+        inv_h, inv_w,
+        height, width, data_im_ptr + cnt * height * width, width, bp_dir);
+      val  += weight * data_col_ptr[col_pos] * mask;
+      cnt  += 1;
+    }
+
+    //grad_offset[index] = val;
+    KERNEL_ASSIGN(grad_offset[index], offset_req, val);
+    if (offset_c % 2 == 0)
+        KERNEL_ASSIGN(grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * height_col + h) * width_col + w], mask_req, mval);
+  }
+}
+
+/*!\brief
+ * gpu function of deformable_col2im_coord algorithm
+ * \param s device stream
+ * \param data_col start pointer of the column buffer to be filled
+ * \param data_im pointer of an image (N, C, H, W, ...) in the image batch
+ * \param data_offset pointer of offset (N, deformable_group*kernel_h*kernel_w*2, H, W, ...) in the offset batch
+ * \param im_shape input image shape in dimensions (N, C, H, W,)
+ * \param col_shape column buffer shape
+ * \param kernel_shape kernel filter shape
+ * \param pad pad shape
+ * \param stride stride shape
+ * \param dilation dilation shape
+ * \param deformable_group #offset group that deformable convolution use
+ * \param grad_offset pointer of the offset (N, deformable_group*kernel_h*kernel_w*2, H, W,...) in the offset batch
+ */
+template <typename DType>
+inline void modulated_deformable_col2im_coord(mshadow::Stream<gpu>* s,
+  const DType* data_col, const DType* data_im, const DType* data_offset, const DType* data_mask,
+  const TShape& im_shape, const TShape& col_shape, const TShape& kernel_shape,
+  const TShape& pad, const TShape& stride,
+  const TShape& dilation, const uint32_t deformable_group,
+  DType* grad_offset, DType* grad_mask, OpReqType offset_req, OpReqType mask_req) {
+  index_t num_spatial_axes = kernel_shape.ndim();
+  index_t num_kernels = col_shape[1] * col_shape[2] * col_shape[3] * 2 * kernel_shape[0] * kernel_shape[1] * deformable_group;
+  index_t channel_per_deformable_group = col_shape[0] / deformable_group;
+  // num_axes should be smaller than block size
+  CHECK_LT(num_spatial_axes, mshadow::cuda::kBaseThreadNum);
+  using namespace mxnet_op;
+  switch (num_spatial_axes) {
+  case 2:
+    // To avoid involving atomic operations, we will launch one kernel per
+    // bottom dimension, and then in the kernel add up the top dimensions.
+    // NOLINT_NEXT_LINE(whitespace/operators)
+
+    modulated_deformable_col2im_coord_gpu_kernel<DType> << <cuda_get_num_blocks(num_kernels), mshadow::cuda::kBaseThreadNum,
+      0, mshadow::Stream<gpu>::GetStream(s) >> >(
+        num_kernels, data_col, data_im, data_offset, data_mask, im_shape[1], im_shape[2], im_shape[3],
+        kernel_shape[0], kernel_shape[1], pad[0], pad[1], stride[0], stride[1],
+        dilation[0], dilation[1], channel_per_deformable_group,
+        col_shape[1], 2 * kernel_shape[0] * kernel_shape[1] * deformable_group, deformable_group, col_shape[2], col_shape[3],
+        grad_offset, grad_mask, offset_req, mask_req);
+    MSHADOW_CUDA_POST_KERNEL_CHECK(modulated_deformable_col2im_coord_gpu_kernel);
+    break;
+  default:
+    LOG(FATAL) << "col2im_nd_gpu does not support computation with "
+      << num_spatial_axes << " spatial axes";
+  }
+}
+
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_CONTRIB_NN_DEFORMABLE_MASKED_IM2COL_CUH_
diff --git a/src/operator/contrib/nn/modulated_deformable_im2col.h b/src/operator/contrib/nn/modulated_deformable_im2col.h
new file mode 100644
index 000000000000..b50eb13a8122
--- /dev/null
+++ b/src/operator/contrib/nn/modulated_deformable_im2col.h
@@ -0,0 +1,291 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ ******************* BEGIN Caffe Copyright Notice and Disclaimer ****************
+ *
+ * COPYRIGHT
+ *
+ * All contributions by the University of California:
+ * Copyright (c) 2014-2017 The Regents of the University of California (Regents)
+ * All rights reserved.
+ *
+ * All other contributions:
+ * Copyright (c) 2014-2017, the respective contributors
+ * All rights reserved.
+ *
+ * Caffe uses a shared copyright model: each contributor holds copyright over
+ * their contributions to Caffe. The project versioning records all such
+ * contribution and copyright details. If a contributor wants to further mark
+ * their specific copyright on a particular contribution, they should indicate
+ * their copyright solely in the commit message of the change when it is
+ * committed.
+ *
+ * LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright notice, this
+ * list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright notice,
+ * this list of conditions and the following disclaimer in the documentation
+ * and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+ * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ * CONTRIBUTION AGREEMENT
+ *
+ * By contributing to the BVLC/caffe repository through pull-request, comment,
+ * or otherwise, the contributor releases their content to the
+ * license and copyright terms herein.
+ *
+ ***************** END Caffe Copyright Notice and Disclaimer ********************
+ *
+ * Copyright (c) 2018 Microsoft
+ * Licensed under The MIT License [see LICENSE for details]
+ * \file modulated_deformable_im2col.h
+ * \brief Function definitions of converting an image to
+ * column matrix based on kernel, padding, dilation, and offset.
+ * These functions are mainly used in deformable convolution operators.
+ * \ref: https://arxiv.org/abs/1811.11168
+ * \author Yuwen Xiong, Haozhi Qi, Jifeng Dai, Xizhou Zhu, Han Hu
+ */
+
+#ifndef MXNET_OPERATOR_CONTRIB_NN_MODULATED_DEFORMABLE_IM2COL_H_
+#define MXNET_OPERATOR_CONTRIB_NN_MODULATED_DEFORMABLE_IM2COL_H_
+
+#include <mxnet/base.h>
+#include <mxnet/operator.h>
+#include <cstring>
+#include <vector>
+#include <cmath>
+#include "../../mxnet_op.h"
+
+namespace mxnet {
+namespace op {
+
+template <typename DType>
+inline DType dmcn_im2col_bilinear_cpu(const DType* bottom_data, const int data_width,
+  const int height, const int width, DType h, DType w) {
+  int h_low = floor(h);
+  int w_low = floor(w);
+  int h_high = h_low + 1;
+  int w_high = w_low + 1;
+
+  DType lh = h - h_low;
+  DType lw = w - w_low;
+  DType hh = 1 - lh, hw = 1 - lw;
+
+  DType v1 = 0;
+  if (h_low >= 0 && w_low >= 0)
+    v1 = bottom_data[h_low * data_width + w_low];
+  DType v2 = 0;
+  if (h_low >=0 && w_high <= width - 1)
+    v2 = bottom_data[h_low * data_width + w_high];
+  DType v3 = 0;
+  if (h_high <= height - 1 && w_low >= 0)
+    v3 = bottom_data[h_high * data_width + w_low];
+  DType v4 = 0;
+  if (h_high <= height - 1 && w_high <= width - 1)
+    v4 = bottom_data[h_high * data_width + w_high];
+
+  DType w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;
+
+  DType val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
+  return val;
+}
+
+/*!
+* \brief deformable_col2im gpu kernel.
+* \brief DO NOT call this directly. Use wrapper function deformable_col2im() instead;
+*/
+struct modulated_deformable_col2im_cpu_kernel {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(const int index,
+  const DType* data_im, const DType* data_offset, const DType* data_mask,
+  const int height, const int width, const int kernel_h, const int kernel_w,
+  const int pad_h, const int pad_w,
+  const int stride_h, const int stride_w,
+  const int dilation_h, const int dilation_w,
+  const int channel_per_deformable_group,
+  const int batch_size, const int num_channels, const int deformable_group,
+  const int height_col, const int width_col,
+  DType* data_col) {
+    // index index of output matrix
+    const int w_col = index % width_col;
+    const int h_col = (index / width_col) % height_col;
+    const int b_col = (index / width_col / height_col) % batch_size;
+    const int c_im = (index / width_col / height_col) / batch_size;
+    const int c_col = c_im * kernel_h * kernel_w;
+
+    // compute deformable group index
+    const int deformable_group_index = c_im / channel_per_deformable_group;
+
+    const int h_in = h_col * stride_h - pad_h;
+    const int w_in = w_col * stride_w - pad_w;
+
+    DType* data_col_ptr = data_col
+      + ((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col;
+    // const DType* data_im_ptr = data_im +
+    //  ((b_col * num_channels + c_im) * height + h_in) * width + w_in;
+    const DType* data_im_ptr = data_im + (b_col * num_channels + c_im) * height * width;
+    const DType* data_offset_ptr = data_offset
+      + (b_col * deformable_group + deformable_group_index) * 2
+      * kernel_h * kernel_w * height_col * width_col;
+
+    const DType* data_mask_ptr = data_mask
+      + (b_col *  deformable_group + deformable_group_index) * kernel_h
+      * kernel_w * height_col * width_col;
+
+    for (int i = 0; i < kernel_h; ++i) {
+      for (int j = 0; j < kernel_w; ++j) {
+        const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_col)
+          * width_col + w_col;
+        const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_col)
+          * width_col + w_col;
+        const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_col) * width_col + w_col;
+        const DType offset_h = data_offset_ptr[data_offset_h_ptr];
+        const DType offset_w = data_offset_ptr[data_offset_w_ptr];
+        const DType mask = data_mask_ptr[data_mask_hw_ptr];
+        DType val = static_cast<DType>(0);
+        const DType h_im = h_in + i * dilation_h + offset_h;
+        const DType w_im = w_in + j * dilation_w + offset_w;
+        // if (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) {
+        if (h_im > -1 && w_im > -1 && h_im < height && w_im < width) {
+          // const DType map_h = i * dilation_h + offset_h;
+          // const DType map_w = j * dilation_w + offset_w;
+          // const int cur_height = height - h_in;
+          // const int cur_width = width - w_in;
+          // val = dmcn_im2col_bilinear_cpu(
+          // data_im_ptr, width, cur_height, cur_width, map_h, map_w);
+          val = dmcn_im2col_bilinear_cpu(data_im_ptr, width, height, width, h_im, w_im);
+        }
+        *data_col_ptr = val * mask;
+        data_col_ptr += batch_size * height_col * width_col;
+        // data_col_ptr += height_col * width_col;
+      }
+    }
+  }
+};
+
+/*!\brief
+ * cpu function of deformable_im2col algorithm
+ * \param s device stream
+ * \param data_im pointer of an image (C, H, W, ...) in the image batch
+ * \param data_offset pointer of offset (C, H, W, ...) in the offset batch
+ * \param im_shape input image shape in dimensions (N, C, H, W,)
+ * \param col_shape column buffer shape (#channels, output_im_height, output_im_width, ...)
+ * \param kernel_shape kernel filter shape
+ * \param pad pad shape
+ * \param stride stride shape
+ * \param dilation dilation shape
+ * \param deformable_group #offset group that deformable convolution use
+ * \param data_col column buffer pointer
+ */
+template <typename DType>
+inline void modulated_deformable_im2col(mshadow::Stream<cpu>* s,
+  const DType* data_im, const DType* data_offset, const DType* data_mask,
+  const TShape& im_shape, const TShape& col_shape, const TShape& kernel_shape,
+  const TShape& pad, const TShape& stride, const TShape& dilation,
+  const uint32_t deformable_group, DType* data_col) {
+  // num_axes should be smaller than block size
+  index_t num_spatial_axes = kernel_shape.ndim();
+  index_t channel_per_deformable_group = im_shape[1] / deformable_group;
+  index_t num_kernels = im_shape[1] * col_shape.ProdShape(1, col_shape.ndim());
+  using namespace mxnet_op;
+  if (2 == num_spatial_axes) {
+    Kernel<modulated_deformable_col2im_cpu_kernel, cpu>::Launch(
+        s, num_kernels, data_im, data_offset, data_mask,
+        im_shape[2], im_shape[3], kernel_shape[0], kernel_shape[1],
+        pad[0], pad[1], stride[0], stride[1], dilation[0], dilation[1],
+        channel_per_deformable_group, col_shape[1], im_shape[1], deformable_group,
+        col_shape[2], col_shape[3], data_col);
+  } else {
+    LOG(FATAL) << "not implemented";
+  }
+}
+
+
+/*!\brief
+ * cpu function of deformable_col2im algorithm
+ * \param s device stream
+ * \param data_col start pointer of the column buffer to be filled
+ * \param data_offset pointer of offset (C, H, W, ...) in the offset batch
+ * \param im_shape input image shape in dimensions (N, C, H, W,)
+ * \param col_shape column buffer shape
+ * \param kernel_shape kernel filter shape
+ * \param pad pad shape
+ * \param stride stride shape
+ * \param dilation dilation shape
+ * \param deformable_group #offset group that deformable convolution use
+ * \param grad_im pointer of a image (C, H, W,...) in the image batch
+ */
+template <typename DType>
+inline void modulated_deformable_col2im(mshadow::Stream<cpu>* s,
+  const DType* data_col, const DType* data_offset, const DType* data_mask,
+  const TShape& im_shape, const TShape& col_shape, const TShape& kernel_shape,
+  const TShape& pad, const TShape& stride,
+  const TShape& dilation, const uint32_t deformable_group,
+  DType* grad_im, OpReqType req) {
+  LOG(FATAL) << "only implemented in GPU";
+}
+
+
+/*!\brief
+ * cpu function of deformable_col2im_coord algorithm
+ * \param s device stream
+ * \param data_col start pointer of the column buffer to be filled
+ * \param data_im pointer of an image (C, H, W, ...) in the image batch
+ * \param data_offset pointer of offset (C, H, W, ...) in the offset batch
+ * \param im_shape input image shape in dimensions (N, C, H, W,)
+ * \param col_shape column buffer shape
+ * \param kernel_shape kernel filter shape
+ * \param pad pad shape
+ * \param stride stride shape
+ * \param dilation dilation shape
+ * \param deformable_group #offset group that deformable convolution use
+ * \param grad_offset pointer of the offset (C, H, W,...) in the offset batch
+ */
+
+template <typename DType>
+inline void modulated_deformable_col2im_coord(mshadow::Stream<cpu>* s,
+  const DType* data_col, const DType* data_im, const DType* data_offset, const DType* data_mask,
+  const TShape& im_shape, const TShape& col_shape, const TShape& kernel_shape,
+  const TShape& pad, const TShape& stride,
+  const TShape& dilation, const uint32_t deformable_group,
+  DType* grad_offset, DType* grad_mask, OpReqType offset_req, OpReqType mask_req) {
+  LOG(FATAL) << "only implemented in GPU";
+}
+
+}  // namespace op
+}  // namespace mxnet
+#ifdef __CUDACC__
+#include "./modulated_deformable_im2col.cuh"
+#endif
+#endif  // MXNET_OPERATOR_CONTRIB_NN_MODULATED_DEFORMABLE_IM2COL_H_
diff --git a/tests/python/gpu/test_gluon_contrib_gpu.py b/tests/python/gpu/test_gluon_contrib_gpu.py
index 1d19d850dd8e..348e9f77acc8 100644
--- a/tests/python/gpu/test_gluon_contrib_gpu.py
+++ b/tests/python/gpu/test_gluon_contrib_gpu.py
@@ -57,6 +57,33 @@ def test_DeformableConvolution():
         y = net(x)
         y.backward()
 
+def test_ModulatedDeformableConvolution():
+    """test of the deformable convolution layer with possible combinations of arguments,
+    currently this layer only supports gpu
+    """
+    net = nn.HybridSequential()
+    net.add(
+        DeformableConvolution(10, kernel_size=(3, 3), strides=1, padding=0),
+        DeformableConvolution(10, kernel_size=(3, 2), strides=1, padding=0, activation='relu',
+                               offset_use_bias=False, use_bias=False),
+        DeformableConvolution(10, kernel_size=(3, 2), strides=1, padding=0, activation='relu',
+                               offset_use_bias=False),
+        DeformableConvolution(10, kernel_size=(3, 2), strides=1, padding=0, activation='relu',
+                               use_bias=False),
+        DeformableConvolution(10, kernel_size=(3, 2), strides=1, padding=0, offset_use_bias=False, use_bias=False),
+        DeformableConvolution(10, kernel_size=(3, 2), strides=1, padding=0, offset_use_bias=False),
+        DeformableConvolution(12, kernel_size=(3, 2), strides=1, padding=0, use_bias=False),
+        DeformableConvolution(12, kernel_size=(3, 2), strides=1, padding=0, use_bias=False, num_deformable_group=4),
+    )
+
+    ctx = mx.gpu()
+    net.initialize(force_reinit=True, ctx=ctx)
+    net.hybridize()
+
+    x = mx.nd.random.uniform(shape=(8, 5, 30, 31), ctx=ctx)
+    with mx.autograd.record():
+        y = net(x)
+        y.backward()
 
 if __name__ == '__main__':
     import nose
diff --git a/tests/python/unittest/test_contrib_operator.py b/tests/python/unittest/test_contrib_operator.py
index e78884f74c71..476dfac24a61 100644
--- a/tests/python/unittest/test_contrib_operator.py
+++ b/tests/python/unittest/test_contrib_operator.py
@@ -23,7 +23,7 @@
 import itertools
 from numpy.testing import assert_allclose, assert_array_equal
 from mxnet.test_utils import *
-from common import with_seed
+from common import with_seed, assert_raises_cudnn_not_satisfied
 import unittest
 
 def test_box_nms_op():
@@ -409,6 +409,42 @@ def test_op_mrcnn_mask_target():
     assert_almost_equal(mask_targets.asnumpy(), gt_mask_targets.asnumpy())
     assert_almost_equal(mask_cls.asnumpy(), gt_mask_cls.asnumpy())
 
+@with_seed()
+def test_modulated_deformable_convolution():
+    for num_batch in [1, 2]:
+        for num_channel_data, num_deformable_group in itertools.product([4, 8], [1, 2]):
+            for input_height, input_width in itertools.product([5, 6], [5, 6]):
+                for dilate in [(1, 1), (2, 2)]:
+                    for grad_nodes in [['im_data'], ['offset_data'], ['weight']]:
+                        output_height = input_height
+                        output_width = input_width
+                        im_data = np.random.rand(num_batch, num_channel_data, input_height, input_width)
+                        offset_data = \
+                            np.random.rand(num_batch, num_deformable_group * 3 * 3 * 2, output_height, output_width)\
+                            * 0.8 + 0.1
+                        mask_data = np.random.rand(num_batch, num_deformable_group * 3 * 3, output_height, output_width)
+                        mask_data = 0.5 * (1 + np.tanh(0.5 * mask_data)) # sigmoid
+                        weight = np.random.normal(0, 0.001, (num_channel_data, num_channel_data, 3, 3))
+                        bias = np.zeros(num_channel_data)
+
+                        im_data_var = mx.symbol.Variable(name="im_data")
+                        offset_data_var = mx.symbol.Variable(name="offset_data")
+                        mask_data_var = mx.symbol.Variable(name="mask_data")
+                        weight_var = mx.symbol.Variable(name="weight")
+                        bias_var = mx.symbol.Variable(name="bias")
+                        op = mx.sym.contrib.ModulatedDeformableConvolution(name='test_op', data=im_data_var,
+                                                                           offset=offset_data_var, mask=mask_data_var,
+                                                                           weight=weight_var, bias=bias_var,
+                                                                           num_filter=num_channel_data, pad=dilate,
+                                                                           kernel=(3, 3), stride=(1, 1), dilate=dilate,
+                                                                           num_deformable_group=num_deformable_group)
+                        if grad_nodes[0] == 'offset_data':
+                            # wider tolerance needed for coordinate differential
+                            rtol, atol = 1.0, 1e-2
+                        else:
+                            rtol, atol = 0.05, 1e-3
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()
diff --git a/tests/python/unittest/test_gluon_contrib.py b/tests/python/unittest/test_gluon_contrib.py
index 123db085e817..fdba553c8560 100644
--- a/tests/python/unittest/test_gluon_contrib.py
+++ b/tests/python/unittest/test_gluon_contrib.py
@@ -402,6 +402,36 @@ def test_contrib_unroll():
         check_unroll(cell_type, num_states, 'TNC')
         check_unroll(cell_type, num_states, 'NTC')
 
+@with_seed()
+def test_ModulatedDeformableConvolution():
+    """test of the deformable convolution layer with possible combinations of arguments,
+    currently this layer only supports gpu
+    """
+    from mxnet.gluon.contrib.cnn import DeformableConvolution
+    net = nn.HybridSequential()
+    net.add(
+        DeformableConvolution(10, kernel_size=(3, 3), strides=1, padding=0),
+        DeformableConvolution(10, kernel_size=(3, 2), strides=1, padding=0, activation='relu',
+                               offset_use_bias=False, use_bias=False),
+        DeformableConvolution(10, kernel_size=(3, 2), strides=1, padding=0, activation='relu',
+                               offset_use_bias=False),
+        DeformableConvolution(10, kernel_size=(3, 2), strides=1, padding=0, activation='relu',
+                               use_bias=False),
+        DeformableConvolution(10, kernel_size=(3, 2), strides=1, padding=0, offset_use_bias=False, use_bias=False),
+        DeformableConvolution(10, kernel_size=(3, 2), strides=1, padding=0, offset_use_bias=False),
+        DeformableConvolution(12, kernel_size=(3, 2), strides=1, padding=0, use_bias=False),
+        DeformableConvolution(12, kernel_size=(3, 2), strides=1, padding=0, use_bias=False, num_deformable_group=4),
+    )
+
+    ctx = mx.cpu()
+
+    net.initialize(force_reinit=True, ctx=ctx)
+    net.hybridize()
+
+    x = mx.nd.random.uniform(shape=(8, 5, 30, 31), ctx=ctx)
+    with mx.autograd.record():
+        y = net(x)
+
 
 if __name__ == '__main__':
     import nose

From b9f3b0618b1909b6354479bb6a69d8c9380eeb0e Mon Sep 17 00:00:00 2001
From: Thom Lane <thom.e.lane@gmail.com>
Date: Mon, 4 Nov 2019 14:47:15 -0800
Subject: [PATCH 566/813] Updated logos. (#16719)

---
 docs/static_site/src/assets/img/logos.png | Bin 493103 -> 113062 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/docs/static_site/src/assets/img/logos.png b/docs/static_site/src/assets/img/logos.png
index e2d58a0ef3d3fa8d10181d29feb2c27a3e1c0fc9..9e07d60b4aeea946798b1e59edc0a9d721b11cc1 100644
GIT binary patch
literal 113062
zcmXuKbzI!u(>4r6O3_k^TX87v?%JZo-L1$1OJSi{Demr8pg?gevbeju?4k=4Tb#vt
z`QE?hoqv*?B%frinYl8VIVVCxO%Vr^3=;_n3FniNoF)<y8UzXHRUZb*%Lv2pD~^{9
zs-3i|G!jy69M*#c+RHJryQZQHQq=^-J`xfNl7@=5{PT-Ey~r~mA<^^Gf4eVN{Wtc1
zC+;5}9q8TIIl1m`Z!s`&udc429`BG@EzZs_E^Z%=1?|}RWQC<vwzpx*$|^{28NlFH
zY-z89tGi?P@#NIZD*@op(C8~b9~8QF`xk9tVY$1jn}LBzOV_X&3Hjf@eOgvA{~uuj
zA_>s*+q_?edq)>!6x0&p;*pWDwY3f2-+lj-R&4B^@Bj+2ltO~QWV1(Czo`Uixd4}T
zuO5_MNg_2aZyt)Yji;uj`Zb|^=@>?`?9lg2(=~Q+X+#>C-|pFjV`Oh&k(;&-%oEb^
zKERLZ8-@$3`%uYL=}3uMI|q8kH&O~)?Sp<>S1cb=8KsQsJ|W;8ub9ovE#{X0N!Z2P
ze|1FZYvij_qF1Y}>4DyE!9J+_xw!$aS~|ot#_XK~qpG)Ll&r+7_Fv(N)0Z4qfye82
zV3l(RAsO|c+Rey`3$@B60t|HYlri+wvoHRYT4f(odtvA`eFOW5ocXI?<3iryRnHqa
zSCezz{paaOAD+%u<B|$2%J+UX%@1r<{Y9c6G*7!q)j2P#goXdOTU$W=tbRMu`?Npk
zlHBSy1=&aYfb?_f>659DUI;U5c*g2R^nj4%@Z7bxymjYU?HVGnVtgwrdkg5u7v6tX
zWvvQ6)ig}M@~7ub5}@$uy>w>G#bP!8*)MC~em7Kd+Y|$_(tl-E7E;*zxK(F@YvYqM
zK}_a9D#*8qj)tLFbeHxTyG$rhYx*CN(CHUXXnEsym|WF7$cof)>1b%@d}TT`<!(IG
zrc)d>R9~<C@1;`&TwC1c<G}XlFfEVnU7qv(lns}Zw^vNoZ7VN&$4JFW9^zMoQPwmf
zGJmA$=y%ZCq>_3EnS^*QR-=MJuZin{n7Ex|X)h%Sl3;AN{||qe;F_?;3ND4nm`(_9
z;x03}&pzZ!zRt6VmP?C8mSetWcT>n%xGt(Gx4kpYPg;)2HYZ#%vwus|BjwRLX<?#@
znFiLiBB?@!E>e`XT>svp{}&kFG}W}uB8|z8kdQtgeUg*b_F6e=e^#f|OXU>NDIHnU
z`Fu-33FM?g#h`mP_CuIkfT@mM+3sl6Jl&ISm(&bv5W;-Ib=I_hIJjflKrtssw0MTn
zL^*nWbNwM%@a_4jtlH{V#q8g+pWnXP5=>z0+$W+<^rQL9-i=qNbc?lS>Ud&eHbJ~$
zPQGm&;51QMU*>R9)?O~r;Befqco)@K^DHC%BAbe6NsstwFA_pc`X0>6{31X-jsFBz
z_3J+wGWqX1>4a99kIeV#EKl8dSWgCwW+6c1I*bvIpGIG{hAqa=fu?OgfBvKeh42T;
zV-K{TBa<zJ+mdt*qw@$4vD3oHw(XM1rNJp51%FI{jeED)zld?`IKDg$sHe{hWXhac
z+Dt>!cKUVK@jF{dIQ%T|2Qc$vNucHX7Xh<Y(6nRaWR|b}3fDsM|4Oejap_W=n3bE&
zx>w^}mR&rYVxsWh3-+wIi83z}70JCG#k6vb`G?NU{`b$1(ti3}2$yu&9uR9sK`OTY
z?YLzYujyih#XEHX^YM#vFE?hoiRzDm`O*HY$WT(=kpClntyw=B*v32BeH~j&_C;)H
zKECXK+7*G+x)|T`@G~nhFYZfM8A%8uG7^9=xA@r~6wV@2aLw5M!w2R6!>eaDEcTjU
ze8Z5xna4pylTS%S^^_Y8t4&P|3?EkQO^ULV0z_E80={4`565&w8-Ld}@z?oprGBbH
z5Kwl=&0afc61i4*Gs`ZCykm>-e@NhT6@~DKlRNJckqv;cu#I|m`jU(wZ+iz7rPYcc
zQeZSrWELP>u4~f&Z;n@YU=l8b($d@;ej(WlY0XGn{;rcgsTEMhN{VABQ%No)9vDMh
zy<+%(K#<jm3TC?63bIqs&A$S=hA4t9iiSK`bpZL0;F)i<#e!eXu)qADZ`9~cvFF!o
zS!S9mPctZMIk?4<Xobb28c+Po#sCKbGZ_~(s+7UZxf+B4Qvbtv|9GxTxKysBs#o+`
zzzdS$1SE?$67n1VVW|lNoV7SO{XZOn&NCFvMN2HH-uaUAs9>Lpp21qwISh;uk;vY^
zNCM8v8eXt?aS9yQMtlXVA42gPudK|+kno!qIS1oWe>Q_1=|81h{7KXvnf`!BZuyp5
zIidA`#(RYw01OhX;~j8q`V&t_-U?Jk^+N@V8nCDWZeYe%h~fVSGJdO7HYWzrz@_6y
z8ML=KKPhtP!Nzjob1YsnTrC3FI4b)*zkkHi1W2oC?u2R_@vENw?*R?3ipb_k+qF=7
zFL{dhOxdIRN<)Ese`@5R=P<b;8Yy}_9@&WD&$Ow4F11XiMdlG4*$kWi*)?MeV1DXB
zA-U80XJa;;isOUeP#uuquisD$>ad6yIQDGFx4(6B^M5ZW=4}7x+o9O?4_7lqBOJqZ
z4{pHcSHO4iho)qVGzdZ~uYLmls89bxy?Y7=@WBqVaah|ad<<8?E9eHcAsZw>GL++>
zv@HzafQ54Wd}l8qj5AcK3H=|il+1m%=XjomVr6tgS3iWZAg((KE!m0h!T*c!61>x(
zIhLm8e{Y}gDTqDPV+l0V5()CM-)<!-XW<Z4*B!C^UtG&N4E|nBN1sG$B8lFz-|VA@
zm*W=Zo0lL9^Rq(}v#^AlXRK4niTER!^j7{Kesil}UThH=GTK<HOyk0g9o;gDpr;n#
z`tC<q^?xQrdy|g$JEW8&N!iGIIwyvrGjhkR#oD3qD_OaU<T#~?^<R%~-!^Gpr>2e~
z@iG(Pp~i%lmc8iqzVJUHd)((LQez(?b}(RE`oOj!e~^}?y~Oy9r9@=xn41L`>z!8L
zsAYM`OF(R5si`uj6RTI+1VPRn_&?$fnz2*FR*^G?Dm3+YR@5H$<!4yQW*rC##9)t8
zh$Pr!g|NTFJv3KJnBVP#ck6QbR{px3Sm>&s_p6i&h6FSMSpS*Q1!WT#!n3b@6s-^3
z*_$K;d9Q?F=bFbALyx6Fh7ipk_0HHyVbw)*OeMilZeK!GxW&Mm1789~>Et=00;vIK
ztQtA9Wji~+USk6Nn`>$vZSCdizQ4^1wQcbX-Sp&zIJhrD+jWP4jifs_vZIi6g#iU!
z{WwtH?6Ld&aU^6BW?Af-;*dQpM?t7OvUwL${?2|Tg<JwmBeT5qi>8=*<ij2UIjvd_
z1kA%IowrOjmXVP<$`bSNvktfFQ@p0*HQ~lG!PhgaRCjAim0UflG*7Ikd~0t_@kM9z
z^};sg+24C(?@y0>LzMQ|>x*3NJPfJi{Mq_{{$@UGPVS_);n3;s8?$tAVlnl;a9mOL
zKQW$4fHyFR30VgGI(I%}af^tvoPlLtGe0UGQie0KANf!G(UlFb=Se0KLTWoZRGTqT
z6+4hqYi~g%yP293LunEg8&jhr^+=cF#w^F{?u(`7h-N~Cn@jjfd{v4yp24tNj$E8`
z?a)2s3T=K5Q#~Al^X<25Z@-Kbiwy31%T+F;?mH@W-R)XioE&q24GYr?eg6Y+TZT_6
zvFBRzcxzRAJzww7&+L>JDeH{xjJi4QI+T7jq;I=G<q_S!ny4CJR}+e9tgIn$7Iuqv
zMtv5gljG%QZ^|b5YNxFCt?~PAz3TC5@_%M%xz~cb&tFO6*GF3n!v$`f6t5owPVV4`
z!~iPX5wJ|x+9~81hi35b^92X_7wpa|V=#Vl(q9LyC^Lfw-C`p;p9a=+mprf`<M3fR
zvaMa;8a}`L2C<hO<Jx9=Hhg?w0$+PQOynj@Eq3<79T37WDWD2Jc~N}eP`z^!XZ~37
z4e)gy*4Vn$TabPtMMus39UQvoLb<DA=iMue-hX`Cnv-0wCz<1T`x1AVka_o*xnrBN
z7q#0zrM4^U2O8kI8^{tq3CcHw0y5@ky-j58l~0y>25X#F|EiksqVyYCXw^?=6xU$=
zYp<%}jUKLWpMiW$)$J;rY(Hz@16NNHE)XvKp3DmzZvU%&b)1<bI^oY3ufmp~pXBk}
z4qdF~{r<dBOh-}XCQ``wCnbz2A*)wPoG9tzBDT8PCk=R^d6OYzZEI8Ae!!tWXlSm@
zK2(i<VIfgy-q+Rb?1St@=_5{U{CG?ny1GL0cOh0<(E4Wj97Y(O30-H<ml@{u0l7k<
zqEsQ3Y0UJ#Sj#^iGPH%}zSZO&Ty_d-Id#^o)-JwvPTS;~9CyZ0;X`$gfEB+d6`4Sy
z_kXHPeCo0DYLsr!Y|bwM*^t1s&fTnZCBvJ6z13U{umu{7w%tf7c17U*$%r@^!TdpA
z%?vJ+d&3Wnw#~Wj{@dHlkD(HHM~kNqmhM487dNwICJ}VjpBrq`msD`7$qyn!`ft6%
z^1y_0y^X)lC9DiA4r@giWLfnM$jv6#ZobA;4Ea5l!i6urN66Pl?!<23Z3<j?6dES2
z*S4hT)WN-H+Oj%&VaN!hY)JKb+vV{W5;r@&48Ybr^4CBwSJwjRfBF=8B|`;bXXNs%
z!P2uZvJnp`Chk91;pzpDP=!}O1EVf0+hRhfR#fzyxB%YIs4(Ky)6aqr^L1NkS5L~N
zL_KaE*z@z_-(VJf#3~XF-J3TP0#le?v}6s-RcAEj2sO)$^27H{gfPlO-K?Usm=wN_
z&dK@8`X_A!;kB^wkeY$?ajtknHEh&RZ)R_omurv(7=R8_<4(dm^u^~%Jw(ZdfC5&Y
z&i58M-sNR};C-$+j(HCb(Up4165g9M|GL?@=7>QnV@L%Fb#JN0sGtAcrmEIs59#Qz
zp3h)5p42_qtWa|E#0w+&y$$jRGuKRX#?pa`uSQpzhpMhF{MzM;D1@&iWEn6zva%T*
zN0P69K6VfB2u?u=bDMeJ7ZIDvwbe8*28biT4OGKW>GVFLb%Vx{zdemwsg%f#PNE#=
zM|R3^Aw)M49e2n47rvHtxvYD|<8_n!b22*+M;I$g)8L{w>xU+<I}Dn^MQIGSs4Vh2
zFB-&IdZ*#Xrq>Oq4WDWD0&6TI`fmwOTSiZYH-)$#=g+s&0;>&c{YF*9*bG@mm(gJ@
zYN`%ly+r)%GvN}v-kTI{lh&-KELx#PZK5bN?hOyF(F|mgMG3W&0WE9}p*b}u&$nT%
zT@pQ$Gam`s&Y>Nuotvi1cLmg(b3&{~7k1oXUnvvsy8|AXLOY+cb~ii2LY_%RnstVi
zT}rU9>});1kLCnt3o%Y72=bz?^M+fQOeR8#!rtd8ze&RnF1|@-g%el%-7?sLqDHx`
zo4uGfu8$hj<*K%d$<zVo`IIz!7r()f>i!W2e&)5reywT}S?zy+k!qRr6b|1Fdy8sR
z+EiF$;Fn){ZwL3jRb-6bSHxLgPMoV}X!BaiJvOgL^IT!-VyGJ(kv9VxbtmTZ3<mnq
zIPhpIhZHnAZNDbJEx~jy=;X(`8_co$nv$R=(YeX9u8*C<S!`)#9pcw<ZJXio2Q2!0
z7nSGsK>2Jx)V!QaY(|^kR36kr!t7)zY$_;sK6OSqy+aX1dPtYGkpgX6uKxgnZ_?b6
zuODrOcJ>WFl79dVz!i%qI0z=jLtDYvzTH<{Y(TR!WM^*uO19jVeperKD^I?D?VZRD
zq6*0!*VNY5E*yp4H-X+pjZP6Ob2G;^!#h1V$h{)_nW_6u<jVy28p--+xmC2+=F}q%
zQBVbeQoyQ!Pu0Iu=d2@6ypCQAR`>0s9dc@Fq&cSN;R2AE?qygm)B-FR4^o`EB#(`6
zGE-3mm)~<j_a-+II;e&p?Z#^x8ylZ5qdFs+8swjM?bt~L%l+M0m#?=R6MndkDw&v+
z=&ymlhQE(>a)^dUA49;rwn=RorbA8?-3CXUK8>&{i(Pce?NFl`(Z+q04~VRFT5y2(
zajWN7yx<D_qmP?kg>JiI7+|P7nKt&?BI^gnqi8{S=wGF5&RXow@J87Eg<3#DjNB`@
zxV$Np89B49H_7inF=iY?ev;@&r_>X5un8mS!a2TAdeEWbeD=2i>dL}KRMA~?;U`Wk
z3a3`0^YhLtpJc>&3-5~#*3xG$h+-2PUh(r=U)3n^zqU8&;f{EA{ZoBb`2x&%bK4}p
zY{I>1sNW4W(b9?Ey<r|DSc~p>V8pc-dCyvg-=JgiY~Z{9Fcg~*ZLK_8sn3`l95jk1
zd=V9Na|OaN7;d+8w29oswfHSU52m()Nz^ek_fI6KPOh=h$)Nqn{oIW!b@m0XKE3MX
z>Ce4#>|_AJb!kIpv_KdpvIDwqWVYA2`I%<YDa*$W2W}@y!S5tIe{@!T6;j%(9lX2@
zyv=mjG%9z{6!g#ANhFSI2+=L8dI7we6C;sN4tv#}EAV^Hltx!wUt+6^gdF-6cl&Rh
zG0?VBxEu#W4ZNHJEfCnD6JVTodgrOA;_MzV(3JYscX`P8iGUG7`6zk~^d#=%kj!HM
z-^0UI1XGjuI#UxMW<7@>utWgS1v!ugfwDI-KR1`XnyRj}4<_p<)G{&{|IVPPp=Ds;
zU8Mpr)E)O<aAGy21k>d_51Ks?Ka8ls_Z3=}ie)K1=)O>cT_Vw%>>+&GH@{cl+yycY
zTQ;f$(!aQWK+wLWfzM;Kp`CZ*dh4b~p0I0slGIhK*JLqv&ICY$$b8=|zZNl#cD+hw
zbZatfnv6#2^LNn|lX6yW*B)H&MAF3wBC^X4vf}Xfv~|vZxffUGSC*Y<GYc5cH+Fs;
zhApt<_v$ea%QF^qZB}gblK{!&w=Fils=>GZ;UJs>yTc0Vyo&WA*fA1dYKG|OTSo$Q
zakVnGKpt?Ef~|wk{nFABRE&(wu>f?je6COG8$47-Cb=|JFsWxDuu<7H*;wl1<m%vQ
z;puP$Xbl>(_L`6E?Mn}y)2m!fUxpRjFu*#t+-kTrj)55JDsd+yOq)~ICtjW9ZOWPJ
z2&ZAc402_~D3h4q7nKeD{b=Mn)i?~S2NxaycS%rPTiXL{_)ZWnzV1BfY6!ME^6ZD>
zQ>?R)XU$w)Eid@K{(2nv(B6I&5$|(6a2`W}_8W%r>tP&N3?`;rmQ^}ah;WXE*eR7X
zm)0lxtkpAta^68VrXRjo^}Fs^Mjk9Gf~OyMT&j^fd~a6)--2NKTsLoV0M*?lijP1O
zIR;{&fv*Fg$-E&`i~`H<YLLiiC_@XTU3c<6BB|ec-GA%vB|pK;lpdkrL`}-DmUS)X
z7B-?5U|3PNt>fngtcjsei9DN&&rT?d$|zwSiVcp8{e&3?AmBM|ZD~<8x-l@g`Ob!`
zb#SSTYQ;b9sy|q|uASJbfQ8j<PXG^I4*`ogOM_>YjXct$p+W)v0&?drW6SkcR(Naa
zt)f>O-Gz)>{1OJ<+d-?SWj_FU)c~TTRm}WaaEOWEL~gs=Hv0W4zv#TGT!V<}vHKTx
zZXSd%UJ`YDeaFm~T+dQ&?-2&(@_9WjYYAZ}Cg#0R-UvZ1iJ~D$mG|Bl7<W|qt+u3*
z#O79|q@wg&iIJ|}R&{~+2U~0{O#?cO_DcP`?s?ri5O?{lK>zaE>WpDvaZV*2rA@4B
z<$1VWnnB~_K5s@#Dgk(LF^^SPL(9y+?WcBHTAo!w!S>al@~8gm^Sk4Q)up>T=b;;?
z{JO@*;wS540vgcdqkW-^iWMu1*BL{Y(?unuX9!AFc6gp)Q*fx^zGsUW<f@A2SJYKb
zL<t3%HD8|dlV}{L4j?A0{XF>C%&7R`1tG-w9Q?2JVu?#O3GtfVwb+wBm9uJ8GBE8}
zcm_^&m%;pj#SUjGW;#Z&4`0=;8-(tyblBOa8#~Iv#f=+;qG41xI#>Kc&g{JjUS>MX
znG_Np=<KJ%-uA8m4d^BbRhU##-h=*=EgTEe)6+wQnih;bVnr<)lUU0vJvYc>BDEDY
zHIE+7mTjEkMa*T-Spvsn^1{1eZ?voi*SxkL{KAaCyZXBVqE*K0KJHTCi>mMl_z%>p
zI{MNr?FpB|+YCKH*c@D@>pQRX{i{|3KC$aK8|K|}usB&a)jy<>nM@Zo(l^ANEHzaJ
zw1MhCiUEgSoY={#jhNff+M8(LG{9+=n1EBIty4~1T^g!OT0xdK4od=&%QM$rXqv+f
z7X@$t-x~>yL*BZ6<Ysa~Nz`bnC#s0FI9yV6G`RIXD#h9+rn7?|G`0NccU?zYur@{Y
z>-qM^j_d|F`221J)4u2KPO#hd!q(4^9rBGKr99kt`{9u@b9VUgxGphq<~SQ-#?HdR
zvN|#Jm_ppNLa!alTqXy!vtAoIamWYf{re|#N)FYhwk7>opm5Kp*PB!DeTQ}6AkXag
zx(UGosJQca_fJy6zw-wquD4`t11aRBuzk#W3yfCU1Lpg%`@6e|nv*i?b=N=6xko2f
z@BO(%s2yYb%MxF|5grnVhfOR=@=)8rs3LGK;!*v6HD!^OHR#STE{vUoVfIHejAFp8
zl~%Djydo}(G>?Ok=T#wWy;odna1T-UqxauZxDp^4yKCYvA%^n-R6H==K54(cctejE
zP(QpdcG0=9&lC6Y)WZa1L;2HX8<dCTx&6Jbm1C^MR#iZke1+G#zc1IGLt?pSo7;LP
z;fWAdQ>7~f?vTVb=|51PzBtR@zQqy%Ja`TNIs-y*ho+4M-08KfJK@<_&_Qel;Rw;(
zgFMaM`8xA~Mr~D%P0a9&@>n?~fBf~2Wr6Rw=*B;6U%8}yg8GvKn!-A<D{ZUdFVs74
z7L~}rg_Bn7Oo&Qf(%~G+)P12wt9prbj5VH|F62S|ezy-G*NE+Z#_E}(vBSMrLEgq)
zDP&3q#ftE}09lUD0L9}+_w@PTiw#!?KKx9gJlD;UPqtwB;6~QwNi+6Eozs0w&aFT8
zz~&ga-K2}(HbU?1?!bPGYZOM-uviE0tP*fj$dAiF$m&w$Zbw;1neD=<?dOI$!v#ES
zUkY|$0f2sDg=TF&;YSUM_mT3iX@vmyImq(LAGC=hqZScS(bmROZWZYl@2~x;mf7l?
zoK6Ln9Lih4hE065$`nnB!3K!s=8w)iy4n6gDD`;PXHpz^Ce_Bz!)D(5skIQJHmsYI
z-5cnIbh>$BvJOw}k#h1+@3s8m(wlH6&`g$P4xdwwvF)5o6}pLd^1JuE>?xei$flxp
zK@0K;;AB2|^!H4I<q9|qlVo=0xNB~zbS79cfT|2%4~eh)S!&xhj-=b`m6TR)bZ+X0
z%=<J_FSj&AkD|P!VSyaWH`32Z1;Y^$5vG9=!*gf7L%;QfH?7~Pp`$a84Zi`&SHVo@
zM8%(a@n+k7;vpAtY1EU7L1S5jXgfO<Zg_Kxi>D*z5lbmoR}AGPDtmcP@RQ8hA-}^_
zm(F%SRwj_IWVr$`fM4db*~)1TI{K-Q4Gi~S6<Y&LkB~KCLK4*)g0l6<;&z8<XdBiR
z!*HoI(aB=v{|WuH%@|z-8-$h~biPTdo)YRk+YwCTT)$5qD&<8*p)e2v55A>3Z6jfu
zsxiiO+xdQglw-s`DbXv$E-+cSYd4|Io+wb<m3I)8)jLeYv;Hn_Qqf9BC+b3{M2VLU
z!8>ek-&`G|&9J2euZ5A;Y>W-|J5&8x(3fETZIM|#|3xqD1EbfdYnIR0XkOF4J{?WZ
zLfspI`pO<P_?!rk78_4N&pph6Kk8m)R}N0rDZMP1*ed%GT{gcI00fOS?k|yqyqQd$
zsGL8_`x>}c%m~dtdqdYVwvGDA$(5ju-heKP@fn9I`5V7<z-@1#iR=9`IaYlfm8y7V
z7Su!6_0c4;bh45yjqJpoCGG4j%Vhas+Ma8v4Y^_tx{nCNhG6^g-Y~G>yV3OB1mJ4H
z?_Sdx{=!_)W{<;#G=U*xWq66^grHZ7k&WiiV+ft%NK?m#1ny5*tVA@q#l2os9jUFg
zg89eLXw6C8e@w-SjpTl?+?AWQgrJy{fQ^Q0#L3cOey4|TkKZ;wQ?XHdL#6!Bu8bP9
zA<s*M`!<oW7QuE$CFt@+_k{fg(R(-qd@+$D`m7sJ)%@#S46nI<Kt0gs)=k&L*&Cd$
zqOEJz2}Kt5?cYwg?{*SE(6PWKM8K&LW<FQ*dS)9i+_mu#I*%9#8?Sa6Fsv0uwGzIK
z>?4r;PV;GHf$rAPDU};SBP4s^<4ZJ~;zF9yM4)hHX6kdB<<fC?kmVBRX?A<}QmPVs
zgGcjGps5Ym#k<JPm<6F6$I*?-;`Lw<$cE)cJ6n6D_ub(p1`R)2ODs!;swaeQYE)E5
zrt3tXCym24Y6vP{0VQRZtbToTz~xNr-+ZjlW*^;TLmAkwII(4`aZ+~4NEbwjc|+F!
zhvh-(Evtw&lm8rLK_3H1R@Vn3`QhJ)Pq0tZMHP5x7<&EFpayOAO;+P>^Si-Dej~=6
zK(pent3Q#yk*@s|$M3PYPTLD-_gKXY)9Z%cymhlhYnqI0aA$OdPXKowkLdD7zkFV|
zZJzrHl%NxyElN7poSoS*cq>DT7qm<nsxc_u^V-4-LaB?Bcu#e`;F8GWzun3H*F$X#
zZKmxVJ;*m_a&H&0_&Ep$0Q8&0M>pA=yS%k?WcO*5$i&n^9pmYdQ%|+`r7pQXNwSXK
zV4G-wySg0Wf(U)pw=wt#;O?LeykOI(^d)@eKm4zOO^imMn!P^C*Z`mUj<&WZ3Qt*U
zpLP<dN`*(tL6NlzNvaV^(@yd8-@}jNiF!!#=HeGg_vN0*mszBVwi)>8Ei?LpmI`QK
zL0d+|kmG48@yFMc8}pwpLXI7k;Hjk<J;G#6eBEuYuge~&=uV-6oTm2_wk4(y52KFj
zR2}F0JEM92-w%5{C>-0-?jw&?vkw6+4wPdFTr?Tg@w0s18d%@T8@MRm)rPE@&NnF?
zy){tzX{N$!VEuj~{a#xqy!Mvm9gpmV?^rU+fs?9E_}@VG+yo;dROUom5sGnRUH7J;
z{Po%@p5Ed(h+stU(y1RCn-jaoFK}3#g(mLPbMPc#qMq`3DA&=+NGs}Y0Z$c$@1=cB
zR+f$Z(%h$i|K9hSgxSZU)aztiiwOt_2?=?mb=Chk?$V7TM2=cDCwH>zd_9<{@7=sx
zCF`t;wMzD;c0JBqnTnbQp(oxro4j$Y*dgfQgS-5Q?n6>^2}VFZgXbR5U)2hC5<L{+
z`Fl4W2c;SACTQ1mM`uC0)<i5W;Zp9R8B5*$U2vsiAFbh>EG<(4MiL1>jELo;b8Hv~
zl(0MqX4-T0B%XVhx7j{)4#iK+FKT$T=E}8Xl?EvHZuLD>t}3+?jfLNnQInGzGVMr!
zrdtIuCPp89e=QFlhP$y~119}>JoB!-asqY>!0H{Vc+GVG9xF2sM!rjY$LuZ1?tjUe
zZU~qV;vM<;l3*8RD{9W<Eukny_)%pWZzCT#%(P5Fed1>&P2H8!@xWFCJ*fGoml_AD
z$18Y$FLBj+t6Z#U+fXy~>0qTHElXkr=OWYdBsF2pWtfFOagHCqw6EqB5N=@cd#GJ+
zN~%F@<?@H1@YfEocSp5@e@B^%mp@5I`Hi00e8z52ub)cW5_V_k9!>PUs}7IwdY4X?
z%KZ5$&uH6l@bX170KKA3Hy(ob(*5~{v+1j~xVuJ3XVPhvm=S8`ffUg@Fte-XEn|gE
znL0|(NY*$E1z-FSr24fB;I5*|k{`ak2bJdREqVWaoDLF{s=39tzks^mY(JoIt)zQ<
zdL1A?<x+}%To@zdu3XE9RWaLJM9=615B1bLm^E**>=#VFAr)gC&OwEJwS7%z^&_bW
zM#@2%xRW^InI9CnHkoTbYwpP;)L))3`v1=bkZqX9{~dRRJ`hrP-w_R)crBALFk#>?
zy4KY5F)0I*lNMZc!h{eM-rU9|AK@Xhv1UbeVWYbmuxtRVXNtgD{YqKaL3utT>rE)y
zy9*0$p}JG;f$0d<URe#Ed)z?3%2R6?ZLMtQuV-DJSY`ivwdu%qn}3@NPe`quS18<N
zhtpVz!6f5_bftEwEVkdO^?vX_mB#xZ&sOjJDEojQ{=7*?jkZGtej(T}1)g-9==VTL
z;O~F)&en&HMuMr8G~hzvSj#aXdfn_--5e3cy>KGvV#mY7s=9nI;_&Axr}01zPBxiE
z<kWAt=m+|!{Ltk4$gmjf-pjbXyRw~a#cNly4}79=%t%<41`mBSdI+ostHkI-BN?%3
zWGXeohOdR6_K)!!ZBMzKFh*PGK@w1*+6c~1E~KoeC_7=lrWCRx54=b9r!Yf3m={k`
z$hNOTE?ks;7$}qk?SX@*1+5S@Rv=EL;rF7`t$2O7dz0$iIM8A(Ap6s6r(CRM?<1N`
zXJMsion}(0Joo;`BblTC`6lxR;jO>!7#{79@TK=YdT%eX+te%s_3uf1qP#Bc9ZmsB
zHN8eGKEY@y`RL^Obg`72(lc{#TIIQFS`ywIDO^R(lbwZYQ-xhoMuXu$&951`(S|8g
zju)4XO=3~uN@0ClPOyoHHZdu<ym?VTgAB=VlijszP2IYFZgz5tU3x)_iNqJbnCvr!
zOFqO}M8t4Gyp?4#$PQ?ncoqEXn6h%VogfLDfS^68{R5=6sR1+-*raJmq`FOK?otlB
zUU1{)5f!gZM4s;z^O8Dg#YXnbL*Gl^zVThsGBslh{le-!`M&E%S^Z<}Yi&GR&k)}%
zkIk$NOattycup{^BfqYt<$5T}h}9U#G@T%l4`#AF^J=TC<(g+hfdM)%R}?(aoS?EJ
zO;ts^01Q1zXiqe|$ervBG_L{fxG3>_y$g_RSZ|HiV!%GlYdEdr=A-&el=$kjFOF+x
zkaKX0+aqV4j#Z!iNQSl-tcW3-N-a@OeP=qUQRyQzI<bMltJ4ZkN1=*ORfZe=XlmV<
za6C=8SE6a17KtA@OC=2{{bRAX3)mVWR-h)q+_6lhfl77#0n>rQoJ2gIa9Avk0IPo=
z1CzPNb6}FEmDGP(_K6<Yy|dZ5SC|CaTce0$wzw_#oYDf*9vCGuzk5!3qq42Sgz~Rj
zwOWJ6{i0Q%`sU4n{=BMI?pTmKbTJ3pl9lPo*Lk(aTJ^0HBRW`GG!g_+fANY>x^aZM
z`Q6*t>|t=ldaPH1yQ!K`(kykZ>s%?Iowfw>`%bH$hsU%tDn!8Q6xhTuk4+E!XVNTh
zl26giCLO{793B^$CPO;N2O%Gw>HfIAre8$?_;rB8`SJ;k7Njt}eYzJDjfWy))i2%a
zs1Ty<Dz1PN)m76aOV|g8mCNR_%vPPgZ_jQEcVUC;FYOdjI|Qf$^bz)B9}HC6iar(|
z&ElUStQ#N#*nsIy%mM=catuAX_}9+SGhV0aY&D!eH$Dfn*rvy-2L6c$a(uUfVw^yx
z9LW*MN8Xk60h8tOMx$7!em?B{pq>lP&(F^O!WyLV6pI1(;5Z2Aq{IItl9)uZk8*D`
z*$aqL&9yZQmK8`#(r$7yYmiG~L{NDBR~~hfB-!{|R5ss_aXurYJ?NDkCs!0NI$IhD
z5%joW<j%)j$3Wi1>}C%YuNIdCjgV4T@POl)b5LI8Qd0N^(@T=m%KsUjjKP^eW}5u+
zxkLbEd{UL0|C)m)?grPY`A%BYB2skSA6e<4dv0m&6=%5Aw97_nG@Lk67t|f?c8{e6
zAto8-7~|oZA%sXM%3%3UI0zPAmv+f94UDytL|Bz91-1Sxw_TN{>Jk4Pz~FoGz0d4z
zX0c~cRfa>)6U8>`Tf5iwxPN|5oBVl6zY*|Nnr87}h{Re8XRCp27Ci_)rqBNOge)-y
z;y5!*31V>_ts39rpaZa-D5$j8-t<ZfjdiwoY-1toDY3drlAjNpukS5$x8w1<bsx8P
zr%}IGOHzU5cHGuw`g}QD1Fb{P;HVj=&Cq$Mk7H2*$1o)mcc4vhC2~$jMMp<RCiK!%
zdfZ`*_jxBT)FSfWC2`sOp#G?u?8M4ReD>Pm(=vXov8mL@SSLSTOP6*1iQ2Q{rHBl*
z{ed;^-oG7H8M1G&QXeCfEJDJ985STNS4AUap7`=>Y|Hf?+qwK6A-@AL=w$2!u{$RN
zFQ9wt{_I13f=gTCisZBur!^$tB;s7<p;9J6UXdduNeKv5G3!px(QrrEZ4f)nWD1>Q
zgTlzNt-s%3cLJA()sIj0D#YoWFR6=^YoIC}Ma_G6XZTy<^luvn_G-f-E!{Iu%yI2<
zapD`<WA70cF44<#3Jd;Is#0!}l187swHG~?oF*9O1}KZeCxj?+kzsHYizZ=A`n<T#
z_4E-sJ{Rm<l%R6=>P%5q`)sj9Cm-1<K8?%t);svCynitF5J@bfF8M1AJ?N<~%;NP-
zj8@@)MG;ZaK`PRkgMsA`z~8$KyyDSGZ%13s&H4eo;i~Hjkug*+;Y@q$yj>Wsf81jd
zFxG)HZ~HF=*gceknBGh#RbbTVGD*}Sz(^IelTe|mQop7@cQw2DeJ6KIX^N1?b?4rN
zEl;eqe8KwA)J20YqyntDPFCY{g_%MrI7!ATT(}ERJvmm?%scW!K#|%D2UYz1gx5<I
z5`Fs5&hNJ8R~=xPE9ZB2$Mf?kSI_?B2Ay2atFZMx1D%2&gG%+U<8I+DowvKIQEB-H
z#f6G-o>^Vz$BGvCxtGL}okPQZh%2L$&Hh&>ljyDgm?!JIzggnE5-p(MpoNEpXboAc
zpc&_&Pk&3kA2{&#r{Nexs7KOxtLs$KvxX_<i*z<i4y52iK&1{i-Y#q*u`MFS{$ZuN
zvk(UGVJU2alCp?cEO>97rQHTQd~sy<C})U$oT_<<t7v7oexGi`m7MS5BCcyMK@QHV
zNrl=NV3kivMO|F{@-yy7nQdGAW1bXD=f29vGb-@<5bIRZhZ$SOTfUKKqTV|L?1+L6
z{;RxV)<;qnRd2VKX$+^P1G!)<<oNLk3Gp+DZGuam@Yn4Z3%RciyP-~=9$1K5@_H*7
zgT1Ge8bt(0$F9BWcm)b4=7YRt>`TF)C8}5Aw+GkQW#St^XkZ`323LJGwR0ayjyWk%
zadpHIqShNBMhDK~e4~jNqSWg&co<==PX0B{=%++@f}sr2C&R9=T-fTuDT&B8UZ1`l
z5+6-**?3?wfw*QkO(hYuZ*NzMipl$ixe|Mb8w$nUV@Qe#PvG!BW;B|qxq}8s6FSL*
z^B{3A9BDx;!C16<*sWOiq)JsfX!}v@fkV<%!M{(xeHS;VWL;{G1U-D!|7;~KFJQ5+
z3TaPp(JL3Xu^Xf)xq^dE`r9*-Gc36I<#XQuOSeV_9Ka{|L2(e{x*|NU)bL|g?pU<A
zZ!ws`_Cin95iQIL<2F&~i=H4s<Jgbg4hB)Ml|C1qtY|xFSu_=w^mTmw&m(_{57NC>
z5^?I5b<P@ngI%4_d@Yx@m%`MiOqLdl&)xi?ye&8^<hK<}OPev$2TMO8y;AT!QgCl7
zwdR44{Xm$ZZXnCzN7<Ii7Ib|5OiC<*nGWT*z$9xZcU8^b$v1?4n17GSWhNDivW;w8
z`rR!SdkVX2TcO9t$JsqD+3m`<^;HR+n5v_3`GG&zSlg&A=xeCiM&u#%1ZRx~p^(w|
zk*Fpgs{-X_nT4An@t<gbt{6e<+@gd2>zxR^XcfIW;_>#--w8gAta+10se;)7(Ixf;
zpzjnVJb^kRgU6zbTDdWQnyt2%xa2*uMHi2tf)X8|ujMKVz$Q{~XI8}8ppVfB<hFyv
zhki!%(dBva`9A1hj#4*xWF*rN#HjJrS=D9->?|*r;A7iqxK}>uhW?Sx<Mq%ZS-dFC
zx-TwD=|B0MZ)Uc0Pu-P_61qLIIv6&|KU-gIeuat&YgF2L0dutnlI|htMUbvzcK_Q1
zeO_3%8YbCb0L7=VRc^6PX78M4i!txBfp_zU*2^qq3o0)?+Ynqa)+tD7Na*!aDziCO
z5FJ0FM&6UQ$;E;Grb3@P9_)M&Oo}4A!KDBfx+AsCj>c@F2a(}!2Qz$vZ+&%@l)IVj
zqF2p^u+#E^)Zm_`%yCKg4n2vPzK!n&NL`C}ZHcG!>l`hvr%Bcj##xg0N)o7^P>;$r
z%SnSC@E!V!1{+Oh?c0Ohm&W444S9CA-LMIu_pEaB1l4^@9(nG`#u>M2aLoF!<9Yrw
zBU3LI_q$_w`&M1LlgYv^$Dd2p?AXiC0zl5P&CAEgILIuoQyc7emNlF5#Ow}Opkj`E
zbo)GwwkKdoFoNZHL=cuzoi8S|Kzgk%4Xy2+JQlr1BRs=sYL1JtLk9VvI;>ASHo`|w
zx%5LJ<DaG=vtYkLA#S6SwxHC<sY`^*Bl20$-<#jR>jWsn9)@bak5djPlQsn0Qr-vM
zZ!?c6ILjo4)$p7`1ZhxDUkl>7+r7bWL4ALGn$nB9esh!jl}4~TEs+JwU)VUGao6lM
zLr-f<7hPAdN(D*j>briUCBNxZtth+QurTo$KQAwTf45ri09iJekC6Ag!>CT{&mT1*
zbV5&GQPH(ux_2Zo2OI3$(($eGB2$c7X+zu?d4Ve+g5gnF0?}EBMKGbSNwyeShIpJU
zDOY|IfvA%qqt@bN-U~m5OOXMpu2vBV5EeSbg%mvbW}Js6Q_udCT`!@cw>hd0(*pgp
zBIg?3iVgKGE~D}CAvQYB*p=T_HX38w_k4s&ueFaNgqpbpUpnO>#6Lqy86mv0nQwi*
z#5d?$mwr;oZoQvwR2z#pa~t$z^y$W|kHb&ZAAh7tqeGCzWshUSo_Z4K6a_jMP1t>5
z6T;p>C%i8N2w=l{9K@E;e&5^!CW^&P>4~7Ka<%M)F(JBA@QBC|16%!BRh)IRtC>6@
zr2&cyo6~R99s>2zHYO(4{27BKtrxx$><b_?TpXV%0ha2Uh$qvT)qp)*{b9z=fWu13
zwyWODzP?Mux_W#bGZh`|Wb=e1Ct!YUZVig77y1F=IdGNtJb*9acC%L5TV)r6@qV0W
zz12VeQ}AP#++k*XukQCU<A3<2(~>`dku)^?lTE|8@`Yz4ihN{se)rtP0^B*J)rNK7
z8Y*sfUbVPLlMM#$xSGEsXpuo5_QPT&)f?Z?aR%|f<;?YICWtxx=bdCkd(i5#@rP;k
zHDr+ncJcwckP59+{ufNJ<xMPjg7Aw(cywp+FXuj_GIuivI3ft<Pn>OIuKcml7|l48
zvCmlro)jG8o94J$ANv78js<^f*CdK~lky!~c6-Ge03ner$~)f2-nvK9x3JwShObyA
zk=HBOy8@6G**3^#eZG*;1M)bB;Uq!7FxqYI?zstOarckIuCLo)C?V>RS7vq?8s{tl
z<P5AdWoBk)W#!=DVE6vi+?}>SRPqP@kjURo&as0DI$Wo#autM~tO`YD-i|P(GJ(vr
zG_Gu^xuU+dfM{n9=@3u9XeExL1iNH6tfs8jQblK=_Nj9arjCF;HCuopaJ?2~jf@fA
z=Dt^X(*H$8Wi=Aw&VT@fQ@DS5T8M`@hZBY}dWpYf)W(WB;xc|(^T6PtQwb3=Wg+Mu
z779rYiwow`@dsm>$nknK4Q|nRwbW`a5hR3mmFuckrjEv_gw?+ThLJ>)w%Pp5yf4m(
zjb)DgvoQYGd3i=iOK@hubE`#+W$XL>n^8<o{8M^@DNBEUxIJPH1-_^DaVpNQ>>f_{
zeyPU@XyFjQ$pLb;7KQXvl7xwBy5kkax8h_zkjKF-r}wB~)wGkj-++0v8TY;i6?S@i
z=*i1~ihfc8NJn`V+t9SeQq1eJz(M59!^w#ra_iY5I2@22Z@o^*n^A`6xVs@dJ%{7y
z)S*D;wehA0&n}~QfEh(}fXQpftQd3+tZe_&<WY@%*g*mI!F`$do^&7TutkSB?&gC3
z`}8PN>St}`9q}BC0bwSsKGhTOVgxxoI7~{4Uf}Gl>9=tDo}#L8hA^I5O5ahE-{)XU
z#7#+Y6?#x0FkkE1ELLjqfiVA$$A1BxP&3)OaF(|cKR^R&hGCa@k}B$3_rCazod#br
z)jxjDBg@ha#)#YbCl{$gk>V=T-B=?V@Aj&ZTXy$56QcT?H^T?;tcwNLRh?2%)v!mJ
zh3Us==*5_=6uil`gBP7tUYKx%H$~Vp-B?yw-Z5Iq8s^y&uSNAhSU_>UCEqYd%q6Ak
z2nII$&z3dR=|%N(%Gz;X0^WqVxi-<Q{wQm3*UAZJ@7<no`k4&2H)C=lL`Cf<nLMSa
z;maflI}k#9Q$o3^T__w@aKORDl0#i+nM)AIdW!Mw@xmAHde}Y%6Nsl=AFcY4NC-^b
zPwO?kp`LV4NS6(n9>~d`%nClls>n@BptI|y4$!@k;lc4!F+4t-A(b_ucD@O!8DA!M
z*Rp~hs~*{$!<XhDuc|Uenr-7<>F`^=L`s;!Q8m2R7bPV&-7=M$m<pag1NS0?UnQlR
zGBfzMjwxGa<9VFVSyOTX-u`A!^f+xsn}@r(f(9}l&gpjMCCXJcMi2nDQAg`INSv#Y
z@{r$>6LSzN1hk_?(Fb&48*isGuD)6*==v2L2Qku8DD2nPwr@1oboinfU|p1X56fwi
z3$GD9;-1bdYeN9tMJt{p<i1ZIJQ00_GtA=;)aTFq<gqh)^Cm-0zLXM}t+Y&_8h!Jb
zn`NjeD*R;`+va=WK9-?sX3UVtNV?SRr36|_z2s0QO~V0uN$u20I7gVAjJ%pZDNCf(
zH?vh!Vi+Kvaj4JjcxQBLhivGvyfAu&#z}PNE8U0S1dGJpssau#(qX8e+$ZQJ8@{3n
z3HXuogl~h0IZ`p;Gu87?<;VH&z^!2q<zQ(1JSO<-*$`POv%-TVP<a4skPE>JVJCN~
zZ_5`E{Ae8ysYIrdV+(Yji(eCT(`=fedMQGKoJy(*b?=%RSgaS5+?A=Z73|Yz6!+vm
z1YT?iSXq%E3qBx4$L12(qMI|P7H8Tx>A5pm?fC^_+MB{NTkSqg&fUQN=vVgo^k%Q4
z<Wvi_x-JT$M=ri<y6EB@fD4ggDyk9_5#8iMJp3@Re(+<#Gea1K9YlAjCNL-LE!zT4
zI&}AuG1RE-9YF4-FJ5(Ax?De~8m2emKd<fnRQ7}4=UTgiR!3Z)Y3Aq{{|`sJ1Z9JY
zu7T9KMV2@fvdFx~L!-uc*6PJ99K(MnDjvHu<|xyquYLg0aVqfAfOy!5k#`*ch|nS6
z<DcO)+_{<3?0XA0e%t?<8ZY22Yz=Svr!2uX%jE>shuKO|*N+4lURY;gk`G<XMZd14
z-jFnWj>MZIl3%j>)E&4rv*nDY{!P1~S?K(;zkwUeeV&=Nv4Tv$&F!s*<5zzJe**(I
ze}k`|UNQnrO>Ip>V_n0Mr?eWJsHltO>bkAShpCnipl?y#@0HEmKqyLCca-UyoiwzX
z0(s26RVm7E5A=d=g*JO{BnJ3FH_-Q<C*{30v0DmZ{rVlrJP8Ef?6_dO$m28V6GNEV
zS*iehmJ-^`QZo*|4*_Qqw{<9GgXb7>E591&hvrVw!}lhf;Tw}{(mko(ui94$`fvrY
zKS?5{dt8?qj|kp&ii_~f?3lyFqhQGZiKC_KrB`wAD<o`u&!zmkji8Gxd224qObbqJ
z5<CjV-azqm%-WKm`Wo+xUFmB!hfHYOtr!!iaZ0rdIgGHKwCQi@F-+y9%jc?eag?sj
zixPv^7GsqY#Egqqtfmpt{pAa~;c<6@9r1I&+1bg&8Mw-F?maMY?96c&eq<mKGfe))
zPmV%@+%rD{_0HFzWGG_oYk0t`gzDzb)^NmO++K%*>t{L^klaX$Itp-MNWS`3OI&^p
zm+W@7$`{~+w}xNg(i}GGh3+!wR`vU}Gu$E{Y$p_CLrL>O$uLKaweDOh3m4=GuXxD)
zj#KeM_}QZ<XzdQA?w*h3BlD`R25>{F8~IvMW*!aArg|<l`TcvbWhx6c6J2yC9Agtr
z8QSQ-QOf7a%2B2k@6Ec?p)BT{HY)C%>=t-G6ZK@G5p?|wHiR~}(L1sX>I=vISFkGl
zJsZ~$>&qwAqz@pK$YZG$B1$V|qqpRb?LNd+W}U8^rUcUf(`J8U1^uF34*y;quiLfP
zi=2?wo~4I{kV9%n5F#*(m_v|zzg-!%v2|a46NB4)iTpQX=mk!0es{U9Z9J-Q>k>aD
z9~owXIu7dxyMFt*^T@MdlzuDRn<AVwUJt49F1~okhiXT@5t3)Qy@+XrU;mb*?Ch9~
z#^EVB@!uI<mINHf%JL4zj!QkseO6tiogNT-x6cfd_V8|C4M_VLenihBz%PifuOX+s
z5%DB}3=UF_`w8*Clt>hWoE!wWTWOL{=IDy^MZM|!K=yW$J$<CUeEjp{k#9C%72Y#0
z{iQ5yCq83m4F^>B#5hGOlBYXQLT*&+u`C-tZI(qy*^_PnwZVG>D}FZ7XrCzv@U4$p
zwd&K?A|&OBY^fWf5;XVVbjH0GZWsKZ+X<})&E3VJk*U_npv#91zRm#{m9Gmgj4=0X
zgjnxfA#K!*nG<g}>U#r5QwgaHn?ocy7G>B>B@blP&CI1boJt_0dsiZN_Z`8+C7b?F
z$o?gDJhYn*9YRCXGIkjoU7uk%Ouu>*wlCTRcM(o-W{`jFKsue8GM5@Z1)h0uwuy#E
z<*{XC65bZc%ij{x6qo2Fr|#4by8L15@lR9u%=$TJrmjHgPr={t_k??CsX8^2w2daH
z9m|?uzl0~ls_}qfCEl}wiyGd~53GvuqV^|Hxwhr}1vP1xLDzUja09t5pNVVmE=NF6
zuD`qB7Ll+oa)|jLn86awteI&6ZCs|!YmSS{iL$Ao85m{sW9seK2zu0gpzuX+5&=z#
zSa?G+XC7@U0XtXEVmc*OTj`XmKK-YL3NE8!(^KB6q%gd_Ws4|YY_}A;v#&e0?vqyE
zs1z=+LpI>CR)@Cwdv0#b@$Ja_Lf<lSgHCUbwV$S(8gc!1YGr%SpaREYLg?hmg>Cj2
zNJlV0>Q@I$Za(3s0b*(84KGTtVJJ=T@NaXfJaURdz&jiwOY&M^FjSxq6!?3+tv(#3
zmU_?D&Z6fX9z6?XTaL2gZ6<f^K!T``Uk4^oUHZA64pE`!zkylp&&_H&I_bJIN<PYz
zwtLn7I7^U(FEtIb=EP&*5~h)T>%~nqICyUpjU2D4w14BJKW6OAz&Z~JxIqzYc#x7=
zKWPoh!0p}6*e!!^z$C^!9wje(NNky3UEES$d;bbv!nah&F5&t8HI(b?&n)=JZYPZ}
zZdD_VGNx~+)7C~Pqs1Vaviicu3n5!l^TI+i6BM;DVP*T&Tg{sh^E;k|o}E)4L6BLe
z$G-Sb_4nDfH*FKck35B^rzOo^J0eD){n@Orz*85>j&Bx-D`lyhMHf?K+fGo(@pbnm
z(!86z=^(Ps=tfyXwc+TyCt>9wx3Vy%!B%NHWN^fG!wROhJr4SpxE*{`F@BGXbnH4G
zpcy$Uwi|D0Y$GjSaAsc?`4)(i$7bO>JJZg1mO@lv%Ac-J88njZ2^NvlM7}3i#l8uP
z5Sq6iO(!A*rEoYrlOMD#)GnKBrJrx==@BW$n`;<le3ROr0h}KKStXALXyIABndV-u
zo&Z#Q8cTD&g87qcX3CX&UJL~MkV$5enu+x<IBOjXn8(<D0?EjQuXiUu4TE@V?qhWa
zzsetu<4M7#^^`^PYMm0udDsG=Dz-d%r@gjUunEqL)9Ew|{i{|+kkr-a<aE#gt*O&9
z>!0u>tUtc%t9~!M=h8hRuDwp`_;U1!xp-1lc>2z%iwZq$I=EM`)qi+>-}J2b#MKv=
zA*qQDPAXfDe3|nbw8|MMgqs`sWt+RACgQH;Yi70t|7_1|G@)6Ntq_!O=-&n|wZxhK
zkBzsCYAfp6c8fa{DDF<t;tr))DOQTRyGwB??q1xXKyfP&+})vgfC9lGH~|95;d$Qo
z8|REOzWhisMzSSy?aa02z3+L+%~)?>Yv=kGf&y9@C)b`FVW^60Fcu{+vT=Wa>vP=f
zb%VC&ekL*!tVgpSDW&_to=maxx*4cznt4^R?9t5D-)E~1mxLQ?%{<E0`fEfS0l5kY
z2t^#gi<VFMVuY4YxU!hwX;K?&L@N3h5Ot;NKOBBUOXD3%R$QX~Us}yR8%bW}Cl*;&
z{98A;mi@3Mit!h*mkkHQB$8MbZ&eUz>$V?^1>pihO(A!KpTRRI&k0|iP-tCQFENFe
zb&*jrQ)cl@-ZE3(4_^0%nO~8M?wVV2)4N|p+w=JT+KJ5e+(#YzUKtH=FaM*PQBSH_
zpss54xFAFVI{8fzgHO@g#_!3*N2^abY%h?f#%>}k+tNUOc-CaiOD2)b8Ra*$Q`4~i
zrmdnal+L`bJdcyrsz-awr*%zVnz+89uAxMd1w<5#Th1UCf72R!r8ONGJh^rFJTEE2
zm0AqGOAtBR3{m%@ci+VIUgF14fmEFJ1*yu*QZLPY8PA8ksY{5j38p3dhqs6oNn)N@
zk+aG3E<};JoMns=w;His0rwpx>y}s1ucFqhpG5CySS7!HHPkmWG&K73uD!wmb2kCD
zD}ep+2MGKYUm(6S$^a;lmj?A;X~%d~$;&?^LwE2%7wqpaD3#x2)|-rKsuXRT$bW}j
zqNsTYbxrI~*~X5is5gK3E~#TgQttCkqnz+Gz%Uwfu#`nXjhjKm85z{%<IrAVgf{bK
zpz&)%#_6tWv@J^5FD>>(yk?h0bIgF97<}cLL#^E0$HCy%<J#nX$2)ABNrMneTa>M_
z40_8+{H_1v1z5bhmu$2w#3G^wmAS-){3Q`AVT#mr3~U4*dNs=y`fuVo7roH$ew3PR
zSrIdPT0T!c)i*HH$A5o)-Um5iB79!q*K0hG3`sn7dD*E|)Q*!G3EA0EDS>ZdOAi&T
z&*3HK9dr5!34i^j{WY+z6#)9!sBx-EDg4#X)PU@nTMJY$V+Go%H$|nh1+Vlt9)h-H
zxkA=nCY1t|lij@yRKZbFtAQ7n=}J0vhKPHUoCMZ6Q2z@fGju+=7sTMQ?g67+PSabp
z#W5pD&9L!R#++6-LzbFz2ZCUD9u)3gW;}+GLRif=QCk%7etLYDtRByI{BUzHWhwQK
zJr5>gg{#9M#tsq9Z^2O=k1rYaC{3R1y6pB5R)-$%Ya}wjH;}C~9bp+cgokMl7e7EB
zJ<8a*P7my<wBDEYw__mq!n?xuTM6s;CM$IJQx(%h3GVbPmng-&QL=+cC4ITjAn@-F
zq)rRlkYh3NWbEuQQUt*F4(|>kzKcM-mh>g!LmJ_)+S=L#jaXl@CX5aA4JSPx*Oz1?
z|6&rx2>YM3+}?(NHY-6}^y0r|bo^9;@sR@lh0$+!%ec`t7tUEmC;z$RgRZMmP4Y#u
z&g$FY(X`dlORs?Qu}#`@lOWi>OV1-}Ij?llhvN1y`q{8a(odV|)?{-daz)vY@M$9$
z>PaN$fN4Un4c%zzv_u3@<7|>RDMUd|lDQ76bE(7-Yhr#szeLocy;ac|n|oj&+#I2O
zW+{8$N4uHFgL-jO=0l$S5ByDU;U@jmZ=Ao+&QcrlMzU2%HOk`vOZx40W7+x$)lA1r
z)=nrA9_*L$tzElB%f)8Q28C(tCIKhC$ldHCTKZeiBdnnnr90n~{pgX=JTCR=1@{dG
ztH=|BZ{BdTC$wmuzTxg{Pw)H4J{Ixr!Iz^rO48X--vCBU(5foFKTz0u{$!+&f?AT2
z_SfOgV0AdQc)$ZxkQw^0Q(yY)5Q0aRN&{h}p-<K6&i9i9$J`HD#I(x(Vd`(Cp_Qm0
zRPAEIS7f{<;B-3M^E4Pd*H!B(+qwTZ*tgVb>*z_Ffu2&3F?2j2z(3hX$sE0pyH}~~
zhUSizXye|0|Ca6D4KHH53E(?nmj#z$kNfGe|3@&1b0l};wopAV99t!mHh;82h}#g=
zCEH)eIj8~ZN0<yP;{eSF5dH!97h7T{?f!6l{9X|U!IrEd`^VJ&D<m_9u4W8;|4fct
zEg39e6qKjJ{EsyBYj;-K#8CEKmYDS5*#;12Bu!)UjlyU4EsvT+8x2|a3if)z-)$A|
z3TyRu*|*Zrs)>RKlAA&%E89fWt+VeUnV@;e^}P>A4c-Z#&kb)yRyL%p>gQ<?+HEd8
zoTj7n7tALCXRzg0soO)rZwyj?1QrWu&)#~J4!$0Z0Bv1=@XaAO{4T#!ng>)o+Zi;0
z-`}m6(Ab*MI6mH)Kpw_k%#>!2`j+PB7bTD?uuAJqs7L{U{(mYCZ;zJdtJ^kIg9E;s
z{;4(lcR$+Zf@er|L`Fq*WVWrN#zJmd-}2{GF%oIx>%!(!<_dy6dT0J4uzgZR_EeZg
z$ZP8JrqaC2T?w2y>)gHx3ah0exd@R=@_Lv&f(Atxcq9Y8O}?~XnO!ZU&+&@vXYICu
zB~ck~@HNT5{-_HmUq!aai=FlqGEIG-UFeQf-p<j!p}oaJ{k4eCz6@I}nk7c)6I;T0
z#{N(zVBvK|6z=nB9;gEDG05ylNOJQ;Tyk%x7G`IAfojZTT)&sBbhpaXOUlST3!GVf
zc_OiOA74H!>M}8#{h^k5a%1yhIhBAYRW%5Xrh0+hW7?Az0#e>1+<Ucc6Tvm*!7)b{
zX}!}7j8~VC`mmYTR)AoZ7|<Xx3qh_Me3054TEKyXgrwtw7vBo(S--hD_A&Ro-3!^k
z=~V$r1`4DAMAzMO++LPiTQ?*mkj@U@K*=9Sz1qoNo6F{!BSE-|N8CX-xfeaJ4%SlJ
zFp;NRmkr;!AK=$KaG&c!MPH)c;IOyQjn&py9<;w4@j8PyJDg3RpfkyH^<U&3^}@Dl
z{T&-)jC3+Di~v8fbGbrN)dt$Y{p|bc{%tvk^+k~!Tl6Uh!^1Dk)vQf$O{{964yi^d
z`PKTDidCJB{^78*+RF2v54K8-uJ31zX9?osFwm?ajh|F^csF9ssmvm>IpgA{)v3uh
zNFq5-Ypr;x#6`myjf8mL4$3d|7vaj}Wq?OMeV`@o<_gs(_VUE_o$85Ez~&p`dnHn6
zr1)~YCAX!T*CEGuk5hQPOD-e+@!4G{D=|fyD^5q(TRyw&iP96o%@zF@lk6$)y}C*8
z70dNJ$pz>9;qtoF;^JQWM)x+>`3o9+YWZ@=r}~!{^U9Acw~uX*$|+5AP|)37R6XAp
zmWs5Bji6auN&#<X8~ewPajVQ@V~w^P1<}Kp3^BS&O03ketxWo7k+w2Qc^HH^jl^Op
z4Gd=rUBz@6(T6R6n0Du7^VIcA%09GafM#SQMODn`ZQhq|4#o{f^BiNVSc2($Fhqc<
zyA+(a#0l{fZW?iBSulyAH$93agX5M>E-8v?jTeFsCrSl7CTNN!Nwu~1v^TV(H_g88
z%-NLIugw+av&~xLe9VTU_&v(&rZ=Du7BD%(=mtL?zOyEMtVqTU`qggdMP+lA!hCx@
z!M(tc53n7Hy|Xi69==^0FZuH*uTNsq)plL$c?E4}1+w{Zuf3?@D=w7nat>)~GZu8_
z8YM=UAm)iH)Ha|zvp97+OT`3NGx+wt44T)C9(*-S68!YkeY>Iv5UBqEhVN>e$0ecb
zmCP1)A0!H`TVCAWdb+HCJTl9B5v3cs8c4fzxPGr9Vf!)64>2=U0(3jL_!&%h-><v3
zGnnKwAONw68O5NyS1vYqIH~0OVx){K#K8<T?_1RhtRSt1Xx&Zod0CrGTMD<Yeu&TO
zK@Sg%jDbGsUmjLkF2OVc$2D9fXq2^ED3$N!EVKgSrU%D;N3H{>q1CC==J)qs<P(1r
zGpCT*iPuwz2<EKn9fc!3j+<gC8~;hUdV2e|vvBQHUUctMdxiL!Mq-Ybq+<9+2c7(o
z&jV{p_}8`ocQ@@wetGramT%5u#n$hIfFnA(F4_>Qr{{@6)1lka`ubmQ3q1B6NL5}I
ziLQ@H3b{599qKv^vfY1k`6AU!ySYujv?+Vgs0@5K8H~aS__$<`poF>LN8YZ`XZhAa
ziMzIeDdTgW!5ce#NggkyTNY(--T1nxxP+Sdt9U>_R!1(MPW7!qe*6PebpOp}VU_-E
znm>_&q2-qsvimr@i=vz6?HfjZDar(-8zH_;$&{Mdl;J83XKOH#;cCXTb8Vf<_taWC
zm6QJ<Eb3&@=u?hF=SA3TH`UIHdTCA(mAdWBKR6AdGa{R0gRL)h$hk>rb^3ng`M7f!
zk8MsPEj{fY2|JB5;uaX1y=>yILGvnG;WUuw$3+E`V84Bdp>(xg*byS8S>Ii0(y~$E
zp*+|DwV{19s8C$xItT~$VSZ(eB~^?b=Kr=l1$w?Xud5b{FH-6*F;Ac->{Y@kI7dwO
zBK&l<<U_xnO!^{BOc6cmXWnK&fa4oe5UD&HN+Z(lQL=<nr9KC&&T(50lGzUM*mez1
zfnK_rDE+ec5yr@Yx>6LkCIQpV$lfBjD;OuW%Jp+T(2{#OeqA-tmDYM~V*>>shwj>o
z{~c*clQW}dvp;Ukez$d?Y<y^@fq8NgDM}%&f?0}$lW2Bj@6k9(S|jqW08}@fOr$;7
z$TK)6pv3peYLOi)e)s{@skQ#9<E*~h5?b5^Tsfy|G<;po4D9gWkeC{tURFenrv}#5
zi}V_tW&+VZ@0wk+9i1Un)Wbv{p<_!?y{%Yky+Kd6$1C&ZY4YH!4GA4W&Qh9S>@6N7
zPMShz^v3-p1+ve0@zsf~I053WxfrPbww+q-|Jl!~B?2S7r02ER1@n|=29fGS+E=xg
zR^EVunAd>G;(W7XFPb!^=jc~y(A@xcxU@9bsZkf%GYy^H5)k3)ZbO4(np^)Wdo43E
zZyAJ)8_dW!<*ogk`In8LIh!RB?mG+8Q{UCj)hPWMb~w!g9K=U>`BA9>8rY!PKVF*f
zO_Xkz7H=PT>SV|`_4qVUXk7wHlEk^e*qyw6<Reu#ng!u5XGP%!E6Mjyz+6plcj{|N
zA<n@*xt6pL!P_{qF3GvHH|{hI2BgI-sPYQ+sRacar4pq$NHGNn%%r40dgrU1aC#G9
zps~$V5%jqU;#?c?fY=XBsvqQDXcZZwa-lOE%xd`vX^${A`3)Yab{g??@j(GdSW1=T
z+^QbkJm1;-IgT+ZYWxG~);%(cJ34a?HLxb?`!_6uBQwPv+e@|Kt2Q0vRIA+B_*yt7
zBCpAh-<x_h%g@dDafkHz$nMKkIEa~KxRVd#-SP%iqhn#19c*HSB=n0I`A$Om?W5mi
zh8|7y{cSW^m4p*x&XFRcANjf;K}J|y3}BV{XqVt3M&6&iq%lKRydcvynq{>B=Snvo
zLy?1A1th?jRTmRF;KPA`Arjbfc&|2hkl0)kV;6&cQBjqurCuJuT@c#%RUV9^h3&@Z
zO(23a!x!Y9q^&g#p=-~zshT^$Wme>1oSEj02d<@gkt!GnC~39<>H2s2`UnB*2EM@Y
zMlSaHl>|z$ze~DfMm7T-A>gRXngvefv0A-qvaiDwsTXKbrKhWs{{rpCu9yj9SN8RG
zs#|qxkJBdQ?`aZ`UrT2&IGX5I<lkj_ZkuGsWrfts@qmKKcGs?t&|ls_XMB%oHHZ9E
zXb$bx;v9j0{7d0Bf>u>7{lx9=WzfRH%E|~U*@v^U3yQ><W)u%eUp8JT8Wg_H7*J4T
zuGL)a>yxrbk!V0H|0{q+!Cmb`h6|Z7<ID#e7!ecSe*f*IGy;#vYUtwfJVt3nkcU|E
z%h=eQvW<=c@E}~dGV>sYoD|)dQGn0gQe9?-87<?LwgCu{Uq$2Ix{R&RtD0u?W~tUN
zYCP`fq|@FyY`Fi>=MFLInZ@YcSteBzCEOwmvp(2&T6?S>35H!5Vdv-PM<I)<<q6L(
zEzK{yO(I^O)x`2k9N$bm!4wv4y}KLENxR|1nN~h_KBh?IxWCIcI}2n(oe*O1e!!yB
zLuzMv`T@)yqYDKd>d{?Q^`PFjN%79My1?{Il<SbUQ_|xZ^ktPDTL3JQ!evCxc4tQ?
z;{1bd(J4<-7r<1OYd(xwv-lZCYurn7Pr6wiHpX)N6`wbieLK-Ylx!v&8dYU>OV()y
zuQ@kA1v&v0FJi1cl{5(k;r@ip!k@R{r|^f~*LU}L@XJE@y$k$4v-f!)F8d(C8U*Rw
z=3xTwWzQ2z#6)J*js@;>lxxtRVH_Rt8m8xYM+SQ(C%=U{Dr$?wF#1e9*JNLLEPYV6
zw*UPF7muZS{i}bHCKH*H&@EHc;XH>$%7JAn5ifRHR@GB#V8v$xq79yy1F}p0xk4n-
zMb=rwVg1o$7tlM4p-VtLD@y?|)tx89;R*pMsP&&XQk|G#;_ySP4mIEDMWWAoV4W1w
zaK5%?O<Ts+H9PG+97+S@L$dK2KM&nZSw-lzF?}b+zqnX5Cc#3bWy^LpDI#nr5Wq~3
zP2+4@GF%mgGk9a|>C=Pb#Chf0+L~?0$9bcrb;B9Xk0;cOHt@k9qGads#nm@=+Ohdj
z*ikZ`zKN!F`y#m82vSAi7PwSaV5LX4ul*G)Sgw}XtRvB$yR!5FTL~pouke*r1@^e>
zVKyZpE|ELvg(c0*!0)#cd9ZPCUam74C><YHu{GAzZhtt9kgS(CB)`oz*pl}BbNQZn
z|K<x=b7+1p8sqr97{zHc9P3-N)5ag1&!26xTkY`q2pV~i6R2XcWRd8e8&vLL6Uz~O
zf+alH-lp@Rj2)K*6Oyj}*vi&qv%}vxciX?Mi3$e;Q!Sic-OY;xAyy#15csaU(nO`&
zOj!JDAaygnhcwPT_L<USj&3l?TF>4^%a=w(_>vh^Ing>R5Mr2*PIV3FhJxf|N}bfM
z#tICiyr*}4uYOm{>Who%R#nn7H6-i@c3@kP704N-(VIYiD9Rha1FVaCaYgx>2BXZ3
zqA^0VQ?BF%!VOD>6SPK2Q;G4wm@ZrLRe2AfRZLPzG*C{*^vWhxBWj@Y`=vld4hw)i
zfJ*6>v5r|Ewa<uG+Rx8VzEthXwP1y;>L^!^NB#YZFh8S%ebGXRUTE2S)CF_+gF_j!
z1<m7r21{nYDqW;SgHMSBT1Ir=nzbDYdo9riI*KX&whX6Ny=r#ce@ac+%_K~+P4fge
z`pI~5X}&c>cGh-sDP+i&-lfQf7T+)SPNHp#_k&wv^v@Jwt@LZX%sL=Jhse@U95WLc
z$o(LSC6n;EfmE%)qVB||q+Ny`@Lxohr0@D#`JYfnOD>(Zla6kN0{uevQs^E6;^Zy=
z&@LyE6Q_g|&aX0-t&EB>2_)rsZ8T<d9K$KIr{sRVeYf^uV>tn380$WximJFs2U-!K
zS(V}5^_`l{I&bYq#9C7h-_~!5m48?TdGBJQ@UbKA?j1=2XFMsF4{KJYOt(J?JD(Y_
z(?%WjtsYu8%nJOJ;R%N>9c9F#JO%8C+-Ic7yJ{S9(sxAu?0jUvX_wGz2kHd4LhtIj
zd~MQ|*eyji%E<LLMKLkk*R3#}DjC*~_$@YuIEI+`X7gXF#iZQag}$zR_z=ftjZFBc
zUS`PXEc8oYIBn1s-|rr?j=+6l)MnFsC|rYMi?;x&^p&Ea-~Tqn`r8b#Lg_2X?SJLe
zqN3zny>dEyKC-0IQby`F89_S-x|o9B#~;>RJSaD37?ep9(!^6C$Y%nxF8@AA?jrRd
zWW|w3Lcd=jPN-Z7`y!2m(oR#Ya{zYcSq|&V?c5n?Gl!+nCaU0$iw1PB$f5m?dZN#f
z+W!)2-g%=$7|!#O%Lrv>%oiZ<J!Z5P$i_+JMC0D_-`@UVMAg^#(_bamb^MGi-a|8e
zbHW+M>^d<>hVk-~BI3n;F+YS6L=R2o5+cd)6d7j-17c3B^ftz(mg$FZY+^3Q>W5TC
zgI~jyMw|?N6)Hk(@fa>Q+GHej>Ji)9uy3(Hvi=+DTMDVxK|>{{6}ms_+oP~4c%9>9
zl>hb(nTzi;r2e7%<H)rV>YlAU^L9--0+<>z{Iw2k|AX&_4>cm4wLOZs!LD56)r_DL
z`%$(*h)~l$QT=NKDRz~G)NcPU_1Q3QrT&;Z=M>y~Y;C2Mzl!mZp~t_lv-=_37c*Z_
zSaSfLZXWSf8fagUD%6)RpO)tOHWH`k*R#8Sf7+|Ay@;tdBUnphTSWSbOr=!UwOx<!
z^(w|Ktxzq_R&X>|xIwpj^m#L)G5zV=7}d6gPW=dVXUCy18zWokPnI}ohXx8n)P2-(
zHignA#aDV{vWA`{<f7~w%y6!g%#msfbIYfJWd+G5{oHXMl<}^;X_A#yTx9j?=eV-`
z8A~25#qZT!I}7DcU8dv(K*mb<SV?L-_sKMaYbjjj@^^4b*UpHj1PmG4jnHE00u*en
z_};ble<%Fy5$;I)vSW>;(H$RgTtfxJC%EHugnoq%JdNB`x3mB9=w#|%<Dsi&+H&N;
zM|M>d;ZaW2n^AduYZOJ2G+6cJ`zsO_RM(L4>H%u|E)ySGVx8C_EDiarrW&58Doc$f
zIW|G$tMlL5Y;^a#fR6fq;gk9Z3`j@hYQM}(-{SxIdD`F@`!TVO(r{fpk?h8lG4zwT
ziw;*Tf`~HH4^|C?|D{W|gfasq+&+Y`{GGS`>WnUuC~@hged4V8aIgl7toQj}SSpgM
zU2(E?ROk0^D=3j4`<~vlUwT#0bK>pjnG4)z(V-Up%wZFXX88}u_Vu&{fzGC;A7w^`
zHwSJM7Ck*RMt|aT{K?nAO3VM;zLOR<JTmufKW9Y2oD+v(n$?Eve?X+@_(DJUI_=cS
zl%~?c*u^Js*yZXBD&41=Q5)0gNE@=YhJ<wf!^j;oGQ;`q&7IvgvnN6zJz59+E786)
zj>E;GG`Wc`rm2%q``_=>-g-qpmpCxbm%&t23SS|qOh}`;rZ)3Dix^c9VmT3wschn;
z;a_6{_Svi}=_miQ<LP(a6E@V+l%#A2bm}rgx(wnss8zsEbx@G|QyQ3PFm2%}_KKul
z-{8OTUjKpVlxLvTwQ4`049+x=_We*rn(2VrHlPbLZ}1uX*8jh-Q7Vq(C{^iWc^A@6
z_rasDul676DESFFaVomDUwMA+lm<BOUjH)xFZuVC`}tq`=6^83uc+GBM=bC^B+~z2
zSpNeI{=X7~{{vn9|8Mqx-|qhl8r~rrc>m4mzlYu#MeY9QlWqS$hm_*|`L|HqxGLwB
z*Bcr-kc2sWcQ;DZ$^IW$@q=6O?Zdd!eS6yIO`$%{c^D0D>wiD^sw*SQrdGhfsCn~u
zb<-C<OM}}3ACKA$%Ov{mnb+V~scFlj+dwMJv+dxHH?&auj$YrJ`B7HpfZL|Z|Duo0
z6hWDo$xTmB=9T?4w8aalNGKbvOBv#gIXRLW&lom`vw~8I2a*8a8#y_!)VDKne656{
zqD$GY8r;by{!RL0Y_UoXNw2gS<UD_Kf$m;1BJaq!;2`Yj&1cJ-13aR3IBSZ|D&RQW
z9u*~m1)=mBbTu)tcV^;d)$I^3(ii>Wv)}H~a(Gfm+HSJm^*H;<%o{9~coO*74Bd<c
z2Iu)fl|8K)%h#oM6ibhQG!AcrpBbKCWSMU?mq`rpQ9A)WyhCy+Icax?U)U&Hb?-s^
z-sJ$ep6CR2$-%2$Khsouz!i~?tz=y#n*vQj)IR*&Ra&PZ5I&GL5`q(aY&K}J4AUGl
z5+BCRt6_d@FdX{nYiGwD<PHi7zB!o9VU&n6GrWDllM0~myqpXp;Wiczcua$mY@m^G
ziG)EedV?`q`M8@@E@S@nee;GuNpiWGYPl!BXTEEPN6!iKFA!0X+iP+bUxqhMtAT^t
z+1%So6L1o6u71Bf=Y5}6lm>06E~lE0+!Cd;up8-&u_%DggRMbejh1{h$<Bb|-9n*W
zwa-EtCqlh&y2UeY<7da$sBQ-bU(V)AIVkkz^n+*nTFQ^pro%G9F0jky=`aN;ncU7B
zj5_~Vnd(<8DtVNAr1dqfljtw~PdnqGyzcOJs0$Iucy(UH<7{=V(PGpZ_r*ftub-qU
z6gZ9)P<(HRQ2ATR*boCZMGmF-RVvE-HWv6cN@rVPHj{Z|rO?ot!ZS@xu=H8x`577L
zfZDGK-#LKNwcf7Dm7g?!zZfKjsJLHU<*^a9*YgeMo7s$(r{FFTOL1ya{+84Uq-~n$
zW+*FroYb?$dUeXS0_~%}Q&BwakG*w;@^~dsL-F8%n|9lkle7)c07B8zQ(C&**{Iv$
z`!uNfuOrA+^9$dDADj<#GY#P!z<6e@I*RX;qAPUjp@}?1D0MfegCb+K(`bWWdmp#3
zJ6+^e-{B@qfofZ^<Bo_MJtw&6>>H$*8-80iZi`-lh$mvKvoGa-<I*65h~E{fFNVGn
zt6SCN+XwbE{P#_q-aL1nEKQqK!X9Vg_998=1S3b1OA;*8-3<;$nep=}>trw#DfpEL
zDMSd9F_58Mhf=|LwqD8x)4ksJ6Jcb|4<H`Y_$L>QK+H|C5_q8J8Us_fy*UX=!X1Q&
z)L7`)bAddT9~dC$lM?70WM9DdI$p#^MS!HK)qDm9qiPoKk&bw;l*zTw5OL~czE8_p
zd6qvN_?v-W(kU*d_c4>AQhWW><2{^zo1|YG-|WUw;Br9TJbG6z11XXc(CulSNbd1g
zsbEy6bIXB?8A~+W^u^H(XMAOceOt!vP35fbFxGzU8uq8<;P@4${Gt5Xjc$G0uO=A)
z=gR~pm8Xee_v7N?8dG*18HNXLkK*g8f(rqsIj`3?>)U*?P@S!mk&)sQi|YJwOE)$4
znWHln0`aQjp!Sw;A}Q`~<IqAQpTQGE)P!YH+kHMShrxH5XQ#tY6=m&ddkCfexS*}O
zNfTVUcD#`3!04+d6F6F*3H-Z`ZFgHO(xN(h-{Q+J$gS8{li=rvtBD+sjfsM+PP)ZT
zIL9Bya~SP&K(EB@4lf8iqy6N10}U$-M1@8%wFh7SY+;rIJJbV8jSfhmx-BQp>XU!I
zb}m<zQJ}W)V`h-bufBIrbH_@M4+oZrYK&!5{tfz~JaFgE)s_YPH@qhn@{?VhdMYjc
zZ!m0h_jz}(s7t55B>J<R-7&9tu0m_=g2BPi*w|YZ?*}+{@N`x_+=d1YupiBC3JXwG
zKkly}3x#~VLLmLfSzRM_%m6W$w7Ub1r`@FwtNmtfm_*-Df);WMx=FxZ%En#>r^Ss!
zyc(gqrcgP2rzwHSfY6I~B!&9eystkYCV%v+n@yYgEiyE|zJoT7l(@`PWe3%>Au;#U
z;XNpl#fDr&XSjL%3N4M|8<^d=5o%FF9KU0D0b?)_fB)D~GbTW`DpytpG`8Sv*e>gf
zmb3a0Yri2m`H8W&<_|i?${&@uEIIJUQ+Q*<KN&`y>CLlUAeSbjS8~Mmg%moPLfE+x
zEPlu~F&zxaWZ4--xX&LZ*q5d5v{O>0o~;jQKvG=`Nre}7Lz0|MChmKzEc^>C_J?9L
zT+Pi#>qiw`IVjCX4aFhE|Hlh}Ag7fs+oc=x1`S=xuQdEKN!njf46W-p5mWrS_&`}G
zBkwp3dW84qiCWPmWr7g-$I~5mft#<J+8>KKYYTou(D-J%I+Cik+7_PsKkN9dtozeI
zSKUHy!ibNI%S7%)M>p=TL<(;t9O}Ehszin(54>83ExNm50$<K3t%1zZw*|;?ecY8m
ze_^0Ph2KG>Vn`irXnuwKcdGL*3Cb~9P<FElNuRiWsy$wsXTJ!BY)idzK$%1Z!L{C~
z&(uq_{o6BCvT>CcRHW?yH>m;1PLn=~T*cT31KhTkF`&X(aoM!9i_EHzxz-zVNtQgG
zRV@Cx*y1tlJLhP88<%lE^SBU?ut}Wj#SPS8Qv;TsDoY{eXdmg!4oWj%xY_fN@cFmb
z?u|O4X3`CK(B%ah8XUF~yp7rM!cTNgd6A&wvCTdT{d=M4b(em77xMG|jB{<nTO4|j
z1a7OnwsDQ{ml>j3w)NV#dt$6I4h9JI$lp|jD%0#E<P@OeESTbgK$popV!)k?G<A-5
zz{TccjFz=|PzOFSh%Eb^S+-8UzHJ5>PDf$Y$e--=1j|%gQ($zeLH#c9hcxktf|hFh
zyD`6|Bobt!g^>Dztj=GhLF?Fb@1-O)UjF!YKO7TBDt=N4vR9e4{*CsgYa`|T0grb%
zW3Z69BF3J~gAO9WUmM;#RPVz>7uSW4-FDvC&(2acUiiTn&QT%$G;V`?t9}|myok-d
zm&h?JW&Vm<iA{k0FcY}MDw+-KWgGrfNK%*R;)1*Z)86x1BKMF~?uJ00{%)i~nXYul
zsY`A%h60(;{+w)8f-xswQZG+}P*|Gs<yV(Xq`lzBcGp7oSZ0S+X-so9F<!yfmb^UT
z0zx7J@!_M?^DMaq$~Z~l2nmVmnd)a|`4oxDyXL&+l>JUWwq+U+T6_u0a^p=QCxnI&
zATJ#XFj$t4=II}29Z>3_=$2!wOoqL~gr5|fKaOdQis6{?V5Kw{%9K#(JcG}PKjl3I
z79vP6`SKXS8SY<{tM3G9E`9CszfhT7Ye35Oj$-#l+T08uQtsPz<sRAF7%P2Fz0>MD
ztHOD6a%c8z(6m7uDn(84ujfSl2*1iZ7lo(5qy<5^pf?2ejWFzPsN$gG@Ni6JZ>8^^
z@AZO#e?q#eWOpKSaa1X$s^uTeCqQbXJ}l|_l^G7rV>Di1AN}HGCuo1FA+=WUbH*gw
zcc2T`vLTZMin0T%kn$CIwCF}ySJ^QV>o8!hke82S_$7fzi^i+4b79FSn)jK}FH$;P
zA<928(vRR=0&uiLfur82mJTgfk;y0s_uBNRIpg{V+tY&^DDK1h>!HWC<>`r*3*&j)
zMMHMj5%13ppHaaTXz8lmxR=uy8v(^8y3D{_``k~($x>e(f)84Q-Z*2^2%r58bw(W9
zdQ-K4;gdLT`i1*#rIV03`LK6TI`=zNm$FZ9lGuRV2x;S%HaG`uWcWLhQ%&Jo@0?gr
zDP~jNsCjXZ^X$&!+1gmb>)}rd;A$GQ^Q|n*&O_3ywZ)Z{w#?ydaet-tu0Ti(G-#Z9
zatoSMXPSHssXb}=*W<0_m{chcbVnx-L8NGSQG@wo#;p!LLPZgCLXoRmh~@!-_KXB5
z6hXYtdsL-}V5yvR-kq5+a4(;QHfW`c-ERB`26xuvn6QejekuMtp}WPS*md78N7}4|
zR>YR-FY>mLPl{f>%C{XqO;!;B-VD~$j*WkYRhS+n+4Pl^N#l8j@eiq)U6rb)ez(5Q
zPdVj!*4UT5O+PEQV>TlKtFDy#p!*WZ0w!H?iX<{t6_ljHea##0kQ;UOxn%$(`CC;b
zFTJq56C=B|g7Qrd^6HbG?Y1`V8dqn?fYy82Otgg(UbIwqDy}zIOV7xyx-N!HwLEha
zsBS?QYQaww4;N}`3A!H40&CQ)(AL6R*KEImPHSubpe`XISNQxH2L*IAjZW&fWL$SO
zJH@zcQ~^2bxRv19XWk=hj;PJ#>&4=#__>-i%`db1Hr&>+`9d<be9r@POfLh(A)TpM
zK=t;OZ-B$x52gh)h(B~5!-~Jop-r_$DP6N4*T4DH!-#u&jqi7QbEEgXUO$jOl3pul
z6A*}@0WQxXzcXtrej4fTzoJ(-2M1_qC4OnSsfSDB?9{1jtcuT10ARBZHsYD(15A($
z57*A8FYA<nX1m8oJ6kK&F_JkN126Mz5uHp0IV$xDD7GAsPenkuEL1b)<rVj}+H+!J
zNR}W^k1z14PL|_*{<pQ2Y$NNhd`{wZptu2ZIla^tPlvTD_viRnzM%IT0XfYtr?C3q
z1h;IzrvNL4>{Z%t1L%pB=cqA}--sV#ye`>P!qqzKe`-xE25GJ*(zSFezsU2%zZV0B
zwGU7oQ;c+j&o<{e5egmx*V#TK37R@aM&d0WW<F5wFDAz#BHNs76ohsPQKPoK4;In^
z_jm%*GM4-Mm#fQW>h9Vxmv-P^al-xAuX0`oIT?)h1w0D5Z-gZDf3IA$Vd_bR9?U4T
z{;AcZx&WB$8f?0lqINWY_5UqIX|D4YYwiWlv4!MFHNVc)#r3Y&$I>#$+1WXxzugp*
zrs5OFe9BsTJ3ephhNV(4nVB4_e6aFPkC&^EvPtK?@I2SdlMM!>P5(31+{;RDGvz=9
zJNfvp(NH9Qc*M~yfB*1j-BfG{(#ak9Fixsr>Kd!oZ6*{k<Og2!SvP_SCp9EhwH~c;
z25Z9fCcyt-*2}lpWpcqWl?E+G$h@y0m@*&Rg}vH^og#X@t42^Q-p(cW_Vl+&;gc{g
zr1z&;wq<L)BY&!3xW7}-xbsrgH`g{qpaatCcg$WZ0&u@()QO)a?9_rs6Wqb!%5t}X
z7IHzD%PArsi6x)v3QxKXaA=o9f>4%#Sr_)0AWqLbIFX%@dn;sPj&7#zX9ZyC$f1t2
zd@y%9Xt~I!`-g3_6_A6uSJ06qGE`C7*UXbTXXB^(Yzm%kj@rS1&0Js*?>rx`n6fcF
zfaDi}ZwU>&P)IU}l8AKH{_t-R_I<Hnz?=2%x<YL`eQD8O9Au1SrObYch0J{#K@%3G
z8;a8^X-qsVDUY4Xwpsp$xRYOUUJ916r?ZEw7oH~pwf~f!+)`btubPw6Ha55(TDj4I
z4!NPlUs*G8wQ>%r|0M-xwLgq$e%o(ccNFCQxY{Y5kla~q+UX-1A%<|_#}|x})NY}j
zFzYm|Cvv@kz4lKvwoVXNF!2zW?g7(JJukFI#jayMhRtrU6fBjrw=v2OJo}5Y9k*nO
zZIb#l60~7;V#HUuUTmbr?+$6du#?JkcVFFJ)n`;x>L#%O9c(Hu9nAUnQi}cE?YT;K
zSJ2r7K6Ii3$LBy}pvxqsQLO=^Y(*3c(9x(%*VV`<pWcn&^nUSNC~J|>k;(X95HEX3
zNl4<ar}oJwSc$bBATZWws(#Wj2qzU`U&E#Q?#^JG)_)SM_rNV1FGDI-abdB)6g3Q&
zNH9oS8<6s(Zf+WYB3G9>`g9SyglNI5Waly=w4bZ&gLM9ySsVV4_ORTJnJ;%qJ4se2
z(v5UPI$D?0pmloPg}0RU)Q;3Ii&?;iw*=$ha-m{vl$ym-0_Es~Crm*g9*fk){Sqd#
zqvp44?nK}g03AV9>|i(Bp8}<4+S!b8MDMp3IzorfoSrcbZCZffw017e4<2ALP6@U(
zhdVH*uk4inJtbg!g`%?;5=5EFKLU=uTPUuk1r9!=bUyh=CN(+<>|?ACbnp1&jym0h
z&Zp(#9t!vys<Nxjyt^L}dRRFUDm?4|V(O`TJa(C8HSogHu+%T%ex7=NyeBkQXNA=x
z7Q0ew;QI1M2jtBS*@`~ARb;)zMmJ|jAgy=|hL5+!twZ=}D=Y?uW7ZQLO(WcJfU}Vx
zJ}IZ5mi$;wLqOqfpwjC^ako+ywWXi!nMraMVfkCOn^e75WER;|hz_{e!>Qikn;;#v
zy{$RJZ8_de%e{)FCnYz(<D_@?h02&efkQ<>6)M~$JzJe_wfT6xD`s-zWP<D<6%2dc
z|4P_)O^IyX)T&ImW=tRip5ZR`W$FpgAR^jDcd9wjwNl>!53vuF3Q95ex&n0E08yEt
z(CeY2(PlOviF{bS{XbYdK-s`#oVuyP(uE@E?3ELf+5-)=WnzNR_)eoizpTF9L=v|B
zu7*AAUlV@Uygtw{&9=)2_Td0-D4mAe`KdTHZf#{zO?Hkt-M6q&38rR&`m+jBidE``
zfebXgDcZNp(mSw`QN1H@ZOw08*Ej1ot)AQzj+z-w@9_L=xSR8Ea~}Jj6We+N{nerO
zgjn0gyAe*leD>$*QwIXDbc1<1HlUR7udPk;8JJ0LowZxGjNIfFO13}3sLZnkvQ+t9
zbv-QkS$jyYbH;L=;PP#v+>%<3Fi>FC*q+-Gu1Yc~n8DfL^nFfwJ*nwPb;kEli5wnh
zdI8&wGYN3pa%^NZZU0I0wfYlyYg+Uh&3vOg1@*AM(ii0-c7GKnrfxSAqQLv;!F*h&
z0ffrar3PgRLk6$WO)mYwDb-~}aFSF{h6E_FRWFU3uMy-UHX0b;D2|v485#74+;1j{
zbvv$EGZFWB3om<bBbdAk_wPcM;d3rPJMJo+Xw);a9kEV=nvdOY{giD}U+;8n_tKh_
zCzw}>-(-+b1W@?SC^G{YO8KYCEC$#KTDlBfb?ROUlNJ-t=7oeeNy?$ZY&XOM-Qx8n
zUhOVbgE5T%4vf({zJ4QYyyLw&y<;`sHVHld2-&t6mtCcfHk*^-6IE0d2y04(F6NBR
z^lP0Rz}mJ-#(8sog;~HMjngjbAZ~v~LU?OxfqZg9wVS&$cwC&)p%;F{P4lvkM($Q(
zl!iM8rg*S>U+MbSN)t`!Xz`b?J@@Cz?&N*GcTSv0Wj*Q^m+y9*Klj@9&1h~5PS;n6
znJnKvNjk7hc2GVT&u!KDFQeA9cufQEh)4NUCF8=VPGSeyx}k9<Z-AM1X(H9vOChC6
z*EclkS1Q$=8t<Jqis}-Ivu15v+lEZL-}h#C{WiB8Jr`;uNk8M_iJ1C>j)1jLLkTWq
z+T+zb(MfRl1x;<FPTmpRKzMt~I<ObWVS{QZ`o+`1MbU&Z8P^ppaQoo+bOTEVaSMX!
zcyTwPjpD7-rx;IMJ$O>l_?;<SY?dNXWJ;U;3*;a9K)$xE&S$8jYy@=f?Cea_69HJt
zaHDafQTof&-QM2*8(BdxuTV*svTN|=2*t_*pnBjX0c{os?0G59UQVybj}@-U!N?=U
zuF=U4+zu){){PMw9HzrD&}g;IReNM=id9ZnkcmHEM~}bq?q!H#y;0-#U`%^*T1tvx
z;y$P-KR6&dRiFukzjy~f#=eDJbrZeuF6DfOjXK$wgfx}d-ir+;78A#7wB)tX@NVO_
zrOR>2UB}W3jxHHQhr}JLlZA`HSi`eU?`hUdra!v;t{Z%xd2AyW>>;3d9$;=K+0?6Q
z-CNt<QHKj#T57y}-&IErv*BWjnn}9vEPV9{9=46WzQTOPUgF+L;!S$HZ611$a{~YI
znoW9>ZAp+?V6frpzej~N)zOlOUlQOww~&Q3glvAIye}{Rcv!nXNM&PQ@{ni-#<taF
zQZ3YJw3Mh$igZ|J$VX)AT<Kg&4!cmNI9NGrMe)9<b5B+9Y}O}Xg()OfZDPmXr*3wh
zvqwSCc3tbY3l_jE8MY`Nwl?A-2K+WS`0*CT1}=q1CMda%v37oxDR|-(!PL9B501(<
zD+TAytxvaljM$~xZi{imw<t>Zte|$+ot$SDI;X7yxz>1hqVxe8cw`0il;FYK1FT5b
z#J`>ZmI6m|W{jG*lqm4D%9P^DLR!OSOn_Q%3W7Ip>FdDGAC?34d<{lS^<5~H=q4h6
znam)73AVTT_WssTjrdb3-cU69f=$}<tp+5FN^;|};I{^ol;gi1TLZYKW>i9gvQ8gQ
zxVOUsZ}X=P)OTH3T2_NQs1t)ecG%^!V0h1pdV2ojR?k-Bvt7=#*~E$)0#3F%b*<`!
zKg%KNWG1Hb0K)Fu45hHEf;L*INPZiOG+&uhol~`JS;A`nCQYv-II**{ZvgJlUj&z;
z9=rQm`KH79=U7}3`sMU+kpa)c+`l)6t;8NFVf5kLgV!DliONBlb@1J5qaM1l()!NU
z*3M;@`g1TQfq}O-$*^2Qb?1i4#E#2{aEXddX#+xMKroYQl*XIGufvoIXr~X&9iNK-
z{;W!D{W_)yNgqBfV+wTQGB#t!U3tT8W^6D=gFNgoY=gU%ynE-Do<3-l4%Yi;%%749
zUK`~hgADC8ZgmGTF$F^W_3jkD|Fc8d7fCD(4Btl36fY9r(r#Lz;EoZi>=24nx)+tc
zkJ9mQN6~VkhP?m$uw`5*g5vGHT2Vs>B;@Df`!tT@N>f6#W#bmg#0e}_JCddR_r=uC
zgacUcxv=yCC92b%lMsxulImI*<m|elB<o^I_+^Y^nRYR?O);1NydBi}+kFKe>9>*u
zXZG07N=s9tK-A<U&t**T%FPu7)s9F#`)Tp#xlMEZ5BN!@ukYk4*2J^;7UfLf&7cB0
z<f5nfHz6wz0FAY6hrh2373@p|ZVhC1ew?}OZa8|woc$isluN#?g6kdgmr50N2hV#T
zXE0)ed~Q2{XWyl~(+l33Pccg=?$OxbIu*|i)b`3+Z_T$-_>2_j^T&~btL`k=`rCo;
zjF&0*bE#{^TY0Iz7pxOCp}EWb7D<|E|EtG<O)vACMqscxT2wAzZd&_#I`6z~D-g2m
zA*Hcb9w1s9^<4&ors|0jdnd?A9gs%&9-wBKFv$wBgCB2QmKqgpL%bFo@RhuhGS9v*
z()XEhki?X#zw-QAhOYyEwf*8$9jzSE>nnE=C_}ha1Z+r*-RtIZpUDFF%Ka^PPRR$1
za<>221=+Gq>_|@T^w{y@fEhRr^SS(1pH<KyM@mQ#J948WYa+1Alz4ALeB%8akBCY$
z0?xOlT|kJWsVf{*xCMWKY+FZ&<tyq}s1jCL*!hm}%2fF(Pn8{e&vl)c6p!z@<f8%#
zzX7w$j;AV2acU?`jJXo&0=eJvqD%zZdpREkmIHfPo(*^axj1x1J~(+XGvsM@abg&+
zDKlAZCkStOW&ND$$J4~0gGXas&Gs;KG<l(<O=K~>(H-2}!uQC>ICZrYlbH4I@S9q&
z|2fl6(=KSiXTnxLAC3(0#Ls6XIc|ucPM$b5w3F4mBd=!06sIS_bRj4uqn7k|yJ%E3
z&CRD*Km;yY+P3-uf8LyZZFoVp4{Qml*jUr-Bz>(aCS-n|hZJ>J3jSTwyJg7B(M5D`
z<38GMh=ncK6b_?9*5sP3{<X&1$Z%0-t}RlWp1;=UeVyr%t9J59DO_Yr>OG^n{n-jk
zPF${cC3)WFmoFd3PuQk@{S@Q;{MHNKfJ0V%=gq0T`;hsTK)9HhIx*&{O6h?}&21c0
zfQ^7_;Sl?qC}<jb9~F!0xo_paAWn@EmT!(L9TF;=Mv@gRVK$Sxp&?-KkImH3&P9^T
zMoLo;bk)**?f&$%+my#SDoltA9JS#OI&e+vlPU$_ATzNQ%rlEA*W_4IQUM|8_t<$8
z*O_hE#Ea)-Q@z`I(tD<Z1M9=WDT3q9-T=s^LhxfC+d`bHj;*RCj^xGXFtIaeK+Yjp
z&!)<?0lb3AJ>Egm)R*t|J0rh19QD010PaK9NBvXGj*Go>Rar>1r}hkS$Cbr;?#6%u
z5X^pVCoGeaxZKrPL$(v7F7XBz+%-c@$=#K`VO)IM6OpuK^bXtx=z(|rFv5~l@LPGh
z^7-@RKoa7gmVWG?*EmgaSqy#5eFrMrP<c!$Sn($%SV;!AHSf3Ybw6yv)d(}kMUR#~
zciDsevbZ*$bdkFg4jQ3HMP+-mU_8?ph{BMYA;#gT1V0-g`!QCOlD6KxP#G|Wv(H{e
z)M%(Ia-FlM!HIK6a!8?)9$1Qxy};NI?xKn1J!yyuF6Cwb`W_w&Fi~vOC#Y7(pu=)v
zySid?z`I@I&p3HS|JYkKH9dV&UA<Q{H8sDTiQHVbipxxh8QC3Zb*_FA*d!+E#|G1y
zq^mUz8r4g8EPdiTw>>E+P;vfMLG;eehv{-A^}61bM8g-hd_1k5$)aQ!xo%LA;jK8B
zR9cbABI-s!DSG4Kp_@7eeR*AAK>XPNc9o>K-U=vD{#IT9gD^T0@K1AHCx+LOq_I0$
zAY!;rOj9U>s^4cyzV8`DfI}wnXF3&V<Gdp6@&kZ|n_aBNcw6+;lx0xMG&jA6>bWyU
zpZZ_jWk}U1)~K#0M!ML}w%74*?13B@00nE1*IQ4wJRj@XVEk*q+uzecWQX*w^>XJs
z=s&KDn8+L)0w<>?xz2khYT!c$R$WtMBV;Yr%SA^^CB((96`pW;y;ZRnx!*iX6&+&=
zN$Cx|h?rV?tg}QVToV5H53Nw9K>z&9@DY>ypJ!UOnj`HbH~%2iy8All_X{r;R}X{T
zJZcMM^r(OhhGl_Bv7NmvQeawh6|wD3o$XKmTdQy=|KY=4{NZ1M#T8+o%#Zu79P&PY
zoPkY@-`u$T-$7dMI@8Ru-_G@7yw+Tk>_H<Enp=GxAt8ZKRDb~f@GDH@T4*T?H30Uy
z2<j_~v<mt$c<!<V1D&@?J%R`^zkgRu7uyPp>Jm<@8W}&eBwKBu$evXSmwqr+DTu;*
zGHi6s434-BIyD^HzqLk_sxwg)Wu)riUUa}yA3bK5Q72ExgkSp0z1&`MD**N?k3%*y
z6?O{RNkGG5n(7@i+R+2lRcpMzfo+?AGh{7Xr8HWvK0Q&*eP;$9&s+cj)1hW$5xwUr
z17VH?tf&xBn^B3F);S&1yVJFw&_ny%1NYR&V&}r77v9#gf6h*bAug&NnJdS<S$~F=
zkly7ByU{n_3?I0QIL+QXugn*TmHIRLb8Q1W_fX>|pKpdr_OkAOn-N_Ea$s0@&SHj{
z9lnpW_;HxAmD(fu<MFDe`_f1pAT3;2F)S79KhCvc--E#FZ8ArLH#E>~*6SC@`{ZX-
zoP`%uxk}F5p#e~9`a?pVLFYjUE@g-nJdjKehf?~I?ZAXN5ea>3tf0#MqcnT~e}^~c
zD$d@&h3ioVd5sLNJM7quw}?#$zz4Xi=s^f1j?XnI1HR>$CK}!feHV}@do*PP^V$yx
zAeA=(9mseQP>Aqd!;E=KFMLS)=S43HlgpBbZ5HYdIE-^gfbMVAX84TqPqxVDaXxfH
zs$*UppU(@uyzCOPJ4XN5ysZ_>z=y6<njMq?7AYp0QMHpG$el}~*_Pf$A^h1Uy($v8
z13s_o^UQt@!1Qhj)W6lS(Pezh8w@m9`~LHUe?Pz9Xe3Jvb*0{$txF?kfXd{@QQPGw
zluHpjVrsf^vX1(n-+A|EK1;TyDOhkdG{nQ!UMiK|d*gZ=p#n3@fI66v>4e&Z+MnmO
zwPA774u9I+Trb%k8#zN<ZQk=8OGC!^n_lEUFP7z;hlzo!7frmk6wIg(zX#x%wpE4V
zQ#b%XK`th%pFagQFk$W9(m(oUOkHU$XZ1gmHn?(t8!JgZ=?EX(-9=UoFJpt#08#Qh
zD3Q!!&g7pK7Ri&-80@I3Vi<ueT7X_<sw#Is>!~n!+9&8JhWl|s(ni`qegW$n=O3Mf
zmR;K?UIu3ZL`C!GT{2533skz>MkzXvH3jG78a#$P_us?x@kDQwRfZh7UDj<*PChI>
z+-Hj#(g{UC+qT&Q_+gO3zS7>uj0ihT02JYDSOAGxN>x{}p5+fx>cC)DeEsCF<v+8v
zxWOjJp1Ln%;AWH=_t(^SW_5GCIGF)%QjQm8U_VfD;7;w>hTRMGzHDscUmwC>3gM3{
zy`rfk2;u$-``NB-A(le@ILo5A-r0h)>zUt`Mt~`Yv=ew4b~nJ7JKE{!H6Ne&{h_ab
z6)L%OJewQ&)?-q8U+ifLw*_aQtrVMgPiEpzgwskBoJsK;%ud^>m+Nn?Y9l}@6{W#$
zuQFF5M4$o1Xg{;5h%k))KYG)i!<2Jy4gpQa4feTdm%{hm!e%9iZWBS4ze^z@7Ux`u
zmb^YOHd@{K$F|OZu`7wdZtJgCZvsyP^X}3|jyRLt3;66)oRMr;-(jwA<l*jq)#}(i
zy(4T}l$q|<wvb`)U|mbN@xaspW0)6g@}o^dl;swxKP5W)5e$!N$n$o^NP^AcG7-Tj
zG!?cm)1Ry{qbs&+4Gu|!u3cTl0}{zP^Zm~09gQ*qn(Bktw#GYOtR^4+k(=K9xXGR$
zCfBg?-TLU}rV?S7PqUM#l6$X*SWYgJSs4mz{SB0Q++k;U|8No&>_;()^L5`iIlQxR
zRa0HnbKghExaY3~U4%dHSQovFU+>-aG}!%&cFcS)5B%D08wciHTFjaYo!ox8?}lta
zr?<U2;Ftq1v%xQXcA=-g!Y&=NOJ47>ifR5oti5GaTtTxoio1l6K=2SWXmFPV_Ym9$
z2@-tJ!QB!F65I*yg9QdAxO;FL+--0M802!!d(N-B?z-z+>-*WWdU|(NSMS~Z)KgVf
zui<GpCwz%$WNsQ{Qy<T`R3<GLw3}U5^*yOkDuczA1D-mgsfLH7JLnq9&C??sts+PH
z$wD8%RH*Pi8mrMV_TBV41{8!Nc9|n5``@t#YPdML-TQ(CUXb(hq~-AE`F3P9)0%p{
zRfe%sDzg!m?`KZ6Hn}9<W4t8f8pg6c%ZrPJBIM3-Rp^71C~U9+4`H7xp&pYHCp#9$
zTx>&Yyy4KEaP%d6Acb&YHdXZt=wun+w*nlO^O<(B>Ia|OQSX3iW#QbXEPwxXBom@`
zIFSv3KA-C&rNitxW39ttig#6Q^5Z#)kCsEyKI6Lpuap@#J^2n1-j_cu=n=OG=8aLm
z)|+n!7n-RI_!|G03!pS8{GCc&j?{hP!nGsYc&g8wHfBSlKz7li@a>)c)WpGX#h%3y
zb|VVx0R>@Umz>;734sP*)6f9vKkT3&s)JE;7mOSZfO>CXG|9yt+bB2U;6L*X+=|mg
zGa7g`GFL|B>wIfYqcS4VEF1&2#4xr+lfLssLzY?r59I4pQODnlL7cdtR4~-G2z%II
z?fUl@Base?L+Vz}ngFpbe}iZ5zp@E|>6(Q6gmWNr#W~<&#X0T|J?v>N@R8++C=pkK
zxAG2$#S{xh*w8pqo#YDUnQ)*WLd5KB%cW&#!;MA@FKfu)qHMKQNavi+GbNiS`DSud
zaOQZH2%jf&5zNIGT@PME*c6>+CG$W4A9RLl8#6PEXLmi$dS~22SYZY^(6|R;V846X
zV*C<K`BjyszPx%D4cAWS&(N#FOAJqN<Ezf?ind4N_wPk{0H!Uie?0!Str)u#J;uA$
z<S48~8{RY)mOjI#Q4VdQZRrkuWa<t84<-Q_WYKtaEnvo;&Qy8fr;2gQQ(`ej=S>I{
z;-&PxJoc~eiP!+cn2(_7>yi$x=GBPb<S8_XrjshzvQk|m{!B5+uY9uY75MNApzPG1
zRT6B;-xx&=QB-1t6%-hb6Eu@HCf3*KF-vUi6tE$j6Ph;%%Hto#f~%tC8BzvVE4z*v
zxN%`ND~k1T+!8tIIT7rVk&jFadhpV>Jk=|`Nxp!e(ZbY3(fRr#8RCdyybfQ$N<IG3
zOxh1l<=Qd`R@&A)HW1{*v%OF&D`GZ}ess=MZ-wVoX%Y_AtbonU<mVBjtD+ab&2ut6
z3mQzC!yw?t><|kI^*I-(hVx73Hq~``@Y7h=&k+F;mdz6UDS)CnnzNh>MP?3U2)d0e
zxP-Ps>M^48Au5^;6a=L!yul(qo9=kot=?Nh5JYBDq6w#jblbf;SG&DU!^0gtw4^M|
zALYga=`^3PY%z}h*x~`n{1EmKO0Ova_B+jC0p>{g=uqKb!^yt^B&@g6xKjjzT%~+0
z=SXU-!vkk5N3#91Y~0ISTUIS-%A5ELlJy%$cF#XS3ds6VO9RNmp%Y+=>4v_c?D&e%
zswz$vN`sSwYqvt2P#(FZ1v-L})Ep{mhf2DVLtE-Cm}es=HC>Gz@Iqw1bQ^<s-Bh5O
z67{?NdzH=yR2$^3af`;kj*$>!kUdBGz_+&cIc0e2XBwhk*-V{B-+T9OpT2Q-EU)8V
zd_SpdQ#kG9#I}g@OrLwzAq|Rj9L0mQZ#8~_P`z~KuoeH_NA$1wH|a=}IS+_(>=Mfa
zP=Ezb8Ty-%@x$ra$=jM^Sm>DAA3oIjcX+e1tX347p$IgM<b0w33XW;Zg9sLfTzTru
z5)izFVPmz3UK6JMuxFQE$3lfK)1XUB;DP3aUkq(}xTV_4$FyYGH!XdHaHD+E^nbbu
z`*A&0((fjrF;m9zE1Y()w*6t$_vGQasVKJnk)`7yuxY(jsm<18RMb*jxs93QC`a$(
zFmyK@mTY8ZupW(ZZq{5q|FdH5^%Pp?Ee!{WiSkSWqslXIJNcDG_tm_Y^`Sf-=<~dF
zMMB_6rWMXvmH3A^bi+T75^0aDwOx0}VXors=86C!(EE~keI&Hfogmb=bC=*Kw?w|Q
zeCQ!%5mn`c+IIg<2B%i+LwOQhEnp3rnFN~iyh!sE8ttnA&CUAgq9gjg&(7+;wYEPr
zvwxrdjfOh+O(D?PG3<5GRcO^x)7NqKzop^hS#wcKvxz?Xfbfcs?DnXwn-%gw=2+-_
z_51D`c%XRrpt&mP5(^ny{In+3(joA=7CznB)=IcPeU%aTD(AOn+*Mz0e~gJdW;<GD
zHqIj%E0_aT3p@GJr@&*t&VGJwH$03kz+qJEs{(NOXYOHvBVPei5^?yvIf4sMMo0d5
z_+6cW8h@hJ^94?jhLthYI(F`)CD%Nh9qNib9~?&0fj~c|mGwh@w^>D*sd)ttpiFJL
zQAPeeP5I*`qTlpOOoF<7+VnH6ETp8U;eDpjqf<z<r@s2zH8+|;%MWk9k*WM>PFcM_
zetyF?DXc|Yg3SUWLjUP>9^zIa*48+RA|wY7JK9a?8_PEUw7#cNH8Zc>Nf>QMxkY8L
zLdAw%VBdq29?0k4GS5HmG&1<HbHYH$$&QDX@Q0;RSLb<1t=4w;N7ZP{1>So{5g}F>
zp$O*Nwb<tbVyPmH<ALh^l{rrv_~o$nysxP!a8tj|B(aw7BrE-uZ+JqN^`iUp@MT-Z
zPk^^*sGs3}G1&$0U*N3UuoLI`h6BMR-h8JHd<o!2L82TKh>+mBF^wca3;p8o27-{U
z8$I7~7ZHYd>@0={8V>zrr!zl8f0B~^>+>Rc|H;4Idxn73>(r&iGZ5v8Y2op9x1kb=
zVTU+Y85^*)XNdFK87{a#)xhAd0H==P*In}<65jJu3Tdd0suQ-i0Q)oIqmAnJ!0g2R
zA9Tzb#k!UwYPkD?Ro{fv>Q!p>wv;e~-$}cMK>ORdz;#f;XYFsp0SZ%Z)q*J-FSjv3
z8~c=RJ{(_^0#|q6HR7oj9GL-$^XF-ThWjKd96P2>07OFg>}jJ-IFJXBsNO&IZeBsK
zj>N%_N4R?d+40TErB`+6Vl5eWO2zb)yIj`m8Lur+|7SJ|hrq}BsTj1m^@@c$D-|^@
zk|}8H;tvlaHpj7{p;!y4$KyR#c<d}$cfpW_Sn;O~I}N5ZQhRZ;3oHryfP^|xzc99-
zrrW)VXmV~R`!+yh)e$SR7=W8&NqE*lfSvG7%0f|Bb{1-jDOhV{W^O${2*~nWaM@gK
zt`;WEHlE#DrC!1Yx?19LJPC*uFm>k_z2L%}+cs(%OANW#9d9C~xtEADPxrh_0t(gM
z9ScHe4UhFrg90ck`uBCCW+E_~Is78%_&9jl$)Y_uo7BmzhaLJ^pS_zj`V0Ew39HUo
z(i+hVs$$waMCd@eo`e0O0k?;l&dy`Gqw;?~L`$8AXyU&!Q)1)d=D?_py!+~?;6AId
zOxxR<z+!i2W7jr`uiVi?F9g^w68|kD{FF=B3$!u?B`At5qg9<K2MO{Nd{aoi=m`Z>
zo8KgN`wv1?%Xh%|N&roYH3se0KLXE{xV5)DK8I^(rDhrYDr(t916f?pc}^cu0omWU
zv8bW<BH6BA8=;{KAkW$C97MvbzDc0k@L&P7A*rMwOtwp9EW``KWk`l9l4C5`pZ|Rh
z@wGw96?!d`O;qRdwtI^sAhMT0xppsglaw^+gl$O^x|_ys{-K7zePyPs_&duX<Ef$8
zk5OwGoc#s7$fd@;Y#x*Bzb+9wW$M-*aHhIFRaJfAqYqh1y1H*(GMsQE`jdM`J3+l}
zP8;iZsekmWaV6SSvNRH^1SdOM0-LB;yf1xZJgy%3^jTxr5z|YPY|?{i$of#;H(s~*
z7u;TTs<At0MgO*(@oFs7cg=0R;Xkje8%s1Acp+1J_+KSLeGk`gXAz>pnxZ`OKFMjL
z@)sDDMdY}7>_fMGXFg$VmW=jt2yM%qkGvcgm?^4UYOf=+glm(~TrNsu98UmC8knFB
zpg1^%#b8Ku`KwmC$85fv{ymRg<G6E15t_N{Wq6oaa{~rUfNsH#_~zbe<2`qY*z(>b
zwx8NI9)8aP#G#x2hWQsMOe~8AHLwP@V)>h<ywESt*Azf&=96b{>cS)6mk8vRE)7I;
zMxu8ukE}f&BM(e0X)&A2VdRlWX*F!T<?Znv9F|_$1kR)|X#b|?QjOSx1J`He!$1#@
z`zLeTc+#x%=2*+F29j)4-0>aJUo!^gR)Vow41Wk)9NdhJJ_Vdijt)0?zZ+&0gUx6^
z=ki}wD~6e09Do{=U*{(~`eA!auV-5kP6q-Ru2N0@24Hp{cOdz!#D=9;PQ+XAb7zws
z+20K|g&X0Z)AbFt9rImB7o#WlnYi)=3S|<^=owM@a0y+y;=wx(eN_FVT#~o{=AuA|
z)nD;I>S?D*ftbSmT^BWHo*lOslzTTWNhh0jO&nM!8V-O=jZ*QkX}EFIYEkk}74U}i
zOyBV)ON!(tP;$+A@>m<-{2<+lSK!5a-b`G?He;QNgu5R*fC2Cs5+_+NCIC7d8fN$0
z!U1W}vhDi|1W>7kUlL+m&$v6B&BE~sUV)A-nAe@Ge>@zolAcF4wLe^w62f?fez@$t
zg7GE!z93be^+rPhEd`a?hWqZN{wxtv2zd_?Lxl?`f@cnD3^|UUBYvcBOf5<dq#P7l
zgdTb4AOv*(&<Uog2wiz;TNUM7X7$@}{mMoX7<>xk2kNhDb=wj=?YXcJKgqeN+v6Mf
ztjZkHTMn^7)RKNX%+a-`Qc-{CcxooSF9=#H21jZ7Z851fA8V`Dnmcw*YGSkbNgo^e
zNnQdb{j5`c_I>0VRgqOt4wIc|=rC&&oD;hh)q(iNq;J<Pg$bMIr>SFbcb!#;&bu{p
zik0^*-o9jzCI<}w+1k5*lLK<Cw`}80wk8$k8n}XK`$RR__6RCzFUfS=ghLJER-gPw
z0~eVk{}i#!))QaMb@h}mU*r+5^=8e8Bt>Vv<+Di{nqa~Rr=n{g7G1{^%m4s@(}IdX
z@erEDbxVfmwRw(jkUrvdD)Ck`n<$&;QSpYEWYA58^XlW?v2%N9bT8e?Q#Y|YAYbb5
z)8mN}9{h15Z*F5%p_><dAsMReFvK9KZys!qV$5Sa+-|<=*K%g;`=WmwQwLJuUUJdk
z+bYQKTxe38G@JWXGovqXKr_mm4L=(YM>w5Yn>tSZ>ANv4P7>+Y(!mpty9{c^t<d2l
ziGN#{HyM8m|CSA68u2$%KXXY^hbg|{Q8)${=Mf2ZD}HPqcP6~$Z18?xb<1P{r&LLy
zGpj{Q0pzs~)c6SOXiE~<XF5k|6)W4BeaAy+7kwCQGP%ohkP5s#-@X{;Xms*~te3cm
zgYU5mp6}7!yH)xY#qDhYzF06Lqx#NPk1RbJ=%TB7xR~FTn;r`#>P;xitYV71b+v!x
zd3H1cx|^CQf3gGG5IkfbxGqXY^6g$EObX_Q*zOG)cShra)cn)se6-x)=w+1yhXYmQ
zjN?gjTL|<I4}VNd(2HLLE$ma4*7&et!9HsJG&T+eKJ7hYvK#BE;8OQpp<En9+zN|w
zj%^;=<&(5XbhFTNv8^`7=hL$t8V!1}Qy5aZY1MM*u$+3n3HCYX9`v)FjkUx<LkXk|
zvUWoGK{^r|>_t{|8+n%XvhQ1hH-XmPOnI;HH(c;X6Ue2KK=FNsUt-zI+&kdbmtL|&
z+txp`mIqyGcEhzlR25L}$x;U!iqf1S7&2A8Zx1PJ%negDxs?_yL>JS1cc{y$3>?!N
zfNh51ZoyLirxhG7H#UDcG5r^t{P#I%;G3aUl`<Zvu$eSp`&K+q^38o+$2vgUFbrFH
z|7N3?K-X*{9eeIqFAkSPnxsY;=Wkdfv#*a2UOq#;mc+{m!tQ!mcP~hZjqt|^=Ez6}
zMqUvCwA7%zE#NqipO;r`Qe9p_o|#l)B4;p1Hs9>q!C-Oo;GmM<Xbp*AW=xwja9kZ>
zKl~V_X2izk`l?R={ZRjob~em-t|;DBjLlJq>!Db@pxap<5_m}xg+P$tn|5|17P7<6
zA3-%}YfeP}28fDJ!mzN8l(ylU3%r&1f96%PUJJYPNsDPmr!L94SD&5W%(j}jD?r>@
zwA!E)OPvM)r^p#~P+`a4TVpO<zF&Z@W|MtYCfTkRYKiYxPsW8r9*RAF7yC`oI{&m)
z8Ne<j@=R}>e(j!q?oSWlseOeGb3E<Gp+Y+R7;nJrc@Uk#@{P1LN%)#w)(!Yt-eD}S
z^a}`gT=CQWuKI&3#j5rvp|%;r&#N$hRfCzM^nmx<HuWkAmrZJ8wQ@owkEt*5Tq$9r
zq@khis@=`Aa_eKszK5P8o>_T%_Z6h1oS7mpBT55xi;3&mXU`lxroK0dqbN`L-)U;W
zNZ#&PqV~);$HAY!$hJHTC)raB(Ui#fquu9}^ma$sm~X#usI{eClZ4tT!@YTP(-!&B
zV*1bF3>s$j14ot?UQSx?c%o77=)JZR6O@cEEYoC51V`IH16ii<>zC;`d>?u271Y$K
z`FNWpDBUvmO=89V$)2D|NQ#EUcRd`(N#0!-oCMyi`wm^41l$Bm_+@s{91`7RhRhdz
z>pe#&D(V09Dh``ku!KrRK&{hIs&O9|;|R}105ijQm^O?@QgaK}rNDi6F?%DsepzY3
zi8x}-iSZ2wXwg-4#Zun*JDG?Ap>gP3Q8j0^g|TNQ+sB;#D9lJyuX8oW({RBx%)B4d
z7PupZ)G+8+L-Y%%ZFH(vQ`%9_*C8-b23xt3h%tc|roeZb$~R<M@UJ#?^PP4_(E^j&
z<X2rCQpFDch8=iiX8neoElnWl&iDbkyYkNjIn-@Q)PE~6-gTirZGrvTX{4Nn5Mn2x
zx$gS2FK+L}DJu2W0pkkboK0Lod%IE^054yomGz>5#TkKy5jb+bo5%E=;Np_2$;?1r
zA8YqGt>B`xct{fMbQizo*Bj3oqIJvi#L8-4VltoH{N9k<%QgF)m<?tKkF%gCboLCP
z)0HjDH@OUE(gh}8`8cwxsU>@jzk^oUfRLsS+XY2O{>RaTb{|yLG10zJ2V8kpPn+xg
z49?!a*4D=YYqPC(e!FCjgyUi4s9JH*Ngb4a$PS|-E%1%r7Y?{5#H1-Uq>h-8bvRVz
zS`+P9wl%almWP1QVV%nnZslmO<wF!WJlGFR*OU*m>3&QcwV4}^CPlGq=P0A&qX_x!
zYBCD6eCJ0Jc<!jvUU=3XlS1Owubq0lxw(XGgbLqbgpKM4<axYMC@o$Cgnguzd~;>T
z%OR6Zy19AU;KD~De~<5TG?H#n$>~6k%6V2boQ)xuiU|*bPq4WE($=k^iIyXp8ala%
za>kSuV`!V?6B6JTq6??WU&nC5vX5M_q8C+QS5jC1MNR-eU8ZHRkMh5N``dR#s(?$N
zOrRKjs8Sa<`-;@t?B6yDD2JU@MO0G3vz)?##gaFC{-7bP=lm+B*Y4xj!1VVN#Riy5
z22~;^Ta0bwF=@oO_c7XOdz$#~#G^#Aa1d{JoI7{`w`?$pVvpExAZShIGyL{;MvW43
z_^niS()>)xWOg9tul{>8gOW`*FO>$0`N7g{CWC&@x&szb34U;*W$1zFO(QJ#zVTdT
z2z2ymIj3dYe2}-{phGw%c|9SSRM<UvHibgGA1C(D>ZqQUmE~9&O{v`G0gR4QiqsG4
z7;BapE*#o$9$LiN!<*^QnMb&*ezmaNv#@i9TrT9KZFm{Ij5Y4~TX=bdDn{&>r<e4i
z*F@K+AC6;?bb631_9BDp7m&sMW$|7T$(clYTH$?ee$fiPqNUSRV)Futm)iLAiEO`+
z_Sn*HuQybT{<vU@xZ1?`L-x18$M)Ku!H6LFz5?YQt=wCK^u?m}$5cf6K&ls?F}1SN
z8L_*pymDm-<iK?1)q*{BSMnqRjZAHixn+@XO!<fu&Ks^2IT$&#9Ct>J2g2><{UVeS
zb;Rot{6zt>r1o0ZV#$Jyr7_s=dxX^y4q_xb>mf2v%=AIn-7TzsHWxNnj<!2D?JlKs
zJJXarlbw6!XgXU%pk7}xX!nezO7idk;*#k!US&gkbTHi?X3oye@+T|wz!uUb#T7gN
zHxCPokd&w%9n~_G-G8^0e=qd<FRS&%;n9(3XZkPfk$3lP!qoKYr+tf2-Q>|?JbP$l
zQN3M~@A@|w=ka4d?D2yL=x#V1Yj8xnyo{WPeSZ#zL%GNy#G9ACZ@YNzBn3oxMMQqB
zJ?oY^PVG(pZ>6*MkWEf$jd2`rlJ&=e3`<K7v)WS}b@@UWTwDG;Q|AvgBHB-N7|T!R
zsjKLQKwn%dv7dXC-}-Y@6TbG~wR=V%2wCLPUbPDwPZYIruHpm1>xY8r2O%|;0_lrc
z-o=Em=XhF?GjZM@PD>bSFu70yv;shSZSThm9C#dY3|mxh15`*;T%CE-H*)Z_YPrW7
zps|9Pji4}GWLdJ9x<5^n!FQG&rbpov9DKBC6BMu9RE!6AQYlTpnryzKcV{SPVVl-3
z36`RW0UHGU9E>QL>Hc0uwC5zl%1KC4$vxJVH!y~IUr~YbWkF2Dgtszc;j)Gx{PEt6
z_4;wLbGf6W^zGn^K*52=^~1wyD$^0RBcDT**u{X7)P!K}PT=qBke#ns=W4|s9~(yy
ziIh{KT6vR6?Myk?aTzkj%SNij7VMgAYQ;Z>{IsxV?~kuCXSba@dE21~v(^EV?L*aK
z@u`#u2j7orHMy5T2(#b6KI2r}`HV=G%vk?HDjIccTp6O%V}Ug0(KCC@NIxUagvCLm
z8#qwvJ(f;>2(}&?h+1!mM4W#qhX(%UYbTCR93JBLIwOohCLt%G>{iXKPu5UBV(4Mv
z&#Emq6>MfNDp)Nhav?}qtAT#h`ThVZcg&vQUBW_fEX^nUfp>;rre~Vl_MUqnVN5cX
z{&t@S%r1L~+c7C0Gp}~&0nJk9w%!V=Innv_kzkkIv%UMMXEL3ueK^?vFF^GVxNo$w
z$I$6dW}|kZ_Jtoe4g?4JeU!ALE*l2{>3@lj;fHFPHUHHmm-~hNCNNGDLiMrD4YE|T
z_ILQF$_5@Egq=MS+T|lTAzwW34!HYJaqO1H8ET}?yiS{~5a$vQ%eXG7A+tc@k$;@q
z>}u+93ilSQo^C4DjpN%Zjf;*lsPD@%MEB(KXIg-DUC><?aYkEz3Mv*nFo-Mo$k$Yw
z)u<TZVyAXkG4<ejebK*N&ZF~k)=Dk<!-ka>^QTsP<bE<V{d0uT25s*yk0>=6wQrR^
zjc(gJeAx0UIFTr~*0-yn>>(GnW#1izL?E42^cKOqmTDMreRDmj7Rjp%hM4t>H`Zx%
zzsuq^eI!^F2_}NZVQzah_Y0j5-Wjk!#XnOeQMV4~3kF}@58TfUq{^fUYej!u*SVEw
z)F!Cz)JN-jrCR2&D@F^wG{{~s8fD)N&_#=xwlDy(Yl?B{v|B5_@ck#N(zf(#v|?#^
zBlY!lB0DG-El3`Moz+7p^wKReQ`oP=#yRuTBAT6kAMrCixB=+Q;NrFR95+b}My2=K
ziLJtc7upa(l_C|aNz;a7{fj3wTlP9_G~{1i;&sU0oF=L<NF-_%xWAaXANcTkufDrm
ziviEq_S5fLPf@+q#Dv|NaSYhpzlIS>c|-p3gJ^ZzoYy+`Ia%dglahQ?ac8)Q1LABp
zL)k_u;J3IQHfdfOu61LN2GWmL7b_UR1%I_R=J3-t^mLtPK%h3=GB-q(`gb)^7JJI=
z)#7nDF48s`n%xH@8ABB-mh9V?ZOqYsOM5YOnGUf~NMnGFwxRT`gth_%GX|p<cx!8x
zLdy7r7wE)seFsBa)J4*)yzBxz!^k{NI?vG)3kvilQD})a`NE*QBg0@>g<|=L0>%(~
zGl33&uW!%6N06X4_cwyN%q7eVedAVe2ULVWgDK6V@@YM#^^<Z1jCjU=J)pGZv0=wb
zu5kO(U{S%(v-S>Nm%-TGAbJe?H&{0tx4(gvEnAwKOG^>(tGU(bBho$JK3m<r91l4-
z8sXvwPL=v%{E?jI<;I`-9*{z2qSxYDjmKA<zA1SJPX>SWEG`Z)ILPpbC_4LErIRmp
z4xVQZc%c#_9%o042DktA5J{AMygQ2ql<Lhv-A0abJ_Dj(BMpD-h02ud<$t>X|2n)g
z8Y#S-rjPmKEa`4H!DuZaf|)IgzF8?*{bM`)Q9E8@d(J*{y}Jn~bDclaH0^`=c(1&|
z;Feo@y+((>+IoO4-=0X>Uk@ANtHi>P6r?w6>*aK9exde`Qu?K)l)w4Y1}Wb}2zy;m
z;wL7qjnvaAAFdJZ`d`uIs~w3ne*z4uE-Vyp!*NNkjU8?V6B814d6#m6kcM3nRoItD
z^d3ILDN_&1PYb4xFCROfO4@YS$G#q1t@S*eYDgK=97xwc9UPU(M32TQDHzdvSltsR
z3&*bV=e-lEA-Ug++{N+>(P?(aGW?B+{QM`tFeG8tuIKGvq9-N!1HDWe9`BDJbMEhh
zMC|mKKe~nrsa}V(u3&CwB$1bIFnLz1OQxnghs_c|=nlAN`e1x(D}k(>J49e{oxp=b
z8Jkq$Fdh~<x;-mHsgL7q-`7odgeF_?u)ZTTW2U+e>iWhrG#~6(*d3X-OGfb__`bL!
zII<vdj~5nD&^T&|^v7V=@f`?=_)pyEN0gh>aYbsPB?(aqqt58go1A7ft#O+nB2)yO
zLbH9nl^2HuE+WMNDAg(qn_(4=E~)9~|Ds;OVR!$fCAaV%i*0*hQ=1P4W=Rh?o5J%w
zlY?HBWx{+9JiShqp*B#&<%(A3cFyR-We&0|RlIdKT;K+=qIL5;4=PEI_BV9mqIX%v
zUtc~)O0ET@EdG|UX*NPfXxZp=*Np7Ql$0aKX+{6czhHj*f$-0%$UXs<680}6mUVQB
z+%N4pcUA{;hnCqN!FNm{0k04~|4rJanBgwE?+R<Z*$KFNB?m>)o<E-~ID)+W2>vxm
zTuJ)l4Fn#v?MD2Zzte*~zYt!+ZSBO8hvoxu)&k141v9rNwOOBCqix~N0~NX50vjCS
zl2EV6@{hd#T$3xgP*T*kbV$>4XYkGB-gvtxFnUDf+CWtfFy`wD?+B;Cwt#;o^D22T
zk~u*9t_I1z#<JjxoxGLhx06*E@sF=+iP>l`p`(3s&x<ll0goIz=S6$i{qVXjS3**D
zeGL6b$=W^%xuDI1#_-<a9&F10deUs|^}dxy3$egn=Kpd5@H4CnSYX^mY!q(o!R5|M
z>Q1qp<mj&-MDD_IeJ83q^51w^Thy9GmRoRsc$u#sgIU;N&Ma>qtkWp&Z<wcVh7M%Y
zEjZIzrGMV1_y{>Ho}x8Cw&k@24}vV`bF<eKDxO<G@K4%sh0^6KVQ(zEz}Am6IzIv=
zt~T*RxxbG*$b#o;$%iW&xZ7TcX<f-b18;y1llh9b$5jYwsS0k6wYG?2PAQ;nv=4_=
z-t0@o2%|4$@wVI8zdk#QxI5Is{nTndv`%y!d^$f<^1H)7t+=AT<qv~7PnX<BB&~Mb
zGeS-88@d3PEl(qD;2<Pr#_iEQSfp@OG`als_0@iT;od)bBE;QPq*vp}Z!L!}63tKl
z=9IRpD>TT6#Qw;xfA~J`*3bN-Yfx-{V)((~k+I6uBD3N*rC_%puXxpe*$47@*E6p@
z);GRWqe1;7$ddbtq^LcY7$DGinR@;+u-8PcY!Tz3@bO6GN<J?z!CY08gV!15{D28P
zuKeHMKGpWhJA-4urw7LERGs!7=B8h^VYC^)anJJ3nI!2FZ>*k&2a;dIevS(N804-i
zpLm~|7(l6GF01?C+G6?)u4E|Pc`WYy_awNEnlnd+E-gNK>VA~B`8QlyqnbensY*>0
zuxI>re)#^1u-ufBulv64AXU44e!D9S7lKd9teW-#6=eS0cou`}xnbUg0sF7vR69)m
ztMUKSDJ=;4f4S4Mxc}dC`rpU>pL8-uga5y!JK8m?d#TCzAIW|HaPUs^y(0PVC)el=
zU&>qX<Ndd8=GiSV6DDNyS$OytA1&mq|DzfQOhIvhVFx??|ELH+nD|oqisXZ=->3ii
z0hc|EnpP)@k>o4m*^Nu^e{|G}XU*|8VWQ_bfwo0Mb_4r=rghDxZ+UlCBsRh&p^mft
z-wOZ^VR|#mEdh}wc`{S{pAiF=i4+4rNKo23JI66wuMH|f|7XxG=24ipczJ_qPELAy
z4lQRCBiC{|*?%NSg~^{V7GCh#jXd<ZLwVg;*#5J^+<#t=#E^}gj%i;DeJr-Yfa!Tr
zF%HVU<E5|T*wi;g_xK`puZGZS>eFZlaEQ2NsxqYpKdW$f;$6Z;YN>^WF>PyBS~F^#
zO;S&35K?)hgUl!Wjiq$G<y+AOlpyt6kG;9jRkL{QcEG20)d$`?55`e$3Ifpsp{Q=1
zXPauy5XXBaJ_!eU`}kPj&F<aWdWlZa`gfkwo04sFsraPhdC@$|Xb;EyqiokiJ2=F-
z>gFpx_IN3d8Z#`MqS`N{aWT9{-e2)-c&-hsUE;qWosK*AtWgMx`XJ=cNR%rzli<Hh
zJa3wr3m=j0IO6o&4@zxebeZb`|7Fg@qj)d26ZTbJ9lbjFezE2=ac%{8zWDQeagzS2
zeu<@QMX9PFJ&fZsL#!;`&0Q8y*P-$2fByIq1cybbGAxvoIIC$eta8TzTHN+Ahf}=`
z)|}#*C{^aZ%0?V`#&nsDP^U=yw+3U5>9lhk@;H5s=MVVwz@JmVLJ{E7fk(>B!D8R;
zvOCFoaJ@g=w1WB9v<=^#E?a*8@e`&sAV*vAtpM*EHxovg*np&iwb?tX=iS@u!gge1
zGc0mmpRL~|4H=JFk#1aVbwR*Q^wqz@9{RGWCN9w6?85JC>)!nweIc<UOSva?sj?rC
zA;zF7<)$(nmypI0fKJ#Mt*vM-MXn-tj%zDfUrO`WKz33?*d?RmF-o@h#JGi^%l~fH
zWQJ=T263S%M1I?l`!VvD5m+L^S!XE=98Mb?2;JSAs})umip%`cuF2fzE=33lrOW*N
zoo{~O2bi4s$R@b>k|CGhVY!7B#{F2EVVMK$!YeL_JAt9BOUf)rKER<j2(p7470}D?
z<hG1c!)PGM^QaXjO6{_0KFcdA0<4{>JD!h|X7P1Mk59jFkPCaBqX`;Z#tj@T#|e%e
zjGJMkVxMXKsheGFc6dAPXMaLLtNw^(BSnM}$ay4Y1;R(;pCGDZnvo{P)Yuct{appe
zU?)IiS)-}HfXClA-BE+LX*X2=`JgS*VNxR5Aw}M3NbaOP?O+aceoMiUH6Ofnrn)sr
zQ$Ahd{B~)A74~s-HQ0kY-aXJ(zI3RLgn_+|kt#hc$XBkFzO_XA>#rSo?InW0#`z|)
zJ&MlQc{J(UvUsG|Zh26jt*<?DXxmOq@?N_B=2CZMth_>|&K(!~1C3)br0PeKygn?L
zAj+iQz+9!yhp}j_u(fTaaK>MqW`&yM6uxp+WU)N*oe@4hYu2oHqe9&hGBVw{0ixov
ziWsfk-4d8UeahE0in77hd-2RG1WJAGy1IdR4fXD~+`K{!Nk6n?=QT=~6BQ)iL|Cm2
zJ~EJVy}hyZ`E(IJmZ@6~%T(&aWjQ+Q$4^3XR=EbAq7hp8q_4$BQgqk!oLfq(`CTm9
zH#xUo?_`4I29Je00o_lg(5iUzyVFsj$oRXabi%36g!X+Mne_bZ0hmA`P>zIW<<wu8
z-W8pgXQg@;gOtsZU-dHwZja@Y@7?nPI><rjZa1$EZC>pN(KRo?yzA4uB`(5{&6yZL
zXB!>_)W8DHH=%+C9~P9LO=@^WVBYlI$7fm&fSwfEdTXui&1n^MY2KC}8L#owpBaM$
zV#eFX1{HR98?)4Yu?)K==bSn~IW|*B!97SpsMCmfHY&b=+c@Z9x8I)+ll>CKuoV;$
z>pBZkMNld2B#hUM?*|V*cu6V8h;%CXZWXv<LlSqSoT!kSbYi$yC+z_j*4<Z`)bMl`
z*!YSyTpR^rV>t|i6j=|)tTgj!pXSF{8h(Ya>pGiccYmpw1p!}5x!blytB*D%D{cwe
z+}I~f2nhgB=vPlmR^zw2=KxgiIJhH(3iL7!JnK|#pLk~OQlCpRZu^#~XN&4`s{hL6
zgS9(2I6BhrE1X*>KniJVZ3CWnKCTA(@G+6_=73ZPO}P+vtcjGCp!~OY$oX^zq5kRu
zYmxK*9ZWhK0Wuo@{lFGkMweZ6{^VWBf}Q47oeS8BvE9kOH|Dz2vWMHzh^}fD4b<B_
zFx2BGX-R2y-j@&>E36w0bi{9WNZ?TJ$}_wWJ4EAwFA?oN=$T^bCv7WI{AcW$ie>uH
zgivn<9rb^P|L9f>Ys~{XJBXJQSN$7P_Qa`U@Z)JWY?&rw6C#!Qa$_I2m8H5QzR><B
zV?g;LoPlu8HY}TUiA#`Lq(1la_DXs{S5=*I|EB%-)ce$ERi=DUsd#Q5NqLjgI?hZw
z6=)x4h85>-U?iL5-0J`{UESk&4FGx=>5VoTzqckp2_&J*rl1mQv-Y!TEx};IUP+)d
zIQ~EgD~RsM{&BO^v@e4q(WV$T`Y#FOS@E8myMOp|;I)1WjqSi~uiVo^`!i{{nJ)CW
zZ6$AQFB%^~R-R~;FM)AOGIrmh+8%7h*wUD$Y7(aRH}pa}KcF>>%KJ7U*KcX3%Hwja
ziPpBQ?e+HE{OrzyP#pR1i>}6&EnJohQm5B~_X{?;SAVCavc&HfK2S9CkxopfbfT)3
zE%MIK+0mx^f8?^T#{_Mo<vomCSy#wua(F=d8UZl8sFG}cdkoO`y_-C32!1a*QT-G{
z?#hn1=_~kZA?-UD(@2{6S0e)@N&~=(rqN>H(R8iq;kd}ZFP9ajGf!`9vD?bauR`G5
z4X!qkd9u?oA3VZrZV1a5a^o#X^%XZ$4Lq3M&ygxI2{^Wy+w=V%{WbRR71#n0t`D56
z{^BVQgXy-ch51GZ@Pq2qS(~ft`5v;~U^Z%4#h%&<2bhglk2qDd?5JBSM?mSDSLz1X
zMKh;n-yA}qoqw6zVW&0>)@EK|y5Y~{AoK<2<$N7_QvKJwXzWW0HYI@gU{!kzgk9mS
z+9-G-<}AI+hQl5U^j+6WJ92)4;b&L&kmpUHdsOIyCl*ZVr`<Xik4d3zBwk*Yi@lSG
zryc!Lry-zHeNuWRg7DCkqQ!_h_8_KHa5e&K_@xmmQ3XSHQInmyd9}Ji<c!_F55MV`
zdKWAGW$I`C`at+XrbK$Swn%bzgx+ahF?wueva8-QYX#LX7^HeK?)s5tiJn&(#g#f6
z`ELo$A)a;y0%XPfmujPxeJ;iWA|<GXiKZUxB#b@X^%gm0kelO@!#FK^?e(>ll{LW4
zYIj6zR{>S$P^_S3i)}*`H3tkLb@BQp<flSh3XM8LY+XT&fQOZ%PjkLjeZkOlHpTZ@
zx7>+ow|2S1<|fmY3;hsw6iqw;4MZpl^>+aMs1A^H-N{63eXQhjR~L_ai+ILpxH$!-
zo3|V5onf)-ZhpYcRAF&kxe{7KL|PWLUn<|y;W*l&2F-8;f`O3xbIejB>ROxm0~WFZ
z6(OP5Avyh_!{>acJ5ukukeY?Ai%jgx?!Aj&Z*nBIz)$KxuP+x4Pu-jQem5U*4<l%Z
z=_JJ=9|#X_h$465rSzL9Ax$|2)2cLswhA{E0gMjhI0!#-(U?JBLm&Aq=H;TxCIRPG
z^=ZDYmOuvwy?TQg$*qyua28m-MZE$(#ji{uGQgntEuR==RV4nfAY2$y;4uD4n@+;V
zeKT1VLQcCyJjA;W@O<U?)nGmN(hJSG{OHuM!@o3Abxe{uZ;bTVow?X(Fg;CvUsZK-
zBwK9ax@-X>o!ZB04UW=i`Zs5NdigKpD#i!xhz`2YtlZ*;s!^i(D=J5aGr2%!LJ?yx
zn-I=nd11}#da+|KQ?}!kASO{^ok6v%g4ED7A~;D`i0^a|q~Zx5C}-?+wwAQey+PmM
zEz#}VA!E?`p*vi+^?Pzhca<puh~3g;wJ`F{L%+Un*ev|lmo^ILszTl3!b-r*sI5^Z
z=i)fw_}&~3+mLcETr3<rHrMaQ=NEiN&ocnB+mW1&ohW*|Hb!%D<n?g1rPN+>+6^M2
zh5eS816@i?-%Iy{iW#<tWUjgmz~Zp_!|ZGp@_-+a8L937Tm5G~&CZxJYvG8@XANRC
z3HGpz%L;|IKVMbCr5@sw37Zim%yE8?jW&JLVqYO<CUjz5Y+>u3a^(GFIlysIG*FUb
zA^E6;l@&I*NMxdCUd_=E7xjlPskn$b&;eyf66VOsNHuJKb_N=2&E|d)Th|3zqXpMH
zadz=CXj!_G6(R?+RT3^hH`L{Pd{a{nL^D(`Q_z^Z>lqmFp4#65_4=fn;y58BwLzJ=
zC`g{iwwB*wyjAz5B{JJJIWdBPXPNwv_QCbXof4(N0AynZ-usD#KTHzbM_eqV0d6&J
z@9S(P9|F(*reBhKWviDgIk+XKLdA{ySZPna&qDULh@(a^<D^L`mung_mfhX(d3>_B
zx3?V<VXAbc2Oec}OI+%R^O35*^rjGA7!3&3MIu<b0~{}?AJpd@`r0Hwy9Dl~XC+M5
zHGUM;tt?a<VX;1xmTMv&vS!C%?aLA4Y*YI+v=-uHUVCfby}szx%ye5>A-G~qe;2>%
zMBJ>40qSwR>ZUmmL9QPry~XxyON4EB+|-PMPZYZzu3~)sCvSoMhm^u6Y^ssSNWBrI
zBXm&$BzX;VJzD}>E9Tn`&;ux?pr~nEmcAm!T)+c-Q3G02fCSTJSThry4$P6%k9HOc
znv}6}WLaMByh2kGJFmUXw84?8Ra~AA-~37_>%Y_@1yIZeiXQhHbg7f2OjK-i)~QOg
zeHg-gG2TL`c$sLhOf|eRNCbLZv9e+zH@4?(xMv#?7m$*r4HOL7hS9O^a?`JrkQNja
zeBY<KftX#6Ws_i@=DzK|=mz7#!01a&f;PCbjID}K(d4?lo}md?^a}|bwMprU?%ztE
znTf47+L*UOP0tm1(z^Wh!^P%KQ9z+Nsf1r;F84+--HoT}tM=x7OSHvOOj>i*q&gj>
zZBnP4BEws=mM4vjw>`xy{_QoSE}7u@9C<=X)d74?`<+sdzKvoqdlS@cSx(;cbqB0$
zQetF3=i}68tC_nis#!4D(A1vc5457ZJge{}S+cIwjbJ6X{4#zmf~`e~+}I$brWYPB
zL_n+GOdqC3ep0<ib3NOYAx!_An9JdiFhD-Q>&(YNih8<4g5mn}2N;zh4=g#7u6%1{
zaC|-#3}Rj<GSZh+jX)#odf4i2VW-={LLBLf0)vj`|5yX1CU_-l{gFL0Zj~=lBrrIi
zIoKH+)p_Z3>kf*gO=We=;Nos)D}?}7urPRjl$SF5Pp`Lk@3I$a6ZR*OUTMOEgz?ps
zolldb!=j$`8PpUN)<(7bAj9iyMyauSM?C|2fZ1r|u%P6j?y_rI#l2O>!?)e%EEdo&
zX(UCaMv%muTauUQ88m$zBcY1O5t;^-RIZ6$e%>eb2D2@`S8%NaJq&oqX%%^YomOrx
zfsh^{NNEF;hcW$!r}&-E{QNXM9%7yT5Blqz$yez@QZyTzdd>_mXD!HuHLzIcyB?oa
zOkkr<t_AP}JYLgsC+eZ9br;0!Eb=QOGdjfdW6~0<zYAejA&QH907EOmTJiR!Yey9<
zVUw#%a+*-5wR{>;?hdjO`oU0GR<%mgh!H=J0`f96faX+{9h_e%P?cK%c{<eW42+^c
z9nDQI%>dt<unr#z{bZ6C&2&)cL%E9yk5<;09T#l`OQv2s7*W9DK%Q+EpXCl?BM)fI
z&!>xqTG!2afo;225;NJ^l(6^2#J>{3py#NgmdJ3HO!6VK;n;_*4l#Qy#Br5rz`~EG
zxYY6*LY=g<nIyNie5r<TU-pn`lAy@Lteb(w<Z(TnMMW*gD!!)E;;fd#Wc@fciQ1%0
ze)^3rHT)-G8?FpKbqKY3VnjiBWR*igEq-R)iNQZYLoc8=a8*%{0}X*59)1$Piv=>#
z=7}9a#M5f;bX=+6+PX)$X0zRRj^sD<DbtRt|NJ!=NX0B81IB<Qz<^<vBJl^)>a*%2
ziN<QXY#MblQ&IHULd0SCuXkR-!OH<|klj&!NO$fM30bQ5#kJ7$JvG|^tK=vsbT)5p
z5Gy-rspH@a5#q~V+u4j2GggbT6C3glnnyPuaW2chk)5qs-PvC8H$T2qSAXIp&h!-@
zbH(!}ZyU?0caPJ02R1#?q=75%?0Z<(`Ahr&1=Xc?OQ>caicBSn7k|utd(mk>uQIpv
z9yWogc0<$aV1Jii{-Sn6bf+ATl<CUBfBsX989QD;jvtLfi89(nH#=LTX&?(O0z^c8
z^P#92vBr)k&~<uq*srvE$l?|xX4_WhwwsKePUlYk*Z(Ru;!lXLJwp?BlXG9X6;7F)
z>MkFkTkQpHhU=utGO7=u0>pPM=5)4j*5M5D(3y1}%hN3{alX$X@U?XKvd4!;04f_Z
z2i`o@L+2?lQ^^7Pj6{yeK~Gr4@j{+%`Y`kSOKN}$EjAkF*Yl(KK&h63qi@R=z$Spf
z9zYCo$nfM7M=%=tuH;7kmlUml{L3l6=dV`*rt${rX+RRe_!!#LZpo{2sV5z8%r$@l
zc9HeTPg*TZ|L!aN*qK&BWe}h-cBc$I?){j{{Jzi|D~(X(hj3`u2Cs8Kd93DhNZ>y)
zRV7FHp<1cr?(DJ){ua`%^_<@@M;v>h_21+%o!{1-K5jvmx64ayf+^X%1Dco1PT$7z
zvC#IV2gzHhDQ@trkS(&Md!DGBugFmtQ{(h@AMy~c^slQ6u~%1d?s%P4Rau=9F|O0%
zM6|F@v5(Fww^8ta)Z*&)jH@8aJoqE*lDxXph!m`J4n=g^03IE-J<qQsvPZF}VdR_1
z4#2=0;O^o3v=i@#t?JEwfn{+q+S&99slOHmui(U5ECn8OgOOt79k#@^2gJ1c?2wY9
z;p5BFvvgJCC<bt7gQS35GuycNLQ;rW?d&(x&*lswG|UtVlGHZ}jgw97BDzEB9G?zl
zEVQiypRt2&*FfL87u;n>&UcUO!(n1r%k`MnWRb|T?)mX-8hAgsvABNZ0p)v8fBSs}
z1>-FaPuz$)lOmcNGIW2!vqsTk2eqq>W<gRlzy3nTa?kX!<k|iDAdH8ot@KKY%QCJf
zw(GfbXRY-(kjM}2d}!<v)t*5?9<ML4!Mv41gxbxYzraC&;>6-Q&%KAB;4S?lt|C}{
z&nCGR+;bNEO@;@q7%ApbaYkM|YOCLu=tn~chG-&pC$;)y-t+UmL-<`|5#BZXzF)3j
zyQTVNDC@%1mc7AEl)wmc(cIVUeZ1qE?^=N7^jWdO=JfLKgr+h>ueQmqQaW~B*LhW?
z*j6yL7nXppA0)>FDW?ofkIct)6TzorhoebU;#_X0^u*w!#dN%3{`gR^P-3sBcB>v9
zSp4s~&k!`swZw1r%*SVB{%*Y!MAUY%9xD)rOf?Nqx<aOIu5px<0`!cPt{`PyDg_7C
zewRDspx3($U31M^KyVRv>E7Nz0kNp-G1L&aHML}4zLdV|m%S1(C98Oho4WMvVw2w;
z?JeAo8K$(nUg^H72{{|;3>71y(_eF13B^q@Sg)oA#B*yT`z|Dhig_N**ZZJ6Y(635
z7<mPxR2LN_(Y>or<B<b6_$_gaik)9Ap}c5IukQCv0YSm46%o*w+an`qYtu5X`kjD7
zjv0WxwdYR<7yaa3riPpf!dBVg4fAr8NN75dKBby&{4L#^V4ER{A?&hinC`kd%YD%1
zGzuug$55Ahml1@AK%|5rL(+UjBB4cDd?xsy-rjfTTuhjtCdycl$eSm(P~RwfTe{<$
z+oM(CWeL?Ed^s}q?hIr_JQnBqy3I=l&a6)ANHZCoS8&fe$`rSPf^+e_Q<b2ftK1*d
z;8i^yAB|tTo##qCEZ8Oa^8Ktl(u9gPf1Cfk|M}0a2<^HNG|0(&zuk^ObQV*ov=HPO
z0i<GRxw1xmDF$n$c6LT*jtQ!*M~s*~tJvfaoifx=WFl<b4FA{0B%=D={q1DgTyi8a
zG!X~_cURf1&wmh{V3+oG`u#P-D&2F-R4UYgJO9FZ3a-0-N5W_OxS8;b`VHBpX<xlI
zgIo=LdYz34mqyfaBv2fQaB^_KpkL`>$pv{m^<DmReRbWgWZUYQi{8Lw>^uvm$=pY)
zDA@#fLc+pAzvj#IfDRcr=-Fw|aeV&jE3*BkqUFnKj9K5ER2n#$=qr!1u`k2b`2F0*
z?dhuSV5p@0?rQ5w)*TOChVz%z`EWUP3UoC{@@<|t`r&6Q;gkK}Fm+u#<c{Dh^_$11
z1>e;);aG=tBMEqMivle%0jk3V;@t-{4AM}F7oXyoXWQ~>ZQjbHnR8mcC7CxSJ93Fi
z6laa@U93|JtFa=L8yNap55PQ5&etQ-1)L2So<v%<&l|SCbG_F$v<2%yeZXM_$?uNY
zCgG7yV`b%!d{gM8-GEJq26dXql=hXj&ePpxnN|raZ22XAa2DwrUElq*gOnrgF&PEj
z7rwz^dcNU@4dNi*V5eqZ+4vX=_+BD-OR#0(8N$<hd;t-1fwq!GOeG`VYB>4C!>yNS
z^oMW8dod8pUb44var5t~+q<}s-Jh<fI-d;sqtlBt;?83x^>i?ua>sYi&~C-9d0EE3
zNs#GvWnF|`VH%-;R=>nTikBvu;XIfHUmHfkP`ADrh5W03xsJfI0r?trST;kKX`6mo
zG;1`4+E+G>ioWzUm^fSE3`M@btAn#hBO>SCRDgKrtZ@IX#4Vdorl4OSUh_dx6GMS~
z!X0=$bNzhow9yH1A|ah6X-nd=)_y(i=C?MmZxADHVFA9WRtcNn%hX~~-J>=2d(TE+
zUv*nwaoSwP!VU@f+!d^&b#=$fX&aOp*iu15>A+53uJPHp9q)br)6qK{=Z{I7uqtFz
zfQV`xbnJ0eyV@AeaQ<5PK2tCL*nbQ0bR}kY@Kk>z7wL;K?<AI6?pu)({8azWmH8Pi
z6{ExB6dV`i#ygbYUlkW?{;L`|Cdgg#{m7bv|DViQiUAI!UPKEOM23kAzh(f{dw&{8
z@NF5-#=Lz=hJM>!2hoC<-kSr3V7%~`V$YzFJ9sMs$-K7GUGm9(eV~5Y3Hz4riMzoG
z*X{ST`^`U)jH$q42hEF73wS>(4BO=ZYS}~t+8JOGSGTUAjs*vIDA4sRY=PX}wI`nX
z_l9fF7b$msuA1PRf|A;Y!JMljZ!?Lw8@SV9Yb)2MU6+@iybJ@G_XxG@^qMGs$FDce
z%*-_Ez)QcMEd@DUOzBlXqIA4m$rdqSn<5ocq$*0$Y446JxAJa6N4YHWrLjdWMHf#C
z6Eq4o1|&NqxK)>WE_l#pKstC3;xE(fLuf1C(W<i^D8>OyaegEFW0yx=uFV4@xuJ~P
zA;U-V#)fYc=OKy?>#;fMmc;6J-_qagbB`#g&UZ*y*T3_`XVH#+x+VXV@Ia2a_o#Ar
zF<FHa;{HkCTW7v)zPc@PM_YMoK6K6HdK>p-pjy7J0RTcGU%(GnDI}u%cu?`Kyt&nT
z#(wBj@oAHrBZ3W-Z{wH6U-SXjQ;tvbqPqELV9oeBzME>f%QSt<U0yup`1Sk2_>zeq
zP{_lP8r%m!kj8qLe(L{n0cbwCIFGqvmmqyo#2{{hVMtWLbi?q#^SwaWu4_6l4?TCi
zjK*&#<-1zq_SkxCiZ34QFnqHaT@njYe{;K0?ni6WZQ*!+&hAIUG0zLeqL!FR0gX$7
zr__t)_y=GUMKd#=iDhJRH}a`B^An#)r&v)nrQ@NKEIp{lsu=prF__A=*E2Ikr8B8X
zFFbm04Q^QOGeeMvS_4xs4c2ATCybxYKOFuay52gf4X9}!EQKP)-JwX4;!-SFp;&Qu
zDGtRgkRZh=THK*{afbwVcM22;?xh4M5+KO(zTfxTJ$v@-e>q96%yVt-%$<3J1YaE~
zNxZ$OAyO3Bk*O8`PPb(y?K7X?BTy9ieMhHNpix}SX_we)cOH?PPYEJztvzq$B-X2T
z@2wB7rv6l1_kCOVeP)aV>{}3nTs|F~&?FjWmh~EOL<5L+HEo(WeL6RGYk%U-&gs03
zRPO&sRgfmuyukBpyE>Xv^}UTUb75+IlwOv+&kqt;9x;DBdPwY%XbCQJHH!pn0(ukI
zW0mBUycQvVP3IpQLr#&TGfpRh0Xo0J_PEpX9{1gsIDoT_@pPH`@v-9a<FvMh3F1DU
zkd2Eoe`Nye(sPvs{#pL`G$`g1MUN-S)lfUWh1@*AfeWZ^ln;O`tqDm2Q4lJeh|WF_
z#Gx6bBh>ua#Y$=gR;d4q97+Q#pr-*qHjq7+q<W9-%38}0seVn?^X>NyT(l7QS6UNO
z2Urezd8!J_i|ESaF}{b?)#hbwyEMQ!jou|OD!i7ApvBRiM<tPN&n>b}A?+BLjtg6s
zx6JHcv)!#M{^>`^!`WqE%mUw6ngCirH+Lrr$L@z;gIAqJ^3dVG_SMWzoAUP5WG>J8
zNFW#qd%yS4Q6a7$R3a11#Gf{#4xh<qp6+rZjShk!@0a9K8a-qGjNCv!%W#|D_0#V+
z1fO)X?gF1~B+gTA%<Lf?nFer@qiqTY`@p1H<2ZNP6kb;ty6s7llH;A(qSF?kZDtqF
zYRNVph=M{OlKE-Uir)jV?n<W(*{~XVNNn6)C;V-vyX)DSg355(kW#<*s7-qyB``eo
z2DYKp03!`+kKAeVaDA`zEv;OTOGb4kZM4Tj-#foO<f44!`pM>7I{Q)p99N14?l&Ah
zP0M5-4H)=97ZnfdY^_qj%<FP>btXlhTQXXweG|WFkOwji8zJ!c!{9kC7;M;;j;|gY
zgr*OO1W@1FLIheTx-2D=1c2kr3I0Bx2W=z6=umlCv)7(Cig|wQ49zz&`VQh&kmTT;
zImP`nz)^E(`v~Z)wjRcjfb1cvMQ?j5iSgZcdDvo^5eMIljlf_dBg@>NHB;!v=3kal
zF-}ih26w5UgFJ#T=$>R#uVzds^;g!-*)6pvHPjsH+y(r>Wwh>hkho@f5~a`QP_LQX
z&d70f`suhCJBHnR4xqW8O2pA=B9y5cddz4F4D#Gf<#z0m>H?-eE`^T^^avD<z9!tW
z*BVM|-%3*oMgq#yz`>HH(b#GJ%<rc<3;Spe@*JUd6kJR>Jc=$qki<hibWH|>jB6a5
zlP+{uaQ9WHjnfwQOG`l}qX_!2N=s)I4_D%WDKEGF!BzV}#2j{;r5X6ah)`4gGi0J)
zoA+DRAdIt#`1<otaOP)d(TRyeF!BQg<!to)Zh}>(7f;`7OtwV<hCn~6IeSYFO`e3h
z6%?DfQJS)z(@@`)Mz#krh>q^uxh0U6lAW3>PT_ZyxFKlDmQoS<ydUmXpYGIOpKY11
zx4pLvK)aNVZI4v!eqg$>4tz@3m>s{dC-$q~qH|hjGh(5H-(66If`jI$*d}Ds#G|br
z?I#;7&baynx8YQD@NM-OJN6w0x#CF3QFV&-^cwYt6Dv^66%No(+$BLFNXG8S#y~VH
zYu2Bz5r(TUA&L$2YoKf=t$1ZdPvGy0#lX&?!_i46Qfk@g20Rx5HjOunI=0GYaG4M4
zF!|Qd48K({7+#OZ)?!Hm7uAm~coIBtNSK8I^2`nM2<;8$UM&2gw@5YZ3CX%?M}Od<
zA>87}5BfLDdb54C^ar!eupwS%ocymHwmP*JWM`z+08U!`QkH>wvj~I}KH;Vox>x^s
zRry~czRiGb`mGfzmY_PQiae*(+$|GxX35q=fE3;vSfehM)oZHc^MkJyN1XCm=`efE
zK4^i`WQhTkTejxqwrrz1dPGV+F7b3SBRw4dWpIN=ai8|OANg^705<JN-~Jf5CqlJ`
z_#h7p7WrbIB`}yHkpHcve1ucHg{Qp;i@T+u-;C`W{+f}^L4viVWX(sECRmVT2vUyt
zPtbLIJ!ruiYW0(8A9troXwzksYiBiOn)b6%(LrEA3zEqK?C3)o5bii{FKPWS_S++p
zwBpJs+!3QjgVg7SjXTD%f{o|PNMw$cjo<s*W{JH8o6WXzu2H+wPhxeP3O9;(SlF&X
zBbJPOZl#IUN@DA7m%7#f&@WNg@<?kR?fqY!`imR)4svT<o4=&|@{(PN2ImiqQV1NA
z2`7}}HgXeC8B(U)bSgs!qJRzFAx13`Eh*<qz)l-=iP=+QEEy%P$P1q9L%EL${>TqA
z)I|L@d1WBCV1}7_YbW{-DHW7G#B?<-^?2UuGUYSp2D>QimjFwKa-|Dm!yf80_x5gY
z%}TRTAj(RCc`08hVd^U3_P{8eVJN+U(Ff;$jdlw1XF@B&DwDn;e*KZ<@V|zR!<EIA
zmDuYLEQvbovQm4L%lzU3_U({gU=J2I_2y^pl+932`ixDNFVJJko(#V)%y=AIqXPzd
zig<|5>0M9Ow#cwa%YQ4*|74;=4c=uWVLyB7)hCh~VEixPq~hK<j~mNdy<V1nw^l!D
z9K?Fob@vW3Wn5@xOU;7TQUW)LVW*&(B>NXHX|cWX0XX0o;|RRu?Ogf0;HoNp0i5?G
zZ!;CT`f4!Q)+MCcbFcwF!)H<&+Bf-OZ`viC()AmKS;Z|Dm_0Pel6nCI+=KX4<0H%!
z@n_SXj7sr8Xs*dcl~AhkP#!-ZSju$w8rD*uiO$vA{;hhs=?C4WH#W_6-=l_*;o(Ul
zSQjSC=wFDe+TQP-9Jd<5^Dtq!EUc{IwRDpFtfBZTL*qZ|PK#Ks2{pniZQ<N)E2rLD
z%6GQ#znx&<>Gmc2z_7R^_6pM`up8rO0F(N+LHro%8$ZhtdKc)%$bvZZk60Q&<!2hH
zbQIWzGXLXMnkI~#H%J`ZYo=?(>HjugBRmDpNYc>2-~as`8gSi((9R+IV#)N2b$Au!
z<sy|K129{Lq5cn%!GPchiw;JmR)frIR^@4zL{k24mfE~Lv|3V4SCIY=1m>+ZvinPx
z$^AF0cuPz49ttWD8K2Afx895yD>N}54XD*Me~FP9!T1cUlZgTE=hqhbPG&lo-6Kvs
zkl50#ZO!bFR0F}h%fE0exz&U#7A*>jZwi?=$qOi&fjy7-g_;~9B<D&UL5;aBt<iI9
z5A8ws;dnbuWY7wNBgE6a0%b#IR>zs!62TBVl9#g8Znp~$2y^ltQl~}<-H0?0@@+I8
zB7?ufw@qgFrAKt$@()Ic?*0k!nM~LdaZXs%kND327gJf>jlAE^zyyb9=<Vz2u#=j?
zm{YT+JDAkZ`Gb+UzRtNKo%D3_D4s*^(wDCO3zy->KPv*BRc&`Zq8U_-EzEypF-1aT
z!uy`&pZ~6ZSnbS6xmy#@2M5aXm7i1{S>M6{z@7Jcx%lNPof9mlRm%cVl8CI*fALf!
z<7SQ27tn;+(24=!3W@>dWWV?C&kp~%grrz!^aj-Bu*K~I<C)vn!ux3>f0XI9!j5NO
zX@|RQ==Ql{f*U8?^tV{X6Q);86K~_*`t4?ObMiOR5P;kZ47ed&)8Tv9KY8SXMm=mu
zfA6F|-5lIiRtG)dM^&5)(yXBis2G3$M{X-UUlNX{gQ4n%P@NJ||L1qY(0{N_Q`csr
z1#}K!VA<k-jY|Y*n~A32^O<QE_D5hUOP)phwReIO%%Q`?_%K;WzoPD&2IUYBYtG2{
zwOCHXu|3rM*Or=jjLPTZo`-H%cGMYDl}f)sNih3`)2pi@T3y{x+#a0Y-^xd2-z}@D
z*#DSGpLp%nAE^8m2TmT9{-}$g{!W_e3$<gMhlB)fsID$8M%IlFb1LrTOz%pkulm1h
z6N}Rhyct~lP?cW^AFu0VEr^sM<z{{-j{&vt)u10CzJH!s7PISu@W>hy)n6ZGN4#!<
zm4~>u{JnV$Kwi^3dl3sI3Gl6z2ZS<h{M56gK2?gqPB}ggNxs6yYY7H@%WwTufu|K(
zxrsPVu%`DnbGyIsiK2U^=80V7TkA~bJpTF>aN{<RNCWRJ7P)0uWlk13$5NAOF=f6(
zYo8(P7upr^NaN7~`6HqXmeJhucaYu`Zo|Q2oJ4*6P&=}ny?1So+$->*uy-X$fa^hN
zc>BK%9N_kHB>;x+cF<;u6W$JK6WM|0$=63Io!`z$%J^1R29zR;hV`B97G1{MgA#q2
zzD4ExT7E+lzr1B4teUfnPtfk*o;%g;6!(zXctQ3kW-BTCHZ7{Eo3*$#NZZq9^HsT(
zXg{49(>?>H=}rs4Z1G|3d-e<QzfK|Z+7m5H+RYOkEc{Bew=dUxK;y3zbp8>=n7F?|
zJs@9y%EAM+^i#SH3W3KRI^pYncmXH~zj@1F45j00(E(v|L_#~puR<{*o0!Kb`~VXs
z{Qlry!`&YupXKOsxk_aM9+>&bMIt#8+^63fS`ONCdPbC<nnM%!;_ZW$&=4^cw|JD8
z6fho1^YJwk0eC`+onic))119oL0iDvMM=W^YmD^Loq59Te6@kH-d<7xRvUy|0Q2_6
z5cE`vK$yD+V)F&h9O=~oTzkLtaFV@Dkb(Q5#UINPdck1-V_vMcb}Xr0>{ll}nTX$+
zZ+%%g%Q&TLVs(PQcJp@MoB^K<538wLmb{sZyIqviV5h_skYm#<Hn~WM+5!o(yVYTj
z1aKP=@tq6!H)~B;w)L;m3)Kr>o@)##`L|@fRLH`JyK1y!pGtt8P~~NV{ZIM21fVwZ
zW`v`GN4<y!25|Y9ovxqgM@^fzMfRC7N@+;-%<TPbD7)4rp7J$Zy#dCNPYhGy8Y*|{
zWsD|AgC~l-Y3p86cuCAmw<l`B277qGx_d`~n>e5qrqRJ8CypYF<y)xCx2-xrQOWen
z_mp*A`i)Gjxl|_~Ge{{&P#Q*f7i#oR@|tNg;guq@Ovp)M)YG!M-q0Zlrbm$P>xds!
zJu704ER_v>f((r@eMvR#10x3!;KeuEg&czrM*hlGirXfm<r<@kR=x2UAUnVN?yIEg
zuK;6Rv1}Q{%<f-t;pDX2cY3_ztnMwpA`PwgJDGW<9P(sL^KzRfxr!P($a!aLd;7%1
zwi`}AzoWzt1vGveIpgqg>jq4C$PqC{2TNoc@EJvX_*dh}y3uR|&xm_rdI~W~eVl4;
zYem4FtNbNDFv3@|vZ^fr4i%&0r%u1GcSM9}kMpB64u`h+ULN(i4N`@JJB0aZvDLx~
zyGFE9RL~Bm--IJR^45PJi5uROln$o98e}3SG(YtU!Fv_t%1n`fzEf}{&HPC-FG`+8
z(egQF^W!S(%h{Wcg1_k!)TJ&=v#suCjwxP^diWSBh|p&#fua(t$F`xX?=dF{rki}_
ztZmz@LU>oV7n(rNPM3cTy{57|?SA~0eh^cqyj&h3CooKw+7)$jTtFb@ItC)O;eXL!
zhhD_E<#@@xY8Os&Ggt*bi#^n0XnkRg$&_yX0g?b~2cDkqfLqd#9JR5&A}~33C!%kV
zk9GR!T0oFL3<r_F&0m_|a0qmprI;1C|B{}Pk}6)7`O($+F%2!vYQH}3L^8J706CzQ
zbIGH57%|dhZqE)QW~M?Qv`}GETSdPSs=hCOb>(BCm7@HaKlwU>WzP2D<@sX?rsuc{
zr)u&yvpaWdvF*2=#IxZUWU-fRJDnQQM3|Zl>XAa=r-xkz3OM%yKN`ET;iDXR{+_VB
z<m#*yIThST|6_y;rR2YPeU7sBO&UL}t$2Acs%_aop#nHIm)g`6ccWX^2oAPme*QNI
zll`vtvHz?>$Yp}99r^pT=>Q*(K)+1Kw3D?=NDxu1MaCaH@D(HMz`&!f{kt5x<(qbR
zrVK-n=V{fZINHF*Nq@Kx|G4w(J!!t039h8_W0F&rd_c$p?K#-d_^PVRU^&U-_1Ku%
zSGFO=9E}UHkM$?{KI)-NECXCXN5A23QhX;E5OuVB;=DI~5+bTZCyAIe`*~+tsHT}w
zDnIzP^Yvs%;Pds0UoX`MBszcjBUp)gA*@tyd)b;T?#d*r?=oe}-p7^9h+JK$dwjlS
z1?FYTL&kiH<RzTGGFt^YZm%RL*<FLN=D(;+%QvJE!nl~|D|Lk{$neuI=e>4qLIE-6
zWlwAc<mu@LtSF0Ecy}KeiC#y@)DNiiDTmbU&H49DKiYfbshar7#2n@-wG$_^a8N08
z_QQBKRcE&X(UQW}tg_+W-f<cz7Z2yK9WayKq>p%b``<ZVOUnJh;F+=5Mq7>I?uR4D
zg2xj(w^Jd{`jof)yeF|Jzv_rrRS-K_l-hSi*B|ta-NT?!Gy{0>Pgk+slM<nTnbp-(
z^XP$<hhCrVATffFe%SnAFIJFOVSeVl#j-jpqq{33!MDNE&hs!SB7<lRZ}CsAXb-)L
zRZFs<{ACaQ8-gnE3DZNpj!>MNYOEWqU55ySOEe>`0e-K%!N&3NP{6eELzG`t<#r!Y
zIVMi0lb@OGCspsE<#8+^;~>GQ%1;MLXogF8+EP@kw#uw`Qmb$73yG-a!53UG!_br8
z*zhYRrU8NfER#n6`4au9ZQ@gjyp5&p5R6tF9&{qv3QLkoDdwTdE0vmM4|1s_3I6zd
z#HXNV6*kJX4G0Q}Zyy*CL^pX=6qdDLkNK?+>6@cb6!I8*%|axJ_?TjJO>7awp+lRQ
zhv!So`IZ0A+XA+SYzR?C1|A`OiqI@w0@&)ihBzJmZTT%-!2VZCiRN*IvM2*qN2;v>
ziY=3A1<sFS)+YmOfEcrWQFYP&wxfc_A5s)J=;Fm}oBR4`=Q~U1!x1vCyDnbIx|Lzy
zJV@RN^zcK)e1k-{)D(TB8oPRvyTqHCKivDT<PBoj*Hy(omg?PhGBIrvpaZYhJrS5_
zBVQ-ZV^3+|RTP*{A?1vtZyg$n9jDJaGQshKQ*Tx_GCcsY4#u~|PRLO9wdh!$q?e0H
zj{~W@zdVkKcj!*{F$6At<~x=M;o^J{Bf7=JIK`&Ts{6%e$U~?ffI2ljo`tR%O~etA
zV4bx5nn^@bL{vGyHrvkkr6R?>?04_gR)~mf?H0qQi`)Id%+YCof-!kY`e0o042}aE
zvhq{^e6{wAHvs|5{<g)Z9cW{cv)HAwK{#%au76MQHL-kK1KG2qsZTzD#C8D`@9K!>
z6Tv-hibv)Bxped1>4xybOnN{6Q#x)N2@jrZ(om2tNwP!UA03GKZsKXY!cZbTe+m|T
zh&L3JBwzP4+$`3O;)Gx!JTa;Q?yIz*OnF+|8^AHn6$pe+Fb14I;ncHbEGrqL^2@rq
z!86J3dS{+Z-!I1Yq%ht{ioPJ59e07`KMnG@*iqLWwVT7b0!SIwfk^TLO~2H}+`vnw
zP-M3TNd`jG2J?mXtz)NXO9YmsHGHxj_yQP*36Z_z73deY>kapumNOxmWuB>wj^=22
zuf#M>&~-evcdWb7>AP}1BJ!_~BH!25(CNB|$W+F%WYX7);$2DUt@sAM^Zqm+Q8up3
z3M&W^Op73k{7rh}p?s}7c5!i!+p+JFidm&y{Pfb0XH6*HYcp?ao^t~$c6r59!Qc)h
zWs8s&ZUOCP+@@3b{s7F}%hOUB`T#FaLteQ@f#|yf72UsYjl?kV{zZXDn`@40c#ab+
z+>o)dexq-c?p=`fqsuNqNAmB99X(xW*e}PCk14xA5e%bmQ4p(#8CyM4(-z!L;Qlb+
zW**iu!=~QOZJc%3MU*Jfe$_J{$YvfGA<p|IGU0yc47$<p?RQ4=M}|7WnUekUR3|;B
zFn4@6An-hPC((UZjwn{oJcss=c^IybE!jc#mcDg;X2|+DH)n&mvP`3F*W)e0ga=pa
z1ja!4KP~LNTV0pToova^ecGFzkR3n=E-dho8$pf^-{ctHRU-KvtxP5f1VD(>-Nu>e
zJrlSZ`z>_}p(YfHER`z);V2}zbW#Vt)--;-Xn#*L)BNfdo>Si3bMYksYBeC!6Z?hG
zqQnA2V9@x#?fm7bk@Ykix|uiKze>T^C*~$6?OdE5v~V3&b1aBmk(I5nh>aNq<O}D=
zi9tPeVxes7Z=a<AcF}3Um^b<n47%R@Fn6F+CxxWKWh{2(T6O#~d<2_CWU-fPC*o>%
zTp&y)&^9qCmr<gpHY(HB5sEQ#UxiUaB)sdcYks$^gL@nBoG0Rs8>{_u=xUMG{+|}N
zu?}gsXPZ1{(~H&+3`!w)Wa^fv#1yS|biF<0G43W`?^g>_Tpv|L*Ovi9zk?Cw5H`ae
z+VDOJK|m8tu^Zy%XZRF%*zL5N%);tRl<xzwTQ!6&^LxrL4}K5RNMA8XW9H)OG9E0b
zr;)kpE4r0&uVw@_!Y6Zn1V_rYUche469|ILTN3OS(ijAADNp^uQ82@MHp|($;cM;q
zM|~fanwECdle(=6=jyr^9{wHnE^Ly(7Rr&SVEm{^U86qGZ_?an+6b}9_#f<k*i*tt
zB6ut;r_%|zJ;Pj^ymm1o0iNS8VCIzQpnM6BftSFvSED*Onpc7W@go0rB1wMpU2qjh
zI|a_2x|l^=DNAnN5R}|-!oB#_KTki?Wi^LkVm0}*aHpgj-k4NmXf13_y|kV(4q<NX
zclr~93e4!oplaNuewUE0c>t%|@|YIrGy7pIAIxb9k!e<z+8#;V__{asV`^@^&O<N{
zW13LG?Dc#u+Q9+C6LItO!2$9ct73J*;JO_B`Gh(*t1VeM6mFhpUit&bGmSAaaqk~x
z>c-QEi*`}U&rn*Iv3h{KyHhFnt`7~zPeU#^e@G!`B_UemX8)M{eJE>;X3y#N2nI6@
zXJhhwWBQXGitt4v9{s-6$i#NG%<B|fBvZj`FjV|GS`qeW>UjtsQMQE0?khCPTZ)ck
z_#$Od<aXP8D`l)4CImZn&9=us3QxKUM7H@6YPTF&osBsqTV#?u5NeC@33Y!GxV9$Q
zegPv?5DXY+c~qY6Dkw3LC+j@kYx&yKn5U}hZBPnY$3d(fG*14FRLpcH+JChA+RhX&
zl2q^mLksuj8;|t89JBByL!eZ_Y3F4?P<O&js6`v{?%`5kaGw4zejBQpx&`g(Q3-)t
z3(U5UhPSQ|OFPoW7zZhN+2fjQpojr~9>RZ7ILoPT^i`g4uqKeBj1R)H5oB8r6P9e!
z=2JL{7Hkwu!8Wy04ILC0oKrWEkgLbJmh4gN{)vB;dOJD&64`#%4Xu$@L&48I>rg2L
zP;7p~JRfG;IgntGqNHN%vhn95K1=$o!6_edA!|lta7n~Z9MZ{S7ptPeQ@w?EQFd??
z6fnWr8rjaf{QuOG24LR!G356~z9V?RV?sHCQHPIs;%)sPK{#^TgJ9diHi~{T@9RUU
zt!-3S_2fAF{zr@D*35%Jbhy0Qrt?;Y`zAb!i6_w_#n6dQn2hm}f&U_t9LCRlW=KE*
z$JYW#UWRl0J}tS)M--qc$U?sGT*$bLDpCyf1gxXO^(WUe{_i?ckMN@KaKpt1=slAI
z9qY7rOu{9u?wDjh++lrt6&+#W=#5F#6}m@fNln|HlJ5OUS2Aez<Dag|l;eQb+AM7x
z>mvA{lZyuT@2Efp8u#69bzYl1dDtB{TE$Eyq=1r*WZQ?B)K;DE_-jR72P)4670W0n
zR4~v8@4E`LfA1i_viCp8ifoT0XwX0a+fmP--GBpx&vgy_&ks>dcraS`|9<lyp`ZU6
z_1~rj5>*v_^3%AkY!JEAQ<6cF?L66+xl%k=O3UB5le&sskuCE_V*gXuC?;HgdP6ek
z>VBb8|J;r~SpNX&l21NX`M*`0i9^RAY80c5qYGV<ciTccpbg#sOrGcN@D%2B<J#W&
zoxFPm%Kr*jz=GsY`}|Kw{=MvzKwjTUdJW}Z<n>o}t-ijop=pi%uQW05mJgK7=|zK5
zCx2+wnM+K21qA%vyFKx0`QIk_lgK%MS^p~ueZl_``u{EBGYtQW$#&1$e=#Ba-){Z?
zVDkSh5ZwMhZTz?3KaVK+Cx>?d4E=yR8Qm?e3U7%DRgmQ@@q!Kx$MF9Y9=RYPFKPHV
zTQ&A^&brXCp(b6hCf!DN2bBIA^a+@*B1ql&Zu~#Z|EH_d<~m{RpEOl9Ysyn*J}zqT
z5Y}L{oAhPBSkk7>voz@!rkZ|qKFk&J{_kLl16w%WX)<Hwus0OHtT&~~*vZ1a%+gi=
zSso7}y-cbiO3wSb&d~nf0k<}eLX&Teo+8;-!I1v)Q|}x0m1BdMT<4IgPt3Z5#(#ks
zxhBX|4RJgS|DgHzs=-#PwroRaBYN%2O)GzHsfwU%?RW1>{wKRRz<<#*d(lsO&%sOh
z38hr)!Z_`14tMHiwOqpd&!kw;LSf{Qehtt$-JbHni(rmFuT(h#3oSgTlDRXhi&SM}
zk&<`%O%1HAoZ3!JTh!C4qGsgeJ^Nkx|9K1WpV4^)aEl8zo4)Jy)5&2ToRP0wFFF6N
zP^zcNLkNDjCU6(ypTg{U?iowtPIzFap5k3g{QVC}w<XnFQQ*UTGOIe>zQ@1ao3bVK
z4g0lEeE>J$t{*1<tJ?xbO2Nr+bp%<bZIS+LNt|c4Nv+XkG@<p>B}KC(Nzsa4Hs}_5
zhG~{kZe%P$aZ5nNRAG_#T94~<q$~MG_&YULXyVdR<!D?jeOFx9Y77H>QQ%+*|7kke
z1kZSHeqP&n2>3j!#4-AQ1RsX5f|M3@$mD8zD|I9UC>T^;(W0y1&qlFGc!m<-s28^s
zk6jtWR(S+yohNBJQ(;7ux@c7QRqB$;rIMts8>lQevM;z`XU}#e;!3Ow%m)}_b#u)d
zQ!i7_XDY#eIXKpuD@duBYHFIAa){X0BpBZWjyx}Oo+`z@4=)xOF3z@%V>OiKD*w7|
zEzMkI$<^{n+!owjUgT_h@&DA*`Z@AZQ>e-ZAY$_<7k4@`Bm`c|^gK}8__<RGT|K_C
zZPb_Z7G~R0kJldQR(}Gtz90I}?9U5jV<FC44(1=q$hb_3%cD%Mm`S$oRGIse9CH9#
z#tg5|Iyu(Ku3~)w+&*W|vNo@7iZcq_k+%iSx2ie{TCR@7TW=;n`E<kZ-Jp+)<*gfm
zNR3LVC1;-{6Z#;;T2Rn4<(gEWowAAP^SbcEWBkHNAA>_k-hg(#8y)dQx|~k`MqBXZ
zr=NNDe?>4QW~K32N;}QoHc<$2ywAA`O_&7fRa@#P8#K4H(iizQm^HtUG4eeJZt$w(
z{W+;vV+o{m&K=TMjhpV~(uDbLFAfdMw={(?QQtAHL1*ISsv});ZZ|_)%kInf{bn<P
zPY)aHYf_Il&K_(_|G}1V6)S&Mrx(~;ZK;|(37RZsHK{E$vou4i8Sej3OJ9?5*`Vag
zZl+f~xC#rrbo{2;e0TIST1M)2zZ$TS7+0}I=G590lGJs6+xqrZJ3a}oNQtxmay?9-
zX)UJIr9*Y}vA3Ym3CZ*)@DncL=_X-1s3hPl{uy^O>4KKLu*El)>m_5~2g40VRCpS1
z#3`++{C<5iR1Y(=(Gyz9+<fZEa5BT`-Da@BkJc|~T37+`m<TP56&elBaQqT^`qK^o
z!u|XAsRoaVb=oB+6g)XLTUUOZD-Fh1nd*W`3-sl7Vq6Eiss&1gSpaCw+I3H{g#o!m
zlD<z?uMyMuqz|jp&ztB!#%N|wp72j$>Qp;MZ+K#A^#5KFjsM^nXDOC$%A1kBiM=5R
zeBGI4QeDt0u(njGHy2ZugV1PvsBV1$r`AK>=VxjeeGZjp*#k5*H29=4A16~UCwj{j
z2q7`vNFkS3<&Np^dY_qax&xlC54WTb8~=fxW(Aj}j!i6%Pj+dXth4G{e>F&nDVPFr
zamLIm!xNtlr6aUGl;blJ<EAXLGOmW372feYfg&4BAvP1kWqR$^ROVAZ&naOX^GzX8
zZyF_5Xtis1oW;{JxfHgG?<L@wQuOvO&h5gJ3%AE6d?bdYVXSm}#=FqcB==?FyiP4v
zr{i3|Jmts4<g3d7u*ejuOGei5(TBzD<@<`PLj5_7B`-0dg*o9YmPQSVf3!bxdjmQh
zOHNRv7v^ppJ$A6mQfdy|c$kLHOXbtvr?F4guPr4Ovddo&4)SzmjDB5`>n?PoXPj*0
z`p<#jH~W!^lLi&FED@Jzx-<%EpjbiY*?RWX5wq|!h=?6morumpjt@jMz0G#9<Hy%v
zP2xH`?(lMrt1MZE3oT7LIJ%Hs4|2Tl&l=ug6sP|M3bubutkf|_GHTN?Z$6vfaKxrG
zd*ur$XeBGkh^ZHn`0^@Seo=9G)69@&m|DAMt%Wpp(VdyL0{_VR$uf|oh#(_#+RiSt
zcR4N1u=_vrQ3TR>b({4&9M4KW%uKe^^6;u3nkOeJ^MP12oUc!#Vsq=zgg06ty3kCy
zMvuSZ`Ax}-eAQ-y*i&VE6fdLAuE6HB*Cx@1A{n}J#GrJ;+M|)Bt0Y~9z>>rI-%W9+
z4MLI*V`D4J8dC|D8+tvt!VN=34ij-zgoBjx4l|D}F934LdllOglKYO5OwN|3rWE<0
z0k>gYx1{aDmWc82qS062%}q-}QnFV>=29_a3s>*BNm8EE5r(P<vntg@TDDA1EGpvx
z^IqqAN58w%TUxz@vfSk`ev^aD)HqF3j;PKbWSNQ2#^`e``~iUx;n?Z>7mms5k^WbU
z=G~U|0{v@0$<!i4HFUl-!@RxPb^MR`N2)T*^UKZjE<KptD=k2S;`^#`oMnQN4ihhv
z^xykk>G5&`EuH;m*{jZJ>lKjGf!9;yIe#yb$MYs0CzfChDp}aZuRTG}azrX}E#%-B
zYumJ(O%FDt-I41_D1nsWPBpBfAcefj5h_HnF_J8lMEI|#Hfsgy7D|#s{+B`{eUIjA
z;WzskL-P#fW!m?PxvgCn!F}6l^^_r?X1#W~dcsL-Kf9l;x~xZU#{;}`hwR+S8cuyP
zMNE3uR&GYgbC_D}%XG};>rht@1iTx3S&TibxWdCrM6uWD8;#McSPg?z!ZgdHF>J-8
znJTy3aD;Jrk8d4u777m})$O>}e9p&ttJ<`XK5LTdMg)RuriV<Fm!gNJqYD*>K1#$`
z(An>WZrz>-tN3erkX4#m{cJ~Qwkd1ux>mIEAY)B&zw^fi<ct$E+lZk#@fEE6uN3B&
z^jNF~uQ*Pzal*=7y1Pu?<%0vM>|#sm<)0)Aukbr@J=iGD4z?{W1*gAEmV}p-hz`G$
zvV;g6(CwlRkj}l!ZcM*Tf;?N?$?{lOB;YnGZm1x+L*0U5%N)&CiM-JposuzRntcwf
zcz$6(exo>-MbsTR7z|Fg>OE|dyq`xv$Jy2kg)5ppX)+_IDi{65if@dgoVzcUJd~Sa
zOMFG4E6k8e*i;&2s;M3@T2lKDQO}~uHE75`ZjFb;P{V8ow2TAX@luJa$}gpa8yW)Q
z;a3@~I=X2A^DT+AN<<$49uJL!e$wCz|4(fwK)1t~r;Ey`8yXv*>x-wG1#)_{BhB9Z
z9u4+p7`8HYsFWF`k>lH$fk-Og%QC;{_7QGtb8|SkxRXbe`qgua6V>J=z>W(w1^joU
zeA!voThK=7v1chOvpXj%YfiGZ7%$&Ip0`Av_nirojH|hGTVzbiJRfO-TbW`|CCm|O
z9AWgSSt+f3d+x95xgaRLp?=*cb_kMxSB+W!#UJ~5NUEy2eRXeO&PE@eTH+0udHYTH
zEE<NN`NH=Nh?emKP#6*vucotEyA5cfuMh|fhMkFt=fJP<PoIzKonIkpveR3M;QWa_
zjjSrD<RBcrd=hrEmo3`g2+~e4S-{pNLacM)kT=!D@Q{dzlnSM(x>d5iIkXWXpXk|E
z#sKLoNd<}P6BP$FwXOO-ns?#YoiT|9UO&>+^-7LC3tB4|)ozY{<9?uV#6*~PUmXMJ
z1Fb>;5&DiovKpy20EqJF51;B<{i;PjQ0LVt*LBkD4KwuoHtY{x<84>AF+6F2+T8Wk
zf>*Do)e7@^0gspYgmu(}aHxurI2>Luy1bmWY#0U1F^M%yirR}1Pv&9^$9_CPLqJoN
zjUBh4)?yKE)C%M-;#HsC8KcmeY&zkqV|KoTOR4C+UIo0HsAuanOUNJ$Gtx@hr=VPY
z34ixO%O;*|y|u7$?ppLlw9Zwjj<?KBk_3}WjgI40PkP#%Ibl-E^yFY=p;7XB;9+l`
zDyVsJzTge#V_FjJV`}E6T0%;!<3h2c_9pz3P5r=SMM@#CAecL|OcR<HSg6D<$0yN_
zYS5JmJ-5-`ng2uy1E$f!GOc_28Ce!@_SI%%v8O2KBf`3FRK9m+HUwZUO9EUanHOPw
zHXcJm)m=Eh%V0dYIxU1SWL{Nu?wU+G;PdKo-3d5zoJOELY37|B;LZl8Q-p@GJ;N`p
z3W&bZ!n?Mx+ohW+Y^u6(;57X9?u%rZS4iqkf=Uy)Rzo_O8hGi*GkIO7LwWvV4NrEY
zJUi4Vokfi`JByG6KH`H4-;fR=^Uifl-gHq3XdA#!cle~kf0SJ1mu_m&o+5bqj<OY7
z0Je8UmEYf);md&mWbTzHA+3u0i<w@SIn6@3Ou0IG@8o&W^Y?GVtM%|h?@ZGc?{?0q
zsla$Io?O;dOnCZY8+>EK67q%dXXe}6{xyGD=a3VfJ1phyKbFV1egr(9vk?^)?=E4p
zJ0qUtj_1&ul)Uv;C)PJL>L;GdGnJ^KTqrJ_@@cXKhXei{L7|eum&^{FiVmkz6quG3
zdOYZEN62f}Zh1aq_;$`oWwZPECXx2FnE+}Q`=+cSyVHXJ>Bw}>QBsmuy9KD|WlM;z
z+N0hPReo=PIp1L=KD@QhRJE&)^LaOhIpyb*%*6)!1PO#AtKO#ecsa07q@`W24EIfZ
zC8zU0=i-u`riFX9RZDjKi;At9IKsPu)=c)%>iJbdn$x4>RZRRXZ2&hm)^}_?jXS#H
zX7eUgt19w4>2+g19bV|$Ep;$=eCAGV-cZgHV?}<8je~~S)`1|-GSflHbVb23!M@=!
zv4D8)R|akl#?@;NlFdejFY~c|7Z|$2{4dC?khL75u%L(I)96{6M836J)8@1S=ZqyY
zbH0GT7ThGbmmkk>qa_%B$TO{5GDzY%x0rnk#Lj<A%svyLp4_|AEI2hyGJTFSr`q^6
zlJ)5P3TBv`Li5`$qTT-0D=N{W5YGs~i<W5$i!wzFp<!FQ1X>PGT<YUQmcBzpPSx*M
zztxV`=l=S^-MYIyE?$GZJ>&8wt64b-ANG{Le94YzKl13;ArlcW?^~uTl?l4yxB70d
zmPIwLi?QVs5A8bP1gd(MTyy<7LBgu=X+k-|kB|xB2hV3}y{V&P^U($67)&nLU+kAl
z=|bSGeumns+f_3%E>xcGhaud`WR;7UmW74}9Hvg6gU4J|OG9GD_Rj~8!c=n7xa`Z_
zo>IxV$ndo66Ws0O2SCEzjRKXZG&an%W)&XIF+U`147XnbQzyVj_zP`>+&^ETnTNQq
zNo`am=QacodY#jxz+bs*)HP*y;bRvu53<h7EI$T)Sln1n8=TCY((8HH9~&D>CKTJP
z{V^MunRObL)7dQFyqwKt+<)aIB3B|MgFd^V2JPadO}Io#$ed~|Yj-H`xZA5#KVMgl
zd2%#HoI`+zB-$HPxi%XIRj%js%`g<W3cbisn!W4H0a>SNb9s^x7g<poY35BASlQq>
z2-$Kkdlx}@4us!dDen)oNYGXq%g^g4&`pAPVj4{B1RQ)%I}r_kL{F3kAFB{y)}5LT
zoGx#7H^C9swBDYTd87t1Hv<*c;mEe?H=Ep4`Jepy10@R_n_fz+!$vYytaWvs4u_lg
z1(rE`*2Q^O`<$+hT*AO2tONKlMTGzt;|RO1l5nb%jOXC}m?NDN(Y}Y-v9XeD4s|*#
zAhM`=D-p?ops%im!8lh8nYqN*9G%|GYEt}g!jIH1V+tHx%8!yS+H?z_J=)GjY!?u0
zy!kB8)VQuBT_^Ezhf>vG!_q&mxYvj&#_T_;;P)qQFhYhKtt+HjCqqDeBq9LsE@eB;
z(OGTAq*^iSXT#c_<gPtNTaa?*6)CX$`LJn)!*1amFs+53WXg)wvP7a5n4*vC)UF-F
zoZh&T=LnyUbM$ZklkctCT|M_H%35YPzSG9%yQo=O&Zl0)y05GGE3n8+l?&ynyq@n4
zwVS@5IPU6~@_&b2pOiW3hFXRGwQUb+NWEQhCOnkZp92>%cScSs3<4fAKQlrk?%Saf
zE@p(J_)nu!GlM%rqc{nxzo?_zf49E3T9Mjt`u<?>d5Vv0==bWRDsU_PgWT`H6Nfq|
z0S*uyF}=)?_XFwI_wYE$nzxnxh2sn7(AtBw&E6xa$HilHb~g8Fc(7i>(2}7vHMdvf
zD#%LG+#2LSDj~2oALh#6Q?_}-sE`O%TZ~DhZ4bzX*r??8A%#jBV<nf6X}*l(#N&xG
z6Jhz}jRskb5cmAmqqnl%d7l|rXs#|3{ax``fRbVO-M7?s0DI|@<-GWX2>}@Y3*e`d
zP7&6z<#@OB!_|<78+KwfV82VyP8{u!f}ujMy85I89<ct`0ufiE%MP_9P<e;Ad4<)8
zp+wt&$#rJShL)V@RalOss7o64M^UT5ps4SKo{OkIY1&3MatSfabBNSQt*l_?UIpNk
z$Jls7wAh_mdIKFz&`fB&<xX(n&Ig6wwAiDW-736mt+9VJaG$L-uv)yv$M#ueE1>eX
zmXwwz%yi_z!_SLBtLa%TCaJcKW}levTiF~;KM#$-9%O%S$AyLQfoT}1xkdbqk!fm8
zs>sg7bzQjoL5$8@_<Lq?NN{y*%Phb?;E{3pvYM_japeF+AA$bq0~x4DobqQ7Lz!Tc
zdw$tcEjCa|ipGIu7SW~<(f^<<GwDSPoApvRuO@}FjGO-6C6ayK$(2N{kW*{Ot!`Rh
z6UtefmhO6L8~V9`?`!lTFmKi_j$7lBFaF8KLvuHUsh2y>RGy~idODZ-9K}~>!*Bi`
z4AbW`*bOK@nzg%V(v(767v4u+>9Zai)W2m_yR6eMYJX;REe0pZsR<Iz)gOe(Ey!~J
z1^sA`dOdWVqv$mHJ?}#U&zs+~8u@j;pQW`&>`y2~KFf42JGt_dI7JcwrIEG2XM2Nh
zJg=Y$Ri?Iq*V!EX7EV%rGLh>eV>x%bv!&x{?lzb%psLMFRWQ)CnWu|ND15w!QCDo8
zBn=xYpU5VaZc0Lsy8t6RS;PqpVZANtC~=+r@T24sS~@#r%}sgtF$O!$!{aqZ8q-7X
zh_u=iGXwQCw6LPkw{$7YOsz3`REKLLWaJggedLDGOyEJSJu2DLl`F}q%6Z9^m+vPs
zuD1_g(b>^0$_Y`(j=jj86d>)2SAn+A)@8HWr4z{)@(*C-n;7!)ApA~I)v&<rx1<2?
zwl8gaHDCKp8^UC$sx6jAB8<`dQaIyWO$_|;J&s?sc4?QgYE5oS;^j&P+)WJjv#RG8
z@Te_8hw&5gg^E*??#~7V?$<KsBc;03MxY7!`2f_@p<6q<DcZ*y!PfTnc2U?DgqKEN
zzVF%9E7PvP9xtniKYhRRG>O(Oqz&MMR^T=!W4=7kboM0gI@D2&0R_U5<ldtx7`$se
z2kr(m)*1{xnsamb%us~<dtmN+d--SJB3Glc!zX;@$TQ$$C%!@VGdD<s!cVS^R-x9T
zVPNXX!3$4O3QzHR63#sP#zNYp$SsBnck!A!4TxOf>rjp4+!C)<8Ec6Gtlo!colNhv
z5`4paYLFw8r-_yE+bxQUxCRJkv5Xsy|Io8Qy<CsR6^N|Qi;rRa$I++|QTUt?Ht=dq
zcsb9t3;Acf`w^E0?i#Seu<fN)(M+VVVXtHWpJ1syaKeCmDbA?x{G@&l+LQ>Su|tAY
zx08wF5j$UvQQ_i-bywd$3VHmoUmOxPgcHPCFR??WRDQ!*9za^_d_s3u;r3W|e~uEQ
z_Ut|yKDO+Q2t?v9QJzlG(oMf5mkNyRY<Ium1rD2t`%mG)*XxO!_xrg27O>nMe$#B+
z*cr&cf)CPy+o)SDMKD;5RV*;#kLSw<JgZh`)at+{PXp4QMam>TThkt%a-HC5_m>c)
zS5K)mNa4Rvcd;$&tPVzEx^I{pw)^?{o)BdkQy#T7wKNE+L@db*VFS}zQdNKTQ=799
zsLMYqXa1l&Eef}WA-weRtIu=eKY`ZNb!)#``DX#jY0h^h`&g<P`7!#T!X(ZLT?Z*G
zJH_+z0WR9&E>hO-B(JWmD)hnwq#L>$3m>L{<^eY+&n(hh1W)~cT-WcYLDnQ(0nO!-
zKyr~~=rnByPqS;(5()g*_aXiEn87Yp|9{?gq7If$wd8&{Y-_y&7wcUfmNzi>@(Rab
zuoefKQk-irz(wDt>7@{VA}6RbQP^MS;x0PC3<cqi*DM=}-)e*hc#x8M@B9&Qz11&=
zJ{lLw6X>6-YhW1+J`W{M%Z&l4iT_j#0cTRe*W)Evw~hI7tcE?3idU^&5r5pKv4A|x
zzK3m6WVv~Y7BOtS_Fy%j@OaS&8z0FC39@WX&NUt0UA>+F3eHPo;e)&?SWv@1d*{8y
zv6Uia^|q0{RF3R?Un{_EAJ5Inw|~5f+%Hdg4}KItOW%lE3yCeWI1>rzxILj`z*l7i
zRkaM&cxPLwuH$B?1E!D~pRIz;DK2KEdBrx?B_#$5d4>LXveD`NE~{Le+JW=y#uR%7
z*z?0qVYkQn<`1O)CqeB@u{S(NqHHd4`D&k<_1yUyjrLqJb9F6hPA%+fiPDf}o9}Jf
znfK+{*BTqH^^KR+Vo-)LXJ{cjnT0kwu-fOO->pqAm0bL_bJ}IJsPcEj%M(*^%j&sf
z&7xR8jXehj-q!Y;N}ye~K!l?wvY*pzLV|K$BB&2s?1j?6-mhWp$y@7Qg{YhH$(b%=
z*Vx8<8AYONqfX|$)slaBaI+<`cvLbNAJ-l=yg&s_?b%zg+W`eY^w3xL0?Aw@)4=eG
zx%VDNk`dzT-5*Wtcq9xB3&`%HB4afmQqP%Gf*h<xz}jvH>y$0ZR!%5c1<u|M<v<Xq
z?U`r824arB|4;W^pA$;aYkGD0hVq8JQ}LYN(dF6wP_z2LRNT&d4bU-c@cPTSa41_e
z?R*IxZd>)n2Fyzy3}#fmzw{{uYc*RK`7_ySpLSfSayKs+qA|DbuoIakH3a_0<iPCy
z#ecZ#Uj!#S$FH_Nsn^uTt#*lcwx{jw-r>|s$Jy)?AI=Ceoxx3)8(5~9yAu1xn9b@4
z3*<O16Lxb%<(E79Yb8E@c6Ozf=Og%?KYJPyI)4zR;iaA{w4OJmih*!a7kc(x+C!H>
zJih`b_j>bc**EDok@3W9#(me{-1t{L`_hzF9L5jU)=E%``RE0~Z}=qKV&75RD<|Nd
zNu#{w6%bw3G7=+ToW_PPr?daoU(ADnF70j5HlH{5Ru3%dK)BMz*~%o;Sbb4150=wD
z*NW9KZb?~CD-2s}5zNH|Npyug8Wb1)Hl~f?X%091$ICZya}F~}!s+bL97rq6sy=U-
z(>c#1dydL+N+L`6)M#Q*ezWy;s(M^E#z<Y2R8^Jq;dh=eb0}7oI>dqFEkbmDd^BJ+
zPhhjf;05*bImpn5)5#`zqVvODtaKZTeaM26+3&XY^nT?l;Ee99W;AVHsYYm-t4Ohr
zOMA-n6u{Py?UTWU2rY#u$uV|$%-x_5Nz(~dd#2(*vSIu*r`*V)bZzR5q`)_-JIN{X
zEXSq>aK=SlBO%hagC4@WJJg#t!n;9gwEX?Fc>9uwT6*4h?ffCgr3L)!oXv)V4=CUA
z5BE>SS}ZS?j^J+5+O|C)pmE(Cy?tO+fEd*g{tasIqXu)5;z!F5`%V9@S@*Mzjd-sn
zY*EjIt)LqwejW~R&a=P!S&@+vuceT%2d5m<$u4n)(--P_q|dQN-{lrq^j1rmE|GV{
z1e@zEGt>zr(x+&wZjBNlABZ?p6K^o`bZc0xcfQek_?xdkc62--D;-tGt7CwtvNr2e
z_a5{0xV8IRy#R!O^L$rFN3OzEIZsJX)5GUMIDMOBt)l4F!~7TRUoO+mxnl2iTYD!9
z3d_s8I2)5Hv$L|OV)7fOP2VH99*fiw+%sQ%K(SEm%Mm$*^wihaN2>fMo)c#wgRIAf
z=ROzZcSj?4=hCA*-8&3?=F#Eh5#*26uCQOe>e|&QN&m=whW2Oq0qZ|4zGJjtLn`$k
z?Izql_v;9>)kzR7oErtmNhdrZ)ZFo<b4VC!!x=a2BPvkyX7Ja@KXm<tqvV}@GjZVE
zM4W83w;Z&NWYiKG1bSdhS7&cir<e3k<dEz%^J0tJlWemS6d0#n<8SVojgODVU~L<g
z2goa9_H-7ZzQ-@jrW^g_8olfi204z;?E4kkx3(r)f{ofqd~VN3gwmz3Gv9fFI{eA=
zO|^l;QT=43y$d?rR2_by_Q$B*FJ{l3-i*+#yBG{}tr7o1W|4VngPOp5eq<jT--g!w
zOWmthZ6JD$yIyiqIM~6@y2Uelh}>|5X!?-#(pnZGOyO`=aan@9Q2S>kwA2d2+gTzT
zw*0DA`LbKg=rax2O@9v`xm~8#9H_oW5)NQ@<f^C~H+`CIEoiizwzrkjU-a_X*A=%*
z2ZV;Nv%j+@vO1=>DVm-HjUF0<>YFU4P<+!h^D7C63&~w)%Vd-HynLIp7-!40ZnIs*
zHzOWjZ$73`!YQ!WzYHBkIlfo5mitr>3pYmJREzut{`&Wd6KH{?K1>!><OGclaalFW
zEZG0YXTs@b7dPsmAVZiXl6O=cm;X2m{)3a?EFS7V9lj}_yl18`aHlCU{;c0kE|BWw
zx8GH{(GR|Ah+AV7;|<Io_$-s^_^d%kbqCs>Hv!9d0Xg&8{5vaYpMXAYz*d)=$Ytsr
z?Me2NtK@^%K*!RJ116v6ds*V32fd*&;Rkqvo)Dp+tO6bd#{l`egiHC(Z?7_d28s|a
zGBSn&HgjbR<1@AfmI8*tLp`ZK3@km(HFl*qD4j8%^cDj@uj!r2;<6=VRo`2mXjY6k
z&cuxIpLdMS*zG3z-383fpXRHPE|`K>K-Gu8=D(Dc8pSMvBRal|!p0pP#V+yzFuAgs
z$7K65i@(t`jgpb6NY0cYk({GZe>Jr}qlvqKDV4L!g_T~@uhafwP0ZiaWr@N7S@1N1
ztk$kw^mf%|cH)XdrKPPJ0)!?7YxLLlVkGQ+;#T_nh1$aZc?&?-m40hYrD&)t3bFB}
zLQTg54xT~FYBb)rhUCoM$T!6n7w}C_Ew6etg`LUkZ5kVjy}EQ5E^RL>EACjax`hi*
zq=9o6CL%Y7aanb)l;&b#{P({PzY!N5zK^@W($zY%m<~-+jk9V^J+X+R|B?`?Mc;@_
zSe{!`b7L5mcH-b}oK*d3HrqHXZuA_rJ;=HO8*5Z(-+3ou`~HfjSx%y}j>*UOCAGR!
z*OcwUTS2bNx8YQ_-ZnfsvbD~<w>vc}^{yLTv?IXWVH&u|-^%1IycPY-7Oc&#4PQ)N
zU$b*Zy#;(q{*7)!Veg8X!<qT+ZolfQ$uekJPjpaQp54BTs+paw8!&Zm;g`58ME3NE
zgXT(dz!#wU>ByKg?sIY=i`|>0s*Ru1SKGSE%@LB5>=i9#{V3nuf{vGhkk`hZVY|8b
zs?SbA=cGMutV*LEQ-*~&GlODB?{S*V@;>GXR&Dm*W(~xBwiCBjK4on-8Ry)FUAlMj
zLVKRV0jo)<fW{uJdb(Od<(;zRn{UKx_;j&x-m!~jQNmg?<X}<{KIf*1%zoCGq^&@a
z1&`;KS*N|<YPq~aBiEwR9K`C&`nfoP*9Y0f?V~REz{q~lVbBgk?L$GG8@p^<q$fJj
zYrjXgGBUu*!Jo6D)YNSK22oh*c>1EAi`kT|f|KQjL@LC<Nb|`{1cp!9ztUIN@PD!Q
zRc~<w?UxXOI|TRO?hxD|1P|`+njnJ{Jh*#+;O;iaAR)NB4Hn!68{Brj@3(uiH~SCl
z%{={7Kh;xRUHw+Sa?U}Aa(qieb$l<7THw1<%#evojxbmK=<$!WytbA@;-1CLx6LBL
zPsZA^Xh0X!ZL0E}GyWXm^iHGKMc==T{Fb$g(P?ys>-U6mWvVLeCkAx^ZwzTNWgGt2
z6=a9v3kGcjZj2~*R|lu=OVj;T2EyU91L3~9jV6QvV=C(Oc+JPW$L=axCtqq8Uyr2a
zhWPTZ6on|V`Y1kr1Z{SC4x!!gEf|~;_D}IJZDAD%aPe05Q|y3YVi1X<+VGYl{N!ee
z4vVkvEL1c?mD^xTdS5YRy<2tXM1=e3@!#Wmr{3shF*--Z<(w+gO=@bYoG*2cDc@@5
zA#*TWttL^7hvoD5JdJVm@cefFfycnS{g0;`tufH;-%xMtVc4>!xDc6z!ciKDsEYo)
zJ;b9}<CPyTNW8@NlPBN>E25S>HJPGytWVl}o(suUw4(V1zfo}}fwNM10h}0Ymnih|
zSpvVLrSbp#)y^l{<wgHAga3Fu0*f;$^zUAc)=X%?aaR9z0OZuYYXnB>KI!|SFViC@
zO@4Lp>&g^E19;impdOk_R+4TOg|dT(nJ(VPUF4b|K+?k&8Guw=j^lw@jmG+QXnCE0
zxp&z-uairZi%B1;pEloK_cu@Sx*q_|>-mIYVre{r20AO6|JH~h-B(u@^c3K<<LImT
z2W{8(%t}s<qwTcJv&rMDkYbw8F(dw7rj|L@ZaXAx%|D<Wl{OH=eO<+cCKOEVL*66z
zTD1y7jr@IF#u5V)(K%Z1x&IRl`D0o`SCl^F*`s>F0p=TJEPU5BI;_EU%TQd2r%1Oh
zFHYV|6oPy8@ov~WY)4SuhQYj`DtF5J$?XKYyMt}J?qvvVSNBzqHWaNjW_;aZuhf6V
zRyB1rw%l_BSih+e91Q}Q4fZcE|4~Xf5v<u|@!&!%(wTfss=vd3A0u8G2*<x*=vJ6w
zFG}|$a}BJohsKW+;9t8q9Vj4w|4Qpv?UH-Za5TNRqMl>&7ixFL&@#)ZG03XRQB$*S
z8d`sci|y@1Su;K5;=)i*?yi83&A+zxj@u|^m9TNrS8)r1PFJXe`>iXYUD<q(Z?^|W
zT-!Kb1GG2qF}3O%>{r4LPB3&qg>sqKQT-;81=VBs2Do!sMm{-GwHsoq1gNd3Hkcsy
z1VhS>^Ta1w$d`Gu)r4{d7u5^;LG~BDOm!_z&|W))z|iQ`hUe#ocyt2gwWJK)6a@*Z
z1mcAoii;yNv7X=PSh4YoIvM1|V~+o(b}jW3ox{sLJeoJwP=e;a^--g9+N6s8h4LBM
zIeb)6IcdmcA3n$}thX1hwRPOdqI^o!dn6=MBn`-s%I~~)@L6*>TiVi7jnrjTjU>Q!
zdz)0?8!5^DD!1_vk=5rdJhD(LQE-eW{v95m`z1M;r}YN`;mVU2!nk>OO4M1RmpsgL
zA1!IIEn!GdXws5xE@IqWMdYSC-|DgIk*(NK7;ZP81zv3R=?xtoE)o5ULO}!tibqz{
z^+<irO@lC2?B#Vxr^!-DCTYnlP`UM)2T2`f`Pbl_d@QZ&WIiuq9E=Ul@pdaLy4PQs
zWWUubm5&qQYv?UCI@{OjtHHm-ZH(Iz?}GWQ%P(Oo2YIaXX89*O^F6=B?yRgeNl@ID
z?=${5Z_DBUOhXpn6CY6o@UAU*zSawVtlEZz1&W${2J(x<ORF>;^H#L|5%tS%*n_Ou
z-&^0>k|2_EwL9`~fh8(L+|RZPz-99uLDvQ+n^GIk-B?Dvn){<z^B!J{>PO(YZCp2r
z`Z7&omK*3EF-F>;=%?DICf>T%AAEhTEV;RUTov*=dW>%#PMo1aT~y;``TYz3T!wej
zJob&%Wg8wS)cM8gzW^a**Ebfu$Obdt3dDQpdMtD8ziSuoe$}VCO64rKMc+{I<(JDZ
zb)7rp{|LQZDD8x=(Pe)?qy6}8A=#nYRT_9A!(Os#1?=Rj+H(o+%zL~936jFd07dnJ
z+!!CNu*{%exXVQRzt`L2XhuuTlDPll$|xo2Y&Pg2pz(^U>tA2eYhkqyJ@q7$h)dGa
zfQeUYXu#KIHGWp3`>OL*!zIMeC0FjdrZzEKDOI%u8(~J#2PQ~Jp#K28v9SvtMry6V
zIo>~x@^W>5g8fLC1xfyAYxOi(OH6hxCcv#ej9{5*_E%YzIdzZ5kToaVZwddWc~85*
zxW?OVcb@wGD?YaWcQ0PIGMyyX;6~6=n?toMecx>>jMs^t3YUAJ!R=Fsh~$>Quk1k!
zAc_+MwK<^(m}b_-fHjV6hiRaJ@+_GfoQ0+W{lfJx;RJGL%z*qXf2DDvu%ssDf3E?l
z=;?X5tI$lnaD9C_s`&Wsl6O$s06e2FqvDn-zy7`MZ2V-mY+m-#a1Vh~+13QJu&uNy
zif`5w4)Ey6>FaLCto~lC7|A(5S=7XdG2n!RAZ<E7x5Z6y9uRFH#ji7A=~+GSxkb!c
zJJho1L^;*6>~l6-#mBAH5>yaNAEYR`02!ay=<kTH;#mDu&10&AGl=KZV^&Ma=XOC*
zocz1l#5?gGi<6vgnxp?z^H_O~xhce<guk~Hav7#cZav>>@8fbR^=@OH>&#P+6@gV+
z(F?2}H!ds5?>LUcO$9)@zXx1vAwYa-PilT=lje6h;gMzj^vOR{dr9(kefq<VV#CSK
zCW_PtiAuaPb&Jml>pT%Rdr3mE*UcBD$<X<9*tzKPwQ-#Y9K<m5GjjFJx$4#4>WKv@
zNWQ-c6l8Gbf_|u@d&$ykY>|*#>5y$WS2S&9W9noD)D@f;AV)?AUli4&F1%z4_yvXX
z8`$K_8Ae^#yVz8p<K_L5hYJ`q$y!`&!_F^-5m_2I&fEpL!qOq<`FWL9A~(&o{CD$r
zzUlO8$kGBm4s5P!?A-kz`A9z#736^_>X%%=mh>6%;1^Q2b1-@_68S;g-obVZEc3th
zbHp|5pJeW)#(=o%mRn@c$W$FJ%g=s30-1BtFEVQSGH1c7o{&|%sjq*8!x1eml;3A>
zM3xVwh7p1sW*q-cI~GpS7fos2`0MJVh);w$JUa0IV6vu@BwW1e&6_!dF>{nvq!;(D
z(^n7zmBx83dyU)HRi4->h=h<oU%plgCJWeGtet)XcG4}cL@h{->xRvs8GPNL1p`h6
z$<9RX!yFt~596x4!=7>;1ng}(UxLkof`YiV?w-dczoobhVL;q{KR=bEjs0!%%J1oH
zy!uQ(nxf)V`~{g>U5xzPc7=*N((s*N^;h4u$D5sZ&f7oFObyq&tairD2kaV8SGn!p
zqw@f-3%aMAgGH7gR%%}24n^my8hs{KXMhuA8&LMqudaVC0WU9(Wt82okNuHl$EqzR
zBHBy7$ISQEuq`OJgMmZSAwx5!tGoB$#Ga`>+>bWY&r`f*g|oMP0WgivTIcEj=V5AB
z$w|FuHor8Vejh#NP*IED^$F!}^7-+~0{aMF=fIfh!h4JiZjayEd+K*SAoU@db9zdc
zrnUMH(>@&GP6_SiiRaUz4>(QlGb(XB&zk24eVQaK5JMA2H_t1{8m*w>6gI#f$bR_f
zt9i-c!S?ycRpLaORmwXZomlN=9wXvC$7m$@dt2S&<N=3%p>--<8^$cCuPcjkEg{r4
zgpF&?c=N}UXu)XL{XXc=f9e<d*4ds;SWRIO#2d2Bq%+2xkj`hjz3PJ**e4ichWyqm
zZwSqqZPLx`iUP3=FSx}^DZmS;z;$UE#5wkcg6r0I@$Tbf8(i>f(#9=Np;wGJfl+#)
z`*R}QYx9Rl<lI*{)Gf*?(#wI2*VJWia>^m<J6&W}0#@)t2)(7n4X|LH-sn>cm{Zd}
zI;SGy;74U_&v!`YdC%KzOJwR9c2HnkVuzzzALsS{Tu0faL=C>swZCO**#m=MV6@@j
zz_y7h`)M|Vg|8hmye0WLzZ+}$_Cs~wBgDzV?ZEs3g2nadLw>m-s)L{h$)yl~J;;4P
zWFGi&XS>t>AtS~)CHT&uJB0#0)QU`&*q1~`$ms8M6X+vdN}e8+2U1OQGGGWTb<obz
z&Dk%|FsU=}&@hB@H5fdv3;O>Nc<@Me2B6ud^;6t71*)CeVouji6=sLZrY!4JVCq{$
zo>o^&A$IXc7jN4rF8~1ojR8aA;9*4sM&&e<-+y^Ja?UHSgyZtx-{~gIRsDtmQ`!l%
z9Ct*nV3<JgXaGuD`}bSCVq~)Ru0cW$F#{4IzoXaH)d^Od^T3mRhL7t6ZlhsqFXKDi
zVfZAB|9~&(QTI{G1ym4|t9y9J#!>4+E7wa9|DXBbsFGIiU)>?Gz1#+}0Ph`V6;eQ-
zJJXBqUQ_T>ZM6X}7(i#w>eGecYyHWOyPD^k9bVj*YWwv%vd-8s*I~kWpqvEh?!1Sn
zFHvkOl{+JAkzMl_c9_6G3r+y8<_+;n5xTV4auhurxV`b6$>Irr?^*G$2k;m)<m!tZ
zHxyqZc2=Xv?_~+!29YNQ_s<m^9*+`sR6x~*x9;-&P_b>KQaK~DuigiTLzMwX9_)K1
z&6=}R*KCIt&&rZ6&weNK4hMFxm~HnuQ9V;r)A1!*DJLbNfS^Rg?ob+teKpbSuJvI1
zXgWvIRVY*=Hhh|#kwpok+B{fg;Qg)n*WF?$!sF4hKqD%o)2nH^?1}>X-L&nJBKZ}#
zRU4;1_Dp!!3J^0WLh{dWTdb}ITjf~eZ#|9jy0%3|z{+b+0kTcDEg}U8B^wZ?sAf+!
zZ=HVb#(9biwDdpQN1y#ZNj>UzmKANg8Tx%}oCe80J6B2JVMYpqwZ=3}G`!qwb-p%_
z*BT=$u}IzO=-H?x@Hm%n72fd@VGy5KV17`Zgj6%I0R|%&YC3PAC+C~@hudnk3$Sp<
z*?ddF_d4@lk<h2qsAkInD~d7WyLKu#Qo#OFx=+fs36kb-B41JsU->K<#O=wg_4ht#
z&=%c$lzO5qlm0RdR$}m2Z;UH*sYAavN4>ML4r|sKjTB!DLg>l2rVizsF5)H=r;Ka^
z=STkt9)QN%4~MAZ?~XbM>#YX2g8Lrahown3MqCV!hPf<%lc~RwuRtkHPw2$8YLFR9
zsPxJ%F@fh+4Rx?tCF5(Pm0$t(OP3fXaG5_{fNNS99UU<a^PX7MbRZJKN`5e;YEe$&
zHt2-p#O%5}$ve|E?aSvNLqk+~W=Ryf(wn?b4LC-ak%foR*sbCC-2Mb-Ccl52TjSSv
zAGtVyIx}KLw@$wFscPE!!D5pzOJQn0YNCF@GTy19eYO!qjo)z)=vEFXl37wEXbFVE
zY<CUSWqFoh-GWAI+v25MWlOH2WBO?TL~<p>|JUAWHHqVQ!|jbQ&9Yph=>-!wm>RYH
z0LOF>DIdcR;puJZ?MV=f;z=b}abSMeww{!TXl^S+3llKCvtOrON8+0?`e?MMUiYmj
z^CYUkc5T0+Tz5H}Y(D0n_L_qxa+?WzM6l&013r}zSK{?>xVU446b7!Ao(mb#RAWhd
z?3|*nmj=lseY^?93h7<pq4_yszu_#!QE9^cB86`R_uRVlpTCLw5vFBH;d|XXhZ{~a
zXu{wW(oOL2B;h9s@wV7mfDdGUb#yf)r+#Pyg@0K;{qy6RJE9o>-O}Qsi<t1*O|@nv
z1hLrnp?UjzLipOF{-Buj>%7?6Id*ftT?|_o?b$ll`msLR-L03}`$q&2<7#2mHq)N~
zn&wqP)b|!ujD$Q@z{OL(DGyxyPtAa7OJth*7j2h+RP&nCr=xTQF}Hm^Abh&^GI5{J
zdyF;tqR$VGHJ+P25Bn{P=RI|fFL{yAl)u^P=uZqF-~(08$v)8t7s6GGUQjm@!UpC_
zifyo8sP#hCZ18rH3LKyg7VVU<<S6BJwFEkrBKsr}{o13~LSzZglan50Id`wJA1C@W
zVpM@fg)U*zrcD!`G0x=7Ov(6u?f|RbqKkgOsZ8D1sBc5w`+ebp@0ZHpZ{qif-F<aN
z>QdO>GPLW@wbbFzqxCK;JP*Tc7`jFQTH)_cD+>gm($-~+Dj)H?Dpx37UFuVH%y?0p
z2DI@f3v^E6DzexBGxy4z_(htniDw*C&2&z~2kiCy5%VG6SBK<kYCKs+Y8;)NJv&bf
zXyEcdG_{K|Zb{PsC|vRwLxmYYUbpt$$lf-)sxbkDgZ81{qk<e3cn&wv8?rx&|4R+@
zV1?Or;_i4yW5Vz>T9anzp3x+z2@E30>?XCkt{_H<a<grc>|VH#ewv+~{geNw+aE>W
zJj)DT%lL!-+xTA7iJ(n3Dk7t>-Vgt<l(!1lXO;ej158=hdOD6bmNye&a>7F@dmHU}
zeBKn>ZONDE8ds>P6Z3!BJ}LX2-|gcZ-S{$5_74i<dKB;&<9hq~^;<zD5-p?OSN6Ng
zcoyR;g@BRt&W7Az0EVrN=6{Gz(-a5gd12aj+qK7ux>4c-j3e3lHB=lU@~0o?s`fAB
zQ*;NcFMBe_f|uR(zpC63u03Hxb}N&jE9L*F4r!|OY(V<(m`Xb;QL&h-DvKits9^98
zieh$mH$i8c<(1lkzVDBU@gxu-LHL~2t6%p<b*PEgZk`QGc|MU?`@W3yX=&EheymMV
za-4y{<Zc2H3MWJr{x+<Y&U0_n&W0nhs1vuPpWpnya~mGyF_W=U=V3XN86VcabO}ql
zia}x`ht%?DBUR(`$vHcuf=s691+fwmOg#=viq9v_d=-7+gd(d9UAvU*#XXb7Zwce5
z3eP$72Klach+?Ac;CBScfn^!B{ONa>izV%8tkCt6c1B`YR^7SDubhwzi2V9@U(>vk
z9_@~p%1A0x{zg(20E;`Go0OynFm+}B))CgcYeNMNnPWSv4dRxV{IvhjR4nnAy&uz)
zb{#JBs`Ga5ysWWFpr|A0@!?I#qv((ko2vWu+EEjmo8gM~t_7BxIdhFh)xPjI{6$3$
z`S8e3{^<5O@}jA8Ot{xeBJtS$oW|rs!q5q#-^lwr5I>45cO*JhDtB8fB1FtBvm`Sv
zhr~j*@C*dPzXpLo(~j5h(jjAkczO{;*DrSDPw8uZMK0q-wPr?IuCjduvQZ`<j8e(7
zFKF#Pj=31k?VGx|9&~CwGA@6E|JYb5GxwLF>q1m=3{_Ih-pf+DDqTm(kyQ$a08e&>
zot}D5Q5_=B3<q>X0mNz1g40%|u!S-=hyN&|RaT;LWy_|}=j8khHc7wIa91R>K?jYX
z(`HS(EIM`=n&8J6)hHNjO&SCnOV!_h4xZ?O0hB1cOe5Ac=xYTTv>FJ^_%o31MD@SM
zTC`4j-NhPH?=dnh4|TV`kQ4pt7@FE`&FlJR*;QU1|0{6GSTHdD7v&T?%V<fj*XAex
z2&10Q2H2a_2h<cQkdYLZ;g4!rreVw&8zl-<;=8pXO9p@|br<7WggfX*>cA7i_O~#w
zktS8cv={t~ZN`t4@7SE}M|x;21yzWpN1?tRBH?~aKvI)jh_fU5gD3wVV+5cY9GI8F
zq&t}<*4&k^AJpAZ{I0n8k7J&C4H9N`-TpvE6CA0SqaORSGwo&Y2iw%Mv$Lt5Okw}h
zLwvM5%`!Mhoy7Bl+A$x2(YXb#rlLx+DXh>cgnM0RG?(S$!Y%yoMnuQ}4*Oh^tOY)}
z>RKg`<Y!En<h>S<NTzvw3)?74s(>fR$*iE+ZEIQjr<c~GsP(t)_gNgp)_40`SIx@}
ze}aQ&!{OYKlruP*saOZZ^oJ!UdwYBTG*~mC>WZlYZwUYCBW@+2DJ4Bg8HkFx{LV-D
z)t6j$3!&js*3SA1t-feY7f}Kh@&ILow?UoU!*hb4P~1rrzImr=-(<4g?G$tOpn@+x
zgsEt2-^jNxA0hLl|6CuCUev}<a=u|v19LEvoc{~2)(bybp^#C{mfgoz;>pL=j&&XT
z<2&x#1gox0nn|$T{G!^4b(&?>9BM>NR>k41E){oI$N53wXpi!|PdzgE-M_Up&EM+4
zO=>yDDRR=t*!Pk*mvg)0ot|>L7!3HO5>v*Eo^W>wLj+&$W7@;6-i+@bAl|qw;C05V
zb`!zCouRQ?_TVRd1PDxOU9-@Y1e|49%KnLcE1nGR_R6OK&Tv5%`Qu<avhEsTdO5{`
zX^8_kbG;s1<P6+prbVzkuM$Z-+y86c>2-$Fc=8(_AXD{ZRM|LA=Iu@GZ*zeC(G(KI
zw(;rtbxjUtcD)Zt8(+2^sao*%o&-0aO?pInJLa;Hyki;w?;v7u0Z=5qXkD=)rxz`;
zPx@dRV1EsDoqOUs`Z1a4h#N8^Hao(NiC#6@Ue<Qr6q}#7(INR>6bdPip*(w$`#QiW
z#7tJ$UP4~22JnMzx|^NlZ?4n3@S*Y%3KpZ`aF>`}NnHiTtyfhfB(v@$p9R_yA&tZ3
z2TMJ7KbjZXH_?st(C`?yU+B7POrmSf7kcI*R44_rD9NK<S{FOae~H0IQpj6`NCh!l
z-ps!Z<fO)O-GGjmLpgAVK!;ZE`tDad?tIhfulSLQqDwy(mIY3~k%3~`9q2L&^~84v
zK7TO?nDvNE(nQ8^^M#LE)X)~_?0qpRw{@$dGfFram;J&wcWKBVW;sA1Ry1=++<vk3
z#||I|g=hAbBr{^JV&bT9yGtd|1orsducs`H6!h=xL{cL^Z@afcm+rmS+W|N$jD}O>
z;{Nx1{7ZES`SuhNFfsc_;dbwcKV3JEWgj3l{%VOGmnl28<iI*wchN8V`vE8cJ+i|k
zjN_|*-|bWA{g%TV*e18I0ugxD+0#oI{>0wVW&VI7Bx&FBsit=j5hs28^W7Asrm>k1
z@){>)*nqxLTlR>T<LMTpq>~2oE1+kYm0?3+6%W#RqipVW{x(;01-vj9_Lbzz-C{!E
z>&14zn(6{Y$Bb_!#BY5Mi-UmLK_S4>(m5~z9$*C4;#~!{2_)1pu~V+4=o;#ra-8G9
z3m}1Mki_eFd(2(c=BU}LsYxJ+IJBGk+M9Fk<{0r|wjo+$6bS2b+`0d38iKbc8MNRb
z;}Vpi&Wx7mV?%aJ4Bw^U@<*h{`DaX{ksf6W)Oy&d!9Lt{8bEd8j~rMq-t*>d9aKVj
zPgZhKR335(ZXDKATYt3R;Dqf%fu?sjZxiBpE+m^<Qms>ReHvdbLi+j;w2-P^gKjqj
zTa$K1iR1)3_17`KK1b>qC8fUC|5!jkm-HAJ%!O|ken)I4KW@ZAn1w7?D88(<7*97C
zOIg^<DrM3g^+L%Yl6krD^iz(_H+NWm|15rq@_iKM>3(38MD=#;kjXL;5WY5`PQvKD
zx9JPvZKUpj){R6osm4x&g5^>c?%S{0S_@s6I)W}tGc?2uIL<~pQCf&{wG}s;M*6-<
zbUEX~!7!Za$FND`>F-rSnhehY#IxrZwiB1=4TCyEUlo@t_cGmqp!n(?T5KBy&n^R{
z1QD)nRbQOlw)~JzTXv`8l`D4CSKi7x2ObRw4UjSL;=XPEPh0nmriA1p_7zkjdsC#7
zMz(HdXq>0zS_H}Z@bZ&2WJ=i-j`fN6@)M%_u!XNXP)7utFzcn}Xac=c<iqIF5<oB?
z-i`63<)vo(#vBWBuydEyj{N;$Q6rFuwY~QR`R;D>U18wm%VyxP;EDHT8>fY27Ln<5
zm;SlXGx)&t_*B7g?2i)LUuDpzghb<D)CHlP)0JP_*zcb`iYMp&-1oyA*43y`FB(<{
z!zu{}=*e$g%I)u>ov@4kRH+oCdH)DjjWsocOaw>ys(DTD9t6EwO5PpytUtsAJ(<3`
z_yRAl(L00gbk*cvdwQDi<G7HCV<(8~ciIgdqR((V<X;x^&6hLgWpG}t!I+TypFi|W
zaiEuELATRp_`np7Pp{5_&uc)h3PfOvLihG)X^pcYSL+rN0^p~T9x~NJ{uU$!ZXJ!(
ze!75EQEViV{AB7&{Q4eiLp$Eqqq}vNqEdqpuM#?#1ZGW6KetPc>wCTje5B&eWoZ(0
zNuNPQ0JtHN?{109TC*dz@C;`?U0um$Zv9cQjKgvt)Iq4$JFDLgnQ<5yS)?bcrEeM8
z)6l^_qDL0dJ+B6cH=pMBGiDGBbQzRUf>!!+dpM7FAiSj;9@n;)bmtFfr2zXOisydm
zGO51x-&=Wa2){*-`h7kInKFim7-)Ot2VaF9>i?fwfTbGS&pUtkx^NpqX*TnHB2Jfv
z7j52eE-G5){@Mfw?PpEEDr~U!ZlL=0SZcvX=J>m7Y0n3%uN=D7gp~__Ha4PDdI@1f
zzLRM(MP=|{=JGep_pWawkb=jhAm7XW&F=f958$_>Ya3%JEzZf`p8K~(h>sfPf!%re
zRb3te3gQNtQ-mfRARteVI%<Ayeow%Y7s!PFmT=#c#cFDQe}6`-vEHqf*WYjZ)TAZQ
zuQE>Bc5bGHV@*8Io9WE`GtWrK4wla^1JjNe#vyhQV37C6Y-oulW5Da+-O|$AKMA8{
z{!(W9&6UR(s<(U2h@e;3x7#+3lPwhYAMk8J;0FC6oR}RHGxTqj2r@sfiCdD^rgGO#
z#jbF$pHP<Lkcf$ZGC(t4mfbde>JXi+jgL7y?IKPAA_jqR7Z*l|TSs;5yM15t+MF1?
zC2k~h<QfZj2S%A<s9#g`1ajELhHk$_O~l?>q4EGQ$3GQ(^-R)9eYa&2=^;vmxK8KZ
z!2Ff{iJMF(Y}Pl5Wlkv?74l(yY0=|X$f=6wx2D)}XGkb`Uwj7CHONvXC_K9<L3S3t
zh4AcP$ORZN|H9pmyUfKdQhlUwsE;)1#HF5@_3NZWMwdPZBJVV(KeUkTyRR$@zp;4$
z@D|ed)jM*~$@Nm}dr6WgngUZj2Nyn5p(VIZPX*n>TLMFi>oTASQ9J@O>5YB`I#qH^
zzA3AvXs%oInrp^oKUFSs$*cRjDxDG;1(_F+r#BN2?(=)pi*{eL)>p&V@55~!T5ivH
zVy^uF?sCVJUiL*sc~xPJymH0A(!*pFs&l3WV_{AdXSe6xz$Q#%VQ4re=qu;zEsM_y
z`@1CYX2;_{!+gOHcE960IWeU_gfzA3z8-vh*QbvXVU%C6pe-iS<JLK6D&hBZJ36j(
zL7kazdXJ*{%NV0eb^(zG&9&Vv{rK2f4DAKduIq_&yU|pVLV?vb|22YpV}XBir7FMl
zNc3{~OP;udm>8%@<~)D!F(K$#H|S=k#-3{m`0|ZP@lyRHNPL^oauI1I1)KX!5O~x1
zbm@9wC8K<ag5!kWEfRS@;~G?zp|&t<boQHnee=;h@OI~A|L6AFv;6|LqF;>@SNTPL
zP*u&#F8xpTa%%SsoEIjIpt$mtJho-sKaxm9euOK3S$~N9Wg90LE65{qdzn_V7_i_r
zloNCs1uN1+Ni89G-eal0($j(;-~gX9b__d)-GEwQic7Y*LqFP^*bfZ20eWV2+^$(1
z0?4A21K6+p^(BDPYE<o%b|o3dVhQ13y-(=|#wI2zapn#6@s*y#rh;Ms;_)2D&xVJ4
zv^S0Wi-O_L1EZ1Qrn1Q^MUkd85uFxmz9ZEXqo)Lh2}tA<-7IptyM8E;sCN`hqSB7K
zFGtuBfvXD75=`~D9<Bhu4~##e5e+f5i2W@uF9!aRA?{X=zK@E^p08Q9t%q^5zvsof
z`N5JA5+Egs#yewg*I#AGj#H<ChdxhFQ_YwV2d~gLk_C++6$&gv;j$t<vk!ouWH(h-
z(S3KyUeUWuL8w-?C}Wc0iHT;(d{Z&?3TI4K760rOGc6cw;*FNmk26ppZ_u^Pz<b_%
z7`!lG^R28(mm9ABHsB`=n9#4kzd}fH@8#u^Fyua^i|!uFt-QC7fu~18k&F8DqRGq4
znwGJDKr}+VT=ho_)F}Oq7aF%X@x^+Q8apiZ^^hp&p%0l#l;8JN^x~H4Z2`LZx?uWT
z|1gA1N%Ev~Y?!cvrS!66yhEOhzhjvoA<kzXUNHYSIK)7ie_s)`994UH$?<f#AS4dG
zKi-iD_<R287{zJpoWf8g!11OGRC#kq8Wi@DT+eHNKsQ!8@t}oK9lqtFdS-o6{57z-
z>+`4_`N0(Q!!n-@t~wdbU?yw$chx40;wtELUOkb452h3EIFxMaZl2aI-DgptkqI~)
z$fEPAc*`iJZ-I%Gx~PMwuaP+6z=L>~B+Gb;RDdPMH=A{69P@|Vfsu3`7fgT-)<q~j
zDd3g|;|BzUrpyGo+x>2F3r}@UT*S8Qs;<Q1ByBTsRdB7y9bbPG^3T9r4}qlnh7XM{
zMk%QQO`3KDg3OcOq<ZO$0l*-`SEPrH*JKBNG6eJmi-zn8H3Fq@Cm)YyJ$6Fo4z0!o
zn0ub(z+6Un{9z1zKrDJ&DEm97E?U~H4Y55N8=TvOhG*J=%!-nEozXn3Y{Y;m_<Wgs
z>^ooBiX@)@n2v6Ai9@d`+g`V<ThJx@9P9pzgz?M8lWW!>@hF%xu=|(&P~WgQRe`Zh
z_t$m`uW{w0+B-BVZhDugDcneAbpU=Nvh+U*-?UM5wb$d$gGe%%BwCR8i>rjIJz46D
z>EO<2&=jp|C{?b-3Jm#^6Eo8po|=~v!`|q{lD0zozV!2iO^!u89|JJ7=6#=W)l$sO
zkATP?oIm#k`(c$4yn$Pb8gXKGVE2WuPJ`wTGYN-2KqhU?&p|9LU9m24+YNROLLH)#
z({Z`%7KNWVQ~FXtKkeWDq8a*XSBiz;Ido*SH|%^nex$XIxty*k(lPPb`Iy^3SZR~I
zM^cbAUz<v&yLj1mVv)7fL>dHHJYi#qt9bjji=2?3-1)t<e7|o@Siwi(#R=G<UY_or
z8gqZ*nT)+R0`^be6=bn;p+V0xZO)mTo-~@M98h<OkoX;KrjTE$`<bH|D1F^7rk15B
zZnQqEL1~z+k6}IXMJ)}>>gY*2{&WW8s$f8RD%!VGT#gD88Zpx9Oxl6G#UW->BfYTk
zc+uY`-ep12asOa$j)WjxYo>PxB8jCK6fDZnUQ~Lp3b<vnOyXtprjtLioFo_)oer0x
zze2z}w%g|nx;fTV>wY}0d%c=DVgv-<16UqdP+dP*L7GvMsMEhRj~$%Afq`H<Tw7h*
zl4T~kpqfe=a@{_PwReD&_PJlu^oNd`a=$KDWkf)T5I(=qvnX}>NpUpFPk0Cb5Wl<b
zJsVqWqAxOK-;Zx-8c#!v$awDs4lnM!LRy$+a(XXeZ^&b(dAOe|C(Zgg+uEy1k7o<J
zz;L<&u^zc(z(<e=GU%kjdL1`<l+|=ccPx<tN?e@lGod#URb2=#KZAAB(+fG80^}u|
z<glyKbflk1{u?n_QJWW5Pjp~HX62HXL5%*(%jMXN$Fk3I<Lg4xS&sCX+h#FA16iSa
zcI8VgDa*W4CUyW{BKXh8vyqs;YWV~=!m+x3&+XHPXs48<C}%Tt@JxMbL%-T@CtmMD
z+C_GsdS1wBs0~F0&Cr}ETUT;~l}u0M11JeOkCTNaOWN{DTFN^#rou7bz|LINC-sM6
zP12Zij<=OZ$kNYhFUJ*SOu(MUDr_5NO|b4~H>!@Ayn|B#fAaz>oTAvAZHu*iztJf%
zq9QTMZZRU3xju*37Uv3N!aSQ91)eE#Mxp~tE+t`ub7l=2EChq74J&J9L1dRCYqO)N
z2+BQ8?QeRg<AmJyj~WuV``GkP!Nr#opZh*wOPCf|kv$TbF7!*gDTXN|2T$C1?3)y|
zEN|h4pxI$<#1FG^W0S7m##=dNL&|m6eX)+SkB2urwY#ozT&TNNX`wYPpK{$^mTbsM
zzkm#CZc~Sieonx{38>K^FyCtRttJ;WQ!dVG`|2=}O%YwO7UN`>(T;ZPt%tk6X%;xq
z=bKiu)!Xan0W?a&0ux?U!dc8c-i-{#I;@mOFezqb*G;Ce`gv}#mV95{bGR-E5%IZf
zqvBYXL4m8%N1P78s)6u^$7MX*ou&B9e_hW*e6cg%K0RA&yL+b^6E}GKr%#2NlDr8c
zhgA4&p0oRDK4q@R6h!aggp?2#bXC31R7C${m8kLCs!nwSVbhK9d&XWjvlxkrj1xJ5
z8mIwk@iVy+Unq=o|K)u}`&;-|kdZGu)wZU-vhLKjE%kjk8?WU=+7?z8a{=Pvq4TX3
zikSTjd<uyj5I4!jlX>FUVc#Ymd{}TjcfT-NR(M>KU(^mk=`v_F{L*AHX9#wC9&B{j
zuHuA0l^Vd=!}V!?yF2Zhb$G-B8jo{M2O9j|k;{BvGx)i62s@=h2ybMHC^mBstLfda
zSiir^_bt?2Y~=y8Pdl=W)_PTKJZPCzzp*ie#o2GCu}o1Wv~Ry-%wCJFe0h?1pbYJN
zUFvyR0m;FyVzSyEFxv-3N&C~dd2Q2n55QIP_9fP$YzSrMGig;lXe_?*8L47Y;O#>R
z!0IYJ2r44ErFV6wqjrl-VG1z|9^Igbc9y&rc4XD8ohnid#ksARvxBH3Zdlmphry1s
zOU^yKk#^eIj8tZS@L*fL%L{&i-hG;iG3FO(P;ry9tO}@_yX@QgBGqw7+3$a_Il07*
zfJuOq*b}P_b{ot}BvVgT@!znm*6nw!)^&s_0eQZ=7U9GX=#*Ca>=Wv9o}|{06&0Kw
zVr1Hl-pr)wF&SlXBH7%c*|J(yiV6zn8%yaFB2|+8xW372m+ThCXPxtB@p-JyEJO%#
ztFBD}JqUf}fS-<3kf9eQ98M8>#Hkp%jpm|P()W)73%W7Bo||@M7$>a{x367ahtD+G
z9-Kgr4<xSeyVJ5lR9%EuUC^)w5$C^X_&lTYGS)ImtA<bHat_<Z6&~+9cyH{py^FSh
zZ)$Rm+8vM3TU*U?h8&8OqP}lQHm*(s)lxMe4Pmr|NDRTguQFy&IrfenR6#C-O*E1H
z*x)v)ln3|fFFKxg3#%<n#}qVC!C_T|hcLk1{q#cMUPC|~mI<qmF!3+L0)){~<%aiK
zft7mT&>8F6Jodj#7doRW&nBo035^3_J8r}Eszga#<li_W9EKrA@=Z4up3&nAN&}-`
zTD2(HQyXKTt}fh$EYn4NV;Vs%sm29++A)YcDK!~i#I~>mw6i-CwgMA;kj@2oZyp{<
zxW=oAKOC55H=dichQ1Tyvreefh^snt3sqPjk32$b&U@#!#LxR$J9sY$Xp{j-XG2$M
zUT~fL!b})TyFD8e#D0j|Hmq)2sP$W>y^$~NBqZt}Rn{&9CSQ37NTe51pw9ZYn0j(Q
z%knb;__r{PRxuG?L#u>&2}t4H{vrCsMej&UOm018Np$vA2UnVg$Mz25%Lm<XO-0&3
zBHv;7{>l>(u2e@PB+k4_VdlFck}hJY*jKwMQWFIB>u`MzdR}-~gO3%E{O14a)Ymd@
zSBiq3kr|Nq(4{DR0C39iRk5Z0@-d3tQZ@XuUDFSF^G;>fUn)FetwMh}xNq>*CiU%W
zB)TV5((O+?4eKZr+4T5^ilX;+qD4GgN-fk-P5-4$QHUx2#&%)wFUbI}AS1ET4ilnl
zjCdpU3H}_CrZ_GBk#cmAqie0*eK<$gllWp$s%;}Zkd^g?SvQIRol*F*IaEYhW1CAD
za9bi7@amOmbwD#InWg~8TgpNeOshiKNruJ54I13P<!`O4n=I?_`1teb=m+u0hI#06
z+`x}H2X~vVOW8NsN}KXIf=3f^Vom{LwSWVQT;KFKFXe`^od}0YM4o?_UgOD^%}c!R
zX4pkUD^^xs$EPdXEMvzRIu*J$cHfhz`Oc+;g=K(DOb^(!F>!6_gPh52Aaw0tL3vLW
zjd{%J(cuJ*dUbiHU2~X_&++&B$J+^L?zRW$gBbF3nG%L?cCN1HMHwzrmR2DoP;+Ef
zmY7dWnx*u8gYU8{+iwcV@{pXCtQK3BnOM{%dePD{eJxH3FlQ(z32fHz2yUEP+htsd
zSa(_jXJ-=fWd`tc8z%GTR2#}H$KQvoRa)^Jsl;1HJa%=Dl()sO`PmMMZL~nJTs;c=
zb22Zy;xs-{6+{jNP`p6h9N6a2E4gQl9!&i-v<oXu&GrE_XQ5AU90upPVG9t1l=XT8
zORM)AQL7?CKSVnKT~$?%{-WHUur9TcGS9504bC=%;zue9Up_^3sMPEl{ccn@t*EzF
z!I49HFB9G1sT9o$YF8^J8CR{3nY&%~;)||oAa@Q-HKAv}QD}K+cq|IsSts-hdrh71
zvS{+oZ%Z)Io?lZD7Aefl`#^)g(aq(AC$vpLT;xr4L&sDONOn2bdpB>F&TU{^lQ_o!
z8>%|biqapAsqa7N3iG&mfXCM!#Q~Oq7S*Lib_Iq?kF)j(WscNGN~I~{g!}-uEagVA
zIQeA7pF(m3Gm@PY8hs*Y6tf>5D>^ioKj2}kQ@rcu!BfLdMK@}811QMt>`NMQu0DI<
zd>)u#+7hn4ZvEcYzCYWvi+4Oi^D^UH`@=-MaapJMU%ADNWwbU#{nQ@k<iL#n&W{%w
zx|zaYW_*687ZLXn`59-y<7cqnAU;EPaef%6YR6w8TUlXidLf$QMKbhBk_SfK6`*J;
zadhi*h%=d@j43yFL{nJnTR1$iG%Jo0v9u|-?Z=-DWqb0OU*jJ)|C0js#1CT0orJcb
zLxefnxiB-s{9)iGuRiYzl6BqQ!1B>(A+Y=t6Ba3d;n?pCy=VJvc6XL>o@u7qR_*G+
zuLg$`t$S4)*hdNW?zx|!5RQQ+dqub4)QtwcmthO*jfGzWdC47sMVP)u99Tpo`F-p6
z^B6DaLjcA0dnY=9?KA5wO7)ZtJH8!bicoetKDfJ!f+~V5$xuIA1|j*8Hclbw!IbXF
zV2|$;)VVW!h<>AB5+EZlF{eHFfP_(gH07u`KP8JlA6we>K4Y$Xm+fehE~UG+)%?c<
zumRL%Ms}C+YHMBag{AF);(pRgx5b!(pAbn=w}zk7h%=VA3~9q!VGVDw706>E4&Em<
zb=Y+<l#hA%m(%a;a-u)X#p+jM-4IEWSmp}(s&Itu$!|gEc%-H{U?_p#-QEXkn%hwU
zC?|^x0?9_=_xAP{YPq|6h!O7gVfO$R>PMYsg6NWGMBkKJflVI(<W<YQ@<9dyLUtGs
z?-a8)atg--CYo0B6=jq`-Laf#jEuZlP^TQW&^f?jTH;Gs6)UH67asu=*aqot4a|ZN
zqjw>oJx<M2A57fA{|PFsiW<;-Is-PwA?DzCw4WVQ9#~q=<jZMc&y)G(d}8NbX3{^g
zYhXPU<1FShe^%SyNzJO`T2-};9A{hii*y50i8vc)lLf~+=8b@mrT^1o@jb*YoyM#_
ztEJBvS8!1m1w0aF+s=EOGi>!ohQiS*tpUoBZyPJ!ZS9^=S~#}NL6vP?2>NkIL2D%h
z!gxX}E28aI_MYHvUFDu}DPZA9Kyqhv!iB@2A<ru+H^Hn*OK@otT0s1HS%v3PwJ;-!
zCW5wwX@5*ZCN~@Ass4HGuXIaC>N?^LzDEngOGP`&i7g)gN|L0}Ic;tpMpWrH84TD4
z6ULIQfqdV3E{<Kh@y)1)&VQQH&5i1^Sl)kTau2#|FvN!F^xgvvBBSbd_mG?=OXMVy
zp&Y-hn7Wlv61+EejbE{=$G0Lj-{2o|7Ujl|>JnFUk@djBX2mi|vYloGFTHy(p*oh*
ze+v=C7&Zslxzh}xpEyhy51ocMd-3mI)aF*wi{I8MRPyalc(5nj_!g~?Z74oYpy!>2
z{`lv(5lnObNzo_r(HC@dslWLt%9qhhu(BNB^rOEsphcR}&W-A`O>Z?%s;XSd4&3e*
z25**X!lwxyga8-~3OIxEB(Ah5H;i($IK8MFio+H3Nhl>YSzoQR?j=WgM*p`kp&r3f
z)uK7goIfZtKZmr$V(@l)87hYERq(yg;)Gvi+Ka}yh&twJV4ZR$aq2XLy>oTO7)Q4w
zFPWPbo!nB+u80_N6Q8sKgJjZN{3OdUy5V3<_OKE=Iigg=PUJ`s{30*hMKNMBdvxuk
zzF-w8c+3caEoL*AGGxR=>0|PUvIL1@KTk{V_iT%22jRVgEf=-&ct+;ayPf!1Mbqrt
zh!1l29FNXv$|1VehQ-E9z$lzd99g?rI-sJ?z3lPx^U7M=lysu&wB@0bcb||yrp(6*
zG((q~hMxh^#1d%sN?IEwryNX|%T`9`hYnhtA|2T-Ui^!Q^x&JFRJQm<?kn1yno?})
z$NM9)9}`?iox<k61YR5y#rXAQWfD$X-CntFqTJIeBp)vCuzC~8)V`V49CBd`03$W+
z^Xu|4Y^K%3aBt`s8FW+f_}|^$MHNI9{FN0B?JQM(6s6^p8e$1}f6wsrD6%v=0w2U6
zgc_eTqCbm1wD#d=#f0r&E^+)moF0O*I^}ddUl+y!5)R`7k%5Z=LL*Mu&xWm%izR$j
z=#_t05ZOV6IliY3*$aq1PV5w&TNOuu4j+YKFEff1A2r`dvv~gSyW9!rLuP;U<j6~`
zk>IaGZrCVO7O=3{qz!Job&lE~<z@YINk(UMgN=-5M;E@2jKaxwE?LTc3e-N0tP8k^
zCq}xfor`I=7(aM0`#a;UTP6%+0qnn#W!vs>mz!oNEDqd3X?f1DDgv%e4iFB_nb~ir
z?aUR#s7`b|*6!k_s`eY<8CBi39f`7sK?_~7pS~&Lt{wR)G(!ukM+v}&Fww0oaV`O*
zr|!E(L7q=S4FY_s$Zp5?%crNO%T5+%PK3_qFP4_;nYE5#^q2{;anQ+0hC^C&CpyX4
zq=uDN8?6;-oR6%B_>tWlJhAQ-0**yb7%r};AlzIRg1Ep$Gk38^sdSH#c^M`QMK=r@
zQ%eo%-MHf6+qjZ!!F1V~03HGgGP>W9a$n-f;+?tZ`{U#;250G(Fc}?g;gRp2W{4hC
zZAj63NAHjkZgIGDEB>G{cvzY}?Q<1wL+kBl(Q&U@S_PR&H*h0e*al3x&*oYG8ihC>
zrx6OKm+Qep7$MQZRNIA@c;#&t5IPBi1F8PXFp>VuMPgnd>!<05Y^ZdYe{NQ1p&h#m
z+n<|#{H+VjgD*jc5%?c_U;Y~75&`<f?dyZ<{`M54P0QXrkSBZOddl&FN||{E`N9cy
zeh>XLuXJ0*ySgFllF}PC1zKMWetn1w4-Sa$+Wz$@Blzn-qX5Wk5NHdW@sh@D!Pbp|
zjoBMk8Ra93Iegyu9zm&zzzu8=0TrE|3P9KDBIs@WZewNhbQ78CPq;gLv`tyc8ecXj
zxhnL~i0a<%_0&HJ2zAmWrntp3la-ru0&zqnN_TIB1ouN11s64ZLE)6Ar}6*ooN_Ds
z(cXWJI5)g-ak<jPcyU@?FEhVxYZ8j%kaI+!597_9Sj3;dE-1&LF|}>NkQa?;*BA(}
z94Xg5BU5$!W=T;&1e=HA(9i3INI#cOLksmDN0o}gmVI~kF?u{fEhaw`>$tz!0`S@l
z3<L?6l%h`}XxUvpeX~S$>(MpV;4Ly&|2I(>km*Ya>32m+|8`{Gmf~rs<^;0)4*NrM
zcwdrFovv)4hX|PWuD3<I`IS@lM<))AbQlWm7KiLl*wK3+I_^i<5j^BSjURiB|I-+T
z;zsnJMw)lva9E@4PdLDT`~2_I|5u;?f64#nT<$8RT=xY2TT+unnc<fv48pvEWdoic
zqZly@liE$+4wuwOWM}D&=lJC!WuZmY_{!M6g=6Iz9)@-H$?_g@!70ihtS>5ZVly7Y
z@|vhiIV+Btp#9<3LklU+|9Lp^iMFO?SMTLi!V^v35|&KgsI(++Vwj}^WqV$uL%L=8
z!t3dvJ=L0e3v}w8&`MPaE)de@_9rFPo5cOZJf>`!^WlwIL4q-;O+(@3e;@xYlMnPp
z*EHZ|;ra*g!=Y)w4VC!)7L^I&Iw!`}{0THpx}tOhL&XzaA9p26iIaAN2NdG#jb`?p
zVu3VCqmKWCe_o`q?FPW6^FND=oJQ>{b<oXCP%NBKU@G1%%EH9+yXhCjs}BJ$$X{}p
zjcsHNN=!f0|5WGUz*mG93pY&Tah-Q8UuuUj78g6>9TUj+seo$7I^;+0&Bd184~q(|
z8-5R+A~vJrZUwr^Mn1)KKP~ahbS8!!@|0;2nj-qVAPoK^0j9{o-Pw*5VSxDy9lytP
z!}$)B=zh2bm6$k|9IODL@uzFd{~cn!8XqwrMRL$jaF)4a{OH5Oa}~yV{yV}1SXLlS
z>fx~nDxwge5|O3e>=Wu4euG%w)t@8dx@to$q;Vl}*kS|17~xkCAZ_~FJiA$-M`Nm{
zVfL&11dkY^b;!}2^EtV;>K*tTZ<XG7)*22lWel@Db1VnUzu*3O@^$8HsSe}+CT{5i
z)fD7T;us)dlU{hw04XSpY_g%5AD|x!jwp`ii~5X_B}m}DdBc-o1j<N|3`d-V|BB`Y
zmA1OLpZyfbNU(F)W@KM8@@y44Cg@M}<R``;jd&3Wky-}|PCeC8xi78c!`;;pqZV$x
zo_q?wS_&dyj3Ed2k;_DhWIDlt`NKE#Q`t4si)SU`y1x$6IaV@|RoV<kzzgQs+~7h3
zw&$wQmU#IDZhE`ON3tl%=@RCZR?T0cP@{4ARL5wgRG^c!?3|o+6SoXKwG>JJrxu_a
zZTZ5wr9FOyOrDFWs+l{k!q$}4rb1NE_Op~W;Z`7kske*pSpyzo%D?}LEja@}7(9@2
zUv$WRElWo!wJ+X<FblU*(unw#*E)YpDs&mZ>D$bOS8`%BHD79C>-+sHDECE?QEOqS
z`_1(A#`JX$_S@tC#{JN(>kA_eJyt4}O71?C)xGbNzvXP1kYZPoT&rMsm1bTH{W_WQ
zn`;crzA#<yMFIqT3JdUL&FLmbhmbB$eXtXxV5SDj*m>NS998QH3)325SDBz3TF+N~
znnI$a*m_r^Db_W+W0X+zX`(=|$=0`OnE_PU0B<>F(c>`wLN$$beSD&(f&k19qOf~b
z_4l<U?y`RUKJ?BLX&;&TL`xvcNOI+4?da0@D5HXwxQ0QC&|I5?dx>058z)iFE$7>s
z1n<Mf-Ru2R%pJ+9LZKqsaq0BbOj2*CWBtq5HFBsd+U!iBJjc5C^<WFcLCA|iM|@7x
zgiZ>!A+AKo1|!oV+%?NA>$Wu>jWuj>T%LN~;>w#!qI@vKJ|R*`pZ=1dAX%5`o0-Xb
zQ{1v)T)pVkU@Ek)I*h!9`vBk3@<u8yPLiM3+6(F<>BNMPZ>ogZZUt)5w`o2A=JuVh
zlk;CT-=3-79`YrM-(ElO`MoU#b=Dnj<Xja8xG0usu4A)_0o`*_L|TO$2U9i<%$$}o
zTwbr@1UK&YCS-5MvtS|qth{%qXgN>DAtG8_mJo^=2*iYAJ-~5884x6>odC`0fGGR+
zKF>;QT~ne%FhY)YOt>eWpzSri9%QWPbph=ul)}L^j96kJA|8y9Fgx)#W<gEsR5!j|
zQ<5<$$e$wDCJ>iqkTbCZ+kJl{8mMU}QQz<qf(WT$F%>Snf(dH3F(<sR(w|a=P`4s<
zyuFIY{}}S0n2x`I<nU8!HN~#%SK|r+MOMkzUdja&Am>ho6AMI%*$w>P{&q~!%b;&O
z@9hana%>(xDXKjvpDIt8Nd9;_zz;110#+)rZsd<nDh1EBC}*0*ls-&0BS6r$x1~M&
zi>rPXG{%<(>dj)ss~N$&ercjK%s=5ykJ79wR7tQ-=vP<?yDo~co}A|+n>G5+?YxEb
zxVsG{m^tPLzWX5H46x`#JbUXy0(4&Grkz&%k1Vq0_e`vH_Ex=}2R&cNFt%!Xyw%;s
zbwuh?Ad!^O%{gKh+avwL)lQprC$T_d?x0f?c6HbLT}sk{!Ma>crkZ5`mHHZKd>olx
zKaFl;W}oNIHH@zjslt<)30GTl>j$M4zdplkiy-IQ!B~&1&z?ar-rZGJ-tkv;{H;LU
z+(urUQVTjbuqOO79wl^+c5I^@zAn#1`nmizlJ`jJ8vV#Iv|WjmY-@yTsn!mRWpzT+
z9rZEimS&=6$qDqu;CNz}L#*LOWxxTzaa+Yt{l7SS%dj|_s9O|*dypW(gS+b>!68_1
zcMCy-4epYl!Ge2$1eX8<gAMNP4DL1%YzTVh{mwnlbMMc4{#IA->Z<Op+Esh)wM=$5
z@$1pe6$&W6vZ0XPZ5`2ybyL@`@eez{YN(Cq$TL}M8B$@l0JL3d&h5Jl!KELG2X8kX
zr+;*HYR;co-2NHxiG}6QV-T*T)7>$mzrj9c7tN<cSMUnFtEea@N7pM{rWO@>s(LT0
zqP<C_g8ephx1!O7ZK3f|7pw7S2s%>R7)c7F>Og-^ZM(<LS!ZYG#Kc4=hibY<d`|#p
z*Uz_znOsp^U>FxWbL!vVE`Rk{GCl?<i6zLlZ>MBaYQJ$(bipLLo-Y^M0S@YM7+D@c
z9iAbUlHc(sCMMD0F+%BM?${q080l=vw~3dl6%Cm%P|gp0x(N}y_zXFHzOPmM?MhC)
zLD`E5!(D*`oiLDKCQUaH5#vwQ<2O$?I2a}mC%2ImM#EKFx#@mABW^9@tJ`O!%^`P#
z8;^fQ;1tl(ra$Mb&gIacZJ9qKsNS)im=}=3A{|-RP2#NPHwGUX#$4s^YcTN2a4Vt<
zX54!*D;l<(O@x$ZF9iI`&?RWch-?`^UVERd1*yI&;gU2MCTaQB!qLC6cR$tGE}S$4
zT4eIXFRm&mTg;pw!3pz+a5g*lT~<w^OZYq$rkBYRH7nlp65d+?ej2ozv$%HTm2e7`
z8%!0KbNtBf{1{wRM1%3{Lqkqqs#kBp<#+9Edr~30-StYjfSHlVE!1kC46uQ=0|*Up
zY2j;c1c%f$scwAEc}N&o(BHB2baE)KKSu>Rl&}JS1y)w)riYNZ1h;oQRZ9omEyTEK
z{kss+6>SSS?~=L|!#nr}W>{`i`g?2ZUmYNJmKB5)6a2Q$6M?+Gmz_2ssIa%$GEjNP
zpM;|{ED;LEF4l>j)WAf~;sH2$uGFEI9%aXd;{P<8_okwnn!89SZnc$vg&ZNJ=XK$`
zsEq$;=N}spT>tgR)owY*=b<Jb=Htni&<93f_&Jh$!J%ka0idHlVc;1Oj6!?)_R6Eb
z+1hEnYM69^STNJd!D)+5O0@ol5g&bf4Z)q|aQtRC7Vyc=?Zlh(xo<8tZ>45r!e{KS
zVSoOw6M#H+evf_7?aiV^-n%<;)4=rL{Gv@M$w#xV_YcVJ&$XXYmRE`kgWc=8<_q0O
z=ByIEgMxhFmi~k2z=X{7DvoLnBkf!lrz7wFQjyhLkg&9QIM4GDm;7zZx`*Whtu#FH
zc=xhztqOXmK@)a%v`daiw@U5Am~>z!m-snVU(YzQ<Sl$uJW_R2%B*Qkt@$H-LYRQ2
z6Y^7zxU}M@GRc$7CsIGJ=qgj(W5nOFz?<sBV{vKU6B!=>kZ|3{pP7ba0zLpX@n^S^
zpIpAbjj{cT&6>5Y8JPU`kj+oWn*y_-8iV{Jn-+#7#k&1JOH|Geq+hv@l(C)736?7b
z18l=mI07ij4}mQNg4;84Neyu>I<8Rv4f+;WGqI>~|L1Qk`LO~Kt+$87ni4Y7N<Kb5
zoe#)Q@Dc)q*9yjjfx&=;>J^#ldhqJf22^PSeiN}a;A-Qvh6?;7-|s&lTS8S4SlxQ5
z2i%JoL@nQU`Sb5D8NAW0vwu&dYwPeiC*VEwNiOTKdyO2L#5?jcXeSc)E~WFj;U%mW
z?d5QD?r@HEWKOq-cd%2&Y(W|1IaSNX9=BQhDh-2JN8)I6^Vfd)IeOz@jtdP9Rc>~V
zpoCSAX8bgQQ2&WnNO>~UZaQsQ#|txM(VrDW_Ru3ESM{+2>_=R<htCc>IxJiR3#inu
zBEwLoNz&~QhlYw5s_e`7p5;KNgjXIBoq|h1pA-D^+VkQaO1119|241l_g&&;2ZgdX
zy*aB7O(?z}hD=P*>#u#A>%ry%R7ZN&+8)<&d~ti5u5VP8Z9+2toEJfMk3=R~chAc;
zE7#`!en5g!E>z5|nO(bXeEz;w{S;BH$hiK?Y^l6Z1M>7r0nW9$@2by#+CK+nHLV3-
zb8%Q4xF#Zot$xw_uOIfw;$K>Y+n2<P=fU%!^R6HbXO;m&L2t56DGw62q+8+{GWBq8
z`&}87=xOVY-85(KSX8FGna>C*LgR`>3%WK=3_UznzsKc{;=DiRg7ytJGoB^gqo6K+
zqp`Ltj`Ucazl+f9Q=BjNr-gE;X7hSPY`3}J<~^;Xx#UA1)+p_B`>5?h^ksICfS9ty
zp>MnM67yKW{O<F!e<POe_HVhnk4ZsJLlR|`U%G!wM5F(?ZbSQ^R{IHG5)Ez@dT?gt
z5&+na<cj6i?Vu@I9&(k0U6dI0d$=!FTW?J_dTR3w<q&jzStE@-lctjD7;>F|g#wQb
z*qedK*!Vj*U0DVJecPH<vL3jZu1<1H1h<f_ACg+CP6hwO2v{@&bUk$HSF~ILVuYn{
zXUxSgmL|HNx?c9(Iz#sAIW}G%5TN)2`$2>_RoJJtri`&mI_L*q-aP-QUjf{6#GW5C
zHGQI$Wf)i~`z(%+j@ZTz5&n04p+;B^&HmOaIy*@|1K1H8&t({2;!ZxwTi6iyf{w8f
z+m_J<-iyC=Kbu*U%PZEQ@99MI`bsEXzucuW!LFTk^_>&N?xLWXG1kQ#o7Da5=5-JH
zDTISRahk-g&%%OTh4EOybL35K_iBuF2Nmd<=!Dn3R&|)xl6Qd;It&Q>{=NX~t$0Vu
z)>*_B$cVD=;1TarUeCL(2A6l3wDl*LFkUv_y0xhV5j;|FeFwsBp@y!tujGAdlh>~e
zNhMs)tdXI1`I0=O-FalI!p&gFlUwjz@9$YLrU9q03u8J$2Pz6uX?q8!7R705U9bt6
z&yES0%!h#6`RsggVNu;(35?Chp(9qD!qk|OR1xT~y52gTM0|58)_5Col@lU9Bg6ci
z7g-Mv5YbL<MFs!Y+Z<s=SG6t)N^1E7Vo9~(6$A9FZKRsPtISZscx*1E7d`jmQ4+R1
zlP^E3r0b>hp8qSX(bBHgp-E!%m5rS)g|zx}>QA5<IU`d9pPLip?hMtJ&q*6tQ(QeB
zSs)z2nXj>AhHM4j(n(Q*zcTqqX97;?e}|`kS1KC7?90ZA_UyOL1l@>puFq3zRdRKV
zp`5zAH(#^oIBxqlM=ok{ej($Kx&6_Jr$xU(SnHBjl;=Jbvq3%9;bv>=;o%_>JJvxd
zrKw>ro`?a=qysETWiqQvd%pDrYssl%hscZ7{9sK~%J6F%-<+uZaZC@Im#XvU2<Weh
z+E?Vu0_zRi;$#ANn$4VBx}WT#&ouUfjV1QSYsvtxekx_xKZRP0j``5eEwp?-X~Jn5
zaCPbWVig4M@h`Co1JBQ2@7F9;9GocWcDhF8eqQd)a|zBhZKAF2^83rG2n7h&3-hgF
zm<3vXa&f65OM4=241;}ZDhihU<q?kJ2VYuQ`Iv#cng5|0Pdz?2-mbPZiN9sGDv0XN
zq)jfGMM)98DydO@^3BWc>1L4*QL5ALx#LWya34$CNNI8O0jkOG&&$Jv#-ZNd^Cfyn
zQm;e(IYGV7WlW*%dkzsY{k)9YsD-d$eDtP5*Y8={1n(OeT!bOrc?%5LlP>$_K0o;(
zYg&pF`Iv1V9Kj#L1Fj=w-@V2?j`g7Jrf&OXFK$YwU<JZA6U^n|O2hbvN;qrdq3$3>
z7r3ol-?&vz#+y@ejqNz&6mwk`r}8kC*XZ7&HLxRgiap$6N7b1+xB~TVXzb`PTHP9@
z2KkeBo2{=4LVbS;8IhQ#kB#x;@s84ga=w+fw5YkaeBN^BR*Tt8On0kjaW^e$!5axZ
zBalAbJ`?tYZ-Bi2T~<@7@^*RcJW-BSgzTjo)O>vJA2mGBbkKgv0cDaY{+)w#y6^y6
zxvV#EZk7GvYG_eIvnsY`rjFvi;gTp?r;$vZ@sozi6jbMDb!C~#@59@|@a2Us{9)Q`
zDn00g1~x~m6XHxDJ;5F^IiFG66$mRxG)@uEid8JhI{Zw_XTN15D1^nyoSJPmE?-jl
zjobhG(I;)gCPHc)6cCS+K<C`13SCKKx*juie~gN#9gV8JF_b-zLi3)!c^}M?cU#q(
z<ijLsGw$G}Fzb8%Z-J`o%XsST&1F%OO|lrZU?Huk(nxrK>Da3k{EaUJb*5k6otZ<@
zI5}C_?;#O+W9g8WN3~X>xl+sTx}i0Tae5ps^ewhw03`54RzQ{Ofl_G-l6zA6(zn_-
z*b3O^lJkYpTlB+f9W45%IH4zxPhYWrJC*e|#godJR>n1_x|bT7Fa6TAs`Fo2wkum?
zf{|fw#X)jXUDSs6lhq_;I(~Abl&vm%O2Q)CL55L+Cz{sADJrz^%)j^DI?*UCqi-5|
z5mTQgcoTp5yvhxM93a>k0`N;zy`x(cBw992A}74pON?M}Vk{=K5!HzQs`iQCZ6XR)
z*xzW2H)!w`nAG?;a!eSb6cs&Tgp|Z<XgQWDfzYoR6yK@B+JTEYdwu?JfIk1HJ<QMt
zK`YYY=2QPgq?L_;-h?w|&n}NF^*Sldu*H|h*3q$(%i~s063~W-gGE8h$A8<S2NQpJ
zAO~H3qh`-`5;&91yXdWV?KgW1d^hXGl?uiRsYXByY|oJ-^&IdTnu)j2@buN{@s?UG
z*dJ78B>5s*u6wr7n$c?dvaHoEsqpV?%2;qkler`<QKyZP8p9%V@9?tEgI%beD$h{8
z^amJXMAs$(kWEc|?ai5;ok^W7F&#b>)~lao>z^fa8;JtPFb)e4&!ci+lZ;sq*BYA;
z?2jx^^G_LVY^>}|R4b^?Q)ZDIy~|-~ubk*pfnp2T?5$#G3f|`ywe9WH@GW{W>u2Lw
z*lrgLYj1Kfg@5?S!aJ>k+`&SL4p$CIZ#eh2;|=_R#Ykm{&51?`rQO=B@z|#s)iCo?
z7X7+uURuyYM!eNG^GTTWgpC^#>*XKO`TAt|HX3im1b0U&6rcfTsGOtVs<*8t!Qo0%
z$})c!Bb#*$(pbfH9$FJIKE&GCjjx_<#=U@9`}wQ)ItxC@^VPS;^j0lirWL)Pe56Ub
z_JlW|9Zb})&xjVDQopXNPvq6C_x1DyIt4sf0;xX+7c?9U?2~3i`z@@<<T_J-B%x&n
z=d7+Cl9%x9NDc+($|FH<s>jbFs&CA4b4mk)ueuBUrZ^pMXVa2g-Hu4QninBj4yray
zy;*?j87of5pv>@x1<39Gk3gbz*DSk<C!+d10XWCvWF9#9AAeA3CUtutjFf@OVA8B>
zWQjQ^q{CHQW4(_4t+t2}%|srU$}5MC9Aptfv%7avOC`<dbga#|{)tdM8Ux!zgds|3
z>w&cX%5iz#*h}UIbo%OisyEMHAjih`=yEhe4D@nPa>t@@R4)@!>=cTt%}V<hgEhzk
zFQR1ev&fOTx<Uqq@_~HpD}PHa?AHZ`C0_Z^gz+HZpe3l(uC#4}X5yb$KnwPSz@UpF
zwF)fg1HTv2rqu6^gW<x2QsBH}Mpmd^ohWA3p?g?B*=m6@BYHJUB%dg`#!vA9+!$Em
zrt8M~*1N)gg$1Z8BK>CqrI!@3>+&LBU&LakZayqs$7OiwF_(?!9rD+Y@k=U{+kk22
zkfEb`A>SpJBO>?bQq>RNO5oXlDDsE#No(1v<E^vgfaB{iTqs+P;SXEbW!8Ia;r`!}
zgp>^IL3dn|Rw|YPMAR*5w*LJKm3>_e{QcrmoS50+JcS~0LX8<?&R%=+(H70AOafzt
zTK2^Z-%?zyVL%jEgX2qK9XP3myD2F2-`mg!ZZzAG;}|x04WUO}2XT?R*($1T`BZS7
zn_%-I+vS40eJEJg^=xV*a#LdxeQ=1&f7?F6zN?jhnQ}-9|3~4?O|(qoDn`x}bDI_W
zek6N!;Se&1V|F)jSv0<+i|)z{fPCgK&w~Gx-%dtvug<KF&g{@*25V)fHxqfg?6<u)
zlu&PCXhE0%(MTyrON|L{@IUB{=cGkL_nO}G!v!ajahF%r$e!n9?<!H~Lv>undm6B>
z>7*Bs?PK3KwV1ISApD^$6(rDz>m!7;t8Z3qN9IKqphnPg6M2%<+MrqsZTC4_7<HQ>
z_1*ibh@&Y#ZU|26)x0BY*Lfm=mLO%2>_9eU#2#KBsVXdnN{{t?!G?9PM8(T7!+*NX
zyxYf@Q^zR@DCe0?|BH-Ka9x!sRGN8~Nq*uUb;%${V^Qh1{LSmLSb}|ly}O%t05|YY
zlR0U3M}EOh0|)~rwgIb4hJ+4QYHquX{B_zS6l>a}IeXGzf4{6dNquh<R{kWn7f>jr
zG&#aOvCLmWYzspZAo}IgsX<HE@BR^I=I@4+<X)ilx7I(yGfzlB(wY2a6F><3AxZk-
zROkDo)boS(A>qOsdd#;zPusFzN2$)%b(8R=#DNg|BA5(8r9ZVlzi~8QXiwk>dr=ho
zmLe*n82>0u&%uelaPakvBiR~XibF_S?=13Y1}^UE?6PA#6vf>^ij`=kd69t-HfCre
z47BmgOL4jXCfmWM2BA%ylEZu^aY?v2qrUh_WzS4(?>YDla-%MpC>Qn?4LG__vjN>*
zJ`c5E3{09u0so2Ta<x<1=O#2KvIX@^xeToSt`KE7P6)wR#eE7`alzVm=MIy6<n;^*
z{?>bUMW+5#eK5K9X&A~QMKHK7g4J8?nW5fM>Rjt|tZ&fR-J$SW8PHQZ%G{d;c8+#+
zou6k^B4gz4Fshmu_@M3&oMiEtlny3L^}5P~uebY9$o8&_zQc;Tv~z7a{6PObl|D28
z8)k_{QEp3P56~fjKIr`3E2^(A+WS;-?wT1On(aJrYaZ@{YBP`ZaJ2VhtRrAvjuvg^
z^Wad`AR6pjyhFQmmY0+hddaxGA3iL<2^Q~Aj0S4SmM9$S2y)A!(Xo_m$|vB6URM_j
zsA{|B)PtOs%aD$W7R5T++&Ft{%&6~AIP(*&Kq34aHmbiq(Sb5l5#oT8XaA*ANJETP
zWszDw=CR+9D56wYO{xVCZ@LnY5_Ku*zMM8DNk2cGBcx+@CObP`KV8ozg*do>ilEIX
z-Apuilf%%59Wp4IyiI3%I{!tiKOw4}LjO02&{!g{I@XUy{O5Eu8|lG{5)z*28t?9T
zg%fOm@Ny9yD4t9CnHK6eD~R7cp0|BghWtLU%m)KRd6Hw9>Ndw3XWv*XWDA?c>?HB9
zyh(jE;eCk6RCjl9rtNSo+mfks!$(4zsn*1B?jfnZG;~6OeeajjC~<Y^wXx0qb8=Rr
zL=Jkn*vRtU-xA5|#GB0dHQp(n^?-*8k9(xJ;5Tu)dKM`lShsG@kQ;j%XwBd;c6hD4
zFU<zx0I}-Mj`z2Hq&E?Az#S@4JHtFSebz|`s5+6L>_4ranm0aP>leRiuly42c0_SJ
z`g09@Hxu9{@i+i5a9wYI92iF&%ynVGw8%i3{O$z`+LI7!gF~*#6fiPez6=XG%V4`p
z@9Ps^x~5YVM!K1QN(--g`%~oT#OW1Wg&=BQckugc+qum^#`<z{@-jDzr$zLkuvtv^
zng}02vB0DQvG3)LGM0)U>dnv=k@p+E?UOCdDs}&+{db$y+2YAKt5uA@d?4S$C(1ns
z99g3u>*(q5?_*fjto5tzj?>`K>$Lqxh$SO=)dEf)GRnbcHO+G#GN{s3s}}ag{VHw9
zC6>aa#wwB_t{zQ^@5^v`m*#6>oT$xw9JR1Jle@8_Fs<1jJ-3xhE;8dGkIDM!OU0uL
zl(J<dKrTY_*!1D*_u;4A!S7vp&38)eSrdJhBPSG^cMe=R0l;_lJ}FE5??-pyTNzYY
zgh5J3P)K{jvgrFtbz+dFTdka6)|%);*4P`O&k%ID6)K#I7}~<=zVR6uHUx$LUBiO)
zQbAX{-;6-P>h2-6SkT(`)1Ecbu{@a@u4m*g_XDk-2F784*Z(*UM5|dA1dzu0gEQle
zKQHc<xf5s|GJF~B=x|(Ra&qYSAq?CPnE;o~q)E2q(#`;4y}<+g9nfN&&+5xmOqx4C
zZ)XuIPWwDO)C4dTT&SK1NGWf#Gumbz^~zKFw~>9_vZj3ku6;X6W)8D)>a;wE`}xWB
zzx!e5L4FF)FmM|#XAtMvpDHh=R21ixxzy{bwlRLj+#}_G1YrQhSJxBk)SzA(pzy`O
zNWBMNRjTQ!TC*|*Ky<jTdr|UN%+;o9Zz{cPj7wvD6?>m8Eb-A_6tqLUg)5~nVs~%J
z%4jL)+>pYB%Z(<);LFo1oHxd)gBkT<-XTWn-!2>$HK|azLc$vYUuG$YC#JC|0=>@u
zqW%1VFGqyhL=k7%qt&!RBRR0y5|LYcB%<8BS?ktkzqC`>Q1`unozy`&pElCXGqWAR
z9l`M2Wn!9Dsc)cR`6CSP0Hy9mQT*t;ps)IJt*NO6VrP9FS%o)9nL}c^^Ysc3yAIzy
zsmhHulrI>g+XqLTkqXK%Hl`g{XJmm7BOyCP&f9C+;~%{^brSYmB!ogZd&geDt0#5e
z2j8m)QjhbSfMSK$qg!xoU~9RpT?em#FcMF4s5cqNNklE@0WZ@?C<{ahz2V^X`@kKN
zD%XYswTS*VcXCd<Q6YXBR@V42xbCMiiz1aRC`wIJvv<R4`I{$?g6)oF*tAgAnMc$z
zZ7GlhXBzX<p3|=kmyY-{A8Pp0?(*h`G8?lFcm0PBC$~<&qx~GEu|H_c#cYMm&%B}V
z&$RSGqn5uMAyABrl1h7?+YK~N+Ur(ciKi(#ETyADk?$%ZVoJ@ZaMy;@ET-v1LjK)X
za93JG+k1XT41VChnW04o0j+}B_X1lgXhWiX`OfI@y?F$ZQ+LrU+s=Bgimg-}{jY!I
zQ0*I>sLw^(!yO-EjwZD*iAb}4>%wCtC@g)`Bfulp&)H)g`QFO;5rF&FMm@DX>rw%_
z-wm?*?0HzyoCv@~eju$gEPbk{r)OZ`>>xky3Q*r+seRQV{j}Stfv#bM1dPv$F{EX4
zbEaG2kmv3b7ABP~ib{}Rdoo(HBn{slcr~P$rAxMH1-WFcOixIlV*4Ah?0YzQd-2!Z
z$EGMep`o5vschwT&aNnk1RYj`*8s?+v<1jcilR*afk3TBJSf`@-wn;Qe758)Ku4!u
zUXQ2CaqW)PlixXe;*u(AlQX;wQhbC!&}fl(gOi+HDS1f>F$jlSEX946k|i;K;)d@v
z5*g6f9=TS(Pl|InQZws1ts3sTnxuQPG>#=Obg$k1Xbn7Bs2*KxZHZS*Iw9Q`;e*4q
zoM!z0w3=)tUXG8tBn%rGpwN=+)tnPt>&0Nuimo*{kBR1T29%q-8CCsuFjIH%`_kd+
zEtBU4_j>$u8{pQCtQX>Vw34GV{wK_2%oe!Vw?bDW+O+_vi$?8414Xde7fXLXG|uTa
znr})@CXRCV%CRGFfzg}*1SeP6k&we5oCjBsA``q20B>;tfS6c{O1ZOiw>fPbH5ER~
z4?s2~ugaf4uTfnXE@e#~y#X?D=ehB@<vXf5O-KH7^L|grOMBL>?*Cu`{F1^5(X54!
zksuy)xuJN%9%fOT{fGI^6SlCv`mb#Ir=FSU;yevl^UgQr0rMT=g#nJ-g!t%sVuwlO
z*C8K;c!~BpRv6DBH*2__H&Zo(?~U0=vHL&FLrK;P=MyBI;tYxIPE$3BMcEXx9%6SA
z!<O(aUQcZfDOR<K3S>-jr<^JdTV9pUM;X!+bUxeSpYiM3TVJEtor}HyCqXrq#j<<Y
zXqA+1E~4Rk)kBK{CssuW#jL=@K~<aaOn1UP{_|TmT$sF#1YKU_xnxe@&52IW_eDMh
z!5hVx)pffMy`r=j99oMp6pZrt__*(M3e9V)&r^=nW5<Lf?CrZ6zu9M@xKAzTmRSJ5
z7|^-*_b~wghuE!Mnwl$S8~jgPNd-1Vo9h9QMv7hjYiO_-7uX`<k6KS@;D-qdqc*Q(
zX(H(6M4&addC)VIBL?#1hb!;5<C_E4pyDHcbE@5YP@PFI9z-``KsY!FK8;E#c1jE+
z@${dc0W>bQ`u+KBsO}IaZG6mhR`+aJ@{n^bQJuP(b1oUQ`>E1X47>UIQyGpZU}?@u
zG8GG!I>ssRIze0F%rv3whk=1k)*k9nqN2|9q472<T$64O75)b+*Cyt6#N0)w=);j;
z!6aGKvOXCIDAgxisL4Y8%(ta0-j4^b<aSr`ZsCy>T!?NNQ|ayqVC&wsw!{vkk5et_
z=Z_QYPB;xf#vIXDolY``k@X4)npLGaY~lkB9ovxC8#cLga35ObH>FYF$8<}5mzhow
z9!Mwk>mFuh%`_ogmTD(B3e@{@sk|T%H|gB8$dG_5@_4z&H2tCRf*d6x4V;Rgg*}X;
zJl7{Xz7XW$<V>qVd}_uR?mRh@_cOB2*aO0Xc2N-zife*+_0E+zrLPOyw2XZ-xNe<R
zbBH^rI?3G~oXoZXfHHv(wisl5!>=(Fi+JjCSv58eX;G7f%3`ppoO+tnefR6e;K5UN
zhI@?l<aFqOuW^}lJpSSKcC}ZhO<#*_G}wQJo*uD~P<+VN_cjz(xazi8OysDZH7OhB
zL=lFYxb9CNFGHX_CY?zad0f3RO5`G-jnFrlzVTTHX7*XaEV@>1)>sm6AXZx2guYHE
zO?TJ4{eOKKWGU^)aS>7+SA-3X^P+{C64)0H56Ql}!_}gab;Ps-<!riAw?xDEyBt=x
zb@P0udDchX!`%8h#)o_!De~DjzVcdFj=+S0Qjv`O;GL})H=3-%0KAOSiD;~&!6f2M
zNdcPv@0}=YeRt2g=)9e7m>G37+sS(O?=wZG6R9VnJ~(y?vwrban%`X_+|*zL66J21
zZAxwbtyZs^exWFTvSZAn5hsEUR}E&U(4KIW6re&MEO&5m`HLv^-XHWi@d2dNG^?m8
zC+lw<LH+2>W(ZirpWlb%of+?W!-{DEq4Ry@!G&)x9uh1!#k_uvX=S^0Uzw+45wyHJ
zlHi@oz=2&W1wRg#lXfO>>#Ob=ezKy^nM@E*4ZIY#vd=mNTe2#Z-0M19aZ|lo%;0Sk
zZ|ly(Ln0lqXBp**#P0swK)F{p3{`ixz$y|eBN<7zvVDIwp2zx()K${Sx?i*Msl@0n
zGS#d-5g*2V;;;zE9A?RL?2~|BB=Pq8)QTyZjYBOU@#0#&pC|*UXi~{|eev>b=HZ;6
z3*+)4Cs|n(S5(<hkfT##nAS@z_f@=w%)=;?dO-i5HPIj0JqQn4r`VOo`nTCSf6Iz+
zi%~kdVO;B?&u5d9gs1T<lM^sF=j35i`f8VyT0v;xDAV2{NK)f4Y=zsovjrUR-kZ=`
zUkZn(mm|xdtkfahO`m^*U=LH$<S5pEeZQh{JgGo?9^tXyT~MC4%R!#TOT^SbKo5`h
z4PNP6YqWbXxQMp|Oy*WCb!&f}q^W_=8=QU!qZZY=M&{4*K~1xI&RFAmN5sgI^a`FV
zsO`)GddN^GfH&b=bValUq&NjVzxS1x6)eJDO#WJys?stZ)X)+)^8U^>>w&7y8M`z2
zy>GpI)&qF$lYgxza<7`uX3yIf?4KNZNYJbIb8KfZPwC+SuRASCx-cH^czUwHv<B^H
z4U!HclBHtrM%m*y4~{>>A)InkhsCVI!tG!!c7%>|zCT}A&+#?9An+1^55u9nsKqt>
zzQKxONQUKU)*}4MBP<}2!@+-wTVS^FFE=98aU`@;8}a57yKa&#gulS#YXZ>?YMid)
z8l?8cy4!4f`6O`sN0~rX{Y~r}qn}OreuljbWIDBu_QIf{`t<(|iy9nmg0ohjI0DgV
z@3#CRDA#xxav$6}YKv9@Z}qq(lW1ULpw4TFkXIi)f^p>f+C!*QS|6dcrqY9n9hVYU
zZa@T{rgKC1>QhZ1HVnh$z$y9Zh8hX}RBhb1WZ_om=oHr@W&Lkq0v5G)m2j0qvKAxa
zG>eBqTg%1Knm5vwBP_kJ|GZv`(49S4>2+&|UjsyjpGY}`GkJ!Q1EGuTX@)qe!7LNm
zw-4f(Np0!_7&H4Tb2UFb$F(f}^rotCDWy6hc3D@vPUF7(NA@tNK+j)aq<xqqB=Ylg
z8t2>os{l!diMgw_r?n?td*01^Q;;?niFv@8(AU>G<Fa?P`POdKJL60I2BnN^zY9|u
zyUpeWgI;eZ-K3Q%f3J4)+&F+^K#^PdM%WtmMr7G!A3gs8>7U<MUAqQz|E0iyZ_EBN
zEN#fZAZK&Y4NKOUFgL>t+pyj<^B?=uiiiwKGG&HNFziNjsUhnij&XZcPO*1QlBRNF
zul=-8Qv-e|;JI!X?Z*j@zHZ)+LOY9DHA}VPt^I5LrY0k-_lu-|-!g)j$)&^f_zN*z
z)RO<t6H}fmYax|If;W||vFT^lt4Qz|vn>o!kbpBrs1*D$K{dm*u8ud!Od9x<&XG|g
zHTgb&GS}RWBhMD6hN2gPVPd{g<d-SltND<4QRS{xPPy4ID99t%NKs7ppDl=RI$9+#
zrf+hP!K<$*Wh?LQxkmBbg&ta?ngr2uQOLlAJEKNf48tt{bmH$_=BER<tDP)BtC#0D
z(*XaYaglZK`Ii|KeRp1acY{IWC?t)t9wk0tL$n63#(w9I#7n@ap+-5C4pM|%me)$E
zhT@<&0Ut0sDyP+iZ}k3rfRlJTF4ORjw`q7<b=uLvXY1iEl532Np?4Q|$JWC~TgsEi
zYzO`)d`?!x(Y_%?BeM6meRtCA^SM~C=+CHp+T@Y6NZP+D_`j?jAQnzGu9rWPw}Q1u
zQ<uU8qYi9QN$D>acoxDMrozCyu_*sCOirosR3MrE{wMsKgoLOlpxKyYBtGA{btev*
zb((ukS_A-K7<DcwAb5qHg;HU$!up*UmBqovO8sAWcit9HT<Zh_&*fySquaD-Rm0?m
z#(X2=Omk*+f3IH)<Lb1g_Y84OH{<=y1TgOX;B~jj2yNextWML;p3KMA44EkAH?xyZ
zr0-tRPY%q#_%EaKnEc2rWm>(KP3<^u*pPFk$iFZQp{Q2iy1=N}zH{!QUSNx3I+_SP
z9dWDJ)laVtdj1dSB2qhT;d?ey3UoF1KbQ$p1bV^*6Ykb}ps;L5@7U=@j9*l+2%3~b
zTCYQl@i5!)(t29V)W$}J4(@r&5Ej+5@&hqk9jHiY?d-Wm-X{X6O?}y#_;LC>g}r;I
zLKi(Bt=FA-jc$PHu=UNOcFpPfF@yf6l`0hNS0MOD_YAKkem#0$?Md(#)5VImwv`|x
zU;=qsM##Nzmu8`?;`pTVGw;h(%gbwQm$U=Z%|t0K4h{|-qmf4SeRHlkvRiV{D3nuY
zc+caFC>9ydc-<6AS&JGz{tQc>E<uoNn&bMG$0sHz3NuhaOG6M+6{xkzU5~D9d#%rB
zQ@HG($)jc4$S>4l-gv|A_**0ueBHMo@}_dFb?LVIN%DSj7&@uMY1!_JlMKIqaQ-%p
z_ovdL%RAXmq%-iFi6bjzNIyO;x~(bm53Faq`6C^_e>prBw)8lUh*YK>$di<Ar%zI>
zq{e#R@TrK~%xvu%(EZ%bhoTwBC*%)Pv$U^?EzfP*n)b`EHBeHYn~W$}4+q^&jv%E~
zyOpX^%GiUMLoa~%_4#y=PwC3fLhW%o$NTmIB5$(@q|~GZN1r`eBzvtJIkPiWbf5~e
zrT4$xotD4omR?XX>j*uyf-%IBf1`j7dwVsElR-a)3u=xjBOQ0=`>Fg*G#p%0$YHx&
z7vw}LTas2>yR>QElIh@M<0hy(cy-kp<&hbM?D+7ukn<l-&PN3nQM7QXkFn@7o`>&E
z7pkn^jL-GvRoAa6PF)%G;$<G)sPNC`<kU6I*xaHvVm#%=_G{&vRlWPtv4Ws+$%v0=
zU0q7leHh@VT|!TdOLQ~n-VCR$SqI72WN(ZyEY@Vd+TbcvAl7lus4?y62{xbl;BzC|
zsCHuf9p9jHyXq03$3JV54z0NK-kV6@Fwddoct*_jI~gKu71DS^iBK~G7%jdvE=9BY
z=ZxAL^JN6M5qDJulmb*S06O>zDy0Wmr5{4A<_~LiFFQ)&u7#3bIFh^wn0tbW&Z|Wy
zYIkXmOn>j@c%J4MM=~JinhtWA=<^CW$IV00&M`d8(=Yh?&o!kUo>n2PAD8QO8h+fZ
z4HnEW6cj^(><9Xx97V5l-0-<b1V>Gxi~k&94`%{IW{1gntB?c>%ungvYh7(|HeWEK
zfr720yQn4^n=Zct!-8StdLOoQoa}QA%sFj*I2jAYGMIalq<=1I)l!u@P!bZUhIrK*
z0;(Gv37{LEId;&cXFvG^4Tm!(CzjHnrZau1m_vYmXGu!^f@%7BmF=ZitR$?nWbElk
zgf`gt)hpWe+leMs)r@dB0Y-A0+@ZNQ=knWCKWHe%hj3L@Rb<Qwn#~EAs;bNyW@InA
zmLBO=?u<)F<Z(uLumt`vN^sYQaNSplsZXk^+U{(x%^T9M?S^Z6{i3nW`jBkYc)t!?
zanPuL?up`|fd1Q#YYEE_xeN(>YeN2T(qbjHxauYss{H;|Q|f8y)>3I7{}rz}!Rsz7
zI^_d0(uNZJeH!{;_oHTg1_;yr3C<o3>0IIP8=^D#-p9n?%%g8%gcjNw`<x7|X7pV<
z-LUgOSNBDdM==mzmF&{b>|U*87xFm&N1CjC^Jh^N2Um8qGLyX8xH$^_A{y;RGlooQ
zrb!xgP`e@Vs&4?_Q<1gMs@d;qEWJ*RYd4>F<?wyyHsjKNpT|eMj!bm<51BHa-I-Kt
znk5^?{pz_mHnf~6yRWX*J&ul;F82Yd88pE$s2i<57M1?*&QuivD%RDdD(IXrIRdI?
zl$p0OcLI+xByYkawGffxSQnZ<Q7ru3#`p)}*te6yhRia0vQY)a`Lt3+<^(<HzuBjC
zq0=ZS-Sjsf$=V4vxe;!~z0~vd%RgKCYn9Z%`{i?@W~}`105+3XMpSr^KP)-Tg~|yW
zx*X}UemiE_73CVKX_bgwUm$!97>8h||H+2Rk#*RVz0Q9QgUE=76Qq%a?vB<iVHrU3
zKLuxT(Z3Y8+l40n9&6|m{ukYi;1&QpG+9}5e?#u5fN-rZQAPLv!h<^gCX7kvaz^W&
z5Zsy|TiR~Jr7jFB!ipc@jciP)%YW@gE_yw<6k&gxNrrp1H2H-ru3guO<q}#7T=yg1
zBTc0&CtuY&`JBUEFrR3arN4J&?5@jS9eCk_u`p3C_f7AgWtmkCZsMt4RBE+NEEACP
zR{r{OzxSMz%PjmX07e_2DKB}%H$&#4zDi%Cb{E#`lUMwwXvW~KHdJzw*VZUGoOgAY
zLP(1>rkq+`072qnz{c6TR(hOG@1%?6_c3`Zj!>6R>2;Zi?U26gBKndK<-2$acjK#|
z8fen@u6Q8tVs)5wWan1jji&U&9Wa8OB@gs94glhGcHYIIEE20@*Xl7v2YjG!u9G3?
z)-IiSMer7d(C*U;Vo|5CpR_P!wNCa!hxt115Fn3HJketL0mG{On(|~42YqOSo+mb+
z^T2kn?9ckdm1s1*Ij7DwZZw<QI?X6K12I>4&^ornEtE15%180C@}`@&`z2XNT9e(9
z_|s@UW0vQ5ca~(8^&=b0>)%d6Hxt&YYZ)JHtUC&h!B_5Fpw-8(##5(?U+S61&Hm|6
zVsxy(>)0zS`Ac78|K?<>g}r$kr~P_jcK0ZThf77Xt5M!nO8tlHy2z}W0Y7z~<lEJt
zj$H@}+%najh-OX??~H$HpQPwFjdCl~Qg+3zC^dmo%w+_Xl^`9lr&(eJ1|Q3{o58on
z$~7`~kp~Y(RY=SF&Fy)x$TNW54al5{M`mN*SZ_bELgB+~P@TEZZz^;ivk7^vQ{8qb
zqNC?&n^-;(Q$!NdX`BhU2oQ+sNlQ1PS;!oFQ^6VdfSo%ia}jCqS78~t)<l=1k=>+D
zw9GeVgktiRoPjw!xxGvT2x@ND-2P$^bTx+yUCi60q3|)BtZrxMwS{+g8Vk#OdZx+?
ze4_v~kQ$S_4I718miw0*ho+<LU$Y&f1O)|I&5gF>nLQ-YeqKdI-?N_h9+5Y&^+Eq@
zh)>^2qdri|)3Dyfe6uNLLyNnE!nDojY`;`>u?FqDe3_aImpbfhSU(QIH2=}OdL)*U
z?K&^#V>fS!*2w!T5hu^x&9CbzMcOpnmCgb9_CK(+t!sy{@5IpAt_&Q51?92}etsy#
z*c72Vj*Pv|#ebZmE7NP`u+=em(1uWB<cr4QE{e!5Q{m4r`ETjJ%&BRv>&Mv&y3T!h
zi)x%8O%iam|AWtK$-J^pkaP3>J>!+{2UThKIh43DKC8nFXKZ9<X+YT;(8Oiz*xU3r
z67X#_;SN7MAn+-);qTTBHKxq^65p)|u&n%BJ$Pek$jb&t!)5Mm!Vo?h5OI8CFEO(e
z73dQU+_BBfH(!mFJ4Rn#i<N(?=;8?kX4><+=ykfzD5`hWu9I4Pud8g}D7a?nFpHF*
zwSun|XSF1`!kP1c7WY!i2)z=5zed4Plj??MXBP}D1R{P`X1v){UO*k$`@tv1)4vpb
z8}sU3l~s^o)E6d19?Yh?d$_6p`BwSxI56Dy=;#3xX3Ll*MYO6!qvp{aQ!ZgmHSvoB
zX@R_+g1O&vIg3-3AI*vakB_%FosCzH&LJUa(JBx#z}u1YSm3F*v>XXdP}OLGQLo--
zIy#cSFt6-*)3aae>^bsL&k$5HeJ|^iD91?<jfB%^<cpVKt|ZBK6X;xFC_;wQR7?oy
zs92@f6O{5ELm*mJeCkt-oL9??^4>i3^TJC$Ba7&*m40mxsnd)WA;1@oi4iGJ7)Qr|
zk1N`M35zy^>sq^lUP~{nOD+{VhlF_I+^#M`%jKUoEy2i}H9GEYNFXJjW&{E15eBB1
zU!8P&XNY?!G8BQyrh~8exHKvju+K_-JuLe0{Nda3ycf7Kc^$votbh!+yP`mj-<ORy
z1P#!Qj|%6#lXc|ZPSEgeXLy~xlGIdopA`@hcKc=idNnfM%m3Fb7<YBds$PZ1=XE|=
zBwSx$7)k5l!Lq!T52L_nk4>}v%_Y;)8866dBggHMl|^~u4n_RZ6OuR~Cp_GC6)bEN
zRy@>3?4zcu<DSYzInQ~WqUAQxW%x^R_ecMF4#<kdJ8EuP1Spte#s^VNrn{p)7HU_u
zbaR;Fs1pi{_g@l~-2<Z>7iL@(MOU8b<qgWBbXXr_EzZW2T9d~8Nv*dk1Cj>38s&V>
z1%wCO*NpS;&mXswUR1c#6}!0LFE#_=EjvvpuPoIdLDUd4MUHz?wCf}4B~5R$)fE;D
za4&h5WG5JZAv5Bw1hNmv2)#Q9p(n-HPQ9@__x2=V{Alg0dgXQkCN3Ww1M2PtV^}7Y
zvusa7@|pi5xap7Q_c7Q>*KCtCKS%oO{n5Q(jM0EikH#qHMgcsSG8(~HC$2d6jb1i-
zV4xV;=3SPljj4YhK;uRKsF6Ok>`2*-WN~1UIn^>a>!wua>DuKJ<E;99#_V)=p@?{z
zse;KnE~oGDYGe2OD6fr0&L}_XtS5frp~zuEQE~GR_nPHd`~9V1VQV~|*k)+Ov@NLh
z_Il>`ufOZMI2Kv9;`8c}x%guB<J7jFVUUhZ8)n_Et5!0;`csIb-t01YT(DM)4^k?8
z5eRH~_aKDnDAX>-S!*&DL?*njMCIB|x<Um-9xiV|E^&NU*GJ{<E<z_7XtVmsd4`w`
zq;wr|_I7#Wr{7@9?X+N*P&+<L%9G2`#y?NiXBBa+PJer#id#+K%!%2At#TQ|d;5;g
zG!f!xYL60gH|Cu@I;`5HyHu43+xBanE;pA1es^>j@tT$!+0n@bo?kKI&&KD1C-a?O
zHUNL17XL27FDT#)=s4<hU&%4TC-A51rRQ8*OfHQ1L~+DB4)kssMX(?rF;Pp+q+k0l
zeA;KQP5Ge#$?LhV0CMe}`G0hh;N4ecSpxun1~mpFR}ct-q&K6D{&*TAmQsM|Sl~gQ
zFfd<Ipy_0cjCvj3LfJGv#LcwyK$1b3FmC43*bGk4r4}{|&rANiI{Wu*Wn#ZfvGoZ;
zYV`853Rqkcu5sF+m9oDb`$`ZIsV-ZHnZ4E9{)$Qarx*6CgT8l>uga1s(dzx<=X5Cy
z#jpv<x!;%=pgB`0BXHt4ERGc;NE}NVv76gSM6U~pv@xX6ifK^wYNSwA;wYD&rg&yz
zn#DX^$fKlDfz=%jcT>F!5wT1gH!TX|rxf_5g#U70syc303<L}`>l;PMDj!adrg5a(
z%HYNM{V0}w3Ni=J473F%=RHY^wLzN$;BXwWnta)V;16oQ#!K3^2l=8GSw~VafK48k
z2^$E604(jvJZ8-mdC3PU2V+kov}Vv%g+C+LHhX-)vI7rvdWp)3MX3Hm#weFevVX>d
zmsQEVd0M{jNQE)%Q+}ZVKJt$*8VrG!G-41_e4Y^Qo(`nR(a~W0WIl&J*9g@x;)3OG
z7z&{B3r9aO^vUan?+k6gNx!;z%KF1+Nraw#d$o)#JLKt0^n#kGS<gftbe#F=BKGG+
za(RWy?j{%_m&6b6pC#883wWzTTbuW)OUi#9qy5ILBRcVMn^hyv>!#Rai&JAh?ce4#
z#uuNhcc(uc4Kyt}#IwUX!c6Q%yA?zPdx?XVNC&gh3;(`?;SD^^u{wi%2pO)v=>Pj-
zxHrg`W2xd-{9Lslq!Ij{n^#rPG1<4kfjrj_IW$L2*|-=TChUI8ndZf4m&3f7@7n)M
zjb@z=AL*tLSJPRgJDciDLwN5G_d6>aBsiugNGS{FsG5925|b>7cT{}W_LC44fDCg)
zhKHgMWJ5>ITbR;sSL$4CdiGt^n_hp!f+b0FH`@IEKH!{{e(CBj9GNxg4!Ks6!fT|Y
zd6NJv{bavxhe5d}Rrh4C_N&*f5RcR5xp{k*v9b7Qm{LM(rt0^FSFUYV)?d5&>8fwl
zvC%I=`O+3IJ9BwaESGLhkIiEQx)UQF@=h|YEn!v*TdtyTQjGVD_Q-Aikk6~{9#Ek=
zoBMJ|H_e68uWiPn{*tRQ?jm4$?s;|eebr)g?bW2Pu~0CE5Av-OwYz$~l0HN-ZbA~<
zonTnSH|l$Hi>7~^G?goU%oBGp8{~;E#;z{JXAnE)Xm%y;Z*ej7>$k~<cLNB|ZjN74
zE=7cL$H_?`I6iEstj)(fvaHOf<Iu3>a_jrNva0t>qx9ubzlNIlRXpwB$Q%oo*?@7#
zC51S!y9RG(1I@2aY#Z}SZ{DA!JOp4YdG*pm+nUe|W-j(Uo!AE%&Ux}3WBRBivQb+#
z$Kkc{rH2FODlVls3Mo<m$rjCb;yf2bWT?I0x?6G}4`438^5}AL@18d_z!;})J?_De
zi*e(l_!<`BAwHCnxNoQw$}0a3B*MtvE1zCZ?E#BrKTc;=6Dr9Q{!2nD^}29nLZ`AS
zmWHY3kIBhObukRftv24j)-hwOxPGPc*lu|lhahB`<@mgUJnDws0g^P!;;{EKRqU$4
z*>PYw<Z;Yt)gj53ufXh<c?+=5-hgx)%WUSWb%rWUBdc2AeTYmUc5ch1WrV&+tvd4U
z6Js|vY3}RdaeBa`hVPpUt`VN0b$J};%dd;ckW7IeDO<k6zSL~&N~WQ~N+eEs4_qF|
zNz9{G#jn3@uG8UO+~*;5AcFh1uS}u=`&-{2{h}!{@i6UcA5V2~F#WIWpy;ji58yUM
zaL@6I6Mk*YRfF}2rei|Jd`K>fsZZ{R46aW4t8wzzu2{=i7+)#-x@=fETP>G~`>u8!
zUB96B<Nl;4tLjM{OYO;0j`)q#tQtmgOWG`mQCgVVe-}s`NPuOofE_Cmje$=XQA@f+
z<%EC5#w`1rbRRN)>;B$)qOJh#uM*bw*6CJS4Bdaj&dk|6DptK#bpKewG45Q*{`1}b
z>YNg@?y=(Ej6JR<0do#Ta9hLL*yq)Ro{a4a{U-mrw%aC)AJtC{j6~lypHYW@dMruu
zb6!0Pe;)m=%t!=zGWn73*Xbh(SBlqU0;MW5hCgt>;n}aBuJ}Gl3{^qY2+~xBG#HIc
z{CH$a{_k=NXAr(%x%zr60yB=V*~!uGpxMa(U^xRun?`aX`X=wrf~ZFX$&-JVtc(go
z6ucw+&(OC`4MYKBH~>+Q{6F_1dW2~3|8wDg*ZjXUfeQQ|<NyC{nDp-G{2wg9|Lc+d
zuk6jNf8W3C%k_HuU*C^Zw0|*Vxf{Owr?CzAZvZ6OKC=xq<SMUFHBhNcAJHM6^(TvL
za8B;w1^9Hr&{Oxvte5Qn+==yZ>fM3azz0uRnx8td|BY0D`NU5s_J*#CQ02uMadM-V
z_J0)}Yb*681nl+o^ip)~^|CB{d=IO>BZhZoU4Lu~^VRB`R8(s<nH7Q=NBaL7>!_|W
zwNz_5cEWy)6MM(yW&S@lG&$HsP7EE!MymAJNeZw2cXP(V%e^EDcJ>qAddNe-A(#KB
zF3;$jdJr$C4cbXf1-qM5NgxH{U0*NR!cnzVM}L);x(_L^*gAPAa&o<6j`(Dd^^*t+
z`~bW4|F?4HrHu*u`6gJP?z+&ix%jB8e^2+j#;AT;+EyQ11s3TW6@HWrqe`gCLFwO_
zfqnf7HHWB8jS4+?$1k_AQ)it7`||P}8bO`;%JE9N7ORH%o-uOQMZfd%s|J)r+nQXC
z`Qr&FwMZUmIN*2jl*jC~7xgk;=OlL-=6R0Zhs4Ws_A;5vc;&9#xx;orXp7UCCzEg0
z*S&ohv<QR?$+Kl`mH?A3qpdrXll6MV3bO>r<6Fj5==Dzl6df)}Jlxs?C73<S6+Jy$
zXQ<m^VfG-^CzP1dgfoATTS-95LbA}jSId!AcD{MywBt&e_})*P)(JCk_}Xvb%T&Dd
zY_NdOfkus>!GNXO#K+t(AlhhkuS6dP=xWIGY{B#O0VNhX=zKH(?7Dm@+W5zRU9DLL
z#|gf2@EjSPCZ+1O-QLmF!4J|U{;kz>H)<L!b|Bg(Br=5mB3K<*zLpP3Fa!9zSXJB&
zoFzY7TEpUlR}1H0O+QMu!K~>H0>L3H`f4A>>8s|SoN^R$jF&qdHlMG~ks7+LuJC*(
z05A9f&ktSGCP637k|2<)d>OZBqx`{V0T2JZuY}_sWp-~NbDuzoYgST7V7Hn`?qIut
zG3xX)+l6?<hnK&_&PJrOrOGEzMW2=vP#<sdX5N|qckCOV&~fI*cWi1=ED!;;iqFj-
zl--9J6JHV7D+sB?E{~C<ju3nfw@(!nbWUe%j7(a>owUd9<do>qsJqG!e@HICruOqv
zQ`~4wT0b?>D|BpgWXR4lFZ;M(c9@^BXDdu~S&q)t5EU3uqL<B)4#@>iwI1vbc(nFX
z)cE=#qCmAXP>lgQQ8T8>EWW!WFU0{wn6c|+J2!l&|3jhuw;U(vbYzi@O2amaTLww0
zo)8<e0^gQ~{V;Z@2>m^i1YH*q?{NGVNmQ_2|MY}E?2YsGU#WKfQ}R_HHVj3)0fWZx
z21X2cen3KKc^|$P9`Vrt9z&PXw1tffAgpjFH=1kzE2D~MuuImTOK2WZnd}cJlk3cy
zRQc+Wm#STSYsUW6^m#6*MPIB^%msP6^Ah^h+WK&JO!UM5hkPiSFk3jBTz-p|zEW@R
zQH0?T`Q`8e`Ni@Od67B3i`70-#FYL{8LN_}Y3qj5a@u)YH|C(yc~B}9a0-2(ZFN|T
zmXvZ-|3?eCHF}CWf$`_nsSnd=D5b8;rtcwB!xQnv#a2Zo&!l|4)}~{)(ktjGPTOw(
z`P}yF36KNns=_gU+?{L;l66TRMC%LOJ6g6?gq{fJbSI#Ux3U2pJfyB+74z&_yv)5j
z9CfJR-znISKTz-Aa2Wcub6WZ;MY{Ec2vCvm;H9uzJ*>yIh8&b{EX$8Q9b9W{WV;d6
z4Bi^84`L^orn}BFi94)n<!}Z7<ci!^-Ua;rBWJUl&G8V3oiuZ|vMLZGV7BMRFiA~;
z8>u%fQNmBmvwrXiaZU!oZ{6B{O)ydj9tahK`PYa_rC1pr6e#KH3J~;I=AyZwRz1Eg
z<W0S}V;+vJ6Uo}b|6jG8=Qo^Rw8x1a2|@H0C5T?4w<LrlT14-i=)+(zW)Qtcgy^D#
zL>Iy^dQGAeeFj6AXfyg?4A<|?{R8e=_j&)Eb=G>GbI#uTd_Vi!op^#?y*Hm+TKEuZ
z7&#o<HnfV#Cw3n9a?Z~ojHzEA7hxwTT#~qx2Y$?}L2#Jk-8J6sFsV#SOUqYcVq<JS
z)wKk@*&Wyxia}MO9~2B3z7Zc1BbMN8uYjP%rKON}Hd|J#{bjrhe)mZ}XSEJX_&2kU
zAxTGO6Dep@&w~Avp2)sAL!9-j39VX?*&qF*QU8z-_m7c`qCICI^+g~daDVgj2R<jJ
zY0_@j8&9tGp<ZbgU&Zf}D&?nqHpet8C0TR2zIN8O9;a}kOD&c`i&pXxIm4Qq8mp}6
z{55W~GnKXvAJrQ7e#Y0)XMOuhx+)5xc3`D5(jN9EJN9tGp+h5fzB0E0<7@(8RSK-k
zMLu+F!1eX#+&H8w*ZZDFK_TR2tQAg=&DPMekpU_nB?($l@taBfxty1b?$O(Yroqo?
z{_fgDz|!xNXcpNr(G0YjFZf;%e;M&I2@4W9LkMR-rhA|9_^Z3s)bIR_G3AGa>ogv|
z5>C|I{l?3v3kTCXzxSa=(O?8R2*1|)Cd3glfcGg>b#g52>(e+R?qx~#XkL>gjGUc`
zu#`MKN--GF)n0k+E;Ke`n(?~{@!I{dneYHB@YCRv`oH3LGsFJvt!qrY=9<aQ#G=>z
ziqBr({5wTIhe&ad@hNl(v4=&zjTFrZ@hpuMy?j)yEJ{tepoF32d5E8|fXSMw!v&lB
zu=IiP&o||d%`o(e@Av4XJ3g|KpbPUvuwKIauGK;+Fx&8fUmta<bD8*_iB^_Lv?Z;t
z&)Gj!`NbbAb{-lE9@_UO{))lui5VQ9Zkm&G8LC=>vFFQo+O1oynv!|W1;>iG*B9Pv
zpuymve@rfofEDzj8A!l#``yN?(SnZ<PAC?<<@QM(et42uCrq)38|<864lEkira3aS
z%(a!GoEc}i2YHzsHjPS6k_GtYdY3#SwQA22;0-#n&I^%X(Hm*<M)o|_t$Ibs(rnh<
zN^L5Q%4U&YL39jh>$n(y`<Oue&s^`e*19ZrEqq<Ub*A8H^Ty!Nkc>*y)F%|5#X~!R
zvELn(Z*|K#-aYIN#eJOxcEVfWaNH&7tuFRewV=+t=?-`c1wv(a+G=oE;~VHxw@v6#
z2D4g7<1L$$9AD`5Xu)<_PtN+l+5yZQdfq5fWK;VDztFM*C7cv>gq>H7L%|#4U1y`d
zP!hYMt)Z~JVTrD=8Yb+Ua+OKo^%bFzsy&9M25iUbQ7Ma=Zq&(*9}uPLD(<s!3>@PO
zvMvF(B+N&4$$Sxf7RvDwwW8b3SF2YZ@N@6Qz#$2!tx3F8n7=Kslk5lqRJMi^)z{iD
zDvIbm0AfKPT!etY;48Jl)UgqCDs%Wr)yM?E@2?r_NcyAHwzM=NL=qpGC=m7@Si~xx
z$~|O0`hrT`f02a+1fuO$p>URPlWt9*9|pia5uK`j(e<frVUHLWcz+q}^xbpyYLi-`
zeSesF`LSuyx#~81U$I*v-)G(m{6?Lj-Il;rmHPFXM3*F~a~#Fl1?$S$qS04_Dv1v{
z!3~uO^mpMFW`P~wruB#ewv-Tz1D1Xmj}Bm*$gPy_h5Fu?x*ptrX#`vwQ2R_ADe+sh
z7M1j^dTpOD$u63zARAD96Ifhc&P*8DD$`sV(|1f|-qdh}j%<7qSjz>-WI1WE%wa!Q
zrKbRJi(UZES95gf@)FmX{nsht#u;gu;u7b>Fb|qi9md@`5=K!pOwd~ekF3An!}tZ>
zVpHfJUR}^8L-h1wnP6375|o5b=AiIC)D@Vo6*EFP$7&w7^?T0jweQj0BElx~3H=TS
z!B^$saqNNc2J?<As??xoahA{A6XE9eoM;|^nN0+mF?#AX-?6~yeKX3+*n0LX^#M{g
zzko`Is481!=y1jKq0|5;NIjiPoBus*c&0}O)X)6ZD-OML&&6hq0S%vQB$;c#XRH$j
zs5-`o01$Jh?vr)5u?Nd>thM<)t0O(@$;8ufkLwTn`t~3EOgUg}N$cZGt+LBgK*%7&
zIM)>;&ZW)6-u~mv2ucB77ue$^ZbBhi>gMaKROq#m=qgP`1VOi+@Ov1Bax<yC7QDpA
zpb!%4gLXTB>k}m}iZ-LSuHG}x5v|(tW+<9tDJA2Ys@2Z<XKub4w-wJuLTlS!co+<G
zwp&sf)!aT(puel0V&fS;vK~$lG3X9{!)J#{Y?`vRaM@5+^Bz(^5nxc-%k<KQIiZEf
z|C0TR>&Yr&C_{^&v*E!2QjEeF2S?hFl+yaq-L!a?VbarTf0*07WhccJV%!Hw%!iUS
zM*H5o0Pd)cI3f%ZPKA-6gjLy`06^>jGt+K@v?D=cZAZNdLl_^gCB{u9RhcY8e6`&a
zPQKc{eALvo>i2be1%~so`T88RUM`yt|L(C;zC~`XU$geGW-KO#@nPPN+L)Ny3%eg3
z;l*8!gS*si$pW5p&$>*f$66fZLN+GYRJaXx5_!1KcunVx*S)?Brrc)z)3<$EmVCma
z@tyDI*{VkD(f$v)ONgwZtPCJ{#!7Sa@`O8_pq)Ute(>Y5nd8c!Gv{slAhM;qXtme@
zGX6~w-d_iP^LJ+%U#5v@>~aK$Y<4O@_j$#ik({PEcIxIY9R)>z^|9&&LR${3{7TFB
zzS0M^>7NaTf`PD}PfIpqHgU>3mP4&|(fHwao8jM-K)VKnqa80<1QItFfwyZRSak-R
zci_akI(Y@r=AFX%?2V(!=8tG*P~IEFjWiH&@ehHW(Hyp90S*d}+o_MW2Yj|RxAUaE
ztfbD<yJWjo9Fo|ye`}>Mr<J$-B_cVoc&|elK=-x|gmL9csEY%Wyc$7Reic1cCmh$b
zc~i@yi=d%bo4tuy6=>fr=5}%G_1rm7+xc;rvicj7BY-q>A=GlENAdbt@2V~EB(crM
zeeKuGAJMs**ucJ4-YlkSOk6>QyVt+>-h(XlO<tat_1nML1r78AYc>#`t3IbcO7rs)
z|74#75%6nq^HGOx2s&Ip`4^xW2%~P=0Z1I7%2i}sKdReCsrURmJ0r_t?U^F!K3_XX
zj~d{?pp(uTY5cBjHX~0J;^1<BqH?6rL&42=dLg=DqEh!{CTRc?s4J&0HdLU%Qossm
zLw@GUc)8%>Q#fJnnjmgPf-zZ7<r@g_mgn9+!CeQe^&9Bt!yv6RSc|v;hd-A)f5pWd
zN5T>Q5#zmmyA&Kuh62AGJ!O-F71;a~=&8<>Cd#`gl=olLye@mAa*syd9Pd%sG<wUR
zp*y`_mfsQ*qVrayYxeN?6-;J1@HeWqQuf<>ujl6FbT*ZHw|*c3>muNU@<?<Zd4-Qj
z1oC0nMOY@PMUDb@ex9q$i@t2X`s)!LgxZSHf+#trmK7X0);M&`Ke25N2;R*V$^Xcp
zENON6_vJ*T2iQXu@#DsKBuCG9322arzPx;MjUjKpy$r!v38{aUc3BIPAUAlzxV}sx
zf*!u)VzCL48WXp#t4t?JST{ocJYU;q<??6MVxYZBz?DgJxe=qqd-pndc4bxV4q3-I
zOD~0KM}TOx{>SqUC2%mU0tg=paFei+nz%a2T)8R=KmU}b4&DAHv0_e*aUx#?<|-S}
zyUXBOa8)|k0tHfMigs?LV^pIo`yy|8Sgt4F-qxbWf^V3$jUlxJwM&}TDf8>e7rc`m
zDq;?b@5fxNN)t34Uq=Y!)jy|R?ZF9(8X{xx-U$TRqN|BWO<sEM(dJF>EKPc(UC;OM
zd$w#>%u3CU#6JdR0B(#oKy90kFh8ba?~#e`R=#atE8mjua$C#_Q>9m2meoIW$j2{`
z!n!DXoi1p5Pyn*!%;)IL`>7>0M!u-iqBtX{amutfl2jxmakz<}UflJKvCPgNiJ3QS
zwa3lQ&$2Rjnq>e*+u_l`IY1rz=L|s!Zw`#nAW(mdWBFv6VD(K*=J7js^i7mB80(({
z8%HO(0iuOhv%!2^E@m)!5cL%Nz%LtUixpu^XFoQ`Ci%54p-VfwKW|f80S6_^qq`S}
zSS93%;Hj;Oh>-qrutYy%Z-&OiQxzc<qE)5^y~c-buN&FbZXpD>M?=dIz;LfT&X>NN
zN<0DhQieDmbt`I|Cg<9eXOAk1(YFyG&EvYUrOWTdDMeR6Y)|{@7N*|Nar3pB<0P<X
zn#!t#E#q!)?IaPV>YRG*!)rir%QSZj@2`$uFMR9@&||X^_+~*D|53BZ&feA)X-6@F
zmbGioTX%|8zLXjwoK>c`d=-E>w~ot43}*JiXO7m40vEF_>w^D%1XhYIHB9mAC~g|%
z5iBWCUJIX#g}1R`<|J&|9+R}&st%>hg1-5B!wg2P!(C##de@6oo<cTnY*m+4%`TWQ
z;%nU}M`-wv|H)ffx8HtTYIKG{g`%a2zWKPJ6F>?THXg|PmnJ|vI4$sKB8hUCvRG%l
zCloO^N+HJc$KL&g{CPHWE4%=|!cCXuu)8pRwm6r~?bJ<3iR*z>9~L5fXy(D)ry4E3
zMKA~+LUdV-+gV+1Cx-1jjWF(iS531>-~78#Wm&VN<`xLbjn(5={YkTzrpb#Jk!`!3
zpt<~{wJJw~7||m8rYR(d5phzcO$V*Jf%n)jw9O%wz9!E^MGBPd!)=E$Y-HMR@E|?A
z>RwLEO|x#Kn%&*=YAO&C?iuFy5FwJ1^4dJ;?0I!Zd1|ui=E67La`r%s(WUiW-Gcmp
zy40@x{HfV!Jbz~<1d<`>n;)jqC^&Bw(3*W+0K$ZD(MwB}!7PQNRvjABN7R$5+19KB
zOWn{J0Ye92mJdZ+#7lPD<eA;2dBa@;tRFX6<KMH?d&nM9QqzvUK(wZL@WQEVLdj8M
zk~lv~DAYR2ga*XWmw$%o-~dv<uCwZnp6V6N_7RY>ebN&8f7y;eVW*`Uf@<xMwVEYt
zM_f9NK~UWH=4<iY@qIKu`Ci*&FdvVj&N5p~jV)NIZ4o$a5nvdk95m){Nc_7LgP%g0
za8_oQxy=>89V@$Sr0Qbvav40xqnWoM9YA@B>Rp?GQF8AZ8^_k$pk_nZlCLSMGxXan
zP&-4mZ+@U}CTCQEMcvpL=bQ%&uC0TS?UlA6-!MDr%ysqAt=&Til4+FXWV}0CX|wHc
z`?`^Hqc3vftN4=!P9ieDm(@83;~s?Eo=6o+vlB@(0lEkb{#J|4@cdV+!sjUY;)3Vy
z=;ggS37HSmVdz2a@dLJWQ&DEc=+X}Yh>;Og;jiP<<ji4)e{;{ziIcmJ^^+svL!UbZ
z^5Ka&?~yl(EAp)HAeT~rU7g0sK%qY$J$Mop9&lcRvkl)m!TFR1Uhg|N<V;)uXm24J
z5<$F{>@=j6pjtAV{gl+t!vvBxMb;*Mr3DX|FLnKJ6{^<K<jw#!!*~H6d6EyFKII*>
zNBAPES7BqVeFCJZkjS`k=53<U<o+yS1bovh#-%9~w$pQN#fVl@AJ3ujDykbqAv4!k
zoaNcwkQQ0Euh<t6gh_zeD<_8Vv%WTfn+zq6k3@_48{x%DI^XI@#f7HM+?Sm>i4ze<
z*pA>l%{X&pPHm*lr;D)a4s3iLCuYZOk7SM_xhkd(dogmNMrdlr-7Mvau2F~QQ=k>8
z`;K<)M*~`gM`Rd6<*JG(WD;fs@%}4%sb=Zp6+HhUTC@GgLsA72{O<vPFf!2-wN6yx
zS+4@+XrL+6?ou1e;7y|Ndz_J(rnjxO0*1z~xb;lEWpz@!*-OW9u#;YgQY0+V;p%%Y
zbzJ7bl_jTgSH*W1(*cAYdl05`(fB9cX{iN^b?PUaE}2iG-Bwo(F8`K(@>pHoI*fSN
zpgn}J(C+^*peQRAI8gFTPN$6tQ%FLUx?4@g_9j8oly=sfhSaQ&5Ya_u2ld3AjZvI5
z2JAO{Jre-G&b$4KTJx|!Cljz@`cwmbIela~P%<}!Anw#x6{^bvcSFE;@lv@_08n5g
z(`Mjd9Trg~{Uo(jZk|zj<wvE)4{q9)iSDn;Nk6_=<#pAtm5Z!mFjsxtR}&rL2zao$
z>(tLWDO|zh$7I+?xRY-g@;>?bxm%XRAUVL{-#gHG$+xulQ=48%T5BQk9){I)?DUYb
z{S8slt^FDvEwYMhnPaHpMNB4i0ews7Ud^dS<5j~n9^H)vyWWSEcrtV#xdBZ@HD$vO
z5a%=l>1ziguN#6n^q;mX*m7_^zb<@=VfM4%7CPdTa8xVnl@T09sxqjxgHQDWaL&?z
zGTY?7A&Yj3*_{40C)N3IrO5CX-qmn;zwU?M(yB-Xu#lD3VmvV1Ll0KR(f_A$bV_=B
z`HD&b*Ao1S(1?XZ`{m440b2IZiSc(n1+H>=B@9@qaLQ4W+efSg8ha%u9m)b|HyMJd
z>>Eh3RJfkYql*by|AbZU(_aiPH9ux@HTkhZQBuNa)t02$Vo8SqxoNOe8(LI%;u!yR
z*0goy@4N_}^FTw5n$$=!dp|-j5nem4sMNXGijcKYVc1GP>8qObS%Ot24aoa(pUro$
zqbdv+c!Hy)q)-DK4(fj4Y~(~aObsmhdHd7Xs{{s<Ay6ff4-Yu)X%SDmIUgr@|J#<>
zTJq6V%#kB;X4c+bn)Rgsbm5W(S?+MU*1Fw-3LbGNZ#rCIJ1#gDt5zGF#CCITT6@<H
z?s70_t#px$aUMWU-eUJD%<66}ju$>}H@L~e&%;6lHV28?%TX$omFBJ8J9J;odHwH4
ziXNo6o>jau|2aEaMdb9{Dp>hK3#i!n=_??KKsnF9t^dkre(5*1(xIwpXSFjS8?VED
zcep#{-|sb&C91LPl|co(Hum81)J>x6<VyPlkOr{OJa*QDX?syTS$5|2d+<QWmibY@
zCa2*fhuB|g6~?KDvvh^tq2X04t`@2f*}HAp!%FI7Rz}#YsBq5(_0d&CL#;Mu3W(>%
zS`Lv5nKISl-W5(>h3HK7S!A2EXp|N`#Yge@Rh^pj!7C$~=`GRtJ!jfDe2uu8bzm{u
zyL5AP0h80YeiRv^hOqX9%>&6zf<UUP5!R_oXXCmHG_2)UjY9ttD63oWG)@n=^f&Gh
zo!1Kl66aJPP5?t{X53R@9^GiQWMk%=u&dE?{^gD%BrNZ5`{Tgqh8-dTU(h$g#UD$O
zYB@9lOXV-}Vvmm{;2cwh$fHoa=vp<*=5G3<<<fa3Pxr}?jRr@pomQws1}K72+DjD8
z^hv8QOnI3ys_AfH7c(VE)0Ck1fKIxDWv>LJhUj<SnhunOCi1>4`2&ALrFHNttN+iu
zRz@aw5C0?GH=uoF6T?`6ceBRHdhVk1$#Whlg^UH7SRd|-={T&-=9BQP8d~4EKGms3
z#6r&Y?j29sNH9O$%>`m}0H8(zGO{BC$X*h?cpz~6K)fz~1m3<TS?v6f&_RyLBJuuq
zzdaP4b+N4?(tfsE&8-o<KjzU{m1gZP>#z}YRcmFbIKAEM5$EuLBRDCN4lRtfT`Cq{
zT|Nnb+$k_QMEJ64&l|@Hmp4f4>->BLTQ{_p#baz>*Z;KDFB<SXfG!^Y*^oPA^<g%e
zt5Vcj1&Rrrl?1^PT{d%ViB9^Pz3Gv|7Atrl0{`W=p@47v*LKpZ#*V`goeOI3q1K(E
zC~klCtLG0XuWdR_%*ap;U~D^ZH!jq=yaxcX(`+ufSdB`kVy+tNsDR^CVqXUrfYjFM
z5qJ4==oU(Bvz@c}^Ao59pJwjcE{xzz-Y>g~N_0Q<>20f4plW7x3zEvKSEzc4%D`LX
z{Pxt!^Rvq^F)2e*;Np~UBJ9bnRamtXsmj}+ZoIh--8=l!)=<dKL{Y>)Y}e@g684B@
zW@+q1**L>9@M|ndWc}Ry{d$XSk6;R36+ZF;ynXTOw|C7-&P(L=MHNb!tUfnzR?cPu
z&`@Nh5K-qB7rM7;s{2RS@=TJ#<{gCZEUiQh2(uU#GenN|HEnJgpR0JbEZv+fpMLw-
zkdT;Yy$N6_vQ9;v!LB^qw~uh#hf)J>F{U@tJi#!trtcZ4=#l?aedKqKR#K3%6g}~p
z8YI|l(`L+iYoqXw6J{^;$rUA6<rMP9XFgy(z0D)9g#~zxVYUl9K$<PcFv3>A7%&c2
zI#i>6#=~|Fwv-hHj?a*ZjXywOW$$C$(zsM1iOL{OOdC*rWqt6%_O|Ig>2Ic@dp=AJ
zqm$`TZiur;$1pkCOM45&()7IvQNOsl-2TjNkD6?MqYbWZ1%^wp@o0)AhA{~6+xwYp
z<v9FMS1p9c?%QmqZ^dQzusLY8e-CF1#Y%rC^cT07-$m@a;jt3S^pKRQN8Cs<gltWf
z84{Y#%!3&MZAxQE!}VpYVzKx2=1b0netITp%0mD3ud)GF^o9Ql!we+ax>Z+~UIn^m
zQ~lc27g)|0RZ(7gg0^L+Ko7#9_ZWNz*`zJMG&WINz2~4FoDd|z?Ay3qxUSmzDT&-j
z9ti%86F;qSH_1F57wf%9deo>y!rNAxZirU2kLDS9eFyKRT7m=n`h`4Dqq_k9RV@Nf
zN4M{wHRmy!PUFjf`Fb5<P}R+eH>LY0)i)Pzn1GCbaW{OCIR%8VM;H`s&T{^Pp7#Ej
zyrW;bor+!yoAt^+S-c$)`!Eyix4BtRL4V;VqTt)eO06EH@#*+;(yiqJySx43^htIj
zcMF_yWLV5&$#4^`#rd0KF4BtJz)v#CnP<+NUyzizlzzlO7{}~M0elOYHW#5RWZSuN
zZ|4eC%peH$$DdkpBd_jBUT3IxG>IdYL=(Jb-XA?t=tX{8w=~B#YnFVT;d)0gy@M)z
zrAg0pXG%ddfs(d!{eAgZY%j!Q#Ks&1!y4VFLbU69x1->CDL~BBXa!mla+{8bh%xV_
z=2PR7wv-mqP(1OZe9T2)fA2-uLx$zH*WPqY@DH~w8Zdc*7|$L~ao!_^ZmRHS>F>{@
z`)r1k_JipyefQ&G0L*4ihU_i=y5Cum$h)uYZgL!d&F&8RaKnpo589a(B3Ku&ZX1Cm
zW-p5J<-n}C<R9bmXTFXAlOYDHojm~V=P(kay244%fak#?J3UdG$8!d8hYx(@rYv#F
zLH>Eh4B~mg1E9BNk`G8cD_NuVSWM0OA3t!d#z8S>JwP`auJO0%!J)XZ<RWD?=&0%}
z(uPaSC3JWMsmhc2yI$$<G8y1nf)jILtb1)j3o4?)t<PlR=PuqC&(#^DYSUgrUP-9$
z=hEQjOgnPxq)XhD4RI~%z0N<?Az$Ynz((dn9dh1}Qnn41@IpFnfjDCL+cftKB~i88
zbY52`0Fahc?sCE_7o63di=||a-<xlP@eVmA3(|v#uZgV0X{cwJI4YFQ--fLP6TnJn
z^T@Lq#?pi4xlH^TzioD16_x26@A8lhIYO2%!XGFyhGbc;G>eI*)>Nmle^*oy?yH5A
zrv}kkp)^eKfe+v!V}#HZBD0kzMRiP=Y=P+t?SE#<plI;*ZM2$^;#7n$_$Kr~$PA?t
zCdQ}jNfCSRmOXn2itRL)tMF`^kZKfQPM-=^)ERm=+*h^`-aYLS%U>uOWy<l3#;$T9
zI-^7+>{Q@RUxd{s$+c7?MGHUHc2!$CdlINjQtm`f`hg7dP#W`4T-+a-^#STW4otG1
zF462z+pu})XKP{H@yFqtQf*QEa+Ufv;M$FNr8xb!uI_A0U~ag;>E-s0$p%H?MZWI>
zu9XI7@Vx%UxceH<okAP(a0{Z`=$#XOnr>*l*<Z|nZBT->{2+}vOIvVw$-neYtnj0}
z!!OT3KwM0Gx5IwKL%+{rfBi$>oJT+Rt&Rc%c_$4SellB4C1~=mupW{ck>&Mmq5$gm
z;uFkP5~J*L(5bV8$Q%Q{VrhsqncZvidUx5SmUJ|<vD`Vk9`g?o=trNo<co~nSe%0E
zFZ&zeAU}S%2v6ShJSfi#G$SLy$j?QCQ-ElSjxXV2e=a{RWp&h)Hj1@VqtuLET61Ji
z8d==K2!qg}FKih2c18G%3-$xLub+^u+Jp-8$H7@wm(hi7QYOX0*Tw^b@aE^mxn4OZ
ztKu^~9g72r#=Zf~BNoy4Nh6NEPEWVGLZujz<X{2$(`ZL#A#6jhzL5ylnhafbXlu0W
zlsHE;290qW5du74y)@J9?QL9N&ynHV9f1vMjWea$2{FaR4y~(wJ~vm9AqAv4*|yJ4
zH!Al^pBe<TUDI7@;+Mmo7P(^8^@))WqxSfi?Z>g<<?EMKZVuzap}1|v5o4*%8SwTr
zq<#e*`jfCez7jJde|0545&mg_UF6D<WR+%_(8my0FaEb!qs6Y4x{oCUgU2P=!-4lG
z+pnKUf2a>ut){^|@k;+I<iEg&?^t&3cYL|+SKw8`hMGJ~@X%vrs#nyEsNi#qj1t5x
ztuAl|nP#9nS2mHah?h4^07V;$?@f0rdzQJDOG`_J>=-{Tj8U5Wa<ALm_4E<U=l+Fh
z-kB0+H_f7vH<MpWG^!FkII2|K%Y2)(WKn~EE1&8Vq`Y^LDvN#5vOHLYRd%qu3w<D1
z?IBCCpoXBPu{c#Dhp7kL%lHE&l>H+ch|YRcFhdFIXWVZ!pdA@+GP_B=*e3YUoi91B
zRd_nbT50W{6mpM(z7=%k!}K|F7H1^|XmIm`D6V$17-cW@DSH@RpIbHb*n@-OlP&C`
z=aCJ%&S8WsN`mES9Ns<}_U;;WRJJe5c5})$_>?6E40#gy+pSs1{M#rlaC=sGo*5K&
z>F9TvjJ0l1P_Y&*6iJ1xJ0X!yUy3=pIaR}`WCHx*#F*{8uE*_o?rX}=*Le}jv5sSR
z*uA(D=7LW|?5<C%>eOVi)ceAK(`a&J*X8k%gcRMO)7kX~x~S`PXHM;$eNG-X+zPAv
z<J1j-2U6NXcTTmrk<jZy+>T}B&sdJ$2pw;Vv+XNWReMLAQgW|DNSsDm)m!gn%9Xt*
zal{+dee|_#Anm=cWly!AW&eHBGg#yzLV8SMYKv@UoD#2z+=c6ee&&qA0)X*Y-8nWd
z-&~tysVUbI`{p5HkV}(FgB(K8&D9lPo}P-_nE}8^KSIA4Xt+WBT|fI0?L?m>RKCxU
z2^0FD#zZyLsBg;#Qw#h~gMm<jV3zs$917BsAx={?y3E?Simf?peY7#~?{u(mS`k`O
z!qo$TnalcvsO(x!)zHZBYa%|Zbq61_6ob|?fK11wQg%Ru!W5VCYbnMz&sS*l5MTS=
zS(r$%2wO<qN_!d%z|v1A_H{ZQ?7le2$0GL64p8aqbBTQTu$+bjn6G1UALPy|=IA7K
zj~r&UCVg`cs*@Trc3Q+mR+45m57nA?>8p17a^w3(+MWVS=5au@+CEvKu?zd2x~)*Y
zhmKsd3*B1C^lsQRZsg&+Ku+cBfyyei7K?8Gli<<~G`+WbyXi-H<EV*R=$p~nZU4$I
zS0=}VCi_Z0<Rt5dWDRzS!rNX0PI3lyu0hjCipw8<uLPP{&!jyYI^0i*^>pE<ke8D8
zs=0M{CHC{azl#6^d%C6p>J~A|ot|r@RVZkw_&8?(i$k?9l&snD^~Tiwx@D+_@mZh&
za$=_y$R{DH%ESUAF25#6P>moYSD_kZW^0}D!POPC!}Rt(gg;-r3zpc>SAC3tA+Mb4
z9+d5`tVt-*=w&I(TEhO_SoqBEwSR07=8#u^zZq444REP<@WB=WQbG4gc9JQa02WPF
zH6J9m6LWBluikDK+pH<dCVFHA>^1_|2a40p5M8}$KRpM6!_Llf))eKUr&Tk}%faz?
z13{k#SErC2%B!|NFwerLQ@}OH%LaXIW-g?wI7n+Q#*Phe?dyXC5jZMuDs?-p?8^`-
z6RUlR=Ev|^S$q5mqUiOH1!s$97f(d=X6Cudd=v+!AwJ(a#3CT(Sr8N3h@7L)ZF{iC
z_bTc5UuW|*xe4xXzDhGPq#8@VA_>qtJS0ixM9IkMCEgU-GuB?pd=gG)1YY04gmw_7
z{x~axT@q<S-&|C{8q}-RbA}GiQ}go9-Nhh=H<}o=zKjQUjIIR5<*OKb9FBDVdF=>u
z<|<xwPqJFR{^m50ttXJ!<K6#EmTGIKx~}@n-Dzo8Su1h8uXX3mn-C1V>?`kVLW4oo
z0)KALpwm3(ep+D>`srH3cnU3T%Kf+@?Xedc8t7Q*0aP)HgS~g*^0$T)lHp(yQ>fc2
z&!BF%%m{EBGuYm`x?4|p%ef@+56otW6JDxoukj6al0~S`J<P|;pO8`VzZ<iZ=S%W*
z!eiDx6e)89Me7j(prD!pHc5tQhHp<5r)KlB2Ex9z9Xi$;6NAJaX`87Cvim7`)zB?W
z#v5Jy9(khNjwHsYcLvue6&YXFU7u->a#RMjAX6%#lE=ilN|*496Q%UAK7!34gc8d%
zX{s-=jOpvO_ks5&kKJu-SXD!W!~7wB>!j67ggt9$xJ%aBdv&3nz{!<|rmKn5Yq*wu
z&o5m%UDGFV$)QVOzUIF6UmRzWPgS57LPJV(KfftRyiai>zRIc3r8xbzR>~6Z{AQy5
z=oUn{OW|uUvE;#IwSb7$Q%1F)R6^rx>lz0KK<2@5X9Qp0H7@v4p_<F>Aq|ecm(vNe
zXbu|BZWdXstrR;6xZaSzHXJ_%W<pLvrA?a6LS<$^W>ZLeJX+<S?f7XyWn3%zN4o&x
zhP`rQ<`$KZP&d!FrPxc!);M`-+?Y@4pDNf2)SP`WW^WbG3Yg0;Zf2Qw6hBiKJiafY
z$V#dG_ZhI0xZ+P4l`Xt3WwvuC^UQ9z^dPR`<bz@Vr=|lTx*|UOpUK;cX|@3}YGe0-
zHvA%WV%<?Y?JsG2WjN-dx6&pm%~5&pp7+&LFr5<h)eU*DWZfCFQDzzE;BYyx<CLID
zbCo`DOwSY$5b*cg+!2%c;`Xj#D#TDQm{|c--sZ7&HEaL{HKgwSP)F+&L2L%_b6t=Q
z*J*fQdd~O?q~;lJ{2ENQG!(zLBUDe*8i|~Kwu7-B>C~=dh{#RT&a#pU6n50PZ)v``
z>0Q;0%xnqC%aP$eAXgTZ*8jEP_mj9?m>+a}dT4l*?w+Al$mhN;Btu-5gYQjC<KpDz
zRuA3g)mk0>K*a%b9+XV7n9>;l<nZ75e$*>1(D;(x>r;Qcz@<-&-fc(jI?{TKNGjg{
zMs#!b^Yx_}I?<d%4HTB2`I|v?e@GMdY5dcIxo^l@(8tS-WylIv?OQiN59a;b<j8sK
zPnw)`$K}FYTdj31cKq_nld(PgJU0PyJ;w;^I(7Blh)l@OT#ni4zhjfM(~CFE^(jAU
z5kQE|+bph8IB{hzS=ZR3Y(iRJM=Dc?Y>0+8Z5VOVXj6`=o#8)goT`s;hed$ieUN(z
z>iDU%xfnw|r**>4m*KNxQe|?7Nh%qZlhC@!nyR}YdjS|N+nyHM?=+=LGdpuYXt0)V
z#K!t-u!^ChVv0R21%|>fH;aGx%v=EN+RD)mb(g>6D81&=1wLY!;0_n$bzJ<nu-@0#
z=O$h~X5Q3wHRJZV&ek@w@|Mc=&0*hlc{y#DblH#X!Y_Ijb)GKPt;`RN_`%xrj*oSd
zGo?RHAKVku^Ud5_%6G=TG%?RQF?1)|dkVl(NyyE1b6TrreSPsuPSBS6d%Yd$*q0$*
z3NAFQT4PPFM46zmRU{nteWoVY?DWr{GaO_m$2YL(8&o^vjcTWsb@<0XJE_C_Nl|*^
za-;fv;g>-mwsq|E{E(84Va{Fmdv1R``)uJ&h=h>z+Dw4&IshHX4!!9%<{#e>;T4i!
zz_N(C6929AK=wAK%;s~OyRjR$sJ^_adA?1UBph~U-p<z2XN|VrlDj*{`}S0;l~vfF
zaR4@?wbc=A)@P&Q4^kIGtM-&3UZGQOwDhtgZF67RR`g;p;v`rS6dZasyZch{Q`SBT
zL+@F;WA$*ovs1;r|E8tQ#=Zt>*`b^BRM(utU3%<Btp6>2wnJbW3Tkx;O88Q)I;>Y{
zZ?BZqhPPjcqHVl-T|e7q#PwH$QAgQXN0dN`wv7Y}EztRtThjdZZo`Ah$kPBLfZ{#1
z(+9In_sx^V#D`R_23)!?*BtAA*)8GPYFJMT-?|eoPa+--f4e^<FYu~58q6-!W}=Ab
zkdxjk6J)@!i*zu9v(1{8w#MsCW(KXwNqQz6ySXE<I;R^01Lc5!J&r~X+MVqZ$tnRw
z&?jgx=Z`H#3Axr$O!t8<s)_SnVO=D+d90PWe)Eogy~6xU!OW%#l#juImn|6h5UZo(
z>pT%?^<n9A)_v|B^metxyLka}u)N!n($e>1o`vM<ACWn59Z>*|#me?iuTJr6q5W>!
zvhaI{6wh=bD+gAz8dd~tLwtrUMtJ2(x;SkmCg7t2%H&Z>2rEmm?_c^d^twwkzBt>5
ziSht9Mgy*1fLSnNa%$c;9azkJI$Dk<4UYJYE@x$rRd7!Vu0^cKlvO%pg_$TQyw3yT
zGOh&j40V3=j_j^AY`|%Ilah3%LCl}Hq6zgZrxjeHV$0r^(yMAZ>z`)LEC*X#E!*2K
zBf3^of~3uM?6uiMo`yE`>jD7vPLg}r?ja_oXz$Tw=e8d|WF>|R{+kLbW|Fdm$D5eX
z=e8Rv>T?fXoheA_n<9Jfg&OMP;IoBS4$nH%Wb*Fc?cvvd<u<Cm5{pr%9wx4n|M@-U
z340e`e24?~foL;nwjj$`Y(LNCu~}$5h*s8QYixSSt$~>OUN>)e_C_oAgmLAiXJ=Q&
zcdtgpzkh!l8$QbG_ekfg8iXn`eAn(0FWb6IKb1tNo~s0(zJ9#YQ})!iswL&+q~uZg
zB&4EaRMhLJ`Fp+m-e^)o6=(f?k0p6V31zc7x8C~wl5<gDZ;AZC;)m7CC9fzlwJ+%u
zH#MK;&!5HxD2{#+Iax?xv@MG;kqZkRT)IYj{(rp)m4$CKocg$U-Q0S1#L7$y_}q9{
zN3}C1O>DFuO)e!G2ES8wH|SH%{Y_!sCae6@a#5vvbl6-?Ia`YtY_9z=Cd0m&+Hiln
zH(=@6NYNdcaO1MXbLvNRaXsEX6h}=z>H|LZ+&+ZIFp}SeDHG!+<z{m!<UhI*D>zzx
zARksI#XP~KWbtI*NZG|xVk;N2iyjFZ!IGLjp}3C7Ev<6oCy0e;n#+$2Qy2@ikAwK9
zVYB%ZL8OQ0)t~(++SmZ+#E{h25n;t*n|FC830qCfa<t8fNXuz*goP64J3<4`Zl)5d
zJBEM5MLi#*$6kuxwQ&STxwQP+veyy4x3Qk{qO5}&SEl}PeA8m*0d;6&?bz4;!x)vr
z1$G}A3Jc+FXL_I<tyai{bP}QB+=~&jZ0<%}X!Wz#v7(Bvu20pD0tbKsUO#>ewy2Qj
zpoycrX`l?3ZGzFYjgN^N>XuL5)^gG2Ma4TmS}JI2LHSTOzWb8#g<9p-bnc&Qc`pN3
zhtcuNQPG%Rk68j54kTOEsr2gYo?t)U4vxyjof9Pm)$8x$&xO`e*)(T`TI&qDi3`F-
zv`$V*s_H*Wm3mwqr}*}{z>=!?7fZ{UjS_p5%X!L1(C^%3s@z#rBsETNtA3dJ^01LT
ztLV0mGiRDzG?xh~lLH@3Hof??b<8AbBK_Im&H6Y+s~-1=v{_Fcj)`BvGRuX!+h4T(
z?=RrxJ_}e*k>&aI3OU8tRMD2<hpb$TTKjM8G0AP88>R^tSQe5$!kS^Ob0^LNzsIF~
zL^Kv9bu7_M^NGS&->Mh-ZO!Aokbat^9IT6>n=?wn?SXUJ)5ewYv{<|P0y_q!sY@QF
z&2+GVOr|8_eWW$?Mc4N3G4BcPzp;lZ&6N^DGPZ6JyyYH>9I0p909`v4wiX5+G8gdt
z$8ec5CLR_;z3(S2H1S^_Rw_0`;`C@K_x>ZUnkr`ey+Rt?*|fiu*gG8byosk*{ExVD
zA0KADBPu2a6qCg}X$JjAT!p@K=B>>55B~XIKI{KXUHyL#Q9}&cy0KrNPdL_mc~Nmq
Zku~5)<Nm}ejp*ii`CMPK_L=>={{bBpb`}5t

literal 493103
zcmeFZbx>U2)-8%_aCZw9EVx^+Bse6v6C4_McefDSJ;B}Gt<i)A8n?!s#^Lh2@7(X*
zs{6k4a{oV7tBay*&u;cwbFDe%7-JKuq9libMv4Xl1B3DDqqG_f4006=4EziV((5~l
z<zd^eKVY5J<UYVuO_J}w{(|c8QQH{?h6wkc7pz6@>}!Yw%qMBdFCMT**~kI<GMz8o
z&48(<BY@Y(>bD`Qbg0xa(%6#N*YRRGQDV8uHAJingAlwg*aW_`QPdX&SF5OjZ{N~%
zf>yqHw?D1E-0)kRGPEC+B-GJ7WUuH>3Obwb`b;hhLMGh(y3Ye?l15`ThRGkQsNrb;
zdW3(MsG+MT_fr!4LBhg;gAMz)<2{_8@!wyK9oY4jdZUeFpT+)fzW^InTdy25A^_zt
z#}(Uazzv)K4*TEU{!>ms$f2JhB7y{h^xuv9l-G!^o$6(ozq?*KZw)WOVdb<u0Ol|6
zB0^#f_cfyVF}eM(Z`>OWx9Y){02}6Sr*=>GpNN!hC!)Vx?m$0fi9XlvYB;#RPAp&f
z*NC+X&6B^)#pzSjk1Z!RUnOAvaxtXy?Ei_-eIC>Q>-7_bMFl-|p`rZWWECpNcigZw
z`Y%Hyx*1;+eN7V<_HTDbU>E&sg#8_h`(Ll$4Z>T!n}>JQFn>L{&HqIFXLI~TzW--)
z{AH#Gpu8Tr|JfXWR|)=8IsQ{Q{(m6Ae=5iS4Xi?edjBmNd61-@Yru%~bni73A%5;1
zKI4T&34zkd9KVBmihl)k&+e111Fx0z;WPD|YdC(Yx)}AIe1EDEUD9V*F`*iiFeBBu
z^agP6)RaUR&eQU)yxZIF(zMx<Jd_`7;4EY5Rn}(X7}$R|ma5K;X*$2j)J^(nCr?8<
zPMz|RHa(6eWmJZg<~H#|z^C1iASN_3m5f6vGh^)F=*OS$M&;<mS&+8#I;cnNB>dv2
z?()fZsT|CVA`_K#J!cih<6}5_?4hZWN@HVV9;|L*hHPi$LH`yQ_5e_d_}Ha4YpLqI
zdEaak_G<Ex2S!3@1<plo!<E;7IZJQhXcOqyuObu}xYJoyC#rsz{?8BWVMk~~1$`;|
zb)a^89iDi0hi&?sTIN76snS=eZI?6(6p2r}57X4}t;S0Z!N%sGwt`ZtG26t|6#u#s
zhewX?r}7SMa(;~h=VF5imCRW{b?i8P(vzT))@0h*@%J0mk0t9_qi0Dwc?ov2Ttw-z
zVYxo4e=r}onb6EtG_Ykotzb)%W#4zzVJC$rpYy^5U$j~X42UXDG&QI%gDzOsg(yi4
zPl#FPFnht3vzh;X4s>7Vpd)VKl*81WE!5_)#?0TIfgq6M2iD679=6bVKYrbmu?1H%
z%j=H3`uK`d<Fy*p{=tx5j7BM=%xECVv34a9XnFv7Q2@mUR~xi)8`87gUD*jka6~aL
z=NEqZS~^l`p6ubwWb>iei=oi5+T7h~{B+-RghzG^MoIM_e6WH?sPhxOq}JTavKb6Z
z3=v?(c+@dJz_^OX?oflakFK7M{6mk2LjMOLMM)tmT5nDeeIijz0T+KHXRy4({7>q*
zT%I>cVJXH;(S$}Bt6k=j0KqhRw}exQ5)ivgXXL*v(k_e%zYN1i*y<N*mlV^Qo|o}~
zxeMZlR6-c}bK;Rbzi(=$<}VW#MJd>#CHxs)(WiK>FU4!a(PMz~K79esF8xj}5L8jg
z%p*5vY-<;9vft#!s`E7#@$jI=R`(5=&Z8v||2<}=mSGm@d$ymg775iw;_-l?U$~ht
zu>yE@p}B^0pL7%-?4U?d16JOSnW)nYmcr=v&TBw2N_TG4eXh7pTe8Xy_+7CqE2I`?
ztGijz))g$XThV?VDf}9?qT7{4S7Ci{c$NeLUi6lLvjUAdg)k-~)z^TrAQ2*#Y*a2b
z9s73CYleT@>P?dcDtN;eyl|7HTSkN2!RiC}5587;+f-uk`HTG2?*Zn@wGB!L_iV6b
zWcYmB?>g2rU?^H%XW;}jP34$$EY8vjZEU=WZ=)56I-%c+aXH^?{3q@W?kBWy8Xe_u
zCe=*9(Pn8v(_Quc1z05FVXk2cbAd=lb}rO|QXGAMzrF5#Ox+_kl0((Oqe_&snse2@
z3TI!d;K(>{rp4zqtF8xJ+iLYIi5$tdO8nck#5%Yn4GoMleXTWzOS3<ti&RX#JjQ-l
z5e{Gm*uzGx`!N=&ua*!W$3+*k#2?P?eixyXqTU;x33hTgk8$d7HxZ1Z5S%^+CkGUl
z{`+D(9V$T^wO73w?Q%@2Bc7f(3;6KeKyXo<JPm3PV&RY4HJjfaS)oWf?T_->V2ML?
z<le#2PKO3qh#!x8;MPY1f28l1WWrTzOJ$yrt#{1nMLAa;eEk#a;b(@i%WBnQqZ>eB
z*Kz()4hLmsEZxL~3wxKnp=cL~s<w-kML6As{n^P`RbzWN&xES%^9N#Glo*z&!Ra!m
zH_R+O0U}YL4Qj+g>OTB9p!E{EL8}m<+k<h_Z%5L==1VqGWg}<jH|0&drgu;jjiU%-
zeSO^DOR6ATW04`ui<Tgo5TE0Z-vp+3SE7-p;~u}DXIT9?j=ojp>xp+RNnKZGj4vSI
zTM3<0TZU9LF?(LNC_FZKc8W%yVpom;|Frzuv-L_H&Lxnf^YUF0sB7R^QwgsXY(z23
z^CjvJt-urfNb7~>QB=z5_zLaZke}o~{jva1=pznx;0eOSW||I1W%{rF+08V2d{ba$
zQ)CN%U=pgWb;SCwP_nc5VnwYQ-ryxm9D4?)h1pIwt^vVMY8{CmXTJ%hOJS{|ZbSG?
zr9TnlArqMNox!Gw351}^2oh`Y!4%^Mq5E(aDKgzG9xV`7pz<v)I*BCI*$X3@QRY(+
z$sO~m%dHKWYPWY%a8%d~UfO_bENu`JKRH_{$=kHCP9Zq$=Xbz7e?iEX#&$eO{nS_(
zL@(?s%h~1TMi57)eM0{Ifk`W~-%&X34T1lIbN*Jp?)1N9CayT(YP<d3{E~@>HHLOV
zNf#17N879g1fpcWC?dpn!Hg0dCTi6pwL&IBIP8@R+^bP-f>AoQPzBN@tNPZ?A*#r)
zxmy#DpwPnC3|0zg6f9qPoP~|*aO`L)Aaw6+f>*AJ$}7p9<%1+ck(uUa6<gv7$M-{k
zIux>BRo@i8<4WVI!)A;n2{8>%HgT{brZ=8sKIYb59<W3MziAkCJKZe=FNc92LFPNh
zsNF7{06+(pN(3j~c>nI75@)Bby|OIY`pWztvGZSWGi@@?7G`xOv>V`^oA0M7O|>h3
z_NLVfL5jevbd@9Addi@!9Zy)FlL;puuX$qpop39=6{URfDYf0KwG=8ukKr>IijeO(
z|4rAqzy^D0@_AqjCETpIRaa$+2`)TA;xU(>`h3=KeLVjVxcY$$$#770<nAo{D4rrD
zCDSi%Z^b#m+EU^qv|hyte6!BMa%^W`Nfbzz@rq;U?ysPKdIi4%QYY*dKPdO<-Lx1?
zboq*tkN<9#jvHCd&a}}{RDTb+*)cJ*Bct@PBYW<u!DQhF75rKD3%|fI7cpcFB$io*
zy|aq>sW9W}Mx#Wb9?2Yx&RHgaMS!@gw#-_}b}NwCz^I<da*QpbpA@hE1d$74Z~Aqn
zBVBcR_Hk0#KDKh@B_SXEG6t_tw05a{W*4rL&R$v2Le@N%Vz>aPcnf~Cel+dHv;`D-
z0b3h^R-R0wt?j{9-yTK6c+~U9uhhdz&bP)H)<YbVtMX_lLo+$SW=_=cCNF|ai2vUN
ziNJSM9v;4pst3xR;i@)r#Ji($wO8_G&lxVV)Gu%VPdtCn)o;qI*3U|bEZzHfZU4Lu
z4uh%<Gg33PPxB-CF^sp4?h<weCjq_xBWF-y*iBa0d{hsPLeCcrL>vJL1SWuL2Uq0e
zCs!o_ZeA9$+m%S&#hoJyH!~=Ad)Gs0q{Z%2T7glNMv=l=L&IA(BVcbmBc{%?D9Sdg
z*86W7l|N2?RwBa1>>v!Bcz#Q*WL>;?@9O6oKG(`lrks0<54XD5;ErBFC-TCq^QODS
ziTlLMOq5cOaB$wd%?K52gW|d;@HmQs#;lRc)rbIzV;hX5Lx1mSDTvnBz;%)qE!6Ls
zn-p_cQ75R`pzWyZ%QLfdVDkO2WBrc;6|h3o6J(oP&o`}C55C_ku4z8~G%0CkR+9y+
zYz}=p$B(=QtMRRTibN;<fyoGKoqVM<o~`$n>G*m2Vr7f{v)kkSVB>;bt{j=8*cpO=
zCVxDqVzeC<WYf>nhCFq(368(R2Cu5!2+0W!|HUdfyQrobJxZ#Q|G!zNw+P=y-&!9Y
zE{a>{JmbFtE=**&uGz#RmetrmW!j+iU!Yz?PLgX)z32gvsQJOHg4x4{;(+Y<0W$d+
zhjUk^C-4+bmcXuq0Bf=&0wxLU{+sYO*NM5R8n`^F)5CW``$0Lm478VKWWrrg*&rRB
zj0~{K+H~A*iGU8+#L~)IPD&lFvQm}D!B^gf-0oPFXI{<{@j&ivhQvx7U#iDY$cOPQ
zwqBO0yxw9ICvO7-lpV7>VhQ=h@3TH{s#vA9Jt@bK2_1YX-?4Zk?rCV@-3rBTu<hFn
z8q4$?WLPyWdc#wNnk`ApA(6CJBuUN#1<=knmq;I<7b`usfuz5OfNdGo<R`?@`DO1T
zx8xqqt56BjcuiSLeuO7(y-U?hJf`e3>$%@9^tN>#wd?)bT>*0}rYLrz!|cwk4QnUP
z$N@e<W<?MlaH<wDs1x3CPMM1vY{j&A=8K9~J@WPO#OZRHgwb<4qF%mRFnI(3*bdac
zjwm+MWz3D>1Qe5BsYX%koBKS~68>%m_Jdaa$u9&yyq93HpLSYV&eg4<bLZF9rd+k|
z{O`z4a%#BwVLT(MUm#@f0Nk%!|1?c9J(_yGld6}KRuskamrHCR&e&Ows3udBGDo$V
z8!QF#j@A9G+y&LtM57pFGY*Rus`v4Rv;rqTC++TO;pBu~DI~dj60wkBi<BZIPM5yq
z$ib9W@54t+KwO^zTP5cg5RYkpn5^yMY=~W!eRCIkkR?wf6e?i&DQeb7MgSDNCO>Xv
z=ff>;b<;`bDc<Z&1cMf+#M+FZFO_jfmRYv)nOlNab!9&N!Bls03M)VFJ#uI$e&aa$
z`#3<}urhJPFLfm2y#y0{M_s5&N5@f+>75(7A?T>#cQ^tU`L~3+yPuwbN0RwS7Z}ML
zNS_3^$uAL+MzJ7q(N^+!2S11oo`)~IULysz@>U2AqQ_~ir-0+8iUK(o482%AL(^QA
z=+vqO#AMj9PUFOBVDXf~(S7k^zIw66ex**_#_RKS<)LkCbJL+WLef}Y%l??U!%(g3
z?Wk|{=G`&8RiHtfLt9Rt3p(VUJt*D)-S0wA_+ZI!a7xTY<ig2Gl;v{Lo3qZ}rsTm{
zYf1Cp@!*&?a9w>Dt87)jK2e9y@o2<IAN>L^LaYh3L=j}2H?K<+UFe@Ze@wMfJWJva
z5J=}yVZRCySV|W(!qSXt=>*K!@#-!mFr6k}+4vlvmm6(tRP|SaGLC(YEnVm5m@kdC
z_z4z|KiEy<p6PiC8)rl#;(yO(V=7GlbE(7vz{_nNJvOZr=+49j?o`pzg18YTOfpDX
zJ^izL01wlIDQnEe?F|{aw3gOUCg|f2Lu9%uZF)Ku@s#>uR_e&6^V+7IzDw94R{GYx
z7w(Jg^qmy!2znTKM#rVejhXICROSR|n|~@Lm!J2X9<MPlWAXxG6$Ig8xU7v}ER<RL
zb{Kzbg%qNxjHs4rXlg~ToJ@u`mxIH{aVVFaim=@Xw>MmHi4>WJ9bY^NyM!!IpYQeX
zYlCo~({ym+knVKRXtNE&*1DP~dV72xucR2ME!i>lf|mX2p@lp)mwq89>p8^5|3-Ln
zSin2mNma{YFSJnhit>%-|9q)7{%oD;RM)n8D0_Syq2SN7?h-eyQFy-A9rnHbh0HG#
zSyvJv&f!(5i9f^Z@DI1xTv;7eGUx3~uV&LFtF>LktkC-!tf&_!yH_NbSdd^hR_{q)
zLckiU2XC7OZ;ba{G#|VlGLNkr_zNks>4@8GA<D=e5Ui~OcGt~)0`&3EIl#FLX)i8O
zt^~^u6)z_K*qL#(1q>ONkkr&^sgX?XjJGg+FBRCd+Mgw5)z*s@bWSnZ9Qf?BK3jNR
zRX;?-KkO&Qz7uz(N7HUmxg(_*Q2I7u`wqi_&>C%ioRs!<jWK1b**VG5vJ{aQV#|K<
zXQP30r{%FIP{7xRU?|zW#dO1G-wF++!G9!UYRA|MEe+!`t{T@)wo(Bm3?=lzoV1wW
z%M$QzAu9tUCo~nJ-7+tQLX64?M}s~;nj7%Gi%wk+i!Kdz1EKWlZ7}_x>>7C$!qU>x
z-f>lS&I#>NePp{>0=Z7W4g^w|mlrTs;n>ssUvR&t3lD{`CsiCf-IDu3Tlgw|^2@?$
zmMvm|;#oZ9flKV9qNuO2VRJQ8?))N0!A>TB;jP8m5p&9XWDkx^p7=he-0w(X2V_%?
z3EM{@Ud>Z^jksgIPWdoJm+Q{X5&{+twdtM5o^efp`#6Ro&ZG=QU~YlS`)&8j@-(>J
zQtY%$aA#l>f>mrfa76cLVo<l6`-Z)EOh-Z27Ozsc@5EMkg|FE#{C9^<SIKN^YN|?#
zj2nR1!dS8YRGKRm5xv=nrn0tlr5ii9^F(1nAdHBJ(ZKyvjl7pZnV0xC1P`IP-nTkh
z{3m{Xg3pkdW3Ae3tYtlZ0sg?7iJt=mjG7z29euD%T^*yYX%hik+HdGa+cKt}mKV20
z-uj_61B`+*!|xL)dL_p!K|&c%c4i>6RAcEZM!1{Ib!)sc-)FdG2UW3md$1cr4;lr0
z;xQf4<f{HOA0l<imi->L_hl6*x^Rgo_;c9%J6%>zJuGIC_gj8c{2v@n&k4#x&to!+
zrpf4cdXM8q(6WW8DcysEPPPL@tZbrEX||<ZpR1}W&N~(s$H(*Gt(L90{;T~tRaKgs
zqIS~?yMM`Huk;o=q*qfbN`Wob`*By*E!AjKiZ$?e7=Hryp-aQ_ZIeD9^dcGLn2Fr}
z6!(BFWd80#+{uaqr%?wX?#(|tF6Od@N={OB>^FTWckRp(c6tZasVJ$U(N+l(t6mLa
zbgFIt()G1gb3Wy9LrIfo2wMtJ$}Z)%XKNPvQpzHXH=uK66X)C<^aC5w6YqkMeIe?l
z;~CcHx!z*amxv0u#qGW|)W_;d(l6yMO@1SMN<AV@At~>T((O|cSm26H$5PPZ@EJ#p
z>ykK&=Qn`RQc<zps2sX0E4DuF3`uT+o;Y;6m#+vT%XVA}xUe~r?)HJvcQbnqIO)Cl
zv06BQ@~|v+OS#uNMhLbl(Vjec9?Cbot$NNY+VD75D?uEh$=|FI8?56Om|)RX1S1gC
zZ->O2Hx`VOiPg131m?JLvO+o)uK)M*Z2A**igmtwX^1!AK+iMH=X~bfTYiCn;u$HR
zvD4=-Z*V-o2u*h2uy86rVl0s}nBA$|DJw8(f&xSKM>}7>MNk!qmcJ78y4wE<9oCDR
zj2}R5KA9ECQq=-in&mWBHFN!6B5s~AEUDa`lU><oXeAWCMXYh}1-osVFr-9T!er*%
zty3H7vgi?H9DVNKz8fcllO+Eao}NDdYo_5d!s_{}%-OxAyzxvK@X6iB)mgjN*c5)n
z*M7K9Mwiq5M0QenCCZZLWZCcGZW{L+aCAp+>W9tBvr<iCb<XM^8Xc-g3W^_G%r0U5
zG!4yzj+axp{WTl(0?!K@J~fviT@}U9=Nyvb7br>^HQ6Q6c^7q1q-U(aLssJLZ93ii
zMlJ<OaEKNrd(B;XdItzbN3@aj=<8Z`E2Z`|D0S8Iaw|;p42hX!L~;x>X{iq9+`_&@
zVPN<3*b?SvfuuYYip>m|=i@%SGubV&+c_5CS*XA;np_0}SHmo{xYo&=%<q2T^Rm-*
zB#H&C3uBHq!lxHuo3O6`mkaE9#{O8mbvs^wRlsnM#PzG+Ca*}8a2NUASuII?0kw#O
zKc@Oa)GO&p16yD6d~EL@E&jNHrPc1n_TY4@AlK_<9~8N`n6P3oQy`NiYsY-jajB%|
zG)jNFNz@%Nn#5>PxUc@dFh(93tl<VVRO+;&#BGth!Lyu7Zm+{`q<a^Q$9GpYiY@D=
zt@(B_d(pxsdx?#IB}hS@>=7PqgM(e#=#dzK@tpUb={EeU73C2thi{kZtCH_GoYMyX
z)j_!`S{heNPW}KyWIKe;Q89Rrfw53qi1BIioE&ZLn4e6>`7T>3buDKg)tJl*ccZGe
z$y3d3esigb(&__XNiR;l=+YT?J!PKh<aVVzjnDP{plv1@I3n%sQgV!=ihv73Q2N_<
z7eTj+jgW#efsiZ(;e^ef7ZU7)zw_$(G1Y7lSC(#4jeH9<xR2dN+vKeDV&4mMD0C`2
zOos}}x}!Cfx0!op%PM7n?Kqt3eATmFIu~;LZrIbq=yi9EB{A?eL^etMV{#UfMz)jJ
zPx#eZE@I5H1ixPzS@Z!8U{1<vI1rBBo{KDm6Lk>i>kzajHVOceK<~hq^CF(LE4$gj
z5p^u?k*2OaBGNS*2d#q#BmFE=smt|<bN>%f|H}OKfT@i@YrA`V4R4zQWI^Fy*XgJ>
zpPA$>-Tl#C&%je@IF61pxUQ>W4ng!oE!N!}ROpSsL~IK%*si+$w2O?R>z20b>DYd&
zth{Pzt*Ej4Uq>-{B7%=h|02ZvsZtc7s`;)u$@zt-_4GwV$Nrf@4P3UtZPU9FqCgu~
zVDCeE?0+x*HnqyU+qTI$;qI)QgE6K@wI3(zJ?_S0DJ`U73x}NFnxEM^357fd!R<v}
zKxDi^?U)x<{T=H!viCNf9lTJ=c*fdWpmoR9tQWugW!(-KC;w|>Wc4mo82gHjyg=A2
ziin*)N;f5dEnUp!Q!B4|uQjULv=dS$fG!lfY9fuG4m?h)wWsAK>|WG^G9Q-tWfynB
z3^}#<cqfD)m3vE@G3t1nRo~0ULYfGq|7LF~&Fpjgc4bBTcE7JebS<mU_Zs^YZmD6%
zmeY36Ld#=drJ|}h_NIs`uU35%WfFsildne0+77i_(hoig4>&i%;C&{D_EdjF4WojE
zK&C6}-k)@sW^ao&|JGV)DH*fKT+~uUUL@_0TEQ}OOSWu;<ElIFbGm&$7B?lIhDxPE
zUfeCCWa^--=t0N&<1Ba|4`ZY?uTv(UtJLV}wDB}QXF;WCx0A)J$>Hi8y|AQe!LX`6
zeKlxRO*2Bp{znVT(Mf0=WMB%31t>{Kf{A^0?i~P*aSrl7k#>@h4JhQP)V^qU;|M0E
z(*k1E<HXRbllw@QduZG=tn@oI3n<69$`LVgML4nFNHYb%PVlFjEM5If+z{KveBa+@
z?CCN0X`vKA0)_It%lTJE97P69At5bYF9JScmG*eU<%+&}_lmSS=unrv4l8e;Z@S|%
zGDsnqk4EGn+V<7j^++HCZ??|MuUnV7FOR(=vR1Fh%|FnrM>r#(+U2KfEfjYFnP@`J
zsaG6Z=ukY-ySIHBdMG~A^E0)Jza+^jF7GVN2Af=*badxXAl%Il)WF8EI$k<o0fGLB
z5IYm_8HC5q;Y1W9nvy;7<i;W9YdI9t!42&HA;Hld(%;bVZe9%Mec=3DzEF3|)0&=6
zENIuh7f+eBbbToRWso^3L<;|xlprkQz1MM8CGYuw8~03*9Tkzn75DkVz=rMCp*x8q
zx;W6y*<y7$8J&T(ooBh-_w?iV_jNI)sL3{Fw@5C`mpP>bHxl=vjpBN4-=r+Is#JTZ
z@Xgs+Izf7R+^8t@BuK_#ZH0)@sh!qs``F|hh}YSv@7Cr0W|?bFd0?(abz-&7?s9gS
z#Bgnu5>^<!v$kkmRxwscSME(T<PHW!^$Z;WZ4VAC=a^!#qOF0vzqhpeP1#8hc$qDg
z$T#7NOn1cE>|0-R$+X%w6zzDn_Ht&8;O}omkGrA97=*O<EPP_q|GR*v%YWw=(;J7q
z2#(KU__=tnB3$-@d@@W{mtj`(2{3VF;y;EZg>4y27#vmye6swnupiC#G2a!<)1EnP
zhD;*l=omz|{`r)}ITEKyk^A7}R;dk~&J2#6_A6h1#7g@r+-dlYNm{tsmpsJy<mAbX
z9nNQPzbqTV-0MvIEz1XO_9FwfsW;_LD<{#}C%$Cn43$tOo$$1(_FHFvvb-Nnl+Kve
z5#VQb@n<#SwQTjS(j==8W}|woe~z&KeS|Z^;#GE^g>pD#H1A1r<cp>@G&gS@8omh0
z#t;XNv$r4RL4LjYmuqZ2Te?T{+uX?l)7L{4({SCJp5B6VSuZ1<7hEUv+_)y)m!6;V
zoC|DK`)rkVBk8VaQC-j4Jbt^G3cqy8<%Qd$)U)nyovdc0%#T7nBSfFLmXKDIV7&=1
z#4r3N%PaTA-lX?!yn5TNH@pGR_HXl(W@JN^x|sr+*GM843r>^z`r&P1ZXrPpeT60X
zMUB}>E^J+>bdUAEibV(bUW)ivzs;()yoR{T6)iS~d98F{bsSFd^?aHV<&_3p3u+@Z
zbMovQSIqH}6;bLtZ~t60@RBsIlH7Zm<;mnRDtH&vBu{VZ7&PFye#m5D)YS20En4hp
z-^wF4Es2<4ZTHoKwX<NUcduZ0Uf@FqP@(0nnhbxoS5I|M+OJR`blU&kRznT)b?+0?
z1WV~n6*NbeG>hWa2qhZ17LCc~__fh<+syDxwPbJMBhK2K>V(~8ZjKa3q@K-Cc$6U?
z|Dt|gL*2RUyTj9}Kgx2pK=W}c5#Vn3cHch-&Qpx6o1<@eVwLQF&mtVt;MBJEQ#q^e
z&$k@;-hOXwCiL4_-3}kC@>^>Va5aAXz`l$kGaP?Qjsh^}n@&#15?M-AG1PH@kWpaO
zrgO8^NZ`t}gVH(km44e^O8rAaA5xWQCCW|pPxq8I1}_UPg<M8!IaZ<<t=|#cVcVRd
zr_GK(<8QJva(Pv4nFaTPAj5<etL=P0;JE}oA_?NxwlXWwfc?nL$#U~Q+C|m>*?$Xe
zr&f-u#HxEHs>!aO2O(jF;wmgIc|YeoK8n7b_?`*ARFCO>+jqAdPwUnL$YKVBSLBz3
zcIp%9fvv^g{TCM<_{4yH<?KLx>MGTt$OT$<8*S$IUfncX5^eWjxe;cee@*6TWfYOS
z?WEf!P?y_B=|lZQSz4TBkF8jNr>i+mOKix+mw(KHfl>-a-gXpaHg0*1ow4xNv{wwI
z#i=t0_Qe%1i}4*DdOvEEZ5+(#ku<+jGY;Q(xrIs7M%sa!O;^GcE$wXj6UyEx<O+={
zrnhp>>vk{VqeZ7y_M}w)8QEDL=nuyn+)M1wMs7c;wK+VfwcW9<SgFB@1+u-LA~2WG
z(EAcGBNscceQwHG(^2sN@YYjO`=QtE$emJc5L^ki9?4s|o*}~D&bMBdh@s*}GnJzX
zT?Pzy42KYG1Yn;LEDE~1-**<t+4bEgHnYkaZ9i<VFdM&+3-4t))Qz*ND$NME5l8ty
z*%f7G;P@X9KyZChiADX*sH!3<yY%KgpD|~Fm6qLG2GS?bj82xm`nQH4Y92*6x2dy+
zrfO7juj``f_?FzAwcq&toi}VfS^jLjORg9lOi0<G`s^zY8PppVQp9NKq{gv7J1w6S
zvIQhn2kL|QG#jlDY{iFM=lxu4QuEgn^OAGH;fKzG-Q0~BC#G<6L&x?23&oVCA8Ib5
zWN+>gG_ea{QsU?5iP<{wB4jrKRhAa6z%U-^q%Ig4aWG5zH%|{d>^xzBH5+Sou{`H{
zGZVDXk7&F!uhcG`a{UA|pgC3&Z%0wvbXYlPzr{({>5C7mfu8{iJ-ug#Cu)9E6NX)-
z<6FfV&YPCiz!njOn<GlwWQz1;!J*8|+QM;8ACF(UHs_r-AFzbols}HQ(?~Qu*D&(x
zVo`cu*#-pv`INM=M^18R1Fm(^eIo|FO_w*ri%D`ck}3yPT>BuoRju6F@eeM9E5hOx
z+f5@LmY1HMH}`zcz_z`Vn43@L)Z#OrIPe*PFD)zmWfuUip+);_o-OSA>=)Fbdh)Fo
zjXa;(+gJGT`e(j}*$hL&;0FQgS(J*U1`5+MttsXJ9ks46Xmi7uy$u_UY0XuLByro9
z!u<09CNu+IXY?ftWf`$X+t5x%BS>Eut~QxP3TPJ!)@jshYX1eNnW<+YINw!^8s}_7
zI~)&}GY7*~xK$C1Q{TsTvu>L^aVQbC#<ubXQixuIIk0GuN#@QjSPid`(fA{Xf670~
zuC(!p!MEMZHa4U2$u_#uSM$FdwjSOHTpgu$^1t)xyAWe%6p6WvgpeLe&tM1RJ<2d@
zUAUX=<<!5i&_;S8?C}ofun4e3;5DonR4vIPNau-TFVvbM16nqac=(+YoTx%}##6T@
zg--`GyDpN&M)bA+X_(yMu*9JF1{wUhzJ2o?7Cdd&`VO31Z>qS6MSh|&Qn|k|&vhid
zxCAc1EvV@SQ$0;p<M8)RyX`!ICgzm-!o?jT4<B3;+d9)NpSo|B;~@rqe@N`_7VQOp
znoxJ$8|KR9Xh*t+gbgu>T-~atwXb6|G&XWS#dWzJ?im}*PhC8+RQHDLCdFq4-ed`r
z!84hkDr&XdDvCV*uGi``&pzaLefn61JSzNV{4h6OEHFP9dE+SEVm(G($tbt|aDOp#
zn*aiXeYS%?2NUZ1%iLk}>m=Y;#s(S27aiSoj<MpN2{8b4#WhQkgZ+>f;#g$)<cko7
zDZ9<#EI<TxyN%joSU#9pMe?pO^3RWV1yXVq`1|A6%H>F{em#}!wGfM1A2Bu%T>@X>
zD<fcXy>j}<Bo1o~r&5lNm{qquoC0^-i7l5{muC}~Ha}rAVD_YVQu-F_hd0+=j=vEm
zncxqi;?*1Gc9V!d+|5<h)(L+76}p~*MG>!EKcpsCGRX2ZuI--B=Rr8W&`S;-v5L#*
zv>oDVKw3%Bb^0vR0_h4J$^V>Epd#XCi}-xJ#kf(l6;9Jl9UdI6rd3-&=zq^Wes!c1
zHNmRP6R7MDU{De_!yl|i@VnhAt+hPY(DT^P@;NGGY|V@jU%eKh^l~AQ$I|mSk@mSd
zz_U93bSOzV_u7Wu&Nov^db0veg<v5$qLgfGoXb~F!-CdTGxh8cP6MBFm)B<bOAM*@
zuJmP76LF507q6c`q<leRrO%@yLVf+@+{>hBS*BOZb56-;z0QifGWChMHP+XADL!u*
z{%F8C4?dKKi5J~gLw3xHJR?9M;hn*7KoowcaSn$2hkI)!vHFtLCF0(t5^^0nFiaei
zK6f}X=B}EH!X~j24KCj)YzZz@vSuv?y$bHer_j0)g!F5=iD(omFbE`$LPMhNODr>^
z;J?c5#?aYG*+D$(+BZvUE(t9SeQ-Pra6Chn($b#`tJD*9TSJa<!*B5SYq|W!7{p(~
zcSHk3&QY<!Pi)lP9~1thd1{-vAV%-uHoME^<3WH-Rhv`GEn*qus!#SEY>h)MgdJE%
zTSG?98Mmj{ES<pZiLW*enx4%dsliZkoK~Z>Pq2An#(zUKac6IB*{LX)x!DK|%{He?
z+g;BoNR&z}xjh!0y`H)(C?pK*q)9N*CE5CTZ>dj5OMbpK89thVp*!8+C0ZbSkht4G
zD>*6omB$+4Gh?jG#u*J0u@Fit{P^v=&|LV)e<a2@R=7lIcs`!a5Z-HLV_mS%?}iF#
z9vOSyec~!ns8tMm_M&GGgDoRBXTzY4Dv@@mqT*#u(>b%Pb=G*Gm7-FI()oSG{{*Bf
zsLO6(JFNyw)tf)wjkYGy+Ef|qri1~2=@*0JR_^bp0{-`Nxo5wWeWQ@z8VJ?@RnvQx
zUfl%H`kt;g+zNGF_J`^`Q@Q2?-`1J|<wGx`7YHd;TA_zx&p1#|0;7Rxnlvs)azYgp
zrCFGsH_17^=LF>ZPnHVl?<duK<kPw>QDqKWtERxaoNo|<1R(kr(?4BHLA{NK{F`y&
zPXyq@0|9K##l<e2_Cuj;?JE3nyZlzc$kH5t)N#$qUPdvmWW3C^?^eTT!t?$jw<wf;
z7uxQ&^M1Ni-w~<inhN={S5Mg3Z1d@%xhI`ee;VF(Z~C4(4nz3%j@R)<*mgyJCy(&h
zZjX*yBd^pIJLYk4P?3<{II^n^fz8^l*9J|A9S${d#H_-=*q-aEZmV_n>6xo;Mik9=
zwcND=OMXz{w-64g{io($L!os$#$OaN>k@c-J4DWT^{QU>xe`qTCgnR_7r^!tuTQi0
zGa(}Ae|UilEw!lJ=5O2pJ!E8s<gQ=CBk|t7cO5hGxgM|I6Dy)T1UVy!S6(X|3cC$q
zD-at75{GWGfipP0z6i>B-Z_qQFvp~s*e!C!_r6*g07nv0D@SbkmRu&%obSrfYCLu6
zWaIM>6VH<^?d4UjE@;B%eUb@6SF<Vtd0y4t_4YL%{-Jtu2Bm6)uhIEZXpR$Cy~LvT
z<iBmZspM1D-fx8Z-PWkZ7<)l$zDNXu0qkS)m-?%2F*`dyl#UL@r6{!wBqTeQHQL{s
zc%aJ8+<R^mR1)`H#8LEbMbUfC88wBv!Eg14p5_Eq`&gw!uSq0w62{JDf8}KNUE}d1
z+z8_Up0jq=Yob!N&)v0zyZ84iZ%t)}5DWk0@H^$na-H``&AA!UYSK6EMosgrTMQgx
z*0KL-Ke%Ut*|JC6Xc^(FapQ@%Ytey!_${KZiDdtQIR`4U&f3#r;|Sm7JVn;v#(SQ6
zDRVh>aF*7`MENje`$hY!OE;*$;d_E%8b4TF_Qz&E5z$r!30{pg5a~;`^5&JZ@)qaf
z$DX*7kwwJP_nF;?nNIw4O75u!!Nw5g2qC5NCKI_2eb-_dvd(xpCM%fru}YWu<FW^Q
zolUq?z7S26O6^c101fSxkujFc^QV4?`x~+Fj!4s)CF5-Ums^pJXJDq|?;WDeg;O8g
z+K((LYqz@6&GQ>Y{?7=}-G0J4hB{+`RQR2O34OZ*aXA7=rE9lV6HoUq)RO$?p7?#0
zqJUjrhh7r7t*p!{$WNuF#?@kTs`!sHGV3GsMupH!jVejtc(v)na;T+VBWB_O#m9yQ
zM}SB57D!0+Xvr+s06wEPPIGO&F%YZtk-JuiCl3Zus!WB;io8roaI(|3C*oB*>Jd%~
zh@3qnGwk_(d}E@x_3Hc8S3I<G2V2KupSVR%26<>73#H8rC({^Ges&6P2c^wwr$urU
zG}$4hoTLTwH@zVKsYG9S4o?cu*Vm5?3q!(u>*vjnc6WDo6_h+|(G7Rg%5Sk!A#ZxL
z;B05pOmQSQJIlhcc9*rl_BlS<?Twua){<^UadB}~OjG?}{cYzU??zRPLuuoW<k2zm
zQba}Nhbt}WDxQ)JhqMzpXDfn~ZsiB_Ia_P%O{0?(gBxC@l;^H6A-BHF=)Dz+c#7ua
z_6h|$?VIG&u7T#YJZ<63gj$Y`W#_3uWU(_?g<P?fKd|66)+Q?7E7fIJkp4oAaevoa
zx&9CWB@yw<3GN#!aT&sYIvM!S0aN;|g2v>+cJCg7FFlT9jdg);6>ANDP{H%_#h#)D
zod9@BpQ}$kXR|j#QdQBD!esJ$P_VR7yI-yMg!X4;a{dh2GX2xV39g5H#+(zTrpS3%
z2<_vKt@vY+A3)vajrp~wnN~#pnWdoXEv<N+ogODlVzfC+mM{tzX8>sSXg!||f8O5}
zlIzg#`Vu#bt8)m&ru5k3+<mDKrerA(HA=`)@wA2a1Y*q4nf$o<gn<#OG~zZqfT>=O
z>3>s^4tUo?`Tb8>utX0LdjLl+=_iVZmh727y4<5@j}PIBGEOVzMaNkf>!RO=J%Q6G
z#p_`<3I5l`fZ^>n#g83z5rDoM(`n(uPhnmW`z*aP{fRf%Q`9eosr6OUp-=MuQVwG>
zX9Sv^jd?nBaN%jM_ARM5K5O;d$q_^f^}id~mDra`d98gQ?&74(J!0?wQKXyi1U7CH
z1<pe@2qVYQ&$n{LVo%pxD8)SPYJEKT&s&2&d|_jYeHxj<P-TM3_Gj!p9dVauIp|Ak
z;sqn88m-(5p)K=?zqj~)V)?##Cd5hCl}@Sn>y-rBEOQjNKg_GpahGEuMz!!Wr;8;j
zM+$?(-5!)Z_!AxrC*V;+v*h8W7KPvMMpf@-bneVBH~Z5KqbK{6OmuVWaZ~QAm*|?k
z3;(&}E1(myH0XQGniTsJ+*FC!&*Y_?E<${1u+|sd48K!RtG~!>OIn4_p0(`;u#MUr
zH}u6O<KtT&7H`3xi=zxuR~AE~3r`gkgY6b%fhZ#0Y$OV-PEPdB8b{11-ZRI%+)ZVF
zLU*yx2In<D(I$xg(P%!_D}@`p1<I4vt^;{no|fi389KrpAFk@vr$g+7V%#CrGV<oa
z1nIs9Jmb4*Y*4Ux>Mmgi^~wiyP=j;#QALWCqh0rm8&pF_v~CSBzj2Y{G?p?EovJ_P
z=VfIk@0%y4pZPs*YheaoCF-&!j1pxsO^i@I{Ks6Ikb=Bn`(lZ(uUkci+!M7NVjSt-
z@Y$?L1$ai`a9!k~vBgx;(eJ`cSJ!bPB*(r1>q~lkgUiu`PIGnkJ~owK#SR4fV~Ah?
zb&DgJjW#xXOJgiY4o*{Wg{n_j9rqSxpw0odVJGRKpFh?+hIr|n2lGP5V}>7mYSQQs
z)5l>Bxo!Q!+5_a~S|OzOC*@S({`jKCU%nV!F;+`>EHYF){4rdB!gE)>FgXp!!CAH*
zM?1d#K-o#o^h(H5uP(%3V2MS#zIY(GqNmbM3XUD!n+Ci2?0Q9`cj_oRXHJ!HkD_UG
zbVu<wQfsV5=#(_>Ry!x?8bpoZ#rOWaF$^$<WCaEQ<%(ay(W}29f?k(N<AtKy?t!hc
z!q9jPA6at=QOMgE0?Lbr`({af5xlc7dm*IrU3ZyI7Epr#?-X{yn+A4AtAY+_s=mkJ
zz7z7ib=6XRpf*_bLFeP?-n*~Ax9l)ab#|On<lngIe(5}HIIc71AhPuvE-0Y=a%S-D
z_fJ)U{i3Az-q2ST^O_-S&F2#M9(^vSNw@U)_b>JH5xRI6oOpLla=aha9T%TFF3_fQ
zv8Hh6Nkl9r$-OwamCbx6pAQWkpRPeS#Gs3pf9UMPs`e^w`Fa!IwTIPkv++H_33g&{
z5>`XJD@7E3F+4^k7oSOitv8r&scJQUJzkZH4|4dOM;lDVQ$(YtJ>WLYnb^!%XfM>6
zjRWF`b0EWU6w$*}{(2fM4{#n-TN%?GeoyzEtK&$xKBwMm{eLtKk69}f9Y&FaEf{k=
zXscQ_(e#{wydJNAQhg@$*2;CwPYSo{CgL;BKXy4<*uz4$E!WE}P5%Ug{SGKF1S}(;
zI(L7^0l>7iIkW}ry=v2&o9Jt?B3{|Vg0@UbVt!%H;OuScZ+Mxh&IuQp;~FMQ_|+VO
zx<?>F_5$OyYteQ}GU%s!$C>siw>f8>EQFix!!G3Mfx8f485@H+##pkg%~*1=AF_tV
zjO>|Tp-&AtO`c_JqAZ#~sLtb4F4oqX*kiA4N_K9vCpp&abqijhC;6&xC>A57Foi|7
z9~$JW>OjF;R}mw}-;>h7&#nuvCVa-s0~R8vArOL4fjOJ8v~qUygH5jR9y#uV(S$<7
z<72oFVBu~(!77^iM${sI&*o*O)#50UD4}X+eRMN>vkCk$9?o3!Sax)&lHu^}TF2AG
z#cF3u7(Bz{kv&SbC`Kz$c0Xt_E2thLGSxr+tzmjt<;TfRbivd|l;=XF3n-`^<qGGu
zOZ7$Y%dKmiRC)H~9$(7IK`?ig7Qfg-TfR;OtvvowomBMR-4|BgS3w&qZqV?1JpWqL
zS_MRe3hC$~+X23y)!b$IxGlG!Y2xEKG|xA*owmBX2&c^t+gMft56ir%tIMurV;DU#
z*d4?E*Z%^*(Qh(A4n3T~uZZ@{SD3S2I@Htdsw<g;Yx@QR`DSD-Cf2d-<g{`Gf1l9H
ziicPW$<d8|zhVPkUSQEYbuhcwop6&`DCIxI_Bsi5b%`{H!BZxU^FA%5|G;-fdvZ;f
zNJVB<kJM~bH3_O%2^7i*6}vnlU2U00w5J`sQy^Y@9v!~N&$0yGnlIa6mMQYZ&JEs=
zGGg@ecR8^~A&+=goiCsE7&O0;d+!Cc$O2`}&%x#d=o)}y4o~%C&>7VPOc}d5OyQX=
zwSnlKVk=Dd+@bhK9y5wEi702gEUO_A<({HHM#?^<8nLuhH<-1zxT8~m?g2LKY+jdI
zb6*HhsUUH?_Jevt$V@eJ^DwP0{r1j!x@sEnN8AqwDG9pUY~d`~?ZA_k(T{bs8O5!+
zR(EL{-(zgOvFJ~jM?1BKaqpb7&cuw3P>=r5pS7^II9Y7sSHm*_QH^A+@L9?)1<{fz
z8e&YV9;8OSI%*5+#!qw>xC1AJjIpR)1W&Es>U%-vLcOCb7(P`K(uk3TYDIpq&6`^`
z-?DNNTwshh(NYIs)A#z^>VDU{K3>a@IjQ0zXpK0wEYfwD&v1^DTbwAYJ%9Gk52a%*
zXBS5{Zev!esmyU$ZBNLd7|vYQm<<2PncJ3C)z2J<%p6CBDZ1r7R7y<ipn_U-OWm@Y
zBd7(<Y@d-nv~E{DbLR1O@TYuE4_l#2mv63Eps=?Z9171JDv`^Tyt^7r^I4H|#HV6f
zi5-;`{XqZf$2fCC8TekQ_2p8tqhlxar<LIz>R5*o_%c|3WZY?Zn*KJOSlbB+iGVV8
zbJ-Z>0o?)I!L6EVuvgMiKW;msY;*XS|8pWU3Y7q2s<+s#nju_Q|55Pj15f1vnnGC%
z3VPW~j0NZgTQRcn;{uOIiY66ey$|0w_zBg#v%_&qmht<F`<n0MXLrKTnJNG0%PWBE
zyzX~C<0Coo`x)JN3(b!EgYwDy%U55{c-7J|WU0~Sd^4J(pz)74<q4(d*bn9!O}+)r
zOnPT~8q%GBZa{u*Z6f?6S$pNmWr4P}MKEel^Rg4VXeh?!l_t?veeM%0gK|#R%JNpJ
zcK9h^)fDgehF9cvJ=o(C$bVzq{Hir##EU|S*}ESvI-{yTKFIbA5RrW`&h^HdAnUDc
zUX6NHhhpI5r{;ASzUlhD0PX#PohJp6udlDYlNzGyJ<c~f<H{rc5t|%pP`-ukS2)-m
zFU1s=uD4jpZP7=yjK*2vknWomBu^PI=;;c)9MzvK^$l9uT`qg+R%dBzD5nc_Im2kh
zfPiv)1;XB!X2BB^z}w%S@bpNb(50ZkqeFvXF=w*gJ#8K3EvT}AGjC<DJLcy}i%i=G
zjMDOp+^2g?+oifn^$tJR=MNCCW$6>!nm~t_o3pJG18(pM*m--0$S~s4qML;b=o@yI
zW$kRHOe`GM_aH5Dw=Case<7d6n97dR=rE0XiymH2b-UBN+wUi!WEp!Vv-ZK{PD6Sh
zZaH<T%g$hyy94w_NeFGQ`;pK`B$bfs$RcjiGw4#^*)#RfuS7JeCl3ulIW276sExj~
z{ecI0xW8jp#}Hly;IB;sM;AutLcSFviuT+GGYcl9JHG`b=s=0N*gDp}8PZ;iCHz_o
z*rTfcu=jXM?XnrZsspIR36zpHDjoBT;QGS%pwzQO3;MP1_4cQV3ZIwR3DWZUgXZ~A
zpQ7HORN7`Js`hxwpS)K3v1oO6b`gIrq^|@UpyuuvU4U=Sxj?R!-*hAeb6W-*Wr9@d
zY7XLxMtxDqpSsuuF3#<rI6EFGn|1mqZ1&E^CcUyVfTbhRA}k8E38m2YkQFAkLr`UD
zv_M;V<NG`TQ02I>>HTgU1$pes9&7C@3J3=K`w9ZOx9DFzLGlyvcC-)`3{?lu_P)lQ
zR-YZWN%h%~t$2#hpv}+;;fM91ix$sB3|d4=JQ$Ln$b$UuXq+AnJJB7d*4B(5YD2AZ
z{5CGi>~-#x-CI-pA6m+Rq8F4-Pkq;?9~?hXHhb;Y9xfG^H#dI@I8lq?v(VUW*S_3c
z=dZTy0iEjf{qkVGSEIgmZn7-jGC#LTDOr0uBe}&Q)hC)+Dbs$IWlqbGIa7r{%38R)
zIWTFGHm6zUU5IL~I3g{k8_F^DY+tmWK}CNAdRSfl)H*#{cw{XE)@((Y+XJq2Pp=t_
zT>r>GfC%wxooQu)G>kNU@+uwMSR>l5-i04K)yOk$ST|te3+uiIYpzz9dU~o)nofP2
zcqfbuc3U9dK|>A-I6q<2vb%I9MLHX*4+PNqxZDYxC#4#nbDZ{$Wdh_{Iw{T{Vf-aY
z8s>_<1CC9}sPdK;`0fRg;cldVT-OIwGZ_9zS#|kDG9fu2;(JCja-f}6DB%p8`W1xo
z`e+q~yyiwDnf*``WOueK13HU5gSI3@Rksi7s(mx%yE(payDlhjGBh<)e4FRzh2*k8
z>)?T_rSYIFQor%AuzV49D9#1D#GWeXr{pTVahFzB&RMptfFB}ys=iqGakf7;_i~y3
z7m4|bA0rn|8=_(C{em^Cy$=jxpkx}?8EzKORTA+R9=3KC#1o$^fo&ZHrgj+R%9pD8
zGo?$?RZY{0I`|*L=pHa#4VSAshA;O-IX)Mke0Eq29XSq(C&Sd01ViWKV>cuYc^ALx
zi&dK5?3d;at)hVln_qiWT(OtO%j~Znd`Y4d&dOl#y6wr5=IY#C(t9Y_et7NW+1+Br
z_to$S&5%|$TjagSX*wkZb?N%|zM3c?C*|(#w$4<UDqhqiM|}N$4KsR89NKl?s^b@s
zu+zNH^!=4Zk8L3bX{K?#GDP|z;?L*tqB8rA*~yIH@ZY8gmH7X(<YI4Cq-oqiM*8mU
z2AO5=kc7&2!gD;rQl=W(c6sgpEL~6PfJ5xjoed2mfV$-?e7a3fYnLaBlUfrgm1~cl
zZQGMb?UTDZJL^&Mt6-UiNkH?|Cag@$cHgDLxc%ZPm$HqVx=-iDJ)9R3#S)1t!AfSe
zc6aZNF$+-sz;Uj$OQzvuIe%+Ba+orBU!74{{^$hU;B$t08->K31nBfa#k?;f#4Rza
zuRfCI(f48FYM8#MtqKTZeujltaeBbAjO}!8Oa49P`mv)c<O6iFOE8m2_~k`Es-W5*
z^BK)+pjc9qVQ|uk)Gs|%hGPK+P0VFl%G7#}_H9lV{QfmMlS;LP!7^ciszJ+7I`(lZ
zEbfZ%NI^qH+E^6eqsuU#Z$wW%ea+?m>GW>-GBhKAkvvnv`ZDLweB(x(utD#u-nh||
z-r(%L$zLuKrAMQ)$)>9+b+bOTE?y-}@%3w{3x!w^7P*w5@(kMJ0f-~t99eEzg@s#w
zKXR*F2(rKMVWv5Z5M(~6|MA@PtcEtO@4#~1g9h?urfMK1HFCPLk~4->A1PDQYrV%O
zr97K`AX8!MEXXFF*+T_kwXK+h|7ji7=Xl&=F54Z|yhfz?c~ec~X@V5(fWv5fepRmg
z6r+Ut@@L6e*N=jy_Nnv{RS3EMZ)QDRNrAQqe<hRMTCMf#HPj1`@8-85sy-VyvTqk1
zJ492nqVjmHY3qBKeGO6&(vCC$P#pTAlt<QTDyY)4aF+Exicv$S49z|@;1B!f`V*lX
zBheS|xV)8BC&uwJ*O!ojmZO;SZ3T350~bMf<YOgn0Y4Ou3MHp{GUYgiEuGABp^?_&
z<1BX!d~e0=<E}QFB7>9`b;w{>DpP0WZk1cVmFtw=yf^O{ZM|&U=LK-w^3=}g(qP;O
zj5jSjK8~G!Sa#OY!8VS1SIK(r@YGS|$J1XF9c;UidDF58Y6!0Iq8>R4&ZYkFJjc1`
zz+RTC#2;K#LAHP!YI{JX-<^OZ^(4o!W=`0z{<4}qZhjh$xu~pPa_xnb3DA%|E<>@E
zWCrY*;>3}i>S3SY%YhwwDW*851iVvqJ0{d&M=r{6y(1@6pSK%xwGln5*bBGSjOSyi
z{sDWKO8HLMh=)02xO>F<ohQ%XGat_%W59j^v&>uGQDv^LkmB`WKK*YPIa<zigr_ax
zH|C!hC6GO48enVGk-Ml$F&;Q#n8b=fxQK#h?-jA6XrPL%5JvZ@5gQJB&DW>iqrK$w
zNH3dSNYR_u6!Y)#1WX4Ys>H9IRK0*Z6MMP`S?GDXhsQOBCB}3k)x)p2BBPY$wZ~ol
zmF4NRrxSN!EZ}Zt4zG<yb>II0nm}d0wMBgDsi&O9_O2b$oeAvn#qQP3lDW5F0-p)j
zhhwO%t;Hul`AKKV{e>@l;gA$@{`~oP>7|$UDX(XreHKfXE_E*4_i0B(-Xj6C{NK8D
z>mK7-1p6&~<Rc&1qiCH?%;KAOgMAUMZ2*W39I?6C6%HH$8=PcqmFpK|vf1yJgY^^o
z?Rm{!Z?D7SfE6@NLjvoYbkkY!In|Yo{S{WWgB>hykCWGTYQ`mgdiCoIBNIDMY3qM-
zPw3u}8w(3op6qvTi{5ufz$S5Qj@6<rwhayZY)`e5DvhfG`6%N((I9%N84xOQy@W=0
zPr>Gow%?Bh8!T+|K^GNz23^^G&z^Z2UYhqh>N(f2iO?4R;U}NLw8<0Mz9sSKQ_ps$
zVfP5;qI=xvQQ6(GqplunH*D;v1MKVMpy#O*C(wnr=kc^NVz)1oCyc|O%7NHgy&Y+a
zhL-oTm8+;L(pZNAr-j9DF5|r>dr!HI6^VV<ew@cStM?|DYjTCYA>b<>!cJ0}jL?82
zlZvqJ(Ku_=i3pwZ6&jO_M$><M3+cKhT3C;xRY?TtwpI-I;g=9P#v3e=Ep?P$*)lEd
z@D;Jn+BJdJ|DmblQGD|^kXrv7Ew+D-^!BxUn4tC(Dy9o?i^n<~Z{|9B6|&B0*EI2s
z#wzA<lkRY{$#?x^8O`7-@#qg+8ljn=adob1BrS=+!6hK!`rz{2_tfqBnZ4H9#kAwQ
z-P|l#xEVjb`57!*zTF9@W~FSwqS<clHi#Fl|NG|>EPb;EAG_vwocr#H2s7S$E}YEy
ztc&rbuRg?!C}-j24kI}qbvAvoxHj=R5=m0H2F8x5M1Elo+BtH^-Ts;{TFZkM$hz7H
zU2M10(s}>}(dydVkuO}d74ax<5WP~kbSeG1HZe#+{l%9y;NP#i7gtgAz2v=9IagN>
z&Y3-)uEi_xU)Mi?O%#jm7<l!ycOH{{+8Vn#cUx?=cdRUmpXIUR&IXG!ckU)9T-)5!
z<~DuTkfy@y+<$i*huwHs`34l{^8tDg@@e&L;m$kfl1(#(8jH{JGY=E_kC`?EpZ(04
zICjR)k#3GS%hGX*cFj)T_mM!Bp^u{MLmYtc#d}xNGITf!6C3f(4^GELlLk_dV=o@J
zTX1uzvv#q%(=|24YO4(VZfD(Wv$N^B$b7TScEf7}4Z2ztbdPC!Fp>q^)EVFEV|G-A
zZQRS?l4s8R-Iy!%Bp=wUPMO8OrDiXyhhFGj7F*95=6R4A=gA<<V~erwb8cf=mxtNe
ziA+1A*c|hH(UfP)%knrhe3&+wljSpa=G0FS>-ar)9H2f91_AQ6`qaWs^8w^v7)`<7
z5XDe`5ehzh0k%B-3Q~9fgMxH`gB+~31<*!c<4rtANiN&-v<)yy@&=lZM#zov#N`)o
zyA1{58Kuz89~fiqd&oWz_Oxf$-(gQN-Wlt9JH{jB_}&?3;oz2X^`HNNxnKPeEg3n0
zXdgxEjJ1LMje3LWc4_O`uG>@q)>*Q*A()?s#+7UF?6qIS>_6XvqEW;5c2~s{0TDRB
z1YX<vdpxoJMzkh3(_0f0=g-^veg@`F!a1XE!qm!3d(E*qwiP?tS71lm3XCnAjr<&1
zK62jcws;-tqwBD}Z3$m1I6pS0#^J4Ag5xoh{wBP*^~YG*@DR=${S!<mKd(#r#|*j>
zgNu&FKbHSHHq#AZ;s_J3?O=cS!yn?*Q%~(8AYX971^C_Xe&==Gb|K;|n80e5$wP*)
zvv4<|hVwt#yu7`IapT6}gcDBaZF8Tmx6W&`N*^_9l(T9+*af=ho_qG$HGE4;3;y=E
zze%`02q$Ow@Zs+K-*wkroc^pG<{I8U?bSVuA3xq%QFmXzyXDHteVh8pL~AFg>@HDl
z>m*=e>yFlysB2q`_CyV@1+|F88(~vW#FA|^u!z#@AE)Tzv%mGO*<gOV=-4wz#yktU
zXK$ObjJ+WAc$OO+fWq8Ll;#g}(^QlfjzekQNQzqrPO!4_G)6a}p?w1yB5TnY*~BTR
zYFO?D4ydN!LUG3Rz1aP(D;fJSShjGeh|^&V!BJ(WV@&xhmUrL#FrvvO4vydCcr`}X
z^ZQn`Mz<rHY^HT;l=+%)ZWGX%m`^V3HwpUkI4HZ6Tq-zK$58SZMLtIz5H68K139d7
z-clMO>sX#`Xrs8ANVl;(HVV!9k+Q)P(QS}Hz!zqjN>EWW7NdDDFtqsCY$Sbl5ZDn}
zkEQj`VMXISY-`_$CJJw9_FGv0Sd#s^v^%1KQ3fS>l^9bp8TIr$m*T*DUy9FH_Ro0T
zr$c!CjU`S9HY;iiCMchD+>Eyjv?->g>p3&ez`b)GbnThNU%hq%-dw(t9!^_Py?qDQ
zn_&$Fbc(?j(7pJs!Nl=6+rn&LfjtG9D7~i;=JX9?Mvg>vO)YDV8HA)c&1D1U$!<n@
zSt;9rjI*|O&zMZi=G7ilS%DElckQR%Q!d)x7X<u+C(x>T5Z!<efq&$Q2uwN~fib7M
zqGX~ml-%(>3fLV2GCPek9JuMD75=WlMC#~2pJ4|c+fLR67dYi<?)%T9Fuen@dp<=A
z77Etp?wf54Vr|GoDOS7AJpL%y7P3Rx{XTY0cemedzzrXA5|4fdfw5=8KWzGLqidrG
z9B2ajey4DtOQ7_+5-?@W-Gtg|yV3LHf<OQDb=>jWS2#aaqH~_+Zaeb!)rYduu$y-|
zPPY<2`Ps90=#gdk!VR-9YGfIvPald4E|`Qr{P9(HfgL->b2nikZ+$}>A9A8@?&pe%
zLW~?)jNIG++S{0m-)@k-a_~NZ*43@F&ThtZT27BXs*ECZn4{fmDHgML#f!$B>^S((
z2Dgv`_{fn393_^fF#IYWePk(axZ!L}KYB3BRf_jg$iDSgFL?X#!f(NiKqL!W+9^YF
z*|uA{EC&}Xsw-Q7qTs9ZHgaH6!imf;)6H}?c<s5R#m2v5rF9smD30I!-_JO4{^~1_
zLo8-Dx!xh9_?h5**HxAIxblkQaoMFuyW?<~?>xwC7W&+40^S+0v*DdRZ%3<P=KlK4
zZSA=6f9ImwHxj;h1Rp<Z6h40HPzq?x6rBtBaAxPn9!G#D7jH*(8)Z0Jistcw&Rl`V
z`8KX9AE%7z>PppaZl7G*fZ8UGOtb5CM?K2cp{~Wx))HImM+pa6jN@-&NO=M1X`jI!
zKCCunkHPzWWS1<47g!)xD6_n2izo5Vd8^$Gu^W@Y8{grjxj1FY2tHymKdwyk!OC*Z
zx(GKub^L8UziK;PUR8q)jqPYmlyHDv0OdI;*w6QrDHS+vH0PPNaThkim=8f#4%_$8
z1qz_nv&x%lUxqhF=6>4dzvgc;!?}?a_K$#_OMC5FE4!y?%^`j2BgOf~{lhB}zVsA?
z>Dnj8xs#6i&yQevq#en!0f@5R1%rHu=TlyK=42Gad}z<%{MmNy_-7pqaoz++PaThF
zyp5LIDGreEAxevXALlO*@WEd}1o{&JvvQvM<sY&sXIs9Di`rfpFkULoP73~}0_?;H
zaU^tz?&v}V^o_wOS4^n3`KA(Wbl>09%onF<VSGP-cYBk*3FkJq_PlTW1n;`@XKue<
z%c2b;AOZ)RKuf$DkFNd-=GXkeZr$AAFT1a#0~}1RxEkk={V|HdgWr;GGp&SH);)~n
zb^pZr`j-(&*K(&6uATNgjw<y!t+G4CDO7A}na??_@4}Mp_i@N#y|chJ0lOx(1b<$9
z3C<q%9a>*s&)Zmc2jF(CDwv87Pn&}WmVbgBz4v!oZD-T%%TKesw9esd2kx$ANpet-
zz4jC=j#*sWeAl_T^z_4ELiSL0)^6IgsmI;3g7l@A_CAonc6|BempNtDwjR^4l{Uw3
zQBl{<!(Jo5%?;MMUR71)EMhGQn|Io*%}snXA#hK@I@Q1Y<uBumGtO}7>?!`+-EJ1%
z_uqeikGmHa7vuQjk9Y3fdx~)Hz4zk!>#v8!Q#f!0?Du6{Kk<oAxcXxAQQOPrMz?_*
zcFye6<~hIWs;f{&>%x5rcD~H=T4MCRP}{q>n<>aWx9&&S(zt+qQ!&?1vFn4M^-a0Z
z@##LY>$+#b2S(QiV4oeYG&{9I{z8-$jB!Hoq{@p>Q8=Odo_n=y0`2Ced012XG&VKN
zqXq0{#A(sz4|ZhCa#?v@nRhK?$7U-W|KX#v#n-p|9+N6A#s!mp$^q0AQuYw)+t*@4
z!%Mt=K2KH)De#-9#{vBuZ@p`tF5BMnc9}CHZZ|IBKp~2AhGRt8shCiCK8BZ^ND+_;
z?nP*g)nFaVvZn5FY;ITpTSkB)rd^VmewIN`<+0?gD2@letZt;+>5aGJ!bv~Jq{{c$
zmVO@^V_Wb__1#!f_Y`8j7`r(#tZUTH2sgn#W!EvnJ__qP53;g1#kOM}+qw8)K8MZZ
z0Erb<$lYAZ_ArVU=gxEDwI@8Urxo>lUSD}l<w*+ix;uH?7?^c+PhsY1C*k>5UiGY>
zDN>tp)!+a1h`T;#+e7P%JpJSoXw=oU|FLFAZO1*DCy?8-p$W`;6DsMd+^Zy3%naw+
zZbuUbf0=L{i9}ILAv^0~)I6dNuBzlQO4`{QwtX4LKm1taeePwp6%?nt4PNgQtg{7V
zQ{>}7pY9>SDa4X*F7M(E(JZ%Z&g#_4=Yckg)?r#$XM^kf&Mw%eYn66gv77H~pv*!%
z$p?_aas*HM5QX;<yNRz&B5-gBsC7FyT;F!8c46{5At)rUov?YIZ~4{pxci<3yfE8b
z)+U(s6eu3iGWfFhPsdeP9Ovd@zU?<J;<h_pcJou;{Nwky2ad6H?`>{+%8J7nJji=!
zY-*0sy;!@uQQNqw5fdj=p{k;Y^VSY<*418a-UZjLpk^_;YHd9qOowCkS>xU2wd?9Q
zqRhmPj=-{-OdR7K#`&jB!WX}A78|_`<~;Z&zJB9FNd5RJ{PA}mM2POlhYl{H%X7M)
zpc-vrpana|B!%NR#TdJ>%m%Xn?-U(7SF=NzqlR|Wwa^-U8(n`7b=Kpay*KxC0=Ug@
zZ6czVzKy_)$J1zNh_GYFOUT4*lOW^q7$2w$aPdVG@S!VbVAN6NUiRGIV%m98c$)+a
zAaD3myii+xwz+m~5JJWmp>;_LpZxPmEN<h&bf6U{j!NRI7fh#{;~3|!E~Zs4NA6Ob
zwRx;<)a-X3ug5EE>Uq(oT_{JB`8g^%oy@@}&a8TiaBYLie(}UsJhYM~C;{H}+DUXH
zaeFupH!5Q1dOPcd)MJku<i|Cq4Z#Ob8BQnI9OS@J!xnrjpXJY^*;)VH7S?9#)a382
zt}Tg=-dAt2z4)nITsb9!lPO$hB}1mjI%@M^Cs5s*#&4e8i2LSl!ICD{T|RJvf;H`G
zEeQr5kd^=W7M6>HQ{FWe7au>6zat99=^XO0I~|L6aDpe>+wavv2G#A}rNm^-1V?k{
z#yQF#1t<ycxueY`aiqVo9@zP?!4<~Lo@uR%*4i5yGjp&)$I0S{{Vq6G$g86KjPq1R
z*oKAKzT22>3v7zq>2(7>ypw<d;as$GB>w+r?>hkFD$cEsv_-q>&9Y?KvgBSdwlTIb
z#U}I)q2+}ZNJs--LJ|`4L&AIM1s)+J0YXCWy#z4D26y9zyKKuvR<*3&w`u=#=4xe0
z-j!u6gTWemwR`WKJ2Q9g)bD)X`O;6GjGR-Z6U26)f%gwMemn*}^=DEO1ZkB`|2fRu
z9dO|EpMIYUTQ7V`@n-WTfvNh5pR$8FKfe)eoO@aW921#uw3FLJ3Pf2rwne^u_FLnk
zg6aF&pB<VMwD{(=P`e9?zg&myk7gCz{5AE^ObhYYfSn2sDq5TIThESU?(y=RZlhti
z2`q-GG@e$aSy||uDnj@0i%F??hLNC+<?wQxaA|wX%K>gQ?NX%REpjSgXCGHwy^m1&
zxR<o<S0;+cpzF$;|IK?cKX}cry5O9pxrn#1&ngJ|56xgaRN0x9pBX7hDPRTIXT=bq
zk;29sHAneWpA^PB>3_(_`CW01zK6=71BLeTm~TFF8#DB`7hFA0J<<BdGO)mUw$Fux
zL1q0Cg^G_qA!z+OHZzXW1YCLyd@liy7j^>FSu-YM&?z&KddyfP49$j<n$ppnr=rMy
zqc#&{pK7DP#9QBhy6uJ7vvd`T-u@7^TM7t7GhVliZ1;Tabj?sQZ;A1>o&A;Yf|l<0
z^dI-vKz|J!dJXKVT||rKZ==e)Ni2V6Y?jd6Mt<<q^L~YsbAG};9vZl<{A7K#tmGe9
zyXP4+_=?Sh!l}R--`nSb?j>{D<B*qflKGsN^8?J^^&5P$=P5J)$rpB8bSviWybYo%
zKY!G{oZl=ESLwt#(s9|iC-BDB6s)QE*AZiReeb>ZV%Dr#VSz7!X$1uZkQMK{@4nl;
zI=UP$U%ng*7A(LSXPjZq%Mw&T_T!H~-X&h=gZJNm9|r=i=gytm`Iauf1-Q${@8J$3
zMvS0quek;w*Pc~Ec7fI<B_)vMxaw!&!i7B+L;k)+>3rL5xAhqBP%ggn&O6w-b7$Bj
zWR?D#-~0x%XU`66k)5BPZ{D%|EBA-Kt$`RStLh_ATFM-1L;46@>HPg^=)1LcG4hwb
zE%H&H+TO-3cyZnJ+_Tgeg9*N_!x92fv<_`sN2ZT;SXuNiCT3lRS-C$ztg~Ilb%0ze
z8a88n`3u;x_ibb8EJZEqG_!57jd30ebduE_AgPYa+Q#HpzJ@VX=NZtr<3gt&e)?zK
zUOf-%%brJJ)l%+#Xf?<X;2u-?n)*5fQA2B>5#B&Ps=fJGRrCmkrJsZo^L|9j=c%1a
z(e<Y+%-57YhE03kK$EY`T<j=M`@Xg79lbkpx>~WR?Dc-&n$?wG{Wck99ztVVE#EcA
zFVnubcV_?4PTO+lsNK0b48PS7)E>0o=jOdFr3Ee13*pPELtt$-9((F#$}bq9eeW##
zZH<Yd*Pb(L2Clg1y#1`QLvPJZCAo7?KNSx@`J6eW@pN}_iMd~CXwN2!@o+TV4YbM*
z1&yCl{)vmD6>Kj;BGXj*okJ7BYlT*i$Fk%$&#yC4NJ;Kj22)LZA%gr!A42dh`-C*%
zw8Ty!>t?8pa4!-~_c=k1(H7gpA!J}~Wxon|+4ouyotzE(<jY~7`Za>qLk>0J{`>oD
z;8WFrWS;%uh^YZier7_|lq?ZA@Q()<<MAg~k(t3FKsFPY=4nmRor2<PViy=2FO-d8
z^r%b>&Q75ZY7)e@Pt;}Q4b)Qeb5oOq0qK?|PIYxNt*GgYmMB|kCx6o;ho@u1`Vv~`
zIXJPmHx+>@OnY0wUISblH#M7i2Dqtipx|a-nIuG4o;u^_1RqbUY=vQY^ou<-omAv$
z9UyW|HR*)7Adxlq&RtcEHPTR_PdRol>grlCc}ljqSI=BRf7zlT7P!+nH=ZPYN-Nf^
zEyv`^IXLCy;k<6eQCtB8I;yU<obG1Q|Mw{;4M*YbDja{jH2^m^dC*MX-Lgu*`oE6H
zgz=USIkPBX2?T&Bt^RN*HK6(vY$Gs7t9!L|PPA+zzkI6%E4Ej_;f{x!nw394_gG}a
z6WD5F+cS0FzBpxkHD{X|3_tp|=)$RtKtM{1!f-*0(@=aRpn0CHu3J=|G^o$M(@t6s
ziw5$e(iALtwixTE+;P_>laSyr5E%0oJjTT^Q&R~e1yf!=NHojnW4_U}v{BIWw-ESt
zkya}@sujz2+wsF^HelH<g6vNAT`sg8qy*RFdg;eUKsY;$>4&PrRi$mX`iVkZzuSY~
zU6_evI}2+Br%L&720?K_TH{8cxUzwujBMt$Ymnu)cC-Ox#dWXW!&#mqFKvw-qI1&d
zP4v`IzRUVw05M&b^L?u!lfj2yB2;4}IQZ`5=Uvxg8AmlJP2NN*@>HpX@w~x_`P+b^
z?TyaE9lTp~Xws($Fh|}i9nC1t34RK)eC^$$p44t`T7Hr=jZ8r?+yt^O5>PW1q7=@0
zTk7DaRi`~xKCr3F&H_ZYdO0RLIF_qzF>SqO`o^Y;Jc|>BCN(vF3%54Uz~{AdKJfE6
zT%RxixyEI>rtp)-Ks~`hzfEelK{oVgW}}YbXW95DrY=N|XXiNKVE!70b&r>MdfCq8
z=iT_~rc+QGK|MW7G}~p687ZE|er<ZCloTYO@1T;E-x1AzXO&O0las#%RQvc*xmE8v
zPkfy;WCZ;kZB)gGHWuh+{$bt%?FCZk83n%Uw<#Bo)nCQ&8bj@8DX{pNcN7%>cpCvu
zdZ^bAJArJbE$w~@Jv8*lYC-H~)N5snppp*rR8);Fd@2Iyq&(v>^?1y4M$uwkgPDlm
z1H5LJRlEw(9}cMoJk_-ZwDwZyAj|2Vf|NS=3XprznHYQH*D+wqMALpF-osZU%V|eq
zJmLmt5#*kM@!z`^TOWG?>;CUyDmIhiF2X!!rO7dX>$u%-%|kzM9kG`CFZ9<ypVq+2
zvPbAc`bSjgA`NQ07MZ%K!{);IBk#ww>>Ik|Cu&y#){BepF@UuIX9Z5@WG{(Bk~4>N
zt_i4XEk>hvPZz({R!r+}M%-9jG5$#mOPP%~xBifGo|H7r^n#TY50XZ<0hf;ZR~PWw
z$+hBzqwm)=iq%I5crB{!V~#nd^P_e7Eg!m9Uwt+1x#u4I{O3Op3vlZO360-=`|S?k
zTIbF^_uL+V>qUze84%U&4lgVet#VI*a9&>CXF?M$fLx$sTwEth89rZ5P7Y?yoQZFK
z^P2{gzV+5y4HdMWJzRL^nP+g*O*bKy>$tuSs{f~+q7p@faM48<nYxnIb)-7g{g+;P
zDc3e?q5Yw+Yrx1~`nYaG3<rH()}uYWwWZH-|5rnId*OCVeOEhLqXxK-)I8*iZonty
zkI-^@B`%uqfT7Bclv8!nPAo5a0GrBRrM!cb<xGRyF78<(-O>B=DwCS&o`y|ff$J?5
z@8DzRU0l1;SXKMm!Rp@UaNwAxUYM?)NAks8wF^+Pb~Vlz^;=BH?8?#+pw;23;zzJ_
z*Wcg?R&~_Hfph6y43%X_%Cz1^{?f(@VrALCv3l<lw63P13*U|0L&~6En{8`Y;|~b}
zv}tbtwj{oxW5(r%Ubpy$)}TJogM?*6;cSehtV)tAOGYgj4euajcmdv-w}6!3Cz8p<
zGD_j;E<wJl=PzE0?K=yNLYd~lP!Q{<?oCZe4xQ@wHj3^lNPCcs(ym2dFM>Uc^|X9k
zD~Nq2WNjTv>eDDBh3@VD{$kaD`^xjs^4uE;Y}yV|@mhta5H8A18JKT>_aF!TbOx{{
zn?=S5IHr7!pf%TP3B6+qIOx3kZ|JXqUe-YGM$TR))*tOqYk-p}C(+PG?u8fD;K^rJ
zlU`UrpJh`et6(!phsjD-$4i&&#FdvHi*(W&pLfm}96xO^eNbD#b#t?qzNU8?pUqiW
zNk~hh6(>VOgO}7%)Y@UvB_-tUX%7>|4aAFN0uvyelVeTBF)q6i$nPLEI9CiNW~;}C
zlF}M(kR8<YwVIUjce$N>o;IxbxCmSF_h9sxftWEp7tcQNbtEUc4dB>9D&%<|78pT<
zo%hb4zYUjMIFVp`9KQd(6L8JdQ<0cxSrV*YSAwluD=eUr3mH+~DhMnP5G_Bgix=<2
zb=ORz%Ll;@1Rhx4DAr4qtZ1pG_sr94FqIVN3Gpuc^3Kx@J-CZNhJyTi-*xAim^g8e
zIW{XKvmnwTCj%{TxJa^=LhVudzdltBXxV7lD)JY>wpTW_;OURHBH&6PIF^Y2J~4`P
zNz@$V!Z1h+UcF8Afi8g!*7_G}iPlwXQQIYE`-RZCeJjWy+m3=cV{YUUR)`{l`l5nQ
zzCpMsB}~Oln-hPXw+rz}Dfs1?IXo92$i+6sc(Ro4UD%hu;wzUJg5&Di@8)}P!;|?a
zt|xdG8*loLox)G~_V!Q^8O8n-ExMNWpjw2DeKWcd4}Y+kzMaU>aCsi$8BYM?;R{wT
z5OiHd0CVf!Cetqk{ISMZE3|@VA03;q&kE@9mF37yS;L2qV}|^abBDneY|&%}$V6e+
zxWjXvz0|TV+l%@F`Uu-yjMkbuv=MwAN*{E=v`jch4TPt$5j)5fz!qWJTK6ZG{6Ijd
zY>J~~!pyd;yor?qF195~DxylrPEcM2-;N?w>@Gn=U6au+PR+_d;;3BMNw4o1n8HOb
ztwT9p3YgXq>mvg0BtWg4Wie{3!zncn>LSNhDILWS1h$EBgX6iUhJL=P$-u#O8sEr#
zdEQ3=*W0cg>}OeQ-Y5*1a{{8%q#exGLWk%+qPu<~vTC&gkp=epTAJV^m9lR$wWfEL
z!9&W-7Nx`d60-&%ebjK+M`R<4?kH@uXcg^}j}jFp)01y2FHM)#J3|)jP6y{Cjw!7K
zd$l!nRaBv2XEDn2x1+wS0x`@pHYpKtnQ4d}ME^rMS#S;@EfVKoCsFR8n;<>wBihr9
zmUTN3EU7~6j$(Kks8~XQvpYQnvAJ2S&!O-Q&V-F%zp<X?3$oe8%D}Qv=)*7g_>g=v
zw0>7TM#ICd62n^dlRnzVHsLMVgBos-n_0#-Zr&a7u}H}sjD(T7bfTIJXKFI(qo^`N
z#WB4oW93Js5uqXhrhPbjKdbIbAJ+fn->6=nPX&(fKu4$o1R#qJeA@r~7{jml^cHze
zg4h#o{)S0w#sB-GX%nG5!hJ`xz13~q!Eu(%8|~|zaPj)j_18du4IEkx2!Q@@*YEM+
z?q6F1OKXW}_Ds<nZ_gWf-+rLAd{_Tx`+s9w&0NbWO~W*ol5SfDre)oLi32Z3dh969
z08w~w)f~LH`)A0BKN&Lz--Mi`8DR@NZO}JKL7s`1w|oorZRJ`%z)$_g4b@MR5;Gnb
zjJ}Uk!9Jm@tgz1?^;cSxHDmqWXN{0OY{H+>$=*<bi}~fJKm94ztXX5Miz5|n<;s<2
z-xyL<6*YENR#rr+@2y+6V)N$Bm@;Kb*d*7lUyp6uwuQZ>lV_cE)@Md--B}>sy+E?2
zz468yx&O5rO6W*+s%PD}apPeD+t;mI7ZH#iFkk?czt3Uch%&%*B;b19ym{t*j2kzu
z`y=)rf6g_a_9uFJeQIlK$xK&c`jy^gPi^yaF7_8Pdmk;?c2s@T`>|PjqtKJK|In9_
zR%n~4YvxZVQuuGp9iEmTDuV0r`ubaN<&>uoW4C02p*tkMTV4D(mhQd}&Hf59KvD+Q
zi?l=c_x4+9Hh4?AM6YexjgNNxp4P{&QIsLW91B?P5~=$^sBR9WURV~Nhk4l=@J{|u
zkxC!fgOVnjoEp6qn4AAI6jZ-!$|LZ;FN0BnLlQ<0IIAy{{lcZScF&{OQ27`Yc-ct#
zU(z=NP<MI~OA?~S$ZM2CNZ=v4;=c3NrJxan&<A#M3mRta!b!E~ATuTlHT8AaT~cbO
zwl$`emhZu9Z@))2(Vh6tbzetp7zH=Yxfh&y8t#4Y5hlLx<!cQxAUzecPYl0qbI8S%
ziprob$rg=!J#(h#KXMh)IjFBVr~t1Z<@XR?5Q@`1e&~Gv_m{5*Y?MtU{_0lLTuSyt
zH7t#Ll1H<>iA}&d@hP5Ln+lHo(OF|)KmHonCSE|fAg#LlLw^k%0X1Naen&t-`io%I
zfWT2nLkb8C|Kp)$26zySwt}YR(Bx;u?);b9Xq?P8Z{3UA@Awb>GEc^FQ-)A;JI(+L
z%RVl|leFAkx2~MpWDeAlBKy(DS2BShHf<_5pp%5?tJjv`(I-}ze0J=tHUO|J2A_WJ
z6C~2tZ&eNbY!MA9E~zCA`4YGYHrLe9&qif4HO<_3?1`0#i*@pPouU7xyq#9g&A9y+
z@6b>51e|>0Fr?F8a5E`tSFSF?3om_wRjZ2VU&Jg;uytD{etg^Oxa`tNw0O=m0Cs+U
z1y-#o!gJ5BA;8{Bi!_RQ2tu!4SH@>pVX9=;u39))md#uC;O`GEp@lbvH(ZQ}zPySS
z<(7qmjd_*erdwV%*4D=yWBKTIvEH<{$;wJJpMAB;ljcAFC}@#j{p$aF$TeWK^EO)Y
z7dJNJe_r278f01>5~LpDZpMwLOrS*y?RPl|%Z^t)z<|u!ZS`S@5&*~cZ7p^?C(QC`
zAt3EFg4UuQb_S|w?dU^u>;OFU;ci?oDUmeYu`IQ$EDa<Xv3)415aqpSi)AwFm)Wcd
z0-5|=r~@}XwGG8hi4;na!64AU=clAo6v9%x2s9A{q#r4jmlQKWdkn9$->^>HZB;B+
zDjr=>kMSAB`0-cr+S5LqUsi9}S?)ziofs2n{mM#{J$ZoEL^N^5IQFTdIcUTn)XUdx
zflD3&i?aN860~kAE<@>pkFoRVx8Pm69x=4=q-q4KD~bT74NG`rqko#X*~h}2HPBf4
z$}%_9YC6}eTA<p2lm&bor~DivWpOPmx1q2YHE%CL<%=JnZuxq&G*+`O@!ioMxvaZ)
zvVS$kL?L0~a7;XRHe$~{1p|+xj~t~zK}8%HH|nCvR6#1#L=EUHpRAJG2p)S0LdR0j
zOp`B^&RdT9k5;07={i)c+d{Ct4o=3mT1lw1I^t8N4#4#H9!6|t3WfTlSLOih;pS6T
z)q-~$=wG{P%_bB*@iyw`E{DIcgqF{IM*dS*5KtfBxE+%ai@_&ONA^W?kbLe;#H6KB
z352m^T}>`-DJ+RrOe`Zl{;=^`q`B>5`nB`dpzLoiv&^gDYpz2hO{@L1bd4j_?P4iB
zv3v{D$0PgF(~x=k3?z-rLtsx8Di*Ck<@?L9``NjOt8HZ6_z~CUL>%j<jsQUmwY7&#
z8Hd4_oPoiYpNqC3>1d{jdb~z47TnL@LEaNhzV9}^hX562oO}<q#uk*US&yA>%tON)
zi{T(p@AnY2=d-F;NpXfGkZ2p2j;Lv4F!ai^Foc%6aRkWeAi#`cj%Wvg6>d6=Hh2H>
zF4XD{_PcV`dTf3CMOsDotau~JZwD#gPWsz#`^@q=@8Ywu;r>U_Qofh#oF3E<)OK21
zIj?Y>=m)M3_7(TX@2`Pg*T8!EnEtT%e>gvIEbcS`+c-zg8F~kf&;C}Iv_xzAa{djd
z^X)RgvMvg=9+h$~&KdD%42T=k5kEi%lmPp1ov#S%Jx^f^{a>Fq@^6@ubyY{4&OgSa
zpNE+ZH{tzVcW@2KE1KUp<gclG2&2=^;`;C6&e5sR>&D4LZ^Nd&uTja8Rv4Yi-zDM^
zb|7nE`B0AJ*LnBu-3Cxg<D9P`(E5~9PQkO!J{$H)JXCmFym)a$;QGT4Ka7}{gu?=3
zkEC$@_1ELgH{Zl7ue=hrSbE+SD^?s9;97L$vQQ5fPCM;1<mKg=OR|E$>#n=Zd+Hvy
zfqpt=6)u0}{o(Ve0quQ7(J$ZdqHh=VyZph6mVV2YEyg#t6eH9JTe;TIcSxc8^C|I{
zFW0jAwWw;|Y97o+OHghFBx=$&OaHCmnqxM_S;P|bLrSWYI&iIT=f`40q!|EZ89uK4
z<sUoR!5xD{2HbB5@a-x1Xyv^Jt4PEBB90q!T}RYbUmf1t`U`BVc*)2vdJEF3{Bk4J
z{W+9y$K1&xsZ=^P&%_6<ZY=D`TR{HYt+#U@xZ8Se?&rb<<Dx@##`}#}g>I6f#U*B=
zu><Qy%De*KRWDLLi=q~>?h#wbf$GJi=V-x7T325{;JU$6j+fVe3wxT^7+^bGdAgqq
zy~pn7_d9NXJO}sTLz8DA$vtGhC`ZeQqPmZ;VXr`IO4Ybmw}?E|Q23Jh--J+e(6MP$
zY6DiBF7)B%9-h~W;8!5oi|X7RxcQjtxs#@AHO^u0(#rZj@6R{Z%WYhXZKCD%T%No5
z+%wxV>-sfe>}X6JHyZ2cTUhHhg=n_3v%fN%vhVQhCS7R3Sze8)4UOUF&#t-2u#9rQ
zjt95;R(!0*#u6jeF0=PD6~RXgqNZ-2@mcq@`j5YSH4sAs5Z71FLCdpmAc_VrQ7%f*
z$fQWCkA3~6w~uY^;oOlk4fYw=!8Yb}vIO?5xZm5P`{VW3Kws5BzhtwoYO+t#;k2U#
zAJcm7g|(<AxNKPihi2)>nP_pQQOZt>wN(U!*RL;!+s)U@2~}EXt*t(^w90Z$mfKMT
zh%4w1x`N{FHZJgN1j+=z0mX%Nxc~meTq98E&&ggssFn1ij}px0oeC1x$oKX`4=*$4
zv{V!gaxDSue?9Us^VC9tR480IOYAD@)55|U+;i_jJivRLJn!d%qLuWs9$NLOOu9$6
z*%^23#xiUo0PUu=v_NV<scvNrP0_r0J}K7;Ok7P1>{Xu>DF?Gi7WIn!d_VHYaz?Op
z@A!jd)1rn#UTPqTN_Fj80s@ry$Qpb2h;&6Ub;N`iRQvtmaMgg-f|NR`RY?IZisUCh
zD!}IAMg(JN>F#O5cTSGM2>MtL=@~i*-SF)ZwxP8o@9e8Lq;#SPM5iUVkeBK-N+K43
ztv*5vNCIU=d+X@Gi1|~o!b?BXUSq`^Ksh)2Z*OcQaP1;6Mr%?5%mlo`2U?`Ee~ihF
zH5!t^Ai*bshPfG9W-7-vdaicke_mXS!WvqiI%`Z<^U+$SO@6d}UW`m?!Q>Gs$jKUx
z7NUlqY%9Uqot0>`rx<I}I_F?MObnd%7X0<yO*rq^OpHk*=)wvUNU7cs>c8R3c<^WS
zPSp1nuis9A2CWY1JX!k$Z6w;*|C8N8j2^T<ZPLN#^u@hHib#|pm~kn{Hq^$!)Wywp
z>9UpBeA{0TShEFfEv*DK2{Ln|E#(aXtN|))q>v-qMs3^HcRwa=v=g-7MFZ!D?(b1=
zRwJ#Kn=prqZIsPW?J9<2VcE0qV%HxZMd60cNFj(D$NRO_a=YWqoIo3Xrc02PLaD|T
z8?k-WHbg%)AC)&=j6vVIm{z{=u+fErE5!OiAHFoxhM(`ck%E9!QlwTbU5moIpG48(
zRcNEuaw_|QKw7s13IvKpt1aaxl}clH(16KNq{f$u1D~6KuZ_YLyXFj*Cs169Ex&w_
zVEbZ3m)9bJ6N8;ngci;lO2t7iPwFfI4}t8~MpVD@7AoFf3_LRdlYaIsWYNEOrN0@;
zE)shiX%EXm1fQY{ZdN&eQIV<mj{A}S#`~lgDZ@aX6<dOfTB=@JFh@~1By|)A8A@8_
zuR`(S4JaNu5bimXkyucP@<q!K!}pTG{@E1gKytuIfS&+9r?CV+d3`IbRF<rPXZ2>3
zf3yNa?)nZ=rc5w`75UflaZdA+njnGQN0tQgaotu>gf(|RiblRKPi-AXgP@^5XRo3X
z0<UXwrsHoH>!@u{EgIfjjGZ5>M%mP{82`r~AbrLpG^?#~UX0>qT0$#UWq<e-H6V-p
z4fj7nWrh~mBT&YLk`+bxlso^s&p>OXD~fV|lQx}tiIIcK>7+li=mXH0M^2&tN`DRX
z*FbM-AS?bD&f|#$e(O7?UoNXS#M&?_t*d~ux@8BR-S}U$P}^gGZl$k1)3nTQ<J^&V
z8z8xJo@Ncm#3U#eg=@f8kJq<-2Z^)_8j<>y&QZ<pPs*RhveLhCKeO+WK=CZs3|eZ>
z+;_75SF^7Q|Jimc_gr5b&{{>GG-(os4<Bv-wf!E7Cj@*}RaKEWY@dHt`Jj#jTq}+F
z^XD7-?Cw@d+8g%d-+JiKp_nlvyfsz0vVB(PuD$kJy!z^^TvKUfw11GrbzcWr-%6n%
zT#)726<1u*aY^s*)KgEzOE10Dajx@^7hZS)Kl#Z|$f|a9E4X&9lf(U6pouKzMV-HN
z=~8_B@yA99LH$XVhuuGiyF5pG`c=&Z1iZ*R<#oa3NJ3mpDq{I@ac}Rkk-^EC0=FZX
z*3b#YBHC#ayP%ukELjWb7D&p>nu(+w?Qf%%ysy^ypDwLigB{iLQQf-T(1S-9B24v5
z#<!*7EleGJ4P|BA+j(exy5V~$X{6FHm6s0`1h~@%!OUMSd$MtPk?7{0$&tdc#<84o
zrbfzVDQUHN2qZVdLq+^nZ$0HK_1y0^AvreNWV4R6>F*cZ$@9&o+~M*z06*v7ILAO_
zCr?3U{3N8t4P(9;thXdnKLV~*FOrQ4v~Km+ac@>aO7e{;sQdtX8aEg~zNdN-xLsPi
zmi1A=`nVb8O$4nEK<Caa80UMw5AGvv1V_bsA{l9yz_MFowq}1R9rFcHf!S?O#Bqba
z(Ix89bl~yV(r@_RDKn5FKI>6`Bvj&mExaVj>R5nTr;Kd3<60-nYsIzXUc86rjhjar
zGHq?xfXBA|3pbAZJ`J4WaLM^+V|(FlLltgh#FYCjU9rmes&<odxqHysVamkuShp$P
zT+*?aP)82S?H;w`I3qpHddKZ&#LiY;u@_@Ujp&FI=|}nAy}V@gLFJ_~Cnd@W9NRyw
z!V~$sj_$0q9+VPDS$PFrqWQ?ws`lR>4z&if$Cvg@lne;fxsSjQf7>_VdFMj}DL053
zKup6;#!Jfd_8WJ?m0bg%pYjIE>TDxVfqmw6u#K3_&Ua)!Yq&?~KX-K1KqRE)=&FSy
z=4q{Ixw^UuD`|x+qbV(Dx^J?>71wAMasi}C-q4b80&8lbHEH{zNCTMYUIiEm=8*NO
z1?cLCLEr)xPTF`1gciMuCVc@fZTzi;7)LUeg?X3-0RacmQE))l3PWYo5=;UbesLn#
zkrp4;u~}&HzSd>}=++!;79$2o*Tm1~WRaN;E$%cyFwbTJ?Xucb&e3))0{M&Qt>ufQ
zQEIGV`HT`E@d<dD;Jq}^n2xd2WuiVVR%8vDnVCrH^dUIstZ^7SCWF4f1!JhVN>Rc3
zYkv0sIXpGM_abXeC$)mrp4OGu<K9Kprp<}*HZQgj7avdRIJO5_AzDETFnQ#qregb<
zzW9>0a*&$1XHLq*<ChJhRkPLRwe}5gL)b(u-?z4w;b%{-Ls1Jimh2x|P^+B;xl8+a
zM+0!$V6KU2XH4*#=-7V6GKwgCmMpfl55GT}!bm$>d9R!FU|s^venORmrtli6(%)TI
zNjD_}XnCrBOh3F@ECpI|^{jOK>eLhr9+=Udel&%m8F*~rHr(+>8L9<)O7k<C6v`~Z
z?xrOC<HODP(<S5KWKurTCp+02)YAJhL~)Z`*3yPo*U@jVgPY8@RMYPRv{{uDc4Sg5
z2Dj7v_hr6cthB5#MV7T1593H-T|aLnR{!cgv@ThJbk>!h8j@a)Pb~yPo!lrpWns@Y
z;$xeV<+OwCN(|8T9B(aYb@=yakD+EH$8V{3IH>GX`RqGb`v3lfnBqN1veV}c%i!f=
z)I|jhzo!+61f4yk1ge!{2sb%?rr$&bgv@O_vF?BGhR55Cu{U1@KV3c~NGicJgDk56
zHQ7cBoc1hOhZR4+8?6MbV<{+bGfk<pdrd#Fl%CY?q>-%Q)zEsf4x$|~#wWBEAnJ9k
zd{!r)Gf+^9^?&*Y>L0oZdD{R0KmbWZK~#SogD7BYigKfrV@0Yf7A`6q1!(dX#kOUD
zS@tWN>Xkx~<W_?8?<~TyvMQYR;IEM|bu^l|a8D$yrh-SlrnxYWt@mL4KVC$|L(d_Z
zUBVj^hidk>=r(%ECeR;Cr2>_xR%{PcsB-eT9keEI+g5}og3Gn+>kgi?IryCHa|smc
zkoBEUNaZRDWb8zkDNaHh%M``@chMrMnRR~ZgTH|%BOXol4M?Q6bQIGFa-NblxxJ(k
zEAF}<#m~QrOdmC<dCkWmuZ9~Y8;6@XD)=~9t`?3H^dP{wkmVxqZENwMZqX8~y~B@b
zzxW}NPM&J~cxzk@a%{6&o_YNKKL=3*l`GewWZq&@`R_lTcTOfii?$(W&c?{EceQZr
z9Oqzvd*~)$)&Jg0y8U{x-uEnc(1tp7;#jlxQa)`IRs;;KTu*PFYaMo;1JR#%F1N3V
zECLjUH0&xy6Y1-lX$kJBrh5aPZ{v7j(j&m{OiF+|BOS4HJrF;1FyeCt!IhTMIjKJX
z?Io>#V^J9zcI-w|smfMifPWuZT-sO{0{2CmCaZXNMw%&?m=jXQj)05Qeti{uw4P|D
ztAU1HMQARqK=U4!qlzPIdmZs5nfByNPK0~l0J<T_K!WOMNH)uH`0B{Z@kZq_<tkJC
zRI+VJF+%T->lqF}^0gjA1qe5p9OND$k?siOcRhx#2>Ln%sQ4smeGZrh891lKTUI6q
zd1IeM&m?d?ChY>OuX@s0H!D#i@Q(Ij-Ja*VP<zKZ(kOsV<y^YQ<jq&l`Ok^z+27s|
zv<_D|0odN?CcL@r2e@&{hh#LdfLZMY7w*0rb9d19GZzFrG=X|wNO2CsW#b-2LQH0t
zB%A%!cy8lW*wr}SED^h0Ji-s+sWg|Hn;Q|h7A2;rj5`O>g$u|QpdBs<sFOu$W@guV
z+OcDYp(YP^kIv1WJ)17K4qrcwl<$Gg%ZIe6xhpCv!baV-YggFwhjQ}0_uiw+uB~B{
zkPq`YbLMopcKPL(M+B~wjx60TxZr{=_w*n1eGQ26TS^YHau*H1fNFu*0&5~EKJ<Ml
z`cv#)1LNsKIWP5O%DpUL+5%j=$2z#<?4&6emw6#teLpn*m>2E5m+xBE^5N47P}Wh~
zQfL5PKf&c!H+&bRjUOM-=e7)G)ON%mGCX|_hNjFyYV2^%Ljx&u>v{dGlE_Nj&;6ap
z-#~yqjaJ_;8B1#)_obQ#_Y0g$9dv_~5jzo6hkOIW)8-(KGLznhti(z5qkRr$=HA45
zc?=7-|AF#B@x+A>sxSGpo?q}glr${u(b8J;yq#rBaO5zLvoJX6cq;D?HVOx}PVPID
zlb&0PApMiVL6DyS_rNX!*NH9*zz<)dFWy-_W0SX>>}(BY{S)WN<a#Ll-lnj2E+t|m
zh9X>hd)~_JIR8*Krwy%f*Jyrpt_KszEXa%`m+>W2XusK<Z@-yeRNSYa@%^&5aMFNN
zFgAIDNod&69OM`5F!B|xvl_TQuIKm|4!G70+VgY|J&mD*G6x#DwB+TXc=D+|=fwRL
z4LheR))e{EHgfj%d<UeZA~8Xx4#E~~7&2%Oin;e;;TbQ0GVVQ!N=h+2Z>WjW|K}iU
zfb*eu`5JgXS`ObvD*ccdG1yEHI5`0}?%nLe2Ejg>KA(?2hQC?IVcLQ(zW{#jNjtX_
zx%9FR%YkFu$cP1M<9^M%m_~vfvhPk&DQ|L~Gy~CeDb_vMN94hE#W`rY_c1u84W#8Y
z<x4e{EL-?~X71MpLKfCBG`^Z}HXJi<Ky=>p?n(C__t(G?R|Aq8_lF~_1{xZu9Y|lH
z2C&xr+l!#ZL1=Q<G_J{BP>GrQ4R|atTJyFnutV1c?P>yUmw+&yAYkh<zxiKj2~5^P
zpiSQ#>;xg31u%vC8nbneiPw=IV=FJFt@FCW0NXqefFVD))*bEXGcl!aC|*=xQzquZ
z^8$RV&|VnE(_JP_-NiV>e3&3>K$+<tS@O{u&&EY{60K>EnKTHepEd$hC+E-?wxy}2
zH1yBhYEo1Q`om{Y1G1n-kboT#;wL}YfxY##5TpetS{iWr@k20z^ud;0=>ChtJ}qCU
zZJCBc8%}e~Hm!Ee<#SS0D=r#003VG@$3q``C;$(&5H)TaDe$Q&%zHZ)*lbUcg6=dx
zIYFt=Z*@+!agX1Q7Z>b6J)yuTv%w*#&weI)<F8MO!T()Oz?)XMK?2312$L1IlV!Pj
z&KPP$2l0ouNJr_WkXij)f%W>)+8UIeWk+soGXZ}sUZ~6wx^5yJ;vdx+HER~poxs{M
zQXaXJXf;b3e{R5h)Do9;_t@d-+{7N{cX)`8aky{S7-JN2xWUkyZ=%oKrGI)5u7w+r
zMc?FAe5ZE$JeKt_NF6O5$rcKw-4rB5bK~Dk1&3&Iert^NaRW^~N;9^q+#0o|WT7C6
zZ2>eMf~<1iP(cuP^{@Vl<l+i$nB26!R$Jo+*-uJtZURXrM~!!;&3^)P+j!kU5GtKg
zaQU>(rA|ieZyrH)Y6dcHyb#p{Y!msu)kXX)hgdoS+l#R1j(ZTbcs<g2Z1gj^ankd~
zu$@ce+*mxD5^BU&Va1$5N;_`I{2aK|I@-tv0)pcLQ&!yf7^)tAf#9zbHKcp1Wj{=`
zlc|95)z>&H@?&NoHfR1Oh^v0v=x;(gEfSj7?!xAu{1ub`dOQ4+awx3fJis}}$+EKx
zp=J3dZ2iZxa8Ym&OR9Yv*%Vwn9~VUspU*|TWv0$+Qj{T(E#?tp5lxUehH>ref1(@r
zkltMNA0$w1=lhbY0D;jhoQI@vqko*mF(?p^hSwG#fA*Wm{r;tJ(N8t?OLQk%$VlL7
zA@K3Hr%?35n;7GdHP5Tj$1T8`@5E1LhB{IIF@7RTB`bQ?o@ozqUy#Fbw3b%kOI#1&
z<h0unKVi5TXS}Q{ml@BLSARH|8rbs0OH@Fn5<(bl`;K&2zjkW7Pq^iJvpFqz>J?-C
zoH!yEaf1dLKh+&^d-0>SvKpoHmtxa{PojFm7J}9X5c(;9`rghtcaBM2Aoil0?<7V4
zX0Cf8Z&v+OrWkeAC8pdx6|0_`?mbIap`4rOnhjeGAZ);313I$9w70F0Ql$~buuKZl
zOt(>CM1XAasA0&MF$IH8orSasqdV^FDenVaGcFcBT!zw*mKq=AEftmUQ?MY~cJ(Wj
zZQt6N1C}WlM<1(rbiY6a8v^VDjz1P7=#zX9sn$MCsNY`5`dNwcB`Z<0af_jLH$XMV
z9Fv*Kp@iG(Dr_AQ*jJm$U|9y8JOgRRj-v&A<oDN`a@OW=N7<r}QL%J2YBq0!sUK?M
z8=q&Y1XDkzU?#bJ<zjjD?g(VN={qfHR2~LSpNy=Nr&B><46m6HzBfha5~phICKSK7
z(6mh{7PN`Aqy1fUi5TWU(FAlKd)s%AchOm4VtjU|PRO|h8!KOC^)+|2C0R%dxIQW8
zM@VuFHiePJ*OaWQ@&4|g8?aba9*k~<=*F`WCiSYoO1ILp0SgNMjI&1k&ItD3-u_d3
zT=owm(3i|fL7=s)&MqDEIKcz<uTDXh)-P<n2HWf30heZ9xS&2faNxim%N-H|hawFg
zJQy=)&NLL-p>v(T1zN9KwF;uJ?i@rRUe^P~=6UCx*E#wT^E)Xi$<$FK;JUiH+5nRh
zaQ8)!0R71)pNyE)C6`>XpY^nWY`vG_;^O`8)B96;vXl?aCHqC{KXFJkAYl5%7hl95
z|M*AaQ(OukI@ceLj2dv-V&Qb>9G1e!f_qlp4^Y{-1Nl{SyFlN~VFOPKp!ISe_3qX?
z4+vUwi(}+#S#gtbe9pHGXziqB^}&W{?wOq28SCD{>c!0a=ahRy2CZcPGd260I63df
zl!5FI;Pf_sS**_(`W+Njk-4gFenhKsf#-ECrC3+;BIo=Fb;nG##Ea(IY1-g#Gu`h|
z*{tuBB76HxM`FBPr2XE)c30a%Adgn|4(_=Vor92<ayCY!TtGR3rC6@Aw-LB6teGoY
zC96JJ-<C@=uAd}OZU?U^eQy7)_o4ND`;(i+cBgA5etVpbdl*;g+Gs3N{1QedjW_ZG
zStxg;)1I6p7Lh_vZ6Vw}vbY{RC=0uG7n`wH<L}1&t;Xs)Gb6I%#fHsWQBhgd0bEOd
zK7Pz716YUh88><)maJGEIyCKc?)(M#Uf$Im=lXy2t_D2ZH#PqD@9-^Ki=g%?l&M7-
zLpZfFnrmopXOvE87!dD<<5<dO@BTT#=rLU~XnE>2H2vvch>D{zNqY#A@;&aBZ@}^A
zpN39#{bu_0Klwdco_Lj1-!zN}1!mpGd)?QakC@X=4xQcaJNEj^(emK)uno)RUX2pL
zYAP3MgU~&wg9)&XrAx|VzeXR{S0g(6nC<|OKySaX`j7i-;E1h(e&G5Dd^L6smWGi+
z6L0(EDG^xYX;crvxSZUzfRZp>_X%(g-J`p8O#q0%UWL%2zyMmjCJ>~{9g4>DCYXhT
zepwq}YYic=nR!u)qG+<N)&fI7GIMLcRp12UF;A7D^8&=c7A6la5%_KHFlAFID48*N
zTZPbRS%F6@eIBUn0>Bky5ibA1Mk|f+Qj%SmFp<=51VK+eX_)a_Ex)o>XqWhyQt6D9
zetS~war(awR}Dz<0YOfhQAt==S&vuORMY%Afo~xiY4&Pdal$lOn1<fb;VMOYx@uGe
z*FxE;4X7MGegqWR2=BRA-s~hUK1emy-XN`;+3p1Z`q}0MjHzvh4Lq%`V?IiOztuma
zQ76Dni||cV%~)R8NVG4><Z9X^<Hgdt^_yo-Kr*Q#JxFGJ`Xpuy^(2zwBc7Vk*G@~r
zvrCGxvzdK}+I8xepcT-zniy;>szdG=Qi&OeE!6(Qmh#|F@<Eyjv_7$*5G~w{u(W)p
zN;BptF}JM|rynzseU<elf7eH8;A<rC$+kpL#oOw^);~NBytfj5Qc|}P{1k&lkk-fb
z9s&#eXG=(hEj^AF^{r@X@F2F9T9!dKYMdTYON;W%K>(GnnEzVH0FXvj3IVz#<VGU_
z)K-@rc2Y?f{`o1`3rc96ODkhqLDv)fBz(=yV+$1|2zq6v!yWHHD;MYfqP<MZj#OGg
zH`^&RpkjoNU~)opGp(Qhj^n0{K>XB+v?f;D16nyh*=Vg-_Rt$}E?jARQwJ${pspx^
z>L97JNxaSeQo~~b*baflY?}hsHEstuaJMG0t!nCEdQqIOq9_pE@dOw955LJiN1J7C
z7Luq5-{_!H3&H6I0@u+ADR2(RpnC!O#IC4;lN!-cv;vQhcB8==gGO&Ft(9%qyKor_
z?|T9nfBT{F1Mg>^nmc@~@3OfcAg**L;-ln>fS%=@d^aAlfV9=4j+D#Mv^tEBiAGaR
z1Dfg@Y4xo>)kbS~C*o-hFW+rWN;6{koQ>>*jj^Qd8<2%YqS5htno!qVL+W+H-lSId
z@m>$#jezaqCtk$h)26^ReiZAEFgGn7-5gs==Pp9oy-y>F@3o3Pwi8%K<utQS;fB7s
znf|kB*&WXfZ#2u~-P?+mnmrsJ=|X@jHyfY5S%XP*9Gq``jO|H}VDv+`(*;HhX$1R5
zP<^y@+lHM*D0zR;0fA##HV>LT6X~RYS16gk1Up`O3vLQ1Tmwk;Nbp*|$K$gz3E<N2
zdP)-PwE7ijDmphm-2;dcys4zzfQtgFn@Y+!9&k=L5NOT(bbsDOXCi&Fm47uAVv63L
zZ!Dm#3C0ww>mSW`OkW^rxDep@RPw<hY=8buRD4XTdzOzkn2ozY%V>^S`t&L=ua-TP
z#zMQ8^|}5p|1xEjb$ZS@ry6jySLt+$RJUaZHb49fispW3tiDxO(d;i4P<{ZQzf(H;
zDKK}}%Wq>h-31Ij<0Kq+*N<q8o!u?g{)cT;UMcxtF$$h}&8j0>RjW=V+_371^KB1`
z16@*79kCp;6qnM%hWj2t%D558yX0&PyX-vEX1c`h;UK{FP&D@=6g>Aj_O4tHS)!|c
zH0Da6DllBWAA1!z7B|y6yRoPg#qTbprL!BU6UJir73U$BZLYVL&r*K)<PU#m8Vewc
zbiF4rB(X{LI!GBoeP`f|slC4Ea77%FI1Q5qehq8({)_XYl?$ti>!30$FL@AW4F6R}
z)>AUC#nR%xquy3*fM&_MR+T+MU)VR&g~gDLcs>2#bcH2Fe?xY{RIDuhHv#Ck4Hc`I
zG<jnPw7zoeGsvZ-Tjx;MT1?RTTI{NyCmq5rLHDs~STyFEfREr1M_SB-32O5KLFM~7
zT+lxBgAYE~54e8&?YF~4H|InXK701;h}S>Q3z8MMsX!GeG;<9ntF71wzBeP?7wNpH
z$LGzP7xuEt<-(<xUb^2kS%k|!_(KmpwBI$Ic;}sW44^u09GSNIL!Z`w6g7VQ;~yJp
zcloFeS?={|CjE&W@imZ>I^6)*5ev%sz)ec>6-5tYOXZspfn43MDcnH@uM=`_A!vOg
z_gs<3pNRQ&yHMX+ivMi>F|Bf&%zWAHvN@DB^YLUYId$Zn;Vyrwb1qV_YcA|52v9vx
zkde&HoSSjV@Y@a)^{BWmtK}7C4`Th^r@8m1e3oo!lH2+Nls5#nqTaual<g}qWAGhR
zK(LI7>RNZ8R<!n9^GRAI`K}B*G^cCLV+^Py;4<%vC(Q4z3mD19+NBC?sO@nqDT6t0
zZsi|YU2m*ei84<S(t?>-pP$dMk$|28^K(%akVwXtz6zSBXB;;Lg<Mn1GFt197VfuR
zeElta_ZwGr0ir{B*3<g-mA44wx1UfwI4BF5Ho2?v;{^KKPA1z`4PA_Bd>4zxs<rF!
z_6HvtzuTeox_`^!Tk$Tt@LV!I9^C$^ds<(H<Hma)LF2FQ=er>j6=f(<VfNu-ChQ2T
zYwyYpY;kI>-GcB2#dUVtFN^V3cu9oN#Jz3HGp{2mk!#*ggHn(#%bX`pL((6A5w=XB
z<Q!v1!g=cPh&k(2v=wb6RXAM(b+)+HCWMsWQ3)Ba&Ablw<NgcL>BB;|_kZ`-z!#<l
zI`yhAOqsvr1q~vDl%j*Ol2BSwZ|FrN=+q=EP)(q-0N&7K7OD(0A#2j+1x@e@W)j!2
z4y;S<zpdCEiG&VA5!>S{pN`O;j+L^K3*BUuNAa|d(4SD8(6_Gbi{Ji>7cCg1{YFz}
z3&7SDD=<O*<d^YJqV)NZpK61DYT_UbxSM`Pv$Enbn*OT4@|DpTJ30eHh7jm82w{6t
z9beri4kg+jKD!!VeFsHb9Kb6}C>U*}K%6FzZRFdXob95<suoZL<0xe1G=KO5+gE#c
z3AJc`OKn0*5-yfQM^>9L$`>6A3PRfkC~j|*rlE!h+)WGPALV0Wn{W05eX|}m?+u`$
ziFwG^J99K2QiYEj5X8hm9HQCSW6X_OG|__8)C$i>)Aw~|21e$jW5>pNws*GyF-2+K
zObglV<x~n7lgHS)S%De$S%}8_TWT<GGZ#m+{FDD(KLJV>iaQPu#-{pk!boZzYtt9X
zzR&Xh;v}R;*ITo3<FfgMSFnS?X{y6b;5vX>AGIj$ar7_UgoNx|WZ!rh9CIeZl|g{E
z)r;!g<=FfBLhOCzJ;YVF(D$YMgh`<yLSWmJp7-`QKE%!yD=|RtLKJZHAqEN+D=EQF
z7?FeVKe&oomLrjzkc3)KGkm1O&wt=qG|pQ^AlycJZc-(2;oVHFU|;cGtb5=&obs2E
zXrmTrGqv)iKGC#bEtWm<tns@Y%MG86N)&Mf;2UT`UQZ!uD=jUvuQ?m(<ER}>Nk=q+
zF98=?kgFdFU{1^%M2c|w^@=6PTd)g7PrXC>Yx?OXb+_!EcgDA|JT6pwn~?IY%aM2a
zS+tZ)<#<8MW&(bm_t#(#eM0+o>>{X4(3vIkvMlt<jm@vUi-fP9g7|Yz;zpUH3G;6t
zK(u4+22%zP`&v*|*W6J1Nu}?ZG7{syeL12hk3b5klUk|ay6vq6*z@~GF_LM=(%-d@
z{oO<AelL{)b`jtoa^V@6^0f<L8#R~!IotpCGL*mj5eonL0!DK@vr*B+Lo;tbtr#k|
z7NT;)HVhg!95LL$L@{4f*JH_V{)Q~FtFRZ*hiVjdvR&kn@88e*kE4a*&>Jp5dR{hy
zu`aZf?nTr5btu07X?XXPz~v_OxUYfrOT_>xj}*M~KB8Ve7ROw3X~&orp{4Ynj!*-;
z-=2ros+yjyt#vPl(%cKrWOKF#w}B^2#je-iBL%+)4TU8TXeXL#Hg+ox-=}E96yL0S
zxTe!}LuJkL0)%b76ds74JAOzOj=lXRlctJO_~!exs_fZHT-4bEjyneFR6^+<3g7qu
zn;v)^m8;jYOq}t!>9%pq_|$>VDIhmw=_i^rW1YN+;}Xjt3)-wxX7nm~(Mxao#}mdH
zU6$#x=I+rNz89&Qvbi~u^8FOP`2p&-?ZWIw?uKZ@BZcxsE3o#ShfuL>4Xr|`e4;w)
ztQjBfMyB5p+S^L!<IZ)YvQ(|zjJ>NiV9R4KV&cuD!lXsG#>}2V@!XHF?%sz{xn?7y
z@!dIS!_<f65_nv(@)J}nUyUt~JdcS#{3eE;cUn(HqP6~NEWYVZH0&-mb#yS)^qmt9
z5FwVucWh>4tCY_15BKj=a&E(>y{~gF-rFHymzS26r4M6j)^$iDO{zkoYY?rlZ^b)1
zZ?%AHF6){CCA6Ua6D}BiU$2U(J&rfnfT!1A$$ri?G4lxp=?W%0^KjYNCy<*wD|Fg?
zS2S(Hiv+Dpn^txKt<{W3VuF#0wA|pjV?*WhY%QE?xkNisLqdWj)ION<oqqc1NKQ_M
zsFA}3`MDN|C!ke9;NFrYOTtAp=T1KP<Su@+BS!q3E_{|?r2L0BH8q8O(pj@+8NcM=
zE?#!oW%$>>{uS2JSCrNAb^Xg<{xaOH{pUVq4eZ{%8`oWTo$+Puqrz2xIC^WKuBE8Q
zWrUU~4J~`{apAwISlnsN(yhw4RHYy81e`PWZj8w|x7*cEcPNl^-nQS6HLED1PiukJ
zLlaLTFdV^J@l#~o=&iu++GU(qBR_9%lI1cN$uW7fwEk{yq8}A;HdelYwN=j&u;%`k
zjf>63$cKd`d&w~8=ZCW{R+Ro3Gl$$kS(}40ur*w}(ocK)I!E^iybc*EY0ssNt&RI+
zOQysV{N1rUZhwvDn0XpWvk~rT{cLVF-%?`NaT{51-dIm%`K509h30xr`!xpbSw@W<
zM!)kk#^}Q^>-eddPuE`MR8|%HQGg4&)$2Ckfk&UhMdzG>EV=@eD>VVnI|_^N!t4J*
zY55)#SH%$cJ#*Sr49U*wQYr!LXP$Zro`3B>9nT<(?&n{96U9`9o^#R($j%}Q808jz
z<|*IWOKG9~(PFlJ)>8^uTh2Y>)GlcrZ3it+zmA4G$sQO_fAo~6>=!gZs$p4;i<h~N
zr#mXwrDwuUz_?eTqoM8}+qY^R8h-H?%hI}YJTd{;vNMqU(613q<%9!;gkS#{wwxUJ
zKm3)YnJVyH3w+A0qEZIKK9v^Mlds_KK?h2z|9XE79I-VJriUJ}Wj#6y#!1yd!S=OR
zPr<r%Wo9B0J&49l1vBYt-u;pVEnYPl80{r9M^LECMTfQc-~xu%16=F`xVVU+??;!*
zf#JhbIg#h!m}3TE)TlJ1rFI?IB_OtzDEihis6Tw(HDKNd7iU%87W`*r4K>?nT}cb<
zpx=fwCJ&`AUA{4bS@JQhH}+XJb>$_C@MwPIBbE|;mVsb{S@d?RIkrvO$ErgzZm4zb
zUs`iUeYd^;Di!u-(=w@tYA)oLN>ftLN()>qs`(Lcp6+Txez^~^JVjer#^oY}^yQ))
zS8N;0r(;KKGLOma%yVX)&iW17+OUlj-a%T?>ZHkQ|KjnU-UWe;OoadGlZxeNr10wQ
z*FQmX6zR>|nh0FdXE7K2^8JmLW?VUI1O~=Zv4m8|N3$-c#xKH@w0dbp)!d~>qVL*Z
z41G+~VtEjKP1o|fb@)(>z5UzBxaxdb*3v?buSA5}xs%2tefA8*jLE^?|9u#7^^LT!
zW&P^CcM5u~Qg?8$U3g3OqWHzRa8cWmwm5u0^a)LCUAvclqNhy6lzVQ2^Q2=<%d@f3
zH8Zp9Nyi}TxJlUfJ6gv*@(R*u0WB+MZ8wu>)m!z>GI;X~VVg|wnt3>B30?H~Yj6|b
z^Adngq4{qE+gmb$;5w%p18%+&dEdDNNkh0eR#S}-WQ`o4HM5?kcKHSb*KK8)xiRHA
zk)sPxZY1zK{+h30(67Hs&^pD$l>u4Qs1eAVH4QE*W~{v9Zlo5={+SfooI9PQ*tS=Z
zdYL}HGYDL3GpITZ?x{oLt`d$hq}yj%?V4Aq_!P)U#xw$H0=m`9v?!9UbrR`uKU}{J
zg|B@C%`7$o`s7zI{<qxpZ8ZKTHzH;FcvJV}kU-+t5g0UOG^&e=QTX~IdM}rSyB|rk
zz;0;tplJs!;MtAzuAHpj{P~L!x2c3K320GHD{L15@?>t@8~J{R|KJ)V|K#hilWtjM
zSDAdd*%*4laY!GNi{*FxADXvrMYb&oMFCo)v+u>zKl0wUm%wwmG^nXeatQsM*&TI3
zR@A!*VyZ5C3IZ+TXi1&@l@mLnr5!UCb_0ZPtQEDGJ`uv9ycU7hcG8c{yyurlq`xc$
zUqd5`-uZ|Ym{O669NGv#4n6;L3WxT^lg0PyU)^g!cMb8q1<%dAr>rzB=z%(Kw!;;H
z*F%=%d$~!Ucg+tl{`=Qq(yiaJ>MPtmJSU)f>5qPe-EYma0Bb2p^$?7*KuGY)wM1th
zV_|P&xdf`#ZrzS$Km9FEd;0$Utic7Gul>^l*!<6@A;0DV&M5`jFF66cqP150P`(`&
z>in$<UOuJGbt>0r#Sq0bqC2+}mc>{`Q`jKO|HVJJi<I9haQtsLW`_}4$clM2{h@Dr
z`c;!>sD8T_Oy#l4#Z^T|xyXRQ`mxH~`8vzMatToH2zqzzN)<Jmwqe1IKf~y6T#l)C
z{`Y=$rF&|(Y{$ay-cF1Ay{2wE()lz$SdQEaPX9D_9+rF3<3?e2?rj95f6@V5^L0|8
zs~q!p{T5#x`!KtL)x%|7y}sfF6k{nBS~#bB32>MH6C+bk$Jhbq_o{sAlbj{BATU8m
z3$)ITn~sY|Ka9+Hi*X^8*tV+o@amQuQS04h@(Z1pp$DfuOwYO*XAS>7saazQz+aB}
zg});$><Y8~ltIRk7BqJsY><Vt=)xmeQE%C@#aQ@Go;=wky>jJBl$Mr8%;re;zbqf$
zkpOx<kgTzty;O*bSOM)91Teq+^2-sUU3uk|9g0fbBb{)<379Zp0@kiw+x?O`e*XFA
zaqF$O;?pQ9giY`$KdJV7%{A8;(E8KRf=lsZ$dDn(p%7gCDK$0KD1(%gl;AIa`3uL3
zu6vN9yt<Fr$M;i-yu4vOHk8x-4drK%g0_?`z8Z5`b56LRxyMH3=`%+Cw$DIo<x^1k
z0j<2>h*&<&U3Rjz%^C4?D(Uu(G@;pBg||2V6it50ShXQLgb+<@>tXb@9n)?(d<aQ>
zDQ;~IG-B1BXW^sLyxNsSdL~G=MOl>gz#2VT$P!o-m<MM`B1WWLfSuKIjS_*HU+5Oi
z{<=;34XvxJ^Zd<|YLAl5X*I1?l68$gO|Z^cDTPw(^v#?7T<|Q}z5uqZd6X|(jX--p
zC-+BZ%{e8MP@jDZ2><$5FT!6RevEQ4$`3h)YHVIbW%~7-^Np|Sc(QWUk}XL-ss$<=
z_dYCvouIRT?TgPjGi-U}D|_>nZP<{%l|I|$%bs=5&(c+Eu#BJ7WJ)2Je+w1%s%vV^
zHRY}Sz=EaAamJjJh&$}t7lzH`OL{UurG<vO?xkO7jxF8Rn?WjrQ`3TSY8qRyzCbw|
zH%AoW&{$(!E)wpzna5pQ-oaK=TXhZU{`)RkNK^hxmd%c!IhJd;q`&_PcFG8Q6C8BM
z)mF9<{<W`K&*CL3Va6C5st~ZA^i`5y%CzM(2xLDDa?=wPoAeoE)}O`ERs;J>UyimK
zI3k}nh!am3j+?(b3-|qfG5yZa%8e73f}z?rzYcA%G)Wvmf3)n-Kf!^rDhv{Q;JnKZ
zg>C$i5Fdlw(Ww|QA`KHKWMLA)>g>TuTx9HP!WOt@)r)1B&IdGy2QU0Mf~w|ARTOKE
zkHV78HCWpyaGF9>&d-Sy#?2lkcu4O`%UXG|6#?>L4$MHy*?oav78fr%CxsEK0AWf&
z`rkHMMccUeC6qvLmUl3I*R<4M+doWCzx8DF4h$a`KL&UvxV5{65=~j7@@|VtpMql^
zez41ng@0LLZnAL`U}}c<wd=2%yi}iF0*6|32B`p`ILbtTTu~GKh?BC}E;Ad`x6D<=
z_!NQfiD~Gsp7IZGZpDIPHwEuJ8*mdSXWm+f)6zRH&5kQb(@(otTJN%teu~__G`F-j
zorcm1ct6<+>b;_zN*k(60k;haPK^2X70CL=MX2KrFO6$sUo(A;xuR(e%vZ^cOb#u9
zYnHA>{j2i~U3a*;QM=OHt=X~*^&fAb<uw=DY*X~8OiM2>n(0UBq&vQkz)6!pH3=j!
zk0`d80onj^)8@>}#IW11L+!G4Xd&$|iLz+*>!q({T1M|_MEQz!7&2v)_MHfjLc3!A
z#~2*tMiqTU$#--V-}`QA#h-Zl%^2{Lt5D^RLY!CH(cD;yEkuL40-w=rKv`e``cex~
zq9u#hAgaEeG}<<h9h|*_l;@4~Z%u2vp?7^7p422X(~3Ekz`dZWW>V1Cksf^DRp(+X
znFTiA`B%gcWcLZQVv@4IuHmyZ6qh5Bl+DWBPRr)lMuN{&<94tm%c{AJC_po*+2dwU
zqaRVuLbMExp>W2dI-!;Gpy|h=c+naHXjSY-Q~(fFJ0%^q%uK`$&7w89kJRYw|9sw7
zmd{Shz|m*U#>)3sBBqIB1L?nI4echVg<8Vme6|4T$pg$MpY+m$s0`b}KI&tKZzelI
z6sh0i&!C0ZPp?KJpE-r`1?ESyE|X$xtZyLok~tV#T8{O1-isF2k)#9iS3ZDGwRa6^
z*x1$v<PKGB_lJY1fvQh7p^B8<2cpAn<2%nHO}INFtt0hBDl)i9GcSteE}&mWj6?q+
zDsLC_Jb|FKXv#yOe90=v-?Bi@NI`(ED?JrM&OD_fN;UmqFZ|yBGeD!U&^^EgTvpQo
z3Psd^1>OoM)hz<kRW=D(MBRO$`qO<XkHBCb!4T1O8#acHl372zwe?u=?Vq9SqvZyy
z?{<0Lp~e_09jIJR(n>p%63sOa6*;{8><7f|yR+k$oeFdn^yyu;a5)B_esafofx}CG
z^uO3iT5+3{>;zhO56oZVTTEIqQpSuxDwP-#DQFN)yn}$V@)vOIZ)ir#-byrbOsXXS
zT)Acgt@Ddbopn#r)FB70?W81^V!}xe{H}`?wTDVW3%`E{Y3&ypa5!w6Yaesn0|&I1
zYYdeghLXlU1J1+*LnAKgbEPj@_r{_!<Ey*!lZ|L%8N<Ct)5e&te6jnRnla<gw3gvJ
z4+VkzD}M23Lw|lCtM5>I3f<fNJIHn=|KFLU$vcw5jO_1I(Ec`dVIK{*K<Q8RJVBP6
z3;DUg6jy#bFBtt-JhJ9YG}-8<P;)yiA70z~T_iX&F*vE0&!x$WL4-DH<Hrv82F@P-
z2gEtktqc4s!Rzvpe_-zR+ex?D*u^?q)ZmWjSe!Zhx0soIi%CN_jvH_>hNXN3tIHn8
z;@$s;I{OX-c1zg&MGgYH&p!L?h`_bL-h~SnA_QE&`|i6Dfw=7KY@B%FiAQX82uSP+
zbQb_AORc^LqF5K`A1(|UGzb@6bWylV=A4A)mtTH)MBsY!=FNt}y{9xHCf<LcOAW|}
z^$&me!%qcTOTj?a@v@%J&CP|V`qiJznw?|MiWMtx-+jGfjObFR{)59>13vl>ZuIU&
zqo)kj&4s9G-hs-d?c8$gMiqYtC>uBsK-<1th*(ZpJs+ES4JKq=5;4xfUXXlZ@y`3W
z!=co#eV*%{hMmCh=mBRVGjU?~O9y?dxpCXx_po%=-%#0-&-pHVrfljA+MRNbNLf!$
zfm1JK9{bjeJ;gm5u2r?{Ky_;gxAkI&V@2x4n)>9^O9&?Lnm@Hw_j=l}wfYqlRK3AA
zK){tJ6;*E~bc??AK4f{Vv-+zvI*hn@-rU7F=9-CS#kKP01;#h#+o6=((+ap~G^0H_
zwfY3&CMCq<zrK1Q@`eskE)j!~Rx?Q@tMc_WOc*;F*IfQpJoVx$loxopPUGH*%I&S(
z_ix`-$iyV~wm@x_-R!j}`yY~(i5su|8WOo~4;KWs-*D9xc=*ZZ40x@*m;uI_zDih0
zaFkr)=3TVu+1t~$b5S{BSZ+jxvv7HR+0M1T@FoJ>??iP|4h~YD6LTu}`InxJXuAFi
z%GE}B1>;sAIynKZi_V}mbmpN|2j4^e9rwUT*<e&6%_a11+C#9p6^Vbk9WkBu`k`w*
zef!r_k-5GEwqYj`u)Yem@#hf`?>pN2p7IV|^RIjczJ-57^x#Qwp7X!7@Xiih>i_Pq
zfkUo=zS+?naxwZ72-Sc<TQeE*&sA4VMP_y)9(Z68wiZ;;Zzh2<POc`xzVR(|>lgCd
z8mB3E<l;{gxcr+4q)ttbMLGe6abpK!%9J4(mX|^b^#pUR%Y!lj%U5@(LiDYLik1^v
z+_eAfa#R1o=T!q*VAAU==5Mro|H-gNK5qvPj6u$zbY7FxjMT#XdoT;a&+B;(Jzx2{
zk-cX3r`0l^zJ{s5VFK$(!EONTP+R9+{OO|>vgrd>6!X{Vt*1MaI<|k(3^Q&0+jm!|
zh`su$JA?GIZ8t#Z%OCH=eai_H5VSU6yy<H!AgR@(TWay0vj!t4A%<^KAfxW>{%pO<
z^h*&bv<?kcP|LNll65GnUHUiXJFBLI*fwe?a!;LQ&Lwc6Omzmd&<~BDG}A<U;i8{w
zB*h{1j2YNPdT!g^I`b~NSB@rffvo|8mNB$=mpVlo{p%#O1n7gBJ?f0<2+kUBHj?Sg
z-%lUQ@{t|O_SMEsQ!tJcaCt*9?AXaDTwg#p>FS38svb~LfxgW5=5IqDA4dMF{Doz_
z?qPnU^`>y%PpN$p<J8mo*nZIrQp4Jj&T-u4;Jar774T{`K8?rv%@3c=$GI%X^z2O@
z)NI?yvQhf)h@+5&Ko92vhnuV#*PVyDf$>Pxyhkf#ufSm{Z=|uU`3Xe(Xkndq*%`=x
z@^v^i?B-^eTAmzKS_u#&)-<AaT>+8^T&ss^l8NzYW7uZ=L^qN`$*CG4^|;^3^0NL+
zdlnMU=jR!?U9kk}n~)KVA>hw<Lx7(4^2~!u9QGj6I45f(DD7=2ugGt`optJN@gkPb
z(L~gs-q*q>V=p8<xD-&-7aM6w>MW~;$5#(8)1p^wQmQfC_+-SLF%7oVMEdw9fX^yv
zbq0)|Y!_J#5(N&@e|E;~;}JDB7uBn`BbMW53#r=Gwt}=Y2cH|m^uYb$U}~W7^>+;b
zdmz9~;9Yks(3sR@B#|D!v4|T+O=^7@1bPY_9yoKVvA#{ER(0pF>s9(eGg8UOz_n<?
zGiM!#_#A5kS+ltS^RNFA0m`xiTE!ZRU4e+S@XkExcw|hQgoNB7a8Sr2z}er_LZ8zW
zq_|#>GWu$+-n50wFOG7Zt%*Aqn7<_?vb$rj>7l2Pke7oo*I&^khAe?s{NnBdg4O~*
zr3{mE)*R&0H*^|V5?pC1X3A+M7+X4Tv7s54FKPLEju<#a$q!$}@=rQ|Yd!ahJO6Az
zYkMTxK=yq-=hz#*hSAqvO26h=5wqxi!B5|18~^?|){%a^vlV<tJeJ8GA8V}5chK+n
zD5kF<aCiy9bopYJ-}JD7<r3BVgqyC#@GCFuqGS;^ey5XOGJCB3<KL10uV*>dgi$-=
z80Vmsdcm`=v#w4;&beRdxLcO-Wgji;0<w2p?DV6J&yc|OB#M1ks`b7)T{b_}=Jh^9
ziU{4We_qE9n;Ykj{6GBrlhesEQ(*uBO=<Fl`PQ}{VNk-c^kbc4iYNcn=a0M}FKzt>
z*K-tnQI1$2Rf4BCUPkNee`9#cscgPIv`eNUk`;m0N%j#qZP+gf9(>)*A))85Z7sq(
z1wX?&`jgZ!&>489_YP8u4`&(viqYw3hi>WkcG(kg!jPLVG2>D!D!PZ%oexn#jO#{C
z|6ky6=9y<w$eW0ID#L{f@4x>(Zn@<a$OrU?AAT4via95m@PPvdM!f!cUyxO~o*`1m
z&CQK;>7dSQ-S*T|Pg(1?Fn5j_GX^_%?!=BAJHlMGP6^zWf^9h3YNh|=lTYHp3okUn
z`iP12UpQb5s2*h*e=wHTSy@@Q^Ugbsa)GSgrR)%n?ZB+x4_Jc!2z^}x+Ox?2v&Y|n
z22VLe{ar(Vy0*0tb*;q&sEfI8tTFouqu|8tlE!cu1+>ZXmGje~glKyd;_ZWRV%~or
zN;3Q1yS<X@g~l}zDZ;tb-~%}5M(*nU?(NNq09{)(`>IjVxDh+67Gg)$d;<9e=GI6Q
z;W8Q!pf_&dCCK0N9~utPh$2G$iN(Zi%fO(-Nxdn~Q4yzwuF15o7frZcpJY>J+|Yr7
zHrbR5m_yYo;|0kK7pi3Q&J~TH&@Fr=<~9?D5v=n}!=%NFj2*JP){(iBFD;5P1G{7l
zH16<90fGBzBQ&#0#<f)t?)W%lr4Pi!@uP79skqbAdbUcHWT1t+Z7=ubew*B+MRWH~
zzu3KG1DG|Pa{Y{S`mcT$@-wZXsD4Zxsmv^*-RvPLN8ldm#Oc#;&S|recmTI438W0a
z>4s}CZ_!e`zhE({C=1X&%%p8<lUcFu>}s?;c5{Dl;`HNi!C7SCl6>W846JVtjWA@{
z+&KifKXJ~SjFe~ZGX@@=ul42c=Eq+^%hPYr?=%H)EK4W^>8dW41}$;l{#xi%ufH8L
zZh$R!rU9<eZt&RaoBJqYu!Unl&29v0is=fm0j}$w<R+$T9rsaI{mC4KHPG{p`6#TA
z&-;-j_}&+SIBm{I977A^*WcKHS6<tIJr$%xrBGd3h_VRPB1_|)b(j9?K$Bp5IvtCI
z_G^r(iA)n%M_~NUAtbbj5~qveQ!p;S+4?69L@RxB$*ci%ma&vG<EcEMGrC(JE@F62
z<!qxyoxo`Ul=8XZbVeKM@0=kiw5CqQxG@8fot=Wgw5A@M9gb$gDif2n0y8vE()oEp
z*OYUo$Lu)W|KoG30r^j=rDf#8LM~JZ1X%+2C{j;FV*sh8rK3q{xM?$=TOmK=jLm{I
zOrG*+EUi_wc>T4$Wv|_Jkr_?vQ`7Gi!Gs{ZjubRIGT^0EtSqDn`I+xfjD!7_&tZBl
z#`1z1-2BpRv~nXL>SVtLAPosBpQi;ghq>{M6LMG_AFr{0bK^*<{K01TrHQXsqP>!j
z{)wYV1=L8b^(YGD?F2$)JMT`UMa<AatSkOzMf#%no>E*$^%IPAv(}^ObBFPU<PIa9
zbRwy&sX5y%C=U&D(pD}j+(jyHfyNHh5nN89<#mm(5tGM`B@i5E0Nge*c-W|A9LtRs
z@n(+41eaOJ0DZnjO&<gQONp>IH^Rk*X0wgrc2QANV>d_vX>43Pwr|}*0SbXxg6lp4
zM?Ol{s{#$k9W@-OLvr9_n^l_&`bigLryohf5}-nB-Z5Wh-tcg4;#Lx#(n_GJfppw1
zS`4VA+3C~Wg&o`&CZA0pob5+*hmD}MpYug6dq6^vn?-K!h=0R7G##nqMxcctbt22s
zCh0u^ZGS^OnzxlIV{S9~4hT#O43XkMt#T&d6wC6bkw%MXGoLkxIG$^xB1shUR8ehl
ztp7xU^OanPt6r%L5TFHnR6OI+%9<PlOqjIndQO&6&qJPWf`M@e(vs0ZYwK2NRkNQM
z_7KtqM-kYMBY+;aw~?wT4qBN52`pDD0Sp%@z6XsNP5OOWa56ogOAk&T*bXXmabk|A
zFL`;GjG?dc)J$4@`|^=MN_B6P2h9ZFquIuM)Cl%X8BcIth3OBysR2(_EsExnHjbcl
zPeJV`dGs)39DnS7(FRPJfMWVM?~5RdWaY($lL7d~e)DS>`Spvb6w=k=w{dp~$`&jO
z4={ErNEC$?c^98$&d5@B*=@f;Q&Fh_H#<e!@24zsWvM;!2RHEkvk^m!=5Qem<1pl`
z+3=J4dpG@Uul~*b5Lg_}YPaKl4hFK;-T2o>k#o+eUHlmDq7uRO=igvIV!w(I1X?@U
zcaQ(wPxn(K&@Hl_NE(rgbh=2GchxQM5nvZRcErF-vak+?om2=YAPsj!&{`4s3<BA=
zKli%v51u}G0+Q)6ATe(Ut>e=Tz#Gl+z|hZY@M9kiXdEMG-LP{vT^AHHjjr=~DF4v6
z*6Mqft?B@-w?6s;cK?U$8>Ebn7<i9p&GYG#S{C_f$BsiXt-%wiaNrt{YLpe!)-B}%
zK7-mDnFQ1}>I!xltMDGn#q?#>p|nH5bsOJD!BelWNIldsuc}<4te^hdpRz3bSPLRP
z%Mo}XE8!TNGvaQ%n17vF!)kt_t`%K>6yUWj-^bS`JZI>*RrE<&S5gnE8E<aCg|0Q4
zjLW&&U?Cp=<O0mf`5~rd-#}$89|SU+Cc?Dzt!AM#2gHrWl+0^!T-FVUcV=|dhUSxX
z70=<l9d|O{?Ut1_?^V#fva}wVd>+mld2fgBZR_g(e-mOdarTHmA}8?#Jh$oVWJhc^
zbMF2zzZhqR4I74O)210K-f$%rJ$NM*3FQYkk`=M;JOBLi`7U~H&*9SlEY8U{_doyn
z&mOZHHEL9k*L!<WbtKF5NI@3t0{J8DWptnFQT{t4h4<cj5Bd4|7|XS4fB1AYpf-K?
z-FNp`&)riN9k`Sdrca-K)Y8RvuanR7xYb{W+7?<$Hx{6(r2y59yU^e*L$jZ(C@ocJ
zrLwDyfSd3q4fm4Qg}`aeL)2mI^B^2;3c=&Jn0+iChn`6gYf7FbXvTRiLSi-o=9!#p
zvy=8;Z+E_>m0+ppzZ*Rj1oDee?<r#a?L<w>E`ADmt<uO$HJ?TTtd*XPn<g9gkY5>b
z7Y!<Wbf@t`kMp37O2^sBlgWNEpmTAK>fiB{I|ax>#?>_U!<6W$k#g*pEURN_$i2U0
z%9>fZ#WC$z`*QtKH2t)W_T8dmvwUxlplFr=Iuv=|jmj(9LD@20Y0b*Mo^I<#Vo~V}
zSXcX@C6Hl0a)*|gH5lKy?FUFrqD!tGz}qTMHCEQF#fqx+*xFcx!iEZz&#a|PfhG1w
z!|8G2_e}qM`z*{%rL2WAkDkJC8VTI`-5XFqD{E1F7Z#UL$&O4pQm*HIVL*Bsh7HZZ
z_%Wl9!QZ_NQVctD&dCOV&)-&nf*pk@<vO8>vKz^V+-?`sOGj?bV2q=?wTuCM*`Iq`
z$|EP9teX8Bwj-+hzMo3W&J(8|R?ynFb`!0wNl{LjQs)rlK2=uMiS$>kesi!PDlro_
zem>Wr4T$8v&IGjWUXHd}GK-}R`&^6EpZS-k2D<i`FVAy*NuI=-x-$mE<D1vdz*jFm
z2J_|@VBSXs*tV?_6?+>fprG)NLVV5gG=8zhx%R<Mc(jFITXw8w15OZ{+`e2KC~Ynk
z6scP<Ahk|gH(P1)*pz19AFVO8%;0$~HuRT^LV_R)L29>Ic?hgv!MKn}N^;YRI-b5d
z5|Nb^M>>`i3>%h?Vf0m<m>^$Vy=jgrK!5n6)_|Wv-jbRoY_B2BC>0}U>qyYWj*%H8
zHKuUSPhUpHT1##3sCd=#X)jH*{jDZ~>^JI8+L($a$@1$XP>?4&1#SAQUKjd~hTF3T
z4v*-_Wi28wM*Z{CRj4#s+gO&>+beLzqopWqi9wvL5uP^E7DX#}YMs*WaSF9&|L1~Z
zkQPs{fr4=EISIUy_M9<})}WzSZ?}>DUR;3~ZgK)>is6{;Yc&+p{v;<_6ZA#};t)UO
zcMJi9Hqy(=5-XZJBU$&hl2W`TiON1@9BcV5jDS#^Gj2v~{7^jr4)z&8HAP#wXG*0O
za6IqV$S>OLAOYA$`a6>qu*UxYt)WHh9@ECFUcTRWY9=#{IBMa_qBr0py9dE(Z!E2h
zX_1{y%WsyDsteqX%7+pGSTDihSW>Mu(Z4lqf(__uqf&}u23cko^Vf4}W6EtR`?dZu
z@K1nEGKP(tHpYfZJcb*HMpITBwWA5zv*byRM2-=$d?r$9$GBM!euDeVhnqVe#|}S1
zX3@z;6NHb8ccOvdtvx!9Ks(zbCqo<4k)^3xXae)%Y)LEeHUkz*>zFR&nGegv{zo+o
zB(S_vCh)QDU6lH(Pl<xn-b(PEez)xe>iyi%SpA8t5H=U%vu<gD=U{qK6neCAKJ`*L
zL4CHFX~z<XXKC0GR2Bl*^f%7gkThW)_EGw?=B_@Pb9e=Tixes|_~ehAU|tl<?&UQy
zwZlO{R4gB&+1?6|BbMdjxJgZKXEO6;xkNSY;d^qm5M(5kaTD9xK6rzf^@ju2K<P(I
zX)R44mqLS{f`Ht?XP#n!zwYtU2wKV)uxPUd1Zv?V%VMq3%o<38f$0F<?)?Akod;lC
z)s_CwXe5n#vn<KBY`Ir#V+^*zfGIZ3rq@tHLI_C~vSgQRDk)2V6jC<He*>F@BoGop
z3Za_r00s=k*kIgym#p5)NHg>QeRnjnB#*{2HVNRl_GsR`ci+AD-FM45=R1XYto#B%
zovS>?t^i`i%@)8n^z2i@R>Zw-)6bpHykxBv)d1SD0baST9(3XiSHiZ(p2M<uIaaYT
zfKPG?h)f?c$mZO8muri=`2c@ptv>S71-Qx{Zi}wD+5GjQQ8nVDCjr+@yGxyw?x@Sp
zb(z`$Pgno%eQv!_A4W~u51IeR|L9f+m`Baid2-;f>`A<vJ-FsS(ECjv61J`sfGcb0
zSZ(iKXIo7@Anr1h2;N|wV(nobCSeUNa9RB<@Y=<crcXOsSkKU?Z>uF}1MAm;+NAf8
zchq=So!<4@UnA`8EokZ9m~O|45x%Fi+;+e43hiQTO(6(Fw{|&24aTSTC4uRFlx^gq
zU1Kq39gZF`#znYYs<LkVW~*4fDyH&>ya!~I6x+=2e#I$7bZhE|?r_?mt8H)7YI|k(
z_YCsK5nt$v*@oIDY~hAm?7Z>+kG&}@z&sY=X}Yh#p4@b^)p~KsO};iylfAU-c6)2j
zZ*9`RO8_P&TD^Zadk!uNiI+JbWvq?PJI#jY%&{~S5xb{d^-FEh);n!o)njfykR^8{
zp(|y+gls#x_{(-u$u01OvDdW7eY@+I+iSai>cY$LdOy@jd+0gmoYN<8T~=0RD^{$q
zHEY&b8;TUsQW4=mPCDtNXqW!w=VVEK<Bc~W2iVwWThs@~9(!z`*AM2x-~RTuKCRbP
zj%dPYmedDRhn`9G+<NP+2Xk9IZ?_(J<dH`l_}}eR%tOUL|M8E1>|(<A$5Tu^wsp+z
zd`}kxtoId-Yi)b=65Cbxx|P+h0f5~LZ%tU1u;|S36EgbHx~!!c-DBZ`|CFRQBztmf
zVo^8*#^I{8vT>vBta-yaI%S5@%2nNC2cGGkxvjm<ngF22>lSVR06+jqL_t&=aED&s
zvd3y#Wo^C#Yjc3=mNG1A2{nbMIq}gVIn`Gp!yTy4w?9Tb$}d;w*}ma+>Zn_(^DKLC
z`IWAWXzl7c2YEVjz?`n1@dL@B6+#F;2%c6i&XMr$o6gcp=_KhB#1q3W>!)m_B$^g@
zE&f(wX)e2K97b+&-zrb?u&}31UDUkj%&fb0tTBsW)H%byWTSItyVo)<_c&YI@DfVo
z3<i~ijAC#`vE}FX4GfpJl-r}bAGc@9{$Zv5YUCA|fH02q2};aGV><XWB7?ZM;%U2Q
z=R%t~V3Pgk=!@)}!q|JSfby{;N4Sp~D4CABNAiF}O_^AcA2)5P`yB4F54CL{sQck_
zu)f$gz#@%9siFP#x3zfiLc%nB<kI2z4%VTZ^!aaEs1XTN_qf)`tGp$HEc@=;FmQ;?
zpC4*F0&9pVzvXrFPC3uK!=@c-aWU>^Q2Y0nZ^yVHektAqF?&F=>UjD<_kcDrZQW+M
zxv6&XMN{m8^N+H!vIg6<snV9O*k!BMmRdz6mJ+osR^Ql$U<^^Y1nCl(vVjYTmUVVl
zd~7y$SE{7zItmx>>d+tkRE{th&B850K<C}Ys@NH9u>2DsAwjFCYPmE?OH1Y(zyy#a
z3wPgzMcFoFa3&ViIW}xq4lY>IER$>5ISBlPvPVtf&HIC@<sP-k@ss`Tfi{4jHKlb}
zg96}aY&uve$;J-?1nm^^c~HdAhzpOpei!`#$rx!l^GDP-KRwJ39IEbn9_EvXbj>lv
z_|8s8(g-0e*W2O5b=(clALX|%o;!tOI*L9?5)lRH$Yk!CvZ^h}HGgN{CBdl$xh_rD
zjI<=cL#$pMa4qnf@-?~LAKj@R`tF^mgCtkUO4yd#;;py<|J`OZO|#Ju1b`)`R>)bU
z@@xWJ0OhVfdxD)drpUcf5A6|i5rC*a{A8bN)`#McdRL!R+*XFL7EZvubs`$NsXkm{
z1J(vA8_ZYdw<N+M_-jzy$o5$a!lVp<XCJ6#CF=9Z0ISX0cH6K@zg4>4bT6^T^Xu)l
zG2Sw<;I80(`H^fSV8SK0R&5aB%>Wn~SZ@c~aKR0z5^QaR!B4h20BnuF)`Xzd)6(*?
zQh<vF<|g1M<1_`NT7WHRD(~ClWF={60H<-UAD5zDEThx0tj_@YEv+oK(SXZhzEWv_
zfN|>u98VIbsgJlx%FwVA7!S^jg|o++QFKVe%@wK;^ieus4s|ou+gh4%sa<KQxx*aD
z>k;UVTd`Ca(k4LlL;!Xj8~a^7Zh^faz<pvpC$smteF+dg8JFAYtwhxape=77;dw_>
zxS(Ge?e3h~rS@rIa_1QPfIYH))^(MmhjYj7hn`9KHNv%Dx?hg*RN3yA%O5@;$)HPK
zAJ&ek^s9aVbf)?;m_yL_JYpsNIE;R2s&BD06uK%HH>y1cWda{cKk0cmD<?*IJuzuh
z0oW6H&u#uDfOzIT$^^**rqr}}>R%7=j<!Ae40+v4>-!$^B@>6LWWn61c?J=S_sqwB
zW3qy7z>>Cc2ksloDy<c71^#+0Es3ovpi(ZV-B@8v3b+-pnVgelX@d$Zd(3d$BTulr
zsS_*{t#}QuzLG%n?T<h20Li}c=)B@m3v5rK?W%bDqt7zV!(%+weUkc3{nBUq4O(|E
zD<A9gkr$tBtM7T>fNrDJ>$U4Pb=|I**44xsj|J$;UAC;WW#xUKB(PL*oMY0I^ov0E
zf?3mDaTUwnacznQ-m08{-Xm+2JX>gO)4Cz01*fH?5-87szySAQB_nr|KfKV+{8DD<
z^BL(By<fF`<R$aFtgZK8UEjDvmhwF<?^U{M7fMg6JJN2w=k9e#K|;y|{yM;E@^vn1
zJ=>)<PbOB;68C64iTEFP-DNiXg0s4E?Re@RuAdWeBi>ii_#cI0gRJ;J3h9hd-@&6s
ztt~4jUc0oqNCKso?!AY-BN><CKVr*cQ728vUtmS)lkLUrciM`w2cXlMom{Lwu-m2-
zT?#-w8!In_XuK488R?w_aO|vG;$m5^sdy9`ubKVKWp`O40{f9!bBSmDT`SItEjW#o
zFKe|WyMAtqcl;1#MEsek2_J!U#u;b07`;tR`@ASb9!q0?;lhQsVZ(;Vsr|kc+q$@z
zHTa*LB+yRnLueUsZGQXhx7(5>OJcTc#E20#b?VfZdHQp)v9Zw}eDJ~kWIg0O4?Xmd
z-F)-SCU@2GbZ|W&P+9$VFyQjsxpSR+_Wpp@YE45!18Yv0KRmeB#dG)Wf%?``TUY+L
ztu24TO6yia3yP+IUPBh3Z<hOmPhs*xB!=TdWL>On{!o&vkc+dAv+UHNhf>Ub_in~*
zchHN9GUP;GsU5}W4dr;gseBgq=zFXg%hv$v(_*<K+jJxheboi(i*Aw<SwE8NBN!!-
zI=%$F3Jto<jvM-E%k&je7oLpjSH`r?*8LvP%b_{5`cd0QJIfy8sc&vrW;^Rw+ur8&
zDB(3bNd=UoBQd2AuGAIQk-l?DvS`W4NYTJ+M=o<sIU=`+b8#YRUcPAF@Oh4P(*58V
zKDZ!t0-*H?<c6ni-Da<r-tWS7g?VH0P|)}omi#a7ea?Qr^?oaBC&U}$Fp;?wWMU>#
zr<D6W#OE$b8Gy&LY!v2w7$^8`FY57cRIjm%man&KicYm3PyUo;CiYDayT*0?anBNY
z$nF;o!}0rB_F-t=zpe;CW6!`Gvn_;_>BbU!P&D>0US@S){GMfg<3_w7lIL(FEgGcy
zui+JC`8r&LEB17F{6qW$V*3ECzhmB^gIvD%>reZOKezU0?qKc3o8q8}X74L-t*3aO
z2KM{aLGf}w&^-|5s6Wt6JklE{h?r^5rmQ2adjfrj;6_`YIdew24yvqd!m^?c0J{lL
zyWUCxwrc=F%J(){eM39vv1g;8pg^WhQGK{zZg*6`zJ7M~b^i1c>qU&T2Q9UM1G8~s
zlHg)bXQcV~olYFFWB_SmkY)i^^U2BPgC$s2R;<msbD{2}sKd`kg<9K0h{V^peJ|r9
zcGL{<x1<zM4WK2MHh?i%%h(R!(h#b_li`Tn5B2NUU=E3^0(=Gx6xheCg(kvEl?QWR
zHh^A;`6TU4*`yB7Lv<6NI_yeGjHdbmEzS<F9aEBS=}`38QUX7VBNKJid)XE^O&KKF
zsuHc(9qG4Hq(2krXK{KSIARYVpJOR{&3!VE1xs7NN}pVibMv2;SJ;>Tw$XOi^KeN#
zhb%lpSioXooxms1mSpoMX4zNHC?GpAbh=eaPaM(74F`H46=2GfnPn-sgX&-*7O>ig
zTW}*Lbzt3Avy-u?!Y!`A>L$iUBmJI3_oo3m3vg}PS!O$%s=!5Hp!(?10Q*APpnMed
z0lkw_tTxby`|Ttfkd$iW%T`&a4rq9Ajx`Z~Ij0>r-@L0P<m0m8^)aqe+nOz5<#z7z
zST)>#3hu8n*f0D@#4~Yy8v>Y-z&9I>Pz<gu57*_DcuB}b&?3t+&-$I#zIvM_&N&(o
z94qHm=0FMnwx)yor-p}>CnS^cBGAVJws{d!1^{Da_uLHg=f#SD2(9wrn){un7ulR~
z*Q4R=$8~xld4d46C}r?`0R(lLUu|o*TGi@JJVBdfrDPzuXoW$=Vj0jkb4)SwEIec6
zDqL}<H6IU@^xl+944;w%c@BJ=%d2PA{Ed|aCbi|@T>`KfyJ@TE9e2PKcT`td$F?$J
zY17Iq0B96dQ|P}|xb2h<;%UP$w@`G)ww>lLt;7Pnh1gnnR^WcQ9dA$1G#}d5bgNq_
zVznWDE!+e3TXx`zc?F6d`-4T@`$(>o3uY1b3Rlq4lEA`&(<i%6_v-@dWX&3AMNy@V
z^^<p{@g&P!)hWO<iFX#$t#<bUJCCd1U1wFe8;f}#^(0~STXODPcUOC3vo&nrP2GL1
zBH_XXa4Lqitd-wuO2t(+58AKW1>i^0*1tb>!21Nqj=bcY$SpCyYgTQrn$;U(_LJVx
zfN7H~e=1BoNio26u6w(5Uh8&ZMyefi>t}4oQ;XcV5x}hXm>^}$$h_w}vUbE?2~;YH
ze$tqb<+EIH3lPtlIM&90@-iEB`2ttIvem5K=<?~fdy<88O5p&Tbkiqn%b%aLs<&5%
z%VA8)O?=mq_g>n?GY3I4)i%Y=9yo2XO}ODoD>-i-Ws4qRJz{(-zIDv8NI8mEEiVEe
zl_XGl{+J)LcDLHw6~A+!wT65EVf^c*KeM(#z0Du}V@vI{n9fZtu?xrlpUoJ2gS}OD
zpKYvu#>FXh;VtMCxhd_Q+BzDsEZc^Mq8Ds+`QL3jz_rKgXWiFW4CmDAm0R%aq!Bi!
z<SRC<=;L?*3Dbk!%j$hBizt=(()RD$-j?OgiZ4=T@4O$z-{Z!OvuV?&*(<NS61Dk?
z1?<PdrLVML!GgXny|3rX%ggNtKlp*AFxT8Z!JHNNuK3Zi5?{G;Wz;pGccU)2-~zmy
zMGx)JJ8!JtFTVJqtzEk|R+htdO&0Kr7A<mM>R~JN2q{Qz@85m*-Gh4CE3UZ0dHCt?
zO|jys_qU8AJzrB>g)Q6lTYG2EgM{1wsz6o0Ld_UnN?^6t^!_A~814h_Bk-+r3wGs%
zP#8|cOI2n{zH?u_w|*UAous7JpOT~J5&d$wt%?=qVsq7tU3*ycnmJ3m_cXlap4WlX
zVjs1?(q{*o!hJ@6Nnw<J3$}Z$Fk^y^E0|9_>+>xqby!!CyX)S<g1a_m`{c%aV8%Gh
zOB>mB-;s8(tzn(LQ2Hm^P_q=ni#C+U;cFxh(0ii8CGU#33U@D)cjxG0y~Iw!SeDny
z#~GI;V$`7@!n7r#QdV8>yv2R%eabjOXWhNsGK;+_NY{G_-?6@W5i+fE=b=s%mfVP^
zOIcySY<~4jEr9NS+V(4Zy7WmxyifwP9G>|(VB8@8&Pd3$QwmPDIR(dCNqVv6;2BOH
z+&Y-ZwS;Bc*1XrAuXx-3uxGJdu=Gy5f7%ypNNV3!-92lM|N3Y4fLw$p&6s2zYd50o
zkk+$mWHimc{FC`#U25q!f6`JfItyU=z;t!bLVEu4!0Wd>{G_!!@JKkWb$H~uOraL!
zSKt4p`8qwL^}O@&{R%ZyTKke;Bg@TT|3Cq{7te9{YKoT>?}7KH2YPbJ?@t}^QV*#I
zB3m{)s5WR32HLS%@Z8?-_GOO)lk>6K>z*Whlxt>LnTgO-{t7Jo+OYZyyRK9j;f=lf
zePI#-0YBFY0H;%&D+>g6xMtKfeS1auMZ2|ILHpXOy2Av9qePUa+jD5`7674ifFfO<
z_@57N5BNd6*6*lt+Q4!!91wT~1CX6_m^9k{#a#nbM+@}Pcn^OCu}EzP*i$^{Fo})T
ztIH#+DX)#gD?tjDudP_Gx+M48OxRWB2o~$SHY2CW0s6WwXvHxCk$NE31Dy4`D87R3
z$OBWTnjBXfF7Y7#VpZ$v@mK=_`JX?(+3tA0hPYaQ?EslbO?fogsnQPG*v99mqD=e6
zB_l0Mj}uS(^?-P6x!m;+&o|HR-Od$uBXN|yN$J+UZLe+q?V~ng${1_T2E3}Ow;Zgq
z6(Xu3SuvdfYq##SZ4WNQq#8h0Za>{x+N~PB42GN5^z5U?S|Zw-&AW-I&H4=t0wCRD
z<%^ctru!bTk^l7xEV{i`fzUc1<%B|nC4LyLG)7RzQ%h~#KVG##l$XqVZexj0r}0g=
zP@8zdF}!duV+%UBc#wrkimdJ3ZRltuGB(<*g*lu|tf1B3`9GUJ=vHeVJH&!8dGh=b
zo(T@l4Ifj!*whe%IRulN1Yn;tb)3~LS`KjEjtg=?{Q#E5!6a+>?bFseW0rNym;i8#
z8+F<%rn8N{NJcxgsixMJeD7{c-BFDuG@S;xm>x<2toB=5ajp$KdK~)ss*Q2YxRqP=
z@IUpRI&S|IW~@2SWx4Hgb7}u<`_AZnxIH<1O7|!53D1&!*}5Mk=Ve>RgrVktHksJf
zO!r`mrNTr6>zZxn?;o?Gb5FO9i6bn)>&|USl3ojf6f8$^2_C=#YU6#6TJwgjR+OAY
z{JUnX-sub8wtsk`<qRLj5pC~YVf?ty9@vScXj^q{RQKQAZxRhtvH-BIFKg89nY$ee
z%uCM3f=r*om}G6;f!Bsuai9atheE*i0h7mvYZjf=Nk7JFiU3Kuv~Jp6M!e=>v9d<J
zCXj#E^NSH?9hk*XUAdj@Yd|5R?3FjMXkg&?kz|GJT%VsbvyU7-Pj7$XITyP+rX?_-
zY{`PtyDXJ^1J;oeWm)a7Yp`_I*fak2OWX6}60F{zv%O1}yS`GNIoekAF?rou(rs<f
zHAf3;9n!kNN6#-s7P*JdBnGu&at}G}cpG@!(fe7eCtyhuEva3tl{L;<f856}v0cx-
zVtVfKrSDiXuEOQMK2nzho7E=%yW|$=-tFQtWy-QUB|q1)Mh>%llo*Dbb^?kSQ}zR>
zRZrc9Eu0sH5<SUMy9loY@+$CAN=bx@IDhQj#8b?&*LVLc%xggc%JTZ{^50oy^F{}5
z56zm<_1HtQrkg&BXIk3$4(`J5w>6ayqb!!q{={0}(c}Q_>eh``j!V^`S1}5uc+6|;
z1UMv|6=<EFFxaLKzRqS9e-;{SNLL+w9kk<>WPR0B_S(*$+t%6_9hoSg{iBu??@{i;
z`vR^N54f*XP*7l}oN`KEm*3a(a%2AWuYdiJ>d4E>vrm2MQ-@SW<Yoc<eOd7q78cs+
zr=K2P=OQ=6_||$Tfc52<Uyhnf^{Bq{&O5K)WkxON2t2uY^JbIMLw}Rpgdc1?@q;P9
zzwJ1Z@@=em*8Z{i4y$e3jG~Yf!lFOD+nz;2h=$->zeO&{f;uT7l~5Adc$><#?6e^O
zuEnhTL$Qc12Cy!&jN}4KOU@&%bG7|;>3nXY4!6fguPf$RkqPi&L$cxj4r5X*>dMA-
zF)f-S)y0ZQ9mx~<j`W+>nMl9socb9KmA)Bh1(_49IOjMUnmx+~Wle@h-JjOnTK%%?
z^GLbTzP0xb%Q=2OWwU5`KWgV>dHmGwKikv0|H#n7SOU48S60dNbs{n;Z)CuV$aH|R
zdVu53f#^VO4QQQGa_F?UbjG~Ksyi<pnsc4MBX>t`VOn(FrgFma1y??02|({ah7d{F
zzCrQ~j{9(?19>$i&K!E3WhUiCvbgU$;-vp}<6XA0VL74F@K;CC@)##tSab2T!g+S(
z@GER^>i!n7NdSNYeL3!PT<$o#X~cQ<>mAS9Z+1Lyw~oCmzMu8z=?~72)QirvmOnfe
zwLvl-=-9N=>TbBzn(z68rGM^fOS$mO_Xc3Y<F&6`Wi1aqYO?6=N{q`8p`+6O?J7(A
z<Rx9F->-vE`9|W-H6YK^WMF`6A&aMY4}6GvfRP(dAM_q@TXNUN9BD0Q-43o@J$jOW
zDoLMivZRcRf1c)-6WVn)EpjZ<#5LRf(Z$GfM1O|(*^gEaC^pQ-N}}NE{o@ufh)YBt
z)UMAdMI7X_LY(T~2Pt`pb>o0*+LnOoXb7v?<UlJ#bShvP4~^7Ok$n}XSX!xS{@6T^
zm0%dLzKIhuoe?dO&|y^#9rmZ^*4VL^jUjIv@HES{z&XbVV715XIG*Ob$CmA>vkqeM
z&ML`+*vG{v@pnbocTYjbD0EG{?w7h9!+eCAZRxHKyZy0^_T<_|sC_gMaSa_Na0JIu
zGsU!dvTaOWgZ=5+VjEVJWlb;)=?5ylTTMs8q27^RbLtQ*p{I|w+9fM34dI;Jh<Z>c
z%3u!EJh0F@2(vKqo1ZrCK&%ucu;3oKX!pOf-d_2y?^(vX+bxIwE(74)FR2pxy@Rop
zJ$8iU&Og~U-2EFH?#siaHj$PYGesyX?f%}q5UkBM;+AWy32R@)e9q$5U~{|eed%>u
z_3fWqF5rhO{!^KkO>p~qr=E$!E%oRzZoDO8d6$rxZpBxeX>YCkzK!(JUh0*r@MfNC
z>!QWhc<q<$sGF{aGd#|GxV-c-rnUY%H(~mwPLb{Hj7NchBAbinonWO8K56NDYD{$`
zVkMpkGn}+?lP&(*5AEome#z1&jKxbsqScZg6=w@!DZBZ{4_NCHuecc2K3sCO1;Vko
z8;Ait_M+3wo0Se2&X|R3l_LW$>yO??=Psm@VkW2HMUKucj+r-dF}&AlB1FH-^#InH
zL(e(UmjCim^AX-75#ZTF|9MhSURb}!mVEYGcFfPdW=)fs`<dZB@&MR}_Ey;D+ka&Z
zzkJN{;l7D1>8vw5a6ezenvpqutfdSYhyqLWx+86je}{V@Kz!3}0Kk0#m63jum2_%h
zz7@|sF>>l(`mH#`JDz^A3ux`DF;ZT1ma&~+ds14O0}kspZ$0oc0}#J+*FBaye)K`P
zb`D_0xcoamC64ynG2<Pp&9Z9F!kfdOlft0h&Zl0q)+*KjG}HS^iWOgcMyKoZzA|;5
zms{)|lo@fI9W^_Blrk{ShRr{{>vA4Idf3HwlzGI|e(SdXab73}pK`qEqk8H#ZL=C&
zoa?)3SE;pC)rKwYooj7Y9Rvi5DXnLdYk1X}KDfxzi;J9cK>E<ZmRc|XA=$p2Tko&?
z@BE`D_4wV@VAfSxelPv{5A2lR{16xFbKIw`hOjIE%Cb1G+q~Vy6W2KxrXtcVt-*j8
zk#?yKu3dohQd&^^ob`NHWvpy&P2JZ{f%j69NKB0(1Yy0G<$w3*=~m>2`Y;K4Yr&YG
z5F>M_E!zHVJcOV|)Xw@0c)c_9s{QVrvuxIon*f*p%`%gVx}H@)^@yC4?a{UWZZ-Cf
zyC-r{<eHr1M8Im%%#tz5Ktg2~rcbe{g&(t{3$N^9Ef%@I_jf~EnXRsP1UKdP*$!f<
zsw@YbNQ(OrO>!6ho$q|dS_so{FlpATS%kM5b}+YmVDgIo&@;>8{7{o(Xg~MdbA1+b
z-F4U5op;{Z=k;h8Hf`EuGiJ=dBV0|i%Puat>K!|Fth+8S`-vx>aF*tBA%5wlm)hXL
zgQMPiB%j>5b0@UB_N)G-{QP_j9-!Y3A}w3C>>zRz#fNz(uLklSaHOZDJAP#^ZN3ve
z4KHPY*3puzc(pEv@UD~tD4pReV!s%~aj|8m08XbAbFR<<(<zBLfTrDqd%X%=Q~fwf
z$?$Vxc-~_YDUy~{!2VM59!rRRTTRPW;&@jaT-(_H#8qh@6p-kEY1*fAED;U_PekcH
z4Zb(SHvnKiA25H2<)#gz-%9u%?)on^DYlVBTL&~_&5{Fx>avhO+?MFkd+URBRy_7^
zHvK<aQuVAQ$O{kXt^jNC*N*LUZv@_6FUqY)dSN1FxUO?L=sXU48SH#M$r>C=4UOZj
z>wGxByIsYE?{?pjtNO8@<u!bMMdNBmhb4Q{ZG7%&R?)n{Hh2~zI!L!^11_;+h-2P8
zl{Ig--*5iDZAH-#R|w%}qHr}?@v^TOd4nw&a(>q>l09x~+G*RH_Mkl8#5_s2A!&s+
zf_dvhzLSXz<3C27Pxuvl+qviNy44*tA&Y5;|H;7%QC~@WUk3wCp&Y7F(jQ2ewn%C~
zfn7bKc-Has-h04z$_bW&_o&vtJY}A2x$f?k7>BaTCSD)j1UFmlXKuBmyC1NOuit1X
zXP(k6<AD!Vj{g<*Pu4COfpn#2zxmEO#WKJBA6=&o_dy6xhM~P1co>v6cCBE+Wbu-$
znctzR?G`Lu6J`cr2t1j1&dljWU%|)(wk@;Z-c{s7#+FrL-r}j|$sc}Lni|}Vhp^r6
zSg56v5PAbGVelkAN6}aNc+6N8g&Of(vTKzEtC+he{d!pcJ^7<7p=dmgT=&hH-em+U
zw^8rA7OLODCDtrr-X{ziX9<PS&e~vlr4U9Up~|hUT?qrn(1&3NHPqT*!R>EZuwotk
zg9o+&<IFqic-$p+Gge?N4DO+gyLir442#kSn|JsO+Bu?EE%BcR>Va^tA1GJ+`UkiN
zSOh;r>BH694^hW{NScZgqm{N`x#^WQquk>X3)vBNqE!2t7(`B}*G0ZY?mLJwBDWmY
zZ(L#ew4}26hbeRDbqHUG*s><C{o|eOcK)$LA)J#1T|2;8@w`ijT|Z~yKwJOH4ogVQ
z23Qm4s+gH6_M64KEq6e+-FV7i%j>?0v#FB98X+`ZSyyJaJh0R1v8uZJlf&%7ql&qo
zO}E!%OOzEGgo@}U^;ECR;WKau?U)MP`p6c0VVwX(X}t<0=Q$vv1^el^10PBga;xqC
zJ~iDY6^8)Iq-iS&Xnaed6S|W6SR`tYexGph8Mgkh*NIoX!+a32S^j`kC3T?K5U@SJ
zc+mDNU13LEeUbTy5$eYsZ4K_dOMm}_<<+!VH2`I^n<p9@-QFDlLL?=@thEz2x#X&I
zto#p8SYl(VH8anAjFlkTOP=~VTm8dd<6d}`jXe7_3z4^u3Ayv7H=Uc;yxKa(SR!CG
zbQx^|CdDmvf{i}!R7)6I=(IDvC}Xq(8V|eV3;_3M%y6lzw=OGxF{g!6aCrS~Tk`fw
zOFwxUae9X!h`}QOgnA;+EZ`;yB^AFf#Y#SPiDeHda6*U7nG-E<&T&@x;Imjv6JH31
zxP^gQ1faX|<)!xK{2Q&{$^|xf(l|z7hc%RzTIn-OtnI~jteBZpF88S@i6l^V4NPS2
zgi$scaNSRCocOvf(wY%OXr%i4tMDSr=?K%g|9pw`{JwAH(I-5|qTfM@fc70W_{3?}
zc=Bx9_Qdn_iwC#tyxKOr7~q>MbLl$!#~Gipu{T_1g=0sX7jV0#p~}`hxyXErS7Ra1
z3X_PdCzN80dEE?Skvn3fjlJ|7-bbg$g?-J6ANA1#6>qMznl*AG4HFk536v|EeVnBY
zW*&S1(w>*!um(IabdO)$yIBG~(~1W6j04S(9Cg{bw)2^ndS~D7x4>S3&(B|RqfNQx
zGdAQ@tV^QAR*u{#C4jP5m)V-Te{ZFWm&R<H?vapPZJhic*E?(DAOdd9g@o=*eWdWn
zH}@oEq}lNE=0?uM{4S#%as{t-r>~@UTRi(%%Pe8v>`eL7j>1y?7*jtd_IhS1IW$n_
z3s?M`O}_bhrx+k>>48{E>(iBJYmjxU%Qd{#64oB~d>r?cS}UtveC=J&zv5h%55I7q
zfUO8Oa(PFMiy8x5V_2@^pS$$4Hu($J69fEI*RKULr<p#H<oX?9SctV>>!8-R{<N!Q
zZ>3eQUT?c;*ANsqhMYFr6{+^h@;p`_3+Pr1_raa_MB4IE`j(Q@$t7R3L77wS$&LSM
zm2ImGd6D}xCDhnUJHKPgOMj0m@2hQc!6mpUJ&JuIEWm;)Lr@+Dq+OpdJTyco5)W6*
z6>YCUpXH~I2I!tcyzFzWBxg1P(r$v0D4p4XkZD)_GFw&g7hARW5o_q!?r3KL)(*6e
za?eNU)KNzrWs@gQwxvs#9@JxfSS@}N87$xW*0=0)pZnZFJ#Sx`AAa~@tFEq&dRbtk
z+>sw_8aZ;L9e@1sF2;DYdj)9!<u8BfvO2%}?z`>pfB(CS*Db5!mtK0we)X$g*?<9k
z3nZhJ`%ym^%Y#6)XIBWg{>HY}{k65Vws`U4XhrpTPS*8$pILa3I+D}dyZ>N|Hr?jD
zANB<#C&B~dq>QjBgDwLQJ<W27DeX(t`rbFK5UWo&RX+b-Z|y2pR;B`feT0QNoT;jL
zv%^i;p88DM|I{aXeDzrnYxCqp!Y3tU;0B%8!YKt<P^0t*m>&lA0s4~)Efs6?<ivFH
z`T8t7)`jxMHONwSIli>7d{Qou5<yXBH+rwH%paliQjWi8!~e6zHP4x+Rlfe<{T+L)
zm&6Z;Fz<U{#8~0;dWl@qI}Lm5RA%wxToxbDdApZ`pvoY12d^ESs=O+OYs#T-C4GzE
zc7%;8;G#leHYe=Dv&<9FT9>=AaO%*TZD#wmPX3nc#TcVo+EDwZ{cYP%tirz+iwtB2
zt{lpuEm9`AyyO!-K<hUu->|>zdeq*oSx1<!R-`j{sA7yJppsvbKFrP^a;jZ2WG>-b
zP@L~Wx#aa_D_od{IVa5Q){>rwtJiPDOKO#kA2Z6Hdj3WG)YX@FWq9G`S8d3kL3Z4<
zDP8B{2k)T=*ax$Je5-}3>#gmDR{>>F1kn5MmAU}zRhz8l@-JBG$Ih{=AAAkNf?i;E
zulsuZg#YW282btln0eb50Bqk!<*xd2YrFsB$d~G<o;ILV;Sydn+h6{fwJ-izcyeLr
z;=B4j^L7KS1FN4l{|i5`P{n4L8A-00$I_nlVU#lAEK5G?R>1CoJszvSKSJf3&A;$l
z7TEe4TE2u)Q#e1Bjf^p2$T5`tbxRmFz0cx8O*Pj3!uPFX)l+E6VbX%!Qeh}$tm9lP
zq~)4-^vRZV%FUKAa8y@0!Ls$%{`_4Q*zywd9VSgcz7`IF`P`aNe4HiC`>J_Mde$AJ
zo$5#BOBg=We3##C9jl+Rj+cKyD8^mX&my4BitC=-L6&^(_su(YjsvUPpS|4z+u!6l
z)kb+e<~^hkDDe2}EottTqK>()2FDM2^Z>6Yp5i?a?}2y^{BwFh1SbYl><xfN#PSt{
z)FD6=rbM7<7;tUpn?+qxyI#ow!JH33P-giYWJV4kHa4mtu8)<%j)2_ZKk<(U9ls4N
zOvl3k!~k)ulL+qH4pHPsTQdL{_pLv_X)kQ7wL!!j4dSkGN<pSwHhTnsZ_utdZInH-
zY_nC-1&M5GiuuzH!Rt>dvfCcrWlLA?uuEnRvC)I`0h1H01%PXFd7ZtqVy!J)7qlw0
zNHN#7Pydd%x0i?P(itP@Lv6-<;i_D%%g+VwB0hB>N&Q5hYjc;yricaQ>x9!=ae0{}
zH%n?a7C}kGX$*zv-vl)60-WOoP9BqQzq@R>9Rq0HguQJFfFRL#>HNdX;L@xQcM>hc
zC*Tj-;1gz8!)GtH4Y&W=GFxzFjpi~ahBXrCIUNoAz_RtWa@p<7jYJdy6oFc7P{?W-
z2a2te$U9F)+U(~Z#XxSCX5ZjEr)|uP0gz2X5yWsYJJAMxe7=<upC^xc6H%OK;~fh;
z&tXVZKfc&@KfVa?Gi1$-gDgw8p%76C#70vr>E?u18=RJ5warzQan{*Zc+CQUR$`Y!
zlm)R|W@ch-IK;->a+SUDU*EUFDn@=XQPv@9lhGhf!d-m=7T(RzzHN1emZs)4Ni#hB
zFJP@{c{cEzlO1qP5H<pO=h&E=uCyJ?)>`7`EeO;AxS2Z*?M+sKup+d()=GcyJKGB^
zD>gxMT&f#sXxL-Z!N?6*BLpuY*de+H+VoFf4beFi0G_;5-->&KG~C@2iQBmf6UPjB
z%f&FLuh_NDT7|VJfw?E5Qmytw2;-rge1Og5_lRJn-$MdAK^oi?;{^+x@)BRJ8B6aL
zye>2+Vwuiu@`upD+mw}|gdHKF1^7sBgSc&lc^LooPh0uct=7J7E#Q%lK8C1;nP}rZ
z<Ww}<_B(!Md(>t(B0`o&MC?rFGyWCa8kkL4UVod_W~bUw|8^N*__4S~C*HLNLT@IC
z_QX@q9@zHivnUV~SN#A?qrl4H7o6Gi=6~U@ZI7TdBBnP+QatTJC(m-$v)!{5pK&t4
z?-{n`;ip{u>+aWj9}5^4u>bO>|I2cS?JY1jcfuG;1rYc8d=|i>s=2I!Jh(J|^KAgv
z&CZHC7ND*=1Xu@J{Q$q8bU?b!RldE(YKYq%D^7CQ_4(v3*W$V_R#HEyu2@NPb40Iq
zE6{%0*KQ(K^cri&GNQLdzOJ|It+Lm@e21;R=l3>v&MYfD;aCSMr{p6z#Vdu@4EHST
z*S(Y08pVZfZ)mg@`cGEcwd*&V;+E@J?!C3_ob&VpJ#I~+++)7}dHct;|FNIdvofmR
z8@BJVS8x87t^VKNIcspevz&=zEF~|OxaH|Ii*-!vm!yuplKQHni4~Q0sSWj;wptDE
zRPRL=+d8K*CVuXkt~*Ns_hn%oQ>g)^1i1;9BE?5Njmev5pPsge*wH_*H%ouXzESVM
zYgu2{c5Jhkci(QW?))DclyS5T&pClujFSKd23gSG$lA?VgD#VRK84tcsos1WkU9!5
ze6kg#Pa~%080V5aRv!jJek*Hu7oqLbwyyGVVuWI0k?40;+D8Id-Mw#otWjs3b=E-v
z*SWdGusr3I?!|m4j)xB)ZnxilyIpnFRfna$if8=iKmWPUvJ^jj>eQ)yUhnII08=Rj
zEL^y-uPpY^Ll4;(zVHQjsU&;x#TVT>eER99ofWy>sT2{;KKtxG?>`bR)YVBS-Tx$@
zTcGmLp+oz1lit_%?c4j6H<C%^EADnlNlE0?5%;~fVKt!j?Es<h+<lND0%*q%JkREh
z{cjhW`cONmp>412ta-Cf%WhY`2n?kdWF(}5r6y%NnjxI|@ceCPErUMlyDZh4LcQY#
zo@u%1B>>Wc*&_$jH~Cn$4&YjBYxBNKKJb-o)vvH$!l#ABCsA(@m|2{CEFgJo>+PsF
ze~3^1W&6YSGzt-3z}-Z>BzR>6Nsg~~FH~cx*LzhqF9s0$PDD14<;&$>LSErLuGNos
zB|@T!AD1E}y^g$OfACtox@u8%DqmG}uS&nWgV*jl@`@E@zsB-D$42LcdAGD)*7l9<
z(C}q{u~*Cfj*&G0Ry$;ISNT|y6IN|<_H0`)^b%@dNC0p>u=RKLmz{qjq@J)g@-U|b
zmZ=}wq2M+&Y_dByZncMYK5yTiaD$zYA6>a&>yDi^Zq!KIU0MbJU4(ZEl+Y<N6<JSh
zeLaz-GOYj$>#-w;+sm)NX+w(#yEc^VEw^fn40dtf>>0<obFC;1w_>;=>+OanLJP9T
zCbM^L*s|3IK+hE7$*&oHL$rS?yl!nBA?e6l51k*j+&0$+SJ)BwCt*O2<v#cmtH151
z)^hKk-8${*rLY|7mWZ40mir&IV0pFW{Pr$O$dZCle^U2ROMc!*sI}D^fAUMqz2|!j
z;{9db{VDZ;c_b_FGrn3S*F~WR?}S}5sDnW-nHDmqgu*fA9Wkq`xQ-QnHUGo6@LMyA
zw$Wb6C0)W$Cb0a^*0y)G`L2ENpuuZ_#|})d-A#K0+IIrZZj(&Gf_a&>-hY+(uKcxm
zN1hn2v)kWb&2IDm@p@-L?n$QqrKkk&10zK)))Zv!)>va5Z~e&<kA)`g1g?WSmRsu|
zt|b;@4U=7bx(d~M(U<0NngzGMVQs&=(vmO!u_YcgKYWe9o($%o)_m51;MS$q`r9il
zRK6Zrpzh}*A)l0D^j>SqtnF_%Tk`2&GXJ6<nAMeY9a#YNc~Go!d0C_UFZ{?XV;}(b
zbzSwu4`TPg{t}hg`QumPJrM7Kcn`ei9*}FGq;}%Zda)M7Lf3~|rWU}Lx<*`?3}izL
z;W8KE4;XCU2H91cKr=Q57Jd=9QwDkMX*`!CxXlKpQ2DfBL;<|Jis*g7O>W=nJP+;p
z-V-R+_<?CQzQAYimbL);iqwW6U@uJInKlN7*v6&JP`mYPe5rQv<Z`?G#L?tQu#<{{
z_Q?}R+V@|mMF5-uIM%>(0gM4Ovs2oWn>*}@joV?AHbGbmA_f%K{*2Y7x+DqJ<xrr;
zssNa8_|r~nB;L@~CzQY_02}g5QV^ItB-sY|nyu0^5G!BYg?4J4!a{HrW+Bu@Uaw_S
zZy*6efHrsm*kMt)ir~}Oumg>D<uMs{`{iS7L}yTIwqQC@Qt&8}a9umXlYq}8Gzf!$
zr^Bzm*y`4Av+ckAD`0VkWnz)n!8`~u@O%)I=`e792rnGBA~3+cJg933K-dFQ66C%3
znJdW<J9Q}@8+_HJHtyz+S=#WyPQzF;K(4TndfV8~U1~L3x7e<Q|F8_N4|=YRxYY?L
z7$jO2mi)=MmQF#Ck^|V?Kz-E!+sT}7Z%-tyb%*U}tFpw26YS`(ea2D%soSwilv_Fv
z?MVQf_7H1zz=bE-u$Aj<!~Z^L*#HwhUz#;D|AMq3nSbeku$o<wDb;m^^gNJ8T=!03
zRrF;`d#h#6oNBYb^f_Dm#oHnB>KNxiYfDM8#*S88(1$D+w>&{S2`IjnlrH2hE=Yeh
z0t^Y19^9H@+n5=Jm(H`iYtJQSJOF?s^{8$Ov=_6iwmA1@+$*i>0QajrwbQvW*CN6-
z(fLYASbxe5nNLh9=YgO3C-+?d)p}M6^iYRXaG35Suj^-7tLq)=18GVfv_lFa+@s{9
zZJKk5v&P!=+y34D@o!(T#JzRE(g5A<Ee<SCq`wD1=myna5X9i()<Sz5AkLFfj7h=k
zfqJ+MB6{dm3v9@>=ehRCy0Jsg=W2@o>8l466L|NcS7W=8=H^ID<syK|4?<EL=)Ftc
zI$*oyePG1-XYAKxfvGd@{1P#dt4*Mzi~SrPmiy(^iHJ42+)Y=#gMtNPSQfgnFcr`l
zu_#u30(cJuTFa%iz~ZT2yn)YWy6W8i*mEwfcC1*`s!y)ZV_Ic*me{akx3d6`X$j0c
z&cs!@ESY;JSzXV%>+AN~e_|<xP^q`OarJ?hPved~w(d6%+4}qbYROqBaRFSX;s)H8
zmxFb9dbmH-SFDG!N^fs$Ha~)=wrW0n`|BG4@u7K`w+fx0ehksBL8qQzxmZ{CPJ=mr
z>}_APH^1>irzFrD=#PNxbsKSAjz^5OzxpHXV!fqJsRIXKg`ET0kQTO(cI{%nM~R@l
zvB~5{ybYIxZ8h}{R99?l^?A5mu!|fIJ!6h#426MLVr`W2$AOYl{6JaaNJ;6$(mZF(
zT{flg3P9?g*t)969e^#xegW7Wo<`fz@QUqfdBrLFe`3nhHZ*G{`vh}?cjASf@*;@o
zuhm$Szryx3zHOVU7un{T7i@1MF6k1W-Pqc+1wuOfQ<B^Q$4UW56U55V^IEL(<c3&a
z(1DVG>)UR-?LgVOuFH+Jtb+TS<jONvYL`p?Zb?cjt<3jBO#*=BI(y>8iFWR}=h`*b
zTw}$>#W9QR-Im^&yT2c~2w%Q@dG9P;zt2Dad}uy|h3_FLE_OynhBZ2Or~4Nmu=A;>
zp0bNBy2x3j2}GA$Yq<#DvSmx$_4NK-W@&^0Ihdp|xOVMYn=<9V7JKyoDL?(`Po3M^
zgQ-J#6|;Q){P|t9@4?>xVa^X6XG^yIFJg3DiHcZ{k_74%q)oB;6MhK4ad7W2N^$Pg
z&RPWT!D_eu_mu~~caDeZ0I-Oa!dynI90z|*p)_Kp1QetNR4<tHlUUgf`!(^{TdQ6X
zs}?)KACAa7Id-0p*wrl!+w8t=4`Fx$Sj+Q^e+?&%avgJ0{H>Vh$WQoCdD9)j_@0hy
z?h+L6(lCY)jT1ob&;)S2r|})zRQm$1yjKBC!;d3BNPyptSY8XPrWjJV&`zv8Qic1D
zTy^<WKW8DQ;g4Hv@y<JKd;P06GKW0}xBF^sJ&GOM8{W2cHH)n-Sn2ANf_oUW?rc1T
ziG#mj*wvKg8ek7^|D!!w_E!|*;rNwb=iQ~w9IBgZ0^v6Yw%Mmw++qJS<v(p+VNd>|
zt{!*vyY@I>`VW77)ULboQd_!Wr424Buo1(C+9L~}waYI!*A_niqFr&}0^7cGx1Dv`
z9DD1XckSIZ>rm(tbmPHwcsNG4%38YoZTr|o=h?%LKW(#T9&4{JTW;xi@7u<`*Ijv;
zEq(*=8w>4{p+oH9C!e;DUv`n*`@o;rR|i-jd#k|nBRP5C)v~dmPQg=E<DGY#f63d(
zSK)J05Ku))9(by@r(d>)Z~n-#|L5CLa`ZmM*pMeoxmKdwND>g<`p1RVIB~pXeBrvt
z>BIYt%WCh;k5eYHUM!~r8(u_Ig@g!(#5?vh^8i?zzXj{&V-QX`i7sxz*O~u`Z!*ln
z`Q&yxappC+;m)*<HBVSz6^e&GJcRC9WBzBqYrcyCU_0l@ff^s8F8?F9V5MA2U!+nd
z9x_h7$rAEMTL72g9dF#nd1y;$6#t`Nwv-#5z_oVnzG9IV`XBy+bMx&XuDpPES*UnN
z&NfdmF2*I3qg|o8&6afBb%60R-Myh^cw6qXCS>r*$j&<&En&n7mN<EV>yN<3#U=}L
z2kb-F`yc%ZVE*_nYi$o6Q9XEz3^jAV<f?MNP9J&z#S?}y$D6CM;(pcDA8vo6`5*r(
z-Hz-pbATn3oPb7FmIb%GjC=A5SEoStj#uus#A%l>*5Za=`(AS2M_T>jDc%F|9*FnA
zKcxpm<E6utv`I({FcSo*4ubydtg5$ZBV<E~jVS<=@HcJkjwbBZGVnHtW<VLPpE7B%
z{o-Yyst_B3fHyQaTd*E&#|rbGpF|E5<0d_5*H2HhFFsLf?Y=@s#2PLhX)Z(yn-|nB
z(T%aV<VgWD-D&<L614D^Ge+85yO!DW+w|ONfQv2GMwVa_!ed7~6avPfSqTv47=;ww
zuaaeb8V0D@Rjn3P&_%0u+5F>2Sq_~TuBFu`6=m2NlLp!!m(^M*1>jFoQ~<Pg?rG<n
zC@z#GHr0z@6=KDG_yQD{J#`W7zv<kecGDRJfZG(Ts(Zg6PQ}x{9&ncPbQE|o?-Qks
zybK%vjT^91_gLve&zXn#HmUS|8ZnRk&gB$p0;!CbfXA93?gTaqE^X$mbRekgz?wg8
z$|yVO+c#R)>Bm?rZldL;O0K#Sa6uD<OKgE5OqqZ+{D1$uy$%qP^5pBd-A02k!)wjB
zCpTPaCkqsYFx1G*)|}&A`myNl5J;*KN{rVDw}0MpP9TmA3|b;!cn6vE4AK-N;*Eu}
zGwtYGuC<m_M&CV8StG6lVK-fyDanDpe&)H0txlIjm_Mft$#|ELw3YmdKb^!H-~nJB
zeEB>W<3@Y?yZ2b$4lKS~0$6}E)BxDidA@cC|7t9pTUh|olYEve%kEYz{TZKCDQ#9b
zf1XYM@h#Sn!8M)@2LW!Fsu$HS%qS}irC3F<9>y4pQTmQtC~<hK0mXtmUX&PPH#N97
zk!McP+QK+*2sY7Q%oXnOlQ$^PpJ|Xuo5YASNgYg`)`nJdtHAPhXX&26yOo7?3uQEm
z8K#0%EaAoUwo-8Fg(upSvYTz|cOS5-J-cXkl4VkM0`H;)MUf!y-N*BJ0IJjEf+*QX
zd^oJE<vjt*_`%m+W7DwQ4&-MThVOVqz;!=$()@Ue(F1aCd>~6v-L5#yL(jn76qcqR
zgtYVNmmKIE3*dFJqel<N3b?1Mt4R9=uFkplF2K*bY|Gyky1CgKn2qF(d<(#ifY_09
z9>y73Igy_+zr(<--?GP!bXLQ|&N;0se_M5}?R@rSz|`nM0%a6{npTMG^E2mk-52X%
z_cJe93vTujQ#;+FMaisq+=^SC%vu&L$;w{wsF!~Ad(IMCR>i$zG=~9uQ7f!$&8^n3
zZI^Q;?m&I5KKzfQh~kPXgS9~4Iw$M(9*HqG{QP<DT95OcKgNCHQY@%*?9FfekTG0_
zvKxwiPHML=w*c)3sNLQKSdMi?{ibd7Bm0pS{BFs$O9}}RJa%taFRKHOJP%xWX4jo^
z!@l?R<@<s9T^TrViXSn?V)wiswd3Nf>2~>~2duQ=9ebzzk61x3wDQLFkX>?%7v5*2
zH<gr-ilQ0<n0}<Nz_-#Wn%3Ix`V|1+uh~vqUDo?|Ba4#Yx3k=yuecv2A9@zKjvj<?
zR4%J~CxOh7xW&D%_4WJAGtYEZx4p~L_)TW5P+bQ~vI_p;4}a)fUJLAvl?3d{k~cC>
zW95xpn>ll)edQ}Co<(_Pm3QKaCq}ZmZ{@rB=9{gotPBI2*zM_lqn=s!%I&hO!euEd
zfL&m<`l3JSoO8}`0H-XOyQj!{&^<1;K<Q}DrMz-6{jraIta}j$bv$a+DECfe!5ht8
zwfar2$kjiWU3QuM{O3Qni!Z*|UVH5|*M7wykEfnJaIo>Jb@%=E-|zbOK;!+p-~Fx~
zY<%lT>z;h_NqhCxSNEF-k?SAjZ@HY_RQV6WZ}q(xYs)=$=(UFpv{rv^s(2oHpzoel
z2(TfU(+M$DVRGB8JtJC@VsA~;4m`-U(!RqdSCLndSV;=W^)00UjHOuU9(aw9Ro1~=
zt8GCk6j$MaSnrE|_2TzyXiiTs@?h%uFy{UJ_P<+QsMWlPjY5vrWK6^J!VW{?YJ7{s
z4}ZhfLwmy4qkMnvuut2Jg83-p^?K*To=!{3wSj4)?Wp|oY<<ns_U4{n!CTkjDFAtf
z%7$0gFlIP(f!9*d7mrS5`X8y0qwsGvEgU-=X#{&!5TI~p!;7}H?nPt{GSfg}hEPM#
z(2B*hygkawTh(<hQQ^DBWK6N*^ijHCTN>8e!rc!!Ig8spU4C8BQ}2WEbw=krD4({5
zn(RN{`H4L^^A0;Idt~?$e>ZL2W)lbjcHUX@Y;VP%oCUNjuV+m^+BR<4=B%x&@m3+F
zbw5S}Qs5I<{p!-U>@(M1Y0C-sR^QOjRbCQwf!6Rh-g+AVy3n>^%{{2Fz)mL=*}Z>w
z$SSI;Y}cMrJ9*Y|wr=BQ%f;Hd9FH5?<FCH-Lf4O7g&pAszEh4jAKsx_o>*kf|9ijr
zm#&0fXYB45Zi;t~oA8!DK4EFsTx3bpV=I7#;0;qRJOf~MulWfB*c}k};DxjC&R<y4
zQKQW__vFL=20ZCGxZVDWE4O3S;}%%+i~~(^1!>-?7h2-fbN8#W{gwa2QUWh7SVJog
zv+vsbyR53cljkF2`n(0+ew2R6w7|;0TX5DVaqm5OzjC6T2&@A5-tiW)EPz@Jcaksr
zsd>lDaap~9$O*Ya0nKlvZoDm4Y_*ORf3c(!Z|Ewj;~nbT{0envvpAsan~s;W3+^)S
zxKq0FxC1d)-8RCG<qrYKZ$wO<&Ae~2#Oars?_8{*k+&%&PX8Eg)9<nNXYSw!@}4Gw
zH1gYb(OvGkVcf&wA5N?-hCDXur2oJG<rBDi4};Eucb{~Y)?o!6GV^@iNpmgf%v%BG
zN4R@KSedu|?&B7$-p0a)5lcPFGsNijjt1C{rvvstubt$8`QsVlJrM7Kcn`ei9!LZr
zD$2ydx*As-#Ci?@QbTds$~{dsAB$d~Y=9vIuKeqw3%k`O0#T%_K-ZH-=h>Ar2G}nb
zQw9J|J8^EM?daTGce|(SP{giFom8)msk<+xJzQ63EB4E$tKs|gr6lXi1fa3coIb(c
z*_&*?d9^VtkWLa9$i_$+34p*JZKgdEcG>jB!1Olz%_qj#zdy9q9$ijMV_ao9GtPtt
z2Oj&-0PA2A4gm1+*Y%G8Yf<s-Fag1iWJ?QG*^RTZ>`Rx;##*zPHi}S3Ie_m};L@*O
zJlVEZzhO(ZLcS~hN2r#vAnsjz#CRwtU?OFDU{pH@(umV$J9~7ded&T>cFHgj3b;9?
z4e`yR$4iX*Lkw0b0Bs}wq&$Gi{#H}~MhvqPzWYVnoRV$ji<Vd=ag5plRTZ~5*^RUC
zKox))Km)vv_m+^6N37#fmVq_#DBOUz;4;3Aax>862*BI~P=WRU=sGzlbD)*6m@|E{
zo$~#!+ScI@*|x`Cu*7XJM7(WCRcj%xP8(XE&Gk(VxYh`*V@_ok;F9gM<8Ay+S6RyJ
zN!A4OmCijP!UVuNUQ#oV0n-floLpRBv%mH^+ckcit^ebb)(VmDs}U?80O)ODj9@k9
zMwp%{;BE$n002M$Nkl<ZJZCi8Lei?Hv6|%_Sed1wB+*X0J2Dfk_%mo84llCx|BH6_
z;x{a<4h9g5alyALD>LY|tX32((8dJHwiH~Prye`mQm;JCre1%UwZkx{0CLM3k`fuq
za!}?4=<UF1Ylwbs0gR@D=v$5Zv}R(tHwV$i*1E!JmJ{`(W`2Nk^*nnrdDHY95c*<#
z6C2wtt%W%tdY6gcPTwa{rs9b=L5Ma1W~Xu=(Y%c=bTcfe!4LSZMS@wz$Aim#56oyI
zgJjJ0ms?g&5$@d|vfb~lLC9X_-j)1Ls4;$GhbJ=sWuf0j40;eg%R6qe6`XmZmE80R
zG(NFj;(dfrD)1r1_6m9DIpS&m9*79(w>^SptzL4h#5+ucsT{W8^jNw6Y1e`XF=UzS
z80i?b%BooaZ(@4i7Cef}Jp1S0aj}Zu{poL9TxIQ1&O({*!;#e2YKyFiGl~b<=#QOm
z<F32hQU-L>o~EKbFTRfDGQb<!=Y1u`z#cSbrlk)l>MKjvc>(uZ)lV|_=qtH*OT6q}
zZo+$KAB-U5%)kHI-u?M+Y|CF4Vkym@i4Y@V+1x$9z+(?{@K7e7+OAmY#dA;UUi5y)
zvOLe5GTz?(pL=cF<Imy91$u+IFH7*=$pPeUbLf4g|Gzc%vd50F!sGWTLnv0dth5tj
zxZ2nA6wgEP1B-?uH7V}3KBo`=ma~lBQMbf)HM|Lkz0Mk1OIgz!+{Y<E(Mj^qA%MCq
z(17=xa;t6K2Eeez$^omZTGw;D6(zJv2UIhR!(cUgmvhkbFYIIi*Y73nabHcIH*cQl
zb0|rcsJGlAcZcs!l7+TD{Y?UDKmYm9_c!}t%_r+~efpCQCU1Yr>z+qE*JnQSneM0h
zJihS43vI%L3HH`oZ`rliUTdeEa!S8}`aTQ&2wo^GL{a)6)1pO-?B09twd=3HKI)xP
zF8$Gueq=xS$xr&_-5^>ava0{aH@;zyKKkep7wdXY9oA!5Tkps6ck3v{Zq7*^-tEkx
z9X9)`ZAZ<L*x)PV-GrhAwx;YcNWLvp80I;9*DN-owsnhbsCXL1_X~Poe$T%%eEBi&
zRe$;6eAkpdVzY*SE@s}td2vV08?bXt4s+<MT(6U19mP8v)>oE~-1%M2W%f+j>(<Ix
zm2z}=xJY(}+(EoFN${;CNRmfseNRAH{$nG*YEuTxcGvZ%vAq`hWjhM;e#<0I`GAz6
zo!(`VY~p}(EkAWEh8y3uCeLmcUQK1o@)~wL{7zg|`lEXJtLogZeXLw0i#(-8&IVA}
zSL>jVT-!t3DB~E!SjZGqNM~Wn<#-=mG@F<^z4N!(t9ze<-a>vOu$x;{p2U}4#p9)4
z+VFCybNb8Ou5KyoHhY`wj<xsN{WJa>1;sEwvVPMRVpC7GJ!N~{x+;aV004U)-jE)C
z@)<k%_~UHNhD{FK-n?zQ4Z}_N9)RruTo-@8607U8PM_my(Vt}ak-g=F2}DM4`Gx1&
z{eSwKoqzTjR#jbNe)umvlNZ@U79LvOUAw_fnLX3iZrE&NMvid3(p6CWAlw7IkJJm!
zu#^R-S?lA^TjLM!C5|!*r&zG}Nc3x{rOjIY{Nw?FYhE<gy8|r!mg}wR%xj&j+6kIe
zpm;OhQ{9cXS>BWPTH@%E9@QMyA6nGAr{2H076PyhZd?Q~i~OLy*^;o<?h(hDv6OW3
z7XZMY6|=%(jVx}%3;hGGnU@_a{|287ua9>@?}XF4K<h}e5@&qe+TXkni{>r#4RX+R
zgf%+>8b$p?KX$DAt2^%|GZMMv^X}Lmv{qrXvnz?GdKb%WDPVD&KwR$RGX#!Hh_^2#
z&Bh31^^;h0ui_eR!#BNvg>?<@9+`!Bd#x2pgnIOjIm41p{rB+i&c6cn6PEnif;-<1
zw-F`L<a6&NisnB0%#$<3ywfkX;KJ`Xt92K4u)efMIq@HT_ds|R?fZKCO1uZ+JrM7K
z!`1^yfL}uk(rse}1REBU&K(e3%G+gaFnBo1g(4Irn&4C{sn$`U`>Izw8M(}r<#HNe
z(swR67LaC*{r0u(SZ}h)V!^E#w9!(#C{^}seeK(P*gGjRR_ah&xXEdmI%1gIl+!Mx
zx|2lJ{Q{HO>2dj-Xg|JuoDEIgXuo}BlkIF`vm?%qESN<;ON&|cyY}ctlHz>>1L7_+
z%kIBsn*CzfF8jZiHrv)3%EYx&2)8612{2TmltH9K%B$Jn0ge_=?I^S9#p(8MXHT*#
zrsYGZavvK!H+K^E5XSV+glvyJ_}Llu)2BArW6QSMjwUQ$Tk<I_LTpneceS^nSqrl=
zXLOc*YTg7pe*$r(e7LdZ`4!tpHn%D}o?`caqjCJi8{)MICQZkkwjTy1ke6noe|n1*
zZrx%vi&t2~D=V$~rRCNPc#{k$n}}s?Q+ApqjV-pK<0o0cyjhli%tT^Z7dcnm8TcRf
z15jX(>%h<e?<D|8<AqU5WsGPJ1p#k;<4bJh_iwU+*Pm<E%U0W70HWGgR#@`x2Fq<m
zMI>Q>Sz(^FPb{)gbB?j$XU;ayxKWmro$kQpbO<%D7zaGb0yqGZ=D`gXu#IKm;>S;X
z;3Ql=7Jd9Y8+7g*t6j0)YTsG~2(^>A)Rop=i%U4>j@)rO7vHoq2%+rF%wjylaM689
zvh3Gf0vHLTV_6InSc2AR@i9l)9zb06Pu0TLEMa|_wcujQ3n=UzkY%mI^DJxnSQ~oc
zOdBwB3Vl_A_k|8XYd~ZO)&NXgvMfTih|q=r?s3yjMZk=UQJU|py%RyHGp9`e?2)x}
zm}!kL!|n8K`@k&o&Yof|%_tAB@bDB2d#s+Oj&k!$vr!XF{}ks+R_PrBGR%9@RBLNM
zTa~I?smq5YcQWmB$}K!!hmU^cd=k$*08qLK@ILLzIW~IUB+DaieccOhTGMOqTEogs
zX7zQrCL`WA7V?cH`IdkBaW>}E>6Ulm6u`h?;W*wbV%a{$&hEgiJI@`YPkZrLYHB<&
z6d1F0#O>Ylk0owgceJ$j*)7UD2<_{<$rJj_^Uu6chKum>r7%Q{s}AOR)WjH+W$^GQ
zZo+%#mq260MQ7U(td6(Cpl|uhlUB8IEpDPwJ`o7ZSeI3GSeVoM?q0vy)-(s4r7;Q?
z0<DwLNFO+Dl9il0&xXxE-O>gX^ty?A$h+xJkK!i0#ofDK2A$|Ed-(ZhM!nqoWceE_
ztQ<>VrM~*f0pTaHc1$_&yH!l@8Fzfy#(w+~*GD^^e$g6n*{<;saqq1O-LHO%G*-`~
z2V<WWupjJvp91ekTyPeyy;++NkTQo4wG(htH~v#s*ycY!Zo8gcY>hkjIAwwN-mXqs
zfOe@(&V{-5e8$wsOBWCpEUXNnc_ScSiV2WI(Gz1pR{A9ffZ~bu9#^m>*(bP<`lACy
z#s;j)9$tHcJ-hwOjD;{wC70oCcphnOZ$Ma#Te<`kvD{|khKmELnQF%E@f7cYk9-e|
zA3xsoiKo7MK-TxN#+O^~{wBE#|Kb<F=)4hJef8DO6}#@$`IReI+CBH&V-G&~pta%Q
z=TOq(#f$BZJMJ)9ZXb-7fkUbDgK%$E(-ueP$7)LgyoZ3{VYxG7W$X8~U3JT>p}o}Y
z5z%gvEI%QXZlm+(+OFDVRw@>)&kdYw@O{s3{H7~ve8B=jSR6XvUxX4@P%*Sb|MZb$
z_59lQpIKJQ5Sv(ZULUy*@9C{oFCDNzVHYC?oD8i;M;;;RwW^g?83e$R@C;f|w1;>Q
z_W}JC5G<ZLOsE4|qwqa%@RePlwSem9cRyh7R6T>YunOcyK}!KZDoP)2N9WJC$@ynH
zi|m55adzg2JM5{gU$N$3sZ+j)SYE4i$x|ecP{mNqVKE-RbxI@2UF!T+K3QIiH+TOz
zcET{O>&7tGt{>q$C3y^&Ao`bIJZn+vP-Sp)U?-k(yvRP}F-`gO`m+fMbMyc};QIBp
zzHu`$jD2N<+rnQIH{YyXX^SgY*l7jFIZt!7ghE^X&Pu$+wb}f6r#eNf+PVf@gg=7?
zb|tblAJNk4Ek7^UDyyoU*O^>EYguJK{KV5%i6<!m*pZZ$l4@n;6*m9O)7-s9xEz1t
zxfhtLUOVg5Id~~awQB18JLQ)GmdoQx1?8TE1$I33-2>u(Q!hLVE9eug;lI9X%?~|^
zrL?c-4e-Yu>$WkBc=ybWSSiFlnuIsQjBkA28ou~_z|1f&<?i8~1j;I`?xt^A-Xs4<
zJpio#q7*FO3?Sc3?K}^%=O8Yz+uyh!h9e?#;Cx#n*Ce}R^N{z{vW@*}ZKwtFk(xc+
z!#<)uL)P50Y*ou5t7}?R7{j6Jy|ky!xeE_9VVzvYT%%ov5l6!CW6V2#-T~_l5o0=7
zqx-0r@Pi2>@vM{{g~z5o@3>Q4jBUd$dZ>voAM}TJ__1nO=Z7z~kLQk-Jh`LHh|%q?
zyIb_9XYynZ;e?nvJtqq)vb%WdUk@BQ1=7EI;<@5I5buF_51210!6sm}yXf7$Eb3UH
z;u1Lo;k&WC1D8|)E?Dj)NRaCY%l$h{%fFU+#jzF?TY$UlpItS<<{dM_9$d2BR&A-Y
z4B!5UjqaIHn23;&jSh`jM__O=1gbP4S8#H8OA43nNL|jzBGibDlV~RlLnsY{*~P3t
zz>X>afP_%Nz*7x!)|vqi#3Ueeb~0M#mySj&a+E#4ywcv?T4A+3UjW-rxit>}m`y24
z>Z}(_TjJLGJph4#%bCf3`|_EiY{3ab?3p)9?YWiJw!YGDHEmd7LpOv3+#y_jbx<6A
z(=EYWf(8u`EO>AyXb2V{xVy8sySr<!1OkNMw%FpZxI2q4E{nTep7*(5-TQu3UGvve
zO-;@GT27xn{Xw~i9mQ;SVW!)x)oe*h$kGJ3ecvrF2;9{_!@I(yS%<tqO)-A|bxQCk
zB0~|Fy%SfH0etA{Bh4+dPC)20e{)z<2R2{2Dv4{npfABkc_*&o_jzToR$DrF$sr5d
z_4nRC$S?dy$h$((z<xN>B^;;ZfrT_4b=1>O#LNfFoU|hWi;HtoBiaF6q>-31eaY#+
z1qnZYCv~94S3Oe_7lFs))_~|Z>c+`CaavY%A`fcOwl;k$xOmS-fe$80(p`|s{NWqO
z=NGQ;3%nct>lid0oW@~FM8o<ej7iI|m=Lp*$4`s8TN&=kwYi9>@b4Y`7fRf91+UL|
z2XLfO@TPC{x8s>Vr}r){YT1rgyNX_2@iY-yf;g1PGs<LA*zJzdb-(n7B8wBK#xJ|d
zzoSF5P+XL1r`v!RrskRt&k=>7?~vP4%<G#_DDo5apX+GMQwG(SMx|>dO0JpFEHi-J
z$ux3>n>PW6uffn`h`yY&0OKM)*W?1$j@9xyAreZ>?8biH)I0LMEGP(<IcIQ**4CMN
z<lOEXhiwbtlWP-FZRUJDK6pwOkzT=6mlu}HC>U|WbnI}|Y{u!uKw;_vj_=dSzKZi#
z6MG<yqRvLRh}6B(BS06fP4##hAF;9Rf_$>Yw;v<T1oVvY4+b-gR~IjN+ir)1WBfw&
zJW(a>yyvUq$oKe~L$S0Fd>*uSYPS3XY0_Kq&2qYgLf}sHhxn2wAMrr*6>tR{(6afa
zE+F2|5}uJL6|QNB;u3{=@UQ!pIV#S(7l&FY>(rg^;A-l0A!CKqvPoqo<nzojw)#oU
z97`Ts-nS3lrN@4z%`U7fsw!a^M;w1xj5GqRdi8<234fI1lHBS{7)l7Yggn-fxDt5#
zkj7>3=rvxLfX>%{-pW7pnwR@1{A3&qT5r054PSYk=su2+1(ny_%LUpF!Y?~<-P~t8
zd#0N@Jb7gMUbtyAoA7Yfueq+pK|Hi=3E8mUxJZ_F==8tU(p`L259&ojIk2Uy13ON6
zRVT)rY~?7(%TA<*@whxsrr5n_+cjJC<zT`wjucgMXzu!$-e&7#s4F9U&NCLRPqaya
zI-u2=yFAv1^2!^?wXO9@NOI?#YiJ*kZei(g0mpIoJ!rJn%3vMZq~G&X6@|Xh^1Dg<
zUiqqd4fI^<Kq2T6WG>3Aey^@E*-lEFwINI@Mk)oOyqPQ}>EkeYfrncD$LrA6qR1pr
z9_#gYJn=@`qa+=`akpPp02W^rNGzQ<*bc>V)daAb3Prx@T|R%O8$LFC+$84pfGta2
z|D4Fb7iIK<f0|%H|7q1~HxKw03aBxS5i`EqH+_$@D%93ox+!2uLbNFFeG^7305%GQ
zl8ZMdFGKD9cwh#mAB+hV?bWaM{*oP6<UH6Nb#hhk`?MgWsySVii<HZ=O{rrGqw+<M
zr#WlDX}*uPtx)#)*|(J}#6*q2i{TXvIcjk@62#?SS~376R6K(!syw^FSi=3tzZ3en
z=ES$YWH|=?QH}v0q)9P%7j<X66~v$oujCCy^^#e!NqAU)77koCM;$NNwdx|(68C4}
z+u-mznnb&VByCS!RV>8~u&p~Hee<Ae<M)q|!OMhrDN=T!&|w3okqy_K-A}QftM}P-
zXHRhg^>1wr${Atrp?;bd-jG(GHv2MN8_R^-=(|&L{`YRba|MrVuP@WtX^E|p4H(Xu
zZ*>n6S%%|?=P{pD+I*cB?uPwb=pQRCmf;9tN>*l4OQmFz6B`bQrQhOPE7dgNmS!XP
z_~^n!)lj*z5WlRJ<QR0`4a@48m*5EY{}B3dyn7g6!Tuc->KKlkBI%`9aQtIT8RoV4
zX8yd%`KpLmz;=&Df~v?JJ~i5fwcQ4mlY-mmc}xyxKM0_YCo`=WV6Cf3Z@-R~Kh?^W
z%ENcMz)Dd@pZF|veN^+VU!lRNP^QJ@VmHr5$Rk4#1ICgXktW*JmP|G#awnzvjAS|P
zQrW%IcblInk|<7P@ifj?{-h75m%1vV)#O2U&mFnjjk_e4XE4{Q@8wNlw(MBc(9trY
za~Aq%VJS&^B7{NerEIujgG{WZytRGlurgqE{2T4L*X9@9Pf#iWo&H*s8%y!tvSAus
zQu4rwUxKjx9N8KjQXA^;<|(m)u2jy|$MUSzUT4pZxEA7hNGrnjrAEi%L^3&txK6*F
zF8ZGI7+gPh|J)~^PDl9t4gcm#)1NG+n@t2na#`g(P|XZ064JRU;EypwKYXQ6$(X?E
zLw#O_Y;?M^eY|^nf7}_Biucc#x7mz*{mOkl@Be9(^Jh}xI{MAv(g02H7GQ63oc+Dp
zfDNEQjgxX<!C6QRDH1ol!n9aYfXLf*70<~{faOSzlpcy9f0rc&J-N9M+=~R9T3gC>
z13-_TbW<Yvv%f3s-RH=!R50(LOmdZjWlxV!akUN_KxPZ9I+p~BqxhZx{y~4*`9U<h
zg$>q^kD4S@nv7I@%jBa&uS?9ELj*obOcR_AZOd3;o#i1;7un0tjV*-|J}l8Sf1_3$
zQUJ=8#MP52eFPrQUiDTYbB9=?Plz$$j)t-Tm$|D5cWZ-sdfk*)d^#IqEuHRt;&H#Y
zSCC|7o7`ubyRH5ulAoijtztET!t9tWm+L}bwwg5D+1FMGUho@Gf-ymY2DWc7vx$)l
zSryrSImTg~GAU?|WTtW?N9AW1khGBEDjf!%oF1Z1>pT+BR*}r0!PUuW2{F4fD*f`h
z$aTtU))cq@Vua>4XkQO*1QG117j!xdYR~jA*lc?LEu)%mhyTlj(V{vu$Wakk@dJlk
z=oEb;eTnv)F5Y|yCTd*Nhp`RJE@X=>!4_^Ckz58_w-UtjSjJ)wMKk)Jepw14_WQWQ
zm)SYX50Nz~-}Uw~TgKjPesw$|kx0`J-<UC5lF_AcGWU}kq5p*8kuZ_?nPmb0_77%F
zVK79GczX4_USNsQ@eZ7T2pnc%9@m@0?o=nO!4#G864-{#Z96Z^*C?jH{*LGP=NP@j
zKl4OeCbp-SQ$zsh(YZ{i8`B`ozL7Peev<0zpLhlSYQFYpXb2p|e-w4;HS+GOt=4mc
zeZEY=-<%u##-;W+%_9&vKY<hPH_;8G;hauUORhlvmdA(N&h*?Ui-Y=`!fW66X5VQ;
z^0aVkxmk;S+;2_i<2OP0V5OEbC>xtI7iaSi_Y48tR_{^dKou6GuQ#7|qAnx)09{U~
zpZgPP%aa1f15@vNqdQhw&)~mU`ra_NDN{`Wn%x7$R$3XT2S{Ac6>d}{kv4fV(k@D?
z!;f2|T-v5>99cCRt+VfHe&agk2djRCg-)iwjz^2SRu@49^=rUtDkRB}>&Q=?zl59E
z86bT}8p{rJL35);xyLlu!1CTQD@}x&Qo;)z&T2wFVUJCcVTquRA@1hu(JLAG@~!8i
z{jE+I)~Pc4B2+Jh0IBA;Vs0EFxKpb`s|;n0TIm23H49<DKd6+W*;SxPFmw1_7ySGI
zowin$e(M~s-NL${;(C7PPEXd>C%u|VoY||F4ZG<FeGR3hYE=43OZDWd;l7ihT9l&|
zk${HaSTv1Hfk9_ASuG26m;ydm_j+|EfASX1xenb#1A<2pJV+OU#yG2CjrFX$smorA
z-Fe!c?AelZSJE<Eh%(mDr;DE0xc-J6d>F3>QF|#)<84Pf$qG!i0~USQJ{^>iBU$~5
zltPf9Nd%0JIcA8r@{z>=;ZmntRS8G!{qTuaT8%xf8vpGrIS@6c$%Y6rKy8hZ(3hYW
zHcmgxFuOI<4zvYD8>ToL!F}tVRc^Bh5x1F*(W!aBj@Xz0z$tpx4wpPn%53G@nO`xZ
z#i<k3QKgFU74@QjVz^w~N!Scyjoa?d**82(O{$q4G_-*eGDrbb`Xq@d4CmWhN8`zR
zhnzLl??^|Qy_uu#RK4{Lj}~9#6*t&V1Dt|P&>#VJ7&88GDzFCXcd`&C>3kraxtJt1
z<W4}+f(W@vy?6_^x=V1grxiz&!unr^njqv)y3D6Tl^M$2gXJ_rCaLD11D^8~)S$Yg
zw@Ew}@SAuWi8WO1@u7Wlvz65;=sV}-*4K+Q`E?C$wssyyRrtW86n#~c^GqH$G7e$u
zAZ6t~b3Sn^hMz|t*OlKqel}z>(br#KPT0c2$k#QS4Lxi=&EaZsAm$hK)pD8#P+Yb*
z_V?W;AY2Es{KW51g)^v<^`BN;{o5cLy`_Ze+G*1LE|XSU?DlWNt4IgD&-FE2x5z-w
z57c)Vkr92|61GqB$<xVhb#!`d>F$c3z`hAmjI<e2%s<U$s(#ldHHO`NrO(^RD|hqG
zpy(xxOfFNE;24Bm>5_&2cFaXHjq|5l*$qwKSk5E4@j}#*MT~%nE|Du!Wl}~W%u{7*
z;sL&ZP(<%V))Dkb4Kw3)UrVNzxGzgB)VA==_lJLpviT_OwPfMMF`FocRZ?q&z`qQ&
zMC-ERNCS24sWsY?J2&*Gb|oKBv1f5pfgGU`F%g9iaHA~@oRXrE1gF0!<scTgGKw|6
zlhkesDK^LYF1JjV#9cg<lWL6Y$+ELy+HI$6l!T`U4;mgHvE+y45Q3I+nEw0}PJ-O`
z%V6TcrOv8boP1iH2Jn>E!ERgw9*Zj}Cy)<wpeAk=jjWwB&GQ==eK$`jV^;+|G}-lA
z@KJd4+(Rh*Wduz0dXl$Q$mv2;ePfs^hc0xk&rt-3c$Gb0+U|q?%JJj(>vKsFCaTgB
z?xF0`T;}<MzX}8gm-dQCTdv?8L;a>%_2i@&YYn})>FH>faaxE}Y@@bJo6#7~ufh*o
z#bjG_CZ7<G54^E*5@vpGw$#?-?KS4jH@wAOoSn4JC36rsR>NRa_Ns|F5&qmk*UG$c
zHlb~$RV2mdD!&;;BPK-krDIxey9G|I0{y#;Rx{~@;Olsv<DN*Ol<&d_o+QP<kA%bL
zx7;s6k^6)a6Muhejn|GTia!|E)NC8S*E!mi1Pr3IhJHwm{}koLDDDlXF<rZZZM0q9
zG=Ya<%Y-z5)%02t)YM-fSsnUjMu|O+XNIPE6FI<heT_!rar)k*&p6=*$XH4Yq)A&U
z_SjvL$u8&{5`AQi&G<u!>&aT%n60;sFv(dUK%RRSWTgng`-g%jUQYQ9^VIO^KvP3S
z1&>*G#uoks@#i&zxyciQ?NNwaPXtcm-Ni1|2rJb+sHbK`vDPkdV<|I`F@|NMIT7De
z2QFN5VKtpF-f+Snu6<X$9hOT?q+g3>j!qR(b&gJ9RdHe^=f%2Sr*c=)G@R8=BV^pG
zc-@^2faiY+xE@33jQ?x*GG5`+m~m8W?|p(63im4vJNVCcJY4A+WtN4BaRbq(t?7>F
zKU$v=-;hNT{>GmQOboqT<u$KcOh0aIe9pfaY0F+Q!-xM+T{|uZYjd$w%3d2e$`u&*
zTAL?5D4ltBd-r)+BB=p<Fp>Tzq$*_mvj%{^=<xKu(+Rz7Zq9tpWwKYw<qhT5ho|s(
z`v}6N3cKZIBa8V#h8;j*81py);$d573%Ood8us<h-2<|<4Jr<eVWgRguM+5n*}Glb
zl8q!=`}I}`uaB0`Mobm?WTtodF=g>pV}Bgk*&aTF`M#%PuXOPSRzS7#@>9xWca<GI
zj{rl#e@j4)zQP$#<impPTlOUS9BA@;lmVK<U!O<ird&wEUgf(ozlothqsJZ1OH%ct
zDdOa-hZ_?qgQg8)%c*Tc1dd!sBj@R9g!EZgM1-ERrn^O1^W!cSqiohz7HAHgy{3?k
zd%!gIW!qPi1I5MmLSTZtfpdajfnR3Ic3g3?M+NnVEKzh1J0_c}*8Dybbc|bw@%Ufk
zB}Z5LODh&#eanXBH4I$}-ndc3joEuxJRLq=x&(}IVc4%WqD(`vvjpRwrU=V8+%`RT
zqlN39(?qj%bgC;%N|<lHdaa>DQfA#;$Dh=tDnY1t;q~Wd0)I*f7*h78W>EkoR%9e2
zr?LA{LLzC$*7|{~<>&#o(ldq^OwWGjV~QE$W9r^xc8@2zc;sSlH=rd~-ybt8FU*~~
zYsqZQIy{df#_k9nO_jF200mAsgOwQ^DEZ4svpv^rWi0u#`8I{SvN8A~dAldtW{wNm
ztsr`g2=9i_yT*BCQ&v-O2V$uppjJPF-rHcr<i#`hy-k6@FwZpn11{4nDCOzbV3b$x
z)^7qsB39*8Z>%?z(sX#ZZ}2=#QA3$Pv22*eT5?2xN%z{5lX16AcX{uU%y|~P$KH#Q
zEMP9tQjI8{_V*>X0)S*%H^-fPt<4tgY-GpaPn}s!4n=AZknX4g(T$On?y>BcUZ98E
z&bP&U|L&T6<)!JCimckKa(7$1m!rYKn7EX?jYhZYPjV~6NlYns+(M4>Ok>vy=itTP
zHu<B^n@8rFOH39?lBw>}SM=~S@Bgu?sKCBP><RgZs2^V==+~w8>pA@P^Z<`UyF|<P
zS75#hqJ+f2l_x?e<X>1m)=@J8G`^ul3HH3LeIsb8H4$xPhW+kU>aD1cmTx&cHmL3f
zWKSyqeutUIowJ&TaoK?Ock_k;BCn!2u2RPe*}PaCPsae`d_N|_DT<p?S^#+2eoZ14
z;kb&Ma}c~a>UR5U+1JC;k`||mE$ehi4Dj}TJ9eMNQ}514Ma~x5(10@~RL;Qd;UL@_
zJdL>kY80#bMTKe4U2a8Wf%U>csppTd^yri87!bK5sHxlPfEsE_UB&k<##p*837^dg
z*4sGC$`5kyMy@zkdD;Q37QaA;A|CNv7yhKE{lN-74sKigl1j(c>ITZ*Sk5>yYS$$z
zO7pX?&tuyxeWPM_HC?xkB)KL_oBT<EO#rJy%a7}zdL?R}+QCr!;scl{cZ`#7n5?r@
zQY=1#ud?ul58S|0^rN&4a$33B;MVDCSz^zXzdd$2=662&?$3hGbK@EJ@R9qV5)07U
zJQ%jM<A3VH4DHM3UPqtLUqo1q&#=w^Ez1plc2h!N{5cJ{UA@+4*)A$5m+)5Ix*Cr6
zEc7;DFW*V^qJE!Hm`j5K)>Gp9rmL9{7L#GJ^jmY6!l<X1bpcdSFpfH3aU&KB>=2E*
z?%$#1-gb~8A!TtrI75vmk&qAJSdrqRy{Bp8eB5*R;0_beYT8K}YjGUIqnv}?Olt4u
z1}!Uts7BYG2*b3|Yu#J4Ho0{FmQrBx`5`<}He)>d_fHUX#rgS+TCDm#$+Y-+IFMQ!
zrAJRGK2OZ!<Kz<S3Enx&lR#RjR>DrMDw}t75W_FG!iCfd9!l6VR*7RGXw}=Yh?#qy
z?R9%I7YX(F%6-5H?RV(f@y1AM6tg)jl%JgsWAbp<LW+D%9!#F~*SG8+bQ2YSwYeU>
zY1lu^mJ{#3%9b;<j}fP$HFEg#uq(xBY{f}5ukPuQ{^)dyRIs3*-8;Ucx#<I->Rn7F
zs5*{-qTV)<ecQP(GpLeMMrc?S9<3ximo2@Jg|v<yHH)+!3p>VaH@dBsT)3L3Q)Q0M
zHD}{NI<tv@8O?k(iUwYQ(p?Ys#<uM2;!slw&MpzoA8m~uA<JrYBJ(8;|DLUXuK?a4
zxQek=u45dw&@UFXTiGimmB*0YQB?i9k5PfBmkzJn#rCH2pX*%)pDw4BsjkLWU8fb5
z2-w-!z&QS=2zp-IY1~GFAQG2X;iyd@#G&VPCRJ!fj_NA@Y7^ZO>*2k=Mx%*JpEAG9
z=bP1fO82D&vSmxMd>qGKkD;H#`v%!5x6^~^YrGpTiEYiO^6pRJwCHl<@GTGG()151
zclGV5J8?bh!TU`%_idie)0)+-gPZc%8!Mc4w}GpcEaSGiyoJT)2w3PiE5K2vVRVAl
z%mtM17wIp8p(ZNzd2oB@EiSRcWY5k$I`KcSIyqys-!%3z!ajvru_P0J?5eav%0-x}
zU78QteHZd%FIoA^=NMjrQ4k5k#(e_+8@j7f=#O}PS0<{$qzFD0AY&Oh1I|51-l7vT
z1J**;V6XeAeN*X-9gIiYptVM*tXl@EMcg`K=3{JX1MCGZtm$9+e_L$94sl^P=Rwwt
zy~lh8K4EiDg-FDv?>tHNID!K)DyrpMvl|6AgtkI$b<GJ~V&k#QrkA*uw_9A0T0j*m
z)o8YJfZkpwQx2P64!Z?VTEPV(=<@vQO>=_@%K4s5i@za)&EetX)KYkzQf}Ol-MCA3
z-<AQ$PQYCq*2Tmy);qS6#z@7DOYQBUQ|n6yJYI;xFIMh3{k5&t>`p-+M;3FiBuFgg
z(>p<})#$|aN+*?>^cI2|O}7AXe)q1D);}M;xqT-Z8`Obk*h;9#zV_MY<2`tIW9>`p
z9`y3>q696oH^wO^X10*xl%49O0pX8N$Vo9{-sU>OZI4gbz~#}_?ViZ?YRt(yd1KB>
zuEBmNsbL)+R--RjU&nLap`iuj@WEBHOcSXstypJosoi!nA*I8W<k7q^X}KYs*op0J
ztW$1|y8u+KqeTvPUuK|OK6}U7uGj8^2OGn#uYaY)(VY*j;GLhokJEs;AyJQHY+jt_
zw$1bB;%#@X5bWm*GE~DmQ_B4M;VuR1I^2Ydg~`#51ooGK=_D;*v5sBpW=oz;b}@yF
zBTX~s*72?Qj3lj_-*xr8WwtMYrKcIG9>ay~0Jb*|tyLfXiAw;~H?1m{7aPZ^uUp)W
zu_FsXlMO!KTr}Txw_&(%77fvljoF!xg|<drI((?`fdQ`q{il+%lp2ovBHMGT5I&tT
zLy8t4SLR~H^beFQU#Yb2*K2w4hxJ@9G(y!|TUQY<++JX{nfA@|t4Z~?yeoNRj_nsg
z)C1X=;g#F%$%KGV0TejQnnQ=URz8ch*tDxj-SYu$5M@H?V%EjGByGDG=?z5_A3v0(
zV_5?^L@DwAvGq*+0rV}Q;i@BC)0msujJ{7IGx|H6D22$c_ZSeq!B0DBf?|*42wbSA
z3F+;0cT4y_9U6L=xQSu6XNx<W)r*})DU(-{IDpP%c3XTGIIu&rQ72GRwBz%552l-(
zmrgipZ&?m#wNYhBW+FBE>DRpr7FFO)O__Y(+0-!tdCMzsdq}rs?v3Zx6QE7!wm3E{
zUoh%j`uz|x6x%Sik?Qokm@VDN8=ke-7TC-9ay}XlG&Q#RW3psQoZb76ygO6B9Ht0+
zU!f%_+j`9AWA|X{H-LTFU%i>Kk{<0BLvmlymfb#86sdce(%1P|Iwmv}1X(>;vm{RN
zfVJ<BQc{qgM7y_J`Hr~NTzXs-P(TM8lWX!=@4PzMDZ8WIVrHfd74LR_3+%4vBQ=NE
z`!bK!@)B(uEL%7YwHBk%5u2EGX`@OBQ-x-6vT|)z=UlJ2a^CEN=dS}Az3u$y&n8+g
z7Re&&pBIP0AH3R{h<$q9glD+1HyCrpqML;R2K^d2KN)#<)SaEliFGx^_W3~<0PE8!
z0g+t-w97zN#lxCk`{uVyp1NKa)q8DCBV8>tE#z!Qp!>Y!=1bm5ukMA(+nZ?@m4-%A
zc2?aW!m{~|1e_NHA!cSykFJY)1?6=>f@sj=vK|9BV9{Vwd!-}K6J33lhlI-pQAbCo
z#`@5mK}n1&APK)F0-G{6K=hh`)cW(|r3(f{BymQ2>`IzCLG3&=jBc32V#^UsaT18O
z=tte~c<J%w=+`y?JnEfEokMaxm?0fwN!kBBwVQ49-cJCNY4B{{fdRkA=9V9y@nvqj
zC&L5r7Xp9Kd*3lR6l0#6mecDhAB|bz_T0g-4iI_$%bgSV%W37<Y?V)!aDs(QoR<=q
zcqL>31LytoR7j-gUBI%ef%@Ypdfqvn64AyN&KINNCCG%+8rFyhdvd*l5|6sdj6u{w
zotVt!WYzSh*Y2;@G)x-ao82#VISIYjbZmxJb3FBCJ7#ZUJSawFj@H&JMAiy2L36XY
ze-~pHVs5DK6WHI^cy)btL$M4<>@O7?t_z?qukt~s@X#N8UK#gim34lQk7T`PT{-~~
zPy{@vTHZ}WoorJDAir^a$PC--Xb-;a7D}=y{~c%;avR-I2u+}ZQqf+=%sUQ&mzCR-
zhOdbwN*i$#``iEmZx13+FW<ns$7GtkpTnRp=1Ap3O^X9jZz)QCB$N?O?OaVKad_(I
zeT0R}4Q$_h2;f#ca_gWGIj^z5mVEpgb%*T<FQiwoVk^{CZr^y$=Ei2GL4KOGI5w_%
zaq*gG5?(r#-BdyIVG3<1+`E1}x^~Q_Sx0jsT_~7eS9$TdXFFN_!8AjkqmSliz-JIk
zK9f<P&f|Vz|Cy(&%+1x`saDj;!2dS!Dmo7TM^E9)3CY{3jP_6zO3!t-E0^n|#oML%
z_VBtfK@4r}hkPv7$^*WRJH?rqne{v$*#2C&ed~6*u3qzz9k}p&cVw~}Cuob{J5NRL
z&JW9)5R?7Yoj5DY@;yWf&W9DuXYN0hHoD43Q{&ID{XDN+N7*2{v`bp%RLNf~!~*rM
ztV8dDlaEwL#GO39Y@jg?QO>E^=N^L7^CBjn#-ya5F0>kN@a(E|L&5R61jLY0pNbNQ
z)>`@KnCfcqp=yhvgQA7NiWrywaFH&oOS<3q`c!#B|88%Ll*QswuIB|~dU&k|?GmH9
zbO-I|k74jm{MYDkR`TalMA!M9AS{o$DZj38AC@w=p0gVNL=~bHt!yp-`+;$4G22Au
z`U;0%_`|L96We6WXsU7K^Dn}cBS%^p$AGgw7i0z5EHRwfb{QP{hKlbN978jvlF84o
z!sMskJg+8`g(60Cy9MY&ZX~;6GoidK@tM!N9ZXQ?<X2VUrHSffhhBSy=#&qhowfXO
zWAyH>ucJS{_lXIGHa*_;q3*3D)0IrXtQrvTk$lNn!gcqoCS-N{n>mrb=J9Cg=i&8d
zNNrgk@9DOmeDB*oc0TW3R<Hf^I3BrT?ejvC2aIl<7pDQU$E{9lJ5*Emt#RRW1i~v?
zg|o(PHHP(b6iHl}bGs-QIcVT7{?A24P3O4)Sz~|Eyu8N1c^%wIPt3CUiV~!2t4iHh
zrhfh&O9g`>Ner|^xi5yjO5D2Rt>(+>51pe0^{52@^)~XItzLNVF3+Q^-EUfn5&ikO
zF$sR_-ddD(^{Wyw2SEt;(}%(NbBpTy{!8B<FV0FFuZl8$mKME3c*vkW`4Xx%e;xqS
zEodP+4tQjaknw_t#2D9HGkB;Ty)3y!MR3o~!KAjkAsyl60;^jYmD<>PydF?(fDpxo
z_B77&Ry#_6taz7)ScD&+e^h!quS2WY-FfZg<BNaT`lVB(p5x9lR+ZD>8UT|u&3e&(
z9;H7`Elxf12^y0VG=={1+htj>OpyQTm>@e}#`o-mX^U9(Tct%iJGDi3RbOU&1Ah=7
zp{;4VC0_Fx`{}kf(RMp(e>&5B%VEqf@LOBTUv3vCc!C%BsF?X^R29uV#J)(A9X0w6
zuM?qXcO5d1Na&YD7J-ysaiPE8kg94wXrH_wz!zG0z-l_oNF*}89X)d~jA^qReB+fq
zar^T86cd^C*ZLv-w9@!UFKR1lzDw*8@pia`#`SuaF*P9Oc&qw_oH$@ByK}nHDc|!E
zLh89+C+4p)wJOehdPB>9XP<ja`4r|#mEhk8*e5;4Wq!@^BRk0?IxXbfzll?*ECexI
z5^SQ3{~)Z_CTP5HPvD()-8dVFwt3uZw(-4NR#}$Oq}X3naT<fK%XfV$4cL}6XIZf)
zS#0(y#T1shJaxLxUh5}ASltHLmxJ3b%A9VJk?pF`Ob<@qxSG#g?Hs2;RIvX+AO6dN
z_%Z^ch%L4s%M71}v}M-3c5^+S?{+6wVcOX3swWC|c|5j6ucaBOo>Lzo<HwH$nNxn^
zD+dEb+iS;-Yw=$`1RFk|XH@FcjkRCR={ogb@ikpl&1e`dIUw92T$D5uJ96n1@L1VY
zNg+VDcx7*XhMn<2pm78hI(W+N-%tyevXyddl<et_mVHqK7N~xECQ4I}fZRf#?gp?e
zL2d*zZ$FxNqEOcLG|`XE_^g-Y6M4~oRq|kWaWYubcwLKCab$`_3c2|?+sEQyY!wsF
zCe0*V%UZ{Vb6batF0;^q8a5#8jmuP!z2(n1!d8+fiixBh(wchMZbi)m&S)ELE3j*=
z@ne9jZuDO4#DA>s+bhVjGcj{9Lcg`M3xQG+P{1!oh`*PL(L9uo$1kgc4;v_S)G_L&
z?eiVZkn6{3Ci3EWYEtGka6Uy<8_JMk(w9~yyM!quiC$?_GIcdb?yk!UJ-<?%?{j{a
z9mQ%LBkGh)I#cMkokY*BUte`}YnJ$??^=*RG3c8$HLYT<MT6p4>qjj7h29cDTexk_
z5*{zQ&G~&6Sa0Xk**RBv`q4+tz37{RSjTO=14a%nWv&OU2Oe!!hRL-b>b+=~;za}p
zmozv+AV_R2bj$;UXvll36Sp5od12u4Hz!@ykNc6QKkDMJK>qWN$^E86FM$UO2Df|X
z%jFfUS?Z1RH2r*cB^SN^{Z~zZNi9spQC8Fl3D0Qwt4tNz+;x!gv<B$pV7MmI>91rC
zDt8oH@5$4&p<{P!>4Id8U3TbXNBvAgL)EV?PV1tFQ}B+(k*(ct-7~#B=wn%zZ9%Ta
zjIu~$Ak7uQ_oJmz=cj6$6=VZ>PBShUcd`KDBR*)9<*w<>sd434<GpU6Q(;WNi(38a
z#rvff9^%2jI)Dso?c+M9Z$k0=E}fwhsktt$SPo8AuMrXNZta#pf{XqpHYz|?b?0IA
z;3phe9v6O?*d4vEFgW7tqNYLYUT~jPGgK74??FFY_nF|(s%ZlQRn#2^Y5OeV?`GuO
z14^8%(K%9m^)oGx4#t~auCxxW_HuIY_4D4dpp|*fRs2ig!?8(pe}^g6(m+@Nj|aw2
ztK-aSy$|`EWp{ilo5H_S0=S_wy96qz4h8h1pXy_;rO|P7&f^t`5Si$+#mRlnTfgvr
zuXR8NqZu%wRXNmwd1KrS2OZPp48P|Vi5Rc+uVw9ZSf90dFdv#|KeR!&SSeZBL2Ian
z2JeWqEd|5Ing%J>7M3ANc9oW7c8$3b&GOvkF^&9<o4@Krqb3CMuf~EdHEup*8;t;-
zjn~&ILjof7D0Qi}+aY$WH?mXDMC6HjN2kbnCc=#cIk;wX(66#mow-<#P?4x+O)3>M
z^S7@aB53|grvu;dI2RzT+C~{=>}dmi27G4<^*4Krd=S8h7E_fT)OmdTf!1U)$AO6d
z_-j2*Di(*@J;di!nMCuW8?BL8#*ScayFrtbrUD2J?FS@7ocz$X@D^I@*A(DD$UAe<
zpV4t){9LKTI^H*(pA`uYhFAIL$^UJ)<FvSi98+_1;e^Z@Mz7i!p08L(wdFV^x9T!S
zW3`2}y+i}rSO_wfGaGnx^fr}AEit91>!=44XlYds1a4Ng!n$kChjS+UUd23U$a&@e
zN`8pWD<wilqQLT%igP~AG=+=x*nh?5EN6OBg{0hcSm9|Va>cQr#4ks%wGFiwH(K;_
zA9t0Nw%&~n^?HrhZ`&T3dkk`zhE>-wwuJ5@3thYeo{;~nBN<O(5!<=U4#SD_XP4Pc
z*knxQbDsZdY(*)37}2<<s92RH!%W~KfK2!+<`yTegAhMPS=dZA{lt%C{P;P^b`8@&
zLDNiS{AZZ`Qf))BlY!A@jbEehwKda)^)Nj{)<Q1t$xkwu2UuS-ux2Qf)g`x0)zyV^
zSHi&xP6Xw$q{x$eHfLZm)rPxA5xl1)kg;!J`IR+~{e%k)rmd%WdLGH2sPJs&fv{$I
zKKCwr)rYPI{n`$3?9#UUnMTxhk!KhiGbS?KjGZr6wpTm#R``62iyts%y`WKpkEN)X
z>6T;J1zBAUilFksq}Ln&U9GS6El|C_vi-HAdFIhVRJGQKV=9}_o1>-#9%g=3;O#r@
zLO`irLjyfMy6j0j%3bGg(mV*p-&sr*w6Yf=vW^#C5YgUmqW7j(eE#drUh#LoA(`mL
z{nOpv&gxnR!G@Y8&0#QCqT3S7$!|f#-><^@AwZv(@O3AAJb{z%QmbB~VY=u|Rf)+t
zffMH!5By&oEDbZb(qc-rP$cRs-al-*wAg!xee_w*u>rWV)#4xKtq-nzb@F5h+p@(Y
z5i03_+#wQf-%<JCQNDF@?XKK8##(}OVTPy;7Ta96<*o6eHo@2nZ}q2L@y3zfOT~H5
zv9TO-pgenGVs|E+xZFZe>Ze;OpvJ$mMe`z#epIL&6fi=LJ{WPmlw6QjukFgf%PR5L
z9Y1VWCY@>k%9H*i81&$Z&DzCOK$V!x14%S$MFYkZA0sO40F@<6X!tkaZzB|hej;|9
zll10yWbcNfjS?KAC>GE?3gHY98=kTM{-}A%uTp~=CP2L|3F_Dvt;{RelVrY($fV1@
z5kDrlVmHnf(cPk&5vMZTsKi8@_j_cSfW>r@Np+4oZsAD9@*x<v^xtpgH*PQ}&%Za~
zLnf%g&tb~mQqZRYI1Ixd-*#Q8Ij*>l?KneN=z&v7xTm4E?H9;Pn`zt}7X9Z3M33eh
zY>)O%B0wIM-uQ)46-v`P4tix-p<=AjE;WM63xU9&{{d<Kf0&cz6d6BK7BArJf&9H_
zRwdiGzkXb{{1SgY!tpesEq=EB;<9)4fZb+;_Fk5GLxoQRXX&U&#jK^<m07NRNc-TB
z^cOoH_|hL4*4y<E?c`?u3=7sicfZ9Dyv$qC_DcVikc%pebo7|c0REstRz0$Ku@jv@
z+w|p~=lIM`+qph^4a-p5n?X0~=tbaa@1bwYYOW4HcF*2Nh>0`+uo!p{Pl5Ukkr#^2
zB@@Mg{E)dE)#NpVqji0XuW9fHL>yBFD-f4f`C+O_ui?Va`LvkgX&><QwFQkvIg)?3
zRX|2l=zi2|OUB1V4MDVJ(7W)F0rC2%>MGULe0Wj%!A}9rw$9F#;JMHO0fu*hy3b$W
zcGKE0_+p!*UZv<pmc%AT^{;+iP};;iJss}@PJ#}7Y=*%WicMp_lz-#et3~DJSDAFW
zO8BWe{WBh2b7AZPT93jXFob^D0eb@^yc_)d8t&RY@6j?C!=E2UF+J%FcN{zkp6jcQ
z70P$|bEW=+K&@lZ8u4Yzfs3vu2?rR)-o-O$@|f705_J#Ch_WzDZVW>)6~t_ky>3SU
z=|*dWa?76H0=4)Lp7==#o;vM72)xYRdM<s&ryJDAtsi4mfCVLro^ZQ>BlAIrVxb!$
z>9Z^+DHeJZfWV4h@(LIdJI=8;got-ke3-_dIlBqCn1hzutF-WL+F-NX)>^m0^jhmD
z!#uGC_okS^-tYv?Y5xx<`yae)6%n2&q}CYAgc%^ae#<`9vK!*-?_)ogLjOuK#H#zv
z#>OW4m!@xnr97q8!07R7%jI%i;z5E)=v23$IP*sz|Avg__gGRS02hY76AeW0MD#v~
zh6bQJ>01iT#4ehiw*N=q^!fdTEo?heEjfHXE2;FdlMHS0tilV=VzUK1{%W$}#spkz
zunL;eHbGJRX)aJVR7`7RnK}SyvRc5ZV${*CV8Ck0gYAWQwx27Z&pVCzaQavS>_%cD
zcBAGiae&k*<;sr^Wm7Q@F~i+_b?ZPU?gxN;C0j<wFulYQ%Gv_?OZeogv#77ThJF0j
z60IG>taNk7Ek=b)89y9h2(=Mhh8z-BRKP~oZV(^>j(*DaSyuK;7hXmbe*3L!!UP~n
zb|n4rs8wv(?Df^`j)&%QS>UO+l&$SfLwdmM!_~)YIqigxZ_wst2}?fWzro`tf8$}h
z<)C#e%0Xr-A2_b}Jw6+@7Ryg%mO`LB+U7O|6ZF#5iu>gIJkDIx_LsB~<Oh#@D<GLe
zm-3iY15v}b4soxu8f-l|PhT@R$<nXWT9;vamAJ#vDUoGbLAjNrpb5bZvlFXWP|F!?
zG1A0T27$;D*w+-~4?pOrxvIS_rfW6lHrOI7{Blf5z8sCcYKL;iNV9Rz>V9D5@xT6o
zXEoGsP1t$&`n@`3iJlv_u3`c>K~Pa?kMt6%G!f52sp#*cHn%bKJ$#qs?^{#xOYYXk
z7ka+PSTAaXZ2|iemqjwhPqU~8NAOQn78TWL;AtFFQI|hm>kkwQy^L42qf~(Wp<@-C
z%;Q?>nLi-*Sx|KfUW<$ZS?Tcx{!xu(&s=>N6KScQ;AKdrmg^(l&1vYH`S<y3t;8D7
z<P^V1KahApEKS3&EklB9nfiH0q<a7*0uNjvAz8xv>7N43ml@V-r=B?FZLrHZe{+i2
z8|v9-Eo;=VLM)Y|APbJ~w`2`|61?zixG<s;UZ<)Y{`>j{bU`Efn`JD;M^y^%p9;YV
z-**+TEs4D%3(_Z_ebQ4N0tiIrDKr?>;%*?rRI<<fKBW;<-y5UMS#<?7Gz0D#=F_%`
zbUIqRO!uk0v!&BFKl!|>^lhi`A=oXSpNvDVx|ofHjDl&z^ev~|N&rMtjc-SKmg$9c
zAp5yrEuFT@Wi(^dO)DVT74;4Kez4Mjycqo90U?I6iL$;npE^g7n?}l2AEXENu<vX(
zq@%k}lLgvWq`X&9spK9@pLg%CsgB+EtGmWlsPxnHC{9Lj0jYP85sxMRR&>n4+5Yui
zs4|xaQJz;5N{pd<`z<@c2=s%mS=%@3p&EAy9^tgd@_O@5U&R+1yuokUEj~8>8-qAc
zj48Tt{Z-FkigE3u)bV9?s_6F0T^fgRzGY@Uv)n|p70m}JHGh^Wx%D-IGUD=$GMeBP
zjiLJullCv68KDVvQaB2g4k#m)jH{0jx$^%Np(dnt$NxtQ;I;F8T8w*2h$*voWJr5`
zd-X*_ILK_&`Bv62d_6XyN0a|ZaeunZo||J^@CF0}8{6si#>M^4pSdGHq4K6L2>A7n
zqK%=(8%yI3XW<D=2+B6`>#-9=6y-gjQD7sHIs4(zO2*f`F#eOSK|WXVi=N}Kd#<I1
z^CHpR29C=k`|D1kh_yhBA)I*pLv{DPn=`syBXJla%j$)$U9^~@t!X^7s+95%LVgr8
z``D3&1HTnRybjB73;WjQcQ~qMz%yvUfK)7tzmTcwO@I1*f3@4%^zx2mYC1GGq<#PE
z%0h##1zmZ_OPfAaOE>bY`oS`;Yqn4{=2x@nO=HD>xCyVhzw~R@^sw9GcbJIwy#LaV
z9}pG&l_4t4)$)sZmx-V7V4{NC^ykGosu|di5{7ek`@x6F?F8q06#k3-S!03ejsl<=
z8EZ`<zI<Evi1F*H)79dB?OGlU<KilDMSHG;L&)-+BY)&ZPqY?Rz`DKSPDP!lZWd8G
zm*Nkd9gPx*v*Gng!fcwHC=~#cv0b~*VC^=KX|5S3%YLVnE-bh8nwpd+Ti58lz|OV>
zrMsQjRG6ZQdkkgO$g`Kvu%P=~VcW{V>93#fF`gc{+S!K%iz~~I6<XMIhgd8`o5wUr
zEr<8rR|tVvv3?RApVHFetP+0cT2g(DuAQvno^Qz56!n>Ptr%gxN%!-B3(1QCKzyR7
z7uz*_x09o^YHT@))W54m02i~~gzkSD5yej}O-Z5|S(^zu1hV%uxX7%BSjK{zHgOqv
z!A&bd36ERZS*T|k>+@b3j$ICaM~8$H3$l4yr;96G<v#^h_*@&W0yTtNP1uR-D!VHG
zG$%nr0-Ep`w*<1*_0-zoM{Fr)alV2_avQF1211WR`i?_}tit{qF8&8c;-u-ctl!)~
z&AlP(&A;bJ_Q{}bzBcG}7k=|{dq{$FFL-;7OkM~~P;IE6IQo(Nz)RbFc5<4)PxV|o
z=v&OACcCrlQd#9HlM$3&Sya#RS_J8$=4cBdDd*pdHF@-N-EIT>YYL3XB8vD2dTZ>t
zzB&`vKPcQRfaR=XHVbi&N}7BnHZVZ}TUR8S92-0*swx@+V`R_f3i~(M<)u$3A4!R(
za(X1NYboOKT7&d}+hB8j=A-<lU4D;1Kdke#$cJw~xUdT4*(ANX_Q_E$o;9$Tgpmrf
zSHsn3Z;U;h1tjV_<8>5lWq1c3D{6<z*~o^KoqYztd&zhF#_M$aex2$ASWYZT|90Hg
z|KK^QB1<R9l{;Ojl&KS*-6YS&<aU?ixLs8=?XG528*LCp&+m+7h<OqGjE<pg+S(j!
zdOY8c%*=mFKmK+R9}0U~F?oE5!W!6&t>y<D#9-8&g^lNjbP?EyV3>NKo?RHS_68$C
zZYAxyKk3(g<H+S`$IO2?S{xWUpF3k9#1R3(2izTg9CLN)Sp9OU={fU9)pw&~T-v=?
z8WTK}=HqzN7E_a@QEhKepmzr)$GqjV`+_Ix2$kcvp5vGw*J248_kYg+vo_yN7DgcY
z*rnz;bc9!oRTqiV(Q@H6lHJ&MwL3E{vr&vQDnlFg0PuT2J1Vtt=OZ2r#VOopzb`Ws
zPH>Vh*P%*hg+x)kxKf(ri17K7-)y*83h>hF_Al-jz5w4Qhyp2FnaTL4$C=giaJzB@
zXwRMxEvebZ5$#ls#1zu&UMQK_t60U(X6PT!l;vw|mAMXoJC1@DlW^&kDc283#M(Q|
zWw!HVd>@3Ae>O)Ewa<y+*R3#QXj|RSIP1CfVPZ1zX>!cb`wT)Ig73wi8;AlP9zK4b
zum9F*L1varh_>1EZu|CDR_RL0IsHI>PzlTDF2#L)U|3r|J21a<>3|4yVtG;11RN+9
z7fecT3>mRJp$Dmk(!G3_B?C`PFQ_wMfwvhjJ7Yh434hSvS=T}kR(Ltv4}7vJF+Kg;
zLS$Ewu+i-Hs(dO*G51|YWn~C|$|MwN|2CU5K%x}8_B_<VqjpJPc*}30w(Y12@yfnu
zIO@%$7Dh2vl+h%sS`z+HL)zE>Drf$~jslRr5v@Be6ZeHNBrOYMjQjX1K`-*EjQAg+
zt8s9K?ZM}K6&|uvjy7xbA2gnjqgVq_NP#pQPzyxmj<obG@)NHuXPENaUl=c_FJE*F
z|G|Pm7Vta`Znujo9X%z_hcX19JkK`SiI8GXvb-|w5ZS-!MsiQc-+}7Cc$TC95VTMM
z-kiS4;^cC2%<<y{?I(KZQLP$Bq2~__7Z1c)DyQ_~J<9LARqKc5N49Ihzy$+REFMQa
z-k~#1uCZ!k-T0n!fTgh+$IyAP>6fl|N=?@u;Y@q0dcm*>X5Dnh<>PnX8!G!mVY;#f
zxwPrA9;2VRFZ?od)ng0F@kmrW;pvISKReC+gr)6e|Hm11s!&fz9}9fEDt#;4`9VX8
zRldXztDf;`sZdD^8<D9qtHCW0hngVnFO!tt&BM5fp`?(g`*dlg6xe2_-0kIRK(KtJ
zdLFHxo@X%u1|I&K>zG@*Cd&Nr+W0Q|YYU@O#|ZB}R;xST2AE9AU1P;U-MZiabf<fI
zex5sk9$^<3qx`n+owY{b=9+3WxN-U1V}=jtjPnTVU(^eMl-I7PV=dG-U^n``P-ODD
zC6)n3m%bhqA$bMzF<f08F9U4X4$gm>^oz3S)?}Lp8TT;Be4@LP?{N3S`UsqFeg^;>
zO{bIUxh=XX{MZ{MU7cTu-)8o5nfR=$^LKIVDn^sb|LRcsb>^;5$w$6sjs8#nAA|XM
zUK50{YgXNZSK^yN<nnU$VO_&M+5)gQo<rnz!rAu*WqhIz>n8i&I?#iQNNth+tsO)q
zLNVLcsXu|;t=k!;qDHf7F~;v<iLqXd-%{79rS}YFWHgS7y#s_XF53ET?FB|hBAc|r
zuidiSp5fR(PTZ2^d1e;tVAuVnRxh#c9B5xAYww2dfBWR&q#C3Rx#`-%4)YM!E4QJH
zj50^M9hDvLbL-HG+-{)$Ac2P{0HDdVF?+O)u8H+HzG$FM%Z*VSMf2UAv~RNXM`Fv$
z42e<B4@!}>D4bDj_&w~Dt)I9lg{gG5HMtp6M|~`{F3|B}t{Jv%Dh8_bL<t^VRk>_$
zpYoY6NN;@b$i6jZ&n7T=8u0bLQ>bnfL$uI@rB~GJbIh^(1Kn?{D~m(sX56&uYv5x%
z=xgdK5rkQ_>G}@7VOQO`=Rw_RM_r=KH<x$CyUUlX&j10IM-I7qEa5yPw2<XG2CrwZ
zR}V!IkNXFehzl8W=#G3bO*TF<sO_YkSg-VdC4B!RdEt2LdtWCxbPQhJ)*u<mIQJOd
zI_89YpJw}Da8LR65Ec}`j|B%dzzBHRhh?2$9WHH<e(g;x*Ng4Fe@I+DBbb;J?>8X7
z9fYC0RAcB3ZsmpfYHi=DoO#}&P<WL?dU(q0|KX}&20a?_vQhF+w+VW-&C8IfD0Vev
zA>)yIO2muBJ|~%6Vh~fCKsEXqw<Az!I?UdK({jeofQU<`isVq6t+ID$@jh89S`$>i
zoMf12e!WHCT@m?wIa2}mAo?Ky=+n1{RLiOnk*+0jSjdUr2P+pBb?$je#J>bL`TUjq
zzXe4+_CNJs1hlJx|7=d<fr$vv=Zg+b%K3?}!`@=_2|QUa&k`{YD0ecsNEx^5Zmr4*
zQf)~Z`^`$=dUIr7+$4t<{q;w}rkbozD$!(GrprC!S3QDN1;-*Sa|xTk0yjpf&r7{(
zl>i&79@q+&QWH?n)>gOL)RLB_{vPX_cu{I!1B)c*$_HWjo3#$a2U3U?=$*4eMHMI-
zN1VWu>h?*sz8?HdZCWbkI8CXEH;F}D{*=RX@BUrYf=RUJAby*|?*@C(7C4+AKlk4D
zS-4DgJ6ljV95*&dpKYa(35MB1@~dM6Bt9otTFFRoQR0Y#lKmRpL`MX@NXEo`$kSOM
zVg30_;z3+tmb(t!v<a16Y4ySGf<?An>)KE`o%TlPO@Iz8)X(MQleZqz>eX23LQ5M_
z0Q3xX%EoO4Y{TN+4*$CC(Tu?&TihNWULii8*XcddF+AU-5v%lr=cmwP!mHaW{c-4v
z3J3^oix~-c>8!nce#0<M9_3PzFVB2BQ<t46ISk84*Jz1CW+<+huki4r_Oc|unejzl
zl)X1kq3_I!QyU7ty^>YxWPo|&v47v9PLe-CEcLxLF*ds^!@DxS@ZCJCS7T@mtgHz#
zWPx@1v5)sSA%yVTo*RwiX@$!npmV+#Q?O1?K~~MtGUg(9yj1Np$x~2>F=O+o#1Sr?
zYuBmj1~oTa*uE@1wbcthsO|WU`%>nxe`Y@8rzxMWL;wUlEsy_ffGrm;qA4x@sA<V=
zvf<OeII*+pTB3;a^wK5MIQL%Bn~wJ5Ts?ZYYor#(%W>)u@hwlRoH7M@UDLN}iP;IF
z_t{RjErusN*gH2rC46A?tc(AX&$n=<A@w+#$wd=*Gm;gA!9Zv%9_rLLI0w0T@+tkd
z==lF({~*2)xO`3*<oDdW?!7T!+M3G<*y5O?i9@NR^K6@I*As`YdA|?Mb!5!?Fx94r
zXM55Lk4IM=Byr*Tb(o7v`mY7uE`aNZJl|!1Cn0@<7Ec(85^L}GLpOg}>2vlR^w~-C
z>~q6y+)?S$YsY~<8nKpNlqWMq_2|lio{MrAo3MF)@<5hRToSJxo#rw7@4x=zG#o$C
z(N7^U`)7{7#<~j_8U5Fr=zOP^_B;-ExaWtCad#6ZZEI}N=fQ|(*1*4QaevBe-MZam
zVht`Id5Q5M+-sYg<n<GMp25NA^70-NalFUOlBqokWhS3&6;nn>mKM2PbnRim8LVq@
zSzHk<<gd|AwW+v>OWI=C?pO;+W2`)`Vhk%bh0IZ73=ITL9=x5vVsI+ccI8_)MsMXc
zwMSts9VK1v{lws$)%SOHHcL-gi%Ec^y3(FAYgry>X3}ceH+sO>f-ugy5PZ`CAc88$
zvbYGAX)?IEWaI_ZbVr?NyOtV!C+M1Q_oYO}X6zJV<j9skp{plSo<O?7i1AoQ*Ky>6
ztL^W<?-DHUAgY6G>SX_MI21kqmEv@*ZJO)Z?t8~puA|>~yNDJ&9*bu;_^J7N|2f;D
za^8>+V>h3VvV!0&z`I}g@vLi`U>5<FX}72oqCzWgU85helaAd;wkSaDd#&bTRM|f+
z>VrwdzW9IGdke3)nrwYI1QIm31`Y1+4#72eaEIXT65QPh8l2$LNaOAvv}xSkU4H$}
z-1qxN@5~=?*J7>iQ|r*(XIE`~YCkmuX&Q|x%j%r70qj|I17NS$+=Pg=!1tmnR7u<G
zKQqiW5K(M8iLqzsgl#{~ZczsD$yIMVjARmzS`Dx#t3;V)OI^}}nC&DvYI7<E?($sa
z@Dd`^KO;D7q$x+yN9NLpCQ`cBrhjPA<ZhwzbwV*2SU_eKI<^ce+_W+MvA?3NZFS#P
zv7b<RN*Mrh*~7C3-H%Iu8nrq;H@Uj7rx`}9XOUroa;EBVQAFgluIgU;rEXmkZtP`)
z*^=R(!cBapf>{>mJ*6d-BwoWew3y#&I~w5qeHcQUoOnXBuS1+(Vk7GPmH~6Jt3yQG
z#AB-W&C2(F@=TeWL!Qhe!|#X*N8bY?nvOLm9#`6YeIPZwe*nnbq2Yp@(D+{x&r;<i
zp6=}?gYP6fSBH1&NOR6NSPERnB#mz0lBc|4zWA!F6T7o|Z#%6q5sjJeKlh_a0*a^1
z$PYP5)p&@9bT8w|HAM~&C<O<WNKhE?k3HDOs~ta2NSytC4X!dlnxBH!oMlA-$=>;}
z1|+VyskRgh|82!?<s%Ha4srgb6ipb{c6MiyTpEc4R3z8F4WSY{i=HEV@LrAah<)Go
z%%lhu9Ur=cuYdEGb1_}II?HQ4!9emR!Kk{OsIEjs8?mI5Bu<-hm+phGbqebqF&3+_
zAQrll&mxAn(a~n52<19$RBfR&RJm?nm^mU3FGPYDn#=sVE2#t_eX>%9{0bstw5%f|
z@3r*Ir|)-T49ii+lePt;@ie!7)bh_k=n4n7Dw9-*Dl!4b&5tV*Xl{7Hn>@vrT@e`s
zEE)j#W!r&j3zcY$1$lPj0r)qC&#kb`LdnnJ7>~xYzhnxiNgL~3xo_9k?c=zkVu#{a
zYMA{QMYz;H{cs%Mp=$!)>+F#0Yl-N4uaigNs&6b23C(3Nq{Y7zX!3qxZ5)x~!7NyH
zS;XWY#hp{nfnS6$<?*cpV^}KM$i~|?9jq-1HJMd>ePL4ddBO5T)jqhqs%(*jxb1~c
z&L74Vy@IL7Qg9^B>JAk;?`p@Io)-~*_c~5uZw2yf^0#0Fo$47qWqz%|aI#UBj9`ph
za$jlH!RQ>Bb$Bw=q#$iaK%$}P{N42qmz4y^_}i>N)cl|@QS8g&hrx+43C1d+kN21S
z>&;#HsoCdow!;cyZmeb>uLZ##77)OKHBlsi|JGg5xp!)AWt^KYDskncXJv=lm1Cf$
zt^qn3AZ=Y{`S~5yuit<lF3v<1wfDto=YDIKl(wj_R$E)4CK=i`ABxHa6tNnsLgfve
z2*<kuU=HP{+I%8dJS4C~?bmzxTX*k%rV04^J$h{&*T{>W6a3;kv_}8(&#3U8I1KmC
zMWKVYK{AviFQl7dcF&>nJRKgpGD1CoW2ckmM853x&*wI0+tEQ)uk2K2j|c;fUyPUu
zJ+60q%sBNWxe25;-!K>1{Hj1M4MAw1Qz<A?f;L_*`azvLuqWE6Qr@s@)V{YqGBBy{
zzbO>i=|H^che(xwX#r9;8qv?fay{#<dedxbo3-+{Dj=3i=TFwEa;zmbA}L0~(6BV-
zfyW@l5ab+ZTi0k?I)GxA5veWcL>ZEVv!0>f5(jFEzdd^tSI+g-r<-GG0B9QHd@*2K
zziYnFfKo89iS0Yz*g)W3=k>Wf4RW-!GD8#a{wj1-X>GsZ`mQ3<iYD%wZm6r)v?c4z
zk`ZVw$5ia6HKEHAp4?DOuc4e2?dz=8p4x2{mQtZ82q2m%M(!8Kk?-BdOi~oG!88>(
zb?9+rLOT{2<wNJbX*CL9CH_XKh!=!K=a%S?^6G1yg;8mvpGlcrIkF*<F=@q8FVD@X
z)DhHOv}?KSx)gf2!iIsP%N$RkJUISg>La*Utf6a6;m(Y3?h>v7aLFx_Tev9ZzGmK`
zBV~01XO_c{e(H4!zfx1ps-8I{<0Aj~m@NA0>!HwTYan+*UQur~xR}uFk1}b=C5||k
z(t!MQwTpOmKam{_uk%`yp>V!rFvbZlO!L{#wtE@NK*K)^N^=l}{47~kV(o~=^ls-f
z{y8_p654nj^nv5Td3|d0VuK=a6&Y~}CL0BfLB`dH#mD-71w*i)fi59acCyZz!_T_6
z{RQy3akhu@^^C)jxg=Bp-Ed?Uk~g3DGNUu?VGM~RFNF<&^{ES7f=yKJZC}eTIWbD^
zEp1|d3hqd{L>vRerWTfBaH;(3rvTq&+2KR1+(>Jkk|okV`U0Pdv_18EZwkYi8oF?Q
z?{qFt0FO-{leY?xK(dYBUAupn0$UKk!$hk`na5J`@WGb}N8Aed7@gv5A)5iEg}CZT
z6DfK8T+$IP4tqUWp;1pg4K@Lxn)!d(HTL)0MXz|@5_~?#iWS?-BvyITKG=+`I9suE
zNkJvdcfRzVZQ4O+)1lyC{*zi`#L6A_m1JuBj?r=|n7BRDF=B{aD)*K(2+@LI>>?|C
zA`8hb*|KUka$@56gB0LM^jDw+8jGOaOgBg%6?k(;VCB*hs!nH4>WcKzRZPZxS$Fn9
za0;-f>0<RkH(V%YZD`f+`&FOLF@me@T)^KIDRCiL$iNvtbNozw5gTsoLpy8=bGsvc
zH|7Y@PvI61S&qu)5ZmGjqDXozVF>L{##czMiM9rZvT&i4c4nl-%y1UR*~)=g2%L=J
z_Ah~^0|W?xohoJrwOIODQm#9ffiThGmErD*Pf4k*Y389s#HwZ#xHZ(KfZ6>E%kT)|
zH<}|YQ;Vv6%Cq)ELX~RIF_quHy%qc(=UxT<n=d)nfmY^9y+kG#TD+PqRUAqrvbB^E
zv&C7g38Y?Rzi!wK7e`vY=z_O;Jr5(FogU2}`+dE-e_YRDJn#`arrC7*dEYLrlEp@b
zd#6)?IKO>?8W(Wlf4N#?-7*QS31#dTx($}p;Xkyh{NaD`NxTxl>hNsHC=hmkNdV>X
zW~w`b)K{Ltho3DPPd8$*E|e|%`n`#HaCOq-bxj5_DafMf0X(DB&&s$6pSK0)F6s3e
z&x-PP=*?;(R3<BHl#sV4Hj$?pwC|ZS*4*qR6n}<86|{q%{uR2PvK9p!qgQtZcU(2B
z3ql8Fw^psyq;ltK7e{%jQGH)<Q3n*eJvtopfKw6N$da66>XArJ3b^#W+e6`MtNcll
z95ENyxgT9QREFBga+VWo7z3Z%lPh3B&Q*v?$NgkzERRRp|3^Ob?}@g%8TI9?Lcx>N
z@Fh7K^V?(TK|S0zCv@-JMs|N}a-bO;xmV`)HS`sk{UnD)MOOl1p{)1%S2P*)OJxfA
za2I_*uESx3?sH<=agD>^sb%O0+;reF+F8)~+ZKwyV#B$O=xfmARF;sgwgBOKYx22g
zwD8%E!s|;{MRHCq@X38F@r|`5ldKtYymet^YjiA7yD4BI+g2{%x{%mGx4qKt#3Ozp
z>&E+#2tU2iDl5Ge9-}<}EO}hmeo68ho`Mo@ulc$hDBZih8#((D@eY<?f|r9Jy(>@g
zZ-OuX6By^>g{%Rkhn=BpJe>J_iJKmV96^lLj$<Aw+mIgL^3|__c25(=?pIT@P4~vd
z#@#_szvId?ha{osb!!9F`zZ_Vna!+ex_^>T${#>F^BVY+JOnCg4rv-RkE~KN#aTvF
zMrjMSos}RYk4z=P&<u@@HdQVu{T%fL3X@tM^k4&R9*h<_FQ*A&3$*KmNGijT6y7ID
zAhdc=5PX@aXxEF#q`HRQ-}*8bswnQ8EdY1!zs*63L1Uwc5zZ}<FgCh5d_G8KMf01>
zu&cX*+IZO#ls)B=^<|eRZQ++@er+q2<V=LC4;D7#^3H>W0PDz+NZ{pp!zI<UatcG?
zh^DSW$|f*&g&m)RW6b@gS$;6j>YQ3PclEVX*8L*<L(|RDro%DEfQl9S)Vcl8HDn`C
zU<QL%%9?t&k^T3Z87H0PW5D4rT<8o^8r{a#YY@&kQr|oZv+W&dXfcA;6wVn(?nAUL
zEdK%{)@75=*>Vl{cF=m0-HF-Lc`$fky7SxqPktDm`raJ}P7z*d#6wJR3;kcg+wKgD
zLt_y0AM?ns@pJ>;rBAiVClun`DE~F)2vYWJyWNIcZL><AnVZ?JNSielWuHK4uv^Vc
zI$54?p+FegPb4vYw3~0I*fGs{671J3PK!1P!Vvgs$C~;m>i|l=<wIL)HrZSa53om4
zfi1K@Nqd_)F3pG$iuIG8PV^r1V5$-6Uw^lf-kV~r?wfigWA2?=u7&x}fjb*~+F1r4
zsy`lAm(UF>l>rr(z~J3H{sNs>|Mq1zM$EA^d*bv&j;%lrGI+ic#CCtgmc>=~>(a`z
zi&B(|2R<`pzWrt36Z3f+ToiXsY;XOBU=sD%uG=e#mSKzZT6)Aa&HuFW{<VA!hL>tO
z8Q(1NIczO%{7BQF=iUW_g!|Qx?<DP%-X0n=$Om&1Tc9IL)Ip&ASqNu6D1F<TJ=e=P
zqDoe8Wh?-*`VNUDI%Pb?Ob%a^#kIysw>3WxIk{eS9B!Vy)0wnl$Z*FK!msIwPy}TT
z-|AotIknJrqqKQG8-k3WY2ouV_F9-uYO>Uo_j$f7n@{vsuZuU`x-s!seZyE9!@BUf
zW$J6Nxo)}!*%>d}IQ1Qc+pSziH52U6rXF<-w0|wW>+F5L72|K5(%zv2H8SgZ=J@n~
z<(HV^mZo?PFXmjo7%5uJcB3&K(TirEcD=|Be@fBQ(+darJ`n_9-c&r}f>|ec4dKRx
z%v(3Nk~dl~QY*wFY-gO#vtHY9J07k{`LE`&zL`F)mzUH3{@u71D)b|g7x?tjJD?z&
z+Q=$Efq^q4n$I!%aDS4}_d=ws7nbw1r~GuZi+H|=VTEdH?U3PJ4KN~p2~|G=xG*@&
z3zii}08Zjyywf1PI&LdGQ1dy0Z&8>)p+ZaxG06aje!61nF+8xZK9Xkmd$BPeZsLBh
zi(w+6I~4g%PVPQ7t0etQuA72sdt1Xq_5pWwR-hGUnPm$}0-l?dnS53*MKjh1dttx1
zjZ$*mEJ+@VAWf&wgi%u;qKLIZ8s%yGN0lgWb=9L@kR*jM9DDa^mzSektdiz)uMd*F
zy5#-^sPW1JgZsFo!5mcCY$stk4RG#z#!S$D4c}U!5YCXl*s0EeA5xGTm(w_TSbmUE
zjMzWsEJ<Rgv+<<b#3*9=&aE4e^Zs>E_RfP9*6o|>dbYVfRF>GG#+!n%zXedxD`U5L
z42H`w?Aq|3H;YjE35D9DjP_}tPG0&-epyOT6Le>{Mc<thI-MtiR)V{`32?kT2gct=
z*w+IKnsf*6T3TGsz2a<pXn>roMUH%dJB)^-2hxe}J-rMN2>TeVXozHu9_mFfjrY^i
zDpOJ!!DDHQneZboThpd@^%l?&awOZhQmaDVci>TIq#O?FUmd%@Huw6yU{2b;!@u0e
zs)hCKYo#%c?Lx{VurEyn#P_Jga@3wJ0(Ia5x~=EwipGwe6%f%kpp2QBhoxm^GiT#<
z&DXbeig+Q8TGFQrvtb-oWz3d~3jHO>xq&PJx(@RZ$;SBfyWw+s_&kfBkoIyyU3ZR2
z5>3Jw(}#}MOl%xOXEsc272tASw(z(Q%UPN(D{;>FsZepPgo2%&4t<v$0`cs;M!RO6
zbw9i9DrvPmSPOx_$NpRe#OKML>3Bbv?XDj=wQ{Og{3`G|_sjTtma)p%U!MNi{i#(N
zUbk%a;wRjs<qw6bKz;}(gXrA`&$B}h4TZa_j{uQ&J8W!K#lT7bn<eN`j3eOy>^>Uc
z){TO5Kf9ayT<EXIG2I*!sW%ys>${^!8TIS8;%LPNS$P_7>-%wMKKUo;gdpK&2c{ce
zFxT-1V1%R^P*c^xT4>nJ{cgxvDa9orYdJ|<&h>N4x^Y%>PrqXPJDc>!x;V>`^;7hs
zG}!%+w|&`yhWj0FQ`=75aRO25#C@r+SplHxQ1l(2m^Xc%(lY}s5Wl7RRQG0CKxXMp
zXd5>rbY1xlr0(S-PPR^$5#AZ6vW9QZ>fQXjSjlD4D*NH@4e$TAv!SHhjAksuyXAg&
z0K318(|;}qT<hE1v~z%$=5z7o4=@whG_L7P(PlQL2fV5*Uds%0!T+Ts&H^fre|9!}
z`o8*oxVchclC(LQG-aY3KqJMVr|f?_4RC=R9-uO6ZgdjYCBD$q{63>m-PlbnFmgyo
z2eYde9=gWGiZDk*j{NeVmKOxdUue(IX6&WORDvNE%)`}xId!6`M(%TKe`MFv#+6Ll
zIPs2{KW)3=1}^SEM_}aI2vuauDIS%EsAp4R)-gKG+l<#Uqz@j})O_t|SPad~q|fx3
z<6d^TMf_$Fy>U*(1-!=NT2&vHp_2b?jF0_e2&2!RZ2~uV$oZ%5E|_VfW?&zEgkFu%
zoem(R>1(VmYh@XUVfzfnhJA*F@FeuKLV;PP-_3OD9Y0|VA2B7(&*wJtDaguBsN9UN
zNIjL1OelCjs$n<G<8;OBcQv6Cz*c>(m@TGJUh9sg;+Xhpgcjk@9(x`!rz+0A?G=@9
z3cZ+j(s4=0A2t)~T38ELUmzbYRgtdm0q8oaJ0I0t=4N~!BVlcLblwzu-h2PXj<L}u
zAtVbwt9Ty`;C{sFVUF&#iG2iIbLp0*GVrdpC-<M?S;SRK351;Lpd8E43)8PpoP;m>
z(+O%^v=p~07^ctr1+Unp+3jYrGeDk{1qFr^bzRL)Blpqq$W@Xf^6wKVNE=@3BFyN)
z;<8&RtG8B%17>ZN#4iBRw}Lp`mLCB(*gy<@#h!gFWJ~5=^<_54y`zS{x(4$-d!pd|
z^)sy4zz!26@8C^pomj_nfB>Sc*V|(T#rdo@$N?Jve}`YEwx3_np|K;XeBMnEsn_eY
z2GLI}7wF(p@=BixIq#?ZV#BR4-@7$fg>EXLM-8g4;ub6f$IM;x%lfBzOC~ONX!%$U
zANnlIsDza`#mf$vD_rFhml^=rvzQ7a!_?#H!rQ}XVxtL|Dv*l6z|yr>);mXIl~9ak
zN_hgQE}p%mQ7KKcZ~~K;Ys^uhzL$Ds{u`G5%i~7fh;+7LhnMEfnvj|wr|r8GL1|SL
z3_CDaQrO^VZ3^otu|3Y48CgmqXs^BD<%I1axG~0-(uu6?<+qUI3*LUen_=xQiOEl?
zX@MR!ezZ&UXGk~D`(ohWJN9u_vT>IM2f8(v8UKd1s1VrvaboLM#&o3u0`}vocr%>b
z^?8@!1%_g7Xb5$n_eH_Kv>z%$qF1%GU>u9%v4=xTZj42l0+5@2D@RtO+N?Zg6-`*}
zft322Kv-;`&38|Gp5U{>ajd7wu6K7cr&XWzY_SC&;#DjtIubG9xh?#>@ozkAoYhN$
zHOLKO`&~ndfxTO-rJP%_#xgYGD>DdZH8byxt&A}bdEkxL`)TQylp{X?;EyaA#MmWL
zK|5BU(pG-`Zxu)KFw!}nTB}EO-G;~L8}ZQ9`6i%=wno*CDJ9*Do9MP$VZS)v@XepQ
zRydL!)?9K)kL(w~@4yS*X%id<>29i5WyKN>nsr=?I_u})^yYTc%kA8=8h_*IcbFbJ
zd%33``7qhfpZK-E*|hF3>bw=NE@SNCRJ6$asm%Cfs@dtJUOuM<Hmf`R;Og%~yT6#x
zIr241*2~4;V%CV)DHHAsfr=Y!6gduKgMQdhrPMu2=~xY#uqFv(^(baNseKEo>AXkx
ze07;a#ebXDK;RIGKkB$-)sz(J1}1MC!jq1p(6Y9A{55&a6Z#T4-PS0-Jwj(kT4Cs?
z(l1KaEcTi<xld4yeT*q+MlxH8i12*c7#CNFs$2qX^_pQ+%748S({9)kW0^$v@H_Os
zCKW!XmvLKVGeeT*vJ;Sfbvh_FA6r&9U)HhBuy2g)&Vip#>2?O&bh#|bo}l!xwx^Gr
zSOX6@;m0=|nRI~|sk*$gwfQmUdABDvhI$gG+n>KEq4l{?nv2`KBOtb#@#(rM4P@~&
z5h~Z!kms84j;W=e6Jq>2YD>pu>0F9Y{5k$j#X{NsOKb8;(>-&a$wvOj=}jNqF=rE(
zt!!gr7KWHMtc_}VM+2}A{*_V(mU<A@+KdInK62CQ9kG?dTUOg%u;&Yg@Ibrs6MNeS
zN0!<!_B&nFGMf%}I-?QeNr1Qf-veFj^1^;7PJwmOG7L_D-JXF_B9&K`@Mk|W#E-Vy
z3OT;j@t!0|U<a`{UcAW^DusLe*4Ik)v3F<qqxIrq*oS1Bh4%T_;SlnM#>RYxkDtAw
zV0%>!mYAGmpomXs-dqwWI%dU=oLKIhy9G378N2G8^$~dYt;?oSxQA8gR2ElTLqpXg
zH_otT*zsiAClH7K^eaBB_7;-3$i!c+!rXpFV73;94@tdO5_5ah6d}HJ3h|B&PgW`)
zFNks-k0h%b{^Vdj`=YjQ16#32pa1S}^4`DZG5(FLP+lopiFp}s+Fw0pB+D#M*Kay~
z<}Ww>#jbQ=JlUY|`J&m949)nPxNg<{gf0BZK27Q#^CX}TJHf#4qb&%$`J(!R(*<>I
z%7{m7%4ukt@NVk(lG#WM?d80RBZ}}?(;4|lKj`A(Ef8rnP(UqOAq?<EL_>%7!)AZ7
zOM|91C?NEu;p6P6u|t+dRcWt9aZEw3SpYyF>dcnnIf^Bd#Xb^&Y&>9zb9*O*#X-jc
z$cRzFpk%a5s6ZOu`Q`m6!zjv(Bd{m@*lo2gt+HivJov6BYJ?CznEIignmLt+&J@z4
zBjf^e(q8qR2XlRQd^PIyc}+74UpvA)2OI$Q`dIpU|BZpL7tcKEN~^Ag#wz`J-}5t8
zQJB}s;%1HI2OcE^b}u!V=VhkE-B!XMT?@+|m|+Z+S9<-h9oJrpo86aGQ*T?L>C{im
zzEMPM!S7txwz%pzDJ)#7E8xx(YhtcPs~M~C`CjEWbH4__FVr_<)2Sc3o-AsEHqw~8
zj1`8KHAkn5GSz2A%&}gMoHDXxuV<q)3E>`Slm{Gk&ObAUbOQ_FH)V%n9YUH<udi+H
z9vX=TGT9`I?`sJc+S|tA7v`#HFx8V|1flqP^1MwQ@`8o%#93a4lY>;!*dw5R4lHfG
z)jCqq*jblE1aHny^>o?-@v7Fz19d+rl^5FT&P;~<3PEPCr<y_sNE15`+00G5ep-a@
zm)MKSm=8i}zA}hQBouK_zzw```jUdv-Pz47bFZn6D)##HM#h3icN89nClf^p_l9i4
zD8hj?B7E5l%C>H0rf%70T9y=R!z7WhnAM5qk7n6_`0JdaSc^_30Q0Irwjg!lOLvO$
z#C&8yT4!#1{`2<#akkE=^86#qFA$0;jPt#Y`FwvFO;VyhyHl_D4bwN$S{@JI!YxR5
zwo?_l{Gi_59SY4E=zop^so?SkVW(r(wG@dWOyyI1o-XJe(tF|vbU+ig$Jf$U1Z`sG
zR48dTusdP~ALbFTD+jT8cn^k&BK_pHp%IU#Hxwm!9l)cSAKp^I^xfqvUW>_L%02sG
zGJ5QD2MHikJ`%Dud-}f=dbr=J&CFnrcdBU+g^EYbwi>E%^tEvl8I<pl$QQ|Pu|e;U
z3sp5ga1-P-EJLv1)P|D!$p+7?>LX8>OxGp2r=1m?NjXGQeUkO^CSBqgl41p#0Uq<%
zF8$YW?kUWi9A8hiov~G!u?Y;!F<L&NXfoUPRTdB9mCJ-kW#r-x%}B-54EGp{cFyO;
zIPKNYO!SLg4Wq2t^k3)Yv<mg@U+$c%7Eo@u6w1IP;s+j>yNfR7HH5h%u8FS$N?R{?
z^2N;$AAS!}ArD;bL*oW|b1{5_c0WkJPUEw~0OfA;ftVGFGg;qK<*<{2Lj+?uq3n=(
z3?H4Q)h_Qq1Yyp@n;IDLiBV31>_TBfg=pp$#IgkjM32X<k(WQ185S9-_{Mc7)T%x@
zq1a|4w}#U~DQq#zzKo8vXCw<K;uE=DdUi54*`AG2JYHrr02|fz#rZ;caoVChAv>@C
z0WAHWOaDMP%*nRZ#}%LEOLdP)iHbkRUxnX7veR{u{GE3~vz0W7u<NhwPAo)sJ?Pdy
z%g(rUF)~KhNx*MYSr|{TQsoEwaOCr(Qp9|0=t5BHz77eF^7xR~TJ)t$Q(nEN2^#;w
z^IP-e{bvyb2sz*eA+E67I+<h&vlT+&^Il~dO^!pdVkSot_N+q+(%RkT@KjX})z<c|
zxzd}{-Jp7rzzAwFuU{+I^I5`?TO=>{dmncrGG<v=#%&ee<nIkX2A&GVGuFV?;DO@0
zQTxMRrfg0-?eJbbS>COsq?AZ|LwZ|HhkYTt1f<<@`s|(hjxF;Xxq^&ZCltG4Sz4TG
z*&b87!_X+WAIobX^mt*|3p+bzletgh_)K*MFWtKnKgDm~@Za?E{{9V^R^tM*^R367
zq&bO}GX-IoA)9$s@))s$Y>N3Wz@hY7ZNAqdyAGnlpRgI16w0isIw%EqwnH-Mu2EQS
z++SHI-g$(*y|4ZmOifdOIV{7Lkl!rKWMpl_hn?DPg|Hc=?He0j^E}?j*stizOFW{X
z&g#koGI(~Ci^SEoyb1q&{&JfWw9Gib3D))@c=wjbqAX0db)0p;`t>aeg~rmzc1`dX
z95s-7htg0M4j6yLx$IHvPnYxWmH=ejzmXHFIDXRflKf=npz)@%y~fLYqvDyXlTwR!
zQfH;PtgCrNL8*Q$Z^QuOi~Mhnp_z>hP7cW#>=|cy!Xd|#QUQ_Q4Rz`WH7xzPwl?`{
zj=<^G)-Wdx!on~s8nOAlW=sbL!1M>lq`7W&Np<XZ-+)&Q4RU83Tx*o`_cXb?Q_)S=
zbA7ox%53lU1k|q5M}4(gg8R^tt}i!KwLLDSPHy6T29Lmkz`n{-E3+F+@C*!#yTPZ&
z4VuA6a6s|o(an_Fh_7;I@s|@o)`o8%&DC`8qQ5MU2E@m>j~O=sb^)f0U5U=(3Mr$f
z@bkHk7X(R3CM`f~DwWE_%0?aYGR-9;6u#Td&h~yz)l(bSRR(nj3kyaB#2IX048WYw
z)~MC^^k5CY45@B{GRci{#|>X+pgB#D8xnz!Z-B4kLGM2>?qMF<kOqjissA%d`!AHw
zzg?kIItQX-7Z!2Y@;=xo^SvrXQCpt&vMV>whu}|(ud|$9v^5L`hkPWlG^Kc5zmxgT
zTD14u(mM|#h`+*e@tr3V&&f4D08<)GtB|ew5)O%0J<$zsZ^;uu$@bg4tJ6M?#CRO-
zPR5@8_*t`#yr-WY2aXiJDBiAaxU~?vwq)$f?_73D9C?{(Kd@Z_ipwi0FnVUT7eeL*
zV5<kvS7fmF%I95<m^}dh3_SeVn)bJt@5%$r7VXnl0^?$BvtC)nH2!*ik3EJ;D_gcz
z-Z~dX=ly&CL*%Cwjzc;-KDiiYp;icCYLh+nr)IW^7s9>i^eZE+a=|3CV@HSlvcu~1
z)(7Bqq4tZM9cvD(o2k&Ab9RsE*b!i4)AVRgM{(ARudXBg^Ul=!e`DGHe!1`Q!E6;b
zof23bm;RIU{0Cq9=jD|Q!~%M48!8q5?L+==?{iO4<Ltq1F@PJ@Kbfxnm7M<eBRnY}
zU|`!tb&1N~)&>8!_mggLkm5DpfR-2SKh=-^vLE}O|G6yzl4k39eEm)HU&#uxSRvvf
z*SXCyt`NZbdpZ7#h5XkW{PiIGf-Y73^an}*wSWmB0)8B?nahs&Q|A8|*Zjk{a1}Yp
zI#7cM+ePNT74X9!dp>=3zgj7Z|EIb5pLxmEK!?mC9ZDoG#eXYcpHPT^+nve*#s4h)
ze?2lu_Xh-#94emmUm1%(Y;^l)h=5ml=9X0c>sd_xV-|;d@)Z6>$oLPU{p+Rh>L-YR
z|4&W+?K1y+ZTLSm`9C%J4<_qRHTyqr@_*jsU%P^Ta@szj!qulozy5O&u?9d5I|YUA
zB-1IqE<1hGY{6dleV#lGKs<Y0Y(YMw9<KDN-G|?Y|B2x#3G)t3%(`U3OXCO&tLFR7
zO=Mdn7A74vEp=X41c|(7D0$MAo|K|1*K2d4+=8{M${zf!;Ka#j>*X99Umcs%ij&=0
zwc*(g@CRuw9QE4>Kho+dxRMP|eYD>fkc<Br`}Z%xvSPWl=!N0oVc)mVp1ABu=CZvi
zMf6~Aju9u;49Ghk8P#Jy2HR{7<Qt5AT%e=<(&|9gu<QVPR9MJ>hDMn9*Kg>A5);On
zheg$aFlK#$!GsbQ{$F3d#lWd*RSf=Mg9TA3z%<x}7U~NjWpf~k1iqhzB>HBbUpih%
zUAtile9xes9&SSgxNwNb0keO8u743}Zp2{`Y0*8_fBGiKkMYNj5~fI=p;0L|8o|=Y
zWoJajijUuWJ|6%`S>d&Z7E_6qHWA&8Fu~yoe7u=0Q;Ls!qo}`%9X0-m=%d=1X=!QL
z_bVmq)-zG^)@RNm3X0Gsm8I6dJoY0jl$y>dF#!PkKRoYWM3TEmMj~@~wCel$N#M3q
zPX|fS&Q2`TjYu>E*@cgDb?@8N4}Lr=+{>lF18V?usPH@E=u`I%_$>!gk#`w1FPDme
zO)(x2x|;ctx$-||x>H0{IQ%dWrk*qZuM_|8C;5+cKrjxc8u25QrJ_+SaZcTJZeufW
z4o6?-d!qpV8mNX0D0olapyx(dKm>pe^sA^wZAQe&i<$-9p#fY38_zqTlRCW!(j;Qn
zv|crVcuqLh!ogz-?z)>+ji4#OXL3d%J8osRi?bO!y7;;lTx?n0;sMt=49JO6vi(GX
zdSDQtvdPHs@EMfu)9G?EM*cp_dZyF317H6$c&gQiPEaQ^f}T;*?L+^<3!1#GC?gx2
zy19{uGTQhtMud`psMSH(1bvrAS#6xY7F1-$rg~ztXWuwN&vUCiXs`he<*NTQ!-<*6
zm5aaHZk{cp4ISL=<8WOw1$U#NHHYW6Ya>30hXz*v&nxo_OpGuLt+WY6>-qa5ReAgZ
z4tU~;xr)OkIJbAv;z^6<IUhRmV?)|nm({T4K`3zU507Nkio^zo>Vd5{yS;B;_1co^
zS5OvsHPAZ9KA5lGQ6k73uURAQf&mQzQg-9njUOGtHJ`Q|a~_|Fh_Jocyl7IM&bR}#
z?&p8)8rah7wc_5ccvZ1Xne2?Fb7v7@c%vj_yn1Lmo}E3(sV-T&Kj&CxoVNeT1)%Ef
zVCtsB+2oQKlA~|bcb8r>SPg6Nl0yH)<-pv^&p=&Ky}88HXyEWM)?fDty(!y%t(FBv
z5$R|W!Tc6+K&9ijJ3(%`gfpFTqkD@>X=qh~Y_w-4lpKf^8@0b2wk+w2O;H(pv{{v}
z@!!Y}$*7WBkccMdJMvGXpo8-Qqu*7sICsqbF2<v5ByW^bbpq;{+P}%Uj@9A7xd#XT
z9GOuxrc!IyixMIl#oc7dYfuNB!kO$~{mlIurkIVE*{h@>#4tpJT3BhHc9~+m?K!(>
z>>#2uzXbqQlhNSczoQ6EYQpXV>=Sk+o*&4Idk}%AR=>YKhwWakl@@1rhf`%Yt=2Hq
zptI3YHGLg10Js}<EAp|V1v$jdK}Fk^Wgc8R{z_NUi|R&TT4<szFt@Gwpk3M|!Q+E|
z=04~$n&vhM_rg0lfUypx_IrOp5KW41cx$~%Rv|L1#ZSzc5^GZ}d4Ba+Mo&oval5f#
z4)v|t9#@j=E!`h>_<vVNhyn{oiVLewYYIKRQA|+JYru|?)FFJFddIEtk;4{+i76<{
z{%x)AQ;m{|LO$)R902u<qzjkbp5#5Mv6lr6y{bVOAui0XFrC_;s#3%=Rb+bKpAZsS
zL<3kWmV!Fz>1q426utf3e^msp3BGPYrMX|?(eLah%|o;K>P2uBKSL|h2Fm9xlph)}
zfdN7`OYGhc`^6Hg#3n~q;*r>08AMn&#qhQq5~M<ag_xH1*q);d*v^3w2XlGGR5~+(
z9c9)bU~#a1tF1V!`{vAuCutBPP&&D#yhjexvDqaT$5TCVH#T}d%uB&~_lCPdk+GX5
zaX!D77RKY*1^<~9f7-MSj@m(&i65x)zxs;*aVOu_;o_WYlzh_J+$w)Ia7xnkrr3cR
zcFcNY(ZwNd<L7*aU3Va3x$Z|0t+bS|xWs!}I@-0xUs$dCG0Rl62%^I$74YHOO_o^0
z$|~!ipU?rJ$Q4G_AC(L#u#&eeEU3eygDaYF^DL3~rl42QJ`8}dGzPY5Xs9<skBFZ5
z+-y~OJ^{-!$-mTErHTkY28H&7K4Z@U)2@boWFV#^tzn@nCz^fn<YyF3#47+ur}415
zNt_7iyApxW`&k-xwmRv!aobr~&Jd866Gi4f3U@*uF9jKIeF2)d8B;mxdn+eLo{dZh
zAd`h&yQVv-Sl5nR_0X`ev;QWs3eJsP<5{CszkNA^Y!>JMtH*eN{Be9&gNyt7kBScj
z_IQ_}dyE56`_pFSIkQznqCAer^8$RL9-`9)6NX0SA=?{!KqycvUf+G3=0j7`PKeG^
zxV8TD-2Z8v{ua%hwiM@qUk#AY5I#1D_J|APl%Dy~`(8cBokPmF*jto767ihAf4YL2
zTDY)@(yWDIXsG&h#irLHMsSxv403*kH>kdp#)g4ji~x+C%6ihERHXH6^Qm-8M6~f%
z+wQ0RvuCe-W?1)ZnIRf!s)HwJ*_Y4gqy;78v~{Lrb-Y1NtsrV7w%2*pZB<-a8QoRz
zmEUo9?DM-J^Qjcj0@rJ{z?4HbOnt{bWvWS16xHQ!PW;V!D1@=+cuWwHh2Eh%f1D5H
zc+vLFp*`m(U}Tun2FQ*q$0i`c0&&O$e?;c#pd^w}bZ{_!Fint9oKC${#+tOM6+P>u
z37SvO8!h<t1<{H_lYzW}YUm<&y=UsVFty~Yq9UrwAgffRnDnx&>Pt$F9qNH8K^2qq
z#oWAJBr?}3(uAe*a&rz)7as~AtUPapi~&1badN31#(T@$IKtFHu(-ImFf@ehw(7$*
zyAB&;kEQ&yBocpT`xzVTqs?L=f*|37*frGFeS;|D_$w(A<s9086G&5~yz12%3Uj|L
z8BEIC+sJF97&sk@ioT|S626pJVr7f=Wn+nf9r*db(x`j5u#4s>a<z9@?J<<ju{c;$
zkCxk-nhTB~o5AO64d?HhAL`p4EZy(!y8@#Wq>V^7q)>4t<vliPO{FM?mTv1NP}$w9
zr^=}=#q}M6e+nwxZ($_G(VLm*^w3IonvJCgsscGvxR;f4#Nq*Q{0h%^#$s$Mu8ro|
zA|9T+4MI1#v{Eo5zMtIbq_)hS@2On*@5#IIdKy=w@Tga6-#MTNA(@!vIbWKQ^IwGh
zgtP0pA*{%>bSPztenwMsWxC<osGs>FbA82$KF4#~8fYB@0F_mdMwql(<awuD-L;$q
zz=F<*+1~r?C1_H@hG}^d!k=Q^Pz95_?^0#^MZ<iDIv-5u$6L>r%ohmX$=i&TJZihm
zO9<-tk}Ig%faq^WT~mh7I(Qz9kH+WAA0h4hh(3~ET69|ox&<$<2$S+(Pf5n#g6#Gb
z-U?JVTDep&+Uase`Q<;Jg*Xut@hmqZ;54q?UU0cGmrwT~?jnZ?=QQ}c-!+;fIR<oQ
zv2E6@jYE#55_hU&rtRko_Sozh@#>y0yV;62n|kdjPn6LiHiH#w`R{@|IT*2M9T8GB
zIOuRYn;)p!d5$YMi3^Js<gSSAuSA@4sb|Yh?}rFny55B(K*$SfKwwY0Iaaf}rh%KH
zj~*a_TLaW^bwywu=Z0D0*9N8aR<9xSqe{xk4*-_g5ouNV+cb9g{qohr+tuh7#Pt(t
zbnV|0BIKSUJe;2zBGYYA=UGXLY^ZqX3*3>gz(%5LJ4|2w$|r=l2G1RTWhF|}OX|CS
z;xF02#<&GLkk>n<*&1q{NmG5g7-ft0X+H*$K^T#%)1S|B5%^Nb8=AI|`R+~pjOj>O
z7>(=Voo^`jiH1D;#DQu0jke!7OyCe8Y<rkkKixr`K}h%^t&4T*vjDoM`}4!!LnO`O
z6uMgSI<kvz2JU)T@kj)TGVGe5w&^swMH?q7q*Wj5d~1Y(jyU4(JFvtCx2xZc5wW(P
z+wb2y68uIcd~5ntQK{XCjFl-ijd{4z#-}wi+it<5x>!8v<j1GHG)jd*RKT!Fuxr=9
zXmtO`Ktm&<@p7WYBd3QJ;LAqkvK4{v1)JRL<S}4FGgGO{=e$4N$Fle}HAS{G%y%sb
z`Hqbfr^AM+KZO9F@&z+?qVhnVYdc1T^#D`I8+~Dn)rungu%}pd3_yio=p^8{WcS}>
z76#u*gpGzI0`C33h>@ov?hR|h(SE9?-A~{TJn8<h@xwyYS31)wkOU`no7}y|EPDM$
zTgNPe(uZx?Cj(wbOX>_klR!>mF_`?hHjVPCmI`YY8@O-FVc-`JqNcn4L3v2N@>Vih
z6lr#)NIJ?U=OArmTA%KbjB&Ux%l%EbK3{mN^NoP<FeVvx6UwIIV?taROT!@~wix&9
z{Q&H3k11yLS}WH?-FClW>gZ%VZ_^ZBW_sjl660E;Y-UtfxZO)9FBhjER8U@ybol|(
zSh6Xfx+`H;T!qj-rr^Del@wpkxGDJEV!%HsCH#%AsvhD;tfTzklUcX+v$vj3Z{MJV
zsMr8Km{F_G37V4=xr(No{J`gKOTwFuCs$jiPi_iHd%e;hG_S0gU5~z&@7~X$meT(^
zXn3lS6*LFjO&7{K^y8W1_5!XB8GxHy4AC`fiaTOa&YmpaBH)`DpZO0;@((cc2W;|*
ziS_0%s9M{Q;oo81Ioc2@945zDg`U9Ue<{wTte1jOty>{QDMOa|ugov`Tk<M;a`oVH
z7xutO%eV16U*~ji@>F#k5S0!FVo~e61_;ehDzy?OIps@K5JB`5UEnxr?_GN!sNsvi
z(oH}9)c*B6n&+Hm7|9~1A7evXS-8QjX8{Kj@XmIkY04z-_J|UBy5q+R+)sTS>ymN`
z6`swPqDh;^Ch}B%(FbYY8Kl}c3!_?nxFLjJ<!sWfkrz6uzl(TMlbyR{<m_*AGtSTx
zcA%pAY!c@B$35H0)jiO3gNl_!Y=A1}RDC(_@-%@Z>>xu+nt@b#tQOQ)%M-LSe!pRT
zCC&-s*2|)}xoMUAENd_ji;hQH#2WFt72O_kN74#>!T5t<%f7tuFwl}M{^ap;U#26o
zLo?jz_*))Q>fO9N00}tH^|-GySND*)k9iX6#&q;vND*<sY@m0srCP9glE*8tkzw|l
zt2q~&n;cT?c!=w)jHZQT#PLCX%LOlOG>DqP+f8R_4VmYqY&fw6n^neY|2BPDvzHWx
zQ~4y>m)s~lER(a%piUuO?ZbkSFtYJ$2_4msG{F+eSq&TlDjI5GWBUHxscuS^#=Nkb
zr&@z4KZ@$`bg-%Q!eWp<Z?Nd?Z&j0)5hfd4+&EP1!n(NbcUJRrs36e?O=;%TguO)X
zJgq8J3fjvIXIF~wb^88<nD#5>&ziOk+vScN)6!x;D6LW~0~Y11zFClIrbFOB>>FvF
zc6=XmVhYN}MM=8ZWp!X;%FB@C#a&$QD5b;FaPY^JmRWb51eH4JEiE3{wa!V)JU`d^
zv^3mh-^b$^X^z8-$1GkKTF#4GYBRaCl9j`!mV>Tvl)N7)(wH%nE@6CUv4RgJF&kQ~
zP-#0&j23y**rrXWOUjZyl&0p6)BPKLk<r$bQjYsxIz`;)Rkd}Gp_~3nHfPJLc!RWn
z&C1iwvqKwbv^ACQ+GZZRg;|a#8wmI^6<+;YnF9`>$mjEmq&m^#FQ4oU#;ZSbe6-Q{
zLpA(+kf55CsA=ogqgP!?0M+OsNRAHuN?t}W1ZLpblfP84{!R8o;13sOw3sTEj<d-K
zDk&=esd#T&7EoZOGAeNk_8|WyDjDS$H)NFE-X7L|#Og;JuEtQ4JyKGV(zIks?isna
z^wmf>L(IW=3Byu)Ins+|<#HuW61pJ6DJ#V2M~#nwv_Xba*a#1{5*=c?jk0dSM65&T
zm<{41gou4#B<iywHx!NY5W^$A!h(#q9X`ByV71vQe||pNLRF3v*RriF58rI{G|mip
zMbrS(`gUsk!w?ne6o-@y-{WzY;Dfq3CZLUaWOn(J!N36nO`4z=O=bJH*hf#d=37Z%
z(~Iv}7c5vEV7*xTabwAb@+8iaQXzAXb>wZo)9YCeitdS1>fVUc{ly5ATVsm?u%uCb
z130cgsk&6|{@vnZr6A`=7PPA&;f=fR#=ORwqENo(t%t$Vrta&vS?(w0jQxTdE=}@v
zpg7HMg^%X|%*5s~wYtD!SkY&nvwJlGmTFxCd0-`h4op&$V3WI*6(otZhxrtjq!IDm
z$jQEs<#@8p=8dv(<oS?L3v;U^uIO9KiCn$_gQ=eo<@?7(!hR6-YdfUV)-D`eb)4h<
z#O%EP+puG<G@0g)d+n>yP~GNzRh8efP&z|lwV1JgLo-cE)Uhpxzt^g_z-U<UpbDub
zN^EKf_gW|R6{ZEd;jsF;yu<2%Qk8jhJwei^&9OmmOb<$yzTs>TkP)<6w&DVg>Y0^e
zHm>$|Z*>$@`Km@p>N%qUStRP?x<iJzGo>7qiag^8kcF;Q>)RhJx4h5aJ;$Wx2UY%%
zXTt=OUlr=<C!A-UwBrksS7n&wRZ=$03a}hp80+fh_<mH{&4lI=4B@#_TB@r%&6Zbn
z<0{o>AgRl$EX(n9!tT%Jhg&zSK)w&lOrAaAC3jc?r#E5R4KA1d>YHt9`rxMO)$*&%
zFOlT{AcL<Q(R#jjbe!gDL)@cXTGjaORCgIVe^ob@9DRa`L8=d4_p^iN>X~PmBVeX~
z)zwIla96ve>Bjml>xprEbs<^hS1*8$8AbUA`aCTBHya6vPQ~!Un3c=)=xf(GtSC+4
z&UA3hHyUYFQWY^Nc8%rp5qtRUh5d_SDR(bi>%+RL-0OYg^HL<<X-84-wXe7;oeD%r
zKL_}srmMn8W_d9pNONaUR{D&nD36Z4PZd4EPV@%Mm;&O4jsD0NBuM$pADjcFD#-mo
z(Fkidkn9I>Hsk$bn<vL~$5U<B1B&VUPkS(Ak{AICi57YlA0<sZ*(zNp8dth+I)w{V
z_J54V+O@czAt16PfwonhAHTqZ*VjX*Vpv)-EG3Au;J@8~L<7KyHbAz-D7QxaneaF8
zlgHA{Y3C!X%bwRXumlNm3JG`qi2^!kza29Z6B3Z!i3^~@U5>vw%BQhgpcl!d2atMd
zl*Bfr_;`4D#PsP_X^)(N9~_r+RKvQm0;2fAc~9p%_qz6d8V=OE-jRIf{f0`gA$V|=
zKFGkzYFkV8d^DufqY3GcF1P74QS`CMp9Hxpoz#d&opp)+PF;dXswvt~ow&?@1|^DX
zP;420Yy47=d{JbDj^D1EB1+1KkVRIeLqYf{Zjbq2&Bqp;b@Zyo;A1Xf2|C3NPAXYT
zJyP>K(?L4MGKZE!aU+trUnODHZ(x)huvUhNef0`dKW=WSbV1ykzmKS}k&<NAcR|<o
z2in$z#Aw_hQRU>LrFRamy2R(2Gt9GD=}x=ddKDL#^RqPM=9ki1kzO{4;q>3p>yGW3
zx=1PtGpT(mM4gFGBt#ctZBwlz5mKyvcu~sTXgWE?U(V}?edNPN59D2s<}xtO+)ta=
z3v9xgxI32&JG52Gc6(lf7Rz>c@6E>!P7Gb6t{Hyjbrt)`mQpmm#FGVbESG7tlVsT>
zl+1QXi6r=8LP{d(sbc-B*+V!rgC~5w8<ELc6~EF*lIxcN87qTM6Kcon3txW}p7EAK
zVdU#>&i56P3Vfy3cun{0uin)Rbp;_!DFHah5#7+_bvdt8(C7EA%WP~!`ud;@C!?+T
z6`1^KPwO0JZ%@-IN=#mR&DXn4l=81N-C+Vm{nB)ma!CiL9j}6U`!4Ii!^*N`ah=u?
zhxn%Lyf9*6MLa5IBoP>@{(2ad4Il-9IAP!)1$1?|!Saey*+eDF%|L)8#g2-LkLayE
zQa)dzKIc}RpWA*8y<G3i?rpi7#F8Ng^>Ng{C;>9O@qY?gR%`HMA&U7NhjXAQj`cM)
zJ9c)cc_Bf??<xTOh;bKD!%h+SyYNwgr9BjTHm5JpL65HdY@2m-6@@VHX;Ppb>!A@|
zBz6#0?7gzh_EyL|b(KEEq*g1!h4D@o`*`)rrTG!wuG;-J-MrWC!Ua(9x#p_9zFf~M
zCNx0Gvx`nUo!T1;b%K1le&(+iks<+z`&8tSy;InYi-0H(6t_g~wTc*C1frvk!X?7S
zE3N*$aaw7)-RDQ9X~9P%G^(ti;|amYfI?XogE{&;3GrsShWUN;GTyDb&3MN1^0KkO
z0dY#yCN@4Qih?PZ=BUr%R&LbC(v5Rei{QlL+zR;f#R~04=s7)}?>!R&w*(3sr}G(!
z+Pwlzb?}Csm=hm%r3oC{A=SMP=)1sAiHCt|?U_eYGrV}`m9Gm*QF!>5ES5?LCj_%A
zG#Wh_M4?kKO^bErn+%?MO;^*h>oHB)Ty{i6UPmm@ms613o~?&AjS>T1&%iwZ5*JC0
zIw-BWZ3QLoXOfVUyE<`ERAxM){eFRzY&#7@J2#GbzBZKk)nRQH%|g?;ZId-Ny)9NU
z6)-pEJmRE#S<Z~UggxzF8*dHQfY``?MxVH;;l5Uk^EL-gAE7OVwYD-+*R8Jx(LWJo
z_<u-$H;W*Q0(@8L2dn?2jT@}CvE=D~Mn-)u+eD{%OA<K7oJh@3Cj4>MkIpUCpuf@?
z#=-nGj3jCgHTWkX8S2M9F<)r`%Rgp%9uAHNl*0I{?)3>Hm^?Q)?}DTzT^`Hjb1$GI
z#I5a)w6zyt4~Y1(?5DwuZOb4i^Sh%}B8_q$@ss<feMY(ESYqTvwJ7d27M5>gU#Awy
z^WNbr@kh`(yHO4w&~M-lu@>Uth45Xi8TNdUE*~z@sW5JZ^Gpc4Ktc|wpI61$;63Wa
zSf6iX8pgF+4znE<)B;~ksRr$;JTK~QIF}J}!XXiGqb5=*RyGQ;vXpZqKL~hUm2j=L
zpXk(oLo;)z?GumMWyH|Q1P3B&@_v8uP90lznb&o{1-t2<SjidrRZ(r~y7lbca*_r^
z=A4TQt%nzOGmR2>=}I|^KXc<hOos6$$lkSos-{g*=oz!ey&|lTDkz=J+~_S8X8Mk%
zj}5A$HYG#Y+s7mo1(xN;4hco%F>q3o?TO@<$0`U~7K-&&d|TbSfgf;Sreh1dAEK-1
zoj&%!1+x(liu%N*$<9-G^S|!pz_faZ3*Ci{uyOpWJlr2T#;@vhrB9rHjnGQu0~}Uz
zyne)Djm2GhkPAAMV`4OEOAnQyAQ^tIGY1cYhbgEK&!c(9mgA10r9#wFglJYfvd+!e
z!xpWZ%X(l>VTwCnTicmDy`l*63nADd)2~9Wwo7DAJzpN$RHo4A0!M`KrJH$u6eh09
zZ^7;g1p&;G8>)ykeU*a)iIru~#ztRi3<E{-A_7{XU@EhMDvL{MEs`@c6C^kKQXgfS
z8Me0`NNVzBrB6=WFQl(LA8m@HW^3JIhi-@vycIq3=Q9u3hG!Eg7o3}RiMDDx^!VJm
zhaFNU3!Qj2(H^;JO2oG_A18!L)^PKf+HvnkQho-=923w^rY0m|Hm(39Rc5Wi9|<P`
zba_(f4K4dcyo^f%?u5=3({{ez;vBDoWDCRaaM?%t@1!quezo$&2|IJ*Q(=)Ol30o7
z@qL>l*#!fA;LzYceCRt{??Ev{xnXBGZ3-s9+E_{+f<bm`5L!;V{YDt(#<&+C+Y$EJ
zSDfPHNf7xvm;+VsWc;05mIdYh`IXK&6%~rl^{hrn#KYx;(7@gK=6au1Nd7xk+cq8O
zzN{wtmJ8q%dif2tc6XRF(qF<>0#O(<yFoD>TMxhz5J>hkbLV7*JzlWkX)*!l-9npq
zZY5L03xDV0LpjdJ`+$1$HRB(V#2+|sFN}_2#bwVPg(7P_d(4jHWF$lk(Y)^+`B?dp
zB(INyF+z~L`iuui*vXqbKAbQg+Q?|Vf6;FFsb1yn-RiI-P4!#GN&%k`)FARy83I=0
zEM_58@PPn$oo@Il%sx&Zm1S+ts?}(yDKIIBqstab4v-P2wn9ADiB32WRLxU@G2?h?
zk4ws{6W^2xzt@fWo)Z_d>Q-ck1b=k-=q#ox<+r8&M^xt{;<uZlFNgFnk><~xP`?<}
z6~&-ko!%>*bGs6V1J5xNH2F(PabW!QQK%r{=!WcmU!1itsM7yKFxhJkXw{SMq7><h
zG~UYC*Ge=%B*oRl$7pdm6{3Me<i2fEZ}2g3Ur!O|%0)g&8!9<QK78HyEX!|p=}0Qu
zf@&s*!8I=dMCFT_M`@tiw&}ZC?`(Ahvy6;1s2RCEK7LLbRu0X&4+^RpBD>cNo#F)H
z)bdp83tjy`vfi>SuB}ZM4FrM(5AN<B+}$A%914O5cXtc!?hxFyaJRw<S~$Vo-FL0k
zy?4K-&kv}1)rT?1e4bl>V{qLyt^B~e;$uc>*Z0L+%kjm>UZA2rHT8ZMoRJZ5F&FqT
zl}GB1hJ-YRly5UN#&Om9l+c8PYusVIEB8f}lc}(ic2m+rf24lKoa}jU+*hOiJi(Ss
ziI}pB0<WTjkqH@r@|9sJb!~v>^tfhvFM=V;k)1;3j)tSSsm1NQ-?+rpo$L?Vrta_v
z2^}3BJUqNeXlGdX`rLHH%K4MRq}zTt)$Oj`Q5VuDsjYk!Sa(B1>Xee??aN%Zzu_Fl
z<!7iFI!lH*@6!5CyDf5h4!!Dn5j8qMMDb7*PEXsdmxF>>^58@Z6V=`|w+)+taiQ_h
zh0-%Rhc()?um7d6K&FW6!`tOpC<d?cWKhxbGI)#2k<GZJZYBw#KiI0{(?8Bl(h5IV
z%aTjEqKZqzO+Im7&J_Kx0pu@Y0Ag31&-u08#mtU!TtdzvJyOCfN3H9%hWFbmW0c>P
z$Ey8dh`Z+&;8Xr-y+8FrWY1P|g~Q0{{ro8A(8veg<J5moDS*$lXG8woN(o?#?E{=X
zy`LE%?6@|7=GslTiT|UW%;nA(CoNXZlX2a2vXa<jK0zx!)0QAhET$is-oErJ`)v67
zQ1b;;sKBT+(N`qMzZIoVB*Q%a9@tq<{`$|m<pvMWwgoQ3eDZl_QpJ1cxqZEl@4wx3
zrXzCM41qscnk6-lT&R{rQ6KDbwpp_}QWK}rQzG}8ZE+BAz{BvDuz|Rp^5E{N*pzHH
zhoUbi<2xeR8ow7S1PCK=8w}Z=j-`!1%BTF`AHm^Y(?Jq#kG#waI=pM+PF~F{6<{tH
zTO}WI1R_jZyJpd!Mz8J<epODGT01!7Yspwe?<>iA<koW|vTkkt<+-w6uCA^fQxmmw
z(RMv4PC7s2BZFf8a)X(m??+@xnQUVw)MRXG(4<mz0$N<Cf*{eSJWUthl_mX#%GPdY
z<cA9a`GR%_sN9F+$nqF5u+);Dn=FEIoL2}IOJb*W{mRKcdfiLmv0-v=-=@diYhX;e
zVraG3bj*b5X3M)xIG_s2h$`@oHZ-V=2#xOknXH2!>9)N2`Os)%g|_zFF*O`&Yh5tj
zXWXCzcV+RMk{HyZ-}%{_A<U`5I9Af2INZt+xEInB^uqK{y?xaKe_YxKh|p=AjKE)y
zMS*?374vK+`<k<5f{U1F7VrIRHM$#9?s8%4>y0(h2Ln5O<k1O&;7?Y(Cmb>gmvkLm
zIa4DvV*0thPlsV!g*hyT6MdA`6QVB=l>K7cr6vhH$LWYn+RAh`nAr|F-K(*AcLtE<
z6@>t%OF#RU-nz^3;n4UrWs0(B2psm{?$(Rw^a1v6tWA2+P>{EdAypS3K9FB2!h!Mk
z@xK@Me<DSH{d<~D`5i`VFl%kcxI*WiH08%eFV`FGE>2Gj*j``3_E%xsM=W_>vG6cb
zC`3_o9KJl{L!Pytw1?xgNnV>F25@CbUY7*oC<<o8V|>TfHj3gO%O|OIOpmKtMFTz9
zW)aES9RiQ>MIjbfj#={rEF(p%Rv08(t_18Y53F1W$Lx;w{dvS;h)7AF|DDoM;lt!w
zh(?yVGyx&d3FKn_*s4BE)tyXg8gOx8He;`%l-811jC;!DZNjp)xx+HXx@w~~hpqyS
zCt6<;9+-_hR4B*Z{`Ll;%QP<;i6y}$dXGIn+i4*oes&ZH9sts;96oiw_sTg+h1rAL
z!H#(7MU8Pg31?G$3W>+T+<=3-#{&v+8>UxQW>T|XNA+UvNDd4Rr9rX$)k*{jYheu&
zT;An?qDW{xnawWBG`X@iX01+qs+DGfNAG2zI6R1K)LQ!5)RTMYL#%nF4nJGmab81u
zs$}cSNYRAYj6m+$vba>V7%jr%TQP$xUSvU?-EXO8R@Z8h6?DwhZFURNtU5cS!*amz
zsbhI1Rjly#)~CeAJ3)9k0Lb>pa~qv?8@HVye#n@v-0JaU892t|05A~5F!%6FbC*c)
zZDF?px_wyM|LzlQpIpOcfpPv6H0HTA;I=?fwHO%cyA;FDFjXkWYkb7LF7*{jx7TV1
zTc2d-$%U9^Aq;_yiT=dglpVE3{TDY<vAoXx*;+hkgo{Eds)wn06`HK!9?QI7TKr+x
z*@Zt?aJu;4UHs<|e5Eo|&-sd@djVr1@CAu)&GRbYd5TH9<)=MC)EQQIOO~3@cg;Wj
z+PuCxsUfpZlA=Z~rT+q2|A|+M*ubRJXot?$bisPGBUzsR+|h8=?^5v{+5tPf9hQ8p
zPcpMAgU|YniF>z_pIoxO(_tiVt1OT_*&aQzgdJqYw(&VrK9suXn@j!W&bSR(zGB5P
zQ!r<^B?)Rd4c_~Fq6yF(OPk4*s{0Gu9C)uOeoq@1v@^jjWJmMbjL>|080bq>5ysJg
z4EP5WUne7_{0YB(I$9!15x{6^Y<^9>iQ#%RG6eF-wU$5G^CuRJioN#rD?X1#NGBFa
zXkyz_lmvQb7>WHrLSW4OrErDFd#)_TA12|f#!0uUrBrQ)@x8+tgkmx?-;#WE;z(Z*
zJ;KMX;xBh!oB<bplvfbTC8Wqy@rX(Ut=nun7#;Q9HX$iSFn*(7t}TIkg0cj#yVfI%
z#BJ{w&LCeX?^h;2+Dd<19#zP@pWOA?5uE~XGznP=8@V^UG3D{>UN7GT<v=j%wzm^L
z(SV52r`O1~tt}R#c!ME+xH}^3)}T_5wZ;okW+*||l8$n5dHD+u-Of|K3hwe_j~CfR
z4GPoWMLHFY$cVNQci`XP553>!Zm##Ixb*^a`w_T`(=kygiQ5L6^~b{{O|wpZ4*<jC
znEyQwJYwjjq;#)Ker2U`>3}HU?r3n{dX^<9rjk5sk%MqHg{mM4b>j-8BT?-&x+Njt
zWy|7@KRW}M&$IYn*Hce?Xpid<xzKgS3O;4k#{o%VgPFzM!DOU{tD-6OZIXc*Z3APG
z^WyFc7nLGv&hIdMZa+h}V~E3wD!`nx=!C>;*x<jq^@taLI&8w$0MKelniZ8Hj_Af&
z@`?G?a-~mPZUnp!IZGC~G|BThZ~7NEWDbh;ZKLH=R^XE6uj;1b`jTeyyacM=9T%1_
zN~=ew0fsi@<s64bex%>@R7c~Gm%<QHMP=+OK2?lQu!y*x**4l?BKUQ`7Mxkts!Tx8
z4>Ncfg`+h!G28qVN4xT@XWutofx}OW+2L>?_%;dypS|>7j>`ZcMd4cb#YaC)PKHry
z&_|8T<NI#MZi_u&>j(vK(cPLVcg`522Sa#SFIB+kcj?>Bta1ex{sY&xGMuAhP~4)J
zdq#?A^X-;r8yRN(9Ayt+5X_Z4D;9y6e)RJ<e2jVJ%`XK#?V4h@z&c~40Q2VW9u%aB
zljY~1TA1%~qU)~l&Y#}mVcjj*0DHQJ(8(@5LZ9Ly%mJ0MN8BiyR7Tg8VSvu|%_G@T
z>84Q|i1`gPPVaN~V}k(fQs6^J!`!=3lKEC!%ew7;`y1(fGYj{IsZ(9gd0O(}q+!bP
zNoU~Uc8!O35A&CrJ!SR#(=m(0Y(=mFVH&?1vu)R-84N7!e3Df)LwSpBwY1W6)jyji
zGPECP_lF|A@nq7q@=SDiWCQ!UKVu%G^6!#px?PcUq3FwW2&y{x#R-6G*Pn3eA=P2U
zt^TwAf6Z?85@e(-<JB$4P&+&O?S%IS+cATuV7-`GWkS&{%3=4%2A5|gh4i<N>*-He
z^n|Mza<GHm`L1B&DZ^g3-1bUk%wux<OYM^WCMTaNqDdBdt5bm}Zn=Z5<=n#3MZk8o
z+Lk9tOmc_XdR!gB{p4C;;cxb~Pc^0FRTF&CZc9LepJUAXNf|a>9k(~s+DySf7OaI;
z!;?gu9?taf(KQw;^Ag9X*U^18KVe^-6K@)!j@x)e{W1Bg6GDAu1ba$^{mwc>&-CZB
z{qD=<ox{cCv_R(U<;`rI^ZvRZ&-Tjd#>UU*im&J-ewQ5ej@PUvKa{;6j>HcZtAS%L
z8Kv-zCNT5|quht{nwyD<L9S7^01^u(p0KA?4$1+07h)039?Q^A0Zl5s2TODq=^m0&
za4$YS_FLb;NAKUIZwBp<xGDVKO<z6LTCLpXj{`dSN%;iO1dYkju~j)eM3b5^X_N*E
zWfQ-ts;Y+Kx~j*fFND*-^*Xn*EW}#AIcyK%vDGjfh;_5w+g!b)iQ+uO3f^fUJ22he
zmN2{|C4DK-CmS-=twipmTXg^teX671u`*s}MZA(<#&GQP0mamYcorDv!6C|rbli!k
z<-tk=tqLIxuzqh@%tNcdDGk$Jox=bQuaI8NV57!ijrA5+87prf7u;?XCFwo3`cSlI
z)LMbgQlRao%I(f_LxocdiPR8`vHCBp5?8wrkrbOKSv^M;+3r!RKwjCu3G&Qk+T95b
zr$maZ?Gc;!#mc!cg!<wGfEKiMh~lU@UeV}>Fr>JBh|QT1tOu?*`^doX?D&Bo^O&@8
z<G)s7dYCJDNDU3BN@koPsERKqhRq@d_3BER(a%*(XRIxBkf0NH<Eg+3?r#b3(EatC
z5>l>7-Y(eT-7<xGuD5{mmZe?&503Gl7a`#xy!Xxg=hdr=?=C;$aPVpbFVW6A5109#
z$AkUebwe74)?!`)+v@J#a)hvG5^gm3>(L&V*@$R>DAK_-&1@t2_p;{3(neRlP=<S<
zc3XiQrTBc%QL)X78S%0FzH5O4mNPn&vJS-5EFnxU?9GyIC)%&Y?o+;d#=f##R}P`v
zYXM%co*Y#=1%S>e@ts*I4<TjE650F=mr&0r>-ksKYg|?9z2<Ko*yG#2()W`*)bv@c
zF#?Hh82p&C_UoLe`AIw1oH)_F1N_X={@n%zy|nw8@6AGQ#LAs77Rhc%o>|v7S3|NO
zl92O~jt}QY;bdkZl7mI=e-GW*ZdW$aC>^<nKHXjRD{96JGAOTEnDLv)_fjU8Um7JG
zuNg0s)cgMImqvjuLO{?O)SCSa69wh8_w}Gj_sjC5+D|(IVd9V7&nUiZY|<!Np-VE?
zSCO^t{_B!I-`<46BJ8)tbgCKfJT5j(D#I?G)N3pbTYq95#A;Mo8iY19G>{!F7svIA
z=|t!(PtVNGBBxGAmq}NLiv32!JNh{oCC>7Sj=9waY4QNeK{of9Ha45ITRB~^8Ts7Z
zK@V&|Ja(#PS${ut2ikyFAT{3cFm0&_?7P|WuZOuD8O_d`Im`VjC_8w3L+?mbVHPCv
zppIy-45=@h@8v|=Ay_{#Pd?V*+p*oIj!#TTii(ONy9UaC5M+g)bbNue`Yvg=K#7UJ
z)6!)8*-Lw>CTEr5k(`3!_~p~M<AlS~fPttJ;qN=`f4Sq2p*^t#>~VTOjXNyEZ2;X%
z9{qTk`e96EA-L0mge1&z68Va&X&1Tk8ZnEEsN?#%G4i1;x$&BQNNxX;&;Nnh{=|bQ
z;QofV2|4qo(a@}_uvjpKND=bm+6g?e+P-gaxE!TD9(Q~SKORU@8#id2Bjp-|^<Y&;
z?`#{4%ftH>R{Mv~g)0fI-54iWL3wPPefXv}?nh&!Z8QYy323QyHQ!2@mMf|)@Q5db
zphJSQu9TFU&9LuAnvRHk&32(H)ve>Jri|~p?ZilsoIfd76oa@=dB!VTJRSqA^u#Op
z&x6T@rL;s7*5WZiaq2PDEHpl4T#lx{+q9cJ1-U?jv(fg{5s#{hPxx|@9XB?^JzbPx
zeC_n3LTY~$f5+*1je|m2kRREV6po&ZH!;G>g+d+1`o+cnR5?j!ef6g8Ygvzbtj)RK
zPPowOW|ms*ZqD)jvcFpEH_XACBOQ2|O+SMqclqZ<i&8Cd;Wl=qV3fb4gMll}s;gkH
z&DwLOsN^DR9ZJX<sY^P0fZDQK$5p}JbN{5#0&Tha>AOX<A@_ciUxy%+p|8*Z_FS!%
zmFDf&{l=Mk$Bvsj&9If^A;ZPXBRCFR{d~eHK}V+v`Rk`48gSTQ!&~2MjY649&<L<c
zO8q$lxsG!1`*eYPXa@c0DE-A%yMadhf|Lv=Z`|M8O2T(W?V!Hj7(umVboOzYJIhU;
zuUvYb*WYRB7$6_WoY;58mKZR>zQMfc*&><k<m7w%R+VnNxGFFKQ%FF;t!Hl>?M~T<
z3PPN9jXlgyt~Dc#Ty4kJCYDyY1YUJdOui$XFI1uXTn=8?KPWp@_s=)A{9)GXCWejL
zrGIUVtK|si(EfCD7EL-7RJHT2yP3gBtJOv5x_9k7Y;KqdrB>X8&+|te6OK~RC>YN9
z%zB`bd8xHQbZeO&@$`OO{2`4^PKP9pV&*|kTpXG#_fvFtchj^XvRUG1o7El6-3ITw
z`2nAst_-xYD+lu`50vvnbQTq<Yug{^6^2ArvbgPyPj{`1wQk|Q>)6ilRyw$!RpiG&
z$swWbIVLp0ehLavDiW)}&_5L7@<2glJ|>r7KIVVHY!o!;c1QV(?PA8ZXJ$JSq-1C1
zy`aa;2K)Omz@Kg(v=o{dfIIN0t$ya0oksEY05N(PitTbPfPz17>3=aF0cHXIaYOux
z<$9QWt}W6EG%t29RWY)*U=Y9)YG?M#^KDIt`_;VL#YP)4bEL=R;5`Hh{W}jLk!a_-
z?XJX}@i>Yixtw{<C3Q6q9DAa45UC+{cYa}NxjEsml+oe1W*|}MrAS<7<&NGBYF_h~
zja8D6(%>2rz7#NfdCA<QR&jMD$FsozcaIzRKj)+e+fYtqE2`CjF*BQUJig@m@+aPQ
zd}A2P2I~j7<#>G-yE1Tm_5t1@AY`{2T2y@vmwfX(_YJl)dyNn)cRFfVUu*jTJ%7b*
z;311VP5Q{veeFA*^(<ZWdqdHe2q(t_fBnS-KgZ+RXJ_xc8fd`PAuF`%dTZ)_8IPbA
z`vhLHqi!_STQg5^{oBa3iHnUM*q}@lEq{RMGsN=y(;9c8EWC7JvLnWz6Vg^KSxHi2
z&7p7OLLOBHW#Wja=G8sbOD^i}(%Y`<LqitHywdvWoe4ErlmI_WAO1<GQNn0)j`thz
z+cssL&?$Skm}LSggM)P;UpPNLm*LVxy_5^rPKx$rZxfNbvxr@=qv}As(J!y_B|qb3
zx0kBx&Y(23;cM)dP|z6xC~ekO|3UL3CFObB1f&<Jbwuj3l5mbZrfnyT{M(9ucWNaS
zl)hmLDR;P4y7l!KNhGq{mOvgn$`!~MbI9;&F1I%OZ1CC@huCpThxO5?7m=RA30YzN
z?S?<gW%&|UL32;K@ls3I?OaOMno+K-e*AG8b<sode)o~GBtET_k3rVntz$o@9EN*C
z41b>m5HaEGcI@6X@{Vd`gf_W2@uqi{TqmqSk1YQWc|0GgJzFg5jUz|$QDzTj&6U72
zz6HA?ue|Q75V2&tD_Ll{sZ&OjuyM~X<uv<m;Z`j4I{zTnO>6EHs_VW4WE9eUnrbDJ
zLzl^vREA)VsUE3a)6`HB*U9{qf5()@pAhAX?R2OnTnS`yfkT99#d~vf!AGp}=Gl+-
zF|JA&J@MOiayUMi<SEmH2|w-=OjLUOkUpXP*PvQv(1!-}wMlN`pP&BE(DNq{U8~iS
zlYNFG<Exko0Wz@w{BvdCPt}TN?rY`l_rrQI2h_7tF4uGm77Qws<CgH)t(7IGV<wy3
z$Ok*K_lQ;`?9B$Seow2hl54s!71PK4jj!uWP-j{{Ul+6}QtqV<d_wUO4GI!k3?<zJ
z!Y`&%W5iW;+KkmXk7E-~u*sLQmmXDLFfkS!D)$ArjqNoTcmZM81ZRGgqJ7JZ%~ZZ{
zwmv%+<B!KA-rSGTp0Op_68oI@0#Ph}FH4O2=5%%A8#zx@TxcK<10Oxi%xPX@68fTG
z(91@fySHG6@Nby~pR22e#TF)%Or!1W25wLOIFnY|Zr!%zM75kQX4vktaf*9wGqhQD
zGrr4T(NXbwZaUNIW?%Y$x@?t*+tQKKI-xKDds7eBxe^uxa(vgjesb`A5}e}yw5vvo
zke=1j4x@Yc%f)BU_Jo#_Op>mtWw^+yfx4G|>d-L?nL}fzBExpO&#Xqou@y%sb|d`K
z$Ma-F7f?vW9PGSK*p3vd{L~^v$FzO3Lzxxe)6OlUBftI<NG@=7V&ch1>yNXxk@Izn
zR-xn7akX|=@~r4uz0LWaCY_18S_v~@_VVDGM65bh=w{NsAIp5RZESKN$!-s~8`Exq
z%+~pSen7lmSRVXiIm(0Q{$`WseOx8FT6OrY_1Y9VreiAp!d3*~9WKVX;ldMJj-Ata
z0RD^3$}c8Le<n4p^Biu}Eids&TK-OBlea4*zEu|-fd_s^^W^bVHY;o$A}_`WF1u<~
z07nBVO|?XhH~W+K!E!}J`xD`8(ORua0h((c0<Fnf`i5WZ)sgJ?ER7(vJ8gw41X1Fl
zyDMxaG3;mKq8Z(Bd3fi$-5(jW)%{aK1Z(Raa0tqRA-DTkL6ek4K{5O&U1~}i-}3d9
z0tF!gkND^7DR{!K@IJzO?-_~DdqGR)HuC>YUE%>fy`6j{5$Kkg|Fs`{fYG2nANmb|
z?J%^fTy87JWcLyvE5WF6-8D7=vwH93hGiM8A800SBrmYPG*Q8s-$YAna7pJp*MGDx
zZIt^@IyHk6f)iOwB94q8(zs3BBFn7`fSkiE76xv~+vZ(OaftnS`nJ|R$LF~XHyyz?
zZAa4UXVBu|FJp7}4o3+6R;IEg{1=A%`e!L>)yYeS+f##MtXqK<b=C{qYP4;B*l6qr
zeY$53y4|IT#}rjW83Tc)44-kfV=NT4ad37t*Ir431zb&k|8i|UFWGqS%gj~kAJDCK
zQ=gOZ^0+FACV!{8Hjq!*?q@OovwObFd~7v%{oC&+L**F5z^j$f<vN=))e1eDu+J5j
zQp<r(e{}m;ATni+^UJqp6OWIQnG?3@2elj}xOR*ivdl|nahg{ES#6geA;<s@`9CZ`
z__caDv=N>}p?T&F20EL{-#H;?(B60(JWs(WxIBiGfae)SmRX6%)sTo)mDB#{mXUT`
zac2h|;y0xcDMVv^IW6Y&4`-v6=<^Br8i9=#)WZo<G4&mlFbs6725}kT(>4HFq6){R
zwDXrhBY~T7ubV${AU5^w9!_5FDM4e2pzWk@P`TcZNZ2X>rW4zr-#+AYyi5H;t99Ix
zPY~5<j*c-I4&ssmZ27*tuc!SAPEy!*n*n|&Sao~GvKGb}Dbv28-;_<=Iz4o3ep{?T
zc2R)8G9m8gC-IH89pzx#kM%>xLyw3TCvbgc+(g%?UirmIu+2l=j{ea{<eV9Iu3K7s
zf|%_Y)<8uaX&PVhH)rkuFzqF9N4WIotz{4^%F)J#jN7fL@yWsqmHg-Ctq=lBFE(_}
zyEoQ?+^CwBxRLpug)ndA`3B)TFGoj_n|VzN>zkj2KChgrvI;b+J`;)k?%cG*bkK)K
z9V-kqq!5+Ly>y~Z=)d^N1PT;H|FK3ki}H{DBVQ^niFvY1D;=?7weH%it8CBkvX3N_
zovlL`H|7jCs=F!i0}HoZBZH{2Wm^4Lgm*V3mZ6t&&mKEVf{cHNjQ<&n@=Qrd%X!E&
zs??an!^@|WN5*t#Ph5wJoD-+UomyA`bU*kT!=*A8o^yY;ou{WB#VrJ0rKwP`S}w9t
za(_fq)?74r{|XAjv@z10G$t=RZGv?|6T4qBawKFfva->XJ9rap-|m=Ima-aRQpk^Z
zd*;2ywd3looYacI_gM9bHQJN5o0oH2eK1=fr0{Jt&S+&m8<Dg*9aSRoAXLJ0AWV)~
zM@w#|Es88XbKFf>Q}@ucKoxj*Br@XTV~4I0xce}f&tTgtY4knv_&$@^`!}|)>c;bI
zS5;~Y7oh1`1?aI=YmArLj$H5PcnNpxw43l!P_u>!o>^aPLjUB@8E~}Tu4y~CBJPRD
z*8Ki>=9K05h%BR!{Lrn~V8y~$V*#6Cc*dSEOY)NHpM^M7c~_elcO&9#$oq>D=kGz1
zSmOvoC=P-ML%6jArvBy)A;W%R>L7vPYd_nMKH8v0SJ<Eyv@;Ase_V{EeK(t+5IXG#
zIx<WIDlGNHp`~91yavbsgS$ID0%YoBEcjMF4l>x+{vdQ&qw1oJfpp*3AiKIBhn_)s
zoFKY))YBD-tzbjxbXn%qVx4E;$Oitokc6PmuB7pwT*~u$^-T0;!I6tJ`>hL6%HO)v
zuPxDMAkDv-xqBSKYyHp9n>o77qB7q|!*{6`0a~AVQ<a4uTYO@um<Ap}29wklim{Z|
z6&-zWW14oBUmO5R+?%sz<Ga9$qx+rx<LFCngTS5-!l^ujb>WT%y~?!nqu4{Ew}tYy
ziMCCwW-gPft@hG-k)U9KHUyk4{@{q?QOo3O^vI&w9zhw^XV;_PNmM}?fd|scfmx}+
zmeQ5Z(rR1Fal4Wumw$bg0cNrO7~qLFw4)D@)A{!Y1uF{|T;}2IN|fT-+}&vuY?HUN
zP{gP;Ppdi4Tf|sUD9ncnuJ$TIhD6?vS<HjVo4)@)=?fZKeQ#(TLAK%yr2a!$o)s^Y
zA=!4Sn(Y=vdDVO$=gUGSXmzQ~M_1xksNrjhjr;j^&4h_DUe7}|hp;DRNiI*l#uMiK
zddzHf2SNpxb+zo&@MLd;ozf6PtniP**M}AFmj-Xs316pOwxM0$y|+ss^%sHrp0$?!
zk@tSD*)FyQyfG53P}+25T&_Zmw$}KB9E)l(BVPm8$#gYVSL%SBaJs0nS5O}n=d-C~
z&e!6Zsz)FWm|82hW#2W+b2oZ1Cr*vQ&zKwL+|)`YjCTpgPN}>|EVX5JKg$)vq@!8j
zMXhR;oh&a9^Fv3)THC_1cIR}nH22HXqlvTSMzxunksQ7H4EP&Uj!q&qLj||3sL=ey
zo<yJyf2)H=yNp7NycBVG1*e++Mx7CoKJi?CkKOdR#jgaP-6zG#!Btzg(>bVpCw4J;
zE`l{Ly9vgD%2Vx9QioN#?$0zA9(M;jVlaugz)S6gNo5bk9WBkDT7Nf|Nj{zv!Pba?
zel}NMX#kF)I|b}LL9~QZX*=C<9d8ae;x2V8$<eo-)P*ZKpc6r4JiRPrXZIKqSE}v3
z9KijFm8g{%`Fou`?E2dSe?lw}!H*@!jv^FA1TspNo0B~l&{$+Qw}*k5^K6b;S3UYe
zD8s8B943WnX)X!yPhu-rc>6_^^dgi!pOM;3YQCzCOMmb6K5V7o@PqBj?CfcI14g5O
z9?i3Am1V|Y$W*-iuI5=djjykP_6oO{mouetmk#_1m&Y%rQrgPpy3&LzB<37lKpg-K
z<<}g){YiKlk8E+5T3Nj@leS7;r$2}5UVwzkEoy=5lHAYSYx>Rs5UhUwP^)>6f;EID
z6=&OKOj_~3uG~CC$R&Q8`Y!_~f{8ZFOmD7N<Gk(+_PwwY!}3<GCu{f<vAg2L29c~U
zhqzNDSC0-vw^(=+f^ZmASsTBz1|Enh{`a%~JGbKRuvCwjON=l=4-yLxDiqY#4{+}T
zm;37zvR0S3WF%=#h?c!6*=@^I%FA}HJ8}10=dk)TpT)W0eaoh*=Zse_56@K9=p7@1
znfZ*)oy^IhvVjLm&}2)ghR=8N7yoy!2mK&#pVNXV%hSZ@*|JsNl)|b8*~+u#+*cBf
z%9XHPD=W;N8sD#jOb#{+PUq3!zq{{i2AC8&VFB&A?!BvkhK|Qk`J*`dh!WcWn+32?
z-+&|wwy^66L&wnvBsSAAAW+77xgD!k$b7&`nXI0j_$*f4&Q1nAqNpzEc+z8<(K&*^
z>r6Qr_B7Sz);8uVO~y1af|mYH^>sudj_)aFr<XH(fT252`kES@dUO_)qnKXGq4(@Z
zsxvP$)$9=%W(H$Ov!Mo&o7=Olm?y04Fbj8KZ}UJ*16RrB!vHkKg2oWA+7XNSgq1~f
zK0dIb#b}93apASh<kaQ#2aNM)wnPG8k}PkpkbX<0F2Uz;#35;2jJl#fTYu{+T{(9O
z@8gW6Xm;dPk9F|YKCw(RZBi<^9<KKQi);`3`6a7iG0dOx&DW*zl1C<S-LD0BTyE-a
zQN&g)SOBef!jk|<ExP&yf*?jk+sCyEyGZ4MX5rV7>jfm7D54N!xZ7g$I7ILFYv{^#
zG#VD}(#kvTxdn`|ry4)7J^LhnTg3fP6*wofX&U{uY3+{^*&L1XD?+P)pQ-hF)*qK?
z?E<!&10iHAPQ5JU+}o1;x0L#~C>OtdWE{NeSpr7yU^CX$kX%2ZdKtaQ5j@itB!hNr
z$xN>N`JT43r7?(J17bzzTX#aWZh!2P|9^+?Cp7fuoV@?W&Hre@bgiU?EBpdBofl*b
z_V))0bDQ_ZqDFyS5jj_(@V=*+;&TW~F`hsZm{lv=S%bgk{(nd#5hQ7mFTg5YX)?Yx
zms92A1^C~p(lR+I)?!0*=88n!XdK-yt$7;B`py%+vsgG_Ij2>gayz@vX9k&sBa>sG
z7r#_|xeY1?FpnAc0?j&mwNgepDJBN>s&`eZc0*S8&-8qWYFT?7^yZBk;YWL93#N8%
zo5;|c!KE{Nw<0IiiMvY>VYDdW?E0cC2(^E&+qe2wn>X}GiJQ85I@5BE&u!q3sEaIm
z*?yx+HCBShBkV1glLcr2m#|5CDMhjGG-WsQl6}OQA@;p}N;u}xz%MYzHq5o*3Q0*A
zHZ|L;xOIs<)o~OHE2VcoR!boRCpMGIG+!7H3{qR1E|`nRdAVV(5^-bZpDoa$G}iTM
zh*J=ZZ&0t6(a&*47h=UA8oF7viW&%UpNbO^;BDa_k_hda1%XRDC$avSr1cpLr;6kC
z!rAZaUB&91syom2H&ts}3ctj@Zb;u0#|@!tkkhkAVX80Vr6IthtLdN;%{8x|r6i?n
zPN0^2XPWrAH_-jPoi?i7n-MWm?qc;gmyDqq?bXw@4+5Ux-J0y~Y!j)tHTbHqp_0tt
zm}d!$hgH2k6rz^(HT|7a5x2jd-=ABt73ViKnthsBk<E(acWkRN-{5PE9uVIq;D(}M
z;3naaX&X~RI^6%3a7GhNxcaR@`o>oU5E_FErQFtZ=Pc5vsuo;nQ0nQ%&QC+J%c2KA
zRm%!<clgnP;D6Yeudq<bmz=On6BQ8XLi#K8ij&-ht{$_wo8}<SDh@0CA+t9g7FgT+
zKsfxY$B!%=aQoY4DhRmq=-@-Gbc`pe-ZZ4MjMD+E{Lj<J1O-KAyH*xYK<<(``#@FQ
z%rG`lV*s32`@Ha{%9(ggjz5EB+o9`XGBifkWr5*<8oZ=Y1u>4$lq_e>ZHWcvy05ZL
zE?L>+@;SO!*(}99OdNCBRV3O{3wVPj_ADo6)b;FD%1{pg!iGFoi@jP67V?UM&&HCg
zF6RnzTt-2W{CC>dZ{@p2opm;!D@)E#c>=`Y729Wnf6dg_dsd|GSL4JU#aN}<O6XX$
z#%t#Eb;-(rUTz3i<s|QT`$G-a&V{#5%M7-P?uA9Ew=MZIT3B+gj;>>5nVhoM`(D5{
zy?`-{Z~B%H6+z~H`r{5g1pA*4pk(Ynly1pFXkEuHyl%GJZ!5{A@znE$*yQi<3N+Nt
z*B6>OptasB3#}JNtw^khm!Bpq{9woI{!;zmR|l`@gdOQ5P5rZsCC}Ulk&T+u7uj_k
zSr0kCt@R9zt-yVJa+Yl8I#ZwGQWDW>aVC1xYn&<V2Tzf3>p?7Q==m~AV|~lf4!FJw
zb{(%ToxePFmR}%*D|&Ne_4A`HtNM6OR$5}xDDJK-9*^5+niT>5oM)4n-u2AVs*P6@
z>Y8}R69!%(KZTZCc4yMM^|%qBu3j)rA?qsiGaceZW(}|A`~or*=$*6X!d@cVmycce
zY1kfzdA~b4*txg`DVGNwJCv3BSLVf11G>i$p89!fR`oP8r}<g)rH($GL++u?P~XO7
zK0E)mle{p`_s=<1I;T$D!NWDfqec&+o&<&hR?2Ov5M}+{C#dVzp3AN!Z>6~K_%ns8
z;m){pa9xKJUiyy38TU0<v)So!$Trib+>=|sJ;|8F^1N(0;dO>a(-g1^Zrls_XXf4J
zv0#DdIR2thkg|w(>1F=HAlPT%UQpyQ(9?dXwdDx2y{_xBBvl2Sv9r9iGZC00TLw*T
z`YxtxDpB1NtLj~SS!8^$biqF(A#w^s|FieM{5k<aLH-@2*VnfJXkTKQcx~Ia_k?O_
zO6G5EDT6O5F_IO56AqVZuaaOo>bge2AeGxVZCz8y57(Hf?40D&v??>eFt(<NyOO@X
zBC%7EqFQ0=kt?0gs-|v}Cz073AL@Ix7Bbl%f((;Ci+6qEYu$fRagw?y<>j)%EEBb=
zYNFu{>^e9Kacp1hkR%*zbf}HFgqs~`imjCgT>#5llLR$`Hkt;bJO>+p*QUCtH<J;=
ztpwec;cT~mX1V}_i$W(NMiu=yepMlBp!%Vd-8of7O)IdauIq&}H&YBZVBk?^LVuE?
zuohW!GPb+Cq=xc!xksV>>+sCq;6JreR9%5lKOSD{I{x!y&$o5-B|c-CJj}|&qucw|
zOyGU!Yf#$lrbLz%GcVD{Cs%2sQ5-@GDy)mBLjh}%PL>Dx1F6^7GGY9>@?8O|DV}Xj
zJ^KtO1OB$h9PVvo7~oZC@Fz4(;4R2td`~Ai;xVnjb2CW=ge%ea7fSGA!;U0h@fh9~
z>?9JEarB;W`P%HsVD?H~C@}H`BfS69J=c(y-$;2Ugm_Kkdet-*U!VVcvVvtSg=(WP
zO(7HuxBXi>+l^RNc=O%(7KX_IN59_iW5hy@^q)AgPg0JrRWi!by+hFlPdkZ<Lsbbb
zdvA|4GsTMHP{&w6ZsR;s=Lh>nB5o82oXpia*-7`U_kEVIe1uBLZF+BF$VF*r4+k&&
z&vzx!V@RyXj}7`7*qME?81Bb@)adKcpm%v<vEOxusI9q&^GK(Qkd`d!1R7<j<(orV
zc^TF)dpjlgFrGuo##~mYs`BHN%s0z)3d{hbbqFc8aX1nsN;0xh{37+f^EjLCaOib&
zu!)@nXm`a+%or^{&oweKTHSkvs4dY)zwv&=!yrxU-X!<bIamf-Wxvd%^5sVzL6WCx
z!wup1oDY69?xx9CKg*{w4_<;u&hPxJCNem2S$~KG-LJ&l@>L_*Qc+Po&-sC#oPFL&
z|N0&czLgnRsr&<S<Vlj&3OI15P~-GzsCY>=ANQ)`bC>z*6CYp=rssR}R75=OX0>Bn
zA6iaCK8L!Yu*uj8VP3_$PnFE*{1V=}s)cBJdj4OW+kZ2%0TeI+P@PBR!5sBYXw6Qk
zx-b$@?LO0XE<%Q6z06}fE%p_s?8X!rkrDDorOicJq{&9nbv^!VkB-;JZLcY9d>q&f
z10Yu}O*u?5Y<zZ=kn<|4qT<b@F}ogzeLz4ewVx##_)O;bqVrqwV_c!|w}p^_6?4$7
z<P<5S2H2XlweT~G^>X;^yMDBr>on5BZYsI&wBF0j<%-tTwx(3n>Bsq+{$l;EVIU4x
zUrsF|SFTtWn%ydc1X=B%y1RcB2B77{L#n16ZuOOil|)E9wjlEeSC~bs<L*VO@DzXB
ziIbpu$yuN6Ud=vme0f#C<WHxoQ}g(V!0P*4J1y;Rx%t^Zs7|AcZUpdF?%-#`p72|v
zb$x6??!xYsd_>t=lR@g2PIt}`L;m2Gt`h+a7iVr}YH<2NX)XSY&fJ3VOIsXx?@JJ4
zYB(pio(I=TSzGn6M4_t<`F(n0O4HclLMBQ4O~JEc-G}n#^X_dqab6qBxocD4^2NE|
zah;`4B^z`44SDs{y*iY>aw3yuYREfpRg56Eub@}o<pwCxM7g-2-Co6TDih8R;~UiY
z$`=Nj&CbW-%v!?}tsO|-2Qf=$aDecO|6TKXM5D6s;4+&r+Wkd;t&`DU0K1Cj{$y^f
zi(0B(=N|rvemzV!iO-iHvk9?WR3U%ml_nV~vhH6d*LNkkzoIE~vM_%Sq+BARXFpwn
zh9MCUh1ZFXPshV)Y#&1yG>s-;aq{9EdPDqUd5#&ioSamMNn$JMzSmxsSA6Sp;grmy
zgV*xt8sX%k3IQ8r#ErJT0MbVf`ufD+Wtkoa=u59sI}-FKH#{@BY|yaOcdNu?4tI7;
zC~~(`<J#(#Wo#e)SVztR1EC9S%f8DTKML5no~>Hkh|UZY5`l_tl&|1r88t?ow1sBO
zwMSbipDA+-WCdqXXA95SnK^QklHUr8i!-#No(NYGJd4h?Z(K=T5K!2e>lPQaULckf
zDPa?Setv@R@Ng-i#JQ9vc6N5l+N8EZ03`I!f7@>&z)I5UUw=s+9>q5!pfUdZf^(sf
zKp5xOFX0AE4Qc1Xt(a^6(m$5#O%$~Igq7Q09L^sLfU{T+Z3EDhCOF2<_x9f1WQo<_
z9YaTDVr^hu*OW%#)?i%##8y+1L2my*_AT6c{USL@7tn=YzJtn3OB-DN1#AnfFQuku
z+Ey$Vw@{&$<P;-welW}zRQ<oGCJG_+l^6)ikdBEMzS#%PmJ%5(;Q3JZV7K;*53f7@
zmVvnaxry&E0qt~aos5d9zv_vb(p@>rxIv7hd^F~0c5K!9rrxR~=9lGitB3=Ifu*d1
zjyaOy>Xk$xFU|KuTrC<!46#GUxg`m8W){}tXP?4Ca~>S4xu%wn!kfRX4lp{7LduNE
z95&Z#%-wDdr?EQQT?T6l;K*rN(F&Mb7uZ&WYQ}i$SQZvGaB@+OgTT#{TV8D(u1(}^
z3OVs<0p|~`HEuL|yn815j7)Bsg++pF#QH_NwJ?x*R0&gk_DYZNH1-b}(H*~(<7!Y@
zMt7H{v_LmCO3~1{KkC#KE#Nva4lC5$>QJ)kHGx#BD0vWbjF)Zi1m(0mA!D<7pwM?O
zx$HArZIF09MHXVKtn#XDT-!S%z$)syy%hmbjF4b^3sHVte_~lvljByvjvWzkl!Ine
zRkDHpPUgNov+<~AS=FPk1ay&K4Q`g3l#P023-SFb$PG_SfJLhw`x?9Q?}PQl7SQvg
z476E@E)71)Y>#%tp!?pP)Bgurea9;`W}4^x26NRE?2H2+rh6=tFlwROB=VD$H#<0Q
z^bHc}<a;6d3--eJryxeRGYPT7-4b+Tq>dg<^0iN&8Ae-((?0&KbKdji#jFws`}4Xx
zg`@8~QM+thN%tLorJDG?=RT2V;9_^<uWX5=D5|@?Ohby+-9g;7>|9Soo9dRngHVH1
zX861r=ZPj8S^yByB9S^DA{4&yya!Np&aSx?M44!{(U!ZNbFn5^aAIRztq{9^K&R|)
z7rG2q(ZoY&N~qFRY4qfjll}B*V2~``cCo$t;dh-u_m_YnQc|!HS#HQMmMU*{H}LLs
zh2S32jYqJmU<XB(^|d7dlSU%GBcr?7Ua1-_P$xxf0FjQ6h{&c;*5@(n>k?S>mE}G*
z8+SuLY?X996;XxR#V)TIw#;}i0_$L*N*8&P@f}(0N=`WVvruH>R|&<nko_qsPwKF+
zFxDf!y)<Jq&_-t?i${?@<Uc65YB*=Ua|ed6&dRM+@n}m7QQX3ZclbQJeq<-BdA>O`
zzL6hdhPg-c3|^$)*LCT>az{oVHzBM~x)QS<lfFX`Qq?$0sGo@urujW8&5F%fggT>)
z0Lpj$fwtJK`S5X8x=Q`OSht8C!nW&m?H?59dMoPr$4VZ!99=ae3!h5vt1^IGw(f8{
zSWehi%Y<!g6&dPgNxR6R;cJ0@O(xr;+x&T6g~ClH$qSA+%&2Rw<<;Pl*)@4<l*xm&
z+6rOt>x`P@-w)(Jm_TdIQT^vpn6w-x*;SR5FwKhI#8X17lOCycKA`OF%lp8PA!}dR
zI0Dcn2)SQbc-%hS0Q3Kj!T}WMP#)=OQl@`1AT_!A3HI$n4+*qvWOY)8Dv9e+n#}n8
zpIH1@izeaMA0C{dNZiy5*1r02SSe-mjRzmdBseHhGmoaezf^s<+~(SU>#S883f@uM
z9_-?l#df1+?#ec{kzYS3g|}XmbID8O?dow_ov+Z1+AgkRK3f=!zP}&~Iy&LNeADX~
z*Rz?q+C>$r64#MeSP4DvK)9b7Y-$m4Yb>e=7T|vw&t;J(ImHR$SdsjDTZOQgW}th>
zLCFY@>FFYtiw4I0gwl<EnoAu1tm|dO*uj*1VVf+EBIi68&5>W>T^f9Mw(*G=>dfys
zx?;kGIeli0<|};3Rm+6zlz5RsuuT|^Q-eW{u!h<0){UzT&-xy>ww@8U-}83vgY}bM
zP-6<tA*OA6(&&+|Rs$LFiW?QdVNMkKdbb<5lB2_`F7}qVp2L)ZmLkga(%DYDcx`(g
zu5c9~j0nLZY%M2jqOP5_)Bsh%^Mz~t&4;aNhz9@m+5LI5k_}|MokAqlb&60H7OmXZ
zbSg{P5DOYusel*Z{e6{7jd%(1pHPZ0OqY0>2ZzpQdm9tK8hMJkUk4pJ8|dHif8NHW
zUibAt4zS&G0)7oc!FalkPFv#=a(mjWzgFIS53?fFo6on{$$u2a_L$_AfmlY}<7!jd
z(RhieC_xdWlqke}bM^rM3FtDKk@BGo+EPpzXq>pl6gs`G4r;i&W6ke+t2WPxq{|aY
zs!f^KkNYzCU*U=DoCL$Gihq39;E5r}xT<qrV!AAmblp<^E#y29rUR7eI7Mq?Vxe$g
zqD%#ir_21<Xj8+S5%VQDo2ZaizgRXR6Eein537z9)cBqnqV<&UnURc)I`PA3nCM%*
zY3a_4cz7*-mUEFIOz;I(f;zkbSpdX`VDkloT(kOctBaqBY2slR_)P|&f;cABiZZ4O
zZ}B*6r!#CUo%kxog=EO^Yyxcq4Oo*0s5ZxS)F-)mO*3`LGRpf=D#F{L7sHb=uZTq;
z1uzBI&k<o_1~uK7klr#v0t@!pC<7Q$xA!`0&<@2G#&CBi3LCT_#uN0R5$fMB*kXDM
z&-dqcaeFf<HxOAchCaO2VeTAVn#;-JG%0WeXcambi9@hEAlTLdz~OA6T_|F1O*If>
z`AmJuWK&AS^G_;^{i5Xrgn$17jW!GkIPBm>a?M<o<FdxKGss$}fYJPHtDCr#Xghw!
zNB0S~h--YMFB3Y!y?6xq^d(xmC1T)qxY}?rBNYmjzRpU}VQki4nyO1bHD;SHlzh&B
zWsTam4JIKWAvzQEr1mv1akUV*tEg2<pqc*si^|;eD#Qxg?nSTFiIHV@f@RITtwCVD
zXH>DEB_ESvA4#JXUG0Ay@d1MH!(&CNU0ILEHLx!Fb7QB!S2ahG@=}a`{~=oKM%A!c
z)U%RVaz6Lj{upZv@x>hf{l-ASJ;Tv(vE@a=PXo3lnRqgt$)^3RxB}3UNT5Ln@Vk9Y
z#P1~J`}sk_!rCoO?OSguAo5K&Br%3$uPI0Py-J=C?qU!~+r7`CbJUb|jiSO6X>~>3
z<?h_rEcDvP<REySJLtmd%BZ{XRJrk9-r)6etGU?<cUG3ji~u`uXWvvH`_0c+PndCX
zwErgfa~S{87@wpuJXVSHZ#-}kWm|R8)dB}A33fL|+rce&jv_%&XlOI+n>3LYY(sMi
z?7p*Q5EWkQFnV9Q+%|sV^&eyvCXXo4$<a8M&^gT&tUEct+Re1%anIx`tJ!zVRY2&_
zI4Jny>aN#<2BkL}Y%uT+!w)dFGbMZ|x5`Q_;f}#;1QkdijQj-)y<kVC)<_?Ru@<~Q
z;2l~TbY?RuSz6nk?#Ihm%llR8JXh;zc@gT7w?T#jyn?3;&7qNxHOe_L<_6h_3`G-J
z3sXK(lIK>vRQoYpM@esp@vmnz8^)6_rZS3C>>X+M2Qvt<${i>;ufkrMP+IDbUAd>#
z?U7I`2WMv(STHIBB7=QmFX7~irv`&P8a+~?=!IB??ffAB=-yA2?c47i8*82I_h-yK
z0nq#NV@lqSIZj>fx!k{QR1Ov1(XEcV!KXdHY@X*inrPtJw}#eXHYpSm)jb)FU&c2O
zBKG9F<u=c^tNoAVIArMW=+f)jaTr(pU7Uf;A3*;4L~24R_HEd*A57e!As<!y&;At`
zq98jOhY^ex$U8$BI92VOekx&KN=>;PH4rDn;nRl5M(Q!wY+<NqQ(K+l1${N-dxa1v
z9UU0+SJmblSpcJhv_1xAfrypII#-hN!2#Vs6wf3N+5l!k(+=7V-+e#oMK`2t5go0O
z^3llIc~CFbpJ(DnbTd_rKloF^`EP%J5A+qo8Yu9mrk{RRJy1~Uun%ipKKwB5E<0Wf
z)$N1i+GhyE48pD0{r9>u{<?K3DGo{|;YQJn=N&uf{!2+aYe~5Sx!B({D=T2NQmzJ6
zb&tMW=p7~G(kg|UHu#S+mHLx=42Wr;$##k*X!h9Cfq^FknBQ91Kqz&Swqp)MbNxvt
zc9h+X>Z9w@lIg^1^t>v2`UZ^oMM9S!$SgwOW>A4{TP~>d`S3kVhrt7KkY_t5NL}IH
zed~iO6Tcefv<UTI`~<7E88q;UO{!ZUKL&;T+S2&a$yh{Vl09S)-(XK`%0T1cU}mi)
zqn~obP$t2|@n^0C^ONLijORKsgKP<4f#+gluO~}<KVIjjxU$;(M=&28O(U{%-^2PX
z=?@?s@A8t-%wPCGFsPPFr^l+3L4@vJON@(r$~50RjmGR$FqBZ~!HmF`uDlR_-|>|7
z{TuYzxw{4Td-3T)%=<H8+QoQ7TK5k&oFD4#B_q3iR?Cojq~7*1;Hp)Fn`OLo5mGf~
zGFK5@95aC)`j&=N{kI)U6((GD{MqeMb_=l8ctAnaSj+ae%4LN)p6^UlUktT_Jbe$=
z>&*fqb;>muq_+KsprSu&vcU03NBrH>$>*jK3Mac4)W~nYe$xq9A@Tb(yY7LtuAhT-
zHWq@(R@bt-`A)`0HGkyOdZ4hWlUtpA!QHF-{pqgR!`*A<dvWS`Ov&pDfl$U}!4eQQ
z%4fAw<)gFZEXK+mF>5=G1;fW`Q=+fQ8apX-HD2bx;`$E#`Br$aZL1-v*<0Nr<5ErJ
zyV%s^DVyy$zzpn0!V}g0&b@T99Fmn~=4~Z^G~XJuOSSz7$@9dudjX-eJ$s1yg87#0
zi8K>_`wj6udoOxXlCd-kg{;go42MaSe$zw*!RN}4N-Y^a@bUk!FBSIXR}8s^)?vW6
zRD-*UZ7e3on;l`IrZa1)@lng^obf;2nNtuLYgrg6!}I16@wwXSOeG2ZSXD9T)8aq#
z31*{r{ZnUq)A#YXx_xJF=wjbn0>>+uMXn3kx1-o}7(8v>p_dpnDOTF2!eY}O>$%72
zb6-1=JEg+{5vdZ@x@rivLdib1Z?3Cw=P5>hx~WtTv-8!Caud!BDf|MBD>v=`EOJCZ
z$d4C!Ii)Q5%^%g%*>aG5l#ZVU)xh^&TD!vfulPA!Y)q>*)o^iOV+-nrth@sY3(l%D
zK>4eMR@eQv*C_dKA3D@17Z$|fZbdrz0(P;q2ob(@{jIrmf8I`Sq!#XPKcVnwKR9PX
z`|%zG9-{j&NWQ}9K#vWA>i@N=kX}?P-~$yoknFbh{)#F)mP)a6)^0T(sk*1A(}ByU
z<CsxpJLhGp`gv+MGdWja7DLCom>s2wq>AI6PS(T7l&P+5Vwn_Hw^3Y#>98Sw$5#$9
z;gEO@-=2y2G~2?E-rp~oau<z~0X-l>3LnmL7)tv-90>d{X*6$bPr!WM_@NRGg|J7(
z6B0V8@xdG_<Nkc3#zY(%m*DOTG*=`R(47Qb1af)k`CHTtrx?fIhJ=PPTxgG`I1ZS)
zzCPV1KcBd&{Xl2c9ZhMH6@m(Y{fd2T3YA^-S(YW`k7`p~A^Mnk9ZD=&4>R_hSb~QC
zRj1c2qQneb5LNRIx`=jV3+zRT&~V)x%WKjblB^f(4mL5uh)=i+lW}TpZf?2m5&E(H
zh+ov^hk-u5$_B{F;<{Pp{jS=*gQ;)jY{8oc1x`)rPVKl+tn=Fp^|2!Z7@;g32;a!-
zAHu5H)4$R#fS3mWOf-KGZ)g0eaQ+Z7l???TZS}+t5LU*F(CYG(?>ZDc+ME)i?ODW*
ze8jyT>V0aa1sinKLFMGSeR<w)rN$bwvdxN+BdX2N0LP+!mTvf<dBUr#=*nO_A|HuZ
zx1)vlG{ernDqTaRVG-0w0R9Y@^nU>2KpnqkAQyy&1!rX)FXsCr+KiWkbPjlD>gO0n
zYocv*Z}r#mmDFy`sPSfA7JOk`NE#NAvRD?4QH^Sc153A`z{DvFDV_fpECX^1@W6=9
z8gN}Zk5Vf|<ABD2Ym@^bAQhpGSS8hzRC*DDUg30O|K1oibTH;DSze(hzb{>`tdZ{=
zej^6=>4D$?e_9C^(sgy50;0u5xB+0PklEzIgoo~}09?QS_4nw~wl#)XiD&!=LGuZ(
zzefwkd}AqXn>SUUwOn|5?0vX>+%v{|a<F98I=ubqG`#raBWBlSZ8q_(_py1`UbE|R
z^7Pm-6|U9ff>=ENOyJt`=|A!E+xTVv5)A0kl>$r$?Am`wwX2AN@{1*j2X0Bxrv>Dx
zNXe;b_;Tj#iZ`??|4j~X{3ywywNgeZ4X*h%(M`Cc_j%+e)XWos+C4kj!Oq{pTJaL}
zR$P1c3--s~afy1XYvWjHWe+bOhFrEnGJYH;_&Yq57HCx{-CD`|uU(Q*08>8u1zUC<
zQL+UW7iYZv{KE*Vs~4zeW#?kozDTx@MlS4AXm9|n;q39tA1iQ@F1mYmX`|e8=bTMu
z8ZyUh-mrlS_YaL4)a7{C9DRGXSFF2x+?#X{U6xh>&*k0s@^C}b#`Wmop(Xk<_H_GJ
zjp6H~S1uA@ee&yHVHT~ojrFiicC?xopjfVD(*peaS$~=V*K%*ewtd*XN8d;B(lz3S
z-U^(KKbM5D&rHFZO?&uU36`@d(|%flA3uKq-8<^7;RG!IZQ*L9q-Ci-y0TCA-hM-Q
zqb-hd2$H|^+0Xc7`tOYME%R>KOT+i7fCdP27Oldg_l-c~h)|21*T$*13-@K5>zFB1
zU_<X7!kr@WCX1laxnF)kF@Y(c>#nOYGfFATNZ-2EY|biVxyPP<8uoO<D{CKluGx$w
zT};r)@cG*DyO%m^1-ND#@(7gW5ww#2TqqTBJ&;KtUr4?)1IdvM+i6QsQ2uJ9aiF$2
zAb_=4GYj?X*|S3NZQ8U^Agk;P1eg^IXjyy8u_aXo#7Y?kl6cYv5{qMzLrPilYt*Qb
z0=}g#0m%(y+AaaL4FIs*H;rX27SKZB;o&NcI^jR^P4(&1N1Yn-3czhRHl*v?wX3>j
z=$lw=o2D_8FX@{$ZEAK;u61Jw0O^k?19a-t$?U%L%|PNVU6cYu89<{;%K;6zzO?+k
zq@~BC9mVuruOPdS@da%En~%~K)bK3Iz>oXiMzDK0S_gH#q&(3I*EmpH9FWcVELvL^
z(5;n#%T<vr-EH|e>+sxw`Ur5Z8W{3%b--)G8e`I2#?CoUcaGEuFQGN{y2xaV?OPwc
z!@aOCAy2$%S)mf-6x(1HU4}p3r~VbQnYPjq?NTjrKo*63iI7K4b4O|c+FDrqizc7~
zwQY4qO~zWRUSE`rE!uYIq<)V*`Uv_D8iaN0wkV-!P3rpe;}0oVF5o6q|KdxGGY7i3
z)2MbhprbGdPUNR!Md5Po)KEn!6l&SqSy$$%BA0ec<ABBijRV&l2a418qagBak#L&z
zT}O+xuFda6>()0>a8vb~)R0kxG(vS~O>i~PH&fokp<~DSJ5ixKDRcw)#ZR-<ufok5
zUbwz*FAVJ26)R0#a*KQK;SY@`$lHbYx~W*riODILzibtD?5A5?3!3smIZ!=v$aU!Y
zO>1mrTxnB!>4Q%(pUQnV4<105ygrCKmw>r{{*A*?75jB#zh3B1fcavmQ=3+J{N6jQ
z@?;BROv}%bf&=_;HX+IKa<%8pX<?o~zL?V6pG`+7l@f;&^mlS{q{U|*R&LmWN&ojD
z;?F0VCN&-x7FMuvGv3n<{yh%Z6c^BxGEd=lvQBsKNQTaZR*^X#)s9C3{OUZo{&c<c
zf?e4`_z;Zk6YPtHaVaR}9=lSqy3f|9F5!27h9Qm;Xr&xfjW?@G%fE~_1khcyY$Lw@
zX#t;+#BFxOL!*ac*kC<)A>c&J>GL>shF0^O7vw)VzHZdhNJ-CPSOFKbXc~?$-g^dL
z(3&~sR6MPoPpcm2D7f$5u{l0_=NScpi}kjUfWTAVdJ?}A2##UAYq8jt7sQ?+7w#B7
z0547$LvY;RY)>k4Qry`jJUnp<)^6tbL-~q;cp-tl827*}v;c35pXV-TKNcystTAWK
zngIr~PyBAyQifnqO@ODHGsfOC90JNuq^0?qO;r35nGNqtl5>g4bXER67XI{}vN}&9
zU_NW!-@H=-v3~8*xs|g1)|Y1fuY`)2`0AJVS<<siTkf@6HV((&{+;pV_Y09t_qO>3
zMTkF_c<ln$e0Hw1Nc13pmBo0p2K(SrdU%U{`)&9#ma{`yj9qi?T0bkL@>D)88%<X@
zI=;)Hk30hR4juR$^bcO#YL~KZ1IedsPfN*KA$}``1l&vcu9dxJH7-`tm#`0o$yEMe
zIUF3#@+#oW?22}xap3ZDz~JKAP^$shHJtyGzXI}_lEE0o^qS?dsd9(qJ;QlrX-u!1
z9!owsw|31?rsZGDv~u6HKi2L`8sVqJ3D@Y-bHM!Vy7cvFHPj*pR-O0*8TpBDv~#P)
zQft2`TN{Gch1pnnVh&mbbuj`HwO^A~qQ-&R<G_Yf>DZfK-SXMA7TIu#8}JFe>!MK&
zwaJ6Mobbl*#+dNyVVq5Kpcn80oJ!8a{?i%gQqL1V>^_V160xeG*0BW@7tz(%>0<0W
zlZnoCugzF%7A*NsoE9V}A!FYj*m-%wtw~eZxmG<cr{|{8>YnKdCbtY}3wOthf0iG;
z|1Rzrbt|@Q-+`ZJ{!Af6woy<ZfOX5(Z7_1gaP;Wj746#V-NId3rb+yp3)}8_Q<GXF
zjRP77G!AGS&^T~SbD%I8j#(RM#b(Y0dpjG{54#UGe&w3}<|(fJGj}&vteX8D2H*WK
z_8k$+!3(Fsy?YRtO~n4A$E;@l<<lwXIAjzoU0<(a>}mN`MNf>m3xj%hHzpB*;M{2+
ztMbkznuEc+=$c!8RZ#=R)}HyvyHzEr_TI;@J*WEn$Fh~xrtM2t=|czhq2>1n)uy#f
zaL=v7uy*SXv&*UUx^u@96V)|q@wUwpmz`uVMyQwP#jl60m8o6(ha4bSR6^_0QYu*s
zm5M-Emk%f3I0A;Wpf$&4DpY1eB^(=Cw%TwEyBuW4_Vp2~>oW<+5k+1ByX03z^|E6z
zzfE_+GzvLRYpVY`a4n#8<cV0k@PE@eZ<IhsMbSQ8TjPbN?!~!;6hsk#-g7Vtf3Dbq
z0`3X*#JHV#1+;1|gpZdi?i?|YfNU26*b*|K4W`XpgvoDyO}^2(o<Q_NcMq!o&N|5T
zyM|))EramPt6$)U-<C0bG2C376bN0fPJr3t7SJyKTrwW`-~VCbjzcP~&W_-5rxy6+
z?WfQvybj}YM`QQ?DCT1<b-HU03k@=Y57N&AhfiYdrrj#9#I_#LyCWKeg<$&3#f(3_
zNnIDP&|tY;%>EvZjK&#)>H?xK`Fk@?(N(y-4+6m7J#w(IeAC7X5I*6h&oFzzYNi#C
zo3X-sx5t+R_C>j7+E0s_T<WobuQ$JeYnW{K90G2>8QBD`4WyLj2<-dQjrfI<%4?EI
z<ZN!V3>9#^I=T>VG*y))N(zQCLH=G4$@s`NUvRz)szghyaX{n1e}Mzyo?N5L!+~;D
zrOQ*FR!c2$KqmFrjKiE5uM8_DJDnq9={CHes0z*3TH=jXjK+a~%7M&+V$3;`Xi)5|
zyf`jSWn=Y7mtb@Y^Qt_Zl{?}7E_mbSM(}pA<9BR_V(75qU?Ssl*`sx^J4Bn*pe<(h
zmt~bu2r%ma-Lw^1NH(q1qg}eR9LS73f_+auifto@;^5>7*gNhaY`=9VPJTWW#k4N5
zq;nbZcxmZG44Kgn<L5nu$LBtPA=CTel|LsVA@jUt@*17jWz3aZZ@m>$-g_6pzFtaj
zVaQlWjrg8@`{KptpGDt3z2NCyHK1<Dr~TD9pm9LsfW`rh0~!Y|HwSEyS5R8vm(Od(
z#hNHI(BBW67yN>;caF3GnyP<J!v^)kA2U8dmv*fyO&s9oi!BRhp?%8>v!Hln^e6~O
zY)N&30<m@BY_w_C#PU{^=RG>M!=^ueCa82_dkf1##bQLj^Xc!tM(bzmaa=v95b(w`
zPhiEYZwOLdoTr=nZo3)R_t9H-TDC#Xv+3^Dl*H}3fZ3K5b0*&Ml6L+tb3m+fZ3xyG
z9X*sGI*z{a&^hwCT6YnqS}kC(B)4TeWV#8vEIpAubFi}`I{14+!gwiCRaaiWwL68h
zoII4Cd0E>0Pbv|RbrRif$DdD8fUK}ttiVQ%eHonwkHz5IpT=`<O~?9e^!y+IK1sYV
z_36?YAHMcD)-C)3@4q@uf!1;_hgRml&0i&lv@*6ugZd%3u5V{`ZuyVMBhQ{s#-A&<
zuxz~*b5B|^w`vw)nN-Eye)QQ7Oh9Wp1qT1}<tu1H7v$pV{KKz)#rC~aN@BULe1>Cg
zzus6*0pG!27p&sks8{NDW|#x9Di_P~DWA_OtKSZ{4(W@ZK7UC8?gB0gm@jU=^YRNZ
za}L3Ek|-55B;eh@C#`rbNZRr0d*2eYCJzY2i)Hq}p6&4M)aT*t>5jQe)~Q(E!kdO+
zbrC`ST+3v`ImVk_)=C-s)c`CgFAHH|@aoayf>>iiz}Sfv+NR&59E%5pN{R1E0j;#f
zRd{WXw85Tj&@qngg_5*YKySx7brg7Q_}(tG94)=Z0gVG12Q&_tb3n7WHs{9`byiGk
zOmWwAC6KhTsI-uq7ftALB|NWdy^Twcr{GKutww1HT^T7bnH%DfEqpPog}GL7WqB;G
zx324n*GDwQ3%?(w_k<E`VZB*-Md%&oiA`}?T=eqGDcfY4<}EEQL{yp$mK{&V@TUHz
zx3uHS$bpQ5`*GmuCs3T03impp&;<sojdMOV^kZgxg{-J!Xz}ic(9s2(kO0$9%o~fN
z=MN&nFC6u~LwU6n8F?9)yMGp<k|OcN$Zz4};;YhM=s$(Dh$yl0z7)|_xJDWWD&>GE
zz|@9&P6`V_7mc+|BCN{IXAF0~+l~|zNlZE!fBy{<ON(r_MZPmfTD7>azn7vd*|`PC
zqw7a|&H=8hLxltQH#)4<Sz`{A=0u|;c^!&U_t3>)nHyXOKiK<pgstBo*f?>1AQdOA
zr;km#;4C$W_y`Kvxx&`}21>m2LiUox75pYec~1YH+t5vPshtmW0oSnvLq1iU`lmE6
z4y7sU&6W$2$Ht3Zj$9h3G$ncGQJPHaAyXm}VdL2awyq8JY0`F}`1~>yXGOtA*6jA)
zu=DB6dT)S@qdDkRT9kug_O~Qu6H0PonV#Q;&J7M;Eol{e9c<m3n$}e5aW-8_Mn}gW
z_VgKKWMt4=2Q9ql8Zww5Tf>G85x{uGLJ72zjX!(Vyc~XqjT$wCtE)bCYJ6Ng5|YX-
zTV;<V0(wbfE&iA&c10dLMnJg4Y=7cQ_sw-fWq#&k;5RR6iul)b%jN6CG<Ef95@}6)
z>{t}evVGz{)sB{2A;H0D(xfr`3ACD$Qaol~Po6vp$tPY>WDM~lK-sr%KREtNW1DFk
z|Leyt1U#p|_c{bDe)r37uxP~^71O%1b_ekpyf)!+Jk0dM>-H_1SC&*=H-ODEX3xdD
zUwl)+`r0&e>!ytf`i{r&!Tn7yn;q8=4Z*fWzhcJh-!bK@@2V`XQCK}Z|HN3_d+YF8
z8~3HpWAC~R4-xF&O84Nq4jf{P*K~N%Qlug4>)*YLa^)?JmiO0^ocKLfM*;-RpYbVP
zeE$=C@#CyYV~V$E+yKvy8;j8+ZbFX{qbtPpUPr6#-d(D<tgEg)+Wp$&fQ+Ly^em>P
zETbbZj;+pFaPp5u_L(O9u8cRVR$MCUO!35Ynd~j>xOVE#7|xp$)63c;bE$<4N=~a-
zO3L2p80VxMj?pf5|Gvmn<_`Ima|tXbr=%0GJx_Puu~@NoC)RA<&F<;@S6En#KUZxn
ze-1XnblxMdEF`jpCJpOj&Npwu-OU`F6PM##b{@puL&sHmdpjrGHmom|p7nFAD!}9)
znI9J|-^4Hh!e_k|^F5=uUa*+QvKh+ixi`MXs*Tc4s_K)U-8;6xj1MQPxYyD*iBY{~
z<1Teys&IF8R_&Byxu1}jip776Rkc`^+o5Bd#%SNV30{6@2E+xpR9U}H5Z-(FQCgZa
z9y`|(a?Rb<g$h6(=KXSX!tm>R!i{y9l8k@Teq4yJep;yN5fHL%i-!1e%0zg2xFVK3
zpZ24;W0w#}4j6u8FSyde{8}clE_RNHfM@saNdN0E^_7X@gfGEtJA&C4LIQ$&cIkr5
z<;%^pmA%{M7xiZ)j46F;>QvZro{D(&RclEu=$f>_seXO9Hfe$!#;WGZ?}EsVF|0iq
zUsp)pTa7dhXdKWupmE@zbKu`!h5hqBXeDSI&^VxRpw>8WCNm$4qmof7Ldq)0*1-vV
z>$&2-4nb9<s{U4&2rs;NQv<v@|2X2aY;ZU(10Cvl!PhAsDY@3&g-fa8Mtt7kBy<b)
zM1WhFMtSwsX(?)n1I3Je7WMxBp*S-G{w-R;&P1^1?Ck>wH#a1$TZJ>Te@6WW9;Upe
z1n;bT4Uq|l(4~GycsP3)H#7n6e(-bi!;aWp_-M^Lcx%W9wbV-UV)AGKzG?GTzB?3N
zSO{wbEbyTqzGH{>#!R_6+1R{wJJWOHWhK2oHIm!4soZkOSWqPgQ3Up*qfaUAWXmMd
z9&sPusbfWL`0})+*s)_LcJ1Dc{fspkf9|{zCb$s9XvUaZo#|GrWBc~-r4_nHm2iMU
zs=Eia!l2F#l|@1&sj9s!LaNmC9K87L8syPRMM;aQ&1F4-#Ejd#{}fj2I*CIk6L2;q
z6X!DXC_LmlN)R{H+m)7M-sso5F8X(9fcha-yP03L{WxYUqzh9AE_qo->jr^%;l94s
zE4R2R0>JHuPOG-;J$4rHsaZIio{N0>Uf7mkx>&3m=!IS_L)n%HG^lG1QdtyN>8bR2
z-R_fEy7f4Y#v~v%B@0P81#qLVxDNYUKiCHY+SbG1u8mPApn5^juA^~y{}&4Q2-;Oo
z1mUR+(59{rdbJKi#}=U#g^mQ$pZ;_i(sT0v(X&-;;oLf`cT?OoxNY@Ky8=>_GGtHD
z$&XNSZXxVS(jXfvL!<=Chk?Yo$UpH3>>{3mUERA;82ba9a+aegUoX(2T$C<JgJAF7
z8{%?NsNnciI2G<ASX!2bE>`pGn?dI{Kum>H8q<GDG7m#{Y$Ctmav_<{-UdYxQ(#vH
zT$g6kHFD%LDw{!;X6MK>VIL?^x-jxJl%AUhdz%7+&}t~v8^|RuKtc4E&^3M)c0of_
zegWT0PQHhd^xeu;rfpezdd-AQ>Jk*id;#0g`=M*_G==2W*Cztl?%1(Qjo%p=nSAF6
zjHrg`h4WES$FYU;PusSw(W_?<&K1$vuyHei<Z@t5(%<XvZv?In96F2x1my|~^-6~_
zF3E4~Ll0O5u5)Px`sbg^lp=~jz^LMtHEm)9t|iU#<tr4xEc23(KBieygof6^X3nV+
zrlUaOA+kO4lR8hHjK+4(PhGlnLN^LAZTT`JG4A1-HS2Kv_;F)@_33R1o}WbLF5L-S
zt17gAYQTXGty|#tucsoN?~eeUyAK{g%$Yc3GB&9l6^p|7e0p{6fWduwsApy<@}WC#
z#T~<_C{ji)R$|!7GnV+!^4oX#C^qlh%Xq0tNT*_=8-b8;DlK$t-v-SaH#8Je<zGiy
z2R?D%X!YAqH|M71dDAs^NML|!Ym)}ym6kK%p)q*$?%U1oOFwH$@?Li9&{q8_%M%-K
zp&Y`5A%S}35sAs2cF+V6<2@<UKoZCL!<Q%F#qkg0@AaE;n6b#k^|w3MWTC-<1ghGh
zWs^q6JYwN(36$HH#m^NN>EBv_=)%LQD1PT!<GR|APM(!U|BwUnh#j5v7KB{hsab~q
z$fI`(Jcay}Iw;O1uxT{OF0W6YmWtq9OUb-)xk$&xH6+j&?&63OIjptV%AUUb)wS9z
z*|(*0GLOp4LL2XYZy%AEl#0in`$)O{cH}xXiJ-Lrvl(m`ABw*1oOkrZPN{EF+*m_d
z19xrT5`lr<_;mX3N})s)AzLt3vn6OP`3pF|&Yri@xQ6lda>K}*`j?em@lO^3>0jrs
z<c(6bmuuRv9{Tkt-$zQ0d|EJn_U#{-wSei!QyDWN>ILJg_a-XPTG9vrzL;*jlT$N!
zMmOOE51X(oZvxIj0;sPdsC|~M&*i=Y$LHH`9;g8PIg8e)avcd6J}~-5`1yEZ{+}Dv
zdPw?Ozitr0Wk>wZbcVH=JL9bn8{A8kZTLqN*w${|i?^o!$nx~_c%2|Wd^P161O@nT
zUdh4pZ%<RM#)ZY9A-=eWuEwt!Qty@Z-r92Q>Q8s!0$dA~l$~2oUHMfTK3Bit!*S}P
zk6_EWL*8mzK8u1BdG6M!nCa5DY(bMxKCzzQBCgs~{t$G>9f%=tT@}gtO~tY<OC$<h
zwgkO>Z@jUpY}!4I0~!Z34rm;>Kn`fY^#w9aORjN1<G|I;0dWzYRbtC^D0k<TkdB~r
z3tvZ!?_L-7H39}IDX8pn|K|RJr{DnNRyU@ba96ri>sZGFD^F#kxFFAJW&y5K3LP+y
z?R~6M9jj@yi<gcAX}fkH<G?=nG-{$iYs<JwE<V1<^$Wn+B@0mht})cwKY@+W>(IbA
zT!Gf&F~dmQ_joybBFv{g)*N4n*wh%*^A0oKuC0TF#6%1oHiANge52sdP<BaSK6>}<
zk9BMQHe5o&xwx|Q1Tri(tdjo5VK-yx!nwxu7kV&b#x%VDJ~yZK6$OhW1RlEg?1@bq
zD3GwAY$^-PoBKO{m^mBUcI>8GyF5!=UFDDsZ<8j?FmC+gc;taGDxR4|QQ9dvAmH%#
zlZPuo|36_!8iDIKzFQ0HBUa5y{(aQ}O!;{ucAlgXf`D*>{3$d{FN{m3kRa*|1sHoz
zVdh`E;p^#&;XRvR;+;LvEX=y+i({vg@Y`~_NOYmlo7U3jIz?0qT#H5Z+|>v1@vob)
zE$SRf6c|qdp|nMzkv2$Vn#j}1`nD|F3okb(4DQqb6YuJYj?HR5CNJK21aJJX9y_B5
z7)W4Sd2!_*esf9u6T><-9XO3yEA}DK+YOK0)E>`{?!`EQj+HX?46TfQV|oSF3m@6m
zDoOQ;79<1>dbO&HcOD&t&duu@vk0(0ll;i#Gp^L#r<5T-sY(F8%BsvOB>3)rRRGr|
znMYA{U>xl7kD!o1bb%h%H1f@GU{@56lE}#@$cp5nC_!PK-W9am7vZukLxjllHc+XJ
z0}5DPei?A>!0Dot@gyrJx|e49LKEQHk!`V6mR5#bwiH?ms4n2D{+gsgVH!#g{tx!4
z%aBcX)cn5!IoO>+@!|2Xv3I9rn?U{HNfajLQphcW?ur?7wi$3f^C{Og`LJ*Nido;J
za8aUKyksd(oH(fj(gr}-vSI<L1>6>EYq4+^a999u1IfH(IycF&<dL6{oLjTBHsqIy
zLoBND2~H~kWZ4Z7J{n2V@Lb9?v{TQ&Qmk6NRw+*vb9^w|Gv?!gv~~UZjkL_71$IaX
z<}X-;EDBjgA%=I2Nu`|vl<MDr5;!@CdsE|Wbs#aWY13|gQ9Dx{@bqxUu<QD(Urksd
z?z1l>j&hskO(DP9qSka9F24&U0dA`UZ%xa5c=D^t!rJuO=n+FP`SFKLFBy;JJzlbE
zo$=go(6LQRbtrrjUROur-u#YX)eG*$a$bJcsH%I`(#y4)w5_td+V!e9V1rVfJ-vQ7
z(PcQ5*NjA~YsVb8)H#OC<L!)BEe~XdD5X+(G3S1rL(LT}EX%pjbMesAi*b!(V$Kn_
z=A57(V67G-ZLDyc7sa>Xo|M#o4Y-zhDJdlbf3Mlj`Q0%7vtiO;A+9G3#A$?Jsz2Ja
zXoTL~+M;`>mW)eX7tT~DS+Q;h-Y=Z3lG^cEwrSPCkj?m4>N<HUUZoYQXo(>`xL+59
z5%{%60w!<XaS#WOMyqx>*gFwS9$;pHENND(+eug6zm&DrhG4WiK7MOFBIv4HNa~eX
z++yj>Gj&aX>6>rt&AC(mz6hYcNMcx%WWEgW^ThRR+qON2k(iREmQR6n4S(m&gXlo2
z7)w{^)5@6Dr&~J(I<MVK5R&{7P`p!{W@y$pOqD19NE_nLCgR!GzhGDcdB;oXg~ADM
zOnwM0nuIGi=5J5^iE*?ysxq9Y>@@MQI}zybV=UupKPaZ`peQYkN+^Y}C2vegxeFgK
zfS!Z=VMhf$Ct53ebn0Z9q(;YLz3oE9^LpdPA^yGhphg?cOIZYv0~r#{lEm6N?z`{c
z+PE>?+qUK7E&m=Y(_YBAj>ZQ81g;a8EP*X!cjxo{C}GTQ=|32r%}8upetf(-l~to7
z0q^?HJ`2Yn>)(tqpLU>eK;wYM0gVF}!~qSsz94p8d5Og}cL^7t0>YKh?VqiUJ%P5$
zg`HPk549F+9H>1G>^+}_jc2k@Tqg9iR<f<V1A<*_@l3B!xH;C|2K>5~{*a%6derm8
zsu*#7C0l934%Kj%e|cS8nXiaWL;nbGH1Vwn{+XoK4r`eMxpAk}GjOCOiEMbSkyu`*
z#l@oFWDL&Gf<8Mx3xOW3I2KpjSjjbiS3g82pTeos=t}}z%ey3-cxkR!f0>eO4jn9j
z5qb3_y@+%4?_qfkNiTx-e?oe2z|JbLKxm!qsc2oZcjHDp{L}>O+JBf;OB#C$W%L_p
zc@CECJaPOOo*XwG8)>;WgK@__H0vAlM@FI0p2&E-_2W9c_F&~!73P_&pXD-sob@lk
zdV15V1r#(r_1Rze>W}RNhdHi_b+nKaDBp|vTUI))khzdix05Ld{ch<VEZ7){*T?k6
zxZAo_cn9o6xro-$aO96KwOFiFMJWVlAA5fh=Bz&=ABw)NviiyaSwmac4qCPfrLhmc
ztv!sz+oJH?9X&8{bWb=-ot2OPU9Zhpg|}zX)v~0aDTs~$W-D54i&eF^yAy#%0_{ne
zID96VmL=IZpP5ULdmUEpj>gQFM<SvUYjd5nTl!p70z?1+KmbWZK~&7|H`K?4bCv$P
zw`_`h<Kj^vmDb$}v?Le7=xQnj44g0%KfinnhV|5gHNwMoVSY%?%2hnHEJucuq^x`-
zX5>;Rs6)7~D*;)tptVfN`uLs%`?yuSafMwe%}+q#fybcBjpW!~v7_?Z$zu>6P_{)N
zlFv8xTk2!j6VR=g{@>`n0OPhNK8I~_3OyxM-hzUnQrHow&fhl<rA66#(7KBBB45uT
zIB$FE8`uV151n@xW8nghEv1$7i4)NktSu#xjNvjj$apS40jnkL$&=CKV-=MeYq<0W
z#kAf#LaSM^#+Ei#M)Hlz{WWXXaaglcN@Laa!N%Sf#a_Lck6=+**)qZu={UbRSXa)e
ztbXnKwao!fcQ+h}j51@w|9w6kBd#AvKr77bx}3Z|^-G*Mb=vH@SX=k(WNtxemOwkX
z`Z*wDvOD9~x_QEep^Xe7iDY;Zx6@9(aVSU&Kwcufwb)i1qh%Z`&86Z`dOma>=786w
zWS$-8k&b>om=|*vnYLW3slAu=@mjx%tJs=?f_xlE%Rm;DklfUMsA8H+c}3>KI>COp
zzJFINUa^%`>GzLLTwCT-bIdSDh_{2+Cq0OJZyQVn4)gVuSUev%a*}zuAR!blOYGLo
z8&+txct4o`*9N4^I*%kVq(_guu0lG)1!bN6hu%6{+8N;MiCYLZ8%WxAIz9oD-uP0*
zmzL{JP7ZkCse5UaUG7aw`tkAfIf#u<)GwI0;P>@*$DlrX;8V&u!+YCybKl2fJMR1X
zGry0whf)fYas`ZT*QzlAZ7=Q*k74^BuHTtf!ubpx)Ps4CFa|gkO<A^p;=|}B+;kr-
zajnJDIx3nVB;OMO?Qb2@2e%LJ&w0BL|M$sEeERJ?<GtH%>WjOtA!};^h?C~eM<K(e
zWbWDp@4kJ7?d8Ph@5t~dKK=V6cfkU9-*AJ~7}4^ch*h;Isc%mf%VnhFp1$7X$;ge4
zM%tV?DC8P2fq#SVxd$!;#0^wRS>3tWvyrlLC0hLOgW+<u<x4^t1q5ujBvE8Eki@BO
z{OPBN96J^^J9k3IumK4R7s9P|Yn4Pm@`7{ckh*4#I=81=^tvy-grK|bG8CcQm&?A>
zK+=CT0-0QsdvaYi4CN{HISgKy4EeRc8V58E{4)+Dr{_@)q=TC%jMp+1(h4mlBM*U;
zyIc(AQRX9MxQn4hEmB-kHZm#aX<E;_7E8VSMO7TJFMk`f8ZQwC1hlrL(?TC7e+0PJ
zLqnfd{970E4t<HV!X$#&<%@(%)C8?qjRRK<2l6RtolWcIVp%+Lvtx~HD0p;tu*IZ4
z^$>E2H{Z?uToLSMNB7}WKC$NFOf`^}TU17weJ}wp_o`2?u$D_+YrW8})d~l!zanNu
zt0d+Bl;5+G*X+qzxnG|7;*Z2-S|H<vM#r2|fOK_nb9L0p4`6J_n3!0`?5zDbyPn+S
zd3bm#SCS5NwO(C&r0089tj3OAd*SR>ZuKL;Mll5+Itp3DGFCQ|;xbOa4*}Q3+HKB{
zKQQ)LUA+6|i=6w+xP9>qy;^cou=CN}ZMdy}3$zFiH0#yrgmh)SP$kbeUIzHM>o?wJ
zxodv%&QI&`)$iN%R?+f&1lXgn$eu2&gJ@MP0yJA%I~NrcBauS2bONNx4YmNvOp`(Y
zd;Dk1vFAhrzM44nqOI$b(y}r9g;{h}ep;2QdcgNdEVXT&Xf?^Uh#<_C;A|1wmz<r4
zRB4N}Spad~%VFDJ`FaI1xdE9%%jL@GmFX+-_RkxbkKY!%d)MHWczk##v~3s!5lR|~
z{5^d>U0HxnnX?7w(sHo*;A!0a;;&fs(Y^4eEAdK+e0lcPE_h&Q2feVwT0)ke&9)vq
zaUN6o9{jmAifu2%gYPfK_J}})6C8JUb;9zgV~j#l%VMPaFHK*KPk!GDdp8|sy?6_n
zR$>7!LP&=)cd(Y_Tq)<$hk~dNV4q(RwC3aFIK-YXrggEc8$sS2*yf%kkk33~oou7G
z2L8|VwJ1f2!ky`(n%z>bvU=q$EUBrN!S^&tN&JXoE3066?kW#ElgIROY#{lBo$RPI
zdv>82aDDLLVa9_#Myum;i(|<n<F&X87XiF8t(i(#KqlQXOPK-`o0e^;<KOx#&tE`m
zA%k+4zAdE+T$lR>Qtt}CCmS2uOOvrO&l!Mu5mp*3$mQ6Uabj)U8G5AsuZHZ}UyTD7
z%>jw&{n3mcjX<LmltDM@U54F(v5a@!w_9h%xpt@P+j#u^$71}shVzjHJxA+od0#cU
zwmBdmkBx62?2=BK_=+eS{|Q#R)<2A*Y!4LWa4l%72UkrwrlYoN(YXxR*Yz~Ld`XXO
z2tfMgpGRBUB&@UX;aqG1s>?9VIjky@{X_=S9i*jYSHFu31yfb|HM!@(_|oF8`sTIU
zl>6&CLB3ecSkY6zp2O?}a=FhL)TbjJyYFVMGq@(UAW<|pc7khKMWrfsbmNG67OB86
z2-%n`Zns&cvPy2-1fAP8tB_7E$Xs^v)LE=rzl+Ifoy|SmjRU(8WG`317cf|?lw-~$
zFii>DX@?;LyW^32h8xRNF2FbJ!uP-asmhTXb~F%Z)4T!dhUgWwB+ZV!N0F96*XV4U
zSYcn+uQTUTTXbsIQ~}%@w;p5&gcb_SpU(KyIb8cneR8c~L>+wo9YJS4Y?)^T6d%~T
z+!|QQU&(&_x?q)RgQN)w^uhRtZpEGh$MEsA-|_pBwPk&v6*_r6<(0=({VepVCb2S3
z-LwfYFT8*Zg10gg?4#wgf46S%z4u-OjT&X_zqkwc9X1Rn=q}v<rkjk{<)D;`AIVFX
z;{1<4A~z`sI=(-KPfylBsVi3CAj@)U*boigd=qxGTyFa9w}>H7eSX?Blpa5h-TnF@
z=>Geab#(#D$lT6QW_;h9&zJ#+fB+;AFi)iQb*@;2v)zU-L1NV>%$<wes3<tojkimi
zHc0v759G6qQpyhu=?a-A?uQ?cMPS~!Su=#c@(SFUwgvN^ruBEiufHO0$`lkbpI46_
z$Ul1)=U89KnKN+h-5d2LO@iN$A*!z#v=EPFo+5hK$&ZayeU*AjSYO80t*Wo?9Xg;6
zEz8gU^b=CavozK#Bb21Gku~P2Q#d?k44m1wpobq;F~oUGqyIPuR_{8EZ~i*U^}HAl
z+Telfn_^(cx@N6D8k>sue%+(?K~zbF8|4e{jO_-WGTCxMD#H@Y*g`Rg%%v=cU*4@f
z!*FY#hGyl+Ng9>LUtrpd94d!#u4)<PjTi3jXqN6!bTXE0=lgUjjb)ney(FQup$-k8
z{_mk~_;lVrdVmw0NG8bYh~A>;4eHJNx*#;LTrO8#F3bBL&Dn!H`!_-~4ahbhh{w~T
z+Hu`!7$GcE*X%szYzF@Cmz{W<ee(Az_w4iD?A;6*=Zm`sl`Fy2ti<Y5#U*87($||Y
z=e6rq;x8BMNBhPB=-Rw$4-D(~$D?=a5G>jpgXs7SjK8Hd_8d<{kCwp|DivV-AfIRN
zHgzi8vvxt+H2Uoo7}}*Cf$MUGF>7i6>(?k{)97mDfar9M@BA*B__c?dg9iry9ff9j
zSa@UxyDS5twh_HrpS{+v3+KS<Q|X9I<D}0;r8N@wMRuG-pXwHhW<Cx$fBw9R!E4Pb
z)LQ-vz2-`7ur72LzVIXkx2A5TEeaBs%DWRvvFS_(decf<qszkq=a5jfi7j9pXlE~P
z3e_tv@bc19=q@T44iVu9$tb3$Vpk+&CL+wYKCSO$ds0p!uush5X2{v=l2}_yO~G_s
zwQch{tXsbkFTeg4qK-wC3Ej)vVR{r&Hw;@guEqKdoACOZ?;w(3gI-8)n(E?@pLyn4
z+;;12j1RgCQ$G5b7HmhWEnfsQc6Q~Up74DTH;SW%4TYz-CtdL7<LJ>N`25SS8UHO?
z4~A0+A#UBi`sz#Eci%l|-mw0~H~MPKN&aLKfPI<PzjNQZ(`>UakM5ygB*1FtES_2_
zzT?7<QVM<kcw-bgG~qXK5p*&xMFFM)Kr7UYN<_YlKR5o)?ifmoU$H6{x7ea=Ct#O=
z>Ff8#U^*?Ww;ws92aIKd!*`*C8!aoJVvSlQ?ghtvu*4j+CZH=ZrXLv97DL#UMs<7?
zh%18p0t#EsCS_pTp)=g1?!{`xsOHN_*UQCdN>|}`U#Hi~w^sR*O_6x#*DcBl+>K>T
zdu9mky1o)un|wZXgS;@|uAUgwtr2d2;}0B;K92)*Ek60n<@oM}5qiE^tBA~ySdVT5
zo~t6+ZU#~LpkMn4jD2qbzF)SV@pv=w#rz%kVBB@;u~g-nT*{VughWW{Lhw<*?yHqb
z>GHee+#krb0Cyc}eP80%8V=!4!q$r$L_1nO73ZKh;}D8ZO`|*RwaUHre~B+jDaFnm
zrNOrV?k(BgER>vI4*T>iR9Z1_DL+m1E(nfQ;xh=getkGzIG{LX8|=<6raRVrB{(tU
z;wMF<Y>x~Q%xI^qr?*nrD4?_{37G3jZx|BGx?zI`aCUK40B{-wkh}Nn!@hm{|1;Ls
zrtOw-ph@FK2n!42=vazVr(!w(@ZF$L)^trE$MTbYhJ*y8DTT2D&L<@$W8eM*%5}O3
zZ#Qh*q}-FrP%EAbx^(WO_+nb8cC2xrrW|M#ULWr~HvyC1`N%9w8e@XLKka+<GrM7N
zGGg!m6_eZIvUYkca)4u<ZJh|{qIQcWkvRuA_lwJLj|O{@7TJZagdI3fh@7dMbUZCM
zkpkyVb;~ba(j$(!>8D}8e+IhcJ7b;gK-MN}6qC6}zhtSl75P-0+LD<3uL0N6K(Q{q
zbHpGukHnlghk28ysyRnM)*ydxy!qV2RzYjYEAwr1Y#fhh(84*WP7n=RXqjwE0^Y7;
zeCMcWrX$GgK=61pEsE_p4_PBwi~q579Wt`@3S6SlX=<G<<w?BgIg3`aJSt?dogsmK
zbiMtc5%85c@x-Y(Jp0-g3UD^mV@D%_o?Y7*P6^i2CHeM)Jl6xy{<Q4w+M&6+@530_
zKYjiZ4j+p~OUCU!aQLLM8kasevJa2me+#m6^00o(Uezxhm0i1cYNf2$4HaZCcJ=$8
z&tg6LcUIaZam7d6_aahK(;-%Hvayi;=S|mj!{`4u0isNBEs$78yENh&=fxKpu13E{
z6A;>#&qUmTo02HE_zoQkC%V#hYiF^a;rQV~FgQT0k(ti+`R9#U<%^UuZ3r;y7!pS0
zZ9?L%yTR+PBZOeJ0NZH<e6#lMMNVX-0<Y^l`Y62m^ik<W!Njda3xHs{QnGpOIrYv-
zIa0p3H@A^?Q(;~}ZU+w!1$qnEF7Kv+#N(n3`)23s3mbkbys5zMhP1p6eey}9u3Ck(
zZQGDQOYc(YF9CY*sZ-%aAm7#y_JZwq4h==%op&lL>bm2{8?C9;=p*AN>#?UyMn{DY
z_by%F7#N5Eg44Dv(>O}8Ph#mV*Mz4UT^SrW5}ShA8)Es=QJ&2GQTG;sX25j}6}WDB
z^)DQWqaqH+6&b63c(xzIjftE^PjlCwz;~;n<;B%s;Qs08sU!^T9?o&id`!-zV*M|x
zB9S5DYlesFnV4Y)Tu+(33vbWfgN$66x8)6#U->)F6O}LA&k=7u*bOW9oWWmvsBC1Y
zTP2V@rew&^zr_WZa7&wi1YEBtSRE1MjxQG<!kZ6tQF5r?*B+<5Ckyux_!kiSU~~!&
z$E4uS0gbSDb2KfR^D%a43!FTg&ht}fOr^M+dqx-=xqL?~{@8F5{o9A)j{c2tA}$>Z
zHbrB9bdpNDfpvB#pd6Q+t;$Nx%){2hiMX+AT{NR*@}`655Y4ik>9Ow40gWM++CTk$
z6y9zQ>U`<eQ;0pEiTiG7rk>sRe;&s1_;h4bY05xr_r(!3ccDT;8qOqUVdv3AwxyxE
z_RGqnw0F><Nj=JUzS@k*x3@uqU{6E@dtery=U0o5;FEFPm7-QSuZhL9JnIbvxu-8Z
zg0G(HjRql}Y*Qqf)%V7YUFxf9CCyA;lYQs|H#WzTEis_Y2X5}w(9o<a_Ae!D8eOd%
zkcqf~PYd|E1RA@TM^}FuX&lfvV9tTGydunxO6K}OHZ7KuGcWO5vgJnVzP5qr5$c8`
z)Y=q95CN?(6>u#r>=x>YMaNUPDCfdJ7B`g<+b6NPJCw!8t}K~$z4kcZ)v+@?TDC@7
z)G;`?xH4`VH+1rh_$*{Wn3Ix>oWuk~+{R5BL7b+J&Cn~NFIGh@BPiz!cPBR{*FRGk
zt26R4=_33ThBUt!^?kx?uc0O-Nj<)P{<!6q5%_w>cQ{%NP%P;K0)sGe<gNJTyB~4*
z$Pw;0Yj4@$;NXacjhm?7tl#F~;K4&RDqr{}KUtLd1_WW&58uMu!ZlvA1`!zZ$P+4^
z^uvbVYj$Qj{#?GA!1etmT+t2{2Wa6tf8#MMSbrF|^yTKij2;@+5#KG}hXag*ZE(+R
zxM%!J0hYKyzF7Ku$8lOv=h4EM3JH9!-gJ?)=>P6S_f{1*>f!~UQ9VEPyZidK_;k)D
zy!72V6yz1)v0FOfvk60uLa_^N&(DAF!<@B;$}O#FQ9Yo2IHo;23=IfaTaq)|5>dxj
z{q7jl8s9D2fyX~xj=J@Hu;}eO&?wZ`GK)ITWm{kSZjBz`qf4j{p1Qtr(AuJemb8eU
z_xf!Z@V_4rPnYI1R~*3oH?~8sc9oAA7xHApg5|Xb`eDJAD99#mIiJm&`Te+YD_0h=
zXp-^Nv>t;iQRB_4?4USd860eLkYmzm0j=TL365>Qqf2L#XKzQh>Hd8wkn4)VLr=pg
zVF3l9CK>-l-pJCzR@&G43+Vhsu&*aw-7zTK{}>#Tms6l@-c|={*Q4H}aA@)}6Z4p)
z3%whKr`o{g*sBEQ^}=7dE&}tC+ypojW|Ak~ID7USk`j~5KxhGfU0hr+>b8*z)RlY&
za;6|~2;EP5dwXHcn)S?QfutJ}{tJJl-F^G?MvordjJ0>{(4LB38?j--CbK?D0+~A_
zsKj>5h~Z}I#s-|L=FOXrT*m#DIZ${ibC!g75>Q+KeT_5@Tr3ABJ@z1D&N^e_Tkoql
ztu-k}lv$p7=w7_{{L|`PtVtd%%~j3;uA%M1o1ti}e$1EN8jfTuBV(4mM>4_d-N-mj
z4=+VrGnjza0%Yb=;OcQlYZ>7#Ewi|nFFv~$rH6lmt}q!5OPmnql!oY1Z(1{o@V+9+
zELT~p#A5RpJr%^#BC{Uns(+Jma`W)~o73nefopOyUX*etJ#iOZ|A$uEtSo}dXV0go
z^dh?ms~5s;sktlCd@AkFp1)FYL1Yf$jB9;;|1Oo5nNI8Hd18f3Ll^;cyLW7fb}btl
z(+gPq;g_?GV6Y?WdvfeZgx4##AWKQh#8WSQT4s@5zRnN}zAo*X8_O!BGU$OL$9XQ6
z;5t>0X>4gvg2>%Ew4}0f4qkb0267mqTCA=6_iTqg1i&}%IHX*bOMdadb$$QNCS@0?
znBF`0NAh<0+C|F9$)$%GAu7$fxjEqpg5Z<JkFLNweWI)LUjeS2!@|_BCOxz#Anet%
zXN9CHc6H;%aN+NQh~tWLcsQI1>W1)F8L9FKunWju6LN3WswT;dX#_lWY1&l%#7ep<
zlD0T--5OveCbzYG9<;oM{4}~MIH1Cl5zJM<FNZaCUe>duv|QZr#&R4vozDAI*5G*j
z)sww2W^hwu-}8ww&REL5Kyg0P80M=&>^)hCRkY^5v1^zytsKbQ>qKLx49*SIx-bK_
z7ZIqP{0+U?iNT_bZ?>|Rm$9~dyyXdr%4f16Bq?QPNoyPrnbz12NlL{M`6-gnPWnAd
z*@cVxBLMFBFE(LV_xkA4EC^pNJb)EDW6?a!2Ty&m0gnx9i96p~hS%@wis?%Z;Sd3A
zPZ~IUI)6Xfg!@p@$_B6gunl$St+Z3qK+2>_AYk~L_jkcx+hPf5H^9Rmt-;_<^^i;c
zimUJsf7=6(`#R&N6_IFM$BPR0RIcV8^NCN_;hSgr<Dm~$;h`H_P%adORuR7VfL7rW
z!Y$C-53^Pt!%|wXHwpE|`z$BO+Xbr$_;+d=pz1a-LHZ@-{<!=oBB{_2;O&NYX6-^}
zw(%!k8&AOhEZumgX5{cWf(8($k(ynIx&f|g4_QR_W*_{v6XQm-!O@sxoMzhkfg(RH
z;d;IZ6Udt;b-nTR;)C#{?5sztV7xhV2a+gLv)9?+t)F+IUH}#7xmL(E0n`ocyVCyF
zo&@?z>-oRf-la<xQ#f%@4Ggk)zk?cCp_f3Ds+1azMP}ME(rL?q>R)7pR<*`~{|E;b
zpG-j#*BGTm<yumvO|!Lk#E=LN+}hliaTa4xM4+aK3kngoUP@}{<Aew=C!C}jT~%?n
zOW5wy**YwvHv6qj{Vrvbw6e=Mprb&&(WIAgVB&Zr96kt-I-!ubOoB$`WuzgKuA_W=
z^+D+9F)D{xi$2@`MVw4Nj%~3!(ZDwh9!?(WUV3giP9&W~i=Z}4BVfR#r|RNtTDIUq
z(_7}R?!2bumvUt%fPwtPg?>O?YM)vNV5F{L+%N{e{08Fa8NuL!Tr|4lVnq0%kyQ?G
zk^AzuYtX+#1iak!Zk5E!cFMy8G4#1v45J{q^cx$q{Cp;|IIk{tv{@dLlcVveEVgWI
zLIG8$mi4TH)+Slhq15;6-F@KW>4x3M;_>NILoOP$&dx31rete5IL~&pZxW2(X-V$w
zQT;eC8^;HScScZv7s7(QD+R5k;VXBZz^<rsik1BdjE|*z@C%`)1nytFhw;3pE}_d#
z8*cD+R0&*PNZFFMUXV9BHVnjy-7z?RHkC29vk+d#`$CJks?(RG?=h9hs|_dZdtl%2
zMRU;FgbOx0C)hWB3HiwzDIj22l5!cTNvg|xD4YEf-~MKxwX{ImstX&3{KQr4o5a!7
zw@bpy7dwQ&G5iThrN3DAuRX!*oY<MLFO8z5wmyT(M{Da+S{_L1GjVZhJT@KgWi0RB
zy=w)~+O+VlT{<H&G79l$<7w4x5agJqxV*<QrwEwdxwE-DK50egP93p#?|x)39-=5$
z3CZ{%)T4VhGtgSDg@=csLBj}IYK!}HgLQ=nj~I@CbCyPW4jjqI#<b&c7sL(^TBdl@
zcYhOi7j&hSo=*)Fa4x8VD<tu_F?V6a;DPw~+n+G^&%Y6U+BzR2>V;zHfIfKq-aFBx
zLHHHYJFS_Qg#+Bf*oHPhzN;5>nN&XF5HF*=jBILjQzy1?^gRbpo1Mrw(T;L=XUex3
zzK3awxlb*POh!p+9z#dy*O!;1-2@DZk2CIa)M73x3z0_wue&Z6T}!#1fp4Y2wSI)I
ztVP_{vH$DOC*bbzu*wo$^6O&dJc}`<7yZ47%+~|PeY>^AnA>ltv~E%I6_7bCJyWI8
zQ3=0EgSuwvWu7`36<cPNTnq<CCycy_7JwEC!G=r*YwD<zXLv~;`eig@Ya3j<%KZ)7
z_F>B|f-h{REfpf0Hx5(rw+&?o0RQTH-(uaC{Y+Cnr^{R^R@4o`>ZqjB{#3U8%vplS
zJhr34XvcQumcEiu;&^}c;{vSTc0i@`_Hf1X{~N<BrP#Ue2)E|)giF~cg4`X;K}$oN
zZdDooQ6Lcal({TVa`^gqqHp*1827*kGcetdUi+(YK;wYMftqkYlzJbTx(3^5D5XGc
zT4PTh)f$fsD<6-f9B~`H@1&&BkBb5%i!!*l^ZtI>kqW?dRdr8WLQfT(y96ZHaj!LD
zWGl35qz}Phny-}Oqo*r98d47Q?CtGv2Q7>R$CmLhkzo5XU#-_GA=%pDMtV)TXHZks
zCYk*uU0s5y|Af+W@^KRZd2yjGR?M3Yo~0}Arnt3t19W-(cO<0c(FM9M9-wve=(qod
z$ZTAksD{RkbT)zLv&lIs1lcL(72sOjZErbrj*8bMI7q-(+>gt6KIxA3I8Qh1_T0k_
zr**biD~rYT(<9r`YW*0M%8O|+&Cmsz`3Mbg$Md5*;Mkc|96Oz+ti3mWI+FF+;=w7a
z=xrpQ?%DI<PH%xLs8BF|L~C>;aJ`w9+Lk19<KRy9(Y(G7f$rTH+_@e$9*oC^17|Vz
z#%8F`x^C;!h+gjYQ30Wdef2|gT4MKYR|lWb-MBM>cCq}n=O$eE(2{_Dw-&*8=)=|U
z_jIC1PaV?fGG1uizIe=j`8wvY!=oRs$4I)DU%&q>S~a-x@qudsxQ>sH!;6zATf9T^
z`d$pl@>@?uT<A@D?s>GjK&`C>^XFsXtXXiR8$}xmcLod`cu~N0Nht;Q>Sx}U5-tGc
z$H-}HXhq3b5$_cg7jmG{_k}|a2QzsU7@LhicX<!ha8!1$tkWicMBAfS-2a)7EnQhy
zi=8d)ltHnkBnwT0pr^7t`E;MMV)-&8a3j&9S5Gu)+N`pq|B34v>FJ1xK8gJY4#1uo
zR=1BH{hw&pwcdyry8c)elSW|J{9QKiMd#pzjsZ@1q+<xqojZ?Ix~>%0S#EBw6$Frm
zJhk=Lft$VVp&p2iOygK#9aJ~eRmw%lLRx3{psSQ1E<QB6^c?VL*$QnxpMj{U?;&H~
z9<D?AjJU|NqkF6R4?co0{&cS8?!-boL-F;fAMwe$DOh)66;7qdre6#^T`;`WC_L5Y
zSz2RX`3s9n$@$@rjzyP|x2X9+<_SkPPcG)nEeE82JI-%@j*Jb=V^gO}MW$M#m$IQ(
zV%Z$m4o4^8V*<FZJ=o9KgTY-J;nu#*FlWtS<=WbK!*IY2z^gy4r~4#l<O)#3ZvcEi
zgTLTHNZf)O7am-c?>&`>EsWvZt);net+Fy7yr}~o#6QzkU%w|tx!>lFL{($YO~wb0
z45|@mZQ8CO-Kw`Hn6s9AR9jFRj3a1m5cZjtQ{C}hgIZzAZ<}$7v9DJVU_MW`-hsaE
z)n%*keIGtc5fT=16IVd3UX4sRAnV?O^Ga}UxGI{>B`yu<hOpedrQx3GUmK?o*m!n;
zL+Wx0Mom}Srl~I9u?QSWJuF;0mQ`=-+JK52evAuxmcjx3MLSwT6*;q?4i!BRP;(19
z_omS06S%hMf?+)`V6KeY29oO{NN*4kZn$LiuYlW)8#m+{>>Tao%7xrk6JLgl(w<<3
z0kNkwk|jCP^`oz^53(5B&=k0qHKSa&MnQBLDZsTxm2)7Kal$twrd6JxnmaNp4G#9j
zV-4#d)WxZq)R*seCifo6?3*b$@cjx=;W<pN{<Gtdpa4vHaiaR2r`LnyF|jJ%wZz2r
z_Vl17WGG|5n!6AFXEkcMuPP33thROafUa?C==Q8duF<Ws#gdQm9tWTE@N(RS^y95j
zlqs%%>GGE2zI|#w^7h8VwIAn}+9t}R#5H*FfqAe?IYPq?%EI{3E01NYt7CCAmfE*K
zR;fMZV`Z{2nTlj!r~wsF=CmUv71z^}(vM!WE;q?s5_uvPZ-4v~tJcq<KAx_4ZPJ6v
ziny}&$b6fYo<-|nTH`UFSk*PM0Imx-28r8mi3=?x%9^8w_gBZ({t>YE*9EJExm>@N
zDr@h7y~~xj<o?`6YmIV0M|vcCaP&}ytkElh=F#iW>mU4p?^ImtvOPoDx<Fi+htb+w
zz-u9Ce{xDXt*NurId7u{U4omE0OgDS-i#@q|H2F<47cHkiR11<+m;P!WtNXakumzb
zcJ^r6sJ=0Fwv>G?AqBhk9hHhyT<`wf+v43<#vw5|Rar{b3k^bOS=??>xZAL8KYnB1
zC7yU|#^KgT<ABBijRRG2K!9f%YaS(N{m155<yxC=8u8eWW^~QnwL;cuw4N=c;;Jb0
zH>%^Q+*$v)@;Jw+BK$@7*GEpJD%a2zGFrKCJ~f+HWsKD!)ZZ2FKibnuD)UQy+SW0<
z5KmC}*=ZZtZZ)B*y6<2->e?h7=aT>3(u%RQ6Y=~vTWFCSfQ57i9(5)S;X(A`K=<4N
zDhqXB%;=qw=h3xU2s$?Lr)BgS+|rYC0(s2;70ugl0z<onVf@Xl@W#xYG#1b+i+{c7
zkOG$55X2oyp#0rgyD_9|eOh4`;rqXi5ZG^{6tBfwn7G^)>-QW2*%BjL+;zV(bGrhK
zThjvjJOTYK&4SS~+#7au?fx@ee{VZ-9$C5N>-l^dL5N4fLVDNB&1YHk<j1FhIJ!U|
z)S(_eq!o9!X0#AzyBBRfNkG1cVd#qCN-OQiGwIlN_#AxP9WiI!aav|)(fgm?GW;+B
z{+*EtXdU5CLlY+j)_141wNTgQfyxTpo)|({fQwQ%Fm+$95?!JHTob@`4uuD6mMt|t
zqEfy<`MJ4<Wov~<&+<Z5Mxvo%&jk^6R~QpA2V?Gkzzn$l@{^BoI`*_$7+Nbs+=m=x
zd=6)V))K>|lncITpM47dfIxn1)@#n^LaBfM{<vZ2O%;n|7O_Axko3*pIT)^tX)OzL
zdusETk_?`qZV_;`=jKD@vt^9FES%4Yr|`%g_1wbCfNE(A<4IC;^=!^*qfFUQmMow>
zoIK&{7|2V8cH1I3KannP)3}fo4waK(ASu6Mek23>#tj>=e(f5>9zTZUghV(I@CgbI
zL8E5P(W7^7v@}r=D4<KD$xlzf{9k8LMs7>@w?3Hm(=X`QxpKEg<~3g4v*X8)W9)rn
zkZ|S{lDP4?arg)gxW2rPz-nzWG5vll8M&fZA%Keo>1b&k;bD*eb*qbvv{b~!$1B&D
zI(}Ck?(Paav&d5Gr=3FFG3RI!N;m<@_gxuD9PEq|2h3&sj3+vTRF+J;UdtSCZP<w5
z^>h@TjYIbFNa$SL=&Gs-be`5N6oWlN@a|0?BO&`7jwT-FIBbii0SxKj>|e_*yE=>W
zWZWtFS)-(+B>ke5Q(|Fm7U2z<Ag!g;u2sqbTBLnEZ!1O(Xh9cEdaYamm)?4C0Npl4
zBUJ=jf_9k_&o>-6&7}aDO^whCr9R>koa37!t{oES0{i+&zu^ACt#Ri-Du*-*qBVC#
zfapT&sVe=do!k`4&Ou15zMI!UA6i4#Hi?_??T6zO4}*Q&s7)JoQB{aVe1GQs{?EO1
z36+DrjCVb_7J+Ngein(Yj*0|!)a2F3gafjmmDpt_V~Z@OOYJ-<8>oRm+n$1alj8pE
zLmT@_6dtHxX=~?%Qg+oqYTIB}xd#GUdurWR(YX?C-pz;wipPwagj~7Q9&XIqRf7}9
zO27*lFYPHHH-Po3D7nUXn+ES6mc=_VY=%acmILy+E<2lyjRe2nZ`%-^X@UJ;pr!Fi
zc>gGcnI;rY8NuA1txc}f?qQ(6ALOTz#(@js0Nv%<we5|fJ?oW>Nlk&)#(VCm?Ojvh
z-EteUPd7sD+4?l9wPz^IB4jROeA-$Ct|_d}PC?PJ6|jlf1YK#4UV&1UlpJ^U(=_8N
zmn5T2X*|{;91aG#m9@58V~8rG1X<j#t){WWsQNWis<c+r#a<KF)vvtw9TJl0VwP(V
zM|$`gd-pK3Y8Fvtks@Eqr(%qN(?Vp4S$t=?mPVJ|3;rT7K{=#7LFPeyIwPXKbvc6G
zI{P#>Y~9QBw9dA7#%;s<!QCXJMJ8Q#Z`-Z6ww8G?B+wVbuG0f1;zIo8cW2=1pBAb5
zl$&wJt3DVR!+6peN|Cc}oj}f&`aOZPU;3U$3uqw`#)pQKTMP=Ayk^sG{EsfjbCrv5
zd)zpn8y>xPxJoZ_p3{s8&dQXMT;qs(st&m+eK~bH0V!!2>b}g|y$M`5YgAADB#nW@
z+FjyxPkjAL6fk^B;|8JE25>FwUH(caca@h%<k*&Eke`WLaclP_y?8t;<+C!}m**<w
zN*rm4Nv)B_fvc4RGUmSW{Wg5RBFYF_kM0+NkH_^ij&p{-pJ0sSzjwtl9c3*9w@377
zfQN=O$FGd{EJI*6f4{CdhS%?_8Q$UV$^DuQz1q>ZNdV~A=uW#ws}MtVGAz>4+5rOO
ziu-J9l*f77l!`8ayRb*tM_*586^puvGMHgK!j&cS>BMY&I=(lnFU1pd7cTVVEv*&E
ze4JL*Jz56g{YScE<?b_h=C-zI-%u}0ZBF;uqxv=^*zAePceKO#lpGA~P*=scetcL9
zbR_sKVDu|@cR*X_iC{Xhu9i5~uix8Ifz;33+D2KR-_ID^jYGZg$rHVmoAT7m0^Co_
z??4|{9EnY5eqTIyS4VtK%V<Z+OeWplR<%?9d4Mk9o6ySIm+{Np7!J?JgNAU7JMBd<
z`q+*Px(aWN+xj)+{OO=9y8E;Vp$Mv!R`LG0k9j37_e*zo!Z&{&LB9bZ2=;MN!2SJ$
zo8c5=rGGZQ7p?G*B83*>%EdeDeD&VW_>LgFfaQ<g)Pi7n5gxjsxv@=G^1(GSQ;6Sw
z{l_Sa6i;dv_a8Zm^Die~4kq>jAeTKw@riGZ3~Sr?xYO9YX@iP)EAcKYX~CS|uzTlj
z#uv~}>^e^3qCIGkrWY0D!(N0m6ky1tEepD(^XI9F+$>#Tfe}c{plb;?E+j31Yt<$K
z-?k<(gz`(Xab4r5a9x9us!uKs<^m}fM-NQw^)Z+1(ob<z4s4BEh0nG=4R`xcOzQO+
z0cmfQSMJ4U#^BYpW0cEreQJG=i;#o%fkW^>hZmVv&po;S)7~lg`@}3-yIBghO4wHe
zm-S^B{yk3%idUY04y*rOhJ0#9@|!2gKcb<_Kjf1;wZOY{>xri(O;!MQRAeNU&YQ~t
zpMo{=F#b#&X3w1Yp9QYP^5Qt-!%7T9c@t%!rqRF8fn5pN*q%rw7JiqOWNYU@t!lb=
z?j8!qqFidY#p=IVCM+gM?C!4D2DZ#s+voK-$%)JGqiKa)$8g0`1(g=&V_kd+`qs}u
zOFzqRu!@Y@t$)OUq7qxgmii+-$d?;pAgIU=LDiJ#knV;?*<9RkptVb<(3%F08r7Tw
z;wJpSfrD7McnJ~`k|A^RmhC(B${n&x;JlHQokaja8*6LMRi-2eozB?CFU?quxo_QW
zTnI~C=?S-W#Vg;eE{_3iS}<L*UMzKQ9Y&W;O|ekoOVc&EfY=!nw0$yn8@^b)3$0mq
zk5+Zjr)@YoH>*RJR0JI48P^iYT5N0NSqVg?FOk}=1KUSbP{1=(SIAA92x@B70=Coi
z9gIuNz`68nE^kWFIy``G&M$tP?$In1-!I-RvO^q+PQ>7@)mwWT`cT#1b4i)lb>h5w
z7b1eZ5aj#MT!2+o*A;nB-aQ*fvsX~8$!v<!_*}V`u&o+|ETy%!TH9U$omav|%XYG_
zm66)lRCbfsb!wb2`EpoR0oPvz78a-0ipZq3wiy^~EzxDXW|cQ(^;dQ8GB#1G$_fXv
zIKEHV9fg_QS|CC^OK7BVK;uBoI3UVQ;Vn=S90vQi6DTa33uHzx8P!!;lul9ciEcC-
z9E6j9JhI~&qA)oCHoFs%e>e$_&Htg|gw&l$&nMBFP<(PD9E#JC&$U^hv_#2~WS-V{
zOOi@#>s}?tu*tTbQO0H3X={Bj-Y+XBk5<c@Qj*a(FaQCSC@vZE)Z&56k3Y}-8_U*g
z<K9N^db=rs({T^nTBBOTBH1t}IdTp*^{gO(?3&Gc7(PG`?m99y^gVR@T^UKd=>>nS
zQ?6PijW1(%jvU(0m|W^fOi81W1_5?*T1Q!FD_SP|`gq_h_i7TKd(PrDsvmZgg%0Y~
z4lhr907FMlQe}v2*4xuub}2Vx?w7Ld***`(v=)+ntysSUk4*fK?!>u1;Qpm?cqra^
z=@FGq(umwQBP&M^`8wDjD6rh?iM$s%dAYJ()ZgcG_VMQa!h%v#Gx72CIrwD8?*s#i
z;pgp+fxSCfq`Ep!i?g$lvUV*_zx}q}k0;~28yUtf3lMF~uz7YgHfl6=D%@J?!6j2}
zl+pq}{`>E7_Uo^yRG!N)yY|G<is0n!jUdL04!-Ly)8yK*#(}Gb12(jho=$h&yQ30h
zIOGST!}aY$@by!Di3L@Zn|%A{VPuwh<8suM;P!rv7<$YLy=YClau4Mc1fYLdc9_=5
zZ4?Mv9i#5Qz8RM7ikAVDV^<Mg|9KbNvC}M@xCd_);fLqxM*B8`z5fOc@72Iq?U*u<
z^#KCBrgSTLAtW)w#|~{_yd{><-CCGSU2mcra09hu-&&TP3LtDsqpxdfJiXWCST5d9
zcj-dccL`H}<)2sXEg$zSt<J>_xh37uEnHoS2q~Xe>IUlPw^!~h_Y^4B;qo&j0ksYE
z<^!D#hw88Cy_fFkXm-zoAox?X95;}F;x}}yZ#cc8|5ij^ujuYn-gu+1gR2v7KYzUQ
z@h8T_U(n+DB!N(|T)yL;d(g3Cr!pwkm_!|j_TSpotFdYQT55-htD%b2qE&0iLd=d6
zxqzytPSHXsHC2JvB4}?;tKr0?M5L#sE1{??!kRR1fkejP%*djqAqO;BWVLM5mYOe)
zYLS-9MP=Rau!@!Oin#k08JdxJ?xb_3#dY}yV!X!#S+u#(8rzhdX+1$*I@x0Io~Kz}
zS36p6m(^ifjeym5)C3aCZd0<OwMnUEo0z~fuW9;py3Zc}@K|gnPsP2S2zU$Wj;WBm
zl4oYeO_Ci!`&_yRS~PzyhTMF!0<NXcg+&GAF<)Vx2p?rY{x6daCk6v2D&xn0x&2pL
z+j6OiG$&GAhj1mq_s5h(vA~rY6i;;tMX-|%4jzqW61v8xwx|<hKRHp;+1dH>xEd7W
z#;$cd5Sfu`{;iuf*mz8L|NPp1J(7r5d(@-H1?^2UvgQ&U?2k;x7r&guKK_;0NPccE
zI`r@Ak8yWJa0BjOR&(mPO#D548>07}MUiMRGK7LX<FqvF7>es2>I`?ka@XQ!8MPB5
z2X<`ThSy$uo$=VV6Py+hkRT$zS+Qs%LyhSwvu-mc&<?C~z{ws9HXg--b%$|F-}24D
zgi+lvk8ZB_#UzTxpY^;Kan+7v?Tm>-kw<HZ<y(&ftY2AR^RLY6yXn4sH)BG7_Qwvo
z4|GB6`hMt6H{t^ri@PJiTMxH?HWo{)q(pJ2Wf(8{w`F+cLe&W$FU9&j(R7zwwH0_N
z1zc?zEBc3*Mk;}tuqJ`Al`=$Sn)6P>5bItUYRbZp0It78(~o7vC5y#K?a;eFuczQK
zOO-pYLpWR+zeyt#4lpE+jZ-jebM_f8@=?Ps=LCw=_Cn`Vxh8x`_DLXpH`lA><CL+G
zY7VNJ&DFiC5>!+6rM@j|bZMTKmlyvn@4H+V;j?%p^7FIqNkiHuv7V1bk-uDnT@0D+
zlP;zJEzf_J13rx77GM!Bq=@fT2KUYhbjxRr^0;Pr<6ty?=xQz)vzAu7rg1>yKsg6&
zot<IdV<>FqeNLB_l-2N7<x>yu&|kN`X9|r*cF=7&*F33V$Xx6JhsMJwOS`~zB=_N^
zSqUgPcMK(Gb_0pWV2@nJ&!$^XnO9_>7tmTYNamTcmhq1O=}l>I-NYsdM@j>DW<Hmc
zr7Z8L)uR$*ve+eYad;piTwkBnbhUR;=E$h%GkEvYpINAW?sTHO?71iJftP2^lnM<p
zQ2FcP;>>+%c|LLLEv~PHR{}D3Y}*7~XaQXrrDbGc!Jiv=TW_5`WMDU=>up0Gku@6X
zETJ;Nk;rIFoi+!v|5%|cs|9@5QI6WPa|=xSV3M+?{(tta0zj&&ZNIa#-OVN}xpYej
z(xqS^qGAztd?pqKcA@wgzuoy6D1rgf(n!OS3%l&H-QD{?&zZTqcV=fc5EW%!n7wuC
zo_p^(?|Gj$c%LN7T8fHGwW3rxj7#l>_l}+}OX2lt*S;f&Z|Vjxf2ln4%4aI%0qcu~
zB{s|BRwJHw*Ub-_B{#1#Oiy?#S{gbltMn@H7d!SGl8et9u4J*9+4+dUy;I)(bg}H-
z58e&^?}2(Bxo2jRmhM{p|Maa9pmYGdUV3%vB&CUovUloK5hxMXQL9$c3n4Jwv2~5m
zS#s7Tzz*K4Fy!;@-CI%sUKf1!nS|VWtAx#(CGL@t`jn@9zpAiMjCgm>WUKC)vNEM?
z&1cyd(JH<yIa%EBOsHJopi|QW2a<9X$^x?|HD?Tr4TYSr;kr5xR<ob4IEbMYk`Le?
zL*VkcPe+q1;+k<?Ou#iX)sJVD!L9T$nE_C`DPnx%d2lQJ%op3C*kw+wpX}5ctV(tq
z$&$;S`W51YS_PIjRj%W@qX+(u9sufhW)T5bey>gdp<MwCFZ<~yd2Rj+U{Iiqh=pw2
z&h6sk?F+^U3>26hE%snJ1S{jun<TyZ_Ay<v(``?`v;fx01c4!&0w{aQ<ntsqCr4_)
z4EYBJ%ZFcoD~~?(u>7>}YjFqgMYkzWzPJFu?OJ)^iO0m-#~VKUD`m{YbLHmS?@$8>
znK-t!c?8xRgI!C==%k;VOpK$JpCGg?(vNx8*BiTLo%YL5Kaq8-Rw_oCHC2Pb9W?A5
zp#ne!d}8d|zfbn<+9|o&+0q*d1!KogP(^g;6f5UWnJV9a`7uICfXWD$L+Rl3rk3=~
z>3t{vhSse}%9mp$u<EzAVy4mwG=8t|17<wfP4*$~U=`v^Q{$Pj;26`;8!?QvMcH4`
z7X!k5<bQAhUV?FpAYEfr1FP%(`6BC*3uI(#+X^g=<#$|beh+Nfog?==zZdaCoFpbH
z1j;w2u{0MJmESH)m7~eUvf%!n;^$*JpN)7WU(EYe^3#eXG%8R$%yAY=3SrUzTcTtp
z=E>!cjTOJpdZ9w|TXSZlT(kUFnR>+x$;!?#E0B07FoS>w1rp9!Ae#tE9GJIl&tPXp
zU;UB0tc5`5!FN}}m37-Xt6*<fLqB!HXu0^I|A|rvK7~_S2&^_1J$r?``TZ6E)LY;l
zDVK6I0OWX{6mFUYoH+oQSHms&>Ya)5^cNct|0zJGkM1GYU(jD7f-M#4ntT7n0L+Wc
zZtzqdyhp)}SVAYJ=E<I;0PoG;yWM(Vnc}4ur+f=domEU1+~9NgV*36;<)>Bwo)~i=
za6>$$EVx&<M@FSI$E$X}2ArlVuwy53<i)Qy$@{<TM1MP>eXerrMV4B=_Ubs}&Lb|g
zA@`TM+vbwNNh+k`z&vs5`++#Qht*xvc4{m0rSjlBG1lY>94WPkxOS<jEW|nm_GA`3
zyQ!Gks(hvEVH>7vQB{9x;)w7tB@otELS@~}TeeChgk{muQTj`2pX;&Tep@f;aM5})
zz%~1h!g*UnYcqx(a?n4w2PQ=X%k8nPZQcOmVlRX%*tg?TgkZLw_8{)-Zn((p>EF=0
zPoK`Q@8|(X59oRT1_RDLhe+v$AI0qi+?PUe4&t&#2;UvNQZbE-PY(Q&W=m~uoRogF
zL7dxm7GrRX7y?>BVG-fC0LTJiLRnbrR+l481pv&Fe5o!>6GMKARA(lNp*US!4du#v
zT{-3s1#z-)3f>ftqiYPRu5TMaM|eaTReMklh#A=Akf2M>5}KlNfIwSN?+_SNboTx8
z#}2y4e)+AhB?BNTHKU!u6Q4VJkW3zTc2iBQyG|=*$}@Sp{%Es@he@W$x%c3aWLdvu
zH_E2lZ+E$3>Nv37+B&MP2(?2cGr9;rl9-~};tA!_E2fUONJQC4Xh?uWh6T&PqseN%
zKNz1ZbDwz^>B#%?9$aM%EUn*sVU7d``77(}5NvZXCbLb4k0mK%6t;t*2O}dwq)l`y
z1$t9i`MNuwQ=om~33vwLGse9|N3@dn7ThQOdUdjB5AQo7EJUDP4w-6=97{~CyUwyZ
zwvCiwgZjww-?m95c!BpmTP#agZdS5hwukEjje-MwRZQ_&m!GGd`uA_qN6x+oFUjzD
zFeKtP3RsO#QhDTZ1t{#*v7@*I+gJAGJ7EkA6yN^+MXtYIvVQqRB5t}#JUevw)B3L}
zFHhnhepqsU{83z^qoqA8#{Gs4{nP3@9Sg33b$klEk*2=-s(N<eH{2i**IlQq<ZZRL
z3Wjz`ue~Pi=FSx-Si?K$kM{r-rtY5FM}FN7kG(J|A}IaLS39Lcw7*<Aw%*#8H7#0y
zR1)C@l!`rgNHY7}ZgsL|#$k>L^^k;gqR+K(1s#X{u@H@@iO+6BIsQF*%>cPzNL$2=
zPLN}eUl5$uh%Tm=ZcT(7vkKF+vpoITR+$VdVNb+Z{;O#ljL8ChT_pxFt@SeY)a+u}
z8J{H+&T4IO{?BgTLd31^4<!RXFBi$oD-&19MHsU>6tWjTxc;_9jBmZ@x1%x@?!W2X
zq&ai~v8#6+%9KfXMh<Gc2mboEvhnu(C0F?|Fy_fmix<ly_uix8ZqTaN%hylRvvc7G
zAC`^yXT--|Qb0x#F$GGBBsMlydiQJS20-6v51lsC3(r0yiAUp=Wo8jHzNXKZCE*c~
zYQoSo3t6@s>b`QuO!@TV4<#3t*No-(;d}4O@X@2?tbv2{#W&cmg|(h;`~ef;CR>tz
zm5ibjIA1LjJ6<j!-t~f@^2$<Ka_|!=gaFM6gCc)P1@X97H^qcBtbl!m*t+#&oJzuD
zt5bDZ)itY&=<WvTDS%qKWU*<`#WSgbrS;8o?vvZ@xWi;gje?kF&+gsw?wfDO=rLmy
zV91IX5AWIeFUgoO<AiZdhYTAkU0}KHAV&{0rw1}hD&&Wwxmc%>{nisTj$LG0rvMoh
z<1g{?@rbQdsunZKEj<``<t7}!^<PQhaP!d(mblxq%Ml~2ei3D_4IE#-Ih-y1!o0=T
zPRtT}c^&7Q*8@exRWkpBBN*3=(xY<(6dH_m<0LIWec^&~M_j6Wvgo+ndS!d?p|!I7
z{jHLhRwV7aw1(o6$)g5q^YZo-AAc`75_epd!Ww(pL!(tA4j(xTfjxvfW+6f~-q+|h
zZDq{Jb8I&f<EYiptI|>V>EA#_1$lA@=7^l!Jj|;m7oVIXeEj@n-lLDmsNrWTMJC>B
zWm&OYIrU<R0~ps7WB+gHn^S%dc$XNDnQrm)<PY##zo5`x`=k4`dboYs5V>YzA6d5P
zkbJ#nzifz0MqH*M6WGfI8Ubvi)97$ozC8B+D)|^->&v%Il=IJNS_4_jt8J?hPpBFm
zI5Z+(WKVF%H0u7*KPEw`mJ3_$<I@(B7>BLjJt{V8f>KQ}QddHYcB!<-W{|4LWZ5DJ
z7T^4FC*nFCwpgs{lXAWw@O?NX7xNuJJn+7*=!4g9KTl%YK;Y+~20h>$HbKgcy@K(k
zNa-OkL3t(OP8RQy%59g6G3F)+eg=xc#Sd!?2xIeiNOi(nVkq1$Wk}Z)S$|{Hb#SUk
zk=oLuV({tSAbVBLCMeuBIDOik2oDdJkdP3`%!IcajNK#(s*8$><?C-2%HTl*r9+2y
zir44$tgLMLecN_9Z~$Jh@SZkB*8RpjRA5R?NtITi^;TF~Imxh`W18lat>tlS|2aJn
zfc5n3_K^sM<t)$bPi)Ze^_lq&;JQJtI8Hcvpot#vfYRK!=~DU8(_*N#trVn6;E=5`
zDcG$=>}w@#GE|6D(m`=4I<915)ks5YFay3*1B@@geyA}S5-4%S#HQrGFr;t-pF;(d
zGR}3J@K`qY74yX%b-)vKq?(|Pus%W={c01Wt+Bf#)CQmkOa8Vlll4x0A#9k6q5akI
z<8oiuF5(70?9Ze!@Zp=c?L!r&Mi4xuwRLm(N6v{<aHOoU9Ew`rSZ{iofor<`{$c5Q
zjjwP306+jqL_t(<%I!D7-xz?ylg13OdplGB`)2WKTsK)~59!}s`u4B{ikaCDuDl6o
zKl1$hDy?ceWRiTaF5us1Tsl#nxNmly%WrQl4;guOUpaL2gqpAS9Xu+#<Km@X&sY`*
zYij`N7mt?*o_<@EmtR;c`QRDYB@DCJyK6gn^_jb*Pmhi&z5Wk*`SBwM$fBj|r3&q5
zJnW>Dj5@1q*8S)`*TA#hQAx{$TnOz<N&&#cS4nj_?iD=!sI&XX+&iz40exD&#?ZIt
zpSsWPpnGp?vUlD(op$U5;M>{H4<4%;TAXTW{^*wRxKls;P_hBSQZ2Cxmf{Eg^B?KI
zdbJn<!k-C5>*<2uf0y{X?*@obtX}2BM;{e$cz*R8V&fafxK;^+y9(eCc;U~4F8XWk
z5o2B(0I^>_JV8d?^Sv-Ga|OoX>ldt1F^?}C+FrGTYmj&0CR`0V%oE}X*U@e3+8E&D
zE>q9xDD%JF19?aVWFzUaX<wQQ?HN&*rm>ygaFNZQExP>4rSj#PqY6|%npz-P1*Os&
zie!HkF=qDM_cqEg81+z5>v_bi?%X;MU^*7Qpi;zW-f=in0oOdHqP@FL4#Zf_F7!TU
zCSGUA3$h0+gCS-0^CdArdRt{4_B-du3e1Ii*X;zT-&(Ry0uYOu<#T`MkqpF_4wIYC
z?<ymDN2{{6dbKk1i^^re!d)_cU~6&pg{KD&kY-|AG?@^pt;^3oe+Ta5HQ?I%{&@dv
z8|%>OW05RFU%pn)zj<3P{rflHrPaMr;JpZLO>V#bTEt?8k|&s!Y16Nii!Qwkz;d`^
zng~+83wI_TyzweD0}-bhjPwXt`)YyDt%|fbq)WS(=FgW8-+5cPzotNDC;-;$5y!(M
zR3Rr+Pjqn|f_OQPK^yGWYiEj6tx<~eA)rV5o_pmr89IDKePZ1nOR0qmrW$hiW-=N)
zWM}$D*^%}e*!>FWA2r4TxUPT(>r%v^PAf?kXQi)786_480?iSC;I@e1cnuaC1oJhj
zLA~~f*O*>UJZ3q1V!XQK;|VI}9j)Enz)V~|{R&uLd+Cz_ydNk%;a)}qZ?ucPNPqs6
zDd+>b95QKTYS$XuW@Kat6$yO7I0f6#5Y(>w!KGnPkbT#M`Z^fHnwIr^S3*NuDJEBc
zy`}p(D60V4f{de1A)f})w6q*YdfPof&^osY@2M4VZG;=}5#b&(y<4ax!(C2BW)>!j
zYNheZZ<pf&t*$+F3M#g1IQ3@-NB9D`&S=&oRa;#t=@rhh<Y<me?`)e<IrS|(vr62$
zH&+fP6iN5kNCjGxq0k687+kS=N;q8k{<JntuDY;|l&0s&(H$owG$KfW))dxjM8=6g
zc1T#DB*2AuR#Kh>LmP9=npJY$wKsq>F)#3HE2Pix39=rFIvzSEO)ICeyi|%H{ek?U
zo-jYSdp8u=+gg^G=BN6sr`z7z6-y;<{~<N6s<xruoM8R&>Kkv$<rhx0Xl`v~85mnC
z6VyK!iv*5?#(Mxv`@ESWrBzV<;)RQ?``%g(;0*#O-J+rKf?9sn-v>O|*k0<Fo?R$=
z<I`l#uH&+CUy|&EoANBW_2&1(HV`BRn0^d#nJ=C9m3%VyBAGh6XNxLzhfvR*Zog~G
zkPq_$$}ZAbSy&eq1b5~9wjoiqzNF>~g1{6I+9D6lN*mv3DXani2;~=-sl^t{@U!7|
zOqGqnVE2(U*{uWSZPv-v562B`d&Y#yb2p5U34<F7ByHwB15Y}6brk1_Y2uRp0p6AR
zF^1r^OYuRej=Mw31)f=q2*p!V3M=bE)x8y%9Qewb*W-43&VUd;`UNRpcNe^$m51kx
zYmfgbKwQgQow-bE)4qeN%ldX`*G{)BJ=P2wI6#*C_>)D`2vV28I%L)AHL`lmT6oid
zGA(Yp6fs4){?ddCO^xTS5LVzrwkVtBY}&j<TDOjtKv?}~MDTpWhD~bmubp#j|D8Q>
zL1c(5ICxwtbbK1`^k890-3>GL^{Vt7*_u-zM~liNA0S5|<~k!jL~jU<!rfh^3tZL?
z3-Omeh;eGoFRe<?m9#QT#nKU>0TONoiT1-2&(}%mvK{eK^D#e614wjJ$0)0})zq8<
z)cZwZT3y?s;GKu{W<sd9JPlT>Itqas+%e!CN%4r~`VB11cjp#KI?}qJ?SYuvdZWGP
zg7@sEa}lg7!eTcY?$uYKAJ%2UVy&oD3ehhF=fdHEqo=>OoEsi2UHmN9=~@BImkyv!
zPooi2HWYnF+M8D--y~<qPIDhjZxbfhL9u|8g!yba^7C2}Jd?1G2vT}rzzjqFjy|3;
z6n(%SpNWJ1z8)}u?>2VsD;2%Z5zn2gr5NLPV^4%5vSKf=)kfzESSD5BIXXcw%!^8}
z3_eADS4<4vg=xzmJ5u<vDu4-bvMExPAT9HNxD4Pf<xoDgB{D(c<PEX3SY{S;a%Qol
zx(lWqdSN?W=^{&#PsqiXSN=>Q$b9+5W97-0KZ4L2;Kk&T(zR2YW>?AnWXx!WH>;>B
zMj0kK1`D9;?Zp6esZBu7jx5{H*S2Cl=iWq&Uu!nNBM+3J-CW(}s%aB#%DY@K=3IaE
zWJ!WW^e1rVU4}7*>(bucJ3;n372YUETC|@AJJ($`S$<r;QIgZLpfp<}TXyWP1Fl)#
zjLRp=&b^1_e?P25Uej|34D^;Ori_z&ZoW)tv2BZJZGPU^!7}QHRk9Lor~B{3<g_~A
znpW3+0KR|m)}t~P!1yL838-lT_ZS%HCqw#mldA!=j~j6ozyquk9Q4O}fMfTcJbu%g
zY4QI3c&h;p;a3eFusR7={IDoKlYr}T$T4dHTC)%M#TdcUl*Y%~1g;787wy?270AQT
zYYsYfJwOmN8lDutdko{qy+2554niJaOq}_`Dy-ezWMtnc*#sbZ^T7-Z#gMNUUF7mn
zonYx}mi?KVGW*;v@+JVzq7nm~jyu6ST3nOBb$LZK)~N8*W%HY>l@frWnR&GGR+FXz
zR{v`LifvrFHA%&frd2hK7=GE3D6f6LSE2#n&m7-Xg;=WqfAH{on<PFp9~R{8q!Z+%
zFMqQW;bm*3bt`Xq@us1&;Hw>Qsh$FmJ6eYI443&h=KVZ5d$6=YZ0TbFiJ$yrt7PPt
z%DtENRp9a4OX5^NbZ;9dkIx(+_rJYS4#Pv{;bc>Y&T(i{ulB)m;UhoGhxd(=+@cDZ
z_55-ud{xQ=SM*nX_Tal4p@`rtb1v-%rR-X{?Ul7s1YY>#`^VuvcB8C3NzN*k7p@;H
z{!l`=`~TLd@#&sRdc(TiMLZyfB0zo5o9ofWLK%nfkvCn?LmvAG9tczNB)_;)0r$DE
zkbi9EK;&O9H($_Q+DD#Qo>k9C;F`=AE!RfF^>=Z8u58`7QI;Tn@|N}M<S|(B4ggU4
z(Yx=;=O4bO0MTLyPG($xqnv-?1+wDTUmG3CjvP89FFf_6EdBXMF<|g1LEa&w$H;=$
z{}1tf?7v(bP$o{kK%SicvOF>O0pu%|)DtOk<E)u-^M7uat7cs*K5!#$O{QT+e-?s#
z3V8rZ^FD3y_eOxobPw)i&@o_`)EkziFZa~%(^Umy-xvU#lZmM;DV7viKeUdCv8aqp
zr3Tz?-Lge~SoA&G&wH#Vg4Z)<&60NQ+pCjXw{DdmzW+|8qlN7BE3Z<Tman`xUv|TS
zyQ;KAoS`v1e&R%V;NeG9`D<6NQpySk5${~PK56p#a?b+~)Yp%4`3|s6bU)a;XD?WU
z?~)}yEP{1@AtoE6L`Aoj**D%O=S`Ya-@s<>t^4ga`3MRR`{MRV1;B8|Wbp@helE(I
zb={5P=WqWVYGxH@e6mBirLsB&R#FNyAS-8jlL%V(2ym5~`b9`KtoRcWkDISiWy8d1
zQ|RjD<<&rm;;-n`UYNZ7T?~>}>#RZyG*u_T>t#uWG7RytqbV40&?)VKgU1T-9vCq;
zxMB^jAB{}a@D#!8BMF6aJiSajz_3)5R*4@3h_)=C$urXGIwK`p7zdlcwE><m09S|y
z-Z@fjt%t<N?UkhC$<nD^YjsZlXT!!#l9HOH#wYE(GX$=~&l#W{)@`lh*78drkj8df
z0N>^uwC?=7Y5nheRR{?WxS1nyY!8iylF4I7sAK&f&Z#w4%2N94j{Qb@0NSdf`?i%U
z$M>>G^udqYWj)+<s~FT4SN^;M0_XgV9?(%$k_Lc!`=JzB{(HPET)STqGYU*DyYVfS
zfuFeb)nzicM;nO@ZT?(I7wF!M<E(wgC?_Eu<=Uaw?w+JHaBW>ao|Y@a@A_EaK*lBo
zVoY$wBBcUx`jWHk*I_fW)yY(hDK$`3;q(+9Sg&AUkqiS%XIM>#Li5+gFjbe-6N16m
zHi0q{uHnZH>}cwbx~q;ITRq^?9^iD|R`IOeD<x&x%L8u@=J?f4PT&avSo6K2Yj6VA
zP8ic`U4q1^Jf+d+;pEv?obp#$R7+6YH5)?U%~Qms)o6g%-cpqpCp7@B1>ksN&)1@$
zzxkkh_pXwVkN_p50~T(&2{7}=vAej~6y|}vy$P%~U=8Ifd3pKfW7{`DVA_<v{yNLd
zMresoKKWc?JI6vW&vNo)KdiV*;ZcXmi4OXA_kbHdKE}aLwo*tUILfu?hsRRogQF=@
zVn?GS59yKtrf$tCl#dhA0APE{qutvnFjeb0g3_C_Eywg2D8>_5e(_+Ue03Z`NgWY{
zCXx5Drfl$H^AB(hVxo}%zzJNRKpXVud9@e7<Wa2x<n_bJ@)7Q%ek3yGWt{H=0O-Fw
zo-UWRZY6U%x3($7tiX5j9$cz_iu+>>a6MtgBy7`OfbX9trptuzAXSb(<_$f4folZj
z^+doq0D{QpVe!5&8J3JX%0k;p0E|C>AQ5qcvl_IWNvTkfiFA8VH}gkfzGmO|1uyNO
zzp)3vhr6A7wUi$}D6Uy2q>_smYcjvw+B<HU0z7&dAQczLq&!{B&z{~dSO{`(2XldK
zGM%q4&nlfd<W=)|?J~9u7&t)UWtA~j4%7r=g=HH5wIsIdvp#E(GPn%?H7irvLpIm@
zPjsv6hVf(0&6i8>ZXIO%?n3}@&$Vt$qsN?230#MV21#B)F?dsiG{JbrHQm;o2jsvJ
zj5Fx#Kq#Y3yI{24vKUt5msOi#>0AnhVFT8nkuv5SxPG=nzTRH4;ECI0_Vn}QSW=n-
zmV4nIyv!l8^x4?P*!I!#@oNvsycgb6bNGnCee_9`757{7=g71RN6P*~iK@IIXZ4Ww
zF-=<>64d_mjYnklh8@b<+}diIGQEL)yUI_WzaTpRj;E$)O8`Phv}+Sl2XI?7#&K|3
z_W<KF2aXvd>F>NF?s&Ip_~bEou(*ab4B>NH-^d?m94*>CyLA(nHf_WW7U%S8Q42#T
zp8*Z!L9B+cz|+6{QmUaqYJc4Q0}XOymF{hW<bye*Wa{Hf0VY>SUU9ix_3W>*`lXBH
z1Ax$#W}`rN=USOHp{x9F<so?m;H}Z1@nZn2!4D~d%kl5mACt$@3l->TuX}&qoG7=y
zu>qb@8m@CVy%s|jk(m#reR^gzy2+p}VbUtF@mT2g>N?r;ULG#;+JA=26QBMrpDsTv
zPt6)2Phy`ybcYB(iA&5;zn?ZG$jY53<SMvG|M=H~a^vLgLRaVS+&xOJes;O6!L^0!
zj>@7Z&r|Hf%;#4~pANyun=79zJ0K5Tb(VzqdqBPwB|oBHKKNz7jKMy^`)95lBsVNr
zCCj%Z%T_25eE-CGa>=}(6|l|wGFg9{th49;Z-<hzh6Q@b>j0(m0Aw?k_@qH?q_Bia
z0wPb$8YnlvyjBjM$dkB)9GP}b2Z=+h^F?@OG%y<5FItB7h>*M9SSNF@=!a+MB0q0V
zkd(|K`E2f3nexa_5(by#Y|9I<KqsI*ZA1r|@!SgeZFh<sL|Ks`4ROJfeFp!Od}kzZ
zO+m%v3oek0rd%uqg#~i&U3bZ90L_I3MRN0utEFS7SlPW}t8%?at%>O~X3Im5J&uVF
z3p6s)c4!-1a$I}mbjeIbEG=kz1tRY9^|#$FH{chbV`^!cXA;n05WzU130cTQUIu`*
zP1|<z(sR!MP~RZsnQ8LWqYuk=0H$w#_>l!*#-?y6vsz!)w~kiNHaPKGeo+`4^)h`W
z@UnAjJk2`vMcD%cRGUz+b(_?A^MI^1ka2=T!y_aUZb@9+0MMY0hwqysk3RLZT`TjQ
zJ9o-!FTNzcUWgCQ7MUAGMtt;`v30=p?p?d&b)@rwyWVP8v?m-qAp7^nNqS0(>O%!Q
z=VZzI|M!}*qV+=Tmbd=9K+2(+W(32dUH`xTzND<-Z@lGJ)$FaCHp%mkKaO(U@!;UT
z2!Ik7FUsXKo*$J4GEW?r+qQ0&j3=Lxnb%#f-ENcH*Is!=Ucqy(C@)b!7#9|<V79U|
z(q+M(-Lhf*26^+{_aw-Mm3WhdI})fKpgYxXVTBAQWu{5m+%HZpZW85Xl>Y#<t}HH+
zLx+#3Z=C`y6(1De3>Q{>-}v7C9TbF#JvPu?wq{o-z^<t_)Iu0rT<a=d!)5p#eIjgv
zkxiB1xNg4(oawFur2g`cjq#JpE!r5R602l35}Ge+bAhR3K@6NwSog#74DTV|KLf0%
zi}G@1!GB+q*A_gh1P@xd`MKHh%(KrafdoS-kSLJ%3k;UtolOcoT3T*<;@tN?0q`)B
z3-WB+wo{g^Tq_espQD{t+uzo#mc4uTDvuKS3mhkH6UV}a*XS9;9@$E7eNO8atqk@@
zDHNWv^9zLT-t_d%!tdlzN<%l{>O0mabnIL90NyofyFPvWXl0q9T}*@J^An$}gOJgv
zowD3Qqge4r$opqfSTHQdf@9^}A+d7r^kMQS+=RdV(+*R+08XT57RXnt_Q{=>4rz8X
zXr<W^G3WOl1F&HBWUyiHak=rrvuu{(Wwsu(b#Wtr^t?PQ{ApR;aGfZTXn!ay%9EiS
zu;XZkvP5y$tgvkr*vPrP323ib%cMh8knNOMl>X<!{&LgBgG|rIb}Ech)?b&sgpTuC
z4;b7+#I5&RQn}|g@iJ^z7SEbYfob%7up5-l;2Tk#I?hA9&I4jh`xx5Qy19S~oVv-v
zDQKuvCBLs#Rkx+Sol9+;7?Sp@b57u!s{oW^DE!9|$Mx~!$Emf?zPSBrtGNy5O>iF6
z=0c{Wr<>lrd%^oquq<1)9QRB65fho_#rk(QDvBOZ07YO8rj@~zJT8PX=#Cvb)m+MJ
zrZjx@u^z&)gZ|w;P-+8|rX|Llorh(0MqZ0<et$uUT)%a{d^E6|w8narRR0!Q7Bz40
zQTaJF+w$ZoK6()#H-YJ<=zmBPh&lRNvz4=!p0U91-f>uV!6Ldj^b78XmeKDI>?*Aq
znGabOKdrgD<7EXb(yfWS?Je65BG!2G>ScZ^5Zs8q|8L(|864bv1vTrs9mjvL2k`AU
z1q6%hMK{7__)}2Ef)bF;B008Ne;qjHDLnrgSP~h)cR9i0$R^p!Zcd1i8+)d|rc+jl
zacIrjP<G^1TPjlwyJfy%gnB=1)@$>mu6(q;1ar|Ra2X!r?k<t$@I^MW{lSw|lAegT
z*8H6ec>*jRHLS1fM?7n~*{+1;Wew)lAAVkk9yDNW=PW}9_LBAhYV8rNrk5;RkIV2X
z1Eqz@<Ayd^cUkA<{M{2^HGfSJ=lMbXy2>wKzKA)#8nU{EYYkV7T@y#4AMk4_`NP}9
zg%gK0nvIs-1h5_S&+Y*l{RCflp>+QIbIJMP2XSrPTB6`8oNmg`7{a&b-L<Q9Lm#BQ
z|Gsj~-s<YB#S39it*IJv?A+zcC2z?R@#xpj`m*D=g*`x?M=i>@FvCwBo68tKppCqB
z#|W7@f0dHgoyaMJ74{;@hI?+!+Im#)Na+aUl}aokw;o7?QW(cV77D5ZWNIb1q*lIK
zb40E?ubc8Jpw&sfN~1ym<j1ohlK{|do6K_>K<mgrHvqVUl?AbNT6!(e%GVWdo$REJ
z>83j($WwlL`aHSz#Z~gkx4TuJh4{Hk5Po#8O>d_K0EKCpOzZXgF7E>?>?##+Iuu|u
zf!~5+cp1XH5#;NxVo~S8Z8u$tUoj>Y064tlffjE(Cd;-ZNv9}3IRvX};$L9^>IrBw
zEiIZoQHQ^`E7qQt&yM}dwPJ2znX;0mrF<*od-TICFdpzkTRjvA?vD919H6!*+Qe?7
zdv98Mr@&j_iL6SvI<H2ZUJ{IZ<Gs^joHtwwK-<sDO}Qth2O74?A6EAtE<G$a;2B2*
zdjoWjmU*zkJ|7n6^eTC#(LYGkacUn80Y}E8Zq+JGo_P8h89aPAgcy}@AzdKbwrs)x
z06_;9a_5X1CHLHSe;rt5ElAh5>IAHXli*^5!1Jwl-6QXP_Jw4mXUKiG-3m7|(`4<+
zl`6THm#4V<`iZ-z58PvW*MZSLBHr_)Q6psj6Z0e-?fSp>Ka?@&oiAR9-?@L^ZY5;V
z*GFcY4-j*c&7Z-EAA&xIX<scLtn=`sx?nqfgE|l=$R4S7S(VR*I<Y-EBDga4yvg{0
z%T(1)_|T64V7~6kE9AzRGh_=uVr$|cLLiY_4rDGE$V@*hcy#TjAj$|}iPzk*pjf|p
zg`^}M7jN{XD;5_7^$0S%fr<I-y?5lb7oL-HSjPtfNO#3Rqg{8!jW7D<>$<+jZ78|D
zv(Z^SH@Y^g!l2_B7=*Yz0Lif!An;9V*9Ff$Ek}+30Br_+{lypZ{F9G?S+BsnRV!_w
zJTc|c>C&!C7Xa@Sa8Kka>sS6N^PhgI8KpUr*z1AK=@49Im*LxncA1&T;QB(Sd}se~
z2{6{kUbq*kgpjjdIBI&a`0dj}0LPBMnsT8Hwi&UJk7I$)cayQGn)9ovESD{rrLqf_
zc@8?YJ<uWA8;gG^2SA8M0M(i}4rb>ROIVPbw2kl(-&Vfj1_mS#7CE-l8xS6#NId=A
zBnS*EY0%ljr85>86f$T;?fZA%dP62(bg6v$`B$>+*X8o=8*j+?36o^ix-D?`W&*95
zz8uy?7b2WOo9IX_d);=#sF4U4Q9mYg%&dlc?b)+t$#e4;sB%C5>@&IM>KSsu#g|Ds
zVvXvpuK7JMtitx4@L-`sYjU%*Wj(x?<mBXHjIHk%?kC0{kllOsnHX$yOUf{Y{tl(D
zEabCJp98mETM*y0#O!{vUzc_mXEn<V&M8h(keekp-Fk<tTDwlgLS4OTmE3j59WwLU
zo23-WKfEtJm71-b^yeJ=x*h<~dBeHAWk9!R-AR#WKKV^fz}>wv8@0HC12Ei!I)}^n
z{_Rxw73<5*J#Hy@L=gMUdnU`Mo~>chYDS4MHqteAOKEto7%c3}-vbseKW~kf<c$25
z79(G+-q&cJAXv3@07%YHFCjHgb{<M?G`+oxRE}J>?I@lHgs@O<=@%PrJ1Od^)&LuR
z3-*4tb34v9(E|opM!WR=RBEE{5GVI=rLW-WZonei^y9uO_zT9RHuii^oLkSpyP9KB
zDnk&}8liZj^Md3QI8182x?^E#IesxChBam3O)Kv*xCaL-EVXX5qWtG9VmXhUfBqzy
zIB~oRhr_X)!f=Alrk`n_A%Vt>86^`Zj8jUe`te-x`QY&>Y}SZM!d<&|R%K}Cv@I2e
zIhR(zB_g-XPfu2~#?hCnrFU%qJw0#?R)Vw`wx(SGUu}Wbepru=fs5-)pgcGc*2Q#H
zZB2CN{Z{-5>(ds;zoupX>7X^;oi_)x)*AM~(NwrVuNPucIC|&y=7ZK+Icac%c=PWE
zq!e>eWAsyMmQB!_dFYDV7HAy{EA5M-LSzblgM<9QS6GgNl=D3VFubX{8*7*2+9~J(
z$We^3y~Q|bmI6O5pAyPfELDTEmKEdut1g7>1Im0Bf*f-y+qqT2D!pm1{@Ai`a}%si
zBI>2|k^~v$mI`;}_49{Svid`<cy*@!fHHw>y#H?>Jt|oMe@<7TEAHE`yF_MQI!<O^
zF&R9ghveoL%F1=Wql4fk9P{qv@n_p@KIhJZN0Ma6o<m3v_x)~0xZiHRa;V*c?Od=0
zTH8tLIPK^Gn>|3Q>sFUtCS3sBwtx0naf`68lwq@gf5Axu`oJIYtUJStY^VQ&=hs0E
z6+Y-9JQs44qjTm6m}BK8oN?nFbn-nwKrz6}MS|e%l3Qzlar&4}^2#k|19bJ0Kp0IC
zw5EJ5&>MNY-C%9qMOEGvUeK->)yY&A(gpE{GK1i1nm^`UwEifJH_9Xg-rNEKw(>{!
z;~Kfc*dWl$8RfEU%b)4ItHc7ty>@&@Sp&;&TXC?dAFq|q`|*O;xKl}V75)%hfM4+N
zFOa(w$#s*u$u;9*<(600DVN&?uuLbIJb6frv<vr@RlCw93G#eT%tPMpCP^muM*-B0
zh5U5dlPl!quXoCA7xe`2pMfxQ=?d^>o1M|mX*s15f1*Hw0dTv)<BJ!S<}j_(^W2zz
zQ3`0j;;H4519#%AmqZ{r0H88?l$$T?A&KzHw|-xS!lG`U(o>%OVw=o_HFN@8kq1ML
zOT#5!c#<J#?Tb)us8#{vd>+$Bb&xj~@0IIcS|is2kZuo9{RG-{c8_o=fcx>w(Ecv1
z0}xL;RJjNz?zIP2+esOPawXoK{jfkE4_D}CBAK4X8FuFsQtW^*eBGKgau8Oy2k<+7
zEM8ej*H)CnEvvVb6c;K0anrhWGJVR0%Dr5-9zEc;IT$aW?QZMSrw?Mq{3<<q^-@BU
zDhNhyo-sqRGSgv|TdDvAcJ(8$-u}-WcPb%_D}d8zXo8XG0&m;zTb0%bU-<LRzd*)K
zm<Shl*^&@{L|GlEgyuis9UHZ{Rxzb*aQuqpu0dYiwUe>9X8pEEwF~825Yke1YMp1s
z>{+sT?HXCL;#cv6C94x8a1{XCeqOu?K>q-DK#0Ez8GY`#a^uan3j3Zk9&yHFzF(vS
zP4B<=o-AMTg94@rT0=BwQ6-Nkj3UGB3MGLX@489Ozvv=7+Y-5H<_yV5J)vB|SE9a<
zR^f8@+(%?M6c~2z*deze);P6`s0Ew>z=kobm8*A@r<F^0^C4h_?z;a$88qZ<SVm{a
z!}s1Tad1UW>qT0UfA`f_GUveuE!t&$z<AdGeerp50{Byn1;oIiL*>=C-i1ry0Lh0t
z!&z5cCULMdcZVzM#osTK+vnUXVc~X!)YgSNj_Z1$2ovMC@vwT(y|)G|x?S<k-P}7&
z+G0ZAzH1M@)p7uFP(UE#W`3o(VVop)#E8-ZgLQ?U;vKrYO@`}{gK%pems>7}prw^p
zVfug=yQkTN53UC9>^DcUr5m)_T*-Yn=#=!pfSv);GuBrQC1y&uPH;non4EPl7v8aK
z#2BPo<6Ez22e-RM@oEzw9l>bCtvM>*a5L!zZx9;sl`Mh_x2)7$=`#^bYM99dq8q{)
zJo3OjGWCjCY8=ymT?+P>{rt21^3#v%b>>s$`|6JQM57A$Dvoa7LFU{EEn_R8AJYsv
zdzkd;)lIhTJ^%$ClTemrWFhY70}srtD;I;PVyKnJ5v_1+*@oZY31rlm@d{l3<>$rn
z*{7e$i!Zz+X()r5iTq|XV*Ip4U%V|V7Jng~I(3pIOBPAO>aXS6hZjhEayr&am8wiF
zJ@f8|E7>t)CQ5Wnj6DD9>oOX1VtDuyaO0L$r|`x3eA$nSWyLQ)tGKB2s6wR^wvTPl
zgYE86TKZw(xAHqePRx7eIk{lepC@?H?&BY|#kdm{<}deOb&f@|x9(4tx8Nq+u8VNY
zT~E#$Ap^U@MfcQ~q<|KYHr_dlLah$+3QJ|yQwwD{T$JBFt@+P|3OwCnLS*HR1d~=7
zK88xx-$FWXNGEyt!_`tq;VH_@g3`$oAFYwM=A7T`l78EHOukqh*LeJIwC}=Uon`g5
zV^V?l^PR=N%b*^un_Y*NaP9VEvh84s8glw|!u%C!$KqPc;Mo3YJz#M07v~Q5iZS|X
zsm|RZwS{}(!aY;*p9Z&3G5YipXIM~+Gr~kvmm=oeA*?s*84)gMtDS=}h?^`o*-(SC
zm$-DECpEigi@W42fZF;7IGqz1FZUTK?vYcaeD^I-Qm{^Cd2Fp5f+89vrq`#@@7v4R
zoHWc!{=5$?sk?UREXjx!nu;(N1qB7@n<@y_p^Xj;>$YuU#1|fsNXZDFq$lQAO2|-7
z)zh${XQR9n$%Wz*0cz`eW?J%bzHmK0Zp>);VeyYTMMZr{_4~HUsK460y_S~I()7)1
z#hI6iAng<Tj@i6%aMcay9Ju6ux@UXw&{_Nu+?})Yi2R1Q&w5&&k*nS>dx{S~!2Pu~
zF=lf$zAKID_p~!2tzjYg!9bJnl4o5wNPujm<$8e>3Z?6-+J;M4#Nj5-w>z&;-i7OL
z#;Dd4-GDQG^~4B_IiR<WoPZl{ON(bOw2@ZXjGY~Va_K644es^TL&?f@w^m*P;!?kl
zI&SG?Ux>E2kv?F4Yr3{wgo^#GKhL<?*KgTx;p&}jT9%q47XaLL(BIetROBBt0pZQ^
z#C_FQQpPdYsyRI7yuhF7ns&x~67OUUz-uOO!uzJ5bm$$B`C3G(lKPDN+*BFnYTd9t
zsP<_um$Vx!@kg>GsT8uJN`N-@Dyh3rw}X_X!m0uGB;n})5{}6YF){E!P`|dbSKMEH
zp5s$QSg^eM%$=%gDjU=7cruhqa0za={bU#{uI&-iuidmmS&A7kE_R2<t}ZQzvu&?j
z$N4k62WqfZs|KH@z9i0Zd>KJ%KE<&!6c>4p;A#PknF`@eldi1YLqo+IF0Z}2b+hO|
zhL)*<%$()cB0ps&EK!w@a$FEfrW!7>`OR9#ab~(2Fwk_d?d%6n9vC#3wh9U<PLMN@
zRO>KyIGcG=o~yxlSF&%P6v8WAEz)|lZ!bQ;0i02nM&#vc(C%vRw+6I{o=-KNfo(HF
zHerMeyb?0s9LRR^H*XdK?*Ze-OVF4xS~1WusgZIR$+0i0Av-p<Y9)UC`-@vtlvJVY
zLddWE&punKAhWK8EQEc)`bg^juyvuV9XxXt>MBNP%#4K#CH0+mqz1<jtYGa@E@kje
z$~LKd?0@WN@+Z@%hx<lbSh4}eJkBVSvdJp6NuB3C;+~n-6@6*Y$);H5zn!MSrSr%>
zQRp*b5Vgt@*ZQ7snA}|^4{nR~4#OjvuCmYgUW5nL8%X{4wsU0O47`I}@L7>*jbH!(
z?jb8)yhxSBBzmCy+(9w2qia}Q9r_})`T_xh(_Obcih@h(E&n&o^i1(keXU||+soJ7
z^FFjHzJCndYUeA<<v;++eLIE7Xt>iR&`ZnXezBp#cD{Z0NI8_0r!1IVfzb@?(n^8S
zZ{0OQd;k=`0c-aC$Kggh*b7$ECfDZ`n7=wi`m5$#f@jmGW3aMpW`0^|k3l`ObbjZa
z5dfV%<g*9IAr5)50=7NP{Sped-5)<NM)mE0&aLEwd&fu?fNjRvrXgh?w6_8=!CJ!_
z{9S;~^vt*5mZ7RmPs|zwIj{@7X!@(QKikE!N)ReVi&6dVJtLLtZ@LLT3h<l2^-l06
z&H$|kcL|YK7Vbv8Z*LW^dsxp1cnLJ!PjmbD-;%Cgs{C)cqh{QP8vwv>zxtoN^4jYX
z2(1xsA1{e)-A4MIHBg3+94Xy<^_E@Rx5=s%D`eZ&Es~miTn@zTL7s2qhFfnz2mk<D
z9F46g1QY2F<XeK+%A6sJ3yUBOsZd&|9tL;W2rD#N$q*zCj|c}C-bGf!3VYLrbtuUo
zzi-+A%?=MtkOs-eME=zmpQ(wW8W}pn@|FU6YpSWnplF(ttS?!(0=K?cP1IU924bwN
zGd8kL%Q|?SKaDV7*(F}HzD^Kbf#X)W0s;c%RRGY>JpP1y`x&fO;L5@c6DnOolmqNv
zjOVmw<qEm|?m061`s*bkGO`Y|{dwt9)t^>HS{`Boptaup_s^A^Zn;GP${eVBKv=dk
z`IrK(`7ED);YArab}afB+DM2g9p0v`BpyD52_MDbnd#javI0&l6)5-Wt7ph1mrPUT
zw1EP`eUHwQtEXP9+Q7kZ%f|I;zp+0nmMxW|h>h+64cPK>#8XEs>oBusAY*$^zW5Tj
zir*qWZazY5#w{B*$oUtW>S(N7$2H3ySO%9MC$KKA!8fT0&yb1|S9K1S0b$+}2bYzM
zdq~iW7X2i>G6bz;eZZV)%%)0kivCpLgUP}KcC?^E_GXvJ!Mrj_freR)5sU=hS88>u
zxX9QiP}36P{T?YY$m-+*8Q0q3R<9*}*H~Il4`-P(yOZ4U^lsU6AVnft!HWh2E*xJ9
zi}3u?^JUE75V>Mflqv?Vn~a^^AMvXT<mmn+2?-B`B7_MrE`{(e6R~mI^aztt*Yvlj
z_JT_;lcx_Imbp(puK+n}#`7I?HG|C-iE+S1Lpc^tZQ6B^|9y#=o;G4(GmnS2pFIB1
zgEH;PS;{h51DOe4E2Rx{!R1)QL_|f))M=N<D+?BY$*Z4-xR5EuVr<WzJ!&i__;4&C
zUfjKW>lQiYV${Y60T-2kc0;+R5{zVFW{T|F7bi}hem2vy{7NX3#bHsHk)0>LzJW6L
zzPsh7|IAT+ptX}A1pA$qbet!-xbwlnvUOwxJSZHfn>)FH<J_8&nju-aEw`rC-^;&p
zAE51{R}Pc#U<eD$M27#tx0l0uAJ%G0D^<q`Xo)bn6NuAW-fNedvn6PK`J-RSFPo3Z
zcN-2NhIOhuHhYxBga<U4Jw7>GzTbElt7GtGB(x<4-&RYBuC^~9-c{aSybT`*zDw|i
zZ~wSMLIS<zu^GcHz6o1pZ{3$9*F5#Ta!&;Y##WNLb5l{z)1R)B3}{(@0k`Itj_oDm
z&T3zGy{R2~d3gBUl~At3v%v4V$$jhQtfulhuAk~2fN%zUtHBB5vRjxqhe6SSKRZ;D
zu}lmlhlTnP8ev$HTA$7s(=3^JV;~ebdO)Cb<SB8<--*yMCSIJpJGp==@sz5tY2w!D
zL8;B#uDk*1*CrUsaj5@Z&DPn-i{Vm{sY-ns(|PSLSs|v;r`J1X)zWEOUSoPQ3x)R*
zEVL4PX({Tr<f}P`)ALdL_U+WK{;K6(78FPm5)v!`X3h@;wdqw!PqfmWcF7d^b@@to
zJ&HH=D?1Fg$$6Odwrv|DV@8isdG&%emcw#6pXiC__|r(gubs2r>ho~FDc(_^o@JTd
z2g`O?*Wox8Ny2!z7NDx0s25TIV3XF!OX1d<AZK&w-Y#t{Kx^hBa6Apx*Ve!_L1X?1
za@%@R+PO#%H@T@pqzpr>YH#!WP>y^gzM9qqS(RAFk7gQMX?dP8q(2_i4K9mKJSETd
zMC@yV*w<}6z(WgSzf8oL9Jv2wTxvbJ;3IkKtS-_8-a#~CobIX7t)zpur_2U`tfx;9
z6Ptj!uTD`>e_iVX#-_fdLzJ8i#Q-mJ9}M{w7N1ts+eYkax+A{^bvzZ9V3B$!z<CF7
zZP6uv%K<I>+{Rue<yb$v{k9PE8(dMdCFU1advCRO!Db`D%P8;~dKOL)K&y$wX2SY{
z>&w$>FlSJQylT||4B>PKTIJndj9q6-fQz5p*vUtp-*;H@Yv^qcd~S1zlZlzC0D2!Q
zbCUUo56gerwU_9>!84oImga29tFPY-cdY0i7XSlipVd=Zh1ln}sSLVq^Bxopg=A+}
z88>o(#aggAbvn{GdO+U;1dhw%<7MBhSyBr*LpkJ|2D8!&LwLBu&2?|MuFhM%T8_?}
zC*{YENqK526gkQJVvO+jm#~{|lJ<{124_Vk(5(`{>0Y?Qu1rdjN)5b4T7Ic6i0|AB
zZo`ki{<;+W`m2<oKE)8?8i|0lJ7Qq_0{HFt!V6NFpD)Lsep+%MYp#H7uomBdEBMXO
z88am2{`-aQwR!Et%P&h&T%35p#WusC<*i>Y9srcXAhQm??mBVCSWttsS*#nD-{e2b
zv!E~n9+T~Ih4<Ic*|Q}CAxsb8eu|MFY_qzS{^5tx5gzqIr%sh>+)oMs<}_H4yGKWh
zGr~slevd!%j2Hn96Ew%iEIq&dR+8q=m#pu;lPbtW2xya7mm7HF){i_Q!Kmx_OD{>r
z`|nE$Kx$r7>TS5kBFJZUPMSnUnAy~|N&xt^ko9Q&MPAm3c6*|~Lax15B4^H&Qt;<-
zc$Q`GG)gev06B~Y<l-F=2Gf7&P*v8M^2Zx81pb<$2!N|i5kcbS0L2l(^|CpAnFOR8
z1Euw8?d=nojSe*_5Zg=hN6))r9_kijDZ6Qp_|_T`P$sz{U5|G4iu&$mi)ez|Bm(^U
za@v>;fz(Cw9}UFT&UwOWn?!J$<PH9#qo*Y>Y(%-*b*7=R0Dl@q!i~2^S~|KpC-L4$
zoI5qrD<|+=Yq%08(G|MZeuC2@;5L5CfpitudtCo$dH1I{nSbNiSYz|6J@d%+TmCQa
z2U_%wffiGj9zCU`Fkjxp;O*h*4eQoi1uh>tctBc3M$5#>ljTyxoCa#7tk6<3GT~wX
z04yvYo#0x)nu-8aKXUIql8*_=i0d`5o;AQan}Qi9EYiOJ-{<OA4$a{qqsGWAOi*{+
za6JYjx)dNg2nG_1GYWoAU?O?w?%S2tbP@dRUkS_Pe*Mp~u2KnN8lE(rsb}b|QrNg)
z<CH}vUjHMXh$`PsB_Sc90FGaf@#jvIw_krve&4Vj_f><q*Y1i5D9Xu}Cmy&DKyeuW
zi`!LyvT@deC5r~@4hUip2&Q#Ai4$%xz-fYkiZD5chC|@U80H{aOS?d0wzd{7nHBqB
zyRKR+vIs0Yn;9mS+pAX}6%#uL7KdbDj)Ezp1)vipd3I>!ij}HO6hM3X_)7;^BbFf6
zHoJ=eYAY}Xnihf!j@PbU<p8ej^rgMisU;Qib5eoSf|=6D2n*23aQA#(y8t-`hL~>t
z`A%7Cwbj406mDwoh*riQ*%m-@9bW)YE(bt$K4Opn#3)5vdRl=qW;*KwXIxi>`u<Mx
zBZS_le4mgj{X!9g;}pAiscYaFxYMJPue>p@x4idFf^3h=gC#I5eE}{6__@dpm&M4n
z7sp^7QUC4xLR;>#N5{&V&vwYcjfqf1$x{mfPq_XWbWwLXXL@h(z`ixjnftJG?%qS5
zeCj#bv3swwBD0Ja%@+et8Sl3@7IT+Oze*l`@IL9#Hp;pvi{l9wPLWUFFOysEnTz)y
z8n))KOpW&V78pJwwtFvm``vfMSezv<J@Yu+>eVw|sxYLh!C1|2hYK=Hm_qm8cFCL`
z8Pq#oiLEq|o@H@9C0&2hEl}_&mw5p4>5km!!uIexC8z_d4J^FcckU{$zy4pT%1o6>
z2)R&c(6KtK59z4|>mXeZU_2VyCq}M5w~y|m$dBvx%l3n*5{h@%9@W5QDiyWNGpfC0
zHqR5ZzUaX(WcAJjn2(y?{g0RKmKCrlo;|U*Oc~KlVp|6*r4X$Ey1q_=+w!Gbj>z+0
zu9u`NEX>V<a3dB7lLmLvlD23&_nP4-GhPyN3Qgsq%%?tDD;sww%FP#?B}03)kx&5H
z8UV$1#GlBPFP80=SHIgT*-$nzp@)rE8yVs!cV9eE?t5#wltPF)^V#p^>&GsUA>CUy
zo)K4z0McBK^c(h{z>z_<Y3hjXP3ChX_;Y$dSy&%?UJPNEi&O9@F}PYTy!EwKWv!8_
zLyv*IwH$khsksE(7VH>dsqN(dtvKaC@iq5%sV+@dJc2U>@6N#^#L1_ds>0xlZJ$rY
zNb|Hh!dk_<K3<~}(zx{d3~x<+8jOUnmgrKK!(zwDQ(U?&z~Ebouo7Ax@a8V|EgYR=
zZjjorNf0iY<~;)E&f)^0pruf}7@m*5gZHF%ZQDv0xb_afyk(CFS}$JmBfQ~MsCh<9
zN1(TD+nBmFfp(r-x(uIs@kMe1R@w=PiOQoDc`9FDA88vCBduFUD<GNz{EOjLOYvK}
z0tkL<<Ox3i!V4yw=Qr)FDCMa7Bg?&W&KWBGOZr-zq!$od3dl^<zI{7*b*isZWki>U
zLLu5gjrYK+j9lTbr8FqWSB3=pYqD6CouF+vK<1W^Ht%U)h;`l=#K3-kKv(g@Sg$8H
ztmjS@O>YAMvJSM?%IzEID|EX}pjuC<h<iv{oRTH9z}C~n0O<9gwf-7G<zWDx^#DG3
zxaDa%G6nhUQG0k8dT&4%@zH_X^oT=Cc58YU?$<k^%|W!h)zQR=AUO;7qbK)M*oXRZ
z9s4KI1CXJ(PP|eo;VH_v^iu#Al_oI6%%5A9sQX$>?7QhQ-iaE-v8)Ae<pirF1J+Nv
zl$J%{i1IS+uektqY}&_PT*fD@Qt_NS6fmm%x{9&ORhX+`35ats2;X;Wr?&Fip`%hj
zLk)n?_DR=RwF{X6WqeqN1fa0d7_WoOvL^dw{QXy|A=li!|0o(lAlg~Z9XqJeMioK1
z`rwg7mEObMDC0*AY&5;&qN4|z>H%8ly2CoT*Z1Ee&V8}$nKn&I;l+(JLJfF3BY@e|
zk3W_K0JH5MeN+O+jg#U72V~#$=~4|Dc{RY~41mU-?b=BMz*7<}s(T<$70Qc$-~q||
z@=KxAkKeiC@h}*4Kv`t~Sa)4;fmA_8s{mW(g(u6lPd=$Em~kjB0E64iohxAgst@0N
zx0J&3Wjn~K{o$FDU~nS9;gsi}SH@Ot=FO96geW7JoxNz0?7!+NNk;wo8#hX)*IpA>
zfaZ+3?GNL)65PXXc>kn@Glvi-0NX_fQCN(h|FB_F0z;+>$dGx>4PbTpH{VF$gb9)Y
zci*X>ekwga`9wSc>XX=qCCGQ=uDhfhR@g41MnUPvQ(D9AcLYH5ti_Av$ZfZ&K4l-c
zLPkW3by|$KL7GtX`Tm(RrFa_@wn5}iy?XYPZU_@ci)*fRt08Y;{OkPy*ENoX7U-Ux
zJ4+X|#Ry)TgREyvjC4o70(i7L0MOnOR_C4n`(N?s*in`3_|vi;;4+as5=m|F0E>V>
z6(A0B1vE3MoXP9FM)@04THqo$!DmhuiO-R$mF}=G_Jh%lhniQh5<~)~;ntRCMqF~x
z-`E4RAiwuA%XK8Jx6fp<Tt%HBJy8jlq(d?BP<XQ+u039O{Bg<L{=0170;>sp{QSTf
z_tq0y{63htf`dclZ4A!mMYoof%YTKHOC<y`4ZF^W0U<oHwb|0T5;3g(;En`VlMtpe
zB(#f%H>{Qr#{f)iD73NY;w~^0t_9$hoU!R>bwG<-jv{0vYYlDyYfHt$-%kQD>76Q~
zb?2#|mX^c8)gIAeV$8VlGIZEEvKqkbI|Q&dZh$*KXDkZX*VXd!3(v{f=M0ygy?fg$
z#_oBG!JMf$Qk#kI0UnZesJ4biVtDHlT9@mK_a!ns5-xw!#2K*?iohTgLQ~BW46QGL
zj8yXR1WYUhEa5&5TFST1o~blx*di|33X2iP0oK^8%Lq{YK-}K?0vvnI>VbtvbESw@
z{|Z>cL=Mje1A|=U$}XXjmX;=m4jneRhUe_9UJla>qP-Q|(h{3#i}5_@a+tzQT6&Vv
z(=0!m)fglqh`c<tSTX?G5I`sM$=K#lQv_>a0@qawRC}6RY9r}Ter}DcEKbOi8J$nv
zSkg$1XUwHeG2ZgrJ>4ZUr(BLE72~}yO1mg8xH@e(N3c`8p`mx)twUtQRedD$c%B*?
zg5f^K6RvR@qsbRsh&a|0WYwzGvJ39AepvD|!urz15Ckn4M|}JP<l>7il)nA?%D{fj
zA0yA7HeGu49{_i$KgrkM!eSKfIXPihFCUq4)pWUV@_7;z7$A9B8S*7GeZe=YFGSx~
z0th~s&n~}gnsn>hMJes*uUqdky}z%wbQ^V%eEM-axU;g|S6V*KSZDX^p&iO?cibhT
zMvsw2-z}0QzbwO|41It$`}+CIrI%hR6A(8wDlA0GN{i)-FTPMQ>rG#@sR&xCp52IB
zyt<0clpXY)8~B&!t{tW3T>Whi>)lp%eRy-DiyHQQkiVzo!EI7YiJH7I@K&JSD!k8X
zl?COR6Zs|bI6&j)zS$(v5LQPcM!Y{3T*V0akepR4=>V6rkwyX5X3$yy>RvIfmkjJ4
z-O^^L^sV70{r&sTmnjeZPx8x&CPGUV+OI2jCaPaJ;!%ac8a)bO7r07H%qo(Ew0tSR
zI1Y1IQ+m!ZR8(t18}>ib2Fr>a39<~}drD4`Oq}zX+<*BHxq3oxi43jRGS+US1VF=D
zxFUc2!!@#zpf$ds2xuU`dFQ#xf=El}*#5OWP@VLNxE5`aihUcU(lts90Rv$D&=c=X
z1m0z6(_>DT@?@#XS|>HxE5)e>p-41I8Jg;b_}4XF-NcF3)pp2W1P~B1PW|j8!MhhE
zPC=vWUgiaZO8|s{ji=EUK*d`_;9&i!Cie~AupGy)DWZoPg33ur$+BVNCW(rQlxX}I
zl1H<kE{5=tUOWyRKB5#r8J<TYC5QoF(5;&t&>aam&qPN@sh^h5ddpavZQI6JpSCzA
zj~5fu#^PL42jNhLZ3z*yZU9`jqyR_S7WKeLSUx}7tAkQTZ%)QIxYGXbp`_*{ZII@H
zt}zWj>jsJb;E90%;q6f@Lep3S*Cpn)(8jF12H6NqH%9C`J#fvm>rqZ)!1Y}8fgZGG
z9tuj^0nk2zIM;gGnO7u}*6x(Ep;Ryc_tguQ<LzPT?Sbz-8ZjLmbo%yy0pPXEh>NAl
zCrAuGy(i8k`BL7L%t_Cj^VYym)<Ti8IuD?*4`M@tA8e@m&s8}yg7;@0_1AQ)4t0Nc
zogNHaA!n)z86?I|Qz7H4UvJU6vzx!K+zI#BA011Qqza>w-2v&UFGZ_PoxuU}7>rOv
zay5!C+ItQ_wvmwQ1=cD5)@MB(_sZaDAtjaJOazwJ{*Af`Cx4%roh#Wn`AXK&qYK=G
zcWiSy-j<_H|F9k)07_7|8eppt-zUE~a(R@+mnS7j0xX_mUwu`A5jK)!1mGF~`F%Fm
z^*FACMR5v%>hNo>QC8QKqtde43EmNnv~)%q{x~vdaylcF5<NO}kO%<M#~*%J0oQ8e
zfUGuq*)j<P_^SvX2VI8Kb+!}aeW5Tm3x+{kHTlvv-;|^$o=`5ojj(<W0T4~%J@^eB
zDy{%)D`16P04wW5kP~(T=u9Bk33C35j0_^gb#0@fzhUiKWuYAcOXnnj@fwlOr6qV7
zlrNJ30*3)`*FbBgq1Cz<+@*IwAMFMZ?+jp?#CEeTx+~|<rS$=~>Z3=w><3pU+R!Dr
zo(!nZ1IAu@_uftuCyM`|K~e;8oHbNKPE-y6z8D~T;ONn&>}ZP{@_EI^iW~Z*Bdp84
zx;vDr**Sz5@&GuORZ&^5R*K<LI=Qe4a)xT`L-_!|qOxkqF0Yk5c;76+^-|1%<yAFG
z&c`j^VxBL;d3v~`9Lw~BM^0SDCu0J#Ss8FADt}Q)n%@$Sov?vjjd%jIc7=$7*J+GN
zK-&Yd4j1hE!t&T3ib-L<Zg4thiv8>h83>PKLJ)2e6DeJH`@)Jl2(h#s<miFFx(6DU
z7XQ_)`3vi#px+f%V%?z;v~cOKl9!teYq?BGK6Xs@!!7BF<l~Zn{fxA9NlQ+`^*m|U
zzJsZS`1oy+AB5(g!9~Z6X;Wpx`(1OJ002M$Nkl<Zx^*(`nwhczT1v?$j;rLOM~swT
zmMoU;-MY(DFU(iK^%KuNCwJd?Ed=htG8Plox1WEaCRl>A-rl~l1e#lSKlF&)Ip^L6
zRaRBgogGfwX-_q-bNsTV>RJp_R_8IMVIV|I5Lg-O$;AoQ$8d9AURAFA>TB09_F^!#
zDg+l<pE_-I$uyv9xovOBRCs~e7(Zd6oCARRD=;R{Klzwck!ip+#(Q4$&DSRdT-&a(
z{;XAV?e=MX4%>UY2WMy-(~5<YOf@8wCS?S&MGdJ8+IhveFD(WM0nEeu<B-tYB4z{N
zsz7b?BC4{yF>T<6b)4yE;t(f#V`ed|QB6WmCs%j4yK|D8dWK0+VS()0O*hAU_n{a7
zB?;3vWzu5P9s~QZ1{%stS6W)8T;ft8fl3b^h<i<|d&U>m1KDIaZ4v7^m{%_Ouv~R^
zLp>;$Oa+(U7RFMxGXH^dHSjdCI=xzkNBT-z8&<u4plC-{^gu|UoBB1kGrRygdG)Aw
zGt-=WoU;gC^LOuk_cbR=v(nJ5_-(iUNB!(&@P`7|rI%lB@3K1Ir%!L;ues#jv8P;t
z-^oxncue8%!ABmpo88^ROD+SL?w}@mpsZ46UU-%a=@ru;VX!~Iw`NjpEr8?}k~gfZ
zKbSj326c;;#}HFG6>;H}OL7Px=&~E@IynOC>LZB%UdOob8i8TWB8zh)?qTA<4)QX9
zTzg}0bDB2ltPb++6PL>D=YNpIGzj%shuKmtwXjT50haGLY@VOl2C6gDiv=eRc=j0F
zJrc?tHL@)p!ih$RVFf;Wc#2&4#6nrR`LMudTpoO9r7T#uMf!CJli27$3G{Y{%gt&@
zK-hs@N77|)Qnrc7!tKVuE!C%wOp$KwTLzFd(j>>F7WP0*;eM&kSgM2&<b9oMlEgVP
zStN5ItaB+;Fv*DMg~N#J)f8HoUmaG(*O(G{mv(nT0M-2OtcA@y?UI*+hus0|6)FSL
zO9!_{j~;{Pn*?PQLn{JuPXwO%Ei;X!MKs-;x=Md2X$1o?aFC-1TG9hk5EGg1z$vuT
z6W_aI#ibGtAT<R_P$^IrN(R3l55=S`D7>ExMPLr=Z)GKXGUYUthpx!&k)O`WSxckE
z*gjCuaL&GG=V6QWzLrPZR&lPgB5Uwjf0^;Rd)aXR%``I+&wB3ecztrUUx5KtZ%OZt
zv7iS4>><H^a!#l}tYllpJ2$AnapF(w0UCa|oHa_STSZINqPN5|?U0m$kKtRxFV6J#
zXh-&%fs|Mm*C2crL2M)3r5YHf&+?PCm)Tqi65~JPeO9~rN_B_H;v7BPW*#b0#KIfL
z1Km2wPbbo3ZDzI<;yqVwMLW1|XBq?eJ%;Ntz$m31lqm)TK)C{Q&_4yR)^472YAM3q
zIl;qQM|kE639_%O!h5d-uu=g~j54lX-8uqyb10B#ed*YqnjYYLR!98aAY%&!AgzJc
zyhd55GraR~7+`9?$7Kl#Ql6eJ?om-pQcwEw>d&e@g25q|Tq4H-5<4*_HqMp<kfuBD
zXoQudD{c~HvSr7QO9fnV$HJ<c$3-xBqswpB&sf!rO>F@1UPt&wTmZ1c{}#S6#3ugf
zCrN?QKiz37GRt!7(zBh&(+cju2{bdNbrD>otFq~G{E9222v*F_u)<cLcU?yF4%*;`
zFq)A!+#u<|AlNr`WL;O?1=Eg2pPR2SKaG6!UpQlpvmJ*4T$7ll8m`n+Kl;c7^rH<V
zT65>FUM=3R+V(xmhL>2C>fiOJ3~N0abW}iAkx~L9App=t1g4ANtrsEI3KckAD;dQE
zq%rP77LAS6z+jQ@T@8(@@D5btS<w?L*E;m(#;xgxYq(CJnOmMCAdSDKEw7;*K1=*p
z=jeS<1Fng8sUO<OG`V2;nBR2Xln)2Erlinl5YF)taceFIg>eB3U4F>C0bKh*=F!T}
zO}sEC2f}M`n?O%#-^vTt+%7OQ@Q`+`ycNjpgLL|M+bq^Wjvn|UJ#fYX*VLf@9`PUc
z?f6}yqT5IS7|(FH3F!{^8<)Yw2i>sJvhpA_rnYX{EP-$>-4s#CLhvdVuB~ZB6pRUo
zF?1DS6!3%PG_A5r0aOv7RaUkb6e=)+8S!z4hJ{NNj;Rqt>+2x6&^F+>3>MjRNo`GP
zfI_((^lXuKU4IQx+EAtm@FrANM;JiMs*0h>QwXc3dW%e7k3jKt3xHNoE*J*=G**|>
z=8kon*p}uJpN$VD$*ZARR#1>H^PhZN-M=d)b_%;#l&<B?E%q--r*2bqJgYNV?{i^b
zngtp*fkHXcKPIQkDQ5}Bdtl+R12?O`-$1$!cTlr>paK*9x5sj&8p2^t)O0Hx;%1Or
zdxnckb%ku+zFjTGs6EI9na0GKURU!=t9?i~BvU~*#g(v(S4s+aSGky@24fkXl44S^
zPXL?GopFfOcaC#RPvSGbu1m1&$tjiNMb2^@La}tj#>y>&@(A*%?@Tc)Sm=X!<a=>S
z2<24Z`~UV>j@*S<$`<d~DXG9wh@%JUdw|R}V@{L!&hwk#)<LIP4`57d8xbH+&K~uL
z8|I4f@T3Uu#SIq?knsaM%In{5QgNS8WCFk@(Nu<GCmAC>q1{kjCIcj`;J*8&D~8IP
z=|kZur=b?GiUkefrvjO=WqkF<X!L*%vi7xE^86=jRa<fp#z6t)<mc2+mz!ll8`=Xv
z^z`%k$kR8Bkr~e{gv0{#0tB3m)fF7zE#EzJxxD!KI(c>BR>{aMl1u>izwl?059lf~
z?Yg0!sUthfW7mz6_L27aQhtAGB@O<Pi{QV4s#p*Dw*rD<Z7%&Q>ixUws7!nx&%Fdf
z3R4TztDrPDEn7tGcxz(3Y6{^DZRdzF%w$bso#x+qeBi*rI@jI2N4_frul2+pQ}3|;
zB=)(^;u@h-rw%f7sC_GSt89+LGrR{FBl&EAo>Sq@Iu>!C^^^dx`lDm1vMN1S3Y(Z4
z^!b{z-_6f{h>hmtZ*Ce|Bikcdl-eS?>9%#hr6)08q?uih+p4F#pJjjR(@Y2tk{l=n
zymAmRNgG+~(4G0#oC5Wu<@;bf%Q>A}OB)A(t?$m$XCLFVF(y`=u6<m}SAHYTTYePh
zsuBR$E#yVu{jL_MKsAgf4DOI%K@r3RV4L3Q`kB@09S5tX6vkiyUK8A1jIkGq!6&lO
z{^9z>7fR@t#zf1w@KA{>gbXmhP_h6%m18aL4zM;D>zFo>-*tq?s3=-c+WEE`t>`qk
z=mE>nnATyEky9-FdN%GMg>!C5ke`GE2S^SSH~aT)JpE~KdyXdj8+rh8`6yU9H%9#a
ztKrGC1}?Q*MEGvq5r3H$&RGCo2{Mxpt%VEnjIY0z)^NQ|VqED=fXIIR`zveWDuA^a
za6fK9NKmdKm4qK<>8+NPoWndWuzh84E1v$zClUs4a8CH{tciv%Zm<CNXx9$m3tXkk
z2Omfl>ZJR0-(kbVBPK?Y;F7!o$7yJ5HOdPbJ65^iriHUB`pFq%QUqLidv)nzS}$8y
z@&}I@&)N^J+Fj8`XqhUjc!uxH2S{BAxx7ywQ;0FPEgNyaqX7Q@Td5rDxkP}{M+&MX
zwFCflX^mv!mk;H6gBuOk;5`)vg1~v9@B{1UN|W+!73RKb{`gV=+o61yYaA}s>6w?|
z>(r0$p#qk%)%ML>oG{6RSq6ax?N}Z1n5T8;8|<sfl-$Hzu_-+pq4Juqt9<6N&=&JW
zB~qxsS}nh{N)lm!1?e9CL%t3SN$ul46u@0uDItjO+zD};dqn$6cNk2a-zQwc%rT^q
z*g=jSIH?{uNh#w=)pc5BqGjb0XjajUXb@Z@7~%5A2_TaPfGt00bNPq1k|<0FUEt=T
z5446TVB)9S6lGyDOV7*@H%vZZ;StK61M@IeXefZ_fPg?rJDwmXpuyD|lTmy;CIA3m
zA%VfD*B1jBY5}-S0bn@lC-5BycO3Re4JVB>%*hJWc60HBn+Uj1DTdn!2vwoSqsBPK
zU@ogHm9+d566J@9$xQCf9?~mfkQ~h2Ek1?-1+wx|ZH=0)wPbZ$HP+RjT&v4G-%?^c
zxDlg9%d5{rvyBCyy#<Ji$AO+;;OR(=uSzD$G=Sq#;yv?(yP6zm>)9eID0ukz;+`;p
z;aT~3`^#4gzr(u_V7g};nJBb#Q1g0VV@5F+pNQ!K0F6ok-Vhew221O3xO3jTbqADF
zN~8inG-HX^lZjd2v0@sy)&Il0sNQq@A&||`1JwF7I#sB0_<T5CxWN|-&l9}klA4P@
z{s>_E;~O6y3S|`jjDbo!FAGdhQgMYO;+KTD_GtjKa{y)&bf-HYlg1prSG+6gdtd`w
zNHvJvwIdhSfN-^YRwFTMTUe|kxuXXf^uXTjo8`(G*Fhl)!l0mF`SRO^(jm+;&S`^8
zjuU@<4`4o>JfxExhgI{@6x;Jua~qYF2hSE998Z#B0HD8c+eEqd%3-n^F|L>INRUGb
z>2feNA7Ridly)-5Qd)bo3H5<ha<Gi)8zU12#lpR|uiaXsLVRU(|Mpl^!lMR&+`h1A
zZi?tO{Ixmf%iRFOf8KaVR_#iV1F(2b&MZ=z(PRkS0MK@e4v@h;V&u}1-K2MHgetac
zRFD++!lDk1?a?ljN!1)+t4;#m56v7YGZ0(+yS4je#kQle_js0M<v|#OzN8Su6YcKa
zI!Fd~jgl$DyGq~AjSFB|PcXoOQGMHr0X${vR`#uFY_;iR&vl6oQvGZMPwna1P=VlN
z%ROE5Ifssss*2-cEZYLtYYYoSUZ2|W<Q*(q4fYlDKY2pzFr7y`acX<FI7gUV3|glC
zcOQj@2200|9puQ7_&UIve7)kg&~X}nqbWLZYvP|^Spu&kXZ7zZg9Z&$0QSGTryO_J
zoE|tA;3s1|+aku14#pVkYs2c8>3>Mhl&9hnpfqmv!CDm-iZTWOyjg;n&@G{0gpB$t
zh~rC3si}KzNqLQx#r|)M`0VI@{Hx>XvJ$b%lVR=N6m87Pm#a6$$y;Z2md;j+2u<a6
zT>tBOz~JRA?&n=CRXvAD&8lz2xPK$8wqTIK@m^~Ncba^EF)zT9t-1m#;%-<I!`Q=s
z&@l$ir=}@Yl}1AVSA^f73*rjjXfbp;SB&8RRj`h3F0m<rkj)I`FBr;!YHZ+HG}H`8
zHMgwOHJukc9liM2ZL)sLUYU0O$R?W^-YQt$dGTIJOiq^zCJb#dy(5962mVM8P{!*F
zYhwbl%^{6vY))D&jSAZdFLLPwq9F(5FjUT%(qDZg5wm6?<d2_JL3W$>+i%hdz%hxj
zt@Bo`Qot>bnTO|cSFBL!IZ9L_PbI9QxdP&-V}M+^9C4^i4jvS5czM+m(^J0Z0gGw6
z_9nT|kZ#5dwa-6Q^zI12n{`!VKO3&mv+%3LSVZe{cjWc!3-3cyrbr0fh5sppy4>+R
zTER*^<+<nT?!N~2nufU61g_~)Js&a33CISH81bhx<8&z04RcOgA7_d1G{VvvF72Vg
zkY2{v)2@<NQ434!a=813)io5ZvM|r(Lz%k-C^o@pdLwZnKteG-*E;AcD!x;$Yd~59
zWXdXJE!85_yaOQEJoT8~wo};yEEuF-LiGpx=5yMd$Cr-GCBvtpeQJ@b+FB)afZ_#j
zbfIt~9s)0(e&~-#gpBJN=_CGF|F=T;!*-!w%HrA^G7KNc1svq)fj`;<XY5-<HyC4~
zJw!k&<3tkTCFV*=ZkFT~<Vbcl7N+q>WEUorUzYr!1STD0yT}jAR{;F7uhq<lv1|J_
zDbCLm-@p**+^s7>X_LhnHEp6}Vx(KIKC)!tH?sKq@8ss&?~qL!HXv86^nf;3KtKS-
z1PlzA>Ih_Z?%7LOh$b94qS_P$;lD;0?o@1p8UV<2Ct`~z^sJ~c)vsMAkQy7>OZH{$
zz~TXx-_Cvzih4;wSspZLs%3x9F6rNTn3h~^r*^$o5_68op7d=fuS`vzl~r(mVv)*p
zKvxq4V7b*(jk5B#Jjd&_UM<9=A8aRvcUf790{>jt@3<dtxI;UgHC*XFIw>^;af_kl
ziwP(+6c+1n8AH%fPh4D)D2xk^2$$;LYvGdJsQRJ|uIbvfw{J<MFUPTOzX$RGP<)q2
z_p)FJuxO<hgqfX#CDz|vcJJ9ER5IcFN8%h^FEllkspf6G?TR5**Xz?TLw)-CedX>2
z-%%~fr4=^;Y$}Bq0kkk~J*}{rhB0l(AaER_f(B!YL}4)=<LM#85Jv>)wgR)W5r-<d
zq)Ltzlqpa<9n4U6DFoz<J&CeRZd_UhX4j%U-y+s^cWAzO06=$;qX+(s9;kv>f}>E(
zDgkiqUj&6Cymt;d4SN6r{kMPFC2#(?gQML^H;{80$3!ls>lQyJSy~7zkuMqDL;bke
z%PTIEGAQ=YZMG3(CEaZMK=9^fcH!MrHIs&Qkx9eMRwBH`hOCI)BHBg<%FR;;sb4AD
zPz0g+|Fd@=fK}D!|Hq%~MG_JSVK3QR5C<;Qsy{~^b=6U=ZMChfty;T(ZLQXhUt4S2
zf3?fiWm~Ov)=|X`2&jO7viArO5<>R;f1Y!5lbcHd34&g@@2_5R$GPWx&+FWA?s?Ah
ze5-u?YguI}`zJ3)SvMY$jA;M&rL%&^!*@kL9=<>FZ+``sufOP+;Id20s8FE3MrUjL
z_m*do=jlgaS2{jz!XSI$*H<?`r3)(@*{LpG|NKQq+vhJj%4y#BYoLpZ{!k`Q>UFxt
zh&a^}%NAMVj+d=r#~f>_SZPW1J2kX*h4$lh(we^?5_Mv%P8e-U(Rg#&V=S@fNgCJh
zRApDh=YF17w>tm4b96#yNl+eq{d)Zd)3|??l~ut$nP*@7XNkXWyi!M|vOV%tl6C0N
zAvQu|2j=H<0@R1{f{)RKdSG<)9<q8n>b8$tecR&I!3j~3(<JLXd!ouwPf>~Qp~~nT
zoRw|``V98zyBfGoZF5p=<WfHT>z<|UCAd9^7}-9h^4<OSP<lLa+Rj{0jrx9U?>T+6
zyf2R&7+f~#H_n^Nu3o6IuHP!zWoz{P-(D-%{=jXER@+~vjce~=?u!)xZU5v!Bdqa?
zuUq2E#n$k~Q<k*+O-oarqYta%Cq3+G&$DIk#lK6{JOjJYGqCmhI6)^5CMtlPtj~ZH
zU9a&>x1J-dY3K=-(r1!_Li^zay5{9}W&bNy=sW6%f47buIV8ArG)?6Fesceb({us=
z4S^3zz&{tFzMJ;zqV*o|fUA#5y-R^-KU(g`<m#3$x9v|o6^vWGLqTdEgS(ri^B`lb
z0@795Uk!g_djC8~z5edIgR!W?{male&*m33|J3qQ+uErOZb$#)L3$rUyQAi1&3&xs
z;J3Br5k9K#0nWyf606Z=yULgEP^S3S3ofuxzxhotesxFl+t>TOBzl2@;~ReedrQ<Z
z-Jn*-GK*!^|BGJ?vc9)J`l$6d>nxpI6bi!c>#}0~c`5qI@5gP!-wAy({TM(vq}G~M
zU#_vCsx(BDk5OHw3UQlME_|ng+!YFD`+ZwHTU+;)>$|K<k5}oqwP$qitk$s<{q;i)
zb&~eu#!Wir>&;`x9)Jnr2)aKQ!`dJ4`*m9r>#w#>qAr?;UevnyiDJ6#*L}j^xOjtN
zTB-)B@3&OVJ5}qGqol4>zpW&%OqB%pQr`pfGBlpGPDXDY+qy72U1h;DRVF++C>x%y
zVLUQDihTfEovi#fVXFGjndX)5oT8)YYgE^L`_3xsM*>%8IAdy>@VhLgI`u(x#>cAt
zn8uSh=e%?Eer~dIon^IM2TnF96MB_`)(aOdwAIU&+1j;hti-dZw`>Xmrr~nG;Xe8s
zDcn3`;NSmxSYzPU*hi)v6O4)N8JFICw>(W}OkI8LXYIMCp3qp<f3<NM1LB<pZ(C}5
zhFx&c#kO9{_RkY<4a&kk`j}&E;LxGAZpBKQ@$7SU(Pfu~9t{7hX+3b=7<_n!?`-{B
zs}I4=WovC>pCeoE)U{7cy51)Dofdou6lC>P$%kUQ^~D=Bo<)YuTJxe^IOdZYTC!Wq
zb9#0wwC|nr8(UGl*orHQb#hRH4yGpAu>8?2(}b@CpPAmLsK9lD-&TKH_WzroFl*-P
z_SGA{Y!_a9iJhU6Eh9#bvh?OsDqFQZ{-`ft&*1W(9<>UDOq{46BGEGmFZ@IJo1z_x
z$hK(FJ62q@RX?DTb#S}Vh7BJTWZ1U8O58wYrB(jjo4;mGJpKfG=3ftK$3<o2b>QxS
zd+xEH{Nfjpvkk7lvtWVBj?~)ZBjRJ+klg^fc*ipvcG@-t%^LLumZ)-J7Z1+0<N9O=
zRStYC#qfKx&205r^Y?U{i@$p#?~A_ekLfd{F+pd_`F?@V(D!FldBAsNd@rL(??n$}
z>r@TDC-gqh>%fEBevsBn<W&Wk`oY7Yv9k3;v3IJ*4(X9*$9gbZ(^qN-XuI}g)+sA{
zoyJ@BzgwyjY=?G!8dNpH&)oDK6yNy{jh!9&$_vfAsb1su)n?cWn|ImS19C%m^6&c=
zShseq?I_(D%*Vec`fI$UJPi-gUaKtMWvkY#Q)LZ5sY0<2RpaQ}r!`}!Qa=VZY}^!l
zR`~P%$5Ek%5Xe<|y6r_<Y{TZFV77gAHgd0CtwTGkQJ}cg&(75kp+SQNS@+!5EYxlK
zJS|cG{ISp%O@a+O>@dx{-j=+(R3(k;En6qq3_EOSFmL~7r-r>K+P<UZIsVx-aNr>A
z3?^GmRi&-lu&MdQ9QrIPEbOH|X4|^8>umMf4Z)BbDSD0j5?8HQZdTUa1`g<N6}!r<
zxKzVWGzYW&{Z**-$kiFne$v(&*#z%TEzj3ISL;!zvwi*ZZ|TzIL0_}FW!s2ht$l6j
zbn}%B>(<(~9i>6=WAKonmYbbr>(_5kIsYnsCMRn>2I^#z&g}$8&-+ljU9FR>Li-;6
z!S#OH`+ffI%l7dE^Ro4$-Y@T~HTUg(bTLCe;{39==GAQU#@O3r9X{?sdB42x<{vH}
zc+)aJjKzN7>)E_O|9s1x5L}Xn=xtPArUUCG`m54~4GOqu8zdxYTP9`ez*fGFZM5!N
ze^_0PmF0Q>+AmY49InrH9k|gxP_@S0^r2Y1`TkKjaGjCSdN3$f`cFR&Zh;5a7KQfL
zBUYog0@S}%7ViL!%N*VOc;KPd`YK?&N~O{x+vuE3JGoGwRUcCGKK1QaU?0`R+eV$p
z_4vkZ_LsF=EZA3x=+}F@Duc)S?T_!vi1~7L{|h8)e>-uczAJ}~v*aQLe-_QQrloIK
z!nQS*p+pMbZSceE`t=$lWcb@HIF#fM=;A+J{5ObKCG}zVJc*&PLB~_dbzg?6&<vPt
zDZ{5)V*a50-+AVUK%5Ks`!Pt~3XUO!F#NZBy_f3tWQ<O>AQ-LZ#X?KcF^}F?TwyzB
z%+QBTs84l@2W(arajwdA7i)a#-a7W~$4#1ajKtT|*EigIKLI3N$LS|1OFKowgYK;b
z>)y^p?$}ZDOzhmFj<S3u+f}Lzw|`sv)Zt5T%u%-!)Gr?!d*hTTHt>cUI-1=+-ydX#
ztE73snP*zDeEevp_q%cPX504g!`A)OQ>|+CY8$5UziA<XZmRy_7rk?WJI3U<jTuBb
zh4(LObUbE9RVaY%!EKL$=#K)~9>5O0Osf^(E>XE|9TE;=(?0BrAI9;Y{J~2}|9G8X
z5Aylp2o(Ueyv$@JEQb2wN51^JCjxj7K26`vnYz+5J5@!0^?mB!xBkxu*tV8b+5OMF
zt4dmlcIn9@tWOUe_ik=4&);a<G)%>%r-V)h3E$t*-<RIrr0J{dvX6{tcaop{{VmJy
zmS$f#e|)e{(e6at8IuC>8MxLP+5?0>u0*)ys9bZd&WIW|Vz`}f$|(vmH3Z`;Z&<h9
z=FWLT!R+O>Q<=dtUwt+B;P9Pj-+}g$;Ndn)+0)NI^Ni)>cDFBlNr7MmFCxE;d_0H~
zrcbxe-*mJ6>%qU;UBA3jLCzvO@8XZ!(bJ|WptdqRtDsbPfzI-}>QkSw|GoJp`{N&e
zXJ?&vUXXbZp2){-^be>e1<2ZM3CT8R!>jf`BQI+6aQBQJcJ#ngg8Ni($+qs=qz|f8
z1qy0yS@9Bke#PVV(J`$-a(#s9tePwvTX=*%23x)&gM&(KW~>`Qf7<I)@Zq|9KOcMe
zA=|KOrQQ9DpV^=O_y;>|*kRUpz~JE1ciED6Y|V;gfpLRAl+!c2**PD(AbOD>%5C4S
z$qL@Q_VUX%<*1{rhf2G6pybE5eqa6ZA7IJyY_yZ~MefbVJ`cVG^gJ&QK7Za^yXNYv
zRc5-zzVg+tS$_qCioL{qdYV0~*G8#=I;Z~68J43mFdNsdu{pD6*~>a3aFWW_|Lx&N
zf_d^!#{%nBs^!0%b}8#g8G0%$d2&ITojW8?C!nb8?6Rf72?LSuO}`#oG)uiyL;p74
z=P&Be<9^1M2hu!99XiX+GoBm${pbJdum9){%^`Rb1Xr7aodACqg+4Qy=N`OSg0txK
z+7AEq8Tsc*gARE6Ne$IC+kNujv&3IF>H54W(3zP7edk9dSiDv1jJmDW^|oFadTW%K
zy-_<en{~ZdXY4g7GuwY?_|CGQEgCK}-l(Aqp59nyNA=9q{8~Q;gBkKqmjqTTAp4!~
zecxuh^r{t?lm^?+x8abXhuL}OpJ!kB@)xW}ONLm3-Fwe{cK6+Xu*C|%hRdURQ1<L|
z&b3><`z`C$BhTi&G22c*=fdD~!#h*0?~b3UJX2~@js5hOzqWN7n*-PV`q?eF{lG3d
z_YAxByWg{W?)$Tq`433F*hY>XW54{x9d^?3#{}#1a}Bw0=iM5UwfW566rB`t&+mR^
zfB5|$?Y}epd#%y3G_1kL^nLWBAN{}zbnx=QyY8}^ZvUyJchm3^dflh!HTaTFHX1v)
zzb$(E4LkWPjoGBvm$xb{_s%=-uupyB68qSB=i7U$)(5-I{%~bkseSyMvxDHoOEaF+
z310uQU;O&+mXnlfYHICQzx=s<=2M@vof=c_<WtYIl9C-k(79Sa#xB)o<qg+-(r$#k
zc{tQ>`!^nukQ7OgrA0zg*6gH`J$rVgu@5Ty7!!(5Wl2KzeP0H{jBN}lvhOp@U`W=P
zv5lF*7=AwYeLv6reZKeid4B)QaXF6nG3I?;=kmJF^L3tQ>y~SVvYX>(xZxPP6Td@f
z+RWCN$m;D8^v1k?+{u9B(Z<-5ki86eOyrX_+~gK7iC?p6!UsiCOzr~VLN8Jq_?^NZ
zxv*?7Vq9xpZ*3+(v_{Tp&4Jl(+*mHXVozY#+3<3Gweu%;J2pHi<ORnun13)Q1bC{8
zFgde6<1>sc!Id+<o*a3AQpOH8A?aA-T8NJkDG_Ejw;%VrPW@ChgEpMe`wpFxVwKC=
zd@ATw>nh@_C1V>tni8^`ED<sl7}*=GtH`Yn2@i&-KDfM@lR<O*qtihFV(+k22f2Kh
zQVlz;Mo|6r#a~Z(@kIq5-r?P;Ux91w^-eXuDIsxIGUZv+gX=1_XeZzBc(0^zi2RL`
z@<JcDO~M1Oev%hE@|2D1##NP9m8s#kysil_pT2UANwa!BV)lYis`q#;VROVexwqDK
zhI%iZhTU=W8TR$p<owxfbXa5eB|gSKM`K2|E4H@$=cg#1$$;`7wu)XURVcEt*EmIu
zH8!f4Lc3QARwH!%HP-++e@ZNed#<;Tx(>V!p{d&KO+am<WHV=`A$4@?nv(84_$I+|
zpw^<hh%J28pt<2F!_9%eymjA-wY+n20!$>uLH3g@%T>IqdDhJW)IOmDn1m$Hrdxj}
zrV2su091&79@j>yS5h>s&Q_>})*C1zJ?TD^JmOFmyHg~nPt^3SY$$llZ_90&n}K}!
zi8Nf3J|<4Jv~2!57w8dEl@<8ogpm_eADs8_p6*N|a(dPtK2sdKQud?&Wky5rr<p)X
zB1@HyOjFbvzVWyrXhP4;GW(5`{DHs@-63>*P2^M1q`k1`wuhQjrS@fPYH9sUu|h>;
z|EewH=~~gN8r^tLb-GI&bIqb^ACc-b?O|Rl>>>8^Cva)KZ`+xw)W$%meAbu^--;5M
zypb%@v`7_s7dg#wr$Ldmdk3)F<5k_Dw2br^I-=QBSVU)}_H!yhPs~q<s(iE~ObcWn
zeToNc>2*SKD?c{JB2jwby`FFS23LG$%-&Sy7e6DUyV8&pK|+SykK!F}R<-YAAyl2+
z%DheR2!vMwpKFp>k^iC9qA}HHb56GrI9orJ9sc{nu#IN#Tk@H<M`ZrVNCf`u&8=;|
zYze}`<J5{UDwh+zSg2aAC*Lj@_ZYu{pzS={-HI>XqN|4X=Vauc`Fwdu-38``^qFZ{
zEbR~bVENUWr6bFhG#-0N!Pe%F2t}_-YwBGHc1p_fawvo=^z(9)xG<M20g7B#X_;Sw
zP7OF6zNi87eudMCya_nSQ9)MH66`=tYt8LoAZ%64ZfEd;pi=mQyH1Bw4(*UXhaiS=
zg%wxC!z^t@V=-fmd-?6Xf$Q~qoa$Z<ZbtAB=qmV+YW6^?vv~a?9MzqEa$+A>_%`5{
zyAa1S@!)}rct!1Dm9<A3+^Z-S))sJ#<}P%Pm9LevK33G31Od;cC&={48FexZ%t4pt
zr=BSEK9n4B(Qt(iO?8Lvh`vm5jM?M3e|9B1ub=qzu3cy4GgC{4&xuH@v?)Q$7hy>W
zE%|I~JHiw>aVg4yE+!SLP?tx_Xa{ZdBgit%<Fj^f+Z@YKWZ85)n<qm7{$fv-K2n94
ze1UWl0sYCr!np!ld>}M7%`woFCWP;_v7TiYI17la?^!>bkO9uBej=^vikMlI$nNY2
zP|nChNV|l&grK9Yi_#<7stmtv-t%g_KxADjic0aF>^$0qsh9B>NnBRec13AY2M_kB
zQv~_wDTAX1X4X!|jSAbWTf8dD5BP5HsO<MQV5ZR0cDIehSwp%S+(PkhFwiOsyvxz<
zn8=PpYMIcJIBEOKYtWp%)eT->jlmi>FKZ8t*=_;KL<L(gS*s;=-h?Y*qjY%}+!h7_
zgQ%HqCiK?GWl!wp8{`d#y50+W&2$=|pbeB00*qIu?8w0_@wF0lTOWnl6)XdXe8W91
z+e`&6C-|@n!A3zVApD*aeM;sqr^xlCN4Dcf&8elKG{CZx--lw;_a~95tuphJ(<=R%
zr$yhMU_LE+PKg<ya{uDY_ILX25$+YLlpXGr4KD2^YQ{3(?8`i(fT1`R=(>|QyGP}Y
zlNOq4z0%CKNk=3FtdFU~UxA*!P~TQY#ED=R+Z&(@PH68BnQ>LOp;&1@q3qm(*PAw2
z{vx45W=YHRY0%rWJ3Wf(^*s9$GlfYN4Y2Apv3(=~RZ9`<Jc$vOA5D9neKzoO_1&4r
zb8~!~&ToqKoiQG*OMV8U8;2ihvz(DB^K_N{<64_?Ybg8zS6o`5OMdOJS?Es9EJs&0
z1AhcuH3oZ>0Z|(og28l>Hal^SNl5dF`2!nI8CLYyUl#*@Ko^@vtixj0^4Ubr^n#a#
z!Tvj)Amq%-#9x0LpoW|Gdi|>{-h!8jQ&z{3BP*CsmgTD1b#ocP^)>~8AeG(eX+ma;
zMntOg5PG;+nd9lk=$1P)?oYSRn!>@00>hgjPqU)iHBMhqtxjMp?g7w73obcwz`q!C
zLvI4YRDV9+*{I%tz>2&KlEt#}PWUHg$%f=I$UhfDZ)3udW=yMzNMc$O0r)vS0)_C2
ziM#R{|K*-zD-7Fq)NZe8Og@@-TAFejydrp}fuQY!?GQi5U*aEpGmTAyVYb>K0`Ql%
z-%RN$2=BYNKL2~cawtb>XlW>m*AVec8=56h0n=xjf>Da-Rd&9dk(l{`S=7-9dgrkn
z$!Vivhna$e@X(4X&O71f@N$00kflE%%aH!+wsp=xyrys+Q&cny4HSa6HCkq3l}TRK
zv{gU3?uOb=p4n;eUC9NFC&&$;w3*~`fFORE>yAZ>M+b{`#P<M`vi^sJDe(^GjH4cl
z)$f%*4n+O(nis6hY@Q>Ac8k^P=a_JoLXu!XWiSZiCBH-|Cm8_2Jt`Dc0OD|^MZQsH
zyQxXC0EDa?iYd>8=Qu2+y`E^8(00z#UX1m+@TYXr^7$%<Vc3EyKB)r}d8Tz!?VxWI
z@u<eZ3w!FC+OT<566e9aoGxEoxC*O|WQGWI*y{zNd(o<T+0p`}i9PZoe*;80+Nr{Q
zVG0Dyv!+1f0~R)!C>&BZV&)K*!<IJLHr?79M&C`&8Q=Yu%&}5h8P6=~7+73c`P~t`
z3TXM$D@7{y^(@gNSKfvW$7@w}IbpmW1Av4z&z;OQ(O_j;akbzyJKJt%-;Gi;#kczY
z<y?ja^@Of9LvPle*-%j1tX+UKQ{9R(ON0y11R@<Qs+`F>!()%8k7yb^ut5vIMEi#>
z?Co%I=<m~UQO8~ReA#FU$DPdZ2094C0>WV!inPJlWaKR>4VjZmt<Q}|p@l7WoU_?#
z!Rmn?;(--)JJyx9z&HM{`}*YErZSb^028~Kr|QD97RSHh>enR#r-p^86ZeoradWmw
zp!uoERQW-gGFTQE7Mdh21BC$qz&@78Eq*OvCdg4ZrNRk8OM#9IpC~K8c&~RIxsgCU
z?t+Y)DuLVx2Hz+u#<{W<A(8vP&$N(qgPxU(gqdnl%`x^aLmK3FjJ5b9^Rmrsj(XS0
z=~TxWk;5J13^mFvyL#tr@P4I>{2<WboF6y{oCw~JhGO5CXnNp}mg4rWJQ;sI=Dke4
z23ktHOx+`(UYKZ7zR5ySmov|Nt=^M%s{17z`ucIrK}H)jAVcX*bes@cymV#0h`?(u
zN7B`7>C6uOji;9N-9ay#Bx(4zOcfj*w5T8aS_te}*6>QWwX&`n7e4n(W?s>C)KoEO
z$w1Nd$FQgtk#{f1>SN>#_rc;2HA6LgCE$2~Lcn9=YR2IZ<QM@5qaP;-1C&Z*Ct9bV
zQ=6U9ED5@UxV~i6?g1>2mZix)>!ruB?h7&zC!7&+O;9^5Ro^Y4dvD*0OD&-pdp<5^
z5JZlK^>hH*h*^jG_;6Y`+ifGlaBl9tjav>NL4E%(#$nLWQ0UG|*~SLPRK?(vAEECs
z%TJF8;_+#c_uNb7qEPNl4JuEJbWSj_oYNA$$g=%4<ot}|ZOIR9QjO{b?z%Axc68r&
zw6O;+V(_d^V-oGiuSWo6bh$?kk(%x%F7lV6&E>(EAYaw6h}4Bc;xsLC@p1MXLdQl1
zWecD6!8Fb#Ry|#uFqw)O{mol%|5F-QZ-WcM!ZL8#J0jV0d8H>>@Hthv4GG%Ds_$6*
zU&?ATt+9~s;^mRgU(mt~eht7LGT?W)K0F%UKZiwE_G*jGiN{|E+bIYytt;BA{rrXB
z$-4<zB4wnKZF(^MW{GL~q1e`s<1oxCs_=5A3wy8USrGt@XO3OqW7XXB8@uL}Zmuu#
znKrYv-Ec#GwbF$km2;D_4uANL4tMU=Ac7@csRucwv*mrV><yccMzF9O&fGMr3(uiv
z#vLrm0KcP-fVyp4(L&YTAos?B3zU102n8n}((6i%)w{b1cUlj&Q!sBK@9A5`t*c|w
z=4k|Z7V4w9`m&QGw;WCMtZmys_yIx*$Uy72tmf-cO<8Nf{B#SU&ch!b1^)fR-d%Qr
z{$c)=4nealcBraRVF<X$fuVVww9k1HtIrj+dar2nc^jW#kZoSqYTNdXsns{=T4HF2
zutsZ%kX$vb%|B^5>=$$GR@JMw=l6F=Zm6c^Hb&irBcAQra@{y#q1dYeAtw$Rc}>oo
zofcPuTrOGZC%sDIMwrDVIuyt>2(zl&?$3?c2T&%oo3=OAI4m4pdPllfV(cU1E~yqT
z&V{yh4K%Lr6OxtTAU*uAteBKEU3)!rs_(0S4-C8%9@d?5VD4fk0sy}baP@le(Gp|r
ze&sz0{-T0;5J0eHUg)tsG-j=YsM61ZLRS|;2Q36MHK#e6`JTC&t*})moOiUc_I$*b
z!B^wxTA`m}H&Q)dWm*#JA~FPBl&Wfw8$prOK?ec=LC7)B)Vm{c5B^k~{kF|^b>umY
z6hh3K;x7HH%Ge$Aq5C@8dGp%Q&k580)w@5E1M6E`SrDqR!m7=S^XA6NKi3&J{7D_W
ztFA}b=U;()$vJf%-8thgh%UL&84z}eZjq}&Kv$O1hMQ8d?g#I-0Z+aQsAo*e@~mh)
zI$j6xs4Spw0I?Ng0`G&i_CAginApCMbGR?)TdxK*7?PEdn0+yo9@yBsmz^&w-v;z;
z+;dgk`jbQ|>lT3p1>AJ7*+M&Kyj<p=yqo+~^RO@}>dUtBxS98>xHhx02rXfU64+Tw
z<J-`(K@I3zV0E36+mMa(qRPE%U#qS#Du;m(@F}P%V*#2bszpl6(!Og4QAmT#@4<JD
z!VC#>@A+q)C}VH^f9p0Xh<V1Iw-h*CjCm7wN`u?CgN{yJK9YDMY-_a=dalujJy(fs
zlo3b<J1XM4nW$P#M_iH1q5URZo4haecbXkqe84r;*@;<Im}<6@E%dWNv@3)Xo?%_Z
z1R=B4igT6zrA`O9kO+)p=twqX%hF&%yQRP1yX7c??u{LPD)ue{37RC!^w)gs-A>^Z
zTR&KIuqpv&m5N+lb_i`^94ZB!g2wPBXb#GbnGvxv)0Zr~f>(SYFhSI=GI)s#b~QZ#
zrEw4diq!vbv>q?QBBZug$P4V4PNV)JzX@m=F;<&rX(3o^atf<17Orw|48gl|K&CQ{
z4>Q!sd@bwcoVO96Y#|Mg{I1Z!al%di7hU{52XOF0bVcN{k>=quySC6Oq#`KZxUvkU
zn**M`)3j;Nny!O?Gy>)2PEWX^YFy+1i^3ES{V3Vu5Yt_Wo&M96?i?#51I;Pyy*w7L
z*%8h6lC5tWa??2TQmbOYGGQZ?>Ex-&-B-5Nhh&;gP*xhF9f?95g6S+n8#8ivW95Y_
zLhbri$)B>|ZKac;D-Dcg%^BYo|Hv3fwy<JoK4Wi)X6N&?ZTlr*tYvr*Ial|z>#>2%
z@8#b55eCuDI;ZqxB-=V0P&eIZ|NN`*NhTrne#SrXcvvp8aJ_^1d6gc8*16;PXGlhz
zSl}1tTH#4*<(%?QRgwa$g{o(p6D`~}&wA3MM7{2L#@<hyo@9=?O;H{9LppWxD)qzE
zrz(Av#CPB)V{F^u06^QhF`+B7dLn#M#iqgdeD)|&>N=Zky76hEr_^v8xeVJ4zA5>X
zQT1Mm#104$Nz4JNkC|0jbi`ZpwdWvQFg~Al-BH;B1+|2}S>WU8h`;X*HM&fid4*cq
zvs?m?`z?gV<b1p+d-mJkIjzbn3H^U9WVZ}F!CzhM?Vl-=o#qvRgdt)yD^*+50k%hn
zR>6mRR{pe^jv)K=csf;$Y-m8hK#dSz0&Z7nxj6cQSFRe2*Pb$QF0)qXR<V2rgg%5P
zd_d|bUOrCiq(nB4bgN?fTMGnLZvIYI@v?XXeP}bdr_v@g#k2Fxt%J5d*<ERy5HwcH
zhd0iWb`qWu*E|I)Ny)wRgq!j_%hwC|MpH~(yZdrfZ9DC_hI1gb`?uX(gd4u$3c!v=
zNCQ|-1PCmsT)#vPeUfuUZ1Ywv%$WTHRs?Fv6Yb`9#CQ*Y2fMdX`t>YBwz8=S{MM6y
zh2tuN9Gi>Q?qlNXp4Khp&Q8J08oy_!BfAv6X8`ZZjcxPh6~N({<ls%@((h}2B<+kh
z@)|pb-j3hp_d91cK6!`NFlgKd68AT&eWm(_`JcZe-$wolY-X+(zqMO+ne8&p5iLL6
zy5sedv&|~KHMTsV0;l5eM}D60fg^s8n-HXka^X}&0(*KVEzkP^IAip0f^x4~sHiFL
zem|=|q~rMk9@_0`XVZG^^w|>6E5wJa9Ibo&4&Z`1>wvA*uu9_76UrCH&3CWKXDmS&
zvHl{Q_xG2P(9{HO@b8@@;(b;{HG=$s*jk&<n)YArUOA+9?7ZI7jjyEpKJ197z5I*o
z(($nE?t?+SByk)|R>MV~#G!zrRq?FxQSO|ZVX_Z`Lyrh)(1OQta48Lj2W!e#){5M`
zobk{3S*M+3@RvV&3!gyk%jLkOs#|4$Wg21X+vIpOu6D@XE0JtpUtCbXze*;XQ5=mY
zoj5gaf-=G%4`vb~+7Cu0?Q%Xpm*83qjFbJu=II^#=+i|U6{r&O@G=qPdDu*z^LvWL
zodoHZ(dtNt&0L3)Cra4;)%_tqGKvD==|Aww9WA=rUNX|_(3v?3?jU6|=r2v-dgrv}
zqP^(U4NRx@8d?;Nf;^zHrOkHi<c<D5oaLxPQqtMoeCXt`a7&e@?@`&p{f-57qv0sj
zS;m))4407OeEzaafpPSvoS{b+|Fb5~GUG#!ac$UR0?s#A2>j4%@9?;L%9Ij{!Ug!T
zQPZJw%5I=q2<|geKF6?w3e}8hytd6S`6V}`Ho(CN!oq)>;Tm5>Njuj6*yk4(2|F8{
za6tKTp2gXYu0*=-HrZ){s@AH6%+{-yc?=I<4V6|CVX5|#v~!{zrtuwyO?)!zAfl7X
z)L?ttWD_{Xb<oNxxcqRCz1(cqtGE+wf4LCZ4Yo-f=#Km74czD1(;`#+R{c<`IiT<d
z){T_avA%8C7!dTx!SDCu5F5be@JB8?igl-1X?mB3xDftI{aN)sr{(?oakxO@Zp0G*
zah&w;(8AUozoy>jn$;&fkksVsI9$=<QtHamRENexYk#ScHsdy9jcLph6ZVRbyBE}d
zuWb+}H6)n7{Yf+Zet+9N$a_m&(gtZ%`?%Bf1G+V7fSjbWKPe%p)CMJa6`Y#7X5(Ax
zIb%Wz85r$h4xch78xn@N38)gi;4^=tU}sH^8x{4`O{atJDfWI5Y0PlpM(^q7iIkZz
zSe&(l*O2~f)vD@o9O*U4q(`4&0j$PN*0vb^?!Ix&&W~u={iMKdr`8fO9fcT?4sD*h
zZ$JKOfAdY`+nt~MxMDWp8Wz@!0C+23_`$+_jNM_VR%1)uPda3;55A?V$<dW+Y(eSd
ziIXBlto~YxaFNM#EEJ{vm2)V69}Ycvw<Sw$qvb-D@*!H3a9pF*YxWs?l9V_gH`Qld
zS#!!Vid5yj;zxeY6y$ZjdtZ$((>+8j3sc?Sx|)-!33W^+Djw|(p}|=BKJ=?qzc<|%
zgb*^Ve|T`4j19K#ny~=2Hu_d~jQG3^v%|&vK4DIvkdB)|2m?#%j=N=l6-?X>YB^yq
zrV+a?#8OH2pWgVr+6f%3$u8%acE?%X+v(#smPctmnh4UDzp-S*FI4?Kl9G~mF_c6b
z9IDTd(U^9<BQaW^R0+{mYSB-_IYws&Zn6^(^&p#7Iwhggj&rr<CSfAHdodvY<KgkH
z%S`>%UwJ&-pIC_MtDXU(2soRE?#xH8$1z4vT|0EHa<HwyXD6H2T%zH?#34QEhAkkj
zV&}o8f>3Cndnk7#9mqNiPU)Zw<9|WGhZ+ehMej=3nR!Xzj%acBWhi7MXa}%C1!0#!
zq17_wFs2vWYrD@n!%bOgvK-;azNq2wo*lD0B>9b0HWV_%FA=Q}akzgi{j-gi?oJPa
zJtPhOVo~1?^;~E3JDK;SN+@H`>|l`~?i9?vH2mzRhT|)80Hlf)6YwR*_+XQlJreVj
z4DJZXI`Lbz_ru5dJs^bZ)WUbKJmE?zPTI&iO6VcPeD`Rd(6KfLWh<gIe>~&Jw)lh5
z)>SC_Ev+z0kE?n1Tj7{h{Tm__lRMs+LI6`mgI;pZf<pVH?gnesJeXQemQ-!iTj-~g
z^mxUep-SZpuICI<W?0dv7A=`eBFjx3vN7=(J&vlsxnGAQUr5Dbe32x+?fDpdE+BD4
z9T)x3B4+bLGmThfW^kDH)H6;%AUEHW7FB$R{mYw6;LFwNd=aunK=C8CB5~n2A00b2
z(T_B3^*=?<pHY#^;eL^>NVw%CgQy;hiF`z-8+rV;LIMpog?Bt)ud(qte8_5%hdDN<
z{;A`qe`L2iXPpVgz00G{ysxOd<9Q_6jM(dKJ8bKEb=dJ+!K%>v&o6j$EA}h_zJbEX
z*2^4s!R#b@aaz^)gvQ%<MYM&7X$R345(`Kvk1D+1ccsnR8DKOp{JJ{`12!b;^{1xS
z$fK2e)R`cwGWosilM{(xm1;Mi8g<#rc$F{j3CE_Uor!NV*Oj&q4>N@vwN#m%K!o=2
zFb=8_4nt}#z7kAcHO!M5r|J@1NNnL1O1|*l;yLxwv{^4!{cC~;6ewZ`THA~*PqrAz
zbur@-@EL(ju*I0|w4P#pCuIR1T}~>FT~x7@shVjV@uXl%$k#=~Eq*MUmgP2zoHIe!
zceGycr`%pq99#Wnom}iS>LgNvE#Xq)4s(<Ewx0HbPZE&5*n$1)Pw}SzA;ti}JvyAs
zl|Gb$Kj=#T`U-hCs4D&D$LT(EimFi9*)GuRArS`OJET{kdPChjzIBIEO3VnhRVUsg
z?Gu?UZ9u-n&re8hI<@)b4b6U$KZ@;$iXtQ)1?y3<X>!24Gckv|-6<u&&$$cf6oIi&
z-1US>E3OR1%@%xv4@a)G*I&)2l5at6P>v<rF@`e}3H<1_+HyeOZPV^NaM*?;$7HHI
z{RZKlX8f}}Qq#67o*kX1kE`Cy|8%Y8yghB1Y8Ji!7*JXwG3*dL|30OZBjNaFY&loc
z*0hxB*N5rJw9ZzP4TCL0*{YCPAv?J#^n+d-U2dPIti%-WF>#7llX~dWwmq@w;`lYb
zwqAUcp{NfthHWZv)gnm!z9qCYV3~X)Y9_*Xpd}z59AZp>bz%-~2P72jrpj|goD`e8
zj8=+2Z7IH4H?=6>+iDGECJjj@snn~sVjdw_a-y{SOTTFOIlU28IcQ|dXM}EIpt_D>
z9vseWG**Lg`mG&^pH#4j{LunLZTKR~QY5MhaoylAT}K*aYc<Llp}`jUsxctPZ4ETs
zA~ygFMnd#B%v?PToo)G9clJ*!XiB&Z<#=bW3Jy{!9$?i54^tDoW==COWw=R@y{d)f
z5bF*N@CyPZg}2knUZ(agf>0U$dQZ;!=5RL(-ah-cUpfxaP%E?|Bs(%^jRQAMb3y8$
zB>CiW>M|Y`3PaX4jE8xz`jNM;buYP=Wd^im;ZW^r?O{Fb5A1BM&S&4)wJYP*LmHV%
zY}i}2fAG#C;pAE4kHFI`fVaM|y<RQqTw{U)DcN;2Kz41DIIR6##%VS?BF7(RDp*GO
z6h2rJ*#FqeK_pXgFdJXjnLP%m?wYvc>!_52km-PBBE9$Czuv5^D!?VGzpfOOq_-jg
zk2GXV_@ctq0fhrHQ-I@UqSd2B0NnKQq-o1fw24?>!sx*e5jS?$IxXlQe!rbfX0F3a
z#wJVMB^qd@)%-+zthdOZ$_mh)D#6=gRa9WEcE8fO*gC}!vzdPV5Pt--XSM4bNU*f5
zvITt7Bwr@K%2dCbQMvkkAOR}PAi7Bdcl`tl0Kp;aRkZ1XegnoJ#(&;d<Y-_c!pg*W
z_0?*TRq<RkTdClMr$({qKJO!!G#o^@W>z}TzfubSTpzM>EXxxCN#UAjycd_mZ%AFI
z-v!t&SEwwXVukj}tKo1QST~Ol4*}{JM*7XBvtTF=m?8(zKWC5M8a05ME|@8;TzY!<
zqCff7^$kdJWRHQujZ$kZmeckBs}}&eAAJd{liV{sDxU@6zuA|Uy1y(oHS?36A&O^V
z9acZBX=t;laTxqrg3*Gv=`lC)INfo|Anq>Zx`~~_oxrk4vU2%m=p+fjwh)Y2Z4z8>
z>?}uDnvOmw117@Q>bnoO5>m{b4v!{p^5GRd%002>9R#yD-&v^_w>LNC4<7w3QV1=a
z(=$3~y?^5^$1cB3mj3%7F@La@YJE%^eJFH;kcXyTtWg<9)0}NZWH1_fF8ibB?w@0h
z>hdg&h-SMWYSd6Zu~GV?HD&bR{IdAv;1Bx($(6=K{dMzE`%(o+1kj;w+7szb|NY2y
zVBt9kA9w*+U;c4;7pzy7Rz5xwik^>H0R?Ak7r)+(KCHGmNQvCs_gK&@H4Bi9a{EeX
z&bhJJ{fOaXsp1P95cjE{9qCX|M-*rUvkk3`&U7sMz4#a)wNlhp9@T8IA?YQTuo3_y
ziz@v?U5+D~uB_2gllwZ!^|SZ^IjHYtCHZf_yOqh*HxI-G*AFu&lF41`rC?8Ye7x}5
zI74HJT*B=&z~ix@7d4Kl7RW@G#OxKx4V;{FNYe<-#t@5+M#gk!vkRT;o!tL{RBNT@
zP^)13Wo`UHF<0a2%?h)dwQTub$u^Z{;o6UVqxEe|q~_9#UyqLrMCEAQq#if2;#M!*
zdn6)$n&+NB<V)m$<BQfIOKVp4>m5uV{<^&He+U$cB^}G~kXKN+%ICz=y+?=}mM#6(
zb4mff&h?qD5|9cxZ6#gw^;c*cZC99EJVwH6x-Z`CjF9K*$y4?H*G$LuFO6rv60R=|
z;EI2~%XG0}MT7n(YB4h~9dJhQVr7O~5-6o90h%?7@SVHlSR#7;zz$!>)XTvg4|2Cj
zl-BNJPq_i{)$7d`-ux+Zs%S)=7bil#*HqA#$i}OCun@sG!wTi)t1%&sZH1fuFmL?W
zF3h^-pI^94DZI7yvQtqkdlB%g*%(dqCeus-Mo7TgpPtfGaNq5)Od_yoLh;y{d{0Rg
zqAlNKv$VjqIoW2HN^z-Nv^B4g%vjCZF#kgaSNh<)sg#1_G;UkZ2Cgm`9nSZp=5<z_
zLxocCgBL4(KB&QP&7pogkxPJ#=_Inf;p{DS@1HCz{FqyBUmkeNy;kBJlFXlhEVO2;
zXx}_vaNE3Sm0)>2Y^<0<?Xu$}eUr!&QRQ47s6A4qU6aCDK6mLhshN^<svGHK!?nKO
zSS2nz1TC#D6W`>tRstq+hSO>v;Xd+-8^aGz?qlC=<<))(6PKLMKM76Lx%yCZY4LZb
z^nUFoqb<p*&Rp|urx6o@ezY$$qhDidDRuufb_A<z0LzShDb=rskQOi~)_2iF9`qV!
z_)m&;z&y((t6KBx7GtblTTFG$W$20Q#bHei$7z-?$-D;F@CAv4g|WTi%Zbw4aHoWW
zn|dRcXJ$9Pt0f<eI6`w84tfc&Dzn8#*$mBszMYdMPC5>yPkEaBtFQkfOZ&Dx@Ae#9
z^7n~XXBR0iXa1=flOoK*@)R>%_^Lj6F2GDY=Fv{BzuEY!2x`jW7|Dy=_D4A6Woq_1
z)AY_UPUsWPDFuIuuWP)X@c!Y@^>2_L-_SlO<+t5Y{^MbwcPFPvM!X>hH-hcKIGkF+
zvUv&5q8JqE8uQ3CM%UyAkVQpeb4SpNG=j0RqxqBtZhhz^;^nMaT^w{%xD&D`Vi9QE
zPA8qM!x>WPxCn;OcCWQi3l=Kv*j%1L@wPs+1Gf*1U93k3aebe54HPtUwojin`lrCV
z|8u;_wtWjAl|dT_pxO*ToUpM7MV5|TiR-s4+hoEIuP|filC-OuL#RKYZZw24g&bw}
zVqn<>{0q6}zQt~3<K%{X&Gsy`w?k-!R08X})A*Z1HFWK~qSZo9tdLS$LGn^K%enjS
zSmQOQEUJ{RtJF#JqlwEkMuCfb)>9M=WVX)ixZuy}2n**+wbcPOPy|&}PN#&gMhg=0
zM`_O+u(hFUwYtRb27;tW@?dR|!E7!5XiI~&-n_~x0$l-Z>)rlpQ}5UDz^=~K%dg8j
zf3yh!#JYc)b@03%&DB8h!c2}9ZJg4K<gus=iwW0iMT8}D38}TCE4N?GD6NJF%;@2H
zA3e$d&gY9MdjIevkEYHZn0$=v55kAEkg#>V@fPij58cABRJWmf+_x|mn91%z6@_or
zLvK42pZC7BqxF9Zz5O$MtJALPHSTeohoy@Q>VD_NJC2@Z`+3i*<SJ9|*gdbnRKl)C
zvDqrc-OKut{ait1#0@iO)?(xGoJ)8T<I_CPX_YHt&&hwgCQBfum!6YLv8#W$2;Diz
z(>XQO96<0MoDPwOW~(G0q|?!1s^R<oUZvAT-LtZqZ$?mT>N;Vw)5>U+Nf3Rf!5XK|
zedyqJMtm}|2}U~Lyi+@-`T9%p`mI_>Y5zC2`V4m4r2)Qw!l8c|`G4fDPDD^y1uU~$
zEwsIc)~RMI@+`B^_I)5<N8^M@mg4<y6HzjEIAwG3HVre`5HFa~mE-J{$+~YsYx5}D
zTLR(X!bzf{24P${shwxscJ;0?MG_uvYb)PH-5JNC>pg;%Ri&3Q!NG1t$fo@9;wvd`
zjARdz<&93@M2c+7+K#DW$jgZMD>o;}zSW3DLH#HIWUYThaRUjYuBm1y486?IF#YgI
z2ZW8TYVlql1;YIuK4iTio)c8KJ9J1rkj2^9c%^2mP4`x;5uKowbsfl-sa12fsU(;c
zuDVwxZa%AkCBqQ3FmuhW@pVgGry!BM91Ba%&?l8mdEItM`|Wl`xn|%Qem;C#>BhM$
zKdqLMOM;|NEza?LLk3nmP!on&#XOUETxeHVxYxd7tg#|@cAK))*LI>fmCF$nj=|t_
zVtQ_q?`2Gr{CJsIem`{n3Hn6J<h;`mlzQ8;XR~lgw6eD+IIy<MPFU{M7X~2_GF6(F
z_r)~m@Sud6>^sZzwQ$SKN`iw2Gl6DZ$W!vToiC>un8zT?(57&{$2A2Bno~ScT3)v%
zr%Q*j589$g6U)M|J{d+M66vScH`UD*(0~q_K2OC%X1v^T0*k905fYcIdzGSO#P3OW
zW(NbS8AFqffukS9Co6pKAmmx|)ZOh(;WJ;xn#<}^yB>`0#tN&UHb0t7I<4;k@5T)+
zY(W^EcbX5z%G2|_ZxmghD3h^r0(wlTSDI{O!;L#UH8~r#7c7jvsU6c?G5~RFW6I0m
z{zGz(o3w*VDg7JSh+;_{a&v0<;eaE4=ajiC^GP`VhLR_P%t@b=l2LPU%M6b`Ci<J^
z)b2ZyCon4VH{7Pc&{mJ1Lo;ql5W1DRl7k7Qy`eN9vVDhB2F8C>*6q_g5+1(nkW!f&
z5=Aqw(gPdy{DNBmT<P~GoA!`IiVY0->?nSy@=W%x|HZQTrxE`<ZvIv)^R)cxH(E6U
z5C2_~1Gh*HlNq1H#5HfI$Mww6^FLKuk3XA#_5Hi&x?2P&CmfUgt;sfGUYMHvi5nrp
zAQ{mcBin;Csqqf4w@-t1ixvZa{CZ&5+%~T92K{YrcrIu?1AyP%Jx?7yh=Vt~r_Ka-
zJZvE+z7L%6xK6sGzHuDishh5}NInIv=M(HBZphq**;S`|LPqd@U6g^Kp?YamN$-Qt
zVJ%zvdV!Q9bze-D-0b1rw!Z37<5lV(Vng6e(*h|21(|6(-0GozW*+TMAOI7F>|OYC
z<og{nQ9U=gH#|1VoLFb%DjVzdBeo?~LwdD92bAPM^hgv5kst#<YN1{N>38gO#_&(V
z-e9I)Ezq&56+x4`9edRM-vfaIQ!XSB4f$MicYH*#euGWU{!jSqJCR?+7fRT@h|0qb
z<ma-J;y7#L#@X=g-!oP=*1l!;F11#Bc|Mhxc}yRz_ag1gi;GOptszrkad$jG!_W&-
z$45ZFao2nN8Hl<nPJ-L{!crYGJP2st)68X2@*2Y*#ElVls3Rc)9ueSOBG|TVNznBB
z%vk59VI$vvs`PhD0|0_xiPv?pP5k!GPKVPamen^WWMyT6qw`6T#O0@d&@KqBlq@HA
z4@oA4*W*u$3`^`jvIR~E2&`3hf1VfMygASnE9A1rij?k=tDb1R(Rgh-B`Z;&S$LXX
zvW)y*5cNF6svqWdq}nMdSy5SI;-pGDIBwg8Ys2%@Q$sSzwV#Ny`1QJ@7BT|v{j+zj
zFzcRqF*e9|FCOU7_UrGNUK9l*CX$MvV%WpGkIl^V%V)qCD9Hc=u~GCbO%UL}iAbn&
zY%z6Hr`ECaira-gj$WF*@(r^eK*$!U#IM|Qsnl|Ddymof@}2mwno=^V`Qbeh_<KG{
z!NH3X-;5Pd^&Zw5kR0(rGGbDalU=wO^m}$qp;dL2<_*$=@P+vF7l@f~(C0YPCqw7h
z%lLZv|1|Mh;tY)4Dglan!CJwK0K!<fkXGBH2I%EpsVZ$g^0tX)W75dvkJ{^VF_N0E
z)^4d1F2zB!c~rt}j%iSek_Djy#m8LKzrH(96AHR7U~9tj+I?s~9*WPOnPf25FRJ?3
zix6;fQo-w?aS0@`uI@9voOKl&)=$Hp`o^h4W0xf3Oofy)^SOI=Sy_uqZY`)-_6-af
zBgzUmNXbE8PZU1PdMUPeus0Qy%%$;zgDe+CSCa$P*HtED44aGrYx#PI?_f=fdcUV>
zKc=di$guCLh5K<6(lYX5iR;?z+x$zur+d?b#<nJ}H^gS~Dd-n_)Hu$->SJldl@X)@
zH87Cq{q+)&J%Vs)`c@Xn%<RDz2RX{?jA!?I!(|ulfPkGm1|Dav`~!vAUN@_-UhAS=
z@x~wi;s@^6cyQDa>5bRf>T>!8k3$KQ))7ns8gn$rK@<?zJfY|-!NH7qkL0WhdQNxw
zW*<EAGcaQCsYAo-hOU_E)4kT*!fKOsvg&^cdM4d8u0NjXeecQ`*GyTK%Ftb7Oyd}B
z7UWwxZggv@Vx_*@o7d9y=7jM6vL;Rgtv2Kj5XE4=D&WoLY&D-m$6av=I+&A!dr!$$
zR93<x9g&qz4|9=#7p(7Oj{dk^Xm6WDS2`>U)#mANo9B#Gt&Xn-PL))%uam=fzhURk
z!`aRXyhzZXmM)>LUGh~?RmJU72>f3qa`yK7@|NH6^VEhX-0H+h1;}^TcbY#-N9TF`
zm<a6XmPrWpF*k?~adk}rAF&~(tlRea>{@(u&NT|Fd%jbr|KYW&A_mI5&(6;p2_tQe
z{vW{NU$K|37ffwDisW3MMMk2N&*py&OXk+}Ui5AAU??0o)Wxq3pF76XD!1K^LR=bp
zT~jA}Vz`H28Cn)uME2Zs3AHO@ZjIuEk#;{^CsI-UmJ%C0^k09N^ndBW@S3&1JP86(
zKQbRbLNQPUluU8?$5a#Hu(7VQy%@D1Nix>usZmPV93|!V$2sLPi4PrQ;n0w#x@;xZ
zsS#m$T5TO~K;~E!R-yb(4Xm{znzrEskYrs4bFj4$EFH#@qKwU)grCk$RhY(GeBR$F
zJIVK}y8bHob){AWK{Vot0WD<hNOYIfx9L(We494fBM2wewW-%xn6;v}-`MiLp~jH%
z<P+v<l#0G{uN&D`)jmd~^4q;XJS8aq*h+Up*TA}_*%~37k~k{x^+V$N2-mUding%`
zcoBcNB-CPNZRg7-sePJi?oO?1M4f&um%lU_mB&?3jTB;HN&l9G9m=Q5A!JIN*bqXt
z)My<vRwjFc_7mC4vS0lL46$PS*8%+dP^d<^+C6+38!bcV(%Zp6^6gRicMkit-Ppoo
zpnD_km647!<TUYCg_ZI~7CQGWWo|YPWBG;WdFY=)iGt=|p_1QJ&1?n<h!h+)zb@B1
z7!cra?yGR=?Zut(8-1oWo6;PYXr?D7`vPQa%wO<w6V9_lDG?5t1n^k7Tx*~tXJoJN
zaRYuLkqg%s^-hG_7^HPP?fAe7Qtr7%DuF=8s-NE6WpEhggp7-->btnR6^cpaR?TxI
zXE|8#*k-eJD@loJgglgC?M?y?f*z$*=hi1cNaSseJtyv15Q@Ary<Kiw1)Tyy(hF*9
zbte}DIC0$_hdXI5G`7{N%G}JDm_M$7qI35fS=PQfH2J-GPbwY#R_`cNC}lL2`+nvN
z@WDo9ird6mhI7P{we8IC>K?$eAAeqbtWxy8l$3l$)nWs65ba6c3Tna1nk50Bg{M;n
zje#L+U0Dj$b?Tb)^n8kHOR$)>C0{h{2||0uZrRSt!FS_=rN^(ggHYe#S`Cyb>nd7P
zcj2wYcu&=+{MTB(mp5^R8db&ft2jErt!V)F?78Y<6&d5b_SU;kk25*c!#8xDKF8`9
zvIuC|n5C1p#E*IB#n?ITF1@4@>}5Ab0(hZVhII-n5(cFn@uYf8Z+Qrav`Sq48G2!-
z{_s63b$t|I)mm_4_x2N0J+k-_`CXR(^gTLczW@AxNXWVSr&-m^>jTGb+8s7PZF-VS
z)*AIA7mad!bRgTjeWT}s<XO%Fn7EDcVHhuXdFl6PYHuA5s(kZw{KiJW;UVLVH~P!9
zWC0Ucj$=?J-x6Sb<Ixpaa)HI}RhWd$d6u)rOx(7b_aPYrGUZ$4mpvUg?ZuC|i^KiB
zDeVEt`q2lLWIHo^+?A6X(+4~kP7E1))R`C(Jvu|9C0QzOSJ#zTTiWS37^S}AwdmE9
zE9<zEF+A3FA-e6|?2MJiP<4fM&r<oFA(?}Bf9ee@hA#=tw^PvDDlJD<Rq!G+R<p=l
z$TC{#b$EDm23vK-ju;~cAeQp^i>qG11X`v9lA#s8t2_e1d>v=O-8Ee}TcUu*&NI~J
zjOJ9ZtDUXtUTDL=89EI%vXN#Av#H8Zl=CMAR|=QY!NFDD##wm7uiieXs^VgFU}@{b
zw%8}j9Pu4n;zUaV<6uzrv@PVD%)^wpe*n_|`f*Quu5+x&BN$P8Tuw4k=_IlvV~OBR
z4lvT0d&<8VOZ0Vk^=?9fQ&mi&qfYj0KjApVE<PzfRLZHJMEjNZU@P0vnx&dn*y(aw
z)UTb@(ZSM_jF_--00xx63a+ajCqf=3v#}%ity8dHgeX?lGn6?$8}nv{*GM3Jw3cBp
z>*zx9EU9X9#WQ1Ob2wPvg2PWX(_r2JQTilG_!tDOgAAUcnpsU~&)?>RAtvGOmW1-C
z^X}B=aQ2k1=Op`Nb3x5<)>O)OpaS~aXK%9NSKQ_6WU9)L4M9Hewb>U1&#(lElXbU`
zPJo*>8m=1-=e4toXFB8e(LuK#EjY*66-e^>_w{MMj6}CNj>W5LFGLO1@H>T09n*9p
zl1W&Aa?=cOZTvd15;j<$*clydorC&E4Dr8xoM&d2oXW%^eEm#b$mSKC7N{2f&MbU#
zlLyb%%=gFB&<iz8m258=a{Tb^_~v`|t5yKG0GT)E?gGXb*ds6{uj6#@`MNa87PdHI
zam&_D<}Mk6=A@Y_fs%ZuK+va^Q81-<{0>~+t`=!#^J@bVKEjE&uyQegOP*`vF6u8>
zUPOS2f4Q7moO}SNQk3Kb4emPx9?V@Kz+>w0*;`1zBzWF0^CVv4tz`C+pV{sAjyqX|
zE05bfcu^;GFTm4Jc^s2W`6r?DpEvV=X(zSfEpzaPvMN1pdcbV|->LSM!nfil{&uzz
z+>e}f0a>KdQf}D*JC$6a8!{$i$p)nl;Zs&|BdrAw7HEp@l|(<nkJj%=p=sL>vuqlh
z3l$p;ej-}N6MkvLET@$k{)uZ@`>v##nIr6;cC)cACOk&m-rClMOHV@k!{#qQ5G6D?
z{4nU*kiX!!B%U3Z#TgB&-E6F2FT8pp`1=RF9!32jQpuNv{{eXZ`!#Yam|xllS;nrO
z*K$U&f5pf@i>s?r*CI;avsuu+HKrtSzv!mqXIzI_(gk;811|8_WNR-+fePEiP3N^6
z1JE)(GN<dr)`5WKkCr^`4nVRH?Wk8Fx*qZM2TPoCo@l5Grpt?Z$sgH!7lf{3n~++2
zKuFE!GoU7$-)#YonJMTYi@B@aPxaW!aBJx*LE3128Fbb$k|Juw?YKzbo73rA-+nDC
z0sX4092#{TJnC%-f!M^}eCn2ZKJE^n5mk1EcC*SWe_!5c3QM9W?}to3{`brM=PIA|
zw7u>pzhlg(5zNF2OfoYAItSKeDNEw(%nDAn2nF@%d{-VQs_bO)%fT)y-Ikd!7R=_4
zwVs*1qV{X8<986;Vv<uzpRd(w?wNFEhkA9868-ftGif+G<oFRRYN#Zu!0?YX`h8t;
zhMH(q;KL(t!OWcV9LBn86D2Yg)d<ueUYMl`X3k6e*a>1iJKt7sSp9)4`#{4n1!Mv3
z&;1e9x8TtFJ6s)x4d;v9$nqGvGxJ=vJmahMZ0_i4vSQxQ`a)RnaHw0B3REK`g}RxY
z=3uXq^sm6^f3;r!R>G?h@DUD+e@LZvd*2%nac8^dz15_V4Q2xtGPP#U3X#!lJtnUq
zhkX4Un}OCn+pKmKxZBFo$|L*B>&F)qt|pI<%T^6(PLK8TxmKLRlhb-qs=K8G({CF=
zV*Zrad8*nBCd%~>42Q1Oq3m4szX;{!?n10nywFC9Z%D?17}dUXUS!lyg<Ic_qPyCu
zmEIykxb80F_)KR=(8s;2|0N*s-`=48s&viKb>SF!eyNxf%xn+Z4c>A8GMbgq_S$fI
z@w$%76dvFfiJqsV<$JHN+DRst&+OMW0<5bBa293vU^+#eonz}1=$Fsde8<6AAiX)n
ziaeXn7J0p-0z*QwR$Hf9;8Y)R#48=Q7y(`;sjnAJh)Z^c`Z?NW#3X#}uBZsIE$Ypb
zIc`}vQ1`^dxJJybBJ#{e{<bZy^d201W<&TgyJGccZ-V06>A$m?{|0pb$136ud;Meg
z<{kN)f6FGd;4L$<tfLW4_sV8R+pD*Z$?bT%%S;JPS#6Zor7Diw9{bThGg-~E&zz3A
zOUEMmV8J$M&Bh_aUC*=#32z!%^TfIa1_AC!cwA0Nv`5;_HM?1TG$M`-EP0p1_xDvQ
z%Drlme>PBmlnYc~w!;j4WL&ay+bM~RmVmNWkoA6d{l8`7KUU{Nm|3b9TvTBxr+>6+
zy@YQau6SE7BXG|~*WOvTRYR+>CG7!yDy7#$Jm?CNcv%9=qC2JxBK%}_x%g1*X1$X!
z<6Fd(qV-~XRPm}>wUbjtd&8Ys9*f`VGiy<`Em5kw{|SHp%Wh5TMx5j|n(7|h{M&5J
zJ>}VK_c7PW;vyx6KdnwlmBCAMse2lIeFKaGe$3Q0YB&17ZTjD*k<0tHaby0Vcl=H5
zoDVHm!||NNwF91f!aH4xsHuySxJT<+e7c)A2Bh~2npc&QnSSx>s7AOSyw^`;x4j{u
zDC@#KjUZh{Y@4RCdZ(JkjAKTTH=*kx*yz^o?4?TU$PkZL2%xuT{V9MW=2`jx?&)*4
zSVN%i#pz>d{g18v-(v2s|F%7#<lDE?$KI}9%fZE3X8^QrYSfu6k`P`GcMRzqSga`!
zA?F5c_~<E?nOL<<{hTF##GcA%dJL%NLmNGLQs09di{il+aLCe9>aQRh@8GG2Ts=m$
zrbyYUJY~PxskGp(onMhNCkZ&IHSdun4zEhzx*HQqPbM@wL9UY@6eyC?*?>nao}?-x
z4tJ?i^`Oo>s?VRCU9b<jn6>&}&f~v}wX+fvGo~fckB#|s*f4V(B>*1iRsfWSHaO1A
zLYAtBd;|0flC37}Xt9Ifek9V=MY*CLVOAVu2Og>y9+T=Eyp)W>xg-V=OFe679|`g?
zHw8auxEAm|ITTGlX3Jms3>-VPypH%8a<>`#MLs?tiL2MBuC1v|dvm|iHA;YWACo$P
zrS_h}na;fY1h>GN3h-t`|G)N{_(^N|nhIUkEj#M?F@Lk%+Po1-0uO1(7iihD8pfKD
zq_Wrl6tG@7UZ}{l))gI!vq^I1Znb$?lF$#!tdC!|#<){cBGF_;$(F|2jxQF!Taai|
zWzsK?1fj1k8$kTUucsja1B0U9GRxY8R?vlYGa0>}-mp6@F*CGQf0N<CA;a3(3*6Wk
z+oHPe<CZ*=!@1sDH!>7&DV?^B5uu?xdOifcJo2IT_rCVx8SD=LC^k8z6a|MWa{XTo
z%HK#M|3n1gPUJo1<2vjUOcPk2rz!O>a;P{ZMVr_B<<a7|aFeh+P4L6#8S#4uR;lST
z>RvE0G5;%r6y0?B_%~6<Msur7`qIy)0isGkr$GSsFFmW9uaSISjf9|&U*A_6uAH3U
z$>?76DXnO*wwjo#Y-ow}-l)DUb5CqyP&m<REC%lCB{OAv`K2kn5zm_TL9x1q(Z6+_
z?a!oCMPKDK44Qlo^;)+f#KZqz6v+Qs9fmEL%ods@yLOIA)OZ$IGhS(BJ(mYcLn8)Z
zN(zd$NZDXjkjHrNXhT3AQ&mgRO)p1|M=>Xx({tQRH#{7<Y*X06uScQN)-Y}Le}1q4
z#FyWtKz;fx&<!$ltp?&>hOeFU9r#JDzHup~8gS~cx@+~sWftr$c8(R0Rls95n}ra`
zPK`3(6tVKJq2@2XAZr~L(H{TvPyMI*9#=FH?fhLuF8(k-R+9+#2;sCcN%JgEc0FL9
zhf-O1fc2g1%fu>C)rr@9hDK+)%6%NLvr7{yMNVV8<H`~PLGN|efF+Y-RqROEZIe!a
z(B66gAOP-dx0rns;*>xc(1X2;R{6P#%yE$WB>#c<LlR&Fy<^(*D%p2P-@E4eg~nO)
z48^<MUozIJYTIU0ZgqzB-N|Cn`XBJ=zty<;meySJCuhU><FK!ZsQH4V$y$<!o&+gD
zYGy)g?-`nx114`A5Ne$xV3<=_H`OFzr1+RTiCJ>76{)Hu+~Dr=3UimigLAQO*F6+(
z$P9d;y#nJ17;h-1dYh2_NZm%8&>HJ^4uvcm*k3FzQwxq3TT^aG@qY8G*+=9f==^`T
zBLDW4&I!!1uaLUJ!VgbGoRsz(eI5e$>~^*Dnu)BN%~qaHUed-z7e6iYgUm{FHsjJq
zui-BAL)*q+`cG2=KX!fB2r|rGERN9-Kt=|>wr!SeOsbC^q<QJ%eW>3*nt46ZLp-vp
z@TSB|3Vz9{?QYv83i|6A_dQ+p0jLGN2ysk5XsuNzWrx@StzTHd@WT8sTvS`Dp90wk
zpg^S-a~O<*jRF+1gmZ9x1u|H;(boEf3|QW@@ue?bGs%0#|Ia;Fk<xmZ2QxGL8@D_Z
zHCWJtczXKACD{8MTiw>Kz|@qnLfI*uZCklQb>~o0VtNj9=_A#O^gj0uaEgaSk78|5
zF&AM<z*+cnl4lag%InI?O#q+UI{4;*`OYu>+JyK)go0#K*5|n*IL-03n$Fa+u2g4J
zW8GlrOIMrnuETXhawTC%$j1GRQVMQxO(pKxOar>mnc)lt&Q7<=2N%G#kEYcKC1!uJ
zJURh^*ymGL_NHz<J!u_eRkB*K_P@{QKUd$GwO>9=1H1Tl|K7~{2kYkFvi3i(=Aw93
zJYUbUO~|DDZHNC^ZvDR<;(!11ocP}$<fRTQ{r~UDw_24P=NjWDq;*jL^FRJCKR)9A
zc~QUrkG;2ki1SJIg})&X+}+*X-5r9v2X}V{2?T<>1t++>YtX@+!3TGDhs*B0ckkJA
z&U^O#1NO(~nd#~NR8?11SJzY#Qsbx3OZ<no|Gkzq(fBaY6%M#hA^&o<{ox;~m}hCK
zg6d!6D*wP`>OMNf{6D!Ze_tGlA|#AHaQLSM=l`&K{x^(3ZOji`<$6E;{|8U{|G@Yt
zo4UcL$i-0}{;zSBec;kwW4nR>?@9C@=pBas;T1U7{nq&(-Le0TOY`Fh20TjnZS=2M
zn+fy5+C;>08?ygTN%_AY)Y5(6(riX}75kU0-IDlVZKj=-=Etq@f57nH>8LP`4_viv
z<_FmSnzjEH*?)`d-|n;jEwcaCvj5hyANe-_*0TS04F2sHko?=175TSg@NdW9BUbYN
z=lB))J_&aJOTxKwt)7I0#6-ORXN1Xl=YrK&P7PBZ=Aw?&9m?~2O7BDOqWk{-ehhSU
zy(p>ZK`=lvBI0|=cNmzNmAC!&(LZ$zH5iy<ov;%pWA?5VdlQ}Oj}cNL<hiI*FjkN@
zbP(<~HbA#-4hXzY(ck~^{=Ys3%18c5>sPk}dqS`nEc-9ot_?52K6+aq9D>oteLGIf
zr-A=zkPRwCdP|igx4fsu|C#MSec>OEPPHgu@-hPo%x^J_V)}^`Rl00il`SgEZFtz0
zB-F(6@1E#Y)BTNsabO$Be+<e+Eop?Y+G+!V`PQ8O$EAOY{u3g;aX7iKeR1geaPtVe
zQ&}#hzn?KaG}&yCMGtnQ%}&gzhU3fl`rzt`q3Iy>Q@?&!d%$*%@;^-WcWR;i4Uw#;
z_`T%6K%^{=!h@9&wTCFkYOk7uH<Q1$HZ(FeIC3i;mz=2e68u*9ck=(^_m$~UOVZ2C
zDm%>9tp5d;?^3w}kabsAn>%J=ca$VQuNOU--bdst>LI~gMhS1p^UQvV|Gc_fm%q`;
zrR7ldU-5VpFYrWweMnFIz*t(%+pM1awxGabb6h6_MV5QloHCUUYuZ9FyQzf<p8B6B
z5wRB)L0@RxRXp_nCl>vUYX1QFzeSD-4Z>ouV4`TCzP|~p{P;?hZFnzdA#{tFxJs(O
zv<`N^?r3Cq92jAMdVqEidGIT#t}ZX8Ua}!AK3UH~$WQe@Ve=r#4L@FX&T(Vr{1+mO
zP56nla+JL42l>|JX!<ud$2A;AyajPX|0Sj_`S!56D7y|;OZ*N&1Lyg;zwD3&Ln@(_
zQc?(+p!(hSD4om=ZHjMT|Cu*|a^xSA%ZYn_f>+IN9P7V+v_y*s>_A?bOr)e~<jnn7
z@c%>$^)H{|m3cG1vRqa-H2Cg2e+8T~uezIi5(uY3B6C|ZEV(r_oD>pa!=L9~u*2A$
zAu4fWw_GznnZY#?Mldx-Ig`=r>FMKZA5{LweH*wOC6cwg`k20W-!X)fFe3Zc8lH0z
zQer#)7_JTrkRskpQN-VbZF0A#G?{so3hBFA{?ifL*#2yMs};G6=TDYN?_c`If9C$6
zD|SV1%oTk4>aK&l#=e^qBkeE*>wx}kVtBK+2X;bmrKi;!y=9#>`}PT=m#o0UU!7nq
zymUx7^^5C>_fBzvYsQxv2D4kzvus_?kmpmanoge(^T^Yk4C9G02%Lp5p<j#d`t#UM
zqQ;5}A2$nESHa=%a9RIEq5mp8#E>I(O@>Y65Y1h67||b1sSfwZM;af3PEnoQxi$|=
zKNEg2UOS^vJ_@<_2Zt!-5XP1)y26C>L=pumOqUoNNB|3SldC0vT2#**g_YY^8~T0y
zhd}A<sCp3beKES?2&{bNAo<uVf1sp3#CjgGCa$NHIUqEXF*MNE#yjC81b$OP!jWjq
zI!$13kjGhzmLHjy-t+fc^JJ+2FCdoebe?!~=M<@Knl4i3NI8IkMo+e4Qa$wN)fQa%
zPK(+-7YlWk^x(`XOCn13{gy$B?+f?Gkhpq{r@kvOK6wGM?P_JUlpR;qFPV5;5^En>
zsG}o_hW*xN69$p12F4U6O98SJZHMRKnVzBnE?$)<k;4N}iuUQfrpwVKv-y>j)o;xp
zvbgxP(4kqENrj7h*B_z$m{@Lf*nx`$b`pbT<(^fQIsCr(w&pP|qO-XFJ<)Te$VfCl
zT6de`lF_ax5(yE-0>FlWZf*BfN(<mEkqczBr3_@LeDB#O9sm=e0-o0*7X2lEB4|dQ
zi?FAW9AZ<Ub#W_N+Ija6*GDlNiYh|fu>EfR*qtbS0WXC~H1pg4XL0mtAY&v3Hk#er
zSpw!YSK0hovRk*);pda8v_9BVBlf`s&2Q@F+}t#1OQW8-isygrOpoXc8E~3no1_l!
z=rnuXI(QE$@fdkAaPvSE`8a&HMV;0VamL=Gt=;Y&5a%!maYfzie-MrMMkVUdK$$Yf
z0*(2mY1Ul9WkzY7)lo8=E<x{HPO*@9bpdFYP;Asz#YS6$N`vF4<}aw#F(-n)LWpg<
zc>0#Eq2SZZMH(k=9u`;fwQWyMn$O#|wf#A!=(ip}vYW#;5;iuW!5{GJ*aWBPd>}?}
z)|eP!7rgs}B4mgq*`G;Falm|NdHLWtk;Buk|F~`cMvDzq5|oUz=;oN2wnSG?(^C3!
zGnFZaT}|wFyLGgxyoSoej}d<<`9O}LfBpjJU$GWqX|}sHjjXk8_Z6<q?Xpj}>M$@z
zGnB5}_UTb-Y)QRhoj5u}+MNM=pk@e?qhO);wTJcB<&ccP4G7NcLeTH2>63QTHjggn
zotU1HWBaXHBvAbS?LcJ(m`uUcpl%O>-qqxl8h<02iP`ouc%`pB*Z2BH3XBIfA953T
zg)-Ie?hV81lMR}u^3>XyrQ}&?!y2_{yVL|t+#PMeEzLzGI@AtXjI0qm)LJy9HaD2+
z8)wY2F<9y-`Lh`W0x5f24O~ZA-!!piq`UzIjhUu`-o^vlP-*p6oas6eLHHVr>$=)@
ziK+ZoF`4RqY~C|2`o+3GzB7oBo7UH!%kWJUDaQw%ZCqb7$YFbXyVvkjpaYf6e_Q5K
zFLM<Kg(wc7XybbjJ)7VHgt$Zd8*%ge6Z-DyUMld92!vucJ5QNwA~o^?{)@8VfWU?d
zWbmPc-`0K7R}}iXM>aExK%H4kZKA_>T<~GQwNb1@<c}8n5h9w$oVYq*u98q@FT)SL
zb_T&#vd2fJ)RUVz%{K#!{%;RX(}ogK)SKm)qAlTAv!LK+L4ESA1XkBaQUSp8Ut=pM
zMfxF1?>6Lvf<oAz+c<&2{`RbGO11#Wc*)0*p~h92n!nHZ_caENki;o$_t}d|*E-!Q
zzI0hLD`rM8@3<E!Wkf9ywHC6?AF+KXFA+-AsroiK2(3JvZGvBtqwIZKhPZ6TA2xRe
zjrv8A!iTaHXVFsaTUnG8H}$?0Th6{%&}D7bifkdpv`k(`1r{bHL!~*Z;R4iz5O_!s
z=eLO~Kc{_3nXAJRWYHja1hKVljth_0lHR#xTqmltGW^&ju8g;MGIO-&tcHW2*w)EX
z=!ZA2y6!i&><WFfMemnLC6B@;9kg&;&zqBtn4)NX+X+ei-t5fD5nvNK?$FR4S=aZ5
z3Dbb99m27%(igIcWG}~nqs==sFx6Vaf8H#)kPzxn%=-5wiJ5()tbdc*Kr%=?Z`Q7x
zH1&v<;1(u6a8WDw-B=qu{++~TbEC1<^bG#+KKtQBJG2At8tVP)xl+X{Ud%c-abLkE
zVFcXrQbs!yLa}Ucz#Wj(_hQOv?D?9-umez2x0u4>qlJW6qc-9~!?;G)=d{r8UWzl{
zgzgP!?rZh4%<bZWD6zj+yw@UE&Okw_d$Mw4NO4&G5pwKl>Zna&-pZSDQH=({#Sc9R
z_CqubP)`Ik9(Emd84|%*Q5CkG<<~ZGhV4LLZx(7)*I~CnanDk_cA<dL0@~=pS*zl+
z%}WDM_o<689|aSCE$Rnjs}ZK|;YiDb?Y;Dy?VP{_t~83Pd!rlp%dgth_G`!<G1VY7
z6V%)@V?XK-{5k;37N^IG*hmT85y|&$hPg#pWVXVLt3fsjU^OY_2|)2uG)T%nN$4Xf
zW)DkH2OzsSo(K96Y0@vG2Q1Bl`zQuK3uh!lQ+Lt3!(_&Rn6Ap4Gn9^_^{akQTJEuf
zwHmoA>Hk|#rx7CX<izf$^32F&<Ceve+e`*qw%bT_9gVPyJFV_JzlzX?sA*WZ_)2*<
z!rl@lS|skeV_!`+<K;7cqSHP8=*1MEe2V-J^l3VGP3h8=EBv-L^Qsf%c*Z>Nod_q!
zHq>3@84L09I{jP0VDV;A_}WPdJ5mwc@SjF6!TY=^z#wmDr60@=6tT!zCUgAkB6gJ;
z@prYAx@01ob<86o+YiT-^Hbp0gqd}wk0$Khm3`T!(=6-)P)Aew4hGyK!?cTq+{?vh
zEpYFEttOYwpL4;-(|%Jh`VJr7^b^{eiX;LY?wHx02rOxv{gPO|qZko>aEtSp2fg{g
zR{Nik+)(?S|62=yUzj`+YXi$umad#7c21QsUb~r>S3w70S-qwuqmMlbY|3hH5(Q7I
zRpb-UyEr0q{=ufN4w$2XtUE+HzX<>8pEMhvb{f2zDL_XvC70cqB)m-<Zyzq5Fe$I;
zLFLYni386cde(<-?ROd;gZ8egz0z&$V!7(*N=R*ns%37jfN%vPN%TqG5gt?Zt!nWF
zkvKHcWf*Wz+#6%?%;NGu;&zDPrLsI1{a<t-U4+ON%$xnON7TpwqoAZD3`h@z{yt>E
z;7_m5S`Bnr?`ULGQ!9kDxJyaIJH3&$<?`}p4oo=H<(OYsz_-lw$lH2tusOFc606T3
zm(`bbO6~VeGdeeH=A029;@5AS5HEN%c`MDop~3Ig&}1ePzdbF5Z^**eVkkUxXx};p
zzULbnRN|;$DK>8Ezb)~YO=cCE6(~9v$Gtux@EG~QA0gl(CTjYYA~q}EtB@<(qLj%0
z=!K?=?wNxXM@@{2a?&+q&YQpW46sxG=^y(w?Yn1An}IiVB`0<ve;H~^)ZdJkJNPSU
zo-9PK+~>x*BU!uf!bUkU8hBoN=6DV+XA%w8;!(2d!j=|1?$v1ds3QS*)~YhbNT(DZ
zgRFP}3BhA6tdk80=P~Nuyy)3g@>jj1BBNhi02|R(Oo#fY)8UVCvVJh-p=s)uPqo>?
zScxH7(la=~<%)`O%9zHf@hfUshu^i;NQ(sQ6Ook8nwoqruE*b`W%yzwEOV8BK33P<
zQ2}d<Yp{U|Yfc*#u&uamIZ|U1ll(a%eC@42jvYDfX2dHm1rF`!Q63%_)-UF{T}$$l
zV!h#0Hv$1gTX(eFAH6LNbpPSy{+~0T@;FREQK9kE&Edn&b}S<&Kd<TNp2CCcK+Z2g
z0N0=qYgM}5+`It|q^}HB@l+WvCq1?h5{GIkQ~P;q6E)Xq>|kp5*&t9UX5h#9d&o0b
zC)}IaK)yCv@`!}Bp4itDbF~Df>u6061&t`y5&Il?@X7vSaokU3VZ7G;=8$5f^uT<o
zy^E3{=d;0_jtv>alFrc6q3xV3Z)g+*Zjr0Sm&|~SAHM5*PG_r~RCl`fvL66SjA3>X
zk%xyz#E1}~%-0#V!!MY2gfn@to%5u1Ea`e%T3Xp=YzbRbEmr=FqTe3+-8DM;t7TiC
zcX~4j`R_6D<uG2csT!oVdI!+tDdrR|=w9(4QMJY}o$BT(6*nQJw7k@x!bkQS>yGeW
z`|r)h%{I$^j>1=N3Qd_&UaH}Sq#h0*791uAi37f<T2C}LoIE{9iv6k8*^oT?TIulf
z`i#%u)!61BWVKUzl)9Q%I~{)Og2ttVLp2?!km;K-sr+MZh&*LJb1_wh?U7mgFPRE!
zb3%I^<fI|pt-p-MURo%2?26kjWc%U*_&)9RCyCu}@Q!-Mt?q5BDhGsL4SoV>u6?Mr
zPBWN(s@-q4+H}3L`uv#vwM~8;2&%e#)+hLS$EKj?y`YB$SnrgFh052}DgGLwZKHQT
zeYbEx!%7<ncu2U966&Ll^Sc*1&~<AE*vCnX-oCJpw~S>kszMztGJx`n-IKKIWlx%O
z6H79QK=(0#^%xrI_OZqFXdJWr>pti|A8YkVASDi(%}24uUXE;e+FQyvOQzNJ?HCLJ
zukHxA0x-8n&aVY((bBmJi9Y&!d)xXpqE>v+0D(-<Lf1!t#CA!v<MjeY;PMi$gU1#f
z>}3^Pw)w~m+E#z@W&rG4ybNnE4smXq@1cLxaw-xS=~7i?+fZ*t@;zE`Z&+Ms%5maK
zK&Z{z)k1tqHI=&SCEmnyhO~wP(-wL<P()unAxmc=MS1#tQggAP+Co!x5{mvJg|R+&
zUlCc9bnXMNHV=b`d6q2IHKRtmQ!XBb57fsyDa;I)Ho1|dkj$yGQ_A{i=r=)0e(g(F
z$Bvt>5x;<tI5LVoT`}B>Bx{S^;_a1Nm5RNJ?~@<ysJ7E|1*F%DC8yyb;Ep+po|M&X
zPr_$wbou3fG3&yh+XYn;mWtv7@;mC1O>b!m61Z{`ILmws4{6`h&#hGwCwO>NXt=O8
zOD=y|>)%mdy<BH@x{|0+4RYPQuDyO<7-80ycL^Kg;n^y{gKlrI*C`(qSJ2m*6}LzT
zZ)~Uk>K0v~P5;Q^5|q!7bm~X4tDSB><A^4v{iQD`+^m-Aa&tsDacsIVtOP4aU?F?O
z;SmWR64w+yjrUTGy~+eL<?4rjbe7&T31FN^Qhz@r^P3%XTWpn9u1-N!c~ae=v9-~b
z=z+Q^ip=m(pJE%NFh;T{yn+p)&OmLI9QCfIo^TZ}!p>Jr_`$A;zC-qKH^PaBg-=9o
z<eC>R>V>ts%AnK*UO%hP72#w3(~w>AzTNyB1sM$>2dkZr-aNWa@V36#h%>s@5m+~x
zSl{wE`L6YBa%bUt*P7RLx2wfHmH%0bZCpu<gdKl|MpCxQ6pbv}GRq5>n5#7fqF`c%
zR2j4zzJ1gGqO~{zgr{N>oS-VYvPsuGh=6#--xSp~nxyi6ommsVTwTbRvUZ0N-`h2j
zwqo%2zqEO&V}R*w=jgjItfW_OHdB<H6cZ5q?Bm^%7q4|`S=pOxFbIYAk-qH<{z8Bh
zt70qTUYexWaq&mO?R+Y4j6SE`1RJYrO^G*@QjZX?tK)G8@#4N5hgVsX+yo;&CwX{(
zj7%w>#m=3Udfa;H%Nh5YXxd>&e0jwh=1(K7v!3iYnttj+WnKX#*xQhrD^lmRT6^}W
zGwd*0^rxQL&X$blx+-A2Yc0h{&bt#T8;!Jkur?<DZ;|&RFTds6v2z*tnBBBy8|1y$
zMy)lY9>0_CF<v(*@#a`U1pau{V=v;vpD3|vpiLpVUt{sD`?oYKS|hTfl=x!zg|vek
z#u(-`B}dfgVw=YCH*cw_rde%nKi!SywAXx7n0{6^%(2q^S$vKY*(11|oT`z-{5f#-
z#4eK;FJ?&a8Kv`_M3E3lax2`bAcsFxKVeoeQ?JFRr7s%J*}e0q)V@y&kq6p2AA+zI
z?~2mGh1i>SX~+U6acoYV0@zP>sm<eIzVj`+Sa_n<E6>jZt*y!^!m)_pyHTp5)mk&Q
z?)_OfmHlfZ?Pl`yyo(a8SSAQ8_3*hqIF>QCZ?nC0%OO?od7Zis^K*N?8juSnnn%G@
zUW;OC?YYc8)al&aJl3cQrgFs<huZoRvXJnGE0=?^=MBtF^SCB`K=JkS)h6?3ysr?U
z0~6LHMC>{rQPq{=T#%b_*GuC@Z+Uy9g`$JYeW>eHZQNARmptCFNeWmacG(@YdN_af
zacc^1$;3ExGIU}hNwLM_U!qdjnWOv@;YBU2HL_b1BC8SQ6#Mp-Ip!!B5UXH>58rUM
zciH!s1II(&eimPT!`J?`gtE}jX0743bo4IPA61EWQ|58YFasHMcZo^D+t>vV57B_>
zkz!9$D)x&RRl{qXF?#2zAlhSr!7g8s=!b>hQ2~VBe5h+HfZY6UXlRN2yBV8`S0H{1
z?{-m4b_dGitSq^*AIu3qN^rG|Vk(xZ*w9I}<@=b{Y82osQ{NBqIQ{QoAQ31O9#<}0
zX&!0!Ce@~-Evh$%y=}zz6a@mb5#AXyt`PKRBxzF3B!2Ul5nik9pSM%Z(t0gg60U2C
zrajUnjR8#2YI}LDksO<Yy#5&=1r2_W2wpcC{X8O=7_l#$dkRHlh3YH4d6;W;-lidG
zr>zlhNWbfB#Z>9=d;od2z!XeIL$dd!{%aWXtO<Scn=^2VgMPW=G(g}8A@A^3wj#qP
zf|?#+n}Q}t=S{*SgbP5HB-0BP>F{)6{`~o6Dn0<4eV88=*AMH}r*V6x<iNY^EAh<Z
z*bt{XtX-g#O_olYt{ZOjp8?4~X_^X!l-}LP`Xpp!4P3XEBjg(k{qR-#=RZG=>JgtX
ztBZBEeY|#l3O(H2zj`^l2>aGq5f9$khs5UK{qVLGbH3g93oSUB-jG$kpW1Tcrqzw3
z`dl<`I$zcOg%D1bVWBHaZ+kREFValhSGt+a?3;K0?fLP&=?W=c^Uihq$YZ3DDKdwH
za;FP&Vmba1SP-%G?R2xl?$fFjLHxGp)nSaZRkHT0=4AM=wb$k~rS?~`>?rK&=qpvu
z;+@%VT%;LnjcS=nVbb3l`NddkKFZ768gca)P4CnE7!*793}xLa7(<n))AV4g%^5B|
zl=IME;!Wtp>cz!!(NpG-6^uOL3%l+(mb#OOEQ$hX$;pwy);)@teX^ix;YqUw_Fv6s
zve7CTb@OBB1jUoP9QNMP#(H}6)PPTAbW?@)nZ^tQvrIu=yvmAaD}-CqH;$RX=R@4H
z=G^O*5iQ_}(Qyrm+f5ByC$nqK{tzNcm8{;HD5eXCy-2LJwm;2k&=2k>-=!+`ddscV
zytwQ{W|r0B=iOx{hIQw-XtlP)Jgz&1ooFx;-}%w^Co+NkJqFIjT3i!f2&KL*vJ$M-
zA?{U=l`Is%rhA@7DdtP%j$-C-EVSm^Cnvu#x(JZUt#ICN@~9^qv(kMmMdhqKdff+p
zF5mWEZ}0|a%Z}YE;h>PDfmN~6qfALo*ASE0?Nf|r#6VsP+ib*JFk;)B?h*hxhm|tu
zf=)`O0aNob61E4$Uh9-~Sw4sv+8)w}!!I3~Yd5D=g^wM+$VA3PItPew%OT8(Z>0y^
z3K}cQHVcr3EGQ<VelF1AI1WCk5*dRm3|G^R9gpcSze9r-rhd0zadoj!ltqVC9F7%w
z&JOsQE(eI|^^HkyXdu&K?m{_7q9Q#U&w~Z_h!@NM5TRKMbVO1p1hKj!l~ly#P^X8d
znAw@jF+2~R2hG})*%7c`;q0H-pYEcLPL6#zf$c%ofxcwuo&4T;(MAUG^nZ^bsiOpL
zOp*KQJdV?t{-)aRdh}<){%o=8xsU|clw1F|V=-P}8F@{(SSPjx!`4ee3Z0^)JOcYZ
zlwr1ml~QE#b8$@nqVXEtnl=-r5cYd3zVO(3uqmoD!YHXLJatU$<Iw211V7rSnj=Aw
zUKOrP;c=djdO^>9w|-0|Am*CyKsBZplYDrt6_wHPDKWU?y-6jREN~#}FaZ1lr9zF)
zYv@|3JkmcjJPhTz&IU87&^}?+rMi-+PS$Z=%A9Pwu&2<;dOu1%)gb#SH*b!yJm}tg
zT|HT>p-7bSdLy9QTtYDYZO*du)q5K2rOu4I?aPzN8Mac=mst0BlV>sYfa_Vlmzr<v
zsxuD+c$VSy8IibsUm&+0trzmV>wgZW!M9}%biFiKK@0g^BSjJEa+a%-ZWBK)F!)~W
zbyk#*LY+2O>60rH!obi9{;K+O4B-Cbt;?yCIA@}>lrN3^@cgVLt=1M3`CSCv(X*kb
z{bFONU#=Mx2b!97IE*|`A?RPza_i}hmPT1@f-N#!!W=(P?5I05W2iKvjkI0rB4RW#
zpw}BWH=4+jnEdWwZ=I}I&M^YTJFEin`^VOJZB$Zbx@g)2?nttgS6^!+*=K;eNT*Gc
zkfy=LecQwVy|K-3t?{saOH}I@#qspi>GqK<x!?R3&_)@iq}%2~RKKx!b1;IYR}xd6
z)$h>F#cNfS#&dn|At=+nw9SOk@=JF(N4Q0YLs#xkr~H6=zskHRZ%fpkic}3!{rYVe
z!*M&baOEQ4UWuWRR%J--ao*J|&m<DR-H_*+u2FwL>6aH^M~6{X2&+8umF1~Ark=SA
z?C^`P7A>7?fR5?04~u!2bW*X@52K>&y(#%b8&Qbts>}}WWn8}o4`{l@TA~N-hG->Q
zjVNTLTwTzsBIN>i$7~QI2=5Ps)zz}3qVDSy{oHUgjYuekAlfeax+&eeT<^RsC+KOC
zb-H&J751y%>!MzgXM%Hf-V}Oce}P!q?~a<A8k*MG(&<Q<CB^~#^MY~3+4os7xN<$L
zF~>j9<1=*b`VPLVNvtN;nnqCe&PziE(0HcAU!!bK410pIJ5I9Bh{zm!;<DeTnW`Ye
zYaRSitej24IPLg-{5f{xBisj}$6r+AOU6YMaLUJkEy*e+w9q8<i>1U;p{2p*P#iBg
zr?XQ4W%#~Iw(rRb^y}IMHe5wsb=+&j23FhLeb(Fwdk%0d#QDhX-6sB)iKoJ{BYKsZ
zjz}BZ<rt1@UwPOEn~=ubDND65f8mEcPEg<9%|w|du(~n#i?ziw9@4q~dRi+#mc4G_
zRI&?Y;iZHnqO8y{@0L6<u8xY#Wuk>jK|j?6n`w1ZO?GJ!C*`LptNPDew4fH@c(e2A
z1k>H|47pMA-IIA*d!1r^^0!-WtqO~meb6}0dpg#z0{-DZ0jHwR`}q$MHtO}#IS1S6
z4xJFDHl@ik8_|s6aFfy<PHYG<-GJvlW;x^CK!9VlGCp7Z-$S+~bP!}Ru6a{MLb6Bn
zQXbT@qLr44cx1FE0)J3Apo5e)hu>eqnv2$hjt?Aa^JK%?u`S1ddj>a7{k+B6BKJqj
z5S7-$jGArGL3t>$Vh`i7shic&qF#?__|rwnN|G5WrloVt@Gio->0#2ik6AMGCy;G2
z2CbwxdY*&>!0PqzPn|1TdHxeUG`{81sYND~4vtb%6tW4Ut!0Ir89!t;Tq=8POt#c&
zt)*cc+I+sv)0tbVU=3l97T74JI1)esW4>~nHoW$AL_gfLyEi;d0_RPAE*`BagUv*Z
zi!Z1R_^Os%Fg8)^FZ!rsHQ~EF+LXO$k|6>WR>Vtblw(;>JT%f;nqYp=!+tfczKN0A
z*dpDKXqE2n^1IAAoV}bGWhSTp5%EG0z4MB6vItw<^<p$vS)RPc3??kqm3Oy}o1SR?
z6f;PnQJQB!S{p`9zt`jV@bJmr>J;y%ilDAW>4YF~V#<pGx^soIQdKY&V0<Yw6RbJ2
z29a*M-L;*WuQH^>YsAvI8@82-QgSy_d*IbSy%I6>q`tM|pnd)qK&Sll7yGtOM$Q9r
zwC`$JrQ&gfP)0LYOcx$^lA^$reMIiv8^{xNxUPb_kZ1QxCO=1<;!evRezT+gNnlL-
zzHm`!UyJde@ZiiAI+YEZ40qID(<58GqLR%Y=956`S<QI{N7AW=!e(uh1p(ju_iPM9
z-vb+6*ZmaQ(4>c*`c%8ZkKlnT-Ol?C?Cay0mY<M;p|rW7fVG>_twZ=iLBg<vV-lkr
z(HlNtJZ~U>@<%09AIPHdU(qW$kAGFE!#FH8kV5D^qUC@N#23a4mK#N8CbAZ9=bL4C
zT-3dTZms1Vp|#e4-LMTlXOR0gS0FU!Cj^Iw1!$fcW(2LcmzT?V>xhPw6|$7&Xx*7n
z)b}=}4hWr6>#)T}=x6`u&!o1X&5ej;7@g)%Z+<?;JLLD}0x7LNyAC#7!^cN*)Z1v6
zx97H^)dvLhB`3XY)p*P8y`W{>DAF#$0~}DooS+v4+_dGSsEo2sd)o}7r`ov>jMWl`
zc$??<mJ^5RIW)elSVlnY=^qt3Uf$W2Kz5>5X9Qn-Z+<6lj=@Ji!DN`N<dyp?xShoM
zws;VzlmGF0dD--~iDWl~Cj?G<l<Qo4Lme1)jH!oA#=1#>sI-<ux_8_6?6i8(Ys8N<
zvmy?IxtpcIGX?F4Q)yxuIVOG~h?+6vzq|>u<70YQAKRy&T1O@xdsT}zx1igOJz9|5
zv`Fk+oIHP9lo_bio`+`j?+iUxde$r)O*Hk2?Rv3~R=peE*Y*PT|HhebW(Q|>;`s43
zsT^;8{*8)&>Y`TbVRME2%$}IOx{bHYttRL>mnBd%2j-pSG*Hw>GYSq0)!jkbqRPZt
zAo;CdYQ_@#6G7ZxJ}cqDgqC|fBHQc>ZbjbKrq3(NPFzb$3+r7%3&Z3RZW#k`U|Dw@
z;h~0qTCQ(wQFISsw12`-hqq`|7sro=F;U2{5{9C9-}vLA;9NL9P(R#Mp;x<0dC=5&
z<=3h=uVp5;Kfd0u<H^ciysH%3)sW+{-*!jsIB8(jmJyKs&D|G`@2Nds8d?d??$P(K
z0QDh*q{g8S%lB^nUPhKTR`eCHeOOE+_1RIH%2CA9CPC=_hU4YDb22V#z_}&qQZF}&
z_2D^ZkA7?S_w!i`&4Gop7(jpx4%kbMijE(uu6R@!Np$8uT_9`PaOoI+ce<QQYQUA8
zqL>vEI7hxt*D93u?U~CSLg^!aU`1c?;p6?bWlSU}$af+WCX(zs%2$x=lXKj1EvgE2
zd`gNGqLbDGr)`8o7XNS4Z{O)0v(|ltNm*fg+(a?`1<+(=_h}}FLp}YN8;XugtK48U
zvbRJTI9+FFON%Ci<PrjQm+`%@jc)cYe8iR%WfoOr((IT91xs(JZav{nzJK?4a~o8B
z<ME~v64hbTVNSyP=u7^hQ;aa^3|H7rZ>@A?2$;t#k;c|7r0m%|sNLova)z)(N1J~w
zl^>0uipIOlZW8+z>S487vZWarWH}d}SO#ukzZ@DH3x<ahMSK)$u-gm;Rkny!eCxE_
zn!NItWMxu8n8;D0^ge=Q9&EHxyf?1Y<&t(4HgX@k(~Tt%R757dQrykgh}@Gg)@f~$
zFl-k9x=gye5Pb5k+Np-B)G_iqOX&@&YeAoR%$iK4oADeh4*14m3iYHef|4@GdmwD|
z6c6czP~39E;JgB}>a#}jz;&s~R9+ZekD<+I?%P!zpc`xOthBKi;P=B(*b9b{4kuvR
zg;4Qex)7@Cmoss-IM_rN)@<MSK5*OX_H{k(S7!LVB@^%jnJ5qdRoQN4g%b3B<lK2#
z4Xk1+=|*8{B;WpK4Y9MuEB{Veg_&9{n9JrhUoJm>#PHH&!tKyulglcQY}m5O&Zd}}
zQQC_O3Fuj_tJJ>SR*!qwdgiowynY+?{I(kAFhJLGyBw%)qa7rxb+^0dxj9@Xyr5H)
zbJ>d_AoR)~EImk?8|_#tq;Ufb`O}f-H}%K~fhj5inV}^7%HeZ=7BbMyEXu}M#P{Wk
z$<f>j3JQvHe3=^<@B5#rGyjJu(2R8wtj1v7qjJAa2frn?1K|QHqLrVY5GDNDXWt7p
zQ^QFwUxe_UtNqTqZ!j@3%AxI09JQXh#PlM69-5HOP`^h%lh3K7IZCKK_@`eta3QFe
zFrOmx!^%N=%b7(}wOk9`9&b4^+cpxlD;Ky!V4?<4(&|}d$<NeMVqymPZX1r2MFbQd
zt7xvY>5zXE0sy4hUL3VKwMzSE5(_)892}L^x!hi~@?<20=V+a%oB8k3m1e6Nc0eP8
zc{)}{gtFUH*igI`8Ktzq9Ap_j=I<_}8uDL+#1~=?0>d?Ks?-?$N;i{XNFUHE(^ujz
zUe=rCon9me-c2s|rP@x4Px{i1>FygaI7TYP%@aHh)*1iO67m;*x;cks_K7a3QkT)4
z<@22TNLhM*;@~J~UY$v3Av)*q34V&?$BG$We+%n`$09cYGD(~Z2Rl$SS9@<7S<M~I
zj3RgGv=LSNU)1-v*6AZv0zDx^Oy`T=o9NMNJNF1&_*_#QdyB6%jn`FvqHLtsu@Erq
z`hpPt=vJ+C6*XRO-y4Q1Wq0an+(Mu4j-99KF7FGrJG%)(hudx(4;<H;N~4Tk8J0KM
z0W5F%>Cr2F@mV3I^j3WaNGhm4Rido}LykSR!-vsF+~t|;O1*78E@lU7f1I|ef;r85
zziEt@orYeVVZ$>li29$D{qDqYW?UangJ1YYj7ik;%`-Pg=SJlC2a)>3gZVBOfe^+7
zo5cy<3b84)8~r?`x7d~U%X7UEs%OH8HyL)ps@#@px7$0SCJDy6K|MW8wf_)6>9M3k
z-g9cV6}hsac%2_s?=G~osb8$~6Jw37JRQ?vC1Yn*$ScQuaSAr^Sf0eDz~`?0;(}fs
zNKa641l!WOsi4I)mnnj<Gdt7S;5-Q#2qaL225S|*t*l)s!)LW-KwU#Q8c&1mq6Yce
zutA?v5@KypftK9D?8h~4DS%75pwq1#zput(Mmi9g-0%|jCDNccOW;Ylk%Z*w(+c@a
z4Wqzk>nPJKX+|B=N{Jn6*`~p+RV-7Zra@O@zs%56s$&7rvy{cL>0sA%%4NIu%Q1RX
zfP%uQ!|CcY<)0tKXynZ*rt{6bFD&r5CK>uXL-^^hVnKxk1#vq(lb1#&3Nt>Dc$&9I
z^W>YGP{YSoBin$a?Ch$exY%Fu1&Y>_%beNHOvy~w71wyc!l>e6%Th5tT5^d@ij^xd
z7jJD;KdJ1fD1@#fu5&&mIl~_~iPtmKJ>pmLYcq~LeHp<C-%OcdJ%aL+FjouT$qLv;
zmfAf4xB3%6%}TJy%%d7ok6U@4r`v1MsSE|_)3W?zvh1F&DVzU^%<a~M*E{=d$1)gj
z+`;zvbh!^zRvAj%GWw+6cm^CCFnt{1L{WW{xS^FyS+Nx{x#MtHMR|R?>37$Pm7Y8^
zh%kRX0~)b<zMWHqL}29a@S^oP4<0k;rd7xKG#LVp#qE~5<WYyEpwLVkjn=chc7@-!
z*pJF!K*V0Bwjld0*R+xSX*Mu!e$?Dbv6d$${Av?%v8l-={By1GESGx#MfD|@ytj8_
zh&GXs8_HSdwPwO^N>0L5CZiB$%U3JXs%S-*V{wT0Xsx$sq)+zq)7>DuKh^N5WG6J#
zm0!#J8!aD!Y3u7Tfv4dMkY91QBhK^uM8Ucy?{7eMUMdXVX80qNnWye|Zz<!fo?542
zV)|OMB|1qQ_)>aJD6vJbEj3kJ+)6j2PT*<#-Ao%tsS_EaHqL9(el_aK!FuzD=JqFR
z^EkAc2Ryg4?Y*`72)=1|;Yr63Z3{9gi?Gzye<F>;kKakPB^J#LMLN@>3h#X1FlNy2
zKmZ&FeU-6nwfr#Uhdr!|Ic7H-pZr*Cc9a`zXWLlhX=Hs{kvo0d23lDAE@sNlH(D)=
zj6ZRf9(#lO@R@~;ercWa#C-P(kSSJP$^Slu2~RAnYrQ7y&H=I$`Z~j%Ra-i#5!T}W
zK59-;spMK~kvDK>-;$t-2$Eg4l7q*zCa*DI7kIr=;*nhV?U=~$e$d!nS<lzOye_Ro
zk1Uq0zaCE6+VAE6nqSC+Lr`hrBhRomLg8%gU=Uz_L{lEsKO=<U0{6y<x0Qoz5K?UT
z6zC@u{y7Q}T&}nKq^}7`ebUEZ>}9LJ+C1JaHOX4Jv;3>FZ_l5#a)fot{<!g8ufh+D
zX(b%L>PV^l<*&Bk`+*M1mCFDnLr3OzAx(Ldp~Twgd#+SHEq{4Ip0h=NPn+RF{W*sb
zg1rd(D&+v|M&t59GoiS+xLlC-+1WfX{X$vksE?;e>Ympvb(>tYVu88-u(y*Ab^sX@
zl%sDmCU-27gd!GWwA7>8<s57zh_HwE=kl}Dn}dKSKW6zzS^osDvtT>@3qq#Av57J;
z;&xckJs?v2GoXQT;s#Vwm9mX@i5wY0pWFiOdkofvVeKBm;a7z%#~fZ=PLH|JWyIRS
zg4{_bYID|xomswnVgh+FUt2|D=SOrQHVIZOJJz0M)Z0OywOKed0@<r|C*@KUbAeOm
zsVrE|fm(mnHg);CF*j7@UfB$%6AF_V!n?U3d~$Al12xc&m|waQzek<;qC><(F#Cvv
z4VOehsu5;h*hQfyA)gAQ1Wo2VV=$IhtcGKi3obY3ktPYZ&9Jo_L>5^cXz(rR>pBKE
zbRD)LJAan|*YpTpj%Fz3);4QdFUb?!+Y^7!0Q=&0R&~O4cVAH{5+e-u6w*6gDWf>}
z?ISi66Kty%s^ihWJYk1xJ9kgiD#-Q?5!ws7K|aT3D{D5J-=0dG=m^|_>=;%W7E}!x
zB4|xn<8yB*iRY4d!V%7VeW<pqmomSm;kTSMd*D|kRZ}KG?z)~aP#Z%L$S38%FHO|V
zWK;AV%orj*Qn~&XcwRQ{oUYJ8@28@mPhAIykvU!b(sx{8%Rq|ZkyL&EHK4$=tKsL<
zApOpCs*bC*IR;nNm+TUlcg%u)lSn!WCy>nJyqdxs@#+s3vRClra~o&KLx5Kp_V`&H
zF08Q7^O_3r^v)TXF@JLC_QFuE(_*X3+K2|&I+NVNeS%mU_m*^zcmy)k@s{5eGgQL!
zi^uRjF;v(^w%KaCTEt`?hL%<Z*Cr;}wfD7pJKHN!*V^P=bg>La%2#5#+7!7=ss)KK
zjak)`Bh!Rfxwk!uni%ZQLO~{#e*ppCuPOaG6I;xbe<W!nTA-;>4ca*0IYxBkpHQ35
zE-8GZb5SutX6mXQ$7$9-q5<_x+(-Wu&QgktI9MxI;R>J1NJyZi5?m34LFH-0nynv!
z^uDwPUAwIERco*|>Vdt7&9Z=PjK_W}KD*77Z|+~7ji3QBX4_`ISc8aGBBE}$LTcgR
z;V{U^T4t%!$fSAr>~FHb7(!HR>`=90MOui(6m3iwNk}|@e}5}*N2@plEvfxSQP>h8
zk}Fi!gpZq0k5f69x`$Ait9~GG`{r;q?k<WVlJwAWrOEzry0QBrCOTTu%&ee|>$qu$
zPq)b|3MWI`If0si;VbGf$X@o1$)K&I3zmqLl{Mmf6Xi*vU@Ow0Qn_X&9Oq52nSl2d
z#Y84=zKk<zUqvEk45RLg-x^sZh06UMActNR)MB)SxhbLy^L9OgAZf^N)5zz)Dzh80
zSLrF_9M+M2J?eO5;8&17Y^&m;eX+v!ax+SnnZFywP!TPecgy~*Ge;zHO@qQVM7b-$
z`8!eDJCdX_oOf$FuwQHmbZRGI#({)NO<J`*Nfp-s9zLGXJl8Q^^R;ZUUym+u-RH3u
ziq!&p5mSGp0PHjcdK!a+vep2|TT`3L0<SybEjGQ{?&?n$hpF|}*7R7Q!ge!k`qkus
z%03~R`L}yvrF_7`#6X!*yK#BlL!$-;>gbjr067<t(QEzjLcW4zlbJl^N>#Pu%P8{Y
zAAACr-~j2`h{-A894{Fih!u;q7VmblgXChw@<dBn9!JVy=9Izejz^-(NpX1A_^#m<
zpLESbknCBHU0b@5#LTjC9SVYc8ZC`W!2B|zcC$0PL(3VRfYZ4;n%G?>j;YDyMi#OV
zkjdl^e#w)`TGMS8jl11?tJW*$Th}MXKA`E*4n7YP_B<o#$S8mx`dWr>UqwaSDX9x9
zx=WI?=(4SUMBrqI^+h0nRSE$&41f!5h(TV}ZUJ$3VF-5FZdCVmQh~hp6rNDSx~t0t
zmHMEfT=mv%pr>xdYy%j`u;34E(wcER*90mct?gGvH-GSLUp^0-4qA@Z`rG1ER>zjN
z%r!lZJR2<I<T13&_gRSXgybYsR>_xlclh)Ad>E1PtV{zIUuEhjycr$Tr%jb`?KYcp
zb6Ne-6HTc=3SCvnEI#>abV?|lGmKUJC=A~n3^K+3?X~wim2rhue-`kg32xCEKhbR`
zhqVXmnW)A}Twxpgd-x6?05IbXSy;3Ux&PPx;k=yTJ0C_0r-!q+9(WRrk$zW{tf=NM
zU6wBKc=2!vGhyH4TvbavtwJ3qwX3bdmFzCt>|nrZwIR~j#ge2{zFeeYI9O1h>1e;!
zgvsxDmbZ|cFSF$<_Cx_!TB$c1ZJ8s4!)8`fm(Dek+=#ggdhfV^5EBzC36}PS!-~wC
z<FTG`_qv&u9;Gw%Qp5=MJXsAtU1=ug;gP7!uWP}IZxwZ|`di<HnBON_li7mF5cbBR
zXEG((>;8KEEm2Dpat2Pj$y#Xgz*TA8h5Y+Kq0@|+>>j9<gVzGG{@b*Ejt)Tpy_Ea@
za{9Z1@mE`CK_=^&AtV-ip`Sl~JO64XgGr>ux=&FdIs@X6?JN#cxLGazMW~Bu<eI8=
zcka{Wx?({=(t+a2C5c7u%bex(U2`5C=WUA1e42%d<|T<TC({fpD!4rT@aNJy(>^Qp
z>0T#+acg<Wj!AphDGI)!wxMgzuLAY-dMRwPo&UUg?Qh8moO$wv?k1b*er451-g`J|
zsS#CM4hF^P(SOv`s)A#c$bD~mS8$SXkhtmQ%$%~;&ugWpe0!5&`25iTvLZw<v4fY1
z&JVVu{x6L_#RE>UhK_nOobEu-_<==7GMQO>ldUW=tODF>-)L~YSprWtcVYv$nH%B9
z7x%u7cfrFN&Ki6BPd}hHdU)42Qj4?27k`fPVEf?-3*1ngWj><vA+&Q$t{-KEqR6e9
z^-^x9$5o3`$PAp`XJTfIQdGs}MD!I{f`PwlXfDVVwn(AD93~yFMXW9ECn9b(E19G{
zI9RFM#bc#tGP<D>{Tug<?Ng-+u6kZ&H+uTkp2i3_%-P-2@m+VIakkWx4afKl?)=|l
z{XK##fN5RxinO$pz=7lYBzb*ia1Fk-dKq_oF4lzI$U_fh2XBfysfAFr;NjMrnr;R<
z7G-tKIJ4jRpMCY2gExbViOi3#Ko(@Qen`)~Wb%|%3+G6h9XZ1f;5Hu9_jvTM7<uK%
zR(t-D#&&I2epmki|LB6q3%*`N(3M#lBj_r!WpQmXRzVEZ%Q&Q@AnNZ%KwJ<6i_DmF
zU2@6{*(m?6c;WS=$%AR-m3FkuV8;8i%w?ZFIAll2fO!b6HIpI*#Ps9y#_b!&4iRs7
z(uz^B1q`u-j%a;HTBJ?#PES**QqY)daG?Ao(vTnlY$UzYVOT=flD$rFk0PR#c~%mF
zvuF0?u^JY5MuW7)yOPg9ACk>=jK_`bGq9vSE@R+R%J^uG4`fA?%U4Pv=&c0R1m`gt
z)7KRq$ISXS`xE??x~n1H9}lcYsV)goLU6+g!`&?uO1egtq&2r8s9QP#t7b+OHRNPu
z7}kuhs4nyAW67oo{aX+H;ph=Vr&GUw7(`mjPviwimXHrOjpq?a0>qTz)94GTcetNO
zp2g^7R}|7YXs4^Rxuqo~OJAe_s^xK0Vmy%|qlRoxW_Hm6s_U2AKul}<27CDySwqw)
z*g(by*h^-tb?I0ljqZ>?O<GbTxIA+Fr^}6r;sND1`(^GFwA~v8RRw@^U+_LjLUDE1
zqmV_%CB#QA77kzBU^Gz`c)J<=s=nz6P^v7CyT71LSU_9ZnGWTNuY07@-gkEs4#9eK
z?0)Cvieup%5|bX06E-KKS?KiL)s>{^x;=WICTxi%t2r?Uz5FWoYSKw1fn83U?e%AV
z02BHE%a@8VD`ca|pF6^`=><)HN5?-EvQAq;S|a;TXpVnnwkhDXrmF8ijUoKt!!RP}
z_+4Q6%2w!61Kerg)zM1W56=AN)1!CqRF2rIOwZp)1YTc8y&gj>r}OnJkBjCWjH?CH
z`l++z2W=f$EoOhCgaB%TUQ`NL;mk|ljxvZv7=AIH*E3Ev6*8J;t<ExlGae&T$k^!c
zm=ywa<GVnu=x7NS=h*8c6CZ_vjk7*NVLk|=2`x15#%B;dRU1LpBhhr_-yVB_Mw_vi
z<x2}>1H>yn5&rc;Tcqrb2sU4;4ZEQ(EAI0>4K*f{XvBwbD62!OV`FXhhiaWFOog)O
zLca^>{cLPh`O(oQ%((g$DDshc)Pbs-ycIJu<k-2^LY|~%-d423!fQT*a;#Q(7Fl>4
z3(v(~<%$QggDb;uHqhPG*<z|B;Vctd?U)gu#c|g%r>|P#wnLO89Q4VSId-c(BT}-x
zWbQIjViU%R1+&LEyc0Gvl*kz|Y3c+F@>L&Gfhn#oW}=|HQXCecZybQ8*r63oT@&67
zPpF;`<A>HFGrx@2E{Hn6ROko%)vV7y#8(ck4u`n_IK|kWY0)06OgLJTt$Sb1sXygG
zv}y$8$Azp3bcdxF_^U_!+E_tJ%;1#ThM-3+uh>1?Qx$3UIK}h5y=AalY(cdw-LOHC
z3kiW|7{=qUiTdaY^wGwaxOZO5K*P#j^;dNe|J9nH0u(5X-O}WYxx1`Ul_(iBKQgV%
zW<qorJ4T8B%rWqAsr7ByG`7?~X+G1-bh*ov+T(mx^smTZ%PMuVqp4bP9m#p#OUT>n
z)z*jf7Dz%NeH`ixVy83~?WYna)_g-hpP<8a;#k2e7THT#8$Sxy^uC_Zc)3ZAAnW_H
zHI{%LUxC}5#NDP!_9Tfv#lNY4PJK6dY})?i;dh>ZdqN<aci9&zY$v^v0{+OJ%)Mww
zKSw+_5-l4&pZ)lgM;tKKcD+X}Gt!&&EWg{iZS{0dyf{uWb&!0}qbM~iQW%`N(u4P_
zCnH#i*~cVG0cDhNgyn&WkO$4axmbl2mIpO)h~cVhm?dsfJMWXnH7MnWJ3PY<-1F!q
z#jl;wu&&)$*%0$LDt;69*Vh^Q7QZc#!8b-#vMSkB=HKxBY^&PB5vU7ZbjD#pKNUHN
z12+*3P*VuP?P4SkZ_rlui)>jRz5VltA&}13WDUOij!r6vUue5AAWcPh2#3%A0Opl8
zV1#nIA$R_C_&sluT~L;zB<^O0uwh8}I-MBo(fnQWhH5k41WP);p(~uV@}BL@B>*k#
z+VbVIf1!3eUqmLh;IYEvf&EyfFZTq2DGnQ++E~Dyu$Ng+ES*qO{;l??1p%We^Lp%P
zv+|h*?!tK*PZSuf<Km*+h&FOdlHynKx@%`|Z<8G!W8=A>c@bIVfP8h@y~$r|4U?Jv
z!nJm$8CipKOa$lx(@m|_F2C)Es^rWnPCog&c33s==563Q6{MG<|J(=_=F&l6rXe@#
za7KRfn`~5Xsb{tw-{OR8Rq)XATfv(ydw+`eBeX7~L4H0|h-L2c@nqoB9L&~sZ7u*@
zTm3}CsvbO+Q8XHV*lv<V-~^rizU5Gp9{lbIYWV~@hbvlT(itGchC*C%+mS$s$>QsK
z<f?4<4j8<x{zaB+@5i~@OjSkiOzrZ6JpS%Xziq1M8A6AHmhC$_?-HE?3_9DRv*&MH
zH12MQO-LiWG?X%&IbT}!t%Av~%>ror>2KcJ+3ySY-+9wCXUi1%vrGx8oL9y@veM^5
zOFv!bOzt?+e%)Gj!lp}PJ<QNxyA&tEnq?vfkhs3-j^bjeA<C~uQ7n*j!^>C~V@1aW
z&=Gk9m8JuPkCWL1_9arI5`u>oG#B9_BCnLVpqqyZ7!vS&J>YbcD%+B|1mpJAh@!s`
z<*n!^0r{#sw|5B>Ek17U_f==DVJ9Bg*rqw3EbMkWYMHSXt#P3uh+XpV5<_0%QW#`m
zK&~dqr3?~`An!rml}b@7d9W!khPDobIy2d%U>azt*ZqR!xopx((qHQ?gn9v^!T=hx
zUv8k)RIli#tQK|C%Sq~J0?mN-?VqW<V8WSChbj8nkU_g<MpO|dha}#G>n77oyQi(D
z%}n&KNp&VVhTZ;tl7o6V-I@2PudR6JejN3`3L8&v;kdT89<;q6+uUovj@Vx>J`1Mo
zG{&_!PF?ZU|EX;6^mX|vqwCzqXtv%EdcJ8X23{SZtmX2LuS&X)m%)T+tCJlX973nI
zmhBL2Jz***l77la%uqXEYOmN|Y$F_|p1go(p3G%o*AOg@NlEsRuyUmmLKRL>_q!vO
z1c?>iYF(h+uZU4;6%bm>pZ!L;V(xxp5~5h5W6h0Lw*fHJXM@&F?Co%!qtPgvw^Uyx
zb>E?<d)r;^=kuKEZ72O-VkdXXnP1DD7!qt>O+fb%4NXdH*DL+f2}>t;Y9pf$6b>8F
z4L+w*3<hthy#qWJot!MSvGs9Vk91sdtdr?3qqie$YvHarvI`0#?M;=|I(vad>O?c8
zTQD$P#oH5`A-LC(I@@8lrP@Nz%~w4E*&P$c<!YA)P8S!jihN%i&>R#3o6OI23@48w
zl~b5HOc$U{CwsPc&6@#+`aVlkq`}?K?&SNdZsJN>2g%Vo484tl(Bv2Y3ur)>zv=8%
z98ZeoD!F{#2ls7{L4B$wJYBJNKax{(&@HfOp+)#rV|(M<xf>9BHWeGUhcec6?Q|Dr
zdryqt?dVbJiTkdgW%Hl3gpX&8^I7QHu_byk4(I5hUC_NtYj|G-Yv*LHwboHN;Qg2H
zEd1uy^K`@YjI;RjZ(DG~!~w-f8>9bvTXPT)+x=KtT1Rtz&Y)aylMj`eJ{6xsSyuG&
z5z9jy=gHqjWME5T3Lc|{%g~07jaBlkijJn@*MDEeuk+?1Gd&H?RLFDTe7m$Ho}QL3
zTZT1j*W#9&Z^j$1zoym{rR6W{xXjgmU$O)*yznAILxSnGjWO}K4smfSouEicO2W@`
z=3vG06?o$D$MNj5&oays!=javBp#m#po^_j_^lAd+tQMJ`wt#E#Jc~5HEY(WY;{;q
zS^biA$vR@PdCL~ue8UZx@%9X5jV+T57s^H&E2Ur;yX&aZ`k{}PL}1d808Ia8GtQn%
zr|_GuNJ~lbWy{FS!6%Gq?e67DZ)OcJd2}a=jf%fVQrwM(7&i^;06r;Ly!9x(GP$Ei
zKvOwYZdyVTomyHXzuT9aS7#_fK&+<!+#aS5$lUEn)ET6tWzw3i_`82N{4|vg<!<ti
zW1$bdne+%OT8k9bSoOW*CmDunb5P;8Q`WCt1Daz@f5!*`M|;rPuQ6OX@6Y0TNs_GV
ze*9xQCXesO7`LTA(~j1E`Q(%DTRu+2C1b&wy}0js$CO7e_Vq8{e24@l<J|K;%E!76
z$C!I-XkRZjHZ{FH33s`C#)n8F*(=|8&qfU}?x|^N&S2elnV0xAX`ucJ$!y;5n6u|_
zgz>S1NM`RlLNc2QhvBE=v3|!P%0ftH%UX_Pwh)C~+O|-Zsv)#kZ9(hT&uGcplGfhU
zk%YV+_1J^>^&iip4(D%Y=`Cl&m*3)s*I#tFdU3s-L=U>M#w;y~R9_U2O7mP67Ihk{
zcODkYCMtZ_!F7z?-N?HhrW3mau1}&EEBTMj`*6=SBT6(e^S8CwNMclx*1Ru0JNjes
zdrvz|?3dQ35)D=0fXtEeI8HlTDL$4}d`OQ#T4}eyAwCCUbsS8O$U7O^dsN>pc8l!_
zIgSH#pDuV&IEe8ByQ^5#b~Bf8P`*_%Z*>F%aY^65b2~(zqCzLXLmL?<TVjg47(8J?
zJK<Ey2oesCkHfH@9nr5#TlHMaZ?VL_bzDhB=q(3nTqc|#y$iMEcdi#L!7qnY`fA~F
zxVj5;4JHaboMe(-;NHkyGbReN6v1qqq|n+#4bwWES|RrkC)XT!RpR7Nh-c-3n{JoP
zsT4BH;@FWS<C%?6O(tEEOdKpl)69C%6Tz6717V$!y<lqSr3iA9hr2}^hl>ge6DgAu
zR$L%N6BjzpTzu!yTBR5{&B;JM;}a@YRETaP$wI{Xw{cu=s|^7|C@*)~ERPCrSHY%u
z8+?vr@Vl|EHv)aBWT9@YJT&Desjo6g`X(Qj6Y<$txjUI2=K0KVvTK`+{>m9gA9ve2
zD)U=DCPorriA;bb$#D_f*b*Hg01=W_Txpl)y4$F)GFx9?Usbo16%x%zvM*$>oHydI
ziI7U%dE1iKMqF+mS_)P)Njt^|R)k6Np%n}a!r8VyIWE@Vl8kbKV_D^-K6x&Lh!<Dw
z#`9}i_vQ3kNHvK&EJVNUGi809f`zzNmUzS!O`{pBSIWQn_9u)bn_gx+LVW(ZV2R4}
z_#^jL>K@2-eDKLvinO**K*|+}cL%=R#!JoNM&ADt81TGkly;+=Z6T$qn#A3>SSlIq
zSe`M<C}SM6*M^)ESLe2Qte;WabJq0;u4~qB#@%;JW(@1{7d^J5wI~)zTgG@OZ5iKi
z>vm*c-naO(iFo&;FY(@6FBKR5+GZ~JT^?y|T-(Q=&%$B4B)3OetNw8wAnl8Fs3==V
zzonl>d82tbx0!C+<ypJqwwu)9@Z*uxfZoh<B`u?6+;F8PsJK6su{MJwfr-zLb=}fd
z3tGjGA2&+XCu7#!dB1VL<Z~nS$)o2@B^h}h#5;hK%!6!jm1p#}TPLY!P%4%9)<!a1
zzc=-1!g$G=xHfR9$X4(^$A$6Yl?VB3%hPR1mbrilBL)Skn7_vH^0uN09ek9FHl|)(
z+gQe!@(LB47njWJ4#NmAL<z?UAub$E(%++WQ9k|6Uki(f=JRfn?{Nv@V$vD6UN^8p
zmg9vLaYvr>pwLqYK61wTuyXC4P3z985ZBqX6kV}qKl|u}Yp&>1h*=vd4fN^W2HW;V
zV#&%qBvAXQF{oGvYWa_QCXb}TL><ihaV?VQ_AMkl9-;i3yLcN~($fEyYX;!%TSq7h
z^b2*V634x1<ND}r6G!~UtNLNif=$>L6j`kv04~&Z{N3x7(aTj0VMXIuS(wPYMyTea
z$y5dl&BWsrklsk^>#~x3Tg4st0}nog{d@ON*y&a#F>BN%-)0wDkH~lT=bz>vC@2Wu
zelrWbdzZA>Fv{55$~X1X&pyYCFTSW0RLdfTt;@^z)``Tnd}rT$^9@=w2jlb4KjU|^
z;%gD>dX0TqZ>;+3FFf+dqli8c<q#%PR^dV!h>Wkl{`w1!Q2BWFH{U4t;bBLPDso-c
zF}L1wtI?F+Rsw-52eif0Uu?kYJyDoKE9cVENyfDPXx=Vb3N<7V?TH&k1QyP{?3EGn
z`g)Sr@?OkdPNjv(1JI&r*`7h7Vv@1x@F|9RX^dgrTiL5tA1eic0o~d#90Fw^a-MUZ
zxP%l$olZda4#i37-4fqgCfH0e_mT5HlqFt>bT~W)Z+*X(-LVkLCWbq?VMOO@C$8Jk
zLreD#&9R5`Ff)xo<oY8{Ct><0i}A_7rxa7Xyb#w}<Ll)8ZbQ;Tlt2$1JI%Q?-^s;W
zaO)L)8EVzO<>IBfrfjbHzN42LV{K?6!8NpJJI1JObE&$d+pPcsF`;3I+Phzoy?K<K
z`O*`MPdmr%ohTTKt8n=n)6YUo%34jx>~LD09wd>yJ1i1MqhfHFf2%g{FYMWADjDAV
z?~iDSO$FZqH{;^!I)Ij~KJ++SnG`UvA3PZ%GLv&7nWF`-UWf6|Ji%iY?@MLXDRGjY
z|MVBU`t>3^QVvT=;d8wB{w@FHd9?PmFQ=HdnqlP5omV{3u%6;|poNmvP8`w`bCzyY
zW4X8szk`<8*5MwK>A6>kYaUdx6E|{QA3b}Q!=!at*LE?_iPx2zM-RZEkVtwMu7f<T
zsTZ!@St@ZY7U;S3oGfI$@K{qP48ADcy@CY!qSYj>iFat^X?}ms&<m|!v9`C8gia-Y
zC!E`;KAn~IySt}{l9_Efz)+y<x2>0%O?yLG2fx>R4~BFPpdyxMu{vw(562}qwe<=w
zK);RP>t$6fKIonOiUyPXbE%f>IV8>uc`Ngn>d^%Q1$<^&(V9fLv7$o!V);oB6~urv
zapEt3g>4y~rOOBvBbzQCve)ees8y*_!DLiP_>BI^32Lu8QTQTmA`0b?l2_`cwRnm8
z<(yHMrDpx_1wue>lll|Lxi2p4tdldfY?^sHp<4@Uty#%=p_2&#es$s7ARVU@osgT8
zr5co$jV{X8_-!1!(7iJ17qVIe>p}=iyf#VV(pqA$i6BOVKt_^$@>|?<3+Y<SQozVm
zg{`#LrcE0hrt3Qqwn%%DWWg@K#R4NRFi;(nKFBdSXCx^vW!koFtIiq69o<S@VnHqS
zdU<)NeGwLkTRzze@oJr<Eu)VH(QT9yoTQux!KAEpl6vHv;4CE7<(!r-U!^Rq1p@;s
zd)F(N6L`i2z8x(}iL;k~gqF=A`{C4<f}S1&N>npy#1KWyz41S4snXJ?toDRtm3Y8%
z&!2eg{xSteB^u&^m4B^QF{-VfKkIxF@iQ7K002M$Nkl<Zj=-IMe7kfCgg@grOOm+8
z5?lD(Ifm8Xf|ngs*DQ;SC)tO7eR`C5#;ohC$i7^=kTf#NG-=#OJ!fJ`otT)cj+KUu
z^>KNhBsFc?gszWVk;D)gsdS5PA=gWeR8lsTll()6LokMl1mz{8Ep_jixl@M#^yuD2
z#rhUfTjF^irq#2Yw~&0rYm|E^*6x4N)%aCcjxB!D?yr)4mTXeLT&oYqv>m(l;ZNRM
zd+iBn?9-$%uD^ObdiCrEFOHXqw6c=;(|_<ePoq|_Z8;G%{ru}9j2SglxnY<3t;bZO
zo1*YC<(5gfn%7Z}E~X^DwzzEHv3nn5Ywl7cy~@kJ-gnnzTIX8E>Ik|cKYl!_*f^-J
ziGA(Yw-+9``!*^WHC1DfJb(KS1YzTr9XN6{Ts<Fh-Eyz`_33GJMQ^Ja0eQY$cz>te
zHwM1+3?wngh0qD3FmlPty_mCbJN6xl#D}llMK{U~%WYli<Nua9gNz{;DareKHZc`T
zR#R(TB~dKlxXx$1fww2FpU_*y(XMQFWW4I!z8QuN3@jL93R<B?;-EeOm918l3#E`T
zC@hAqz&&x*xFRA;aJ=>ULD;f890!A=5kiH4E_4-oIVE{s?!09f#*OHP-L$mdzWXTl
z2Swrdi8DAAn~V=;{Y7PgQ~2KtQ<RIl>b`byiNA>MDksyWy{)2zC=LwmABbNTZ`H(g
zbpv06>Z`-|ES<_DABLY)F|x0xm5;q7^C@wCefu4EB8-;Kj*`aKb;<KC*4YOS9Kc<7
z--Ee7|HN3&-K?{ex-awfFTVT||Ni&?DQlLpEWAsr#4>Mk!taZJqf@zDd_Q|OyuB-~
zjA>oJ%o#Rq+Kl_|yI(2yRt1-Rd-mYYyYIrhUw*+o+)9Wh@c?Bg1zWeP_Z>&TznM42
z^lpt=e+HFFT<-`vMeDZn@NMXU2PgG$r~y~Xm~I-?NeK<U_-!wqy<;#Mc$V&cV9}-{
zNQqCylXP2Nzl?kiHU2lt)S-1#wBkH0;&dYMCV|Z<*5TtZ#fWQ}!yRCFLOHJz$DE<P
zsy0U{3udv#H|J6Dn{zEAxmG~w&?r;hR*XBIx_vC}`R@-L|2SxK{U~$1-!~qjb)qL;
zd+fSGrN8p(x;)RD6hZyq4MXwNJM)Q6K8e6YEZuMbx6x}%O-dVfAEDdmlZDR=pCH^f
zY3Sel{?(*ARCZtDBchN^Ie?20hfE^Fmbp!3ldM6-g|hsOF0itdxXsr*{T@OR&m(}=
zxI?<N$05dmKE|-GCr%u~#tnP8(4-uQ-X@yS0=u(+GxX@t3O(AlrpJet2x#G3X3T7(
zuTC6Sj9A1XM@gdrRB}wLL&UW#cH`N<c)n|;CUQS+v>wkTcVsW6QZ_Sp)pl|?r1?0P
zn#@?umU$XTR1d+=e=^)HuXEFWzB5N6(T`TYM%r~a97iK#EM>|4j2YMqBl_@Le2J6D
z5FE9(HoAJ#*#3Cur)5YL<4k@(mTf(N*UqP*fmd-7H<^~nOE&FO*DUe0MRCD+W{kQV
z-HKs^fwd!LhWF_R4|@0$*ZAkzx6QmKw#2ovoEK#Ux_TFv?Ex*CvX6e)7aWNr(PtFl
z-KSHV!sb$VJ-cZ^E@ZfPdLG-qd*Qj-zN3lj%G^PjJn_LSHF2;Jm)|C<*m34e#yF)l
zwYV9lUm8VFi&lrY+s=zVTWVEyPZ$=>v>pNDc_?X~WaMPCkW`oM*~ZmYc`QP|I=$Li
zi0iUG3VcaS61$HJ2s7gyD9NEwhs5<R0fEJ`QY^i7y08eyU6eohWEr<XtgmI?Ah3Bi
zILI4kdEz1``Su(Al%{3hwl~s$IexK{kk&%f1_T5sBHH-4h=4jFAwjtX7eS6S(INz-
z<P*1eO`A5Y_O3Q)&;VjBE$v&o-j<lF@$vC07Pb(-(!MPrEY~7^67sk*Ny<z65`WrS
zh$9$C3~OoAmb{jBq@0lc?c2A9r)SXwK*}9Cazwdfmwh7%zJj4B2VBm?^^#>P@%|@Y
ztNUb-lm-UHw<KbS$0PUOiODx#SNv4Lubj1iAn&^m;ljmxHpgP8>+gW;l!r>>9Witu
zO#k-}#xnj;S<95wt`MIKep{wUlBcGTyk;L9Nokw}&HnKh#XH-}FZGNVHW2rdG;G(_
zzgQM=alLfeD*QZeF_jv`0zDs(Ja89oxNc&xd{zA+c(!iY91juG-n5{tOH1~2DoTj^
z>My?fUb)&X3mdtHzP)?kUXr8%ZQIbhP8~(KuUfMK-_N1zPJYAfj#eDh#MQea#I;<b
zkX20>N9plL?!(AogNqRxGVW}p#quX~D-{zHSAv|C@)WCJ;qS{ZZY(VZ#r0+dk|-BQ
z4Pr^$zI|I-F^|NXbnPwE09!s#@aRk>gjZg827b+a3tI_*Kl+m(7GnCPSKmh5nX@cg
zG>Xgqc|I@Vb>Ly%iwL^ODmrVaM($M`|5kYP!Mh6UZA4AvmM#1+oX>OeJyWr6<5q0g
zv>jbL2V&fq5rz3qos6-o-zOTw;!PmH>iLuS>0(VSv9WjWJ%|;It?uM(!s8F$TUejo
zxiAR0aNc<B6}{1{X>kqn+eqd;_J7~ef;t}WeX$I0zc`hiPs$#LL|`edo=?Rd#{R=m
z6fl|{p4+6H&rr{|7i03-l8EJ9xxA~bTQtPIQ%0(1Jc(g^X8p8|Zcjlg=3aRIar<L;
zdG#9S#Ui91=i>i-=qlq-wQc8;7!?7GJ3TqATz8<57#BfM0)-+H^(QMkAHUP;`f?+#
z4G{epTl!jBo6DcLU)!<w7(So<7oVYMtlAiiKEH1#!9Co_UTxc83iv~g#^9!Fow069
zaIx`Gp7j=f4e=}6J{6mY7R~Gvn$^}BUC|{&K$c%(1@<MaDB5wo-<?E=ZIXHaGtWF*
zCTXpdAUQWMlB}`qxz~2)d?18zod5aEGgvr(z9MIhGWNDI-w-$B;vQVNV6?M#7h+wk
zpDanf<ea;f(!RJCUqRR9ufOpo-kC9@<buy$C+r-PIYeY+6rOzQNyNp*DvKrCY?4Q<
zhm>&F(l1VE6)v{IZowt!;6Xgjc-HgjhFv^a=~O)kJaSDB-1zbmghnNx6Rn+X)7-y;
z@dDk6FMNFx+BYvg7qHE1`&;I8H;?H=8;M+evt$qcaqB?4bA>D_h|FFUOj5uTcTebQ
zTaEr*4g#K3IPTr4H6mlrSv)ZE9p4`sjVTlQ6_z}ca30~&@%)yF)P|%I1x<r`v@JYe
zn|H<y?2Jcm9)Zv1Z&GBrT#K0sIJ1{*R|=OeKYFdh^UdnU!9s%YLvjg|yk8TBbVJX;
ztt7c<na=-a{k9o5jOmTKoL|<2B<^{?p(VJ;JtfK9x+mS(kLqhN{J7YCw6<Wq*lpL=
z_9Rr7K}iVldxpy9$y7e~rXqTEq?2b7F!hDc5JC&wHf{Z|aK@9gu=eLVC<mv{CNV@p
z4BauG#Ga4{9OU-I>9bh3?I6~z->s~c#m%^%PXh!pW_H(hEzzfA8xq>B;7=n(pN0(G
zYIL5RJ#{dcWOWk1R}y4~>s2*pa*!M0v)rUneRwiFnv}Jd#NxUhV?NihGiQ_hYjy`A
zR9s85PtEA@=jx%o3ac5}yE8*PwB{I3MS-kr{Ip_AG2(h}SR{=p=~<7Yw~Y4_26dNN
zUtxJ;CxKpt1Uu{NmUA*U6NR8Af{Bs2s#{K`6ByEN?#itki)pZBTOZr3OT4Ze(u3hf
zipHcaZCheQuZ~#0aUV=PA4&J<8~26MZF=8gHEi4)g5!*tuCAG~z8gmMBfjp%@;LN^
zYi^0Z{>!Ru>f2$jpYH6(_`y9;m)-^?b!h2J*Y(Y@`|xps2gb4v#(md~ENt!^jbpY4
z9aHn^uI-vLj<}DK-45q<?W61S?6eFjAcVr2xIRpU*@zf&i@g8!XlXsH*QFT;3ooJ?
zcQkQbjbIj5MQwX0YA=dQaU+@J<1T}mSSrht%t6QG?E$A2KFHo3W;tj5gv;A*{kxzd
zA*S;<p-lf^K61mNVHWb*TIgpCoP4oWn|zs#qy~_JL9R~kK)#^m45A#koW#W$2Mi%Y
zWiV3LAQf8GYt(PqvK3ja>vTa8kh^O984VRaG6g%f=Z$<9Ys*Jm5$n<qNrnBh{8axM
z=d2E0&|R54v~Al~5yjRG$%nTI<DQ8iM+jSM;#%^_d2tzjA&6_KUt(R0089F3w5{GZ
zo(m5TFC?suV^T))O21mQY9&7_oJ8;?WOb)bj5Ss;E*O;vG41c~uZU}D!?s;1+p%Lu
zMGhKeWGm&`v}uF=bbDx9PJ~Q?;e~LyZrz7VwZC}jpURcC+&^o)O7_R%PWv&EcDLPn
zL&?)TlCydVa%X>lJYtr`u~uju*Wc%mz@~-#)JIB`89rnn{yY6?y!zG$NTY>tS?vpX
zz3BJl%#)9QJpQ0V<n@M4+i;o-2t#OL-2ov(ubDVj#d)@UjwGL0?A}L0yDu$Vm#<ud
zp53}QXf15rO4<AJtTm#$^|xM|&iJ`43o}bx<nd!iGFEY8JoA6A6p+t_rwez4NEJ)*
zw_csjccO0L@n*h0xNGXoYC`<U7vC0=KZO|zc7&XFB&mKf=H-mnUO>mdfD%OnKOt@<
zhV`@m_bM&c<4W91(Re?4EP@Im;dHg`m=>(GEAOUQH&3`?w6cizqLuUB{f7`75>|pB
zm;4RrBI_k8L|Bv7)*bHJxg(x_>JdzT>8--?$v7{4T(@B>?!9xe1#{bhp3ih57j;7T
zw-0D*6f4|%d)LRLYp+y)g$E0E(&!f7-fBq2K|T~*jJP(+ig$>?1N*8!<CxxFSOjDY
z%A{*9+tjK>W88D=aJ=;HVys~Znlp4u>1RW3+m;dGox~|#wkAkf&L)z0^`Rwqc`211
zh4vqgRM`gfZeL#Z66fVTkiOk|-JrsA65o3M(!E%<{vhrm>Dkh+YQ&>hGF5kqC#IWs
zgeuFS6EWxT#>dNTnxHsD=J5Omtl4xJkK8$q;VMd!YBq&x^-mF`3>(l9UFhEQw#Poj
z$(R&uV2tkzL0tdw*FLyY=<&;<Z6pLs-d|DbkPt7+R`16{cZ{jkxc(hGFKZjl^3<iv
z+{~HdL}^LRCsH{01!FJ1*QE{It>)UIoI2~9Z?O8WRdy|{WzH;ff*w73piAe@%8FUm
z35VzsTogp*9kwN~ooJ=BdE-WW{>2yg_w&z})-84X3>9Nv`tQp~J)g?<T}$N6rSfTG
zD!C0FG#H&bcj7##E{;b=V8@P~YMmr~vsHei+CFB@nuRf=M`Pl|D@)Yr$N^E{efyoa
z5yaTm_AIS)C@l4$o5Z0*hM-rkUJ94k*cj}lW%a>>hm^9dt#>TJWz&WYm^pJMp8MCo
z9O;J6;{XEV`un3jmAgLweLp^aa%`~@OST-tTR-i<0+QCf+E<Qz5Fz#L<GYc#-iHt8
z?ZPv+4J_7%{8+vt0*69l@xTqe(Y|HF66f`S@(~!?GXTG>KV-?sc|lP4DT*n%_AD6N
zLLy>tE|nU4GH;-8z7>_iIx>vl<&vJeXFTHQGI+t714ZODEw8_!f>}Dp?>8U6{(^XC
zDGNVYZy!AohdclCt^I7gH`DK*NEg7(?4Pfuqq4U0@^r(4Hx0+Mw|=EyFBc(^XH*RG
z8^`vprdFy}%^te3-?Bek&3P>~(NotA!>FuUj-0z{&Dt$(Hs|4^pVwmEs@)}I1~O~t
z(#}uaH@k)Z#s_^nw1*o#tL1WDByy$XxL9nQ^&PH#;khcKaw<L%H$V3gc90Zo$(Yvj
z-+D@s)>5$>3B<N^6E1%f23tI_WpeHldp4QR`DyG8i=elODDKA~GA14ywjIKT^|Z_u
z?uBG_v&P<NPq*SiWcTjqj}Ekk-SN$jd{6R8`7op!Wr9v#P2k+fv2T-*#xowDj7cO{
zWw<RhaoX8{A!zNT@Ba`b9IicB!Igu0u#ZJbV?yxWcEw<>rH_-7a=`++&z^qo^@@C#
zx=x)*DyoumyZ{ob_R?ej`xfhW{y%$H0UpKGMQ@xyumlna1WSP6?(PmPE-fuB)TldE
z>il);UZ4tXDYTRd?(Xgcf`kwvgb>ev&f82fyF0ttKoV^3m&we&c{X?6JUdr*!ZmF!
zP^{vi{^2l7{@=%wpj=TfFe8N}4*+x?{me&3Rl$Tj6LZxvvS-#?c#hoqa1B2a+UE1A
zO%0-T<5@7vZ3|q_TCh&t6AxHp1$H;GXYQKcxIp7j;%a*_*B>?M14J-b7oeVjzz28y
zG)0Q}msXij0fpx6H^Lk^HRRVr<DDAw6igs(+ZQ3nP6LF-yQG<LO;~{f1s}}(7aG->
zvH<Vlp^>@s?KL|NN(Rh_vyC>)wiKW;yAJ*pOpCe>7*U{)z>bC=s|E~X(9c9ipqY(O
zWuXDOW8=aNonB@%Itjb>jR1uf+tdt=FyrWHN#B25Nj)pI2`vheSOFHru@KdFdIYn}
zLW3IQSuIH1XaYRj|ACf;)fs*bJOa_tCZtKR+j>j#jDI8x6Sy4k+A1=Buq;k;QI1K4
z%K@@lJ6MupJ<eJ)qV_ed9`SsoNUNWAW>svfqunctI&uKx_u<Th9f9c+Cr&8MRlTM$
z!ASa;=Gh3GyWILyJCp!4{jO=iHS?s_a$H=T%3dGN0B%?jTqYoODa7ZXwzQ+?VV$`7
zqckq?+!`^gEPS`{z7Y`&=ccS}I{qf8dOgzYKjzESkH1#$&Z-Z#eOR<XZUjt(#^=ak
zgKY1r-)RfyiUqXJkPNg}61TonFyZm}^G6GKJ?37!ez;wm7V_e=0Iy$vSDCLi0-052
z%n#u8?*Q7lAbs?~yX-a}wRk-9c@{5QY3XG2H*ihBH?&4onEf8_upMbh8o?wskHR3<
zqj%4)R-m=M9Gbaq)uO4aShZd~yZ#=p**A=VPi8%6t&cOHZ%_Gc&R=rk<Z0_MmXYv^
zs7dY$v78Baj4=eQ_33G9{ek=Lkmn}<&#FJr;;>b0*u2dp;F@KYg*Nl^Pd_Xzn_F)(
zEQMCH(RfaN_V3dJ8qmR7k%s4Xty@T=hIN(sa(>?$vCW(~bxIDwq<2Z0__pczc|R8B
z;~#zMC1r7-TbB+(ApuuNS%$ED7FN;OKYsYh*YIa~SO)d)CAGpr(XMR@wn}0CxKbP_
z4&PjC18K0_lg!1H3q;WI{V)EO<kT$r?za{4=-vJDO!edMtK=k1lzKwTzDv70dB!)2
zz|C7dQTzJeB|Bis?V^15%W|3Y;7$75SH(320%GE@@P*0L5d&c&PA}Yw8uF`%feG~4
zf3F9B5@gM$1Jbu!u0;M-@eqYn34g4CYGdOt@-$tSq^fqvKC+;QzTM};xA^482E$aj
zWAkj&W-dqjSs}L!ZExqBtf1;*=2Ng7U{8>aucvG=n4rYm*%R&N6a=YGp&hwlE`@H!
zo)E^Z^BvF@Sp`ju?&T~Si0uC&BO~Rr&%eO;X}=Xip+%bep8Vnqu&&ZAM|+y-2wwmB
z=RA4!_1EPv{5R8wqMp()SDZe5hKw0K+M!@8*A}y9&z6nr*W0D%bGo6NQKLu6q^F;@
zGCR)lqhM-#`n2it2DGYaMM)2y^IM`@ppQTPSh{xYqSgZXy5)7vc?A7^|NQezW&YeY
zekS<Rwq09!Y4VHGv7@!s8J7FUAAid8FTN;8jvP_z7kzs2<(qG($*57IB&2e#fTKQ+
zd7VEF_+YQ=$x$ujzV{Z&<HMW7EURTRmSFO0->;RQCl8dm)$BE~^HU1J>)ZM@mKp!-
zkgw)#m40o)q+)<&FDn&ui2u#nD1If2$)iJ><|kV-u8srEV9~}0{tc2dxK9E<&yi=9
zx!NG?6OuOVIi|E&35HOcmGP;meZ3F`+unM3h?)=lws^Y^yiS9b-)6W)N|E=U7+$m*
z#^`I~6H{c-1^^mHgueD}Xo?pFQR}*IyZTb6+7bYGQL4x<X0MR`nBx=_ICVMCL2c`d
z><v%)U#}TbPWrShoc)G8>xuWa6K;`q9y)ComETT0tgo76&w-$@8WPsNt?c;gAMwUM
zqNlfytomk#l&e-n+T1eIK7orNClzS@K0xa|QUM_BFH;_p=5=d0O<jyiIM8h6L7&j1
z6j+`)9u*_>L%d`EG1+?XxH2;y0nOHx>vzdYXlD!RO#G)rMvC}Xs3(DG$r7A)L5?T;
z%Bq*YmM@>aLut%9EgbEq6hKS=(v~sKX<UPw`yx)tqV>B~Jzy_aZt30DF4nN_tz^oK
zc@hhYWRcJ;pSygMj33lR#Y>|?BuHENvTrn^(-8DIb0QkZe>PBzaT=Y0@~K=7V7`{#
zDD5SOIoi{~x6l0^qcVHr{Z+15ULpy|<J~OUxEsFDV{?3V^LpV%xC5gZGCX_x=5?fg
zXq8K@qFP~-7e>T-a=mG@R_NNip1k$lUy_A%M@~kg?>H{48`M-eEJEEfVCtL#0$p3w
z%gMDx-B1ZGUlwclSd7m{A$S!hLH?HY#j?$K|Kt@w?alzjJsrHmoXoDF3;Wg5HB|Yl
zwyJ0#nI~f;D?Ukq!x`ul(zhPQ2c!G=(J@JB3-FT#e<8Mw7_%qi$75s10vRK;nq0c+
zF)MrwSuycB=`233b@||faLcyq?*=Iyk7^YpZ9M>WIIuUOEcmc?uToC3ptX{|4HG9v
z9R?F?^<^9YVkQ+n2M7~rZ<ozNyVgqPZ`&~KUQwL2@>x&TkHj!uYxULQ@Lo|7H(neO
zH(mVw{e_wkdf=LyDO#8#px05Ju7+2vSW)?MV_EvLZ2@cMN3fbEw=Y+F*%>dWNx{Qt
zQ`aa=drsbSl+IasTKa44OhEGk0A_qo&Yn->3(Xl1A7X4`HtoOPC2iVN$wXcnXsz*s
ztyY28fBh_}Xv5yO+=mZI`?4&t+O}#A)6S2}<X7L3^ANOfRv(rz7pBb_hv2oNc1{1(
z`wtvOJbua6#L`FLSsVer*0Brk7d3oaG_x!&9F@|vQ3F}AdcECkz87jA*A2H!673=&
zfX^3RD@;zFqSkq?5QkA*M`0`{1VC_)uALl3u?lbBrX@^u*Mvs&ekBBAbrX$&@5B}I
zg73>`pM1~`v`&Xq?9Sc$AfayGA5x;TH#D<dp>Cbq%a(1J^xF}98pG6AI5f-W{k_oc
zF546RWq<b7ck=zuv!!X{`p`CS4Rh@cl$O3-g6x3mFfq;r3SPM#rC>9jWzi&fU6>fB
zC4$y1n@V*od`&8P4%l>0IThH!O;B_mTugb}N^9rDvc3p~u;q|`E#;@V8)Vw771Fp~
zHR;+ucM)eDTVxIV8h`oyQj`NX2CZw!rD$B+=E>hxDwUHFgIdWKKdg}7{@N%v^==_`
zV3P1ssi1``C=Boi%zsm3fxziw_ZkEIoCY0&HqZ7wC*f~-jr8i=5MzLY#daPKcd6;}
z$o7(M?}SCFnSZR4C-3Wre%;qF4{G9b>?#I#O6->ll4zFQ5B~8wwQ;^_I;dAu`E2?E
zS-yIY^zQ0>>?+9mnR#6r2RKT417IG7@BgMif2oLdGuuu)HoBGpkp3p(ltjUYfR}^z
zl!p1%B53e*1#r!;jC1_I|NcjgMI03`Zg%R3bF;=xn#fN-d@r?X)zaPJl+R)v>Nfal
zZr-A~j2(NMvT&sLP0jgt1T>@n{^uXLYr+Jp^x6-)f0;Gw7rQwm@4KUOc;LYY<n`BH
zvlEPE`4wPl`?1F!l`v>XjUPWAbIl8C&adT7?U*&|*2%_=8>L;lc3QAYXAMwKh>x?=
zw&pyqcb`7;&DUQUTEC*pq*0?rO5M73;WPXe*|T>K!0X)o4f5sK(WCMg%$eVF_uVel
z$BdLW2S#+REpPq2L8kn)UOsuG2j<8E-;1Z@n<sl+HfXJt?t}fa0c}I&li6F;{7(!2
z9lovCZ8|EG#<c^eR!$2x&-vs)by&jTvG)kAJY`BOw5t!oG`I#_Z-IqJ^<tSXUT}vd
zj@L{1Ec&X6?gQZB-N%MWacCrdH-AHp_AY&G{{hQ1A~R*`lOv&_S%h2XoSrxsQyA`S
zjbD(Ar2r@0&d_J{6Yu}7c(eV$Nm;OVkM!%{SU|5J>)<G_Jx5N<e{1$Y*uVm~p@WcH
z`?tRGf)z&Z4PjImFv*?+N&(^V2PI-71Vv(CW*Z<Q_b28&|Ek1Ao|LZl-z`4atH}>3
zP3xzo%G!;4q&!S|&wS&-96&HX`7ka`P={5nppO3SEkaTkq0AQv<-^0LV`RZ!OJv^M
z|0E`(n8bN{%W-#KIqq3fHhBO{q*No!Rd<VcO}=<`ymW6_-)Zr-0@F?-+uWSFV67y?
zpXUNw8iiMrb}%VzOVlp!3$xlk%v*{6#U1_KO1Zs%N2SeM68@G+?0Puoib=Sje%t;V
z*0qJyt6EWL)}6x9M*+D0xpJ#gUZk*DK@zplYr&`UTMv$c5;1CK^9j+UIyL7@8?5=t
z!+K!<_KoGUng0Rr@{!YU(e&Ti9da9<m-H_L4o*j%127EJ>{;nDx_3LPIYxe-gY~2@
z^fx9Bx8z|^PnrlXoy}t{?`CzXOQj%xi3A9p2K-^g)_tm-(qh+A2wd=GREAqAy*Y52
zr*Bm+OnyJLPND$5Z-A-s{vDdinRD^7Vee72IZwPBQQF*EIoHFv2uI)Rfn6>>12<J6
ziIViz&*I&yr6m8f08$TWO41`I2TTIoN?A5lGS4PDo|xn$$i7I0#wR5s2ey;U)3MNk
zhM53%i0ol-&VX;V95U!Q6qwBjxaR@W7U`Q0WcN-vsd6tMso?=rK$&qDBz3_im^R0N
z;b0)fOb?pbNtU!+wU|_>93Y3a!?qxXvpHN1Z^5>Xw)MIi>S)V9Y9ZT##atZFY#%pO
z9BCTc=9Ayw1P-fJt0sH*?sb}!8%L}U<MP_1E60Iff1f8bA?*ri&G-8NK<lA{2N+hE
zpmn+f&{|8t?}gxXvH3FrUc+qonETN0TMG(Nvv@MW>(>BY!vv%e;H>4x4bng6|EJ<S
z{J>p?K9f&Joq=|2ylQh=I-c3~{IG~<o(Ev8Pg7Xe<ZVD6W|;f~U_Hda*hJu(&*F>%
z0{kr<tFuT>?r~AptSS83VB+iwag((T1QB-c#TacvoTO9hiR7#ga0g$ycWDpswwX;1
z7_On6bmmMn!tHxu@{^|T^Yo#OHgNLPX$V>vpvcJiz`?^ZcFb@o?mHdU7Lx4R5c@F-
z0#H+G`O0<bC{wzmw18%KukM`zwl}tWKKjOPP(NI@?bxFPJZv9~ZJNi9oqJ@*uD$Zr
zw?9a|y0ziYyR-7cUJ9GrCKU|_E<#H9A~dS`o~api5_48+1AqVPYE-~Ynm4L0fdQ_+
zkNa-#A)B@zmHkJ~%9F4BBzKPP3{BxiQnhmVoCi7saOl^+*2`A_t)V0$p;i3lxrcAc
zxnB?$w+!tdf5IH}sWXZ4*^Gtq{wrhc+Nmq$kT2xP`Yi_?bxqh)aFaS=!45%K+j8h%
znLeBeCim}dZDBzlms1Dc-?r-n%&Z=l1BXvZILv70m-q}3$InU{n9Y94mT+6uy?j32
zvuz%({-zoChn|}uQ85=$r)YT`;B~_~xk4-4Fq}Pqy=>Wa4E%=gHh}d7NtXOKDDCyU
zOjMRF<0qY<5k#v1CU9MlZ=rDV^1_&SAN(SYfCi490<Z~n6E>Wx0?2-T?-7YXe`jPm
zoCehP!~9<~Hadd<jBq{i-@*lU!!N(5GG)rh|E5f_1FiMta(+Pb;U9c3Rc^j{u*Aj3
zV_BcOSxBv!#Y>jR1OUsna|8;<9tE(y4St_Ju(_!xZd7*b(E}RIlkGrjeLQMe59r??
z8rhTN=}D8U%I2!%!ubSQ3e(*A1+Hn8Y5DRMN~=>}9>3RWSo6I1-n)jNwHB8=XxOlU
zy!+1EGJM2Hj6V=kVP}_36c|{xY`FsT_59NAo9Fy<pcsI>DdRiH;1}k~SooN3T{l>s
z9cQoQmH(WrQv~_@$xA3ld!OeLF3NM?tdbhl%gbZKTUbXnf8~n<)S7Kx2m22ZXO-Di
zf*IQnL;~~(6<QJ52XIiWCGjo!`glNNF?U?eS3y^KWNH__4l6o70BOJaeKmj^3Myk}
zqrhwUQTLrAZ$2^@;7M*@TWF62URQ)moKe_E)^%Yy#c{S(t>WcbGswxz#fw2*8p)@#
zmdar&TwxCX1*{+SfhM+5{anoQvZKxTbFCz&aSaNNpxx3M!;mXz!kogg{UB6J6YjPk
zH8Ja|m+4;UvKe+2XHcPbO&RvqtMcOmPe>{R;yf_F;^Ju0ho8xTRcjS^UAJ3j1(xRZ
z7$*TJ4}1CpS-WAk1i*UiPp{pt{2k|&E<g9_ueds_IK?epER`1jAwyzMNGj%viSFKV
z8eK<5Xl?Oo+7ea@PQcvrIXQClr1YBjhD>>2ggiXDpYjErpYrmGOB2&GVG5fTf4O!%
z1I3fUPr&UU7h*c1*6%TBkJJ5Z7OvOAv@}grw{2JpX3qopX<G;lb_+~ZkM3nVo@dGZ
zBYNp_6j``_hy1yGvsGAu{XqTwp?EnE!0e?E&4qtCXGsnpep|9Z#`f!otg>J#eFIL|
zSI0h9a4;=3T!tVq=BWXtipzj}*0WrhlG3_fsQh|jE$}%HSq7imPu$vHnWo<k&3<-m
zZR&?f0QSf9)U#E6`5o_@fcZjb{S&y}0d4K5=s1-o1i~6k!>j9)T=Un~E0$~iaf;{x
zY&rm=Qr$3-PyKhjr2MrO9Rq*`8wovT;hycAAPZoaHW4?<D<_OI19T$3@M+@Rxv8YV
zH+J&Z^OaNu<It&m`tD-@v}2TSYSkg39IgM!$tU~5vq3dUS+o&$PSeo9tvfX>kq4w{
zGEVHY)ZZv0Ci8(*2R30c<1e6>Mo*1}A-vAp7H9r0AP$h_`h(f>x9#g;7^iYqiX7EZ
zU(f8R=9Q|VVY!-lY<`p6L?ZFbO%87O6edyIn`XFUp@~S4+=y7e^5x5`%`Kz*W?12K
zAUQcj_U=0%AAR<1o}e{rLt%&e?z&9|59(`Jxy%E*ByIYKhMLy43D_c&F~Rcv=T}J&
zt|Fe@2iZn}S?ngzynakxoibHvwQB9yE+PU5^3D5ukq{6x(gZ&jiwTe+LGvcCJba^;
zF}z!pfz(>eYZKDh7|XP7c0H)|6L9#wT*|NJZ#9?<w;P*T3G_#aiAmW4HkLlhsBT=G
z4D8p_=oU<To|i-eFqx4rUc4y3!jgbXr20sMIv^<e>^TVvEGJ_|4hDeyV|IP*b7lL~
zG}$R6v0(9XZ0awC_H{$K9e%Q#z|^}&1l8}p<5qd}>B*>P3ep+($y#5XjP|`{+fJnw
z{=?6|$;jb1$-urnU7K@hg_`Glae(iWfc5>8z6JP!g)!O!ft0iGO`V>}-}D{rCr>{x
z5E^l=FGFbN`SZ8#lvm#S9RT=l`Doe_nE`X%l^}^+9A+zNvj5bXI5``ejQVCt(*_~(
z>QlqfPZew*6@Yq-8Qf0Z{_G!FylRiE-nd`dwXm0Jcg8eYJRoBht=OZU#eN3I)R=?7
zpWu(5-Y-osUw4J(LfhKS!%ISfOG}H!HJm2t)Fxa$`1)Tte<5AwE!=Vmz%|?;;Tax!
ze!7#5TzBcywSm0&_z<UcDTtc^{w3w%yL!v3@6DCXJ5R{1_kSWav3Xbt8-;9>Cr-u6
z?)@jQ>E$gwI@FPy`xGqLu?>C}j~qKA@4bAhOOJEQkoI!R{U6KDy^&Hs9DTiu<ZL{5
zF$*(VJ~+S;rY-!@4aFvq5fQ>@SgM4Kf*H!G@C{4zjJCx2!#RvsCop#;aLqaX?%jLr
z09y75-FtMGZe6?BW?}c6V`1}V&1BG^K}rkR9X_Zv;$|fMJ)eThj^N;6Em)l?Xt@hz
z)BTa{yvYZ1)w?H5P;P{*5*hrU<)_hOM$0sqCEvSmua(L049rn??%Ji+OtxhhWTk@G
zuA`%)vAG+8y*q1xOm~0*Lx&8Onqgs%lIMhzFEk_G4JOv-{QkR@DRi1*+z<2W6qa-5
zHYz7G^TH(#^luX?1ECQ;@%@Fe`lHc>qLpephkt4MYB?BjPUgKe6dSt6)-_zJyctPx
zpbh2=-~WwGd_`HZm0H%IgOSm4Iwk@06^ohhT2%t2N)ZF5sv<@7kD&BR_w-j9#vlH?
zRB0V@K1uMJX2UB0On%}v<8@U*R*?W`C0OWq0{*wJ7V^V9o4%u;`(&Qt#|G><uiAD<
zN?f5mUur1MS-MrtlgT5R;vS88U0HxfSB&u926t-;K)>D<tHQNhaNX`*Wb79o$Zt=*
zAZIZb@TS{B9FbeM%Z$4olxk4UXfga|sRQeEe&vh>H)#HO_|sEm4M6KM<x0yBuTGS1
zIy1`!!Jp_O5wdOGeAzO4o<!|Gh;;?rs{%0g#Qe(dLag+F0RD)#pO=8zwdDflevxM^
zX2!R|U-t4%dliWK!0_G$Y8hbBhMltKV59|*#(XOBRE$I(k1~E-&atrm=D3=Q`hEZR
zDg|8AjJgj@m8U>?ky_h-t=b|ljK4`r7T1rl_8Vd;5KgzOW9{d^#udn4DA$@95G3-z
zH=bW=4bZo7?W(eA*AazhE!(mWf&|gXFH@F7yIEm$5Gozmz6s22m(FkYR`FR+?mxLY
z@6{4_ZBbW#{SUx3+R(<mN8wW7g3woWJhkWXj%ZHYmfAP2CBD!yzX*YamD^xJ5oIsm
zvRCL*hwp+K*=4gWE!(EOR(}_P$k?^Y8YlI}2Ane2?Ieo21&KX3KWJCkfY$0BIs`Y^
zrq2S%n+3A~Y?O*9j%D=CYxg~|!45NYiqN=oA~Ov%t~1ZZN$ScS>UI`3@w8+rz~-Mj
zwN&DuCCxHz38u*1p&{WOQXcgK5bcOqZQRd<KO9QBQCgc)D<%~#2l&RwW$j>TTA2RK
z@GC|7dhl91v^s0QuT=dUmFsA*;oz=;A&$~sZaB-J`EQnEyGUo=4CBCbxhBFdx1Kk8
z`b21j{Ig)Gn)KwgiOlxYrd2bCpf!1!v1gkU6MOUEj`+-P2Q5L;cWrSLMVW>k1(43^
zAdsD!&tPos8QnX_Cj;Wx;){vw)nq2tpqf`t5MEk-tu^>SJDcy?h`9OesGneb_7y=8
zEC>s&W|Ul+Ds|R&HO=Kb&*-a+DJ#dP{VgMhKqz7WCh{;Th3~-`u}&<L=gnJp%43sW
zlAnH=W0j;q{W|i@6AxnJ8FLgD7BS=#{kk8IJSA_x|CzEzKn-w{3Z4UOcglwovOaM-
zPL4nu_bBFvC!*r;4zi?KgUWLI@V4^9#}gH(nBN)%V$<-wmu`{QCk=(K;_?!okb;GJ
zlx*4&DLeL^hF``csZljRp1QA(eEIeSXk#0LO?j28gkktB5Q;IPVFOx87)+C5rYxUK
zTL7Tm$voL61NNP@;Agoe>R&4~2xlIFhDY7nmBkk(tz98%n=f3xLwWUK{qhnDsD#bu
z4z0q~GcH`d6P7h#{mDhr-ZS4dO>f7aPcl5tUr13x3@&D?qLDw$*Aed><>d)2^8r1Z
z$v5xcB^_GUP+I<b4n)bqmHTAT%Kfqz?W7beU5y*wMy5PB3jE3c28g4w*<bzg$69II
zyp{w9!McYFL=*FMar_1g0r??rtdsvH6%7XngtqoCWvEH5)qwu4TS0506a{s(K$|nS
zl47y;=Xi7m7E|aS+SdO(HU4^Z@8R<MrRH<z&YiGf>ZQg^tw@fuk;jk2jQBP!DCf+!
zdh+B6$)F`T9Wk6BceCcrbfL~K10ZbMs#Qy@`?+bEO9alNVbO)cM6MF~LQ679Nf+#%
zpCEaM4jo<1N`dH|I(4#(&#xvLWyC{6-J}BK02urJ16^P?HBnv$pj_DW=c<G9!K_X4
zDEw>pYh9zTi@PE6Z&Eu1#uG6n;@jn3-d^}JMwg&8ue0DcSP}O5S~skLy>;jF_-mbE
z9A78h(OVwBwL7*p`K*@lmV$&I0L)&v4(3~nfGi-0qY<SDDqv6qwV5lIhvpyJ0{QaA
zuPZRu;^<cN^dkf<;x9nSgAEn$tgN56jO^9C=qqAV;+5iuO*O9BpS3!)mAhw7m!^XU
zs&!*J_MzM{2OuzfWZedt`RF8>+Ha`*{KzEP`p0~Ui8!7O)(I>w#mR_CAIK{BGA<2Y
z%QIe?D81TTh8Ar$7gHh7e{%O;Sv})N`TF*I<iml(<?lD%mFUAq0Id6{y|6S`cyWhv
zQHOiS%eWuDQJ^)Gmw>=W?W(~ts8e%!al#P!bLx{aVNlls-#?oHli%#S`8^Z^1{G2{
zP(q-9XmnJm5~NIPE8%}=w$E9%NlwGebc4_krTt5jWp3a%f$(qsSeng+tE(@{u2h~U
znh_t?wWU(7WZ#|;o1jdD(`5MhI14cB34V>}**ed>3M)d#CUvBENeJAaerMqJmwv04
zL-QIN%~Aq_I2{a3zSpV}EcI$sRshRhxGUOqI8uS#mcChp8rjzyxvuJPy*>HmJP$QQ
z(6FqCU@bSeY{QMdaVAB_;#s2-ROc|@V#9}^vp@rb=C~D5Ovx3iB9IaD!p2_~HiAeV
z!2$50T~e~nU$m6X`=~?O%AHbdXlKcc#>ePj(~arfVILnmL%g)3EKEW?>sAMN4apPR
zXR?cp07znb)U1qe1?DLrsbf;%b3kias#%~otLB2=4A<i1RU6Lv;w}O>Qy7GCDrzLM
zxs>(=_+VR@K>=DWJoC_!@T|XQko1ZMar1_UEitUHHw#(D>C>mdXLn^v&`}*2r&K9?
zzb0J~4wQg@ar*ZCXTcJSu#Q1{(JIExxHW4x%B<i1Fw|&fJom7=k_;_GnwYdT>7(VN
zKWpERXCq9+9MHxs+vVq9=i+0tZ_9Q`P%h0f8xc1QjfmMMB|+<#W0{S1j8Xg>J`697
z0uTA2qzf0(Z&F0Tp6DN#8|p@V6=PFczX~+AeSP&(vbp(i!`oSZ=ImZ>E{?Cb*~2nO
z?9cAM=MI?c4wvtK{8gC{=ZL@slcV}G9l_?*luY^L^XUNZipfm_dNWv2mrm`}^L-6X
z?p13yD9ukLIOn9jtMV|lTC!}l(*B<O>|;4~)bcgYm&XB`Uv2^OlDVUNZhmYB6`}on
zxxy}N`!_+WqG#tuasVbQx9vPCahOM!g5+_7I#s21Xa(hGIJe~dU%mG92uX%erXzob
z^ke+>J9kME=kREAUJxpEm016`C*P{tw=3Tn%~rZXWlQ_Xj1TWq-&+s>epl#)M{kw~
z?(BuSz={VpS6rnoFmwIa_m3gEQSYSB#SNP_#$$*+mj3!)n1sJ|SVw5<Q_w}5IqG9L
zzpe+c|C1@><nYlm$_zTS4%p_xVRF3=%%K;w2EJCCiQ{_atKD()|C1?qz;XoIkc(6q
z+T-)Se^Sa7f$4P@t90E*bH}<UAP*r2w$hRqk4yV_%LQm|7?}$vqf)Xzmt<^krYWt6
zT=sD+^!6z(l`COyz(r!8S}vd*()lQ1G<~v^<cqR=#d7J}w~szpjky=$kDL9Lo>)H3
zl;`C(MW06X=@o<YLD|=C5Lmfr*+ezC>nMm?;OvX^#CmxuOH-~3A+cV85Zp5&nrWw1
z8IuZ@1C`5{lFuIPA;TvBAuYly%1s>##}D$}V=*%RfB#6&meu9eJ3AEYL)^f*RffP-
zEw~5V3accXOCN`6+Y@JEp?Mt%u4MNW2yfRo?1tgeqX4cGp^f<2uLOh$&O+-Cial>n
zUmzWu)`B~wf)jqbR+vgZvSSCdme;>optP=+!{uZH%&WXGpGl)7p(3IfSo!?vpY?bz
zmNMPo(Dml7O{5acLYs8$bHG2Ol8pK29cgmY09pR!w{isgXz38P^TeJM_ZJf2#%|-R
z-)z0(V_eZ1H#%_|t1%ljwr#tyZM(6Xrm@l3wrx*rv*9EY`{umoJ@<aOf53d0wfBD3
zT0ff2Gc^q1q|nkWmC{lYuqP=g6^@=(!4-OaF?@Q=LuVxwD$Ze9mL7Oz`0B%PTmY1n
zKll<U=p?OpvC2gKaGvZs@{JOY@zE+g(-p(<yheE@JGU8iL!R?=TbFmsujGe)2A481
zKtxK)jiws@9;#Ij;5z#+DKy2GI_8oAxb2*{M!-TF;aUh|+X8hE(IhpguVnm}-Sb4U
z&Buw$`#i<JJ~*Qzi`<0dcnIj9m_hcn$dPZ~PxJ_MDjVIOo+?{inM!o%=bDNQ9=;VK
z`xo5Cbjo~DldSwcpqF?DakSWK;BGyiYji}PstVLmr<LBc>8nTYE^pJko5**s_c$hV
zIFi{s?PK0G=)0W#w^!gg@3h)#FN7swMD^aoQO~r;4iNRchQeO1R$=I1U;Y_ru6eB!
z@H@Y{=Ryju5!i)~kM-k@&03TAZ;)Dl;RrH;e0J3U<&Au&*&H+~RmkO6$U(=`H<#TZ
z>x4S4{$Xklj~LwC2Ik0bZE(VngvbK+H-d|`*4bHqy|KZQqWblF5Wm)gr`y}x`gl5Z
zow*B2tR4OM?*+Z6azyMIp7L*~jU1%p2NaQsS~GKU%n13(1jYFJ`?_p1?L3~+AsAf)
z9Yf$!AJ&Zew%0fq7X`xGM4_vM^s%81y@Q)m6rhfZ4oj-9ySuKdLFfON4&@HtVw#>$
z&T<!3CQ^Iu9`ws>jYslO*-`#&Y|LPCso@%=VPRtKFUWIGSiD&&GJS`ib15RzGEt3p
z?OkrceZAsL`Q&F(%SZx>)_$`A81d%vN&Gk!+*8TJQ5q|1q&A25yw^h#Sl*y0(f=WJ
z9<`_;B_>p6iw!o2DRp$>);iyCdS5TLBuC;|-kIoSn4NEzWZr_-Lxe6$?N;YXTV^$7
z$)eaWFfl78Mb;_Vq0YIT9-Tt<(;5N?m=lxdznqchGZoiZ$ORPM6+gqbN;>g;uyO{q
z38i;)vmaq%>|`z-y<yPP8dhhGZ{Hg@pv2Uq70aj3<+H!L&$heS(b?}f`dmCK)LWg%
zuD1K74)(EgACtrgTgpA9gDHL0=UpJ0MmZ3w4Acz*yc5ntv2Oc^=*%|5r(9kKY0RXQ
z2~F{y?a0SRrBlu6md5A4+U`JghNxh%on!C_3#Uq{C_4l4qjzlMr9?0}I&ShOAW#`}
z7QBH4;PQ}nL5^P)eX^?lr4*cG-Uq3&SP$sLVIuEN0)zW5zx;%)?nvD~Ys{;Q&Ar)W
zvJGhwjm?RxF~W#NX<=INH~$<%!ZB~iI8T=CC6|f4K&Q6`jHe&6Q0yYnpqyaVo|I{4
z)lPhg_pOhxMfkfqY)Y`VlC;;W5U1}?kMr5+sZ*XufWo-GT&Z0FjT9xeqN<eQtWILm
zcC-Tgc@X<&pCE!4*oF)6S_gkM1b^YYc}lQSA3D;0%x<MQmnNO}zL@OrcHXfFTm;5A
zQ0{L%>WYrP$glxcU}R=%F}NRxgm&^rHH(Gj;f}JV>?ecwm|)6umTIVUQL{u-@MbDx
zQt!|Ze!N%lq=qwB3dz@VK}&m+&$7*QZn^)X2+R^gpr8!wf_nLT3$fMdut*U3l?TXM
zuZj5d^PbkqDm%i&qFw&Qv`H+Go>~RM@U_`0N_;3k_BfmJ+1t?36K}o89U|4l!n24+
zxy8rw%LuRE$M41Z>(uSTopFu?T%v-lHn?8XOJj42?9@$N&#{^ajKjv79<P2`b&&Yu
zv){2o`ty*bD&g;+g39;TFd8Ba`<Jpi&}>+@ws=-OWimL2-!>vB2EueOHum*HB~qfU
zza#`ZW#}J$Rz*QMsIYw8hkI~5gv>W=cBYj5Q0edR2!8_islr@NF=i@jV6o?rEB>9j
z$;_XVE&`h`7)-@}R5x3pih0#@U;VA~TNf)>`?+PQ%LO^RtDL`qjKlRKH|u&Cq0Q8Y
z`jF0h?<yO->v_Y%!$@`An<OcC6EBa?1$f{i$e3?7g3B`Ip_-oXAmIt(VRWJnDTN-N
z=)X`*MvxT!UCSEjxjXuUyJ6oAIQ#ZF31w3)+|8%+@AhDctAdmqF*eUF`oe&pDwILE
zixcp#1_Lg2r6>;Jmx#e>EE8dsFCaxFgHkE#;F)#Q(&smLG+s&$vFkFiM5j@5T%fxZ
z<i{rHSdMD=7`Qvahl0Q~cz(Lu6)w8}{*&C4QO{9HyBD~R^5LP1K_KuLN3$rrvqVJV
z@f_LEx&)!ChkVmgED(*Fu*|-D;qw5v77mfs?IIv$^VLhGA)_?NAx$62r+`W%lWqMh
zm)6YBa1$gDV9ye;fsWF=agx!86(V8&njJaK9;LHE;V;G$sw7c@e4)|a+B)#ZWY|Bc
zgv&C9msJVWe5WppYNOw9IT-~MZPI&Y+r5DZ)y(C5+=0b}TUg*MyW#;<R|w`<VgT1F
zn!7a6!H*-F8A&OT%TWusaZR0G(>%=It}a-AR}7X)&f*RGMqjpNcp48CSew}(oCet{
z>Tr)*;fIE)a4LL!>Yf)+#OoR4)}44=4t}xQt)S`usRq_X+r2aXK-u80jm?<pewa<Q
z0atVptc&6)Ud+Y{erWua>Jy4(@P5Fjx3;p>o-f`bXX7N8cKjq)baRaN+$Y4(uBrG2
zkTaWUXFknw==NVonlbl6bKd<){H_=~WRg-UO~OcN)Y_8<jWKEh1Pu&(aYw<=4=2?U
zTtF0VVYFmOm|MCF&Hg8oVC@;|D;sItGw*8iDH%uQIG9}4_L?DAi;PY)8X+1IG{6oo
z`fQbtiDGMx&$DrvT1T<*lg@k0bY&tcHmu~EISc(WFI4Z5NM3}#&;1xaU{$yIJZ>~G
z4oiZ(1tu$f+gqBaMz2969O=|n)6UPz<wm;y+f8pCdAUOMcB}e5NT`q=@@kzeKLCf3
zBgmF~IL(*}P97zVuvLV6Cz{8t)bPt=PE1IEjZeN|*C84+3D!R*LA@ISW&Gh%^SWz{
z*gFA@k?@1aI)=c@Zf0P?5QPqUWK>9PN6eh6n%Q#oUsfmK0R7T^Ii1c`sDQ;Xnz6k@
zdvfVumwOU!($Bka3M?}HnfC~f{uAFpl+Y2y)(XG;)E3r~O>+2lW{aIlcSRGk<+0mj
z9+&-A=ve68M!4GFxinw~c14ym2z=Sa{VA*YBO4b4oQw|xp``LIB1q0qUHaA|c^fkx
z&6I&jUUf9#uR1z^XOvxV&orx)`a%fLi8_Ff>+N0WPqUt5tW@H2-E(_P6+}9aYC-@A
zQ3|*l1{xTx)BeO6iqvU-1(3^!q*`8x!h4?GhLw%grftV|>&w`C9X)}5SD>S+F>N~4
z^>(H8pW`ONWZ1SAeqr3bUFBEPL<=HS$9@wU<6D#ylrAHRGJ*u;GTB>TsdQzY3w2rk
zhPfi8ewH%N>q>D-<-oUA8|suJ89K){)VvEZ*UP>sxa=~3bhJ&jKrPN>mm%aLbnS`3
z5N5x-IUS4~d}H~U#~Hoxg^t`9SW?*5f#<2s1xonoK#J{<$BO5y+$IO+h+92)Vxqjp
zK-p=M-s&IiIlPb5MaFXcGdtN|xUVDrlRMK><`3QQc8t%X&zliRWhXHJ*^h_b?}wQ$
zncFxOzCfrAzoU?x!})D&IXEluQV=$?-#WEO=?(YruSLH55X)M~e?~oFoeDt`;VM8s
zNI2bpj7X;`FnyncjdygJY0KVSGo3y{(z~R?K!STaS-@Phtb$y4qlFmXG=PoI%&mr#
z)p870z4`EO0kKz$`3{1WbZQF9eA~~EyAA*Es=Vbt@k>kuI$9h^YufCMd;XUNuqiao
zC>pDQe6UIKj@`Z{BVGT!OqHfd3UTD-0sBgZnDBrpUZoBL+0)RjV?*o`AuikK@lJAi
z8}n-)fr}K>byq_yMb|?n7<3Lh_V<=3g5d~OOVCGxG-!O8EeOi(C;K{08~|LZBjO9&
zm~2wep=~n`LN%(j?6OIyd-+A-_sGZA>sj`~e66BABkI<BRgn7S3s*G1&5uBjdzCS6
z$S*R%!+_=nj09yEX*aY#OFv<=$<j{^I6eDM!cj(`h?JhI3sPEF(=F-*uRiViRbjg-
zbSrzB1aqmxyXp|Fsi_MAOJ%4N2}0w86#YayKEuuM6E|mPHR?<JIJj_+yiEft#a0|V
zHQOqL3|Tc%5v+*8KO9E07~;&Y<oARVNgASco+d*IfpQa>#%q3GPiP;ddR&{0(E5yJ
z|KSuTy^keQCv$Rel;0J~n4_;O{mSH}$UrJl;!Uh3gtO{S+aBUm6VlXSYPOfEz#HHZ
zc|M53OM|u?ON$R_(&e$N$p$peF3t7$DYeDS*c#kq(nz{Cc0uUGc9HZnGO<y&RfY|p
zOw{y1F@qu3n0tgr2E%%D{kyQNF61Bc=-Dl87b_u{o%q0$_92saPW<w@z+}e1YoafV
zj?7EW_SGMJz8@-APmd#=0cN-GFP@GQL1A(lqkRKX>X~(2Bbp=ze3OhToWhxD>)|(C
zToGqWWi%1wF~-#<L?QdV<%?&ZLO9qZE`&!-VPWl)mF^~9#@_SSVTJW1n(Hn=mi;=@
zNMs10=r20QC;>-lMT~PxOq}1SC6)ZGTE{vu5-`bhM6s994PnWali+<Cfd!OK$joEp
z(tFVBJ`jYFhUh-H<^|(BXqsZd)v&i_Y5VG(?*C1PDim6q0XPPYa8n%w?9`^tueJUS
zcqzjhX44W5qIs7ERPZormMV*UMphp{AHA&+=6LtPG*7;qF|~oVeDv!}u~2tjQn&pj
zvMY-?>areNwOY%yT@?V!SrM=>u(HhZMWD@&W!dka$!9r+0dESIxLk}%GwH5XpJ9Y!
zh5=I%;YoL#IO1YOI(PWqN5)e!H=6$3y>~O3v3E)6Odm7?B$<>q+k^-`=}!&NCc0aA
zRvIWi&M3kQH(*pSVvP>7QL8-<*E0y#IJxl07m`S&!t@WM!wH@c1hz+qmFrDui~c+N
zyq`rbwcpRE%Gi5E3<CEs^E%z2=F_?v4%XW$D5!`LNBQpw5Hb{QG%w@C;;~9==?&UA
zSTBn`VD$f_Z>x_&1hfj<x}Hh9>jA8~%%5!V9xj0{^ia5P_l3OF*-67=Ga43~ERH){
zG{;g1Gnj)aH*BvHPf09i`_Rz=+?EizNBnU&=%`GN*EA>8MZu7ir52^3>^7vy&OeZ9
zD2M8}G?bG>E?5HFwTV;ieIdN2VdS{>TjBXNzgYd4zrFALs%r*(H^tt;-!*-1Y{(Ge
znf~<I0y)uy(@~ch%Mws_9xK}9!g`ySl2E&SJv!Md)bxwxGZAe)EEnGooUMXCaJ|DP
z$B&FXBv@jPa3MI10k3VDvRKtWfGI>N@|EN0WcI90bqzH7`-odZ-bT8%E$7P)t2yz_
zc7djJA@Va_=6+>5Wu4-LLQn>lQDu-`prs<b#!otO>j1kzAYzB8kGra9%51whxA3PV
zztDr>=dV*UXYLX1D$&gN1FT{HREf5+x*$04CYjJ@3!$Z%&hswXxsE*%DSzsGdij1A
zMmlw}740BEtTQs49u2l-GgeyOF3%@^1vfm#Tp;I6lO;SK<BxIw#GvXS<3Kh_70Ayd
z!7gT&6Cs(qyu#a8VNm85rGvN{3dRu(w8|GIk1Y?{GpMU9tXiY7j<X9*MBC_(P^#iJ
z7+?=0pjh9S$O{qQx&$O|(j9SnfgBVly`Osk@#HgxJA$tUKK+V4e7@9PeQ^5Eno_x6
zkT0A(sANt6U6@7*{L5K?fpI7a?Iya_Id;_<vm#zirB&)G{Qb21c(k(5Tc&#`Wmq7C
zDGIjx&LIkJ&Yn$WO^;ioo0Hjo)sL)&BMD=tzyWAD^K5UYSISj`o0f{g!;wd)mI^RM
zU{(BkH}=u_WTE_kSVa!DVTGmt&BC1F^y*STh9J|kKmlwhx|h6Roq-i7Oyt%R2M^C$
zlX0RTQb@5ASE>eP`k34QwG~=etZxL`0AatWHS1Uy(3^$Z^U`;GN>naWt70R6%Uaj_
za$bRFGv_){O6F15*^VlT+l>ojAl8p*{rILluk-u|4WtWd*l3Yyv)GjMeu{p`O+SUS
zLM|K4glHA={Rri!ZXwkCy!kQ_a5}copua(Lvd0icCp`#0ie~?`%by4BBfY}BJ---!
z2PzB$S{WjTdZQ)naOS%|B>F`w21>jzBVv^p4wQPbP~Ph!8mViQGrk7Ol%vF9y}Q1P
zc0jJQOuksgyM>WF{B(CchV(#hfcn?LcRHzE77kP7k7*F4)ay^aru0|GSQW~@^v|zg
z)W5uq5NQT-!`m-ugIQ5cI=D_wYW_U&X~BFG8rAAYbGl8;N91RHod!QIon?XwYr!G*
z14{QdCwk)PZzZ#IECc^JGcxlyZMp`!R!GkDBL|)ZH83|=9^_;YIM2sQ$xuN5g%!JL
z=dDb2c5s8uv_mY|KU`K*ae=US3$(|@SpLxzSFG3E^HWF6db_);QojNJ+I9JB7K!~@
ztIU@FF_HJ-VJh;?93+j~-Ifn%EM-bJsYilAFW@M{coqEBWjXZA^G?NmJH4+ur}cu?
zz3`cyHLCe?QsyWDr$Z9gR@%4El(3Awu7U{Zz%gK|;ZN-tsLuO(ymf)zZ4Cxp?!Ohh
z<I6~%f1%iS1<t}CSfB5n=H7PG!Se_2Gl24=pRny|KIA55(32sq1X1`K#>wNSml>%r
zV?m^?(q*~@`IVc_ST`V~^P%Hv5qN|q2dysBs16G-FQzN8sKiFwo^f76*a5A?#D|TL
zW*~=R2GU39dyo4?VeQ%9-*u1cZG>D5py0J7qReoa`77Em{l@eU_2D2hkO`~zDltzQ
z!enSG&Ak>UHXXI@#&0=^So)Ls-1bJ~FL8xwt}f^nz4MGza<EHpSwLr@@Ee8<zymbn
zJ`7G^cN$zuFX1O!wx&|iE`KPiT5J;cm>fZwX7c1&c0k)93NQ<gF!`V$sn)QTsb-zK
z=2-%91B|PG_eS$m0;;WiTC5LjW@TM}ipOfyWBVtR&A6N&;PDWok{*r&2KqbCy7L4M
zEUe5w-E$i6(8ArA#oa)NECur3NhLI>2I{MFR%H()WR#?(D@;GvV+-a3TZr%wh{2`d
zPb$q@1@_)Ul(}cA6qdOPch8dXyvs6I&^S3ObC?lPF2wT|^j&Dm@QVS|fw@Q;c`V6_
zHr?DV&u9<(mI|e_6faXH9{puE8WE5LcIrV~>u>GXB=+$?cSsVx<llgBA)Zl7=gRg$
z_DyOx?HKT>(bHD&ub&|nfIlei`|tW_mHF&dett2FR8AL_ln2hh4h4Z-k@;=No_X1E
z2x}|az~aW$paY4^zQaLKl@G<8VH$MN7bjl{hq!T%ngb@{CI+JxzA3O%-GnwDulCyh
z@+jutdQcZvlx0-Lo^}bXurpvROFpmRx(h^A1e4v4MKyRW;_YrlN=&vy!lCfzrOTy+
z;-gjB-9MY7;umV5H84C+2fbX7QpiXAX_D%@lGJ?>FPFOcf%qN_uade{h477(v2!3%
z^{JaXOm5gg=<3$I1Y4(>FQZDDl%gOHPD<?t<sW10@Sy3_-Scc?i&XT|ph$Fdbkz<I
zd%nQ12k+Rwf~W${S4ZKp#^@82Cr#yTG{fZV>_5UlTV1+uOW7d=s_a8@SG@G}m9^y-
zM`z#QU~ez-sQUiHG`n8m8-C}g7W#PRTbv$_!L!f|)_e#m4{*=C4tgvu(!!p6lZt%!
z=Yy6?Qp7P^cst?PNf#N}*lfJ%QDw@ii;9QjI)SRtsiq)n4<dNG+^CD5e7weyhL9|C
z7A1*Dw9L?p84`b(UYDd*F!E=@pB=n$3H&qfpGK*o61O6L_i!%oovG(4G>I|U7D7BK
zKp^Mof=Z;}@hNpX$=Os&cH3YR(0v5?h+`y~m9cbtObF+1v)!IeePB-{{j!8qRgKzR
zXlwH$B1|J$?5fLt@i;GgAu#)OjgR#t5{}TufNct_es+*~A|&E^&ffbtjAqnon4Yps
zo0R4ylKu12oyYeUm+E<IuEbI^FFDvoU99oDhGj<41E<bT?xs~|{@5E&z&l+!+CS^{
z_BJFER%LvyN=vJZtn_*M_Sucle(5l4iGkC;Ns|9wtv|=)qH{0i%{gi1UtQw?N0cDj
z5PX-Xzc3&3H%h~!_5Zl9M_>yGhK2sjVMH|XbspeBZs}+kte~AE7>15YqKB!%^%6c>
z=J>^2gqYD^H1TP{k#2i#`xs7P=J@V8i%p$*JsSDmRJ9ylcy6m5(Kor+0S9$B9k(hg
z*;FQ8Y^cG9#hGwpm4+5&O?Hf$wSq8)TRtCtlM!qOt1^j&@_4ov_)XzZT5lT-z_D1N
zH;U|)!Ff!Eu^BAykN3B`z9V-s=Um+0vvlA?Dz%XQH>m7vhMLVq6=LGGNhijmQiK}~
zk|e&8S17~PuV%xA-Ky1<)~jRaH3;9b-jwOi>8dxqkya0Frc{_aSh1Y@^9EfC&|i`r
zcL#W)=A>2+!Z+)mXB3$bzO{NZ&8<LG9Y#EzQ81!I_U@>u%+yD}n6JNMuDG@|Dxv&k
zKddGYjmpPYoM_EB-D}KWzETLT7@+bam0P~V;!0~Pz7m(jZw!Zc|LhYK8Ad+M7w$1>
z2S$to25m}(`|r!|wd+dqCqoSTQaKO%X2GX}@lOr|l|87n#bchR$zu;%_ZR|K{4uX&
z1YHiEw-<&2Tq3G@xMN>vAE^E80Jpyn$H&DAo=f-L<g>)Hd8jRSBKTl}KNcPtQ6;8i
z4_}j;YpIj%+-?t6-oc}3-8`;_n+zRt9%RqAhWQ4Su@!@sz^LpV*IND<UgHP>YTBlW
zoBS84+k}n?51@=hccRu&OCo5hSu0bMF~vdk<IfV!hS{m9sj(`N|DrYq@n)24rT5Ig
ztqU`!WjnQF>3xVVIy&l|#56b%UBo%nzp}@oNw{(9Qnj)*CwTk!RY$8rYp?>$YK>J}
zFov8QaRcqGuJT;-P;JP;mviD?xt_If1r3;==NB*T0=ktGzKW$gk68WI_nGWiW4jO?
zJ__GM!u~ncqoWZt21f=Gg_jKM1TZm%%gw^LrGJbx-Nfu$FiJ_dzMWm=`9_;s`YD}C
zekf#ZnYDu2rsUcZ^zT+<)oQ)IC+1lpoHI-!BKsnRsh5TPqha}zxuUOuL$(<#C8!S3
zrQTjglX#CE$ic8@(J+uU{H+<&Z$C{{Lu7VzL4FBvUeS)2Z0wLpSzf9)8I}Ux?iWm#
z>SS@XwuxS_0}^64_s{D*%N08hsToVq0{ReD5nCHd#I2~gO$y#Tc=&qCe9!l~5fe!*
zLaO$&oCrO!cLZD$#SH6(8d}I@235Zyp-rd%svTvN*-s7izz#ZWHH&5YoCJVR5tU@W
z;JgZ~r+6}aP@z6n3%=Xo%lp26L2EC56#GuaD5!XSO2OW8+sXQKX*UFp8`?U0j}M`s
z=cKM6f+pu*vvX@NpZoJ4e!&y-lh3w}#sQTw6By~GpR@t<a45v_$;{N<_9KiGVGxS+
z&?h0h_fN}aV7A!2fF}4cg%|%6LS9HC!p!$boVml6wj1;Jo+vEbt!8yZ^YCsYN}ub^
ztgw_jP{8B}72hPcGmNR2o$h)wD534<U0v|d+h?kMs@`TwvPgfkTLIBpxd&yEIaw25
z{nmR5WO@STKsZ>k909T8CM;}mBC`7t-mD!2jzJ_sQNdLQpm;$L;%ek&RrXrwLCRC%
zTk9txZZV4`|GvAGsic(7YVxjVOS*ei*gOzIGR13MBs_XuJJEBW{U)6_99`t|;J|wP
zu)22%m3L*grx)k?@}ZFaEee|<LDvC~+<v{62=Ud4S~SyZDx(R!|C#pMDLDmYcO0W5
z$nW~*0v`ObTJK(Qd$Oq;a8}+~%<Rxot^Xg>RK4AaDqXa-U<u@1UUawFH3pubT^hUN
z7NJFtoGjLoa)oAfk~TB7zte|*h1*(4yJ^f^kD-wDLTw(lSvF!iltJz8%NEpTp~kNs
z{<{-a?Pdm5L9bf6#_}u>HKm?(d}_BorQvIx0x{97UUpZNeGIH7ndVbCu$w^-M)A!#
zl7ocVuvq9AqgK|rYt~I`nnHQL!JX)uNx%Q}T2WN`WD|tlj|GY$MATZivNxa>$6v0t
z``W_n@03IOWj`L>Yl;J-51FJj%#9NkOR}qxzoIwzc*jBEU|nb#io5Yo<XTrhcOr=)
zP&>?ck@a)uaoE>ME>x)+cM%**Kk=|y`X=K{n|x>r+E>j9A~j-_N3+zNXCzn>f$BbY
z$bj&6A;quO{^TX{Agk&k>9kjHkb{~ptkSx=zX~Rt@mzuIt_`QU&O7^h`RP;w(c#$*
zr!_*E?p+uoxJmAYb`jJAc^p1@t~Yv1)6QzDvB+aJ_#HZhVVst_!9=y(L`2A2{y(GG
z|G0kd+u&|XIOA-GrBJ_MYW8|Eyf^uB2A$bHU9@R=KGzq+dSxF37s_UPr=kB2g~H#F
zb4r`%dT*BBcd7Zp%C@0C7ih8W+qgX$LF(ZTHq)#6eC*HdU;`CQuFz@In}`V5n5)UY
z{v~nlGf6;tE=SW1LI(UJk-*O%3WZJWwMY(}*Pe^z>gw@?N##GvzVUgS*Z!Wkr8rXQ
z@qhc49#;W56@bDs)1s+)$IyNEUSe~j@?lVgvHJ$T@Z-3UD&oM`Dacr~f4_o9#@XEs
z)TlOqn|b!Ly6z`QN6c6nh(L;a0pWSdT!d!SpK;p7QzAfzx-}u!F@RSZKmgwh!p4oE
zk?EhS;J}Rfb&vBD$DYix$>eo2_stIqA*=9Msz#vr$bs9QWak`Ph(*??*|b>R=4^oJ
zxLvNqqyFcn<dfsBi<U>Ne@!JSjQM9oK^2P4vMUtKq9(?8XKknM{pr86YXeUZ4{1#o
zkGi1Ov3g@z2%!99943182>lqcaSfjHX6KjaE)0U|%mMe!(lbYL+4C{1(887~X761l
z$o~(&ixkRdpt45`&P7b1k8`_wFD)~kMISut%5elVZd_@VeZqe~b9t1F6~djLG)ToL
zBO2ADYN!$>rfTc>EysxAw01r=boehX&uB3}4vm9dL>EbEEE<N3cj!6FQ<)4=@>ad9
z6ptN#J5MW}*_<uaq`KQK|7O}#ouP+$zfNFQOz5;@|E-?e*Yha>DgR>&(Q0u=Q~83#
zUe4vXS{eG<$~phb_xkEKL1E_GHuU^1`;>HTCUz<7{Jaq(R%W5#eP(N;g*qF(eD=>%
znWpYJFdI-}Ajrqj=G&Al+*oJ}j&#(Q?Xijd^@$6=Q-a{x22H}a<8G~L=)+@qJo?QH
z4TcQajH2%-3y8?!)^ivb?w9;HPCR<n+Z61_&p+E&>K|HP>%AhNlb>*m+<yHC@z$x9
ziuoIp+_a85w(9l#(p<>{pOgZ=iO0FT1fdOn|GuSM;02nUpF7(s(|8f&c`Ar0VzyAo
z<kuLVXFdL!?+mp*UitAE#w$@t;(k%YT;5>q8CtpJfeRX_*Esayfe&c%1AUB^4U_9S
zFCv&9c4&f>g#(Ni6ebegeJ&<GNlK+C72pO)ssqU8R}A!W&k`K%^oWOou8L5~KnS|6
z`lkE|yq08a2<9dtT`VC`CH&3&;ZX<Amhqn_sYwGTztbLQD6jF2iaRc5z@ZFGFm*;x
z&%XKlrGLbo=dfl?C0;ve=LmNsNN7D$VCYAZWJ`hg>MmiYHHscaqQoLZW>b=A{)crX
z61}%DSp86{VV|9*S{I2mlb-&(6}v=SmFl%^#?e#5u9LMV@6~+(*z7|(NvUQReNDER
z^3_sX81Y7u=eElJKT<Oe%x<WV{6~+hO)lyD;6`?>#ev(fBHP+d>qYtnaX|*6i`Ah>
zYeoO|XD3(b3KB$)a-jjlM0f;S00)<dv@caxx<i$3M*LwhyvTlkZfWWFNBgc)fTG_!
zeX7pFtz(r+*=Oe}Zd1bvNQWHqdI8JQ%P6;r4!{uO&8UNNOXd~rpOCS<VdeHXRqByS
zb8M{0)6-}-gztS9pr-!foEG3w%#JE(@=rM3>)|35T8E9mpDR|#7gauZgsw`n02FqO
zBqF+1OD6lf1Am$7gyThR-D_zJ2MArOPu4v!-uLw&9_~_Al(V|fnG)rtH-cJG8StE9
z-vL0-4OyNPgNk5SPBGA(!Xd=U?ONWCr;f|<r<lQ*(euA_ZFsA2?(19WM}8yT<dtay
z1n^)uPhR<`y5^prGYQ8HAitFlTUPzJm~1B0Ba{9&uuk!x-T1#K%T8FGlXMt>C-~ZQ
zdaNOzi^ZE}V>i2rX=w;m12F}bvWnv=jbtS6GnZ*hj!7XUg2Y>VDgw8dI%As3c`Cn3
zmk?qp<q7(YQ|CB7=y#=Vi^0@aL@|J-;dXE<mGWkpnw0nHSjoW?PBxrjE`#NbJieh8
z@iBpX0bJ|d-Hu)p`;wAcac5qy?VG?&`#<iPe8t+zXRpT2>o*<m+kqkQMdt;$|H8gV
z=?s4b*D2AxLus6DUSlHTFZGUVeI386tu2jyzX5#SKO0}u6!9w4j6T7JUBJ<d9;&d@
z!n&BwLWPGuYpAWt{oj&ls?n1)fpUg&xWy$)y;R}-tXy}ewgfO|r5<O68n=&Vvu(;j
zk{b`M_fi_@(c(21BB%HQhhU`rTzQ>$)dE+8QW@6G?z<4vN<xAAUrqdVe!aK1J5WWv
zU$iYvJ;kmlYmA;;=o(Q4+cpU#LWO~b`R<4{yr1}Axk2I*^DaA|ZK#*uREIfG>`DR$
z*PiB6_fSqvOOjMbr}SNG?48Dq3O7I&wy?3FvnVJFpf&)MxZ?&!MWnF5AO>^;d>}~a
z0v6Mp4gfRXN2>CB9u?9_4Uh*aWvBl9PCnqFqp)98<Zam-wYkoWh5#Zb3_wTug}b4F
znvoCoZ~|N?D~0zbp-Ql%6^;1FA=V0J>ky<h%v~GO3fO~e1IH11!~gm|1Bd0MtV}kD
z1QCC43rTN~A^X3l*H9wnQfNfTj`Qt*2gk_t{+?kyrwv54mg`$?vV@{%6~^hwW*VD+
zuX|ju;E_Ii7}nogJe`}&u8^M!U~~>tBO4E}QTxV0HlaAi{02JT;a8$%>KsT=o<^lT
z2`bKfyKJ~AuvTY!FtPIYT4s#XkXlN-+s*5-z}6u&z=3hT3CSdYF8K1un>naDpW$r#
zxkG>VSJr;7GaWgX4{dO$v^*S7zR<$J!1(rAa9eJG?^G%Pb(2rx@D?7U4{ISD@0{xy
z%lOY<z-*k4gr-XULg0jM2jwD8@@^p6(8|xtU|BA;Mnk6xCr`yDsYXx$0;n<oJJ#j7
z=`ohulwwGeRuBMl)V1w<Y^q%cTio}Zc}T)f@R#YAumWCBI|k29XT-ID@Ol2m*QN#)
zmCoaH!Flcu(0R5-b_4hHrS-{^0xI!}<~P7mb(puU?u7+I_H6o_vh<<RBTN8>MQv4>
zT2}JKd3HCUP#Wo>&l}0wDMH#uchbW+arRv$CNUL||0&MK);^bLt6ITT8$xJ>0(oXB
zwW|8*{Tz}3g7{HVv_doJ<5mS@$57)5Hs}3~NtSdlAxA~F8EzlxiP)FrI1g<&`p9%M
zjohO2f4g%W)KFcDU+GUunc=mE?Rl2Hfv^cmv=I^#cs&8YOgpu&F5fqVGDTb=;w8=L
zQ3A)#yd@|-sFegNGh2e{w<E28Fe08pJqhDP7$a?j{aLo4Za<f+8M4;7(i}`*4nwE6
zyYIcPH^-O<43FSxfEZ5?^Eb;*vK7N(HyZDoDXzm}5vNRxxvz)9ktPP<(?=B6a@$t^
zn<-kn+t<&nG&doMewRUxlPEEJs$#r8R#NT_|FFhN!11$!v{&zOPa&etY5Yf^y}j0-
zQXQ7u(Utro(26TYtA%o?ZAK&`k_}|T_F3+j&$(G6ZO9pkAlEXxUPb*vZeYS(jUQB-
zr4`(lpd$xkv}Ic?57E5G<Fy8zyBnAvG((m{KH%;2i}(%5k%5uhjiPl*nQH}mB0hcB
z<6^fro8vxA@I$$NitpE0J*A1X>EX!NLo=fF--hkFF`7;7(nm!Tgxq+3ip1WQ;h|wa
z;yg)u6m%A<c3nQBu>Q01%W!b#no`Rnu3tCRgd2FSPnam~dXQIjCUO6j#s3NWmSwg(
zYVJ&h-4_|_OY83lxnZxNyU2n_#fF*ltT(p=LN}kw%+veMCh(;p%`L55Mz=v%)*l?F
z_v!$_UY_@N<i#d9gqy!Wo+|^-balahngRQP#a9)IkAuiMuM7{yi6}9u@{!`&0T1N{
zc(vN(LeN+r|Le)d+ng^HSRE;iL9yoMS|)!4HRYmCT!EQM@hQ-ekl)F0te%Oi5#@V~
zaoJMy7|ZfFGtl%X-B0`-HD<&DnAf*?mhnOh)HNHAY=jMT+8d!dLa=scD~B3Li=GwG
z1a~KKSMqU%!i(nw0=X0)+g8N@S|7fD23i;7;gONuS3TJwOpE8iCPZzs`5#q_nZ}<e
zBZ8U5)0$-R9@D7==I+K!J*C4D+Dx&KL&os@@zgQTC#SMdYAlp)%3r+5<nsF~okj9i
zGyOYN06W02BcSXgZR=lpqi@ZKYperN-KA?H*Vgk4N@wxv@h97TFl0#*f@ikegpfk7
zOIbrMq$zU7LhhL2GaSpneD8u82RN;9JsOK;3(%xuWSf<7l6RKWSugIfd%i8svG6Cc
z8?j40NEB|6RFdC^!kne>H>8g+ZXWa(e+jn(S@(1;K18}SM#r5KV2v6-Jv)lY6$mw4
zuvTYg-nqJ%4$Ov^3ksYOp($%zcKS7Tvfa5p`Sg9e4LT2T;fS^0Z{P<vxsEiC5BeBA
zxjcVAU4tUH`0aVSpNozwnltNtEgm(^CbE&56CnDO`+j}AD%-HN_#jIh*8}x@mvjwC
z`x2U%lsgcmH1<2$)_mz)$eeF-AQ9+&glIrRa?agiwWXEqdtd;g!v@rqLQck3Aefjm
zJf_WAV>h3ESFk7EIsZiuT1ED+q8lwh-C|(K;PAj&MB@5CjCJ5x5J3`DQC*6Erz9m0
zstA~4g}&*rcAA8Zdusz9gCc}(Eq1k;$2sQhwE-w=^aN&oI!rwztfcn-`GPy`ZZ{9+
z>eFst_d@YjM!R}C&cXC7T>j-6+q&XfZ*g~O3)^b3Nn>;mmSd860tbQDY-?oB+i+8#
z`gueob;?7-7NM`~gO_x4TXm-I0P;Y;JF3S{K}I!ytrGg1*S8+Bx$3;G-up$>V}deb
za-Sw;on<|BCk-NawT0{-$~50>7HivIZ=YMU{1}$<M0t+=XFnrZOkY}3$gbvnue=)V
zwp``?4Lc<`&v|Lh<-VpG*q_N70+!gx47w|~i?DZ9c?GxsXgX+~k*8viCQuKT2eQKG
z)}VJ~$k~4XUiOvp{i$EH#Pi<2w&3Stm5Rz|-<$|WozkS6UMg$~gBD^|BQ*0{vNIz{
zL}&49a(;lVl%P%3-qFoS=@vubrx_=!`Bc_rX^3K(M>y4xuH`au>;Wm6cX5)X-+elr
zMFB<6kZ@)ob9p)O>(IQBvD@xc4#izf(;n;%OZay}>_02&>P>kTZWemMSpkpa*FJ|R
z*OwdOrS^ZjxkNW)`vVyF*DyFwH$%q1Po3TVl^UF?!EJ1Xz$O@KQmFWtdZhJvh3ZD$
z{5~y7fw{Je7iZ!MwQ`|dQqIbj{bkDjobgPp!uB%CG8@QYEjOb-G!H&1OxbCZk?4nL
zrX}{RO2M|!uk_gFU3g-$fsmwZpU^xrDH3sFIl<NtEl&w6YkzHSIXAg=pE(`Z)%hEJ
zdeIr4>Y18}N14a5R%<Mlm+oN0G{4*=;Tk)&s&AFRM@kCFq&(DzEYu<owviury&FU(
zC$H6DdpbTowlqh{x=v4JOWbBBQ&ih+?&X*-pTY<1{v?gwtt8;+Y&te0!ix~R&qDX^
zZ9mP4IvFLm9DK%9o=Y8hl<ag$0p2;)_@ttgcVXHo=A{UQLcr@RBiGQ>DERB73whk}
z;4tVzpuLjcz2AWbzYDB`830uvM&TJ9x`?R5criGc<9mCvPpEA7;b$@-AF*{jwiLx@
z&YrelBt5(ywA_eCRbjw3F@`FQR!3FuVkX>uJSgBDxtqXvPh0L!RPD^^T&c0ZSFT#$
zKqTQc${9|jkY22P7Dpv3Ja?=4IYmsKlECw*=evH0V9wy9*}g26^b^0`;gCZ$-7!4(
zvkj4@>TSrxwOJnE$gTN!Qt(*0!*s&Y#y*#v>n>36F4xU!Th#)X&cx>dN?yQ<IpLXI
zc+kMuaomT!!@@nE#BqIKB!Ykk#Xp8Zn;DOVCTwj$f64~@9EutB(Eky$nB}@%&Pcv%
z*NT6RMg}SJ7_nA3BZ2y*^3ppOyNLyW<7#Il60{W&iU^V}SeTFEYOC8`ZnfJr_Gf@@
z^JN?+wNjbF{RWPM%$SEhB&g8Aiy?39&4Ky23IxQBI#k}-ADuO=$D+DfuG+4Y|7F-3
z7kbW&I!ik~IvRUYq46dDa!0C1aC4}q(iEASie>D7kfDPfO45+;PMyuyLlvzip6oOV
zcJfPFb0Yvgnx3KqoYMuZvjhKi_e-IAg$DTf%yBe043UxsUC@Fhd$#iX@io5lpMwov
z#4`?^3+uVUI{t^r2A6q-5HkT^lb-vZ&N7Pmfj*bKf{14~Fo-x~URIuo+&E-K#){?W
z=5QP$%nbB3Y-c8Q?@8uyI7c1f74w}gQ(a2k{I4@1bIta;SBD(htybvS$PTktc3D!1
zOlxaW*Dz-!HgeB!Nr_});k_G=iD<&r@tJwv?&{9DY6WP!#&e59L98o@b}BWr-INQ6
zZ;;2=5;Ep;*g%FH7#tTOc#5TJi&<LG7Oftm=<KJ+%j8RFYADI3$x07{oe7?Tb+DeU
zQJX61V**a;xh&^1cd`+gc+m7JDEp1>0TcW!c=<e}iF04fycDuf_(N3{_Tro2>+CpO
zj-DS1LuAGAc<pce(DuU;1KE|0MaW4n*_~X?Oi2kaQN}sv=dj;7Jp{D7+%{=zyF0JB
z-)Y>?R^q`P7XshoCJRc2s@$Aq(i`T`&WEIBV0DE6Z}CQCIv5r$0<@2EufJ01i-+25
z+=R8qtW{sPAZGq-(ct-fLx3hNzIVTt3YQtKhL?G=tnv~YXT%gVAUw<oi_{xy0ilDc
zr+zSCl6=UkzUw-&e4QSVZ~acWo&J3*PLQqaiW@C8i>Bv1CiUwGgt+Lhpeji|t#Xfx
z-9z%-_ba(|LB|_KOD40?v#0_}N@M^G2lcE&SKJona95@VIHh`~w_2GsAaSNfGdppB
z1uyPri?orA`}}m}XJlZFTh#^mh3(hM!S5m`A#kWVPccV@$-PK^<0o&O=xK|b&R#ja
zbuv_N2pLdx8a0VZJ!y=VrY1$kZ@m9u`&=5O^=WK(F@l%wGu9=o+eSOxZ<Wn|umAHG
ziBS2z$1FT37D0aCwty$IH~g)XyAm53HYE{%u|hW5Mm-rex}YAIqx7R!utyC8xB%G<
z!?3?&%14x$*a?)Vr{-juNTRv;u3O>ca6ddmt%beZI&dqvf?I^$N+%w02rbn#rG;`q
z`Fp?*GFz=cD#u@j({96ry0P!L^(=;EhLfaR;uCHm6e5d2C4nLJ$L=1V`}72%1@BH|
zyq2T=5Tvedh=PXS))Ll51mUKxO%Fi;3{mPF`SH;sJqgH46m#MS2gUF7hJNpMiN@vX
zETVO;)0S7hCW#4Rv<f<(Hi}D)<aP<eqlj0r2(sN&!ILahV{GrAlv+jdJF7PJKsQo$
z+v>2w2O6H2lO-1-pfosoCKp)n{}*@oga>mOlHBW#57PytDDX`4>LC;0<o*>^5VKEh
zq0~^lr^0)n!5cJKRK0TgG*^bF+^T3<s=Cr1Od&hQm?j5@Bm*3_yZs84KwIMqXcfcW
zy_MQ*hkADWy|DIzMynz4p-14W-2p84I)_bO?Pyk)W6fV7J>Z~2^M5mBxO`+3AjrDv
z3XLjAqizw1^Aw6SGO`*{_$Fi$PQtsp(6}oeJZK}o#q!1chuMqpyQ3~l>hTv)cc!H^
zz3u01lhyK)q~3qvr&bj&)iGIZ#Mg=pxaR?N3<gE_(-jq16vEucM9X3e=>HF6`H$%D
ze-wxf$^mL*dpSvw6<un(TLnb`o1w3R1Jg-IEKM#`39O(I-J5P=>(2(t4869rZ#X5>
z;ZLB5JQX5RyohfQzI@(9N!5x|Sh2Js3Elg(L-gD?MMRc1Vu!}VL(K@MTup6xi^*T6
zY`LVdimO(Py-}!j=Eo1bJS;BN>&mKScCT5=ob`J#Ji_ldp^}YAw5uKUJC_{Qlm%4-
zw(-^*J6j=yUBo&R<ia$V3fGo%ZsY#eBF(h_u5l~fr8D=_pD!<2G$hv9(%Gb^)aq{n
zH`dZQA5q19vC{i8y{%>LVYM!s!D$nSJj89stKlRQti3<eCnmKM@MxOaXl&K|)XXwq
z_S(v#0~QrhqoeB7NaJN}Vh$du#>IC1TF6-r2k|yB+g!BjGUe8$^8SQO{%BAoYwmu)
zRV64WVnYn0aE@aeuC6I1a-={^YYYp|js+bb`;`v5ynqzUcEil0oJN>i_Nu2`#w^Er
z^M8h!Do<~+B4U~`ks6P3_b+bBOUW~D{Ug1Y7cntV{Aph*k`5bR@YrXCj2c`O-Df7b
z;&fJ|#!_r2AUu^pP4Y8L5_uC><<WMd)wx{<&rmfaedG}1Yz<wQ1=UjA)&NU+|MiCw
zVTT_X{4qjZrQ%=SvXG43&+-~4b<C2DqpP-wl)Kb9C-X!S9GdDR+hLafdzk(|WY~Y<
zx6gq)?%exD_2(h2`(i2*8jtxj!XX#$VX8THuLkP9O>R>G99aTfAQ$=GpWnOaIx2Ih
zgcA@%$cfH<NYR!pb2CW$r)ki=Yh9{=UYtnD3q4%9#LY9glo`7L4p}fSa>J4Hdj<``
zUgC>!f9xX|fF^0Zq*veM|D51}0s9Tpy6h!LCYd5IlMv0p=P5>M4Glh$Um73Rw1<M&
zeRakqcw(o*hotz;Ob;YZeiXDemYvcK?IQ9qnHW63dQ~;4X4`{~lXJYc4|<y>Asni&
zN15!`>Nia+^tQLxEP|1RZJ?tG_*5@@jsIV2Ko1|zWIxet?o6mpG+e?b=LmP8Tr;lv
z;;byBE&KQHL`#DY?N1MXczBOFCuT5@cL}Gvxo|9%?7T9a0OM}(WLlFb3UMT)@_zJy
z9M#5}0<HS@3W+*Cr8lr+8Mdkc?45&yCsPkZM2zHE9VW|M5fQST_P;LRv0O+ybKZ7d
z5W-69S6jdzvMB=f<@^Kht;RQaKpED_3@<;hHRjv-770yGK+g}I{0$bdHJ;xoHCKLc
zN@ERb{(U|{Tl7?!<~ud@B3@rlHk<Mm%Cu<=z4gaN*^VpYqj#=#nf`PqEVdd?6h~gg
z_Z^xeZrD@_5u7b-bHMCNsv{)9QvU7O8AD}@qCuqZtL?|g$}!Va*V7T=@dg(Xh8<$-
zCN{WTpu;nOUNPQ+=PBdaT4bN5=KnbUf5YQ{ugs@}cyR;*v$ea>6B4sX24G*8`;`Q$
zp&yXMF=fUDx%N{z^Uye80vTWGl9^gEahG_}7=sNDf9>*@9D8iW#H22{-7j+pB%mzx
zcfEcoL<!#oa!!@GrAzdh>FEF9h=FU2r?ss1^1+25KG^xKDhj_F5$A$t_CR#51v)xL
zmKH)dP3^K3w7I~~44bZYE(rlO!a>du#NSZ(D;0m!nQEG_3-r%G9jctVG2M4oPt1l6
z{AR@zGIs3|lk2JUw&aZczFuF_&>zrA9;LXxRr%WJSG2Q`W;S-Zi>xu+*!|}pBg6eW
z)4ppW#Ay^<rb6M+yzI13+VDSv&*VE&`rl+7MTJvUn>QjB(jabaNd19SA2>mM^%BMv
z5I<OGhPTp?PBC!s#;K+E^CU7)wZJBKToCH!4z=f57amK<*)ZU13d)19QqO(EkFp<%
z+5S$X8<<{W{fB&Jdxee%1DcLF!JCq9w~yMbWLKQ+$)H;OmzaqjIZqvk<qMsqlwOys
zsZ@n1%}dq9rdPnSoCYXCtz?f8uT@b&E;V&4o#C_7^$S1%=g0D?TiLM}AQ~rk_vt>W
zEk)|l7gdN$tlR&Cu}p6^?KoA(z_^Idk4od7h+kW4HS?*xHwZ&(U|Y8+_*@`BB;jg`
zARVbh`}#<W54B5jEg3OZ-$>=3i`f!WkF1>?(q!n_J+pFfL~6@IxpqVEI4c|L|L>T2
z-V0>ebMq50?Gr*4HuMazyT^rqzv>egUVC`YEPN~&7;GR0M6fejyNjPgkT&{I1~`Xc
zM_BGVU6<m0yEN|GGoVBRu=YAZu3p(P&|DpUQGfuUAP;pI%2{cj<q876YLArx(t&bo
zvT08qFpX4KB5Fsg;&E#Nqe9e%T#(l*No>BH#Sk;uVxzJuoCqfxlTxhufKJ!B%+Cf}
zi25v2HsG@uNwNNM2^xbTL)C@jGp<}1tOu5_32-C(J4yAQWP9p7`@vt$gB~%=7s?g`
z_5<~xbt;a4%ulUo+(eLt0{*zb{)mNbg%y$GrC)|&7PCX+7oFq!!Os6KNUynbFOGv-
z>0jL%4|Dh1^95mI!foAB7`gn~IBMtz8`I0`A!{va@nXn7TU|`5CJn~}hyc|RW1_3c
zkPr%7QZHd`Av~7SX!deV_3oUmIb$LL(!tZ9?!V$fm$PV`l$56SwdNx#kH3UU{Lenn
znGHKm0tbal)eG1x_bF+4MaBkH`S_8ISjzC&vntF&j#Or^MQgdB&VGp(iJ%xu=u!98
zV@xdlgIt1hzQ$PqCOVN}=EN0(y8xfHH0=!I#ae^L0*a95p5oSkTWts0N*Q-3EB$7-
z>SEdnGe1=)B_(<#Iw1(`?6fWPWLj<d<F2bWWq7jQZB$VxH1JgMN-JVLN^nOBaL`(W
z$1*zAvi>RC6$;09sfsUl+yE{Y{4{8G=l{Ji7d_`>auu3POUYz&j4OINuWJPyg<p>4
zMtc2M)vLcS{}9mAyXN1`3h&Tm^e|a{RY}i91Ji=<844}qoYufelL}?`@gGfwlF%EL
zU@gOiR>uS=sJoqj!=?}>-SU6m?tiTi|MyfRkD}!)93iR`mMH|@2?UN#C$!@mv2dva
z%xr}nu>%kHfSa^iL=1>^mAHZ9nGbY<IyvUp*sjFPx>DfaC9xB2pe{JKnUm9kwXQ8b
zIqRE)|IkpEuQj3hkBx-@u5#CDA;?*NHv&1H2SPn%L_KRKD0{bv6K7BQ(M8p)RW!3>
z)FW$Q2ZJ^RDmnolu}Bpa(8AMMkydU|HmuB4F615n=Vk5Q=+Yyf(zP3bK_2RlK7$5S
z1)kwV$nrD)Q)W_jc5#0!wbumK*?y6SQNnHMAvMQ@c4LU@JR2n=jhKD#>5Xb+Q*pB2
z0NSkv`c%Mw$TurOT!B3c_otK8d38^qe_-#Im(vbVtAqoQz>g@K>>5%y_<QbeQ9Q;{
zo>NZ$S+#0T6fY<LngXOr_Y+%nrR>Ip!UBbL6d7juz?bCj#iAbuo$5b5>)<SjJ4$kh
zm@6iRPZNxeUDYgP)YDL?M|A{qlkA|Jc&}p&WTP_n`zG6L4|YBy90Kljb0!lrUz+1|
z!y8kdqMFD1zjjc3W7WC*d2Z`{aJe;CFH`yo8W1X!39*?gu{BM<d5~PKzrhtgqgbuu
zQ{D3NfDU{>1i#}1Oh`FyWZ%%sX<WwZzr;?cJ)kCOyDpEXT%e2g-d$36r&y8@7Y6!6
z_RU)`q|){sz5}=X0MhE&FC-rdo8yf1T_HdE5Po+_oYxIv6hzh);&fSml+tifwYAAO
z$ea9R8NzkMD7cn1UDi^rBhCL#+U2Qb^rHAC1WYGN(@)7)_1FYeG_biIB|Z2~L%L48
zW*;x+7um8u4TgjW`&AItbFo=6EItG{_oP&B<SH2kWNrmqQgnU17Ti5&9X_vjsd22N
z<g_>?-#NEdw>~}^UGv#zPNg6V`AR#!fn#G&L_6{x3P|{iF3Lk5`u-pG-ZQGnbPF4{
zAu0kkKtPH*DlMQWpman;L5lQVL^`2|UIYeF1W{=sQj}f;0TLjDfCwnPCWH_I(n&&3
z0tAvbXU>^%=Dc&(tnbJ7@BKm6v*1~<?|tum?Q8FQvu|V)DeRq;>E5hg&6XVexP3kD
zx?(6@%i6o+t6g~WH&UQdUP0QrU^V~Qh%EvuXl3cFfBThdN8By#<xcMP_cIEj_RDIU
z8%=EP)7^7wGE+(SSvemuHlUpnE-HHKL5kZ%nl1d%<>zE}Pn+%eUPcx0LP=4MTxUbs
z)+pn)UYy`9BV@zA4`OZOi*TFrlQw0Xi`<=)f&;@5=r7PjzHr6@)s_BPCXJLcV8(B=
zT$Y1Wt%i;^jblu&l6VhF@kTsk*o1`4a97l-Tz(tv_Y^5g&9}kHR6oHc@`O5-WrYH=
zUH4%~TnWLezSYya1mj6udc&>(+7P|wKGo7^*ogEKIvtX9;LtJQXTN=@E}R|XjwObL
z<H1V0x*5DF!yJ~mvQOvWH)T;-&gz;R=k1~vfer{qS@Y-jv^(yVafA+3PJKn!q;3L^
znP6Em12UbpVa=~mD;}+~%x-q$BSQ<duPn{Gj~s&n!KALZf)(yxxx+15u_P0lpvTRw
zmRHrT+dlMgf4N0UbNC{7_60_xB5KY_0GfFr<s?TsLu=c9b72o0hs0w()h1cW<e=3h
zcjornpoA^gSU~EdBb)u$^MiW}rJx6l&%!}Up*qPX=z>(8g^jZt1W_v#PtSNxB92!a
zKI+?~miy{N(Av1vQ^q_s@QAtJW47F|5$Ot*?I<>Q@q<n*sWs;4VydaF={E)d;6e{r
z6P6s{ezSHaibH%#t^a<Ri~s87;nD8suk4Av+#@=RgGDyO?C5M*9bOr4O+^+*AjC*z
z{#Qi=R%|~u?+S67)vltmTbn%E?<Y1iF;I>hQewoyLD-Pu4C-c!*Y1!;>9UO^XA_+m
z$jKT-m@AV#dT<#VNm;T>%?32JcZ^cz52Cx`&nWF~@$XX7r5;DXv$;sTqm#<JpA4f<
zI`>D=M~sVD9PWb6i~;?!l1<YcH*=LPW3kSFm^%)U)jMW*Ig8`|wqVw3r2$%=SA@th
zVu;BWtIDQdqH?b7sb?JA9y>m3QR>mvnJQE=7PP68UgFi?WrtJQB_HOtQn8Fn;C~V|
z1=yV>M-Ck1E~X64XSeRWAaxGL3FYGV7tg941x?Fuf|U%z5xCo7NjQB%Bz3pis@^|*
ztr~xCibly)VPJQIHQulwTMrGSNA51jdvB?-3yfI~J4+7fHHSN1XAE)iL!E-*?SYXY
zVBH$KJr-wy(_wTWTUs*Re<7uX)ZpqD1Ki(@RQ{~RPuQkLhHFJubq0Dijp%lNkrfQd
z^Q^A!RsnV})~jm{Ehwx+prXm*{@pOJezh^8u@^;Ff|pG3;1Tj`6-1NI$*-04a~d~l
zr>JYmwsb@x68b<>??NixI-3f4mcv7aI{3Y10ehL(TkTky5-DoNnBbi&HkX>FK1N6d
z7PVD2S61puA-3KzJ1Ht?LSg!P$~*Z(6lYlrvx?r3Rk>~QGDaJ16`oq%Ajy94$SKtW
zzkiI`JZm9SuEJ&7Yd(cbESFoitjFb4g}mvzna370zza82{`SoiZqohZTgXFC_lNwK
zC3lwZ!UwV*%Zog8&KPh^V*y_IqK?x_g-X2K+M5&GpWSKjsm4~8A*c}r9ds|)&ci3&
zZM5YXA6mJMC+cQV;25!H>&NKO_qbI%Zc8wHfKsYzE>WHwm$ulD+G{asR%n!*7``GW
zT3b71HtybkaC{J~8ZpT{`Z3*h3FW<r>=V$@yZRye6(_ht$8}zwpxptfz%r>A`YcB*
z7eMlH|BsXo3b-fGAxSBe^41xCP8NClU6<)`k-cZ=WB!+-w562s9Y-#F(qt%UT)QCm
zkU8`o9yIwfVA>itnq&iN*iRNhxr6)1E#}dI+dad&gD*RU$d!|U0US|*$*SXGok2%8
z?{_OwrZx$FX-j>0t0J|-rBISnH&0O&PX9f0FnrhI0DSyVkC$1=nqkOXrw4R(3y4$%
zTDEy2Ho#lTlVs6Op>szn!aseA3Bj7KJUwN$;)%(=NL%e-(BU14%itPaC5dkv<Q>Hq
z{536)eI8pON*j0bb9c-T5n@xl3+tM$u8Ja#*_j_Qvm;6U%8~Vs&Wghm>7t^qfuvzW
z!-e7w&(~xLKhBNarV=9)stSEMAc_#A$hXybcrRSSg+!AZPPnFXo|h>@Xn4=&rS8&*
zFVS0q^^{c~knG41gVqxYOB<l<K;2ROSNVNiDHEx!(ws_n4D+5-M{^=}UE!aE&un?k
z6ox2mt)hwWQsWlph}I9i8#mbEBj=ZgCAL3~FlZ=S|A8?zd8|YnM;Go<6GrSwFT_D%
zNV+)EJ<5yozTCJk1G-+L6471~f;ZPZ%E`GB>KeE}=0DQZkd=Ol`mDyeA$0S=r1;JB
z#V%@2v6%S9)n2a)X7EYR=g@gxx@Jb_;8|a_<r8dAOfjeQGNHGQ<ZL?Nj|ZX1PrJ*T
zoqLZ0!0!4Zlg#)(LkbK`FxDh2_eQ4kNSGCCPezuM%6VSr;MZ#HYr8};I$DL41kI(I
zE*C$PJ;L{29ujpRiXhNJ41w6bb{Qt-S6pY_DOOu!V$i4Uw^}YUxtn{YA5mx*=8A+&
z_ks<AyOFSko>XTsB99*N?!&cUdd<YgipcqNFQq!N0$lh^r3ma9+)+0)r3OJ}!Bq*-
z%_k&@6EYeZj{JaPo6jTBj<SM*1lv!<zC-v5SRWiFI)y$d{_0ZW^n1ZwWH33z*Y)d0
z<xJNvq{4#=D|G(ZxQaPB7Z~iF?NwyhbTmmvd`~e5h}FGX-h@D`YhvdKoY-q`gWi6@
z)LvyzSX9arH+oD&hJ`|d2O9J9?`deTq@E-Oj!8++ci&sV03fnQC|NC04)<MOIIgCp
z%xev<zyUP{*2h!QQ<vL>n#Q89E{v;WV(C}6mVnwPSKX|`ULQC_(`@mI5SNld@u>Ku
z?PBUjCM&mzQsiY~LB%1oNt?*A^4GPwF3rK-8!8g7f{5Sf%U<0kW9-G7+~7gAG<&Nt
zMUkb0eOmB`pui7QQ5EI7{?({U4DpVlP}NPC%HDhWeh+`^Hrl3<w#@mln3^qn(X6h2
zCW$%J(T^VttJ=Za$3`Y`?WJ<}q*<;)uebr)#>=Af0WPE{_sHh8kC*)y>px+g#DeB{
zmzp^uDNqyy7HR~goOd=Kh-_GBnDXP47789)%VMnn>cRvRbK{yRI}@k)i?V~>H1Fb&
zt~HLILNh>8h?N4UA+*^_#c;QZB`G|?ca_oK>}RasN8{>lqA&7zj>eT6?Oa(9<Vq2x
z4xjw8@%t=*)u~BaJggR%A3Xbs^~k{>Jg3wQ-C6vmjVERy9QghkXBZgj8hnitrFYxK
ztV)W0*-=h2#jLJ0#sj`H-?4w<xwY7ZYuJT#(43dxH>tMG=F^Mz%r<zq+I}F#c_K-@
z0ijYB*qSIoE94gl5vMC%JM(IF&8pjLLuH2Ox0MQ>82dzx>AU!EF7+FsYN7ME2!JLS
z`%8*SRSHB>Imj}SbPq5)gV}kH-f_CJEiIGAj@^~lUonJForaXVd71NrnLmC6JAP=v
zsb~KrW;gFLt3}4@eSRryj1RX5bZ+k}Mmq&-LA2+2FX3>p{#~l21a<gk=~-DM^>XCt
zF3Fy4>FKcf7gC;cO%^gaPOZy!k39HIdueYlRpvSjYRVT}x5V(op#u1^E|`Uo7o}J=
zl_gBY_kmp2t$H&?)d9-Zs7V_3P)$bK1}<V7I%mHcGOQd)pGJz&8`thI+&|{5xc5Y^
zfZ2+eL;7;|Hy?~d$dRZZkDT|DHz9Z1LZN*xD8Uq?veMFbDlW&G_*vpB44$jF2<Wv)
zjZ@0ISQ_PRRu@knI!3H~7Kx+`jqOkdyY&X8JVT9&1m%>!arEg?g<b6lP6t=-ivw)D
z@R^7#|DLFj7U|bgif~ZCw2Dx~9g!WPV?J9<TP$H5hZcNBjtB~<QkndS)MD(!z%5uR
zmD)m_d#Q4Kp5b}G7~{tqc0SFcavrwECEj(1Y1As!uxe#{Oy|!0{)s;0jk5t8R*t;W
zUOkqBZm&(9Bsn@hQC>!L4Y&2F1QZ4HZiXq0L(({_s>=~4sSMgyD6nVcjHRAyS;X$1
z<ZKi%fxNSo%)ucjPC7cv&!Bn9Zz{!^?W|6)KQOflyL;+=9~jS^0D5B&#ku+x|8=cI
z{2HqeaKfmpv#9BGQ$S3;V|t*t$m!K6VsVycg@qw-ZiKdA%9C<+)utho)&RO_Lj)b{
zZ&Yq5g|9$1*6T|0Icj~^Sg|os_k)amYA#u{NEam)N%y0Letv>xyTSSgnP(cHziX6I
z&hnVXZA4Q5;Xt*u@Ona2G~uo}yd|0J>ltwR7oO7|8J!C?ErG?i(*ZmbVsx5EjKI2M
zrlA&iQbb4{{^K>j5q+k=cyC^>WDhQ6x+4cR!I)O+FBM5vwbWG%!_vJ1T17*~M7V6j
z3*%L8n``jo6pzgwk5AyuUwjc2pCIrt6Q3@+7+MwN&C;oRD;X<k5sYZlFJXBU=0OzR
z&cdJSr)k?gO?uxPW)v9iV-Uz*_Cmz!socGLpS?s~Na0lchrKqbwt1$hg<DK-L08!p
zx{;KHH{>g4qz0dL6;~=k4A2*h8%$$R(1(1#uYC>5vVm#Y;o834X7i4wCLTlTJ$yrO
z*2=*#%M^?|mq2cES(3;3Z{pH!Sn?*U6F03x=r>H1;c6<h{mW{qkk&r-LfuTT<!aE~
zh3p$Xacb`PX0Qt0_u$kXe1a1G!5QBd;uovDm0^4Ipf6`_Uy#E~$Ab%6dbBe(u1=}s
z>dEIK8;(tN*2r|CjeYlS&?G%0y^`{7bl5}Wa{n(8Q|r8qILJ%vYg2f;=K#4H5}slB
zTF8rb76qnn)dS$|9q~3E8Oz9lGA{{sC}xtg|3W{k`ZDWcDw-R+jjM?P?V8~j?iiF{
z0(Bny0E+l7i}Y^wTN(4P1V?T)B;{1Z<%H=d3vQoIyFzkAh3~lvY0?_f=a-_(B1BXo
zntRKXsbzMNxFKq5Oz*ncqk;gxwUTQW^1UYalBR;BY@mJ8kVyUnM^eYIt!??F$z)he
z>hYXAp&1duSO*K&m4=!3>>Z1`taAKb1mf7~{TO`g)9wZoDKh8G_Ra;hQ@Du!6Pf(A
z8AjVTz|_yra=1C`e0N!Qbyz5&5gx{|PqL%+uqCR}u4qLfQXJf*71=P;&!-qPpu4c3
z{%6{(+QRa!J_3$p&GxY#Q_O3ToH)YU4;XHf<uG68O0H<445wvz9Q1&ToP%t%XE$8n
zyYt`%Ke*R&cFKOp56lV<+-~c*PVsQ|8XV;IplD{M)v-C~Q1w!m0p0K`C$j1!o{bGU
z7;jW>TJ)3l>t3m-1#jF}_#_cOcPX`EVWaF`gJ|r+T);)Zv2T8mNfUP@g4*Uc^6ZJg
z__a#S^jpiV8s?+L_r800ULfO^%#_E7o4dOfwqn=_i)lq=uW9fLB-O{p!Gya>@k8}V
z!V>|NawCnwFAW}7|FF}6s3VHUmS!YFWj*X^vvGQ(LaSl&-=0!DFlzf{TZkMSPXhS@
zWc4e(Zu?u?{Y8G;#@pv<m~;Tl?GY?&vuzh6BsjW2-|Uw~`9I<>btjXhtco2xnq~y*
zYyvo@ue2b7y+anKCe@~HsYJd_G!K84SVycT#SpKK@K-9yi5Tv~8R2)Cy=Mc7XzMpQ
z#&PDs%gBAd($wx|OVjYFAOYD3v+HS~4E&4nAzhW7Q{e<n>Y_Lvp`Ak;<5er!beB{x
z$1`>gb5}{BSSJI^D`|_o+}jm>QfHnft1vbc!^xq6%WOHfJG=ZcJ55z`h3Lx%#nb7|
zjuj~sW1-s*&vFVhgEGGGWV5Ukz&9~dlQ~{tOP6rI1ocK5-IaT9GXE7m)c?&6v75Yt
zsYq3uZaiVj)9%n3!d8?B;}1y6zHPus4}I=vp@#zCcdME=lOvbk7TLglJpN?oK{))J
za-+Z8vxyRhqf?#9G^C<y#wd;)tCtO&`|elNV_S<>*~hi*5=}j8q?*kIB4^JBAB8$+
z+eS?L?CuB&4hsaTxVHrxv4rQq?Lhh;CTc47&!?pWZ&K;uQ_&7FKCMXlKC<kKXF@T|
zuijn7_x=D#h2A{*W!^?v@HUWf>>g=i{zaE>FVt`4Wk8cl1<T(3bKs#<Ms8IqjA@rg
znvWFk<}fBejax(7ywU|SJga}o7**}A9co>w!euD;&1cjl?`#+Ax9E1cDbv-j6!rJA
zgzc|H!XZ=UV;T#BWgA|R`QDxdra*nCCBBlej<99Rf~u^wqmJ#C^KQF@nNr52ttAV>
zvZ7`3Y33QR{U0=AUyYR~_p$x^;UVqvqgG^VM|X(_o;<iFM}XhwS%+2QC1<IVlQ3$V
zb}BkzmQ|}+9%+mm&kS@PhUh%x%DVY*4!^VA=QL9jE?o8LVgZd?wghgCZS?MemS+k8
zL#)FsY{Q=*KD^8RZa;H%JYmP(5guZFI|qzkElsj$k>~DOJTLB`9Bfn)lIEM#^O~5!
zB)7lSpae8qZdzo1INNI01?)UaNtk^?Q(nb`_$pGM&g~)3y+%B>$ac`Aw@rB_hz8wk
z9~}Nr_uI79C4~>@o8IBr9r8|q;tO*#lZm1_QjW9HLb|9Gj9QKA7?G@g7AV1eyu+QM
z%+p7}G=XGK>Nl{w*&IfDmDkzPpl8sYhUXuV9mA}=q~6-p>nXoxP9Fp^#wp)#<|w_l
zt+$;-8_Q0DVk>H@YN2|Z&5rZPAnwZYsXFTS?!fdhA2APQ#P`0UE-gPU@LtVc5IPpy
zFI987+wP7fa@7)>$v+RUIGe&J<lyj;CchsPnv1c81)ki*S|C4l?7o9O+6SuZY_C3B
ztzp>AWrpr-PwdPj=kzfd=o+@c7GqF`2b+O~5(Nt1W$gvy<&>7sTdqGN6TEtcf)}!Q
z5(HFS22=%n2U_J9KKl80iC!y%Kp+nOwQSVS9dysgt=QOB+bJLZ6MY;R`&q!qILvC5
zN)Zvy5t)L;n_km9&$qXq290zG<7L1lQz86=NwxT3y@0Vq)YC{!dg%C*D<6jWR3NbX
zoJ$Qj1A5@3xSt7OM-J@*0wMT_76y310(tsWl)Tg4vnl-ZFR@K3oGM7+ZCGLbZMQbx
zUq~G}P6c~-ZTOvt#TGFE+0rB}w%vNW;5wT+vMIBThT(}GLo&s<P}sFGp>wZQzDV+z
ztf9dfL-PDl9p&W%AXsFi%kL#hW)@^>hs{`{lQfIQL!A67hQ!erv*EOt_B>8cIuqDB
zAi4-?J&Z+vfG6(K;gIh_3LdSo?*$@0&{}l4Aq2Mo<z$vzKL-yUtHElvD+>GySkv>6
zo?nOfB@OpstNLzz?{fWvs=OY<x953KCrn`0e4b^37?vxRJU|B97DRPjhILim&U(xc
zdBM!g>_%X4mG^6s?ZU#k?deE?#CAa2)iNK%CHc^VL<j59-Otk$ce@k6(~<$7BZ}9t
zvWpq3)~?^7379YFo&G?I)hNP>K9)7#I`;)-bD$pKs&K+3WUBVFU6^CPmM2zm%?k5w
zE{oAq^N}qRdrsE2Ff^7uc%(4+`FiCCYKf9_j`Bb>4VbBi&zcW+^Fk;in5i>i+*~E_
zDX)a-sYjB;;l;zdT+PO=Y9^tfo#HBft2WrA9V{iVWq~ZL0%RQD_Oe3;S;dV7EgY$U
z39y0<Za8g;=mhVI!XM||o12@Xz>)iSHf-9|XfyX3lF`IBZt5AT>zD2^@|OF&a)`fe
zd-;!F88)V9Spo~&C7R1o@cwky)GFUAQ!KlMsP%qcr;)=!oPwvksnAE_jwIYOP*GYt
zXRkGHcb7G3=pNPtOUY$<#aN%UO5__+pSiy^*vh&!#LaeBFVe40ncUVHt#w|`%BNP@
ze|)>*)vn6H8~o8kIVz$wG&>92R>44Lhra({DB5dQZ^OAqF(+4*a^o3W-{onGhok3y
zP`s9!<}&?<8H%6tjSp|H7<VphrS)%!OF|;S5kSITXUfiQM{4I{kHh_Wl^?vnm+VyS
zR8A0gOTR=Atu%4|0qPYH0^kKP%9>W11BDY1COPR?U1USgB=}vD1_BP44Wx&zjWb!y
zF7v9#{-$#~eIJryNCmByEFZ$71gxw?txWy*yp8vkLJG=fZGFct6cqMzO4r{^6ZrhA
zV@l{|4G#k^Rah6O))n{(vD8qogNb6<LrXHs(jzA^V223Pjm;x7W|~j<T+j~vyoQBs
zG#*&jx9|P^H-9&--?GjD%#ppOqRcG9YrwbhRS#MfY}eAts{E6B+)Bl0&19g6z7ysv
zPqTA*nmJcuKV$27IBnSh)RAxHEr(oACAgD)*w*Ite(XD4w4MKMUbeM~)En+!4b@py
z34WwczKDDj+7DtszTb}<mT?Kv3*Vo)<{`jwNo7?hGE5gNWmS}^vMI>EikIhST(V^F
zNGWL9()Mc_e^^opv2rF$Y3}&5MzA&eE4nHn_0P9f3qt{!w0d>crR7b|Ua$$9i4g%|
zyI-WdMxv32q$JT}_J~{gwgt_;y*}d}qhb+jNU37m9M1|!hY~hy<8d2#zDuXeLgrPb
za>y4HdHvAOy<?m>RUg4m&j4`u)SX-u_6FJpY&z(LUPs%aBbGJAc3Vo~1>|g|m6f9H
zD${5BP$LV`{geWQ2`N+Q2{|D#n8Lfw!7l>j&a`z9vDQ4}m;M7%C&g&THBX(#FGV+S
zs@o{9)yG6Lubh*r@eNc?%Sr`yStym1C&JS(MtLJ_HGq00G(o%0ri=xz4(!^f%Z>^Z
zFmI`;2u$%IA+sZ@J1psSDzq%D4R_O*j%sBb^UL1lrIrIQLMpmoJL*5t@ZXr{ZxiLK
zz{nawfUZRM!R5A^%8nbE-G^Meygk4I%uHWu-Y8#B+Z<^8(K+ZqTUtmvw*G@S8bT~+
zm#2+oIsd{S<Na<$4u&KOsOa0oD|ndWb|^KGea9dLIr~eFCwAX4a;F-c9bZO}V~0m7
zBr1i>-oQg)PxejKR53MQ5tp8W;Z=zjj0%Lpe%#BVw!=ZIBe7~z@{Aq!bRn)swDF4m
z#oFWQx2pP~Vmt&nnZ=wm;C?0}FeD-%`}7|baQSD<@*d3R6og%eyQxOFLW=*X7Pf4U
z$C>syr=-%jEos$YF_2SaqD|MM;#M;I$#V&ES#>uy_asqoaH&?lz2;){si&cc3gVP5
z+<`FWy4e~(o+7~6XiA}UEWSLsbS-k$5?XojygL^5wrUFG0&l77HP^p;19_o8OTC5J
zS;Wb}_2oR-ttHud@ptb2dhtfq9mpeumSxUtz-vTd`p@XXd}^s=xfCF1i@I6`BNV(p
z9QFF*8=zlBah2&~8X|%b$|3RbT+<2`@k{+mvQ7cHCbHSBpjOi(T_el37zL?dtW6Qb
zDnuTCvAG7QzJ&7ab@}8d!zgkzV2}u_IQs#4q+leSBGXCO!7wR4vMJv$N#Ijn$DA17
z%2S^5*&r{Ct{DsG*2OO-j;(8};lv}>K{HCbyFf#7oK#K&c_x!V)v)&eA(D8$e*YAg
zrSlJ#KM<frnMc@rKj7Oix3K5SSr*<7$`zHT4(}aNo_(pqP^ks_WXx!W48NacRq{kG
z(SUiSJ7`p^dF{UT)Og21N`K&J00n?c>y?#->1L40J5JqN0d-CVJe3ANzVoWxufu)$
zzy`gRoTk>Sfl1|0Evc~!cSt4~T9}L1&CWdWLYGHo*Xa;G9@@#RqfXi`WHOxQ@ifAl
zL<a##<}Vx&pYnbo%aYSd<2%;hq<a2}TmJ$tf3v83>xGxQpmJGs!Jd4}ZSf@~`$x98
z^|1>|)bSHp-eXe%)R}%x#3wer^96J^6w9Uz_If};dxJX*5*}F?VxdZ#6_CIN#A?VY
z;!FXCyX8`o1^98uP74UTa{_-2_-4gltSrW(hX^G_pS@3bce8_##MxBYUH;3CYw~mV
z<sL4JTHl)H&F{(MeSM*SWTH{~5~@?XqzeaxI`6!g#c(2L#t(O~sLg%L&DH|fp21EA
zcxytF;<C&UqH=nf$uSdOVf34p`0{d=Q8x9dPvr=#26t`x0Wf?TrqydDVvdYXTTNDq
zYideHatcyh>7Y>SgtPUYNd<X&o9b>0K_+xu7p~}xWn^5~yYQ6IF=uA75Yr85w0l(W
zdeQ5khwYo^e*uVp9o`Dqw8VS?VmVho77`@0i--2exoNy-(qyb%AM(msDL)KUQnH0j
zY-+)y2!H^<@-z?kvR>L_r3JKJ2`0@Q(ozRYvCa#z8$Tl;-w2K`CvLAmnb}$S<I5og
zAGYgqV{4!t;d&_{=(`0V7NOTa+^QaGag@9W6=F%RYW6Zm>oqkQ+3u3gDS2Qpun8G!
zIU7(y;-_AHXKd7sgTLeQSpOk7qryieu=X-=vNOMrh3GM{)Yy^hZr=TXvL4-35N@>?
ztr~E>n#7qYV@cp0ip@I#%PQ#&mAcbhlO}~D;nrR_Xk+Q(;q>GQKd0WS1w3Gr;?ky3
z--?CtcDP+;nh!}W+@k1gu^0y-KBjSLa)*69MZ);1Jud~5mMQFUf#BU_hRbu!x(T^g
zbh1vGn&5z2dVNFS{vRiGzaiG*mqP(FJNr_SsPW#!*4`y$ldTc7soM&~Eqn*^5Zii7
zwy3<+#%Sg_8ReMFIYQz@e&XFkFRwigx9lZgP-i<SrkvfSl@Om2{$$Znx$*14C|R_u
z>tG{aR@h>$NjGshk+qZ)W=BQz1FAOgWIhx!wCcea#a;)5;I14ymjdvD0+nRLOKd*Y
zX)(4@!3q)@$`fhGrBVkFsXu$*q3t$YIk>Mi?O`*}rOBEc*Z&h~DOm>|cfjlg#9%k_
zHUk~!Z|bt;we74|1~#swEkdrj%b6btHI4GLG!=nvzU!G5oI4SWJQ0mDcTD+!w5rbQ
zAXUj>mia{ArcJtD9}+L-N%ldgFLLaujd(WR-h3wA{aF%)<i54|^2$S*qPAAB<KtWT
z$-oN)>g!_t9BF#jVoTiH%0E}Y|7EFFS*qUOyDDviry2&O^Oamk=`}XiwHLoRd4fNT
zJMot_rYG`~NPawml?sB{j^^l_TSWv|;#q44Lde>VMkBdnqIOcb17xTr!~WgwPXD;>
zwVMIq(|})qN3^vr)BFuPw4TxAH+pa8JNMXvZTG@IG%0SkfhEyHbuu3)1Wvb+H>G<X
zB;`mVB5u)ytyv0dR+KdE{znz&nQG@b;S*%p*V@Gs9SM>(H_G=phQg+Hf#uiCP-)Z~
zd4ZU{{3oVr_EPW>C^4DkYfUC0pmBF=-og<uDIj(%$O{L?9V_;-ZphjSI___=z_n<6
zW^j}>aM<T?{8l%wj23pCLHw$fSn}ifuYI9LOLzW2?BD!Y)s*{~B-N~4MMxoJ<82$v
zASh#1iaxt8e0u_#0&{kvuDRQyICuv&d2*!hM@Ih83Gda4OXB;;b_VZ>K_zn#i=q&X
zEStt@=AYvn-Mn0TtQX?3pwbsgCKdP{P>J2NtjZJae2Ql9cPQbs;8$h8A^rWu#_z2Y
z{br);-(|TerV-R~`i?En*d8|hZa)}B^-`ru1B+dazQ%pc`?;C^52CC`vRB@OPp)R2
zu$+>&GS2Asm^9y6`J(ELwXsyaehwxKc5HOwujtvX{19HkL6*QSSqa|aIMf--i|0O%
zH{My%clD8UvED}D4x_xTFwaPrd9mvPIlmBVH^at09eEy*wDjDeks#i=HL#}Td4Ex#
zHLmYJy(xb{-kR)@cR=YJalrNcx0@Ws#aTXia$cVLj8Y)_lXB$r5gE%L?RpHa`&Dxv
zo;xXB2Ysr%LmjA45``H|L);B?08xalq5AvevH|T))KSMaSYJKARGL@0sS2C}RTJu3
z*1#rZoaOyZc)yc&ZCQ<fW*5fzfN^W4Og{CnM}`^KzoyJP{Is1G`dMbol_jbdXq*6v
z8=^XfFw^HpvtL?!n=S@qu6uniXn1B9@Cp~aeC;@$6wC@Z_fljca=|gl=t<z0-8@qK
zxRX5$bHDzpu0#NYBwGVxpwc)o=dn+%VEx0GLT*grnf-E!AjU>9?v;L!rn)os?Cz^S
zbn*L9lfs$X^SNsAu;dcrjG-y0CO!s0I@VpTOs#+0xc?SYIyqE~RF?1TfUo3T3>V+1
zDUM6$hDj_n7EYW;-+&8(;#tzs731Zdwl)w@vo+#@<+J^{JMR9JUq6YBx64$3$G0aO
zfKAKbJ-T_Xtqr2gc=M+XtFr#aIym02yuHl4^&;@CC#*gRF{9@CiX1E)SOoKdri}KD
ztjf>GyI;wIWCqw+h#upk-5vva2>@x$z@og+;wMe4gut|@$pCnh=3k-sH#{C&J=FWs
zsBAr7Zsb;RX(!afS%{Uk$)lsMW1ZYgiE{?6)@+hrvT=Bgt_r5vlhnsXQ45FA^yxS~
zp`!O!0>W1N(v6V{>T?)rsUGu+_A=OF@`4b`ZJ&fFk&r?qsrbBebN{dvYpdiq6Nn_Q
z*J3TDhJV)LC`td~u1kU{pf8jtck#G$N@4j%Pi;p?D+3<DJPk0+vFy2rgh23h1-wP`
zBWFT*^WIkhMeMFLzgys%efLu~KHAHBWkgpt3uj=@=d3zAA~81yzWlWh^`tcf5Bsci
z?g$=JxP9{XDES9zKBa00JF;OK`Xm;ee<IDw(i-d@xm{ce+*=5<G{u%f-l5dvvVz9!
zvVyk`?vCOMU4M*4@Qvf24~n}eb-g-QZ9m(T5Y)`9w}6}N=9-F-jgM>*<cz&p0cAN0
zZ`!V{N(H}^g27B2bzI&yzu;qVeHLWiS<vrUs)Wits>&TxCaLPY($)q*FK!UV8+~nl
zI->visAv(lxFy4)#RtT(7$g7b;|){(<B)Ph@SCw_JI$k8JuG$mzvfP{flw=2#?5>0
zoYi1YE#efl8@#QSC&n!pwHT%I&BOD~msp}U_L?x(5<HOg=)2*K4@=Vaze#=jzuBSi
zGu0yQ_wXmlDG%It_dw$=4yIm^1LQ7pUC4Ux=<ce5&A?kBxHT8o71=`n;Hn&LG-{Dt
zFd8yjpy5X8iUt5Y&|u}zR{Lc~^c!V+2L~9i3~(UZQ)?+0t6Mr{F(Ox~sGB}+Hm0)E
zt-ZU=DLB{>eIR&R-o&eXxRXJ<m~}nkndkPvV62d9#}{_3Tfb~|MU%&^V1Vn{IZL5J
zN;Yu`1aSZU*71G6t|n6aX%q$`UPh?3_siohpZI8-;QW(Ka$G&NPM`1})<KY#ZVHrp
z2{bC$994|iYdw+5C62I5O<k{Ke^+>uAS-TZnA5Ec4c_yU!J3VUhUNoyJ)(e|o^kQ<
zMq%rs@d7vGMZmZ)yb+sCk>5+S;8%*&cJ|w>NHhIA$~)fe971*K=^hSudjs_zL;F_W
z)Pc4`&OD5zQ49u`G>P7K`cLWY5w5U(u@v<s_`Uf=!b*jWHQwE6uBCcxIsqB+lY!Bf
z_9eXa)c?*Sy$%T~(!b-REQJH3cBy3p2I9#5i3CSVMDt3J2bAuTv|wXvZ$5I_f3rvr
zYW1p$TLNDUHtX=4u<@E0$z9@}pb(cm3p83IP7+6T;xJ<<%>)wO4QZ3%uC|7?)%OyY
zvT??Yg8^n`mpO@G$dC6=WLL;6vdRHSo4MW1yc%**9Dbk+RObdII9ir}rG6~n-)gC0
z^QWHm1NE(629d64&WfFYdQ3A4dq#UB!yg`&u*-hvxYa~%Xsrws@Ks%{*i5y!vr%)E
z9qq>U4ZrjC0>J9AKmrnneJ5jMS+8r01ZnAiY4W0;QMwaeq6Nqd+p7Zd;wl23%l%9i
zX*$Bi+$p_Io#vcQB$$o=2D>xLSbTl!(UqL6lC3W?T^{34S{=G8q4hrAuZprnzV^!-
zEETu|-dLA@rV3V!y>&Rp3nghsY&r~&>?Jv;SpTbM=L|MI1oKeso0gYzro<Q!gPCK1
z>ORkKj(sxe(0YvyU+&aGG(VU=NSVWSR8kIv4mz$rUcYi}m3hsQ@cLS}me-g|E8(bP
znR_Z3qWe7s5^!lMa4tkP0Clq)nA4<E(e;*gg1GrjP2HF>*L_axdsff()1UF|XJ~0@
z=BXltr{N~@pYR9DdtPY@|0vJt<m?gB0amIrA`#{t3}LmuI+}&1fnHiCZ3OcX`QD_T
zOqvrvHt+1QNxafLoHn380kJ{=76@d!Ap5eqo`n+bpa{ooM@OtQSGx}gEH?W7qVb38
zg*oJ#->OBezG@|1n{^kB*Npx(=_*_|wRV4Tu=kzDxGN}o%$_!-w)rE-h@D$lZ%n4r
zH*__9nU91}F-`N?FBMR}`Ug#+88dtB1m~$OULm8;t=0nl5RgRf15l}@_3vi^=O^;L
z`{RY%LNqhk54BU~6wcLDGq3Mrv5B5I^zEXMLMRWQ#^;r^I7(bGAKWX{UD1iIyOx=L
z#|s-~pk}@W71|X18<_s9gN(iXS1Z%^rrxF2pdn?GTIXxntU^qAlD2?B@x;m@GTeM$
zcQ~NqQ=eDg>T|LLX{r*v7baK_{r(Ae?4)xzmcFK71l%zpeD|8oQVw+<ci*8ysl0y*
zNIyf<U!I}_juEGgCImbR*mO7!4Xg3yn^mD(vkva;dsz#Js??48!K3Dd58&LO0?o&R
z(*l$YL*x7qZJfvSk?N}4e;0TE6p0Q*-92~+9n07BU#Zf+UzxJL^rosbtRK;Au<l~k
zdEwT;{b06|J8y?l&}r`a_!9!R^PR7!kzduAy3XbbF5?il#{7h^ySzC?fpUo|xq;Vv
zJ6}go>bUp@%B<oNh-V*#wx+qeJX)STt{x34y2-1==;*Rja`3>_vG<1fkH52{&B>Ys
z0Ily9>GBCCsE{07kk>;C067MVo^3>oexPO&|A1CRTiShqg}Fe2w)GPSBcVG1s`}ps
zI+r5sQDT|dBQN9FtG~q91FR7mH|HxKk5OdMcQ?9zJ<CA^w?e^E%;|iDv+|BVs_c3Q
zqBqmrdj`bV33$Y)Y;epU9#X|PvVota*m90Z|CP}H{sz$}%H?)Bk8|a~EAA#rZXK+c
zCD<7-SzI?tZlzVR>!jBSZ-C@fN_*>cT5Ids)nIlzP5@g={wAuBhAe5aXx{u;&?d@N
z=Vfg~2-=_j7b)~5C?ee%Y?8(D0+D*Ied|}2N@hMce)uadn+F;-2c5=RyAhC^C31lC
zlR8wz^+LLRQrM>IPMehE$Up>6Cd6A$i9T_+qO6ilnG&7>f0`#48u9iLK%IbqdS`WM
z{b-eq4D3H?oYHQ5F3D~E1E5t}c>~AT+tH9QpPFRP4iAr=i2GT5V4i1e2cUdjvnZ+k
z)dl`-6?RF-gS_h_`=!aHBACX$7c4-{lPjN^jU6qE1e6QGV&bp-_ocE84E-dl6g-WD
z$PmX(fRp+#d#wi{n}s01Q`O2hS(NDn7(dlx;*~L;iqn?pZm?aOJp45Vm~kVf?AF8~
z^g`CH`{AJh-CzTC5A#+0w<sll`tiKlr^b@sh&M+@<lLbwdD`)L%U*=-jnU4M&rL1_
z$bPBTXeTt4^IHz6=U7(2h_^uGnlL+*oXsTtS;53(SwV|ab{Tn{oFjKS69<G=BXw%h
zJx4;5gm|B3d0AhS`ssBPIMdblBaSWpzT-bB(!ZWUHCwcOLtRN<(sZ4p&rI*Q+VJiD
zNWSm>IdzmiB$b`gkDK%*=_u^KKkQF6^oud>wW`vcIcjWvG(efvQVT;7sxBuW73H-f
z!?zQ2M*<_@%~`4Z%UeInxk7-0A=Zk)9`ws>F6oxn5swxkmOR5((mJE3>Ycq8EC+Zj
z1Dk^zyuEA$aVnb&P?E!fP@!}Az3t7k*a+tQVW7&q(jPY462UF(jmY6VkomVDVCQur
zXdYBH@7RWY<W}{FkdzZyylaxS2#4v^-6*;Ybat(a-^(<-$}#%Ganz&(ZqtqO9Bc*5
z*e<A0dlUG^tToMYgY57?461d3J5UnpJNeznq`#^0?!JUsme)nX44FA5oPk)f61Y_g
zrW2C-T_37tZ0v=3%{7d4d{=(rp+Y9uKxGVR6%zeTKVxsGDHG=>$&RF--}=)NQ7$S9
zAzprj3iT;LTn2%9OGRFLk&=_nk{l6;aMFhvBlMdAKw$IJR>C4GcrUhpp`e2;lQmoK
z<Mn$g4FpGW&AUm#3z8m>Ejzk@O%gOHNWOM**a;iZ9K6?*&eD|x^PSW*hf+D|y9V?;
z;bYBi>J>-HB?FrMckT(nr}P1~NMH|e)vLMRk_W=0CIrmHvJfntHhMfbyZ(ga^4Y2c
zuMYyurk!JdA66y{nVsUCChCS-Fw2Y;(@CoeS|PLZ3TSpX)T=up8|hn<omJO0uWcc^
z`lflk`s}%`=P4eKJaMH-wo1<JR{>Drpv<lr`PHw3A>pkd0@9!RQ9Jt@e$o{z?(VmM
zlYC0#W$Ex$$^v<OLdJ5z+>Y=8k#CXdp1JjsjT0Sa$p|;(IhqsKX1~AYHN3UFSN8$#
zACnl2%Wt!QG$2&BGt=e}-a3hV0K=v#KphEShR|@AMRDnJ*c6rD+l7!pAK;%u;NfGQ
zPFH_=2GK7nS43YnQ^46WyRIMBcl2lR>ZfNhb06@DpcYD9EMa8iy}qNy%MMNg*OB5)
zerwOxNwQ2LpHgvLm$q-l7&~_@<$#a*L&=!&!)QN%nU%#kc1TfMwxsny8W{7rbzh9F
zaJD1U#!k%#d?&kBIN)pa;7lBOqLCEbT?P=&8aO6?WirpQdf6jW8>qxOuGma(=YD2m
zX<cN))t(k?UGfAAl@qF=^%n4_K5#0RQYzQOLYho1Oek(_7Mv@VpPH4rEXdbfXKebb
z=M-<N^qo{x$ovaN9<vJk=%A+e=f(XUTpoy$6jd!*lMEiixc-qX{JmM6Qq7$?cB+2m
zc4u_<PsRSPCu@UeEueHoBJtKAF8}v<bxfFv=>K1&oodt;3<%)+Uqb$|%EjA<j#Mi2
zRp(y&lZ*bnxT@*rguQWBI29BU|Cf-z7d?*oUtn?)fd#XHv;V9wZRUyK%DcF%tiL5N
ze@ObT$R+=piR!QYBo>eVv%apdF!lAltxxyzA5#7Ox|k(;P3AUq-r?Rl^3VEu$J7^m
z*50}3?|tJ>;zqr`tUA>=bAc@+`k(dnkf|^0jjEdAf8K`9)68x7pP~Iv0vtTjn9tPL
z|1;bFXSR#~&ukAI`u`1WPm$h*_WmQzw7~BRXX=XAl~{b|5>GLMYh9#Cjf0Ut4zr4m
z=V^U<Fl$`{UPTF9^xpSWf)IKB0pYCg31@1F4v)F0EepnP)vDH!0<UHmH9}3^4oSyq
zX~SRKTfpfYLT6FO2L1jCgRe(<KYkm3l(Y5oSvDpXe`QUo{0Gn+NM&id7gShjlJQ*L
z@dIB>T#B)5rm+a0VST;PTZnY5b~l&gMMC3cMkVw2ebbC<{-X%+Kk%(3;E;}=Q|VG_
ziJKMNVhNC$0)*y*#7f?b9-~T6Pbvmgcz=adzq-O^$3rIOnK9JF?)blpf1cRW`?a6r
z@~I1Q3NAC{nl<u|z>CNJL37Ml`dZb^uMS1u%8ZvAEy3KZ+~h?8P7~f}fB_5RXTkf7
z+*rUzf#b1UN%vAj%8c;t48ymcsig9(hcs>BCZudnz(0dOOiX`s>#&&Ch!~faICuJ!
z8?VeXAtomjD<1@BTuSANH}N;DFE{&SX7;#DO(2PRW|L;@CtKpuc6Ibo@M48$+71<R
zpx_Ht`g+;lQvW}}e3a7Vfu^UGd_^iHZxSs92r6TO>Rbec2X9vp5-!&EQOosrPqRfi
zNpLD;L2iHvsU?{5*$x@4a+^Y8=@l)YRoPdzq+()K9+%K<kADAzx+aH%TCqrnyC(k_
zH$$ykI?6rY??gMAYI-Cr-{3CxM2-ZaqEX6Yb?GO)un-dq#RkvVcWh2#6;)!d6}mfG
z3m<?ii}u&ETgY^M=gj|M41a{nwbw@sGErg;0p=_A;2L(Bu3K^<$yy@kUYk)sZ!fhw
zo-o}qYN85j&`v%sN2`j9+N5DCS-vQ>=n&2yN;$F09G(8B()iy;{}#z3?+B*FK9l}3
zfFR=%ayOTjtSH}cQFn%UHfZg_*E~{PXMW8IQIGzIuO7=4LE?=m24QPvsR?(6Aka>6
zD<jA&z{Yy>8R3}_+lMs*|Kw^xmWO(~=S!#OG)L+eW!j~W7u4_;5bZCUvl2Fi>(Y<*
zD=o-QAM}WP8qGrqKJ}GD)Ty9#c&Yr7@nIw1fpsyZ*<ZvJCij8lSFx0j%-F8v{Qbi}
zu~$1zwtP7-x)Dn^9DIuSVDQjUJxvpMdf8<}FbznGTFM9BydYI~a`_3Zr=uJy;&L@Z
zUv@CR+|jWUXhkgVcQ!{0)>483N9H$|mf|CpK^W6>ODG3U>c>$8*S{=<3ox(oCv*He
z3KbR3^oBh|eBtuQdtI?7t;<0YpMPb|rg0dTGhDdH%z0hl*7y_?v2#+#J75%H5UQiG
zPZlk1T%-FPq&?E~<n>S)kI1;$*&~%_zbEh+@TQ_FACuS^2Gn8YCOzt_#5!i0j2n*$
z{3m-FP&;x)e4)gZa;`f~w<P~7Bu`uHoRD8aqfk)vlkn%Ph@Qd^F;o7VyBY?D6;BU(
zf_bwOQ$;Ln3e1w|Vtlu3=Fav#F05wGpm8epp$uzD<rF-SJ@?c{(@CjhC`M1e$<{MV
z;3*YuBqzc(S->Hu@P8i%{zXF7Yp1{E*U>9iOI`LAWqy5S6tK-Jrm*E7t1X^V2{sx2
z;i@GOY1~Ul7s1wQTx9bMU0Ms<{8Vu^?XiHUm|^~B@Q-lKvY_o>+F1q9rSN&#(4V{6
z?x_@*_L)eWd+aoIaTzq7F0lwK=y0W3F;jBoYtAu$`TKwP*-uZe5|4UrZkQ7E!^AP}
z!sm?{Dbw-=)hn_RCNn*8BenjGmzLy(N-atrT|K`rZp$juvRUn(Iptta!#=p};b0Lr
z^07y3(!E}bL(#avLGQ818N)XIq_Tiz>v(hX^>;Tb4ZklwZ2hoZ?ZqVLtCn8SpV`<?
z5&rT7KlSa_h)WwDSu*6)Sr#@#OJSA`2)50ZiGDdoM$Ar!VTgB~Mj=pxax6}ra!i~U
z<rYdhO-d-RDq>DX0Lfma;Vn=QNP*ey&s9=Jckaz;HsK*vNk+aizM~A9i41zQm>Sny
zR+X5lgM}T65Fyz)c4CIh!{M&~EZec#(^b#>NZkyB+H$y7M^^Ft+)ZQJgb-J$B~Hvv
z;PtOuNt!XwK++DM7JqogpEDJ?05}mnnsE;yts`@yfbU#gZos7s1F=-7FjS+O#0dK3
z$!4svbO5ALz%LfUeG<PlrDt@@uC^<wu~?@l2oC#~&Vcr)KTU=Bn+;@Y4-R<49Kh$>
zEziYyp==i|^ccq$Xm-`J=AEE7hx6P)!jI4I4P;k9P52-#_B&s?UL+kmgjQCrOJ}97
zSH!5gls+sO2WeGi6%@`%iCfl)9n%jzg*`m>$>YJ5!Qv6W1Tj5?u030rSB5V<u^{|;
zh2MZDHG>!5z=Wef&u@JH1CF8se_0!INOIlTm<wUh@ZgP&jlfz-?tkZexixH0Z;HNB
z(KM!ZAxd*7rUfKq0($qg$T2hLymm2OP<*^*PNDbX1_OR>k?IKDQz^PKo{wcyx5lUA
z%^Fr81bb<eap>3f-;UJ|F*^_n<#XEC9<RNp^YYiB@3rYgCJ%op^=P*v-=66{(cV2$
zyUoQ<*gemG7#lra8-7W3DkSH6&FQ})cNF5Ho!r2}N-myX+&SytUPDxmDg!rLz;FSX
z9j^t-sEF0@#>LOC{I{p$SPS&1_HtwA{!Kju?&nS?b{f%$tA1*PH;(r;C2ls}uGE(j
zpHW&8V>1gh9R6}G)r=B->AOYum)9aV=}Hj&(cl>{SGADxEXYrqd2Pwtcp>l7C*k+8
zW*COaOnIbOvWt=5mqWOvgm~s%4!-rJeLJO`Z-5yXQH@4p(D~%RP|agjNNmz|THIfZ
zqKS=VIrGf!HXzWQ4godJ)vOLz&pdBtqrS7g`!CR5)H)=z^G$OrPG!%YAlW(Nqx0d`
z!*rXFl0Jo1MRs24rv&Gw|C}V?BnO&)umeLDX<hl5DeJkQTTe4kKJA*>rI45DqV`vi
znNIjJmqNAUuS}fFCt#zjoPsu26@}{i?l24C(xvK@7v>!Ss6!)z5v*m~Z>ZviwiV+g
zNXP=&4YK)&(<c4C!%)lRk(n*2Bi&~A_HcbTdy3SAeaKzS|F)<xohTvPO^wLlDHff~
zx_jwiw0?`)kQq<O?;ii&D1VNqUsEN+DHvxEsSdpy%_ga@uCBhYcpGu=w?H)|a3Y`I
z9hk6$$LgbAS`Ca36)3q_-=c@53i-tHIqm7ZMAOmI1%%P}5LJJXGbx)KmMwRj<RZ9u
zEP2<5CN6vOj=weQdD5Tu5SVoDYI(|+F3&38>HZ1gV-J4x?$OvT+EMJ<2=S=+&1`dJ
z9r8_~THl!?75M4tFzr`o>m#(SdJ_J!Ny1e~78I~@SZXCx3=_+S(Rg7@kaZpH_^^)y
zK>zLTEjDM1D=ST;U9UWH{m&u~Mm3t!OT72p*rc&z!`$B`L|=*rw0sv+*bXlZU5A~h
z2>!0jX0ESr;YhxcowZxkvdhGw5k~~q@*S;D%B<!O*L_YHb386CepKLiB_4a{>6mtu
z<pH>;qxpDmU1{XHsTR7zbna!2X>tAC8*ILzYx1rKNsYWzkvc~mo>Qy0;VzngI&k<m
zXsa%q4kD)RSil34pYmKud9>8y&_O7!PSGPNYQD+7^skqijHA5Y@8!YVI`)2BN0y;*
zoofN#f}-b@F8~*TuJQdY?yhoqRoqo;*Qi;fNYgZX_X{Wl&mG2rK_+Q%C-glB*x?xZ
zsYZ#%3%_^3`wBlqdn~z>PaGQ6=#mARJRS9K=R>nxX$7oQJnhhWZuzV+VtjRk^i`^a
z_=2;{b6F}C0xU9gO?6r}rY<9vBZjVdgdZ0-^_0^arj#-<4AIry^<VU!QrqHZA~<pM
zdUpk?LlQpUbD8UvK{HTKp+@;Hs9|TzHiD!VKXEh3Q@jlvoO5~GmDFVLGWBM87}ZF|
z^6Q;*)k9O<wUVNu&@=db34Yow(Z_jP%L}Q{>e87T-Cv;|9%Y9;)lDp&Kx5Js;+n-S
zBj#FOHsxbO&M8_Uv+|B=ZfvjP3`!$+l1gvygvfBp8%%RaVNMD3rln??1Wg+ia46uT
zQSAl}iI;F8B=>icbxNM21cSKDtiLied8F#~>^kRc#~Lw7z|i#NqyF@)C!D|enf%9=
z4>InxzH?n1Xes$$f3wpTxIs(b>JT}T$yr%sif*bwK|YU-sy}IpTMXhmGMlw|&8skd
zEb6i={tq9%y^D(zm)-DiL_IQeX+N80j2P^S<NQ2#==WIwaU&@dO_X4^G4M+J@Iwu^
zc~QiaP3)^iQQ2igJXfk&yLb0EvpQbW%LBA1E8IycBM!VueR&S+Y;S35;v-YyL%(?<
z-Kf|V!yj<AuJ;kvomr8i@&UyEt9&>%d?)~*Zpl|Zn`sSRYXQZwREh)3rT=rD9%?bQ
zO8_%3BR=c@ueleW;sgz>t#R3tM+m<+Rh)g`S<BxJ;q5NXF7V!T60dT+=++I@je8;{
z6#4oCw{k^LqfJRUYvpBG6VRiKcgpeXp-BeXf{%+^yIxuDcy}jpbW~A%%9%<5(WkqE
zgcTeQ5(m193jFgvx9PH@X4Jg<Cw0db64|o#>+7x7)y`JjYhc%LRP&a)Hd(t}Fmb0i
zmCegym#@=9idi6?d{tlmU)5Z@{u5)M!y1>ffj~l?T<`Z<<G`8MRHd?>@!vMmDz~}W
zhJdOVCc*HPHT}w#$yarT*ySd2mxhbP9eAq$10%v~eJs*CAUBI!O2;0^0YXR5l#-YO
zsgtnY`qeg<xg-gWQ8S|4s?ecnZaWUz<uj&XU!Ptv)iu6loaZF8IevH`O-TUy4AfNo
zK%>3A^|IT+Mg2GC0>-GWI62E_7Vg=m#rg0NhXk$onbweD?sHNQqMOu18^s@Ah2aWM
zH<|zHwl`bIr3JJp*nT$X_I_Re`XpHa?}r-5X1XH9=>2~Y!~uJj=UYps6EvJ2y8}zS
zhQ@X31fXF5-#WB%H&6V!=UEah>)dxCoJldz)t!Q6$-43nRcM(0+Na;&Mc)Z}`5)Z%
zGR|EmU$Z#j#yrQ}{~@eT3=|73S^3<0R>H2Srz36B1%rY*(O?bVAQkFLogyi22Ick*
zAKs;92^^`&i|+tR5L~P<Lz7);oCbxp$kgr1TF8_}!z1E@TOu;qb!6rhl>?zI_rzJV
z#d%i#4_n_DUfH^I+Z}Xl+qTV)jgFHQ+ji2iZ9C~W>DcMmwyhQ0x!Gr*eeU`0cYm(u
znLpO7w`SF-QRA%=vWPL~sCLtsKL1|0gCDL*z&p%?U)x!kR&F-=M@qF?=?zD|V}9M}
zeI2!iMC2d$`zNsIeZUl2;ntZurU278&@JD~;L1uZm3Yw=KeExVxpGtBKks}&tn8{S
zcFLz@IsH2K)o;X4yDv6j&?@g0hhO{P5&ZkRZVeP9j+^@Q103GYt0-Hu>%HOUZivs-
zWb*tPDk>^EJwLPlPL6u=kcE~UWj4>>Pka_i60Mi=f2@n)AdUPksg!9(TU2V=FiceE
zSp8jHU{U{ouiZ%5VBkHdMd;p2jBa*MYvn6qs3;mfLI$H6!VK4#?IB8=n?(mCDN08x
z*R3>S*I#dp-!4sz(&-qM3SMRWvHnd9>pDnPir2cRuQGom8BMi;w^V(eG3ZkiBvZ%>
zu-P9=DVtlsIG&cjV|Y_0D~MJqtZ<NLtceEsWX!=a+yDO4|EWKh1VCcidDT9g2Y1uC
zO-q?&U6}GzlC|tCJA_?K-?lcAxIIo0vpmP#*4>s%pU=f7Ya@_QQC0=SqbmQEo~%~t
zC0*B@)h;pFty2HOKsrv-qTJ6%1!zqb4F70xPStbI`+ccqYc%3U_@x%rXfmwoBh6s6
zJ*N}Y!+MI=lS(y~nU#J}Qlex>CrhbP^D1{OjJU6s79>GDbT<^4X-&1}2#4LV>kfb{
z`e<S)Q7%`=^p&}wJ^Wy;<HxL9;n*S@o^7C^ZS(l`qlq--?o6kF-zcMzQkzL|d%k)}
zigzwcrPwCGx6G~yKn>u~y_+TY(I1uqKKodE_}L}8;F4bWJ8=I8Wn7|z&#=oaH=Vo5
zY}Sge*p@c6?HL<Jf~%1`2)tT+uTkx~tg#v=r_-tx^7Va1OLC@}51bCT?KB*;ybe^v
zW@(mZgxB=cQSbDrL$6x-l>m<wr>o;x#{}ocN4%v$%nEiNBDgxU@KS)F<DLOve=QMs
zGglDy+UW~xz3hQ;et$arYs|AwT9EhOY*wz0UtfB^MdnlDZ}eRF{_vzSjRHGL8XlT@
zQyAJoX<AI3LZ{v2wWmDU2oWnUV7`>`Y_gDbtwlL9ekOw$-RDNWQJ)FcmEYz?Ea)|j
zKO0lPQjC>MmC>E(&rLPfap;Dub2M^!tPRylaM&qlUSe}ansX?hn;x63dqEfDW~gc8
zZEjXrd8}2226pbzsVWtSlN4_v2UR?_pu+F*QDqJXvp>5m9j3S%F#nTH>|RA|-hMzh
zF@ez>{as3TVugWrI`TPkUu6l{3|<=$0mYu&aGLM0Q&bb8lXdA{InP0HI~MumVe-t2
zmV{wBmdl^cXpuXh#dYPQvi`#&%?8*wcRX$kbXsI#X|Ql^;P<==d!DA4Xn1U{jQBhF
zbpsGDGt>Y+Py1U}Sr=a76xnY1^eubHS<VX@CyUjafF<JczrTXq1SlhxvNVULd?_k4
zs&QkPk`7YW^I~J)k(H%cp~kwTC!3Z!u4X7MBSZC&nXomuVg7;-+doGY;4O9R)u!UB
zU@w~!PrR7cAM~D*SCNxAwT{DyDKHOu_2b7tcv0HMGa{fiOrv#CPe8Z3Pd=ZiRmO&}
z{^<R^vu4`WxbnHu<bzJis_G?`z((m`)gbjFy@pTk0QTDsN6b-ZFTxa;sjOGZDO%hA
zxJHMkv2`KeRh;p0FG7!arUnsD1xK)?wV3H#7%gDd?4goaN)<P^(~xodT2bWTaUhH{
z>5-LwQsI@!fxzz>+}L1(<7##AikAL#!0c&?f#-j{-6;1tXKF6=hUe0{{WNFusZcJ5
zQ}YmC_Fn7DAi`gP)vtqEL07jr1mA<w?e=0**EHL`n7-py3D5WSikgOIwS3iN|KBM4
zhu;9#Y@+&YI(Wk8<*obAX%qPE<A@4*TiO$#{gvO-mLu1XtY#hZ4w8S?G33Y|js&k#
z<>r8zSi4Ja!7pT8RaoZCr@#T0JkG3b)|c0g4Zo@m`7bZmuM6r-`3p98Os=@JvuX9$
zOuZgURchof{Y3i1>eKG%w;=wKRUO0T#Qb#<;C8$p^!)6*UX;e3yymGUH>F{UYvU_H
z7vc9m)8i?bU+aL0_wHn}`56X+rvJjA2G#{n*F6#3sWI6G^a@e|1TY>2zE<Qk@}N?^
znu-PE*nYI$XWD;`X2BXBWJ8GEWp2W-akS88xZ-}rCb?z3z%=N2HQ?WV*om&*;nDE^
zI4JNf=4y8kxaK$s$6SlRb6T^diMzOae}|;+`7f)zNayNLM1*T5JJms?(ama>77AZ6
zmp2<9TDxeb)E?TE4FlBC8)XrC#?bBLR=M<>TNap0B2GI^wU(I6`4F>ym+8cbL<yq>
zB-X}n5aGOWXGnpUWFYa#%Y<0rHS&8FCEE7-uka5vgrBXkD4GU!ycV0n_^c)?HEvi{
zj4jlSEfDP4Ld^Es<7lmFwwAIJyfm8PysVT5Oy8$|`X!&(@{{V^TH;)#uZOTe+nqie
z8`<3YK#1qvUB70%QKcCERM*WJBHw=lN<TT0Ex$t!jLH#Xnbl#RB4f49=OF1u7)^|G
zg}ITym^$XI_s6}KK{I9RfDQFlRfG;!arrUzUtFV$r31y_`vcZlExFB=e{9Aof6>k_
zQxKHl&<m)9z#n>*OeHP;Z8d=2LWRclV2tmpA)fP`V(aVmXoLOkfcGX+W9Z*F+y@ad
z$q8m+(SNqlsAnMko~eiiEgW5=rj_YapO_9zlAWWOsjU8;aiCGL46=w$djG{4aVT0%
z-R`qYGWFlGpObP8K}MgWKe3#gXJ41{)cV|42%|Z8da%D@j84!**aBYZtrHiTgv3CQ
zdwa0&0)PDVM0<-HCt5?-<5#JPGh<GUnnx_pj~NmQWA8g3PXgjv;vnf?x$yzZECNv4
z7g&RG4d@@B4Ib7FolhN}PMZO<Dgbol+9;$$*I=uSncTsgY|*V&Wjsah|Dr}eXPZ7g
zM1r?`sY6h+HtuBLd?WmnB~IF0NMx;hy)5g#Dkpr_O6e@*$HfRad2I6y>4rspht-<Z
z&KW2E5!hs+_EGpt(kS8xGokN%uAXmW|F+fyn8(9;Wf%&D#mQW0S?Lwbk!>{_2Z!ZI
zBGsRONMMuckkY>lpCwPYQ4phq|Joov=HF9#q7oj%rMbOhGotBSRiCsQ9YUAT>QQRY
zAzto2Jj-4Uo=zu$2WOnPQvYKbV2HNPr~@ZCI?;%{1%+cmjjw6CJhP5UUG<x=Im=3O
zjRy0<V>Dhb@+8vO^@Y2qar?mONA1jp6s^w+$cifZ+ChVQ<L{yl)bONF%Aib_o$OIS
zm5uVJ+<XPX1Y0kFtK!$3{~9{#!&XZr4{n5OXTbCykI%ia0eG<;>v#+Wmp%1QtydYY
znkIJ^Do%B6YS8H)T%Nb398Tj9@=CntYO(({p5gOlyR$J;Ey=q0P`J%p@^&gn20DwP
z5?98Xq^(YYU26~RRTqG&UNP7yYu(NtrnpR+re?QVniwp9cLOg~!JluB3trCK<fW5o
zr}-Y%3VdI83DTJigOe~CWU88$DcPRcx7Ghf<I0eaOLN2Xqn~Xhnd$7efcSO%Hh8gU
z%{B>aSAPSQ?}-{J)_#^SV5<f0`>;s_S48tcv~^sGkjRV~){wuB(y_wvyh_s-Cw~jN
z6eJ-&*q3~lVx>TpwXMx4u&ktP&apMAByCY6me<f*W~sEUu*_&;#aAtR5P^|nP@H3K
zM^Uxlg$(zctKSq#zU`2t8g0Hu-os~<S5NHF+KiK0T)iC!TI)0dMh~KY+&AiZrp4nd
z{@R`|Ez9(ZtNeK{2c}tt+@7FPV`YjYu|iPOwegn8@33zBxs+CO^z+V}8D1@nxb1{a
z9>-zjIc=M$z<*N)pLy5~?N@s6!5SJg(32B?o^mwYhi3f3CWqA|6gB48vOgF@C$qf#
z^3o#f-#Gt1=qE(l8<WKxrH=30oyU30;qC3LJg^m5H9HhX@*s+_^zCmPk|jl0`zSll
z`yP`!ISl|!ibB^iTmD^12f5bn0ATDe+v0>#Se526l_BM`<D1<GM7{70&3Oj@v!4i_
z-COEGrrKGtbUBRv2oH`mr!iUFC{7)xFoU0FRL6Y3wq`WRzu`Q3SX5gRPZoeme`YFl
zo#LP8)In@Ngg=g+OONf4n4ThAcWV97v{6@6DdT3IWbB5ZW-t!2>QVqcZ=GtLfe+-M
z<*Xi``%!)zB>o}bx%d^azrkhlcTkbqS0TmIj(&7Q1}f~?NT%Y4ja6xPci-z7Pnnyd
z^i)j4$Xe2SWX}sUAw<;U4tf}vk0%oyFtEbUO-1FS6BA=}j>YSqlMh)4LG~6hWm!Yt
zUKRVCLA!Z!ejn^>T&}b^9-H$i*U{P+b2P}Ht0X~RSE|>#=&EGQaR>br79`3=RDh%e
zLj{5U`mdi;&5(9_2CSG{!U!j=#pn7$V?7#ha+o!Uq=oakWeru%<!@;JJWH6c7H!0e
z&15!3D3itvzS?NrYuoh}ESW(5XHq_>)K0Ik^WV>I^@s|#XR@SBhLGJub|gFMU7Cwk
z1NFu&TT+ee?JaOSlI6R}DhQyZqj2|+SrN=Zp)90tJyLme$dQUA=u^yyN}wggNZbje
z91^zXo|y;A*?TlXt&!)jC3Un#cG3xaRu|F!gkA4qo1d*9KOcbVM`_9!@Z{WuHZ{Oa
zd?U*DoC#RL0avsn%wAs;>{)|4K<A?bJ@@Y^Xv@Wrw{iIzXy<BUt_DvN8R4I1Y($q)
zzTaXu>C_vejkE|ic$vvSM8*aa)5VCV`%!~jW%4b_b?8cE+JVU^ZrqX`f1yIAY)Q0I
z>A%hRk5%D>1>H>Io)^SkUTKJ;M2O0&O4Cws#@FQGES2kSPBa5obpLY#QPDq}nmEX1
zQh)0DJc*Lj3cTGa!xHdk>OcKh8x@0S3qx(TID@ayuImWHjpBy=*Kyf#gFLmd7d)DQ
zIiZg@<Zw!TOE<Dx#ZSB<7m}r4QP+f(?lU>+$mr#2rWrQ)J*911)aUx1*Kg-=@9&tA
z$2hCCLiAvrN9$%krb{^<7Sj3F@Y57ia$l)g;e7R1K_UkX<K?P`E;LJk8ZdK=(wraj
z`6r-8Y)sr7P)1Hi-`B85L(SIR0BoQPRsR}5mSs3+fV{thR23jSxJ^kl7D8si0p%1H
zcNU18z#~I<re8)o9TF>VvW{I#@bofw^Lk5@dv7Thb!nL+e&(J{NT`|<5<|LGCEnRL
zgVwfnEN5<)MpQ;+Ux&Dgq<F=No1{LQZISW6oi-Qo7YE3=H=iGI{5);}>MSxp7fsWI
z@xDgIUZ=c!TbM|u)W6+37m+gs5fTQbmn4SQKZ!=o!eNJ@^O2a__1ue|O|fAgSFn40
zzW-c@a;1)%>aVZDuQ>Qi+HoeFuQ_RrpJAi__qKWxr?V-D1!N?c!>L%nqI(GY6weCu
z1HRf6Ogc>vD|-A=yx*Em-PVF3Ncfmvx@mo10s8Sp56H;RoF$QqV`Iv@b4bGwOMS})
zd6HUb9T0OSJlG73%{DqQ!A%!htEV(dUdD**6{;G>>ewxA4+G0U*Cya&1{inPHON)t
z0gDEtleZv+RFZ2jnFCAaHM72<mh9g2SY?~w#d$C1GF6QdaeJsW+x4TBMr3WJQU{Vk
zpL0hZPZLY{`FmrQV)NPihyG+IW^>DrNK^0P1uaU9PMh98EN8`yH2*gY{NumSejY@O
zt@55=j|uJQ@}EV{y$IP{OXlS+Rk}=OavYdTZj5-=r~=l1;>V>c;Z`5id8hZwP87#R
zcQTz;joCEu+ULwCNa*+)3~HStNZ6xQaFP%m5i%L$K-WB7n3G2|5Lx7}ouF9(-;WC_
zX4{kKkUSL)hC^!IEmMWLW5E^@a!n$S$3#5TZ1p0Kvl+o22!&)9kCE~E>RKs1*o+67
zdJLxmDA_z1<x9!0bcC<D1vWvn4w%#tCW7~en3rg|u=Oi#7vSXh&lJmCMfDvxq3_@P
zm55qkt{a*cg_SIkx1G)xLZXa1Kw~?OX%aOZCFEvT(?6utyF3Xz933~;)}=Kl;36M$
z^7ZY2>>@lm;M;9_HSu7F9_0y02hr>4d*-ig@ZqL9Ma98ChfXWWk6-6t1c+#8Agbt#
zujL>cxV4W-DmO6wUM_IqNDTW+3;$=({oC3n{y(G8FWXA@>R%*y4H8()DgYzRhH_@d
z7eV(#<94E((U>pmj!UkrNYYKX!uS*<C-EYhuak7Fjcj(wLdc`%Ys}r974OvloZ=R)
zSa)wPsG7EY(0y1;%;0b1QC5wsJK!P-gM3g2XNdf|zzi#mBr+Yd6bX-EghG@un*|%Y
zzAm;x&abVxzCS$S=!fOp6pdQCvTfzm($BdLoNUF^BdBb%ENs=}u$C1uIQvUQ@RFKb
zr1usHC-K!Hyh@W!rwzY;JYUWi4V3jN`pBv$3b-&`oBdi*dTLxvITp2yw-ckzf&<g`
zxD(%4U?cT<k{Oi<AoJqT_Js4-%845a=PBfK=o{rW1%foYV1Aht;vlj<*N<OkkD{gj
zjZWkWWOal0#kL8^5!@J<)Nd^A6-HxdNr86n080GC2}j9gkO+>#P^RK*?9bBj#E;{k
z+3s{t1gFp%{~&&l<+7IwE10Kjc4<Rj#_9Y?{}##@NxypB)4bfO8lY#hjN;yw^2L0R
zN{4=90XN_|sAn!6j4kY@eI3}qj_q90<?M->K~+#6to=Pa&I;1wP;bLv!%JH2bYS))
zEl-j(6^o;)ih;tND^%MJl_D+QFn+q$`;yD8uuSql;pacew!0<trwrv=qYVRpb{+aE
z@vee4VIV67sL@QMA>0{4l;Im@POEF%r^C;F<<MEdI7@#S9?&cyP&@CN_&#{r#|FH;
zy6f)G`fiz}d-m%efAVwu>bJF}w`M<xw{p1kCm>b8m)~SEJL+@fdF)a?Pl}W*x?ja9
zFYoIugyzF9wV3;(g%-G#25Y~cCj54;1y0Q}Gt6(;Ng+-5M!l4O060`hM{v!JMNrlE
zH*_>iCBMK3F2)D)chTwOfWs%Fjf*aMiG8vV@^~hZ(1m?fZ<x657zCq(7!Z!Ju@u>n
zMY&~F3!+X|)#FWw(>_(?YjXqOuFm(QVLHxL<C5qLNfDstK@}l^s&Ejb>vIYSDEMb5
z(TE(N#lNFgm7x^{<I7vUgcGqiLpGKdu@IbG2Dc6l%5aqWHEE!lJ@LJ9Y6r2(68ct}
zM;QhjV>Y$>v(eOyjSqJwAT|XxJ=i&p7~OZwc<mP1&|Ii%Pz_dMU}}x}(f1DaIUc!)
zy*&kV8D!c`z--!m#>~sUd^{X9skiAP*dwFjB!emQ8K?2MnnktQ;DkhYI8)OwCg=y9
z`#j1P78{#;4OVghf;}&q%d9+gFb){BL#UKZd&OoHYgp4@AGe|grwX)IThWW|tD(Ne
z_lX{xlRAKIt+mqKj%$I0F|9=9)ET2`w<74B9!12WAbwLmQIxV?-HOkWx%R;44Os(w
z=tNPdIWt9_`8haXnwUV3!DY!GJV=JnZTMJ6x%7mRnQ$LhcyJ>EX}$pzHR_6}^kU3(
znap8D-)5tmB)|YQ9y$&?Tie&f{O&>Gxr;#=wi@m=67m`4NK34%{4Q~vaICmWH8Z4+
z&}@9oj<=>ipgFN*hf129C50;vByEV%&KFE$Rb3^0@Ew}0{0~E?*@y}oRs4PuSCxO+
z_tH@r%Xp3<<GFmL5wPTF4oeWa^VS*S%Sy_M!D}l(v-j=<AeOEFzYg!``^C?Ev~fGT
z$#i9Wr5`7KpkAe@zJj-62|F4s1AfQ7E)1~rj=4eyt9E-4DBapvrFido)o91d=loR)
zuZlPGwe5MQ3NnwQ>AzCBF4ECV5zOArUV^;AXGbTM%aI(N0T8*gr0(IXF|pXIbXN7v
zFLKQ4_ITM3;Oi2z)f+b$8=oAeN(VY!q?{^-F127^seQFj1$#=x$OLJ%j%ra>n*PSZ
z=dll|Zz}IjYDi8m-7|xFzfKvWWhzf8E_5x+{A)<nX{G(P{t|bsi3p{iv@u9~M#-C^
zdij2y%6%!tra$q|J82$u@ss$C-ROzaUi|sutyhDGMU@r9sI~`f9DcIg&_Y~bGJ7^v
zP8ArBV>lxRJK83~-9TUCIU0`AI0I2YFNuNOtva(gJ;iMWnFjUE2SkryY<8S9$RyMi
zR4JN84LaIxt*JoYt7yiOi$|jBlI912|3&cli#;VVdiC4O1~fQcRM=rHF2$tyOikR(
zdjOMqy8%mQ&NFf_U`CooCEEKVufTXPJ2qAIVkNn9&3`ca1ruCYAzS96<2{zR5>JnH
z$Mw%2&>=uA?AI6psH|zohxhMsJRg`Hg%R6cywJ(LZCf)%4DG>yh)6C1^9Sv`2CzmQ
zX?=@jxbu!`VS5YOOaqRo*uvBp!IrAJxDFTST^(JB6rYD&N*@kdqzL^H*+3LHg1~Nc
zHd`hM$`3oliP51LK(-=Tp;*R4u;<|wCEbYlz3xRGD_wkHLy~=+^dn}$$QMcLYW;`a
z;dz{6n2iu+A@<>8C4PRmDRAdN%R|4l#o$b5m_OoQZ!VV))?p9m?=2UQy}_Pt4zD>i
z|EyJcljN`5Mkw_;qi_vgZ)G>x(Ey$=Xg5+}zf9uPvk&V#5x2g&ETg^vW%jV4EBhZE
zhw;}q^%3bx%Tho~W%(=!{&&H&)g!p&CWTI+!nJ*T-V-J)r#Gz}1PTrR$|X(60?7<T
z*#-A1p-wzO5ia$kua;CqgkmcrC<93Zq*bjA5w;MbVaS)`;bw0y6CUcAOTG1t(WGL{
z4@4Si)Cda~=E>m$gFLF4@r$Z>>AwTKEdJNJ<;c(BcosSJ^{lc|+D3ilH?LOLKUt?i
zt?*WG<@p?Tv0^3$m!WdvGx6#u!O6bGFV5JPWny}w^`GK!#ZRXN!o5h&lTm=aHOZd|
z+zGNLlRJL&XnVq!iWk}e6Vw|kZ+|Q+^)bcwOxnnm9M;`Jm<JLWOXJ;1?ht8-^u&4h
z$|IobTbc3GBRJF1P!)9t53&~$Et0?Wy(T!<_G)DRp<aF20||3cr=v7Tn~4&ox{1r2
z;h#U)bw?bFfQhwb+^M}QZ6+Ox)bp@fB*!9ixsY^dA*VVYF~;g=tWWoH{7{d2g5kJE
zu1y%?KEozFfRkV?*K>FI9#CCO*PV*=HZo*Hwa|TUw%Xt=(%BXatU^DlSv37_WApX>
z<=$2OarMQ{rBRoL4bPJ<e8EMd%NzmU;{+5ru^qm_b2!ZCm9P1&xVC{#_)JY2ObQxC
z^6`BbqFWJFOgygrOwe}E?=}?xVrarFxv6ASl{t8~u5Yu6QydMN8?)vQva#!dO>SEG
z_-!DJq1UtTZAd*HCZ&%L+g-=1$jMFeE3KzVFLqao!!F9P_Zx0_Jc(Y^su<UW87CU^
zfU_jh1>ogQzBQt+g~kcrZwkUWphZ;o2-ZZMSS^2R3i(V!zjlf-ln46jS)pt2OYDn2
z^tu1(xTAuX6UDC8>~33P&ptQFtJvDmtJvPzI;0jYu*j=94Y>X-%73FjDMgjafuo|p
zss&Et=Aid>$<U5@{w7ULx;<mYrol6uy@<rdf6#!UvS}+w&Br(-V1aPHFB36dbAtfc
zQjWiW&VXue4^3+OY#W-xmF06&T{AZhBC4Q9q9B8l*(@%xjsbgJT|xZ+ulnKy1&Pt-
zg9JW^8j?XB+*bzJjptS!4ciBmlw)bQql-`!8uF0!H4*SfgAH^pwCfpx$PF;vzQ-j>
zJoS>{u+$p8@xOR1(}QBezc##4?H>|WX0`C%T|pg9<u7At7PHfUO)!AX%qYdB{qZc+
zc5r<V)KgGsVT-Cu%}fl;#Erw_OZwbtv3;of@j^|;g=Jof#`i}q32+l15dota6g1zi
z4B99bNU#%Viq<DjHv$U}<yWHiHjjslIoV8@6DWa3RcI_NH&OnSLY>=^tM>Htr2p9l
zUJ)nnQQ`hTioHpVO9N$)cO4;KjNZe++VMyD=4PNq4TWcaMw3z)cv+!jC2`;(wRch~
z!NM<spo_dQufudJ-dA(L%53yImiIY(V+s*UTapxC{M!lAfsjKR{$l3i_u$qRmC)~e
z6x;~rWTe(s;MiEWBV{2F!1AQ0MvlGPE@9y_sRn&D@)v($z1e^pt;P;s`;JhDy}^n#
zfEC4%R`Zct#WxogQQSu-k-jPiWhEwJ$ED50FKxdO!tUVDxo_e0I;rL2>U&2XhMq-m
z3`lADpiV_7FkX=|VqV(ZvNtZf^rHgNQ<*H&I^HzBxuvXKbJH!%IyT)6XzL!jk;Z2=
zBmgbACSw3qzh-`CGw#R7=H1_MpeNQwB=+S9_|bo=_~I(c1D(aqb9}tHbIOXpO0T*G
z?RJW93c0{<_VQv9E$4V!G2=J4<*#{wmMLhA62<ynB3ex|rH44)hofQJpY5GM$LOa_
z13iKdoX_GhLo&xlx4CYm++xxr6H&zV#$<^o$l|xnC3HE#JJke0tEF=U7e*$xk(;t6
zX^|Jb0Q)JZJzQ6Wl4pxsYwa|dtMEaVi<WsTExCzeoMfulJDERx?U-BQIE#rv3{_Xv
zJNJilDS2Dtz+AtDn;}}G1X%J5H!RJ6<8XuRAfUH~h))GXztVMCW^b&xa7U&?M20v<
z+*1yZicO5P?5#Pahq#2<ep!2lUqGxGh^-K)At-W!7X|qaqAnRuv(vgZuXvMBTxB_M
zn7K(^ct;S?v@Hr@t~1P{c`9k;^@4siP=|`%J0^)o;6VfS=$_~LfEd^SB?dDPZ;7@O
zl}l*rlDhWTsg}~u<-4N-`X8F@pIZEo6676MdCdC`{xm9FTn%|~W5-=bRBB?Zf8o+l
zy_N>K+(mqwkgv-6={7qKN?S1&*<mHlC|Z$USZ`DuvP75$&Zgt)*bv3;E%rQ`Z@x)5
zcbJgzY?R?@|1d|D6CXu#{KNZ!FBq0UQQ!N!kCkQ@{KJM14K&R#;->qBcc$f|ZAUnN
zjV*yqmp$=sW)@(QF8RpjlNoeXmfQD^>&3+5H;zqCxW)AQ>h?<K4EEHkRc$b^Z>k$V
z$~PH!@4(nAw6yj+Z<1-hFP`(2tre?MgMoeBxA%V6g%H;KxL9p@+^W-n%r%ht6cw4R
zmvL!=aeP-V*8>B8CT8qL;NLh;CyA?*<PADI4#VGw?w{@S4IS$LHt>#CO;)zGv+n0(
zcHvEZVU9IY89G8OY{bqN5`<8qJnKvco9b;&IBzp@HrL&+LLs3J1C-2HNN?D>7QDT!
z&dHM#%Y*xVNTKJ8{u0hk(?|xooQQDGT8l7TA5>eZ5vQ7h0o3GVhva*;xOa*ir5ng~
zzv9fE^dY|q2YopCY@MyC_^9Q;o2uInzls#1ai7qW+UvZ%n>tDxC~+J2#UPvTOn|<)
z3{=nRhDd|r&GZfJoJdY?BNm+oJ#IF<Fkau#*%c{T1ogSPnnehDJ-;iXsV?4y^!aq%
zo(v-4C3IKFEwz0>Vh1#%TYVNFbcGULb{iDj9`hT%hi<$tedEs7`72$?kf~;|YNy+N
zO4#Dbr|S_tByo1dMvX5&iH6oYzfMp@&nTx=rIvq?9?R@-k-}~mUI-s##^jZ3hXp;f
zavf9tmH>DH-XXo{eWAHBOmf{ei8qQPvt;+Tg{7GYXt+7a7$=-(gp7dWjL-EyH&`&`
zY<#9Nw9R9b0+C0&QVz%pD0ex-fIm{*EHUDK&@Tv+)Nl@@yXHX)hlw-te%pjO+?v2P
z&uTMiMc2|2fz_7qAgPDorN^%(if!>?cRHr}VV(=Z410<1&1T%cUT>;%|GR%G;u=%J
z2>~CL;$bBS=@(hOtWT2z%U9WV^^CQz;iQkhlRPnJpD{+NGq39xaFNXHmHINOVw0&l
zJMoVe=rKt`oP?{-t*0ub9bJ6nG<+l@GYjW<`v!~^Y#73BMCx5wPD<CrrIAJ++&!C#
z>Wgd>dgbwA|95t?b3wecvmYRfRMSz0-U>X;t*T_I7sv5>{K*2}N3Oqp@Lj!im+>B7
zR?vfdT}@7o)XroWkjRcyQqWX@L$1Amz#xNP+CNZLir*khG?j>0qvUYq*LH5kdfm!B
z(@K=emRO(MYCQ9%E6-mQ=d`FTP)jsk8rzgt*no<Tf)}Ml4hYFH`2g5nw+a``A$%gx
zEq~$MQ0_V85VslUf+KM0o?!gW{4Yec;R(vjtdd3qwg_~c&JvEk$gA&WqLNqFU@j^@
zHTtcawUHT4R*KC=+_WTHa}%@utY)q8)^U>R>5a6anc?f#A*VwlGMI`T`6Mm5$O))#
zWvq6C-#jH1Ff?M-1z)ejCAC+Fk7Y)lERv@G(DbQ<RCTf?EGU`4xPq=E?DNyRKfzm^
zZ99q0XkI(tGUZj4;F}+lR;4k7fISN&?Vu=^W)p|w-;$DV^K73W!I4+)tM7TVTbv>W
z%BKx~uP0*S(rHue-^zE0w_FyR$ZwPaNxB)ewXXoqmXky~SP@E|M+6s>DB3sQ_;DOR
zjx;Y(NCGp+qkCu=HfE>tRo<}?8J}MMI6g9}ziSWK>p(H1kBP^k16ROuXefy9fC!S*
zvnXk=KMr0ArcSF4^=wu_&q>HhOrM-uikidr?!q9A#=LPh)OaCdgqnIV=b*T>e1fHI
zK#~xC(bIylXR+ten21o11ZW`?u(HLEa@$q%)zad?po%LjcS%4W`Vv*hvPL#84+S;g
zKsCK{D4ndLEaUi!ZY;Q?y6A=K_>95&DV*RyciBIzkoCA$1{e=&ax0~D%Lr?2c2a12
z+%KN|#^wGpy1|!@%Du)L`sXqsN<p_lR1j&gLG%Q7Ro<pb#qxf)G{w|xV@^}Xa>Fd$
zl&w<6OF~t5rVf-IXBH>mrHSMUHy2OHMGhZcoLtes5U24hQb^kX-Qjm#Tw-F_81A?1
zLjd<#46Oox5JQHK_sUk&1wld5W}5ExA*M_j*Z*6we+}y9itIk=P;@)m-DIjT)zWsh
z$KbWw7v%Nbh0K%~p9wt$WeN+Gk`ecC>0~bj9v+8(wu>;A+wiypS$RTYWY}bh?CufG
zXs<OTfG;*(XRPwT;QDqZ2}|)eVSvCxpW({n0AV#wK)p--nc0s-;%Yp-U$&nPo+}5L
zdL;Dr7{D<{HMzW@A)!lmz`ARMgO$NOR^XET$yTY+^HHN*AG;4dR_{!&H@;7!kCe@|
z50k;Op=2?~X+$%+Eb(x=p#PTAbuThBWZO(%bYNQ@sYXAKfZki-^+?Mze!_bn*mv?k
zmi8nK8*^E$(R+yzdt7Cw6_J{kqAom9RJ|WoKZ;UtQ4xK7&U9!+$*(0kR@X*8ce{k^
z!<C-0Ly`wxscA>L0z8|Az&yXN%+1{-Gu~E2mv!bN@9;T*Mcd|*x{6^-ArOe2AIREu
z88I67^7MKi8P`;3M1q`OU=swu#xHg*ZbCfx7-FgrLOcO|7l|=ZW7}Qi`221+H}W}d
zNt^F6WlPU(ZbJdEt9fXQ=!FG|g{pCwpwkKi)B}_8ax46+zL(W5P^$$1oTGO0!kH8m
zNMP>y<*73oUuRn$CkqnRXqU+Jv`*xg?rw<o*Re>~>Kk*Y3rqZ<jdh%$Nb{_YN7JY|
z_9=<NzMuiIT?!%b`5L7#AfGLKn8(0KaZ>{@b(-;UX`Y#Om`YQ5?5hQ?mWy1O#%Du;
ztvhqHyx-xI(IIhre#$YEG}wnLUZj;8GA{W3La5ErDKMbBf|HjwUO$`_dIaY6_6A*{
z?Tg8j`4|=@T{5p-YQjaqB%gVtVx0?~-ZXgJSKoe`e{@!Jp@92*XX8lUo4->052yKe
zWAZN)lxYg-1O+Fe3Ha)x(}~wbzZ_mJrW^QFy%voqWjb;=6TzKnDqfggj`-+_NJ&y%
z^7fOcH+Zo{3BugER-v^aZsyP13l1p)K!pW6qTNPjW@TDV6u=YvIqvCekf1dZCpJp8
zyB5dILokkUa#ognPDG`(MpR3Vo5-r9ng_l^)e^99&&6<l+s3KqHtwsY%#o5*kn6n#
zC)xHea|7n$j6|||T-cQ_rFnb90Oso9>PMbQ?$5w97yM=PFmh_9IUJf6mfM(sF$^2w
zAD|N^BSX2F55s$>+P>ezNj*X~@3t~RIg!=GG!Y19V3aq!uOq3yML*BG;vsF_xjw$+
zOc~rly49P*1(ZN|Rb$6Qnr~XR+Yh|DGrFEa2KchLMPnfVYt2$)f=P1lXX!ng2uFJ(
z9JV?BSROgsF{j7YJ*z48vR~`6V_LNg0j}_%qpb?GA4HlTI^b>i=tIanU<_4l{pp?{
z_gii-HAe6!0lTI92)0UR{QJ5sD#PN+V!U4lrZz6k1Tc%c?@==;h0mh}V!Hx1wn@k7
z@;e1*R{HfsN_A--wO=Tqjs(?FVu-x&e~d)W)}*iAu?Ajt9-w1pfSU^%(`kYA^_!Er
zODZ7pio?=?GP+EFgo~(8-Gzm)Ds@=}XmY~&1$U9j(I{1NW<U<X5c4wQ$Glo{J6zwW
zC&35R6CQtd%g&pPG3jyE1CoV3L_{g{N9IJ;?-zXn#ru>&v>|PR0}6^FQg{gN+lBVC
z>2mgpD>IF<g51@rHp)YRBU``zI`Pj`N=ri6OAiIEI2K|L<*80=Xt+JQ+HG1VU~Cp{
ztO4}M+7JG<^B){;myCGn>>A{zNHM&JL4m7``CWd1mpb@R14J8bwGeS?2*_hQ_;Dfu
zu-&Ku+|=7x%{(_sE-vyEJIhiO=k47s6ISA#sq8g?LfuvV<;+li1+`F^99J5Jg(f}g
z<6~3N*_RJ@Go*jC0>Ky?8wfzsp9lkMtKQz)S!bpbwsCZ@eEPea%Zc4zTuJR^*e&V%
zqSP{2MsXIyg$gDuo;@#1y&(M;Q+m_QnHqyw?_J3aUlGV4YEOT?d!5Ob6d4<=R@qIs
zF~PHaEO&I^i94UX$T;koA8TmJ06QS^xjJjEZjYv$<RN6<j<7ZY=u4oLq@%(GcN()K
zH0!=LzJfS;z6y<}_Bc)n?%Avc#|MOLB_>7PYOqY7zK3iXIRsx6w#8m|J?M?d1PAHc
zO0(0>Lt4g6FUDnd#^X=$4{2PXzy^^fh#q7FqsZ01#Gzxrr`*47t$3qqb1S0f<ix+i
zswWgEW}-*rSCA%ZayAyCwnPumaTmahUK_pw5i!$){zx!uCX!pZYYeS7Kt8>we#N??
zq|RUC>nO^4oD|6(FLkz+O57Z82h@<`eqR}rZ`S3G;*i>|VCi<=bMyH5Fv~~_DG<7b
zHhNS_H$d|3k!dX|A>vJQlnXg3aa_w>&p`BYTjJ40>DvmMj$3$h6;zg^ofe4kb+)@X
z_l_KCZxmjNPsi%+A5Ss;zNxCj2kM?3G|$VMi;P-Jaa4z+7QOttv}v=zk~YaY^0@EF
z%&m=*fG4^3+C}T=?t&-uaqOB#_j#XxCt3voFssw-K{O2Fuq+yv+E~*BH{~M6^l?fm
z>K=sx2qi1gB`pIX#!{fF2a5LcvXbO~gaU^+AUkkMimg{UX->SGcFJcekerCWXx!-v
z?>d^+r9kfn?&|rq3LADgfzp7kkA;lK>l=Bz{>&s>Q9urwx=J3jA9+ak#+x{!nV@Uv
zbCJxrl$RwGIMrkm>A+=D^B)hFNYdm6x76^aij)){FK^x{-XQ|Naj&Pd5~MX#I9#UW
z5{PX+OQkJ~8c*_thAJgwJ_Kin-q2X(K0R_ivYeIFVC`hMc)2^-BG#=g-lfns9?!X^
z(7O0)Ejk0UoTTu{Y2p4M;*%q&_B+2m%Br^~^n4u`gqYC03fKGt=feZB_uYK@61-B0
zypLM94%L`7>7)ddi=v@lyPSylqYe^36Y9@rO3^ysPQl033$Nc#Tz83L@lpf_aR`lH
z=nK!6;66G#*r3i#5^Vdznl^)!ZQyHc1%4;f`adM8{n-K{Y-XH)U#X{J+&hd#Y$US1
z%b}bY^_y(Ykd)B{De#5-o%{ir@(8JJ9;l$8M{=kQV);nEhrjgY@Ic?rW)^26Z~d5W
z{p-O3hKjFXJ0QF+A<oy0gf<bh310w`0(p&nU<-Xl6Mt$V@f*WDPp@&eTXO>M;+3oZ
zslL(JJ91GXZ_|wN>Zws+>7<@Vly2p`N@jL&WR5SX1^^7Dn&VKvSJKRNwlQhyiq!;t
zzxQcu(bK)hONqPvS*(mMyqQ0y$}e*bg!k?ur`Fk&RSfRD7-^w5#O1q(34Q0I2bND-
z6O^KO{E<qfA;sVq1lL36_*VFf(C|>Z4GHcaii%-N4HW1B4Cm_hZO=vjPO@;qKi`PP
zO`j)USqpiR^3fvA#A&(-U1I$uBo1o>vd(`58V#h6<QnTs4+LHtK$tbK!LZkH<ST@d
zB+~|RrcEvA1!K@X|M-svru)fX&_)Q!0lLD99s-|PvfF;_=>*TAQ?F`e`4;gPo%@@7
z{K6TJi+@bDZPXUVtf1rJz4iRLvKYY8;3XYuQLQb<@Vhl{W&c!{t8QCLB#Exd=9RsD
zD34D?nd}O;C_oJ+YUN*$KxFI0{ySmLGbz!5k<G*DL!6Y*`8VGIm|kspL*e`V0oEo}
z!YUX_QGOa~geQ;qWtG^-hJnDD6aL^H$MEK4*I7?xlUTv1#R<jD4LtkJEEnSPg~9ng
zmqZ}zr|1U;-MV5iE@>q9XY&bXm-Beiccc~Kns5gxBqTI(2ky>r>wA@ZhrqdoJe>?Z
znUCwubR>xethrz@q1^e@ZO%>?5~=tEq^Fze%abK|F$9FvbcjEP!9XgkcTgw;90zo|
z{6ohtYY?!3j=;vtjwc_Z*V`hU;*9J?xzz<?J|0lOu5Hk#4B}Dt9mAoCR&2PD%&zeu
zKbCvl1tep;5XE39agf-v7x!qrJ@?@<U}F~Hn;OFrEbMT1%Y1x~7wTbgLcN@*B!K*O
zCv4o8J$_0RDn)@h4;ix{ee*e%{u0**REFq`RmQ=!Lg;)(V=UzpeBiVq<a#|;w##Ad
zrhm)(ycCtkN`}^|q-oNGYJR0W>DQN39_P%MY5i>lAdPU={Zs5uD-}k*Nz-IPT}Fy+
zKaK5c^dkz^qy9+%rl$r$=a=7C=ZWBt&gbZT6otK3<Qn6g^YHO2SL>J|W)0|uPbYlg
z%{H)%Dp>yti$5VH7TrAo-#k+J><SAJM<af38@UX)Xg;OU{zMo)b`etxjBAG{5A%*2
zmhBxM`uY>|3ww#NZYLW=3-0ay^@F{bUntd(FiZi9rlYUG$o!96J)Q@B-8LVX{20|t
z#J#*c-%Y^LO3Pb|@0UBPJXog>^?H!2E6I|isd6<n*we+A`a6rDf3W~&)R3p)+<uvv
zUIVQ84v;w^))_BsN#B(R^AI1kkT_BP<-%%_UC;zR_d(>&p5zLhFDKINcPAezZ5d(!
zf%jjH9pu-o?AZ&#n!fMju9!?7G7WcJ?0ZJ0EIto#OHHOthV$l|;qClz$PL1u!JQD~
z*hwFNZYuP18Onz~GHB#+8bFS>hB>R)!@5Ww_HeYBK0=;kECLwpYtL-_6}wk56my7S
z|NDEWrD9|OgyAx&OHha9qoU|C+v!`AwdQXF7I<9liDxI+Me~-eFiW0GhqE8vZ=p?2
z{UXmbYoY}@{G^p_t~OB^*`1X;Rr|h@SFL5hnhTRna^s9&Mq542*VI|wV3~Yt)B6iJ
zHNb$$t;AG+5`jsv!<UO0>ibS3L_<vi@$C^6N}|ogryJNK9i8o&4u~<l)}F)0kfVlI
zw2t2_XH3-y7vC+eS1((=khNRyLI$_Ww`ZIQubUV`&bq}?DGBI_&?5r94vb1OEp_DB
zoo*Mz9B8E;j}n2BZOD%$fs=^tV3L{l28zC0zKS^PH)%#iyv^CQRqM~X-xlRSoBiS%
z9GNkdJ>jn#tI^K2BFxWZLWpNYze~z&3M*x1FN>-czCH4VlfbW%4PX_l#^ib7B5%9b
z4%%5wc*#C)L^)>AiNo`}P~<_)<%R-pwn))udu<jsC~wcP!d-Q{kTkf!-41u3Qn^e~
zY}aU0btu8l^&hI}foiNf_QT_6eG~o9<KnSNN#bePJnl20D!=moC5Gw-4-)jKMI)dP
zEy&5>2-o4g@)W|BS|*|%+3n-w(NLALEaJwAEpFNt^dbL-wa%n@0opqp-OfTVrXy)&
zIs>zs1=d6g<Efxg&o6h_Y6kx;VfhN0{P=8mqLXB^1^)WtCRuMjeH^zf8@rDaDPoYI
zwPP4%QQ(7{00Kd@mc;91so&xz8K+vc@2<sXyX^iiHQSw-pwU*RX8qG#A=#$w_qL$<
zHr^yfBD*{i1w41}MhW1mZKm5m>Vld!SLZ@uLuOaod7Caz6dX}C+sre|071p47P&sz
zfR~r=s+XZ_A-z5OljCKDQP^N*%LHkVB(f7CBy1nL(VDq(#^rmjfSp$O0a;fCW|O#m
z&jdu;D+?hoBpxj(3PBo|w0gOjmFa9*PXKG;S+>TQ7)^hS<65c9QiuEYm@u^Mj|nr+
zmUJRL-4m63w;!JnD;d3sdl=kuJT;I22zRFKnp5zn-#{C6L4A?;;c=uFGoXYKHDRW1
z6`crbN`b)JYE^3v-lHBG9v?jS7H<yOU6~=OP(joTVi#xHPb3@WRk*-EyXwwb?B3ke
z(sF$!z|#z5{cd@{qoVrM;lzoa61r<owSqhQ=Mr=6sT(KteSS1?^MSUjK+^^m%#8tq
zxwxS&gD=_pCc=ZrybqDIS~CJ{pzZ#A34c6f6^-JRLOUWb5%uW)e0E2##QTBn^}GXv
zUZ?f5361i4?v9Mr<pr}?xJz0cp(i6NdJ;LgweVu#28)AWs0TCRxcPbeEhTSj3LQ0&
z#l=O5Hj;ZIz-wxWvzoln>8V9@kZ8nFBGiIJfY@AD<k%*woFlTb?N8k*St4dhJx7jW
z5$tkmmm{Bp>c>|dG3e@XrEeBaJFnD+nlac2lhbPE*$YKKV5G9%`NCb49JF@~a5A?Y
zU##tHkn&YDGIL^vE{Y3MiQFPE-)k4o67ourf=J^KVpcs7^SnLGy5@7jA%9Vri}Fqk
z>w554FknqVy}c|ko|Vi$UWatq?TBD&>qOnw@OuV2V_~)zV0GdTMi=QFZWLfL^;M1Q
zHtNv*TWl;?iL@l6k-msbGMyE8$9X~c8k_Lb30_2+Ht2M#2B%LbI!rahIwF(ti_OvA
z^rtS`Dm5buj3uW(V9m=e6|fAt=3P4wA67sa{8_CDODKiZ1fa^uTd8?yuR;WKyHrf%
zkOp)hOTGO#(!RzsBBYn)<I0f3_jKP$2jwcjzGc5tu8x-f^jMObT?yP|D=3IPjX}u(
zlks-@V^KVMGnu(RG+@}_KLgs|uV0Qad7UsPzL{qUABz)Jvam2)SXGH>`tmnScymRy
zAAlWwjvXypxyH+0tnBTXDxWOn3^x6wwOECRfET6{J$MNeS<^Sq2f*t788#q|TP)Xz
z;$H=5cThn0bS5#bsotR?fV9#)S4Y!g?ua|@G;l6i9@?;h{01dkbS8R|A9NU%C@Sme
zN{>@Q0C{Pr=uA51EcsjvtcBBj&m}ZNh@=6}Oezgdj?__mi7tbU`wQI~jxFxQcVrxI
zPv&%cQ*qPwwZ)P@<0{~p8ITgE_i}i`S1*xGa+bM8Q{jgnH{d?m9q}n`S!Sq)zaId;
zkEBrEn#t5^M{bpJ?IxrEuY*SFrqPjIig<fq$49@=KudCK<2mGDfP81j1-~znjZKL`
zjhfca6$MMM9e5GF!D<-B^Zr4--1Nf22@W|y6C>7eEE_V5foeNTB-lw8>+Qa$>h894
zLE?G0Tk(7aVZ-3t>aOCZyED79b%^4v<jbxLb0fM(@ZzjQ+>%HRQ)vD5I`H9b&#eVl
z$hDW4z&~AFyqLZ#-ree`C4Ee)clxLUqWP>*zu&lyZv*K`zv_H$>?wVh&DW?N4hD{$
zPrZP3BO*dSOv$C=HgAmEj1j(IoNh+9%Z@6b;*`3Z!kE)tRY<RQP-*#~XnWqzM^_jA
zyxwA=^z3x}WSNn#*To*!99Y&ovWfF!uA6W24G6c9xn)}6vnz!g+OH|X%MyTH*?u(d
z7g%7<X3C8Kx+NsDc8*x#+X^B=gqNan7sHXcB2KqU&P;RX!^@iFjp+7=eu45I9y}Cu
z^M!T9@euoTi}mjS`zZf^W&wQlgwizi^R3@}rNM(B;+&ZKQ#&1DdmP^J3I8okHPI?Q
zaXYIqSRcvrOTFPEJ<V4i(sW%|i;&8o<)xy!Yb=?b^vP|(qiNm4;Elxjt73{fA!+&z
z`N*D?S6I?y)a}ZjE}TlKA*M<NSGXOG)tAsc4DOOk=JfsS11<E%HK-JwEU%O;^N~#2
znB(AIJqixo@dlcgBe7Q!iBt#wVdf68Kq^7JrW!?AJ{g#yE$|o}Zzz8+zKOa5g>e<g
zOixHickd6prtN8poSs7hUs#ykb2byHg2U0}PleH0JMbN%zCV2kINY=moSnJlREV(Q
z1EagpGy7au-Bp>y+gz}Ay_pRFG>}&ZX)`c*Yj)x`{Po`2n2_~%tOi%jx!>?AvbGlT
z?<_=`#x_URY>bWB99DbASbtUh9y4`6*^+UbGCos)aTd<K<|*OFznNE2Z#g}==b80j
z!9(A0Khxiv1$Ex&fy2(j9af)r<$wHU;&lV<pYf7Y8v6Upl)E#T=xZwb9hpq$jX-F$
z<kZQt<(Uoddv(m!Q^;iZQ{kbdhMz^=2IQ$$13uIXyy5DpfZbK)qUC<5ky|yyd0#RI
zzo0(gE4<GYoCp60sy0aapRM!gT!;(tp2LrTS;hEOE_aEKdz>Y5nV*B9ic@W#-4Df{
zapDN4r(}-$1~MlgVV*jPF+EW<v0a9EV|bnl2HHD@u7wybzwYgUqn8Y)zDcNiGeVRI
zMXE4T5t4wL<zEGUfhdxe6c`-ztHaKNUopsXau4t2_Ab^zw04-Nr<CCmu6MspjAJ)j
zoz6gH8&dQf5&SZyl9ScvwTWLygun=MAgb)JZAZdS6m~7#y}=;1zvD(_@$H@xg2dxn
ztA*kkE?s)vwH+cc&q+v*CZybHh0RQW`6Npshq!LF@H>$d!`wa+1Zti3@u#k^&m;Be
zRLMo_cPJ0U_wGbg=lc7>FzUjxJ;XFwfZHRR*{pGnCE|@?z+1VwvqRY*3hI%kyf6a&
zl=bLLk6uU+wdi~M8Z9~a6N$xW*M-b+bZ=;3CBK~)6^)Dzx(BsnDYL$(gj<Av0(QG-
zl33yRrzaGM+KxUDlAJRJ4%-;7fo-HPI}H9c9yB0RHytFN1pVjutC92y)K?({ARl<-
zJTbrwx3u*t%@=9k^1zggIL2^#F`-}?3)JSpJUZ=(YH7^W6>1a0Ks2@I_pDR>0(u(m
zz$82^ZGS>GQ3;&Yr2W#5n}3VnbHhjwPY=U30i9?u-rN|K-(N51?t0T7k;;3OAzAT3
zDp%`;WLRGU=1GaFgNFE&xs|z0M|9`!pFK3y=*!8kpUFM;+piB+jr(eFfgk)?N-d$#
zUHt{p62Le^`pTNlDgg^=z+!`TL)Yi)PSqnm-aRC7BIQ5}+`GPdbxe)LY<R$S9c`a~
zbM^3SZRT8*(wCO7NGst}#>P1bzvp2=lB~9wy6hkdn?;XGuXo>f2#GaE!=O#V#`(&^
zr_m5b{EimF9=C5(ktm+8apSYwPlP-=@Qrd8N}qRsog&V*)?j(W8avNxOM&*>XOHgn
zao%_9;&5JlycpKq(d(njcel7rS7;3?4z5Z6PkxjWbfy%;h4@etYkaMELBr!K?XN@Q
zUZh$Z7?%m1Nc45?JY(B7gKF1|g(Htex}}$=Ma_0#K#ot?xx4^KLK6op>o-`RTOv~L
z8@!{+FarQWTGy+8J_^N_^BLID`WmLK2jtjU8=>dCFsk*cAaa_vD$yZ#<&)h?9UOQY
zNAK_;LGhG}9Xt@s@J5x<?d8w#Jl?m(vZwO+9uU+L@_b4%d{KMFngNk+haF0pM2M#8
zw${+K3_p>kI!-L``kxR*e{?+W>vTVO`H}-(;+SqI5(^9cK+>j^MSFk;8Cx3B^>W$`
z-)g(Y3raZASh?#(7kO-E3j6m@!$lrjtBZT%<DzPa{ts90z+GqjHjOrR)7VyHn@t*H
zH@0otw%yo`ZS0thjmF+FcAT92AH3&1=M&s(-D~|W%*-_dHRIMxOY!&i_AD<)jehs}
zY<q26=~jyAgT<J!^t}!e&ytrV@mEWNOUupB#g3tw50zGx3*t|Wb!-c?{b)5tAKc|W
zxc8o1%1Yg*o4OH!>Kd}Sk5R*_v%46=L1KWohY9*u@j-)uv?3|_iJ(wsibQg5cx|T0
z93(-0>5(`Z<MkR`9lpX&-|lB`J&J^!oLJL|Gzw16>b1RZ(R?^KIFHXy<oTLkjH4gP
zSXcs*=wdNII;Zh*afWZMAS`ch@1185&4Q*|%}wAT`_f=Gud`)WqPnMYEHEc0ht+Wx
zx@}&xs<g@XMZxA@OZE7VFx+4r_nblN@@~%0Xh2Sr^$BKR5Nmqe@C;cnfl|!4HVot^
z2Hd?;aJhRH2L%(_)+hJ{iuU0RP8!6@By>FexH#oAdO<rnTuD1p$3=sJhprM83^D1I
z!Y0FaXb-6D{ZfRG<;-russT^?uvknnTF?Y$>>*>R|72VQs-Y-zNS~zUlncu$XR$&V
zE~OlaJMajOHmi4kEz<oT@#X?vpq_1TJ$Nr1lnpTb>?g<Y8@Ih;5m7O{p9$&}+?d>3
zjx=6(XQBRrPafS9f3oYv$`l^!h;O!2CmdgEqPpH`q^%`83HR@3{CrOZ(p*vsKJVOI
z#Yq_%V+YIBgO7fkYSpd*FW_x9J4oUMZqYo2pv*g^=B(uE1P?2+Dv^%dbdOz3%m@wK
zp<y4bSlxYb-PK2J&$*A>yLlhzf41@)5C1-?GC5r7DSaG+P&xK-&u6aDmX6%$kgs2S
z`vS@sSQIIDHWy5)w!uAODQo~~^z!z2droiNOmK2)k8V8|k3WqsA2G(6(Exg+EJp^Z
zx03?POrk-`c@)#LD;`rit617p#hGPgf@$@}-nzgchrMXjyd{C^W!sGrv-LV-b?6K4
zGa~d!=0pv<uJ%d=A}vMYSC|c_OKT~Goosy2v*i*Seq!79V){k3_59+yD(Sb4dPb(6
zCqA3!3!JJ)AAIVrCjo&IO~&#eDyUx~wY#Hujpgyam~f?X;>$F|VPg{*UZ|+hLJq=8
zez^$i!}64IvId$!;-wW_4?qUIbJ<kNJveweWPN+d9^PrWR^<Afh-Iznd!ZzUr`hq7
z-GZrEzQvXr=B$BOS2NjQb0>~VHZVP7=|(E0ZL#H5C*|-UpNBhZb3GJ!a=SQjno6a%
z$$<vrU@%r?jDsbvWxW^8q5Ig#%W~cHKo;?ssN06ML7m~K%Z4?(Eibt&9eaMQdW5!y
z!SdYerysy5%hr)Rd~UD6+kmunSE}>yHL{K9#Sr;w<f_wtE?b;vec|DAzjpLRjCMAX
zHtB1%iSAt|`3St%CYKW7?^|CdVhr~E1luL=j+YSs@xxIxi3c2-o?A3^C8H;?eZxu)
zdJIvaq&6ev<;20GEUQjY%)cdyS%^W%ztijFn*6rJ7cfUKIrDcb`I-Wf<t&LwOVRTw
zbY+rfsd9;E5Z%j@gnx=4kBFg>z53oH^5>YoHJEmH4dT`Foe_2B6%r}e_X37EQlP(5
zb}qBQ9tWMDh2>i}Zmr=)s<GqHLAoVW*{M^m<)?g0VPJP?qD}i>72}XrkNf53@k;$b
zX2l;zZ#z3Cz2}FC(b3T=N8mj7>9_GV%p!?69$)2lPP~{G@arvegR%-t4+mlbF4%p`
z*SwvuS%0I&7cN#7d9c9zoL#lPtm*nF6WE&>Oe(IU4)F_0y_*m(npCTMbBhf*9S4p)
zMQ_oF(YHOCS!qT3nUR86_QcTl4@<hE9_1!hDhHh=35e%A?)G6U^7#5sMLHcL<Lf|V
z0)7pW;-Q2TroJ3M#+{#7qrhyZRU^f{ZO#!dmr(IzNzYuUC7r59v!bHFVR@a}|DSrb
z_bIkFy7pC_Kj1lGSyT&nGpwD#|FSFr(>IddXJz5Cnjt-EjR)v{TdU;3k+L4CNANs9
zvr!vthWd8ztM)B{HyVY2Yr8DZPt?SOGL1pA-&6FmQ7N6S$?;o4`#)Z!rF_|ur7~Lg
ziAK57aE@jUzoSEJQhu5mn<=Sm$&d_8NjNgF4iA80vs_dvYPkg`(vBJ3>qnrU3)A(|
z(wLWi(&jF&mwW~Rb?el?^sH-^F}?r(9IU$7-V|P2UDpyZY{*f)ZVXu`tMn*{JR-%*
zH+c|37$<3)Y8XE0=giwx;+{b5lZ#7v=1MF;?qEakh}^Uz0pkG<b>x?3bDP8-borjt
z9fFD3SXaCWbeF~UwiH?4q||W7-256bxygH>YleWMTyP|7bjdfQbQ=@Ss^k&+VJwJA
zAEL#j7wyrL+ERgV?MrWzb!jc6f-b*#q0hUb@HMY?IDGp1(^-fuKd^vZ=Lcu8vV2R*
zt%Wi$2N1MD1Q6zGudis&pE%+7Lq?}4boAN#TJ^fGt@AX8!7?Wzsq|7=6Tr!hI)cL>
zy0r5>X<0viV&I{FbsE*{0>fpQNDB3<1o#z31&=9+3CyD;^agDw7S;>8sh_#+Hb+F6
z-yisgF;Zx-jGc0}4xv4Vfcw<NP%!6EFQD(qoNAVsGDXdMo+8B+J$C(P`+#1#1YPSh
zcae=-)(_)=UiNP`GYSa4RR|}U<+31gGf7veZ}Z>iyEBB%$R3B&zn=(>x*7zJ|Gbdo
z7)8(cuWLQDB;`LemE{hh@VoPT;O%G-d{$|__S|gphK!5nPq5-Q5V-S(m2tjX|HRO=
z*qHr^0dbJW7xVY8fX^Lp=<%jA_0Ejb`1PQh#YzM9?(!eL?HWhz)oxOpf{hN{r+}yb
z)HU3JgDbPxm7Jen?K6oyLX8B5l72%oj->kT`OYSgt6FS(Vk#yPT1LT+4E-^PfeE}+
zEGS3)QnGz`Dkoc|uy!;CH)1q79Q=6jAg=-BMITZdk8}nSc^f6><Va3b3^)2bm$3k6
zK(1v1vl+8R9=eaDf`Z+!JZCv%s|ES_UszdLUk8mgUxjDNmEpP``)}L8Yn9fc`ft=^
z`9wL@rw@F-vS|FyS={!PERKvsa@V;v&bzYq|JGd1DQn<R@FTtwt?gayM9f!`Rz>45
zmvcM5fodCR^Y*q3cD?>}ph!te?-+O#ad{*`B+0}Ei%=T3F~})M*0}h2ObU!2)fH2s
zFWJ*b7#Ry0H_eDhgLoZ%5xOe@vRx9qUOz~_%243m@@Y$1;+?Qb|A9X-@lC=;LaI(9
zL4XgI4J1ZFV&@S#G)NC|*I<oJ!@YcVm&M<uEkx7Hj=wNp1@SR6=NW!SOib4onGh#{
zTQ_TK``z59V+=5{5_uK#H)-e_Pat4(!=S*d=c@rEdDk>7HWd-lwz4M4uZ@Fb*UknJ
zP|Pu^6+o6dx_MyC@#aa{)lA=nj$)nmGz){&X=`a%Y$n2)(f!yi;1sw~hJ~Kb(~k;y
zVztAq86?%;dZjm8L!lsdvgq8#lpeXJ{$CWY&J6rQc$Ztk(h-J(yYLk$pX*7~kL=zQ
z*6d=SGV!ZVzfAM}tE9+tXs7$-@P7{OJ#P=m1O#;W|IEzHG=M%{g~v<1p+!l74_=d2
zY+oVQSzFxm72jJE^4ac|mmGg&133D#`1!#YMIKNi1qUN;_Fu1t!hXtZBZ?_64|*Ze
zCcoUYAbZS0R1j0GSW}i`{OmbRw9CD-XcBG=hv_tsW<yZ*k}lQy4470TA8v7K4(DjR
zR01uHSE-t$#SHOMkjGXTziX+hOb~UtAqIP(%$H!GC62J}ETyy5=`SnGlpc&HI@1i^
z3^Trr!&_*!I=e&H01w2ccHC!%Lk}7(qHiuH%m}wj=v(s>oZpl7@lQ6JV_O*ff6wO^
z7>ECwbk>Ym1uk6-%DZ<5)#$dNg8taA_0SV`K9J0g&s=YSkJl8EJ8SJd4{o0nO}9Il
z^#&&&x}tpH;u<o!7<B8g07Akrd0iirPYKTi+$pt^X>eEBKGKD+7vWn=b(k@mPad#G
zI64j=y2~n?x^FcQ5S{?jGV_7}XRf|#Z3}Nc$9Jp0P_2e)I*{mKNrQWJVWWj2_w>ns
zE|~&>T=@G<?;0_4R}?ER7nm-d`$BEaCVqNbJ?c8J&KmT^)7dbCBg%Z)4cnFK0}bZf
z8{uDr*QvzvAMQeEBPx>BbtDlk9qWjNqxwh1zQ@FgAnd86&#X-JCbY*`*qWEDvW=uN
z$0KAK&O3{vp~Sdk<IH<LCw#L<Is)hHY_w1y_qZ`To(DXRpnKxB)2Fe4?}d&{Ak#<`
z=UC$LVQMRDC{O(6l7Ej-u~x@V<xV_nU?4ng0tAs!d0UccPp;6aGWk%{yi!pM%3LQd
za)`2;E{BAth5{zSded`P=pgR13{^)t=^%|FTX$=_KLy)V0u+2*A8Yb$E++1iPE6s<
zl<`h;xS5)s8ueZzhs>)(>Ax`b9Tskn&J9d_tOQGtkrk+PxaQb9IhjcQL5ab-h>U>A
zu(}$p*FD_b*@t!4!Vm8HTZj}NFUl4_^*ll{QF*<aP$R65y9&*WGUx7b<fIt;KYj!r
zOJAM<-?awR_=pdhE$e)@uC4~!-r_pHVZGT4+}<TOkF8j|ybh%=f0{h5SsW~ullCMm
zDLofuIQ`{J#3L{NG!08nZi_RyPQ7LvI%L##e0_9mvRQ3VSj0&f)$H-1x^3=tE&*^`
zTs^<&c$P!HfX_z|hSAEcwNDqWkZg{GLz_x*B>0EZI|Au&*zDv=(Gqar(u%r&&m!@m
z7P~3Z?Q3vKkNl|BFDEAYYxkJCxmqIndi~0ul(6iwGpwHJn9@w!#~x@)_V4*Av}wci
zbnh%sMT?%8ktrlDWJ^M}g_@|wb+<_n{|F!<fxTr4D#vfgf@-#)q$*(<49$s1?`z_&
zhSv^n5oxU<VznBiI$j*8xl_zrd$<rDT=^M^H7ZDkrwt8PAXyTpuX->-Lv4Jy8H{y7
zKqfBe0R&$zM@wieqQ;mMio`jdKQl%MVvi&cXf_oy%)+bM+XI@AEo++pLzOl~JycbA
zQBs!~9M!y;o~N_T&kfFX%&`Xn;|U`T|CYJ_P7Erli^lsHo=={WG?(ISGSdYb(t|Hh
zkJ$OrU}ZA#(dbVRdjv?x`sS7t+B|y2&zv7~3YwI&LghBQOv}Kp&T@nkBHDn<ig%p*
zVw?atM9-WFp>?;j1NIB(Ii7q36qI4m$AjEz{((v)R(cX1jRjRL)tGn2Qpea>xf>M3
z1vLiSdy|&!dKe-{OcsaPzJZH{zg50oeQ^|UzrEcPq+pvcEvqCuXGbKx>xXksO1;_D
z%%CyRCg&8l_o>!q$*RoJCmYvpg6|ja?l2y#i?$B+A6lb-N2jQh*LE{}$bE~5i839H
zbD`Uh2+Wu6w9~rcJKZQJ3e%Y*nGA-cGpo6yXA~4`JWhA-h@De6!;j*bir(v@MiXoA
zfrO&}+E0g7;`cvC`MsV|BD$lXEPJ5XZIciQ>U~my*u?L*9e#g(IZ%VlV$gpiY2S#5
zV#TAecW{OuH#$<2%2J0TV@(&l(opw;n1lq#u`dzPhH;m6r7&myO5R3+3r3ne<fA#-
zJdQdXJLz-6@zH78`0}-98=vmqg$OZJS|#N{vOjhd<YM;;#orL?aB8><yJ<;K-KZRW
z8#+MiZ9DSA+f$Ca_;K1_4{#m6cRjZ!{MIVBExPXUMb@o_5;VScT{Y}qq_{f26XvzU
zGONVw1pmBoE*>UVkRFwNF=YyH4q5+t<NW8)C;K7MR~)g+cl{gknRPrBL*}5x?l4Fq
znLUDm-WY2Pq-yP+`c|_Axju4TOi4CDBaf>JG^!Y*LWIQkoa9+Rll&Rj8@BbKQHC~t
z`TAgZ-c~WDb+kATe%Dt<IY{F1Dj4^Hq^VA;=LzDU{9%7{Z+xn=8Ty+n*$QG_`1$K<
z$`}8Oz4V>_1lS~<KVKXU*ipAzKf1yPKJ8*sown0ASf$sgZ!vT1JBYo^G2;H_#?S3t
zuiSzqk3Al$uR!y%>m$v|G(5sD$ZXh5qEq^nc(92Q@3h(4Il?5X*2}%Vy&`R35|Nt@
zURC-WL%Vh97b!DsH|(f+Wy1-n9qv8Ak)zOvs4<=p{r;)zu1|DwG=Z_Tx3b>`i3ISA
zm(xp9@1Z~?#kE)6!BGLrF3SzKt;lBJ8_P@v+$&Ea*sTyEbGdoBw^>_V^6N!j{2vnk
zA9HIc`_1JwbUG7zsQt;#`Ee;zPXnfxfY8v>(SJ1^nBBv%ZZ>m0+|0Vc!aaGxhqj`h
z_t)rnBb`N%zfMQC!UG1Ab6X8MvDKe*)M*s`cw>^&S2fx^XukfW+^(4DFdW^!$iABF
zn|9{fS#BqAyt6>0v6D4C9PB8|id=G=p*D<_pwZzhC9(U>0}sVywPk_odi{*IQY-j;
zZy7DGO>s4gBU@+o4?<k$!#90QvvQ2JUeIP`CV9~Hts*J?)w&iK$dtoxc(y`+sxFn|
zoXX~Q=VtPW(o0ozO)aKPI9$#An0)Ed?Q}a>%I)Ftd*$(Yf5O2ZQ}(pR-ixW#>&9-~
z^m0Cs=X}Qofr=#Ktw!>)v#Z8hmoo}}$6j9}fSsqEGsJ0AtF>EccRTQ_PCNM0m%@R{
ztNo#GSq~kp;$bDwk`_KqjieYatBRhDzVvs9_k&rHiW=%kmNeAT0l8^BxCor>{n1ds
zbI*<1jNr!c$e1>bD1~YCN-Kg~6suY^^$8@lnPArX7EC4ba554b%7d?sf(S!uA-Jan
z9KYK2-m%f0FJ6Z-q|3969b-Hx>y1~iJ(*Gfw3HOeE2|ql&dg@-9bX2*?na1O6^dOQ
z{>A2-y|SUou9VX!2A+;l?(eNkNG?RRZHmu5KKTO=My)=m$<|6;zxRARLnVc)%H9(e
zy6&6n`%Js<Fe#8@SwnGEKY%QPx%ATh0Ja5B;%6yUYw@`yC`c#NTih?nmOfhCrVe-+
zG!V2>S(CRamu!kt?@Akf6sFux#OKW$b$IP+!Gm~ju?@l5p~z2Y(}d{ZQJg6`7%jhE
zY6qAbaV&mbnHq-~&E4;DifWwxp`}WC^nXfy@VMap@*PT~Y)Iz?Jn@jwz>cL4cnQrV
zlQc-SPX0LlnC^)>XfkHd2bh_0t+SIf9x+LfqmVT5{8ZJEMW_-ETwhi;IvL6HAIVS@
zbGXb&Mq6WR^KmR=G&eEZNnIA6i$NJTHZ1=Ams`IpIv9@PdJJd&8!=jmZHuv61uof~
ze_kuJVYSw7&ANTBs=A)mQBRsj9J59nVWZhc;Bm&M5amKe&<TrYYSL~pt3MnTmO?rO
z(6e(oUqc}|@vhT^?teuA4Fm#FVZoOJ2>BFUTXR<f+$2^Q3awkW#csL&zqy7I<$eFh
z0!IWxOk~vhgx}|5a(R(BQ$h>)<kq}CosJ@^1$ZkybpMTe=l{IOjmvvDcY9E$!q5B*
zn$qsRLbZ~`dWoS*ryV^r(+z4L2{Y~%TvtNFY&>}MVNW*_y+a70ioZUQ5pPT&IFe)Z
z{d{MqB5{SBhH-96)B)~>!m-BJ^2@pG+&UQ&Q&hU03Jb;oEneqB*jZ0|H(b>Cu)lej
zYRs8sq|L|ZMar$+FQb3djWIER5;r}GuBB->fX$?#V0Qd=Ho}1*;QUt}m1QGWSi^{(
z8>V5h@p4OjRFcuiDZ3w%aLIwOAHmrCT50aT^_|FYnw@T1VfKl!Yxh-+_GhI*?^Tnz
zJdT<gQz6^YKV{d!?3<6%Z}{!nJz)gYF-u(y>Q$>Hy3T7Jjo7xd`Vy%V*`P$nw_;G3
z@!yDdEH|_=f$)Oh79B(DbH64lybw!e{r>$<y&VCG1~*jsy!LFmOzPnRvzYB<E0-as
ze-Vl>!iA#0@;JbA(bw-;@iUb#82AJ%7p&_a3rKl$8G|vEBVWq_i8wmM?DeODkL0gX
zH(pET!n<)t7xbO5uVP{>Y5Vs<+(La*TR38Xqnto)zzj<gZ(ZE7J_<$->(@6Pq!Jg{
z)rVOe3*Ske5IxSc@kEvUqP>8R;z$S37LF!mU3KS!=yX;iB_~vi=|p0a5sZLDT*wjD
zp4890I}fuHui>mTRIS$Pho|o{(h^ITzygm;;)3#mB4}P(d#fAoKHtB%pW==FjOCPM
zo=m4A<8Su^$onjA?yLvHy8JC-58)S~*1SgvRgXUzypP!Tlh5pj+Gh%~0d#prsFKD@
zH1o3s_U<k_l2rVVQEl(xHPD{^<IuJ~qI|=;v*Gn*UG+yI48(q?+6?;6-_m1`1CR2$
zl(atv{&GG#t<innqnK4$LREutp;RyM{Hnycysl0hPX6S29Otqg|D!moB1+V-jZAm_
zh+f;7+2N6IQ0_Wb`Mhi9!CG#Mx}~k$_nKE)4KfyAZS^4RdD#rd6+gM|km*aqQW^=}
zYf@Wl_C^g$_V}PtB9<mdTouA_{8woXa5-Ot@dlp>c4vmTpdIvsWOSHynlwQ9C8eda
z&d#ma)~|Q{(_Lnt4#s!=f9ozso!^#EIWt&xI@rxsytB~)hwfV9sK#EkG~hehIt<S$
zC_D<RN!hA~G?MJa_j4V(Hn=eJn$Ds~=6A15pB?uVYfDp-`moU9THf%s_~y-qAfg4o
z0z%K@NN$Wi1vaIjX%W{u9=ZFj{M~q%J+l!YB|fxh_m<eX$1FUMS5U!DZu&0D%b|Ck
z;mFjRO(^o<f%5(Kl*0J9oA+qo0X=vB<WRHTV6zlDuhAUOSN;7k6JOh;Ch?ne0Qr-$
z^83()+Fop{usHSJzJ=b!0ehYflYVhRrSN2S_?GmkzW+JThU+?t3gXQyc;OUBRGlql
zTE#Sz=AqI^TeWhq=uvDKoe!6|Jk@rj`zBYZ7OcNcO5ABASLbj|x8c2PvcYkJZ_8HV
zRC5Cy8u%BtLrXZ6u)R0kGvtQe#TJkfnb|OSBrAW4*?9u-aoCxc>=4T^P*PlKeL`th
zD97&Jr&Og4#EKX2;IAjvRx?#anzQRGW@AzYz9*`kyd2-l1&7?6GpR@HsSw2q#-)F}
zKrkMoZnu|_m;kE|S4`u`-7Yrc<AvVTCPriwtb=`lQAZM^ZoECF^vhO9;#2Wbm+1hf
zzd3v@LqH4U>(OBMnVCAc3(rT-$%Osh0(gA+Ra|0fys6ZiRAw>8|E-|&BHJbE`tx6l
z>297yDXS%Qy)@@}>QBG_CWI_(YV^D{dwqYo%m6SqWMpJeyT{<HL718z%!s)#8W~~y
z$xvw~Ycen*fm#DL;)bz`MK^0ly}|KjA8Ck75h-RS@oLb}S^u^SsPQzH?XnJ&N1wL!
z7iRv<KL(rn+dr~16vxNEf<xjxT;{X1OReQyGaBz5-9)V$V~Z8{vUwxf*T?_$u0}+i
zTodbAn7Q@i>^wzGV{Hv8?~O%yFJ0r%JEgMN&?q~rL&b+XI`J~07!#h4pF5nXKXry?
z5gWo}s~a?GHN3e>PYd~Bh-yO{4A)NZZsjQ%tF?cn^mESBBXXR!-K)2@hc2Dg5j9;T
zY-v7R8gHJ>oZCm;@+KPb!zu|YQTpV;omM{jX+E+(rBLYo=W?b?D;jqD$EmQ`c5R2_
zWFA)+)&TbHuZVZzvct=1rL75<UZr4L?W8V0G{CEM6m=1k6oXz1-ao$9StHkp`qr<u
zQ|**w^sc`veh60fqb$e$EEtlZXytVoZJuWJy<25Io++iew$<G-V$SffQ-LS;JCNFU
zinHnC23k7pq$}X7yS)~ujCP(yde=wj4X>NO6$rjC^&C7moExUcg=iam0s9IP4RLHo
zWi|%X*HX-a4+*w6=~ULIsThzC!%0Nn1A|9*UtOhtQdW`mU3I%~=ybT$h>)gZZS=wz
zH-35C^nyb?w#*SLQI1SK=XykML&Zae+Oy$TLcmhoO|KrT_e4_s>-|?$ulOmV;MirI
za`;|IytIsjKd{8!KPWpy1zK-`$e6fCnc{}%z@)^~U@c3(_pkZ0?eqo|1`4N21I4da
znmW<y*>4Fw!5Xuk4Py1i<{WxHzSwECYh`2nJ-6d1ZQ$|u75`(NT%T*n;jV~$*F@sn
ziz&8gO>n(N)VH~Q)M-Jk+?NOIrk0#?(n#ljjozUDc;n?s!n<5<qRf~2PU5&3hN{Vi
zYr?7^ARw$rTpqfDmaQGF>CaRyMsfwc>-<0bbQa10?alZ4DPvEcsy_>&hYFecL`q&X
z_rePK*cPS)=GmFF0&t&riIS|89VHcws+RXNp#CeNB;fATsmsy$K*5ui%XV#Wh6n+^
z)W+U6N$M|?eowz1_Vny$2nk4BM^@7FV#BknV&6WJfQdM;CZ$HH{2e|b{e<~?3>QFu
zqRs2z>0?dUVv1SVUKyEVk8XS?AlScqDd+(G;QTUQt-VXG&fJwl80*{N!|%z6>O)Ng
zS1YP>$vFBW_nQ^XvNb_75fAi^Yktw98-ldGu#EBnepI4Z<?gQ{yj`4)f&DO5@l^+-
z>`kRj?oL}zTw8Epx|GWzyKTYp^(`|n7E?<=P4Z4vy{SnZmg^xQq!N(ys#<coq~2_=
z3aXd@W{y0Qpr^vXkW;9}#Z*@$B!l+f(b0v|>nlq?1k{oCt_LaH7ZZwhvIbus=gYTl
ziLs{-*P4~Bnp;yVkj@f-AJ@yjTQB_eyt38a7BL#ij8!Y72R_X$TT;~1n_QmoUmqrE
ztAtw>&Rdp$WP1WjlyXlOxl1k3?@|BxYd2rUR;)2zw{j~kZET*gxBheVamHbo@1ooE
zxYtym%lS=N{;8gwcs_psig3|<a$|PIibkXf$yxEWJz0^X6q48Dz`WfrZwmoqv4fuM
z<7Lr?0l-?+ZtjhOBf+^&b9H&M&7Yvd9?Hh~?^yqbbK>xYt{|^vHktDpSZhWeONy$j
zQ5|lV|2P$Xh`;I$gnHbI7iRg4=r~)c57zBwe-NC0D^g;_gdVhh{_QBna{U`E7BRxN
z%8aJD*))wQY~esq-qeEu(PSzcgXKm!&qdkWT`oW6;bgDmImL{`csygUSlIW>PZ<t%
zWAynZTKwYq?)<)7vCh%(QC`x^JD)^vk4F*x{EI<~aI=&?>Mf4A0i*N_=r{H3x~9<X
zF?o5%UNRV;N0{O^G|9`wIy}q1Ehcw)$?A60F9|T5E9$kJGFbFcVs0Ova8d~UOPAk@
zuIQDkT)l=7Pg^Zj&;LmX6KLR0nUDZ0%CgMudO3$3edrUgn|z*Tt$V0X6}j-ffoE?i
zL44EMv)s9#>H^xIVBI<0!LJEQ#O<g|Kz6gSdU4;_uS~#9k66{83PDw+c#b7hNo#%!
z*lc?pd#OvgmE$*K4oQ6&GMIvjf@>)b$o<E1bztte9iEri<BZDoHN^PH`jY%Eiq*n#
z)dJUof$V4~wqfIjmRz{N{LCWpRmeK>o|iQypWt9r*Zcl+IZq|tzQC%OFj`qKrd(r3
z4P$?#s3kyO%(2T3n3)}*KTG%cj!rN1O5${4Ni+w<Ve)+?#x2j@kqSjNe|XbYPMq>w
z9NF?F?wNcg%v#_^`}1sBl+O!-9~I*kVEdZq+m0+Ntzmw&U!-5l(sjEwc|4f0FmGPn
z$4~MeDPXnVsr*$3eG5%%mt7^f9+$HLPS5v(Z#bq3NR{0E^Zr3pQe@XqPala{J2BC3
zf0oAKc&31k45PLt_+xt0^~<4)+YU{`gJ?toX+-zKrZ?6<s2nqGqW8iQEuyB15-mPj
z)lTrHx4jXyn#pJa75|5`-3`^2T+h#68}q~NKzHEL=H=!?&ou9J0hWp)#Bz^ck4CGV
z>y)ShP+61bpZd-~#g?+~^(Sr`<EEVO-syT7Rlem5*cRsIsxO_Df@Ctny!ty2#UbtK
zM_d$s|8$GfF#Zc}_r5wZ_P4os_g~p`Cd12~H@V5hy;S-iF|^*quAqfk`f5MM2v~)L
z3Qb&E>Ul(Pylmp*trde{9{bJ@#6RD?T%x3OKqfs7a;WXG!K~Mhg;6AawAxU@5;{Y#
zf3ilmWn(rT@f=}NZHQQ-Pllkgr{V}~`+SffRWo1pg3M+a&jn}0qO-NYQx3(Z;Pzq<
zEsfjIusaRGGr?bR3)`~E<Y*%}5gJ^+47|j)XU=`KL4N@P@>?Gk=+y(pyg%o^lph{Q
zeYBFFT*i4Y>RTNJ#hm!1rs9q$13#$v!HJtXravZx57Y+Twd#$qeJ!TbSHJ&wJ`@t<
z->fC0JAW5)M;m#vcH3$PS`H{29ISizEw!=NyA8M<3|-wKkYHx~5c9t1@J$|;RZOuv
zT{`zZ=@v*e;78$#**8!9gg~k&O!p<mrFCcD{;uAp1PyF@iMJN{^kiPFyNF-2=(oGE
zYc<=%)a0yXFtpumxn^*9!YVy#Pj-^bABAD1cC5Q!s9PMCDZFdcdn~(DTvMh4&<X&R
z>aEmnti$b``iR=jG1@Jc^O)+wswjW(e{tqpZL=;1aCtE1-#<$03*E54+uf*}%?Dcx
zRKqm+InPW;9h4SDAnFO<r1w$q2F}d<y0ETN?mZ>qcblo>d{4{{=R5A=BsKU{;HMC)
zq8TjfI4pPZntihV-x)4v6_z~HSrr$5HrQ#mK>t0c%{Qt&Xx5$SSUVU3b?1|dw4|!`
zfeseqXXYlh-moFIr7zS?$R%)Z%9tC{Q%Edo^=W{YqNp`8JDj9P`$LIPXyQv_!ZaU{
za6yxc1dCIabux{xbp-`O9p#OVUjMi#6Rv9~X98u;tZDa;RUuKf2;%bZwH+*1=``jt
z=`!+mUhu{nyv<WAG*H!`lf<iJvyQGxFqwk<DZ$OATvANlyphW_6ea^^8*AMp*i8=7
zhu~;-!3iimAJ`gX+A>CUFmXRP)1N$ZiovfMzew=NrU*u>%n{&mv9HPFe#W5pVnRu$
zR#~j8D_9DPX#ZYVQ9FbGjV{B!v{v*Z9XK-X8lLz&EsTp1^6KSp>$ZLmrbN7cKs^Qy
ztDtzn!_0+LJ=<<+@l4Q%fNLG1mNmVkV9zDK#lj!5Qr`y@913bR3LOkYc<mJOX#aX@
z4sg2ZkR<elP?SJG>^-9;%n6WBaKt*9ZKF-Y;QaUfaAQm{qI{d%-;7TVc6&UPl!EG3
zL95#=XIHmcSLQ<UlS^DwP>M$?2+T=j&G@y7igQW1H$Pt-vFo}&ibHB{E&Ee>gt!02
zlbU<(D<$O)uaa0bN^~Eb9@e#dX2litjLnbfha2c>EYja1?7^C|jlYNGe99TL|6;$v
zt9@)ML(#ty7;%8w(Tm@+@*i&1;|-6Sl_PeY_Z^%V-ldQwsd~PkxH6z@x#)Gq;5n0q
zI@Zd6$>7~%cOtD1AMpoQOV4QevH#wZcT_S?-z8TOcx;?&Jf4Ey2Q3sdCoX+>OOHrh
z`bZpCHbVTEgf|t+y|6o;<QWm$@<>$KhJx2?kqEsxNFhfDY3JKK5rBs$uI9A{5!=3F
zU$6OVnsQbj`O$gJ)6-)*tu@3N9L5Vz@2p~KsK2|250jt62hgTCjU(kd<u=T@%0_eg
z^DVK4J^Co+Y(-l<r}pGzpJDl0w$h}mLmnee1ha~$E9>h$`{GfIO)rPvxr=tvEN4gn
z*BH$v-&%!n3~!hIt>MA&sjVGmoIffRX!M$c#x*F|TE%76=RKt2cZEBz9PvHnGWjMb
zEvhq%1OSv_Id|omc}>;KP8UP`K45!)kcs6~ZE0MSc87~}sA!kWK@>;sl}e37j?5{J
zDpUom%I915sNy8uZXWc&p3%DX@%V-j2aiONoL*3W(S7&%xc^?39jt~>mo^JoFd8Qw
z9l=qA8z0=5dbTLe{2g6KGua=zsi3#eZGPRWhF~4n0s0BTqTq)---C>=w)-?1&0-O?
zgg47{b8;#m{`0|HDU=l_lD$|~F~zx7WHWy-1th|-m8>WJqVWCL_Z1H;$*aIU4c2#p
zjUFe?vIv+5F*=vakW!3yxMf&ZG{8t3^l<3c=W}N0*+xy|Cz2L;PLG-0gNOoW5ik4!
z7AO00|7|1-o@XMW87+$=RA!w(6y#7GfLdRL;9AkVlv03o;L=I<R*)rd?|4eg!)%b=
zVn&PG>~$a)<X_@n`7th%8{!B*(`1Wk%;VPy85o0h-kX83beCqW0VMZ(c1vhLcp2^l
z8r`-iBQm!2C_p!1&&8rjxS0z?PCc)p-gOQJqzKc{EYh6>(!C4MXbyy-{Azci%I2eD
zwM8lU!z~^Yh!@uV{-7Mv->g1;!Q*h1p9p714gU0Dy1If-VaVhT7A+LT=n2J`^0h$n
ztzSTe|6|lbuJqKa+isMqz%OIU)tTd1BZ*96<)&@br-M@L%!(z?SCrP&1a2S~f32B-
zuE?C8WCM89e=C)M#QFXlUGsMVGsIlOXyM7=tPJOtCe??vEIV41NN;f%BGIzEEjuMZ
z#_g-c@qS<&6V*3^qYxFZ4A{BG=kP1iv*`H#dle<k+f~sCSRRa9CrT_2NMmC-J}pD9
z4MqXmqsZ2rF05&gZe}D;R+0$5qwaXt#^;7VR=V{NV@QbUldL=g11M<<FvXfk7r)HS
znJLs1gLGs<6i=Da)$JgRIiTm-9-h?paNh?#{3J5vX?RI@x|%@<l=eJy@^^-0z%IkN
zk15AcEb<CaII1sR7ZR;|$s3{{vv9yXU*{0p+SGL!PhnKX*X3&o!ooQscgkD;G{7P+
zGIps&flPnL@>OKaZ%~q$kLdKa``h$=P8jg+Rw_zc>aRQYrg0(2_?zpK6g^*2pn{2z
zFS;IV29d4wSPOI6VL$)Hr8r{808VN~s4?O@BK!TWnS4(1gNd-6@>_4Ujr-js;#zzi
zgv%n;5fQVaJtrO-zQr}i&8+vnRp-Ze=U+i^x=@j3`r124sVA%c7}V&q=w-7?ZG%zH
z*OBOLjOY(YE_)w*JW<^iA3$&xFREqYs5)M(7Q}-X|9<pFXN>;VY`}Zj7=HI`G$G(_
zK9w6DaHUG8I&(r7xB0BEurnbOG!tyCFMOlD15GPWuxR={68uM%T_jSM(Q(N>EM!*s
z`3slL=Fl&qovO+g?c|nsy+`J?>os&f2ed6Gp~v&Gu`VXTl*=su9;)OV7WjBB*LQX)
z)XRRgt3Ijhy~s*rG2lNB77z#9;c|K1OXGcTL)FFULF)Hz9A_DP1Q~Ox_rqFV9sf$J
z2PgEXIbewyd?~rjr#K(_{1jp(vb0X#Ad!4!`5AJexT_|M%c=UGO7owoL1eKvIUhnD
zRUJ9&@v9&1Eu$YHH<p;EPP1QZc6oY*3NgSROvWYmL1ah+Rd2JgY#{-cfS<EI3LB|3
zmFdlbB7q`yoVeH4l#N_v$8>c*(6+kz#2YIZ0(IS0bGIgvO_re-RIpA5K7)NZd&8M@
zFcKT64_AO){+jo*Bb8GN#Sh!HZL5f}d}H7_JpaB2rE~UZ@@S?7AbD<cuOSOX=TKb|
z^WvG|!41Q1R=YQ`&Sg?s*0(yFmeqpyM{ki}o(9zf8OC5R9BBCPe(T^m6=hm2NW6NP
zVmqN|@9OzIzqxCFXrAI%?<xC{z0u7><+VyYtjB#x%i4fSeBKw72&&FXYbJ|Ac2m7F
zDm)A=tUdfsV`6q{-zO||3}kNZ{{De__e-I>llcjcgR=!=$@oO-*RCI#w(Bi>i+#b!
zbMjQ##XEhG$uzNMFOMm#ziX%uYat>7_lIMV)N#y_*9c-7BVQF~KzKTJn61sC==R{T
zt~~r?IY_kzTy0FO=sn5l-Q*}!*0D-9l(az$S`6Wo%E0j!NHz0aja?$S4p)egq+JI=
z#>kYN;$KE`)-XDzF_`sgO)YgHM1lLz!<q_3j$(hrVa@)bh~ZeP4=HzYoD2a<sSEJ~
zt(|e8$`s&?{tXjFN!IKTGF-U;?QnTrST@?)7Sh=^OQh5{envJ{66L7_H~i1Nr7SY~
z5};kGq<ghUn$m)p1RiX)ImPLNLOusgHIXBuyM$$I2&Xb1RtbKD;4Lo_#qi7HFWGZ|
zmlj4AqfwEZhBf^(27OuM0G1rDBxVU{J*sHqjgPP*TaekBYRX=N&?aKt>qdZH#@)RO
zA*o0$O2YVX*5Ie*hXs|;yG8L2Uu6WgLv)?4_$LP*z7~7S3S96Re+&`yl&Nn4eN3>K
zF-)ZL+c?r&?A-6v!c@D)())Vb5j~&xMVF9JN&FZ=N)AzotS$c|lz)fvzI$z<<TkG>
z?z7#p{5eX6m0|bRd*=@{2Cc*%j9wiRoSUZjX2(#=6Y6%ona=4k*#t;Jk~!Bqe_B`6
zy{TF<-NJ=9Y%DhFzUKOu#zXqpuh;9F`$108+?pOuw?``;WmyR<Yi{1%5X?)JD}@Dg
zV;CD&7XI%mt$-yCfpxoFi6aY!+b!Gr=%Tq=vzhT)s%1H}tQKA_gskPvzo%l;FTXLd
z7FjY}uF?JMO9XWeJEDX<4!ss0bu`+tr)U3Xu9uz>alBZJdEFJ1BjLwl7k>X5Jc9h=
zA2y0p>BYEUMUU~;@Ws=FgUJ)0lI3)tYr_I3RlvF08hx2g`LpY<LN$~I%p@4j^(Qi#
zTQcI6xrq*9%gKAH<f+o5_78W1O(n{a!M-@>6Rta~l!MK@7Rp3hhs~5#o`zeNA0YGD
zC(EFlii`yj)?{8Qi9`#LFGmD1`p?7bK!de-kbx=#MoaOj+#byN;b9DIUN^KhzjYVC
z*;nwlCO0=0=<0T1X+Q;sz%mf;M8dVZCCVhhlY^X~A-yJKH}r>azH*-Q@$*8u$3b<T
z!`;ts_Jp1)DviY2C{i(I*T`JdqUBnZX7a|GR`{ITm-=Y2_+0ymOc5oDQ@@+7Im4-i
zmy@V<R&ZRXFv3yiwb|K}xOwC4>D8)!(g_CNi+|yY1XX`-90uY<@859aunLM%FlV$v
z2=Ixa_i%TxX$5?p>^bS63B;K&eDtvK5$Knksoz?&N;s9^uY6r@37_?NLlRr33>W`d
zY~*uuLcy_5aSB%73$$0Sra|F&To?PMiL3C@jwP>L0xjzw>B$IPtFvcjr()01S-6$4
znYD`srxux+NKc#r4})#96tO`|YX>A$Gg`y8XFAer?Y**XeiT2wt@uXLVyHIDed!-l
z#$Yg95iNWZ<yCB-{i_*<*fn1|!H&q?w+GqB!N7(<YQ0runt1llm7<kw;ZWcIJrUhr
z=hQM<hns-wJYZ%Ziv~K!a)ZNfgWoe~Nwl*kgqb!ov*S&g#0eGuZSz}~&@pdOelwT{
zIw4OE>We$R4DM%!+`YEr`rj)owj?w$SiFFx-|zcRT>Gvyt0_F%9edcpiTT^0--YAm
zqW!wBomNos_kN&-dJ2erW|}xPi$kioOxo+ii7CoqyjsnoD5HpcU4^O1K(P^rr*Y16
zlY5Q-?pyzvYz4ldq~zB-N!M<mNEqi_to6u4)F!tpFveiQH(g{;NVyAL>Ex;$;9qsr
zHSHV*EiGCtaA@RVN}yWhe6+v@1qIEtIT|Aq@nNC^wZO6$;u{+qqxs6OdcS{xD*mUH
z1ylrvmqI|)ZatksdAz*rmR)ZdADuGt8P`*rx~;3{i2)#2xTpltnl5p%{U3Q1X0AS%
z%9}Zjq+ic~&|22RC2zDZY&$r$2Is|>b>}Zz-!RZ%^x5|A^X`P9X7~=>F#X;==)Ph!
z`5m6CtITL=CsN0$Zm)$HMUiXvmNW(~vwEyB*lhWR>1nu)M@Rn}(qhv)c%1dctDQwM
zrePRd$~oGm%x~5k39>DoL;Cz*<B!KVLQg%9@NbvR1d46Am=bH_@H(A%p$Rkm5p6`#
z>xz!l+{5=BlTUO6eM5rT*K55k@PAIDwb9%S%yp67pbb<!**NR${=s^s>2k4oB5~{>
zX{XYq#_nSYNgGKE%gw@xU6?YkrEhk7;oAoZgLC-Z$>`|DJ%p60zMh4}W_c9*mwhod
zrNX`6=UU1bT>p$Y@1AuFf$cFIcpk1Ow=}aLjg!Ntx7+B$G4IFnav*osZ1!MgG^8P3
zK53)RU0mxIKnn8RP1Q|et<z_^2wS|b*Z~FHv`GL?Fok4y;bqbrTEyhM=*StRwdJ<~
zM#kncT{iM0e72C_u1y<DLl<?6u*M0gs#N49Uc1Do%*X;Mv)tfJH7Uq1#C)R6zTFc5
zKMGYk_1^UXSZBp22mjhNy6+XnktjA6)=A|7?U#Nmir3GaX3$D1Z%2z$eFG!XRE)$s
zUNoUley-c>e{>tQ^cJ0OLiDu831p~^E(VQ0`i#RgC6qj9^SLa$wOcKWD7m?%y9!`j
z7Qt6(I+0KR>UEI>WSJeDON5N6P`FsZR}oT#{El27r92K0S1<Qt>pXQ{&EpJ56U`Zh
z$*o0&uINzH`u#()ec449?PzOHWiR)nFy6*FH>Euha37%HT+XZUG-u*z^Zt5_GC5V%
z`Two!pOcsGd_bAG<6rHrv!1}^`BEQSJvr;^rX|Q~)BfNs4~(J_-=w1rBqTlh{QH3S
z(dl_D5l}l4&QYg-@)7CcUrA9Y^u1T``T7Hg8bZl@{tqahPi-WR%kej_PZwNs2SE3q
z)c@;a!LtGUVxVsMY0%nTOD^|4Dnm~57t6t_rV>QWyWAHq!tqwyDE?XZx`)eyHow=|
zwXFof>Pkt4?bAqS=$+`ITR5><&;3e=1;k4l=da`#TTKr=y$8<fS8I=G7W0+CEp!XU
z82G<tB{8DfmrWq5<MR;v6WskJEj%~~&uux{AdH#Kc31sSW=q6~{=T|Wq*V(+;}pPg
z&y}CKkCz^V)W4k+7+V+rR8vw?Ld0g;3Wt#e?{0)@-}M;rajQk6C47_XpG|A3U*ggb
zcp!RAXU0u!%*vuzYc7wKNtM~VJE@GIJ_w`(<ihUm?bSOiTE!4>=fZyT>Lpk-w6>1^
zc)Nt6%C%Col;pxJEGoLa@4ztVoth#BgWbdzT^0DHrN<&74L4Q=em=JvRv}4BjtIz@
zn!28?{D^$<KAIv5ZXhEoSn=N`HJ-(X1H=*Whke2^cnirS-bE59JIYHVQn`jncaGP)
z;seNqth%eM-Q6-Y)_(|Zm7u2^3Pvn^QN7Qi3!A%td9VvLdr%^&w#|gltL!)p&`6p8
z(7Kj8Ts~wk6fXDm_VnxxGi_zFyB<lm?+v!~)vkt=$mtG(D3RR;0QoYo(PG+Xxf7?J
z5glx8ECkY-V(b}n^CNYnXfbzxJ9X*JpFBB5>*c*9?fqJcl2HBKB_Q-E$0{G(T!bTl
z8S=hv?{RQ*8!rMEWl{AW2My69Q<DN;dXdsujTSs$s?O(&C}Q1De^``|q)6%5w8u3N
zKWlbJk^S%#CizT%sumge*87;g^+A2^@#pIb-Ez&Z!3V>Qo7bP?G8+f4$bNBzf|1Y^
zWEg{4p4+leH=tosp#EizMw1I?4ZB?{(=VHX!LRA<2UJ5mcC>rj2@l<Xt|FLip^@9%
z#y`nnPu>cJiIFX%?K@OPq{J~VcjM7-D08<m7V{0X)0Uc$J3shtlef;tlmm~qTD^Pt
zrYn6p0sn7@!IXeHpjT7r1>wL6gkG@>%+USj;>=w>oC`y;c-w2b<XDXLlabpwkr1(#
z-&qbrR?W>!rtvM6W_w%+SZ=U8S*7tGE+?FM?)MCCX~L*j{8JFHRCAZDD!wS9hyUkS
z0pu32bN8eyPvSFoK1C3QHg8Ldua;|3;D3>uUz}I^GbsBf2LI~?5D=V9vH9x3llI#1
z4TMwEC*Lq6_%TI_$($1{%j?}y1X`~xkO)_EFyY}u&71oOEyxmty@t1cw9$WL*U69V
z?uIa6%o6opwjC)XIqFMvtlxS%q)@3kw0C;}c>Ta?>p@i)Suz95Bl}*WhKT<hm`dvn
zh395r_vm|m#2oP+=rq18xXrkpkl^86H8}vi(G%oa#)R|jH-rD|+WS!Ge7R9J54yfC
zHeGSuCJcm<<oVWx5ueL3%r9SXUuHvDR&27(^s&2#B&j{<&Km?$aBy%l@R`$yQ)!V^
z^(5r13sYGbi6f$+|H4T8U3yHq&2bNQUI7>Gla_=JJxv+YFBFeH6t76i&o^kYTNm2G
z;eM*-uwTLizTY?}f|*e>LZh*b_YNod3<CXVZn7SZh9?U{{W0#Em#3GWE<oB}<+C{Z
zo?Qtt?INrqXqsrZy(aPJLMBXNrSgQjZx@qG*MJ{2ENgLCByHVZ;2BJ1VWa{Gexr!y
zoT|D8c^EIfk=>VG`0S{?x1=vEa3~Rj1H6e3u1NS=ZCAwq`X>>Xza`l1UIa-gwKBJ1
z1yZ%z?HM`IMa>3mQ{i}1F@X#C9aqq@0t~OISpP2Ls8*d{n?7P~kj<ozX|#rkZvXZj
zKS&zIUG{n2$4rb(FuJ?4%TPjo%aQrM95i@11*n16XIBcT_p;Pktnj3u@QjyK+LLho
zm8jPuI#z|cW-T&@K{LHOSnm9NO5FX8k8`%fnP8z(mniqu5BRgIE7iHsT{gj)slHs8
z@TCMI)I-lW5k$_!^rPLZX(Dn~48FU=)R|it7I`-QOV!iZ&_rL9dov51#m^<W)Ypl5
zZ(t$y*cku^hla!MSJpHg#J^_bN)JuA<bLfbWj%rbwvq8vx{Sp<S}0oI;2P(<HnlVv
zuOrC}N1<F6aW~4zQvhP*X3_C%b^9g<*1-$b6mu*C;!^)we$;;hhyR6C_Tqns_xM+C
z>fF}WvCrGjQDc8|Ylc1JHGH;(EKq$<;OBeHLPaZl_56l5!16M3^1O)BBN-A-!A3Hz
z1yOzU+K5YMVVf_+Nu~`&OiXOCTtftlj2rM-TiLp%-@NsIrPlmkD^)4%;N!<C*>sW3
zP=f}GFH?{A2YuJwW+nU|bzO4}(@Gph?@EO|)a~d_ciLb*rFQks(|GZf4i^l%#oD-Z
z$!^34bYj?p1?YMFUxC*19keQOHn`y%QT^V=AN`EOG|d$GWoWCNa^`{DdUi#{{;aO4
zqR9r85G|ZA2g#xH2r|nRYS_&8++Sg31U~&7rnYlfZyor_i9ftMadJH?5CjAp^k9vR
z(Ce%(dH=p!6O`xJ_ifH1UP}mMH+4>eG`OVhx#e%^Y7Yf}<HLXbI=nx&<#g5Fc|M+~
zvmsIJ*WK~>m|SnMRa2d~;Vqkl@=ma9pRqW4I2rkwc^HnfSB+INI5dRzcP!6zTWypv
zTi74M^>ksX)w=*mADG+Q94NzH?+W6I;BKj`tW@CN&1v%sUSR$6)8Q`^(OT;~t^H~A
zuSENmx-kp>W~(KUYAsIr&sk)0M#M&j4;wCQ+hm2vXQ7IXtNCH%HUGO@_csJ?0$ED5
zKlGCxOmDKD1|sFV-!Bcqlc9@8^x%hwNBJX?=IIS`e|aU(bvLxakt@w%R$Mh1WoCG>
z6CQam`e?Qbbl2vva_vJ-X;IQP?eHzb{$N!f+QUawZ9Cj#+|KdvY;CBMw1)#!MIngG
z(f~Z7%sqtA$G6gZq`cp;d`PtItut#ov*<UyUtHlCJsM^j37$~sEaiZc8Jq-Mmn!c*
z6P_5ocF{kW@57VvY>=NjCNI5hbOg8op}ATgxd~$)53DQoAQ3-#+<g$#tqcSgiR5ss
zN>EXYNg4|yHsW5`p#2np9HBodFlTBsaxbl@wF6faOM>o6odx>T^lTw{5dZJQA1Mi?
zlo$vjm2b55&92lLoRu2GHfzlf4uNGBXC28Bh0^7zQ{EEqz->tPa<%^k)cy;pGqV(d
z(gV%_nV~m0smQqPN%u?3b#CV-VX8T+Gm-S*as?~Pm5u=@7U!jqiJ?n7ds>Rf$XQ&r
zQGRdF1(NZ^-vHMCAB?Ur@GwWK&?gl!Vux4_!M>KpXe|x;Jj>NOdX15wNQX*)aW(&&
zdAA1H=^xdb84>jEeW9#ZW`*o_Sa1I7Np(9tv$?%Vps3~D#pag@HKM`HMs?@z`unw%
z?kOW^Xg$vwDnde(lX%nQo8E$G5AQpjIb4i8k6IcD;5?GwflUAEn8Fi1XfN^zwyC#E
zQ*5@$@r$7Od;-(;FI7d(gmBnTZ|}-4a1GKmPxBCi8tT0jT((-q?_KRHJ-)ZT5dRMV
z13~=0t@9Y7(X7Zw#xf<9_QMkSP43yV*R1Y4v~N$(Oi!a_#k>8$!9xalM~)md9$TE}
zqE)Mw@b)&3PG!fAT?h#YF=$_01G{(cmP1cwq=1@ah6bD&qq=9MQdn~-6s_m)#rW-~
z(A~=(yV_Skhte*nPO6*@4Qt>XKX06*ckA=V1JV1)8T@nm4n3~ZbK_RCFDrVl`a9So
zh;eMwNyRT<Ht934p7;Fz$Il`rF$1&9)kHr6&<b5NJ?3+SAfN!(;^Cb}BPbDxA1q#;
zovfT#r#OC&xQgX5x8R~(FQ);+0|`lCOvKw&7G#gJNIc>%CVq&LB|H`HS5w}*S@%p*
zvs3R^uieC~U*==Th%pEaHNA;jSiOm1Pa;`WjEmgAv&K=7PvdRz(qoKu@wm=U#4|WQ
zuWV@}nIRYqgqNup2^3c3I^^YTAZh&Fr1H0AS}Y)O1+A@%&X!Zo%UxxTS-(B~z6)|r
zG-ZvV;A~hty^Ty;zf1MIssU94ss>aIs2aGh8W8Y${*vV=QL<=_h_wf;(1Xu5jA^Z1
z#@Mld*B_4k*jQGPrKzBGk*V^rYA7He0At3C;Xc70Y}>XCBS(%@LF>or46Bn-HK1xh
z)qtu2b2Z@YTL#tY*MKLTc%B52y|Gedy%lP3l_9TiXT0ck=?d5XV^u4S8@vCmIfT_4
zkHDGnsCAsOxT~1P*w#<=Y)A@b3;ex!4{Qi<7~j&QT3Fe=xEtEE)}>C+C@efY7XSNf
z5hA1GNzqyrZw&6J$<q0hP2AF@-0}7s9dY2TAc70VPkBOOGL9U-j3Y;{AT0VWs#YkD
zwykQQT-g#j?V0mM<{c9gk83w?6Jtq(8>w(T-JR%hW~&pcSjyd|hoJ!cPEPj9rugY|
z*RgZ=IRsseRw&e4wWx&F&8pLj%Qz?!pO8dqbX#teXKmZc=K8gp(Fh5<4HtTSRbgCi
z8&c_-5*g>dLl?0BP!JO1Qs7tK1D)H~BJHzzAfTysALaB470RP><w`ht@>HJFIC0`6
zru{IT@ikB3xo4k7MQ&C+RN=+Gefth{>C*Y(41?G6j-9(OY}k-|`!7|>3vJr8!jh%S
zmEXE{1^Hd9C0%<b#=_Q?)vMR=H^NMPVN##oz4TK}eqT<SZ~>|HK@g$872sNxd((gw
z??vU^9MRsd1Xct`<L8q%vEsrljHq7$uhuOON8W?iMnvM9;9Izuz)%kSuAEf@()wb@
z)8xnd+rAivXOIgcTQXCUaXHf(FQ2}M2^Gs>eCf*Yw{gIXsB1{z!YGq~rU0uhme%yP
zCg8{(EC|fWS?^)xijMZ~_>pn4e@VKHK?J?6NzrVM$Y!B2NhbY`Lix<TA7|;@v9&@c
z{rAEZ^q4(JTIySzZq{$fm^W-zS|*O448#}T{D>)&#unO}*qB}gNEYT5Q(_8SH&xzL
zy0jyUY)$wGQ{@VhZpzRhiLuuUE8^vB>^#W$i&;L{Yg5LN7tmjn=SJ~R=NwhJ{~CzD
zn}U=A8h|?U9&W9-hP}hJtAqk_cHp{LFHel%lDT%ap&^Bp-gTQK*ErV!_C}bR;<%1a
z=GdIb=wWAL{9diEp;*?5O-xemVjc_yWUO8NI(dDM;F>y_Mk2B%cXzgjeIDy??RHYR
zc8}AG5wD%EeQZphntain=r*r1nzHgKeM;XhWIoZ?wRSn}V|fzGJX5mgDS9(k-zn@X
z-pqLxWK7!Q%j;PBm+Qox?_8Sqro75)=Oxf^Es58qlf4aWi_pkNyASpF@z8(<yw1<_
zx-hfVfYu#4cPUJDnB#d}sD@cd1+5FKxyQ3!Ng8iR+-ji$Z`G<5S$&)z&xxwDQZ=Az
zK-GY%0aXL422>5)M-7;_vvD5>@VGQBm_k_i9n6@ufg8Mr8>9j_d3rjdbK9CYdNLSi
zFNVOuj_S|FB63tAZKb4VqHVJ(@NhF+<jRKDocTL&@zPDWa%1SJ?)42e#c~?sfhEC&
zssUwDhqU7*OXzwt3D~}R{V{y^^IDueAA$^SddcRE#N~FMxdHEu=!Tb`ZKd6u$zu(5
z`Cp6oVc)*<2#t&<C~d_J4r>DWHYi_?bhIVhV5`L7W(-c!Ti(1LgTH?trs!kx_p7o0
z;3WtpvWRS;&G~0Lnl%W(H=jRGk5YHVyFC1MEDjyHh%LJU5yv>5fBrN~@$g!-Vm}ry
z+lSNVZ*lXQu{gOESGT$^zMA+P!3JF*S^)LA3wGeQIn-lNC^rxpACkb1ot-si{<ayz
zU+=)!;tvS`HL2kTlVMBG&Ss>sI(6!dqDN!B%BFmDbTkgpGx*TK!}#^r-|_zY?_%hX
zx0G{Xtj>K;<CwPZ*okBUA2NprFPS(<eT0Wcpk&GX4L<tx?u{k%N-z|m3*XLo^zk&_
za8k16H?e8+79JbkFiMy9;tO#L!)A=6+_-TQJE$YWb}Uejl<xrnRgBb9lM12%4IuSY
zKpCvK6sFLA-^#GU1m3SYRxE{p;?5|=>ob7>gaTR{0j!n&c;7Bg@Sy~$nFHf^PvnNY
zwg@;K%X`-Q7lLt&_rY-lumkL!F!9z!1lbbAVtsege<$&GL8t>(1gHBDv~FhSf^R}E
zVQyR)ESOiDv9-+=cQ<2YGjaV!2#u6d6`E*s+q&0s8a)@b9f=970i6rdhQx0kJ9;>2
zprdi@#2IWPE%?#nrx6-{TQM#$2ApPOq+{FmeWdQ*k4_!Td!CvzCZX+C;w?uTmUghW
zhci8pwPQ#dmNG9z?mB+HK6x5}nb%ToDWkO9ylg|mA{6ksFy~i1+=W#@f6k&Ky9fMC
zaLDO1%djDL=HNQR7A}kn&xvX$7xLJG272YWZZmG&SI9=yY9=)B?yQwqx#J`(Y;#|q
z<XV;WV}{&og%v|fG8sBz{<s&>p<X4eRN$9&2k_lro6x^=LwrB9w@zNfodon4_d7~^
zx?tHSFXyc9cb^Hu>t8NF@3wXD)vLNjW;e;$(Rs|T^zu)^nn|xAARnXC-DiR^Yt>%t
zJQaipK3|04$=kyjT^m%vh`uf1?`@nBMrWYeUlQ5pFY69s;kIKqPlMG2GEd2SL}|9y
zrX~%xx-~|VfO5vF=A`X7c?E;MUVu!3`1(4`DI^DcH~Qz}7tyAcaYM;-*TT__dZ=5;
z2mg$DAzvVV*!1Ptb|?@FKOKZ-)qHij(*GiW^XxSTuxQ6|T;Mzt`5s}-d3y6awdHtZ
zo((Je=vUMEzWL}yy!p*SWD@wzbDn%>TsHm{G^_5XQ~zGSjQ({EmaIR7@o)CTYh4=^
zsJStJtjE#~hcJ2MQ+U2z-2&BB3qN@pxF6v4y@J-7vmoF#IcaE2__-$Oo<CL4`kqzz
zSTx|^;D8n_THu*yo<Zl%ol&Mtna5&Y>LgSR+)oW$x^hEdsj41OUS}Us-l@-=3!)>H
z7hIejb%t<*q0=I6$Dmr(@(g2T+DS>L(tY?M`yYpo2GW=@6-^t}&@oU?;+pkrREiQR
zRNfV2KC*7~b+UxJZSH&9_4qX0N~F=JmJPDJB0BI@qnf)79S|&VIx-n4TpMcIv12>_
zu2!(O(#0Ll$D|-OMatz)meo+w-V%P!x$iELc8X)W#=fms*x$txmE5%0VzaAfjV(=i
zPmm;O*4xn%rDbqh5^*;jVToy|;hFayY+!UUT)4j_ZRpFbgf!eq$v^<-tS|AY8Mt^S
z6}3EU6emFa{P4SJ2#!lffSWbj*VRcX&x6eMMnXDlEG<xpdaLM?d(FrW+nGojXVRcp
zd;dYsL0O)wQhIaM?_2$@YCzS%{nvo*9q0b{_t^Fzo<2+eJ%FI#Fm6cN>F!7JrY9b6
zmFdM>y0iyo{Ye0d8`<2UF|H4mx8_5rMPuV2(w*45ShMjcH)83H!Hw&hHGGY?qm}AT
zuje^GzX3<m&T2%!+i3)>r~SAdmh}Ga(7Gy$bEE0Np-TuOI2=!U+6iASQ8rfwz0g)u
z&FE(|y$>dSwG2yE9zYt$-?e>BF4l|V%$ch=a_kE3-p#<tb2pHF>MEaj7PQ*WfR(ic
zCVeyj$GCC!{wE6%P5Rq<wS7^On@6`JW3gv{An8dnuzTMb#tfg231j-=eSRK0a|4zZ
z^fY5$SsxGVX1U4Vuf_4R*J;gaugoPqnHx%+(_wm%e>h<Q{+cy}^yyi1kKv}^`{Ng2
z)#`(aH+bJ(_2A**O3z^$6a?NN0F#0#Kdgo$DcWCtuC>wmKJJOVdi5Zc{aIz2LPskx
zmv7z-K`a3>L(o&=sYc?Xk3LpF+lil!FEaIrm!HI})?P&9)wrBHq1rA=?{5K*Jv`i%
z#b#c8OP(x9FI>1_7I59NWpj9!Da|-<hP2&RuU^IB!$%=r*&>Gy9mbU_SCnI&fB0K!
z*RCCk7dMY)Wz(k3h+=G6V<P8V=yFvl8V$6i$F&bxCT=9qlbY8>2^#Ns^ZqpLTsXRT
zyI^kZ${2L=BChiqCy%i~rxwL5Q8D{|efe$@pBcQd>I*@V3|<5C2wGf7ynrcH%43dS
zZF~`W85=Vb;cRJzyL|eHuk`?HH#D+w#>AWFvHWh7GA7qd3skntGLF!1+SqsBa)ui)
za?Rho8H&IQmr<`y4P({rOPUquW=!siuFQ}-j5AEt={~DjPksYp?3JH*db*=SyB5k{
z$gME!*>@O=m#*P6<f2kl^r9{Db<Mi1MG9Oyk#^pzWO3XKp@9U~7#b1qwp!In_<X_#
zI_aAHf_{qUuu-77pbcr2_w^}5-OH<@mQYETE(hZl!E5hQCABj0KNLfR<0npQt0Air
z&v8lW$P{Z!{w)`-gbmkb#Y+(UyU6=??x|6pWo3EapE~&y#SlRMIw@#xQs0pqrRu%A
zpqeXOc{fLUcyXO<k?nCR_Ym>gy>dMQ_Vn7V<ilsN*7?q)ai}fVqWaRcVHJ%1Z7t?*
zI)V?MZi6z#U2}?fxjVzx%N@&@x1V_}Ym`^=ekb4<cqIf=M)b`ot2tP*>lDtMyo`=*
z>!K3Z-ljx6Za<l`0aO0jiVS*>m-Q>9fUV+ne2#PYVcsVEz2z9D4ex^&%m62&<L}~`
zN%OF2`w7@{4t4##QIhW)G4y&q%lDyQm+r*xYY$-D%boBk|4d0@GS_Y)k+3H_Q)P}{
z@AjEfO^i}(O)!5*A@TOU&i9w=H;&<d)S<pnKu$y12ccxGx^XLt?<9uLVC}Mzv5Dya
z8AIdjJ`G12;MU<hOSn2h=6~jD7-lWnhTqog!}vj6G4A>H+EsOq#cNsS=}53!i~~%~
zQ!<2=t0+Z`8|G{}jOg$f{Iq%>UhY`mz}r8s{%-T#=_bb>Yo^C|UTvv)Plg5ryq>ps
z8D1RlRGu-f&Cy0tf!3P7-WWPuIV=kBS_Q2&T75D<WsjnE?b_(yzdw4@D7{9F2lp=Y
zWNKWU_2Z_2zZY-Btlw8+>*^n9j9V^edKWHTN9Vqy@czhW@$Rr^bJFGAXUM2;82T;(
zyEgm;E|_xi9^`?HW5bpM@GVyw<$X&l>}1;g2gjwM)ry-)paX+A6llc9p%}*3al~uY
zvey37PTs-PK<*dOkxJQ|kb-2*t7@NuQ_uJih)ze>wV}9`%$E)gOCI~K>28IeTDYTm
zsob!l!*)etOPIW0YrTxv*yClN49sbl<yf;L<Sxb@x`RF8T>EmrN&2)*PexxqM||1H
zjevNrKBt|G!PIjcn_w66(nBHci+5`{VqASU?#XARtqe}Yh=Xz1+}Q)ow8o=U$o?lV
zhT#6H+jzf*1IE^K*UnOohwQqI-JwZ1^Ry4V^uXdX1fH9(ion>~j`*~phe6#XR}wJn
z;9YF$=z<PD4mtU2uO{G=Be6IVCFAEg;CQXlQ#e-#OsMaQY6QX)h)eX{5Q@u*GMB9R
zC=FQI;H%m;7+u#br#<yR)qtvjhob@GJHHQSERS@rclbiTZ1o}7*^~BF>p7uJiub=X
zJ{Ouatfa&uJW1d+Pf9wLcO>;(?OMJDIuRPY3m0zSdT2B^9IO;da<7txH-7z!@=S4a
z%>^hV-s7BqcH+Aq*TIV(fKxty0qt7V<TKR**KXX#2cOJWVrYxE^mjjPpaoeKlp*MD
zOrj#=@b;K_*tq2cN_n|q+E*{4ZOg1hds0#=7B1g|&nGWeESxgA5mT*78GJgrpF+=<
z#0T-9;j@s&O{4dRcf-gxyV6q4TA@GPx%Vtaj{cKfT3|0dB9^W^gx_Wk!KYs>#>%ya
z2`t;v>o*?5KAej-O{?I`2~VT4e;LHmdt||)otQOe8@<u2aq8?f(vP1-_b&Ar=Q|aX
zzWo;~7-PJ=ZwbtpG7ydG`zw9i;l|V#->x8y`z{&<*kB5E)0pS13OA^pG-=tg1>{d7
zcaxG378=U9Ogr$y^dE8Q(j`rn)@LTZ|9%FVG;V}$-MVU}7yht(c|WXOv&w8wKUqL(
zyp#*yt5!|^aGoU%`X?Wc)k-vdDE&J)=vsuDD(&In0f}`z_pf<eTk764#B*!iy7kZi
z*K5|SRlK<j+G4fdy?YuY8%q%IY5DShnWfv8ly!G^L*KrAj8#+93aJ4P(rI?AP!fNf
z4OJHV<y;)#L<`f!*JAMdl_)H0Sss7Zs)RwO2wt-f@u1cbTG9p(B#9v)7sdOd0m!E*
z%xfi+ECUC5TAw{}9$#1X#n)v5;2&`vB`j%`%5twcl|lt8JB$el#JZ&DEGlt6KgwF$
z!`nLdWk=Jbsn7C$<rwn89`R%VkZWBda=k8Evg&~X*UoyD*UDHpq%eY>=CCdV1z+R4
z3%zqmrJgGme6H9Rn6bf|tOo<0>W-e>I$-Vkt@v*GuQbGp)zOjs?oOQvL?R8s9JCC`
za{Dg$kx)ce_AgJ5?=w1;1>6xsf;)F&QKPzX12~-u58+GAngNQJaH`_@o;3iurozG_
zv2oK5418%ofkt)w#95p;6=?W7;WbgWR<#1PRaAxR)U1l#dk*O|CvyzCc7xaHZ8U9C
zPbW|P^556M*YtG$>Q&wK&7RZ2X#MtdG^$b#TPDA5FbVNy)|cwOrO<<6aF!9&UA*%o
z-g&BJP7x{nX7?rpt`B40mZJ)A9U2{nfA^dw6>WKR)brjJ(0Sh0qsqqJkRDBqdLl^U
za?5^i)+)@HzZqr9d13OKJ<y|ZRfQ&7@^3}O;untRb9#cm{KY)pu`=;uyS%T_FA0Ee
z+IAcr+tkL)k^OW$wFT^5fA~Bm(o;7-Ds9C$k@u};RsFEx%U5MMCZ(8W)6Pf<QMDzX
zo#}j!_+iNoRP*&xbZMlhk|d#<s4zYY49}5vor%=hb{YqCYhMr3X*f`!RPOtbc*w6i
z7>LjQT%WIpw`QKL=!w2+;y~>#436`fr$J#e5*BPfuDml<ui=Mdf!82j?)ukKUd<@f
z;?$L~>p8D_YTlErfeQZq_Y7VaHE69lBSisTSFK*1`zedz=AQpvjHX}p^U>9S81rkS
zT)A=#vDKaX1ka*Tqef)YG5>u^(}wz~YCzQh(5Y=BvX+toXXg<e;Cl16L)<Usp4g`S
z81dGC9CoRz*TWdz@;qL6wj<xEAKvdSk)d|vy{=aEiY8BEQicV(m$gB^@>%<X;yL_l
zU_6HGk0)qKmP>-XbO=VV^i+J+&;_1$0&vqin}43RPRg1~*6-rIUC+Y`!)uWJj^D+@
z_}kNocxg*Gj`sI~n{5^_E?(KCY%?&aVJ_I3NzlBqb5{Nu0=fOS#y}j7Kd<M6mfm(q
z=2pm>tBII#E)j=sM__%|5~xDYWf5g;8L9ZPfh$VTYg^i19+Zd)r&Cbfoo!Xj!WU#2
zeb>SkqxME(Yp>Gu7SHNWX(YRy_xIjLjD>^Ib>!LT6l}PWgd{5mEF}m%v_`H$Z%!Ko
z_CG(7h{~=uc%gDGt|9rNBW>dVN&D-3EC%k51E+||4V=)#%Z`kP=~#E|E@qxj;vQKv
z=6A?)kV+=jU)!}9-mc}6)t0o$Ni`~N=>08itD<T^)xd+*fC^j}fxd+z`|yciT%pyy
zJ*lSkMQ)^g*H=K*sHixE+=?(wsmikQ04z&dY>7>+FVfd>dMIadvp1udHOiKDg_A?U
zZm81!g-f?EmB6emY0W#ft*!iN`YKCrhtVT@;-$BKL@I&hxYz`&Tz3dVU+tnPlJl8N
zAoz#b8?b51ae~A)82CcV9MD<{3HA0fPc=s%>AUAI-mQ3v2VaXMz;8p(loEJm&<{wa
z^}c^aZ@e+Eg92JhJ=u6_N{^Jbt!rU9DZm9tUb*flULV+w-cz1gaXmQ?d6*7*u_ea7
z*H@}3;^tzH;cs@s)*WYXAt(&RSmw~N%jnXvE_Uq;!~)WZyExl%TuljDSJG5tngiZ?
zwH-EWJH<K2Bc7X4fBmxypO1Scr_7UjV9%E^KR-VV8svvgojc;0r=Q0OB?dN?t0e-C
z3b5U&Q%BR+xK{c64vTT2<>~49z`f<P$0ZcA?)Pa!q7OmmzX)9GYLUJ3+TOfn3z-U%
z2v}!i(S$QiU;GFpHgDEcKiTN_ix)4EieDE*(G(PK#3@t0RW@(s{-7=5_5SWVZ$sj8
zt5Prx^r%z{f1SF9Gy=#~OSm9`0K=`sRNRIYUf2_irLD^2m+Jlsczp}jdf+vwjGP=0
zMo+YOg?={gjdh889YQbU!Hj!7vYa=DlG>SUNXpAWEEoC$T5m}vn9BAP;cu&sy^FS8
zX}*&afL1(t4<0$0{f3ZLMPjaQ*t`oJ+qXpfwz&o``8IeD1xnK+S0Bt5pd~2y2F_o&
zf?5O(O-Wo_0=;u36oT%xF3J#3Y9(f{smghz3kCF3eYz^Y=T9e1Qw-HK#iggaNy!AX
z?-UtuEp4}M)dUL`uhevt^O?Z-=~KS@8Plgu%%Y{v$@y;_1XQUAUtXh^uUykRkD2)M
zuf=H9yb&r?@HK0&aSW^Q!^}S*p-=Qh;siHqQeQv$fqZY-ydh@&I-m4$V)>pc=^W>j
zX+LB3k6)p<XD$GiTS7hhuWBGGF0k&s*ivq^6xp;fhpj;~x7WKi#<I;vF=xvW4DH!e
ziD|9LYgMZPs#o*FQi7%5lGb_E{&R@FMP{4l+8e|d-g71xNBI{}r7SwvudHdp;B(Q=
z6PU4RJA4RQulwqC)S$Puwv_g8!6z?vM3u57@$%>M@b-*<3C5SD2XvN)hIUyw7VlvJ
z-7RU9_4VsLbU<s#_hh<wZFeF4xTBqU?<ob<q4Z-+toXZ6<A0-Ou0R#OYqY6tdQ;I@
zeeE=f1HN?kNm$d9eDWJTbU<q<C!W{hz1+1?0F40hrbyJZTU7JZw3pxKUB0XRy8a+4
z@Z8QM)pz?*KPhze1p%(}>qE`IzZ!UR_z1l4%0Q!?xYGEvD5Nt%xF*e-8*R!lJ9LrJ
z%zP3Byxy>L58`8E^C@XO+lFf^PfycK62@zpmnz_O@2C3HnM`;6aO1{x3>iEScVc4l
zY^a>CADr$XcJa4A&O{2=hQ_2gV<fBc-_k(cx^<Pc{fjR$1W1Pt${xjkYlP}(R1G|O
z8mLvX61?dkwRYn^?!{*9>B^q>>h*iLw$8xmGna8LFc>wn0pi_zPmmEb16@0sw+D8=
zG$QZzvPYel$X<1$lC~HWkQ@H9O&NRCS{9Dg1dBD^$(EUE7|1du?8N23P|~^A;_qUG
zK~-|w5!&lfN0T9-ucjRtQ*kau2W$1GX)T;|-f!-Tf)~N@4P6LMYf*A{^sz^0A3O9R
zs6AqL1lD#d4XbSKa?RL;Zd{}fy_L)TeG_Z070Usxm5kJ69JphNALv2;KRu7?xdf5d
z+~Vjg<auXkZ187b9MUW-;ZZDwz<vt7(OqDp&7IC#3Y<@egEx0ap#pW$RNu2(YhULw
zX~jqEk3}_t(QCStL^-Ei*sDu9N1l&lg+5)3F=anV^jp!{Qb)haoYa3*4X7IUPiR2b
z6y`rMq(^TMvRNuImnDw1Y(^LpcUK4a`nciR&1lkaClHu5UQix3>3qV~tyL~3uAO#!
z8O*yF98G3fl8>`LS8k+ObAzTJA{62C7wtwY>AkJ^GT5_gU7hN(F;|6g+T2NxEQ<o-
z8wBP_^UdQNuLNa-&Jr<q;<0Y?aX2{H5csu6yOxHYO)|c2o$IkJ3&o>5nss&_xJ-}k
z3pjo<7}oScY|}gduH59(7E4-cHL6`s(Us^mmSDq4dbZ?@frag8WoIk~w}nL}1i%&@
ziO9o9=Kc5LJt#(S)q(&|+txLeJk3ASN6F%DC_x&)44F5<^JAy4Q`foHwVJX|@@E-u
zZ_<K)sGLhfJ!InOv17(*tqlvCm6EQvnP66>A}nM8ZWUqMO`0@Dc|Sj+EndESg>>y_
zaN_t01O){dDPw_-9Xk+|G8T8(u=v`w>j;G3HOyd2M8s|U^6T&T<(J=-c;&zHSo1gY
zr&-vsbEnaIYGU3RXiZj!08i4&(!04DnJum{_D{TkYXq6kC1#-iu3$J?SmRfDVWaVY
zh0unJ@MdJmGJcGcdzHou$wK3#wHVJTr1dY$TQHur1@Bod?CT2aMDc!SNzXLqU{=Ic
zY|SKTI6bp9V!=gvJ%@sAcG{4Nt7rEPM#hq<+$TT%;s@;BYuq?O({E9IRt>1cdyw7|
zhB^{(c=m69D>1iC^|ff}YFrMwt_PU1G#?iMm5c)%J9Ztw_|K*)F_=xYt4Xg#FK5w}
z&Vqn#i(!w16)8$-Sict8S*o!sU0uja*pXvrFy@oV4837IJb?B%A|fI&@r&<y|30mq
zeBX}67^-*AP6qAE{Z_{N;kYkx>B?1ueEo#D_ykP&e465QE_2ovp-gYms4nlZWwnzZ
z*l{KQ3TRNTmXUKq<H3u;cyILQG-MQ-_q;_s=O=wVLy3`|cS$w(VQ3()TXSCHX-6=%
zPIW(=AS=)whSkv)JFc4tcWQvhklR>DPuT_ZYIfq<^qJPRwTmgoe{4LA41%kJJ2ixZ
z34l0}z~yIuZ&u2GGo+URXsz9-^zjxwz@tbD{mo+2&wc@PV@Q>4!%aJ(2R9`GVCUy?
zZK`fz(q+w9l>qHK{aPWB2B-t4EJQF_stPMI$2;*!u%*GRfYhc$@@xzEK4z+|qE6p>
z{1VOwURMkWTG4C2HNEy%(}Vvq&#@|xh6bFu)-TO?xyJs?WqQhQrqYhF_9e~5)zJGX
zzxK5AoeA!mYCFI5`vqES#^+V4l)=~<Hg1Bs3zu-8CGU0ehP7)jYWPqcLq$!y=6uTB
zOwC^mhgJD+X<+>L@mRHLl@h~R4C_@<HK1zX-ZkK0Z;Q_D8sW(C^SI8~)gtFFUc)hl
zRD0=}PRcv=nhkq%I$FDNFAaa4(Xd{Yk-F?rFI}-6gNJ^B7hau!Ie)LkqGem?#l1wS
zvwPnOO!{Un-{E!FJS*1h!j$jllMT&un6cn%q1>;&K&P*3G(^sNhnGG7Yd6AZe0`Je
zakptSAENvvYn%geMyff;+K<+Ji%#PED))A5b?^WS+?9Bn6jOVT0)lHqJhzWTq@Y+@
z3fycgG|9?m_V%}Otlic!dNSWkx5roXp4JAfHI=*iI$~I5TWku;WZSuWMgkdY#+J^P
zp8a8rw_VJJv7hxocJ6byTcqHN6Y)4rYHy9)U>xnShhpJsnFb4bZ|h4m-JR!NOh7kp
zYkX423CH5BurtK)oMaZW!jJ&2Q`qnG+oN!mRP3f?&V@MKAw~PNCT=>QwWfR>qWj*C
zx)_SKezo<csiS_X8c;Rx@HC*i)A{hm@ksY9;O>E=R|z1C#g5)o_+1_zcC^BGcXLE&
zL<~1EbssL;H6%yc^D1t9F)Ym|ODr~m%W^DU1$nin-L~dfVmxo$aay6#rV$%#?d%a1
zoxsiFI6lzt@Bti+ZS?Ypp=X$4X~j6GiS(=x09SkDvQK{gQaElhKAe?2d~M`U_W>Y%
zim_D5;x3GzO)3;V5v;hG9Lk6MnvKWV9=)cSX32PvnlZ>{WK=w3UEac0QhaOrluiFT
z1lu*nS2BM$XIr>98Cq>gzd~QD5%J)bINXPi1u0{+x3xqj`;~sAKlux1T)dsT&mf$O
z8V!|u8W$)<4K!%R^eBJUt5*-MCN?qdk_uP6dsWF_1G16RsWXAVoU1&yjx54AZrFr%
z>o+P!F}XQeM`B<iajDG_`R)F@a+MLlQL8q8FS1C}|C16sc9`D0)@ngL)YE_+!RV&~
z$|$K-JRET;HW@-iq)2KC&L*Uz|L$NoTUg=O8WmBB_bCOu<~`Dd$6mZIULiQ3q-ovH
zl=|`<;eFS_)(%!YuFGf4=jFYzsZkAlQK>x7bupx{PAKo)uM{tfx|syFv#o+H7{}R_
z_<2QJU1Oo_k>&Z(sY7d2B0aSsP@1X4Paie<GtBsD4r7eoGH53*E*_h=?#A~se!u7Y
zYK>}@X`St5@ca|-a{I3R5Rd1ZA)z{BkpBOey9hI9&s9KVolJg_-&FIa4fK<7gE73n
z{`My<Td@Hz4}OOp!AlTMpx2aK4!TCVaz3ZE&lyjb74=@!v7&{VaL~)fX|=O-aR7tg
zd>{X=+JHzS0Coat-?#7FhhguG!{TLYG3?!svGm`y%6-7p{E8~QSGSHlhglR|+HJ}`
zDDeChyfJh%$Fh#kXG4!CVWSWuiMK|Kp|}4|j#u-;%5BM-MjS7_(630?KXO|&|457%
z<lGpGuPx&}LL-4!UVo3FO;#Gco+L!a{sTub{M}FR&*D}1;G-|_2ffiHWQ;10z6OL1
z;PtMJkjZ=WADavfex7bs8}42n7|)p0y9qq@X;Bm9O6r0r61RBezB6!VyyWNG)-kD+
zy=Q}Q=KKxR2q=fXq$tl%hWBa)2ga;kzV|dDXl!asoCxH)INISZS($#PH*ZmF@)Y!a
z#@M*z<w#clUohTu-;ND%_3}*&`eq^GsprCo3&RpQ(?~X+?+|}%G(1m*Y4btTZO>YF
z05&{Vnz-eIXcTdmp_YV-UKLdXss@Z}Kw?*$175FNwUY5yhZGvTHr~6Ms%k*hfU1Ef
zM+2QZG~wPr26pZ_rc8SM=KXxf2SyHg8jTuM!>YBrd5_IN#O-M8J$M@JTGxj&nd!uV
z?1OPLFy!5-IC<t0A|hij?z7n#_Wm@?{$qtw$H~zF-_KmEeAj3+DmoVLkDZ}_<;IxS
z?vfF7`Kle<&j?eP(!_yk#p<276`J+_9}^RYD24@#i%&!(+mb(H%wN`YrdoR~2_No{
z!u$IoF?44H25gRiOR;qHtB|!1BYWFv4C&T(WeBP)xQ2iwVR&u(ZOu@XPdh6Mg-Sc%
zpX;cz_y+1MxrzDLfmi))5#XN7_G8V)E+tq%$|-+KBQWz!7Td{Q0?tn9@n}cyYE#lj
zD9s5FAIb)<HDhbH>K1A&ypFo;yZ@F*l(WmgYgP4}XE={Bb?C6cPoWIg0Kxy;ABphf
zOpLF~JhA`^@UJb)38;r<*yB}tiN8|C1+E0YXPq^|(86)mU`*=2+j(+-3wUvB1O&j>
z7ID}R*&CXSGFHj-L^nNG?Rui$EupDd`WM|T4kdkhf_4%0Sk-{4frqF8LzM9$4(3tp
zTEJ9!u*pM9e<NQrAE7w0w6;-VXU5)%BXFy`pRFk;pK==)0NBUdv}JRe)~03Fty#N;
zypQF9A9(QwVi+$^ywzm<{a&3xlve|~%LoW2XQacLR$x-bg4SRDWjqPYlgc)GBVPKI
zec~vB;B@SR6Xz63@HgmsltNdiKMEUClG|`ziKNLD?`Mh0ca^cUt*8uZrvEy3J7)d4
zO97iTj$<+g@d~uIv_^881>EfI=?&+D6M?xNN>*$)Umfs?YfXUX{KXK&(>u$CVA9>Y
zsp$L4boQ5Bo8#x?xZp@*e*(6%T|RBgc^4<$dG2CBe_l`Ock!Y(>FLkaig@s<Vx9(i
z_3BAa=iiNNcw}PX!o@UX5o<!-1)j9*?_Uv(8B@y~IZ0agQwp`aIi>Z=@OQC){{h8Y
z+n&EcRSKp7iE};s=oNUAk>X$&V<B1dev*=HMN05GFu?{-@4AYGE&T91J+EIseHk}-
zx?LILz=JoV3#5GJO*6Ms{=Njr&ZJjaDc;Mvd%2@8!HueXDZ5FJ?%ys%Vdc#@Osr87
zj`o@O;QUp_z4pU&pXwNM;~chU#wm6ZzQyd|V^i?+>B#uZ?+hOd0jQG{ARzbJl;?v`
zRR2tRWTCIFPFis<uM!Gy7t8Pq5?5Miyu}k%D7jyJE;qzO?gY&{a^!q_bZv)Ozb)Xc
zn<%`N$T(K4-h{pT52JR?YD!-TG^ja$;Sw%h3eI_sX;o3i)VN_C((_i<PF6g(znDCO
zmenDA=4BuxG#uYdn~nMZEJy1WjnRUX)>SH3P@WS)0e$%BNlg9W7Xx5iUZi|{O3{GL
z^s`;3;{tqX-?}+EcWjjf;A*W8qzM5?uMkKdH{lzUE9;G_Rs2!L+e?Y#E#UjP^Owl%
za6wtWNZD9|`{O_Rj<jqSFzW5sxhcRG*!v{Ki@RgQkb(G!Ug7%pI2ntKPrTg!nCqjA
zyHu$XWMHvW0C+i%XU<)a$F?#@{UORV_H@7QXxg}*e%1s1-i(G2&kX3n>t%&<{b+YC
zbG${5Yw@NQgNmvF{wQ0<TcN6tjEbgpe-KWcmbl$nbChfNTV8we8+xDTrg(7=?dH_u
zM^FR(Th_v-WjA5v-ZQvP!`8C&s@8~430L%L&iJV-_M#X)sReA*m!-Q;BZd^>uRYxs
zWe7BzlD*>5O*V?Q^zs(KJU^*e+7s2vm%`!GK{y>8#(GW$Rh;P|^IDh2_~hr6_~f^>
z*hX-@ALrA&x}QQD?!^2837>6oG}JmBtZQ(j-G+?Ip7E>m6EW@(@8?<X^uwi_5!g-u
zUgCStdb?kKRSojp9J!g+r6E55Wexr}Yc)2LVe2Ux5j3mji;8?l5CFeG!g=1IcaN!g
z%Cn*(4H1gUOa0unaBSFn7M+_`Q)u92tlda!?(OG=KN&}TRR30xbE%4|0aXKr8W8YW
zVpzZO?0`IJ!v(w+!<(ryW-99x!<N((RRgL9R1K&aFr|U|b*sR`-I4FI2k>maj`)`#
zaf7-6DCgsaUR_(@-7zy1ujk-vx0L<lZk?JbO|9Fs5Azmn#_%`#;DdKwWY|s{oIM|m
z!9%{Fhja@6Qc<T?CG_du9CH_}$D4!uz^kN((*B*;M2vi^KkwPH5v(g)cN|8|Y84sI
z(kCaQQpK`ZM>_Mqy;`GI4S&T`JB=IRGEP$>K=RSssfq`(yw6$kdv#A+d|J<iAZ*s!
zOVYWqgG#Q}j1A2_dG39elg)K45v<qMhOw$yX4TcZin0B7O>8lwx=W5zmlT(_B;0ki
zY#Q)PvW#lz$7546;LP@&>1=08qz^FOc1&{C+Ey7G*VF-SRyY@(ilB5y^eB^tY0W%P
zk#yZU!f^{_`S`jPc<)3y=F{uh%f1-q-vHjL=78?>=pJ=Y*BDm7?OEqY>205ZUgfhw
z6Ah~5fS=CO5sGx*q7!Yo%L)xk+F@p6Ck#H2gps==@q616)Pe4tVj1q#jdQW*e9aNj
zujoa9aURnDgH9fFbW=sufU1H2h6eQRHvf%5JxZexiy_&lPa!pkDNg~p;R^sPAnjcO
zw|NT7O04KoCG{+rv?__mvWVQD#j8s;(`A#^9Fg&!zf9|-ge1jNxm;Nfd^_o7(sVoW
zK_|CqEy=#mOUqvL@<>Td*M-lRZz+S2df8QX?m2_59cpVeBJ`-FQwfR2t%w9T5~Oyd
z=R)mjWwCSD8HJ8pn)qyVFSO(aodD|w!hHTuK;MS6Fiy0_(*-FE%IhUaT?>NnS3~ab
zT&2Q_57{b}N@L1r&nt0B^rx+K#`%baS#f%b8_?0}wf`i3y%ib?p)WNh2G^3Qf@(S%
zG-!azl`G-gxj+MLTnP#?NK+E{qv_hU3-1-?0gwN#Sb0w#uHr#|;lf4Ku3gLcc+^yr
z8mL*)72W(x!;LRem*VI-DmNr;;Y-!IL>oM_I|z$fmdBi0{&<1lbty3p;fvHof>oJf
z#6#~<-i%<CmWo!qU-v8JiB=x2H144VZV0J_k6*)%&^RO#j1I6#M@uhfd_;ijIy-#h
z>}5>k%hoq#t7B5=Mf{s`2i0s{6i;pKp_qHzx@8j#d-G*{`@^p}_x{|vSzh}wyg%%v
zx2Q&B+Bx?H-gL$5xdCae11eY46e;j$|EIcR<(kb3-EvOb*{ujD7;!rao44;#QZ)VR
zm&?keaTE3X`rzmIoX%Yq3HUMVxA|n^5O2b)XH!=FOj=xr<DVs~6@Xrhn;Zz}CleSF
za4~Bwko{219*;cdx^`}-yl7@871@V`wfNurLlAs5E55g8teOrqbG~*xMDc{rdFClf
z$tsmohnX^R@xB&dc*+-J;ajeZrl}(T?9r_~&IVq@UkjES0d})=DDw)wb~E={%N}>m
znDs^_&+LZvYh&13uju7GoUg;)d<B7lmuc0{xa#~ZYm4T363TEfiqNd3H0u!EIm&Mf
zSiN=&jSbG@+por<a-|B|&8f$am<EJm``I>i@Wb4VSj2eI?~&eGTZH0UMAp&5e55TY
zG_?Gap4yfKR$rs%c3zU@HEoe6xUF4Z*eA*o2tUj|Z$+72>wWZWTgBM+$K|`RZQogi
zt%>f*@MEmPhLy{rXOpV@s|GhGGuA46=IuYn7>2J-(H=_Hv0V5x)H*nQBpNdAwmFhY
z<1V4^o;&(Ebo_uZ<(KY4wQ?o#cHfrfRM#o<@e3UkAo*t+5o{*K_GZQlmw8qokS_D=
zMNqgq=jzJ$n|y`y+;#Gb>3K5$*vxlOr=oYhO_@_*4#DXZK9^qYl6#G~^L)s4H+9ZN
ztUDNpr;Sk1o71*hM%BQRpn>W&YnlaKuU)x9QHKg%KM7aG!=GbHitd`=;g3iifvSPR
zYrxgTiC)wV_}+d9+jbtIVKoi!Ck{~>Y}=~7vIbkZc9-Ju>_W!P#`MA#S+ijuJX{^f
zdhx6RT1#5BDt-j3{b|5{O1>&G^!0vNPHOOZ|E$NT5zpbbzgDAeZGW~?LsKN@QykJ{
z4?r8VmP{FA*)m?Zb}fwTb7d7rth9VW8ZxXj-md9{|J7wEO0H#vC9gQ+Sj)bEM&xbR
zieT}acBKjQ8V5^L2-vnOV}peqz3};wXncG!1$Vh8u1hyg&u#Wmj*bh<I(uoF&-wIr
zr15u(1;XxTz|Yw@c5t1v-mtPonQR(uWo#KK99ua=CS{_<>Iix}TcCuAaVBw`!)v<Y
zpUWY5@8Df9oHqRJEbwuI61bTl&N#ZV_f`@PMrNRahXp?4yhKM~R9bQ|Tg<x<k53!A
z>sHAAHi+Kd7YJ;Bbpfb*>JA(=0Cl!ry&SBtmkuK_1nULB&QC<YO-fo}sS&;9S69tf
zHK1zX{%at2hxY#W_1N~1#to$md1z@pfQ(Jn*k-mtIkq7i=g#!bGbNU^00Q^WEx551
z8WxSa+}M)XAm)f{lHQ4<*B|S1*zk0BLG=KitazOHG>{yVOP3)JF&RUxYGqJ{z)Vbh
zJV6#Ktln@O?HN0|Lz|j8twlw};nz8vQH-6YrWeBpL%T5UV+DF>$0-~90)ChA_JCix
zEGw&=ibf6;`c)(8`6bDwUpx&A#G6~T<wakWE0iMDx~>&he(k9FSsIx4*WWanF!92~
zYpi6+{LiJT%Pb8@tXBb{&z(EZ1m%~NuWE#HxL3~}`IeIciHV6=vt}KS4BHmKEucz3
zHqd6eIk|5fJ$g(9u1%}RWR8Sec)gw<BxdPFQiUrwApu8oP6rS8{5Y4ycs+ZrVo?h}
z{21Ve<D{U9VW@&ja^K>;wLFb}I(fRGF9GXH1W7N%rr`Utx3TzYG=jw-imV>WO`Vp-
z1N=P@#~4CeZ^!Unp9z5-KD-!=c=q$DPZc<aT|ymu)82U|C!>`y=;fyskJxz&{>=uY
zb0(Bkir0(=FqH)DTuF~5z3kNQf58t^KcxYYdCxDc?v=w*^yVHl@->VZKZTyrDLVZr
zeX<_~>C@|9w;lSo@fkGmrT(NM)1}%C55J9N%hxLT(v-Gj%`Gc2iGX#I+z+!0Y0q2g
zh+&zZuMfI+X{+5tksg;|c#*F^{~zA@U?M@@8wMb-bSC4{8Jpgmv<D<Qk@I5DIP3RQ
zq@TBk4OT*vtXjL3<Kx0YPn0ohpMX5ZU5x7^@m!wx@hG@B8+wWAHT^)o$~Y!X_yF&J
zG>OIl7YRagy{IPznWL;V#q-=;!zI10)UW?B4Giww5I_C98-FrZwNP}6r?AMm>)}{K
zW6*%gKDf@c{>;_;(SsncMs}YL#xef2ZCp(OPc`YrJ{1E@{%(Y2$y|}JMZEdZJH+&c
zkA&6va6lU+zV@~gm$CWSMS{=QaE_aL!Iy4g@rFaF;_r>`hW19+hNeAIlrgbCKgOnh
zuC1=|tCGjD3z+WhY1*jQ)Vu@${&~~@JUd|yKAOE6ReinCsa|DM#f_#5Q`6Yz+LPM7
zF=U=wa^@M1D;I8H;f8~#=3fTWhWA0o{MzO`E0!sar&}2@1mrx$^T*5G+2}Y5ni$Ra
z+;cY_A(Mz7dJ|mNme;#A#*fQ(;TO`p58zm`-chwnsK=@XR1Ih}AmH_1i<V;D>Xn)#
zlb<J#9Yb9jC99%p;C^Yq!NCC?I&`4Xti%24LTy*ozysGn_s-3*c=<L={Q3_#F}z8K
zwhfgrl<_Wsmd$G8uYWcuai&`~s{>CO?~91RxIK*q?ey4hbl!JaPQ0$WbZCq@^tvul
z!kymRH}KQc_l$a53!|JV#xXZHXJt=W>D-Ke<dxNhz^N%fTDys4f~R?jCVMnN@Iw*d
z7<(caH9Qm0(@&Qj%~UI$ND=<i72xlSaTrt2I3WC6U>s}-qPHlmyH7;lFH~`}#*8M;
zcy@Op-Xi7sy!IsxJe>t!u;F_B`(~bKzA}tVgLYWi)>-iuzZz#avfl!^OU3t$v(YIy
zMjCh#@lv<4u*AHJ$#}1}tD-++dYRbBgWz>IMjc2%yHecK=f0Wdymu|@fYsqvSavxc
zgY_xRjWxzJ8M`VEPy=Z^_sKVCZNdFfQsu+eho1#~|KYo8LJ-?vTiSa?G7W{sm+!EG
zomq0P(SrDz6@M$1{3~V#29&{ZC9>@dGG$tO(z}%7(+2!x&+6_~UUzg%B8@$)iGSFY
zGGBq|Y(H7*CuEJ!u52OZZ+>@=ddaNakw<-wQsZ_206+jqL_t&*4|)7T`C*J=L^CYz
z`zZ9-X=xdJux8P2X!4AG%I1N1T^kc=%hkn>zeC1_=8y5}tvmE8i-T`j^EUqFek^pR
zvKb}ur4zZ~E>!0di?Kk$hSEuCQigPPV?OAE(G&im*LkX9B{g#N9Q5hY5ETeSN5|d8
z`i;kM{Pb1Evh={aL%Lwl3vF;YD4fqIq4}mo6=PFG#gGoy43Y6GLOlo)lEEjq{Fkq=
zyyy$`PLz2|?8pRC61wWqkL26V6J8*m1`4xg{eoY9{f(M1u6KKaYT`j$6e7Qc0$-a&
z(Xmz=0ubD9>CvM*rceKoUeQ_JRr&Q-y?QmXLFB#r_T%#9%Z5!-x81vS!|d5VWdn1@
z&!?thhA}n-O{V&prv}99I+j+ko}?VAL5rW%G%Lj-HkB)637x=b3`nIl>*aV0ys|eK
zbDR62UO6Y6qs6ayRf~6ZYfo3+D=n}&G!~<SLUAaH_b^!`X&5L{xcK2Ybw(PV_w&G-
z@F+xa(Vr=cB1#JHw;x}=fryk;j4WRfrlE*Dr;?K?zs(Ouy`@lJ&;EUZV#s0)bj+!s
zcOYfOBm0lJi}BWLFBE#yyi2Dx%1i54-^^5=Wd#|7%t1nYy!^sbiU+yg5Fx<$v~NDe
zkF(}r@4h37eoeNozgW(0OxkyIamFWO-iCn7_fz~U`r+s4lazSe+ji_@R}un*i|PW1
zMkl>{cEpG8z6lqO{eFuW&`cONN};}<JAawNyi%BP%Y0ilZ>Z>6&fR}We0)k{#&;7j
z`MaO7cKvo`XoZ_2J@-4d#)t2{b>FY2|C0V5=dskP;EfKXZeB-^>+L75pmT#PP*niO
zM8=o?X2Q$(d+Tv5+I$2juiQed3Z)fw%;9&r|G&|#QNBzLei9p->);DFZ=0pcWN!Xk
zBn3HnxeelK8*89AJ(~smls^dtbTQ;M)*m{LA6M=n%ToxR|6)G&em??L>G5n*XjoOn
zr-Txl+GLqRX7nSq_k>rwVcg7rF?jMqZ2NWyDiJ&`sECLBGxScDzZ9A0jR>qe6v%n*
z#aV`7dT!Eu?E7Iv-feT9)d-$T4DS0aD;WR$7ROkHam{D1J)n3HY5Ej{UJn`qY$phR
z{7MMwRxG2*Qa@D<s2VV$fvVN3L;h4zHSnM`plxg>>+@Q*_?#Uu0KI$nMs;3`lBSBP
zfyYGyjT=<wyNxTuTHZvvR<#wHaLwF$cW+5=>?|g?Zk?KHlF_hURnm(e!p1EJ(7khW
zC0%|u5j5TxgGX%{{zgCaer7a2{_Iy&@GZsnsoZw7%Q=zBD4O9tjY$~yS1Ob**m_C4
zh|Li@GK88V0_^_WsyNQ93difa8S|U;))idx-^=ezFt@qu9ejEslN8)G7*H`6@J{AF
z-;@)v_?KYs@XD4-zOk`9<L``v;mMeK$pVc|-oZPy`F>$Unvyd7_g3zVL7hop-ME3x
z)wonFxtfe;eQoe%W3T_ScNYLwmFXYHAEt+4=#cIf0SSvzR7~vd?yg;Hbywa0+U?ql
zwYDybi6R)F0@4Bk(lyio6HNYp&$(Q<GfWW!%<z46m>VZQXYS0s=Xaj>-4r@|x8FX#
z^i{uR+Nbl<?aa1CUx@AwFAk_@b3a{TU#0kYeq|gRo6qyql*M-AuUXbW)#FDu+q?~4
zbolg?wf5D*3>%_u#*;MksJEae*^Tq_t(*ls3|=u(S2^h1w>}qHp3b+gn`2i0FonQH
z=U_!{QQBOEzY{!c+cKI6znuK#=dZzF8a?RYb-V)Rsx7tt63)$~YPnHM^jTXy1+4pB
zUGWaod=Z~xbM!l6^%y<2@wJzxX{xJttEw%E+|&XO=4+X&f7J4Iv4(G#7oo1x@@nl@
zU^4D(79hCZY_UzZ{0(EAs&=DwHU1V?d4!kf<0$KgbA!UtWL4fMzxI5g*vY+HYp-zd
zKXQe~Yw+rZ9{V)0x898jez|8UbRMmfCe54n39qfh?^^A~F`IqaQMO>wnjkc;QKRTT
z2$2gb>dkSC_WQ(Rdt1W>HSAwcerl7a&bMrZEie7&8^xMhoA#q~`-XPi6-U|GryO9d
zT7<4bsipwGR?Q?`v8FCT6h8ZS*JW>I`{N(EJb&X-Q{BjT;P1B=EL>yD6!@2Azay8k
zC*B^d;B%U~v7WB4??#SNm!~ttP~0)7V!d>gd8<~fb_ms?MT_m+^DfXa3%#Kz>q7l+
zQ}5or?CPtoDC;amPp1Wa_W76Uig?LZRVw+yTDELyci(kKnYO*_K3#0@z4x}?bd`ST
zEfhG%m$6FqqYpm{x*6SKxiUKO#1nL3z*f9D*kU0(P*#CtjaU8Beob|$j{jQpK(UH<
zTA>So)MJOf`<DmVY5D_EP1V)jl~j9uaC&8*b~br+hTSrDiG8;`-4?3Lqu}M|m2=s8
z(-+xPbv?LolI>MB#@?NlqJ{cWL(RJyPf(?&cYWo}m%qKu|4v<|(0T99?G^66W|JoC
z4V#wh!&j8l{BMbWRZH>@Oa*_J<f!{w4|^k<-;X?b6ZxFqKI!-)HGXqrd*Yc_Y{JB;
z!Lt3NeBot>=L?pv-EZ2sp<Qvw**5qXy-6;j`8Ialr=val#C_&L@EaP}deY?SmZQMJ
zujmb3)mw1;!;1;;gIE9Fe(P20j;wR|sbarx+V_Y<<IBnQH}qv}O|(ys{_Af0<n!S+
z<hA#8k=VR!y?Hd->lQbDI;fVCwyIi+4yO}EwT7CjPV2O4DiKpqYK*BRF@&TjPOIpI
zmZGYr1Tlw1q^gFRYe)zZ^OVFC2@>I#bMC$G`n~tP|75LiWXZ~Yp69ba`?L46_qTqd
zz7=4bgR(-f?zTCs!y^&;pr<BR9T94YWHqt46&1lp|L`(sIslAry{~OEu_cO3PEM}O
zD8&!p7-NQGZAAY{k1o-RSCmG@YAjoe%u3Q>jdduL?u;#=^-9D2`dVf;DgJGRKUO|@
zrN<)FV7oz{_%=QH9>0gUdj~ur>xG$*PV`=UIX0)bHMD7RA0sTAaaVeZ_l0E+Kv2))
zt5m!*b+J6r`Bu;bYlpKHBH`DzQf?SXxLEjJ79y*y(a+~BmY*k4r8Ou{HbN{3(BYa`
zX#fu?O~V04T43u4S(CA!JJKWCxNsm)q^ClCvAL<$Pq`=&D7h++yM?Oz4hGm^^P3lg
z3=2HOqE#>o`V1A7#mK1$-gx{y*xbkX)Y`%oTEVePl$J{b2i7T+D-t}Gp3NH{;S3t6
zST0Wq?UH=Ez7Rz4mHPdg<ev@MSb|Gsa0UB(VgVC4f1&zZ{2c}FIKR+tCFh+<5b(Dj
zLzkhZOM9RbC-OvZu&!4y231<^Hx#i;fA~(gKat<F|Hv(uR*``stJZWiZ`W2icyT6-
z;$1^S@KQGALRw2R^Ujt92`w(3<MZLr8gn8Scy>KX$c9QZ56RU03!sl?Ykj8Y&voD2
zvVhOU1TZS-ay3CcABtIf;KOOo$>s9MkNG`YLPkYYJ?-H$Krk{#)SyS7f6(M1FvL>H
z^6W#UOqpHxQ8iZBcfXzQ?%DwjB(CO?9HQ_?PZGDVSWWHXO>(7o+&TYEtzBQ>Mdxks
zOQ6+X(#PEwDxFOe>$Zmsq3c@Lf|D*gK4B(!*{V~V799G;`A@4eHC<yP5!Nv-P5^g3
z$Ys=4Xb(ob5OPV}*}EXg-ZTDz1B5_tbx4h_3^)-`_F`dGqZUy*Mx@Oq5c#?(+1Vl-
z_o>^<bZ~16#?eDFxuYs_u>~#4`e9LB(NZg(eRSns&$2~X+ShTdu>A#I>)S`5LRmH!
zx=X3}IGnP|Nt4VG{b9KeWu&Z6pWF(t!yl~v=)^y<Dy})Oj$pkG*9iO&H(K$-l#VHC
zrAgcNnC*Dv$=--alzpf&7(UqFlbpY4#^chp6I;Pe%dzQyF$>=N>d-T03=c>ksuJT)
z^5Yfo-a58t+!~x@w3F-nn`VVBEk=M^_Evr+WymWsZE6%s`1QFW$28TEjmN#T+cU(g
zvd~|i-PCFF9ds0Gu?Iof&+=TemF!dVJIGIgpHA)Xkh3;EK$d$3zW`g3z9HY;!TM&M
z*|Bl0zN&q-+N#PId}fKaoIzKiNIPtV!4sGz(}3gKVwOKcYII>e`wkKn6fc?)51vQn
z#)D=J{t~&Y>tbeVN<P`8zD^M#KK(8sxe9*wcwXovy6k+#<N(?L9t<f#(SJ`Q7b_E?
z*TaX%@0bHq071jNbE($jXAOR46h2Xp!1L7!`g&H6Sh|qm2$p=|-W$I4?^057Q8f>`
zrgW^!!UVhkbwq1tUQWZCavRTCYbrKP;M$<gDoWp@F{?PpyeX_P@M>N7mqAM`HfXM-
zY-sZ&Z=zrU#>_L`&vOV|5L({4H}~i4!?iaqv$q_GqLk`Vw7>JQY3B>`lkS}=H`umK
z(TbNRoh+q}99Ol=i=0nTX{c(IL*6GYUpV!``G*{Diw(ilDa^N2{Z)YF=2SzVGql!h
zUv`9kQ@|pH8_nwJ&*<v{4f%CMdvvAe9dYN_^ej&Nyd#wrV49(!1a$%=Cu?#4<oQLH
zG8CF`I)R|FCEgu<Hl>c<O~g|<_hLYXQFekWG>;>Ba@OOjOYgIv?an^<wamDL{OF$0
zUl9$ETIG(N!q65F&0Y}?-d~s(Gyn8`{N|WG+T;d0H%oJS!#q_yJK3&Edp^q<SwnbP
zv!Y_a_fN9<4D(2^*KnFL*)1h%ZOMiDY*B&iDCuNY&56Vk_$lRw6#Lj1@p*3^Z23#z
z#wD5j)sWVt?8ghIb~f(Glrq;MH!=_gHkW>7s8LPEkZe_X+7SBrd55%a+32wIyQI+1
z^NqpxO43?75DUY|;kM=0c@*7}!@@}T^0`%}XDd~DRG!_lhGOD#<HZ<|ebr)6`tdQK
z%`{H;_w@yU*5(<R-I2#Kd{%%4Y;-^Lh6edM)G$5%$V6}d?%h^8yFR1<X1EF8%;C3N
zS2IS)ZnVf~ZzI<nrMCWZ|9+hN^z8fakwo*;5zl22e;WAFZzlfY2aUg}aCN^%;<q#%
zI}M;Y$!Fsel=W{iUh8Yjm+irQMz1SCi1Xv)dxZA`mnPh1TFErbK(Sr!fZtXB=q6!K
zB_F;Fai*U{tPcLkGnG0Q3St<(v+Rw|&a3}ccPHT@?0L=B{CV;67H*wF$=0fTZvYZJ
zfOC2mmpV7Xl$sc{*#xJO%8eXM?CKP1H&t{ADose331kQH6MpDs=(i}1y9UMx-<HC;
zJ@Pm7O!WM{$Qlh!1L&lr#1JyIE=WhGmbORweB^IHQr;d=?;K$HS2rcZ%O`tBZ7>yM
z7vP+{+jOGG!9#N_7}Y$#VM9|^Dsf1&;gB?^7&|QC;dp6JE$D&*eqmrIl>zcj>%*b$
z^IV{h^c;a>J>s%{*z6~NmIrP2tP!dF?x&Jj&Vivl6^SE`oqg#8Xm4XwdIWc%t`Q-5
zW7sTgNM%2Qi<<IFMRglP9Oq=RcYoX-%L&+BbWkhwi{D_lROYJ;&G1f9GqMNWdGm)V
zi@_ZPMhw13tR?=ifz*z^=T8NlrBV=M?2QD}vhfz{<x}as2hTkFwsba-3=SFg!;a;b
zCYtD*W8uC^4pHAYqNkA$kQp~ZUc_ZGfG&^V>V<AG$%}3>!M}oy*ndd*vlmGfnojj2
zJFDZ!=-+f4q>e6+R1;*wyi_7df_A1$6ax(DA&7Pl=*FC$5*Jtb(zeVOU-UmOXv>m~
zSas`F+6tHS$fJ+8AB|rk78L*uva>!hq$Z@sBDNPeKY;_Lp+Dc4oCog5qO$@dKpwvO
zZUsw;^k(=$CZX78bjkh+$=c1@xCW7Uj9Knv#~FHcI#HO=t-S1bzOcSj7Q-@VuBuU=
z^8KO8W|rTBFvqkOIQ`SrhIvomO1mHBp1>^Qr}s{IXj-fO;i)GjWhu6_XVZkgIOKia
zW;j=ZJhVsbSaH%?EbN|)MzT8i2bb`ro<Q{=VHSnLJq%PvTv|2Su>9j$QvPz|&LdjG
zPMMkB$kGnWkco!vh!W#V8KTwVlm-xwTrPvOD_{p88t~DJ@<}BD$q;#i^o&QjX(eI^
zISQ(SWsGkf&Xc9@A0&CBM3%pz*md4p8b0N5^P<@Sy&F}<lS-m~2SPd&##2`5bMuZK
zXB{xQX8lu6hnP9H?b78H4Z@E3b35ys>k-`p=tVtS4s<@kq}MIaiFo3MLrRZ!k2QRE
z<(LLC*|A1nhNsoFx8$W0xmxXN=gXtoG}H{btoc|KV-V+6>yI>bUSZ?BshxS0o{y9h
zrt~g@R%6Xy2u;A)9POOY{T(Iy>8rXYuZ$CibRGSNt7^LVL6*~28UDPUP1Jx|1~D{I
z{g-WbHL6ue@7(d7VL;DoOh-}0x-ofQ!Ub*BLB|x)iCGq7@N+7g-ruNynwtRZ2XIx0
zJCB+gP6xEg4WHMwdqd+t9*Dl9$Yk#(9gaRq*j8H#ZXn!z{M_s%%%;#GSST)@gdyJO
zqg46YK88kTSM5IOJPco7P$>p&*ueVLV-g&Z(=l@U83j*Dk*9R}2UhD$GaGvKcf+Hp
zua6S|a-5z%+u!N^8n#utlfy$1+3U!nE=2&P%Ey-tJ=SHa*S57RoGV$g@zBB|xNHFk
zbw=p?6kqDMyu?XpIENXK1VtY@AgA3chrHbgEs}DyE>~0`jvf%xju2jko)Ql{3URFU
zdZR!=fP_MS8Cc!veE8kSfhZ>D#ouOO+(k+wn~i0mU-i~k&Sw~<yVm4G$fYC!8+6T5
z+AIULsW{bt9gk@wC}^zo8Wr1=^cmsLdCcbc9rtq38CG8YMOl*5>eAm1g08gObPk|=
zH_Y4B7k#J*<waU&5E!`&2@Jdtnt6;}v1;AZ0we2-F^NH-5je}ruSMJMIZ><QSB6}+
zxqKjHabMRS_FG2x#~>wRP@c^%H|V<N)Z2?<jPLnkR~rjd3TC6mEn~z<DV==3;kDx#
z8hk*80kXX0o~f8ZT%<0Qut=`vFzp@aHeE4gRfVQn8;p0%)6g-(f>F%Hr)d@IxN)!<
z;Gt%_+zE_jDAA?S)uH0*ov{zRJ1*sh*B0DN$wgo*!x<8hzbe9V(!(8acJiw?Yux<O
zY@&3*J#VigcqmWvTlH}YS9`GtM<UyiW_j?j6e%v%zkJ!S+0RbuxGK2X2N?5h%qRq~
zDy3|H>=lx57nA@e?+&g%f@8Ss0i(M&*nd8ZL_gp>WG@@-*5BA17L`Z$_F$BcTwdxj
z{ZW$=+~4|K5TxZYYTd-K7fe!xr*5lQEX&YIrR2FpNARonUl*sijkcsN;8~}KvCs+j
z-|29<O`cy)c2VL;UG)ePI98Au(Fv2iM6At0()iJs;KZI93u3`sx5Hz!O_+*WSn5}|
zIdBKor%2RxorP03Fp1sx$iowF0l}|f6B7hRO)IrBO@Ft!!E)TzWSQM_v(}+()oBbq
z{EIgt38;UhG9Lzzez!PSRtXr!KG2yX;+OR10(piV{8oswy^I(u2!+Edk8W27ZoI}Y
z-UYNc@Vb?vRk5Px5)B5(EouBrtT}_!FJ2?($Bx2(&@dT`gQGF;V)Lq3mf!Z}kC5ij
zXq-n|Nx{fj&D{|v?(`*-6`zcUJ106V(-x4CTQ~6FMt=~L?&O$J{Rgjzh;=`N?0_3Y
z$~Zr)hL$R)>hOByYBpM^r6K|FjJwbnQEB~MSbgMIk)4jDE0aG=CqC9D|0ZxWWmfxr
z28KAIh9T_-Dvs@kl|75^H<qg*>TmdNY4P6(m;R!x-$h$MhQGWatTC81t~1i$vaEuj
znH&*2K;6{&xF*NXFBb%pXQ^gdh+9!B_}<BAJR+w&ey)hisI!-Pe-W5So~wwS>w3$!
zQ>x(NSDxCV>_aAhCdQ1{tpvT<2q4|;)-0S4>PW>L(F$IgZIzIg+}h$Q02Q4_Esc7%
z>dsVy2B9$TlG59Py+*FU!w0I~caEN^^?!X5IK39Yw6U6cK)!m!k+<dmsqIpYp${K$
zhf04RNwNwVH&!<}{8II$15zh;R>GWOA~ntZh=~vY?*aSL>v-30nNd`EJ!Z^UDvciI
z`61Yq??U~Oimb&M%F`~NtsT-_+ls?o9(U%EYls`|SjmrJjppY2gy!DRA3SvDmF032
zu3KI9=syQ)WR^${mFZgNon9*H-scX8B$zC11Epye*#+kmGdghF`!holNOM)v;HuL?
z1Jv!`X1(k5ouDujssQSwYfRrrONk$HDw5SSr^)y`1fE9I)CbvN;mn&QMi@g(y;&~@
zlabS^Ba~q=p6v+ySdcUb^B&WW{DkP4S(r?tj77SMK4{O3-s`Y)=kliRM*ErZ6m4OS
zzr=pbA?>ZI)#a9aEk65gk%36%Y@Y2#WICjHPFus1`jr3a1XMB(mpJ%hczyjU&^n-O
zI4Di(U}<E|6Yd`3c;_CJLdI8ePRQ;@2tao&rzN8_PqEY9&pu#U%;1zdeCaxVy#-Vp
z{`89&gVx+2tvX!au~bA<DMRY%59}a#R~(-2b8_d8Pi0e3DfHuOOHwGazii8v>Ctzt
zz(&Bk)z#dlB~VRQcD8F*`qt2sX5!}LagIFvu{oUG^~SJpB4^&nJkpgZNvaAU-*gq@
zW`1#2$VGm9WUkMO)mt5}TU#~g%yExbY!&Ua_#x|z_=5N-8xm{||AStXR~8<JTgNX?
zBs1hgKKVE`OAlXXo6JpIieK39tdWhZ-90xPlo8@KW;<9XdgAKU6OAz`ilw!kJFCg?
zmzf@(1`|?p5xabV&gEKaO^N*K*q=IE(-IwOL4rod#qf=#S#YJ)$a=@)mGaj2S}xp`
zrsM?2_t|QC6Pr^nBa(zW%)jlPV1S7@0{meB9J02T*P498_g<WG!_UEtPb>1`gw(g|
zI{Jph^B{MR5l<F*$P;<BMj85!Ia=DqtcQs)7dcR7M>E~Gjgg|^6P7enm=M?>O7_GS
z`=i-9kok%fuB2V_0q+QggPOP%{vj@Q><yeDzb>urdnCfI<0(G`M}_)#6lct?W(NRX
z^C8*F?)Wyh{!aJ$iIir!%Up7jk3Yz%#J>7>dT)d2O7(ftM9Uj_F6_s3QI#eYWiLvV
z<qB}Kj#;;SmddHBaQvfOy<_9$2F%Z1$l2kBe7}uy)D6Ir|8BVFb3Thm+pnhpesWtL
zVvFbIXX@4kNb&H>kNLdUDt?(bp1ze~NC)Ber8x~#>+gyCbTqN8rCN=p5$k$7Qw^F7
z-W4Qe3suPRc|@!YTFW+J+=J%oS)cXT2dq3?H=~BmrDnc_Wb@bX1l~_C9&O$m#(m~*
z7&7cM3<gVub7Qdypsy6Pb7Hl*<X<-ZCr2*HRfRlB5Ya28UGZK7H^wZjWH)`kvFUkA
z-W*>Oeq@+5>*hd==qQ41X9~2i^L*xDxQS2pt6vD8!;sx2sbil8Lb6>xJ;4NT@Er^J
z5euwrrlQus>zP&I)P`(2u-b}(AX%}4N?esMH}I-4zUc~U&cHi}3bXtZ{tc$`yM;ZW
zINOZ}vKenF&Bp6}n~M_63s56ujz1i<tR~Q!e)~so0v$XqY0ra&np<C}#w~W;sJIhH
z+Sx?Xc$Djd@G&7zSy?T+7g&fjCO^vzEvaKVUw`M9IS+4w!6}!7y><w`Tp30Ud<Gt<
zxJ^H3Dr0vFJW%x}c`L&7I3so7B~1goQPk6aRH!>|31SFf?{!#1#dN(qy@6Njs6E!5
zyIG$o3M@8W3ULhFxw3ZDKP}@H#h35_s@x))M5xeq8aD5a?ya)`vN8;aN{!Vjiubc$
zxQgz`4OPj%K)k8`KD}P&0XZ%=2iaFIJu<>Iu-+O~tJ2FcZr=$QB#Hcxj58J#r2Q26
z4qW#Rvyyay=rV5h{Ls+Y4U~D6QR3fzLp?D&xt$C$l^njaTzBW~qpZG$v<dPZ?}YI%
zgW=PQ4LYhfx2$R@ZOFS*>*uyj-8wR%lY2xy&?&{kI7)8eyM2o`5lwNY9dZ@%((1*2
z3CBfvFVfr4EI#1zT#_ksyPrEEp@Mj%KEP*J9ESp)Hg&*wrmez;kZJh{;(bQP#5=VE
zYzs8{%v4^1GN2{rlRmsCm3VuswHft=E1cMv>6i8?q>MROR+`mQu_`e;bVCJztzv)V
z%qD<@Cj5J47h8xq<>9RHOZfZ;VBgAFl}c_kn!?aBVb1r<RZ#UD79R&0p;4v&1oZl8
zs2zPSF2eUMVPx5zhr+W+CDKDebEjRC^|xN22*-e@aP8grH5!c2(j<5DbCAWODwm0;
zJX+ZkP9Hs&Q3FXTr!4yDRfPjEMwpGEbLb284nv7D(hmxjLL?7lM*7IxJ*fIvDdiyG
z#XB5hH{WIy6xNj&Q>B-<?%@%Gi_UbzG^n=ke5mZy598&#=)u<v5Y+(~Cp}3=dfJny
zhMS|BG+YSX13UWkU5m2<FXLP0jF!*9W&8veB3h4tk#J)kogY+j2QPpJ^YXGEvo)t;
zgyqrW+_~G`8Q7cm_O!k+mMh<iI_BS%P5gE@k=xMbUhOm3$DtvK$7W}X){YD6WaVye
z_&Jf6K%xAl<%Ag3QX}w_`Q=zd$d>T?VB}O~xy;9(=ssrZo5kVDIXl=Z<&`=M7LkmN
zHMPSVj$JOPaJKn6ylU!0aY(SbYRICFp<ZP6l~Kh{b>MWBY&8U2#^Q}obix(&oxuad
zhY>pV5<rXW-hu?eHV12SXM)jee0*C53_81JhblvC<~@?$Rcut&su*Xv+p%X*l+vQG
z-L>!6;9NF_xd0%7$UB44qJNMV2xTE7But1gd~H#}8eEL-G4b*QjDn<6=Z2=_UsaqH
zsgK`YHZ~~)DxEYKF_8H>Um3sPO41f-%&zA=Ki1mb%@%#gX7~TlA}xK7uOjurx0P%#
zpSKW+t9)`lV0iKh@>|Sf`Gl*L@7<o%;!Ke%6>iM*Rx0^{{(zAxz5lw($pI6o@>z<6
zd{PCd3WJH*j=6veua7$&q_+Grk!R<e&CF_8`W;f>QeQi(+>m{KP%;9}^t`s;+){!*
zIUHZA^~+zo$l0=oQuq_O_&sKrvkgT&noezGBZC{i`GAWf-YO%!N*1|-6hRIZ2R$#g
zs(Rz(KK@@$%!E(69q6-<Wr8tI)5mrH`kO936TC1Gd(q_LN-}MyPn=0>(hl*d=J}m*
zc>XQx%(&Y__+Vu=ZT;$1ZM#Nhg9sc%&vg!8(Q2wl(VCvW@%X{2)T`s>!0oxZUfp(;
zdd0QVv8@a5s`Vq4Sv{{TQS05N{o2)f4kNnLsGQraDU|q|kv3~AEW(3~Ka2?}OeK`x
z>T6|Fuc=h&JIBX$RPPsBSQV>*CBmoI_mg$T%iy;thQRM*8~%@S33U4HK|I$|{PxIC
zkxLPWn-Ib^)d9w);&QB@t8coHOZ_)0{Fc6}FD6xGntrJ5O_Hmk`k)hr+fHDPaMBf%
z%3?b}6k{VKa3;9EWZ+%_WOpr{u-0;r-d9wITk5GE7^E}OXtTrHv5I~xpG!FyVs_qA
zh0&C+0c$)A#x5~pm;<38lb4knJDM%}+rFQgQKBY*bSqE^sM<#qKC@sF!zQvC0W}c|
z_Y;7ZC+9-%^4IYS*p2d1o?7+3@S2)(kq9&}0f5t%(|3KzfllT-NuNu=M2<u<=x~3X
z#!_O$YtlOpBdT1Hi!SA%^@YF@8nt5R$Metlw_>OUTg(%VfBoWR{N!7sK_*=~L)9%R
ztQnS4xa~5!@{8DAg%F1Jxn&?fkxM$tX3%81t6bFL?oX7$E|{eb3aK;Srq#jNen!6B
zSFy8W2N1lm$1<x^!3hZ<_Hz!rggP`ZXq#rV6Zuj3`enGf$@FTGFIIScIY1t<P8@gq
z64Fm8wzG;ICPY8^D17*&%L{F%5lThmrx$JVul>NiCs<c*?rcdpR0Y0K_oh5f@N5EX
z#-6371cv!Gb$&z+y}x2r60+<xF_^N+Z#ypz8szR1!L#El1N&9tv-IHh85%eg`Y^NK
zJYHx_Euh>vT<>(w(kTCCAFSk7NBTX^q~#cMC6&#fE_)8uv%{4Ng~f=j1FeMa6^Uj9
z`}1y2+hE3-G4NX%KNT~($GacZdbk=TZgUG8%QPJ__LBlz-5*w^ZYu+kXU{)Ys+m%+
zg8*%H{sx&R`|I~^+5#tG-d5YZR~1WLgE4@rnJbXuchNumxeBAodOij`x1FaDN?EGx
zSCK_OZztKvK&%F*0VgBBGa_GOy_`Z2F4Tzh8BrAuuE~{j<iQzz=>T9b|3xHov?+WD
z#AUEbaA$WT8=sm0HwLKas1SLCOREPg?mKKbz5Xky(2V`9*W7gzUg4Ud%wR8-BtV>U
zA{fs~N!F6oH&U1r>iof0*~f~K#mN~Om>)|7G@UirRj`g_39|<f-DO85JcN05He(%I
zu^j}8dbxh4{m?>uFxDo|`kQ8v!;PwJZvDea#Nt<DMR2{NqfB7}Qigm_0LYhgOX8;>
z)^Cu-s0EBcx%uXVEAi68IqC@X?6+3u<jT4ZXkXrk8s~VA)SLuKdo=mlKVs(J$YEi*
zzd=U5J9R;q<$dYOpI^>C`&N2%#m6(*SFN=6!u(ywev4aRV0KRB>~n=w^%{I$j}>Sn
zvm-#}o&EgeVv$G+)O9qb<Z|X?vF~fqA00kwp0PJtvRTf#?=P9SRiXAE>ViUqdFDxJ
zVI9v*|J@UgS7kqu2R&61P@bud`g%sGVR*&f=h_buX1dOn#D%tBHNbDJ{hJK>Ds09S
zWBV3#O3DWjeeS+w$}VF4HX1}sl|{A~83xLpe%9vA#{KN<u7)TZ^p)1HSm4)iXJMmH
z#+dt|Pr%kDAP{wTW5@!|9Ftda0B3K$kj9*D>-{N<N#ZDUla!=~W~9WbSZ2!(pA#f$
z7Xc;mp_^_z4$m8v)orQqMJoOFAM!G+=|%!Gm7a8{B?Q6a%QIAvaFEoiAJ4zLn9ih}
zKzvym)5JWCtw9|fNZD9<^UkjbMU1=}PScbp-o}5udRJ0R8gk~WzVgAZk%yO2<u5oC
z0d(oZH7hp^c80SM3nm#vm%Q&wqb{$EmoHXD;p*^<lTD1#O76(?l_2I~M%++m-4#PD
zt?_9!psr3p3)3!gxCv<viPD1u?qPA@z^x!o6DCLve7Te3pQ~g*Ily6h?S@S}bU?kk
z0nlUDiSd1l-G3?>&mw-^sv^}Pdum`CewQdzK_yLxxAN<^Z(VZ-n6MZwyTmd#j#Tz2
zmnnii4<v4`DQ4)wlMpb;-V`Gl-OZEWXJs>o##d1F=eUFo{oc(5Z=DF4r_i%M9!a0C
zkL1DrhJ!KZcOWNdqG^_H-Z#jg=bAqqQ@ed#XO2;M-|_C|PfR@0P41Hvj14`l>h-#%
z+V5!A`(?b~R`Q^?2nLA-cqmuKE$Vo8-Vq+jq?uKdJ+JmRf<p3lxGP6Skd=!eU!5hV
zf8ExBo<&&G55PM1Fr$@Qu$vhbG7OoFzrEj?GS7pd(29*?lDQVx>sUQ^14WkVs2y*1
zS<u~;ArjRl$AdL<?JoWONu7hm4r%#&3xSP8Bz+Bau|4}?6}PgwJ(YR4=Tw28XY?$>
zE<BAf6e)#Vo(DDuwUcvh%Pu4J@9%lKkA2<#!uy%y_&mA8o?X_xCFj(@7JjN-YEHp+
z;lKBD(~w1F+UnMOn6U+e@OV^tlcK{;VolJULXpv!*~c?afDW<QN?ac0r^wIz_`RWk
zw0h6+bRK;tf;rOHJ7t(x7rlhr$KKXBFmne(cxWK~X^JGQ5KWg2{6I#vZ$v9*Tg|K%
zpZT2V_!a5VWm6l;eZd<6W3wI?IIq1D^vW0!NhVD6hmGrQwk>hK(YkAhJA7FayK0gX
za_Qyx9~RTso3MI<u6WSW?=<I_^2!_6uUo6jLaKr4y0-?$j8BtH!B3=|!3(UO4|Q!V
zrkhQIDTxp<I=L){vn(inS&}UNj`ePCw>B~FPdTq|*4olT+i98_JRr7#Di_qtS6=K&
z#n__n_pnoQN|&Bmn`RU#w@F=f@^ibST;>c+d4tpFnv{IzdF879UEbC}jMM@8J>a<R
zESbhkd&0VE3Y;Ts_<2NfmQPy$#oJ$0w184@4p2qyf1xWpj@3J}q1q5xzp5KMEqbCv
zq+Y|{r!0czkGYD;RS*h5oO_fM&696^P<f?4t*9M`V(XO=1a8KD?0wpr$@Quqdb7>|
zm;U1nZu9=~@^bAZRqfeEOfpl$mWYV#h4Nu4g&%~hF<Fk2A9{Df9q=&l3Qp1E=m@(k
zSxKhA>2Q1Bw&&A@zC6@#WO>f*u52_IpQ_y5E5C7F&A{o7Qmdz_S?P(>t=F%{-GhC3
z781l=s*2@w58UBQs`4m_&gK<>xsvkD;=_<npS(;V*3}+^5`N3!gC*}8C<CNglSPk<
zmPAa%pC`>Mm_K5gQU8ube~Ws!f19r(76m^0Z7v#|)2)5<UqR~UXU{+m7=D=Bk>bxc
z?cHvX^+H*{UlDm3q<NruF^B9UcU(FooORrRN+FHg3*Ab+?nt+CDRq4iYb8>u_raeK
zCOdM>5q)I9CP_qy+WtNIrN4Zuy`Im3t|njiD>haW;jXMM`lL<KUc<Go0PFqayoV|J
zhNXM^vs*gLSeS4p;7_5}JWie7nbHE0$z)os;oz_YBZ)_?ek@cZq~Ehx>6dkkbYiiO
z+aD584KUdHxfD8no)_3wO)4?z=+nGy6-w@Ka$guE>ExC?)QkxrvU0&i{zMZWx%SXo
z2rLIa`qd!Dk5Tl*o)$h6Cwn3~wF;4CB#6%yqHm#E-aHt%b8~sO;o5vrZUPELM_o$h
z4jzlnx~lM9zbx@zY*>)0%Tm4-NGRCd&%p67)l#9H;!}Ue_kw@oc<U^v{h(0um7H<=
zJE=CT-(s<-*W%c914-ZAV@G`<j5!>i?R&&7u0UZkG9v~e)Y)PEBok{h@$vS|nOd6<
zj@8YxGXW-n%%C~1kBYj_dj|ZJMw%7_$LD|(Pwb7moHEEwCD#vK-YH;S-h<I!8bg<W
z17QXZSf??`<32#^9Ngmt9~tgUfHXGT8B(meSTBOV)!ze^<fnbPHq=^2A1#k{FYMcj
zJ$pV{WMXgsiJ(i?NAW?o=~uxyYg`@L2wi^LSEgQN_n7^Arz$2zts+6?q?gRofo3d+
zU=*MKoOI#C>Ty9t5VrRxuKk(~(mct3<l6j!deagg8_<+*-+#8(zwPm#$EY#;Z}`1E
z_N6K_cc?%ZBKrHJ#d1SNjZV&+=*OAM>vQUi62Fy+W1xWW@OWz5Jy2P(atAO}T8*s?
zvDBG&ggk%(1D4S<-UjE4=%0<9k^b4TI7N8-_k*P&D{qoO<AF2H@#qA)cxu)g$?f7e
zkmVhz^gqyOCf4AjU-=?_cl{mUFzy#8dv333zq?@S+3&y+b;v`qpz6WR`Ed5*CS%hp
z*M^q@FDdG@#))MYtG)(N%nf=jL*LaeMo=U8eSIn~rWfzQ`o=3>8Cop5e*9a|DOB5w
zc$2(qSD1s|MbQK9*Y7y}LH}Ox9jTzuC0=>{KfC2WQPe-7)ENt{{e5$V3BG{hY8|=p
zi$#6fs9uY7xzm?44(TmRwwJ$D=vQ+Nt6CoxX{&Z|7pQ2|e32DdEo$)Kti69%Z^Lse
z&4}p3WN(U#v^8~%88t{arZq*C=B<9+UC!z+fp&0Hi<F%;)%nHYXH@5UE@-T?R$<fy
zehD;zThoZY^q1Gp4JOO<)AgQ7NyjP(?ZK3ZCneWhta1=SGJa4;4+!7z)Ixpg9{5nd
zyU}Kmq2czZTxO<!N7eHMpEtOC^`b({PI!|Gz3(2e%IkmrMC+$fuU;LN(eVAy^}#ZH
z)%Zez%f>(ZKq2>*f85^rtsEZ+09PS-LQ1nC=%EP|lPQI=<}CVV_uxmBgZkG@e8Ltb
z!<dhiLOcpho@SHqO3CQlaqXh7TG*w!^)QhIt7;7DG(ZzDMTQW<q<QHzgM0Ur9ZYSw
zhxM~8I!=f&%7weJ($y%m@$)W&vYm%7Nt^vD^_<L^xtZZa@@YSP4Ok%i-j6R+!57+3
zCZ?5~(~$`d^i23AsnG9nwPF|U>-7<TIN8>*ZtPXRD8EdfsRS`9?Xo_DT&qhSVIyT-
zvm-RRXFI_@ii0zC$x-}PMl)wp<~(CvG+P|iDIYd|RmVtj?*GVs|7d^zdExAj5|#`7
zmZUX%QfKn7^S)b8*?-y=y1O&$B}VHO6$Xj_wkhLU)Zy6L@^-u~>!JDE6VB!|h$LaN
zmXcoM@1>ose^<*s?mN9gEn6RSC+nla*G}6b-1dPGc^&xJvIqKZhSKiCje;wu$xrqE
z<eUM2`~-xc)k7a*_t|%A$4*I9DNR6q9X(zjQt0H2e-fCF`!gpc{?_|rbCEbTEYct%
z2aI4T*1*{Itk+~`fD@Du_~@nH##$hL3+KitY`Qp7;QXr62RL<mt2%$xzj;pC)*adH
z;K3K4_ezu=s7OJRD8fd_nr_+jp*qiX#pEt(yZbC1n&p}199*kpYUjDjizqI!{8Mk5
zn9E2EL6o5e-&PqcQf-LGpR!n2&Bne0w5u}4f@jV^MsUAuN)vwm+dKWojQ_%w!rHxp
zna>9`5MAl&C&Y__f^SF8_vUbBGXf|@1zA1?nK1jhrSZ2HOroKQCsn@-W`l%5`o7+)
zD@~3)|BtS5WiRfef@$LuryqALfev1C38F99p@XTY2TrH<u9ep7g_`-4*bNMZ-s@7V
z3QAo>_ug@lGeZZe6&`0NR6f=4PcTURywvVy{~UK&#e3KtVRm+NvD`je0S_EHC9P@8
z8y_reQ2B^EspOgZoyWEJ6o@-1hoB#BT{hSw|1OxA<;R%0q+CN6=nP60wwZ-e@rcLJ
z@_X+IETSsx$HW*TDI>XZRbr<~68&&+201UZfWZu~y*W%kKl6)#%YQb)Q4|9ZkQXnW
zaGv3&ORaQ<ljH^n+Zqk;F9;f<&0q+eOj(4j6l5g7T#wsR$t<=KFMH%{RwiUZw&%um
zetxT~s@%=-u^1DS!uu28Rpbw2i*ZqL|2Ja&!-1cDle-w$qqkuz7*tj)$dza8bpyQZ
z(V9DLVs4H-pR8LkdPjeI#Rxx>>QF4`U4aK;l4csFivL-!&k;h`(~8zY{R%sLd+OFC
zOaQ)my=>I<Ng<-#-PTf@gT>dha~>^>EDrYDaTQ9#o-u&52BkVVN32yf^pa^wYseoC
z==P=h_ZGL939m@uK9?&^oIkfpw(zcfcUs64$P77)YxA;0dRs|DJ7b}z_LFBK0_)ds
z##kFpwx<eKW+`;6<;rI>v%Y7H`}sGH@93}0w{o~aAWA0ov?rPG-ZrZ&S_M}9Wyu-a
zuwquQ#LFwqblL<%TRi#2o@mdQtN59vU7w>s#$_RzPG#79gYSY5uY(W(sb?O>PgXR=
z48k|&2=E%Q2G}e|(BJq!Uhm&d5T?0jw?1q666)`>ee3zHm;+*r{0#q>p+9<pj2(Q7
z!@aFVkt)|3#Tz6ft-mas#=SIkb-k%x9}{MG`tP7(PNX=y#K7l%U<&yFuvI@Pc=p)X
zl?A=>{Nz%Ux0*(crn~EG#}xr-h^mSk9UHBZb50=&Z``L}2HF~ftePutulzWXUehvr
z54({XPxlrnCCEGtIMlxFDy?`jKps`*v|2(Ec;7iU>o5WrRH@B>FB-0XvqUYbE!Un>
zrAtiQyAxGyN{{2T-$)ywEg@-fC==$7=e7i`R~4(ou5wSOo%T}v_MbZJ=XM;fPHu5{
z_2xKRvP97!|ADht|N3C5sgX|D%8o$VrrE0N5e+4~G)H%EdBuNLv$`3?M28w^<xCMg
z=1RZRkWvW2d+Jhmweb!jKO8(BTLXRhAo2g+k7bt*Zr1%KkQo@ddx*A=r`z6ex-@AY
zLVk{wJu;F}^Lou*8q>MFv!b-Bn#tb^Ao58Mt*lDiJ5phc@>Y96G7me{D<-2c1agja
z&%K*i0i~1d1anIKR>m7i^<gX*imzl^Xs>S2>;s-);%l+s&m?RBZJbl~sL7YTIB4k3
zKIQr8sI?~Vfj$#M-0|`D_J&cz=7dIOl4D!bovOSnuO;s8cnzX}M|^k@5*Htond@);
z*xn)tg7hTq#X=Y2cHMVB!JHpXvy`*fhwrgAXd5M&n}IH6U^#1y;5M^uT+bAJ9f~)4
z`m{pbzgS}F->#(Q&KZX(Ezc`oyV(QwN+wZ$H6b%4FQ@zLYxYuCgtDEWVE6J_hk|dK
zb(>oJ@92|4zg~3RW*rE?rvQP!rsQmmFU*Gt%{k4~=Qd4_KB!b*JU3c3=EP=Bgk_OH
zL2iJb<<*^UH*9`0uzYv9s@obgc}N6D?irYpeM!5NHT?UL(`JNn7I(8KLvH47-J!<|
zrc5*j=?O64e{}cswEKi8%*}OUevI$IE`c3}w9@n-R{7nPo0~&t>bt40-9S!HpO1Yh
zFOx#|Z4I#G$QwYzw8=W@XZk9{mziPhQLuiX@j{o^A(d(7=y_-I%JRjL64bg=(JD@c
zgz*j_M5wy~0}2!?|3U75ajAbHlVErFW<N_l71=Ba8q!&=xB86Tr&3$XtQlbleC@&L
zX%pqTANrf)F?$tR%9YFbWqtf?Iz&USA$Z}T4v$e8^Lj*n9pwcGrw<gl@wseK(uA8e
z{6oaBd)lyx3gNMwg{IFyn_SrR8`F+enSBQimn9qQ4EYQcKGGZwjM3ZKoFWz5-N;af
zq!Ko$@W$}(dTP&ALcK?7O2U2v3#M)I<_vbdcQ;!NuAX;uQ%_GlB`+T!_+UU;z(h*e
z>zy<~i|?#Rj$KFRz&-X~ut6YbxZm8s?^0&)+c40?#4Mc!b&g~Yat5r-W94W679<5f
z+u_n{)N5Ew^zh->Dr)`s6G-XA`h9Oe6affiKmDYT24P6x<afyAH+j&n5ZM>$7i(he
zN}MJ<vCF)*fT|tIoFzi!RmOA{CCTQFO3>g*A^M{D=*A%aJgC95ns@n=e^nSag4xk7
zU)7nHu58lZ*0hhUy>H#Pera~07Wnm9m6wjy88eWpQv~&v%-ZM-sVLIQfv9gX*e}Bf
z*xiVtKJysa7n13J4~Rxxig84Lk%ByMtcui#h|ViDt>i)>L#6^EHmtRyz|rioTr{hH
z$)D@8gtgo;A0dmRcH|1`n<@*OHv38mY@h?)pFlm`x?ghI&tCm%@?iOc{|4B9@V5Q;
z@kVrfzu>&XEE*$bKYCJ#yZit=t6L}L$ydE=92lQX7S5>-A@e5_R1N^iazmwHyvD)z
zg!_~Xm1O~E5Wh`d8p8_%M~S;Tcs*9<4eewsRi8V5KI7&SSBpEc;^~nM8#JWB!NkaV
zA3!^yZ+Ikx7Ky!bReZzo!|T^a_D>*pwhGvc;UKHDF*)0fh5k#MYrWjD5L)#}08wu!
zUsyikTH#IuZwVh0wp=?UE~D!DN16C;>%LWE`AD^cC6UFx092%ocJRE50B1x|^?DH-
zsx^eL>QKQL?`S9!zueI1wjJ{i8h*18oxbwHG+Rn~gyP9@ivMzaHheHB_*6rLfE|uH
zya7Q9dVurjnbZN@)70TEEF^}b9yC?ctoW*wQtINx9GM1EtHTQ??R$jXd?60G);Y`+
z%9aeeFN9MA`pjOqbUD=MZZli?rk7yu0M<B*-?_tEGEYs-dK7a2Ryij}=#7hHJM_wi
z6yuI<bUa+o>XXAQKhRknqNe6Z{a_;_$KoQjNT>zWvE(xT84r)S`E#jWI<*0p@VCJ6
z@>NLQco(;3DRrasb%kpp=|-ayGUR>Nr)q1d@*h63z^f+vsI(^?tFYC&cwhSDN5FrD
zlm9$MF;8qVu{6fBec}g2jOxD(H;n|cN~;N9!dqUQf{jNB`xO2>Epz?xUtzVU{pHre
z$rNfAHolWJ+a-$I<SaD`X7xx1Uy#fC`(%lszBWmA?raJW*;j?Q{q%@9T)k4bhZ->^
zXizMQQz9<m=+Q32tym6IKz=e4Wpzj>RA@7PeTh;H>2=4d%tebTADrE|Ek*O75x?_h
zz^U-+4Li?F_kjA<I3<UgPTj}O6^~!YhKxQjytNFRqzdZXPeowBc-WC=@H;i2G;)c9
zX5pegrD_3|X>|BRx4}Q>9A0{hlI=l990DrB-OH2hz~;l2Rj4mbz<~=3p-@;-xX8UN
zn|^f2QT7g`{Yx<yArkB0n^hUu41I6|s0~S%{nh^PNoFO<H&&sNfDV*58o{cfQtiM@
z$a|cq3d+3`WUHrrd=Wl>!uloFy0GM>zkT`MX0H=EdghE<QR(XFVLfc7?>-*WPaw4v
z2}IxeYA>gro$#qu4I{(V{vM?FPw5^CYI+zhzw6s7KW3sQ^2ab3`^m+sK&dP?0c0ED
z_~3|BE4t&!Zrv_7;{~Uy?FvcI+*oH~NZ@n!!1^yjJJt^jw3az#RkXC2a{C)(*myB{
zt-(|k=MoXRm&yHsl(Rk!N%rb!`)oc`@{mAL{V#SUc#HyH9?z+aub12F9KMhzRP(#8
ze`UxZ6YLGIu1<!_4oAF#ILC!_5nQ6Wu~+`&^iuFs{(^BC(_b@|lI8TY4>r0*yy{Bq
zYW*r6Puh7c+)S;mO~x|m$|%9kDgDlwBSh;sHZ!N}&C}Jc%;#_akTAb`4UcJ!p=Tp^
zw1&jx-A0Tc*<`#jAfBZEF2JbvjxtT21&t?5WyLN3D@1+|z7=K3{nYiY^-5<V1f?*w
znA3WjY~hD!sC6iBZ#<T9c{~$ehhJq^N;+ziPaE?6o&+s@YGp}UV{EE}CHZ<Ay_9|2
z2tgL9sI)ynv?Q`4;_%V~sFa+ZN|m!yQKlm1)q!v`v#N7K8brLpnz!85fIZ7g@LSnt
zq+Eyze2aaQGy3`{ss5fU-qOvcCj<LkpBMh=_Ee~h!K%5;4OnV51nfNJYCPPuk?Ahw
z$gcNmqqvmIU_P&B9X}>{4PL9u!P;rel0VvjCqn77&=Sa8KB>$y5)Hca^X<FI8288i
zZPf$pk2Mj%bR}^8X#N8SugW=0MKDx2D;I5#y7)sdbsAsfT~~*@oev&qA(fiungW9M
z{m<3RRC-;&vX(msXT8SVlgM0G{t`x}QX`}>YDvD-l$v<#!oq@~1!JN{(-}7LO)G+y
zEt+g1>y&CR>-@$rc(d+pgR%0ZwVHs+5GE6x36F(67F{j?S@AZhR{0MOWto|oQC%r*
zTidW{?Q;QASxLNquaQETAtC%R#Mg`Mb!@t0SOf~Gx(uy3cj?cE7oN5=dY{(gSG<%o
zO*)blH$(7kVOXAFtn-CxD3J~*thqFLr`EYPb-->U|0TW8KGB~_D9`We-?_ZWQ7$D)
zUHMNhfNt`u{tvS0n)&wLLCbG?BpjD7#_MTm{VF90_;av}6r7zBz)U;AX6)e2YGxG4
zLz{<!uG*+PKN}F~X|5OCfcstAueE4o1vwUk8VeTO<I2@+R;6ZsZ<*aw19F9LaEGPO
zAYP&>Tg^YAHAm`jN4~xczt&rot{=KD>s`S$ZQA5O52CoGKj|0i5VhG*)f8P3#n<s;
zXRNrMXl?qJA^w|Oy?X|_3)dW{)|;&S`0+z2b!sf8;Op1@$?$`YH*Gm(WX1Q9xGmP4
zFgT8kadsvgQN{{rF-1Fs^uk*m@CLcP+Byy^Fw2Zq{Su=yv)(h=oDg$?w}nr03jg``
z?b~Qx1E09Vg#?Z$c16DO$JB6Ur(DSbMpfJOFAO~R9{=qCzXXLk<3rd>|05BYS#Y0T
z@x1PipRF&`;!oKWX_Ogf#^b7p-iZeo$BundUa`c*s3Fw*>QDH>6iLK`oOG3bbW!Ak
zMFpI!zZSiVWY!-pnG}`>1xmta$(knq@>yS|mn|s(OsA9HL*AErk-p=<3(e3a<9Jh5
zO8d{PV?&dKVHMAk<q09Yd=7FW)2~0j2c$Xig3}f`H&h4G^%Rx*I@Fe(KQdh;T6`zr
z0BqMte#Br&ALwA)>`&mCcJzrVm<3dbI!hgs1YZc%Ro1WvC3Ar(IhkwA2pX;1Sl_8n
znOCe1u@-CTQTEpUN09!1aPD)xP(?+BFF!of!X_axQK|P1EObonCj1*+sRFXR!qRuX
z*~vjc00>O|+I{?77MwwaFN@*C0{Zh!uTj|mgY6^x+w@zYVc4pwDi=w)ZrYPp**m4(
z8(sT0{@h2C|99qA=gv2D4q0VD{*g-Yzz|7&12{g7#pY~9sN)oWm&45DDUJ`)Dxd%5
z16j6*mnaXs><_LJNq*<x+jRfs#|w3tINj_o?uysD%t;&JM<skBS8<oGnvZ-vY#tNk
zr3(gp;=@ik)Te?7@uZwG+)L`a=_`#%qTtsyEf*M;?~Xby$HnW|oDCu?E5#^5hU**j
z*ccY`yP{Lsdmx0-Om3()UN#^*A%X-oh;NdaoQ!1b%|w~NesF+21-Jg$9v2uYOKlGH
zVs7Xai(}hbo09!j!J=J2sRIJ`XdN;mh7C;@{(nsSw@xwiaj(;r%-5eqRMeMSMq)<W
zRx|le0C`Q1O5^KeGYn2FUDweaqwR1sSr*%Zz@kh*$8<h4r{Q;Zc*0Q}!8^90syf>U
z$4_bQ3!|YW_J+;XpoDnrjr!{}Y`$rRKgEDH8yGS>;k}vk4~4r`cj$MoKkWdR$irTk
zt5mAOe64nSEa2Cg+KDQVp79;WSCR!QwMhnZV{hqoZK!$Xm?5BfDzfkh5C~f3TiD9_
ztBOq1#RtNH3(QB*_qSPo-oTWt3u?bURZy~Kd^>Q8L%RFP_^mxG1xBeLHS8&`MN`Jl
zUf``|+|yX?3~M|&)SAuyKrNdc&J-m2FoR|`q5RaY^vatjCEyTc&SlZ4_z7xE<5|A;
z;^Khpu#k}*sax%TLd5aoEB*}*RbG#RW;+j|l5X6%p$RRL&FX9N0w4)t?57h?3JLaF
zr@b_NPb!c;2<F50nuew+gBe^pMiW!ZS!!~?`V+4FF0MkXZ-{TDaIR7JvF;`w7eC%F
z;K9JSu;$tjS*L;|v+e$Wt=!P5y>hn8Z6{{7`csXN`_V(cqM*~Tv?_VIwt#@k%XE?G
z&A+bcKjn{7e?RhM_*;H!ZF$C_KhU&oemJ(gKu*uu&7yTE*Bt5L2V_TZ4+#%h378C-
z&xS%qYTiU{&nCjwR(THL9!V{sJy$G%1U~+Kz@WN#NcKK$-`y{^d;b-L{g)|5u8ijd
z_hjkC<Za&U(N~6AD(RY4C(957w;%Vu6M&$+;e|?&Kiq=J?ABR6-0}vo(kRUQE~-+6
z;B)N+WVBtcs`S{eJFcM_dbZhGK^{?l$`P&Dt_)$8fU+uXt8M&81C0_ne=87dD4r5e
zjsz^a>>?O@GjyN45#z0C;%C>IN~-tLH}ttbyYUPyhl+@5y<*jJ=ZHyHNdK3d5(nbh
zlRWK7z2zEaNr*&NT0n^d)15hT90{GPQTkN(zYm5+lo3Er?ZYp{M1#M>XwlU(n&;0q
zCFyg$Btxys1<K0s2URzb2RD3&%ElYwNq&N{@hA)-dL%=5=<g7DEOaWdF)c(^-}T2|
zpM7*bxCrwlmiCUK(M=_S(V2#^SkPZY4h1q1uNj%G2j}FMmk{V{U%!5J!BY`DrfL7C
zm^@AAdU3KNXu^L`w6jh2KNQeK>fruJeOgeD9yPlYh<D)^KULgcKe94wO1DchOJok7
zr45%GZ<Mu&L54w#`nvAT(lXrP(1hgVa;qd-kD0;3EvV>wU)G7@stYDoO6G2dH)}N;
zUX}Q#U(nH{b!)M2>3j~smt<w0`k!{W42ZgDnm#1JRhOW-Zn4|m05`o)>}TAUilt?P
zXwZn(+$V_#Q;(LPACl-qe<Z+H=CYCcn`;a5h|NcILEI3ybM?Z7X2kZYu|sX(qq5V^
zsmMqk6KnVvL$X((5nh*%5C2=v76gxWQ!R;1LD(w3_bIJsWep^_BP(|s9=bCKavq-l
zl^au9uR>UVnAw^D$cL{sOT`?a#Lxv37>8e-x=|d-<1|(@Y)*=1sCmB?6o0oLlaTQH
z_2mwyMJ8bALYm{T&KrT_x6pzU*bBx5wElP@5z`tu5}^+mi_8?<i}x+l4GG`nZwUID
z6Cl9`&hvwd4gCL;+i(GiUTa~O>hE=^6L_5^azQQ-E$vB5`rz7nxqj#A=I^}84BAya
zx0o>=2Ea(bToM`_lFAG!%Zyw!<ZP|H;4JMT%3lbIZ5OK-PN3cYX#T_QQwxgY<4e#~
zsq>Eh>a$An+)cV*-dg!zhwWdFj6e1Okl6z9L}%u`mG9y}5p2W{D`^YI`j15il8hGL
znd@sEECmR}pK(=;2j@ORV9PNx1OB=xOBOcth$A?C`}fp>f(6@9@1zb_us$^l{RJb)
zT^{YsD~=DNxVYFwALFY$8qw(&Fg!dg)DSPfAuNahWuK25#T_p1(dS*=eEH&q>hkyP
zc16r#(#B{|&#>TIPellc_8Cx3aq-;~FoE@iC#pfu4c2>&8bO;{G<ljnpn<o(;3mg`
zUq-UJG=<--FO7}dO_Y}0zc3N@Z~q{05`yZI&)YU2NmyTd!O}}Hm&}oi<DlqL$7&Ml
z!Uc*W5SWdiPSrSp<?jGrh_qle#9RzM2N>h$i}#Q~QxAFTEUDrOLFGa<=B_C@RMW(7
zX<ITiXSK}D4P`6Y0odz{tXz5os|_p6%*=%7h1XOAc7sYve|I0>EEA*s2W|fU6deZG
zBTu6!z9Ha-@CP?UCI!NO-vNN!Y3sW@n{akz2%afP(T4@oNhQqZ5@y!`uqHpoP`Tl2
z#@XeSiKdwHlBS-pL6&OkX^G;deSeFml!mQYnT+f1FT>as@pBVTP=rYep2-R0413DU
zTyuFADX^U(6eifV(Vs_~JOijHSmA!TSMZa}NBIAby|)ajGfTRL1A(By-Q5YU!Gc3@
zcXxN!;O_43?jGFT-QC^cJJU0r?w+UTo$LEQf4J^rH+$FKr)uq0tLg-7;h4BSzg&->
z0t{Zo{r<uLUfQN=yMA`@u-3`{L`qFf4L-}+dgp#Cdcp_8Mg)Gs=?2?yx!n&Ix5tI!
z0LU5nyxtevRZQjM|4t;f$arnIC?1_>+8gfgEPcg&(Q8<HjVCUlUrGTROTw(aHDDWR
zwlg#7|9qw&aC>e>ojePhK0Y>7zj&>%kO39H*!4)xY&%A;+R8CX&rmZ%?NqK~Rzudi
zSaqaP<PgT+@{-MGw(hR->G}%xZ(~TD^v05DMO{I<%g`A2yPNCntYX7LZ;D0pAddTu
z^zm|orBq&PF9^r)0|sOVMuF{++ehtM#zU))uAX~3pBE#z$7?0q(M&~xF2Bv%>HH{P
zk^|<{?*qnkT+g^WU=W$hGS~guC<%64?+A4~lNEL5xa{uWOa1My$+dsgETBT>=I4Jh
zy+1NI7v-q_YEhB>b~~@>v)^`<^hchkQWvzaajh!7#mrbkyUl7)^y_w4f!Sm>^3hVk
z1`~uwrmgL@kIB)<XSUAkI#b04j6wM__v7ZdRsr`C)q?nD9LdUJ3`{Z-3Edr|{FTqn
zb8V)wrfa2ZUzS-ELVp8G|MutS7~I8W$vDULh?YDTN&R}cVnsTDWuZC<e%X%ThUsg?
zVYAVj$h#j~Aer<2{j(?JfIM;Bf$;tj#l&QBbNfN^J~hpJ1^8!}a}?JDU8Vhf^4PhX
zCec>+B`nxF+pC*K1glrvJnM>A*V^Kyb>~NnV^^&J>iF)Df9s6oNW9fPXWd_P!!GMa
zaH9Cm`rVrYTIj%}<1!jaZ5I5q`VO#gy^v&9L&Ne9NM+=Z`ieM8&Uo^@8M4Ep6WkQ#
zp)jArFgfBg${uTOlynZ*ra3ew(bM}$hvu)`l{>fUs6OXmQqAL5$W$&&lQz6yz1CPK
zi!aw&tG-+U58vHB-l#amx_r%#OX0D4xVpanYW!Jt*qoZmsQ^OHaDVCeJhZk2kiRLG
z4_hw2b;)ciI%G=BfoqZ7H@GYRaqR0`raG6uYxVc1a!8U&t4&@^F1N<lt6`e>^mGgR
zqnRr2k6<eAZ?Dwk<V8PDKc|HJX6v6{P%e1b>}!>IZEGI-3=V6Mcok>eS0&Oi!(@5n
z+iDh*HId*d6rIW<S*zldnULknGmPWegkp@6-9RVmU}%Rm4C3o}uWy!Gb&pAoFMA;y
zk=b3f4Rus&7M4`n<CRMy_T;_VwA*n^bM22UACu2Vqmd%=$6?H4z;=&4r(Yo3a*%@i
z3rV*yFf1H>=3?^o!8+@z=`}s&_zT;ep&IKxf&@4AXn|4137pV-FDw{+y^d|a>p$7O
z%V-c(2{XC$NE1y;)=WjP@L<q<KV5G_&ay}I`xlk-*VD19FHa1K_zA!Z!M5K8VjGbD
z(`}MT0#$e@oOje~=K&kZQ&pf>T(EYQYEb))Li(4~%dwVzU^5L<UVU>hOItj`MJ8jJ
zIF&O``mvYXHmX2wrop1VS;Koy1-cSNEp%TpBel@lFjq5RRt9{z^ieJ^shoy0fle~w
zy@+S&<9T_OJ#op8<%|$nrUn-hnEI-@Z;%yC5f$87S@qwuoMY!Qa(7+ua0IdpiJrsT
z%QBEu^bv&vBkDTVXcxj+?Qm5y+O<<0#%a`oWD-6D8Ur+nUo#;7t#kTg*L(B0se^W#
z%5%06A<4PUUi;^{h_NY%iLYvuXR3Uw;!dwrB%>Nrh&;`5tnJb{&t7ghzdJg0^Hch!
zxDi#4V^)N~UY}fCmSW+?8__EVD{r`Oq<m@4mv!SGJ5fsq`P0Xui%EUrb7MSi3Kryu
zt~(d(XkEKj8)T!8#fZC#iZcY9T0+9f7Y#t~1NqbUHwXXQgS91k`kEgJ>Pal*jfF((
zDvTAD>g-J9*JfhB${7B-a=*{!Ckil~hI*Okk(mAUY1~Xzb4C>&5>G!F)g4_Wp(<_r
zY2O5^5YT;b1u~XlGI7cCg_hXKKu~uBvXwKGBi)I&ZD?^NgPeC-N%2wf=xy!~L(K}X
zrEhp`*6CV(fu29CR?aC!M`a4r^S=zcv(?%l<28O!TvKnkc)B%N$hprjrB*r7Kg7{U
zxFm?&ATGWv1NeA7CX<69q+denKb-wvu9hj_N<J)>l-SDIV;58vv;i-F+@vUuzB_AZ
z%VyWytY(2tzqVVZEOa+JDrA3>X~ZrWIzbyRLo%|It}b%!l3(~HUoV-1Qs7)8uYIZ>
z>#BsEM{%sSdVg8bU<Ch7g*LxVNA5j+LR44vUY=#=*^CYh_CyH?AQ?SwQl%~njy{T~
zK3cSe)ztfpIR_=LA=J7TysQM<cx^{K_>;>HQ6m*T;_q^Gf2=~R^w0j<@X5rfNywbG
zuT~Yl3g2p-Gf%0D@|vMEv5L(kuu&nibdSxU%aPeh9Tg_p_&Xbl>)RlgK3>#KkIWrY
zn&#!tcU2<^S3Ik0>ejh|u1>0G!YP=dXhwi(0fYf?IXtTCK6|^VI4;LcYGLN5*A<Tf
z!{ZZn#n7<L7#Riq?&`(OW5a8#xa&iFUK`CEa*jFrf`@){(s9!?T-H%cCQ9p545AgS
z3RQKpMQM7g&9w_*cfR6(8z_GT&%#7tNPk3(<L}#&uKKl#68&UocNX1k#EkSMYwB5-
zPM`~rg{Ig@kWD@xmI~xAuqvEu6b|Zn56M6`*LEHEc>M+;lQ<*q;>#o1RN`j2JS%bJ
zmHLfFPA*)wj!J-+`E13vsTGhHaU@&^u*+^AUYv`eE0$?)8o~D3)6lQ$)Q5SmFD#oN
z4hs0^;dk~Ory7cO>rqfzhe5Aq%DT=OP?mRD+H`E+)yzcXw^{5jaht5yo_Q@S%5TqR
z0VM0DXh`tZlD{yn#s7Ql%zV5eq)%r@aS*^(y(z8kzO=TbSXn+68;s5Bu6=f@1vae6
zzWdVcj&shuk`W?PSId3AS&dUq@zmdfdH<EGgJypJ`jE5nODoQ~3y0(~d>%l5;GR-!
zlmuW+DpW^t?}w~tAYk|I?Td!3JUJ3+0u_Bk9u4~;g%j&Z!JT&PN#!*<?nd<I%rg|?
z<I7Q%>8K@X9^Sy}32VRf1qA#ZcKzcBS1<Fk1qwi@r-1e1NCfa5Z&ZbCOSuZv1&pu#
zxUAshSGacOJh;nbJFrkw4|k2LrRgq!!kK>6Y7T=p+Pr<Rac76k^3gX*;efvH_o1-L
z&|F5o(Ji;^d|4ZP?CXE!Dq8^oFCPwbxL36@8aaNtyOK><(hfM$HJ!Jd@FVbG6YW%B
zBJYG@&(!S%YYO-so&52=__wRrm=B%yY@KIgA!V8s{+yTN*C#(1z!sir&4x#Zq0Gza
z%{o@I)Co=3T`E?cQ5TqhKOO(tk^e+;zaAFy{|1A!EJn+TNEPc`Ex8xpoX-zYFfmP&
zFk3A>K<6PVz_1oNx3-wWNh_J4^i}@b9eK1c0W)p@Dytqrz{E7)PhFaSyFS^S%eMz~
zMT$3`Eg@dQvnQ1JhsySkRrtFrhj<>^AB}x%XSv7{tICx6oGlqVL&0@-6Nnj0Pcve!
zPnXx!6v3t#4jnl?D8Eqq@1FdxmXu-x1uv)Ew_e?A6BQS)!C;E6clI#g|F2`HSMaJ{
zDnl;WnO2ll-28RY{g0p1GK7RvI~O~JtSmn;@*tx4sjY+f|K5Lag>t~Tv2jOM)eL_>
z^gp?Io?b+}#&W0rP-?;dID+3_pojx(lsxv-6X^dEz5%s4mw*MMT%*$s`7c8SB-ccV
z0c=wd4C&W@W}g3Q<v$G-@PZr%AU*^;jx6Z>mxYGo$&>*EamC{9!Lfha?tk+TSrXu#
zx3C%N(f->8`iE%&82^8!{hw+7w`)QE=MVrQ{r@?H|M^+~*7#o-1Hgp;IfVZ?gnvM&
z|9{{MFMrFNUiEKz*gu28H~}p{y2@eQ{a%@umsfu=FRdC-A6;LI&*y&<YkwLV2Y|`Y
z`hU=|e*^S>gTn&<V4Rn34yUQ<=&A<xovp^<GD|Kvw^?fv{wrMmX+a67%v;96V21v$
zP;r3)fPwErw?{Mx{)%G%W?(I4A3AR>T#XiJ{|m6C=-?3(3zaXD$-6oyb5+U)Ao3s%
zj-Wr=>7SE8!jH6!FQ`!S6kUHE?ojq9-#wgETfN@rySKeT1E}2IGTP2RRF;q}dsxt@
z)`&2-eZwFEfs4`r1^Jeod2_A^1mzZ(jf)rf1GI8lE;bwwIEz9(`U8F>oD8_{P5Nr=
z>FM1w_jsx+vm;B&s(7_;<Oo0wGBlEOHL{Y#cFu7A6$L27OW-Gg7JMbR5(pKrD(}zZ
zy4c<L%Eh#^XUBrBrD_$xv;X#{Uk?u;K&s_F4PJ~NPs|QD+3k5pc&3+*LFbJ=w~jeE
ziK8r=G6}l?yJ80X=|BAI!<D84T0zI^H5%qc|M~t8An<VT)LEkb{B=kroE*~unyrHj
z5x5#fYR_mgLxeLucZsA<7HR+Y?i1!9(St<QYFN!}5-O|oB4^FvTc1II&BfviWNOo6
z7ag8OEZl}Vrj`{*8IFQY6&+Jd3>9gLs5AcWt$w!*iZ>1~5Buzs6B+lkg4>zBzabZ7
z&r}j?H9BsPDP+H}o~rpV4)B_w?!PSg`#hKNX)RV*`Ew>uDH;OTi;F3!G~2NVD&4to
zku15;seZV0{s0Z^)$wmdS>$jyQ9?pSHfOMIce>P>nVrpQY(xXpCM@mIGqSMg0<yKs
zsS)N9PGH<dLC4t8jpAd!*6r7l{Bs#myvMnK`wkk!ei+IQM7NFUeY_a-U<K8|Wu)qo
z4SDyuzI&k;j0@%U$CzKE5;41;Fzh&~2l+oYNf+elv2i8cQo6TnIb}X+Sob(>YdBK5
z<N&DM8*iq*!$W_PmXY}pfa`$<5X2jW6%-XgXufQrR_P5Q<RnoOJjbhAbZ!RVwvYN=
z0#vYm0Ie4U7`7c8pako2uD1S8Z!DFK^)NR^jElX?#B_UExm$J1#jp%Lh2}4t@-O3Z
zPyx{^q&?2^VB#KlsH<UO&M&5OKE-r0daF^wQ4OgPQ5)9375cWgm(99u-tm9-RtqQa
zBT4JB5B_Ik_xgE}VfOX9h=#U~E{f2=t#rJQh63p2Ycr1|$2;5aYUd|Rde?@3UC&?J
z0Vw2WL7-PwVbrHl2m=OTS)<)J1ExWvMTmQXp0u(WCw**)V*$xcpCizVCwj7r0AuXb
zE~Zb2Mdr%Kl2wrAQ-7M&2^V;yD!6GgN&0k2TTt3#YE3F_w{cRya`VLy<_^c0HDo!p
zR-{DtcnDF0E55pM_6BuwoPiT!7-GVv6dMb_e>%I(x?X=j-*=7`8Glrbih%2%r=+U3
z$}79D*in5JS+2pg;zS7Mj0MZ*DXVWPhmLmRql%8<oZp-%)6%;#zdFA$b}MZ?P4lnk
zoB!wON`!wPPC!3XJrw~=Rv@_lgFxz9DQ<c#?Du4a?KS=Po|0iw!Z?wih}kRjnE5X<
zTas+|2WpF!e_cy&Nf>WCFR0IKjxpEH0+^Nr;l{T~DmC<+WyC=Qm9oMGQ$(fC5!#$O
zIU>y0!z1@Hm#iG^A6j?v8OYZPJpHwsL_+b4rn$IZbZU_;>3iFvM{NXz7!pFTon0I3
z2?8l8DDY&L3DjB$Jdq6ms%_O76OA^PI=a(Uc@Bag*&?K;+v9+)t}f;W7Dh&ZIvj_V
z<ma-JS$8ksdHk9!^{A;UOJn0j9ahwWv)J!wCop3O^DTVT%k=-*4_1nUqj?iD6_8sn
zn6Lst@)8e~iZgCC%~k+@$uIq9avvoB3-QG#;zluy;-x@F;`^EYs5A@Nr#t+AGt3!b
z<c^rQprxCCqJ%8|Qc|F^(=;h8LJ-8$6hDli9YF(Azpc>;iiv53#(bdP@Vw615&O(6
zi`Hc}@x{|e;a*utF-i`irlM$dIVE3+?qKT$Z!oPBoLw@QhbW2sFoSw<s~UM|2useI
zuk7&EP^e#e?%VG<iDF8Tb*ADFH?bhCk&V)AgS306(^zBYCbIZ0cVZ+h1l8W(?mPNx
zji=$l1V3k`CyXd$XxNT28LS^!v99daR4I*|6s=1C><vNPuAP=(&}&hh3-3BiM*#}l
zIHOpJPk%36Y3R8%%*!|9qW+JOWMLD0Vj`ysDk<+S%+A(G7D1L`z4tZb-EVRB8kz<h
z8VNs6yh}3vYs#%Pw<$zykK#U>Y1I6j@?V3K+ZOl(>udB>ZN8=fj?k$|zyad7QzKp1
zHXsva*=-er_9D8pEi6lY{m*;U$^njCYCgmzbGoPg7(@qcZoL>bD?obF)(8t+fnbsh
z-omUum?(XeG!}gcVbH?1VKE}2Gf}?MYea=SP5WB0*iSid0o<VVrXT9ooT??0p0%PB
zcm=ig+{(&I-dw$~dQ=Obw2bK!G};776S-{u294(<R5KrAgR>PBDnU+OCv|o!<vjXk
z`3o~MbFx#toCC|-*oAfX38WCf^FY=a4gbm2cnN4x!VlQ!m}z7*S8dr-=G1rovSsCW
z{EbZ<KM_d0x{ll6zF8-&$()DrBbLIVm~V+)-#yvM2xE{UMGwe<$OSSuByG2Q`02GP
z;k>Cg^X&iRg#HozGpK-p$}neya;pNRD(=!9;t`uQ#w=!1n<jrqa7ZwWb<%Pa8CcvS
z@Og8GM;3sF+1nSETJX=I?zKv~$^*K2zlCmDp@dbja;ac<3k?_OZbr*rOrICN-tnLF
zrU!BEGiW3utgo)hE2{ey$rfw@YGPK!Qd~?c4`GZcZJwjQf9V7i>@JZU9UYmdxt`WV
zqBzl_nQb?ep$<iWAC3thp7ovfO(R>cu0C)bZ=O`_)oA!IzyD<p^+4Xq+^Z%%`)wXh
zr_3DYxK+Qw8o+ygp80sy7vB-L1?E6Fj=3ULlscnF8KFgq1?rNIO4>~G4bs#Qm3|~@
znqGQN#+W}e!x)rn|Kv~jbHzQfhImwnC!Z!SNmf%cA_A4Fr%M2;%4SzoPC9aFVZk@H
z)}HJ`7(W3SHTAcSc6U0r9&j-<ZXAGkD?5^jUp!CPBld3Pk5KI$4!5DhcPgE>+e-D_
zc7ubJ!@+_g|53GGpD<b+bHlQbzuE%3f1S+V&sBROz3rkYi;&LHnmnDFtk=|XoELlF
z8(n4PGWx@~o=hKf(@6L<9-E9x>43Q&mqrx~jbu`ITfnW5buL;bmi$>w4-+ILq}4EW
z1Dfr^(s2`M;97XGdhU(WSV}oSZavy++5TwY29O}GPCc!iXuIkL5CUKsHq4_TWv64o
zXy=u_sns1DX%$~pNjJA@ouRGi@A1Jq9A3Cg+0Xy0ry&Y1s>i7t$S2rS*37m)-k^>E
zbSg#$5XW&{Nm@2HUrp9(DLms$MLGI|n<gd9I4AGeCC`cK910=?`Y|GlNE44rm^7Fo
z0*%{6&oK~{*31lEk}2ev>+0n8#A=_I*8N;4OAS0+tTEg!Ygq3Sy583vj3%Brv0=RY
z3}@oVVLV%DY))!IWx}8VeV`b=*)6m&;RD;}4d#KPD;VLwZYki>BF|UtSNG2y;a$e#
zwOvYMRPQ8W9J#w7I)tu~^ht6eT*CHq^>Kct#oIDjv8r#E7!#?7M$eC3;n*;N%|t|6
z?Heg*94<(|O=uifeG-|Zz)c}~@+Fx0faH2_9=Me$j7}pUjFcl<{XVe9jnHj6GI=c&
zCKHvxwAwO4;OFxWZjYC3-0kM$a)yu_7Yw8%@cFd`f`Q?op(XxzfDUcr?fLwjf66>2
zvh9El<b<lBKXc`lAerf!ki%)6VFiiHoY|jy|3$WO)#n<a<2Y_Ee&E-}YJmlo2Z%u?
z+1nE*XIESZht3-v@5x)Ki++LHLb@1y%u__w(VReo3Q{dA>o8$<gSNZ~zM4paoFWJ)
zbT39BKgabj;?MSJ-J+Bh{fhjJ#|646A#Sj)?N9`?mTMd@*O#QB2DkmVG_+k7)>8<j
z>=Hut5cNgO)ZJ<aA*TV~YMrueR$Z>0?^h4WC@6X~T*G?d1F*v@(%cmyPNx%xV>$of
zX@2|CZZJgfM1L$4R209>4M81Xj;bu@tGC9cN5;+3p6v%Oa|UwvNqu)lFtF6-7WZ?l
zsk=~UgKcd<dVPiwnmh=(D3gWlYJOtXaQ8${PKg13s$+5RvhBh06rbL@OvH`K)-W<D
z?&<%osKrx->NE6R*d?IJUMxB_m|^^7<YP-3=)+w@N7!Ht>2}?`#_{E22xF5#D6ajC
zw7HG4va<1j%_-sOw#FXkVeV#`U5gFDj4CRb1DujApYrac#$uYE?Au50)f{T&t!5v8
z9ZR4Z!_odWPuJ)YukF1&ldEHfg~5F5H-Un?2iTRZKf$V>;e2?^ujYC<u5he}b~&pt
zyUxR)>-;;J-41FbW}?5`*#i<h9Gm*IOaOu=J}_ekRkkunR&0*3Tr`_47Y@S8REh&x
zk0f0;^WiuW@b`o-y=k6rPzEDzxc0a9Wv4+`GC7yT*-O?O#YI)GLFo=kCXeRqjy@RG
zDPeN>$CyWS?tQL%*AFq0TcHdR-E4-Qqlufj4SxiLg;RKNCql-<QQ`oiyr^<MgKplQ
zxiNuk9T*PzYw5^hfgxN$U3Son1R}eI05mXV?!$u*jx`c2ikW%UQ7JtysNMb80U_fY
zkhUmax{J|9eQhtZb&ry5`7NTb0iD0IT9SHn88?LZs5&{TL?UgFb=h@Aj$ir;+wweL
zk2Q4x2ZO@0vQxTr5v;k+TG5=g+UvAENtQJyLKGT}m!L%Oj+5%)5sf8Pf*t{07AB_6
ze}v%O(Q(zc$Bj-O>-{uG44g0T9&b%k6I`1BX|7eo>0}!2`<R18&~aLspu>+BH%3dk
zv!-W=a+IKy6@CKk@(1CKQS$Q?+;Px%Ta^fVim%_S%~5`xA1aseB6rt<XIV6Ss794<
z-xjIeny$3Ek7;u5@=f7A?(CgP2M1U4sTY3y1{A$lF@PYkGL)InuZ#CKFkREa+`r#t
zCCc=5*L|-`;QemAA4o8RZDJ^kLC!wO9>QY?ZghU+$#qA=A?&U5(J$6tpIOu7dlSkI
z{iFqd^QlY2)1g0BL|=sWiYhcAzFE8iFjB$YK>KO^S6cI>uZ<6?>s_6i4?mozJbr^j
z-jcvT)K>(OhI8vq&Sws)Nww1=LAhDCJ9A%3YB&-#qMm_MggNxk(#TSU?8<wZv#^UY
zn{euHm229n&W3$5h+v0u*2iDivLcB~jIWCA(1i8$3?;I5mJ0MOrl$FG$d*88+s)f|
z$|?!uYeota=EPO4V{y6L<$m0wMY}Q#+lB1m_vss53K(cL_S1)cvOH_wZV_J>At}sd
zJf#D#B@qbIcd={WYKdti9d;|(phzH(LD&xPGNLiDKwiy9HzSvT-TH)>-C1D!9U3(C
zaUvT=zO*R^%6dmhjqT&59}QmJox0Q3EI9qCUM-fq_a!3~*9zQ3eroA^DG#kUo$gYI
z;1cU`c4a-J?sub8Jsxz0d3l1fps1+3&J>s_j3zNZMAA8Nsp8cl=qD6@sXkh18A^2i
z39K-@=De;G|NH(>bltnm_NyUES6|&|5_@UsDExrB1c!D&6)*vg>q(X52>-*R5N=wA
zTEPBPZu@rlS1DQ9>8uWmJ3y@hpy=id<@K5q$7#d+Wmx@g1#pHio3&8wtj)Mxe}7_C
zN!~VicsQDu1h~iZps(*Yu;q|N{i^=0pZGPNUorGbYko&90N^J?2$yk-<K{lJiA!%+
z5^eD?-wobzO+x?f%Jd_rFVT>1{f(=&Vf}}HKMpS)Q$<4`v0??Ui#u^w##bv-oEq(4
zK=Go80FTZ%HJO>FEHxAzbAP*(vm>-dDAl)flM7sCn7#J{0>U$SM{Lp_6=(y3hKeEl
z0s?T%y3@z|HAmdNv9y5D;lxI7eHx9hF~INkZTKYuLoC|IGVHTUJKggNc`R|zc>2YB
zs^7-{e6m=xzp-N59{!!CeF5AGxr_a`qs~MOxRDrip`^?_QNdfddvYM}elm_ocxHQ~
z?dl*)fGR>_)4nhdNt1kD{pLkfB62`!`PA(x5eA}QPaMiED?jC+Sg*Jt$`fsfM^qb8
z8FGf$CHqn}B>rN(Dw?|GVw1CUYPBdufz!ww{tduk!)K&5*%kXe60RQE8l!6$lIrdD
zS}R9S9j$-<L=}=!&-Y*xzO^|=oN~68zEscmQ`=-U7JDPf$~29nNZahFh6!~!J~I7E
zG$Y9xv6Q*+IGLr#xCPBFS(sTbIXpQ6-3B>H6%2!0*80IDKF9eIb0#?*1~=}CBWFyI
ziXMqhlkFoaUS`0kr~oyVRLW%bCjnkd!7Q?mi$pGYbA&4NSb=w)UO+zZ>Qi3vAm6`L
ztHgARHTRl1Vp9bZh!Fqeih>(*MNMLzW+Xq$2|BQd3pzZNI0Hb0o?MZbi<@alOO_%k
z^W)=+4xEhVmHj22Mx9>B6|3&gWg&=5PG>)%;OCE6T)K+hm()|2`Od!V*6p5vco&D&
zH9n6=WMyS7Lf?>jJX)pNT}AB$4Og^&@N9pTmQ$S9K1!)!<uSbAUgzkJo=@8_Zu{i}
zFGTRA-Xos9-Z?<e5BD=KRzl#G8cspla_pw%F{M3+$GyDy=q{e$=P`0mf^l_EV<HH?
zcl?NHBo;pBD)Uff4;oJk6O7(CEWq&#AnB*JZX?b;asSsLctIInSlS5#n!gAQv77T6
zw`XV8a_g8-qBFrCJ7mpUhLA)2d!-mezFy*!k|DfNgcnE@EXM=ho5OgU(H>3!x`|p{
zHL9TSU_4;M+DV>#ibyfqSMy@tOpGqK)lP%rOlheIy_o9J26;p8X&T@92+J7?%as_A
zq2Y*qw_*$HSN%^e?FJ3_#9`r*m8eQ_X=3VAe&_-vf)p-Yp9fu;dJN?ubdXApb4jm`
z8^^|kYZ+88n!00P2qROG`;5xk4qD2S=r$4qx7^JwS;q;@M-tDJLVoz3vQAq`dpoK7
zQ-{qrh41=ZU4vE4rZEbrQIEm~_2Gy!tdcR6_n`7JDjxW@l=K{YShk`k{x@=mfM3C|
zkJLvao)(aSo~(<RYv`ko*pkCfEP$@_Z==X57x&0B?0Ef>4uKYCJsrMs;FM(#Dmv6z
zd0g*<1TVcj$-uR@UW*q!h~<M=CCDgOAyjzw(djvi*J+{(`pgg=5j=8cPGg!}6F%A-
zA=aR%QK)p*Bk%D{&npU}-+_;cBME4yKqMB7knDUK3?7!LRF){W>55m+G1{DS7ZyL=
zZfn6b0*DOec@tJ>6M`SjUtdALIC8Fz0Iz1g<Dr*^7SK6GPEIaT_h_u5?e=mGF{-N{
z*OR0DdG)-0Z8``gA#emh%-lL4ZkrRV`G*Mmp8hmmS-!Aa7Sc0uceGxfBXB^qbI7qF
zX>vJwb0yK)_E)*)V?T(5V7)#;RDnn1(t-QZapksRznoK8T-;k*WE+}6T%n3zP5KK#
zEog%~g@i<{R>A~ztozr&z93H`N(fF0B;eWXR9%>96R6tuTf(ciKzc8q2Q(-un`+o=
zxQ&JhYg~SwS$V^}X}V-7^LQjxE<XuvV?wuwT?va<eDkp)=$pNB)1)FFgf$rd!?`B$
z@F<9oQ|A{>he+Xt=7mvOAD41(_s&{wu1pqeG<pO~@90KWTl6b6^7l=(+(`*neC2|4
zkwxNFrrR;hZsv{>V`gE&?0Q;zK0n9vP^22qkx=*6H}#QTro%}|X9^Acf<Ewhykt(Q
zdB2*Z`MADg&Q(J0d^|mDxV#a#qv_T_46P%=cG^SrAL!aUoyY4{B6+FZ<|t(ZPq-_I
zqaX`rl-gc9yy8p4!3#n2V=TV7K1uRChVd>VzvH|J-?IIo-Be@;R{m%BA`4mHsBn^g
znM35Jv5{eR|32HRx>W}W4ht8FHCe{xvS-2T<i~URb0Rf)2@=a@JI{fFMZZCxI|1g#
ztD)YB?>cI}rOhrhb+s{UUA4<eX9QZwx#h=VUqHZv>?p=kS_{oAG*+yZT^LtramOOz
z6zFk~gWo?X#>LDRS;z$jEL|6OgrdLmZ`I)VQEWp;(!$(t>AW0N4fwJ~=(dOwwe@97
z1U`T^S#@sjW5H0z8b+$+cbJl_6@eDm4TT&L#O2pB2N_D@g-8Wnb5l!g*Ds<Y`@dWJ
zZv$DM>8t;It>u;+t^@tH1|^-aDij^X`l(fAr4quB6!e+X?m%kAZd$xCF<wWEd*kJ2
z^XJfk!>cQs8bb$H-utWooGw6>*?myV9H93LD+-m;1o`C`o3^Yh?^CJ{zdGu@X8U72
z#0?5r!_B?XlmH%{=tlMRMa_|N&Hh!2O-h<JKTup6L((X3;iKy>FTu*2aqkxs+8?bK
z@5c$9U%>~D*;}vgu&#C4-?L?<f3{)%1qbxwZJbZThPj2%yV_f?AMK%L@a?Tj0y1qM
zr5;Alvo~sMHYnHl$w;}910L2bz+l*c+s3#oYs644*Or9Nh2pihG&|t3+E^gira#{r
zc{mUpoxekI)HHGfT0F9ChBLUg4G0W3&rPgReRJ*pb*Rjs2gYfCb|GUOePYZUx+ww)
zH6BrZ=h~~t2zZBn18#4aXQ6AhBC9hqT8d8AMiRIE&IgIB?=SO?RF08#T%d-M@)9nT
zSzeFABDKKoJcMZm9&WXaEQG<<>>KmQqEb_sBH>hrFXTUn2x>tyA;*73cR#x^B7atB
z+uw0&8=5j+mUlN5$K47!|MJk!>YXh7PtMO9=bP7R_@ul+LLO)Kthk7*yg^6W+Ng+r
z%;d3z46^O_eQgd2?gC+ZAC;=m(Zfw<W5JZ4*zR<hEdo9xmaeU#3DyVB`-I2jY^2p3
z$3{oNM$E5u$Me0?yD_pO)%+A;(gtf-@(Au~kn)@LJ8Cu>>Mc2pRAu(NGeO^FLm3CH
zE*tz;@*+Bg&@&Br?uu;5FZEi|^1n&!vTX;b77%!+VaS-2fm<lLWZ%j|(+q20Flr8c
zz#gy5Em1WNdN9}>E1dql7r>`gZP{nt6v2TJR{<d~UL$DREKWr^ei<rMh!f~#!FVsj
zhscESfa=ucs2+#o?m^#agH#oVGbfSg=f*78xfntExgG?fCeM1=ZDot0G6ZRE9QZ!&
zO0pkq{s}p?v|Waw((X?sZcX&fK?}N+z=rA0`E=-CrkF8So6~;E#`=krsJI&Je}y0H
zc$%$n0Pm#SdRjEh5Mad`8|O{2ef8MwFb?^_ytc>Nb4%T@qEk<A_0&DueGtw#y&Q9c
z9q@9pUI(CeJ7AV$Gn?4<N~v@`jTAl^*B%a3anDEORni9E9v5G1b*$5l`w@OqyLp!|
z6q>w@7{#wBau%=IWRky4e0_7%l~^Gz*Ej5_uLruVV;r8G@KvG;cjE9y8ciQq3Hs!)
zYueSWOKW7+5pH8}{wLOV0u<OA;+b#^(Egc5!=wEIRn7YIy#!?T+qolw^=@zB^D2$B
z)7>Lk>xC*fG^Y2F<_ic;a>R*!_b9fB-uLe<p&m>HSV74t9zWh9I3sq3c8%P2JaiE_
zs%_9^hzdi>n{mFi?=aa)o-k^TY^j;AnDWjW47HGj-`4+*(0{@R@hmaPB-6QST-7Q6
z5TNl()JTquZG0I;w$xs0hP4`+*uI>VF3^b>&<bxtioR-7SYj3o5sY>r<XPKXY=H0x
z1}7rhZhU0v=*}IC33KC99z><bU~?0CwB2P$?hcZ(?qxz~Y(O<(5G&!=@PI6FbqN%L
zSX%y)Obc{S;Q3UOX0f8=r}_3|K!Jg@S;+@`MkaKZ{u}4BKmh`sCMGkR2s$5g5`(q1
zh|j!wR*LD;Pn0r?TKs)lX$Wz8vZzDWv(IUFQM|xI@*M_(oL9v?4>3n)vg_C_0r}FH
z(fqJr=U@rc+QQ+Ytd>aDkY>!#FA&^}6ybJQJx+adwB`k6vJBcvQ0MA+5J7cs6s}c`
zmh^hn#(&7rBhFG5jHGpFre2NYLczniki7JXk=RO$VLRkN$tS33UuHoS{n4N^4zfy?
zO^Hz*!c#~y`93j|r6E(hM(El?k8{cfl{xr(RHM?xfG5o^5x&ak<x@@S-XV^N>DHKD
z8ox7*`IJm<bJJjv#T9m#Hv{h+IAU>GNj?cXB2kV(v_8+N(3x(J#+iNF`G7RqOasWI
zRV2xwGD?h4U%`oTg8)b^>@A>Km=7R(;g}K|H=9!7aa%H>vH1AqThqLx#}y)=m^n+9
zp6xy9lnvGz))xLO4Ss$#NYg!d3%;SA$z9WN>#fwk3h)J}+hZSS2;6M3cm|`OED=Wd
zy2rHA+2C&ViuMWiOTWCLCsfSa^OwQhTy(pLuWwS&*j$zF7tHen74HeI8r&~GZCP(a
zb*0xTSn@l*qS_j`hwcu~W!C#AD-%t``Fj|hdYXAS91t{9Td#gnaMk|B!deh+P#&Al
zaRiKWfCVL;6FfffkvUsa1b19wGjhDaI=xxbM0>s<Q=GM9dlQC#yV~7{Z$FUXhv6~{
zG3JM1NBWGa7P{c$m~-_q4exnv&!1v6X0)ROV)2Ci$@3I=K<&Xl2FAzn0Nj_$lU7HP
z3mEs_`Cm}^w}b%`2=7XS)Ls`S09x-6%f?p9olnqn9u_$FFY!a2rMweqXfPpjr&>Y4
zVB5fPl56CDkRve77*SEt-Z3mYYM|_(-*np7d=Ey|aG9({p0=6!G-E}Cbw1E^Z*S+v
zXvR8rx?*c0Y{6l^HBcw9Efk@q=3ncu&9sTV7*cBm{DzmDoRTOP&wIzdGQdf6{xFTp
zJS}Vz6WaDKHG?;gN)Mq3+ocV<y?K+Ny&Ow|VVi46l}tgRr$htE)g)aeHM>-J6dC=k
zyE<EVuYZqT23fCu!V(kg^%7iYXg@LaAZfB~o_Q)qtV9YI)BgP4&qOiJ^MMv7#mM@y
z>O+Irz7o~*T4%;{-pw?_k-6nEG^zQJFS>yWG)&I&;h|M`UCRWEw^-L0xpy?2HpS;I
z2G41-8E1_<Qs)*pppuS9HL6Wopw8lYU86-bNuTF&cEb&-Yq#r)D{TvkW!_delJR5j
zeWjBZgmb>eLR2?9^~=?Ygk5u(45t{rCXZR;%rj$ip3miN=dcl=BI@}Vi8xofoeK6e
zjf%z3IaZrJ7+m_Hk28=(=vj54_GL<1C+?JF=<6sq=6B8z+RiPlr3Ril;s67x!<qKl
z#{spb435U*#JiwO1yi*+TyCr$kHeZ6Z{+LmXD#P_B^B*YK`G$llGEv)^x%MmaZKl}
zY>+$+vAzB0!koUEv9Q5t>K%T{2oqsi7A9uhy--+mE}!gdU@TX)dxcFHoGD!Hhs*bk
z*L$uW8qYgJIXSbPhw}6cPOb=g)y&E66Bh0Gs^j`kY7CHHwHqQ?T$g{r5ESn*_=~BL
z$P8Tzt8r<vYz6z(qJFLI1{$7s!}pM>VGkqllX#JeiJN<D;r5$U=PXKs%au4vgG3aB
z`6AQ|frATOxV9aeo5!u73#(njcp5O|0&KAH<hX;^k>fydS48r?PQ?0UKRdsY+lGH7
z2-4ty1~ZI#=_!G%BxHlP!~z1x{D~g<z0L<UG|`5S=QmD+4_WOt_KhLw&F<>+5n#JT
z8<!aN7#w+h&Ena4HgF@jg28ZICI+O4R^m^32Y88KjqOda_B?GD12D^(8YN~vrD1X~
z6d=T<7!*CTv4u2ceXu!uU1_?a)zb1*bG&7eLn7;;?s)usucjv4>GCP=n8NyX+{3@+
ztnuS65{&9Ur3v-OK*nE4je(nj6<87FeoVoUS<sq4ky9TMpo<XD=W-Q8^~2A@g&h^>
zVYK35@O~re;S6-&G8SJKy0k}?Xv+&U1=ZEcKgHK3>#EKpmgp^J)v1k+(hi`AvLnLc
zJ)a_=7}>ijI7^5vYEO&UI$U5IUJ(}CpRy5|WZXxnW?jOHu;td1nb*1cYE98VyZy}&
zJb42rH?W|V<@tu31du#I6k%lSl^V9$2^O4M3h-!ygeT$A5SVh7N!!thQH+K(u(z*G
zUW(S@w9()%Wt<88K}tD5Z(qU1(%ZOO6P*bw&?_+Gdxt;f=MyGw%~&_#LUk-vm&vGq
zpKn@#Wdem-haX_YvUH}_gV)vQbrp?JPaM%1cp42|CpPev!w=k$llrO^JFQiDN;F`T
ze#8=5sUr&rVienbqzFYU7a3}M>sEvClh#Qly|1yy$~kP}gZQ$Jk4P2pa^8$RykiYq
zzpZKvpm`(+K>p%dK+?^0KDCdk7R%yvvB69>n$C&Y@p@|6QdYORr|nubjSonlrIm};
zbMm=JTMr&>ZIRt&u~}RL&wh)gm!E9VL{}&;-O$VN-;RXMcz$7xwYoS9U#`r;h~T^e
z-jf+t=}wyaoY%BfsQJ!?MsN4w(`O2yb1r>G0i18Xh!Jq33FS)dD6nhyc8=K%+P{by
zE%*ayp!UE#;BQ0;)ZZ%JTYyFxH&0)?X^atG*l*e|6`VvEQB&QT9~obtjc3%{U`)!N
zfqqD~0c%!N!|0C9%w-;tDt4iQXU?y`2mO%kSRzrorKu2lNFyJ3*<j}nXYEUblf3jB
z>23vZFTWH<SilGdGB)I=#Kw<KM8xA#vD8@&ED~9XBcfO0^C|gKN%*<M{6!{#z|z6H
zA|_ywnO2Sr==+8+;3=DD1Q%za=#BgB&*CZ8i@*Ut2YNRUMof^$I<_|l+Lno9JZ)tu
zZN?V*mYnuqzT%OqC5?M$aDSMeX50(h>=tJi!geM?p;GIyg;-(W&<tEYZ-rus`Wf%6
zcKn-7e%9mRO<8E$6Xtg?z?RIFZqibeDZ~FbX(;T;Ei0uLL71FDJDZBy*^L~!Wb!r|
z2*CgNtn;wpO?ECD+^#PW==*|Yi~QXq;GnPAH5@T0kM?LWca##iHj6H-=~_{1aB5d+
z+#IoylJ2z)jm`<K-RTCOj1ro4r*6S)nso0z1Us*PO%A*L@TO*_T|B3W+}j{}n5mAg
zR88J{)|673FS|7;xhX~Mt;v=}+;(GZq4N<8HQ-sq?nxNVFd8BhjUKKa#}@AslCM-%
z7K$xu>CY3;5;()N=~|Du3PpGrZY#|brdrrh>1#Hi#<cQmx^Q&cc&*3ax<Yd!Ei)n&
zwtaZxQ>afy&50@-Y#0t-W4cgDq5C`#`=V&Tbx!*PM>J~@LgGvL&2@A5NTzj!hs?UW
zZkYCp6jlkU({{adQ>=}&Wbe%gHGo7B@0R>?e1o0&0`B_TbG}Q-s0DTz0lln6l6x1c
zB!Y^tBU*8xnW^T^S-VY}*I_9j7Gok5mhW$Y><wqsFyn=tQr(Nuz_?lwEXScB^DkOJ
z1q+YWjMC*$`QLTcmwyFU1N1aaWVff_=P`vB5-QKj16_aFW3hfZENHkaFDmLpR#f}8
zI}~h@bUJs=hEOGnX3n!R_=U>4Ek=Ie&OnHpJ?Q|4wi0W-b*J=8j_acz3wk;>8Q76K
zN$44UE3n(mCIW}mxh$Yv9*b<i2hf!aZYwO9uU8+Sap?ln`1-L!?i%X<B%<>^75ceu
z`N3_Q<R2Nj9EjED;$x|ApASy3JHAb83uSHi(ln6BSPh{ItJxw}cQgX}hrWaFI<C$i
zC3sdxc|<iaY&NC4;@1<FrD(dtTla<eMD%>fCq6xn2S_Pf=A9mfp7(`x{~(Pj5%KXh
zYjT4Ttd2PDB}DOccu2Y!fCiDs!FSyk!}ZVxsgQ?a@_1shRTg<}^9#$mSqrWBxRaT8
z`HUaY3!ENHK}v&vSPKUTIU?uLVAI~)QHwJvZVS%a_-8Vb?oD}|=7RU=(5*NupB7eB
zFxVgKO_%km<rA|eFc3jToYh(ELv`OKzKF5nr~;Z7(v8!4J)0fdI^RDuriyJp71cRh
z4QlqyazDWbdGXqIG&f^dw%?L$D}HylDl~e#?t)^y@EYH&m6c@LJW2THc|!$+f`xe_
zI3=UIT0f*{bVV$QzMpp}PS&3>ca5>GO7_GRI%+{=2ZLNTR?}4^QV>{;EFO<RI3&%(
z66y|z?Zf^=_Ja0vSZC!FE$^E_U(eep>60=^ZP9MBxD{1=JteA4Osv^t;fTkV0lL+F
zN$nx9lFMf>65Yw-x)UnI&;)*4_Fy9|&^I~<&l7H1e=Y4mU6;sb#IJe{f)=><5-3?o
zcS56Tc#&E449_YxLiAr{E}QHxsn%2#f($j1!_jLJGIh)mWllnvOr^v$YG8w=t6J2Z
zxsY%WW;B+H?p$LXO)71ZCt?ROy{vILTt36BJHqDj<tbH_f1(*z3i1?B|L7Tb3GZ*m
z7}Azs-o0MI3B)%PQuGanj>C&@jp4Nw#D2VOExP0E{dzXZ>E+Q_NnX0UIi|iPBJrYI
zz)eIGDjEl&BXz>aVmiro`hY65o_W<aLYs_=te2spBfxMiG2(HU*$P82ZHEU%yoXTK
zY-*7;N-sI7a4;gYxIlTBYsp!17^IOzQsu+Xm+x$nP$Ua*qh0h#R<LjZ!nC0^5*%2X
zUoC)1ps%a*o5-THYnINFmph%5Bu+`GG;(I)4H~f6gLN<Uc}ronrT|or0Mv0=B+*yv
zE0P5a*%3>mf=~#ZlgPC|DG@wjWEa@7M<a3HelzNAAI<KnuRvzvBk8q5nE=%)%eouG
z82d4mQH9B7#<?DW+snh{!=a>SvgdMY#U^iZP6QVQX{uG1fyb%kfunSPnH)|YGWO_s
z=`tI<)q1VN*vg`sIWq9mq;Xul+K-Rz>`IPaOEUb($XHkp1#PG+oCS&%#XW*1a_9F=
zZ7i-QX$^+kn_w9^&d}wzIxf0>F#Sn}8D5-1FsoE%&zMV4L-uEQ8yd?wzoZaUz^g($
zC5!F?y~5KsgyQLFW&7=pYXP4%*^!tO-JsgRNC`$bOVsC&hf1of4+%hUnoyZ?`m*Qa
z-uGRSbVtoQ{N6L}@!ii@Cho!JYcE5@GYqzFZVsXHHaClBs75pOo2F*Dwct1&X<W?H
zYf*o3(hqj<ky<N(%H)Bu0#-q>0yx9Ohq`wKxlNYJFL$C=;+^#neWskl-{yQrO$hn<
zfUjhJ*nFCzCUqd?Qa>bbg7%w33cE^U>$MUMvRu82=1EOnAt{6?a(!WqXmUKUCgtO)
z4h_I{2D#>X8UXKQnZ;H0ifU;@XO0_TAFT^o$-A^azvSHsgwQ_Q1-{%Eu(V6tNAfqW
z?IWkj6VX2NdV94dt_x|m>$)uKT5W!ES-C0E)P8eQ?jL5G?h|gT#ByA@H9P^dMn+@T
z3p_TB_-$U9%iukT)>KN>AeXF6em<aMz|;s{=wCo&aY?d8?nFa_O$w?E&?UAB`?vTp
zAWSAcK?N6eV)c~nGAX_Ov~_Ynk$O33m93l~Xvae}AE68Rj3j+(WLIO0h(7=ne^WEL
z_6aqOsmIO9Zp*W>xq2w{8WOPpg|P<WYS?Kbij;0+OIA405I*0blumA}a7LcpqytFz
zY=!hzEuM6=+Q4|LnQgPPdbZ9f)%TWAvLjrMY{>OCP|6jJh=p^>fd5fj({yL;q(t>u
zyEn}?`1|AhSh97rExEFNR+9xeJZYeaIf4ILkFuzNe<X3aNZ%o$a`J|&({A(U!r~%9
zXOYB5*ssP-QI_4-gI&mlW%!g5-|4xA&`0b7py2e{RiR#>A4Q{;auf~6Hs+N-^zyV5
zbXGGlGs?=gh3^5f`4?PYG$uyG4Cab@V7My;1WZk3sUH~>tY;J&52U^bkDk~?#~U)`
z5sVcR&dPhe_!GpUKP%;<zo$MhawW1s%)32fov;|-i3bkrvGhxE<s@E<m?j7rkLQsI
zs4(`gM9gQm6MHF%Lcul;cq5}_58YON14?z9f<WUZ=EWsVQQ5~M)lo|gv9*A?E5QWE
zvKhc{eAo)P?B{-~3KOgyCRTS`wP!3?H>^qIH#(kH2lUip=eX=al)yYW<6~Zd5x~2b
zUCJ_E>fyo%0d&p4wuo+Xb7V}d!>xas*ox*oqm$OMnVXpK?RdY1sPVM-P$+2S!oFX>
zM!Ii}T&$%Ef1lERc$0shrk+x{G-9eera##z`*Zet8U6y~@yAF4R~w*rf&aPpmg>20
zfeYcBwcpcHOgPW#`Z=%7qs+FZ!?n55;{CNz)9t5PG57A3>4dQq%SZApL>Qh$>V}l(
z!QEjg$MFnl>nmxc@kXgSAc4@Y077G%CMHJN;!5vI>uli<(fNrRxD%^}^C+vxW)(P3
zQOkwAoZ&YU>41l|lLELIK5>Gm1$s;i-!TjoTU>yG%VWMTq5(OqN@LKLl%%)FQ5~W%
z4n`o0<bz*<e{Otb!nkZ@yU6;IvkV<t0km?%GyxuUx)6^0$e?CRGd-|HeVT&g{mjY)
zD0kUgvmQXuF&ReYIP-0D<hqr1f3M5fE<7MeKKC}AP;i4~MwI=qho&xi?#gXzFv!XM
zDpouT*^f(1_PXH~lX;`b^F3v9e`}8KUo5!=L8i8hE28Tw{#5A7eL_j&7LlAjh1&Di
zf@pH0B4iPjiV)aCzB|54r;tSrdKoD%!LzEBpZGOV@uCz*vQDmNGB1~Fq*Z$AAE*3#
ziJXVeCKC-&Udc|CVLC*`m*#Miv4nmGD6YSuUB@eA(+xW6KZP88Vk)dd7Upv&%oocD
z$ctOaN%3`#Pj15zDAZoD>kk&jDr};)1=1wrMVJW@VL;t>gl+c)%Wy`tUz1_fFFM8g
z@{R8!Q;Z1DFhvqdk;w7Na6|yc?9_|{cXPa^AjZ*%Yx2^OM*DO?<E=s@NOP^HZcn}6
z(wISonerDVcP7H!rr23V55}odhty|Y(_x9IWt-;nQ0l$v+qL`>>I`B;HO7q)8OpS!
ztNb@%JVcOF`SPa7@kA~|(4Dxvoa^%A)Kg-Sn2l0W<DHVxLKsdJ@yr`J!Te1KTkm!&
zon3=EE3exW9gSRaaY0pz+d!rQIOt(4#~!#<g85gXci9;=OS9}d2VoRp`jrUIz{TN`
zi-3TP?i2mvFoTvIQn9d0^RC<+y2%ZZml(4grk<Bk$Gse`Uh^qoj{xJoW-_#{z$^Mx
zhpzToH`Sr~Bz{`F>*=XI1@}Uwl%>i1nG<D2a6mi1{BAstFQsVgc18gns;<)}Dwfnb
zU%6VF^^YC!K|+%(!YQe-e(G^$F%(o)6a`VH9Ar-hCXpSqCO&ibY(uPEH;~d@x53&&
zRkXd-Pu5CU!@_2ahX7JiknIj<A~Q1%BgRJWN1>l)BvO|Z9T9q5{>-?GgK*^O4=}Re
zkIv4OhhSMVBEYxDCft6r>7`Ew@4zbQphbx@^hBqAcm9;eUC?wMtx?!~!;w2zDrj<w
zA?Nw*=Hy&))3;7=H+5b3ur#RTKA}WZ-VP0s>@_^K61NqK<58OeO>bW})0l6}%4PUR
zb&rDrNN!tf{)O@nJ+`U-ml!(9BY5d};V9;~%){{zM$jZ>6Gn7mQ*xDkuBkp0=Tn6F
z8{=c5M>$}G@b6NCrpAj-MZ`qcIfqoBi8nVgi6&@0snD`f2Xe~8`jQQ$%MkF5_ixYA
z>aw%(Crro*C2eln0m}<=C3_y_U7sI+0wnEYqe6c6W5_X=rihAfHD-#CUE>h_xB(q+
zk6%t&&Otb5j|{9O2VBqE=7W~oN*=DZ^_t&ZUaYB9S{FWR{|c3bUm>hohh>%-`OvFe
zNMON4(T89r?QCXH`LY744U!hlPWWWkjV)T>A;Fpy?XZ)NS!6<({h|Y5;Z4e(*l+-q
zMjfx07-H{M#1KLz`uzYdrZmH-QQ|14UGBA<UD^bcB}U8R;!vb;+}HTk*Ey%7Z-c5X
zh>xhPOROTC|CFJkH7v}Yj)<7M;Lw5r3=Wb=bat}1!hY&X1`5N;dP<Dr=>$p4<D8JF
zdqxyy+K#ELFn61a|FsQ%)L&W*ix--jVXrTiEl4a0$B|n@!!;--;}De0@%|byTVPtN
z6x4f9FxF~ol10Yr!!WTLlWtUBRk_`l>Ul|1!=V($XnV~bd)KMDSEi2+N6=hEozpW!
zbMzmK>0($&zKmdAW6he8BW|k|UP~6-rkO%%e?buLNlCJ-$3+njHs3;=TCz;G-__p=
zB4R2WG>-Ha1X3jObu%6-Doh)XNZ14(F=R~5Vp2+G9-n~3+P-nFGm3<d2EU);cjDM(
zX%)uf4JVv{Vu)AR_G2;7ebX7@NwlJ$6XTD|rJX~^9mS<o=f+;#C=g2tSYj2Qs<Ge&
zir)Qff6(tf1-od72D4@eem{9PZPt?T61?Z8Vwfq)SQe-#QA1C9SE35Oqk3xo?p0E|
z`1UrZ5zwU7V_*FIV!As2Hh;RzFZ68j?55&WWnbO~VaK`3^;#BUJr7-`G1jaO?rr2<
zQ{-lA3kCKTPkqKxMqqmJec<hrIuUOSNfS(+<vRTh?gZE|MTs@;;Bk{Jiq;Y4IbADb
z^K0V)X2Iq}H@9K>^nC7nd&oZo+q3iPQ!eA_zcjW#2`&clBaKb?wGOO6L~uwGzWngq
zz$2wAv)ISsjACdHug0_x98QUebbit_ay}J!(DeNOsCozP%DQN4x1x&8if!ArQ58F>
z*fuM+lO5Z(ZQHh;9p~mf=icvY_ZRH7TU%|eF=p@m>Bw@O<5c>RMA~Xmk80XhS+}Yc
z;uQsO=L}q!5ID<3801pkdukb+(YzRo96AxfzWipYvF&8i^tr3{@kjnd{W%?Ouo-;l
zI=-F_|3@U7^7DJ@PE0U~>S|PIOc8YekoV%mFRpa~j-;sEj@ud03#lFsJnNAE0){oa
zMYUyW=yr?1cdavIz3zhlO~CbVx*3a_XT6nhyL>hH_IL`yKqg;LBgH?pV>ipwUK(1W
zHg-NgAgecez;9)|x>V_uM5j@(p>Z`rEESjORyE?KmUjTytjU3qea9K*)ZC_usB2+8
zx7N#3PE`J<5XYGzGUmc6po?B=QVA)(&-2+6S@l&&TL%mRNF+*cRpsTwG7R03v-#qO
zZ)=YK>t!PYHBs1YfD(<1Bvo3q{VUm#96x}w(ToN*9xl0Kub`*5&R^y|aCW6kO7(#&
zV#&UwzxZlVQE5?>0azQmxE8$_`R7UkWbxT;Y_>aTae$*EN-wj|{8TPe?k+`6{h6P6
zQVQ|8rd7`r<4Mj@Y{ttgio!ICL@HdrMv%VW!qDT{E8HO@_HBXQh8&#jeGhdj4(4PV
zmg|-x{-e1fE)?Nju&dO;{^&iSQ4={(h|yMi!OS)3od%W$F5PnN;9gDn`a~saV839d
zm=1S1wreckA2k2vDBjx|iqQfw5@o9X^D_NH<g0FL#nX)|m?bL!Y0^a|f5SUCCLC_^
zRn+*tD+|?g8*FOe^lw&6V_5dBsbr4~&gQv!Uuy1$qHMxV=Xi;d>_qc2-g}V!$$B{!
zkp+$A6`3gRZ%z@zLDN=jBHZfUt$a@-eRHIsg|g%**U4pOZSb60M#1~Tv>%wbGiC6W
z0c^t<f)&^nI7+p-2$q(6q-m`qS>P@zsFefgiFUhz1ofB|gU`9%wZIyFZ9-&5Sd{nh
znoLKgWowQ7QWEF8>EfcEekM-A%4gD-WWF&Oy8(X^`-@zol_OZCArHio&RYeO&CaW=
zb?lQ<c7?%>CcSmPo^SRolh?Cdy_pdXK1WQ+<)1%)(gxwVW9GrXNH$Q4zNzVt9L1iG
z8$M#64&3}-@Av98`a+g#ZOz{XyifKv1>JJngtWU5e2<ZVa)wQ<P7i0xj!OLYmoTy_
z*V><oV^ewz#7tGf<)x((8rO4l$%^HGcvcW%%n@_>bKa5O+Y=+izWtakW7g*4u3LWJ
zhK^~*?UrX*uiYKFau2o2ik|(L&d}b#w^OD6z3u$p{O&V2l4O;3^aD&6W%tKVWdd)%
zqf@jT8}$&V;~Bd7E#ZTk{LJ56_Ux#ZU5YQyds=R58b>6TXWya;;p0Jm=($Gll)MKQ
z5RPIDjU61gikMsP8#QMn@8>(CKdi8em9m$pA-z1f<qq2r5cJC98!_a|_wNQ1A;`D-
zHWc3l&8uG=OL-Ii|J?}x3lu*$Ann|UjRAypyJjqGCE@=PsL~D0)9CfXp6iJy5nN46
zIdIL-4fqMc%u@X*{F&O+>HuCk)tr?gC1ezzaLmPZP-LidGqr3Pu0U2-pdnLsU~H+(
z8kW|Z{y5KAd?%98xvXaQx<z4Re5!b#@ox&on)D&vu+Eg{7bv2@9U{qwRi%?^8nRG&
zzPvlJf7p@q(TO%d@y^n@)zMMma%_Pg>=+cw_Sol~giB$v_tjD0JX_n5Et>ysL~g5<
z#hm=(HCc-h80bEQ8w1Q-U>|Hgm4)SGcDpMrwmYLL)_=a)O&>J1{hx!@e+mgn<B}xz
zoJ@lxej0T|9~XMLUw{MSEebE19|FW;+(o{uOhLL-U;#<aacX>-W3P)suQ!A2d{(x7
zM+yK;raJx6Kk;Vv%aC+@o|Lwc#f2N)bWO-*oU~A?Zf>UWSKzspSBveQ?`#kLj7<8T
zcK*;*ED6i#$`3YYG4i2Ry`QB)TH|sN+1}pdV{b_YvWcEzkn;<MQ*jDh0ZSd9bzU;d
z@#@<vR!>|{p28Dg&|{o(niQ5b$9{zLypK?DR19L~_Dp$^B!YgdF*ipe&i6i36;L!u
zJR&H3<sUJvu~VrNKPP=uYAYCji{>r$ld#oD^?CjkPCvvfExc*4u-Q}Wnp+zZeSY*C
zS5)B>i8`k}{x6`(8YDN^INKkxi=!uiEg~&FLn9g90vR9iL?!l*`jB~@zQ0oQpt!Ta
zyFw!EZE8;TwM!(g<=~;8F;5T5pb5-KH`7`U`wBmq=Kz}cw7fXYgFY5YJdUXhNW2*+
zSrg?|;|2EVHr-uAt`SPf_KefAqf6e8Sy8fse+Z_eFn@&%!_q+J?rFN_h+joj1l;O#
z_WBmopMqG^bweDkXngj1J?lFW2S<boDfRKathBU)h2ACeSgIGIT+cKk;MK(id>NLk
z2|%x|Ph7j-bYhS?!WA^9%Bqi!OtgJJ9*e5(98X`*vwT9nm7zBib4c?mU(1G`zgJ$X
zb$?sl@5b*Fm~Sop^R%Yev)WICJ{B(w(FODc^;C#EK}WrSStOY$d(q*gDK^U;&C`)X
z>B3<xx{SV5M-#ecs@eZqEKa_3yu@+lG;{R|q3xqAU%wmc@A_Lj?VKJX|38iLzgSza
zU{M#s=pbwyq0r{t)bE0$NpY`U2};?4*qt|XVokpppKOVp@nRDGOZWoYb_hBNf{+n^
z$mXsNX{@YkQBh2t8@YQapLb+{IZDTs0!KRG;CA<rnK~p8JLOCAycN0cj2Z}vnazba
z>HE&c=wXY7FFt=Tp*8QTC%p3#HgsV@qQ)LFbOHax|3>04lM8j#i3my}(XXXFhh5C~
zf8uw7F~owoIYU(8kW##sa0_o#WEJl~<xon4lknt5H;+{o(n>vvKOJ%8a;+(b|A>(R
z@Q-XNvDzX+4zknA8}g3DJ}@}*UQ<v+rDywbZHR;A%wng7$uUnQ4@{f15T)W^k-}qe
z*$PXuq83I0pJ-=}0%Nfp(v??O_|H2^9W@Rp@_6o`^tir1JtRDEtf>$os;7{~H=1FB
zN_7SnWHl(?o37@Ca&mJ)zPk(90=%H_GsE|k;L8_JHR+G1?b2cKHccHQ?j`BHY<&La
zjl?X4cHlX@)rcVUIxp*r1o_Ls!pc2wEZ=X`d!PUt+1>TGWLe2|6Jk-FEk85$=h}Q+
zhv47{_&1)fORCYN@*mHaadvA`thGSB(<12@7>eseYsRfY3#v;hSh3otziWiLyx*`v
zoKldj{{Cq6U(>XS6QPum4u4s-zh8Yk=g>c`D|}8})8STIg@wt(V$>%bipHNDg!_<f
zZLX8V`?<W;R)*PVze_DkGE3P=JI)Q4?W^((MZs6jO>3%S^@%g$YdC%k9|ixdj7*Y%
zECk>CVqG;l{$7vE=~l?**NSccBQP(s=ypvDQFZ5V4c~TtIZZG#0<3MjSk5~cy;Tsb
z_RsP*L;Z<PVjZWOpL;#Tu-Dt}B2$8+rxDyH1{^F=AKP#ucg$s6ZWc3a;j!VCmube9
z>}SoI)TTAWx4uU%#u3|ZN-iGT)ua|xh?$mDC&)L2T6U2ncx8U}GNWwcv?@5ot|KKx
z7E;;7K#4I|fmonYNRgUKhU2g-PCPy!s_iqP90rcTw)nEn=lx}lx)0Z@^?cTbolIiT
z(+J~^zJ{Ix6XmlcFp2A=zPFda3+;TO9Tv3EPWcDhGF;m*DaL;9{z?9uU;L0WSp+tu
zZVZQQnB`Z<JJ3EWXS+EB_9S58;AwV)n*vFEiT=A0Qwn*bYN&o}K<0jQutNtjThd|d
zqZ1kcOR+@@c=ka#0l8T1Mhk33%1tG&NeY?L#@srZl{H`@9woQvdXt*66e3!W#}KUX
zL2u7q2GIizd`&Vm+x~$GYNlsLZ%BBXo~PQvIv-djvc{|+xxa?P4hzw-;H7psP&#;3
z%>D@S7TE)6ZIonNtie@ct<^15)0@x8340cQxYjazGDKvy=2}c@34iaBX<25zfK3n(
zjZ7P#(e@xZcGlz7M?=^&B@}-+76xnbr^FQgr;brSqj$^5KOa~l|L27tERu)gkHwNT
zA}C(k`Bv8RL&aGqPf?pEwY9zRCEc-=?Jt+X<HdQcW>_Ld<C7oB;+y%91XEi7jAHVU
zJYnhe#@oD+;8(WW9<1K?bB`HQ4jx}HGXnw<wwmQ?I2+qAeL5y&A2NDd?m=W})E`DE
zYSu-~82*oVU4uh1!|aWXZTr1p5=@E}S&)Q3I80%x3VcJUi!=Ot{6q50`g}<JhkJ%G
zJzKG)4$7)=qW}at!86?yDogVB<{nHI658oKt<^m%ol1QQ{R0w{nR5i%3D=#gtxs>P
z47!m#^DCic<y{i>X1^ZV_WAz__{KtKZ?%AolEEUUv%o@J%ZXfk{0ilJBJ7CSiilz*
zI=<Y5jI&2oeBZ}GB2TmazjqKGDE_1&{Ig4SIcbFre{kzrL*7x$@1M`cq@LIJBx)O6
z=y=h%e>M^jd#fxg&4JSC3B_viplv9F(WuJERkeuo^UL+R2L#L+cLaIjD#ywhIFl-l
z7^vEFkcjOzNm?m{2mA#T$ttbL7ZSlRa6Kv6eQWAgvBgw5cW=V#1nYjD{BBq(%bg*x
z!<iBLKk)+0V$JOe9#ul=;7RY{2D6Aa{!pLu^MM~tXUCw^sMWcw;79JX26k}nVXDE^
zQ~g*^Dw`=4^0{BLx6$Tpn{HD$)QPx=%{crP@dm#>PC_U*`kH93yTAB9UxD4}P7f`p
zru2N@)<BLFU+6vb>S&cMH!#yI9}j$~6O)4ko-y8|ziM9FR$7=l*Ak@iyvV~}x-MMu
zFLzQp<h)<|F}J?U+LmV|JI5P|-GgwGkGh_ZY@F{k{cT=8mS7t$!+5E8M_R$Z*KGU1
zH}1~$oFnwN*PQP98MGUq5{hJd&#M&z*7QTHW4s5Sy@&QoNMd?whj9ayNF<IVY8Q`X
zlIcmRmTNM!XGGlfdGY&0kv(_5qX!Tkk85>g5{>!8EvB#Y)BNM?CxhSZ7<Tv7K4GBo
zTkCteZo6a~-qU=P)CKPP+#gSxz9MZ7>kJ=G0z!S>8@irp9&s32t~zo$cvYQO#iZxU
zX?%g1D|bGAM<EA8M+ZHf(lZ+mx<*4gh~00Mo|etrFokHt!ZD$iOOTK}UEi#Y{c=iy
zIb*s5{FyDg<D$O8#>u$(SdRwj$p}UmV?%8tuC)0`jL_2LwBX=5G`6Dp4GB%akj<Zn
z64qz~KugdPO~MPmoXS1`%neJz?|Lo|HlGv|5=b>PY{?PNdc;@y>M&7hE<gJYbin0o
znuwkGWpVMEni`(8yCD?5{K`j~W|Be5Ct)&$R{}1M{X4L8IYBVI?zT@7I!xzCVBkH%
z?C*BLb|q1DCT^1t&G6c>=IJ<Vs}BeM)xH(fC0aF?1ae$?UaM#=MxjjmhJXJH9w&0!
zf!jL!8bF;xA##aDE++xr#3q#=l7T_Wc~T}^*e5Z#1u7=OM3&4I!_+3@lol$@`$})h
zp)8a(|7IpE`X?CP$3}Jrr^zq&ky?cT4{0M}ks!AR`?=$NUYefPq-460jb=Sk_F+&4
z5_*Fz&si&PYONSXd)QdJWGeeKx0IjG#Dn%BCOO`+!+3~EQBU%)9%pAbOhy>TARv!o
zRFU}XXpq+&DE*LVIcmsKmh^^9&RI0=$o)vd`zy&#Wot~TE)IbA=;Dw6Y`bm3IdAzM
zYD^kmnV|mufn$@O3+T9!m=k)z@HiGNp!F%E>u(#~M*BmWraQ(VfP!D~Lqp^Ua3-lM
zv|k2So*$Jm)53oTzrhabU1{A<vo=;1><lnC?7#O%hxOPE8n7pZ{dI~4VPX0x>8bWa
zo3X^iAX+|;(Ol~D>FJ!wbJN8v{&#EmkG!PMAx6mRL2L1Xq1)00Dn}wt^19BW#B0a+
z9qqfRT=af3hn0W!V<3D@(CfLcC&+!};K;q8;7I#%$yT+_pVWT#Z(|D4R=1AR_GTth
zd2&)f;vq0?<nw}~vY@dqTymMh3kf1PW$plZKY7W`VUn8l>)8Q~cAtx_28=fU|E7IU
z%)vp$Po(0C3f8?k*$Nn9uB)=6hEw<>q;QIF*&%WkDEERj(9Bc}rIqrK1Ep1EpgqDJ
zOdjH66Z=ljSoa=TrKIkPdi4Im#&9AYU%jCxDsy@-KHC~(Z{^!f=Se8dYKK^nu-%ls
zEDEy}FhfY?)+-4cb)cx8$pqMqvWlSHcs}!Y<hVflygpUm^zDG%R@NTtfAM>!nYNab
zLOezHX#MjD-uE45`5|VtNfGAPuq}$;)L=Io5LZ%e&Q)+-<fNk!uC>ZVpf(hLT&V&*
z5|z+B<!sLD0OI(`ov0G{pb7XXaCDO2-16-E-N@W>Rm`D(xz-r1JshPMcCsNyf!Wzw
zS)Gx<{*pHsQOgn5jdvIy1}<kz@66~7V;k=i>&fSHo0`wFC@8Ijqdaf~Xtb0BJzzYT
z7ec`m-~XN#YD2;AWU4#*T5#aIs=J=Bqu=&0Zx0jXFLo0%P6{r8NWc}ZliU!cKRSOI
znMJLuayem%7R)l5IIsH=Z+9i`Z>uS?SPQ-ot2^ex%y7CEry5HL#hHhbkX3(I^<RHI
zOT51aXUBJ(t9w<{%(dt?PtE3CZ?;F8WDE-1q?a3L<CH>o+V2m^U2+IeH_IJgndd6l
zD=ArK-3Pk3R5djiE=t$K`)TP?@UAEMF#I%uJdfTBYg#wz%4cl9;CrGYR$C+pSAVS8
zbTDS|waCZT<x7NoYjJCJ9LAEQ9^%xuu?jWVJv)51yLDhyoT7!L7`lQ=kDvG}5rB-6
zfLrff_b(4-?xfPTXkuKQH;SrWZKu#xz;L;E&|FuOJ69V3X4X9iHFT{MZSVOY{#Y)j
zt~Kq(%gQ4^PR)c;e?6fQO7evBom~%+<diX~MALjmH=qWgccP(#OWzg*T+*HY8Evge
zkC;erb$!CQraQnQAkAIH@gC2k%wKYsQc8?iEO4L1y;E1!>|>mO!#sO#D3SC1Nt-MJ
z&d$VIH2uP|PqvSlM|zfq=)JHR=gfBekn{I<+Tsx(ccY%S<}l{*ld5;b24-%$a#m^=
zF|fvlccQ#vxS+K5g3Nv64|Z7u+-1EZbap8Yy&fa%Rx5TdEi?2>qk62pBT;s!*)HXW
zF<;7|H=XK@;<_e`?Hu*A1&CK{1XQctOzaVO-|+j55<f-4g=|O*lqRBC-=;#stt?Bj
ziLW>;N-rs}^{Asd?i`#qBm2edBX}|l7QGRRDD1~MN$(yYS(@jf;a4x{iQd}kFRK#U
zp>viL{n1y=Diuk?>)uZLA>{p<uM?jW72#GnArLwhH0+FwF?;>#Kk&EA^|Ht0M$P}Z
zn^w5NxwG7@ba2@p<OBSkDiCT|1WsZ<ZZ1^5#CVej3YLp@EsuRx4-}8OvOQnBGej)}
z_b?&Z@z_F}GE1zfV}BM#OIilSX4k()p#Hfk{uql;;ovI~qFA=X+2xYRt*g^rF*-oH
z_-gGmR~{ys=it+y{=d_mC&>=oWsnc>6ZL$$_-LHRmbme|-^?ib8YOYgR?q*@cHa0|
zr(0xy#Xea72x-$u-af3s#^V)w;OZIvogyRP?%IV2Ykxgo@+^6kWmm5eZd4>%m}~rq
zj{2(BYc<pb9-KMqe7CgJ4xE`!om2xtG9+T>TwpF7+5<@q=Nm>?EGDQd3qwd|v#=FO
zbEdY}OE$f;ZK)?F(-ucALLx3`(a={yR>a42NXm#IM7!Gx6-yg*Xl~)<4*9IqLqyX`
z6qt?c^Glw@ymDEq(@_FUjMMRXbfw-7yvps!Vxh8H93bq4xLr<VS~!yoLUq9gP!%7k
zbTx%Za;FcgnFtIAySv|N7%NTfG{BF>_kfyOzc-dW;nS@?J<`!EXpC9ogDb7jYz9L{
z@~0%e-iLtY7?oxvu*im7`lB_Bb4417ZvOARNZ5VKuks)OM;tL-GKxXrW!R-|d4=hD
zaN2gDM2TcwJlmCQ?-J<yj=H4!o70`u{%B4O(4qR2!E4LS@{-^Ed3Js-<x#zwJJ868
z3=PO34%&Zcf7E}uJ51&G=KimZe&b^jF)0J5`{9h{J(C$ooi9D&8*+fOK39J=S*ikW
zPFFwICugEBrkm}wmy*$T*?UlM>NV{R+^g!|nq-d4F?=x=<`A4;2m6UIFhdj=C1sV?
zTvbxSdQb(!5ao#6`Dbx)JqhPgt;&i~kN<hhvU)FaUVab+61QgXS!{|E7CO=NDM&tl
z-SBye*BC~FP3DIzBb~J3)<e3JR{gCJHcVbYp8<+RYqAag*-*+_?nHydw#EmB)8(gb
z;UL|l0#}kmkwIgu7&LXHS6*Dr?KWQbzgD7HsjM-p`WzHF4VCPH_(o?aF`CjaY(`I1
z*J#AJVzC7Eu^6^La;Z$V5GD*&qAJ4PDJ)l`4q*)zZ$Nq?Ix1_cPxb};m7|U$*S@DA
z`}V3_=xSN5&@rq@jd|?w>~u_y`3@(-^+>)~^qmp=52hSnrXfVUfU{Q1mTji<6$MM`
zG{Pk-vmS}@a>fE*g2Xvom(YQdl}XaZF)_2(I_L)qA{}E{w*(8aUW*B>gj~$Vj*<Xa
z4&BUXzTn5gm(wk*m3E{L87XZQeueNPSj!G9<{E+g1KY?;3T*gXLSaDCfjLO?%9+^R
z{P=e5`@hPU%le87J2wLxM!w9l*?ac>v~5cJ9rN;ezWHFSYfk&d?v&$AkFpo2V#Xs#
zG7s%a?d``&`;Klqc-1?+u2jH-Y@Lckp>ctiJ!_cX5&r_wlq)EySG~4SEO|6!6oK)?
zm<aX)fTmVkS~UV*Y)J-e^E~blKx@bfyS;HCkx3z56=+{mSy-}4aN{`ZM{8}|svf&5
z_OBd%Ok5lFZT9Sl^@vU<W-wxzM40X)l_LOyUNV%C0iq(1_=v2c&A8L-9CNa+#fXc-
zRyM`mL^x7wfUTyj4Vs}|r*KvNt!^tCO8^%#np)&qIGJvO8vz~b(u3cyX5SSOiz~-G
z+U&nIt;QK@g(Ii3-?A7U@3;#O`2Gm}ZT5tcz;nzYsyJ^!>_1pGwbMKEp8T+Xi|kXd
zc}5`TzlGt?81F3SLOKZ&0;^gLF`$uAQylrQkrW($>5CI^F`r8@*uZ9Gb6*sVvAr!J
zYUOhr3OSl@?!fGFn%m`k9|4;<aHC21|1r6UcM3H+w*GUJnP&M7(R4vGKu`x2XD*s+
z@S&YNydGZH+CRZ9d|nLSaLfawKeeoi6<qrh?5U>KaD2dRyFX7J%LVLYSnBCp=R<I^
zJz=6!z6A77pdPx;L1Z*t!<Vj3)2wxa7jV>i?w_cqWIPeT5QEUN_O{f0HvVAE+L8vm
zpe|;~3=J87`?W8?p;BGtYcbKZEd@@8L{YQUKM7>DZ*h~^{t${*W|f2#b^a0;=Qp2E
zw=IvCQQjFxg~o|`LYx-UA2;8P_&pS#sE5zFONiSJoJF&`bC2JQCy9{6Fz3v5f{Q+L
zh5Bs#)DSH^?dKHJqzMf+V8JN=nhlxLO-SY{_DjOn)-dtQ`+b#;HS9>E*XN02vcEt?
zv71+Gnv;0YB5}$@V6EHqE6F*V7nFFkptn8JxXj)HyR4l(DOWr{oww{V)ls+oxCj|b
zk5AIJkxMMm@#VNp%y(2-p8r-<+h-NXXtqPhVH`4Sn(d2k+wqbd*KP%)&zv?cbESW`
zAFTKaXB5CBKnk5gr?=~IwUciwMrvc`qpB`556nDe;U=rv&SqOT+k+{llv9e(4o%oF
zQ=#Mg9S#6s@4Xe3DvXXN7Hz!Mn;M<Y6|Q_|e?2M5C;-P<z6iiApdXRhA3owh;JT|`
zhWwnm=9XAcP~|DzPS*1D$FV@FY{ZXp)ykn(y)W#l*Iq=;kLIs`e-{PT<!Ld7FI_i-
zx;Na5dpq)5HjA!*{~Ps|=xy4|nwKxEea)0%5c=}@kj*|HxPIp9rbWaQByADkrW25g
za-hk$dEfe`7MTE2Fx=@V0<>$t9&LU50c@6&|IL5<yn8eWz0xY(``~Y1{GFaSUz_(4
z-8v{CYt{UPA3+}wr0Icfu0D7sbO5=05|&aO91&s7x=hZf-k?YdnSfL@(njyatNtla
zMrw`GjHc{2U$o%572E7wldsd7=17ww4K*)xISMXj&sE49GQ*cM=TvS-e1JJO88)Rv
z_$9F@>aut1^6gf?bj@{%5392r*8}q3)Xtq>zgg$JSb>^T<bN@`f7vRRH`!JT!xeDd
zf+7QCdHUYdRR@K<S8_hFwyHzBIW!4LgtE!au@AbY0jLcFbtSu>%Z?ao23G;t@w*Gl
ze|)PEC!=Vx$7INp4z4-Jp0uOm0%4d+6sUp|X82Q$&9XrWq+wB@?o$&tAM_LdwT>7y
zN)`*u_*@L30J5)-F4}*2PBo*(wx1qG6f5?;1iOSqD@9j-<f~Lftio~_o?a>tZpMql
zO088H!wIQ`v1ZE~a?{-b#GngMGwv0ssua+sx4y}K4!wRFxI0IaRKzMS8Nn4p#!Q&Z
z75!uJgpB|x8pn#6yl&bUAs^+2U{u&zZ##^`d%I7uZgP5dt;Q<s@nuzmr4Sk=B^o++
zdhr8QdLauNnf11ce+~(Il*B~zyvV1MuMw2q^tuz0?dhRK8b8f9%klXQGp8~-2O4_w
z5i<Pp5e1MwUeO3Ov4oYF)Hv#*3%`>;L71&5D}mqV;EW2C6My)_Z))}|1vGyw;Cu`4
zih|Mf33MW@(a+d{YKr$wrsXTEc!GK<<jVJ_K--a4hH)^F%HEgfA%uEo848WsZ@Wij
zSUogiEcTB3l)Pvd!pm>X#4eYp>YJIRP1t#0&Spp)CoJO#x47u6-r0r{fk8VQeh7GX
zuj#6TK@_;bHJN_JI@WnkQguASD>VAQv0Z&vVM0b@tXE!6WSlM!(6diq`T`bSs3hi_
zFeDHwtPjUVY!gVIH%i+z0XzuT9YWuUaH+EZ3%~L}>gAJg*1)4<*%IOl16pYiJp9!F
z?GJW+iG;85B#O1xjDC2(?w!9Q(gX<esk&X7rXZjj49=$H{*3`+8Vhx*Aj}>9(S)o4
z?!pXCy0ai`o)+X)eFwHt??!A=?K>SAs04^i#7H%cKL~rYZZILGb$#k{DM8Gq>W>|=
zqTpLJl8h)01HGLs^duy<wzhzoo#Bv6v>>pCVIzwrAFW*`v^l<w3pFz*R!W^v{1r!l
z%hMSi0mQ0BwxKp0Q>A*ABay7ktzd<9mv5z}^ZPlQVJ<FG6x^qA`b8-KQ_gbk!U{^P
zB#`s#s4VJFYk?dXGrYJdF&t^lmeW*vFrmv;^U-pp07PQ9eQW;O&&&}aWRt-Kv$tNv
zs)4lP%%cXJ{q@`B?-23rH|UiS)Dbm(kNK3QZ-a&0hXYKWyND0l_9$~#tiX4GFEJee
zpv0s;;jC4-sDdOTBYQskWvaiYqF_%j>mi$V_mjSD2lN|V>0AH1QT<>vOo8i#{IfXQ
z2YbuYwus5OOJfJW<#w*T+VJSD9!muyUm}`$;I(YAyd+l1Qg#GLYTs$`XtD8<(w*vE
zxJdn50aMqGpZ;yim76qT*v|Km;NFqE*7KXv<<8qV{Iuz^mOp*Lq_f;piL@fVmCXkU
z<a;Kh|GIMZ{IPl^y8F)VMVhF<dBq8b!(PFIX#EGvTr0aHh;Nag`1G-nYtMs|ZBv13
zwbrop5A?;H|6;YB7CZoK+OAsP7$B)z-*e{4iH~D#CID<EH(yViH^ZUOz*2C~F625P
zh3@$Qt($*4RjcfYl0Za}n?ATJaS<eJ(dxlWI#!4~7-Y7#u0AaNNlqc3*>A)*mIoGR
zT_VAVcZ`MG;}3Zm(m5O>YVQ7P42J6o1I^%i*Jq)6WQNzBExgrCTNT!%T0P1j;?;12
zGJd2mNc$mqH&mgVh8F*qx`h_l@I^O8+1Z>M0u&AM|HlG2?sp~~GMZqBkc^*~*wY0p
zh9#o~XbbK-bw02f2_W_bA8W{q0u1TY$9#-$u?J=Hmq;67IT1OnzT%$O%tMU2Yr&Ls
zaha~~*J3nMEs!IcaN>%ZE4oHXV<cfcAK&<P;z<GNY3ee;1uT;#mh2?&{Xrn4Y!tJ+
zq2!yHFLTMLXt#L6SO8}8+swPmLv30qx>q+jybkzANhL>(1C~JS<JkN3vBLUROG9;T
zdl+VDK$K|<>%DOKMbSIaQg^nybZI4$<C#3kMCYIi@kkKYyrmRUy=0t<{}v^$wwp3m
zB<gk?7MIhHW9JQ#bm!xg#tb)<upA7Wno06~C<rVL=bDu2@_w1>>AWK^D99+f0k!BA
zNRqME8voRPBoG<p=35d`4ub{{^?zpn$KuKt=J)aEkra?EtZg5^^gZp_i)dO3bD7x*
zNS?P5$Yo5Vu@Tg7ePNZWs_qdW@<v2-J2ttOs<A^Np4#&3{N+lL)$iR<V|3YOuxH%T
zZZA{QkWy1>c#W9R?XrR|k_AMYtC*%I72F@u_g&j|BL3&Yi2TKq$5!!wmCc^3NI|>n
zTS}5$Eyt<RlCB5dM1e<YP#vV9!DH>vmBi&8_je-FWO^ZwevjwZWLwP0aFW3mRxqdx
zZ;<fm&XVzG6d8=%r}+DartmFV5xWh39eyLQ3tcE<fGXiVGEmqy4Oaedl;RM&t`#Cm
z7M#?%J`slRX-ElyGZqXdu;G6;{6(bL9Mu5(v3d>GxIr46rM+D-)Z4zZ<=GziViEt6
z+IKQ*S%yiTG29m6;=kCxGv!p@v{hQ9e4!H-RU{)us?Rfu;)s`h=It#WM~tm{q?S^F
z@=UZw`;Hp)OGQmsn658iXl}}a>9OQ!5NT@>EF%7%s-P~qGU*a;h7RL$j%n!ja^0j5
zZ~PWycniL~pW7uY_HJK+Ni2t<zFNgXX884bd;x;m1CJf<6|@{}8_??!2YY3BBD(Db
zAnX6)s@i+CVqiK>HI7{@3yzdw8N}fT<fB$;><^G78u(ia6X024z1Q(v^QxwZ&~^5N
ztydIJ$0E+}?owQrpWAwq>2xeuvs`)Y<>>$Y6}Vb4VzQjVodB?nU+8>%t-c}rC@nkx
zuzj^M!s%qt@sI!)D=1Y*VslYKmAhafpCvs?$s>O$PAwvDQm=7U0*RXQjQc+3#;^LC
zPVc|E-b|7=|GH}E3z0b*JT+=fcV2JZPq|KWjVCjdqqOLbeyTUSRG!91M#4qrVBAe;
z)K?^j4=qKY+P+BP73%P~-x<45QH^o!j@25~hM0`$;eSz&hY90A(zpMsa>UNU&90P|
zj^?mjva*OL`1U^74>C(c;rjDjx8zIDw0ZTf9UTaEx4~fe6__)I%P`<Yx!)hpZ(NbH
zRDIW?<2{_NqJlvpRFv<PO@|z&1H(4U<VcHoM&RsuCCfRHsg(scQEw#T4hpZ0G~sfm
zSeDl)0~WlJ>uUT|DSAarpVf~R{uZ@{lBfS=Pv0R%4S6=uKL)+FLyj&OwYi6v)_CX@
zDTHF33U0?|^Y`XY9(G>2%%wVqQneW!E}q9E6lAPR-6xBBl-^rgW9;{b^<W?N%kVJE
zKoe4y=8g{w*l>E0q<z14D=3~J;L)+-E@i_n;5sEsM<DiGLSd~I0H>omYw~<=KpMlY
z@v`>E@wTzuDj#-8F*wBbxyE>glBSq1VAF}3d1&C#Sepx>6I5Ai$L$^hXOHoZz1wS>
z2Cgg6{9KbWMO{-sYEHb&Y<VIir}uz3cC`s3@C7nE6${ves0p%x{E3#pD|EIxB6a>D
zqP}+K%z7Opb?*0!Yg6JF_hfL#H&{4$<KP(JB{dGLuhn#n1pPSh>;U(`13wg5kVdQv
z0W+}Raa5`0NFph;qX@`KuHbEkK)Q*7j34Q^H$gZ0<pS7aAT_HbTxYF&!uX3P0{$A<
zP;uyNSFgvk3GOz>g^>YXCc}j=dL4IMKhNsU{5GK{41|_i7%CFhVdfxiu-F-j?aG|V
z6+;EfD0Z9=U_B3;Nr<-MrP(F&BiLP8P1lR8Ua}f)i^8#&&&J*GJ``*)UxF%SCA-dX
zX&mmC!_gGgP|Js4s6P&i%@GU$b(Z%hsTa*O%^bP;ts<E7SpVfy4%d*({<sO;_8XGJ
zP93}`(i*~ox0EY@J+AIaul63B-%pnXTIogeX&o5=oBmQP;J%#3T@%yQy%Ji^H$e!U
z(cJEb*wD=1P4UHsP<%aMW6-JF3;!iWA*1}CpM-`XrL-egCM^9DTzET<sJYw<`{2;=
zNAgBeMbBdmI?CpNeG%~e@u{Jr)K|DwYsc<_coDF{V=I13K|q*+xr|jt9TE=gdvT}y
zd=M$ZGrg-}VL1Gi$_MX`xQ+Ogk+va5*Km7+v{sJ;X@ebcGG$!?jKSRD3od$|<s!Yx
zI7JIZvz2gL<O{`M@Q*2j5m!`#jA92YfmSkjA-Us)V4()1IFkwofI`D~@lXjO6N|+L
zC_Uq!|2bk12Fd?{Ubce;a%SVp|Jij#=4CsjaC6iYb2>aDK}W?A6oYo7OlkElHnzZS
z3J3+kErpOnS|@khT>w{BTtW#SAvKLP59kP0Trc5NkHm;xI+?Gb_*=ffS;wNcBxqL{
zC@hE57*e3d+OYXUd*y}Lf^xLPoC@BTMGuA%Y(hq?-uSh<XeNzX%?0#Gera25hFdZ!
zOYnwqt+=U&;FbShH^Q?3m}WgvQSIlMx|(CbDD!Rc5?btwL0TX`3a4@;?ciCG5imkL
z*(dy$fIxiuXvE8@xov>I!pA*9BWR^JxRuY_^L~*HN^-h>nX$f;u4-8t_<36)HDw8W
z5nkI4nC{hT`}B|HHb?HjA6Os*cM2QfKBiF>;Xv@@F;bgDM(-n98yAu<sa9p+)n=^J
zO<POomHh(#C3(rh!`NxevBzfsgnE-z>*%`N*ASoQli)JF4ph7ueyHJKcv31o%bqjR
zyVZPg0{P$?h_NE<M|<;z^yM5$QSW(9s-{V={Q!R~hQag|-%bD96-#=csG)V@GthB*
z)*>H*{7;&}>Pp$&Y4b_ZRysGX;&xv0a#0zmvC2sZ#RQN;QYKvpa`K7dFY(Ewa2O40
z&=edR*ZhmL16|c_aUE!rRaG23C**Cw4^tSS11Hcqso!_b820<>wTSBoXuC-qCOd-W
zXk<;woMv(hR}TSIk+sH`^s-s4_*yW~isYtD-GrLWmVQ&2*gL^Y$N8S?!L0ps_qz*A
zMdwH_=~vr8S|`ippYmg6ItvpncKhWMZnmqnh>pqD19y|2yZat^N8W~jrHs*gkAYnY
z#F5@=TfBgFhI4=VsQ#PaQ;D=6uBo_wG5g(#JFS^rjhh)|R;<->xJf=q&@OZTxZV#0
zgb@Nt7EX$O*%%akZ-{RtSzeNLj%r4-dUcpF0*~Cumn$*xskEDZ%U+O;N(%ft-=BDQ
z#i9piT}-01dqut{)%b?8(n?%-6fYCKV5_u)5ak^a_G}BA*ugYxp9M}tMN2Me{!w{;
zNuSP5@Dhlwl$kn-L{&6aQ?(Gef$R16kWmAOSZS+mJUX)JKX5$050P^GG=vm1tS@*u
z$^w-O|6sVr%&f9{x>5k2y009~ZztmbR+k`YtY+|4U4berAB5gxS>uA)cTNy%^QZJ6
z5ggLzdxur%BqWO%a)ZHOoz!+?^jDx|gr^NZ0bPAsb)X;T`az-d58(q7jm9IHC;x_R
zR3agNaEP-E&<s$N-E~QSPP0z0QxBG14(p{Kz!`8i5r9BgNP`E}^QX@8PPMPc>~l)i
z-Jn;?sF~hlrqV4X8@|YcRGt0eZ@@=%s!>9+PV4N2I?vIFo1MK*gFac8W(PLp({V17
z>tIy-B@F32sXMPzj>}^pc=fis5sJx_x|M9Qz}H0kLpCa-PxbyS$}l(t3Yw%M;eU_y
znL-uI{p#?~7wGy-&;|K6tP><|>kTTAe5gX#V$UZ{;r(w9xFu-ZD*H>5D84Udx1B)L
z`1>X8yyqsijgheN!05n?6kbE$_6KI%>raz*_aJV$A*o*)z3;_riJhh-#QnC54FGu!
z#CF5qh}64#K&?R)Wbl3>I66Mrg59F25*j<9*|AveHAbZx;c#}U)Pe?HfBG-T3=h>S
z;G&|{3_%H<&y2O}Gc*cX`rI!|A(`RX4+@>%pD0Ad(~_8%C0wAUk$xZHJ<Eb1RMl9N
z(f8mA(C>HL7UqoTVqU5BrtF`c2E0r7TmHuMrc!9h4&_dCmSv+MpfdhCN`MB^naf<o
zKbpE?;1k%2_h)$-vn^eljiKNXH?wunSaSxH%n7H{ivEMocpzSNf4Q;Aig#V}eS=?k
zF!x%?Y>g(X(0`)2Dl1z1FU}nl#mI!joPvAIW|M!=ns_qXi!ER8oF%8^m}`>2=ow-E
zK@(n1B3xTwyFLC#Nhs({G0BlWOJS|+4~ET-9d$G!fYwE@s0Mm9mNmS0t_Y%Cvj>HH
zF_R1S`8uEn2c@NG2L&oyUtd4duEtPjE)jFgvF#DE*6KLHV=!i*ihi$ZP^*8IH+EAT
z7>2`hrd{j>DGeQbJw7S+_H<d9UVK~a6*%_rG(#l9WIty-7U-4wuzK9`1hAY5E2`Ny
z*#7)@HXKVK<?01l((%bY%vYyBtKer8jgb&2%@@p5E772ffIn*zT3}SwanNiQ2Ucy?
z=+6qz!9RbuY%sY@VC9RsGeg*{)<Kl3RyVOyYv`}LSf2{CPmUV9_nEKKvQ#xnQDl34
zEcRCwJOL%W#OqJ<#!&!pzF+-__WO^^YC6G5LW|SU$2RZYrDu3NSzhw&Rc8ikPIbRB
zHBKM#Uq!%^pwZUMtp_m3Lv_%^nbXsH%_CmhpwN{?G(XENpgaJ=JL`jw=`{?E`g|J;
z(khGK$oUPrD9)BZ%wCOGO5N^E%d3UL5!_9%%Ckid>@^<?Zw9?iXqpt?pz=189JLsF
zv*k8qY~vQC*mlfHtp?H&Nq9{klIN4`)#*)R7lvicCD)16h?=Wvh8~3+t(^KmK;Ns^
zN>eoyYSDzmbrqMHsLI0Hf_Oo4P8<0orE-b+dXu?`v925acQ;G8TszQ+U&qB}Elvi&
zX<srE5<m<(OV$872poC-N8W==WMf@iWD}cy9RBg}R}N}vw5jy4bit0EE-B0+;93_G
z!*(-p*@V6isPdXN=`pu}A&U*^+-NR!{HGF`Ew+6~g0)VroYg9KY)`35#fr7_65#;#
zp|BBqo2ak*Vct6uvri2E`#+$Y2d+cajsI?m;*A-;6hBY4HVY$<$TnlGn0mW?`u(Ri
zlrE|wIXk(%wbf5^rp_4k8;9ZR^quM9MDRIx-~3n88aHz&XV@Kl!cN|!ITnvFj6Qzz
z6UI({d#mN<{gac10t_BPQ^j&k<kF4!+yvf$q9%J)=)wZiJ1?c~!#y#|4Ub6M_mGlx
z{GlJ+X+BeedVQc)@aG>9VoH|VHe;{vIwTX)tN&)Q9w}u)5bbPIknZRMegL}r)%G<Z
zf+mNhjk#tDWaa*2JjPQdP&>x=)0!6s5!2|dm<9gco6K9EfW}Ib4~!qM6kB~J$%@$a
zy-pcNjXqsY4$3fi`9<M0Ir=-~=!(P8w|bdD%y4J<d)dUiU7?q36M_+R0Lfg}gBd>u
zV<zlRYCvwy!uB~*LEFPgQadRk@x)lOhX>pAL?AsDjuC{J)52~Z6$Rg&Kz_OPb0nSr
z%$&P~P1|$e=+2`4R7fm?PYD5)cqIzti67Sxe$CDi%8PTB@PAmp7RP5Rvi@e8IrutJ
z#51&l4qkV_d@Xztrxn!*Pim38OcCU|ogLd%>x6m4@cBR3H<Kj%fkN7N@@D!K7e<ix
z{K2vFz5{>2R^VQ-izy;l&yNNCx_N{0`h@OybIA{NlpNd1z0r31#T={$?&>LHc4D;z
zN+!^O=P$1l$LmDumE94uV@oh}wf%vTbstP}2uN3CHV4&?IPUwL*CW|~11{l#pm1d(
ze@~>>f^LW3oB>Pk?d9HhV?oQ;<aN%o5YCmbbmo>_<$yxb|M`m8azG4>4;4c}`&y$d
ztWh24xR~+j>U`iA5u}Fvm{cs9yUx26k`l^eZMVQgQxlf<A3&i63(DZwK&E(Iz7AdI
z*$(#6(7D}qR=ELkP3sSL;bC=|&{v)CfYM<%4RuKpnOLQTxw?4CTjxR1Te}e1T)Cm^
zwp~DXv>l0bv>SrDU|i?VBn9aEV6K|OXm_!HFdNC=cqq<0+SxYa%#yX&BUdZ0S+K8T
z3EAV;l<J6})XF165Npfa-QA_}KuUNKmeJO`XmIS&$h{1UuNdwPW>ZJF!6m43Onf~=
zI&HX4Z~ko9sy9XS`5f0XB>S-7G2e6AnB>@tzV@jGWh9b2FwOE@G6N|Kb>m-hzp(B2
zzPUe4#iUa0Dh(O<JS;<#aVg0mba4S7l>Pe_f2Rp<_B(n5XBx~^tX4b1e#lc{&};q%
z`8LJa;5AvV%a5&^8`L1Txm-Cok(`yBr9+ceZ@2V5Gz#AC_J^eT#3~$sfV~=h_5~Fc
zB!Q=!PN%7SKR2MP$$J=K#MjjeRW!zC)2uAzUIpC%eRvp_q08)DV?8w4?k7v_Liykz
z8LJ*YcE7psM?t_4v@i20P0z*CwKryj`OwD?4#bo8UA-@#pJ3DcIM(Y8rA4VLZskYj
zmA3V?)`TykB8dI_hvI|)t7=^1A-<m5`;lqiXpoOLH6Lw`WeH3zju)SF$KB^p<rahE
zXQUm>0#Icb|GrPW^nBlA)sw{e*w23?4FSR+zvYBPg6v@sFz3+Rm|XB_2f`<LYF*y=
z(Vk00XtMjYfjV<rh32PJ^J;r#2Az}(d5`FTV_k{m1o-bZj}1DbIH4KWkE2UB$2sB(
z@|*MGzo_lr{Mw=zhw*}hwJC(Gmx;gb@8vB}-om-6ct<N*CuAId6|vhv&z`@Ovm5P>
z(P1sMvn|`U-S50%k#-ZJ&LV4A@1Cb=2!PQ8UTjj6!fm*skSWO(CqL|nG!v)~!*=XC
z^Y3IXDW2O>t`(SiPXjY7f!92Ogj*rJujBN&?hkXqFGEFC7m~;sQVpWW4n}*4=vZ61
zOd!UV7w0-~@oKS)X>|lId2KLC0S{ycxiIFzS~!3O$I%7Gt3`S+4(%mGjRp1MGM+aP
zO6$j7qkI#d%56!l4I!UlnS|3j1|8v=HJM3Za5Qnt#sgoaJ2?hx%&#{RD=pj!c@n>k
z3L%US1%kX}-gPO>V97VAw<OMP4S^-=Rk`gG{34h6?#ABCG{ni``8;{$a6CC2wmw&h
zD8YO3$C36zl1xA6pC2Kftz3lCf7}d^=!;C1)1D1}9YAs+<sCqTWX>ZVKV5~vfo^~-
z5wW!s$(1@ff(-jLl#7uRv#Z&4jEHuz>;3Oyy1)m4(&M~2|B!e@IrF;e+ZlyEzLXxO
zne)g8+z$PD$XZFGmLu1WvCz?l@#S#`kA|M4KS#lOD|PfuDUnEK8a|vqz*`B2rzzg)
zxpFp9E6g$%-mbZmK|8n<eXGJGEEhVzlbU$x4TJz6(HrdQe3<NqU`>a8>0AI!*}eqy
zos0()v(Z7=c60qIeBir%eU5p##P{#9<Z(44>*I(4J%=TJUS}34x`08>m~XRQfdl-l
z&Gl}>1G=~x$;yADMz%YaGBnuMWC#R|D3WF}rjd0Od2?Mrmq^^!xaOk`CNo<K8Y5&6
z(iWd=l`Yxv5cZ20q?HDIVhIW{86<_9FTi`drMw@k%Vm~CzCyq}UHd=BSZ;jZ5kN~H
zV?}@PxL-Jz2)qm9`dISvetCelnBbmmx@l?LZ!82aRc|3)iP-gXCn60rc|RaXLwT)J
zo+wee?~}E7Jow*d5#+b_oFQb6%?&He*|eZ{z0~ijZtz@*J-xG0zTB~hZhPZ(Q6=nV
zFiXLhg>Jd=)DgV?;nmnn&GE|>Y)Ji_P~CtCxu!9~-;kU<_hV;=Ng#fpYrYlsUlKWo
zYlwc0KptHm#FKn8k8An&d494?m!t~A4Cv9V7K&nvd6hZ6PUgQR{YTVqt&msxZQtP$
z4}FzMozhF5Iy$WKfvFt>4boR4$ZW-<QLF*8AYY;E;W;Io4})|~wMFpeT~TH+$QS5Y
z3oem`2=`&qT;W|9D-C)qT064clNw2y?t+=sCj46ML<?Rl?>(jrN}g2~PMibh3cjf<
z^N<`LdB&2BpOLG_A0?TYmB2}%AlzU#8jukpe53uoAoRPFipB-)CanNZ3hG7SZGL6z
zsDCqh4X4fBVNL@~pvB(t49G=M$#_{lN)2X|b;a|`i>6ysMJ9_Y96z|UbbVmKne+a@
zdFJz4mH--FEfprmCSt3iHc$5)rx&Z-lf#CL%A_dwTok&fr#gFDu7{+ynPStMJ2Tl3
z`FSgy=RUiAV7f&RK4sn5EU`_d5?wCVVA@<c@%f+INZJ?H{6rf{Kf9%?#nyKIi|VSt
z15seDHQQXJYZsm3K!m`m$>2L^O0Fr(SPk45VAD{4kz9X;blvcGghXUlDwa=|x3e?1
zvdKF=2}@u+svDn84R8(8X?NYf-TuNKWSzHye)_XD>s&U;SH6WlkDXm!R51Tg9I1_=
zNYtwE#qUjcX%xiJ^^Bqx(}|+{%9W)02>0q2Jzt~K?oy)H&Hr?{u2xlNF+B=7T3aNW
zZ<*m`qp6QqU-Ge4FA?+2;X(KnkfMtp(;pN9U-CNTd2Mu)BZSV5l_*wqD8)2+JQUbN
z1@gQI5f%8W-UOQ_<jiM20^1bV2<Ec1(wn)*8G?G~wS@1K?<t(g?I`V7U3eii$@lEE
z_;!8!9lNyQ?l)7Sv(?}_g~MRQ0>PSOj&{^hnRzWUiz^(6XnkK_9xByts(FR$W$NrF
zB#Gpwge&Goi7lp{1b-U$l3np??B<GGbF8j0*?k&czW5qcmD7xbOIXZjz$Km85f8*0
zOun1zoE5ER?Jpij8K@ug@F5!et*agiKiuEIphyYwhODT$8>fYKgVCS&%E?ln77fsj
z34YSq299k>5=)+vY|APTOr+#6yF*aONrsrw4PF&=TUwy3KPv);4qQehZboj4*g7fA
z`YH6LgkoHx;xY!|TJTp!)nvGOhh9Cb&;peCR?`y$XaPkC<Ov*BBa;UQ?uCyuQD$jF
z$>U&3;Rsnu8`SAWsoVpfHLDdWf?Gz9MIWi1ZY(CWE78iJT|}N#{?{mHhV>O9HccEc
z1&>&lhWFv;3RZkvb)O58dzu1H2{BK~utOp<?4q%HFEaGk1*i8ebXvJx!}8fjTb$=w
z)?IVK2!S}cypb~alk&=3c|PDH5--rkJbb@T2>HOzv=mtoN#lx`K<;i`ze|!phNAHP
ziJLQj(ec>|&mANj9CMG6w4;tch1^~r>lOdSwa|xj%YJa4v*mm|KrTpo3fSl`8UD0b
z^z(C1#w*=It5@oi#l5h_l=HEap8Y9qGJ*PPc0BacI($mrfM04OLkRW<vX2^gSn|5L
za0cG=?G$^h_#RKAd;bo`JGtDy|1Ks<JYh)v&fAJlsm5zdYQU!j-VjtPSIjVam$)YF
zmphk{36tgjOy;T_vk!eWvQ|e#NZ+mwXlUXvCIgmhuc@A?1~fXKjrONC$t8@5dGQ<g
z&$w-`e@>9)Y&QHsbh>h+;ze0wG_!X<e+=WSVaUEcdCS+OzVh55A0Kw%O~8&834S_8
z&}80K!TfdfP{~0qd^DidV5KcmHjrN(2nsubyTR6pwmzaL9jGQG8#R>B;H`EnVF!dG
za8ZJNyRv~M${?CCe<%4#pRNAguSWnA{KC?adYbL+q}<NU5CG%|xmg+_{B(-Otem%C
z*x`wqd|tvVbS26LTnB+yy;vJ+B)={-Q_uQUCxG7J;T8RAfD)Kf@%^M^rvC*?F_}%3
zIFTB#N3ad;bR9M1>l2PMU%o}UoA>1OozdHh;ak>0I6g*|eVv(6GP^5swz2trE8)KM
zwV$xkZUG_f<AABnWo=IW?r;x#x#6#Dp^wS`1iPX}FJ=d%Zvn8E6Q>MReu=?I&T~u3
zS^hr&zCc00hd*W>obzK{1WVIep!EX;tuHXznAVDS;<-ojsP(jX0wQZ?Ae_666L^WW
zmTDE1#z0CY>@B;)(fcwuJDp1g=;$>xID0(oi$5@?03DQu*vC8qXNPl+S*g(6*I`@u
zo|TTpIj{URY;F3Lr3-Cef$+P(l)OqZmTE&pLK~Vcj%GH4i*>Z0bv|rKG4saNrmFfu
zX#G5HPI=gX1~T;*%^3jq4S%V!-BslfSvwv0>~GY1p^!>GhtK~$oc+dB74?MgMfPt(
z@cBEq(E1Dj7|3h|&+X4az<*UCT2cVtOZUQFLZ5h2vpvorzkZRF2Jnpg8|)kp3eo*r
z5P1D3#-yV7u;U3%<#*wa;OsX1c#{23#FeRV1@W^o;hjo>XS<9RXpz?p8xQY9fDzpG
z5+lfz2+<NgT4|2;SKqGct4N>}*-2Trw9{COYde-eWhq`<_!?eaH36mE+~wuw5q%Qd
zf-a;vE&tku)bf@YQ?C)85?ZH#H<?1_L*c`CdHrjcxqBYQwHb?RyIq0{a?Zx{t3SlD
zyk)qk)eziz)-_1?q%lwCL6N7EW=pl<B1|nGHGsx5k5%==^V3NIjY0j?E%9Sqc%ph}
zLN;B-Kju8vf921W+@w)l>$&`tUh|!+aUIQJ$Aj^;sdTY1AZ=FxwhDzsMflBQk73*P
z?MCim;*^!aG$vWBY$b^0qbaTo+$f75ucIDTkyhzXR}ej7)lrdGU3n!wqWNuKX^GK1
zpEhGUE*v!q8#iypr=Namz#upMVUM5&w3AwIae%{WZ{1Y-;K76Va>^7#LrFN!8E`h5
zmE&mznvWbY9PfSbfr%qu*PAzQK|2D*CmICKmJemV%gf)aa9)mR>Oz8QWn;^LK@}n&
z_eB|9%cZ<++&IjiHxEnX_nZH|oH7+Xd-cZ6H(XyXmQGEDtYf7o$8{MWuNop8C#sXp
z+>2#xNT5Gfd8*T?2xafjsmT0WohP-A>1IQ4qBi*$byb18y!O!dbXrmdGU%K*8j}~-
z)Kn+UfTyKNlbAv*uBRF4E)4F|9=QkeY3-ztATwQru<yBI!XGTemb^T4Y?{HSA-wj*
z8cgGu+`N4o4(D<c8t2R~<2k53?5B2Yh=8YE%mn|dZVJ(eKvVk1rdF`>a1f-Fujnw%
zP&?TM>f++C--*SW4`Ri}J!sOqB@<EWni}LbS$3fX6&u%XWv9;oJ1rVIWLl4!=XUY}
z4sNKG`;mw?e^MwU>S&`<YMSMCGeo9BzO%DKHe^w5-XNXsN@#|A){3n-6iFq>P8mDj
z;}Csr=N3EZ^P5`UE{<?+U<E<p5bNNzbK{<`fG)uWGHZ*U+NW}?Hu6+2jydSa+z7~$
z-!SL&8EriwL@Vf+`pQgfw8Qxz>Swyxl;WVNc3}glZt_6VR8CGY?_(@^)d#kfH6&X`
zXqUX3m@S(v70E0+%%6+Dh<G;A-%Y+wA;38>K)QID24QX`pu3hRLFE?N9yHHPhnp4`
z9P}-%DIiKZhaKz>n(1!Suo(Hn&c<8QH_-hkTf*X;jKq;=tli}Li}3Hym*F8Q{57th
zV*18GD>>4v7ImWR%OLxT@H%aT^9HrTb*^eFFRhVL0{7bJY95=LtfL<BXPw2aICc{u
zL4gDX>Yf4#;JWT<ddhN?>7Sk3_gYLFr^#%ZL`l!6y&x(-?~vIs#p#(7ub4koRev<)
za_*);E~9=+6dc?MKNmqer3B^8@#tE9D3X)2O*{v8)s+te&hAc7eFIH&*y$(9kFOT3
zsx@#uclHu2m`f&@jDmJdUU%aq)YPs)TcUiyiSyG&f&HaXJUF|QroqXIaQ2WOM;g5C
z>By#pjKk902yz)mBkJ+R0W_m|mN9L##GiZ+5IALL2=fV?tK0#N92nU`X7^4$?=vk2
z(Io!M^h#I^l(K&WqDu2xrgf(63gd2WKzMaTGBUaAQ9-8Pm$MH^UJRhw_xdzbbSlHv
z!x2p05i*+`nsTgM)6Rt~*7r;a@jf_}V^n70=j^qFTHUklXr8K1mP9zyC=jNZ@clQB
zg=5J=r2PE_)Vt}nvL=Rh?LYxdcq2U%*-!q5KniyfA0rff_zrASCnG%SG85BIjp@P<
z-ZuWPYf7rTv!D784qiSC1rOW-_s7#96XMS1Es*wu`w>k^L+SnB#lc&yLW4Jt_~Nc9
z&&0Dcn*!02UG!;v4}~jV5d@+QHp6?Ke47nuO>N6fqwXhYJ&b}8&4VYAIr-1vytfga
z@*G@6`>5?e1Ak&R7-TultW3<_&P|@qg53zN`T~*e7s7Sn{jg;;HSywqqJ;<G$lXdm
zcjRfc0QhRB1r#mJg)?^x(^30`=^{*5VMY&@BC?O0SC7$5$y8Kx=k}!tub7NblTL6@
zBi-3yP`rw@K8C*f5RO9!sNJfzFxjf9aP3(SThpVhGE|vq=^@w;Y^J6u+eBt0RCKV)
zXeV<symTU*hj!7@j3uyg9nM2|<9s-~4y!s_X~FE>2pffj<-c>Pj(qf8eGi3G4wJ~%
z_)v^!(1-Beb(Dii@OpHbn(oL~UX*l7geoXtK(&}4kl!6NIEgEBF2Om?`r_lw|HXS-
zKgXWZT}I$6e~D2FpE((DCXk4O9YM_*i5276oZe)ak96=OI6%Rui-PM>H1T~?=PPhl
zS_^zYf7P!pdK>EsHc*&K;U$5wFoo4o&H_;iNd=mODL4*FC{CfX`0Ef&O1e0INCSwG
zsJ&yu7T-?%a@9XEaL?zst-}rY?YZ~Us#q}v`6+NxlgS9c6<;%p#tUCse$klkU<vIL
z;hRAL$xGZGYV6UErU{x{<G~W7m@bUpGE#2cS3E^E=Xx4XXB{<%bm`I=-~ZnCj7%n;
ze#p6l@%Fp#VAsxFEZ2iReYhF1v9*c+%{Sk|y!i{5r5P|*5>p>bkRLC>Jc!-bIY+$t
z8y{!pdHjz_#6Pjb)tRP_M?WP;wWr($qSmcjp?}{#)F_^1Os~o$w}9*UG?8A+cSv<@
z)UY8g7&`Rm>Zqyuon(-<_1d-TO!p;G+k4QUb10wE`)OsaaWQo0`Nq$)0K5D{1@yf=
z#rUK?QIONa{x*SSglRH-_ny7gwxW5nW;lPy5Hm*PGd4Fj57VYkElXc*!dgDiaESlP
zCy|--hko)C+;P`kI83w7qQ>}FUo|G)TeoTzqYv+z-4f+fp4_<r#oKPV#h8cp(;RhW
z8uJY>)*(B#?{IPjB~l+B1=!VMz2noJ5=(2<CV4LBI1kNAd#RyplSb<?f{hku?9_;s
zELbbJz{Gm>yfpPpIU>zDhoV&3<=QRqH%t)KUqYVAmW8>?sj2+c2AUS%k3&U%t<13V
zod^poriQke3OSgK(Q7gStQPqQ3dFcp+i2m4%p2um<Qu4XMFg1ag4Y1#s&0fJ_P+dL
z%v-Vs{W>;5{WO<Z^Uc6Fz$P;jb0L6Qd5cHp*mqE?SQ@y-Iso5iJDEcL(@PcAyTMb!
zqUPvgxzb=R^;7XHGt~gl;#us}{1x<TV;lT5Bkm>-F6%4~mZOd2^4%N`C=Zp*_>3#h
z)AB1W`m>1mv~J!@y)%Ng9fly{i5P=CkO<_U#<|uBTiDMwODkPflNP*Wx{SyF`sQLP
z&Vx?+sV4l-emHiB$$(8%w!2awW^ejC$^uoA2q=Hp#(y+5zoq>gC8+HoaGpXMdFfkP
z-w~=)RHoCZHSeOIa&0QS@ch2mPLu01mu;n{JvR%G27b2BL7}HMsIK365EG`Zq_v!E
zw51<xJNYkdJTT>oCTg?VYv`jgX#Sq{HgkTg>J|;-jfmZhUzQ!KyI&R|agv}wf&wQ%
zfn%>CCy;3($*G}0qsH~|!yk{sQ~!9K{$=tA46ng6Prr%VZod*@s%r`}r!T^*FHhjE
zE$%-fK;5^07hG}W@Yv*RrF6Ny$mui~&2oBS>$*Ao&8A5bnmXZPs}C$UeShS19E8>#
z&*6DR!MG;!KK;6yov+V7^EN-u{rK>`FSO3#Qu?^ADa@a{3@`uZL&I=tH`lc{j5cP%
zYf6)N_VrNU{mntF&5t69Glv9oKk4Ph_d2_g7W13DjwZw(nB^mo7D7jY*<udsBFJyu
zql<iaZWV<^WV9&%L=H{H3~ff{DIwpflEixV1X4dit?JG+izBVyZG{oMu|7yshh&o3
zhx7JDs09*WKUyEl@>hN*wdF<c4=+r@1vG<rs=|`oFupsb6x-!cmotTU@6K5c3~8F^
z(Bq-`2EKDQ(j>X4_~f}>+|Z#m9}>q?LL%<hO@RnaLLRu|MtHuO5B&7kNG}IkM=9LO
zy`KQ`*3C2%PE+AskC-y7uHK;?+>!p%yAesxLgT+aV|G{X9^3;V-t+e94_C|9)g?9e
zJzcsZ^>@^u{K1XLz3+B3dg~KzPPXO>0qQ{l>G;#HO`RK!f1jG3GO3ja|BDoe6z@i0
z)xB`}7S}OoO>mGtZ8wd&54oL2oIGevASy8V@4(ELX=*NjVgiz4U*r={3KV97<qZjQ
zf_zpQpx~5x3$CS~z`uJv+*kh=_7>gBbuq<5jHe$(Fea~Z4{JD$rz79ZRF;6LTy#$K
z$}<j(ipO&CkLLf%LFN!k4e%a>dpkFVzWEb`ulq9`?a!$kwc6_lwTQ#3r*lIp8_u9Z
z63sIQmwXI&ErDyb-l7GC@kV{2j@Kw|BnT{_n*!}_WRNV*P9b7=F#-23L#q|8NxRKO
zA(as(nw0!Qdg_}emX*0HK1$y}<%w)~Tc+pTvY>PInzU%uKZ@g4;#vfaF^#K4sEPul
zq4mG)p#+IdoQ-jNr<*XW$q-D~{wY4#IT5+RBBas8sGdugM*y|a^wAYN#7-Kshxi>q
zts{O&oL#>ku4;E7hBh3GJ-&nZ(U*_n^PO`T&rZOae6jl2N#<GDH$}q6`K5%?6nIK_
zt3H;PI4S|SiNyTX<?MfoMEQ$iZtf~9FZeCaYtR=rw!a2lk~*?3>T65TY%-U$oB8Ul
zgr$k_&7puc(`Tfo6QtutmC8W^ZcRAsHFGq5&ubmy!3e>5FBZ*;M-4lgnt$Nl`;eWT
z9gk^_IXO9a>Yx9_RDz%lV&JU&vd$ui`||klW=xh9pt4OJtb-||LT<V!D>xRZ+`afC
zbIZyrJ-zk{5lVKlK}|5#;9MLJ0&x2YxSl=BkaFe9Rn)pXh#50yS&<pBcaNT^mqv}m
z%0aX*Pyfo#YjGXm`u_g=9~#YW$+ztES-NV?8tmM)i}I~fqr@SAUt>Z?Kg}cyY@0QE
zw$Y%^%&6Hca&=V@7;lXyb(IY(SFSWZuccL9Idtya8GrtN|F7)!nzd^&b=p)j9IQWO
zk52ByjNgh`bnMvCSP6LkKQ9_<0Q>jv!^<zff?xgLFO3|~bbxsml<p)uBUZlN!;3Gv
zkQ)QqR;<h^ztRBZPxUy-rkwJseoY0CV<L$N@|ky7T#yC+3Ai;@im2%1tOdwP1zP4K
zS`+BzzoHU5HtyJiz4-+QQad)xb=znI6Ij(ko8<|kuq2F^CN03K4F^zEO7q&xBtYQV
zK_1XSu$zX*&5A1j(qdr6wW%9z1jw~MOPiUx%7#}XE=-V=?=ww=i>sDp9j(f;dNFnW
za-844D+YFMOn#93zm&JkVqb1pFk_QH$BLcvf??@cuHI#mhajodL9roih7_j_`fYZi
z_;7&j3}D^f68e`;;k-iaUTu<P1kp3aOP++9#&%iXFdOMaCvKW!A<pDv9He|NDB&oX
zPx*U~*UdCm#frHw-!g0yk5(NRPrMDU1<)#QdxY+G%%sd`qJhQyu<q9Ph}vW$jv(&E
zq9mi8MDVc<9uh~J4Ai%J&kZ2X-4J#=iN-pJewqW_G&!D3@Y+Gp+>l<T%vl!~`89ty
z2|c=`<2HLwZYV6q!nJ#_r-+*!7%{~0;^kI_5I0UwoVmfQ=i@Hwfflq_>*fXrl`HGu
zPJN${#+8H0S1@#`{4jBypg@8Gr;P$fb3&)B=tMy$LV@Nj8XLdSPyPKh`t&)7rC+VZ
zlk_Wn+Z|W&<L78bS%R7AQx@R$S3flqe`;#70fk3jc0PA3X+bzT2$b&7eFRd{>SO8L
zx47_X@vx(7-^&fCeRPT=cY5^d#9f%z;@PL)GXAOsUc0%=Zq!BR9*I=$Y{9(cc<BY2
zQzTO$!O(R#(4^Y%v&+RuoPLui@YMk$y)hu@aB4e0(4(R$aDml-80f*IZQL1qmKzP~
zNMm;yLCzP~(z^{Ai%d%RGFYtqGat=fe3Pj4&8SP`WSh}^S8dwVvm3Dgk@-G6zlOqW
z(s1Io5Pr~wn-8cVA{ndbY6P_wpLR+bp?>@0SAL_pFaOVtlkGUn&9AX<fKyWDnVP9+
z7JnCNDI-5qNpZ%u>);~~!cFby=v@yY`{7?zXlMRok5F^^GuVInE2Ld>V})l`T||98
z?7RJX*r>ss_0DH-HnKE%vY&Ys`B#m^em-mZ;g`n*o9lh&2IOs~=I5V&gS<z7f$ZNL
z5umA#2a5~ge)=!S{l$;a;E8`#Rh7iOGo1p(+YwoH7d)l&s7XLRvbqIZ_bF_OaM!#M
ztQ?Cj66B<$W5-1gps>qG`eCCGi=wBhf}6r41h}{yBZQ_t56pOx<_9ITvA|#nprXyY
zBHFqqqRBKzNT1i(xfYHcOW>wyVIKu8CH?@?4y-|F?Tkvm^)U*pgU6ctZK>IaWVfg&
zP+|S$Cp&s*4?I5l-=q*rruW>3a}oOJ_wd~LN||}<V^t%xd@|hrL!cp@vY?G^(WRfm
zHS~7aNSCiW1lP@k{UA++OIQ~R0SUpl`PR8-0~}2|#%~`vQXuAN)Q*<L3rv5PJ&>S`
z8sT<ozAHP)tD^iCijvMottb}J4AqqSWR@5|7LJr2<7G&jXv=QoQ(2dIq-u@<4mw+g
zH5XSCC2_kP1&knA9mKhyZF)1@-{lT!O^?Kj8{fqAJ@W`kQ_IT8l_}t)))sZ~2w+>A
zUPIKtX_M9tSLIxeF|973>2YrSrX+V${$_l!cL6uU5`q>pB|)#iWHSyVJmoEZgxEN?
zBnc03vkP^v%KBD9Y01Xp!3aB<$ipASr<k;FE<x<27~OOTZt8Fi`SQj_$gKfrG_M%X
z{41A7Jg>y@>!W~+0`q3nZr#3PhgAtJ5DfZjx=I6s_oP`@c5R+2PV8P;WF@lh-GA@B
z=-2Ov8wlkY4>{Ca&AIi~_$6k~os0kb?V|=LwTiduU`<f5b5pv`%NM3BOs%Zjv3i+w
zCihreWy*TFD8!Fzcbmtj;g86t8djbq&pr2C0`;##Cc8!ZKRxjTRuITljRkIu8FTs3
zwTm_N=m*DlXU^PtSi7Fu)+&wvi>O&V{?*q=A{G#%mGwv<k&qnn3Op=du>w=3PQ{g1
zTycB_#$tD9-yW^l_uFX-UPRfnWec`!-AaGe?T(pWh?`ig`W-WBts{um6&FY=sX(M#
zZob)=3E#ANv)SzR!ABp_|MeB9pUrhK9!!6jT;g|5{74#yVkYxXJd+bi$wtK6QAbTV
zky?C{(-_%<$i$aG|Foi&5(PeZ*pFQYifFBZ;4n3>j}@Za&?Nv<1TW#{ksSwoSh#Ex
zw(dGe{*D4b@$=f0NF}Tk0@)4frC`I(Tzs`T7X`(o#{9I*IY(_=(}TsxPIaMK{WMF#
zMF29_Z();W^QRbSU9&hlfnbWj2+_JwOpWUe+jHTghO@wCJKLl+V0T^#zFM;t-P>eS
zc1D1epBw>xTlo27Qns0`CLf{t3*eMiu%-qTNG`;@N4nG$VyrMRUqAU2UrCVK!KK)^
zTPDJ{VKcRYBP>|fTx8Ze!nV4o9h{Mt%sl{1%>SB#WO!WfyR?&|E<09l+>J)mOqWk@
z7lCkXc2bBPgxMPzn|w?M$sB3E)i;@g50nS7dSf-FQHD0;iLbHNDYZ*=j#8uB$4{-@
zc|`<^X|`OzI`5xsD5eSbR%sq?5a#$`XGCP;T|cw&fNhAQ{VYi3Nz+_-fi@PD=eLM#
z!%c1R-t8J;*TA+MKc&cDdjLLaWd}-qW>o4svxFwrC(l`p4$U)=+>>(ZEc&mOFqHkK
zjS3ol>OV7=R%%>T_bNq6TqY=xpuj1lK-H3-Q&?ak>yxKI(`F5E-;Zv_zn**@yLRly
z`n6l}#2;V8&9{s}$Fp)O6i`y)LqS0yf#bnw)HoZR&T7LQyjd0ERCm#|bq{RYxBw-G
zb|5*u1$14T(5-t1eD4QW8}Qmkb3X6A{W(%nk}>$aJ{6KL;O@1TUU=Uu`~t?Wx&9(t
zFd_k3S0K_i=t7HmA$Jvv*Avs!pc<3T-LoCguP(!igJA=fd&tB5<s1*rr>6C;Vwte1
z%TmtFyv#R1rKa7~!Hw59P<x$>>?WG)-bt<Jv$9SfF~vYzn%2PCH2MDA&?IBZJV`be
z6Cpu?1O>_{kpG8Y!~XKW5x(|TG<fvS)_n@7e2W*L_}$kLc;P7oY4$4Z?7_5l#t&$H
z*w+y9p7<TS3+Dj${|w1PhnK}nK7SZZd_N5Q?RQX|>|Z=u_N>Yi*1<dQToh$wA@a{Z
z5~wah%2n6HLyc|KA)9qQK>uT^dw#=4$b9(MRc4d8d1g{TAO8FH%%@IrON2Z*++9<B
zJ0(Ez+72S4^AbxkQGHbN#7?b?((bpz+hYs}*%|>xbxFiq+MSA1eF$%ugTR;17)@*O
zHUh39SuNnDX>D7_!8AE2KXuGO4=Lb^Y+HuV^cUbESS?N2Kn|@CopXKl1)QQMUe+^o
z)=+rHK5|qEHVS?s1-lUbY62Y8BG=p?FxR_pJ;E!$gnhtON2RHJBT8+L(2|Lg6Ph62
zPeQ&B|9A_wBTSRw&VDB*ps!rkiCzmdh<x?2(Wue(ggAeydL~hD87A;v6>x1!N=Ned
zo@RGin=#|%M=5v=%zh27FaJri<7_8s96fg&TxWlWeIFx4EV2Y>@K&vzC{627^Dc0Y
zdorGNjYlH@bR1i-TVp2wYY$_ZF?zct8t$B)gU9>+1T*%}!D|~Q;H!LsTpSeIq^(!C
zBvgtFcRDU<Jpwnj8;cgn4UGwKcC#`4nZ#T?)ZU5E&t?+A?V!fH1h~?UW*ki-(p1v9
zw2u_xLS1uF%^yp92#~k+l5h!L+x8I_>|crRw7&u)n+~FToOBAzCGMAowhCoLm6HhH
z2nq<ymFeoa^y{mE6dMXh1lt;1e1WYI!77_;tLm07-m)j=rhJdyNPobWU3$qe+b043
zhZ82?iNF0Fg@u+`W@`YJ%Ttqw%2cRuZf=;{R-IyL;&IJDj@?uJdD^H!1IlYETGX>L
zIQ@TRcDieqE|@lB27Q*6VB*9{tOVb7zJ#66ItyoY>Qq)KwZ5!ptc$W|$9p1$kJsLK
z!vdt^y-MPUr!hXHb+7zd5F??f0n2{x{SPqavdav(e!OL;Q?q~c#TVh<&pt=ci{{7k
z^DzFk*YL>Cer7ZSYs&8ks1h3sHASxFGmVKO`BqGnnwpAx?z$5{{n^hc$D=P=*7py8
z`lGQRpt@T=c14^r0#(f5TXZFgmc!4!MKwQNRpsg>lvj)wxv?OBTj-P8M)Tbpx9>Bq
z1*0CVfHeiaU)*r%;2Inyin?XnUhFwoi0%9HQ7lbj`Z^BDBsAB@Wb*YL8YI)ab|&7M
zJP-MFPZ^=R4haS2($Yl$Gs_#qIh~r}lJonZL3S#<^lzAy;-#V#*LDKw$>g_;f-UD0
zJ3-<w6{sTI#I|)`5niMfvE{pc$crTNyMTEU@GK_a`Nh0txMXm5)T2zhlwjz_UHh?h
zcdlk7@(nbjP4HAJfi}SETtK!gsMy&aGwo^);XgZp;!rS*Lx=LQ|G)t(UcVcgcORtI
zG<|UsboY>FDJd>M16pn<EIf>QJZ2Ew4N)6fV6&6Y#VyE{k@71zT!IfKO~sb&?YVIz
zi<VWzw^&SszJV0tV62mQP;G_0kKptu&&Ye27u}AQjni-?=_LzGN~U)>!a9p3iEsnp
zmi>n@VcHUY6VV-tE7@9aOztQChn}Tz<++`a)1n#MBrZr}NM-8CGt$-2&RnS-JkdB4
z$ZSP1^-vSsw8G`2JC_Rwbu*^g_ZBX|+O0+0G?9*wHmQ;>5vLEE_5d%uJ)7gwiF10i
zMN^~r#|FFPf*`?%$4H$xBq;EWqd)?<{>F7l?J6V?`u+!R#&iFA2OHLJqku3MfBVbJ
z7;)hsj2Lwe($cIC4{2Cman*?0Wm+kgJ`Or`AC9H7#+#%0_^uSEy2~CtJK<KEFn{5h
zcM;@nwAaUfjQR~S4REeBix#ZFi_g7BX4lfR7HB<uWbMrtR#$G~{;8vYK0V}zIHH}2
z<WJ}A3u8M?)3i*pn4(ixGH{rHxq|#^_b1R<k6R=4QxGsKv(t*xjGDV0>2o^~YNkLv
zYOJRaB+jFMWC?l$=3}GgG$-<OM^OYD4n-iX>OKveG-+z(qTbXmOs-Sr!PD87`Xxew
z0tpHnp+Lb?e}$hK&9-3|8_+tiWD$x!{Sd_;y#vQ40#J+^Y1$mN-8*q`><C0#v_jhG
zG4Px-1fKr%$49`pd?@+yGX($gD8d8IMbn@Ew)`V=p7pceVE4RPa6a)EN(Kx<^6(2Q
z#0k?Jpl{(^1m;XfA?sn=zSTSrunxihJdFT<!8YxYdim8zz2pjHJ^Cjc*teHv!+(Q<
zv~*<L{Xm8M5*KGS1$Ji+#^Ka{2yz2sO+gwG4NK-AGxCL5&ufaSXOumlUdJ}bNF``k
zCb_64m6jDQHq{AVDZxVM^QYmY`D-8JxwRlRYy-~?Pr%l=y)lWWoXf9Hj<(#K(X2B<
z|9ui6g5J(Czl1F%vziK=;@k4kR>^fwyhOV<6}C}7M5Lq$o`r8wTOy`Wu!$}QY5`nF
zHZKJBt~Z*@PSVBE>k@?5&qAaqpG8FBU-S{2eJ-PPiwxn{6r%e!AhdBF0cZ-~B(&@_
z1mOc)f!wX8-qEin!ZqX;T98PuSVKwWk~1sxt0JhM?Um>mbraG|t=)#-iiNN~bg`5O
zr-lMH`93sR^RG0usGa1b@O*gV^U*)659aP&gz=kS$CA9wNU|qmY>NwUMXS;1)u223
zSstq?UKcPeO)TX@Fe*%;XP5$7g%Bm74sMb$((Smp2m#g{I`WsOIVGz5F%a7%s`3w4
zYNiP>Upn1_mzqVJ{5$aI%0FSk)=9Xz{Z$y*Y>=so#<AIj!9%rkR+VKWZk{R%$bWD*
z0;q3jtzq}aMvGZj%)pVvJRTTF!yz8WI_JY!6!oe6T92c)Y8+6AC#`(U&(FtmFT8+P
zUVYt49xo<-Gzm2Rj#TAH9_4SULj1+o%6NSl#bjJ|-%f4cmPk!G7Ql3*lsY?$wMkWq
z2hm6Vy{8kn7C2)ns{_}fv(Fwt7kNiq&?tGF=B7=t{Ni~X&vEmXEm*%{y;UM<)xCQ+
z4CvRNAQ9(+c!<-E*D_~2d-iOsrm1d)wLE^cWT`QAZf+dMAB~S;7hHf>sA*nIuwDT0
z2OoZf7R_7WySLs_<CxJnje}FB^2bqbwNd;<1A_nnKmbWZK~&A$#>T3>RRq;_#PDI5
zG-)#D#3(h+SDJBV`ipaGwF#@pt2Vc)GMQeRbSD$X45jKxa57n+A!%tSobQITc7-pK
z%j8<@!IlH1)R^AR<xKvTxiw1~wKWBfpK!PoN$yly><qx;Ng{aZ;D$?rzuXK>=_CD7
z2YEjkZeKBm_w0b}1yM{}Nfa{d;{;#<tjq+-K_|MlZibsL9!1UCdYlpnRHr3z156QI
zOz&+#0Gs?nSQbV3&PlsD!o1xnSxC(cqZ^gH_WHM?;6RDloa&cvX#(f#wjIR$6`PPk
zcOMHEFUAKm*JJi70@rMJfEwCDd)?G54~duH&uCh6`>u^U=mcWxy@*J2gJqhr%!IO(
z5VU(Npqn2zVQw;YI$iLjW}txJcrsm;4D8kt4YR%2pD!(J@!RA*SZD2@ktVy7v^r35
z7?bDG!Vs_Hyo0sTMnq?PmZkePT8fc5bZJmKcuywPWg=bfO9r0R976{7Wt}WQTokp=
z=n<U#w>7r}6K1YL2|;&x(>8N$O?_I6XZ7rd(V<Z?veGjV=7s{BHocIJq8CwXLt6)V
zds*4geGW6TIYC+TQlr9@;!-CFj3+JWUL1pacg5ZVA-q?#7@Kx*)0Zm+0Vmz+aYL)y
zTOVupa1-ID^NCUTaS?&;taL8|wg5S_isMvti-$N}<JXB}f&vK&oL&l8UuUPc%tUE*
zM1joAblh|Q4KxG(3FggNW;B;4eKG?JxFc-P;NIxfyE7U$sn132#Jf4WVGcU=z6?4Z
zZ|F;(*Tsb;7*F5Y0;pel{(U_1o4c@i(+<4wYz(yKB6{_;3DEj@Y5R4>zp$kfZ*G*Z
z2iQu`TE2CE*u#xcG#j20^K*SF>K11k#GZ)3Dj!P8DgSh?4~zGQjd}9F4)T_NoH(z7
z0{cp$2I!ZJUM#Tp6lpqtyfuWM&-2lgqaB}Jnu7XOXz8D#qN`|i;#PtJ-yRASzVkW?
z|L;LW>SZFSaZ~KSnjrF$h2|5@-r+2G@BJx~FT50<p1n~rYdZXMX23V~a}@pi?+8Bg
zckJ^fBYDU$q>LO5H$iNh+k?D&Zl!tLrfB%Dm&<%om#2vQR5#+Y1Ka?YckgX*PMAt_
zYcz#6e>O_~I}xQbzC?0KF+aokvD~c}JojFWq`^b^8v@_5CGdUr2_lmxAoy2mZS#j-
z{b1|T1Ah9_4zVpVOP+ewb>)jpoS(@Qkl=wlo{=up)K12ckVm05f&pbKX@(9ae;sQI
zVjQXo;Vp|1*}9m58iBfu#P`H;>Ca%7|7KHA6$vS*wkM~<eKFt9!vr9*npY9&8*qzK
z9h(GVbn_Ej{2oaaH0A=M1$$@`kX)7errJQ`%8TCTW;2S~7&V%d2G@wY5DEC;S@VSf
z!JXT`LU`vYI9hhEHkVWVAh`5XxG9LD*pf0U2b@E`2dtY8$Mol^VH<@fp8)onnQ-?$
zwsvE@uH^+)x>pmG;%O4cuY&>vk|=x?QYcLwJI#D*Oh+jsPNRS7QO$><cf&52xcv*%
zPi~CiP0pd&ZtmKY;5aTAm6$!&!8Uga_h85`rL@PWJw+`mO)^HKTOT2c7yFrcD)w$j
zbZT+Z)zQFASbP{Y@16XCx>>$El7f{*>+tZ>Kj8BnXXC~;SD;J%PQ1^;jwR%;uBgQQ
zI;Vh_J~eyv=z)6m>Y=cxh~rBWB7d4cb!5<AGbVUXTGslfYyFGI)zHbH=G?Cyc?1oq
z*<4dtwR$xkef)8(UbB|@nA}adD$*)U$5<VBPq!-s6OngJoY)iHv#M*|h{rMatq0O%
zPD?w@z_p4t#p_M07%m+>8gISzHg@dZ&DV_YCF`2Oei%7?xJB=Z(c>8%b+@)Rg5-B6
zfA$$QoeNA|WN!Dqd+tI1zI|)VLZ-YQ`uWdI9|?&1&&w}!%ycJ+#X00SL%X(ZaqF$O
z;NQ<YOZJ-}6Z`NVPd;h<UEg}kIP~OLtQ<CO+=v%n8DEYj<vY4^?Al&44|RyfN9B$w
zm&LtzQLeXn3)ZfqX?6CI;>U(;xmYKEDmtuRzaD<NJMvS*+q`d;vQ-?>zhj3E=6$X7
z_=GM+NmCZ{PK2r`Kv0nzD9xJ6?k5z?4@AP%T*u^pS$wpULPI;Xplb|Vh@;7AC=~w7
zIjE4}btvScKjLJX2B#VDV5xDt;qsNDSDOYHOd0bFQ<hWP+C@OSfR>fGaf4;q$qi;_
zg>cEm=c7UW05`Z4Beh-@>*YjR1~+r+TZ2Hm_J~EuKiF6|$)Q5bH$o7%K~^$Gj~Ix2
znqt{KeK|FBsijL_%6_V6hq%f1{m<v2d*==Wi@m0uCFa|OAaJsa?IqvLJhYi5BvUSC
zZe{(*SLmKhT!&b8lst%er3l7~g^^%0Ea4`fOXHk6B}OAVjaua0TV~+e^E#kK!(<#P
zA#hFJhStLkpzL6tL26J(`NG+psXQ`&rZq#A*%6jDBv&;|;4>oBtd}&v8J97%*)L4G
z*=S{^sPGU%+L$W3=%3QDeG&eQNpkW?MT{3<9USaWLrW{?6aHIB3pd398+_DwH?&b%
z%8hl6ke)6owWzK-*Za+bMwp4CcB;?R1=`3Mq8W6D%YkH9nuZ{*8a4oJnl{4U{__!5
zZ=nr!X9+d!)9AX2KsGIDEn1%kA6;4PKd=|qjXe)3nGFq(R^8m9YE|`2_Y)yOfdmCk
zJq1o2(LD8aOO#Lx3M8i_;k)#2oztc{KK^hDii>^7rCF5;A5O(56goF*T;G7#+1Z(7
z;!@%9a%UE2^U6DY)6>(?tw)EWN|cs$^PJvC-H+c`v|uHcE?L7}Y(AP{ahrPd>eCtZ
z>s#6<LoVo#9Xs~W&-Oz4DLRDrX*OK<l4-6*W>T)Yb|gkpNS+Aa4hme*+=&}HIPu~d
znu+D?v60%e_ssC2YnC0C=D2WKYbSk`h^08aA<mP<kEgfvZ+2-iWm-N6=>ONs0GV*>
zZfBZxZ;^UxDR*jfFW<Vvd4dApehT>J&O*UM_n42gFinXSzwsiREpw1`=Z}mgv!_>I
z*eFbmhm;W)LBHrPA4hQGdX!H23^r;;mrVNtq0c@>fV;6B_0nN4$V1B0ufWwRC!X}E
zqqAjeq(1&v9J=!+?7j4SglVSC?JGejBin%BuD%12F>DlE?K<#7*7_(`CP`-xGQW(6
zeg!`@!;7UEKItQbS$7E>*%;*i^lmtt(FF6r!AF&wxN#;@fKmk$;>YUScwB45v9qhb
zph{0d%ReLB6{bE0H*isa73n+zPMQa=7PKy(o<80TXk8{1D*y1CbMBzFaDZkJZ4`o=
zK#dC{6|%%bRaP{ze+we(=TeYF@K<zcKM1x4Z43Y$Byg=Ra0cl&Zt29Y4Y-cdlw$a+
z$p(j%rhK?Dg^6u?Q)4;_zF8FFQwxV8FGLo9glNx;*riA2t126vxcRN3Ks2V&XXXwH
zUj<%ULz>z|<a;IH%=R>(Kk4g@pff@+I>gA*SdyZ&U8YSb@TE+Y$!K1b`6cn9#4ar#
z3WBw4@f%ovHw8x8DDaipaE<+Fm}yyt(fYPgqfGaBLqb&Lt@~C4J!2YPhY5oD7}vov
zq~#sqx%9|%{@>hLSdhB}_jVnJt6N`g^7~eG#u;2$X?kT6aF*}km8(`+E#;hVz-e9a
z+>F5(4KzM1fxiYq?4~)`@WcD=MaPaE%ipYUu5mZ{v(NFTzx)NcbhV=EcqT^#=DMfb
z6*4!s%(yLoTj$Ed+~Xf}<Tb~fMr`@}O53(=+oR+|CgW52hgC5xGJh%-l$Di*o;`bF
zCxL6h+A@DDKhQZjITa(uuS~n*7sr3hBh9Bwon|E@eOt6_fi|sMA2Xvt=L|G{(0A_K
zNt)WQX3ZMx+_?*Fa*mz0a^{lHjl1zi?B2Bt?|neaMzMaLI(<5pE?b7qozFtcmMt~?
zAiy<U{;)T1-HQGD5AcqNNIEf%%*>xm5WQ;?<p2Hl@7{(7AEK)v)zyr*c-r`}HaS%j
zCzhW;{NMe*KN?Nd2sLG6EoGwe1BdPAvp+oc7zPijE7>CRkG(@DIlQWA;J5#_0X|71
z`EE9P#RNCQB9^*es3JjMyB5?Mf}({=3CxmL4O0u&<>7jqM9`Rdg}7nT#kD%sO__9t
z3pZTaA4LRLznHUt=AY{``xFBP3xLj`C5FzeveB_+D>TeVp?_+E*#vb10-jmEfDfC^
zMWrrk8nc1|diBn^l9`x*vMfq=Zrcof&T55K8@3Vf+>fv;gYDAV@4(`<TQPU}dJO8?
zn(<2M_c|X1hYARo(oZwTe~|pOw4Q~G+r>K=MuHmM1hS*lJhzcC(S{h6X`|M(0$FV4
ztqr^uFq<qN)#1Y!-m5)s88ZYevfM~d_rNPF6Al8`rq1Lw$g>cFCZA4CVS5^L;1*7O
zZ^r}k)#?<VLv-7jM2&EQ#WDx3jV7`hV`DxtrS9hD)~GMvfcinwOH7pLZs+{~ty4Lp
zK4XPOX5pntPv#|F(g`1<&NLMH!^RIfhYvN}NgMX9*4H5EV`E)oQy}Rl8nP(;)HeRK
zRl%4==e@XJZ&geEn4RiGkB&`n`*ox6r>EY+o`4rFZaygHCJ4KSZDK#I+H(k{bJruS
zVFQ|#ZwN2%Yt7fMawt#7%Fhzl2?``AaOx?L0Ip9xjZb+Ann;IncUYIM?eN*8Sy;Sq
zC4FxM%-XewzBl&LZ-yp#P4rp_Oo$zHN4vn%GgI)`?|(>T6E3#L5$4WWiq$lm5~PV0
z)nn0uRrvJd>9}mn1-M}N*(Rs4SC7Kl)tj-G=FDf!T#WOE_QQP-++;MbhYqvi97mCf
zs9z%m#9aNhzZXdkKVDoLG|ZFyPOr#|VCg|Wo?R8dm^Lol*~Mkdiq}<D%Y(0;O1y-a
z6EQCu;;LV5gZw+ow3URiXKK)EzM;U{y?BUflA8~Q@&2ZeG4oFTpBhQPm>eff5uF+u
zsNa;ce&zd3Cd1<;p26flQ-@WeU8j%&erhfsxOps+30V91lJHdly7WZ$Bagw=wFjJy
zn^ar6KHl6NI>C{V319sNkZJGW>}l}&OA(^kwn$b3B=x9%BU(ITZ}(ovr)h5+cOeTN
z*GICab2lVkbQ!!uh96~uJf2QR0qO##1#G8WFcP_s|B{;G3(OqtWt-HthVOm`Pmf;Z
zlO@g*6gd495b(NVd0Zw*Aqica5#T&7)7N4?zTqKqcpsvEpW*%`7>Xp(#5w`FnnGw9
zO>9#up_K1_hyq9_&4g=yv9}w5NM=j8^0w2|BEKM(O@?#m_h3t_UsHCc<XLFlOgIX*
zaT6YeYvf_<JuW5_NbQzpXTjF04?LUcyPU#9#}=AA-?tHHQaxaFN-Fp*7#pXg-KQy2
z>&@fevR5!Rren7(@|Qg{Lf*1bUv^t!T`Pp?GdoOSrHumHs5Hnp?)AB0d}q?kcZ4=7
zZ5&SmvhD5&{QT~91U-n#k`O^6f!vO`%$UO9Fo9_cOJ|-E{!(ZyP&-~>V+zw09fp^h
z;D-XmSaN7J#zrrp06Za{F{Jqh{1Lz)t?Tyf+GEw~RhHnKv#A+5@xc?XC%-AngxhhU
zJ_n-iUHhG@ap@)1TYm8S{dn&A=kdaeFA+b{W(PA^tekbKvUpEgb!EP@<8iFPZk28B
zF@f&I!6P&K$MZ88S<mD3X52LTwr<v>dj2vVwJwhq5Mj|QUc1WqpLg!L_~g@1d7ClV
zw(i}#LT0fm2a}hHqiEITRykG0>lw3VVdJJvCcXN7;DEEKh;i(-P1^WZTz(mzeEMmV
zHjlokWj<V{*W+MH#gb~i@_0OW_@_UmUuzHEegAz<FVuP@Ae&cEfZ22AV%F?AWf_>R
zk2P1yCaQNTeekw!-MTE!@tzp^mC;aOQO)!`f5;G=bN1PoGk2~TX{J1;Iaa!3)yt$e
z4OE{-aER?UFM@fuG>#QCF03<+d8N_2R98Wop4Pj>qs|kdA_YiuI~AfrG?gs_pSFmf
z+QVGasHrTCU}@|6{gg>_EY}n?pQh2jvow{X%qTz*n<yjaLb`$B{br3Dp~qRRap{oe
zXisICr{AAPkdg6eHoVA@%{7!(Qi3IDKo>cq&goAJe@<lbN2}Wew^cWu6RNh$W|$Ug
z`mffzDSTG&F;{m2wz9OsO{}ya*@rUqy@x~ehdhyMJvX*#b0oEb-E=YY-|2JEwZ%xZ
z&S{SBeM+%t(PEU+5`bNRH5YmD2R6FNG#cH8*Ct^ktE3s~xVX*Q&n+;V^B>EVHnxYA
z&7$x4el*!VqF-B@(DoubGa2>LQ>o$2^rUB$8|dQN+hIR}T>|h*8(~8n<Wr*LJIw3K
zKW2?$+sw5&5)9@=ki3Zc#zp$-<tdf6GdI_H+}tc6VIBDoX@Ez_*SNWnPuRPjyO}PD
z*I*xqNIUahFo)_wZQg&mT`4W4H7E)i?Aq|Bt|4G8{z&~JpXDiZUy<zP#sK!AdF!nP
zTJ4HW13`O${Y$NTZXB>VN?ElC&C0uJVm%$_4(P<XUXEY>c@By~g-D{CDL?tGF#TO0
zCOuc}^x>tq7NK6)gG)!!l?=yp9JtoG+M#12Bq)%ez}HKG#Fx?6OaD48y>XKU7<bzh
z7)zhlt5<Ep3hoHoxnnO13ybM5#7A@8+yN)^BxdrpmNR4igUoQi?}m?l2#*)Ic9FRV
z@#9?#wI+Ejz(p+Ytv4p|e%NSJCntMx$>l@w+%xZ(Mdq`KGx7bquSF~RA4!C79|fe9
z^c#ZL!>E1z-0C1^?+w96|Iz}0<uh5@ykG7N;SXnfa9-2ur<ipGVa(YZ=FT?@aBfr2
z&iD=_#ksl|X?7Jyu#Vs>c^C7|Fl~333~9u~>p^Yd+03Je<qITL+$p<4XhL7tg=BsU
zdEZMOtY1U2Gvd$6g^#z=R}SxuZdK8gmxR}I*d|%+k>BM$4J35367HpmZRMX&XP7GQ
zZ2H^oQQv{3^s$}Gdzl_9zGCb1W+DB6A7(w8(KmQ~B0!&P3z_z|qv`DPn>mbzmz6W)
z($x8q1AG@*uIk#IZBzay8dPO{V?9JO>Z-rK0rXS71r`_c{Nk1ljO$df{WX=R{ATP9
zVNJeF2La8gsXdD3;|uv6u>7E<b<&IU5*=1E&RYIdp0vBa+!;orBpZ6xx07BGW5!dy
zYGiVL8fmKh;%Vb`5G`i0y!F)VSAWP?cE5%WbR%sl2R#=Ji%IVe^m8uD^`Da7D*G&6
zYb=T;^Eh5gDSXVKWdK@sGI2zgR%td|)Y4`AaPQ$b`Y*?@!2To_b$&A^)MuwFEa!Nd
z&$e(ADl$mRku=4x<2;T_(WEWkl^p&m2W<$@_?^6+yYLJ75x@qw&)`^N+tm+=@Xe=y
zgF>?Ue|-i;Q$B}%+ZF_99z48q2}-ZI2z%WGh1<4=vv+@Zd-o&A))(%aw#E#2XwNQ`
zPNk{v_uoQr=^~_3OS+WtQ!ct3u6_ei`0PL6*|!@9AG!~X-k4x~hE+w4D76}L8OM`%
z0KSGzsO9|t0t@D#_{+&~&6$Q=njm-e=!2B8*C2W5NI07|N0htcgWI>lzj!_Z%a<a!
zU^aYPHX)sL2=L?6nUQ5oi$~hDLpIHqtBr|}pupEnfk<&aY=`z5!I<XCus03f7G1vX
zY8_|!TGgr#(EvBC%otB$8HI;j8`RFI%%Im42v~>c8`{!ZpwOmKJGeRyG%0MU*>Lu_
z5cX-$qJ-<H=kOkc)=Y=1|CKf6SohB;*a|NEkb*i(%O~i~;AWUnRvigmJNsUS$fh}l
zukZv)I5$mz_mZE~eLbqlF-Y1(cCSUrs}E9OX8s=)(;*<U3pM<EjXvsG;>KyD0C`W-
zM`S`Z=5=Xq&`f8P_Z;CQ3WFomj;480MktZ+HsHd5R^3sW8YZ8+#t4#~)D%+&(zZ4}
zusJsE0uC8s2XlV(A8UX}812+nH7m){(GqvQHVU|C@_FdcA^3FCrv`*M0tlC{5=V~t
zSQKDqb)&|TK!v`2`r!V1?=|2_`4sW<^)$2m$G<#*1&bCLA-TD4GKmY#<1u5yBUX;?
zs*EE;CRPWf<<EMir$=H~l`D76Skja1*r`*E+Tbdyu7lYO#kNE#LkiQx^O4OQM@kwz
z=p1zK)(tDD?cS(SBaFFh%#nw&Gl8{gqgo|R;}Zdk4<A0vO#?6T-PLAqf+fkx7%^<v
zQDs$k<AR|>&8CBr;$kyzy!XNT7&&qT8Z~TKU6LB_YqP}9AASe|?x#+hM(}<jwGz1j
zjs0r%p8m#q-^@EeUz#)@H1KTt*Y0a*R$~D*JQO{R#<m$l))=fIIsq^j9{TZvxbvQS
zv76fQGLar&e}<(oc@p2A0_xOCfsWQ_=7pN9VBP`i)S?1oaK4uLW7Vgwf?b-IdY>4q
zaaLD3pBedNq<C=ah@LdLTxv|Xi&o}!jEz#YQe7ngy5_)0j7VxzrcBLWf}<$}D;s8J
zp+2pKHf`JxndvFW$aHgEbz)GLj>sUITSCURgzvnaV6>agjaxLzLcfk#NKYg9$_*e6
z0_NJ#!PS{TGD(%dy0DTOTh@J^@R=-<Y90}u+qy{>E*{tssnH^A*?Exfp4I>1U?Cc$
z(-ShsSeMrIk(TU6yEKC5#U+%3)66%)WETNwnXFg225e?=aS&HyNbX2<?V!-c%kT8o
z*<hXosJQ_qH8qVswB4x3%_nK8Nd#@_lbf2>r4*PoY8=7PK1H-7(}v~I^fduy`Kp%u
zi`z?C2<I*{R;#CVNET&8XQrj?8E(dtHn<;Y^*rd_F^6uDGI3syR%p{O2}N{`5zr<O
zk&EwvWc@0GJ*)!jB-HnYa9*GGNX@8+{@vRkvz`lka|`H>Nc<P;>2~3&!R;X>DG9P1
zlo1;ZYg0OD7~7oX5@^k~+qCJ>^s`k(59h{g3i3wK8f@lc%`tz$LLA7YUvkn@rrphK
zPa3+}=^GE^8utw9BLkAh=9u!JwJ9TZzfK5{$Rt651O;kM0lnRca0XFe{kpAKL(tmA
zT~pWHFq(^<*MQbOuDJSX*PFm=F{IiN_v$MXkb8i_J5JJ7F=Sd?7tUXSF5S4Oq$VFl
zDNW`|n?}>$_dam5F#)axPQlJaAwI$P?%M^8Xv%#5zC5g2xd8?Fhsk_U>mw1qwG<E#
zsNV_#mTzqgV#3xC<U5&`eW=+R!JX56cw=Oe0bt8FvVh?BpO*OX_NE{X5bQNP79$9-
z?w;kq|MhcYaFZib(`Va5%%7&o$w&&c`{68~F=Hd(?)BkGcyKn&<1-&=4bR*Y#%DW9
zjmEbGwEtRZfz#uMC1EIm_IMDp^3q!RzG0e+30#-wzb3Q1?@udbS@fOV%Y!}yN)<jK
z(EZB=e(Wfc-yX}HPc}6zu50havV&0rhz@Gvz=Vsf_k^z*9;ScimDEP>#@(R;iv>oi
zta=2*KViJ?1icUXB6wuJ9}@|(9wyKpXE)P`yk4H;#A5@z2DCJlIDtP!+!&xdRjz<y
zwM%*5*4~Am_wm$P+gg45quHgH%8dguH_mL#&e6f#Y|uH=f${5tMg!~JQOP)$bol4;
z0N!OEi^l5nA51Sr^AsE2yD%BQnD2u&0VI(qYPSG=_3it7rnCyn2;M$Iddqitu~a@8
z)P^k5{E}8q{Hh;!qDs>x7Ua+JhpgY0Ls7E<Vn4@(@=$0(6Hx!X%<{2)->g#XWFPix
zXoqOCn41Lz>av=+rd@~{=q}SAYq+sOxoaG0Bg8Z3dn=4flW`sY;f7^AzSxIXNXtVU
z3zU7EI&>oNeranLUgiD0r4jss{r2OY6?ZSI{@N_?&ZZLlcLz=1>Z6GD6a5F1rhlRD
z_ZF$*{1f4uOaYnF)-UOjD@-*b)ZFl|TaEDARY;yW4Ux4gQTRGdhrjj$IPGvU$$+Ct
zGnxorhYSjdBH0b#C7|qK`j+PrByP$LVjK&FFFa-ZW_hMeM!~=Sj?DXhTqPOw%+5H{
zSCG%^jC&c6>wIW8f$<q%!aMOJ1n12}n5N8k@B`7=t|KCQcLUU>PUjdr%sP2HbwToo
zi{U<hIGS`g3!byO!bUCd@*jUy7M{47pup**Kq>v4`Af|D=-?Q)r#FNxIpcKJ;H1jZ
z>u;l<xoAlN1w33pV?pnk0PDzstq9L~1CcdTXugo^0pCx*Ga2r#7Qh>iU-=kXJrnla
zO@>!UreH1HbvR8fq?JW;_8bl0>{sctu-MEkkwqUM+WS(@7e_>ZWsy(ONody^*tRY-
zb@XzL3wJyZj_g)trMWtvkCMz5)C}KY_!8Ul&k-5=J=jw$H%4U<Ptl24y9;D}b8sec
z(`~p(Hnwfs+1R#i+s-E0SkDvNwrzW3n;YB7&HG(_x9-1FQ&Tllzv<Jb`}Ap-U0vp~
zHIzbf0!aq|NOeWgtd)1~ldup}gLuLzsr;0e&{5Y*GIFW=&BsqAkbfmo)3MmxSj!*U
zx^qTckVi$HE)ym1pD(|Czg5SwO08!*EkE{o)8r|*VzAW;=FK9@t|hK&pV{!6<llbZ
zYm~+1Yi7CfB6nk>oh&ucYO^^6$z^b+1ZAjCRbFXUOe}c({#8}LWp8!<`7D8m6ew9%
zPux(N_SPZrE^rRuwte^|qU&x<Za96z+}9Zgku@SDTf|zVEi&S58e;)$*5x@=`piy3
zJ4V2<Wt)gQ2>3UX&SoEd!`NkNsto2;n5WAfZXfVIaFvkZgH(3!w+=Y!pbh(RkNw&2
zGNWmTOs%MvIc`N`z&m>N`FS(4OH4!#fNr$}0#&%Ro0hq&boP@lzeHk`K-DwBB=^~@
z6|!xS9JhM{40K%{)PA@oh<i#|MSzy7TKtuGIA1G*O>eObs+<HG?3>1Z6k+P`Fy(c|
zyJ$5O%f+om<1Ou`mE`mj+k+sLVv;$g$X*L@pIWZ&GP6dBVY#dpVa?V!G~x2^y$t@T
z^>R?%UOJCz|Jf68u7t-x$F^#QEGicgi8e~I<9*z8tZb8v3qfsiG|D+2bujYtrki`|
zDQE8`p8y<3Mjd_kIUxy!5xtP_^{;Dd;GwBClP6!9Z%ca~qMq(JSzFGTdAwi?N37Rq
zgSpEFIr6z1l3;9b=1tq8>d(xAmskS)*bUjDdcp7A=c9Ok!@7$|@1yVvGc$i`AAsWR
zqOiSuo$EmbqKPf}qk3@E)6sF$Ta5_;$jA#wBEzw`!OibGSu?lNW_V|=&&oV3xMtvr
zRg{pB$F!WMxn(r>j-<Yx)D&i>C#NY4jg~9cvQ97m{jBM}JT0%T+45;p9t`#VCq!Fh
z)s#(C#f&i2%ZPY-PE<?GzbC0{VjM=%`d5&_cn?)4?L-{HFL)%-_GtjE9o4R2QrA^T
zjLAN$maflldWO=FZ?00jenBog;gbGe{*-2CME)<sD()@Oy40HfhA^6mAqc|BLKO^#
zZW6bdA!q4*-1IdVnAzvpmWk`nDl%GL_~ivf?}8z$*R-R%KrZEMN4}`C+7J~s1^q4@
zjF$aZ_bwv2wU#nOc#laKFo|~&AMHYnqgRw`x}thr%W!uEZ0DO-oMiwmB{PJ=--03;
zoD$dmhB3wNF(^dgz4RoeE?8LdN#DGO6Tg^1@mymfq)&)aEEX&3X^@~P+?I(r0lMBu
z;%9vOd08B`^57u<%vVub4vaw#-o3;`viRxiEM%1D>o4C8vNU!R>hUsik4=}V!X;?J
z#xz7vz{117MKC1-zrFDYbQOG_=EO3U+%`yo4PKAS6om9v1fh=GOXatuoDQA01Niu)
z9DApM#^_O_l~i9NuWVmFA_hyGu(jH!>9GXr4*2?7m_~iv9T9*%)ad0Q5spJzHmQR?
zUPYs9#LuSuQw5zE|2iqK({Us0F%3W@%nHHxN){(9cKNb2oUm5hbRNMyFQPDot?hV5
zOc9&pQ|x3yalL0ndFvKsoAjUq>Urh)B<IbA4Wg!!%NxN<U37)2Z3;Ep_+W@GgRP(?
zFE9$CMPWHL>1RyJC@Crywy>p3=IAP);B!0pNz36|A*3r*p;r}?@Acwm!ci~Kwd=q`
z4Nc|X#~jQ)#wcoE&l_@rMF=Yy-4$2B$HLwvEZrnWiY5q3ykZZkDSy-1MSKA<^z0w0
zK|RwOp&YuQFh(4#^&cX9*SjP?#FbnD5ATkICMHdX+}0^7-xd&tfl83p@^}2tkX(jv
zRgH}mMs0`smcmy*PWjv$pkSe$4@BrBbJcw<d!JR0geua_*u$Ege&~McZjB;bIHD~6
z2~2c5CC&i^i*jR<n(h$b^b8bKLu%BZl<00it>;}#L1N6%PXQxDa@*oM50R->8)Tdp
zKSWnmOsZyY|MZZJ)?;Hpeea~+i^;bKAD&kgxZ1@~qgTd%k(#|4@MAN{(a`HrfEuQ@
zd%le~NYUbl)?`*f@iAiDi0>lXP3}-7f63qozuE!4k$chKkO>%`>QuDtdXqWV>q(-?
zYDZ@|yqK6m7SKPx(DOD_-lX7W<fnJSd<y<!+`8(RPCYZsZPTJpR8-q)$4T*R@e&z9
z$qRiwc;;WgPmoIVL}x(fb=66n%srob^j8lLza#u?E>9Uzdu=*7NzSIpw>pcV$(U5|
z%lYU4hmteV%TRVtH90<NNIThWez3zr&S($k3IyPWhJnnS7dD3790yTT@)l!Uvt52d
zAzaYkWy`17dw|Ar$eS0sbvj;qzqEGku-%$5XqygM1+6)-+N6vZ-v?NtrqlBx@Jx{p
z)Ed=2%)lGQAA*6mSsWEC^0$mxS${-5>x?7A{pdz5p7J}hh-iwcBP!)HgH4X{ppxIh
zM91r-;>iqv;Ck*a%1th=$NR&KMwz<k%NlNj`=dE+mZv;YjGnWlU0uAfRrw$J;@}`X
z#&Cbl!LKR%co}Qgl$s~`BnJ<LB-FN%qz9IqIB*Wx10dv+SO6yi2LX#BoV5=PB*}M^
zAg_#ycX7v|jl2;crCVW8o&GUG@a5Iz8BM<}9_&JzYVdO5XHuv(ksO*XMC-Zj&ncwu
zcfpR7fK^a-8xN<$D!>Zq0O^)fUL&X_?l%m&Pc0pU*N4d>npR&XN|P6<%B>07^jccx
zQw<)GmaJ3@lN7AE0O~ZZpcF)=5M}HehMC03mW{K7zutlvLo?5xgMT`ekwUFth01I6
zyOn$VDC|k<T3-lskn-N2+AeLVsGynC&nks|7V&)*u1xd3Z{`Iq28W#|XHnz1{UTMd
zECWDfuSq<1oDaVU%wrU3DKS5KP8ANo@t(n0q~auyVKa)+!HHu$`11#1%)lRSBcHKZ
zuwm`z{5ExjycERgSsxdcDl<*6mPmXJUQ7G=jrA@JW)4%TTgst_F|idw3lTeDuTUCH
zFF5zxXXPjk+zTI8&I)RH%pU11HLen#a3yslKh5P;Q(^>h4?nSVu2r0uss#{|8ZtVN
znAupbs^oG*79xi&2bQt=oNWwlbSZ34*;L2uGz`h|g2jl%h062^eh>0FiAk>rz9BJ%
z^RB}GN^VgL#v2=C%(RRFwb%8NqJxiUEa?z3QOXQ`T<8T2G!DA?P!^mKS#HWD0}Zo+
z?XIjU8&TzK0Zt+`_3d|r8`;jtU<us=ipG)KbA6$Z9G$#)hE#NhJ;=#XC<f@c=853l
z>5-pGGLNhFe?Z_JnJN3^&>*gQ7mv6%h*i!5cgDMbm8RU<UGRMJMeu5)sp?<&WMlM~
z?fDan%Nof|Z}ez2{@Y<yfIkJI00&ORCKDGlgbPz|5ST%06znPed?KR#Qj5hCDAFo@
zq)hdU8ViIZT=&92QKQE8x}hb{yy_zYQUKex`0_a?7hakrouZuZ_mbt$aC3z4BmC=8
zQrO$X(-RB99k(*%8p&qbTu>R6-K$C$liEKluWf}GY=%<9&TsYxDI4sx8cX;O<BM{5
zw<EB|XhPcfwD*64YTpt6WrCBqtQsMXvk`dZ*mFSh1AKEtbT-JGFa>`Ait$GDwkNOW
z<?IYluF05zN?&)F?YSS3phzhAy)=^ojAOuWr`_^x-9dKNfFP`y@q+lXWY;h}Fl?Ds
z>?d$&r@h_5J|s)zo|Vm6lB1A&+KSsl1Djj5RniRaB&)~py{KOYfdIEow>lY1<t#hn
zUn_~+zA(|UbLpx4i{#=)3Kw>O%YYDbFNPxvkqLh~M|^#Qq!J9!&UH?8>t0o^rWAuK
zd|J{HL0%mGmKwX=L}t6Je*~q$E@UY=f#Mua`NeF^*wA)f)?z@x01v<JP6U(`iixk-
z`kbJnCT!IYORlS$9`_if&m}2o%HRzSvwe&>PPe#D8G36?_!ZYlTGD`VrS#o4P1);?
zN9J4TOF7Q9r`g}SFV+<j&3pV?fj*O+8k0OTOYX@2v^loJXP6MSI#V<?!XoYHbV%1c
z3`1?(CP4%CscM|xGn0%t-Ge7ujc+^~9lCIWG7tfvyPpP@-HgM29|W~NSn3e2r5n&(
z^UQf6acBAd&i8F2<fs(wHvC^W?}{xEYfMdhnT*|KOihoYK<D$hZ0GyBZRx5Se>7NM
z3Fc=*l=G8<r~RT0N;Lydz1XhzOivGc_-b`T`W5|x;uS`0nnj$#F-FX$l#s0tGD!Yb
zw^?Xa3J6~&R?9xoP(lc*5u(p?;7)4opn;cr%`?5xcCP6(dt;Dngc_gsPir^FpD`iq
z;;(zBJj%vDHlt#3@Wqh(JPaG2TSS1^A4G3R0TbUi(WT^RE$hqOu1A_Z<$@Fk8Avvg
z5}lIjBC7kQU>5GNsmd;JJlH#I&;sx4;DHEC0O~wWlahJ-T{aF?;6=H)G~4c*>Q{t|
z9{*ZQ`Dlcsps6C_jY{7lt)adiDIM?<7li-mL@kJL^O4BU_yVg0OiT5BG~wGS@keZH
zxu}{e*H$SES|pEOa!;jwAR?wL!X;-1K<UH`b9GVD?fWt`Aim2gq+`wOVh8gQod8?I
zi_@>pA?Me{$QJ${qX#K*Zc;Uv&m^m0F=7WP=xi|gmhFhK#V#Zpcx-!x>2jmZWQNze
zEHj8{&SbLlzROB-_o13+D*5JFq6;xQ=9CF?r%Hqd>gIVl#ah5SIgR8Aw7%C*Ot+<7
z2L^hph(oNjWFdH|3|qT8#|Tm*gQUqUA$Ju|+0y|ISpRxLV9yZq`*RI5aAUdPSr3c<
zY5;TL^b}8^W54tZ#yY~wgrCROM_Of|({D5m!g@1avT5qqiw2bC4%dH~m&U<WStKgN
zm}ny_k{H?Ax)|DyNU<-CeBzTawnPU1sm#Qsq#kc`vZi6py6I%kp37u7u7inyH&F(>
z<pr4_tC(N1Dk9*nM>cCEgo#LsQ0)ZPacw4sr1V4V1_8=NhktUD{(3^L(T8Wc97#gs
zrE39<n(0I%cAy^>fkg@>kdky>&vOa8#p7KF<3<GIOX$Mw79PR=t3#65nKIl%)`CA|
zxrE=t*P{p}b}SI*uQIBwQ!jTx^}uVt={6mKEbRE%P&w)y2_)9m#xFshh0fH282`HX
zI3KT&;hlg!7^sx7jwyF`+OF;cw4t1-#4j~i(5Y8clqOt2dUbg`38lU6^R+oY2mWsK
zoMm+!(v-?FvwPlg&nGR^0*XMf{x&<w8V>`J6Pt%{YIJln31e}a#3g~+pTH+tm(-ot
zy<c9h`;8F5)+5)S06_{q0)J2XE-4ZJhY7M9QuM)tcf%Q0QqEc(7;>Z-!p`wW-I@M5
zngcve3ju0qp}VNYmUUxpF0av2YR9kjZ&kR>t7`U-fC;h}F84Gz*2Slm2Pyo-F*Y&=
zss3$fJDVTr<;8Jk2(JFX%S2?NktxCLMYhAP6T{^5u+uZkAXwat0o7-{DlU*_NfJ{w
z@TpuNo*6DwVCE+96x_+75s1^1V~ZljKOKT-`HCixCFrstrdKhS2*qW($3XX|k5JAJ
z*jmzN|3lu^0Guxwtl%j~?}Wb3Mm#@#ITT#wHJl=7QZUBOn&A!)lVNtd5nJ+L9*SeQ
zs+B=r>?Gs3RPGBpT!TS5HT;YNrtrmK{Y4Gi9A`Wuw%vZP$=HrQ&kqmV@?RHt<9S)=
zjNtE9m2x$T0Td9kPOyVc7Q$HEJCx^?PsVT?>s_EMV-1s4iOvgAiCd6BurI>HzMm02
zu52)G(o%rZ!~Enkd#i<mjaKfe$otnEy?C0eLEJVPCU&&du`0z{6ph4)1_Q+HQVlYr
zuQ9h_e|_Oj*dtq0CH~Vdl4gx;pvl*3N8$UfRMN$>PL0$#;APM68YbzFmCR4~g|YTK
z;$Q@vpz%L}?4U<yMqumd889k7p3N2n7!Jb(b&K@kq-~8cgAmdG0^{}N6#EK2%6`u+
zQAt`EaSuUQ%QzD2T8@fPmQpw)jfv5Ai!(K?e(=;a;X^dA4KeCE*F5XvUBko>XOq;>
zk06B0K4&m%vd|iFrslxRk70TCu@P7O*5sH(BYY3lsaRl9Nm^g(aEe`tB@9f$u$0XG
zz);9;rR<6Z4ZYzvRzDx{flVeZuY;ZEai_<e$!;=sSm={gw%4KjA(Gv0M}G9$Q^xGj
z7%6GaGm~(7Yx!`-1rEA3jYn>bgSU<4v)3C8Nyo%PGF8Do&>D~mSG_G^T=jn8$PBH5
zBfwL9$QzS>B5FHwurybE*1&Ss>-*xku2KJbLQ%I*-urV(x!A-ywut$wl4AA6d;rTD
zW0xKr!)0f>B1CM5N;K>)1cbf1!}XGk)nbO3V$@5@OK<8eaHKUklgt5jeyTuxv+y!T
z(TUS;i%#L!N3`um2qxbjLu{&$EHS3ie#gOntuQZyH$Jxc8$9qC7Xi8|Cz^BI)70hE
z^O4`?mIw%-PF2C;f~2{~RIFg0sZ{OQ+6`oUB7`dCmA%1{e)>{<@?c3ouE~K2q4R6*
zqE|^g>T2`_iV2djae|DoRKP+N_Pgh2y#jwj@7yW@${fF?FYe)s=VTot*b_IJ;e2VD
z?T}&Sj6dR9@4+89zKJ;G1%mE{eH<lUT_7|^5q#3Rr}#eJv`-TEnIN$&9P7TTld0iv
zVWv+rZgNN}^#VTVssmOg_6~T4wQmJWZ#BfAXeLd<ZM~tb8o$XvDk}np>UaxY6B39{
zcf$%%P8Lf&zh#$;7)TwJ9P|()OV{?LvP&D?#Gg=Tx|9p!F^;{Fb{Q=N)74ka|GaVl
zL}U&!_1CK#{4p}<YkT!}Z7dx28fB^2#h=pzCqjipFPCKMv2s<p#ENPHD%72v*TEZE
zt@}IcHq%itCUWC=(mw*IuVDQ+wfVN>P;062ScopFb<Ve<9OFNik+|cSPReB>K-ldq
z1`e3m0qcR`eh49UjqyL}wE)R%HIssS$wr)#tMo*H+_UXi+SL?RcA4zmK7qLvLGRxr
z!$&|CFCz$|1e+@l2glP^Jha#({oXWVF7^|okY>tn{^=0{Y40BOyI%w-SQyQMKuKho
z3L0h~kc_V*E5eq{pI;!813bYZeT%tvXweUd4vQo1ErV!baN|qi;k>8V@G=_gt8vV5
z2|$c$)IoosIAY1Xv}(Jchi}ZoQ*e}I<d|=G?0R2c^`}1)S>bCFL~PCc^#^q15#M_o
zc1bhUUKH!*S*04{RwGB40roKm3cVY9#Sl8p+3@8BzDF7De;gh5L$i-^kdii6X27Bg
zgHM(E81ityM0>kw^l^7rmWm-r+K%I;U)f&F<8h4v&6z|LwvQ8nJ`Zrov`x=$PLvgg
zg&ts!3%njLTLqSdDl#b^q#Q>u<T13e_^aul4-O;QDb%f=lGG2-EcL!vJpOnZ1dd$`
zrIe-Y;hpOkNp~(0X4BX)m&;ess!NMFr?e|(U26mfdMlT~0Dd1^EesRK+^Dib=ayOt
zlgKc0q5|4a^ki(&pF<po9#x=fb5UUH-U%#`8M$Z&n}($1v<uX%jW1R{^C4ll_rnZK
z-IHudJ5=Nw(a^b)0SE1S73;!Q8Wk|OjeYHIb+=AaFPKo2#_cg^I$<)4q8dtO%v(8b
zvYpqEQ=e%;I&ag5#;YCU;m|KfnnSQEK4^WxA6TOodcX84yBy%8)DSxKIlsI8eY$zX
zWN$v}{_(+KE$HRZCLjV+@F>s_8I&5Gv=rH>d+Ba>tCm$;ii>&&%d72aQI&!@RQO<6
zWp8yW$opNsbmOH*m5ihr1{4G72#M{?O$!clurUm{_T;j16>r^!V3-;in3<r*_^ILe
znk6l|id8X_8v+{qoBUGEnH$t~3oHE%u}fp6Wp)MbXqR$PgvGo{f%s{^n4rpkDutfb
zM27d`w`?=WBr~L_l*r>Cen)emD)0AVo5f0Uh4<4q;k$YB>)wTPL;?Bal&>rFL#WhG
z;l-E&0@WbZAPaN{rpxjXkpnOBA6J}Y#)>c^%``06X31kBtXXU9Y{%ldZb^;yZWtIg
zkmd8Z-wbdk03OtuT%r-XkReB2oNa75{7wmxnL7xyfy5*s`{jdgSbj&U1dZTXyl>C(
zhbSd#K0{I<T8q$qy{F6h$jAK8augF{fRaFC0Qc%|3C`nc%?uPcT-XS+@p2L{=Q2P3
zWAGVoatM}sU2<wpx^N&1r1Kpe_#tP6W+>>{E{s1U2>YBNUr=4QQXDZgPz<(_Oaxmi
zq+d2^Xpf4Q^d`>-7?0HHUhYg`^w}#U**?GeAjBQZ>P|Kl!=G4>gs895On6Hp1dm;?
zXk<L-lJ}Ni#XX%b<)~f4{@bZ(#k|Q=aM8nk4PC2+e+k+WVj3^#_v$ArqfCn87*g&`
zypFg5`Mt|jycdlO|Lcy9R-11DPxVj2g{Q56;6nB?N_d>5<bX~Nbt`GYU*5u{zoz0d
zzy-Xa8B$maNfnhsyLE{=P=P3DPFYDw_)El?(v_q%cwpQ1PowMCc`{&+WZ}B$4aG3y
zqk=he_n4E!Xd}Qyu)AvO_>n%)y)q*_`q>#)b|>u8As3E*yV>?njN;C?frwif+FL`6
zbcnoBOUvSHVe*=OGEO<88VFGDpDyBLjS_u@E!eZFu~veO0}`yhJ)*Gpl#`EQu+gy#
z;l@10C1rNmCEMrCns7*~ro7@&vh!FtgU7V3pcsdHt<3Bpv+zkJ9Fm5uq12+fVa3!J
z{v2Aq8w6zcrxkqKJ0xK8YbrW=MTk<Od|us!NM3S@TB@ddzb2Y9Cj@P$Ah8x`;_{G?
z^Jw-ea`h6P!qcKXBz=)DD7>ZwZC#(;h<7}K`F?X?ZY@b~GQm>Js_1*kurOSZW4CPQ
zUc%Z4|5o*Ru-k1@hFXHTEBAl4UJxQhiU`C960&vK1*_XT^(pkYdO~a4X~NH}MxlUF
zGI#RJn(w?29x7o^R(UV?ip*3~xcx4P#+Oayn)eAeJ0@|f#8{<&QGeeQ4~L}4o*~mz
z`DaY5Dg?8&X=CJC`@AzVfim+MBi$D`;}Rg-sR^_}ON)s5>YQqqnY^Y9whE7VE8!gE
zIrdxRe9i0268ONcD|tebF}~?0`GtEd4D@Q5Gc^wyTnc;l(!D?<$AUeJ_7BzU(2x%_
ze}b;v9I|nfpe|of(zld-bFWg2T2GB&0f3Ct?qL@?s#<Xsha}n*M;3f-8q2Y48A<Ei
zSdkrkXeA%tTB+tWWI{qLxYGM`@(X<Qy%h#*${fU+FH4@$Ofe-yLdLWP&_y;LUFC-K
z<<0fp?yZ^vtsl!-NG^V!YE^Xj)?M&w2D~aEc(LTRUGz7AERBr*ChY<@ZGvZg(El_7
zEP2ZCz{1aqCBlL&v=cZzIKEs16&8Ny8It26?gEs9)BLM+h_bNj8yXHbpSDuIPrJZ4
zSLY75(3DSN{_pg_5x=r*;&R)4Kcy^*#@mc>Pjn>YNh|R*FQ>_S8ZjZB<ebD*G7UsR
zgr02j2l*hD2k~$0pM?$~>ZOd|U7#aFnV|>Tn%X*<8cQ-P63&H2-WEk4rwQb)RxVU5
z-o?zY1!tRe@zX81GQq!E@Z-mn<lWdUIV?@>&vLQySANEW1CGbDV9$iM&%<VH_MK#l
zF!>o{UMIy8@epwlwO=ET_>XLIL#Mn|_3&_sW<%qgDiM(TIP$7iYB$FV-F6b&W}a(3
zurNY7Sb>l~F=_DMz?!gV;@e7NyfKYIhr?J{G!uVt)*T?vq-FH-PjG^Vvd^bFm9$Nb
zM5Pc?jKR<GFnL7R`}SsNEao-l7tE`5W|QklM4IV)yh^t8EyC86{<P)IFl&w(8S3mw
zZ00X;{HXd5!Lx(N7l4^|r*H5483Dq}18m%Ca_RBC|MWccN&ira!J&43@>>q7tu8Qi
zSShXEc<PP0J;KeAl5ZALKNbP*E{xH+@gr13N`+uzR&o)_e65_2ZH&sqI9OYsDRLv7
zXsuJ2aN@3&;0k>z?<esWkq)FVRZM!xJFhk6{@TRTo`GZApPEU~wbU2sVAJM;n0w=*
zvv!6PVtKL+NmjFS=6;um+dNli0X9}c=6i{%beIMueOdx_mI{8|7fdms+&ttF+x@VP
za|t?x;TqiX-I(RzoJm_(lVI{>7&R8h>>%f7lbIS*Md>$z8vLdgLKxMb1<5>8Bk8mM
z_zi*{DEC-YvJZ)#3PMA#iL<3b?;41T`w5oAyQ`miD|wq1Ne2)t%}`s)TRHwtOB6Fs
zF^AP3V&`jNF`eyy$OBI`af5u|U<C}Taw+_I^MY=8^YRGIs=}p*Au-mC{^ojI%<2yF
z`$aUcZ533Cgc(4A@ms!ox?iE`e9z%5;@)0|TV_KZ)X!?D=?As#rK;PJKPHrr9iN6R
zb|Jr=e?_1=nO`3t?U|wASYFR-s~To;SYmiRT_6V>QP=g_wj$twf`itutv%Hr<Uss|
z2DG(;*h`*|%wjSq2uq-L#lcJ|Y&-E*n93AC5WTBlZp`OdwJHEzGAFG8iC{7efBj%|
z*5Lu2X;+a`KGF}=%wa8(pNX79+~;xR^5LEVt2A3V0;nT_K2BE}uoYX%9c|whkH!y2
zKQRNw+*`M}J4GtXtc*^D5j$rSoAI`)CAG9)uS?jF0(`COHp}CH?$axV%>THzkKM;s
z*$j;RS3`zO{Uat;C|(4*sF^hc&}B6&p&*((Rw0@Ki98VoxuHys3Kf|6Yegc5!064(
zD;i?l<HxhJXot2Mbxh|P+|Pq@tWo^fP72ytn(<+gB2$oIZTMcSiG?Q1g7?;$bSgD7
zbA!bLp-cZFQv?As8NAvDx}24!vQb22pc;Dvb1+LvRy%H6{#cn6x#2%ZMEC$O0KT4=
zj<R9V<B{@q8g5>>4^_3{^Ah}9uT7J@jSx!?u_n8WUEDlE>OP8i7}eUjE>jUJbF%y@
zj`-8xacT+2Z6BV+V~cOK+mH8^^ZOLrLbS_u?6%t&dE;U*fEjIf``sQi%#15rTH%&C
z3SaC{z-&;`fN{on7pGyPa~tBNq8|O@+gH=xU$vFp$bbyBU6ixL7%uOZmPqn%dq~bG
zvYIc3jJVJKpX3Y{%+&KE^cZX)o1+iP;0E%I?$4p{v;yu7Eh%ZTSbhz}Zd3{+?wi!2
z&Ac(h1nV)XSD=OQE~Va05{6guVg1O63w}Vcm!0&%!)_P&zC7*v^f67&*fW(fPJM-J
zcz%y*(@G2@x425`+6p4xU3`MCE~Z#NjWNzXa`#MWd#YGc3=a_$5Aoo>3AGn8FJ^%@
zH*#UPD+87~3X#B8m#Pny&5n0Ei?XGQK>WL{ovu5Bt@vhX@RH?%o5Xr$>vXwQX)2XC
zta%UCh3*1ZGjU;$deR>Xxcjt(!&^<L|0H(rq>A^l$1f>zGLAvh^NIi43jhI$3zGg!
z>)PyN{=f?W1eWkjH4eKSO#4)aUY#W}!Qd1v0-*?AV_2T1P$zxfxn#|Dy|Uqedqps@
zy$9`>K;8Qy`t()?cw2Omsa|h{XV_^CGRCon5zIsCseijx2ymIAFSY(YcHPmzW0nBd
zRjequu<u<h@hoh=sgLxDdU{qbfn`3w>UjoeFUr$MDGDO492_T{Qj6t(ADxJarABZl
zCttiFd{<lu{L`sXE1Sm1<<75{Z7YF=5st5)hohw!mF=59d)D&uU?=5)o%~=aHEdyi
zR^UFVGbdI|Lh?D2k1Q;|@d({yo2coU`_P`qMmc}_r%eeqOh3&`fwnPRpPh$nANDi;
zFkidfm=oZT3vu8(N@l;N|7FnwwOpKgM%4F??YP@p&GzXg4QTpx8DULy8@J1!8}W46
z#N^3tPn?v*Ki(5m<GLqduvE31(@p{vIW2+zVY(~>O=(t|@O_o-a1;0G&7&5sHx%E%
zej+U3%CY|%DEH!wk_s`r7@9um-O9Bu{Z25ojfYAf67i+=R;P^hc2RD0qlkT_lAI?p
zB-)$dBwFcX_}lfwu@e{bgLhP?Nw<<HrBi~H2{TUWR!|_*_X!;E(zjmjw805JUu(<T
zM6baBOpVQ&QrXp6z+SE@G3|NqAnl^49=LfS<lnoa;_v_t9#<vvRhLs!#QQv0sQ3K}
z(FLsvF$S3I^+ny3Yt%FSRbG*gNKko^0moisVo)j{`W;A5vQl0WQ8k}r{N<IYJ(?UB
zR`0gUm5LmRZt>;Agowt3jhe6xZ@dD#JyVU!Oa8PX<kXi^*ZOm~uQ@$tNWCOU)4fLV
zz)AMe$tmEgo~!09V^-A5^csJaJe%+f;R5C#78ZttFTD>nCrE;oNL++V9tg@N`+?Ee
zmnU3f%N-e%oip8s^!E)>O={c1W7xZ!9rC`s7()9CwT1h33y=pNsKW=E3({QoVI2R~
zPWN(dBkMF1fKd}YsMHay41EqBs-VcHyk3f(XXb|f(~fl{>^M>TYnYBPJ2ZLF*xc|(
z+bvQX2K+Soh(A=YN?KE5N7#G7X8L~Ls4KZ;EBP84-$akkrcL03((t=@x4YZgmFXZx
zHRZVtz__wIMY3i>hjl5Fhr!RFJ%mR(+kJaE6=v#kI!MUZxiGAR)u^kbj~c9h38~}h
zQ4-pJ2xAcFM>_u{9GFZTiaOO!g`MiZV&4W();-VX?UBT%r_*?BdWG>lOtNZD#-)fr
zFeH|16%_08I-4q9W3!r%8|Zrq#o}@2ZzcSud`>(Ti9+4`OQ3Jg*YZpyJP^$~kK2Mf
z-QeuoYQ{8!D-jKy$%%T4#1O)8PSF#w7tEU~cV!8|28I)ag2dFeK5H6jC<Z|&bJVg%
zzdM0aD>GQF`l|{ojxHJX`=6e1W~ST1#j24NZ~;Jn`HGfH*bq69Ay4j)wvvbm52zYU
z!^kh3j$A8Ido<YQfJ3w^N##nr$-|>kp$YRR>fy~|<i?!~+NgHEh(*f^FA()A`HwcN
z2R}J70F3eG|BkTZVgo{qgZM^DPL=~2+INzR+hWqFOpF-qx@rZOKHPCbm5s-%4B`DT
z2O)9FlrvXThT>jCe}<3*zdBfA9Zz}wg%JR6n#M+psCk6z6mD9O<MZM{lX2M|u!)og
z6DwC%tvi_364(}7lq-}E^-H@k*D|7f2>K5Hj`B!UhJ@2nZtVb<2#_f?u^;@NtgH=Y
z+gl>_-iP8^&8Ag&Q(QqQ*84-HE-f6+^QE+8{KT&^E}P!$!D5%IeuB;}H_mbTp*Q#2
z2SczuFOC+%cP|dl&dSU*0vc*J1tNz+L<BAK1^6Zhv0B{YmCP^`78u84xJ*rQF$Z{b
z;<?R@sQ3afdzb80&K?=RBZLrKF6}Q8)86>T10F6|T@KV!&$^KYuY+s}C%rlj-?Cc6
zn5elc%S752xP0PD)QLD5ZBF79IFD&v#yQzI{c#gdhs_bv>emY>QY1KHK-;i*3Xz}D
zo5z{rgTC~0YJ3g1S$;{_|E6|IN6UdndD=ePWIC=I<}>R~CH*T8h=FSOxb467<xbpB
ziQ294;v8K?6k1~l6L8{`ExF_|gCm;I?b8}}M7`d7$Wwptb8$X=RF}7WTr-6_z=Mi;
z5QF*^IVpg9v^fu~a(00Y>uhIHptx|Ipr8McZs;Md9=ILQFCtdR&nfOA{09=lp+EQ&
zkBVR_um_U$?dWb6jT5Q|$ZUrn*?RQxLbxG=`|TY$KJLp6LU<)orx{c{E42{NHC2ES
z!lckBX!kEk6V7?Q(Y6gTiZR_VZ?r#HKM|%McbOgm7Ri3#ivHOcsR{{SqYMe!T5$x8
zV%f5r*J6on4Ckr)K=wWT%5+a4Id5<8hi?9w6xce7&HV#K+Bec2Z?`MS?GT=Ci|M8y
zrVM6?M^(&#$kt=>&8IC9maap=+u^f;<w2LiV9Fw=0`c>XNuJQT2P258Um1z1<i6(%
zjg3db6igM@aF`C4nK+O!xB*cHilH|Z4Odx!4!eX_1UoebMc>)!B(19mT%5t=X^n^~
zf1OkW1+*w$4Zbjs$biMTJ*}5u&0F~~srdeg42vDk$NIro&*TR3KU}lT&7?JmgX|`^
zOt7d8M#Tqyjy)^hx|EOJc1)Vi5dz1Fp3U?aGo1>#c3pB-es|be$I+M;VR;F1w2O7=
znpOz&J6V6CY;G_CQ?c0f$MUZNPI+6F=Jj2)6cONSXQIHLFpt#(L)yGuE3b(Pts!J%
z!Aor)&!A%x28RZmDtIH05k=OUrC!XkXsaFid_=izAbF*^m)IF9cvBrBSSH9R7K?1p
zqynXlRY__093MklwfK=t6p#5yVji@BHIQe2vgxi0q1s^k?Ix6Ev!)ds%sL`UZ6UL0
z(m&mkCVQ*(y`$KxFA|BzBOgXGcF;L1@FdeG1I`kHAEw-C6D3F#PRyl^H25}4x=f_D
zLlA5+8aT%VQlW210_^X;d2V){sJ-DOJV)L_TVNcTi6^>UO>8p$n&zMNirp)pRh9hh
zQno}ji-nxsw)~xMXsWnXB={PjNUf%`L|R?uG12%>Wpj-K=zp&Mq1Kz;ph*2op+cpU
z1AM#nf!o8~f|&&Hzk_jt<;ZjrgMC0A%SG$Cv0^mJi?J9a<^pbM741cr3}fTHu|_`g
z%wuqFcdi1UDm*N%e(`GNZ2kSNJAGOpg+HzkBYTU3uByTOjWojJT@|c_#Mo!m{>1?9
zMFKNCYQh?zq*IJ|WE4P<FN0*>yk5y^I>BNh+Y=^cn8&0H#_G0<^lDp-_cwc?{PZsq
zjjk>eMJJv)LL75lj5~b65F{-BW)>9*QvGOKxnloXCCxbwF^gx{Bn0xhB?he8FXAVa
zMh@{MRHaJ(h5uB-kin1@DqK`6s*5*}b5F~2iYI{~A)ZbHQ0Dgb1Z;&3Ldt|GCK3uX
za#pyF1d>!PC`{DM@_+Px2{QbfFB*jEp?)7f#6`23T9lVd=ZK#fkpq11bEdsbNAw!b
zAmiB1z>?48QVZYUm_E5059~Fx#1#lIihlQ!;+Ng*V<Y2vD_HsShi_8sG4%0d+ovtW
z-%CWqB7T9<&4b$Rf}Y9deK23Q2vmA%URRcCF5r>L((0=ygEl&zZ5o$9alnREy2R>M
z*Ef<>USeRG9j5n^-EIy$ecC)1=DvAWbn~Z|I3%>)9bSuVAEDsN-FtMGe%cSan^maN
zQ%IMk)Y2+Q)~zwD*-NOul3=`SiDVk2@pS<g7^FQpIkY{2odNz{*|%*|k8@}oAAAC{
z)5K4I=s=+91=jd)cG-8rdU)k?lQ|rf(_U6$XMGpo0y<Tq`5MR+9)nH4?&fsG@b1!`
z;w6;55GOSyj!e8d8Kje;iDhk9mPW4)eM(FN2TLA5AuI<JR`=D+tF;Ix3bigMc}FCB
zym$4m&W!y2j<cOH&5Lf)ifx(<2ASPL5~HCubmlh?X1m>pIOB(f3R~hey&9YlkUmXL
zCw2AS;v;Rcnarq_*m8bk5`t1FvccI9ni=B{lEW_>-4N%z`!Y%B5vE6wcP`W)<pUMv
zUWEHk!llCk1vqn0_i2c#yGG)qj-DCG&jvgOo83bXjNRNeMwRy3O-oHT1hiv6U*b^K
z_eqePm>ByE-KU2Wo_C^{!L5AjoRmLep2&_zA^>*}79_y#1QR<R>34b$seU!mp=4dP
z!&+h1q`pKk{1e8QKw+ZJFGh5#qOpD`i<1G?LPL4`SzR0O)E^G1nqXG=qZU%t<kPz|
zkro?QgKqz7Nou4xxLPJPD7rVVJ?w&4Z3)BmuFBxBo<gl`TRV()QpvY}0C;}FSa%qP
zd#WoWuUx<i5xnJjT_PVN#4ivvsHG8A`(4}EL3+ulEZOs&bH||vBMb!mwKJ}n=OZ#&
z<-~}-vg+q^7*Td2*eHiyZynGX`gL)^l(Ry&>Tz4!s-d8ix~9}pBdzDu+{@ap>PA)U
zZ6gd{S1iKnI~I_)8Uv}!Eiu$!9m(~&fx(U$VS?LKub~j>y%l}1!m^x3*1YXUzeK~H
zmm1ma`0(_H(be#PqHfzC`iCW6L=uw`WW!7|LP~_$wzb>(pdjl;gRgNL%5o&pc8~nV
z)utUs*@_(<1^1m#EXMK9Ta;XhLNYkm33yxQ9}kqCI(0Tfu!T%QPC|}Z={+(=!~(vO
z#AK|!vLN$AFg($fJh3Vi6N4$GegDI~4N(aITtxs3-GwV~ro}%_tb^55W}3lu#J5_M
z8n2E=l@H>w<CuWk{k`@5dcgE>hK-U^;`4D%=60-P-}G$BPTKJ9QfXOjlb3ioWuFlb
zBi&Jh6Sg62!!}ywv<*I!)t*L8Va3m+ct0)Px013=8o>8-&I;Iizkf>+4D~MfE{pwF
zz6tuxNdK!R>H*Pj@iEcGqbKA=YFNE?=ssO(=1I7RtCXwWd6JsW_`?bwIH`tGB(u!H
z;^>I@u%~)*SOy)$Dj3LU&@NxaQk|VnXBBfc7IHIBzCS93G#|=tIB86kvT2>t+4-Kk
ziumnPqmBS!Z+)ps(Jk;>{My(O!lJj4S&G%#6~NU|U#C|cbY(*z0ec^{38&q3WjC5)
zMzkt#B%A_^ic7I}Ky!c}bjNH8BLXJX(f4ox01-QtR2h-|HaI8y)GomSrV?ufcYH&O
z7-<I_$ZFZv1Gt6eGW0^nO{zf}E225{YToIg$J_9D$E^Mb^4cmHQrYer^CE+X@S7*g
zo_hb6O9oQIVr8)NVt9*W*gvrev8pw1$vcKdI}NkvL<bAZqxOH4D+k_|!3K~dIfE8f
z;5Vl_%f-<0`e3i<jCQ5)J>%d(tWq1+y{v4CIw=qjZ>{$CQ1W{z+Q$q0H3;)tWLi@B
ze~w6*73Tjn{q5e_Q$drqwdCj)BJs<`9R71OX7Fzp0BfUl*fYz<;j0ZRJF0`8yfAu+
zmzOXTVQ(E&A~c`+q|qqIXZf4|R~W_cz&sP8Srsm^4LqIfPM1WQ%EXQhA86}S@FSnp
zl_MD}8R_2~{U*U!0;Z_Xmt0LRmnz&I_|*QPerWw3;_JU#girKM&25H-)c%O(J6&To
z;xG2#3~cP5%+*7nbWFL;*z7fBL4MTZv%AZofj-CX?5p+_>w+5{?xSOITS=2M0?8r4
zZ-Y8r#e<)pPGvl|6iyrtc{rD=?ON{vKEg#6&I&<R*NUq0dC|{qv_rQeJ(B&kHVc$r
zeF9QTDJJAsz_0M*-43&>uV0ifL<2zkHkEU?Gg|C#Rl20?8?12D+#61iOC(5ydis)0
zsv>00T*8vHARncj6=wL?a8H!OdjpEXlntEq?WqO?p$-2#326-EzDBH(l}@0aGaV1U
zLK2TL{%b0i_7f{lb3BHQA<qW)9`+dFTkRmBCT-gZ+rYS}c5(?9^f?g1!eIBzJ&TL6
z8~zZT?^q;~{(%9;U}OhMaP*6y7=3fxwAJq>U?^it#Ws->sq^~foC^k4`lOfJ{;v6g
zaw+8B(dk}G?+}8@+2!GO{|57iq)8oGW6Q;_Y7L6c0<+>Vd$VTVwK2g=Ob{JgNQ$kG
z4vh$pV;Wrqfp^4UU55qi;+6R*YU20AF1LWOte_!2tr0TBkMhc_AI4wybrj183Z58o
zt)9y%7IX|WWtx;~u1)CW07IIbyVkHNC2hwz-aaIELkjq8_)8+KYs`vC?>$*28%K#>
zybB#1{%?!6V=To(a?Vy4QKtRXW%bX`JanRy10C{xU4(Q7SE^hPd$l0f2~dQI17Qdg
zd*m`Vd{0sc7-;sJ&A+a7M;iVhdb+*5m=T!dJmFsk)%NZSvbc5c5YeU4K)tL6VBK+;
zlLu9`m!u6vb4I$g)hfPv^Z;Qy+@h!y2(ACaX#aPF`A@*1p#Z}paM@Y^nc;@6B1*5O
zg_L?bpqqAmA)9*f2T@aY?*{Cy=#MrP0u)=#-aZ*Ow^G`p{aR;2VF@RIhAUgZqs{l@
zP_^y0yxZBSr<z=y`w|$zV)JU8bU8=dW%|q>Z1%paV)ir*9=;!iqQ7GlFyNW@yE(sp
zD9@g=DJL;8kS~}lo!cczD?sfhL~O8V!7w|_gyyv|8OwxlJ&QnujWxzHgq8IM@!|6Q
zhZeu3gbp(|u0GTuQJ*F(OCp+OJ2xpNuq3`soO&)rRsZCrfGWk5eC%EoX{4_$Y*rNA
z@C!3OKQp%)fh2{ZV}kYCN?7IJf}ld+)tw~$=WXSiZ}OB+^b=0RSs@D@f9~W$BY*S|
zI>$zVZglQI4Qf!qi~zY;4IkwvfghQ}i*dxv7>370Gr;i<AHej^Q>Uf?Z_~=is)Epa
z--6g(Z8M_}8Z+Hq-(sPYRJ`@hzPH+=fS|j9Z0@g8I2~O?RD;rg>l2HYT<O4{A{)OR
z3vWZl%b<^U!5i^#%#Ct)tpd0mu^5;;3;r^K*y&j8@=YBbGb_4t@*|V-6#za8N$3jK
z8rS;|urrR<v1vl*YdJu~I@vM#XbbQw&7qVV<2mEZ{7S8U#9wE;<R%QSOs89_gj<Ai
z@hy0UKr0*O<Z|al9o%xyU3uMLSXy9%b0+<PZjKxO*Tc8Rv!sV~OHCmB*HfV}k|qUs
zQDLQh^=ELJrO%a;@tHTg<aVGKL{j<AM1xVRra-4iOxrn;qsuxR>M}ed8d><p-7b&s
zLnBvmKoLga_mt_ukG;+_(mM4}f$ev^F`qZ{2<}>(C@G;O5s?L_E3P{Be^^?|2_ycN
z`NyAU6cN8#f52)874B$1OMIXncm^bmyxn0MXI!&w$$-T$)EE9nU3i~v9G2Q3^jE}L
ze#sG_m}cJ&y5?Zh=pr@1fO=O=;o0Fii1pFbJ);ytNIh&`AN+MqoEp*7rHI@%zlFR!
z*%Jk2qGEiG{SH;<LM#N+2Y$EjRTC4h8LcvTrX!rZW5tVcc=*t%XUyP3g;nC=zX^@i
z^TUKW8l9rwZ+1@K$GL(ns)mw5LihUPWjUt5rogS^={#X1$5%QH;i=Co{e~r~gqIiL
z$D@Qf<?jKaUE6HVc}JT+@%Me5=DK;lm0?{{L=e*znxgad`gj}t8y?$OZKtniQ>xw-
z{~h>qg-Nf+D04<rnN7$zcDAa8VReE+WE2!qxLuA^i6W+D6G_aqrz;h+-rk943!Wh7
zit#4}cEv)0MwycZ0t`?T_juP{_^9AHPO;Y${?{p_ESLK*fSjuYg^_O=)GDLjD|#1>
z$H1!{i^r035pz}|8l&HvK`D;!;v)Er!GPP;F(jz9&#g=QuIyb((-zN-kY9LhrI{7@
zx4s`mzGun^T>`H#uu>i-%Qu|MKnVf<caJYqxGkv-cglm<Qb2cXS)2X^+Z7~(Z>fg-
z)5%M}$g&O=sDXNMuV5fvmVk^dCSv?^hd-^8A5<TvhV1TOI5Be8`@tQ+<<4=;^G2)e
z<+W#E4&U!|2F2&TwpHJ+1IcPC#2sl@?XuGVVBf7$Z}CaC<+0P_^vITX1Li?7iGP-h
z%!LRYPgUe~%}P>5%{lo`N9CW?<Y8Q7&D+CXaGB#3Luv}Ymn8B5Kd)>zS#e~OyiA1E
z8vgpxUKpV{Tw|0y`^fH?+o1Dd&fwC8c4C{L?;B^hDZbiKO;z2^o?Io2!4&?`(z|e-
z;{0kXOBD1Hj@2ISeJ+XciNmr|*ja28jC#k)k8R5CbU9T&f_rNBbmr+Xw7jMOr1j=l
zCMyI$+D0UObL7guWJPmPHw=krt}s5TtzQ+_kG+r$Oypp8F07jWT2Qxpw}Y#wKT`X^
z_lD5HZ(|Py^=-5%Zx$>oBk%M0b)vS+Z;ICQotQuDF&*?e355_C*+}gcaYU!iLJaI`
zk`fU|MVqN2v7arm?Pj-P+rXX>s^pO;Ss?Ufd<NvrM)QE5I7q!La2saV)pkRSsM=2#
zfO+sJ0<e(dn!JQBa63!Ip6<@if3&xAv^ArMzyQ@j*V<KIjMNZT(mKKg07nigXg+sp
z81G*K;si5hJ<C--4)D2p=%pmxqDa1n15}n=1A)8Bt^XfkAi;Lw%!`n;V72hTA|h`3
z6|{t{W6;_zhlz=5D1}k`Dw_45*UkiLee#Uu@dU_oe4p{(&G*T18Aa}+^w^7}^IUm^
z|Ep>KKg+SVBE2{{MBTXJGIq~dv2D3BWi*jJ=hED(Un~hT{PCSSS76Mh&m+@1ZL*y?
z-HcnHDN+x_vx)Q+@mO(TGq(P>$&3kj&-2ZK7~f^8W1JwcyB(>y6Ib>skx&X|5aR{y
zhBD0xPPDhs<sd8&KNuE(e}NQR!p;%DoR`EthH#Y18yONFL*0T6OnnFT_$^6Jo|Tk)
ze=$aCzW}u45dc~N<!p(+rQ8_4J0EL~k0_kIELn0U1J2HAu*|pkk0%`4h|pXEadb=F
z2M5x<8C)}k-Vma5+NxG8I#ycWPvDk6(y`{9Mzi20gW%pe4<c<K1z9&8q_%ZQRFgO`
zHyRT1Pl|EXvi7^1Hd-O9m5k^3HK~lTTip{*Q7xQ+%56eBMNwtR!JKR4co2xaY30qe
z*7=IzE|~v#C*lqfuE0qBrzVX=RKU0Pv#@YfB~F0hrdE{obKPNg2?Q2p0aC2Oi+eGS
z4-NDVXFoqIQ)BF!=)oi?2D1C_q7=X8l;LH$$~z1AW6ami=73jvyW79f90DIz@27ue
z6bm~4i@N+DCL>rRB#644bflb=le;k9U8;a-w&rslhKff0@zP3|%f15)O1Xzq&&;_>
ze%k(!Mj+%XXKUm2u%ho`yu{<H@%V4a%C}yUi#Q!_dwvPzM)x1i-)#+_+Zt!rEx0jH
zoV6e=Et<2x76hoUbPcM0do`w6(t3C>F8=RcUPaRdsMZkEKfuVUE{J^2hA6V}=Xg}9
zP#}rZXDBob?L~me-Z}ggd3k!HA9iK1g2Lls^bVKhSIWNVe=(9+4|K~WqSVR9-a&4(
zOYu<Ni8aM1^p!W#jyTt}i6n@zMIfoF>GMuWo<wS+`PAl|>TVMgUo@QgE%{$G=CT3l
zM#Ri88Mpkn7ijTK!@n_*;`BEpimrlW%EfX7QlMv#AAoURQt50~bUD$uly6Ujfo?pS
ztvmBP&!n7V<}SEgL2@S!fl(RJNGs`KzSB(C=0=%bcw7qg>C~<7MuI)v&V-XT9P%<I
z2;YbK|4HoFz>*L^<#aXLWz_w_p8jpZq!V(yJO?`%acs~V_;O%VOuVx^&*_gp;j>pQ
z*Q_Ie(#=s-#csIXp-1*I2t>G!{kNxylH^iQPDpD*{d8)+AbMOM4nMBaAcOkg4mO$i
z;k0oVKe)4T8j?Q=%#qjPi4)HygD6cZ_SNP!A)|-kuL6H^l6txK`aT=qgS3KxX|jK+
z0T@I(ldW@-R>^&kE9TezJin8o$ibZaHDwJA@qGj6spBo#4ydVsUL=RNFTO#pLn+wl
zjI#RX!PsCGo{Aim+=gONkojwt<J-EMC`idKSkW91BvGr0?0(E*rz#~uz3|I=lwIEU
z@c}(g*|Mq6T{L~26s_Su$YW%1S`VGFms4<-TB*U7c{HndLOt&|_Pvr~wI(G^wmSuI
z`dwcx3M3|GC&Es=(uv}|CH`;h`A_1U%R-VHaW?L`IIeVb`HQzSB{Od8OZ@hB<JKU4
z*>{!4|Gbbcv*#!KThR~MsBNbi$q0pTS_vuFzyIxfCPINx_xPwj`oD#8Jzl8|9WR$r
zmLb09@s-b+eb$idJcWZ}6y&LD+UAltchAIxxH~2GXE(Am#W-1ga9L`P3|q~$Yq~y2
z#R!Ut^b(y?p+|7UjgX#k+9d>su@<_7H3O8{wP1APY0}>fJx^KjXFvf<BMRB^^g1o$
zpIuO7w0CHgAH3v6+HO``jb#fJP=t5o&?qaA6z8zg4-?Z_KBoK07l5VKJT`|75QlcQ
z;~Cw`ffYxOnQp&&`+W>4hc!_g_R^9_CI#A#ZAsVXQ6;S=>#BunJ(aon3}Yd#iMldl
z)gQixM@M6O=oNzzPzR6~YsR=#kb4Q^(=#)`!wC%;b@$|jnr*>!b~zEgi3oyJpR28|
zdZT^u)A<raIwi&b!QNK}#kFSbCP0wj5Fof~aQ8-n1q<%(?ruSXJAuaC-QC^Y-QC@}
zotZmx=bSn7)%?9xAGM1gT{PW$?X{kFt!G&P_Cd%fN(%w5+bojtc)B<DOE>By;@sBC
z-5QW4sjfXUWvL#qKE9Krz&CLUSpiDSL|r3#2F_4@-Erd?E^C?yzFVP8QPS1%j8?CA
z`Ad!IEc4~Q)xY5rsqk-ny5Eci?yoe~p(c$K4*Pt$e6)+6tHv{pDpV-yZ)t0JzwJ<S
zS*Qv87f+3kgGjq1^`qQYg!nG;UA`(xxs`Z{OgPJ7=35@pkKXi_g)3`B5831GkYE^+
z(vpL5*5-FygLy^_K`tM6YVudq)k~eb$n9VTZcKjb6d2LF-Vq<*(C{$z!)3_Z;3E9d
z&a2yL)ccpG{0~$7(X_$)?&PEgC;s>w2~37V(<V&%Ypq({DQQ;mhP@ov$-;Tr^0Buk
zOM|iF+$CyFaa42D-)Jaii2$$HG46l^a!ls@w(N+85X%#p_j38cJ`cGWsb#%h<VtXu
zM0G2S-;Qs=h?WVEODgRTY+a5{oO1N~Zb8)iDUeDb+DCx_KFASe`0aTwDf->iq?hNT
zP2`b-3@URyn#oDrSnItMnI}L-oq@r24>v2vrK<OkhdUaSUuLE_8HmXnEMBN0CFB)z
zwLi<W{=xK9d&PBcnRwlrJzHz7^-!>FZYp;y^5%%Ge`2d|n&)TJ5Oi~Sa{*@m*QAd6
zi4kJGp{u~U^sVh?IGQaMt3?UUS}St4RcBvj(|HQ+7T(D}a%P~eHeHBoY)R5q=N(Gd
zy;6(yt3{%-mnXA<k%6BJEU=obs5_}oWkA;(vsj~<M)QBshe1s5Zkeo|>G?Z(n&mlu
zV(VL^cL}l|4EGocH2F?GX>d}QMKMwYl8%3}-iPp-5ISN488qLAuY41B-fQK!5NShb
zYNnK8i#we;GxgE{U;@)#;M`G{&!K#QT3z^bp@Mj2B_qH78IZ@#ap*X~&S39$#r7@U
zE!(qY|Ex@rx?1PO9@x`<8OF!R@JqD%hlJyYk;p5FNOxwtbV&y5Eq*1)s68;TU%Mq!
z8{I?~pSg)fVtLR9z%O_$kM|h$Zn>N7$cIYmUWHxlkNlWPs06p^VLzH9KHGj*EYb9R
zG_O40%b6@uGpg<}7yPR0;jD>NZ#qxEVRW@i!)Vn3rc!NOVZK_a60WcRIlIQi!=}Wh
z0=Cp;on79zXe9Spu`ZzYRj7py#j=QhN%eQp%x{YFpXYpg89eB$d0Q;qS1}*^kfIuL
zVp-IDwF}&|UIfXz6xb_ynAU@3FY_-Ln2ZQPO#mzV`dnF<_{0gyve1G}r_%}s8HlR+
zXB!~#k<a{2z2JihO*C(yM0Fd)Ga7J;9&l{FJ6O&VQ3CaK9*{|5>1NdM^Wp<+WOv8j
zRLI`wg>Fw)>C)UU3zi-3j0@t_Nzc`FIWnYCj+3@C3Ny}u{CG(&!O<Am#DP}dM}ERW
z_d<+DrNd=kxx8H7u%YVTCO7B?z+9M>;UZLxX+(l|JAVJrOphtaK3l9tM=q7@16eOu
zYq5q<weggr)Bek){9B70A(HWGbHcgZ3Cmpm2lS%3(%26Km2BpV)a2o~F~!N|@zG;w
zWl(dtbz-4Yl%?~^K@kzY4-bpBI>6G&mrhIBDnlAE(LfAS2T&aIPkH`by!SP~j~cC`
zSRU~c)3u76Wsfxn6mTyc(1;dp@ti~5e>w5l*Py#^5A9lo#&692PVuzA!GV!&n4vUr
z@3y+Ae?l_kjnZgx9FV3zJMk_!fAyt}!?vo&%cc?2$jyMP(uyu3>5m+O6x$LG+t#9A
zX|WwKjW<oDoKyE8jc%uAE3ASLJ>6&VAJiZ0|CMoJD3wYVITmX0jd*pQX=^6V5}8`l
z;50%1`TmTogZo~;*=mX36amQp@<dd`uZiX7y1^)#oyHkUIh8jCwr~;|uMETi#1X=x
zzf#{EYJLbiot$rij`@b4ObnrGQ^H<nt5at=nW4dzV{!Jg%_;G9_eGm_e4QnChU>9{
zObi#+;*~u<ey?U`?hZ$TS|jXF%IG2p0`)!k$3Z!prrU#95oMZ!KmNr(v{#-FLLg{;
zG8l?(oR@elx@YKD&`vd3n8Ez{7k*ViJIp=N(+}cg4^Svg21UEJ2a)`jl*7SeNdRfc
z>m%YB*>xwZy)NGkuSA<D!v@KzjcaM#uylkFVnSTFjHizNb%dLe70Za{yS6QZ$%v`$
z1N(l-bGhX<D=Xr#G<_D{pB!e|8*;Ced!tF@Tuvu4yO~@Lt$e(=PX*NK^`an;`okq>
zqsevpJJ-Et!gjZyG>G_L(t!~GE8G2GmOndtXqdm9z3rkbgQ-75;{VMm@a2f4mp7PY
ziOI~XICQ3G@_+IDek&#=RcgM}pte4%%2{JYr+|XOuPVp1KoASRI#1v(#wTZ2V=<%{
zot#W05`h<U(yvC7gF7k5v;;S8j23S>8qMQ&$A2`#9pFqNW1yO`0=Kq6(gzz5$v$mV
z>&J*;m|}w$%O8R6nSa8Fwm1ok{z7dnO+&m6zCcnE+e`CN05ucBSAPsN$fRdTG5Et`
zT?%J)oF`AyP_&S^!A&B|eX(LU%TN%>X4a?ttbft=zuO7gNAC>5fNwxrjceuFP$g0_
zvfNM(Do0z89FC8&a(>H!iU7>+&TTv8=HZgB>GOdsaU*4GC{7wluW}D2W17PIB->u1
zmTDsDTf@hl96W~2N}&`Tt}Rac_x}P^zs2{~1RdG!(6nOvd~ajZIcyt(PV?#WH{wMc
z&qOr`f7L}iv2UPF&xHy*=NnzoQ8wM|S<o8b;s8-|C^F|+%XyNd*T+nq>!iie;0nkK
zGwZ!ge|*y>a8&+&x5o~GsS?&A{XN5>;|054(#O0Ww9y%Yv@nt91jAWOv)-m*!nB%=
z>Y%ynWHqKUk+7T&p8|#`Jsvek%vg(K4yp{hFl?3LCCW1UpEifMUS{``|McClyf66(
zI5;pvq+^D%j5Q2N1=f|M3gk1tWC;C+*l%C8?<mu_TiBH#rTU|b$c%<Wu&+qh<U=VK
z?iR$$VZWZ#adF!cH0=)*VLmlqPG_ncTYl0wd&0ilc`$H^-k~LvnOK;?jC<R%j2NCj
zq9inUNY->7FFMS-6_?o<DzS7}LD=!Di>Z8rf_)d*G4OnDCluRrkNL#S_`!nKBYgAQ
zm#2mYDDKM@ggWP76T1cR8IC_a>P%ujcw^scQe-pud+S^oYo<@f2@ILfqD#b+{>Tme
zUu_o=7!!E?G)k3&!a`!%m2h|~*oD>2&D-<N7clQ9jvGdFfTlA=f)7h{*mG8X17|Bj
z#`YN1J<CHwLjea@A3*$AoZ~o!v=9fg$|_Y$!gPs-$n6;y1(V^ZEJXbQ-NJBD-JgZ}
z_fUW!ou%|km%=3DZ`Pxde2RST?H4h2#1je401*kl7Z;t<EdJHN8m&D{@qJ1aU(e=e
z3RMgx^4+%W6Nd-c6@#b%E_BwJj)9t>ChkhwE^?<@$8XJ9_M37OTM(j}acTSI6zZqR
zvBU^CboHE?roE|4H=@Nr<xd#7CpUw4P0O}|OnK9RH1*%RwL7IE%sW|HYI5YFlY3%y
zler)yZ@CP2gE9EZTO2k=E!?EvKLucH4>am&CV0gvEGkwFP*QUfz-aZTjMql1Wy;Ab
zLgZf<+&40-YQstdCY@Zkw(MGJXL32`J96VmTQe?P+kH;-dAPc~q$f;4Oan^YpH&xG
z7-QL`ftH$CNp)6n+pS6HStgXSS7mS;4a&(b_Om8Ae6<{@Wz<B5ml-Mc-($S`j#ta0
zn(l5$VbNgd+VWffK+oDc*yl%v#_*oAj0Z6K)607gML9VRJYH|H`P0=FHPt$E!+SSj
z0FDJjFQbX<(|pj@zO?yxs}$TP5!6LU$a7ZehkZM`{c)*MTn=>;lPsdh1@8;eC*faj
zC*47ecC$G4hDJWPBCY78Rb8i%I!Xw!lreezsUA<;T@ipN2B$UBf`S<ZEsNWM3Dc!o
zJ@V?3I`P<TP``OuYN14~ctIky2A6|Rag6@Xp#!7yxc2?`@GwQc@V~@aG%W9ZCLc6a
zeHwKkfgf};0SJbow%?+=wS8X9{k0bqWLPCS6TDGp0}${O=w4>ziu_ZUrasl}==V*8
zqRLh&)@+iC1%LUP#<d6S-ZDaC#*hA5lO%3wxe>{I4jMf)5UXlK-{dr!L)joOYPP_z
zcQZ&8DNW$$1zWWl<!#t4_84b?5SlmulpKt^rc!)J_bSfzfo?6T5j>9fF3Rtw9I(Jx
z!pzAq=pn$|ojWt%d1phT{OO0!m`z}xuJ@8Er~Z>z(zJAWq1#z2M}sZJ5_t@51f3&8
z7YDk6k`e+m4!84nP4^2S1KVFcdD+?ddZzjeIPBJZsji2V_Z!19B<I8t+(Z^jwF-Op
zHLf?NE)P3Eg*8Fh$A`T%AQp>h{uOBPM9jrj-%v7#o!?DlJ1Kv1V9;$iF4v%fk_IV_
zS}j~$Ps&eNjKL(UR?OoeWvwR1^EFJ;=8InJO7n$E?ZtkQV>>8BJnAgruxPL6EqqYK
zGMR58X31tU`StPU2o&_+1XGhaOEjQUE2j@?FI?ALT~A4i`$EF|m=HM`4#yMXyPtap
z8{+BS?Po>!lP;C(_&%RldC8$ZaU)}3kpJFNjnr4({rC_6mWm#XAp4@24IuO}_&te+
zbYD-e80f?GXBHhQod<Le+-#(0>f#?l*}(M54+I+&@1s`BU5aoxqB9;Rt9!$un4e4d
zP0X{-=an&@1?Ie|tF^ls!Sp1439ijvT-f+io-u|kN)8Cr<>Q}hJQiIWN|Ki7cs$Ih
zo%J`uC+(a#tpO+MmPu;N=4hwiWKCi9FFNz?I)4Jhr7TnHH&G2veI6LiUb(9WBZP`s
z;{^eQ5(Q`TH6pAQt3)ZB=%Fz3m>HRW8)Ej4`}rrfe&<IX?{@gmLLVzs{PRkw-$;*K
ze9y9EXICvCMWJGEOoLX-lT`>(b5g@!b3rLqGWxmet~E*$+o=%a5fncS3P!B@4+P%J
z&hgkij$L+jyD-`-<73k2yA@m=`0ZhM>$xOHA|HHAo1A8r^7FquYWY%&{ywiobokXl
zg*Uf>+SaSd=fmf`5HgmMh~38>d7Qj7`XHma{;d?a$z%JWVu^>qy)2ARnh@5di+nUs
zT&h2{L`>E3`(9L%AzvG!rRU^Yp(900LF4;|(*RnM)mQQQ4yvk&h0ZgD3Z01L8)D~D
zDV!{!^_oqNCFZNmDk;*Ek_w4>!}XwrBQCoMM*3r^Jcr2z9=*1VjEwqor5cx0(trU_
z;$=J0tQ3Ydx|O%id?6Fvmf>=`K(50siVHNJ4Ne@pU`5zPt;RH`QYMC`dU?G1SJ3vw
z-I^C&-I{09vddmdlmd*{y;bXjuv98{!D1H#wb$-&g6-7^8^$*);bQd$*^RBPZtkOH
zD4ObtED>=B1IQWC9!6G+#ZTr;Qxz7wVDGA^{|SsB3im12=X*JRXy1H@2XwyL0fg6R
z)$V9aiCSHO=SA?BQu@k5#z>&rHLPNY!Y$stQ0v<U)IdPauU3sF(Xi=X+W2O(d*B;K
zz9r{i#Otg=7ItuB>mn-4cizUkuL&OntCDlZxf|w=qN;`Aci7r6Nv)o*a8;Ttv)rEt
zlrinjTAb@7q06YoOH&B4G3ZpNrsqdThoeoZ4EY?T4$?;m)=O|gb$84?rCK`+3|#G~
z&uuY1E-;7iX`}x=H2;nDJ3oJrl@7r!e3U<J<`~l73-_>H2^0L4P+ditICX#o<!;~8
zILaGUZS&%oQ!`sH_LAPO<#CZ^zS1nh>13hT9KV&*R!t^8BJjKS;`rEnq1>~GtW@$l
zRY$FrMpJXuDh;S{Ar^}7oz~<yEhWQG@<?M@cN7V0e80B?)|q0)`c@4TKKlTFt#sTc
z@X2HEvO}_Q_10`}jf87MtVfzuSo~f`iRbIW4|3l9kFbNP_T$7S+UT4#L8wMKur3s4
z_I3g0{v20Ez+I}5qb6<i=K9Eppo+BHMuP{PH0Gb?;D-zPpaF7>6)Tw8FN4II{m_iC
z+c(oZcn#&bl5xbNko<0Znlap@6Vib3B<cHX*6PUxVN>WN@7)Hrtts@+BR*sME$x|f
zhleU(n$_n0pJTLlxv)P{0NEHv8A_#bo8*Ih+EiW-Ea&iU22bTYO=Qrr@fZ6XFR>g2
zmN3s_fDcg3n~;zg{Qf`d74x_~Nu{Yp-61uf(rj9=$W(I#9g;46d9f|jA-tBE2SzVd
zb3X4(bh9m;WxhB)k1d=<(dHL<@ibjFy`V9_1nsa$;!M8VVJOFmp7F4jMhWSnS4;Cf
zVnsF_D^MAI37;v^akc^+#3<BDmB?6KZTlRhf1K8eg?yh#ak5lb=6*AyWQr0WbLe(=
z3Xd{31f)~4(BU4K6AD46tXi99H6++Dlj^RsTv0WF#piWbPIcad5&DtjXHlYBlPws4
zF!<@P(cws51@v=ySXA~ch0`J1NIDUch3rx#RVA09qD%_yQCG_(cgGWi`e0vwME%-R
zrW4d(1igz2)7H)|qrvfOtYGJT7a~C3nb~Z1n1bY-$7+>ZNHyt1XJPs6q7Q$k@u(Db
zDw2um^0_w*TY*zfs@A0^7>&_-oi8?2b-#O>drWk&qvstcbY=;e{6~Hx{|B@+T%NG0
zH|{>8p1O!`eLIWa2fr%Jdv~LCrZjL{%OqAyij_wD=!5d{i<fPa{WA4^)Fv@oTQp+q
zO6L|{71*|kwa_;N*mML@-$oYw?`<J|kUb-eVcolIhj7#40xCH=Gr#0!!e}7p(E*Sy
zfmy@LR0q>a{2ar&$ubEuyhHK^8zEa#^y{Lse6e+AE3(3x9yy^;&#)?0o=NVnGi<g)
zl5<5)=EV5Omm6bbQ$}O4atRBQ%_e#Wiw(rxti4qf3Zy@~kOn&4PVWFxQ8#96{oxvJ
zISL`U+!p;sx7WbG3Yhcfq5YUXp{Xg-te6l*f>OtJMs!gfz}x6)EXq8UZ`bB2O|`M;
zC5id5%M)_R{M4%l4Ja+bl7fbb_p8k=z-cRG@zaL0$0^$Q&&u?^-<KiYOfX8B;;_Lm
zd`a9JL_(YExwTFcVu)$@%~uyY3x@r_Od_+;-S>O}6>rLP8!$R&hO&U!PLaKxqJEiK
zH%rtZ6CDiL-5cbMOj76|#2-O>P(ll7p8cZO=nXOI4SxuDNzP+#Bu5PE`xHgdd68?v
zacL^8L};O=V2rF4&lb~5(CCGj`QmS~aJjvyE@Zw6*O2wo9I3o5VC}T5wuux*pG$1t
ztGKqEnq}D6H~%eNtJuAHfQEb})7=9=&nD8^N}`#n`iq>zf*c`8&PCD4d8dxO?lfOZ
zHa*mXQEQtep4zLT>5C|Dga|HOSwGMf2Hn7s2leZ*+R^hRWc7TRC7dIM&ZB0;INU9A
zWS@ff9(jHe)|gwBJL65T^8p{%o)L$8;{wf@uD8I7^WX<b!~WMZP$)avLyNimj;z!B
zAWerT)w2~NOf*vEspWDrnA@ezs#tw$I`Zje#^o}C<$=+WueiF<G)4q{yT6{svbw|g
z2%G&4rMy13Ku(eX6el5upoYSHHLZFhTOq{o{nUmSLK65M<D<m#L7deOhmWUOZDoTo
zHzFQ`eYbwNc!Ue1J;-d*V{sGbGcTBjSkSi5JpJ8ffrj~R!P)oSLz|<Fx{dXqMiCF%
zm~%-{(a7Op36=&)i?!7jH&`(ZQ@A1+N9Z0JmYh0^rCjC&vEfOGdd$hmN%>d0)_VcZ
zhaC)|dE3fnueaCBdA$f76ke4C*t}gv7_;bF``C+7t}}}0*$w(rr>f>jOBJZ0Tro?O
ziwr_rfwVp`F|o_Ew`UQH1;a$-G-$0fb3}toM@JGHNuR_MBhTpL?w>9osG*wXJcGbb
zj`D^bR94ha=dh2dCriypTsKrjnFEoKe{+ubNcn$k(-<rEY&=#hB9gTLi<L=5bcDL4
z4VB;*O2%zRfuQ9*-|XV2S)V@Lddz9D2{2Zan_O%fFrE&*4Vo+Sn=Dj*U@j~wYIR<!
zO{shjo55HG8j4(VXbaj<DfaSs<NIRA{*B}(@^~J@{YCiaV_3=n&H<XSE-dI)$%QZv
zG#&SfH9^iB2GxLjzQXcu>S_X?mxJ{89;9>tMExg-1O|^>_6qvvMP%acgK9y%%U#xm
z7NfLc)e6}#ngE0bd6Tq&X}nsu>wB04?gx<s9;e!DHY+%vw)EC=zkm9*|MUZY(~ck_
z>|-3W65j?@lIX$$2Y*ymrR_GT$-0>UDr*{HOFDHJE+W*nn;GgBKYCbBMP}4r#K358
zkPZrA=t7UYOL{$!?%@K)1_eB9A5;k6f3cNEg2-Dj%r~{fRePoo5P*mq%TmNPgdW+&
z!J&NxwsEdQ?D=KWPVn;5o^`RS@jxWh`^I9e7U~rcQMjdrfD^@1#F{aj5??*4Xfx3&
z`b{o;<_p_EYfEo;YZz-3vU_~HcEfc0=*U1<1;znuVRavv#cIpZkK;8b3e}1D>mP;(
zxhb|_7Hoq|WMr9Svr%a1@1^QOO;3NN`S%WJHTF_4o=^O2xP%Z@W22>H*K$GkBO6$&
z0M|3vEq8W#?ffhx-v+xGv%wdqg$KbfT%ATdB2AzL@JZX>x)+NaCc_+T3U4iVU;rl%
z#bIZyNf^1GG%w)SU>g0n$s&~~==(VF)8(>9F%bbVrIPU*7Od|5Rf(k#PR!?22%<!+
zjfZ>q_$x^#LA|8b;-zfmovrsavak?zJ6X-ky8=v!78$dbTpsF-XxmRfUtRv<U~aHx
zN4_hdw?&3acWR9V<A-<@C{qhLNPpsSB2S!o*Yl+(X)U0HFSoQ~cog-IJw8zD8=kjq
z6vZi2*dAs6)kuhX^aoiFYG?~I>(ypn_rql{uPKr>z;b+9^^O>y7rbmLEIO^#b|efz
zGC+70JCQ>l{S;;uFcNKX2{dpy^jz2T4_@Rjo<mL1)qjk=xFnC)Tzltr2oRWZ|EkyI
z&OjlBcZigQ4)q;U6Lzru>D2Yt60+(F%evWf(@;E{aOn@jM-pSLF-VyadW<C#g7z{&
zRXIELlyNOkidrguO5QnN#Wb<RFnyN=u)EE<#g572bgF)u`Nl)LRA;5Mm*Brm6J-2Y
zZO93UGKk1?9l3ZJ|Mj6iiWp61B*;EDOYt%RrG^?bf4R-YSTn#gSan>;Yx&pD%c#KI
zXG8j$ff2phsNG3S<*_ifRSL-@R)r_-R7Qi~1ZHz_pER;w71kYXkL5gcE-@=eQ?(ds
zqx5C!2$One0>GN{Xi?0g`2WpxfaaBa=EWo|_s#g!8?*n$r&*@nAkg|~*zEo?;&gd4
z_w4>fOW?}+LbfY9C?R&~D|KVzQTWpK;fB&TBJ}8!)hZ>=cy1wHtS8LNP@n<D5F*%v
z&+W-h)Y^-pYMq3V$A_m-f%hJPbH1<O2VvjG+X}WKsd;m=n&CnbQ<)q^y`L)f7t=cz
z0GJ-XzTx+43t{0;e&=Osi-xsTcN`#SxSs4Nv`S($C=7jb%q;B2A0N%NI_gq>H^*L*
zQT+qP8am9jxsW=}$o+iNdpt|T2}D``+7Sm1%vz{@SIDQP3VQvwWZG{)1es2WjYk}<
zWN*~g<>3&1e`JOSU;AlP>=0XfTAYaNhWJq-GI)+o*WzW5QH()XqlM{Y4T3}*JT@<1
zVx1^#%n!etIjz_1eod)f)%H#M=JZ&&HbW%ltj?E*=;M_86SxPL4ys>4K*eVkY3Uf`
zIfTw^R~7|Yv30h!dwJybH&;CziEs`b!=@TW>`@3kK;k>E39fCQ(GvY0#zguZ@e=96
zJhCB{In7Mb{Q^1k&lmCZWg2;n>VQLAlZna-Dp35@bNNzNWzdh-vUB<6(FX@W`F7Xo
zCQHg8GdWTF;Yu+dZIky`LUJ+zP-8;m@fx0OufcJ|aAvo~4IvnQcXS!AuKFyJc7b3_
zOmm5WL(=Clt<1~E@zIaurfu~e=fbf+g_dp25p6U~A^UO4t5v#CL0YjgLF;Nym*uq&
zAe#27F@8EUY}`_k4Lu!H*QsP58v5g4m&<5}BGZAnUp!)r2wNw*i4(dTUkB3tk~Xsb
zyF=Lfq5ADeh9FR#d`?~3<p997$uGAL6RX|*Hg^lb#Z@eoy8Yq(jkI(~1qr*!?Gs?U
z9hZd%N_EC1m;^z&7Qv9TK@9Ag5~=$J%&;Zx@Ysj)NaDFMjE`w*+BNr<UNRuYx+hEE
zLtNc?xrkyr$<2#J3L|$qha~D;8`Ons-!P6)dLnNkhmJFLb(@K76Rq`599cPLZ9BxR
zaAaMZZ|!}o2_zXcXx_UoN#!?{Mmo9P{%QH*AUIm)ZQ;n~X4Y#XDbzic56uqr95rP}
z{HI45CNGbN?(s->{jU8IavPxLB`Ti;tSJNudAz<%KP9EzB6eFpuJoSPn`|Z1zNHx=
z=-L;ucn2p)Md)!hT_$ZFJggZ74aEldJ7x2)5n>BsiE^-9h;tci=<n%6O|Xe$baG$t
z$B$dfb2U~~lggOy&w|gxaLof(3*@0A=_NrGl(<}u>veldo&^i<OwpP)L}F0K6^axU
zXemW=W%ce$n@&4@O1t>+r)h!WCSDT(Of{jBERH|_F$+KiVPFxRMpb(`QWZKvv&D5_
z4DePwc|qc#Gc|pG068c&Vl=1&IXGUf(<PufPckG1N3IK&+<j@3=8;?ZT{98MYN@tF
z#cxq6jh9E1NRF#TuP;0}TdT#jCfaMZSmk4c=}f3ia6*DZz768J*_8h&UvVdQci`8T
z4;bp{?jcM(LD!(|c`%aENptJnL8vknOha@33`aggpQpqUvd)sWG&%wOZ?FU5v3m7w
zV+$-D;$Sg7_7PQ~`r5sXZ<Zq>!lKOlwFpmCw{X!zauD!1i_tkMyhK41Cru(-%&0N}
z`j)M~u}u=`Cvz@`;~(H5)fRKX+~tdO>}IDAIpB|+(we->&eaOAdBaQ@ONNrzLpeaW
zIR7xL{MVV|sqf9m^JA=qbU*|=3I>}BS+tOj&;5B%7o(kbtK$5cOZ1RDi8k^ooHlwD
zaZ@ItbaF*Xz>fZ<QVncsi3lhJ_cm`EYjh;!$?s$3UjjB)p5vXz_mbj8NTve*l6@B-
zLSlf|A~Y!eB%mNEhT_TlaL#42)Y`Dj=vuFQaVhpTY4vu+B$TUIrm0}r@IocjJu37J
z(8T`Y1u`<ipb<Wc<GI%};u}WZ!`UwzW|f@P5r_kIYZ5VYS})3Y?%U|i5PkX8)02;5
zOIo^{?)ZTh?*-aMrt4FgZEBk@H=!=i_39`mn-x#wkwyf!yY702!rB@H2BOqi#9}+j
z93vgTnG=9Cnsf&=*kVJolH2QaBKoh(JD1Y^MjhQYLyBPv&pO6NV}j3IR-Uxdyj;Sv
zvW4x>Ca4dmZwp+g?fHdnZ*P@nFk+$tD9}$-l?4RB!3yd}sva|q(~3{3YGy}At4eRT
zEpsEaWsWvlsb6Lq6=)P`)`JP4E6;Rb-bY7zr0K^!d#y3qk+F3_fI-Cjop}$m+$FCq
z@ZOOz*|pu>ag|Zin;B>E_>tl>ImQ=j)kzlKmdK-L8BE7#OnzU>2797PC=zhVy#=@o
zvd0hQhAr)Q172pFWyUz&5<hNn2vfh=A8=lg@+w;CI^X09#g(ev_Rwii(eb`XgJv`m
z?4>$2K1B1HR<G^{hDE_}p7w+&9xNs~Ji*L0ImQ-<)GLvtat~e);Cn>8>t1NHQhG1b
zvlzmn&nnycF%DQHauQ%byO1Z4^GKV|R=!(=rIy<VJqCBSB5rT7SK-a_lTdRrRle1g
zj|=sy_&hN+%VE3Wn;PFJRzMv<s!I`Ir&+n2uhaD=*pdmg@4h}@A=mV=5ZVNRa5~ri
zrc_FSxMj`&Wp=%{6wMucVOITiUv<X96@RE9mE|Lpsn)oDg<&<5POGRfVpN+2VBH&n
zb@W%v^v%4=><VvlONB6N^F|7chwjTh$q5FlXn8w(j6%7vj}cR+wNr^nun)iz3%e|%
zo#(P>Y*9bWHCTEs@=SRO8%FcmX3?|gwoQrWbOHh5KI4EPIunc|9GQVgt>?hFFATuV
zcfP$By;FzfkJEXzR@)f1_mJ@L)MlZJ_v-jOHzsxnA7Q-}QEW9A_>T4=f0?*$9~?}N
zrI|<Wjo=-kZqnf3U?>}lpV=~ZZ`dK7=S9e6X=z(f`K)sXq)usx5sN@vr=44uKRiF8
zZZ!s$?b2;jY^=dzO`jR4bRmScu^$g_5`D|e*f?^@s;xyl$rwVXJ;h8&ZOO{xNX^hv
z*$xXPld<>F7yp@$krC`D*wj-^Es3wbmhDIy@M6Ek;jw6vHy|g?W3Y=jW(Pg0q;B)m
zWaza$IkFvTbst&=VFRlV>4dOQuf?hwe(Y?Bu5G#6moqFZZ1?>S>rB94g6Izj&_xvR
z?zNP7W1|3;mi;XqyvvAMt<Ul$t(o(hzfxxRr||OQID4yC-@cnX{Xy51GZObR`4a-i
zNwvwY!Ep_-M!tyC1aUN=Fv?T*mx&yC0*0*x<!#h)yl@y+0kW|0BU4zcS1LcY$J^jc
zz?5a<1*aaUwEGtI9xOW7=N6;1k$T*J<(>v`*I>sCPUm%52DQ2nFw(~=wxH?Kvxlp)
z2wH?r9MeR?2lj^xDGK7Y4IDqJEA*LvgUnJupc!rupF-<x*I_rvB-FL}V-w(QA&mth
zL8;Yc4>&xcxvi(Mul4*)h*k?6YSTEo1M_@J!#|W<b9^{>dlDXR;%p<BgYI~<8v|#2
zJBiq-`~nC#tUQikaqV(TTJ{)YBb9#o>hlf(@pSEF*T!YX>a3s59(P|E%^~0unrO2B
zG7f9=_qmYM!;N1Bn@qi)lig95Cry?gJumRm@utq6?JC0U;@$V$v=DX?r*ASgbXq_Y
z9f<Mr^70Z}PcR&zN5CPokKlckAC{93IO{?rIXsg&Ju;DJFka1qkV^?$+b$>DuF0zV
z+1fmOB~lMDs&u#<kI66R_EwqM@@-<UuHs#GKRrARUCktMi__NcOT>SWbM?m+q5o3v
zTft9l{qeR#;Pe^>3`v)efG8y{Nw>MMBBCZa!z#C+J_(4ELs<QSH*LoPS1*ZJxbNKf
zF+sG;p~Yl|${@%UUJqV33H@Mk9!WYN_hBwpkzBnhyjlrOtQaPse1R6%w+&70=)6`r
z$*r`sC6uMg2{i0|V=+%sg^Jcobz`cgz=KoT%WCPUr4gZS>?>`#<BA1gdTKPP9y|2}
zhlhiE-j1tA0H!W8cUy{BRcEfg!8;k;6Zf8pQNtV4GaQ@w!Y2c%(Qww)Gde4~p-=Ed
z7E_%omRp1#3I4PG>?<0FmZ7tFdp99jx;vg4bDWA+&nsDYN9WZdK68#u{`!7<^=K);
z0;pHhn{W7edjfCFev<c1epm15T%0YuOguxl8&@qB_dT;yBt7(Wo1f))-qNO#b{||V
zCYKsxB)#T+tu2I%oe7E6>(LZ8P8>FVVtjLOs6j5-SP4a%W3N4n1=%;#SxYVa5X44B
z7iqKfR?9dUHtiA~{vo=>&)cwy4G#|Hj@p_UJIYTudf{_gUM&){^u?nP84IqNSuL=%
z_3fX!)Ed*%2|j<rHPhB|TrlVu`}!GnwoP~`A4>W}EQW00<!;RiG%p2bbp>fu1|h=9
z*+6jFA3B128nMo?*3NyI{5=Z6dldF?h;vr-TS0jv{yjY2^p4N`rA=gSO&I|EGi*`z
z;Hk3=)`dG%1wRW#wxv-#U&to|9f;3k9g81p)4B4u@RPg%kvmA$O~XTY@vRqKDs#04
zVb!2|3abdNI*o#bTF#*ckv|$CA6S$``qh#KNknBt=tz-`*@lJroMCMjsP2?SE8Avk
zA;CY4;#2XLFhBBcU=i?w6qrIhFc9Q=RWBzgCP%jmOPfl1*4893WR!G^?DI6NG8Jlk
zQZ2LwEUJIm7o<zPDJJCG5@v2~<~liMx32mu9Vea4Oy(?zYj7UzsrJ(hdZV7ud@7!y
zYa0Af0~H0w<<`)frVkx&5>EsCSmGw#F;^xl086K>x_a0-Ug5MT7Pk?Pm3ulRljU%j
z*UI1hzQdMM!OA459s1?xs=0_uz0JI<BX(&j%@%7VOgqy9*^Tzg*^Y(nRVO+}5rN{I
z_3eQ861O3ZzH?=#+pPdvR)sKgbHW(TCu01=#m)#T`=L9sz9D8bdm|GHqv8}e25jB?
z?c>khfe+QeA;5yW+v{azyCj%e#3K040O{f23D!kYk(P96Sm^b75y-s_@D)f#)<-iq
z29UyBJ;I=UCDiO1S|sx;^q+Ux6DhGhx_6d?+^C|-#)TfACB||%VR-CJoQFo{Rhn~<
znr=I<nih1nOHsm;n?Sp2BOsXXnsv2WdYwS%4YQ9PeFB3|3ctyeyXklbeSmL5ar!{D
z*_+RfLl1j<Qik@r7t5JC=U5-xwB+2?1>P#fQ=;{qZkPJTUU=UriJq1TAt^(R3y_57
z8v2|xbM#oI#=X1Y8{!YST*vZ8RX*<7+Ey>w%GzNx+NUMIE^KV24of_#Pz-x*vef5@
z+?>r)`~>eR^>JDde~W6md|fo-HlwfHTj{L^tMj?-8tXB-7WuFN0Xw%<T>Jif!!XPk
zy3zT~8!6=bx^o*2gRUR4%7(@Bq&QtdGbjNFpqSL1Wo>4iO%TR?!h@V(Qy2Jb?(ScH
zrnll^7*K>wOUJ>XV%e~R{$f7)(B#7ahs|mZuq)PqKRb<!Lw7lAxf?e62plr_YEbM1
ze`b_a?pc!pd)RU_tLD?77w&jW(T6p!JheEg2O93ytG8GSerDQCp9;#eV9U6kE;rQ9
zL+^sK=u<V%5~>sP*BcW3&H4hh!BVs+1tAwy#vA5t+^VVs^1{A#KE9&RIsn&ZShFq9
z>Fcw0=bi8Z@Ej~Ej%4Osc?wMmkJpUu90Z^CIl<m!q|G~L^zdLJ-~Hjj!3X<P>XyLO
z8S_*870EzDJXu<x&N%)&4juSxw;E8nuB9y3IEB8}r%~#7<7|r=aZI8yRn%H4Bj~DR
zmVbiVk<^|4Uai<^WqFz?tWlHI@5q<@Z81^f80<xlM%qX~bS0If*aV*LY#c(SwXjl{
zXWXL^L8WRnFd{ZH^Z7fI9crevb?UF<<ti;qCMgLpWwv_$F5b59#}h0?OQ8X$XkB_c
zZF%Ha6Ik-E;&&O~ZMnKfS5%5b!?!7iCQrU5o;u7jE!J<giqXqe!=z(H<xgIkbt;0G
zJ{K#Eam<~v#49-hPGSDjo-#t`Ml+CjRc&0v6M+pB(lTe-&X;6PFE@NL^rS_25Qvtc
zgzAaKseKW{Qor8nibtUP%e^BXmzYqtTW6pdRtyD##%Kd4#usGB{MgNr#LDSO2aA7(
z=~eTJ_8E1VWIY3!P#k!6DR$PQfC|e2KRi-vU#_N5e6|M0Z3y)p1++(Nai6|~mv7vS
zN{rufRm0aICy*nC#$A2Xn-*U*nq3fngDzM}-#XZBC6pjh>APHC7$Ey~mqax@wsgRX
zH<5P;4hG;L?cnCqhP#34?9f{=#umWc0`uBlf;e+^_Txh39w@^?F506|MZw)O@aHu@
z4`4#wxjU>t%(d?B^+mW#P9!0ok;C5PS?udV9%bgor)}rlm$v9^{GrQzeKzjU6=iEb
z`r*X$%pJ#<UtVPH5!$NFxJ=2Pa;xl65L)6T<lSt!KNNkpQuYT}^!=ymp}-;K2rRZ?
zLMh^ICrQb$V&#fX=vE{v8CLguYdJaqV6EEMA(?QW$lh5_E+#woF)Q#h@yq)Ny(DcY
z8*cT(fiTy3zWUaO-6YA+XVxe4-1EQtm=L}01;eOf2*$XtLSe|m8RQid1~z*_cA~|f
zW%SlvUMEEGKLBEW^sT+UIxYPg5aA(l4i!AE>vRwj5(3_|b$k%unXT}TM<jD5)R8z}
z??q2tn3cmm($4bOW{Ucw?*FSPTuXux*1bOMk`S3p-{u}?gu@&$ixh48vE)G^VrH{x
zHP!y8QG(LXEeODXVAm*8JPC;SOieD0g*%(YL`o`iX&-j%OS<*5#KMW~^z>4&rLXG-
zwUqYKS`PEe2vK$8*k0_T>sw+%hQ;(8+oM9mxDS0FRHKYXdL_Li!xsS<zED4Uk0a+a
z`GR~nes<<#7KomJF|p`P*EHtB{*;nFr1%sVqMfX&82zM0?H9dbO58*Eq@p8s08V*F
z<M7YMRMum2pSjD>=3g(}QflER=4d>CdMcqwL?p1MQ2vU`JQW#X<ywag4tE)u<Whwa
zvnHrse}|+k@TnhsFP$F-_;uK1gmgmDdK8^S_OIQH$Oh}k9LGN59iI!=UQGfhZQFt$
zN12rucsx8S%}cHd0$c+^Wu#4>O-#pY+AUhN3PceuFa#dFC;50VkRs0#i}fZSwBgO5
z`y7XBPuZk`fplNMmw34k@H?KtMg*N(&xT2u!p9o{$%*M;tri7#ZYTZh#LK`!AnzeT
zHG8)`2@b%<veIO20q@aEqGb~q3wL!k%i&Z}qR%0aL-1Jy%xs3(SMTep85j4Zw`=~}
z-8ICPM;LeRtKhpM+fL%otL;cx78DlEYwc55Fq^EM8eZoeP#S~CtFFWG484$GHwAgA
zRG@4&%SrpyeGqwS<eB>(S1t~q9FLhhrDPwz0zIQLf@GuR^O1}bM&j}Ut2qMzdQKVm
z_!C1D+7;N<wfJ!k-|M*o`QXG2a4ZP;!Z+N@t?kRP;(Amf{8g*LHs`I%dlwO#br|~f
zGdLcY^XtQ&Z`XURz(vJ+k$y*Kdtz@w?x_^K_1qtTzsE^``TG&>{l__E+d^5{XqHgN
zaF{)hS&lqBd-*OA1jUFWs)QMEwdMU9wr0e|a|Xt96AJs{Iv#Dl!eWgo6)RS@Z^FWP
zPWP!RXq7GX)#9J0z~N~G=N7}m!x=K>`a*GMd#VcyOVxB*LkJNcA>KQ~U^r+}>Ga`H
z83!;4h@OCxibk*0#AJJMAd*dYHenMzbf5^>29P<Q@9IvR@O4&5B3Y;OQDE*P&6MEQ
zLg{{Zzf5_ViTeeeYw5@L(3<p=frFFdYd^<D`#Qkao^wKt6tSlGO>aN@0N={jhea#T
zjou*P^K}6ym?H(FjxcTspL{%8LN@!;a@Z86MMp9}elKl{Bp5-@t<Qb>$->KLR}cie
zg6=giXEhm_jDAYcbEAGWsZ*1jZ-Fb;UGSCG8v<K0aQt@*MrkaXz30Ncjq~Tb{{5kT
zyz7+kbo<r^83A~Vot0H^L}w{Zs|UlJ$1kCwa9bZ<c9X0U$Iy_H#l^)(nm72vE(i(z
z?tBq>>(|)d&I17b&kt9U8qNt_F*u>-f%elP8rYrgoqjOF`LtiYFr95th@{?#LJ`w<
z!4h_DG)QhdmP^xpiT+PW({4jPt`Nq4+I6r5{=*2_GWC|ZW7d^;L-UL@4WP#B{wDVz
zI_Kz^TUe;-vN-`X=(}C%IOf1e&Nvc0i<{Hb(C&;_^dW;sew{Pg8@Mz$GLOr~mSIz^
zMLYYryMXKLLVROC@925E%N%}D*=Ia-s;0KRp#Stfr@I^CVfle6C#OIXZ;GYC2^g1T
zy}u-1?)e-A>|Oy)l2Q?NG+QdS(B*P`hQ<w0T6Mh=T&Q%(gK1iwAE$=hql>C3Vk;6N
zH+zduscDe@?WwnjtaHE%wlyx2*M&(#Khg7@-E_x=lfVCxxXFV^*DNBL#;dWmgyP$k
z$mvkn-Pf07YX-hC^RbE^r&OoQ>v1X#D+<G+9f7bu&GXSDfz3)2!|LRyv}x3tX9?l>
zsSLLZRL3EyG2M3*Bh4EHq9mr{m!Rggu}@zXYC(fJ(Vs@`Zx@W9*6s-Nv+$j{=(Jjw
zxAhRpDXFNUkB^UO>70$07_uax*P*e)kq%f@tn6_F(|LD($-$|GV#D;Yc06H;C(d(}
zXf(~D+lc=;^7wyjz&I4l_*NrOO4YZNyhTr~z#n17tHrQvu4wb}HPtf+>(*^27`_qD
zp(s0>Y+xX!Z;(Z+Sv1w_R8r-7!zfyd{yWxTl0E#0><!-_Xr-EnttWr!^6ib;P6D|W
zP$A7CI-~2dXJ*mR+(z&@R7K90NnA_}#o6ZVrorVmKX5oRLj=^YDV0cI)l@87H0O>*
zMQQAv>j~S5k5}G$k7q9GgtUsy;Hp%%NC8xL3}20mn0l0<LO-L|6N0{iKMJWS^?w(>
z^mZ9Mb6Lw;R*}YO;gjsOfhpTqB*9@G?e=;#CaiDuy#w<ukBMn&#<y?=dMu6W&3>U;
zrzu>7MCu6+R5a#!kz<s^(&_b7)JMufBQ`g2dzhGI315UDL#Wx;Jm9Ajp~U+czgIoa
z?B|_1?)f;Li^OkvKB4AM)9;s^SPvyUFxdK*ri@N|!SGr8J6VG)?Rz=TQE^qH#Am#D
z<p>v0DqR@PKmX{HVaWxko^)7s?!30wbzYec3+Ai`$x0*~hw^u1o9y-aP#e4;@JYla
zzur7-9A6-gxI-Mf));kTums86B4r7DK_C>LQ9rQNYc_#Zu|(BivAc0W@mK44Cv3{d
zhr2w91l?Vq$bHcyRtrWt==IOoD1IJBoOjE1JD}1cW(&R6{WY@l9@wKmVjb!^b-OT{
z-MHT*)?Z)g5y<qXUqJJooM7heDk?;#tqFO(?mO~qABQQU6Jl2l%&}+D#D4`hZIOo_
zM{AW376WNCh^xj@XvGFn@Km{+j-$}hN~e%AZW?d(d_u7SvSiY@d3dT%upLiSruqlb
zGqReRCSSLsvO@G1DN(C6=W0w_4jF0$TFN(aQZmd8q|+G0Dl3_`Z<H`TcG|8v5A6su
z*Mj>2@2<vLDKXUDb3<|2mJLIePNFb&TMN|cmfeS9sXq1%0yMpp98c$I>O4<WgB1Di
z-|#%dE@7{>2aCp-G-W#m8><{|&~JQRzY3jI_4r@=m$&IpP(7IUxV9j#ru%rLCi6VW
z->8NF=QLG}zur$7CR2l|@ngJNUMhwg_Tdt&Cn=1oIy?fW&ROY}nxqrR22AoKe{~4?
zZiltCk>`!ZQ&f#68d`dexovqYlp;bbKc3C)k6dnRdz}7u$=aiktc&bSW10lZ$(}ko
z2k^xJ#ct0qeAL3mrp*C_{LpyJ#*r8A9K#X7tmNe6=*`yKy)~LEeLr~P1~#5+3*$o9
zAoUS`67)lmL>ehE##S|q*mxh#TU1zhtwReO@CPz3!q1VxA!RO?F2OG?FE7(xQPI({
zrB0R$acG{LoUk)nWLj+h6pxLK?HJ#q2nY;sP`$wQ&{<{eAjVE64AyG~ZDkANKJPR_
zAt_!6vnBh@j{NoI-75B@(_xL<3Wwz%QCGVS**h7u%>v0$q;*!rPFC|`-0m507p^SH
zW`gZbgxi%F{-j!ur8GOUCw>pJxfmOqVOIOq{l40e*CuSb!S5I_xA{=WST75oN=wml
zb+)JxQ`&Y6mNx$<J+(qkw_d3O?KA%$aPE%`=u3F}s}1?n$|nxO|8Q&ne#_pXWPBT(
z(PN`FI7uLXmsRP?lla^Pc@s55>}BZ%PJoe<vl;8OnhAm;c;$rlqW<N`+&}(qFauzp
z5BOwK#@sLb*Jk!_<`+%{@`^;ztk%qz^UNeIl`jw}S8faJ{Q^hT=4gKPK6=O&FXj3V
z`bt@kg)7KvHcA!VSpW}%PgLs!W@+6N3he>vrgkg^<tr$q`{^z;VKcqCt%h~xYv-^0
zCnkTC!~Z)6{`5K$CWJ1r-e;Vi3!?Gma%oVtG6|(YdbZ(qq7I+Fu;)bn!Q`@hi9y1q
ziPcOc1zN1y%+eywiNy$*H}(??+7f88u2A$8<TpKyhKpP&(Qm-eV$jx~r~UoUV%YSe
zCp@yM&jlnTxuU!6L?#xcGoFJrYhYp$qx2SNWo}BFo0~bt*AhWt;_?+u%0rAbC14hH
zlBQHn@z2BFf;}s<L^mu=BpeRuU;DZT9(T$H#u<!6<>jlH^XoM$+<b47%61Yav-?U$
zW}>5b<QG+sdbUjFEPS`OBVyy{@3(%Yz5GY_<&SO+1?V}D-y4ii${Tg>o+QQfXHS(y
zk)TB)_bwG>GB>B)ojywlOD7Y@?x-msg-3rz3nbLu`ng3@w8kL70CD=9DORaBMT8m8
zCJVV6!Jw~$ii`RMp2e;E=xTqcNOKtvE%~m$bTfxa8!;++bBEDj7bC1i?MWCnB2SXl
zY{>-NHpjm28)hMqYtQM8@Yk{uY8jaq`vtYiZ<CC+)vt^^sXBgEp**a-D(Y0hg6XR*
zTTGa|mT|7yUg;k2ah0-x3of#Xm5TnM+&z8_5ez(6Q2#K}Ki=lQ#)*Fes0*U@?;Cv~
z?hw}@HF<X@D-;(&2b&6r`f06^>y#;(t5Y0Qyie}9!tu8c?y6uNja|;)#RI%jjus1L
zi9dDua&oW?O=G)Enh%U(d*@CM8~GVe`|iV!9hjrlZtN`2E@m&0p#2KKQrphS-AlA}
zHm@`5KS$m&64&fthi7ZmY-v{0-}YPZv^qg|HdFd$Z=MO)Pb>T0Ei(8tAsS#`>1s}F
z6?n1eU*|N^n&RorNvkr+1WQWG+cRv@-+$1UrvJE$_}^Kz_m;?y&YKH8uVuH>?-!W5
z)=5PZ&O&vy%n-fVoA#k=h?s<}bylugUEUZW;(}&<<k{bTma5<;MUu?y^h5*nGyRV(
z88?{+zmF9Wc36CypkJNYS*ngOBz7)Jt3|GwnmOW7Xvy)=1o|V0@HpFsd>n5$6l;;i
zGQBH=PW^%na1*4uvy9HjGKz1lEA~{Q8mj#q#qBmO3a_(j?sw>yEgv+6w5>KfjG-j%
zLI2Fw%Va%HF(ndErXefKM8e8S%MRd@vK0F_0QpOt3CDk5KVBF)&%n=i6tPA>e|16h
zgQZ9VOBOqmNx)vO<PhPeTiCPt<KTIG@8h@)%s~D3&;VOAyOvU}6idJ3>1!O5afcFx
z@VHMmakuqqGh_?SCVEmxeM#6w%ybiUWJe4rvt`1{XSt8x#6-VMA+3W;@Om;&$0o2w
zS4Z?VekA79qozfhRZB9{-;3*^(jWUZoSZ>r4J<&yE%QK}zFn*-`-Uoov$}D?X&873
z6J%n@`x)vnC`^(bZ~nR#foKomztUbDB@p2k@x{~8lLCT3xvp+Hu<r|_<K+i_5XocW
zTl*gY?~nKw1=0(L?G)7Td~C4uRevBWPfpYOZu9?2tM;=X>U$mVd2Glx%aI3@GjD>N
zcR7syi?@2eN`XGr3(%E}|6koI$X36y{|=x%eb~AGi~s+(BW?c-f<pc7j%oPee_)IL
zfCzkksOi?Y8{~mq#Q%OM{^cwGdn5n*ME;;c|LMH>U)B0wWB;#!>wi(}{~P!Pl}X@5
z<VlCd_>%nf;gH2oaBIvw$olVoOC$MrHMK9GbYo-$;8of2c%G1jg+*3LiJX#>GCDSv
z^x@%wQLh&+0mQd`LPvL8LlXZ>Pe(L84G9zwN=uRC@&A$dLRx^}<mAlG&i<O}ajW9;
zbW&%q)f+asxJbpDHrD(d4-L&gb7Eow1=L;(s!NDbsWKcGVOyhSG8zp%hOxE(Yv;il
z0wVLfl2xR{_{bx%ae%^K+!x_@EiF1}-iK`Ni*D3Pha*LsrWN60sc+w+mzUKC`uii_
zgF}jni-)w=78ZV6kA)}126L2>3U}Xt)r#agtrrxIz*~`|8z%c}fwqeO4#Ihya5(<x
zHh|EzTyqxidJ(Y$Dr^Sc-Z~Ns6GPk!VQ7#Tr8vZeVzCSc&ceXJ_&Agjssm$V2ck*E
z^<SSKE?=L{u=%lf$kW^_pKt5hV(rFw*gB0yH~!jVXwLwJ-yX)z#*%*&HXj%d(GR5c
zquG)eE3c;|VZU3~7DmJ2&kwz9t%P%$ZYBCgM&jn?#RSys+(juc4urbIzwSGK!tQiH
z;WIDA$A8A;w9kLO1h)P9&6=wGeEMtgkLSUBuRop2i}3w>9mhYJioma6=hd_{G_lxL
z%`)h8n&y57;&d(Yo-apQL!gp;QeNKHzApcD4Gj&N-)!x6>(ePJaESE7hY!D7dIowh
zw3fFVjYa=_Q-A*!Q4$24(NKLAe-;U!7Rh&z!aA*a)PagzWIRCav_pc3JR~4B<06^B
zvI_vR%ytOZU$~vMD9OsoN>E&+y<X?_W;M}WZp8n)=KR)gf?w}`9(^a`{`1rKCMH;S
zY}&~IYDVv+d96CDjwLXOc-|CgiFtaydJ=XZ<|I{DGd~O}wGsgyCn6-cj^@gMTF;vp
zhAYQ{CISD!6-EjI`G+E#DjV^CvaLsw_kPfHbacP~e9!q}86Hp)6cvw~j;>5;HwLue
z=yzAKy2FeDn72Dfg(|f>d^Amx!v5L|Xulvqa^vPQTKl^!Jz{>09mVV}8WzwL#b9~N
z926DgE^Id>F8v?2zA`S#HvEz<r3Iv=5v03Ox<N`B3CW?mM;htw?v(EC?g3^<0YSPu
zc6eXk{qOE?zwilo2A=z>bDeYUzRjz*O03ImQBbptzIX(gVfmQ3@;8LhBBC`H2_-CB
z5`!m%r5)~gc60y!q79TUw)^vCnrYGN29JB1roZ|^KdQFzNJ>Z`Mq!%Kad4=1+^-yu
z+#h$cpvXK8QAG_MKrO%c_-^e|jY<FibtnV{I!;ji;<Uqhc?2|n2-;Ms9hJw&auw7V
zZ!0dfTU5;*l_U&+X+zH>Wa7p572L4f395!4P*S472qOZ0bfnXyv56@RDS;{MZo0tN
zS^V-ribRxry$))-k*Fo3Vt=2zD}bQOoHUlkqqK%AU~;iL?J%prg9>VD_+uw-#pnWI
z0z(xOVY=Rz^Jh>yfRUL3In(ps<Lu6RY7q3sfB*dd_v_RS3N$hti^Abwjt<aRtZVj2
zCC4MF+}qXPTE9)5p+P;QaomjmSlJ%DkOSwWoPQOxP?jO)ytiZty(*brVSc>EbDZpU
zt6zzRmO}eX^7rr}hvaDfWCpbagUR5)Ibb9-`fpUmD0J<d4}&UnOrfIom;AYt?il&z
z&6|t6Yzjj<YU&~AF=c=*IqK#X7Dii-JLEQ67G!Ea{{Q~zKw}xdu&(O-@s5`hn2m{5
z%utb-@T|+kxTa54K<rY+;vDV~ar-G?k17jA+}1XjVG+3HEHRvk?rC~F;GlKWch7Fp
zW0>MGO`YQM=glx#y6Jcm&A+J=7(%mt>0t^L&VGhg8faNzH=9VO!g#g1Vq$D$6s{~}
z)Q3Wq$fDo&<p`>`WnNubnKu>wzq9Ipy{tGTbo7dG)8PFFALYeBaH)VWk&o+rDBGz@
zXb5sa-#sKKs}{n4Pj7<UcQAA5X)<}op7Olt$?iTe>;OVHlb-JD9yFpq@c#?HWBh;-
ztc0!(6jq?s;Kf%Nqbws+?bFa{dHDyRT?FgK_VzZkR9PaNXU-L&|M#IpuMt2Rnj<<i
zkp3O9T~@Q3pGW&7iZV`4;ksWq<!Ld;gwprQ#r`LWJw`&aiI1!7zrZIiCtNGffePjh
zrpdg3Ro?;zi+Pj}lZc1Gm?uAf7TTilX;|Yb)u_G}#pv^CihQ$OY`W_{AG_P9uS#h-
zq<=pH2?rcjB_ktaM3S?lWKe!V!KLh)i{8N$6Q~8sW#iem(A3maZ92jOTL=98Q^lXE
z^S(52bEVo2Ts<}}H0{Qc>o@9Lo?wJVMiC@kNk9+BZVknMhXW}5G8dDJ07?FN6(r2S
zmM{;klg0b9;nC5)m0zK6yZN1jv8IJcZ9*-7#7;g{YlE0|SvaaPn0HpVb&$4915(eE
z|2{gB7`9wUPgBz@V1H}NL`Fg3FrhkIZ<!Nwg%VSLL3(_A9IEo1VSKE{{`+{|Y1DNU
z8^LIo5)ZK*+JXyk@5(ez!J4i2S=3?w=Y5+vjKkid!7HIB@tuj=xc^Q^G)$nCKKx5W
z+I`3FBKT!mRT16z^uSWGqDo%<-Ferd8m^&niHcmP{R@cs3k$)TJ5av!EIzNn$9(s#
z+dumy=no@P!cY)?=ZYQOW+$6MA@s=~_V5U_In7*MTr^Krf~r>CF2P3s5~yAnj1F)r
zq$-6mwP(n3KC;JxA>X7f=LYIO?>Nhamu?Cl(#k%w{h#&f23k0OC;S6^&|v{n+w0H3
zw!eo<8yAx422M>}4dk$XuIvHz+KVKAKkeGqJhewSY)6T(Xd$L}0~knH{(1YNB2+M6
z`B+#c-4x~*7KmAPSCexud(ax0pu1yCyY6#v*zfjfI>_Im48#rX0<~zDSOZXHhb|A9
zH__YcysBQy1`S@u(0M6-(O02mOe3OZ<kn{<=g@v<bS(*y66SBPD@HhJ9!oiZ58|Hm
zvFWW|X>|Ss$l&h2YneTyLf_S?qHFXxsIn@J?M$pl+PlJUE;%vb<0%{B{CZz^$k#Yw
z&wF^#!6Zd0ppmvCIN)R6QJV#S2<hlq3TPnoAgRPSO9${@XIxEqL6}Ec(|*)9Hn==B
zD46;1>ZF++R--5Fj5TgAQu5u`nSxfeR`pUi$gj8qtvcyUSC#^<9J8+g#LF*;vM4XK
zR`jZT{fC5hAqKZEvPjSJ^q^h>na$P*tqO~XF}76scxJ?xuh2YSotJIrI}Tjmy4(K^
zPVXcbP&zX`Ju6S1rIl3yv`K}Mz@4G}B2U?;Mg;|h8pj<)M-E0hx}t?oapb3dOaGm<
z3ktX&BQB$=X*?m+2$V_18e1CJt-bpRJXeXbPV(|q<Fv9OjQJ&RFIP&6WOdcTgHVqd
ze6F;mtappC?9lA590~brZe7StC(e$pGW?yu2u>De_DSCHRmChx3yfixA4AYAri1TS
zJhFB$A&Dcq@$!V}(V&87<&V=#NsenvGEH7vfeqW`j@j%*q*uN5w)+qt>kRm%+(?B6
zvWA98z%1C%>O7sBn<bs}tQ_FF-O#b3Enp@TPeCzhVRl^s*u{5FceX!}Zy5W{7X^T<
z005-c8Qk1!zn3=cbgYbb5Qz^2qI&<b`<z~&Iaib9e|Geb0WiV@lfEW7ZQjpt_Kltw
zKkU~CDl&?Y_6-&eO5*4`F2i-525;GS_Mw*s8y6v7|823>7qLR7i6L>c3b7X#P8L>H
zaW7UrP*W7wz6?m(#|b9Tif*G##v1~Hmkw1Py6JzH{y+38C#7ugJOlaB3KI?bWo$+=
zy~Qc&*+8Vx>F)hrcsVc@pTJfyTeE4jpfXRPFds7-)-tb5*>%l4N-mdLajkZ8f+e1!
z#G<CGdc%+F$G(+mwTG8xW5dYLaM(h97o!>2{huehI6uGVKFSBJrr+~67-iH;WGZm8
z)I~Bi-cEQD(PAP2#}pLm&m`TeKUmSn6-K&*MusIWMt(W{v(p8>;(l*y#nw{~m^G*`
zQ{MaFcmz3JIs}fCN34K-(wz@FGLlxnUab<sv9(M(znA&<b#z+mU9J|vzZQ>rY3tfr
z1tF`zhKYvqjPcsRi3-wJX9vO!8+*Ki){vhr4P#3|_0<4@sKbtmg^UOQi*9pmC15fV
zd2r$kzTSA>!P6S`okxMb#mt5!fq_+;_#c0*yC7;68C1$s`xPly-iBTiTWV-GwZR|q
zUEn1_PCcX4H8zZncDm^$c3hO4)(jvgJSD#yXl%UQ8W(A!QZL|g7@w?+zM-x>Um~C6
z+7W*Vm>7AOo=d2{%oN%x)i`X+x<5Z&BU1Eou(M-$E?&_p?}9c@hrT1eLeO9MBIS8?
zuoS$4{2y(lD^%3b)-i6m(Iaqf&woPLn>@nO;!PF=K=#n`b&j{$ZujI}kdQ*e=i|$v
z1gb&ln6NP^s(u7A-d@KAMOYMXCYA2HXYQ(`$Qw5f&}GH=S+z&d8rQAb`-5$+#;Aei
zJI^$Y<X-NJ<*!FM?pKcK+c?@}sZjzvP0R-x#POds<JwfJ74YZdOAQZa%Q0j^!Y(1Z
zwLeZ)Z5OCCc_z?uyuoZs6{PSD6Kzx8Rs`V8@nE6Y+T|k%lojI6y|$DzUfbZZUsvru
zQNh%Bd01UvJ=h?~Nj|u;^aU~nFxSc1o-i(|a3ts6ZEW4DJGAL+Z5eU}@sYIp&o^$n
zc+z{5nG1dp5QHPu&$@BZ46)tBurSd4P-9%>=BgQ1)uF<#t=8n?dGSc2k*gX<%^;+1
z6uc!fwo8G-<rCl}|CYiYaS2C(@BU=HR-@`=&}MwQgz{1h#L0k(clhp#3x2C7N3^F-
zZj`ou?ShmcXURJ?Nr`gC&T-2ZX)Lm5ep~uSf#ZKGQ5O(3zp!uwI&FPm)F_XJ+DDB*
zr!?xu6&DDQku>`akMF@aM{8gsmtaMf?~{AA(QW*DRg5XLAAiS_^*&k{zTJ6%PQFGb
zPiA~F<qiRQ+;~0L+2l+{Y&lt3EgG^btjRVNPuon3%spp%liVl;E14pLl?C@aSnVB)
z%JIIr_oS5NWz)7hy55nx{n@W{LBgaIrdKY9@s||}XO^emnvKui2d@<U@e}a~^i$2Q
z{hoL3n0DBgxUuMySZfM>3$;4&8)3z+jiWmEvRmV&I3;IwvTedA*$#0fmg%?`{6G(M
zTiJwK=av1VDlJtRU#CA*b}LKl))|hq_Y?ezItS#%ALc-0!DYtxHQYInqmc|iAeiJ}
z5d;lO-a1=Pub*e|OQhwMr2c?SLx_L9Yw>o@)r9c@<PHCXe?lM_;?i`e2gfV6`b+Ou
zX6F~d<5KvzFi`b4bLcdj;NtgAy1Q}q#rIEP;#RazE_FUF`p+OIX=l&iI@5fa-{tQ=
z-;6WTY=+0lyZ^*-m0o}QGS7#L))K#TK)i_vsg|kRp#oVkhodf~$@{{IdgRv~%lrl*
zmr*OHb<1}<X*67n+f7lS@c&kMUVD_j{{CTSsImE^@O^oFV&cdq)Vx1Q*CABvzNL}A
zuCZ|x%0{sUO}zjhRnUo5YHNN)XVd7PZ16H=l_0#suq{(v(w68-D;l3--GCa>xI|i)
zm&6`2h$fZVy#_WFS;PbpXX&)bq+SYXskdfoKqf22$R!y8=FzPpxAb3-V(7HR!lI-V
z6RhTONlKL4JA9jVg_icN!sG%E^B0@4g<K;sVHx+Jan|20-lFWO!{YSg<dR$?X?S!S
zVyYvT(PwFr6+j>H0U1^nj}ArIt?6SGenlxxgOY*A`-YMtni=37Dv#ux)dKg-uPHLJ
z^tr4jv-cQ__;;T?8$`??+?d?lOM(#YGe+&@&(ph!-W4_$&i5wT`mt{c>5w3G&W6M`
zYfDA5NDUKPb+x802cQY?qR#;+k=&fv(&}E6w2m0u02tMp1XVZuwExWq0Hn-<8kXXi
z_Li>Fb<FhAe6xkE39ZEr0V%Z#N7wAI?PDuhUbKgfJE1#yc(GG6%qni#63@(ag8|2{
zI(_f<nYkD=G@HF<^1s(3PqsOU%gC!&sCC`})h2W(d!No}39tP3dw6<uad^X^2mrZ6
zlXdD#%N&Ue{B~LA^9!OhdZi?%$U6-_3;KdNO-5qlxsETBvr{k8%k)nt*rh`2=UfP-
zGFh~hA9vi_K#kTDmZ8Ye*^bDw);o4`nVtyp#vslN0oO{w^Dz$Ex<6wn9NSR4B;nX^
zT1kJq^E)BnTlKqNYB#tB;cBJI=o>8(i0ic86xMEDPCTyHz0tSQ68lAs*3m8%JnTYZ
zM%ggMQ?BMR?cg~bPc!cf?3^EUq`rGtjc6ePX~a&}WE=w~cqP{*t}eko1JYA`hRpJV
zNq%@cKb>jQj>l?yGG;;GKpPdzt9>F%Q=32Ev{sc1_+x_*7S3(j%T>q<etix;t%~IS
z<t!C!(01k0SA4S-)ns;GZ(Qxo7iGU>grrnenYaMbl!*%)kbuY85}BZnj&pp-(m8Ar
z+lROu;6G~Vqy+;OJtXl6wDhJwV6s%jO0=e#xc*RDX2_-)aNp`>iAwlXVFhur5xdUt
z4m=WozGu=XCb5o{K@e@4S=1rO!WdEmZGsvb&~<hUU5d>B)#MYa4Hkf}xDykJ5SO3z
z!wo<{)6*wN?cgHi693g=gVkvI6$oU}`?yNAu2ri7Cim7CJZ4GwQuVBD4*cQFgPO!!
zc_yh8tX6lIuM%jLg}DGUr)GI1)!-r?Je<B$mT#4FSvk)a?;nt!)>|-dpID2sNj2j}
zy&!^4yzN1;=r5_e>y*xqt0L=h<GjY$ogBBK^;sS-01bE0HN?=zRY0;$17q@W7x8w4
z+;%H<VFhUP?-VUsg)0pmTXbxi`^)Xyp_Ep4s3Oc{D4ODIll=M1FXxJpUWG3UcTfUr
z4AdiMXJ&Dc9t!#vj#Fug-P;ZJc)aurFD*k7N?*yYfUz>LO}$!^Ot<!P;nvm&^JPut
zXNqriVFO|_Grfv*YEHL?N+nPjZq%jSL(G8Fy6@}SNO01%Z)uV9NA1*ygSwRNC!xLf
z*J;~XLy=e9&942Q2}_N2)U_y7?rwiww84dHV#oct!TGs>*r0ei@jIAUuZca)Y&#pt
zTl6$RsQ*mXJydN^3~!j>EzPb!nu;Xrh0aFNh^k%q94X2okiaIjIKxs?NcO2iU@Oo$
z;cUe>A(UT;7+b)jQHQ*kK1Q>WltxRY`eO{cZk|SVovDlP6)%(?(N<rf-*K*3a!`IA
zqRH2De?Hb7dRAY;$8KY2`Lncs5LDCP{xL6_)U4T&{M*Fl(G|dP!Xa&EnSWGF>x%#l
zQV`vLUI125`qa2%;*!Urw!l9vp2cSQ!b(Sl8N?pERAUDSN?f{|c(4OFYaZ2YKW$*2
zaB;~&HXZxVABb`GEoav#vfMCoFE%n6o`luvJPN>wK<z5F2s|?5r`#Lg&WB%hPu?o0
zM3cRSe}JT&ZrbdV8I*riCCOI}wSFLA_`{G-3%oWj|MXA0CJm$1-~RAp!F1D8=fYoN
zD*$;ASvyOZ?a1EqLQI8cE&C*J{~e207ysOr3I_2S#Jb536Zb!W;T2qyjH<HoyX#fY
zs&H}<{nXUdt?SO`cCY9AwWQM0QZX_(uz%4dlt}yj1&4&9dQvkNwAarMlg~rVJDHwC
zht+U@e^+`4g0P5nzWn4+JQ5xPL7CKkC3n)Be!3w5odP}zWCI73W;1<l=w`;UtVgL-
zQUuj1N`=S#5EHlZ+N^8%DKb^2PA+MM?lzF>6T$k2{9`#<LZf1`^?*jHao-o>IWprx
zl4MGMm?P(Rfw()?gd-XEypm|+=axEIa|dz9<$4U5hYX1f=%`s(q-Eb@y`?`&P`A^;
zsi3Y9VOrrO+A;moKt`a?B67P71ekyFy^oj;7m*ca03;xVd;zNmd+2%?bJ<48xy&^f
z<>x3k6AFOq98p(}G^b;#OWU+M9yLPW@VNEoLB-xHwppnZEWTfpz5e9O*B;rp9v<>w
zO0@>pSV2VMp*@G0clhuyoJ-DT-5_*I<DRtXt;ZD5L3VLjv&nF7fk+8KkoT6ni@cS<
zwU-AVt*raONI%cbZ}uyvRG}V65~nRX`CJ2xr1p2M9)6lmUcq&xP3^tgiIx%N*+~KK
zrli&C^fB+Q%o@KNKU7RJ9nHGzau?PsxQLudCU+tY+Dsh$>7%lX-eFY~e3#Dl@^UWJ
z;a-t>D!t4ALCDv^f&8HJld7UEf|3Z)3i6;l<6IhHHpS0Kh0@=iU`pPX#SM%|KEiB0
zQ*PT_4-MDS4VgaMqjtEHx((e8{}~tQWO~8jngTMbb^^Jox@n5&b$)w2IwBwW7yOq*
z<Pfmfz0S6V9oD?Bhe=(RDf;{S|AGDs71Fk#7GCkMSlX0-l3jJjDjkbg>bGl?dF)bb
zHlt$;a&f8qoW*4gDe$c1Q>+}IkNem6IsAtgRinOJKI<%#7BU-ReQ#wG>vU;IR$;_?
zb}iRrUzLJcuAh_Jutr{?-@j@8$n2Ttp`BF);8pdnbbee6n*Thb?0a(DP+V-_OQ9iY
zv9^o67WB9lt3n@}6-z0?XBuQ(Qjy?+S9PxX*;IT0#+SlZ5&<^ERk4~yZLAjVtERy7
z5=MQ7L4)Q~6QI4$;>*cFGQ#;g#7_a6SyBmqxC0P1ucek{L+5?+xX3BFp?e=bUiMb3
z$_#?oM6mq<GWuRTXiOo<1Myg{SQDK2G^rRKJGOPA^8M4GO_W$;L+8{JWN!Oh^T&v(
zXGSEy;$YdJ>e;XDBdx&LxPWRy;cv#NKM#Mw+~*upkh@DaER*m?W&n6cU*<=!b7Jt(
zb^iDiKX1e30e5;t{VN_?vv#}fm3p&tOpUeS8IZkAhBw%|)fsI6yo+V-dkk;Uc`Y1T
zVx`=zo%N-=YV2c6kFl0>OOSJlhqmVhW`GtUe8S1rkK6l=XHTQd@6#geH?6p)Ptm)X
zQ2#Xj1#f5*jy{f)mib*)p4x5>igpR_g)G6ooHY_}IAY;NYTHNC4j+qCvd4)}9w+V>
zagjdjGV(IfTd)kum?O~#-CFGCCYf82LkY_{@TQ{rLHXZ`MrPNZ%e)?b(MgU;a$vQp
z)_yiw+{%sC=p`>SElT0vkZYYpBsh6bqWKPYf^eao;=dJTHUVnizyMLyZc&yG*iDB;
zt44g)`&v({!wZURaY!+@9<cL0Ud$>C9dB)Itw)qMG;o(xRwgDeYNk%D+qRts#U~^X
zR_%BDBc~~E@GRSjMFkw|r(>l3MgM?49}Sy=kOs@>P*=-QRw6i+*UCghOq$XKn)a_3
z`0)~-b2kQETl8=WR+l7EuJ_GXTyxfP+JP>u96c=%+*)K8&9MiRcQDvfXx=%!)utJr
zD45ak0!V(9;XTlPGaWvjQCwg>nQjT<E5B%&&C=IHZdV{TvUX^cQ`aerZ%r-GUC5}g
zYm}`J{xDsPsajWJzK>glU2ZQm6gk*%NS@SbJEZy>DUwX`V?OAj-lDmtx<%osK2K(0
z@F*HKl+EEf#oxqS^80(&r=Nh-3X4}b1mL|l@f8szW_N!+^o|KLS|$40Sg9Q=hfBmw
z92bTz#l{`@X3y5`u&{|tlf`cD9=y6O)V*IIB|(a-=jfeV4sy|()fZa&8irsch!7pO
zOR!tf{s$R=_<Ed~fxgO8v$csc_g#P}Z$sV(_1@;jJ(80~xC*1Va?JeKAyKso*`liP
zb7kiPSB3aX({qJ8vrPIGZ>s17RRKRPJ_0u^hGPpNiQZEF4)mRGp*C!Ns$g}$!(EVS
z5O&H-LTfpnBp<ix>3w~(c5phPcxdKnoOUqF)@UJu${_ulx^Fb(LYy!)mfphtiS<OW
zV{!1Ioj=RFf|-M$uB>~HhJ_DX0U76s)(A%L-gbV!UgMr42qp(mK>s6L%ubjs;!Nrb
z9H6S5_UN8G9s?cyD2&Um7tF^&A-2%qEbX4ApXVW*s~&JP^@FF5yOPWCas88qfFSRt
zEB_3R(8^&#{Y*zyVoyCT^L5m}L}M1>m(3|KPbpiYJ=x&lwhZpr)N6`2akz{;=+yxJ
z5P^?Z^A^Tr%p1JQIF8sB((jL=2b{d|SgXNCkOItYIN|k+j830fH(!C#f0Z-P1S<tq
zQ`Tj&83&DJ2xvk#oFc=}QNT?6kEVgQ`tR{HI(TG^cx)<3>BQBaV`F2`84<svyqt5R
z%k>a!+GiPn8tlp)aCGc_?8)x<drS<c;`B*JRh7!w*?DGa%5cv}4=RDN1{zNM0|mY0
zbF5FIo!Xm_OVT){n2ozkR3I9=f`fEjyf&4#FKsI)r<;m(o7dsREVWo~l<T9->ig?O
z{b+x#2H*ByamEi!tC4;XDjGuxVMaH82*<CYZ%!+BRq(o+rLQc`egrHs%F8CgwI|?s
z6m;RcG(FiVd?n<rBC0PFZECHT;cny{`CX{JRJ^g}+Q4NIzxH-G@_H@q511$QW0U!;
zVK&wC$B)`=f<6{8<!ePoO`}v)H8`f?-*T~~;X&QwK7dbqXP7f0nk$Xmg|UBnM3j2v
zXs)2{m>OTzeX}^J`F_WzW6{(G7dT&SEWZM<DE~f&CpA_C${;p$8~a*hO9q!$SVoIj
zulmELvWa;pfY=^v!1NL3(dGyI``XF_%klGngbF-`A+)U!!4b*9N;(>q#)XKXxFk7-
z{NX$mWM@IBRlJ5`i`VnkMz`oOlSjwuvflewS97v5j%j?pSeR`1>C5!$NSl{H&5-M&
z*U||;f=otvFRh*b2sN%%t<ezg>*4$m5w*<BA#3PN5!QFWRReu=_H7_EZV%fyA1&SX
z5940~Wq+?<0DKyBP3E&2G859;MW(!MzSVL#%7%?iL)Z|0XJRCnx~I1Fyb%WQ$h14+
z6dMe2*B9Vq>4L1;)3K;3fabYJSQZXus_!7Mo0B^QBbtMl*Tg3~UvbSnH@QxipQWDH
zU;+?$hHZLze#jLnO6;TIRbW@b#t>bE8|lHG2mNk?n$p0?+8;g8JgsRxS1WZ!-JK}I
z;i8gIy--H6&0YRklS%o*1T1U%Ubf5~RV+-BN)1AYybZlA^Lf3|=DdPX<p;`?$`m`j
zQqws-z24C|@T|itu3yfJtdP9cBx|PnJ@hXwb@vK3;GB%xI{6<00}GVNL2GgYUHa3t
zx*p?)%zmjQ;_*4@L5FL@q9oBl0nZjH#Oc_!t21}p7~#^k6>W4^XL1X+t@+#oHrvrp
z!P_&$1|Gc(5@ON#czDB52_T>+Q3-0$Wk*%osk|fj9~L%*nqRD(fkW(L5oM`a<^etW
z1$(dplx$i@$(-XMAfZDpE^&lo3n4J6FWgP|H{7cVzn$e{?MN!GiP@|vpgh`9GUP*s
zRKxPB&oqu|u}HSQC6x?Yb!;J~bhyIg=gCJ(3%kbQN)SGt62!#gTjuR+i$N2eb2*JC
z{q*m6)T47ZUs8m>P%W2KPm>4~nU(De(cyh#yAj=9*L_qd)hrA%z7e$A%T-_LuJ%0%
zirM?w;?q!>0odO?4Y_#xUiR8kD6JGJ*2St6`L%uVZDEDqXPYtpAO4#c5JU|BpLr%m
zia~t@NyDoejlN`H?T8?yC-HXaDk5&_)>#n1vqj?mZNPR@eU1coF*1j^;g)gB=1iQ4
zxpuCH(*4{%SPiRDSQjSBY|$_mEAIK{*Cekm)7kro_W~j8KVIFlWgAGZma>dw;zyxP
zB<x+1(QUGS9)C-8;iDVTp=P{*#voK`SFwh3(UtYU6y~L&#}WltWz=DnjNkms#+EaU
z`5PN>A)BOV9S63yS!R3&*DBc%bBKTYy~mD#k&TtU??h_iR%~UepmNsKkWmcK)rg-h
zFc!c`ZpM>!ZsmEWdtcRrFK%JNPYHK;%z`zU2eo$75Ntv6o=oj20Oc=S3nzK(?*pHC
zaLiE6<Qpa18k#e1Gkodh6ng`VBdq2kCuI|wfI>giW@@o#!VoazvxAfFfW%+l|1<b>
zol^WfxWucu%W#&W5$8p)YV_#}BDl;ySBia4pqQwBTpp8PTaoxhaEVa5nn=I6iaKf%
z;0A<*mnj}H_WtJmcW-hB3%h*r{figWl%9x!DEG3)baV@f?Iq64eBQ)ffe!1Tp`nuH
zFZ!i)hmrRV1B7NnP4=7eK1!fIq*51j(p#tp#V|akpdn~5XpsAp!Y@fOed)K8(EH#N
zo-u`QvUT0J6TUGWh2qksIz0XFonflq>fm6-LlJkSdes<jC)OOIbo_ypO(KySr)AT(
zBo7^t8k+>{>vBSyYh%(wE2(ghbHKF$!5o?}C==2l29uR{V8*ahTrp&E9v*_0gUe8a
z9hFVsES(qhR5@kWsoqjCI|)GdK&M!)(;;sPmVQ$P_w_qk6bfHPDJBWVR4~cONTgh}
zQ#;6rQ=hZ#Jq7h<q`cy%*q|syPB2v)u$OimBXZD-Y>shi5eWg(WhTC>{f#UHGd$lI
zxEPrAR$%Obu{KW`m%R=UVm#Gj=+6q0WbkiK6(tiB@Js_2<gc!jL|PH#IXE52Jl)HF
z+v2Bv=B>}gUYLUOxUPB?RhX;Z7KHVqLnREM%JjqPz6kP@J=hUJY4>%zD;Fb=o<4mE
zhTA7AdY3~x3x!%QAM@Z!MjLH5?=&F}jDy?R*byXL2jZM|HmZ=pA`_a>kx`(pOMF96
zR=;Y$=XU#<2Pu&xFf}>2<D#~iRB*9h{QmK)s#AKA77%LysO?htEg2{SId3k`)i~1W
z)G1YGZKPPwTc55#-56rinEL_p$=z_TGNKkdX{>kK*0hq_inND%$lDqE2~4DSGK+i6
z4=cR&K-rJ7UMVActacd4mggijDac4Qe*PlhyZ5JB^>w_XV=;oD4tEH&k5>mVpF)Rj
z8t-fuJN}cgj|E|%r%r(;4`p0}$lGyFo#$f$9|0Ax*6Q|`eyax?>L|v~8`|V{hWv#H
z(530P_jl+zZ+IA0kkdh4%I<-GU%;~W)VMG6<@mm%M4dafigv$lHx)L#xPn<>ZlwMb
zmseYv^}g1YU#=Iplg8}grC}u4mHxQI0yohny*h}@YDet)`|a=)3zPicvC-}WDwO5(
z9%a#ZAUr09N@|2_LDzUy2b+>)bqa+kGd|v=Eq8-3f?)50lVa=!YHRk&{cM;{VbR{r
zMhE*vSRI>|W_1Upp2XlWY79X&zy-OvpI)vdfnIDEbBfmaD~!<tOnUe?rDsoKn}d*B
z_P`KonKk2DwF3cV@KV0jDJ5bc;WLGgOYup7&ZApGv7TDewN)n;RvBjgp-R=kvH8%c
zAUd*lafze|ah>_wGkHLfz~r$QAns}~$PW*1nppe~p87mPENG+w8bKdh@~0Onf5i%a
zDc+tkbngE;y&da3zWFwEA$&2}Z?Sm$<Th&dQ+bxKXyCr?V;iq{Rnf+Ki`b&fh2|Dp
z6P5P28IP7+ao^pWzHoh9t}wS7o<M4Rv(RE<RWIkl>6TwU%-G9rv^`1iI!0W*EUZjB
zziE|fpCJ`GL+V^{8>1UX=?Zb{b#ScsW}$QvTQLxAUyeK1)V1lgM|iWpXVmZDppp`c
zysx_TX86LgH2Z<Ywv@{#74If5*PCc$kxr><wP>~FCSxzUHte&hSLO3Z%!I`4S|NqA
z?n+uMm1$Z$rWVDjND-yHoGg{(51QfQ@wr_N&RlyHZ88$O<6O*3pSNS#Bc${Tdi26^
zo*vx;+4%*upf^>HX4WuRSWi2r<9Rt41)E6n$Z5%?y(yqY4E!P>3@^98VC8mV%AiYy
z;G!n_K}i|*{@^2)=Z4<lvw$Dd5#I(m*}FyUHG12m`M1+xpVYzTx0$i;s4g>gO2g*J
zE~hHhieh31zAeokB-(3v=+8QRm%Qws&#Cz1-kbH#@eObA?yJoY7BfSbX|k6TC{^y!
z-DuI~JalXtLHWOIrNTWn@AK6b+AAI0FT0W3wWlJv7mMGK&0y_GSn@z6RF9|}yXm-2
zHsv)d#Q?#drj2h(50$qApV-JbG-k6a4yzyu#@QA&9`aoa`IvHoURC0a^Kxlgvn^)o
z_D2k9-rpBf>{?u*(N{)qcV<6v@*{hJYoe*$#qKJ7knK)K&8}C1Xq7yQjg^!U?gzg2
z2bRu<8m+xo*T~E*yPs;E7OVH3-LAzp?%c9qul`xp2g?{f<sHk4xyK2=3ywN$j~nxl
zxeNF`yputl<a_*?(Bmwc8GZA&tAWz`eDQMEacY*{(n3-1>A#ek3l+Gg7~?<-c~~Qt
zlfqwLCs1>`k6ZScHv#A@`#n9MZlOHU4+eKLNhZ5fIUaEg10n0X$ogbhqe&mMD5C_=
zLQv^CzV;$DKVCr;o}g@4-{d?)uD^&)h&)^q_H$Ki1`%1Kc<F@@*1mdHqJVTIvCxE@
zx+@`9-P`ddz{4;*FCIm_{oHJR<8uyjana^lBOqoRP~osvx-Ho@fw}2UTB#l~&tkno
zopr>Rgzv31sqytJmqN@`aoR57O>!g;EEOn_RD4|)sA(lSq@%Nzomom#Q{JEmR^JMr
ze{jR|duKf$Iatb*N48mDWCYj1pgmaf8RhuPL+#g``9=dUr-@UAhq<k~%7{+cKHh$R
zG$8>Cm0GlrvmY}{Ue#9eqZmX7I34*XbYA@|Noj<O(>a*4jIjb!zxnPKt18qTb>RN`
zT8?46dmE3TrdQzYLT#k@;Bt;}a`HYwKBRt?JV|4SJV{+vfkQCa`RT5##?3RXT$Sx%
z-b%8>55&yVS*@y3R(z;&kKy*CEGA~yX@dOFGNC|#%ZoR*biubJw+m$fvIpK;jT$vs
zZICPhTv$HeWrqaGUY8OD2aD@N<~}G@l_Z%}5!sab97X_YazJ#<=uzRGn!-ZJ!s<Wf
zKQ4xQd#l$jpW8IYAI!^FM6K=O<MoU>de_J<d<(LRFd<Cw?}}1l+|MtsUwLjV8pD3{
z_#_(}J|Z1M>P!SjGE%c@#qLW(;>#dh28fYF&xmj?h&i0T%}R)4SQD~HW@hx0!h7&F
zvlCJ;;q7oTh{)gDxis1SekK1&LR8`tkBb53N{fiARA8o5)ta$x)Ys7Z@@K%VXmcIC
z(plWW<rIad&T<RrO89oL(y|?2!SUPcc{evhtLBI6I&2T7I~o_NDJxH`a)k%i3@TaK
zOIYB&`tH+G{>EPa(Y2lH>)~*`>1BJL$0$N`n!;YnsjHDVoJO;<x?OlOU^YB50NK}t
zNLwIZeSuEO1WA1M!XWv|^8CjP@BJc&l0=u}^D2T5V?Cx2cR|aCk80u68C>cPmHgM)
z-m}@RsOz2{-rntQoRr8<g-rTw;{E$S@{3d!JzO~lJGjsm1~d6QU-<YyD%ziBv-(;O
zzdn1jPIO*fVDl8C8<Or*dzYS%9Q*1|bezhci<{fl5r+cY;)!p@+rG8DiDV4vqj30J
z0O{TV?RZ8!!eA*cj(x5L+)w)nBIt#nw0uXeJt)8Ed(eCI1s4a08eNBB8d{QD<0v88
z&Zo<&g0?nYpR3v|!fE1tga4>BqP`R-8=0ToznWFMLBMlmPJXDD?M;e^=@I+e#B@r3
z*cFz4NfG6^1ilBon$B{i7pXqF6x-JrLDKWRi7HDP&G67I)~}Ja*AuK0&<S+A85?k}
zX)Dn;y8L!IRp<2HqBv>C^$oG@@=~Bg#=x>bAwqgC1^)n>u<=rb!Rr~gcE5KF9lAAA
zWFMFlv~64YTxA#-#FDvl>pj$nW3>BesC4%)4H2!SBd|~@$ZrjNV5>=uxW(;5LKv)D
z)W%kpzzjO6X<Hd*pHtE^-;1QNs`o45$&wn+erxf<4}e+CQG8XZ8TJY;(LR5UD|+7Q
zdTW_FRCS|H2JsBxkkr9CEs{@m&;m5#>^el)LgrG7X8e3Qq3CW8i0JDZ3?WRr?GMF?
zLOUn7woQz3<@s|-#>B*M7daA!<D3a{E~wAwVoBzHpU1)KEB%dOai6ycFMoKjAj08Z
z#C4?5GRh%*dac*ISxl&H&}3j(B#d|~RNZ-|uuUVead>hKLM4)(z@oy?@$qLr+T>ft
zrd!Uj(_L78zQdEXox^xj&kHgl5USK?|1D~_2G1?32=klU?&bmGC4;v&C7b?*jdg`8
zTDN0-ea(SMmlpmr<1Mpc%4rLyR^;Y{3u1;IpQpp75n_+lT|I*;>odx%zNDS|QyP;n
z1rDD&^iK%_NSKV=wEb$j22Ac=eQ&iYCE8l4(a!MiRJsUsu_a5PTR<P>*}+!0sR}f|
z+=`5;iXx?izw{ESsqYC%C`JW3x;y)qtH3_b9dz*z@XACsCs%iA)Fx?gkErf#KUTzr
zy)o<ZghY<0RK*1_Z8a1@+e=W$@rq_&#lCi$K{%OOU$cgkXNl_{TU^CA%=(VMoPP9l
z2~WK8*&O346tT|~6RP@}ncaDPiYqm`c<=LWI`qkTcN%TBn!+!5;<WWg023umx^!sI
zWvEjFpC&Y{e8gZ@d-Tzd{i6umV+0S$h6xj?8XumJ2%W(ptLE&v5Ez~l_(7c~J7e*v
z1pmK6N}xY>m+Ini+j-=&@AIQ%U8b;i3+khg<sAK{sdx4ridbew1_0DJ0M-(@>BdC(
z+IQT%Lk1m@G{-z3$fc9)f9u!oD};5X7DLTY>;xMDi<&Wpx8+}>(zaX~lS-0|Q~D~R
z62+IPF}wzuZ7sNSx(pasKfqtW{>=0`4_fBrWD=Wre-N%4WVh9*lx_*?)L#Ho``#R=
zro_gX^<{bBO>OX5ULYWgU$n~$Rm^gdTwp2+h@q+R8t8UNJ=u;*YAI_|n&G`>>nX}Z
zbB$sH+F>HxnZA2oDxbM5dv=}0NNH@8HBzHj2vvyY#S6|(>6zqa#?PF`vZl}wlbaXB
zON||?M$4X}Q!DOrHWSQoCN7{XXMSf~NuX=>WHocsyBC8trEOuMmvCTTKeE`>i|x}r
zBSO3L1W<L{KC51GIR3@n{5)$Js&Q;U6<z$N?jvg9C3&U_J&)o@LYe`DS#D%SvM#7F
zHTew{-S%GnZB<6iM=^&^r^mO1#S|r-m7mUQYFzNfSlRKqy~&m|_G9>ZYfV<sH`PoC
zn!WWyKhRsKPg-0KsMq#vy><VyCMnzUNtB6oQF)%hz&%ggrA*U37|o1oHgCJREO@j|
z5IwkZOcBKT%htL?6Vu!@`_SuWk@^$Qds+Q<pLD^N7Re*6CLQ9>s3hN`f<9*EUOtsO
z>Z(p-Xqp^q-tGyqC6!-b$V_$cfVf8|Xp_RFXA9t!F+XL}qIhuu4jFd2O9)!RyoW#z
z<>y{5iiYh5oh5uMg|W9^IqWIn0e_GlD=RJ7_hsmau+eHU;`cM%eMEN-Iuq+&_fw#X
zoxkcKtr6{w-rRtHzFR)oy&C3gCOnhCtFYIjNIBnI<1&W&=4o2prncbUHq{L4#+_6S
z+(O-&eqo?3Xb-YH-w&EqO8O%`<Zh9Jc?2g|n9%fG7hba-EFQ8p39-U{5SxA)z`ZjJ
zkP{TrT_9rm1@79z4|$4k9;1+lIKC}1<+XK7?hV*Jo_ZyYZhY}2OL02;p=FwhsQX$h
zO86nx`Idz6thY+WOA$ZSj1p)p3svT`^^Z@y3_|Nes0Z4OIvSG(bSFed5nJ<gmgkaN
zBRu5Ksjp{>*p=cDeAZDiC2*0m=x2Wm{>3UNfMBq;`#KO=KIHh}!RvP1*2fq$h2g4N
z-}<KrN;!ir@2GVeZ4zDtQqU_K*>53hPv1TTyJ`mCQrp#`x?M^#*wp-C;`*20PYHM8
z1Aq$P^Og@8NbZFgvEkZ8#q4;B7xrTBh@SPHMOkd>_orHKvbaEUYI2whmvfz(t6}UV
z8XG5jF~;9F1KrT0j-(bUV&?ZG9)V#$u=4rq3>c#kBb<>p52pCtLx*YQ`9=y~t5tjU
zYELI97U)T!*9|*8pnPGQ`U3z2u2rBD#d;`IdugndV~Q0A*YAnh@6C`D={GmU{#2~r
ztN1ugl$(eBb7Q4=K{+?9%x%s88>+2Y6i95!)TFNz97aWDASR)HikjnxEs1}eqO7r5
zN19)m1wWlC@)h&z@T=L}OYOkV1v2}&9_1L;;CJG0LuQokRASRenC`Q~Ej8c9X$DrZ
zF_26`%B|FS>W7yZrKG=lv?bn}v;@6{E*$&68i#T}!U=uKAt<CPCMZ@CioQ%8{6741
z8Y4J=aOB4Na_e=tyJm*mk(*~@Jh%B5J9IIoJ;J9rj#nlgK4GA#6f3Tf&L`mqz&jMz
z8t^xoLD~8snrq8WA41)Vx=_I7VQjg6S(Sl+uEw!df&o+ITmhU{Z1h_ic#e6=!+lPY
zXQ_Kj^(K&W>vSkp#b?`nH1z%mWGtttyZfEEVR()?Pb~=>M{bgVq3)j-<TF^8xmQs-
zzI$F984ukZ9u|7d3e2kAuG%iC?uJNQC!Oc%w^0gIH#rNUcDnq+udB6|tZmHM?n&tv
z@{=NU1$lL<*l7H4OIbW4a+eSG@wW+HkHi+t@4;1(!gnG);*Zs<?=0N%Mr0wT66HH~
zuVwCW>&fW=g!rfF8WJon!c6#N5Ur_E*1whebvZ2}w528eu)Y<~LtDQt*ctDFX!{~M
zF<G{+K6EzF)&xU{X;AY9r1$D(sVxq8Lx;(Vx$S?=7NZ?{I`M@?5Jk4R6}dTAT)%x(
zOy>WZ{`zK_Fjml_d{cY(me*VcuKlB5mV<{s<v%2Rpt1CNUh|LsA}g#*$sXjTn|!zC
z+r|a*2J=IZv)i2|oYYC%#=<j5v#87d-^nX|9bp|1aajeuQ-b<*FD0<P#B4m()JC2Z
zjmV4E&+YMfsTHHEG_2fv#`8EI*W>tI_RNP6QqS&&urR>NFYtcnUL{mY<BZK{bW9(X
zu&P53REB=1&~2yfQ7cqV#<E3&04*AA%bq(E=_!1!N6RU<ztbI|(#%mPm4gv_gK9jD
zTp~Sd+6EFK&P_W_SHVU8on=7^et4j7Dx=fjcNQ+tYj$cw>8@x|O&U={*0E7J21^nX
zEieTi2d(~9QsPifDVk~Iea$W}`fF~5`c9zM5lQm3FrDD%p7kVcplI-LJdq_0OnK+0
z7TcN`*i5d`f=8qj)h02|L(m45sm{laxeds;0$<?zGv6<r$)l7k<gTH*J=rCccWs@%
zelvA8J+8t%y>hTw-v$PTj~Rd(hSgj;Lg9mY1$bwThsdTZb*}9>mt}S3r%ROdy8*{<
zUyGMHG7qW3$6mXu(Yga`aOzwr=0YW>4`8B+V6t&PT6IZ{BhaC$)pi$)pgABYhoR$2
zi!sT%6>0n))R8O7vM{8px~MYe!a2x+x7w0%F(KYNqE`{7Cx!3gDQJ>BW8kW=SuK(|
z0E0cWvJT&|jD^FVj1g^+gRN*@WjmUgB)^Kf;s)+EeGqTC7?C`gf-(G!jmfbgzu(o8
zYdBdx8t098LW9oPN%$ZM_qx+CTV$1heQq~6-eDN|<X|1;6)LNak9K$?f*Y?K*Uj-n
z87Kyzs$!VgR<0r3P7Ry7pwG?)(i{+X8;vWgp($v_FYM8B5l5^-zb$aEclvqLl>JmJ
zFgmEKNuW_Y4nLyIo`t-6`DeqYxP*2X6q|x(j<){w^Utb)?m;IN(y&0wyau;aj2a9-
zdm}9xuQ<<<H|Ekxug?<Q4A39uoBVi|YDuWkr1Z|yCFc>-Fo?R6&H^5$n41bH5NNw_
z<-O|i3oi=cwJeI5VAj0*$^Cp~gyiyJm)(C3e0gmJo65~=Y<0R{%|;1==zR-B9I0*n
zEMb87p%@S!$V&KhQ{3@uC#WHt-E^FZT}?M!#6=EfveBns>Srn<$89MhI4L%_sv;MM
zOPiKEc#0IZQ|>5_PWHldA&j6y1mh;CF3%pN8D<;}S(vNAqaD!r!wP}K5p{Ph%~(Hr
zEe8h4?oj|`X{aaKj{;T<PKXAWJ7%=hA&3o+?Vapql5RoiwME$bl}s=P63#o!s}kyv
zFF2{ss`~vW<NP}@xFCersBdvtsqJ!Q{JJh}J0EGCS$T-a$e5v58AufU?iMJeqU%vn
zQE`BpbB>-*im=>Y9mYZLhaAqAiQ|_w3b_7+%4uw^sk26%$$IbpA@r458sjL<Jn85o
zs~O?`0W;gt(ahkm`a@2|?^mplxUw#21hT$F8U`vBy|73}XIqnF*+X;^lZaV9s<Rkr
zb+icbS_cP=t`5%Q*B#LO$b0OV5w*tc_TG%a+q=={Se$KveB`HhNtd>WReG?I3a|Yo
zi$c+0oqT=FMf~W#hgFCwsW5ek7bkGP+f7&z2v8QwD|;p1!kvfl_-41_oHcS#63&te
zJ~N7@s}>Pl>I?Be5iWw&7E7BZoQx;>sfa*yDo9$=iXs{ch|wOJfGI8Mlbao%>$~*B
z|G-r(Q>sAkHiJN!!&Hw|ha^6Z^^~dr1|c+}@^HZcF9#Z@iPxlYM$MNh)w27uE=G`s
znSz&Wq*76lJzi~3xcTy+0^i%686V=jvMeaA1ePvpqloU*ogv*WXtB+R45`|0CcQ<=
zh}HczQ~0eJM%>e1=@ZO&q*XlP_|6%TU;Z4@H>{`EukAEzK2n%CDi(Ov6se^=YZ!^9
zFz+2Rc4uCAp>JD-D+w<`{opHy5JNBtVHP6;1%8Vtp-Kpmew}HR!H9>WD4hC;CLTz8
zDHKO(>Xgi5@ZR*u(=t4&%~7Rox!ykOO)ne;(BO^Cl_uPms?xZele`KP_pL4-S0f6q
z@i@8>NC10ZSKI9gjLo-?dT&swBs8(H6kvy=Nx9a0g;ur)I<U`lKNrHiS)%1!rJQG-
zZYwL7bV>ROFBeoB{^|mV@AC8Y6$jw0DRNI126*$0^*nh)jzfF_lE`<Ei5%{3x1H5`
z6$E{Ovh3iC3!?PiEmb^>cXC?Un>qC|^fz9SoLu?==rAgoHl<{RYGJClbrnTYH*%7u
zA#Kj|nCxQr5~@oYpP91qw@1m5#4X%6s+x;`V{P{7qjGLTHMH>i1*;`iV33z|eFQ?q
zX-iG#=}Am9@exjE?Zlu!(IsjyqkoC%_Xr=Crp1*`O$`lZb-UT_tF7<jLQd-2e@ZTC
zGVD#WG(LYuV>DJb&%*e%x%uNlID<y@dOt@`@t35n9MmR{;OR?ZbTk&?mCowIBvjK_
zP**2^sR;xEe?{`iXqME~sSO32UVndu<?Czk^QXaTUAc;0#C1X?mS{ZkXg4}qdrah^
zd>xPMTyfk>NCzQA<{~tBwdxVEHcwr7letSGWEclYLdJ^;;eV`($5U-LZ$n5Oem@Yl
zmsm>b{Tt7Q((BYQqU!1i*5EoB;JjGSYwV}U0N#`=E~~_|Bs~=s3Y`XPHotu;>|NC4
zhPqsRd=|Nw9PdYJ!67lY$7SlSLG|SoeuIm>KYI?qMlwLUQ&-N%&#Z3%7Ug(6=J4Yu
z7}2V2P3y+d|9--QxGvUYsmINb>Y5Kih))%a>tbL9#X!l+i{fOL*C=h#+@hO&Z+`b(
zS~1mab|Sn(zDFGL$iHW%!u#z-Y$7UZ*m|K`N^7TJSV{tOyLTX8pR6}KM{htTk53;o
zYIyU$(*JOwX22u#<RY;awh~i<^fkW=j<R3<NahKKP9V}WO^@wq7B^X}lV6)Y4Bnd)
z`Ee=BUbt+RK1bSbAsmRhxP2}NJ$=U{^_6<_d}1<RN<_T^r5Kgra}Z?glW23kzR?B7
zkEQZYbg{z}_s}unD64{IO(a)o%`5OUw`;1hCBsmt8DAsR!{$*?kp0@9M&qW#?==yg
zp{Gx~<!TsG&wDWa)}}<<L{poK|5OfZR~t)j{HkNY|GOUbaw{IS?0bJWq*Y{Kr>#*n
zf)6shvW7IV>;;>sRpKD(Q(klEMF0H92N>-t*{bLj#Q7R0-{yx{jraA=^o$SYN|6q6
zok~#we|*s|M#?PXKUf;ozJ6Ef+JDD4y6q2#XReTPMz%jQII2!7ws~_N4rh<-C#T$s
zRM@!C6@EPhV@u=fu!J_nPvFO^0F}mXG<tq^3qf#i%ENe5NJD|>4?`W7oY`4xh(qF*
z+vW~qgJ9^Ak(~<?#!{!TkM=TW_D$0JVD)Nb+sJQ0lUBUgC5P7>9r&R;ijcJ(iJd1m
z(u3YR$5Yfl_4jW9N<Ad`UIzD%a^Tn|)ZEIL3N%yI+f+9oU4GFOf-XKeg$xX20bhfm
znKxTFs|ard1?IRCZCxrg@`qtchP{m#oI<OQKVlG3iA!PMA)QC2xu{t0K9!<RH_p>e
z=Z~F}>MIW3Fg7boOOyH1`4<a};gOM^pndJjNL;%D)2f>rw-@@G3H17c-`5K@ya*W!
z<ywqT$AY-<aCs-HT`5t7HfZ@`oGr{?rjiW*nbLqEyw`cRC%4o9XqD}4dNH)v+TNyk
zu&S3y+jfL&BFDw0a@?6n0Ky{|H$6bf!}M#uo$>`c%gM*g!2Q$B!O;}6OxTl$lP<oM
z$Z~_uj|n1FRANprE9hzRxp{ePlbQ6Yh4S62*}c7W2o;VF4%<*?c8;WoV@DGcYF*Ej
z(k=6jX*op<mKgodGI>P**5X}soNSkiMBB!%-*eF^JxdiyOu%vxixFT*$XULw*|J?K
zuVaAO-?)1TcHWQFUBe8ndXB%wLw&8SKe*WHq!J!@NDKwX?LzM~c`^Hi(xWOl*gmIE
z;n4T?9D9z|{A7(|@*z`T&#&r)tG<`Kmo-7D%lv9IyYC7b_-*8Yi=TP3`CWwrKPfD&
zPNED~dr@)&9r~!76&hA*R_U9vUIr0wSXIYqT8c%)RCzf^RC+7X_;ELGxHMwob&)rY
zawqy)(Nv7t8x=4dmL6JLQsfH{i3Fc|?+)wyiRiW<a9jf=Pu&iW>t9Q~IA`TLhmc4`
zaDKXlPjObpdh!%W6Up;<c2Nt$D%>e(Fujt}WtdVTEg_W?#gh8I7*g?|)4F+ElY&iV
zB6tUML3lfUm80?X4Q7CtT#`B6rg9#E0=$(t$|qh{_0vE;GfTU;<ix|uM-?4aaI79i
z+$60s>86O;;=8vCMR)-@SBIIp=Fhc;>k@Cr(G9`N6tHM^bOt!*i>azqG+eAYfbXk>
zzSaTAV^WPB0-j%8(zT~B=vkvyVG4~OI4k-clFRfnv=tM~=Vaish*h40eMVKBZ3d7c
zX@BOD?-N!ZISd>6^3`UEjNPbyCptuE(nEH&i>zzMc$!(Hrt0yc9wVkLS1>h|*CU&y
zf;ZJt42`9_hn3oD&&igr+#^QfIF3eo!=tf<ZyY|p^W8}lQc~dc?Mw6@@I|<LRJcN)
z+d~nqX3B5z;2|tua;;2NYDPynST&w(OiO%%I)VaUMONZxsJCF_l~^9hc6+5Sxk-77
zvRT|T$|UZN*LQ8Sic)PGbIdPmMxHrymmb=nj7eF#Af9U2ln}2!K5cqgA$DhGvJJ2-
zew(k5q~w?%oQisKYH&v|tj=o84I0veJ<hcI>!X`9gY6H*;oN*8kU=owhhT77NpqfU
zH{;eHim%s#9?O29loY}m^J$&f)YQ^dK!ZSqh54f@Q-?2<E{!%vi{-Usnp9s+CYgN?
zm2D(!{V$@<fx8YaTGu;n8rx{x*j8iPc4IcS8{1}M+t!Y;W2><m>+bKIamM`-Ypglv
zi|627D*W+B$+8YVL#*9;i~Kv4LblPuu)2;;CcW-Mce~;9UKA508JWnIw?jR+#)o#w
z;r5_2Av>EMOja(W*Qyf^Lna~%M<z-Ht91IecL<X=Ptf4^XLd4Ql9--Ob9cN==KHdv
z-~3~Bb+zKW$)q$bOA;Lj7T$~fpj&D??Q<J)ErMfsey4)Cg<2`B>r^?xN_`9BCD!rC
zSKnHXXbH;yd3ksg@jYy!&WSE8WtVG!QnuB<BvaFS_n_Bg>O-052O@%P-Ab!P;Qx;1
z;D1~d{+druMTP9-<YYj9W6`5@+#&P)X!>gUH~zA@xw&-%5#a`#ju!js$mSKeL&o}B
zK_e^fqjv`MELB#)k{qqouVfBBS+k3puX@d6J6c@ruND2QZnO2S^&)h_dYBiqH@-y~
ziq_8B7@}>U8JOiQYv-Ac=?8;ly55otbBTr4X|+|>vG_iC8VIiRb^${DmDXO0R7_b6
z>`0E4Bggvn?R_%t*DO~aZOCqGr@`lB;UHeXQQeb*<sZmQEt`spVI2I9B*!=-oFBKS
z?kE{}M3p|tiH#6Ct!Y>FU+bYiz8tCx%RY_|(XXmP$lJuOUlV<6le7)OWeXIxu0h8<
zxTBPl!r10#jV<+z<~!|#zQ((W|LVjZqf_(2p5MJ_ngk_p&V||slfEj}1IXdXr6O7W
zf)K#ThR}bNW1w+H&Q=qqGLUN9B<HqUVLP=4@i|65pu<rdWt~YuRKxp4ukyBNwx1Yz
z9_46An%6LwdVtAQpsAkY!VWrr(v+;LFB40o-*KasP(V~-jdD^ALD-z3_pJhU_2>MG
z#&=Y>5d$*~!bze*f7jyH#-Ug4iN8`L5qFPDnLX^S_R{NL8tBF|dt~F6$56cx;Z6b=
zo~#e;69NTupW4S`=XFZH%ej@SH#gLF$&A20_uG0fOnNLs7B4?><ojWW{Eb=@1!@Qg
zQxsM;qrQp5R^??Z>Q>A3J&JZ{T}ibw)8b;|$(X51mz>j=ix321bJ~qm{_=3689T-k
zXXRNmMEA*Gt^OzvYHg6%+)$?0fvFvKF<ve2H-wG(v~EHZ`o~uF$DD7copLEy5@IrF
z0jHrObVN$9#}ZD)ph8717~K-i<rdcgr5(QSMU_vb{`tqkmY|kClcB`{9kFd6t-s6H
z{UKs__OwG8*<C>^$34+z>d^1Vx$5MTzuHU|!e%xIhHU;FFj5G5DO&}-rf3?dXAVH?
zyPB-ea)p%F^NikqdEE^^Wc7Xd#TJm2kwN+S@z^^BXf*fAJ+t!caoVRcpUBE@EP1}R
z_?NSRI7kwKKZAhNQ*boXWzj>CgL4du!eP?TnXK*YK_wWvo62t`Ry>wedRx*oOahC4
zOc(66@NM9%4pYT5`&3~PckA*UIf{VK)mArP@{`>E8JL&C*&4agpZDN``EQfBRIogl
zo!Yt%LNBjOahj&}zd4mU2O8afEoLf}u>Bvs<0jS*UQ>4~2a1W5TaJLUt*2ixy#KqL
z?mQ&}s4F*?x-I9(wChb^CUecxeKpVmh}kTnZ#4zHA8Z>*=yW*>;^;O<v72kXgW907
zg%qk>w*>l^cbxX512U0>4eFHE0$gpV$C*YXqMV=N_<xi)bSMkH^XRvnGYqe}ok8MP
z4svK824hyXFlq@uI0s*i3UlG%UNI#}hDC;lf>z*l-tDonNI&;K*T0!Fe{+yWW`>u!
zE4Q!HIXF6{@inU9sEOn9Gng0ZP`8=k&2h4m+7XNIo9#2&y^^xhvuwLn)rY~@VJG)H
z4CuE=<+;9V$L@2h3s2J%xE|B<P0zrmmeho_I7cC6ACYFdno2orPhp;^4g{UEmDjHY
zeIy*qi$Ml{<rLL#Bi>f!0;mjG59)1FP5|Y}Hg+|!vao$;!@YLb_rnC2q#;V68zCBY
z6p@5cAP<J`kyJvakXIeklmQIGA_1{Wv$Qpbn)rugWkk^1YeZ;)c(E5x0<|IKoBHB)
zdLphSN^J4h!iJhB4v-~ysZa>?Cs<O0yz%jZUj@sH&}I^~W{BDy&wgyR1<Kwyxx^b6
zR0AB;eUpm&M>+brOA?|xLSH%t{W%U`CunL@&@J`(GCA#9^7+Yw1rEFK_WDv&(bB;?
z&=@ie+nI^<24X`|U9(_CjR)+0;hJ9*J8nkFB769s&}ZP~t)AX~`z{l`Cor?i8Ah*m
zELcbf@V1dZ{Og~PpG6+2tJxMWVA!h_JDfE?8!uQaKtX8XE#oop^Dn~Qpt+k|W3f7D
z495!QmaVC7HCGGymLZ6q^C(Rw`)64qp9lw4srABwCu!C86J&dhae-6b;Y_7Xvd`1u
zL!(wFn~-}qX{2gYF=RfIm=CtnVYG}=7BBQs8vMgIgkeo_N$6W+)csp74tjva6y4$b
zB4jj7cAa^?II;+ou9GQra0h*0YEbariW*f+oo)X3_D~_4=bm5-?QhXvK~FR76~JfW
zYUo9oQcoMACXIKLwXELW-lym1?@8MmG*ho#k|YZqCw7pN;1iG=!EC0}mk;M8uBp)D
z<KxQ0M!Q6~V-ShYb>N0Ga4M3-fi;GbC8y^k3wz|t%e)ufpsR2u_Gqs6B@avpo4`J^
z;&gKc^?t$^ezr?+2_FTHp-%!2!F18K&s{ZVK8Ji@9I=4T%e}19)%@kAcXEbv_j!AJ
z`z?4j23EIDJ7`+uurO4{5b~%@X+B{)=Z;KWzP~;$6#c6(y%Hn!S|VqqH3eU4!RM~?
z>_H>Xt2MJ|-KMzx`b2nn>U)Voz3+w-t9VeZ!T+}i_r>?kf#37Ue;Vn3sbTAeO2y=R
zek;a{o&Mn4P513_$}~MPMgE%*c?`2`DOEOJQc_{d$Y=}$h5U=TMbKS?p!2O9(lywi
z%3+0!fY2CgqL(*>uGCkv1UVZ)5z9*tO<j@<EbUhBt6qnyx2Dukm(o3Owya5-6-Ie>
zb1l{5rBhJAb2=aeMNlxMV(AA}$<oJ1;uG8fD5*Yn_wXhe`8cHkfEa*f-SB|exf<YO
zVN~3xe+Jhu&vIsy#$iS}z+(Igms7@Ity$iWb}R)y9j({?+Ogzt#YpFkig4(v_8!uE
zCS_n}Q58M^h$UiY<0wex+0dD#>2Hp@q4eLR9U@GZ-Te$yl6;CM1n~F^mjoh*;@^c^
z(KYb_1Oe(EVB>!XYn+L5T*u>-((5?UH6c0g$K^BkN3sGyo&d@Dg8JUhHaSky4Gjok
zzO6>nG?vs0y7o5gzF$d@t8>?VZQ5aQg`W?WFg=erO9P+0*5V86RQQB{AzgOy9jor>
z!chJmh}h<`2ku}>&@7g~!OS6YBW%#GVsWT^skR<5c1u>|?!hrQy_>DtWLwT2*P)mk
z)#itowC+iT-)tGcoy@vIj5y{=K}yt^WVJLef%OguCw~Lll#Z#3sp<jU?YzX|eKHNo
z0ESd0pC?P9ht8Z6os6}ULnw3-@rqq(ozV!O07pS6dDCR`olc+^D-_{ky9P3YVb8V>
z&wW=8>Q2OcbKu9;Ewv#Np!ejKqp8Yu$_DOQD1>#9R$d;IBXkQ|gIoXcJF8M`fc}?)
ze#xmK?jORn^-AN*uHM?C$E^C4F7*!`E4k1FLl@b)8wdjHw(0O!+t&9v&i8z$sh<_<
zUjk3%_!{&opp9hYFX(ozGCwxmHAUERW(>teeYF+tedrJKweb9r$peoD%$!`p8DP3d
zngC*jhp}FIHi~_2CW5BZpKsIYbg`E&jjcqFk6%kD>yZdJGn*qbnBI8}{hkk5jR!mq
z^D?iXewdEzZ@5oqyG+{G;=1khLAZEX<74pmfE&p$m1M!R^TuuePhW6cM&%u15L9c(
z1g@t92yd{Y%W6Bt%J;5q6w6PYv~GFn+EFb3IGI<ODqIbKfI4_NtCid`wl;mN?fsz5
z^L=Pvo#EVf*y_?w$+;)DTWt_ul3Xr3lDIpo?Nw0VyI^<C^PlE9CIsWlN^ZgJ95l1~
zwJ68`{^{DuR|#>ok|3|ND7X|Fwmv*jd&x)N68z6)I3GtgAHav}f^uY>=xiH!wnb|K
z{-8^;$zQ@BLDu;9|Etpkd<)zchRpPA2)*rtLZMmM$qrG-U=_ERO#cQQVU=qO-t%w4
z%uYHI4cSzBWxgpKMm_N9LRT}e&-L?z`R7*>U2va^vS4f5-I~EX4$(b1^-8v_3pKC1
zD+)gQn1O}_zbNErH*{OwF=R^l(Fm_)npI!3jt8ezYlbFFeS9hQ5SmbH8*wk0^i)l9
z<;)44%g-Ug$NTdW$PQz)PQ<ABYUqBYbj)<Cgv>{LqhXrfV&Yzj)ii_ex(}G+%XDn!
zTkY_(Iw)51_Styo+~X9?G^9$G_<}kgoi3eK;HJ`2fe7b?cr<)VkHqo4klGr^{e1YC
zoB60tmrzi3`FHs~d|0b`3#i7;S)YfHmMc=BpFEU9k_t%o%2avp05v`c3bl`92ABem
z(>lyojhM-w6}{%Tdu1-v@G#fth&#@8ajlbI;e%}AV8_dA6113*n&t!beSLnKSn)QE
z9i3a-ZiI)iy|}|LLlP38UBZ@ir#$riY=Y#O`oX};i%HW(TigB%P;y4moVddvtfB$M
ziVGP<31QD}9?Kl<icpbRGFFUNg{q|`APrEe5mutA-y5f=%P*OF)w!2{J~7$L0g*|`
zcm`u?ENn0rAd3ymYj>frv!zIIVKhs$Bum$YS5BT|&|DEts$+|N@xfW<oUrj`qagAt
ziy&g<h<VM!KSEGi9@@_4c|H|lumN_VLe8HBu&MH1lv#OpeoMM3@N2*XbV%%kf2OQt
z=c{W5S2ycel5+2(aTAmLCC`c~xQNYKY;%?9)XG#Q#R%?7R<ui<%<dft2#qgJbS^oR
zxAVG>Mvc+|VTk&u+v((mkNmjVAyW4XvzmmggV-<aSH9gz4Ze<_)@lpphP6cc@hhCR
zu>=N$qeJ#b%5(*F3?qxm8GZPhuK*zw`<z`Tl^NX@;RvUfSJc(th3ZSfPFcvhjW5;B
zlt^0ton$97IU3~)xE-1~+$m|QF}87n2DrB0;MhpznbTb?Zw;IN$~S6oE!eI$ED<Um
z7m&c~U~p4Q)u@Vy1a16UkiY%6peQSkl;fRG6wJg?ed5i_+5mQag>VG%xE%EjYr^(D
zAbnN)$scsJI0R;!Y238fZ<fZs4YvE~>FI%g``xOA^VOfg)1}I#BbVE69yytr78q?7
z_&x5?gC`(y#{cUA=*UAf11wVys&bxMg1_KCc&5Onp<h<z$H;uh$lj@hEaSYZ($d)A
z*NU|BV&;G(@&4b{2&^vsqYzel0&B6V_=_8%)B5|1rT^iEQnc9>Y<a9G+qz9hk^ghJ
z@1C8({g-Ct|9INJt@VGe+!RZ}SYA*R(ieRHr`oLhmS*Ue;CXkHLHENi8+YBi7`_SY
z8CEITwZ8&i<FY*QsKcPWvY;)sl8*YiyY-T#H9)m!l|d!58x>_EZ3J|IynQ*jn$%Z5
zE=t3Byr&1vVtpSW6WY_WK{i2E_yqOB7klN{n&p8YNai3R#u}^?K51p~46~!FWEU#Q
ziHkIpb36I5vbb*$os`UdLQ0oZ9!OJX_vXWU=}oE@V#8}WTfaSgqyYN)g?YxrZ^s+f
z0pS5j*X{W&6;Sb=N^L3W#23^qWbesg%_JKNZKP-t9y%g95H){I+fug)pC}mzS8Bfo
zxD%ywIMa_kJ<TlP8|~~N2h73Qa6w$w>|Gr(5`X#C-<CkiEk7GJsOCxIQ^ZE%JNYN>
zGo57ys~(qoJ27q`h$qmZgc#zZXz*9#8f~I&>5OH_nV8)Hd_s?u*zcy4#GOuj>_V8x
zV;u(Nj|epAwsAZbG$E4M^vb#;5gd#SW(i^YfK~eT;1DmgA&Z_Wme?^HL*wcL(}m=Y
zaMo691M;uj6i4bZ7=4d(%6=36oPwLP$kZsP6U@wGs&C1#?HW4zj5;Bp-UZ{^7rKD%
zzU~x7XM2$qiR~?;;bb|S=wlBJGnyJyldqawx~p0_R)>}%YaBeo=n*f&&(8p#CW>l3
zMCs~23CP6+M@;izGZ}K-&iRFS>O&}2ftiei9}Vo4QVnvXd|;T|*|D4X#HJ`&!S2<v
zj$HNMhLSot1KEjWkxUD&o86?ykSeOdUmfx$AQ{{zQNcD&$`lre9%qAJbw25dD0V)J
z@mxQWvr?t>@%?#|0NXfiNS5t8=1%Z238`y{{Pvno8NaI8&8emL<NPs>Wzx$z$J2Bk
zd7V^Cy={e@koo;EoxXnkie*`8!`vu<NW?3~bZZ=!&4z=Gow<fO6wt%?t)P~*(Jm+c
zvbhXGUrnuek1a1doAAuit|lDO$}o0i1vldNZ-8TV1wB1@W?mvEW1n41?5{H(#B`iu
zK9Xe4(AlN{rIEIPKGac(E2Gx3?eRwVz{n-q9&FiHp!_+k6*_Z(O%dgPPYf*hK*tMD
zv@{iI$^hpSsN4TSEhrS`)U|#;mUf}et~=!VYX84eGx%d`h34k*Jt;|BG)pb*G4R-h
zc^d4%P10rrHKW&3%V$#5_Po$eM(Q)sY1SlzE2qUW*ugL`9UHN1r}{+Wjoln@k9KYM
zLuR)3Wji`9ZrR=mLdpXn&q--1*WJSN+8V@mDPSb@TOfuOB=U5P^B@eo^Tc&L2uq7z
zn4_=B25HjlqmENo@Z-C@rAZe|rW8-;{p}oz6|22tU7Kjqx#dEO@9^!Lx3^rB;p|q_
zeNwmso?lbrMY<3bRPLpM^D17w^YkK(I^zqc9rUaUpt13F0cd{KgRY&Ij^a3g#0r%d
z7`n7kXrbM}4}-r-=5A;euuO!o(zdWpIXfdHd`ZkA@sZS~4#GSftaWH#CA4*@^>!#H
z5k#By#tisnKKnaV(HbDZF^d=*^!@s-sQX~AO&AY*2SzEQG#M=c-eV|_7qGyybgitf
zO09h@nvGIdU)ZBuw<#GF+y;#bZ?CkR3ZM;~k;q1?vStoktJ%T%B7Y+FBohl}y>c_5
z-0zo?HIQjTQh)$%FmL$OI1uULcvkZn9@?bz)+mTh3+Vd}@DpijU#3Jc6wOes)hS?)
zrlooPZ?_VoJODP#w1-5217+FR2ms8UCiNvWx3Xf`qkeVNy#YgZz%=xyMs@#_u;K|6
zXtjA(M1o8*VBP9azQNJEo}alnZqKxx`2ONU<$3=Wk%w|kbqm6=x@iy(sOC?R_U#*E
zWSW6SYF4qKjp;u2$=TUA)BN@~gg3d)%t?zZ;>^8KB-jO!cV&NhK{X%1Tg}toeVZ7T
zkB0M7R@7XkV8h#Glp`rD!GQfBU-PnFYEIm?e(H{vlK{a9j3~N;^(qtd((KF$9cRg8
zpz5$H>j6{%z$0$ii%6t^#ZTKOy{nEdhqihzpg|>y7v|!;{^Ep{a!3)jNMN2;7;EIY
zQ>Mb4K`tV#fZwO7qYHnIlPMZL7~WfzNZ!>5lOeX{n@DY^c^27|ucvLg?f0_y;IT9U
zj?Cveq|*jy!qLg3qkpZ%PQ&O<mKA+2BWHVP46%bGsK;!%$*JadC3E8hF_YriXR?b=
z2yuGZ*R(vQgDhNOgiMQH=ukmJ&m{t4TsqK>_ioedi+-HFY1ndQ5mR~QS!#l740~F+
ziGO~=F=DlS)ARr1N#P;SB@Gh0+w<XU`JL}=XIGg*2Dv&u{+iO<+;T&-XyICeqLoNJ
z`AJYL-PZ#$k^27!;tc3amVn6(1Q(W<-woli1H)9_<HOaiCdLk#3j4;)j{K;HSqaH7
z)%b`zOVL*zG)whNq8L=8<$Sc1#?d{m9cSh*hdG4OZ|y_b*QbA8WTfPD4ONe_Bpnl7
z03Y2N$ufRA)yaok!U$#JZ=(7XUS(;Bd1$18Xw?`u<YN-}e@R@_%L&&szL$93t7vbK
z8bC1aksQ(96m^;Zwffi#%6ZT)5ZGz;F>cwE-q=N?=U$VSnEaXyfl+t1so-^{2v=MM
zh#Dhh^8eZXECjVyBKKAz=I6=xN8iI8Qlijj;kbGG{&iSrp2AM^Okp0z0TCUi#&P9G
zZ%-B-pC2vfXxCc;cf{X64EgC$PAc&RmAO!PvWn=`yl|YFQlB?1haX4XuO(edS;d}3
z`ft0#ACk$we%3K<yPmJ1|7-<f%akPcz$l|6&}OGQ!n=u+id^4+wD8mH3z<CIGs#Sr
zsE7#VBt2`xg+*{4Te(vwPpOeKTWKL22g2Uk(g7?q{FkBg1S2!~4yuohJ<XooF4jus
zog3us&Ytg%TgNMPvAEE|^Lj32fRp7H_8v0Pb$>~%^OY~@dvP}jk_OwMp}KuvJTxDF
zs*UM9`Q)<nygyd|snpTlq>(N^^gwxz3E_+Z&cOlsDxq+$MIbkd9VoCB7%Y{t^W=tL
z1EPL%4ybuB06x^7VJ!xDmh!3G<g0^yK3pom$|f-8m|CltT>%L`TMzy3*%`{U7yV?I
zL(;z&9V?BsKh$Z63I}VP`KrJWuN#^4HG69}R0sN>dW$oL_UAazNG9$9EdW0HRPG9Z
zT&akS2Lb0JVP8G2kt;c==+19|G|^%lV{t~9EV;F3*hzoZrz51?3WUzeTeB3=2sm(z
z#B8chZ3FP&<UEECJeN{*e-`yAmir3NTw+6K4y-pNR*Q0xWibqFX^YkK>W>_$d7~W<
zD8140!FWttltyEBFK3u)MmJ-C+GXO!1qeh?ng+s`IZP}HJ4)=GCf4dk5z<#C-TJx$
z2D^(5^TW@XY6gk6C~=wgppcF{8b_L?lH;N#BOjC8F=Tfaf&;k7z9szxVadp7ck8jv
zY98k3sWEL6SH#zX>uP_8Cv5~eurx2aqw<H#HKX@cF-P+7LfatIBt0OBgRqN%!S{vZ
z7k7n0W_ErVv;`VG46IWIuSUL>*pm^JyyRMnl>#Ktbz5d;k$p&TZ7MS^2)W7@;sQ-`
zL<ghU=+8WGJKPiF{CGJ87|AfMdN;B&VjEJZA?4f2sGlvK%@)QJUtI@#SCs=BTAR=_
zmxodH&nyB}nz~DP#2e^aaqHRJE1jKMAsbb`@Nue<ZcRG+w=?78A65{z)fUzfPX;G6
z3(@3e{NiB_pjtkr3>nAhRGBkThUKu|y<;A;hZnY(vT}%&efccLzsl`VJDbm@V#vBg
z9c5vP82tBhh@&mfWHq^6Ih#`mPKe=tFF>Jhwf3TI^3o?xMGW{*@UvhG<9&}=5_}(6
zOyrfZq1({d+B_8h0KM5BT8(e>3UY;~rD<wOu;jT+EAC})EiY@rA`@EnxHshC_T4*y
zAip9+6$469J7n#daWDulLgh6ssGGD`c|4>iux!>2;$kD!n8Y=2C#J{MKQ7b5`1DN*
zi+B%|<Mhp~PEIEDf&@J4CVK=09ob2W+4;|<O%=1Vv-zc^<x?&qI?blm?yW}^2q%T)
zGFYWyQ3wNI`Ylsx1-h#C7fs=mbaakg4-RrWJw@`h_wCoZEvA55Hg2&@Z?BY21B@k|
zS8dJu$5u>v(|b!GV>nBV*z;|FVUWWbq)SSKH7OXmu9N|7hzj*WUB)@2FY$mXZ($~a
zP}VrzO>U5EtY=%bCcdPcy@hX}u0koEF^t>P^ltg6=BxW}E}jUn-K9|y{$e$QEpk7A
zW_zy+9b6Y2)QK`r45xlFNA~ujP~-C{lltbeuwwtn^~#kDjC{PEJnYy}WC7GnrAhiU
zT;4%Gzh$uV)Qvv~$KyF}vA>)Px8L4)oN!HZ?yPA4n25qx0~_KJ$d^jk1Ff?7-?Pn*
zV$fJ0KeRggM>c{v?99xvTwZ4%N#qhfFnkVD)8JOrYwry)rTijWUfBRg>$VWftK&<X
zw6d@B{aqQgjq*EQql%Ob`=g*K#MbVx{`Q=<^N$$2^@a0watOWFnQrb*g%20~ME;Kj
znt-<l-^w)?D}sR-!P<=m#WtsW@*OXMHPavXI2K}%FKxsuj_>_N-}f*M$hdkO%>W+j
znYfyxB9Pg2_F#ii2K0XMzeHI^7APQnoS;Mlzq1d&)1DX}pIg=2!@2{l!04TypPyYt
zpZ*cJb|{$jyMn*&e|<!eF5ucM<MZ-ne_u$uv$9~2`=)E!ZIXP<)w+GBJh-}giHc-@
z0DQkq1mH-xt1rn2p>W;w4NP50hZB5N?R;9CD`4gC*K{MI9ra$LKTNrAI5s-Dg4A;f
zOdoWRcL~WP5#YzZD$TbWk2ZH5wC*B!i31pFJ_B7vA<W>$KfCK?D|VQYfy?Szzu&#Y
z!?Fdb)X)o~Z``<s9VpIV(0@PPt7a}UQQ0T32ADwCBJ#3OD1}bV3H6J4Xs8$I;a-tH
zA|eE~)ck?W2V*DV8YA<U-smPH94494qVa>2ruM#G&qKYpSsW2O(f7Dd1JH;dNO_^~
z4wh@jA)MeJdC=N^2qSQ~u;i)Ri9w+?r^`XjVmy9@(7OOKX$1ToG6F2;_|VE&XBLY+
z$4SJFMdoeN+cI`MuM;};dE7Sw_b8BzoUC$Ar~(x*OGyBBK?jlP@Dpkp`qU>W>96Um
zhZlRW)a~!D{7z*R$^O|{fSomc<W!S8_FH8~20=3At!Z>yP80=F&4U6}ax*@&)O3`%
zdBFU5!`FQF{&SQ^BMh8vEX%K4eN8WaX;3^}6$pLoy=9PXLmYy&oisvjsnajvV*b@9
zyiEfd)Ei9Ev^zjWe$%hykUnO5`Ds-Q6)H30;vtS@5k4|ywTQQmDn7r6*L($1BmO*d
z9AV6J;=e(%?Yi#lmy4i6i05lHAD0i+o<^vUW4{%Na>iQH-JuskoO??%<T6OtTii6$
zrYkgcwEt*)Z|P^*Lw@#w5n7GWdUF&hVp-C#!<?;sn*3to5*Q-$b@I^%u-%Fn8Ro`S
z2i+uOQTT`B=h!bqGu>JHy}}e+b`CNn1t8t<t1_)Ur(*<YWh8?Eo?R2dP|DK~e|Cgn
zR2xajz}-E_MB)@~kN48EeA_XK8+07C@cU0O<u2mQ<Qw)C0yk&cs)_gHz`w^DKA0VE
zi}xCB!!sYdP!j1a-tAo82A_|Q50Sfr7N9r+=JbRP{_*>_Z*`F7@rivmf!KtSC`B+N
zduzqPDm}(oYL{!&NIcg>01QUtI`Jz)bQ*);*NB;+xjhb_d~V`z04sev&Mi1%PZ5ya
zO0nPv=<%Zch-^U@!7u?0fWuH;Sg2y(d1oLkEv-({3Gf%vzP%&>5Es!G5j}K)5$V_)
z?>~7Qck&~TkzClLwzX;CefRB3bsO!w-)@*tg!ilWhkbz|8`^ujKXwEpEadp)8M};P
zLqbhjHMxEZLis=4;AlU*$2Hi@5C<%+yu;1&{f2@=8CUGBGWs179y%U!V|o~LH<J*t
zKU(hb@bL`8|M4(u`m{o_1^t!pyTXp>pvYlz1jfN0!nAle$Jmzb+R%E7fby2^EgEbM
zGONqW39HXaJm+KFzdh8oF)rD(@me}fsN|VGxFj7@Eg6wl3S~D#pLWcg3UW?HF(CNT
zl@i6st+=;`94FG<`2^+p9@DyZk**?l@ih40bR496fWoEsQ}TQZs{}f@r$QxNO4-2y
zWf>{185sk~`^3^xuhaPF(T#92)1jOvL0|KOSef?Q4aWDcA>_F36C0QJ<C163DlSjM
zp3)5$uNoKYv9|*oiY<PcReo#^h?>H+bl61~B{7CL6S7=X(&Ea;+fAnpRL@6-8M8Xm
zDeIqHN>ql<zYbgcO+z<?Tr1z4ZsLn_)4FeY4ff2bm&c&~*d<Z>5mWB^#!VimU`&pD
zyslAbzZ>^VwgW$I781tD$jfCk=%+D$0w9jJ2^jj71)%y!q%)$MHq!w8yI>j*2RzD`
zFYzDm%~M|pshzXU&&D>s@Pn_`QdZW-M@L^r+d+LC3o3lC;L^$@o7!#-m3phB<Nq=c
zd|(Bv&aVPEF|mVl&Su{7pLT8yMehmr%K*;&wT#)v;)PLU0{}>A;z=ws2Ko_GD(RIq
zHDw9&VG^snU3ve`c0C^wu(z?Iwtb6y0eXYUB(Ruur?ld;6blgx6hJ3l9EWFxHCfOJ
z!@^B2$H`55)rL0b1PU4OCv6ce4r3dh`lF52RPUb}0V5e-(2(Fn=G;Dl`@kFTaWZp~
zt`hkbg8)t3G|7_HvwHhYxMX4l(+BEMJq6YbyJkZwLei;=Hqg7pLsc+me38{KSNGPK
zZ`Lfjffi49RPe3&qw80Xmv)D0&EWCk>y`az$8ctGH+tCB1!2{rhmpXMN0AnZVf)V^
zZcjST)_->ob8FK#8d6347Zd@CL5R1m37S;mW5d4hI}SQ*lGS8;B~yW%4-Hk$mLLsj
zKY1v*Ctmo6u1M-WachkQMfPng>kY8RRDk`%=7apE7DHH}dK)qT$Q-bo8R(3ZY~EY+
zTnfuy0j^TF4vM17ma7!}Qh#4hsj!aLTuLrLWNru#U}Y7dGN94>6vk=yk&2fKEJ?WX
zpF+o}a^=10p%w3r{@R=wMVgaU**GMk`+`cJ%aJt_Q!_1(9$beY`Iry|_dF=aDHl3;
z-r9Z#Yb3+AdnsJ0t}mBE1aaBQ_&Y7!ADD<$z9cp3H7LgGKM~;pHO~VKH4Q$Tt~W+U
zLUNZ(c0l2TQxl2W&`-g5B4Yn786wOwu`XZ9$2xat!hoo+a0#$G7T))8?f(V<5|#wS
zB~}zoUSd?`rsZlVn)ZxHGfZek`DpFw1Z>{#^b7hjPx_JseV%S)N(AC!3eWtk_(EMJ
z<gz~k>z76@kbT(DCg{xQBf)n|6|m>kMq7~K`SL_%KY4pou9Zl#wptBUG|Sn2aQ6>#
z)lfUwgC0fL_Na)=W?MC^6Ea`n+D)^kd?p$(?7I^+12XH*zR{ab{-4RT6VI&xm!#k7
zuMLxVw<QtRmFNTJqRQ>bCZ-`6{Z5W#xDHCk=#~(U7b|)t8x>lkBdFSh6$>Tb?%<p%
zx55zkv#PF!zmXbbMg|WVKW*+2&Ne;;xZ-tq*~ylxoGtSni;*rPw5d8d)wfrxYYtWf
zA(7cD8yY&qj?yS7j3VEfb>GMA(<2-&{5o+xtG@H}6kAjlaubt_+aDUe{7(E+#460q
zaSqG_`>a+hHd%0U3SdI{2mEXBtaz5@sUI{e0dO-Igq8Wrfg?SZavas@|H$52CEk~o
zrc(|*bK#p%?YRg7$+|Yf&g*Pdjv`loO{X9={J;&zpVR*q)Y#Nndq;|^ry5LTM}&H3
zDK;}PUM?^+%ny0q`A9LITkLzgiem`uc4vxjoXIp-bau%u+4f~Z!09H3Hd0<{dEO_l
z$gO;_us)oa0wy{1=u^pYT{M+Dk6yG2XSL;JZ0{=2lA=ruula;lvoCRQ$nfzA2#EH)
z9O<?nRR9k`Sq>qv_3s!o6Cd4=LQ0<KwEpfl=+DP&imMYfKx0Z~q34mTE86xmKZ^7`
z4l85aIwLq-^X$Y-DiZ&PP(9b-66g13&8WCIy9Ek8vy$xdgcR3YuoKAB^SQ<T{-q8*
z9o;#=s;o%FNLO|4#@jaM50_j11AFKY%=%OVgl{&!_<ByE4chmg6MPn0zn0M-=2w7*
zGn_WDjGo-$R0fzqKbDoPqx6%(_yl~<Um&-}2S?4s&o>K3PMSsF;C5^i-Rk_O*;t`B
zvZK;0Wd*HOvcExxZjEOL7*xa1B5T!&-;TE4^67d{3?Ka~>Bpa@rJ-}$+AZu}(q;`}
zZr4i4+9FujS`?dr_m*SEZ3cqg(;oHg%dwOzqHtxTS*6#f2sPV~zgwlPmj`ymJiO>T
z#hF9n+*6@R|87#Niv)Q}Bsd~;*1h<#jBX^Cr%Mgb|D(AjCMF($Ywgtn9>Mj)=4(w>
zUjdJSq2#GW`T4?}_8T-pZ`Z+9x~&SQMt0%+;0nY)lcBo7``Fref5M<U7y?D;sd`g&
zDXwtRAlhn{1gmj%jHK}<%-1$L*C*ptL=+;d>vyV9pt;eV2O(J^uJ(@vhOd3~i~8Vn
zj-&#@mW9)&+`=sMf2ygjQxLf0$Y`qvdkPpT4u<Y`m)B7ZLZ+9Mb!qsm#Y26r$Dm`M
zf5Ql0BbIat?8V)AIVu)e?dp_rj<hZ=tdQn6*5F|8QxChpPrG40Bf{E)pADoExF_^_
zkcz2o{3ABLmLq<<c}hQ=UFzz7&Xmw}E)&tn%2ji05I1FVh~?TK(5~h-5NGv6o8b+H
zQzk#JQ!Ko2i(JHbWD~*MJp|tQ32xJn9;ReP4xl`If0G(`4KwY12#Dt-z2dE)3-syI
z%fBF^!dgOVAkeuxL;sc)MB7|OjDNtmFe)*Y?_qdzQyeGS&E68{1HxiH>|v@#6zoPg
zRI&)gOdBMmSBe4{Mvnq4<-?+_R5llKj1uhx5vxZxIxtJkxGrhqp|sUi^deshCv!bG
zKYA`%xu+#19#G-|=}SxN2BRC%e0kOS)CdO8d5dN8Ph`h21auA?zTs`6-wr*BaD9?U
z2n8V45<)r=dC@3#;HyF3jkjh8?lpvS9_$?Bzjlq15YePMu+mZDyeIO3Vg>ZFAxcHE
zkck4wN3=p<;T0-u{2o1G$Up00uIZ#OfaeeDX+Qch``Do}Ar!dwb;YjHci%qPp|H*J
z);18!I!lyI=zWS#O{H{MWA5BjxR@@fdj}NVkzUrB0bTENaIryF7XTC-OCax9bCHOd
zg-8G`4j=OYt?7V3^-6yS#p%=SD$6a$%f0izg&(tpmn1vi2K(p+M%8cDGBm$?Vj)?J
z#=R6m{NkgWz_6%ZZ}A3ss->sVx6}Lp{fJ$^wWg><EM0>51?Ry%Vf66%G%_+!0rw!0
z@wyb)Z*Y5J4xc$mBmSFyeg<pp+~`Z`nvA2(+;={G!IuR0(a@&}l5jC;4l@P1HE4Zb
zKtN1=!`ROZSmm9@HGO+LRAeI^?;h0eKCzt@4^E!jUoz0}C|)NjDcTWBSB#rWQ1SQG
zuRmo;Gg+_Uo=9N)4@KX)3!X0+jFm2p2#jw$Khd9Z1vv;|kL0(}>`AV}BzPmt=gtN>
zxe%yks6$@HFB@c5wN>C}yRfOY&mjC-X?o9otPrl?^mK&xUEMPuU<Gt#r0tcks2v-4
z)cF4;pbCIWdBgv<D5W*bg4Itigr)sH7qUsx_na3#)EDlZV||85WO<9Ug}=<*NC^Rl
zW5_R?>E+PN5;)T{41YoP_N#m*4<hD_(CKSdY#ZY|Iwy9Lm=^b>kEG7qe!dT$QPlet
zF*Yvl3M`b7?q;ul?>zry6>~P-0-1w_MQNnWc_=|27+iz-#Wurlxn>j0g)H2H3S-Ng
zwWk%)xxKjL2|NXd0n`m{Ns$v_5SoU>xlyjZ-fV&*j$_g#bL0qsIrkHJlcSLge3bhI
z$0a_8u#~Wu2iA7jpu`{HF-%JL4+4qx0@AnJZ(1g~ZD8Y|Vq@$|^;s;wVOHFpF?Z+A
zaLeIP&IyC(-G)JK>Z)sKIKxLr9D;1_3r}R(K@G&Cd}2;gQB#I25O@$Ndwe>_%a1Ph
zG3xS}%G9G%vG<p$73wS!)y+$AYwJ`BHG`BNIOf5d{&E(n-e7*__PikA4(7Y$EE^lV
zAjx_%N59!F>BiL_i!0@kI$2nx^4~v+x@GllMNE4PS2SZE>N7}tKaTJGfV!veaNS5~
zQzicF{dohy^JUBSeT_hP6?|rP=3+>n4{2j$BG&%nY|H($P2d3RxF<L)c>Samg>7Yu
zYp%jHedEdQ`p3=RZ~<MkFN7xzv25Lu&(QX-7?I}l7IG>yK){C_?!G^Em-yvZenz`j
zhs-w1&4XEWrn#pw<qK=ioMbq+esLF4A(dZ}&#iyCg|ZoyI?&<ja3cDQY|?o2G$a!g
zH@U$GO~LRT`J-f}r#TKWi<tBWz|jh}m$Vz1syynjGp&*0F`z}1jRG`fe>e5-zU!}j
z00{daWb^Mr4N}s0S{Q8}Xlo@yA)Xher*GLelZm}{OKY1qQOoD^o4JnHbdEB%9<sj|
z0ekaISG!_(LIsdOy?g8B?nfhG`teySH4s5}bak_JZDQwKm;BqPly0lqO5UhlYOroy
zynW$J)*m8eKi<3|Px|qiG`IrK_m#LJ3cunDBn_g&8TZ{21osveyDjC7T*N=oRwu%L
z^0p^ZqCW6V7xG$(N4bf&(RaAg{EB$o_6{;Jv<%p?jay=?(5O!rvyXfgg1T99FMfXs
zo5|);-2w-?5BFaxYkO2MKEPH1-7T;~>F_kXb9p=|=_lmMM#(gbzK>7We8a~e$w&U1
zjcal^n!VJH6opdA6ppMc(40xr)pd{h?l02SKfMlLim`UMs8?%B!m9!t_9YXD*u*D;
zk2*%(9=(jBOP6W%?NNP$9tX%bVO^`zRhs@>(URM1IzH&vqd63<u^~bn8;qlG;!(wi
zYQT4<3jkn`Y9G#X4Y~$l*YG8YWZ@wA&}u@mu%!cn3fuX^*jG@Z+uMAk&9U80!4NNd
z$H4%V>*uB@b@JRjGuG?-nk~n&ndbyVp~I%;269`ehK9Xyv(K$+hfgHQW0lD`%J*O2
zQ2iX^KYfzvi{PNg?9z}hg&c4^U(YaCGxY+GcPWl@@#)?56_nQ1oeMVKAGzEijVxZU
zPcZhVb^&T1H(U17{K6#=z$Fs6gA6F8ZQ08X=ltluB)_NlTL`Fp<7(GZsU7^f4OLv~
z3Z#w~bmTUe;<*GB6O`Y54_jnm$Ks)dCd-?VP&zEjV@Bms@;=fT^FA|?RZooq-16&p
z4MC|PMWRVyBcnl$0JQ;`NbF=}w23F-M*+X}js9B1)J@EjLjA>xs2mO+Ce2M<sb-wu
z(eq4Cvb8-X6w3TE0oV8u6r9;<eE&n;>*hXp7D&G%>E;q#uM`LwEhuT>zYAkzE#{LA
z8N<8pNyDYsP%tOKoMMXhGoX@uZ@-|x?=A2xXX)^<W5%tVy+r+Pacf~|w#LuXq5hnv
zT$5_ByRJP#BMmR^9X*RxUKHbxs|tSgV(=2+hqZxA#Go<+XmQ@0Opm+=YHz-{@dcGT
zzqTJqWD~jyuNEesLzjmkL&=73i)bds_scwKiTwO%cG5Kw0YQ!3skxp#yNW)sM}kDC
z7yQGQ=G~$`w$w@_7dX6-7&Z8(qm0O!wW8@LWli%XDRrVPt0uY{o)=H<v6H#C*xSxO
z)qJOiCd7V!>1O(1D^FISNA_rXzECTc)W@nt*Q&B}a)_HG#VK?ZwXwPom~FPkV-<N7
zz8m5aRNO8m)z|RW0hznAUwGmI0l%0c5RS_;v`~)r5#t3R9|x}u6aqh;9yFm7JP-EG
zd2(*L^x^}oer+$Cg~%Q@dTFRRzOu3eMf`iIBc`_@tpdnN7+?gytFM=ha_3!yzUj?<
z!IQLlV+$B)Ano=`9v;3iw!3WfdGU>+I90Qm((x@FG~>Qoh&Vw!)q{-fftq~^kMJJm
z<X~))+l3`~zc&U{b(;>|CyQHe%q71WI$aZx#iBJ9JMB;H#eYUg^{(H<$D!<Wfa`^=
zw<dU~*O891fN7s)N4?|{h&Y;>Tv#KXH()}-QmIYmL@xg`rusdL=?HCWNe3Zj>g@<w
z`$;JQ<yY*GaTk=TbLUuKJA<@8&PX&~H8mfVB*<Z%({dzOO96BPo|O}AYT9+CMi~&)
zb#*KEF4tQX6?hJlIR$GNQlQ4EQ6|!Q-)^H2Nd)zlQe6D)QU6viivAsppv!$j=}G<9
z`=*~MGqK267;9blc*uV%y8e4)9$>duD)37AT+vxF^WG}GnCH;%A@aK8HuR@*7iHs-
z`ed8w2gmL7{B70_yz06rEQ$Xb_c?dju+JF-*IE^<Vb9(kx^|;|aFs#hbd|hFb)vuE
zCkftcZC*6qX1#^pcR9k3LkAR~SGqPe6FLr(UY(2W#CHtyqkmQacQ_i7g++>T2uJz6
z?W|VY6cYar`3Fjj<XPT1)+Z#Nf8)KfEW9Z~8{CRF4aX>FQhg6yE<g97Om0FYOhDuu
z45Av<+6W{^;%$mk`&7?<&ok(&)FPMd2qf5if5B@SYsMvrOqK2E9YqlK>AjAq+)h#(
zA>;G@L$bI@?rwEN%XZY@I#>|PuTL?LSNw%)$ke^H*F;H0h4vMNFnQ@3XS!(v&=-dS
zr{DQ_9vbnGXA8NU+Kw^QV|E$J`Z}q7bZqXq4dPiqyeRc_)qPVau8R~&r(EPKAZFzD
zg-#h@cVojwA*$zVWQ!!iBR9V3PfSw*2%Tkzx#n3W6l_2BpbNB_223V;GrSR}9wZwT
z)Z97il%CD=98?`_mWXw;-EuKm$Y>kr0trb3Q)&O`V^K|)Zg!7W9R1U^h1cOTMfG2Z
zdOxgs_ML=M@3v(T&7rg?OUrhVEcSUb%dC3lHYC;+oM+4%WnXwZt$zKbaf-_~Ap3DU
z&KyBF>(<)tpI#X)$foqp*ujLI3F@RUC)m=*==QvVz+)b77404K;(!0GIAI*oQ)JcQ
zJBumwys$DQVw9TP(XH}O&Gf0?TysC(Z;aqWxIxck86W<~AxW}_<U17A%1?%H0_a>`
zF6BfGh`Og7tDE5PHF`{TQSaD~nk!%bzgK$b<ej;R050WNu<zZju-G-r9GE&D0*^6y
ze$P^Mm~aeNx1zAULO6&_#M`8*l!37VelTNT(s>HW1-RSf%e%qCBscY2D%J8|i{Jb+
zyl3?0CO&-u5N-}->^K|TJSUWZLqqyDlluATZ}<@Ca*(kE?XJ)Y^8;#VK0ZC2|KiO|
z76=2);nIy`Vy=@Hc8awBSnUtwf7hj16~AYW+9dtnKHb37tQV9|RC!t<*~F_7Y%2*i
zn2VH)Yle41Q}X~UiJ*U}%XlwL9DYwf+{%)XwZe|_fEJW8Lt1qal#y@9YsCa&1IA`H
zIA8%b!r{WzGTzYsCZvrYv;13Wf%Ry&e@btWEmaNq$wwwr{KU8@v4BF`TMT&5EA-Xa
z-@ZY>^nt6Mv{u|4d)|fH-)^4eSzUj9VMkUbup~&N#~J&s2<f%fjXlm(k#v@)70knN
zIEN?*9oCOX4<F0L1;s9JmGJt}sGqCDtTKX(tYQ>vV*4U0|IpgQLqWnYBhrIQ=Q0>*
zAn{J5^adc_{oq`@OZ@|yNNSA>d*I=svx;sri%-`ho9Rx2ZqM)0Vk31T5*=0!z=C}0
z`Xcu2V^qj|EOUte`Sqg6jE4Q2HvyKZ(<@dRgZCw{w!F~H|9<v4Iyx%FFxL;=dA`qq
zk~s$oT1L35bxxxSIO%!*-PXghGb3$3ptT^{X=~^vyxJKFlvLyMz#}~|30h+@6-}Md
zbC1~&{P|$KfX}7QML{{s>}RDI;p12)vooUOV@w(Ns@kTq_eDutRPjb(|6TpP^OV35
zL-IkMH=xXQ4*%aMW6(9bhGmo7$|!T-DMk~rSVr^+hamZ>djGM^#<eeg1$|J30g_cb
z^i}Y^o;(wryltmQb$Wfj#F=~g*rIejle#{BqXx^E5*_-dbytQkf{Qui+fQ!t3Y7qy
zYwKrT__cUKin{&5wS`d8cXPR7PLf9YqSQDI$&wfawDgH0)`q7+$8N-ArXO|;S0HxK
zvX?WE9}koX)4ab9{a1R8sM?cR-zI}Sqe1Xx!RYx5HaWIjaT3wt&{<3IP`FW!P@GrB
zKjzXMo{y%4xRR>L=Wm&CQ&em{6VtScm0fBS4HDe)>y=$i!8}Yr=XCpKp1RGl4juC)
zszj$b14xC-J>FX<t2LiW55A*5cD{8KPI9B_mVW<=HpDi0*+C~rPG;U=Lpp|~)p?Z^
zDh2Yql++ipUn1{ocLEaMe#LOOoooF^qr*G?_WK=-71M;6`S&_0zu4Z+GXcpG)$aXh
z4_No5;N;Yx8dy4=NTEvx>Od|G5v!=G9)5mYT}_B%*JF1KQT7yW!4)6-JSVGC!5=q`
zCXU3Bpj27_DdA)?yjC@94H&@DP*(d5Zg><FRr4c>hicu{@|YufZ<9R*&+~f8sk`?d
z!LWm@YhEtuZM!}<dygrHoIO{1hNLV0_e+`?)$JMNk3mUE_-)gMx6Q@$9#WY`tXZ!h
za1a9C?;pm1($Wk%O}|*=PR3{SxXQyitQF+nf%o><d)LJGr>J>7O{RL3ZF7qS3~KUv
zQPY*#eHTs3v{N|GurOF)MxzBG=W0rtAzANRiwd##E#6%rK41MZHe;gZ=PT0^gf?6L
z_?={)hYmPHHY5dYFdByk)3|+SP9F<8Kq1*iL)&GwKeuf6+giHippvX*@=?A0`gMsS
zmuM@Bsk>s*z|ikx7&LBY&^Y)vTpo7Wz%_L06|q?R_W1MO@cAP=kHvu8W0O7#f`5VV
zB|k9yO3nR3TBTv`Ov-Pgyp6D@%b{<mklb)TdKqnw{?!+S|LbhW<i@zF+t0~#s@AFA
z@zU{MkQcT|IQ@!`{i5G*A39|ZfVI8qOcPaje}MAKGiNlNoVz#mEEBCBS;G4RrO*{J
z#vj{-Q4M(IS%J`jQ4KOXtvDa7gS=c!=MW^^s<qklBtj3*XyC0-DR9~qG{u%YL+u@D
zn5>OR+Dv%WdYW<}J%7&~-<asWtol%asa}F~dnmN9z7%0}6Nk`f{&1muartflk=64(
zxf&QbwaR0H{n*Dvj%j3z+~l+u(*KbXHwr948D2x`>h+xQD}cApd5L*Up!O%KZu)c2
ztULM}8pYG=9@o=j#BH_rN28_itkW*h_-!20zAfd6o0f1}*8G5PQt708a#O`WcbJ-c
zAH@)Ij(sbWkAf0%v%gkrVO6mc`pO%mirL+w$hOu?j8`=Fis4uNY#Se<Hhjy^3%#6E
z)1$~&TKgJtHiU6P@Q$p!UjHodIINSwUUcyb<UF>Yv6)?`2=Xr?@qP!e{ot&Za^Uo0
zvf$%}i(PDZwT-MF`@Ju?-xbJnoJ|f+WUKJaTiM4@4V<_~b?$&u8kLjH%1K$IZj#u}
zRnG*=VbJuHbv|4^^_`JJ{oIKtn7KY1`8VO)@ITU%N-Q*`;|<SXDSP}&099;vmF6d>
za^Orw5kM;TNAP?0VbDSmC2Nx_)cW_qU`hzVI+yyJj7t(-{IkP_yalt3S<*4_YK3)7
zu!0l%*1>~w#|)W@ycdbJM=Hc+dD0y8g*{kb3S))JB>T6Kb#uEEjZdJ?ykB~S-*riK
zBC<QpfixgF>C4FD5N$i0+|OlWeifL;<Hd;@2WLobJ!(oU#@k2n&VxXL@QL&v*GV3j
zVQch2nt;CXj9KR+eq2W>1?3d;8}rR+5zm*Y%|ydU7%5voz8v8W255Ofx~t$&A#XMN
zQv(X-dw99Jb-CsbL12NvR*<-CA{5RsiYOBFps_s;iu3@a0Hz`1m*qj6pH7}caxMGV
zw~Xb#;?smSnH0A#VHoUC*hVWdsh+og!W}fGJj=Lc))A`VSrKm#l&)me&FTfSVh0*h
z?j^nDBS|gX@c!<X-}bI=-<?~=pzK7|V9MO7K-S5y_lR48?b_L2#Jb2QMymLlYG`??
zp8*?|u~XG$@qCA%dmwIkkMesifW!qt@jA(?s=mZz3OZ%U?*RmNL$~vqsI&IZ+;=~t
z@gQ)Rq|Ei3V3D$xH}3Z5llEk5t4_}FE2a#|5_`CUr#9;Pb2y!*>sLK46M+gs86uBB
z9nSzw1?5+kG8pguMT)@9zgZ-!@zit(_`?YAbp2ANJT{+goDtKQe@Qyk4CV^OFhGVL
z)AvME-W*JI{jko<`^vz~`~tV|8EpQs$SxzYF1)P7m9#R<FE%46AX7?0svdIV(u{;9
zuc(oMH%$~(Q-z$~y-M-9(cG^2nPzq!a6ED(WNdx$$V16omA<K=LKT5nXn<kxG@AC`
zOhDlcQsljR{Ocbv0Q*lKJl*|CFKY-YW{t|GcKW0A<P-x*brjX@ibj^hsV6V~?k+Ff
zIEwDvm-eq}iUUD-w<@caTuX*Id46gl%*&$tl?Y*qkz$P1$6nR)uEV};Os=sjrMxY~
z9E@+W^Mm)iTExw`rQBW2>fwN14S@?WrZ^2hhLqs%i5=mVK8Ri~AdKpmdPE9fm$srh
z=S5g~HkBZ4%gwPPx+7vp>GeTeG0Xk`1Fk?(zkQMNBy=jS(iD2SILBS9ebY>doAOqO
z?D;=wC_e}#B#cucFBI1ikKnF#Ydu_UNB`cOIp+Y3O_Wl&CnbQZw{rrv+1UWy@jmN6
zmWFfD{QP_cT+>z74L95%FTC(CDJm*51He>9OioT#g}1~=2;#=X#K;v_T%p>XUOPQc
z{hCKxVYb5Ujc1*f^Y-hmn<p>5^s-X;)4^*3*PM^*eEF`h4*W5U8_Wsr`}%6*yRl=(
zD7Rje7kQ8vmx_!ereCpQg-n_>QMm+DfxYO@;YanosG`<YDPO-$Qty2NraT+ePGie{
zm@i4-nWss~kE_(KR~J8D@t!bJ>V8}!9<!&3lZS^m*46>+)`)`+`h3WK_BHYBA15JK
zoNt|7x39UAqPI{JXYqE>jA^H-ojBA2)=d{~+PqDHq_%udzw_P_`Q_IY>cu5{PKvJr
z08andT$JmK80WG}E&_->N*;Ll38gixbsF#&`r^wnY}gPPYNr;v-idUSXE;n(Q9GJi
z*PFL&Q=Jk!n{NP|cLrR)xA;S~6N$XlAV7`qFe%3FT54TC^5A_PWot8>9U%dK`nJ|P
zOeb{mcQ2T)fN*;jE(w-D`PhRpdC~;<CEu$y3fSwjyE9qYEc8hlSX;>*)TWVU56ghN
zd<j}-?#;FfxMudJT|Gs1EZ-&j)@cKU?i8uMx?WDZW~z)jW4PVPQ@5uHW#K~eS4WMo
zLtn}JsAX_MF#htn3}yP-R$)JRDotkG5Fu_@m^&8q)$<u`vc2;0xK2T9%LMjni>K|X
zl<mt)%t_t7o#g&6hj$8E>j~Vj$ojxn!)3w1^-^7?4KVj_tdQL+OFIQjbsqK}<3_Xv
zF-!A%&)C6U8FVFHZAFtjF?)yn?Tg_Zww=<vdU^DW?UKH;?V@^D@;Q>Yiyt^-P9D(5
z6{h>)rlyppNJZX%=n&f02=jg8<iwIdaB#5Pe*5h*fBt+0Cf{+#9rFD1&&!l4Q&dGb
z#?y%!($7BoEOu2dR$%qCY10%4t>3rj$O$}6i_@IgkRe0N6JzGXAHj5v`7HCbhp!RG
zkbtEGzTG8~%zc(nrX3U^GL%19m+S+D&`9hNg6quVmYN%D<-Z@NU@@@m_yQ9o{MR&Q
zTj*013xTcx9?v^HvW=E7E9L|sf*RP=o-V7x!aUNg-T@8lf@+uz_qO?dac;aPwM>!}
zC=P#h(43h%%3rRU9WVagt^gvqm@Tpuntac`vkTYE$wB$ge&+7cQVTVAFZfMnIT?}W
z%wPIoFU(eJAQUyj1ALri((oX;^o%&^50k7OZqAC&K4@kCuqI1>-kh(@R8uQBJ>Oa0
zffjLOh?j%|=<aD^&(;T~WL2eD7yPKv-V8Z#BW-zAqb&S<Km1hVTL%!Abw#iYi1UWL
zV`XT(pHOSNzP?!w!tW`4cQ0F;E9p7ZEbn-ofeMe-1Uy(Kj|!D5&We{I;Oh%h<J6-5
zZF8Rd_oKa#B^D7;hNQH5*_KoUfIAY;SYxytX5l~mEeB=ff+5T32-D)eFi#!@zuUo(
zA2u}LnPleID1Kk9%z)N@BTR+&l`|&wF&Cx0vO!+`WG}Szp+Us@;u-q;xX6Xmqh!{E
za2Ws}-I%5+86~Gz$Y<CHu@a`qOW-4x_07k|ihsPbTVA+#ocO|TcW<I@`+8RIh$e{z
z=gyH(C@cK5W2eO6yL;oAGsO>!$i0cGu#WWo=B;Rp+B~C8-LR;=YS$jg!a{#!C+^m4
zS%u&lfk7QhpbCB0rouY8b5)*X)@nYUxlM>a5;vI;ltwSk?f~O=d{3|mhwE-^rUaka
ze>f7I21rPtTC=o({*XYl0uJdbnH)5}O)4(<p}W(xtsS9ljXo6N)Vi+4p8?*U;)y=V
zk`6z<BX{O)nL<0))9FVQ&m0YD%;%=~JeV2bu8Pni>Da*Y(8?_Mj7GU2hbT;2Up*^E
z1_Z*b7loseY238rZ8_`tGcM27T^#jm#RGwQJl@yuk+*)X9~ovzdOC(#p0jBBwU$R~
zxh%u=bH!6_7GpY7z8X_?OYu;7s6Z9g%28cv`Kbn?U8_?~DCV-5L1io1DXuE6CMS)9
z1z()1Bqo0P18n;w^|Z{H2I)~k6NqhMZDwnEt;?3F6qzcf4yOF{Z1pI{@$eFG^o|q9
zVGpX!kd&iCQ>8RzFB1nhY%UC%Bo4LNQos3+;?h_ErAoY7*f8mt{hl<qhl=ySyHp!I
zd=9_g-7w%Fx86Em-hA^dwRu_xXnE9$?-A#t{EZql5;)8-{gyH-e$PF3tMt^i*6<n)
z<YgqL)qk{XHgDdwX3$q>;_U3A;^;VsBl=@**tRC};oO<`nU7W`jlXqVVjmhmt>rVV
zi<Mi=L4yVf%T5jHRJcG<^9sC()D*WQJzrW&x!{5eplFGSl!*v{&!2ybsy}^v^L^8Q
zjKZ|MwpAaU$BBGADS_+esv1eT?`dhSs4|1r&M>FaR91mO0w$iAJh>;tNPwIwwcoE4
z_fy7*$AG?4w{4GvUw?@t-*UeiY*;>c)vcqUq(1nZ_)H!r?lG;C63ckTxqT9tHFKIW
z3%w(8mwEyDQt#cD+)e}d5KKb6`ObSP4mq>axbm_~<o>_jt6pCFa?@h<jgOZXpMO?v
zz2lF{6gFS{DwtDz@xQOiEB}2#`TFS!zmDQ^$IvxbU9Nm->z&PZ)RRCi7Y}U_>;AJD
zoX1Xl_%Eo(riOBuHD_n~-S<C2)BAFXkB_sJ+-BI44?l&8^!;YpQPcYN`8Uc<H_Wpc
z|8Rk`&pt!=GwA3@fD1ZDlbRZuWcP|B8G8C)yRvcfbd!1iyi#7i@pVbvk#1KS%Sd%q
zbu#|^F>>j{7h#dmG^(|X(82kx->}=z{^%WW={)#o%e(Uu9NQSB`StS6zuPX}hV^xq
zyhGeTAu!ioFW>z)6I#Bpzk5N;3+ol|t7q&N<|60c8?9gV=yBa=1#S4vxhqNn`*dyP
zo#w+Y_;ZYW@NkNm7x{Lk>1&Q`=Sfi(7f)Ns!^ifwq&0?dRu3Tf@8>7VUp^it6V4BA
zBap((T6q*c)U%VZ_<sy&%d#R#THUJociF>n*kIHkOCHkN(WbA9BzZ%b44Bxtm5gIh
zd?U+FB)|#j+_`g=#`ViDzbu<KZ<Z^syizW@=pyBtIy^jFXhwYN)~)i{XP>Ef93yh#
z`@|DZw3~=`lAZasJ|Q*-7$=4VdQbxJ&Lm9$t_iO0&J{_*C-gI{QTyQcr<o$BAKtK3
z50<1mtN;=KyzZ1R2Bl_hZC$e@9V(N706Z;+v8Rm<m#<f5%1)TC<{S^J3i9o$Oj&T*
zAk0hKb1ww&`0J)z3~jOCgb1!KPIAtaC>cEvYU>u1SFC}WV5&kr0ai!B@2EBXur>#I
zm#Mr6b@&1NzU`8J0DaqzlUU}#alYy|epsM9^Y$Leff@AX#u`}%&H42^3*_9>qLkLR
zbsk4@O&NGAfK?X;U9U9@eMS5FxXPA2g>nEIdD<oc4EeEWc-Ev)x$~;Q5^CWq9Bx8o
z%-}%vn>Z|3{td8x*TE_c{Iyj;O>E{n1c!#g*YvY6jqYZl4Ic<C=Zj~?NK$GkwCwW~
zK;F_&CqHh?kyFQnY2bEwtt|X9*#xX>YYzmiAH8KHe4bl86I3=T%u}wJ6EA~se}8(p
zTsSS(Tn1{5@7P}~>#<3)xe09#^Y9>F7nwh|zs$8Toz4U<u))vr4ow&V|J$k9WRWbD
zRoVu{q?A(m27u7jv*XOXd-VzA)FI)X7aE5S!m_<xcIs61>oCm;eq;UKeR2>R-1NSo
zO?A*;l-AVB@~zwDhBMF!8`Pr`$cLqehc*{U3ch<D*kPjpXsi+wP^R5ZehXUw)V2Wh
zBe2{Ipqf6Yc~0QD3GI^*Hv4M+O#c<2t$jaQ`$@KX&g`1esGX7C1$^*TP+&K4ADR-T
zyACcHoeB$jOv!GK<35tcPo1hTeGO8-uia@MPY>6ROxIBwhFj;ubO)f3KL6WP*_n$z
z3vnh!y2?LhMag8ijUd_Lr-xbQr}NgstdEKr{^;>|t)J`s^fKy4J&u0gGQNJU$I*|L
zX?d<+Tk~Li>o7g8_Hg*r^=Hi37KC8t&-j)x^fDn>BkTLBCGn;n!GfpFQ(N&Erdp3h
z3R66`Y12J(N^@bf=qlVQ%z}||+64)410OdaawAtuZGqIUohNS9dthZBn_>n(APydV
z#I?Byu4Xi*Xfo1(jeRY-KVrupT#-5)UvMAiv48v9ee&zCzhREMMGB$JN59VcdqHqE
zIyzdWPMxBBJQF;&B;Nb$U;iqrSFeKU@KwqZ8Ljj3z0lL1if{hp$&+p3DsgZn!uj{Q
zb?X#{LKEaehYqpKLY=cb7hZUQEM5Ao$|E#1RA$dU+x$LTCKxhgFhJ(>;ErpfGSN-*
z<ni(GmQmDsKe)bS-RZV#{rU}(kpU$k%=LBPo@)a>K0erBK3HbVm>~%X3GH}Li~PR(
z{)%;jEwXdxPNDWS<;VI{R+A=8lDN3IcIkRUK--qk8){@n9r8b3Ds`K8nSENbb9E01
zlAzn?Ny+EmOUvFAsoK6rVjumZB>%Ebf-gBoYH%I>#2=+{%PweJ*Ngj<aZ<5<t65E-
z+0xv&OKzd$yz;KZKl3*`N@7ImNCJ+SP{!k>OWe8JJm4sSA98E@;G<8K9|ZMbM~6OQ
z_)xj`o&`2L73;LNuEWAY<*`TZmz!_B3mr+d>PXhD->7_HPns~^R+4Uo^^K1ccL3Pc
zG|L2{KkZuVn5AE7TeKLytu4(!EV%7v$<E1@MQ^>U%E(Ud!;e3c`|tact>o>(3OH<h
z0ssI&07*naRI&5+!%sdl%a(xu6m+sT-*|nyxJE$lmw*#?h3)!vkDT|%bHs!89qpjd
zxDZ*eXuiC5>mm%0Ors?`(lypM%A||N%MJg!#_qhxmS)2DZ#V!G&nM+)%MQu*T$IoL
zo+fowt(zq#UK9-f&`}D=({uU!*>pMU)<~hB<=>O`(_}N}QYIRIUa+`o4x;oR^RWR7
z%S#?fF<*CdGHy<gO(1#yM$N>xo^Hxzq55f;j#DoWZ3A3e^X(Ed8<CsfBa9mfEGc2E
zkLBfYdCtj|CB6NZgK$5<5n1O%Guqvuapwlf!L1c$f8aC&|Lojda?g8%<<u*}6wBO0
zHS!2RYfB&MG)F)Ex^Ve<QBHTV?NS<!XO=v4$eboL-d$$h6d`QSOW}8(e!5AEA54)a
zeroRu#hlPMIj$u@(_fE0_L$mm`NkV>s0r+=uf8ga7A;bfPj1*GSgl)L;$-x>=blsk
zv5)JD8HF<>fWDn>m30Nca&+q@$Hs6)m!5^A6E!gi4YZO16V#vsOSSZ!haXKgf4Dqb
zR&LFgJFgrp)1hUpYvKm^y2>~Js{2yPqz-DnG}TQodEU7R3Pf(@teqW%S?|qBuzUqo
z8RkC%Zbk;iwcn6rZMDq$`$=x~0D#ve7Dz=6%L+X|Xm4M6MwDCx?cKxCl#yX_^9A+t
z&&B&NFU5mHnU}52l1U>%Bs|EzIl<xhx4DPKz;>`B!nnp>3$xnWpsn7}OpU|_l;1^$
z#Cgd**9=i+v)k~oxEMJgQ0}~Zpgj1>PAMqHY77w}E3({Nz+3LF4$wf4Z39|cCYdxm
zMAqymko;mS7Narm1~^=cO}@S`&AxgY!wDIop<}%w02>9bJ1?OvXl=<1=i^`=oj*&e
zg<0~ie$7yAgaUBKriJrie%>0iwoJqGOJ>E%|Fd@;fKgRh`%CY=kc9LQdPh)DumFmR
z3MlH@*WTOOcJ1AD-CtdM*Im1UpeUk(s3;)4*N}wt-h2DMb7$`4Wtd4OkRZYwn7sGy
zyX~EK-)-M1>AAJ|cu@wcK(MOO1&deaGCU4m%;T^nwv+gXa6EhUmGE%4{hVT<ATB(N
zd}!A`Z!lOdl+Ay4D<y$Oj<quH60*KuR$Wb#<%OuAB7>M7k7M~UR35NL#<D!PiL3#1
ziC<V=flQh`6%FYCT5n8<j6k^A1gO3I90{NI+X@1s@>T8I?oH|2@CD;sHsz%K-XC+M
z9!FvvffU`@Cy2m%8?7-AJBB;^eF2wc3?#m+z-j>|1f*_efE!;^D?20wYRg-bNX$o?
zpc*r&MdigcM<9XlKz`2w6k73PbElUC_%h6k?N_XRNPKDU^~}4PeMvEWCS*}a!H;py
zj57)2O3p%RpH%u;0|_u=i;UsMbo$(QU#B;Q8P8cm>?*Fs?711(OxG3tB0MlFH4rz9
zi$*jBmDsyV*S`-L^RRa=iS#{v)EQY{>u~*@>-+ks@5|8`X7AiOp7pgco!k@JCye};
zXWw2@k6oqp)I9e@40)1P)~Gb>`_l!8H~Tp;<G&)e8kJlF9uw_@P+zSb$~GY5JU{(S
zMfKQQ-iV`Ujiiqs8w&H5woyuVAfhp<ni$7Li68E3(m0hm2>>l->!tUzya#i;kRFyL
zs2de*U%RRB7r;KPe~>T3O|cu8Us_cyzX0yr2m0_^kaj7#ney99MU_HYsZ9#-MHnR;
zO?P<DPTNxnoqsGGXf5A@){TEht6LB}#=d}-!e8K3w-wd(tsDbsWns_%;4$zPVgZ`7
zrli8&R#zYLn>^<`GHC-_vi-wPLOF(>efBJzamI9I4!g3lib8@7%2IA{a0tS}LOX3E
zK0e-<I&~_JKmG&)-$lywxG4XLpX`W;h)$nZz+EYK+O$(tTkT9cSuB*vPk>=jJhazt
zJw3e$a?eoK(1h34V7rmwV(Itz<BwNAF$G>hcdfN_ZzAdZ{rw=k5dgk3^5OhJz<K$}
z+Mub4LZ^Js!UL)2;Y#e;jv(SU-O9;n!GGr{DLDP)GR<q)m4V7%SE2uh|3UF53sCUU
ze0aui!S=vS2s&;oJo|*hJ2@U@)TmB=>z~N|zc*>BVc!X&m<%ua{2Rpn?J9T>40qE0
zbD)GDv#}cv4D7fOFD9<VAGRJKWwCL?wO2W09=x+!jTkl*GtN960*dQ_V8Pdm(38M*
zy_lNUJ36e4_2R}@zn#0%uxRlT)geiT**7K%r%gLqnfMm4T1<Y+<Yd7&i<BmJTwLcH
z6q5H(KmV%Cm22Kf`PW^2g-yvMV_lr*hbITb_iw?TLTp;H8RO16%HfBMOp3tmAKj>Y
zWILK@%9SS~Zg5|?i)v0c==XW6QBhP0Pai#D??z&0REIfGoK=r+U(K<^mj%%Uk0v92
zh#!WX6wEea0t~CUIVdK(#b@-vq>}88n#EEtdn7>Lyr=ityKM;%j`XkzTz4poi-5Sn
zwgrnU8QD3BN%duWr{A$g(}*TK?kN&;)-PYZ9b$r9Q*e|gMxJbcV@=2VqmS0>)^A^K
zZ+E>_x0nIGxZ?TL4#2ed9)I@2t!S)k#engF>Rwr{Y)YF*-re6m9s)5He=vY+(a7Jk
zmbP}ybpAt0$|t*+xIg>v33%uB-RjP!#bwyIqzuDPq8(l*{V6#hCc~e2;t5KYABWdp
ze_d%_%OX~jEU5eU?~mEDXXA=1u25#u4z&#kU03JP$pL#D;POoNQ4eVn&EC-i0uU3{
zahz}AMr?gU6KbhhCnmr57Ko2w?kCVhbWU+Ap8H@6{JmXiiaWyce5X$6i|?1`Bb%Ga
z0(#~X<XlM&=99<wwZxGV@iDz(Qz5nFxUo!edI49b938Wd&*~0|I=R@BQ%yg~RQ*${
zX96F76u27OM*-T#^|W;s0<H~63PitHA8bk!aK0Iv_Ee#$tgh#QYcj7=X$tEaQFD+W
zjA&tJ=G9;;wb->ub}_wv>GWg*!(Gq&2hgPUnG<91&Vnqo*wCEsxUVak?R;lL+uxZr
zPZFa8XtLW2g=GXaxgl6uNzHHy2@3tZu8@CC^=B0^tX~L@A7c`HQxP2e(?G4L^*co~
zpRJ7TFFM={Co-P~0_5hsf4e!&I6fA0mSl3nQtIJC)9MXK<NF_ML+HV7CV#dYY^VO*
z^qo3tghMr#y^&jAdlSpI?^FPpaLSuw>gzY(fPt~5Z*e}A6Q2BRE<r|7KLG_9Jn<&)
zM+61o={aBEOKMQ%QX@?A5(?v9)D)U8z3=>U74WPA&3|56@D1KuvJ@_CFA`hgestFz
z3iN)8$^zf7qd+9VYq=-gVH7v0&p3K4?mh1ugwU18{!lp;7k*f`K{_N;axU8X_l-s{
zH@<)0#knHuB53IEw6M3E3J<9<?Pf>!r#xqxjvVk~-*r-~H<lBWRT|a2Eq#sr2pJN@
zHYql_@x6%eYGo>%?YKpRn%H-U+3!Xo+H~H4AnwJ)(Op5JzdND`E(Qo_%`lns34rJ#
zZIEfzP|Bl`WIs;?cI0(S1b3#<dwjBYC_jB|OlR-h2+$bQ$+^9I*5T@!&*w|<_0(j4
z{B43ij;2M5cnWuU@wtqhoUsghb<*Lw9P2bXT;l0_I!xDFuH{@`>$p1HScbhg)_F=i
zYqHKy-`C;lElN7p=h2^YF>h-HeSL>uQbHguo1a19EslMo!|>P%aVX|^dHI)_$gOO_
zkC!A7xNZlg1adB<3HBSm&BW@=di>{%SX?$V1SR#&xaY@QY|X8LC&w3=VKh-FubJb_
zNy!0th~q~y+sl78XCSY>2_6Kz+*lW}2GB%&BiYXzm-p{0)q@}3-^#)=Jh>?g1B3nW
z+_)49Pnh=bvglgxAFFo5hi&!6qX!_&n`1Q_q2wbC@#P&^_^qH4*9?roUlJmfXpNha
zBJ0?GHGiDPjvQ#o{t_My(!X;Lsb~dUefuhl3S_quZmIJKf?J=7X!IWp|N5<{=RT9L
zR|J{d;%1^{+rQva{wrD|PlbEGtN2Rz-5o+CYh1A~Ccph5PfriT#Kfpychie@v!mpQ
z>siUGGZ5TXSprha??6cA$!=~vEMR&d73#&b-9rv~u_6Z}>sCr~)TL#jX47_MBHUP}
z0M4~PtwQ=;Phh}@FCc98X|$~1f!JHGP|>{NqS0Jdj-==AN9O(ihql#QR35Hu^sNR-
zPA)aznpXtzp&fR{6k`$lb?&IY;Qh+{5Kfd%+y5IjZc>XysisJf3>=W!p~hRc?mz*h
zU`-PXR}30B07s8=9CYiDnbpPFGtb25bLT59XUTu{nzhQ5Lst?oR@vCSTt)K^7M}*3
z6~shG*#+D`{P<JUQ-V@RCOosxo~a~qL@MX>Q%}aHpMRl1?MedJpYZ&S+itR3uD<cx
z@|F6OdAef6@L}|QIK&cmFiyHNU>uD49Li;g-?xK7OW*qiqfZ^#El}A%)F1M5l#)g!
zI2zRwue!7v%RX5F(F{Kr<Yq^b|6r8;N0;%*BN-4aR85mE=%YYuxxVO$WZZqsT73&&
zKAVNpZ|aM{@UAx%Er|~Bq?sFiM)DFd^j;zjG)wmG9^6>qM(6&LC;eTEwE-bxzMbVa
z=gCYgekV`$`?7eH%?LTFMKh1$r#WLIeI2Qd%l4cu{Z|S+Jomq@?KvHuXHm&Mc;c5)
z9ZZ39{f-x}*iH)qZWe#!^7I}AB<`L&6k|>e!}{+_dz~RIRM+^>U{=2GKwnI{uzf>(
z+I2A$7}<-`922nq!M%GheEFDOua~oc-s6Dyo}M*p7Nx(ZVb`u**s^5{%4mg0B&rh<
z5|n>00k~yBtiJu;tBNycCkKvL4!F2^Qfsy!CQ!@R7Kt`<en}nH?<mEKG}By46W^jG
zozG$6oiEeq-#FN80vkw<4WRGjaLikpO%PHx%3JW({N0#LEi>6<(zJ)#)(d{lC(ue$
zkkl>~;9SgJyPN$e>v)bH_vBO&U}e1&dcnO;3hO`q_GSXZ?I4D&yu@Wsvpiq1320}9
zDO~MAligJqloY(*^|4i!0D!ys+{Vp*FV3w5JTIYt>Am?>&F6qD?yLHSxg#|`kid;h
zfI25XA9su%7=r&T%p^|NRebRQNZ;QCQ0!4KO?LZm9wlpJa)a;9gL?v|`RB%6Wv2SG
z&fYXLe$?RoQ&ebbZlQKOYe1e*?+wLe4S4>eZK|I)^2b&k<q35h8Xi`@&A03-KfHl!
z^D~Q9vr(hk7Jnp66a<!S*^1qTMG8z&@z}0J>sbTWo4DV!EjO1&22AsFHa2`f*KEtT
z?ZC>kG?NM}-;bseYKDFK>k6#jla4PRxEIl(?Sg{>6mQR^RUWOFkORE2=sWyx@ls^a
z(wz!7=OsDjQ9<MN?-pZ6b`Czi`)~AB-~G+!pEhkq4)-Ue4Jl&%PdjQ1QmEwc`|dpq
zGPT*dCR$P8-c#zC(@pW5e|v-jeq0+)r3<NlaeG1m*OVV1#-sq`Ru=+z1h1vbmo8bf
zuv@52EjE)}8L5s!v)(izo*YD>sR$no4)#Pa`x6CnGY_*%VRJntK)!ks-*poO71(9-
z+!XB2KGK7rq?9H7f@QAA`}*1vrZQ;{H(%~^(COrct#b(zl6>{K3NVMMbKZ*y_Q8WE
z#ItT(N3(tc+8Xndc)D)JaGj^Vr}8uW(dBk_-w@q$Y8~Hrt@D(~`rLTml1Uf$JFC0S
zPvoHXR5mG%>spRkmHZ8f4#x1lf%tMq4L;gZiBO+hWL39dZ&fQ~Yv$dJWmuk7jnh&?
zkR0lZXK5K_VOk|t7PO#&FaN1u^01j;^|<&TyuYptNs(T-e^NX(w_UN8THF6zR!9x~
zO5A;H3;a0-4haiEG{>QDvI?=crV+zK{BeArFpLTJ!-mod{4XsZ7bQjWeNoAee?+@q
zKU*`e^VzRd5a_Pwc_Ho8`*8p4<6Sw(t!v=-(1tDLb)4I{;+yOe?5!}lXdM>lhqF>*
zjI4B?+u?v~%xMIym!j1@7#^XcXcjyY&8fG+C2J0vytpnLaz9+?%i3e?i)h~YEL=*~
z!qtcKk%Y_P;^7bXt#?!4jx&U!b#MtE54Vuvc1m&vJ2`OZbKu~h0EY-4TG@$Kty~Wm
z9!*1Sd&OiBX=JBf_0vjZ{^Oqns2{WpqB@p)NH7`;is3t`AA;U|0%17?DEwd^>UO4g
z0K(lkAXl&1)FZ&PXwizF=gx@Z0vBK&m~zqyG?B2cd9lvY9?W}spzf+nF&FW~KmSet
z%~dL@NE#kBdL&Z&rF2NTea8;NqyiX!%vc3*cZjr~7vk?aB{>P(=r>3P=)$66<Zv<4
zKegX}=GdNWZ&o(m|L_ybz%SF#2@{Ulr@cvvJ?R-(xabE3Obh=aBEpq6xX$e_l+yij
z!9rzXLM90FzFdF{&Y!J-a2?0`C?>~u?@5<gsJg{$F>dS_hc*VR^V;8Q;pr+01BfXN
zJpr<nm)w(0gYlF|PE3rq73GKs^Y)j;ta-uGOHV8s&}nPZ@%#KWm@xb3UMgk(vR(Z4
zPbf?;JV@HsF7WbemuB6+8vi*{<?gD*l6Uhhm3QNU{Je*fEZ5>sckG#A5dXSD0-Vly
zaxeb+?*s1_EroP(;uqX;+ESX^Q3Jb+<lVY$EK9)VO8OWM40BvUQy11iv!b=-=1nmM
z8uQh0Y#qg?xb`b<>%Oh9>9@w>gp2l1a@AIThih0kH6$MYc{CoJvL1UkbE8~{Lt<@(
zS!;!tUVg6l$Ctwosz&oq|I0_tcF~wK(O(`(;=HE4?-#T9;_v#^Yj>&$J6BZV_b-e0
z@zJf*JCA=34#-?cOoxveH44Lr4_EUdne)l~R|Gem<m7;p1HI3I2AadPN1~w{A4P33
z{se@0{GDwCt~qFMosv$#IIFNmfxJ5Xna9Ut@$dO)tZ3rU(uNFb6R+7;ig5&{g=(o0
z^&7RYtLxdn%UXwFlgCo?B)t3JmCBHRDykc4Zc&~=)^}|LMO&yrZ64orq)v`X)0uS<
z60p6HTD+oVY;9V(lPtQtCH>kug8%Z&T;LJRXeCcg!mm>ID*C#XwVx|}I!d^=m*f4P
zKeZGiI7iGW+V?$LW8Xer0!rJju7K%AAp_y3m|~PRqRgqAlZv)@Y#;lUf9#haIHn*^
z@+0w?tEhp$ZfCo-71Is-f3=dW#AxPwDy8r3!=0r4cyhdSlvG@W8k+gcghun-s%;zZ
z^ZW(wr6`R-a~o9+Mb3qIY-?`94r(vI_|?~V{L+g|;ruIFizbB9RF>5Bv5!7gU!x_G
z@kH`uUO#W$hL3+<hAU2<V!Z3n`Ml-7ci0|qq0q<4qem%|>H;FwQIlLqv~j<rcKP+E
zQ}fO_r*Me3jzsd}{Csez7ls597%Zcr4cE=8Ik9hPrpp6&qR--s){TI*yzVBdSvS+7
zLa2`mj_E^_u~FVgpoX;%1@onk>d+-IZ588|(kyT$HCxlFnvg^7*kW4$s9|`cWXPWj
z<9Twf=)=C~c=knS#RU^Cm;TL^p;<?JpT|-6_X%weuy@~9xUDdqlRT3Er-}i5Let(}
zhbOg!Nk00%KG)YeKjYC}xz^!2FJriMzSeP!_jNjb)OE1F?kZg0m*dYF<+yuc2DVi5
zT~SEt!_~!z@-kTip`toX%e&y0tU9DuHq-40eW-Ix+F3?nIktm|aRGe8KmhmuJw3fC
z#86A2fHn+>^g&FJH*%{Q+1A(|8LN`gQFYu)EvynS83?5|wZ9UOaKWjuq3X5lD6d31
z<BbXN#q7jzrF~sZOEa4(2=^tW^N#Nm%AA;3NZJ+Ye>-!qfNuOmi&sGPht}@K`0zkP
zanCY<1xYXH=31x96!Z4}UY;rkAC~PT$2i~?Is$DI=aY{#!p@(N`@mb#*8c|bT}-~4
z@^KC92aJ9RZf&#(L=)lCm$c;lz?)*Rp@qVcVs%0I&i7K61y^qh4jf{>uT4+l{Oja^
zeGWK2^Ru7FpXN?$O&!V?|E9ZLOTFcrccZQR=*wm!jQ*^>V!7!nvkn^*J24p^HhnVk
zUwt2iAI?V$J9?8rAWL@Yq$!IMmpwrLeg9)ir_^b795jN00x@;+iJeCKBX5d^vG|R)
zN8;OH?fQ+%Z}Y}YTP+h8(K^5Gnk)D57m!bFYfUng_Df0Dmj`_G-~=)uJ`US=>{Rt=
zY-~hvNhyKr&a>{!groQ>T(x>Fo_gjv<mDGwo=^O0pLWVrn;C!j(PspuO+1sy(af`^
zBRsravRj_{<S8dA@LCqs6&02E?DKiJ<)&+G<|l#G)io$9<?Px_GVe`Dh|`w`<tUTE
z#Y=v|k{^HWvOM9b0QC3Vd0Urp57!8O!8Bbc9hG^`*jvvj8V(DdTZlmu2OuOWxVMVw
zR=#^TXW_?pe};!w_W{Fh6{I7R&x+sr`UpCH^guc_blMx)<jd)UivAXjbm3#kSoIZw
zYjd=PH1B-o?Qsa}dvL)1*g+jP2shDG^7#)Y8LMbH_o3gx|CStQxz;DkKQF>7*X+=j
zc8{@xd?CP~CMoOK*^&Cv;iH&yHj?Dq(<C74g^wrWj9U(FUw$CZA!Cn`-pCpcFKVVc
zLhs#`cED!4gZp^m(WRsC$SE5rv||#K(`EZm^ZJ2>BQS8n!4n2-qBVh!AI{KKK;lS$
zOgLv>?LYZ0zn}?xE!6Pg{d+L>j4;j>Wsq>vpP2(P-|1`~`DcDMXWg9~I9xa&;B$D8
zJ56beNoTITY6vcHgV#u@@c|f|6huGC#RS1j%9e8$XIX&jGR9x<OE$L~sBO(PyyQ2W
zT2$_$GPj>30?a1J%#?uwESR@F;KI5X7sI}XtA`g#2wXQSaMiJK**ZU4*Bf`1(d4>P
zGiJS95x_Bj<N%YYL>a@BoEC>NStrUrJ=e6AmvxwI%Bx{eCKuMjA%l!$4P+bd**_Pc
z-BdRnN6J&0kvgCoN2@BC-V-;Mt*dS?ON(_VR-&Fh4$BB|9PU(0;rkWbn^7RKjfa&s
z)Ssq91AIHKU2H_P8`L6ufE<gx+})MA?+W^#6u;DJZ&0UVoNw2x!@X1%@U`PpQrfU0
zYQp+)4IAX^3(?qa5RGgp!%PxSK;_G)PVUw`E}xp#qH%4>i_e}I8HrK-lHp4WEhCeY
zu$;V7_gS83iErJTh2cqwx@hO|068G$m4moFF*C^zYf5BK*uw-E60GJeg4Xmh%MPN6
z{jdl+g|RC=J<$&*(`0r+fIGoP_JYz(3?F?hg;2JZ)?!;lGj`JqbT)zN3a$_8xsNJf
zq<rNq9CyUjvuJrM6XEPA8(CH!0q!YLfe74Bt6S0*ZLKAYVLFZu)7PD`(ilglGsZWD
z$+^TAGKL%D>vLniI?UeDn3uhD#yGkj#xQ$%TZh}r-xzNM1+%U>E)F*>$|m3l+%-7?
zGx`VOvoA9ElACee=svh{OeEglT!w3DDts`796p+nqy!Yi_jLt@-#pygaMQRj{Fq&X
z1RqyiKHLN_ugs$m6M5LcwzMq0LKunFbX`KJ8^i><E3JNEaXmGrMRU52TGy^@qaqL?
zVe-s_f&*}Dq(4gNJH3onE-dp1)2qRxm@u?*zx;z;dDQlI!8s|>7*7A!$@FJ!j)DxD
zInS#p#Uz@7z95PC3dzva)wT0a=NxcxZx?24t>Qji=0|i_%Xb=a5?rHCkyopTLl)Y~
zh+AZTPGI|Hnh-w$&He6xOXVuKhn)=9puuR%dke0mkD@K`CLosgoG+tu<~#2mQXDw4
zfNO$}HM{u1TfG9+xz^UB@a6xZ`nNTReef1|^)+>_R#DYuGeJL(U``57JO=f<GEtYE
zhgJfzEoPJ9ve0TVn|8L9zB^%h;O;67hj>awAG!+`F<T~@<L6y?ncYXJrk|c-`VkV+
z1HKy<4R_vt3&v36+M2{qw1qh6`f@?glN1op(fn~;oh-ENlhk?rhD~_r(I@wb)XYi1
zo(xLWZKaudE?Cri<!B+AG=ID0h7Mq~PMnds7hiw7i0ch$u`WuRe)g;x`nKhG0sSm}
zOH)(z_4Udu%NO&%B+z>{A|v<R@Rmf4Y^U;*35$u^&{HPRb&if49c|kZ&tg{Hk&F++
zI04~oi<bA*dk-kSu8a1T;PXf4;ffb7`NKER>$w2?`2NpPTUkfo`XCKg<g?;;@3Hxi
z%vnrMFZ-lj7|4~9+X9f4<QV!{*4#go<}9a$V(mgo*^-(l3I6G$8MyA%0qVN9|0Is^
zM*z)7R+X5>yKT!VkiNbONh1UHDOwg@0j#eziLYWuX!zt1EO{s267`!Gv-b<Q{`x=J
zmOSJ{@^yrKsX63LK-!vGC|pocAjalQ#62<md+k{C?ccLwpw7Ryj@An%c~_1C*Pb-t
z-4i4l^p7kViAPV{i0!{}AxQEMb;pB?MxftOfjv=#!+CuFMlOmnxUpcS3m;2Tn+N(@
zAH`(-c@)<8&!yW`Q0B%O{PJ-jCjF(4j^aEzIpE}glLJl;^b!Zely`K5H~rOe9Hu|$
zmR64rTDVfIm_<E(Vl39~C_{CfXxp_><GL7m#dYWt>do*{njWX<JJn`g-Mug%F+iCZ
zKaeW>dsF+0bGI4}t^$T9P&@pz<6_|NL$_w_i#~^nps{WYjHAZ0I8*FFdh3gwT-WLa
z4d-aSo;YfVNef-HoPB-Vl%Gaf^yC!QqOKDyc<XA_5#T8(rd2&YYj5qxx)U%Ep43ad
z_!#12+T>T<x;9;3yD2}RrkaY6>sYp_3#KKieX4H0F0}#OpIUf_HE};AgGvk2?t94Q
z+(_QLEBB4=IcFyBK4;ecvw{e~sbfYf-_!BjODUvZ>IXmg2w$&S(+(1|tX!J<E+BZE
z6xGSxi+No>^+en-{WRq_Tk3z?>u+IA+Mf2j_*{GP^C6&ocg%$^UcUjw)zr{8Hx>~*
znN0uRer6zj+EJsioaW0lu-(LX0+2g_>-}#U`=&JYW#^`N<E`!0$ff*}JHcep*mvVm
z`q6rtfc7ECJR#a07xnkYurMD2ebPtq+n0Bz7DH@Lw9d7T?i$vz56h+Hi7Eo!jcgmO
z1iqU^1($7!*2gI*!#})s5pbNHP#e#s7L61#8W#}&fA&wRR?OQ7y2|%qO>$p{+lp%|
z%$SGjYt4D+ysVEpzA>(Z*@|Z@Pp6ZkuA?zb;u_=Y{O!e;aASO(mriFq>U;WVyl2cq
z!Zk^_@mil-hnv#3(O0zxhO^I?p(N6~4bxKt=_48_q@@(0{vlrQ<Cs`n%Y7J@6X)lF
zaE_0B3jW%#v7iQb{ZxeekBcNg%{d9(Tg=;4MW5I86ojLYKdq*mJ1h)0jE#`wxbMeo
z>?+DfY=CKBB#&6dgKKu9jN@@7?aq6%tnUiS@mp~vHNV~Q@Te4AJs=i->{r$liBN`=
z+Wx%B0Y20OZ=-4OT;)^!gh;8s7lkM4kj*-a$@~)bX)+htLv8M@<yCx6PsI4P1F_7~
zNe*(Lt%hz;mR|&qrYzN^w`G2emLU(rJ>@b>4lQMy;F>cRZLzcA5)uLT0XM35&!(wJ
zwLRhxs!du_IZvD%_;YaJARz&V1~XhJeds?p6%CuYXfjuch8L8XZ=ZoF2t9KO!e^WU
zIq&e58>-(%0J4=61Ha>shVR(nXv!}_LuM{2S8qVsvQ=m!m?{$y*8`CBG!#d$G7#bm
zSWRw>Hw@=8FiM=r2-CvPaO;pd6<}4s*v?1~@~&LK^@$9}-)_ALr%sz{Gl@3`M+>n>
z)|$zdume1)s^Vh6K;mz(Gm^SzWo09qzKU#-+}F=11BN?&sNZ(;b$q^)ZAHgNpM0*g
zqvfXf_d1J<o+ulOqalL_;)F@Z;=3PyR3LW=1uN#v`x4h(eTB_@J=s>|Cxe(wWMl$V
zS=p{tXfshS1p4{;ngH|mIgrg##P?c!T&&G|M@nD_ecp7`qv*XS_vFUcs;|}{W<V59
zzj<2k71Tw}ZJZ2$`p8`DT9tN?OoW>%BzzWrA8GW#eS2g@Vj<DIzWZoy(l|gryp@L;
zcgF9do$Wx<ZbuQb$tPYKjW7Q%OQn_d`zth?{P<6!DBRF9V5Y9r(K9087qSaAWhRh(
z>(3SV{>@xWy^<!D9Hb@h<zd73?Hbpfl!%__Ai#JTeO22kOE!J)9J?BO)|$pBF&DYu
zxh!1s^1%@vaPZOpG|_x1eJ()t6K8G0-c8kb_=k~57}im6t+yXW%*lWDSY~@M9G`6o
zW9SIUqdV5`;|J3zR1pCYK5~+i15OS&IpE|#?{PptiQ-ZUGs^^oYbZI|^20od8dK3>
zyRnPDqJ^qy3i_L0b8z0&L@ZpEqvrKu>4-{77(XmR`Jz@4_w!F*nh6aK@IqOY_^NJ3
zG5t%5`SHGy)POR~@9bsIswgwcqGT-_)S^vjbX8$5oulDzeX$#Bw(I$jn0%(ItP$Zf
z?`>Mo2L*T%Y!9IJdW8b?a|-HFPLrnsz;{LReAxxn)J7D))qFR-NGSyL*K8NzP5S&K
z>aaVz<9>moMT?nn7rrMG5J7%WOLF9Z5G2Mp20+WZk^KZQ6D}me5=R0V_fxE&SLVlM
zv@EyZM1IDGhT`SxuTk38l25RoA0GJ2d04z|1K+pVvH}}iBemSCxwmDP_(6?HNyal*
z{MBNfTeP6>o;?faKJ`q8WRh<kf87DD=dD=TArC<_rqlJCrYR$b<MEF_gSp21rdqLJ
z)oR>HVBZlR;W{7Zv1<+_2GKg?5P#gYvKpQQ{RDcHwpdH>xqqMwt{)PBQxkk4xMPRU
zl%H%)brZg$#_XEnMwIepi6ze#g4Hcd%Pf>etQ;E&P|MgOKy?!XL>Ni_HF5l*H!DPN
z{(o*tghA<mfo76#MuAp3fJft=qz+bqmW{Y_-#Wc9Oy9Fkufuiz#x%xr>v&yV>->z*
zWlUo{*Kv(U<9&T@eY6!{$I<ufT^s8qaV4HHOx_`bkX=E5m-{V|Y%?Kr1yxYpz&?cn
zaTGrI_sRmy-Clu}xiz#3?S@tPbvSSC9t;Whzyp)wu&t17jr+Nk)W&b)7!d61j=ZX7
z0?_Wrui>~t!KlyH<xxm3kL}R~8>?9$LFiaN6a3@Pcqc}OpoC-CPle?uWLMVL&y(i9
z0}w&smvyC;cz0(mE=Y-jm)R1G5s#43#x(Nq`PkH?J&HN;6-DLvGN)XPM`FY;ks8>W
z%Br!vuo7{s<I`gYApBriQPeN$FgUhk%~PPY2to>ZQcb^Q_uFt!ynw3nbZy#@1^4(f
z(HcFKey=G^)l!SLilwv&&OOb5@zMx^K9{2{h`57y6gvRE@P>4>c_z~gxdBr-40z>y
zgu{~qM-*`F?(d7BlO~|_)34Rrl*OXo$iWCX<#_lGND<*RlzsmTia%L^n7giq2TgCQ
zkKFw3+ZvnDO39&`4O_AIAOBJywRG6heY9~FA_J1CD`97n(AS*mzz&Z;`~ZdxrD+G|
z-5H4=rr_X1A8fJp9C|R<&leaFh}mbKh4X11IwqPOs~w7>iET|Xc&6=1*Ov!;Y@$YT
z=H4s|-#sbx5FTbSW!72dww})zUz>mwf8956QFP)76OFem=ULgg`0AT)&E>nm-_IYj
zxzMmj7hiZjmMs05zRfs!VO#lp&U~DE_DqD?sI$MnA3{PyP+Df%JdklLEv@HxXRnUN
zaN)zH7hQn!&pq41W8;16^AP%%KaxoS*J4Y`B!t=PtPp?K3!eLynuq>4{-Wc0y*yh5
z%ztJ9mVLbZpn}#?k2do8P%)Eki!Z$qwqr#FR*U)X{Ulj*32=7X`-As0hwdetac7(Y
zkn1W<AH=Sem3Zd-EvkHPKkf>2LY3SYSYV|AZ7;gyb@tyA@L!sx*7Vx-JK;-{o{pFd
zUp|*6$*<m_ld9wNTVfFuAsegvPqION%_{?N&-gW_eRF2D=(Su-A)uZz+OZAK{V)CS
zOeydBz6HZjme+_rNu3L+^#%vNejyv>`FeYiC9u0+^jZ!scqG|oao@g@+aWw}R~;79
zI>5<S#dHXFUN|}6<baa{P7d@S2iP~KWmTh~oNImp2BJ;m>+OO-YC~C*_{Bc;*uL01
zcc=1=EdHXG(C@W?$!m91Q6Y-vH{Cpu7){f$$3$Bv@AmcJln_LQ`p}<tBj*MLO&Z;>
zjsR;cfnPD@*%f8xR}<`R#K``kJqv<zUHa;rUHI_ZbTpblR$2Q;QuF+;XQo)TTB5^!
zFgQ5~>vvUi2&7qDs%(9*FrC1)KLPoD`D=WhwCpP8pGlq?#!+L09y}jMAp_Bvm#rBA
z`B!f(rf@<vQVD2xhdc>14e!V2-A$mL+HrZswKOeXg#HOZ4rL>L(|2Z8u<ba<eTNw4
zNF?r0j809lfYv%mWN;9IC~#jd))nQ&J3z9}c>J)T7SLKJNuW7z@kuP&wEIgL1qJwx
zR{WIeon*rADT(`H;|C42WH~e;L1|a7-pw_Pl*Qk6Dp;)9m4*padT`Q#b3iu*83!&K
z6pSBp>#;DK;31cytrRRdE5QeMjS4^#wP7iz!lt9!4wXiz<+>oF8jG@PQA(v+X)ofh
zTDg2+JTc)cK(+KS5=MKCJU1VC-40xf$>&zwIV6(6pogUl0U6tOrcKY<UVi(v^u<DQ
z&v<T%Ax+D)$E~mJ9j)UT%d);_eJy!Pn2@b>I=()th~_^!zV$tQZGDtvI?R}l4ws|O
zQ{OknmvdtreXYavwJ{H4nCV>FK^rp4=nj$qL<qs;Ku;HJD{4YD9a@V}Q-5ma2N2M%
zpj8ji&rb~YMpO`eSX01@0oYwqhe*2Gpxgr+NHc<{c(S*m7O7!g$SI=>61Kk!DLgkg
z!cUn1-?5bIM%QL)Tnqfu27g+o7#kUaK0aQ^t#8EM+A@p|^T+9N5eks5p|W01T@$|8
zTZB_$`>56=<r>K-Ds6f$7mSSvp=t3Jn$Y*eM7kXm(0hAj4IWsVhK$-K)HbuiPT<<e
zMu*PZnx(C@M^&Q-uiGk?!zJw!zHeHM3%U|6$<OdzQE*}R4REQR%Snnm+TxyuOY{}e
z@ZsVU$M((B!pA91`2-O7B-$eWCbul4=eCvr=ZTX8hX4nTDB#+a9dqC~I=QEb3K#mT
zkq@*!J0CeO{TD6NuJx3UnExVgyBx(Ieu<DXrobyYk`oCT_$l#5|G_&ixg7!HMk4Hj
zvrzc{T(l6(mV2B%SYD@0ECP=jZn-{?lhDv$o8aSt<oQRWmBDNrr9f}I?&=Pc0SRBf
zaTA_>?j^ENx?qtw_P~XIITzylxicC#V1Okr>3~<PT#Xy9ZBgd8J4@%tz4VMs>`Bj1
z`AZ!|o4qp-UFRuU*}+`gSd;h-2@K@KH8FwO(Z^vDHKYUl?JsaY`D_lV=m$wAJp%I1
zJnMAjbK5#!eLZr-a7;PrL@fO72PIKkSX6|$^S{DXmtU%*SdLzlqDx6iGy(n0U&>hV
z`zo3{Yf_r~mWaK4A^^HAV7-@%{ZnQyK&oG`AF4}aGpD_;>8*!RlTY@KpFB7R%?-_%
ze8ma9Rn$KDws2Ex-qT;<yVn*&0Q7@N(*Fyek2H<z5ANM_z-<2IqXNvjCmsny?e}X3
zT!=Y?kSH%)^+JD(sbmTMeO|HBLcZkb6pWp|uk{gWS3iGPi1+`IhSHq&jZGT&K<d#H
zQnN#6-WH3M^NO(JH<MM07A_#4Id==zUs;Ot9!NxZ+`cBSN^=|V$s-wT!yUoJ#1Vct
zmuY%}`i~1#+SuQ{ZYo2DPXX2cxqP%*koQCxM_L}9+-dqFK<gU%u@j%w)+FzC_I-))
zpca;q>L^ja^ndxrAayP#`%k<is>dx4>{F04z{vq82b>%@f;eE>fHrBnt=nFLN8j8?
zEmzqsCUFuG6XAonDBG?&W}FnK0NVx%7R#>VUV_7~6U1#+<{Dk$?M@T2BO(wIc;Ff@
zzFuw^KQs)Rca@>0-j#dVO?c-0ZHSNdSDL)`G;(DPy(gy%&-`}_wxrkK;#1>s{wawF
zumQ%aXYS;W=9R2fL=*VejhuIVoq=t8D$u~qY!iuza5r3cPAbB=8L!FL+a1RY3&-b+
z_tFF@b+=lZ@YC9IjOdqzbEYK1%W5pt>59tg@!nTy*jpfG!gU$?ZqFZ2o7k7-?LsYG
z<;VtaR(>tsn!giI+%QUMtlKKFl)h@0t}ei|2{9COFs*HTXeH>JsqxsbXB#!<8c<i?
zhA)=vMLgTmNn>L=Y$O}=l=rnItsHm%XB}z?EbQ7_iR)$$uv@k<?cs2)l>lr#iz`8r
zaPGGp2>E-rw@ICkv}?6C)&UM4{cTw5bLGC0fRwW5)d7icFi(lLXw7<5HE=B>jX{X{
z4Npu|rpuL&ZU%``%P{UciCJ=UEMjD?m``)zPT*Q`?STIn+eC;b@X~RiG@V?FjOu0_
z7wx6Atb;tc(BylPS7q8!=AI?3NxZhL8apd!HIwg&7$aQRb*WK;@#H95%T&5n$7a4k
z0kP$xh5uT4L;1O8Jn@NrM!YZ1NerguE90<zseV}URBg=kn7R#{`e;*_uD|s?W4I}%
zWZMp4>vQXP`dZQ$&yCl*e0^VEOC0NZ=yW<<U&~QQr`36xLOYhN)9Sd!I6BNay>%R0
zd03~>ag1r@+`0_YwYd0f#ZFPNCj~1onwDH>ETon~2uTF3pPL%1OmHvDr1@{UCAnsF
z7-kQo4{P(1a7w>mjEQr_qI6nxq1JRfg(FVr8%9jn1$QjYV~e2$Sej(3tD_b-^Q>ce
z?0Aqs;d`E-fZ_MA-ia#*CBTdG7}1(`W9Dz}$Y$JT+&3}>7pFv`gub*JInGIMr6Q>_
zS{tE6MXA~Kq_oYFh9(3M49}wb)wg%$BhlZJfOjYYso5RLy=M>AepDv%$4TAffLrVt
zXv};czFt*IfJOEO8slfNPbjB<0{%q8rFy-}wXLxTE@j_~xdv)eQb4lwYqa*c!qj$J
zs`zcPPx7R4pce%a+(VS|W@}>sTs^3y*HQ{peZoe0qiQeRtfn)%lLNiRfrE<*^x9)=
zy=7FJQMWaU7Ap>=Xee5sKq>B}xLa|D;x561v`8sd+}$O(ySuvwx8Ux4>3Pq0?zwl|
zKN%!22HDSEGWXhR&B+sAi7{$<cOx6{TKJ*x85n+nJoR0(l+O97RK$q80e6NaudjB`
z?*|fv7Xf-g*}CJ~3j+^;wBoB?5*jOLSAM3V8YgBWdQ9r*W`lw#+%HFx<k#(Ei#T5v
z1!dAU>1ZOF)Z*9U^AjZu6k+KCX*kgN^)~9@It4C8(BxICi9EIBTiC?QOBfKTqBktc
z0-^ov0_uLW@mQMU+CU-T?uSs1vCk^?E<&fyuKV;#nByhUk$m%bycgDIrs}oGk@U9-
zAZ{S}k_I=b5_IN1)r01K_tJi<LvwkSyPcFkWL~SMWT!8*3mSy)qY9CQP!p88U&GLH
z3;_Bm*TzFQEYof<cfXqV(qXK$8pl`^|8)Vaf4#X^SbNiMxeb4~@oy5xxhAyK>$vIp
z-V^-0o6e*i=jT)E(`@{lQ`XdD%9k!4+KO)EDf37lf0#&3DzM+h{Sk22r)|!fSKNRO
z5au8%*)0#^&ttn^B>RpBkH1lU_9&GV9x7XDiALXIc7fs8f0bf#ZCcR3S^`sqw2sNI
zgM$qzW<<36vXxGAJnUv`IOCAb0)0Q8KjLdr2r|pjt4YG|Uo`L<a+IgGg5N2oR-$Er
z1lg_va41@1(aN%0uNN8;NgK^#An{=fZDo1(C!PJsIzLMld#)}7z4N{3!Qv+ou9q>w
zElHBE^Bz*PeyJQ&8Mb|hoI9ff3mxI8Ru!^tHn{^d_A8IG4(=go4HVg->2g69K$r@B
zpXsE4!ILXRzAo;xC@oJl?|ZVu*VEQQmOPgloaNla82eINHJ_0LU;O}SqKWMU|M-@K
z$OlGMM10@l>)HIv=+Zadk1|+e4V+^*%N~dSLD*!TWM_L#rj*D-+|HLZA*8pXsjyqn
zB)O+6iBl0UCq3H%l0CI$oi<l2C#U09d2V);pOT=@veIuL|Bz+ZtUyO~*@+lw1ohb`
zpAuVToK6US<l!$~BpOpK4O!t4NcndDmyFroS;Rrmv%}C`S-;JB33#ukiUnL~sbp46
zh}!4a-F-x7%qRbw1@ir;n$EE1INc0)%|@NM`o@!GNQ0kY4Yr7?G^+<%vvmW*^Xi2_
z%cCjDAB(#09JK}+86nZdZ*;j!gnPW}<&Br}ci%l^<)6y@EHwV2Q}e=@$WmTNvCPg`
z>8QaVV|l2PzI>;ESg7av+=$3F+vgm)KR$W~pQTxu<ZWjy=B!Gd{`!Mk-JYozO!QVo
zdkLXK)+Za(VChDjeh*Rqu{(R|F}hVaihiqW`29Dx{}m0w#evLz`fNw!Om2I*Ex8?!
zFJ&#T^%LD(udaicKIcm>pWT95h*R><*8LfR6EvJs<7R;D>soirbA=Ii7J~wr^8w^0
zq@FA1&xxo42~pmYCG@szjc9b#x9TGzK*_5${#0SV9pfgmu!aI#Z<*aoQLw*=Vk{&K
z2fR6N-{x<aaoku#+O2Amja_JHq9d3Yme@dlIR(U(U%qFuq|<$;pS(cNtlRnq0@kr-
zP;GNww6>gnCfpkGS<J4rKJP%@a~=NF$kOLgGYant|EI_IjG6k^0_*P*WR0C+ZMy-G
z2LRy=)KHDiYF{Q|8cOP2sKvy@!&dpJQcip3%6e3G(KP-gC+_*D_oGv})>YX(){gE;
zD8q&0RgCxeZ>hUKGWBTs-%Zc65vA{w!}jp<%deHYa*thInH@QJNfb9px3mqQUJr#(
z%J~PKsoT<NS6A?56pzH0UD}O^SR*-mfq9Pu0hc5`-TpYJ%FWg-%UI772OHwa&mApJ
zci2OtLSTUFcUziIP&eY0kZRFI7in;QA93ZFm8tW{FTk)^!x}x-+Bp~^#KX{u-QS0t
zoxXM9WOZ&o0Pd?=&C~4TqVe+V{-x(QGT@LKloD`FO056QsEWiGbBaT3K;%$&h&M89
z?`NdJ46Ax=H7zf#%$5O;0+tR9k#}l*aJduDxZ&DV89+NDVi5H6Gi8qfd={PaK3(i;
zfJlY#mm96*4`Vr^WDb}V==F}}Ilys#0;i&HstYvk`TC<0I@sTJyU?^qaDWA#31_(o
zk`w2t4lIUVo5PrCX6WTF$R8;wF(WdRSyh%jF-7bdE$7PoWnU~UFz)zusqBJe*8{zK
z&Ct+-t@gsUVP!_=_1_3(vqZyawpXUrm3#BG+huPbj%ieb-dP#XtEzl_IGv|{u9L*^
zK13_jZp7Lt_xAhwp$Iiy0Nw+sReXBsoktaQq<|aWN)ZF-V-zc`5F!G?-vooqV%jD1
zY-It9b-3V?qzr@KQ&UdEnJ2QsYuVyQ9~TifkX>32hlU-tz-~^sLxb;qA>NPVW=g|%
zg_Ai)W~_ck()0>l{TU<5z~~y*whKOq$%4pwOiQh1Vy)GM3d?F@p7?i0*#`v~*^2yv
zf218ZF8wD8GDgM4kw-zW=`%3G57OO~2le0_Ij*pnJOPAt17^+NvMdj>!R}6u0-SBV
zS00XzA%SkJtq(y6`#Y_C2NRoqQh)og!bEQRQh#Z)CG9sUGoA)>C*b{E4y`^F#CVR7
zIf>)5(Qm?ZrpO3BI_5pQs;gF>W1_7$XN+<EPl~ubl|MZztUih8FKx!>3s`I}{AAfx
zJ2;#r?;1LHIR;_+u+*V93Cj-6e~cShcWc4^){P#WIfJMn=UqDWM?WpTS>uHtW(a={
z2l9<P><@}>VA^B#(4aiYqeyyg4jS~YCtcPV;=`y<5fn}}^^1J?M&=wR97P(SmszMM
z)78g(lrxA4PwdF7dX=jxN4}?vzf8=;J2JjD#bAN3z7ue6mY7#F6Q<oINFi0WGP{ov
z+`ydi5pEvCzzTq3cHy;JSBd~7QvQ~Fb&sFKK@7kIyi!|x+8bqF_WZ;DF9c-!vQTY%
z`u@$v0CjI2^+1FDgqwt7l5Gd-+9V7L_#fDGwJKZJdE>6<^~(~KyZ3>7iQ`(rZexUs
zWTYXo`Js*0kJo};P6+QDRGY@$a-i>1lD41Nf!_;${C=NVr)&|N`967aw=R$hp5@D>
zmE*y$b_Pl}3mKfU4+EcQ=xWQ8qrO#uFs7Uu@^|xV{HE<k6}xqR?iD$T{65~ov`?la
zl;6N?mQ_;gzRroRi&?cMvMOyd+#C=>&FzZ?ZSHc3oJY2ec1VVP!JKrVr5*4dGmn#3
z;%?duq3ZC3MfXCFmHEH<&oc?T3gxMBGQzz&s6H59MW4S}MZC7u;k1`BeC2p{r@y@}
z5MF<0MWT@t4adrj^<ArJH1TdZ-CQwaknU;6oX!NFbD8^yQDa?&y)#Ih3BAHa{=-A>
zqluKvXYLoh3TJy{t^&0QK-F0`CaZ69&~F@ooG^<z7~1`_#i5_}mm@|Xh72RBc%^OZ
z^bOR+-mZ((*_Dcy%a3H?Wu@FI5Brp_bWvSy6u4_@C)%}C=wKjc;u9=bY5U-iVXFH&
zqtIU8orZ0E;&rWXqguRLFKqkkV}g?E)ZJpzlC&3@bd0`Iz|OLp;m`mmPWmldTEXf%
zoUkanvT~pf>V?OjMf-!jz#rZ?;CWyB!j8vgsE52#$}O}J!1Ns7li;(8q>&Qf)MFKh
zP(~a3oY`b1HW;^x3E$5}Mt&0Vnv)U|0LOY+QishY3(4+h@Y)+$`o>p=wSTyzFJPjD
z*Z-hhPz~gWXV{BYk6{Cl+l8^7hdG8FY_GunXxif-hb>)*d###9baVf)Ne-H0!6m8e
zR}vL3zMVoQ1$_SgQa904|C`Nb=lsbdsit@$jH>deQvmQ>i)wHGv0o4W!SL;>uXjrx
zQv2S_=E+ue<LPlA@>Q^)U-A;CjXBi}A^u_Tdy>xPgG9|IDUm91{$V!WLBI>rP#}#W
zQ6GMUjOuKu>2X~CEm!jG3q$=kG%ed4G$-75jJa{a8gAR0cn_`V?+Lperqy)KaFOqH
z*>dbZjs~D)WmYDE)4vhgj0Xa*SBP%KxhmuM`r_h>P+fQ|Ut#@hj-QsgB271M?30W`
zE#y7@j~BO_BzBIv1T$weP%TucmzjV1fa!~Fp(;;!%gbh}_1_=pmW(0lg5Oj4Im=rW
zHx(T|v;pcPhNvDjbj%s)Q9R=jC4Y?La#@pj(}e{kcziMXQ3OYH5>ol`(Ul?D7lKM;
zH4yL9Dkkl3zVd3HOi(UT9c;^>IP|%jQ;1}8Uhl`q#a|wxubgp0UY`(I`bz^tefHH?
z+YDNIs762C_w@FugeKz&3V!WZMs%n0MKH$TRQbFP>vx4_ZrGM2VvGq&T7D~$MnHK9
zPpMl^GB;4d7t}d#MjxiHcwMJf%&d#|q2c)`1)>WR)`oA=%Zq!|lmFf{Bs%nJ{DL4W
zbfK$)LeZ3a)3A$%W=;tJ@u9?V@Y>057#ZDRfPTx8e3*2BG})rO+>cay_e(fpL1V|D
z!haB@bMRggvlqn#Kmrx!wtVUB`=;LR5c7{xP%?re1##!Pz0K8SNdK@#TZC@%y9e03
zh~A!X*mx5H%6Ws653ZQ3hN@H;y@H$4vi1~GkjMJS@|KGe@CdDXi~P&c=H?9aVe>pg
zGV7I1@7E4rNfdqX`s-W-5VdB`a$5_a%Yl%+b3}Ud{^LIRde9|>Ob@lJ#0`%H7|MBH
zuKfOU+w&rf_`|RrQIBY^@{ug@^2a1&kpBPW0>~FPS&ECk^7Y_BGgnd)m0Q3F33(8N
zF7=;b3dd5JH;*XFB`;2wg?EZa(x<%3dRvm~TT?!gS^sDIY3sj>+6X8=VlKbV3#fp<
z{%qe1LfHuHd0z^M|5GcfdWJsU{elYr?OI6}5zTL6qyJCws@~#JJdhEj{n)hiHaYC&
z(I6Oi{a9D&6Q>Ydj*95EBQs6@PZLYyr>iX+#D@6ayD>sYWugd0v$ktjw!7N4Zt8^U
zc4d7Mc2cC4G@~Ie;f5Bz@l4vS#bc$uTBi7%m9l5ZAU&TN3)XlrYqmHm0+lYJ3>o|n
zRTI$p{x!A3r`iEqu;49dZwe>CpjagFu4icAL1jc)rfp5k=^?;#V>>73{00&K=;z!6
zaZtgVt&`!PiFsGb(O;oKJRW1UsBx`@QM!u46SSJmV6r$)q{Cd@ctQ#JdsuDbQ1SCu
z+m^$Tw~{8S;d?<Ju`=G=c|<ExvxULcqTB|?9>{ayu?4r8DiR7kejv?w`YVnJf(H)*
z=JYo2+?$P=A^vbG$!}<pHwIHAK13M?Xx>M;UufQ&V#;{E!~kxJpW^LlELJ~y$G4z*
zvz6V=`rIvT@R(VQANB9^krzvP*oPyGQFLBi7tdN$)N4><N&?lV6t=w$@H!4dA?in7
z-Na6CWH>(c^xVhg=Q$cPK?pZ$^~_A2);iWb#q7y<FNcN1OzMoq;?OvM1fIFG$Flsd
zO3Zq)Gv_Vo-x%>E;p`Y8*%1dl1&+L@8Ef+><?>-7?Uc$Xcwc^MG5S3Rl>NFQ&UaRY
zwAeQ=1agpe|4KvzYLgf6OG0br=Ibu1)KJja^J|5;^`Qd5L?M}mRMK+rFbpYg9SDFg
zC(e&|Oymm2f9=n01?A-6e6pj~Cb4`YfWRJnC8Ca8Yp&G}zhchAw}juRKclkSjJ^Ke
z*`|~`;?LFB_p91!6ZF@r!x-m%O~yZB=Qq2rk=>2d&@L9o>KwzKPJ@iSdn@<quVj9s
zJswT*jnM8t^R24QgkGfi^nV&wd!ufNC04#eS^tAYNs60*RA#uMEf;T*lg=pgP2K8f
z-mFb9vlP}<_g$e9vP5V_AKIjm6xv09g`w}rA8bhvJ&cz|2K-Yc_Qzi4q@n&t!XmaA
zvQs%q1?K^entu5EFks5GBa%=QuYU{wv(?5}YgR=yye~SZI!L1WJeB?d(0|j18ALb6
z=H17Ji9ZA8PQr+MK}F?JGiC5_&|dIgvhtDCIbOmt8pEJR!J<PUTLuXxa`K^?HmWc(
z+#9@h)Ey*rx0e1i)3^gBOK(Sz6lv+kMv31OCfg6PO6&|>XJ#=ypnQ}Zg8~>YKf$gk
zhQ7?j`H<sfG@+zlg@Qg##o{Y=%tJn@sKtmMvbMhj*|$H!Pb;KPnF6`(7lR_vep+3Q
zO&KZh9v~&7pC-}e)|8buyk+7gF3)j^mm)9ItB1$Xgd~~E*Xy+B=8Eaw&&!^zV6d`>
zOVQO;r8^Ttkn15%g5Dt8N3!a;jj~nChT9j(sQCD@i)iz!%gb&sx1MB?`*Ih0-39O8
z&uq#p`BT^1HgI`=g5OdMSob@{@s(#66_y^6#Yu8SVzk}ABT5za%6Lm-gC<`S8Uh?`
z=DloFF<td|l*h7;;8uD#dBzs*EUV#IafHgF7D&6(Ukjk%1@(9L#>5=Ii867i5V<ns
z?18vo5NQdwJ>skT*1n@wpa{w_4-=XC_>gvaeW)&dJ$o&slNJ$YP@0Xl$IMYFC#|5c
zwt9?;3}m`|4*T>?w##7SGU{uUC0%}NIE_$Bv-Qx!Cxm%OOQH#zM(cU-7n_I5x7`Xy
z4bs-13JS(i$neD=V<r`5Mrb~s!hn>7_?%A~x$*KeUg?uXLhn7CJ}NZupck1e`|FeU
zN9B;p1Jgl@2uQFSf^dP-hpP9+EUh6^6Ui%$19HUVewF4yCWmTl=qvNLo?9P5FPyBm
z{p6D1x<q;dG02iJi-75fzt-!1_ygai&E8i>S3!BNJ#B=1U4_nmpM2io24Y-IwgRcu
z$K76=mAg60OONKd+M1oORyqUrPm8!X)tlcgGZsUq?e?KFO&Ti>FOE(fgZ>m7aQ!6r
z^c_WE{A*Xs2M3wWACoHi_^iWo=x**eQ2MQi0ivS?I}5~vr8;niY^O(x9ZmK~&mjsS
z1tIqRU(E*}S7Puhy`7^>1^0$9yEl-kRkz4fBHbN*yk|dYYu>vQe4%EB>h`Ju7ZRFH
zPy#oKQY1FPtQKY|Uo9<7sp0XTGk5O}^`=yEtwS#=1!)*?Zl8c3yDZU{DJ-YA=WZLv
z!CcL~VfFlh6)lZsXFG42?*`L93m{CRRrK6sJtX|%i1j<0t`F<<!kCJ!14{EgyWlml
z!T03L=`JP5O?-7p<OVs>e<+ItoOt`x5-$_|%`q{mA};Ul;Z)N2VFRd})0-rqb3Yl&
zIm?LLFSe28MISC)nV`BaPB0!?dumncBv&?SOXTIHAWBa*e29Nb_@4dq+i?i*yK?U~
z(If)ak3~`H=G5g}?v3^5Ade}4d%%=F_D*9=#9J&5+|Sw-PK1w9H^5*|CwwQP6tuaI
zNMI)KalyK&Vyl2RJTLM~V=KgrWH|4jZQ_`&@ooJx^FD3Oc&E1IB5=2S$vP>8K$(sS
zyr{<nyl_ixG%73Sp^FGw&1c&j*{^Hm)=na*^GWrQe>&v5zv1+-4Q|hCPo<#Gdi{Ru
zm`D>&M+_m)8yh-2($~0mQ%8YmL|&F%%-jM=Ku4vY(j+2aF@vOgdnGY4m2^uBR=qI(
zITAOc;F^QI&x771i#U+ukc9eklmhQ3sl;t(|MKwp-~(Qd_?r!*Cip0C!A7FWlr2$d
zTl{Yy3u)(soUE7EPv!mjnif;yH$w?Eu#B3s3@PC+C?k3qHhe5L8(M<8Mmaf<PnaJ=
zRM6mAcIZ*ABaLh@<O#6&;!Pu9!ag_=Mvx=s9*pH`T)_G-Nr{4wXIfLTs<<mUmiD3i
z_(36x&=eH<!loyCMR-?rZagyk&NR_Ywm_kVQFKVyK`zD4S8H<JFMymIh<fHYn4{fU
zz~F73-WQfky6bVI@^Vqx84QO@iZ0_tA&_Dw;K=PGvNJioe?k}SN$t(y=at$&iC-by
zXjZ|=5A}zEa(&PGkjdloRC5I^GBK>Hw}<XHOe?D5_mN~_+X-kY`i|Vkuk%_R);G%*
zV0)!rj_O<H$8&CucLXJ41_*jqR<ktU#6Q1o#EM9h_6m73b&VxkwE#QzD6WQARXL(w
z5`wXa!$uLl<E`7$p<bWmC{E4;smBt1iuI~(+L;YPf+;{#*^NxU%zlU=jR|bkq!%X-
zNqXyL2$+6pd+^Lc$TaQnJq~xXn0w#}2wH&jG~9JvqPE%f-i9V5PuFh7jPDvQN02w1
zH<dVCE#$<`RkaSO&(9cA)TTn*9|dETs^r|MX~sSFY`KBMsZFh4$k13nG4|~ljMSq_
zxI0|#W<8#ER%CtY;8B1)S0L3x?y=*(y5CLGXOy_@DB`S1!Cx&h-y1l0+j77g6w_&5
z969g#2HJ9b03NF)Y%Q2XX7TbaXXf7V8=OB{wVcm2Yu5YZe13aykaV|)k4???VNEl3
z)cIU=ho+wwC+fX_VXV-=YP=-AGJ(evqLh3h0|P_HWmRN?N$&~-H*O^uPAeLrB1hFy
z`!f}ekiXDM!<SZwnGD{%+FVw4AzmZ@<=-|IMA%55a-zN%1ju~09-z(9^lA|u(s-tE
z=S}nXj;+P~WCAOaBx!qkV<`$F1I|sL-Lh2N`uo)Hc5`dK*Nt3IO*MFHq4~yHsoP_u
z$mnl|lAaoS@jmxLI7Y4C=Q=jK%o~DxWG}<rwDUuHkd3$zK8Z!y)bM=xF+|jM?L<)v
zK?o~(L1+${jaBla<qtT0SwJM+WDlFFxqz3=!ABpj{D!WK=Pa*XOmX~L1gro%A2%G5
z!zFG=PknU`j0z-#XRFTD_VrA2c`Nn~;OlYA7H`A96-pbOhR=4t_Mx2e(e@B_U~n5W
zX{YqzPwCf^!N_xI%_oJB6pnNZ*ESo)`QBGQJ1n+87Fl$HmX`bF?Xi1$74!DD&(54u
z6XM0jVnm}6Hx=t!T6DE2quDfLt^|c#5D5qfa(|^t{?{ST#-62Yg>hZkn-@d#-S%mq
zgOz(YG1;G3P5w-#lp?F*-5eJqgq7<zA-fkZVPXj1A}JkK22O`_FpllqgybkQbe=S^
zbGXiazpN6C$o}Dk6PqavN4@DuqVI7HrQJ&<D9;=z_Z=3&tW6>*%$t{p`KxR!vGNA(
z@8d)*bzz!raR-Fi(zG!9UEJMq{4KR%1FL_mKbn7{%XgXxss-C4lhSGeo1=hA<-&D|
z8qN@6Wdnd+G=3o#WdrLjSbU2=I$!oSA-6Te09vgW)?ZGFQRym4EHex(0!y96|HQ}r
zpwE+>+=nkI!MHA%CM2l%<GI-C04+W_L^S#rgRT(?hB&^Cw}i%Jx`3CM!`?L>`<Y2_
zz=w|$qP5r?KtD_(GJo&2$1<`Aa$>@}!vve7fzs@QYdR~fX~~cNPPiw31te}TdP@f#
zORGUyehWfv?oUC=LrC{4v|0l!1eCfRpBNR-0M$8XNT9eI?A~1EsF}~FKKv^Xw+%Tr
z3kSpQbQhbUm0^iVAvZJ8Zq8|iJ~o%v0ue<$9j)b)fw4YS7tw+Vl*Z0RQ6CRvpQRRJ
z_Wt_uNFd3-1K?jD)!iT89&|h1rxqOoNGMLiMLuvYDBw?kk6uIQRSbWhqmfK?m5V9=
z%5OkYT0Bd3n|X0&kAls-nER)L6O)dDtx?{rFSPlbMPq46#dYS;%<LToAxZb)eY*Y}
zg%!i3rvbA2V<cnVV%}me$GYJJzN`l-7I3ajdyq~ad=4oejQX5bJ|;VL$hX*3a0<;~
zOT&uE;XG*C&$$Na<p)5Wt{YiIgNK2xKqf1wPV8R|g9u<P8M~7eDo0gLWKIeed?EX=
zX(><ysh=Xp7m8PwHhfQtyCYt`-osSrf7&VFyQUMeO^H36TKL<vK}jhOaLbG!xU6YL
zIyDT%*&V*7gQVPLaiN(ey;e2q%CN6rrxM-c`f=gz37hreDRW@FGeWy0=T#k5s5f$M
z(0uc8S95ue(Kd0sr)g(q<~$kMg$?q>d_%w`0g-Tc0cXEnTVIMC%n+eFLbf0|@s@|A
zQF4=rMHIQVOmX{3ijy4}Nikd05K}0ldK%3-FJ4ov6S{p|>~KH*?w+z7b1Pye0#?TK
z|J4n@N==?Rg;lB2HI|B=;I{hL=Tg+6zzo=WZ!(in_?Bgs6zziK&{iP9kZo*JCZoeK
za%GK)u4}$jN3o+J8uS-97$lRVNE;Lx$sL5hUxVz90q3!I%)Ql=VyBT`K|5Z2Xtna#
zK|Mrtu^kg3+3qu_n|OceL;i(_P5)t<_3nOtXT{I9YDVM@?0TgwVjVc1lGn6ZPGmAT
zm7&&}>%-gFcz5~;U;yudG8PD^%q9R$uYjxC+2027&-oqJlPzl%M(kycFY2bdj{#ee
z-4mU^zNd-Hw$znjE%G+bDCma*?!zuhrzT~Sz9iwmR4H}rdKRn4sx)jOgU3Hu=&7g_
zAAS%>=P_!*<Jh+o=~ooM`h|j-dvJPu=rMI~17Uff*c(Z%R8-&GdP*vw=q0~hk(9T8
zV5Ns_(|24m`!<szzR5Lyn%w&CLnQ!X#hTutBB=20Jzb~?RMGo#z$W`*2dRwthUn~g
zOJ0AX8V~fl(<I75m0YUJB}_;CeR>MP^{XGdb!dHOyX}wvKy9xB4~Sxq5dPb9TTA~u
zA-$Ja&ktB!cG_3H)A&hCakjggo{&MbVww<ro3fu#kd^N*Y{zw;n(;nQ_5d1@!kZpE
zv90zaBe2~!ckZ^+JI^>m$=Z#=qVa<+4^UhLcjavN)G!L}@B(TZEmT?HtpSiq>)txD
z57NG*q*^;~n6tbF^2o=Ne+--wzJH09o^F87P*vchP2Il%&Nn6+|K>!HymSiaPiwV@
zuiY`12OXg%xWp#_DPR>TY?L29-<6d7Fj!k~yl#$^FDV7v6Fd%Qid4^Gbn7}?n6Cng
zyQK#i{)iL=Hiv*c$qge#3d9fE6&Im2e%7HDlXzucIJbpNadIYlsk%bu^Bo#_KKH5r
zISEEnViwlG^@+peDtm508*@nz!*{=#LAr+8Ej@<_pGuaokJ7ZIKhh&j5V#g%IUNn$
z_sKmB#~VnSBFZrF)b9oX_u2}xo-9IHZL^FK+6ikoW#6eRPhp6_M~n~uXmg&YUB%CL
zJ?i9(BqTZxzRZb<Rj>WSfPpsGn;gAAaS7m9wF}KYKv`OYRrOj!KI!pN&(1NY7$M^Q
zq7=@evI<VPd`7(^mt(*9aAmSeP9outes75<)tNh-FPhp<9fxS3yxi1{1=s%TD|*E$
z*OpvQz~YJGAqnaEq~h>jEzN<kBuE5~a0aW7-Ud>c{K|f1`;;X5pfafb&ninI&Q`E}
zzL$}Y1oQ4`k$Q5u>Gv|uL9CP;3UTM9E6+mzQKFfyXHNJ(D$lxXLUY)Axp!N4764>J
z?MQb|R{2wtLu)ncxbmX6(8sxRm(^?MeQR<>0~ldm{$-p4^}z+LTn02J0Cq!|NCs@c
zQiXpqX{i&McXaXL!{*iQuWjSXpFBU#OK|vLLNRv`KL4N_5iK$O&_W_`Z%y(0n2Oe~
zgF{d8`xKTNJn~dwMC7Y2V`p-U)yT4*JW-#^yre3`%{w9R2g1aBWT=EA;%L8WA|qD^
zB9*8<(@&_JTXz@PSoqQ~Zu8Y^;sT0BBwB%M9m<zs?TYFKcu7YgI_;^eFoo$YiXrnR
z4z<p>;J`OSH{^pe6nRV8*Q~cR=Fc!8TE=rzbu=T(po{jJ!FNJ?8eSM@&LYTj6S=U$
zi9zan@(2&&`#V2$CNFDJHX#AHf<E%(Im?}**P89L4kE%{w@%PzW@Uc=-$ut3sD*V?
z@X*w+`{ac-nKeJ1QL?s*7Y@n0_qv<&S)-gdEb_z-48HyyH^>$k&EaN!_C_hD59-Mw
ztUhG6@O$_ce0P`a!pDye>Mi}e6{lsIkwlIb=I=cpq|anRBySMMv+tq3u|M)X91c=P
zXSskY<QXD-wrtcalI@PvxVLAvsL3;BoYQPK{(6GXVK!1QZ%#?!U-N<z{=TVVCSmu?
zovH$zMbTSEr$j5}6Gu;_60)y4F?W<%xT6kUl5+kv%{huEE$<~R$S6L!y~>E)rZFYa
zl~b&*VxgOcI6FKP6;Q@yHVLA?eM<@V`2OYeo`}$aF_PA>SfKB;WNzIpdHfxhr<a%N
z#ITxW8HWQ^IXy)(HbO*DP|!Mz&}f%kp;|*XG8n_~`9AeN{`Lcmv!v^iU(Sh)c_!3n
zk@gsJ>Xj%>Kz-IJnX$nkv$gakO$f$((Q=XT+MT2LGHlGHduh@ip|{ZJ%nhZ@E1Bz{
z<_;f_3o-d)p6<ZzFy)AUHZRK(JCGLLGC#gM@+(ooEp}T^&-Xa4;pC8sOx|gsmsH3U
zHNNF?8W65ie;IfD70%+G!5F`&q*#)fvsB(T=#suK%8gza8@?G^*~-~A@N8cCKQko%
z)d@|8PWIPPwWe56_}Rr~r2%(1p1J#EwOMWUN12d|g=7hF@VB%yAdfc>B}t#+iWH=I
z#l<Zk+M*)pVj4&M=;iyx`yK-)Y~so&Ol52zjL{nJcLR-Pl-$>d;?9fXD2Od`EbqvQ
zwg9*VLE2-X@q0;oy=7;#A3ghe77&YNG&6~|=k?IBZ-Sm)W@s=%MH8v^?R9(v#U&Xn
zz_(PMe<JxZ$}Wx%+s5_`0oJ4>XPnHd2OwTGq1#Y8C9O^h3uZ^}!ga?lx36n=K5Q0t
zlizJmx_|cpT^-Sk`5n()3op8^aDvwiHWEgb2^>?S)7Tqbwxq*^1C8FGY*Jat6~u8H
zX}NO7U)1L$zXU+_(7p~OtOdc<tM@oak&+(<S%&z(e|HdU0bqQqWcl^8^cFu`JpIn0
zS3F;$R&IH?WZGP!L`F)CR=W6}t-v9V6UQ#oJfA%;cXR~3QJ*u&Hf`*E|D2$n0Z4HZ
z`q^veu;HPNrCYxtq5xW0Zkxv*4~Aw(_B~w|O>9gp_$@vQIE*f|p=Mr-IKrQI1fY7i
z!D`$qIR@!!p%%%3rzw=17`B>GdPC%zF>xst+8_BD*2vATb4>BIDR`L{F^y5h2hWs4
z4Q|$B(<0PyJ<rrg=GavbE7<DMO&Hc`w`JunX4<lN{(QY=d8nii0iPmis}XS1jY#hp
zdBQptltdpb&<$x-sU~Brv9oIa@Ex&~WE&!n#!Mr*7uFXbz&Nxtva++#`X%42FNvK0
z&JCS2>hy6pdDP#Yo%{91Ftww0(-8ah`~!}l%LmRNC*(}4@fBt`7CUOW`B_>2r<8k7
zw7>4ps03Al;=)CrqwALB36S#oxGbXA%-=@ZP`<+(^|d5YaB^CY-0Hx^*2RE6zqt{8
zjWqare><#nc2iKOTZazbi9;AaXW{pKnGJo@XthPfYk2ipOy861n|bP6ZmqmqoDj>B
zh#w^0d_jxtR&**l1_t)R2&bri4C|$So161mZ&Y?9dshG<Xcp9inY4J^lggmj{MuZ%
z03=w$RNJ^&%d(HpV4!d3JO<IEckST3GbT5KzUMU%S<Y=&bvYsV^!E8#fsEbmlfExh
z&x-8SuvI>Wo=u)AQbR{)Ss!QoisqZ2F?O<8lG9X3!%H$#RN{NchxFD^Q2OLy(s5oj
z)~n4J5svDH3ZQ7X!O`TiXROEH%7~+m>)1j1_0?OUjtD&jzItP~C5lhEbJvUPw$^{K
ziNB!Qw_FIU<+wKAp_UgGDgoJQF`@oLHhsUZ|GIt<h&Y@^@ol}LO6B&-pHi<^6Svlv
zCL3($|E{7km9`(Ow{mCX`1=G9*m8WIN|qcTi&Yf-Lr3X3@Q@$7C>f|t%x&j`ZV=cG
zd-VU^#OvU+uC(!0N($Ak?cR*mq0w@hF4gzteqdze=;rA_$%DdN@I`>^EYtLzq=>ag
zF-GJUORd|WFyq)@qLpM)+!LaJ_in6LJFKNBtCrWhI$BLlaDjRGwT&3uf%w_aj%4zJ
z{Dc461#FzZj+A<NxI3M$RCtUzD_ec`XQ-Pd7Nt$^sUPdsE9HDIJv6a%X2ImE>{+pG
zy4DPnCVlLQ^S)XON^AK6$xYhzk~gjLdHeUd8FcYwE^3c-n!SQu7(&3f^Y;h9Y&XC}
z&M56EjqSyvr4vLs?EJ-!SdW?|r=<0gUjDmj{QyP0+H>m*4akN*(q~|0)0+NHZil|^
zxYMO5ZXXbk!~map@oB$R<XBb4bZ2zxGr7e!+KWrbx_r%2nq{iXox{4Ob_FqqGu*A5
z9?qf3Pm`X!A!JeN&uMd9Kit0i95N+njAI=5dXLgIqe$Lu9w#B<%4q<EY(BB3#t%J)
znXk&df49MO=-L5^3>Og`-4ugOE-8Hk)9C*t!7$!ROnM)vx=9u5YXzJ>RJ2lpJ0VUR
zy0QKyxqY*p_&F>THTnbtenm7vU(LJTfSo7(nG_jw(F}PczCz5`{>fG|8ljGv?gTvK
zpnn60(h^gY*PkJ1HE2TLBOzi$9F=cry&@~}QMLXQk0<;#K4+A@)`Z=Oc8u_TJi_Sa
z!YzoOcOsO`0b$;3_s?SGUsbvy15pXCVs-m(qjPD6<71-MdV;zP;C*63_}9*&Gir-t
zc1r0aM(Cg$HU3;h1&;gTIm3#Y76O9Ik{{eJ05g9G(hBHyG=c54#Qtow9%4cduW$~u
zr6Rr$DEu&pW^Me9Od*MnxY{f|%UdVq1o+JT`EQ#Y+F|HTorTqKHSf2{Nsz;VIMMnn
z_nNxp;5(a@8u6sblj#CzV4GhZeqUtwR7S%2Vw<WZG7@+A?(sMs@O=9#=`=UVRwOTp
zc`6rXcQ8<K>kAK$lw~)E>QA+FHmZ?rgW0IP8ONUayVH{St{5Tk@)8exmPF@Hg1!wA
z+}r^l|ASKXQ<W@h?z`)0j?1+X;BO}NQe9i3hWHi)1cZJ3Co8L#4FACG<><V2rD}Tl
zBsEBao9)@<Wv|~$Q6k^HSgPmH_W(DlN~ZhZiJ1nAmSu#B9Ul#vaU6i#55&-u*a0%<
zhmYRXCX-n(GTUoB?8(PXM7cZ0C*c1Kdcu-4N($l^ZW%>Ijmz5H++3{QP+7A3kf7^C
zHWLB|W?a7I{rD#_ew{gF7|A~Qg)?l(X@ZpOH`+<KZex*zd=387uVs+ujRIS1v^75W
zAIP&6_p_3d+h3@aK~s5yyC>CrhWffzdF;u^%%Hz8J<7L6dPj`gG$g{J0<&zUNMoSU
z#Q^TR@b<EFxu-G+-`3lw3ZEPVMWj2lt+km%pXX1~<9ueH<SmGlKabR)(6OMhq&P+I
zv<abIT=%9`n;Gwn>B&IV+V;dwr1O-DM5j$xu$BL?h#~w?RJfTK%WmehmihhLP@lbm
zpVeSiHr<+r8SBj>&~A~6mJeMcO#pev<+9+eldnfo!my9+G2Tco-@;Vb^&DBVon9}F
z;W~yla5oUJhvt?h81wap3!~ioz3aI&y|(#IOI<fS^j<UA`ovV`wfxl+x8llJ2V=v1
zLhs9N$v8N&PXN}Lo|p=C0C5Cq^~0(n=hv0h4Sg<s?l)714xl5(@Q8xQn;|G;M%fhc
zCHJmr+8AGY+ni(?m*2_NwmD4*{x=*QAH68-J#4hjV_SL!?`jtj(1>4c#4gJMioa==
z;m?K68hY#88@}cVfmYJ&8lbpy?$?u~!SQ4&&`mAZW@pvI0&2YiwB^JU>n`ovD-sz*
z05)8mIh#>YvTT~9d-np8NbxGtFAHY(qDQ&;g=mGevu*l$+HNuOYK%s31Rh>9^iL>r
zOvuTPa(`?1TQQYcWxNSaL2A{ckhLlx|HUN+js%VqDt)8qJ`o=;FYYFwxtZqP>|``s
zjmooq-H?o&a0jQ5We0VE88}5Z+}i`Rfh0a}${H!-awD==#`&bU9m|NSsS#%L-UB!)
zgx_H8M+V&Sq|nund+2*`okucX$C`(Qgar8#VPNarzjK)UuCHHacmHrl#Zp!dwpc}v
zx%sOAPR*Byc>@x2Z<wla<G_cXsjhaeh#}3`yyT;14;@FPaYX#SBrYR^w`u%XEUzwk
zL1624&eYaU(?uI{elElA<9o?NHWl|HVZEq2T|6yuOZ_4-xLftvahx*cEv&HIVE$r@
z8hYHeJ_yioHXr;SaN9ZvpCoDe<A=jZ1BRBCR$)<*h=arDJqyQFQu^EXlq+BHGhgq=
zeb0uN$_qzNbnIIR=k>jgg`D*r&#9MSeX#fWPEdA}zPH{F2L+EkG52%Rio)S;YaS>s
zFE0&y>0S<#;zY;z<=h^7Bg_BNnO|PXrQ@kAG)`DXi~c4OyJHz!-tYFc-VJc3rgqTW
z5BZJ&f51#o6touV_fcHw4U!|AIPMvh6M1VLG5K=XK6!5+Bnr1LB6|QeJ3_Mq7bTa0
zky6dmo(he2hTsLNRk0;aiG!C7?=!48Fu3Sd-g?VDLX*1Ja_SW4@)ikaKzqHyE1cHI
zeu~xJ-K|`tr-{3RNo?$7;n?Thi7I`U2@s>Wj%6o4VLD}l6N{Gq(RtszHRy#~yVRl7
zzWg|Y=O(!B)QKDpy8%ZqQMMPGR<EPXgkxsNOJl0dM3O8`cDdGIe?pi4_6RsU-c+~B
z2j<s}8%2jkjuAB7vPKfdzqXi1dW}lk|K#P%)=m6A6nNgri8OBhQaVl3FyHRd-gym8
zTz_zpubL7>TE5oKu9GCU#!U54Xp?}0N5bJ5Mw+^526zu2Nr`!2ghucWv!o9TW*|SE
z!bCU-e?q-4>c7HU;LPprN3XDEDMyvj4h;HS3U>AcuX>IbH_U~aTfy#erI1&W36LYy
zJW>ydHu2>SGE|lwpTCr5aX~tT37+r^?nLJa-Uh}$^h+p|iEcPmmGI5AS}L?|sn1TL
zZ>jB+x~1;^SX*EER(|Pn%9bcP|Nc8w$#Kk5{GRK8;NmE0P3e>%l)70IQWqQh!#@AI
z1G^bc*SGxYRu*a@$;<t0Lf7p2;(3J)(-Q)fzc(`hytj9z_c_&;-5JA|y?vEEArE5q
zf%MqDcV#9+rSyYCK)r)>x4*u40cied57&VnU0?Z=eIh4M1s?RuQ@z;DZckQi2mXFg
zuQnScE3q8ow4PhYOVGE{skghnWFq({9Si-T%GlkK^b49U=(W^*+Lp3$`}vurD1I}I
z4_KA4N@{fN<pvX3u^EbGD7%!u;RBfRSaP*b)FKa$6#j}taK?O2F<Yv!ycwj=xa)8A
zx~-FCX{1T1t|CP+ukQdW`j|KqJs))yIcN;J_nhqOEI&M#9WH`Ybp;Xrvx5Ix2IUzO
z>b4O1BTT^6o3HmEDAasN!aq}}YTE3{WvqxxnwQ+1F?R+d>_KVyqUF8$CLa)F?z!JP
zyRUoq_;-oC{L~0GpEtk`iEO91bqkB~@~`8rsXG-)H+OXzY*|s<H=77uWe8UBZazBu
zopUONojyIEWm^-2-Nen-;AV_6o=4_t-{NozH%}^;{Y@Ki&^WU_bH!c0fBH4R+CKOG
z%D6RR`r*>2fm>Xx!7Tzx{hp6&R_z7a@NvFxKi6Iy8;fsre8bK$1NLuuM{^d)VFvki
zU6){jlm+5-D(H3AiDG-XJK8qux;K^vR!HM}IajHt%l&3pyFmfd@f}U$pYMwxhn7@0
zA7(jd0UjC)alJvs_dlQ6PYbrLomH2walO+#+E;wQ5Zrg4G!3`<!mD9i@XJ{98&czO
zu3#mdIvYzt&#MYp)aeVtBsD|Pn@uT`)`#sIA4}}`ajk;@!o9rV;GtffBh=@X&Y|^u
zsnn+Oo9ne{nIJXdzi<1`hyL@j9*bZu#LU0Lun7O}GfHlRIH+1pP}<?*QzTJSv9*R;
z#&DKjqgWuGtMV{Oozn^9aw9|MxMr!Ya>*>;8mM(zuzd+-481f(Z?*^PNYHTDy!?vw
zPk()}WgcT(%_{%RLRHf6bLez(wM7y}sl?@>`U<mmVf+f34&JjsbNy*uA{Co@y&gG}
zYF?~(6BeR0nslAn8sBoAXS1+ku?|s%Q>%=6rE4m=6rY7`J~I$n3&5;&_Gmr`mJAa2
zl=S%WUke$t_ow{*Je~2fU6P@u_WZA23gbhb=ASF{8kYose)!&X1D>YXi${E3pKp)H
z_)kZ0)4Nt08hE_+vx2>w9l+9{+f_I75<cyIGYa$~HTai3;nzitu#(z)*e}e*L*I?I
zSeLb_cL+OfiuqEg<(BZZXjPlN?6r5<)C&4uj&ir^EZ5mSo2%(rYmxD?<M7NQ7ps4?
z$)T{=;0GP(=d7l1Vf61lVXp2q5#0vEeEC0~@XxCQ&~bEwt<K)Vo<EN)65r}n=R2Ru
z9u@3{Kf8t^#bo-;#pxK0z;qM=uU60(<3FIgD;i<d&=Yy^-t($0^pEY0*sb~TS!cI;
znYLE5>5Tl%4(YV*WP&W%;as!%d=Ogd5;s-nd+YP~sv%?=qT{CZK|{;wasR_RB7-$o
zJGryC**um{rlbirRU06Q1)!*}J<)4hAmnzUO;l(_L0+MHV?MlMba+L3{{$IUwA{C=
zUDDky-x;Lnwmrs%f(FvMmq#2De_s7gM2-2s=EeM+jib%nJd3vH*8Z4QMDAvxL54hR
zg)3y#g#<7xA*|DO%CJCMj*gD@*2uwF(T+_*{DIt|&9eIdEw8^hUVdD!mx*J1xeVET
z3px=aoNq!&bDPK%nah(Uf^gDbdRsxkXFP>N>U)`)qS15x>N_=Mvfy6X_)o;1GOwEb
zV=3I@!E?nj<!UAI*hSX!WygDZt)8_8Igc;_ECuYHt*tH4=koF50W(U6G7N%0AvHrg
z?nOJguR_kg|7&L`|8gTdSu@(}CK19mXF$~-y>_z{m=VwAJVGWrwxd125w%eV8da<V
z9b>6p*J%4OjYPO*RTihps<Bv_mc%&{PFjPtr;A_O;uT&eYf%(y88Mk2Cmxz3_jIc5
z!k^C#!qawcOEjB*%0Fa`+e|rUFb3D1_#T>(ztOjHB#Nu%OPH&polaOBc)hIRm;6)W
zU1tAF;;qB-&~g(p6qE-9x|IbQe5$*f;{<o^T-#pXTCY+;&4bit=Pv$dE070@GqIMe
z=L?moMJ>ot8CvWnvjgotz&JNBaS2qe`|)JS+1?z<$h!N>y_Dh_H8k*$Rp2xt3RVqF
z=j*}Y?zkRO2U8iJtp6}Cl>C_Pb;4^m_LiRKMtt{=b>JLKTsXS*dQRsZKK|Bt(RlH$
z`fPO{EV&#-@Sjwe4#(d+GsLz(9F`o<>Dbr8@K8UXoNGT>hj7m-rtFzXt=Q$L)Qm53
zZf>o?F(+dSrq5K<7f$+kX-SW~fIc=$r<>KbOU`B-Ab8e+=rLC(@Vx2%&t?7}=lS=O
zc`ID!wbm>1_8V9n_9j%SJ;Tmmt<qa|ZB`H9wQnsVD#Sh#v?$R{IomB8diXg-l<2jy
zR?O8hu?wo|AaO0T=S)^}Ue?e#mfbwgv|ep>JTSZMa&3Ubdw*OMdhRDEnaZr<JYHHb
zd$Lw|Sehk@W!d~?kGwwJH&St6Kk5Kgw-!A5%%qu{8Rx3D5PSous%0tlxw@dYc}++3
z0ox!iE3+wc?PM6-jmtN2)Qn5`q_^z??b`M>PMShvndEALb~wE6{&{~t5`kt3iI!+2
zx9yTk7ytK#63V-m$J2V40^*c*tyNrG#<$fdYRM(Y%MG`|{Yty9FW|5^?FJ^4Xakdm
z{wbd!w5sp1Ls(+a-o|M;)dSl<Gy9hFAh1FE8v46W7NEx)`Ha&a*k3esCB?+<YV&*_
z4(LhQ&B6?Fnw7G?wf*Evl)N+m&Z@N`=z?3WFAd_>VOU9f1|3Q`#MGi+V-fY*Vz<iY
z1p^<`CB15Kg60vAyr*F<3diLWg6}UTlh>2oPS;wwY?nBe8=dK)+9+SW{|m1DuY3dN
z9|H?_8L`3Puuc5>LO4M(wR&=5Z1VbKcfvZQCdQgkJ+!2|JjB@WXaDmpm5a!{koQ=R
zy{?**2S9WDZr%o9Sgupi8MxD2DmB<uhS!u1ez60C)7r;su?&pC<*f@t4WB)SFTg&n
zP}wr;o!yoN6+MSmH}hy_S8Fvz?@h?fC<<iJ^m%E)pkU>6DK~#fgeaA%WsVD+vATdf
z%g^;>Q=%Jp{d1g18`AHolgCG>JA2KiflCop@~D>L&^Q?B|6c~^heXg7|53Tiq)@G{
zIN$4P-v|bwhTw9aVen`RQ3tv0L0go$NzL>Lrv8)CwOWwjv-Gb*W>5>hFIERz6)UJy
zKEXzgw7Y{aLGG~N1T+X2{4szdEV(Ol_?8~~p6b%42@A{){$md21<mWZk#lF1Y24-x
z*#{oL5`9$?yZOW6LHq0T=|S9DaV!O&OJVB<!Zm|xzB@)O`}{;}QCd^b&$E%^@n4@?
zq@t+myl(Ae<>YDt0+4=Mv-Sc0KQ4qy>A>i3utwoqEVvl)Z+(AZ72hihE6nmj^QetR
zXcqa#Qee43Luc))LiOQj$$f1eAxHUxYdF}Ixr^0>xl;|OM^)CqoYK0YCJk^MSF4I9
zlv#+Zj@K#GnfC1EE-C%mX#sFLg;YcI8*OU@fuqhh7XXRnW$c&XIo2f@({26rrJVPs
zDCzDTn1b_;kkt8<|K_@<ac)4(mAkpPGS0zS-CAW<eMIYEH?#^fl=~j4B2{*?-AL>d
zov`x$Kg&TvC?>F*FH3t>ne8FvviTf12;;-9w_UD7ZoVH!519?(M+IN!7K8i4N%wwf
z2lf}Is#la$XlB4vM(tp+iW9kMsVqyz)V5@Eu2vm3=}(-*RQBusyQv-wP=6|z0$Bmn
zG_0|}o#4+p)^7cMeGk~RZ`v=%_`%-y7j3hRUB~O*S^kab7|(oHx!M>h|1p&RSGY<o
zgCm?TVCsAMXZ=EWO2fz26h}s+MD}%QDfoKBXQ{}|p68&we5B<n;}i_=7T9p(J&|^K
zU2Xu4j*wSg?SXS;?a|^&M8oUM7hTrx-20m2vMw!+t=PG0d0^#o5czVAO*Q6IhR@BI
z=$7r=UAZ3gj6>Fid308$r2(V;l)|l<`Dxtvas5F0B=GSwz|XDW;0wTe-jdWlN*TB8
zuqkS`v9zuv607>`w?hdeD;>0{9bMwdUqUqWjQrzV(p@uv-%a_Hmix_rg0R0I!Y(Y@
zKU}A%@q*XYY^z=`658+PmSIPO8~Ac<?+lB=V)waHEuj&+iA?{vPg-9<+|MH_7GU)a
zZ6ciyuKTE%9ikb+i(4_zWN&R((`Ly!g{%(1#ZJgeofCDioA3!njj|)1aQn5E#qvRN
za`H3^8yF_*{DlJI1UjS&F^$Y=FQNAU!+S%CZg0t&up@E*cd+>fY2o}!k$gM?sY(Rh
zDgM2uG{7KWH5hq#+uEn!dMwZH{c$vSxB%42(m$&`&7?Q$^5~!N(A`cZxQfyT_0hD4
zog_+ur}6O(oRjtNtm-!i)^p&HlAtw<Q>-)#+$h}q+A@C36T@r0OhM}>ZuK%xt>&9a
z{Wed2=HuU-`%3fU&9>LB&6z)Np-x`(5IbFKGmT=6!E)2I^{(4^4en||{+lJJTll%P
zHgrY4nX_(K?`s*bKA~CWXkdT(@jUO0e~6r#-h=HwfE|@k36{^*t-lN3t^c~Y-^+N!
zQHSc6wBG(9WIC#y*T3Aj_Jc=!TAJ-VM{jgHTG-IBvrc!~S&1e+ao$(rbcUhg=gXvD
zhndN|F0ZPBV}oo-do!HH2rY83{toVYN}a5_G3Wh>4-i*f{-STXBK5@;swyf^By;5a
zu5!Zq<DkdmdZ8Be3Gj7CDB;$g9Ul(%f1f1&*O?+0q2PAtRl2~K`k$3_*wZTQRcX)d
zyaKBtgB9yysTp{i-l}o3-i=c+K@s+5wh44zu4RbmB%7~G-InDvIQ1&kQ^9whakR%v
zrvQ7s#!<88J>S&EE-DN$?V*kDL@tt=S=oU{Xa3z>4`ubGY8mluPD^<Qb$fe)H)GxS
zHn7W0&(e|}JMK*ItgXg!y$p;o8LFtqdHrSHRd-w-1WIOpI8`z5NK2019cvsPYObvh
zjT>GJh2WMgX$v_S{|8<;P{FH_a9hm`z~r*!4afz}M&1J?m|E0;m5detYC%f@X!u9*
zI(Fw!DeG`^TS%>S0=7Hh8*{>$Aa%~*bYbTK@+RhzeqP>PG3O$24|Ut2U&TigTLcmY
zZmYnXNlCzBi^rvRqCur`?^SJw`FOhRxZrIn_I|C;Ex3NoQ};K35AgqI&Pwn<p-Pw(
z)c=rAWyj91<>d%We%q6P(@*zO7nYEa;zU9@){NG!>jxKW`zBy{?M-c8K>wOBmqDU<
zzu2N8)O7*!7=Fbn(N7R^zU=apd~f}HDS~`cAG50!VEqWrdI!-Kg&AG>_5bPaOrx60
zvOTUS1;(bL0@5X5ENMZZ5Ze}1hA=2a2{I@ofdm4G3^D~oK!z{}EGfWJ5Cy>j5FsRC
z3P}i~fFdfRB#<!4JcB^OB=Anqw_YjlL-*Ithr6<JZ|*&J-+lJ}?f*IJo_rMD&YHW>
z&rCP<=4M!LTkpTa4i?=b9(1F;sote1O<H^swgbCRy)=fz7!dTWVtd|SgadU+&;?E8
zpSZdpE^03{pJNeGD$*K$d2ul99!dadp!h!TzA|R=p>uixEcA0rOtI?*$xcI+H+p%0
zt_3{x)mT&?2Nk~X;!?&WC3KBw{P1Mmbj7Ntc^JhGtK8=JAs3+pqWgn(Qcy9sp>wdB
zdiZ-Ltimbic&%|eeVjmJ`u>2pbPr>pi}~f+Gt{iM`xrycSPUDD<7Cymwn>f{eK&Bv
zX?Z*^!-}^1X-53PemyTS!Ux@3B-OmNW;MbfnWMqMyF}?<b*GF!_F+qUM@-C#JQ_@>
zpFK+KKHkHy_Q-R??>nmYv!E4M_m~+?f}$K999$-LP_rY3JyWqtR>qcf%+PSX@YTi2
zjVZQQ$oldK2_RPp-i^R!`p-0;U^DoHmv|~d<<hgLBt=96ncfcxozWeosFra7P_UQb
zm)iM^;p(+-(O1yD9*Q5L!+q|*ivpMbJS@~AoI&a}XES(UDR9-^QfiKmSpfbOu762p
zCE5IuJ69!WX!KGhv6#%7)SKCBGi!M(J>%gSQ9)?z3zjhREfAVaQFY^#FZQ?rVB{&+
z+TwW#BSXVE1WN!OTwG;$4BeGM(!osFJbvo{fRA4<m(bz-81Dp`3R;{_3t^r#AM!<d
zw6ysTZvURu0cZ$?JNSAlbi6d*Oxn&_IzXFH8-?x2L&qvUpXCS3z>EDBRYU?#d<iUP
zlEPTI*vgP{kM2^{8ZyK|(hx;NzK{M<))lHb8%8<7T6c{rzxrlhY;0^%E`qq?_Yn-d
z<PQ$E%>>^4n3=t#H`4gNS!?KOW>!`w*zew7`#Z8CIADT9B|;Y^XD8(3<)fR`74l^w
zze&{sLu14GQYu9SMm~GgjD9{(EW|#nea!4_@vHgeQ#UGo)#)tq^!iZMS1rXt2uy3{
z_XN#!B2YGtxXLyDE`L-9kw;wRO^igkPGxShB2~95mVt$n;q-J3fAaf>{~Dp6l~{4L
znFMNrEKOPT8P(M9SHH-Syy3RB9|kOZuEjUwi_>YWf}XN~qBxnetref=j8ftFz?Ii1
zTf8ICndZ5XKC?e<X1Z@S_3_)Pef<(UM8L0Sb_+uoh6AbT%xd(4=ddD0PWj!M|8Z#d
z<7x9njaf#>^Ht|Bj&(;i{Bg+2oAi*CYnX~)2Fa>O{_Q!5zWv88*NU`rF4%$!H$J{Z
z8duhmYmswB!+*M1d;L>_TEN^@0bPQs@xgE?kXY8>fJ{T%Mo>rdd;pu)$MhH_A2;s3
ze@_JMpz%O%!F$vil7=`!&bMY~E6(RdlW|}!34gvxd)21c$v5tR>STYcN{LnwzeC(q
z)4rSOPxI_9%?f=1S)F^ULW`Hx;5^ZdbZ>d4Z)Myxzv)NcY;=Cu&Z0L~RZ9sT9plIo
zRm$o*iI$XW@lw8WmT&}SQcx_HGr}@1W$uE;=D!&IjXB4EOKQ6lfBd&IBc6Xs>j{!s
zz1((Ty6bYe-xS*X$ee#KeuVe}EE^oy^^@eF;%Ik?M{4hLeE-0Jak%FchdKq`;Ybad
zX1qqpERMCk&P7qCBRzyGL7>WSNK#sTf+E$ndxB|wo&1k6u#{oMlh5EzR(0qB^TU2?
zJa)Fopt_Y=QrdDs1yXWh>C*r{Yf%Ii{3JX0@9-<NqP;6+#;Iy7hL5wLUenIb%~X{D
zZ_ed<urdswp>WcO?B8q>8gp19r@L?5v+;Sa$^yk<WjYQ0oPwb>BFWZcP2w`*D*+(=
zhdc&_K6kVwgJfS5{WLY;k1bv=Dg&Y?US^l6z$HfRhv{`SZqPCiFJzquVk&S<wV8rM
zxd0}Am~!j|FE18BuM)7-I2O6e)>IwYG~F44oe-(xlRKOcM#KZl1SxF(>NK<I*|QKU
z&vQRd1AS*ICp&$vx}+qh&@!AFU4bNelL#cg0Z5kQ--!KaH@n60_P6A6KAH#w9NQg1
zorR7ifjn2(s2WJeO;3GZybP$05N0v`aT|};&GKcVhByZEik2)6_W-0zN(DPv^U3Lb
z%JLzbrU`nS^l8pZ!ful91dCq-)n2;81g^-Lj2UjzQYwQ+PiKMp0DcB_l!8AMdinBQ
zT+TYspjOv>yB!-6-}Ghfv0-OdVUh+@Fv@kd!Jq0DCLoP9&R=reU&-f?MHM@fA$541
zu8<t%+4s*)@?j|4$v@Hp51w<toCtU->%EaB`=$q-vV7^V<}Y0|ZQKpV9n6(=Pvl|p
z&5*K-<XwUe-1o~KGx}#iNc~rn<^TmbRVjIrxX-FOk8_1sdb5k}U6E_$L6<)5>5ePs
zAp^Z+yPoS|3AB|C8tyUF&B4JzSzi7XO#;e_W%<3&*sGcSS{-Za*6l84!v#}G@$Ej%
zh0Ggct-z8;VTmFieNWhqTB~E{j|H=qRZ%A@ORkN&iFqX4x;2}?Gia``PM{`4To-OO
zg)e7HDk0xRLt&m%T-qZ0r$7_J5PA_<2CDrzf2y0w#*7^_v8W?2|MX2)-1M%noucIB
zAVGe7<SZ2UDPTILm+LGC_itx2ZFs)7p#@F*qwSO@u1MZSCu^u<Vu`6vH>)??8Bnsg
zz$Aw@L`R)7a-(9zq=#!<5HRAnB}})@ySWkbl*IC7sk3rRSp2-`VKFh^BF@bDris80
zcQNg)xqir-<mHH`)ROiP45<NzU${gBNjHD&k5FV48FHPv8`aKpNYHWuJcf!koOq{z
zAbKI<l=8`z1<&3;I@?AwY$}7<3P9_-TMFMsdbwj4T(D>4_b{vuuCwT29gTd=+&0b0
zNB$jkaP8Rrl%^f-!mPdaf9^*dZAJ4u?PYPE&UFn3E7wN@<~#<~HVc);Ct)FK0ESox
zLqB`lUef|2h?T=sbfi)kLiIZ1`cNLWJdg!=2ln>~#ytY7wCqtdOC71pXe70O065T?
zH3caY7Z-0+qOCOs%*I4aFnmT*6e}bj5M>#9Yxm+tM_rVx3Nw54f6Oja@3eM5_kWcB
z<y-jtxv;*K&r5KhWI?R-7J}eIH_V{0E2emlQsoDQ&8<Je-uQ3#+hZAH5M!00binep
zZn9OKdH@RHCnK>LAw(aY7IGcyE_5_<;}qvW<0HK+w?VM&h8j!tZW6*nl(M<o(5xaz
zm8A7V>5c9Ht>XBS=tf59#=3dB3F#G^Vmm{j{2F&)>}UQl=v9#|J?jikSd`qv#c7=u
z2oo}rW}=#QOFY*4b0d#e9i9A|(EXiHwPKWwH)PM2@`rKuk_Y$Of1Iuqk~)v6G2xM>
z#7Ly;<+m&Avb;<L=MJC$O-DCBmoQ(mq$)Hv*_D0q!(GjjCtE4LYjsk#mLNbDX`VWJ
zwo~3XWsv)bc#?^tV3GND)xRt{@A%#gL=PPTd1iQKlX7%^2v1Ah=iZ~R+kJhIP~tr%
zFC}Y0A6F!FkdNf7s!*a8i8VDZZ7}U#QV+AyKvyDtN)pSv!2T6kc;)vpb>;c56sx1Q
zv&4hzBoJVMnzJ-M7Bo}Nrr6u$S{#EJ64U6cN-S;f!rk$0+jhENIInBrnT7`=PW)CQ
zm1SQBST`8g1Yga&=Hd0VLjSAhdYuHN!@aS5F#!@J2w(m&yCa$)?eQ_$$bES-sRYmS
zGtx>N)|apwQL8tB<|b6cG#rLRVLHcwT*ehs|3GaJQ?$BzDo(d?pfaLMU0@D@h{0VL
zdYjA@jFa3`Fy*(iAsGO`M%m@aBr2<_r})}E91hF4?kIiA<qkmN2hHj*Jd7dn{ezw>
z0=yEkKJDePdD^|dT`-}_f?6d|>M8}udunU+x_ze=I6bxh*ljewx0sm7Dorl!6Gu2Q
z@$nx1f6`=kX<yO5EmhJlc^gxQ_C`{@C7I2_?ODXclKL07g0Hi!|JNc>5EuMJZSDEg
ze>y}Pr>VDt^mq--hpE8de5yytb7^_z2L|h{09pxh#(pU&XJLqhXj;VUznbz(OwhS6
zw3BVsn*U|&Oewth!zpeuy=fn9x^U-W!CU-4j!e!s$#(fSOWcTIWTI8P41U!|Uv%Fd
zt9;OnU{s6#MDItISzl7ZRy+tju6bCK*fy*A)yW3Wg(oITw8d~DiLGeu8M(uUPX@ba
zxvw4n3Y*__<U%DKuQZ!u{4L#E5Iw&+a!<YZ_J*#xsH6*9g_11?|7WL-n9#I$yu9Ut
z-(4T{z)7G0JELx|ec!Y#M{eIacRs97-PH1-#Fn;g`ST$rcov;WSG50)7HIB0L~j&F
z%Ikk?oByW)d+NyojLzzS{u?bY1!->nztglEyH(P=4Srv@+t>y^7xYcellAN({s;C^
BJ6`|*


From c583e44816a5e383493f35e69daaa92a47e40e39 Mon Sep 17 00:00:00 2001
From: Xinyu Chen <xinyu1.chen@intel.com>
Date: Tue, 5 Nov 2019 13:57:06 +0800
Subject: [PATCH 567/813] fix requantize flaky test (#16709)

---
 tests/python/quantization/test_quantization.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/python/quantization/test_quantization.py b/tests/python/quantization/test_quantization.py
index 6d6ba41ab7fb..723873ac6fb3 100644
--- a/tests/python/quantization/test_quantization.py
+++ b/tests/python/quantization/test_quantization.py
@@ -178,7 +178,7 @@ def check_requantize_with_symbol(shape, min_calib_range=None, max_calib_range=No
                                                                           max_range.asscalar(),
                                                                           min_calib_range=min_calib_range,
                                                                           max_calib_range=max_calib_range)
-        assert_almost_equal(qdata_int8.asnumpy(), qdata_int8_np)
+        assert_almost_equal(qdata_int8.asnumpy(), qdata_int8_np, atol = 1)
         assert_almost_equal(min_output.asnumpy(), np.array([min_output_np]))
         assert_almost_equal(max_output.asnumpy(), np.array([max_output_np]))
 

From 0c5677ed333b3b503898240782ab206f902e9858 Mon Sep 17 00:00:00 2001
From: Przemyslaw Tredak <ptredak@nvidia.com>
Date: Mon, 4 Nov 2019 23:38:50 -0800
Subject: [PATCH 568/813] Faster GPU NMS operator (#16542)

* Adding second NMS op

* NMS kernel

* Removing second sort

* Optimization

* Adding out-of-place ability to SortByKey

* Optimization pt2

* Optimizations pt3

* Do not recompute other boxes area every time

* Sort only topk results during second sorting

* Cleaning

* Fixes from rebase

* Fix lint and more fixes from rebase

* Fix typo

* Early exit in Triangle kernel

* Fixes

* Fix sort

* Fix from rebase

* Fix for the mixed naming convention

* Fix the index_t with int comparisoon
---
 src/operator/contrib/bounding_box.cc |   1 +
 src/operator/contrib/bounding_box.cu | 689 ++++++++++++++++++++++++++-
 src/operator/tensor/sort_op-inl.cuh  | 138 ++++--
 src/operator/tensor/sort_op.h        |  50 +-
 4 files changed, 840 insertions(+), 38 deletions(-)

diff --git a/src/operator/contrib/bounding_box.cc b/src/operator/contrib/bounding_box.cc
index 3ab11bb2d6f9..8b1d53506c47 100644
--- a/src/operator/contrib/bounding_box.cc
+++ b/src/operator/contrib/bounding_box.cc
@@ -34,6 +34,7 @@ DMLC_REGISTER_PARAMETER(BoxOverlapParam);
 DMLC_REGISTER_PARAMETER(BipartiteMatchingParam);
 DMLC_REGISTER_PARAMETER(BoxDecodeParam);
 
+
 NNVM_REGISTER_OP(_contrib_box_nms)
 .add_alias("_contrib_box_non_maximum_suppression")
 .describe(R"code(Apply non-maximum suppression to input.
diff --git a/src/operator/contrib/bounding_box.cu b/src/operator/contrib/bounding_box.cu
index b20c570ea417..ffc48d7d5a44 100644
--- a/src/operator/contrib/bounding_box.cu
+++ b/src/operator/contrib/bounding_box.cu
@@ -24,14 +24,701 @@
   * \author Joshua Zhang
   */
 
+#include <cub/cub.cuh>
+
 #include "./bounding_box-inl.cuh"
 #include "./bounding_box-inl.h"
 #include "../elemwise_op_common.h"
 
 namespace mxnet {
 namespace op {
+
+namespace {
+
+using mshadow::Tensor;
+using mshadow::Stream;
+
+template <typename DType>
+struct TempWorkspace {
+  index_t scores_temp_space;
+  DType* scores;
+  index_t scratch_space;
+  uint8_t* scratch;
+  index_t buffer_space;
+  DType* buffer;
+  index_t nms_scratch_space;
+  uint32_t* nms_scratch;
+  index_t indices_temp_spaces;
+  index_t* indices;
+};
+
+inline index_t ceil_div(index_t x, index_t y) {
+  return (x + y - 1) / y;
+}
+
+inline index_t align(index_t x, index_t alignment) {
+  return ceil_div(x, alignment)  * alignment;
+}
+
+template <typename DType>
+__global__ void FilterAndPrepareAuxDataKernel(const DType* data, DType* out, DType* scores,
+                                               index_t num_elements_per_batch,
+                                               const index_t element_width,
+                                               const index_t N,
+                                               const float threshold,
+                                               const int id_index, const int score_index,
+                                               const int background_id) {
+  index_t tid = blockIdx.x * blockDim.x + threadIdx.x;
+  bool first_in_element = (tid % element_width == 0);
+  index_t start_of_my_element = tid - (tid % element_width);
+
+  if (tid < N) {
+    DType my_score = data[start_of_my_element + score_index];
+    bool filtered_out = my_score <= threshold;
+    if (id_index != -1 && background_id != -1) {
+      DType my_id = data[start_of_my_element + id_index];
+      filtered_out = filtered_out || (my_id == background_id);
+    }
+    if (!filtered_out) {
+      out[tid] = data[tid];
+    } else {
+      out[tid] = -1;
+      my_score = -1;
+    }
+
+    if (first_in_element) {
+      index_t offset = tid / element_width;
+      scores[offset] = my_score;
+    }
+  }
+}
+
+template <typename DType>
+void FilterAndPrepareAuxData(const Tensor<gpu, 3, DType>& data,
+                             Tensor<gpu, 3, DType>* out,
+                             const TempWorkspace<DType>& workspace,
+                             const BoxNMSParam& param,
+                             Stream<gpu>* s) {
+  const int n_threads = 512;
+  index_t N = data.shape_.Size();
+  const auto blocks = ceil_div(N, n_threads);
+  FilterAndPrepareAuxDataKernel<<<blocks,
+                                   n_threads,
+                                   0,
+                                   Stream<gpu>::GetStream(s)>>>(
+    data.dptr_, out->dptr_, workspace.scores,
+    data.shape_[1], data.shape_[2], N,
+    param.valid_thresh, param.id_index,
+    param.score_index, param.background_id);
+}
+
+template <bool check_topk, bool check_score, typename DType>
+__global__ void CompactDataKernel(const index_t* indices, const DType* source,
+                                   DType* destination, const index_t topk,
+                                   const index_t element_width,
+                                   const index_t num_elements_per_batch,
+                                   const int score_index,
+                                   const index_t N) {
+  const index_t tid_start = blockIdx.x * blockDim.x + threadIdx.x;
+  for (index_t tid = tid_start; tid < N; tid += blockDim.x * gridDim.x) {
+    const index_t my_element = tid / element_width;
+    const index_t my_element_in_batch = my_element % num_elements_per_batch;
+    if (check_topk && my_element_in_batch >= topk) {
+      destination[tid] = -1;
+    } else {
+      DType ret;
+      const index_t source_element = indices[my_element];
+      DType score = 0;
+      if (check_score) {
+        score = source[source_element * element_width + score_index];
+      }
+      if (score >= 0) {
+        ret = source[source_element * element_width + tid % element_width];
+      } else {
+        ret = -1;
+      }
+      destination[tid] = ret;
+    }
+  }
+}
+
+template <bool check_score, typename DType>
+void CompactData(const Tensor<gpu, 1, index_t>& indices,
+                 const Tensor<gpu, 3, DType>& source,
+                 Tensor<gpu, 3, DType>* destination,
+                 const index_t topk,
+                 const int score_index,
+                 Stream<gpu>* s) {
+  const int n_threads = 512;
+  const index_t max_blocks = 320;
+  index_t N = source.shape_.Size();
+  const auto blocks = std::min(ceil_div(N, n_threads), max_blocks);
+  if (topk > 0) {
+    CompactDataKernel<true, check_score><<<blocks, n_threads, 0,
+                                            Stream<gpu>::GetStream(s)>>>(
+      indices.dptr_, source.dptr_,
+      destination->dptr_, topk,
+      source.shape_[2], source.shape_[1],
+      score_index, N);
+  } else {
+    CompactDataKernel<false, check_score><<<blocks, n_threads, 0,
+                                             Stream<gpu>::GetStream(s)>>>(
+      indices.dptr_, source.dptr_,
+      destination->dptr_, topk,
+      source.shape_[2], source.shape_[1],
+      score_index, N);
+  }
+}
+
+template <typename DType>
+void WorkspaceForSort(const index_t num_elem,
+                      const index_t topk,
+                      const int alignment,
+                      TempWorkspace<DType>* workspace) {
+  const index_t sort_scores_temp_space =
+    mxnet::op::SortByKeyWorkspaceSize<DType, index_t, gpu>(num_elem, false, false);
+  const index_t sort_topk_scores_temp_space =
+    mxnet::op::SortByKeyWorkspaceSize<DType, index_t, gpu>(topk, false, false);
+  workspace->scratch_space = align(std::max(sort_scores_temp_space, sort_topk_scores_temp_space),
+                                   alignment);
+}
+
+template <int encode, typename DType>
+__global__ void CalculateGreedyNMSResultsKernel(const DType* data, uint32_t* result,
+                                                 const index_t current_start,
+                                                 const index_t num_elems,
+                                                 const index_t num_batches,
+                                                 const index_t num_blocks_per_row_batch,
+                                                 const index_t num_blocks_per_row,
+                                                 const index_t topk,
+                                                 const index_t element_width,
+                                                 const index_t num_elements_per_batch,
+                                                 const int coord_index,
+                                                 const int class_index,
+                                                 const int score_index,
+                                                 const float threshold);
+
+template <typename DType>
+__global__ void ReduceNMSResultTriangleKernel(uint32_t* nms_results,
+                                               DType * data,
+                                               const index_t score_index,
+                                               const index_t element_width,
+                                               const index_t num_batches,
+                                               const index_t num_elems,
+                                               const index_t start_index,
+                                               const index_t topk);
+
+template <typename DType>
+__global__ void ReduceNMSResultRestKernel(DType* data,
+                                           const uint32_t* nms_results,
+                                           const index_t score_index,
+                                           const index_t element_width,
+                                           const index_t num_batches,
+                                           const index_t num_elements_per_batch,
+                                           const index_t start_index,
+                                           const index_t topk,
+                                           const index_t num_blocks_per_batch);
+
+template <typename DType>
+struct NMS {
+  static constexpr int THRESHOLD = 512;
+
+  void operator()(Tensor<gpu, 3, DType>* data,
+                  Tensor<gpu, 2, uint32_t>* scratch,
+                  const index_t topk,
+                  const BoxNMSParam& param,
+                  Stream<gpu>* s) {
+    const int n_threads = 512;
+    const index_t num_batches = data->shape_[0];
+    const index_t num_elements_per_batch = data->shape_[1];
+    const index_t element_width = data->shape_[2];
+    for (index_t current_start = 0; current_start < topk; current_start += THRESHOLD) {
+      const index_t n_elems = topk - current_start;
+      const index_t num_blocks_per_row_batch = ceil_div(n_elems, n_threads);
+      const index_t num_blocks_per_row =  num_blocks_per_row_batch * num_batches;
+      const index_t n_blocks = THRESHOLD / (sizeof(uint32_t) * 8) * num_blocks_per_row;
+      if (param.in_format == box_common_enum::kCorner) {
+        CalculateGreedyNMSResultsKernel<box_common_enum::kCorner>
+          <<<n_blocks, n_threads, 0, Stream<gpu>::GetStream(s)>>>(
+            data->dptr_, scratch->dptr_, current_start, n_elems, num_batches,
+            num_blocks_per_row_batch, num_blocks_per_row, topk, element_width,
+            num_elements_per_batch, param.coord_start,
+            param.force_suppress ? -1 : param.id_index,
+            param.score_index, param.overlap_thresh);
+      } else {
+        CalculateGreedyNMSResultsKernel<box_common_enum::kCenter>
+          <<<n_blocks, n_threads, 0, Stream<gpu>::GetStream(s)>>>(
+            data->dptr_, scratch->dptr_, current_start, n_elems, num_batches,
+            num_blocks_per_row_batch, num_blocks_per_row, topk, element_width,
+            num_elements_per_batch, param.coord_start,
+            param.force_suppress ? -1 : param.id_index,
+            param.score_index, param.overlap_thresh);
+      }
+      ReduceNMSResultTriangleKernel<<<num_batches, THRESHOLD, 0, Stream<gpu>::GetStream(s)>>>(
+          scratch->dptr_, data->dptr_, param.score_index,
+          element_width, num_batches, num_elements_per_batch,
+          current_start, topk);
+      const index_t n_rest_elems = n_elems - THRESHOLD;
+      const index_t num_rest_blocks_per_batch = ceil_div(n_rest_elems, n_threads);
+      const index_t num_rest_blocks = num_rest_blocks_per_batch * num_batches;
+      if (n_rest_elems > 0) {
+        ReduceNMSResultRestKernel<<<num_rest_blocks, n_threads, 0, Stream<gpu>::GetStream(s)>>>(
+            data->dptr_, scratch->dptr_, param.score_index, element_width,
+            num_batches, num_elements_per_batch, current_start, topk,
+            num_rest_blocks_per_batch);
+      }
+    }
+  }
+};
+
+template <int encode, typename DType>
+__device__ __forceinline__ DType calculate_area(const DType b0, const DType b1,
+                                                const DType b2, const DType b3) {
+  DType width = b2;
+  DType height = b3;
+  if (encode == box_common_enum::kCorner) {
+    width -= b0;
+    height -= b1;
+  }
+  if (width < 0 || height < 0) return 0;
+  return width * height;
+}
+
+template <int encode, typename DType>
+__device__ __forceinline__ DType calculate_intersection(const DType a0, const DType a1,
+                                                        const DType a2, const DType a3,
+                                                        const DType b0, const DType b1,
+                                                        const DType b2, const DType b3) {
+  DType wx, wy;
+  if (encode == box_common_enum::kCorner) {
+    const DType left = a0 > b0 ? a0 : b0;
+    const DType bottom = a1 > b1 ? a1 : b1;
+    const DType right = a2 < b2 ? a2 : b2;
+    const DType top = a3 < b3 ? a3 : b3;
+    wx = right - left;
+    wy = top - bottom;
+  } else {
+    const DType al = 2 * a0 - a2;
+    const DType ar = 2 * a0 + a2;
+    const DType bl = 2 * b0 - b2;
+    const DType br = 2 * b0 + b2;
+    const DType left = bl > al ? bl : al;
+    const DType right = br < ar ? br : ar;
+    wx = right - left;
+    const DType ab = 2 * a1 - a3;
+    const DType at = 2 * a1 + a3;
+    const DType bb = 2 * b1 - b3;
+    const DType bt = 2 * b1 + b3;
+    const DType bottom = bb > ab ? bb : ab;
+    const DType top = bt < at ? bt : at;
+    wy = top - bottom;
+    wy = wy / 4;  // To compensate for both wx and wy being 2x too large
+  }
+  if (wx <= 0 || wy <= 0) {
+    return 0;
+  } else {
+    return (wx * wy);
+  }
+}
+
+template <int encode, typename DType>
+__launch_bounds__(512)
+__global__ void CalculateGreedyNMSResultsKernel(const DType* data, uint32_t* result,
+                                                 const index_t current_start,
+                                                 const index_t num_elems,
+                                                 const index_t num_batches,
+                                                 const index_t num_blocks_per_row_batch,
+                                                 const index_t num_blocks_per_row,
+                                                 const index_t topk,
+                                                 const index_t element_width,
+                                                 const index_t num_elements_per_batch,
+                                                 const int coord_index,
+                                                 const int class_index,
+                                                 const int score_index,
+                                                 const float threshold) {
+  constexpr int max_elem_width = 20;
+  constexpr int num_other_boxes = sizeof(uint32_t) * 8;
+  __shared__ DType other_boxes[max_elem_width * num_other_boxes];
+  __shared__ DType other_boxes_areas[num_other_boxes];
+  const index_t my_row = blockIdx.x / num_blocks_per_row;
+  const index_t my_block_offset_in_row = blockIdx.x % num_blocks_per_row;
+  const index_t my_block_offset_in_batch = my_block_offset_in_row % num_blocks_per_row_batch;
+  const index_t my_batch = (my_block_offset_in_row) / num_blocks_per_row_batch;
+  const index_t my_element_in_batch = my_block_offset_in_batch * blockDim.x +
+                                      current_start + threadIdx.x;
+
+  // Load other boxes
+  const index_t offset = (my_batch * num_elements_per_batch +
+                         current_start + my_row * num_other_boxes) *
+                         element_width;
+  for (int i = threadIdx.x; i < element_width * num_other_boxes; i += blockDim.x) {
+    other_boxes[i] = data[offset + i];
+  }
+  __syncthreads();
+
+  if (threadIdx.x < num_other_boxes) {
+    const int other_boxes_offset = element_width * threadIdx.x;
+    const DType their_area = calculate_area<encode>(
+        other_boxes[other_boxes_offset + coord_index + 0],
+        other_boxes[other_boxes_offset + coord_index + 1],
+        other_boxes[other_boxes_offset + coord_index + 2],
+        other_boxes[other_boxes_offset + coord_index + 3]);
+    other_boxes_areas[threadIdx.x] = their_area;
+  }
+  __syncthreads();
+
+  if (my_element_in_batch >= topk) return;
+
+  DType my_box[4];
+  DType my_class = -1;
+  DType my_score = -1;
+  const index_t my_offset = (my_batch * num_elements_per_batch + my_element_in_batch) *
+                            element_width;
+  my_score = data[my_offset + score_index];
+#pragma unroll
+  for (int i = 0; i < 4; ++i) {
+    my_box[i] = data[my_offset + coord_index + i];
+  }
+  if (class_index != -1) {
+    my_class = data[my_offset + class_index];
+  }
+  DType my_area = calculate_area<encode>(my_box[0], my_box[1], my_box[2], my_box[3]);
+
+  uint32_t ret = 0;
+  if (my_score != -1) {
+#pragma unroll
+    for (int i = 0; i < num_other_boxes; ++i) {
+      const int other_boxes_offset = element_width * i;
+      if ((class_index == -1 || my_class == other_boxes[other_boxes_offset + class_index]) &&
+          other_boxes[other_boxes_offset + score_index] != -1) {
+        const DType their_area = other_boxes_areas[i];
+
+        const DType intersect = calculate_intersection<encode>(
+            my_box[0], my_box[1], my_box[2], my_box[3],
+            other_boxes[other_boxes_offset + coord_index + 0],
+            other_boxes[other_boxes_offset + coord_index + 1],
+            other_boxes[other_boxes_offset + coord_index + 2],
+            other_boxes[other_boxes_offset + coord_index + 3]);
+        if (intersect > threshold * (my_area + their_area - intersect)) {
+          ret = ret | (1u << i);
+        }
+      }
+    }
+  }
+  result[(my_row * num_batches + my_batch) * topk + my_element_in_batch] = ~ret;
+}
+
+template <typename DType>
+__launch_bounds__(NMS<DType>::THRESHOLD)
+__global__ void ReduceNMSResultTriangleKernel(uint32_t* nms_results,
+                                               DType * data,
+                                               const index_t score_index,
+                                               const index_t element_width,
+                                               const index_t num_batches,
+                                               const index_t num_elements_per_batch,
+                                               const index_t start_index,
+                                               const index_t topk) {
+  constexpr int n_threads = NMS<DType>::THRESHOLD;
+  constexpr int warp_size = 32;
+  const index_t my_batch = blockIdx.x;
+  const index_t my_element_in_batch = threadIdx.x + start_index;
+  const index_t my_element = my_batch * topk + my_element_in_batch;
+  const int my_warp = threadIdx.x / warp_size;
+  const int my_lane = threadIdx.x % warp_size;
+
+  __shared__ uint32_t current_valid_boxes[n_threads / warp_size];
+  const uint32_t full_mask = 0xFFFFFFFF;
+  const uint32_t my_lane_mask = 1 << my_lane;
+  const uint32_t earlier_threads_mask = (1 << (my_lane + 1)) - 1;
+  uint32_t valid = my_lane_mask;
+  uint32_t valid_boxes = full_mask;
+
+  uint32_t my_next_mask = my_element_in_batch < topk ?
+    nms_results[my_element]:
+    full_mask;
+#pragma unroll
+  for (int i = 0; i < n_threads / warp_size; ++i) {
+    uint32_t my_mask = my_next_mask;
+    my_next_mask = (((i + 1) < n_threads / warp_size) &&
+                    (my_element_in_batch < topk)) ?
+      nms_results[(i + 1) * topk * num_batches + my_element]:
+      full_mask;
+    if (my_warp == i && !__all_sync(full_mask, my_mask == full_mask)) {
+      my_mask = my_mask | earlier_threads_mask;
+      // Loop over warp_size - 1 because the last
+      // thread does not contribute to the mask anyway
+#pragma unroll
+      for (int j = 0; j < warp_size - 1; ++j) {
+          const uint32_t mask = __shfl_sync(full_mask, valid ? my_mask : full_mask, j);
+          valid = valid & mask;
+      }
+      valid_boxes = __ballot_sync(full_mask, valid);
+    }
+    if (my_lane == 0 && my_warp == i) {
+      current_valid_boxes[i] = valid_boxes;
+    }
+    __syncthreads();
+    if ((my_warp > i) && (((~my_mask) & current_valid_boxes[i]) != 0)) {
+      valid = 0;
+    }
+  }
+  if (my_lane == 0) {
+    nms_results[my_element] = valid_boxes;
+  }
+  if (valid == 0) {
+    data[(my_batch * num_elements_per_batch + my_element_in_batch) * element_width +
+         score_index] = -1;
+  }
+}
+
+template <typename DType>
+__launch_bounds__(512)
+__global__ void ReduceNMSResultRestKernel(DType* data,
+                                           const uint32_t* nms_results,
+                                           const index_t score_index,
+                                           const index_t element_width,
+                                           const index_t num_batches,
+                                           const index_t num_elements_per_batch,
+                                           const index_t start_index,
+                                           const index_t topk,
+                                           const index_t num_blocks_per_batch) {
+  constexpr int num_other_boxes = sizeof(uint32_t) * 8;
+  constexpr int num_iterations = NMS<DType>::THRESHOLD / num_other_boxes;
+  constexpr int warp_size = 32;
+  const index_t my_block_offset_in_batch = blockIdx.x % num_blocks_per_batch;
+  const index_t my_batch = blockIdx.x / num_blocks_per_batch;
+  const index_t my_element_in_batch = my_block_offset_in_batch * blockDim.x +
+                                      start_index + NMS<DType>::THRESHOLD + threadIdx.x;
+  const index_t my_element = my_batch * topk + my_element_in_batch;
+
+  if (my_element_in_batch >= topk) return;
+
+  bool valid = true;
+
+#pragma unroll
+  for (int i = 0; i < num_iterations; ++i) {
+    const uint32_t my_mask = nms_results[i * topk * num_batches + my_element];
+    const uint32_t valid_boxes = nms_results[my_batch * topk + i * warp_size + start_index];
+
+    const bool no_hit = (valid_boxes & (~my_mask)) == 0;
+    valid = valid && no_hit;
+  }
+
+  if (!valid) {
+    data[(my_batch * num_elements_per_batch + my_element_in_batch) * element_width +
+          score_index] = -1;
+  }
+}
+
+template <typename DType>
+TempWorkspace<DType> GetWorkspace(const index_t num_batch,
+                                  const index_t num_elem,
+                                  const int width_elem,
+                                  const index_t topk,
+                                  const OpContext& ctx) {
+  TempWorkspace<DType> workspace;
+  Stream<gpu> *s = ctx.get_stream<gpu>();
+  const int alignment = 128;
+
+  // Get the workspace size
+  workspace.scores_temp_space = 2 * align(num_batch * num_elem * sizeof(DType), alignment);
+  workspace.indices_temp_spaces = 2 * align(num_batch * num_elem * sizeof(index_t), alignment);
+  WorkspaceForSort(num_elem, topk, alignment, &workspace);
+  // Place for a buffer
+  workspace.buffer_space = align(num_batch * num_elem * width_elem * sizeof(DType), alignment);
+  workspace.nms_scratch_space = align(NMS<DType>::THRESHOLD / (sizeof(uint32_t) * 8) *
+                                      num_batch * topk * sizeof(uint32_t), alignment);
+
+  const index_t workspace_size = workspace.scores_temp_space +
+                                 workspace.scratch_space +
+                                 workspace.nms_scratch_space +
+                                 workspace.indices_temp_spaces;
+
+  // Obtain the memory for workspace
+  Tensor<gpu, 1, DType> scratch_memory = ctx.requested[box_nms_enum::kTempSpace]
+    .get_space_typed<gpu, 1, DType>(mshadow::Shape1(workspace_size), s);
+
+  // Populate workspace pointers
+  workspace.scores = scratch_memory.dptr_;
+  workspace.scratch = reinterpret_cast<uint8_t*>(workspace.scores) +
+                                                 workspace.scores_temp_space;
+  workspace.buffer = reinterpret_cast<DType*>(workspace.scratch +
+                                              workspace.scratch_space);
+  workspace.nms_scratch = reinterpret_cast<uint32_t*>(
+                            reinterpret_cast<uint8_t*>(workspace.buffer) +
+                            workspace.buffer_space);
+  workspace.indices = reinterpret_cast<index_t*>(
+                            reinterpret_cast<uint8_t*>(workspace.nms_scratch) +
+                            workspace.nms_scratch_space);
+  return workspace;
+}
+
+template <typename DType>
+__global__ void ExtractScoresKernel(const DType* data, DType* scores,
+                                     const index_t N, const int element_width,
+                                     const int score_index) {
+  const index_t tid = blockIdx.x * blockDim.x + threadIdx.x;
+  if (tid < N) {
+    scores[tid] = data[tid * element_width + score_index];
+  }
+}
+
+template <typename DType>
+void CompactNMSResults(const Tensor<gpu, 3, DType>& data,
+                       Tensor<gpu, 3, DType>* out,
+                       Tensor<gpu, 1, index_t>* indices,
+                       Tensor<gpu, 1, DType>* scores,
+                       Tensor<gpu, 1, index_t>* sorted_indices,
+                       Tensor<gpu, 1, DType>* sorted_scores,
+                       Tensor<gpu, 1, char>* scratch,
+                       const int score_index,
+                       const index_t topk,
+                       Stream<gpu>* s) {
+  using mshadow::Shape1;
+  constexpr int n_threads = 512;
+  const index_t num_elements = scores->shape_.Size();
+  const index_t num_elements_per_batch = data.shape_[1];
+  const index_t num_batches = data.shape_[0];
+  const int element_width = data.shape_[2];
+  const index_t n_blocks = ceil_div(num_elements, n_threads);
+  ExtractScoresKernel<<<n_blocks, n_threads, 0, Stream<gpu>::GetStream(s)>>>(
+      data.dptr_, scores->dptr_, num_elements, element_width, score_index);
+  *indices = mshadow::expr::range<index_t>(0, num_elements);
+  for (index_t i = 0; i < num_batches; ++i) {
+    // Sort each batch separately
+    Tensor<gpu, 1, DType> scores_batch(scores->dptr_ + i * num_elements_per_batch,
+                                       Shape1(topk),
+                                       s);
+    Tensor<gpu, 1, index_t> indices_batch(indices->dptr_ + i * num_elements_per_batch,
+                                          Shape1(topk),
+                                          s);
+    Tensor<gpu, 1, DType> sorted_scores_batch(sorted_scores->dptr_ + i * num_elements_per_batch,
+                                              Shape1(topk),
+                                              s);
+    Tensor<gpu, 1, index_t> sorted_indices_batch(sorted_indices->dptr_ + i * num_elements_per_batch,
+                                                 Shape1(topk),
+                                                 s);
+    mxnet::op::SortByKey(scores_batch, indices_batch, false, scratch,
+                         0, 8 * sizeof(DType), &sorted_scores_batch,
+                         &sorted_indices_batch);
+  }
+  CompactData<true>(*sorted_indices, data, out, topk, score_index, s);
+}
+
+}  // namespace
+
+void BoxNMSForwardGPU_notemp(const nnvm::NodeAttrs& attrs,
+                             const OpContext& ctx,
+                             const std::vector<TBlob>& inputs,
+                             const std::vector<OpReqType>& req,
+                             const std::vector<TBlob>& outputs) {
+  using mshadow::Shape1;
+  using mshadow::Shape2;
+  using mshadow::Shape3;
+  CHECK_NE(req[0], kAddTo) << "BoxNMS does not support kAddTo";
+  CHECK_NE(req[0], kWriteInplace) << "BoxNMS does not support in place computation";
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 2U) << "BoxNMS output: [output, temp]";
+  const BoxNMSParam& param = nnvm::get<BoxNMSParam>(attrs.parsed);
+  Stream<gpu> *s = ctx.get_stream<gpu>();
+  mxnet::TShape in_shape = inputs[box_nms_enum::kData].shape_;
+  int indim = in_shape.ndim();
+  int num_batch = indim <= 2? 1 : in_shape.ProdShape(0, indim - 2);
+  int num_elem = in_shape[indim - 2];
+  int width_elem = in_shape[indim - 1];
+
+  MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+    Tensor<gpu, 3, DType> data = inputs[box_nms_enum::kData]
+     .get_with_shape<gpu, 3, DType>(Shape3(num_batch, num_elem, width_elem), s);
+    Tensor<gpu, 3, DType> out = outputs[box_nms_enum::kOut]
+     .get_with_shape<gpu, 3, DType>(Shape3(num_batch, num_elem, width_elem), s);
+
+    // Special case for topk == 0
+    if (param.topk == 0) {
+      if (req[0] != kNullOp &&
+          req[0] != kWriteInplace) {
+        out = mshadow::expr::F<mshadow_op::identity>(data);
+      }
+      return;
+    }
+
+    index_t topk = param.topk > 0 ? std::min(param.topk, num_elem) : num_elem;
+    const auto& workspace = GetWorkspace<DType>(num_batch, num_elem,
+                                                width_elem, topk, ctx);
+
+    FilterAndPrepareAuxData(data, &out, workspace, param, s);
+    Tensor<gpu, 1, DType> scores(workspace.scores, Shape1(num_batch * num_elem), s);
+    Tensor<gpu, 1, DType> sorted_scores(workspace.scores + scores.MSize(),
+                                        Shape1(num_batch * num_elem), s);
+    Tensor<gpu, 1, index_t> indices(workspace.indices, Shape1(num_batch * num_elem), s);
+    Tensor<gpu, 1, index_t> sorted_indices(workspace.indices + indices.MSize(),
+                                           Shape1(num_batch * num_elem), s);
+    Tensor<gpu, 1, char> scratch(reinterpret_cast<char*>(workspace.scratch),
+                                        Shape1(workspace.scratch_space), s);
+    Tensor<gpu, 3, DType> buffer(workspace.buffer,
+                                 Shape3(num_batch, num_elem, width_elem), s);
+    Tensor<gpu, 2, uint32_t> nms_scratch(workspace.nms_scratch,
+                                         Shape2(NMS<DType>::THRESHOLD / (sizeof(uint32_t) * 8),
+                                                topk * num_batch),
+                                         s);
+    indices = mshadow::expr::range<index_t>(0, num_batch * num_elem);
+    for (index_t i = 0; i < num_batch; ++i) {
+      // Sort each batch separately
+      Tensor<gpu, 1, DType> scores_batch(scores.dptr_ + i * num_elem,
+                                         Shape1(num_elem),
+                                         s);
+      Tensor<gpu, 1, index_t> indices_batch(indices.dptr_ + i * num_elem,
+                                            Shape1(num_elem),
+                                            s);
+      Tensor<gpu, 1, DType> sorted_scores_batch(sorted_scores.dptr_ + i * num_elem,
+                                                Shape1(num_elem),
+                                                s);
+      Tensor<gpu, 1, index_t> sorted_indices_batch(sorted_indices.dptr_ + i * num_elem,
+                                                   Shape1(num_elem),
+                                                   s);
+      mxnet::op::SortByKey(scores_batch, indices_batch, false, &scratch, 0,
+                           8 * sizeof(DType), &sorted_scores_batch,
+                           &sorted_indices_batch);
+    }
+    CompactData<false>(sorted_indices, out, &buffer, topk, -1, s);
+    NMS<DType> nms;
+    nms(&buffer, &nms_scratch, topk, param, s);
+    CompactNMSResults(buffer, &out, &indices, &scores, &sorted_indices,
+                      &sorted_scores, &scratch, param.score_index, topk, s);
+
+    // convert encoding
+    if (param.in_format != param.out_format) {
+      if (box_common_enum::kCenter == param.out_format) {
+        mxnet::op::mxnet_op::Kernel<corner_to_center, gpu>::Launch(s, num_batch * num_elem,
+          out.dptr_ + param.coord_start, width_elem);
+      } else {
+        mxnet::op::mxnet_op::Kernel<center_to_corner, gpu>::Launch(s, num_batch * num_elem,
+          out.dptr_ + param.coord_start, width_elem);
+      }
+    }
+  });
+}
+
+void BoxNMSForwardGPU(const nnvm::NodeAttrs& attrs,
+                      const OpContext& ctx,
+                      const std::vector<TBlob>& inputs,
+                      const std::vector<OpReqType>& req,
+                      const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mshadow::expr;
+  using namespace mxnet_op;
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 2U) << "BoxNMS output: [output, temp]";
+  if (req[1] == kNullOp) {
+    BoxNMSForwardGPU_notemp(attrs, ctx, inputs, req, outputs);
+    return;
+  }
+  BoxNMSForward<gpu>(attrs, ctx, inputs, req, outputs);
+}
+
+
 NNVM_REGISTER_OP(_contrib_box_nms)
-.set_attr<FCompute>("FCompute<gpu>", BoxNMSForward<gpu>);
+.set_attr<FCompute>("FCompute<gpu>", BoxNMSForwardGPU);
 
 NNVM_REGISTER_OP(_backward_contrib_box_nms)
 .set_attr<FCompute>("FCompute<gpu>", BoxNMSBackward<gpu>);
diff --git a/src/operator/tensor/sort_op-inl.cuh b/src/operator/tensor/sort_op-inl.cuh
index b20b466d9c2b..c157de99a4e1 100644
--- a/src/operator/tensor/sort_op-inl.cuh
+++ b/src/operator/tensor/sort_op-inl.cuh
@@ -95,13 +95,22 @@ SortPairsWorkspaceSize(const size_t num_keys) {
 
 template <typename KDType, typename VDType, typename xpu>
 inline typename std::enable_if<std::is_same<xpu, gpu>::value, size_t>::type
-SortByKeyWorkspaceSize(const size_t num_keys) {
+SortByKeyWorkspaceSize(const size_t num_keys,
+                       const bool keys_in_place,
+                       const bool values_in_place) {
 #ifdef SORT_WITH_THRUST
   return 0;
 #else
   size_t keys_bytes, values_bytes;
   WorkspaceSize4KeysAndValues<KDType, VDType>(num_keys, &keys_bytes, &values_bytes);
-  return keys_bytes + values_bytes + SortPairsWorkspaceSize<KDType, VDType>(num_keys);
+  size_t ret = SortPairsWorkspaceSize<KDType, VDType>(num_keys);
+  if (keys_in_place) {
+    ret += keys_bytes;
+  }
+  if (values_in_place) {
+    ret += values_bytes;
+  }
+  return ret;
 #endif
 }
 
@@ -111,7 +120,9 @@ inline typename std::enable_if<!(std::is_same<KDType,mshadow::half::half_t>::val
 SortByKeyImpl(mshadow::Tensor<gpu, 1, KDType> keys,
               mshadow::Tensor<gpu, 1, VDType> values, bool is_ascend,
               mshadow::Tensor<gpu, 1, char>* workspace,
-              const int begin_bit, const int end_bit) {
+              const int begin_bit, const int end_bit,
+              mshadow::Tensor<gpu, 1, KDType>* sorted_keys,
+              mshadow::Tensor<gpu, 1, VDType>* sorted_values) {
   CHECK_EQ(keys.CheckContiguous(), true);
   CHECK_EQ(values.CheckContiguous(), true);
 #if CUDA_VERSION >= 7000
@@ -135,12 +146,29 @@ SortByKeyImpl(mshadow::Tensor<gpu, 1, KDType> keys,
           NULL, NULL, NULL, NULL,
           keys.size(0), begin_bit, end_bit, stream);
     }
+
+    size_t required_storage = sortpairs_bytes +
+                              (sorted_keys == nullptr ? keys_bytes : 0) +
+                              (sorted_values == nullptr ? values_bytes : 0);
+
     // Check that we have enough storage
-    CHECK_GE(workspace->size(0), keys_bytes + values_bytes + sortpairs_bytes);
-    //
-    KDType* keys_out_ptr = reinterpret_cast<KDType *>(workspace->dptr_);
-    VDType* values_out_ptr = reinterpret_cast<VDType *>(workspace->dptr_ + keys_bytes);
-    void* temp_storage = reinterpret_cast<void *>(workspace->dptr_ + keys_bytes + values_bytes);
+    CHECK_GE(workspace->size(0), required_storage)
+      << "Workspace given to SortByKey is too small: requested " << required_storage <<
+      " B and got " << workspace->size(0) << " B.";
+
+    size_t start_keys = 0;
+    size_t start_values = start_keys +
+                          (sorted_keys == nullptr ? keys_bytes : 0);
+    size_t start_scratch = start_values +
+                           (sorted_values == nullptr ? values_bytes : 0);
+    KDType* keys_out_ptr = sorted_keys == nullptr ?
+                           reinterpret_cast<KDType *>(workspace->dptr_ + start_keys) :
+                           sorted_keys->dptr_;
+    VDType* values_out_ptr = sorted_values == nullptr ?
+                             reinterpret_cast<VDType *>(workspace->dptr_ + start_values) :
+                             sorted_values->dptr_;
+
+    void* temp_storage = reinterpret_cast<void *>(workspace->dptr_ + start_scratch);
     // Sort
     if (is_ascend) {
       cub::DeviceRadixSort::SortPairs(temp_storage, sortpairs_bytes,
@@ -152,17 +180,31 @@ SortByKeyImpl(mshadow::Tensor<gpu, 1, KDType> keys,
         keys.size(0), begin_bit, end_bit, stream);
     }
     // Copy result back to [keys, values]
-    mshadow::Tensor<gpu, 1, KDType> keys_out(keys_out_ptr, mshadow::Shape1(keys.size(0)),
-      keys.stream_);
-    mshadow::Tensor<gpu, 1, VDType> values_out(values_out_ptr, mshadow::Shape1(keys.size(0)),
-      keys.stream_);
-    mshadow::Copy(keys, keys_out, keys.stream_);
-    mshadow::Copy(values, values_out, values.stream_);
+    if (sorted_keys == nullptr) {
+      mshadow::Tensor<gpu, 1, KDType> keys_out(keys_out_ptr, mshadow::Shape1(keys.size(0)),
+        keys.stream_);
+      mshadow::Copy(keys, keys_out, keys.stream_);
+    }
+    if (sorted_values == nullptr) {
+      mshadow::Tensor<gpu, 1, VDType> values_out(values_out_ptr, mshadow::Shape1(keys.size(0)),
+        keys.stream_);
+      mshadow::Copy(values, values_out, values.stream_);
+    }
   } else {
 #endif // SORT_WITH_THRUST
     // No workspace, sort using thrust
-    thrust::device_ptr<KDType> key_iter = thrust::device_pointer_cast(keys.dptr_);
-    thrust::device_ptr<VDType> value_iter = thrust::device_pointer_cast(values.dptr_);
+    auto* k = &keys;
+    auto* v = &values;
+    if (sorted_keys != nullptr) {
+      k = sorted_keys;
+      mshadow::Copy(*sorted_keys, keys, keys.stream_);
+    }
+    if (sorted_values != nullptr) {
+      v = sorted_values;
+      mshadow::Copy(*sorted_values, values, values.stream_);
+    }
+    const auto key_iter = thrust::device_pointer_cast(k->dptr_);
+    const auto value_iter = thrust::device_pointer_cast(v->dptr_);
     if (is_ascend) {
       thrust::stable_sort_by_key(
         thrust::cuda::par.on(stream),
@@ -187,14 +229,25 @@ inline typename std::enable_if<((!std::is_same<KDType,mshadow::half::half_t>::va
 SortByKeyImpl(mshadow::Tensor<gpu, 1, KDType> keys,
               mshadow::Tensor<gpu, 1, VDType> values, bool is_ascend,
               mshadow::Tensor<gpu, 1, char>* workspace,
-              const int begin_bit, const int end_bit) {
+              const int begin_bit, const int end_bit,
+              mshadow::Tensor<gpu, 1, KDType>* sorted_keys,
+              mshadow::Tensor<gpu, 1, VDType>* sorted_values) {
   CHECK_EQ(keys.CheckContiguous(), true);
   CHECK_EQ(values.CheckContiguous(), true);
 #if CUDA_VERSION >= 9000
   cudaStream_t stream = mshadow::Stream<gpu>::GetStream(keys.stream_);
-  thrust::device_ptr<KDType> key_iter = thrust::device_pointer_cast(keys.dptr_);
-  thrust::device_ptr<__half> value_iter = thrust::device_pointer_cast(
-    reinterpret_cast<__half*>(values.dptr_));
+  auto* k = &keys;
+  auto* v = &values;
+  if (sorted_keys != nullptr) {
+    k = sorted_keys;
+    mshadow::Copy(*sorted_keys, keys, keys.stream_);
+  }
+  if (sorted_values != nullptr) {
+    v = sorted_values;
+    mshadow::Copy(*sorted_values, values, values.stream_);
+  }
+  const auto key_iter = thrust::device_pointer_cast(k->dptr_);
+  const auto value_iter = thrust::device_pointer_cast(reinterpret_cast<__half*>(v->dptr_));
   if (is_ascend) {
     thrust::stable_sort_by_key(
       thrust::cuda::par.on(stream),
@@ -216,14 +269,25 @@ inline typename std::enable_if<(std::is_same<KDType,mshadow::half::half_t>::valu
 SortByKeyImpl(mshadow::Tensor<gpu, 1, KDType> keys,
               mshadow::Tensor<gpu, 1, VDType> values, bool is_ascend,
               mshadow::Tensor<gpu, 1, char>* workspace,
-              const int begin_bit, const int end_bit) {
+              const int begin_bit, const int end_bit,
+              mshadow::Tensor<gpu, 1, KDType>* sorted_keys,
+              mshadow::Tensor<gpu, 1, VDType>* sorted_values) {
   CHECK_EQ(keys.CheckContiguous(), true);
   CHECK_EQ(values.CheckContiguous(), true);
 #if CUDA_VERSION >= 9000
   cudaStream_t stream = mshadow::Stream<gpu>::GetStream(keys.stream_);
-  thrust::device_ptr<__half> key_iter = thrust::device_pointer_cast(
-    reinterpret_cast<__half*>(keys.dptr_));
-  thrust::device_ptr<VDType> value_iter = thrust::device_pointer_cast(values.dptr_);
+  auto* k = &keys;
+  auto* v = &values;
+  if (sorted_keys != nullptr) {
+    k = sorted_keys;
+    mshadow::Copy(*sorted_keys, keys, keys.stream_);
+  }
+  if (sorted_values != nullptr) {
+    v = sorted_values;
+    mshadow::Copy(*sorted_values, values, values.stream_);
+  }
+  const auto key_iter = thrust::device_pointer_cast(reinterpret_cast<__half*>(k->dptr_));
+  const auto value_iter = thrust::device_pointer_cast(v->dptr_);
   if (is_ascend) {
     thrust::stable_sort_by_key(
       thrust::cuda::par.on(stream),
@@ -246,15 +310,25 @@ inline typename std::enable_if<(std::is_same<KDType,mshadow::half::half_t>::valu
 SortByKeyImpl(mshadow::Tensor<gpu, 1, KDType> keys,
               mshadow::Tensor<gpu, 1, VDType> values, bool is_ascend,
               mshadow::Tensor<gpu, 1, char>* workspace,
-              const int begin_bit, const int end_bit) {
+              const int begin_bit, const int end_bit,
+              mshadow::Tensor<gpu, 1, KDType>* sorted_keys,
+              mshadow::Tensor<gpu, 1, VDType>* sorted_values) {
   CHECK_EQ(keys.CheckContiguous(), true);
   CHECK_EQ(values.CheckContiguous(), true);
 #if CUDA_VERSION >= 9000
   cudaStream_t stream = mshadow::Stream<gpu>::GetStream(keys.stream_);
-  thrust::device_ptr<__half> key_iter = thrust::device_pointer_cast(
-    reinterpret_cast<__half*>(keys.dptr_));
-  thrust::device_ptr<__half> value_iter = thrust::device_pointer_cast(
-    reinterpret_cast<__half*>(values.dptr_));
+  auto* k = &keys;
+  auto* v = &values;
+  if (sorted_keys != nullptr) {
+    k = sorted_keys;
+    mshadow::Copy(*sorted_keys, keys, keys.stream_);
+  }
+  if (sorted_values != nullptr) {
+    v = sorted_values;
+    mshadow::Copy(*sorted_values, values, values.stream_);
+  }
+  const auto key_iter = thrust::device_pointer_cast(reinterpret_cast<__half*>(k->dptr_));
+  const auto value_iter = thrust::device_pointer_cast(reinterpret_cast<__half*>(v->dptr_));
   if (is_ascend) {
     thrust::stable_sort_by_key(
       thrust::cuda::par.on(stream),
@@ -273,8 +347,10 @@ SortByKeyImpl(mshadow::Tensor<gpu, 1, KDType> keys,
 template<typename KDType, typename VDType>
 inline void SortByKey(mshadow::Tensor<gpu, 1, KDType> keys, mshadow::Tensor<gpu, 1, VDType> values,
                       bool is_ascend, mshadow::Tensor<gpu, 1, char>* workspace,
-                      const int begin_bit, const int end_bit) {
-  SortByKeyImpl(keys, values, is_ascend, workspace, begin_bit, end_bit);
+                      const int begin_bit, const int end_bit,
+                      mshadow::Tensor<gpu, 1, KDType>* sorted_keys,
+                      mshadow::Tensor<gpu, 1, VDType>* sorted_values) {
+  SortByKeyImpl(keys, values, is_ascend, workspace, begin_bit, end_bit, sorted_keys, sorted_values);
 }
 
 }  // namespace op
diff --git a/src/operator/tensor/sort_op.h b/src/operator/tensor/sort_op.h
index 6d4675a0775a..11aea9db09ec 100644
--- a/src/operator/tensor/sort_op.h
+++ b/src/operator/tensor/sort_op.h
@@ -49,11 +49,17 @@ namespace op {
  * \param keys the keys to sort
  * \param values the values that sorts w.r.t the key
  * \param is_ascend whether to sort key in ascending order
+ * \param begin_bit The beginning bit of the different values in keys. Default 0.
+ * \param end_bit The ending bit of the different values in keys. Default to 8 * sizeof(dtype of key).
+ * \param sorted_keys If specified, keys will be sorted out of place.
+ * \param sorted_values If specified, values will be sorted out of place.
  */
 template<typename KDType, typename VDType>
 inline void SortByKey(mshadow::Tensor<cpu, 1, KDType> keys, mshadow::Tensor<cpu, 1, VDType> values,
                       bool is_ascend = true, mshadow::Tensor<cpu, 1, char>* workspace = NULL,
-                      const int begin_bit = 0, const int end_bit = sizeof(KDType)*8) {
+                      const int begin_bit = 0, const int end_bit = sizeof(KDType)*8,
+                      mshadow::Tensor<cpu, 1, KDType>* sorted_keys = nullptr,
+                      mshadow::Tensor<cpu, 1, VDType>* sorted_values = nullptr) {
   CHECK_EQ(keys.CheckContiguous(), true);
   CHECK_EQ(values.CheckContiguous(), true);
   CHECK_EQ(keys.size(0), values.size(0))
@@ -62,6 +68,12 @@ inline void SortByKey(mshadow::Tensor<cpu, 1, KDType> keys, mshadow::Tensor<cpu,
   std::vector<size_t> idx(keys.size(0));
   std::vector<KDType> keys_vec(keys.size(0));
   std::vector<VDType> values_vec(values.size(0));
+  if (sorted_keys == nullptr) {
+    sorted_keys = &keys;
+  }
+  if (sorted_values == nullptr) {
+    sorted_values = &values;
+  }
   for (index_t i = 0; i < keys.size(0); i++) {
     idx[i] = i;
     keys_vec[i] = keys[i];
@@ -77,18 +89,28 @@ inline void SortByKey(mshadow::Tensor<cpu, 1, KDType> keys, mshadow::Tensor<cpu,
                        {return keys_vec[i1] > keys_vec[i2]; });
   }
   for (index_t i = 0; i < values.size(0); i++) {
-    keys[i] = keys_vec[idx[i]];
-    values[i] = values_vec[idx[i]];
+    (*sorted_keys)[i] = keys_vec[idx[i]];
+    (*sorted_values)[i] = values_vec[idx[i]];
   }
 }
 
 /*!
  * \brief CPU/GPU: Return the amount of temporary storage in bytes required for SortByKey
  * \param num_keys number of keys to sort
+ * \param keys_in_place Whether the sorting of keys will happen in place.
+ *                      Default true. If set to false, subsequent
+ *                      call to SortByKey needs to specify the
+ *                      sorted_keys parameter.
+ * \param values_in_place Whether the sorting of values will happen in place.
+ *                        Default true. If set to false, subsequent
+ *                        call to SortByKey needs to specify the
+ *                        sorted_values parameter.
  */
 template <typename KDType, typename VDType, typename xpu>
 inline typename std::enable_if<std::is_same<xpu, cpu>::value, size_t>::type
-SortByKeyWorkspaceSize(const size_t num_keys) {
+SortByKeyWorkspaceSize(const size_t num_keys,
+                       const bool keys_in_place = true,
+                       const bool values_in_place = true) {
   return 0;
 }
 
@@ -97,18 +119,34 @@ SortByKeyWorkspaceSize(const size_t num_keys) {
  * \param keys the keys to sort
  * \param values the values that sorts w.r.t the key
  * \param is_ascend whether to sort key in ascending order
+ * \param begin_bit The beginning bit of the different values in keys. Default 0.
+ * \param end_bit The ending bit of the different values in keys. Default to 8 * sizeof(dtype of key).
+ * \param sorted_keys If specified, keys will be sorted out of place.
+ * \param sorted_values If specified, values will be sorted out of place.
  */
 template<typename KDType, typename VDType>
 inline void SortByKey(mshadow::Tensor<gpu, 1, KDType> keys, mshadow::Tensor<gpu, 1, VDType> values,
                       bool is_ascend = true, mshadow::Tensor<gpu, 1, char>* workspace = NULL,
-                      const int begin_bit = 0, const int end_bit = sizeof(KDType)*8);
+                      const int begin_bit = 0, const int end_bit = sizeof(KDType)*8,
+                      mshadow::Tensor<gpu, 1, KDType>* sorted_keys = nullptr,
+                      mshadow::Tensor<gpu, 1, VDType>* sorted_values = nullptr);
 /*!
  * \brief CPU/GPU: Return the amount of temporary storage in bytes required for SortByKey
  * \param num_keys number of keys to sort
+ * \param keys_in_place Whether the sorting of keys will happen in place.
+ *                      Default true. If set to false, subsequent
+ *                      call to SortByKey needs to specify the
+ *                      sorted_keys parameter.
+ * \param values_in_place Whether the sorting of values will happen in place.
+ *                        Default true. If set to false, subsequent
+ *                        call to SortByKey needs to specify the
+ *                        sorted_values parameter.
  */
 template <typename KDType, typename VDType, typename xpu>
 inline typename std::enable_if<std::is_same<xpu, gpu>::value, size_t>::type
-SortByKeyWorkspaceSize(const size_t num_keys);
+SortByKeyWorkspaceSize(const size_t num_keys,
+                       const bool keys_in_place = true,
+                       const bool values_in_place = true);
 
 }  // namespace op
 }  // namespace mxnet

From 3c404a512829d2894ffe3612dc3cb29a12a36597 Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Tue, 5 Nov 2019 08:59:05 -0800
Subject: [PATCH 569/813] Mixed data type binary ops (#16699)

* support mixed-precision binary operations

* improvement for documentations and error messages
---
 python/mxnet/ndarray/numpy/_op.py             |  40 ++
 python/mxnet/numpy/multiarray.py              |  40 ++
 src/common/utils.h                            |  30 +-
 src/operator/mshadow_op.h                     |  94 +++++
 src/operator/mxnet_op.h                       |  14 +-
 .../numpy/np_elemwise_broadcast_op.cc         | 100 ++++-
 .../numpy/np_elemwise_broadcast_op.cu         |  40 +-
 src/operator/numpy/np_elemwise_broadcast_op.h | 341 ++++++++++++++++++
 src/operator/numpy/np_true_divide.cc          |   2 +-
 src/operator/tensor/elemwise_binary_op.h      |   2 +
 tests/python/unittest/test_numpy_op.py        |  63 ++++
 11 files changed, 745 insertions(+), 21 deletions(-)
 create mode 100644 src/operator/numpy/np_elemwise_broadcast_op.h

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index 9a106083a10e..30b3d2f4d90b 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -523,6 +523,14 @@ def add(x1, x2, out=None, **kwargs):
     -------
     add : ndarray or scalar
         The sum of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars.
+
+    Notes
+    -----
+    This operator now supports automatic type promotion. The resulting type will be determined
+    according to the following rules:
+        * If both inputs are of floating number types, the output is the more precise type.
+        * If only one of the inputs is floating number type, the result is that type.
+        * If both inputs are of integer types (including boolean), not supported yet.
     """
     return _ufunc_helper(x1, x2, _npi.add, _np.add, _npi.add_scalar, None, out)
 
@@ -549,6 +557,14 @@ def subtract(x1, x2, out=None, **kwargs):
     -------
     subtract : ndarray or scalar
         The difference of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars.
+
+    Notes
+    -----
+    This operator now supports automatic type promotion. The resulting type will be determined
+    according to the following rules:
+        * If both inputs are of floating number types, the output is the more precise type.
+        * If only one of the inputs is floating number type, the result is that type.
+        * If both inputs are of integer types (including boolean), not supported yet.
     """
     return _ufunc_helper(x1, x2, _npi.subtract, _np.subtract, _npi.subtract_scalar,
                          _npi.rsubtract_scalar, out)
@@ -576,6 +592,14 @@ def multiply(x1, x2, out=None, **kwargs):
     out : ndarray or scalar
         The multiplication of x1 and x2, element-wise. This is a scalar if both x1 and x2
         are scalars.
+
+    Notes
+    -----
+    This operator now supports automatic type promotion. The resulting type will be determined
+    according to the following rules:
+        * If both inputs are of floating number types, the output is the more precise type.
+        * If only one of the inputs is floating number type, the result is that type.
+        * If both inputs are of integer types (including boolean), not supported yet.
     """
     return _ufunc_helper(x1, x2, _npi.multiply, _np.multiply, _npi.multiply_scalar, None, out)
 
@@ -603,6 +627,14 @@ def divide(x1, x2, out=None, **kwargs):
     -------
     out : ndarray or scalar
         This is a scalar if both x1 and x2 are scalars.
+
+    Notes
+    -----
+    This operator now supports automatic type promotion. The resulting type will be determined
+    according to the following rules:
+        * If both inputs are of floating number types, the output is the more precise type.
+        * If only one of the inputs is floating number type, the result is that type.
+        * If both inputs are of integer types (including boolean), the output is of float32 type.
     """
     return _ufunc_helper(x1, x2, _npi.true_divide, _np.divide, _npi.true_divide_scalar,
                          _npi.rtrue_divide_scalar, out)
@@ -633,6 +665,14 @@ def true_divide(x1, x2, out=None):
     -------
     out : ndarray or scalar
         This is a scalar if both x1 and x2 are scalars.
+
+    Notes
+    -----
+    This operator now supports automatic type promotion. The resulting type will be determined
+    according to the following rules:
+        * If both inputs are of floating number types, the output is the more precise type.
+        * If only one of the inputs is floating number type, the result is that type.
+        * If both inputs are of integer types (including boolean), the output is of float32 type.
     """
     return _ufunc_helper(x1, x2, _npi.true_divide, _np.divide, _npi.true_divide_scalar,
                          _npi.rtrue_divide_scalar, out)
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 0580e139df31..9439e751f1be 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -2402,6 +2402,14 @@ def add(x1, x2, out=None, **kwargs):
     add : ndarray or scalar
         The sum of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars.
 
+    Notes
+    -----
+    This operator now supports automatic type promotion. The resulting type will be determined
+    according to the following rules:
+        * If both inputs are of floating number types, the output is the more precise type.
+        * If only one of the inputs is floating number type, the result is that type.
+        * If both inputs are of integer types (including boolean), not supported yet.
+
     Examples
     --------
     >>> np.add(1.0, 4.0)
@@ -2440,6 +2448,14 @@ def subtract(x1, x2, out=None, **kwargs):
     subtract : ndarray or scalar
         The difference of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars.
 
+    Notes
+    -----
+    This operator now supports automatic type promotion. The resulting type will be determined
+    according to the following rules:
+        * If both inputs are of floating number types, the output is the more precise type.
+        * If only one of the inputs is floating number type, the result is that type.
+        * If both inputs are of integer types (including boolean), not supported yet.
+
     Examples
     --------
     >>> np.subtract(1.0, 4.0)
@@ -2476,6 +2492,14 @@ def multiply(x1, x2, out=None, **kwargs):
     out : ndarray or scalar
         The difference of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars.
 
+    Notes
+    -----
+    This operator now supports automatic type promotion. The resulting type will be determined
+    according to the following rules:
+        * If both inputs are of floating number types, the output is the more precise type.
+        * If only one of the inputs is floating number type, the result is that type.
+        * If both inputs are of integer types (including boolean), not supported yet.
+
     Examples
     --------
     >>> np.multiply(2.0, 4.0)
@@ -2514,6 +2538,14 @@ def divide(x1, x2, out=None, **kwargs):
     out : ndarray or scalar
         This is a scalar if both x1 and x2 are scalars.
 
+    Notes
+    -----
+    This operator now supports automatic type promotion. The resulting type will be determined
+    according to the following rules:
+        * If both inputs are of floating number types, the output is the more precise type.
+        * If only one of the inputs is floating number type, the result is that type.
+        * If both inputs are of integer types (including boolean), the output is of float32 type.
+
     Examples
     --------
     >>> np.true_divide(x, 4)
@@ -2548,6 +2580,14 @@ def true_divide(x1, x2, out=None):
     out : ndarray or scalar
         This is a scalar if both x1 and x2 are scalars.
 
+    Notes
+    -----
+    This operator now supports automatic type promotion. The resulting type will be determined
+    according to the following rules:
+        * If both inputs are of floating number types, the output is the more precise type.
+        * If only one of the inputs is floating number type, the result is that type.
+        * If both inputs are of integer types (including boolean), the output is of float32 type.
+
     Examples
     --------
     >>> x = np.arange(5)
diff --git a/src/common/utils.h b/src/common/utils.h
index b919cb301dff..0e3e35430652 100644
--- a/src/common/utils.h
+++ b/src/common/utils.h
@@ -365,6 +365,30 @@ inline bool ContainsStorageType(const std::vector<int>& ndstypes,
   return false;
 }
 
+inline std::string dtype_string(const int dtype) {
+  switch (dtype) {
+    case mshadow::kFloat32:
+      return "float";
+    case mshadow::kFloat64:
+      return "double";
+    case mshadow::kFloat16:
+      return "half";
+    case mshadow::kUint8:
+      return "unsigned char";
+    case mshadow::kInt8:
+      return "char";
+    case mshadow::kInt32:
+      return "int";
+    case mshadow::kInt64:
+      return "long long";
+    case mshadow::kBool:
+      return "bool";
+    default:
+      LOG(FATAL) << "Unknown type enum " << dtype;
+  }
+  return "unknown";
+}
+
 /*! \brief get string representation of dispatch_mode */
 inline std::string dispatch_mode_string(const DispatchMode x) {
   switch (x) {
@@ -842,7 +866,7 @@ inline bool is_float(const int dtype) {
   return dtype == mshadow::kFloat32 || dtype == mshadow::kFloat64 || dtype == mshadow::kFloat16;
 }
 
-inline int more_precise_type(const int type1, const int type2) {
+inline int get_more_precise_type(const int type1, const int type2) {
   if (type1 == type2) return type1;
   if (is_float(type1) && is_float(type2)) {
     if (type1 == mshadow::kFloat64 || type2 == mshadow::kFloat64) {
@@ -870,12 +894,12 @@ inline int more_precise_type(const int type1, const int type2) {
   return mshadow::kInt8;
 }
 
-inline int np_binary_out_type(const int type1, const int type2) {
+inline int np_binary_out_infer_type(const int type1, const int type2) {
   if ((type1 == mshadow::kUint8 && type2 == mshadow::kInt8) ||
       (type1 == mshadow::kInt8 && type2 == mshadow::kUint8)) {
     return mshadow::kInt32;
   }
-  return more_precise_type(type1, type2);
+  return get_more_precise_type(type1, type2);
 }
 
 }  // namespace common
diff --git a/src/operator/mshadow_op.h b/src/operator/mshadow_op.h
index 1ece97b0efd8..b8db165675a0 100644
--- a/src/operator/mshadow_op.h
+++ b/src/operator/mshadow_op.h
@@ -194,6 +194,100 @@ MXNET_BINARY_MATH_OP_NC(right, b);
 
 MXNET_BINARY_MATH_OP_NC(mul, a * b);
 
+#ifndef _WIN32
+struct mixed_plus {
+  template<typename DType,
+           typename std::enable_if<std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static mshadow::half::half_t Map(DType a, mshadow::half::half_t b) {
+    return static_cast<mshadow::half::half_t>(a) + b;
+  }
+
+  template<typename DType,
+           typename std::enable_if<std::is_same<DType, mshadow::half::half_t>::value ||
+                                   std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static float Map(DType a, float b) {
+    return static_cast<float>(a) + b;
+  }
+
+  template<typename DType,
+           typename std::enable_if<std::is_same<DType, mshadow::half::half_t>::value ||
+                                   std::is_same<DType, float>::value ||
+                                   std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static double Map(DType a, double b) {
+    return static_cast<double>(a) + b;
+  }
+};
+
+struct mixed_minus {
+  template<typename DType,
+           typename std::enable_if<std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static mshadow::half::half_t Map(DType a, mshadow::half::half_t b) {
+    return static_cast<mshadow::half::half_t>(a) - b;
+  }
+
+  template<typename DType,
+           typename std::enable_if<std::is_same<DType, mshadow::half::half_t>::value ||
+                                   std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static float Map(DType a, float b) {
+    return static_cast<float>(a) - b;
+  }
+
+  template<typename DType,
+           typename std::enable_if<std::is_same<DType, mshadow::half::half_t>::value ||
+                                   std::is_same<DType, float>::value ||
+                                   std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static double Map(DType a, double b) {
+    return static_cast<double>(a) - b;
+  }
+};
+
+struct mixed_rminus {
+  template<typename DType,
+           typename std::enable_if<std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static mshadow::half::half_t Map(DType a, mshadow::half::half_t b) {
+    return b - static_cast<mshadow::half::half_t>(a);
+  }
+
+  template<typename DType,
+           typename std::enable_if<std::is_same<DType, mshadow::half::half_t>::value ||
+                                   std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static float Map(DType a, float b) {
+    return b - static_cast<float>(a);
+  }
+
+  template<typename DType,
+           typename std::enable_if<std::is_same<DType, mshadow::half::half_t>::value ||
+                                   std::is_same<DType, float>::value ||
+                                   std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static double Map(DType a, double b) {
+    return b - static_cast<double>(a);
+  }
+};
+
+struct mixed_mul {
+  template<typename DType,
+           typename std::enable_if<std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static mshadow::half::half_t Map(DType a, mshadow::half::half_t b) {
+    return static_cast<mshadow::half::half_t>(a) * b;
+  }
+
+  template<typename DType,
+           typename std::enable_if<std::is_same<DType, mshadow::half::half_t>::value ||
+                                   std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static float Map(DType a, float b) {
+    return static_cast<float>(a) * b;
+  }
+
+  template<typename DType,
+           typename std::enable_if<std::is_same<DType, mshadow::half::half_t>::value ||
+                                   std::is_same<DType, float>::value ||
+                                   std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static double Map(DType a, double b) {
+    return static_cast<double>(a) * b;
+  }
+};
+#endif
+
 MXNET_BINARY_MATH_OP_NC(div, a / b);
 
 MXNET_BINARY_MATH_OP_NC(plus, a + b);
diff --git a/src/operator/mxnet_op.h b/src/operator/mxnet_op.h
index 5d297a547c8f..b15117f9f83b 100644
--- a/src/operator/mxnet_op.h
+++ b/src/operator/mxnet_op.h
@@ -859,14 +859,17 @@ struct op_with_req {
 
   /*! \brief inputs are two tensors with a float output tensor */
   template<typename DType,
-           typename std::enable_if<std::is_integral<DType>::value, int>::type = 0>
+           typename std::enable_if<std::is_same<DType, mshadow::half::half_t>::value ||
+                                   std::is_integral<DType>::value, int>::type = 0>
   MSHADOW_XINLINE static void Map(index_t i, float *out, const DType *lhs, const float *rhs) {
     KERNEL_ASSIGN(out[i], req, OP::Map(lhs[i], rhs[i]));
   }
 
   /*! \brief inputs are two tensors with a double output tensor */
   template<typename DType,
-           typename std::enable_if<std::is_integral<DType>::value, int>::type = 0>
+           typename std::enable_if<std::is_same<DType, mshadow::half::half_t>::value ||
+                                   std::is_same<DType, float>::value ||
+                                   std::is_integral<DType>::value, int>::type = 0>
   MSHADOW_XINLINE static void Map(index_t i, double *out, const DType *lhs, const double *rhs) {
     KERNEL_ASSIGN(out[i], req, OP::Map(lhs[i], rhs[i]));
   }
@@ -883,14 +886,17 @@ struct op_with_req {
 
   /*! \brief inputs are two tensors with a float output tensor */
   template<typename DType,
-           typename std::enable_if<std::is_integral<DType>::value, int>::type = 0>
+           typename std::enable_if<std::is_same<DType, mshadow::half::half_t>::value ||
+                                   std::is_integral<DType>::value, int>::type = 0>
   MSHADOW_XINLINE static void Map(index_t i, float *out, const DType *lhs, const float value) {
     KERNEL_ASSIGN(out[i], req, OP::Map(lhs[i], value));
   }
 
   /*! \brief inputs are two tensors with a double output tensor */
   template<typename DType,
-           typename std::enable_if<std::is_integral<DType>::value, int>::type = 0>
+           typename std::enable_if<std::is_same<DType, mshadow::half::half_t>::value ||
+                                   std::is_same<DType, float>::value ||
+                                   std::is_integral<DType>::value, int>::type = 0>
   MSHADOW_XINLINE static void Map(index_t i, double *out, const DType *lhs, const double value) {
     KERNEL_ASSIGN(out[i], req, OP::Map(lhs[i], value));
   }
diff --git a/src/operator/numpy/np_elemwise_broadcast_op.cc b/src/operator/numpy/np_elemwise_broadcast_op.cc
index c206ad453ba6..c32820d27361 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op.cc
+++ b/src/operator/numpy/np_elemwise_broadcast_op.cc
@@ -23,8 +23,7 @@
  * \brief CPU Implementation of basic functions for elementwise numpy binary broadcast operator.
  */
 
-#include "../tensor/elemwise_binary_broadcast_op.h"
-#include "../tensor/elemwise_binary_scalar_op.h"
+#include "./np_elemwise_broadcast_op.h"
 
 namespace mxnet {
 namespace op {
@@ -55,17 +54,102 @@ bool NumpyBinaryScalarType(const nnvm::NodeAttrs& attrs,
   .add_argument("data", "NDArray-or-Symbol", "source input")        \
   .add_argument("scalar", "float", "scalar input")
 
+bool NumpyBinaryMixedPrecisionType(const nnvm::NodeAttrs& attrs,
+                                   std::vector<int>* in_attrs,
+                                   std::vector<int>* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 2U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  const int ltype = in_attrs->at(0);
+  const int rtype = in_attrs->at(1);
+  if (ltype != -1 && rtype != -1 && (ltype != rtype)) {
+    // Only when both input types are known and not the same, we enter the mixed-precision mode
+    TYPE_ASSIGN_CHECK(*out_attrs, 0, common::np_binary_out_infer_type(ltype, rtype));
+  } else {
+    return ElemwiseType<2, 1>(attrs, in_attrs, out_attrs);
+  }
+  return true;
+}
 
-MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_add)
-.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, op::mshadow_op::plus>)
+#ifdef _WIN32
+#define MXNET_OPERATOR_REGISTER_NP_BINARY_MIXED_PRECISION(name)                \
+  NNVM_REGISTER_OP(name)                                                       \
+  .set_num_inputs(2)                                                           \
+  .set_num_outputs(1)                                                          \
+  .set_attr<nnvm::FListInputNames>("FListInputNames",                          \
+    [](const NodeAttrs& attrs) {                                               \
+      return std::vector<std::string>{"lhs", "rhs"};                           \
+    })                                                                         \
+  .set_attr<mxnet::FInferShape>("FInferShape", BinaryBroadcastShape)           \
+  .set_attr<nnvm::FInferType>("FInferType", NumpyBinaryMixedPrecisionType)     \
+  .set_attr<nnvm::FInplaceOption>("FInplaceOption",                            \
+    [](const NodeAttrs& attrs){                                                \
+      return std::vector<std::pair<int, int> >{{0, 0}, {1, 0}};                \
+    })                                                                         \
+  .set_attr<FResourceRequest>("FResourceRequest",                              \
+  [](const NodeAttrs& attrs) {                                                 \
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};          \
+  })                                                                           \
+  .add_argument("lhs", "NDArray-or-Symbol", "First input to the function")     \
+  .add_argument("rhs", "NDArray-or-Symbol", "Second input to the function")
+#else
+#define MXNET_OPERATOR_REGISTER_NP_BINARY_MIXED_PRECISION(name)                \
+  NNVM_REGISTER_OP(name)                                                       \
+  .set_num_inputs(2)                                                           \
+  .set_num_outputs(1)                                                          \
+  .set_attr<nnvm::FListInputNames>("FListInputNames",                          \
+    [](const NodeAttrs& attrs) {                                               \
+      return std::vector<std::string>{"lhs", "rhs"};                           \
+    })                                                                         \
+  .set_attr<mxnet::FInferShape>("FInferShape", BinaryBroadcastShape)           \
+  .set_attr<nnvm::FInferType>("FInferType", NumpyBinaryMixedPrecisionType)     \
+  .set_attr<nnvm::FInplaceOption>("FInplaceOption",                            \
+    [](const NodeAttrs& attrs){                                                \
+      return std::vector<std::pair<int, int> >{{0, 0}, {1, 0}};                \
+    })                                                                         \
+  .add_argument("lhs", "NDArray-or-Symbol", "First input to the function")     \
+  .add_argument("rhs", "NDArray-or-Symbol", "Second input to the function")
+#endif
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_MIXED_PRECISION(_npi_add)
+#ifndef _WIN32
+.set_attr<FCompute>(
+  "FCompute<cpu>",
+  MixedBinaryBroadcastCompute<cpu, op::mshadow_op::plus, op::mshadow_op::mixed_plus,
+                              op::mshadow_op::mixed_plus>)
+#else
+.set_attr<FCompute>(
+  "FCompute<cpu>",
+  MixedBinaryBroadcastCompute<cpu, op::mshadow_op::plus, op::mshadow_op::plus,
+                              op::mshadow_op::plus>)
+#endif
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_broadcast_add"});
 
-MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_subtract)
-.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, op::mshadow_op::minus>)
+MXNET_OPERATOR_REGISTER_NP_BINARY_MIXED_PRECISION(_npi_subtract)
+#ifndef _WIN32
+.set_attr<FCompute>(
+  "FCompute<cpu>",
+  MixedBinaryBroadcastCompute<cpu, op::mshadow_op::minus, op::mshadow_op::mixed_minus,
+                              op::mshadow_op::mixed_rminus>)
+#else
+.set_attr<FCompute>(
+  "FCompute<cpu>",
+  MixedBinaryBroadcastCompute<cpu, op::mshadow_op::minus, op::mshadow_op::minus,
+                              op::mshadow_op::minus>)
+#endif
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_broadcast_sub"});
 
-MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_multiply)
-.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, op::mshadow_op::mul>)
+MXNET_OPERATOR_REGISTER_NP_BINARY_MIXED_PRECISION(_npi_multiply)
+#ifndef _WIN32
+.set_attr<FCompute>(
+  "FCompute<cpu>",
+  MixedBinaryBroadcastCompute<cpu, op::mshadow_op::mul, op::mshadow_op::mixed_mul,
+                              op::mshadow_op::mixed_mul>)
+#else
+.set_attr<FCompute>(
+  "FCompute<cpu>",
+  MixedBinaryBroadcastCompute<cpu, op::mshadow_op::mul, op::mshadow_op::mul,
+                              op::mshadow_op::mul>)
+#endif
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_broadcast_mul"});
 
 MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_mod)
diff --git a/src/operator/numpy/np_elemwise_broadcast_op.cu b/src/operator/numpy/np_elemwise_broadcast_op.cu
index a682ec989ea8..153ffd0048dd 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op.cu
+++ b/src/operator/numpy/np_elemwise_broadcast_op.cu
@@ -22,20 +22,50 @@
  * \file np_elemwise_broadcast_op.cu
  * \brief GPU Implementation of basic functions for elementwise binary broadcast operator.
  */
-#include "../tensor/elemwise_binary_broadcast_op.h"
-#include "../tensor/elemwise_binary_scalar_op.h"
+
+#include "./np_elemwise_broadcast_op.h"
 
 namespace mxnet {
 namespace op {
 
 NNVM_REGISTER_OP(_npi_add)
-.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, op::mshadow_op::plus>);
+#ifndef _WIN32
+.set_attr<FCompute>(
+  "FCompute<gpu>",
+  MixedBinaryBroadcastCompute<gpu, op::mshadow_op::plus, op::mshadow_op::mixed_plus,
+                              op::mshadow_op::mixed_plus>);
+#else
+.set_attr<FCompute>(
+  "FCompute<gpu>",
+  MixedBinaryBroadcastCompute<gpu, op::mshadow_op::plus, op::mshadow_op::plus,
+                              op::mshadow_op::plus>);
+#endif
 
 NNVM_REGISTER_OP(_npi_subtract)
-.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, op::mshadow_op::minus>);
+#ifndef _WIN32
+.set_attr<FCompute>(
+  "FCompute<gpu>",
+  MixedBinaryBroadcastCompute<gpu, op::mshadow_op::minus, op::mshadow_op::mixed_minus,
+                              op::mshadow_op::mixed_rminus>);
+#else
+.set_attr<FCompute>(
+  "FCompute<gpu>",
+  MixedBinaryBroadcastCompute<gpu, op::mshadow_op::minus, op::mshadow_op::minus,
+                              op::mshadow_op::minus>);
+#endif
 
 NNVM_REGISTER_OP(_npi_multiply)
-.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, op::mshadow_op::mul>);
+#ifndef _WIN32
+.set_attr<FCompute>(
+  "FCompute<gpu>",
+  MixedBinaryBroadcastCompute<gpu, op::mshadow_op::mul, op::mshadow_op::mixed_mul,
+                              op::mshadow_op::mixed_mul>);
+#else
+.set_attr<FCompute>(
+  "FCompute<gpu>",
+  MixedBinaryBroadcastCompute<gpu, op::mshadow_op::mul, op::mshadow_op::mul,
+                              op::mshadow_op::mul>);
+#endif
 
 NNVM_REGISTER_OP(_npi_mod)
 .set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, mshadow_op::mod>);
diff --git a/src/operator/numpy/np_elemwise_broadcast_op.h b/src/operator/numpy/np_elemwise_broadcast_op.h
new file mode 100644
index 000000000000..1d36c6ff881e
--- /dev/null
+++ b/src/operator/numpy/np_elemwise_broadcast_op.h
@@ -0,0 +1,341 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file np_elemwise_binary_op.h
+ * \brief Function definition of elemwise and broadcast operators
+ */
+#ifndef MXNET_OPERATOR_NUMPY_NP_ELEMWISE_BROADCAST_OP_H_
+#define MXNET_OPERATOR_NUMPY_NP_ELEMWISE_BROADCAST_OP_H_
+
+#include <vector>
+#include <string>
+
+#include "../tensor/elemwise_binary_broadcast_op.h"
+#include "../tensor/elemwise_binary_scalar_op.h"
+
+namespace mxnet {
+namespace op {
+
+inline void PrintErrorMessage(const std::string& name, const int dtype1, const int dtype2) {
+  LOG(FATAL) << "Operator " << name << " does not support combination of "
+             << common::dtype_string(dtype1) << " with " << common::dtype_string(dtype2)
+             << " yet...";
+}
+
+#ifndef _WIN32
+template<typename xpu, typename OP>
+void MixedAllRealBinaryElemwiseCompute(const std::string& op_name,
+                                       const OpContext& ctx,
+                                       const TBlob& lhs,
+                                       const TBlob& rhs,
+                                       const TBlob& out,
+                                       const OpReqType req) {
+  using namespace mshadow;
+  using namespace mxnet_op;
+  CHECK_EQ(lhs.type_flag_, out.type_flag_);
+
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+
+  MSHADOW_REAL_TYPE_SWITCH(out.type_flag_, DType, {
+    const size_t size = (ElemwiseBinaryOp::minthree(out.Size(), lhs.Size(), rhs.Size())
+      + DataType<DType>::kLanes - 1) / DataType<DType>::kLanes;
+    if (size == 0) return;
+
+    switch (lhs.type_flag_) {
+      case mshadow::kFloat32:
+      {
+        if (rhs.type_flag_ == mshadow::kFloat16) {
+          MXNET_ASSIGN_REQ_SWITCH(req, Req, {
+            Kernel<mxnet_op::op_with_req<OP, Req>, xpu>::Launch(
+              s, size, out.dptr<float>(), rhs.dptr<mshadow::half::half_t>(),
+              lhs.dptr<float>());
+          });
+        } else {
+          PrintErrorMessage(op_name, lhs.type_flag_, rhs.type_flag_);
+        }
+        break;
+      }
+      case mshadow::kFloat64:
+      {
+        if (rhs.type_flag_ == mshadow::kFloat16) {
+          MXNET_ASSIGN_REQ_SWITCH(req, Req, {
+            Kernel<mxnet_op::op_with_req<OP, Req>, xpu>::Launch(
+              s, size, out.dptr<double>(), rhs.dptr<mshadow::half::half_t>(),
+              lhs.dptr<double>());
+          });
+        } else if (rhs.type_flag_ == mshadow::kFloat32) {
+          MXNET_ASSIGN_REQ_SWITCH(req, Req, {
+            Kernel<mxnet_op::op_with_req<OP, Req>, xpu>::Launch(
+              s, size, out.dptr<double>(), rhs.dptr<float>(),
+              lhs.dptr<double>());
+          });
+        } else {
+          PrintErrorMessage(op_name, lhs.type_flag_, rhs.type_flag_);
+        }
+        break;
+      }
+      default:
+      {
+        PrintErrorMessage(op_name, lhs.type_flag_, rhs.type_flag_);
+        break;
+      }
+    }
+  });
+}
+
+template<typename xpu, typename OP>
+void MixedIntRealBinaryElemwiseCompute(const OpContext& ctx,
+                                       const TBlob& lhs,
+                                       const TBlob& rhs,
+                                       const TBlob& out,
+                                       const OpReqType req) {
+  using namespace mshadow;
+  using namespace mxnet_op;
+  CHECK_EQ(lhs.type_flag_, out.type_flag_);
+
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+
+  MSHADOW_REAL_TYPE_SWITCH(out.type_flag_, FType, {
+    const size_t size = (ElemwiseBinaryOp::minthree(out.Size(), lhs.Size(), rhs.Size())
+      + DataType<FType>::kLanes - 1) / DataType<FType>::kLanes;
+    if (size == 0) return;
+
+    MXNET_INT_TYPE_SWITCH(rhs.type_flag_, IType, {
+      MXNET_ASSIGN_REQ_SWITCH(req, Req, {
+        Kernel<mxnet_op::op_with_req<OP, Req>, xpu>::Launch(
+          s, size, out.dptr<FType>(), rhs.dptr<IType>(),
+          lhs.dptr<FType>());
+      });
+    });
+  });
+}
+
+template<typename xpu, typename LOP, typename ROP>
+void MixedBinaryElemwiseCompute(const nnvm::NodeAttrs& attrs,
+                                const OpContext& ctx,
+                                const std::vector<TBlob>& inputs,
+                                const std::vector<OpReqType>& req,
+                                const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mxnet_op;
+  CHECK_EQ(inputs.size(), 2U);
+  CHECK_EQ(outputs.size(), 1U);
+
+  const TBlob& lhs = inputs[0];
+  const TBlob& rhs = inputs[1];
+  const TBlob& out = outputs[0];
+
+  if (common::is_float(lhs.type_flag_) && common::is_float(rhs.type_flag_)) {
+    if (lhs.type_flag_ == out.type_flag_) {
+      MixedAllRealBinaryElemwiseCompute<xpu, ROP>(attrs.op->name, ctx, lhs, rhs, out, req[0]);
+    } else {
+      MixedAllRealBinaryElemwiseCompute<xpu, LOP>(attrs.op->name, ctx, rhs, lhs, out, req[0]);
+    }
+  } else if (common::is_float(lhs.type_flag_) || common::is_float(rhs.type_flag_)) {
+    if (lhs.type_flag_ == out.type_flag_) {
+      MixedIntRealBinaryElemwiseCompute<xpu, ROP>(ctx, lhs, rhs, out, req[0]);
+    } else {
+      MixedIntRealBinaryElemwiseCompute<xpu, LOP>(ctx, rhs, lhs, out, req[0]);
+    }
+  } else {
+    PrintErrorMessage(attrs.op->name, lhs.type_flag_, rhs.type_flag_);
+  }
+}
+
+template<typename xpu, typename OP>
+void MixedAllRealBinaryBroadcastCompute(const std::string& op_name,
+                                        const OpContext& ctx,
+                                        const TBlob& lhs,
+                                        const TBlob& rhs,
+                                        const TBlob& out,
+                                        const OpReqType req,
+                                        const int ndim,
+                                        const mxnet::TShape& new_oshape,
+                                        const mxnet::TShape& new_lshape,
+                                        const mxnet::TShape& new_rshape) {
+  using namespace mshadow;
+  using namespace mxnet_op;
+  CHECK_EQ(lhs.type_flag_, out.type_flag_);
+
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+
+  BROADCAST_NDIM_SWITCH(ndim, NDim, {
+    mshadow::Shape<NDim> oshape = new_oshape.get<NDim>();
+    mshadow::Shape<NDim> lstride = mxnet_op::calc_stride(new_lshape.get<NDim>());
+    mshadow::Shape<NDim> rstride = mxnet_op::calc_stride(new_rshape.get<NDim>());
+    switch (lhs.type_flag_) {
+      case mshadow::kFloat32:
+      {
+        if (rhs.type_flag_ == mshadow::kFloat16) {
+          mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, OP>, xpu>::
+          template LaunchEx(s, new_oshape.Size(), req, rstride, lstride, oshape,
+          rhs.dptr<mshadow::half::half_t>(), lhs.dptr<float>(), out.dptr<float>());
+        } else {
+          PrintErrorMessage(op_name, lhs.type_flag_, rhs.type_flag_);
+        }
+        break;
+      }
+      case mshadow::kFloat64:
+      {
+        if (rhs.type_flag_ == mshadow::kFloat16) {
+          mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, OP>, xpu>::
+          template LaunchEx(s, new_oshape.Size(), req, rstride, lstride, oshape,
+          rhs.dptr<mshadow::half::half_t>(), lhs.dptr<double>(), out.dptr<double>());
+        } else if (rhs.type_flag_ == mshadow::kFloat32) {
+          mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, OP>, xpu>::
+          template LaunchEx(s, new_oshape.Size(), req, rstride, lstride, oshape,
+          rhs.dptr<float>(), lhs.dptr<double>(), out.dptr<double>());
+        } else {
+          PrintErrorMessage(op_name, lhs.type_flag_, rhs.type_flag_);
+        }
+        break;
+      }
+      default:
+      {
+        PrintErrorMessage(op_name, lhs.type_flag_, rhs.type_flag_);
+        break;
+      }
+    }
+  });
+}
+#endif
+
+template<typename xpu, typename OP, typename LOP, typename ROP>
+void MixedBinaryBroadcastCompute(const nnvm::NodeAttrs& attrs,
+                                 const OpContext& ctx,
+                                 const std::vector<TBlob>& inputs,
+                                 const std::vector<OpReqType>& req,
+                                 const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mxnet_op;
+  CHECK_EQ(inputs.size(), 2U);
+  CHECK_EQ(outputs.size(), 1U);
+
+  const TBlob& lhs = inputs[0];
+  const TBlob& rhs = inputs[1];
+  const TBlob& out = outputs[0];
+
+  if ((out.shape_.Size() == 0U) || (req[0] == kNullOp)) return;
+
+  if (lhs.type_flag_ == rhs.type_flag_) {
+    BinaryBroadcastCompute<xpu, OP>(attrs, ctx, inputs, req, outputs);
+    return;
+  }
+
+#ifndef _WIN32
+  mxnet::TShape new_lshape, new_rshape, new_oshape;
+  int ndim = BinaryBroadcastShapeCompact(lhs.shape_, rhs.shape_, out.shape_,
+                                         &new_lshape, &new_rshape, &new_oshape);
+  if (!ndim) {
+    MixedBinaryElemwiseCompute<xpu, LOP, ROP>(attrs, ctx, inputs, req, outputs);
+  } else {
+    mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+    if (common::is_float(lhs.type_flag_) && common::is_float(rhs.type_flag_)) {
+      if (lhs.type_flag_ == out.type_flag_) {
+        MixedAllRealBinaryBroadcastCompute<xpu, ROP>(
+          attrs.op->name, ctx, lhs, rhs, out, req[0], ndim, new_oshape, new_lshape, new_rshape);
+      } else {
+        MixedAllRealBinaryBroadcastCompute<xpu, LOP>(
+          attrs.op->name, ctx, rhs, lhs, out, req[0], ndim, new_oshape, new_rshape, new_lshape);
+      }
+    } else if (common::is_float(lhs.type_flag_) || common::is_float(rhs.type_flag_)) {
+      CHECK(lhs.type_flag_ == out.type_flag_ || rhs.type_flag_ == out.type_flag_)
+        << "One of the input type should be the same as the output";
+      BROADCAST_NDIM_SWITCH(ndim, NDim, {
+        mshadow::Shape<NDim> oshape = new_oshape.get<NDim>();
+        mshadow::Shape<NDim> lstride = mxnet_op::calc_stride(new_lshape.get<NDim>());
+        mshadow::Shape<NDim> rstride = mxnet_op::calc_stride(new_rshape.get<NDim>());
+        if (lhs.type_flag_ == out.type_flag_) {
+          MSHADOW_REAL_TYPE_SWITCH(out.type_flag_, LType, {
+            MXNET_INT_TYPE_SWITCH(rhs.type_flag_, RType, {
+              mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, ROP>, xpu>::
+              template LaunchEx(s, new_oshape.Size(), req[0], rstride, lstride, oshape,
+              rhs.dptr<RType>(), lhs.dptr<LType>(), out.dptr<LType>());
+            });
+          });
+        } else {
+          MSHADOW_REAL_TYPE_SWITCH(out.type_flag_, RType, {
+            MXNET_INT_TYPE_SWITCH(lhs.type_flag_, LType, {
+              mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, LOP>, xpu>::
+              template LaunchEx(s, new_oshape.Size(), req[0], lstride, rstride, oshape,
+              lhs.dptr<LType>(), rhs.dptr<RType>(), out.dptr<RType>());
+            });
+          });
+        }
+      });
+    } else {
+      PrintErrorMessage(attrs.op->name, lhs.type_flag_, rhs.type_flag_);
+    }
+  }
+#else
+  mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+  if (common::is_float(lhs.type_flag_) || common::is_float(rhs.type_flag_)) {
+    TBlob temp_tblob;
+    // one is float, the other is bool
+    CHECK((out.type_flag_ == lhs.type_flag_) || (out.type_flag_ == rhs.type_flag_))
+      << "This case out type should be same as the float type";
+    if (lhs.type_flag_ == out.type_flag_) {
+      MSHADOW_REAL_TYPE_SWITCH(lhs.type_flag_, LType, {
+        Tensor<xpu, 1, LType> temp_tensor =
+          ctx.requested[0].get_space_typed<xpu, 1, LType>(Shape1(rhs.Size()), s);
+        temp_tblob = TBlob(temp_tensor);
+      });
+      CastCompute<xpu>(attrs, ctx, {rhs}, {kWriteTo}, {temp_tblob});
+      BinaryBroadcastCompute<xpu, OP>(
+        attrs, ctx, {lhs, temp_tblob.reshape(rhs.shape_)}, req, outputs);
+    } else {
+      MSHADOW_REAL_TYPE_SWITCH(rhs.type_flag_, RType, {
+        Tensor<xpu, 1, RType> temp_tensor =
+          ctx.requested[0].get_space_typed<xpu, 1, RType>(Shape1(lhs.Size()), s);
+        temp_tblob = TBlob(temp_tensor);
+      });
+      CastCompute<xpu>(attrs, ctx, {lhs}, {kWriteTo}, {temp_tblob});
+      BinaryBroadcastCompute<xpu, OP>(
+        attrs, ctx, {temp_tblob.reshape(lhs.shape_), rhs}, req, outputs);
+    }
+  } else {
+    PrintErrorMessage(attrs.op->name, lhs.type_flag_, rhs.type_flag_);
+  }
+#endif
+}
+
+template<typename xpu, typename LOP, typename ROP>
+void MixedBinaryBackwardUseIn(const nnvm::NodeAttrs& attrs,
+                              const OpContext& ctx,
+                              const std::vector<TBlob>& inputs,
+                              const std::vector<OpReqType>& req,
+                              const std::vector<TBlob>& outputs) {
+  CHECK_EQ(inputs.size(), 3U);
+  CHECK_EQ(outputs.size(), 2U);
+
+  const TBlob& lhs = inputs[1];
+  const TBlob& rhs = inputs[2];
+  if (lhs.type_flag_ == rhs.type_flag_) {
+    BinaryBroadcastBackwardUseIn<xpu, LOP, ROP>(attrs, ctx, inputs, req, outputs);
+    return;
+  }
+
+  PrintErrorMessage(attrs.op->name, lhs.type_flag_, rhs.type_flag_);
+}
+
+}  // namespace op
+}  // namespace mxnet
+#endif  // MXNET_OPERATOR_NUMPY_NP_ELEMWISE_BROADCAST_OP_H_
diff --git a/src/operator/numpy/np_true_divide.cc b/src/operator/numpy/np_true_divide.cc
index d2135befef42..35df2c467e7b 100644
--- a/src/operator/numpy/np_true_divide.cc
+++ b/src/operator/numpy/np_true_divide.cc
@@ -31,7 +31,7 @@ namespace op {
 int TrueDivideOutType(int ltype, int rtype) {
   if (common::is_float(ltype) && common::is_float(rtype)) {
     // If both inputs are float, return the one with the higher precision
-    return common::more_precise_type(ltype, rtype);
+    return common::get_more_precise_type(ltype, rtype);
   } else if (common::is_float(ltype) || common::is_float(rtype)) {
     // If only one of the inputs is float, return that float type
     return (common::is_float(ltype)) ? ltype : rtype;
diff --git a/src/operator/tensor/elemwise_binary_op.h b/src/operator/tensor/elemwise_binary_op.h
index 6f444aed21fe..da088c1dcc39 100644
--- a/src/operator/tensor/elemwise_binary_op.h
+++ b/src/operator/tensor/elemwise_binary_op.h
@@ -99,11 +99,13 @@ class ElemwiseBinaryOp : public OpBase {
     return a1.var() == a2.var();
   }
 
+ public:
   /*! \brief Minimum of three */
   static MSHADOW_XINLINE size_t minthree(const size_t a, const size_t b, const size_t c) {
     return a < b ? (a < c ? a : c) : (b < c ? b : c);
   }
 
+ private:
   template<typename xpu, typename LOP, typename ROP, typename DType>
   static void BackwardUseNone_(const nnvm::NodeAttrs &attrs,
                                const OpContext &ctx,
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index e6d12da23582..32cd5b10717e 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -1650,6 +1650,69 @@ def hybrid_forward(self, F, a, b, *args, **kwargs):
             check_binary_func(func, lshape, rshape, low, high, lgrads, rgrads, dtypes)
 
 
+@with_seed()
+@use_np
+def test_np_mixed_precision_binary_funcs():
+    def check_mixed_precision_binary_func(func, low, high, lshape, rshape, ltype, rtype):
+        class TestMixedBinary(HybridBlock):
+            def __init__(self, func):
+                super(TestMixedBinary, self).__init__()
+                self._func = func
+
+            def hybrid_forward(self, F, a, b, *args, **kwargs):
+                return getattr(F.np, self._func)(a, b)
+
+        np_func = getattr(_np, func)
+        mx_func = TestMixedBinary(func)
+        np_test_x1 = _np.random.uniform(low, high, lshape).astype(ltype)
+        np_test_x2 = _np.random.uniform(low, high, rshape).astype(rtype)
+        mx_test_x1 = mx.numpy.array(np_test_x1, dtype=ltype)
+        mx_test_x2 = mx.numpy.array(np_test_x2, dtype=rtype)
+        rtol = 1e-2 if ltype is np.float16 or rtype is np.float16 else 1e-3
+        atol = 1e-4 if ltype is np.float16 or rtype is np.float16 else 1e-5
+        for hybridize in [True, False]:
+            if hybridize:
+                mx_func.hybridize()
+            np_out = np_func(np_test_x1, np_test_x2)
+            with mx.autograd.record():
+                y = mx_func(mx_test_x1, mx_test_x2)
+            assert y.shape == np_out.shape
+            assert_almost_equal(y.asnumpy(), np_out.astype(y.dtype), rtol=rtol, atol=atol,
+                                use_broadcast=False, equal_nan=True)
+
+        np_out = getattr(_np, func)(np_test_x1, np_test_x2)
+        mx_out = getattr(mx.np, func)(mx_test_x1, mx_test_x2)
+        assert mx_out.shape == np_out.shape
+        assert_almost_equal(mx_out.asnumpy(), np_out.astype(mx_out.dtype), rtol=rtol, atol=atol,
+                            use_broadcast=False, equal_nan=True)
+
+    funcs = {
+        'add': (-1.0, 1.0),
+        'subtract': (-1.0, 1.0),
+        'multiply': (-1.0, 1.0),
+    }
+    shape_pairs = [((3, 2), (3, 2)),
+                   ((3, 2), (3, 1)),
+                   ((3, 1), (3, 0)),
+                   ((0, 2), (1, 2)),
+                   ((2, 3, 4), (3, 1)),
+                   ((2, 3), ()),
+                   ((), (2, 3))]
+    itypes = [np.bool, np.int8, np.int32, np.int64]
+    ftypes = [np.float16, np.float32, np.float64]
+    for func, func_data in funcs.items():
+        low, high = func_data
+        for lshape, rshape in shape_pairs:
+            for type1, type2 in itertools.product(itypes, ftypes):
+                check_mixed_precision_binary_func(func, low, high, lshape, rshape, type1, type2)
+                check_mixed_precision_binary_func(func, low, high, lshape, rshape, type2, type1)
+
+            for type1, type2 in itertools.product(ftypes, ftypes):
+                if type1 == type2:
+                    continue
+                check_mixed_precision_binary_func(func, low, high, lshape, rshape, type1, type2)
+
+
 @with_seed()
 @use_np
 def test_npx_relu():

From 58b824f8c2c82a35b65426fbba40c6f240c2eeab Mon Sep 17 00:00:00 2001
From: Thomas Delteil <thomas.delteil1@gmail.com>
Date: Wed, 6 Nov 2019 11:12:37 -0800
Subject: [PATCH 570/813] fix R docs (#16733)

---
 docs/python_docs/python/api/ndarray/index.rst | 2 +-
 docs/static_site/src/pages/api/api.html       | 2 +-
 docs/static_site/src/pages/api/r/index.md     | 4 ++--
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/docs/python_docs/python/api/ndarray/index.rst b/docs/python_docs/python/api/ndarray/index.rst
index 46b6ef8f589a..cfd206eeb767 100644
--- a/docs/python_docs/python/api/ndarray/index.rst
+++ b/docs/python_docs/python/api/ndarray/index.rst
@@ -65,7 +65,7 @@ Tutorials
 
    .. card::
       :title: NDArray Guide
-      :link: ../../guide/packages/ndarray/
+      :link: ../../tutorials/packages/ndarray/
 
       The NDArray guide. Start here!
 
diff --git a/docs/static_site/src/pages/api/api.html b/docs/static_site/src/pages/api/api.html
index 824756898606..6f87cf654fca 100644
--- a/docs/static_site/src/pages/api/api.html
+++ b/docs/static_site/src/pages/api/api.html
@@ -58,7 +58,7 @@
   tag: julia
 - title: R
   guide_link: /api/r
-  api_link: https://s3.amazonaws.com/mxnet-prod/docs/R/mxnet-r-reference-manual.pdf
+  api_link: /api/r/docs/api/R-package/build/mxnet-r-reference-manual.pdf
   tutorial_link: /api/r/docs/tutorials
   description:
   icon: /assets/img/R_logo.svg
diff --git a/docs/static_site/src/pages/api/r/index.md b/docs/static_site/src/pages/api/r/index.md
index 1f8ba603a4eb..01aee98801aa 100644
--- a/docs/static_site/src/pages/api/r/index.md
+++ b/docs/static_site/src/pages/api/r/index.md
@@ -26,7 +26,7 @@ tag: r
 
 # MXNet - R API
 
-See the [MXNet R Reference Manual](https://s3.amazonaws.com/mxnet-prod/docs/R/mxnet-r-reference-manual.pdf).
+See the [MXNet R Reference Manual](/api/r/docs/api/R-package/build/mxnet-r-reference-manual.pdf).
 
 MXNet supports the R programming language. The MXNet R package brings flexible and efficient GPU
 computing and state-of-art deep learning to R. It enables you to write seamless tensor/matrix computation with multiple GPUs in R. It also lets you construct and customize the state-of-art deep learning models in R,
@@ -49,4 +49,4 @@ You can perform tensor or matrix computation in R:
 ```
 ## Resources
 
-* [MXNet R Reference Manual](https://s3.amazonaws.com/mxnet-prod/docs/R/mxnet-r-reference-manual.pdf)
+* [MXNet R Reference Manual](/api/r/docs/api/R-package/build/mxnet-r-reference-manual.pdf)

From da33da3cb8774b92892ed675531b43d2d5d2e967 Mon Sep 17 00:00:00 2001
From: Caenorst <cfujitsang@nvidia.com>
Date: Wed, 6 Nov 2019 21:00:43 -0500
Subject: [PATCH 571/813] Add MXNet Ops for fast multihead attention (#16408)

* add MXNet Ops for fast multihead attention

* add cutlass as 3rdparty dependency

* add cutlass to compilation flags

* remove all cutlass stuff

* add better error message and description and remove cutlass from compilation flags

* change credit for the approach since the code have changed

* fix typos

* correct another typo

* Add all the cuda/cublas helper functions

* remove tests using kAddTo

* only use cublasStridedBatchedGemm if CUDA >= 9.1

* add equivalent mxnet code in description of mha ops

* remove a wrong copy-paste

* add _contrib for namespace and add GPU only on description

* add warning in bwd_ignore_zero_init description, also test with fp32

* add error return if bwd_ignore_zero_init is used without MXNET_EXEC_ENABLE_ADDTO

* remove std::move for clang

* remove bwd_ignore_zero_init flag

* remove bwd_ignore_zero_init in test_operator_gpu.py

* fix typo

* fix another typo
---
 src/common/cuda_utils.h                |  74 ++++
 src/operator/contrib/transformer-inl.h |   9 +
 src/operator/contrib/transformer.cc    | 270 ++++++++++++
 src/operator/contrib/transformer.cu    | 560 +++++++++++++++++++++++++
 tests/python/gpu/test_operator_gpu.py  | 316 +++++++++++++-
 5 files changed, 1228 insertions(+), 1 deletion(-)

diff --git a/src/common/cuda_utils.h b/src/common/cuda_utils.h
index 2f7254040475..ccf0931f2480 100644
--- a/src/common/cuda_utils.h
+++ b/src/common/cuda_utils.h
@@ -187,6 +187,69 @@ namespace mxnet {
 namespace common {
 /*! \brief common utils for cuda */
 namespace cuda {
+/*!
+ * \brief Converts between C++ datatypes and enums/constants needed by cuBLAS.
+ */
+template<typename DType>
+struct CublasType;
+
+// With CUDA v8, cuBLAS adopted use of cudaDataType_t instead of its own
+// datatype cublasDataType_t.  The older cudaDataType_t values could be
+// included below, but since this class was introduced to support the cuBLAS v8
+// call cublasGemmEx(), burdening the class with the legacy type values
+// was not needed.
+
+template<>
+struct CublasType<float> {
+  static const int kFlag = mshadow::kFloat32;
+#if CUDA_VERSION >= 8000
+  static const cudaDataType_t kCudaFlag = CUDA_R_32F;
+#endif
+  typedef float ScaleType;
+  static const float one;
+  static const float zero;
+};
+template<>
+struct CublasType<double> {
+  static const int kFlag = mshadow::kFloat64;
+#if CUDA_VERSION >= 8000
+  static const cudaDataType_t kCudaFlag = CUDA_R_64F;
+#endif
+  typedef double ScaleType;
+  static const double one;
+  static const double zero;
+};
+template<>
+struct CublasType<mshadow::half::half_t> {
+  static const int kFlag = mshadow::kFloat16;
+#if CUDA_VERSION >= 8000
+  static const cudaDataType_t kCudaFlag = CUDA_R_16F;
+#endif
+  typedef float ScaleType;
+  static const mshadow::half::half_t one;
+  static const mshadow::half::half_t zero;
+};
+template<>
+struct CublasType<uint8_t> {
+  static const int kFlag = mshadow::kUint8;
+#if CUDA_VERSION >= 8000
+  static const cudaDataType_t kCudaFlag = CUDA_R_8I;
+#endif
+  typedef uint8_t ScaleType;
+  static const uint8_t one = 1;
+  static const uint8_t zero = 0;
+};
+template<>
+struct CublasType<int32_t> {
+  static const int kFlag = mshadow::kInt32;
+#if CUDA_VERSION >= 8000
+  static const cudaDataType_t kCudaFlag = CUDA_R_32I;
+#endif
+  typedef int32_t ScaleType;
+  static const int32_t one = 1;
+  static const int32_t zero = 0;
+};
+
 /*!
  * \brief Get string representation of cuBLAS errors.
  * \param error The error.
@@ -218,6 +281,17 @@ inline const char* CublasGetErrorString(cublasStatus_t error) {
   return "Unknown cuBLAS status";
 }
 
+#if CUDA_VERSION >= 8000
+/*!
+ * \brief Create the proper constant for indicating cuBLAS transposition, if desired.
+ * \param transpose Whether transposition should be performed.
+ * \return the yes/no transposition-indicating constant.
+ */
+inline cublasOperation_t CublasTransposeOp(bool transpose) {
+  return transpose ? CUBLAS_OP_T : CUBLAS_OP_N;
+}
+#endif
+
 /*!
  * \brief Get string representation of cuSOLVER errors.
  * \param error The error.
diff --git a/src/operator/contrib/transformer-inl.h b/src/operator/contrib/transformer-inl.h
index da3d14e33cf4..da48ffa52dca 100644
--- a/src/operator/contrib/transformer-inl.h
+++ b/src/operator/contrib/transformer-inl.h
@@ -34,6 +34,15 @@
 namespace mxnet {
 namespace op {
 
+struct InterleavedMatMulParam : public dmlc::Parameter<InterleavedMatMulParam> {
+  int heads;
+  bool bwd_ignore_zero_init;
+  DMLC_DECLARE_PARAMETER(InterleavedMatMulParam) {
+    DMLC_DECLARE_FIELD(heads)
+    .describe("Set number of heads");
+  }
+};
+
 template<typename xpu>
 static void DivSqrtDimForward_(const nnvm::NodeAttrs& attrs,
                   const OpContext& ctx,
diff --git a/src/operator/contrib/transformer.cc b/src/operator/contrib/transformer.cc
index 00085c0dc7aa..2ca6f8c71093 100644
--- a/src/operator/contrib/transformer.cc
+++ b/src/operator/contrib/transformer.cc
@@ -29,6 +29,276 @@
 namespace mxnet {
 namespace op {
 
+DMLC_REGISTER_PARAMETER(InterleavedMatMulParam);
+
+static bool InterleavedMatMulSelfAttQKShape(const NodeAttrs& attrs,
+                                            mxnet::ShapeVector* in_shape,
+                                            mxnet::ShapeVector* out_shape) {
+  const auto& params = nnvm::get<InterleavedMatMulParam>(attrs.parsed);
+  CHECK_EQ(in_shape->size(), 1U) << "Input:[queries_keys_values] currently have, "
+                                 << in_shape->size() << " inputs";
+  auto qkv_shape = in_shape->at(0);
+  CHECK_EQ(qkv_shape.ndim(), 3U)
+    << "Input queries_keys_values should be 3D in seq_length-batch-proj_dim, "
+    << "currently is: " << qkv_shape.ndim() << "D";
+  out_shape->resize(1);
+  SHAPE_ASSIGN_CHECK(*out_shape, 0,
+    mxnet::TShape({params.heads * qkv_shape[1], qkv_shape[0], qkv_shape[0]}));
+  return true;
+}
+
+static bool InterleavedMatMulSelfAttValAttShape(const NodeAttrs& attrs,
+                                                mxnet::ShapeVector* in_shape,
+                                                mxnet::ShapeVector* out_shape) {
+  CHECK_EQ(in_shape->size(), 2U) << "Input:[queries_keys_values, attention] currently have, "
+                                 << in_shape->size() << " inputs";
+  auto qkv_shape = in_shape->at(0);
+  auto att_shape = in_shape->at(1);
+  CHECK_EQ(qkv_shape.ndim(), 3U)
+    << "Input queries_keys_values should be 3D in seq_length-batch-3*proj_dim, "
+    << "currently is: " << qkv_shape.ndim() << "D";
+  CHECK_EQ(att_shape.ndim(), 3U)
+    << "Input attention should be 3D in batch-seq_length-seq_length, "
+    << "currently is: " << att_shape.ndim() << "D";
+  CHECK_EQ(qkv_shape[0], att_shape[1])
+    << "queries_keys_values.shape[0] and attention.shape[1] should be the same, "
+    << "currently are " << qkv_shape[0] << " and " << att_shape[1];
+  CHECK_EQ(qkv_shape[0], att_shape[2])
+    << "queries_keys_values.shape[0] and attention.shape[2] should be the same, "
+    << "currently are " << qkv_shape[0] << " and " << att_shape[2];
+  CHECK_EQ(qkv_shape[2] % 3, 0)
+    << "queries_keys_values.shape[2] should be a multiple of 3, "
+    << "currently is " << qkv_shape[2];
+  SHAPE_ASSIGN_CHECK(*out_shape, 0,
+    mxnet::TShape({qkv_shape[0], qkv_shape[1], qkv_shape[2] / 3}));
+  return true;
+}
+
+static bool InterleavedMatMulEncDecQKShape(const NodeAttrs& attrs,
+                                           mxnet::ShapeVector* in_shape,
+                                           mxnet::ShapeVector* out_shape) {
+  const auto& params = nnvm::get<InterleavedMatMulParam>(attrs.parsed);
+  CHECK_EQ(in_shape->size(), 2U) << "Input:[queries, keys_values], currently have "
+                                 << in_shape->size() << " inputs";
+  auto q_shape = in_shape->at(0);
+  auto kv_shape = in_shape->at(1);
+  CHECK_EQ(q_shape.ndim(), 3U) << "Input queries should be 3D in seq_length-batch-proj_dim, "
+                               << "currently is " << q_shape.ndim() << "D";
+  CHECK_EQ(kv_shape.ndim(), 3U) << "Input queries should be 3D in seq_length-batch-2*proj_dim, "
+                                << "currently is " << kv_shape.ndim() << "D";
+  CHECK_EQ(q_shape[2] * 2, kv_shape[2])
+    << "keys_values.shape[2] should be equal to queries.shape[2] * 2, "
+    << "currently are: " << kv_shape[2] << " and " << q_shape[2];
+  CHECK_EQ(q_shape[1], kv_shape[1])
+    << "queries.shape[1] should be equal to keys_values.shape[1], "
+    << "currently are: " << q_shape[1] << " and " << kv_shape[1];
+  SHAPE_ASSIGN_CHECK(*out_shape, 0,
+      mxnet::TShape({q_shape[1] * params.heads, q_shape[0], kv_shape[0]}));
+  return true;
+}
+
+static bool InterleavedMatMulEncDecValAttShape(const NodeAttrs& attrs,
+                                               mxnet::ShapeVector* in_shape,
+                                               mxnet::ShapeVector* out_shape) {
+  const auto& params = nnvm::get<InterleavedMatMulParam>(attrs.parsed);
+  CHECK_EQ(in_shape->size(), 2U) << "Input: [keys_values, attention], currently have "
+                                 << in_shape->size() << " inputs";
+  auto kv_shape = in_shape->at(0);
+  auto att_shape = in_shape->at(1);
+  CHECK_EQ(kv_shape.ndim(), 3U)
+    << "Input keys_values should be 3D in seq_length-batch-2*proj_dim, "
+    << "currently is " << kv_shape.ndim() << "D";
+  CHECK_EQ(att_shape.ndim(), 3U)
+    << "Input attention should be 3D in batch-seq_length-seq_length, "
+    << "currently is " << att_shape.ndim() << "D";
+  CHECK_EQ(kv_shape[0], att_shape[2])
+    << "keys_values.shape[0] should be equal to attention.shape[2], currently are "
+    << kv_shape[0] << " and " << att_shape[2];
+  CHECK_EQ(kv_shape[1] * params.heads, att_shape[0]) << "attention.shape[0] "
+    << "should be equal to keys_values.shape[1] * heads, currently are: "
+    << att_shape[2] << " and " << kv_shape[1];
+  SHAPE_ASSIGN_CHECK(*out_shape, 0,
+      mxnet::TShape({att_shape[1], kv_shape[1], kv_shape[2] / 2}));
+  return true;
+}
+
+NNVM_REGISTER_OP(_contrib_interleaved_matmul_selfatt_qk)
+.describe(R"code(Compute the matrix multiplication between the projections of
+queries and keys in multihead attention use as self attention.
+
+the input must be a single tensor of interleaved projections
+of queries, keys and values following the layout:
+(seq_length, batch_size, num_heads * head_dim * 3)
+
+the equivalent code would be:
+tmp = mx.nd.reshape(queries_keys_values, shape=(0, 0, num_heads, 3, -1))
+q_proj = mx.nd.transpose(tmp[:,:,:,0,:], axes=(1, 2, 0, 3))
+q_proj = mx.nd.reshape(q_proj, shape=(-1, 0, 0), reverse=True)
+q_proj = mx.nd.contrib.div_sqrt_dim(q_proj)
+k_proj = mx.nd.transpose(tmp[:,:,:,1,:], axes=(1, 2, 0, 3))
+k_proj = mx.nd.reshap(k_proj, shape=(-1, 0, 0), reverse=True)
+output = mx.nd.batch_dot(q_proj, k_proj, transpose_b=True)
+
+This Op is GPU only
+)code" ADD_FILELINE)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<InterleavedMatMulParam>)
+.set_attr<nnvm::FListInputNames>("FListInputNames", [](const NodeAttrs& attrs) {
+  return std::vector<std::string>{"queries_keys_values"};
+})
+.set_attr<nnvm::FListOutputNames>("FListOutputNames", [](const NodeAttrs& attrs) {
+  return std::vector<std::string>{"output"};
+})
+.set_attr<mxnet::FInferShape>("FInferShape", InterleavedMatMulSelfAttQKShape)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
+.set_attr<nnvm::FGradient>("FGradient",
+  ElemwiseGradUseIn{"_backward_interleaved_matmul_selfatt_qk"})
+.add_argument("queries_keys_values", "NDArray-or-Symbol", "Interleaved queries, keys and values")
+.add_arguments(InterleavedMatMulParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_backward_interleaved_matmul_selfatt_qk)
+.set_num_inputs(2)
+.set_num_outputs(1)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr_parser(ParamParser<InterleavedMatMulParam>);
+
+NNVM_REGISTER_OP(_contrib_interleaved_matmul_selfatt_valatt)
+.describe(R"code(Compute the matrix multiplication between the projections of
+values and the attention weights in multihead attention use as self attention.
+
+the inputs must be a tensor of interleaved projections
+of queries, keys and values following the layout:
+(seq_length, batch_size, num_heads * head_dim * 3)
+
+and the attention weights following the layout:
+(batch_size, seq_length, seq_length)
+
+the equivalent code would be:
+tmp = mx.nd.reshape(queries_keys_values, shape=(0, 0, num_heads, 3, -1))
+v_proj = mx.nd.transpose(tmp[:,:,:,2,:], axes=(1, 2, 0, 3))
+v_proj = mx.nd.reshape(v_proj, shape=(-1, 0, 0), reverse=True)
+output = mx.nd.batch_dot(attention, v_proj, transpose_b=True)
+output = mx.nd.reshape(output, shape=(-1, num_heads, 0, 0), reverse=True)
+output = mx.nd.transpose(output, axes=(0, 2, 1, 3))
+output = mx.nd.reshape(output, shape=(0, 0, -1))
+
+This Op is GPU only
+)code" ADD_FILELINE)
+.set_num_inputs(2)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<InterleavedMatMulParam>)
+.set_attr<nnvm::FListInputNames>("FListInputNames", [](const NodeAttrs& attrs) {
+  return std::vector<std::string>{"queries_keys_values", "attention"};
+})
+.set_attr<nnvm::FListOutputNames>("FListOutputNames", [](const NodeAttrs& attrs) {
+  return std::vector<std::string>{"output"};
+})
+.set_attr<mxnet::FInferShape>("FInferShape", InterleavedMatMulSelfAttValAttShape)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<2, 1>)
+.set_attr<nnvm::FGradient>("FGradient",
+  ElemwiseGradUseIn{"_backward_interleaved_matmul_selfatt_valatt"})
+.add_argument("queries_keys_values", "NDArray-or-Symbol", "Queries, keys and values interleaved")
+.add_argument("attention", "NDArray-or-Symbol", "Attention maps")
+.add_arguments(InterleavedMatMulParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_backward_interleaved_matmul_selfatt_valatt)
+.set_num_inputs(3)
+.set_num_outputs(2)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr_parser(ParamParser<InterleavedMatMulParam>);
+
+NNVM_REGISTER_OP(_contrib_interleaved_matmul_encdec_qk)
+.describe(R"code(Compute the matrix multiplication between the projections of
+queries and keys in multihead attention use as encoder-decoder.
+
+the inputs must be a tensor of projections of queries following the layout:
+(seq_length, batch_size, num_heads * head_dim)
+
+and a tensor of interleaved projections of values and keys following the layout:
+(seq_length, batch_size, num_heads * head_dim * 2)
+
+the equivalent code would be:
+q_proj = mx.nd.transpose(queries, axes=(1, 2, 0, 3))
+q_proj = mx.nd.reshape(q_proj, shape=(-1, 0, 0), reverse=True)
+q_proj = mx.nd.contrib.div_sqrt_dim(q_proj)
+tmp = mx.nd.reshape(keys_values, shape=(0, 0, num_heads, 2, -1))
+k_proj = mx.nd.transpose(tmp[:,:,:,0,:], axes=(1, 2, 0, 3))
+k_proj = mx.nd.reshap(k_proj, shape=(-1, 0, 0), reverse=True)
+output = mx.nd.batch_dot(q_proj, k_proj, transpose_b=True)
+
+This Op is GPU only
+)code" ADD_FILELINE)
+.set_num_inputs(2)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<InterleavedMatMulParam>)
+.set_attr<nnvm::FListInputNames>("FListInputNames", [](const NodeAttrs& attrs) {
+  return std::vector<std::string>{"queries", "keys_values"};
+})
+.set_attr<nnvm::FListOutputNames>("FListOutputNames", [](const NodeAttrs& attrs) {
+  return std::vector<std::string>{"output"};
+})
+.set_attr<mxnet::FInferShape>("FInferShape", InterleavedMatMulEncDecQKShape)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<2, 1>)
+.set_attr<nnvm::FGradient>("FGradient",
+    ElemwiseGradUseIn{"_backward_interleaved_matmul_encdec_qk"})
+.add_argument("queries", "NDArray-or-Symbol", "Queries")
+.add_argument("keys_values", "NDArray-or-Symbol", "Keys and values interleaved")
+.add_arguments(InterleavedMatMulParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_backward_interleaved_matmul_encdec_qk)
+.set_num_inputs(3)
+.set_num_outputs(2)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr_parser(ParamParser<InterleavedMatMulParam>);
+
+NNVM_REGISTER_OP(_contrib_interleaved_matmul_encdec_valatt)
+.describe(R"code(Compute the matrix multiplication between the projections of
+values and the attention weights in multihead attention use as encoder-decoder.
+
+the inputs must be a tensor of interleaved projections of
+keys and values following the layout:
+(seq_length, batch_size, num_heads * head_dim * 2)
+
+and the attention weights following the layout:
+(batch_size, seq_length, seq_length)
+
+the equivalent code would be:
+
+tmp = mx.nd.reshape(queries_keys_values, shape=(0, 0, num_heads, 3, -1))
+v_proj = mx.nd.transpose(tmp[:,:,:,1,:], axes=(1, 2, 0, 3))
+v_proj = mx.nd.reshape(v_proj, shape=(-1, 0, 0), reverse=True)
+output = mx.nd.batch_dot(attention, v_proj, transpose_b=True)
+output = mx.nd.reshape(output, shape=(-1, num_heads, 0, 0), reverse=True)
+output = mx.nd.transpose(output, axes=(0, 2, 1, 3))
+output = mx.nd.reshape(output, shape=(0, 0, -1))
+
+This Op is GPU only
+)code" ADD_FILELINE)
+.set_num_inputs(2)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<InterleavedMatMulParam>)
+.set_attr<nnvm::FListInputNames>("FListInputNames", [](const NodeAttrs& attrs) {
+  return std::vector<std::string>{"keys_values", "attention"};
+})
+.set_attr<nnvm::FListOutputNames>("FListOutputNames", [](const NodeAttrs& attrs) {
+  return std::vector<std::string>{"output"};
+})
+.set_attr<mxnet::FInferShape>("FInferShape", InterleavedMatMulEncDecValAttShape)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<2, 1>)
+.set_attr<nnvm::FGradient>("FGradient",
+    ElemwiseGradUseIn{"_backward_interleaved_matmul_encdec_valatt"})
+.add_argument("keys_values", "NDArray-or-Symbol", "Keys and values interleaved")
+.add_argument("attention", "NDArray-or-Symbol", "Attention maps")
+.add_arguments(InterleavedMatMulParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_backward_interleaved_matmul_encdec_valatt)
+.set_num_inputs(3)
+.set_num_outputs(2)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr_parser(ParamParser<InterleavedMatMulParam>);
+
+
 // relu
 MXNET_OPERATOR_REGISTER_UNARY(_contrib_div_sqrt_dim)
 .describe(R"code(Rescale the input by the square root of the channel dimension.
diff --git a/src/operator/contrib/transformer.cu b/src/operator/contrib/transformer.cu
index 6ed073db6011..e152669478dd 100644
--- a/src/operator/contrib/transformer.cu
+++ b/src/operator/contrib/transformer.cu
@@ -22,12 +22,572 @@
  * \file transformer.cu
  * \brief GPU implementation of the operators used in Transformer
  */
+
+#include <cuda.h>
+#include <cuda_runtime.h>
+#include <cuda_fp16.h>
+#include <cuda_profiler_api.h>
+
 #include <mxnet/base.h>
 #include "./transformer-inl.h"
+#include "../../common/cuda_utils.h"
 
 namespace mxnet {
 namespace op {
 
+// Approach in gemm_switch_fp32accum is coming from MLPerf v0.6 submission repository from NVIDIA
+// by https://github.com/kevinstephano
+template<typename DType>
+void CublasStridedBatchedGemm(mshadow::Stream<gpu>* s, bool transA, bool transB,
+                              int32_t m, int32_t n, int32_t k,
+                              float alpha, const DType* a, int32_t lda, int32_t strideA,
+                              const DType *b, int32_t ldb, int32_t strideB, float beta,
+                              DType *c, int32_t ldc, int32_t strideC, int32_t batchCount,
+                              cublasGemmAlgo_t algo = CUBLAS_GEMM_DEFAULT_TENSOR_OP) {
+#if CUDA_VERSION >= 9010
+  using namespace mxnet::common::cuda;
+  CHECK_EQ(s->blas_handle_ownership_, mshadow::Stream<gpu>::OwnHandle)
+      << "Must init CuBLAS handle in stream";
+
+  cublasHandle_t blas_handle = mshadow::Stream<gpu>::GetBlasHandle(s);
+  auto err = CUBLAS_STATUS_SUCCESS;
+  // TODO(cfujitsang): handle computation_precision
+  err = cublasGemmStridedBatchedEx(
+      blas_handle, CublasTransposeOp(transA), CublasTransposeOp(transB),
+      static_cast<int>(m), static_cast<int>(n), static_cast<int>(k),
+      reinterpret_cast<void*>(&alpha),
+      a, CublasType<DType>::kCudaFlag, static_cast<int>(lda), strideA,
+      b, CublasType<DType>::kCudaFlag, static_cast<int>(ldb), strideB,
+      reinterpret_cast<void*>(&beta),
+      c, CublasType<DType>::kCudaFlag, static_cast<int>(ldc), strideC,
+      static_cast<int>(batchCount), CUDA_R_32F, algo);
+  CHECK_EQ(err, CUBLAS_STATUS_SUCCESS) << "Cublas gemmEx fail.";
+#else
+  LOG(FATAL) << "Not implemented with CUDA < 9.1";
+#endif
+}
+
+template<typename DType>
+void gemm_switch_fp32accum(mshadow::Stream<gpu>* s, bool transA, bool transB,
+                           int32_t m, int32_t n, int32_t k,
+                           float alpha, const DType *a, int32_t lda,
+                           int32_t strideA, const DType *b, int32_t ldb,
+                           int32_t strideB, float beta, DType *c, int32_t ldc,
+                           int32_t strideC, int32_t batchCount) {
+  cudaStream_t stream = mshadow::Stream<gpu>::GetStream(s);
+  if (!(lda & 0x7) && !(ldb & 0x7) && !(ldc & 0x7)) {
+    CublasStridedBatchedGemm(s, transA, transB, m, n, k, alpha, a, lda, strideA, b, ldb,
+      strideB, beta, c, ldc, strideC, batchCount, CUBLAS_GEMM_ALGO0_TENSOR_OP);
+  } else {
+    CublasStridedBatchedGemm(s, transA, transB, m, n, k, alpha, a, lda, strideA, b, ldb,
+      strideB, beta, c, ldc, strideC, batchCount);
+  }
+  CHECK_CUDA_ERROR("Error at InterleavedMatMul");
+}
+
+// TODO(cfujitsang): use scale as optional ?
+void InterleavedMatMulSelfAttQKGPU(const nnvm::NodeAttrs& attrs,
+                                   const OpContext &ctx,
+                                   const std::vector<TBlob> &inputs,
+                                   const std::vector<OpReqType> &req,
+                                   const std::vector<TBlob> &outputs) {
+  const auto& params = nnvm::get<InterleavedMatMulParam>(attrs.parsed);
+  mshadow::Stream<gpu>* s = ctx.get_stream<gpu>();
+  MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    const DType* queries_keys_values = inputs[0].FlatTo2D<gpu, DType>(s).dptr_;
+    DType* output = outputs[0].FlatTo2D<gpu, DType>(s).dptr_;
+    const int32_t qkv_seq_len    = inputs[0].shape_[0];
+    const int32_t sequences      = inputs[0].shape_[1];
+    const int32_t output_lin_dim = inputs[0].shape_[2];
+    const int32_t embed_dim      = output_lin_dim / 3;
+    const int32_t head_dim       = embed_dim / params.heads;
+    const int32_t attn_batches   = params.heads * sequences;
+    const int32_t lead_dim       = attn_batches * 3 * head_dim;
+    const int32_t batch_stride   = 3 * head_dim;
+    const float beta             = req[0] == kAddTo ? 1.f : 0.f;
+    const float scale            = 1.0 / sqrt(static_cast<float>(head_dim));
+
+    if (req[0] == kNullOp)
+      return;
+
+    gemm_switch_fp32accum(s,
+                          true,
+                          false,
+                          qkv_seq_len,
+                          qkv_seq_len,
+                          head_dim,
+                          scale,
+                          queries_keys_values + head_dim,
+                          lead_dim,
+                          batch_stride,
+                          queries_keys_values,
+                          lead_dim,
+                          batch_stride,
+                          beta,
+                          output,
+                          qkv_seq_len,
+                          qkv_seq_len * qkv_seq_len,
+                          attn_batches);
+  })
+}
+
+void BackwardInterleavedMatMulSelfAttQKGPU(const nnvm::NodeAttrs& attrs,
+                                           const OpContext &ctx,
+                                           const std::vector<TBlob> &inputs,
+                                           const std::vector<OpReqType> &req,
+                                           const std::vector<TBlob> &outputs) {
+  const auto& params = nnvm::get<InterleavedMatMulParam>(attrs.parsed);
+  mshadow::Stream<gpu>* s = ctx.get_stream<gpu>();
+  MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    const DType* output_grads        = inputs[0].FlatTo2D<gpu, DType>(s).dptr_;
+    const DType* queries_keys_values = inputs[1].FlatTo2D<gpu, DType>(s).dptr_;
+    DType* queries_keys_values_grads = outputs[0].FlatTo2D<gpu, DType>(s).dptr_;
+    const int32_t qkv_seq_len    = inputs[1].shape_[0];
+    const int32_t sequences      = inputs[1].shape_[1];
+    const int32_t output_lin_dim = inputs[1].shape_[2];
+    const int32_t embed_dim      = output_lin_dim / 3;
+    const int32_t head_dim       = embed_dim / params.heads;
+    const int32_t attn_batches   = params.heads * sequences;
+    const int32_t lead_dim       = attn_batches * 3 * head_dim;
+    const int32_t batch_stride   = 3 * head_dim;
+    const float scale            = 1.0 / sqrt(static_cast<float>(head_dim));
+    const float beta = req[0] == kAddTo ? 1.f : 0.f;
+
+    if (req[0] == kNullOp)
+      return;
+
+    if (req[0] == kWriteTo) {
+      cudaMemsetAsync(queries_keys_values_grads, 0, outputs[0].shape_.Size() * sizeof(DType),
+                      mshadow::Stream<gpu>::GetStream(s));
+    }
+
+    gemm_switch_fp32accum(s,
+                          false,
+                          false,
+                          head_dim,
+                          qkv_seq_len,
+                          qkv_seq_len,
+                          scale,
+                          queries_keys_values + head_dim,
+                          lead_dim,
+                          batch_stride,
+                          output_grads,
+                          qkv_seq_len,
+                          qkv_seq_len * qkv_seq_len,
+                          beta,
+                          queries_keys_values_grads,
+                          lead_dim,
+                          batch_stride,
+                          attn_batches);
+    gemm_switch_fp32accum(s,
+                          false,
+                          true,
+                          head_dim,
+                          qkv_seq_len,
+                          qkv_seq_len,
+                          scale,
+                          queries_keys_values,
+                          lead_dim,
+                          batch_stride,
+                          output_grads,
+                          qkv_seq_len,
+                          qkv_seq_len * qkv_seq_len,
+                          beta,
+                          queries_keys_values_grads + head_dim,
+                          lead_dim,
+                          batch_stride,
+                          attn_batches);
+  })
+}
+
+void InterleavedMatMulSelfAttValAttGPU(const nnvm::NodeAttrs& attrs,
+                                       const OpContext &ctx,
+                                       const std::vector<TBlob> &inputs,
+                                       const std::vector<OpReqType> &req,
+                                       const std::vector<TBlob> &outputs) {
+  const auto& params = nnvm::get<InterleavedMatMulParam>(attrs.parsed);
+  mshadow::Stream<gpu>* s = ctx.get_stream<gpu>();
+  MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    const DType* queries_keys_values = inputs[0].FlatTo2D<gpu, DType>(s).dptr_;
+    const DType* attention_maps      = inputs[1].FlatTo2D<gpu, DType>(s).dptr_;
+    DType* output                    = outputs[0].FlatTo2D<gpu, DType>(s).dptr_;
+    const int32_t qkv_seq_len    = inputs[0].shape_[0];
+    const int32_t sequences      = inputs[0].shape_[1];
+    const int32_t output_lin_dim = inputs[0].shape_[2];
+    const int32_t embed_dim      = output_lin_dim / 3;
+    const int32_t head_dim       = embed_dim / params.heads;
+    const int32_t attn_batches   = params.heads * sequences;
+    const int32_t lead_dim       = attn_batches * 3 * head_dim;
+    const int32_t batch_stride   = 3 * head_dim;
+    const float alpha            = 1.f;
+    const float beta             = req[0] == kAddTo ? 1.f : 0.f;
+
+    if (req[0] == kNullOp)
+      return;
+
+    gemm_switch_fp32accum(s,
+                          false,
+                          false,
+                          head_dim,
+                          qkv_seq_len,
+                          qkv_seq_len,
+                          alpha,
+                          queries_keys_values + 2 * head_dim,
+                          lead_dim,
+                          batch_stride,
+                          attention_maps,
+                          qkv_seq_len,
+                          qkv_seq_len * qkv_seq_len,
+                          beta,
+                          output,
+                          head_dim * attn_batches,
+                          head_dim,
+                          attn_batches);
+  })
+}
+
+void BackwardInterleavedMatMulSelfAttValAttGPU(const nnvm::NodeAttrs& attrs,
+                                               const OpContext &ctx,
+                                               const std::vector<TBlob> &inputs,
+                                               const std::vector<OpReqType> &req,
+                                               const std::vector<TBlob> &outputs) {
+  const auto& params = nnvm::get<InterleavedMatMulParam>(attrs.parsed);
+  mshadow::Stream<gpu>* s = ctx.get_stream<gpu>();
+  MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    const DType* output_grads              = inputs[0].FlatTo2D<gpu, DType>(s).dptr_;
+    const DType* queries_keys_values       = inputs[1].FlatTo2D<gpu, DType>(s).dptr_;
+    const DType* attention_maps            = inputs[2].FlatTo2D<gpu, DType>(s).dptr_;
+    DType* queries_keys_values_grads       = outputs[0].FlatTo2D<gpu, DType>(s).dptr_;
+    DType* attention_maps_grads            = outputs[1].FlatTo2D<gpu, DType>(s).dptr_;
+    const int32_t qkv_seq_len    = inputs[1].shape_[0];
+    const int32_t sequences      = inputs[1].shape_[1];
+    const int32_t output_lin_dim = inputs[1].shape_[2];
+    const int32_t embed_dim      = output_lin_dim / 3;
+    const int32_t head_dim       = embed_dim / params.heads;
+    const int32_t attn_batches   = params.heads * sequences;
+    const int32_t lead_dim       = attn_batches * 3 * head_dim;
+    const int32_t batch_stride   = 3 * head_dim;
+    const float alpha            = 1.f;
+    if (req[0] != kNullOp) {
+      if (req[0] == kWriteTo) {
+        cudaMemsetAsync(queries_keys_values_grads, 0, outputs[0].shape_.Size() * sizeof(DType),
+                        mshadow::Stream<gpu>::GetStream(s));
+      }
+      const float beta = req[0] == kAddTo ? 1.f : 0.f;
+      gemm_switch_fp32accum(s,
+                            false,
+                            true,
+                            head_dim,
+                            qkv_seq_len,
+                            qkv_seq_len,
+                            alpha,
+                            output_grads,
+                            head_dim * attn_batches,
+                            head_dim,
+                            attention_maps,
+                            qkv_seq_len,
+                            qkv_seq_len * qkv_seq_len,
+                            beta,
+                            queries_keys_values_grads + 2 * head_dim,
+                            lead_dim,
+                            batch_stride,
+                            attn_batches);
+    }
+    if (req[1] != kNullOp) {
+      const float beta = req[1] == kAddTo ? 1.f : 0.f;
+      gemm_switch_fp32accum(s,
+                            true,
+                            false,
+                            qkv_seq_len,
+                            qkv_seq_len,
+                            head_dim,
+                            alpha,
+                            queries_keys_values + 2 * head_dim,
+                            lead_dim,
+                            batch_stride,
+                            output_grads,
+                            head_dim * attn_batches,
+                            head_dim,
+                            beta,
+                            attention_maps_grads,
+                            qkv_seq_len,
+                            qkv_seq_len * qkv_seq_len,
+                            attn_batches);
+    }
+  })
+}
+
+
+void InterleavedMatMulEncDecQKGPU(const nnvm::NodeAttrs& attrs,
+                                  const OpContext &ctx,
+                                  const std::vector<TBlob> &inputs,
+                                  const std::vector<OpReqType> &req,
+                                  const std::vector<TBlob> &outputs) {
+  const auto& params = nnvm::get<InterleavedMatMulParam>(attrs.parsed);
+  mshadow::Stream<gpu>* s = ctx.get_stream<gpu>();
+  MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    const DType* queries     = inputs[0].FlatTo2D<gpu, DType>(s).dptr_;
+    const DType* keys_values = inputs[1].FlatTo2D<gpu, DType>(s).dptr_;
+    DType* output            = outputs[0].FlatTo2D<gpu, DType>(s).dptr_;
+    const int32_t q_seq_len         = inputs[0].shape_[0];
+    const int32_t sequences         = inputs[0].shape_[1];
+    const int32_t output_lin_q_dim  = inputs[0].shape_[2];
+    const int32_t kv_seq_len        = inputs[1].shape_[0];
+    const int32_t output_lin_kv_dim = inputs[1].shape_[2];
+    const int32_t embed_dim         = output_lin_q_dim;
+    const int32_t head_dim          = embed_dim / params.heads;
+    const int32_t attn_batches      = params.heads * sequences;
+    const int32_t lead_dim_q        = attn_batches * head_dim;
+    const int32_t lead_dim_kv       = attn_batches * 2 * head_dim;
+    const int32_t batch_stride_q    = head_dim;
+    const int32_t batch_stride_kv   = head_dim * 2;
+    const float beta                = req[0] == kAddTo ? 1.f : 0.f;
+    const float scale               = 1.f / sqrt(static_cast<float>(head_dim));
+
+    if (req[0] == kNullOp)
+      return;
+
+    gemm_switch_fp32accum(s,
+                          true,
+                          false,
+                          kv_seq_len,
+                          q_seq_len,
+                          head_dim,
+                          scale,
+                          keys_values,
+                          lead_dim_kv,
+                          batch_stride_kv,
+                          queries,
+                          lead_dim_q,
+                          batch_stride_q,
+                          beta,
+                          output,
+                          kv_seq_len,
+                          kv_seq_len * q_seq_len,
+                          attn_batches);
+  })
+}
+
+void BackwardInterleavedMatMulEncDecQKGPU(const nnvm::NodeAttrs& attrs,
+                                          const OpContext &ctx,
+                                          const std::vector<TBlob> &inputs,
+                                          const std::vector<OpReqType> &req,
+                                          const std::vector<TBlob> &outputs) {
+  const auto& params = nnvm::get<InterleavedMatMulParam>(attrs.parsed);
+  mshadow::Stream<gpu>* s = ctx.get_stream<gpu>();
+  MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    const DType* output_grads = inputs[0].FlatTo2D<gpu, DType>(s).dptr_;
+    const DType* queries       = inputs[1].FlatTo2D<gpu, DType>(s).dptr_;
+    const DType* keys_values   = inputs[2].FlatTo2D<gpu, DType>(s).dptr_;
+    DType* queries_grads       = outputs[0].FlatTo2D<gpu, DType>(s).dptr_;
+    DType* keys_values_grads   = outputs[1].FlatTo2D<gpu, DType>(s).dptr_;
+    const int32_t q_seq_len         = inputs[1].shape_[0];
+    const int32_t sequences         = inputs[1].shape_[1];
+    const int32_t output_lin_q_dim  = inputs[1].shape_[2];
+    const int32_t kv_seq_len        = inputs[2].shape_[0];
+    const int32_t output_lin_kv_dim = inputs[2].shape_[2];
+    const int32_t embed_dim         = output_lin_q_dim;
+    const int32_t head_dim          = embed_dim / params.heads;
+    const int32_t attn_batches      = params.heads * sequences;
+    const int32_t lead_dim_q        = attn_batches * head_dim;
+    const int32_t lead_dim_kv       = attn_batches * 2 * head_dim;
+    const int32_t batch_stride_q    = head_dim;
+    const int32_t batch_stride_kv   = head_dim * 2;
+    const float scale               = 1.f / sqrt(static_cast<float>(head_dim));
+
+    if (req[0] != kNullOp) {
+      const float beta = req[0] == kAddTo ? 1.f : 0.f;
+      gemm_switch_fp32accum(s,
+                            false,
+                            false,
+                            head_dim,
+                            q_seq_len,
+                            kv_seq_len,
+                            scale,
+                            keys_values,
+                            lead_dim_kv,
+                            batch_stride_kv,
+                            output_grads,
+                            kv_seq_len,
+                            kv_seq_len * q_seq_len,
+                            beta,
+                            queries_grads,
+                            lead_dim_q,
+                            batch_stride_q,
+                            attn_batches);
+    }
+    if (req[1] != kNullOp) {
+      if (req[1] == kWriteTo) {
+        cudaMemsetAsync(keys_values_grads, 0, outputs[1].shape_.Size() * sizeof(DType),
+                        mshadow::Stream<gpu>::GetStream(s));
+      }
+      const float beta = req[1] == kAddTo ? 1.f : 0.f;
+      gemm_switch_fp32accum(s,
+                            false,
+                            true,
+                            head_dim,
+                            kv_seq_len,
+                            q_seq_len,
+                            scale,
+                            queries,
+                            lead_dim_q,
+                            batch_stride_q,
+                            output_grads,
+                            kv_seq_len,
+                            kv_seq_len * q_seq_len,
+                            beta,
+                            keys_values_grads,
+                            lead_dim_kv,
+                            batch_stride_kv,
+                            attn_batches);
+    }
+  })
+}
+
+void InterleavedMatMulEncDecValAttGPU(const nnvm::NodeAttrs& attrs,
+                                      const OpContext &ctx,
+                                      const std::vector<TBlob> &inputs,
+                                      const std::vector<OpReqType> &req,
+                                      const std::vector<TBlob> &outputs) {
+  const auto& params = nnvm::get<InterleavedMatMulParam>(attrs.parsed);
+  mshadow::Stream<gpu>* s = ctx.get_stream<gpu>();
+  MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    const DType* keys_values    = inputs[0].FlatTo2D<gpu, DType>(s).dptr_;
+    const DType* attention_maps = inputs[1].FlatTo2D<gpu, DType>(s).dptr_;
+    DType* output               = outputs[0].FlatTo2D<gpu, DType>(s).dptr_;
+    const int32_t kv_seq_len        = inputs[0].shape_[0];
+    const int32_t sequences         = inputs[0].shape_[1];
+    const int32_t output_lin_kv_dim = inputs[0].shape_[2];
+    const int32_t attn_batches      = inputs[1].shape_[0];
+    const int32_t q_seq_len         = inputs[1].shape_[1];
+    const int32_t embed_dim         = output_lin_kv_dim / 2;
+    int32_t head_dim                = embed_dim / params.heads;
+    const int32_t lead_dim_kv       = attn_batches * head_dim * 2;
+    const int32_t batch_stride_kv   = 2 * head_dim;
+    const float alpha               = 1.f;
+    const float beta                = req[0] == kAddTo ? 1.f : 0.f;
+
+    if (req[0] == kNullOp)
+      return;
+
+    gemm_switch_fp32accum(s,
+                          false,
+                          false,
+                          head_dim,
+                          q_seq_len,
+                          kv_seq_len,
+                          alpha,
+                          keys_values + head_dim,
+                          lead_dim_kv,
+                          batch_stride_kv,
+                          attention_maps,
+                          kv_seq_len,
+                          kv_seq_len * q_seq_len,
+                          beta,
+                          output,
+                          head_dim * attn_batches,
+                          head_dim,
+                          attn_batches);
+  })
+}
+
+void BackwardInterleavedMatMulEncDecValAttGPU(const nnvm::NodeAttrs& attrs,
+                                              const OpContext &ctx,
+                                              const std::vector<TBlob> &inputs,
+                                              const std::vector<OpReqType> &req,
+                                              const std::vector<TBlob> &outputs) {
+  const auto& params = nnvm::get<InterleavedMatMulParam>(attrs.parsed);
+  mshadow::Stream<gpu>* s = ctx.get_stream<gpu>();
+  MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    const DType* output_grads   = inputs[0].FlatTo2D<gpu, DType>(s).dptr_;
+    const DType* keys_values    = inputs[1].FlatTo2D<gpu, DType>(s).dptr_;
+    const DType* attention_maps = inputs[2].FlatTo2D<gpu, DType>(s).dptr_;
+    DType* keys_values_grads    = outputs[0].FlatTo2D<gpu, DType>(s).dptr_;
+    DType* attention_maps_grads = outputs[1].FlatTo2D<gpu, DType>(s).dptr_;
+    const int32_t kv_seq_len        = inputs[1].shape_[0];
+    const int32_t sequences         = inputs[1].shape_[1];
+    const int32_t output_lin_kv_dim = inputs[1].shape_[2];
+    const int32_t attn_batches      = inputs[2].shape_[0];
+    const int32_t q_seq_len         = inputs[2].shape_[1];
+    const int32_t embed_dim         = output_lin_kv_dim / 2;
+    int32_t head_dim                = embed_dim / params.heads;
+    const int32_t lead_dim_kv       = attn_batches * head_dim * 2;
+    const int32_t batch_stride_kv   = 2 * head_dim;
+    const float alpha               = 1.f;
+
+    if (req[0] != kNullOp) {
+      if (req[0] == kWriteTo) {
+        cudaMemsetAsync(keys_values_grads, 0, outputs[0].shape_.Size() * sizeof(DType),
+                        mshadow::Stream<gpu>::GetStream(s));
+      }
+      const float beta = req[0] == kAddTo ? 1.f : 0.f;
+      gemm_switch_fp32accum(s,
+                            false,
+                            true,
+                            head_dim,
+                            kv_seq_len,
+                            q_seq_len,
+                            alpha,
+                            output_grads,
+                            head_dim * attn_batches,
+                            head_dim,
+                            attention_maps,
+                            kv_seq_len,
+                            kv_seq_len * q_seq_len,
+                            beta,
+                            keys_values_grads + head_dim,
+                            lead_dim_kv,
+                            batch_stride_kv,
+                            attn_batches);
+    }
+    if (req[1] != kNullOp) {
+      const float beta = req[1] == kAddTo ? 1.f : 0.f;
+      gemm_switch_fp32accum(s,
+                            true,
+                            false,
+                            kv_seq_len,
+                            q_seq_len,
+                            head_dim,
+                            alpha,
+                            keys_values + head_dim,
+                            lead_dim_kv,
+                            batch_stride_kv,
+                            output_grads,
+                            head_dim * attn_batches,
+                            head_dim,
+                            beta,
+                            attention_maps_grads,
+                            kv_seq_len,
+                            kv_seq_len * q_seq_len,
+                            attn_batches);
+    }
+  })
+}
+
+NNVM_REGISTER_OP(_contrib_interleaved_matmul_selfatt_qk)
+.set_attr<FCompute>("FCompute<gpu>", InterleavedMatMulSelfAttQKGPU);
+
+NNVM_REGISTER_OP(_contrib_interleaved_matmul_selfatt_valatt)
+.set_attr<FCompute>("FCompute<gpu>", InterleavedMatMulSelfAttValAttGPU);
+
+NNVM_REGISTER_OP(_contrib_interleaved_matmul_encdec_qk)
+.set_attr<FCompute>("FCompute<gpu>", InterleavedMatMulEncDecQKGPU);
+
+NNVM_REGISTER_OP(_contrib_interleaved_matmul_encdec_valatt)
+.set_attr<FCompute>("FCompute<gpu>", InterleavedMatMulEncDecValAttGPU);
+
+NNVM_REGISTER_OP(_backward_interleaved_matmul_selfatt_qk)
+.set_attr<FCompute>("FCompute<gpu>", BackwardInterleavedMatMulSelfAttQKGPU);
+
+NNVM_REGISTER_OP(_backward_interleaved_matmul_selfatt_valatt)
+.set_attr<FCompute>("FCompute<gpu>", BackwardInterleavedMatMulSelfAttValAttGPU);
+
+NNVM_REGISTER_OP(_backward_interleaved_matmul_encdec_qk)
+.set_attr<FCompute>("FCompute<gpu>", BackwardInterleavedMatMulEncDecQKGPU);
+
+NNVM_REGISTER_OP(_backward_interleaved_matmul_encdec_valatt)
+.set_attr<FCompute>("FCompute<gpu>", BackwardInterleavedMatMulEncDecValAttGPU);
+
 // relu
 NNVM_REGISTER_OP(_contrib_div_sqrt_dim)
 .set_attr<FCompute>("FCompute<gpu>", DivSqrtDimForward_<gpu>);
diff --git a/tests/python/gpu/test_operator_gpu.py b/tests/python/gpu/test_operator_gpu.py
index 8b6928a2aa39..fe74eed727e5 100644
--- a/tests/python/gpu/test_operator_gpu.py
+++ b/tests/python/gpu/test_operator_gpu.py
@@ -2502,13 +2502,327 @@ def test_arange_like_dtype():
         x = mx.sym.Variable('x', dtype=t)
         y = mx.sym.reshape(x, shape=(0, 0, -1))
         z = mx.sym.contrib.arange_like(y, axis=-1)
-    
+
         mod = z.simple_bind(ctx=mx.gpu(0), x=(3, 4, 5, 6), grad_req='null')
         mod.arg_arrays[0][:] = np.random.normal(size=mod.arg_arrays[0].shape).astype(t)
         out = mod.forward(is_train=False)
         for v in out:
             assert v.dtype == t
 
+@with_seed()
+def check_multihead_attention_selfatt(dtype):
+    def convert_weight(F, q_weight, k_weight, v_weight, num_heads):
+        q_weight = F.reshape(q_weight, shape=(num_heads, -1, 0), reverse=True)
+        k_weight = F.reshape(k_weight, shape=(num_heads, -1, 0), reverse=True)
+        v_weight = F.reshape(v_weight, shape=(num_heads, -1, 0), reverse=True)
+        all_weights = F.concat(q_weight, k_weight, v_weight, dim=-2)
+        all_weights = F.reshape(all_weights, shape=(-1, 0), reverse=True)
+        return all_weights
+
+    def convert_bias(F, q_bias, k_bias, v_bias, num_heads):
+        q_bias = F.reshape(q_bias, shape=(num_heads, -1))
+        k_bias = F.reshape(k_bias, shape=(num_heads, -1))
+        v_bias = F.reshape(v_bias, shape=(num_heads, -1))
+        all_bias = F.stack(q_bias, k_bias, v_bias, axis=1)
+        all_bias = F.reshape(all_bias, shape=(-1,))
+        return all_bias
+
+    batch_size = 2
+    qkv_length = 7  # length of a sequence
+    qkv_dim = 9     # dimension of encoding
+    num_heads = 3   # number of attention head
+    head_dim = 5    # head size
+    out_dim = 13 * num_heads
+    qkv_units = num_heads * head_dim
+
+    arg_params = {
+        'qkv': mx.nd.array(np.random.rand(*(batch_size, qkv_length, qkv_dim)).astype(dtype) * 0.1, dtype=dtype),
+        'q_weight': mx.nd.array(np.random.rand(*(qkv_units, qkv_dim)).astype(dtype) * 0.1, dtype=dtype),
+        'k_weight': mx.nd.array(np.random.rand(*(qkv_units, qkv_dim)).astype(dtype) * 0.1, dtype=dtype),
+        'v_weight': mx.nd.array(np.random.rand(*(qkv_units, qkv_dim)).astype(dtype) * 0.1, dtype=dtype),
+        'q_bias': mx.nd.array(np.random.rand(*(qkv_units,)).astype(dtype) * 0.1, dtype=dtype),
+        'k_bias': mx.nd.array(np.random.rand(*(qkv_units,)).astype(dtype) * 0.1, dtype=dtype),
+        'v_bias': mx.nd.array(np.random.rand(*(qkv_units,)).astype(dtype) * 0.1, dtype=dtype),
+        'out_weight': mx.nd.array(np.random.rand(*(out_dim, qkv_units)).astype(dtype) * 0.1, dtype=dtype),
+        'out_bias': mx.nd.array(np.random.rand(*(out_dim,)).astype(dtype) * 0.1, dtype=dtype),
+        }
+
+    qkv = mx.sym.Variable('qkv')
+    sonde = mx.sym.Variable('sonde')
+    q_weight = mx.sym.Variable('q_weight')
+    k_weight = mx.sym.Variable('k_weight')
+    v_weight = mx.sym.Variable('v_weight')
+    q_bias = mx.sym.Variable('q_bias')
+    k_bias = mx.sym.Variable('k_bias')
+    v_bias = mx.sym.Variable('v_bias')
+    out_weight = mx.sym.Variable('out_weight')
+    out_bias = mx.sym.Variable('out_bias')
+    qkv_weight = convert_weight(mx.sym, q_weight, k_weight, v_weight, num_heads)
+    qkv_bias = convert_bias(mx.sym, q_bias, k_bias, v_bias, num_heads)
+    qkv = mx.sym.transpose(qkv, axes=(1, 0, 2))
+    qkv_proj = mx.sym.FullyConnected(qkv, weight=qkv_weight, bias=qkv_bias, flatten=False,
+                                     num_hidden=qkv_units * 3, no_bias=False)
+    att_score = mx.sym.contrib.interleaved_matmul_selfatt_qk(
+            qkv_proj, heads=num_heads)
+    att_score = att_score + sonde
+    weighted_value = mx.sym.contrib.interleaved_matmul_selfatt_valatt(
+            qkv_proj, att_score, heads=num_heads)
+    output = mx.sym.FullyConnected(weighted_value, weight=out_weight, bias=out_bias, flatten=False,
+                                   num_hidden=out_dim, no_bias=False)
+    output = mx.sym.transpose(output, axes=(1, 0, 2))
+    output = mx.sym.Group([output, att_score])
+    executor = output.simple_bind(ctx=mx.gpu(0),
+                                  qkv=(batch_size, qkv_length, qkv_dim),
+                                  q_weight=(qkv_units, qkv_dim),
+                                  q_bias=(qkv_units,),
+                                  k_weight=(qkv_units, qkv_dim),
+                                  k_bias=(qkv_units,),
+                                  v_weight=(qkv_units, qkv_dim),
+                                  v_bias=(qkv_units,),
+                                  type_dict={'qkv': dtype,
+                                             'q_weight': dtype,
+                                             'k_weight': dtype,
+                                             'v_weight': dtype,
+                                             'q_bias': dtype,
+                                             'k_bias': dtype,
+                                             'v_bias': dtype,
+                                             'sonde': dtype},
+                                  grad_req='write', force_rebind=True)
+    output_shape = executor.outputs[0].shape
+    output_grads = np.random.rand(*output_shape).astype(dtype) * 0.1
+    executor.copy_params_from(arg_params, {})
+    executor.arg_dict['sonde'][:] = 0.
+    executor.arg_dict['sonde'].wait_to_read()
+    executor.forward(is_train=True)
+    output_opti = executor.outputs[0].asnumpy()
+    att_score_opti = executor.outputs[1].asnumpy()
+    executor.backward([mx.nd.array(output_grads, dtype=dtype),
+                       mx.nd.zeros(att_score_opti.shape, dtype=dtype)])
+    grads_opti = {k: v.asnumpy() for k, v in executor.grad_dict.items()}
+    qkv = mx.sym.Variable('qkv')
+    sonde = mx.sym.Variable('sonde')
+    q_weight = mx.sym.Variable('q_weight')
+    k_weight = mx.sym.Variable('k_weight')
+    v_weight = mx.sym.Variable('v_weight')
+    q_bias = mx.sym.Variable('q_bias')
+    k_bias = mx.sym.Variable('k_bias')
+    v_bias = mx.sym.Variable('v_bias')
+    out_weight = mx.sym.Variable('out_weight')
+    out_bias = mx.sym.Variable('out_bias')
+
+    q = mx.sym.FullyConnected(qkv, weight=q_weight, bias=q_bias, flatten=False,
+                              num_hidden=qkv_units, no_bias=False)
+    k = mx.sym.FullyConnected(qkv, weight=k_weight, bias=k_bias, flatten=False,
+                              num_hidden=qkv_units, no_bias=False)
+    v = mx.sym.FullyConnected(qkv, weight=v_weight, bias=v_bias, flatten=False,
+                              num_hidden=qkv_units, no_bias=False)
+    q = mx.sym.reshape(q, shape=(0, 0, num_heads, -1))
+    q = mx.sym.transpose(q, axes=(0, 2, 1, 3))
+    q = mx.sym.reshape(q, shape=(-1, 0, 0), reverse=True)
+    k = mx.sym.reshape(k, shape=(0, 0, num_heads, -1))
+    k = mx.sym.transpose(k, axes=(0, 2, 1, 3))
+    k = mx.sym.reshape(k, shape=(-1, 0, 0), reverse=True)
+    q = mx.sym.contrib.div_sqrt_dim(q)
+    att_score = mx.sym.batch_dot(q, k, transpose_b=True)
+    att_score = att_score + sonde
+    v = mx.sym.reshape(v, shape=(0, 0, num_heads, -1))
+    v = mx.sym.transpose(v, axes=(0, 2, 1, 3))
+    v = mx.sym.reshape(v, shape=(-1, 0, 0), reverse=True)
+    weighted_value = mx.sym.batch_dot(att_score, v)
+    weighted_value = mx.sym.reshape(weighted_value, shape=(-1, num_heads, 0, 0),
+                                    reverse=True)
+    weighted_value = mx.sym.transpose(weighted_value, axes=(0, 2, 1, 3))
+    weighted_value = mx.sym.reshape(weighted_value, shape=(0, 0, -1))
+    output = mx.sym.FullyConnected(weighted_value, weight=out_weight, bias=out_bias, flatten=False,
+                                   num_hidden=out_dim, no_bias=False)
+    output = mx.sym.Group([output, att_score])
+    executor = output.simple_bind(ctx=mx.gpu(0),
+                                  qkv=(batch_size, qkv_length, qkv_dim),
+                                  type_dict={'qkv': dtype},
+                                  grad_req='write', force_rebind=True)
+    executor.copy_params_from(arg_params, {})
+    executor.arg_dict['sonde'][:] = 0.
+    executor.arg_dict['sonde'].wait_to_read()
+    executor.forward(is_train=True)
+    output_orig = executor.outputs[0].asnumpy()
+    att_score_orig = executor.outputs[1].asnumpy()
+    executor.backward([mx.nd.array(output_grads, dtype=dtype),
+                       mx.nd.zeros(att_score_orig.shape, dtype=dtype)])
+    grads_orig = {k : v.asnumpy() for k, v in executor.grad_dict.items()}
+    assert_allclose(att_score_orig, att_score_opti, rtol=1e-2, atol=1e-3)
+    assert_allclose(output_orig, output_opti, rtol=1e-2, atol=1e-3)
+
+    for k in grads_opti.keys():
+        assert(grads_orig[k].dtype == grads_opti[k].dtype)
+        assert(grads_orig[k].shape == grads_opti[k].shape)
+        assert_allclose(grads_orig[k], grads_opti[k], rtol=1e-2, atol=1e-3)
+
+def test_multihead_attention_selfatt():
+    for dtype in ['float16', 'float32']:
+        check_multihead_attention_selfatt(dtype=dtype)
+
+def check_multihead_attention_encdec(dtype):
+    def convert_weight(F, k_weight, v_weight, num_heads):
+        k_weight = F.reshape(k_weight, shape=(num_heads, -1, 0), reverse=True)
+        v_weight = F.reshape(v_weight, shape=(num_heads, -1, 0), reverse=True)
+        all_weights = F.concat(k_weight, v_weight, dim=-2)
+        all_weights = F.reshape(all_weights, shape=(-1, 0), reverse=True)
+        return all_weights
+
+    def convert_bias(F, k_bias, v_bias, num_heads):
+        k_bias = F.reshape(k_bias, shape=(num_heads, -1))
+        v_bias = F.reshape(v_bias, shape=(num_heads, -1))
+        all_bias = F.stack(k_bias, v_bias, axis=1)
+        all_bias = F.reshape(all_bias, shape=(-1,))
+        return all_bias
+
+    batch_size = 2
+    qkv_length = 7  # length of a sequence
+    qkv_dim = 9     # dimension of encoding
+    num_heads = 3   # number of attention head
+    head_dim = 5    # head size
+    out_dim = 13 * num_heads
+    qkv_units = num_heads * head_dim
+
+    arg_params = {
+        'q': mx.nd.array(np.random.rand(*(batch_size, qkv_length, qkv_dim)).astype(dtype) * 0.1, dtype=dtype),
+        'kv': mx.nd.array(np.random.rand(*(batch_size, qkv_length, qkv_dim)).astype(dtype) * 0.1, dtype=dtype),
+        'q_weight': mx.nd.array(np.random.rand(*(qkv_units, qkv_dim)).astype(dtype) * 0.1, dtype=dtype),
+        'k_weight': mx.nd.array(np.random.rand(*(qkv_units, qkv_dim)).astype(dtype) * 0.1, dtype=dtype),
+        'v_weight': mx.nd.array(np.random.rand(*(qkv_units, qkv_dim)).astype(dtype) * 0.1, dtype=dtype),
+        'q_bias': mx.nd.array(np.random.rand(*(qkv_units,)).astype(dtype) * 0.1, dtype=dtype),
+        'k_bias': mx.nd.array(np.random.rand(*(qkv_units,)).astype(dtype) * 0.1, dtype=dtype),
+        'v_bias': mx.nd.array(np.random.rand(*(qkv_units,)).astype(dtype) * 0.1, dtype=dtype),
+        'out_weight': mx.nd.array(np.random.rand(*(out_dim, qkv_units)).astype(dtype) * 0.1, dtype=dtype),
+        'out_bias': mx.nd.array(np.random.rand(*(out_dim,)).astype(dtype) * 0.1, dtype=dtype),
+        }
+
+    q = mx.sym.Variable('q')
+    kv = mx.sym.Variable('kv')
+    sonde = mx.sym.Variable('sonde')
+    q_weight = mx.sym.Variable('q_weight')
+    k_weight = mx.sym.Variable('k_weight')
+    v_weight = mx.sym.Variable('v_weight')
+    q_bias = mx.sym.Variable('q_bias')
+    k_bias = mx.sym.Variable('k_bias')
+    v_bias = mx.sym.Variable('v_bias')
+    out_weight = mx.sym.Variable('out_weight')
+    out_bias = mx.sym.Variable('out_bias')
+    kv_weight = convert_weight(mx.sym, k_weight, v_weight, num_heads)
+    kv_bias = convert_bias(mx.sym, k_bias, v_bias, num_heads)
+    kv = mx.sym.transpose(kv, axes=(1, 0, 2))
+    kv_proj = mx.sym.FullyConnected(kv, weight=kv_weight, bias=kv_bias, flatten=False,
+                                    num_hidden=qkv_units * 2, no_bias=False)
+    q = mx.sym.transpose(q, axes=(1, 0, 2))
+    q_proj = mx.sym.FullyConnected(q, weight=q_weight, bias=q_bias, flatten=False,
+                                   num_hidden=qkv_units, no_bias=False)
+    att_score = mx.sym.contrib.interleaved_matmul_encdec_qk(
+            q_proj, kv_proj, heads=num_heads) 
+    att_score = att_score + sonde
+    weighted_value = mx.sym.contrib.interleaved_matmul_encdec_valatt(
+            kv_proj, att_score, heads=num_heads)
+    output = mx.sym.FullyConnected(weighted_value, weight=out_weight, bias=out_bias, flatten=False,
+                                   num_hidden=out_dim, no_bias=False)
+    output = mx.sym.transpose(output, axes=(1, 0, 2))
+    output = mx.sym.Group([output, att_score])
+    executor = output.simple_bind(ctx=mx.gpu(0),
+                                  q=(batch_size, qkv_length, qkv_dim),
+                                  kv=(batch_size, qkv_length, qkv_dim),
+                                  q_weight=(qkv_units, qkv_dim),
+                                  q_bias=(qkv_units,),
+                                  k_weight=(qkv_units, qkv_dim),
+                                  k_bias=(qkv_units,),
+                                  v_weight=(qkv_units, qkv_dim),
+                                  v_bias=(qkv_units,),
+                                  out_weight=(out_dim, qkv_units),
+                                  out_bias=(out_dim,),
+                                  type_dict={'q': dtype,
+                                             'kv': dtype,
+                                             'q_weight': dtype,
+                                             'q_bias': dtype,
+                                             'k_weight': dtype,
+                                             'k_bias': dtype,
+                                             'v_weight': dtype,
+                                             'v_bias': dtype,
+                                             'out_weight': dtype,
+                                             'out_bias': dtype,
+                                              },
+                                  grad_req='write', force_rebind=True)
+    output_shape = executor.outputs[0].shape
+    output_grads = np.random.rand(*output_shape).astype(dtype) * 0.1
+    executor.copy_params_from(arg_params, {})
+    executor.arg_dict['sonde'][:] = 0.
+    executor.arg_dict['sonde'].wait_to_read()
+    executor.forward(is_train=True)
+    output_opti = executor.outputs[0].asnumpy()
+    att_score_opti = executor.outputs[1].asnumpy()
+    executor.backward([mx.nd.array(output_grads, dtype=dtype), mx.nd.zeros(att_score_opti.shape, dtype=dtype)])
+
+    grads_opti = {k: v.asnumpy() for k, v in executor.grad_dict.items()}
+
+    q = mx.sym.Variable('q')
+    kv = mx.sym.Variable('kv')
+    sonde = mx.sym.Variable('sonde')
+    q_weight = mx.sym.Variable('q_weight')
+    k_weight = mx.sym.Variable('k_weight')
+    v_weight = mx.sym.Variable('v_weight')
+    q_bias = mx.sym.Variable('q_bias')
+    k_bias = mx.sym.Variable('k_bias')
+    v_bias = mx.sym.Variable('v_bias')
+    out_weight = mx.sym.Variable('out_weight')
+    out_bias = mx.sym.Variable('out_bias')
+
+    q = mx.sym.FullyConnected(q, weight=q_weight, bias=q_bias, flatten=False,
+                              num_hidden=qkv_units, no_bias=False)
+    k = mx.sym.FullyConnected(kv, weight=k_weight, bias=k_bias, flatten=False,
+                              num_hidden=qkv_units, no_bias=False)
+    v = mx.sym.FullyConnected(kv, weight=v_weight, bias=v_bias, flatten=False,
+                              num_hidden=qkv_units, no_bias=False)
+    q = mx.sym.reshape(q, shape=(0, 0, num_heads, -1))
+    q = mx.sym.transpose(q, axes=(0, 2, 1, 3))
+    q = mx.sym.reshape(q, shape=(-1, 0, 0), reverse=True)
+    k = mx.sym.reshape(k, shape=(0, 0, num_heads, -1))
+    k = mx.sym.transpose(k, axes=(0, 2, 1, 3))
+    k = mx.sym.reshape(k, shape=(-1, 0, 0), reverse=True)
+    q = mx.sym.contrib.div_sqrt_dim(q)
+    att_score = mx.sym.batch_dot(q, k, transpose_b=True)
+    att_score = att_score + sonde
+    v = mx.sym.reshape(v, shape=(0, 0, num_heads, -1))
+    v = mx.sym.transpose(v, axes=(0, 2, 1, 3))
+    v = mx.sym.reshape(v, shape=(-1, 0, 0), reverse=True)
+    weighted_value = mx.sym.batch_dot(att_score, v)
+    weighted_value = mx.sym.reshape(weighted_value, shape=(-1, num_heads, 0, 0),
+                                    reverse=True)
+    weighted_value = mx.sym.transpose(weighted_value, axes=(0, 2, 1, 3))
+    weighted_value = mx.sym.reshape(weighted_value, shape=(0, 0, -1))
+    output = mx.sym.FullyConnected(weighted_value, weight=out_weight, bias=out_bias, flatten=False,
+                                   num_hidden=out_dim, no_bias=False)
+    output = mx.sym.Group([output, att_score])
+    executor = output.simple_bind(ctx=mx.gpu(0),
+                                  q=(batch_size, qkv_length, qkv_dim),
+                                  kv=(batch_size, qkv_length, qkv_dim),
+                                  type_dict={'q': dtype,
+                                             'kv': dtype},
+                                  grad_req='write', force_rebind=True)
+    executor.copy_params_from(arg_params, {})
+    executor.arg_dict['sonde'][:] = 0.
+    executor.arg_dict['sonde'].wait_to_read()
+    executor.forward(is_train=True)
+    output_orig = executor.outputs[0].asnumpy()
+    att_score_orig = executor.outputs[1].asnumpy()
+    executor.backward([mx.nd.array(output_grads, dtype=dtype), mx.nd.zeros(att_score_orig.shape, dtype=dtype)])
+    grads_orig = {k : v.asnumpy() for k, v in executor.grad_dict.items()}
+    assert_allclose(att_score_orig, att_score_opti, rtol=1e-2, atol=1e-3)
+    assert_allclose(output_orig, output_opti, rtol=1e-2, atol=1e-3)
+
+    for k in grads_opti.keys():
+        assert(grads_orig[k].dtype == grads_opti[k].dtype)
+        assert(grads_orig[k].shape == grads_opti[k].shape)
+        assert_allclose(grads_orig[k], grads_opti[k], rtol=1e-2, atol=1e-3)
+
+def test_multihead_attention_encdec():
+    for dtype in ['float16', 'float32']:
+        check_multihead_attention_encdec(dtype=dtype)
 
 if __name__ == '__main__':
     import nose

From ecb7a3ada1ce7dbbae695bdcb2a767e3fda92fed Mon Sep 17 00:00:00 2001
From: Dick Carter <dcarter@nvidia.com>
Date: Wed, 6 Nov 2019 18:21:47 -0800
Subject: [PATCH 572/813] Update submodule dmlc-core (#16742)

---
 3rdparty/dmlc-core | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/3rdparty/dmlc-core b/3rdparty/dmlc-core
index 0e13243556d7..ca9f9329654d 160000
--- a/3rdparty/dmlc-core
+++ b/3rdparty/dmlc-core
@@ -1 +1 @@
-Subproject commit 0e13243556d7de754a923c8f97998721d694f29b
+Subproject commit ca9f9329654dbd42f06988276c6f74eb8bdd5038

From d967be912ebd962f2794bfaa5624e1ddca67336e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E5=A4=8F=E9=B2=81=E8=B1=AB?= <fiercex@outlook.com>
Date: Thu, 7 Nov 2019 11:10:08 +0800
Subject: [PATCH 573/813] [BUG FIX] Always preserve batch dimension in batches
 returned from dataloader (#16233)

* fix dataloader

* add unittest

* fix test
---
 python/mxnet/gluon/data/dataloader.py    |  1 -
 tests/python/unittest/test_gluon_data.py | 17 +++++++++++++++++
 2 files changed, 17 insertions(+), 1 deletion(-)

diff --git a/python/mxnet/gluon/data/dataloader.py b/python/mxnet/gluon/data/dataloader.py
index cb6ae5a164b9..7394f7d5d3d9 100644
--- a/python/mxnet/gluon/data/dataloader.py
+++ b/python/mxnet/gluon/data/dataloader.py
@@ -507,7 +507,6 @@ def __next__(self):
                 batch = ret.get(self._timeout)
             if self._pin_memory:
                 batch = _as_in_context(batch, context.cpu_pinned(self._pin_device_id))
-            batch = batch[0] if len(batch) == 1 else batch
             self._rcvd_idx += 1
             return batch
         except multiprocessing.context.TimeoutError:
diff --git a/tests/python/unittest/test_gluon_data.py b/tests/python/unittest/test_gluon_data.py
index f87228739c36..c3ae2de41722 100644
--- a/tests/python/unittest/test_gluon_data.py
+++ b/tests/python/unittest/test_gluon_data.py
@@ -162,6 +162,23 @@ def test_multi_worker():
         for i, batch in enumerate(loader):
             assert (batch.asnumpy() == i).all()
 
+
+@with_seed()
+def test_multi_worker_shape():
+    for thread_pool in [True, False]:
+        batch_size = 1024
+        shape = (batch_size+1, 11, 12)
+
+        data = ArrayDataset(np.ones(shape))
+        loader = gluon.data.DataLoader(
+            data, batch_size=batch_size, num_workers=5, last_batch='keep', thread_pool=thread_pool)
+        for batch in loader:
+            if shape[0] > batch_size:
+                assert batch.shape == (batch_size, shape[1], shape[2])
+                shape = (shape[0] - batch_size, shape[1], shape[2])
+            else:
+                assert batch.shape == shape
+
 class _Dummy(Dataset):
     """Dummy dataset for randomized shape arrays."""
     def __init__(self, random_shape):

From c38b52784325380f79cafb9f0407ad327554fe6b Mon Sep 17 00:00:00 2001
From: Zhennan Qin <zhennan.qin@intel.com>
Date: Thu, 7 Nov 2019 12:35:04 +0800
Subject: [PATCH 574/813] [MKLDNN] Fix int8 convolution/fc bias overflow
 (#16734)

* Fix int8 convolution bias overflow

* Fix when data is too small

* Fix CI

* Fix

* Add fc fix

* Add round
---
 .../quantization/quantization_utils.h         |  18 +--
 src/operator/subgraph/mkldnn/mkldnn_conv.cc   |  39 ++++---
 src/operator/subgraph/mkldnn/mkldnn_fc.cc     |  48 +++++---
 tests/python/mkl/test_subgraph.py             | 110 +++++++++++++++++-
 4 files changed, 174 insertions(+), 41 deletions(-)

diff --git a/src/operator/quantization/quantization_utils.h b/src/operator/quantization/quantization_utils.h
index 857841d0db1b..5230576ce594 100644
--- a/src/operator/quantization/quantization_utils.h
+++ b/src/operator/quantization/quantization_utils.h
@@ -32,6 +32,9 @@
 namespace mxnet {
 namespace op {
 
+using mshadow::red::limits::MaxValue;
+using mshadow::red::limits::MinValue;
+
 static const float kUint8Range = 255.5;
 static const float kInt8Range = 127.5;
 static const size_t kInt32Range = 0x7fffffff;
@@ -80,8 +83,6 @@ MSHADOW_XINLINE float MinAbs(T a, T b) {
 
 template<typename T>
 MSHADOW_XINLINE T FloatToQuantized(float input, float min_range, float max_range) {
-  using mshadow::red::limits::MinValue;
-  using mshadow::red::limits::MaxValue;
   float real_range = MaxAbs(min_range, max_range);
   float quantized_range = MinAbs(MaxValue<T>(), MinValue<T>());
   float scale = quantized_range / real_range;
@@ -90,8 +91,6 @@ MSHADOW_XINLINE T FloatToQuantized(float input, float min_range, float max_range
 
 template <typename T>
 MSHADOW_XINLINE float QuantizedToFloat(T input, float min_range, float max_range) {
-  using mshadow::red::limits::MinValue;
-  using mshadow::red::limits::MaxValue;
   float quantized_range = MinAbs(MinValue<T>(), MaxValue<T>());
   float real_range = MaxAbs(min_range, max_range);
   float scale = real_range / quantized_range;
@@ -129,8 +128,6 @@ MSHADOW_XINLINE void RequantizeManyInNewRange(size_t count, T2* output, const T1
  */
 template<typename T>
 MSHADOW_XINLINE float FloatForOneQuantizedLevel(float range_min, float range_max, bool all_sign) {
-  using mshadow::red::limits::MinValue;
-  using mshadow::red::limits::MaxValue;
   float range_data = MaxAbs(range_min, range_max);
   float range_T = all_sign ? MinAbs(MinValue<T>(), MaxValue<T>()) : MaxValue<T>();
   return range_data / range_T;
@@ -140,8 +137,6 @@ template <typename TA, typename TB, typename TC>
 MSHADOW_XINLINE void QuantizationRangeForMultiplication(float min_a, float max_a, float min_b,
                                                         float max_b, float *min_c, float *max_c,
                                                         bool all_sign) {
-  using mshadow::red::limits::MaxValue;
-  using mshadow::red::limits::MinValue;
   const float a_float_for_one_quant_level = FloatForOneQuantizedLevel<TA>(min_a, max_a, all_sign);
   const float b_float_for_one_quant_level = FloatForOneQuantizedLevel<TB>(min_b, max_b, all_sign);
   const float range_c =
@@ -215,6 +210,13 @@ static mshadow::TypeFlag GetQuantizeOutputType(const Param &param) {
   return out_type;
 }
 
+static inline float GetQuantizeScale(const int dtype, const float data_min, const float data_max) {
+  const float real_data_range = MaxAbs(data_min, data_max);
+  const auto quantized_data_range = (dtype == mshadow::kInt8) ? kInt8Range : kUint8Range;
+  // If real_data_range == 0, to avoid `inf` in scale, use a large number here, which is MAX_INT.
+  return real_data_range ? quantized_data_range / real_data_range : MaxValue<int32_t>();
+}
+
 }  // namespace op
 }  // namespace mxnet
 #endif  // MXNET_OPERATOR_QUANTIZATION_QUANTIZATION_UTILS_H_
diff --git a/src/operator/subgraph/mkldnn/mkldnn_conv.cc b/src/operator/subgraph/mkldnn/mkldnn_conv.cc
index e1f9174898c7..f5bbd5044446 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_conv.cc
+++ b/src/operator/subgraph/mkldnn/mkldnn_conv.cc
@@ -82,9 +82,11 @@ static inline size_t GetInSumIndex(const MKLDNNConvFusionParam &param) {
 }
 
 template <typename DType>
-static std::vector<float> GetWeightScales(const NDArray &weight, bool weight_channelwise_scale) {
+static std::vector<float> GetWeightScales(const NDArray &weight, const NDArray *bias,
+                                          const float data_scale, bool weight_channelwise_scale) {
   std::vector<float> weight_scales;
   const DType *weight_ptr = weight.data().dptr<DType>();
+  const DType *bias_ptr = bias? bias->data().dptr<DType>() : nullptr;
   size_t channel = weight.shape()[0];
 
   // TODO(Zhennan): Handle the case weight is not in dims 4.
@@ -103,9 +105,19 @@ static std::vector<float> GetWeightScales(const NDArray &weight, bool weight_cha
 
   if (weight_channelwise_scale) {
     weight_scales.resize(channel);
+#pragma omp parallel for num_threads(engine::OpenMP::Get()->GetRecommendedOMPThreadCount())
     for (int c = 0; c < static_cast<int>(channel); ++c) {
-      DType weight_range = MaxAbs(weight_c_min[c], weight_c_max[c]);
-      weight_scales[c] = kInt8Range / weight_range;
+      float scale = GetQuantizeScale(mshadow::kInt8, weight_c_min[c], weight_c_max[c]);
+      if (bias_ptr) {
+        // avoid overflow on bias
+        // TODO(zhennan): mkldnn has bug to handle INT_MAX in bias, so set the maximum value of bias
+        // to INT_MAX / 2.
+        float scale_max =
+            static_cast<float>(bias_ptr[c] > 0 ? MaxValue<int32_t>() : MinValue<int32_t>()) / 2 /
+            bias_ptr[c] / data_scale;
+        scale = Min(scale, scale_max);
+      }
+      weight_scales[c] = scale;
     }
   } else {
     DType total_min = weight_c_min[0];
@@ -115,8 +127,7 @@ static std::vector<float> GetWeightScales(const NDArray &weight, bool weight_cha
       if (total_max < weight_c_max[c]) total_max = weight_c_max[c];
     }
     weight_scales.resize(3);
-    DType weight_range = MaxAbs(total_min, total_max);
-    weight_scales[0] = kInt8Range / weight_range;
+    weight_scales[0] = GetQuantizeScale(mshadow::kInt8, total_min, total_max);
     weight_scales[1] = total_min;
     weight_scales[2] = total_max;
   }
@@ -331,27 +342,21 @@ void SgMKLDNNConvOperator::Forward(const OpContext &ctx,
         post_requantize_ = true;
         weight_channelwise_scale = true;
       }
-      auto data_range = (data.dtype() == mshadow::kInt8) ? kInt8Range : kUint8Range;
-      data_scale_ = data_range / MaxAbs(cached_data_min_, cached_data_max_);
+      data_scale_ = GetQuantizeScale(data.dtype(), cached_data_min_, cached_data_max_);
       MSHADOW_REAL_TYPE_SWITCH(cached_weight_.dtype(), DType, {
-        weight_scales_ =
-            GetWeightScales<DType>(cached_weight_, weight_channelwise_scale);
+        weight_scales_ = GetWeightScales<DType>(cached_weight_, has_bias ? &cached_bias_ : nullptr,
+                                                data_scale_, weight_channelwise_scale);
       });
       // Collect scale.
       size_t channel = cached_weight_.shape()[0];
       float sum_in_scale = 1.0;
-      float out_range;
-      float quantized_out_range;
       float output_scale;
       if (mkldnn_param.with_sum) {
-        auto quantized_sum_range =
-            (inputs[in_sum].dtype() == mshadow::kInt8) ? kInt8Range : kUint8Range;
-        sum_in_scale = quantized_sum_range / MaxAbs(cached_sum_min_, cached_sum_max_);
+        sum_in_scale = GetQuantizeScale(inputs[in_sum].dtype(), cached_sum_min_, cached_sum_max_);
       }
       if (post_requantize_) {
-        quantized_out_range = IsOutputUInt8(param_) ? kUint8Range : kInt8Range;
-        out_range = MaxAbs(cached_output_min_, cached_output_max_);
-        output_scale = quantized_out_range / out_range;
+        output_scale = GetQuantizeScale(IsOutputUInt8(param_) ? mshadow::kUint8 : mshadow::kInt8,
+                                        cached_output_min_, cached_output_max_);
         full_conv_param.requantize_scales.resize(weight_channelwise_scale ? channel : 1);
         for (size_t c = 0; c < full_conv_param.requantize_scales.size(); c++) {
           full_conv_param.requantize_scales[c] = output_scale / data_scale_ / weight_scales_[c];
diff --git a/src/operator/subgraph/mkldnn/mkldnn_fc.cc b/src/operator/subgraph/mkldnn/mkldnn_fc.cc
index 971dd0505a6d..349d18f4a3a2 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_fc.cc
+++ b/src/operator/subgraph/mkldnn/mkldnn_fc.cc
@@ -66,6 +66,7 @@ class SgMKLDNNFCOp {
   nnvm::Symbol subgraph_sym_;
   MKLDNNFCFullParam full_param_;
   std::shared_ptr<MKLDNNFullyConnectedForward> fwd_;
+  std::shared_ptr<NDArray> cached_weight_;
   NDArray cached_bias_;
   float cached_min_data_;
   float cached_max_data_;
@@ -114,7 +115,7 @@ void SgMKLDNNFCOp::Forward(const OpContext &ctx,
   CHECK_EQ(out_data.size(), total_num_outputs);
 
   NDArray data = in_data[fullc::kData];
-  NDArray weight = in_data[fullc::kWeight];
+  NDArray weight = cached_weight_ ? *cached_weight_ : in_data[fullc::kWeight];
   NDArray output = out_data[fullc::kOut];
 
   mkldnn::memory::desc out_md = GetMemDesc(output);
@@ -143,24 +144,40 @@ void SgMKLDNNFCOp::Forward(const OpContext &ctx,
 
     if (mkldnn_param.quantized) {
       CHECK(data.dtype() == mshadow::kInt8 || data.dtype() == mshadow::kUint8);
-      auto data_range = (data.dtype() == mshadow::kInt8) ? kInt8Range : kUint8Range;
-      float data_scale  = data_range / MaxAbs(cached_min_data_, cached_max_data_);
-      float weight_scale = kInt8Range / MaxAbs(cached_min_weight_, cached_max_weight_);
-      float quantized_out_range = IsOutputUint8(full_param_)? kUint8Range : kInt8Range;
-
+      float data_scale  = GetQuantizeScale(data.dtype(), cached_min_data_, cached_max_data_);
+      float weight_scale = GetQuantizeScale(mshadow::kInt8, cached_min_weight_, cached_max_weight_);
       if (has_bias) {
         NDArray bias = in_data[fullc::kBias];
-        float bias_int32_rescale = data_scale * weight_scale *
-            MaxAbs(cached_min_bias_, cached_max_bias_) / kInt8Range;
-
-        cached_bias_ = NDArray(bias.storage_type(), bias.shape(),
-                               bias.ctx(), true, mshadow::kInt32);
+        float bias_scale = GetQuantizeScale(mshadow::kInt8, cached_min_bias_, cached_max_bias_);
+        float bias_int32_rescale = data_scale * weight_scale / bias_scale;
+        // TODO(zhennan): mkldnn has bug to handle INT_MAX in bias, so set the maximum value of bias
+        // to INT_MAX / 2.
+        float bias_max_rescale =
+            MaxValue<int32_t>() / 2 / MaxAbs(cached_min_bias_, cached_max_bias_) / bias_scale;
+        if (bias_int32_rescale > bias_max_rescale) {
+          // avoid overflow on bias
+          bias_int32_rescale = bias_max_rescale;
+          float weight_rescale = bias_int32_rescale * bias_scale / data_scale / weight_scale;
+          cached_weight_.reset(new NDArray(weight.storage_type(), weight.shape(), weight.ctx(),
+                                           true, mshadow::kInt8));
+          int8_t *weight_ptr = weight.data().dptr<int8_t>();
+          int8_t *quantized_weight_ptr = cached_weight_->data().dptr<int8_t>();
+          size_t weight_size = weight.shape().Size();
+#pragma omp parallel for num_threads(engine::OpenMP::Get()->GetRecommendedOMPThreadCount())
+          for (index_t i = 0; i < static_cast<index_t>(weight_size); ++i) {
+            quantized_weight_ptr[i] = std::round(weight_ptr[i] * weight_rescale);
+          }
+          weight_scale *= weight_rescale;
+          weight = *cached_weight_;
+        }
+        cached_bias_ =
+            NDArray(bias.storage_type(), bias.shape(), bias.ctx(), true, mshadow::kInt32);
         int8_t *bias_ptr = bias.data().dptr<int8_t>();
         int32_t *quantized_bias_ptr = cached_bias_.data().dptr<int32_t>();
         size_t bias_size = bias.shape().Size();
         #pragma omp parallel for num_threads(engine::OpenMP::Get()->GetRecommendedOMPThreadCount())
         for (index_t i = 0; i < static_cast<index_t>(bias_size); ++i) {
-          quantized_bias_ptr[i] = bias_ptr[i] * bias_int32_rescale;
+          quantized_bias_ptr[i] = std::round(bias_ptr[i] * bias_int32_rescale);
         }
       }
 
@@ -172,9 +189,10 @@ void SgMKLDNNFCOp::Forward(const OpContext &ctx,
         full_param_.output_scales.resize(0);
         cached_min_output_ = mkldnn_param.min_calib_range.value();
         cached_max_output_ = mkldnn_param.max_calib_range.value();
-
-        full_param_.requantize_scales[0] = quantized_out_range /
-          MaxAbs(cached_min_output_, cached_max_output_) / data_scale / weight_scale;
+        float out_scale =
+            GetQuantizeScale(IsOutputUint8(full_param_) ? mshadow::kUint8 : mshadow::kInt8,
+                             cached_min_output_, cached_max_output_);
+        full_param_.requantize_scales[0] = out_scale / data_scale / weight_scale;
       } else {
         Stream<cpu> *s = ctx.get_stream<cpu>();
         if (data.dtype() == mshadow::kInt8) {
diff --git a/tests/python/mkl/test_subgraph.py b/tests/python/mkl/test_subgraph.py
index 2fb7d4939135..4a2aedf58281 100644
--- a/tests/python/mkl/test_subgraph.py
+++ b/tests/python/mkl/test_subgraph.py
@@ -302,7 +302,7 @@ def head_symbol(data_shape):
   weight = mx.symbol.Variable('weight', dtype='float32')
   return data, weight
 
-# single conv fuision case
+# single conv fusion case
 def single_conv(no_bias, data_shape):
   attr = {'conv': []}
   data, weight = head_symbol(data_shape)
@@ -887,6 +887,114 @@ def test_float64_fallback():
     ex.forward()
     ex.outputs[0].wait_to_read()
 
+
+def helper_quantized_conv_bias_overflow(data_min, data_max, weight_min, weight_max):
+  data_shape = (1, 32, 2, 2)
+  data = mx.symbol.Variable('data', shape=data_shape, dtype='float32')
+  weight = mx.symbol.Variable('weight', dtype='float32')
+  bias = mx.symbol.Variable('bias', dtype='float32')
+  sym = mx.symbol.Convolution(data=data, weight=weight, bias=bias, name='conv', num_filter=64,
+                               kernel=(1, 1), stride=(1, 1))
+  data_nd = mx.random.uniform(data_min, data_max, shape=data_shape, ctx=mx.cpu())
+  weight_nd = mx.random.uniform(weight_min, weight_max, shape=[64, 32, 1, 1], ctx=mx.cpu())
+  bias_nd = mx.random.uniform(-1, +1, shape=[64], ctx=mx.cpu())
+  arg_params = {
+      'data': data_nd,
+      'weight': weight_nd,
+      'bias': bias_nd
+  }
+
+  ex = sym.bind(mx.cpu(), arg_params, args_grad=None)
+  ex.forward()
+  ex.outputs[0].wait_to_read()
+  sym_sg = sym.get_backend_symbol(QUANTIZE_SG_PASS_NAME)
+  batch = mx.io.DataBatch([data_nd], [])
+  calib_data = CalibIter(batch, data_shape, 1)
+  qsym, qarg_params, qaux_params = mx.contrib.quant.quantize_model(sym=sym_sg,
+                                                                   arg_params={
+                                                                       'weight': weight_nd,
+                                                                       'bias': bias_nd
+                                                                   },
+                                                                   aux_params={},
+                                                                   ctx=mx.cpu(),
+                                                                   excluded_sym_names=None,
+                                                                   excluded_op_names=None,
+                                                                   quantized_dtype='int8',
+                                                                   calib_mode='naive',
+                                                                   calib_data=calib_data,
+                                                                   label_names=None,
+                                                                   num_calib_examples=1,
+                                                                   quantize_mode='full')
+  qsym = qsym.get_backend_symbol(QUANTIZE_SG_PASS_NAME)
+  qarg_params['data'] = data_nd
+  qex = qsym.bind(mx.cpu(), qarg_params, args_grad=None)
+  qex.forward()
+  qex.outputs[0].wait_to_read()
+  assert_almost_equal_with_err(ex.outputs[0].asnumpy(), qex.outputs[0].asnumpy(),
+                               rtol=1e-2, atol=1e-2, etol=0.01)
+
+def helper_quantized_fc_bias_overflow(data_min, data_max, weight_min, weight_max):
+  data_shape = (1, 32)
+  data = mx.symbol.Variable('data', shape=data_shape, dtype='float32')
+  weight = mx.symbol.Variable('weight', dtype='float32')
+  bias = mx.symbol.Variable('bias', dtype='float32')
+  sym = mx.symbol.FullyConnected(data=data, weight=weight, bias=bias, name='fc', num_hidden=64)
+  data_nd = mx.random.uniform(data_min, data_max, shape=data_shape, ctx=mx.cpu())
+  weight_nd = mx.random.uniform(weight_min, weight_max, shape=[64, 32], ctx=mx.cpu())
+  bias_nd = mx.random.uniform(-1, +1, shape=[64], ctx=mx.cpu())
+  arg_params = {
+      'data': data_nd,
+      'weight': weight_nd,
+      'bias': bias_nd
+  }
+
+  ex = sym.bind(mx.cpu(), arg_params, args_grad=None)
+  ex.forward()
+  ex.outputs[0].wait_to_read()
+  sym_sg = sym.get_backend_symbol(QUANTIZE_SG_PASS_NAME)
+  batch = mx.io.DataBatch([data_nd], [])
+  calib_data = CalibIter(batch, data_shape, 1)
+  qsym, qarg_params, qaux_params = mx.contrib.quant.quantize_model(sym=sym_sg,
+                                                                   arg_params={
+                                                                       'weight': weight_nd,
+                                                                       'bias': bias_nd
+                                                                   },
+                                                                   aux_params={},
+                                                                   ctx=mx.cpu(),
+                                                                   excluded_sym_names=None,
+                                                                   excluded_op_names=None,
+                                                                   quantized_dtype='int8',
+                                                                   calib_mode='naive',
+                                                                   calib_data=calib_data,
+                                                                   label_names=None,
+                                                                   num_calib_examples=1,
+                                                                   quantize_mode='full')
+  qarg_params['data'] = data_nd
+  qsym = qsym.get_backend_symbol(QUANTIZE_SG_PASS_NAME)
+  qex = qsym.bind(mx.cpu(), qarg_params, args_grad=None)
+  qex.forward()
+  qex.outputs[0].wait_to_read()
+  assert_almost_equal_with_err(ex.outputs[0].asnumpy(), qex.outputs[0].asnumpy(),
+                               rtol=1e-2, atol=1e-2, etol=0.01)
+
+@with_seed()
+def test_quantized_conv_bias_overflow():
+    helper_quantized_conv_bias_overflow(-1, 1, 0, 0)
+    helper_quantized_conv_bias_overflow(-1, 1, -1e-6, +1e-6)
+    helper_quantized_conv_bias_overflow(0, 0, 1, 1)
+    helper_quantized_conv_bias_overflow(-1e-6, +1e-6, -1, 1)
+    helper_quantized_conv_bias_overflow(-1e-6, +1e-6, -1e-6, +1e-6)
+    helper_quantized_conv_bias_overflow(0, 0, 0, 0)
+
+def test_quantized_fc_bias_overflow():
+    helper_quantized_fc_bias_overflow(-1, 1, 0, 0)
+    helper_quantized_fc_bias_overflow(-1, 1, -1e-6, +1e-6)
+    helper_quantized_fc_bias_overflow(0, 0, 1, 1)
+    helper_quantized_fc_bias_overflow(-1e-6, +1e-6, -1, 1)
+    helper_quantized_fc_bias_overflow(-1e-6, +1e-6, -1e-6, +1e-6)
+    helper_quantized_fc_bias_overflow(0, 0, 0, 0)
+
+
 if __name__ == "__main__":
   import nose
   nose.runmodule()

From 5dfa1219effa4e10c59c138c382ad17ca8355217 Mon Sep 17 00:00:00 2001
From: Wuxun Zhang <wuxun.zhang@intel.com>
Date: Fri, 8 Nov 2019 09:30:02 +0800
Subject: [PATCH 575/813] [MKLDNN] use dim_t instead of int in slice/transpose
 operators (#16737)

* use dim_t instead of int

* fix same issue in pooling

* rebase code

* trigger CI
---
 src/operator/nn/mkldnn/mkldnn_pooling.cc   | 2 +-
 src/operator/nn/mkldnn/mkldnn_slice.cc     | 2 +-
 src/operator/nn/mkldnn/mkldnn_transpose.cc | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/operator/nn/mkldnn/mkldnn_pooling.cc b/src/operator/nn/mkldnn/mkldnn_pooling.cc
index f9dbe5bbfd8f..6eda2aa33b34 100644
--- a/src/operator/nn/mkldnn/mkldnn_pooling.cc
+++ b/src/operator/nn/mkldnn/mkldnn_pooling.cc
@@ -127,7 +127,7 @@ mkldnn::algorithm GetMKLDNNPoolAlgo(const PoolingParam &param) {
   }
 }
 
-static inline int GetPaddingSizeFull(int x, int padl, int padr, int k, int s) {
+static inline int GetPaddingSizeFull(dim_t x, int padl, int padr, int k, int s) {
   if ((x + padl + padr - k) % s != 0) {
     return (padr + s - ((x + padl + padr - k) % s));
   } else {
diff --git a/src/operator/nn/mkldnn/mkldnn_slice.cc b/src/operator/nn/mkldnn/mkldnn_slice.cc
index dba10f8b6cd5..575554a25c88 100644
--- a/src/operator/nn/mkldnn/mkldnn_slice.cc
+++ b/src/operator/nn/mkldnn/mkldnn_slice.cc
@@ -41,7 +41,7 @@ MKLDNNSliceFwd::MKLDNNSliceFwd(const SliceParam &param,
   mkldnn::memory::dims dims(N);
   mkldnn::memory::dims offsets(N);
   for (int i = 0; i < N; ++i) {
-    int s = 0;
+    dim_t s = 0;
     if (i < param.begin.ndim() &&  param.begin[i]) {
       s = *param.begin[i];
       if (s < 0) s += ishape[i];
diff --git a/src/operator/nn/mkldnn/mkldnn_transpose.cc b/src/operator/nn/mkldnn/mkldnn_transpose.cc
index 2ec38d586552..ee9c06d49744 100644
--- a/src/operator/nn/mkldnn/mkldnn_transpose.cc
+++ b/src/operator/nn/mkldnn/mkldnn_transpose.cc
@@ -73,7 +73,7 @@ class MKLDNNTransposeForward {
 
     mkldnn_dims_t strides;
     mkldnn_dims_t sh;
-    unsigned int total_stride = 1;
+    dim_t total_stride = 1;
     for (int i = data_ndim - 1; i >= 0; i--) {
       sh[i] = shape[i];
       strides[axes[i]] = total_stride;

From a37dcd46cba88dfd0478b601fb8fbf9b425780c0 Mon Sep 17 00:00:00 2001
From: Anirudh Subramanian <anirudh2290@ufl.edu>
Date: Thu, 7 Nov 2019 18:41:18 -0800
Subject: [PATCH 576/813] Fix SliceChannel Type inference (#16748)

* Refactor elemwise_op_common and change SliceChannel InferType

* Add gluoncv models

* Comment Faster RCNN models
---
 .../amp_model_conversion.py                   | 139 +++++++++++++++++-
 src/operator/elemwise_op_common.h             |  27 +++-
 src/operator/slice_channel-inl.h              |  15 +-
 tests/python/gpu/test_contrib_amp.py          |   9 ++
 4 files changed, 167 insertions(+), 23 deletions(-)

diff --git a/example/automatic-mixed-precision/amp_model_conversion.py b/example/automatic-mixed-precision/amp_model_conversion.py
index fcc2ad69dd62..b363e0244a10 100644
--- a/example/automatic-mixed-precision/amp_model_conversion.py
+++ b/example/automatic-mixed-precision/amp_model_conversion.py
@@ -58,13 +58,129 @@ def save_params(fname, arg_params, aux_params, logger=None):
                        'imagenet1k-resnext-101-64x4d',
                        'imagenet11k-place365ch-resnet-152',
                        'imagenet11k-place365ch-resnet-50']
-    gluon_models = ['resnet18_v1',
+    # Faster RCNN and Mask RCNN commented because of model loading issues
+    # https://github.com/dmlc/gluon-cv/issues/1034
+    gluon_models = [#'faster_rcnn_fpn_resnet50_v1b_coco',
+                    'mobilenetv2_0.75',
+                    'cifar_resnet56_v1',
+                    'mobilenet0.25',
+                    'mobilenet1.0',
+                    #'mask_rcnn_fpn_resnet50_v1b_coco',
+                    'simple_pose_resnet152_v1b',
+                    'ssd_512_resnet50_v1_voc',
+                    #'faster_rcnn_resnet50_v1b_voc',
+                    'cifar_resnet20_v1',
+                    'yolo3_darknet53_voc',
+                    'resnet101_v1c',
+                    'simple_pose_resnet18_v1b',
+                    #'mask_rcnn_resnet50_v1b_coco',
+                    'ssd_512_mobilenet1.0_coco',
+                    'vgg19_bn',
+                    #'faster_rcnn_resnet50_v1b_coco',
+                    'cifar_resnet110_v1',
+                    'yolo3_mobilenet1.0_voc',
+                    'cifar_resnext29_16x64d',
+                    'resnet34_v1',
+                    'densenet121',
+                    #'mask_rcnn_fpn_resnet101_v1d_coco',
+                    'vgg13_bn',
+                    'vgg19',
+                    'resnet152_v1d',
+                    'resnet152_v1s',
+                    'densenet201',
+                    'alexnet',
+                    'se_resnext50_32x4d',
+                    'resnet50_v1d_0.86',
+                    'resnet18_v1b_0.89',
+                    'yolo3_darknet53_coco',
+                    'resnet152_v1',
+                    'resnext101_64x4d',
+                    'vgg13',
+                    'resnet101_v1d_0.76',
+                    'simple_pose_resnet50_v1d',
+                    'senet_154',
                     'resnet50_v1',
-                    'resnet101_v1',
+                    'se_resnext101_32x4d',
+                    'fcn_resnet101_voc',
+                    'resnet152_v2',
+                    #'mask_rcnn_resnet101_v1d_coco',
+                    'squeezenet1.1',
+                    'mobilenet0.5',
+                    'resnet34_v2',
+                    'resnet18_v1',
+                    'resnet152_v1b',
+                    'resnet101_v2',
+                    'cifar_resnet56_v2',
+                    'ssd_512_resnet101_v2_voc',
+                    'resnet50_v1d_0.37',
+                    'mobilenetv2_0.5',
+                    #'faster_rcnn_fpn_bn_resnet50_v1b_coco',
+                    'resnet50_v1c',
+                    'densenet161',
+                    'simple_pose_resnet50_v1b',
+                    'resnet18_v1b',
+                    'darknet53',
+                    'fcn_resnet50_ade',
+                    'cifar_wideresnet28_10',
+                    'simple_pose_resnet101_v1d',
+                    'vgg16',
+                    'ssd_512_resnet50_v1_coco',
+                    'resnet101_v1d_0.73',
                     'squeezenet1.0',
-                    'mobilenet1.0',
+                    'resnet50_v1b',
+                    #'faster_rcnn_resnet101_v1d_coco',
+                    'ssd_512_mobilenet1.0_voc',
+                    'cifar_wideresnet40_8',
+                    'cifar_wideresnet16_10',
+                    'cifar_resnet110_v2',
+                    'resnet101_v1s',
+                    'mobilenetv2_0.25',
+                    'resnet152_v1c',
+                    'se_resnext101_64x4d',
+                    #'faster_rcnn_fpn_resnet101_v1d_coco',
+                    'resnet50_v1d',
+                    'densenet169',
+                    'resnet34_v1b',
+                    'resnext50_32x4d',
+                    'resnet101_v1',
+                    'resnet101_v1b',
+                    'resnet50_v1s',
+                    'mobilenet0.75',
+                    'cifar_resnet20_v2',
+                    'resnet101_v1d',
+                    'vgg11_bn',
+                    'resnet18_v2',
+                    'vgg11',
+                    'simple_pose_resnet101_v1b',
+                    'resnext101_32x4d',
+                    'resnet50_v2',
+                    'vgg16_bn',
                     'mobilenetv2_1.0',
-                    'inceptionv3']
+                    'resnet50_v1d_0.48',
+                    'resnet50_v1d_0.11',
+                    'fcn_resnet101_ade',
+                    'simple_pose_resnet152_v1d',
+                    'yolo3_mobilenet1.0_coco',
+                    'fcn_resnet101_coco']
+    # TODO(anisub): add support for other models from gluoncv
+    # Not supported today mostly because of broken net.forward calls
+    segmentation_models = ['deeplab_resnet50_ade',
+                           'psp_resnet101_voc',
+                           'deeplab_resnet152_voc',
+                           'deeplab_resnet101_ade',
+                           'deeplab_resnet152_coco',
+                           'psp_resnet101_ade',
+                           'deeplab_resnet101_coco',
+                           'psp_resnet101_citys',
+                           'psp_resnet50_ade',
+                           'psp_resnet101_coco',
+                           'deeplab_resnet101_voc']
+    calib_ssd_models = ["ssd_512_vgg16_atrous_voc",
+                        "ssd_300_vgg16_atrous_voc",
+                        "ssd_300_vgg16_atrous_coco"]
+    calib_inception_models = ["inceptionv3"]
+    gluon_models = gluon_models + segmentation_models + \
+                   calib_ssd_models + calib_inception_models
     models = symbolic_models + gluon_models
 
     parser = argparse.ArgumentParser(description='Convert a provided FP32 model to a mixed precision model')
@@ -106,14 +222,23 @@ def save_params(fname, arg_params, aux_params, logger=None):
     else:
         assert args.model in gluon_models, "Please choose one of the available gluon models: {} \
                                             If you want to use symbolic model instead, remove --use-gluon-model when running the script".format(gluon_models)
+        shape = None
+        if args.model in segmentation_models:
+            shape = (1, 3, 480, 480)
+        elif args.model in calib_ssd_models:
+            shape = (1, 3, 512, 544)
+        elif args.model in calib_inception_models:
+            shape = (1, 3, 299, 299)
+        else:
+            shape = (1, 3, 224, 224)
         net = gluoncv.model_zoo.get_model(args.model, pretrained=True)
         net.hybridize()
-        result_before1 = net.forward(mx.nd.zeros((1, 3, 224, 224)))
+        result_before1 = net.forward(mx.nd.random.uniform(shape=shape))
         net.export("{}".format(args.model))
         net = amp.convert_hybrid_block(net, cast_optional_params=args.cast_optional_params)
         net.export("{}-amp".format(args.model), remove_amp_cast=False)
         if args.run_dummy_inference:
             logger.info("Running inference on the mixed precision model with dummy inputs, batch size: 1")
-            result_after = net.forward(mx.nd.zeros((1, 3, 224, 224), dtype=np.float32, ctx=mx.gpu(0)))
-            result_after = net.forward(mx.nd.zeros((1, 3, 224, 224), dtype=np.float32, ctx=mx.gpu(0)))
+            result_after = net.forward(mx.nd.random.uniform(shape=shape, dtype=np.float32, ctx=mx.gpu(0)))
+            result_after = net.forward(mx.nd.random.uniform(shape=shape, dtype=np.float32, ctx=mx.gpu(0)))
             logger.info("Inference run successfully")
diff --git a/src/operator/elemwise_op_common.h b/src/operator/elemwise_op_common.h
index dc83a4b1f87f..6711297718b2 100644
--- a/src/operator/elemwise_op_common.h
+++ b/src/operator/elemwise_op_common.h
@@ -116,10 +116,10 @@ template<typename AttrType, bool (*is_none)(const AttrType&),
          bool (*assign)(AttrType*, const AttrType&), bool reverse_infer,
          std::string (*attr_string)(const AttrType&),
          index_t n_in = -1, index_t n_out = -1>
-inline bool ElemwiseAttr(const nnvm::NodeAttrs& attrs,
-                         std::vector<AttrType> *in_attrs,
-                         std::vector<AttrType> *out_attrs,
-                         const AttrType& none) {
+inline bool ElemwiseAttrHelper(const std::string& node_name,
+                               std::vector<AttrType> *in_attrs,
+                               std::vector<AttrType> *out_attrs,
+                               const AttrType& none) {
   AttrType dattr = none;
   size_t in_size = in_attrs->size();
   size_t out_size = out_attrs->size();
@@ -133,7 +133,7 @@ inline bool ElemwiseAttr(const nnvm::NodeAttrs& attrs,
   auto deduce = [&](const std::vector<AttrType>& vec, size_t size, const char *name) {
       for (size_t i = 0; i < size; ++i) {
         CHECK(assign(&dattr, vec.at(i)))
-          << "Incompatible attr in node " << attrs.name << " at " << i << "-th "
+          << "Incompatible attr in node " << node_name << " at " << i << "-th "
           << name << ": " << "expected " << attr_string(dattr)
           << ", got " << attr_string(vec.at(i));
       }
@@ -145,7 +145,7 @@ inline bool ElemwiseAttr(const nnvm::NodeAttrs& attrs,
   auto write = [&](std::vector<AttrType> *vec, size_t size, const char *name) {
       for (size_t i = 0; i < size; ++i) {
         CHECK(assign(&(vec->at(i)), dattr))
-          << "Incompatible attr in node " << attrs.name << " at " << i << "-th "
+          << "Incompatible attr in node " << node_name << " at " << i << "-th "
           << name << ": " << "expected " << attr_string(dattr)
           << ", got " << attr_string(vec->at(i));
       }
@@ -158,6 +158,21 @@ inline bool ElemwiseAttr(const nnvm::NodeAttrs& attrs,
   return true;
 }
 
+
+template<typename AttrType, bool (*is_none)(const AttrType&),
+         bool (*assign)(AttrType*, const AttrType&), bool reverse_infer,
+         std::string (*attr_string)(const AttrType&),
+         index_t n_in = -1, index_t n_out = -1>
+inline bool ElemwiseAttr(const nnvm::NodeAttrs& attrs,
+                         std::vector<AttrType> *in_attrs,
+                         std::vector<AttrType> *out_attrs,
+                         const AttrType& none) {
+  return ElemwiseAttrHelper<AttrType, is_none,
+                            assign, reverse_infer,
+                            attr_string, n_in,
+                            n_out>(attrs.name, in_attrs, out_attrs, none);
+}
+
 template<index_t n_in, index_t n_out>
 inline bool ElemwiseShape(const nnvm::NodeAttrs& attrs,
                           mxnet::ShapeVector *in_attrs,
diff --git a/src/operator/slice_channel-inl.h b/src/operator/slice_channel-inl.h
index e37ffdcf1b91..6f2aa2f4d17c 100644
--- a/src/operator/slice_channel-inl.h
+++ b/src/operator/slice_channel-inl.h
@@ -36,6 +36,7 @@
 #include <utility>
 #include "./operator_common.h"
 #include "./channel_op_common.h"
+#include "./elemwise_op_common.h"
 
 namespace mxnet {
 namespace op {
@@ -176,16 +177,10 @@ class SliceChannelProp : public OperatorProperty {
   bool InferType(std::vector<int> *in_type,
                  std::vector<int> *out_type,
                  std::vector<int> *aux_type) const override {
-    CHECK_EQ(in_type->size(), 1U);
-    int dtype = (*in_type)[0];
-    CHECK_NE(dtype, -1) << "First input must have specified type";
-    out_type->clear();
-    out_type->reserve(param_.num_outputs);
-    for (int i = 0; i < param_.num_outputs; ++i) {
-      out_type->push_back(dtype);
-    }
-    aux_type->clear();
-    return true;
+    std::string node_name = "slice_channel_node";
+    return ElemwiseAttrHelper<int, type_is_none,
+                              type_assign, true,
+                              type_string, 1>(node_name, in_type, out_type, -1);
   }
 
   bool InferShape(mxnet::ShapeVector *in_shape,
diff --git a/tests/python/gpu/test_contrib_amp.py b/tests/python/gpu/test_contrib_amp.py
index c49fa9b49865..74fb29c3f6f6 100644
--- a/tests/python/gpu/test_contrib_amp.py
+++ b/tests/python/gpu/test_contrib_amp.py
@@ -475,6 +475,15 @@ def test_fp16_casting():
     exe = final_res.simple_bind(ctx=mx.gpu(), data=(1, 2), data2=(1, 2))
     assert exe.arg_arrays[0].dtype == np.float16
 
+    # Check for symbol which has slice channel
+    data = mx.sym.var("data")
+    data2 = mx.sym.var("data2")
+    data._set_attr(__dtype__="-1")
+    data2._set_attr(__dtype__="-1")
+    concat_res = mx.sym.concat(data, data2)
+    out = mx.sym.split(concat_res, axis=1, num_outputs=2)
+    final_res = amp.convert_symbol(out)
+
 
 if __name__ == '__main__':
     import nose

From 57b9b2c769ce53506ad45051f5fe3b542d9bc2da Mon Sep 17 00:00:00 2001
From: Hu Shiwen <yajiedesign@gmail.com>
Date: Sun, 10 Nov 2019 09:03:03 +0800
Subject: [PATCH 577/813] change _generate_op_module_signature get_module_file
 open with encoding="utf-8",it fix some encode error in Chinese windows
 system. (#16738)

---
 python/mxnet/base.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/mxnet/base.py b/python/mxnet/base.py
index 35acba3dbe53..1c46e16e22d0 100644
--- a/python/mxnet/base.py
+++ b/python/mxnet/base.py
@@ -672,7 +672,7 @@ def get_module_file(module_name):
         module_path = module_name.split('.')
         module_path[-1] = 'gen_' + module_path[-1]
         file_name = os.path.join(path, '..', *module_path) + '.py'
-        module_file = open(file_name, 'w')
+        module_file = open(file_name, 'w', encoding="utf-8")
         dependencies = {'symbol': ['from ._internal import SymbolBase',
                                    'from ..base import _Null'],
                         'ndarray': ['from ._internal import NDArrayBase',

From 74529dcd6b69fdf2643b6e79b25dfa982ef9f86b Mon Sep 17 00:00:00 2001
From: vexilligera <vexilligera@gmail.com>
Date: Sun, 10 Nov 2019 18:29:10 +0000
Subject: [PATCH 578/813] [NumPy] NumPy support for linalg.inv (#16730)

* Add NumPy support for inv

* fix CUDA float64 memory alignment bug

* make test_mixed_precision more tolerant
---
 python/mxnet/ndarray/numpy/linalg.py          | 44 +++++++++-
 python/mxnet/numpy/linalg.py                  | 44 +++++++++-
 python/mxnet/numpy_dispatch_protocol.py       |  1 +
 python/mxnet/symbol/numpy/linalg.py           | 44 +++++++++-
 src/operator/linalg_impl.h                    |  3 +-
 src/operator/tensor/la_op-inl.h               |  6 ++
 src/operator/tensor/la_op.cc                  |  1 +
 .../unittest/test_numpy_interoperability.py   |  6 ++
 tests/python/unittest/test_numpy_op.py        | 80 ++++++++++++++++++-
 9 files changed, 224 insertions(+), 5 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/linalg.py b/python/mxnet/ndarray/numpy/linalg.py
index b7766ad4700e..d443d68cda0d 100644
--- a/python/mxnet/ndarray/numpy/linalg.py
+++ b/python/mxnet/ndarray/numpy/linalg.py
@@ -21,7 +21,7 @@
 from . import _op as _mx_nd_np
 from . import _internal as _npi
 
-__all__ = ['norm', 'svd', 'cholesky']
+__all__ = ['norm', 'svd', 'cholesky', 'inv']
 
 
 def norm(x, ord=None, axis=None, keepdims=False):
@@ -200,3 +200,45 @@ def cholesky(a):
            [ 4., 10.]])
     """
     return _npi.cholesky(a)
+
+
+def inv(a):
+    r"""
+    Compute the (multiplicative) inverse of a matrix.
+
+    Given a square matrix `a`, return the matrix `ainv` satisfying
+    ``dot(a, ainv) = dot(ainv, a) = eye(a.shape[0])``.
+
+    Parameters
+    ----------
+    a : (..., M, M) ndarray
+        Matrix to be inverted.
+
+    Returns
+    -------
+    ainv : (..., M, M) ndarray
+        (Multiplicative) inverse of the matrix `a`.
+
+    Raises
+    ------
+    MXNetError
+        If `a` is not square or inversion fails.
+
+    Examples
+    --------
+    >>> from mxnet import np
+    >>> a = np.array([[1., 2.], [3., 4.]])
+    array([[-2. ,  1. ],
+           [ 1.5, -0.5]])
+
+    Inverses of several matrices can be computed at once:
+
+    >>> a = np.array([[[1., 2.], [3., 4.]], [[1, 3], [3, 5]]])
+    >>> np.linalg.inv(a)
+    array([[[-2.        ,  1.        ],
+            [ 1.5       , -0.5       ]],
+
+           [[-1.2500001 ,  0.75000006],
+            [ 0.75000006, -0.25000003]]])
+    """
+    return _npi.inv(a)
diff --git a/python/mxnet/numpy/linalg.py b/python/mxnet/numpy/linalg.py
index 402171e24aed..b0552ee9f319 100644
--- a/python/mxnet/numpy/linalg.py
+++ b/python/mxnet/numpy/linalg.py
@@ -20,7 +20,7 @@
 from __future__ import absolute_import
 from ..ndarray import numpy as _mx_nd_np
 
-__all__ = ['norm', 'svd', 'cholesky']
+__all__ = ['norm', 'svd', 'cholesky', 'inv']
 
 
 def norm(x, ord=None, axis=None, keepdims=False):
@@ -218,3 +218,45 @@ def cholesky(a):
            [ 4., 10.]])
     """
     return _mx_nd_np.linalg.cholesky(a)
+
+
+def inv(a):
+    r"""
+    Compute the (multiplicative) inverse of a matrix.
+
+    Given a square matrix `a`, return the matrix `ainv` satisfying
+    ``dot(a, ainv) = dot(ainv, a) = eye(a.shape[0])``.
+
+    Parameters
+    ----------
+    a : (..., M, M) ndarray
+        Matrix to be inverted.
+
+    Returns
+    -------
+    ainv : (..., M, M) ndarray
+        (Multiplicative) inverse of the matrix `a`.
+
+    Raises
+    ------
+    MXNetError
+        If `a` is not square or inversion fails.
+
+    Examples
+    --------
+    >>> from mxnet import np
+    >>> a = np.array([[1., 2.], [3., 4.]])
+    array([[-2. ,  1. ],
+           [ 1.5, -0.5]])
+
+    Inverses of several matrices can be computed at once:
+
+    >>> a = np.array([[[1., 2.], [3., 4.]], [[1, 3], [3, 5]]])
+    >>> np.linalg.inv(a)
+    array([[[-2.        ,  1.        ],
+            [ 1.5       , -0.5       ]],
+
+           [[-1.2500001 ,  0.75000006],
+            [ 0.75000006, -0.25000003]]])
+    """
+    return _mx_nd_np.linalg.inv(a)
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index ae92b9885217..025982cfc7a5 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -126,6 +126,7 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'zeros_like',
     'linalg.norm',
     'linalg.cholesky',
+    'linalg.inv',
     'trace',
     'tril',
     'meshgrid',
diff --git a/python/mxnet/symbol/numpy/linalg.py b/python/mxnet/symbol/numpy/linalg.py
index d06e2d9d4aac..3c8118550559 100644
--- a/python/mxnet/symbol/numpy/linalg.py
+++ b/python/mxnet/symbol/numpy/linalg.py
@@ -22,7 +22,7 @@
 from . import _op as _mx_sym_np
 from . import _internal as _npi
 
-__all__ = ['norm', 'svd', 'cholesky']
+__all__ = ['norm', 'svd', 'cholesky', 'inv']
 
 
 def norm(x, ord=None, axis=None, keepdims=False):
@@ -187,3 +187,45 @@ def cholesky(a):
            [ 4., 10.]])
     """
     return _npi.cholesky(a)
+
+
+def inv(a):
+    r"""
+    Compute the (multiplicative) inverse of a matrix.
+
+    Given a square matrix `a`, return the matrix `ainv` satisfying
+    ``dot(a, ainv) = dot(ainv, a) = eye(a.shape[0])``.
+
+    Parameters
+    ----------
+    a : (..., M, M) ndarray
+        Matrix to be inverted.
+
+    Returns
+    -------
+    ainv : (..., M, M) ndarray
+        (Multiplicative) inverse of the matrix `a`.
+
+    Raises
+    ------
+    MXNetError
+        If `a` is not square or inversion fails.
+
+    Examples
+    --------
+    >>> from mxnet import np
+    >>> a = np.array([[1., 2.], [3., 4.]])
+    array([[-2. ,  1. ],
+           [ 1.5, -0.5]])
+
+    Inverses of several matrices can be computed at once:
+
+    >>> a = np.array([[[1., 2.], [3., 4.]], [[1, 3], [3, 5]]])
+    >>> np.linalg.inv(a)
+    array([[[-2.        ,  1.        ],
+            [ 1.5       , -0.5       ]],
+
+           [[-1.2500001 ,  0.75000006],
+            [ 0.75000006, -0.25000003]]])
+    """
+    return _npi.inv(a)
diff --git a/src/operator/linalg_impl.h b/src/operator/linalg_impl.h
index 97d40099d4fd..d83eb0d08815 100644
--- a/src/operator/linalg_impl.h
+++ b/src/operator/linalg_impl.h
@@ -1598,7 +1598,8 @@ void linalg_batch_inverse<xpu, DType>(const Tensor<xpu, 3, DType>& A, \
     get_space_typed<xpu, 1, DType>(Shape1(workspace_size), s); \
   const Tensor<xpu, 2, int> pivot(reinterpret_cast<int *>(workspace.dptr_), \
                                   Shape2(A.size(0), A.size(1))); \
-  const Tensor<xpu, 3, DType> LU(reinterpret_cast<DType *>(pivot.dptr_ + pivot.MSize()), \
+  int offset = pivot.MSize() & 1 ? pivot.MSize() + 1 : pivot.MSize(); \
+  const Tensor<xpu, 3, DType> LU(reinterpret_cast<DType *>(pivot.dptr_ + offset), \
                                  A.shape_); \
   Copy(LU, B, s); \
   linalg_batch_getrf(LU, pivot, true, s); \
diff --git a/src/operator/tensor/la_op-inl.h b/src/operator/tensor/la_op-inl.h
index c3ed4fef5cd3..35edbbd42565 100644
--- a/src/operator/tensor/la_op-inl.h
+++ b/src/operator/tensor/la_op-inl.h
@@ -463,6 +463,9 @@ struct inverse {
                  const OpContext& ctx, const nnvm::NodeAttrs& attrs) {
     // Since inverse(A) = trans(inverse(trans(A))), so we don't need to transpose
     // A even if we are using the col-major version of getrf and getri routines.
+    if (B.shape_.Size() == 0U) {
+      return;
+    }
     linalg_batch_inverse(A, B, ctx);
   }
 };
@@ -882,6 +885,9 @@ struct inverse_backward {
                  const Tensor<xpu, 3, DType>& dB,
                  const OpContext& ctx, const nnvm::NodeAttrs& attrs) {
     // Backward of A = inverse(B)
+    if (dB.shape_.Size() == 0U) {
+      return;
+    }
     Stream<xpu> *s = ctx.get_stream<xpu>();
     Tensor<xpu, 3, DType> temp = ctx.requested[0]
       .get_space_typed<xpu, 3, DType>(A.shape_, s);
diff --git a/src/operator/tensor/la_op.cc b/src/operator/tensor/la_op.cc
index 3d0e43251e03..8f9da23711a0 100644
--- a/src/operator/tensor/la_op.cc
+++ b/src/operator/tensor/la_op.cc
@@ -893,6 +893,7 @@ NNVM_REGISTER_OP(_backward_linalg_syevd)
 
 NNVM_REGISTER_OP(_linalg_inverse)
 .add_alias("linalg_inverse")
+.add_alias("_npi_inv")
 .describe(R"code(Compute the inverse of a matrix.
 Input is a tensor *A* of dimension *n >= 2*.
 
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index d2620cf8551e..6d9c63f9f857 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -273,6 +273,11 @@ def _add_workload_linalg_cholesky():
         OpArgMngr.add_workload('linalg.cholesky', np.array(a, dtype=dtype))
 
 
+def _add_workload_linalg_inv():
+    OpArgMngr.add_workload('linalg.inv', np.array(_np.ones((0, 0)), dtype=np.float32))
+    OpArgMngr.add_workload('linalg.inv', np.array(_np.ones((0, 1, 1)), dtype=np.float64))
+
+
 def _add_workload_trace():
     OpArgMngr.add_workload('trace', np.random.uniform(size=(4, 1)))
     OpArgMngr.add_workload('trace', np.random.uniform(size=(3, 2)))
@@ -1216,6 +1221,7 @@ def _prepare_workloads():
     _add_workload_zeros_like(array_pool)
     _add_workload_linalg_norm()
     _add_workload_linalg_cholesky()
+    _add_workload_linalg_inv()
     _add_workload_trace()
     _add_workload_tril()
     _add_workload_outer()
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 32cd5b10717e..1b710e30390e 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -1669,7 +1669,7 @@ def hybrid_forward(self, F, a, b, *args, **kwargs):
         mx_test_x1 = mx.numpy.array(np_test_x1, dtype=ltype)
         mx_test_x2 = mx.numpy.array(np_test_x2, dtype=rtype)
         rtol = 1e-2 if ltype is np.float16 or rtype is np.float16 else 1e-3
-        atol = 1e-4 if ltype is np.float16 or rtype is np.float16 else 1e-5
+        atol = 1e-3 if ltype is np.float16 or rtype is np.float16 else 1e-5
         for hybridize in [True, False]:
             if hybridize:
                 mx_func.hybridize()
@@ -3046,6 +3046,84 @@ def check_cholesky(L, data_np):
         check_cholesky(L, data_np)
 
 
+@with_seed()
+@use_np
+def test_np_linalg_inv():
+    class TestInverse(HybridBlock):
+        def __init__(self):
+            super(TestInverse, self).__init__()
+
+        def hybrid_forward(self, F, data):
+            return F.np.linalg.inv(data)
+
+    def get_grad(A):
+        if 0 in A.shape:
+            return A
+
+        dA = _np.ones_like(A)
+        A_inv = _np.linalg.inv(A)
+        dA_inv = -_np.matmul(_np.matmul(A_inv, dA), A_inv)
+        return _np.swapaxes(dA_inv, -1, -2)
+
+    def check_inv(A_inv, data_np):
+        assert A_inv.shape == data_np.shape
+        # catch error if numpy throws rank < 2
+        try:
+            A_expected = _np.linalg.inv(data_np)
+        except Exception as e:
+            print(data_np)
+            print(data_np.shape)
+            print(e)
+        else:
+            assert A_inv.shape == A_expected.shape
+            assert_almost_equal(A_inv.asnumpy(), A_expected, rtol=rtol, atol=atol)
+
+    shapes = [
+        (0, 0),
+        (4, 4),
+        (2, 2),
+        (1, 1),
+        (2, 1, 1),
+        (0, 1, 1),
+        (6, 1, 1),
+        (2, 3, 3, 3),
+        (4, 2, 1, 1),
+        (0, 5, 3, 3),
+        (5, 0, 0, 0),
+        (3, 3, 0, 0),
+        (3, 5, 5),
+    ]
+    dtypes = ['float32', 'float64']
+    for hybridize, dtype, shape in itertools.product([True, False], dtypes, shapes):
+        atol = rtol = 1e-2
+
+        test_inv = TestInverse()
+        if hybridize:
+            test_inv.hybridize()
+        # use LU decomposition to generate invertible matrices
+        if 0 in shape:
+            data_np = _np.ones(shape)
+        else:
+            n = shape[-1]
+            L = _np.tril(_np.random.uniform(-10., 10., shape))
+            U = _np.triu(_np.random.uniform(-10., 10., shape))
+            data_np = _np.matmul(L, U)
+        data = np.array(data_np, dtype=dtype)
+        data.attach_grad()
+        with mx.autograd.record():
+            A_inv = test_inv(data)
+
+        # check cholesky validity
+        check_inv(A_inv, data_np)
+        # check backward. backward does not support empty input
+        mx.autograd.backward(A_inv)
+        backward_expected = get_grad(data.asnumpy())
+        assert_almost_equal(data.grad.asnumpy(), backward_expected, rtol=rtol, atol=atol)
+        # check imperative once again
+        A_inv = np.linalg.inv(data)
+        check_inv(A_inv, data_np)
+
+
 @with_seed()
 @use_np
 def test_np_vstack():

From 927def2b3891e519310ee51a4f484b80c7fb7b64 Mon Sep 17 00:00:00 2001
From: reminisce <wujun.nju@gmail.com>
Date: Sun, 10 Nov 2019 13:31:42 -0800
Subject: [PATCH 579/813] Fix rtrue_divide grad (#16769)

* Fix rtrue_divide_scalar

* More tests
---
 src/operator/numpy/np_true_divide.cc        |   2 +-
 tests/python/unittest/test_numpy_ndarray.py | 107 +++++++++++++++++---
 tests/python/unittest/test_numpy_op.py      |   7 +-
 3 files changed, 99 insertions(+), 17 deletions(-)

diff --git a/src/operator/numpy/np_true_divide.cc b/src/operator/numpy/np_true_divide.cc
index 35df2c467e7b..1e46cc9d13b5 100644
--- a/src/operator/numpy/np_true_divide.cc
+++ b/src/operator/numpy/np_true_divide.cc
@@ -126,7 +126,7 @@ NNVM_REGISTER_OP(_npi_rtrue_divide_scalar)
   })
 #endif
 .set_attr<FCompute>("FCompute<cpu>", TrueDivideScalarCompute<cpu, mshadow_op::rtrue_divide>)
-.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_rdiv_scalar"})
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_rdiv_scalar"})
 .add_argument("data", "NDArray-or-Symbol", "source input")
 .add_argument("scalar", "float", "scalar input");
 
diff --git a/tests/python/unittest/test_numpy_ndarray.py b/tests/python/unittest/test_numpy_ndarray.py
index 239f300e028e..8e46f03e79bc 100644
--- a/tests/python/unittest/test_numpy_ndarray.py
+++ b/tests/python/unittest/test_numpy_ndarray.py
@@ -27,7 +27,7 @@
 from mxnet.gluon import HybridBlock
 from mxnet.test_utils import same, assert_almost_equal, rand_shape_nd, rand_ndarray, retry, use_np
 from common import with_seed, TemporaryDirectory
-from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf, assert_exception, is_op_runnable
+from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf, assert_exception, is_op_runnable, collapse_sum_like
 from mxnet.ndarray.ndarray import py_slice
 from mxnet.base import integer_types
 import scipy.stats as ss
@@ -281,6 +281,62 @@ def test_np_ndarray_binary_element_wise_ops():
             '<=': _np.less_equal
         })
 
+    def _get_grad_func(op, scalar=None, reverse=False):
+        if op == '+':
+            if scalar is None:
+                return lambda ograd, x1, x2, out: (collapse_sum_like(ograd, x1.shape),
+                                                   collapse_sum_like(ograd, x2.shape))
+            elif not reverse:
+                return lambda ograd, x1, x2, out: ograd
+            else:
+                return lambda ograd, x1, x2, out: ograd
+        elif op == '-':
+            if scalar is None:
+                return lambda ograd, x1, x2, out: (collapse_sum_like(ograd, x1.shape),
+                                                   -collapse_sum_like(ograd, x2.shape))
+            elif not reverse:
+                return lambda ograd, x1, x2, out: ograd
+            else:
+                return lambda ograd, x1, x2, out: -ograd
+        elif op == '*':
+            if scalar is None:
+                return lambda ograd, x1, x2, out: (collapse_sum_like(ograd * x2, x1.shape),
+                                                   collapse_sum_like(ograd * x1, x2.shape))
+            elif not reverse:
+                return lambda ograd, x1, x2, out: ograd * x2
+            else:
+                return lambda ograd, x1, x2, out: ograd * x1
+        elif op == '/':
+            if scalar is None:
+                return lambda ograd, x1, x2, out: (collapse_sum_like(ograd / x2, x1.shape),
+                                                   collapse_sum_like(-x1 * ograd / (x2 * x2), x2.shape))
+            elif not reverse:
+                return lambda ograd, x1, x2, out: ograd / x2
+            else:
+                return lambda ograd, x1, x2, out: -x1 * ograd / (x2 * x2)
+        elif op == 'mod':
+            if scalar is None:
+                return lambda ograd, x1, x2, out: (collapse_sum_like(ograd, x1.shape),
+                                                   collapse_sum_like(-ograd * _np.floor(x1 / x2), x2.shape))
+            elif not reverse:
+                return lambda ograd, x1, x2, out: ograd
+            else:
+                return lambda ograd, x1, x2, out: -ograd * _np.floor(x1 / x2)
+        elif op == 'pow':
+            if scalar is None:
+                return lambda ograd, x1, x2, out: (collapse_sum_like(ograd * x2 * _np.power(x1, x2 - 1), x1.shape),
+                                                   collapse_sum_like(ograd * out * _np.log(x1), x2.shape))
+            elif not reverse:
+                return lambda ograd, x1, x2, out: ograd * x2 * _np.power(x1, x2 - 1)
+            else:
+                return lambda ograd, x1, x2, out: ograd * out * _np.log(x1)
+        elif op in ('==', '!=', '<', '<=', '>', '>='):
+            if scalar is None:
+                return lambda ograd, x1, x2, out: (_np.zeros_like(x1), _np.zeros_like(x2))
+            else:
+                return lambda ograd, x1, x2, out: _np.zeros_like(ograd)
+        return None
+
     def get_np_ret(x1, x2, op):
         return np_op_map[op](x1, x2)
 
@@ -364,13 +420,15 @@ def check_binary_op_result(shape1, shape2, op, dtype=None):
             mx_input1 = abs(_np.random.uniform()) + 1
             np_input1 = mx_input1
         else:
-            mx_input1 = rand_ndarray(shape1, dtype=dtype).abs() + 1
+            mx_input1 = (rand_ndarray(shape1, dtype=dtype).abs() + 1).as_np_ndarray()
+            mx_input1.attach_grad()
             np_input1 = mx_input1.asnumpy()
         if shape2 is None:
             mx_input2 = abs(_np.random.uniform()) + 1
             np_input2 = mx_input2
         else:
-            mx_input2 = rand_ndarray(shape2, dtype=dtype).abs() + 1
+            mx_input2 = (rand_ndarray(shape2, dtype=dtype).abs() + 1).as_np_ndarray()
+            mx_input2.attach_grad()
             np_input2 = mx_input2.asnumpy()
 
         scalar = None
@@ -382,7 +440,9 @@ def check_binary_op_result(shape1, shape2, op, dtype=None):
             scalar = mx_input1
             reverse = True
 
+        grad_func = _get_grad_func(op, scalar, reverse)
         np_out = get_np_ret(np_input1, np_input2, op)
+        ograd = _np.ones_like(np_out)
         for hybridize in [True, False]:
             if scalar is None:
                 get_mx_ret_np = TestBinaryElementWiseOp(op)
@@ -390,26 +450,49 @@ def check_binary_op_result(shape1, shape2, op, dtype=None):
                 if hybridize:
                     get_mx_ret_np.hybridize()
                     get_mx_ret_classic.hybridize()
-                mx_out = get_mx_ret_np(mx_input1.as_np_ndarray(), mx_input2.as_np_ndarray())
+                if grad_func is None:
+                    mx_out = get_mx_ret_np(mx_input1, mx_input2)
+                else:
+                    with mx.autograd.record():
+                        mx_out = get_mx_ret_np(mx_input1, mx_input2)
+                    mx_out.backward()
                 assert type(mx_out) == np.ndarray
-                assert np_out.shape == mx_out.shape
                 if op in logic_ops:
                     assert np_out.dtype == mx_out.dtype
-                assert_almost_equal(mx_out.asnumpy(), np_out, atol=1e-6, rtol=1e-5)
+                assert_almost_equal(mx_out.asnumpy(), np_out, atol=1e-6, rtol=1e-5, use_broadcast=False)
+
+                if grad_func is not None:
+                    x1_grad_expected, x2_grad_expected = grad_func(ograd, np_input1, np_input2, np_out)
+                    assert_almost_equal(mx_input1.grad.asnumpy(), x1_grad_expected, atol=1e-5, rtol=1e-3,
+                                        use_broadcast=False)
+                    assert_almost_equal(mx_input2.grad.asnumpy(), x2_grad_expected, atol=1e-5, rtol=1e-3,
+                                        use_broadcast=False)
             else:
                 get_mx_ret = TestBinaryElementWiseOp(op, scalar=scalar, reverse=reverse)
                 if hybridize:
                     get_mx_ret.hybridize()
                 if reverse:
-                    mx_out = get_mx_ret(mx_input2.as_np_ndarray())
-                    assert type(mx_out) == np.ndarray
+                    mx_input = mx_input2
                 else:
-                    mx_out = get_mx_ret(mx_input1.as_np_ndarray())
-                    assert type(mx_out) == np.ndarray
-                assert np_out.shape == mx_out.shape
+                    mx_input = mx_input1
+
+                if grad_func is None:
+                    mx_out = get_mx_ret(mx_input)
+                else:
+                    with mx.autograd.record():
+                        mx_out = get_mx_ret(mx_input)
+                    mx_out.backward()
+                assert type(mx_out) == np.ndarray
+
                 if op in logic_ops:
                     assert np_out.dtype == mx_out.dtype
-                assert_almost_equal(mx_out.asnumpy(), np_out, atol=1e-6, rtol=1e-5)
+                assert_almost_equal(mx_out.asnumpy(), np_out, atol=1e-6, rtol=1e-5, use_broadcast=False)
+
+                # check grad
+                if grad_func is not None:
+                    x_grad_expected = grad_func(ograd, np_input1, np_input2, np_out)
+                    assert_almost_equal(mx_input.grad.asnumpy(), x_grad_expected, atol=1e-5, rtol=1e-3,
+                                        use_broadcast=False)
 
     dtypes = [_np.float32, _np.float64, None]
     ops = np_op_map.keys()
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 1b710e30390e..59bf75ddc6e1 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -1572,8 +1572,8 @@ def hybrid_forward(self, F, a, b, *args, **kwargs):
                                             rtol=1e-1, atol=1e-2, equal_nan=True, use_broadcast=False)
                         if rgrads is None:
                             assert_almost_equal(mx_test_x2.grad.asnumpy(),
-                                               collapse_sum_like(rgrad(y.asnumpy(), np_test_x2, np_test_x1), mx_test_x2.shape),
-                                               rtol=1e-1, atol=1e-2, equal_nan=True, use_broadcast=False)
+                                                collapse_sum_like(rgrad(y.asnumpy(), np_test_x2, np_test_x1), mx_test_x2.shape),
+                                                rtol=1e-1, atol=1e-2, equal_nan=True, use_broadcast=False)
                         else:
                             assert_almost_equal(mx_test_x2.grad.asnumpy(),
                                                 collapse_sum_like(rgrad(y.asnumpy(), np_test_x1, np_test_x2), mx_test_x2.shape),
@@ -1594,7 +1594,6 @@ def hybrid_forward(self, F, a, b, *args, **kwargs):
                 assertRaises(NotImplementedError, getattr(np, func), mx_test_x1, mx_test_x2,  order='C')
                 assertRaises(NotImplementedError, getattr(np, func), mx_test_x1, mx_test_x2,  order='mxnet')
 
-
     funcs = {
         'add': (-1.0, 1.0, [lambda y, x1, x2: _np.ones(y.shape)], None),
         'subtract':
@@ -1603,7 +1602,7 @@ def hybrid_forward(self, F, a, b, *args, **kwargs):
         'multiply': (-1.0, 1.0, [lambda y, x1, x2: _np.broadcast_to(x2, y.shape)],
                                 [lambda y, x1, x2: _np.broadcast_to(x1, y.shape)]),
         'divide': (0.1, 1.0, [lambda y, x1, x2: _np.ones(y.shape) / x2],
-                               [lambda y, x1, x2: -x1 / (x2 * x2)]),
+                   [lambda y, x1, x2: -x1 / (x2 * x2)]),
         'mod': (1.0, 10.0,
                 [lambda y, x1, x2: _np.ones(y.shape),
                  lambda y, x1, x2: _np.zeros(y.shape)],

From 618ce8b0e36c3f032505bfcfcc74998eaaf13c58 Mon Sep 17 00:00:00 2001
From: alicia <32725332+Alicia1529@users.noreply.github.com>
Date: Mon, 11 Nov 2019 06:40:35 +0800
Subject: [PATCH 580/813] add numpy op nan_to_num (#16717)

---
 python/mxnet/ndarray/numpy/_op.py             | 102 +++++++++++++-
 python/mxnet/numpy/multiarray.py              |  94 ++++++++++++-
 python/mxnet/symbol/numpy/_symbol.py          |  67 ++++++++-
 src/operator/mshadow_op.h                     |  25 ++++
 .../numpy/np_elemwise_unary_op_basic.cc       |  29 ++++
 .../numpy/np_elemwise_unary_op_basic.cu       |   6 +
 src/operator/tensor/elemwise_unary_op.h       | 129 ++++++++++++++++++
 tests/python/unittest/test_numpy_op.py        | 103 ++++++++++++++
 8 files changed, 552 insertions(+), 3 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index 30b3d2f4d90b..f9164855dfe9 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -38,7 +38,8 @@
            'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
            'around', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
            'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal',
-           'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory', 'may_share_memory', 'diff', 'resize']
+           'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory', 'may_share_memory', 'diff', 'resize',
+           'nan_to_num']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -5208,3 +5209,102 @@ def resize(a, new_shape):
            [0., 1., 2., 3.]])
     """
     return _npi.resize_fallback(a, new_shape=new_shape)
+
+
+@set_module('mxnet.ndarray.numpy')
+def nan_to_num(x, copy=True, nan=0.0, posinf=None, neginf=None, **kwargs):
+    """
+    Replace NaN with zero and infinity with large finite numbers (default
+    behaviour) or with the numbers defined by the user using the `nan`,
+    `posinf` and/or `neginf` keywords.
+
+    If `x` is inexact, NaN is replaced by zero or by the user defined value in
+    `nan` keyword, infinity is replaced by the largest finite floating point
+    values representable by ``x.dtype`` or by the user defined value in
+    `posinf` keyword and -infinity is replaced by the most negative finite
+    floating point values representable by ``x.dtype`` or by the user defined
+    value in `neginf` keyword.
+
+    For complex dtypes, the above is applied to each of the real and
+    imaginary components of `x` separately.
+
+    If `x` is not inexact, then no replacements are made.
+
+    Parameters
+    ----------
+    x : ndarray
+        Input data.
+    copy : bool, optional
+        Whether to create a copy of `x` (True) or to replace values
+        in-place (False). The in-place operation only occurs if
+        casting to an array does not require a copy.
+        Default is True.
+    nan : int, float, optional
+        Value to be used to fill NaN values. If no value is passed
+        then NaN values will be replaced with 0.0.
+    posinf : int, float, optional
+        Value to be used to fill positive infinity values. If no value is
+        passed then positive infinity values will be replaced with a very
+        large number.
+    neginf : int, float, optional
+        Value to be used to fill negative infinity values. If no value is
+        passed then negative infinity values will be replaced with a very
+        small (or negative) number.
+
+        .. versionadded:: 1.13
+
+    Returns
+    -------
+    out : ndarray
+        `x`, with the non-finite values replaced. If `copy` is False, this may
+        be `x` itself.
+
+    Notes
+    -----
+    NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
+    (IEEE 754). This means that Not a Number is not equivalent to infinity.
+
+    Examples
+    --------
+    >>> np.nan_to_num(np.inf)
+    1.7976931348623157e+308
+    >>> np.nan_to_num(-np.inf)
+    -1.7976931348623157e+308
+    >>> np.nan_to_num(np.nan)
+    0.0
+    >>> x = np.array([np.inf, -np.inf, np.nan, -128, 128])
+    >>> np.nan_to_num(x)
+    array([ 3.4028235e+38, -3.4028235e+38,  0.0000000e+00, -1.2800000e+02,
+            1.2800000e+02])
+    >>> np.nan_to_num(x, nan=-9999, posinf=33333333, neginf=33333333)
+    array([ 3.3333332e+07,  3.3333332e+07, -9.9990000e+03, -1.2800000e+02,
+            1.2800000e+02])
+    >>> y = np.array([[-1, 0, 1],[9999,234,-14222]],dtype="float64")/0
+    array([[-inf,  nan,  inf],
+        [ inf,  inf, -inf]], dtype=float64)
+    >>> np.nan_to_num(y)
+    array([[-1.79769313e+308,  0.00000000e+000,  1.79769313e+308],
+        [ 1.79769313e+308,  1.79769313e+308, -1.79769313e+308]], dtype=float64)
+    >>> np.nan_to_num(y, nan=111111, posinf=222222)
+    array([[-1.79769313e+308,  1.11111000e+005,  2.22222000e+005],
+        [ 2.22222000e+005,  2.22222000e+005, -1.79769313e+308]], dtype=float64)
+    >>> y
+    array([[-inf,  nan,  inf],
+       [ inf,  inf, -inf]], dtype=float64)
+    >>> np.nan_to_num(y, copy=False, nan=111111, posinf=222222)
+    array([[-1.79769313e+308,  1.11111000e+005,  2.22222000e+005],
+       [ 2.22222000e+005,  2.22222000e+005, -1.79769313e+308]], dtype=float64)
+    >>> y
+    array([[-1.79769313e+308,  1.11111000e+005,  2.22222000e+005],
+       [ 2.22222000e+005,  2.22222000e+005, -1.79769313e+308]], dtype=float64)
+    """
+    if isinstance(x, numeric_types):
+        return _np.nan_to_num(x, copy, nan, posinf, neginf)
+    elif isinstance(x, NDArray):
+        if x.dtype in ['int8', 'uint8', 'int32', 'int64']:
+            return x
+        if not copy:
+            return _npi.nan_to_num(x, copy=copy, nan=nan, posinf=posinf, neginf=neginf, out=x)
+        return _npi.nan_to_num(x, copy=copy, nan=nan, posinf=posinf, neginf=neginf, out=None)
+    else:
+        raise TypeError('type {} not supported'.format(str(type(x))))
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 9439e751f1be..b6816d75a98e 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -57,7 +57,7 @@
            'blackman', 'flip', 'around', 'arctan2', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril',
            'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less',
            'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory',
-           'may_share_memory', 'diff', 'resize']
+           'may_share_memory', 'diff', 'resize', 'nan_to_num']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -7202,3 +7202,95 @@ def resize(a, new_shape):
            [0., 1., 2., 3.]])
     """
     return _mx_nd_np.resize(a, new_shape)
+
+
+@set_module('mxnet.numpy')
+def nan_to_num(x, copy=True, nan=0.0, posinf=None, neginf=None, **kwargs):
+    """
+    Replace NaN with zero and infinity with large finite numbers (default
+    behaviour) or with the numbers defined by the user using the `nan`,
+    `posinf` and/or `neginf` keywords.
+
+    If `x` is inexact, NaN is replaced by zero or by the user defined value in
+    `nan` keyword, infinity is replaced by the largest finite floating point
+    values representable by ``x.dtype`` or by the user defined value in
+    `posinf` keyword and -infinity is replaced by the most negative finite
+    floating point values representable by ``x.dtype`` or by the user defined
+    value in `neginf` keyword.
+
+    For complex dtypes, the above is applied to each of the real and
+    imaginary components of `x` separately.
+
+    If `x` is not inexact, then no replacements are made.
+
+    Parameters
+    ----------
+    x : scalar
+        ndarray
+        Input data.
+    copy : bool, optional
+        Whether to create a copy of `x` (True) or to replace values
+        in-place (False). The in-place operation only occurs if
+        casting to an array does not require a copy.
+        Default is True.
+        Gluon does not support copy = False.
+    nan : int, float, optional
+        Value to be used to fill NaN values. If no value is passed
+        then NaN values will be replaced with 0.0.
+    posinf : int, float, optional
+        Value to be used to fill positive infinity values. If no value is
+        passed then positive infinity values will be replaced with a very
+        large number.
+    neginf : int, float, optional
+        Value to be used to fill negative infinity values. If no value is
+        passed then negative infinity values will be replaced with a very
+        small (or negative) number.
+
+        .. versionadded:: 1.13
+
+    Returns
+    -------
+    out : ndarray
+        `x`, with the non-finite values replaced. If `copy` is False, this may
+        be `x` itself.
+
+    Notes
+    -----
+    NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
+    (IEEE 754). This means that Not a Number is not equivalent to infinity.
+
+    Examples
+    --------
+    >>> np.nan_to_num(np.inf)
+    1.7976931348623157e+308
+    >>> np.nan_to_num(-np.inf)
+    -1.7976931348623157e+308
+    >>> np.nan_to_num(np.nan)
+    0.0
+    >>> x = np.array([np.inf, -np.inf, np.nan, -128, 128])
+    >>> np.nan_to_num(x)
+    array([ 3.4028235e+38, -3.4028235e+38,  0.0000000e+00, -1.2800000e+02,
+            1.2800000e+02])
+    >>> np.nan_to_num(x, nan=-9999, posinf=33333333, neginf=33333333)
+    array([ 3.3333332e+07,  3.3333332e+07, -9.9990000e+03, -1.2800000e+02,
+            1.2800000e+02])
+    >>> y = np.array([[-1, 0, 1],[9999,234,-14222]],dtype="float64")/0
+    array([[-inf,  nan,  inf],
+        [ inf,  inf, -inf]], dtype=float64)
+    >>> np.nan_to_num(y)
+    array([[-1.79769313e+308,  0.00000000e+000,  1.79769313e+308],
+        [ 1.79769313e+308,  1.79769313e+308, -1.79769313e+308]], dtype=float64)
+    >>> np.nan_to_num(y, nan=111111, posinf=222222)
+    array([[-1.79769313e+308,  1.11111000e+005,  2.22222000e+005],
+        [ 2.22222000e+005,  2.22222000e+005, -1.79769313e+308]], dtype=float64)
+    >>> y
+    array([[-inf,  nan,  inf],
+       [ inf,  inf, -inf]], dtype=float64)
+    >>> np.nan_to_num(y, copy=False, nan=111111, posinf=222222)
+    array([[-1.79769313e+308,  1.11111000e+005,  2.22222000e+005],
+       [ 2.22222000e+005,  2.22222000e+005, -1.79769313e+308]], dtype=float64)
+    >>> y
+    array([[-1.79769313e+308,  1.11111000e+005,  2.22222000e+005],
+       [ 2.22222000e+005,  2.22222000e+005, -1.79769313e+308]], dtype=float64)
+    """
+    return _mx_nd_np.nan_to_num(x, copy=copy, nan=nan, posinf=posinf, neginf=neginf)
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 1eab6ad0342a..0d7303865b92 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -41,7 +41,7 @@
            'around', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
            'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal',
            'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'shares_memory', 'may_share_memory', 'diff',
-           'resize']
+           'resize', 'nan_to_num']
 
 
 def _num_outputs(sym):
@@ -4824,4 +4824,69 @@ def resize(a, new_shape):
     return _npi.resize_fallback(a, new_shape=new_shape)
 
 
+@set_module('mxnet.symbol.numpy')
+def nan_to_num(x, copy=True, nan=0.0, posinf=None, neginf=None, **kwargs):
+    """
+    Replace NaN with zero and infinity with large finite numbers (default
+    behaviour) or with the numbers defined by the user using the `nan`,
+    `posinf` and/or `neginf` keywords.
+
+    If `x` is inexact, NaN is replaced by zero or by the user defined value in
+    `nan` keyword, infinity is replaced by the largest finite floating point
+    values representable by ``x.dtype`` or by the user defined value in
+    `posinf` keyword and -infinity is replaced by the most negative finite
+    floating point values representable by ``x.dtype`` or by the user defined
+    value in `neginf` keyword.
+
+    For complex dtypes, the above is applied to each of the real and
+    imaginary components of `x` separately.
+
+    If `x` is not inexact, then no replacements are made.
+
+    Parameters
+    ----------
+    x : Symbol
+        Input data.
+    copy : bool, optional
+        Whether to create a copy of `x` (True) or to replace values
+        in-place (False). The in-place operation only occurs if
+        casting to an array does not require a copy.
+        Default is True.
+    nan : int, float, optional
+        Value to be used to fill NaN values. If no value is passed
+        then NaN values will be replaced with 0.0.
+    posinf : int, float, optional
+        Value to be used to fill positive infinity values. If no value is
+        passed then positive infinity values will be replaced with a very
+        large number.
+    neginf : int, float, optional
+        Value to be used to fill negative infinity values. If no value is
+        passed then negative infinity values will be replaced with a very
+        small (or negative) number.
+
+        .. versionadded:: 1.13
+
+    Returns
+    -------
+    out : ndarray
+        `x`, with the non-finite values replaced. If `copy` is False, this may
+        be `x` itself.
+
+    Notes
+    -----
+    NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
+    (IEEE 754). This means that Not a Number is not equivalent to infinity.
+
+    """
+    if isinstance(x, numeric_types):
+        return _np.nan_to_num(x, copy, nan, posinf, neginf)
+    elif isinstance(x, _Symbol):
+        if not copy:
+            return _npi.nan_to_num(x, copy=copy, nan=nan, posinf=posinf, neginf=neginf, out=x)
+        return _npi.nan_to_num(x, copy=copy, nan=nan, posinf=posinf, neginf=neginf, out=None)
+    else:
+        raise TypeError('type {} not supported'.format(str(type(x))))
+
+
+
 _set_np_symbol_class(_Symbol)
diff --git a/src/operator/mshadow_op.h b/src/operator/mshadow_op.h
index b8db165675a0..0477d8553898 100644
--- a/src/operator/mshadow_op.h
+++ b/src/operator/mshadow_op.h
@@ -52,6 +52,7 @@ const float SELU_ALPHA = 1.6732632423543772848170429916717;
 const float SELU_LAMBDA = 1.0507009873554804934193349852946;
 const float SQRT_2 = 1.4142135623730950488016887242096;
 using std::isnan;
+using std::isinf;
 #endif
 using std::enable_if;
 using std::is_unsigned;
@@ -1012,6 +1013,30 @@ namespace isnan_typed {
   }
 };  // namespace isnan_typed
 
+namespace isinf_typed {
+  template<typename DType>
+  MSHADOW_XINLINE bool IsInf(volatile DType val) {
+    return false;
+  }
+  template<>
+  MSHADOW_XINLINE bool IsInf(volatile float val) {
+    return isinf(val);
+  }
+  template<>
+  MSHADOW_XINLINE bool IsInf(volatile double val) {
+    return isinf(val);
+  }
+  template<>
+  MSHADOW_XINLINE bool IsInf(volatile long double val) {
+    return isinf(val);
+  }
+
+  template<>
+  MSHADOW_XINLINE bool IsInf(volatile mshadow::half::half_t val) {
+    return (val.half_ & 0x7fff) >= 0x7c00;
+  }
+};  // namespace isinf_typed
+
 MXNET_UNARY_MATH_OP_NC(relu, isnan_typed::IsNan(a) || (a > DType(0)) ? a : DType(0));
 
 /*! \brief used for computing gradient of relu operator */
diff --git a/src/operator/numpy/np_elemwise_unary_op_basic.cc b/src/operator/numpy/np_elemwise_unary_op_basic.cc
index c980dcfaab5d..cad736aab65b 100644
--- a/src/operator/numpy/np_elemwise_unary_op_basic.cc
+++ b/src/operator/numpy/np_elemwise_unary_op_basic.cc
@@ -419,5 +419,34 @@ NNVM_REGISTER_OP(_npi_around)
 .add_arguments(AroundParam::__FIELDS__())
 .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes);
 
+DMLC_REGISTER_PARAMETER(NumpyNanToNumParam);
+
+NNVM_REGISTER_OP(_npi_nan_to_num)
+.describe("" ADD_FILELINE)
+.set_attr_parser(ParamParser<NumpyNanToNumParam>)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"data"};
+  })
+.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
+.set_attr<FCompute>("FCompute<cpu>", NumpyNanToNumOpForward<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_npi_backward_nan_to_num"})
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::pair<int, int> >{{0, 0}};
+  })
+.add_argument("data", "NDArray-or-Symbol", "Input ndarray")
+.add_arguments(NumpyNanToNumParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_npi_backward_nan_to_num)
+.set_attr_parser(ParamParser<NumpyNanToNumParam>)
+.set_num_inputs(2)
+.set_num_outputs(1)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FCompute>("FCompute<cpu>", NumpyNanToNumOpBackward<cpu>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/np_elemwise_unary_op_basic.cu b/src/operator/numpy/np_elemwise_unary_op_basic.cu
index 44743ed94be8..af8834f01664 100644
--- a/src/operator/numpy/np_elemwise_unary_op_basic.cu
+++ b/src/operator/numpy/np_elemwise_unary_op_basic.cu
@@ -110,5 +110,11 @@ MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_arctanh, mshadow_op::arctanh);
 NNVM_REGISTER_OP(_npi_around)
 .set_attr<FCompute>("FCompute<gpu>", AroundOpForward<gpu>);
 
+NNVM_REGISTER_OP(_npi_nan_to_num)
+.set_attr<FCompute>("FCompute<gpu>", NumpyNanToNumOpForward<gpu>);
+
+NNVM_REGISTER_OP(_npi_backward_nan_to_num)
+.set_attr<FCompute>("FCompute<gpu>", NumpyNanToNumOpBackward<gpu>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/tensor/elemwise_unary_op.h b/src/operator/tensor/elemwise_unary_op.h
index 188ccd68a340..27013dfb98ae 100644
--- a/src/operator/tensor/elemwise_unary_op.h
+++ b/src/operator/tensor/elemwise_unary_op.h
@@ -34,6 +34,7 @@
 #include "../mshadow_op.h"
 #include "../mxnet_op.h"
 #include "../elemwise_op_common.h"
+#include "../../common/utils.h"
 #include "../../ndarray/ndarray_function.h"
 
 #if MSHADOW_USE_MKL == 1
@@ -660,6 +661,134 @@ void AroundOpForward(const nnvm::NodeAttrs& attrs,
   }
 }
 
+struct NumpyNanToNumParam : public dmlc::Parameter<NumpyNanToNumParam> {
+  bool copy;
+  double nan;
+  dmlc::optional<double> posinf, neginf;
+  DMLC_DECLARE_PARAMETER(NumpyNanToNumParam) {
+    DMLC_DECLARE_FIELD(copy)
+    .set_default(true)
+    .describe("Whether to create a copy of `x` (True) or to replace values"
+              "in-place (False). The in-place operation only occurs if"
+              "casting to an array does not require a copy."
+              "Default is True.");
+    DMLC_DECLARE_FIELD(nan)
+    .set_default(0.0)
+    .describe("Value to be used to fill NaN values. If no value is passed"
+              "then NaN values will be replaced with 0.0.");
+    DMLC_DECLARE_FIELD(posinf)
+    .set_default(dmlc::optional<double>())
+    .describe("Value to be used to fill positive infinity values."
+              "If no value is passed then positive infinity values will be"
+              "replaced with a very large number.");
+    DMLC_DECLARE_FIELD(neginf)
+    .set_default(dmlc::optional<double>())
+    .describe("Value to be used to fill negative infinity values."
+              "If no value is passed then negative infinity values"
+              "will be replaced with a very small (or negative) number.");
+  }
+};
+
+template<int req>
+struct nan_to_num_forward {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int i,
+                                  DType* out_data,
+                                  const DType* in_data,
+                                  const DType nan,
+                                  const DType posinf,
+                                  const DType neginf) {
+    DType val = in_data[i];
+    if (mshadow_op::isnan_typed::IsNan<DType>(val))  val = nan;
+    if (val > 0 && mshadow_op::isinf_typed::IsInf(val))  val = posinf;
+    if (val < 0 && mshadow_op::isinf_typed::IsInf(val))  val = neginf;
+    KERNEL_ASSIGN(out_data[i], req, val);
+  }
+};
+
+template<typename xpu>
+void NumpyNanToNumOpForward(const nnvm::NodeAttrs& attrs,
+                            const OpContext& ctx,
+                            const std::vector<TBlob>& inputs,
+                            const std::vector<OpReqType>& req,
+                            const std::vector<TBlob>& outputs) {
+  using namespace mxnet;
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 1U);
+  CHECK_EQ(req.size(), 1U);
+  mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+  const TBlob& in_data = inputs[0];
+  const TBlob& out_data = outputs[0];
+  const NumpyNanToNumParam& param = nnvm::get<NumpyNanToNumParam>(attrs.parsed);
+  using namespace mxnet_op;
+
+  if (!common::is_float(in_data.type_flag_) && req[0] == kWriteInplace) return;
+  if (!common::is_float(in_data.type_flag_)) {
+    copy(s, out_data, in_data);
+    return;
+  }
+
+  MSHADOW_REAL_TYPE_SWITCH(out_data.type_flag_, DType, {
+    DType defaultnan = static_cast<DType>(param.nan);
+    DType posinf;
+    DType neginf;
+    if (param.posinf.has_value()) {
+      posinf = static_cast<DType>(param.posinf.value());
+    } else {
+      posinf = mshadow::red::limits::MaxValue<DType>();
+    }
+    if (param.neginf.has_value()) {
+      neginf = static_cast<DType>(param.neginf.value());
+    } else {
+      neginf = mshadow::red::limits::MinValue<DType>();
+    }
+    MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
+      Kernel<nan_to_num_forward<req_type>, xpu>::Launch(
+          s, out_data.Size(), out_data.dptr<DType>(), in_data.dptr<DType>(),
+          defaultnan, posinf, neginf);
+    });
+  });
+}
+
+template<int req>
+struct nan_to_num_backward {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int i,
+                                  DType* in_grad,
+                                  const DType* out_grad,
+                                  const DType* in_data) {
+    DType val = out_grad[i];
+    if (mshadow_op::isnan_typed::IsNan(in_data[i]))  val = 0;
+    if (val > 0 && mshadow_op::isinf_typed::IsInf(in_data[i]))  val = 0;
+    if (val < 0 && mshadow_op::isinf_typed::IsInf(in_data[i]))  val = 0;
+    KERNEL_ASSIGN(in_grad[i], req, val);
+  }
+};
+
+template<typename xpu>
+void NumpyNanToNumOpBackward(const nnvm::NodeAttrs& attrs,
+                             const OpContext& ctx,
+                             const std::vector<TBlob>& inputs,
+                             const std::vector<OpReqType>& req,
+                             const std::vector<TBlob>& outputs) {
+  CHECK_EQ(inputs.size(), 2U);
+  CHECK_EQ(outputs.size(), 1U);
+  CHECK_NE(req[0], kWriteInplace);
+  mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+  const TBlob& out_grad = inputs[0];
+  const TBlob& in_data = inputs[1];
+  const TBlob& in_grad = outputs[0];
+  CHECK_EQ(common::is_float(in_data.type_flag_), true);
+  using namespace mxnet_op;
+  MSHADOW_TYPE_SWITCH(out_grad.type_flag_, DType, {
+    MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
+      Kernel<nan_to_num_backward<req_type>, xpu>::Launch(
+          s, in_grad.Size(), in_grad.dptr<DType>(), out_grad.dptr<DType>(),
+          in_data.dptr<DType>());
+    });
+  });
+}
+
 /*! \brief Unary compute */
 #define MXNET_OPERATOR_REGISTER_UNARY(__name$)                      \
   NNVM_REGISTER_OP(__name$)                                         \
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 59bf75ddc6e1..79988dd71942 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -4304,6 +4304,109 @@ def hybrid_forward(self, F, x, *args, **kwargs):
         assert_almost_equal(ret.asnumpy(), expected_ret, atol=1e-5, rtol=1e-5, use_broadcast=False)
 
 
+@with_seed()
+@use_np
+def test_np_nan_to_num():
+
+    def take_ele_grad(ele):
+        if _np.isinf(ele) or _np.isnan(ele):
+            return 0
+        return 1
+    def np_nan_to_num_grad(data):
+        shape = data.shape
+        arr = list(map(take_ele_grad,data.flatten()))
+        return _np.array(arr).reshape(shape)
+
+    class TestNanToNum(HybridBlock):
+        def __init__(self, copy=True, nan=0.0, posinf=None, neginf=None):
+            super(TestNanToNum, self).__init__()
+            self.copy = copy
+            self.nan = nan
+            self.posinf = posinf
+            self.neginf = neginf
+            # necessary initializations
+
+        def hybrid_forward(self, F, a):
+            return F.np.nan_to_num(a, self.copy, self.nan, self.posinf, self.neginf)
+
+    src_list = [
+        _np.nan,
+        _np.inf,
+        -_np.inf,
+        1,
+        [_np.nan],
+        [_np.inf],
+        [-_np.inf],
+        [1],
+        [1,2,3,4,-1,-2,-3,-4,0],
+        [_np.nan, _np.inf, -_np.inf],
+        [_np.nan, _np.inf, -_np.inf, -574, 0, 23425, 24234,-5],
+        [_np.nan, -1, 0, 1],
+        [[-433, 0, 456, _np.inf], [-1, -_np.inf, 0, 1]]
+    ]
+
+    dtype_list = ['float16', 'float32', 'float64']
+    # [nan, inf, -inf]
+    param_list = [[None, None, None], [0, 1000, -100], [0.0, 9999.9, -9999.9]]
+    copy_list = [True, False]
+    hybridize_list = [True, False]
+    atol, rtol = 1e-5, 1e-3
+    
+    src_dtype_comb = list(itertools.product(src_list,dtype_list))
+    # check the dtype = int case in both imperative and sympolic expression
+    src_dtype_comb.append((1,'int32'))
+    src_dtype_comb.append(([234, 0, -40],'int64'))
+
+    combinations = itertools.product(hybridize_list, src_dtype_comb, copy_list, param_list)
+
+    numpy_version = _np.version.version
+    for [hybridize, src_dtype, copy, param] in combinations:
+        src, dtype = src_dtype
+        # np.nan, np.inf, -np.int are float type
+        x1 = mx.nd.array(src, dtype=dtype).as_np_ndarray().asnumpy()
+        x2 = mx.nd.array(src, dtype=dtype).as_np_ndarray()
+        x3 = mx.nd.array(src, dtype=dtype).as_np_ndarray()
+
+        expected_grad = np_nan_to_num_grad(x1)
+        x2.attach_grad()
+        # with optional parameters or without
+        if param[0] !=None and numpy_version>="1.17":
+            test_np_nan_to_num = TestNanToNum(copy=copy, nan=param[0], posinf=param[1], neginf=param[2])
+            np_out = _np.nan_to_num(x1, copy=copy, nan=param[0], posinf=param[1], neginf=param[2])
+            mx_out = np.nan_to_num(x3, copy=copy, nan=param[0], posinf=param[1], neginf=param[2])
+        else:
+            test_np_nan_to_num = TestNanToNum(copy=copy)
+            np_out = _np.nan_to_num(x1, copy=copy)
+            mx_out = np.nan_to_num(x3, copy=copy)
+
+        assert_almost_equal(mx_out.asnumpy(), np_out, rtol, atol)
+        # check the inplace operation when copy = False
+        # if x1.shape = 0, _np.array will not actually execute copy logic
+        # only check x3 from np.nan_to_num instead of x2 from gluon 
+        if copy == False and x1.shape!=():
+            assert x1.shape == x3.asnumpy().shape
+            assert x1.dtype == x3.asnumpy().dtype
+            assert_almost_equal(x1, x3.asnumpy(), rtol=rtol, atol=atol)  
+        # gluon does not support nan_to_num when copy=False
+        # backward will check int type and if so, throw error
+        # if not this case, test gluon
+        if not (hybridize== False and copy == False) and ('float' in dtype):
+            if hybridize:
+                test_np_nan_to_num.hybridize()
+            with mx.autograd.record():
+                mx_out_gluon = test_np_nan_to_num(x2)
+            assert_almost_equal(mx_out_gluon.asnumpy(), np_out, rtol, atol)
+            mx_out_gluon.backward()
+            assert_almost_equal(x2.grad.asnumpy(), expected_grad, rtol=1e-3, atol=1e-5)
+                                        
+        # Test imperative once again
+        # if copy = False, the value of x1 and x2 has changed
+        if copy == True:            
+            np_out = _np.nan_to_num(x1)
+            mx_out = np.nan_to_num(x3)
+            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 177fd3e432bf158b842d8183849732a77eb84f37 Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Sun, 10 Nov 2019 20:29:54 -0800
Subject: [PATCH 581/813] Support boolean elemwise/broadcast binary add,
 multiply and true_divide (#16728)

* support pure boolean elemwise/broadcast binary op

* switch to unique_tpr

* fix the test error
---
 src/operator/mshadow_op.h                     | 22 +++--
 .../numpy/np_elemwise_broadcast_op.cc         | 29 +++----
 .../numpy/np_elemwise_broadcast_op.cu         | 19 ++---
 src/operator/numpy/np_elemwise_broadcast_op.h | 81 ++++++++++++++++---
 src/operator/operator_tune-inl.h              |  9 ++-
 src/operator/operator_tune.cc                 | 10 +--
 .../tensor/elemwise_binary_broadcast_op.h     | 32 ++++++++
 src/operator/tensor/elemwise_binary_op.h      | 28 +++++++
 tests/python/unittest/test_numpy_op.py        | 56 +++++++++++++
 9 files changed, 236 insertions(+), 50 deletions(-)

diff --git a/src/operator/mshadow_op.h b/src/operator/mshadow_op.h
index 0477d8553898..f9d4e23a3a6f 100644
--- a/src/operator/mshadow_op.h
+++ b/src/operator/mshadow_op.h
@@ -98,6 +98,18 @@ using std::is_integral;
     } \
   }
 
+#define MXNET_BINARY_MATH_OP_NC_WITH_BOOL(name, expr) \
+  struct name : public mxnet_op::tunable  { \
+    template<typename DType, \
+             typename std::enable_if<!std::is_same<DType, bool>::value, int>::type = 0> \
+    MSHADOW_XINLINE static DType Map(DType a, DType b) { \
+      return (expr); \
+    } \
+    MSHADOW_XINLINE static bool Map(bool a, bool b) { \
+      return (expr); \
+    } \
+  }
+
 #define MXNET_BINARY_LOGIC_OP_NC(name, expr) \
   struct name : public mxnet_op::tunable  { \
     template<typename DType> \
@@ -193,8 +205,6 @@ MXNET_BINARY_MATH_OP_NC(left, a);
 
 MXNET_BINARY_MATH_OP_NC(right, b);
 
-MXNET_BINARY_MATH_OP_NC(mul, a * b);
-
 #ifndef _WIN32
 struct mixed_plus {
   template<typename DType,
@@ -289,11 +299,13 @@ struct mixed_mul {
 };
 #endif
 
-MXNET_BINARY_MATH_OP_NC(div, a / b);
+MXNET_BINARY_MATH_OP_NC_WITH_BOOL(mul, a * b);
+
+MXNET_BINARY_MATH_OP_NC_WITH_BOOL(div, a / b);
 
-MXNET_BINARY_MATH_OP_NC(plus, a + b);
+MXNET_BINARY_MATH_OP_NC_WITH_BOOL(plus, a + b);
 
-MXNET_BINARY_MATH_OP_NC(minus, a - b);
+MXNET_BINARY_MATH_OP_NC_WITH_BOOL(minus, a - b);
 
 MXNET_UNARY_MATH_OP(negation, -a);
 
diff --git a/src/operator/numpy/np_elemwise_broadcast_op.cc b/src/operator/numpy/np_elemwise_broadcast_op.cc
index c32820d27361..a76e59d30dc6 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op.cc
+++ b/src/operator/numpy/np_elemwise_broadcast_op.cc
@@ -70,7 +70,7 @@ bool NumpyBinaryMixedPrecisionType(const nnvm::NodeAttrs& attrs,
   return true;
 }
 
-#ifdef _WIN32
+#ifndef _WIN32
 #define MXNET_OPERATOR_REGISTER_NP_BINARY_MIXED_PRECISION(name)                \
   NNVM_REGISTER_OP(name)                                                       \
   .set_num_inputs(2)                                                           \
@@ -85,10 +85,6 @@ bool NumpyBinaryMixedPrecisionType(const nnvm::NodeAttrs& attrs,
     [](const NodeAttrs& attrs){                                                \
       return std::vector<std::pair<int, int> >{{0, 0}, {1, 0}};                \
     })                                                                         \
-  .set_attr<FResourceRequest>("FResourceRequest",                              \
-  [](const NodeAttrs& attrs) {                                                 \
-    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};          \
-  })                                                                           \
   .add_argument("lhs", "NDArray-or-Symbol", "First input to the function")     \
   .add_argument("rhs", "NDArray-or-Symbol", "Second input to the function")
 #else
@@ -106,6 +102,10 @@ bool NumpyBinaryMixedPrecisionType(const nnvm::NodeAttrs& attrs,
     [](const NodeAttrs& attrs){                                                \
       return std::vector<std::pair<int, int> >{{0, 0}, {1, 0}};                \
     })                                                                         \
+  .set_attr<FResourceRequest>("FResourceRequest",                              \
+  [](const NodeAttrs& attrs) {                                                 \
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};          \
+  })                                                                           \
   .add_argument("lhs", "NDArray-or-Symbol", "First input to the function")     \
   .add_argument("rhs", "NDArray-or-Symbol", "Second input to the function")
 #endif
@@ -114,13 +114,12 @@ MXNET_OPERATOR_REGISTER_NP_BINARY_MIXED_PRECISION(_npi_add)
 #ifndef _WIN32
 .set_attr<FCompute>(
   "FCompute<cpu>",
-  MixedBinaryBroadcastCompute<cpu, op::mshadow_op::plus, op::mshadow_op::mixed_plus,
-                              op::mshadow_op::mixed_plus>)
+  NumpyBinaryBroadcastComputeWithBool<cpu, op::mshadow_op::plus, op::mshadow_op::mixed_plus,
+                                      op::mshadow_op::mixed_plus>)
 #else
 .set_attr<FCompute>(
   "FCompute<cpu>",
-  MixedBinaryBroadcastCompute<cpu, op::mshadow_op::plus, op::mshadow_op::plus,
-                              op::mshadow_op::plus>)
+  NumpyBinaryBroadcastComputeWithBool<cpu, op::mshadow_op::plus>)
 #endif
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_broadcast_add"});
 
@@ -128,13 +127,12 @@ MXNET_OPERATOR_REGISTER_NP_BINARY_MIXED_PRECISION(_npi_subtract)
 #ifndef _WIN32
 .set_attr<FCompute>(
   "FCompute<cpu>",
-  MixedBinaryBroadcastCompute<cpu, op::mshadow_op::minus, op::mshadow_op::mixed_minus,
+  NumpyBinaryBroadcastCompute<cpu, op::mshadow_op::minus, op::mshadow_op::mixed_minus,
                               op::mshadow_op::mixed_rminus>)
 #else
 .set_attr<FCompute>(
   "FCompute<cpu>",
-  MixedBinaryBroadcastCompute<cpu, op::mshadow_op::minus, op::mshadow_op::minus,
-                              op::mshadow_op::minus>)
+  NumpyBinaryBroadcastCompute<cpu, op::mshadow_op::minus>)
 #endif
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_broadcast_sub"});
 
@@ -142,13 +140,12 @@ MXNET_OPERATOR_REGISTER_NP_BINARY_MIXED_PRECISION(_npi_multiply)
 #ifndef _WIN32
 .set_attr<FCompute>(
   "FCompute<cpu>",
-  MixedBinaryBroadcastCompute<cpu, op::mshadow_op::mul, op::mshadow_op::mixed_mul,
-                              op::mshadow_op::mixed_mul>)
+  NumpyBinaryBroadcastComputeWithBool<cpu, op::mshadow_op::mul, op::mshadow_op::mixed_mul,
+                                      op::mshadow_op::mixed_mul>)
 #else
 .set_attr<FCompute>(
   "FCompute<cpu>",
-  MixedBinaryBroadcastCompute<cpu, op::mshadow_op::mul, op::mshadow_op::mul,
-                              op::mshadow_op::mul>)
+  NumpyBinaryBroadcastComputeWithBool<cpu, op::mshadow_op::mul>)
 #endif
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_broadcast_mul"});
 
diff --git a/src/operator/numpy/np_elemwise_broadcast_op.cu b/src/operator/numpy/np_elemwise_broadcast_op.cu
index 153ffd0048dd..a0a277df211f 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op.cu
+++ b/src/operator/numpy/np_elemwise_broadcast_op.cu
@@ -32,39 +32,36 @@ NNVM_REGISTER_OP(_npi_add)
 #ifndef _WIN32
 .set_attr<FCompute>(
   "FCompute<gpu>",
-  MixedBinaryBroadcastCompute<gpu, op::mshadow_op::plus, op::mshadow_op::mixed_plus,
-                              op::mshadow_op::mixed_plus>);
+  NumpyBinaryBroadcastComputeWithBool<gpu, op::mshadow_op::plus, op::mshadow_op::mixed_plus,
+                                      op::mshadow_op::mixed_plus>);
 #else
 .set_attr<FCompute>(
   "FCompute<gpu>",
-  MixedBinaryBroadcastCompute<gpu, op::mshadow_op::plus, op::mshadow_op::plus,
-                              op::mshadow_op::plus>);
+  NumpyBinaryBroadcastComputeWithBool<gpu, op::mshadow_op::plus>);
 #endif
 
 NNVM_REGISTER_OP(_npi_subtract)
 #ifndef _WIN32
 .set_attr<FCompute>(
   "FCompute<gpu>",
-  MixedBinaryBroadcastCompute<gpu, op::mshadow_op::minus, op::mshadow_op::mixed_minus,
+  NumpyBinaryBroadcastCompute<gpu, op::mshadow_op::minus, op::mshadow_op::mixed_minus,
                               op::mshadow_op::mixed_rminus>);
 #else
 .set_attr<FCompute>(
   "FCompute<gpu>",
-  MixedBinaryBroadcastCompute<gpu, op::mshadow_op::minus, op::mshadow_op::minus,
-                              op::mshadow_op::minus>);
+  NumpyBinaryBroadcastCompute<gpu, op::mshadow_op::minus>);
 #endif
 
 NNVM_REGISTER_OP(_npi_multiply)
 #ifndef _WIN32
 .set_attr<FCompute>(
   "FCompute<gpu>",
-  MixedBinaryBroadcastCompute<gpu, op::mshadow_op::mul, op::mshadow_op::mixed_mul,
-                              op::mshadow_op::mixed_mul>);
+  NumpyBinaryBroadcastComputeWithBool<gpu, op::mshadow_op::mul, op::mshadow_op::mixed_mul,
+                                      op::mshadow_op::mixed_mul>);
 #else
 .set_attr<FCompute>(
   "FCompute<gpu>",
-  MixedBinaryBroadcastCompute<gpu, op::mshadow_op::mul, op::mshadow_op::mul,
-                              op::mshadow_op::mul>);
+  NumpyBinaryBroadcastComputeWithBool<gpu, op::mshadow_op::mul>);
 #endif
 
 NNVM_REGISTER_OP(_npi_mod)
diff --git a/src/operator/numpy/np_elemwise_broadcast_op.h b/src/operator/numpy/np_elemwise_broadcast_op.h
index 1d36c6ff881e..1a4596fba91c 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op.h
+++ b/src/operator/numpy/np_elemwise_broadcast_op.h
@@ -34,8 +34,8 @@
 namespace mxnet {
 namespace op {
 
-inline void PrintErrorMessage(const std::string& name, const int dtype1, const int dtype2) {
-  LOG(FATAL) << "Operator " << name << " does not support combination of "
+inline void PrintErrorMessage(const std::string& op_name, const int dtype1, const int dtype2) {
+  LOG(FATAL) << "Operator " << op_name << " does not support combination of "
              << common::dtype_string(dtype1) << " with " << common::dtype_string(dtype2)
              << " yet...";
 }
@@ -218,7 +218,11 @@ void MixedAllRealBinaryBroadcastCompute(const std::string& op_name,
 }
 #endif
 
+#ifndef _WIN32
 template<typename xpu, typename OP, typename LOP, typename ROP>
+#else
+template<typename xpu, typename OP>
+#endif
 void MixedBinaryBroadcastCompute(const nnvm::NodeAttrs& attrs,
                                  const OpContext& ctx,
                                  const std::vector<TBlob>& inputs,
@@ -233,13 +237,6 @@ void MixedBinaryBroadcastCompute(const nnvm::NodeAttrs& attrs,
   const TBlob& rhs = inputs[1];
   const TBlob& out = outputs[0];
 
-  if ((out.shape_.Size() == 0U) || (req[0] == kNullOp)) return;
-
-  if (lhs.type_flag_ == rhs.type_flag_) {
-    BinaryBroadcastCompute<xpu, OP>(attrs, ctx, inputs, req, outputs);
-    return;
-  }
-
 #ifndef _WIN32
   mxnet::TShape new_lshape, new_rshape, new_oshape;
   int ndim = BinaryBroadcastShapeCompact(lhs.shape_, rhs.shape_, out.shape_,
@@ -317,6 +314,72 @@ void MixedBinaryBroadcastCompute(const nnvm::NodeAttrs& attrs,
 #endif
 }
 
+#ifndef _WIN32
+template<typename xpu, typename OP, typename LOP, typename ROP>
+#else
+template<typename xpu, typename OP>
+#endif
+void NumpyBinaryBroadcastCompute(const nnvm::NodeAttrs& attrs,
+                                 const OpContext& ctx,
+                                 const std::vector<TBlob>& inputs,
+                                 const std::vector<OpReqType>& req,
+                                 const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mxnet_op;
+  CHECK_EQ(inputs.size(), 2U);
+  CHECK_EQ(outputs.size(), 1U);
+
+  const TBlob& lhs = inputs[0];
+  const TBlob& rhs = inputs[1];
+  const TBlob& out = outputs[0];
+
+  if ((out.shape_.Size() == 0U) || (req[0] == kNullOp)) return;
+
+  if (lhs.type_flag_ == rhs.type_flag_) {
+    BinaryBroadcastCompute<xpu, OP>(attrs, ctx, inputs, req, outputs);
+    return;
+  }
+
+#ifndef _WIN32
+  MixedBinaryBroadcastCompute<xpu, OP, LOP, ROP>(attrs, ctx, inputs, req, outputs);
+#else
+  MixedBinaryBroadcastCompute<xpu, OP>(attrs, ctx, inputs, req, outputs);
+#endif
+}
+
+#ifndef _WIN32
+template<typename xpu, typename OP, typename LOP, typename ROP>
+#else
+template<typename xpu, typename OP>
+#endif
+void NumpyBinaryBroadcastComputeWithBool(const nnvm::NodeAttrs& attrs,
+                                         const OpContext& ctx,
+                                         const std::vector<TBlob>& inputs,
+                                         const std::vector<OpReqType>& req,
+                                         const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mxnet_op;
+  CHECK_EQ(inputs.size(), 2U);
+  CHECK_EQ(outputs.size(), 1U);
+
+  const TBlob& lhs = inputs[0];
+  const TBlob& rhs = inputs[1];
+  const TBlob& out = outputs[0];
+
+  if ((out.shape_.Size() == 0U) || (req[0] == kNullOp)) return;
+
+  if (lhs.type_flag_ == rhs.type_flag_) {
+    BinaryBroadcastComputeWithBool<xpu, OP>(attrs, ctx, inputs, req, outputs);
+    return;
+  }
+
+#ifndef _WIN32
+  MixedBinaryBroadcastCompute<xpu, OP, LOP, ROP>(attrs, ctx, inputs, req, outputs);
+#else
+  MixedBinaryBroadcastCompute<xpu, OP>(attrs, ctx, inputs, req, outputs);
+#endif
+}
+
 template<typename xpu, typename LOP, typename ROP>
 void MixedBinaryBackwardUseIn(const nnvm::NodeAttrs& attrs,
                               const OpContext& ctx,
diff --git a/src/operator/operator_tune-inl.h b/src/operator/operator_tune-inl.h
index 1dbcf4298918..122ec0487044 100644
--- a/src/operator/operator_tune-inl.h
+++ b/src/operator/operator_tune-inl.h
@@ -124,7 +124,8 @@ class OperatorTune : public OperatorTuneByType<DType> {
     if (!initialized_) {
       initialized_ = true;
       // Generate some random data for calling the operator kernels
-      data_set_.reserve(0x100);
+      data_set_ =
+        std::unique_ptr<DType[]>(reinterpret_cast<DType*>(new char[0x100 * sizeof(DType)]));
       std::random_device rd;
       std::mt19937 gen(rd());
       if (!std::is_integral<DType>::value) {
@@ -136,7 +137,7 @@ class OperatorTune : public OperatorTuneByType<DType> {
             --n;
             continue;
           }
-          data_set_.emplace_back(val);
+          data_set_[n] = val;
         }
       } else {
         std::uniform_int_distribution<> dis(-128, 127);
@@ -147,7 +148,7 @@ class OperatorTune : public OperatorTuneByType<DType> {
             --n;
             continue;
           }
-          data_set_.emplace_back(val);
+          data_set_[n] = val;
         }
       }
       // Use this environment variable to generate new tuning statistics
@@ -517,7 +518,7 @@ class OperatorTune : public OperatorTuneByType<DType> {
   /*! \brief Number of passes to obtain an average */
   static constexpr duration_t OUTSIDE_COUNT = (1 << OUTSIDE_COUNT_SHIFT);
   /*! \brief Random data for timing operator calls */
-  static std::vector<DType> data_set_;
+  static std::unique_ptr<DType[]> data_set_;
   /*! \brief Operators tuned */
   static std::unordered_set<std::string> operator_names_;
   /*! \brief Arbitary object to modify in OMP loop */
diff --git a/src/operator/operator_tune.cc b/src/operator/operator_tune.cc
index d0642eedfdf3..633f63026bc0 100644
--- a/src/operator/operator_tune.cc
+++ b/src/operator/operator_tune.cc
@@ -39,7 +39,7 @@ double OperatorTuneBase::tuning_weight_scale_ = 0.0;
  */
 #define IMPLEMENT_OPERATOR_TUNE_STATICS_FOR_TYPE(__typ$) \
   template<> bool OperatorTune<__typ$>::initialized_ = false; \
-  template<> std::vector<__typ$> OperatorTune<__typ$>::data_set_ = {}; \
+  template<> std::unique_ptr<__typ$[]> OperatorTune<__typ$>::data_set_ = nullptr; \
   template<> volatile tune::TuningMode OperatorTuneByType<__typ$>::tuning_mode_ = tune::kAuto; \
   template<> volatile int OperatorTune<__typ$>::volatile_int_ = 9;  /* arbitrary number */ \
   template<> std::unordered_set<std::string> OperatorTune<__typ$>::operator_names_({}); \
@@ -314,10 +314,10 @@ IMPLEMENT_UNARY_WORKLOAD_FWD_WITH_BOOL(mxnet::op::mshadow_op::np_logical_not);
 IMPLEMENT_UNARY_WORKLOAD_BWD(mxnet::op::mshadow_op::nt);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::clip);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::clip);  // NOLINT()
-IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::plus);  // NOLINT()
-IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::minus);  // NOLINT()
-IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::mul);  // NOLINT()
-IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::div);  // NOLINT()
+IMPLEMENT_BINARY_WORKLOAD_FWD_WITH_BOOL(mxnet::op::mshadow_op::plus);  // NOLINT()
+IMPLEMENT_BINARY_WORKLOAD_FWD_WITH_BOOL(mxnet::op::mshadow_op::minus);  // NOLINT()
+IMPLEMENT_BINARY_WORKLOAD_FWD_WITH_BOOL(mxnet::op::mshadow_op::mul);  // NOLINT()
+IMPLEMENT_BINARY_WORKLOAD_FWD_WITH_BOOL(mxnet::op::mshadow_op::div);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::true_divide);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::minus_sign);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::rminus);  // NOLINT()
diff --git a/src/operator/tensor/elemwise_binary_broadcast_op.h b/src/operator/tensor/elemwise_binary_broadcast_op.h
index ad06df8d92be..b48ed389ba98 100644
--- a/src/operator/tensor/elemwise_binary_broadcast_op.h
+++ b/src/operator/tensor/elemwise_binary_broadcast_op.h
@@ -347,6 +347,9 @@ void BinaryBroadcastCompute(const nnvm::NodeAttrs& attrs,
   } else {
     if (req[0] != kNullOp) {
       mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+      if (outputs[0].type_flag_ == mshadow::kBool) {
+        LOG(FATAL) << "Operator " << attrs.op->name << " does not support boolean type";
+      }
       MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
         BROADCAST_NDIM_SWITCH(ndim, NDim, {
           mshadow::Shape<NDim> oshape = new_oshape.get<NDim>();
@@ -361,6 +364,35 @@ void BinaryBroadcastCompute(const nnvm::NodeAttrs& attrs,
   }
 }
 
+template<typename xpu, typename OP>
+void BinaryBroadcastComputeWithBool(const nnvm::NodeAttrs& attrs,
+                                    const OpContext& ctx,
+                                    const std::vector<TBlob>& inputs,
+                                    const std::vector<OpReqType>& req,
+                                    const std::vector<TBlob>& outputs) {
+  if (outputs[0].shape_.Size() == 0U) return;
+  mxnet::TShape new_lshape, new_rshape, new_oshape;
+  int ndim = BinaryBroadcastShapeCompact(inputs[0].shape_, inputs[1].shape_, outputs[0].shape_,
+                                         &new_lshape, &new_rshape, &new_oshape);
+  if (!ndim) {
+    ElemwiseBinaryOp::ComputeWithBool<xpu, OP>(attrs, ctx, inputs, req, outputs);
+  } else {
+    if (req[0] != kNullOp) {
+      mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+      MSHADOW_TYPE_SWITCH_WITH_BOOL(outputs[0].type_flag_, DType, {
+        BROADCAST_NDIM_SWITCH(ndim, NDim, {
+          mshadow::Shape<NDim> oshape = new_oshape.get<NDim>();
+          mshadow::Shape<NDim> lstride = mxnet_op::calc_stride(new_lshape.get<NDim>());
+          mshadow::Shape<NDim> rstride = mxnet_op::calc_stride(new_rshape.get<NDim>());
+          mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, OP>, xpu>::
+          template LaunchEx(s, new_oshape.Size(), req[0], lstride, rstride, oshape,
+          inputs[0].dptr<DType>(), inputs[1].dptr<DType>(), outputs[0].dptr<DType>());
+        });
+      });
+    }
+  }
+}
+
 template<typename xpu, typename OP>
 void BinaryBroadcastComputeLogic(const nnvm::NodeAttrs& attrs,
                                  const OpContext& ctx,
diff --git a/src/operator/tensor/elemwise_binary_op.h b/src/operator/tensor/elemwise_binary_op.h
index da088c1dcc39..c046a28f16b2 100644
--- a/src/operator/tensor/elemwise_binary_op.h
+++ b/src/operator/tensor/elemwise_binary_op.h
@@ -485,6 +485,9 @@ class ElemwiseBinaryOp : public OpBase {
       Stream<xpu> *s = ctx.get_stream<xpu>();
       CHECK_EQ(inputs.size(), 2U);
       CHECK_EQ(outputs.size(), 1U);
+      if (outputs[0].type_flag_ == mshadow::kBool) {
+        LOG(FATAL) << "Operator " << attrs.op->name << " does not support boolean type";
+      }
       MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
         MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
           const size_t size = (minthree(outputs[0].Size(), inputs[0].Size(), inputs[1].Size())
@@ -499,6 +502,31 @@ class ElemwiseBinaryOp : public OpBase {
     }
   }
 
+  template<typename xpu, typename OP>
+  static void ComputeWithBool(const nnvm::NodeAttrs &attrs,
+                              const OpContext &ctx,
+                              const std::vector<TBlob> &inputs,
+                              const std::vector<OpReqType> &req,
+                              const std::vector<TBlob> &outputs) {
+    using namespace mxnet_op;
+    if (req[0] != kNullOp) {
+      Stream<xpu> *s = ctx.get_stream<xpu>();
+      CHECK_EQ(inputs.size(), 2U);
+      CHECK_EQ(outputs.size(), 1U);
+      MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
+        MSHADOW_TYPE_SWITCH_WITH_BOOL(outputs[0].type_flag_, DType, {
+          const size_t size = (minthree(outputs[0].Size(), inputs[0].Size(), inputs[1].Size())
+          + DataType<DType>::kLanes - 1) / DataType<DType>::kLanes;
+          if (size != 0) {
+            Kernel<mxnet_op::op_with_req<OP, Req>, xpu>::Launch(s, size,
+            outputs[0].dptr<DType>(),
+            inputs[0].dptr<DType>(), inputs[1].dptr<DType>());
+          }
+        });
+      });
+    }
+  }
+
   template<typename xpu, typename OP>
   static void ComputeLogic(const nnvm::NodeAttrs &attrs,
                            const OpContext &ctx,
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 79988dd71942..fb022e06158c 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -1690,6 +1690,7 @@ def hybrid_forward(self, F, a, b, *args, **kwargs):
         'subtract': (-1.0, 1.0),
         'multiply': (-1.0, 1.0),
     }
+
     shape_pairs = [((3, 2), (3, 2)),
                    ((3, 2), (3, 1)),
                    ((3, 1), (3, 0)),
@@ -1697,6 +1698,7 @@ def hybrid_forward(self, F, a, b, *args, **kwargs):
                    ((2, 3, 4), (3, 1)),
                    ((2, 3), ()),
                    ((), (2, 3))]
+
     itypes = [np.bool, np.int8, np.int32, np.int64]
     ftypes = [np.float16, np.float32, np.float64]
     for func, func_data in funcs.items():
@@ -1712,6 +1714,60 @@ def hybrid_forward(self, F, a, b, *args, **kwargs):
                 check_mixed_precision_binary_func(func, low, high, lshape, rshape, type1, type2)
 
 
+@with_seed()
+@use_np
+def test_np_boolean_binary_funcs():
+    def check_boolean_binary_func(func, mx_x1, mx_x2):
+        class TestBooleanBinary(HybridBlock):
+            def __init__(self, func):
+                super(TestBooleanBinary, self).__init__()
+                self._func = func
+
+            def hybrid_forward(self, F, a, b, *args, **kwargs):
+                return getattr(F.np, self._func)(a, b)
+
+        np_x1 = mx_x1.asnumpy()
+        np_x2 = mx_x2.asnumpy()
+        np_func = getattr(_np, func)
+        mx_func = TestBooleanBinary(func)
+        for hybridize in [True, False]:
+            if hybridize:
+                mx_func.hybridize()
+            np_out = np_func(np_x1, np_x2)
+            with mx.autograd.record():
+                y = mx_func(mx_x1, mx_x2)
+            assert y.shape == np_out.shape
+            assert_almost_equal(y.asnumpy(), np_out.astype(y.dtype), rtol=1e-3, atol=1e-20,
+                                use_broadcast=False, equal_nan=True)
+
+        np_out = getattr(_np, func)(np_x1, np_x2)
+        mx_out = getattr(mx.np, func)(mx_x1, mx_x2)
+        assert mx_out.shape == np_out.shape
+        assert_almost_equal(mx_out.asnumpy(), np_out.astype(mx_out.dtype), rtol=1e-3, atol=1e-20,
+                            use_broadcast=False, equal_nan=True)
+
+
+    funcs = [
+        'add',
+        'multiply',
+        'true_divide',
+    ]
+
+    shape_pairs = [((3, 2), (3, 2)),
+                   ((3, 2), (3, 1)),
+                   ((3, 1), (3, 0)),
+                   ((0, 2), (1, 2)),
+                   ((2, 3, 4), (3, 1)),
+                   ((2, 3), ()),
+                   ((), (2, 3))]
+
+    for lshape, rshape in shape_pairs:
+        for func in funcs:
+            x1 = np.array(_np.random.uniform(size=lshape) > 0.5)
+            x2 = np.array(_np.random.uniform(size=rshape) > 0.5)
+            check_boolean_binary_func(func, x1, x2)
+
+
 @with_seed()
 @use_np
 def test_npx_relu():

From 27672b99000df92f18c199bdfca48370f426b978 Mon Sep 17 00:00:00 2001
From: Robert Stone <talby@trap.mtview.ca.us>
Date: Mon, 11 Nov 2019 13:23:31 -0800
Subject: [PATCH 582/813] [Perl] - ndarray operator overloading enhancements
 (#16779)

---
 perl-package/AI-MXNet/lib/AI/MXNet/NDArray.pm | 19 +++++++++--
 .../AI-MXNet/lib/AI/MXNet/TestUtils.pm        |  9 +++++-
 perl-package/AI-MXNet/t/test_ndarray.t        | 32 ++++++++++++++++++-
 3 files changed, 56 insertions(+), 4 deletions(-)

diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/NDArray.pm b/perl-package/AI-MXNet/lib/AI/MXNet/NDArray.pm
index 1d968c14a487..abad4471a8e4 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/NDArray.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/NDArray.pm
@@ -65,7 +65,7 @@ use AI::MXNet::NDArray::Slice;
 use AI::MXNet::Context;
 use Mouse;
 use AI::MXNet::Function::Parameters;
-use overload
+use overload (
     '""' => \&stringify,
     '+'  => \&add,
     '+=' => \&iadd,
@@ -86,7 +86,16 @@ use overload
     '<=' => \&lesser_equal,
     '.=' => \&set,
     '@{}'=> \&split_array,
-    '='  => sub { $_[0] };
+    '='  => sub { $_[0] },
+
+    'sqrt' => sub { $_[0]->sqrt() },
+    'abs' => sub { $_[0]->abs() },
+    'sin' => sub { $_[0]->sin() },
+    'cos' => sub { $_[0]->cos() },
+    'atan2' => \&atan2,
+    'log' => sub { $_[0]->log() },
+    'exp' => sub { $_[0]->exp() },
+);
 
 extends 'AI::MXNet::NDArray::Base';
 has 'writable' => (is => 'rw', isa => 'Int', default => 1, lazy => 1);
@@ -763,6 +772,12 @@ method stringify($other=, $reverse=)
     sprintf("<%s %s @%s>", ref($self), join('x', @{ $self->shape }), $self->context);
 }
 
+method atan2($other=, $reverse=)
+{
+    my $val = $reverse ? $other / $self : $self / $other;
+    return __PACKAGE__->arctan($val);
+}
+
 method iadd(AI::MXNet::NDArray|Num $other, $reverse=)
 {
     confess('trying to add to a readonly NDArray') unless $self->writable;
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/TestUtils.pm b/perl-package/AI-MXNet/lib/AI/MXNet/TestUtils.pm
index 3131692fc981..2b7249fea8ee 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet/TestUtils.pm
+++ b/perl-package/AI-MXNet/lib/AI/MXNet/TestUtils.pm
@@ -46,9 +46,16 @@ use constant default_numerical_threshold => 1e-6;
     b : pdl
 =cut
 
+func isNaN(PDL $a)
+{
+    # Only NaN is not greater, equal to, or less than zero.
+    return !(($a > 0) + ($a <= 0));
+}
+
 func same(PDL $a, PDL $b)
 {
-    return ($a != $b)->sum == 0;
+    my $rv = ($a != $b) - isNaN($a) * isNaN($b);
+    return $rv->sum == 0;
 }
 
 =head2 allclose
diff --git a/perl-package/AI-MXNet/t/test_ndarray.t b/perl-package/AI-MXNet/t/test_ndarray.t
index 1e290b4bc715..c71ce15b66d1 100644
--- a/perl-package/AI-MXNet/t/test_ndarray.t
+++ b/perl-package/AI-MXNet/t/test_ndarray.t
@@ -19,7 +19,7 @@ use strict;
 use warnings;
 use AI::MXNet qw(mx);
 use AI::MXNet::TestUtils qw(almost_equal same rand_ndarray randint zip);
-use Test::More tests => 261;
+use Test::More tests => 280;
 use PDL;
 use File::Temp qw(tempdir);
 use IO::File;
@@ -217,6 +217,35 @@ sub test_histogram
     ok(same($bins->aspdl, pdl([10, 20, 30, 60])));
 }
 
+sub test_overload
+{
+    # much of this depends on PDL being sane as well.
+    my $px = PDL->new([ 2, -5, 11, 17 ]) / 2;
+    my $nx = mx->nd->array($px, dtype => 'float64');
+    my $py = PDL->new([ -3, 7, 13, 19 ]) / 4;
+    my $ny = mx->nd->array($py, dtype => 'float64');
+
+    ok(same(($nx + $ny)->aspdl(), $px + $py), 'overloaded add');
+    ok(same(($nx - $ny)->aspdl(), $px - $py), 'overloaded sub');
+    ok(same(($nx * $ny)->aspdl(), $px * $py), 'overloaded mul');
+    ok(same(($nx / $ny)->aspdl(), $px / $py), 'overloaded div');
+    ok(same(($nx % $ny)->aspdl(), $px % $py), 'overloaded mod');
+    ok(same(($nx ** $ny)->aspdl(), $px ** $py), 'overloaded pow');
+    ok(same(($nx->copy() += $ny)->aspdl(), $px + $py), 'inplace add');
+    ok(same(($nx->copy() -= $ny)->aspdl(), $px - $py), 'inplace sub');
+    ok(same(($nx->copy() *= $ny)->aspdl(), $px * $py), 'inplace mul');
+    ok(same(($nx->copy() /= $ny)->aspdl(), $px / $py), 'inplace div');
+    ok(same(($nx->copy() %= $ny)->aspdl(), $px % $py), 'inplace mod');
+    ok(same(($nx->copy() **= $ny)->aspdl(), $px ** $py), 'inplace pow');
+    ok(same(cos($nx)->aspdl(), cos($px)), 'overloaded cos');
+    ok(same(sin($nx)->aspdl(), sin($px)), 'overloaded sin');
+    ok(same(exp($nx)->aspdl(), exp($px)), 'overloaded exp');
+    ok(same(abs($nx)->aspdl(), abs($px)), 'overloaded abs');
+    ok(same(log($nx)->aspdl(), log($px)), 'overloaded log');
+    ok(same(sqrt($nx)->aspdl(), sqrt($px)), 'overloaded sqrt');
+    ok(same(atan2($nx, 1.0)->aspdl(), atan2($px, 1.0)), 'overloaded atan2');
+}
+
 sub test_array_overload
 {
     # array conversions are largely calls to mx->nd->split(), but have
@@ -242,4 +271,5 @@ test_linalg_gemm2();
 test_image_to_tensor();
 test_buffer_load();
 test_histogram();
+test_overload();
 test_array_overload();

From b9b56e66281f9ddf9372ee753360b4a9c8fb16ec Mon Sep 17 00:00:00 2001
From: vexilligera <vexilligera@gmail.com>
Date: Tue, 12 Nov 2019 15:42:07 +0800
Subject: [PATCH 583/813] fix inv test flakiness using random matrices
 generated by SVD (#16782)

---
 tests/python/unittest/test_numpy_op.py | 18 +++++++++++++-----
 1 file changed, 13 insertions(+), 5 deletions(-)

diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index fb022e06158c..d7a775e38792 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -3155,14 +3155,22 @@ def check_inv(A_inv, data_np):
         test_inv = TestInverse()
         if hybridize:
             test_inv.hybridize()
-        # use LU decomposition to generate invertible matrices
+        # generate well-conditioned matrices with small eigenvalues
         if 0 in shape:
             data_np = _np.ones(shape)
         else:
-            n = shape[-1]
-            L = _np.tril(_np.random.uniform(-10., 10., shape))
-            U = _np.triu(_np.random.uniform(-10., 10., shape))
-            data_np = _np.matmul(L, U)
+            n = int(np.prod(np.array(shape[:-2]))) if len(shape) > 2 else 1
+            # eigenvalues
+            D = _np.array([_np.diag(_np.random.uniform(-10., 10., shape[-1])) \
+                             for i in range(n)]).reshape(shape)
+            # orthogonal matrix through householder transformation
+            I = _np.array([_np.eye(shape[-1]) for i in range(n)]).reshape(shape)
+            v = _np.random.uniform(-10, 10,
+                    int(np.prod(np.array(shape[:-1])))).reshape(shape[:-1] + (1,))
+            v = v / _np.linalg.norm(v, axis=-2, keepdims=True)
+            v_T = _np.swapaxes(v, -1, -2)
+            U = I - 2 * _np.matmul(v, v_T)
+            data_np = _np.matmul(_np.matmul(U, D), _np.swapaxes(U, -1, -2))
         data = np.array(data_np, dtype=dtype)
         data.attach_grad()
         with mx.autograd.record():

From 02f4f0503de60cce41989f1bb779ee8c7b943e4b Mon Sep 17 00:00:00 2001
From: Xi Wang <xidulu@gmail.com>
Date: Tue, 12 Nov 2019 17:07:02 +0800
Subject: [PATCH 584/813] [Numpy] Add sampling method for bernoulli (#16638)

* add frontend interface for bernoulli

* bernoulli backend done

* frontend done, test to be added

* finish tests, fix indicator initialization bug

* test with native numpy

* fix indent, change test name

* resolve comments

* add raise test

* modify raise test
---
 python/mxnet/base.py                          |   2 +-
 .../mxnet/ndarray/numpy_extension/__init__.py |   1 +
 .../mxnet/ndarray/numpy_extension/random.py   | 104 +++++++++
 python/mxnet/numpy_extension/__init__.py      |   2 +-
 python/mxnet/numpy_extension/random.py        |  56 ++++-
 .../mxnet/symbol/numpy_extension/__init__.py  |   1 +
 python/mxnet/symbol/numpy_extension/random.py | 104 +++++++++
 src/operator/numpy/random/dist_common.h       |  31 ++-
 src/operator/numpy/random/np_bernoulli_op.cc  |  69 ++++++
 src/operator/numpy/random/np_bernoulli_op.cu  |  35 +++
 src/operator/numpy/random/np_bernoulli_op.h   | 200 ++++++++++++++++++
 src/operator/numpy/random/np_normal_op.h      |   1 +
 src/operator/numpy/random/np_uniform_op.h     |   1 -
 tests/python/unittest/test_numpy_op.py        |  35 +++
 14 files changed, 635 insertions(+), 7 deletions(-)
 create mode 100644 python/mxnet/ndarray/numpy_extension/random.py
 create mode 100644 python/mxnet/symbol/numpy_extension/random.py
 create mode 100644 src/operator/numpy/random/np_bernoulli_op.cc
 create mode 100644 src/operator/numpy/random/np_bernoulli_op.cu
 create mode 100644 src/operator/numpy/random/np_bernoulli_op.h

diff --git a/python/mxnet/base.py b/python/mxnet/base.py
index 1c46e16e22d0..3ed68dbe8b8a 100644
--- a/python/mxnet/base.py
+++ b/python/mxnet/base.py
@@ -747,7 +747,7 @@ def write_all_str(module_file, module_all_list):
 _NP_OP_SUBMODULE_LIST = ['_random_', '_linalg_']
 
 _NP_EXT_OP_PREFIX = '_npx_'
-_NP_EXT_OP_SUBMODULE_LIST = ['_image_']
+_NP_EXT_OP_SUBMODULE_LIST = ['_image_', '_random_']
 
 _NP_INTERNAL_OP_PREFIX = '_npi_'
 
diff --git a/python/mxnet/ndarray/numpy_extension/__init__.py b/python/mxnet/ndarray/numpy_extension/__init__.py
index 5be34ac9b3d5..afa81edc3820 100644
--- a/python/mxnet/ndarray/numpy_extension/__init__.py
+++ b/python/mxnet/ndarray/numpy_extension/__init__.py
@@ -19,6 +19,7 @@
 
 from . import _op
 from . import image
+from . import random
 from . import _register
 from ._op import *  # pylint: disable=wildcard-import
 
diff --git a/python/mxnet/ndarray/numpy_extension/random.py b/python/mxnet/ndarray/numpy_extension/random.py
new file mode 100644
index 000000000000..b0472a4ab122
--- /dev/null
+++ b/python/mxnet/ndarray/numpy_extension/random.py
@@ -0,0 +1,104 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Namespace for operators used in Gluon dispatched by F=ndarray."""
+from __future__ import absolute_import
+from ...context import current_context
+from ..numpy import _internal as _npi
+
+
+__all__ = ['bernoulli']
+
+
+def bernoulli(prob, logit, size, dtype, ctx, out):
+    """Creates a Bernoulli distribution parameterized by :attr:`prob`
+    or :attr:`logit` (but not both).
+
+    Samples are binary (0 or 1). They take the value `1` with probability `p`
+    and `0` with probability `1 - p`.
+
+    Parameters
+    ----------
+    prob : float, ndarray
+        The probability of sampling '1'.
+        Only one of prob or logit should be passed in.
+    logit : float, ndarray
+        The log-odds of sampling '1'.
+        Only one of prob or logit should be passed in.
+    size : int or tuple of ints, optional
+        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k`` samples are drawn.  Default is None, in which case a
+        single value is returned.
+    dtype : dtype, optional
+        Desired dtype of the result. All dtypes are determined by their
+        name, i.e., 'int64', 'int', etc, so byteorder is not available
+        and a specific precision may have different C types depending
+        on the platform. The default value is 'np.float32'.
+    ctx : Context, optional
+        Device context of output. Default is current context.
+    out : symbol, optional
+        The output symbol (default is `None`).
+
+    Returns
+    -------
+    out : ndarray
+        Drawn samples from the parameterized bernoulli distribution.
+
+    Examples
+    --------
+    >>> prob = np.random.uniform(size=(4,4))
+    >>> logit = np.log(prob) - np.log(1 - prob)
+    >>> npx.random.bernoulli(logit=logit)
+    array([[0., 1., 1., 1.],
+        [0., 1., 1., 1.],
+        [0., 1., 0., 0.],
+        [1., 0., 1., 0.]])
+
+    >>> npx.random.bernoulli(prob=prob)
+    array([[0., 1., 0., 1.],
+        [1., 1., 1., 1.],
+        [1., 1., 1., 0.],
+        [1., 0., 1., 0.]])
+    """
+    from ...numpy import ndarray as np_ndarray
+    tensor_type_name = np_ndarray
+    if (prob is None) == (logit is None):
+        raise ValueError(
+            "Either `prob` or `logit` must be specified, but not both. " +
+            "Received prob={}, logit={}".format(prob, logit))
+    if dtype is None:
+        dtype = 'float32'
+    if ctx is None:
+        ctx = current_context()
+    if size == ():
+        size = None
+    if prob is not None:
+        is_tensor = isinstance(prob, tensor_type_name)
+        if is_tensor:
+            return _npi.bernoulli(prob, prob=None, logit=None, is_logit=False,
+                                  size=size, ctx=ctx, dtype=dtype, out=out)
+        else:
+            return _npi.bernoulli(prob=prob, logit=None, is_logit=False,
+                                  size=size, ctx=ctx, dtype=dtype, out=out)
+    else:
+        is_tensor = isinstance(logit, tensor_type_name)
+        if is_tensor:
+            return _npi.bernoulli(logit, prob=None, logit=None, is_logit=True,
+                                  size=size, ctx=ctx, dtype=dtype, out=out)
+        else:
+            return _npi.bernoulli(prob=None, logit=logit, is_logit=True,
+                                  size=size, ctx=ctx, dtype=dtype, out=out)
diff --git a/python/mxnet/numpy_extension/__init__.py b/python/mxnet/numpy_extension/__init__.py
index 879ab4d56a46..c4b90f2f8d35 100644
--- a/python/mxnet/numpy_extension/__init__.py
+++ b/python/mxnet/numpy_extension/__init__.py
@@ -22,12 +22,12 @@
 from __future__ import absolute_import
 from . import _op
 from . import image
+from . import random  # pylint: disable=wildcard-import
 from . import _register
 from ._op import *  # pylint: disable=wildcard-import
 from ..context import *  # pylint: disable=wildcard-import
 from ..util import is_np_shape, is_np_array, set_np, reset_np, get_cuda_compute_capability
 from ..ndarray import waitall
 from .utils import *  # pylint: disable=wildcard-import
-from . import random  # pylint: disable=wildcard-import
 
 __all__ = []
diff --git a/python/mxnet/numpy_extension/random.py b/python/mxnet/numpy_extension/random.py
index 9c059ca9ade4..316760b6e2d6 100644
--- a/python/mxnet/numpy_extension/random.py
+++ b/python/mxnet/numpy_extension/random.py
@@ -19,9 +19,10 @@
 
 from __future__ import absolute_import
 from .. import random as _mx_rand
+from ..ndarray import numpy_extension as _mx_nd_npx
 
 
-__all__ = ['seed']
+__all__ = ['seed', 'bernoulli']
 
 
 def seed(seed, ctx='all'):  # pylint: disable=redefined-outer-name
@@ -72,3 +73,56 @@ def seed(seed, ctx='all'):  # pylint: disable=redefined-outer-name
     array(0.9894903, ctx=gpu(0))
     """
     _mx_rand.seed(seed_state=seed, ctx=ctx)
+
+
+def bernoulli(prob=None, logit=None, size=None, dtype=None, ctx=None, out=None):
+    """Creates a Bernoulli distribution parameterized by :attr:`prob`
+    or :attr:`logit` (but not both).
+
+    Samples are binary (0 or 1). They take the value `1` with probability `p`
+    and `0` with probability `1 - p`.
+
+    Parameters
+    ----------
+    prob : float, ndarray
+        The probability of sampling '1'.
+        Only one of prob or logit should be passed in.
+    logit : float, ndarray
+        The log-odds of sampling '1'.
+        Only one of prob or logit should be passed in.
+    size : int or tuple of ints, optional
+        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k`` samples are drawn.  Default is None, in which case a
+        single value is returned.
+    dtype : dtype, optional
+        Desired dtype of the result. All dtypes are determined by their
+        name, i.e., 'int64', 'int', etc, so byteorder is not available
+        and a specific precision may have different C types depending
+        on the platform. The default value is 'np.float32'.
+    ctx : Context, optional
+        Device context of output. Default is current context.
+    out : symbol, optional
+        The output symbol (default is `None`).
+
+    Returns
+    -------
+    out : ndarray
+        Drawn samples from the parameterized bernoulli distribution.
+
+    Examples
+    --------
+    >>> prob = np.random.uniform(size=(4,4))
+    >>> logit = np.log(prob) - np.log(1 - prob)
+    >>> npx.random.bernoulli(logit=logit)
+    array([[0., 1., 1., 1.],
+        [0., 1., 1., 1.],
+        [0., 1., 0., 0.],
+        [1., 0., 1., 0.]])
+
+    >>> npx.random.bernoulli(prob=prob)
+    array([[0., 1., 0., 1.],
+        [1., 1., 1., 1.],
+        [1., 1., 1., 0.],
+        [1., 0., 1., 0.]])
+    """
+    return _mx_nd_npx.random.bernoulli(prob, logit, size, dtype, ctx, out)
diff --git a/python/mxnet/symbol/numpy_extension/__init__.py b/python/mxnet/symbol/numpy_extension/__init__.py
index 5be34ac9b3d5..afa81edc3820 100644
--- a/python/mxnet/symbol/numpy_extension/__init__.py
+++ b/python/mxnet/symbol/numpy_extension/__init__.py
@@ -19,6 +19,7 @@
 
 from . import _op
 from . import image
+from . import random
 from . import _register
 from ._op import *  # pylint: disable=wildcard-import
 
diff --git a/python/mxnet/symbol/numpy_extension/random.py b/python/mxnet/symbol/numpy_extension/random.py
new file mode 100644
index 000000000000..a557a75d56f7
--- /dev/null
+++ b/python/mxnet/symbol/numpy_extension/random.py
@@ -0,0 +1,104 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Namespace for operators used in Gluon dispatched by F=symbol."""
+
+from __future__ import absolute_import
+from ...context import current_context
+from ..numpy import _internal as _npi
+
+__all__ = ['bernoulli']
+
+
+def bernoulli(prob=None, logit=None, size=None, dtype=None, ctx=None, out=None):
+    """Creates a Bernoulli distribution parameterized by :attr:`prob`
+    or :attr:`logit` (but not both).
+
+    Samples are binary (0 or 1). They take the value `1` with probability `p`
+    and `0` with probability `1 - p`.
+
+    Parameters
+    ----------
+    prob : float, ndarray
+        The probability of sampling '1'.
+        Only one of prob or logit should be passed in.
+    logit : float, ndarray
+        The log-odds of sampling '1'.
+        Only one of prob or logit should be passed in.
+    size : int or tuple of ints, optional
+        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k`` samples are drawn.  Default is None, in which case a
+        single value is returned.
+    dtype : dtype, optional
+        Desired dtype of the result. All dtypes are determined by their
+        name, i.e., 'int64', 'int', etc, so byteorder is not available
+        and a specific precision may have different C types depending
+        on the platform. The default value is 'np.float32'.
+    ctx : Context, optional
+        Device context of output. Default is current context.
+    out : symbol, optional
+        The output symbol (default is `None`).
+
+    Returns
+    -------
+    out : _Symbol
+        Drawn samples from the parameterized bernoulli distribution.
+
+    Examples
+    --------
+    >>> prob = np.random.uniform(size=(4,4))
+    >>> logit = np.log(prob) - np.log(1 - prob)
+    >>> npx.random.bernoulli(logit=logit)
+    array([[0., 1., 1., 1.],
+        [0., 1., 1., 1.],
+        [0., 1., 0., 0.],
+        [1., 0., 1., 0.]])
+
+    >>> npx.random.bernoulli(prob=prob)
+    array([[0., 1., 0., 1.],
+        [1., 1., 1., 1.],
+        [1., 1., 1., 0.],
+        [1., 0., 1., 0.]])
+    """
+    from ..numpy import _Symbol as np_symbol
+    tensor_type_name = np_symbol
+    if (prob is None) == (logit is None):
+        raise ValueError(
+            "Either `prob` or `logit` must be specified, but not both. " +
+            "Received prob={}, logit={}".format(prob, logit))
+    if dtype is None:
+        dtype = 'float32'
+    if ctx is None:
+        ctx = current_context()
+    if size == ():
+        size = None
+    if prob is not None:
+        is_tensor = isinstance(prob, tensor_type_name)
+        if is_tensor:
+            return _npi.bernoulli(prob, prob=None, logit=None, is_logit=False,
+                                  size=size, ctx=ctx, dtype=dtype, out=out)
+        else:
+            return _npi.bernoulli(prob=prob, logit=None, is_logit=False,
+                                  size=size, ctx=ctx, dtype=dtype, out=out)
+    else:
+        is_tensor = isinstance(logit, tensor_type_name)
+        if is_tensor:
+            return _npi.bernoulli(logit, prob=None, logit=None, is_logit=True,
+                                  size=size, ctx=ctx, dtype=dtype, out=out)
+        else:
+            return _npi.bernoulli(prob=None, logit=logit, is_logit=True,
+                                  size=size, ctx=ctx, dtype=dtype, out=out)
diff --git a/src/operator/numpy/random/dist_common.h b/src/operator/numpy/random/dist_common.h
index a3c48f5b4d5c..199b55b9afb3 100644
--- a/src/operator/numpy/random/dist_common.h
+++ b/src/operator/numpy/random/dist_common.h
@@ -27,7 +27,6 @@
 #ifndef MXNET_OPERATOR_NUMPY_RANDOM_DIST_COMMON_H_
 #define MXNET_OPERATOR_NUMPY_RANDOM_DIST_COMMON_H_
 
-#include <mshadow/base.h>
 #include <mxnet/operator_util.h>
 #include <algorithm>
 #include <string>
@@ -172,10 +171,36 @@ inline bool TwoparamsDistOpShape(const nnvm::NodeAttrs &attrs,
     } else if (in_attrs->size() == 0) {
       // Two scalar case.
       SHAPE_ASSIGN_CHECK(*out_attrs, 0, TShape(0, -1))
-      return true;
     }
   }
-  return out_attrs->at(0).ndim() != 0U;
+  return shape_is_known(out_attrs->at(0));
+}
+
+template <typename DistParam>
+inline bool UnaryDistOpShape(const nnvm::NodeAttrs &attrs,
+                             std::vector<TShape> *in_attrs,
+                             std::vector<TShape> *out_attrs) {
+  const DistParam &param = nnvm::get<DistParam>(attrs.parsed);
+  if (param.size.has_value()) {
+    // Size declared.
+    std::vector<dim_t> oshape_vec;
+    const mxnet::Tuple<int> &size = param.size.value();
+    for (int i = 0; i < size.ndim(); ++i) {
+      oshape_vec.emplace_back(size[i]);
+    }
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, TShape(oshape_vec));
+    for (size_t input_idx = 0; input_idx < in_attrs->size(); input_idx++) {
+      CheckBroadcastable((*in_attrs)[input_idx], (*out_attrs)[0]);
+    }
+  } else {
+    if (in_attrs->size() == 1U) {
+      // One param from ndarray.
+      SHAPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0))
+    } else {
+      SHAPE_ASSIGN_CHECK(*out_attrs, 0, TShape(0, -1))
+    }
+  }
+  return shape_is_known(out_attrs->at(0));
 }
 
 }  // namespace op
diff --git a/src/operator/numpy/random/np_bernoulli_op.cc b/src/operator/numpy/random/np_bernoulli_op.cc
new file mode 100644
index 000000000000..d67ad1b8d7f6
--- /dev/null
+++ b/src/operator/numpy/random/np_bernoulli_op.cc
@@ -0,0 +1,69 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_bernoulli_op.cc
+ * \brief Operator for numpy sampling from bernoulli distributions
+ */
+
+#include "./np_bernoulli_op.h"
+#include "./dist_common.h"
+
+namespace mxnet {
+namespace op {
+
+DMLC_REGISTER_PARAMETER(NumpyBernoulliParam);
+
+NNVM_REGISTER_OP(_npi_bernoulli)
+.set_num_inputs(
+  [](const nnvm::NodeAttrs& attrs) {
+    const NumpyBernoulliParam& param = nnvm::get<NumpyBernoulliParam>(attrs.parsed);
+    int num_inputs = 1;
+    if (param.logit.has_value() || param.prob.has_value()) {
+      num_inputs -= 1;
+    }
+    return num_inputs;
+  }
+)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    const NumpyBernoulliParam& param = nnvm::get<NumpyBernoulliParam>(attrs.parsed);
+    int num_inputs = 1;
+    if (param.logit.has_value() || param.prob.has_value()) {
+      num_inputs -= 1;
+    }
+    return (num_inputs == 0) ? std::vector<std::string>() : std::vector<std::string>{"input1"};
+  })
+.set_attr_parser(ParamParser<NumpyBernoulliParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", UnaryDistOpShape<NumpyBernoulliParam>)
+.set_attr<nnvm::FInferType>("FInferType", NumpyBernoulliOpType)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const nnvm::NodeAttrs& attrs) {
+      return std::vector<ResourceRequest>{
+        ResourceRequest::kRandom, ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", NumpyBernoulliForward<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.add_argument("input1", "NDArray-or-Symbol", "Source input")
+.add_arguments(NumpyBernoulliParam::__FIELDS__());
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/random/np_bernoulli_op.cu b/src/operator/numpy/random/np_bernoulli_op.cu
new file mode 100644
index 000000000000..a73bf9929db3
--- /dev/null
+++ b/src/operator/numpy/random/np_bernoulli_op.cu
@@ -0,0 +1,35 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_bernoulli_op.cu
+ * \brief Operator for numpy sampling from bernoulli distributions
+ */
+
+#include "./np_bernoulli_op.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(_npi_bernoulli)
+.set_attr<FCompute>("FCompute<gpu>", NumpyBernoulliForward<gpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/random/np_bernoulli_op.h b/src/operator/numpy/random/np_bernoulli_op.h
new file mode 100644
index 000000000000..aa8e344e2842
--- /dev/null
+++ b/src/operator/numpy/random/np_bernoulli_op.h
@@ -0,0 +1,200 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_bernoulli_op.h
+ * \brief Operator for numpy sampling from bernoulli distribution.
+ */
+#ifndef MXNET_OPERATOR_NUMPY_RANDOM_NP_BERNOULLI_OP_H_
+#define MXNET_OPERATOR_NUMPY_RANDOM_NP_BERNOULLI_OP_H_
+
+#include <mxnet/operator_util.h>
+#include <algorithm>
+#include <string>
+#include <vector>
+#include "../../elemwise_op_common.h"
+#include "../../mshadow_op.h"
+#include "../../mxnet_op.h"
+#include "../../operator_common.h"
+#include "../../tensor/elemwise_binary_broadcast_op.h"
+#include "./dist_common.h"
+
+namespace mxnet {
+namespace op {
+
+struct NumpyBernoulliParam : public dmlc::Parameter<NumpyBernoulliParam> {
+  dmlc::optional<float> prob;
+  dmlc::optional<float> logit;
+  std::string ctx;
+  int dtype;
+  bool is_logit;
+  dmlc::optional<mxnet::Tuple<int>> size;
+  DMLC_DECLARE_PARAMETER(NumpyBernoulliParam) {
+    DMLC_DECLARE_FIELD(prob);
+    DMLC_DECLARE_FIELD(logit);
+    DMLC_DECLARE_FIELD(size)
+        .set_default(dmlc::optional<mxnet::Tuple<int>>())
+        .describe(
+            "Output shape. If the given shape is, "
+            "e.g., (m, n, k), then m * n * k samples are drawn. "
+            "Default is None, in which case a single value is returned.");
+    DMLC_DECLARE_FIELD(ctx).set_default("cpu").describe(
+        "Context of output, in format [cpu|gpu|cpu_pinned](n)."
+        " Only used for imperative calls.");
+    DMLC_DECLARE_FIELD(dtype)
+        .add_enum("uint8", mshadow::kUint8)
+        .add_enum("int32", mshadow::kInt32)
+        .add_enum("float32", mshadow::kFloat32)
+        .add_enum("float64", mshadow::kFloat64)
+        .add_enum("float16", mshadow::kFloat16)
+        .add_enum("bool", mshadow::kBool)
+        .set_default(mshadow::kFloat32)
+        .describe(
+            "DType of the output in case this can't be inferred. "
+            "Defaults to float32 if not defined (dtype=None).");
+    DMLC_DECLARE_FIELD(is_logit);
+  }
+};
+
+inline bool NumpyBernoulliOpType(const nnvm::NodeAttrs &attrs,
+                               std::vector<int> *in_attrs,
+                               std::vector<int> *out_attrs) {
+  const NumpyBernoulliParam &param = nnvm::get<NumpyBernoulliParam>(attrs.parsed);
+  int otype = param.dtype;
+  (*out_attrs)[0] = otype;
+  return true;
+}
+
+namespace mxnet_op {
+
+struct prob_to_logit {
+  MSHADOW_XINLINE static void Map(index_t i, float* uniforms) {
+    float prob = uniforms[i];
+    uniforms[i] = log(prob) - log(1 - prob);
+  }
+};
+
+template <int ndim, typename IType, typename OType>
+struct bernoulli_kernel {
+  MSHADOW_XINLINE static void Map(index_t i,
+                                  const Shape<ndim> &stride,
+                                  const Shape<ndim> &oshape,
+                                  IType *inputs, float* threshold, OType *out) {
+    Shape<ndim> coord = unravel(i, oshape);
+    auto idx = static_cast<index_t>(dot(coord, stride));
+    out[i] =  inputs[idx] > threshold[i] ? OType(1) : OType(0);
+  }
+};
+
+template <typename OType>
+struct scalar_bernoulli_kernel {
+  MSHADOW_XINLINE static void Map(index_t i, float inputs, float *threshold,
+                                  OType *out) {
+    out[i] = inputs > threshold[i] ? OType(1) : OType(0);
+  }
+};
+
+template <typename IType>
+struct check_legal_prob_kernel {
+  MSHADOW_XINLINE static void Map(index_t i, IType *scalar, float* flag) {
+    if (scalar[i] < 0.0 || scalar[i] > 1.0) {
+      flag[0] = -1.0;
+    }
+  }
+};
+
+}  // namespace mxnet_op
+
+template <typename xpu>
+void NumpyBernoulliForward(const nnvm::NodeAttrs &attrs,
+                         const OpContext &ctx,
+                         const std::vector<TBlob> &inputs,
+                         const std::vector<OpReqType> &req,
+                         const std::vector<TBlob> &outputs) {
+  using namespace mshadow;
+  using namespace mxnet_op;
+  const NumpyBernoulliParam &param = nnvm::get<NumpyBernoulliParam>(attrs.parsed);
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  index_t output_len = outputs[0].Size();
+  Random<xpu, float> *prnd = ctx.requested[0].get_random<xpu, float>(s);
+  Tensor<xpu, 1, float> workspace =
+      ctx.requested[1].get_space_typed<xpu, 1, float>(Shape1(output_len + 1), s);
+  Tensor<xpu, 1, float> uniform_tensor = workspace.Slice(0, output_len);
+  Tensor<xpu, 1, float> indicator_device = workspace.Slice(output_len, output_len + 1);
+  float indicator_host = 1.0;
+  float *indicator_device_ptr = indicator_device.dptr_;
+  Kernel<set_zero, xpu>::Launch(s, 1, indicator_device_ptr);
+  prnd->SampleUniform(&uniform_tensor, 0.0, 1.0);
+  if (param.prob.has_value()) {
+    // scalar prob input
+    CHECK_LE(param.prob.value(), 1.0) << "ValueError: expect probs >= 0 && probs <= 1";
+    CHECK_GE(param.prob.value(), 0.0) << "ValueError: expect probs >= 0 && probs <= 1";
+    MSHADOW_TYPE_SWITCH_WITH_BOOL(outputs[0].type_flag_, OType, {
+      Kernel<scalar_bernoulli_kernel<OType>, xpu>::Launch(
+        s, outputs[0].Size(), param.prob.value(),
+        uniform_tensor.dptr_, outputs[0].dptr<OType>());
+    });
+  } else if (param.logit.has_value()) {
+    // scalar logit input
+    // sigmoid(x) > u  <=> x > logit(u)
+    Kernel<prob_to_logit, xpu>::Launch(s, outputs[0].Size(),
+                                         uniform_tensor.dptr_);
+    MSHADOW_TYPE_SWITCH_WITH_BOOL(outputs[0].type_flag_, OType, {
+      Kernel<scalar_bernoulli_kernel<OType>, xpu>::Launch(
+        s, outputs[0].Size(), param.logit.value(),
+        uniform_tensor.dptr_, outputs[0].dptr<OType>());
+    });
+  } else {
+    if (param.is_logit) {
+      // tensor logit input
+      Kernel<prob_to_logit, xpu>::Launch(s, outputs[0].Size(),
+                                         uniform_tensor.dptr_);
+    } else {
+      // tensor prob input
+      // sigmoid(x) > u  <=> x > logit(u)
+      MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, IType, {
+        Kernel<check_legal_prob_kernel<IType>, xpu>::Launch(
+            s, inputs[0].Size(), inputs[0].dptr<IType>(), indicator_device_ptr);
+      });
+      _copy<xpu>(&indicator_host, indicator_device_ptr);
+      CHECK_GE(indicator_host, 0.0)
+          << "ValueError: expect probs >= 0 && probs <= 1";
+    }
+    mxnet::TShape new_lshape, new_oshape;
+    int ndim = FillShape(inputs[0].shape_, inputs[0].shape_, outputs[0].shape_,
+                         &new_lshape, &new_lshape, &new_oshape);
+    MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, IType, {
+      MSHADOW_TYPE_SWITCH_WITH_BOOL(outputs[0].type_flag_, OType, {
+        BROADCAST_NDIM_SWITCH(ndim, NDim, {
+          Shape<NDim> oshape = new_oshape.get<NDim>();
+          Shape<NDim> stride = calc_stride(new_lshape.get<NDim>());
+          Kernel<bernoulli_kernel<NDim, IType, OType>, xpu>::Launch(
+              s, outputs[0].Size(), stride, oshape, inputs[0].dptr<IType>(),
+              uniform_tensor.dptr_, outputs[0].dptr<OType>());
+        });
+      });
+    });
+  }
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_NUMPY_RANDOM_NP_BERNOULLI_OP_H_
diff --git a/src/operator/numpy/random/np_normal_op.h b/src/operator/numpy/random/np_normal_op.h
index 678f0ec2fb47..deb603ac5270 100644
--- a/src/operator/numpy/random/np_normal_op.h
+++ b/src/operator/numpy/random/np_normal_op.h
@@ -158,6 +158,7 @@ void NumpyNormalForward(const nnvm::NodeAttrs &attrs,
   Tensor<xpu, 1, float> indicator_device = workspace.Slice(output_len, output_len + 1);
   float indicator_host = 1.0;
   float *indicator_device_ptr = indicator_device.dptr_;
+  Kernel<set_zero, xpu>::Launch(s, 1, indicator_device_ptr);
   prnd->SampleGaussian(&normal_tensor, 0.0, 1.0);
   mxnet::TShape new_lshape, new_hshape, new_oshape;
 
diff --git a/src/operator/numpy/random/np_uniform_op.h b/src/operator/numpy/random/np_uniform_op.h
index 580dc5d05eaa..1df0c39d4e57 100644
--- a/src/operator/numpy/random/np_uniform_op.h
+++ b/src/operator/numpy/random/np_uniform_op.h
@@ -25,7 +25,6 @@
 #ifndef MXNET_OPERATOR_NUMPY_RANDOM_NP_UNIFORM_OP_H_
 #define MXNET_OPERATOR_NUMPY_RANDOM_NP_UNIFORM_OP_H_
 
-#include <mshadow/base.h>
 #include <mxnet/operator_util.h>
 #include <algorithm>
 #include <string>
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index d7a775e38792..9aabdfd4cabc 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -2484,6 +2484,41 @@ def hybrid_forward(self, F, x):
                     assert_almost_equal(mx_ret.asnumpy(), np_ret, atol=1e-4, rtol=1e-3, use_broadcast=False)
 
 
+@with_seed()
+@use_np
+def test_npx_random_bernoulli():
+    def _test_bernoulli_exception(prob, logit):
+        output = npx.random.bernoulli(prob=prob, logit=logit).asnumpy()
+
+    shapes = [(), (1,), (2, 3), (4, 0, 5), 6, (7, 8), None]
+    dtypes = ['float16', 'float32', 'float64', 'int32', 'bool']
+    for shape, dtype in itertools.product(shapes, dtypes):
+        prob = np.random.uniform(size=shape)
+        logit = np.log(prob) - np.log(1 - prob)
+        expected_shape = shape
+        if not isinstance(shape, tuple):
+            expected_shape = () if shape is None else (shape,)
+        out_prob = npx.random.bernoulli(prob=prob, size=shape, dtype=dtype)
+        assert out_prob.shape == expected_shape
+        assert int((out_prob.asnumpy() == 0).sum() + (out_prob.asnumpy() == 1).sum()) == out_prob.size
+        out_logit = npx.random.bernoulli(logit=logit, size=shape, dtype=dtype)
+        assert out_logit.shape == expected_shape
+        assert int((out_logit.asnumpy() == 0).sum() + (out_logit.asnumpy() == 1).sum()) == out_logit.size
+        # Test Exception.
+        assertRaises(ValueError, _test_bernoulli_exception, prob, logit)
+        if prob.size > 0:
+            # larger than 1
+            assertRaises(MXNetError, _test_bernoulli_exception, prob + 2.0, None)
+            # smaller than 0
+            assertRaises(MXNetError, _test_bernoulli_exception, prob - 2.0, None)
+            # mixed case
+            low, high = (-1.0, 2.0)
+            # uniform(-1, 2)
+            scaled_prob = low + (high - low) * prob
+            if not ((scaled_prob.asnumpy() >= 0).all() and (scaled_prob.asnumpy() <= 1).all()):
+                assertRaises(MXNetError, _test_bernoulli_exception, scaled_prob, None)
+
+
 @with_seed()
 @use_np
 def test_np_random():

From 3885bbed8c526c5e89ea87ff10457b3dc0e6e7ea Mon Sep 17 00:00:00 2001
From: chinakook <chinakook@msn.com>
Date: Tue, 12 Nov 2019 19:03:10 +0800
Subject: [PATCH 585/813] [Done] BilinearResize2D optimized (#16292)

* Add align_corners parameter to bilinear resize op.

* Add align_corners parameter to bilinear resize op.

add BilinearResize2D test

fix BilinearResize2D align_corners test

fix code layout

fix resize gpu and cpu consistency

fix bilinear reisze test bug

close transforms.resize test

* optimize BilinearResize2D backward performance; improve fp16 performance

* optimize BilinearResize2D forward kernel

* lint

* fix forceinline and add

* lint

* remove commented out code and remove transforms.Resize gpu test

* Dtype to float in cpu implementation

* retrigger CI

* retrigger CI

* Update resize.cu

* retrigger CI

* Update resize.cu

fix resize.cu
---
 python/mxnet/gluon/data/vision/transforms.py |   8 +-
 src/operator/contrib/bilinear_resize-inl.cuh | 201 +++++++++++-----
 src/operator/contrib/bilinear_resize-inl.h   |  82 ++++++-
 src/operator/contrib/bilinear_resize.cc      |  39 ++--
 src/operator/contrib/bilinear_resize.cu      | 230 ++++++++++++++-----
 src/operator/image/resize-inl.h              |   5 +-
 src/operator/image/resize.cu                 |  13 +-
 tests/python/gpu/test_gluon_transforms.py    |  15 +-
 tests/python/gpu/test_operator_gpu.py        |  14 +-
 tests/python/unittest/test_operator.py       |  34 ++-
 10 files changed, 457 insertions(+), 184 deletions(-)

diff --git a/python/mxnet/gluon/data/vision/transforms.py b/python/mxnet/gluon/data/vision/transforms.py
index d2c7f906caac..935ce2738a6f 100644
--- a/python/mxnet/gluon/data/vision/transforms.py
+++ b/python/mxnet/gluon/data/vision/transforms.py
@@ -257,9 +257,7 @@ class CropResize(HybridBlock):
         interpolation. See OpenCV's resize function for available choices.
         https://docs.opencv.org/2.4/modules/imgproc/doc/geometric_transformations.html?highlight=resize#resize
         Note that the Resize on gpu use contrib.bilinearResize2D operator
-        which only support bilinear interpolation(1). The result would be slightly
-        different on gpu compared to cpu. OpenCV tend to align center while bilinearResize2D
-        use algorithm which aligns corner.
+        which only support bilinear interpolation(1).
 
 
     Inputs:
@@ -348,9 +346,7 @@ class Resize(HybridBlock):
         Interpolation method for resizing. By default uses bilinear
         interpolation. See OpenCV's resize function for available choices.
         Note that the Resize on gpu use contrib.bilinearResize2D operator
-        which only support bilinear interpolation(1). The result would be slightly
-        different on gpu compared to cpu. OpenCV tend to align center while bilinearResize2D
-        use algorithm which aligns corner.
+        which only support bilinear interpolation(1).
 
 
     Inputs:
diff --git a/src/operator/contrib/bilinear_resize-inl.cuh b/src/operator/contrib/bilinear_resize-inl.cuh
index 0f1605549d0b..b82a71e3ce4e 100644
--- a/src/operator/contrib/bilinear_resize-inl.cuh
+++ b/src/operator/contrib/bilinear_resize-inl.cuh
@@ -42,7 +42,7 @@ enum ImageLayout {
 
 template<typename In, typename Out>
 struct ScalarConvert {
-  static __host__ __device__ __forceinline__ Out to(const In v) { return (Out) v; }
+  static __host__ __device__ MSHADOW_FORCE_INLINE Out to(const In v) { return (Out) v; }
 };
 
 // The maximum number of threads in a block
@@ -60,21 +60,64 @@ static unsigned getNumThreads(int nElem, const bool smaller) {
   return smaller ? (MAX_BLOCK_SIZE >> 1) : MAX_BLOCK_SIZE;
 }
 
-// caffe_gpu_interp2_kernel overloading with Tensor<xpu, 3, DType>
+__device__ MSHADOW_FORCE_INLINE size_t
+idx(const size_t nc,
+  const size_t height,
+  const size_t width,
+  const size_t y,
+  const size_t x) {
+  return (nc * height + y) * width + x;
+}
+
+template <typename Acctype>
+__host__ MSHADOW_FORCE_INLINE static Acctype cu_area_pixel_compute_scale(
+  int input_size,
+  int output_size,
+  bool align_corners) {
+  if (output_size > 1) {
+    return align_corners
+      ? (Acctype)(input_size - 1) / (output_size - 1)
+      : (Acctype)input_size / output_size;
+  } else {
+    return static_cast<Acctype>(0);
+  }
+}
+
+template <typename Acctype>
+__device__ MSHADOW_FORCE_INLINE static Acctype cu_area_pixel_compute_source_index(
+  Acctype scale,
+  int dst_index,
+  bool align_corners,
+  bool cubic) {
+  if (align_corners) {
+    return scale * dst_index;
+  } else {
+    Acctype src_idx = scale * (dst_index + static_cast<Acctype>(0.5)) -
+      static_cast<Acctype>(0.5);
+    // See Note[Follow Opencv resize logic]
+    return (!cubic && src_idx < static_cast<Acctype>(0))
+      ? static_cast<Acctype>(0)
+      : src_idx;
+  }
+}
+
+// caffe_gpu_interp2_kernel overloading with Tensor<xpu, 4, DType> for NHWC layout
 template<typename xpu, typename Dtype, typename Acctype>
 __global__ void
 __launch_bounds__(cuda::kMaxThreadsPerBlock, 1)
 caffe_gpu_interp2_kernel(const int n,
     const Acctype rheight, const Acctype rwidth,
-    const Tensor<xpu, 3, Dtype> data1,
-    Tensor<xpu, 3, Dtype> data2,
-    ImageLayout layout) {
+  const bool align_corners,
+    const Tensor<xpu, 4, Dtype> data1,
+    Tensor<xpu, 4, Dtype> data2) {
+
   int index = threadIdx.x + blockIdx.x * blockDim.x;
-  const int channels = data1.size(2);
-  const int height1 = data1.size(0);
-  const int width1 = data1.size(1);
-  const int height2 = data2.size(0);
-  const int width2 = data2.size(1);
+  const int batch_size = data1.size(0);
+  const int height1 = data1.size(1);
+  const int width1 = data1.size(2);
+  const int height2 = data2.size(1);
+  const int width2 = data2.size(2);
+  const int channels = data1.size(3);
 
   if (index < n) {
     const int w2 = index % width2;  // 0:width2-1
@@ -83,50 +126,55 @@ caffe_gpu_interp2_kernel(const int n,
     if (height1 == height2 && width1 == width2) {
       const int h1 = h2;
       const int w1 = w2;
-      for (int c = 0; c < channels; ++c) {
-        const Dtype val = data1[h1][w1][c];
-        data2[h2][w2][c] = val;
-      }
+    for (int b = 0; b < batch_size; ++b) {
+    for (int c = 0; c < channels; ++c) {
+      const Dtype val = data1[b][h1][w1][c];
+      data2[b][h2][w2][c] = val;
+    }
+    }
       return;
     }
     //
-    const Acctype h1r = rheight * h2;
+    const Acctype h1r = cu_area_pixel_compute_source_index<Acctype>(
+    rheight, h2, align_corners, /*cubic=*/false);
     const int h1 = h1r;
     const int h1p = (h1 < height1 - 1) ? 1 : 0;
     const Acctype h1lambda = h1r - h1;
     const Acctype h0lambda = Acctype(1) - h1lambda;
     //
-    const Acctype w1r = rwidth * w2;
+    const Acctype w1r = cu_area_pixel_compute_source_index<Acctype>(
+    rwidth, w2, align_corners, /*cubic=*/false);
     const int w1 = w1r;
     const int w1p = (w1 < width1 - 1) ? 1 : 0;
     const Acctype w1lambda = w1r - w1;
     const Acctype w0lambda = Acctype(1) - w1lambda;
+  for (int b = 0; b < batch_size; ++b) {
     for (int c = 0; c < channels; ++c) {
-      const Acctype val = h0lambda * (w0lambda * data1[h1][w1][c]
-                            + w1lambda * data1[h1][w1+w1p][c])
-                            + h1lambda * (w0lambda * data1[h1+h1p][w1][c]
-                            + w1lambda * data1[h1+h1p][w1+w1p][c]);
-      data2[h2][w2][c] = ScalarConvert<Acctype, Dtype>::to(val);
+      const Acctype val = h0lambda * (w0lambda * data1[b][h1][w1][c]
+                        + w1lambda * data1[b][h1][w1 + w1p][c])
+                        + h1lambda * (w0lambda * data1[b][h1 + h1p][w1][c]
+                        + w1lambda * data1[b][h1 + h1p][w1 + w1p][c]);
+      data2[b][h2][w2][c] = ScalarConvert<Acctype, Dtype>::to(val);
     }
   }
+  }
 }
 
-// caffe_gpu_interp2_kernel overloading with Tensor<xpu, 4, DType>
+// caffe_gpu_interp2_kernel overloading with Tensor<xpu, 3, DType> for HWC layout
 template<typename xpu, typename Dtype, typename Acctype>
-__global__ void
+__global__ void 
 __launch_bounds__(cuda::kMaxThreadsPerBlock, 1)
 caffe_gpu_interp2_kernel(const int n,
-    const Acctype rheight, const Acctype rwidth,
-    const Tensor<xpu, 4, Dtype> data1,
-    Tensor<xpu, 4, Dtype> data2,
-    ImageLayout layout) {
+  const Acctype rheight, const Acctype rwidth,
+  const bool align_corners,
+  const Tensor<xpu, 3, Dtype> data1,
+  Tensor<xpu, 3, Dtype> data2) {
   int index = threadIdx.x + blockIdx.x * blockDim.x;
-  int batch_size = (layout == NHWC) ? data1.size(0) : data1.size(0);
-  int channels = (layout == NHWC) ? data1.size(3) : data1.size(1);
-  int height1 = (layout == NHWC) ? data1.size(1) : data1.size(2);
-  int width1 = (layout == NHWC) ? data1.size(2) : data1.size(3);
-  int height2 = (layout == NHWC) ? data2.size(1) : data2.size(2);
-  int width2 = (layout == NHWC) ? data2.size(2): data2.size(3);
+  const int height1 = data1.size(0);
+  const int width1 = data1.size(1);
+  const int channels = data1.size(2);
+  const int height2 = data2.size(0);
+  const int width2 = data2.size(1);
 
   if (index < n) {
     const int w2 = index % width2;  // 0:width2-1
@@ -135,54 +183,83 @@ caffe_gpu_interp2_kernel(const int n,
     if (height1 == height2 && width1 == width2) {
       const int h1 = h2;
       const int w1 = w2;
-
-      for (int n = 0; n < batch_size; ++n) {
         for (int c = 0; c < channels; ++c) {
-          if (layout == NHWC) {
-            const Dtype val = data1[n][h1][w1][c];
-            data2[n][h2][w2][c] = val;
-          } else {
-            const Dtype val = data1[n][c][h1][w1];
-            data2[n][c][h2][w2] = val;
-          }
+          const Dtype val = data1[h1][w1][c];
+          data2[h2][w2][c] = val;
         }
-      }
       return;
     }
     //
-    const Acctype h1r = rheight * h2;
+    const Acctype h1r = cu_area_pixel_compute_source_index<Acctype>(
+      rheight, h2, align_corners, /*cubic=*/false);
     const int h1 = h1r;
     const int h1p = (h1 < height1 - 1) ? 1 : 0;
     const Acctype h1lambda = h1r - h1;
     const Acctype h0lambda = Acctype(1) - h1lambda;
     //
-    const Acctype w1r = rwidth * w2;
+    const Acctype w1r = cu_area_pixel_compute_source_index<Acctype>(
+      rwidth, w2, align_corners, /*cubic=*/false);
     const int w1 = w1r;
     const int w1p = (w1 < width1 - 1) ? 1 : 0;
     const Acctype w1lambda = w1r - w1;
     const Acctype w0lambda = Acctype(1) - w1lambda;
-
-    for (auto n = 0; n < batch_size; ++n) {
       for (int c = 0; c < channels; ++c) {
-        if (layout == NHWC) {
-          const Acctype val = h0lambda * (w0lambda * data1[n][h1][w1][c]
-                            + w1lambda * data1[n][h1][w1+w1p][c])
-                            + h1lambda * (w0lambda * data1[n][h1+h1p][w1][c]
-                            + w1lambda * data1[n][h1+h1p][w1+w1p][c]);
-          data2[n][h2][w2][c] = ScalarConvert<Acctype, Dtype>::to(val);
-        } else {
-          const Acctype val = h0lambda * (w0lambda * data1[n][c][h1][w1]
-                            + w1lambda * data1[n][c][h1][w1+w1p])
-                            + h1lambda * (w0lambda * data1[n][c][h1+h1p][w1]
-                            + w1lambda * data1[n][c][h1+h1p][w1+w1p]);
-          data2[n][c][h2][w2] = ScalarConvert<Acctype, Dtype>::to(val);
-        }
+        const Acctype val = h0lambda * (w0lambda * data1[h1][w1][c]
+                          + w1lambda * data1[h1][w1 + w1p][c])
+                          + h1lambda * (w0lambda * data1[h1 + h1p][w1][c]
+                          + w1lambda * data1[h1 + h1p][w1 + w1p][c]);
+        data2[h2][w2][c] = ScalarConvert<Acctype, Dtype>::to(val);
       }
-    }
   }
 }
 
+// caffe_gpu_interp2_kernel overloading with Tensor<xpu, 4, DType>
+template<typename xpu, typename Dtype, typename Acctype>
+__global__ void caffe_gpu_interp2_kernel(
+  const size_t nc,
+  const int height1,
+  const int width1,
+  const int height2,
+  const int width2,
+    const Acctype rheight,
+  const Acctype rwidth,
+  const bool align_corners,
+  const Dtype* __restrict__ idata,
+  Dtype* __restrict__ odata) {
+  const size_t i_numel = nc * width1 * height1;
+  const size_t o_numel = nc * width2 * height2;
+  for (size_t index = blockDim.x * blockIdx.x + threadIdx.x; index < o_numel;
+    index += blockDim.x * gridDim.x) {
+    size_t index_temp = index;
+    const int w2 = index_temp % width2;  // 0:width2-1
+    index_temp /= width2;
+    const int h2 = index_temp % height2;  // 0:height2-1
+    const size_t nc = index_temp / height2;
+
+    const Acctype h1r = cu_area_pixel_compute_source_index<Acctype>(
+      rheight, h2, align_corners, /*cubic=*/false);
+    const int h1 = h1r;
+    const int h1p = (h1 < height1 - 1) ? 1 : 0;
+    const Acctype h1lambda = h1r - h1;
+    const Acctype h0lambda = static_cast<Acctype>(1) - h1lambda;
+    //
+    const Acctype w1r = cu_area_pixel_compute_source_index<Acctype>(
+      rwidth, w2, align_corners, /*cubic=*/false);
+    const int w1 = w1r;
+    const int w1p = (w1 < width1 - 1) ? 1 : 0;
+    const Acctype w1lambda = w1r - w1;
+    const Acctype w0lambda = static_cast<Acctype>(1) - w1lambda;
+
+        const Acctype val = h0lambda * (w0lambda * *(idata + idx(nc, height1, width1, h1, w1))
+                          + w1lambda * *(idata + idx(nc, height1, width1, h1, w1 + w1p)))
+                          + h1lambda * (w0lambda * *(idata + idx(nc, height1, width1, h1 + h1p, w1))
+                          + w1lambda * *(idata + idx(nc, height1, width1, h1 + h1p, w1 + w1p)));
+        *(odata + idx(nc, height2, width2, h2, w2)) = ScalarConvert<Acctype, Dtype>::to(val);
+  }
+}
+
+
 }  // namespace op
 }  // namespace mxnet
 
-#endif  // MXNET_OPERATOR_CONTRIB_BILINEAR_RESIZE_CUH_
\ No newline at end of file
+#endif  // MXNET_OPERATOR_CONTRIB_BILINEAR_RESIZE_CUH_
diff --git a/src/operator/contrib/bilinear_resize-inl.h b/src/operator/contrib/bilinear_resize-inl.h
index 4da12cbdf280..2167f2558a05 100644
--- a/src/operator/contrib/bilinear_resize-inl.h
+++ b/src/operator/contrib/bilinear_resize-inl.h
@@ -59,6 +59,7 @@ struct BilinearSampleParam : public dmlc::Parameter<BilinearSampleParam> {
   dmlc::optional<float> scale_height;
   dmlc::optional<float> scale_width;
   int mode;
+  bool align_corners;
   DMLC_DECLARE_PARAMETER(BilinearSampleParam) {
     DMLC_DECLARE_FIELD(height).set_default(1).set_range(1, 10000)
     .describe("output height (required, but ignored if scale_height is defined or mode is not "
@@ -97,9 +98,63 @@ struct BilinearSampleParam : public dmlc::Parameter<BilinearSampleParam> {
               "(if original height is odd then result height = original height - 1);"
               "\"to_odd_up\" - resize input to nearest odd height and width "
               "(if original height is odd then result height = original height + 1);");
+  DMLC_DECLARE_FIELD(align_corners).set_default(true)
+    .describe("With align_corners = True, the interpolating doesn't proportionally align the"
+              "output and input pixels, and thus the output values can depend on the input size.");
   }
 };
 
+template <typename DType>
+static inline DType area_pixel_compute_scale(
+  int64_t input_size,
+  int64_t output_size,
+  bool align_corners) {
+  /* We view each pixel as an area, idx + 0.5 as its center index.
+   * Here is an example formula in 1D case.
+   * if align_corners: center of two corner pixel areas are preserved,
+   *     (0.5, 0.5) -> (0.5, 0.5),
+   *     (input_size - 0.5, 0.5) -> (output_size - 0.5)
+   *     scale = (input_size - 0.5 - 0.5) / (output_size - 0.5 - 0.5)
+   *     src_index + 0.5 - 0.5 = scale * (dst_index + 0.5 - 0.5)
+   * if not align_corners: the whole range is scaled accordingly
+   *     scale = input_size / output_size
+   *     src_idx + 0.5 = scale * (dst_index + 0.5)
+   */
+  if (output_size > 1) {
+    return align_corners
+      ? static_cast<DType>(input_size - 1) / (output_size - 1)
+      : static_cast<DType>(input_size) / output_size;
+  } else {
+    return DType(0);
+  }
+}
+
+template <typename DType>
+static inline DType area_pixel_compute_source_index(
+  DType scale,
+  int64_t dst_index,
+  bool align_corners,
+  bool cubic) {
+  if (align_corners) {
+    return scale * dst_index;
+  } else {
+    DType src_idx = scale * (dst_index + 0.5) - 0.5;
+    // [Note] Follow Opencv resize logic:
+    // We allow negative src_idx here and later will use
+    //   dx = src_idx - floorf(src_idx)
+    // to compute the "distance"(which affects weights).
+    // For linear modes, weight distribution doesn't matter
+    // for negative indices as they use 2 pixels to interpolate.
+    // For example, [-1, 0], they both use pixel 0 value so it
+    // doesn't affect if we bound the src_idx to 0 or not.
+    // TODO(chinakook): Our current linear mode impls use unbound indices
+    // where we should and then remove this cubic flag.
+    // This matters in cubic mode, as we might need [-1, 0, 1, 2]
+    // to interpolate and the weights can be affected.
+    return (!cubic && src_idx < 0) ? DType(0) : src_idx;
+  }
+}
+
 static inline bool IsWriting(const OpReqType ort) {
   return ort == kWriteTo || ort == kWriteInplace;
 }
@@ -107,25 +162,29 @@ static inline bool IsWriting(const OpReqType ort) {
 template<typename xpu, typename DType, typename AccReal>
 void SpatialUpSamplingBilinearUpdateOutput(mshadow::Stream<cpu> *s,
                                            const std::vector<TBlob> &input,
-                                           const std::vector<TBlob> &output);
+                                           const std::vector<TBlob> &output,
+                                           bool align_corners);
 
 template<typename xpu, typename DType, typename AccReal>
 void SpatialUpSamplingBilinearUpdateGradInput(mshadow::Stream<cpu> *s,
                                               const std::vector<TBlob> &input,
                                               const std::vector<TBlob> &output,
-                                              bool modeLike);
+                                              bool modeLike,
+                                              bool align_corners);
 
 #if MXNET_USE_CUDA
 template<typename xpu, typename DType, typename AccReal>
 void SpatialUpSamplingBilinearUpdateOutput(mshadow::Stream<gpu> *s,
                                            const std::vector<TBlob> &input,
-                                           const std::vector<TBlob> &output);
+                                           const std::vector<TBlob> &output,
+                                           bool align_corners);
 
 template<typename xpu, typename DType, typename AccReal>
 void SpatialUpSamplingBilinearUpdateGradInput(mshadow::Stream<gpu> *s,
                                               const std::vector<TBlob> &input,
                                               const std::vector<TBlob> &output,
-                                              bool modeLike);
+                                              bool modeLike,
+                                              bool align_corners);
 #endif  // MXNET_USE_CUDA
 
 template <typename xpu>
@@ -138,9 +197,16 @@ inline void BilinearSampleOpForward(const nnvm::NodeAttrs& attrs,
   size_t expected = param.mode == bilinear_resize::like ? 2 : 1;
   CHECK_EQ(inputs.size(), expected);
   CHECK_EQ(outputs.size(), 1U);
+  CHECK_EQ(inputs[0].CheckContiguous(), true);
+  if (expected == 2) {
+  CHECK_EQ(inputs[1].CheckContiguous(), true);
+  }
+  CHECK_EQ(outputs[0].CheckContiguous(), true);
+
+  bool align_corners = param.align_corners;
   mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
   MSHADOW_REAL_TYPE_SWITCH_EX(inputs[0].type_flag_, DType, AccReal, {
-    SpatialUpSamplingBilinearUpdateOutput<xpu, DType, AccReal>(s, inputs, outputs);
+    SpatialUpSamplingBilinearUpdateOutput<xpu, DType, AccReal>(s, inputs, outputs, align_corners);
   });
 }
 
@@ -154,6 +220,7 @@ inline void BilinearSampleOpBackward(const nnvm::NodeAttrs& attrs,
   const BilinearSampleParam& param = nnvm::get<BilinearSampleParam>(attrs.parsed);
   CHECK_EQ(inputs.size(), 1U);
   bool modeLike = param.mode == bilinear_resize::like;
+  bool align_corners = param.align_corners;
   size_t expected = modeLike ? 2 : 1;
   CHECK_EQ(outputs.size(), expected);
   mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
@@ -164,7 +231,8 @@ inline void BilinearSampleOpBackward(const nnvm::NodeAttrs& attrs,
     })
   }
   MSHADOW_REAL_TYPE_SWITCH_EX(inputs[0].type_flag_, DType, AccReal, {
-    SpatialUpSamplingBilinearUpdateGradInput<xpu, DType, AccReal>(s, inputs, outputs, modeLike);
+    SpatialUpSamplingBilinearUpdateGradInput<xpu, DType, AccReal>(s, inputs, outputs
+      , modeLike, align_corners);
   });
 }
 
@@ -254,7 +322,7 @@ static bool BilinearSampleOpInferShape(const nnvm::NodeAttrs& attrs,
 inline uint16_t BilinearSampleOpNumInputs(const NodeAttrs& attrs) {
   auto& param = nnvm::get<BilinearSampleParam>(attrs.parsed);
   if (param.mode == bilinear_resize::like) {
-      return 2;
+    return 2;
   } else {
     return 1;
   }
diff --git a/src/operator/contrib/bilinear_resize.cc b/src/operator/contrib/bilinear_resize.cc
index 346324753a26..0351e479290d 100644
--- a/src/operator/contrib/bilinear_resize.cc
+++ b/src/operator/contrib/bilinear_resize.cc
@@ -33,7 +33,8 @@ using namespace mshadow;
 template<typename xpu, typename DType, typename AccReal>
 void SpatialUpSamplingBilinearUpdateOutput(mshadow::Stream<cpu> *s,
                                            const std::vector<TBlob> &input,
-                                           const std::vector<TBlob> &output) {
+                                           const std::vector<TBlob> &output,
+                                           bool align_corners) {
   Tensor<xpu, 4, DType> itensor = input[0].get<xpu, 4, DType>(s);
   Tensor<xpu, 4, DType> otensor = output[0].get<xpu, 4, DType>(s);
   int nbatch = otensor.size(0);
@@ -69,23 +70,25 @@ void SpatialUpSamplingBilinearUpdateOutput(mshadow::Stream<cpu> *s,
     }
     return;
   }
+  const float rheight = area_pixel_compute_scale<float>(
+    inputHeight, outputHeight, align_corners);
+  const float rwidth = area_pixel_compute_scale<float>(
+    inputWidth, outputWidth, align_corners);
 
-  const float rheight =(outputHeight > 1) ? static_cast<float>(inputHeight - 1)/
-                       (outputHeight - 1) : 0.f;
-  const float rwidth = (outputWidth > 1) ? static_cast<float>(inputWidth - 1) /
-                       (outputWidth - 1) : 0.f;
 #pragma omp parallel for num_threads(nthreads)
   for (int index = 0; index < output_elems_per_channel; index++) {
     const int h2 = index / outputWidth;
     const int w2 = index % outputWidth;
 
-    const float h1r = rheight * h2;
+  const float h1r = area_pixel_compute_source_index<float>(
+    rheight, h2, align_corners, false);
     const int h1 = h1r;
     const int h1p = (h1 < inputHeight - 1) ? 1 : 0;
     const DType h1lambda = h1r - h1;
     const DType h0lambda = (DType)1. - h1lambda;
 
-    const float w1r = rwidth * w2;
+  const float w1r = area_pixel_compute_source_index<float>(
+    rwidth, w2, align_corners, false);
     const int w1 = w1r;
     const int w1p = (w1 < inputWidth - 1) ? 1 : 0;
     const DType w1lambda = w1r - w1;
@@ -95,8 +98,8 @@ void SpatialUpSamplingBilinearUpdateOutput(mshadow::Stream<cpu> *s,
 
     for (int c = 0; c < channels; ++c) {
       *pos2 = h0lambda * (w0lambda * (*pos1) + w1lambda * *(pos1 + w1p))
-                  + h1lambda * (w0lambda * *(pos1 + h1p * inputWidth)
-                  + w1lambda * *(pos1 + h1p * inputWidth + w1p));
+            + h1lambda * (w0lambda * *(pos1 + h1p * inputWidth)
+            + w1lambda * *(pos1 + h1p * inputWidth + w1p));
       pos1 += input_elems_per_channel;
       pos2 += output_elems_per_channel;
     }
@@ -107,7 +110,8 @@ template<typename xpu, typename DType, typename AccReal>
 void SpatialUpSamplingBilinearUpdateGradInput(mshadow::Stream<cpu> *s,
                                               const std::vector<TBlob> &input,
                                               const std::vector<TBlob> &output,
-                                              bool modeLike) {
+                                              bool modeLike,
+                                              bool align_corners) {
   Tensor<xpu, 4, DType> gradOutput = input[0].get<xpu, 4, DType>(s);
   Tensor<xpu, 4, DType> gradInput = output[0].get<xpu, 4, DType>(s);
 
@@ -144,23 +148,24 @@ void SpatialUpSamplingBilinearUpdateGradInput(mshadow::Stream<cpu> *s,
     }
     return;
   }
-  const float rheight =(outputHeight > 1) ? static_cast<float>(inputHeight - 1)/
-                       (outputHeight - 1) : 0.f;
-  const float rwidth = (outputWidth > 1) ? static_cast<float>(inputWidth - 1)/
-                       (outputWidth - 1) : 0.f;
-
+  const float rheight = area_pixel_compute_scale<float>(
+    inputHeight, outputHeight, align_corners);
+  const float rwidth = area_pixel_compute_scale<float>(
+    inputWidth, outputWidth, align_corners);
 #pragma omp parallel for num_threads(nthreads)
   for (int index = 0; index < output_elems_per_channel; index++) {
     const int h2 = index / outputWidth;
     const int w2 = index % outputWidth;
 
-    const float h1r = rheight * h2;
+    const float h1r = area_pixel_compute_source_index<float>(
+        rheight, h2, align_corners, false);
     const int h1 = h1r;
     const int h1p = (h1 < inputHeight - 1) ? 1 : 0;
     const DType h1lambda = h1r - h1;
     const DType h0lambda = (DType)1. - h1lambda;
 
-    const float w1r = rwidth * w2;
+    const float w1r = area_pixel_compute_source_index<float>(
+        rwidth, w2, align_corners, false);
     const int w1 = w1r;
     const int w1p = (w1 < inputWidth - 1) ? 1 : 0;
     const DType w1lambda = w1r - w1;
diff --git a/src/operator/contrib/bilinear_resize.cu b/src/operator/contrib/bilinear_resize.cu
index 0753c47a4bd7..1a6e6964ddfb 100644
--- a/src/operator/contrib/bilinear_resize.cu
+++ b/src/operator/contrib/bilinear_resize.cu
@@ -32,6 +32,72 @@ namespace op {
 
 using namespace mshadow;
 
+// fastSpecializedAtomicAdd adapted from Torch
+// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cuda/KernelUtils.cuh
+template <
+  typename Dtype,
+  typename std::enable_if<std::is_same<mshadow::half::half_t, Dtype>::value>::type* =
+  nullptr>
+  __device__ MSHADOW_FORCE_INLINE void fastSpecializedAtomicAdd(
+    Dtype* tensor,
+    size_t index,
+    const size_t numel,
+    Dtype value) {
+#if (                         \
+    (CUDA_VERSION < 10000) || \
+    (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ < 700)))
+  atomicAdd(
+    reinterpret_cast<mshadow::half::half_t*>(tensor) + index,
+    static_cast<mshadow::half::half_t>(value));
+#else
+  bool low_bit = (index % 2 == 0) &&
+    (reinterpret_cast<std::uintptr_t>(tensor) % sizeof(__half2) == 0);
+
+  if (low_bit && index < (numel - 1)) {
+    __half2 value2;
+    value2.x = value;
+    value2.y = __int2half_rz(0);
+    atomicAdd(reinterpret_cast<__half2*>(tensor) + index / 2, value2);
+  } else if (!low_bit && index > 0) {
+    __half2 value2;
+    value2.x = __int2half_rz(0);
+    value2.y = value;
+    atomicAdd(reinterpret_cast<__half2*>(tensor) + index / 2, value2);
+
+  } else {
+    atomicAdd(
+      reinterpret_cast<__half*>(tensor) + index, static_cast<__half>(value));
+  }
+#endif
+}
+
+template <
+  typename Dtype,
+  typename std::enable_if<!std::is_same<mshadow::half::half_t, Dtype>::value>::type* =
+  nullptr>
+  __device__ MSHADOW_FORCE_INLINE void fastSpecializedAtomicAdd(
+    Dtype* tensor,
+    size_t index,
+    const size_t numel,
+    Dtype value) {
+  atomicAdd(tensor + index, value);
+}
+
+template <class Dtype>
+__device__ MSHADOW_FORCE_INLINE void fastAtomicAdd(
+  Dtype* tensor,
+  size_t index,
+  const size_t numel,
+  Dtype value,
+  bool fast_atomics) {
+  if (fast_atomics) {
+    fastSpecializedAtomicAdd(tensor, index, numel, value);
+  } else {
+    atomicAdd(tensor + index, value);
+  }
+}
+
+
 template<typename xpu, typename Dtype, typename Acctype>
 __global__ void like_mode_kernel_backward(const int n,
     Tensor<xpu, 4, Dtype> dataLike) {
@@ -52,86 +118,109 @@ __global__ void like_mode_kernel_backward(const int n,
   }
 }
 
+// caffe_gpu_interp2_kernel_backward adapted from Torch
+// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cuda/UpSampleBilinear2d.cu
 // Backward (adjoint) operation 1 <- 2 (accumulates)
 template<typename xpu, typename Dtype, typename Acctype>
-__global__ void caffe_gpu_interp2_kernel_backward(const int n,
-    const Acctype rheight, const Acctype rwidth,
-    Tensor<xpu, 4, Dtype> data1, const Tensor<xpu, 4, Dtype> data2) {
-  int index = threadIdx.x + blockIdx.x * blockDim.x;
-  const int batchsize = data1.size(0);
-  const int channels = data1.size(1);
-  const int height1 = data1.size(2);
-  const int width1 = data1.size(3);
-  const int height2 = data2.size(2);
-  const int width2 = data2.size(3);
-  if (index < n) {
-    const int w2 = index % width2;  // 0:width2-1
-    const int h2 = index / width2;  // 0:height2-1
-    // special case: just copy
-    if (height1 == height2 && width1 == width2) {
-      const int h1 = h2;
-      const int w1 = w2;
-      for (int n = 0; n < batchsize ; n++) {
-        for (int c = 0; c < channels; ++c) {
-          const Dtype val = data2[n][c][h1][w1];
-          data1[n][c][h2][w2] += val;
-        }
-      }
-      return;
-    }
+__global__ void caffe_gpu_interp2_kernel_backward(
+  const size_t nc,
+  const int height1,
+  const int width1,
+  const int height2,
+  const int width2,
+    const Acctype rheight,
+  const Acctype rwidth,
+  const bool align_corners,
+  Dtype* __restrict__ idata,
+  const Dtype* __restrict__ odata) {
+  const size_t o_numel = nc * width2 * height2;
+  const size_t i_numel = nc * width1 * height1;
+  for (size_t index = blockDim.x * blockIdx.x + threadIdx.x; index < o_numel;
+    index += blockDim.x * gridDim.x) {
+    size_t index_temp = index;
+    const int w2 = index_temp % width2;  // 0:width2-1
+    index_temp /= width2;
+    const int h2 = index_temp % height2;  // 0:height2-1
+    const size_t nc = index_temp / height2;
     //
-    const Acctype h1r = rheight * h2;
+    const Acctype h1r = cu_area_pixel_compute_source_index<Acctype>(
+      rheight, h2, align_corners, false);
     const int h1 = h1r;
     const int h1p = (h1 < height1 - 1) ? 1 : 0;
     const Acctype h1lambda = h1r - h1;
-    const Acctype h0lambda = Acctype(1) - h1lambda;
+    const Acctype h0lambda = static_cast<Acctype>(1) - h1lambda;
     //
-    const Acctype w1r = rwidth * w2;
+    const Acctype w1r = cu_area_pixel_compute_source_index<Acctype>(
+      rwidth, w2, align_corners, false);
     const int w1 = w1r;
     const int w1p = (w1 < width1 - 1) ? 1 : 0;
     const Acctype w1lambda = w1r - w1;
-    const Acctype w0lambda = Acctype(1) - w1lambda;
-    //
-    for (int n = 0; n < batchsize ; n++) {
-      for (int c = 0; c < channels; ++c) {
-        const Dtype d2val = data2[n][c][h2][w2];
-        atomicAdd(&data1[n][c][h1][w1],
-                  ScalarConvert<Acctype, Dtype>::to(h0lambda * w0lambda * d2val));
-        atomicAdd(&data1[n][c][h1][w1+w1p],
-                  ScalarConvert<Acctype, Dtype>::to(h0lambda * w1lambda * d2val));
-        atomicAdd(&data1[n][c][h1+h1p][w1],
-                  ScalarConvert<Acctype, Dtype>::to(h1lambda * w0lambda * d2val));
-        atomicAdd(&data1[n][c][h1+h1p][w1+w1p],
-                  ScalarConvert<Acctype, Dtype>::to(h1lambda * w1lambda * d2val));
-      }
-    }
+    const Acctype w0lambda = static_cast<Acctype>(1) - w1lambda;
+
+    const Dtype d2val = odata[index];
+    fastAtomicAdd(
+      idata,
+      idx(nc, height1, width1, h1, w1),
+      i_numel,
+      ScalarConvert<Acctype, Dtype>::to(h0lambda * w0lambda * d2val),
+      true);
+    fastAtomicAdd(
+      idata,
+      idx(nc, height1, width1, h1, w1 + w1p),
+      i_numel,
+      ScalarConvert<Acctype, Dtype>::to(h0lambda * w1lambda * d2val),
+      true);
+    fastAtomicAdd(
+      idata,
+      idx(nc, height1, width1, h1 + h1p, w1),
+      i_numel,
+      ScalarConvert<Acctype, Dtype>::to(h1lambda * w0lambda * d2val),
+      true);
+    fastAtomicAdd(
+      idata,
+      idx(nc, height1, width1, h1 + h1p, w1 + w1p),
+      i_numel,
+      ScalarConvert<Acctype, Dtype>::to(h1lambda * w1lambda * d2val),
+      true);
   }
 }
 
 template<typename xpu, typename DType, typename AccReal>
 void SpatialUpSamplingBilinearUpdateOutput(mshadow::Stream<gpu> *s,
                                            const std::vector<TBlob> &input,
-                                           const std::vector<TBlob> &output) {
+                                           const std::vector<TBlob> &output,
+                                         bool align_corners) {
   Tensor<xpu, 4, DType> idata = input[0].get<xpu, 4, DType>(s);
   Tensor<xpu, 4, DType> odata = output[0].get<xpu, 4, DType>(s);
   int outputHeight = odata.size(2);
   int outputWidth = odata.size(3);
+  int nbatch = idata.size(0);
+  int channels = idata.size(1);
   int inputHeight = idata.size(2);
   int inputWidth = idata.size(3);
 
-  const AccReal rheight = (outputHeight > 1) ? (AccReal)(inputHeight - 1)/
-                         (outputHeight - 1) : AccReal(0);
-  const AccReal rwidth = (outputWidth > 1) ? (AccReal)(inputWidth - 1)/
-                         (outputWidth - 1) : AccReal(0);
-  const int num_kernels = outputHeight * outputWidth;
+  const AccReal rheight = cu_area_pixel_compute_scale<AccReal>(
+    inputHeight, outputHeight, align_corners);
+  const AccReal rwidth = cu_area_pixel_compute_scale<AccReal>(
+    inputWidth, outputWidth, align_corners);
+
+  const int num_kernels = nbatch * channels * outputHeight * outputWidth;
   const int num_threads = getNumThreads(inputHeight*inputWidth, false);
   dim3 blocks(static_cast<int>(num_kernels / num_threads) + 1);
   dim3 threads(num_threads);
   cudaStream_t stream = mshadow::Stream<gpu>::GetStream(s);
-  ImageLayout layout = NCHW;
   caffe_gpu_interp2_kernel<xpu, DType, AccReal>
   <<<blocks, threads , 0, stream>>>(
-    num_kernels, rheight, rwidth, idata, odata, layout);
+    nbatch * channels,
+    inputHeight,
+    inputWidth,
+    outputHeight,
+    outputWidth,
+    rheight,
+    rwidth,
+    align_corners,
+    idata.dptr_,
+    odata.dptr_);
   MSHADOW_CUDA_POST_KERNEL_CHECK(SpatialUpSamplingBilinearUpdateOutput);
 }
 
@@ -139,23 +228,38 @@ template<typename xpu, typename DType, typename AccReal>
 void SpatialUpSamplingBilinearUpdateGradInput(mshadow::Stream<gpu> *s,
                                               const std::vector<TBlob> &input,
                                               const std::vector<TBlob> &output,
-                                              bool modeLike) {
-  Tensor<xpu, 4, DType> data1 = output[0].get<xpu, 4, DType>(s);
-  Tensor<xpu, 4, DType> data2 = input[0].get<xpu, 4, DType>(s);
-  int height1 = data1.size(2);
-  int width1 = data1.size(3);
-  int height2 = data2.size(2);
-  int width2 = data2.size(3);
-  const AccReal rheight = (height2 > 1) ? (AccReal)(height1 - 1)/(height2 - 1) : AccReal(0);
-  const AccReal rwidth = (width2 > 1) ? (AccReal)(width1 - 1) / (width2 - 1) : AccReal(0);
-  const int num_kernels = height2 * width2;
-  const int num_threads = getNumThreads(height1*width1, false);
+                                              bool modeLike,
+                                            bool align_corners) {
+  Tensor<xpu, 4, DType> gradOutput = input[0].get<xpu, 4, DType>(s);
+  Tensor<xpu, 4, DType> gradInput = output[0].get<xpu, 4, DType>(s);
+  int outputHeight = gradOutput.size(2);
+  int outputWidth = gradOutput.size(3);
+  int nbatch = gradInput.size(0);
+  int channels = gradInput.size(1);
+  int inputHeight = gradInput.size(2);
+  int inputWidth = gradInput.size(3);
+
+  const AccReal rheight = cu_area_pixel_compute_scale<AccReal>(
+    inputHeight, outputHeight, align_corners);
+  const AccReal rwidth = cu_area_pixel_compute_scale<AccReal>(
+    inputWidth, outputWidth, align_corners);
+  const int num_kernels = nbatch * channels * outputHeight * outputWidth;
+  const int num_threads = getNumThreads(inputHeight*inputWidth, false);
   dim3 blocks(static_cast<int>(num_kernels / num_threads) + 1);
   dim3 threads(num_threads);
   cudaStream_t stream = mshadow::Stream<gpu>::GetStream(s);
   caffe_gpu_interp2_kernel_backward<xpu, DType, AccReal>
   <<<blocks, threads, 0, stream>>>(
-    num_kernels, rheight, rwidth, data1, data2);
+    nbatch * channels,
+    inputHeight,
+    inputWidth,
+    outputHeight,
+    outputWidth,
+    rheight,
+    rwidth,
+    align_corners,
+    gradInput.dptr_,
+    gradOutput.dptr_);
 
   if (modeLike) {
     Tensor<xpu, 4, DType> dataLike = output[1].get<xpu, 4, DType>(s);
diff --git a/src/operator/image/resize-inl.h b/src/operator/image/resize-inl.h
index 4ebebbfb272c..aa381811195d 100644
--- a/src/operator/image/resize-inl.h
+++ b/src/operator/image/resize-inl.h
@@ -68,10 +68,7 @@ struct ResizeParam : public dmlc::Parameter<ResizeParam> {
         "INTER_AREA - resampling using pixel area relation"
         "INTER_CUBIC - a bicubic interpolation over 4x4 pixel neighborhood"
         "INTER_LANCZOS4 - a Lanczos interpolation over 8x8 pixel neighborhood"
-        "Note that the GPU version only support bilinear interpolation(1)"
-        " and the result on cpu would be slightly different from gpu."
-        "It uses opencv resize function which tend to align center on cpu"
-        "while using contrib.bilinearResize2D which aligns corner on gpu");
+        "Note that the GPU version only support bilinear interpolation(1)");
   }
 };
 // handle the keep ratio param
diff --git a/src/operator/image/resize.cu b/src/operator/image/resize.cu
index f045f3b238ea..a40966d1b5e4 100644
--- a/src/operator/image/resize.cu
+++ b/src/operator/image/resize.cu
@@ -40,24 +40,21 @@ void ResizeImplCUDA(mshadow::Stream<gpu> *s,
   int outputWidth;
   int inputHeight;
   int inputWidth;
-  mxnet::op::ImageLayout layout;
   if (std::is_same<T, Tensor<gpu, 3, DType>>::value) {
-    layout = HWC;
     outputHeight = output.size(0);
     outputWidth = output.size(1);
     inputHeight = input.size(0);
     inputWidth = input.size(1);
   } else {
-    layout = NHWC;
     outputHeight = output.size(1);
     outputWidth = output.size(2);
     inputHeight = input.size(1);
     inputWidth = input.size(2);
   }
-  const AccReal rheight = (outputHeight > 1) ? (AccReal)(inputHeight - 1)/
-                         (outputHeight - 1) : AccReal(0);
-  const AccReal rwidth = (outputWidth > 1) ? (AccReal)(inputWidth - 1)/
-                         (outputWidth - 1) : AccReal(0);
+  const AccReal rheight = cu_area_pixel_compute_scale<AccReal>(
+    inputHeight, outputHeight, false);
+  const AccReal rwidth = cu_area_pixel_compute_scale<AccReal>(
+    inputWidth, outputWidth, false);
   const int num_kernels = outputHeight * outputWidth;
   const int num_threads = getNumThreads(inputHeight * inputWidth, false);
   dim3 blocks(static_cast<int>(num_kernels / num_threads) + 1);
@@ -65,7 +62,7 @@ void ResizeImplCUDA(mshadow::Stream<gpu> *s,
   cudaStream_t stream = mshadow::Stream<gpu>::GetStream(s);
   caffe_gpu_interp2_kernel<gpu, DType, AccReal>
   <<<blocks, threads , 0, stream>>>(
-    num_kernels, rheight, rwidth, input, output, layout);
+    num_kernels, rheight, rwidth, false, input, output);
   MSHADOW_CUDA_POST_KERNEL_CHECK(caffe_gpu_interp2_kernel);
 }
 
diff --git a/tests/python/gpu/test_gluon_transforms.py b/tests/python/gpu/test_gluon_transforms.py
index acdd22ac39ec..e777f0bb90e2 100644
--- a/tests/python/gpu/test_gluon_transforms.py
+++ b/tests/python/gpu/test_gluon_transforms.py
@@ -48,14 +48,14 @@ def test_resize_gpu():
     data_in_3d = nd.random.uniform(0, 255, (300, 300, 3))
     out_nd_3d = transforms.Resize((100, 100))(data_in_3d)
     data_in_4d_nchw = nd.moveaxis(nd.expand_dims(data_in_3d, axis=0), 3, 1)
-    data_expected_3d = (nd.moveaxis(nd.contrib.BilinearResize2D(data_in_4d_nchw, height=100, width=100), 1, 3))[0]
+    data_expected_3d = (nd.moveaxis(nd.contrib.BilinearResize2D(data_in_4d_nchw, height=100, width=100, align_corners=False), 1, 3))[0]
     assert_almost_equal(out_nd_3d.asnumpy(), data_expected_3d.asnumpy())
 
     # Test with normal case 4D input float type
     data_in_4d = nd.random.uniform(0, 255, (2, 300, 300, 3))
     out_nd_4d = transforms.Resize((100, 100))(data_in_4d)
     data_in_4d_nchw = nd.moveaxis(data_in_4d, 3, 1)
-    data_expected_4d = nd.moveaxis(nd.contrib.BilinearResize2D(data_in_4d_nchw, height=100, width=100), 1, 3)
+    data_expected_4d = nd.moveaxis(nd.contrib.BilinearResize2D(data_in_4d_nchw, height=100, width=100, align_corners=False), 1, 3)
     assert_almost_equal(out_nd_4d.asnumpy(), data_expected_4d.asnumpy())
 
     # Test invalid interp
@@ -89,17 +89,6 @@ def py_bilinear_resize_nhwc(x, outputHeight, outputWidth):
                             w1lambda*x[b][h1+h1p][w1+w1p][c])
         return y
 
-    # Test with normal case 3D input int8 type
-    data_in_4d = nd.random.uniform(0, 255, (1, 300, 300, 3)).astype('uint8')
-    out_nd_3d = transforms.Resize((100, 100))(data_in_4d[0])
-    assert_almost_equal(out_nd_3d.asnumpy(), py_bilinear_resize_nhwc(data_in_4d.asnumpy(), 100, 100)[0], atol=1.0)
-
-    # Test with normal case 4D input int8 type
-    data_in_4d = nd.random.uniform(0, 255, (2, 300, 300, 3)).astype('uint8')
-    out_nd_4d = transforms.Resize((100, 100))(data_in_4d)
-    assert_almost_equal(out_nd_4d.asnumpy(), py_bilinear_resize_nhwc(data_in_4d.asnumpy(), 100, 100), atol=1.0)
-
-
 @with_seed()
 def test_crop_resize_gpu():
     test_crop_resize()
diff --git a/tests/python/gpu/test_operator_gpu.py b/tests/python/gpu/test_operator_gpu.py
index fe74eed727e5..075261300eff 100644
--- a/tests/python/gpu/test_operator_gpu.py
+++ b/tests/python/gpu/test_operator_gpu.py
@@ -1307,15 +1307,23 @@ def test_bilinear_resize_op():
                 {'ctx': mx.gpu(0), 'data': (2, 2, 20, 20), 'type_dict': {'data': np.float32}}]
 
     data = mx.sym.Variable('data')
-    sym = mx.sym.contrib.BilinearResize2D(data, height=10, width=5)
+    sym = mx.sym.contrib.BilinearResize2D(data, height=10, width=5, align_corners=True)
     check_consistency(sym, ctx_list)
 
-    sym = mx.sym.contrib.BilinearResize2D(data, None, scale_height=2, scale_width=0.5, mode='odd_scale')
+    sym = mx.sym.contrib.BilinearResize2D(data, height=10, width=5, align_corners=False)
+    check_consistency(sym, ctx_list)    
+
+    sym = mx.sym.contrib.BilinearResize2D(data, None, scale_height=2, scale_width=0.5, mode='odd_scale', align_corners=True)
+    check_consistency(sym, ctx_list)
+
+    sym = mx.sym.contrib.BilinearResize2D(data, None, scale_height=2, scale_width=0.5, mode='odd_scale', align_corners=False)
     check_consistency(sym, ctx_list)
 
-    sym = mx.sym.contrib.BilinearResize2D(data, None, scale_height=0.5, scale_width=2, mode='to_even_up')
+    sym = mx.sym.contrib.BilinearResize2D(data, None, scale_height=0.5, scale_width=2, mode='to_even_up', align_corners=True)
     check_consistency(sym, ctx_list)
 
+    sym = mx.sym.contrib.BilinearResize2D(data, None, scale_height=0.5, scale_width=2, mode='to_even_up', align_corners=False)
+    check_consistency(sym, ctx_list)
 
 @with_seed()
 def test_global_pooling():
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 376e177d0659..14e5ad5e9fb4 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -7807,7 +7807,38 @@ def check_bilinear_resize_op(shape, height, width):
         x_scale = width / shape[-1]
         y_scale = height / shape[-2]
         y = mx.nd.contrib.BilinearResize2D(x, scale_height=y_scale, scale_width=x_scale)
-        assert_almost_equal(y, py_bilinear_resize(x.asnumpy(), height, width))
+        assert_almost_equal(y.asnumpy(), py_bilinear_resize(x.asnumpy(), height, width))
+
+    def check_bilinear_resize_align_corners_op():
+        img_shape = [1, 1, 3, 2]
+        data = [64, 32, 32, 64, 50, 100]
+        target_height = 6
+        target_width = 4
+        expected_data = {}
+
+        # align_corners = False
+        expected_data[0] = [
+            64.000, 56.000, 40.000, 32.000, 56.000, 52.000, 44.000, 40.000, 40.000, 44.000, 52.000, 56.000,
+            36.500, 45.625, 63.875, 73.000, 45.500, 56.875, 79.625, 91.000, 50.000, 62.500, 87.500, 100.000
+        ]
+
+        # align_corners = True
+        expected_data[1] = [
+            64.000, 53.333, 42.667, 32.000, 51.200, 49.067, 46.933, 44.800, 38.400, 44.800, 51.200, 57.600,
+            35.600, 47.467, 59.333, 71.200, 42.800, 57.067, 71.333, 85.600, 50.000, 66.667, 83.333, 100.000
+        ]
+
+        x = np.array(data, dtype=np.float32).reshape(img_shape)
+        x_nd = mx.nd.array(x)
+        
+        y0 = np.array(expected_data[0]).reshape((1, 1, target_height, target_width))
+        y0_nd = mx.nd.contrib.BilinearResize2D(x_nd, height=target_height, width=target_width, mode='size', align_corners=False)
+        assert_almost_equal(y0, y0_nd.asnumpy(), atol=1e-3)
+
+        y1 = np.array(expected_data[1]).reshape((1, 1, target_height, target_width))
+        y1_nd = mx.nd.contrib.BilinearResize2D(x_nd, height=target_height, width=target_width, mode='size', align_corners=True)
+        assert_almost_equal(y1, y1_nd.asnumpy(), atol=1e-3)
+
     def check_bilinear_resize_modes_op(shape, scale_height=None, scale_width=None, shape_1=None, mode=None):
         x = mx.nd.random.uniform(shape=shape)
         original_h = shape[2]
@@ -7891,6 +7922,7 @@ def check_bilinear_resize_modes_op(shape, scale_height=None, scale_width=None, s
     shape_1 = (2, 2, 10, 10)
     check_bilinear_resize_modes_op(shape_0, shape_1=shape_1, mode='like')
     check_bilinear_resize_modes_op(shape_1, shape_1=shape_0, mode='like')
+    check_bilinear_resize_align_corners_op()
 
 def test_multi_proposal_op():
     # paramters

From 52716deeaef0bf3360c3748f23b80a16765b1bea Mon Sep 17 00:00:00 2001
From: JackieWu <wkcn@live.cn>
Date: Tue, 12 Nov 2019 23:17:15 +0800
Subject: [PATCH 586/813] [MXNET-1426] Fix the wrong result of sum, mean,
 argmin, argmax when inputs contain inf or nan (#16234)

* fix meansum nan

* remove print in testcase

* update to avoid assignment

* update

* fix argmin and argmax, update julia unittest

* update argmin/argmax docs in julia bindings

* debug

* update

* update test

* fix sum merge

* update testcase

* update including sign

* fix allclose

* ci

* use constants

* fix build for isinf and isnan

* ci

* ci
---
 3rdparty/mshadow/mshadow/base.h               | 106 +++++++++++++++---
 .../mshadow/extension/reduce_with_axis.h      |   2 +-
 3rdparty/mshadow/mshadow/half.h               |   2 +
 julia/src/ndarray/reduction.jl                |   6 +-
 julia/test/unittest/ndarray.jl                |  16 +--
 python/mxnet/ndarray/ndarray.py               |   1 +
 src/operator/contrib/allclose_op-inl.h        |   2 +-
 src/operator/mshadow_op.h                     |  72 +++---------
 src/operator/tensor/elemwise_unary_op.h       |  12 +-
 tests/python/unittest/test_ndarray.py         |  54 +++++++--
 tests/python/unittest/test_operator.py        |  17 ---
 11 files changed, 167 insertions(+), 123 deletions(-)

diff --git a/3rdparty/mshadow/mshadow/base.h b/3rdparty/mshadow/mshadow/base.h
index ec798337e7d0..5f477e52801d 100755
--- a/3rdparty/mshadow/mshadow/base.h
+++ b/3rdparty/mshadow/mshadow/base.h
@@ -606,6 +606,64 @@ struct divto {
   typedef op::div OPType;
 };
 }  // namespace sv
+
+#ifndef __CUDA_ARCH__
+using std::isnan;
+using std::isinf;
+#endif
+
+/*! \brief
+ *  determines if the given floating point
+ *  number is not a number */
+namespace isnan_typed {
+  template<typename DType>
+  MSHADOW_XINLINE bool IsNan(volatile DType val) {
+    return false;
+  }
+  template<>
+  MSHADOW_XINLINE bool IsNan(volatile float val) {
+    return isnan(val);
+  }
+  template<>
+  MSHADOW_XINLINE bool IsNan(volatile double val) {
+    return isnan(val);
+  }
+  template<>
+  MSHADOW_XINLINE bool IsNan(volatile long double val) {
+    return isnan(val);
+  }
+  template<>
+  MSHADOW_XINLINE bool IsNan(volatile mshadow::half::half_t val) {
+    return (val.half_ & (~MSHADOW_HALF_SIGN_BIT)) > MSHADOW_HALF_EXPONENT_BITS;
+  }
+}  // namespace isnan_typed
+
+/*! \brief
+ *  determines if the given floating point
+ *  number is a positive or negative infinity */
+namespace isinf_typed {
+  template<typename DType>
+  MSHADOW_XINLINE bool IsInf(volatile DType val) {
+    return false;
+  }
+  template<>
+  MSHADOW_XINLINE bool IsInf(volatile float val) {
+    return isinf(val);
+  }
+  template<>
+  MSHADOW_XINLINE bool IsInf(volatile double val) {
+    return isinf(val);
+  }
+  template<>
+  MSHADOW_XINLINE bool IsInf(volatile long double val) {
+    return isinf(val);
+  }
+  template<>
+  MSHADOW_XINLINE bool IsInf(volatile mshadow::half::half_t val) {
+    return (val.half_ & (~MSHADOW_HALF_SIGN_BIT)) == MSHADOW_HALF_EXPONENT_BITS;
+  }
+}  // namespace isinf_typed
+
 /*! \brief namespace for potential reducer operations */
 namespace red {
 namespace limits {
@@ -674,6 +732,12 @@ template<>
 MSHADOW_XINLINE double NegInfValue<double>(void) {
   return -HUGE_VAL;
 }
+/*! \brief negative infinity value of float16 */
+template<>
+MSHADOW_XINLINE half::half_t NegInfValue<half::half_t>(void) {
+  return half::half_t::Binary(
+      MSHADOW_HALF_SIGN_BIT | MSHADOW_HALF_EXPONENT_BITS);
+}
 
 /*!
  * \brief maximum value of certain types
@@ -740,6 +804,11 @@ template<>
 MSHADOW_XINLINE double PosInfValue<double>(void) {
   return HUGE_VAL;
 }
+/*! \brief positive infinity value of float16 */
+template<>
+MSHADOW_XINLINE half::half_t PosInfValue<half::half_t>(void) {
+  return half::half_t::Binary(MSHADOW_HALF_EXPONENT_BITS);
+}
 
 }  // namespace limits
 
@@ -755,7 +824,11 @@ struct sum {
   MSHADOW_XINLINE static void Reduce(volatile DType& dst,  volatile DType src, volatile DType& residual) { // NOLINT(*)
     DType y = src - residual;
     DType t = dst + y;
-    residual = (t - dst) - y;
+    if (isinf_typed::IsInf(t)) {
+      residual = 0;
+    } else {
+      residual = (t - dst) - y;
+    }
     dst = t;
   }
   /*! \brief combine the results of two reducers */
@@ -767,10 +840,15 @@ struct sum {
   template<typename DType>
   MSHADOW_XINLINE static void Merge(volatile DType& dst_val, volatile DType& dst_residual, volatile DType& src_val, volatile DType& src_residual) { // NOLINT(*)
     DType t1 = dst_val + src_val;
-    DType e = t1 - dst_val;
-    DType t2 = ((src_val - e) + (dst_val - (t1 - e))) + dst_residual + src_residual;
-    dst_val = t1 + t2;
-    dst_residual = t2 - (dst_val - t1);
+    if (isinf_typed::IsInf(t1)) {
+      dst_val = t1;
+      dst_residual = 0;
+    } else {
+      DType e = t1 - dst_val;
+      DType t2 = ((src_val - e) + (dst_val - (t1 - e))) + dst_residual + src_residual;
+      dst_val = t1 + t2;
+      dst_residual = t2 - (dst_val - t1);
+    }
   }
   /*! \brief finalize reduction */
   template<typename DType>
@@ -807,12 +885,9 @@ struct maximum {
   /*! \brief do reduction into dst */
   template<typename DType>
   MSHADOW_XINLINE static void Reduce(volatile DType& dst,  volatile DType src) { // NOLINT(*)
-    using namespace std;
-#ifdef __CUDACC__
-    dst = ::max(dst, src);
-#else
-    dst = max(dst, src);
-#endif  // __CUDACC__
+    if (!isnan_typed::IsNan(dst)) {
+      if (!(dst >= src)) dst = src;
+    }
   }
   /*! \brief do reduction into dst */
   template<typename DType>
@@ -863,12 +938,9 @@ struct minimum {
   /*! \brief do reduction into dst */
   template<typename DType>
   MSHADOW_XINLINE static void Reduce(volatile DType& dst,  volatile DType src) { // NOLINT(*)
-    using namespace std;
-#ifdef __CUDACC__
-    dst = ::min(dst, src);
-#else
-    dst = min(dst, src);
-#endif  // __CUDACC__
+    if (!isnan_typed::IsNan(dst)) {
+      if (!(dst <= src)) dst = src;
+    }
   }
   /*! \brief do reduction into dst */
   template<typename DType>
diff --git a/3rdparty/mshadow/mshadow/extension/reduce_with_axis.h b/3rdparty/mshadow/mshadow/extension/reduce_with_axis.h
index 54bcc750cfc5..26b6156ad6f9 100644
--- a/3rdparty/mshadow/mshadow/extension/reduce_with_axis.h
+++ b/3rdparty/mshadow/mshadow/extension/reduce_with_axis.h
@@ -112,7 +112,7 @@ struct Plan<ReduceWithAxisExp<Reducer, SrcExp, DType, dimsrc, mask, dimdst>, DTy
         index_t z = (x*size_+k)*trailing_+y;
         DType tmp = res;
         Reducer::Reduce(res, src_.Eval(z/last_, z%last_));
-        if (tmp != res) {
+        if (tmp != res && !isnan_typed::IsNan(tmp)) {
           idx = k;
         }
       }
diff --git a/3rdparty/mshadow/mshadow/half.h b/3rdparty/mshadow/mshadow/half.h
index 2dded0a7752e..1cc53ae0460f 100644
--- a/3rdparty/mshadow/mshadow/half.h
+++ b/3rdparty/mshadow/mshadow/half.h
@@ -349,6 +349,8 @@ MSHADOW_HALF_OPERATOR(bool, <=)
 
 #define MSHADOW_HALF_MIN mshadow::half::half_t::Binary(0xFBFF);
 #define MSHADOW_HALF_MAX mshadow::half::half_t::Binary(0x7BFF);
+#define MSHADOW_HALF_SIGN_BIT 0x8000
+#define MSHADOW_HALF_EXPONENT_BITS 0x7c00
 }  // namespace half
 }  // namespace mshadow
 #endif  // MSHADOW_HALF_H_
diff --git a/julia/src/ndarray/reduction.jl b/julia/src/ndarray/reduction.jl
index 833b483ca321..2045ce231674 100644
--- a/julia/src/ndarray/reduction.jl
+++ b/julia/src/ndarray/reduction.jl
@@ -47,8 +47,7 @@ broadcasted(::typeof(min), x::NDArray{T}, y::NDArray{T}) where {T} =
 """
     argmax(x::NDArray; dims) -> indices
 
-Note that `NaN` is skipped during comparison.
-This is different from Julia `Base.argmax`.
+Note that `NaN` is treated as greater than all other values in `argmax`.
 
 ## Examples
 
@@ -77,8 +76,7 @@ Base.argmax(x::NDArray; dims = :) = _argmax(x, dims) .+ 1
 """
     argmin(x::NDArray; dims) -> indices
 
-Note that `NaN` is skipped during comparison.
-This is different from Julia `Base.argmin`.
+Note that `NaN` is treated as less than all other values in `argmin`.
 
 ## Examples
 
diff --git a/julia/test/unittest/ndarray.jl b/julia/test/unittest/ndarray.jl
index 638963f1b8aa..599b0a65bfc4 100644
--- a/julia/test/unittest/ndarray.jl
+++ b/julia/test/unittest/ndarray.jl
@@ -1515,8 +1515,8 @@ function test_argmax()
          4 2 6]
     x = NDArray(A)
 
-    @test copy(argmax(x, dims = 1)) == [2 1 2]
-    @test copy(argmax(x, dims = 2)) == reshape([2, 3], :, 1)
+    @test copy(argmax(x, dims = 1)) == [x[1] for x ∈ argmax(A, dims = 1)]
+    @test copy(argmax(x, dims = 2)) == [x[2] for x ∈ argmax(A, dims = 2)]
   end
 
   @info "NDArray::argmax::NaN"
@@ -1525,8 +1525,8 @@ function test_argmax()
          NaN 2 6]
     x = NDArray(A)
 
-    @test copy(argmax(x, dims = 1)) == [1 1 2]
-    @test copy(argmax(x, dims = 2)) == reshape([2, 3], :, 1)
+    @test copy(argmax(x, dims = 1)) == [x[1] for x ∈ argmax(A, dims = 1)]
+    @test copy(argmax(x, dims = 2)) == [x[2] for x ∈ argmax(A, dims = 2)]
   end
 end
 
@@ -1537,8 +1537,8 @@ function test_argmin()
          4 2 6]
     x = NDArray(A)
 
-    @test copy(argmin(x, dims = 1)) == [1 2 1]
-    @test copy(argmin(x, dims = 2)) == reshape([1, 2], :, 1)
+    @test copy(argmin(x, dims = 1)) == [x[1] for x ∈ argmin(A, dims = 1)]
+    @test copy(argmin(x, dims = 2)) == [x[2] for x ∈ argmin(A, dims = 2)]
   end
 
   @info "NDArray::argmin::NaN"
@@ -1547,8 +1547,8 @@ function test_argmin()
          NaN 2 6]
     x = NDArray(A)
 
-    @test copy(argmin(x, dims = 1)) == [1 2 1]
-    @test copy(argmin(x, dims = 2)) == reshape([1, 2], :, 1)
+    @test copy(argmin(x, dims = 1)) == [x[1] for x ∈ argmin(A, dims = 1)]
+    @test copy(argmin(x, dims = 2)) == [x[2] for x ∈ argmin(A, dims = 2)]
   end
 end
 
diff --git a/python/mxnet/ndarray/ndarray.py b/python/mxnet/ndarray/ndarray.py
index f656daea3016..7d8cc524c817 100644
--- a/python/mxnet/ndarray/ndarray.py
+++ b/python/mxnet/ndarray/ndarray.py
@@ -4935,6 +4935,7 @@ class DLDataType(ctypes.Structure):
         "bool": (1, 1, 1),
         "uint32": (1, 32, 1),
         "uint64": (1, 64, 1),
+        'float16': (2, 16, 1),
         "float32": (2, 32, 1),
         "float64": (2, 64, 1),
     }
diff --git a/src/operator/contrib/allclose_op-inl.h b/src/operator/contrib/allclose_op-inl.h
index a10c7795e568..c54b2630924c 100644
--- a/src/operator/contrib/allclose_op-inl.h
+++ b/src/operator/contrib/allclose_op-inl.h
@@ -84,7 +84,7 @@ inline bool AllCloseType(const nnvm::NodeAttrs& attrs,
   return (*out_attrs)[0] != -1;
 }
 
-using namespace mshadow_op::isnan_typed;
+using mshadow::isnan_typed::IsNan;
 
 template<int req>
 struct allclose_forward {
diff --git a/src/operator/mshadow_op.h b/src/operator/mshadow_op.h
index f9d4e23a3a6f..4ae587188d1b 100644
--- a/src/operator/mshadow_op.h
+++ b/src/operator/mshadow_op.h
@@ -27,6 +27,7 @@
 #define MXNET_OPERATOR_MSHADOW_OP_H_
 
 #include <mxnet/base.h>
+#include <mshadow/base.h>
 #include "math.h"
 #include "math_functions-inl.h"
 #include "special_functions-inl.h"
@@ -41,6 +42,9 @@ namespace mxnet {
 namespace op {
 namespace mshadow_op {
 
+using mshadow::isnan_typed::IsNan;
+using mshadow::isinf_typed::IsInf;
+
 #ifdef __CUDA_ARCH__
 __constant__ const float PI = 3.14159265358979323846;
 __constant__ const float SELU_ALPHA = 1.6732632423543772848170429916717;
@@ -51,8 +55,6 @@ const float PI = 3.14159265358979323846;
 const float SELU_ALPHA = 1.6732632423543772848170429916717;
 const float SELU_LAMBDA = 1.0507009873554804934193349852946;
 const float SQRT_2 = 1.4142135623730950488016887242096;
-using std::isnan;
-using std::isinf;
 #endif
 using std::enable_if;
 using std::is_unsigned;
@@ -1001,61 +1003,13 @@ struct product {
   }
 };
 
-namespace isnan_typed {
-  template<typename DType>
-  MSHADOW_XINLINE bool IsNan(volatile DType val) {
-    return false;
-  }
-  template<>
-  MSHADOW_XINLINE bool IsNan(volatile float val) {
-    return isnan(val);
-  }
-  template<>
-  MSHADOW_XINLINE bool IsNan(volatile double val) {
-    return isnan(val);
-  }
-  template<>
-  MSHADOW_XINLINE bool IsNan(volatile long double val) {
-    return isnan(val);
-  }
-
-  template<>
-  MSHADOW_XINLINE bool IsNan(volatile mshadow::half::half_t val) {
-    return (val.half_ & 0x7fff) > 0x7c00;
-  }
-};  // namespace isnan_typed
-
-namespace isinf_typed {
-  template<typename DType>
-  MSHADOW_XINLINE bool IsInf(volatile DType val) {
-    return false;
-  }
-  template<>
-  MSHADOW_XINLINE bool IsInf(volatile float val) {
-    return isinf(val);
-  }
-  template<>
-  MSHADOW_XINLINE bool IsInf(volatile double val) {
-    return isinf(val);
-  }
-  template<>
-  MSHADOW_XINLINE bool IsInf(volatile long double val) {
-    return isinf(val);
-  }
-
-  template<>
-  MSHADOW_XINLINE bool IsInf(volatile mshadow::half::half_t val) {
-    return (val.half_ & 0x7fff) >= 0x7c00;
-  }
-};  // namespace isinf_typed
-
-MXNET_UNARY_MATH_OP_NC(relu, isnan_typed::IsNan(a) || (a > DType(0)) ? a : DType(0));
+MXNET_UNARY_MATH_OP_NC(relu, IsNan(a) || (a > DType(0)) ? a : DType(0));
 
 /*! \brief used for computing gradient of relu operator */
 struct relu_grad : public mxnet_op::tunable {
   template<typename DType>
   MSHADOW_XINLINE static DType Map(DType a) {
-    if (isnan_typed::IsNan(a)) {
+    if (IsNan(a)) {
       return a;
     } else {
       return a > DType(0) ? DType(1) : DType(0);
@@ -1067,7 +1021,7 @@ struct relu_grad : public mxnet_op::tunable {
 struct maximum : public mxnet_op::tunable {
   template<typename DType>
   MSHADOW_XINLINE static DType Map(DType a, DType b) {
-    if (isnan_typed::IsNan(a)) {
+    if (IsNan(a)) {
       return a;
     } else {
       return (a > b ? a : b);
@@ -1079,7 +1033,7 @@ struct maximum : public mxnet_op::tunable {
 struct minimum : public mxnet_op::tunable {
   template<typename DType>
   MSHADOW_XINLINE static DType Map(DType a, DType b) {
-    if (isnan_typed::IsNan(a)) {
+    if (IsNan(a)) {
       return a;
     } else {
       return DType(a < b ? a : b);
@@ -1092,13 +1046,13 @@ struct nansum {
   /*! \brief do reduction into dst */
   template<typename DType>
   MSHADOW_XINLINE static void Reduce(volatile DType& dst, volatile DType src) { // NOLINT(*)
-    if (isnan_typed::IsNan(src)) return;
+    if (IsNan(src)) return;
     dst += src;
   }
   /*! \brief do reduction into dst */
   template<typename DType>
   MSHADOW_XINLINE static void Reduce(volatile DType& dst, volatile DType src, volatile DType& residual) { // NOLINT(*)
-    if (isnan_typed::IsNan(src)) return;
+    if (IsNan(src)) return;
     DType y = src - residual;
     DType t = dst + y;
     residual = (t - dst) - y;
@@ -1144,7 +1098,7 @@ struct nansum {
 struct nansum_grad : public mxnet_op::tunable {
   template<typename DType>
   MSHADOW_XINLINE static DType Map(DType a, DType b) {
-    return isnan_typed::IsNan(a) ? DType(0) : DType(1);
+    return IsNan(a) ? DType(0) : DType(1);
   }
 };
 
@@ -1153,7 +1107,7 @@ struct nanprod {
   /*! \brief do reduction into dst */
   template<typename DType>
   MSHADOW_XINLINE static void Reduce(volatile DType& dst, volatile DType src) { // NOLINT(*)
-    if (isnan_typed::IsNan(src)) return;
+    if (IsNan(src)) return;
     dst *= src;
   }
   /*! \brief do reduction into dst */
@@ -1327,7 +1281,7 @@ struct sum {
 struct nanprod_grad : public mxnet_op::tunable {
   template<typename DType>
   MSHADOW_XINLINE static DType Map(DType a, DType b) {
-    return isnan_typed::IsNan(a) ? DType(0) : b / a;
+    return IsNan(a) ? DType(0) : b / a;
   }
 };
 
diff --git a/src/operator/tensor/elemwise_unary_op.h b/src/operator/tensor/elemwise_unary_op.h
index 27013dfb98ae..577c994a8ee1 100644
--- a/src/operator/tensor/elemwise_unary_op.h
+++ b/src/operator/tensor/elemwise_unary_op.h
@@ -699,9 +699,9 @@ struct nan_to_num_forward {
                                   const DType posinf,
                                   const DType neginf) {
     DType val = in_data[i];
-    if (mshadow_op::isnan_typed::IsNan<DType>(val))  val = nan;
-    if (val > 0 && mshadow_op::isinf_typed::IsInf(val))  val = posinf;
-    if (val < 0 && mshadow_op::isinf_typed::IsInf(val))  val = neginf;
+    if (mshadow_op::IsNan<DType>(val))  val = nan;
+    if (val > 0 && mshadow_op::IsInf(val))  val = posinf;
+    if (val < 0 && mshadow_op::IsInf(val))  val = neginf;
     KERNEL_ASSIGN(out_data[i], req, val);
   }
 };
@@ -758,9 +758,9 @@ struct nan_to_num_backward {
                                   const DType* out_grad,
                                   const DType* in_data) {
     DType val = out_grad[i];
-    if (mshadow_op::isnan_typed::IsNan(in_data[i]))  val = 0;
-    if (val > 0 && mshadow_op::isinf_typed::IsInf(in_data[i]))  val = 0;
-    if (val < 0 && mshadow_op::isinf_typed::IsInf(in_data[i]))  val = 0;
+    if (mshadow_op::IsNan(in_data[i]))  val = 0;
+    if (val > 0 && mshadow_op::IsInf(in_data[i]))  val = 0;
+    if (val < 0 && mshadow_op::IsInf(in_data[i]))  val = 0;
     KERNEL_ASSIGN(in_grad[i], req, val);
   }
 };
diff --git a/tests/python/unittest/test_ndarray.py b/tests/python/unittest/test_ndarray.py
index 9b3ce3192b9e..4c6d9f7b8ef2 100644
--- a/tests/python/unittest/test_ndarray.py
+++ b/tests/python/unittest/test_ndarray.py
@@ -21,6 +21,7 @@
 from itertools import permutations, combinations_with_replacement
 import os
 import pickle as pkl
+import random
 import functools
 from nose.tools import assert_raises, raises
 from common import with_seed, assertRaises, TemporaryDirectory
@@ -31,7 +32,7 @@
 from mxnet.test_utils import same
 from mxnet.test_utils import random_sample, rand_shape_nd, random_arrays
 from mxnet import runtime
-from numpy.testing import assert_allclose
+from numpy.testing import assert_allclose, assert_array_equal, assert_array_almost_equal
 import mxnet.autograd
 from mxnet.base import integer_types
 from mxnet.ndarray.ndarray import py_slice
@@ -580,13 +581,40 @@ def test_dot():
 
 @with_seed()
 def test_reduce():
-    sample_num = 200
-    def test_reduce_inner(numpy_reduce_func, nd_reduce_func, multi_axes):
+    sample_num = 300
+    def test_reduce_inner(numpy_reduce_func, nd_reduce_func, multi_axes,
+                          allow_almost_equal=False, check_dtype=True):
+        dtypes = [(np.float16, 1),
+                  (np.float32, 5),
+                  (np.double, 6)]
         for i in range(sample_num):
+            dtype, decimal = random.choice(dtypes)
             ndim = np.random.randint(1, 6)
             shape = np.random.randint(1, 11, size=ndim)
-            dat = np.random.rand(*shape) - 0.5
+            dat = (np.random.rand(*shape) - 0.5).astype(dtype)
             keepdims = np.random.randint(0, 2)
+
+            allow_nan = np.random.randint(0, 2)
+            if allow_nan:
+                total_nans = np.random.randint(0, dat.size//10+1)
+                dat.ravel()[np.random.choice(
+                    dat.size, total_nans, replace=False)] = np.nan
+
+            allow_inf = np.random.randint(0, 2)
+            if allow_inf:
+                r = np.random.randint(0, 3)
+                total_infs = np.random.randint(0, dat.size//20+1)
+                if r == 0:
+                    total_pos_infs, total_neg_infs = total_infs, 0
+                elif r == 1:
+                    total_pos_infs, total_neg_infs = 0, total_infs
+                else:
+                    total_pos_infs = total_neg_infs = total_infs // 2
+                dat.ravel()[np.random.choice(
+                    dat.size, total_pos_infs, replace=False)] = np.inf
+                dat.ravel()[np.random.choice(
+                    dat.size, total_neg_infs, replace=False)] = -np.inf
+
             if multi_axes:
                 axis_flags = np.random.randint(0, 2, size=ndim)
                 axes = []
@@ -601,16 +629,22 @@ def test_reduce_inner(numpy_reduce_func, nd_reduce_func, multi_axes):
                 axes = np.random.randint(0, ndim)
             numpy_ret = numpy_reduce_func(dat, axis=axes, keepdims=keepdims)
 
-            ndarray_ret = nd_reduce_func(mx.nd.array(dat), axis=axes, keepdims=keepdims)
+            mx_arr = mx.nd.array(dat, dtype=dtype)
+            ndarray_ret = nd_reduce_func(mx_arr, axis=axes, keepdims=keepdims)
             if type(ndarray_ret) is mx.ndarray.NDArray:
                 ndarray_ret = ndarray_ret.asnumpy()
             assert (ndarray_ret.shape == numpy_ret.shape) or \
                    (ndarray_ret.shape == (1,) and numpy_ret.shape == ()), "nd:%s, numpy:%s" \
                                                          %(ndarray_ret.shape, numpy_ret.shape)
-            err = np.square(ndarray_ret - numpy_ret).mean()
-            assert err < 1E-4
+            if check_dtype:
+                assert ndarray_ret.dtype == numpy_ret.dtype,\
+                        (ndarray_ret.dtype, numpy_ret.dtype)
+            if allow_almost_equal:
+                assert_array_almost_equal(ndarray_ret, numpy_ret, decimal=decimal)
+            else:
+                assert_array_equal(ndarray_ret, numpy_ret)
     test_reduce_inner(lambda data, axis, keepdims:np_reduce(data, axis, keepdims, np.sum),
-                      mx.nd.sum, True)
+                      mx.nd.sum, True, allow_almost_equal=True)
     test_reduce_inner(lambda data, axis, keepdims:np_reduce(data, axis, keepdims, np.max),
                       mx.nd.max, True)
     test_reduce_inner(lambda data, axis, keepdims:np_reduce(data, axis, keepdims, np.min),
@@ -619,10 +653,10 @@ def test_reduce_inner(numpy_reduce_func, nd_reduce_func, multi_axes):
     # Force numpy to match mxnet's float32.
     test_reduce_inner(lambda data, axis,
                              keepdims:np_reduce(np.float32(data), axis, keepdims, np.argmax),
-                      mx.nd.argmax, False)
+                      mx.nd.argmax, False, check_dtype=False)
     test_reduce_inner(lambda data, axis,
                              keepdims:np_reduce(np.float32(data), axis, keepdims, np.argmin),
-                      mx.nd.argmin, False)
+                      mx.nd.argmin, False, check_dtype=False)
 
 
 @with_seed()
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 14e5ad5e9fb4..7b0404d8abb7 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -9327,23 +9327,6 @@ def test_sample_normal_default_shape():
     assert s.shape == (1, 1)
 
 
-def test_min_max_inf():
-    dtypes = [np.float32, np.double]
-    elem_list = [-1, 1, 0, np.inf, -np.inf]
-
-    for dtype in dtypes:
-        for a in elem_list:
-            for b in elem_list:
-                data_np = np.array([a, b], dtype=dtype)
-                data_mx = mx.nd.array(data_np, dtype=dtype)
-
-                min_data_np, max_data_np = data_np.min(), data_np.max()
-                min_data_mx, max_data_mx = data_mx.min(), data_mx.max()
-
-                assert_array_equal(min_data_np, min_data_mx.asnumpy())
-                assert_array_equal(max_data_np, max_data_mx.asnumpy())
-
-
 def test_large_tensor_disabled_err_msg():
     LARGE_X = 4300000000
     MEDIUM_X = 1000000000

From e3f5dade609f1eed3a26f0baaf2eaa7187c5bd24 Mon Sep 17 00:00:00 2001
From: Przemyslaw Tredak <ptredak@nvidia.com>
Date: Tue, 12 Nov 2019 10:06:59 -0800
Subject: [PATCH 587/813] Fix (#16781)

---
 src/executor/pointwise_fusion_pass.cc | 27 +++++++++++++++------------
 1 file changed, 15 insertions(+), 12 deletions(-)

diff --git a/src/executor/pointwise_fusion_pass.cc b/src/executor/pointwise_fusion_pass.cc
index c6e2405cb2a4..6fe21402cb3a 100644
--- a/src/executor/pointwise_fusion_pass.cc
+++ b/src/executor/pointwise_fusion_pass.cc
@@ -83,15 +83,7 @@ namespace {
     auto node = nnvm::Node::Create();
     subgraph_sym.outputs = subgraph.outputs;
     node->attrs.subgraphs.emplace_back(std::make_shared<nnvm::Symbol>(subgraph_sym));
-    std::ostringstream name_oss;
-    // the name of the new node will be the concatenation of all the node names in the subgraph
-    DFSVisit(subgraph.outputs, [&name_oss](const nnvm::NodePtr n) {
-      if (n->op() != nullptr)
-        name_oss << n->op()->name << "_";
-    });
-    auto subgraph_name = name_oss.str();
-    subgraph_name.pop_back();
-    node->attrs.name = subgraph_name;
+    node->attrs.name = "FusedOp";
     node->attrs.dict["num_inputs"] = std::to_string(inputs_size);
     node->attrs.dict["num_outputs"] = std::to_string(subgraph.outputs.size());
     node->attrs.op = Op::Get("_FusedOp");
@@ -152,7 +144,8 @@ Graph ReplaceSubgraphsPointwise(Graph&& g, const std::vector<NodeRawPtrSet>& sub
         auto it = node->control_deps.begin();
         static auto& is_fusion = Op::GetAttr<exec::TIsFusionHelper>("TIsFusionHelper");
         std::vector<nnvm::NodePtr> new_control_deps;
-        while (it != node->control_deps.end()) {
+        // Use the first control dependency to get the inferattr helper
+        if (it != node->control_deps.end()) {
           if (subgraph_set.count(it->get())) {
             new_control_deps.push_back(*it);
           } else {
@@ -160,8 +153,7 @@ Graph ReplaceSubgraphsPointwise(Graph&& g, const std::vector<NodeRawPtrSet>& sub
               uint32_t node_id = subgraph_node->control_deps.size();
               subgraph_node->control_deps.push_back(*it);
               auto helper_node = op::MakeNode("_FusedOpOutHelper",
-                                              subgraph_node->attrs.name + "_"
-                                              + node->attrs.name + "_outhelper",
+                                              "FusedOp_" + node->attrs.name + "_outhelper",
                                               nullptr,
                                               nullptr,
                                               nullptr);
@@ -180,6 +172,17 @@ Graph ReplaceSubgraphsPointwise(Graph&& g, const std::vector<NodeRawPtrSet>& sub
       }
     });
 
+    std::ostringstream name_oss;
+    // the name of the new node will be the concatenation of all the node names in the subgraph
+    DFSVisit(subgraph.outputs, [&name_oss](const nnvm::NodePtr n) {
+      if (n->op() != nullptr) {
+        name_oss << n->op()->name << "_";
+      }
+    });
+    auto subgraph_name = name_oss.str();
+    subgraph_name.pop_back();
+    subgraph_node->attrs.name = subgraph_name;
+
     const auto& index = subgraph.indexed_graph();
     DFSVisit(g.outputs, [&subgraph_node, &subgraph_set, &index](const nnvm::NodePtr& node) {
       for (auto &e : node->control_deps) {

From 2c02bfffb2bdb4e8f1e5159e08e3250482e5f603 Mon Sep 17 00:00:00 2001
From: Dick Carter <dcarter@nvidia.com>
Date: Tue, 12 Nov 2019 14:59:06 -0800
Subject: [PATCH 588/813] Speed fused_op compilation by caching ptx and
 jit-compiled functions (#16783)

---
 src/operator/fusion/fused_op-inl.h |   4 +-
 src/operator/fusion/fused_op.cc    |  50 +++----
 src/operator/fusion/fused_op.cu    | 225 +++++++++++++++++------------
 src/operator/fusion/fused_op.h     |  43 +++---
 tests/python/gpu/test_fusion.py    |  25 +++-
 5 files changed, 205 insertions(+), 142 deletions(-)

diff --git a/src/operator/fusion/fused_op-inl.h b/src/operator/fusion/fused_op-inl.h
index 3085bfd1dc07..2966fe2ae910 100644
--- a/src/operator/fusion/fused_op-inl.h
+++ b/src/operator/fusion/fused_op-inl.h
@@ -982,11 +982,9 @@ const char kernel_begin[] = R"code(
 const int tid = threadIdx.x + blockIdx.x * blockDim.x;
 for (int i = tid; i < N; i+= gridDim.x * blockDim.x) {
     int offset = i*nvec;
-
 )code";
 
-const char kernel_end[] = R"code(
-}
+const char kernel_end[] = R"code(}
 }
 )code";
 
diff --git a/src/operator/fusion/fused_op.cc b/src/operator/fusion/fused_op.cc
index 071215b840a5..5c83c30308c7 100644
--- a/src/operator/fusion/fused_op.cc
+++ b/src/operator/fusion/fused_op.cc
@@ -49,31 +49,30 @@ void FusedOpParamParser(nnvm::NodeAttrs* attrs) {
   attrs->parsed = FusedOpPtr(new FusedOp(attrs, param));
 }
 
-FusedOp::FusedOp(const nnvm::NodeAttrs* attrs, const FusedOpConfig& config) {
-  this->inputs_ = std::vector<FusedOpEntry>(config.num_inputs);
-  this->outputs_ = std::vector<FusedOpEntry>(config.num_outputs);
-  this->subgraph_ = nnvm::Graph();
-  this->subgraph_.outputs = attrs->subgraphs[0]->outputs;
-  this->initialized_ = false;
-  this->cc_major_ = -1;
-  this->cc_minor_ = -1;
+FusedOp::FusedOp(const nnvm::NodeAttrs* attrs, const FusedOpConfig& config) :
+    initialized_(false),
+    kernel_function_dev_id_(-1) {
+  inputs_ = std::vector<FusedOpEntry>(config.num_inputs);
+  outputs_ = std::vector<FusedOpEntry>(config.num_outputs);
+  subgraph_ = nnvm::Graph();
+  subgraph_.outputs = attrs->subgraphs[0]->outputs;
 }
 
 bool FusedOp::InferShape(const nnvm::NodeAttrs &attrs,
                          std::vector<mxnet::TShape> *in_attrs,
                          std::vector<mxnet::TShape> *out_attrs) {
-  this->subgraph_.attrs.erase("shape");
-  this->subgraph_.attrs.erase("shape_inputs");
+  subgraph_.attrs.erase("shape");
+  subgraph_.attrs.erase("shape_inputs");
   std::vector<mxnet::TShape> input_shapes(*in_attrs);
-  this->subgraph_ = mxnet::exec::InferShape(std::move(this->subgraph_),
-                                          std::move(input_shapes),
-                                          "__shape__");
+  subgraph_ = mxnet::exec::InferShape(std::move(subgraph_),
+                                      std::move(input_shapes),
+                                      "__shape__");
 
-  const auto& g = this->subgraph_.indexed_graph();
+  const auto& g = subgraph_.indexed_graph();
   const auto& input_nids = g.input_nodes();
 
   std::vector<mxnet::TShape> out_shapes;
-  const std::vector<mxnet::TShape> shapes = this->subgraph_.GetAttr<mxnet::ShapeVector>("shape");
+  const std::vector<mxnet::TShape> shapes = subgraph_.GetAttr<mxnet::ShapeVector>("shape");
   for (auto& e : g.outputs()) {
     out_shapes.push_back(shapes[g.entry_id(e)]);
   }
@@ -105,18 +104,18 @@ bool FusedOp::InferShape(const nnvm::NodeAttrs &attrs,
 bool FusedOp::InferType(const nnvm::NodeAttrs &attrs,
                         std::vector<int> *in_attrs,
                         std::vector<int> *out_attrs) {
-  this->subgraph_.attrs.erase("dtype");
-  this->subgraph_.attrs.erase("dtype_inputs");
+  subgraph_.attrs.erase("dtype");
+  subgraph_.attrs.erase("dtype_inputs");
   std::vector<int> input_types(*in_attrs);
-  this->subgraph_ = mxnet::exec::InferType(std::move(this->subgraph_),
-                                         std::move(input_types),
-                                         "__dtype__");
+  subgraph_ = mxnet::exec::InferType(std::move(subgraph_),
+                                     std::move(input_types),
+                                     "__dtype__");
 
-  const auto& g = this->subgraph_.indexed_graph();
+  const auto& g = subgraph_.indexed_graph();
   const auto& input_nids = g.input_nodes();
 
   std::vector<int> out_types;
-  const std::vector<int> types = this->subgraph_.GetAttr<nnvm::DTypeVector>("dtype");
+  const std::vector<int> types = subgraph_.GetAttr<nnvm::DTypeVector>("dtype");
   for (auto& e : g.outputs()) {
     out_types.push_back(types[g.entry_id(e)]);
   }
@@ -149,10 +148,9 @@ template <typename Attr>
 std::tuple<const nnvm::NodePtr,
            std::vector<Attr>,
            std::vector<Attr>>
-  FusedOp::GetAttrs(const std::string& attr_name,
-                                                                  const uint32_t node_id) {
-  const auto& g = this->subgraph_.indexed_graph();
-  const std::vector<Attr> attrs = this->subgraph_.GetAttr<std::vector<Attr>>(attr_name);
+FusedOp::GetAttrs(const std::string& attr_name, const uint32_t node_id) {
+  const auto& g = subgraph_.indexed_graph();
+  const std::vector<Attr> attrs = subgraph_.GetAttr<std::vector<Attr>>(attr_name);
   const auto& node = g[node_id];
   std::vector<Attr> inputs, outputs;
   for (const auto& e : node.inputs) {
diff --git a/src/operator/fusion/fused_op.cu b/src/operator/fusion/fused_op.cu
index f6df38bac247..78988f13510e 100644
--- a/src/operator/fusion/fused_op.cu
+++ b/src/operator/fusion/fused_op.cu
@@ -163,9 +163,33 @@ void AddPointerAndShape(const TBlob& data,
   });
 }
 
+// Obtain compilation log from the program.
+std::string GetCompileLog(nvrtcProgram program) {
+  size_t log_size_including_null;
+  NVRTC_CALL(nvrtcGetProgramLogSize(program, &log_size_including_null));
+  // For most std::string implementations, this is probably 1 char bigger than needed.  OK though.
+  std::string log(log_size_including_null, '\0');
+  NVRTC_CALL(nvrtcGetProgramLog(program, &log[0]));
+  // Make sure the string reflects the true size (so minus the null terminator).
+  log.resize(log_size_including_null - 1);
+  return log;
+}
+
+// Obtain compilation result (ptx assembly) from the program.
+std::string GetPtx(nvrtcProgram program) {
+  size_t ptx_size_including_null;
+  NVRTC_CALL(nvrtcGetPTXSize(program, &ptx_size_including_null));
+  // For most std::string implementations, this is probably 1 char bigger than needed.  OK though.
+  std::string ptx(ptx_size_including_null, '\0');
+  NVRTC_CALL(nvrtcGetPTX(program, &ptx[0]));
+  // Make sure the string reflects the true size (so minus the null terminator).
+  ptx.resize(ptx_size_including_null - 1);
+  return ptx;
+}
+
 }  // namespace
 
-void FusedOp::GenerateCode(int kernel_index, const std::vector<OpReqType> &req,
+std::string FusedOp::GenerateCode(const std::vector<OpReqType> &req,
                            const std::vector<int> &in_dtypes,
                            const std::vector<int> &out_dtypes,
                            const std::vector<int> &in_ndims,
@@ -175,7 +199,7 @@ void FusedOp::GenerateCode(int kernel_index, const std::vector<OpReqType> &req,
                            const int nvec,
                            const std::string &kernel_name,
                            std::vector<uint32_t>* check_shapes) {
-  const auto& g = this->subgraph_.indexed_graph();
+  const auto& g = subgraph_.indexed_graph();
   std::string code = "";
   int temp_name_counter = 0;
   using NodeEntry = nnvm::IndexedGraph::NodeEntry;
@@ -459,16 +483,11 @@ void FusedOp::GenerateCode(int kernel_index, const std::vector<OpReqType> &req,
     ++counter;
   }
 
-  this->code_[kernel_index] = code;
-
   // Add boilerplate and type information
-  if (dmlc::GetEnv("MXNET_FUSION_VERBOSE", false)) {
-    LOG(INFO) << code_[kernel_index];
-  }
   std::string kernel_params = "";
   std::string tensor_params = "";
   nnvm::Symbol sym;
-  sym.outputs = this->subgraph_.outputs;
+  sym.outputs = subgraph_.outputs;
   const std::vector<std::string> input_names = sym.ListInputNames(nnvm::Symbol::kAll);
   size_t num_params = in_dtypes.size() + out_dtypes.size();
   size_t i = 0;
@@ -513,85 +532,102 @@ void FusedOp::GenerateCode(int kernel_index, const std::vector<OpReqType> &req,
   }
   kernel_params += tensor_params;
 
-  code_[kernel_index] = std::string(fusion::fp16_support_string) + "\n" +
-          fusion::type_support_string + "\n" +
-          fusion::function_definitions + "\n" +
-          fusion::backward_function_definitions + "\n" +
-          aux_code + "\n" +
-          "__launch_bounds__(" + std::to_string(FusedOp::NTHREADS) + ")\n" +
-          "__global__ void FusedKernel_" + kernel_name +
-          "(size_t N, " + kernel_params + ") {\n" +
-          fusion::kernel_begin + "\n" +
-          code_[kernel_index] + "\n" +
-          fusion::kernel_end;
+  // Create kernel source (minus the common header)
+  return aux_code + "\n" +
+         "__launch_bounds__(" + std::to_string(FusedOp::NTHREADS) + ")\n" +
+         "__global__ void FusedKernel_" + kernel_name +
+         "(size_t N, " + kernel_params + ") {\n" +
+         fusion::kernel_begin + "\n" +
+         code + "\n" +
+         fusion::kernel_end;
 }
 
-void FusedOp::CompileCode(int kernel_index, const std::string &kernel_name) {
+CUfunction FusedOp::CompileCode(const std::string &code,
+                                const std::string &kernel_name,
+                                int dev_id) {
   // Guard NVRTC calls
   std::lock_guard<std::mutex> lock_nvrtc(mutex_);
-  nvrtcProgram program;
-  NVRTC_CALL(
-      nvrtcCreateProgram(&program,                                  // prog
-                         &code_[kernel_index][0],                                 // buffer
-                         (kernel_name + "_kernel.cu").c_str(),      // name
-                         0,                                         // num headers
-                         NULL,                                      // headers
-                         NULL));                                    // include names
-  std::string gpu_arch = "--gpu-architecture=compute_" +
-                         std::to_string(this->cc_major_) +
-                         std::to_string(this->cc_minor_);
-
-  const char *opts[] = {gpu_arch.c_str(),
-                        "--std=c++11",
-                        "-default-device"};
-  const std::string kernel_name_demangled = "FusedKernel_" + kernel_name;
-  NVRTC_CALL(nvrtcAddNameExpression(program, (kernel_name_demangled).c_str()));
-
-  nvrtcResult compileResult = nvrtcCompileProgram(program,  // prog
-                                                  3,        // num options
-                                                  opts);    // options
-  // Obtain compilation log from the program.
-  size_t log_size;
-  NVRTC_CALL(nvrtcGetProgramLogSize(program, &log_size));
-  std::string log(log_size, '\0');
-  NVRTC_CALL(nvrtcGetProgramLog(program, &log[0]));
-  CHECK_EQ(compileResult, NVRTC_SUCCESS)
-    << "NVRTC Compilation failed. Please set environment variable MXNET_USE_FUSION to 0.\n" << log;
-  // Obtain PTX from the program.
-  size_t ptx_size;
-  NVRTC_CALL(nvrtcGetPTXSize(program, &ptx_size));
-  ptx_[kernel_index].reserve(ptx_size);
-  NVRTC_CALL(nvrtcGetPTX(program, &ptx_[kernel_index][0]));
-  const char *name;
-  NVRTC_CALL(nvrtcGetLoweredName(program,
-                                 kernel_name_demangled.c_str(),
-                                 &name));
-  kernel_name_[kernel_index] = name;
-  // Destroy the program.
-  NVRTC_CALL(nvrtcDestroyProgram(&program));
-  int device;
-  CUdevice cu_device;
-  CUcontext context;
-  CUmodule module;
-  CUDA_CALL(cudaGetDevice(&device));
-  CUDA_DRIVER_CALL(cuDeviceGet(&cu_device, device));
-  CUDA_DRIVER_CALL(cuDevicePrimaryCtxRetain(&context, cu_device));
-  CUDA_DRIVER_CALL(cuModuleLoadData(&module, &ptx_[kernel_index][0]));
-  CUDA_DRIVER_CALL(cuModuleGetFunction(&kernel_[kernel_index],
-                                       module,
-                                       kernel_name_[kernel_index].c_str()));
+  // Local class for value type of compile cache
+  struct KernelInfo {
+    std::string mangled_name;
+    std::string ptx;
+    std::vector<CUfunction> functions;
+  };
+  // Maps from the cuda source code (minus header) to the ptx and jit-compiled CUfunctions.
+  using KernelCache = std::map<std::string, KernelInfo>;
+  // Per-gpu-architecture compiled kernel cache with jit-compiled function for each device context
+  static std::map<int32_t, KernelCache> compiled_kernels;
+  int sm_arch = SMArch(dev_id);
+  KernelCache& compiled_kernels_this_arch = compiled_kernels[sm_arch];  // make null map as needed
+  KernelInfo& kinfo = compiled_kernels_this_arch[code];                 // make KernelInfo as needed
+  if (kinfo.ptx.size() == 0) {
+    // It's the first time we've seen this kernel, so we need to generate the ptx and mangled_name.
+    static std::string common_header =
+        std::string(fusion::fp16_support_string) + "\n" +
+        fusion::type_support_string + "\n" +
+        fusion::function_definitions + "\n" +
+        fusion::backward_function_definitions + "\n";
+    std::string code_with_header = common_header + code;
+    // If verbose mode, output kernel source, though not including the common header
+    if (dmlc::GetEnv("MXNET_FUSION_VERBOSE", false)) {
+      LOG(INFO) << "\n" << std::string(80, '-') << "\n" << code;
+    }
+    if (compiled_kernels_this_arch.size() == CACHESIZE_WARN_THRESHOLD + 1 &&
+        dmlc::GetEnv("MXNET_FUSION_SIZE_WARNING", true)) {
+      LOG(WARNING) << "The number of different fused ops exceeds " << CACHESIZE_WARN_THRESHOLD
+                   << ".  Set MXNET_FUSION_SIZE_WARNING=0 to quiet this warning.";
+    }
+    nvrtcProgram program;
+    NVRTC_CALL(nvrtcCreateProgram(&program,                                  // prog
+                                  &code_with_header[0],                      // buffer
+                                  (kernel_name + "_kernel.cu").c_str(),      // name
+                                  0,                                         // num headers
+                                  NULL,                                      // headers
+                                  NULL));                                    // include names
+
+    std::string gpu_arch_arg = "--gpu-architecture=compute_" + std::to_string(sm_arch);
+    const char *opts[] = {gpu_arch_arg.c_str(),
+                          "--std=c++11",
+                          "-default-device"};
+    const std::string kernel_name_demangled = "FusedKernel_" + kernel_name;
+    NVRTC_CALL(nvrtcAddNameExpression(program, (kernel_name_demangled).c_str()));
+
+    nvrtcResult compileResult = nvrtcCompileProgram(program,  // prog
+                                                    3,        // num options
+                                                    opts);    // options
+    CHECK_EQ(compileResult, NVRTC_SUCCESS)
+        << "NVRTC Compilation failed. Please set environment variable MXNET_USE_FUSION to 0.\n"
+        << GetCompileLog(program);
+
+    kinfo.ptx = GetPtx(program);
+    const char *mangled_name;
+    NVRTC_CALL(nvrtcGetLoweredName(program,
+                                   kernel_name_demangled.c_str(),
+                                   &mangled_name));
+    kinfo.mangled_name = mangled_name;
+    // Destroy the program.
+    NVRTC_CALL(nvrtcDestroyProgram(&program));
+  }
+  // Ensure function array is deep enough to index by dev_id
+  while (kinfo.functions.size() <= static_cast<size_t>(dev_id))
+    kinfo.functions.push_back(static_cast<CUfunction>(nullptr));
+  // Jit-compile ptx for the device as needed
+  if (kinfo.functions[dev_id] == static_cast<CUfunction>(nullptr)) {
+    // Make sure driver context is set to the proper device
+    CUdevice cu_device;
+    CUcontext context;
+    CUDA_DRIVER_CALL(cuDeviceGet(&cu_device, dev_id));
+    CUDA_DRIVER_CALL(cuDevicePrimaryCtxRetain(&context, cu_device));
+    // Jit-compile ptx for the driver's current context
+    CUmodule module;
+    CUDA_DRIVER_CALL(cuModuleLoadData(&module, kinfo.ptx.c_str()));
+    CUDA_DRIVER_CALL(cuModuleGetFunction(&kinfo.functions[dev_id],
+                                         module,
+                                         kinfo.mangled_name.c_str()));
+  }
+  return kinfo.functions[dev_id];
 }
 
-bool FusedOp::CheckComputeCapability(const OpContext &ctx) {
-  const int dev_id = ctx.run_ctx.ctx.dev_id;
-  const int cc_major = ComputeCapabilityMajor(dev_id);
-  const int cc_minor = ComputeCapabilityMinor(dev_id);
-
-  const bool ret = cc_major == this->cc_major_ && cc_minor == this->cc_minor_;
-  this->cc_major_ = cc_major;
-  this->cc_minor_ = cc_minor;
-  return ret;
-}
 
 void FusedOp::CheckShapesAndTypes(const std::vector<TBlob> &inputs,
                                   const std::vector<TBlob> &outputs,
@@ -665,23 +701,30 @@ void FusedOp::Forward<gpu>(const nnvm::NodeAttrs& attrs,
   const auto& node_shapes = intermediate_shapes_[0].internal_attr;
   const auto& node_dtypes = intermediate_dtypes_[0].internal_attr;
 
-  // Check and save compute capability of the current GPU
-  if (!CheckComputeCapability(ctx)) initialized_ = false;
+  int dev_id = ctx.run_ctx.ctx.dev_id;
 
+  // A change between training and inference modes may require different kernel functions
   initialized_ = initialized_ && (req == saved_reqs_);
   saved_reqs_ = req;
 
   if (!initialized_) {
-    this->GenerateCode(0, req, in_dtypes, out_dtypes, in_ndims, out_ndims,
+    const auto& code = GenerateCode(req, in_dtypes, out_dtypes, in_ndims, out_ndims,
                        node_shapes, node_dtypes, nvec, attrs.name, &check_shape_args_);
-    this->CompileCode(0, attrs.name);
+    kernel_functions_[fusion::kGeneral] = CompileCode(code, attrs.name, dev_id);
     if (check_shape_args_.size() > 0) {
-        this->GenerateCode(1, req, in_dtypes, out_dtypes, in_ndims, out_ndims,
+      const auto& code = GenerateCode(req, in_dtypes, out_dtypes, in_ndims, out_ndims,
                            node_shapes, node_dtypes, nvec, attrs.name, NULL);
-        this->CompileCode(1, attrs.name);
+      kernel_functions_[fusion::kShapeOptimized] = CompileCode(code, attrs.name, dev_id);
     }
     initialized_ = true;
+    kernel_function_dev_id_ = dev_id;
   }
+
+  // A change in device would force recompiling, but this is unexpected so signal as an error
+  if (dev_id != kernel_function_dev_id_)
+    LOG(FATAL) << "Fused op compiled for device " << kernel_function_dev_id_
+               <<  ", not expecting switch to device " << dev_id;
+
   Stream<gpu>* s = ctx.get_stream<gpu>();
   auto stream = Stream<gpu>::GetStream(s);
   std::vector<void*> args;
@@ -713,18 +756,18 @@ void FusedOp::Forward<gpu>(const nnvm::NodeAttrs& attrs,
   for (auto &ptr : ptrs) {
     args.push_back(reinterpret_cast<void *>(&ptr));
   }
-  int kernel_index = 0;
+  int kernel_variant = fusion::kGeneral;
   if (check_shape_args_.size() > 0) {
-      kernel_index = 1;
+    kernel_variant = fusion::kShapeOptimized;
       for (const auto &shape_id : check_shape_args_) {
           const auto& shape = node_shapes[shape_id];
           if (shape[shape.ndim()-1] % nvec != 0) {
-              kernel_index = 0;
+            kernel_variant = fusion::kGeneral;
           }
       }
   }
   CUDA_DRIVER_CALL(
-      cuLaunchKernel(kernel_[kernel_index],
+      cuLaunchKernel(kernel_functions_[kernel_variant],
         num_blocks, 1, 1,          // grid dim
         FusedOp::NTHREADS, 1, 1,   // block dim
         0, stream,                 // shared mem and stream
diff --git a/src/operator/fusion/fused_op.h b/src/operator/fusion/fused_op.h
index 035e5432fca4..24603ac1932f 100644
--- a/src/operator/fusion/fused_op.h
+++ b/src/operator/fusion/fused_op.h
@@ -34,6 +34,12 @@
 
 namespace mxnet {
 
+namespace fusion {
+  enum KernelVariants {kGeneral, kShapeOptimized,
+    kNumKernelVariants  // Not a variant- leave this at the end
+  };
+}
+
 struct FusedOpConfig : public dmlc::Parameter<FusedOpConfig> {
   int num_inputs;
   int num_outputs;
@@ -53,6 +59,7 @@ struct FusedOpEntry {
 class FusedOp {
  public:
   static const int NTHREADS = 512;
+  static const int CACHESIZE_WARN_THRESHOLD = 10000;
 
   explicit FusedOp(const nnvm::NodeAttrs* attrs, const FusedOpConfig& config);
   ~FusedOp() {}
@@ -120,20 +127,20 @@ class FusedOp {
   }
 
  private:
-  void GenerateCode(int kernel_index,
-                    const std::vector<OpReqType> &req,
-                    const std::vector<int> &in_dtypes,
-                    const std::vector<int> &out_dtypes,
-                    const std::vector<int> &in_ndims,
-                    const std::vector<int> &out_ndims,
-                    const mxnet::ShapeVector &node_shapes,
-                    const std::vector<int> &node_dtypes,
-                    const int nvec,
-                    const std::string& kernel_name,
-                    std::vector<uint32_t> *check_shapes);
-  void CompileCode(int kernel_index,
-                   const std::string &kernel_name);
-  bool CheckComputeCapability(const OpContext &ctx);
+  std::string GenerateCode(const std::vector<OpReqType> &req,
+                           const std::vector<int> &in_dtypes,
+                           const std::vector<int> &out_dtypes,
+                           const std::vector<int> &in_ndims,
+                           const std::vector<int> &out_ndims,
+                           const mxnet::ShapeVector &node_shapes,
+                           const std::vector<int> &node_dtypes,
+                           const int nvec,
+                           const std::string& kernel_name,
+                           std::vector<uint32_t> *check_shapes);
+
+  CUfunction CompileCode(const std::string &code,
+                         const std::string &kernel_name, int dev_id);
+
   void CheckShapesAndTypes(const std::vector<TBlob> &inputs,
                            const std::vector<TBlob> &outputs,
                            std::vector<int> *in_dtypes,
@@ -145,7 +152,6 @@ class FusedOp {
   std::vector<FusedOpEntry> inputs_;
   std::vector<FusedOpEntry> outputs_;
 
-  std::string code_[2];
   nnvm::Graph subgraph_;
 
   template <typename T>
@@ -173,12 +179,9 @@ class FusedOp {
   std::vector<uint32_t> extra_shape_args_;
   std::vector<uint32_t> check_shape_args_;
 
-  std::string ptx_[2];
-  std::string kernel_name_[2];
-  CUfunction kernel_[2];
+  CUfunction kernel_functions_[fusion::kNumKernelVariants];
   bool initialized_;
-  int cc_major_;
-  int cc_minor_;
+  int kernel_function_dev_id_;
 
   static std::mutex mutex_;
   std::mutex my_mutex_;
diff --git a/tests/python/gpu/test_fusion.py b/tests/python/gpu/test_fusion.py
index 6adf935fb29c..5606eb19a9c5 100644
--- a/tests/python/gpu/test_fusion.py
+++ b/tests/python/gpu/test_fusion.py
@@ -28,6 +28,7 @@
 def check_fused_symbol(sym, **kwargs):
     inputs = sym.list_inputs()
     shapes = {inp : kwargs[inp].shape for inp in inputs}
+    ctx = kwargs.get('ctx', mx.gpu(0))
     # Double identity so that there is always something to fuse
     test_sym = mx.sym.Group([mx.sym.identity(mx.sym.identity(s)) for s in sym])
     rtol = {'float16' : 1e-2,
@@ -43,9 +44,9 @@ def check_fused_symbol(sym, **kwargs):
         for grad_req in ['write', 'add']:
             type_dict = {inp : dtype for inp in inputs}
             os.environ["MXNET_USE_FUSION"] = "0"
-            orig_exec = test_sym.simple_bind(ctx=mx.gpu(0), grad_req=grad_req, type_dict=type_dict, **shapes)
+            orig_exec = test_sym.simple_bind(ctx=ctx, grad_req=grad_req, type_dict=type_dict, **shapes)
             os.environ["MXNET_USE_FUSION"] = "1"
-            fused_exec = test_sym.simple_bind(ctx=mx.gpu(0), grad_req=grad_req, type_dict=type_dict, **shapes)
+            fused_exec = test_sym.simple_bind(ctx=ctx, grad_req=grad_req, type_dict=type_dict, **shapes)
             fwd_orig = orig_exec.forward(is_train=True, **data)
             out_grads = [mx.nd.ones_like(arr) for arr in fwd_orig]
             orig_exec.backward(out_grads=out_grads)
@@ -218,6 +219,26 @@ def test_fusion():
     check_binary_ops()
     check_other_ops()
 
+@with_seed()
+def test_fusion_compiler_cache():
+    # Stresses the internal cache of CUfunctions by creating the same kernel multiple times and
+    # on multiple GPUs if available.
+    a = mx.sym.Variable('a')
+    b = mx.sym.Variable('b')
+    shape = rand_shape_2d()
+    arr1 = mx.random.uniform(shape=shape)
+    arr2 = mx.random.uniform(shape=shape)
+
+    # Invoke the same model twice, second time will exercise compile cache
+    check_fused_symbol(a+b, ctx=mx.gpu(0), a=arr1, b=arr2)
+    check_fused_symbol(a+b, ctx=mx.gpu(0), a=arr1, b=arr2)
+
+    # On multi-GPU systems, invoke the same model on other GPUs
+    num_gpus = mx.context.num_gpus()
+    if num_gpus > 1:
+        check_fused_symbol(a+b, ctx=mx.gpu(1), a=arr1, b=arr2)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 47fd3a02f4fa981578c3eeb6ab9f1fba3a198c82 Mon Sep 17 00:00:00 2001
From: Talia <31782251+TEChopra1000@users.noreply.github.com>
Date: Tue, 12 Nov 2019 17:58:35 -0800
Subject: [PATCH 589/813] Link fixes4 (#16764)

* a new round of link fixes

* fixing merge conflict

* Nudging test.

* Update julia/docs/src/tutorial/char-lstm.md

* fixing mkldnn version to match upstream/master
---
 .../gluon_from_experiment_to_deployment.md    |  5 +-
 .../logistic_regression_explained.md          |  2 +-
 .../getting-started/to-mxnet/pytorch.md       |  4 +-
 docs/python_docs/python/tutorials/index.rst   | 12 ++--
 .../gluon/blocks/activations/activations.md   |  8 +--
 .../python/tutorials/packages/gluon/index.rst |  2 +-
 .../tutorials/packages/optimizer/index.md     | 56 +++++++++----------
 .../tutorials/mxnet_cpp_inference_tutorial.md |  7 ++-
 docs/static_site/src/pages/api/faq/float16.md |  4 +-
 julia/docs/mkdocs.yml                         |  2 +-
 julia/docs/src/tutorial/char-lstm.md          |  2 +-
 11 files changed, 52 insertions(+), 52 deletions(-)

diff --git a/docs/python_docs/python/tutorials/getting-started/gluon_from_experiment_to_deployment.md b/docs/python_docs/python/tutorials/getting-started/gluon_from_experiment_to_deployment.md
index 47b629991650..20e9cabcdaf8 100644
--- a/docs/python_docs/python/tutorials/getting-started/gluon_from_experiment_to_deployment.md
+++ b/docs/python_docs/python/tutorials/getting-started/gluon_from_experiment_to_deployment.md
@@ -267,8 +267,7 @@ finetune_net.export("flower-recognition", epoch=epochs)
 MXNet provides various useful tools and interfaces for deploying your model for inference. For example, you can use [MXNet Model Server](https://github.com/awslabs/mxnet-model-server) to start a service and host your trained model easily.
 Besides that, you can also use MXNet's different language APIs to integrate your model with your existing service. We provide [Python](/api/python.html),    [Java](/api/java.html), [Scala](/api/scala.html), and [C++](/api/cpp) APIs.
 
-Here we will briefly introduce how to run inference using Module API in Python. There is more detailed explanation available in the [Predict Image Tutorial](https://mxnet.apache.org/tutorials/python/predict_image.html).
-In general, prediction consists of the following steps:
+Here we will briefly introduce how to run inference using Module API in Python. In general, prediction consists of the following steps:
 1. Load the model architecture (symbol file) and trained parameter values (params file)
 2. Load the synset file for label names
 3. Load the image and apply the same transformation we did on validation dataset during training
@@ -311,7 +310,7 @@ probability=9.798435, class=lotus
 
 ## What's next
 
-You can continue to the [next tutorial](https://mxnet.apache.org/versions/master/tutorials/c++/mxnet_cpp_inference_tutorial.html) on how to load the model we just trained and run inference using MXNet C++ API.
+You can continue to the [next tutorial](/api/cpp/docs/tutorials/cpp_inference) on how to load the model we just trained and run inference using MXNet C++ API.
 
 You can also find more ways to run inference and deploy your models here:
 1. [Java Inference examples](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer)
diff --git a/docs/python_docs/python/tutorials/getting-started/logistic_regression_explained.md b/docs/python_docs/python/tutorials/getting-started/logistic_regression_explained.md
index 36f2e5a68062..8cd29f2a32b3 100644
--- a/docs/python_docs/python/tutorials/getting-started/logistic_regression_explained.md
+++ b/docs/python_docs/python/tutorials/getting-started/logistic_regression_explained.md
@@ -55,7 +55,7 @@ batch_size = 10
 
 ## Working with data
 
-To work with data, Apache MXNet provides [Dataset](https://mxnet.apache.org/api/python/gluon/data.html#mxnet.gluon.data.Dataset) and [DataLoader](https://mxnet.apache.org/api/python/gluon/data.html#mxnet.gluon.data.DataLoader) classes. The former is used to provide an indexed access to the data, the latter is used to shuffle and batchify the data. To learn more about working with data in Gluon, please refer to [Gluon Datasets and Dataloaders](https://mxnet.apache.org/tutorials/gluon/datasets.html) tutorial.
+To work with data, Apache MXNet provides [Dataset](https://mxnet.apache.org/api/python/gluon/data.html#mxnet.gluon.data.Dataset) and [DataLoader](https://mxnet.apache.org/api/python/gluon/data.html#mxnet.gluon.data.DataLoader) classes. The former is used to provide an indexed access to the data, the latter is used to shuffle and batchify the data. To learn more about working with data in Gluon, please refer to [Gluon Datasets and Dataloaders](/api/python/docs/api/gluon/data/index.html).
 
 Below we define training and validation datasets, which we are going to use in the tutorial.
 
diff --git a/docs/python_docs/python/tutorials/getting-started/to-mxnet/pytorch.md b/docs/python_docs/python/tutorials/getting-started/to-mxnet/pytorch.md
index 1ab490fbaa42..ec4bdfcdc77e 100644
--- a/docs/python_docs/python/tutorials/getting-started/to-mxnet/pytorch.md
+++ b/docs/python_docs/python/tutorials/getting-started/to-mxnet/pytorch.md
@@ -106,7 +106,7 @@ mx_train_data = gluon.data.DataLoader(
 
 Both frameworks allows you to download MNIST data set from their sources and specify that only training part of the data set is required.
 
-The main difference between the code snippets is that MXNet uses [transform_first](https://mxnet.apache.org/api/python/docs/api/gluon/_autogen/mxnet.gluon.data.Dataset.html) method to indicate that the data transformation is done on the first element of the data batch, the MNIST picture, rather than the second element, the label.
+The main difference between the code snippets is that MXNet uses [transform_first](/api/python/docs/api/gluon/data/index.html#mxnet.gluon.data.Dataset.transform_first) method to indicate that the data transformation is done on the first element of the data batch, the MNIST picture, rather than the second element, the label.
 
 ### 2. Creating the model
 
@@ -143,7 +143,7 @@ We used the Sequential container to stack layers one after the other in order to
 
 * After the model structure is defined, Apache MXNet requires you to explicitly call the model initialization function.
 
-With a Sequential block, layers are executed one after the other. To have a different execution model, with PyTorch you can inherit from `nn.Module` and then customize how the `.forward()` function is executed. Similarly, in Apache MXNet you can inherit from [nn.Block](https://mxnet.apache.org/api/python/docs/api/gluon/mxnet.gluon.nn.Block.html) to achieve similar results.
+With a Sequential block, layers are executed one after the other. To have a different execution model, with PyTorch you can inherit from `nn.Module` and then customize how the `.forward()` function is executed. Similarly, in Apache MXNet you can inherit from [nn.Block](/api/python/docs/api/gluon/nn/index.html#mxnet.gluon.nn.Block) to achieve similar results.
 
 ### 3. Loss function and optimization algorithm
 
diff --git a/docs/python_docs/python/tutorials/index.rst b/docs/python_docs/python/tutorials/index.rst
index af906deba22b..c130ce607ea6 100644
--- a/docs/python_docs/python/tutorials/index.rst
+++ b/docs/python_docs/python/tutorials/index.rst
@@ -56,13 +56,13 @@ Packages & Modules
 
    .. card::
       :title: Symbol API
-      :link: packages/symbol/index.html
+      :link: /api/python/docs/api/symbol/index.html
 
-      How to use MXNet's Symbol API.
+      MXNet Symbol API has been depricated. API documentation is still available for reference.
 
    .. card::
       :title: Autograd API
-      :link: packages/autograd/autograd.html
+      :link: /api/python/docs/tutorials/packages/autograd/index.html
 
       How to use Automatic Differentiation with the Autograd API.
 
@@ -86,13 +86,13 @@ Performance
 
    .. card::
       :title: Compression: int8
-      :link: performance/int8.html
+      :link: performance/compression/int8.html
 
       How to use int8 in your model to boost training speed.
 
    .. card::
       :title: MKL-DNN
-      :link: performance/backend/mkl-dnn.html
+      :link: performance/backend/mkldnn/mkldnn_quantization
 
       How to get the most from your CPU by using Intel's MKL-DNN.
 
@@ -172,4 +172,4 @@ Next steps
    packages/index
    performance/index
    deploy/index
-   extend/index
\ No newline at end of file
+   extend/index
diff --git a/docs/python_docs/python/tutorials/packages/gluon/blocks/activations/activations.md b/docs/python_docs/python/tutorials/packages/gluon/blocks/activations/activations.md
index e33e94182156..755253708b43 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/blocks/activations/activations.md
+++ b/docs/python_docs/python/tutorials/packages/gluon/blocks/activations/activations.md
@@ -270,7 +270,7 @@ visualize_activation(mx.gluon.nn.Swish())
 ## Next Steps
 
 Activations are just one component of neural network architectures. Here are a few MXNet resources to learn more about activation functions and how they they combine with other components of neural nets.
-* Learn how to create a Neural Network with these activation layers and other neural network layers in the [gluon crash course](http://beta.mxnet.io/guide/getting-started/crash-course/2-nn.html).
-* Check out the guide to MXNet [gluon layers and blocks](http://beta.mxnet.io/guide/packages/gluon/nn.html) to learn about the other neural network layers in implemented in MXNet and how to create custom neural networks with these layers.
-* Also check out the [guide to normalization layers](http://beta.mxnet.io/guide/packages/gluon/normalization/normalization.html) to learn about neural network layers that normalize their inputs.
-* Finally take a look at the [Custom Layer guide](http://beta.mxnet.io/guide/packages/gluon/custom_layer_beginners.html) to learn how to implement your own custom activation layer.
+* Learn how to create a Neural Network with these activation layers and other neural network layers in the [gluon crash course](/api/python/docs/tutorials/getting-started/crash-course/index.html).
+* Check out the guide to MXNet [gluon layers and blocks](/api/python/docs/tutorials/packages/gluon/blocks/nn.html) to learn about the other neural network layers in implemented in MXNet and how to create custom neural networks with these layers.
+* Also check out the [guide to normalization layers](/api/python/docs/tutorials/packages/gluon/training/normalization/index.html) to learn about neural network layers that normalize their inputs.
+* Finally take a look at the [Custom Layer guide](/api/python/docs/tutorials/extend/custom_layer.html) to learn how to implement your own custom activation layer.
diff --git a/docs/python_docs/python/tutorials/packages/gluon/index.rst b/docs/python_docs/python/tutorials/packages/gluon/index.rst
index c41bf3d9d116..e2bdb1856953 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/index.rst
+++ b/docs/python_docs/python/tutorials/packages/gluon/index.rst
@@ -167,7 +167,7 @@ Training
 
    .. card::
       :title: Autograd API
-      :link: ../autograd/autograd.html
+      :link: /api/python/docs/tutorials/packages/autograd/index.html
 
       How to use Automatic Differentiation with the Autograd API.
 
diff --git a/docs/python_docs/python/tutorials/packages/optimizer/index.md b/docs/python_docs/python/tutorials/packages/optimizer/index.md
index b52f212b9a35..b7b6c7453a89 100644
--- a/docs/python_docs/python/tutorials/packages/optimizer/index.md
+++ b/docs/python_docs/python/tutorials/packages/optimizer/index.md
@@ -19,9 +19,9 @@
 
 Deep learning models are comprised of a model architecture and the model parameters. The model architecture is chosen based on the task - for example Convolutional Neural Networks (CNNs) are very successful in handling image based tasks and Recurrent Neural Networks (RNNs) are better suited for sequential prediction tasks. However, the values of the model parameters are learned by solving an optimization problem during model training.
 
-To learn the parameters, we start with an initialization scheme and iteratively refine the parameter initial values by moving them along a direction that is opposite to the (approximate) gradient of the loss function. The extent to which the parameters are updated in this direction is governed by a hyperparameter called the learning rate. This process, known as gradient descent, is the backbone of optimization algorithms in deep learning. In MXNet, this functionality is abstracted by the [Optimizer API](http://beta.mxnet.io/api/gluon-related/mxnet.optimizer.html).
+To learn the parameters, we start with an initialization scheme and iteratively refine the parameter initial values by moving them along a direction that is opposite to the (approximate) gradient of the loss function. The extent to which the parameters are updated in this direction is governed by a hyperparameter called the learning rate. This process, known as gradient descent, is the backbone of optimization algorithms in deep learning. In MXNet, this functionality is abstracted by the [Optimizer API](/api/python/docs/api/optimizer/index.html#module-mxnet.optimizer).
 
-When training a deep learning model using the MXNet [gluon API](http://beta.mxnet.io/guide/packages/gluon/index.html), a gluon [Trainer](http://beta.mxnet.io/guide/packages/gluon/trainer.html) is initialized with the all the learnable parameters and the optimizer to be used to learn those parameters. A single step of iterative refinement of model parameters in MXNet is achieved by calling [`trainer.step`](http://beta.mxnet.io/api/gluon/_autogen/mxnet.gluon.Trainer.step.html) which in turn uses the gradient (and perhaps some state information) to update the parameters by calling `optimizer.update`.
+When training a deep learning model using the MXNet [gluon API](/api/python/docs/tutorials/packages/gluon/index.html), a gluon [Trainer](/api/python/docs/tutorials/packages/gluon/training/trainer.html) is initialized with the all the learnable parameters and the optimizer to be used to learn those parameters. A single step of iterative refinement of model parameters in MXNet is achieved by calling [`trainer.step`](/api/python/docs/api/gluon/trainer.html#mxnet.gluon.Trainer.step) which in turn uses the gradient (and perhaps some state information) to update the parameters by calling `optimizer.update`.
 
 Here is an example of how a trainer with an optimizer is created for, a simple Linear (Dense) Network.
 
@@ -35,7 +35,7 @@ optim = optimizer.SGD(learning_rate=0.1)
 trainer = gluon.Trainer(net.collect_params(), optimizer=optim)
 ```
 
-In model training, the code snippet above would be followed by a training loop which, at every iteration performs a forward pass (to compute the loss), a backward pass (to compute the gradient of the loss with respect to the parameters) and a trainer step (which updates the parameters using the gradient). See the [gluon Trainer guide](http://beta.mxnet.io/guide/packages/gluon/trainer.html) for a complete example.
+In model training, the code snippet above would be followed by a training loop which, at every iteration performs a forward pass (to compute the loss), a backward pass (to compute the gradient of the loss with respect to the parameters) and a trainer step (which updates the parameters using the gradient). See the [gluon Trainer guide](/api/python/docs/tutorials/packages/gluon/training/trainer.html) for a complete example.
 
 We can also create the trainer by passing in the optimizer name and optimizer params into the trainer constructor directly, as shown below.
 
@@ -45,14 +45,14 @@ trainer = gluon.Trainer(net.collect_params(), optimizer='adam', optimizer_params
 ```
 
 ### What should I use?
-For many deep learning model architectures, the `sgd` and `adam` optimizers are a really good place to start. If you are implementing a deep learning model and trying to pick an optimizer, start with [`'sgd'`](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.optimizer.SGD.html#mxnet.optimizer.SGD) as you will often get good enough results as long as your learning problem is tractable. If you already have a trainable model and you want to improve the convergence then you can try [`'adam'`](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.optimizer.SGD.html#mxnet.optimizer.Adam). If you would like to improve your model training process further, there are a number of specialized optimizers out there with many of them already implemented in MXNet. This guide walks through these optimizers in some detail.
+For many deep learning model architectures, the `sgd` and `adam` optimizers are a really good place to start. If you are implementing a deep learning model and trying to pick an optimizer, start with [`'sgd'`](/api/python/docs/api/optimizer/index.html#mxnet.optimizer.SGD) as you will often get good enough results as long as your learning problem is tractable. If you already have a trainable model and you want to improve the convergence then you can try [`'adam'`](/api/python/docs/api/optimizer/index.html#mxnet.optimizer.Adam). If you would like to improve your model training process further, there are a number of specialized optimizers out there with many of them already implemented in MXNet. This guide walks through these optimizers in some detail.
 
 ## Stochastic Gradient Descent
-[Gradient descent](https://en.wikipedia.org/wiki/Gradient_descent) is a general purpose algorithm for minimizing a function using information from the gradient of the function with respect to its parameters. In deep learning, the function we are interested in minimizing is the [loss function](http://beta.mxnet.io/guide/packages/gluon/loss.html). Our model accepts training data as inputs and the loss function tells us how good our model predictions are. Since the training data can routinely consist of millions of examples, computing the loss gradient on the full batch of training data is very computationally expensive. Luckily, we can effectively approximate the full gradient with the gradient of the loss function on randomly chosen minibatches of our training data. This variant of gradient descent is [stochastic gradient descent](https://en.wikipedia.org/wiki/Stochastic_gradient_descent).
+[Gradient descent](https://en.wikipedia.org/wiki/Gradient_descent) is a general purpose algorithm for minimizing a function using information from the gradient of the function with respect to its parameters. In deep learning, the function we are interested in minimizing is the [loss function](/api/python/docs/tutorials/packages/gluon/loss/loss.html). Our model accepts training data as inputs and the loss function tells us how good our model predictions are. Since the training data can routinely consist of millions of examples, computing the loss gradient on the full batch of training data is very computationally expensive. Luckily, we can effectively approximate the full gradient with the gradient of the loss function on randomly chosen minibatches of our training data. This variant of gradient descent is [stochastic gradient descent](https://en.wikipedia.org/wiki/Stochastic_gradient_descent).
 
 Technically, stochastic gradient descent (SGD) refers to an online approximation of the gradient descent algorithm that computes the gradient of the loss function applied to a *single datapoint*, instead of your entire dataset, and uses this approximate gradient to update the model parameter values. However, in MXNet, and other deep learning frameworks, the SGD optimizer is agnostic to how many datapoints the loss function is applied to, and it is more effective to use a mini-batch loss gradient, as described earlier, instead of a single datapoint loss gradient.
 
-### [SGD optimizer](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.optimizer.SGD.html#mxnet.optimizer.SGD)
+### [SGD optimizer](/api/python/docs/api/optimizer/index.html#mxnet.optimizer.SGD)
 
 For an SGD optimizer initialized with learning rate $lr$, the update function accepts parameters (weights) $w_i$, and their gradients $grad(w_i)$, and performs the single update step:
 
@@ -101,9 +101,9 @@ To create an SGD optimizer with momentum $\gamma$ and weight decay in MXNet simp
 sgd_optimizer = optimizer.SGD(learning_rate=0.1, wd=0., momentum=0.8)
 ```
 
-### [Nesterov Accelerated Stochastic Gradient Descent](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.optimizer.NAG.html#mxnet.optimizer.NAG)
+### [Nesterov Accelerated Stochastic Gradient Descent](/api/python/docs/api/optimizer/index.html#mxnet.optimizer.NAG)
 
-The momentum method of [Nesterov](https://goo.gl/M5xbuX) is a modification to SGD with momentum that allows for even faster convergence in practice. With Nesterov accelerated gradient (NAG) descent, the update term is derived from the gradient of the loss function with respect to *refined parameter values*. These refined parameter values are computed by performing a SGD update step using the momentum history as the gradient term.
+The momentum method of [Nesterov] is a modification to SGD with momentum that allows for even faster convergence in practice. With Nesterov accelerated gradient (NAG) descent, the update term is derived from the gradient of the loss function with respect to *refined parameter values*. These refined parameter values are computed by performing a SGD update step using the momentum history as the gradient term.
 
 Alternatively, you can think of the NAG optimizer as performing two update steps:
 * The first (internal) update step approximates uses the current momentum history $v_i$ to calculate the refined parameter values $(w_i + \gamma \cdot v_i)$. This is also known as the lookahead step.
@@ -132,7 +132,7 @@ nag_optimizer = optimizer.NAG(learning_rate=0.1, momentum=0.8)
 
 The gradient methods implemented by the optimizers described above use a global learning rate hyperparameter for all parameter updates. This has a well-documented shortcoming in that it makes the training process and convergence of the optimization algorithm really sensitive to the choice of the global learning rate. Adaptive learning rate methods avoid this pitfall by incorporating some history of the gradients observed in earlier iterations to scale step sizes (learning rates) to each learnable parameter in the model.
 
-### [AdaGrad](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.optimizer.AdaGrad.html)
+### [AdaGrad](/api/python/docs/api/optimizer/index.html#mxnet.optimizer.AdaGrad)
 
 The AdaGrad optimizer, which implements the optimization method originally described by [Duchi et al](http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf), multiplies the global learning rate by the $L_2$ norm of the preceeding gradient estimates for each paramater to obtain the per-parameter learning rate. To achieve this, AdaGrad introduces a new term which we'll denote as $g^2$ - the accumulated square of the gradient of the loss function with respect to the parameters.
 
@@ -152,7 +152,7 @@ To instantiate the Adagrad optimizer in MXNet you can use the following line of
 adagrad_optimizer = optimizer.AdaGrad(learning_rate=0.1, eps=1e-07)
 ```
 
-### [RMSProp](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.optimizer.RMSProp.html)
+### [RMSProp](/api/python/docs/api/optimizer/index.html#mxnet.optimizer.RMSProp)
 
 RMSProp, introduced by [Tielemen and Hinton](http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf), is similar to AdaGrad described above, but, instead of accumulating the sum of historical square gradients, maintains an exponential decaying average of the historical square gradients, in order to give more weighting to more recent gradients.
 
@@ -186,7 +186,7 @@ rmsprop_optimizer = optimizer.RMSProp(learning_rate=0.001, gamma1=0.9, gamma2=0.
 
 In the code snippet above, `gamma1` is $\beta$ in the equations above and `gamma2` is $\gamma$, which is only used where `centered=True`.
 
-### [AdaDelta](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.optimizer.AdaDelta.html)
+### [AdaDelta](/api/python/docs/api/optimizer/index.html#mxnet.optimizer.AdaDelta)
 
 AdaDelta was introduced to address some remaining lingering issues with AdaGrad and RMSProp - the selection of a global learning rate. AdaGrad and RMSProp assign each parameter its own learning rate but the per-parameter learning rate are still calculated using the global learning rate. In contrast, AdaDelta does not require a global learning rate, instead, it tracks the square of previous update steps, represented below as $\mathbb{E}[\Delta w^2]$ and uses the root mean square of the previous update steps as an estimate of the learning rate.
 
@@ -205,7 +205,7 @@ Here is the code snippet creating the AdaDelta optimizer in MXNet. The argument
 adadelta_optimizer = optimizer.AdaDelta(rho=0.9, epsilon=1e-07)
 ```
 
-### [Adam](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.optimizer.Adam.html)
+### [Adam](/api/python/docs/api/optimizer/index.html#mxnet.optimizer.Adam)
 Adam, introduced by [Kingma and Ba](https://arxiv.org/abs/1412.6980), is one of the popular adaptive algorithms for deep learning. It combines elements of RMSProp with momentum SGD. Like RMSProp, Adam uses the RootMeanSquare of decaying average of historical gradients but also explicitly keeps track of a decaying average of momentum and uses that for the update step direction. Thus, Adam accepts two hyperparameters $\beta_1$ and $\beta_2$ for momentum weighting and gradient RMS weighting respectively. Adam also accepts a global learning rate that's adaptively tuned to each parameter with the gradient RootMeanSquare. Finally, Adam also includes bias correction steps within the update that transform the biased estimates of first and second order moments, $v_{i+1}$ and $\mathbb{E}[g^2]_{i+1}$ to their unbiased counterparts $\tilde{v}_{i+1}$ and $\tilde{\mathbb{E}[g^2]}_{i+1}$
 
 The Adam optimizer performs the update step described the following equations:
@@ -223,7 +223,7 @@ In MXNet, you can construct the Adam optimizer with the following line of code.
 adam_optimizer = optimizer.Adam(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08)
 ```
 
-### [Adamax](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.optimizer.Adamax.html)
+### [Adamax](/api/python/docs/api/optimizer/index.html#mxnet.optimizer.Adamax)
 Adamax is a variant of Adam also included in the original paper by [Kingma and Ba](https://arxiv.org/abs/1412.6980). Like Adam, Adamax maintains a moving average for first and second moments but Adamax uses the $L_{\infty}$ norm for the exponentially weighted average of the gradients, instead of the $L_2$ norm used in Adam used to keep track of the gradient second moment. The $L_{\infty}$ norm of a vector is equivalent to take the maximum absolute value of elements in that vector.
 
 $$ v_{i+1} = \beta_1 \cdot v_{i} + (1 - \beta_1) \cdot grad(w_i) $$
@@ -238,7 +238,7 @@ See the code snippet below for how to construct Adamax in MXNet.
 adamax_optimizer = optimizer.Adamax(learning_rate=0.002, beta1=0.9, beta2=0.999)
 ```
 
-### [Nadam](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.optimizer.Nadam.html)
+### [Nadam](/api/python/docs/api/optimizer/index.html#mxnet.optimizer.Nadam)
 Nadam is also a variant of Adam and draws from the perspective that Adam can be viewed as a combination of RMSProp and classical Momentum (or Polyak Momentum). Nadam replaces the classical Momentum component of Adam with Nesterov Momentum (See [paper](http://cs229.stanford.edu/proj2015/054_report.pdf) by Dozat). The consequence of this is that the gradient used to update the weighted average of the momentum term is a lookahead gradient as is the case with NAG.
 
 The Nadam optimizer performs the update step:
@@ -262,7 +262,7 @@ Training very deep neural networks can be time consuming and as such it is very
 
 While all the preceding optimizers, from SGD to Adam, can be readily used in the distributed setting, the following optimizers in MXNet provide extra features targeted at alleviating some of the problems associated with distributed training.
 
-### [Signum](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.optimizer.Signum.html)
+### [Signum](/api/python/docs/api/optimizer/index.html#mxnet.optimizer.Signum)
 In distributed training, communicating gradients across multiple worker nodes can be expensive and create a performance bottleneck. The Signum optimizer addresses this problem by transmitting just the sign of each minibatch gradient instead of the full precision gradient. In MXNet, the signum optimizer implements two variants of compressed gradients described in the paper by [Bernstein et al](https://arxiv.org/pdf/1802.04434.pdf).
 
 The first variant, achieved by constructing the Signum optimizer with `momentum=0`, implements SignSGD update which performs the update below.
@@ -281,7 +281,7 @@ Here is how to create the signum optimizer in MXNet.
 signum_optimizer = optimizer.Signum(learning_rate=0.01, momentum=0.9, wd_lh=0.0)
 ```
 
-### [LBSGD](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.optimizer.LBSGD.html)
+### [LBSGD](/api/python/docs/api/optimizer/index.html#mxnet.optimizer.LBSGD)
 LBSGD stands for Large Batch Stochastic Gradient Descent and implements a technique where Layer-wise Adaptive Rate Scaling (LARS) is used to maintain a separate learning rate for each layer of the neural network. LBSGD has no additional modifications to SGD and performs the same parameter update steps as the SGD optimizer described above.
 
 LBSGD was introduced by [You et al](https://arxiv.org/pdf/1708.03888.pdf) for distributed training with data-parallel synchronous SGD across multiple worker nodes to overcome the issue of reduced model accuracy when the number of workers, and by extension effective batch size, is increased.
@@ -308,7 +308,7 @@ LBSGD has a number of extra keyword arguments described below
 * `updates_per_epoch` - How many updates to the learning rate to perform every epoch. For example during warmup the warmup strategy is applied to increase the learning rate a total of `warmup_epochs*updates_per_epoch` number of times.
 * `begin_epoch` - The epoch at which to start warmup.
 
-### [DCASGD](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.optimizer.DCASGD.html)
+### [DCASGD](/api/python/docs/api/optimizer/index.html#mxnet.optimizer.DCASGD)
 
 The DCASGD optimizer implements Delay Compensated Asynchronous Stochastic Gradient Descent by [Zheng et al](https://arxiv.org/pdf/1609.08326.pdf). In asynchronous distributed SGD, it is possible that a training worker node add its gradients too late to the global (parameter) server resulting in a delayed gradient being used to update the current parameters. DCASGD addresses this issue of delayed gradients by compensating for this delay in the parameter update steps.
 
@@ -328,7 +328,7 @@ Before deep neural networks became popular post 2012, people were already solvin
 
 The class of optimization algorithms designed to tackle online learning problems have also seen some success in offline training of deep neural models. The following optimizers are algorithms taken from online learning that have been implemented in MXNet.
 
-### [FTRL](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.optimizer.Ftrl.html)
+### [FTRL](/api/python/docs/api/optimizer/index.html#mxnet.optimizer.Ftrl)
 
 FTRL stands for Follow the Regularized Leader and describes a family of algorithms originally designed for online learning tasks.
 
@@ -351,7 +351,7 @@ Here is how to initialize the FTRL optimizer in MXNet
 ftrl_optimizer = optimizer.Ftrl(lamda1=0.01, learning_rate=0.1, beta=1)
 ```
 
-### [FTML](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.optimizer.FTML.html)
+### [FTML](/api/python/docs/api/optimizer/index.html#mxnet.optimizer.FTML)
 
 FTML stands for Follow the Moving Leader and is a variant of the FTRL family of algorithms adapted specifically to deep learning. Regular FTRL algorithms, described above, solve an optimization problem every update that involves the sum of all previous gradients. This is not well suited for the non-convex loss functions in deep learning. In the non-convex settings, older gradients are likely uninformative as the parameter updates can move to converge towards different local minima at different iterations. FTML addresses this problem by reweighing the learning subproblems in each iteration as shown below.
 
@@ -379,7 +379,7 @@ Here `beta1` and `beta2` are similar to the arguments in the Adam optimizer.
 ## Bayesian SGD
 A notable shortcoming of deep learning is that the model parameters learned after training are only point estimates, therefore deep learning model predictions have no information about uncertainty or confidence bounds. This is in contrast to a fully Bayesian approach which incorporates prior distributions on the model parameters and estimates the model parameters as belonging to a posterior distribution. This approach allows the predictions of a bayesian model to have information about uncertainty, as you can sample different values from the posterior distribution to obtain different model parameters. One approach to close the bayesian gap in deep learning is to incorporate the gradient descent algorithm with properties that allow the model parameters to converge to a distribution instead of a single value or point estimate.
 
-### [SGLD](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.optimizer.SGLD.html)
+### [SGLD](/api/python/docs/api/optimizer/index.html#mxnet.optimizer.SGLD)
 Stochastic Gradient Langevin Dynamics or SGLD was introduced to allow uncertainties around model parameters to be captured directly during model training. With every update in SGLD, the learning rate decreases to zero and a gaussian noise of known variances is injected into the SGD step. This has the effect of having the training parameters converge to a sufficient statistic for a posterior distribution instead of simply a point estimate of the model parameters.
 
 SGLD performs the parameter update:
@@ -401,14 +401,14 @@ If you would like to use a particular optimizer that is not yet implemented in M
 
 Step 1: First create a function that is able to perform your desired updates given the weights, gradients and other state information.
 
-Step 2: You will have to write your own optimizer class that extends the [base optimizer class](http://beta.mxnet.io/api/gluon-related/_autogen/mxnet.optimizer.Optimizer.html#mxnet.optimizer.Optimizer) and override the following functions
+Step 2: You will have to write your own optimizer class that extends the [base optimizer class](/api/python/docs/api/optimizer/index.html#mxnet.optimizer.Optimizer) and override the following functions
 * `__init__`: accepts the parameters of your optimizer algorithm as inputs as saves them as member variables.
 * `create_state`: If your custom optimizer uses some additional state information besides the gradient, then you should implement a function that accepts the weights and returns the state.
 * `update`: Implement your optimizer update function using the function in Step 1
 
 Step 3: Register your optimizer with `@register` decorator on your optimizer class.
 
-See the [source code](http://beta.mxnet.io/_modules/mxnet/optimizer/optimizer.html#NAG) for the NAG optimizer for a concrete example.
+See the [source code](/api/python/docs/api/optimizer/index.html#mxnet.optimizer.NAG) for the NAG optimizer for a concrete example.
 
 ## Summary
 * MXNet implements many state-of-the-art optimizers which can be passed directly into a gluon trainer object. Calling `trainer.step` during model training uses the optimizers to update the model parameters.
@@ -421,9 +421,9 @@ See the [source code](http://beta.mxnet.io/_modules/mxnet/optimizer/optimizer.ht
 
 ## Next Steps
 While optimization and optimizers play a significant role in deep learning model training, there are still other important components to model training. Here are a few suggestions about where to look next.
-* The [trainer API](http://beta.mxnet.io/api/gluon/mxnet.gluon.Trainer.html) and [guide](http://beta.mxnet.io/guide/packages/gluon/trainer.html) have information about how to construct the trainer that encapsulate the optimizers and will actually be used in your model training loop.
-* Check out the guide to MXNet gluon [Loss functions](http://beta.mxnet.io/guide/packages/gluon/loss.html) and [custom losses](http://beta.mxnet.io/guide/packages/gluon/custom-loss/custom-loss.html) to learn about the loss functions optimized by these optimizers, see what loss functions are already implemented in MXNet and understand how to write your own custom loss functions.
-* Take a look at the [guide to parameter initialization](http://beta.mxnet.io/guide/packages/gluon/init.html) in MXNet to learn about what initialization schemes are already implemented, and how to implement your custom initialization schemes.
-* Also check out the [autograd guide](http://beta.mxnet.io/guide/packages/autograd/autograd.html) to learn about automatic differentiation and how gradients are automatically computed in MXNet.
-* Make sure to take a look at the [guide to scheduling learning rates](https://mxnet.apache.org/versions/master/tutorials/gluon/learning_rate_schedules.html) to learn how to create learning rate schedules to supercharge the convergence of your optimizer.
-* Finally take a look at the [KVStore API](http://beta.mxnet.io/api/gluon-related/mxnet.kvstore.KVStore.html#mxnet.kvstore.KVStore) to learn how parameter values are synchronized over multiple devices.
+* The [trainer API](/api/python/docs/api/gluon/trainer.html) and [guide](/api/python/docs/tutorials/packages/gluon/training/trainer.html) have information about how to construct the trainer that encapsulate the optimizers and will actually be used in your model training loop.
+* Check out the guide to MXNet gluon [Loss functions](/api/python/docs/tutorials/packages/gluon/loss/loss.html) and [custom losses](/api/python/docs/tutorials/packages/gluon/loss/custom-loss.html) to learn about the loss functions optimized by these optimizers, see what loss functions are already implemented in MXNet and understand how to write your own custom loss functions.
+* Take a look at the [guide to parameter initialization](/api/python/docs/tutorials/packages/gluon/blocks/init.html) in MXNet to learn about what initialization schemes are already implemented, and how to implement your custom initialization schemes.
+* Also check out the [autograd guide](/api/python/docs/tutorials/packages/autograd/index.html) to learn about automatic differentiation and how gradients are automatically computed in MXNet.
+* Make sure to take a look at the [guide to scheduling learning rates](/api/python/docs/tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html) to learn how to create learning rate schedules to supercharge the convergence of your optimizer.
+* Finally take a look at the [KVStore API](/api/python/docs/tutorials/packages/kvstore/index.html) to learn how parameter values are synchronized over multiple devices.
diff --git a/docs/static_site/src/pages/api/cpp/docs/tutorials/mxnet_cpp_inference_tutorial.md b/docs/static_site/src/pages/api/cpp/docs/tutorials/mxnet_cpp_inference_tutorial.md
index 6d9998d7a7a9..dcc96d4547ca 100644
--- a/docs/static_site/src/pages/api/cpp/docs/tutorials/mxnet_cpp_inference_tutorial.md
+++ b/docs/static_site/src/pages/api/cpp/docs/tutorials/mxnet_cpp_inference_tutorial.md
@@ -34,12 +34,13 @@ We will focus on the MXNet C++ API. We have slightly modified the code in [C++ I
 ## Prerequisites
 
 To complete this tutorial, you need to:
-- Complete the training part of [Gluon end to end tutorial](/api/python/docs/tutorials/getting-started/gluon_from_experiment_to_deployment.html)
-- Learn the basics about [MXNet C++ API](/api/cpp)
+- Complete the training part of [Gluon end to end tutorial](/api/python/docs/tutorials/getting-started/gluon_from_experiment_to_deployment.html).
+- Learn the basics about [MXNet C++ API](/api/cpp).
 
 
 ## Setup the MXNet C++ API
-To use the C++ API in MXNet, you need to build MXNet from source with C++ package. Please follow the [built from source guide](/get_started/ubuntu_setup.html), and [C++ Package documentation](/api/cpp)
+
+To use the C++ API in MXNet, you need to build MXNet from source with C++ package. Please follow the [built from source guide](/get_started/ubuntu_setup.html), and [C++ Package documentation](/api/cpp).
 The summary of those two documents is that you need to build MXNet from source with `USE_CPP_PACKAGE` flag set to 1. For example: `make -j USE_CPP_PACKAGE=1`.
 
 ## Load the model and run inference
diff --git a/docs/static_site/src/pages/api/faq/float16.md b/docs/static_site/src/pages/api/faq/float16.md
index 6ffb04054554..8a6d413449a6 100644
--- a/docs/static_site/src/pages/api/faq/float16.md
+++ b/docs/static_site/src/pages/api/faq/float16.md
@@ -59,7 +59,7 @@ net.cast('float16')
 data = data.astype('float16', copy=False)
 ```
 
-If you are using images and DataLoader, you can also use a [Cast transform]({{'/api/python/docs/api/gluon/_autogen/mxnet.gluon.data.vision.transforms.Cast.html#mxnet.gluon.data.vision.transforms.Cast'|relative_url}}).
+If you are using images and DataLoader, you can also use a [Cast transform](/api/python/docs/api/gluon/data/vision/transforms/index.html#mxnet.gluon.data.vision.transforms.Cast).
 
 3. It is preferable to use **multi_precision mode of optimizer** when training in float16. This mode of optimizer maintains a master copy of the weights in float32 even when the training (i.e. forward and backward pass) is in float16. This helps increase precision of the weight updates and can lead to faster convergence in some scenarios.
 
@@ -67,7 +67,7 @@ If you are using images and DataLoader, you can also use a [Cast transform]({{'/
 optimizer = mx.optimizer.create('sgd', multi_precision=True, lr=0.01)
 ```
 
-You can play around with mixed precision using the image classification [example](https://github.com/apache/incubator-mxnet/blob/master/example/gluon/image_classification.py). We suggest using the Caltech101 dataset option in that example and using a ResNet50V1 network so you can quickly see the performance improvement and how the accuracy is unaffected. Here's the starter command to run this example.
+You can play around with mixed precision using the image classification [example](https://github.com/apache/incubator-mxnet/blob/master/example/image-classification/train_imagenet.py). We suggest using the Caltech101 dataset option in that example and using a ResNet50V1 network so you can quickly see the performance improvement and how the accuracy is unaffected. Here's the starter command to run this example.
 
 ```bash
 python image_classification.py --model resnet50_v1 --dataset caltech101 --gpus 0 --num-worker 30 --dtype float16
diff --git a/julia/docs/mkdocs.yml b/julia/docs/mkdocs.yml
index 383505621540..880fad24d5b8 100644
--- a/julia/docs/mkdocs.yml
+++ b/julia/docs/mkdocs.yml
@@ -16,7 +16,7 @@
 # under the License.
 
 site_name: MXNet.jl
-repo_url:  https://github.com/dmlc/MXNet.jl
+repo_url:  https://github.com/apache/incubator-mxnet/tree/master/julia#mxnet
 
 theme: material
 
diff --git a/julia/docs/src/tutorial/char-lstm.md b/julia/docs/src/tutorial/char-lstm.md
index 1109f3554c17..c7dc9d6c07db 100644
--- a/julia/docs/src/tutorial/char-lstm.md
+++ b/julia/docs/src/tutorial/char-lstm.md
@@ -31,7 +31,7 @@ networks yet, the example shown here is an implementation of LSTM by
 using the default FeedForward model via explicitly unfolding over time.
 We will be using fixed-length input sequence for training. The code is
 adapted from the [char-rnn example for MXNet's Python
-binding](https://github.com/dmlc/mxnet-notebooks/blob/master/python/tutorials/char_lstm.ipynb),
+binding](https://github.com/apache/incubator-mxnet/blob/8004a027ad6a73f8f6eae102de8d249fbdfb9a2d/example/rnn/old/char-rnn.ipynb),
 which demonstrates how to use low-level
 [Symbolic API](@ref) to build customized neural
 network models directly.

From 7d4f2f3b1045fd515bca6fe0fb38d94ef4f2ff60 Mon Sep 17 00:00:00 2001
From: Bojian Zheng <bojian.zheng@mail.utoronto.ca>
Date: Wed, 13 Nov 2019 12:28:19 -0500
Subject: [PATCH 590/813] [MXNET-1421] Added (CuDNN)BatchNorm operator to the
 list of mirrored operators (#16022)

* Added (CuDNN)BatchNorm operator to the list of mirrored operators

* ci

* Enable the auxiliary state locking only in the backward mirroring mode

* retrigger CI
---
 src/executor/graph_executor.cc               |  2 --
 src/operator/nn/cudnn/cudnn_batch_norm-inl.h | 18 +++++++++++++++++-
 2 files changed, 17 insertions(+), 3 deletions(-)

diff --git a/src/executor/graph_executor.cc b/src/executor/graph_executor.cc
index 7fa1de373d07..b31ae7cf0cd7 100644
--- a/src/executor/graph_executor.cc
+++ b/src/executor/graph_executor.cc
@@ -364,8 +364,6 @@ nnvm::Graph GraphExecutor::InitFullGraph(nnvm::Symbol symbol,
     if (type == "FullyConnected") return false;
     if (type == "Concat") return false;
     if (type == "SoftmaxOutput") return false;
-    if (type == "BatchNorm") return false;
-    if (type == "CuDNNBatchNorm") return false;
     return true;
   };
 
diff --git a/src/operator/nn/cudnn/cudnn_batch_norm-inl.h b/src/operator/nn/cudnn/cudnn_batch_norm-inl.h
index 3fc91196708c..881d3d2247da 100644
--- a/src/operator/nn/cudnn/cudnn_batch_norm-inl.h
+++ b/src/operator/nn/cudnn/cudnn_batch_norm-inl.h
@@ -55,6 +55,7 @@ class CuDNNBatchNormOp {
     dtype_param_ = (dtype_ == CUDNN_DATA_HALF) ? kFloat32 : DataType<DType>::kFlag;
     CUDNN_CALL(cudnnCreateTensorDescriptor(&io_desc_));
     CUDNN_CALL(cudnnCreateTensorDescriptor(&mean_desc_));
+    internal_aux_states_lock_ = false;
   }
 
   void Init(const BatchNormParam &param) {
@@ -122,6 +123,13 @@ class CuDNNBatchNormOp {
         Tensor<gpu, 1, DTypeParam> save_inv_var =
           out_data[cudnnbatchnorm::kInvVar]
           .get_with_shape<gpu, 1, DTypeParam>(Shape1(shape_[1]), s);
+        // If the lock on the auxiliary states is set,
+        // then this implies that the preceding call is also a `Forward()` call,
+        // which further indicates that we are in the backward mirroring mode,
+        // and therefore update to the auxiliary states is disabled.
+        // This is done by setting the `momentum` to `1` (or `factor` to `0`).
+        float factor = (dmlc::GetEnv("MXNET_BACKWARD_DO_MIRROR", 0) && internal_aux_states_lock_) ?
+            0 : (1 - param_.momentum);
         CUDNN_CALL(cudnnBatchNormalizationForwardTraining(s->dnn_handle_,
                                                           mode,
                                                           &a,
@@ -133,7 +141,7 @@ class CuDNNBatchNormOp {
                                                           mean_desc_,
                                                           gamma.dptr_,
                                                           beta.dptr_,
-                                                          1 - param_.momentum,
+                                                          factor,
                                                           moving_mean.dptr_,
                                                           moving_inv_var.dptr_,
                                                           param_.eps,
@@ -156,6 +164,10 @@ class CuDNNBatchNormOp {
                                                            param_.eps));
       }
     })
+    // Set the lock on the auxiliary states.
+    // If the next call to the operator is a `Forward()` call,
+    // then `momentum` will be set to `1` and hence auxiliary states will not be updated.
+    internal_aux_states_lock_ = true;
   }
 
   void Backward(const OpContext &ctx,
@@ -230,6 +242,9 @@ class CuDNNBatchNormOp {
         global_stats ? nullptr : save_inv_var.dptr_));
       if (param_.fix_gamma) dgamma = 0.f;
     })
+    // Release the lock on the auxiliary states, so that the next forward pass
+    // will be able to update the auxiliary states normally.
+    internal_aux_states_lock_ = false;
   }
 
  private:
@@ -262,6 +277,7 @@ class CuDNNBatchNormOp {
   cudnnTensorDescriptor_t io_desc_, mean_desc_;
   mshadow::Shape<4> shape_;
   BatchNormParam param_;
+  bool internal_aux_states_lock_;
 };
 #endif  // defined(__CUDACC__)
 

From 9b25db05b4de18b5bfed8467cfd934f2bfa4f11a Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Wed, 13 Nov 2019 11:06:49 -0800
Subject: [PATCH 591/813] Fix numpy-compatible mean output type for integer
 inputs (#16792)

* fix mean output type for integer inputs

* enable for windows
---
 src/ndarray/ndarray_function-inl.h            |   2 +-
 src/operator/numpy/np_broadcast_reduce_op.h   |  12 +-
 .../numpy/np_broadcast_reduce_op_value.cc     |  11 +-
 src/operator/tensor/broadcast_reduce-inl.cuh  |  13 ---
 src/operator/tensor/broadcast_reduce-inl.h    |  15 +--
 src/operator/tensor/broadcast_reduce_op.h     |   6 +-
 tests/python/unittest/test_numpy_op.py        | 103 ++++++++++++------
 7 files changed, 88 insertions(+), 74 deletions(-)

diff --git a/src/ndarray/ndarray_function-inl.h b/src/ndarray/ndarray_function-inl.h
index d494f0882bbb..6ac586acd99c 100644
--- a/src/ndarray/ndarray_function-inl.h
+++ b/src/ndarray/ndarray_function-inl.h
@@ -379,7 +379,7 @@ void EvalRandom<DEVICE, GenNegBinomialDistribution>(
 template<>
 void Eval<DEVICE>(const real_t &rhs, TBlob *ret, RunContext ctx) {
   mshadow::Stream<DEVICE> *s = ctx.get_stream<DEVICE>();
-  MSHADOW_TYPE_SWITCH(ret->type_flag_, DType, {
+  MSHADOW_TYPE_SWITCH_WITH_BOOL(ret->type_flag_, DType, {
     ret->FlatTo2D<DEVICE, DType>(s) = DType(rhs);
   });
 }
diff --git a/src/operator/numpy/np_broadcast_reduce_op.h b/src/operator/numpy/np_broadcast_reduce_op.h
index 4951f62457a7..3566323f1eb3 100644
--- a/src/operator/numpy/np_broadcast_reduce_op.h
+++ b/src/operator/numpy/np_broadcast_reduce_op.h
@@ -52,6 +52,7 @@ struct NumpyReduceAxesParam : public dmlc::Parameter<NumpyReduceAxesParam> {
       .add_enum("int8", mshadow::kInt8)
       .add_enum("int32", mshadow::kInt32)
       .add_enum("int64", mshadow::kInt64)
+      .add_enum("bool", mshadow::kBool)
       .set_default(dmlc::optional<int>())
       .describe("The type of the returned array and of the accumulator in which the elements are "
                 "summed. The dtype of a is used by default unless a has an integer dtype of less "
@@ -221,15 +222,15 @@ void NumpyReduceAxesCompute(const nnvm::NodeAttrs& attrs,
                             const std::vector<TBlob>& inputs,
                             const std::vector<OpReqType>& req,
                             const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
   if (req[0] == kNullOp) return;
   const NumpyReduceAxesParam& param = nnvm::get<NumpyReduceAxesParam>(attrs.parsed);
   if (param.initial.has_value()) {
     LOG(FATAL) << "initial is not supported yet";
   }
+  Stream<xpu>* s = ctx.get_stream<xpu>();
   if (inputs[0].shape_.Size() == 0 && outputs[0].shape_.Size() != 0) {
     using namespace mxnet_op;
-    using namespace mshadow;
-    Stream<xpu>* s = ctx.get_stream<xpu>();
     MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
       Kernel<set_zero, xpu>::Launch(s, outputs[0].shape_.Size(), outputs[0].dptr<DType>());
     });
@@ -246,6 +247,13 @@ void NumpyReduceAxesCompute(const nnvm::NodeAttrs& attrs,
       LOG(FATAL) << "Only reduce op: `sum` is supported for boolean ndarrays";
     }
     TVMOpReduce(ctx, inputs[0], param.axis, outputs[0], req[0], reducer_name);
+    if (normalize) {
+      using namespace mshadow::expr;
+      MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, OType, {
+        auto out = outputs[0].FlatTo2D<xpu, OType>(s);
+        out /= scalar<OType>(inputs[0].Size()/outputs[0].Size());
+      });
+    }
     return;
   }
 #endif
diff --git a/src/operator/numpy/np_broadcast_reduce_op_value.cc b/src/operator/numpy/np_broadcast_reduce_op_value.cc
index 435fe1df1134..fb133568a7a5 100644
--- a/src/operator/numpy/np_broadcast_reduce_op_value.cc
+++ b/src/operator/numpy/np_broadcast_reduce_op_value.cc
@@ -257,13 +257,14 @@ inline bool NumpyMeanType(const nnvm::NodeAttrs& attrs,
   const NumpyReduceAxesParam &param = nnvm::get<NumpyReduceAxesParam>(attrs.parsed);
 
   if (param.dtype.has_value()) {
-    if (IsIntType(in_attrs->at(0)) && !IsIntType(param.dtype.value())) {
-      LOG(FATAL) << "Output cannot be float type when input is integer type for now";
-    }
     TYPE_ASSIGN_CHECK(*out_attrs, 0, param.dtype.value());
   } else {
-    TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
-    TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
+    if (common::is_float(in_attrs->at(0))) {
+      TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
+      TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
+    } else {
+      TYPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::kFloat32);
+    }
   }
 
   return out_attrs->at(0) != -1 && in_attrs->at(0) != -1;
diff --git a/src/operator/tensor/broadcast_reduce-inl.cuh b/src/operator/tensor/broadcast_reduce-inl.cuh
index fc863a972670..360420a1eca7 100644
--- a/src/operator/tensor/broadcast_reduce-inl.cuh
+++ b/src/operator/tensor/broadcast_reduce-inl.cuh
@@ -619,8 +619,6 @@ void Reduce(Stream<gpu> *s, const TBlob& small, const OpReqType req,
   ReduceImplConfig<ndim> config =
     ConfigureReduceImpl<ndim, DType>(small.shape_, big.shape_, NULL, NULL);
   if (safe_acc) {
-    // TODO(haojin2): Use real-only type swtich for windows temporarily due to CI issues.
-#ifndef _WIN32
     MXNET_ACC_TYPE_SWITCH(mshadow::DataType<DType>::kFlag, DataType, AType, {
       typedef typename std::conditional<safe_acc, AType, DataType>::type AccType;
       MSHADOW_TYPE_SWITCH(small.type_flag_, OType, {
@@ -630,17 +628,6 @@ void Reduce(Stream<gpu> *s, const TBlob& small, const OpReqType req,
           stream, small, req, big, workspace, config);
       });
     });
-#else
-    MXNET_REAL_ACC_TYPE_SWITCH(mshadow::DataType<DType>::kFlag, DataType, AType, {
-      typedef typename std::conditional<safe_acc, AType, DataType>::type AccType;
-      MSHADOW_TYPE_SWITCH(small.type_flag_, OType, {
-        typedef typename std::conditional<safe_acc, OType, DataType>::type OutType;
-        config = ConfigureReduceImpl<ndim, AccType>(small.shape_, big.shape_, NULL, NULL);
-        ReduceImpl<Reducer, ndim, AccType, DataType, OutType, OP>(
-          stream, small, req, big, workspace, config);
-      });
-    });
-#endif
   } else {
     ReduceImpl<Reducer, ndim, DType, DType, DType, OP>(stream, small, req, big, workspace, config);
   }
diff --git a/src/operator/tensor/broadcast_reduce-inl.h b/src/operator/tensor/broadcast_reduce-inl.h
index 415059ac12a0..e203da2a127e 100644
--- a/src/operator/tensor/broadcast_reduce-inl.h
+++ b/src/operator/tensor/broadcast_reduce-inl.h
@@ -241,28 +241,15 @@ void Reduce(Stream<cpu>* s, const TBlob& small, const OpReqType req,
       N, M, req == kAddTo, big.dptr<DType>(), small.dptr<DType>(),
       big.shape_.get<ndim>(), small.shape_.get<ndim>(), rshape, rstride);
   } else {
-    // TODO(haojin2): Use real-only type swtich for windows temporarily due to CI issues.
-#ifndef _WIN32
     MXNET_ACC_TYPE_SWITCH(mshadow::DataType<DType>::kFlag, DataType, AType, {
       typedef typename std::conditional<safe_acc, AType, DataType>::type AccType;
-      MSHADOW_TYPE_SWITCH(small.type_flag_, OType, {
+      MSHADOW_TYPE_SWITCH_WITH_BOOL(small.type_flag_, OType, {
         typedef typename std::conditional<safe_acc, OType, DataType>::type OutType;
         seq_reduce_compute<Reducer, ndim, AccType, DataType, OutType, OP>(
           N, M, req == kAddTo, big.dptr<DataType>(), small.dptr<OutType>(),
           big.shape_.get<ndim>(), small.shape_.get<ndim>(), rshape, rstride);
       });
     });
-#else
-    MXNET_REAL_ACC_TYPE_SWITCH(mshadow::DataType<DType>::kFlag, DataType, AType, {
-      typedef typename std::conditional<safe_acc, AType, DataType>::type AccType;
-      MSHADOW_TYPE_SWITCH(small.type_flag_, OType, {
-        typedef typename std::conditional<safe_acc, OType, DataType>::type OutType;
-        seq_reduce_compute<Reducer, ndim, AccType, DataType, OutType, OP>(
-          N, M, req == kAddTo, big.dptr<DataType>(), small.dptr<OutType>(),
-          big.shape_.get<ndim>(), small.shape_.get<ndim>(), rshape, rstride);
-      });
-    });
-#endif
   }
 }
 
diff --git a/src/operator/tensor/broadcast_reduce_op.h b/src/operator/tensor/broadcast_reduce_op.h
index 414b606bbe0a..27e22491ca35 100644
--- a/src/operator/tensor/broadcast_reduce_op.h
+++ b/src/operator/tensor/broadcast_reduce_op.h
@@ -617,7 +617,7 @@ void ReduceAxesComputeImpl(const OpContext& ctx,
   BroadcastReduceShapeCompact(inputs[0].shape_, small, &src_shape, &dst_shape);
   Stream<xpu> *s = ctx.get_stream<xpu>();
   MSHADOW_TYPE_SWITCH_WITH_BOOL(inputs[0].type_flag_, DType, {
-    MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, OType, {
+    MSHADOW_TYPE_SWITCH_WITH_BOOL(outputs[0].type_flag_, OType, {
       const TBlob in_data = inputs[0].reshape(src_shape);
       const TBlob out_data = outputs[0].reshape(dst_shape);
       BROADCAST_NDIM_SWITCH(dst_shape.ndim(), NDim, {
@@ -1045,8 +1045,8 @@ inline void BroadcastComputeImpl(const nnvm::NodeAttrs& attrs,
   mxnet::TShape src_shape, dst_shape;
   BroadcastReduceShapeCompact(outputs[0].shape_, small, &dst_shape, &src_shape);
   Stream<xpu> *s = ctx.get_stream<xpu>();
-  MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, IType, {
-    MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, OType, {
+  MSHADOW_TYPE_SWITCH_WITH_BOOL(inputs[0].type_flag_, IType, {
+    MSHADOW_TYPE_SWITCH_WITH_BOOL(outputs[0].type_flag_, OType, {
       mshadow::Shape<MXNET_SPECIAL_MAX_NDIM> in_shape;
       mshadow::Shape<MXNET_SPECIAL_MAX_NDIM> out_shape;
       for (int i = 0; i < MXNET_SPECIAL_MAX_NDIM; ++i) {
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 9aabdfd4cabc..48e08e5148cb 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -614,52 +614,83 @@ def hybrid_forward(self, F, a, *args, **kwargs):
     def is_int(dtype):
         return 'int' in dtype
 
+    is_windows = sys.platform.startswith('win')
     in_data_dim = random.choice([2, 3, 4])
     shape = rand_shape_nd(in_data_dim, dim=3)
     acc_type = {'float16': 'float32', 'float32': 'float64', 'float64': 'float64',
-                'int8': 'int32', 'int32': 'int64', 'int64': 'int64'}
+                'bool': 'int64', 'int8': 'int32', 'int32': 'int64', 'int64': 'int64'}
+    ft_types = ['float16', 'float32', 'float64']
+    it_types = ['bool', 'int8', 'int32', 'int64']
     for hybridize in [False, True]:
         for keepdims in [True, False]:
             for axis in ([i for i in range(in_data_dim)] + [(), None]):
-                for itype in ['float16', 'float32', 'float64']:
-                    for dtype in ['float16', 'float32', 'float64']:
-                        if is_int(dtype) and not is_int(itype):
-                            continue
-                        # test gluon
-                        test_mean = TestMean(axis=axis, dtype=dtype, keepdims=keepdims)
-                        if hybridize:
-                            test_mean.hybridize()
-                        if is_int(itype):
-                            x = _np.random.randint(-128, 128, shape, dtype=itype)
-                            x = mx.nd.array(x, dtype=itype)
-                        else:
-                            x = mx.nd.random.uniform(-1.0, 1.0, shape=shape, dtype=itype)
-                        x = x.as_np_ndarray()
-                        x.attach_grad()
+                for itype, dtype in itertools.product(ft_types, [None] + ft_types + it_types):
+                    if dtype == 'bool':
+                        continue
+                    # test gluon
+                    test_mean = TestMean(axis=axis, dtype=dtype, keepdims=keepdims)
+                    if hybridize:
+                        test_mean.hybridize()
+                    x = np.random.uniform(-1.0, 1.0, size=shape).astype(itype)
+                    x = x.as_np_ndarray()
+                    x.attach_grad()
 
-                        expected_ret = _np.mean(x.asnumpy(), axis=axis, dtype=acc_type[itype], keepdims=keepdims)
-                        expected_ret = expected_ret.astype(dtype)
-                        with mx.autograd.record():
-                            y = test_mean(x)
-                        assert y.shape == expected_ret.shape
-                        assert_almost_equal(y.asnumpy(), expected_ret, rtol=1e-3 if dtype == 'float16' else 1e-3,
-                                            atol=1e-5 if dtype == 'float16' else 1e-5)
+                    expected_ret = _np.mean(x.asnumpy(), axis=axis, dtype=acc_type[itype], keepdims=keepdims)
+                    expected_ret = expected_ret.astype(dtype)
+                    with mx.autograd.record():
+                        y = test_mean(x)
+                    assert y.shape == expected_ret.shape
+                    assert_almost_equal(y.asnumpy(), expected_ret, rtol=1e-3 if dtype == 'float16' else 1e-3,
+                                        atol=1e-5 if dtype == 'float16' else 1e-5)
 
-                        y.backward()
-                        N = x.size / y.size
-                        assert same(x.grad.asnumpy(), _np.ones(shape=x.shape, dtype=x.dtype) / N)
+                    y.backward()
+                    N = x.size / y.size
+                    assert same(x.grad.asnumpy(), _np.ones(shape=x.shape, dtype=x.dtype) / N)
 
-                        # test numeric
-                        if itype == 'float32' and dtype == 'float32':
-                            x_sym = mx.sym.Variable("x").as_np_ndarray()
-                            mx_sym = mx.sym.np.mean(x_sym, axis=axis, dtype=dtype, keepdims=keepdims).as_nd_ndarray()
-                            check_numeric_gradient(mx_sym, [x.as_nd_ndarray()],
-                                                   numeric_eps=1e-3, rtol=1e-3, atol=1e-4, dtype=_np.float32)
+                    # test numeric
+                    if itype == 'float32' and dtype == 'float32':
+                        x_sym = mx.sym.Variable("x").as_np_ndarray()
+                        mx_sym = mx.sym.np.mean(x_sym, axis=axis, dtype=dtype, keepdims=keepdims).as_nd_ndarray()
+                        check_numeric_gradient(mx_sym, [x.as_nd_ndarray()],
+                                               numeric_eps=1e-3, rtol=1e-3, atol=1e-4, dtype=_np.float32)
 
-                        # test imperative
-                        mx_out = np.mean(x, axis=axis, dtype=dtype, keepdims=keepdims)
-                        np_out = _np.mean(x.asnumpy(), axis=axis, dtype=acc_type[itype], keepdims=keepdims).astype(dtype)
-                        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+                    # test imperative
+                    mx_out = np.mean(x, axis=axis, dtype=dtype, keepdims=keepdims)
+                    np_out = _np.mean(x.asnumpy(), axis=axis, dtype=acc_type[itype], keepdims=keepdims).astype(dtype)
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+
+                for itype, dtype in itertools.product(it_types, [None] + ft_types + it_types):
+                    if dtype == 'bool':
+                        continue
+                    # test gluon
+                    test_mean = TestMean(axis=axis, dtype=dtype, keepdims=keepdims)
+                    if hybridize:
+                        test_mean.hybridize()
+
+                    if itype == 'bool':
+                        x = np.array(_np.random.uniform(size=shape) > 0.5)
+                    else:
+                        x = np.random.uniform(-128, 127, size=shape).astype(itype)
+
+                    expected_ret = _np.mean(x.asnumpy(), axis=axis, dtype=dtype, keepdims=keepdims)
+
+                    if itype == 'bool':
+                        if is_op_runnable() and (not is_windows) and dtype not in ['float16', 'int8']:  # special handling of boolean ndarray
+                            y = test_mean(x)
+                            assert y.shape == expected_ret.shape
+                            assert_almost_equal(y.asnumpy(), expected_ret, rtol=1e-3 if dtype == 'float16' else 1e-3,
+                                                atol=1e-5 if dtype == 'float16' else 1e-5)
+                        continue
+
+                    y = test_mean(x)
+                    assert y.shape == expected_ret.shape
+                    assert_almost_equal(y.asnumpy(), expected_ret, rtol=1e-3 if dtype == 'float16' else 1e-3,
+                                        atol=1e-5 if dtype == 'float16' else 1e-5)
+
+                    # test imperative
+                    mx_out = np.mean(x, axis=axis, dtype=dtype, keepdims=keepdims)
+                    np_out = _np.mean(x.asnumpy(), axis=axis, dtype=dtype, keepdims=keepdims).astype(dtype)
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
 
 
 @with_seed()

From e88e97fa4874bd1cddf65ac1c40ac87637261e03 Mon Sep 17 00:00:00 2001
From: Xingjian Shi <xshiab@connect.ust.hk>
Date: Wed, 13 Nov 2019 11:11:40 -0800
Subject: [PATCH 592/813] [Numpy] Fix collect_params().zero_grad() in gluon
 numpy interface (#16716)

* fix zero_grad

* Update parameter.py

* add test

* fix
---
 python/mxnet/gluon/parameter.py           | 12 ++++++++----
 tests/python/unittest/test_numpy_gluon.py | 17 +++++++++++++++++
 2 files changed, 25 insertions(+), 4 deletions(-)

diff --git a/python/mxnet/gluon/parameter.py b/python/mxnet/gluon/parameter.py
index 957dc2cd69b7..067a357e179c 100644
--- a/python/mxnet/gluon/parameter.py
+++ b/python/mxnet/gluon/parameter.py
@@ -27,7 +27,6 @@
 from collections import OrderedDict, defaultdict
 import warnings
 import numpy as np
-import mxnet as mx
 
 from ..base import mx_real_t, MXNetError
 from .. import symbol, ndarray, initializer, context
@@ -896,15 +895,20 @@ def zero_grad(self):
                 continue
             for g in p.list_grad():
                 if g.stype == 'row_sparse':
-                    mx.ndarray.zeros_like(g, out=g)
+                    ndarray.zeros_like(g, out=g)
                 else:
                     arrays[g.context].append(g)
 
         if len(arrays) == 0:
             return
 
-        for arr in arrays.values():
-            mx.nd.reset_arrays(*arr, num_arrays=len(arr))
+        if is_np_array():
+            for arr in arrays.values():
+                for ele in arr:
+                    ele[()] = 0
+        else:
+            for arr in arrays.values():
+                ndarray.reset_arrays(*arr, num_arrays=len(arr))
 
     def reset_ctx(self, ctx):
         """Re-assign all Parameters to other contexts.
diff --git a/tests/python/unittest/test_numpy_gluon.py b/tests/python/unittest/test_numpy_gluon.py
index 12e89a2d9b39..15914db2e9ec 100644
--- a/tests/python/unittest/test_numpy_gluon.py
+++ b/tests/python/unittest/test_numpy_gluon.py
@@ -179,6 +179,23 @@ def hybrid_forward(self, F, x, weight):
         assert_almost_equal(out.asnumpy(), (x.asnumpy() + const_arr), atol=1e-5, rtol=1e-4, use_broadcast=False)
 
 
+@use_np
+def test_parameters_zero_grad():
+    for hybridize in [False, True]:
+        net = gluon.nn.HybridSequential()
+        for _ in range(5):
+            net.add(gluon.nn.Dense(10))
+        if hybridize:
+            net.hybridize()
+        net.initialize()
+        out = net(mx.np.ones((32, 8)))
+        for v in net.collect_params().values():
+            v.grad()[()] = 1
+        net.collect_params().zero_grad()
+        for v in net.collect_params().values():
+            assert_almost_equal(v.grad().asnumpy(), mx.np.zeros_like(v.grad()).asnumpy())
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 42d3182e5abd2ebbacb45027a08c793d30d46a50 Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Wed, 13 Nov 2019 15:01:51 -0800
Subject: [PATCH 593/813] migrate cudaMemcpy to
 cudaMemcpyAsync+cudaStreamSynchronize (#16790)

---
 src/kvstore/kvstore_utils.cu                  |   9 +-
 src/ndarray/ndarray_function.cu               |  10 +-
 src/operator/contrib/adamw-inl.h              |  11 +-
 src/operator/contrib/adamw.cc                 |   2 +-
 src/operator/contrib/adamw.cu                 |   8 +-
 src/operator/contrib/boolean_mask.cu          |  16 ++-
 src/operator/contrib/index_array.cu           |  19 +--
 src/operator/contrib/multi_proposal.cu        | 110 ++++++++++--------
 src/operator/contrib/proposal.cu              |  33 +++---
 src/operator/numpy/np_boolean_mask_assign.cu  |  11 +-
 src/operator/numpy/np_nonzero_op.cu           |  14 ++-
 src/operator/numpy/np_unique_op.cu            |  17 ++-
 src/operator/numpy/random/dist_common.cc      |   4 +-
 src/operator/numpy/random/dist_common.cu      |  14 ++-
 src/operator/numpy/random/dist_common.h       |   4 +-
 src/operator/numpy/random/np_bernoulli_op.h   |   2 +-
 .../numpy/random/np_multinomial_op.cu         |   8 +-
 src/operator/numpy/random/np_multinomial_op.h |   4 +-
 src/operator/numpy/random/np_normal_op.h      |   4 +-
 src/operator/tensor/cast_storage-inl.cuh      |   8 +-
 src/operator/tensor/dot-inl.cuh               |   8 +-
 .../tensor/elemwise_binary_op_basic.cu        |   5 +-
 src/operator/tensor/indexing_op.cu            |  15 ++-
 src/operator/tensor/matrix_op.cu              |   5 +-
 src/operator/tensor/square_sum.cu             |   4 +-
 25 files changed, 200 insertions(+), 145 deletions(-)

diff --git a/src/kvstore/kvstore_utils.cu b/src/kvstore/kvstore_utils.cu
index 2dab5bc1802d..92b203ca161f 100644
--- a/src/kvstore/kvstore_utils.cu
+++ b/src/kvstore/kvstore_utils.cu
@@ -82,16 +82,17 @@ size_t UniqueImplGPU(NDArray *workspace, mshadow::Stream<gpu> *s,
 #else
   thrust::sort(thrust::cuda::par.on(stream),
                dptr, dptr + size, thrust::greater<IType>());
-  CUDA_CALL(cudaMemcpy(sort_output_ptr, dptr, sort_output_bytes,
-                       cudaMemcpyDeviceToDevice));
+  CUDA_CALL(cudaMemcpyAsync(sort_output_ptr, dptr, sort_output_bytes,
+                            cudaMemcpyDeviceToDevice, stream));
 #endif
   // execute unique kernel
   cub::DeviceSelect::Unique(temp_storage, unique_temp_bytes, sort_output_ptr, dptr,
                             num_selected_ptr, size, stream);
   // retrieve num selected unique values
   size_t num_selected_out = 0;
-  CUDA_CALL(cudaMemcpy(&num_selected_out, num_selected_ptr, num_selected_bytes,
-     cudaMemcpyDeviceToHost));
+  CUDA_CALL(cudaMemcpyAsync(&num_selected_out, num_selected_ptr, num_selected_bytes,
+                            cudaMemcpyDeviceToHost, stream));
+  CUDA_CALL(cudaStreamSynchronize(stream));
   return num_selected_out;
 }
 
diff --git a/src/ndarray/ndarray_function.cu b/src/ndarray/ndarray_function.cu
index da7b60db7f13..79bc3453cbc9 100644
--- a/src/ndarray/ndarray_function.cu
+++ b/src/ndarray/ndarray_function.cu
@@ -129,12 +129,13 @@ void ElementwiseSumRspImpl(mshadow::Stream<gpu>* s,
       IType* row_flg = NULL;
       void* d_temp_storage = NULL;
       size_t temp_storage_bytes = 0;
+      cudaStream_t stream = mshadow::Stream<gpu>::GetStream(s);
       cub::DeviceScan::InclusiveSum(d_temp_storage,
                                     temp_storage_bytes,
                                     row_flg,
                                     row_flg,
                                     num_rows,
-                                    mshadow::Stream<gpu>::GetStream(s));
+                                    stream);
       mshadow::Tensor<gpu, 1, char> workspace = rsc
           .get_space_typed<gpu, 1, char>(mshadow::Shape1(num_rows * sizeof(IType) +
                                                          temp_storage_bytes), s);
@@ -158,11 +159,12 @@ void ElementwiseSumRspImpl(mshadow::Stream<gpu>* s,
                                     row_flg,
                                     row_flg,
                                     num_rows,
-                                    mshadow::Stream<gpu>::GetStream(s));
+                                    stream);
       // Get total number of output non-zero rows from GPU and allocate out data and row_idx
       dim_t nnr_out = 0;
-      CUDA_CALL(cudaMemcpy(&nnr_out, &row_flg[num_rows-1], sizeof(dim_t),
-                           cudaMemcpyDeviceToHost));
+      CUDA_CALL(cudaMemcpyAsync(&nnr_out, &row_flg[num_rows-1], sizeof(dim_t),
+                                cudaMemcpyDeviceToHost, stream));
+      CUDA_CALL(cudaStreamSynchronize(stream));
       out->CheckAndAlloc({mshadow::Shape1(nnr_out)});
       IType* out_row_idx = out->aux_data(kIdx).dptr<IType>();
       DType* out_data = out->data().dptr<DType>();
diff --git a/src/operator/contrib/adamw-inl.h b/src/operator/contrib/adamw-inl.h
index fd139de3390f..6f483333314b 100644
--- a/src/operator/contrib/adamw-inl.h
+++ b/src/operator/contrib/adamw-inl.h
@@ -442,14 +442,15 @@ static inline void MultiAdamWUpdate(const nnvm::NodeAttrs& attrs,
 }
 
 template<typename xpu>
-void GetScaleFloat(const TBlob &scale_blob, float *pScalef);
+void GetScaleFloat(mshadow::Stream<xpu> *s, const TBlob &scale_blob, float *pScalef);
 
 template<typename xpu>
-bool PrepareInputBlobs(const std::vector<TBlob> &inputs,
+bool PrepareInputBlobs(const OpContext &ctx,
+                       const std::vector<TBlob> &inputs,
                        std::vector<TBlob> *inputs_wo_scale,
                        float *pScalef) {
   const size_t num_in = inputs.size() - 1;
-  GetScaleFloat<xpu>(inputs[num_in], pScalef);
+  GetScaleFloat<xpu>(ctx.get_stream<xpu>(), inputs[num_in], pScalef);
   if (!std::isfinite(*pScalef) || *pScalef == 0)
     return false;
 
@@ -468,7 +469,7 @@ inline void MPUpdate(const nnvm::NodeAttrs& attrs,
                      const std::vector<TBlob> &outputs) {
   std::vector<TBlob> inputs_wo_scale;
   float scalef;
-  if (!PrepareInputBlobs<xpu>(inputs, &inputs_wo_scale, &scalef))
+  if (!PrepareInputBlobs<xpu>(ctx, inputs, &inputs_wo_scale, &scalef))
     return;
 
   F::Forward(attrs, ctx, inputs_wo_scale, req, outputs, scalef);
@@ -482,7 +483,7 @@ inline void multiMPUpdate(const nnvm::NodeAttrs& attrs,
                           const std::vector<TBlob> &outputs) {
   std::vector<TBlob> inputs_wo_scale;
   float scalef;
-  if (!PrepareInputBlobs<xpu>(inputs, &inputs_wo_scale, &scalef))
+  if (!PrepareInputBlobs<xpu>(ctx, inputs, &inputs_wo_scale, &scalef))
     return;
 
   if (!MP)
diff --git a/src/operator/contrib/adamw.cc b/src/operator/contrib/adamw.cc
index 2c730f0b3e7b..effae5c42d19 100644
--- a/src/operator/contrib/adamw.cc
+++ b/src/operator/contrib/adamw.cc
@@ -119,7 +119,7 @@ the update is skipped.
 .add_arguments(AdamWParam::__FIELDS__());
 
 template<>
-void GetScaleFloat<cpu>(const TBlob &scale_blob, float *pScalef) {
+void GetScaleFloat<cpu>(mshadow::Stream<cpu> *s, const TBlob &scale_blob, float *pScalef) {
   MSHADOW_REAL_TYPE_SWITCH(scale_blob.type_flag_, DType,
     *pScalef = static_cast<float>(*scale_blob.dptr<DType>());
   )
diff --git a/src/operator/contrib/adamw.cu b/src/operator/contrib/adamw.cu
index 81b13c98ede4..2b0040e5f6ac 100644
--- a/src/operator/contrib/adamw.cu
+++ b/src/operator/contrib/adamw.cu
@@ -29,11 +29,13 @@ namespace mxnet {
 namespace op {
 
 template<>
-void GetScaleFloat<gpu>(const TBlob &scale_blob, float *pScalef) {
+void GetScaleFloat<gpu>(mshadow::Stream<gpu> *s, const TBlob &scale_blob, float *pScalef) {
   MSHADOW_REAL_TYPE_SWITCH(scale_blob.type_flag_, DType, {
     DType scale = 0;
-    CUDA_CALL(cudaMemcpy(&scale, scale_blob.dptr<DType>(), sizeof(DType),
-                         cudaMemcpyDeviceToHost));
+    cudaStream_t stream = mshadow::Stream<gpu>::GetStream(s);
+    CUDA_CALL(cudaMemcpyAsync(&scale, scale_blob.dptr<DType>(), sizeof(DType),
+                              cudaMemcpyDeviceToHost, stream));
+    CUDA_CALL(cudaStreamSynchronize(stream));
     *pScalef = static_cast<float>(scale);
   })
 }
diff --git a/src/operator/contrib/boolean_mask.cu b/src/operator/contrib/boolean_mask.cu
index a5ef4a70d99b..95f5614ba44d 100644
--- a/src/operator/contrib/boolean_mask.cu
+++ b/src/operator/contrib/boolean_mask.cu
@@ -46,6 +46,7 @@ inline void BooleanMaskForward<gpu>(const nnvm::NodeAttrs& attrs,
   CHECK_EQ(data.shape()[axis], idx.shape()[0]);
   CHECK_EQ(idx.shape().ndim(), 1U);
   Stream<gpu>* s = ctx.get_stream<gpu>();
+  cudaStream_t stream = Stream<gpu>::GetStream(s);
   // count the number of 1s in `idx`, so that we could know the output dimension
   size_t idx_size = idx.shape()[0];
   int32_t valid_num = 0;
@@ -58,7 +59,7 @@ inline void BooleanMaskForward<gpu>(const nnvm::NodeAttrs& attrs,
                                 prefix_sum,
                                 prefix_sum,
                                 idx_size,
-                                Stream<gpu>::GetStream(s));
+                                stream);
   size_t buffer_size = idx_size * sizeof(int32_t);
   temp_storage_bytes += buffer_size;
   // Allocate memory on GPU and allocate pointer
@@ -76,9 +77,11 @@ inline void BooleanMaskForward<gpu>(const nnvm::NodeAttrs& attrs,
                                 prefix_sum,
                                 prefix_sum,
                                 idx_size,
-                                Stream<gpu>::GetStream(s));
-  CUDA_CALL(cudaMemcpy(&valid_num, &prefix_sum[idx_size - 1], sizeof(int32_t),
-                       cudaMemcpyDeviceToHost));
+                                stream);
+  CUDA_CALL(cudaMemcpyAsync(&valid_num, &prefix_sum[idx_size - 1], sizeof(int32_t),
+                            cudaMemcpyDeviceToHost, stream));
+  CUDA_CALL(cudaStreamSynchronize(stream));
+
   // Set the output shape forcefully
   mxnet::TShape data_shape = data.shape();
   data_shape[axis] = valid_num;
@@ -110,6 +113,7 @@ inline void BooleanMaskBackward<gpu>(const nnvm::NodeAttrs& attrs,
   const NDArray& idx = inputs[2];
   const NDArray& igrad_data = outputs[0];
   Stream<gpu>* s = ctx.get_stream<gpu>();
+  cudaStream_t stream = Stream<gpu>::GetStream(s);
   // Count the number of 1s in `idx`, so that we could know the output dimension
   size_t idx_size = idx.shape()[0];
   int32_t* prefix_sum = nullptr;
@@ -121,7 +125,7 @@ inline void BooleanMaskBackward<gpu>(const nnvm::NodeAttrs& attrs,
                                 prefix_sum,
                                 prefix_sum,
                                 idx_size,
-                                Stream<gpu>::GetStream(s));
+                                stream);
   size_t buffer_size = idx_size * sizeof(int32_t);
   temp_storage_bytes += buffer_size;
   // Allocate memory on GPU and allocate pointer
@@ -139,7 +143,7 @@ inline void BooleanMaskBackward<gpu>(const nnvm::NodeAttrs& attrs,
                                 prefix_sum,
                                 prefix_sum,
                                 idx_size,
-                                Stream<gpu>::GetStream(s));
+                                stream);
   size_t input_size = igrad_data.shape().Size();
   size_t col_size = input_size / idx_size;
   // Backward pass
diff --git a/src/operator/contrib/index_array.cu b/src/operator/contrib/index_array.cu
index ddba6a87309a..dae61ca71b6d 100644
--- a/src/operator/contrib/index_array.cu
+++ b/src/operator/contrib/index_array.cu
@@ -41,7 +41,8 @@ void IndexArrayForwardGPU(const nnvm::NodeAttrs &attrs,
   const TShape inshape = in_data.shape_;
   const int ndim = inshape.ndim();
 
-  Stream<gpu> *stream = ctx.get_stream<gpu>();
+  Stream<gpu> *s = ctx.get_stream<gpu>();
+  cudaStream_t stream = Stream<gpu>::GetStream(s);
 
   using namespace mxnet_op;
 
@@ -55,24 +56,24 @@ void IndexArrayForwardGPU(const nnvm::NodeAttrs &attrs,
     IndexArrayBuildSelectedAxesWorkspace(axes, index_products, cpu_workspace.data(), ndim);
 
     Tensor<gpu, 1, int64_t> workspace =
-        ctx.requested[0].get_space_typed<gpu, 1, int64_t>(Shape1(2 * naxes), stream);
+        ctx.requested[0].get_space_typed<gpu, 1, int64_t>(Shape1(2 * naxes), s);
 
-    CUDA_CALL(cudaMemcpy(workspace.dptr_, cpu_workspace.data(), sizeof(int64_t) * (2 * naxes),
-                         cudaMemcpyHostToDevice));
+    CUDA_CALL(cudaMemcpyAsync(workspace.dptr_, cpu_workspace.data(), sizeof(int64_t) * (2 * naxes),
+                              cudaMemcpyHostToDevice, stream));
 
     MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
-      Kernel<IndexArrayKernel<req_type>, gpu>::Launch(stream, in_data.Size(),
+      Kernel<IndexArrayKernel<req_type>, gpu>::Launch(s, in_data.Size(),
           out_data.dptr<int64_t>(), naxes, workspace.dptr_);
     });
   } else {
     Tensor<gpu, 1, dim_t> workspace =
-        ctx.requested[0].get_space_typed<gpu, 1, dim_t>(Shape1(ndim), stream);
+        ctx.requested[0].get_space_typed<gpu, 1, dim_t>(Shape1(ndim), s);
 
-    CUDA_CALL(cudaMemcpy(workspace.dptr_, inshape.data(), sizeof(dim_t) * ndim,
-        cudaMemcpyHostToDevice));
+    CUDA_CALL(cudaMemcpyAsync(workspace.dptr_, inshape.data(), sizeof(dim_t) * ndim,
+                              cudaMemcpyHostToDevice, stream));
 
     MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
-      Kernel<IndexArrayDefaultKernel<req_type>, gpu>::Launch(stream, in_data.Size(),
+      Kernel<IndexArrayDefaultKernel<req_type>, gpu>::Launch(s, in_data.Size(),
           out_data.dptr<int64_t>(), ndim, workspace.dptr_);
     });
   }
diff --git a/src/operator/contrib/multi_proposal.cu b/src/operator/contrib/multi_proposal.cu
index 4552ae49d70b..1aa852af8d16 100644
--- a/src/operator/contrib/multi_proposal.cu
+++ b/src/operator/contrib/multi_proposal.cu
@@ -324,7 +324,8 @@ __global__ void nms_kernel(const int n_boxes, const float nms_overlap_thresh,
   }
 }
 
-void _nms(const mshadow::Tensor<gpu, 2>& boxes,
+void _nms(mshadow::Stream<gpu> *s,
+          const mshadow::Tensor<gpu, 2>& boxes,
           const float nms_overlap_thresh,
           const int rpn_post_nms_top_n,
           int *keep,
@@ -349,10 +350,13 @@ void _nms(const mshadow::Tensor<gpu, 2>& boxes,
                                   mask_dev);
   FRCNN_CUDA_CHECK(cudaPeekAtLastError());
   std::vector<uint64_t> mask_host(boxes_num * col_blocks);
-  FRCNN_CUDA_CHECK(cudaMemcpy(&mask_host[0],
-                              mask_dev,
-                              sizeof(uint64_t) * boxes_num * col_blocks,
-                              cudaMemcpyDeviceToHost));
+
+  cudaStream_t stream = mshadow::Stream<gpu>::GetStream(s);
+  FRCNN_CUDA_CHECK(cudaMemcpyAsync(&mask_host[0],
+                                   mask_dev,
+                                   sizeof(uint64_t) * boxes_num * col_blocks,
+                                   cudaMemcpyDeviceToHost, stream));
+  FRCNN_CUDA_CHECK(cudaStreamSynchronize(stream));
 
   std::vector<uint64_t> remv(col_blocks);
   memset(&remv[0], 0, sizeof(uint64_t) * col_blocks);
@@ -476,8 +480,12 @@ class MultiProposalGPUOp : public Operator{
                                 sizeof(float) * num_images * count_anchors * 5));
     Tensor<xpu, 3> workspace_proposals(workspace_proposals_ptr,
                                        Shape3(num_images, count_anchors, 5));
-    FRCNN_CUDA_CHECK(cudaMemcpy(workspace_proposals.dptr_, &anchors[0],
-                                sizeof(float) * anchors.size(), cudaMemcpyHostToDevice));
+
+    cudaStream_t stream = mshadow::Stream<gpu>::GetStream(s);
+
+    FRCNN_CUDA_CHECK(cudaMemcpyAsync(workspace_proposals.dptr_, &anchors[0],
+                                     sizeof(float) * anchors.size(),
+                                     cudaMemcpyHostToDevice, stream));
 
     // Copy proposals to a mesh grid
     dim3 dimGrid((count + kMaxThreadsPerBlock - 1) / kMaxThreadsPerBlock);
@@ -529,50 +537,50 @@ class MultiProposalGPUOp : public Operator{
     FRCNN_CUDA_CHECK(cudaMalloc(&keep, sizeof(int) * rpn_pre_nms_top_n));
 
     for (int b = 0; b < num_images; b++) {
-        CheckLaunchParam(dimGrid, dimBlock, "CopyScore");
-        CopyScoreKernel << <dimGrid, dimBlock >> >(
-            count_anchors, workspace_proposals.dptr_ + b * count_anchors * 5,
-            score.dptr_, order.dptr_);
-        FRCNN_CUDA_CHECK(cudaPeekAtLastError());
-
-        // argsort score, save order
-        thrust::stable_sort_by_key(thrust::device,
-            score.dptr_,
-            score.dptr_ + score.size(0),
-            order.dptr_,
-            thrust::greater<real_t>());
-        FRCNN_CUDA_CHECK(cudaPeekAtLastError());
-
-        // Reorder proposals according to order
-
-        dimGrid.x = (rpn_pre_nms_top_n + kMaxThreadsPerBlock - 1) / kMaxThreadsPerBlock;
-        CheckLaunchParam(dimGrid, dimBlock, "ReorderProposals");
-        ReorderProposalsKernel << <dimGrid, dimBlock >> >(
-            rpn_pre_nms_top_n, workspace_proposals.dptr_ + b * count_anchors * 5,
-            order.dptr_, workspace_ordered_proposals.dptr_);
-        FRCNN_CUDA_CHECK(cudaPeekAtLastError());
-
-        // perform nms
-        std::vector<int> _keep(workspace_ordered_proposals.size(0));
-        int out_size = 0;
-        _nms(workspace_ordered_proposals,
-            param_.threshold,
-            rpn_post_nms_top_n,
-            &_keep[0],
-            &out_size);
-
-        // copy nms result to gpu
-        FRCNN_CUDA_CHECK(cudaMemcpy(keep, &_keep[0], sizeof(int) * _keep.size(),
-            cudaMemcpyHostToDevice));
-
-        // copy results after nms
-        dimGrid.x = (param_.rpn_post_nms_top_n + kMaxThreadsPerBlock - 1) / kMaxThreadsPerBlock;
-        CheckLaunchParam(dimGrid, dimBlock, "PrepareOutput");
-        PrepareOutput << <dimGrid, dimBlock >> >(
-            param_.rpn_post_nms_top_n, workspace_ordered_proposals.dptr_, keep, out_size, b,
-            out.dptr_ + b * param_.rpn_post_nms_top_n * 5,
-            out_score.dptr_ + b * param_.rpn_post_nms_top_n);
-        FRCNN_CUDA_CHECK(cudaPeekAtLastError());
+      CheckLaunchParam(dimGrid, dimBlock, "CopyScore");
+      CopyScoreKernel << <dimGrid, dimBlock >> >(
+          count_anchors, workspace_proposals.dptr_ + b * count_anchors * 5,
+          score.dptr_, order.dptr_);
+      FRCNN_CUDA_CHECK(cudaPeekAtLastError());
+
+      // argsort score, save order
+      thrust::stable_sort_by_key(thrust::device,
+          score.dptr_,
+          score.dptr_ + score.size(0),
+          order.dptr_,
+          thrust::greater<real_t>());
+      FRCNN_CUDA_CHECK(cudaPeekAtLastError());
+
+      // Reorder proposals according to order
+
+      dimGrid.x = (rpn_pre_nms_top_n + kMaxThreadsPerBlock - 1) / kMaxThreadsPerBlock;
+      CheckLaunchParam(dimGrid, dimBlock, "ReorderProposals");
+      ReorderProposalsKernel << <dimGrid, dimBlock >> >(
+          rpn_pre_nms_top_n, workspace_proposals.dptr_ + b * count_anchors * 5,
+          order.dptr_, workspace_ordered_proposals.dptr_);
+      FRCNN_CUDA_CHECK(cudaPeekAtLastError());
+
+      // perform nms
+      std::vector<int> _keep(workspace_ordered_proposals.size(0));
+      int out_size = 0;
+      _nms(s, workspace_ordered_proposals,
+           param_.threshold,
+           rpn_post_nms_top_n,
+           &_keep[0],
+           &out_size);
+
+      // copy nms result to gpu
+      FRCNN_CUDA_CHECK(cudaMemcpyAsync(keep, &_keep[0], sizeof(int) * _keep.size(),
+                                       cudaMemcpyHostToDevice, stream));
+
+      // copy results after nms
+      dimGrid.x = (param_.rpn_post_nms_top_n + kMaxThreadsPerBlock - 1) / kMaxThreadsPerBlock;
+      CheckLaunchParam(dimGrid, dimBlock, "PrepareOutput");
+      PrepareOutput << <dimGrid, dimBlock >> >(
+          param_.rpn_post_nms_top_n, workspace_ordered_proposals.dptr_, keep, out_size, b,
+          out.dptr_ + b * param_.rpn_post_nms_top_n * 5,
+          out_score.dptr_ + b * param_.rpn_post_nms_top_n);
+      FRCNN_CUDA_CHECK(cudaPeekAtLastError());
     }
     // free temporary memory
     FRCNN_CUDA_CHECK(cudaFree(keep));
diff --git a/src/operator/contrib/proposal.cu b/src/operator/contrib/proposal.cu
index 446c92b35ce6..b107dfaae650 100644
--- a/src/operator/contrib/proposal.cu
+++ b/src/operator/contrib/proposal.cu
@@ -305,7 +305,8 @@ __global__ void nms_kernel(const int n_boxes, const float nms_overlap_thresh,
   }
 }
 
-void _nms(const mshadow::Tensor<gpu, 2>& boxes,
+void _nms(mshadow::Stream<gpu> *s,
+          const mshadow::Tensor<gpu, 2>& boxes,
           const float nms_overlap_thresh,
           const int rpn_post_nms_top_n,
           int *keep,
@@ -330,10 +331,12 @@ void _nms(const mshadow::Tensor<gpu, 2>& boxes,
                                   mask_dev);
   FRCNN_CUDA_CHECK(cudaPeekAtLastError());
   std::vector<uint64_t> mask_host(boxes_num * col_blocks);
-  FRCNN_CUDA_CHECK(cudaMemcpy(&mask_host[0],
-                              mask_dev,
-                              sizeof(uint64_t) * boxes_num * col_blocks,
-                              cudaMemcpyDeviceToHost));
+  cudaStream_t stream = mshadow::Stream<gpu>::GetStream(s);
+  FRCNN_CUDA_CHECK(cudaMemcpyAsync(&mask_host[0],
+                                   mask_dev,
+                                   sizeof(uint64_t) * boxes_num * col_blocks,
+                                   cudaMemcpyDeviceToHost, stream));
+  FRCNN_CUDA_CHECK(cudaStreamSynchronize(stream));
 
   std::vector<uint64_t> remv(col_blocks);
   memset(&remv[0], 0, sizeof(uint64_t) * col_blocks);
@@ -456,9 +459,10 @@ class ProposalGPUOp : public Operator{
     float* workspace_proposals_ptr = NULL;
     FRCNN_CUDA_CHECK(cudaMalloc(&workspace_proposals_ptr, sizeof(float) * count * 5));
     Tensor<xpu, 2> workspace_proposals(workspace_proposals_ptr, Shape2(count, 5));
-    FRCNN_CUDA_CHECK(cudaMemcpy(workspace_proposals.dptr_,
-                                &anchors[0], sizeof(float) * anchors.size(),
-      cudaMemcpyHostToDevice));
+    cudaStream_t stream = mshadow::Stream<gpu>::GetStream(s);
+    FRCNN_CUDA_CHECK(cudaMemcpyAsync(workspace_proposals.dptr_,
+                                     &anchors[0], sizeof(float) * anchors.size(),
+                                     cudaMemcpyHostToDevice, stream));
 
     // Copy proposals to a mesh grid
     dim3 dimGrid((count + kMaxThreadsPerBlock - 1) / kMaxThreadsPerBlock);
@@ -471,9 +475,10 @@ class ProposalGPUOp : public Operator{
 
     // im_info is small, we want to copy them to cpu
     std::vector<float> cpu_im_info(3);
-    FRCNN_CUDA_CHECK(cudaMemcpy(&cpu_im_info[0], im_info.dptr_,
-                                sizeof(float) * cpu_im_info.size(),
-                                cudaMemcpyDeviceToHost));
+    FRCNN_CUDA_CHECK(cudaMemcpyAsync(&cpu_im_info[0], im_info.dptr_,
+                                     sizeof(float) * cpu_im_info.size(),
+                                     cudaMemcpyDeviceToHost, stream));
+    FRCNN_CUDA_CHECK(cudaStreamSynchronize(stream));
 
     // prevent padded predictions
     int real_height = static_cast<int>(cpu_im_info[0] / param_.feature_stride);
@@ -543,7 +548,7 @@ class ProposalGPUOp : public Operator{
     // perform nms
     std::vector<int> _keep(workspace_ordered_proposals.size(0));
     int out_size = 0;
-    _nms(workspace_ordered_proposals,
+    _nms(s, workspace_ordered_proposals,
          param_.threshold,
          rpn_post_nms_top_n,
          &_keep[0],
@@ -552,8 +557,8 @@ class ProposalGPUOp : public Operator{
     // copy nms result to gpu
     int* keep;
     FRCNN_CUDA_CHECK(cudaMalloc(&keep, sizeof(int) * _keep.size()));
-    FRCNN_CUDA_CHECK(cudaMemcpy(keep, &_keep[0], sizeof(int) * _keep.size(),
-                                cudaMemcpyHostToDevice));
+    FRCNN_CUDA_CHECK(cudaMemcpyAsync(keep, &_keep[0], sizeof(int) * _keep.size(),
+                                     cudaMemcpyHostToDevice, stream));
 
     // copy results after nms
     dimGrid.x = (param_.rpn_post_nms_top_n + kMaxThreadsPerBlock - 1) / kMaxThreadsPerBlock;
diff --git a/src/operator/numpy/np_boolean_mask_assign.cu b/src/operator/numpy/np_boolean_mask_assign.cu
index e3b0330d79dd..2ccc4ffcd98d 100644
--- a/src/operator/numpy/np_boolean_mask_assign.cu
+++ b/src/operator/numpy/np_boolean_mask_assign.cu
@@ -113,6 +113,7 @@ size_t* GetValidNumGPU(const OpContext &ctx, const DType *idx, const size_t idx_
   void* d_temp_storage = nullptr;
   size_t temp_storage_bytes = 0;
   Stream<gpu>* s = ctx.get_stream<gpu>();
+  cudaStream_t stream = Stream<gpu>::GetStream(s);
 
   // Calculate total temporary memory size
   cub::DeviceScan::ExclusiveSum(d_temp_storage,
@@ -120,7 +121,7 @@ size_t* GetValidNumGPU(const OpContext &ctx, const DType *idx, const size_t idx_
                                 prefix_sum,
                                 prefix_sum,
                                 idx_size + 1,
-                                Stream<gpu>::GetStream(s));
+                                stream);
   size_t buffer_size = (idx_size + 1) * sizeof(size_t);
   temp_storage_bytes += buffer_size;
   // Allocate memory on GPU and allocate pointer
@@ -144,7 +145,7 @@ size_t* GetValidNumGPU(const OpContext &ctx, const DType *idx, const size_t idx_
                                 prefix_sum,
                                 prefix_sum,
                                 idx_size + 1,
-                                Stream<gpu>::GetStream(s));
+                                stream);
 
   return prefix_sum;
 }
@@ -174,8 +175,10 @@ void NumpyBooleanAssignForwardGPU(const nnvm::NodeAttrs& attrs,
     MSHADOW_TYPE_SWITCH(mask.type_flag_, MType, {
       prefix_sum = GetValidNumGPU<MType>(ctx, mask.dptr<MType>(), mask_size);
     });
-    CUDA_CALL(cudaMemcpy(&valid_num, &prefix_sum[mask_size], sizeof(size_t),
-                        cudaMemcpyDeviceToHost));
+    cudaStream_t stream = mshadow::Stream<gpu>::GetStream(s);
+    CUDA_CALL(cudaMemcpyAsync(&valid_num, &prefix_sum[mask_size], sizeof(size_t),
+                              cudaMemcpyDeviceToHost, stream));
+    CUDA_CALL(cudaStreamSynchronize(stream));
   }
   // If there's no True in mask, return directly
   if (valid_num == 0) return;
diff --git a/src/operator/numpy/np_nonzero_op.cu b/src/operator/numpy/np_nonzero_op.cu
index c732d2c78493..a31222ebb4c4 100644
--- a/src/operator/numpy/np_nonzero_op.cu
+++ b/src/operator/numpy/np_nonzero_op.cu
@@ -63,6 +63,7 @@ void NonzeroForwardGPU(const nnvm::NodeAttrs& attrs,
   }
   int32_t valid_num = 0;
   Stream<gpu>* stream = ctx.get_stream<gpu>();
+  cudaStream_t cuda_stream = Stream<gpu>::GetStream(stream);
   int32_t* prefix_sum = nullptr;
   void* d_temp_storage = nullptr;
   size_t temp_storage_bytes = 0;
@@ -72,7 +73,7 @@ void NonzeroForwardGPU(const nnvm::NodeAttrs& attrs,
                                 prefix_sum,
                                 prefix_sum,
                                 in_size,
-                                Stream<gpu>::GetStream(stream));
+                                cuda_stream);
   size_t buffer_size = in_size * sizeof(int32_t);
   temp_storage_bytes += buffer_size;
   // Allocate memory on GPU and allocate pointer
@@ -90,17 +91,18 @@ void NonzeroForwardGPU(const nnvm::NodeAttrs& attrs,
                                 prefix_sum,
                                 prefix_sum,
                                 in_size,
-                                Stream<gpu>::GetStream(stream));
-  CUDA_CALL(cudaMemcpy(&valid_num, &prefix_sum[in_size - 1], sizeof(int32_t),
-                       cudaMemcpyDeviceToHost));
+                                cuda_stream);
+  CUDA_CALL(cudaMemcpyAsync(&valid_num, &prefix_sum[in_size - 1], sizeof(int32_t),
+                            cudaMemcpyDeviceToHost, cuda_stream));
+  CUDA_CALL(cudaStreamSynchronize(cuda_stream));
   // 0-dim
   if (0 == in.shape().ndim()) {
     mxnet::TShape s(2, 1);
     if (valid_num) {
       const_cast<NDArray &>(out).Init(s);
       int64_t temp = 0;
-      CUDA_CALL(cudaMemcpy(out.data().dptr<int64_t>(), &temp, sizeof(int64_t),
-                           cudaMemcpyHostToDevice));
+      CUDA_CALL(cudaMemcpyAsync(out.data().dptr<int64_t>(), &temp, sizeof(int64_t),
+                                cudaMemcpyHostToDevice, cuda_stream));
     } else {
       s[0] = 0;
       const_cast<NDArray &>(out).Init(s);
diff --git a/src/operator/numpy/np_unique_op.cu b/src/operator/numpy/np_unique_op.cu
index 4d90a4542503..22fd1d12ef18 100644
--- a/src/operator/numpy/np_unique_op.cu
+++ b/src/operator/numpy/np_unique_op.cu
@@ -97,6 +97,7 @@ void NumpyUniqueGPUNoneAxisImpl(const NumpyUniqueParam& param,
                                 const std::vector<NDArray> &outputs) {
   MXNET_NO_FLOAT16_TYPE_SWITCH(outputs[0].dtype(), DType, {
     mshadow::Stream<gpu> *stream = ctx.get_stream<gpu>();
+    cudaStream_t cuda_stream = mshadow::Stream<gpu>::GetStream(stream);
     auto policy = thrust::cuda::par.on(stream->stream_);
 
     DType* input_data = inputs[0].data().dptr<DType>();
@@ -120,8 +121,9 @@ void NumpyUniqueGPUNoneAxisImpl(const NumpyUniqueParam& param,
     thrust::device_vector<int32_t> prefix_sum(input_size, 0);
     thrust::inclusive_scan(policy, mask.begin(), mask.end(), prefix_sum.begin());
     int32_t valid_num = 0;
-    CUDA_CALL(cudaMemcpy(&valid_num, thrust::raw_pointer_cast(&prefix_sum[input_size - 1]),
-                          sizeof(int32_t), cudaMemcpyDeviceToHost));
+    CUDA_CALL(cudaMemcpyAsync(&valid_num, thrust::raw_pointer_cast(&prefix_sum[input_size - 1]),
+                              sizeof(int32_t), cudaMemcpyDeviceToHost, cuda_stream));
+    CUDA_CALL(cudaStreamSynchronize(cuda_stream));
     // set the output shape forcefully
     mxnet::TShape s(1, valid_num);
     const_cast<NDArray &>(outputs[0]).Init(s);
@@ -180,6 +182,7 @@ void NumpyUniqueGPUImpl(const NumpyUniqueParam& param,
     using namespace mshadow;
     using namespace mshadow::expr;
     Stream<gpu> *stream = ctx.get_stream<gpu>();
+    cudaStream_t cuda_stream = Stream<gpu>::GetStream(stream);
     auto policy = thrust::cuda::par.on(stream->stream_);
     const index_t actual_axis =
         param.axis.value() + ((param.axis.value() < 0) ? inputs[0].shape().ndim() : 0);
@@ -214,8 +217,9 @@ void NumpyUniqueGPUImpl(const NumpyUniqueParam& param,
     thrust::device_vector<int32_t> prefix_sum(temp_shape[0], 0);
     thrust::inclusive_scan(policy, mask.begin(), mask.end(), prefix_sum.begin());
     int32_t valid_num = 0;
-    CUDA_CALL(cudaMemcpy(&valid_num, thrust::raw_pointer_cast(&prefix_sum[temp_shape[0] - 1]),
-                          sizeof(int32_t), cudaMemcpyDeviceToHost));
+    CUDA_CALL(cudaMemcpyAsync(&valid_num, thrust::raw_pointer_cast(&prefix_sum[temp_shape[0] - 1]),
+                              sizeof(int32_t), cudaMemcpyDeviceToHost, cuda_stream));
+    CUDA_CALL(cudaStreamSynchronize(cuda_stream));
     // store the temp output data, reuse the space of 'input_tensor'
     Tensor<gpu, 3, DType> temp_tensor(workspace.dptr_,
         Shape3(valid_num, temp_shape[1], temp_shape[2]), stream);
@@ -282,11 +286,12 @@ void NumpyUniqueGPUForward(const nnvm::NodeAttrs& attrs,
     CHECK(!param.axis.has_value() || param.axis.value() == -1 || param.axis.value() == 0)
       << "Axis can only be -1 or 0 for scalor tensor";
     Stream<gpu> *s = ctx.get_stream<gpu>();
+    cudaStream_t stream = Stream<gpu>::GetStream(s);
     mxnet::TShape shape_1(1, 1);
     const_cast<NDArray &>(outputs[0]).Init(shape_1);
     MSHADOW_TYPE_SWITCH(outputs[0].dtype(), DType, {
-      CUDA_CALL(cudaMemcpy(outputs[0].data().dptr<DType>(), inputs[0].data().dptr<DType>(),
-                           sizeof(DType), cudaMemcpyDeviceToDevice));
+      CUDA_CALL(cudaMemcpyAsync(outputs[0].data().dptr<DType>(), inputs[0].data().dptr<DType>(),
+                                sizeof(DType), cudaMemcpyDeviceToDevice, stream));
     });
     int output_flag = 0;
     if (param.return_index) {
diff --git a/src/operator/numpy/random/dist_common.cc b/src/operator/numpy/random/dist_common.cc
index 925565654a11..18a20855bab9 100644
--- a/src/operator/numpy/random/dist_common.cc
+++ b/src/operator/numpy/random/dist_common.cc
@@ -30,12 +30,12 @@ namespace mxnet {
 namespace op {
 
 template <>
-void _copy<cpu>(float *dst, float *src) {
+void _copy<cpu>(mshadow::Stream<cpu> *s, float *dst, float *src) {
   *dst = *src;
 }
 
 template <>
-void _copy<cpu>(double *dst, double *src) {
+void _copy<cpu>(mshadow::Stream<cpu> *s, double *dst, double *src) {
   *dst = *src;
 }
 
diff --git a/src/operator/numpy/random/dist_common.cu b/src/operator/numpy/random/dist_common.cu
index 7dde0123e099..dbd313be37e2 100644
--- a/src/operator/numpy/random/dist_common.cu
+++ b/src/operator/numpy/random/dist_common.cu
@@ -30,13 +30,19 @@ namespace mxnet {
 namespace op {
 
 template <>
-void _copy<gpu>(float *dst, float *src) {
-CUDA_CALL(cudaMemcpy(dst, src, sizeof(float), cudaMemcpyDeviceToHost));
+void _copy<gpu>(mshadow::Stream<gpu> *s, float *dst, float *src) {
+  cudaStream_t stream = mshadow::Stream<gpu>::GetStream(s);
+  CUDA_CALL(cudaMemcpyAsync(dst, src, sizeof(float), cudaMemcpyDeviceToHost,
+                            stream));
+  CUDA_CALL(cudaStreamSynchronize(stream));
 }
 
 template <>
-void _copy<gpu>(double *dst, double *src) {
-CUDA_CALL(cudaMemcpy(dst, src, sizeof(double), cudaMemcpyDeviceToHost));
+void _copy<gpu>(mshadow::Stream<gpu> *s, double *dst, double *src) {
+  cudaStream_t stream = mshadow::Stream<gpu>::GetStream(s);
+  CUDA_CALL(cudaMemcpyAsync(dst, src, sizeof(double), cudaMemcpyDeviceToHost,
+                            stream));
+  CUDA_CALL(cudaStreamSynchronize(stream));
 }
 
 }  // namespace op
diff --git a/src/operator/numpy/random/dist_common.h b/src/operator/numpy/random/dist_common.h
index 199b55b9afb3..a28ce5f7bcce 100644
--- a/src/operator/numpy/random/dist_common.h
+++ b/src/operator/numpy/random/dist_common.h
@@ -41,10 +41,10 @@ namespace mxnet {
 namespace op {
 
 template <typename xpu>
-void _copy(float *dst, float*src);
+void _copy(mshadow::Stream<xpu> *s, float *dst, float*src);
 
 template <typename xpu>
-void _copy(double *dst, double*src);
+void _copy(mshadow::Stream<xpu> *s, double *dst, double*src);
 
 
 inline int FillShape(const mxnet::TShape &lshape, const mxnet::TShape &rshape,
diff --git a/src/operator/numpy/random/np_bernoulli_op.h b/src/operator/numpy/random/np_bernoulli_op.h
index aa8e344e2842..0df10896c99a 100644
--- a/src/operator/numpy/random/np_bernoulli_op.h
+++ b/src/operator/numpy/random/np_bernoulli_op.h
@@ -173,7 +173,7 @@ void NumpyBernoulliForward(const nnvm::NodeAttrs &attrs,
         Kernel<check_legal_prob_kernel<IType>, xpu>::Launch(
             s, inputs[0].Size(), inputs[0].dptr<IType>(), indicator_device_ptr);
       });
-      _copy<xpu>(&indicator_host, indicator_device_ptr);
+      _copy<xpu>(s, &indicator_host, indicator_device_ptr);
       CHECK_GE(indicator_host, 0.0)
           << "ValueError: expect probs >= 0 && probs <= 1";
     }
diff --git a/src/operator/numpy/random/np_multinomial_op.cu b/src/operator/numpy/random/np_multinomial_op.cu
index 6aa163978774..132d67beeb6a 100644
--- a/src/operator/numpy/random/np_multinomial_op.cu
+++ b/src/operator/numpy/random/np_multinomial_op.cu
@@ -28,10 +28,12 @@ namespace mxnet {
 namespace op {
 
 template<typename DType>
-void CheckPvalGPU(DType* input, int prob_length) {
+void CheckPvalGPU(const OpContext& ctx, DType* input, int prob_length) {
   std::vector<DType> pvals_(prob_length);
-  CUDA_CALL(cudaMemcpy(&pvals_[0], input, sizeof(DType) * prob_length,
-    cudaMemcpyDeviceToHost));
+  cudaStream_t stream = mshadow::Stream<gpu>::GetStream(ctx.get_stream<gpu>());
+  CUDA_CALL(cudaMemcpyAsync(&pvals_[0], input, sizeof(DType) * prob_length,
+                            cudaMemcpyDeviceToHost, stream));
+  CUDA_CALL(cudaStreamSynchronize(stream));
   DType sum = DType(0.0);
   for (int i = 0; i < prob_length; ++i) {
     sum += pvals_[i];
diff --git a/src/operator/numpy/random/np_multinomial_op.h b/src/operator/numpy/random/np_multinomial_op.h
index 2350d208e43c..9c5c73f3f6b4 100644
--- a/src/operator/numpy/random/np_multinomial_op.h
+++ b/src/operator/numpy/random/np_multinomial_op.h
@@ -100,7 +100,7 @@ inline bool NumpyMultinomialOpType(const nnvm::NodeAttrs& attrs,
 }
 
 template<typename DType>
-void CheckPvalGPU(DType* input, int prob_length);
+void CheckPvalGPU(const OpContext& ctx, DType* input, int prob_length);
 
 template<typename DType>
 void CheckPval(DType* input, int prob_length) {
@@ -188,7 +188,7 @@ void NumpyMultinomialForward(const nnvm::NodeAttrs& attrs,
        if (std::is_same<xpu, cpu>::value) {
          CheckPval<DType>(inputs[0].dptr<DType>(), prob_length);
        } else {
-         CheckPvalGPU<DType>(inputs[0].dptr<DType>(), prob_length);
+         CheckPvalGPU<DType>(ctx, inputs[0].dptr<DType>(), prob_length);
        }
       Kernel<multinomial_kernel, xpu>::Launch(
         s, num_output, num_exp, prob_length,
diff --git a/src/operator/numpy/random/np_normal_op.h b/src/operator/numpy/random/np_normal_op.h
index deb603ac5270..f9618febd458 100644
--- a/src/operator/numpy/random/np_normal_op.h
+++ b/src/operator/numpy/random/np_normal_op.h
@@ -183,7 +183,7 @@ void NumpyNormalForward(const nnvm::NodeAttrs &attrs,
         Kernel<check_legal_scale_kernel<IType>, xpu>::Launch(
             s, inputs[0].Size(), inputs[0].dptr<IType>(), indicator_device_ptr);
       });
-      _copy<xpu>(&indicator_host, indicator_device_ptr);
+      _copy<xpu>(s, &indicator_host, indicator_device_ptr);
       CHECK_GE(indicator_host, 0.0) << "ValueError: scale < 0";
     } else {
       scalar_pos = 1;
@@ -208,7 +208,7 @@ void NumpyNormalForward(const nnvm::NodeAttrs &attrs,
       Kernel<check_legal_scale_kernel<IType>, xpu>::Launch(
           s, inputs[1].Size(), inputs[1].dptr<IType>(), indicator_device_ptr);
     });
-    _copy<xpu>(&indicator_host, indicator_device_ptr);
+    _copy<xpu>(s, &indicator_host, indicator_device_ptr);
     CHECK_GE(indicator_host, 0.0) << "ValueError: scale < 0";
     int ndim = FillShape(inputs[0].shape_, inputs[1].shape_, outputs[0].shape_,
                          &new_lshape, &new_hshape, &new_oshape);
diff --git a/src/operator/tensor/cast_storage-inl.cuh b/src/operator/tensor/cast_storage-inl.cuh
index ee1531dbd94e..4c5d0d8570bb 100644
--- a/src/operator/tensor/cast_storage-inl.cuh
+++ b/src/operator/tensor/cast_storage-inl.cuh
@@ -162,7 +162,9 @@ void CastStorageDnsRspGPUImpl_(const OpContext& ctx,
 
   // Get total number of non-zero rows from device
   dim_t nnr = 0;
-  CUDA_CALL(cudaMemcpy(&nnr, &row_flg[num_rows - 1], sizeof(dim_t), cudaMemcpyDeviceToHost));
+  CUDA_CALL(cudaMemcpyAsync(&nnr, &row_flg[num_rows - 1], sizeof(dim_t),
+                            cudaMemcpyDeviceToHost, mshadow::Stream<gpu>::GetStream(s)));
+  CUDA_CALL(cudaStreamSynchronize(mshadow::Stream<gpu>::GetStream(s)));
 
   // Allocate rsp tensor row index array and fill
   rsp->CheckAndAllocAuxData(rowsparse::kIdx, Shape1(nnr));
@@ -555,7 +557,9 @@ inline void CastStorageDnsCsrImpl(const OpContext& ctx,
 
         // Receive total number of nnz values from device
         IType nnz = 0;
-        CUDA_CALL(cudaMemcpy(&nnz, &(indptr[num_rows]), sizeof(IType), cudaMemcpyDeviceToHost));
+        CUDA_CALL(cudaMemcpyAsync(&nnz, &(indptr[num_rows]), sizeof(IType), cudaMemcpyDeviceToHost,
+                                  mshadow::Stream<gpu>::GetStream(s)));
+        CUDA_CALL(cudaStreamSynchronize(mshadow::Stream<gpu>::GetStream(s)));
 
         // Allocate column index array and data array of the csr matrix
         csr->CheckAndAllocAuxData(csr::kIdx, Shape1(static_cast<dim_t>(nnz)));
diff --git a/src/operator/tensor/dot-inl.cuh b/src/operator/tensor/dot-inl.cuh
index d6fed4af82ae..b8244d3c45a7 100644
--- a/src/operator/tensor/dot-inl.cuh
+++ b/src/operator/tensor/dot-inl.cuh
@@ -702,7 +702,8 @@ inline void DotCsrDnsRspImpl(const OpContext& ctx,
                                     nnr_ptr, nnz, stream);
           // retrieve num non-zero rows
           size_t nnr = 0;
-          CUDA_CALL(cudaMemcpy(&nnr, nnr_ptr, nnr_bytes, cudaMemcpyDeviceToHost));
+          CUDA_CALL(cudaMemcpyAsync(&nnr, nnr_ptr, nnr_bytes, cudaMemcpyDeviceToHost, stream));
+          CUDA_CALL(cudaStreamSynchronize(stream));
           // allocate data
           ret->CheckAndAllocData(mshadow::Shape2(nnz, num_cols_r));
           // generate lookup table
@@ -817,8 +818,9 @@ inline void DotCsrRspRspImpl(const OpContext& ctx,
                                           num_cols_l,
                                           mshadow::Stream<gpu>::GetStream(s));
             dim_t nnr_out = 0;
-            CUDA_CALL(cudaMemcpy(&nnr_out, &row_flg_out[num_cols_l-1], sizeof(dim_t),
-                                 cudaMemcpyDeviceToHost));
+            CUDA_CALL(cudaMemcpyAsync(&nnr_out, &row_flg_out[num_cols_l-1], sizeof(dim_t),
+                                      cudaMemcpyDeviceToHost, mshadow::Stream<gpu>::GetStream(s)));
+            CUDA_CALL(cudaStreamSynchronize(mshadow::Stream<gpu>::GetStream(s)));
             if (0 == nnr_out) {
               FillZerosRspImpl(s, *ret);
               return;
diff --git a/src/operator/tensor/elemwise_binary_op_basic.cu b/src/operator/tensor/elemwise_binary_op_basic.cu
index f88b8ebd69ac..e39f7e92c011 100644
--- a/src/operator/tensor/elemwise_binary_op_basic.cu
+++ b/src/operator/tensor/elemwise_binary_op_basic.cu
@@ -115,8 +115,9 @@ void ElemwiseBinaryOp::RspRspOp(mshadow::Stream<gpu> *s,
                                       num_rows,
                                       mshadow::Stream<gpu>::GetStream(s));
         nnvm::dim_t nnr_out = 0;
-        CUDA_CALL(cudaMemcpy(&nnr_out, &common_row_table[num_rows-1], sizeof(nnvm::dim_t),
-                              cudaMemcpyDeviceToHost));
+        CUDA_CALL(cudaMemcpyAsync(&nnr_out, &common_row_table[num_rows-1], sizeof(nnvm::dim_t),
+                                  cudaMemcpyDeviceToHost, mshadow::Stream<gpu>::GetStream(s)));
+        CUDA_CALL(cudaStreamSynchronize(mshadow::Stream<gpu>::GetStream(s)))
         output.CheckAndAlloc({mshadow::Shape1(nnr_out)});
         Kernel<FillRspRowIdxKernel, gpu>::Launch(
           s, num_rows, output.aux_data(kIdx).dptr<IType>(), common_row_table, num_rows);
diff --git a/src/operator/tensor/indexing_op.cu b/src/operator/tensor/indexing_op.cu
index 3ccf1f39d4f7..8250efb5ca4e 100644
--- a/src/operator/tensor/indexing_op.cu
+++ b/src/operator/tensor/indexing_op.cu
@@ -154,8 +154,9 @@ bool CheckIndexOutOfBound(mshadow::Stream<gpu> *s, const DType* data_ptr, size_t
   int32_t is_valid = 0;
   Kernel<set_zero, gpu>::Launch(s, 1, is_valid_ptr);
   Kernel<is_valid_check, gpu>::Launch(s, data_size, is_valid_ptr, data_ptr, min, max);
-  CUDA_CALL(cudaMemcpy(&is_valid, is_valid_ptr, sizeof(char),
-            cudaMemcpyDeviceToHost));
+  CUDA_CALL(cudaMemcpyAsync(&is_valid, is_valid_ptr, sizeof(char),
+                            cudaMemcpyDeviceToHost, mshadow::Stream<gpu>::GetStream(s)));
+  CUDA_CALL(cudaStreamSynchronize(mshadow::Stream<gpu>::GetStream(s)));
   return is_valid == 0;
 }
 
@@ -307,8 +308,9 @@ void SparseEmbeddingDeterministicKernelLaunch(const OpContext& ctx,
       grad_row_idx, grad_row_idx + data_size, data_size, Stream<gpu>::GetStream(s));
 
   dim_t nnr = 0;
-  CUDA_CALL(cudaMemcpy(&nnr, grad_row_idx + data_size, sizeof(RType),
-      cudaMemcpyDeviceToHost));
+  CUDA_CALL(cudaMemcpyAsync(&nnr, grad_row_idx + data_size, sizeof(RType),
+                            cudaMemcpyDeviceToHost, mshadow::Stream<gpu>::GetStream(s)));
+  CUDA_CALL(cudaStreamSynchronize(mshadow::Stream<gpu>::GetStream(s)));
   CHECK_EQ(output.shape().ndim(), 2) << "Unexcepted ndim";
   output.CheckAndAllocData(Shape2(nnr, output.shape()[1]));
   output.set_aux_shape(kIdx, Shape1(nnr));
@@ -410,8 +412,9 @@ inline void SparseEmbeddingOpBackwardRspImpl<gpu>(const bool deterministic,
                                       num_rows,
                                       mshadow::Stream<gpu>::GetStream(s));
         dim_t nnr = 0;
-        CUDA_CALL(cudaMemcpy(&nnr, &prefix_sum[num_rows-1], sizeof(dim_t),
-            cudaMemcpyDeviceToHost));
+        CUDA_CALL(cudaMemcpyAsync(&nnr, &prefix_sum[num_rows-1], sizeof(dim_t),
+                                  cudaMemcpyDeviceToHost, mshadow::Stream<gpu>::GetStream(s)));
+        CUDA_CALL(cudaStreamSynchronize(mshadow::Stream<gpu>::GetStream(s)));
         if (nnr == 0) {
           FillZerosRspImpl(s, output);
           return;
diff --git a/src/operator/tensor/matrix_op.cu b/src/operator/tensor/matrix_op.cu
index b382c55ce74a..239e42c14136 100644
--- a/src/operator/tensor/matrix_op.cu
+++ b/src/operator/tensor/matrix_op.cu
@@ -114,8 +114,9 @@ void SliceDimTwoCsrImpl<gpu>(const mxnet::TShape &begin, const mxnet::TShape &en
                                       Stream<gpu>::GetStream(s));
         // retrieve nnr
         RType nnr = 0;
-        CUDA_CALL(cudaMemcpy(&nnr, &out_indptr[indptr_len-1], sizeof(RType),
-            cudaMemcpyDeviceToHost));
+        CUDA_CALL(cudaMemcpyAsync(&nnr, &out_indptr[indptr_len-1], sizeof(RType),
+                                  cudaMemcpyDeviceToHost, mshadow::Stream<gpu>::GetStream(s)));
+        CUDA_CALL(cudaStreamSynchronize(mshadow::Stream<gpu>::GetStream(s)));
 
         // returns zeros in csr format if nnr = 0
         if (nnr == 0) {
diff --git a/src/operator/tensor/square_sum.cu b/src/operator/tensor/square_sum.cu
index 0b40786dbd2e..83287e0de3a9 100644
--- a/src/operator/tensor/square_sum.cu
+++ b/src/operator/tensor/square_sum.cu
@@ -42,7 +42,9 @@ void CheckSameIdx<gpu>(const OpContext& ctx,
     mxnet_op::Kernel<mxnet_op::set_zero, gpu>::Launch(s, 1, is_diff_ptr);
     mxnet_op::Kernel<CheckSameIdxKernel, gpu>::Launch(s, idx_size,
       ograd_idx, in_idx, is_diff_ptr);
-    CUDA_CALL(cudaMemcpy(&is_diff, is_diff_ptr, sizeof(int32_t), cudaMemcpyDeviceToHost));
+    CUDA_CALL(cudaMemcpyAsync(&is_diff, is_diff_ptr, sizeof(int32_t),
+                              cudaMemcpyDeviceToHost, mshadow::Stream<gpu>::GetStream(s)));
+    CUDA_CALL(cudaStreamSynchronize(mshadow::Stream<gpu>::GetStream(s)));
     CHECK_EQ(is_diff, 0) << "SquareSumRspGradImpl only supports"
                             " equal ograd_row_idx and input_row_idx"
                             " when ograd and input are both"

From 017f6fa7996048cc429b02dac9369af6778811ec Mon Sep 17 00:00:00 2001
From: Tao Lv <tao.a.lv@intel.com>
Date: Thu, 14 Nov 2019 09:33:34 +0800
Subject: [PATCH 594/813] Static link MKL-DNN library (#16731)

* static link mkldnn.a

* MSVC build

* check USE_OPENMP

* remove the test of mkldnn.so installation
---
 CMakeLists.txt                                |  6 +-
 Makefile                                      | 11 +---
 cd/mxnet_lib/dynamic/Jenkins_pipeline.groovy  |  1 -
 cd/mxnet_lib/static/Jenkins_pipeline.groovy   |  2 +-
 ci/docker/runtime_functions.sh                |  3 -
 ci/jenkins/Jenkins_steps.groovy               |  4 +-
 mkldnn.mk                                     | 25 ++++++---
 .../assembly/src/main/assembly/assembly.xml   |  2 -
 .../mxnet/util/NativeLibraryLoader.scala      |  2 -
 tests/nightly/JenkinsfileForBinaries          |  2 +-
 tests/python/mkl/test_mkldnn.py               |  4 +-
 tests/python/mkl/test_mkldnn_install.py       | 56 -------------------
 tools/pip/setup.py                            |  5 --
 tools/staticbuild/build_lib.sh                |  9 ---
 14 files changed, 28 insertions(+), 104 deletions(-)
 delete mode 100644 tests/python/mkl/test_mkldnn_install.py

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 0eba24f61d14..4e808094dd96 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -258,15 +258,15 @@ endif()
 if(USE_MKLDNN)
   # CPU architecture (e.g., C5) can't run on another architecture (e.g., g3).
   if(MSVC)
-    set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} /EHsc")
-    set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} /EHsc /Gy")
+    set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} /EHsc /MT")
+    set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} /EHsc /Gy /MT")
   endif()
 
   set(MKLDNN_BUILD_TESTS OFF CACHE INTERNAL "" FORCE)
   set(MKLDNN_BUILD_EXAMPLES OFF CACHE INTERNAL "" FORCE)
   set(MKLDNN_ARCH_OPT_FLAGS "" CACHE INTERNAL "" FORCE)
-  set(MKLDNN_USE_MKL NONE CACHE INTERNAL "" FORCE)
   set(MKLDNN_ENABLE_JIT_PROFILING OFF CACHE INTERNAL "" FORCE)
+  set(MKLDNN_LIBRARY_TYPE STATIC CACHE INTERNAL "" FORCE)
 
   add_subdirectory(3rdparty/mkldnn)
 
diff --git a/Makefile b/Makefile
index 4746cc434de2..841ba7e322ca 100644
--- a/Makefile
+++ b/Makefile
@@ -151,7 +151,7 @@ ifeq ($(USE_MKLDNN), 1)
 	CFLAGS += -DMXNET_USE_MKLDNN=1
 	CFLAGS += -I$(ROOTDIR)/src/operator/nn/mkldnn/
 	CFLAGS += -I$(MKLDNNROOT)/include
-	LDFLAGS += -L$(MKLDNNROOT)/lib -L$(MKLDNNROOT)/lib64 -lmkldnn -Wl,-rpath,'$${ORIGIN}'
+	LIB_DEP += $(MKLDNNROOT)/lib/libmkldnn.a
 endif
 
 # setup opencv
@@ -595,11 +595,6 @@ lib/libmxnet.so: $(ALLX_DEP)
 	@mkdir -p $(@D)
 	$(CXX) $(CFLAGS) -shared -o $@ $(filter-out %libnnvm.a, $(filter %.o %.a, $^)) $(LDFLAGS) \
 	-Wl,${WHOLE_ARCH} $(filter %libnnvm.a, $^) -Wl,${NO_WHOLE_ARCH}
-ifeq ($(USE_MKLDNN), 1)
-ifeq ($(UNAME_S), Darwin)
-	install_name_tool -change '@rpath/libmkldnn.1.dylib' '@loader_path/libmkldnn.1.dylib' $@
-endif
-endif
 
 $(PS_PATH)/build/libps.a: PSLITE
 
@@ -689,10 +684,6 @@ rpkg:
 	cp src/io/image_recordio.h R-package/src
 	cp -rf lib/libmxnet.so R-package/inst/libs
 
-	if [ -e "lib/libmkldnn.so.1" ]; then \
-		cp -rf lib/libmkldnn.so.1 R-package/inst/libs; \
-	fi
-
 	if [ -e "lib/libtvm_runtime.so" ]; then \
 		cp -rf lib/libtvm_runtime.so R-package/inst/libs; \
 	fi
diff --git a/cd/mxnet_lib/dynamic/Jenkins_pipeline.groovy b/cd/mxnet_lib/dynamic/Jenkins_pipeline.groovy
index af68314cacf5..6a51b69cfe50 100644
--- a/cd/mxnet_lib/dynamic/Jenkins_pipeline.groovy
+++ b/cd/mxnet_lib/dynamic/Jenkins_pipeline.groovy
@@ -30,7 +30,6 @@ licenses = 'licenses/*'
 
 // libmxnet dependencies
 mx_deps = ''
-mx_mkldnn_deps = 'lib/libmkldnn.so.1'
 
 // library type
 // either static or dynamic - depending on how it links to its dependencies
diff --git a/cd/mxnet_lib/static/Jenkins_pipeline.groovy b/cd/mxnet_lib/static/Jenkins_pipeline.groovy
index ac2e45071d15..470bef703613 100644
--- a/cd/mxnet_lib/static/Jenkins_pipeline.groovy
+++ b/cd/mxnet_lib/static/Jenkins_pipeline.groovy
@@ -31,7 +31,7 @@ licenses = 'licenses/*'
 
 // libmxnet dependencies
 mx_deps = 'lib/libgfortran.so.3, lib/libquadmath.so.0'
-mx_mkldnn_deps = 'lib/libgfortran.so.3, lib/libquadmath.so.0, lib/libmkldnn.so.1, 3rdparty/mkldnn/build/install/include/mkldnn_version.h'
+mx_mkldnn_deps = 'lib/libgfortran.so.3, lib/libquadmath.so.0, 3rdparty/mkldnn/build/install/include/mkldnn_version.h'
 
 // library type
 // either static or dynamic - depending on how it links to its dependencies
diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index 581bb2fd5280..78d541e0ad5f 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -878,9 +878,6 @@ build_ubuntu_gpu_cmake_mkldnn() {
         /work/mxnet
 
     ninja -v
-    # libmkldnn.so.1 is a link file. We need an actual binary file named libmkldnn.so.1.
-    cp 3rdparty/mkldnn/src/libmkldnn.so.1 3rdparty/mkldnn/src/libmkldnn.so.1.tmp
-    mv 3rdparty/mkldnn/src/libmkldnn.so.1.tmp 3rdparty/mkldnn/src/libmkldnn.so.1
 }
 
 build_ubuntu_gpu_cmake() {
diff --git a/ci/jenkins/Jenkins_steps.groovy b/ci/jenkins/Jenkins_steps.groovy
index 0770320f1407..81479328f054 100644
--- a/ci/jenkins/Jenkins_steps.groovy
+++ b/ci/jenkins/Jenkins_steps.groovy
@@ -35,8 +35,8 @@ mx_cmake_lib_no_tvm_op = 'build/libmxnet.so, build/libmxnet.a, build/libsample_l
 mx_cmake_lib_cython = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/tvmop.conf, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
 // mxnet cmake libraries, in cmake builds we do not produce a libnvvm static library by default.
 mx_cmake_lib_debug = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/tvmop.conf, build/libsample_lib.so, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests'
-mx_cmake_mkldnn_lib = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/tvmop.conf, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so, build/3rdparty/mkldnn/src/libmkldnn.so.1'
-mx_mkldnn_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, lib/tvmop.conf, libsample_lib.so, lib/libmkldnn.so.1, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a'
+mx_cmake_mkldnn_lib = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/tvmop.conf, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so'
+mx_mkldnn_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, lib/tvmop.conf, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a'
 mx_tensorrt_lib = 'build/libmxnet.so, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/tvmop.conf, lib/libnvonnxparser_runtime.so.0, lib/libnvonnxparser.so.0, lib/libonnx_proto.so, lib/libonnx.so'
 mx_lib_cpp_examples = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, lib/tvmop.conf, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, 3rdparty/ps-lite/build/libps.a, deps/lib/libprotobuf-lite.a, deps/lib/libzmq.a, build/cpp-package/example/*, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
 mx_lib_cpp_examples_no_tvm_op = 'lib/libmxnet.so, lib/libmxnet.a, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, 3rdparty/ps-lite/build/libps.a, deps/lib/libprotobuf-lite.a, deps/lib/libzmq.a, build/cpp-package/example/*, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
diff --git a/mkldnn.mk b/mkldnn.mk
index aa92108e33b0..4f4afb555e39 100644
--- a/mkldnn.mk
+++ b/mkldnn.mk
@@ -20,11 +20,24 @@ ifeq ($(USE_MKLDNN), 1)
 	MKLDNN_BUILDDIR = $(MKLDNN_SUBMODDIR)/build
 	MXNET_LIBDIR = $(ROOTDIR)/lib
 	MXNET_INCLDIR = $(ROOTDIR)/include
-ifeq ($(UNAME_S), Darwin)
-	MKLDNN_LIBFILE = $(MKLDNNROOT)/lib/libmkldnn.1.dylib
-else
-	MKLDNN_LIBFILE = $(MKLDNNROOT)/lib/libmkldnn.so.1
+	MKLDNN_LIBFILE = $(MKLDNNROOT)/lib/libmkldnn.a
 endif
+
+mkldnn_FLAGS  = -DCMAKE_INSTALL_PREFIX=$(MKLDNNROOT)
+mkldnn_FLAGS += -DCMAKE_INSTALL_LIBDIR=lib
+mkldnn_FLAGS += -B$(MKLDNN_BUILDDIR)
+mkldnn_FLAGS += -DMKLDNN_ARCH_OPT_FLAGS=""
+mkldnn_FLAGS += -DMKLDNN_BUILD_TESTS=OFF
+mkldnn_FLAGS += -DMKLDNN_BUILD_EXAMPLES=OFF
+mkldnn_FLAGS += -DMKLDNN_ENABLE_JIT_PROFILING=OFF
+mkldnn_FLAGS += -DMKLDNN_LIBRARY_TYPE=STATIC
+
+ifneq ($(USE_OPENMP), 1)
+	mkldnn_FLAGS += -DMKLDNN_CPU_RUNTIME=SEQ
+endif
+
+ifeq ($(DEBUG), 1)
+	mkldnn_FLAGS += -DCMAKE_BUILD_TYPE=Debug
 endif
 
 .PHONY: mkldnn mkldnn_clean
@@ -33,11 +46,9 @@ mkldnn_build: $(MKLDNN_LIBFILE)
 
 $(MKLDNN_LIBFILE):
 	mkdir -p $(MKLDNNROOT)/lib
-	cmake $(MKLDNN_SUBMODDIR) -DCMAKE_INSTALL_PREFIX=$(MKLDNNROOT) -DCMAKE_INSTALL_LIBDIR=lib -B$(MKLDNN_BUILDDIR) -DMKLDNN_ARCH_OPT_FLAGS="" -DMKLDNN_BUILD_TESTS=OFF -DMKLDNN_BUILD_EXAMPLES=OFF -DMKLDNN_ENABLE_JIT_PROFILING=OFF
+	cmake $(MKLDNN_SUBMODDIR) $(mkldnn_FLAGS)
 	$(MAKE) -C $(MKLDNN_BUILDDIR) VERBOSE=1
 	$(MAKE) -C $(MKLDNN_BUILDDIR) install
-	mkdir -p $(MXNET_LIBDIR)
-	cp $(MKLDNN_LIBFILE) $(MXNET_LIBDIR)
 	cp $(MKLDNN_BUILDDIR)/include/mkldnn_version.h $(MXNET_INCLDIR)/mkldnn/.
 
 mkldnn_clean:
diff --git a/scala-package/assembly/src/main/assembly/assembly.xml b/scala-package/assembly/src/main/assembly/assembly.xml
index 060a97b82064..6b8480c53f8f 100644
--- a/scala-package/assembly/src/main/assembly/assembly.xml
+++ b/scala-package/assembly/src/main/assembly/assembly.xml
@@ -57,8 +57,6 @@
         <include>libtvm_runtime.so</include>
         <include>libgfortran.so.3</include>
         <include>libquadmath.so.0</include>
-        <include>libmkldnn.so.1</include>
-        <include>libmkldnn.1.dylib</include>
       </includes>
       <outputDirectory>lib/native</outputDirectory>
     </fileSet>
diff --git a/scala-package/core/src/main/scala/org/apache/mxnet/util/NativeLibraryLoader.scala b/scala-package/core/src/main/scala/org/apache/mxnet/util/NativeLibraryLoader.scala
index 49e5d685adfe..4b03fd2ef82f 100644
--- a/scala-package/core/src/main/scala/org/apache/mxnet/util/NativeLibraryLoader.scala
+++ b/scala-package/core/src/main/scala/org/apache/mxnet/util/NativeLibraryLoader.scala
@@ -89,8 +89,6 @@ private[mxnet] object NativeLibraryLoader {
     saveLibraryToTemp("libtvm_runtime.so", "/lib/native/libtvm_runtime.so", false)
     saveLibraryToTemp("libgfortran.so.3", "/lib/native/libgfortran.so.3", false)
     saveLibraryToTemp("libquadmath.so.0", "/lib/native/libquadmath.so.0", false)
-    saveLibraryToTemp("libmkldnn.so.1", "/lib/native/libmkldnn.so.1", false)
-    saveLibraryToTemp("libmkldnn.1.dylib", "/lib/native/libmkldnn.1.dylib", false)
     val tempfile: File = saveLibraryToTemp(libname, libFileInJar, true)
 
     loadLibraryFromFile(libname, tempfile)
diff --git a/tests/nightly/JenkinsfileForBinaries b/tests/nightly/JenkinsfileForBinaries
index 48db4457c1ac..a7fcee622aff 100755
--- a/tests/nightly/JenkinsfileForBinaries
+++ b/tests/nightly/JenkinsfileForBinaries
@@ -20,7 +20,7 @@
 
 mx_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a'
 mx_cmake_lib = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so'
-mx_lib_cpp_example_mkl = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, build/cpp-package/example/imagenet_inference, lib/libmkldnn.so.1'
+mx_lib_cpp_example_mkl = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, build/cpp-package/example/imagenet_inference'
 
 node('utility') {
   // Loading the utilities requires a node context unfortunately
diff --git a/tests/python/mkl/test_mkldnn.py b/tests/python/mkl/test_mkldnn.py
index e43daf12c464..6424990d254d 100644
--- a/tests/python/mkl/test_mkldnn.py
+++ b/tests/python/mkl/test_mkldnn.py
@@ -28,7 +28,6 @@
 from mxnet import gluon
 from mxnet.gluon import nn
 from mxnet.test_utils import *
-import test_mkldnn_install as install
 curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
 sys.path.append(os.path.join(curr_path, '../unittest/'))
 from common import with_seed
@@ -637,4 +636,5 @@ def check_elemwise_add_training(stype):
         check_elemwise_add_training(stype)
 
 if __name__ == '__main__':
-    install.test_mkldnn_install()
+    import nose
+    nose.runmodule()
diff --git a/tests/python/mkl/test_mkldnn_install.py b/tests/python/mkl/test_mkldnn_install.py
deleted file mode 100644
index c2f26df72f2e..000000000000
--- a/tests/python/mkl/test_mkldnn_install.py
+++ /dev/null
@@ -1,56 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-"""
-MKL-DNN related test cases
-"""
-
-import sys
-import os
-import logging
-
-
-def test_mkldnn_install():
-    """
-    This test will verify that MXNet is built/installed correctly when
-    compiled with Intel MKL-DNN library. The method will try to import
-    the mxnet module and see if the mkldnn library is mapped to this
-    process's address space.
-    """
-    logging.basicConfig(level=logging.INFO)
-
-    if not sys.platform.startswith('linux'):
-        logging.info("Bypass mkldnn install test for non-Linux OS")
-        return
-
-    try:
-        #pylint: disable=unused-variable
-        import mxnet as mx
-    except (ImportError, OSError) as e:
-        assert 0, "Import mxnet error: %s. Please double check your build/" \
-            "install steps or environment variable settings" % str(e)
-
-    pid = os.getpid()
-    rc = os.system("cat /proc/" + str(pid) +
-                   "/maps | grep libmkldnn > /dev/null")
-
-    if rc == 0:
-        logging.info("MXNet is built/installed correctly with MKL-DNN")
-    else:
-        assert 0, "MXNet is built/installed incorrectly with MKL-DNN, please " \
-            "double check your build/install steps or environment " \
-            "variable settings"
diff --git a/tools/pip/setup.py b/tools/pip/setup.py
index dd430f5a6f87..f1d3a38d40e7 100644
--- a/tools/pip/setup.py
+++ b/tools/pip/setup.py
@@ -147,11 +147,6 @@ def has_ext_modules(self):
                 'dmlc_tracker': []}
 if variant.endswith('MKL'):
     if platform.system() == 'Darwin':
-        shutil.copy(os.path.join(os.path.dirname(LIB_PATH[0]), 'libmkldnn.1.dylib'), os.path.join(CURRENT_DIR, 'mxnet'))
-        package_data['mxnet'].append('mxnet/libmkldnn.1.dylib')
-    else:
-        shutil.copy(os.path.join(os.path.dirname(LIB_PATH[0]), 'libmkldnn.so.1'), os.path.join(CURRENT_DIR, 'mxnet'))
-        package_data['mxnet'].append('mxnet/libmkldnn.so.1')
     shutil.copytree(os.path.join(CURRENT_DIR, 'mxnet-build/3rdparty/mkldnn/build/install/include'),
                     os.path.join(CURRENT_DIR, 'mxnet/include/mkldnn'))
 if platform.system() == 'Linux':
diff --git a/tools/staticbuild/build_lib.sh b/tools/staticbuild/build_lib.sh
index 4a82b80d00ba..4935d67b9316 100755
--- a/tools/staticbuild/build_lib.sh
+++ b/tools/staticbuild/build_lib.sh
@@ -34,16 +34,7 @@ $MAKE DEPS_PATH=$DEPS_PATH $PWD/3rdparty/tvm/nnvm/lib/libnnvm.a
 $MAKE DEPS_PATH=$DEPS_PATH PSLITE
 
 if [[ $VARIANT == *mkl ]]; then
-    if [[ $PLATFORM == 'linux' ]]; then
-        MKLDNN_LIBFILE='libmkldnn.so.1'
-    else
-        MKLDNN_LIBFILE='libmkldnn.1.dylib'
-    fi
     $MAKE DEPS_PATH=$DEPS_PATH mkldnn
-    if [ ! -d lib ]; then
-        mkdir lib
-    fi
-    cp 3rdparty/mkldnn/build/install/lib/$MKLDNN_LIBFILE lib
 fi
 
 >&2 echo "Now building mxnet..."

From 5139e2cf5191da9fb7b66fbb21a2a04a3b1b2d19 Mon Sep 17 00:00:00 2001
From: Xi Wang <xidulu@gmail.com>
Date: Fri, 15 Nov 2019 04:20:18 +0800
Subject: [PATCH 595/813] add gammaln, erf, erfinv (#16811)

---
 .../tensor/elemwise_unary_op_basic.cc         |  3 ++
 tests/python/unittest/test_numpy_op.py        | 54 +++++++++++++++++++
 2 files changed, 57 insertions(+)

diff --git a/src/operator/tensor/elemwise_unary_op_basic.cc b/src/operator/tensor/elemwise_unary_op_basic.cc
index 56674409601c..7a509933651e 100644
--- a/src/operator/tensor/elemwise_unary_op_basic.cc
+++ b/src/operator/tensor/elemwise_unary_op_basic.cc
@@ -877,6 +877,7 @@ The storage type of ``fix`` output depends upon the input storage type:
 
 // erf
 MXNET_OPERATOR_REGISTER_UNARY(erf)
+.add_alias("_npx_erf")
 .describe(R"code(Returns element-wise gauss error function of the input.
 
 Example::
@@ -898,6 +899,7 @@ MXNET_OPERATOR_REGISTER_BINARY(_backward_erf)
 
 // erfinv
 MXNET_OPERATOR_REGISTER_UNARY(erfinv)
+.add_alias("_npx_erfinv")
 .describe(R"code(Returns element-wise inverse gauss error function of the input.
 
 Example::
@@ -929,6 +931,7 @@ MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_gamma,
 
 // gammaln
 MXNET_OPERATOR_REGISTER_UNARY_WITH_SPARSE_DR(gammaln, cpu, mshadow_op::gammaln)
+.add_alias("_npx_gammaln")
 MXNET_ADD_SPARSE_OP_ALIAS(gammaln)
 .describe(R"code(Returns element-wise log of the absolute value of the gamma function \
 of the input.
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 48e08e5148cb..2a23c976a092 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -24,6 +24,7 @@
 import platform
 import mxnet as mx
 import scipy.stats as ss
+import scipy.special as scipy_special
 from nose.tools import assert_raises
 from mxnet import np, npx
 from mxnet.gluon import HybridBlock
@@ -2550,6 +2551,59 @@ def _test_bernoulli_exception(prob, logit):
                 assertRaises(MXNetError, _test_bernoulli_exception, scaled_prob, None)
 
 
+@with_seed()
+@use_np
+def test_npx_special_unary_func():
+    def check_unary_func(func, ref_grad, shape, low, high):
+        class TestUnary(HybridBlock):
+            def __init__(self, func):
+                super(TestUnary, self).__init__()
+                self._func = func
+
+            def hybrid_forward(self, F, a, *args, **kwargs):
+                return getattr(F.npx, self._func)(a)
+
+        np_func = getattr(scipy_special, func)
+        mx_func = TestUnary(func)
+        np_test_data = _np.random.uniform(low, high, shape).astype(_np.float32)
+        mx_test_data = mx.numpy.array(np_test_data)
+        for hybridize in [True, False]:
+            if hybridize:
+                mx_func.hybridize()
+            if ref_grad:
+                mx_test_data.attach_grad()
+            np_out = np_func(np_test_data)
+            with mx.autograd.record():
+                y = mx_func(mx_test_data)
+            assert y.shape == np_out.shape
+            assert_almost_equal(y.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+            if np_out.dtype == np.bool_:
+                assert y.dtype == np.bool_
+
+            if ref_grad:
+                y.backward()
+                assert_almost_equal(mx_test_data.grad.asnumpy(), ref_grad(np_test_data), rtol=1e-1, atol=1e-2, equal_nan=True)
+
+        np_out = getattr(scipy_special, func)(np_test_data)
+        mx_out = getattr(mx.npx, func)(mx_test_data)
+        assert mx_out.shape == np_out.shape
+        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+
+    import math
+    funcs = {
+        'erf' : (lambda x: 2.0 / math.sqrt(math.pi) * _np.exp(-(x ** 2)), 0.5, 0.5),
+        'erfinv' : (lambda x: 0.5 * math.sqrt(math.pi) * _np.exp(scipy_special.erfinv(x) ** 2), 0.5, 0.5),
+        'gamma' : (lambda x: scipy_special.gamma(x) * scipy_special.psi(x), 0.5, 0.5),
+        'gammaln' : (lambda x: scipy_special.psi(x), 0.5, 0.5)
+    }
+    ndim = random.choice([2, 3, 4])
+    shape = random.choice([rand_shape_nd(ndim, dim=3), (1, 0, 2)])
+    for shape in [rand_shape_nd(ndim, dim=3), (1, 0, 2)]:
+        for func, func_data in funcs.items():
+            ref_grad, low, high = func_data
+            check_unary_func(func, ref_grad, shape, low, high)
+
+
 @with_seed()
 @use_np
 def test_np_random():

From 7e21bda4ae7b0e3ab74fd626c12c1faa89aafac0 Mon Sep 17 00:00:00 2001
From: Haozheng Fan <fhztc1997618@gmail.com>
Date: Fri, 15 Nov 2019 05:38:25 +0800
Subject: [PATCH 596/813] Fix nightly build (#16773)

* Remove dependency on tvmop.conf

* Fix binaries dependencies for ni nightly

* Add comments

* Update tvmop.py

* Fix rebase
---
 python/mxnet/tvmop.py                              | 14 ++++++++++----
 tests/nightly/JenkinsfileForBinaries               |  6 +++---
 .../JenkinsfileForMBCC                             |  2 +-
 3 files changed, 14 insertions(+), 8 deletions(-)

diff --git a/python/mxnet/tvmop.py b/python/mxnet/tvmop.py
index 9ec278afb7a0..29e6f0d76f32 100644
--- a/python/mxnet/tvmop.py
+++ b/python/mxnet/tvmop.py
@@ -21,6 +21,7 @@
 
 if Features().is_enabled("TVM_OP"):
     import json
+    import logging
 
     from ._ctypes.space import _set_tvm_op_config
     from .base import check_call, _LIB, c_str
@@ -31,7 +32,12 @@
     check_call(_LIB.MXLoadTVMOp(c_str(_LIB_TVM_OP[0])))
 
     # op sch config
-    _CONF_TVM_OP = find_conf_path("tvmop")
-    with open(_CONF_TVM_OP[0], "r") as f:
-        ret = ConfigSpaces.from_json_dict(json.load(f))
-    _set_tvm_op_config(ret)
+    try:
+        _CONF_TVM_OP = find_conf_path("tvmop")
+    except RuntimeError as e:
+        logging.warning("TVM config file missing, falling back to default schedule", exc_info=True)
+    else:
+        logging.info("TVM op config has been loaded")
+        with open(_CONF_TVM_OP[0], "r") as f:
+            ret = ConfigSpaces.from_json_dict(json.load(f))
+        _set_tvm_op_config(ret)
diff --git a/tests/nightly/JenkinsfileForBinaries b/tests/nightly/JenkinsfileForBinaries
index a7fcee622aff..06ccb0a03bd4 100755
--- a/tests/nightly/JenkinsfileForBinaries
+++ b/tests/nightly/JenkinsfileForBinaries
@@ -18,9 +18,9 @@
 //
 //This is a Jenkinsfile for nightly tests. The format and some functions have been picked up from the top-level Jenkinsfile
 
-mx_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a'
-mx_cmake_lib = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so'
-mx_lib_cpp_example_mkl = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, build/cpp-package/example/imagenet_inference'
+mx_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, lib/tvmop.conf, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a'
+mx_cmake_lib = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/tvmop.conf, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so'
+mx_lib_cpp_example_mkl = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, lib/tvmop.conf, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, build/cpp-package/example/imagenet_inference'
 
 node('utility') {
   // Loading the utilities requires a node context unfortunately
diff --git a/tests/nightly/model_backwards_compatibility_check/JenkinsfileForMBCC b/tests/nightly/model_backwards_compatibility_check/JenkinsfileForMBCC
index 725261d904f2..e419aa7424be 100644
--- a/tests/nightly/model_backwards_compatibility_check/JenkinsfileForMBCC
+++ b/tests/nightly/model_backwards_compatibility_check/JenkinsfileForMBCC
@@ -18,7 +18,7 @@
 //
 //This is a Jenkinsfile for the model backwards compatibility checker. The format and some functions have been picked up from the top-level Jenkinsfile.
 
-mx_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so,lib/libtvmop.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a'
+mx_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, lib/tvmop.conf, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a'
 
 node('restricted-utility') {
   // Loading the utilities requires a node context unfortunately

From 33602e510b0b1906990c9497c12d997213a87f22 Mon Sep 17 00:00:00 2001
From: reminisce <wujun.nju@gmail.com>
Date: Thu, 14 Nov 2019 15:59:39 -0800
Subject: [PATCH 597/813] Update TVM submodule (#16777)

* Update tvm

* update tvm for cuda with fp16
---
 .gitmodules          | 2 +-
 3rdparty/tvm         | 2 +-
 Makefile             | 2 +-
 cmake/BuildTVM.cmake | 3 +++
 4 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/.gitmodules b/.gitmodules
index 90ef157f0eec..1900820d4c86 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -19,7 +19,7 @@
 	branch = master
 [submodule "3rdparty/tvm"]
 	path = 3rdparty/tvm
-	url = https://github.com/dmlc/tvm
+	url = https://github.com/apache/incubator-tvm.git
 [submodule "3rdparty/onnx-tensorrt"]
 	path = 3rdparty/onnx-tensorrt
 	url = https://github.com/onnx/onnx-tensorrt.git
diff --git a/3rdparty/tvm b/3rdparty/tvm
index afd4b3e44509..5d66e7a695d1 160000
--- a/3rdparty/tvm
+++ b/3rdparty/tvm
@@ -1 +1 @@
-Subproject commit afd4b3e4450984358e9d79a7e8e578483cb7b017
+Subproject commit 5d66e7a695d1d40e08cf2da802b5e2b7015b758a
diff --git a/Makefile b/Makefile
index 841ba7e322ca..925cc3528375 100644
--- a/Makefile
+++ b/Makefile
@@ -612,7 +612,7 @@ lib/libtvm_runtime.so:
 	[ -e $(LLVM_PATH)/bin/llvm-config ] || sh $(ROOTDIR)/contrib/tvmop/prepare_tvm.sh; \
 	cd $(TVM_PATH)/build; \
 	cmake -DUSE_LLVM="$(LLVM_PATH)/bin/llvm-config" \
-		  -DUSE_SORT=OFF -DUSE_CUDA=$(TVM_USE_CUDA) -DUSE_CUDNN=OFF ..; \
+		  -DUSE_SORT=OFF -DUSE_CUDA=$(TVM_USE_CUDA) -DUSE_CUDNN=OFF -DUSE_OPENMP=ON ..; \
 	$(MAKE) VERBOSE=1; \
 	mkdir -p $(ROOTDIR)/lib; \
 	cp $(TVM_PATH)/build/libtvm_runtime.so $(ROOTDIR)/lib/libtvm_runtime.so; \
diff --git a/cmake/BuildTVM.cmake b/cmake/BuildTVM.cmake
index db8b33b84596..7fcf706ffb7c 100644
--- a/cmake/BuildTVM.cmake
+++ b/cmake/BuildTVM.cmake
@@ -133,3 +133,6 @@ set(USE_VTA_TSIM OFF)
 
 # Whether use Relay debug mode
 set(USE_RELAY_DEBUG OFF)
+
+# Use OPENMP thread pool to be compatible with MXNet
+set(USE_OPENMP ON)

From b972406979ba4e94d75cdf49392880e58eb3f14e Mon Sep 17 00:00:00 2001
From: Xingjian Shi <xshiab@connect.ust.hk>
Date: Thu, 14 Nov 2019 16:10:33 -0800
Subject: [PATCH 598/813] clean TVM (#16814)

---
 Makefile | 1 +
 1 file changed, 1 insertion(+)

diff --git a/Makefile b/Makefile
index 925cc3528375..bcb5a41de9bc 100644
--- a/Makefile
+++ b/Makefile
@@ -769,6 +769,7 @@ clean: rclean mkldnn_clean cyclean testclean $(EXTRA_PACKAGES_CLEAN)
 	cd $(DMLC_CORE); $(MAKE) clean; cd -
 	cd $(PS_PATH); $(MAKE) clean; cd -
 	cd $(NNVM_PATH); $(MAKE) clean; cd -
+	cd $(TVM_PATH); $(MAKE) clean; cd -
 	cd $(AMALGAMATION_PATH); $(MAKE) clean; cd -
 endif
 

From 4a27b5c32b1f75f3101f4aeccbf186775aeb0151 Mon Sep 17 00:00:00 2001
From: Xingjian Shi <xshiab@connect.ust.hk>
Date: Thu, 14 Nov 2019 21:12:13 -0800
Subject: [PATCH 599/813] [Fix] Add ctx to the original ndarray and revise the
 usage of context to ctx (#16819)

* try to fix warning

try to fix warning

try to fix all warnings

use ctx

* try to fix warnings

* try fo fix warnings
---
 python/mxnet/gluon/block.py      |  6 +++---
 python/mxnet/gluon/parameter.py  |  8 +++----
 python/mxnet/ndarray/ndarray.py  | 37 +++++++++++++++++++++++---------
 python/mxnet/numpy/multiarray.py | 11 +++++-----
 4 files changed, 40 insertions(+), 22 deletions(-)

diff --git a/python/mxnet/gluon/block.py b/python/mxnet/gluon/block.py
index 629ff22ec4e0..731295eacb99 100644
--- a/python/mxnet/gluon/block.py
+++ b/python/mxnet/gluon/block.py
@@ -117,7 +117,7 @@ def _gather_type_ctx_info(args):
         Context of the first appeared NDArray (for backward-compatibility)
     """
     if isinstance(args, NDArray):
-        return False, True, {args.context}, args.context
+        return False, True, {args.ctx}, args.ctx
     elif isinstance(args, Symbol):
         return True, False, set(), None
     elif isinstance(args, (list, tuple)):
@@ -1141,7 +1141,7 @@ def forward(self, x, *args):
                 if len(ctx_set) > 1:
                     raise ValueError('Find multiple contexts in the input, '
                                      'After hybridized, the HybridBlock only supports one input '
-                                     'context. You can print the ele.context in the '
+                                     'context. You can print the ele.ctx in the '
                                      'input arguments to inspect their contexts. '
                                      'Find all contexts = {}'.format(ctx_set))
                 with ctx:
@@ -1324,7 +1324,7 @@ def __init__(self, outputs, inputs, params=None):
 
     def forward(self, x, *args):
         if isinstance(x, NDArray):
-            with x.context:
+            with x.ctx:
                 return self._call_cached_op(x, *args)
 
         assert isinstance(x, Symbol), \
diff --git a/python/mxnet/gluon/parameter.py b/python/mxnet/gluon/parameter.py
index 067a357e179c..b0f8fef2c0dd 100644
--- a/python/mxnet/gluon/parameter.py
+++ b/python/mxnet/gluon/parameter.py
@@ -369,10 +369,10 @@ def _init_grad(self):
             if self._grad_stype != 'default':
                 raise ValueError("mxnet.numpy.zeros does not support stype = {}"
                                  .format(self._grad_stype))
-            self._grad = [_mx_np.zeros(shape=i.shape, dtype=i.dtype, ctx=i.context)
+            self._grad = [_mx_np.zeros(shape=i.shape, dtype=i.dtype, ctx=i.ctx)
                           for i in self._data]
         else:
-            self._grad = [ndarray.zeros(shape=i.shape, dtype=i.dtype, ctx=i.context,
+            self._grad = [ndarray.zeros(shape=i.shape, dtype=i.dtype, ctx=i.ctx,
                                         stype=self._grad_stype) for i in self._data]
 
         autograd.mark_variables(self._check_and_get(self._data, list),
@@ -522,7 +522,7 @@ def row_sparse_data(self, row_id):
             raise RuntimeError("Cannot return a copy of Parameter %s via row_sparse_data() " \
                                "because its storage type is %s. Please use data() instead." \
                                %(self.name, self._stype))
-        return self._get_row_sparse(self._data, row_id.context, row_id)
+        return self._get_row_sparse(self._data, row_id.ctx, row_id)
 
     def list_row_sparse_data(self, row_id):
         """Returns copies of the 'row_sparse' parameter on all contexts, in the same order
@@ -897,7 +897,7 @@ def zero_grad(self):
                 if g.stype == 'row_sparse':
                     ndarray.zeros_like(g, out=g)
                 else:
-                    arrays[g.context].append(g)
+                    arrays[g.ctx].append(g)
 
         if len(arrays) == 0:
             return
diff --git a/python/mxnet/ndarray/ndarray.py b/python/mxnet/ndarray/ndarray.py
index 7d8cc524c817..dc50635dd8a3 100644
--- a/python/mxnet/ndarray/ndarray.py
+++ b/python/mxnet/ndarray/ndarray.py
@@ -245,7 +245,7 @@ def __repr__(self):
         shape_info = 'x'.join(['%d' % x for x in self.shape])
         return '\n%s\n<%s %s @%s>' % (str(self.asnumpy()),
                                       self.__class__.__name__,
-                                      shape_info, self.context)
+                                      shape_info, self.ctx)
 
     def __reduce__(self):
         return NDArray, (None,), self.__getstate__()
@@ -729,14 +729,14 @@ def _prepare_value_nd(self, value, bcast_shape, squeeze_axes=None):
         `squeeze_axes`: a sequence of axes to squeeze in the value array.
         """
         if isinstance(value, numeric_types):
-            value_nd = full(bcast_shape, value, ctx=self.context, dtype=self.dtype)
+            value_nd = full(bcast_shape, value, ctx=self.ctx, dtype=self.dtype)
         elif type(value) == self.__class__:  # pylint: disable=unidiomatic-typecheck
-            value_nd = value.as_in_context(self.context)
+            value_nd = value.as_in_context(self.ctx)
             if value_nd.dtype != self.dtype:
                 value_nd = value_nd.astype(self.dtype)
         else:
             try:
-                value_nd = array(value, ctx=self.context, dtype=self.dtype)
+                value_nd = array(value, ctx=self.ctx, dtype=self.dtype)
             except:
                 raise TypeError('{} does not support assignment with non-array-like '
                                 'object {} of type {}'.format(self.__class__, value, type(value)))
@@ -1220,7 +1220,7 @@ def _get_index_nd(self, key):
 
         shape_nd_permut = tuple(self.shape[ax] for ax in axs_nd_permut)
         converted_idcs_short = [
-            self._advanced_index_to_array(idx, ax_len, self.context)
+            self._advanced_index_to_array(idx, ax_len, self.ctx)
             for idx, ax_len in zip(idcs_permut_short, shape_nd_permut)
         ]
         bcast_idcs_permut_short = self._broadcast_advanced_indices(
@@ -1229,7 +1229,7 @@ def _get_index_nd(self, key):
 
         # Get the ndim of advanced indexing subspace
         converted_advanced_idcs = [
-            self._advanced_index_to_array(idx, ax_len, self.context)
+            self._advanced_index_to_array(idx, ax_len, self.ctx)
             for idx, ax_len in zip(adv_idcs_nd, [self.shape[ax] for ax in adv_axs_nd])
         ]
         bcast_advanced_shape = _broadcast_shapes(converted_advanced_idcs)
@@ -2433,6 +2433,23 @@ def context(self):
             self.handle, ctypes.byref(dev_typeid), ctypes.byref(dev_id)))
         return Context(Context.devtype2str[dev_typeid.value], dev_id.value)
 
+    @property
+    def ctx(self):
+        """Device context of the array. Has the same meaning as context.
+
+        Examples
+        --------
+        >>> x = mx.nd.array([1, 2, 3, 4])
+        >>> x.ctx
+        cpu(0)
+        >>> type(x.ctx)
+        <class 'mxnet.context.Context'>
+        >>> y = mx.nd.zeros((2,3), mx.gpu(0))
+        >>> y.ctx
+        gpu(0)
+        """
+        return self.context
+
     @property
     def dtype(self):
         """Data-type of the array's elements.
@@ -2580,7 +2597,7 @@ def astype(self, dtype, copy=True):
         if not copy and np.dtype(dtype) == self.dtype:
             return self
 
-        res = empty(self.shape, ctx=self.context, dtype=dtype)
+        res = empty(self.shape, ctx=self.ctx, dtype=dtype)
         self.copyto(res)
         return res
 
@@ -2646,7 +2663,7 @@ def copy(self):
         array([[ 1.,  1.,  1.],
                [ 1.,  1.,  1.]], dtype=float32)
         """
-        return self.copyto(self.context)
+        return self.copyto(self.ctx)
 
     def slice_assign_scalar(self, value, begin, end, step):
         """
@@ -2904,7 +2921,7 @@ def _full(self, value):
         """
         This is added as an NDArray class method in order to support polymorphism in NDArray and numpy.ndarray indexing
         """
-        return _internal._full(self.shape, value=value, ctx=self.context, dtype=self.dtype, out=self)
+        return _internal._full(self.shape, value=value, ctx=self.ctx, dtype=self.dtype, out=self)
 
     def _scatter_set_nd(self, value_nd, indices):
         """
@@ -4542,7 +4559,7 @@ def concatenate(arrays, axis=0, always_copy=True):
         assert shape_rest2 == arr.shape[axis+1:]
         assert dtype == arr.dtype
     ret_shape = shape_rest1 + (shape_axis,) + shape_rest2
-    ret = empty(ret_shape, ctx=arrays[0].context, dtype=dtype)
+    ret = empty(ret_shape, ctx=arrays[0].ctx, dtype=dtype)
 
     idx = 0
     begin = [0 for _ in ret_shape]
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index b6816d75a98e..1df1a0360913 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -921,7 +921,7 @@ def __repr__(self):
         elif dtype not in (_np.float32, _np.bool_):
             array_str = array_str[:-1] + ', dtype={})'.format(dtype)
 
-        context = self.context
+        context = self.ctx
         if context.device_type == 'cpu':
             return array_str
         return array_str[:-1] + ', ctx={})'.format(str(context))
@@ -929,7 +929,7 @@ def __repr__(self):
     def __str__(self):
         """Returns a string representation of the array."""
         array_str = self.asnumpy().__str__()
-        context = self.context
+        context = self.ctx
         if context.device_type == 'cpu' or self.ndim == 0:
             return array_str
         return '{array} @{ctx}'.format(array=array_str, ctx=context)
@@ -994,7 +994,7 @@ def astype(self, dtype, **kwargs):  # pylint: disable=arguments-differ,unused-ar
         if not copy and _np.dtype(dtype) == self.dtype:
             return self
 
-        res = empty(self.shape, dtype=dtype, ctx=self.context)
+        res = empty(self.shape, dtype=dtype, ctx=self.ctx)
         self.copyto(res)
         return res
 
@@ -1051,7 +1051,8 @@ def argmax(self, axis=None, out=None):  # pylint: disable=arguments-differ
 
     def as_in_context(self, context):
         """This function has been deprecated. Please refer to ``ndarray.as_in_ctx``."""
-        warnings.warn('ndarray.context has been renamed to ndarray.ctx', DeprecationWarning)
+        warnings.warn('ndarray.as_in_context has been renamed to'
+                      ' ndarray.as_in_ctx', DeprecationWarning)
         return self.as_nd_ndarray().as_in_context(context).as_np_ndarray()
 
     def as_in_ctx(self, ctx):
@@ -1864,7 +1865,7 @@ def _full(self, value):
         Currently for internal use only. Implemented for __setitem__.
         Assign to self an array of self's same shape and type, filled with value.
         """
-        return _mx_nd_np.full(self.shape, value, ctx=self.context, dtype=self.dtype, out=self)
+        return _mx_nd_np.full(self.shape, value, ctx=self.ctx, dtype=self.dtype, out=self)
 
     # pylint: disable=redefined-outer-name
     def _scatter_set_nd(self, value_nd, indices):

From 32561313875e798937d9fff7eef043e5a9315a24 Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Fri, 15 Nov 2019 22:29:47 -0800
Subject: [PATCH 600/813] refactor and reduce float types for some functions,
 also add bitwise_xor (#16827)

---
 python/mxnet/ndarray/numpy/_op.py             |  40 ++-
 python/mxnet/numpy/multiarray.py              |  42 ++-
 python/mxnet/numpy_dispatch_protocol.py       |   1 +
 python/mxnet/symbol/numpy/_symbol.py          |  35 +-
 src/operator/elemwise_op_common.h             |   3 +-
 .../numpy/np_elemwise_broadcast_op.cc         | 238 --------------
 .../numpy/np_elemwise_broadcast_op.cu         |  71 ----
 src/operator/numpy/np_elemwise_broadcast_op.h |  10 +
 .../np_elemwise_broadcast_op_extended.cc      | 305 ++++++++++++++++++
 .../np_elemwise_broadcast_op_extended.cu      | 108 +++++++
 src/operator/operator_tune.cc                 |   4 +-
 .../tensor/elemwise_binary_broadcast_op.h     | 110 ++++---
 src/operator/tensor/elemwise_binary_op.h      | 148 +++++----
 .../tensor/elemwise_binary_scalar_op.h        |  20 ++
 .../unittest/test_numpy_interoperability.py   |  15 +-
 tests/python/unittest/test_numpy_op.py        |   9 +-
 16 files changed, 728 insertions(+), 431 deletions(-)
 create mode 100644 src/operator/numpy/np_elemwise_broadcast_op_extended.cc
 create mode 100644 src/operator/numpy/np_elemwise_broadcast_op_extended.cu

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index f9164855dfe9..70cf3f6d2333 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -36,7 +36,7 @@
            'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate', 'append',
            'stack', 'vstack', 'column_stack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax',
            'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
-           'around', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
+           'around', 'hypot', 'bitwise_xor', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
            'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal',
            'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory', 'may_share_memory', 'diff', 'resize',
            'nan_to_num']
@@ -4291,6 +4291,44 @@ def hypot(x1, x2, out=None, **kwargs):
     return _ufunc_helper(x1, x2, _npi.hypot, _np.hypot, _npi.hypot_scalar, None, out)
 
 
+@set_module('mxnet.ndarray.numpy')
+@wrap_np_binary_func
+def bitwise_xor(x1, x2, out=None, **kwargs):
+    r"""
+    Compute the bit-wise XOR of two arrays element-wise.
+
+    Parameters
+    ----------
+    x1, x2 : ndarray or scalar
+        Only integer and boolean types are handled. If x1.shape != x2.shape,
+        they must be broadcastable to a common shape (which becomes the shape of the output).
+    out : ndarray, optional
+        A location into which the result is stored. If provided, it must have a shape that the
+        inputs broadcast to. If not provided or None, a freshly-allocated array is returned.
+
+    Returns
+    -------
+    out : ndarray
+        Result.
+
+    Examples
+    --------
+    >>> np.bitwise_xor(13, 17)
+    28
+
+    >>> np.bitwise_xor(31, 5)
+    26
+    >>> np.bitwise_xor(np.array([31,3], dtype='int32'), 5)
+    array([26,  6])
+
+    >>> np.bitwise_xor(np.array([31,3], dtype='int32'), np.array([5,6], dtype='int32'))
+    array([26,  5])
+    >>> np.bitwise_xor(np.array([True, True], dtype='bool'), np.array([False, True], dtype='bool'))
+    array([ True, False])
+    """
+    return _ufunc_helper(x1, x2, _npi.bitwise_xor, _np.bitwise_xor, _npi.bitwise_xor_scalar, None, out)
+
+
 @set_module('mxnet.ndarray.numpy')
 @wrap_np_binary_func
 def ldexp(x1, x2, out=None, **kwargs):
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 1df1a0360913..09ff22e66702 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -54,8 +54,8 @@
            'tensordot', 'histogram', 'eye', 'linspace', 'logspace', 'expand_dims', 'tile', 'arange',
            'split', 'vsplit', 'concatenate', 'stack', 'vstack', 'column_stack', 'dstack', 'mean', 'maximum', 'minimum',
            'swapaxes', 'clip', 'argmax', 'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming',
-           'blackman', 'flip', 'around', 'arctan2', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril',
-           'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less',
+           'blackman', 'flip', 'around', 'arctan2', 'hypot', 'bitwise_xor', 'rad2deg', 'deg2rad', 'unique', 'lcm',
+           'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less',
            'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory',
            'may_share_memory', 'diff', 'resize', 'nan_to_num']
 
@@ -6198,6 +6198,44 @@ def hypot(x1, x2, out=None, **kwargs):
     return _mx_nd_np.hypot(x1, x2, out=out)
 
 
+@set_module('mxnet.numpy')
+@wrap_np_binary_func
+def bitwise_xor(x1, x2, out=None, **kwargs):
+    r"""
+    Compute the bit-wise XOR of two arrays element-wise.
+
+    Parameters
+    ----------
+    x1, x2 : ndarray or scalar
+        Only integer and boolean types are handled. If x1.shape != x2.shape,
+        they must be broadcastable to a common shape (which becomes the shape of the output).
+    out : ndarray, optional
+        A location into which the result is stored. If provided, it must have a shape that the
+        inputs broadcast to. If not provided or None, a freshly-allocated array is returned.
+
+    Returns
+    -------
+    out : ndarray
+        Result.
+
+    Examples
+    --------
+    >>> np.bitwise_xor(13, 17)
+    28
+
+    >>> np.bitwise_xor(31, 5)
+    26
+    >>> np.bitwise_xor(np.array([31,3], dtype=np.int32), 5)
+    array([26,  6])
+
+    >>> np.bitwise_xor(np.array([31,3], dtype='int32'), np.array([5,6], dtype='int32'))
+    array([26,  5])
+    >>> np.bitwise_xor(np.array([True, True], dtype='bool'), np.array([False, True], dtype='bool'))
+    array([ True, False])
+    """
+    return _mx_nd_np.bitwise_xor(x1, x2, out=out)
+
+
 @set_module('mxnet.numpy')
 @wrap_np_binary_func
 def ldexp(x1, x2, out=None, **kwargs):
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index 025982cfc7a5..694d1d40143b 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -225,6 +225,7 @@ def _register_array_function():
     'ceil',
     'trunc',
     'floor',
+    'bitwise_xor',
     'logical_not',
     'equal',
     'not_equal',
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 0d7303865b92..0a42a620c14e 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -38,7 +38,7 @@
            'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate', 'append',
            'stack', 'vstack', 'column_stack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax',
            'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
-           'around', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
+           'around', 'hypot', 'bitwise_xor', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
            'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal',
            'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'shares_memory', 'may_share_memory', 'diff',
            'resize', 'nan_to_num']
@@ -4058,17 +4058,16 @@ def hypot(x1, x2, out=None, **kwargs):
 
     Parameters
     ----------
-    x1, x2 : array_like
+    x1, x2 : _Symbol or scalar
         Leg of the triangle(s).
-    out : ndarray, None, or tuple of ndarray and None, optional
+    out : _Symbol or None, optional
         A location into which the result is stored. If provided, it must have
         a shape that the inputs broadcast to. If not provided or `None`,
-        a freshly-allocated array is returned. A tuple (possible only as a
-        keyword argument) must have length equal to the number of outputs.
+        a freshly-allocated array is returned.
 
     Returns
     -------
-    z : ndarray
+    z : _Symbol or scalar
         The hypotenuse of the triangle(s).
         This is a scalar if both `x1` and `x2` are scalars.
 
@@ -4080,6 +4079,30 @@ def hypot(x1, x2, out=None, **kwargs):
     return _ufunc_helper(x1, x2, _npi.hypot, _np.hypot, _npi.hypot_scalar, None, out)
 
 
+@set_module('mxnet.symbol.numpy')
+@wrap_np_binary_func
+def bitwise_xor(x1, x2, out=None, **kwargs):
+    r"""
+    Compute the bit-wise XOR of two arrays element-wise.
+
+    Parameters
+    ----------
+    x1, x2 : _Symbol or scalar
+        Only integer and boolean types are handled. If x1.shape != x2.shape,
+        they must be broadcastable to a common shape (which becomes the shape of the output).
+    out : _Symbol or None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned.
+
+    Returns
+    -------
+    out : _Symbol or scalar
+        Result.
+    """
+    return _ufunc_helper(x1, x2, _npi.bitwise_xor, _np.bitwise_xor, _npi.bitwise_xor_scalar, None, out)
+
+
 @set_module('mxnet.symbol.numpy')
 def unique(ar, return_index=False, return_inverse=False, return_counts=False, axis=None):
     """
diff --git a/src/operator/elemwise_op_common.h b/src/operator/elemwise_op_common.h
index 6711297718b2..2cdd73a95801 100644
--- a/src/operator/elemwise_op_common.h
+++ b/src/operator/elemwise_op_common.h
@@ -209,7 +209,8 @@ inline bool ElemwiseIntType(const nnvm::NodeAttrs& attrs,
   CHECK(in_attrs->at(0) == mshadow::kInt64 ||
         in_attrs->at(0) == mshadow::kInt32 ||
         in_attrs->at(0) == mshadow::kInt8 ||
-        in_attrs->at(0) == mshadow::kUint8) << "Only supports integer types.";
+        in_attrs->at(0) == mshadow::kUint8 ||
+        in_attrs->at(0) == mshadow::kBool) << "Only supports integer types.";
   if (n_in != -1) {
     CHECK_EQ(in_attrs->size(), static_cast<size_t>(n_in)) << " in operator " << attrs.name;
   }
diff --git a/src/operator/numpy/np_elemwise_broadcast_op.cc b/src/operator/numpy/np_elemwise_broadcast_op.cc
index a76e59d30dc6..4e4734c143a4 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op.cc
+++ b/src/operator/numpy/np_elemwise_broadcast_op.cc
@@ -28,16 +28,6 @@
 namespace mxnet {
 namespace op {
 
-bool NumpyBinaryScalarType(const nnvm::NodeAttrs& attrs,
-                           std::vector<int>* in_attrs,
-                           std::vector<int>* out_attrs) {
-  CHECK_EQ(in_attrs->size(), 1U);
-  CHECK_EQ(out_attrs->size(), 1U);
-  TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
-  TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
-  return in_attrs->at(0) != -1;
-}
-
 #define MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(name)              \
   NNVM_REGISTER_OP(name)                                            \
   .set_num_inputs(1)                                                \
@@ -156,62 +146,6 @@ MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_mod)
 MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_power)
 .set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, mshadow_op::power>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_broadcast_power"});
-
-MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_copysign)
-.describe(R"code()code" ADD_FILELINE)
-.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, mshadow_op::copysign>)
-.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_copysign"});
-
-NNVM_REGISTER_OP(_backward_npi_copysign)
-.set_num_inputs(3)
-.set_num_outputs(2)
-.set_attr<nnvm::TIsBackward>("TIsBackward", true)
-.set_attr<nnvm::FInplaceOption>("FInplaceOption",
-  [](const NodeAttrs& attrs){
-    return std::vector<std::pair<int, int> >{{0, 1}};
-  })
-.set_attr<FResourceRequest>("FResourceRequest",
-  [](const NodeAttrs& attrs) {
-    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
-  })
-.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastBackwardUseIn<cpu, mshadow_op::copysign_grad,
-                                                                  mshadow_op::copysign_rgrad>);
-
-NNVM_REGISTER_OP(_npi_lcm)
-.set_num_inputs(2)
-.set_num_outputs(1)
-.set_attr<nnvm::FListInputNames>("FListInputNames",
-[](const NodeAttrs& attrs) {
-     return std::vector<std::string>{"lhs", "rhs"};
-})
-.set_attr<mxnet::FInferShape>("FInferShape", BinaryBroadcastShape)
-.set_attr<nnvm::FInferType>("FInferType", ElemwiseIntType<2, 1>)
-.set_attr<nnvm::FInplaceOption>("FInplaceOption",
-[](const NodeAttrs& attrs){
-     return std::vector<std::pair<int, int> >{{0, 0}, {1, 0}};
-})
-.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
-.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, mshadow_op::lcm>)
-.add_argument("lhs", "NDArray-or-Symbol", "First input to the function")
-.add_argument("rhs", "NDArray-or-Symbol", "Second input to the function");
-
-NNVM_REGISTER_OP(_npi_lcm_scalar)
-.set_num_inputs(1)
-.set_num_outputs(1)
-.set_attr_parser([](NodeAttrs* attrs) {
-    attrs->parsed = std::stod(attrs->dict["scalar"]);
-  })
-.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
-.set_attr<nnvm::FInferType>("FInferType", ElemwiseIntType<1, 1>)
-.set_attr<nnvm::FInplaceOption>("FInplaceOption",
-  [](const NodeAttrs& attrs){
-    return std::vector<std::pair<int, int> >{{0, 0}};
-  })
-.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
-.add_argument("data", "NDArray-or-Symbol", "source input")
-.add_argument("scalar", "int", "scalar input")
-.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::lcm>);
-
 MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_add_scalar)
 .set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, op::mshadow_op::plus>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_copy"});
@@ -244,177 +178,5 @@ MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_rpower_scalar)
 .set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::rpower>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{"_backward_rpower_scalar"});
 
-MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_copysign_scalar)
-.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::copysign>)
-.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_copysign_scalar"});
-
-MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_rcopysign_scalar)
-.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::rcopysign>)
-.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_rcopysign_scalar"});
-
-MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_backward_npi_copysign_scalar)
-.set_attr<FCompute>("FCompute<cpu>",
-                    BinaryScalarOp::Backward<cpu, mshadow_op::copysign_grad>);
-
-MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_backward_npi_rcopysign_scalar)
-.set_attr<FCompute>("FCompute<cpu>",
-                    BinaryScalarOp::Backward<cpu, mshadow_op::rcopysign_grad>);
-
-inline bool IsFloatType(const int dtype) {
-  return (dtype == mshadow::kFloat16 ||
-          dtype == mshadow::kFloat32 ||
-          dtype == mshadow::kFloat64);
-}
-
-inline bool Arctan2OpType(const nnvm::NodeAttrs& attrs,
-                          std::vector<int>* in_attrs,
-                          std::vector<int>* out_attrs) {
-  CHECK_EQ(in_attrs->size(), 2U);
-  CHECK_EQ(out_attrs->size(), 1U);
-
-  TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
-  TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(1));
-  TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
-  TYPE_ASSIGN_CHECK(*in_attrs, 1, out_attrs->at(0));
-  // check if it is float16, float32 or float64. If not, raise error.
-  CHECK(IsFloatType(in_attrs->at(0))) << "Do not support `int` as input.\n";
-  return out_attrs->at(0) != -1;
-}
-
-NNVM_REGISTER_OP(_npi_arctan2)
-.set_num_inputs(2)
-.set_num_outputs(1)
-.set_attr<nnvm::FListInputNames>("FListInputNames",
-  [](const NodeAttrs& attrs) {
-    return std::vector<std::string>{"x1", "x2"};
-  })
-.set_attr<mxnet::FInferShape>("FInferShape", BinaryBroadcastShape)
-.set_attr<nnvm::FInferType>("FInferType", Arctan2OpType)
-.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, mshadow_op::arctan2>)
-.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_arctan2"})
-.set_attr<nnvm::FInplaceOption>("FInplaceOption",
-  [](const NodeAttrs& attrs) {
-    return std::vector<std::pair<int, int> >{{0, 0}};
-  })
-.add_argument("x1", "NDArray-or-Symbol", "The input array")
-.add_argument("x2", "NDArray-or-Symbol", "The input array");
-
-NNVM_REGISTER_OP(_backward_npi_arctan2)
-.set_num_inputs(3)
-.set_num_outputs(2)
-.set_attr<nnvm::TIsBackward>("TIsBackward", true)
-.set_attr<FResourceRequest>("FResourceRequest",
-  [](const NodeAttrs& attrs) {
-    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
-  })
-.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastBackwardUseIn<cpu, mshadow_op::arctan2_grad,
-                                                                  mshadow_op::arctan2_rgrad>);
-
-MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_arctan2_scalar)
-.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::arctan2>)
-.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_arctan2_scalar"});
-
-MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_rarctan2_scalar)
-.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::rarctan2>)
-.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_rarctan2_scalar"});
-
-MXNET_OPERATOR_REGISTER_BINARY(_backward_npi_arctan2_scalar)
-.add_argument("scalar", "float", "scalar value")
-.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = std::stod(attrs->dict["scalar"]); })
-.set_attr<FCompute>("FCompute<cpu>",
-                    BinaryScalarOp::Backward<cpu, mshadow_op::arctan2_grad>);
-
-MXNET_OPERATOR_REGISTER_BINARY(_backward_npi_rarctan2_scalar)
-.add_argument("scalar", "float", "scalar value")
-.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = std::stod(attrs->dict["scalar"]); })
-.set_attr<FCompute>("FCompute<cpu>",
-                    BinaryScalarOp::Backward<cpu, mshadow_op::arctan2_rgrad>);
-
-bool HypotOpType(const nnvm::NodeAttrs& attrs,
-                 std::vector<int>* in_attrs,
-                 std::vector<int>* out_attrs) {
-  CHECK_EQ(in_attrs->size(), 2U);
-  CHECK_EQ(out_attrs->size(), 1U);
-
-  TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
-  TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(1));
-  TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
-  TYPE_ASSIGN_CHECK(*in_attrs, 1, out_attrs->at(0));
-
-  CHECK(IsFloatType(in_attrs->at(0))) << "Do not support `int` as input.\n";
-  return out_attrs->at(0) != -1;
-}
-
-// rigister hypot that do not support int here
-NNVM_REGISTER_OP(_npi_hypot)
-.set_num_inputs(2)
-.set_num_outputs(1)
-.set_attr<nnvm::FListInputNames>("FListInputNames",
-  [](const NodeAttrs& attrs) {
-    return std::vector<std::string>{"x1", "x2"};
-  })
-.set_attr<mxnet::FInferShape>("FInferShape", BinaryBroadcastShape)
-.set_attr<nnvm::FInferType>("FInferType", HypotOpType)
-.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, mshadow_op::hypot>)
-.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_hypot"})
-.set_attr<nnvm::FInplaceOption>("FInplaceOption",
-  [](const NodeAttrs& attrs) {
-    return std::vector<std::pair<int, int> >{{0, 0}, {1, 0}};
-  })
-.add_argument("x1", "NDArray-or-Symbol", "The input array")
-.add_argument("x2", "NDArray-or-Symbol", "The input array");
-
-NNVM_REGISTER_OP(_backward_npi_hypot)
-.set_num_inputs(3)
-.set_num_outputs(2)
-.set_attr<nnvm::TIsBackward>("TIsBackward", true)
-.set_attr<nnvm::FInplaceOption>("FInplaceOption",
-  [](const NodeAttrs& attrs) {
-    return std::vector<std::pair<int, int> > {{0, 1}};
-  })
-.set_attr<FResourceRequest>("FResourceRequest",
-  [](const NodeAttrs& attrs) {
-    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
-  })
-.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastBackwardUseIn<cpu, mshadow_op::hypot_grad_left,
-                                                                  mshadow_op::hypot_grad_right>);
-
-MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_ldexp)
-.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, mshadow_op::ldexp>)
-.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_ldexp"});
-
-MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_ldexp_scalar)
-.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::ldexp>)
-.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_ldexp_scalar"});
-
-MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_rldexp_scalar)
-.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::rldexp>)
-.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_rldexp_scalar"});
-
-NNVM_REGISTER_OP(_backward_npi_ldexp)
-.set_num_inputs(3)
-.set_num_outputs(2)
-.set_attr<nnvm::TIsBackward>("TIsBackward", true)
-.set_attr<nnvm::FInplaceOption>("FInplaceOption",
-  [](const NodeAttrs& attrs){
-    return std::vector<std::pair<int, int> >{{0, 1}};
-  })
-.set_attr<FResourceRequest>("FResourceRequest",
-  [](const NodeAttrs& attrs) {
-    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
-  })
-.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastBackwardUseIn<cpu, mshadow_op::ldexp_grad,
-                                                                  mshadow_op::ldexp_rgrad>);
-
-MXNET_OPERATOR_REGISTER_BINARY(_backward_npi_ldexp_scalar)
-.add_argument("scalar", "float", "scalar value")
-.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = std::stod(attrs->dict["scalar"]); })
-.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Backward<cpu, mshadow_op::ldexp_grad>);
-
-MXNET_OPERATOR_REGISTER_BINARY(_backward_npi_rldexp_scalar)
-.add_argument("scalar", "float", "scalar value")
-.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = std::stod(attrs->dict["scalar"]); })
-.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Backward<cpu, mshadow_op::rldexp_grad>);
-
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/np_elemwise_broadcast_op.cu b/src/operator/numpy/np_elemwise_broadcast_op.cu
index a0a277df211f..5c9dc97377cf 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op.cu
+++ b/src/operator/numpy/np_elemwise_broadcast_op.cu
@@ -70,29 +70,6 @@ NNVM_REGISTER_OP(_npi_mod)
 NNVM_REGISTER_OP(_npi_power)
 .set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, mshadow_op::power>);
 
-NNVM_REGISTER_OP(_npi_copysign)
-.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, mshadow_op::copysign>);
-
-NNVM_REGISTER_OP(_npi_lcm)
-.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, mshadow_op::lcm>);
-
-NNVM_REGISTER_OP(_backward_npi_copysign)
-.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastBackwardUseIn<gpu, mshadow_op::copysign_grad,
-                                                                  mshadow_op::copysign_rgrad>);
-
-NNVM_REGISTER_OP(_npi_arctan2)
-.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, mshadow_op::arctan2>);
-
-NNVM_REGISTER_OP(_backward_npi_arctan2)
-.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastBackwardUseIn<gpu, mshadow_op::arctan2_grad,
-                                                                  mshadow_op::arctan2_rgrad>);
-NNVM_REGISTER_OP(_npi_hypot)
-.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, mshadow_op::hypot>);
-
-NNVM_REGISTER_OP(_backward_npi_hypot)
-.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastBackwardUseIn<gpu, mshadow_op::hypot_grad_left,
-                                                                  mshadow_op::hypot_grad_right>);
-
 NNVM_REGISTER_OP(_npi_add_scalar)
 .set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, op::mshadow_op::plus>);
 
@@ -117,53 +94,5 @@ NNVM_REGISTER_OP(_npi_power_scalar)
 NNVM_REGISTER_OP(_npi_rpower_scalar)
 .set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::rpower>);
 
-NNVM_REGISTER_OP(_npi_copysign_scalar)
-.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::copysign>);
-
-NNVM_REGISTER_OP(_npi_rcopysign_scalar)
-.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::rcopysign>);
-
-NNVM_REGISTER_OP(_backward_npi_copysign_scalar)
-.set_attr<FCompute>("FCompute<gpu>",
-                    BinaryScalarOp::Backward<gpu, mshadow_op::copysign_grad>);
-
-NNVM_REGISTER_OP(_backward_npi_rcopysign_scalar)
-.set_attr<FCompute>("FCompute<gpu>",
-                    BinaryScalarOp::Backward<gpu, mshadow_op::rcopysign_grad>);
-
-NNVM_REGISTER_OP(_npi_arctan2_scalar)
-.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::arctan2>);
-
-NNVM_REGISTER_OP(_backward_npi_arctan2_scalar)
-.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::arctan2_grad>);
-
-NNVM_REGISTER_OP(_npi_rarctan2_scalar)
-.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::rarctan2>);
-
-NNVM_REGISTER_OP(_backward_npi_rarctan2_scalar)
-.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::rarctan2_grad>);
-
-NNVM_REGISTER_OP(_npi_lcm_scalar)
-.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::lcm>);
-
-NNVM_REGISTER_OP(_npi_ldexp)
-.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, mshadow_op::ldexp>);
-
-NNVM_REGISTER_OP(_npi_ldexp_scalar)
-.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::ldexp>);
-
-NNVM_REGISTER_OP(_npi_rldexp_scalar)
-.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::rldexp>);
-
-NNVM_REGISTER_OP(_backward_npi_ldexp)
-.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastBackwardUseIn<gpu, mshadow_op::ldexp_grad,
-                                                                  mshadow_op::ldexp_rgrad>);
-
-NNVM_REGISTER_OP(_backward_npi_ldexp_scalar)
-.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Backward<gpu, mshadow_op::ldexp_grad>);
-
-NNVM_REGISTER_OP(_backward_npi_rldexp_scalar)
-.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Backward<gpu, mshadow_op::rldexp_grad>);
-
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/np_elemwise_broadcast_op.h b/src/operator/numpy/np_elemwise_broadcast_op.h
index 1a4596fba91c..2ad8b88bdb1c 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op.h
+++ b/src/operator/numpy/np_elemwise_broadcast_op.h
@@ -34,6 +34,16 @@
 namespace mxnet {
 namespace op {
 
+inline bool NumpyBinaryScalarType(const nnvm::NodeAttrs& attrs,
+                           std::vector<int>* in_attrs,
+                           std::vector<int>* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
+  TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
+  return in_attrs->at(0) != -1;
+}
+
 inline void PrintErrorMessage(const std::string& op_name, const int dtype1, const int dtype2) {
   LOG(FATAL) << "Operator " << op_name << " does not support combination of "
              << common::dtype_string(dtype1) << " with " << common::dtype_string(dtype2)
diff --git a/src/operator/numpy/np_elemwise_broadcast_op_extended.cc b/src/operator/numpy/np_elemwise_broadcast_op_extended.cc
new file mode 100644
index 000000000000..84c47e597883
--- /dev/null
+++ b/src/operator/numpy/np_elemwise_broadcast_op_extended.cc
@@ -0,0 +1,305 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file np_elemwise_binary_op_extended.cc
+ * \brief CPU Implementation of extended functions for elementwise numpy binary broadcast operator.
+ */
+
+#include "../../common/utils.h"
+#include "./np_elemwise_broadcast_op.h"
+
+namespace mxnet {
+namespace op {
+
+#define MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(name)              \
+  NNVM_REGISTER_OP(name)                                            \
+  .set_num_inputs(1)                                                \
+  .set_num_outputs(1)                                               \
+  .set_attr_parser([](NodeAttrs* attrs) {                           \
+      attrs->parsed = std::stod(attrs->dict["scalar"]);             \
+    })                                                              \
+  .set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>) \
+  .set_attr<nnvm::FInferType>("FInferType", NumpyBinaryScalarType)  \
+  .set_attr<nnvm::FInplaceOption>("FInplaceOption",                 \
+    [](const NodeAttrs& attrs){                                     \
+      return std::vector<std::pair<int, int> >{{0, 0}};             \
+    })                                                              \
+  .add_argument("data", "NDArray-or-Symbol", "source input")        \
+  .add_argument("scalar", "float", "scalar input")
+
+MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_copysign)
+.describe(R"code()code" ADD_FILELINE)
+.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, mshadow_op::copysign>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_copysign"});
+
+NNVM_REGISTER_OP(_backward_npi_copysign)
+.set_num_inputs(3)
+.set_num_outputs(2)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs){
+    return std::vector<std::pair<int, int> >{{0, 1}};
+  })
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastBackwardUseIn<cpu, mshadow_op::copysign_grad,
+                                                                  mshadow_op::copysign_rgrad>);
+
+NNVM_REGISTER_OP(_npi_lcm)
+.set_num_inputs(2)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+[](const NodeAttrs& attrs) {
+     return std::vector<std::string>{"lhs", "rhs"};
+})
+.set_attr<mxnet::FInferShape>("FInferShape", BinaryBroadcastShape)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseIntType<2, 1>)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+[](const NodeAttrs& attrs){
+     return std::vector<std::pair<int, int> >{{0, 0}, {1, 0}};
+})
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastIntCompute<cpu, mshadow_op::lcm>)
+.add_argument("lhs", "NDArray-or-Symbol", "First input to the function")
+.add_argument("rhs", "NDArray-or-Symbol", "Second input to the function");
+
+NNVM_REGISTER_OP(_npi_lcm_scalar)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser([](NodeAttrs* attrs) {
+    attrs->parsed = std::stod(attrs->dict["scalar"]);
+  })
+.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseIntType<1, 1>)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs){
+    return std::vector<std::pair<int, int> >{{0, 0}};
+  })
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.add_argument("data", "NDArray-or-Symbol", "source input")
+.add_argument("scalar", "int", "scalar input")
+.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::lcm>);
+
+NNVM_REGISTER_OP(_npi_bitwise_xor)
+.set_num_inputs(2)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+[](const NodeAttrs& attrs) {
+     return std::vector<std::string>{"lhs", "rhs"};
+})
+.set_attr<mxnet::FInferShape>("FInferShape", BinaryBroadcastShape)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseIntType<2, 1>)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+[](const NodeAttrs& attrs){
+     return std::vector<std::pair<int, int> >{{0, 0}, {1, 0}};
+})
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastIntCompute<cpu, mshadow_op::bitwise_xor>)
+.add_argument("lhs", "NDArray-or-Symbol", "First input to the function")
+.add_argument("rhs", "NDArray-or-Symbol", "Second input to the function");
+
+NNVM_REGISTER_OP(_npi_bitwise_xor_scalar)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser([](NodeAttrs* attrs) {
+    attrs->parsed = std::stod(attrs->dict["scalar"]);
+  })
+.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseIntType<1, 1>)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs){
+    return std::vector<std::pair<int, int> >{{0, 0}};
+  })
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.add_argument("data", "NDArray-or-Symbol", "source input")
+.add_argument("scalar", "int", "scalar input")
+.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::ComputeInt<cpu, mshadow_op::bitwise_xor>);
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_copysign_scalar)
+.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::copysign>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_copysign_scalar"});
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_rcopysign_scalar)
+.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::rcopysign>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_rcopysign_scalar"});
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_backward_npi_copysign_scalar)
+.set_attr<FCompute>("FCompute<cpu>",
+                    BinaryScalarOp::Backward<cpu, mshadow_op::copysign_grad>);
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_backward_npi_rcopysign_scalar)
+.set_attr<FCompute>("FCompute<cpu>",
+                    BinaryScalarOp::Backward<cpu, mshadow_op::rcopysign_grad>);
+
+inline bool Arctan2OpType(const nnvm::NodeAttrs& attrs,
+                          std::vector<int>* in_attrs,
+                          std::vector<int>* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 2U);
+  CHECK_EQ(out_attrs->size(), 1U);
+
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(1));
+  TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
+  TYPE_ASSIGN_CHECK(*in_attrs, 1, out_attrs->at(0));
+  // check if it is float16, float32 or float64. If not, raise error.
+  CHECK(common::is_float(in_attrs->at(0))) << "Do not support `int` as input.\n";
+  return out_attrs->at(0) != -1;
+}
+
+NNVM_REGISTER_OP(_npi_arctan2)
+.set_num_inputs(2)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"x1", "x2"};
+  })
+.set_attr<mxnet::FInferShape>("FInferShape", BinaryBroadcastShape)
+.set_attr<nnvm::FInferType>("FInferType", Arctan2OpType)
+.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, mshadow_op::arctan2>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_arctan2"})
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::pair<int, int> >{{0, 0}};
+  })
+.add_argument("x1", "NDArray-or-Symbol", "The input array")
+.add_argument("x2", "NDArray-or-Symbol", "The input array");
+
+NNVM_REGISTER_OP(_backward_npi_arctan2)
+.set_num_inputs(3)
+.set_num_outputs(2)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastBackwardUseIn<cpu, mshadow_op::arctan2_grad,
+                                                                  mshadow_op::arctan2_rgrad>);
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_arctan2_scalar)
+.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::arctan2>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_arctan2_scalar"});
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_rarctan2_scalar)
+.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::rarctan2>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_rarctan2_scalar"});
+
+MXNET_OPERATOR_REGISTER_BINARY(_backward_npi_arctan2_scalar)
+.add_argument("scalar", "float", "scalar value")
+.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = std::stod(attrs->dict["scalar"]); })
+.set_attr<FCompute>("FCompute<cpu>",
+                    BinaryScalarOp::Backward<cpu, mshadow_op::arctan2_grad>);
+
+MXNET_OPERATOR_REGISTER_BINARY(_backward_npi_rarctan2_scalar)
+.add_argument("scalar", "float", "scalar value")
+.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = std::stod(attrs->dict["scalar"]); })
+.set_attr<FCompute>("FCompute<cpu>",
+                    BinaryScalarOp::Backward<cpu, mshadow_op::arctan2_rgrad>);
+
+bool HypotOpType(const nnvm::NodeAttrs& attrs,
+                 std::vector<int>* in_attrs,
+                 std::vector<int>* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 2U);
+  CHECK_EQ(out_attrs->size(), 1U);
+
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(1));
+  TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
+  TYPE_ASSIGN_CHECK(*in_attrs, 1, out_attrs->at(0));
+
+  CHECK(common::is_float(in_attrs->at(0))) << "Do not support `int` as input.\n";
+  return out_attrs->at(0) != -1;
+}
+
+// rigister hypot that do not support int here
+NNVM_REGISTER_OP(_npi_hypot)
+.set_num_inputs(2)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"x1", "x2"};
+  })
+.set_attr<mxnet::FInferShape>("FInferShape", BinaryBroadcastShape)
+.set_attr<nnvm::FInferType>("FInferType", HypotOpType)
+.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, mshadow_op::hypot>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_hypot"})
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::pair<int, int> >{{0, 0}, {1, 0}};
+  })
+.add_argument("x1", "NDArray-or-Symbol", "The input array")
+.add_argument("x2", "NDArray-or-Symbol", "The input array");
+
+NNVM_REGISTER_OP(_backward_npi_hypot)
+.set_num_inputs(3)
+.set_num_outputs(2)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::pair<int, int> > {{0, 1}};
+  })
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastBackwardUseIn<cpu, mshadow_op::hypot_grad_left,
+                                                                  mshadow_op::hypot_grad_right>);
+
+MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_ldexp)
+.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, mshadow_op::ldexp>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_ldexp"});
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_ldexp_scalar)
+.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::ldexp>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_ldexp_scalar"});
+
+MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_rldexp_scalar)
+.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::rldexp>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_rldexp_scalar"});
+
+NNVM_REGISTER_OP(_backward_npi_ldexp)
+.set_num_inputs(3)
+.set_num_outputs(2)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs){
+    return std::vector<std::pair<int, int> >{{0, 1}};
+  })
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastBackwardUseIn<cpu, mshadow_op::ldexp_grad,
+                                                                  mshadow_op::ldexp_rgrad>);
+
+MXNET_OPERATOR_REGISTER_BINARY(_backward_npi_ldexp_scalar)
+.add_argument("scalar", "float", "scalar value")
+.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = std::stod(attrs->dict["scalar"]); })
+.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Backward<cpu, mshadow_op::ldexp_grad>);
+
+MXNET_OPERATOR_REGISTER_BINARY(_backward_npi_rldexp_scalar)
+.add_argument("scalar", "float", "scalar value")
+.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = std::stod(attrs->dict["scalar"]); })
+.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Backward<cpu, mshadow_op::rldexp_grad>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_elemwise_broadcast_op_extended.cu b/src/operator/numpy/np_elemwise_broadcast_op_extended.cu
new file mode 100644
index 000000000000..f858fb4a4e79
--- /dev/null
+++ b/src/operator/numpy/np_elemwise_broadcast_op_extended.cu
@@ -0,0 +1,108 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file np_elemwise_broadcast_op_extended.cu
+ * \brief GPU Implementation of extended functions for elementwise binary broadcast operator.
+ */
+
+#include "./np_elemwise_broadcast_op.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(_npi_copysign)
+.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, mshadow_op::copysign>);
+
+NNVM_REGISTER_OP(_npi_lcm)
+.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastIntCompute<gpu, mshadow_op::lcm>);
+
+NNVM_REGISTER_OP(_npi_bitwise_xor)
+.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastIntCompute<gpu, mshadow_op::bitwise_xor>);
+
+NNVM_REGISTER_OP(_backward_npi_copysign)
+.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastBackwardUseIn<gpu, mshadow_op::copysign_grad,
+                                                                  mshadow_op::copysign_rgrad>);
+
+NNVM_REGISTER_OP(_npi_arctan2)
+.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, mshadow_op::arctan2>);
+
+NNVM_REGISTER_OP(_backward_npi_arctan2)
+.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastBackwardUseIn<gpu, mshadow_op::arctan2_grad,
+                                                                  mshadow_op::arctan2_rgrad>);
+NNVM_REGISTER_OP(_npi_hypot)
+.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, mshadow_op::hypot>);
+
+NNVM_REGISTER_OP(_backward_npi_hypot)
+.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastBackwardUseIn<gpu, mshadow_op::hypot_grad_left,
+                                                                  mshadow_op::hypot_grad_right>);
+NNVM_REGISTER_OP(_npi_copysign_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::copysign>);
+
+NNVM_REGISTER_OP(_npi_rcopysign_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::rcopysign>);
+
+NNVM_REGISTER_OP(_backward_npi_copysign_scalar)
+.set_attr<FCompute>("FCompute<gpu>",
+                    BinaryScalarOp::Backward<gpu, mshadow_op::copysign_grad>);
+
+NNVM_REGISTER_OP(_backward_npi_rcopysign_scalar)
+.set_attr<FCompute>("FCompute<gpu>",
+                    BinaryScalarOp::Backward<gpu, mshadow_op::rcopysign_grad>);
+
+NNVM_REGISTER_OP(_npi_arctan2_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::arctan2>);
+
+NNVM_REGISTER_OP(_backward_npi_arctan2_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::arctan2_grad>);
+
+NNVM_REGISTER_OP(_npi_rarctan2_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::rarctan2>);
+
+NNVM_REGISTER_OP(_backward_npi_rarctan2_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::rarctan2_grad>);
+
+NNVM_REGISTER_OP(_npi_lcm_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::ComputeInt<gpu, mshadow_op::lcm>);
+
+NNVM_REGISTER_OP(_npi_bitwise_xor_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::ComputeInt<gpu, mshadow_op::bitwise_xor>);
+
+NNVM_REGISTER_OP(_npi_ldexp)
+.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, mshadow_op::ldexp>);
+
+NNVM_REGISTER_OP(_npi_ldexp_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::ldexp>);
+
+NNVM_REGISTER_OP(_npi_rldexp_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::rldexp>);
+
+NNVM_REGISTER_OP(_backward_npi_ldexp)
+.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastBackwardUseIn<gpu, mshadow_op::ldexp_grad,
+                                                                  mshadow_op::ldexp_rgrad>);
+
+NNVM_REGISTER_OP(_backward_npi_ldexp_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Backward<gpu, mshadow_op::ldexp_grad>);
+
+NNVM_REGISTER_OP(_backward_npi_rldexp_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Backward<gpu, mshadow_op::rldexp_grad>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/operator_tune.cc b/src/operator/operator_tune.cc
index 633f63026bc0..e2a4c8af3099 100644
--- a/src/operator/operator_tune.cc
+++ b/src/operator/operator_tune.cc
@@ -396,10 +396,10 @@ IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::logical_or);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::logical_or);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::logical_xor);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::logical_xor);  // NOLINT()
-IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::bitwise_xor);  // NOLINT()
+IMPLEMENT_BINARY_WORKLOAD_FWD_WITH_BOOL(mxnet::op::mshadow_op::bitwise_xor);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::smooth_l1_loss);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::smooth_l1_gradient);  // NOLINT()
-IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::lcm);  // NOLINT()
+IMPLEMENT_BINARY_WORKLOAD_FWD_WITH_BOOL(mxnet::op::mshadow_op::lcm);  // NOLINT()
 IMPLEMENT_BLANK_WORKLOAD_FWD_WITH_BOOL(mxnet::op::mxnet_op::set_to_int<0>);  // NOLINT()
 IMPLEMENT_BLANK_WORKLOAD_FWD_WITH_BOOL(mxnet::op::mxnet_op::set_to_int<1>);  // NOLINT()
 IMPLEMENT_BLANK_WORKLOAD_FWD(mxnet::op::PopulateFullIdxRspKernel);  // NOLINT()
diff --git a/src/operator/tensor/elemwise_binary_broadcast_op.h b/src/operator/tensor/elemwise_binary_broadcast_op.h
index b48ed389ba98..958432b2c4b2 100644
--- a/src/operator/tensor/elemwise_binary_broadcast_op.h
+++ b/src/operator/tensor/elemwise_binary_broadcast_op.h
@@ -332,6 +332,37 @@ struct csr_dns_map_kernel {
 
 }  // namespace mxnet_op
 
+template<typename xpu, typename OP>
+void BinaryBroadcastIntCompute(const nnvm::NodeAttrs& attrs,
+                               const OpContext& ctx,
+                               const std::vector<TBlob>& inputs,
+                               const std::vector<OpReqType>& req,
+                               const std::vector<TBlob>& outputs) {
+  if (outputs[0].shape_.Size() == 0U) return;
+  mxnet::TShape new_lshape, new_rshape, new_oshape;
+  int ndim = BinaryBroadcastShapeCompact(inputs[0].shape_, inputs[1].shape_, outputs[0].shape_,
+                                         &new_lshape, &new_rshape, &new_oshape);
+  if (!ndim) {
+    ElemwiseBinaryOp::ComputeInt<xpu, OP>(attrs, ctx, inputs, req, outputs);
+  } else {
+    if (req[0] == kNullOp) return;
+    mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+    if (outputs[0].type_flag_ == mshadow::kBool) {
+      LOG(FATAL) << "Operator " << attrs.op->name << " does not support boolean type";
+    }
+    MXNET_INT_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+      BROADCAST_NDIM_SWITCH(ndim, NDim, {
+        mshadow::Shape<NDim> oshape = new_oshape.get<NDim>();
+        mshadow::Shape<NDim> lstride = mxnet_op::calc_stride(new_lshape.get<NDim>());
+        mshadow::Shape<NDim> rstride = mxnet_op::calc_stride(new_rshape.get<NDim>());
+        mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, OP>, xpu>::
+        template LaunchEx(s, new_oshape.Size(), req[0], lstride, rstride, oshape,
+        inputs[0].dptr<DType>(), inputs[1].dptr<DType>(), outputs[0].dptr<DType>());
+      });
+    });
+  }
+}
+
 template<typename xpu, typename OP>
 void BinaryBroadcastCompute(const nnvm::NodeAttrs& attrs,
                             const OpContext& ctx,
@@ -345,22 +376,21 @@ void BinaryBroadcastCompute(const nnvm::NodeAttrs& attrs,
   if (!ndim) {
     ElemwiseBinaryOp::Compute<xpu, OP>(attrs, ctx, inputs, req, outputs);
   } else {
-    if (req[0] != kNullOp) {
-      mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
-      if (outputs[0].type_flag_ == mshadow::kBool) {
-        LOG(FATAL) << "Operator " << attrs.op->name << " does not support boolean type";
-      }
-      MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
-        BROADCAST_NDIM_SWITCH(ndim, NDim, {
-          mshadow::Shape<NDim> oshape = new_oshape.get<NDim>();
-          mshadow::Shape<NDim> lstride = mxnet_op::calc_stride(new_lshape.get<NDim>());
-          mshadow::Shape<NDim> rstride = mxnet_op::calc_stride(new_rshape.get<NDim>());
-          mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, OP>, xpu>::
-          template LaunchEx(s, new_oshape.Size(), req[0], lstride, rstride, oshape,
-          inputs[0].dptr<DType>(), inputs[1].dptr<DType>(), outputs[0].dptr<DType>());
-        });
-      });
+    if (req[0] == kNullOp) return;
+    mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+    if (outputs[0].type_flag_ == mshadow::kBool) {
+      LOG(FATAL) << "Operator " << attrs.op->name << " does not support boolean type";
     }
+    MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+      BROADCAST_NDIM_SWITCH(ndim, NDim, {
+        mshadow::Shape<NDim> oshape = new_oshape.get<NDim>();
+        mshadow::Shape<NDim> lstride = mxnet_op::calc_stride(new_lshape.get<NDim>());
+        mshadow::Shape<NDim> rstride = mxnet_op::calc_stride(new_rshape.get<NDim>());
+        mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, OP>, xpu>::
+        template LaunchEx(s, new_oshape.Size(), req[0], lstride, rstride, oshape,
+        inputs[0].dptr<DType>(), inputs[1].dptr<DType>(), outputs[0].dptr<DType>());
+      });
+    });
   }
 }
 
@@ -377,19 +407,18 @@ void BinaryBroadcastComputeWithBool(const nnvm::NodeAttrs& attrs,
   if (!ndim) {
     ElemwiseBinaryOp::ComputeWithBool<xpu, OP>(attrs, ctx, inputs, req, outputs);
   } else {
-    if (req[0] != kNullOp) {
-      mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
-      MSHADOW_TYPE_SWITCH_WITH_BOOL(outputs[0].type_flag_, DType, {
-        BROADCAST_NDIM_SWITCH(ndim, NDim, {
-          mshadow::Shape<NDim> oshape = new_oshape.get<NDim>();
-          mshadow::Shape<NDim> lstride = mxnet_op::calc_stride(new_lshape.get<NDim>());
-          mshadow::Shape<NDim> rstride = mxnet_op::calc_stride(new_rshape.get<NDim>());
-          mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, OP>, xpu>::
-          template LaunchEx(s, new_oshape.Size(), req[0], lstride, rstride, oshape,
-          inputs[0].dptr<DType>(), inputs[1].dptr<DType>(), outputs[0].dptr<DType>());
-        });
+    if (req[0] == kNullOp) return;
+    mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+    MSHADOW_TYPE_SWITCH_WITH_BOOL(outputs[0].type_flag_, DType, {
+      BROADCAST_NDIM_SWITCH(ndim, NDim, {
+        mshadow::Shape<NDim> oshape = new_oshape.get<NDim>();
+        mshadow::Shape<NDim> lstride = mxnet_op::calc_stride(new_lshape.get<NDim>());
+        mshadow::Shape<NDim> rstride = mxnet_op::calc_stride(new_rshape.get<NDim>());
+        mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, OP>, xpu>::
+        template LaunchEx(s, new_oshape.Size(), req[0], lstride, rstride, oshape,
+        inputs[0].dptr<DType>(), inputs[1].dptr<DType>(), outputs[0].dptr<DType>());
       });
-    }
+    });
   }
 }
 
@@ -406,20 +435,19 @@ void BinaryBroadcastComputeLogic(const nnvm::NodeAttrs& attrs,
   if (!ndim) {
     ElemwiseBinaryOp::ComputeLogic<xpu, OP>(attrs, ctx, inputs, req, outputs);
   } else {
-    if (req[0] != kNullOp) {
-      mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
-      MSHADOW_TYPE_SWITCH_WITH_BOOL(inputs[0].type_flag_, DType, {
-          BROADCAST_NDIM_SWITCH(ndim, NDim, {
-            mshadow::Shape<NDim> oshape = new_oshape.get<NDim>();
-            mshadow::Shape<NDim> lstride = mxnet_op::calc_stride(new_lshape.get<NDim>());
-            mshadow::Shape<NDim> rstride = mxnet_op::calc_stride(new_rshape.get<NDim>());
-            mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, OP>, xpu>::
-            template LaunchEx(s, new_oshape.Size(), req[0], lstride, rstride, oshape,
-                              inputs[0].dptr<DType>(), inputs[1].dptr<DType>(),
-                              outputs[0].dptr<bool>());
-          });
-      });
-    }
+    if (req[0] == kNullOp) return;
+    mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+    MSHADOW_TYPE_SWITCH_WITH_BOOL(inputs[0].type_flag_, DType, {
+        BROADCAST_NDIM_SWITCH(ndim, NDim, {
+          mshadow::Shape<NDim> oshape = new_oshape.get<NDim>();
+          mshadow::Shape<NDim> lstride = mxnet_op::calc_stride(new_lshape.get<NDim>());
+          mshadow::Shape<NDim> rstride = mxnet_op::calc_stride(new_rshape.get<NDim>());
+          mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, OP>, xpu>::
+          template LaunchEx(s, new_oshape.Size(), req[0], lstride, rstride, oshape,
+                            inputs[0].dptr<DType>(), inputs[1].dptr<DType>(),
+                            outputs[0].dptr<bool>());
+        });
+    });
   }
 }
 
diff --git a/src/operator/tensor/elemwise_binary_op.h b/src/operator/tensor/elemwise_binary_op.h
index c046a28f16b2..bc5140a5d75f 100644
--- a/src/operator/tensor/elemwise_binary_op.h
+++ b/src/operator/tensor/elemwise_binary_op.h
@@ -474,6 +474,30 @@ class ElemwiseBinaryOp : public OpBase {
                                        std::vector<int> *in_attrs,
                                        std::vector<int> *out_attrs);
 
+  template<typename xpu, typename OP>
+  static void ComputeInt(const nnvm::NodeAttrs &attrs,
+                         const OpContext &ctx,
+                         const std::vector<TBlob> &inputs,
+                         const std::vector<OpReqType> &req,
+                         const std::vector<TBlob> &outputs) {
+    using namespace mxnet_op;
+    if (req[0] == kNullOp) return;
+    Stream<xpu> *s = ctx.get_stream<xpu>();
+    CHECK_EQ(inputs.size(), 2U);
+    CHECK_EQ(outputs.size(), 1U);
+    MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
+      MXNET_INT_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+        const size_t size = (minthree(outputs[0].Size(), inputs[0].Size(), inputs[1].Size())
+        + DataType<DType>::kLanes - 1) / DataType<DType>::kLanes;
+        if (size != 0) {
+          Kernel<mxnet_op::op_with_req<OP, Req>, xpu>::Launch(s, size,
+          outputs[0].dptr<DType>(),
+          inputs[0].dptr<DType>(), inputs[1].dptr<DType>());
+        }
+      });
+    });
+  }
+
   template<typename xpu, typename OP>
   static void Compute(const nnvm::NodeAttrs &attrs,
                       const OpContext &ctx,
@@ -481,25 +505,24 @@ class ElemwiseBinaryOp : public OpBase {
                       const std::vector<OpReqType> &req,
                       const std::vector<TBlob> &outputs) {
     using namespace mxnet_op;
-    if (req[0] != kNullOp) {
-      Stream<xpu> *s = ctx.get_stream<xpu>();
-      CHECK_EQ(inputs.size(), 2U);
-      CHECK_EQ(outputs.size(), 1U);
-      if (outputs[0].type_flag_ == mshadow::kBool) {
-        LOG(FATAL) << "Operator " << attrs.op->name << " does not support boolean type";
-      }
-      MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
-        MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
-          const size_t size = (minthree(outputs[0].Size(), inputs[0].Size(), inputs[1].Size())
-          + DataType<DType>::kLanes - 1) / DataType<DType>::kLanes;
-          if (size != 0) {
-            Kernel<mxnet_op::op_with_req<OP, Req>, xpu>::Launch(s, size,
-            outputs[0].dptr<DType>(),
-            inputs[0].dptr<DType>(), inputs[1].dptr<DType>());
-          }
-        });
-      });
+    if (req[0] == kNullOp) return;
+    Stream<xpu> *s = ctx.get_stream<xpu>();
+    CHECK_EQ(inputs.size(), 2U);
+    CHECK_EQ(outputs.size(), 1U);
+    if (outputs[0].type_flag_ == mshadow::kBool) {
+      LOG(FATAL) << "Operator " << attrs.op->name << " does not support boolean type";
     }
+    MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
+      MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+        const size_t size = (minthree(outputs[0].Size(), inputs[0].Size(), inputs[1].Size())
+        + DataType<DType>::kLanes - 1) / DataType<DType>::kLanes;
+        if (size != 0) {
+          Kernel<mxnet_op::op_with_req<OP, Req>, xpu>::Launch(s, size,
+          outputs[0].dptr<DType>(),
+          inputs[0].dptr<DType>(), inputs[1].dptr<DType>());
+        }
+      });
+    });
   }
 
   template<typename xpu, typename OP>
@@ -509,22 +532,21 @@ class ElemwiseBinaryOp : public OpBase {
                               const std::vector<OpReqType> &req,
                               const std::vector<TBlob> &outputs) {
     using namespace mxnet_op;
-    if (req[0] != kNullOp) {
-      Stream<xpu> *s = ctx.get_stream<xpu>();
-      CHECK_EQ(inputs.size(), 2U);
-      CHECK_EQ(outputs.size(), 1U);
-      MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
-        MSHADOW_TYPE_SWITCH_WITH_BOOL(outputs[0].type_flag_, DType, {
-          const size_t size = (minthree(outputs[0].Size(), inputs[0].Size(), inputs[1].Size())
-          + DataType<DType>::kLanes - 1) / DataType<DType>::kLanes;
-          if (size != 0) {
-            Kernel<mxnet_op::op_with_req<OP, Req>, xpu>::Launch(s, size,
-            outputs[0].dptr<DType>(),
-            inputs[0].dptr<DType>(), inputs[1].dptr<DType>());
-          }
-        });
+    if (req[0] == kNullOp) return;
+    Stream<xpu> *s = ctx.get_stream<xpu>();
+    CHECK_EQ(inputs.size(), 2U);
+    CHECK_EQ(outputs.size(), 1U);
+    MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
+      MSHADOW_TYPE_SWITCH_WITH_BOOL(outputs[0].type_flag_, DType, {
+        const size_t size = (minthree(outputs[0].Size(), inputs[0].Size(), inputs[1].Size())
+        + DataType<DType>::kLanes - 1) / DataType<DType>::kLanes;
+        if (size != 0) {
+          Kernel<mxnet_op::op_with_req<OP, Req>, xpu>::Launch(s, size,
+          outputs[0].dptr<DType>(),
+          inputs[0].dptr<DType>(), inputs[1].dptr<DType>());
+        }
       });
-    }
+    });
   }
 
   template<typename xpu, typename OP>
@@ -534,23 +556,22 @@ class ElemwiseBinaryOp : public OpBase {
                            const std::vector<OpReqType> &req,
                            const std::vector<TBlob> &outputs) {
     using namespace mxnet_op;
-    if (req[0] != kNullOp) {
-      Stream<xpu> *s = ctx.get_stream<xpu>();
-      CHECK_EQ(inputs.size(), 2U);
-      CHECK_EQ(outputs.size(), 1U);
-      MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
-        MSHADOW_TYPE_SWITCH_WITH_BOOL(inputs[0].type_flag_, DType, {
-            const size_t size = (minthree(outputs[0].Size(), inputs[0].Size(), inputs[1].Size())
-            + DataType<DType>::kLanes - 1) / DataType<DType>::kLanes;
-            if (size != 0) {
-              Kernel<mxnet_op::op_with_req<OP, Req>, xpu>::Launch(s, size,
-                                                                  outputs[0].dptr<bool>(),
-                                                                  inputs[0].dptr<DType>(),
-                                                                  inputs[1].dptr<DType>());
-            }
-        });
+    if (req[0] == kNullOp) return;
+    Stream<xpu> *s = ctx.get_stream<xpu>();
+    CHECK_EQ(inputs.size(), 2U);
+    CHECK_EQ(outputs.size(), 1U);
+    MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
+      MSHADOW_TYPE_SWITCH_WITH_BOOL(inputs[0].type_flag_, DType, {
+          const size_t size = (minthree(outputs[0].Size(), inputs[0].Size(), inputs[1].Size())
+          + DataType<DType>::kLanes - 1) / DataType<DType>::kLanes;
+          if (size != 0) {
+            Kernel<mxnet_op::op_with_req<OP, Req>, xpu>::Launch(s, size,
+                                                                outputs[0].dptr<bool>(),
+                                                                inputs[0].dptr<DType>(),
+                                                                inputs[1].dptr<DType>());
+          }
       });
-    }
+    });
   }
 
   template<typename xpu, typename OP>
@@ -560,22 +581,21 @@ class ElemwiseBinaryOp : public OpBase {
                                const std::vector<OpReqType> &req,
                                const std::vector<TBlob> &outputs) {
     using namespace mxnet_op;
-    if (req[0] != kNullOp) {
-      Stream<xpu> *s = ctx.get_stream<xpu>();
-      CHECK_EQ(inputs.size(), 2U);
-      CHECK_EQ(outputs.size(), 1U);
-      MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
-        MSHADOW_TYPE_SWITCH_WITH_HALF2(outputs[0].type_flag_, DType, {
-          const size_t size = (minthree(outputs[0].Size(), inputs[0].Size(), inputs[1].Size())
-          + DataType<DType>::kLanes - 1) / DataType<DType>::kLanes;
-          if (size != 0) {
-            Kernel<mxnet_op::op_with_req<OP, Req>, xpu>::Launch(s, size,
-            outputs[0].dptr<DType>(),
-            inputs[0].dptr<DType>(), inputs[1].dptr<DType>());
-          }
-        });
+    if (req[0] == kNullOp) return;
+    Stream<xpu> *s = ctx.get_stream<xpu>();
+    CHECK_EQ(inputs.size(), 2U);
+    CHECK_EQ(outputs.size(), 1U);
+    MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
+      MSHADOW_TYPE_SWITCH_WITH_HALF2(outputs[0].type_flag_, DType, {
+        const size_t size = (minthree(outputs[0].Size(), inputs[0].Size(), inputs[1].Size())
+        + DataType<DType>::kLanes - 1) / DataType<DType>::kLanes;
+        if (size != 0) {
+          Kernel<mxnet_op::op_with_req<OP, Req>, xpu>::Launch(s, size,
+          outputs[0].dptr<DType>(),
+          inputs[0].dptr<DType>(), inputs[1].dptr<DType>());
+        }
       });
-    }
+    });
   }
 
   template<typename xpu, typename OP>
diff --git a/src/operator/tensor/elemwise_binary_scalar_op.h b/src/operator/tensor/elemwise_binary_scalar_op.h
index 834bbdbfc3d1..3e8702813a7c 100644
--- a/src/operator/tensor/elemwise_binary_scalar_op.h
+++ b/src/operator/tensor/elemwise_binary_scalar_op.h
@@ -244,6 +244,26 @@ class BinaryScalarOp : public UnaryOp {
     });
   }
 
+  template<typename xpu, typename OP>
+  static void ComputeInt(const nnvm::NodeAttrs &attrs,
+                         const OpContext &ctx,
+                         const std::vector<TBlob> &inputs,
+                         const std::vector<OpReqType> &req,
+                         const std::vector<TBlob> &outputs) {
+    DCHECK_EQ(inputs.size(), 1);
+    DCHECK_EQ(outputs.size(), 1);
+    using namespace mshadow;
+    using namespace mshadow::expr;
+    Stream<xpu> *s = ctx.get_stream<xpu>();
+    const double alpha = nnvm::get<double>(attrs.parsed);
+    MXNET_INT_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+      MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
+        mxnet_op::Kernel<mxnet_op::op_with_req<OP, Req>, xpu>::Launch(
+          s, inputs[0].Size(), outputs[0].dptr<DType>(), inputs[0].dptr<DType>(), DType(alpha));
+      });
+    });
+  }
+
   template<typename xpu, typename OP>
   static void ComputeLogic(const nnvm::NodeAttrs &attrs,
                       const OpContext &ctx,
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index 6d9c63f9f857..658c7a37e354 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -264,7 +264,7 @@ def _add_workload_linalg_cholesky():
         a = _np.matmul(a.transpose(t).conj(), a)
 
         OpArgMngr.add_workload('linalg.cholesky', np.array(a, dtype=dtype))
-    
+
     # test_0_size
     for dtype in dtypes:
         a = np.zeros((0, 1, 1))
@@ -816,6 +816,18 @@ def _add_workload_lcm():
     OpArgMngr.add_workload('lcm', np.array(195225786*2, dtype=np.int32), np.array(195225786*5, dtype=np.int32))
 
 
+def _add_workload_bitwise_xor():
+    OpArgMngr.add_workload('bitwise_xor', np.array([False, False, True, True], dtype=np.bool),
+                           np.array([False, True, False, True], dtype=np.bool))
+    for dtype in [np.int8, np.int32, np.int64]:
+        zeros = np.array([0], dtype=dtype)
+        ones = np.array([-1], dtype=dtype)
+        OpArgMngr.add_workload('bitwise_xor', zeros, zeros)
+        OpArgMngr.add_workload('bitwise_xor', ones, zeros)
+        OpArgMngr.add_workload('bitwise_xor', zeros, ones)
+        OpArgMngr.add_workload('bitwise_xor', ones, ones)
+
+
 def _add_workload_ldexp():
     OpArgMngr.add_workload('ldexp', np.array(2., np.float32), np.array(3, np.int8))
     OpArgMngr.add_workload('ldexp', np.array(2., np.float64), np.array(3, np.int8))
@@ -1236,6 +1248,7 @@ def _prepare_workloads():
     _add_workload_inner()
     _add_workload_hypot()
     _add_workload_lcm()
+    _add_workload_bitwise_xor()
     _add_workload_ldexp()
     _add_workload_subtract(array_pool)
     _add_workload_multiply(array_pool)
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 2a23c976a092..405d4a4cca6d 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -1650,6 +1650,7 @@ def hybrid_forward(self, F, a, b, *args, **kwargs):
         'power': (1.0, 2.0, [lambda y, x1, x2: _np.power(x1, x2 - 1.0) * x2],
                              [lambda y, x1, x2: _np.power(x1, x2) * _np.log(x1)]),
         'lcm': (-100, 100, [None], None, [[_np.int32]]),
+        'bitwise_xor': (-100, 100, [None], None, [[_np.int32]]),
         'maximum': (-1, 1, [lambda y, x1, x2: _np.ones(y.shape) * (x1 >= x2)],
                            [lambda y, x1, x2: _np.ones(y.shape) * (x1 < x2)]),
         'minimum': (-1, 1, [lambda y, x1, x2: _np.ones(y.shape) * (x1 <= x2)],
@@ -4389,7 +4390,7 @@ def hybrid_forward(self, F, a):
                         mx_out.backward()
                         if (np_out.size == 0):
                             np_backward = _np.zeros(shape)
-                        else:                    
+                        else:
                             np_backward = np_diff_backward(_np.ones(np_out.shape, dtype=itype), n=n, axis=axis)
                         assert x.grad.shape == np_backward.shape
                         assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=rtol, atol=atol)
@@ -4535,7 +4536,7 @@ def hybrid_forward(self, F, a):
     copy_list = [True, False]
     hybridize_list = [True, False]
     atol, rtol = 1e-5, 1e-3
-    
+
     src_dtype_comb = list(itertools.product(src_list,dtype_list))
     # check the dtype = int case in both imperative and sympolic expression
     src_dtype_comb.append((1,'int32'))
@@ -4582,10 +4583,10 @@ def hybrid_forward(self, F, a):
             assert_almost_equal(mx_out_gluon.asnumpy(), np_out, rtol, atol)
             mx_out_gluon.backward()
             assert_almost_equal(x2.grad.asnumpy(), expected_grad, rtol=1e-3, atol=1e-5)
-                                        
+
         # Test imperative once again
         # if copy = False, the value of x1 and x2 has changed
-        if copy == True:            
+        if copy == True:
             np_out = _np.nan_to_num(x1)
             mx_out = np.nan_to_num(x3)
             assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)

From ec2f3a61109f68013ac6b0a9e68db7f9ccc9811a Mon Sep 17 00:00:00 2001
From: Xinyu Chen <xinyu1.chen@intel.com>
Date: Sat, 16 Nov 2019 16:59:47 +0800
Subject: [PATCH 601/813] [Model] Implement Neural Collaborative Filtering with
 MXNet (#16689)

* add ncf model with movielens dataset

* import lint

* add licsence header for README

* address comment
---
 .../neural_collaborative_filtering/README.md  | 101 +++++++++++
 example/neural_collaborative_filtering/ci.py  |  64 +++++++
 .../neural_collaborative_filtering/convert.py | 127 +++++++++++++
 .../core/dataset.py                           |  99 ++++++++++
 .../core/evaluate.py                          | 105 +++++++++++
 .../core/load.py                              |  74 ++++++++
 .../core/model.py                             | 107 +++++++++++
 example/neural_collaborative_filtering/ncf.py | 170 ++++++++++++++++++
 .../neural_collaborative_filtering/train.py   | 163 +++++++++++++++++
 9 files changed, 1010 insertions(+)
 create mode 100644 example/neural_collaborative_filtering/README.md
 create mode 100644 example/neural_collaborative_filtering/ci.py
 create mode 100644 example/neural_collaborative_filtering/convert.py
 create mode 100644 example/neural_collaborative_filtering/core/dataset.py
 create mode 100644 example/neural_collaborative_filtering/core/evaluate.py
 create mode 100644 example/neural_collaborative_filtering/core/load.py
 create mode 100644 example/neural_collaborative_filtering/core/model.py
 create mode 100644 example/neural_collaborative_filtering/ncf.py
 create mode 100644 example/neural_collaborative_filtering/train.py

diff --git a/example/neural_collaborative_filtering/README.md b/example/neural_collaborative_filtering/README.md
new file mode 100644
index 000000000000..819f4d94dff9
--- /dev/null
+++ b/example/neural_collaborative_filtering/README.md
@@ -0,0 +1,101 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Neural Collaborative Filtering
+
+[![Build Status](https://travis-ci.com/xinyu-intel/ncf_mxnet.svg?branch=master)](https://travis-ci.com/xinyu-intel/ncf_mxnet)
+
+This is MXNet implementation for the paper:
+
+Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu and Tat-Seng Chua (2017). [Neural Collaborative Filtering.](http://dl.acm.org/citation.cfm?id=3052569) In Proceedings of WWW '17, Perth, Australia, April 03-07, 2017.
+
+Three collaborative filtering models: Generalized Matrix Factorization (GMF), Multi-Layer Perceptron (MLP), and Neural Matrix Factorization (NeuMF). To target the models for implicit feedback and ranking task, we optimize them using log loss with negative sampling. 
+
+Author: Dr. Xiangnan He (http://www.comp.nus.edu.sg/~xiangnan/)
+
+Code Reference: https://github.com/hexiangnan/neural_collaborative_filtering
+
+## Environment Settings
+We use MXnet with MKL-DNN as the backend. 
+- MXNet version:  '1.5.1'
+
+## Install
+```
+pip install -r requirements.txt
+```
+
+## Dataset
+
+We provide the processed datasets on [Google Drive](https://drive.google.com/drive/folders/1qACR_Zhc2O2W0RrazzcepM2vJeh0MMdO?usp=sharing): MovieLens 20 Million (ml-20m), you can download directly or 
+run the script to prepare the datasets:
+```
+python convert.py ./data/
+```
+
+train-ratings.csv
+- Train file (positive instances).
+- Each Line is a training instance: userID\t itemID\t 
+
+test-ratings.csv
+- Test file (positive instances). 
+- Each Line is a testing instance: userID\t itemID\t 
+
+test-negative.csv
+- Test file (negative instances).
+- Each line corresponds to the line of test.rating, containing 999 negative samples.  
+- Each line is in the format: userID,\t negativeItemID1\t negativeItemID2 ...
+
+## Pre-trained models
+
+We provide the pretrained ml-20m model on [Google Drive](https://drive.google.com/drive/folders/1qACR_Zhc2O2W0RrazzcepM2vJeh0MMdO?usp=sharing), you can download directly for evaluation or calibration.
+
+|dtype|HR@10|NDCG@10|
+|:---:|:--:|:--:|
+|float32|0.6393|0.3849|
+|int8|0.6366|0.3824|
+
+## Training
+
+```
+# train ncf model with ml-20m dataset
+python train.py # --gpu=0
+```
+
+## Calibration
+
+```
+# neumf calibration on ml-20m dataset
+python ncf.py --prefix=./model/ml-20m/neumf --calibration
+```
+
+## Evaluation
+
+```
+# neumf float32 inference on ml-20m dataset
+python ncf.py --batch-size=1000 --prefix=./model/ml-20m/neumf
+# neumf int8 inference on ml-20m dataset
+python ncf.py --batch-size=1000 --prefix=./model/ml-20m/neumf-quantized
+```
+
+## Benchmark
+
+```
+# neumf float32 benchmark on ml-20m dataset
+python ncf.py --batch-size=1000 --prefix=./model/ml-20m/neumf --benchmark
+# neumf int8 benchmark on ml-20m dataset
+python ncf.py --batch-size=1000 --prefix=./model/ml-20m/neumf-quantized --benchmark
+```
diff --git a/example/neural_collaborative_filtering/ci.py b/example/neural_collaborative_filtering/ci.py
new file mode 100644
index 000000000000..81f1a27e606b
--- /dev/null
+++ b/example/neural_collaborative_filtering/ci.py
@@ -0,0 +1,64 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# 
+import mxnet as mx
+from core.model import get_model
+
+def test_model():
+    def test_ncf(model_type):
+        net = get_model(model_type=model_type, factor_size_mlp=128, factor_size_gmf=64, 
+                        model_layers=[256, 128, 64], num_hidden=1, max_user=138493, max_item=26744)
+        mod = mx.module.Module(net, context=mx.cpu(), data_names=['user', 'item'], label_names=['softmax_label'])
+        provide_data = [mx.io.DataDesc(name='item', shape=((1,))),
+                        mx.io.DataDesc(name='user', shape=((1,)))]
+        provide_label = [mx.io.DataDesc(name='softmax_label', shape=((1,)))]
+        mod.bind(for_training=True, data_shapes=provide_data, label_shapes=provide_label)
+        mod.init_params(initializer=mx.init.Xavier(magnitude=2.))
+        data = [mx.nd.full(shape=shape, val=26744, ctx=mx.cpu(), dtype='int32')
+                for _, shape in mod.data_shapes]
+        batch = mx.io.DataBatch(data, [])
+        mod.forward(batch)
+        mod.backward()
+        mx.nd.waitall()
+
+        data_dict = {'user': data[0], 'item': data[1]}
+        calib_data = mx.io.NDArrayIter(data=data_dict, batch_size=1)
+        calib_data = mx.test_utils.DummyIter(calib_data)
+        arg_params, aux_params = mod.get_params()
+        qsym, qarg_params, qaux_params = mx.contrib.quant.quantize_model_mkldnn(sym=net,
+                                                                                arg_params=arg_params,
+                                                                                aux_params=aux_params,
+                                                                                ctx=mx.cpu(),
+                                                                                quantized_dtype='auto',
+                                                                                calib_mode='naive',
+                                                                                calib_data=calib_data,
+                                                                                data_names=['user', 'item'],
+                                                                                excluded_sym_names=['post_gemm_concat', 'fc_final'],
+                                                                                num_calib_examples=1)
+        qmod = mx.module.Module(qsym, context=mx.cpu(), data_names=['user', 'item'], label_names=['softmax_label'])
+        qmod.bind(for_training=True, data_shapes=provide_data, label_shapes=provide_label)
+        qmod.set_params(qarg_params, qaux_params)
+        qmod.forward(batch)
+        mx.nd.waitall()
+
+    for model_type in ['neumf', 'mlp', 'gmf']:
+        test_ncf(model_type)
+
+if __name__ == "__main__":
+    import nose
+    nose.runmodule()
+
diff --git a/example/neural_collaborative_filtering/convert.py b/example/neural_collaborative_filtering/convert.py
new file mode 100644
index 000000000000..4c64d2cdedab
--- /dev/null
+++ b/example/neural_collaborative_filtering/convert.py
@@ -0,0 +1,127 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# 
+import os
+import urllib
+import zipfile
+from argparse import ArgumentParser
+from collections import defaultdict
+import numpy as np
+import pandas as pd
+from tqdm import tqdm
+from core.load import implicit_load
+
+MIN_RATINGS = 20
+
+USER_COLUMN = 'user_id'
+ITEM_COLUMN = 'item_id'
+
+TRAIN_RATINGS_FILENAME = 'train-ratings.csv'
+TEST_RATINGS_FILENAME = 'test-ratings.csv'
+TEST_NEG_FILENAME = 'test-negative.csv'
+
+def parse_args():
+    parser = ArgumentParser()
+    parser.add_argument('--dataset', nargs='?', default='ml-20m', choices=['ml-1m', 'ml-20m'],
+                        help='The dataset name, temporary support ml-1m and ml-20m.')
+    parser.add_argument('path', type=str, default = './data/',
+                        help='Path to reviews CSV file from MovieLens')
+    parser.add_argument('-n', '--negatives', type=int, default=999,
+                        help='Number of negative samples for each positive'
+                             'test example')
+    parser.add_argument('-s', '--seed', type=int, default=0,
+                        help='Random seed to reproduce same negative samples')
+    return parser.parse_args()
+
+def get_movielens_data(data_dir, dataset):
+    if not os.path.exists(data_dir + '%s.zip' % dataset):
+        os.mkdir(data_dir)
+        urllib.request.urlretrieve('http://files.grouplens.org/datasets/movielens/%s.zip' % dataset, data_dir + dataset + '.zip')
+        with zipfile.ZipFile(data_dir + "%s.zip" % dataset, "r") as f:
+            f.extractall(data_dir + "./")
+
+def main():
+    args = parse_args()
+    np.random.seed(args.seed)
+
+    print("download movielens {} dataset".format(args.dataset))
+    get_movielens_data(args.path, args.dataset)
+    output = os.path.join(args.path, args.dataset)
+
+    print("Loading raw data from {}".format(output))
+    df = implicit_load(os.path.join(output,"ratings.csv"), sort=False)
+
+    print("Filtering out users with less than {} ratings".format(MIN_RATINGS))
+    grouped = df.groupby(USER_COLUMN)
+    df = grouped.filter(lambda x: len(x) >= MIN_RATINGS)
+
+    print("Mapping original user and item IDs to new sequential IDs")
+    original_users = df[USER_COLUMN].unique()
+    original_items = df[ITEM_COLUMN].unique()
+
+    user_map = {user: index for index, user in enumerate(original_users)}
+    item_map = {item: index for index, item in enumerate(original_items)}
+
+    df[USER_COLUMN] = df[USER_COLUMN].apply(lambda user: user_map[user])
+    df[ITEM_COLUMN] = df[ITEM_COLUMN].apply(lambda item: item_map[item])
+
+    assert df[USER_COLUMN].max() == len(original_users) - 1
+    assert df[ITEM_COLUMN].max() == len(original_items) - 1
+
+    print("Creating list of items for each user")
+    # Need to sort before popping to get last item
+    df.sort_values(by='timestamp', inplace=True)
+    all_ratings = set(zip(df[USER_COLUMN], df[ITEM_COLUMN]))
+    user_to_items = defaultdict(list)
+    for row in tqdm(df.itertuples(), desc='Ratings', total=len(df)):
+        user_to_items[getattr(row, USER_COLUMN)].append(getattr(row, ITEM_COLUMN))  # noqa: E501
+
+    test_ratings = []
+    test_negs = []
+    all_items = set(range(len(original_items)))
+
+    print("Generating {} negative samples for each user"
+          .format(args.negatives))
+
+    for user in tqdm(range(len(original_users)), desc='Users', total=len(original_users)):  # noqa: E501
+        test_item = user_to_items[user].pop()
+
+        all_ratings.remove((user, test_item))
+        all_negs = all_items - set(user_to_items[user])
+        all_negs = sorted(list(all_negs))  # determinism
+
+        test_ratings.append((user, test_item))
+        test_negs.append(list(np.random.choice(all_negs, args.negatives)))
+
+    print("Saving train and test CSV files to {}".format(output))
+    df_train_ratings = pd.DataFrame(list(all_ratings))
+    df_train_ratings['fake_rating'] = 1
+    df_train_ratings.to_csv(os.path.join(output, TRAIN_RATINGS_FILENAME),
+                            index=False, header=False, sep='\t')
+
+    df_test_ratings = pd.DataFrame(test_ratings)
+    df_test_ratings['fake_rating'] = 1
+    df_test_ratings.to_csv(os.path.join(output, TEST_RATINGS_FILENAME),
+                           index=False, header=False, sep='\t')
+
+    df_test_negs = pd.DataFrame(test_negs)
+    df_test_negs.to_csv(os.path.join(output, TEST_NEG_FILENAME),
+                        index=False, header=False, sep='\t')
+
+if __name__ == '__main__':
+    main()
+
diff --git a/example/neural_collaborative_filtering/core/dataset.py b/example/neural_collaborative_filtering/core/dataset.py
new file mode 100644
index 000000000000..56f04fd53811
--- /dev/null
+++ b/example/neural_collaborative_filtering/core/dataset.py
@@ -0,0 +1,99 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+import os 
+import mxnet as mx
+import numpy as np
+import pandas as pd
+import scipy.sparse as sp
+
+class NCFTestData(object):
+    def __init__(self, path):
+        '''
+        Constructor
+        path: converted data root
+        testRatings: converted test ratings data
+        testNegatives: negative samples for evaluation dataset
+        '''
+        self.testRatings = self.load_rating_file_as_list(os.path.join(path, 'test-ratings.csv'))
+        self.testNegatives = self.load_negative_file(os.path.join(path ,'test-negative.csv'))
+        assert len(self.testRatings) == len(self.testNegatives)
+
+    def load_rating_file_as_list(self, filename):
+        ratingList = []
+        with open(filename, "r") as f:
+            line = f.readline()
+            while line != None and line != "":
+                arr = line.split("\t")
+                user, item = int(arr[0]), int(arr[1])
+                ratingList.append([user, item])
+                line = f.readline()
+        return ratingList
+    
+    def load_negative_file(self, filename):
+        negativeList = []
+        with open(filename, "r") as f:
+            line = f.readline()
+            while line != None and line != "":
+                arr = line.split("\t")
+                negatives = []
+                for x in arr:
+                    negatives.append(int(x))
+                negativeList.append(negatives)
+                line = f.readline()
+        return negativeList
+
+class NCFTrainData(mx.gluon.data.Dataset):
+    def __init__(self, train_fname, nb_neg):
+        '''
+        Constructor
+        train_fname: converted data root
+        nb_neg: number of negative samples per positive sample while training
+        '''
+        self._load_train_matrix(train_fname)
+        self.nb_neg = nb_neg
+
+    def _load_train_matrix(self, train_fname):
+        def process_line(line):
+            tmp = line.split('\t')
+            return [int(tmp[0]), int(tmp[1]), float(tmp[2]) > 0]
+        with open(train_fname, 'r') as file:
+            data = list(map(process_line, file))
+        self.nb_users = max(data, key=lambda x: x[0])[0] + 1
+        self.nb_items = max(data, key=lambda x: x[1])[1] + 1
+
+        self.data = list(filter(lambda x: x[2], data))
+        self.mat = sp.dok_matrix(
+                (self.nb_users, self.nb_items), dtype=np.float32)
+        for user, item, _ in data:
+            self.mat[user, item] = 1.
+
+    def __len__(self):
+        return (self.nb_neg + 1) * len(self.data)
+
+    def __getitem__(self, idx):
+        if idx % (self.nb_neg + 1) == 0:
+            idx = idx // (self.nb_neg + 1)
+            return self.data[idx][0], self.data[idx][1], np.ones(1, dtype=np.float32).item()  # noqa: E501
+        else:
+            idx = idx // (self.nb_neg + 1)
+            u = self.data[idx][0]
+            j = mx.random.randint(0, self.nb_items).asnumpy().item()
+            while (u, j) in self.mat:
+                j = mx.random.randint(0, self.nb_items).asnumpy().item()
+            return u, j, np.zeros(1, dtype=np.float32).item()
+
diff --git a/example/neural_collaborative_filtering/core/evaluate.py b/example/neural_collaborative_filtering/core/evaluate.py
new file mode 100644
index 000000000000..02a1379f6454
--- /dev/null
+++ b/example/neural_collaborative_filtering/core/evaluate.py
@@ -0,0 +1,105 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# 
+import math
+import heapq
+import random
+import logging
+import mxnet as mx
+import numpy as np
+
+def get_movielens_iter(filename, batch_size, ctx, logger):
+    """Not particularly fast code to parse the text file and load into NDArrays.
+    return two data iters, one for train, the other for validation.
+    """
+    logger.info("Preparing data iterators for " + filename + " ... ")
+    user = []
+    item = []
+    score = []
+    with open(filename, 'r') as f:
+        num_samples = 0
+        for line in f:
+            tks = line.strip().split('\t')
+            if len(tks) != 3:
+                continue
+            num_samples += 1
+            user.append((tks[0]))
+            item.append((tks[1]))
+            score.append((tks[2]))
+    # convert to ndarrays
+    user = mx.nd.array(user, dtype='int32').as_in_context(ctx)
+    item = mx.nd.array(item, dtype='int32').as_in_context(ctx)
+    score = mx.nd.array(score).as_in_context(ctx)
+    # prepare data iters
+    data = {'user': user, 'item': item}
+    label = {'softmax_label': score}
+    iter = mx.io.NDArrayIter(data=data, label=label, batch_size=batch_size)
+    return iter
+
+
+def predict(model, users, items, batch_size=1000, ctx=mx.cpu()):
+    user = mx.nd.array(users, dtype='int32').as_in_context(ctx)
+    item = mx.nd.array(items, dtype='int32').as_in_context(ctx)
+    label = mx.nd.zeros(len(user)).as_in_context(ctx)
+    data = {'user': user, 'item': item}
+    label = {'softmax_label':label}
+    eval_iter = mx.io.NDArrayIter(data=data, label=label, batch_size=batch_size)
+    preds = []
+    for batch in eval_iter:
+        model.forward(batch)
+        outp = model.get_outputs()[0].asnumpy()
+        preds += list(outp.flatten())
+    return preds
+
+def _calculate_hit(ranked, test_item):
+    return int(test_item in ranked)
+
+
+def _calculate_ndcg(ranked, test_item):
+    for i, item in enumerate(ranked):
+        if item == test_item:
+            return math.log(2) / math.log(i + 2)
+    return 0.
+
+def eval_one(rating, items, model, K, batch_size, ctx):
+    user = rating[0]
+    test_item = rating[1]
+    items.append(test_item)
+    users = [user] * len(items)
+    predictions = predict(model, users, items, batch_size, ctx)
+
+    map_item_score = {item: pred for item, pred in zip(items, predictions)}
+    ranked = heapq.nlargest(K, map_item_score, key=map_item_score.get)
+
+    hit = _calculate_hit(ranked, test_item)
+    ndcg = _calculate_ndcg(ranked, test_item)
+    return hit, ndcg, len(predictions)
+
+def evaluate_model(model, ratings, negs, K, batch_size, ctx, logger=None):
+    hits, ndcgs, num_preds = [], [], []
+    index = 0
+    for rating, items in zip(ratings, negs):
+        index += 1
+        hit, ndcg, num_pred = eval_one(rating, items, model, K, batch_size, ctx)
+        hits.append(hit)
+        ndcgs.append(ndcg)
+        num_preds.append(num_pred)
+        if index % batch_size == 0:
+            logger.info('evaluating test data {} / {}'.format(index, len(ratings)))
+
+    return hits, ndcgs
+
diff --git a/example/neural_collaborative_filtering/core/load.py b/example/neural_collaborative_filtering/core/load.py
new file mode 100644
index 000000000000..de47af9fb36b
--- /dev/null
+++ b/example/neural_collaborative_filtering/core/load.py
@@ -0,0 +1,74 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# 
+from collections import namedtuple
+
+import pandas as pd
+
+
+RatingData = namedtuple('RatingData',
+                        ['items', 'users', 'ratings', 'min_date', 'max_date'])
+
+
+def describe_ratings(ratings):
+    info = RatingData(items=len(ratings['item_id'].unique()),
+                      users=len(ratings['user_id'].unique()),
+                      ratings=len(ratings),
+                      min_date=ratings['timestamp'].min(),
+                      max_date=ratings['timestamp'].max())
+    print("{ratings} ratings on {items} items from {users} users"
+          " from {min_date} to {max_date}"
+          .format(**(info._asdict())))
+    return info
+
+
+def process_movielens(ratings, sort=True):
+    ratings['timestamp'] = pd.to_datetime(ratings['timestamp'], unit='s')
+    if sort:
+        ratings.sort_values(by='timestamp', inplace=True)
+    describe_ratings(ratings)
+    return ratings
+
+
+def load_ml_1m(filename, sort=True):
+    names = ['user_id', 'item_id', 'rating', 'timestamp']
+    ratings = pd.read_csv(filename, sep='::', names=names, engine='python')
+    return process_movielens(ratings, sort=sort)
+
+
+def load_ml_20m(filename, sort=True):
+    ratings = pd.read_csv(filename)
+    ratings['timestamp'] = pd.to_datetime(ratings['timestamp'], unit='s')
+    names = {'userId': 'user_id', 'movieId': 'item_id'}
+    ratings.rename(columns=names, inplace=True)
+    return process_movielens(ratings, sort=sort)
+
+
+DATASETS = [k.replace('load_', '') for k in locals().keys() if "load_" in k]
+
+
+def get_dataset_name(filename):
+    for dataset in DATASETS:
+        if dataset in filename.replace('-', '_').lower():
+            return dataset
+    raise NotImplementedError
+
+
+def implicit_load(filename, sort=True):
+    func = globals()["load_" + get_dataset_name(filename)]
+    return func(filename, sort=sort)
+
diff --git a/example/neural_collaborative_filtering/core/model.py b/example/neural_collaborative_filtering/core/model.py
new file mode 100644
index 000000000000..b516e5039fed
--- /dev/null
+++ b/example/neural_collaborative_filtering/core/model.py
@@ -0,0 +1,107 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# 
+import mxnet as mx
+import numpy as np
+
+@mx.init.register
+class golorot_uniform(mx.init.Initializer):
+    def __init__(self, fan_in, fan_out):
+        super(golorot_uniform, self).__init__(fan_in=fan_in, fan_out=fan_out)
+        self._fan_in = fan_in
+        self._fan_out = fan_out
+    def _init_weight(self, _, arr):
+        limit = np.sqrt(6. / (self._fan_in + self._fan_out))
+        mx.random.uniform(-limit, limit, out=arr)
+
+@mx.init.register
+class lecunn_uniform(mx.init.Initializer):
+    def __init__(self, fan_in):
+        super(lecunn_uniform, self).__init__(fan_in=fan_in)
+        self._fan_in = fan_in
+    def _init_weight(self, _, arr):
+        limit = np.sqrt(3. / self._fan_in)
+        mx.random.uniform(-limit, limit, out=arr)
+
+
+def mlp(user, item, factor_size, model_layers, max_user, max_item):
+    user_weight = mx.sym.Variable('mlp_user_weight', init=mx.init.Normal(0.01))
+    item_weight = mx.sym.Variable('mlp_item_weight', init=mx.init.Normal(0.01))
+    embed_user = mx.sym.Embedding(data=user, weight=user_weight, input_dim=max_user,
+                                  output_dim=factor_size, name='embed_user'+str(factor_size))
+    embed_item = mx.sym.Embedding(data=item, weight=item_weight, input_dim=max_item,
+                                  output_dim=factor_size, name='embed_item'+str(factor_size))
+    pre_gemm_concat = mx.sym.concat(embed_user, embed_item, dim=1, name='pre_gemm_concat')
+
+    for i, layer in enumerate(model_layers):
+        if i==0:
+            mlp_weight_init = golorot_uniform(2 * factor_size, model_layers[i])
+        else:
+            mlp_weight_init = golorot_uniform(model_layers[i-1], model_layers[i])
+        mlp_weight = mx.sym.Variable('fc_{}_weight'.format(i), init=mlp_weight_init)
+        pre_gemm_concat = mx.sym.FullyConnected(data=pre_gemm_concat, weight=mlp_weight, num_hidden=layer, name='fc_'+str(i))
+        pre_gemm_concat = mx.sym.Activation(data=pre_gemm_concat, act_type='relu', name='act_'+str(i))
+
+    return pre_gemm_concat
+
+def gmf(user, item, factor_size, max_user, max_item):
+    user_weight = mx.sym.Variable('gmf_user_weight', init=mx.init.Normal(0.01))
+    item_weight = mx.sym.Variable('gmf_item_weight', init=mx.init.Normal(0.01))
+    embed_user = mx.sym.Embedding(data=user, weight=user_weight, input_dim=max_user,
+                                  output_dim=factor_size, name='embed_user'+str(factor_size))
+    embed_item = mx.sym.Embedding(data=item, weight=item_weight, input_dim=max_item,
+                                  output_dim=factor_size, name='embed_item'+str(factor_size))
+    pred = embed_user * embed_item
+
+    return pred
+
+def get_model(model_type='neumf', factor_size_mlp=128, factor_size_gmf=64,
+              model_layers=[256, 128, 64], num_hidden=1, 
+              max_user=138493, max_item=26744):
+    # input
+    user = mx.sym.Variable('user')
+    item = mx.sym.Variable('item')
+
+    if model_type == 'mlp':
+        net = mlp(user=user, item=item,
+                  factor_size=factor_size_mlp, model_layers=model_layers,
+                  max_user=max_user, max_item=max_item)
+    elif model_type == 'gmf':
+        net = gmf(user=user, item=item,
+                  factor_size=factor_size_gmf,
+                  max_user=max_user, max_item=max_item)
+    elif model_type == 'neumf':
+        net_mlp = mlp(user=user, item=item,
+                      factor_size=factor_size_mlp, model_layers=model_layers,
+                      max_user=max_user, max_item=max_item)
+        net_gmf = gmf(user=user, item=item,
+                      factor_size=factor_size_gmf,
+                      max_user=max_user, max_item=max_item)
+
+        net = mx.sym.concat(net_gmf, net_mlp, dim=1, name='post_gemm_concat')
+
+    else:
+        raise ValueError('Unsupported ncf model %s.' % model_type)
+
+    final_weight = mx.sym.Variable('fc_final_weight', init=lecunn_uniform(factor_size_gmf + model_layers[-1]))
+    net = mx.sym.FullyConnected(data=net, weight=final_weight, num_hidden=num_hidden, name='fc_final') 
+   
+    y_label = mx.sym.Variable('softmax_label')
+    net = mx.symbol.LogisticRegressionOutput(data=net, label=y_label, name='sigmoid_final')
+
+    return net
+
diff --git a/example/neural_collaborative_filtering/ncf.py b/example/neural_collaborative_filtering/ncf.py
new file mode 100644
index 000000000000..0fd9f733a1bd
--- /dev/null
+++ b/example/neural_collaborative_filtering/ncf.py
@@ -0,0 +1,170 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# 
+import os
+import time
+import argparse
+import logging
+import math
+import random
+import numpy as np
+import mxnet as mx
+from core.model import get_model
+from core.dataset import NCFTestData
+from core.evaluate import *
+from mxnet.contrib.quantization import *
+
+logging.basicConfig(level=logging.DEBUG)
+
+parser = argparse.ArgumentParser(description="Run matrix factorization with embedding",
+                                 formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+parser.add_argument('--path', nargs='?', default='./data/',
+                    help='Input data path.')
+parser.add_argument('--dataset', nargs='?', default='ml-20m',
+                    help='The dataset name.')
+parser.add_argument('--max-user', type=int, default=138493,
+                    help='max number of user index.')
+parser.add_argument('--max-item', type=int, default=26744,
+                    help='max number of item index.')
+parser.add_argument('--batch-size', type=int, default=256,
+                    help='number of examples per batch')
+parser.add_argument('--model-type', type=str, default='neumf', choices=['neumf', 'gmf', 'mlp'],
+                    help="mdoel type")
+parser.add_argument('--layers', default='[256, 128, 64]',
+                    help="list of number hiddens of fc layers in mlp model.")
+parser.add_argument('--factor-size-gmf', type=int, default=64,
+                    help="outdim of gmf embedding layers.")
+parser.add_argument('--num-hidden', type=int, default=1,
+                    help="num-hidden of neumf fc layer")
+parser.add_argument('--topk', type=int, default=10,
+                    help="topk for accuracy evaluation.")
+parser.add_argument('--gpu', type=int, default=None,
+                    help="index of gpu to run, e.g. 0 or 1. None means using cpu().")
+parser.add_argument('--benchmark', action='store_true',  help="whether to benchmark performance only")
+parser.add_argument('--epoch', type=int, default=0, help='model checkpoint index for inference')
+parser.add_argument('--prefix', default='./model/ml-20m/neumf', help="model checkpoint prefix")
+parser.add_argument('--calibration', action='store_true', help="whether to calibrate model")
+parser.add_argument('--calib-mode', type=str, choices=['naive', 'entropy'], default='naive',
+                    help='calibration mode used for generating calibration table for the quantized symbol; supports'
+                            ' 1. naive: simply take min and max values of layer outputs as thresholds for'
+                            ' quantization. In general, the inference accuracy worsens with more examples used in'
+                            ' calibration. It is recommended to use `entropy` mode as it produces more accurate'
+                            ' inference results.'
+                            ' 2. entropy: calculate KL divergence of the fp32 output and quantized output for optimal'
+                            ' thresholds. This mode is expected to produce the best inference accuracy of all three'
+                            ' kinds of quantized models if the calibration dataset is representative enough of the'
+                            ' inference dataset.')
+parser.add_argument('--quantized-dtype', type=str, default='auto',
+                    choices=['auto', 'int8', 'uint8'],
+                    help='quantization destination data type for input data')
+parser.add_argument('--num-calib-batches', type=int, default=10,
+                    help='number of batches for calibration')
+
+if __name__ == '__main__':
+    head = '%(asctime)-15s %(message)s'
+    logging.basicConfig(level=logging.INFO, format=head)
+
+    # arg parser
+    args = parser.parse_args()
+    logging.info(args)
+
+    max_user = args.max_user
+    max_item = args.max_item
+    batch_size = args.batch_size
+    model_type = args.model_type
+    model_layers = eval(args.layers)
+    factor_size_gmf = args.factor_size_gmf
+    factor_size_mlp = int(model_layers[0]/2)
+    num_hidden = args.num_hidden
+    benchmark = args.benchmark
+    calibration = args.calibration
+    calib_mode = args.calib_mode
+    quantized_dtype = args.quantized_dtype
+    num_calib_batches = args.num_calib_batches
+    ctx = mx.cpu() if args.gpu is None else mx.gpu(args.gpu)
+    topK = args.topk
+
+    # prepare dataset
+    if benchmark or calibration:
+        logging.info('Prepare movielens dataset')
+        val_iter = get_movielens_iter(args.path + args.dataset + '/test-ratings.csv', batch_size, ctx=ctx, logger=logging)
+    else:
+        logging.info('Prepare validation dataset')
+        data = NCFTestData(args.path + args.dataset)
+        testRatings, testNegatives= data.testRatings, data.testNegatives
+        logging.info("Load validation data done. #user=%d, #item=%d, #test=%d" 
+                    %(max_user, max_item, len(testRatings)))
+        logging.info('Prepare validation dataset completed')
+    
+    # construct the model
+    net, arg_params, aux_params = mx.model.load_checkpoint(args.prefix, args.epoch)
+    if ctx == mx.cpu() and calibration:
+        net = net.get_backend_symbol('MKLDNN_QUANTIZE')
+
+    # initialize the module
+    mod = mx.module.Module(net, context=ctx, data_names=['user', 'item'], label_names=['softmax_label'])
+    provide_data = [mx.io.DataDesc(name='item', shape=((batch_size,))),
+                    mx.io.DataDesc(name='user', shape=((batch_size,)))]
+    provide_label = [mx.io.DataDesc(name='softmax_label', shape=((batch_size,)))]
+    mod.bind(for_training=False, data_shapes=provide_data, label_shapes=provide_label)
+    mod.set_params(arg_params, aux_params)
+
+    if calibration:
+        logging.info('Quantizing FP32 model')
+        excluded_sym_names = ['post_gemm_concat', 'fc_final']
+        cqsym, cqarg_params, aux_params, collector = quantize_graph(sym=net, arg_params=arg_params, aux_params=aux_params,
+                                                                    excluded_sym_names=excluded_sym_names,
+                                                                    calib_mode=calib_mode,
+                                                                    quantized_dtype=args.quantized_dtype, logger=logging)
+        max_num_examples = num_calib_batches * batch_size
+        mod._exec_group.execs[0].set_monitor_callback(collector.collect, monitor_all=True)
+        num_batches = 0
+        num_examples = 0
+        for batch in val_iter:
+            mod.forward(batch)
+            num_batches += 1
+            num_examples += batch_size
+            if num_examples >= max_num_examples:
+                break
+        logging.info("Collected statistics from %d batches with batch_size=%d"
+                    % (num_batches, batch_size))
+        cqsym, cqarg_params, aux_params = calib_graph(qsym=cqsym, arg_params=arg_params, aux_params=aux_params,
+                                                      collector=collector, calib_mode=calib_mode,
+                                                      quantized_dtype=args.quantized_dtype, logger=logging)                                                       
+        sym_name = '%s-symbol.json' % (args.prefix + '-quantized')
+        cqsym = cqsym.get_backend_symbol('MKLDNN_QUANTIZE')
+        mx.model.save_checkpoint(args.prefix + '-quantized', args.epoch, cqsym, cqarg_params, aux_params)
+    elif benchmark:
+        logging.info('Benchmarking...')
+        num_samples = 0
+        for ib, batch in enumerate(val_iter):
+            if ib == 5:
+                num_samples = 0
+                tic = time.time()
+            mod.forward(batch, is_train=False)
+            mx.nd.waitall()
+            num_samples += batch_size
+        toc = time.time()
+        fps = num_samples/(toc - tic)
+        logging.info('Evaluating completed')
+        logging.info('Inference speed %.4f fps' % fps)
+    else:
+        logging.info('Evaluating...')
+        (hits, ndcgs) = evaluate_model(mod, testRatings, testNegatives, topK, batch_size, ctx, logging)
+        hr, ndcg = np.array(hits).mean(), np.array(ndcgs).mean()
+        logging.info('Evaluate: HR = %.4f, NDCG = %.4f'  % (hr, ndcg))
+
diff --git a/example/neural_collaborative_filtering/train.py b/example/neural_collaborative_filtering/train.py
new file mode 100644
index 000000000000..0b0cfad1ef39
--- /dev/null
+++ b/example/neural_collaborative_filtering/train.py
@@ -0,0 +1,163 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# 
+import os
+import time
+import argparse
+import logging
+import math
+import random
+import numpy as np
+import mxnet as mx
+from mxnet import gluon
+from core.model import get_model
+from core.dataset import NCFTrainData, NCFTestData
+from core.evaluate import *
+
+
+logging.basicConfig(level=logging.DEBUG)
+
+parser = argparse.ArgumentParser(description="Run matrix factorization with embedding",
+                                 formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+parser.add_argument('--path', nargs='?', default='./data/',
+                    help='Input data path.')
+parser.add_argument('--dataset', nargs='?', default='ml-20m',
+                    help='The dataset name.')
+parser.add_argument('--batch-size', type=int, default=2048,
+                    help='number of training examples per batch')
+parser.add_argument('--eval-batch-size', type=int, default=1000,
+                    help='number of evaluate examples per batch')                  
+parser.add_argument('--model-type', type=str, default='neumf', choices=['neumf', 'gmf', 'mlp'],
+                    help="mdoel type")
+parser.add_argument('--num-negative', type=int, default=4,
+                    help="number of negative samples per positive sample while training.")
+parser.add_argument('--layers', default='[256, 128, 64]',
+                    help="list of number hiddens of fc layers in mlp model.")
+parser.add_argument('--factor-size-gmf', type=int, default=64,
+                    help="outdim of gmf embedding layers.")
+parser.add_argument('--num-hidden', type=int, default=1,
+                    help="num-hidden of neumf fc layer")
+parser.add_argument('--log-interval', type=int, default=100,
+                    help='logging interval')
+parser.add_argument('--learning-rate', type=float, default=0.0005,
+                    help='learning rate for optimizer')
+parser.add_argument('--beta1', '-b1', type=float, default=0.9,
+                    help='beta1 for Adam')
+parser.add_argument('--beta2', '-b2', type=float, default=0.999,
+                    help='beta1 for Adam')
+parser.add_argument('--eps', type=float, default=1e-8,
+                    help='eps for Adam')
+parser.add_argument('--topk', type=int, default=10,
+                    help="topk for accuracy evaluation.")
+parser.add_argument('--gpu', type=int, default=None,
+                    help="list of gpus to run, e.g. 0 or 0,2. empty means using cpu().")
+parser.add_argument('--workers', type=int, default=8, help='thread number for dataloader.')
+parser.add_argument('--epoch', type=int, default=14, help='training epoch')
+parser.add_argument('--seed', type=int, default=3, help='random seed to use. Default=3.')
+parser.add_argument('--deploy', action='store_true', help="whether to load static graph for deployment")
+
+
+def cross_entropy(label, pred, eps=1e-12):
+    ce = 0
+    for l, p in zip(label, pred):
+        ce += -( l*np.log(p+eps) + (1-l)*np.log(1-p+eps))
+    return ce
+
+if __name__ == '__main__':
+    head = '%(asctime)-15s %(message)s'
+    logging.basicConfig(level=logging.INFO, format=head)
+
+    # arg parser
+    args = parser.parse_args()
+    logging.info(args)
+
+    mx.random.seed(args.seed)
+    np.random.seed(args.seed)
+    batch_size = args.batch_size
+    eval_batch_size = args.eval_batch_size
+    model_type = args.model_type
+    model_layers = eval(args.layers)
+    factor_size_gmf = args.factor_size_gmf
+    factor_size_mlp = int(model_layers[0]/2)
+    num_hidden = args.num_hidden
+    learning_rate=args.learning_rate
+    beta1=args.beta1
+    beta2=args.beta2
+    eps=args.eps
+    ctx = mx.cpu() if args.gpu is None else mx.gpu(args.gpu)
+    topK = args.topk
+    num_negatives = args.num_negative
+    num_workers = args.workers
+    epoch = args.epoch
+    log_interval = args.log_interval
+
+    # prepare dataset
+    logging.info('Prepare Dataset')
+    train_dataset = NCFTrainData((args.path + args.dataset + '/train-ratings.csv'), num_negatives)
+    test_data = NCFTestData(args.path + args.dataset)
+    train_dataloader = mx.gluon.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers, last_batch='rollover')
+    logging.info('Prepare Dataset completed')
+    # construct the model
+    net = get_model(model_type, factor_size_mlp, factor_size_gmf, 
+                    model_layers, num_hidden, train_dataset.nb_users, train_dataset.nb_items)
+
+    # initialize the module
+    mod = mx.module.Module(net, context=ctx, data_names=['user', 'item'], label_names=['softmax_label'])
+    provide_data = [mx.io.DataDesc(name='item', shape=((batch_size,))),
+                    mx.io.DataDesc(name='user', shape=((batch_size,)))]
+    provide_label = [mx.io.DataDesc(name='softmax_label', shape=((batch_size,)))]
+    mod.bind(for_training=True, data_shapes=provide_data, label_shapes=provide_label)
+    mod.init_params()
+    mod.init_optimizer(optimizer='adam', optimizer_params=[('learning_rate', learning_rate), ('beta1',beta1), ('beta2',beta2), ('epsilon',eps)])
+    
+    metric = mx.metric.create(cross_entropy)
+    speedometer = mx.callback.Speedometer(batch_size, log_interval)
+    best_hr, best_ndcg, best_iter = -1, -1, -1 
+    logging.info('Training started ...')
+    for epoch in range(epoch):
+        metric.reset()
+        for nbatch, seqs in enumerate(train_dataloader):
+            user_id, item_id, labels = seqs
+            batch = mx.io.DataBatch(data = [item_id.astype('int32').as_in_context(ctx),
+                                            user_id.astype('int32').as_in_context(ctx)],
+                                    label = [labels.as_in_context(ctx)])
+            mod.forward(batch)
+            mod.backward()
+            mod.update()
+            predicts=mod.get_outputs()[0]
+            metric.update(labels = labels, preds = predicts)
+            speedometer_param = mx.model.BatchEndParam(epoch=epoch, nbatch=nbatch,
+                                                       eval_metric=metric, locals=locals())
+            speedometer(speedometer_param)
+        
+        # save model
+        dir_path = os.path.dirname(os.path.realpath(__file__))
+        model_path = os.path.join(dir_path, 'model', args.dataset)
+        if not os.path.exists(model_path):
+            os.makedirs(model_path)
+        mod.save_checkpoint(os.path.join(model_path, model_type), epoch)
+        # compute hit ratio
+        (hits, ndcgs) = evaluate_model(mod, test_data.testRatings, test_data.testNegatives, topK, eval_batch_size, ctx, logging)
+        hr, ndcg = np.array(hits).mean(), np.array(ndcgs).mean()
+        logging.info('Iteration %d: HR = %.4f, NDCG = %.4f'  % (epoch, hr, ndcg))
+        # best hit ratio
+        if hr > best_hr:
+            best_hr, best_ndcg, best_iter = hr, ndcg, epoch
+
+    logging.info("End. Best Iteration %d:  HR = %.4f, NDCG = %.4f. " % (best_iter, best_hr, best_ndcg))
+    logging.info('Training completed.')
+

From 69fd01838b8bc5cda86a281f25acee65414c3164 Mon Sep 17 00:00:00 2001
From: Wuxun Zhang <wuxun.zhang@intel.com>
Date: Sat, 16 Nov 2019 20:20:59 +0800
Subject: [PATCH 602/813] fix expand_dims fall back when input's ndim is 0
 (#16837)

---
 src/operator/nn/mkldnn/mkldnn_reshape.cc | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/operator/nn/mkldnn/mkldnn_reshape.cc b/src/operator/nn/mkldnn/mkldnn_reshape.cc
index 0fc9f20703af..81d96bb70670 100644
--- a/src/operator/nn/mkldnn/mkldnn_reshape.cc
+++ b/src/operator/nn/mkldnn/mkldnn_reshape.cc
@@ -127,7 +127,8 @@ void MKLDNNReshapeForward(const nnvm::NodeAttrs& attrs,
                           const NDArray &output) {
   // For mkldnn non-supported input, it shouldn't hold mkldnn memory, so let's simply fallback to
   // naive implement.
-  if (input.shape().ndim() > 4 || !SupportMKLDNNQuantize(input.dtype())) {
+  const int input_ndims = input.shape().ndim();
+  if ((input_ndims < 1 || input_ndims > 4) || !SupportMKLDNNQuantize(input.dtype())) {
     if (req != kWriteInplace) {
       FallBackCompute(UnaryOp::IdentityCompute<cpu>, attrs, ctx, {input}, {req}, {output});
     }

From 93f338dde1048217ffb10923ad1f8ce8487cb8b3 Mon Sep 17 00:00:00 2001
From: Leonard Lausen <lausen@amazon.com>
Date: Sat, 16 Nov 2019 21:20:26 +0800
Subject: [PATCH 603/813] [Gluon] Improve estimator usability and fix logging
 logic (#16810)

gluon.contrib.estimator used a global Logger obtained via
`logging.getLogger('gluon.contrib.estimator.event_handlers')`. This logger used
to be configured every time a gluon.contrib.estimator.LoggingHandler was
created, which is a bug. We can't modify a global Logger instance whenever the
user creates an Estimator and a LoggingHandler.

Instead, this commit separates the LoggingHandler (responsible for logging
metadata during estimator.fit) from the configuration of the Logger.

We expose the Logger as attribute of the Estimator, and configure it to output
to stdout by default. Instructions are given how users can configure the
Estimator.logger to log to a file instead.
---
 .../gluon/contrib/estimator/estimator.py      |  49 +++++--
 .../gluon/contrib/estimator/event_handler.py  | 135 +++++++-----------
 tests/python/unittest/common.py               |   2 +-
 tests/python/unittest/test_gluon_estimator.py |   5 +-
 .../unittest/test_gluon_event_handler.py      |   9 +-
 5 files changed, 97 insertions(+), 103 deletions(-)

diff --git a/python/mxnet/gluon/contrib/estimator/estimator.py b/python/mxnet/gluon/contrib/estimator/estimator.py
index 4f2b8fd99cac..83b954d02e10 100644
--- a/python/mxnet/gluon/contrib/estimator/estimator.py
+++ b/python/mxnet/gluon/contrib/estimator/estimator.py
@@ -20,6 +20,8 @@
 """Gluon Estimator"""
 
 import copy
+import logging
+import sys
 import warnings
 
 from .event_handler import MetricHandler, ValidationHandler, LoggingHandler, StoppingHandler
@@ -57,6 +59,25 @@ class Estimator(object):
         Trainer to apply optimizer on network parameters.
     context : Context or list of Context
         Device(s) to run the training on.
+
+    """
+
+    logger = None
+    """logging.Logger object associated with the Estimator.
+
+    The logger is used for all logs generated by this estimator and its
+    handlers. A new logging.Logger is created during Estimator construction and
+    configured to write all logs with level logging.INFO or higher to
+    sys.stdout.
+
+    You can modify the logging settings using the standard Python methods. For
+    example, to save logs to a file in addition to printing them to stdout
+    output, you can attach a logging.FileHandler to the logger.
+
+    >>> est = Estimator(net, loss)
+    >>> import logging
+    >>> est.logger.addHandler(logging.FileHandler(filename))
+
     """
 
     def __init__(self, net,
@@ -65,13 +86,15 @@ def __init__(self, net,
                  initializer=None,
                  trainer=None,
                  context=None):
-
         self.net = net
         self.loss = self._check_loss(loss)
         self._train_metrics = _check_metrics(metrics)
         self._add_default_training_metrics()
         self._add_validation_metrics()
 
+        self.logger = logging.Logger(name='Estimator', level=logging.INFO)
+        self.logger.addHandler(logging.StreamHandler(sys.stdout))
+
         self.context = self._check_context(context)
         self._initialize(initializer)
         self.trainer = self._check_trainer(trainer)
@@ -243,8 +266,7 @@ def evaluate(self,
         for _, batch in enumerate(val_data):
             self.evaluate_batch(batch, val_metrics, batch_axis)
 
-    def fit_batch(self, train_batch,
-                  batch_axis=0):
+    def fit_batch(self, train_batch, batch_axis=0):
         """Trains the model on a batch of training data.
 
         Parameters
@@ -257,13 +279,15 @@ def fit_batch(self, train_batch,
         Returns
         -------
         data: List of NDArray
-            Sharded data from the batch.
+            Sharded data from the batch. Data is sharded with
+            `gluon.split_and_load`.
         label: List of NDArray
-            Sharded label from the batch.
+            Sharded label from the batch. Labels are sharded with
+            `gluon.split_and_load`.
         pred: List of NDArray
-            Prediction of each of the shareded batch.
+            Prediction on each of the sharded inputs.
         loss: List of NDArray
-            Loss of each of the shareded batch.
+            Loss on each of the sharded inputs.
         """
         data, label = self._get_data_and_label(train_batch, self.context, batch_axis)
 
@@ -304,7 +328,11 @@ def fit(self, train_data,
             Number of epochs to iterate on the training data.
             You can only specify one and only one type of iteration(epochs or batches).
         event_handlers : EventHandler or list of EventHandler
-            List of :py:class:`EventHandlers` to apply during training.
+            List of :py:class:`EventHandlers` to apply during training. Besides
+            the event handlers specified here, a StoppingHandler,
+            LoggingHandler and MetricHandler will be added by default if not
+            yet specified manually. If validation data is provided, a
+            ValidationHandler is also added if not already specified.
         batches : int, default None
             Number of batches to iterate on the training data.
             You can only specify one and only one type of iteration(epochs or batches).
@@ -405,11 +433,6 @@ def _prepare_default_handlers(self, val_data, event_handlers):
         event_handlers.extend(added_default_handlers)
 
         if mixing_handlers:
-            msg = "The following default event handlers are added: {}.".format(
-                ", ".join([type(h).__name__ for h in added_default_handlers]))
-            warnings.warn(msg)
-
-
             # check if all handlers have the same set of references to metrics
             known_metrics = set(self.train_metrics + self.val_metrics)
             for handler in event_handlers:
diff --git a/python/mxnet/gluon/contrib/estimator/event_handler.py b/python/mxnet/gluon/contrib/estimator/event_handler.py
index 7e143d6f19aa..3cdc407407c1 100644
--- a/python/mxnet/gluon/contrib/estimator/event_handler.py
+++ b/python/mxnet/gluon/contrib/estimator/event_handler.py
@@ -19,14 +19,13 @@
 # pylint: disable=wildcard-import, unused-argument, too-many-ancestors
 """Gluon EventHandlers for Estimators"""
 
-import logging
 import os
 import time
 import warnings
 
 import numpy as np
 
-from ....metric import EvalMetric, CompositeEvalMetric
+from ....metric import CompositeEvalMetric, EvalMetric
 from ....metric import Loss as metric_loss
 from .utils import _check_metrics
 
@@ -34,6 +33,7 @@
            'StoppingHandler', 'MetricHandler', 'ValidationHandler',
            'LoggingHandler', 'CheckpointHandler', 'EarlyStoppingHandler']
 
+
 class EventHandler(object):
     pass
 
@@ -194,7 +194,6 @@ def __init__(self,
         # order to be called among all callbacks
         # validation metrics need to be calculated before other callbacks can access them
         self.priority = -np.Inf
-        self.logger = logging.getLogger(__name__)
 
     def train_begin(self, estimator, *args, **kwargs):
         # reset epoch and batch counter
@@ -211,7 +210,7 @@ def batch_end(self, estimator, *args, **kwargs):
             for monitor in self.val_metrics:
                 name, value = monitor.get()
                 msg += '%s: %.4f, ' % (name, value)
-            self.logger.info(msg.rstrip(','))
+            estimator.logger.info(msg.rstrip(','))
 
     def epoch_end(self, estimator, *args, **kwargs):
         self.current_epoch += 1
@@ -228,12 +227,6 @@ class LoggingHandler(TrainBegin, TrainEnd, EpochBegin, EpochEnd, BatchBegin, Bat
 
     Parameters
     ----------
-    file_name : str
-        File name to save the logs.
-    file_location : str
-        File location to save the logs.
-    filemode : str, default 'a'
-        Logging file mode, default using append mode.
     verbose : int, default LOG_PER_EPOCH
         Limit the granularity of metrics displayed during training process.
         verbose=LOG_PER_EPOCH: display metrics every epoch
@@ -247,25 +240,10 @@ class LoggingHandler(TrainBegin, TrainEnd, EpochBegin, EpochEnd, BatchBegin, Bat
     LOG_PER_EPOCH = 1
     LOG_PER_BATCH = 2
 
-    def __init__(self, file_name=None,
-                 file_location=None,
-                 filemode='a',
-                 verbose=LOG_PER_EPOCH,
+    def __init__(self, verbose=LOG_PER_EPOCH,
                  train_metrics=None,
                  val_metrics=None):
         super(LoggingHandler, self).__init__()
-        self.logger = logging.getLogger(__name__)
-        self.logger.setLevel(logging.INFO)
-        self._added_logging_handlers = [logging.StreamHandler()]
-        # save logger to file only if file name or location is specified
-        if file_name or file_location:
-            file_name = file_name or 'estimator_log'
-            file_location = file_location or './'
-            file_handler = logging.FileHandler(os.path.join(file_location, file_name), mode=filemode)
-            self._added_logging_handlers.append(file_handler)
-        for handler in self._added_logging_handlers:
-            self.logger.addHandler(handler)
-
         if verbose not in [self.LOG_PER_EPOCH, self.LOG_PER_BATCH]:
             raise ValueError("verbose level must be either LOG_PER_EPOCH or "
                              "LOG_PER_BATCH, received %s. "
@@ -281,24 +259,18 @@ def __init__(self, file_name=None,
         # it will also shut down logging at train end
         self.priority = np.Inf
 
-    def __del__(self):
-        for handler in self._added_logging_handlers:
-            handler.flush()
-            self.logger.removeHandler(handler)
-            handler.close()
-
     def train_begin(self, estimator, *args, **kwargs):
         self.train_start = time.time()
         trainer = estimator.trainer
         optimizer = trainer.optimizer.__class__.__name__
         lr = trainer.learning_rate
-        self.logger.info("Training begin: using optimizer %s "
-                         "with current learning rate %.4f ",
-                         optimizer, lr)
+        estimator.logger.info("Training begin: using optimizer %s "
+                              "with current learning rate %.4f ",
+                              optimizer, lr)
         if estimator.max_epoch:
-            self.logger.info("Train for %d epochs.", estimator.max_epoch)
+            estimator.logger.info("Train for %d epochs.", estimator.max_epoch)
         else:
-            self.logger.info("Train for %d batches.", estimator.max_batch)
+            estimator.logger.info("Train for %d batches.", estimator.max_batch)
         # reset all counters
         self.current_epoch = 0
         self.batch_index = 0
@@ -311,13 +283,7 @@ def train_end(self, estimator, *args, **kwargs):
         for metric in self.train_metrics + self.val_metrics:
             name, value = metric.get()
             msg += '%s: %.4f, ' % (name, value)
-        self.logger.info(msg.rstrip(', '))
-        # make a copy of handler list and remove one by one
-        # as removing handler will edit the handler list
-        for handler in self.logger.handlers[:]:
-            handler.close()
-            self.logger.removeHandler(handler)
-        logging.shutdown()
+        estimator.logger.info(msg.rstrip(', '))
 
     def batch_begin(self, estimator, *args, **kwargs):
         if self.verbose == self.LOG_PER_BATCH:
@@ -334,14 +300,14 @@ def batch_end(self, estimator, *args, **kwargs):
                 # only log current training loss & metric after each batch
                 name, value = metric.get()
                 msg += '%s: %.4f, ' % (name, value)
-            self.logger.info(msg.rstrip(', '))
+            estimator.logger.info(msg.rstrip(', '))
         self.batch_index += 1
 
     def epoch_begin(self, estimator, *args, **kwargs):
         if self.verbose >= self.LOG_PER_EPOCH:
             self.epoch_start = time.time()
-            self.logger.info("[Epoch %d] Begin, current learning rate: %.4f",
-                             self.current_epoch, estimator.trainer.learning_rate)
+            estimator.logger.info("[Epoch %d] Begin, current learning rate: %.4f",
+                                  self.current_epoch, estimator.trainer.learning_rate)
 
     def epoch_end(self, estimator, *args, **kwargs):
         if self.verbose >= self.LOG_PER_EPOCH:
@@ -350,7 +316,7 @@ def epoch_end(self, estimator, *args, **kwargs):
             for monitor in self.train_metrics + self.val_metrics:
                 name, value = monitor.get()
                 msg += '%s: %.4f, ' % (name, value)
-            self.logger.info(msg.rstrip(', '))
+            estimator.logger.info(msg.rstrip(', '))
         self.current_epoch += 1
         self.batch_index = 0
 
@@ -424,7 +390,6 @@ def __init__(self,
         self.max_checkpoints = max_checkpoints
         self.resume_from_checkpoint = resume_from_checkpoint
         self.saved_checkpoints = []
-        self.logger = logging.getLogger(__name__)
         if self.save_best:
             if mode not in ['auto', 'min', 'max']:
                 warnings.warn('ModelCheckpoint mode %s is unknown, '
@@ -443,14 +408,16 @@ def __init__(self,
             else:
                 # use greater for accuracy and f1 and less otherwise
                 if 'acc' or 'f1' in self.monitor.get()[0].lower():
-                    self.logger.info("`greater` operator will be used to determine "
-                                     "if %s has improved, please use `min` for mode "
-                                     "if you want otherwise", self.monitor.get()[0])
+                    warnings.warn("`greater` operator will be used to determine if {} has improved. "
+                                  "Please specify `mode='min'` to use the `less` operator. "
+                                  "Specify `mode='max' to disable this warning.`"
+                                  .format(self.monitor.get()[0]))
                     self.monitor_op = np.greater
                 else:
-                    self.logger.info("`less` operator will be used to determine "
-                                     "if %s has improved, please use `max` for mode "
-                                     "if you want otherwise", self.monitor.get()[0])
+                    warnings.warn("`less` operator will be used to determine if {} has improved. "
+                                  "Please specify `mode='max'` to use the `greater` operator. "
+                                  "Specify `mode='min' to disable this warning.`"
+                                  .format(self.monitor.get()[0]))
                     self.monitor_op = np.less
 
     def train_begin(self, estimator, *args, **kwargs):
@@ -501,9 +468,9 @@ def _save_checkpoint(self, estimator):
         prefix = "%s-epoch%dbatch%d" % (self.model_prefix, save_epoch_number, save_batch_number)
         self._save_params_and_trainer(estimator, prefix)
         if self.verbose > 0:
-            self.logger.info('[Epoch %d] CheckpointHandler: trained total %d batches, '
-                             'saving model at %s with prefix: %s',
-                             self.current_epoch, self.current_batch + 1, self.model_dir, prefix)
+            estimator.logger.info('[Epoch %d] CheckpointHandler: trained total %d batches, '
+                                  'saving model at %s with prefix: %s',
+                                  self.current_epoch, self.current_batch + 1, self.model_dir, prefix)
 
         if self.save_best:
             monitor_name, monitor_value = self.monitor.get()
@@ -519,18 +486,18 @@ def _save_checkpoint(self, estimator):
                     self._save_params_and_trainer(estimator, prefix)
                     self.best = monitor_value
                     if self.verbose > 0:
-                        self.logger.info('[Epoch %d] CheckpointHandler: '
-                                         '%s improved from %0.5f to %0.5f, '
-                                         'updating best model at %s with prefix: %s',
-                                         self.current_epoch, monitor_name,
-                                         self.best, monitor_value, self.model_dir, prefix)
+                        estimator.logger.info('[Epoch %d] CheckpointHandler: '
+                                              '%s improved from %0.5f to %0.5f, '
+                                              'updating best model at %s with prefix: %s',
+                                              self.current_epoch, monitor_name,
+                                              self.best, monitor_value, self.model_dir, prefix)
                 else:
                     if self.verbose > 0:
-                        self.logger.info('[Epoch %d] CheckpointHandler: '
-                                         '%s did not improve from %0.5f, '
-                                         'skipping updating best model',
-                                         self.current_batch, monitor_name,
-                                         self.best)
+                        estimator.logger.info('[Epoch %d] CheckpointHandler: '
+                                              '%s did not improve from %0.5f, '
+                                              'skipping updating best model',
+                                              self.current_batch, monitor_name,
+                                              self.best)
 
     def _save_symbol(self, estimator):
         symbol_file = os.path.join(self.model_dir, self.model_prefix + '-symbol.json')
@@ -538,9 +505,11 @@ def _save_symbol(self, estimator):
             sym = estimator.net._cached_graph[1]
             sym.save(symbol_file)
         else:
-            self.logger.info("Model architecture(symbol file) is not saved, please use HybridBlock "
-                             "to construct your model, and call net.hybridize() before passing to "
-                             "Estimator in order to save model architecture as %s.", symbol_file)
+            estimator.logger.info(
+                "Model architecture(symbol file) is not saved, please use HybridBlock "
+                "to construct your model, and call net.hybridize() before passing to "
+                "Estimator in order to save model architecture as %s.",
+                symbol_file)
 
     def _save_params_and_trainer(self, estimator, file_prefix):
         param_file = os.path.join(self.model_dir, file_prefix + '.params')
@@ -579,7 +548,7 @@ def _resume_from_checkpoint(self, estimator):
                 msg += "%d batches" % estimator.max_batch
             else:
                 msg += "%d epochs" % estimator.max_epoch
-            self.logger.info(msg)
+            estimator.logger.info(msg)
         else:
             msg = "CheckpointHandler: Checkpoint resumed from epoch %d batch %d, " \
                   "continue to train for " % (self.trained_epoch, self.trained_batch)
@@ -607,7 +576,7 @@ def _resume_from_checkpoint(self, estimator):
             assert os.path.exists(trainer_file), "Failed to load checkpoint, %s does not exist" % trainer_file
             estimator.net.load_parameters(param_file, ctx=estimator.context)
             estimator.trainer.load_states(trainer_file)
-            self.logger.warning(msg)
+            estimator.logger.warning(msg)
 
     def _find_max_iteration(self, dir, prefix, start, end, saved_checkpoints=None):
         error_msg = "Error parsing checkpoint file, please check your " \
@@ -672,7 +641,6 @@ def __init__(self,
         self.stopped_epoch = 0
         self.current_epoch = 0
         self.stop_training = False
-        self.logger = logging.getLogger(__name__)
 
         if mode not in ['auto', 'min', 'max']:
             warnings.warn('EarlyStopping mode %s is unknown, '
@@ -688,14 +656,16 @@ def __init__(self,
             self.monitor_op = np.greater
         else:
             if 'acc' or 'f1' in self.monitor.get()[0].lower():
-                self.logger.info("`greater` operator is used to determine "
-                                 "if %s has improved, please use `min` for mode "
-                                 "if you want otherwise", self.monitor.get()[0])
+                warnings.warn("`greater` operator will be used to determine if {} has improved. "
+                              "Please specify `mode='min'` to use the `less` operator. "
+                              "Specify `mode='max' to disable this warning.`"
+                              .format(self.monitor.get()[0]))
                 self.monitor_op = np.greater
             else:
-                self.logger.info("`less` operator is used to determine "
-                                 "if %s has improved, please use `max` for mode "
-                                 "if you want otherwise", self.monitor.get()[0])
+                warnings.warn("`less` operator will be used to determine if {} has improved. "
+                              "Please specify `mode='max'` to use the `greater` operator. "
+                              "Specify `mode='min' to disable this warning.`"
+                              .format(self.monitor.get()[0]))
                 self.monitor_op = np.less
 
         if self.monitor_op == np.greater:  # pylint: disable=comparison-with-callable
@@ -733,5 +703,6 @@ def epoch_end(self, estimator, *args, **kwargs):
 
     def train_end(self, estimator, *args, **kwargs):
         if self.stopped_epoch > 0:
-            self.logger.info('[Epoch %d] EarlyStoppingHanlder: early stopping due to %s not improving',
-                             self.stopped_epoch, self.monitor.get()[0])
+            estimator.logger.info('[Epoch %d] EarlyStoppingHanlder: '
+                                  'early stopping due to %s not improving',
+                                  self.stopped_epoch, self.monitor.get()[0])
diff --git a/tests/python/unittest/common.py b/tests/python/unittest/common.py
index 06fb16288649..816508fbc3c8 100644
--- a/tests/python/unittest/common.py
+++ b/tests/python/unittest/common.py
@@ -251,7 +251,7 @@ def setup_module():
 
 try:
     from tempfile import TemporaryDirectory
-except:
+except:  # Python 2 support
     # really simple implementation of TemporaryDirectory
     class TemporaryDirectory(object):
         def __init__(self, suffix='', prefix='', dir=''):
diff --git a/tests/python/unittest/test_gluon_estimator.py b/tests/python/unittest/test_gluon_estimator.py
index bae576734a3e..aaf9839b29f3 100644
--- a/tests/python/unittest/test_gluon_estimator.py
+++ b/tests/python/unittest/test_gluon_estimator.py
@@ -346,10 +346,7 @@ def test_default_handlers():
     train_metrics = est.train_metrics
     val_metrics = est.val_metrics
     logging = LoggingHandler(train_metrics=train_metrics, val_metrics=val_metrics)
-    with warnings.catch_warnings(record=True) as w:
-        est.fit(train_data=train_data, epochs=num_epochs, event_handlers=[logging])
-        # provide metric handler by default
-        assert 'MetricHandler' in str(w[-1].message)
+    est.fit(train_data=train_data, epochs=num_epochs, event_handlers=[logging])
 
     # handler with all user defined metrics
     # use mix of default and user defined handlers
diff --git a/tests/python/unittest/test_gluon_event_handler.py b/tests/python/unittest/test_gluon_event_handler.py
index b29c72a0f908..17c75813d516 100644
--- a/tests/python/unittest/test_gluon_event_handler.py
+++ b/tests/python/unittest/test_gluon_event_handler.py
@@ -16,6 +16,7 @@
 # under the License.
 
 import os
+import logging
 
 import mxnet as mx
 from common import TemporaryDirectory
@@ -143,16 +144,18 @@ def test_logging():
         ce_loss = loss.SoftmaxCrossEntropyLoss()
         acc = mx.metric.Accuracy()
         est = estimator.Estimator(net, loss=ce_loss, metrics=acc)
+
+        est.logger.addHandler(logging.FileHandler(output_dir))
+
         train_metrics = est.train_metrics
         val_metrics = est.val_metrics
-        logging_handler = event_handler.LoggingHandler(file_name=file_name,
-                                                       file_location=tmpdir,
-                                                       train_metrics=train_metrics,
+        logging_handler = event_handler.LoggingHandler(train_metrics=train_metrics,
                                                        val_metrics=val_metrics)
         est.fit(test_data, event_handlers=[logging_handler], epochs=3)
         assert logging_handler.batch_index == 0
         assert logging_handler.current_epoch == 3
         assert os.path.isfile(output_dir)
+        del est  # Clean up estimator and logger before deleting tmpdir
 
 
 def test_custom_handler():

From 5a97cf5c92dbee098850a40b9fb201482175e0c3 Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Sat, 16 Nov 2019 22:55:04 -0800
Subject: [PATCH 604/813] bump up atol for gradient check (#16843)

---
 tests/python/unittest/test_gluon.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/python/unittest/test_gluon.py b/tests/python/unittest/test_gluon.py
index 5d15b27fa7ea..3a56dc887647 100644
--- a/tests/python/unittest/test_gluon.py
+++ b/tests/python/unittest/test_gluon.py
@@ -1672,7 +1672,7 @@ def test(net, x):
 
     assert_almost_equal(y1.asnumpy(), y2.asnumpy(), rtol=1e-3, atol=1e-5)
     for key in grads1:
-        assert_almost_equal(grads1[key].asnumpy(), grads2[key].asnumpy(), rtol=1e-3, atol=1e-5)
+        assert_almost_equal(grads1[key].asnumpy(), grads2[key].asnumpy(), rtol=1e-3, atol=1e-4)
 
 @with_seed()
 def test_hybrid_static_memory():

From 9e1ab18e2709cd7528c365ea77fdfb469d609841 Mon Sep 17 00:00:00 2001
From: "Huang, Guangtai" <guangtai@amazon.com>
Date: Mon, 18 Nov 2019 12:28:02 +0800
Subject: [PATCH 605/813] [Numpy][Operator] 'where' Implementation in MXNet
 (#16829)

* init

* fix

* use mxnet backend

* fix

* disable some test

* fix according to reviews

* fix doc

* change variables' name
---
 python/mxnet/ndarray/numpy/_op.py             |  74 +++++-
 python/mxnet/numpy/multiarray.py              |  71 +++++-
 python/mxnet/numpy_dispatch_protocol.py       |   1 +
 python/mxnet/symbol/numpy/_symbol.py          |  28 ++-
 src/operator/numpy/np_where_op-inl.h          | 227 ++++++++++++++++++
 src/operator/numpy/np_where_op.cc             | 133 ++++++++++
 src/operator/numpy/np_where_op.cu             |  38 +++
 .../unittest/test_numpy_interoperability.py   |  23 ++
 tests/python/unittest/test_numpy_op.py        |  50 +++-
 9 files changed, 638 insertions(+), 7 deletions(-)
 create mode 100644 src/operator/numpy/np_where_op-inl.h
 create mode 100644 src/operator/numpy/np_where_op.cc
 create mode 100644 src/operator/numpy/np_where_op.cu

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index 70cf3f6d2333..a8179c2b70cf 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -39,7 +39,7 @@
            'around', 'hypot', 'bitwise_xor', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
            'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal',
            'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory', 'may_share_memory', 'diff', 'resize',
-           'nan_to_num']
+           'nan_to_num', 'where']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -5346,3 +5346,75 @@ def nan_to_num(x, copy=True, nan=0.0, posinf=None, neginf=None, **kwargs):
         return _npi.nan_to_num(x, copy=copy, nan=nan, posinf=posinf, neginf=neginf, out=None)
     else:
         raise TypeError('type {} not supported'.format(str(type(x))))
+
+
+@set_module('mxnet.ndarray.numpy')
+def where(condition, x=None, y=None):
+    """where(condition, [x, y])
+    Return elements chosen from `x` or `y` depending on `condition`.
+
+    .. note::
+        When only `condition` is provided, this function is a shorthand for
+        ``np.asarray(condition).nonzero()``. The rest of this documentation
+        covers only the case where all three arguments are provided.
+
+    Parameters
+    ----------
+    condition : ndarray
+        Where True, yield `x`, otherwise yield `y`.
+    x, y : ndarray
+        Values from which to choose. `x`, `y` and `condition` need to be
+        broadcastable to some shape. `x` and `y` must have the same dtype.
+
+    Returns
+    -------
+    out : ndarray
+        An array with elements from `x` where `condition` is True, and elements
+        from `y` elsewhere.
+
+    Notes
+    -----
+    If all the arrays are 1-D, `where` is equivalent to::
+
+        [xv if c else yv
+        for c, xv, yv in zip(condition, x, y)]
+
+    Examples
+    --------
+    >>> a = np.arange(10)
+    >>> a
+    array([0., 1., 2., 3., 4., 5., 6., 7., 8., 9.])
+    >>> np.where(a < 5, a, 10*a)
+    array([ 0.,  1.,  2.,  3.,  4., 50., 60., 70., 80., 90.])
+
+    This can be used on multidimensional arrays too:
+
+    >>> cond = np.array([[True, False], [True, True]])
+    >>> x = np.array([[1, 2], [3, 4]])
+    >>> y = np.array([[9, 8], [7, 6]])
+    >>> np.where(cond, x, y)
+    array([[1., 8.],
+           [3., 4.]])
+
+    The shapes of x, y, and the condition are broadcast together:
+
+    >>> x, y = onp.ogrid[:3, :4]
+    >>> x = np.array(x)
+    >>> y = np.array(y)
+    >>> np.where(x < y, x, 10 + y)  # both x and 10+y are broadcast
+    array([[10,  0,  0,  0],
+           [10, 11,  1,  1],
+           [10, 11, 12,  2]], dtype=int64)
+
+    >>> a = np.array([[0, 1, 2],
+    ...               [0, 2, 4],
+    ...               [0, 3, 6]])
+    >>> np.where(a < 4, a, np.array(-1))  # -1 is broadcast
+    array([[ 0.,  1.,  2.],
+           [ 0.,  2., -1.],
+           [ 0.,  3., -1.]])
+    """
+    if x is None and y is None:
+        return nonzero(condition)
+    else:
+        return _npi.where(condition, x, y, out=None)
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 09ff22e66702..bbbb0fd2fd6f 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -57,7 +57,7 @@
            'blackman', 'flip', 'around', 'arctan2', 'hypot', 'bitwise_xor', 'rad2deg', 'deg2rad', 'unique', 'lcm',
            'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less',
            'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory',
-           'may_share_memory', 'diff', 'resize', 'nan_to_num']
+           'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -7333,3 +7333,72 @@ def nan_to_num(x, copy=True, nan=0.0, posinf=None, neginf=None, **kwargs):
        [ 2.22222000e+005,  2.22222000e+005, -1.79769313e+308]], dtype=float64)
     """
     return _mx_nd_np.nan_to_num(x, copy=copy, nan=nan, posinf=posinf, neginf=neginf)
+
+
+@set_module('mxnet.numpy')
+def where(condition, x=None, y=None):
+    """where(condition, [x, y])
+    Return elements chosen from `x` or `y` depending on `condition`.
+
+    .. note::
+        When only `condition` is provided, this function is a shorthand for
+        ``np.asarray(condition).nonzero()``. The rest of this documentation
+        covers only the case where all three arguments are provided.
+
+    Parameters
+    ----------
+    condition : ndarray
+        Where True, yield `x`, otherwise yield `y`.
+    x, y : ndarray
+        Values from which to choose. `x`, `y` and `condition` need to be
+        broadcastable to some shape. `x` and `y` must have the same dtype.
+
+    Returns
+    -------
+    out : ndarray
+        An array with elements from `x` where `condition` is True, and elements
+        from `y` elsewhere.
+
+    Notes
+    -----
+    If all the arrays are 1-D, `where` is equivalent to::
+
+        [xv if c else yv
+        for c, xv, yv in zip(condition, x, y)]
+
+    Examples
+    --------
+    >>> a = np.arange(10)
+    >>> a
+    array([0., 1., 2., 3., 4., 5., 6., 7., 8., 9.])
+    >>> np.where(a < 5, a, 10*a)
+    array([ 0.,  1.,  2.,  3.,  4., 50., 60., 70., 80., 90.])
+
+    This can be used on multidimensional arrays too:
+
+    >>> cond = np.array([[True, False], [True, True]])
+    >>> x = np.array([[1, 2], [3, 4]])
+    >>> y = np.array([[9, 8], [7, 6]])
+    >>> np.where(cond, x, y)
+    array([[1., 8.],
+           [3., 4.]])
+
+    The shapes of x, y, and the condition are broadcast together:
+
+    >>> x, y = onp.ogrid[:3, :4]
+    >>> x = np.array(x)
+    >>> y = np.array(y)
+    >>> np.where(x < y, x, 10 + y)  # both x and 10+y are broadcast
+    array([[10,  0,  0,  0],
+           [10, 11,  1,  1],
+           [10, 11, 12,  2]], dtype=int64)
+
+    >>> a = np.array([[0, 1, 2],
+    ...               [0, 2, 4],
+    ...               [0, 3, 6]])
+    >>> np.where(a < 4, a, np.array(-1))  # -1 is broadcast
+    array([[ 0.,  1.,  2.],
+           [ 0.,  2., -1.],
+           [ 0.,  3., -1.]])
+    """
+    return _mx_nd_np.where(condition, x, y)
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index 694d1d40143b..ef9ed6cc363f 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -136,6 +136,7 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'may_share_memory',
     'diff',
     'resize',
+    'where',
 ]
 
 
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 0a42a620c14e..7da771966f1f 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -41,7 +41,7 @@
            'around', 'hypot', 'bitwise_xor', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
            'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal',
            'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'shares_memory', 'may_share_memory', 'diff',
-           'resize', 'nan_to_num']
+           'resize', 'nan_to_num', 'where']
 
 
 def _num_outputs(sym):
@@ -4868,7 +4868,7 @@ def nan_to_num(x, copy=True, nan=0.0, posinf=None, neginf=None, **kwargs):
 
     Parameters
     ----------
-    x : Symbol
+    x : _Symbol
         Input data.
     copy : bool, optional
         Whether to create a copy of `x` (True) or to replace values
@@ -4891,7 +4891,7 @@ def nan_to_num(x, copy=True, nan=0.0, posinf=None, neginf=None, **kwargs):
 
     Returns
     -------
-    out : ndarray
+    out : _Symbol
         `x`, with the non-finite values replaced. If `copy` is False, this may
         be `x` itself.
 
@@ -4911,5 +4911,27 @@ def nan_to_num(x, copy=True, nan=0.0, posinf=None, neginf=None, **kwargs):
         raise TypeError('type {} not supported'.format(str(type(x))))
 
 
+@set_module('mxnet.symbol.numpy')
+def where(condition, x, y):
+    """
+    Return elements chosen from `x` or `y` depending on `condition`.
+
+    Parameters
+    ----------
+    condition : _Symbol
+        Where True, yield `x`, otherwise yield `y`.
+    x, y : _Symbol
+        Values from which to choose. `x`, `y` and `condition` need to be
+        broadcastable to some shape. `x` and `y` must have the same dtype.
+
+    Returns
+    -------
+    out : _Symbol
+        An array with elements from `x` where `condition` is True, and elements
+        from `y` elsewhere.
+
+    """
+    return _npi.where(condition, x, y, out=None)
+
 
 _set_np_symbol_class(_Symbol)
diff --git a/src/operator/numpy/np_where_op-inl.h b/src/operator/numpy/np_where_op-inl.h
new file mode 100644
index 000000000000..c019a523fddd
--- /dev/null
+++ b/src/operator/numpy/np_where_op-inl.h
@@ -0,0 +1,227 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2017 by Contributors
+ * \file np_where_op.cc
+ * \brief Function definition of numpy operator where
+ */
+
+#ifndef MXNET_OPERATOR_NUMPY_NP_WHERE_OP_INL_H_
+#define MXNET_OPERATOR_NUMPY_NP_WHERE_OP_INL_H_
+
+#include <mxnet/operator_util.h>
+#include <algorithm>
+#include <string>
+#include <utility>
+#include <vector>
+#include "../../common/utils.h"
+#include "../mxnet_op.h"
+#include "../mshadow_op.h"
+#include "../operator_common.h"
+#include "np_broadcast_reduce_op.h"
+
+namespace mxnet {
+namespace op {
+
+using namespace mshadow;
+
+template<int ndim>
+struct numpy_where_kernel {
+  template<typename CType, typename DType>
+  MSHADOW_XINLINE static void Map(index_t base, OpReqType req, const Shape<ndim> &cstride,
+                                  const Shape<ndim> &xstride, const Shape<ndim> &ystride,
+                                  const Shape<ndim> &oshape, CType *datac, DType *datax,
+                                  DType *datay, DType *out) {
+    Shape<ndim> coord = mxnet_op::unravel(base, oshape);
+    auto cidx = static_cast<index_t>(mxnet_op::dot(coord, cstride));
+    auto xidx = static_cast<index_t>(mxnet_op::dot(coord, xstride));
+    auto yidx = static_cast<index_t>(mxnet_op::dot(coord, ystride));
+    KERNEL_ASSIGN(out[base], req, datac[cidx] != CType(0) ? datax[xidx] : datay[yidx]);
+  }
+};
+
+template<int ndim, bool is_left>
+struct numpy_where_backward_kernel {
+  template<typename CType, typename DType>
+  MSHADOW_XINLINE static void Map(index_t base, OpReqType req,
+                                  const Shape<ndim> &cstride, const Shape<ndim> &oshape,
+                                  CType *datac, DType *datao, DType *grad) {
+    Shape<ndim> coord = mxnet_op::unravel(base, oshape);
+    auto cidx = static_cast<index_t>(mxnet_op::dot(coord, cstride));
+    if (is_left) {
+      KERNEL_ASSIGN(grad[base], req, datac[cidx] != CType(0) ? datao[base] : DType(0));
+    } else {
+      KERNEL_ASSIGN(grad[base], req, datac[cidx] == CType(0) ? datao[base] : DType(0));
+    }
+  }
+};
+
+template<typename xpu>
+inline void NumpyWhereOpForward(const nnvm::NodeAttrs& attrs,
+                                const OpContext& ctx,
+                                const std::vector<TBlob>& inputs,
+                                const std::vector<OpReqType>& req,
+                                const std::vector<TBlob>& outputs) {
+  CHECK_EQ(inputs.size(), 3U);
+  CHECK_EQ(outputs.size(), 1U);
+  if (outputs[0].shape_.Size() == 0U) return;  // zero-size tensor
+  CHECK_LE(outputs[0].shape_.ndim(), broadcast::MAX_DIM);
+
+  const TBlob& cond = inputs[0];
+  const TBlob& x = inputs[1];
+  const TBlob& y = inputs[2];
+  const TBlob& out = outputs[0];
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  std::vector<Shape<broadcast::MAX_DIM>> in_strides;
+  in_strides.resize(3);
+  for (int i = 0; i < 3; ++i) {
+    TShape expanded_ishape(broadcast::MAX_DIM, 1);
+    const TShape& ishape = inputs[i].shape_;
+    const int ndim_delta = expanded_ishape.ndim() - ishape.ndim();
+    for (int j = 0; j < ishape.ndim(); ++j) {
+      expanded_ishape[j + ndim_delta] = ishape[j];
+    }
+    in_strides[i] = mxnet_op::calc_stride(expanded_ishape.get<broadcast::MAX_DIM>());
+  }
+  TShape expanded_oshape(broadcast::MAX_DIM, 1);
+  const int ndim_delta = expanded_oshape.ndim() - out.shape_.ndim();
+  for (int j = 0; j < out.shape_.ndim(); ++j) {
+    expanded_oshape[j + ndim_delta] = (out.shape_)[j];
+  }
+  Shape<broadcast::MAX_DIM> oshape = expanded_oshape.get<broadcast::MAX_DIM>();
+  MSHADOW_TYPE_SWITCH_WITH_BOOL(out.type_flag_, DType, {
+    MSHADOW_TYPE_SWITCH_WITH_BOOL(cond.type_flag_, CType, {
+      mxnet_op::Kernel<numpy_where_kernel<broadcast::MAX_DIM>, xpu>::Launch(
+        s, out.Size(), req[0],
+        in_strides[0], in_strides[1], in_strides[2], oshape,
+        cond.dptr<CType>(), x.dptr<DType>(),
+        y.dptr<DType>(), out.dptr<DType>());
+    });
+  });
+}
+
+template<typename xpu>
+inline void NumpyWhereOpBackward(const nnvm::NodeAttrs& attrs,
+                                 const OpContext& ctx,
+                                 const std::vector<TBlob>& inputs,
+                                 const std::vector<OpReqType>& req,
+                                 const std::vector<TBlob>& outputs) {
+  CHECK_EQ(inputs.size(), 2U);
+  CHECK_EQ(outputs.size(), 2U);
+  CHECK(common::is_float(inputs[0].type_flag_)) << "Backward only supports float types!";
+  if (inputs[0].shape_.Size() == 0U) return;  // zero-size tensor
+
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  const TBlob& ograd = inputs[0];
+  const TBlob& cond = inputs[1];
+  const TBlob& dx = outputs[0];
+  const TBlob& dy = outputs[1];
+  // get expanded oshape
+  TShape expanded_oshape(broadcast::MAX_DIM, 1);
+  int ndim_delta = expanded_oshape.ndim() - ograd.shape_.ndim();
+  for (int j = 0; j < ograd.shape_.ndim(); ++j) {
+    expanded_oshape[j + ndim_delta] = (ograd.shape_)[j];
+  }
+  Shape<broadcast::MAX_DIM> oshape = expanded_oshape.get<broadcast::MAX_DIM>();
+  // get cond stride
+  TShape expanded_cshape(broadcast::MAX_DIM, 1);
+  ndim_delta = expanded_cshape.ndim() - cond.shape_.ndim();
+  for (int j = 0; j < cond.shape_.ndim(); ++j) {
+    expanded_cshape[j + ndim_delta] = (cond.shape_)[j];
+  }
+  Shape<broadcast::MAX_DIM> cstride =
+      mxnet_op::calc_stride(expanded_cshape.get<broadcast::MAX_DIM>());
+  // get expanded lshape
+  TShape expanded_lshape(broadcast::MAX_DIM, 1);
+  ndim_delta = expanded_lshape.ndim() - dx.shape_.ndim();
+  for (int j = 0; j < dx.shape_.ndim(); ++j) {
+    expanded_lshape[j + ndim_delta] = (dx.shape_)[j];
+  }
+  // get expanded rshape
+  TShape expanded_rshape(broadcast::MAX_DIM, 1);
+  ndim_delta = expanded_rshape.ndim() - dy.shape_.ndim();
+  for (int j = 0; j < dy.shape_.ndim(); ++j) {
+    expanded_rshape[j + ndim_delta] = (dy.shape_)[j];
+  }
+
+  MSHADOW_TYPE_SWITCH_WITH_BOOL(ograd.type_flag_, DType, {
+    MSHADOW_TYPE_SWITCH_WITH_BOOL(cond.type_flag_, CType, {
+      Tensor<xpu, 1, char> largespace;
+      Tensor<xpu, broadcast::MAX_DIM, DType> workspace;
+      size_t ws_size = 0;
+      if (!(ograd.shape_ != dx.shape_) || !(ograd.shape_ != dy.shape_)) {
+        size_t ws_size1 = broadcast::ReduceWorkspaceSize<broadcast::MAX_DIM, DType>(
+            s, expanded_lshape, req[0], expanded_oshape);
+        size_t ws_size2 = broadcast::ReduceWorkspaceSize<broadcast::MAX_DIM, DType>(
+            s, expanded_rshape, req[1], expanded_oshape);
+        ws_size = std::max(ws_size1, ws_size2);
+      }
+      // process left output
+      if (ograd.shape_ == dx.shape_) {
+        mxnet_op::Kernel<numpy_where_backward_kernel<broadcast::MAX_DIM, true>, xpu>::Launch(
+          s, ograd.Size(), req[0], cstride, oshape,
+          cond.dptr<CType>(), ograd.dptr<DType>(), dx.dptr<DType>());
+      } else {
+        largespace = ctx.requested[0].get_space_typed<xpu, 1, char>(
+            Shape1(ograd.shape_.Size() * sizeof(DType) + ws_size), s);
+        workspace = Tensor<xpu, broadcast::MAX_DIM, DType>(
+            reinterpret_cast<DType*>(largespace.dptr_ + ws_size),
+            expanded_oshape.get<broadcast::MAX_DIM>(), s);
+        mxnet_op::Kernel<numpy_where_backward_kernel<broadcast::MAX_DIM, true>, xpu>::Launch(
+          s, ograd.Size(), req[0], cstride, oshape,
+          cond.dptr<CType>(), ograd.dptr<DType>(), workspace.dptr_);
+        if (NeedSafeAcc<true>(dx.type_flag_, dx.type_flag_)) {
+          ReduceAxesComputeImpl<xpu, mshadow_op::sum, true>(ctx, {TBlob(workspace)}, {req[0]},
+              {dx.reshape(expanded_lshape)}, expanded_lshape);
+        } else {
+          ReduceAxesComputeImpl<xpu, mshadow_op::sum, false>(ctx, {TBlob(workspace)}, {req[0]},
+              {dx.reshape(expanded_lshape)}, expanded_lshape);
+        }
+      }
+      // process right output
+      if (ograd.shape_ == dy.shape_) {
+        mxnet_op::Kernel<numpy_where_backward_kernel<broadcast::MAX_DIM, false>, xpu>::Launch(
+          s, ograd.Size(), req[1], cstride, oshape,
+          cond.dptr<CType>(), ograd.dptr<DType>(), dy.dptr<DType>());
+      } else {
+        largespace = ctx.requested[0].get_space_typed<xpu, 1, char>(
+            Shape1(ograd.shape_.Size() * sizeof(DType) + ws_size), s);
+        workspace = Tensor<xpu, broadcast::MAX_DIM, DType>(
+            reinterpret_cast<DType*>(largespace.dptr_ + ws_size),
+            expanded_oshape.get<broadcast::MAX_DIM>(), s);
+        mxnet_op::Kernel<numpy_where_backward_kernel<broadcast::MAX_DIM, false>, xpu>::Launch(
+          s, ograd.Size(), req[1], cstride, oshape,
+          cond.dptr<CType>(), ograd.dptr<DType>(), workspace.dptr_);
+        if (NeedSafeAcc<true>(dy.type_flag_, dy.type_flag_)) {
+          ReduceAxesComputeImpl<xpu, mshadow_op::sum, true>(ctx, {TBlob(workspace)}, {req[1]},
+              {dy.reshape(expanded_rshape)}, expanded_rshape);
+        } else {
+          ReduceAxesComputeImpl<xpu, mshadow_op::sum, false>(ctx, {TBlob(workspace)}, {req[1]},
+              {dy.reshape(expanded_rshape)}, expanded_rshape);
+        }
+      }
+    });
+  });
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_NUMPY_NP_WHERE_OP_INL_H_
diff --git a/src/operator/numpy/np_where_op.cc b/src/operator/numpy/np_where_op.cc
new file mode 100644
index 000000000000..6cca0c5fd985
--- /dev/null
+++ b/src/operator/numpy/np_where_op.cc
@@ -0,0 +1,133 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2017 by Contributors
+ * \file np_where_op.cc
+ * \brief CPU Implementation of numpy operator where
+ */
+
+#include "np_where_op-inl.h"
+
+namespace mxnet {
+namespace op {
+
+inline bool NumpyWhereOpShape(const nnvm::NodeAttrs& attrs,
+                              mxnet::ShapeVector* in_attrs,
+                              mxnet::ShapeVector* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 3U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  mxnet::TShape& operand1 = (*in_attrs)[0];
+  mxnet::TShape& operand2 = (*in_attrs)[1];
+  mxnet::TShape& operand3 = (*in_attrs)[2];
+
+  if (operand1 == operand2 && operand2 == operand3) {
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, operand1);
+    return shape_is_known(out_attrs->at(0));
+  }
+  mxnet::TShape out(std::max({operand1.ndim(), operand2.ndim(), operand3.ndim()}), -1);
+  const int b1 = out.ndim() - operand1.ndim();
+  const int b2 = out.ndim() - operand2.ndim();
+  const int b3 = out.ndim() - operand3.ndim();
+  for (int i = 0; i < out.ndim(); ++i) {
+    int s1 = 1, s2 = 1, s3 = 1;
+    if (i >= b1) s1 = operand1[i-b1];
+    if (i >= b2) s2 = operand2[i-b2];
+    if (i >= b3) s3 = operand3[i-b3];
+    if (!(s1 == s2 && s2 == s3)) {
+      CHECK((s1 == 1 && s2 == 1) || (s1 == 1 && s3 == 1) || (s2 == 1 && s3 == 1) ||
+            (s1 == 1 && s2 == s3) || (s2 == 1 && s1 == s3) || (s3 == 1 && s1 == s2))
+        << "Operands could not be broadcast together.";
+      out[i] = std::max({s1, s2, s3});
+    } else {
+      out[i] = s1;
+    }
+  }
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, out);
+  return shape_is_known(out);
+}
+
+inline bool NumpyWhereOpType(const nnvm::NodeAttrs& attrs,
+                             std::vector<int>* in_attrs,
+                             std::vector<int>* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 3U)
+    << "where operator takes 3 arguments (" << in_attrs->size() << " given)";
+  CHECK_EQ(out_attrs->size(), 1U);
+  std::vector<int> sub_in_attrs(in_attrs->begin() + 1, in_attrs->end());
+  bool flag = ElemwiseType<2, 1>(attrs, &sub_in_attrs, out_attrs);
+  return flag && (in_attrs->at(0) != -1);
+}
+
+NNVM_REGISTER_OP(_npi_where)
+.set_num_inputs(3)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"condition", "x", "y"};
+  })
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyWhereOpShape)
+.set_attr<nnvm::FInferType>("FInferType", NumpyWhereOpType)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs){
+    return std::vector<std::pair<int, int> >{{1, 0}, {2, 0}};
+  })
+.set_attr<FCompute>("FCompute<cpu>", NumpyWhereOpForward<cpu>)
+.set_attr<nnvm::FGradient>("FGradient",
+  // Use the following lambda function instead of ElemwiseGradUseIn
+  // for best efficiency. grad[condition] = 0; to calculate grad[x] and grad[y]
+  // we need only condition from input.
+  [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
+    std::vector<nnvm::NodeEntry> ret;
+    // make zero grad node for grad[condition]
+    auto p = MakeNode("zeros_like", n->attrs.name + "_cond_backward",
+                      {n->inputs[0]}, nullptr, &n);
+    ret.emplace_back(p);
+
+    // make grad nodes for grad[x] and grad[y]
+    std::vector<nnvm::NodeEntry> heads(ograds.begin(), ograds.end());
+    heads.push_back(n->inputs[0]);  // only need condition to calculate gradients
+    p = nnvm::Node::Create();
+    p->attrs.op = nnvm::Op::Get("_backward_np_where");
+    p->attrs.name = n->attrs.name + "_backward";
+    p->attrs.dict = n->attrs.dict;
+    if (p->op()->attr_parser != nullptr) {
+      p->op()->attr_parser(&(p->attrs));
+    }
+    p->control_deps.emplace_back(n);
+    p->inputs = std::move(heads);
+    ret.emplace_back(p, 0, 0);
+    ret.emplace_back(p, 1, 0);
+    return ret;
+  })
+.add_argument("condition", "NDArray-or-Symbol", "condition array")
+.add_argument("x", "NDArray-or-Symbol", "input x")
+.add_argument("y", "NDArray-or-Symbol", "input y");
+
+NNVM_REGISTER_OP(_backward_np_where)
+.set_num_inputs(2)
+.set_num_outputs(2)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FCompute>("FCompute<cpu>", NumpyWhereOpBackward<cpu>)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  });
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_where_op.cu b/src/operator/numpy/np_where_op.cu
new file mode 100644
index 000000000000..6d3da4477112
--- /dev/null
+++ b/src/operator/numpy/np_where_op.cu
@@ -0,0 +1,38 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2017 by Contributors
+ * \file np_where_op.cu
+ * \brief GPU Implementation of numpy operator where
+ */
+
+#include "np_where_op-inl.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(_npi_where)
+.set_attr<FCompute>("FCompute<gpu>", NumpyWhereOpForward<gpu>);
+
+NNVM_REGISTER_OP(_backward_np_where)
+.set_attr<FCompute>("FCompute<gpu>", NumpyWhereOpBackward<gpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index 658c7a37e354..79a478679aaf 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -1140,6 +1140,28 @@ def _add_workload_less_equal(array_pool):
     # OpArgMngr.add_workload('less_equal', np.array([np.nan]), np.array([np.nan]))
 
 
+def _add_workload_where():
+    c = np.ones(53).astype(bool)
+    d = np.ones_like(c)
+    e = np.zeros_like(c)
+    OpArgMngr.add_workload('where', c, e, e)
+    OpArgMngr.add_workload('where', c, d, e)
+    OpArgMngr.add_workload('where', c, d, e[0])
+    OpArgMngr.add_workload('where', c, d[0], e)
+    # OpArgMngr.add_workload('where', c[::2], d[::2], e[::2])
+    # OpArgMngr.add_workload('where', c[1::2], d[1::2], e[1::2])
+    # OpArgMngr.add_workload('where', c[::3], d[::3], e[::3])
+    # OpArgMngr.add_workload('where', c[1::3], d[1::3], e[1::3])
+    # OpArgMngr.add_workload('where', c[::-2], d[::-2], e[::-2])
+    # OpArgMngr.add_workload('where', c[::-3], d[::-3], e[::-3])
+    # OpArgMngr.add_workload('where', c[1::-3], d[1::-3], e[1::-3])
+    c = np.array([True, False])
+    a = np.zeros((2, 25))
+    b = np.ones((2, 25))
+    OpArgMngr.add_workload('where', c.reshape((2, 1)), a, b)
+    OpArgMngr.add_workload('where', c, a.T, b.T)
+
+
 def _add_workload_nonzero():
     OpArgMngr.add_workload('nonzero', np.random.randint(0, 2))
     OpArgMngr.add_workload('nonzero', np.random.randint(0, 2, size=()))
@@ -1295,6 +1317,7 @@ def _prepare_workloads():
     _add_workload_greater_equal(array_pool)
     _add_workload_less(array_pool)
     _add_workload_less_equal(array_pool)
+    _add_workload_where()
     _add_workload_diff()
     _add_workload_resize()
 
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 405d4a4cca6d..5e973d130de5 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -4567,11 +4567,11 @@ def hybrid_forward(self, F, a):
         assert_almost_equal(mx_out.asnumpy(), np_out, rtol, atol)
         # check the inplace operation when copy = False
         # if x1.shape = 0, _np.array will not actually execute copy logic
-        # only check x3 from np.nan_to_num instead of x2 from gluon 
+        # only check x3 from np.nan_to_num instead of x2 from gluon
         if copy == False and x1.shape!=():
             assert x1.shape == x3.asnumpy().shape
             assert x1.dtype == x3.asnumpy().dtype
-            assert_almost_equal(x1, x3.asnumpy(), rtol=rtol, atol=atol)  
+            assert_almost_equal(x1, x3.asnumpy(), rtol=rtol, atol=atol)
         # gluon does not support nan_to_num when copy=False
         # backward will check int type and if so, throw error
         # if not this case, test gluon
@@ -4592,6 +4592,52 @@ def hybrid_forward(self, F, a):
             assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
 
 
+@with_seed()
+@use_np
+def test_np_where():
+    class TestWhere(HybridBlock):
+        def __init__(self):
+            super(TestWhere, self).__init__()
+
+        def hybrid_forward(self, F, cond, x, y):
+            return F.np.where(cond, x, y)
+
+    dtypes = [np.int8, np.uint8, np.int32, np.int64, np.float16, np.float32, np.float64, np.bool]
+    shape_configs = [
+        [(), (2, 3), (4, 1, 3)],
+        [(), (4, 1, 3), (2, 3)],
+        [(2, 3), (4, 1, 3), ()],
+        [(4, 1, 3), (2, 3), ()],
+        [(2, 3), (), (4, 1, 3)],
+        [(2, 3), (2, 3), (2, 3)],
+        [(2, 3), (2, 1), (2, 3)],
+        [(2, 1), (2, 3), (2, 3)],
+        [(2, 3), (2, 3), (2, 1)]
+    ]
+    flags = [True, False]
+    for ctype, dtype, shape_pair, hybridize in itertools.product(dtypes, dtypes, shape_configs, flags):
+        cond = np.random.uniform(low=0, high=100, size=shape_pair[0], dtype='float64').astype(ctype)
+        x = np.random.uniform(low=0, high=100, size=shape_pair[1], dtype='float64').astype(dtype)
+        y = np.random.uniform(low=0, high=100, size=shape_pair[2], dtype='float64').astype(dtype)
+        cond.attach_grad()
+        x.attach_grad()
+        y.attach_grad()
+        test_mod = TestWhere()
+        if hybridize:
+            test_mod.hybridize()
+        with mx.autograd.record():
+            ret = test_mod(cond, x, y)
+        same(ret.asnumpy(), _np.where(cond.asnumpy(), x.asnumpy(), y.asnumpy()))
+        if dtype in [np.float16, np.float32, np.float64]:
+            ret.backward()
+            same(cond.grad.asnumpy(), _np.zeros(shape_pair[0], dtype=ctype))
+            same(x.grad.asnumpy(), collapse_sum_like(_np.broadcast_to(cond.asnumpy(), ret.shape), shape_pair[1]))
+
+        # check imperative again
+        ret = np.where(cond, x, y)
+        same(ret.asnumpy(), _np.where(cond.asnumpy(), x.asnumpy(), y.asnumpy()))
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 8cc4c168e7d404a2f9a6c5e36173f2b75af661a7 Mon Sep 17 00:00:00 2001
From: Xi Wang <xidulu@gmail.com>
Date: Mon, 18 Nov 2019 14:27:58 +0800
Subject: [PATCH 606/813] [Numpy] Random.normal() with backward (#16330)

* normal gradient mostly done

* fix #input bug

* normal gradient finished
---
 src/operator/numpy/random/dist_common.h   |   6 +-
 src/operator/numpy/random/np_normal_op.cc |  37 ++++++-
 src/operator/numpy/random/np_normal_op.cu |   3 +
 src/operator/numpy/random/np_normal_op.h  | 126 ++++++++++++++++++++--
 tests/python/unittest/test_numpy_op.py    |  40 +++++++
 5 files changed, 200 insertions(+), 12 deletions(-)

diff --git a/src/operator/numpy/random/dist_common.h b/src/operator/numpy/random/dist_common.h
index a28ce5f7bcce..6394375883aa 100644
--- a/src/operator/numpy/random/dist_common.h
+++ b/src/operator/numpy/random/dist_common.h
@@ -144,7 +144,6 @@ inline bool TwoparamsDistOpShape(const nnvm::NodeAttrs &attrs,
                                  std::vector<TShape> *in_attrs,
                                  std::vector<TShape> *out_attrs) {
   const DistParam &param = nnvm::get<DistParam>(attrs.parsed);
-  CHECK_EQ(out_attrs->size(), 1U);
   if (param.size.has_value()) {
     // Size declared.
     std::vector<dim_t> oshape_vec;
@@ -173,7 +172,10 @@ inline bool TwoparamsDistOpShape(const nnvm::NodeAttrs &attrs,
       SHAPE_ASSIGN_CHECK(*out_attrs, 0, TShape(0, -1))
     }
   }
-  return shape_is_known(out_attrs->at(0));
+  if (out_attrs->size() == 2U) {
+    SHAPE_ASSIGN_CHECK(*out_attrs, 1, out_attrs->at(0));
+  }
+  return true;
 }
 
 template <typename DistParam>
diff --git a/src/operator/numpy/random/np_normal_op.cc b/src/operator/numpy/random/np_normal_op.cc
index 1a5df95ff9e8..992e93c8af16 100644
--- a/src/operator/numpy/random/np_normal_op.cc
+++ b/src/operator/numpy/random/np_normal_op.cc
@@ -40,7 +40,11 @@ NNVM_REGISTER_OP(_npi_normal)
     return num_inputs;
   }
 )
-.set_num_outputs(1)
+.set_num_outputs(2)
+.set_attr<nnvm::FNumVisibleOutputs>("FNumVisibleOutputs",
+    [](const NodeAttrs& attrs) {
+  return 1;
+})
 .set_attr<nnvm::FListInputNames>("FListInputNames",
   [](const NodeAttrs& attrs) {
     const NumpyNormalParam& param = nnvm::get<NumpyNormalParam>(attrs.parsed);
@@ -60,10 +64,39 @@ NNVM_REGISTER_OP(_npi_normal)
         ResourceRequest::kRandom, ResourceRequest::kTempSpace};
   })
 .set_attr<FCompute>("FCompute<cpu>", NumpyNormalForward<cpu>)
-.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseInOut{"_backward_broadcast_normal"})
 .add_argument("input1", "NDArray-or-Symbol", "Source input")
 .add_argument("input2", "NDArray-or-Symbol", "Source input")
 .add_arguments(NumpyNormalParam::__FIELDS__());
 
+NNVM_REGISTER_OP(_backward_broadcast_normal)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr_parser(ParamParser<NumpyNormalParam>)
+.set_num_inputs(
+  [](const nnvm::NodeAttrs& attrs) {
+    const NumpyNormalParam& param = nnvm::get<NumpyNormalParam>(attrs.parsed);
+    int num_inputs = 6;
+    if (param.loc.has_value()) num_inputs -= 1;
+    if (param.scale.has_value()) num_inputs -= 1;
+    return num_inputs;
+  }
+)
+.set_num_outputs(
+  [](const nnvm::NodeAttrs& attrs) {
+    const NumpyNormalParam& param = nnvm::get<NumpyNormalParam>(attrs.parsed);
+    int num_outputs = 2;
+    if (param.loc.has_value()) num_outputs -= 1;
+    if (param.scale.has_value()) num_outputs -= 1;
+    return num_outputs;
+  }
+)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", NormalReparamBackward<cpu>)
+.add_arguments(NumpyNormalParam::__FIELDS__());
+
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/random/np_normal_op.cu b/src/operator/numpy/random/np_normal_op.cu
index 6cdf9f9c4eae..0eab089abbc9 100644
--- a/src/operator/numpy/random/np_normal_op.cu
+++ b/src/operator/numpy/random/np_normal_op.cu
@@ -31,5 +31,8 @@ namespace op {
 NNVM_REGISTER_OP(_npi_normal)
     .set_attr<FCompute>("FCompute<gpu>", NumpyNormalForward<gpu>);
 
+NNVM_REGISTER_OP(_backward_broadcast_normal)
+.set_attr<FCompute>("FCompute<gpu>", NormalReparamBackward<gpu>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/random/np_normal_op.h b/src/operator/numpy/random/np_normal_op.h
index f9618febd458..8cc42887e5f4 100644
--- a/src/operator/numpy/random/np_normal_op.h
+++ b/src/operator/numpy/random/np_normal_op.h
@@ -26,6 +26,7 @@
 #define MXNET_OPERATOR_NUMPY_RANDOM_NP_NORMAL_OP_H_
 
 #include <mxnet/operator_util.h>
+#include <cstdio>
 #include <algorithm>
 #include <string>
 #include <vector>
@@ -78,6 +79,7 @@ inline bool NumpyNormalOpType(const nnvm::NodeAttrs &attrs,
   } else {
     (*out_attrs)[0] = mshadow::kFloat32;
   }
+  (*out_attrs)[1] = mshadow::kFloat32;
   return true;
 }
 
@@ -130,7 +132,7 @@ template <typename IType>
 struct check_legal_scale_kernel {
   MSHADOW_XINLINE static void Map(index_t i, IType *scalar, float* flag) {
     if (scalar[i] < 0) {
-      flag[0] = -1.0;
+      *flag = -1.0;
     }
   }
 };
@@ -146,22 +148,18 @@ void NumpyNormalForward(const nnvm::NodeAttrs &attrs,
   using namespace mshadow;
   using namespace mxnet_op;
   const NumpyNormalParam &param = nnvm::get<NumpyNormalParam>(attrs.parsed);
-  CHECK_EQ(outputs.size(), 1);
   Stream<xpu> *s = ctx.get_stream<xpu>();
-
   // Generate base random number.
   Random<xpu, float> *prnd = ctx.requested[0].get_random<xpu, float>(s);
-  index_t output_len = outputs[0].Size();
   Tensor<xpu, 1, float> workspace =
-      ctx.requested[1].get_space_typed<xpu, 1, float>(Shape1(output_len + 1), s);
-  Tensor<xpu, 1, float> normal_tensor = workspace.Slice(0, output_len);
-  Tensor<xpu, 1, float> indicator_device = workspace.Slice(output_len, output_len + 1);
+      ctx.requested[1].get_space_typed<xpu, 1, float>(Shape1(1), s);
+  Tensor<xpu, 1, float> normal_tensor = outputs[1].FlatTo1D<xpu, float>(s);
+  Tensor<xpu, 1, float> indicator_device = workspace;
   float indicator_host = 1.0;
   float *indicator_device_ptr = indicator_device.dptr_;
   Kernel<set_zero, xpu>::Launch(s, 1, indicator_device_ptr);
   prnd->SampleGaussian(&normal_tensor, 0.0, 1.0);
   mxnet::TShape new_lshape, new_hshape, new_oshape;
-
   // [scalar scalar] case
   if (inputs.size() == 0U) {
     CHECK_GE(param.scale.value(), 0.0) << "ValueError: scale < 0";
@@ -228,6 +226,118 @@ void NumpyNormalForward(const nnvm::NodeAttrs &attrs,
   }
 }
 
+template<typename xpu, int ndim, typename DType>
+inline void NormalReparamBackwardImpl(const OpContext& ctx,
+                                      const std::vector<TBlob>& inputs,
+                                      const std::vector<OpReqType>& req,
+                                      const std::vector<TBlob>& outputs,
+                                      const mxnet::TShape& new_lshape,
+                                      const mxnet::TShape& new_rshape,
+                                      const mxnet::TShape& new_oshape) {
+  using namespace mshadow;
+  using namespace mshadow::expr;
+  using namespace broadcast;
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  const TBlob lgrad = outputs[0].reshape(new_lshape);
+  const TBlob rgrad = outputs[1].reshape(new_rshape);
+  const TBlob ograd = inputs[0].reshape(new_oshape);
+  // Mean
+  const TBlob lhs = inputs[2].reshape(new_lshape);
+  // Variance
+  const TBlob rhs = inputs[3].reshape(new_rshape);
+  const TBlob samples = inputs[4].reshape(new_oshape);
+  const TBlob noise = inputs[5].reshape(new_oshape);
+  size_t workspace_size_l = ReduceWorkspaceSize<ndim, DType>(
+      s, lgrad.shape_, req[0], ograd.shape_, lhs.shape_, rhs.shape_);
+  size_t workspace_size_r = ReduceWorkspaceSize<ndim, DType>(
+      s, rgrad.shape_, req[1], ograd.shape_, lhs.shape_, rhs.shape_);
+  size_t workspace_size = std::max(workspace_size_l, workspace_size_r);
+  Tensor<xpu, 1, char> workspace =
+      ctx.requested[0].get_space_typed<xpu, 1, char>(Shape1(workspace_size), s);
+  Reduce<red::sum, ndim, DType, op::mshadow_op::identity>(s,
+          lgrad, req[0], workspace, ograd);
+  Reduce<red::sum, ndim, DType, op::mshadow_op::mul, op::mshadow_op::left>(
+      s, rgrad, req[1], workspace, ograd, noise, rhs);
+}
+
+template<typename xpu, int ndim, typename DType>
+inline void ScalarNormalReparamBackwardImpl(const OpContext& ctx,
+                                            const std::vector<TBlob>& inputs,
+                                            const std::vector<OpReqType>& req,
+                                            const std::vector<TBlob>& outputs,
+                                            const mxnet::TShape& new_ishape,
+                                            const mxnet::TShape& new_oshape,
+                                            const bool loc_is_tensor) {
+  using namespace mshadow;
+  using namespace mshadow::expr;
+  using namespace broadcast;
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  const TBlob igrad = outputs[0].reshape(new_ishape);
+  // inputs: [grad_from_samples, grad_from_noise(invisible), input_tensor,
+  //          samples, noise]
+  const TBlob ograd = inputs[0].reshape(new_oshape);
+  const TBlob itensor = inputs[2].reshape(new_ishape);
+  const TBlob samples = inputs[3].reshape(new_oshape);
+  const TBlob noise = inputs[4].reshape(new_oshape);
+  size_t workspace_size =
+      ReduceWorkspaceSize<ndim, DType>(s, igrad.shape_, req[0], ograd.shape_);
+  Tensor<xpu, 1, char> workspace =
+      ctx.requested[0].get_space_typed<xpu, 1, char>(Shape1(workspace_size), s);
+  if (loc_is_tensor) {
+    Reduce<red::sum, ndim, DType, op::mshadow_op::identity>(s, igrad, req[0],
+                                                            workspace, ograd);
+  } else {
+    Reduce<red::sum, ndim, DType, op::mshadow_op::mul, op::mshadow_op::left>(
+        s, igrad, req[0], workspace, ograd, noise, noise);
+  }
+}
+
+// Allow normal sampling to be differentiable,
+// using reparameterization trick described in:
+// Auto-encoding variational bayes.
+// Kingma, D. P., & Welling, M. (2013).
+template<typename xpu>
+void NormalReparamBackward(const nnvm::NodeAttrs& attrs,
+                           const OpContext& ctx,
+                           const std::vector<TBlob>& inputs,
+                           const std::vector<OpReqType>& req,
+                           const std::vector<TBlob>& outputs) {
+  // skip kernel launch for zero-size tensors
+  if (inputs[0].shape_.Size() == 0U) {
+    return;
+  }
+  // [scalar scalar] case
+  if (outputs.size() == 0U) {
+    return;
+  }
+  const NumpyNormalParam &param = nnvm::get<NumpyNormalParam>(attrs.parsed);
+  // [tensor tensor] case
+  if (inputs.size() == 6U) {
+    mxnet::TShape new_lshape, new_rshape, new_oshape;
+    int ndim = FillShape(outputs[0].shape_, outputs[1].shape_, inputs[0].shape_,
+                         &new_lshape, &new_rshape, &new_oshape);
+    MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+      BROADCAST_NDIM_SWITCH(ndim, NDim, {
+        NormalReparamBackwardImpl<xpu, NDim, DType>(
+          ctx, inputs, req, outputs, new_lshape, new_rshape, new_oshape);
+      });
+    });
+  }
+  // [tensor scalar], [scalar tensor] case
+  if (inputs.size() == 5U) {
+    mxnet::TShape new_ishape, new_oshape;
+    int ndim = FillShape(outputs[0].shape_, outputs[0].shape_, inputs[0].shape_,
+                         &new_ishape, &new_ishape, &new_oshape);
+    bool loc_is_tensor = !param.loc.has_value();
+    MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+      BROADCAST_NDIM_SWITCH(ndim, NDim, {
+        ScalarNormalReparamBackwardImpl<xpu, NDim, DType>(
+          ctx, inputs, req, outputs, new_ishape, new_oshape, loc_is_tensor);
+      });
+    });
+  }
+}
+
 }  // namespace op
 }  // namespace mxnet
 
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 5e973d130de5..76b38eb45205 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -2605,6 +2605,46 @@ def hybrid_forward(self, F, a, *args, **kwargs):
             check_unary_func(func, ref_grad, shape, low, high)
 
 
+@with_seed()
+@use_np
+def test_np_normal_grad():
+    class TestNormalGrad(HybridBlock):
+        def __init__(self, shape):
+            super(TestNormalGrad, self).__init__()
+            self._shape = shape
+
+        def hybrid_forward(self, F, loc, scale):
+            return F.np.random.normal(loc, scale, self._shape)
+
+    dtypes = ['float16', 'float32', 'float64']
+    param_shape = [
+        [(3, 2), (3, 2)],
+        [(3, 2, 2), (3, 2, 2)],
+        [(3, 4, 5), (4, 1)],
+    ]
+    output_shapes = [
+        (3, 2),
+        (4, 3, 2, 2),
+        (3, 4, 5)
+    ]
+    for hybridize in [False, True]:
+        for dtype in dtypes:
+            for ((shape1, shape2), out_shape) in zip(param_shape, output_shapes):
+                test_normal_grad = TestNormalGrad(out_shape)
+                if hybridize:
+                    test_normal_grad.hybridize()
+                loc = np.zeros(shape1)
+                loc.attach_grad()
+                scale = np.ones(shape2)
+                scale.attach_grad()
+                with mx.autograd.record():
+                    samples = test_normal_grad(loc, scale)
+                samples.backward()
+                assert loc.grad.shape == shape1
+                assert scale.grad.shape == shape2
+                assert_almost_equal(loc.grad.asnumpy().sum(), _np.ones(out_shape).sum(), rtol=1e-3, atol=1e-5)
+
+
 @with_seed()
 @use_np
 def test_np_random():

From 94e7ba76986e87ad63cde2db3bc2065b824c40fa Mon Sep 17 00:00:00 2001
From: perdasilva <perdasilva@gmail.com>
Date: Mon, 18 Nov 2019 14:54:21 +0100
Subject: [PATCH 607/813] Python 2 compatibility fix in base.py

* Use open from io to support encoding parameter in python 2

* Adds ignore to pylint rule for unicode in python2 compatibility code
---
 python/mxnet/base.py | 25 ++++++++++++++++++++++++-
 1 file changed, 24 insertions(+), 1 deletion(-)

diff --git a/python/mxnet/base.py b/python/mxnet/base.py
index 3ed68dbe8b8a..c97250028cb8 100644
--- a/python/mxnet/base.py
+++ b/python/mxnet/base.py
@@ -20,6 +20,7 @@
 """ctypes library of mxnet and helper functions."""
 from __future__ import absolute_import
 
+import io
 import re
 import atexit
 import ctypes
@@ -76,6 +77,28 @@ def data_dir():
     """
     return os.getenv('MXNET_HOME', data_dir_default())
 
+class _Py2CompatibleUnicodeFileWriter(object):
+    """
+    Wraps a file handle decorating the write command to unicode the content before writing.
+    This makes writing files opened with encoding='utf-8' compatible with Python 2
+    """
+
+    def __init__(self, file_handle):
+        self._file_handle = file_handle
+        if sys.version_info[0] > 2:
+            self.unicode = str
+        else:
+            from functools import partial
+            # pylint: disable=undefined-variable
+            self.unicode = partial(unicode, encoding="utf-8")
+            # pylint: enable=undefined-variable
+
+    def write(self, value):
+        self._file_handle.write(self.unicode(value))
+
+    def __getattr__(self, name):
+        return getattr(self._file_handle, name)
+
 
 class _NullType(object):
     """Placeholder for arguments"""
@@ -672,7 +695,7 @@ def get_module_file(module_name):
         module_path = module_name.split('.')
         module_path[-1] = 'gen_' + module_path[-1]
         file_name = os.path.join(path, '..', *module_path) + '.py'
-        module_file = open(file_name, 'w', encoding="utf-8")
+        module_file = _Py2CompatibleUnicodeFileWriter(io.open(file_name, 'w', encoding="utf-8"))
         dependencies = {'symbol': ['from ._internal import SymbolBase',
                                    'from ..base import _Null'],
                         'ndarray': ['from ._internal import NDArrayBase',

From 135c42ca717f3de6e0d217a6bb5e5187420fc351 Mon Sep 17 00:00:00 2001
From: Minghao Liu <40382964+Tommliu@users.noreply.github.com>
Date: Tue, 19 Nov 2019 03:36:43 +0800
Subject: [PATCH 608/813] Add OP diag [numpy] (#16786)

* Add numpy.diag method

* Fix ci diag problem

* Merge and fix ci Diag problem

* Fix format issue

* Modified Diag method

* Fixed the Diag kernel and name

* Fixed sanity problem

* change K param

* change unittest and fix

* Fix diag code format

* update 3rd party
---
 python/mxnet/_numpy_op_doc.py                 |  35 +++
 python/mxnet/numpy_dispatch_protocol.py       |   1 +
 src/operator/numpy/np_matrix_op-inl.h         | 205 ++++++++++++++++++
 src/operator/numpy/np_matrix_op.cc            |  23 ++
 src/operator/numpy/np_matrix_op.cu            |   6 +
 .../unittest/test_numpy_interoperability.py   |  31 +++
 tests/python/unittest/test_numpy_op.py        |  61 ++++++
 7 files changed, 362 insertions(+)

diff --git a/python/mxnet/_numpy_op_doc.py b/python/mxnet/_numpy_op_doc.py
index 33158baf10a5..9e237cb0049b 100644
--- a/python/mxnet/_numpy_op_doc.py
+++ b/python/mxnet/_numpy_op_doc.py
@@ -1087,3 +1087,38 @@ def _npx_reshape(a, newshape, reverse=False, order='C'):
     (8, 3, 2, 4, 4, 2)
     """
     pass
+
+
+def _np_diag(array, k = 0):
+    """
+    Extracts a diagonal or constructs a diagonal array.
+    - 1-D arrays: constructs a 2-D array with the input as its diagonal, all other elements are zero.
+    - 2-D arrays: extracts the k-th Diagonal
+
+    Parameters
+    ----------
+    array : ndarray
+        The array to apply diag method.
+    k : offset
+        extracts or constructs kth diagonal given input array
+
+    Examples
+    --------
+    >>> x = np.arange(9).reshape((3,3))
+    >>> x
+    array([[0, 1, 2],
+           [3, 4, 5],
+           [6, 7, 8]])
+    >>> np.diag(x)
+    array([0, 4, 8])
+    >>> np.diag(x, k=1)
+    array([1, 5])
+    >>> np.diag(x, k=-1)
+    array([3, 7])
+
+    >>> np.diag(np.diag(x))
+    array([[0, 0, 0],
+           [0, 4, 0],
+           [0, 0, 8]])
+    """
+    pass
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index ef9ed6cc363f..e233c9702c11 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -93,6 +93,7 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'concatenate',
     'copy',
     'cumsum',
+    'diag',
     'dot',
     'expand_dims',
     'fix',
diff --git a/src/operator/numpy/np_matrix_op-inl.h b/src/operator/numpy/np_matrix_op-inl.h
index a9828f40436d..508968718af0 100644
--- a/src/operator/numpy/np_matrix_op-inl.h
+++ b/src/operator/numpy/np_matrix_op-inl.h
@@ -28,9 +28,14 @@
 #include <vector>
 #include <algorithm>
 #include <string>
+#include <utility>
 #include "../tensor/matrix_op-inl.h"
 #include "../nn/concat-inl.h"
 #include "../../common/utils.h"
+#include "../mxnet_op.h"
+#include "../operator_common.h"
+#include "../elemwise_op_common.h"
+#include "../tensor/broadcast_reduce_op.h"
 
 namespace mxnet {
 namespace op {
@@ -945,6 +950,206 @@ void NumpyConcatenateBackward(const nnvm::NodeAttrs& attrs,
   });
 }
 
+struct NumpyDiagParam : public dmlc::Parameter<NumpyDiagParam> {
+  int k;
+  DMLC_DECLARE_PARAMETER(NumpyDiagParam) {
+    DMLC_DECLARE_FIELD(k).set_default(0)
+      .describe("Diagonal in question. The default is 0. "
+                "Use k>0 for diagonals above the main diagonal, "
+                "and k<0 for diagonals below the main diagonal. ");
+  }
+};
+
+inline mxnet::TShape NumpyDiagShapeImpl(const mxnet::TShape &ishape,
+                                        const int k) {
+  CHECK_LE(ishape.ndim(), 2) << "Input must be 1- or 2-d";
+
+  if (ishape.ndim() == 1) {
+    auto s = ishape[0] + std::abs(k);
+    return mxnet::TShape({s, s});
+  }
+
+  auto h = ishape[0];
+  auto w = ishape[1];
+
+  if (k > 0) {
+    w -= k;
+  } else if (k < 0) {
+    h += k;
+  }
+  dim_t a = 0;
+  auto s = std::max(std::min(h, w), a);
+  // s is the length of diagonal with k as the offset
+
+  int32_t n_dim = ishape.ndim() - 1;
+  mxnet::TShape oshape(n_dim, -1);
+  oshape[n_dim - 1] = s;
+  return oshape;
+}
+
+inline bool NumpyDiagOpShape(const nnvm::NodeAttrs &attrs,
+                             mxnet::ShapeVector *in_attrs,
+                             mxnet::ShapeVector *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+
+  const mxnet::TShape &ishape = (*in_attrs)[0];
+  if (!mxnet::ndim_is_known(ishape)) {
+    return false;
+  }
+
+  const NumpyDiagParam &param = nnvm::get<NumpyDiagParam>(attrs.parsed);
+  mxnet::TShape oshape = NumpyDiagShapeImpl(ishape, param.k);
+
+  if (shape_is_none(oshape)) {
+    LOG(FATAL) << "Diagonal does not exist.";
+  }
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, oshape);
+
+  return shape_is_known(out_attrs->at(0));
+}
+
+inline bool NumpyDiagOpType(const nnvm::NodeAttrs &attrs,
+                            std::vector<int> *in_attrs,
+                            std::vector<int> *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, (*in_attrs)[0]);
+  TYPE_ASSIGN_CHECK(*in_attrs, 0, (*out_attrs)[0]);
+  return (*out_attrs)[0] != -1;
+}
+
+template <int ndim, int req, bool back>
+struct diag {
+  template <typename DType>
+  MSHADOW_XINLINE static void Map(index_t i, DType *out, const DType *a,
+                                  index_t stride, index_t offset) {
+    using namespace mxnet_op;
+    index_t j = offset + stride * i;
+
+    if (back) {
+      KERNEL_ASSIGN(out[j], req, a[i]);
+    } else {
+      KERNEL_ASSIGN(out[i], req, a[j]);
+    }
+  }
+};
+
+template <int req, bool back>
+struct diag_gen {
+  template <typename DType>
+  MSHADOW_XINLINE static void Map(index_t i, DType *out, const DType *a,
+                                  mshadow::Shape<2> oshape, int k) {
+    using namespace mxnet_op;
+
+    auto j = unravel(i, oshape);
+    if (j[1] == (j[0] + k)) {
+      auto l = j[0] < j[1] ? j[0] : j[1];
+      if (back) {
+        KERNEL_ASSIGN(out[l], req, a[i]);
+      } else {
+        KERNEL_ASSIGN(out[i], req, a[l]);
+      }
+    } else if (!back) {
+      KERNEL_ASSIGN(out[i], req, static_cast<DType>(0));
+    }
+  }
+};
+
+template <typename xpu, bool back>
+void NumpyDiagOpImpl(const TBlob &in_data,
+                     const TBlob &out_data,
+                     const mxnet::TShape &ishape,
+                     const mxnet::TShape &oshape,
+                     index_t dsize,
+                     const int &k,
+                     mxnet_op::Stream<xpu> *s,
+                     const OpReqType &req) {
+  using namespace mxnet_op;
+  using namespace mshadow;
+  if (ishape.ndim() > 1) {
+    index_t stride1 = ishape[1], stride2 = 1;
+    // stride1 + stride2 is the stride for
+    // iterating over the diagonal in question
+
+    // the extra index offset introduced by k
+    index_t offset;
+    if (k > 0) {
+      offset = stride2 * k;
+    } else if (k < 0) {
+      offset = stride1 * -k;
+    } else {
+      offset = 0;
+    }
+
+    MSHADOW_TYPE_SWITCH(out_data.type_flag_, DType, {
+      MXNET_ASSIGN_REQ_SWITCH(req, req_type, {
+        if (back && req != kAddTo && req != kNullOp) {
+          out_data.FlatTo1D<xpu, DType>(s) = 0;
+        }
+
+        Kernel<diag<2, req_type, back>, xpu>::Launch(
+            s, dsize, out_data.dptr<DType>(), in_data.dptr<DType>(),
+            stride1 + stride2, offset);
+      });
+    });
+  } else {
+    MSHADOW_TYPE_SWITCH(out_data.type_flag_, DType, {
+      MXNET_ASSIGN_REQ_SWITCH(req, req_type, {
+        Kernel<diag_gen<req_type, back>, xpu>::Launch(
+            s, dsize, out_data.dptr<DType>(), in_data.dptr<DType>(),
+            Shape2(oshape[0], oshape[1]), k);
+      });
+    });
+  }
+}
+
+template <typename xpu>
+void NumpyDiagOpForward(const nnvm::NodeAttrs &attrs,
+                        const OpContext &ctx,
+                        const std::vector<TBlob> &inputs,
+                        const std::vector<OpReqType> &req,
+                        const std::vector<TBlob> &outputs) {
+  using namespace mxnet_op;
+  using namespace mshadow;
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 1U);
+  CHECK_EQ(req.size(), 1U);
+  CHECK_EQ(req[0], kWriteTo);
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  const TBlob &in_data = inputs[0];
+  const TBlob &out_data = outputs[0];
+  const mxnet::TShape &ishape = inputs[0].shape_;
+  const mxnet::TShape &oshape = outputs[0].shape_;
+  const NumpyDiagParam &param = nnvm::get<NumpyDiagParam>(attrs.parsed);
+
+  NumpyDiagOpImpl<xpu, false>(in_data, out_data, ishape, oshape,
+                              out_data.Size(), param.k, s, req[0]);
+}
+
+template <typename xpu>
+void NumpyDiagOpBackward(const nnvm::NodeAttrs &attrs,
+                         const OpContext &ctx,
+                         const std::vector<TBlob> &inputs,
+                         const std::vector<OpReqType> &req,
+                         const std::vector<TBlob> &outputs) {
+  using namespace mxnet_op;
+  using namespace mshadow;
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 1U);
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+
+  const TBlob &in_data = inputs[0];
+  const TBlob &out_data = outputs[0];
+  const mxnet::TShape &ishape = inputs[0].shape_;
+  const mxnet::TShape &oshape = outputs[0].shape_;
+  const NumpyDiagParam &param = nnvm::get<NumpyDiagParam>(attrs.parsed);
+
+  NumpyDiagOpImpl<xpu, true>(in_data, out_data, oshape, ishape,
+                             in_data.Size(), param.k, s, req[0]);
+}
+
 }  // namespace op
 }  // namespace mxnet
 
diff --git a/src/operator/numpy/np_matrix_op.cc b/src/operator/numpy/np_matrix_op.cc
index 3967cde91d2a..912b32c2e8fb 100644
--- a/src/operator/numpy/np_matrix_op.cc
+++ b/src/operator/numpy/np_matrix_op.cc
@@ -36,6 +36,7 @@ DMLC_REGISTER_PARAMETER(NumpyMoveaxisParam);
 DMLC_REGISTER_PARAMETER(NumpyRot90Param);
 DMLC_REGISTER_PARAMETER(NumpyReshapeParam);
 DMLC_REGISTER_PARAMETER(NumpyXReshapeParam);
+DMLC_REGISTER_PARAMETER(NumpyDiagParam);
 
 
 bool NumpyTransposeShape(const nnvm::NodeAttrs& attrs,
@@ -1302,5 +1303,27 @@ NNVM_REGISTER_OP(_npi_hsplit_backward)
 })
 .set_attr<FCompute>("FCompute<cpu>", HSplitOpBackward<cpu>);
 
+NNVM_REGISTER_OP(_np_diag)
+.set_attr_parser(ParamParser<NumpyDiagParam>)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+    [](const NodeAttrs &attrs) {
+    return std::vector<std::string>{"data"};
+})
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyDiagOpShape)
+.set_attr<nnvm::FInferType>("FInferType", NumpyDiagOpType)
+.set_attr<FCompute>("FCompute<cpu>", NumpyDiagOpForward<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_diag"})
+.add_argument("data", "NDArray-or-Symbol", "Input ndarray")
+.add_arguments(NumpyDiagParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_backward_np_diag)
+.set_attr_parser(ParamParser<NumpyDiagParam>)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FCompute>("FCompute<cpu>", NumpyDiagOpBackward<cpu>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/np_matrix_op.cu b/src/operator/numpy/np_matrix_op.cu
index 7ca205565413..33f5aab7717c 100644
--- a/src/operator/numpy/np_matrix_op.cu
+++ b/src/operator/numpy/np_matrix_op.cu
@@ -118,5 +118,11 @@ NNVM_REGISTER_OP(_npi_hsplit_backward)
 NNVM_REGISTER_OP(_npx_reshape)
 .set_attr<FCompute>("FCompute<gpu>", UnaryOp::IdentityCompute<gpu>);
 
+NNVM_REGISTER_OP(_np_diag)
+.set_attr<FCompute>("FCompute<gpu>", NumpyDiagOpForward<gpu>);
+
+NNVM_REGISTER_OP(_backward_np_diag)
+.set_attr<FCompute>("FCompute<gpu>", NumpyDiagOpBackward<gpu>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index 79a478679aaf..7584564bf387 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -57,6 +57,35 @@ def get_workloads(name):
         return OpArgMngr._args.get(name, None)
 
 
+def _add_workload_diag():
+    def get_mat(n):
+        data = _np.arange(n)
+        data = _np.add.outer(data, data)
+        return data
+
+    A = np.array([[1, 2], [3, 4], [5, 6]])
+    vals = (100 * np.arange(5)).astype('l')
+    vals_c = (100 * np.array(get_mat(5)) + 1).astype('l')
+    vals_f = _np.array((100 * get_mat(5) + 1), order ='F', dtype ='l')
+    vals_f = np.array(vals_f)
+
+    OpArgMngr.add_workload('diag', A, k= 2)
+    OpArgMngr.add_workload('diag', A, k= 1)
+    OpArgMngr.add_workload('diag', A, k= 0)
+    OpArgMngr.add_workload('diag', A, k= -1)
+    OpArgMngr.add_workload('diag', A, k= -2)
+    OpArgMngr.add_workload('diag', A, k= -3)
+    OpArgMngr.add_workload('diag', vals, k= 0)
+    OpArgMngr.add_workload('diag', vals, k= 2)
+    OpArgMngr.add_workload('diag', vals, k= -2)
+    OpArgMngr.add_workload('diag', vals_c, k= 0)
+    OpArgMngr.add_workload('diag', vals_c, k= 2)
+    OpArgMngr.add_workload('diag', vals_c, k= -2)
+    OpArgMngr.add_workload('diag', vals_f, k= 0)
+    OpArgMngr.add_workload('diag', vals_f, k= 2)
+    OpArgMngr.add_workload('diag', vals_f, k= -2)
+
+
 def _add_workload_concatenate(array_pool):
     OpArgMngr.add_workload('concatenate', [array_pool['4x1'], array_pool['4x1']])
     OpArgMngr.add_workload('concatenate', [array_pool['4x1'], array_pool['4x1']], axis=1)
@@ -89,6 +118,7 @@ def _add_workload_concatenate(array_pool):
 
 
 def _add_workload_append():
+
     def get_new_shape(shape, axis):
         shape_lst = list(shape)
         if axis is not None:
@@ -1226,6 +1256,7 @@ def _prepare_workloads():
     _add_workload_copy()
     _add_workload_cumsum()
     _add_workload_ravel()
+    _add_workload_diag()
     _add_workload_dot()
     _add_workload_expand_dims()
     _add_workload_fix()
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 76b38eb45205..1e1b53e88c92 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -4529,6 +4529,67 @@ def hybrid_forward(self, F, x, *args, **kwargs):
         assert_almost_equal(ret.asnumpy(), expected_ret, atol=1e-5, rtol=1e-5, use_broadcast=False)
 
 
+@with_seed()
+@use_np
+def test_np_diag():
+    class TestDiag(HybridBlock):
+        def __init__(self, k=0):
+            super(TestDiag, self).__init__()
+            self._k = k
+
+        def hybrid_forward(self, F, a):
+            return F.np.diag(a, k=self._k)
+            
+    shapes = [(), (2,), (1, 5), (2, 2), (2, 5), (3, 3), (4, 3)]
+    dtypes = [np.int8, np.uint8, np.int32, np.int64, np.float16, np.float32, np.float64]
+    range_k = 6
+    combination = itertools.product([False, True], shapes, dtypes, list(range(-range_k, range_k)))
+    for hybridize, shape, dtype, k in combination:
+        rtol = 1e-2 if dtype == np.float16 else 1e-3
+        atol = 1e-4 if dtype == np.float16 else 1e-5
+        test_diag = TestDiag(k)
+        if hybridize:
+            test_diag.hybridize()
+
+        x = np.random.uniform(-2.0, 2.0, size=shape).astype(dtype) if len(shape) != 0 else np.array(())
+        x.attach_grad()
+
+        np_out = _np.diag(x.asnumpy(), k)
+        with mx.autograd.record():
+            mx_out = test_diag(x)
+        assert mx_out.shape == np_out.shape
+        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
+        
+        # check backward function 
+        mx_out.backward()
+        if len(shape) == 0:
+            np_backward = np.array(())
+        elif len(shape) == 1:
+            np_backward = np.ones(shape[0])
+        else:
+            np_backward = np.zeros(shape)
+            h = shape[0]
+            w = shape[1]
+            if k > 0:
+                w -= k
+            else:
+                h += k
+            s = min(w, h)
+            if s > 0:
+                if k >= 0:
+                    for i in range(s):
+                        np_backward[0+i][k+i] = 1
+                else:
+                    for i in range(s):
+                        np_backward[-k+i][0+i] = 1
+        assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=rtol, atol=atol)
+
+        # Test imperative once again
+        mx_out = np.diag(x, k)
+        np_out = _np.diag(x.asnumpy(), k)
+        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
+
+
 @with_seed()
 @use_np
 def test_np_nan_to_num():

From 4f14bf4fe51b586f86a1e7bd38b8282997e6ffbb Mon Sep 17 00:00:00 2001
From: Wuxun Zhang <wuxun.zhang@intel.com>
Date: Tue, 19 Nov 2019 17:10:00 +0800
Subject: [PATCH 609/813] Add large tensor nightly tests for MKL-DNN operators
 (#16184)

* merge ut into original script

* use LARGE_X to define the input shape

* add inline comments

* address comments

* rebase code
---
 tests/nightly/test_large_array.py | 104 +++++++++++++++++++++++-------
 1 file changed, 82 insertions(+), 22 deletions(-)

diff --git a/tests/nightly/test_large_array.py b/tests/nightly/test_large_array.py
index 74ac179a7e60..2780b0d45d62 100644
--- a/tests/nightly/test_large_array.py
+++ b/tests/nightly/test_large_array.py
@@ -212,8 +212,15 @@ def test_dot():
 def test_FullyConnected():
     a = nd.ones(shape=(LARGE_X, SMALL_Y))
     b = nd.ones(shape=(SMALL_Y, SMALL_Y))
-    res = nd.FullyConnected(a, b, num_hidden=b.shape[1], no_bias=True)
-    assert np.sum(res[-1].asnumpy() == SMALL_Y) == b.shape[1]
+    c = nd.ones(shape=(b.shape[0],))
+
+    # w/o bias
+    res = nd.FullyConnected(a, b, num_hidden=b.shape[0], no_bias=True)
+    assert np.sum(res[-1].asnumpy() == a.shape[1]) == b.shape[0]
+
+    # w/ bias
+    res = nd.FullyConnected(a, b, c, num_hidden=b.shape[0], no_bias=False)
+    assert np.sum(res[-1].asnumpy() == a.shape[1] + 1) == b.shape[0]
 
 
 def test_broadcast():
@@ -272,6 +279,7 @@ def test_slice_assign():
 def test_expand_dims():
     a = nd.ones(shape=(LARGE_X, SMALL_Y))
     res = nd.expand_dims(a, axis=1)
+    res.wait_to_read()
     assert a[0][0][0] == 1
     assert res.shape == (a.shape[0], 1, a.shape[1])
 
@@ -401,10 +409,14 @@ def test_unravel_index():
 
 
 def test_transpose():
-    b = create_2d_tensor(rows=LARGE_X, columns=SMALL_Y)
-    t = b.T
-    assert np.sum(t[:, -1].asnumpy() == (LARGE_X - 1)) == b.shape[1]
-    assert t.shape == (SMALL_Y, LARGE_X)
+    test_dtypes = [np.float32, np.int64]
+    for dtype in test_dtypes:
+        b = create_2d_tensor(rows=LARGE_X, columns=SMALL_Y, dtype=dtype)
+        t = b.T
+        assert t.shape == (SMALL_Y, LARGE_X)
+        ref_out = np.transpose(b.asnumpy())
+        assert_almost_equal(t.asnumpy(), ref_out, rtol=1e-10)
+        
 
 
 def test_swapaxes():
@@ -423,9 +435,10 @@ def test_flip():
 
 def test_softmax():
     input_data = mx.nd.ones((SMALL_Y, LARGE_X))
-    true_output = np.full((SMALL_Y, LARGE_X), (1 / SMALL_Y))
-    output = nd.softmax(input_data, axis=0)
-    assert_almost_equal(output.asnumpy(), true_output, rtol=1e-5, atol=1e-5)
+    for axis in [0, 1]:
+        true_output = np.full((SMALL_Y, LARGE_X), (1 / input_data.shape[axis]))
+        output = nd.softmax(input_data, axis=axis)
+        assert_almost_equal(output.asnumpy(), true_output, rtol=1e-5, atol=1e-5)
 
 
 def test_argsort():
@@ -619,12 +632,19 @@ def testSoftmaxOutput():
 
     sym = mx.sym.SoftmaxOutput(data=x, label=label, ignore_label=0,
                                use_ignore=False)
+
     ex = sym.bind(ctx=default_context(), args={'x': x_nd, 'label': label_nd},
-                  args_grad={'x': grad_x})
+                  args_grad=None)
+    ex.forward(is_train=False)
+    softmax_out = ex.outputs[0][0].asnumpy()
+    expected_softmax_out = (1 / SMALL_Y) * mx.nd.ones((SMALL_Y)).asnumpy()
+    assert np.isclose(softmax_out, expected_softmax_out).all()
 
+    ex = sym.bind(ctx=default_context(), args={'x': x_nd, 'label': label_nd},
+                  args_grad={'x': grad_x})
     ex.forward(is_train=True)
     softmax_out = ex.outputs[0][0].asnumpy()
-    expected_softmax_out = (1/SMALL_Y)*mx.nd.ones((SMALL_Y)).asnumpy()
+    expected_softmax_out = (1 / SMALL_Y) * mx.nd.ones((SMALL_Y)).asnumpy()
     assert np.isclose(softmax_out, expected_softmax_out).all()
 
     ex.backward(is_train=True)
@@ -782,8 +802,29 @@ def test_activation():
 # in future, we could test if mean, var of output
 # matches target output's mean, var
 def test_batchnorm():
-    shape = (LARGE_X, SMALL_Y)
+    def get_np_mean_var(data, running_mean, running_var, eps, use_global_status=True):
+        if not use_global_status:
+            # train mode, calculate the real mean and var
+            mean = np.mean(data, axis=(0, 2, 3))
+            mean_broad = np.expand_dims(mean, axis=0)
+            mean_broad = np.expand_dims(mean_broad, axis=2)
+            mean_broad = np.expand_dims(mean_broad, axis=3)
+            mean_broad = np.broadcast_to(mean_broad, data.shape)
+            var = np.square(data - mean_broad)
+            var = np.mean(var, axis=(0, 2, 3))
+        else:
+            # inference mode, use running_mean and running_var instead
+            mean = np.full((data.shape[1],), running_mean)
+            var = np.full((data.shape[1],), running_var)
+
+        # calculate the inverse of standard variance
+        invstdvar = 1. / np.sqrt(var + eps)
+        return mean, invstdvar
+
+    # Here use 4D input to cover mkldnn BN and non-mkldnn BN
+    shape = (1, 2, LARGE_X, SMALL_Y)
     axis = 1  # default
+    eps = 1e-3
 
     nch = shape[axis]
     data = mx.nd.ones(shape=shape)
@@ -793,8 +834,21 @@ def test_batchnorm():
     bn_running_var = mx.nd.ones(nch)
 
     output = mx.nd.BatchNorm(data, bn_gamma, bn_beta,
-                             bn_running_mean, bn_running_var)
-    assert output.shape == shape
+                             bn_running_mean, bn_running_var, output_mean_var=True)
+    assert output[0].shape == shape
+    mean, invstdvar = output[1], output[2]
+
+    np_mean, np_invstdvar = get_np_mean_var(data.asnumpy(), bn_running_mean.asnumpy(), bn_running_var.asnumpy(),
+                                            eps, use_global_status=True)
+    assert_almost_equal(mean.asnumpy(), np_mean)
+    assert_almost_equal(invstdvar.asnumpy(), np_invstdvar)
+
+
+def test_elemwise_add():
+    a = nd.ones(shape=(LARGE_X, SMALL_Y))
+    b = nd.ones(shape=(LARGE_X, SMALL_Y))
+    res = nd.elemwise_add(a, b)
+    assert np.sum(res[-1].asnumpy() == 2) == a.shape[1]
 
 
 def test_add():
@@ -944,19 +998,25 @@ def test_reshape_like():
 
 
 def test_flatten():
-    a = create_2d_tensor(rows=LARGE_X, columns=SMALL_Y).reshape((LARGE_X//2, 2, SMALL_Y))
-    b = nd.flatten(a)
-    assert b[-1][-1] == (LARGE_X-1)
-    assert b[-1][0] == (LARGE_X-2)
-    assert b.shape == (LARGE_X//2, SMALL_Y*2)
+    test_dtypes = [np.float32, np.int64]
+    for dtype in test_dtypes:
+        a = create_2d_tensor(rows=LARGE_X, columns=SMALL_Y, dtype=dtype).reshape((LARGE_X//2, 2, SMALL_Y))
+        b = nd.flatten(a)
+        # Here we removed the value asserts due to different precision of `int64` and `float32`.
+        # For `float32`, it will lose some precision when `LARGE_X` is too large, that is `LARGE_X-1`
+        # and `LARGE_X-2` can not represent the accurate value in the current situation.
+        assert b.shape == (LARGE_X//2, SMALL_Y*2)
+        assert_almost_equal(b[-1,-1].asnumpy(), a[-1,-1,-1].asnumpy(), rtol=1e-8)
 
 
 def test_concat():
     a = nd.array(np.ones((SMALL_Y, LARGE_X)))
     b = nd.array(np.zeros((SMALL_Y, LARGE_X)))
-    c = nd.concat(a, b, dim=0)
-    assert c.shape == (b.shape[0]*2, LARGE_X)
-
+    for axis in [0, 1]:
+        c = nd.concat(a, b, dim=axis)
+        c.wait_to_read()
+        assert c.shape[axis] == b.shape[axis] * 2
+        assert c.shape[1-axis] == b.shape[1-axis]
 
 def test_stack():
     a = nd.array(np.ones((SMALL_Y, LARGE_X)))

From 60f53ed5b143c7a3dbff42b90c68d84bea396814 Mon Sep 17 00:00:00 2001
From: kshitij12345 <kshitijkalambarkar@gmail.com>
Date: Wed, 20 Nov 2019 00:49:52 +0530
Subject: [PATCH 610/813] [fix] missing input log higher order. (#15331)

* fix missing input

* update comments

* update comments

* retrigger CI

* retrigger CI

* update node_op_util

* use NodeOpGen for _backward_log*

* retrigger cI

* retrigger CI
---
 src/nnvm/node_op_util.h                       | 12 +++
 .../tensor/elemwise_unary_op_logexp.cc        | 73 +++++++++----------
 2 files changed, 45 insertions(+), 40 deletions(-)

diff --git a/src/nnvm/node_op_util.h b/src/nnvm/node_op_util.h
index 8d5916aafff9..54a96336fb94 100644
--- a/src/nnvm/node_op_util.h
+++ b/src/nnvm/node_op_util.h
@@ -68,6 +68,18 @@ class NodeOpGen {
                                                    dependent_node->attrs.name + "_square",
                                                    {x}, nullptr, &dependent_node)};
     }
+
+    nnvm::NodeEntry reciprocal(const nnvm::NodeEntry &x) {
+        return nnvm::NodeEntry{mxnet::op::MakeNode("reciprocal",
+                                                   dependent_node->attrs.name + "_reciprocal",
+                                                   {x}, nullptr, &dependent_node)};
+    }
+
+    nnvm::NodeEntry negative(const nnvm::NodeEntry &x) {
+        return nnvm::NodeEntry{mxnet::op::MakeNode("negative",
+                                                   dependent_node->attrs.name + "_negative",
+                                                   {x}, nullptr, &dependent_node)};
+    }
 };
 
 }  // namespace util
diff --git a/src/operator/tensor/elemwise_unary_op_logexp.cc b/src/operator/tensor/elemwise_unary_op_logexp.cc
index 65394826276f..7ca12e0b248b 100644
--- a/src/operator/tensor/elemwise_unary_op_logexp.cc
+++ b/src/operator/tensor/elemwise_unary_op_logexp.cc
@@ -25,6 +25,7 @@
 #include "elemwise_unary_op.h"
 #include "./elemwise_binary_op-inl.h"
 #include "../nn/mkldnn/mkldnn_ops-inl.h"
+#include "../../nnvm/node_op_util.h"
 
 namespace mxnet {
 namespace op {
@@ -110,25 +111,23 @@ MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_log,
 .set_attr<nnvm::FGradient>("FGradient",
   [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
     // ograds[0]: dL/dxgrad
-    // inputs[0]: dL/dy
-    // inputs[1]: x
+    // inputs[0]: dL/dy (ygrad)
+    // inputs[1]: x (ElemewiseGradUseIn)
     // f(x) = y = log(x)
     // f'(x) = 1/x
     // f''(x) = -1 * (f'(x) * f'(x))
+    auto x = n->inputs[1];
     auto dydx_mul_dldy = nnvm::NodeEntry{n};  // f'(x) * head_grads
-    auto dlogx = MakeNode("reciprocal", n->attrs.name + "_dlogx",
-                            {n->inputs[1]}, nullptr, &n);
-    auto d2ydx2_mid = MakeNode("elemwise_mul", n->attrs.name + "_d2ydx2_mid",
-                            {dydx_mul_dldy, nnvm::NodeEntry{dlogx}}, nullptr, &n);
-    auto d2ydx2 = MakeNode("negative", n->attrs.name + "_d2ydx2",
-                        {nnvm::NodeEntry{d2ydx2_mid}}, nullptr, &n);
+    auto op = mxnet::util::NodeOpGen{n};
+
+    auto dlogx = op.reciprocal(x);
+    auto d2ydx2_mid = op.mul(dydx_mul_dldy, dlogx);
+    auto d2ydx2 = op.negative(d2ydx2_mid);
 
     std::vector<nnvm::NodeEntry> ret;
+    ret.emplace_back(op.mul(ograds[0], dlogx));
+    ret.emplace_back(op.mul(ograds[0], d2ydx2));
 
-    ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "_backward_grad_grad",
-                             {ograds[0], nnvm::NodeEntry{dlogx}}, nullptr, &n));
-    ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "_backward_grad_grad_inp",
-                             {ograds[0], nnvm::NodeEntry{d2ydx2}}, nullptr, &n));
     return ret;
   });
 
@@ -137,27 +136,24 @@ MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_log10,
 .set_attr<nnvm::FGradient>("FGradient",
   [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
     // ograds[0]: dL/dxgrad
-    // inputs[0]: dL/dy
-    // inputs[1]: x
+    // inputs[0]: dL/dy (ygrad)
+    // inputs[1]: x (ElemewiseGradUseIn)
     // f(x) = y = log10(x)
     // f'(x) = 1 / (log(10) * x)
     // f''(x) = -1 * (f'(x) * 1/x)
+    auto dldy = n->inputs[0];
+    auto x = n->inputs[1];
     auto dydx_mul_dldy = nnvm::NodeEntry{n};  // f'(x) * head_grads
-    auto dydx = MakeNode("elemwise_div", n->attrs.name + "_dydx",
-                            {n->inputs[0]}, nullptr, &n);
-    auto dlogx = MakeNode("reciprocal", n->attrs.name + "_dlogx",
-                            {n->inputs[1]}, nullptr, &n);
-    auto d2ydx2_mid = MakeNode("elemwise_mul", n->attrs.name + "_d2ydx2_mid",
-                            {dydx_mul_dldy, nnvm::NodeEntry{dlogx}}, nullptr, &n);
-    auto d2ydx2 = MakeNode("negative", n->attrs.name + "_d2ydx2",
-                        {nnvm::NodeEntry{d2ydx2_mid}}, nullptr, &n);
+    auto op = mxnet::util::NodeOpGen{n};
+    auto dydx = op.div(dydx_mul_dldy, dldy);
+    auto dlogx = op.reciprocal(x);
+    auto d2ydx2_mid = op.mul(dydx_mul_dldy, dlogx);
+    auto d2ydx2 = op.negative(d2ydx2_mid);
 
     std::vector<nnvm::NodeEntry> ret;
+    ret.emplace_back(op.mul(ograds[0], dydx));
+    ret.emplace_back(op.mul(ograds[0], d2ydx2));
 
-    ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "_backward_grad_grad",
-                             {ograds[0], nnvm::NodeEntry{dydx}}, nullptr, &n));
-    ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "_backward_grad_grad_inp",
-                             {ograds[0], nnvm::NodeEntry{d2ydx2}}, nullptr, &n));
     return ret;
   });
 
@@ -166,27 +162,24 @@ MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_log2,
 .set_attr<nnvm::FGradient>("FGradient",
   [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
     // ograds[0]: dL/dxgrad
-    // inputs[0]: dL/dy
-    // inputs[1]: x
+    // inputs[0]: dL/dy (ygrad)
+    // inputs[1]: x (ElemewiseGradUseIn)
     // f(x) = y = log2(x)
     // f'(x) = 1 / (log(2) * x)
     // f''(x) = -1 * (f'(x) * 1/x)
+    auto dldy = n->inputs[0];
+    auto x = n->inputs[1];
     auto dydx_mul_dldy = nnvm::NodeEntry{n};  // f'(x) * head_grads
-    auto dydx = MakeNode("elemwise_div", n->attrs.name + "_dydx",
-                            {n->inputs[0]}, nullptr, &n);
-    auto dlogx = MakeNode("reciprocal", n->attrs.name + "_dlogx",
-                            {n->inputs[1]}, nullptr, &n);
-    auto d2ydx2_mid = MakeNode("elemwise_mul", n->attrs.name + "_d2ydx2_mid",
-                            {dydx_mul_dldy, nnvm::NodeEntry{dlogx}}, nullptr, &n);
-    auto d2ydx2 = MakeNode("negative", n->attrs.name + "_d2ydx2",
-                        {nnvm::NodeEntry{d2ydx2_mid}}, nullptr, &n);
+    auto op = mxnet::util::NodeOpGen{n};
+    auto dydx = op.div(dydx_mul_dldy, dldy);
+    auto dlogx = op.reciprocal(x);
+    auto d2ydx2_mid = op.mul(dydx_mul_dldy, dlogx);
+    auto d2ydx2 = op.negative(d2ydx2_mid);
 
     std::vector<nnvm::NodeEntry> ret;
+    ret.emplace_back(op.mul(ograds[0], dydx));
+    ret.emplace_back(op.mul(ograds[0], d2ydx2));
 
-    ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "_backward_grad_grad",
-                             {ograds[0], nnvm::NodeEntry{dydx}}, nullptr, &n));
-    ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name + "_backward_grad_grad_inp",
-                             {ograds[0], nnvm::NodeEntry{d2ydx2}}, nullptr, &n));
     return ret;
   });
 

From 98893102ac58f37e4888408ba0ebe37da633f16e Mon Sep 17 00:00:00 2001
From: reminisce <wujun.nju@gmail.com>
Date: Tue, 19 Nov 2019 18:10:49 -0800
Subject: [PATCH 611/813] Initial checkin (#16856)

---
 src/operator/tensor/matrix_op.cc       |  2 +
 tests/python/unittest/test_numpy_op.py | 54 ++++++++++++++++++++++++++
 2 files changed, 56 insertions(+)

diff --git a/src/operator/tensor/matrix_op.cc b/src/operator/tensor/matrix_op.cc
index eee5ea67f6e1..b09b332765f8 100644
--- a/src/operator/tensor/matrix_op.cc
+++ b/src/operator/tensor/matrix_op.cc
@@ -367,6 +367,8 @@ static void ExpandDimEx(const nnvm::NodeAttrs& attrs,
                         const std::vector<NDArray>& outputs) {
   CHECK_EQ(inputs.size(), 1U);
   CHECK_EQ(outputs.size(), 1U);
+  // skip zero-size tensor
+  if (inputs[0].shape().Size() == 0U) return;
   // If inputs are supposed to be in MKLDNN format and
   // MKLDNN support the data type or the shape. Then convert
   // it to the output format and shape
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 1e1b53e88c92..071300290099 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -4739,6 +4739,60 @@ def hybrid_forward(self, F, cond, x, y):
         same(ret.asnumpy(), _np.where(cond.asnumpy(), x.asnumpy(), y.asnumpy()))
 
 
+@with_seed()
+@use_np
+def test_np_expand_dims():
+    class TestExpandDims(HybridBlock):
+        def __init__(self, axis):
+            super(TestExpandDims, self).__init__()
+            self._axis = axis
+
+        def hybrid_forward(self, F, x):
+            return F.np.expand_dims(x, self._axis)
+
+    dtypes = [np.int8, np.uint8, np.int32, np.int64, np.float16, np.float32, np.float64, np.bool]
+    shapes = [
+        (),
+        (0,),
+        (0, 1),
+        (3,),
+        (1, 2, 3),
+    ]
+    flags = [True, False]
+    for dtype, shape, hybridize in itertools.product(dtypes, shapes, flags):
+        ndim = len(shape)
+        for axis in range(-ndim-1, ndim+1):
+            x_np = _np.random.uniform(0, 100, size=shape).astype(dtype)
+            expected = _np.expand_dims(x_np, axis)
+            for req in ['write', 'add']:
+                test_expand_dims = TestExpandDims(axis)
+                if hybridize:
+                    test_expand_dims.hybridize()
+
+                x = np.array(x_np)
+                x.attach_grad(req)
+                initial_grad = np.random.uniform(0, 10, size=x.shape).astype(x.dtype)
+                x.grad[()] = initial_grad
+                with mx.autograd.record():
+                    y = test_expand_dims(x)
+                y.backward()
+
+                assert_almost_equal(y.asnumpy(), expected, use_broadcast=False)
+                if req == 'null':
+                    assert same(x.grad.asnumpy(), initial_grad.asnumpy())
+                elif req == 'write':
+                    assert same(x.grad.asnumpy(), _np.ones_like(x.asnumpy()))
+                else:
+                    assert_almost_equal(x.grad.asnumpy(), initial_grad.asnumpy() + _np.ones_like(initial_grad.asnumpy()),
+                                        atol=1e-2 if dtype is np.float16 else 1e-4,
+                                        rtol=1e-2 if dtype is np.float16 else 1e-4,
+                                        use_broadcast=False)
+
+                # check imperative again
+                y = np.expand_dims(x, axis)
+                assert_almost_equal(y.asnumpy(), expected, use_broadcast=False)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 55fe7c5b9d7e0e28667e19fdfe98eedb1f0ca438 Mon Sep 17 00:00:00 2001
From: Zach Kimberg <zachary@kimberg.com>
Date: Tue, 19 Nov 2019 18:51:49 -0800
Subject: [PATCH 612/813] Fix IndentationError in setup.py (#16857)

---
 tools/pip/setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/pip/setup.py b/tools/pip/setup.py
index f1d3a38d40e7..4a8b49eb01eb 100644
--- a/tools/pip/setup.py
+++ b/tools/pip/setup.py
@@ -147,7 +147,7 @@ def has_ext_modules(self):
                 'dmlc_tracker': []}
 if variant.endswith('MKL'):
     if platform.system() == 'Darwin':
-    shutil.copytree(os.path.join(CURRENT_DIR, 'mxnet-build/3rdparty/mkldnn/build/install/include'),
+        shutil.copytree(os.path.join(CURRENT_DIR, 'mxnet-build/3rdparty/mkldnn/build/install/include'),
                     os.path.join(CURRENT_DIR, 'mxnet/include/mkldnn'))
 if platform.system() == 'Linux':
     shutil.copy(os.path.join(os.path.dirname(LIB_PATH[0]), 'libgfortran.so.3'), os.path.join(CURRENT_DIR, 'mxnet'))

From f1c6880ccc2cf167eda24e1cdcccef42f69d663e Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Tue, 19 Nov 2019 19:36:08 -0800
Subject: [PATCH 613/813] Fix a few np issues (#16849)

* bump up einsum test atol

* fix several numpy issues
---
 python/mxnet/ndarray/numpy/_op.py             | 44 ++++++++++-
 python/mxnet/numpy/multiarray.py              | 73 +++++++++++++++----
 python/mxnet/numpy_dispatch_protocol.py       |  1 +
 python/mxnet/util.py                          | 29 +++++---
 .../unittest/test_numpy_interoperability.py   | 47 +++++++-----
 tests/python/unittest/test_numpy_op.py        | 29 ++++++--
 6 files changed, 168 insertions(+), 55 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index a8179c2b70cf..3cc5b85c8384 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -28,7 +28,7 @@
 from . import _internal as _npi
 from ..ndarray import NDArray
 
-__all__ = ['zeros', 'ones', 'full', 'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power',
+__all__ = ['shape', 'zeros', 'ones', 'full', 'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power',
            'arctan2', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs',
            'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2',
            'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
@@ -43,7 +43,41 @@
 
 
 @set_module('mxnet.ndarray.numpy')
-def zeros(shape, dtype=_np.float32, order='C', ctx=None):
+def shape(a):
+    """
+    Return the shape of an array.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array.
+
+    Returns
+    -------
+    shape : tuple of ints
+        The elements of the shape tuple give the lengths of the
+        corresponding array dimensions.
+
+    See Also
+    --------
+    ndarray.shape : Equivalent array method.
+
+    Examples
+    --------
+    >>> np.shape(np.eye(3))
+    (3, 3)
+    >>> np.shape([[1, 2]])
+    (1, 2)
+    >>> np.shape([0])
+    (1,)
+    >>> np.shape(0)
+    ()
+    """
+    return a.shape
+
+
+@set_module('mxnet.ndarray.numpy')
+def zeros(shape, dtype=_np.float32, order='C', ctx=None):  # pylint: disable=redefined-outer-name
     """Return a new array of given shape and type, filled with zeros.
     This function currently only supports storing multi-dimensional data
     in row-major (C-style).
@@ -77,7 +111,7 @@ def zeros(shape, dtype=_np.float32, order='C', ctx=None):
 
 
 @set_module('mxnet.ndarray.numpy')
-def ones(shape, dtype=_np.float32, order='C', ctx=None):
+def ones(shape, dtype=_np.float32, order='C', ctx=None):  # pylint: disable=redefined-outer-name
     """Return a new array of given shape and type, filled with ones.
     This function currently only supports storing multi-dimensional data
     in row-major (C-style).
@@ -110,8 +144,9 @@ def ones(shape, dtype=_np.float32, order='C', ctx=None):
     return _npi.ones(shape=shape, ctx=ctx, dtype=dtype)
 
 
+# pylint: disable=too-many-arguments, redefined-outer-name
 @set_module('mxnet.ndarray.numpy')
-def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):  # pylint: disable=too-many-arguments
+def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):
     """
     Return a new array of given shape and type, filled with `fill_value`.
     Parameters
@@ -163,6 +198,7 @@ def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):  # pylin
         ctx = current_context()
     dtype = _np.float32 if dtype is None else dtype
     return _npi.full(shape=shape, value=fill_value, ctx=ctx, dtype=dtype, out=out)
+# pylint: enable=too-many-arguments, redefined-outer-name
 
 
 @set_module('mxnet.ndarray.numpy')
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index bbbb0fd2fd6f..e94d4c8341b4 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -46,7 +46,7 @@
 from ..ndarray.numpy import _internal as _npi
 from ..ndarray.ndarray import _storage_type
 
-__all__ = ['ndarray', 'empty', 'array', 'zeros', 'ones', 'full', 'add', 'subtract', 'multiply', 'divide',
+__all__ = ['ndarray', 'empty', 'array', 'shape', 'zeros', 'ones', 'full', 'add', 'subtract', 'multiply', 'divide',
            'mod', 'remainder', 'power', 'arctan2', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10',
            'sqrt', 'cbrt', 'abs', 'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log',
            'degrees', 'log2', 'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative',
@@ -67,7 +67,7 @@
 
 # This function is copied from ndarray.py since pylint
 # keeps giving false alarm error of undefined-all-variable
-def _new_alloc_handle(shape, ctx, delay_alloc, dtype=mx_real_t):
+def _new_alloc_handle(shape, ctx, delay_alloc, dtype=mx_real_t):  # pylint: disable=redefined-outer-name
     """Return a new handle with specified shape and context.
 
     Empty handle is only used to hold results.
@@ -89,7 +89,7 @@ def _new_alloc_handle(shape, ctx, delay_alloc, dtype=mx_real_t):
     return hdl
 
 
-def _reshape_view(a, *shape):
+def _reshape_view(a, *shape):  # pylint: disable=redefined-outer-name
     """Returns a **view** of this array with a new shape without altering any data.
 
     Parameters
@@ -462,7 +462,7 @@ def __getitem__(self, key):
         """
         # handling possible boolean indexing first
         ndim = self.ndim
-        shape = self.shape
+        shape = self.shape  # pylint: disable=redefined-outer-name
 
         if isinstance(key, list):
             try:
@@ -804,7 +804,7 @@ def __int__(self):
 
     def __len__(self):
         """Number of elements along the first axis."""
-        shape = self.shape
+        shape = self.shape  # pylint: disable=redefined-outer-name
         if len(shape) == 0:
             raise TypeError('len() of unsized object')
         return self.shape[0]
@@ -1165,7 +1165,7 @@ def reshape_like(self, *args, **kwargs):
         """
         raise AttributeError('mxnet.numpy.ndarray object has no attribute reshape_like')
 
-    def reshape_view(self, *shape, **kwargs):
+    def reshape_view(self, *shape, **kwargs):  # pylint: disable=redefined-outer-name
         """Returns a **view** of this array with a new shape without altering any data.
         Inheritated from NDArray.reshape.
         """
@@ -1519,13 +1519,15 @@ def mean(self, axis=None, dtype=None, out=None, keepdims=False):  # pylint: disa
         """Returns the average of the array elements along given axis."""
         return mean(self, axis=axis, dtype=dtype, out=out, keepdims=keepdims)
 
-    def std(self, axis=None, dtype=None, out=None, ddof=0, keepdims=False):  # pylint: disable=arguments-differ
+    # pylint: disable=too-many-arguments, arguments-differ
+    def std(self, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
         """Returns the standard deviation of the array elements along given axis."""
         return std(self, axis=axis, dtype=dtype, ddof=ddof, keepdims=keepdims, out=out)
 
-    def var(self, axis=None, dtype=None, out=None, ddof=0, keepdims=False):  # pylint: disable=arguments-differ
+    def var(self, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
         """Returns the variance of the array elements, along given axis."""
         return var(self, axis=axis, dtype=dtype, out=out, ddof=ddof, keepdims=keepdims)
+    # pylint: enable=too-many-arguments, arguments-differ
 
     def cumsum(self, axis=None, dtype=None, out=None):
         """Return the cumulative sum of the elements along the given axis."""
@@ -1854,7 +1856,7 @@ def squeeze(self, axis=None):  # pylint: disable=arguments-differ
         """Remove single-dimensional entries from the shape of a."""
         return _mx_np_op.squeeze(self, axis=axis)
 
-    def broadcast_to(self, shape):
+    def broadcast_to(self, shape):  # pylint: disable=redefined-outer-name
         return _mx_np_op.broadcast_to(self, shape)
 
     def broadcast_like(self, other):
@@ -1916,7 +1918,7 @@ def tostype(self, stype):
 
 
 @set_module('mxnet.numpy')
-def empty(shape, dtype=_np.float32, order='C', ctx=None):
+def empty(shape, dtype=_np.float32, order='C', ctx=None):  # pylint: disable=redefined-outer-name
     """Return a new array of given shape and type, without initializing entries.
 
     Parameters
@@ -2020,7 +2022,41 @@ def array(object, dtype=None, ctx=None):
 
 
 @set_module('mxnet.numpy')
-def zeros(shape, dtype=_np.float32, order='C', ctx=None):
+def shape(a):
+    """
+    Return the shape of an array.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array.
+
+    Returns
+    -------
+    shape : tuple of ints
+        The elements of the shape tuple give the lengths of the
+        corresponding array dimensions.
+
+    See Also
+    --------
+    ndarray.shape : Equivalent array method.
+
+    Examples
+    --------
+    >>> np.shape(np.eye(3))
+    (3, 3)
+    >>> np.shape([[1, 2]])
+    (1, 2)
+    >>> np.shape([0])
+    (1,)
+    >>> np.shape(0)
+    ()
+    """
+    return _mx_nd_np.shape(a)
+
+
+@set_module('mxnet.numpy')
+def zeros(shape, dtype=_np.float32, order='C', ctx=None):  # pylint: disable=redefined-outer-name
     """Return a new array of given shape and type, filled with zeros.
     This function currently only supports storing multi-dimensional data
     in row-major (C-style).
@@ -2061,7 +2097,7 @@ def zeros(shape, dtype=_np.float32, order='C', ctx=None):
 
 
 @set_module('mxnet.numpy')
-def ones(shape, dtype=_np.float32, order='C', ctx=None):
+def ones(shape, dtype=_np.float32, order='C', ctx=None):  # pylint: disable=redefined-outer-name
     """Return a new array of given shape and type, filled with ones.
     This function currently only supports storing multi-dimensional data
     in row-major (C-style).
@@ -2106,8 +2142,9 @@ def ones(shape, dtype=_np.float32, order='C', ctx=None):
     return _mx_nd_np.ones(shape, dtype, order, ctx)
 
 
+# pylint: disable=too-many-arguments, redefined-outer-name
 @set_module('mxnet.numpy')
-def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):  # pylint: disable=too-many-arguments
+def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):
     """
     Return a new array of given shape and type, filled with `fill_value`.
 
@@ -2160,6 +2197,7 @@ def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):  # pylin
            [2, 2]], dtype=int32)
     """
     return _mx_nd_np.full(shape, fill_value, order=order, ctx=ctx, dtype=dtype, out=out)
+# pylint: enable=too-many-arguments, redefined-outer-name
 
 
 @set_module('mxnet.numpy')
@@ -4224,7 +4262,7 @@ def tensordot(a, b, axes=2):
 
 
 @set_module('mxnet.numpy')
-def histogram(a, bins=10, range=None, normed=None, weights=None, density=None):  # pylint-disable=too-many-arguments
+def histogram(a, bins=10, range=None, normed=None, weights=None, density=None):  # pylint: disable=too-many-arguments
     """
     Compute the histogram of a set of data.
 
@@ -4383,6 +4421,7 @@ def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis
     return _mx_nd_np.linspace(start, stop, num, endpoint, retstep, dtype, axis, ctx)
 
 
+# pylint: disable=too-many-arguments
 @set_module('mxnet.numpy')
 def logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None, axis=0, ctx=None):
     r"""Return numbers spaced evenly on a log scale.
@@ -4457,6 +4496,7 @@ def logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None, axis=0,
     array([ 100.     ,  215.44347,  464.15887, 1000.     ], ctx=gpu(0))
     """
     return _mx_nd_np.logspace(start, stop, num, endpoint, base, dtype, axis, ctx=ctx)
+# pylint: enable=too-many-arguments
 
 
 @set_module('mxnet.numpy')
@@ -5421,8 +5461,9 @@ def mean(a, axis=None, dtype=None, out=None, keepdims=False):  # pylint: disable
     return _npi.mean(a, axis=axis, dtype=dtype, keepdims=keepdims, out=out)
 
 
+
 @set_module('mxnet.numpy')
-def std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
+def std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):  # pylint: disable=too-many-arguments
     """
     Compute the standard deviation along the specified axis.
     Returns the standard deviation, a measure of the spread of a distribution,
@@ -5489,7 +5530,7 @@ def std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
 
 
 @set_module('mxnet.numpy')
-def var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
+def var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):  # pylint: disable=too-many-arguments
     """
     Compute the variance along the specified axis.
     Returns the variance of the array elements, a measure of the spread of a
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index e233c9702c11..f58159303d0f 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -128,6 +128,7 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'linalg.norm',
     'linalg.cholesky',
     'linalg.inv',
+    'shape',
     'trace',
     'tril',
     'meshgrid',
diff --git a/python/mxnet/util.py b/python/mxnet/util.py
index 3a85e31e7e43..aa9f5241cd5f 100644
--- a/python/mxnet/util.py
+++ b/python/mxnet/util.py
@@ -35,6 +35,9 @@
     'subok': True,
 }
 
+_set_np_shape_logged = False
+_set_np_array_logged = False
+
 
 def makedirs(d):
     """Create directories recursively if they don't exist. os.makedirs(exist_ok=True) is not
@@ -87,13 +90,16 @@ def set_np_shape(active):
     >>> print(mx.is_np_shape())
     True
     """
+    global _set_np_shape_logged
     if active:
-        import logging
-        logging.info('NumPy-shape semantics has been activated in your code. '
-                     'This is required for creating and manipulating scalar and zero-size '
-                     'tensors, which were not supported in MXNet before, as in the official '
-                     'NumPy library. Please DO NOT manually deactivate this semantics while '
-                     'using `mxnet.numpy` and `mxnet.numpy_extension` modules.')
+        if not _set_np_shape_logged:
+            import logging
+            logging.info('NumPy-shape semantics has been activated in your code. '
+                         'This is required for creating and manipulating scalar and zero-size '
+                         'tensors, which were not supported in MXNet before, as in the official '
+                         'NumPy library. Please DO NOT manually deactivate this semantics while '
+                         'using `mxnet.numpy` and `mxnet.numpy_extension` modules.')
+            _set_np_shape_logged = True
     elif is_np_array():
         raise ValueError('Deactivating NumPy shape semantics while NumPy array semantics is still'
                          ' active is not allowed. Please consider calling `npx.reset_np()` to'
@@ -678,11 +684,14 @@ def _set_np_array(active):
     -------
         A bool value indicating the previous state of NumPy array semantics.
     """
+    global _set_np_array_logged
     if active:
-        import logging
-        logging.info('NumPy array semantics has been activated in your code. This allows you'
-                     ' to use operators from MXNet NumPy and NumPy Extension modules as well'
-                     ' as MXNet NumPy `ndarray`s.')
+        if not _set_np_array_logged:
+            import logging
+            logging.info('NumPy array semantics has been activated in your code. This allows you'
+                         ' to use operators from MXNet NumPy and NumPy Extension modules as well'
+                         ' as MXNet NumPy `ndarray`s.')
+            _set_np_array_logged = True
     cur_state = is_np_array()
     _NumpyArrayScope._current.value = _NumpyArrayScope(active)
     return cur_state
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index 7584564bf387..e52d25239d22 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -66,24 +66,24 @@ def get_mat(n):
     A = np.array([[1, 2], [3, 4], [5, 6]])
     vals = (100 * np.arange(5)).astype('l')
     vals_c = (100 * np.array(get_mat(5)) + 1).astype('l')
-    vals_f = _np.array((100 * get_mat(5) + 1), order ='F', dtype ='l')
+    vals_f = _np.array((100 * get_mat(5) + 1), order='F', dtype='l')
     vals_f = np.array(vals_f)
 
-    OpArgMngr.add_workload('diag', A, k= 2)
-    OpArgMngr.add_workload('diag', A, k= 1)
-    OpArgMngr.add_workload('diag', A, k= 0)
-    OpArgMngr.add_workload('diag', A, k= -1)
-    OpArgMngr.add_workload('diag', A, k= -2)
-    OpArgMngr.add_workload('diag', A, k= -3)
-    OpArgMngr.add_workload('diag', vals, k= 0)
-    OpArgMngr.add_workload('diag', vals, k= 2)
-    OpArgMngr.add_workload('diag', vals, k= -2)
-    OpArgMngr.add_workload('diag', vals_c, k= 0)
-    OpArgMngr.add_workload('diag', vals_c, k= 2)
-    OpArgMngr.add_workload('diag', vals_c, k= -2)
-    OpArgMngr.add_workload('diag', vals_f, k= 0)
-    OpArgMngr.add_workload('diag', vals_f, k= 2)
-    OpArgMngr.add_workload('diag', vals_f, k= -2)
+    OpArgMngr.add_workload('diag', A, k=2)
+    OpArgMngr.add_workload('diag', A, k=1)
+    OpArgMngr.add_workload('diag', A, k=0)
+    OpArgMngr.add_workload('diag', A, k=-1)
+    OpArgMngr.add_workload('diag', A, k=-2)
+    OpArgMngr.add_workload('diag', A, k=-3)
+    OpArgMngr.add_workload('diag', vals, k=0)
+    OpArgMngr.add_workload('diag', vals, k=2)
+    OpArgMngr.add_workload('diag', vals, k=-2)
+    OpArgMngr.add_workload('diag', vals_c, k=0)
+    OpArgMngr.add_workload('diag', vals_c, k=2)
+    OpArgMngr.add_workload('diag', vals_c, k=-2)
+    OpArgMngr.add_workload('diag', vals_f, k=0)
+    OpArgMngr.add_workload('diag', vals_f, k=2)
+    OpArgMngr.add_workload('diag', vals_f, k=-2)
 
 
 def _add_workload_concatenate(array_pool):
@@ -1202,6 +1202,12 @@ def _add_workload_nonzero():
     OpArgMngr.add_workload('nonzero', np.array([True, False, False], dtype=np.bool_))
 
 
+def _add_workload_shape():
+    OpArgMngr.add_workload('shape', np.random.uniform(size=()))
+    OpArgMngr.add_workload('shape', np.random.uniform(size=(0, 1)))
+    OpArgMngr.add_workload('shape', np.random.uniform(size=(2, 3)))
+
+
 def _add_workload_diff():
     x = np.array([1, 4, 6, 7, 12])
     OpArgMngr.add_workload('diff', x)
@@ -1349,6 +1355,7 @@ def _prepare_workloads():
     _add_workload_less(array_pool)
     _add_workload_less_equal(array_pool)
     _add_workload_where()
+    _add_workload_shape()
     _add_workload_diff()
     _add_workload_resize()
 
@@ -1381,11 +1388,11 @@ def _check_interoperability_helper(op_name, *args, **kwargs):
     expected_out = _get_numpy_op_output(onp_op, *args, **kwargs)
     if isinstance(out, (tuple, list)):
         assert type(out) == type(expected_out)
-        for arr in out:
-            assert isinstance(arr, np.ndarray)
         for arr, expected_arr in zip(out, expected_out):
-            assert isinstance(arr, np.ndarray)
-            assert_almost_equal(arr.asnumpy(), expected_arr, rtol=1e-3, atol=1e-4, use_broadcast=False, equal_nan=True)
+            if isinstance(arr, np.ndarray):
+                assert_almost_equal(arr.asnumpy(), expected_arr, rtol=1e-3, atol=1e-4, use_broadcast=False, equal_nan=True)
+            else:
+                _np.testing.assert_equal(arr, expected_arr)
     else:
         assert isinstance(out, np.ndarray)
         assert_almost_equal(out.asnumpy(), expected_out, rtol=1e-3, atol=1e-4, use_broadcast=False, equal_nan=True)
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 071300290099..d019081ec3ee 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -758,6 +758,26 @@ def legalize_shape(shape):
                                 assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol, use_broadcast=False, equal_nan=True)
 
 
+@with_seed()
+@use_np
+def test_np_shape():
+    shapes = [
+        (),
+        (0, 1),
+        (2, 3),
+        (2, 3, 4),
+    ]
+
+    for shape in shapes:
+        mx_a = np.random.uniform(size=shape)
+        np_a = _np.random.uniform(size=shape)
+
+        mx_shape = np.shape(mx_a)
+        np_shape = _np.shape(np_a)
+
+        assert mx_shape == np_shape
+
+
 @with_seed()
 @use_np
 def test_np_linspace():
@@ -4112,7 +4132,7 @@ def dbg(name, data):
             for config in configs:
                 (subscripts, operands) = config
                 rtol = 1e-2 if dtype == 'float16' else 1e-3
-                atol = 1e-4 if dtype == 'float16' else 1e-5
+                atol = 1e-3 if dtype == 'float16' else 1e-4
                 grad = []
                 x_np = []
                 for shape in operands:
@@ -4539,7 +4559,7 @@ def __init__(self, k=0):
 
         def hybrid_forward(self, F, a):
             return F.np.diag(a, k=self._k)
-            
+
     shapes = [(), (2,), (1, 5), (2, 2), (2, 5), (3, 3), (4, 3)]
     dtypes = [np.int8, np.uint8, np.int32, np.int64, np.float16, np.float32, np.float64]
     range_k = 6
@@ -4559,8 +4579,8 @@ def hybrid_forward(self, F, a):
             mx_out = test_diag(x)
         assert mx_out.shape == np_out.shape
         assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
-        
-        # check backward function 
+
+        # check backward function
         mx_out.backward()
         if len(shape) == 0:
             np_backward = np.array(())
@@ -4593,7 +4613,6 @@ def hybrid_forward(self, F, a):
 @with_seed()
 @use_np
 def test_np_nan_to_num():
-
     def take_ele_grad(ele):
         if _np.isinf(ele) or _np.isnan(ele):
             return 0

From 61c8bafdcfee129e4f7a491438a2402e6762ddd9 Mon Sep 17 00:00:00 2001
From: Przemyslaw Tredak <ptredak@nvidia.com>
Date: Tue, 19 Nov 2019 23:24:43 -0800
Subject: [PATCH 614/813] Add unoptimized symbol to executor for sharing
 (#16798)

* Add unoptimized symbol to executor for sharing

* Copy the symbol in Reshape

* Added test for multiple reshapes
---
 src/executor/graph_executor.cc  | 20 ++++++++++++--------
 src/executor/graph_executor.h   |  5 ++++-
 tests/python/gpu/test_fusion.py | 25 +++++++++++++++++++++++++
 3 files changed, 41 insertions(+), 9 deletions(-)

diff --git a/src/executor/graph_executor.cc b/src/executor/graph_executor.cc
index b31ae7cf0cd7..7d5c2af0f881 100644
--- a/src/executor/graph_executor.cc
+++ b/src/executor/graph_executor.cc
@@ -50,7 +50,7 @@ static const std::string GetDefaultSubgraphBackend() {
 #endif
 }
 
-GraphExecutor::GraphExecutor() {
+GraphExecutor::GraphExecutor(const nnvm::Symbol& symbol) {
   log_verbose_ = dmlc::GetEnv("MXNET_EXEC_VERBOSE_LOGGING", false);
   need_grad_ = false;
   is_dynamic_ = false;
@@ -60,6 +60,7 @@ GraphExecutor::GraphExecutor() {
     LOG(INFO) << "MXNET_SUBGRAPH_BACKEND=NONE is detected, subgraph backend is not in use";
   }
   engine_ref_ = Engine::_GetSharedRef();
+  symbol_ = symbol.Copy();
 }
 
 GraphExecutor::~GraphExecutor() {
@@ -888,10 +889,9 @@ Executor* GraphExecutor::Reshape(const bool partial_shaping,
                                  std::vector<NDArray>* arg_grads,
                                  std::vector<NDArray>* aux_states) {
   nnvm::Graph g;
-  g.outputs = std::vector<nnvm::NodeEntry>(graph_.outputs.begin(),
-    graph_.outputs.begin() + num_forward_outputs_);
   nnvm::Symbol symbol;
-  symbol.outputs = g.outputs;
+  symbol.outputs = symbol_.outputs;
+  g.outputs = symbol_.outputs;
   const nnvm::IndexedGraph& idx = g.indexed_graph();
   mxnet::ShapeVector arg_shapes(idx.input_nodes().size(), mxnet::TShape());
   for (size_t i = 0; i < num_forward_inputs_; ++i) {
@@ -975,8 +975,8 @@ Executor* GraphExecutor::Reshape(const bool partial_shaping,
       }
     }
   }
-  auto exec = new GraphExecutor();
-  exec->Init(symbol, default_ctx, ctx_map,
+  auto exec = new GraphExecutor(symbol);
+  exec->Init(symbol.Copy(), default_ctx, ctx_map,
              *in_args, *arg_grads, grad_req_types, *aux_states,
              this);
   return exec;
@@ -1967,7 +1967,7 @@ Executor *Executor::SimpleBind(nnvm::Symbol symbol,
                                std::vector<NDArray>* aux_states,
                                std::unordered_map<std::string, NDArray>* shared_buffer,
                                Executor* shared_exec) {
-  auto exec = new exec::GraphExecutor();
+  auto exec = new exec::GraphExecutor(symbol);
   bool init = false;
   if (!exec->subgraph_property().empty()) {
     static int verbose = dmlc::GetEnv("MXNET_SUBGRAPH_VERBOSE", 1);
@@ -1987,6 +1987,8 @@ Executor *Executor::SimpleBind(nnvm::Symbol symbol,
       symbol = exec::BuildSubgraph(symbol, backend, arg_shape_map, arg_dtype_map, arg_stype_map,
                                    default_ctx, group2ctx, &tmp_in_arg_ctxes, &tmp_arg_grad_ctxes,
                                    &tmp_grad_req_types, &tmp_aux_state_ctxes, verbose);
+      // Subgraph cannot be recreated from unoptimized symbol
+      exec = new exec::GraphExecutor(symbol);
       exec->Init(symbol.Copy(), default_ctx, group2ctx, tmp_in_arg_ctxes, tmp_arg_grad_ctxes,
                  tmp_aux_state_ctxes, arg_shape_map, arg_dtype_map, arg_stype_map,
                  tmp_grad_req_types, shared_arg_names, &tmp_in_args, &tmp_arg_grads,
@@ -2041,7 +2043,7 @@ Executor *Executor::Bind(nnvm::Symbol symbol,
                          const std::vector<OpReqType> &grad_req_type,
                          const std::vector<NDArray> &aux_states,
                          Executor* shared_exec) {
-  auto exec = new exec::GraphExecutor();
+  auto exec = new exec::GraphExecutor(symbol);
   static int verbose = dmlc::GetEnv("MXNET_SUBGRAPH_VERBOSE", 1);
   std::vector<NDArray> tmp_in_args = in_args;
   std::vector<NDArray> tmp_arg_grad_store = arg_grad_store;
@@ -2056,6 +2058,8 @@ Executor *Executor::Bind(nnvm::Symbol symbol,
       symbol = exec::BuildSubgraph(symbol, backend, default_ctx, group2ctx, &tmp_in_args,
                                    &tmp_arg_grad_store, &tmp_grad_req_type, &tmp_aux_states,
                                    verbose);
+      // Subgraph cannot be recreated from unoptimized symbol
+      exec = new exec::GraphExecutor(symbol);
     }
   }
   exec->Init(symbol.Copy(), default_ctx, group2ctx, tmp_in_args, tmp_arg_grad_store,
diff --git a/src/executor/graph_executor.h b/src/executor/graph_executor.h
index f150165796ad..bfa6980a8e29 100644
--- a/src/executor/graph_executor.h
+++ b/src/executor/graph_executor.h
@@ -58,7 +58,7 @@ class GraphExecutor : public Executor {
  public:
   using Executor::MonitorCallback;
 
-  GraphExecutor();
+  explicit GraphExecutor(const nnvm::Symbol& symbol);
   virtual ~GraphExecutor();
   void Forward(bool is_train) override;
   void PartialForward(bool is_train, int step, int *step_left) override;
@@ -267,6 +267,9 @@ class GraphExecutor : public Executor {
   std::string subgraph_property_;
   // ref of engine
   std::shared_ptr<Engine> engine_ref_;
+  // Unoptimized copy of the symbol for sharing with
+  // child executors
+  nnvm::Symbol symbol_;
 };
 
 }  // namespace exec
diff --git a/tests/python/gpu/test_fusion.py b/tests/python/gpu/test_fusion.py
index 5606eb19a9c5..693336f22496 100644
--- a/tests/python/gpu/test_fusion.py
+++ b/tests/python/gpu/test_fusion.py
@@ -239,6 +239,31 @@ def test_fusion_compiler_cache():
         check_fused_symbol(a+b, ctx=mx.gpu(1), a=arr1, b=arr2)
 
 
+@with_seed()
+def test_fusion_reshape_executor():
+    a = mx.sym.Variable("data1")
+    b = mx.sym.Variable("data2")
+    c = a + b + 1
+    sym = mx.sym.relu(c)
+    orig_shape = (10,10)
+    e = sym.simple_bind(ctx=mx.gpu(), data1=orig_shape, data2=orig_shape)
+    data = mx.nd.zeros(orig_shape, ctx=mx.gpu())
+    out = e.forward(is_train=False)
+    assert out[0].sum().asscalar() == 100
+    changed_shape = (80, 2)
+    new_shape = {'data1': changed_shape, 'data2': changed_shape}
+    data = mx.nd.zeros(new_shape['data1'], ctx=mx.gpu())
+    f = e.reshape(allow_up_sizing=True, **new_shape)
+    out = f.forward(is_train=False, data1=data, data2=data)
+    assert out[0].sum().asscalar() == 160
+    # Reshape again
+    changed_shape = (30, 5)
+    new_shape = {'data1': changed_shape, 'data2': changed_shape}
+    data = mx.nd.zeros(new_shape['data1'], ctx=mx.gpu())
+    f = e.reshape(allow_up_sizing=True, **new_shape)
+    out = f.forward(is_train=False, data1=data, data2=data)
+    assert out[0].sum().asscalar() == 150
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From ccf0411d452c4c2f958c73ec219e10eb38798796 Mon Sep 17 00:00:00 2001
From: Pedro Larroy <pedro.larroy.lists@gmail.com>
Date: Wed, 20 Nov 2019 12:56:03 -0800
Subject: [PATCH 615/813] Fix test_gluon.py:test_sync_batchnorm when number of
 GPUS > 4 (#16834)

---
 tests/python/unittest/test_gluon.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/tests/python/unittest/test_gluon.py b/tests/python/unittest/test_gluon.py
index 3a56dc887647..0163341dc9bb 100644
--- a/tests/python/unittest/test_gluon.py
+++ b/tests/python/unittest/test_gluon.py
@@ -868,11 +868,13 @@ def _syncParameters(bn1, bn2, ctx):
 
     cfgs = [(1, False)]
     num_gpus = mx.context.num_gpus()
+    batch_size = 24
     for i in range(1, num_gpus + 1):
-        cfgs.append((i, True))
+        if batch_size % i == 0:
+            cfgs.append((i, True))
     for ndev, cuda in cfgs:
         # check with unsync version
-        for shape in [(24, 2), (24, 3, 4), (24, 4, 4, 4), (24, 5, 6, 4, 4)]:
+        for shape in [(batch_size, 2), (batch_size, 3, 4), (batch_size, 4, 4, 4), (batch_size, 5, 6, 4, 4)]:
             print(str((ndev, cuda, shape)))
             for i in range(10):
                 _check_batchnorm_result(mx.nd.random.uniform(shape=shape,

From 7c9cb6b7feebcb75465074f68fd97f81c19e2c2c Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Wed, 20 Nov 2019 15:15:58 -0800
Subject: [PATCH 616/813] Mixed precison binary op backward (use in) for numpy
 (#16791)

* mixed precison binary op backward

* reduce unix cpu runtime
---
 .../numpy/np_elemwise_broadcast_op.cc         |  17 ++-
 .../numpy/np_elemwise_broadcast_op.cu         |   4 +
 src/operator/numpy/np_elemwise_broadcast_op.h | 104 +++++++++++++++++-
 .../tensor/elemwise_binary_broadcast_op.h     |  26 +++++
 src/operator/tensor/elemwise_unary_op.h       |   4 +-
 tests/python/unittest/test_numpy_op.py        |  20 ++--
 6 files changed, 162 insertions(+), 13 deletions(-)

diff --git a/src/operator/numpy/np_elemwise_broadcast_op.cc b/src/operator/numpy/np_elemwise_broadcast_op.cc
index 4e4734c143a4..f2adfc125d02 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op.cc
+++ b/src/operator/numpy/np_elemwise_broadcast_op.cc
@@ -137,7 +137,22 @@ MXNET_OPERATOR_REGISTER_NP_BINARY_MIXED_PRECISION(_npi_multiply)
   "FCompute<cpu>",
   NumpyBinaryBroadcastComputeWithBool<cpu, op::mshadow_op::mul>)
 #endif
-.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_broadcast_mul"});
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_broadcast_mul"});
+
+NNVM_REGISTER_OP(_backward_npi_broadcast_mul)
+.set_num_inputs(3)
+.set_num_outputs(2)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs){
+    return std::vector<std::pair<int, int> >{{0, 1}};
+  })
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", NumpyBinaryBackwardUseIn<cpu, mshadow_op::right,
+                                                              mshadow_op::left>);
 
 MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_mod)
 .set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, mshadow_op::mod>)
diff --git a/src/operator/numpy/np_elemwise_broadcast_op.cu b/src/operator/numpy/np_elemwise_broadcast_op.cu
index 5c9dc97377cf..59dfc25db963 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op.cu
+++ b/src/operator/numpy/np_elemwise_broadcast_op.cu
@@ -64,6 +64,10 @@ NNVM_REGISTER_OP(_npi_multiply)
   NumpyBinaryBroadcastComputeWithBool<gpu, op::mshadow_op::mul>);
 #endif
 
+NNVM_REGISTER_OP(_backward_npi_broadcast_mul)
+.set_attr<FCompute>("FCompute<gpu>", NumpyBinaryBackwardUseIn<gpu, mshadow_op::right,
+                                                              mshadow_op::left>);
+
 NNVM_REGISTER_OP(_npi_mod)
 .set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, mshadow_op::mod>);
 
diff --git a/src/operator/numpy/np_elemwise_broadcast_op.h b/src/operator/numpy/np_elemwise_broadcast_op.h
index 2ad8b88bdb1c..a2b7877dc444 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op.h
+++ b/src/operator/numpy/np_elemwise_broadcast_op.h
@@ -25,6 +25,7 @@
 #ifndef MXNET_OPERATOR_NUMPY_NP_ELEMWISE_BROADCAST_OP_H_
 #define MXNET_OPERATOR_NUMPY_NP_ELEMWISE_BROADCAST_OP_H_
 
+#include <algorithm>
 #include <vector>
 #include <string>
 
@@ -391,11 +392,13 @@ void NumpyBinaryBroadcastComputeWithBool(const nnvm::NodeAttrs& attrs,
 }
 
 template<typename xpu, typename LOP, typename ROP>
-void MixedBinaryBackwardUseIn(const nnvm::NodeAttrs& attrs,
+void NumpyBinaryBackwardUseIn(const nnvm::NodeAttrs& attrs,
                               const OpContext& ctx,
                               const std::vector<TBlob>& inputs,
                               const std::vector<OpReqType>& req,
                               const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mxnet_op;
   CHECK_EQ(inputs.size(), 3U);
   CHECK_EQ(outputs.size(), 2U);
 
@@ -406,7 +409,104 @@ void MixedBinaryBackwardUseIn(const nnvm::NodeAttrs& attrs,
     return;
   }
 
-  PrintErrorMessage(attrs.op->name, lhs.type_flag_, rhs.type_flag_);
+  const TBlob& ograd = inputs[0];
+  const TBlob& lgrad = outputs[0];
+  const TBlob& rgrad = outputs[1];
+
+  if (common::is_float(lhs.type_flag_) || common::is_float(rhs.type_flag_)) {
+    // If any of the inputs is a float, it's the same type as the output
+    // So 2 of the 3 tensors have the same data type
+    Stream<xpu> *s = ctx.get_stream<xpu>();
+    mxnet::TShape new_lshape, new_rshape, new_oshape;
+    using namespace broadcast;
+    const bool need_bc = BinaryBroadcastShapeCompact(lgrad.shape_, rgrad.shape_, ograd.shape_,
+                                                     &new_lshape, &new_rshape, &new_oshape) != 0;
+
+    // Prepare all the temporary memory
+    size_t workspace_size_l = 0, workspace_size_r = 0;
+    TBlob temp_tblob;  // The TBlob for casted input data
+    TBlob temp_igrad;  // The TBlob for casted grad results
+    size_t tensor_size = (lgrad.type_flag_ != ograd.type_flag_) ? lgrad.Size() : rgrad.Size();
+    Tensor<xpu, 1, char> workspace;
+
+    MSHADOW_TYPE_SWITCH(ograd.type_flag_, OType, {
+      BROADCAST_NDIM_SWITCH(new_oshape.ndim(), ndim, {
+        workspace_size_l = ReduceWorkspaceSize<ndim, OType>(
+          s, new_lshape, req[0], new_oshape, new_lshape, new_rshape);
+        workspace_size_r = ReduceWorkspaceSize<ndim, OType>(
+          s, new_rshape, req[1], new_oshape, new_lshape, new_rshape);
+      });
+      size_t workspace_size = std::max(workspace_size_l, workspace_size_r);
+      size_t cast_tensor_size = tensor_size * sizeof(OType);
+      // Allocate the temporary memories now
+      Tensor<xpu, 1, char> temp_space =
+        ctx.requested[0].get_space_typed<xpu, 1, char>(
+          Shape1(workspace_size + cast_tensor_size * 2), s);
+      // Tensor for temp_tblob
+      Tensor<xpu, 1, OType> temp_tblob_tensor(
+                              reinterpret_cast<OType*>(temp_space.dptr_),
+                              Shape1(tensor_size), s);
+      // Tensor for temp_igrad
+      Tensor<xpu, 1, OType> temp_igrad_tensor(
+                              reinterpret_cast<OType*>(temp_space.dptr_) + tensor_size,
+                              Shape1(tensor_size), s);
+      temp_tblob =
+        TBlob(temp_tblob_tensor)
+          .reshape(((lgrad.type_flag_ != ograd.type_flag_) ? lhs.shape_ : rhs.shape_));
+      temp_igrad =
+        TBlob(temp_igrad_tensor)
+          .reshape(((lgrad.type_flag_ != ograd.type_flag_) ? lhs.shape_ : rhs.shape_));
+      if (temp_igrad.Size() != 0) {
+        Kernel<set_zero, xpu>::Launch(s, temp_igrad.Size(), temp_igrad.dptr<OType>());
+      }
+      workspace =
+        Tensor<xpu, 1, char>(temp_space.dptr_ + 2 * cast_tensor_size, Shape1(workspace_size), s);
+    });
+    // Cast the input that does not have consistent type to temp_tblob
+    CastCompute<xpu>(
+      attrs, ctx, {((lgrad.type_flag_ != ograd.type_flag_) ? lhs : rhs)}, {kWriteTo}, {temp_tblob});
+    if (!need_bc) {
+      if (lhs.type_flag_ != ograd.type_flag_) {
+        ElemwiseBinaryOp::BackwardUseIn<xpu, LOP, ROP>(
+          attrs, ctx, {ograd, temp_tblob, rhs}, {kWriteTo, req[1]}, {temp_igrad, rgrad});
+      } else {
+        ElemwiseBinaryOp::BackwardUseIn<xpu, LOP, ROP>(
+          attrs, ctx, {ograd, lhs, temp_tblob}, {req[0], kWriteTo}, {lgrad, temp_igrad});
+      }
+    } else {
+      if (lhs.type_flag_ != ograd.type_flag_) {
+        MSHADOW_TYPE_SWITCH(ograd.type_flag_, DType, {
+          BROADCAST_NDIM_SWITCH(new_oshape.ndim(), NDim, {
+            BinaryBroadcastBackwardUseInImplWithWorkspace<xpu, NDim, DType, LOP, ROP>(
+              ctx, {ograd, temp_tblob, rhs}, {kWriteTo, req[1]}, {temp_igrad, rgrad},
+              workspace, new_lshape, new_rshape, new_oshape);
+          });
+        });
+      } else {
+        MSHADOW_TYPE_SWITCH(ograd.type_flag_, DType, {
+          BROADCAST_NDIM_SWITCH(new_oshape.ndim(), NDim, {
+            BinaryBroadcastBackwardUseInImplWithWorkspace<xpu, NDim, DType, LOP, ROP>(
+              ctx, {ograd, lhs, temp_tblob}, {req[0], kWriteTo}, {lgrad, temp_igrad},
+              workspace, new_lshape, new_rshape, new_oshape);
+          });
+        });
+      }
+    }
+
+    // If both inputs are floating numbers, cast the igrad to the input that has
+    // the different data type
+    if (common::is_float(lhs.type_flag_) && common::is_float(rhs.type_flag_)) {
+      if (lhs.type_flag_ != ograd.type_flag_) {
+        CastCompute<xpu>(attrs, ctx, {temp_igrad}, {req[0]}, {lgrad});
+      } else {
+        CastCompute<xpu>(attrs, ctx, {temp_igrad}, {req[1]}, {rgrad});
+      }
+    }
+  } else {
+    // Case where both inputs are integer types, should not even do
+    // backward computation for this case.
+    PrintErrorMessage(attrs.op->name, lhs.type_flag_, rhs.type_flag_);
+  }
 }
 
 }  // namespace op
diff --git a/src/operator/tensor/elemwise_binary_broadcast_op.h b/src/operator/tensor/elemwise_binary_broadcast_op.h
index 958432b2c4b2..ffd0f123070a 100644
--- a/src/operator/tensor/elemwise_binary_broadcast_op.h
+++ b/src/operator/tensor/elemwise_binary_broadcast_op.h
@@ -699,6 +699,32 @@ BinaryBroadcastBackwardUseNone(const nnvm::NodeAttrs& attrs,
                                const std::vector<OpReqType>& req,
                                const std::vector<TBlob>& outputs);
 
+template<typename xpu, int ndim, typename DType, typename LOP, typename ROP>
+void BinaryBroadcastBackwardUseInImplWithWorkspace(const OpContext& ctx,
+                                                   const std::vector<TBlob>& inputs,
+                                                   const std::vector<OpReqType>& req,
+                                                   const std::vector<TBlob>& outputs,
+                                                   const mshadow::Tensor<xpu, 1, char>& workspace,
+                                                   const mxnet::TShape& new_lshape,
+                                                   const mxnet::TShape& new_rshape,
+                                                   const mxnet::TShape& new_oshape) {
+  using namespace mshadow;
+  using namespace mshadow::expr;
+  using namespace broadcast;
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  const TBlob lgrad = outputs[0].reshape(new_lshape);
+  const TBlob rgrad = outputs[1].reshape(new_rshape);
+  const TBlob ograd = inputs[0].reshape(new_oshape);
+  const TBlob lhs = inputs[1].reshape(new_lshape);
+  const TBlob rhs = inputs[2].reshape(new_rshape);
+  if (ograd.Size() != 0) {
+    Reduce<red::sum, ndim, DType, op::mshadow_op::mul, LOP>(s, lgrad, req[0], workspace,
+      ograd, lhs, rhs);
+    Reduce<red::sum, ndim, DType, op::mshadow_op::mul, ROP>(s, rgrad, req[1], workspace,
+      ograd, lhs, rhs);
+  }
+}
+
 template<typename xpu, int ndim, typename DType, typename LOP, typename ROP>
 inline void BinaryBroadcastBackwardUseInImpl(const OpContext& ctx,
                                              const std::vector<TBlob>& inputs,
diff --git a/src/operator/tensor/elemwise_unary_op.h b/src/operator/tensor/elemwise_unary_op.h
index 577c994a8ee1..05e10ffa4e16 100644
--- a/src/operator/tensor/elemwise_unary_op.h
+++ b/src/operator/tensor/elemwise_unary_op.h
@@ -454,8 +454,8 @@ void CastCompute(const nnvm::NodeAttrs& attrs,
     Tensor<xpu, 1, DstDType> out = outputs[0].FlatTo1D<xpu, DstDType>(s);
     MSHADOW_TYPE_SWITCH_WITH_BOOL(inputs[0].type_flag_, SrcDType, {
       Tensor<xpu, 1, SrcDType> data = inputs[0].FlatTo1D<xpu, SrcDType>(s);
-      if (outputs[0].type_flag_ != inputs[0].type_flag_ ||
-          req[0] != kWriteInplace) {
+      if ((outputs[0].type_flag_ != inputs[0].type_flag_ ||
+          req[0] != kWriteInplace) && outputs[0].Size() != 0) {
         Assign(out, req[0], tcast<DstDType>(data));
       }
     });
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index d019081ec3ee..ff9dd4119968 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -1705,7 +1705,9 @@ def hybrid_forward(self, F, a, b, *args, **kwargs):
 @with_seed()
 @use_np
 def test_np_mixed_precision_binary_funcs():
-    def check_mixed_precision_binary_func(func, low, high, lshape, rshape, ltype, rtype):
+    itypes = [np.bool, np.int8, np.int32, np.int64]
+    ftypes = [np.float16, np.float32, np.float64]
+    def check_mixed_precision_binary_func(func, low, high, lshape, rshape, lgrad, rgrad, ltype, rtype):
         class TestMixedBinary(HybridBlock):
             def __init__(self, func):
                 super(TestMixedBinary, self).__init__()
@@ -1739,13 +1741,15 @@ def hybrid_forward(self, F, a, b, *args, **kwargs):
                             use_broadcast=False, equal_nan=True)
 
     funcs = {
-        'add': (-1.0, 1.0),
-        'subtract': (-1.0, 1.0),
-        'multiply': (-1.0, 1.0),
+        'add': (-1.0, 1.0, None, None),
+        'subtract': (-1.0, 1.0, None, None),
+        'multiply': (-1.0, 1.0, lambda y, x1, x2: _np.broadcast_to(x2, y.shape),
+                                lambda y, x1, x2: _np.broadcast_to(x1, y.shape))
     }
 
     shape_pairs = [((3, 2), (3, 2)),
                    ((3, 2), (3, 1)),
+                   ((3, 0), (3, 0)),
                    ((3, 1), (3, 0)),
                    ((0, 2), (1, 2)),
                    ((2, 3, 4), (3, 1)),
@@ -1755,16 +1759,16 @@ def hybrid_forward(self, F, a, b, *args, **kwargs):
     itypes = [np.bool, np.int8, np.int32, np.int64]
     ftypes = [np.float16, np.float32, np.float64]
     for func, func_data in funcs.items():
-        low, high = func_data
+        low, high, lgrad, rgrad = func_data
         for lshape, rshape in shape_pairs:
             for type1, type2 in itertools.product(itypes, ftypes):
-                check_mixed_precision_binary_func(func, low, high, lshape, rshape, type1, type2)
-                check_mixed_precision_binary_func(func, low, high, lshape, rshape, type2, type1)
+                check_mixed_precision_binary_func(func, low, high, lshape, rshape, lgrad, rgrad, type1, type2)
+                check_mixed_precision_binary_func(func, low, high, lshape, rshape, lgrad, rgrad, type2, type1)
 
             for type1, type2 in itertools.product(ftypes, ftypes):
                 if type1 == type2:
                     continue
-                check_mixed_precision_binary_func(func, low, high, lshape, rshape, type1, type2)
+                check_mixed_precision_binary_func(func, low, high, lshape, rshape, lgrad, rgrad, type1, type2)
 
 
 @with_seed()

From 95c3d03a13f775f143731fdedb01f8cc72e59a22 Mon Sep 17 00:00:00 2001
From: Serge Panev <spanev@nvidia.com>
Date: Thu, 21 Nov 2019 00:21:25 +0100
Subject: [PATCH 617/813] Enforces NDArray type in get_symbol (#16871)

Signed-off-by: Serge Panev <spanev@nvidia.com>
---
 python/mxnet/autograd.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/python/mxnet/autograd.py b/python/mxnet/autograd.py
index 6f1cc4367821..cb2153d4d94d 100644
--- a/python/mxnet/autograd.py
+++ b/python/mxnet/autograd.py
@@ -360,6 +360,8 @@ def get_symbol(x):
     Symbol
         The retrieved Symbol.
     """
+    assert isinstance(x, NDArray), \
+       "get_symbol: Invalid argument type, expecting %s, got %s"%(NDArray, type(x))
     hdl = SymbolHandle()
     check_call(_LIB.MXAutogradGetSymbol(x.handle, ctypes.byref(hdl)))
     return Symbol(hdl)

From 307037ec4382172898d87b9a297090782aaa30c7 Mon Sep 17 00:00:00 2001
From: Dick Carter <dcarter@nvidia.com>
Date: Wed, 20 Nov 2019 15:43:44 -0800
Subject: [PATCH 618/813] USE_NVRTC -> ENABLE_CUDA_RTC to fix maven build.  Add
 compile-guard to fusion. (#16838)

* Rename USE_NVRTC -> ENABLE_CUDA_RTC to fix maven build.  Compile-guard fusion framework.

* Fix fusion-not-supported warning.

* Fix compile guards

* Fix cmake build so -DMXNET_ENABLE_CUDA_RTC=1 is passed to nvcc

* Minimize side-effects of prev change
---
 CMakeLists.txt                        |  6 ++++--
 appveyor.yml                          |  2 +-
 ci/build_windows.py                   | 12 ++++++------
 make/maven/maven_darwin_mkl.mk        |  2 +-
 make/maven/maven_linux_cu90mkl.mk     |  2 +-
 make/maven/maven_linux_cu92mkl.mk     |  2 +-
 make/maven/maven_linux_mkl.mk         |  2 +-
 src/executor/exec_pass.h              |  5 +++++
 src/executor/graph_executor.cc        |  9 +++++++--
 src/executor/pointwise_fusion_pass.cc | 22 +++++++++++++++++++---
 src/imperative/cached_op.cc           | 13 ++++++++-----
 src/operator/fusion/fused_op-inl.h    |  4 ++--
 src/operator/fusion/fused_op.cc       |  4 ++--
 src/operator/fusion/fused_op.cu       |  5 +++++
 src/operator/fusion/fused_op.h        |  7 +++----
 15 files changed, 66 insertions(+), 31 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 4e808094dd96..1d3ce94e7ebf 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -633,6 +633,10 @@ if(USE_CUDA)
   else()
     list(APPEND CUDA_INCLUDE_DIRS ${INCLUDE_DIRECTORIES})
     # define preprocessor macro so that we will not include the generated forcelink header
+    if(ENABLE_CUDA_RTC)
+      add_definitions(-DMXNET_ENABLE_CUDA_RTC=1)
+    endif()
+    # Create '.cmake' files for cuda compiles given definitions added thus far
     mshadow_cuda_compile(cuda_objs ${CUDA})
     if(MSVC)
         if(ENABLE_CUDA_RTC)
@@ -640,7 +644,6 @@ if(USE_CUDA)
             list(APPEND mxnet_LINKER_LIBS ${CUDA_nvrtc_LIBRARY})
             set(CUDA_cuda_LIBRARY "${CUDA_nvrtc_LIBRARY}/../cuda.lib")
             list(APPEND mxnet_LINKER_LIBS ${CUDA_cuda_LIBRARY})
-            add_definitions(-DMXNET_ENABLE_CUDA_RTC=1)
         endif()
         FIND_LIBRARY(CUDA_cufft_LIBRARY nvrtc "${CUDA_TOOLKIT_ROOT_DIR}/lib/x64"  "${CUDA_TOOLKIT_ROOT_DIR}/lib/win32")
         list(APPEND mxnet_LINKER_LIBS "${CUDA_cufft_LIBRARY}/../cufft.lib") # For fft operator
@@ -652,7 +655,6 @@ if(USE_CUDA)
         list(APPEND mxnet_LINKER_LIBS cufft cusolver)
         if(ENABLE_CUDA_RTC)
             list(APPEND mxnet_LINKER_LIBS nvrtc cuda)
-            add_definitions(-DMXNET_ENABLE_CUDA_RTC=1)
         endif()
         link_directories("${CUDA_TOOLKIT_ROOT_DIR}/lib64")
     endif()
diff --git a/appveyor.yml b/appveyor.yml
index d44f52a0a9a9..9fa495002a1f 100644
--- a/appveyor.yml
+++ b/appveyor.yml
@@ -69,7 +69,7 @@ before_build:
 
         set OpenCV_DIR=%APPVEYOR_BUILD_FOLDER%/%MXNET_OPENCV_DIR%/build
 
-        cmake .. -DOPENCV_DIR=%OpenCV_DIR% -DUSE_PROFILER=1 -DUSE_CUDA=0 -DUSE_CUDNN=0 -DUSE_NVRTC=0 -DUSE_OPENCV=1 -DUSE_OPENMP=1 -DUSE_BLAS=open -DUSE_LAPACK=1 -DUSE_DIST_KVSTORE=0 -G "Visual Studio 12 2013 Win64"
+        cmake .. -DOPENCV_DIR=%OpenCV_DIR% -DUSE_PROFILER=1 -DUSE_CUDA=0 -DUSE_CUDNN=0 -DENABLE_CUDA_RTC=0 -DUSE_OPENCV=1 -DUSE_OPENMP=1 -DUSE_BLAS=open -DUSE_LAPACK=1 -DUSE_DIST_KVSTORE=0 -G "Visual Studio 12 2013 Win64"
 
 build_script:
     - cmd: >-
diff --git a/ci/build_windows.py b/ci/build_windows.py
index 4673bd535e3e..ce77c316ab20 100755
--- a/ci/build_windows.py
+++ b/ci/build_windows.py
@@ -54,7 +54,7 @@ class BuildFlavour(Enum):
     'WIN_CPU': (
         '-DUSE_CUDA=OFF '
         '-DUSE_CUDNN=OFF '
-        '-DUSE_NVRTC=OFF '
+        '-DENABLE_CUDA_RTC=OFF '
         '-DUSE_OPENCV=ON '
         '-DUSE_OPENMP=ON '
         '-DUSE_BLAS=open '
@@ -67,7 +67,7 @@ class BuildFlavour(Enum):
     , 'WIN_CPU_MKLDNN': (
         '-DUSE_CUDA=OFF '
         '-DUSE_CUDNN=OFF '
-        '-DUSE_NVRTC=OFF '
+        '-DENABLE_CUDA_RTC=OFF '
         '-DUSE_OPENCV=ON '
         '-DUSE_OPENMP=ON '
         '-DUSE_BLAS=open '
@@ -80,7 +80,7 @@ class BuildFlavour(Enum):
     , 'WIN_CPU_MKLDNN_MKL': (
         '-DUSE_CUDA=OFF '
         '-DUSE_CUDNN=OFF '
-        '-DUSE_NVRTC=OFF '
+        '-DENABLE_CUDA_RTC=OFF '
         '-DUSE_OPENCV=ON '
         '-DUSE_OPENMP=ON '
         '-DUSE_BLAS=mkl '
@@ -93,7 +93,7 @@ class BuildFlavour(Enum):
     , 'WIN_CPU_MKL': (
         '-DUSE_CUDA=OFF '
         '-DUSE_CUDNN=OFF '
-        '-DUSE_NVRTC=OFF '
+        '-DENABLE_CUDA_RTC=OFF '
         '-DUSE_OPENCV=ON '
         '-DUSE_OPENMP=ON '
         '-DUSE_BLAS=mkl '
@@ -106,7 +106,7 @@ class BuildFlavour(Enum):
     , 'WIN_GPU': (
         '-DUSE_CUDA=ON '
         '-DUSE_CUDNN=ON '
-        '-DUSE_NVRTC=ON '
+        '-DENABLE_CUDA_RTC=ON '
         '-DUSE_OPENCV=ON  '
         '-DUSE_OPENMP=ON '
         '-DUSE_BLAS=open '
@@ -122,7 +122,7 @@ class BuildFlavour(Enum):
     , 'WIN_GPU_MKLDNN': (
         '-DUSE_CUDA=ON '
         '-DUSE_CUDNN=ON '
-        '-DUSE_NVRTC=ON '
+        '-DENABLE_CUDA_RTC=ON '
         '-DUSE_OPENCV=ON '
         '-DUSE_OPENMP=ON '
         '-DUSE_BLAS=open '
diff --git a/make/maven/maven_darwin_mkl.mk b/make/maven/maven_darwin_mkl.mk
index a7f2bdb027d4..9bf3fc46ce0b 100644
--- a/make/maven/maven_darwin_mkl.mk
+++ b/make/maven/maven_darwin_mkl.mk
@@ -77,7 +77,7 @@ USE_CUDNN = 0
 # CUDA_ARCH :=
 
 # whether use cuda runtime compiling for writing kernels in native language (i.e. Python)
-USE_NVRTC = 0
+ENABLE_CUDA_RTC = 0
 
 # use openmp for parallelization
 USE_OPENMP = 0
diff --git a/make/maven/maven_linux_cu90mkl.mk b/make/maven/maven_linux_cu90mkl.mk
index e9ba46509973..e8caf73f186e 100644
--- a/make/maven/maven_linux_cu90mkl.mk
+++ b/make/maven/maven_linux_cu90mkl.mk
@@ -80,7 +80,7 @@ USE_NCCL = 1
 
 # whether use cuda runtime compiling for writing kernels in native language (i.e. Python)
 USE_NVTX=1
-USE_NVRTC = 1
+ENABLE_CUDA_RTC = 1
 
 # use openmp for parallelization
 USE_OPENMP = 1
diff --git a/make/maven/maven_linux_cu92mkl.mk b/make/maven/maven_linux_cu92mkl.mk
index caa1c59c01d5..930341e71cb1 100644
--- a/make/maven/maven_linux_cu92mkl.mk
+++ b/make/maven/maven_linux_cu92mkl.mk
@@ -80,7 +80,7 @@ USE_NCCL = 1
 
 # whether use cuda runtime compiling for writing kernels in native language (i.e. Python)
 USE_NVTX=1
-USE_NVRTC = 1
+ENABLE_CUDA_RTC = 1
 
 # use openmp for parallelization
 USE_OPENMP = 1
diff --git a/make/maven/maven_linux_mkl.mk b/make/maven/maven_linux_mkl.mk
index 3c8534a7e2aa..10aee5f35a46 100644
--- a/make/maven/maven_linux_mkl.mk
+++ b/make/maven/maven_linux_mkl.mk
@@ -76,7 +76,7 @@ USE_CUDNN = 0
 # CUDA_ARCH :=
 
 # whether use cuda runtime compiling for writing kernels in native language (i.e. Python)
-USE_NVRTC = 0
+ENABLE_CUDA_RTC = 0
 
 # use openmp for parallelization
 USE_OPENMP = 1
diff --git a/src/executor/exec_pass.h b/src/executor/exec_pass.h
index a5f125affcb0..55d431cf3298 100644
--- a/src/executor/exec_pass.h
+++ b/src/executor/exec_pass.h
@@ -221,6 +221,11 @@ Graph FusePointwiseForward(Graph&& g);
  */
 Graph FusePointwiseBackward(Graph&& g);
 
+/*!
+ * \brief Issue a one-time warning that fusion is not possible for this platform or build.
+ */
+void WarnFusionNotSupported();
+
 /*!
  * \brief Infer shapes in the graph given the information.
  * \param graph The input graph.
diff --git a/src/executor/graph_executor.cc b/src/executor/graph_executor.cc
index 7d5c2af0f881..a57d6c25a912 100644
--- a/src/executor/graph_executor.cc
+++ b/src/executor/graph_executor.cc
@@ -999,7 +999,7 @@ Graph GraphExecutor::InitGraph(nnvm::Symbol symbol,
   // setup gradient
   nnvm::Graph g = InitFullGraph(symbol, grad_req_types);
 
-#if MXNET_USE_CUDA && !defined(_WIN32)
+#if MXNET_USE_CUDA && MXNET_ENABLE_CUDA_RTC && !defined(_WIN32)
   if (default_ctx.dev_mask() == Context::kGPU && dmlc::GetEnv("MXNET_USE_FUSION", true)) {
     nnvm::Graph unoptimized_graph;
     common::CopyGraph(&unoptimized_graph, g, false);
@@ -1032,7 +1032,12 @@ Graph GraphExecutor::InitGraph(nnvm::Symbol symbol,
         << "Graph contains duplicate names for some of its inputs - fusion is NOT enabled!";
      }
   }
-#endif  // MXNET_USE_CUDA
+#else
+  // Only warn user if MXNET_USE_FUSION env var is explicitly set
+  if (default_ctx.dev_mask() == Context::kGPU && dmlc::GetEnv("MXNET_USE_FUSION", false)) {
+    WarnFusionNotSupported();
+  }
+#endif  // MXNET_USE_CUDA && MXNET_ENABLE_CUDA_RTC && !defined(_WIN32)
 
   // create "device" and "context" attrs for the graph
   g = AssignContext(g, default_ctx, ctx_map,
diff --git a/src/executor/pointwise_fusion_pass.cc b/src/executor/pointwise_fusion_pass.cc
index 6fe21402cb3a..6a0d5f4efe87 100644
--- a/src/executor/pointwise_fusion_pass.cc
+++ b/src/executor/pointwise_fusion_pass.cc
@@ -36,10 +36,26 @@
 #include "../operator/fusion/fused_op.h"
 #include "../operator/operator_common.h"
 
-#if MXNET_USE_CUDA
-
 namespace mxnet {
 namespace exec {
+
+void WarnFusionNotSupported() {
+  static bool issued_warning = false;
+  if (!issued_warning) {
+    issued_warning = true;
+#if defined(_WIN32)
+    LOG(WARNING) << "Omitting dynamic fused op creation- not enabled on Windows.  "
+                 << "Unset env var MXNET_USE_FUSION=1 to quiet this message.";
+#else
+    LOG(WARNING) << "Omitting dynamic fused op creation- needs MXNet lib built with "
+                   << "USE_CUDA=1 and ENABLE_CUDA_RTC=1.  Unset env var MXNET_USE_FUSION=1 "
+                   << "to quiet this message.";
+#endif  // defined(_WIN32)
+  }
+}
+
+#if MXNET_USE_CUDA && MXNET_ENABLE_CUDA_RTC
+
 namespace {
   bool IsFusionCompatible(nnvm::Node* n) {
     using namespace mxnet::fusion;
@@ -304,8 +320,8 @@ Graph FusePointwiseBackward(Graph &&g) {
   ret.outputs = g.outputs;
   return ret;
 }
+#endif  // MXNET_USE_CUDA && MXNET_ENABLE_CUDA_RTC
 
 }  // namespace exec
 }  // namespace mxnet
 
-#endif  // MXNET_USE_CUDA
diff --git a/src/imperative/cached_op.cc b/src/imperative/cached_op.cc
index 269729c18f58..24270f210888 100644
--- a/src/imperative/cached_op.cc
+++ b/src/imperative/cached_op.cc
@@ -167,10 +167,8 @@ void SetRefCounts(nnvm::Graph* fwd_graph, const nnvm::Graph& full_graph) {
 
 void OptimizeGraph(nnvm::Graph * full_graph, nnvm::Graph * fwd_graph, nnvm::Graph * grad_graph,
                    const Context& context, size_t num_forward_outputs, const bool inlining) {
-#if MXNET_USE_CUDA && !defined(_WIN32)
-  if (context.dev_mask() == kGPU &&
-      !inlining &&
-      dmlc::GetEnv("MXNET_USE_FUSION", true)) {
+#if MXNET_USE_CUDA && MXNET_ENABLE_CUDA_RTC && !defined(_WIN32)
+  if (context.dev_mask() == kGPU && !inlining && dmlc::GetEnv("MXNET_USE_FUSION", true)) {
     nnvm::Graph unoptimized_graph;
     common::CopyGraph(&unoptimized_graph, *full_graph, false);
 
@@ -202,7 +200,12 @@ void OptimizeGraph(nnvm::Graph * full_graph, nnvm::Graph * fwd_graph, nnvm::Grap
         << "Graph contains duplicate names for some of its inputs - fusion is NOT enabled!";
      }
   }
-#endif  // MXNET_USE_CUDA
+#else
+  // Only warn user if MXNET_USE_FUSION env var is explicitly set
+  if (context.dev_mask() == kGPU && !inlining && dmlc::GetEnv("MXNET_USE_FUSION", false)) {
+    exec::WarnFusionNotSupported();
+  }
+#endif  // MXNET_USE_CUDA && MXNET_ENABLE_CUDA_RTC && !defined(_WIN32)
 
   *fwd_graph = nnvm::Graph();
   fwd_graph->outputs = std::vector<nnvm::NodeEntry>(full_graph->outputs.begin(),
diff --git a/src/operator/fusion/fused_op-inl.h b/src/operator/fusion/fused_op-inl.h
index 2966fe2ae910..e86ce7682ad8 100644
--- a/src/operator/fusion/fused_op-inl.h
+++ b/src/operator/fusion/fused_op-inl.h
@@ -24,7 +24,7 @@
 #include <map>
 #include <vector>
 
-#if MXNET_USE_CUDA
+#if MXNET_USE_CUDA && MXNET_ENABLE_CUDA_RTC
 
 namespace mxnet {
 
@@ -992,6 +992,6 @@ const char kernel_end[] = R"code(}
 
 }  // namespace mxnet
 
-#endif  // MXNET_USE_CUDA
+#endif  // MXNET_USE_CUDA && MXNET_ENABLE_CUDA_RTC
 
 #endif  // MXNET_OPERATOR_FUSION_FUSED_OP_INL_H_
diff --git a/src/operator/fusion/fused_op.cc b/src/operator/fusion/fused_op.cc
index 5c83c30308c7..5e2d782dd9e0 100644
--- a/src/operator/fusion/fused_op.cc
+++ b/src/operator/fusion/fused_op.cc
@@ -23,7 +23,7 @@
 #include "../operator_common.h"
 #include "../../executor/exec_pass.h"
 
-#if MXNET_USE_CUDA
+#if MXNET_USE_CUDA && MXNET_ENABLE_CUDA_RTC
 
 namespace mxnet {
 
@@ -302,4 +302,4 @@ NNVM_REGISTER_OP(_FusedOpOutHelper)
 
 }  // namespace mxnet
 
-#endif  // MXNET_USE_CUDA
+#endif  // MXNET_USE_CUDA && MXNET_ENABLE_CUDA_RTC
diff --git a/src/operator/fusion/fused_op.cu b/src/operator/fusion/fused_op.cu
index 78988f13510e..62f340d0e00b 100644
--- a/src/operator/fusion/fused_op.cu
+++ b/src/operator/fusion/fused_op.cu
@@ -17,6 +17,9 @@
  * under the License.
  */
 
+// Additional use of MXNET_USE_CUDA is not needed to guard a '.cu' file.
+#if MXNET_ENABLE_CUDA_RTC
+
 #include <sys/stat.h>
 #include <nvrtc.h>
 #include <cuda.h>
@@ -787,3 +790,5 @@ NNVM_REGISTER_OP(_FusedOp)
 .set_attr<FCompute>("FCompute<gpu>", FusedOpForwardGPU);
 
 }  // namespace mxnet
+
+#endif  // MXNET_ENABLE_CUDA_RTC
diff --git a/src/operator/fusion/fused_op.h b/src/operator/fusion/fused_op.h
index 24603ac1932f..7d714677e941 100644
--- a/src/operator/fusion/fused_op.h
+++ b/src/operator/fusion/fused_op.h
@@ -20,7 +20,6 @@
 #ifndef MXNET_OPERATOR_FUSION_FUSED_OP_H_
 #define MXNET_OPERATOR_FUSION_FUSED_OP_H_
 
-
 #include <mxnet/operator.h>
 #include <nnvm/graph.h>
 #include <vector>
@@ -29,8 +28,7 @@
 #include <mutex>
 #include <tuple>
 
-#if MXNET_USE_CUDA
-
+#if MXNET_USE_CUDA && MXNET_ENABLE_CUDA_RTC
 
 namespace mxnet {
 
@@ -202,5 +200,6 @@ using FusedOpHelperParamPtr = std::shared_ptr<FusedOpHelperParam>;
 
 }  // namespace mxnet
 
-#endif  // MXNET_USE_CUDA
+#endif  // MXNET_USE_CUDA && MXNET_ENABLE_CUDA_RTC
+
 #endif  // MXNET_OPERATOR_FUSION_FUSED_OP_H_

From a8b31a239f5d5ed0ebff0f3be44b5e5534e0b3f5 Mon Sep 17 00:00:00 2001
From: Przemyslaw Tredak <ptredak@nvidia.com>
Date: Wed, 20 Nov 2019 20:53:42 -0800
Subject: [PATCH 619/813] Fix InferAttr/InferShapeAttr not calling inference
 for all nodes in a graph (#16836)

* Fix the attribute inference omitting nodes

* Add test

* Cleaning

* Fix lint

* Fix TransposeShape

* Fix WhileLoopType

* Changing a/b test for fusion to a/(b+1) to increase numerical stability
---
 src/executor/infer_graph_attr_pass.cc | 201 ++++++++++++++++++--------
 src/operator/control_flow.cc          |   2 +-
 src/operator/tensor/matrix_op-inl.h   |   4 +-
 tests/python/gpu/test_fusion.py       |   2 +-
 tests/python/unittest/test_symbol.py  |  17 ++-
 5 files changed, 159 insertions(+), 67 deletions(-)

diff --git a/src/executor/infer_graph_attr_pass.cc b/src/executor/infer_graph_attr_pass.cc
index 80e4084c478e..4b6ee2e1dc0d 100644
--- a/src/executor/infer_graph_attr_pass.cc
+++ b/src/executor/infer_graph_attr_pass.cc
@@ -67,6 +67,7 @@ template<typename AttrType, typename IsNone>
 inline void GetAttrFromForwardNode(const uint32_t nid,
                                    const nnvm::IndexedGraph &idx,
                                    std::vector<AttrType>* rshape_ptr,
+                                   std::vector<bool>* inference_finished,
                                    IsNone fis_none) {
   std::vector<AttrType>& rshape = *rshape_ptr;
   const nnvm::IndexedGraph::Node& inode = idx[nid];
@@ -83,18 +84,23 @@ inline void GetAttrFromForwardNode(const uint32_t nid,
   // input gradient list
   const std::vector<nnvm::NodeEntry>& igrad = fgrad[fwd_ptr->op()](fwd_ptr, ograd);
   const nnvm::Node* igrad_node = nullptr;
+  bool all_attrs_known = true;
   // Input gradient assignement
   for (size_t i = 0; i < igrad.size(); ++i) {
     if (igrad[i].node->op() == inode.source->op()) {
       uint32_t eid = idx.entry_id(nid, igrad[i].index);
-      if (fis_none(rshape[eid])) {
-        rshape[eid] = rshape[idx.entry_id(fnode.inputs[i])];
-      } else if (!fis_none(rshape[idx.entry_id(fnode.inputs[i])])) {
+      if (fis_none(rshape[idx.entry_id(fnode.inputs[i])])) {
         // Need to skip empty forward shape, because it may not be
         // available now and it is possible to infer the forward
         // shape in one of the next a few passes
-        CHECK_EQ(rshape[eid], rshape[idx.entry_id(fnode.inputs[i])])
-            << "Backward shape inconsistent with the forward shape";
+        all_attrs_known = false;
+      } else {
+        if (fis_none(rshape[eid])) {
+          rshape[eid] = rshape[idx.entry_id(fnode.inputs[i])];
+        } else {
+          CHECK_EQ(rshape[eid], rshape[idx.entry_id(fnode.inputs[i])])
+              << "Backward shape inconsistent with the forward shape";
+        }
       }
       if (igrad_node == nullptr) {
         igrad_node = igrad[i].node.get();
@@ -113,14 +119,20 @@ inline void GetAttrFromForwardNode(const uint32_t nid,
       if (fis_none(rshape[eid])) {
         rshape[eid] = rshape[idx.entry_id(inode.control_deps[0], e.index)];
       }
+      if (fis_none(rshape[eid])) {
+        // If the attr is still unknown
+        all_attrs_known = false;
+      }
     }
   }
+  (*inference_finished)[nid] = all_attrs_known;
 }
 
 template<typename FAccessSubgraphType, typename AttrType, typename IsNone>
 void GetAttrFromFusedNode(uint32_t nid,
                           const nnvm::IndexedGraph& idx,
                           std::vector<AttrType>* rshape_ptr,
+                          std::vector<bool>* inference_finished,
                           IsNone fis_none,
                           const std::string& infer_fusion_name) {
   std::vector<AttrType>& rshape = *rshape_ptr;
@@ -147,19 +159,24 @@ void GetAttrFromFusedNode(uint32_t nid,
   // input gradient list
   const std::vector<nnvm::NodeEntry>& igrad = fgrad[fwd_ptr->op()](fwd_ptr, ograd);
   const nnvm::Node* igrad_node = nullptr;
+  bool all_attrs_known = true;
   // Set the attributes of output gradients
   // using attributes of forward node inputs
   for (size_t i = 0; i < igrad.size(); ++i) {
     if (igrad[i].node->op() == inode.source->op()) {
       uint32_t eid = idx.entry_id(nid, igrad[i].index);
-      if (fis_none(rshape[eid])) {
-        rshape[eid] = input_attrs[i];
-      } else if (!fis_none(input_attrs[i])) {
+      if (fis_none(input_attrs[i])) {
         // Need to skip empty forward shape, because it may not be
         // available now and it is possible to infer the forward
         // shape in one of the next a few passes
-        CHECK_EQ(rshape[eid], input_attrs[i])
-            << "Backward shape inconsistent with the forward shape";
+        all_attrs_known = false;
+      } else {
+        if (fis_none(rshape[eid])) {
+          rshape[eid] = input_attrs[i];
+        } else {
+          CHECK_EQ(rshape[eid], input_attrs[i])
+              << "Backward shape inconsistent with the forward shape";
+        }
       }
       if (igrad_node == nullptr) {
         igrad_node = igrad[i].node.get();
@@ -180,8 +197,13 @@ void GetAttrFromFusedNode(uint32_t nid,
       if (fis_none(rshape[eid])) {
         rshape[eid] = output_attrs[e.index];
       }
+      if (fis_none(rshape[eid])) {
+        // If the attr is still unknown
+        all_attrs_known = false;
+      }
     }
   }
+  (*inference_finished)[nid] = all_attrs_known;
 }
 
 template <typename FProvideSubgraphType, typename AttrType>
@@ -270,6 +292,9 @@ nnvm::Graph InferAttr(nnvm::Graph &&ret,
       Op::GetAttr<nnvm::TIsBackward>("TIsBackward");
   // reshape shape vector
   AttrVector rshape;
+  // vector holding information which operators
+  // finished attribute inference
+  std::vector<bool> inference_finished(idx.num_nodes(), false);
   // dispatch mode vector
   DispatchModeVector dispatch_modes;
   if (ret.attrs.count(attr_name) != 0) {
@@ -340,6 +365,7 @@ nnvm::Graph InferAttr(nnvm::Graph &&ret,
 
   // inference step function for nid
   auto infer_step = [&](uint32_t nid, bool last_iter) {
+    if (inference_finished[nid]) return;
     const auto& inode = idx[nid];
     const uint32_t num_inputs = inode.inputs.size();
     const uint32_t num_outputs = inode.source->num_outputs();
@@ -355,6 +381,9 @@ nnvm::Graph InferAttr(nnvm::Graph &&ret,
           CHECK(is >> rshape[out_ent_id]) << "Invalid attribute";
         }
       }
+      if (!fis_none(rshape[out_ent_id])) {
+        inference_finished[nid] = true;
+      }
       // assign a default value to node attribute
       if (dispatch_mode_name != nullptr) {
         op::dispatch_mode_assign(&dispatch_modes[nid], default_mode_val);
@@ -370,47 +399,66 @@ nnvm::Graph InferAttr(nnvm::Graph &&ret,
 
       static auto& is_fusion_helper = Op::GetAttr<exec::TIsFusionHelper>("TIsFusionHelper");
       if (!is_fusion_helper.get(fwd_ptr->op(), false)) {
-        GetAttrFromForwardNode(nid, idx, &rshape, fis_none);
+        GetAttrFromForwardNode(nid, idx, &rshape, &inference_finished, fis_none);
       } else {
-        GetAttrFromFusedNode<FAccessSubgraphType>(nid, idx, &rshape, fis_none, infer_fusion_name);
+        GetAttrFromFusedNode<FAccessSubgraphType>(nid, idx, &rshape, &inference_finished,
+                                                  fis_none, infer_fusion_name);
       }
     } else {
       DispatchMode* dispatch_mode = nullptr;
-      bool forward_known = true;
       // Forward operator inference.
       ishape.resize(num_inputs, empty_val);
       for (uint32_t i = 0; i < ishape.size(); ++i) {
         ishape[i] = rshape[idx.entry_id(inode.inputs[i])];
-        if (fis_none(ishape[i])) forward_known = false;
       }
       oshape.resize(num_outputs, empty_val);
       for (uint32_t i = 0; i < oshape.size(); ++i) {
         oshape[i] = rshape[idx.entry_id(nid, i)];
-        if (fis_none(oshape[i])) forward_known = false;
       }
       if (dispatch_mode_name != nullptr) {
         dispatch_mode = &dispatch_modes[nid];
-        if (dispatch_modes[nid] == DispatchMode::kUndefined) forward_known = false;
       }
       auto finfer = finfer_shape.get(inode.source->op(), fdefault);
-      if (!forward_known) {
-        if (finfer != nullptr) {
-          // Call inference function of the operator.
-          try {
-            static auto& is_fusion = Op::GetAttr<exec::TIsFusion>("TIsFusion");
-            if (is_fusion.get(inode.source->op(), false)) {
-              ProvideAttrToFusion<FProvideSubgraphType>(nid, idx, rshape, provide_fusion_name);
-            }
-            forward_known = ApplyOpInferAttr(ret, finfer, inode.source->attrs,
-                                             nid, &ishape, &oshape, dispatch_mode);
-          } catch (const std::exception& e) {
-            throw dmlc::Error("Error in operator " + inode.source->attrs.name + ": " + e.what());
+      if (finfer != nullptr) {
+        // Call inference function of the operator.
+        try {
+          static auto& is_fusion = Op::GetAttr<exec::TIsFusion>("TIsFusion");
+          if (is_fusion.get(inode.source->op(), false)) {
+            ProvideAttrToFusion<FProvideSubgraphType>(nid, idx, rshape, provide_fusion_name);
           }
-        } else {
+          ApplyOpInferAttr(ret, finfer, inode.source->attrs,
+                           nid, &ishape, &oshape, dispatch_mode);
+          bool finished = true;
+          for (const auto& attr : ishape) {
+            if (fis_none(attr)) finished = false;
+          }
+          for (const auto& attr : oshape) {
+            if (fis_none(attr)) finished = false;
+          }
+          inference_finished[nid] = finished;
+        } catch (const std::exception& e) {
+          throw dmlc::Error("Error in operator " + inode.source->attrs.name + ": " + e.what());
+        }
+      } else {
+        // Operator does not provide sttribute inference function,
+        // so we need to test if everything was inferred by other operators
+        bool all_attrs_known = true;
+        for (const auto& attr : ishape) {
+          if (fis_none(attr)) {
+            all_attrs_known = false;
+          }
+        }
+        for (const auto& attr : oshape) {
+          if (fis_none(attr)) {
+            all_attrs_known = false;
+          }
+        }
+        inference_finished[nid] = all_attrs_known;
+        if (!all_attrs_known) {
           CHECK(!last_iter)
               << "Attribute " << infer_name
-              << " is not registed by op " << inode.source->op()->name
-              << " we are not able to complete the inference because of this";
+              << " is not registered by op " << inode.source->op()->name
+              << ". We are not able to complete the inference because of this";
         }
       }
       // Save to the result map.
@@ -427,16 +475,18 @@ nnvm::Graph InferAttr(nnvm::Graph &&ret,
   size_t num_unknown_dispatch_mode = dispatch_mode_name ? node_end - node_start : 0;
   size_t num_unknown_entry_attr = entry_end - entry_start;
   size_t num_unknown = num_unknown_entry_attr + num_unknown_dispatch_mode;
+  bool last_iter = false;
+  bool do_next_iteration = true;
   int i = 0;
   do {
     if (i % 2 == 0) {
       for (uint32_t nid = node_start; nid < node_end; ++nid) {
-        infer_step(nid, false);
+        infer_step(nid, last_iter);
       }
     } else {
       // backward inference
       for (uint32_t i = node_end; i != node_start; --i) {
-        infer_step(i - 1, false);
+        infer_step(i - 1, last_iter);
       }
     }
     last_num_unknown = num_unknown;
@@ -451,8 +501,18 @@ nnvm::Graph InferAttr(nnvm::Graph &&ret,
         if (dispatch_modes[i] == DispatchMode::kUndefined) ++num_unknown;
       }
     }
+    do_next_iteration = num_unknown > 0 && last_num_unknown > num_unknown;
+    if (!do_next_iteration && !last_iter) {
+      // Check if every op agrees that it should be
+      // the end of attribute inference. If not,
+      // perform one final step
+      for (const bool done : inference_finished) {
+        do_next_iteration = do_next_iteration || !done;
+      }
+      last_iter = true;
+    }
     ++i;
-  } while (num_unknown > 0 && last_num_unknown > num_unknown);
+  } while (do_next_iteration);
   // set the shapes
   ret.attrs[attr_name] = std::make_shared<any>(std::move(rshape));
   // set the shapes
@@ -517,6 +577,9 @@ nnvm::Graph InferShapeAttr(nnvm::Graph &&ret,
       Op::GetAttr<nnvm::TIsBackward>("TIsBackward");
   // reshape shape vector
   AttrVector rshape;
+  // vector holding information which operators
+  // finished attribute inference
+  std::vector<bool> inference_finished(idx.num_nodes(), false);
   // dispatch mode vector
   DispatchModeVector dispatch_modes;
   if (ret.attrs.count(attr_name) != 0) {
@@ -594,6 +657,7 @@ nnvm::Graph InferShapeAttr(nnvm::Graph &&ret,
 
   // inference step function for nid
   auto infer_step = [&](uint32_t nid, bool last_iter) {
+    if (inference_finished[nid]) return;
     const auto& inode = idx[nid];
     const std::string name = inode.source->attrs.name;
     const uint32_t num_inputs = inode.inputs.size();
@@ -613,6 +677,9 @@ nnvm::Graph InferShapeAttr(nnvm::Graph &&ret,
           }
         }
       }
+      if (!fis_none(rshape[out_ent_id])) {
+        inference_finished[nid] = true;
+      }
       // assign a default value to node attribute
       if (dispatch_mode_name != nullptr) {
         op::dispatch_mode_assign(&dispatch_modes[nid], default_mode_val);
@@ -628,14 +695,15 @@ nnvm::Graph InferShapeAttr(nnvm::Graph &&ret,
 
       static auto& is_fusion_helper = Op::GetAttr<exec::TIsFusionHelper>("TIsFusionHelper");
       if (!is_fusion_helper.get(fwd_ptr->op(), false)) {
-        GetAttrFromForwardNode(nid, idx, &rshape, fis_none);
+        GetAttrFromForwardNode(nid, idx, &rshape, &inference_finished, fis_none);
       } else {
-        GetAttrFromFusedNode<exec::FAccessSubgraphShape>(nid, idx, &rshape, fis_none,
+        GetAttrFromFusedNode<exec::FAccessSubgraphShape>(nid, idx, &rshape,
+                                                         &inference_finished,
+                                                         fis_none,
                                                          "FAccessSubgraphShape");
       }
     } else {
       DispatchMode* dispatch_mode = nullptr;
-      bool forward_known = true;
       // Forward operator inference.
       ishape.resize(num_inputs, empty_val);
       bool is_input_dynamic_shape = false;
@@ -644,16 +712,13 @@ nnvm::Graph InferShapeAttr(nnvm::Graph &&ret,
         if (!mxnet::ndim_is_known(ishape[i]) && is_dynamic[idx.entry_id(inode.inputs[i])]) {
           is_input_dynamic_shape = true;
         }
-        if (fis_none(ishape[i])) forward_known = false;
       }
       oshape.resize(num_outputs, empty_val);
       for (uint32_t i = 0; i < oshape.size(); ++i) {
         oshape[i] = rshape[idx.entry_id(nid, i)];
-        if (fis_none(oshape[i])) forward_known = false;
       }
       if (dispatch_mode_name != nullptr) {
         dispatch_mode = &dispatch_modes[nid];
-        if (dispatch_modes[nid] == DispatchMode::kUndefined) forward_known = false;
       }
       auto finfer = finfer_shape.get(inode.source->op(), fdefault);
       if (finfer == nullptr || is_input_dynamic_shape) {
@@ -662,25 +727,27 @@ nnvm::Graph InferShapeAttr(nnvm::Graph &&ret,
             is_dynamic[idx.entry_id(nid, i)] = 1;
           }
         }
-      } else if (!forward_known) {
-        if (finfer != nullptr) {
-          // Call inference function of the operator.
-          try {
-            static auto& is_fusion = Op::GetAttr<exec::TIsFusion>("TIsFusion");
-            if (is_fusion.get(inode.source->op(), false)) {
-              ProvideAttrToFusion<exec::FProvideSubgraphShape>(nid, idx, rshape,
-                                                               "FProvideSubgraphShape");
-            }
-            forward_known = ApplyOpInferAttr(ret, finfer, inode.source->attrs,
-                                             nid, &ishape, &oshape, dispatch_mode);
-          } catch (const std::exception& e) {
-            throw dmlc::Error("Error in operator " + inode.source->attrs.name + ": " + e.what());
+        inference_finished[nid] = true;
+      } else {
+        // Call inference function of the operator.
+        try {
+          static auto& is_fusion = Op::GetAttr<exec::TIsFusion>("TIsFusion");
+          if (is_fusion.get(inode.source->op(), false)) {
+            ProvideAttrToFusion<exec::FProvideSubgraphShape>(nid, idx, rshape,
+                                                             "FProvideSubgraphShape");
           }
-        } else {
-          CHECK(!last_iter)
-              << "Attribute " << infer_name
-              << " is not registed by op " << inode.source->op()->name
-              << " we are not able to complete the inference because of this";
+          ApplyOpInferAttr(ret, finfer, inode.source->attrs,
+                           nid, &ishape, &oshape, dispatch_mode);
+          bool finished = true;
+          for (const auto& attr : ishape) {
+            if (fis_none(attr)) finished = false;
+          }
+          for (const auto& attr : oshape) {
+            if (fis_none(attr)) finished = false;
+          }
+          inference_finished[nid] = finished;
+        } catch (const std::exception& e) {
+          throw dmlc::Error("Error in operator " + inode.source->attrs.name + ": " + e.what());
         }
       }
       // Save to the result map.
@@ -695,18 +762,20 @@ nnvm::Graph InferShapeAttr(nnvm::Graph &&ret,
 
   size_t last_num_unknown;
   size_t num_unknown = static_cast<size_t>(-1);  // Infinity
+  bool last_iter = false;
+  bool do_next_iteration = true;
 
   int i = 0;
   do {
     if (i % 2 == 0) {
       // forward inference
       for (uint32_t nid = node_start; nid < node_end; ++nid) {
-        infer_step(nid, false);
+        infer_step(nid, last_iter);
       }
     } else {
       // backward inference
       for (uint32_t i = node_end; i != node_start; --i) {
-        infer_step(i - 1, false);
+        infer_step(i - 1, last_iter);
       }
     }
     last_num_unknown = num_unknown;
@@ -723,8 +792,18 @@ nnvm::Graph InferShapeAttr(nnvm::Graph &&ret,
         }
       }
     }
+    do_next_iteration = num_unknown > 0 && last_num_unknown > num_unknown;
+    if (!do_next_iteration && !last_iter) {
+      // Check if every op agrees that it should be
+      // the end of attribute inference. If not,
+      // perform one final step
+      for (const bool done : inference_finished) {
+        do_next_iteration = do_next_iteration || !done;
+      }
+      last_iter = true;
+    }
     ++i;
-  } while (num_unknown > 0 && last_num_unknown > num_unknown);
+  } while (do_next_iteration);
   // set the shapes
   ret.attrs[attr_name] = std::make_shared<any>(std::move(rshape));
   // set the shapes
diff --git a/src/operator/control_flow.cc b/src/operator/control_flow.cc
index fd087ef39679..a9e9038e6c51 100644
--- a/src/operator/control_flow.cc
+++ b/src/operator/control_flow.cc
@@ -761,7 +761,7 @@ static bool WhileLoopType(const nnvm::NodeAttrs& attrs,
   std::vector<int> func_in_type;
   extract_by_loc(*in_type, params.cond_input_locs, &cond_in_type);
   extract_by_loc(*in_type, params.func_input_locs, &func_in_type);
-  std::vector<int> cond_out_type = {0};
+  std::vector<int> cond_out_type = {-1};
   CHECK(params.sync_in_out(in_type, out_type, is_udf));
   bool succ_0 = InferSubgraphDataType(*attrs.subgraphs[0], &cond_in_type, &cond_out_type);
   CHECK(params.sync_in_out(in_type, out_type, is_udf));
diff --git a/src/operator/tensor/matrix_op-inl.h b/src/operator/tensor/matrix_op-inl.h
index 801e4e7126b4..0fee2a26c0ed 100644
--- a/src/operator/tensor/matrix_op-inl.h
+++ b/src/operator/tensor/matrix_op-inl.h
@@ -420,11 +420,9 @@ inline bool TransposeShape(const nnvm::NodeAttrs& attrs,
   mxnet::TShape& shp = (*in_attrs)[0];
   mxnet::TShape& out_shp = (*out_attrs)[0];
   CHECK_LE(shp.ndim(), 6) << "Transpose support at most 6 dimensions";
-  CHECK_NE(shp.ndim(), 0) << "Number of dimensions cannot be 0";
-  CHECK_NE(out_shp.ndim(), 0) << "Number of dimensions cannot be 0";
   if (shp.ndim() == -1 && out_shp.ndim() == -1)
     return false;  // none of the shapes is known
-  if (out_shp.ndim() > 0 && shp.ndim() > 0)
+  if (out_shp.ndim() >= 0 && shp.ndim() >= 0)
     CHECK_EQ(out_shp.ndim(), shp.ndim());
   mxnet::TShape get(std::max(shp.ndim(), out_shp.ndim()), -1);
   mxnet::TShape ret(std::max(shp.ndim(), out_shp.ndim()), -1);
diff --git a/tests/python/gpu/test_fusion.py b/tests/python/gpu/test_fusion.py
index 693336f22496..24e33019f617 100644
--- a/tests/python/gpu/test_fusion.py
+++ b/tests/python/gpu/test_fusion.py
@@ -172,7 +172,7 @@ def check_binary_ops():
     check_fused_symbol(3-a, a=arr1)
     check_fused_symbol(a*b, a=arr1, b=arr2)
     check_fused_symbol(a*3, a=arr1)
-    check_fused_symbol(a/b, a=arr1, b=arr2)
+    check_fused_symbol(a/(b+1), a=arr1, b=arr2)
     check_fused_symbol(a/3, a=arr1)
     check_fused_symbol(3/a, a=arr1)
     check_fused_symbol(a**b, a=arr1, b=arr2)
diff --git a/tests/python/unittest/test_symbol.py b/tests/python/unittest/test_symbol.py
index a2aad2c079fc..8e4fe11905cf 100644
--- a/tests/python/unittest/test_symbol.py
+++ b/tests/python/unittest/test_symbol.py
@@ -413,7 +413,7 @@ def test_gen_atomic_symbol_multiple_outputs():
     p = mx.sym.Variable('param')
     h0 = mx.sym.Variable('h0')
     h1 = mx.sym.Variable('h1')
-    s = mx.sym.RNN(data, p, h0, h1, state_size=10, num_layers=2, 
+    s = mx.sym.RNN(data, p, h0, h1, state_size=10, num_layers=2,
                    bidirectional=True, state_outputs=True, mode='lstm')
     atomic_sym = s._gen_atomic_symbol()
 
@@ -542,6 +542,21 @@ def get_net():
                     assert out_shapes[0] == (batch_size, num_hdidden)  # output
                     assert len(aux_shapes) == 0
 
+def test_infershape_happens_for_all_ops_in_graph():
+    v = mx.sym.Variable('V')
+    s = mx.sym.transpose(v)
+    x = mx.sym.Variable('x')
+    s2 = x + v
+    s3 = s + s2
+    with discard_stderr():
+        try:
+            # This should throw an exception as you cannot add arrays
+            # with shapes [2,3] and [3,2]
+            e = s3.simple_bind(ctx=mx.cpu(), x=(2,3), grad_req='null')
+        except:
+            return
+
+    assert False
 
 if __name__ == '__main__':
     import nose

From ece027c12cb1b720ab643303c4541cfcac41e1af Mon Sep 17 00:00:00 2001
From: "Wentao,Xu" <46397251+CassiniXu@users.noreply.github.com>
Date: Fri, 22 Nov 2019 02:40:19 +0800
Subject: [PATCH 620/813] add numpy op diagflat [numpy] (#16813)

* Feature: diagflat

* Fix codes position error

* Fix redefinition error

* Fix style error&redefinition error

* Fix style error

* Fix python style error

* Fix python style error

* Fix npi error
---
 python/mxnet/_numpy_op_doc.py                 |  38 +++++-
 python/mxnet/numpy_dispatch_protocol.py       |   1 +
 src/operator/numpy/np_matrix_op-inl.h         | 127 ++++++++++++++++++
 src/operator/numpy/np_matrix_op.cc            |  23 ++++
 src/operator/numpy/np_matrix_op.cu            |   6 +
 .../unittest/test_numpy_interoperability.py   |  29 ++++
 tests/python/unittest/test_numpy_op.py        |  42 ++++++
 7 files changed, 265 insertions(+), 1 deletion(-)

diff --git a/python/mxnet/_numpy_op_doc.py b/python/mxnet/_numpy_op_doc.py
index 9e237cb0049b..cf991fc8949f 100644
--- a/python/mxnet/_numpy_op_doc.py
+++ b/python/mxnet/_numpy_op_doc.py
@@ -1089,7 +1089,7 @@ def _npx_reshape(a, newshape, reverse=False, order='C'):
     pass
 
 
-def _np_diag(array, k = 0):
+def _np_diag(array, k=0):
     """
     Extracts a diagonal or constructs a diagonal array.
     - 1-D arrays: constructs a 2-D array with the input as its diagonal, all other elements are zero.
@@ -1122,3 +1122,39 @@ def _np_diag(array, k = 0):
            [0, 0, 8]])
     """
     pass
+
+
+def _np_diagflat(array, k=0):
+    """
+    Create a two-dimensional array with the flattened input as a diagonal.
+    Parameters
+    ----------
+    arr : ndarray
+        Input data, which is flattened and set as the `k`-th
+        diagonal of the output.
+    k : int, optional
+        Diagonal to set; 0, the default, corresponds to the "main" diagonal,
+        a positive (negative) `k` giving the number of the diagonal above
+        (below) the main.
+    Returns
+    -------
+    out : ndarray
+        The 2-D output array.
+    See Also
+    --------
+    diag : MATLAB work-alike for 1-D and 2-D arrays.
+    diagonal : Return specified diagonals.
+    trace : Sum along diagonals.
+    Examples
+    --------
+    >>> np.diagflat([[1,2], [3,4]])
+    array([[1, 0, 0, 0],
+           [0, 2, 0, 0],
+           [0, 0, 3, 0],
+           [0, 0, 0, 4]])
+    >>> np.diagflat([1,2], 1)
+    array([[0, 1, 0],
+           [0, 0, 2],
+           [0, 0, 0]])
+    """
+    pass
\ No newline at end of file
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index f58159303d0f..67af2724503e 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -94,6 +94,7 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'copy',
     'cumsum',
     'diag',
+    'diagflat',
     'dot',
     'expand_dims',
     'fix',
diff --git a/src/operator/numpy/np_matrix_op-inl.h b/src/operator/numpy/np_matrix_op-inl.h
index 508968718af0..fee534315b77 100644
--- a/src/operator/numpy/np_matrix_op-inl.h
+++ b/src/operator/numpy/np_matrix_op-inl.h
@@ -1150,6 +1150,133 @@ void NumpyDiagOpBackward(const nnvm::NodeAttrs &attrs,
                              in_data.Size(), param.k, s, req[0]);
 }
 
+struct NumpyDiagflatParam : public dmlc::Parameter<NumpyDiagflatParam> {
+  int k;
+  DMLC_DECLARE_PARAMETER(NumpyDiagflatParam) {
+    DMLC_DECLARE_FIELD(k).set_default(0)
+      .describe("Diagonal in question. The default is 0. "
+                "Use k>0 for diagonals above the main diagonal, "
+                "and k<0 for diagonals below the main diagonal. ");
+  }
+};
+
+inline mxnet::TShape NumpyDiagflatShapeImpl(const mxnet::TShape& ishape, const int k) {
+  if (ishape.ndim() == 1) {
+    auto s = ishape[0] + std::abs(k);
+    return mxnet::TShape({s, s});
+  }
+
+  if (ishape.ndim() >= 2) {
+    auto s = 1;
+    for (int i = 0; i < ishape.ndim(); i++) {
+      if (ishape[i] >= 2) {
+        s = s * ishape[i];
+      }
+    }
+    s = s + std::abs(k);
+    return mxnet::TShape({s, s});
+  }
+  return mxnet::TShape({-1, -1});
+}
+
+inline bool NumpyDiagflatOpShape(const nnvm::NodeAttrs& attrs,
+                                 mxnet::ShapeVector* in_attrs,
+                                 mxnet::ShapeVector* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+
+  const mxnet::TShape& ishape = (*in_attrs)[0];
+  if (!mxnet::ndim_is_known(ishape)) {
+    return false;
+  }
+  const NumpyDiagflatParam& param = nnvm::get<NumpyDiagflatParam>(attrs.parsed);
+
+  mxnet::TShape oshape = NumpyDiagflatShapeImpl(ishape, param.k);
+
+  if (shape_is_none(oshape)) {
+    LOG(FATAL) << "Diagonal does not exist.";
+  }
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, oshape);
+
+  return shape_is_known(out_attrs->at(0));
+}
+
+inline bool NumpyDiagflatOpType(const nnvm::NodeAttrs& attrs,
+                                std::vector<int> *in_attrs,
+                                std::vector<int> *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, (*in_attrs)[0]);
+  TYPE_ASSIGN_CHECK(*in_attrs, 0, (*out_attrs)[0]);
+  return (*out_attrs)[0] != -1;
+}
+
+template<typename xpu, bool back>
+void NumpyDiagflatOpImpl(const TBlob& in_data,
+                         const TBlob& out_data,
+                         const mxnet::TShape& ishape,
+                         const mxnet::TShape& oshape,
+                         index_t dsize,
+                         const NumpyDiagflatParam& param,
+                         mxnet_op::Stream<xpu> *s,
+                         const std::vector<OpReqType>& req) {
+  using namespace mxnet_op;
+  using namespace mshadow;
+  MSHADOW_TYPE_SWITCH(out_data.type_flag_, DType, {
+    MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
+      Kernel<diag_gen<req_type, back>, xpu>::Launch(
+        s, dsize, out_data.dptr<DType>(), in_data.dptr<DType>(),
+        Shape2(oshape[0], oshape[1]), param.k);
+    });
+  });
+}
+
+template<typename xpu>
+void NumpyDiagflatOpForward(const nnvm::NodeAttrs& attrs,
+                            const OpContext& ctx,
+                            const std::vector<TBlob>& inputs,
+                            const std::vector<OpReqType>& req,
+                            const std::vector<TBlob>& outputs) {
+  using namespace mxnet_op;
+  using namespace mshadow;
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 1U);
+  CHECK_EQ(req.size(), 1U);
+  CHECK_EQ(req[0], kWriteTo);
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  const TBlob& in_data = inputs[0];
+  const TBlob& out_data = outputs[0];
+  const mxnet::TShape& ishape = inputs[0].shape_;
+  const mxnet::TShape& oshape = outputs[0].shape_;
+  const NumpyDiagflatParam& param = nnvm::get<NumpyDiagflatParam>(attrs.parsed);
+
+  NumpyDiagflatOpImpl<xpu, false>(in_data, out_data, ishape,
+                                  oshape, out_data.Size(), param, s, req);
+}
+
+template<typename xpu>
+void NumpyDiagflatOpBackward(const nnvm::NodeAttrs& attrs,
+                             const OpContext& ctx,
+                             const std::vector<TBlob>& inputs,
+                             const std::vector<OpReqType>& req,
+                             const std::vector<TBlob>& outputs) {
+  using namespace mxnet_op;
+  using namespace mshadow;
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 1U);
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+
+  const TBlob& in_data = inputs[0];
+  const TBlob& out_data = outputs[0];
+  const mxnet::TShape& ishape = inputs[0].shape_;
+  const mxnet::TShape& oshape = outputs[0].shape_;
+  const NumpyDiagflatParam& param = nnvm::get<NumpyDiagflatParam>(attrs.parsed);
+
+  NumpyDiagflatOpImpl<xpu, true>(in_data, out_data, oshape,
+                                 ishape, in_data.Size(), param, s, req);
+}
+
 }  // namespace op
 }  // namespace mxnet
 
diff --git a/src/operator/numpy/np_matrix_op.cc b/src/operator/numpy/np_matrix_op.cc
index 912b32c2e8fb..f07cb0e5f4b2 100644
--- a/src/operator/numpy/np_matrix_op.cc
+++ b/src/operator/numpy/np_matrix_op.cc
@@ -37,6 +37,7 @@ DMLC_REGISTER_PARAMETER(NumpyRot90Param);
 DMLC_REGISTER_PARAMETER(NumpyReshapeParam);
 DMLC_REGISTER_PARAMETER(NumpyXReshapeParam);
 DMLC_REGISTER_PARAMETER(NumpyDiagParam);
+DMLC_REGISTER_PARAMETER(NumpyDiagflatParam);
 
 
 bool NumpyTransposeShape(const nnvm::NodeAttrs& attrs,
@@ -1325,5 +1326,27 @@ NNVM_REGISTER_OP(_backward_np_diag)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
 .set_attr<FCompute>("FCompute<cpu>", NumpyDiagOpBackward<cpu>);
 
+NNVM_REGISTER_OP(_np_diagflat)
+.set_attr_parser(ParamParser<NumpyDiagflatParam>)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"data"};
+  })
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyDiagflatOpShape)
+.set_attr<nnvm::FInferType>("FInferType", NumpyDiagflatOpType)
+.set_attr<FCompute>("FCompute<cpu>", NumpyDiagflatOpForward<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_np_diagflat"})
+.add_argument("data", "NDArray-or-Symbol", "Input ndarray")
+.add_arguments(NumpyDiagflatParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_backward_np_diagflat)
+.set_attr_parser(ParamParser<NumpyDiagflatParam>)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FCompute>("FCompute<cpu>", NumpyDiagflatOpBackward<cpu>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/np_matrix_op.cu b/src/operator/numpy/np_matrix_op.cu
index 33f5aab7717c..6f292ab95802 100644
--- a/src/operator/numpy/np_matrix_op.cu
+++ b/src/operator/numpy/np_matrix_op.cu
@@ -124,5 +124,11 @@ NNVM_REGISTER_OP(_np_diag)
 NNVM_REGISTER_OP(_backward_np_diag)
 .set_attr<FCompute>("FCompute<gpu>", NumpyDiagOpBackward<gpu>);
 
+NNVM_REGISTER_OP(_np_diagflat)
+.set_attr<FCompute>("FCompute<gpu>", NumpyDiagflatOpForward<gpu>);
+
+NNVM_REGISTER_OP(_backward_np_diagflat)
+.set_attr<FCompute>("FCompute<gpu>", NumpyDiagflatOpBackward<gpu>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index e52d25239d22..088306ff01c8 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -1201,6 +1201,34 @@ def _add_workload_nonzero():
     OpArgMngr.add_workload('nonzero', np.array([False, False, False], dtype=np.bool_))
     OpArgMngr.add_workload('nonzero', np.array([True, False, False], dtype=np.bool_))
 
+def _add_workload_diagflat():
+    def get_mat(n):
+        data = _np.arange(n)
+        data = _np.add.outer(data,data)
+        return data
+
+    A = np.array([[1,2],[3,4],[5,6]])
+    vals = (100 * np.arange(5)).astype('l')
+    vals_c = (100 * np.array(get_mat(5)) + 1).astype('l')
+    vals_f = _np.array((100 * get_mat(5) + 1), order='F', dtype='l')
+    vals_f = np.array(vals_f)
+    
+    OpArgMngr.add_workload('diagflat', A, k=2)
+    OpArgMngr.add_workload('diagflat', A, k=1)
+    OpArgMngr.add_workload('diagflat', A, k=0)
+    OpArgMngr.add_workload('diagflat', A, k=-1)
+    OpArgMngr.add_workload('diagflat', A, k=-2)
+    OpArgMngr.add_workload('diagflat', A, k=-3)
+    OpArgMngr.add_workload('diagflat', vals, k=0)
+    OpArgMngr.add_workload('diagflat', vals, k=2)
+    OpArgMngr.add_workload('diagflat', vals, k=-2)
+    OpArgMngr.add_workload('diagflat', vals_c, k=0)
+    OpArgMngr.add_workload('diagflat', vals_c, k=2)
+    OpArgMngr.add_workload('diagflat', vals_c, k=-2)
+    OpArgMngr.add_workload('diagflat', vals_f, k=0)
+    OpArgMngr.add_workload('diagflat', vals_f, k=2)
+    OpArgMngr.add_workload('diagflat', vals_f, k=-2)
+
 
 def _add_workload_shape():
     OpArgMngr.add_workload('shape', np.random.uniform(size=()))
@@ -1263,6 +1291,7 @@ def _prepare_workloads():
     _add_workload_cumsum()
     _add_workload_ravel()
     _add_workload_diag()
+    _add_workload_diagflat()
     _add_workload_dot()
     _add_workload_expand_dims()
     _add_workload_fix()
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index ff9dd4119968..7dd165b5421f 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -4165,6 +4165,48 @@ def dbg(name, data):
                     assert_almost_equal(grad[0][iop], grad[1][iop], rtol=rtol, atol=atol)
 
 
+@with_seed()
+@use_np
+def test_np_diagflat():
+    class TestDiagflat(HybridBlock):
+        def __init__(self, k=0):
+            super(TestDiagflat,self).__init__()
+            self._k = k
+        def hybrid_forward(self,F,a):
+            return F.np.diagflat(a, k=self._k)
+    shapes = [(2,),5 , (1,5), (2,2), (2,5), (3,3), (4,3),(4,4,5)] # test_shapes, remember to include zero-dim shape and zero-size shapes
+    dtypes = [np.int8, np.uint8, np.int32, np.int64, np.float16, np.float32, np.float64] # remember to include all meaningful data types for the operator
+    range_k = 6
+    for hybridize,shape,dtype, in itertools.product([False,True],shapes,dtypes):
+        rtol = 1e-2 if dtype == np.float16 else 1e-3
+        atol = 1e-4 if dtype == np.float16 else 1e-5
+
+        for k in range(-range_k,range_k):
+            test_diagflat = TestDiagflat(k)
+            if hybridize:
+                test_diagflat.hybridize()
+
+            x = np.random.uniform(-1.0,1.0, size = shape).astype(dtype)
+            x.attach_grad()
+
+            np_out = _np.diagflat(x.asnumpy(), k)
+            with mx.autograd.record():
+                mx_out = test_diagflat(x)
+
+            assert mx_out.shape == np_out.shape
+            assert_almost_equal(mx_out.asnumpy(),np_out,rtol = rtol, atol = atol)
+
+            mx_out.backward()
+            # Code to get the reference backward value
+            np_backward = np.ones(shape)
+            assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=rtol, atol=atol)
+
+            # Test imperative once again
+            mx_out = np.diagflat(x, k)
+            np_out = _np.diagflat(x.asnumpy(), k)
+            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol) 
+
+
 @with_seed()
 @use_np
 def test_np_rand():

From 4da14a22385622c35e9a5c9c3e8a17c07f718cad Mon Sep 17 00:00:00 2001
From: Yiyan66 <57363390+Yiyan66@users.noreply.github.com>
Date: Fri, 22 Nov 2019 02:41:29 +0800
Subject: [PATCH 621/813] add op bitwise_or [numpy] (#16801)

* solve conflict

* change test

* add protocol

* less blank line

* update submodule

* update 3rd party
---
 python/mxnet/ndarray/numpy/_op.py             | 47 +++++++++++++++++--
 python/mxnet/numpy/multiarray.py              | 46 ++++++++++++++++--
 python/mxnet/numpy_dispatch_protocol.py       |  1 +
 python/mxnet/symbol/numpy/_symbol.py          | 29 ++++++++++--
 src/operator/mshadow_op.h                     |  2 +
 .../numpy/np_elemwise_broadcast_op.cc         |  1 +
 .../np_elemwise_broadcast_op_extended.cc      | 35 ++++++++++++++
 .../np_elemwise_broadcast_op_extended.cu      |  6 +++
 src/operator/operator_tune.cc                 |  1 +
 .../unittest/test_numpy_interoperability.py   | 11 +++++
 tests/python/unittest/test_numpy_op.py        |  1 +
 11 files changed, 168 insertions(+), 12 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index 3cc5b85c8384..d31681113933 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -36,11 +36,10 @@
            'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate', 'append',
            'stack', 'vstack', 'column_stack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax',
            'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
-           'around', 'hypot', 'bitwise_xor', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
-           'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal',
-           'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory', 'may_share_memory', 'diff', 'resize',
-           'nan_to_num', 'where']
-
+           'around', 'hypot', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity',
+           'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal',
+           'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory', 'may_share_memory',
+           'diff', 'resize', 'nan_to_num', 'where']
 
 @set_module('mxnet.ndarray.numpy')
 def shape(a):
@@ -4365,6 +4364,44 @@ def bitwise_xor(x1, x2, out=None, **kwargs):
     return _ufunc_helper(x1, x2, _npi.bitwise_xor, _np.bitwise_xor, _npi.bitwise_xor_scalar, None, out)
 
 
+@set_module('mxnet.ndarray.numpy')
+@wrap_np_binary_func
+def bitwise_or(x1, x2, out=None, **kwargs):
+    r"""
+    Compute the bit-wise OR of two arrays element-wise.
+
+    Parameters
+    ----------
+    x1, x2 : ndarray or scalar
+        Only integer and boolean types are handled. If x1.shape != x2.shape,
+        they must be broadcastable to a common shape (which becomes the shape of the output).
+    out : ndarray, optional
+        A location into which the result is stored. If provided, it must have a shape that the
+        inputs broadcast to. If not provided or None, a freshly-allocated array is returned.
+
+    Returns
+    -------
+    out : ndarray
+        Result.
+
+    Examples
+    --------
+    >>> np.bitwise_or(13, 17)
+    29
+
+    >>> np.bitwise_or(31, 5)
+    31
+    >>> np.bitwise_or(np.array([31,3], dtype='int32'), 5)
+    array([31,  7])
+
+    >>> np.bitwise_or(np.array([31,3], dtype='int32'), np.array([5,6], dtype='int32'))
+    array([31,  7])
+    >>> np.bitwise_or(np.array([True, True], dtype='bool'), np.array([False, True], dtype='bool'))
+    array([ True, True])
+    """
+    return _ufunc_helper(x1, x2, _npi.bitwise_or, _np.bitwise_or, _npi.bitwise_or_scalar, None, out)
+
+
 @set_module('mxnet.ndarray.numpy')
 @wrap_np_binary_func
 def ldexp(x1, x2, out=None, **kwargs):
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index e94d4c8341b4..3ad9254eb0e6 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -54,10 +54,10 @@
            'tensordot', 'histogram', 'eye', 'linspace', 'logspace', 'expand_dims', 'tile', 'arange',
            'split', 'vsplit', 'concatenate', 'stack', 'vstack', 'column_stack', 'dstack', 'mean', 'maximum', 'minimum',
            'swapaxes', 'clip', 'argmax', 'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming',
-           'blackman', 'flip', 'around', 'arctan2', 'hypot', 'bitwise_xor', 'rad2deg', 'deg2rad', 'unique', 'lcm',
-           'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less',
-           'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory',
-           'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where']
+           'blackman', 'flip', 'around', 'arctan2', 'hypot', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad',
+           'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal',
+           'greater', 'less', 'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero',
+           'shares_memory', 'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -6277,6 +6277,44 @@ def bitwise_xor(x1, x2, out=None, **kwargs):
     return _mx_nd_np.bitwise_xor(x1, x2, out=out)
 
 
+@set_module('mxnet.numpy')
+@wrap_np_binary_func
+def bitwise_or(x1, x2, out=None, **kwargs):
+    r"""
+    Compute the bit-wise OR of two arrays element-wise.
+
+    Parameters
+    ----------
+    x1, x2 : ndarray or scalar
+        Only integer and boolean types are handled. If x1.shape != x2.shape,
+        they must be broadcastable to a common shape (which becomes the shape of the output).
+    out : ndarray, optional
+        A location into which the result is stored. If provided, it must have a shape that the
+        inputs broadcast to. If not provided or None, a freshly-allocated array is returned.
+
+    Returns
+    -------
+    out : ndarray
+        Result.
+
+    Examples
+    --------
+    >>> np.bitwise_or(13, 17)
+    29
+
+    >>> np.bitwise_or(31, 5)
+    31
+    >>> np.bitwise_or(np.array([31,3], dtype=np.int32), 5)
+    array([31,  7])
+
+    >>> np.bitwise_or(np.array([31,3], dtype='int32'), np.array([5,6], dtype='int32'))
+    array([31,  7])
+    >>> np.bitwise_or(np.array([True, True], dtype='bool'), np.array([False, True], dtype='bool'))
+    array([ True, True])
+    """
+    return _mx_nd_np.bitwise_or(x1, x2, out=out)
+
+
 @set_module('mxnet.numpy')
 @wrap_np_binary_func
 def ldexp(x1, x2, out=None, **kwargs):
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index 67af2724503e..13e186f4008a 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -230,6 +230,7 @@ def _register_array_function():
     'trunc',
     'floor',
     'bitwise_xor',
+    'bitwise_or',
     'logical_not',
     'equal',
     'not_equal',
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 7da771966f1f..734242f4e86d 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -38,12 +38,11 @@
            'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate', 'append',
            'stack', 'vstack', 'column_stack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax',
            'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
-           'around', 'hypot', 'bitwise_xor', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
-           'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal',
+           'around', 'hypot', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity',
+           'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal',
            'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'shares_memory', 'may_share_memory', 'diff',
            'resize', 'nan_to_num', 'where']
 
-
 def _num_outputs(sym):
     return len(sym.as_nd_ndarray())
 
@@ -4103,6 +4102,30 @@ def bitwise_xor(x1, x2, out=None, **kwargs):
     return _ufunc_helper(x1, x2, _npi.bitwise_xor, _np.bitwise_xor, _npi.bitwise_xor_scalar, None, out)
 
 
+@set_module('mxnet.symbol.numpy')
+@wrap_np_binary_func
+def bitwise_or(x1, x2, out=None, **kwargs):
+    r"""
+    Compute the bit-wise OR of two arrays element-wise.
+
+    Parameters
+    ----------
+    x1, x2 : _Symbol or scalar
+        Only integer and boolean types are handled. If x1.shape != x2.shape,
+        they must be broadcastable to a common shape (which becomes the shape of the output).
+    out : _Symbol or None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned.
+
+    Returns
+    -------
+    out : _Symbol or scalar
+        Result.
+    """
+    return _ufunc_helper(x1, x2, _npi.bitwise_or, _np.bitwise_or, _npi.bitwise_or_scalar, None, out)
+
+
 @set_module('mxnet.symbol.numpy')
 def unique(ar, return_index=False, return_inverse=False, return_counts=False, axis=None):
     """
diff --git a/src/operator/mshadow_op.h b/src/operator/mshadow_op.h
index 4ae587188d1b..a0a424b6209b 100644
--- a/src/operator/mshadow_op.h
+++ b/src/operator/mshadow_op.h
@@ -562,6 +562,8 @@ MXNET_BINARY_MATH_OP(logical_xor, (a || b) && !(a && b) ? DType(1) : DType(0));
 
 MXNET_BINARY_MATH_OP(bitwise_xor, static_cast<int64_t>(a) ^ static_cast<int64_t>(b));
 
+MXNET_BINARY_MATH_OP(bitwise_or, static_cast<int64_t>(a) | static_cast<int64_t>(b));
+
 MXNET_UNARY_MATH_OP(square_root, math::sqrt(a));
 
 MXNET_UNARY_MATH_OP(square_root_grad, 0.5f / math::id(a));
diff --git a/src/operator/numpy/np_elemwise_broadcast_op.cc b/src/operator/numpy/np_elemwise_broadcast_op.cc
index f2adfc125d02..fbe04eeb4eff 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op.cc
+++ b/src/operator/numpy/np_elemwise_broadcast_op.cc
@@ -161,6 +161,7 @@ MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_mod)
 MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_power)
 .set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, mshadow_op::power>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_broadcast_power"});
+
 MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_add_scalar)
 .set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, op::mshadow_op::plus>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_copy"});
diff --git a/src/operator/numpy/np_elemwise_broadcast_op_extended.cc b/src/operator/numpy/np_elemwise_broadcast_op_extended.cc
index 84c47e597883..34ff20e733e5 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op_extended.cc
+++ b/src/operator/numpy/np_elemwise_broadcast_op_extended.cc
@@ -118,6 +118,24 @@ NNVM_REGISTER_OP(_npi_bitwise_xor)
 .add_argument("lhs", "NDArray-or-Symbol", "First input to the function")
 .add_argument("rhs", "NDArray-or-Symbol", "Second input to the function");
 
+NNVM_REGISTER_OP(_npi_bitwise_or)
+.set_num_inputs(2)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+[](const NodeAttrs& attrs) {
+     return std::vector<std::string>{"lhs", "rhs"};
+})
+.set_attr<mxnet::FInferShape>("FInferShape", BinaryBroadcastShape)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseIntType<2, 1>)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+[](const NodeAttrs& attrs){
+     return std::vector<std::pair<int, int> >{{0, 0}, {1, 0}};
+})
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastIntCompute<cpu, mshadow_op::bitwise_or>)
+.add_argument("lhs", "NDArray-or-Symbol", "First input to the function")
+.add_argument("rhs", "NDArray-or-Symbol", "Second input to the function");
+
 NNVM_REGISTER_OP(_npi_bitwise_xor_scalar)
 .set_num_inputs(1)
 .set_num_outputs(1)
@@ -135,6 +153,23 @@ NNVM_REGISTER_OP(_npi_bitwise_xor_scalar)
 .add_argument("scalar", "int", "scalar input")
 .set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::ComputeInt<cpu, mshadow_op::bitwise_xor>);
 
+NNVM_REGISTER_OP(_npi_bitwise_or_scalar)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser([](NodeAttrs* attrs) {
+    attrs->parsed = std::stod(attrs->dict["scalar"]);
+  })
+.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseIntType<1, 1>)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs){
+    return std::vector<std::pair<int, int> >{{0, 0}};
+  })
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.add_argument("data", "NDArray-or-Symbol", "source input")
+.add_argument("scalar", "int", "scalar input")
+.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::ComputeInt<cpu, mshadow_op::bitwise_or>);
+
 MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_copysign_scalar)
 .set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::copysign>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_copysign_scalar"});
diff --git a/src/operator/numpy/np_elemwise_broadcast_op_extended.cu b/src/operator/numpy/np_elemwise_broadcast_op_extended.cu
index f858fb4a4e79..90f11b1cd93a 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op_extended.cu
+++ b/src/operator/numpy/np_elemwise_broadcast_op_extended.cu
@@ -37,6 +37,9 @@ NNVM_REGISTER_OP(_npi_lcm)
 NNVM_REGISTER_OP(_npi_bitwise_xor)
 .set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastIntCompute<gpu, mshadow_op::bitwise_xor>);
 
+NNVM_REGISTER_OP(_npi_bitwise_or)
+.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastIntCompute<gpu, mshadow_op::bitwise_or>);
+
 NNVM_REGISTER_OP(_backward_npi_copysign)
 .set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastBackwardUseIn<gpu, mshadow_op::copysign_grad,
                                                                   mshadow_op::copysign_rgrad>);
@@ -85,6 +88,9 @@ NNVM_REGISTER_OP(_npi_lcm_scalar)
 NNVM_REGISTER_OP(_npi_bitwise_xor_scalar)
 .set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::ComputeInt<gpu, mshadow_op::bitwise_xor>);
 
+NNVM_REGISTER_OP(_npi_bitwise_or_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::ComputeInt<gpu, mshadow_op::bitwise_or>);
+
 NNVM_REGISTER_OP(_npi_ldexp)
 .set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, mshadow_op::ldexp>);
 
diff --git a/src/operator/operator_tune.cc b/src/operator/operator_tune.cc
index e2a4c8af3099..249da3d049c1 100644
--- a/src/operator/operator_tune.cc
+++ b/src/operator/operator_tune.cc
@@ -397,6 +397,7 @@ IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::logical_or);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::logical_xor);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::logical_xor);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD_WITH_BOOL(mxnet::op::mshadow_op::bitwise_xor);  // NOLINT()
+IMPLEMENT_BINARY_WORKLOAD_FWD_WITH_BOOL(mxnet::op::mshadow_op::bitwise_or);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::smooth_l1_loss);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::smooth_l1_gradient);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD_WITH_BOOL(mxnet::op::mshadow_op::lcm);  // NOLINT()
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index 088306ff01c8..87c48ab5fd79 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -845,6 +845,16 @@ def _add_workload_lcm():
     OpArgMngr.add_workload('lcm', np.array([12, 120], dtype=np.uint8), np.array([20, 200], dtype=np.uint8))
     OpArgMngr.add_workload('lcm', np.array(195225786*2, dtype=np.int32), np.array(195225786*5, dtype=np.int32))
 
+def _add_workload_bitwise_or():
+    OpArgMngr.add_workload('bitwise_or', np.array([False, False, True, True], dtype=np.bool),
+                           np.array([False, True, False, True], dtype=np.bool))
+    for dtype in [np.int8, np.int32, np.int64]:
+        zeros = np.array([0], dtype=dtype)
+        ones = np.array([-1], dtype=dtype)
+        OpArgMngr.add_workload('bitwise_or', zeros, zeros)
+        OpArgMngr.add_workload('bitwise_or', ones, zeros)
+        OpArgMngr.add_workload('bitwise_or', zeros, ones)
+        OpArgMngr.add_workload('bitwise_or', ones, ones)
 
 def _add_workload_bitwise_xor():
     OpArgMngr.add_workload('bitwise_xor', np.array([False, False, True, True], dtype=np.bool),
@@ -1337,6 +1347,7 @@ def _prepare_workloads():
     _add_workload_hypot()
     _add_workload_lcm()
     _add_workload_bitwise_xor()
+    _add_workload_bitwise_or()
     _add_workload_ldexp()
     _add_workload_subtract(array_pool)
     _add_workload_multiply(array_pool)
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 7dd165b5421f..dc99fc6fc251 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -1671,6 +1671,7 @@ def hybrid_forward(self, F, a, b, *args, **kwargs):
                              [lambda y, x1, x2: _np.power(x1, x2) * _np.log(x1)]),
         'lcm': (-100, 100, [None], None, [[_np.int32]]),
         'bitwise_xor': (-100, 100, [None], None, [[_np.int32]]),
+        'bitwise_or': (-100, 100, [None], None, [[_np.int32]]),
         'maximum': (-1, 1, [lambda y, x1, x2: _np.ones(y.shape) * (x1 >= x2)],
                            [lambda y, x1, x2: _np.ones(y.shape) * (x1 < x2)]),
         'minimum': (-1, 1, [lambda y, x1, x2: _np.ones(y.shape) * (x1 <= x2)],

From e3a7141c953c7199a9ae05bcc0176dd80e346dad Mon Sep 17 00:00:00 2001
From: Przemyslaw Tredak <ptredak@nvidia.com>
Date: Fri, 22 Nov 2019 17:18:54 -0800
Subject: [PATCH 622/813] Add support for boolean inputs to FusedOp (#16796)

* Support bool in fusion

* Added tests

* Use use_np decorator vs calling the numpy convention by hand

* Debug TVM failure

* Fix

* Do not invoke TVM broadcast kernel
---
 src/operator/fusion/fused_op.cu | 10 +++++++-
 src/operator/fusion/fused_op.h  |  3 ++-
 tests/python/gpu/test_fusion.py | 43 +++++++++++++++++++++++++++++++++
 3 files changed, 54 insertions(+), 2 deletions(-)

diff --git a/src/operator/fusion/fused_op.cu b/src/operator/fusion/fused_op.cu
index 62f340d0e00b..34360544cd11 100644
--- a/src/operator/fusion/fused_op.cu
+++ b/src/operator/fusion/fused_op.cu
@@ -53,6 +53,8 @@ inline std::string mshadowTypeToString(int type) {
       return "int";
     case mshadow::kInt64:
       return "long long";
+    case mshadow::kBool:
+      return "bool";
     default:
       LOG(FATAL) << "Unknown type enum " << type;
   }
@@ -75,6 +77,8 @@ inline int mshadowTypeToVectorLength(int type) {
       return 1;
     case mshadow::kInt64:
       return 1;
+    case mshadow::kBool:
+      return 4 / sizeof(bool);
     default:
       LOG(FATAL) << "Unknown type enum " << type;
   }
@@ -159,7 +163,7 @@ void AddPointerAndShape(const TBlob& data,
                         std::vector<std::vector<int>>* shapes,
                         mshadow::Stream<gpu> * s) {
   using namespace mshadow;
-  MSHADOW_TYPE_SWITCH(data.type_flag_, DType, {
+  MSHADOW_TYPE_SWITCH_WITH_BOOL(data.type_flag_, DType, {
     Tensor<gpu, 1, DType> tensor = data.FlatTo1D<gpu, DType>(s);
     ptrs->push_back(tensor.dptr_);
     AddShape(data.shape_, shapes);
@@ -650,7 +654,9 @@ void FusedOp::CheckShapesAndTypes(const std::vector<TBlob> &inputs,
     in_ndims->push_back(blob.ndim());
     in_shapes.push_back(blob.shape_);
     initialized_ = initialized_ && blob.type_flag_ == inputs_[counter].dtype;
+    initialized_ = initialized_ && blob.ndim() == inputs_[counter].ndim;
     inputs_[counter].dtype = blob.type_flag_;
+    inputs_[counter].ndim = blob.ndim();
     *nvec = max(*nvec, mshadowTypeToVectorLength(blob.type_flag_));
   }
 
@@ -660,7 +666,9 @@ void FusedOp::CheckShapesAndTypes(const std::vector<TBlob> &inputs,
     out_ndims->push_back(blob.ndim());
     out_shapes.push_back(blob.shape_);
     initialized_ = initialized_ && blob.type_flag_ == outputs_[counter].dtype;
+    initialized_ = initialized_ && blob.ndim() == outputs_[counter].ndim;
     outputs_[counter].dtype = blob.type_flag_;
+    outputs_[counter].ndim = blob.ndim();
     *nvec = max(*nvec, mshadowTypeToVectorLength(blob.type_flag_));
   }
 
diff --git a/src/operator/fusion/fused_op.h b/src/operator/fusion/fused_op.h
index 7d714677e941..12964724b093 100644
--- a/src/operator/fusion/fused_op.h
+++ b/src/operator/fusion/fused_op.h
@@ -50,8 +50,9 @@ struct FusedOpConfig : public dmlc::Parameter<FusedOpConfig> {
 };
 
 struct FusedOpEntry {
-  FusedOpEntry() : dtype(-1) {}
+  FusedOpEntry() : dtype(-1), ndim(-1) {}
   int dtype;
+  int ndim;
 };
 
 class FusedOp {
diff --git a/tests/python/gpu/test_fusion.py b/tests/python/gpu/test_fusion.py
index 24e33019f617..9a37c0e844a0 100644
--- a/tests/python/gpu/test_fusion.py
+++ b/tests/python/gpu/test_fusion.py
@@ -238,6 +238,49 @@ def test_fusion_compiler_cache():
     if num_gpus > 1:
         check_fused_symbol(a+b, ctx=mx.gpu(1), a=arr1, b=arr2)
 
+@with_seed()
+@use_np
+def test_fusion_boolean_inputs():
+    from mxnet.gluon import HybridBlock
+
+    class Foo(HybridBlock):
+        def __init__(self, prefix=None, params=None):
+            super(Foo, self).__init__(prefix=prefix, params=params)
+
+        def hybrid_forward(self, F, valid_length):
+            mask = valid_length.astype(np.float32)
+            mask2 = valid_length.astype(np.float32)
+            mask = mask * F.np.expand_dims(mask2, axis=-1)
+            return mask
+
+    foo = Foo()
+    foo.hybridize(static_alloc=True)
+    out = foo(mx.np.ones((10,), ctx=mx.gpu(), dtype=np.bool))
+    mx.npx.waitall()
+
+@with_seed()
+def test_fusion_different_dimensions():
+    from mxnet.gluon import HybridBlock
+
+    class Foo(HybridBlock):
+        def __init__(self, prefix=None, params=None):
+            super(Foo, self).__init__(prefix=prefix, params=params)
+
+        def hybrid_forward(self, F, x):
+            mask2 = x.astype(np.float32)
+            mask = F.expand_dims(mask2, axis=-1)
+            return mask
+
+    foo = Foo()
+    foo.hybridize(static_alloc=True)
+    # Pass 1-D data
+    out = foo(mx.nd.ones((10,), ctx=mx.gpu()))
+    assert np.all(out.asnumpy() == np.ones((10,1)))
+    assert out.shape == (10,1)
+    # Pass 2-D data
+    out = foo(mx.nd.ones((10,10), ctx=mx.gpu()))
+    assert np.all(out.asnumpy() == np.ones((10,10)))
+    assert out.shape == (10,10,1)
 
 @with_seed()
 def test_fusion_reshape_executor():

From 20f8bbc2f99350250554aa8e9ea2c377ad0d7358 Mon Sep 17 00:00:00 2001
From: Disi A <adis@live.cn>
Date: Sat, 23 Nov 2019 02:59:13 -0500
Subject: [PATCH 623/813] Refactoring names for mxnet version of nnvm to avoid
 conflicting with the original tvm/nnvm. (#15303)

---
 src/imperative/cached_op.cc       |  4 ++--
 src/imperative/imperative_utils.h |  2 +-
 src/nnvm/graph_algorithm.h        |  6 +++---
 src/nnvm/plan_memory.cc           | 30 +++++++++++++++---------------
 4 files changed, 21 insertions(+), 21 deletions(-)

diff --git a/src/imperative/cached_op.cc b/src/imperative/cached_op.cc
index 24270f210888..ec5a79a2e675 100644
--- a/src/imperative/cached_op.cc
+++ b/src/imperative/cached_op.cc
@@ -473,7 +473,7 @@ bool CachedOp::SetForwardGraph(
     storage[idx.entry_id(idx.outputs()[i])] = exec::kExternalStorageID;
   }
 
-  auto mem_plan = PlanMemory(
+  auto mem_plan = MXPlanMemory(
       &g, std::move(storage), g.GetAttr<std::vector<uint32_t> >(AddPrefix(prefix, REF_COUNT)),
       AddPrefix(prefix, STORAGE_PLAN));
   g.attrs[AddPrefix(prefix, MEM_PLAN)] =
@@ -601,7 +601,7 @@ bool CachedOp::SetBackwardGraph(
   for (const auto i : idx.input_nodes()) storage[idx.entry_id(i, 0)] = exec::kExternalStorageID;
   for (const auto i : idx.outputs()) storage[idx.entry_id(i)] = exec::kExternalStorageID;
 
-  auto mem_plan = PlanMemory(
+  auto mem_plan = MXPlanMemory(
       &g, std::move(storage),
       g.GetAttr<std::vector<uint32_t> >(AddPrefix(BACKWARD, REF_COUNT)),
       AddPrefix(BACKWARD, STORAGE_PLAN),
diff --git a/src/imperative/imperative_utils.h b/src/imperative/imperative_utils.h
index b609c54b50f3..edc2f2adf4c2 100644
--- a/src/imperative/imperative_utils.h
+++ b/src/imperative/imperative_utils.h
@@ -830,7 +830,7 @@ inline std::vector<Context> PlaceDevice(const nnvm::IndexedGraph& idx) {
 }
 
 
-inline MemoryPlanVector PlanMemory(
+inline MemoryPlanVector MXPlanMemory(
     nnvm::Graph* p_g,
     nnvm::StorageVector&& storage,
     const std::vector<uint32_t>& ref_count,
diff --git a/src/nnvm/graph_algorithm.h b/src/nnvm/graph_algorithm.h
index d1590c3b9846..031e62254db7 100644
--- a/src/nnvm/graph_algorithm.h
+++ b/src/nnvm/graph_algorithm.h
@@ -42,7 +42,7 @@ namespace pass {
  * \param path the output path of nodes.
  * \return the total reward of best path.
  */
-inline uint32_t FindBestPath(
+inline uint32_t MXFindBestPath(
     const IndexedGraph& graph,
     const std::vector<uint32_t>& node_reward,
     std::vector<uint32_t>* path) {
@@ -89,7 +89,7 @@ inline uint32_t FindBestPath(
  * \param color the color index of each of the node.
  * \return the total number of colors.
  */
-inline uint32_t ColorNodeGroup(
+inline uint32_t MXColorNodeGroup(
     const IndexedGraph &graph,
     std::vector<uint32_t> node_importance,
     uint32_t max_ncolor,
@@ -105,7 +105,7 @@ inline uint32_t ColorNodeGroup(
   // All the nodes in the path cannot run in parallel.
   for (cindex = 0; cindex < max_ncolor - 1; ++cindex) {
     std::vector<uint32_t> path;
-    uint32_t reward = FindBestPath(graph, node_importance, &path);
+    uint32_t reward = MXFindBestPath(graph, node_importance, &path);
     if (reward == 0) break;
     for (uint32_t nid : path) {
       if (node_importance[nid] != 0) {
diff --git a/src/nnvm/plan_memory.cc b/src/nnvm/plan_memory.cc
index 427d1c751cba..c89eefc6d1ac 100644
--- a/src/nnvm/plan_memory.cc
+++ b/src/nnvm/plan_memory.cc
@@ -38,7 +38,7 @@ namespace pass {
 namespace {
   using namespace nnvm::top;
 // Return bytes of data flag.
-static int GetDTypeSize(int type_flag) {
+static int MXGetDTypeSize(int type_flag) {
   switch (type_flag) {
     case kUint8:
     case kInt8:
@@ -62,7 +62,7 @@ static int GetDTypeSize(int type_flag) {
 }
 
 // simple graph based allocator.
-class GraphAllocator {
+class MXGraphAllocator {
  public:
   // storage id equals integer.
   using StorageID = int;
@@ -131,7 +131,7 @@ class GraphAllocator {
   }
 
   // constructor
-  explicit GraphAllocator(const IndexedGraph* idx, const size_t match_range) : idx_(idx) {
+  explicit MXGraphAllocator(const IndexedGraph* idx, const size_t match_range) : idx_(idx) {
     this->Init(match_range, dmlc::GetEnv("NNVM_EXEC_NUM_TEMP", 1));
   }
 
@@ -146,7 +146,7 @@ class GraphAllocator {
         if ((*idx_)[nid].source->is_variable()) continue;
         importance[nid] = 1;
       }
-      num_match_color_ = pass::ColorNodeGroup(
+      num_match_color_ = pass::MXColorNodeGroup(
           *idx_, importance, num_match_color_, &node_color_);
     }
   }
@@ -190,12 +190,12 @@ class GraphAllocator {
 /*
  * Internal method to perform the memory allocation for a graph
  * */
-size_t AllocMemory(const Graph& ret, const IndexedGraph& idx,
+size_t MXAllocMemory(const Graph& ret, const IndexedGraph& idx,
                    const std::pair<uint32_t, uint32_t>& node_range,
                    StorageVector* storage_ptr,
                    std::vector<int>* storage_inplace_index_ptr,
                    const std::vector<uint32_t>& entry_ref_count,
-                   GraphAllocator* allocator) {
+                   MXGraphAllocator* allocator) {
   static auto& finplace_option = Op::GetAttr<FInplaceOption>("FInplaceOption");
   static auto& finplace_identity = Op::GetAttr<FInplaceIdentity>("FInplaceIdentity");
   static auto& fignore_inputs = Op::GetAttr<FIgnoreInputs>("FIgnoreInputs");
@@ -213,7 +213,7 @@ size_t AllocMemory(const Graph& ret, const IndexedGraph& idx,
     device_vec = &(ret.GetAttr<DeviceVector>("device"));
   }
   size_t num_not_allocated = 0;
-  std::vector<GraphAllocator::StorageID> storage_ref_count(idx.num_node_entries(), 0);
+  std::vector<MXGraphAllocator::StorageID> storage_ref_count(idx.num_node_entries(), 0);
 
   for (uint32_t nid = node_range.first; nid < node_range.second; ++nid) {
     const auto& inode = idx[nid];
@@ -247,13 +247,13 @@ size_t AllocMemory(const Graph& ret, const IndexedGraph& idx,
                              shape_vec[eid_out].Size() == shape_vec[eid_in].Size() &&
                              dtype_vec[eid_out] == dtype_vec[eid_in];
         if (taken[kv.first] == false &&
-            sid_out == GraphAllocator::kBadStorageID &&
+            sid_out == MXGraphAllocator::kBadStorageID &&
             sid_in >= 0 &&
             ((storage_ref_count[sid_in] == 1 && !ignore_all_inputs) || real_identity) &&
             entry_ref_count[eid_out] > 0 &&
             shape_vec[eid_out].Size() == shape_vec[eid_in].Size() &&
              (dtype_vec[eid_out] == dtype_vec[eid_in] ||
-             GetDTypeSize(dtype_vec[eid_out]) == GetDTypeSize(dtype_vec[eid_in]))) {
+             MXGetDTypeSize(dtype_vec[eid_out]) == MXGetDTypeSize(dtype_vec[eid_in]))) {
           // inplace optimization
           taken[kv.first] = true;
           storage[eid_out] = sid_in;
@@ -272,7 +272,7 @@ size_t AllocMemory(const Graph& ret, const IndexedGraph& idx,
     for (uint32_t index = 0; index < inode.source->num_outputs(); ++index) {
       uint32_t eid = idx.entry_id(nid, index);
       // only request memory for kBadStorageID
-      if (storage[eid] == GraphAllocator::kBadStorageID) {
+      if (storage[eid] == MXGraphAllocator::kBadStorageID) {
         auto &eshape = shape_vec[eid];
         size_t esize = ndim_is_known(shape_vec[eid]) ? eshape.Size() : 0;
         eids.insert(std::make_pair(esize, eid));
@@ -317,7 +317,7 @@ size_t AllocMemory(const Graph& ret, const IndexedGraph& idx,
         // use -2 to indicate that the node was never touched.
         storage_inplace_index[eid] = -2;
       }
-      if (storage[eid] == GraphAllocator::kBadStorageID) {
+      if (storage[eid] == MXGraphAllocator::kBadStorageID) {
         ++num_not_allocated;
       }
     }
@@ -327,7 +327,7 @@ size_t AllocMemory(const Graph& ret, const IndexedGraph& idx,
 
 
 // function to plan memory
-Graph PlanMemory(Graph ret) {
+Graph MXPlanMemory(Graph ret) {
   // setup ref counter
   const IndexedGraph& idx = ret.indexed_graph();
   static auto& fignore_inputs = Op::GetAttr<FIgnoreInputs>("FIgnoreInputs");
@@ -380,11 +380,11 @@ Graph PlanMemory(Graph ret) {
     std::vector<int> storage_inplace_index(idx.num_node_entries(), -1);
 
     // the allocator
-    GraphAllocator allocator(&idx, match_range);
+    MXGraphAllocator allocator(&idx, match_range);
 
     // number of entries that are not statically allocated.
     size_t storage_num_not_allocated =
-      AllocMemory(ret, idx, node_range, &storage_vec, &storage_inplace_index,
+      MXAllocMemory(ret, idx, node_range, &storage_vec, &storage_inplace_index,
                   ref_count, &allocator);
     size_t storage_allocated_bytes = allocator.TotalAllocBytes();
 
@@ -406,7 +406,7 @@ Graph PlanMemory(Graph ret) {
 
 NNVM_REGISTER_PASS(MXPlanMemory)
 .describe("Plan the memory allocation of each node entries.")
-.set_body(PlanMemory)
+.set_body(MXPlanMemory)
 .set_change_graph(false)
 .depend_graph_attr("dtype")
 .depend_graph_attr("shape")

From 6c20fb9e6a712d5883233cd024dd37958f128102 Mon Sep 17 00:00:00 2001
From: Xi Wang <xidulu@gmail.com>
Date: Sat, 23 Nov 2019 16:17:09 +0800
Subject: [PATCH 624/813] [Numpy] Implementation npx.{sample}_n (#16876)

* sample_n done

* add gpu register

* move randomness tests to nightly test

* add empty line
---
 .../mxnet/ndarray/numpy_extension/random.py   | 165 +++++++++++++++++-
 python/mxnet/numpy_extension/random.py        | 127 +++++++++++++-
 python/mxnet/symbol/numpy_extension/random.py | 165 +++++++++++++++++-
 src/operator/numpy/random/dist_common.h       |  44 +++++
 src/operator/numpy/random/np_normal_op.cc     |  40 +++++
 src/operator/numpy/random/np_normal_op.cu     |   5 +-
 src/operator/numpy/random/np_uniform_op.cc    |  36 ++++
 src/operator/numpy/random/np_uniform_op.cu    |   3 +
 tests/nightly/test_np_random.py               |  83 +++++++++
 tests/python/unittest/test_numpy_ndarray.py   |  34 ----
 tests/python/unittest/test_numpy_op.py        |  39 +++++
 11 files changed, 703 insertions(+), 38 deletions(-)
 create mode 100644 tests/nightly/test_np_random.py

diff --git a/python/mxnet/ndarray/numpy_extension/random.py b/python/mxnet/ndarray/numpy_extension/random.py
index b0472a4ab122..5520a1f1b4d0 100644
--- a/python/mxnet/ndarray/numpy_extension/random.py
+++ b/python/mxnet/ndarray/numpy_extension/random.py
@@ -21,7 +21,7 @@
 from ..numpy import _internal as _npi
 
 
-__all__ = ['bernoulli']
+__all__ = ['bernoulli', 'normal_n', 'uniform_n']
 
 
 def bernoulli(prob, logit, size, dtype, ctx, out):
@@ -102,3 +102,166 @@ def bernoulli(prob, logit, size, dtype, ctx, out):
         else:
             return _npi.bernoulli(prob=None, logit=logit, is_logit=True,
                                   size=size, ctx=ctx, dtype=dtype, out=out)
+
+
+def uniform_n(low=0.0, high=1.0, batch_shape=None, dtype=None, ctx=None):
+    r"""Draw samples from a uniform distribution.
+
+    Samples are uniformly distributed over the half-open interval
+    ``[low, high)`` (includes low, but excludes high).  In other words,
+    any value within the given interval is equally likely to be drawn
+    by `uniform`.
+
+    Parameters
+    ----------
+    low : float, ndarray, optional
+        Lower boundary of the output interval.  All values generated will be
+        greater than or equal to low.  The default value is 0.
+    high : float, ndarray, optional
+        Upper boundary of the output interval.  All values generated will be
+        less than high.  The default value is 1.0.
+    batch_shape : int or tuple of ints, optional
+        Batch shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k * broadcast(low, high).size`` samples are drawn.
+        If size is ``None`` (default),
+        a scalar tensor containing a single value is returned if
+        ``low`` and ``high`` are both scalars. Otherwise,
+        ``np.broadcast(low, high).size`` samples are drawn.
+    dtype : {'float16', 'float32', 'float64'}, optional
+        Data type of output samples. Default is 'float32'
+    ctx : Context, optional
+        Device context of output. Default is current context.
+
+    Returns
+    -------
+    out : ndarray
+        Drawn samples from the parameterized uniform distribution.
+
+    See Also
+    --------
+    randint : Discrete uniform distribution, yielding integers.
+    rand : Convenience function that accepts dimensions as input, e.g.,
+           ``rand(2,2)`` would generate a 2-by-2 array of floats,
+           uniformly distributed over ``[0, 1)``.
+
+    Notes
+    -----
+    The probability density function of the uniform distribution is
+
+    .. math:: p(x) = \frac{1}{b - a}
+
+    anywhere within the interval ``[a, b)``, and zero elsewhere.
+
+    When ``high`` == ``low``, values of ``low`` will be returned.
+    If ``high`` < ``low``, the results are officially undefined
+    and may eventually raise an error, i.e. do not rely on this
+    function to behave when passed arguments satisfying that
+    inequality condition.
+    """
+    from ...numpy import ndarray as np_ndarray
+    input_type = (isinstance(low, np_ndarray), isinstance(high, np_ndarray))
+    if dtype is None:
+        dtype = 'float32'
+    if ctx is None:
+        ctx = current_context()
+    if batch_shape == ():
+        batch_shape = None
+    if input_type == (True, True):
+        return _npi.uniform_n(low, high, low=None, high=None, size=batch_shape,
+                              ctx=ctx, dtype=dtype)
+    elif input_type == (False, True):
+        return _npi.uniform_n(high, low=low, high=None, size=batch_shape,
+                              ctx=ctx, dtype=dtype)
+    elif input_type == (True, False):
+        return _npi.uniform_n(low, low=None, high=high, size=batch_shape,
+                              ctx=ctx, dtype=dtype)
+    else:
+        return _npi.uniform_n(low=low, high=high, size=batch_shape,
+                              ctx=ctx, dtype=dtype)
+
+
+def normal_n(loc=0.0, scale=1.0, batch_shape=None, dtype=None, ctx=None):
+    r"""Draw random samples from a normal (Gaussian) distribution.
+
+    Samples are distributed according to a normal distribution parametrized
+    by *loc* (mean) and *scale* (standard deviation).
+
+
+    Parameters
+    ----------
+    loc : float, optional
+        Mean (centre) of the distribution.
+    scale : float, optional
+        Standard deviation (spread or "width") of the distribution.
+    batch_shape : int or tuple of ints, optional
+        Batch shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k * broadcast(low, high).size`` samples are drawn.
+        If size is ``None`` (default),
+        a scalar tensor containing a single value is returned if
+        ``low`` and ``high`` are both scalars. Otherwise,
+        ``np.broadcast(loc, scale).size`` samples are drawn.
+    dtype : {'float16', 'float32', 'float64'}, optional
+        Data type of output samples. Default is 'float32'
+    ctx : Context, optional
+        Device context of output, default is current context.
+
+    Returns
+    -------
+    out : ndarray
+        Drawn samples from the parameterized normal distribution.
+
+    Notes
+    -----
+    The probability density for the Gaussian distribution is
+
+    .. math:: p(x) = \frac{1}{\sqrt{ 2 \pi \sigma^2 }}
+                     e^{ - \frac{ (x - \mu)^2 } {2 \sigma^2} },
+
+    where :math:`\mu` is the mean and :math:`\sigma` the standard
+    deviation. The square of the standard deviation, :math:`\sigma^2`,
+    is called the variance.
+
+    The function has its peak at the mean, and its "spread" increases with
+    the standard deviation (the function reaches 0.607 times its maximum at
+    :math:`x + \sigma` and :math:`x - \sigma` [2]_).  This implies that
+    `numpy.random.normal` is more likely to return samples lying close to
+    the mean, rather than those far away.
+
+    References
+    ----------
+    .. [1] Wikipedia, "Normal distribution",
+           https://en.wikipedia.org/wiki/Normal_distribution
+    .. [2] P. R. Peebles Jr., "Central Limit Theorem" in "Probability,
+           Random Variables and Random Signal Principles", 4th ed., 2001,
+           pp. 51, 51, 125.
+
+    Examples
+    --------
+    >>> mu, sigma = 0, 0.1 # mean and standard deviation
+    >>> s = np.random.normal(mu, sigma, 1000)
+
+    Verify the mean and the variance:
+
+    >>> np.abs(mu - np.mean(s)) < 0.01
+    array(True)
+    """
+    from ...numpy import ndarray as np_ndarray
+    input_type = (isinstance(loc, np_ndarray), isinstance(scale, np_ndarray))
+    if dtype is None:
+        dtype = 'float32'
+    if ctx is None:
+        ctx = current_context()
+    if batch_shape == ():
+        batch_shape = None
+    if input_type == (True, True):
+        return _npi.normal_n(loc, scale, loc=None, scale=None, size=batch_shape,
+                             ctx=ctx, dtype=dtype)
+    elif input_type == (False, True):
+        return _npi.normal_n(scale, loc=loc, scale=None, size=batch_shape,
+                             ctx=ctx, dtype=dtype)
+    elif input_type == (True, False):
+        return _npi.normal_n(loc, loc=None, scale=scale, size=batch_shape,
+                             ctx=ctx, dtype=dtype)
+    else:
+        return _npi.normal_n(loc=loc, scale=scale, size=batch_shape,
+                             ctx=ctx, dtype=dtype)
diff --git a/python/mxnet/numpy_extension/random.py b/python/mxnet/numpy_extension/random.py
index 316760b6e2d6..3735abe14d51 100644
--- a/python/mxnet/numpy_extension/random.py
+++ b/python/mxnet/numpy_extension/random.py
@@ -22,7 +22,7 @@
 from ..ndarray import numpy_extension as _mx_nd_npx
 
 
-__all__ = ['seed', 'bernoulli']
+__all__ = ['seed', 'bernoulli', 'normal_n', 'uniform_n']
 
 
 def seed(seed, ctx='all'):  # pylint: disable=redefined-outer-name
@@ -126,3 +126,128 @@ def bernoulli(prob=None, logit=None, size=None, dtype=None, ctx=None, out=None):
         [1., 0., 1., 0.]])
     """
     return _mx_nd_npx.random.bernoulli(prob, logit, size, dtype, ctx, out)
+
+
+def uniform_n(low=0.0, high=1.0, batch_shape=None, dtype=None, ctx=None):
+    r"""Draw samples from a uniform distribution.
+
+    Samples are uniformly distributed over the half-open interval
+    ``[low, high)`` (includes low, but excludes high).  In other words,
+    any value within the given interval is equally likely to be drawn
+    by `uniform`.
+
+    Parameters
+    ----------
+    low : float, ndarray, optional
+        Lower boundary of the output interval.  All values generated will be
+        greater than or equal to low.  The default value is 0.
+    high : float, ndarray, optional
+        Upper boundary of the output interval.  All values generated will be
+        less than high.  The default value is 1.0.
+    batch_shape : int or tuple of ints, optional
+        Batch shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k * broadcast(low, high).size`` samples are drawn.
+        If size is ``None`` (default),
+        a scalar tensor containing a single value is returned if
+        ``low`` and ``high`` are both scalars. Otherwise,
+        ``np.broadcast(low, high).size`` samples are drawn.
+    dtype : {'float16', 'float32', 'float64'}, optional
+        Data type of output samples. Default is 'float32'
+    ctx : Context, optional
+        Device context of output. Default is current context.
+
+    Returns
+    -------
+    out : ndarray
+        Drawn samples from the parameterized uniform distribution.
+
+    See Also
+    --------
+    randint : Discrete uniform distribution, yielding integers.
+    rand : Convenience function that accepts dimensions as input, e.g.,
+           ``rand(2,2)`` would generate a 2-by-2 array of floats,
+           uniformly distributed over ``[0, 1)``.
+
+    Notes
+    -----
+    The probability density function of the uniform distribution is
+
+    .. math:: p(x) = \frac{1}{b - a}
+
+    anywhere within the interval ``[a, b)``, and zero elsewhere.
+
+    When ``high`` == ``low``, values of ``low`` will be returned.
+    If ``high`` < ``low``, the results are officially undefined
+    and may eventually raise an error, i.e. do not rely on this
+    function to behave when passed arguments satisfying that
+    inequality condition.
+    """
+    return _mx_nd_npx.random.uniform_n(low, high, batch_shape=batch_shape, ctx=ctx, dtype=dtype)
+
+
+def normal_n(loc=0.0, scale=1.0, batch_shape=None, dtype=None, ctx=None):
+    r"""Draw random samples from a normal (Gaussian) distribution.
+
+    Samples are distributed according to a normal distribution parametrized
+    by *loc* (mean) and *scale* (standard deviation).
+
+
+    Parameters
+    ----------
+    loc : float, optional
+        Mean (centre) of the distribution.
+    scale : float, optional
+        Standard deviation (spread or "width") of the distribution.
+    batch_shape : int or tuple of ints, optional
+        Batch shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k * broadcast(low, high).size`` samples are drawn.
+        If size is ``None`` (default),
+        a scalar tensor containing a single value is returned if
+        ``low`` and ``high`` are both scalars. Otherwise,
+        ``np.broadcast(loc, scale).size`` samples are drawn.
+    dtype : {'float16', 'float32', 'float64'}, optional
+        Data type of output samples. Default is 'float32'
+    ctx : Context, optional
+        Device context of output, default is current context.
+
+    Returns
+    -------
+    out : ndarray
+        Drawn samples from the parameterized normal distribution.
+
+    Notes
+    -----
+    The probability density for the Gaussian distribution is
+
+    .. math:: p(x) = \frac{1}{\sqrt{ 2 \pi \sigma^2 }}
+                     e^{ - \frac{ (x - \mu)^2 } {2 \sigma^2} },
+
+    where :math:`\mu` is the mean and :math:`\sigma` the standard
+    deviation. The square of the standard deviation, :math:`\sigma^2`,
+    is called the variance.
+
+    The function has its peak at the mean, and its "spread" increases with
+    the standard deviation (the function reaches 0.607 times its maximum at
+    :math:`x + \sigma` and :math:`x - \sigma` [2]_).  This implies that
+    `numpy.random.normal` is more likely to return samples lying close to
+    the mean, rather than those far away.
+
+    References
+    ----------
+    .. [1] Wikipedia, "Normal distribution",
+           https://en.wikipedia.org/wiki/Normal_distribution
+    .. [2] P. R. Peebles Jr., "Central Limit Theorem" in "Probability,
+           Random Variables and Random Signal Principles", 4th ed., 2001,
+           pp. 51, 51, 125.
+
+    Examples
+    --------
+    >>> mu, sigma = 0, 0.1 # mean and standard deviation
+    >>> s = np.random.normal(mu, sigma, 1000)
+
+    Verify the mean and the variance:
+
+    >>> np.abs(mu - np.mean(s)) < 0.01
+    array(True)
+    """
+    return _mx_nd_npx.random.normal_n(loc, scale, batch_shape, dtype, ctx)
diff --git a/python/mxnet/symbol/numpy_extension/random.py b/python/mxnet/symbol/numpy_extension/random.py
index a557a75d56f7..8d2dc4e03fee 100644
--- a/python/mxnet/symbol/numpy_extension/random.py
+++ b/python/mxnet/symbol/numpy_extension/random.py
@@ -21,7 +21,7 @@
 from ...context import current_context
 from ..numpy import _internal as _npi
 
-__all__ = ['bernoulli']
+__all__ = ['bernoulli', 'normal_n', 'uniform_n']
 
 
 def bernoulli(prob=None, logit=None, size=None, dtype=None, ctx=None, out=None):
@@ -102,3 +102,166 @@ def bernoulli(prob=None, logit=None, size=None, dtype=None, ctx=None, out=None):
         else:
             return _npi.bernoulli(prob=None, logit=logit, is_logit=True,
                                   size=size, ctx=ctx, dtype=dtype, out=out)
+
+
+def uniform_n(low=0.0, high=1.0, batch_shape=None, dtype=None, ctx=None):
+    r"""Draw samples from a uniform distribution.
+
+    Samples are uniformly distributed over the half-open interval
+    ``[low, high)`` (includes low, but excludes high).  In other words,
+    any value within the given interval is equally likely to be drawn
+    by `uniform`.
+
+    Parameters
+    ----------
+    low : float, ndarray, optional
+        Lower boundary of the output interval.  All values generated will be
+        greater than or equal to low.  The default value is 0.
+    high : float, ndarray, optional
+        Upper boundary of the output interval.  All values generated will be
+        less than high.  The default value is 1.0.
+    shape : int or tuple of ints, optional
+        Batch shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k * broadcast(low, high).size`` samples are drawn.
+        If size is ``None`` (default),
+        a scalar tensor containing a single value is returned if
+        ``low`` and ``high`` are both scalars. Otherwise,
+        ``np.broadcast(low, high).size`` samples are drawn.
+    dtype : {'float16', 'float32', 'float64'}, optional
+        Data type of output samples. Default is 'float32'
+    ctx : Context, optional
+        Device context of output. Default is current context.
+
+    Returns
+    -------
+    out : ndarray
+        Drawn samples from the parameterized uniform distribution.
+
+    See Also
+    --------
+    randint : Discrete uniform distribution, yielding integers.
+    rand : Convenience function that accepts dimensions as input, e.g.,
+           ``rand(2,2)`` would generate a 2-by-2 array of floats,
+           uniformly distributed over ``[0, 1)``.
+
+    Notes
+    -----
+    The probability density function of the uniform distribution is
+
+    .. math:: p(x) = \frac{1}{b - a}
+
+    anywhere within the interval ``[a, b)``, and zero elsewhere.
+
+    When ``high`` == ``low``, values of ``low`` will be returned.
+    If ``high`` < ``low``, the results are officially undefined
+    and may eventually raise an error, i.e. do not rely on this
+    function to behave when passed arguments satisfying that
+    inequality condition.
+    """
+    from ..numpy import _Symbol as np_symbol
+    input_type = (isinstance(low, np_symbol), isinstance(high, np_symbol))
+    if dtype is None:
+        dtype = 'float32'
+    if ctx is None:
+        ctx = current_context()
+    if batch_shape == ():
+        batch_shape = None
+    if input_type == (True, True):
+        return _npi.uniform_n(low, high, low=None, high=None, size=batch_shape,
+                              ctx=ctx, dtype=dtype)
+    elif input_type == (False, True):
+        return _npi.uniform_n(high, low=low, high=None, size=batch_shape,
+                              ctx=ctx, dtype=dtype)
+    elif input_type == (True, False):
+        return _npi.uniform_n(low, low=None, high=high, size=batch_shape,
+                              ctx=ctx, dtype=dtype)
+    else:
+        return _npi.uniform_n(low=low, high=high, size=batch_shape,
+                              ctx=ctx, dtype=dtype)
+
+
+def normal_n(loc=0.0, scale=1.0, batch_shape=None, dtype=None, ctx=None):
+    r"""Draw random samples from a normal (Gaussian) distribution.
+
+    Samples are distributed according to a normal distribution parametrized
+    by *loc* (mean) and *scale* (standard deviation).
+
+
+    Parameters
+    ----------
+    loc : float, optional
+        Mean (centre) of the distribution.
+    scale : float, optional
+        Standard deviation (spread or "width") of the distribution.
+    shape : int or tuple of ints, optional
+        Batch shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k * broadcast(low, high).size`` samples are drawn.
+        If size is ``None`` (default),
+        a scalar tensor containing a single value is returned if
+        ``low`` and ``high`` are both scalars. Otherwise,
+        ``np.broadcast(loc, scale).size`` samples are drawn.
+    dtype : {'float16', 'float32', 'float64'}, optional
+        Data type of output samples. Default is 'float32'
+    ctx : Context, optional
+        Device context of output, default is current context.
+
+    Returns
+    -------
+    out : _Symbol
+        Drawn samples from the parameterized normal distribution.
+
+    Notes
+    -----
+    The probability density for the Gaussian distribution is
+
+    .. math:: p(x) = \frac{1}{\sqrt{ 2 \pi \sigma^2 }}
+                     e^{ - \frac{ (x - \mu)^2 } {2 \sigma^2} },
+
+    where :math:`\mu` is the mean and :math:`\sigma` the standard
+    deviation. The square of the standard deviation, :math:`\sigma^2`,
+    is called the variance.
+
+    The function has its peak at the mean, and its "spread" increases with
+    the standard deviation (the function reaches 0.607 times its maximum at
+    :math:`x + \sigma` and :math:`x - \sigma` [2]_).  This implies that
+    `numpy.random.normal` is more likely to return samples lying close to
+    the mean, rather than those far away.
+
+    References
+    ----------
+    .. [1] Wikipedia, "Normal distribution",
+           https://en.wikipedia.org/wiki/Normal_distribution
+    .. [2] P. R. Peebles Jr., "Central Limit Theorem" in "Probability,
+           Random Variables and Random Signal Principles", 4th ed., 2001,
+           pp. 51, 51, 125.
+
+    Examples
+    --------
+    >>> mu, sigma = 0, 0.1 # mean and standard deviation
+    >>> s = np.random.normal(mu, sigma, 1000)
+
+    Verify the mean and the variance:
+
+    >>> np.abs(mu - np.mean(s)) < 0.01
+    array(True)
+    """
+    from ..numpy import _Symbol as np_symbol
+    input_type = (isinstance(loc, np_symbol), isinstance(scale, np_symbol))
+    if dtype is None:
+        dtype = 'float32'
+    if ctx is None:
+        ctx = current_context()
+    if batch_shape == ():
+        batch_shape = None
+    if input_type == (True, True):
+        return _npi.normal_n(loc, scale, loc=None, scale=None, size=batch_shape,
+                             ctx=ctx, dtype=dtype)
+    elif input_type == (False, True):
+        return _npi.normal_n(scale, loc=loc, scale=None, size=batch_shape,
+                             ctx=ctx, dtype=dtype)
+    elif input_type == (True, False):
+        return _npi.normal_n(loc, loc=None, scale=scale, size=batch_shape,
+                             ctx=ctx, dtype=dtype)
+    else:
+        return _npi.normal_n(loc=loc, scale=scale, size=batch_shape,
+                             ctx=ctx, dtype=dtype)
diff --git a/src/operator/numpy/random/dist_common.h b/src/operator/numpy/random/dist_common.h
index 6394375883aa..e8358294eaf0 100644
--- a/src/operator/numpy/random/dist_common.h
+++ b/src/operator/numpy/random/dist_common.h
@@ -205,6 +205,50 @@ inline bool UnaryDistOpShape(const nnvm::NodeAttrs &attrs,
   return shape_is_known(out_attrs->at(0));
 }
 
+
+// Infer Shape function for sample_n Op.
+// i.e. output_shape = (shape,) + broadcast(param1.shape, param2.shape)
+template <typename DistParam>
+inline bool TwoparamsDistOpConcatShape(const nnvm::NodeAttrs &attrs,
+                                       std::vector<TShape> *in_attrs,
+                                       std::vector<TShape> *out_attrs) {
+  const DistParam &param = nnvm::get<DistParam>(attrs.parsed);
+  // broadcast(param1.shape, param2.shape).
+  mxnet::TShape param_broadcast_shape;
+  if (in_attrs->size() == 2U) {
+      // Both params from ndarray.
+      mxnet::TShape &param1 = (*in_attrs)[0];
+      mxnet::TShape &param2 = (*in_attrs)[1];
+      mxnet::TShape out(std::max(param1.ndim(), param2.ndim()), -1);
+      InferBroadcastShape(param1, param2, &out);
+      param_broadcast_shape = out;
+    } else if (in_attrs->size() == 1U) {
+      // One param from ndarray.
+      param_broadcast_shape = in_attrs->at(0);
+    } else if (in_attrs->size() == 0) {
+      // Two scalar case.
+      param_broadcast_shape = TShape(0, -1);
+    }
+  if (param.size.has_value()) {
+    // Size declared.
+    std::vector<dim_t> oshape_vec;
+    const mxnet::Tuple<int> &size = param.size.value();
+    for (int i = 0; i < size.ndim(); ++i) {
+      oshape_vec.emplace_back(size[i]);
+    }
+    for (int i = 0; i < param_broadcast_shape.ndim(); ++i) {
+      oshape_vec.emplace_back(param_broadcast_shape[i]);
+    }
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, TShape(oshape_vec));
+  } else {
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, param_broadcast_shape);
+  }
+  if (out_attrs->size() == 2U) {
+    SHAPE_ASSIGN_CHECK(*out_attrs, 1, out_attrs->at(0));
+  }
+  return true;
+}
+
 }  // namespace op
 }  // namespace mxnet
 
diff --git a/src/operator/numpy/random/np_normal_op.cc b/src/operator/numpy/random/np_normal_op.cc
index 992e93c8af16..e2aa1397af67 100644
--- a/src/operator/numpy/random/np_normal_op.cc
+++ b/src/operator/numpy/random/np_normal_op.cc
@@ -69,6 +69,46 @@ NNVM_REGISTER_OP(_npi_normal)
 .add_argument("input2", "NDArray-or-Symbol", "Source input")
 .add_arguments(NumpyNormalParam::__FIELDS__());
 
+NNVM_REGISTER_OP(_npi_normal_n)
+.describe("Ndarray behavior normal")
+.set_num_inputs(
+  [](const nnvm::NodeAttrs& attrs) {
+    const NumpyNormalParam& param = nnvm::get<NumpyNormalParam>(attrs.parsed);
+    int num_inputs = 2;
+    if (param.loc.has_value()) num_inputs -= 1;
+    if (param.scale.has_value()) num_inputs -= 1;
+    return num_inputs;
+  }
+)
+.set_num_outputs(2)
+.set_attr<nnvm::FNumVisibleOutputs>("FNumVisibleOutputs",
+    [](const NodeAttrs& attrs) {
+  return 1;
+})
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    const NumpyNormalParam& param = nnvm::get<NumpyNormalParam>(attrs.parsed);
+    int num_inputs = 2;
+    if (param.loc.has_value()) num_inputs -= 1;
+    if (param.scale.has_value()) num_inputs -= 1;
+    if (num_inputs == 0) return std::vector<std::string>();
+    if (num_inputs == 1) return std::vector<std::string>{"input1"};
+    return std::vector<std::string>{"input1", "input2"};
+  })
+.set_attr_parser(ParamParser<NumpyNormalParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", TwoparamsDistOpConcatShape<NumpyNormalParam>)
+.set_attr<nnvm::FInferType>("FInferType", NumpyNormalOpType)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const nnvm::NodeAttrs& attrs) {
+      return std::vector<ResourceRequest>{
+        ResourceRequest::kRandom, ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", NumpyNormalForward<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseInOut{"_backward_broadcast_normal"})
+.add_argument("input1", "NDArray-or-Symbol", "Source input")
+.add_argument("input2", "NDArray-or-Symbol", "Source input")
+.add_arguments(NumpyNormalParam::__FIELDS__());
+
 NNVM_REGISTER_OP(_backward_broadcast_normal)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
 .set_attr_parser(ParamParser<NumpyNormalParam>)
diff --git a/src/operator/numpy/random/np_normal_op.cu b/src/operator/numpy/random/np_normal_op.cu
index 0eab089abbc9..d45bc2321bd7 100644
--- a/src/operator/numpy/random/np_normal_op.cu
+++ b/src/operator/numpy/random/np_normal_op.cu
@@ -29,10 +29,13 @@ namespace mxnet {
 namespace op {
 
 NNVM_REGISTER_OP(_npi_normal)
-    .set_attr<FCompute>("FCompute<gpu>", NumpyNormalForward<gpu>);
+.set_attr<FCompute>("FCompute<gpu>", NumpyNormalForward<gpu>);
 
 NNVM_REGISTER_OP(_backward_broadcast_normal)
 .set_attr<FCompute>("FCompute<gpu>", NormalReparamBackward<gpu>);
 
+NNVM_REGISTER_OP(_npi_normal_n)
+.set_attr<FCompute>("FCompute<gpu>", NumpyNormalForward<gpu>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/random/np_uniform_op.cc b/src/operator/numpy/random/np_uniform_op.cc
index 7307b7744d5e..1f4be230996c 100644
--- a/src/operator/numpy/random/np_uniform_op.cc
+++ b/src/operator/numpy/random/np_uniform_op.cc
@@ -65,5 +65,41 @@ NNVM_REGISTER_OP(_npi_uniform)
 .add_argument("input2", "NDArray-or-Symbol", "Source input")
 .add_arguments(NumpyUniformParam::__FIELDS__());
 
+NNVM_REGISTER_OP(_npi_uniform_n)
+.describe("numpy behavior uniform")
+.set_num_inputs(
+  [](const nnvm::NodeAttrs& attrs) {
+    const NumpyUniformParam& param = nnvm::get<NumpyUniformParam>(attrs.parsed);
+    int num_inputs = 2;
+    if (param.low.has_value()) num_inputs -= 1;
+    if (param.high.has_value()) num_inputs -= 1;
+    return num_inputs;
+  }
+)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    const NumpyUniformParam& param = nnvm::get<NumpyUniformParam>(attrs.parsed);
+    int num_inputs = 2;
+    if (param.low.has_value()) num_inputs -= 1;
+    if (param.high.has_value()) num_inputs -= 1;
+    if (num_inputs == 0) return std::vector<std::string>();
+    if (num_inputs == 1) return std::vector<std::string>{"input1"};
+    return std::vector<std::string>{"input1", "input2"};
+  })
+.set_attr_parser(ParamParser<NumpyUniformParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", TwoparamsDistOpConcatShape<NumpyUniformParam>)
+.set_attr<nnvm::FInferType>("FInferType", NumpyUniformOpType)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const nnvm::NodeAttrs& attrs) {
+      return std::vector<ResourceRequest>{
+        ResourceRequest::kRandom, ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", NumpyUniformForward<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.add_argument("input1", "NDArray-or-Symbol", "Source input")
+.add_argument("input2", "NDArray-or-Symbol", "Source input")
+.add_arguments(NumpyUniformParam::__FIELDS__());
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/random/np_uniform_op.cu b/src/operator/numpy/random/np_uniform_op.cu
index d997bc57d3be..be21eae55647 100644
--- a/src/operator/numpy/random/np_uniform_op.cu
+++ b/src/operator/numpy/random/np_uniform_op.cu
@@ -31,5 +31,8 @@ namespace op {
 NNVM_REGISTER_OP(_npi_uniform)
 .set_attr<FCompute>("FCompute<gpu>", NumpyUniformForward<gpu>);
 
+NNVM_REGISTER_OP(_npi_uniform_n)
+.set_attr<FCompute>("FCompute<gpu>", NumpyUniformForward<gpu>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/tests/nightly/test_np_random.py b/tests/nightly/test_np_random.py
new file mode 100644
index 000000000000..345fb86b1222
--- /dev/null
+++ b/tests/nightly/test_np_random.py
@@ -0,0 +1,83 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# pylint: skip-file
+from __future__ import absolute_import
+from __future__ import division
+import itertools
+import os
+import sys
+from os import path
+curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
+sys.path.append(os.path.join(curr_path, '../python/common/'))
+sys.path.append(os.path.join(curr_path, '../python/unittest/'))
+sys.path.insert(0, os.path.join(curr_path, '../../../python'))
+import unittest
+import numpy as _np
+import mxnet as mx
+from mxnet import np, npx, autograd
+from mxnet.gluon import HybridBlock
+from mxnet.test_utils import same, assert_almost_equal, rand_shape_nd, rand_ndarray, retry, use_np
+from common import with_seed
+from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf, assert_exception, is_op_runnable, collapse_sum_like
+from mxnet.ndarray.ndarray import py_slice
+from mxnet.base import integer_types
+import scipy.stats as ss
+
+
+@retry(5)
+@with_seed()
+@use_np
+def test_np_uniform():
+    types = [None, "float32", "float64"]
+    ctx = mx.context.current_context()
+    samples = 1000000
+    # Generation test
+    trials = 8
+    num_buckets = 5
+    for dtype in types:
+        for low, high in [(-100.0, -98.0), (99.0, 101.0)]:
+            scale = high - low
+            buckets, probs = gen_buckets_probs_with_ppf(lambda x: ss.uniform.ppf(x, loc=low, scale=scale), num_buckets)
+            buckets = np.array(buckets, dtype=dtype).tolist()
+            probs = [(buckets[i][1] - buckets[i][0])/scale for i in range(num_buckets)]
+            generator_mx_np = lambda x: mx.np.random.uniform(low, high, size=x, ctx=ctx, dtype=dtype).asnumpy()
+            verify_generator(generator=generator_mx_np, buckets=buckets, probs=probs, nsamples=samples, nrepeat=trials)
+
+
+@retry(5)
+@with_seed()
+@use_np
+def test_np_normal():
+    types = [None, "float32", "float64"]
+    ctx = mx.context.current_context()
+    samples = 1000000
+    # Generation test
+    trials = 8
+    num_buckets = 5
+    for dtype in types:
+        for loc, scale in [(0.0, 1.0), (1.0, 5.0)]:
+            buckets, probs = gen_buckets_probs_with_ppf(lambda x: ss.norm.pdf(x, loc=low, scale=scale), num_buckets)
+            buckets = np.array(buckets, dtype=dtype).tolist()
+            probs = [(buckets[i][1] - buckets[i][0])/scale for i in range(num_buckets)]
+            generator_mx_np = lambda x: np.random.normal(loc, scale, size=x, ctx=ctx, dtype=dtype).asnumpy()
+            verify_generator(generator=generator_mx_np, buckets=buckets, probs=probs, nsamples=samples, nrepeat=trials)
+
+
+if __name__ == '__main__':
+    import nose
+    nose.runmodule()
diff --git a/tests/python/unittest/test_numpy_ndarray.py b/tests/python/unittest/test_numpy_ndarray.py
index 8e46f03e79bc..2c94ec06f4c7 100644
--- a/tests/python/unittest/test_numpy_ndarray.py
+++ b/tests/python/unittest/test_numpy_ndarray.py
@@ -974,40 +974,6 @@ def test_np_save_load_ndarrays():
             assert _np.array_equal(v.asnumpy(), arr_dict[k].asnumpy())
 
 
-@retry(5)
-@with_seed()
-@use_np
-def test_np_uniform():
-    types = [None, "float32", "float64"]
-    ctx = mx.context.current_context()
-    samples = 1000000
-    # Generation test
-    trials = 8
-    num_buckets = 5
-    for dtype in types:
-        for low, high in [(-100.0, -98.0), (99.0, 101.0)]:
-            scale = high - low
-            buckets, probs = gen_buckets_probs_with_ppf(lambda x: ss.uniform.ppf(x, loc=low, scale=scale), num_buckets)
-            buckets = np.array(buckets, dtype=dtype).tolist()
-            probs = [(buckets[i][1] - buckets[i][0])/scale for i in range(num_buckets)]
-            generator_mx_np = lambda x: mx.np.random.uniform(low, high, size=x, ctx=ctx, dtype=dtype).asnumpy()
-            verify_generator(generator=generator_mx_np, buckets=buckets, probs=probs, nsamples=samples, nrepeat=trials)
-
-    # Broadcasting test
-    params = [
-        (1.0, mx.np.ones((4,4)) + 2.0),
-        (mx.np.zeros((4,4)) + 1, 2.0),
-        (mx.np.zeros((1,4)), mx.np.ones((4,4)) + mx.np.array([1, 2, 3, 4])),
-        (mx.np.array([1, 2, 3, 4]), mx.np.ones((2,4,4)) * 5)
-    ]
-    for dtype in types:
-        for low, high in params:
-            expect_mean = (low + high) / 2
-            expanded_size = (samples,) + expect_mean.shape
-            uniform_samples = mx.np.random.uniform(low, high, size=expanded_size, dtype=dtype)
-            mx.test_utils.assert_almost_equal(uniform_samples.asnumpy().mean(0), expect_mean.asnumpy(), rtol=0.20, atol=1e-1)
-
-
 @retry(5)
 @with_seed()
 @use_np
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index dc99fc6fc251..8adf9d78720b 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -2670,6 +2670,45 @@ def hybrid_forward(self, F, loc, scale):
                 assert_almost_equal(loc.grad.asnumpy().sum(), _np.ones(out_shape).sum(), rtol=1e-3, atol=1e-5)
 
 
+@with_seed()
+@use_np
+def test_npx_sample_n():
+    def shape_formatter(s):
+        if s is None:
+            return ()
+        if isinstance(s, tuple):
+            return s
+        # scalar case
+        return (s,)
+
+    class TestSampleN(HybridBlock):
+        def __init__(self, shape, op_name):
+            super(TestSampleN, self).__init__()
+            self._shape = shape
+            self._op_name = op_name
+
+        def hybrid_forward(self, F, param1, param2):
+            op = getattr(F.npx.random, self._op_name, None)
+            assert op is not None
+            # return param1 + param2 + op(batch_shape=self._shape)
+            return op(param1, param2, batch_shape=self._shape)
+
+    batch_shapes = [(10,), (2, 3), 6, (), None]
+    event_shapes = [(), (2,), (2,2)]
+    dtypes = ['float16', 'float32', 'float64']
+    op_names = ['uniform_n', 'normal_n']
+    
+    for bshape, eshape, dtype, op in itertools.product(batch_shapes, event_shapes, dtypes, op_names):
+        for hybridize in [True, False]:
+            net = TestSampleN(bshape, op)
+            if hybridize:
+                net.hybridize()
+            expected_shape = (shape_formatter(bshape) +
+                              shape_formatter(eshape))
+            out = net(np.ones(shape=eshape), np.ones(shape=eshape))
+            assert out.shape == expected_shape
+
+
 @with_seed()
 @use_np
 def test_np_random():

From 971ea4f0adb5bd52cdcdee3e15f390ac05a3d379 Mon Sep 17 00:00:00 2001
From: Xingjian Shi <xshiab@connect.ust.hk>
Date: Sat, 23 Nov 2019 06:20:20 -0800
Subject: [PATCH 625/813] Add arange_like to npx (#16883)

---
 src/operator/tensor/init_op.cc | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/operator/tensor/init_op.cc b/src/operator/tensor/init_op.cc
index 7f60bf4e2246..6db89a5cda61 100644
--- a/src/operator/tensor/init_op.cc
+++ b/src/operator/tensor/init_op.cc
@@ -103,6 +103,7 @@ NNVM_REGISTER_OP(_arange)
 .add_arguments(RangeParam::__FIELDS__());
 
 NNVM_REGISTER_OP(_contrib_arange_like)
+.add_alias("_npx_arange_like")
 .describe(R"code(Return an array with evenly spaced values. If axis is not given, the output will 
 have the same shape as the input array. Otherwise, the output will be a 1-D array with size of 
 the specified axis in input shape.

From 85d3ef3a40da20a4aac3030950aa0f37f8cb89c5 Mon Sep 17 00:00:00 2001
From: Rohit Kumar Srivastava <srivastava.141@osu.edu>
Date: Sat, 23 Nov 2019 22:19:07 -0800
Subject: [PATCH 626/813] Lamb optimizer update (#16715)

* initial commit lamb optimizer

* fixing base lamb optimizer

* adding API doc for Lamb Phase 1 and 2
---
 python/mxnet/optimizer/optimizer.py     |  52 ++++++-
 src/operator/optimizer_op-inl.h         | 186 ++++++++++++++++++++++++
 src/operator/optimizer_op.cc            |  81 +++++++++++
 src/operator/optimizer_op.cu            |   7 +
 tests/python/unittest/test_optimizer.py |  73 ++++++++++
 5 files changed, 397 insertions(+), 2 deletions(-)

diff --git a/python/mxnet/optimizer/optimizer.py b/python/mxnet/optimizer/optimizer.py
index b7311b29fc9e..00d130bb5534 100644
--- a/python/mxnet/optimizer/optimizer.py
+++ b/python/mxnet/optimizer/optimizer.py
@@ -34,14 +34,14 @@
                        multi_sgd_update, multi_sgd_mom_update, multi_mp_sgd_update,
                        multi_mp_sgd_mom_update, preloaded_multi_sgd_update,
                        preloaded_multi_sgd_mom_update, preloaded_multi_mp_sgd_update,
-                       preloaded_multi_mp_sgd_mom_update)
+                       preloaded_multi_mp_sgd_mom_update, lamb_update_phase1, lamb_update_phase2)
 from ..ndarray import sparse
 from ..random import normal
 from ..util import is_np_array
 
 __all__ = [
     'AdaDelta', 'AdaGrad', 'Adam', 'Adamax', 'DCASGD', 'FTML', 'Ftrl', 'LARS', 'LBSGD',
-    'NAG', 'NDabs', 'Nadam', 'Optimizer', 'RMSProp', 'SGD', 'SGLD', 'Signum',
+    'NAG', 'NDabs', 'Nadam', 'Optimizer', 'RMSProp', 'SGD', 'SGLD', 'Signum', 'LAMB',
     'Test', 'Updater', 'ccSGD', 'create', 'get_updater', 'register'
 ]
 
@@ -1244,6 +1244,54 @@ def update(self, index, weight, grad, state):
             kwargs = {}
             sgd_update(weight, grad, out=weight, lr=lr, wd=wd, **kwargs)
 
+
+@register
+class LAMB(Optimizer):
+    """LAMB Optimizer.
+    """
+    def __init__(self, learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-6,
+                 lower_bound=None, upper_bound=None, bias_correction=True, **kwargs):
+        super(LAMB, self).__init__(learning_rate=learning_rate, **kwargs)
+        self.beta1 = beta1
+        self.beta2 = beta2
+        self.epsilon = epsilon
+        self.lower_bound = lower_bound
+        self.upper_bound = upper_bound
+        self.bias_correction = bias_correction
+
+
+    def create_state(self, index, weight):
+        stype = weight.stype
+        dtype = weight.dtype
+        return (zeros(weight.shape, weight.context, dtype=dtype, stype=stype),
+                zeros(weight.shape, weight.context, dtype=dtype, stype=stype))
+
+    def update(self, index, weight, grad, state):
+        assert(isinstance(weight, NDArray))
+        assert(isinstance(grad, NDArray))
+        self._update_count(index)
+        lr = self._get_lr(index)
+        wd = self._get_wd(index)
+        t = self._index_update_count[index]
+
+        kwargs = {'beta1': self.beta1, 'beta2': self.beta2, 'epsilon': self.epsilon,
+                  'bias_correction': self.bias_correction, 't': t,
+                  'rescale_grad': self.rescale_grad}
+        mean, var = state
+        if self.clip_gradient:
+            kwargs['clip_gradient'] = self.clip_gradient
+        g = lamb_update_phase1(weight, grad, mean, var, wd=wd, **kwargs)
+
+        kwargs = {}
+        if self.lower_bound:
+            kwargs['lower_bound'] = self.lower_bound
+        if self.upper_bound:
+            kwargs['upper_bound'] = self.upper_bound
+        r_1 = weight.norm()
+        r_2 = g.norm()
+        lamb_update_phase2(weight, g, r_1, r_2, lr=lr, out=weight, **kwargs)
+
+
 # pylint: enable=line-too-long
 @register
 class DCASGD(Optimizer):
diff --git a/src/operator/optimizer_op-inl.h b/src/operator/optimizer_op-inl.h
index c211d32c2c42..698f7977a963 100644
--- a/src/operator/optimizer_op-inl.h
+++ b/src/operator/optimizer_op-inl.h
@@ -1563,6 +1563,192 @@ inline void AdamUpdateEx(const nnvm::NodeAttrs& attrs,
   }
 }
 
+struct LambUpdatePhaseOneParam : public dmlc::Parameter<LambUpdatePhaseOneParam> {
+    float beta1;
+    float beta2;
+    float epsilon;
+    float t;
+    bool bias_correction;
+    float wd;
+    float rescale_grad;
+    float clip_gradient;
+    DMLC_DECLARE_PARAMETER(LambUpdatePhaseOneParam) {
+      DMLC_DECLARE_FIELD(beta1)
+      .set_default(0.9f)
+      .describe("The decay rate for the 1st moment estimates.");
+      DMLC_DECLARE_FIELD(beta2)
+      .set_default(0.999f)
+      .describe("The decay rate for the 2nd moment estimates.");
+      DMLC_DECLARE_FIELD(epsilon)
+      .set_default(1e-6f)
+      .describe("A small constant for numerical stability.");
+      DMLC_DECLARE_FIELD(t)
+      .describe("Index update count.");
+      DMLC_DECLARE_FIELD(bias_correction)
+      .set_default(true)
+      .describe("Whether to use bias correction.");
+      DMLC_DECLARE_FIELD(wd)
+      .describe("Weight decay augments the objective function with a "
+                "regularization term that penalizes large weights. "
+                "The penalty scales with the square of the magnitude of each weight.");
+      DMLC_DECLARE_FIELD(rescale_grad)
+      .set_default(1.0f)
+      .describe("Rescale gradient to grad = rescale_grad*grad.");
+      DMLC_DECLARE_FIELD(clip_gradient)
+      .set_default(-1.0f)
+      .describe("Clip gradient to the range of [-clip_gradient, clip_gradient] "
+                "If clip_gradient <= 0, gradient clipping is turned off. "
+                "grad = max(min(grad, clip_gradient), -clip_gradient).");
+    }
+};
+
+struct LambUpdatePhaseTwoParam : public dmlc::Parameter<LambUpdatePhaseTwoParam> {
+    float lr;
+    float lower_bound;
+    float upper_bound;
+    DMLC_DECLARE_PARAMETER(LambUpdatePhaseTwoParam) {
+      DMLC_DECLARE_FIELD(lr)
+      .describe("Learning rate");
+      DMLC_DECLARE_FIELD(lower_bound)
+      .set_default(-1.0f)
+      .describe("Lower limit of norm of weight. If lower_bound <= 0, Lower limit is not set");
+      DMLC_DECLARE_FIELD(upper_bound)
+      .set_default(-1.0f)
+      .describe("Upper limit of norm of weight. If upper_bound <= 0, Upper limit is not set");
+    }
+};
+
+struct LambUpdatePhaseOneKernel {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int i, DType* out_data,
+    DType* mean_data, DType* var_data, const DType* weight_data, const DType* grad_data,
+    const DType clip_gradient, const DType rescale_grad,
+    const DType beta1, const DType beta2, const DType wd,
+    const DType epsilon, const DType t,
+    bool bias_correction, const OpReqType req) {
+    using namespace mshadow_op;
+
+    DType grad_rescaled = grad_data[i] * rescale_grad;
+    if (clip_gradient >= 0.f) {
+      grad_rescaled = clip::Map(grad_rescaled, clip_gradient);
+    }
+
+    mean_data[i] = beta1 * mean_data[i] + (1.f - beta1) * grad_rescaled;
+    var_data[i] = beta2 * var_data[i] + (1.f - beta2) * grad_rescaled * grad_rescaled;
+
+    DType g = mean_data[i] / (square_root::Map(var_data[i]) + epsilon) + wd * weight_data[i];
+
+    if (bias_correction) {
+      DType mean_hat = mean_data[i] / (1. - power::Map(beta1, t));
+      DType var_hat = var_data[i] / (1 - power::Map(beta2, t));
+      g = mean_hat / (square_root::Map(var_hat) + epsilon) + wd * weight_data[i];
+    }
+    KERNEL_ASSIGN(out_data[i], req, g);
+  }
+};
+
+template<typename xpu>
+inline void LambUpdatePhaseOne(const nnvm::NodeAttrs& attrs,
+                       const OpContext &ctx,
+                       const std::vector<TBlob> &inputs,
+                       const std::vector<OpReqType> &req,
+                       const std::vector<TBlob> &outputs) {
+  using namespace mxnet_op;
+  const LambUpdatePhaseOneParam& param = nnvm::get<LambUpdatePhaseOneParam>(attrs.parsed);
+  Stream<xpu>* s = ctx.get_stream<xpu>();
+  MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    Tensor<xpu, 2, DType> weight = inputs[0].FlatTo2D<xpu, DType>(s);
+    Tensor<xpu, 2, DType> grad = inputs[1].FlatTo2D<xpu, DType>(s);
+    Tensor<xpu, 2, DType> mean = inputs[2].FlatTo2D<xpu, DType>(s);
+    Tensor<xpu, 2, DType> var = inputs[3].FlatTo2D<xpu, DType>(s);
+    Tensor<xpu, 2, DType> out = outputs[0].FlatTo2D<xpu, DType>(s);
+
+  Kernel<LambUpdatePhaseOneKernel, xpu>::Launch(s, weight.shape_.Size(),
+    out.dptr_, mean.dptr_, var.dptr_, weight.dptr_, grad.dptr_,
+    static_cast<DType>(param.clip_gradient), static_cast<DType>(param.rescale_grad),
+    static_cast<DType>(param.beta1), static_cast<DType>(param.beta2),
+    static_cast<DType>(param.wd), static_cast<DType>(param.epsilon),
+    static_cast<DType>(param.t), static_cast<bool>(param.bias_correction), req[0]);
+  });
+}
+
+inline bool LambUpdatePhaseTwoShape(const nnvm::NodeAttrs& attrs,
+                            mxnet::ShapeVector* in_attrs,
+                            mxnet::ShapeVector* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 4U);
+  CHECK_EQ(out_attrs->size(), 1U);
+
+  mxnet::TShape expected_out(in_attrs->at(0).ndim(), -1);
+
+  mxnet::TShape& weight_shape = in_attrs->at(0);
+  mxnet::TShape& g_shape = in_attrs->at(1);
+  CHECK_EQ(weight_shape.ndim(), g_shape.ndim())
+           << "total no. of dimensions for weights and g must match";
+  for (int i=0; i < weight_shape.ndim(); ++i) {
+    CHECK_EQ(weight_shape[i], g_shape[i])
+           << "weight and g dimension size mismatch at " << i << "-th index";
+  }
+  mxnet::TShape& r1_shape = in_attrs->at(2);
+  mxnet::TShape& r2_shape = in_attrs->at(3);
+  CHECK_EQ(r1_shape[0], 1U) << "r1 shape incorrect";
+  CHECK_EQ(r2_shape[0], 1U) << "r2 shape incorrect";
+  for (int i=0; i < expected_out.ndim(); ++i) {
+    expected_out[i] = weight_shape[i];
+  }
+
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, expected_out);
+  return shape_is_known(expected_out);
+}
+
+struct LambUpdatePhaseTwoKernel {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int i, DType* out_data,
+    const DType* weight_data, const DType* g,
+    const DType* r1, const DType* r2,
+    DType lr, const DType lower_bound,
+    const DType upper_bound, const OpReqType req) {
+    using namespace mshadow_op;
+
+    DType new_r1 = r1[0];
+    if (lower_bound >= 0) {
+      new_r1 = maximum::Map(new_r1, lower_bound);
+    }
+    if (upper_bound >= 0) {
+      new_r1 = minimum::Map(new_r1, upper_bound);
+    }
+    if (new_r1 == 0.0f || r2[0] == 0.0f) {
+      lr = lr * 1.0f;
+    } else {
+      lr = lr * new_r1 / r2[0];
+    }
+
+    KERNEL_ASSIGN(out_data[i], req, weight_data[i] - lr * g[i]);
+  }
+};
+
+template<typename xpu>
+inline void LambUpdatePhaseTwo(const nnvm::NodeAttrs& attrs,
+                       const OpContext &ctx,
+                       const std::vector<TBlob> &inputs,
+                       const std::vector<OpReqType> &req,
+                       const std::vector<TBlob> &outputs) {
+  using namespace mxnet_op;
+  const LambUpdatePhaseTwoParam& param = nnvm::get<LambUpdatePhaseTwoParam>(attrs.parsed);
+  Stream<xpu>* s = ctx.get_stream<xpu>();
+  MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    Tensor<xpu, 2, DType> weight = inputs[0].FlatTo2D<xpu, DType>(s);
+    Tensor<xpu, 2, DType> g = inputs[1].FlatTo2D<xpu, DType>(s);
+    Tensor<xpu, 2, DType> r1 = inputs[2].FlatTo2D<xpu, DType>(s);
+    Tensor<xpu, 2, DType> r2 = inputs[3].FlatTo2D<xpu, DType>(s);
+    Tensor<xpu, 2, DType> out = outputs[0].FlatTo2D<xpu, DType>(s);
+
+  Kernel<LambUpdatePhaseTwoKernel, xpu>::Launch(s, weight.shape_.Size(),
+    out.dptr_, weight.dptr_, g.dptr_, r1.dptr_, r2.dptr_,
+    static_cast<DType>(param.lr), static_cast<DType>(param.lower_bound),
+    static_cast<DType>(param.upper_bound), req[0]);
+  });
+}
+
 // This RMSProp code follows the version in
 // http://arxiv.org/pdf/1308.0850v5.pdf Eq(38) - Eq(45)
 // by Alex Graves, 2013.
diff --git a/src/operator/optimizer_op.cc b/src/operator/optimizer_op.cc
index 01410863640f..9cf32778b15c 100644
--- a/src/operator/optimizer_op.cc
+++ b/src/operator/optimizer_op.cc
@@ -43,6 +43,8 @@ DMLC_REGISTER_PARAMETER(FtrlParam);
 DMLC_REGISTER_PARAMETER(SignSGDParam);
 DMLC_REGISTER_PARAMETER(SignumParam);
 DMLC_REGISTER_PARAMETER(AdagradParam);
+DMLC_REGISTER_PARAMETER(LambUpdatePhaseOneParam);
+DMLC_REGISTER_PARAMETER(LambUpdatePhaseTwoParam);
 
 NNVM_REGISTER_OP(signsgd_update)
 .describe(R"code(Update function for SignSGD optimizer.
@@ -921,5 +923,84 @@ Note that non-zero values for the weight decay option are not supported.
 .add_argument("history", "NDArray-or-Symbol", "History")
 .add_arguments(AdagradParam::__FIELDS__());
 
+NNVM_REGISTER_OP(lamb_update_phase1)
+.describe(R"code(Phase I of lamb update it performs the following operations and returns g:.
+
+Link to paper: https://arxiv.org/pdf/1904.00962.pdf
+
+.. math::
+    \begin{gather*}
+    grad = grad * rescale_grad
+    if (grad < -clip_gradient)
+    then
+         grad = -clip_gradient
+    if (grad > clip_gradient)
+    then
+         grad = clip_gradient
+
+    mean = beta1 * mean + (1 - beta1) * grad;
+    variance = beta2 * variance + (1. - beta2) * grad ^ 2;
+
+    if (bias_correction)
+    then
+         mean_hat = mean / (1. - beta1^t);
+         var_hat = var / (1 - beta2^t);
+         g = mean_hat / (var_hat^(1/2) + epsilon) + wd * weight;
+    else
+         g = mean / (var_data^(1/2) + epsilon) + wd * weight_data[i];
+    \end{gather*}
+
+)code" ADD_FILELINE)
+.set_num_inputs(4)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<LambUpdatePhaseOneParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<4, 1>)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<4, 1>)
+.set_attr<FCompute>("FCompute<cpu>", LambUpdatePhaseOne<cpu>)
+.set_attr<nnvm::FMutateInputs>("FMutateInputs",
+  [](const nnvm::NodeAttrs& attrs) {
+    return std::vector<uint32_t>{2, 3};
+  })
+.add_argument("weight", "NDArray-or-Symbol", "Weight")
+.add_argument("grad", "NDArray-or-Symbol", "Gradient")
+.add_argument("mean", "NDArray-or-Symbol", "Moving mean")
+.add_argument("var", "NDArray-or-Symbol", "Moving variance")
+.add_arguments(LambUpdatePhaseOneParam::__FIELDS__());
+
+NNVM_REGISTER_OP(lamb_update_phase2)
+.describe(R"code(Phase II of lamb update it performs the following operations and updates grad.
+
+Link to paper: https://arxiv.org/pdf/1904.00962.pdf
+
+.. math::
+    \begin{gather*}
+    if (lower_bound >= 0)
+    then
+         r1 = max(r1, lower_bound)
+    if (upper_bound >= 0)
+    then
+         r1 = max(r1, upper_bound)
+
+    if (r1 == 0 or r2 == 0)
+    then
+         lr = lr
+    else
+         lr = lr * (r1/r2)
+    weight = weight - lr * g
+    \end{gather*}
+
+)code" ADD_FILELINE)
+.set_num_inputs(4)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<LambUpdatePhaseTwoParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", LambUpdatePhaseTwoShape)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<4, 1>)
+.set_attr<FCompute>("FCompute<cpu>", LambUpdatePhaseTwo<cpu>)
+.add_argument("weight", "NDArray-or-Symbol", "Weight")
+.add_argument("g", "NDArray-or-Symbol", "Output of lamb_update_phase 1")
+.add_argument("r1", "NDArray-or-Symbol", "r1")
+.add_argument("r2", "NDArray-or-Symbol", "r2")
+.add_arguments(LambUpdatePhaseTwoParam::__FIELDS__());
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/optimizer_op.cu b/src/operator/optimizer_op.cu
index 2c72462de016..a602b649b63d 100644
--- a/src/operator/optimizer_op.cu
+++ b/src/operator/optimizer_op.cu
@@ -277,5 +277,12 @@ NNVM_REGISTER_OP(ftrl_update)
 NNVM_REGISTER_OP(_sparse_adagrad_update)
 .set_attr<FComputeEx>("FComputeEx<gpu>", AdagradUpdateEx<gpu>);
 
+NNVM_REGISTER_OP(lamb_update_phase1)
+.set_attr<FCompute>("FCompute<gpu>", LambUpdatePhaseOne<gpu>);
+
+NNVM_REGISTER_OP(lamb_update_phase2)
+.set_attr<FCompute>("FCompute<gpu>", LambUpdatePhaseTwo<gpu>);
+
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/tests/python/unittest/test_optimizer.py b/tests/python/unittest/test_optimizer.py
index f9adf63b24e2..4dbf25128c5c 100644
--- a/tests/python/unittest/test_optimizer.py
+++ b/tests/python/unittest/test_optimizer.py
@@ -425,6 +425,79 @@ def test_nag():
                 continue
             compare_optimizer(opt1(**kwarg), opt2(**kwarg), shape, dtype, rtol=1e-3, atol=1e-4)
 
+
+# LAMB optimizer
+class PyLAMB(mx.optimizer.Optimizer):
+    """
+	Python reference implementation of LAMB optimizer.
+    """
+    def __init__(self, learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-6,
+                 lower_bound=None, upper_bound=None, bias_correction=True, **kwargs):
+        super(PyLAMB, self).__init__(learning_rate=learning_rate, **kwargs)
+        self.beta1 = beta1
+        self.beta2 = beta2
+        self.epsilon = epsilon
+        self.lower_bound = lower_bound
+        self.upper_bound = upper_bound
+        self.bias_correction = bias_correction
+
+    def create_state(self, index, weight):
+        stype = weight.stype
+        return (mx.nd.zeros(weight.shape, weight.context, dtype=weight.dtype, stype=stype),
+                mx.nd.zeros(weight.shape, weight.context, dtype=weight.dtype, stype=stype))
+
+    def update(self, index, weight, grad, state):
+        self._update_count(index)
+        lr = self._get_lr(index)
+        wd = self._get_wd(index)
+        t = self._index_update_count[index]
+
+        grad *= self.rescale_grad
+        if self.clip_gradient is not None:
+            grad = mx.nd.clip(grad, -self.clip_gradient, self.clip_gradient)
+
+        mean, var = state
+        mean[:] = self.beta1 * mean + (1. - self.beta1) * grad
+        var[:] = self.beta2 * var + (1. - self.beta2) * mx.nd.square(grad)
+
+        mean_hat = mean
+        var_hat = var
+        r1 = weight.norm()
+        if self.lower_bound:
+            r1 = mx.nd.maximum(r1, self.lower_bound)
+        if self.upper_bound:
+            r1 = mx.nd.minimum(r1, self.upper_bound)
+        if self.bias_correction:
+            mean_hat = mean / (1. - mx.nd.power(self.beta1, t))
+            var_hat = var / (1. - mx.nd.power(self.beta2, t))
+
+        g = mean_hat / (mx.nd.sqrt(var_hat) + self.epsilon) + wd * weight
+        r2 = g.norm()
+        # calculate lamb_trust_ratio
+        r = 1. if r1 == 0. or r2 == 0. else r1 / r2
+        lr *= r
+        # update weight
+        weight[:] -= lr * g
+
+    def update_multi_precision(self, index, weight, grad, state):
+        self.update(index, weight, grad, state)
+
+@with_seed()
+def test_lamb():
+    opt1 = PyLAMB
+    opt2 = mx.optimizer.LAMB
+    shape = (3, 4, 5)
+    cg_options = [{}, {'clip_gradient': 0.4}, {'clip_gradient': 0.5}]
+    rg_options = [{}, {'rescale_grad': 0.14}, {'rescale_grad': 0.8}]
+    wd_options = [{}, {'wd': 0.03}, {'wd': 0.05}, {'wd': 0.07}]
+    bc_options = [{}, {'bias_correction': False}, {'bias_correction': True}]
+    lb_options = [{}, {'lower_bound': None}, {'lower_bound': 1e-3}]
+    ub_options = [{}, {'upper_bound': None}, {'upper_bound': 10}]
+    for params in itertools.product(cg_options, rg_options, wd_options, bc_options, lb_options, ub_options):
+        kwarg = {k: v for param in params for k, v in param.items()}
+        compare_optimizer(opt1(**kwarg), opt2(**kwarg), shape, np.float32)
+
+
 #SGLD
 class PySGLD(mx.optimizer.Optimizer):
     """python reference implementation of SGLD"""

From 11edb70fc99236281010577c769d06fc1cf75671 Mon Sep 17 00:00:00 2001
From: vexilligera <vexilligera@gmail.com>
Date: Mon, 25 Nov 2019 05:47:02 +0800
Subject: [PATCH 627/813] [Numpy] Add NumPy support for np.linalg.det and
 np.linalg.slogdet (#16800)

* det test

* slogdet test

* add more tests

* interoperability test

* fix tests

* beautify

* fix slogdet

* remove slogdet numeric gradient test because it doesn't have that in the first place

* why CI's dead

* CI please

* size_t
---
 python/mxnet/ndarray/numpy/linalg.py          | 112 +++++++++++++++++-
 python/mxnet/numpy/linalg.py                  | 112 +++++++++++++++++-
 python/mxnet/symbol/numpy/linalg.py           | 112 +++++++++++++++++-
 src/operator/tensor/la_op-inl.h               |  12 ++
 src/operator/tensor/la_op.cc                  |   2 +
 src/operator/tensor/la_op.h                   |  14 ++-
 .../unittest/test_numpy_interoperability.py   |  12 ++
 tests/python/unittest/test_numpy_op.py        | 100 ++++++++++++++++
 8 files changed, 470 insertions(+), 6 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/linalg.py b/python/mxnet/ndarray/numpy/linalg.py
index d443d68cda0d..74ba41f22979 100644
--- a/python/mxnet/ndarray/numpy/linalg.py
+++ b/python/mxnet/ndarray/numpy/linalg.py
@@ -21,7 +21,7 @@
 from . import _op as _mx_nd_np
 from . import _internal as _npi
 
-__all__ = ['norm', 'svd', 'cholesky', 'inv']
+__all__ = ['norm', 'svd', 'cholesky', 'inv', 'det', 'slogdet']
 
 
 def norm(x, ord=None, axis=None, keepdims=False):
@@ -242,3 +242,113 @@ def inv(a):
             [ 0.75000006, -0.25000003]]])
     """
     return _npi.inv(a)
+
+
+def det(a):
+    r"""
+    Compute the determinant of an array.
+
+    Parameters
+    ----------
+    a : (..., M, M) ndarray
+        Input array to compute determinants for.
+
+    Returns
+    -------
+    det : (...) ndarray
+        Determinant of `a`.
+
+    See Also
+    --------
+    slogdet : Another way to represent the determinant, more suitable
+    for large matrices where underflow/overflow may occur.
+
+    Notes
+    -----
+    Broadcasting rules apply, see the `numpy.linalg` documentation for
+    details.
+    The determinant is computed via LU factorization using the LAPACK
+    routine z/dgetrf.
+
+    Examples
+    --------
+    The determinant of a 2-D array [[a, b], [c, d]] is ad - bc:
+    >>> a = np.array([[1, 2], [3, 4]])
+    >>> np.linalg.det(a)
+    -2.0
+
+    Computing determinants for a stack of matrices:
+    >>> a = np.array([ [[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]] ])
+    >>> a.shape
+    (3, 2, 2)
+
+    >>> np.linalg.det(a)
+    array([-2., -3., -8.])
+    """
+    return _npi.det(a)
+
+
+def slogdet(a):
+    r"""
+    Compute the sign and (natural) logarithm of the determinant of an array.
+    If an array has a very small or very large determinant, then a call to
+    `det` may overflow or underflow. This routine is more robust against such
+    issues, because it computes the logarithm of the determinant rather than
+    the determinant itself.
+
+    Parameters
+    ----------
+    a : (..., M, M) ndarray
+        Input array, has to be a square 2-D array.
+
+    Returns
+    -------
+    sign : (...) ndarray
+        A number representing the sign of the determinant. For a real matrix,
+        this is 1, 0, or -1.
+    logdet : (...) array_like
+        The natural log of the absolute value of the determinant.
+    If the determinant is zero, then `sign` will be 0 and `logdet` will be
+    -Inf. In all cases, the determinant is equal to ``sign * np.exp(logdet)``.
+
+    See Also
+    --------
+    det
+
+    Notes
+    -----
+    Broadcasting rules apply, see the `numpy.linalg` documentation for
+    details.
+    The determinant is computed via LU factorization using the LAPACK
+    routine z/dgetrf.
+
+    Examples
+    --------
+    The determinant of a 2-D array ``[[a, b], [c, d]]`` is ``ad - bc``:
+    >>> a = np.array([[1, 2], [3, 4]])
+    >>> (sign, logdet) = np.linalg.slogdet(a)
+    >>> (sign, logdet)
+    (-1., 0.69314718055994529)
+
+    >>> sign * np.exp(logdet)
+    -2.0
+
+    Computing log-determinants for a stack of matrices:
+    >>> a = np.array([ [[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]] ])
+    >>> a.shape
+    (3, 2, 2)
+
+    >>> sign, logdet = np.linalg.slogdet(a)
+    >>> (sign, logdet)
+    (array([-1., -1., -1.]), array([ 0.69314718,  1.09861229,  2.07944154]))
+
+    >>> sign * np.exp(logdet)
+    array([-2., -3., -8.])
+
+    This routine succeeds where ordinary `det` does not:
+    >>> np.linalg.det(np.eye(500) * 0.1)
+    0.0
+    >>> np.linalg.slogdet(np.eye(500) * 0.1)
+    (1., -1151.2925464970228)
+    """
+    return _npi.slogdet(a)
diff --git a/python/mxnet/numpy/linalg.py b/python/mxnet/numpy/linalg.py
index b0552ee9f319..fbe3631eb6e6 100644
--- a/python/mxnet/numpy/linalg.py
+++ b/python/mxnet/numpy/linalg.py
@@ -20,7 +20,7 @@
 from __future__ import absolute_import
 from ..ndarray import numpy as _mx_nd_np
 
-__all__ = ['norm', 'svd', 'cholesky', 'inv']
+__all__ = ['norm', 'svd', 'cholesky', 'inv', 'det', 'slogdet']
 
 
 def norm(x, ord=None, axis=None, keepdims=False):
@@ -260,3 +260,113 @@ def inv(a):
             [ 0.75000006, -0.25000003]]])
     """
     return _mx_nd_np.linalg.inv(a)
+
+
+def det(a):
+    r"""
+    Compute the determinant of an array.
+
+    Parameters
+    ----------
+    a : (..., M, M) ndarray
+        Input array to compute determinants for.
+
+    Returns
+    -------
+    det : (...) ndarray
+        Determinant of `a`.
+
+    See Also
+    --------
+    slogdet : Another way to represent the determinant, more suitable
+    for large matrices where underflow/overflow may occur.
+
+    Notes
+    -----
+    Broadcasting rules apply, see the `numpy.linalg` documentation for
+    details.
+    The determinant is computed via LU factorization using the LAPACK
+    routine z/dgetrf.
+
+    Examples
+    --------
+    The determinant of a 2-D array [[a, b], [c, d]] is ad - bc:
+    >>> a = np.array([[1, 2], [3, 4]])
+    >>> np.linalg.det(a)
+    -2.0
+
+    Computing determinants for a stack of matrices:
+    >>> a = np.array([ [[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]] ])
+    >>> a.shape
+    (3, 2, 2)
+
+    >>> np.linalg.det(a)
+    array([-2., -3., -8.])
+    """
+    return _mx_nd_np.linalg.det(a)
+
+
+def slogdet(a):
+    r"""
+    Compute the sign and (natural) logarithm of the determinant of an array.
+    If an array has a very small or very large determinant, then a call to
+    `det` may overflow or underflow. This routine is more robust against such
+    issues, because it computes the logarithm of the determinant rather than
+    the determinant itself.
+
+    Parameters
+    ----------
+    a : (..., M, M) ndarray
+        Input array, has to be a square 2-D array.
+
+    Returns
+    -------
+    sign : (...) ndarray
+        A number representing the sign of the determinant. For a real matrix,
+        this is 1, 0, or -1.
+    logdet : (...) array_like
+        The natural log of the absolute value of the determinant.
+    If the determinant is zero, then `sign` will be 0 and `logdet` will be
+    -Inf. In all cases, the determinant is equal to ``sign * np.exp(logdet)``.
+
+    See Also
+    --------
+    det
+
+    Notes
+    -----
+    Broadcasting rules apply, see the `numpy.linalg` documentation for
+    details.
+    The determinant is computed via LU factorization using the LAPACK
+    routine z/dgetrf.
+
+    Examples
+    --------
+    The determinant of a 2-D array ``[[a, b], [c, d]]`` is ``ad - bc``:
+    >>> a = np.array([[1, 2], [3, 4]])
+    >>> (sign, logdet) = np.linalg.slogdet(a)
+    >>> (sign, logdet)
+    (-1., 0.69314718055994529)
+
+    >>> sign * np.exp(logdet)
+    -2.0
+
+    Computing log-determinants for a stack of matrices:
+    >>> a = np.array([ [[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]] ])
+    >>> a.shape
+    (3, 2, 2)
+
+    >>> sign, logdet = np.linalg.slogdet(a)
+    >>> (sign, logdet)
+    (array([-1., -1., -1.]), array([ 0.69314718,  1.09861229,  2.07944154]))
+
+    >>> sign * np.exp(logdet)
+    array([-2., -3., -8.])
+
+    This routine succeeds where ordinary `det` does not:
+    >>> np.linalg.det(np.eye(500) * 0.1)
+    0.0
+    >>> np.linalg.slogdet(np.eye(500) * 0.1)
+    (1., -1151.2925464970228)
+    """
+    return _mx_nd_np.linalg.slogdet(a)
diff --git a/python/mxnet/symbol/numpy/linalg.py b/python/mxnet/symbol/numpy/linalg.py
index 3c8118550559..cf33777b2637 100644
--- a/python/mxnet/symbol/numpy/linalg.py
+++ b/python/mxnet/symbol/numpy/linalg.py
@@ -22,7 +22,7 @@
 from . import _op as _mx_sym_np
 from . import _internal as _npi
 
-__all__ = ['norm', 'svd', 'cholesky', 'inv']
+__all__ = ['norm', 'svd', 'cholesky', 'inv', 'det', 'slogdet']
 
 
 def norm(x, ord=None, axis=None, keepdims=False):
@@ -229,3 +229,113 @@ def inv(a):
             [ 0.75000006, -0.25000003]]])
     """
     return _npi.inv(a)
+
+
+def det(a):
+    r"""
+    Compute the determinant of an array.
+
+    Parameters
+    ----------
+    a : (..., M, M) ndarray
+        Input array to compute determinants for.
+
+    Returns
+    -------
+    det : (...) ndarray
+        Determinant of `a`.
+
+    See Also
+    --------
+    slogdet : Another way to represent the determinant, more suitable
+    for large matrices where underflow/overflow may occur.
+
+    Notes
+    -----
+    Broadcasting rules apply, see the `numpy.linalg` documentation for
+    details.
+    The determinant is computed via LU factorization using the LAPACK
+    routine z/dgetrf.
+
+    Examples
+    --------
+    The determinant of a 2-D array [[a, b], [c, d]] is ad - bc:
+    >>> a = np.array([[1, 2], [3, 4]])
+    >>> np.linalg.det(a)
+    -2.0
+
+    Computing determinants for a stack of matrices:
+    >>> a = np.array([ [[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]] ])
+    >>> a.shape
+    (3, 2, 2)
+
+    >>> np.linalg.det(a)
+    array([-2., -3., -8.])
+    """
+    return _npi.det(a)
+
+
+def slogdet(a):
+    r"""
+    Compute the sign and (natural) logarithm of the determinant of an array.
+    If an array has a very small or very large determinant, then a call to
+    `det` may overflow or underflow. This routine is more robust against such
+    issues, because it computes the logarithm of the determinant rather than
+    the determinant itself.
+
+    Parameters
+    ----------
+    a : (..., M, M) ndarray
+        Input array, has to be a square 2-D array.
+
+    Returns
+    -------
+    sign : (...) ndarray
+        A number representing the sign of the determinant. For a real matrix,
+        this is 1, 0, or -1.
+    logdet : (...) array_like
+        The natural log of the absolute value of the determinant.
+    If the determinant is zero, then `sign` will be 0 and `logdet` will be
+    -Inf. In all cases, the determinant is equal to ``sign * np.exp(logdet)``.
+
+    See Also
+    --------
+    det
+
+    Notes
+    -----
+    Broadcasting rules apply, see the `numpy.linalg` documentation for
+    details.
+    The determinant is computed via LU factorization using the LAPACK
+    routine z/dgetrf.
+
+    Examples
+    --------
+    The determinant of a 2-D array ``[[a, b], [c, d]]`` is ``ad - bc``:
+    >>> a = np.array([[1, 2], [3, 4]])
+    >>> (sign, logdet) = np.linalg.slogdet(a)
+    >>> (sign, logdet)
+    (-1., 0.69314718055994529)
+
+    >>> sign * np.exp(logdet)
+    -2.0
+
+    Computing log-determinants for a stack of matrices:
+    >>> a = np.array([ [[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]] ])
+    >>> a.shape
+    (3, 2, 2)
+
+    >>> sign, logdet = np.linalg.slogdet(a)
+    >>> (sign, logdet)
+    (array([-1., -1., -1.]), array([ 0.69314718,  1.09861229,  2.07944154]))
+
+    >>> sign * np.exp(logdet)
+    array([-2., -3., -8.])
+
+    This routine succeeds where ordinary `det` does not:
+    >>> np.linalg.det(np.eye(500) * 0.1)
+    0.0
+    >>> np.linalg.slogdet(np.eye(500) * 0.1)
+    (1., -1151.2925464970228)
+    """
+    return _npi.slogdet(a)
diff --git a/src/operator/tensor/la_op-inl.h b/src/operator/tensor/la_op-inl.h
index 35edbbd42565..d580cced4ec5 100644
--- a/src/operator/tensor/la_op-inl.h
+++ b/src/operator/tensor/la_op-inl.h
@@ -502,6 +502,9 @@ struct det {
   static void op(const Tensor<xpu, 3, DType>& A, const Tensor<xpu, 1, DType>& det,
                  const Tensor<xpu, 3, DType>& LU, const Tensor<xpu, 2, int>& pivot,
                  const OpContext& ctx, const nnvm::NodeAttrs& attrs) {
+    if (A.shape_.Size() == 0U) {
+      return;
+    }
     Stream<xpu> *s = ctx.get_stream<xpu>();
     Tensor<xpu, 1, DType> sign = ctx.requested[0]
       .get_space_typed<xpu, 1, DType>(det.shape_, s);
@@ -524,6 +527,9 @@ struct slogdet {
                  const Tensor<xpu, 1, DType>& logabsdet, const Tensor<xpu, 3, DType>& LU,
                  const Tensor<xpu, 2, int>& pivot, const OpContext& ctx,
                  const nnvm::NodeAttrs& attrs) {
+    if (A.shape_.Size() == 0U) {
+      return;
+    }
     Stream<xpu> *s = ctx.get_stream<xpu>();
     Copy(LU, A, s);
     linalg_batch_getrf(LU, pivot, false, s);
@@ -921,6 +927,9 @@ struct det_backward {
     using namespace mshadow;
     using namespace mshadow::expr;
     using namespace mxnet_op;
+    if (dA.shape_.Size() == 0U) {
+      return;
+    }
     // compute inverse(A) and stores it to LU
     linalg_batch_det_backward_helper(LU, pivot, det, dA, DType(0), ctx);
     const_cast<Tensor<xpu, 3, DType>&>(dA) = broadcast_to(reshape(det * ddet, \
@@ -949,6 +958,9 @@ struct slogdet_backward {
     using namespace mshadow;
     using namespace mshadow::expr;
     using namespace mxnet_op;
+    if (dA.shape_.Size() == 0U) {
+      return;
+    }
     // compute inverse(A) and stores it to LU
     linalg_batch_det_backward_helper(LU, pivot, logabsdet, dA, DType(-INFINITY), ctx);
     const_cast<Tensor<xpu, 3, DType>&>(dA) = broadcast_to(reshape(dlogabsdet, \
diff --git a/src/operator/tensor/la_op.cc b/src/operator/tensor/la_op.cc
index 8f9da23711a0..8407307bdd6d 100644
--- a/src/operator/tensor/la_op.cc
+++ b/src/operator/tensor/la_op.cc
@@ -945,6 +945,7 @@ NNVM_REGISTER_OP(_backward_linalg_inverse)
 
 NNVM_REGISTER_OP(_linalg_det)
 .add_alias("linalg_det")
+.add_alias("_npi_det")
 .describe(R"code(Compute the determinant of a matrix.
 Input is a tensor *A* of dimension *n >= 2*.
 
@@ -997,6 +998,7 @@ NNVM_REGISTER_OP(_backward_linalg_det)
 
 NNVM_REGISTER_OP(_linalg_slogdet)
 .add_alias("linalg_slogdet")
+.add_alias("_npi_slogdet")
 .describe(R"code(Compute the sign and log of the determinant of a matrix.
 Input is a tensor *A* of dimension *n >= 2*.
 
diff --git a/src/operator/tensor/la_op.h b/src/operator/tensor/la_op.h
index e024693e3819..5fe7a92e2a12 100644
--- a/src/operator/tensor/la_op.h
+++ b/src/operator/tensor/la_op.h
@@ -26,6 +26,7 @@
 #define MXNET_OPERATOR_TENSOR_LA_OP_H_
 
 #include <mxnet/operator_util.h>
+#include <mxnet/imperative.h>
 #include <vector>
 #include <algorithm>
 #include "../mshadow_op.h"
@@ -428,7 +429,11 @@ inline bool DetShape(const nnvm::NodeAttrs& attrs,
   CHECK_EQ(in[ndim-2], in[ndim-1]) << "Input A's last two dimension must be equal";
   mxnet::TShape out;
   if (ndim == 2) {
-    out = mxnet::TShape(1, 1);
+    if (Imperative::Get()->is_np_shape() || in.Size() == 0U) {
+      out = mxnet::TShape(0, 1);
+    } else {
+      out = mxnet::TShape(1, 1);
+    }
   } else {
     out = mxnet::TShape(in.begin(), in.end() - 2);
   }
@@ -896,19 +901,22 @@ void LaOpDetBackward(const nnvm::NodeAttrs& attrs,
                      const std::vector<OpReqType>& req,
                      const std::vector<TBlob>& outputs) {
   using namespace mshadow;
+  if (outputs[0].shape_.Size() == 0U) {
+    return;
+  }
   Stream<xpu> *s = ctx.get_stream<xpu>();
   CHECK_EQ(inputs.size(), onum + 3);
   CHECK_EQ(outputs.size(), 1);
   MSHADOW_SGL_DBL_TYPE_SWITCH(outputs[0].type_flag_, OType, {
     std::vector<TBlob> tspace(outputs);
-    for ( int i = 0; i < onum; ++i ) {
+    for ( size_t i = 0; i < outputs.size(); ++i ) {
       if ( req[i] == kAddTo ) {
         tspace[i].dptr_ = ctx.requested[0]
                              .get_space_typed<xpu, 1, OType>(Shape1(outputs[i].Size()), s).dptr_;
       }
     }
     LaOpDetBackwardCaller<xpu, OType, onum, laop>::op(inputs, tspace, attrs, ctx);
-    for ( int i = 0; i < onum; ++i ) {
+    for ( size_t i = 0; i < outputs.size(); ++i ) {
       if ( req[i] == kAddTo ) {
         Tensor<xpu, 1, OType> out = outputs[i].FlatTo1D<xpu, OType>(s);
         out += tspace[i].FlatTo1D<xpu, OType>(s);
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index 87c48ab5fd79..206686f68de8 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -308,6 +308,16 @@ def _add_workload_linalg_inv():
     OpArgMngr.add_workload('linalg.inv', np.array(_np.ones((0, 1, 1)), dtype=np.float64))
 
 
+def _add_workload_linalg_det():
+    OpArgMngr.add_workload('linalg.det', np.array(_np.ones((2, 2)), dtype=np.float32))
+    OpArgMngr.add_workload('linalg.det', np.array(_np.ones((0, 1, 1)), dtype=np.float64))
+
+
+def _add_workload_linalg_slogdet():
+    OpArgMngr.add_workload('linalg.slogdet', np.array(_np.ones((2, 2)), dtype=np.float32))
+    OpArgMngr.add_workload('linalg.slogdet', np.array(_np.ones((0, 1, 1)), dtype=np.float64))
+
+
 def _add_workload_trace():
     OpArgMngr.add_workload('trace', np.random.uniform(size=(4, 1)))
     OpArgMngr.add_workload('trace', np.random.uniform(size=(3, 2)))
@@ -1332,6 +1342,8 @@ def _prepare_workloads():
     _add_workload_linalg_norm()
     _add_workload_linalg_cholesky()
     _add_workload_linalg_inv()
+    _add_workload_linalg_det()
+    _add_workload_linalg_slogdet()
     _add_workload_trace()
     _add_workload_tril()
     _add_workload_outer()
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 8adf9d78720b..608116d893f5 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -3412,6 +3412,106 @@ def check_inv(A_inv, data_np):
         check_inv(A_inv, data_np)
 
 
+@with_seed()
+@use_np
+def test_np_linalg_det():
+    class TestDet(HybridBlock):
+        def __init__(self):
+            super(TestDet, self).__init__()
+
+        def hybrid_forward(self, F, a):
+            return F.np.linalg.det(a)
+
+    # test non zero size input
+    tensor_shapes = [
+        (2, 0, 2, 2),
+        (4, 4),
+        (0, 2, 2, 2),
+        (3, 3, 3),
+        (0, 2, 2),
+        (2, 2, 2, 2, 2),
+        (1, 1),
+    ]
+    types = [_np.float32, _np.float64]
+    grad_reqs = ['write', 'add', 'null']
+
+    for hybridize, dtype, shape, grad_req in itertools.product([True, False], types, tensor_shapes, grad_reqs):
+        a_shape = (1,) + shape
+        test_det = TestDet()
+        if hybridize:
+            test_det.hybridize()
+        a = rand_ndarray(shape=a_shape, dtype=dtype).as_np_ndarray()
+        a.attach_grad(grad_req)
+        np_out = _np.linalg.det(a.asnumpy())
+        with mx.autograd.record():
+            mx_out = test_det(a)
+        assert mx_out.shape == np_out.shape
+        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-1, atol=1e-1)
+        if grad_req != 'null':
+            print(shape, grad_req)
+            mx_out.backward()
+
+        # Test imperative once again
+        mx_out = np.linalg.det(a)
+        np_out = _np.linalg.det(a.asnumpy())
+        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-1, atol=1e-1)
+
+        # test numeric gradient
+        a_sym = mx.sym.Variable("a").as_np_ndarray()
+        mx_sym = mx.sym.np.linalg.det(a_sym).as_nd_ndarray()
+        if 0 not in shape and grad_req != 'null':
+            check_numeric_gradient(mx_sym, [a.as_nd_ndarray()],
+                                    rtol=1e-1, atol=1e-1, dtype=dtype)
+
+
+@with_seed()
+@use_np
+def test_np_linalg_slogdet():
+    class TestSlogdet(HybridBlock):
+        def __init__(self):
+            super(TestSlogdet, self).__init__()
+
+        def hybrid_forward(self, F, a):
+            return F.np.linalg.slogdet(a)
+
+    # test non zero size input
+    tensor_shapes = [
+        (2, 0, 2, 2),
+        (5, 5),
+        (0, 2, 2, 2),
+        (3, 3, 3),
+        (0, 3, 3),
+        (2, 2, 2, 2, 2),
+        (1, 1),
+    ]
+
+    types = [_np.float32, _np.float64]
+    grad_reqs = ['write', 'add', 'null']
+
+    for hybridize, a_shape, dtype, grad_req in itertools.product([True, False], tensor_shapes, types, grad_reqs):
+        test_slogdet = TestSlogdet()
+        if hybridize:
+            test_slogdet.hybridize()
+        a = rand_ndarray(shape=a_shape, dtype=dtype).as_np_ndarray()
+        a.attach_grad(grad_req)
+
+        np_out = _np.linalg.slogdet(a.asnumpy())
+        with mx.autograd.record():
+            mx_out = test_slogdet(a)
+        assert mx_out[0].shape == np_out[0].shape
+        assert mx_out[1].shape == np_out[1].shape
+        assert_almost_equal(mx_out[0].asnumpy(), np_out[0], rtol=1e-1, atol=1e-1)
+        assert_almost_equal(mx_out[1].asnumpy(), np_out[1], rtol=1e-1, atol=1e-1)
+        if grad_req != 'null':
+            mx_out[1].backward()
+
+        # Test imperative once again
+        mx_out = np.linalg.slogdet(a)
+        np_out = _np.linalg.slogdet(a.asnumpy())
+        assert_almost_equal(mx_out[0].asnumpy(), np_out[0], rtol=1e-1, atol=1e-1)
+        assert_almost_equal(mx_out[1].asnumpy(), np_out[1], rtol=1e-1, atol=1e-1)
+
+
 @with_seed()
 @use_np
 def test_np_vstack():

From 9b49cfeaf6176b87ff8673b564b7de687ed9349c Mon Sep 17 00:00:00 2001
From: Xingjian Shi <xshiab@connect.ust.hk>
Date: Sun, 24 Nov 2019 21:44:39 -0800
Subject: [PATCH 628/813] try to move shape_array to npx (#16897)

---
 src/operator/tensor/elemwise_unary_op_basic.cc | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/operator/tensor/elemwise_unary_op_basic.cc b/src/operator/tensor/elemwise_unary_op_basic.cc
index 7a509933651e..1dd185f349a0 100644
--- a/src/operator/tensor/elemwise_unary_op_basic.cc
+++ b/src/operator/tensor/elemwise_unary_op_basic.cc
@@ -565,6 +565,7 @@ void ShapeComputeCPU(const nnvm::NodeAttrs& attrs,
 }
 
 NNVM_REGISTER_OP(shape_array)
+.add_alias("_npx_shape_array")
 .describe(R"code(Returns a 1D int64 array containing the shape of data.
 
 Example::

From 436967bee9e3e3844486fd872b0065902d4324dd Mon Sep 17 00:00:00 2001
From: rongzha1 <rong.a.zhang@intel.com>
Date: Mon, 25 Nov 2019 19:18:41 +0800
Subject: [PATCH 629/813] Mkldnn fullyConnect bwd bug fix (#16890)

* fix mkldnn fc bwd bug due to data inplace

* enable mkldnn fc bwd
---
 src/operator/nn/fully_connected.cc            |  9 ++---
 .../nn/mkldnn/mkldnn_fully_connected.cc       | 36 +++++++++----------
 2 files changed, 20 insertions(+), 25 deletions(-)

diff --git a/src/operator/nn/fully_connected.cc b/src/operator/nn/fully_connected.cc
index 5d722581257f..a7e63f6d4139 100644
--- a/src/operator/nn/fully_connected.cc
+++ b/src/operator/nn/fully_connected.cc
@@ -147,10 +147,7 @@ void FullyConnectedGradComputeExCPU(const nnvm::NodeAttrs& attrs,
                                     const std::vector<NDArray> &inputs,
                                     const std::vector<OpReqType> &req,
                                     const std::vector<NDArray> &outputs) {
-  // TODO(rongzha1): disable due to flakiness in cpp test IMPERATIVE.FullyConnectedOp
-  // Will be fixed when we decide to enable the backward of FC.
-  bool mkldnn_fc_backward_enable = false;
-  if (mkldnn_fc_backward_enable && SupportMKLDNNFC(inputs[0])) {
+  if (SupportMKLDNNFC(inputs[0])) {
     MKLDNN_OPCHECK_INIT(true, outputs.size(), inputs, outputs);
     MKLDNNFCBackward(attrs, ctx, inputs, req, outputs);
     MKLDNN_OPCHECK_RUN(FullyConnectedGradCompute<cpu>, attrs, ctx, inputs, req,
@@ -232,12 +229,10 @@ static bool BackwardFCStorageType(const nnvm::NodeAttrs& attrs,
   uint32_t out_expected = param.no_bias ? 2 : 3;
   CHECK_EQ(in_attrs->size(), 3U);
   CHECK_EQ(out_attrs->size(), out_expected);
-  // TODO(zhengda) let's disable MKLDNN for FullyConnected for now.
-  // It seems there is a bug.
   bool dispatched = false;
   if (!dispatched && common::ContainsOnlyStorage(*in_attrs, mxnet::kDefaultStorage)) {
     dispatched = storage_type_assign(out_attrs, mxnet::kDefaultStorage,
-                                     dispatch_mode, DispatchMode::kFCompute);
+                                     dispatch_mode, DispatchMode::kFComputeEx);
   }
   if (!dispatched && common::ContainsStorageType(*in_attrs, mxnet::kRowSparseStorage)) {
     dispatched = dispatch_fallback(out_attrs, dispatch_mode);
diff --git a/src/operator/nn/mkldnn/mkldnn_fully_connected.cc b/src/operator/nn/mkldnn/mkldnn_fully_connected.cc
index 1403cd114201..7627d02c4702 100644
--- a/src/operator/nn/mkldnn/mkldnn_fully_connected.cc
+++ b/src/operator/nn/mkldnn/mkldnn_fully_connected.cc
@@ -290,24 +290,6 @@ void MKLDNNFCBackward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
       data, weight, param.no_bias ? nullptr : &in_grad[fullc::kBias], GetMemDesc(out_grad));
 
   CHECK_NE(req[fullc::kWeight], kWriteInplace) << "cannot write weight inplace";
-  if (req[fullc::kData]) {
-    mkldnn::inner_product_backward_data::primitive_desc ipBwdData_pd = GetFCBwdData(
-        data, weight, out_grad, fwd_pd);
-    auto out_grad_mem = out_grad.GetMKLDNNDataReorder(
-        ipBwdData_pd.diff_dst_desc());
-    auto weight_mem = weight.GetMKLDNNDataReorder(ipBwdData_pd.weights_desc());
-    auto in_grad_mem = CreateMKLDNNMem(in_grad[fullc::kData],
-                                       ipBwdData_pd.diff_src_desc(),
-                                       req[fullc::kData]);
-    mkldnn_args_map_t args = {
-      {MKLDNN_ARG_DIFF_DST, *out_grad_mem},
-      {MKLDNN_ARG_WEIGHTS, *weight_mem},
-      {MKLDNN_ARG_DIFF_SRC, *in_grad_mem.second}
-    };
-
-    MKLDNNStream::Get()->RegisterPrimArgs(mkldnn::inner_product_backward_data(ipBwdData_pd), args);
-    CommitOutput(in_grad[fullc::kData], in_grad_mem);
-  }
   if (req[fullc::kWeight]) {
     mkldnn::inner_product_backward_weights::primitive_desc ipBwdWeights_pd
       = GetFCBwdWeights(data, weight, param.no_bias ? nullptr : &in_grad[fullc::kBias],
@@ -336,6 +318,24 @@ void MKLDNNFCBackward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
     CommitOutput(in_grad[fullc::kWeight], in_grad_weight);
     CommitOutput(in_grad[fullc::kBias], in_grad_bias);
   }
+  if (req[fullc::kData]) {
+    mkldnn::inner_product_backward_data::primitive_desc ipBwdData_pd = GetFCBwdData(
+        data, weight, out_grad, fwd_pd);
+    auto out_grad_mem = out_grad.GetMKLDNNDataReorder(
+        ipBwdData_pd.diff_dst_desc());
+    auto weight_mem = weight.GetMKLDNNDataReorder(ipBwdData_pd.weights_desc());
+    auto in_grad_mem = CreateMKLDNNMem(in_grad[fullc::kData],
+                                       ipBwdData_pd.diff_src_desc(),
+                                       req[fullc::kData]);
+    mkldnn_args_map_t args = {
+      {MKLDNN_ARG_DIFF_DST, *out_grad_mem},
+      {MKLDNN_ARG_WEIGHTS, *weight_mem},
+      {MKLDNN_ARG_DIFF_SRC, *in_grad_mem.second}
+    };
+
+    MKLDNNStream::Get()->RegisterPrimArgs(mkldnn::inner_product_backward_data(ipBwdData_pd), args);
+    CommitOutput(in_grad[fullc::kData], in_grad_mem);
+  }
   MKLDNNStream::Get()->Submit();
 }
 

From ca76bf11c8f085266384952c47a0c45cc633e6c0 Mon Sep 17 00:00:00 2001
From: Tao Lv <tao.a.lv@intel.com>
Date: Mon, 25 Nov 2019 19:22:43 +0800
Subject: [PATCH 630/813] Upgrade MKL-DNN dependency to v1.1 (#16823)

* upgrade to v1.1 and handle renaming

* softlink for mkldnn_version.h; make clean

* libmkldnn.a -> libdnnl.a
---
 .gitignore                                  | 3 ++-
 3rdparty/mkldnn                             | 2 +-
 CMakeLists.txt                              | 2 +-
 Makefile                                    | 2 +-
 cd/mxnet_lib/static/Jenkins_pipeline.groovy | 2 +-
 cd/python/pypi/pypi_package.sh              | 6 ++++--
 include/mkldnn/dnnl.h                       | 1 +
 include/mkldnn/dnnl.hpp                     | 1 +
 include/mkldnn/dnnl_types.h                 | 1 +
 include/mkldnn/mkldnn.hpp                   | 1 +
 include/mkldnn/mkldnn_dnnl_mangling.h       | 1 +
 include/mkldnn/mkldnn_version.h             | 1 +
 mkldnn.mk                                   | 7 +++++--
 13 files changed, 21 insertions(+), 9 deletions(-)
 create mode 120000 include/mkldnn/dnnl.h
 create mode 120000 include/mkldnn/dnnl.hpp
 create mode 120000 include/mkldnn/dnnl_types.h
 create mode 120000 include/mkldnn/mkldnn.hpp
 create mode 120000 include/mkldnn/mkldnn_dnnl_mangling.h
 create mode 120000 include/mkldnn/mkldnn_version.h

diff --git a/.gitignore b/.gitignore
index 94cc9baccad9..eb3225f47968 100644
--- a/.gitignore
+++ b/.gitignore
@@ -164,4 +164,5 @@ coverage.xml
 cmake_options.yml
 
 # header file generated at compile time
-include/mkldnn/mkldnn_version.h
+include/mkldnn/dnnl_version.h
+include/mkldnn/dnnl_config.h
diff --git a/3rdparty/mkldnn b/3rdparty/mkldnn
index a0a87d662ede..52c3052df8ec 160000
--- a/3rdparty/mkldnn
+++ b/3rdparty/mkldnn
@@ -1 +1 @@
-Subproject commit a0a87d662edeef38d01db4ac5dd25f59a1f0881f
+Subproject commit 52c3052df8ec1d5b8b45cb6c350a952840eabd42
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 1d3ce94e7ebf..2bb1f225ad13 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -273,7 +273,7 @@ if(USE_MKLDNN)
   include_directories(3rdparty/mkldnn/include)
   include_directories(${PROJECT_BINARY_DIR}/3rdparty/mkldnn/include)
   add_definitions(-DMXNET_USE_MKLDNN=1)
-  list(APPEND mxnet_LINKER_LIBS mkldnn)
+  list(APPEND mxnet_LINKER_LIBS dnnl)
 endif()
 
 # Allow Cuda compiles outside of src tree to find things in 'src' and 'include'
diff --git a/Makefile b/Makefile
index bcb5a41de9bc..d60c0fcbf1da 100644
--- a/Makefile
+++ b/Makefile
@@ -151,7 +151,7 @@ ifeq ($(USE_MKLDNN), 1)
 	CFLAGS += -DMXNET_USE_MKLDNN=1
 	CFLAGS += -I$(ROOTDIR)/src/operator/nn/mkldnn/
 	CFLAGS += -I$(MKLDNNROOT)/include
-	LIB_DEP += $(MKLDNNROOT)/lib/libmkldnn.a
+	LIB_DEP += $(MKLDNNROOT)/lib/libdnnl.a
 endif
 
 # setup opencv
diff --git a/cd/mxnet_lib/static/Jenkins_pipeline.groovy b/cd/mxnet_lib/static/Jenkins_pipeline.groovy
index 470bef703613..84466dd551e7 100644
--- a/cd/mxnet_lib/static/Jenkins_pipeline.groovy
+++ b/cd/mxnet_lib/static/Jenkins_pipeline.groovy
@@ -31,7 +31,7 @@ licenses = 'licenses/*'
 
 // libmxnet dependencies
 mx_deps = 'lib/libgfortran.so.3, lib/libquadmath.so.0'
-mx_mkldnn_deps = 'lib/libgfortran.so.3, lib/libquadmath.so.0, 3rdparty/mkldnn/build/install/include/mkldnn_version.h'
+mx_mkldnn_deps = 'lib/libgfortran.so.3, lib/libquadmath.so.0, 3rdparty/mkldnn/build/install/include/dnnl_version.h, 3rdparty/mkldnn/build/install/include/dnnl_config.h'
 
 // library type
 // either static or dynamic - depending on how it links to its dependencies
diff --git a/cd/python/pypi/pypi_package.sh b/cd/python/pypi/pypi_package.sh
index 581bf694cb31..c32200a53fd6 100755
--- a/cd/python/pypi/pypi_package.sh
+++ b/cd/python/pypi/pypi_package.sh
@@ -26,9 +26,11 @@ export mxnet_variant=${1:?"Please specify the mxnet variant"}
 # mxnet-build/3rdparty/mkldnn/build/install/include
 # The artifact repository stores this file in the dependencies
 # and CD unpacks it to a directory called cd_misc
-if [ -f "cd_misc/mkldnn_version.h" ]; then
+# Nov. 2019 Update: With v1.1, MKL-DNN is renaming to DNNL. Hence changing the prefix of file name. 
+if [ -f "cd_misc/dnnl_version.h" ]; then
   mkdir -p 3rdparty/mkldnn/build/install/include
-  cp cd_misc/mkldnn_version.h 3rdparty/mkldnn/build/install/include/.
+  cp cd_misc/dnnl_version.h 3rdparty/mkldnn/build/install/include/.
+  cp cd_misc/dnnl_config.h 3rdparty/mkldnn/build/install/include/.
 fi
 
 # Create wheel workspace
diff --git a/include/mkldnn/dnnl.h b/include/mkldnn/dnnl.h
new file mode 120000
index 000000000000..44625f5e28e3
--- /dev/null
+++ b/include/mkldnn/dnnl.h
@@ -0,0 +1 @@
+../../3rdparty/mkldnn/include/dnnl.h
\ No newline at end of file
diff --git a/include/mkldnn/dnnl.hpp b/include/mkldnn/dnnl.hpp
new file mode 120000
index 000000000000..4dfc038d850e
--- /dev/null
+++ b/include/mkldnn/dnnl.hpp
@@ -0,0 +1 @@
+../../3rdparty/mkldnn/include/dnnl.hpp
\ No newline at end of file
diff --git a/include/mkldnn/dnnl_types.h b/include/mkldnn/dnnl_types.h
new file mode 120000
index 000000000000..750b64cc4bc3
--- /dev/null
+++ b/include/mkldnn/dnnl_types.h
@@ -0,0 +1 @@
+../../3rdparty/mkldnn/include/dnnl_types.h
\ No newline at end of file
diff --git a/include/mkldnn/mkldnn.hpp b/include/mkldnn/mkldnn.hpp
new file mode 120000
index 000000000000..2cb212af8666
--- /dev/null
+++ b/include/mkldnn/mkldnn.hpp
@@ -0,0 +1 @@
+../../3rdparty/mkldnn/include/mkldnn.hpp
\ No newline at end of file
diff --git a/include/mkldnn/mkldnn_dnnl_mangling.h b/include/mkldnn/mkldnn_dnnl_mangling.h
new file mode 120000
index 000000000000..876ad649aa8f
--- /dev/null
+++ b/include/mkldnn/mkldnn_dnnl_mangling.h
@@ -0,0 +1 @@
+../../3rdparty/mkldnn/include/mkldnn_dnnl_mangling.h
\ No newline at end of file
diff --git a/include/mkldnn/mkldnn_version.h b/include/mkldnn/mkldnn_version.h
new file mode 120000
index 000000000000..76927f2ef2e2
--- /dev/null
+++ b/include/mkldnn/mkldnn_version.h
@@ -0,0 +1 @@
+../../3rdparty/mkldnn/include/mkldnn_version.h
\ No newline at end of file
diff --git a/mkldnn.mk b/mkldnn.mk
index 4f4afb555e39..056cb4e51051 100644
--- a/mkldnn.mk
+++ b/mkldnn.mk
@@ -20,7 +20,7 @@ ifeq ($(USE_MKLDNN), 1)
 	MKLDNN_BUILDDIR = $(MKLDNN_SUBMODDIR)/build
 	MXNET_LIBDIR = $(ROOTDIR)/lib
 	MXNET_INCLDIR = $(ROOTDIR)/include
-	MKLDNN_LIBFILE = $(MKLDNNROOT)/lib/libmkldnn.a
+	MKLDNN_LIBFILE = $(MKLDNNROOT)/lib/libdnnl.a
 endif
 
 mkldnn_FLAGS  = -DCMAKE_INSTALL_PREFIX=$(MKLDNNROOT)
@@ -49,10 +49,13 @@ $(MKLDNN_LIBFILE):
 	cmake $(MKLDNN_SUBMODDIR) $(mkldnn_FLAGS)
 	$(MAKE) -C $(MKLDNN_BUILDDIR) VERBOSE=1
 	$(MAKE) -C $(MKLDNN_BUILDDIR) install
-	cp $(MKLDNN_BUILDDIR)/include/mkldnn_version.h $(MXNET_INCLDIR)/mkldnn/.
+	cp $(MKLDNN_BUILDDIR)/include/dnnl_version.h $(MXNET_INCLDIR)/mkldnn/.
+	cp $(MKLDNN_BUILDDIR)/include/dnnl_config.h $(MXNET_INCLDIR)/mkldnn/.
 
 mkldnn_clean:
 	$(RM) -r 3rdparty/mkldnn/build
+	$(RM) -r include/mkldnn/dnnl_version.h
+	$(RM) -r include/mkldnn/dnnl_config.h
 
 ifeq ($(USE_MKLDNN), 1)
 mkldnn: mkldnn_build

From 6bff547465c83ed343a5ef8241d22f56738534bd Mon Sep 17 00:00:00 2001
From: liuzh91 <liuzhuanghua1991@gmail.com>
Date: Tue, 26 Nov 2019 01:34:44 +0800
Subject: [PATCH 631/813] Add evaluation_loss to the estimator base class.
 (#16888)

* Add evaluation_loss to the estimator base class.

* Update the base estimator class to support the separate evaluation loss.

* Add evaluation loss to the base estimator class.

* Add unittest for evaluation loss in the test_evaluation function

* Update estimator.py

* Update estimator.py
---
 python/mxnet/gluon/contrib/estimator/estimator.py | 11 +++++++++--
 tests/python/unittest/test_gluon_estimator.py     |  4 +++-
 2 files changed, 12 insertions(+), 3 deletions(-)

diff --git a/python/mxnet/gluon/contrib/estimator/estimator.py b/python/mxnet/gluon/contrib/estimator/estimator.py
index 83b954d02e10..54a0b165016e 100644
--- a/python/mxnet/gluon/contrib/estimator/estimator.py
+++ b/python/mxnet/gluon/contrib/estimator/estimator.py
@@ -59,6 +59,9 @@ class Estimator(object):
         Trainer to apply optimizer on network parameters.
     context : Context or list of Context
         Device(s) to run the training on.
+    evaluation_loss: gluon.loss.loss
+        Loss (objective) function to calculate during evaluation. If set evaluation_loss
+        None, it will use the same loss function as self.loss
 
     """
 
@@ -85,12 +88,16 @@ def __init__(self, net,
                  metrics=None,
                  initializer=None,
                  trainer=None,
-                 context=None):
+                 context=None,
+                 evaluation_loss=None):
         self.net = net
         self.loss = self._check_loss(loss)
         self._train_metrics = _check_metrics(metrics)
         self._add_default_training_metrics()
         self._add_validation_metrics()
+        self.evaluation_loss = self.loss
+        if evaluation_loss is not None:
+            self.evaluation_loss = self._check_loss(evaluation_loss)
 
         self.logger = logging.Logger(name='Estimator', level=logging.INFO)
         self.logger.addHandler(logging.StreamHandler(sys.stdout))
@@ -228,7 +235,7 @@ def evaluate_batch(self,
         """
         data, label = self._get_data_and_label(val_batch, self.context, batch_axis)
         pred = [self.net(x) for x in data]
-        loss = [self.loss(y_hat, y) for y_hat, y in zip(pred, label)]
+        loss = [self.evaluation_loss(y_hat, y) for y_hat, y in zip(pred, label)]
         # update metrics
         for metric in val_metrics:
             if isinstance(metric, metric_loss):
diff --git a/tests/python/unittest/test_gluon_estimator.py b/tests/python/unittest/test_gluon_estimator.py
index aaf9839b29f3..cf913a6161c0 100644
--- a/tests/python/unittest/test_gluon_estimator.py
+++ b/tests/python/unittest/test_gluon_estimator.py
@@ -83,13 +83,15 @@ def test_validation():
     ctx = mx.cpu()
     loss = gluon.loss.L2Loss()
     acc = mx.metric.Accuracy()
+    evaluation_loss = gluon.loss.L1Loss()
     net.initialize(ctx=ctx)
     trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.001})
     est = Estimator(net=net,
                     loss=loss,
                     metrics=acc,
                     trainer=trainer,
-                    context=ctx)
+                    context=ctx,
+                    evaluation_loss=evaluation_loss)
     # Input dataloader
     est.fit(train_data=dataloader,
             val_data=dataloader,

From f11592da86f24ddbc7ea63d0e7d6a5c84153f606 Mon Sep 17 00:00:00 2001
From: Minghao Liu <40382964+Tommliu@users.noreply.github.com>
Date: Tue, 26 Nov 2019 06:31:56 +0800
Subject: [PATCH 632/813] Op Unravel_index PR [Numpy] (#16862)

* unravel_index pr

* fix line too long
---
 python/mxnet/ndarray/numpy/_op.py             | 48 +++++++++++++++--
 python/mxnet/numpy/multiarray.py              | 40 ++++++++++++--
 python/mxnet/numpy_dispatch_protocol.py       |  1 +
 python/mxnet/numpy_op_fallback.py             | 34 ++++++++++++
 python/mxnet/symbol/numpy/_symbol.py          | 42 +++++++++++++--
 .../unittest/test_numpy_interoperability.py   | 10 ++++
 tests/python/unittest/test_numpy_op.py        | 52 +++++++++++++++++++
 7 files changed, 212 insertions(+), 15 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index d31681113933..295c83ef47a0 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -35,11 +35,11 @@
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot', 'histogram', 'eye',
            'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate', 'append',
            'stack', 'vstack', 'column_stack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax',
-           'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
-           'around', 'hypot', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity',
-           'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal',
-           'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory', 'may_share_memory',
-           'diff', 'resize', 'nan_to_num', 'where']
+           'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'unravel_index', 'hanning', 'hamming', 'blackman',
+           'flip', 'around', 'hypot', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril',
+           'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less',
+           'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory',
+           'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where']
 
 @set_module('mxnet.ndarray.numpy')
 def shape(a):
@@ -3828,6 +3828,44 @@ def ravel(x, order='C'):
         raise TypeError('type {} not supported'.format(str(type(x))))
 
 
+def unravel_index(indices, shape, order='C'): # pylint: disable=redefined-outer-name
+    """
+    Converts a flat index or array of flat indices into a tuple of coordinate arrays.
+
+    Parameters:
+    -------------
+    indices : array_like
+            An integer array whose elements are indices into the flattened version of an array of dimensions shape.
+            Before version 1.6.0, this function accepted just one index value.
+    shape : tuple of ints
+            The shape of the array to use for unraveling indices.
+
+    Returns:
+    -------------
+    unraveled_coords : ndarray
+            Each row in the ndarray has the same shape as the indices array.
+            Each column in the ndarray represents the unravelled index
+
+    Examples:
+    -------------
+    >>> np.unravel_index([22, 41, 37], (7,6))
+    ([3. 6. 6.]
+      [4. 5. 1.])
+    >>> np.unravel_index(1621, (6,7,8,9))
+    (3, 1, 4, 1)
+    """
+    if order == 'C':
+        if isinstance(indices, numeric_types):
+            return _np.unravel_index(indices, shape)
+        ret = _npi.unravel_index_fallback(indices, shape=shape)
+        ret_list = []
+        for item in ret:
+            ret_list += [item]
+        return tuple(ret_list)
+    else:
+        raise NotImplementedError('Do not support column-major (Fortran-style) order at this moment')
+
+
 @set_module('mxnet.ndarray.numpy')
 def hanning(M, dtype=_np.float32, ctx=None):
     r"""Return the Hanning window.
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 3ad9254eb0e6..1c6873d342d1 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -53,11 +53,11 @@
            'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'append',
            'tensordot', 'histogram', 'eye', 'linspace', 'logspace', 'expand_dims', 'tile', 'arange',
            'split', 'vsplit', 'concatenate', 'stack', 'vstack', 'column_stack', 'dstack', 'mean', 'maximum', 'minimum',
-           'swapaxes', 'clip', 'argmax', 'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming',
-           'blackman', 'flip', 'around', 'arctan2', 'hypot', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad',
-           'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal',
-           'greater', 'less', 'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero',
-           'shares_memory', 'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where']
+           'swapaxes', 'clip', 'argmax', 'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'unravel_index',
+           'hanning', 'hamming', 'blackman', 'flip', 'around', 'arctan2', 'hypot', 'bitwise_xor', 'bitwise_or',
+           'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer',
+           'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum',
+           'true_divide', 'nonzero', 'shares_memory', 'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -5759,6 +5759,36 @@ def ravel(x, order='C'):
     return _mx_nd_np.ravel(x, order)
 
 
+def unravel_index(indices, shape, order='C'): # pylint: disable=redefined-outer-name
+    """
+    Converts a flat index or array of flat indices into a tuple of coordinate arrays.
+
+    Parameters:
+    -------------
+    indices : array_like
+            An integer array whose elements are indices into the flattened version of an array of dimensions shape.
+            Before version 1.6.0, this function accepted just one index value.
+    shape : tuple of ints
+            The shape of the array to use for unraveling indices.
+    order : Only row-major is supported currently.
+
+    Returns:
+    -------------
+    unraveled_coords : ndarray
+            Each row in the ndarray has the same shape as the indices array.
+            Each column in the ndarray represents the unravelled index
+
+    Examples:
+    -------------
+    >>> np.unravel_index([22, 41, 37], (7,6))
+    [[3. 6. 6.]
+      [4. 5. 1.]]
+    >>> np.unravel_index(1621, (6,7,8,9))
+    [3, 1, 4, 1]
+    """
+    return _mx_nd_np.unravel_index(indices, shape, order=order)
+
+
 @set_module('mxnet.numpy')
 def hanning(M, dtype=_np.float32, ctx=None):
     r"""Return the Hanning window.
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index 13e186f4008a..1f68ca3c522a 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -121,6 +121,7 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'tile',
     'transpose',
     'unique',
+    'unravel_index',
     'var',
     'vdot',
     'vstack',
diff --git a/python/mxnet/numpy_op_fallback.py b/python/mxnet/numpy_op_fallback.py
index b98a211c7169..f3fd2bf6ae6c 100644
--- a/python/mxnet/numpy_op_fallback.py
+++ b/python/mxnet/numpy_op_fallback.py
@@ -80,3 +80,37 @@ def infer_shape(self, in_shape):
 
     def create_operator(self, ctx, in_shapes, in_dtypes):
         return Resize(self._new_shape)
+
+
+@use_np
+class Unravel_index(operator.CustomOp):
+    """Fallback to NumPy Unravel_index operator."""
+    def __init__(self, shape):
+        super(Unravel_index, self).__init__()
+        self._shape = shape
+
+    def forward(self, is_train, req, in_data, out_data, aux):
+        out = np.unravel_index(in_data[0].asnumpy(), self._shape)
+        self.assign(out_data[0], req[0], _mx_np.array(out, dtype=out[0].dtype, ctx=out_data[0].ctx))
+
+    def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
+        raise NotImplementedError('Operator Unravel_index does not support gradient computation')
+
+
+@register('unravel_index_fallback')
+class Unravel_indexProp(operator.CustomOpProp):
+    """Fallback unravel_index operator properties."""
+    def __init__(self, shape):
+        super(Unravel_indexProp, self).__init__(need_top_grad=True)
+        self._shape = ast.literal_eval(shape)
+
+    def list_arguments(self):
+        return ['indices']
+
+    def infer_shape(self, in_shape):
+        dim_list = (1,) if np.isscalar(self._shape) else (len(self._shape),)
+        out_shape = dim_list + tuple(in_shape[0])
+        return (in_shape[0],), (out_shape,), ()
+
+    def create_operator(self, ctx, in_shapes, in_dtypes):
+        return Unravel_index(self._shape)
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 734242f4e86d..3790078bd397 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -37,11 +37,11 @@
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot', 'histogram', 'eye',
            'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate', 'append',
            'stack', 'vstack', 'column_stack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax',
-           'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
-           'around', 'hypot', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity',
-           'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal',
-           'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'shares_memory', 'may_share_memory', 'diff',
-           'resize', 'nan_to_num', 'where']
+           'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'unravel_index', 'hanning', 'hamming', 'blackman',
+           'flip', 'around', 'hypot', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril',
+           'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less',
+           'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'shares_memory',
+           'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where']
 
 def _num_outputs(sym):
     return len(sym.as_nd_ndarray())
@@ -3657,6 +3657,38 @@ def ravel(x, order='C'):
         raise TypeError('type {} not supported'.format(str(type(x))))
 
 
+def unravel_index(indices, shape, order='C'): # pylint: disable=redefined-outer-name
+    """
+    Converts a flat index or array of flat indices into a tuple of coordinate arrays.
+
+    Parameters:
+    -------------
+    indices : array_like
+            An integer array whose elements are indices into the flattened version of an array of dimensions shape.
+            Before version 1.6.0, this function accepted just one index value.
+    shape : tuple of ints
+            The shape of the array to use for unraveling indices.
+
+    Returns:
+    -------------
+    unraveled_coords : ndarray
+            Each row in the ndarray has the same shape as the indices array.
+            Each column in the ndarray represents the unravelled index
+
+    Examples:
+    -------------
+    >>> np.unravel_index([22, 41, 37], (7,6))
+    ([3. 6. 6.]
+      [4. 5. 1.])
+    >>> np.unravel_index(1621, (6,7,8,9))
+    (3, 1, 4, 1)
+    """
+    if order == 'C':
+        return _npi.unravel_index_fallback(indices, shape=shape)
+    else:
+        raise NotImplementedError('Don not support column-major (Fortran-style) order at this moment')
+
+
 @set_module('mxnet.symbol.numpy')
 def hanning(M, dtype=_np.float32, ctx=None):
     r"""Return the Hanning window.
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index 206686f68de8..930ad5260430 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -57,6 +57,15 @@ def get_workloads(name):
         return OpArgMngr._args.get(name, None)
 
 
+def _add_workload_unravel_index():
+    OpArgMngr.add_workload('unravel_index', indices=np.array([2],dtype=_np.int64), shape=(2, 2))
+    OpArgMngr.add_workload('unravel_index', np.array([(2*3 + 1)*6 + 4], dtype=_np.int64), (4, 3, 6))
+    OpArgMngr.add_workload('unravel_index', np.array([22, 41, 37], dtype=_np.int32), (7, 6))
+    OpArgMngr.add_workload('unravel_index', np.array([1621],dtype=_np.uint8), (6, 7, 8, 9))
+    OpArgMngr.add_workload('unravel_index', np.array([],dtype=_np.int64), (10, 3, 5))
+    OpArgMngr.add_workload('unravel_index', np.array([3], dtype=_np.int32), (2,2))
+
+
 def _add_workload_diag():
     def get_mat(n):
         data = _np.arange(n)
@@ -1310,6 +1319,7 @@ def _prepare_workloads():
     _add_workload_copy()
     _add_workload_cumsum()
     _add_workload_ravel()
+    _add_workload_unravel_index()
     _add_workload_diag()
     _add_workload_diagflat()
     _add_workload_dot()
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 608116d893f5..9b7f7036bcda 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -4998,6 +4998,58 @@ def hybrid_forward(self, F, x):
                 assert_almost_equal(y.asnumpy(), expected, use_broadcast=False)
 
 
+@with_seed()
+@use_np
+def test_np_unravel_index():
+    class TestUnravel_index(HybridBlock):
+        def __init__(self, shape, order='C') :
+            super(TestUnravel_index, self).__init__()
+            self._shape = shape
+            self._order = order
+             
+        def hybrid_forward(self, F, a):
+            return F.np.unravel_index(a, self._shape, self._order)
+     
+    in_shapes = [
+        2, 5,
+        (), (1,), (4,),
+        (2, 2), (2, 4), (3, 5),
+        (2, 2, 2), (2, 3, 2), (2, 3, 4),
+    ]
+    unravel_shapes = [
+        10, (15,),
+        (3, 4), (4, 5),
+        (2,3,4)
+    ]
+    dtypes = [np.uint8, np.int8, np.int32, np.int64] 
+    for hybridize, ishape, dtype, rshape in itertools.product([False, True], in_shapes, dtypes, unravel_shapes):
+        rtol = 1e-2 if dtype == np.float16 else 1e-3
+        atol = 1e-4 if dtype == np.float16 else 1e-5
+        test_unravel_index = TestUnravel_index(rshape)
+        if hybridize:
+            test_unravel_index.hybridize()
+        if type(ishape) == int and hybridize:
+            x = np.array([ishape], dtype=dtype)
+            np_out = _np.unravel_index(x.asnumpy(), rshape)
+        else:
+            x = np.random.uniform(0, 8, size=ishape).astype(dtype)
+            np_out = _np.unravel_index(x.asnumpy(), rshape)
+        mx_out = test_unravel_index(x)
+        assert len(mx_out) == len(np_out)
+        for elem_mx, elem_np in zip(mx_out, np_out):
+            assert elem_mx.asnumpy().shape == elem_np.shape
+            assert_almost_equal(elem_mx.asnumpy(), elem_np, rtol=rtol, atol=atol)
+        # no backward function for unravel_index operator
+ 
+        # Test imperative once again
+        mx_out = np.unravel_index(x, rshape)
+        np_out = _np.unravel_index(x.asnumpy(), rshape)
+        assert len(mx_out) == len(np_out)
+        for elem_mx, elem_np in zip(mx_out, np_out):
+            assert elem_mx.asnumpy().shape == elem_np.shape
+            assert_almost_equal(elem_mx.asnumpy(), elem_np, rtol=rtol, atol=atol)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 6c7ce24725fd2fe32b1e1294b45e672d85545b92 Mon Sep 17 00:00:00 2001
From: Tao Lv <tao.a.lv@intel.com>
Date: Tue, 26 Nov 2019 19:27:57 +0800
Subject: [PATCH 633/813] Revert "Mkldnn fullyConnect bwd bug fix (#16890)"
 (#16907)

* Revert "Mkldnn fullyConnect bwd bug fix (#16890)"

This reverts commit 436967bee9e3e3844486fd872b0065902d4324dd.

* ci
---
 src/operator/nn/fully_connected.cc            |  9 +++--
 .../nn/mkldnn/mkldnn_fully_connected.cc       | 36 +++++++++----------
 2 files changed, 25 insertions(+), 20 deletions(-)

diff --git a/src/operator/nn/fully_connected.cc b/src/operator/nn/fully_connected.cc
index a7e63f6d4139..5d722581257f 100644
--- a/src/operator/nn/fully_connected.cc
+++ b/src/operator/nn/fully_connected.cc
@@ -147,7 +147,10 @@ void FullyConnectedGradComputeExCPU(const nnvm::NodeAttrs& attrs,
                                     const std::vector<NDArray> &inputs,
                                     const std::vector<OpReqType> &req,
                                     const std::vector<NDArray> &outputs) {
-  if (SupportMKLDNNFC(inputs[0])) {
+  // TODO(rongzha1): disable due to flakiness in cpp test IMPERATIVE.FullyConnectedOp
+  // Will be fixed when we decide to enable the backward of FC.
+  bool mkldnn_fc_backward_enable = false;
+  if (mkldnn_fc_backward_enable && SupportMKLDNNFC(inputs[0])) {
     MKLDNN_OPCHECK_INIT(true, outputs.size(), inputs, outputs);
     MKLDNNFCBackward(attrs, ctx, inputs, req, outputs);
     MKLDNN_OPCHECK_RUN(FullyConnectedGradCompute<cpu>, attrs, ctx, inputs, req,
@@ -229,10 +232,12 @@ static bool BackwardFCStorageType(const nnvm::NodeAttrs& attrs,
   uint32_t out_expected = param.no_bias ? 2 : 3;
   CHECK_EQ(in_attrs->size(), 3U);
   CHECK_EQ(out_attrs->size(), out_expected);
+  // TODO(zhengda) let's disable MKLDNN for FullyConnected for now.
+  // It seems there is a bug.
   bool dispatched = false;
   if (!dispatched && common::ContainsOnlyStorage(*in_attrs, mxnet::kDefaultStorage)) {
     dispatched = storage_type_assign(out_attrs, mxnet::kDefaultStorage,
-                                     dispatch_mode, DispatchMode::kFComputeEx);
+                                     dispatch_mode, DispatchMode::kFCompute);
   }
   if (!dispatched && common::ContainsStorageType(*in_attrs, mxnet::kRowSparseStorage)) {
     dispatched = dispatch_fallback(out_attrs, dispatch_mode);
diff --git a/src/operator/nn/mkldnn/mkldnn_fully_connected.cc b/src/operator/nn/mkldnn/mkldnn_fully_connected.cc
index 7627d02c4702..1403cd114201 100644
--- a/src/operator/nn/mkldnn/mkldnn_fully_connected.cc
+++ b/src/operator/nn/mkldnn/mkldnn_fully_connected.cc
@@ -290,6 +290,24 @@ void MKLDNNFCBackward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
       data, weight, param.no_bias ? nullptr : &in_grad[fullc::kBias], GetMemDesc(out_grad));
 
   CHECK_NE(req[fullc::kWeight], kWriteInplace) << "cannot write weight inplace";
+  if (req[fullc::kData]) {
+    mkldnn::inner_product_backward_data::primitive_desc ipBwdData_pd = GetFCBwdData(
+        data, weight, out_grad, fwd_pd);
+    auto out_grad_mem = out_grad.GetMKLDNNDataReorder(
+        ipBwdData_pd.diff_dst_desc());
+    auto weight_mem = weight.GetMKLDNNDataReorder(ipBwdData_pd.weights_desc());
+    auto in_grad_mem = CreateMKLDNNMem(in_grad[fullc::kData],
+                                       ipBwdData_pd.diff_src_desc(),
+                                       req[fullc::kData]);
+    mkldnn_args_map_t args = {
+      {MKLDNN_ARG_DIFF_DST, *out_grad_mem},
+      {MKLDNN_ARG_WEIGHTS, *weight_mem},
+      {MKLDNN_ARG_DIFF_SRC, *in_grad_mem.second}
+    };
+
+    MKLDNNStream::Get()->RegisterPrimArgs(mkldnn::inner_product_backward_data(ipBwdData_pd), args);
+    CommitOutput(in_grad[fullc::kData], in_grad_mem);
+  }
   if (req[fullc::kWeight]) {
     mkldnn::inner_product_backward_weights::primitive_desc ipBwdWeights_pd
       = GetFCBwdWeights(data, weight, param.no_bias ? nullptr : &in_grad[fullc::kBias],
@@ -318,24 +336,6 @@ void MKLDNNFCBackward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
     CommitOutput(in_grad[fullc::kWeight], in_grad_weight);
     CommitOutput(in_grad[fullc::kBias], in_grad_bias);
   }
-  if (req[fullc::kData]) {
-    mkldnn::inner_product_backward_data::primitive_desc ipBwdData_pd = GetFCBwdData(
-        data, weight, out_grad, fwd_pd);
-    auto out_grad_mem = out_grad.GetMKLDNNDataReorder(
-        ipBwdData_pd.diff_dst_desc());
-    auto weight_mem = weight.GetMKLDNNDataReorder(ipBwdData_pd.weights_desc());
-    auto in_grad_mem = CreateMKLDNNMem(in_grad[fullc::kData],
-                                       ipBwdData_pd.diff_src_desc(),
-                                       req[fullc::kData]);
-    mkldnn_args_map_t args = {
-      {MKLDNN_ARG_DIFF_DST, *out_grad_mem},
-      {MKLDNN_ARG_WEIGHTS, *weight_mem},
-      {MKLDNN_ARG_DIFF_SRC, *in_grad_mem.second}
-    };
-
-    MKLDNNStream::Get()->RegisterPrimArgs(mkldnn::inner_product_backward_data(ipBwdData_pd), args);
-    CommitOutput(in_grad[fullc::kData], in_grad_mem);
-  }
   MKLDNNStream::Get()->Submit();
 }
 

From c9585bdfb2b25eda5aa92523859af41e0e4a7455 Mon Sep 17 00:00:00 2001
From: quyan <quyan.bupt@qq.com>
Date: Tue, 26 Nov 2019 23:24:18 +0800
Subject: [PATCH 634/813] Fix the problem in printing feature in c++ API
 examples : feature_extract (#15686)

* fix the problem in printing feature vectors.

Fixed a run-time error caused by mismatch of output dimensions.

* ci

* ci

* ci

* ci
---
 cpp-package/example/feature_extract/feature_extract.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/cpp-package/example/feature_extract/feature_extract.cpp b/cpp-package/example/feature_extract/feature_extract.cpp
index c23623e23bd1..d614fd576238 100644
--- a/cpp-package/example/feature_extract/feature_extract.cpp
+++ b/cpp-package/example/feature_extract/feature_extract.cpp
@@ -106,6 +106,7 @@ class FeatureExtractor {
     /*print out the features*/
     auto array = executor->outputs[0].Copy(Context(kCPU, 0));
     NDArray::WaitAll();
+    array = array.Reshape({2, 1024});
     for (int i = 0; i < 1024; ++i) {
       cout << array.At(0, i) << ",";
     }

From a11b7eaca6cda5f560c315a30093256f9a455cc2 Mon Sep 17 00:00:00 2001
From: Xingjian Shi <xshiab@connect.ust.hk>
Date: Tue, 26 Nov 2019 11:04:37 -0800
Subject: [PATCH 635/813] Try to fix CI (#16908)

* try to fix TVM build in CI

* Update ubuntu_core.sh
---
 ci/docker/install/ubuntu_core.sh | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/ci/docker/install/ubuntu_core.sh b/ci/docker/install/ubuntu_core.sh
index 3cb806e0aadd..8426b539f4c4 100755
--- a/ci/docker/install/ubuntu_core.sh
+++ b/ci/docker/install/ubuntu_core.sh
@@ -41,6 +41,10 @@ apt-get install -y \
     libopencv-dev \
     libturbojpeg \
     libzmq3-dev \
+    libtinfo-dev \
+    zlib1g-dev \
+    libedit-dev \
+    libxml2-dev \
     ninja-build \
     software-properties-common \
     sudo \

From 6b00b2ca2a9aa9df30e8f2bd5274ba9b0df24d03 Mon Sep 17 00:00:00 2001
From: Jiading Gai <jiading.gai@gmail.com>
Date: Tue, 26 Nov 2019 15:26:02 -0800
Subject: [PATCH 636/813] Allow loading from model files with empty weights.
 (#16061)

* Allow loading from model files with empty weights.

* ci

* ci

* CI
---
 python/mxnet/model.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/python/mxnet/model.py b/python/mxnet/model.py
index 9ec66e8cf286..f718db080a9c 100644
--- a/python/mxnet/model.py
+++ b/python/mxnet/model.py
@@ -431,6 +431,7 @@ def load_params(prefix, epoch):
     aux_params = {}
     if not save_dict:
         logging.warning("Params file '%s' is empty", '%s-%04d.params' % (prefix, epoch))
+        return (arg_params, aux_params)
     for k, v in save_dict.items():
         tp, name = k.split(":", 1)
         if tp == "arg":

From d2d4876f5d867d2a6d5b7bb5d1f587b910179f7c Mon Sep 17 00:00:00 2001
From: kevinzh92 <52752874+kevinzh92@users.noreply.github.com>
Date: Tue, 26 Nov 2019 15:29:33 -0800
Subject: [PATCH 637/813] Fix memory leak reported by ASAN in NNVM to ONNX
 conversion (#15516)

* Fix memory leak reported by ASAN in NNVM to ONNX conversion

* Update template type

* ci

* CI
---
 src/operator/subgraph/tensorrt/nnvm_to_onnx.cc | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/operator/subgraph/tensorrt/nnvm_to_onnx.cc b/src/operator/subgraph/tensorrt/nnvm_to_onnx.cc
index 4107c58df65f..19d0f26eae66 100644
--- a/src/operator/subgraph/tensorrt/nnvm_to_onnx.cc
+++ b/src/operator/subgraph/tensorrt/nnvm_to_onnx.cc
@@ -579,7 +579,7 @@ void ConvertConstant(
   auto size = shape.Size();
 
   if (dtype == TensorProto_DataType_FLOAT) {
-    std::shared_ptr<float> shared_data_ptr(new float[size]);
+    std::shared_ptr<float[]> shared_data_ptr(new float[size]);
     float* const data_ptr = shared_data_ptr.get();
     nd.SyncCopyToCPU(static_cast<void*>(data_ptr), size);
 
@@ -587,7 +587,7 @@ void ConvertConstant(
       initializer_proto->add_float_data(data_ptr[blob_idx]);
     }
   } else if (dtype == TensorProto_DataType_FLOAT16) {
-    std::shared_ptr<uint16_t> shared_data_ptr(new uint16_t[size]);
+    std::shared_ptr<uint16_t[]> shared_data_ptr(new uint16_t[size]);
     uint16_t* const data_ptr = shared_data_ptr.get();
     nd.SyncCopyToCPU(static_cast<void*>(data_ptr), size);
     for (size_t blob_idx = 0; blob_idx < size; ++blob_idx) {

From 8f10d557912c1a9a18a79243a3219d2bd84660c9 Mon Sep 17 00:00:00 2001
From: Xingjian Shi <xshiab@connect.ust.hk>
Date: Tue, 26 Nov 2019 15:34:44 -0800
Subject: [PATCH 638/813] [Numpy] Fix imperative basic indexing in numpy
 (#16902)

* fix bug

add test case

fix

Update test_numpy_ndarray.py

* revise function name
---
 python/mxnet/ndarray/ndarray.py             | 81 +++++++++++++--------
 src/ndarray/ndarray.cc                      |  5 +-
 src/operator/nn/mkldnn/mkldnn_base-inl.h    |  4 +
 tests/python/unittest/test_numpy_ndarray.py | 20 +++--
 4 files changed, 71 insertions(+), 39 deletions(-)

diff --git a/python/mxnet/ndarray/ndarray.py b/python/mxnet/ndarray/ndarray.py
index dc50635dd8a3..e66742eeef15 100644
--- a/python/mxnet/ndarray/ndarray.py
+++ b/python/mxnet/ndarray/ndarray.py
@@ -847,26 +847,32 @@ def _basic_indexing_slice_is_contiguous(slc_key, shape):
         """Whether indexing with the given key results in a contiguous array.
 
         The rule is: From right to left, if in an axis, a slice produces a
-        proper subset, no later axis can produce a proper subset or use
-        a step different from 1.
+        proper subset, the later slice must have <=1 elements.
 
         The ``slc_key`` sequence must have the same length as ``shape`` and
         only contain `slice` objects.
         """
         assert len(slc_key) == len(shape)
-        subset = False
+        is_subset = False
+        total_sliced_elements = np.prod([_get_slice_len(slc, n)
+                                         for slc, n in zip(slc_key, shape)])
+        if total_sliced_elements in (0, 1):
+            return True
         for idx, n in zip(reversed(slc_key), reversed(shape)):
-            start, stop, step = idx.indices(n)
-            if step > 0:
-                num = int(np.ceil(max(stop - start, 0) / step))
-            else:
-                num = int(np.ceil(min(stop - start, 0) / step))
-
-            if num != 1 and (subset or step != 1):
+            _, _, step = idx.indices(n)
+            num_elements = _get_slice_len(idx, n)
+            if num_elements == 0:
+                return True
+            elif num_elements > 1 and (step > 1 or step < 0):
+                # We do not support the case of reverse slicing of multiple elements and
+                # forward slicing of #elements > 1 and step > 1
                 return False
-            if num != n:
-                subset = True
-
+            elif is_subset:
+                if num_elements > 1:
+                    return False
+            else:
+                if num_elements < n:
+                    is_subset = True
         return True
     # pylint: enable=invalid-name
 
@@ -875,14 +881,9 @@ def _basic_indexing_sliced_shape(slc_key, shape):
         """Return the shape after slicing with the given key."""
         assert len(slc_key) == len(shape)
         sliced_shape = []
-        for idx, n in zip(slc_key, shape):
-            start, stop, step = idx.indices(n)
-            if step > 0:
-                num = int(np.ceil(max(stop - start, 0) / step))
-            else:
-                num = int(np.ceil(min(stop - start, 0) / step))
-            sliced_shape.append(num)
-
+        for slc, n in zip(slc_key, shape):
+            num_elements = _get_slice_len(slc, n)
+            sliced_shape.append(num_elements)
         return tuple(sliced_shape)
 
     # pylint: disable=invalid-name
@@ -890,15 +891,17 @@ def _basic_indexing_sliced_shape(slc_key, shape):
     def _basic_indexing_contiguous_flat_begin_end(slc_key, shape):
         """Return the flat indices of begin and end for contiguous slicing."""
         assert len(slc_key) == len(shape)
-        begin, end, _ = slc_key[0].indices(shape[0])
-        flat_begin, flat_end = begin, end - 1
-        for idx, n in zip(slc_key[1:], shape[1:]):
+        flat_begin, flat_end = 0, 0
+        for slc, n in zip(slc_key, shape):
             flat_begin *= n
             flat_end *= n
-            begin, end, _ = idx.indices(n)
-            flat_begin += begin
-            flat_end += end - 1
-
+            begin, _, _ = slc.indices(n)
+            num_elements = _get_slice_len(slc, n)
+            if num_elements == 0:
+                return 0, 0
+            else:
+                flat_begin += begin
+                flat_end += begin + num_elements - 1
         return flat_begin, flat_end + 1
     # pylint: enable=invalid-name
 
@@ -1062,7 +1065,7 @@ def _get_nd_basic_indexing(self, key):
         for ax in new_axes:  # pylint: disable=invalid-name
             final_shape.insert(ax, 1)
 
-        if final_shape == []:
+        if len(final_shape) == 0:
             # Override for single element indexing
             final_shape = [1]
         return sliced.reshape(final_shape)
@@ -3125,6 +3128,26 @@ def _get_dim_size(start, stop, step):
     return dim_size
 
 
+def _get_slice_len(slc, seq_length):
+    """Given a python slice object and the length of the sequence, calculate the number of elements
+     in the slice.
+
+    Parameters
+    ----------
+    slc : py_slice
+        The slice object
+    seq_length : int
+        The length of the object you are going to apply the slice on
+
+    Returns
+    -------
+    ret : int
+        Total number of elements in the slice
+    """
+    start, stop, step = slc.indices(seq_length)
+    return max(0, (stop - start + (step - (1 if step > 0 else -1))) // step)
+
+
 def _get_broadcast_shape(shape1, shape2):
     """Given two shapes that are not identical, find the shape
     that both input shapes can broadcast to."""
diff --git a/src/ndarray/ndarray.cc b/src/ndarray/ndarray.cc
index 6dc6bafa7288..9375bed5a79b 100644
--- a/src/ndarray/ndarray.cc
+++ b/src/ndarray/ndarray.cc
@@ -293,10 +293,9 @@ NDArray NDArray::Slice(index_t begin, index_t end) const {
 NDArray NDArray::SliceWithRecord(index_t begin, index_t end) {
   NDArray ret = this->Slice(begin, end);
   if (!Imperative::Get()->is_recording()) return ret;
-  // fake a slice_axis op
+  // fake a slice op
   nnvm::NodeAttrs attrs;
-  attrs.op = nnvm::Op::Get("slice_axis");
-  attrs.dict.insert({"axis", "0"});
+  attrs.op = nnvm::Op::Get("slice");
   attrs.dict.insert({"begin", std::to_string(begin)});
   attrs.dict.insert({"end", std::to_string(end)});
   attrs.op->attr_parser(&attrs);
diff --git a/src/operator/nn/mkldnn/mkldnn_base-inl.h b/src/operator/nn/mkldnn/mkldnn_base-inl.h
index 0f371d174e40..9bfc20cd7bb5 100644
--- a/src/operator/nn/mkldnn/mkldnn_base-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_base-inl.h
@@ -125,6 +125,10 @@ static inline bool SupportStorageMKLDNN(int stype) {
 
 static inline bool SupportMKLDNN(int dtype, const mxnet::TShape &shape) {
   int ndim = shape.ndim();
+  if (ndim == 0 || shape.Size() == 0) {
+    // MKLDNN currently does not support 0-dim Tensor and 0-size Tensor
+    return false;
+  }
   return dtype == mshadow::kFloat32 && (ndim == 1 || ndim == 2 || ndim == 4);
 }
 
diff --git a/tests/python/unittest/test_numpy_ndarray.py b/tests/python/unittest/test_numpy_ndarray.py
index 2c94ec06f4c7..06c01fa0e09e 100644
--- a/tests/python/unittest/test_numpy_ndarray.py
+++ b/tests/python/unittest/test_numpy_ndarray.py
@@ -642,13 +642,18 @@ def test_getitem(np_array, index):
             )
         np_indexed_array = np_array[np_index]
         mx_np_array = np.array(np_array, dtype=np_array.dtype)
-        try:
-            mx_indexed_array = mx_np_array[index]
-        except Exception as e:
-            print('Failed with index = {}'.format(index))
-            raise e
-        mx_indexed_array = mx_indexed_array.asnumpy()
-        assert same(np_indexed_array, mx_indexed_array), 'Failed with index = {}'.format(index)
+        for autograd in [True, False]:
+            try:
+                if autograd:
+                    with mx.autograd.record():
+                        mx_indexed_array = mx_np_array[index]
+                else:
+                    mx_indexed_array = mx_np_array[index]
+            except Exception as e:
+                print('Failed with index = {}'.format(index))
+                raise e
+            mx_indexed_array = mx_indexed_array.asnumpy()
+            assert same(np_indexed_array, mx_indexed_array), 'Failed with index = {}'.format(index)
 
     def test_setitem(np_array, index):
         def assert_same(np_array, np_index, mx_array, mx_index, mx_value, np_value=None):
@@ -768,6 +773,7 @@ def test_setitem_autograd(np_array, index):
         np_int(slice(1, 5), np.int32),
         np_int(slice(1, 5), np.int64),
         slice(1, 5, 2),
+        slice(1, 2, 2),
         np_int(slice(1, 5, 2), np.int32),
         np_int(slice(1, 5, 2), np.int64),
         slice(7, 0, -1),

From a98cefc74c10985a78050b35d9f888d215a3ff8e Mon Sep 17 00:00:00 2001
From: Xingjian Shi <xshiab@connect.ust.hk>
Date: Tue, 26 Nov 2019 22:58:01 -0800
Subject: [PATCH 639/813] [Numpy] Basic indexing in symbolic interface of
 DeepNumpy (#16621)

* add symbolic indexing, waiting for split and concatenate to be merged

Change the error raised in _op.split

Fix sanity issues

* try to fix

update

Update _symbol.py

fix

Update _symbol.py

fix

Update symbol.py

Update symbol.py

update

update

try to fix

fix bug

Fix for hsplit and split

fix

try to fix

try to fix

try to fix

try to fix

fix bug

fix lint

fix slicing

fix concatenate

try to not use output_is_list

Update test_numpy_gluon.py

try to fix the basic indexing

try to fix

fix

fix

debug

fix bug

debug

debug

debug

try to fix

fix

skip the zero-size ndarrays in MKLDNN

Update multiarray.py

fix bug

Update ndarray.py

remove debug

fix bug

try to fix

Update test_numpy_gluon.py

fix

add back

Update test_numpy_gluon.py

Update test_numpy_gluon.py

fix bug

try to fix

fix test

* fix lint

Test for case2

fix lint

fix

accerate testing

move check_gluon_hybridize_consistency to test_utils

update docstring

Use Size in the implementation

fix bug

* Address comments

* Update _symbol.py

* add load_json

* address comment
---
 python/mxnet/__init__.py                  |   1 +
 python/mxnet/_ctypes/ndarray.py           |   4 +-
 python/mxnet/_ctypes/symbol.py            |   8 +-
 python/mxnet/base.py                      |  18 ++
 python/mxnet/cython/base.pyi              |   2 +
 python/mxnet/cython/ndarray.pyx           |   4 +-
 python/mxnet/cython/symbol.pyx            |  12 +-
 python/mxnet/gluon/block.py               |  21 ++-
 python/mxnet/ndarray/numpy/_op.py         |  18 +-
 python/mxnet/ndarray/register.py          |   7 +-
 python/mxnet/symbol/numpy/_symbol.py      | 212 ++++++++++++++++++++--
 python/mxnet/symbol/register.py           |  11 +-
 python/mxnet/symbol/symbol.py             |   2 +-
 python/mxnet/test_utils.py                |  78 ++++++++
 src/operator/numpy/np_matrix_op.cc        |   4 +-
 tests/python/unittest/test_numpy_gluon.py | 207 ++++++++++++++++++++-
 16 files changed, 553 insertions(+), 56 deletions(-)

diff --git a/python/mxnet/__init__.py b/python/mxnet/__init__.py
index 088ffc1ea9d0..6d9704161cde 100644
--- a/python/mxnet/__init__.py
+++ b/python/mxnet/__init__.py
@@ -38,6 +38,7 @@
 from . import name
 # use mx.sym as short for symbol
 from . import symbol as sym
+from .symbol.numpy import _symbol as np_symbol
 from . import symbol
 from . import symbol_doc
 from . import io
diff --git a/python/mxnet/_ctypes/ndarray.py b/python/mxnet/_ctypes/ndarray.py
index 0d5dade2f163..f71e63d7fd41 100644
--- a/python/mxnet/_ctypes/ndarray.py
+++ b/python/mxnet/_ctypes/ndarray.py
@@ -77,7 +77,7 @@ def _set_np_ndarray_class(cls):
     _np_ndarray_cls = cls
 
 
-def _imperative_invoke(handle, ndargs, keys, vals, out, is_np_op):
+def _imperative_invoke(handle, ndargs, keys, vals, out, is_np_op, output_is_list):
     """ctypes implementation of imperative invoke wrapper"""
     if out is not None:
         original_output = out
@@ -109,7 +109,7 @@ def _imperative_invoke(handle, ndargs, keys, vals, out, is_np_op):
     create_ndarray_fn = _np_ndarray_cls if is_np_op else _ndarray_cls
     if original_output is not None:
         return original_output
-    if num_output.value == 1:
+    if num_output.value == 1 and not output_is_list:
         return create_ndarray_fn(ctypes.cast(output_vars[0], NDArrayHandle),
                                  stype=out_stypes[0])
     else:
diff --git a/python/mxnet/_ctypes/symbol.py b/python/mxnet/_ctypes/symbol.py
index 01ba18b38963..68f847fb675e 100644
--- a/python/mxnet/_ctypes/symbol.py
+++ b/python/mxnet/_ctypes/symbol.py
@@ -123,7 +123,7 @@ def _set_np_symbol_class(cls):
     _np_symbol_cls = cls
 
 
-def _symbol_creator(handle, args, kwargs, keys, vals, name, is_np_op):
+def _symbol_creator(handle, args, kwargs, keys, vals, name, is_np_op, output_is_list=False):
     sym_handle = SymbolHandle()
     check_call(_LIB.MXSymbolCreateAtomicSymbol(
         ctypes.c_void_p(handle),
@@ -144,4 +144,10 @@ def _symbol_creator(handle, args, kwargs, keys, vals, name, is_np_op):
         s._compose(name=name, **kwargs)
     else:
         s._compose(name=name)
+    if is_np_op:
+        # Determine whether the symbol is a list.
+        if s.num_outputs > 1:
+            return list(s)
+        elif output_is_list:
+            return [s]
     return s
diff --git a/python/mxnet/base.py b/python/mxnet/base.py
index c97250028cb8..6478b00e5047 100644
--- a/python/mxnet/base.py
+++ b/python/mxnet/base.py
@@ -774,12 +774,30 @@ def write_all_str(module_file, module_all_list):
 
 _NP_INTERNAL_OP_PREFIX = '_npi_'
 
+_NP_OUTPUT_IS_LIST_OPERATORS = {'_npi_split', '_npi_hsplit'}
+
 
 def _is_np_op(op_name):
     return op_name.startswith(_NP_OP_PREFIX) or op_name.startswith(_NP_EXT_OP_PREFIX)\
            or op_name.startswith(_NP_INTERNAL_OP_PREFIX)
 
 
+def _output_is_list(op_name):
+    """ Whether the output of the operator is a list.
+
+    Parameters
+    ----------
+    op_name : Name of the operator
+
+    Returns
+    -------
+
+    """
+    if _is_np_op(op_name):
+        return op_name in _NP_OUTPUT_IS_LIST_OPERATORS
+    return False
+
+
 def _get_op_submodule_name(op_name, op_name_prefix, submodule_name_list):
     """Get the submodule name of a specific op"""
     assert op_name.startswith(op_name_prefix)
diff --git a/python/mxnet/cython/base.pyi b/python/mxnet/cython/base.pyi
index 0a35555aabb3..c7cdea7a0bef 100644
--- a/python/mxnet/cython/base.pyi
+++ b/python/mxnet/cython/base.pyi
@@ -68,6 +68,8 @@ cdef extern from "nnvm/c_api.h":
                     const char ***arg_descriptions,
                     const char **return_type);
     int NNSymbolFree(SymbolHandle symbol);
+    int NNSymbolGetNumOutputs(SymbolHandle sym,
+                              nn_uint* output_count);
     int NNSymbolCompose(SymbolHandle sym,
                         const char* name,
                         nn_uint num_args,
diff --git a/python/mxnet/cython/ndarray.pyx b/python/mxnet/cython/ndarray.pyx
index 74ad1c4c3d49..8d924e170187 100644
--- a/python/mxnet/cython/ndarray.pyx
+++ b/python/mxnet/cython/ndarray.pyx
@@ -184,7 +184,7 @@ cdef class CachedOp:
                                            _ctypes.c_int(monitor_all)))
 
 
-def _imperative_invoke(handle, ndargs, keys, vals, out, is_np_op=0):
+def _imperative_invoke(handle, ndargs, keys, vals, out, is_np_op=0, output_is_list=0):
     """cython implementation of imperative invoke wrapper"""
     cdef unsigned long long ihandle = handle
     cdef OpHandle chandle = <OpHandle>ihandle
@@ -235,7 +235,7 @@ def _imperative_invoke(handle, ndargs, keys, vals, out, is_np_op=0):
 
     if original_output is not None:
         return original_output
-    if num_output == 1:
+    if num_output == 1 and not output_is_list:
         return NewArray(p_output_vars[0], p_output_stypes[0], is_np_op)
     else:
         return [NewArray(p_output_vars[i], p_output_stypes[i], is_np_op) for i in range(num_output)]
diff --git a/python/mxnet/cython/symbol.pyx b/python/mxnet/cython/symbol.pyx
index 86fe8ae6db4f..1b42b1e4ff77 100644
--- a/python/mxnet/cython/symbol.pyx
+++ b/python/mxnet/cython/symbol.pyx
@@ -104,7 +104,7 @@ cdef NewSymbol(SymbolHandle handle, int is_np_sym=0):
     return sym
 
 
-def _symbol_creator(handle, args, kwargs, keys, vals, name, is_np_op=0):
+def _symbol_creator(handle, args, kwargs, keys, vals, name, is_np_op=0, output_is_list=0):
     cdef unsigned long long ihandle = handle
     cdef OpHandle chandle = <OpHandle>ihandle
     cdef vector[string] ckeys
@@ -113,6 +113,7 @@ def _symbol_creator(handle, args, kwargs, keys, vals, name, is_np_op=0):
     cdef vector[SymbolHandle] sym_args
     cdef SymbolHandle ret_handle
     cdef string cname = c_str(name)
+    cdef nn_uint nout
 
     for i in keys:
         ckeys.push_back(c_str(i))
@@ -151,4 +152,11 @@ def _symbol_creator(handle, args, kwargs, keys, vals, name, is_np_op=0):
         &csym_keys[0] if csym_keys.size() != 0 else NULL,
         &sym_args[0] if sym_args.size() != 0 else NULL))
 
-    return NewSymbol(ret_handle, is_np_op)
+    sym = NewSymbol(ret_handle, is_np_op)
+    if is_np_op:
+        CALL(NNSymbolGetNumOutputs(ret_handle, &nout))
+        if nout > 1:
+            return list(sym)
+        elif output_is_list:
+            return [sym]
+    return sym
diff --git a/python/mxnet/gluon/block.py b/python/mxnet/gluon/block.py
index 731295eacb99..de523567f3eb 100644
--- a/python/mxnet/gluon/block.py
+++ b/python/mxnet/gluon/block.py
@@ -27,7 +27,7 @@
 from collections import OrderedDict, defaultdict
 
 from ..base import mx_real_t, MXNetError
-from .. import symbol, ndarray, initializer
+from .. import symbol, ndarray, initializer, np_symbol
 from ..symbol import Symbol
 from ..ndarray import NDArray
 from .. import name as _name
@@ -979,7 +979,10 @@ def _deferred_infer_shape(self, *args):
     def _call_cached_op(self, *args):
         if self._cached_op is None:
             self._build_cache(*args)
-        assert self._cached_op, "cached op is not None"
+        assert self._cached_op, "Gluon failed to build the cache. " \
+                                "This should never happen. " \
+                                "Please submit an issue on Github" \
+                                " https://github.com/apache/incubator-mxnet."
         if self._callback:
             self._cached_op._register_op_hook(self._callback, self._monitor_all)
             if len(self._flags) >= 2 and (self._flags[1] or self._flags[0]):
@@ -1253,7 +1256,10 @@ def imports(symbol_file, input_names, param_file=None, ctx=None):
         ...     'net1-symbol.json', ['data'], 'net1-0001.params')
         >>> out2 = net2(x)
         """
-        sym = symbol.load(symbol_file)
+        if is_np_array():
+            sym = np_symbol.load(symbol_file)
+        else:
+            sym = symbol.load(symbol_file)
         if isinstance(input_names, str):
             input_names = [input_names]
         if param_file is None:
@@ -1261,7 +1267,7 @@ def imports(symbol_file, input_names, param_file=None, ctx=None):
             inputs = [symbol.var(i, dtype=mx_real_t) for i in input_names]
         else:
             # Do not specify type, rely on saved params type instead
-            inputs = [symbol.var(i) for i in input_names]
+            inputs = [symbol.var(i).as_np_ndarray() if is_np_array() else symbol.var(i) for i in input_names]
         ret = SymbolBlock(sym, inputs)
         if param_file is not None:
             ret.collect_params().load(param_file, ctx=ctx, cast_dtype=True, dtype_source='saved')
@@ -1287,8 +1293,6 @@ def __init__(self, outputs, inputs, params=None):
 
         syms, self._in_format = _flatten(inputs, "input")
         out, self._out_format = _flatten(outputs, "output")
-        out = symbol.Group(out, _check_same_symbol_type(out))
-
         input_names = set()
         for i in syms:
             assert len(i.get_internals().list_outputs()) == 1, \
@@ -1297,11 +1301,16 @@ def __init__(self, outputs, inputs, params=None):
 
         # check if any symbol is row_sparse
         row_sparse_storage = ndarray.ndarray._STORAGE_TYPE_STR_TO_ID['row_sparse']
+
         for i in out:
             for j in i.get_internals():
                 assert(j.attr("__storage_type__") != str(row_sparse_storage)), \
                     "SymbolBlock doesn't support Parameter '%s' because its storage " \
                     "type is 'row_sparse'." % j.name
+        if len(out) > 1:
+            out = symbol.Group(out, _check_same_symbol_type(out))
+        else:
+            out = out[0]
 
         # Infer type of parameters. Without this, every parameter will be created with
         # default type i.e., fp32
diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index 295c83ef47a0..aed88eaaa56b 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -21,7 +21,7 @@
 
 from __future__ import absolute_import
 import numpy as _np
-from ...base import numeric_types
+from ...base import numeric_types, integer_types
 from ...util import _sanity_check_params, set_module
 from ...util import wrap_np_unary_func, wrap_np_binary_func
 from ...context import current_context
@@ -2763,9 +2763,8 @@ def split(ary, indices_or_sections, axis=0):
         If `indices_or_sections` is given as an integer, but
         a split does not result in equal division.
     """
-    indices = []
     axis_size = ary.shape[axis]
-    if isinstance(indices_or_sections, int):
+    if isinstance(indices_or_sections, integer_types):
         sections = indices_or_sections
         if axis_size % sections:
             raise ValueError('array split does not result in an equal division')
@@ -2774,10 +2773,10 @@ def split(ary, indices_or_sections, axis=0):
     elif isinstance(indices_or_sections, (list, set, tuple)):
         indices = [0] + list(indices_or_sections)
     else:
-        raise ValueError('indices_or_sections must either int, or tuple / list / set of ints')
+        raise ValueError('indices_or_sections must be either int, or tuple / list / set of ints')
     ret = _npi.split(ary, indices, axis, False)
-    if not isinstance(ret, list):
-        return [ret]
+    assert isinstance(ret, list), 'Output of split should be list,' \
+                                  ' got a return type {}'.format(type(ret))
     return ret
 # pylint: enable=redefined-outer-name
 
@@ -2875,12 +2874,11 @@ def hsplit(ary, indices_or_sections):
     >>> np.hsplit(x, [2, 2])
     [array([0., 1.]), array([], dtype=float32), array([2., 3.])]
     """
-    indices = []
     axis = 1
-    if (len(ary.shape) == 1):
+    if len(ary.shape) == 1:
         axis = 0
     axis_size = ary.shape[axis]
-    if isinstance(indices_or_sections, int):
+    if isinstance(indices_or_sections, integer_types):
         sections = indices_or_sections
         if axis_size % sections:
             raise ValueError('array hsplit does not result in an equal division')
@@ -2891,8 +2889,6 @@ def hsplit(ary, indices_or_sections):
     else:
         raise ValueError('indices_or_sections must either int or tuple of ints')
     ret = _npi.hsplit(ary, indices, axis, False)
-    if not isinstance(ret, list):
-        raise NotImplementedError('single output from hsplit is not supported yet...')
     return ret
 # pylint: enable=redefined-outer-name
 
diff --git a/python/mxnet/ndarray/register.py b/python/mxnet/ndarray/register.py
index bdbfa1584ca6..c2f595aa1c83 100644
--- a/python/mxnet/ndarray/register.py
+++ b/python/mxnet/ndarray/register.py
@@ -24,7 +24,7 @@
 from ._internal import NDArrayBase, _imperative_invoke # pylint: disable=unused-import
 from ..ndarray_doc import _build_doc
 
-from ..base import mx_uint, check_call, _LIB, py_str, _init_op_module, _Null, _is_np_op  # pylint: disable=unused-import
+from ..base import mx_uint, check_call, _LIB, py_str, _init_op_module, _Null, _is_np_op, _output_is_list  # pylint: disable=unused-import
 from ..util import use_np_shape  # pylint: disable=unused-import
 
 
@@ -176,6 +176,7 @@ def _generate_ndarray_function_code(handle, op_name, func_name, signature_only=F
 
     code = []
     is_np_op = _is_np_op(op_name)
+    output_is_list = _output_is_list(op_name)
     doc_str_idx = 1
     if is_np_op:
         doc_str_idx = 2
@@ -241,8 +242,8 @@ def %s(%s):"""%(func_name, ', '.join(signature)))
     {verify_fn}("{op_name}", "{func_name}", ndargs, out)
         """.format(verify_fn=verify_ndarrays_fn, op_name=op_name, func_name=func_name))
         code.append("""
-    return _imperative_invoke(%d, ndargs, keys, vals, out, %s)"""%(
-        handle.value, str(is_np_op)))
+    return _imperative_invoke(%d, ndargs, keys, vals, out, %s, %s)"""%(
+        handle.value, str(is_np_op), str(output_is_list)))
     else:
         code.append("""
     return (0,)""")
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 3790078bd397..8b64bbf86556 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -22,13 +22,19 @@
 import ctypes
 import numpy as _np
 from . import _op as _mx_np_op
-from ...base import _LIB, SymbolHandle, numeric_types, mx_uint
+from ...base import _LIB, SymbolHandle, numeric_types, mx_uint, integer_types, string_types
+from ...base import c_str
+from ...base import py_str
 from ...util import check_call, set_module, _sanity_check_params
 from ...util import wrap_np_unary_func, wrap_np_binary_func
 from ...context import current_context
-from ..symbol import Symbol
+from ..symbol import Symbol, Group
 from .._internal import _set_np_symbol_class
 from . import _internal as _npi
+try:
+    from __builtin__ import slice as py_slice
+except ImportError:
+    from builtins import slice as py_slice
 
 __all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power', 'arctan2',
            'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs', 'absolute', 'exp',
@@ -43,31 +49,130 @@
            'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'shares_memory',
            'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where']
 
-def _num_outputs(sym):
-    return len(sym.as_nd_ndarray())
-
 
 @set_module('mxnet.symbol.numpy')
 class _Symbol(Symbol):
-    def __getitem__(self, key):
-        num_outputs = _num_outputs(self)
-        if num_outputs == 1:
-            raise NotImplementedError
-        if not isinstance(key, int):
-            raise NotImplementedError
-        if key >= num_outputs:
-            # Important, python determines the end by this exception
-            raise IndexError
-        handle = SymbolHandle()
-        check_call(_LIB.MXSymbolGetOutput(
-            self.handle, mx_uint(key), ctypes.byref(handle)))
-        return _Symbol(handle=handle)
+    def __getitem__(self, key): # pylint: disable = too-many-return-statements, inconsistent-return-statements
+        """Return self[key].
+
+        If the symbol is a symbol list, it returns the i-th symbol or a list of symbols
+        selected by key.
+
+        Otherwise, it outputs a symbol that slice the input by the given key. Currently, this
+        function supports the following types of key:
+
+        - integer types, e.g., int, long, np.int32, np.int64
+        - slice containing integer constants, e.g., slice(0, None, None)
+        - tuple contaning the above elements, which is used for multidimensional indexing
+
+        Parameters
+        ----------
+        key : int, slice, or tuple of all previous types
+            Indexing key.
+
+        """
+        num_outputs = self.num_outputs
+        if num_outputs > 1:
+            num_outputs = self.num_outputs
+            if isinstance(key, integer_types):
+                key = int(key)
+                if key < -num_outputs or key >= num_outputs:
+                    raise IndexError('list index out of range')
+                if key < 0:
+                    key += num_outputs
+                ret_handle = SymbolHandle()
+                check_call(_LIB.MXSymbolGetOutput(self.handle, mx_uint(key),
+                                                  ctypes.byref(ret_handle)))
+                return _Symbol(handle=ret_handle)
+            elif isinstance(key, py_slice):
+                start, stop, step = key.indices(num_outputs)
+                return Group([self[i] for i in range(start, stop, step)], _Symbol)
+            else:
+                raise TypeError('indices of symbol group must be integers or slices, not {}'
+                                .format(type(key)))
+        else:
+            if isinstance(key, integer_types):
+                sliced = _npi.slice(self, [key], [key+1])
+                return _npi.reshape(sliced, (-3, -4))
+            elif isinstance(key, py_slice):
+                if key.step is None or key.step != 0:
+                    start = [None] if key.start is None else key.start
+                    stop = [None] if key.stop is None else key.stop
+                    return _npi.slice(self, start, stop, key.step)
+                else:
+                    raise ValueError("slice step cannot be zero")
+            elif isinstance(key, tuple):
+                begin = []
+                end = []
+                step = []
+                new_shape = ()
+                if len(key) == 0:
+                    return self
+                for index in key:
+                    if isinstance(index, py_slice):
+                        if index.step is not None and index.step == 0:
+                            raise ValueError("slice step cannot be zero")
+                        begin.append(index.start)
+                        end.append(index.stop)
+                        step.append(index.step)
+                        new_shape += (-2,)
+                    elif isinstance(index, integer_types):
+                        if index >= 0:
+                            begin.append(index)
+                            end.append(index+1)
+                            step.append(1)
+                        else:
+                            begin.append(index)
+                            end.append(index - 1)
+                            step.append(-1)
+                        new_shape += (-3,)
+                    else:
+                        raise IndexError('Only integer, slice, or tuple of these types'
+                                         ' are supported! Received key={}'.format(key))
+                new_shape += (-4,)
+                sliced = _npi.slice(self, begin, end, step)
+                return _npi.reshape(sliced, new_shape)
+            else:
+                raise IndexError('Only integer, slice, or tuple of these types are supported! '
+                                 'Received key={}'.format(key))
 
     def __setitem__(self, key, value):
         raise NotImplementedError
 
+    def __repr__(self):
+        """Gets a string representation of the symbol."""
+        if self.num_outputs > 1:
+            name = ', '.join([str(ele_sym) for ele_sym in self])
+            return '<%s group [%s]>' % (self.__class__.__name__, name)
+        else:
+            return '<%s %s>' % (self.__class__.__name__, self.name)
+
+    @property
+    def name(self):
+        """Gets name string from the symbol, this function only works for symbols
+         that are not a list (grouped symbols).
+
+        Returns
+        -------
+        value : str
+            The name of this symbol, returns ``None`` for list symbol.
+        """
+        if self.num_outputs > 1:
+            raise AttributeError('This is a Group Symbol that contains {} elements and'
+                                 ' does not have a name. Use str(sym) to print the name of '
+                                 'all the elements instead.'.format(self.num_outputs))
+        ret = ctypes.c_char_p()
+        success = ctypes.c_int()
+        check_call(_LIB.MXSymbolGetName(
+            self.handle, ctypes.byref(ret), ctypes.byref(success)))
+        assert success.value != 0,\
+            'Fail to infer the name of a symbol that is not a list!'
+        return py_str(ret.value)
+
     def __iter__(self):
-        raise AttributeError('_Symbol object has no attribute __iter__')
+        if self.num_outputs == 1:
+            raise TypeError("'{}' is not iterable.".format(self))
+        return iter((self[i] for i in range(self.num_outputs)))
 
     def __add__(self, other):
         """x.__add__(y) <=> x + y"""
@@ -158,7 +263,17 @@ def __le__(self, other):
         return less_equal(self, other)
 
     def __len__(self):
-        raise NotImplementedError
+        if self.num_outputs == 1:
+            raise TypeError('{} is not a list and does not support len().'.format(self))
+        return self.num_outputs
+
+    @property
+    def num_outputs(self):
+        """The number of outputs of a symbol. If the symbol is not a symbollist, it returns 1.
+        Otherwise, it returns the number of elements of the list."""
+        output_count = mx_uint()
+        check_call(_LIB.MXSymbolGetNumOutputs(self.handle, ctypes.byref(output_count)))
+        return output_count.value
 
     def as_nd_ndarray(self):
         """Convert _Symbol to mxnet.symbol.Symbol to use its convenience fluent methods."""
@@ -4989,4 +5104,61 @@ def where(condition, x, y):
     return _npi.where(condition, x, y, out=None)
 
 
+@set_module('mxnet.symbol.numpy')
+def load(fname):
+    """Loads symbol from a JSON file.
+    You can also use pickle to do the job if you only work on python.
+    The advantage of load/save is the file is language agnostic.
+    This means the file saved using save can be loaded by other language binding of mxnet.
+    You also get the benefit being able to directly load/save from cloud storage(S3, HDFS).
+
+    Parameters
+    ----------
+    fname : str
+        The name of the file, examples:
+        - `s3://my-bucket/path/my-s3-symbol`
+        - `hdfs://my-bucket/path/my-hdfs-symbol`
+        - `/path-to/my-local-symbol`
+
+    Returns
+    -------
+    sym : _Symbol
+        The loaded symbol.
+
+    See Also
+    --------
+    _Symbol.save : Used to save symbol into file.
+    """
+    if not isinstance(fname, string_types):
+        raise TypeError('fname needs to be string')
+    handle = SymbolHandle()
+    check_call(_LIB.MXSymbolCreateFromFile(c_str(fname), ctypes.byref(handle)))
+    return _Symbol(handle)
+
+
+@set_module('mxnet.symbol.numpy')
+def load_json(json_str):
+    """Loads symbol from json string.
+
+    Parameters
+    ----------
+    json_str : str
+        A JSON string.
+
+    Returns
+    -------
+    sym : Symbol
+        The loaded symbol.
+
+    See Also
+    --------
+    _Symbol.tojson : Used to save symbol into json string.
+    """
+    if not isinstance(json_str, string_types):
+        raise TypeError('json_str needs to be string')
+    handle = SymbolHandle()
+    check_call(_LIB.MXSymbolCreateFromJSON(c_str(json_str), ctypes.byref(handle)))
+    return _Symbol(handle)
+
+
 _set_np_symbol_class(_Symbol)
diff --git a/python/mxnet/symbol/register.py b/python/mxnet/symbol/register.py
index a17dd79048d4..b9950e542016 100644
--- a/python/mxnet/symbol/register.py
+++ b/python/mxnet/symbol/register.py
@@ -27,7 +27,7 @@
 from ..attribute import AttrScope
 from ..base import mx_uint, check_call, _LIB, py_str
 from ..symbol_doc import _build_doc
-from ..base import _Null, _init_op_module, _is_np_op
+from ..base import _Null, _init_op_module, _is_np_op, _output_is_list
 from ..name import NameManager
 # pylint: enable=unused-import
 
@@ -144,6 +144,7 @@ def _generate_symbol_function_code(handle, op_name, func_name, signature_only=Fa
     signature = ndsignature + signature
 
     is_np_op = _is_np_op(op_name)
+    output_is_list = _output_is_list(op_name)
     verify_symbol_fn = _verify_np_symbol.__name__ if is_np_op else _verify_legacy_symbol.__name__
     code = []
     if arr_name:
@@ -191,8 +192,8 @@ def %s(*%s, **kwargs):"""%(func_name, arr_name))
             key_var_num_args, key_var_num_args))
 
             code.append("""
-    return _symbol_creator(%d, sym_args, sym_kwargs, keys, vals, name, %s)"""%(
-        handle.value, str(is_np_op)))
+    return _symbol_creator(%d, sym_args, sym_kwargs, keys, vals, name, %s, %s)"""%(
+                handle.value, str(is_np_op), str(output_is_list)))
     else:
         code.append("""
 def %s(%s):"""%(func_name, ', '.join(signature)))
@@ -244,8 +245,8 @@ def %s(%s):"""%(func_name, ', '.join(signature)))
     if not hasattr(NameManager._current, "value"):
         NameManager._current.value = NameManager()
     name = NameManager._current.value.get(name, '%s')
-    return _symbol_creator(%d, None, sym_kwargs, _keys, _vals, name, %s)"""%(
-        func_name.lower(), handle.value, str(is_np_op)))
+    return _symbol_creator(%d, None, sym_kwargs, _keys, _vals, name, %s, %s)"""%(
+        func_name.lower(), handle.value, str(is_np_op), str(output_is_list)))
 
     if signature_only:
         code.append("""
diff --git a/python/mxnet/symbol/symbol.py b/python/mxnet/symbol/symbol.py
index 27d79fc03c31..c7c45a4be909 100644
--- a/python/mxnet/symbol/symbol.py
+++ b/python/mxnet/symbol/symbol.py
@@ -97,7 +97,7 @@ def __iter__(self):
         >>> out
         <Symbol Grouped>
         >>> for i in out:
-        ...     i
+        ...     print(i)
         ...
         <Symbol d>
         <Symbol _plus0>
diff --git a/python/mxnet/test_utils.py b/python/mxnet/test_utils.py
index 6c8fefca4490..063d65659687 100644
--- a/python/mxnet/test_utils.py
+++ b/python/mxnet/test_utils.py
@@ -2384,3 +2384,81 @@ def is_op_runnable():
             print('Cuda arch compute capability: sm_{}'.format(str(cc)))
             return cc >= 53
     return True
+
+
+@use_np
+def check_gluon_hybridize_consistency(net_builder, data_l, numpy_func=None, test_grad=True,
+                                      rtol=1E-4, atol=1E-4):
+    """Check whether a HybridBlock has consistent output between the hybridized
+     v.s. non-hybridized versions
+
+    The network should not contain any random number generators.
+
+    Parameters
+    ----------
+    net_builder : function
+        The builder of the HybridBlock that we are going to check the consistency.
+        Inside the implementation, we will call net_builder() to construct the hybrid block.
+        Also, the net_builder will need to support specifying the params
+    data_l : list of mx.np.ndarray
+        List of input ndarrays.
+    numpy_func : function, optional
+        The ground truth numpy function that has the same functionality as net_builder().
+        Default None.
+    test_grad : bool, optional
+        Whether to test the consistency of the gradient. Default True.
+    rtol : float, optional
+        The relative error tolerance, default 1E-4. Default 1E-4.
+    atol : float, optional
+        The absolute error tolerance, default 1E-4. Default 1E-4.
+    """
+    class _NumpyParamDictInit(mx.init.Initializer):
+        """Initializes parameters with the cached numpy ndarrays dictionary
+        """
+        def __init__(self, np_params):
+            super(_NumpyParamDictInit, self).__init__()
+            self._np_params = np_params
+
+        def _init_weight(self, name, arr):
+            arr[()] = self._np_params[name]
+    saved_out_np = None
+    saved_grad_np_l = None
+    params_init = None
+    use_autograd_flags = [False, True] if test_grad else [False]
+    for hybridize in [False, True]:
+        for use_autograd in use_autograd_flags:
+            net = net_builder(prefix='net_')
+            if params_init is None:
+                net.initialize()
+            else:
+                net.initialize(params_init)
+            if hybridize:
+                net.hybridize()
+            in_data_l = [ele.copy() for ele in data_l]
+            if use_autograd:
+                for ele in in_data_l:
+                    ele.attach_grad()
+                with mx.autograd.record():
+                    out = net(*in_data_l)
+                out.backward(out)
+            else:
+                out = net(*in_data_l)
+            if params_init is None:
+                np_params = {k: v.data().asnumpy() for k, v in net.collect_params().items()}
+                params_init = _NumpyParamDictInit(np_params)
+            if saved_out_np is None:
+                saved_out_np = out.asnumpy()
+            else:
+                # Check for correctness
+                assert_almost_equal(out.asnumpy(), saved_out_np, rtol=rtol, atol=atol)
+            if use_autograd:
+                if saved_grad_np_l is None:
+                    saved_grad_np_l = [ele.grad.asnumpy() for ele in in_data_l]
+                else:
+                    # Check for correctness
+                    for data, saved_grad_np in zip(in_data_l, saved_grad_np_l):
+                        assert_almost_equal(data.grad.asnumpy(), saved_grad_np,
+                                            rtol=rtol, atol=atol)
+    if numpy_func is not None:
+        numpy_out = numpy_func(*[ele.asnumpy() for ele in data_l])
+        assert_almost_equal(saved_out_np, numpy_out, rtol=rtol, atol=atol)
diff --git a/src/operator/numpy/np_matrix_op.cc b/src/operator/numpy/np_matrix_op.cc
index f07cb0e5f4b2..e496202a0b41 100644
--- a/src/operator/numpy/np_matrix_op.cc
+++ b/src/operator/numpy/np_matrix_op.cc
@@ -233,7 +233,7 @@ bool NumpyXReshapeInferShape(const mxnet::TShape& src,
       } else if (proposed_dim == -3) {
         // skip the source dimension if and only if it is one
         CHECK_EQ(src[src_inx], 1)
-          <<"-3 index should only be used to skip dimension size 1";
+          << "-3 index should only be used to skip dimension size 1";
         src_inx++;
       } else if (proposed_dim == -4) {
         // copy all remaining dims from source
@@ -347,7 +347,6 @@ bool NumpyXReshapeShape(const nnvm::NodeAttrs& attrs,
 
 NNVM_REGISTER_OP(_np_reshape)
 .describe(R"code()code" ADD_FILELINE)
-.add_alias("_npi_reshape")
 .set_num_inputs(1)
 .set_num_outputs(1)
 .set_attr_parser(ParamParser<NumpyReshapeParam>)
@@ -373,6 +372,7 @@ NNVM_REGISTER_OP(_np_reshape)
 
 NNVM_REGISTER_OP(_npx_reshape)
 .describe(R"code()code" ADD_FILELINE)
+.add_alias("_npi_reshape")
 .set_num_inputs(1)
 .set_num_outputs(1)
 .set_attr_parser(ParamParser<NumpyXReshapeParam>)
diff --git a/tests/python/unittest/test_numpy_gluon.py b/tests/python/unittest/test_numpy_gluon.py
index 15914db2e9ec..6ce9e18e2624 100644
--- a/tests/python/unittest/test_numpy_gluon.py
+++ b/tests/python/unittest/test_numpy_gluon.py
@@ -19,11 +19,14 @@
 from __future__ import absolute_import
 from __future__ import division
 
+import os
+from uuid import uuid4
 import numpy as _np
 import mxnet as mx
 from mxnet import gluon, autograd, np
-from mxnet.test_utils import use_np, assert_almost_equal
+from mxnet.test_utils import use_np, assert_almost_equal, check_gluon_hybridize_consistency
 from common import with_seed
+import random
 
 
 @with_seed()
@@ -196,6 +199,208 @@ def test_parameters_zero_grad():
             assert_almost_equal(v.grad().asnumpy(), mx.np.zeros_like(v.grad()).asnumpy())
 
 
+def check_gluon_save_load(net_builder, data_l):
+    """Verify the consistency between the loaded network and the original network.
+
+    Known limitations: Currently it only supports loading
+
+    Parameters
+    ----------
+    net_builder : function
+        The builder of the HybridBlock.
+    data_l : list of numpy.ndarray
+        List of the input data that we will use to verify the correctness of the loaded network.
+
+    """
+    net = net_builder()
+    net.hybridize()
+    net.initialize()
+    out = net(*data_l)
+    out_np = out.asnumpy()
+    prefix = str(uuid4())
+    net.export(prefix)
+    input_names = 'data' if len(data_l) == 1 else ['data{}'.format(i) for i in range(len(data_l))]
+    net_imported = gluon.SymbolBlock.imports('{}-symbol.json'.format(prefix),
+                                             input_names, param_file='{}-0000.params'.format(prefix))
+    # Clean up the directory
+    os.remove('{}-symbol.json'.format(prefix))
+    os.remove('{}-0000.params'.format(prefix))
+    loaded_out = net_imported(*data_l).asnumpy()
+    assert_almost_equal(out_np, loaded_out)
+
+
+def hashable_index(tuple_idx):
+    """Return an hashable representation of a tuple of slice object
+
+    We add this because the slice object in python is not hashable.
+
+    Parameters
+    ----------
+    tuple_idx : tuple
+        A tuple of slice/int objects
+
+    Returns
+    -------
+    ret : tuple
+        A hashable representation of the slice data
+    """
+    l = []
+    for ele in tuple_idx:
+        if isinstance(ele, slice):
+            l.append(ele.__reduce__())
+        else:
+            l.append(ele)
+    return tuple(l)
+
+
+@with_seed()
+@use_np
+def test_symbolic_basic_slicing():
+    def random_slice_index(shape):
+        index = []
+        step_switch = random.randint(-1, 1)
+        for i in range(len(shape)):
+            if shape[i] == 0:
+                index.append(slice(None))
+                continue
+            r = random.randint(0, 5)
+            if r < 2:
+                index.append(random.randint(1 - shape[i], shape[i] - 1))
+                continue
+            elif r < 3:
+                index.append(slice(None))
+                continue
+            s = random.randint(0, shape[i] - 1)
+            e = random.randint(s + 1, shape[i])
+            if step_switch == 1:
+                index.append(slice(s, e, 1))
+            elif step_switch == -1:
+                e -= 1
+                s -= 1
+                index.append(slice(e, s, -1))
+            else:
+                index.append(slice(s, e, 2))
+        return tuple(index)
+
+    shapes = [
+        (4, 6, 8, 5),
+        (1, 1, 1, 6),
+        (5, 6, 4),
+        (5, 6),
+        (10,),
+        (100, 0, 10, 0, 0),
+        (100, 0, 0),
+        (0, 0, 0),
+        (),
+    ]
+    for shape in shapes:
+        cache = set()
+        x = mx.np.random.normal(0, 1, shape)
+        y = mx.np.random.normal(0, 1, shape)
+        for _ in range(200):
+            index1 = random_slice_index(shape)
+            index2 = random_slice_index(x.asnumpy()[index1].shape)
+            if (hashable_index(index1), hashable_index(index2)) in cache:
+                continue
+            cache.add((hashable_index(index1), hashable_index(index2)))
+            # Test basic slicing on a single symbol
+            class TestSlicingSingleSymbol1(gluon.HybridBlock):
+                def hybrid_forward(self, F, x, y):
+                    return x[()][index1] + y[()][index1]
+
+            # Test basic slicing on a single symbol
+            class TestSlicingSingleSymbol2(gluon.HybridBlock):
+                def hybrid_forward(self, F, x, y):
+                    return (x[()][index1] + y[()][index1])[index2]
+
+            check_gluon_hybridize_consistency(TestSlicingSingleSymbol1, [x, y],
+                                              numpy_func=lambda a, b: a[()][index1] + b[()][index1])
+            check_gluon_hybridize_consistency(TestSlicingSingleSymbol2, [x, y],
+                                              numpy_func=lambda a, b:
+                                              (a[()][index1] + b[()][index1])[index2])
+        # Test for split/hsplit/vsplit
+        class TestSlicingWithSplit(gluon.HybridBlock):
+            def hybrid_forward(self, F, x):
+                x = F.np.split(x, shape[2], axis=2)
+                x = x[1:-1]
+                x = F.np.concatenate(x, axis=2)
+                return x
+
+        class TestSlicingWithSplit2(gluon.HybridBlock):
+            def __init__(self, prefix=None, params=None):
+                super(TestSlicingWithSplit2, self).__init__(prefix=prefix, params=params)
+                with self.name_scope():
+                    self.layer = gluon.nn.Dense(16, flatten=False, params=params)
+
+            def hybrid_forward(self, F, x, y):
+                x = F.np.split(x, 1)
+                x = x[0]
+                return self.layer(x[:, -1, :] + y[:, -1, :])
+
+        class TestSlicingWithHSplit(gluon.HybridBlock):
+            def hybrid_forward(self, F, x):
+                x = F.np.hsplit(x, shape[1])
+                x = x[1:-1]
+                x = F.np.concatenate(x, axis=1)
+                return x
+
+        class TestSlicingWithVSplit(gluon.HybridBlock):
+            def hybrid_forward(self, F, x):
+                x = F.np.vsplit(x, shape[0])
+                x = x[1:-1]
+                x = F.np.concatenate(x, axis=0)
+                return x
+
+        if len(shape) > 2 and shape[2] > 2:
+            check_gluon_hybridize_consistency(
+                TestSlicingWithSplit, [x],
+                numpy_func=lambda a: _np.concatenate(_np.split(a, shape[2], axis=2)[1:-1],
+                                                     axis=2))
+        if len(shape) == 3 and shape[0] > 0 and shape[1] > 0 and shape[2] > 0:
+            check_gluon_hybridize_consistency(TestSlicingWithSplit2, [x, y])
+
+        if len(shape) > 1 and shape[1] > 2:
+            check_gluon_hybridize_consistency(
+                TestSlicingWithHSplit, [x],
+                numpy_func=lambda a: _np.concatenate(_np.hsplit(a, shape[1])[1:-1], axis=1))
+        if len(shape) > 1 and shape[0] > 2:
+            check_gluon_hybridize_consistency(
+                TestSlicingWithVSplit, [x],
+                numpy_func=lambda a: _np.concatenate(_np.vsplit(a, shape[0])[1:-1], axis=0))
+
+
+@with_seed()
+@use_np
+def test_net_symbol_save_load():
+    class Case1(gluon.HybridBlock):
+        def __init__(self, prefix=None, params=None):
+            super(Case1, self).__init__(prefix=prefix, params=params)
+            with self.name_scope():
+                self.layer = gluon.nn.Dense(64, flatten=False, params=params)
+
+        def hybrid_forward(self, F, x, y):
+            x = F.np.split(x, 1)
+            x = x[0]
+            return self.layer(x[:, -1, :] + y[:, -1, :])
+    check_gluon_save_load(Case1, [mx.np.random.normal(0, 1, (10, 5, 8, 6)),
+                                  mx.np.random.normal(0, 1, (10, 5, 8, 6))])
+
+    class Case2(gluon.HybridBlock):
+        def __init__(self, prefix=None, params=None):
+            super(Case2, self).__init__(prefix=prefix, params=params)
+            with self.name_scope():
+                self.layer1 = gluon.nn.Dense(64, flatten=False, params=params)
+                self.layer2 = gluon.nn.Dense(64, flatten=False, params=params)
+
+        def hybrid_forward(self, F, x, y):
+            x = F.np.split(x, 1)
+            x = x[0]
+            return self.layer1(x[:, -1, :]) + self.layer2(y[:, -1, :])
+    check_gluon_save_load(Case2, [mx.np.random.normal(0, 1, (10, 5, 8)),
+                                  mx.np.random.normal(0, 1, (10, 5, 8))])
+
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 7713a43353b06b91ae7d5d508868b42ce557e487 Mon Sep 17 00:00:00 2001
From: Kellen Sunderland <kellen.sunderland@gmail.com>
Date: Wed, 27 Nov 2019 09:51:00 -0800
Subject: [PATCH 640/813] Update TensorRT tutorial to build-from-source.
 (#14860)

---
 .../performance/backend/tensorrt/tensorrt.md  | 54 +++++++------------
 1 file changed, 18 insertions(+), 36 deletions(-)

diff --git a/docs/python_docs/python/tutorials/performance/backend/tensorrt/tensorrt.md b/docs/python_docs/python/tutorials/performance/backend/tensorrt/tensorrt.md
index 63dd678f3f5f..44082f977eb7 100644
--- a/docs/python_docs/python/tutorials/performance/backend/tensorrt/tensorrt.md
+++ b/docs/python_docs/python/tutorials/performance/backend/tensorrt/tensorrt.md
@@ -15,31 +15,16 @@
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
 
-# Optimized GPU Inference
+# Optimizing Deep Learning Computation Graphs with TensorRT
 
-NVIDIA's TensorRT is a deep learning library that has been shown to provide large speedups when used for network inference. MXNet 1.3.0 is shipping with experimental integrated support for TensorRT. This means MXNet users can noew make use of this acceleration library to efficiently run their networks. In this blog post we'll see how to install, enable and run TensorRT with MXNet.  We'll also give some insight into what is happening behind the scenes in MXNet to enable TensorRT graph execution.
+NVIDIA's TensorRT is a deep learning library that has been shown to provide large speedups when used for network inference. MXNet 1.5.0 and later versions ship with experimental integrated support for TensorRT. This means MXNet users can now make use of this acceleration library to efficiently run their networks. In this tutorial we'll see how to install, enable and run TensorRT with MXNet.  We'll also give some insight into what is happening behind the scenes in MXNet to enable TensorRT graph execution.
 
 ## Installation and Prerequisites
-Installing MXNet with TensorRT integration is an easy process. First ensure that you are running Ubuntu 16.04, that you have updated your video drivers, and you have installed CUDA 9.0 or 9.2.  You'll need a Pascal or newer generation NVIDIA gpu.  You'll also have to download and install TensorRT libraries [instructions here](https://docs.nvidia.com/deeplearning/sdk/tensorrt-install-guide/index.html).  Once your these prerequisites installed and up-to-date you can install a special build of MXNet with TensorRT support enabled via PyPi and pip.  Install the appropriate version by running:
-
-To install with CUDA 9.0:
-```
-pip install mxnet-tensorrt-cu90
-```
-
-To install with CUDA 9.2:
-```
-pip install mxnet-tensorrt-cu92
-```
-
-If you are running an operating system other than Ubuntu 16.04, or just prefer to use a docker image with all prerequisites installed you can instead run:
-```
-nvidia-docker run -ti mxnet/tensorrt python
-```
+To use MXNet with TensorRT integration you'll have to follow the MXNet build from source instructions, and have a few extra packages installed on your machine. First ensure that you are running Ubuntu 18.04, and that you have updated your video drivers, and you have installed CUDA 10.1 or newer.  You'll need a Pascal or newer generation NVIDIA GPU.  You'll also have to download and install TensorRT libraries [instructions here](https://docs.nvidia.com/deeplearning/sdk/tensorrt-install-guide/index.html).  Once you have these prerequisites installed you can follow the [recommended instructions for building MXNet for NVIDIA GPUs](https://mxnet.apache.org/get_started/build_from_source#recommended-for-Systems-with-NVIDIA-GPUs-and-Intel-CPUs) but include the additional CMake flag -DUSE_TENSORRT=1.
 
 ## Sample Models
 ### Resnet 18
-TensorRT is an inference only library, so for the purposes of this blog post we will be using a pre-trained network, in this case a Resnet 18.  Resnets are a computationally intensive model architecture that are often used as a backbone for various computer vision tasks. Resnets are also commonly used as a reference for benchmarking deep learning library performance.  In this section we'll use a pretrained Resnet 18 from the [Gluon Model Zoo](/api/python/docs/api/gluon/model_zoo/index.html) and compare its inference speed with TensorRT using MXNet with TensorRT integration turned off as a baseline.
+TensorRT is an inference only library, so for the purposes of this tutorial we will be using a pre-trained network, in this case a Resnet 18.  Resnets are a computationally intensive model architecture that are often used as a backbone for various computer vision tasks. Resnets are also commonly used as a reference for benchmarking deep learning library performance.  In this section we'll use a pretrained Resnet 18 from the [Gluon Model Zoo](/api/python/docs/api/gluon/model_zoo/index.html) and compare its inference speed with TensorRT using MXNet with TensorRT integration turned off as a baseline.
 
 ## Model Initialization
 ```python
@@ -63,7 +48,6 @@ In our first section of code we import the modules needed to run MXNet, and to t
 input = mx.nd.zeros(batch_shape)
 
 # Execute with MXNet
-os.environ['MXNET_USE_TENSORRT'] = '0'
 executor = sym.simple_bind(ctx=mx.gpu(0), data=batch_shape, grad_req='null', force_rebind=True)
 executor.copy_params_from(arg_params, aux_params)
 
@@ -83,26 +67,23 @@ end = time.time()
 print(time.process_time() - start)
 ```
 
-For this experiment we are strictly interested in inference performance, so to simplify the benchmark we'll pass a tensor filled with zeros as an input.  We then bind a symbol as usual, returning a normal MXNet executor, and we run forward on this executor in a loop.  To help improve the accuracy of our benchmarks we run a small number of predictions as a warmup before running our timed loop.  This will ensure various lazy operations, which do not represent real-world usage, have completed before we measure relative performance improvement.  On a modern PC with a Titan V GPU the time taken for our MXNet baseline is **33.73s**.  Next we'll run the same model with TensorRT enabled, and see how the performance compares.
-
-While TensorRT integration remains experimental, we require users to set an environment variable to enable graph compilation.  You can see that at the start of this test we explicitly disabled TensorRT graph compilation support.  Next, we will run the same predictions using TensorRT.  This will require us to explicitly enable the MXNET_USE_TENSORRT environment variable, and we'll also use a slightly different API to bind our symbol.
+We are interested in inference performance, so to simplify the benchmark we'll pass a tensor filled with zeros as an input.  We bind a symbol as usual, returning an MXNet executor, and we run forward on this executor in a loop.  To help improve the accuracy of our benchmarks we run a small number of predictions as a warmup before running our timed loop.  On a modern PC with an RTX 2070 GPU the time taken for our MXNet baseline is **17.20s**.  Next we'll run the same model with TensorRT enabled, and see how the performance compares.
 
 ## MXNet with TensorRT Integration Performance
 ```python
 # Execute with TensorRT
 print('Building TensorRT engine')
-os.environ['MXNET_USE_TENSORRT'] = '1'
-arg_params.update(aux_params)
-all_params = dict([(k, v.as_in_context(mx.gpu(0))) for k, v in arg_params.items()])
-executor = mx.contrib.tensorrt.tensorrt_bind(sym, ctx=mx.gpu(0), all_params=all_params,
-                                             data=batch_shape, grad_req='null', force_rebind=True)
+trt_sym = sym.get_backend_symbol('TensorRT')
+arg_params, aux_params = mx.contrib.tensorrt.init_tensorrt_params(trt_sym, arg_params, aux_params)
+mx.contrib.tensorrt.set_use_fp16(True)
+executor = trt_sym.simple_bind(ctx=mx.gpu(), data=batch_shape,
+                               grad_req='null', force_rebind=True)
+executor.copy_params_from(arg_params, aux_params)
 ```
 
-Instead of calling simple_bind directly on our symbol to return an executor, we call an experimental API from the contrib module of MXNet. This call is meant to emulate the simple_bind call, and has many of the same arguments.  One difference to note is that this call takes params in the form of a single merged dictionary to assist with a tensor cleanup pass that we'll describe below.
-
-As TensorRT integration improves our goal is to gradually deprecate this tensorrt_bind call, and allow users to use TensorRT transparently (see the [Subgraph API](https://cwiki.apache.org/confluence/display/MXNET/MXNet+Graph+Optimization+and+Quantization+based+on+subgraph+and+MKL-DNN) for more information).  When this happens, the similarity between tensorrt_bind and simple_bind should make it easy to migrate your code.
+We use a few TensorRT specific API calls from the contrib package here to setup our parameters and indicate we'd like to run inference in fp16 mode. We then call simple_bind as normal and copy our parameter dictionaries to our executor.
 
-```
+```python
 #Warmup
 print('Warming up TensorRT')
 for i in range(0, 10):
@@ -118,7 +99,8 @@ for i in range(0, 10000):
 end = time.time()
 print(time.process_time() - start)
 ```
-We run timing with a warmup once more, and on the same machine, run in **18.99s**. A 1.8x speed improvement!  Speed improvements when using libraries like TensorRT can come from a variety of optimizations, but in this case our speedups are coming from a technique known as [operator fusion](http://dmlc.ml/2016/11/21/fusion-and-runtime-compilation-for-nnvm-and-tinyflow.html).
+
+We run timing with a warmup once more, and on the same machine, run in **9.83s**. A 1.75x speed improvement!  Speed improvements when using libraries like TensorRT can come from a variety of optimizations, but in this case our speedups are coming from a technique known as [operator fusion](http://ziheng.org/2016/11/21/fusion-and-runtime-compilation-for-nnvm-and-tinyflow/).
 
 ## Operators and Subgraph Fusion
 
@@ -136,8 +118,8 @@ The examples below shows a Gluon implementation of a Wavenet before and after a
 ## After
 ![after](wavenet_optimized.svg)
 
-## Future Work
-As mentioned above, MXNet developers are excited about the possibilities of [creating APIs](https://cwiki.apache.org/confluence/display/MXNET/MXNet+Graph+Optimization+and+Quantization+based+on+subgraph+and+MKL-DNN) that deal specifically with subgraphs.  As this work matures it will bring many improvements for TensorRT users.  We hope this will also be an opportunity for other acceleration libraries to integrate with MXNet.
+## Subgraph API
+As of MXNet 1.5, MXNet developers have integrated TensorRT with MXNet via a Subgraph API.  Read more about the design of the API [here](https://cwiki.apache.org/confluence/display/MXNET/MXNet+Graph+Optimization+and+Quantization+based+on+subgraph+and+MKL-DNN).
 
 ## Thanks
-Thank you to NVIDIA for contributing this feature, and specifically thanks to Marek Kolodziej and Clement Fuji-Tsang.  Thanks to Junyuan Xie and Jun Wu for the code reviews and design feedback, and to Aaron Markham for the copy review.
+Thanks to NVIDIA for contributing this feature, and specifically thanks to Marek Kolodziej and Clement Fuji-Tsang.  Thanks to Junyuan Xie and Jun Wu for the code reviews and design feedback, and to Aaron Markham for the copy review.
\ No newline at end of file

From 32a9baac3b7b58b133bfc4eb9426ccba75986eb3 Mon Sep 17 00:00:00 2001
From: Rohit Kumar Srivastava <srivastava.141@osu.edu>
Date: Wed, 27 Nov 2019 14:53:00 -0800
Subject: [PATCH 641/813] Adding user guidelines for using MXNet built with
 Large Tensor Support (#16894)

---
 .../src/pages/api/faq/large_tensor_support.md | 287 ++++++++++++++++++
 1 file changed, 287 insertions(+)
 create mode 100644 docs/static_site/src/pages/api/faq/large_tensor_support.md

diff --git a/docs/static_site/src/pages/api/faq/large_tensor_support.md b/docs/static_site/src/pages/api/faq/large_tensor_support.md
new file mode 100644
index 000000000000..4c77ede24b02
--- /dev/null
+++ b/docs/static_site/src/pages/api/faq/large_tensor_support.md
@@ -0,0 +1,287 @@
+---
+layout: page_category
+title: Using MXNet with Large Tensor Support
+category: faq
+faq_c: Extend and Contribute to MXNet
+question: How do I use MXNet built with Large Tensor Support
+permalink: /api/faq/large_tensor_support
+---
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Using MXNet with Large Tensor Support
+
+## What is large tensor support?
+When creating a network that uses large amounts of data, as in a deep graph problem, you may need large tensor support. This is a relatively new feature as tensors are indexed in MXNet using INT32 indices by default. Now MXNet build with Large Tensor supports INT64 indices.
+
+It is MXNet built with an additional flag *USE_INT64_TENSOR_SIZE=1*
+in CMAKE it is built using *USE_INT64_TENSOR_SIZE:“ON”*
+
+## When do you need it?
+1. When you are creating NDArrays of size larger than 2^31 elements.
+2. When the input to your model requires tensors that have inputs larger than 2^31 (when you load them all at once in your code) or attributes greater than 2^31.
+
+## How to identify that you need to use large tensors ?
+When you see one of the following errors:
+
+
+1. OverflowError: unsigned int is greater than maximum
+2. Check failed: inp->shape().Size() < 1 >> 31 (4300000000 vs. 0) : Size of tensor you are trying to allocate is larger than 2^32 elements. Please build with flag USE_INT64_TENSOR_SIZE=1
+3. Invalid Parameter format for end expect int or None but value='2150000000', in operator slice_axis(name="", end="2150000000", begin="0", axis="0"). *_Basically input attribute was expected to be int32, which is less than 2^31 and the received value is larger than that so, operator's parmeter inference treats that as a string which becomes unexpected input.`_*
+
+## How to use it ?
+You can create a large NDArray that requires large tensor enabled build to run as follows:
+
+```python
+LARGE_X=4300000000
+a = mx.nd.arange(0, LARGE_X, dtype=“int64”)
+or
+a = nd.ones(shape=LARGE_X)
+or
+a = nd.empty(LARGE_X)
+or
+a = nd.random.exponential(shape=LARGE_X)
+or
+a = nd.random.gamma(shape=LARGE_X)
+or
+a = nd.random.normal(shape=LARGE_X)
+```
+
+## Caveats
+1. Use `int64` as `dtype` whenever attempting to slice an NDArray when range is over maximum `int32` value
+2. Use `int64` as `dtype` when passing indices as parameters or expecting output as parameters to and from operators
+
+The following are the cases for large tensor usage where you must specify `dtype` as `int64`:
+
+
+* _randint():_
+```python
+low_large_value = 2*32*
+*high_large_value = 2*34
+# dtype is explicitly specified since default type is int32 for randint
+a = nd.random.randint(low_large_value, high_large_value, dtype=np.int64)
+```
+* _ravel_multi_index()_ and _unravel_index()_:
+```python
+x1, y1 = rand_coord_2d((LARGE_X - 100), LARGE_X, 10, SMALL_Y)
+x2, y2 = rand_coord_2d((LARGE_X - 200), LARGE_X, 9, SMALL_Y)
+x3, y3 = rand_coord_2d((LARGE_X - 300), LARGE_X, 8, SMALL_Y)
+indices_2d = [[x1, x2, x3], [y1, y2, y3]]
+# dtype is explicitly specified for indices else they will default to float32
+idx = mx.nd.ravel_multi_index(mx.nd.array(indices_2d, dtype=np.int64),
+                                  shape=(LARGE_X, SMALL_Y))
+indices_2d = mx.nd.unravel_index(mx.nd.array(idx_numpy, dtype=np.int64),
+                                  shape=(LARGE_X, SMALL_Y))
+```
+* _argsort()_ and _topk()_
+
+They both return indices which are specified by `dtype=np.int64`.
+```python
+b = create_2d_tensor(rows=LARGE_X, columns=SMALL_Y)
+# argsort
+s = nd.argsort(b, axis=0, is_ascend=False, dtype=np.int64)
+# topk
+k = nd.topk(b, k=10, axis=0, dtype=np.int64)
+```
+* _index_copy()_
+
+Again whenever we are passing indices as arguments and using large tensor, the `dtype` of indices must be `int64`.
+```python
+x = mx.nd.zeros((LARGE_X, SMALL_Y))
+t = mx.nd.arange(1, SMALL_Y + 1).reshape((1, SMALL_Y))
+# explicitly specifying dtype of indices to np.int64
+index = mx.nd.array([LARGE_X - 1], dtype="int64")
+x = mx.nd.contrib.index_copy(x, index, t)
+```
+* _one_hot()_
+
+Here again array is used as indices that act as location of bits inside the large vector that need to be activated.
+```python
+# a is the index array here whose dtype should be int64.
+a = nd.array([1, (VLARGE_X - 1)], dtype=np.int64)
+b = nd.one_hot(a, VLARGE_X)
+```
+
+## What platforms and version of MXNet are supported ?
+You can use MXNet with large tensor support in the following configuration:
+
+*MXNet built for CPU on Linux (Ubuntu or Amazon Linux), and only for python bindings.*
+*Custom wheels are provided with this configuration.*
+
+These flavors of MXNet are currently built with large tensor support:
+
+1. MXNet for linux-cpu
+2. MXNet for linux_cu100
+
+Large tensor support only works for *forward pass*. 
+Backward pass is partially supported and not completely tested, so it is considered experimental at best.
+
+Not supported:
+
+* GPU and MKLDNN. 
+* Windows, ARM or any operating system other than Ubuntu
+* Any permutation of MXNet wheel that contains MKLDNN. 
+* Other language bindings like Scala, Java, R,  and Julia.
+
+
+## Other known Issues:
+Randint operator is flaky: https://github.com/apache/incubator-mxnet/issues/16172
+dgemm operations using BLAS libraries currently don’t support int64.
+linspace() is not supported.
+```python
+a = mx.sym.Variable('a')
+b = mx.sym.Variable('b')
+c = 2 * a + b
+texec = c.bind(mx.cpu(), {'a': nd.arange(0, LARGE_X * 2, dtype='int64').reshape(2, LARGE_X), 'b' : nd.arange(0, LARGE_X * 2, dtype='int64').reshape(2, LARGE_X)})
+new_shape = {'a': (1, 2*LARGE_X), 'b': (1, 2*LARGE_X)}
+texec.reshape(allow_up_sizing=True, **new_shape)
+
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+  File "/home/ubuntu/incubator-mxnet/python/mxnet/executor.py", line 449, in reshape
+    py_array('i', provided_arg_shape_data)),
+OverflowError: signed integer is greater than maximum}
+```
+Symbolic reshape is not supported. Please see the following example.
+```python
+a = mx.sym.Variable('a')
+b = mx.sym.Variable('b')
+c = 2 * a + b
+texec = c.bind(mx.cpu(), {'a': nd.arange(0, LARGE_X * 2, dtype='int64').reshape(2, LARGE_X), 'b' : nd.arange(0, LARGE_X * 2, dtype='int64').reshape(2, LARGE_X)})
+new_shape = {'a': (1, 2 * LARGE_X), 'b': (1, 2 * LARGE_X)}
+texec.reshape(allow_up_sizing=True, **new_shape)
+
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+  File "/home/ubuntu/incubator-mxnet/python/mxnet/executor.py", line 449, in reshape
+    py_array('i', provided_arg_shape_data)),
+OverflowError: signed integer is greater than maximum
+```
+
+## Working DGL Example(dgl.ai)
+The following is a sample running code for DGL which works with int64 but not with int32.
+```python
+import mxnet as mx
+from mxnet import gluon
+import dgl
+import dgl.function as fn
+import numpy as np
+from scipy import sparse as spsp
+
+num_nodes = 10000000
+num_edges = 100000000
+
+col1 = np.random.randint(0, num_nodes, size=(num_edges,))
+print('create col1')
+col2 = np.random.randint(0, num_nodes, size=(num_edges,))
+print('create col2')
+data = np.ones((num_edges,))
+print('create data')
+spm = spsp.coo_matrix((data, (col1, col2)), shape=(num_nodes, num_nodes))
+print('create coo')
+labels = mx.nd.random.randint(0, 10, shape=(num_nodes,))
+
+g = dgl.DGLGraph(spm, readonly=True)
+print('create DGLGraph')
+g.ndata['h'] = mx.nd.random.uniform(shape=(num_nodes, 200))
+print('create node data')
+
+class node_update(gluon.Block):
+    def __init__(self, out_feats):
+        super(node_update, self).__init__()
+        self.dense = gluon.nn.Dense(out_feats, 'relu')
+        self.dropout = 0.5
+
+    def forward(self, nodes):
+        h = mx.nd.concat(nodes.data['h'], nodes.data['accum'], dim=1)
+        h = self.dense(h)
+        return {'h': mx.nd.Dropout(h, p=self.dropout)}
+update_fn = node_update(200)
+update_fn.initialize(ctx=mx.cpu())
+
+g.update_all(fn.copy_src(src='h', out='m'), fn.sum(msg='m', out='accum'), update_fn)
+print('update all')
+
+loss_fcn = gluon.loss.SoftmaxCELoss()
+loss = loss_fcn(g.ndata['h'], labels)
+print('loss')
+loss = loss.sum()
+print(loss)
+```
+
+## Performance Regression: 
+Roughly 40 operators have shown performance regression in our preliminary analysis: Large Tensor Performance as shown in table below.
+
+|Operator                                |int32(msec)|int64(msec)  |int64/int32  |int32+mkl(msec)|int64+mkl(msec)|int64+mkl/int32+mkl|
+|----------------------------------------|-----------|-------------|-------------|---------------|---------------|-------------------|
+|topk                                    |12.81245198|42.2472195   |329.74%      |12.728027      |43.462353      |341.47%            |
+|argsort                                 |16.43896801|46.2231455   |281.18%      |17.200311      |46.7779985     |271.96%            |
+|sort                                    |16.57822751|46.5644815   |280.88%      |16.401236      |46.263803      |282.08%            |
+|flip                                    |0.221817521|0.535838     |241.57%      |0.2123705      |0.7950055      |374.35%            |
+|depth_to_space                          |0.250976998|0.534083     |212.80%      |0.2338155      |0.631252       |269.98%            |
+|space_to_depth                          |0.254336512|0.5368935    |211.10%      |0.2334405      |0.6343175      |271.73%            |
+|min_axis                                |0.685826526|1.4393255    |209.87%      |0.6266175      |1.3538925      |216.06%            |
+|sum_axis                                |0.720809505|1.5110635    |209.63%      |0.6566265      |0.8290575      |126.26%            |
+|nansum                                  |1.279337012|2.635434     |206.00%      |1.227156       |2.4305255      |198.06%            |
+|argmax                                  |4.765146994|9.682672     |203.20%      |4.6576605      |9.394067       |201.69%            |
+|swapaxes                                |0.667943008|1.3544455    |202.78%      |0.649036       |1.8293235      |281.85%            |
+|argmin                                  |4.774890491|9.545651     |199.91%      |4.666858       |9.5194385      |203.98%            |
+|sum_axis                                |0.540210982|1.0550705    |195.31%      |0.500895       |0.616179       |123.02%            |
+|max_axis                                |0.117824005|0.226481     |192.22%      |0.149085       |0.224334       |150.47%            |
+|argmax_channel                          |0.261897018|0.49573      |189.28%      |0.251171       |0.4814885      |191.70%            |
+|min_axis                                |0.147698505|0.2675355    |181.14%      |0.148424       |0.2874105      |193.64%            |
+|nansum                                  |1.142132009|2.058077     |180.20%      |1.042387       |1.263102       |121.17%            |
+|min_axis                                |0.56951947 |1.020972     |179.27%      |0.4722595      |0.998179       |211.36%            |
+|min                                     |1.154684491|2.0446045    |177.07%      |1.0534145      |1.9723065      |187.23%            |
+|sum                                     |1.121753477|1.959272     |174.66%      |0.9984095      |1.213339       |121.53%            |
+|sum_axis                                |0.158632494|0.2744115    |172.99%      |0.1573735      |0.2266315      |144.01%            |
+|nansum                                  |0.21418152 |0.3661335    |170.95%      |0.2162935      |0.269517       |124.61%            |
+|random_normal                           |1.229072484|2.093057     |170.30%      |1.222785       |2.095916       |171.41%            |
+|LeakyReLU                               |0.344101485|0.582337     |169.23%      |0.389167       |0.7003465      |179.96%            |
+|nanprod                                 |1.273265516|2.095068     |164.54%      |1.0906815      |2.054369       |188.36%            |
+|nanprod                                 |0.203272473|0.32792      |161.32%      |0.202548       |0.3288335      |162.35%            |
+|sample_gamma                            |8.079962019|12.7266385   |157.51%      |12.4216245     |12.7957475     |103.01%            |
+|sum                                     |0.21571602 |0.3396875    |157.47%      |0.1939995      |0.262942       |135.54%            |
+|argmin                                  |0.086381478|0.1354795    |156.84%      |0.0826235      |0.134886       |163.25%            |
+|argmax                                  |0.08664903 |0.135826     |156.75%      |0.082693       |0.1269225      |153.49%            |
+|sample_gamma                            |7.712843508|12.0266355   |155.93%      |11.8900915     |12.143009      |102.13%            |
+|sample_exponential                      |2.312778   |3.5953945    |155.46%      |3.0935085      |3.5656265      |115.26%            |
+|prod                                    |0.203170988|0.3113865    |153.26%      |0.180757       |0.264523       |146.34%            |
+|random_uniform                          |0.40893798 |0.6240795    |152.61%      |0.244613       |0.6319695      |258.35%            |
+|min                                     |0.205482502|0.3122025    |151.94%      |0.2023835      |0.33234        |164.21%            |
+|random_negative_binomial                |3.919228504|5.919488     |151.04%      |5.685851       |6.0220735      |105.91%            |
+|max                                     |0.212521001|0.3130105    |147.28%      |0.2039755      |0.2956105      |144.92%            |
+|LeakyReLU                               |2.813424013|4.1121625    |146.16%      |2.719118       |5.613753       |206.45%            |
+|mean                                    |0.242281501|0.344385     |142.14%      |0.209396       |0.313411       |149.67%            |
+|Deconvolution                           |7.43279251 |10.4240845   |140.24%      |2.9548925      |5.812926       |196.72%            |
+|abs                                     |0.273286481|0.38319      |140.22%      |0.3711615      |0.338064       |91.08%             |
+|arcsinh                                 |0.155792513|0.2090985    |134.22%      |0.113365       |0.1702855      |150.21%            |
+|sample_gamma                            |0.137634983|0.1842455    |133.87%      |0.1792825      |0.172175       |96.04%             |
+|sort                                    |0.864107016|1.1560165    |133.78%      |0.8239285      |1.1454645      |139.02%            |
+|argsort                                 |0.847259507|1.1320885    |133.62%      |0.842302       |1.1179105      |132.72%            |
+|cosh                                    |0.129947497|0.1727415    |132.93%      |0.1192565      |0.1217325      |102.08%            |
+|random_randint                          |0.822044531|1.085645     |132.07%      |0.6036805      |1.0953995      |181.45%            |
+|arctanh                                 |0.119817996|0.1576315    |131.56%      |0.115616       |0.111907       |96.79%             |
+|arccos                                  |0.185662502|0.2423095    |130.51%      |0.238534       |0.2351415      |98.58%             |
+|mean                                    |1.758513477|2.2908485    |130.27%      |1.5868465      |2.530801       |159.49%            |
+|erfinv                                  |0.142498524|0.184796     |129.68%      |0.1529025      |0.1538225      |100.60%            |
+|degrees                                 |0.12517249 |0.1576175    |125.92%      |0.1166425      |0.1199775      |102.86%            |
+|sample_exponential                      |0.07651851 |0.0960485    |125.52%      |0.0885775      |0.095597       |107.92%            |
+|arctan                                  |0.120863522|0.1496115    |123.79%      |0.1161245      |0.17206        |148.17%            |
+|prod                                    |1.147695002|1.408007     |122.68%      |1.0491025      |1.4065515      |134.07%            |
+|fix                                     |0.073436997|0.089991     |122.54%      |0.0390455      |0.099307       |254.34%            |
+|exp                                     |0.047701993|0.058272     |122.16%      |0.0397295      |0.0506725      |127.54%            |

From 5fb29167a1a66480864486bf59c6b4e980ce7daa Mon Sep 17 00:00:00 2001
From: Zhennan Qin <zhennan.qin@intel.com>
Date: Fri, 29 Nov 2019 13:24:06 +0800
Subject: [PATCH 642/813] [MKLDNN] Use MKLDNNRun (#16772)

* Use MKLDNNRun

* Fix lint

* Run CI
---
 src/ndarray/ndarray.cc                        |  2 +-
 src/operator/leaky_relu.cc                    |  4 +-
 src/operator/nn/activation.cc                 |  7 +--
 src/operator/nn/batch_norm.cc                 | 31 +----------
 src/operator/nn/concat.cc                     |  4 +-
 src/operator/nn/fully_connected.cc            |  4 +-
 src/operator/nn/lrn.cc                        | 10 +---
 src/operator/nn/mkldnn/mkldnn_act.cc          | 52 +++++++------------
 src/operator/nn/mkldnn/mkldnn_base-inl.h      | 15 +++++-
 src/operator/nn/mkldnn/mkldnn_base.cc         | 19 ++++++-
 .../nn/mkldnn/mkldnn_batch_norm-inl.h         | 50 ++++++++++--------
 src/operator/nn/mkldnn/mkldnn_copy.cc         | 13 ++---
 .../nn/mkldnn/mkldnn_fully_connected.cc       |  6 ---
 src/operator/nn/mkldnn/mkldnn_lrn-inl.h       | 26 +++++-----
 src/operator/nn/mkldnn/mkldnn_ops-inl.h       | 20 +++----
 src/operator/nn/mkldnn/mkldnn_slice-inl.h     |  2 +-
 src/operator/nn/mkldnn/mkldnn_slice.cc        |  3 +-
 src/operator/nn/mkldnn/mkldnn_softmax.cc      |  8 +--
 src/operator/nn/mkldnn/mkldnn_sum.cc          | 25 ++++-----
 src/operator/nn/softmax.cc                    |  2 +-
 .../mkldnn/mkldnn_quantized_flatten.cc        |  2 +-
 src/operator/softmax_output.cc                |  2 +-
 src/operator/subgraph/mkldnn/mkldnn_conv.cc   |  4 +-
 .../tensor/elemwise_binary_op_basic.cc        |  6 +--
 src/operator/tensor/elemwise_sum.cc           |  2 +-
 .../tensor/elemwise_unary_op_basic.cc         |  8 ++-
 src/operator/tensor/matrix_op.cc              |  8 +--
 27 files changed, 150 insertions(+), 185 deletions(-)

diff --git a/src/ndarray/ndarray.cc b/src/ndarray/ndarray.cc
index 9375bed5a79b..ce0f74437ea7 100644
--- a/src/ndarray/ndarray.cc
+++ b/src/ndarray/ndarray.cc
@@ -684,7 +684,7 @@ void NDArray::CopyFrom(const mkldnn::memory &mem) {
     ptr_->Reorder2Default();
 
   const mkldnn::memory *this_mem = GetMKLDNNData();
-  MKLDNNCopy(mem, this_mem);
+  MKLDNNMemoryCopy(mem, this_mem);
 }
 
 mkldnn::memory *NDArray::CreateMKLDNNData(const mkldnn::memory::desc &desc) {
diff --git a/src/operator/leaky_relu.cc b/src/operator/leaky_relu.cc
index 4d1c5ca10a30..49ba95d306f4 100644
--- a/src/operator/leaky_relu.cc
+++ b/src/operator/leaky_relu.cc
@@ -95,7 +95,7 @@ static void LeakyReLUComputeExCPU(const nnvm::NodeAttrs& attrs,
   CHECK_EQ(inputs.size(), expected);
   if (SupportMKLDNNLeakyRelu(param, inputs[0])) {
     MKLDNN_OPCHECK_INIT(false, outputs.size(), inputs, outputs);
-    MKLDNNLeakyReluForward(attrs, ctx, inputs[0], req[0], outputs[0]);
+    MKLDNNRun(MKLDNNLeakyReluForward, attrs, ctx, inputs[0], req[0], outputs[0]);
     MKLDNN_OPCHECK_RUN(LeakyReLUCompute<cpu>, attrs, ctx, inputs, req, outputs);
     return;
   }
@@ -111,7 +111,7 @@ void LeakyReLUGradComputeExCPU(const nnvm::NodeAttrs& attrs,
   if (SupportMKLDNNLeakyRelu(param, inputs[0])) {
     std::vector<NDArray> in_data{inputs[0], inputs[1]};
     MKLDNN_OPCHECK_INIT(true, outputs.size(), inputs, outputs);
-    MKLDNNLeakyReluBackward(attrs, ctx, in_data, req[0], outputs[0]);
+    MKLDNNRun(MKLDNNLeakyReluBackward, attrs, ctx, in_data, req, outputs);
     MKLDNN_OPCHECK_RUN(LeakyReLUGradCompute<cpu>, attrs, ctx, inputs, req, outputs);
     return;
   }
diff --git a/src/operator/nn/activation.cc b/src/operator/nn/activation.cc
index 5abb6670c9b0..ce5fb3e45322 100644
--- a/src/operator/nn/activation.cc
+++ b/src/operator/nn/activation.cc
@@ -102,7 +102,7 @@ static void ActivationComputeExCPU(const nnvm::NodeAttrs& attrs,
   CHECK_EQ(outputs.size(), 1U);
   if (SupportMKLDNNAct(param, inputs[0])) {
     MKLDNN_OPCHECK_INIT(false, outputs.size(), inputs, outputs);
-    MKLDNNActivationForward(attrs, ctx, inputs[0], req[0], outputs[0]);
+    MKLDNNRun(MKLDNNActivationForward, attrs, ctx, inputs[0], req[0], outputs[0]);
     MKLDNN_OPCHECK_RUN(ActivationCompute<cpu>, attrs, ctx, inputs, req, outputs);
     return;
   }
@@ -118,10 +118,7 @@ void ActivationGradComputeExCPU(const nnvm::NodeAttrs& attrs,
   CHECK_EQ(inputs.size(), activation::GradNumInputs(param.act_type));
   if (SupportMKLDNNAct(param, inputs[0])) {
     MKLDNN_OPCHECK_INIT(true, outputs.size(), inputs, outputs);
-    // XXX: for y = relu(x), y is passed as "in_data" to Backward()
-    const bool relu = param.act_type == activation::kReLU;
-    MKLDNNActivationBackward(attrs, ctx, inputs.at(0), relu ? inputs.at(1) : inputs.at(2), req[0],
-                             outputs[0]);
+    MKLDNNRun(MKLDNNActivationBackward, attrs, ctx, inputs, req, outputs);
     MKLDNN_OPCHECK_RUN(ActivationGradCompute<cpu>, attrs, ctx, inputs, req, outputs);
     return;
   }
diff --git a/src/operator/nn/batch_norm.cc b/src/operator/nn/batch_norm.cc
index 3214e3b9b9ac..04e45d4acfed 100644
--- a/src/operator/nn/batch_norm.cc
+++ b/src/operator/nn/batch_norm.cc
@@ -394,17 +394,11 @@ void BatchNormComputeExCPU(const nnvm::NodeAttrs &attrs,
                            const std::vector<NDArray> &outputs) {
   CHECK_EQ(inputs.size(), 5U);
   const BatchNormParam &param = nnvm::get<BatchNormParam>(attrs.parsed);
-
   if (SupportMKLDNNBN(inputs[0], param)) {
-    std::vector<NDArray> in_data(inputs.begin(), inputs.begin() + batchnorm::kInMovingMean);
-    std::vector<NDArray> aux_states(inputs.begin() + batchnorm::kInMovingMean, inputs.end());
-
-    if (inputs[0].dtype() == mshadow::kFloat32) {
       MKLDNN_OPCHECK_INIT(false, outputs.size(), inputs, outputs);
-      MKLDNNBatchNormForward<float>(ctx, param, in_data, req, outputs, aux_states);
+      MKLDNNRun(MKLDNNBatchNormForward<float>, attrs, ctx, inputs, req, outputs);
       MKLDNN_OPCHECK_RUN(BatchNormCompute<cpu>, attrs, ctx, inputs, req, outputs);
       return;
-    }
   }
   FallBackCompute(BatchNormCompute<cpu>, attrs, ctx, inputs, req, outputs);
 }
@@ -414,33 +408,12 @@ void BatchNormGradComputeExCPU(const nnvm::NodeAttrs &attrs,
                                const std::vector<NDArray> &inputs,
                                const std::vector<OpReqType> &req,
                                const std::vector<NDArray> &outputs) {
-  CHECK_EQ(inputs.size(), 8U);
   const BatchNormParam &param = nnvm::get<BatchNormParam>(attrs.parsed);
-
-  mxnet::TShape shape = inputs[0].shape();
-
   if (SupportMKLDNNBN(inputs[0], param)) {
-    std::vector<NDArray> out_grad(1);
-    std::vector<NDArray> out_data(3);
-    std::vector<NDArray> in_data(3);
-    std::vector<NDArray> aux_states(2);
-    out_grad[0] = inputs[0];
-    out_data[batchnorm::kMean] = inputs[1];
-    out_data[batchnorm::kVar] = inputs[2];
-    in_data[batchnorm::kData] = inputs[3];
-    in_data[batchnorm::kGamma] = inputs[4];
-    in_data[batchnorm::kBeta] = inputs[5];
-    aux_states[batchnorm::kMovingMean] = inputs[6];
-    aux_states[batchnorm::kMovingVar] = inputs[7];
-    const std::vector<NDArray> &in_grad = outputs;
-
-    if (inputs[0].dtype() == mshadow::kFloat32) {
       MKLDNN_OPCHECK_INIT(true, outputs.size(), inputs, outputs);
-      MKLDNNBatchNormBackward<float>(ctx, param, out_grad, in_data,
-                                     out_data, req, in_grad, aux_states);
+      MKLDNNRun(MKLDNNBatchNormBackward<float>, attrs, ctx, inputs, req, outputs);
       MKLDNN_OPCHECK_RUN(BatchNormGradCompute<cpu>, attrs, ctx, inputs, req, outputs);
       return;
-    }
   }
   FallBackCompute(BatchNormGradCompute<cpu>, attrs, ctx, inputs, req, outputs);
 }
diff --git a/src/operator/nn/concat.cc b/src/operator/nn/concat.cc
index 4d90810915a2..1eeef7db5cb5 100644
--- a/src/operator/nn/concat.cc
+++ b/src/operator/nn/concat.cc
@@ -270,7 +270,7 @@ static void ConcatComputeExCPU(const nnvm::NodeAttrs& attrs,
 #if MXNET_USE_MKLDNN == 1
   } else if (SupportMKLDNNConcat(inputs)) {
     MKLDNN_OPCHECK_INIT(false, outputs.size(), inputs, outputs);
-    MKLDNNConcatForward(attrs, op_ctx, inputs, req, outputs);
+    MKLDNNRun(MKLDNNConcatForward, attrs, op_ctx, inputs, req, outputs);
     MKLDNN_OPCHECK_RUN(ConcatCompute<cpu>, attrs, op_ctx, inputs, req, outputs);
   } else if (common::ContainsOnlyStorage(inputs, kDefaultStorage)) {
     FallBackCompute(ConcatCompute<cpu>, attrs, op_ctx, inputs, req, outputs);
@@ -288,7 +288,7 @@ static void ConcatGradComputeExCPU(const nnvm::NodeAttrs& attrs,
                                    const std::vector<NDArray>& outputs) {
   if (SupportMKLDNNConcat(inputs)) {
     MKLDNN_OPCHECK_INIT(true, outputs.size(), inputs, outputs);
-    MKLDNNConcatBackward(attrs, ctx, inputs, req, outputs);
+    MKLDNNRun(MKLDNNConcatBackward, attrs, ctx, inputs, req, outputs);
     MKLDNN_OPCHECK_RUN(ConcatGradCompute<cpu>, attrs, ctx, inputs, req, outputs);
     return;
   }
diff --git a/src/operator/nn/fully_connected.cc b/src/operator/nn/fully_connected.cc
index 5d722581257f..1632486e0a82 100644
--- a/src/operator/nn/fully_connected.cc
+++ b/src/operator/nn/fully_connected.cc
@@ -102,7 +102,7 @@ void FullyConnectedComputeExCPU(const nnvm::NodeAttrs& attrs,
       common::ContainsOnlyStorage(outputs, kDefaultStorage)) {
     if (SupportMKLDNNFC(inputs[0])) {
       MKLDNN_OPCHECK_INIT(false, outputs.size(), inputs, outputs);
-      MKLDNNFCForward(attrs, ctx, inputs, req, outputs);
+      MKLDNNRun(MKLDNNFCForward, attrs, ctx, inputs, req, outputs);
       MKLDNN_OPCHECK_RUN(FullyConnectedCompute<cpu>, attrs, ctx, inputs, req,
                          outputs);
     } else {
@@ -152,7 +152,7 @@ void FullyConnectedGradComputeExCPU(const nnvm::NodeAttrs& attrs,
   bool mkldnn_fc_backward_enable = false;
   if (mkldnn_fc_backward_enable && SupportMKLDNNFC(inputs[0])) {
     MKLDNN_OPCHECK_INIT(true, outputs.size(), inputs, outputs);
-    MKLDNNFCBackward(attrs, ctx, inputs, req, outputs);
+    MKLDNNRun(MKLDNNFCBackward, attrs, ctx, inputs, req, outputs);
     MKLDNN_OPCHECK_RUN(FullyConnectedGradCompute<cpu>, attrs, ctx, inputs, req,
                        outputs);
     return;
diff --git a/src/operator/nn/lrn.cc b/src/operator/nn/lrn.cc
index 3a3ca59f2be1..41337352df63 100644
--- a/src/operator/nn/lrn.cc
+++ b/src/operator/nn/lrn.cc
@@ -110,11 +110,10 @@ void LRNComputeExCPU(const nnvm::NodeAttrs &attrs,
                      const std::vector<NDArray> &inputs,
                      const std::vector<OpReqType> &req,
                      const std::vector<NDArray> &outputs) {
-  const LRNParam &param = nnvm::get<LRNParam>(attrs.parsed);
   if (SupportMKLDNN(inputs[0])) {
     // We only need to test one output array.
     MKLDNN_OPCHECK_INIT(false, 1, inputs, outputs);
-    MKLDNNLRNForward(ctx, param, inputs[0], req[0], outputs[0]);
+    MKLDNNRun(MKLDNNLRNForward, attrs, ctx, inputs[0], req[0], outputs[0]);
     MKLDNN_OPCHECK_RUN(LRNCompute<cpu>, attrs, ctx, inputs, req, outputs);
     // Copy outputs[1] from opcheck reference as backward check needs it.
     MKLDNN_OPCHECK_COPY_RESULT(outputs, std::vector<size_t>{1});
@@ -128,14 +127,9 @@ void LRNGradComputeExCPU(const nnvm::NodeAttrs &attrs,
                          const std::vector<NDArray> &inputs,
                          const std::vector<OpReqType> &req,
                          const std::vector<NDArray> &outputs) {
-  const LRNParam &param = nnvm::get<LRNParam>(attrs.parsed);
-  const NDArray &out_grad = inputs[0];
-  const NDArray &in_data = inputs[1];
-  const NDArray &in_grad = outputs[0];
-
   if (SupportMKLDNN(inputs[0])) {
     MKLDNN_OPCHECK_INIT(true, outputs.size(), inputs, outputs);
-    MKLDNNLRNBackward(ctx, param, out_grad, in_data, req[0], in_grad);
+    MKLDNNRun(MKLDNNLRNBackward, attrs, ctx, inputs, req, outputs);
     MKLDNN_OPCHECK_RUN(LRNGradCompute<cpu>, attrs, ctx, inputs, req, outputs);
     return;
   }
diff --git a/src/operator/nn/mkldnn/mkldnn_act.cc b/src/operator/nn/mkldnn/mkldnn_act.cc
index 6ea7ac4a30b3..7cf94790ed0d 100644
--- a/src/operator/nn/mkldnn/mkldnn_act.cc
+++ b/src/operator/nn/mkldnn/mkldnn_act.cc
@@ -151,13 +151,8 @@ void MKLDNNActivationForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
   const ActivationParam& param = nnvm::get<ActivationParam>(attrs.parsed);
   MKLDNNActParam param_;
   param_.alg = GetMKLDNNActAlgo(param);
-
-  NDArray in_buffer = in_data;
+  const NDArray& in_buffer = in_data;
   MKLDNNStream *stream = MKLDNNStream::Get();
-
-  if (in_data.IsView() && in_data.IsMKLDNNData())
-    in_buffer = in_data.Reorder2Default();
-
   auto input_mem = in_buffer.GetMKLDNNData();
   MKLDNNActForward &fwd = GetActForward(param_, ctx, in_buffer, *input_mem);
   auto out_mem_t = CreateMKLDNNMem(out_data, fwd.fwd_pd.dst_desc(), req, &in_buffer);
@@ -235,22 +230,18 @@ static inline MKLDNNActBackward &GetActBackward(const MKLDNNActParam &param,
 
 // For backward relu activation, it's okay to pass "out_data" as "in_data" to this
 // function, since the computation only involes non-zeros.
-void MKLDNNActivationBackward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
-                              const NDArray &out_grad, const NDArray &in_data,
-                              const OpReqType &req, const NDArray &in_grad) {
-  if (req == kNullOp) {
+void MKLDNNActivationBackward(const nnvm::NodeAttrs &attrs, const OpContext &ctx,
+                              const std::vector<NDArray> &inputs, const std::vector<OpReqType> &req,
+                              const std::vector<NDArray> &outputs) {
+  if (req[0] == kNullOp) {
     return;
   }
-
-  NDArray out_buffer = out_grad;
-  if (out_grad.IsView() && out_grad.IsMKLDNNData())
-    out_buffer = out_grad.Reorder2Default();
-
-  NDArray in_buffer = in_data;
-  if (in_data.IsView() && in_data.IsMKLDNNData())
-    in_buffer = in_data.Reorder2Default();
-
   const ActivationParam& param = nnvm::get<ActivationParam>(attrs.parsed);
+  // XXX: for y = relu(x), y is passed as "in_data" to Backward()
+  const bool relu = param.act_type == activation::kReLU;
+  const NDArray &out_buffer = inputs[0];
+  const NDArray &in_buffer = relu ? inputs[1] : inputs[2];
+  const NDArray &in_grad = outputs[0];
   MKLDNNActParam param_;
   param_.alg = GetMKLDNNActAlgo(param);
   TmpMemMgr::Get()->Init(ctx.requested[activation::kTempSpace]);
@@ -264,7 +255,7 @@ void MKLDNNActivationBackward(const nnvm::NodeAttrs& attrs, const OpContext &ctx
       GetActBackward(param_, ctx, in_buffer, out_buffer, *input_mem);
   MKLDNNStream *stream = MKLDNNStream::Get();
   mkldnn_output_t diff_src_memory =
-      CreateMKLDNNMem(in_grad, bwd.bwd_pd.diff_src_desc(), req);
+      CreateMKLDNNMem(in_grad, bwd.bwd_pd.diff_src_desc(), req[0]);
   mkldnn_args_map_t args = {
     { MKLDNN_ARG_SRC, *input_mem },
     { MKLDNN_ARG_DIFF_DST, *diff_dst_memory },
@@ -278,19 +269,16 @@ void MKLDNNActivationBackward(const nnvm::NodeAttrs& attrs, const OpContext &ctx
 void MKLDNNLeakyReluBackward(const nnvm::NodeAttrs& attrs,
                              const OpContext &ctx,
                              const std::vector<NDArray>& inputs,
-                             const OpReqType &req,
-                             const NDArray &output) {
-  if (req == kNullOp) {
+                             const std::vector<OpReqType> &req,
+                             const std::vector<NDArray> &outputs) {
+  if (req[0] == kNullOp) {
     return;
   }
-  CHECK_GE(inputs.size(), 2U);
-  NDArray out_buffer = inputs[0];
-  if (inputs[0].IsView() && inputs[0].IsMKLDNNData())
-    out_buffer = inputs[0].Reorder2Default();
-
-  NDArray in_buffer = inputs[1];
-  if (inputs[1].IsView() && inputs[1].IsMKLDNNData())
-    in_buffer = inputs[1].Reorder2Default();
+  CHECK_EQ(inputs.size(), 2U);
+  CHECK_EQ(outputs.size(), 1U);
+  const NDArray& out_buffer = inputs[0];
+  const NDArray& in_buffer = inputs[1];
+  const NDArray &output = outputs[0];
 
   const LeakyReLUParam& param = nnvm::get<LeakyReLUParam>(attrs.parsed);
   MKLDNNActParam param_;
@@ -308,7 +296,7 @@ void MKLDNNLeakyReluBackward(const nnvm::NodeAttrs& attrs,
       GetActBackward(param_, ctx, in_buffer, out_buffer, *input_mem);
   MKLDNNStream *stream = MKLDNNStream::Get();
   mkldnn_output_t diff_src_memory =
-      CreateMKLDNNMem(output, bwd.bwd_pd.diff_src_desc(), req);
+      CreateMKLDNNMem(output, bwd.bwd_pd.diff_src_desc(), req[0]);
   mkldnn_args_map_t args = {
     { MKLDNN_ARG_SRC, *input_mem },
     { MKLDNN_ARG_DIFF_DST, *diff_dst_memory },
diff --git a/src/operator/nn/mkldnn/mkldnn_base-inl.h b/src/operator/nn/mkldnn/mkldnn_base-inl.h
index 9bfc20cd7bb5..da9b8166a849 100644
--- a/src/operator/nn/mkldnn/mkldnn_base-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_base-inl.h
@@ -446,7 +446,7 @@ enum OutDataOp {
 };
 
 typedef std::pair<OutDataOp, mkldnn::memory *> mkldnn_output_t;
-void MKLDNNCopy(const mkldnn::memory &mem, const mkldnn::memory* this_mem);
+void MKLDNNMemoryCopy(const mkldnn::memory &mem, const mkldnn::memory* this_mem);
 
 /*
  * Here we want to get MKLDNN memory whose desc is exactly the same as
@@ -688,6 +688,19 @@ void MKLDNNRun(mxnet::FComputeEx fn,
                const std::vector<mxnet::OpReqType> &req,
                const std::vector<mxnet::NDArray> &outputs_);
 
+using FComputeExUnary = std::function<void (const nnvm::NodeAttrs& attrs,
+                                       const OpContext& ctx,
+                                       const NDArray& input,
+                                       const OpReqType& req,
+                                       const NDArray& output)>;
+
+void MKLDNNRun(FComputeExUnary fn,
+               const nnvm::NodeAttrs &attrs,
+               const mxnet::OpContext &ctx,
+               const mxnet::NDArray &inputs_,
+               const mxnet::OpReqType &req,
+               const mxnet::NDArray &outputs_);
+
 }  // namespace mxnet
 #endif
 #endif  // MXNET_OPERATOR_NN_MKLDNN_MKLDNN_BASE_INL_H_
diff --git a/src/operator/nn/mkldnn/mkldnn_base.cc b/src/operator/nn/mkldnn/mkldnn_base.cc
index 1b147c69ba62..8ee9e48b6f11 100644
--- a/src/operator/nn/mkldnn/mkldnn_base.cc
+++ b/src/operator/nn/mkldnn/mkldnn_base.cc
@@ -85,7 +85,7 @@ mkldnn::memory *TmpMemMgr::Alloc(const mkldnn::memory::desc &md) {
   }
 }
 
-void MKLDNNCopy(const mkldnn::memory &mem, const mkldnn::memory* this_mem) {
+void MKLDNNMemoryCopy(const mkldnn::memory &mem, const mkldnn::memory* this_mem) {
   MKLDNNStream *stream = MKLDNNStream::Get();
   mkldnn::memory::desc from_desc = mem.get_desc();
   mkldnn::memory::desc this_desc = this_mem->get_desc();
@@ -227,7 +227,7 @@ void CommitOutput(const NDArray &arr, const mkldnn_output_t &res) {
     auto mem = arr.GetMKLDNNData(res.second->get_desc());
     if (mem == nullptr) {
       auto tmp_memory = TmpMemMgr::Get()->Alloc(target_pd);
-      MKLDNNCopy(*res_memory, tmp_memory);
+      MKLDNNMemoryCopy(*res_memory, tmp_memory);
       res_memory = tmp_memory;
       mem = arr.GetMKLDNNData();
     }
@@ -606,6 +606,21 @@ void MKLDNNRun(mxnet::FComputeEx fn,
   }
 }
 
+void MKLDNNRun(FComputeExUnary fn,
+               const nnvm::NodeAttrs &attrs,
+               const mxnet::OpContext &ctx,
+               const mxnet::NDArray &input,
+               const mxnet::OpReqType &req,
+               const mxnet::NDArray &output) {
+  auto mkldnn_input = input;
+  if (input.IsView() && input.IsMKLDNNData()) {
+    mkldnn_input = input.Reorder2Default();
+    fn(attrs, ctx, mkldnn_input, req, output);
+  } else {
+    fn(attrs, ctx, input, req, output);
+  }
+}
+
 }  // namespace mxnet
 
 #endif
diff --git a/src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h b/src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h
index 26637c7c0b65..23e327389f46 100644
--- a/src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h
@@ -147,11 +147,12 @@ static MKLDNNBNForward &GetBNForward(const BatchNormParam& param,
 }
 
 template <typename DType>
-void MKLDNNBatchNormForward(const OpContext &ctx, const BatchNormParam &param,
-                            const std::vector<NDArray>   &in_data,
-                            const std::vector<OpReqType> &req,
-                            const std::vector<NDArray>   &out_data,
-                            const std::vector<NDArray>   &aux_states) {
+void MKLDNNBatchNormForward(const nnvm::NodeAttrs &attrs, const OpContext &ctx,
+                            const std::vector<NDArray> &inputs, const std::vector<OpReqType> &req,
+                            const std::vector<NDArray> &outputs) {
+  const BatchNormParam &param = nnvm::get<BatchNormParam>(attrs.parsed);
+  const std::vector<NDArray> in_data(inputs.begin(), inputs.begin() + batchnorm::kInMovingMean);
+  const std::vector<NDArray> aux_states(inputs.begin() + batchnorm::kInMovingMean, inputs.end());
   TmpMemMgr::Get()->Init(ctx.requested[batchnorm::kTempSpace]);
   mkldnn::normalization_flags flags = _GetFlags(in_data,
                                                 aux_states,
@@ -159,7 +160,7 @@ void MKLDNNBatchNormForward(const OpContext &ctx, const BatchNormParam &param,
                                                 ctx.is_train && !param.use_global_stats);
   const NDArray &data = in_data[batchnorm::kData];
   auto &fwd = GetBNForward<DType>(param, ctx, data, flags);
-  const NDArray &out = out_data[batchnorm::kOut];
+  const NDArray &out = outputs[batchnorm::kOut];
 
   // for output memory
   auto out_mem = const_cast<NDArray &>(out).CreateMKLDNNData(fwd.GetPd().dst_desc());
@@ -201,8 +202,8 @@ void MKLDNNBatchNormForward(const OpContext &ctx, const BatchNormParam &param,
     net_args[MKLDNN_ARG_DST] = *out_mem;
 
     if (!ctx.is_train || param.use_global_stats) {
-      DType* omean    = out_data[batchnorm::kMean].data().dptr<DType>();
-      DType* ovar     = out_data[batchnorm::kVar].data().dptr<DType>();
+      DType* omean    = outputs[batchnorm::kMean].data().dptr<DType>();
+      DType* ovar     = outputs[batchnorm::kVar].data().dptr<DType>();
       DType* inmean   = aux_states[batchnorm::kMovingMean].data().dptr<DType>();
       DType* invar    = aux_states[batchnorm::kMovingVar].data().dptr<DType>();
       // to align with origin implmentation: batch_norm.cc: L164
@@ -215,8 +216,8 @@ void MKLDNNBatchNormForward(const OpContext &ctx, const BatchNormParam &param,
       MKLDNNStream::Get()->RegisterPrimArgs(fwd.GetFwd(), net_args);
       MKLDNNStream::Get()->Submit();
     } else {  // training
-      const NDArray &outMean  = out_data[batchnorm::kMean];
-      const NDArray &outVar   = out_data[batchnorm::kVar];
+      const NDArray &outMean  = outputs[batchnorm::kMean];
+      const NDArray &outVar   = outputs[batchnorm::kVar];
       net_args[MKLDNN_ARG_MEAN] = *(outMean.GetMKLDNNData());
       net_args[MKLDNN_ARG_VARIANCE] = *(outVar.GetMKLDNNData());
       MKLDNNStream::Get()->RegisterPrimArgs(fwd.GetFwd(), net_args);
@@ -278,18 +279,25 @@ static MKLDNNBNBackward &GetBNBackward(
 }
 
 template <typename DType>
-void MKLDNNBatchNormBackward(const OpContext &ctx, const BatchNormParam &param,
-                             const std::vector<NDArray>    &out_grad,
-                             const std::vector<NDArray>    &in_data,
-                             const std::vector<NDArray>    &out_data,
-                             const std::vector<OpReqType>  &req,
-                             const std::vector<NDArray>    &in_grad,
-                             const std::vector<NDArray>    &aux_states) {
+void MKLDNNBatchNormBackward(const nnvm::NodeAttrs &attrs, const OpContext &ctx,
+                             const std::vector<NDArray> &inputs, const std::vector<OpReqType> &req,
+                             const std::vector<NDArray> &outputs) {
+  CHECK_EQ(inputs.size(), 8U);
+  const BatchNormParam &param = nnvm::get<BatchNormParam>(attrs.parsed);
+  std::vector<NDArray> out_grad(1);
+  std::vector<NDArray> out_data(3);
+  std::vector<NDArray> in_data(3);
+  std::vector<NDArray> aux_states(2);
+  out_grad[0] = inputs[0];
+  out_data[batchnorm::kMean] = inputs[1];
+  out_data[batchnorm::kVar] = inputs[2];
+  in_data[batchnorm::kData] = inputs[3];
+  in_data[batchnorm::kGamma] = inputs[4];
+  in_data[batchnorm::kBeta] = inputs[5];
+  aux_states[batchnorm::kMovingMean] = inputs[6];
+  aux_states[batchnorm::kMovingVar] = inputs[7];
+  const std::vector<NDArray> &in_grad = outputs;
   TmpMemMgr::Get()->Init(ctx.requested[batchnorm::kTempSpace]);
-  CHECK_EQ(out_grad.size(), 1U);
-  CHECK_EQ(in_data.size(), 3U);
-  CHECK_EQ(out_data.size(), 3U);
-  CHECK_EQ(in_grad.size(), 3U);
   mkldnn::normalization_flags flags = _GetFlags(in_data,
                                                 aux_states,
                                                 param,
diff --git a/src/operator/nn/mkldnn/mkldnn_copy.cc b/src/operator/nn/mkldnn/mkldnn_copy.cc
index cf8daa4b45df..a67847f9c882 100644
--- a/src/operator/nn/mkldnn/mkldnn_copy.cc
+++ b/src/operator/nn/mkldnn/mkldnn_copy.cc
@@ -33,22 +33,17 @@ namespace op {
 void MKLDNNCopy(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
                 const NDArray &in_data, const OpReqType &req,
                 const NDArray &out_data) {
+  if (req == kNullOp || req == kWriteInplace) return;
   TmpMemMgr::Get()->Init(ctx.requested[0]);
-
-  // If the input data is a view of an MKLDNN array, we should create a new
-  // NDArray with reordered data.
-  NDArray data = in_data;
-  if (data.IsMKLDNNData() && data.IsView())
-    data = data.Reorder2Default();
-  auto in_mem = data.GetMKLDNNData();
+  auto in_mem = in_data.GetMKLDNNData();
   if (req == kAddTo) {
     TmpMemMgr::Get()->Init(ctx.requested[0]);
     // We should try and force the input memory has the same format
     // as the input output. If not, we'll have to reorder memory.
     auto out_mem = out_data.GetMKLDNNData();
-    in_mem = data.GetMKLDNNData(out_mem ->get_desc());
+    in_mem = in_data.GetMKLDNNData(out_mem ->get_desc());
     if (in_mem == nullptr)
-      in_mem = data.GetMKLDNNDataReorder(out_mem->get_desc());
+      in_mem = in_data.GetMKLDNNDataReorder(out_mem->get_desc());
     MKLDNNSum(*out_mem, *in_mem, *out_mem);
   } else {
     const_cast<NDArray &>(out_data).CopyFrom(*in_mem);
diff --git a/src/operator/nn/mkldnn/mkldnn_fully_connected.cc b/src/operator/nn/mkldnn/mkldnn_fully_connected.cc
index 1403cd114201..8c401a879f15 100644
--- a/src/operator/nn/mkldnn/mkldnn_fully_connected.cc
+++ b/src/operator/nn/mkldnn/mkldnn_fully_connected.cc
@@ -166,12 +166,6 @@ void MKLDNNFCFlattenData(const FullyConnectedParam &param,
                          mkldnn::memory::desc *out_md) {
   const mxnet::TShape ishape = in_data->shape();
   const mxnet::TShape oshape = out_data.shape();
-
-  // If the input data is a view of an MKLDNN array, we should create a new
-  // NDArray with reordered data.
-  if (in_data->IsMKLDNNData() && in_data->IsView())
-    *in_data = in_data->Reorder2Default();
-
   if (ishape.ndim() != 2) {
     if (!param.flatten) {
       *in_data = in_data->MKLDNNDataReshape(Shape2(ishape.ProdShape(0, ishape.ndim()-1),
diff --git a/src/operator/nn/mkldnn/mkldnn_lrn-inl.h b/src/operator/nn/mkldnn/mkldnn_lrn-inl.h
index ca7095fd3f02..6f7a1d917734 100644
--- a/src/operator/nn/mkldnn/mkldnn_lrn-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_lrn-inl.h
@@ -27,6 +27,7 @@
 
 #if MXNET_USE_MKLDNN == 1
 #include <utility>
+#include <vector>
 #include <mkldnn.hpp>
 #include "../lrn-inl.h"
 #include "./mkldnn_base-inl.h"
@@ -171,9 +172,10 @@ static MKLDNNLRNFwd &GetLRNFwd(const LRNParam& param,
   return it->second;
 }
 
-void MKLDNNLRNForward(const OpContext &ctx, const LRNParam &param,
+void MKLDNNLRNForward(const nnvm::NodeAttrs &attrs, const OpContext &ctx,
                       const NDArray &in_data, const OpReqType req,
                       const NDArray &out_data) {
+  const LRNParam &param = nnvm::get<LRNParam>(attrs.parsed);
   auto in_buffer = in_data;
   if (in_buffer.IsView() && in_buffer.IsMKLDNNData())
     in_buffer = in_buffer.Reorder2Default();
@@ -244,22 +246,21 @@ static MKLDNNLRNBwd &GetLRNBwd(const LRNParam &param, const NDArray &in_data,
   return it->second;
 }
 
-void MKLDNNLRNBackward(const OpContext &ctx, const LRNParam &param,
-                       const NDArray &out_grad,
-                       const NDArray &in_data,
-                       const OpReqType req,
-                       const NDArray &in_grad) {
-  if (req == kNullOp) {
+void MKLDNNLRNBackward(const nnvm::NodeAttrs &attrs, const OpContext &ctx,
+                       const std::vector<NDArray> &inputs, const std::vector<OpReqType> &req,
+                       const std::vector<NDArray> &outputs) {
+  if (req[0] == kNullOp) {
     return;
   }
+  const LRNParam &param = nnvm::get<LRNParam>(attrs.parsed);
+  const NDArray &out_grad = inputs[0];
+  const NDArray &in_data = inputs[1];
+  const NDArray &in_grad = outputs[0];
   // TODO(alex): (MXNET-846) figure out why in_grad output incorrect when in_data is nchw8c
-  auto in_buffer = in_data;
-  if (in_buffer.IsMKLDNNData()) {
-    in_buffer = in_data.Reorder2Default();
-  }
+  const auto in_buffer = in_data.Reorder2Default();
   MKLDNNLRNBwd &bwd = GetLRNBwd(param, in_buffer, in_grad, out_grad);
   mkldnn_output_t diff_src_mem =
-      CreateMKLDNNMem(in_grad, bwd.bwd_pd.diff_src_desc(), req);
+      CreateMKLDNNMem(in_grad, bwd.bwd_pd.diff_src_desc(), req[0]);
 
   bwd.Execute(out_grad, in_buffer, in_grad, diff_src_mem);
 }
@@ -267,4 +268,3 @@ void MKLDNNLRNBackward(const OpContext &ctx, const LRNParam &param,
 }  // namespace mxnet
 #endif  // MXNET_USE_MKLDNN == 1
 #endif  // MXNET_OPERATOR_NN_MKLDNN_MKLDNN_LRN_INL_H__
-
diff --git a/src/operator/nn/mkldnn/mkldnn_ops-inl.h b/src/operator/nn/mkldnn/mkldnn_ops-inl.h
index 71f3eafa8ee9..1ce36303689d 100644
--- a/src/operator/nn/mkldnn/mkldnn_ops-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_ops-inl.h
@@ -76,16 +76,18 @@ void MKLDNNDeconvolutionBackward(const nnvm::NodeAttrs& attrs, const OpContext &
 void MKLDNNActivationForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
                              const NDArray &in_data, const OpReqType &req,
                              const NDArray &out_data);
-void MKLDNNActivationBackward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
-                              const NDArray &out_grad, const NDArray &in_data,
-                              const OpReqType &req, const NDArray &in_grad);
+void MKLDNNActivationBackward(const nnvm::NodeAttrs &attrs, const OpContext &ctx,
+                              const std::vector<NDArray> &inputs,
+                              const std::vector<OpReqType> &req,
+                              const std::vector<NDArray> &outputs);
 
 void MKLDNNLeakyReluForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
                             const NDArray &in_data, const OpReqType &req,
                             const NDArray &out_data);
-void MKLDNNLeakyReluBackward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
-                             const std::vector<NDArray>& inputs, const OpReqType &req,
-                             const NDArray &output);
+void MKLDNNLeakyReluBackward(const nnvm::NodeAttrs &attrs, const OpContext &ctx,
+                             const std::vector<NDArray> &inputs,
+                             const std::vector<OpReqType> &req,
+                             const std::vector<NDArray> &outputs);
 
 /* For softmax */
 void MKLDNNSoftmaxForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
@@ -99,9 +101,9 @@ void MKLDNNSoftmaxOutputForward(const nnvm::NodeAttrs& attrs, const OpContext &c
                                 const std::vector<NDArray> &out_data);
 
 /* For sum */
-void MKLDNNSumForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
-                      const std::vector<NDArray> &inputs, const OpReqType &req,
-                      const NDArray &out_data);
+void MKLDNNSumForward(const nnvm::NodeAttrs &attrs, const OpContext &ctx,
+                      const std::vector<NDArray> &inputs, const std::vector<OpReqType> &req,
+                      const std::vector<NDArray> &outputs);
 
 /* For copy */
 void MKLDNNCopy(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
diff --git a/src/operator/nn/mkldnn/mkldnn_slice-inl.h b/src/operator/nn/mkldnn/mkldnn_slice-inl.h
index e6258c8c3f43..0bb432da9f7f 100644
--- a/src/operator/nn/mkldnn/mkldnn_slice-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_slice-inl.h
@@ -57,7 +57,7 @@ typedef ParamOpSign<SliceParam> MKLDNNSliceSignature;
 MKLDNNSliceFwd &GetSliceForward(const SliceParam &param, const bool is_train,
                  const NDArray &in_data, const NDArray &out_data);
 
-void MKLDNNSlice(const SliceParam &param, const OpContext& ctx,
+void MKLDNNSlice(const nnvm::NodeAttrs& attrs, const OpContext& ctx,
                  const NDArray &in, OpReqType req, const NDArray &out);
 
 }  // namespace op
diff --git a/src/operator/nn/mkldnn/mkldnn_slice.cc b/src/operator/nn/mkldnn/mkldnn_slice.cc
index 575554a25c88..26d4f096bef1 100644
--- a/src/operator/nn/mkldnn/mkldnn_slice.cc
+++ b/src/operator/nn/mkldnn/mkldnn_slice.cc
@@ -90,8 +90,9 @@ MKLDNNSliceFwd &GetSliceForward(const SliceParam &param, const bool is_train,
   return it->second;
 }
 
-void MKLDNNSlice(const SliceParam &param, const OpContext& ctx,
+void MKLDNNSlice(const nnvm::NodeAttrs& attrs, const OpContext& ctx,
                  const NDArray &in, OpReqType req, const NDArray &out) {
+  const SliceParam& param = nnvm::get<SliceParam>(attrs.parsed);
   MKLDNNSliceFwd &fwd = GetSliceForward(param, ctx.is_train, in, out);
   auto in_mem = in.GetMKLDNNData();
   auto out_md = out.GetMKLDNNData()->get_desc();
diff --git a/src/operator/nn/mkldnn/mkldnn_softmax.cc b/src/operator/nn/mkldnn/mkldnn_softmax.cc
index 5b43cb0b0864..a3558847fba1 100644
--- a/src/operator/nn/mkldnn/mkldnn_softmax.cc
+++ b/src/operator/nn/mkldnn/mkldnn_softmax.cc
@@ -76,12 +76,7 @@ void MKLDNNSoftmaxForward(const nnvm::NodeAttrs& attrs,
   CHECK_NE(req, kAddTo);
   const SoftmaxParam& param = nnvm::get<SoftmaxParam>(attrs.parsed);
   int axis = CheckAxis(param.axis, in_data.shape().ndim());
-  NDArray data = in_data;
-  if (in_data.IsView() && in_data.IsMKLDNNData()) {
-    data = in_data.Reorder2Default();
-  }
-
-  auto data_mem = data.GetMKLDNNData();
+  auto data_mem = in_data.GetMKLDNNData();
   auto pd = GetSoftmaxFwdPd(ctx.is_train, axis, *data_mem);
   auto out_mem = CreateMKLDNNMem(out_data, pd.dst_desc(), req);
   MKLDNNStream *stream = MKLDNNStream::Get();
@@ -94,4 +89,3 @@ void MKLDNNSoftmaxForward(const nnvm::NodeAttrs& attrs,
 }   // namespace op
 }   // namespace mxnet
 #endif
-
diff --git a/src/operator/nn/mkldnn/mkldnn_sum.cc b/src/operator/nn/mkldnn/mkldnn_sum.cc
index 5027bcbaabb1..747dde69ce13 100644
--- a/src/operator/nn/mkldnn/mkldnn_sum.cc
+++ b/src/operator/nn/mkldnn/mkldnn_sum.cc
@@ -46,8 +46,8 @@ void MKLDNNSum(const mkldnn::memory &arr1,
   if (input_pds[0] != output_pd) {
     auto tmp_memory1 = TmpMemMgr::Get()->Alloc(output_pd);
     auto tmp_memory2 = TmpMemMgr::Get()->Alloc(output_pd);
-    mxnet::MKLDNNCopy(arr1, tmp_memory1);
-    mxnet::MKLDNNCopy(arr2, tmp_memory2);
+    MKLDNNMemoryCopy(arr1, tmp_memory1);
+    MKLDNNMemoryCopy(arr2, tmp_memory2);
     input_pds[0] = tmp_memory1->get_desc();
     input_pds[1] = tmp_memory2->get_desc();
     in_mem1 = tmp_memory1;
@@ -98,37 +98,30 @@ static MKLDNNSumFwd &GetSumForward(
 }
 
 void MKLDNNSumForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
-                      const std::vector<NDArray> &inputs, const OpReqType &req,
-                      const NDArray &out_data) {
+                      const std::vector<NDArray> &inputs, const std::vector<OpReqType> &req,
+                      const std::vector<NDArray> &outputs) {
   TmpMemMgr::Get()->Init(ctx.requested[0]);
   const int num_inputs = inputs.size();
+  const NDArray &out_data = outputs[0];
   std::vector<mkldnn::memory::desc> data_md;
   std::vector<const mkldnn::memory *> data_mem;
   std::vector<float> scales(num_inputs, 1);
-  std::vector<NDArray> in_bufs(num_inputs);
 
   data_md.reserve(num_inputs);
   data_mem.reserve(num_inputs);
 
   for (int i = 0; i < num_inputs; ++i) {
-    const mkldnn::memory *in_mem;
-    if (inputs[i].IsMKLDNNData() && inputs[i].IsView()) {
-      in_bufs[i] = inputs[i].Reorder2Default();
-      in_mem = in_bufs[i].GetMKLDNNData();
-    } else {
-      in_bufs[i] = inputs[i];
-      in_mem = inputs[i].GetMKLDNNData();
-    }
+    const mkldnn::memory *in_mem = inputs[i].GetMKLDNNData();
     mkldnn::memory::desc tmp_md = in_mem->get_desc();
     data_md.push_back(tmp_md);
     data_mem.push_back(in_mem);
   }
 
-  MKLDNNSumFwd &fwd = GetSumForward(scales, in_bufs, data_md);
+  MKLDNNSumFwd &fwd = GetSumForward(scales, inputs, data_md);
   mxnet::mkldnn_output_t out_mem = CreateMKLDNNMem(out_data,
                                                    fwd.fwd_pd.dst_desc(),
-                                                   req,
-                                                   &in_bufs[0]);
+                                                   req[0],
+                                                   &inputs[0]);
   mkldnn_args_map_t net_args;
   net_args.insert({MKLDNN_ARG_DST, *out_mem.second});
   for (int i = 0; i < num_inputs; ++i) {
diff --git a/src/operator/nn/softmax.cc b/src/operator/nn/softmax.cc
index ce19dda23d87..57edab7037d5 100644
--- a/src/operator/nn/softmax.cc
+++ b/src/operator/nn/softmax.cc
@@ -45,7 +45,7 @@ static void SoftmaxComputeExCPU(const nnvm::NodeAttrs& attrs,
   const SoftmaxParam& param = nnvm::get<SoftmaxParam>(attrs.parsed);
   if (SupportMKLDNNSoftmax(param, inputs[0], outputs[0])) {
     MKLDNN_OPCHECK_INIT(false, outputs.size(), inputs, outputs);
-    MKLDNNSoftmaxForward(attrs, ctx, inputs[0], req[0], outputs[0]);
+    MKLDNNRun(MKLDNNSoftmaxForward, attrs, ctx, inputs[0], req[0], outputs[0]);
     auto fn = SoftmaxCompute<cpu, mxnet_op::softmax_fwd>;
     MKLDNN_OPCHECK_RUN(fn, attrs, ctx, inputs, req, outputs);
     return;
diff --git a/src/operator/quantization/mkldnn/mkldnn_quantized_flatten.cc b/src/operator/quantization/mkldnn/mkldnn_quantized_flatten.cc
index c059f9868ea0..11a960e3b9e0 100644
--- a/src/operator/quantization/mkldnn/mkldnn_quantized_flatten.cc
+++ b/src/operator/quantization/mkldnn/mkldnn_quantized_flatten.cc
@@ -42,7 +42,7 @@ static void MKLDNNQuantizedFlattenForward(const nnvm::NodeAttrs& attrs, const Op
                                           const std::vector<NDArray>& inputs,
                                           const std::vector<OpReqType>& req,
                                           const std::vector<NDArray>& outputs) {
-  MKLDNNReshapeForward(attrs, ctx, inputs[0], req[0], outputs[0]);
+  MKLDNNRun(MKLDNNReshapeForward, attrs, ctx, inputs[0], req[0], outputs[0]);
   outputs[1].data().dptr<float>()[0] = inputs[1].data().dptr<float>()[0];
   outputs[2].data().dptr<float>()[0] = inputs[2].data().dptr<float>()[0];
 }
diff --git a/src/operator/softmax_output.cc b/src/operator/softmax_output.cc
index ec2670974f49..0bf6e2a014a6 100644
--- a/src/operator/softmax_output.cc
+++ b/src/operator/softmax_output.cc
@@ -144,7 +144,7 @@ void SoftmaxOutputComputeExCPU(const nnvm::NodeAttrs &attrs,
   const SoftmaxOutputParam &param = nnvm::get<SoftmaxOutputParam>(attrs.parsed);
   if (SupportMKLDNN(inputs[0]) && !ctx.is_train && SupportMKLDNNSoftmaxOutput(param)) {
     MKLDNN_OPCHECK_INIT(false, outputs.size(), inputs, outputs);
-    MKLDNNSoftmaxOutputForward(attrs, ctx, inputs, req, outputs);
+    MKLDNNRun(MKLDNNSoftmaxOutputForward, attrs, ctx, inputs, req, outputs);
     MKLDNN_OPCHECK_RUN(SoftmaxOutputCompute<cpu>, attrs, ctx, inputs, req, outputs);
     return;
   }
diff --git a/src/operator/subgraph/mkldnn/mkldnn_conv.cc b/src/operator/subgraph/mkldnn/mkldnn_conv.cc
index f5bbd5044446..f3a7d2c4e914 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_conv.cc
+++ b/src/operator/subgraph/mkldnn/mkldnn_conv.cc
@@ -278,7 +278,7 @@ void SgMKLDNNConvOperator::Forward(const OpContext &ctx,
                                                   CpuEngine::Get()->get_engine(),
                                                   out_mkl_mem->get_data_handle()));
         MKLDNNStream::Get()->RegisterMem(tmp_mem);
-        mxnet::MKLDNNCopy(*in_mkl_mem, tmp_mem.get());
+        MKLDNNMemoryCopy(*in_mkl_mem, tmp_mem.get());
         output = NDArray(tmp_mem);
       }
     }
@@ -416,7 +416,7 @@ void SgMKLDNNConvOperator::Forward(const OpContext &ctx,
       mkldnn_mem_ptr new_out_mem(new mkldnn::memory(data_md, CpuEngine::Get()->get_engine(),
                                                     output_mem->get_data_handle()));
       MKLDNNStream::Get()->RegisterMem(new_out_mem);
-      mxnet::MKLDNNCopy(*tmp_out_mem, new_out_mem.get());
+      MKLDNNMemoryCopy(*tmp_out_mem, new_out_mem.get());
       output = NDArray(new_out_mem);
     }
   }
diff --git a/src/operator/tensor/elemwise_binary_op_basic.cc b/src/operator/tensor/elemwise_binary_op_basic.cc
index 50772bc075d4..98cf7f067527 100644
--- a/src/operator/tensor/elemwise_binary_op_basic.cc
+++ b/src/operator/tensor/elemwise_binary_op_basic.cc
@@ -45,7 +45,7 @@ static void ElemwiseAddEx(const nnvm::NodeAttrs& attrs,
   CHECK_EQ(outputs.size(), 1U);
 #if MXNET_USE_MKLDNN == 1
   if (SupportMKLDNNSum(inputs[0]) && SupportMKLDNNSum(inputs[1])) {
-    MKLDNNSumForward(attrs, ctx, inputs, req[0], outputs[0]);
+    MKLDNNRun(MKLDNNSumForward, attrs, ctx, inputs, req, outputs);
     return;
   } else if (inputs[0].storage_type() == kDefaultStorage
              && inputs[1].storage_type() == kDefaultStorage) {
@@ -123,8 +123,8 @@ static void _backward_ElemwiseAddEx(const nnvm::NodeAttrs& attrs,
   CHECK_EQ(outputs.size(), 2U);
 #if MXNET_USE_MKLDNN == 1
   if (inputs[0].IsMKLDNNData()) {
-    MKLDNNCopy(attrs, ctx, inputs[0], req[0], outputs[0]);
-    MKLDNNCopy(attrs, ctx, inputs[0], req[1], outputs[1]);
+    MKLDNNRun(MKLDNNCopy, attrs, ctx, inputs[0], req[0], outputs[0]);
+    MKLDNNRun(MKLDNNCopy, attrs, ctx, inputs[0], req[1], outputs[1]);
     return;
   } else if (common::ContainsOnlyStorage(inputs, kDefaultStorage)) {
     FallBackCompute(
diff --git a/src/operator/tensor/elemwise_sum.cc b/src/operator/tensor/elemwise_sum.cc
index d1b86d161e89..b07c9590e8f5 100644
--- a/src/operator/tensor/elemwise_sum.cc
+++ b/src/operator/tensor/elemwise_sum.cc
@@ -125,7 +125,7 @@ void ElementWiseSumComputeExCPU(const nnvm::NodeAttrs& attrs,
     mxnet::ndarray::ElementwiseSum<cpu>(s, rsc, inputs, &out_nd);
 #if MXNET_USE_MKLDNN == 1
   } else if (IsMKLDNNData(inputs)) {
-    MKLDNNSumForward(attrs, ctx, inputs, req[0], outputs[0]);
+    MKLDNNRun(MKLDNNSumForward, attrs, ctx, inputs, req, outputs);
   } else if (common::ContainsOnlyStorage(inputs, kDefaultStorage)) {
     FallBackCompute(ElementWiseSumCompute<cpu>, attrs, ctx, inputs, req, outputs);
 #endif
diff --git a/src/operator/tensor/elemwise_unary_op_basic.cc b/src/operator/tensor/elemwise_unary_op_basic.cc
index 1dd185f349a0..c8f597393ff8 100644
--- a/src/operator/tensor/elemwise_unary_op_basic.cc
+++ b/src/operator/tensor/elemwise_unary_op_basic.cc
@@ -208,13 +208,11 @@ static void CopyEx(const nnvm::NodeAttrs& attrs,
   const auto in_stype = inputs[0].storage_type();
   const auto out_stype = outputs[0].storage_type();
   if (inputs[0].IsMKLDNNData()) {
-    MKLDNNCopy(attrs, ctx, inputs[0], req[0], outputs[0]);
+    MKLDNNRun(MKLDNNCopy, attrs, ctx, inputs[0], req[0], outputs[0]);
     return;
   } else if (in_stype == kDefaultStorage && out_stype == kDefaultStorage) {
-    // This happens if inputs are supposed to be in MKLDNN format
-    // but MKLDNN doesn't support the data type or the shape. We're
-    // forced to convert it to the default format.
-    FallBackCompute(UnaryOp::IdentityCompute<cpu>, attrs, ctx, inputs, req, outputs);
+    if (req[0] != kNullOp && req[0] != kWriteInplace)
+      FallBackCompute(UnaryOp::IdentityCompute<cpu>, attrs, ctx, inputs, req, outputs);
     return;
   }
 #endif  // MXNET_USE_MKLDNN == 1
diff --git a/src/operator/tensor/matrix_op.cc b/src/operator/tensor/matrix_op.cc
index b09b332765f8..5b702fbaa2d6 100644
--- a/src/operator/tensor/matrix_op.cc
+++ b/src/operator/tensor/matrix_op.cc
@@ -118,7 +118,7 @@ static void ReshapeComputeExCPU(const nnvm::NodeAttrs& attrs,
   // If inputs are supposed to be in MKLDNN format and
   // MKLDNN support the data type or the shape. Then convert
   // it to the output format and shape
-  MKLDNNReshapeForward(attrs, ctx, inputs[0], req[0], outputs[0]);
+  MKLDNNRun(MKLDNNReshapeForward, attrs, ctx, inputs[0], req[0], outputs[0]);
 }
 
 inline static bool ReshapeStorageType(const nnvm::NodeAttrs& attrs,
@@ -211,7 +211,7 @@ static void FlattenEx(const nnvm::NodeAttrs& attrs,
   // If inputs are supposed to be in MKLDNN format and
   // MKLDNN support the data type or the shape. Then convert
   // it to the output format and shape
-  MKLDNNReshapeForward(attrs, ctx, inputs[0], req[0], outputs[0]);
+  MKLDNNRun(MKLDNNReshapeForward, attrs, ctx, inputs[0], req[0], outputs[0]);
 }
 
 static inline bool FlattenStorageType(const nnvm::NodeAttrs& attrs,
@@ -372,7 +372,7 @@ static void ExpandDimEx(const nnvm::NodeAttrs& attrs,
   // If inputs are supposed to be in MKLDNN format and
   // MKLDNN support the data type or the shape. Then convert
   // it to the output format and shape
-  MKLDNNReshapeForward(attrs, ctx, inputs[0], req[0], outputs[0]);
+  MKLDNNRun(MKLDNNReshapeForward, attrs, ctx, inputs[0], req[0], outputs[0]);
 }
 
 inline static bool ExpandDimStorageType(const nnvm::NodeAttrs& attrs,
@@ -432,7 +432,7 @@ void SliceExCPU(const nnvm::NodeAttrs& attrs,
 #if MXNET_USE_MKLDNN == 1
   } else if (in_stype == kDefaultStorage) {
     if (SupportMKLDNN(inputs[0])) {
-      MKLDNNSlice(param, ctx, inputs[0], req[0], outputs[0]);
+      MKLDNNRun(MKLDNNSlice, attrs, ctx, inputs[0], req[0], outputs[0]);
     } else {
       FallBackCompute(SliceOpForward<cpu>, attrs, ctx, inputs, req, outputs);
     }

From 78a25236e40ff555b6b1e2827bdff58cbbabfb2f Mon Sep 17 00:00:00 2001
From: Pedro Larroy <pedro.larroy.lists@gmail.com>
Date: Mon, 2 Dec 2019 10:53:52 -0800
Subject: [PATCH 643/813] [docs] Fix runtime feature detection documentation
 (#16746)

* add runtime docs

* Add example for documentation

* Fix lint
---
 docs/python_docs/python/api/mxnet/index.rst   |  3 ++-
 .../python/api/mxnet/runtime/index.rst        | 23 ++++++++++++++++
 python/mxnet/runtime.py                       | 27 ++++++++++++++++++-
 3 files changed, 51 insertions(+), 2 deletions(-)
 create mode 100644 docs/python_docs/python/api/mxnet/runtime/index.rst

diff --git a/docs/python_docs/python/api/mxnet/index.rst b/docs/python_docs/python/api/mxnet/index.rst
index c70e128c3b0e..96f20e683818 100644
--- a/docs/python_docs/python/api/mxnet/index.rst
+++ b/docs/python_docs/python/api/mxnet/index.rst
@@ -40,6 +40,7 @@ mxnet
     mxnet.kvstore
     mxnet.kvstore_server
     mxnet.libinfo
+    mxnet.runtime
     mxnet.lr_scheduler
     mxnet.log
     mxnet.metric
@@ -70,4 +71,4 @@ mxnet
    :maxdepth: 2
    :glob:
 
-   */index
\ No newline at end of file
+   */index
diff --git a/docs/python_docs/python/api/mxnet/runtime/index.rst b/docs/python_docs/python/api/mxnet/runtime/index.rst
new file mode 100644
index 000000000000..4231e8690210
--- /dev/null
+++ b/docs/python_docs/python/api/mxnet/runtime/index.rst
@@ -0,0 +1,23 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+mxnet.runtime
+===============
+
+.. automodule:: mxnet.runtime
+    :members:
+    :autosummary:
diff --git a/python/mxnet/runtime.py b/python/mxnet/runtime.py
index f2e98fe674fa..bfe911080b23 100644
--- a/python/mxnet/runtime.py
+++ b/python/mxnet/runtime.py
@@ -19,7 +19,32 @@
 
 # pylint: disable=not-an-iterable
 
-"""runtime querying of compile time features in the native library"""
+"""Runtime querying of compile time features in the native library.
+
+With this module you can check at runtime which libraries and features were compiled in the library.
+
+Example usage:
+
+.. code-block:: python
+
+    import mxnet
+    features=mxnet.runtime.Features()
+
+    features.is_enabled("CUDNN")
+    False
+
+    features.is_enabled("CPU_SSE")
+    True
+
+    print(features)
+    [✖ CUDA, ✖ CUDNN, ✖ NCCL, ✖ CUDA_RTC, ✖ TENSORRT, ✔ CPU_SSE, ✔ CPU_SSE2, ✔ CPU_SSE3,
+    ✔ CPU_SSE4_1, ✔ CPU_SSE4_2, ✖ CPU_SSE4A, ✔ CPU_AVX, ✖ CPU_AVX2, ✔ OPENMP, ✖ SSE,
+    ✔ F16C, ✔ JEMALLOC, ✔ BLAS_OPEN, ✖ BLAS_ATLAS, ✖ BLAS_MKL, ✖ BLAS_APPLE, ✔ LAPACK,
+    ✖ MKLDNN, ✔ OPENCV, ✖ CAFFE, ✖ PROFILER, ✖ DIST_KVSTORE, ✖ CXX14, ✖ INT64_TENSOR_SIZE,
+    ✔ SIGNAL_HANDLER, ✔ DEBUG, ✖ TVM_OP]
+
+
+"""
 
 import ctypes
 import collections

From 02b4d2bddf1f3fbfa711c49874e0c5a29d2dd2a9 Mon Sep 17 00:00:00 2001
From: moin <asmushetzel@yahoo.de>
Date: Wed, 4 Dec 2019 18:56:33 +0100
Subject: [PATCH 644/813] fix for enable model parallelism for non-fp32 data
 (#16683)

---
 src/operator/cross_device_copy.cc            | 10 ++++++
 tests/python/unittest/test_model_parallel.py | 38 +++++++++++---------
 2 files changed, 32 insertions(+), 16 deletions(-)

diff --git a/src/operator/cross_device_copy.cc b/src/operator/cross_device_copy.cc
index f8c7ced5058d..ee37d1fcec57 100644
--- a/src/operator/cross_device_copy.cc
+++ b/src/operator/cross_device_copy.cc
@@ -60,6 +60,16 @@ class CrossDeviceCopyProp : public OperatorProperty {
     return true;
   }
 
+  bool InferType(std::vector<int> *in_type,
+                 std::vector<int> *out_type,
+                 std::vector<int> *aux_type) const {
+    CHECK_EQ(in_type->size(), 1) << "Input:[data]";
+    if (in_type->at(0) == -1) return false;
+    out_type->clear();
+    out_type->push_back(in_type->at(0));
+    return true;
+  }
+
   OperatorProperty* Copy() const override {
     return new CrossDeviceCopyProp();
   }
diff --git a/tests/python/unittest/test_model_parallel.py b/tests/python/unittest/test_model_parallel.py
index 8ff09d5fcb56..6036caf0a5ba 100644
--- a/tests/python/unittest/test_model_parallel.py
+++ b/tests/python/unittest/test_model_parallel.py
@@ -17,6 +17,7 @@
 
 import numpy as np
 import mxnet as mx
+from mxnet.test_utils import *
 
 def reldiff(a, b):
     diff = np.sum(np.abs(a - b))
@@ -26,16 +27,14 @@ def reldiff(a, b):
     reldiff = diff  / norm
     return reldiff
 
-def test_chain():
-    ctx1 = mx.cpu(0)
-    ctx2 = mx.cpu(1)
+def test_chain(ctx1=mx.cpu(0), ctx2=mx.cpu(1), dtype=np.float32):
     n = 2
-    data1 = mx.sym.Variable('data1')
-    data2 = mx.sym.Variable('data2')
-    data3 = mx.sym.Variable('data3')
+    data1 = mx.sym.Variable('data1', dtype=dtype)
+    data2 = mx.sym.Variable('data2', dtype=dtype)
+    data3 = mx.sym.Variable('data3', dtype=dtype)
     with mx.AttrScope(ctx_group='dev1'):
         net = data1 + data2
-        net = net * 3
+        net = net * dtype(3)
 
     with mx.AttrScope(ctx_group='dev2'):
         net = net + data3
@@ -45,19 +44,19 @@ def test_chain():
     shape = (4, 5)
     with mx.Context(ctx1):
         for i in range(n):
-            arr.append(mx.nd.empty(shape))
-            arr_grad.append(mx.nd.empty(shape))
+            arr.append(mx.nd.empty(shape, dtype=dtype))
+            arr_grad.append(mx.nd.empty(shape, dtype=dtype))
     with mx.Context(ctx2):
-        arr.append(mx.nd.empty(shape))
-        arr_grad.append(mx.nd.empty(shape))
+        arr.append(mx.nd.empty(shape, dtype=dtype))
+        arr_grad.append(mx.nd.empty(shape, dtype=dtype))
 
     exec1 = net.bind(ctx1,
                      args=arr,
                      args_grad=arr_grad,
                      group2ctx={'dev1': ctx1, 'dev2': ctx2})
-    arr[0][:] = 1.0
-    arr[1][:] = 2.0
-    arr[2][:] = 3.0
+    arr[0][:] = dtype(1)
+    arr[1][:] = dtype(2)
+    arr[2][:] = dtype(3)
     arr2 = [a.copyto(ctx1) for a in arr]
     arr_grad2 = [a.copyto(ctx1) for a in arr_grad]
     exec2 = net.bind(ctx1,
@@ -70,12 +69,19 @@ def test_chain():
     exec2.forward(is_train=True)
     assert reldiff(exec1.outputs[0].asnumpy(), exec2.outputs[0].asnumpy()) < 1e-6
     out_grad = mx.nd.empty(shape, ctx1)
-    out_grad[:] = 1.0
+    out_grad[:] = dtype(1)
     exec1.backward([out_grad])
     exec2.backward([out_grad.copyto(ctx1)])
     for a, b in zip(arr_grad, arr_grad2):
         assert reldiff(a.asnumpy(), b.asnumpy()) < 1e-6
 
+def test_chain_type_device():
+    ctx_pairs = [(mx.cpu(0), mx.cpu(1))]
+    if default_context().device_type == 'gpu':
+        ctx_pairs = ctx_pairs + [(mx.gpu(0), mx.gpu(0)), (mx.cpu(0), mx.gpu(0)), (mx.gpu(0), mx.cpu(0))]
+    for ctx1, ctx2 in ctx_pairs:
+        for dtype in [np.float16, np.float32, np.float64]:
+            test_chain(ctx1, ctx2, dtype)
 
 if __name__ == '__main__':
-    test_chain()
+    test_chain_type_device()

From edb583b627f06e0e2a9851873081050a40e18649 Mon Sep 17 00:00:00 2001
From: Jake Lee <gstu1130@gmail.com>
Date: Wed, 4 Dec 2019 22:22:01 -0800
Subject: [PATCH 645/813] Fix scala publish & nvidia-docker cublas issue
 (#16968)

* fix scala pipeline libcuda.so.1 not found issue

* retrigger CI
---
 ci/docker/Dockerfile.build.centos7_gpu       |  4 +++
 ci/docker/Dockerfile.build.ubuntu_build_cuda |  4 +++
 ci/docker/Dockerfile.build.ubuntu_gpu_cu101  |  4 +++
 ci/docker/install/centos7_cublas.sh          | 27 ++++++++++++++++++++
 ci/docker/install/ubuntu_cublas.sh           | 26 +++++++++++++++++++
 ci/publish/Jenkinsfile                       |  2 +-
 6 files changed, 66 insertions(+), 1 deletion(-)
 create mode 100755 ci/docker/install/centos7_cublas.sh
 create mode 100755 ci/docker/install/ubuntu_cublas.sh

diff --git a/ci/docker/Dockerfile.build.centos7_gpu b/ci/docker/Dockerfile.build.centos7_gpu
index 7e49e88b3a52..a784e872083d 100644
--- a/ci/docker/Dockerfile.build.centos7_gpu
+++ b/ci/docker/Dockerfile.build.centos7_gpu
@@ -33,6 +33,10 @@ ENV CUDNN_VERSION=7.6.0.64
 COPY install/centos7_cudnn.sh /work/
 RUN /work/centos7_cudnn.sh
 
+# hotfix nvidia-docker image come with wrong version of libcublas
+COPY install/centos7_cublas.sh /work/
+RUN /work/centos7_cublas.sh
+
 ARG USER_ID=0
 COPY install/centos7_adduser.sh /work/
 RUN /work/centos7_adduser.sh
diff --git a/ci/docker/Dockerfile.build.ubuntu_build_cuda b/ci/docker/Dockerfile.build.ubuntu_build_cuda
index ce6d0007875e..ecc2b0f1498a 100644
--- a/ci/docker/Dockerfile.build.ubuntu_build_cuda
+++ b/ci/docker/Dockerfile.build.ubuntu_build_cuda
@@ -54,6 +54,10 @@ RUN /work/ubuntu_cudnn.sh
 COPY install/ubuntu_nvidia.sh /work/
 RUN /work/ubuntu_nvidia.sh
 
+# hotfix nvidia-docker image come with wrong version of libcublas
+COPY install/ubuntu_cublas.sh /work/
+RUN /work/ubuntu_cublas.sh
+
 # Keep this at the end since this command is not cachable
 ARG USER_ID=0
 ARG GROUP_ID=0
diff --git a/ci/docker/Dockerfile.build.ubuntu_gpu_cu101 b/ci/docker/Dockerfile.build.ubuntu_gpu_cu101
index 7e0f8d93ed37..d005312ffe5b 100644
--- a/ci/docker/Dockerfile.build.ubuntu_gpu_cu101
+++ b/ci/docker/Dockerfile.build.ubuntu_gpu_cu101
@@ -69,6 +69,10 @@ ENV CUDNN_VERSION=7.5.1.10
 COPY install/ubuntu_cudnn.sh /work/
 RUN /work/ubuntu_cudnn.sh
 
+# hotfix nvidia-docker image come with wrong version of libcublas
+COPY install/ubuntu_cublas.sh /work/
+RUN /work/ubuntu_cublas.sh
+
 # Always last
 ARG USER_ID=0
 ARG GROUP_ID=0
diff --git a/ci/docker/install/centos7_cublas.sh b/ci/docker/install/centos7_cublas.sh
new file mode 100755
index 000000000000..0e0f91fc441f
--- /dev/null
+++ b/ci/docker/install/centos7_cublas.sh
@@ -0,0 +1,27 @@
+#!/usr/bin/env bash
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# build and install are separated so changes to build don't invalidate
+# the whole docker cache for the image
+
+set -ex
+
+# fix nvidia docker image come with wrong version of libcublas
+yum -y downgrade libcublas-devel-10.2.1.243-1.x86_64
+yum -y downgrade libcublas10-10.2.1.243-1.x86_64
\ No newline at end of file
diff --git a/ci/docker/install/ubuntu_cublas.sh b/ci/docker/install/ubuntu_cublas.sh
new file mode 100755
index 000000000000..eb6c4f145fae
--- /dev/null
+++ b/ci/docker/install/ubuntu_cublas.sh
@@ -0,0 +1,26 @@
+#!/usr/bin/env bash
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# build and install are separated so changes to build don't invalidate
+# the whole docker cache for the image
+
+apt update
+# fix nvidia docker image come with wrong version of libcublas
+apt install -y --allow-downgrades libcublas-dev=10.2.1.243-1
+apt install -y --allow-downgrades  libcublas10=10.2.1.243-1
diff --git a/ci/publish/Jenkinsfile b/ci/publish/Jenkinsfile
index 2b91f4f749d4..4d82b1524d28 100644
--- a/ci/publish/Jenkinsfile
+++ b/ci/publish/Jenkinsfile
@@ -84,7 +84,7 @@ for (x in labels) {
   toDeploy["Scala Deploy ${label}"] = wrapStep(nodeMap['cpu'], "deploy-scala-${label}") {
     withEnv(["MAVEN_PUBLISH_OS_TYPE=${scalaOSMap[label]}", "mxnet_variant=${scalaVariantMap[label]}"]) {
       utils.unpack_and_init("scala_${label}", mx_scala_pub, false)
-      utils.docker_run("publish.ubuntu1604_${label}", 'publish_scala_deploy', false, '500m', 'MAVEN_PUBLISH_OS_TYPE MAVEN_PUBLISH_SECRET_ENDPOINT_URL MAVEN_PUBLISH_SECRET_NAME_CREDENTIALS MAVEN_PUBLISH_SECRET_NAME_GPG DOCKERHUB_SECRET_ENDPOINT_REGION mxnet_variant')
+      utils.docker_run("publish.ubuntu1604_${label}", 'publish_scala_deploy', label == 'gpu' ? true : false, '500m', 'MAVEN_PUBLISH_OS_TYPE MAVEN_PUBLISH_SECRET_ENDPOINT_URL MAVEN_PUBLISH_SECRET_NAME_CREDENTIALS MAVEN_PUBLISH_SECRET_NAME_GPG DOCKERHUB_SECRET_ENDPOINT_REGION mxnet_variant')
     }
   }
 }

From 34e71163b1863558b2d17636385dfa05fcabf4f9 Mon Sep 17 00:00:00 2001
From: "Hao.Chen" <11779668+ewail@users.noreply.github.com>
Date: Thu, 5 Dec 2019 18:27:52 +0800
Subject: [PATCH 646/813] [R package] Make R package compilation support opencv
 4.0 (#16934)

* Make R package compilation support opencv 4.0

Make R package compilation support opencv 4.0

* move opencv definition

* CI

* ci
---
 R-package/src/im2rec.h | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/R-package/src/im2rec.h b/R-package/src/im2rec.h
index a6abe1146b56..b9e82da0f079 100644
--- a/R-package/src/im2rec.h
+++ b/R-package/src/im2rec.h
@@ -29,6 +29,12 @@
 
 #include <Rcpp.h>
 #include <string>
+#include <opencv2/core/version.hpp>
+#if CV_VERSION_MAJOR >= 4
+#include <opencv2/opencv.hpp>
+#define CV_IMWRITE_PNG_COMPRESSION cv::IMWRITE_PNG_COMPRESSION
+#define CV_IMWRITE_JPEG_QUALITY cv::IMWRITE_JPEG_QUALITY
+#endif  // CV_VERSION_MAJOR >= 4
 
 namespace mxnet {
 namespace R {

From b5ee43e6feaca368b952135d857edf5d22563792 Mon Sep 17 00:00:00 2001
From: Jerry Zhang <jerryzh.cn@gmail.com>
Date: Thu, 5 Dec 2019 02:31:00 -0800
Subject: [PATCH 647/813] add aligned roi introduced in Detectron2 (#16619)

* add aligned roi introduced in Detectron2

* lint

lint

* ci

* ci

* ci
---
 src/operator/contrib/mrcnn_mask_target-inl.h |  4 ++
 src/operator/contrib/mrcnn_mask_target.cu    | 27 ++++++----
 src/operator/contrib/roi_align-inl.h         |  4 ++
 src/operator/contrib/roi_align.cc            | 51 +++++++++++-------
 src/operator/contrib/roi_align.cu            | 54 +++++++++++---------
 5 files changed, 89 insertions(+), 51 deletions(-)

diff --git a/src/operator/contrib/mrcnn_mask_target-inl.h b/src/operator/contrib/mrcnn_mask_target-inl.h
index af297e745487..2318691be9fe 100644
--- a/src/operator/contrib/mrcnn_mask_target-inl.h
+++ b/src/operator/contrib/mrcnn_mask_target-inl.h
@@ -46,6 +46,7 @@ struct MRCNNMaskTargetParam : public dmlc::Parameter<MRCNNMaskTargetParam> {
   int num_rois;
   int num_classes;
   int sample_ratio;
+  bool aligned;
   mxnet::TShape mask_size;
 
   DMLC_DECLARE_PARAMETER(MRCNNMaskTargetParam) {
@@ -58,6 +59,9 @@ struct MRCNNMaskTargetParam : public dmlc::Parameter<MRCNNMaskTargetParam> {
     .describe("Size of the pooled masks height and width: (h, w).");
     DMLC_DECLARE_FIELD(sample_ratio).set_default(2)
     .describe("Sampling ratio of ROI align. Set to -1 to use adaptative size.");
+    DMLC_DECLARE_FIELD(aligned).set_default(false)
+    .describe("Center-aligned ROIAlign introduced in Detectron2. "
+    "To enable, set aligned to True.");
   }
 };
 
diff --git a/src/operator/contrib/mrcnn_mask_target.cu b/src/operator/contrib/mrcnn_mask_target.cu
index 779fb0141e64..0db581ce8954 100644
--- a/src/operator/contrib/mrcnn_mask_target.cu
+++ b/src/operator/contrib/mrcnn_mask_target.cu
@@ -119,6 +119,7 @@ __device__ void RoIAlignForward(
     const int sampling_ratio,
     const int num_rois,
     const int num_gtmasks,
+    const bool continuous_coordinate,
     T* out_data) {  // (B, N, C, H, W)
   // Update kernel
   for (size_t index = blockIdx.x * blockDim.x + threadIdx.x;
@@ -135,14 +136,21 @@ __device__ void RoIAlignForward(
 
     const T* offset_rois = rois + batch_idx * (4 * num_rois) + n * 4;
     // Do not using rounding; this implementation detail is critical
-    T roi_start_w = offset_rois[0];
-    T roi_start_h = offset_rois[1];
-    T roi_end_w = offset_rois[2];
-    T roi_end_h = offset_rois[3];
+    T roi_offset = continuous_coordinate ? static_cast<T>(0.5) : static_cast<T>(0);
+    T roi_start_w = offset_rois[0] - roi_offset;
+    T roi_start_h = offset_rois[1] - roi_offset;
+    T roi_end_w = offset_rois[2] - roi_offset;
+    T roi_end_h = offset_rois[3] - roi_offset;
+
+    T roi_width = roi_end_w - roi_start_w;
+    T roi_height = roi_end_h - roi_start_h;
 
     // Force malformed ROIs to be 1x1
-    T roi_width = max(roi_end_w - roi_start_w, (T)1.);
-    T roi_height = max(roi_end_h - roi_start_h, (T)1.);
+    if (!continuous_coordinate) {  // backward compatiblity
+      // Force malformed ROIs to be 1x1
+      roi_width = max(roi_width, (T)1.);
+      roi_height = max(roi_height, (T)1.);
+    }
     T bin_size_h = static_cast<T>(roi_height) / static_cast<T>(pooled_height);
     T bin_size_w = static_cast<T>(roi_width) / static_cast<T>(pooled_width);
 
@@ -190,6 +198,7 @@ __global__ void MRCNNMaskTargetKernel(const DType *rois,
                                       DType* sampled_masks,
                                       DType* mask_cls,
                                       const int total_out_el,
+                                      const bool aligned,
                                       int batch_size,
                                       int num_classes,
                                       int num_rois,
@@ -202,7 +211,7 @@ __global__ void MRCNNMaskTargetKernel(const DType *rois,
   // computing sampled_masks
   RoIAlignForward(gt_masks, rois, matches, total_out_el,
                   num_classes, gt_height, gt_width, mask_size_h, mask_size_w,
-                  sample_ratio, num_rois, num_gtmasks, sampled_masks);
+                  sample_ratio, num_rois, num_gtmasks, aligned, sampled_masks);
   // computing mask_cls
   int num_masks = batch_size * num_rois * num_classes;
   int mask_vol = mask_size_h * mask_size_w;
@@ -251,8 +260,8 @@ void MRCNNMaskTargetRun<gpu>(const MRCNNMaskTargetParam& param, const std::vecto
 
     MRCNNMaskTargetKernel<<<dimGrid, dimBlock, 0, stream>>>
     (rois.dptr_, gt_masks.dptr_, matches.dptr_, cls_targets.dptr_,
-    out_masks.dptr_, out_mask_cls.dptr_,
-    num_el, batch_size, param.num_classes, param.num_rois,
+    out_masks.dptr_, out_mask_cls.dptr_, num_el, param.aligned,
+    batch_size, param.num_classes, param.num_rois,
     num_gtmasks, gt_height, gt_width,
     param.mask_size[0], param.mask_size[1], param.sample_ratio);
     MSHADOW_CUDA_POST_KERNEL_CHECK(MRCNNMaskTargetKernel);
diff --git a/src/operator/contrib/roi_align-inl.h b/src/operator/contrib/roi_align-inl.h
index b28e437a7e09..8b3b914435cc 100644
--- a/src/operator/contrib/roi_align-inl.h
+++ b/src/operator/contrib/roi_align-inl.h
@@ -48,6 +48,7 @@ struct ROIAlignParam : public dmlc::Parameter<ROIAlignParam> {
   float spatial_scale;
   int sample_ratio;
   bool position_sensitive;
+  bool aligned;
   DMLC_DECLARE_PARAMETER(ROIAlignParam) {
     DMLC_DECLARE_FIELD(pooled_size)
     .set_expect_ndim(2).enforce_nonzero()
@@ -61,6 +62,9 @@ struct ROIAlignParam : public dmlc::Parameter<ROIAlignParam> {
     .describe("Whether to perform position-sensitive RoI pooling. PSRoIPooling is "
     "first proposaled by R-FCN and it can reduce the input channels by ph*pw times, "
     "where (ph, pw) is the pooled_size");
+    DMLC_DECLARE_FIELD(aligned).set_default(false)
+    .describe("Center-aligned ROIAlign introduced in Detectron2. "
+    "To enable, set aligned to True.");
   }
 };
 
diff --git a/src/operator/contrib/roi_align.cc b/src/operator/contrib/roi_align.cc
index ee91561a6818..eeb9f622c2be 100644
--- a/src/operator/contrib/roi_align.cc
+++ b/src/operator/contrib/roi_align.cc
@@ -143,6 +143,7 @@ void ROIAlignForward(
     const T* bottom_data,
     const T& spatial_scale,
     const bool position_sensitive,
+    const bool continuous_coordinate,
     const int channels,
     const int height,
     const int width,
@@ -175,14 +176,22 @@ num_threads(engine::OpenMP::Get()->GetRecommendedOMPThreadCount())
     }
 
     // Do not using rounding; this implementation detail is critical
-    T roi_start_w = offset_bottom_rois[0] * spatial_scale;
-    T roi_start_h = offset_bottom_rois[1] * spatial_scale;
-    T roi_end_w = offset_bottom_rois[2] * spatial_scale;
-    T roi_end_h = offset_bottom_rois[3] * spatial_scale;
-
-    // Force malformed ROIs to be 1x1
-    T roi_width = std::max(roi_end_w - roi_start_w, (T)1.);
-    T roi_height = std::max(roi_end_h - roi_start_h, (T)1.);
+    T roi_offset = continuous_coordinate ? static_cast<T>(0.5) : static_cast<T>(0);
+    T roi_start_w = offset_bottom_rois[0] * spatial_scale - roi_offset;
+    T roi_start_h = offset_bottom_rois[1] * spatial_scale - roi_offset;
+    T roi_end_w = offset_bottom_rois[2] * spatial_scale - roi_offset;
+    T roi_end_h = offset_bottom_rois[3] * spatial_scale - roi_offset;
+
+    T roi_width = roi_end_w - roi_start_w;
+    T roi_height = roi_end_h - roi_start_h;
+    if (continuous_coordinate) {
+      CHECK_GT(roi_width, 0.);
+      CHECK_GT(roi_height, 0.);
+    } else {  // backward compatiblity
+      // Force malformed ROIs to be 1x1
+      roi_width = std::max(roi_width, (T)1.);
+      roi_height = std::max(roi_height, (T)1.);
+    }
     T bin_size_h = static_cast<T>(roi_height) / static_cast<T>(pooled_height);
     T bin_size_w = static_cast<T>(roi_width) / static_cast<T>(pooled_width);
 
@@ -322,6 +331,7 @@ void ROIAlignBackward(
     const int /*num_rois*/,
     const T& spatial_scale,
     const bool position_sensitive,
+    const bool continuous_coordinate,
     const int channels,
     const int height,
     const int width,
@@ -349,14 +359,19 @@ void ROIAlignBackward(
     }
 
     // Do not using rounding; this implementation detail is critical
-    T roi_start_w = offset_bottom_rois[0] * spatial_scale;
-    T roi_start_h = offset_bottom_rois[1] * spatial_scale;
-    T roi_end_w = offset_bottom_rois[2] * spatial_scale;
-    T roi_end_h = offset_bottom_rois[3] * spatial_scale;
-
-    // Force malformed ROIs to be 1x1
-    T roi_width = std::max(roi_end_w - roi_start_w, (T)1.);
-    T roi_height = std::max(roi_end_h - roi_start_h, (T)1.);
+    T roi_offset = continuous_coordinate ? static_cast<T>(0.5) : static_cast<T>(0);
+    T roi_start_w = offset_bottom_rois[0] * spatial_scale - roi_offset;
+    T roi_start_h = offset_bottom_rois[1] * spatial_scale - roi_offset;
+    T roi_end_w = offset_bottom_rois[2] * spatial_scale - roi_offset;
+    T roi_end_h = offset_bottom_rois[3] * spatial_scale - roi_offset;
+
+    T roi_width = roi_end_w - roi_start_w;
+    T roi_height = roi_end_h - roi_start_h;
+    if (!continuous_coordinate) {  // backward compatiblity
+      // Force malformed ROIs to be 1x1
+      roi_width = std::max(roi_width, (T)1.);
+      roi_height = std::max(roi_height, (T)1.);
+    }
     T bin_size_h = static_cast<T>(roi_height) / static_cast<T>(pooled_height);
     T bin_size_w = static_cast<T>(roi_width) / static_cast<T>(pooled_width);
 
@@ -460,7 +475,7 @@ void ROIAlignForwardCompute(const nnvm::NodeAttrs& attrs,
     DType *top_data = out_data[roialign::kOut].dptr<DType>();
 
     ROIAlignForward<DType>(count, bottom_data, param.spatial_scale, param.position_sensitive,
-                           channels, height, width, pooled_height, pooled_width,
+                           param.aligned, channels, height, width, pooled_height, pooled_width,
                            param.sample_ratio, bottom_rois, rois_cols, top_data);
   })
 }
@@ -509,7 +524,7 @@ void ROIAlignBackwardCompute(const nnvm::NodeAttrs& attrs,
         Fill<false>(s, outputs[0], kWriteTo, static_cast<DType>(0));
       }
       ROIAlignBackward<DType>(count, top_diff, num_rois, param.spatial_scale,
-                     param.position_sensitive, channels, height, width,
+                     param.position_sensitive, param.aligned, channels, height, width,
                      pooled_height, pooled_width, param.sample_ratio, grad_in,
                      bottom_rois, rois_cols);
     }
diff --git a/src/operator/contrib/roi_align.cu b/src/operator/contrib/roi_align.cu
index a0fd6f93686c..3d085d3031b5 100644
--- a/src/operator/contrib/roi_align.cu
+++ b/src/operator/contrib/roi_align.cu
@@ -109,6 +109,7 @@ __global__ void RoIAlignForwardKernel(
     const T* bottom_data,
     const T spatial_scale,
     const bool position_sensitive,
+    const bool continuous_coordinate,
     const int channels,
     const int height,
     const int width,
@@ -133,18 +134,19 @@ __global__ void RoIAlignForwardKernel(
     }
 
     // Do not using rounding; this implementation detail is critical
-    T roi_start_w = offset_bottom_rois[1] * spatial_scale;
-    T roi_start_h = offset_bottom_rois[2] * spatial_scale;
-    T roi_end_w = offset_bottom_rois[3] * spatial_scale;
-    T roi_end_h = offset_bottom_rois[4] * spatial_scale;
-    // T roi_start_w = round(offset_bottom_rois[1] * spatial_scale);
-    // T roi_start_h = round(offset_bottom_rois[2] * spatial_scale);
-    // T roi_end_w = round(offset_bottom_rois[3] * spatial_scale);
-    // T roi_end_h = round(offset_bottom_rois[4] * spatial_scale);
-
-    // Force malformed ROIs to be 1x1
-    T roi_width = max(roi_end_w - roi_start_w, (T)1.);
-    T roi_height = max(roi_end_h - roi_start_h, (T)1.);
+    T roi_offset = continuous_coordinate ? static_cast<T>(0.5) : static_cast<T>(0);
+    T roi_start_w = offset_bottom_rois[1] * spatial_scale - roi_offset;
+    T roi_start_h = offset_bottom_rois[2] * spatial_scale - roi_offset;
+    T roi_end_w = offset_bottom_rois[3] * spatial_scale - roi_offset;
+    T roi_end_h = offset_bottom_rois[4] * spatial_scale - roi_offset;
+
+    T roi_width = roi_end_w - roi_start_w;
+    T roi_height = roi_end_h - roi_start_h;
+    if (!continuous_coordinate) {  // backward compatiblity
+      // Force malformed ROIs to be 1x1
+      roi_width = max(roi_width, (T)1.);
+      roi_height = max(roi_height, (T)1.);
+    }
     T bin_size_h = static_cast<T>(roi_height) / static_cast<T>(pooled_height);
     T bin_size_w = static_cast<T>(roi_width) / static_cast<T>(pooled_width);
 
@@ -253,6 +255,7 @@ __global__ void RoIAlignBackwardKernel(
     const int num_rois,
     const T spatial_scale,
     const bool position_sensitive,
+    const bool continuous_coordinate,
     const int channels,
     const int height,
     const int width,
@@ -273,18 +276,19 @@ __global__ void RoIAlignBackwardKernel(
     if (roi_batch_ind < 0) continue;
 
     // Do not using rounding; this implementation detail is critical
-    T roi_start_w = offset_bottom_rois[1] * spatial_scale;
-    T roi_start_h = offset_bottom_rois[2] * spatial_scale;
-    T roi_end_w = offset_bottom_rois[3] * spatial_scale;
-    T roi_end_h = offset_bottom_rois[4] * spatial_scale;
-    // T roi_start_w = round(offset_bottom_rois[1] * spatial_scale);
-    // T roi_start_h = round(offset_bottom_rois[2] * spatial_scale);
-    // T roi_end_w = round(offset_bottom_rois[3] * spatial_scale);
-    // T roi_end_h = round(offset_bottom_rois[4] * spatial_scale);
-
-    // Force malformed ROIs to be 1x1
-    T roi_width = max(roi_end_w - roi_start_w, (T)1.);
-    T roi_height = max(roi_end_h - roi_start_h, (T)1.);
+    T roi_offset = continuous_coordinate ? static_cast<T>(0.5) : static_cast<T>(0);
+    T roi_start_w = offset_bottom_rois[1] * spatial_scale - roi_offset;
+    T roi_start_h = offset_bottom_rois[2] * spatial_scale - roi_offset;
+    T roi_end_w = offset_bottom_rois[3] * spatial_scale - roi_offset;
+    T roi_end_h = offset_bottom_rois[4] * spatial_scale - roi_offset;
+
+    T roi_width = roi_end_w - roi_start_w;
+    T roi_height = roi_end_h - roi_start_h;
+    if (!continuous_coordinate) {  // backward compatiblity
+      // Force malformed ROIs to be 1x1
+      roi_width = max(roi_width, (T)1.);
+      roi_height = max(roi_height, (T)1.);
+    }
     T bin_size_h = static_cast<T>(roi_height) / static_cast<T>(pooled_height);
     T bin_size_w = static_cast<T>(roi_width) / static_cast<T>(pooled_width);
 
@@ -397,6 +401,7 @@ void ROIAlignForwardCompute(const nnvm::NodeAttrs& attrs,
           bottom_data,
           param.spatial_scale,
           param.position_sensitive,
+          param.aligned,
           channels,
           height,
           width,
@@ -466,6 +471,7 @@ void ROIAlignBackwardCompute(const nnvm::NodeAttrs& attrs,
         num_rois,
         param.spatial_scale,
         param.position_sensitive,
+        param.aligned,
         channels,
         height,
         width,

From 10903e81100bb02eee3ecc033f858b846311f763 Mon Sep 17 00:00:00 2001
From: reminisce <wujun.nju@gmail.com>
Date: Thu, 5 Dec 2019 14:02:46 -0800
Subject: [PATCH 648/813] Fix ndarray indexing bug (#16895)

* Fix indexing bug

* More test cases

* Add test from 16647
---
 python/mxnet/ndarray/ndarray.py             | 42 +++++++-------
 src/ndarray/ndarray.cc                      | 10 +++-
 tests/python/unittest/test_ndarray.py       | 16 ++++++
 tests/python/unittest/test_numpy_ndarray.py | 63 +++++++++++++--------
 4 files changed, 85 insertions(+), 46 deletions(-)

diff --git a/python/mxnet/ndarray/ndarray.py b/python/mxnet/ndarray/ndarray.py
index e66742eeef15..260d9d3bfd11 100644
--- a/python/mxnet/ndarray/ndarray.py
+++ b/python/mxnet/ndarray/ndarray.py
@@ -905,6 +905,17 @@ def _basic_indexing_contiguous_flat_begin_end(slc_key, shape):
         return flat_begin, flat_end + 1
     # pylint: enable=invalid-name
 
+    @staticmethod
+    def _drop_int_axes(indexed_shape, int_axes):
+        """drop the axis of indexed_shape corresponding to int axes"""
+        bcast_shape = []
+        for i, size in enumerate(indexed_shape):
+            if i not in int_axes:
+                bcast_shape.append(size)
+        if not bcast_shape:
+            bcast_shape = [1]
+        return tuple(bcast_shape)
+
     def _set_nd_basic_indexing(self, key, value):
         """This function indexes ``self`` with a tuple of ``slice`` objects only."""
         for idx in key:
@@ -946,14 +957,10 @@ def _set_nd_basic_indexing(self, key, value):
             if type(value) == self.__class__:  # pylint: disable=unidiomatic-typecheck
                 if value.handle is not self.handle:
                     # Need to do this before `broadcast_to`.
-                    tmp_shape = _shape_for_bcast(
-                        value.shape, target_ndim=self.ndim, new_axes=int_axes
-                    )
-                    value = value.reshape(tmp_shape)
-
-                    if value.shape != self.shape:
-                        value = value.broadcast_to(self.shape)
-                    value.copyto(self)
+                    bcast_shape = self._drop_int_axes(indexed_shape, int_axes)
+                    value_nd = self._prepare_value_nd(value, bcast_shape=bcast_shape, squeeze_axes=new_axes)
+                    value_nd = value_nd.reshape(indexed_shape)
+                    value_nd.copyto(self)
 
             elif isinstance(value, numeric_types):
                 self._full(value)
@@ -969,9 +976,10 @@ def _set_nd_basic_indexing(self, key, value):
 
             else:
                 # Other array-like
-                value_nd = self._prepare_value_nd(
-                    value, bcast_shape=self.shape
-                )
+                # drop the axis of indexed_shape corresponding to int axes
+                bcast_shape = self._drop_int_axes(indexed_shape, int_axes)
+                value_nd = self._prepare_value_nd(value, bcast_shape=bcast_shape, squeeze_axes=new_axes)
+                value_nd = value_nd.reshape(indexed_shape)
                 value_nd.copyto(self)
 
         elif isinstance(value, numeric_types):
@@ -979,16 +987,8 @@ def _set_nd_basic_indexing(self, key, value):
 
         else:
             # drop the axis of indexed_shape corresponding to int axes
-            bcast_shape = []
-            for i, size in enumerate(indexed_shape):
-                if i not in int_axes:
-                    bcast_shape.append(size)
-            if bcast_shape == []:
-                bcast_shape = [1]
-            bcast_shape = tuple(bcast_shape)
-            value_nd = self._prepare_value_nd(
-                value, bcast_shape=bcast_shape, squeeze_axes=new_axes
-            )
+            bcast_shape = self._drop_int_axes(indexed_shape, int_axes)
+            value_nd = self._prepare_value_nd(value, bcast_shape=bcast_shape, squeeze_axes=new_axes)
             value_nd = value_nd.reshape(indexed_shape)
             self.slice_assign(value_nd, begin, end, step)
 
diff --git a/src/ndarray/ndarray.cc b/src/ndarray/ndarray.cc
index ce0f74437ea7..27b4d58aec30 100644
--- a/src/ndarray/ndarray.cc
+++ b/src/ndarray/ndarray.cc
@@ -243,8 +243,14 @@ NDArray NDArray::MKLDNNDataReshape(const mxnet::TShape &shape) const {
 
 NDArray NDArray::Reshape(const mxnet::TShape &shape) const {
   CHECK(!is_none()) << "NDArray is not initialized";
-  CHECK_GE(shape_.Size(), shape.Size())
-    << "NDArray.Reshape: target shape size is larger current shape";
+  if (Imperative::Get()->is_np_shape()) {
+    CHECK_EQ(shape_.Size(), shape.Size())
+        << "NDArray.Reshape: target shape must have the same size as "
+        << "current shape.";
+  } else {
+    CHECK_GE(shape_.Size(), shape.Size())
+        << "NDArray.Reshape: target shape size is larger than the current shape";
+  }
   NDArray ret = this->Detach();
   // If the shape doesn't change, we can just return it now.
   if (ret.shape_ == shape)
diff --git a/tests/python/unittest/test_ndarray.py b/tests/python/unittest/test_ndarray.py
index 4c6d9f7b8ef2..d097799da286 100644
--- a/tests/python/unittest/test_ndarray.py
+++ b/tests/python/unittest/test_ndarray.py
@@ -154,6 +154,22 @@ def test_ndarray_setitem():
         assert x.shape == trivial_shape
         assert same(x.asnumpy(), x_np)
 
+    # test https://github.com/apache/incubator-mxnet/issues/16647
+    dst = mx.nd.zeros((1, 3, 1))  # destination array
+    src = [1, 2, 3]
+    dst[0, :len(src), 0] = src
+    assert same(dst.asnumpy(), np.array([1, 2, 3], dtype=dst.dtype).reshape(dst.shape))
+
+    dst = mx.nd.zeros((1, 3, 1))  # destination array
+    src = [1, 2, 3]
+    dst[0, :len(src), 0] = mx.nd.array(src)
+    assert same(dst.asnumpy(), np.array([1, 2, 3], dtype=dst.dtype).reshape(dst.shape))
+
+    dst = mx.nd.zeros((1, 3, 1))  # destination array
+    src = [1, 2]
+    dst[0, :len(src), 0] = src
+    assert same(dst.asnumpy(), np.array([1, 2, 0], dtype=dst.dtype).reshape(dst.shape))
+
 
 @with_seed()
 def test_ndarray_elementwise():
diff --git a/tests/python/unittest/test_numpy_ndarray.py b/tests/python/unittest/test_numpy_ndarray.py
index 06c01fa0e09e..d7b32c4d382e 100644
--- a/tests/python/unittest/test_numpy_ndarray.py
+++ b/tests/python/unittest/test_numpy_ndarray.py
@@ -696,26 +696,37 @@ def _is_basic_index(index):
         np_indexed_array = _np.random.randint(low=-10000, high=0, size=indexed_array_shape)
         # test value is a native numpy array without broadcast
         assert_same(np_array, np_index, mx_array, index, np_indexed_array)
+        # test value is a list without broadcast
+        assert_same(np_array, np_index, mx_array, index, np_indexed_array.tolist())
         # test value is a mxnet numpy array without broadcast
         assert_same(np_array, np_index, mx_array, index, np.array(np_indexed_array))
         # test value is an numeric_type
         assert_same(np_array, np_index, mx_array, index, _np.random.randint(low=-10000, high=0))
-        if len(indexed_array_shape) > 1:
-            np_value = _np.random.randint(low=-10000, high=0, size=(indexed_array_shape[-1],))
-            # test mxnet ndarray with broadcast
-            assert_same(np_array, np_index, mx_array, index, np.array(np_value))
-            # test native numpy array with broadcast
-            assert_same(np_array, np_index, mx_array, index, np_value)
-
-            # test value shape are expanded to be longer than index array's shape
-            # this is currently only supported in basic indexing
-            if _is_basic_index(index):
-                expanded_value_shape = (1, 1, 1) + np_value.shape
-                assert_same(np_array, np_index, mx_array, index, np.array(np_value.reshape(expanded_value_shape)))
-                assert_same(np_array, np_index, mx_array, index, np_value.reshape(expanded_value_shape))
-            # test list with broadcast
-            assert_same(np_array, np_index, mx_array, index,
-                        [_np.random.randint(low=-10000, high=0)] * indexed_array_shape[-1])
+
+        np_value = _np.random.randint(low=-10000, high=0,
+                                      size=(indexed_array_shape[-1],) if len(indexed_array_shape) > 0 else ())
+        # test mxnet ndarray with broadcast
+        assert_same(np_array, np_index, mx_array, index, np.array(np_value))
+        # test native numpy array with broadcast
+        assert_same(np_array, np_index, mx_array, index, np_value)
+        # test python list with broadcast
+        assert_same(np_array, np_index, mx_array, index, np_value.tolist())
+
+        # test value shape are expanded to be longer than index array's shape
+        # this is currently only supported in basic indexing
+        if _is_basic_index(index):
+            expanded_value_shape = (1, 1) + np_value.shape
+            assert_same(np_array, np_index, mx_array, index, np.array(np_value.reshape(expanded_value_shape)))
+            assert_same(np_array, np_index, mx_array, index, np_value.reshape(expanded_value_shape))
+            if len(expanded_value_shape) <= np_array[index].ndim:
+                # NumPy does not allow value.ndim > np_array[index].ndim when value is a python list.
+                # It may be a bug of NumPy.
+                assert_same(np_array, np_index, mx_array, index, np_value.reshape(expanded_value_shape).tolist())
+
+        # test list with broadcast
+        assert_same(np_array, np_index, mx_array, index,
+                    [_np.random.randint(low=-10000, high=0)] * indexed_array_shape[-1] if len(indexed_array_shape) > 0
+                    else _np.random.randint(low=-10000, high=0))
 
     def test_getitem_autograd(np_array, index):
         """
@@ -905,6 +916,9 @@ def test_setitem_autograd(np_array, index):
         range(4),
         range(3, 0, -1),
         (range(4,), [1]),
+        (1, 1, slice(None), 1),
+        (1, 1, slice(None, 3), 1),
+        (1, 1, slice(None, 8, 3), 1),
     ]
     for index in index_list:
         test_getitem(np_array, index)
@@ -925,8 +939,8 @@ def test_setitem_autograd(np_array, index):
 
     # test zero-size tensors get and setitem
     shapes_indices = [
-                        ((0), [slice(None, None, None)]),
-                        ((3, 0), [2, (slice(None, None, None)), (slice(None, None, None), None)]),
+        ((0), [slice(None, None, None)]),
+        ((3, 0), [2, (slice(None, None, None)), (slice(None, None, None), None)]),
     ]
     for shape, indices in shapes_indices:
         np_array = _np.zeros(shape)
@@ -1164,11 +1178,14 @@ def test_np_ndarray_pickle():
     a = np.random.uniform(size=(4, 5))
     a_copy = a.copy()
     import pickle
-    with open("np_ndarray_pickle_test_file", 'wb') as f:
-        pickle.dump(a_copy, f)
-    with open("np_ndarray_pickle_test_file", 'rb') as f:
-        a_load = pickle.load(f)
-    same(a.asnumpy(), a_load.asnumpy())
+
+    with TemporaryDirectory() as work_dir:
+        fname = os.path.join(work_dir, 'np_ndarray_pickle_test_file')
+        with open(fname, 'wb') as f:
+            pickle.dump(a_copy, f)
+        with open(fname, 'rb') as f:
+            a_load = pickle.load(f)
+        same(a.asnumpy(), a_load.asnumpy())
 
 
 if __name__ == '__main__':

From 8dd70513af4f1cdbcb06b0f8b8232e032b594505 Mon Sep 17 00:00:00 2001
From: Jake Lee <gstu1130@gmail.com>
Date: Thu, 5 Dec 2019 16:19:01 -0800
Subject: [PATCH 649/813] Downgrade the cublas version for nightly gpu (#16985)

---
 ci/docker/Dockerfile.build.ubuntu_base_gpu    | 4 ++++
 ci/docker/Dockerfile.build.ubuntu_nightly_gpu | 4 ++++
 2 files changed, 8 insertions(+)

diff --git a/ci/docker/Dockerfile.build.ubuntu_base_gpu b/ci/docker/Dockerfile.build.ubuntu_base_gpu
index 94e6437e578b..daf6fa342d43 100644
--- a/ci/docker/Dockerfile.build.ubuntu_base_gpu
+++ b/ci/docker/Dockerfile.build.ubuntu_base_gpu
@@ -29,6 +29,10 @@ ENV CUDNN_VERSION=7.6.0.64
 COPY install/ubuntu_cudnn.sh /work/
 RUN /work/ubuntu_cudnn.sh
 
+# hotfix nvidia-docker image come with wrong version of libcublas
+COPY install/ubuntu_cublas.sh /work/
+RUN /work/ubuntu_cublas.sh
+
 ARG USER_ID=0
 ARG GROUP_ID=0
 COPY install/ubuntu_adduser.sh /work/
diff --git a/ci/docker/Dockerfile.build.ubuntu_nightly_gpu b/ci/docker/Dockerfile.build.ubuntu_nightly_gpu
index 5e812c433b43..9a6781685031 100644
--- a/ci/docker/Dockerfile.build.ubuntu_nightly_gpu
+++ b/ci/docker/Dockerfile.build.ubuntu_nightly_gpu
@@ -72,6 +72,10 @@ ENV CUDNN_VERSION=7.6.0.64
 COPY install/ubuntu_cudnn.sh /work/
 RUN /work/ubuntu_cudnn.sh
 
+# hotfix nvidia-docker image come with wrong version of libcublas
+COPY install/ubuntu_cublas.sh /work/
+RUN /work/ubuntu_cublas.sh
+
 ARG USER_ID=0
 ARG GROUP_ID=0
 COPY install/ubuntu_adduser.sh /work/

From ae472c266d6b8f343d9f4f91a7acdb611812ad25 Mon Sep 17 00:00:00 2001
From: Sam Skalicky <samskalicky@gmail.com>
Date: Thu, 5 Dec 2019 21:34:47 -0800
Subject: [PATCH 650/813] dynamic custom operator support (#15921)

* fixed example to use absolute path

* added example for custom ops, added support for custom op registration

* added fcompute registration for loaded operators
moved library import order to after ndarray/symbol

* changed dynamic ops to be contrib

* added num in/out

* removed contrib op registration
re-registered ops from mx.nd.op to mx.nd

* added support for infer shape, updated example to call operator

* fixed whitespace

* fixed whitespace

* fixed whitespace

* added temporary support for operator multi-registration

* insanity checked

* update docblocks

* small format fix

* fix unittest with correct library

* implement InferType

* initial support for resource manager, temp space

* fixed formatting

* changed decltype to typedef

* fixed whitespace

* Added windows declaration types, change APIs to return MXReturnValue instead of int

* added library version number, API to get, and check to validate

* Changed CMakeLists to build lib_ops instead of lib_api, updated lib_api example, fixed whitespace

* add prototype of subgraph op

* implement FMutateInput as optional attribute

* fix sanity check

* replace fcompute to fcomputeEx and implement simple finferstoragetype

* changed fcompute to forward

* initial commit with fgradient support

* enabled gradient registration

* fixed whitespace

* fixed example to use absolute path

* added example for custom ops, added support for custom op registration

* added fcompute registration for loaded operators
moved library import order to after ndarray/symbol

* changed dynamic ops to be contrib

* added num in/out

* removed contrib op registration
re-registered ops from mx.nd.op to mx.nd

* added support for infer shape, updated example to call operator

* fixed whitespace

* fixed whitespace

* fixed whitespace

* added temporary support for operator multi-registration

* insanity checked

* update docblocks

* small format fix

* fix unittest with correct library

* implement InferType

* initial support for resource manager, temp space

* fixed formatting

* changed decltype to typedef

* fixed whitespace

* Added windows declaration types, change APIs to return MXReturnValue instead of int

* added library version number, API to get, and check to validate

* Changed CMakeLists to build lib_ops instead of lib_api, updated lib_api example, fixed whitespace

* add prototype of subgraph op

* implement FMutateInput as optional attribute

* fix sanity check

* replace fcompute to fcomputeEx and implement simple finferstoragetype

* changed fcompute to forward

* initial commit with fgradient support

* enabled gradient registration

* fixed whitespace

* prototype of createopstate and fstatefulcompute

* make custom state op interface work

* subgraph forward

* refactor stateful forward and add op resource

* wip gemm backward

* stateful backward and subgraph test

* implement gemm and state gemm, refactor test files

* add body to pure virtual destructor

* subgraph passing from python to custom lib

* rm lib_api c++11 dep, rm warpctc, add rm flag

* fix conflict

* subgraph json parsing utility

* add data size and fix unsigned warnings

* use c++ struct and fix cpplint

* refactor op registry

* fix line length and win array of ci; condense lines

* add mxnet_extension dir

* fixed extension to be dll for windows

* updated examples to use the same format as the example in the top-level Makefile: "lib<name>.so"

* removed destructor for CustomStatefulOp

* fix error in gemm test and clear up subgraph test

* lib path fix

* add unittest for custom op

* update Makefile revolve merge

* fix test and rename folder

* fix makefile rename

* fix cmake rename

* add explicit cpu context

* wkcn feedback: change mxtensor func name. use c++11 flag

* add operator keyward test and refine info print

* using typedef in forward

* small refine of docblock

* change names

* add separate stateful compute and pass state_op ptr

* user example using opresource alloc

* added DLTensor into MXTensor

* fixed whitespace

* added error check when DLTensor does not support MXNet data type

* changed to throw runtime exception

* changed include to stdexcept

* retrigger CI

* empty commit

* empty commit

* remove merge conflict

* add setdltensor for easy use and add docs

* CI

* re-trigger CI

* ci

* ci
---
 CMakeLists.txt                                |   2 +-
 Makefile                                      |  10 +-
 example/{ => extensions}/lib_api/Makefile     |   8 +-
 .../lib_api/init_lib.cc}                      |   8 +-
 example/{ => extensions}/lib_api/libtest.cc   |   4 +-
 .../lib_api/test_loading.py}                  |   6 +-
 example/extensions/lib_custom_op/Makefile     |  27 +
 example/extensions/lib_custom_op/gemm_lib.cc  | 235 +++++
 .../extensions/lib_custom_op/subgraph_lib.cc  | 113 ++
 example/extensions/lib_custom_op/test_gemm.py |  82 ++
 .../extensions/lib_custom_op/test_subgraph.py |  60 ++
 include/mxnet/lib_api.h                       | 988 +++++++++++++++++-
 python/mxnet/__init__.py                      |   3 +-
 python/mxnet/library.py                       |  23 +-
 src/c_api/c_api.cc                            | 582 ++++++++++-
 tests/python/gpu/test_operator_gpu.py         |   2 +-
 ..._library_loading.py => test_extensions.py} |  41 +-
 17 files changed, 2160 insertions(+), 34 deletions(-)
 rename example/{ => extensions}/lib_api/Makefile (80%)
 rename example/{lib_api/mylib.cc => extensions/lib_api/init_lib.cc} (91%)
 rename example/{ => extensions}/lib_api/libtest.cc (95%)
 rename example/{lib_api/test.py => extensions/lib_api/test_loading.py} (87%)
 create mode 100644 example/extensions/lib_custom_op/Makefile
 create mode 100644 example/extensions/lib_custom_op/gemm_lib.cc
 create mode 100644 example/extensions/lib_custom_op/subgraph_lib.cc
 create mode 100644 example/extensions/lib_custom_op/test_gemm.py
 create mode 100644 example/extensions/lib_custom_op/test_subgraph.py
 rename tests/python/unittest/{test_library_loading.py => test_extensions.py} (50%)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 2bb1f225ad13..aec5f21131d1 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -699,7 +699,7 @@ else()
 
 endif()
 
-add_library(sample_lib SHARED ${CMAKE_CURRENT_SOURCE_DIR}/example/lib_api/mylib.cc)
+add_library(sample_lib SHARED ${CMAKE_CURRENT_SOURCE_DIR}/example/extensions/lib_custom_op/gemm_lib.cc)
 target_include_directories(sample_lib PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/include/mxnet)
 set(MXNET_INSTALL_TARGETS mxnet)
 if(UNIX)
diff --git a/Makefile b/Makefile
index d60c0fcbf1da..639f259487ab 100644
--- a/Makefile
+++ b/Makefile
@@ -662,6 +662,10 @@ cpplint:
 pylint:
 	python3 -m pylint --rcfile=$(ROOTDIR)/ci/other/pylintrc --ignore-patterns=".*\.so$$,.*\.dll$$,.*\.dylib$$" python/mxnet
 
+# sample lib for MXNet extension dynamically loading custom operator
+sample_lib:
+	$(CXX) -shared -fPIC -std=c++11 example/extensions/lib_custom_op/gemm_lib.cc -o libsample_lib.so -I include/mxnet
+
 # Cython build
 cython:
 	cd python; $(PYTHON) setup.py build_ext --inplace --with-cython
@@ -705,10 +709,6 @@ rpkgtest:
 	Rscript -e 'require(testthat);res<-test_dir("R-package/tests/testthat");if(!testthat:::all_passed(res)){stop("Test failures", call. = FALSE)}'
 	Rscript -e 'res<-covr:::package_coverage("R-package");fileConn<-file(paste("r-package_coverage_",toString(runif(1)),".json"));writeLines(covr:::to_codecov(res), fileConn);close(fileConn)'
 
-
-sample_lib:
-	$(CXX) -shared -fPIC example/lib_api/mylib.cc -o libsample_lib.so -I include/mxnet
-
 scalaclean:
 	(cd $(ROOTDIR)/scala-package && mvn clean)
 
@@ -760,6 +760,7 @@ clean: rclean cyclean $(EXTRA_PACKAGES_CLEAN)
 	cd $(NNVM_PATH); $(MAKE) clean; cd -
 	cd $(TVM_PATH); $(MAKE) clean; cd -
 	cd $(AMALGAMATION_PATH); $(MAKE) clean; cd -
+	$(RM) libsample_lib.so
 	$(RM) -r  $(patsubst %, %/*.d, $(EXTRA_OPERATORS)) $(patsubst %, %/*/*.d, $(EXTRA_OPERATORS))
 	$(RM) -r  $(patsubst %, %/*.o, $(EXTRA_OPERATORS)) $(patsubst %, %/*/*.o, $(EXTRA_OPERATORS))
 else
@@ -771,6 +772,7 @@ clean: rclean mkldnn_clean cyclean testclean $(EXTRA_PACKAGES_CLEAN)
 	cd $(NNVM_PATH); $(MAKE) clean; cd -
 	cd $(TVM_PATH); $(MAKE) clean; cd -
 	cd $(AMALGAMATION_PATH); $(MAKE) clean; cd -
+	$(RM) libsample_lib.so
 endif
 
 clean_all: clean
diff --git a/example/lib_api/Makefile b/example/extensions/lib_api/Makefile
similarity index 80%
rename from example/lib_api/Makefile
rename to example/extensions/lib_api/Makefile
index e5893c8065c4..cb529390b77f 100644
--- a/example/lib_api/Makefile
+++ b/example/extensions/lib_api/Makefile
@@ -16,16 +16,16 @@
 # under the License.
 
 all:
-	g++ -shared -fPIC mylib.cc -o mylib.so -I ../../include/mxnet
+	g++ -std=c++11 -shared -fPIC init_lib.cc -o libinit_lib.so -I ../../../include/mxnet
 
 test:
-	g++ -std=c++11 -O3 -o libtest libtest.cc -ldl -I ../../include/mxnet
+	g++ -std=c++11 -O3 -o libtest libtest.cc -ldl -I ../../../include/mxnet
 
 windows:
-	cl /LD mylib.cc
+	cl /LD init_lib.cc
 
 win_test:
 	cl libtest.cc
 
 clean:
-	rm -rf mylib.so libtest
+	rm -rf *.so libtest
diff --git a/example/lib_api/mylib.cc b/example/extensions/lib_api/init_lib.cc
similarity index 91%
rename from example/lib_api/mylib.cc
rename to example/extensions/lib_api/init_lib.cc
index e67560a87f3d..6a040ffa2ecb 100644
--- a/example/lib_api/mylib.cc
+++ b/example/extensions/lib_api/init_lib.cc
@@ -19,19 +19,19 @@
 
 /*!
  * Copyright (c) 2015 by Contributors
- * \file mylib.cc
+ * \file init_lib.cc
  * \brief Sample library file
  */
 
 #include <iostream>
 #include "lib_api.h"
 
-int initialize(int version) {
+MXReturnValue initialize(int version) {
   if (version >= 10400) {
     std::cout << "MXNet version " << version << " supported" << std::endl;
-    return 1;
+    return MX_SUCCESS;
   } else {
     std::cout << "MXNet version " << version << " not supported" << std::endl;
-    return 0;
+    return MX_FAIL;
   }
 }
diff --git a/example/lib_api/libtest.cc b/example/extensions/lib_api/libtest.cc
similarity index 95%
rename from example/lib_api/libtest.cc
rename to example/extensions/lib_api/libtest.cc
index 8bdf36c05d37..0b2c6f64789c 100644
--- a/example/lib_api/libtest.cc
+++ b/example/extensions/lib_api/libtest.cc
@@ -40,10 +40,10 @@ int main(void) {
   // Get a handle to the library.
 #if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
   HINSTANCE handle;
-  handle = LoadLibrary(TEXT("mylib.dll"));
+  handle = LoadLibrary(TEXT("libinit_lib.dll"));
 #else
   void *handle;
-  handle = dlopen("mylib.so", RTLD_LAZY);
+  handle = dlopen("libinit_lib.so", RTLD_LAZY);
 #endif
 
   if (!handle) {
diff --git a/example/lib_api/test.py b/example/extensions/lib_api/test_loading.py
similarity index 87%
rename from example/lib_api/test.py
rename to example/extensions/lib_api/test_loading.py
index d73d85c02ced..d2fc2185716c 100644
--- a/example/lib_api/test.py
+++ b/example/extensions/lib_api/test_loading.py
@@ -26,6 +26,8 @@
 import os
 
 if (os.name=='posix'):
-    mx.library.load('mylib.so')
+    path = os.path.abspath('libinit_lib.so')
+    mx.library.load(path)
 elif (os.name=='nt'):
-    mx.library.load('mylib.dll')
+    path = os.path.abspath('libinit_lib.dll')
+    mx.library.load(path)
diff --git a/example/extensions/lib_custom_op/Makefile b/example/extensions/lib_custom_op/Makefile
new file mode 100644
index 000000000000..66079a16a338
--- /dev/null
+++ b/example/extensions/lib_custom_op/Makefile
@@ -0,0 +1,27 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+all: subgraph_lib gemm_lib
+
+gemm_lib:
+	g++ -shared -fPIC -std=c++11 gemm_lib.cc -o libgemm_lib.so -I ../../../include/mxnet
+
+subgraph_lib:
+	g++ -shared -fPIC -std=c++11 subgraph_lib.cc -o libsubgraph_lib.so -I ../../../include/mxnet
+
+clean:
+	rm -rf libsubgraph_lib.so libgemm_lib.so
diff --git a/example/extensions/lib_custom_op/gemm_lib.cc b/example/extensions/lib_custom_op/gemm_lib.cc
new file mode 100644
index 000000000000..3835207e2a16
--- /dev/null
+++ b/example/extensions/lib_custom_op/gemm_lib.cc
@@ -0,0 +1,235 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file gemm_lib.cc
+ * \brief Sample 2D gemm custom operator implementation library file
+ */
+
+#include <iostream>
+#include "lib_api.h"
+
+// main matrix multiplication routine
+void gemm(const float* A, const float* B, float* C,
+          const unsigned n, const unsigned k, const unsigned m) {
+  unsigned i, j, kk;
+  for (i = 0; i < n; i++) {
+    for (j = 0; j < m; j++) {
+      C[i*m+j] = 0;
+      for (kk = 0; kk < k; kk++) {
+        C[i*m+j] += A[i*k+kk] * B[kk*m+j];
+      }
+    }
+  }
+}
+
+void transpose(const float* A, float* At, const unsigned n, const unsigned m) {
+  unsigned i, j;
+  for (i = 0; i < n; i++) {
+    for (j = 0; j < m; j++) {
+      At[i*m+j] = A[j*n+i];
+    }
+  }
+}
+
+/*
+ * Executes C = A * B
+ * inputs[0] = A; inputs[1] = B; outputs[0] = C
+ */
+MXReturnValue forward(std::map<std::string, std::string> attrs,
+                      std::vector<MXTensor> inputs,
+                      std::vector<MXTensor> outputs,
+                      OpResource res) {
+  // simple example of using runtime data type
+  if (inputs[0].dtype == kFloat32) {
+    typedef float DType;
+    // extract data pointers from tensors
+    // if using dltensor repr, below lines can be changed to something like
+    // DType* A = reinterpret_cast<DType*>(inputs[0].dltensor.data);
+    DType* A = inputs[0].data<DType>();
+    DType* B = inputs[1].data<DType>();
+    DType* C = outputs[0].data<DType>();
+    // set tensor shapes
+    unsigned n = inputs[0].shape[0];
+    unsigned k = inputs[0].shape[1];
+    unsigned m = inputs[1].shape[1];
+
+    gemm(A, B, C, n, k, m);
+  }
+  return MX_SUCCESS;
+}
+
+/*
+ * Executes dA = dC * B.T; Executes dB = A.T * dC
+ ***** gradient inputs
+ * inputs[0] = dC
+ ***** original inputs
+ * inputs[1] = A; inputs[2] = B
+ ***** original outputs
+ * inputs[3] = C
+ ***** gradient outputs
+ * outputs[0] = dA; outputs[1] = dB
+ */
+MXReturnValue backward(std::map<std::string, std::string> attrs,
+                       std::vector<MXTensor> inputs,
+                       std::vector<MXTensor> outputs,
+                       OpResource res) {
+  // extract data pointers from tensors
+  float* dC = inputs[0].data<float>();
+  float* A = inputs[1].data<float>();
+  float* B = inputs[2].data<float>();
+  float* dA = outputs[0].data<float>();
+  float* dB = outputs[1].data<float>();
+  // set tensor shapes
+  unsigned n = inputs[1].shape[0];
+  unsigned k = inputs[1].shape[1];
+  unsigned m = inputs[2].shape[1];
+  // allocate temporary workspace memory through resource manager
+  // for multiple arrays better to request a big memory pool
+  void *workspace = res.alloc((k*n + m*k) * sizeof(float));
+  float *At = static_cast<float*>(workspace);
+  float *Bt = static_cast<float*>(workspace) + (k*n);
+
+  transpose(A, At, k, n);
+  transpose(B, Bt, m, k);
+  gemm(dC, Bt, dA, n, m, k);
+  gemm(At, dC, dB, k, n, m);
+
+  return MX_SUCCESS;
+}
+
+MXReturnValue parseAttrs(std::map<std::string, std::string> attrs, int* num_in, int* num_out) {
+  *num_in = 2;
+  *num_out = 1;
+  return MX_SUCCESS;
+}
+
+MXReturnValue inferType(std::map<std::string, std::string> attrs,
+                        std::vector<int> &intypes,
+                        std::vector<int> &outtypes) {
+  // validate inputs
+  if (intypes.size() != 2) {
+    std::cout << "Expected 2 inputs to inferType" << std::endl;
+    return MX_FAIL;
+  }
+  for (unsigned i = 0; i < intypes.size(); i++) {
+    if (intypes[i] != kFloat32) {
+      std::cout << "Expected input " << i << " to have float32 type" << std::endl;
+      return MX_FAIL;
+    }
+  }
+
+  outtypes[0] = intypes[0];
+  return MX_SUCCESS;
+}
+
+MXReturnValue inferShape(std::map<std::string, std::string> attrs,
+                         std::vector<std::vector<unsigned int>> &inshapes,
+                         std::vector<std::vector<unsigned int>> &outshapes) {
+  // validate inputs
+  if (inshapes.size() != 2) {
+    std::cout << "Expected 2 inputs to inferShape" << std::endl;
+    return MX_FAIL;
+  }
+  if (inshapes[0].size() != 2 || inshapes[1].size() != 2) {
+    std::cout << "Expected 2D matrices for both inputs to inferShape" << std::endl;
+    return MX_FAIL;
+  }
+
+  unsigned n = inshapes[0][0];
+  unsigned k = inshapes[0][1];
+  unsigned kk = inshapes[1][0];
+  unsigned m = inshapes[1][1];
+  if (k != kk) {
+    std::cout << "Exected first input axis 1 equals to second input axis 0" << std::endl;
+    return MX_FAIL;
+  }
+
+  outshapes[0] = {n, m};
+  return MX_SUCCESS;
+}
+
+REGISTER_OP(my_gemm)
+.setForward(forward)
+.setBackward(backward)
+.setParseAttrs(parseAttrs)
+.setInferType(inferType)
+.setInferShape(inferShape);
+
+/* ------------------------------------------------------------------------- */
+
+class MyStatefulGemm : public CustomStatefulOp {
+ public:
+  explicit MyStatefulGemm(int count) : count(count) {}
+
+  MXReturnValue Forward(std::vector<MXTensor> inputs,
+                        std::vector<MXTensor> outputs,
+                        OpResource op_res) {
+    ++count;
+    std::cout << "Info: keyword + number of forward: " << count << std::endl;
+    std::map<std::string, std::string> attrs;
+    return forward(attrs, inputs, outputs, op_res);
+  }
+
+  MXReturnValue Backward(std::vector<MXTensor> inputs,
+                         std::vector<MXTensor> outputs,
+                         OpResource op_res) {
+    std::map<std::string, std::string> attrs;
+    return backward(attrs, inputs, outputs, op_res);
+  }
+
+  ~MyStatefulGemm() {}
+
+ private:
+  int count;
+};
+
+MXReturnValue createOpState(std::map<std::string, std::string> attrs,
+                            CustomStatefulOp** op_inst) {
+  int count = 0;
+  if (attrs.count("test_kw") > 0)
+    count = std::stoi(attrs["test_kw"]);
+  *op_inst = new MyStatefulGemm(count);
+  std::cout << "Info: stateful operator created" << std::endl;
+  return MX_SUCCESS;
+}
+
+MXReturnValue mutateInputs(std::map<std::string, std::string> attrs,
+                           std::vector<int> &input_indices) {
+  // input_indices.push_back(1);  // mark mutate input
+  return MX_SUCCESS;
+}
+
+REGISTER_OP(state_gemm)
+.setParseAttrs(parseAttrs)
+.setInferType(inferType)
+.setInferShape(inferShape)
+.setMutateInputs(mutateInputs)
+.setCreateOpState(createOpState);
+
+MXReturnValue initialize(int version) {
+  if (version >= 10400) {
+    std::cout << "MXNet version " << version << " supported" << std::endl;
+    return MX_SUCCESS;
+  } else {
+    std::cout << "MXNet version " << version << " not supported" << std::endl;
+    return MX_FAIL;
+  }
+}
diff --git a/example/extensions/lib_custom_op/subgraph_lib.cc b/example/extensions/lib_custom_op/subgraph_lib.cc
new file mode 100644
index 000000000000..8e7e8833745a
--- /dev/null
+++ b/example/extensions/lib_custom_op/subgraph_lib.cc
@@ -0,0 +1,113 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file subgraph_lib.cc
+ * \brief subgraph operator implementation library file
+ */
+
+#include <iostream>
+#include "lib_api.h"
+
+MXReturnValue parseAttrs(std::map<std::string, std::string> attrs,
+                         int* num_in, int* num_out) {
+  *num_in = 1;
+  *num_out = 1;
+  if (attrs.count(SUBGRAPH_SYM_JSON)) {
+    // example of subgraph json parsing
+    JsonParser jp;
+    JsonVal val = jp.parse_to_json(attrs[SUBGRAPH_SYM_JSON]);
+    int input = 0;
+    for (auto &item : val.map[JsonVal("nodes")].list) {
+      if (item.map[JsonVal("op")].str == "null")
+        input++;
+    }
+    int output = val.map[JsonVal("heads")].list.size();
+    *num_in = input;
+    *num_out = output;
+  }
+  return MX_SUCCESS;
+}
+
+MXReturnValue inferType(std::map<std::string, std::string> attrs,
+                        std::vector<int> &intypes,
+                        std::vector<int> &outtypes) {
+  outtypes[0] = intypes[0];
+  return MX_SUCCESS;
+}
+
+MXReturnValue inferShape(std::map<std::string, std::string> attrs,
+                         std::vector<std::vector<unsigned int>> &inshapes,
+                         std::vector<std::vector<unsigned int>> &outshapes) {
+  outshapes[0] = inshapes[0];
+  return MX_SUCCESS;
+}
+
+class MyStatefulOp : public CustomStatefulOp {
+ public:
+  explicit MyStatefulOp(std::string sym) : subgraph_sym(sym) {}
+
+  MXReturnValue Forward(std::vector<MXTensor> inputs,
+                        std::vector<MXTensor> outputs,
+                        OpResource op_res) {
+    std::cout << "Info: subgraph symbol is: " << std::endl;
+    std::cout << subgraph_sym << std::endl;
+    float* in_data = inputs[0].data<float>();
+    float* out_data = outputs[0].data<float>();
+    std::cout << "Info: output is: " << std::endl;
+    for (int i = 0; i < inputs[0].size(); i++) {
+      out_data[i] = in_data[i];
+    }
+    return MX_SUCCESS;
+  }
+
+ private:
+  std::string subgraph_sym;
+};
+
+MXReturnValue createOpState(std::map<std::string, std::string> attrs,
+                            CustomStatefulOp** op_inst) {
+  std::string serialized_subgraph = "[empty]";
+  // MXNet subgraph is stored as Symbol in operator node attrs subgraphs field
+  // custom subgraph is stored as json string in custom operator attrs map entry
+  if (attrs.count(SUBGRAPH_SYM_JSON)) {
+    // user can now parse json and run other custom ops inside subgraph
+    serialized_subgraph = attrs[SUBGRAPH_SYM_JSON];
+  }
+  *op_inst = new MyStatefulOp(serialized_subgraph);
+  std::cout << "Info: stateful operator created" << std::endl;
+  return MX_SUCCESS;
+}
+
+REGISTER_OP(_custom_subgraph_op)
+.setParseAttrs(parseAttrs)
+.setInferType(inferType)
+.setInferShape(inferShape)
+.setCreateOpState(createOpState);
+
+MXReturnValue initialize(int version) {
+  if (version >= 10400) {
+    std::cout << "MXNet version " << version << " supported" << std::endl;
+    return MX_SUCCESS;
+  } else {
+    std::cout << "MXNet version " << version << " not supported" << std::endl;
+    return MX_FAIL;
+  }
+}
diff --git a/example/extensions/lib_custom_op/test_gemm.py b/example/extensions/lib_custom_op/test_gemm.py
new file mode 100644
index 000000000000..9a588255032f
--- /dev/null
+++ b/example/extensions/lib_custom_op/test_gemm.py
@@ -0,0 +1,82 @@
+#!/usr/bin/env python3
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# coding: utf-8
+# pylint: disable=arguments-differ
+
+# This test checks dynamic loading of custom library into MXNet
+# and checks end to end compute of a simple 2D gemm custom op
+
+import mxnet as mx
+import os
+
+#load library
+if (os.name=='posix'):
+    path = os.path.abspath('libgemm_lib.so')
+    mx.library.load(path)
+elif (os.name=='nt'):
+    path = os.path.abspath('libgemm_lib.dll')
+    mx.library.load(path)
+
+a = mx.nd.array([[1,2,3],[4,5,6]])
+b = mx.nd.array([[7],[8],[9]])
+
+print("--------start ndarray compute---------")
+print(mx.nd.my_gemm(a,b))
+print("--------")
+print(mx.nd.state_gemm(a,b,test_kw=100))
+
+print("--------start symbolic compute--------")
+s = mx.sym.Variable('s')
+t = mx.sym.Variable('t')
+c = mx.sym.my_gemm(s,t)
+d = mx.sym.state_gemm(s,t,test_kw=200)
+
+in_grad = [mx.nd.empty((2,3)),mx.nd.empty((3,1))]
+in_grad2 = [mx.nd.empty((2,3)),mx.nd.empty((3,1))]
+
+exe = c.bind(ctx=mx.cpu(),args={'s':a,'t':b},args_grad=in_grad)
+exe2 = d.bind(ctx=mx.cpu(),args={'s':a,'t':b},args_grad=in_grad2)
+
+out = exe.forward()
+print(out)
+print("-------")
+
+out2 = exe2.forward()
+out2 = exe2.forward()
+print(out2)
+print("-------")
+
+# baseline forward
+e = mx.sym.linalg.gemm2(s,t)
+in_grad3 = [mx.nd.empty((2,3)),mx.nd.empty((3,1))]
+exe3 = e.bind(ctx=mx.cpu(),args={'s':a,'t':b},args_grad=in_grad3)
+out3 = exe3.forward()
+print(out3)
+
+print("--------start backward compute--------")
+out_grad = mx.nd.ones((2,1))
+exe.backward([out_grad])
+print(in_grad)
+print("-------")
+exe2.backward([out_grad])
+print(in_grad2)
+print("-------")
+exe3.backward([out_grad])
+print(in_grad3)
diff --git a/example/extensions/lib_custom_op/test_subgraph.py b/example/extensions/lib_custom_op/test_subgraph.py
new file mode 100644
index 000000000000..2625b13f6794
--- /dev/null
+++ b/example/extensions/lib_custom_op/test_subgraph.py
@@ -0,0 +1,60 @@
+#!/usr/bin/env python3
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# coding: utf-8
+# pylint: disable=arguments-differ
+
+# This test checks if dynamic loading of library into MXNet is successful
+# and checks the end of end computation of custom operator
+
+import mxnet as mx
+import os, ctypes
+from mxnet.base import _LIB, check_call, mx_uint, c_str, c_str_array, SymbolHandle
+
+# load library
+if (os.name=='posix'):
+    path = os.path.abspath('libsubgraph_lib.so')
+    mx.library.load(path)
+elif (os.name=='nt'):
+    path = os.path.abspath('libsubgraph_lib.dll')
+    mx.library.load(path)
+
+a = mx.sym.var('a')
+b = mx.sym.var('b')
+c = a + b
+d = mx.sym.exp(c)
+ret = mx.sym.log(d)
+
+op_names = ['exp','log']
+out = SymbolHandle()
+
+check_call(_LIB.MXBuildSubgraphByOpNames(ret.handle,
+                                         c_str('default'),
+                                         mx_uint(len(op_names)),
+                                         c_str_array(op_names),
+                                         ctypes.byref(out)))
+partitioned_sym = mx.sym.Symbol(out)
+json_sym = partitioned_sym.tojson()
+
+mystr = json_sym.replace("_CachedOp","_custom_subgraph_op")
+mysym = mx.sym.load_json(mystr)
+
+exe = mysym.bind(ctx=mx.cpu(), args={'a':mx.nd.ones((3,2)), 'b':mx.nd.ones((3,2))})
+out = exe.forward()
+print(out)
diff --git a/include/mxnet/lib_api.h b/include/mxnet/lib_api.h
index ca3b2952eafa..290a63518373 100644
--- a/include/mxnet/lib_api.h
+++ b/include/mxnet/lib_api.h
@@ -18,33 +18,999 @@
  */
 
 /*!
- * Copyright (c) 2015 by Contributors
+ * Copyright (c) 2019 by Contributors
  * \file lib_api.h
  * \brief APIs to interact with libraries
+ * This API specifies function prototypes to
+ * register custom ops for library authors
  */
+
 #ifndef MXNET_LIB_API_H_
 #define MXNET_LIB_API_H_
 
+#include <stdint.h>
+#include <stdlib.h>
+#include <vector>
+#include <map>
+#include <string>
+#include <iostream>
+#include <utility>
+#include <stdexcept>
+
+#define MX_LIBRARY_VERSION 1
+
+/*
+ * Import from DLPack https://github.com/dmlc/dlpack/blob/master/include/dlpack/dlpack.h
+ */
+#ifndef DLPACK_VERSION
+#ifdef __cplusplus
+#define DLPACK_EXTERN_C extern "C"
+#else
+#define DLPACK_EXTERN_C
+#endif
+
+/*! \brief The current version of dlpack */
+#define DLPACK_VERSION 020
+
+/*! \brief DLPACK_DLL prefix for windows */
+#ifdef _WIN32
+#ifdef DLPACK_EXPORTS
+#define DLPACK_DLL __declspec(dllexport)
+#else
+#define DLPACK_DLL __declspec(dllimport)
+#endif
+#else
+#define DLPACK_DLL
+#endif
+
+#include <stdint.h>
+#include <stddef.h>
+
+#ifdef __cplusplus
+extern "C" {
+  #endif
+  /*!
+   * \brief The device type in DLContext.
+   */
+  typedef enum {
+    /*! \brief CPU device */
+    kDLCPU = 1,
+    /*! \brief CUDA GPU device */
+    kDLGPU = 2,
+    /*!
+     * \brief Pinned CUDA GPU device by cudaMallocHost
+     * \note kDLCPUPinned = kDLCPU | kDLGPU
+     */
+    kDLCPUPinned = 3,
+    /*! \brief OpenCL devices. */
+    kDLOpenCL = 4,
+    /*! \brief Vulkan buffer for next generation graphics. */
+    kDLVulkan = 7,
+    /*! \brief Metal for Apple GPU. */
+    kDLMetal = 8,
+    /*! \brief Verilog simulator buffer */
+    kDLVPI = 9,
+    /*! \brief ROCm GPUs for AMD GPUs */
+    kDLROCM = 10,
+    /*!
+     * \brief Reserved extension device type,
+     * used for quickly test extension device
+     * The semantics can differ depending on the implementation.
+     */
+    kDLExtDev = 12,
+  } DLDeviceType;
+
+  /*!
+   * \brief A Device context for Tensor and operator.
+   */
+  typedef struct {
+    /*! \brief The device type used in the device. */
+    DLDeviceType device_type;
+    /*! \brief The device index */
+    int device_id;
+  } DLContext;
+
+  /*!
+   * \brief The type code options DLDataType.
+   */
+  typedef enum {
+    kDLInt = 0U,
+    kDLUInt = 1U,
+    kDLFloat = 2U,
+  } DLDataTypeCode;
+
+  /*!
+   * \brief The data type the tensor can hold.
+   *
+   *  Examples
+   *   - float: type_code = 2, bits = 32, lanes=1
+   *   - float4(vectorized 4 float): type_code = 2, bits = 32, lanes=4
+   *   - int8: type_code = 0, bits = 8, lanes=1
+   */
+  typedef struct {
+    /*!
+     * \brief Type code of base types.
+     * We keep it uint8_t instead of DLDataTypeCode for minimal memory
+     * footprint, but the value should be one of DLDataTypeCode enum values.
+     * */
+    uint8_t code;
+    /*!
+     * \brief Number of bits, common choices are 8, 16, 32.
+     */
+    uint8_t bits;
+    /*! \brief Number of lanes in the type, used for vector types. */
+    uint16_t lanes;
+  } DLDataType;
+
+  /*!
+   * \brief Plain C Tensor object, does not manage memory.
+   */
+  typedef struct {
+    /*!
+     * \brief The opaque data pointer points to the allocated data. This will be
+     * CUDA device pointer or cl_mem handle in OpenCL. This pointer is always
+     * aligns to 256 bytes as in CUDA.
+     *
+     * For given DLTensor, the size of memory required to store the contents of
+     * data is calculated as follows:
+     *
+     * \code{.c}
+     * static inline size_t GetDataSize(const DLTensor* t) {
+     *   size_t size = 1;
+     *   for (tvm_index_t i = 0; i < t->ndim; ++i) {
+     *     size *= t->shape[i];
+     *   }
+     *   size *= (t->dtype.bits * t->dtype.lanes + 7) / 8;
+     *   return size;
+     * }
+     * \endcode
+     */
+    void* data;
+    /*! \brief The device context of the tensor */
+    DLContext ctx;
+    /*! \brief Number of dimensions */
+    int ndim;
+    /*! \brief The data type of the pointer*/
+    DLDataType dtype;
+    /*! \brief The shape of the tensor */
+    int64_t* shape;
+    /*!
+     * \brief strides of the tensor (in number of elements, not bytes)
+     *  can be NULL, indicating tensor is compact and row-majored.
+     */
+    int64_t* strides;
+    /*! \brief The offset in bytes to the beginning pointer to data */
+    uint64_t byte_offset;
+  } DLTensor;
+#ifdef __cplusplus
+}  // DLPACK_EXTERN_C
+#endif
+#endif
+
+/*!
+ * \brief Tensor data type, consistent with mshadow data type
+ */
+enum MXDType {
+  kFloat32 = 0,
+  kFloat64 = 1,
+  kFloat16 = 2,
+  kUint8 = 3,
+  kInt32 = 4,
+  kInt8  = 5,
+  kInt64 = 6,
+};
+
+enum MXReturnValue {
+  MX_FAIL = 0,
+  MX_SUCCESS = 1,
+};
+
+/*!
+ * \brief Tensor data structure used by custom operator
+ */
+struct MXTensor {
+  MXTensor() : data_ptr(NULL) {}
+
+  MXTensor(void *data_ptr, const std::vector<int64_t> &shape, MXDType dtype)
+  : data_ptr(data_ptr), shape(shape), dtype(dtype) {}
+
+  /*! \brief populate DLTensor fields */
+  void setDLTensor() {
+    dltensor.data = data_ptr;
+    dltensor.ctx.device_type = kDLCPU;
+    dltensor.ctx.device_id = 0;
+    dltensor.ndim = shape.size();
+    dltensor.shape = const_cast<int64_t*>(shape.data());
+    dltensor.strides = NULL;
+    dltensor.byte_offset = 0;
+    dltensor.dtype.lanes = 1;
+    switch (dtype) {
+    case kFloat32:
+      dltensor.dtype.code = kDLFloat;
+      dltensor.dtype.bits = 32;
+      break;
+    case kFloat64:
+      dltensor.dtype.code = kDLFloat;
+      dltensor.dtype.bits = 64;
+      break;
+    case kFloat16:
+      dltensor.dtype.code = kDLFloat;
+      dltensor.dtype.bits = 16;
+      break;
+    case kUint8:
+      dltensor.dtype.code = kDLUInt;
+      dltensor.dtype.bits = 8;
+      break;
+    case kInt32:
+      dltensor.dtype.code = kDLInt;
+      dltensor.dtype.bits = 32;
+      break;
+    case kInt8:
+      dltensor.dtype.code = kDLInt;
+      dltensor.dtype.bits = 8;
+      break;
+    case kInt64:
+      dltensor.dtype.code = kDLInt;
+      dltensor.dtype.bits = 64;
+      break;
+    default:
+      dltensor.dtype.code = 0;
+      dltensor.dtype.bits = 0;
+      throw std::runtime_error("Error! Invalid dtype flag: "
+                               + std::to_string(static_cast<int>(dtype))
+                               + " when constructing MXTensor");
+    }
+  }
+
+  /*! \brief helper function to cast data pointer */
+  template<typename data_type>
+  inline data_type* data() {
+    return reinterpret_cast<data_type*>(data_ptr);
+  }
+
+  /*! \brief helper function to get data size */
+  inline int64_t size() {
+    int64_t size = 1;
+    for (unsigned int i = 0; i < shape.size(); i++) {
+      size *= shape[i];
+    }
+    return size;
+  }
+
+  // data is flatten 1D repr of tensor, elements are in continuous memory
+  // user can access each element using the shape of tensor
+  void *data_ptr;
+
+  // shape is in [2,3,4] format to represent high-dim tensor
+  std::vector<int64_t> shape;
+
+  // type can only be MXDType enum types
+  MXDType dtype;
+
+  // corresponding DLTensor repr of MXTensor
+  // easy way to reuse functions taking DLTensor
+  DLTensor dltensor;
+};
+
+/*!
+ * \brief resource malloc function to allocate memory inside Forward/Backward functions
+ */
+typedef void* (*xpu_malloc_t)(void*, int);
+
 /*!
- * \brief Following are the APIs implemented in the external library
+ * \brief provide resource APIs memory allocation mechanism to Forward/Backward functions
+ */
+class OpResource {
+ public:
+  OpResource(xpu_malloc_t cm, void* ca) : cpu_malloc(cm), cpu_alloc(ca) {}
+
+  /*! \brief allocate memory controlled by MXNet */
+  void* alloc(int size) {
+    return cpu_malloc(cpu_alloc, size);
+  }
+
+ private:
+  xpu_malloc_t cpu_malloc;
+  void* cpu_alloc;
+};
+
+/*!
+ * \brief Json utility to parse serialized subgraph symbol
+ */
+/*! \brief Macro to help passing serialized subgraph through attribute dict */
+#define SUBGRAPH_SYM_JSON "subgraph_sym_json"
+
+/*! \brief Types of JSON objects */
+enum JsonType {ERR, STR, NUM, LIST, MAP};
+
+/*! \brief definition of JSON objects */
+struct JsonVal {
+  JsonVal() : type(ERR), num(-1), str("") {}  // default constructor
+  // construct a JSON object by type
+  explicit JsonVal(JsonType t) : type(t), num(-1), str("") {}
+  // construct a string JSON object
+  explicit JsonVal(std::string s) : type(STR), num(-1), str(s) {}
+  // construct a number JSON object
+  explicit JsonVal(int n) : type(NUM), num(n), str(std::to_string(n)) {}
+  // complex constructor
+  JsonVal(JsonType t, int n, std::string s) : type(t), num(n), str(s) {}
+  bool operator<(const JsonVal &o) const {
+    // for string JSON objects compare the string
+    if (type == STR) return type == o.type && str < o.str;
+    // for number JSON objects compare the number
+    if (type == NUM) return type == o.type && num < o.num;
+    // for list JSON objects, compare the size of list, and then each object in the list
+    if (type == LIST) {
+      if (list.size() != o.list.size()) return false;
+      for (unsigned int i=0; i< list.size(); i++)
+        if (list[i] < o.list[i])
+          return false;  // if we find an object that doesnt match return
+      return true;  // all objects in lists matched
+    }
+    // for map JSON objects, compare the size of map, and then each key/value in the maps
+    if (type == MAP) {
+      if (map.size() != o.map.size()) return false;
+      for (auto &item : map) {
+        // if one map is missing a key in another return
+        if (o.map.find(item.first) == o.map.end()) return false;
+        if (item.second < o.map.at(item.first)) return false;
+      }
+      return true;
+    }
+    return type < o.type;
+  }
+  JsonType type;
+  int num;
+  std::string str;
+  std::vector<JsonVal> list;
+  std::map<JsonVal, JsonVal> map;
+};
+
+/*! \brief functions used for parsing JSON */
+struct JsonParser {
+  JsonVal parse_to_json(std::string json) {
+    unsigned int idx = 0;
+    return parse(json, &idx);
+  }
+  void print_json_val(JsonVal val) {
+    std::cout << json_val_string(val) << std::endl;
+  }
+  // debug function to convert a JSON object to a string
+  std::string json_val_string(const JsonVal &val) {
+    std::string ret;
+    switch (val.type) {
+    case ERR:
+      ret = "json(Error)";
+      break;
+    case STR:
+      ret = "json(STR:" + val.str + ")";
+      break;
+    case NUM:
+      ret = "json(INT:" + val.str + ")";
+      break;
+    case LIST:
+      ret = "json(LIST:[";
+      for (auto &item : val.list)
+        ret += json_val_string(item) + ",";
+      ret += "])";
+      break;
+    case MAP:
+      ret = "json(MAP:{";
+      for (auto &item : val.map)
+        ret += json_val_string(item.first) + " : " + json_val_string(item.second) + ",";
+      ret += "})";
+      break;
+    }
+    return ret;
+  }
+  // parse a string JSON object
+  JsonVal parse_string(std::string json, unsigned int* idx) {
+    JsonVal ret(STR);
+    while (*idx < json.size()) {
+      if (json[*idx] == '"') {
+        ++(*idx);
+        return ret;
+      } else {
+        ret.str += json[*idx];
+        ++(*idx);
+      }
+    }
+    std::cout << "Error! Unable to parse string" << std::endl;
+    return JsonVal();
+  }
+  // parse a number JSON object
+  JsonVal parse_num(std::string json, unsigned int* idx) {
+    JsonVal ret(NUM);
+    while (*idx < json.size()) {
+      if (json[*idx] >= '0' && json[*idx] <= '9') {
+        ret.str += json[*idx];
+        ++(*idx);
+      } else {
+        break;
+      }
+    }
+    ret.num = std::stoi(ret.str);
+    return ret;
+  }
+  // parse a list of JSON objects
+  JsonVal parse_list(std::string json, unsigned int* idx) {
+    JsonVal ret(LIST);
+    while (*idx < json.size()) {
+      if (json[*idx] == ']') {
+        ++(*idx);
+        return ret;
+      } else {
+        JsonVal item = parse(json, idx);
+        if (item.type != ERR)
+          ret.list.push_back(item);
+      }
+    }
+    std::cout << "Error! Unable to parse list" << std::endl;
+    return JsonVal();
+  }
+  // parse a map of JSON objects
+  JsonVal parse_map(std::string json, unsigned int* idx) {
+    JsonVal ret(MAP), key;
+    while (*idx < json.size()) {
+      if (json[*idx] == '}') {
+        ++(*idx);
+        return ret;
+      } else {
+        JsonVal item = parse(json, idx);
+        if (key.type == ERR) {
+          key = item;
+        } else {
+          ret.map[key] = item;
+          key.type = ERR;
+        }
+      }
+    }
+    std::cout << "Error! Unable to parse map" << std::endl;
+    return JsonVal();
+  }
+  // generic parse function
+  JsonVal parse(std::string json, unsigned int *idx) {
+    JsonVal ret;
+    while (*idx < json.size()) {
+      if (json[*idx] == '"') {
+        ++(*idx);
+        ret = parse_string(json, idx);
+      } else if (json[*idx] >= '0' && json[*idx] <= '9') {
+        ret = parse_num(json, idx);
+      } else if (json[*idx] == '[') {
+        ++(*idx);
+        ret = parse_list(json, idx);
+      } else if (json[*idx] == '{') {
+        ++(*idx);
+        ret = parse_map(json, idx);
+      } else if (json[*idx] == ']' || json[*idx] == '}') {return ret;}
+      if (ret.type != ERR) return ret;
+      ++(*idx);
+    }
+    return ret;
+  }
+};
+
+/*!
+ * \brief An abstract class for library author creating stateful op
+ * custom library should override Forward and destructor, and has an
+ * option to implement Backward
+ */
+class CustomStatefulOp {
+ public:
+  virtual MXReturnValue Forward(std::vector<MXTensor> inputs,
+                                std::vector<MXTensor> outputs,
+                                OpResource op_res) = 0;
+  virtual MXReturnValue Backward(std::vector<MXTensor> inputs,
+                                 std::vector<MXTensor> outputs,
+                                 OpResource op_res) {
+    std::cout << "Error! Operator does not support backward" << std::endl;
+    return MX_FAIL;
+  }
+};
+
+/*! \brief StatefulOp wrapper class to pass to backend OpState */
+class CustomStatefulOpWrapper {
+ public:
+  explicit CustomStatefulOpWrapper(CustomStatefulOp* inst) : instance(inst) {}
+  CustomStatefulOp* get_instance() { return instance; }
+ private:
+  CustomStatefulOp* instance;
+};
+
+/*! \brief Custom Operator function templates */
+typedef MXReturnValue (*fcomp_t)(std::map<std::string, std::string>,
+                                 std::vector<MXTensor>, std::vector<MXTensor>,
+                                 OpResource res);
+typedef MXReturnValue (*parseAttrs_t)(std::map<std::string, std::string>,
+                                      int*, int*);
+typedef MXReturnValue (*inferType_t)(std::map<std::string, std::string>,
+                                     std::vector<int>&, std::vector<int>&);
+typedef MXReturnValue (*inferShape_t)(std::map<std::string, std::string>,
+                                      std::vector<std::vector<unsigned int> >&,
+                                      std::vector<std::vector<unsigned int> >&);
+typedef MXReturnValue (*mutateInputs_t)(std::map<std::string, std::string>,
+                                      std::vector<int>&);
+typedef MXReturnValue (*createOpState_t)(std::map<std::string, std::string>,
+                                      CustomStatefulOp**);
+
+/*!
+ * \brief Class to hold custom operator registration
+ */
+class CustomOp {
+ public:
+  explicit CustomOp(const char* op_name) : name(op_name),
+    forward(NULL), backward(NULL), parse_attrs(NULL), infer_type(NULL),
+    infer_shape(NULL), mutate_inputs(NULL), create_opstate(NULL) {}
+  ~CustomOp() {}
+  CustomOp& setForward(fcomp_t fcomp) {
+    forward = fcomp;
+    return *this;
+  }
+  CustomOp& setBackward(fcomp_t fcomp) {
+    backward = fcomp;
+    return *this;
+  }
+  CustomOp& setParseAttrs(parseAttrs_t func) {
+    parse_attrs = func;
+    return *this;
+  }
+  CustomOp& setInferType(inferType_t func) {
+    infer_type = func;
+    return *this;
+  }
+  CustomOp& setInferShape(inferShape_t func) {
+    infer_shape = func;
+    return *this;
+  }
+  CustomOp& setMutateInputs(mutateInputs_t func) {
+    mutate_inputs = func;
+    return *this;
+  }
+  CustomOp& setCreateOpState(createOpState_t func) {
+    create_opstate = func;
+    return *this;
+  }
+
+  /*! \brief operator name */
+  const char* name;
+  /*! \brief operator functions */
+  fcomp_t forward;
+  fcomp_t backward;
+  parseAttrs_t parse_attrs;
+  inferType_t infer_type;
+  inferShape_t infer_shape;
+  mutateInputs_t mutate_inputs;
+  createOpState_t create_opstate;
+};
+
+/*!
+ * \brief Registry class to registers things (ops, properties)
+ *        Singleton class
+ */
+template <class T>
+class Registry {
+ public:
+  /*!
+   * \brief get singleton pointer to class
+   * \returns pointer to class
+   */
+  static Registry* get() {
+    static Registry inst;
+    return &inst;
+  }
+  /*!
+   * \brief add a new entry
+   * \returns new object associated with registered name
+   */
+  T& add(const char* name) {
+    T *entry = new T(name);
+    entries.push_back(entry);
+    return *entry;
+  }
+  int size() {
+    return entries.size();
+  }
+  T& get(int idx) {
+    return *(entries[idx]);
+  }
+
+ private:
+  /*! \brief constructor */
+  Registry() {}
+  /*! \brief destructor */
+  ~Registry() {}
+  /*! \brief map of entries in registry */
+  std::vector<T*> entries;
+};
+
+/*!
+ * \brief Macros to help with string concat
+ * Annoyingly, the concat_ and concat macros are necessary to
+ * be able to use __COUNTER__ in an identifier name 
+ */
+#define MX_STR_CONCAT_(__a, __b) __a ## __b
+#define MX_STR_CONCAT(__a, __b) MX_STR_CONCAT_(__a, __b)
+
+/*! \brief convert a token to a string */
+#define MX_STRINGIFY(x) #x
+#define MX_TOSTRING(x) MX_STRINGIFY(x)
+
+/*! \brief declare a variable with custom name */
+#define MX_REGISTER_NAME_(Name) MXNet ## _CustomOp ## _
+#define MX_REGISTER_DEF_(Name) CustomOp MX_REGISTER_NAME_(Name)
+
+/*! \brief assign a var to a value */
+#define REGISTER_OP(Name) MX_STR_CONCAT(MX_REGISTER_DEF_(Name), __COUNTER__) = \
+    Registry<CustomOp>::get()->add(MX_TOSTRING(Name))
+
+/* -------------- BELOW ARE CTYPE FUNCTIONS PROTOTYPES --------------- */
+
+/*!
+ * \brief Following are the C type APIs implemented in the external library
  * Each API has a #define string that is used to lookup the function in the library
  * Followed by the function declaration
  */
+#define MXLIB_OPREGSIZE_STR "_opRegSize"
+typedef int (*opRegSize_t)(void);
+
+#define MXLIB_OPREGGET_STR "_opRegGet"
+typedef int (*opRegGet_t)(int, const char**, fcomp_t*, fcomp_t*,
+                          parseAttrs_t*, inferType_t*,
+                          inferShape_t*, mutateInputs_t*,
+                          createOpState_t*);
+
+#define MXLIB_OPCALLFREE_STR "_opCallFree"
+typedef int (*opCallFree_t)(void*);
+
+#define MXLIB_OPCALLPARSEATTRS_STR "_opCallParseAttrs"
+typedef int (*opCallParseAttrs_t)(parseAttrs_t, const char* const*, const char* const*, int,
+                                  int*, int*);
+
+#define MXLIB_OPCALLINFERSHAPE_STR "_opCallInferShape"
+typedef int (*opCallInferShape_t)(inferShape_t, const char* const*, const char* const*, int,
+                                  unsigned int**, int*, int,
+                                  unsigned int***, int**, int);
+
+#define MXLIB_OPCALLINFERTYPE_STR "_opCallInferType"
+typedef int (*opCallInferType_t)(inferType_t, const char* const*, const char* const*, int,
+                                  int*, int, int*, int);
+
+#define MXLIB_OPCALLFCOMP_STR "_opCallFCompute"
+typedef int (*opCallFComp_t)(fcomp_t, const char* const*, const char* const*, int,
+                           const int64_t**, int*, void**, int*, int,
+                           const int64_t**, int*, void**, int*, int,
+                           xpu_malloc_t, void*);
+
+#define MXLIB_OPCALLBKWD_STR "_opCallBackward"
+typedef int (*opCallBkwd_t)(fcomp_t, const char* const*, const char* const*, int,
+                            const int64_t**, int*, void**, int*, int,
+                            const int64_t**, int*, void**, int*, int,
+                            xpu_malloc_t, void*);
+
+#define MXLIB_OPCALLMUTATEINPUTS_STR "_opCallMutateInputs"
+typedef int (*opCallMutateInputs_t)(mutateInputs_t, const char* const*, const char* const*, int,
+                                    int**, int*);
+
+#define MXLIB_OPCALLCREATEOPSTATE_STR "_opCallCreateOpState"
+typedef int (*opCallCreateOpState_t)(createOpState_t, const char* const*, const char* const*, int,
+                                     void**);
+
+#define MXLIB_OPCALLFSTATEFULCOMP_STR "_opCallFStatefulCompute"
+typedef int (*opCallFStatefulComp_t)(bool, void*, const int64_t**, int*, void**, int*, int,
+                                     const int64_t**, int*, void**, int*, int,
+                                     xpu_malloc_t, void*);
+
 #define MXLIB_INITIALIZE_STR "initialize"
 typedef int (*initialize_t)(int);
 
+#define MXLIB_OPVERSION_STR "_opVersion"
+typedef int (*opVersion_t)();
+
 extern "C" {
-    /*!
-     * \brief Checks if the MXNet version is supported by the library.
-     * If supported, initializes the library.
-     * \param version MXNet version number passed to library and defined as:
-     *                MXNET_VERSION = (MXNET_MAJOR*10000 + MXNET_MINOR*100 + MXNET_PATCH)
-     * \return Non-zero value on error i.e. library incompatible with passed MXNet version
-     */
+  /*! \brief returns MXNet library version */
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+  __declspec(dllexport) int __cdecl
+#else
+  int
+#endif
+  _opVersion() {
+    return MX_LIBRARY_VERSION;
+  }
+
+  /*! \brief returns number of ops registered in this library */
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+  __declspec(dllexport) int __cdecl
+#else
+  int
+#endif
+  _opRegSize() {
+    return Registry<CustomOp>::get()->size();
+  }
+
+  /*! \brief returns operator registration at specified index */
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+  __declspec(dllexport) void __cdecl
+#else
+  void
+#endif
+  _opRegGet(int idx, const char** name, fcomp_t* fcomp, fcomp_t* fgrad,
+            parseAttrs_t* parse, inferType_t* type,
+            inferShape_t* shape, mutateInputs_t* mutate,
+            createOpState_t* create_op) {
+    CustomOp op = Registry<CustomOp>::get()->get(idx);
+    *name = op.name;
+    *fcomp = op.forward;
+    *fgrad = op.backward;
+    *parse = op.parse_attrs;
+    *type = op.infer_type;
+    *shape = op.infer_shape;
+    *mutate = op.mutate_inputs;
+    *create_op = op.create_opstate;
+  }
+
+  /*! \brief calls free from the external library for library allocated arrays */
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+  __declspec(dllexport) void __cdecl
+#else
+  void
+#endif
+  _opCallFree(void* ptr) {
+    free(ptr);
+  }
+
+  /*! \brief returns status of calling parse attributes function for operator from library */
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+  __declspec(dllexport) int __cdecl
+#else
+  int
+#endif
+  _opCallParseAttrs(parseAttrs_t parseAttrs, const char* const* keys,
+                    const char* const* vals, int num,
+                    int* num_in, int* num_out) {
+    // create map of attributes from list
+    std::map<std::string, std::string> attrs;
+    for (int i = 0; i < num; i++) {
+      attrs[std::string(keys[i])] = std::string(vals[i]);
+    }
+
+    return parseAttrs(attrs, num_in, num_out);
+  }
+
+  /*! \brief returns status of calling inferShape function for operator from library */
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+  __declspec(dllexport) int __cdecl
+#else
+  int
+#endif
+  _opCallInferShape(inferShape_t inferShape, const char* const* keys,
+                    const char* const* vals, int num,
+                    unsigned int** inshapes, int* indims, int num_in,
+                    unsigned int*** outshapes, int** outdims, int num_out) {
+    // create map of attributes from list
+    std::map<std::string, std::string> attrs;
+    for (int i = 0; i < num; i++) {
+      attrs[std::string(keys[i])] = std::string(vals[i]);
+    }
+
+    // create a vector of shapes for inputs
+    std::vector<std::vector<unsigned int> > in_shapes(num_in);
+    for (int i = 0; i < num_in; i++) {
+      for (int j = 0; j < indims[i]; j++) {
+        in_shapes[i].push_back(inshapes[i][j]);
+      }
+    }
+
+    // create a vector of shapes for outputs
+    std::vector<std::vector<unsigned int> > out_shapes(num_out);
+
+    int retval = inferShape(attrs, in_shapes, out_shapes);
+    if (!retval)
+      return retval;
+
+    // allocate space for output dims, shape
+    *outdims = static_cast<int*>(malloc (num_out * sizeof(int)));
+    *outshapes = static_cast<unsigned**>(malloc (num_out * sizeof(unsigned*)));
+
+    // copy output shapes
+    for (int i = 0; i < num_out; i++) {
+      (*outdims)[i] = out_shapes[i].size();
+      (*outshapes)[i] = static_cast<unsigned*>(malloc ((*outdims)[i] * sizeof(unsigned)));
+      for (int j = 0; j < indims[i]; j++) {
+        (*outshapes)[i][j] = out_shapes[i][j];
+      }
+    }
+
+    return retval;
+  }
+
+  /*! \brief returns status of calling inferType function for operator from library */
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+  __declspec(dllexport) int __cdecl
+#else
+  int
+#endif
+  _opCallInferType(inferType_t inferType, const char* const* keys,
+                   const char* const* vals, int num,
+                   int* intypes, int num_in, int* outtypes, int num_out) {
+    // create map of attributes from list
+    std::map<std::string, std::string> attrs;
+    for (int i = 0; i < num; i++) {
+      attrs[std::string(keys[i])] = std::string(vals[i]);
+    }
+
+    // create a vector of types for inputs
+    std::vector<int> in_types(num_in);
+    for (int i = 0; i < num_in; i++) {
+      in_types[i] = intypes[i];
+    }
+
+    // create a vector of types for outputs
+    std::vector<int> out_types(num_out, -1);
+
+    int retval = inferType(attrs, in_types, out_types);
+    if (!retval)
+      return retval;
+
+    // copy output types
+    for (int i = 0; i < num_out; i++) {
+      outtypes[i] = out_types[i];
+    }
+
+    return retval;
+  }
+
+  /*! \brief returns status of calling Forward/Backward function for operator from library */
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+  __declspec(dllexport) int __cdecl
+#else
+  int
+#endif
+  _opCallFCompute(fcomp_t fcomp, const char* const* keys,
+                  const char* const* vals, int num,
+                  const int64_t** inshapes, int* indims,
+                  void** indata, int* intypes, int num_in,
+                  const int64_t** outshapes, int* outdims,
+                  void** outdata, int* outtypes, int num_out,
+                  xpu_malloc_t cpu_malloc, void* cpu_alloc) {
+    // create map of attributes from list
+    std::map<std::string, std::string> attrs;
+    for (int i = 0; i < num; i++) {
+      attrs[std::string(keys[i])] = std::string(vals[i]);
+    }
+
+    // create a vector of tensors for inputs
+    std::vector<MXTensor> inputs(num_in);
+    for (int i = 0; i < num_in; i++) {
+      inputs[i].data_ptr = indata[i];
+      inputs[i].dtype = (MXDType)intypes[i];
+      for (int j = 0; j < indims[i]; j++) {
+        inputs[i].shape.push_back(inshapes[i][j]);
+      }
+      inputs[i].setDLTensor();
+    }
+
+    // create a vector of tensors for outputs
+    std::vector<MXTensor> outputs(num_out);
+    for (int i = 0; i < num_out; i++) {
+      outputs[i].data_ptr = outdata[i];
+      outputs[i].dtype = (MXDType) outtypes[i];
+      for (int j = 0; j < outdims[i]; j++) {
+        outputs[i].shape.push_back(outshapes[i][j]);
+      }
+      outputs[i].setDLTensor();
+    }
+
+    OpResource res(cpu_malloc, cpu_alloc);
+
+    return fcomp(attrs, inputs, outputs, res);
+  }
+
+  /*! \brief returns status of calling mutateInputs function for operator from library */
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+  __declspec(dllexport) int __cdecl
+#else
+  int
+#endif
+  _opCallMutateInputs(mutateInputs_t mutate, const char* const* keys,
+                    const char* const* vals, int num,
+                    int** mutate_indices, int* indices_size) {
+    // create map of attributes from list
+    std::map<std::string, std::string> attrs;
+    for (int i = 0; i < num; i++) {
+      attrs[std::string(keys[i])] = std::string(vals[i]);
+    }
+
+    // create a vector of mutate input indices
+    std::vector<int> mut_ind;
+
+    int retval = mutate(attrs, mut_ind);
+    if (!retval)
+      return retval;
+
+    // output the input indices
+    *indices_size = mut_ind.size();
+    *mutate_indices = static_cast<int*>(malloc (*indices_size * sizeof(int)));
+    for (int i = 0; i < *indices_size; i++) {
+      (*mutate_indices)[i] = mut_ind[i];
+    }
+
+    return retval;
+  }
+
+  /*! \brief returns status of calling createStatefulOp function for operator from library */
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+  __declspec(dllexport) int __cdecl
+#else
+  int
+#endif
+  _opCallCreateOpState(createOpState_t create_op, const char* const* keys,
+                       const char* const* vals, int num,
+                       void** state_op) {
+    // create map of attributes from list
+    std::map<std::string, std::string> attrs;
+    for (int i = 0; i < num; i++) {
+      attrs[std::string(keys[i])] = std::string(vals[i]);
+    }
+
+    // void pointer to hold custom state op instance created in custom library
+    CustomStatefulOp** op_ptr = reinterpret_cast<CustomStatefulOp**>(state_op);
+    return create_op(attrs, op_ptr);
+  }
+
+  /*! \brief returns status of calling Stateful Forward/Backward for operator from library */
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+  __declspec(dllexport) int __cdecl
+#else
+  int
+#endif
+  _opCallFStatefulCompute(bool is_forward, void* state_op,
+                          const int64_t** inshapes, int* indims,
+                          void** indata, int* intypes, int num_in,
+                          const int64_t** outshapes, int* outdims,
+                          void** outdata, int* outtypes, int num_out,
+                          xpu_malloc_t cpu_malloc, void* cpu_alloc) {
+    // create a vector of tensors for inputs
+    std::vector<MXTensor> inputs(num_in);
+    for (int i = 0; i < num_in; i++) {
+      inputs[i].data_ptr = indata[i];
+      inputs[i].dtype = (MXDType)intypes[i];
+      for (int j = 0; j < indims[i]; j++) {
+        inputs[i].shape.push_back(inshapes[i][j]);
+      }
+      inputs[i].setDLTensor();
+    }
+
+    // create a vector of tensors for outputs
+    std::vector<MXTensor> outputs(num_out);
+    for (int i = 0; i < num_out; i++) {
+      outputs[i].data_ptr = outdata[i];
+      outputs[i].dtype = (MXDType) outtypes[i];
+      for (int j = 0; j < outdims[i]; j++) {
+        outputs[i].shape.push_back(outshapes[i][j]);
+      }
+      outputs[i].setDLTensor();
+    }
+    OpResource res(cpu_malloc, cpu_alloc);
+    CustomStatefulOp* op_ptr = reinterpret_cast<CustomStatefulOp*>(state_op);
+    if (is_forward) {
+      return op_ptr->Forward(inputs, outputs, res);
+    }
+    return op_ptr->Backward(inputs, outputs, res);
+  }
+
+  /*!
+   * \brief Checks if the MXNet version is supported by the library.
+   * If supported, initializes the library.
+   * \param version MXNet version number passed to library and defined as:
+   *                MXNET_VERSION = (MXNET_MAJOR*10000 + MXNET_MINOR*100 + MXNET_PATCH)
+   * \return Non-zero value on error i.e. library incompatible with passed MXNet version
+   */
 #if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
-    __declspec(dllexport) int __cdecl initialize(int);
+  __declspec(dllexport) MXReturnValue __cdecl
 #else
-    int initialize(int);
+  MXReturnValue
 #endif
+  initialize(int version);
 }
 #endif  // MXNET_LIB_API_H_
diff --git a/python/mxnet/__init__.py b/python/mxnet/__init__.py
index 6d9704161cde..fb9680a6db18 100644
--- a/python/mxnet/__init__.py
+++ b/python/mxnet/__init__.py
@@ -27,7 +27,6 @@
 from .util import is_np_shape, set_np_shape, np_shape, use_np_shape
 from .util import is_np_array, np_array, use_np_array, use_np
 from . import base
-from . import library
 from . import contrib
 from . import ndarray
 from . import ndarray as nd
@@ -88,6 +87,8 @@
 
 from . import gluon
 
+# Dynamic library module should be done after ndarray and symbol are initialized
+from . import library
 from . import tvmop
 
 __version__ = base.__version__
diff --git a/python/mxnet/library.py b/python/mxnet/library.py
index 9ebf2c2bc580..8ea0bc2ae0a5 100644
--- a/python/mxnet/library.py
+++ b/python/mxnet/library.py
@@ -19,8 +19,11 @@
 """Library management API of mxnet."""
 from __future__ import absolute_import
 import ctypes
+import sys
 import os
-from .base import _LIB, check_call, MXNetError
+from .base import _LIB, check_call, MXNetError, _init_op_module
+from .ndarray.register import _make_ndarray_function
+from .symbol.register import _make_symbol_function
 
 def load(path):
     """Loads library dynamically.
@@ -47,3 +50,21 @@ def load(path):
     byt_obj = path.encode('utf-8')
     chararr = ctypes.c_char_p(byt_obj)
     check_call(_LIB.MXLoadLib(chararr))
+
+    #regenerate operators
+    _init_op_module('mxnet', 'ndarray', _make_ndarray_function)
+    _init_op_module('mxnet', 'symbol', _make_symbol_function)
+
+    #re-register mx.nd.op into mx.nd
+    mx_nd = sys.modules["mxnet.ndarray"]
+    mx_nd_op = sys.modules["mxnet.ndarray.op"]
+    for op in dir(mx_nd_op):
+        func = getattr(mx_nd_op, op)
+        setattr(mx_nd, op, func)
+
+    #re-register mx.sym.op into mx.sym
+    mx_sym = sys.modules["mxnet.symbol"]
+    mx_sym_op = sys.modules["mxnet.symbol.op"]
+    for op in dir(mx_sym_op):
+        func = getattr(mx_sym_op, op)
+        setattr(mx_sym, op, func)
diff --git a/src/c_api/c_api.cc b/src/c_api/c_api.cc
index ba33084a026d..24374cf19cdc 100644
--- a/src/c_api/c_api.cc
+++ b/src/c_api/c_api.cc
@@ -1,3 +1,4 @@
+
 /*
  * Licensed to the Apache Software Foundation (ASF) under one
  * or more contributor license agreements.  See the NOTICE file
@@ -49,9 +50,11 @@
 #include "../initialize.h"
 #include "./c_api_common.h"
 #include "../operator/custom/custom-inl.h"
+#include "../operator/operator_common.h"
 #include "../operator/tensor/matrix_op-inl.h"
 #include "../operator/tvmop/op_module.h"
 #include "../common/utils.h"
+#include "nnvm/pass_functions.h"
 
 using namespace mxnet;
 
@@ -92,16 +95,593 @@ inline int MXAPIGetFunctionRegInfo(const FunRegType *e,
 
 // NOTE: return value is added in API_END
 
-// Loads library and initializes it
+/*!
+ * \brief Loads dynamic library and initializes it
+ * \param path library path
+ */
 int MXLoadLib(const char *path) {
   API_BEGIN();
   void *lib = LibraryInitializer::Get()->lib_load(path);
   if (!lib)
     LOG(FATAL) << "Unable to load library";
 
+  // check that library and MXNet use same version of library API
+  opVersion_t opVersion = get_func<opVersion_t>(lib, const_cast<char*>(MXLIB_OPVERSION_STR));
+  int libVersion =  opVersion();
+  if (MX_LIBRARY_VERSION != libVersion)
+    LOG(FATAL) << "Library version (" << libVersion << ") does not match MXNet version ("
+               << MX_LIBRARY_VERSION << ")";
+
+  // initialize library by passing MXNet version
   initialize_t initialize = get_func<initialize_t>(lib, const_cast<char*>(MXLIB_INITIALIZE_STR));
   if (!initialize(static_cast<int>(MXNET_VERSION)))
     LOG(FATAL) << "Library failed to initialize";
+
+  // get C type interface functions
+  opCallFree_t callFree = get_func<opCallFree_t>(lib, const_cast<char*>(MXLIB_OPCALLFREE_STR));
+
+  opCallParseAttrs_t callParseAttrs =
+    get_func<opCallParseAttrs_t>(lib, const_cast<char*>(MXLIB_OPCALLPARSEATTRS_STR));
+
+  opCallInferShape_t callInferShape =
+    get_func<opCallInferShape_t>(lib, const_cast<char*>(MXLIB_OPCALLINFERSHAPE_STR));
+
+  opCallInferType_t callInferType =
+    get_func<opCallInferType_t>(lib, const_cast<char*>(MXLIB_OPCALLINFERTYPE_STR));
+
+  opCallFComp_t callFComp =
+    get_func<opCallFComp_t>(lib, const_cast<char*>(MXLIB_OPCALLFCOMP_STR));
+
+  opCallMutateInputs_t callMutateInputs =
+    get_func<opCallMutateInputs_t>(lib, const_cast<char*>(MXLIB_OPCALLMUTATEINPUTS_STR));
+
+  opCallCreateOpState_t callCreateOpState =
+    get_func<opCallCreateOpState_t>(lib, const_cast<char*>(MXLIB_OPCALLCREATEOPSTATE_STR));
+
+  opCallFStatefulComp_t callFStatefulComp =
+    get_func<opCallFStatefulComp_t>(lib, const_cast<char*>(MXLIB_OPCALLFSTATEFULCOMP_STR));
+
+  // get number of operators registered in the library
+  opRegSize_t opRegSize = get_func<opRegSize_t>(lib, const_cast<char*>(MXLIB_OPREGSIZE_STR));
+  int numOps = opRegSize();
+  LOG(INFO) << "Found " << numOps << " operators in library";
+
+  /*
+   * Get all custom operators implementation from custom library
+   * loop and register each operator in the library to NNVM
+   */
+  opRegGet_t opRegGet = get_func<opRegGet_t>(lib, const_cast<char*>(MXLIB_OPREGGET_STR));
+  for (int i = 0; i < numOps; i++) {
+    const char* name;
+    // function pointers holding implementation from custom library
+    fcomp_t fcomp_fp = nullptr;
+    parseAttrs_t parse_fp = nullptr;
+    inferType_t type_fp = nullptr;
+    inferShape_t shape_fp = nullptr;
+    // optional attributes
+    fcomp_t fgrad_fp = nullptr;
+    mutateInputs_t mutate_fp = nullptr;
+    createOpState_t create_opstate_fp = nullptr;
+
+    // get custom operator implemenation from the dynamic library
+    opRegGet(i, &name, &fcomp_fp, &fgrad_fp, &parse_fp, &type_fp, &shape_fp,
+                &mutate_fp, &create_opstate_fp);
+
+    // validate custom operator functions from the dynamic library
+    CHECK(fcomp_fp != nullptr || create_opstate_fp != nullptr) << "Error loading '" << name
+                            << "' custom op, Forward or CreateOpState function was not set.";
+    CHECK(parse_fp != nullptr) << "Error loading '" << name
+                            << "' custom op, ParseAttrs function was not set.";
+    CHECK(type_fp  != nullptr) << "Error loading '" << name
+                            << "' custom op, InferType function was not set.";
+    CHECK(shape_fp != nullptr) << "Error loading '" << name
+                            << "' custom op, InferShape function was not set.";
+
+    LOG(INFO) << "\tOp[" << i << "] " << name;
+    std::string name_str(name);
+
+    /*
+     * Below are a series of lambda functions that will be registered in the NNVM op registration
+     * Each one has the standard MXNet signature and converts to types supported by externally
+     * registered operators. 
+     */
+
+    // lambda function to call parse attributes
+    auto attr_parser = [=](const NodeAttrs* attrs) {
+      // convert attributes to vector of char
+      std::vector<const char*> attr_keys, attr_vals;
+      for (auto kv : attrs->dict) {
+        attr_keys.push_back(kv.first.c_str());
+        attr_vals.push_back(kv.second.c_str());
+      }
+      // convert subgraph symbol from node attributes to char*
+      std::string subgraph_json;
+      if (!attrs->subgraphs.empty()) {
+        nnvm::Graph g;
+        g.outputs = attrs->subgraphs[0].get()->outputs;
+        subgraph_json = nnvm::pass::SaveJSON(g);
+        attr_keys.push_back(SUBGRAPH_SYM_JSON);
+        attr_vals.push_back(subgraph_json.c_str());
+      }
+
+      int num_in = -1;
+      int num_out = -1;
+      CHECK(callParseAttrs(parse_fp, attr_keys.data(), attr_vals.data(), attr_keys.size(),
+                           &num_in, &num_out))
+      << "Error calling ParseAttrs for custom operator '" << name_str << "'";
+
+      // return type void
+    };
+
+    // lambda function to call parse attributes and return the number of inputs
+    auto num_inputs = [=](const NodeAttrs& attrs) {
+      // convert attributes to vector of char
+      std::vector<const char*> attr_keys, attr_vals;
+      for (auto kv : attrs.dict) {
+        attr_keys.push_back(kv.first.c_str());
+        attr_vals.push_back(kv.second.c_str());
+      }
+
+      int num_in = -1;
+      int num_out = -1;
+      CHECK(callParseAttrs(parse_fp, attr_keys.data(), attr_vals.data(), attr_keys.size(),
+                           &num_in, &num_out))
+      << "Error calling ParseAttrs::num_inputs for custom operator '" << name_str << "'";
+
+      return num_in;
+    };
+
+    // lambda function to call parse attributes and return the number of outputs
+    auto num_outputs = [=](const NodeAttrs& attrs) {
+      // convert attributes to vector of char*
+      std::vector<const char*> attr_keys, attr_vals;
+      for (auto kv : attrs.dict) {
+        attr_keys.push_back(kv.first.c_str());
+        attr_vals.push_back(kv.second.c_str());
+      }
+
+      int num_in = -1;
+      int num_out = -1;
+      CHECK(callParseAttrs(parse_fp, attr_keys.data(), attr_vals.data(), attr_keys.size(),
+                           &num_in, &num_out))
+      << "Error calling ParseAttrs::num_outputs for custom operator '" << name_str << "'";
+
+      return num_out;
+    };
+
+    // lambda function to call parse attributes and return the number of inputs and outputs
+    // for backward computation
+    auto num_inouts = [=](const NodeAttrs& attrs) {
+      // convert attributes to vector of char*
+      std::vector<const char*> attr_keys, attr_vals;
+      for (auto kv : attrs.dict) {
+        attr_keys.push_back(kv.first.c_str());
+        attr_vals.push_back(kv.second.c_str());
+      }
+
+      int num_in = -1;
+      int num_out = -1;
+      CHECK(callParseAttrs(parse_fp, attr_keys.data(), attr_vals.data(), attr_keys.size(),
+                           &num_in, &num_out))
+      << "Error calling ParseAttrs::num_outputs for custom operator '" << name_str << "'";
+
+      return num_in + num_out;
+    };
+
+    // lambda function to call infer shape
+    auto infer_shape = [=] (const nnvm::NodeAttrs& attrs,
+                            mxnet::ShapeVector *in_shape,
+                            mxnet::ShapeVector *out_shape) {
+      // convert attributes to vector of char*
+      std::vector<const char*> attr_keys, attr_vals;
+      for (auto kv : attrs.dict) {
+        attr_keys.push_back(kv.first.c_str());
+        attr_vals.push_back(kv.second.c_str());
+      }
+
+      std::vector<uint32_t*> inshapes(in_shape->size());
+      std::vector<int> indims(in_shape->size());
+
+      // determine amount of memory needed to store all the input shapes
+      size_t buff_size = 0;
+      for (const auto& i : *in_shape) buff_size += i.ndim();
+
+      // copy input shapes from ShapeVector to raw memory layout
+      std::vector<uint32_t> inbuff(buff_size);
+      uint32_t *ptr = inbuff.data();
+      for (size_t i = 0; i < in_shape->size(); ++i) {
+        inshapes[i] = ptr;
+        indims[i] = (*in_shape)[i].ndim();
+        for (int j = 0; j < (*in_shape)[i].ndim(); ++j, ++ptr) {
+          *ptr = static_cast<uint32_t>((*in_shape)[i][j]);
+        }
+      }
+
+      // output shapes will be allocated by infer shape function
+      uint32_t** outshapes = nullptr;
+      int* outdims = nullptr;
+
+      CHECK(callInferShape(shape_fp, attr_keys.data(), attr_vals.data(), attr_keys.size(),
+                           inshapes.data(), indims.data(), in_shape->size(),
+                           &outshapes, &outdims, out_shape->size()))
+      << "Error calling InferShape for custom operator '" << name_str << "'";
+
+      std::vector<uint32_t*> out_shapes(out_shape->size());
+      // determine amount of memory needed to store all the output shapes
+      buff_size = 0;
+      for (unsigned i = 0; i < out_shape->size(); i++) {
+        buff_size += outdims[i];
+      }
+
+      // copy output shapes from custom op memory to MXNet memory
+      std::vector<uint32_t> outbuff(buff_size);
+      ptr = outbuff.data();
+      for (unsigned i = 0; i < out_shape->size(); ++i) {
+        out_shapes[i] = ptr;
+        for (int j = 0; j < outdims[i]; ++j, ++ptr) {
+          *ptr = static_cast<uint32_t>(outshapes[i][j]);
+        }
+      }
+
+      // assign output shapes to ShapeVector
+      for (unsigned i = 0; i < out_shape->size(); ++i) {
+        SHAPE_ASSIGN_CHECK(*out_shape, i,
+                           mxnet::TShape(out_shapes[i], out_shapes[i]+outdims[i]));
+      }
+
+      // free memory used by custom op to allocate shapes/dims
+      callFree(outdims);
+      for (unsigned i = 0; i < out_shape->size(); i++) {
+        callFree(outshapes[i]);
+      }
+      callFree(outshapes);
+
+      return true;
+    };
+
+    // lambda function to call infer type
+    auto infer_type = [=] (const nnvm::NodeAttrs& attrs,
+                            std::vector<int> *in_type,
+                            std::vector<int> *out_type) {
+      // convert attributes to vector of char*
+      std::vector<const char*> attr_keys, attr_vals;
+      for (auto kv : attrs.dict) {
+        attr_keys.push_back(kv.first.c_str());
+        attr_vals.push_back(kv.second.c_str());
+      }
+
+      // copy input types from in_type
+      std::vector<int> intypes(*in_type);
+
+      // output types will be populated by inferType function
+      std::vector<int> outtypes(out_type->size());
+
+      CHECK(callInferType(type_fp, attr_keys.data(), attr_vals.data(), attr_keys.size(),
+                           intypes.data(), in_type->size(),
+                           outtypes.data(), out_type->size()))
+      << "Error calling InferType for custom operator '" << name_str << "'";
+
+      // copy and assign output types from custom op to MXNet memory
+      for (size_t i = 0; i < out_type->size(); i++) {
+        TYPE_ASSIGN_CHECK(*out_type, i, outtypes[i]);
+      }
+
+      return true;
+    };
+
+    // lambda function to convert from external fcompute to internal MXNet types
+    auto fcomp_lambda = [=](fcomp_t fcomp_fp,
+                            const nnvm::NodeAttrs& attrs,
+                            const OpContext& ctx,
+                            const std::vector<NDArray>& inputs,
+                            const std::vector<OpReqType>& req,
+                            const std::vector<NDArray>& outputs) {
+      // convert attributes to vector of char*
+      std::vector<const char*> attr_keys, attr_vals;
+      for (auto kv : attrs.dict) {
+        attr_keys.push_back(kv.first.c_str());
+        attr_vals.push_back(kv.second.c_str());
+      }
+
+      std::vector<void*> in_data, out_data;
+      std::vector<const int64_t *> in_shapes, out_shapes;
+      std::vector<int> in_dims, out_dims;
+      std::vector<int> in_types, out_types;
+
+      // convert input tensors to constituent parts
+      for (size_t i = 0; i < inputs.size(); i++) {
+        in_data.push_back(inputs[i].data().dptr_);
+        in_shapes.push_back(inputs[i].shape().data());
+        in_dims.push_back(inputs[i].shape().ndim());
+        in_types.push_back(inputs[i].dtype());
+      }
+
+      // convert output tensors to constituent parts
+      for (size_t i = 0; i < outputs.size(); i++) {
+        out_data.push_back(outputs[i].data().dptr_);
+        out_shapes.push_back(outputs[i].shape().data());
+        out_dims.push_back(outputs[i].shape().ndim());
+        out_types.push_back(outputs[i].dtype());
+      }
+
+      // get memory resource
+      const Resource &resource = ctx.requested[0];
+      mshadow::Stream<mxnet::cpu> *cpu_stream = ctx.get_stream<mxnet::cpu>();
+
+      // create lambda that captures stream & resource objects
+      // this temp workspace holds memory allocated by custom library via OpResource
+      auto cpu_alloc = [&](int size) {
+        mshadow::Tensor<mxnet::cpu, 1, char> workspace =
+          resource.get_space_typed<mxnet::cpu, 1, char>(mshadow::Shape1(size), cpu_stream);
+        return workspace.dptr_;
+      };
+
+      // create lambda without captures so that we can cast it to function pointer
+      // this needs to be a lambda function so that we can do the decltype cast
+      typedef decltype(cpu_alloc) alloc_type;
+      auto cpu_malloc = [](void* _cpu_alloc, int size) {
+        // cast the void* argument to the type for the cpu_alloc lambda function
+        alloc_type* cpualloc = static_cast<alloc_type*>(_cpu_alloc);
+        // call cpu_alloc to actually allocate memory and get the pointer
+        void* ptr = (*cpualloc)(size);
+        return ptr;
+      };
+
+      // call fcompute function
+      CHECK(callFComp(fcomp_fp, attr_keys.data(), attr_vals.data(), attr_keys.size(),
+                      in_shapes.data(), in_dims.data(), in_data.data(),
+                      in_types.data(), in_data.size(),
+                      out_shapes.data(), out_dims.data(), out_data.data(),
+                      out_types.data(), out_data.size(), cpu_malloc, &cpu_alloc))
+      << "Error calling FCompute for custom operator '" << name_str << "'";
+
+      // return type void
+    };
+
+    auto forward_lambda = [=](const nnvm::NodeAttrs& attrs,
+                              const OpContext& ctx,
+                              const std::vector<NDArray>& inputs,
+                              const std::vector<OpReqType>& req,
+                              const std::vector<NDArray>& outputs) {
+      return fcomp_lambda(fcomp_fp, attrs, ctx, inputs, req, outputs);
+    };
+
+    auto backward_lambda = [=](const nnvm::NodeAttrs& attrs,
+                              const OpContext& ctx,
+                              const std::vector<NDArray>& inputs,
+                              const std::vector<OpReqType>& req,
+                              const std::vector<NDArray>& outputs) {
+      return fcomp_lambda(fgrad_fp, attrs, ctx, inputs, req, outputs);
+    };
+
+    // lambda function to convert from external mutate_inputs to internal MXNet types
+    auto mutate_inputs = [=](const nnvm::NodeAttrs& attrs) {
+      // convert attributes to vector of char*
+      std::vector<const char*> attr_keys, attr_vals;
+      for (auto kv : attrs.dict) {
+        attr_keys.push_back(kv.first.c_str());
+        attr_vals.push_back(kv.second.c_str());
+      }
+
+      // C type placeholder for mutate input indices vector
+      int* mutate_indices = nullptr;
+      int indices_size = 0;
+
+      // call mutate inputs function
+      CHECK(callMutateInputs(mutate_fp, attr_keys.data(), attr_vals.data(), attr_keys.size(),
+                      &mutate_indices, &indices_size))
+      << "Error calling MutateInputs for custom operator '" << name_str << "'";
+
+      std::vector<uint32_t> mutate_indices_list(indices_size);
+      for (int i=0; i < indices_size; i++) {
+        mutate_indices_list[i] = static_cast<uint32_t>(mutate_indices[i]);
+      }
+
+      return mutate_indices_list;
+    };
+
+    // lambda function to set storage types
+    auto infer_storage_type = [=](const nnvm::NodeAttrs& attrs,
+                                const int dev_mask,
+                                DispatchMode* dispatch_mode,
+                                std::vector<int>* in_stypes,
+                                std::vector<int>* out_stypes) {
+      // TODO(ziyimu): remove this dense enforce check after supporting sparse tensor
+      CHECK(mxnet::common::ContainsOnlyStorage(*in_stypes, mxnet::kDefaultStorage))
+      << "Error input tensors are not dense for custom operator '" << name_str << "'";
+      // set outputs as dense
+      return op::storage_type_assign(out_stypes, mxnet::kDefaultStorage,
+                                     dispatch_mode, DispatchMode::kFComputeEx);
+    };
+
+    // FGradient register lambda
+    auto grad_reg = [=](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
+        // copy gradients first
+        std::vector<nnvm::NodeEntry> heads(ograds.begin(), ograds.end());
+        // copy inputs second
+        for (auto& h : n->inputs) {
+          heads.push_back(h);
+        }
+        // copy outputs last
+        uint32_t n_out = n->num_outputs();
+        for (uint32_t i = 0; i < n_out; ++i) {
+          heads.emplace_back(n, i, 0);
+        }
+        std::string grad_name = "_backward_" + name_str;
+        return mxnet::op::MakeGradNode(grad_name.c_str(), n, heads, n->attrs.dict);
+    };
+
+    auto resc_req = [=](const NodeAttrs& attrs) {
+      return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+    };
+
+    // library author should implement and return a 'state' which points to an instance
+    // in lambda we create OpStatePtr using the returned 'state'
+    auto create_opstate = [=] (const NodeAttrs& attrs,
+                                Context ctx,
+                                const std::vector<TShape>& in_shapes,
+                                const std::vector<int>& in_types) {
+      // convert attributes to vector of char*
+      std::vector<const char*> attr_keys, attr_vals;
+      for (auto kv : attrs.dict) {
+        attr_keys.push_back(kv.first.c_str());
+        attr_vals.push_back(kv.second.c_str());
+      }
+
+      // convert subgraph symbol from node attributes to char*
+      std::string subgraph_json;
+      if (!attrs.subgraphs.empty()) {
+        nnvm::Graph g;
+        g.outputs = attrs.subgraphs[0].get()->outputs;
+        subgraph_json = nnvm::pass::SaveJSON(g);
+        attr_keys.push_back(SUBGRAPH_SYM_JSON);
+        attr_vals.push_back(subgraph_json.c_str());
+      }
+
+      // create a pointer to hold custom op state object
+      void* state_op_inst = nullptr;
+      CHECK(callCreateOpState(create_opstate_fp, attr_keys.data(), attr_vals.data(),
+                              attr_keys.size(), &state_op_inst))
+      << "Error calling CreateOpState for custom operator '" << name_str << "'";
+
+      CHECK(state_op_inst != nullptr)
+      << "Error custom library failed to create stateful operator '" << name_str << "'";
+
+      CustomStatefulOp* state_op = reinterpret_cast<CustomStatefulOp*>(state_op_inst);
+      return OpStatePtr::Create<CustomStatefulOpWrapper>(state_op);
+    };
+
+    // stateful forward and backward
+    auto fstateful_lambda = [=](bool is_forward,
+                                const OpStatePtr& state_ptr,
+                                const OpContext& ctx,
+                                const std::vector<NDArray>& inputs,
+                                const std::vector<OpReqType>& req,
+                                const std::vector<NDArray>& outputs) {
+      std::vector<void*> in_data, out_data;
+      std::vector<const int64_t *> in_shapes, out_shapes;
+      std::vector<int> in_dims, out_dims;
+      std::vector<int> in_types, out_types;
+
+      // convert input tensors to constituent parts
+      for (size_t i = 0; i < inputs.size(); i++) {
+        in_data.push_back(inputs[i].data().dptr_);
+        in_shapes.push_back(inputs[i].shape().data());
+        in_dims.push_back(inputs[i].shape().ndim());
+        in_types.push_back(inputs[i].dtype());
+      }
+
+      // convert output tensors to constituent parts
+      for (size_t i = 0; i < outputs.size(); i++) {
+        out_data.push_back(outputs[i].data().dptr_);
+        out_shapes.push_back(outputs[i].shape().data());
+        out_dims.push_back(outputs[i].shape().ndim());
+        out_types.push_back(outputs[i].dtype());
+      }
+
+      // get memory resource
+      const Resource &resource = ctx.requested[0];
+      mshadow::Stream<mxnet::cpu> *cpu_stream = ctx.get_stream<mxnet::cpu>();
+
+      // create lambda that captures stream & resource objects
+      // this temp workspace holds memory allocated by custom library via OpResource
+      auto cpu_alloc = [&](int size) {
+        mshadow::Tensor<mxnet::cpu, 1, char> data =
+        resource.get_space_typed<mxnet::cpu, 1, char>(mshadow::Shape1(size), cpu_stream);
+        return data.dptr_;
+      };
+
+      // create lambda without captures so that we can cast it to function pointer
+      // this needs to be a lambda function so that we can do the decltype cast
+      typedef decltype(cpu_alloc) alloc_type;
+      auto cpu_malloc = [](void* _cpu_alloc, int size) {
+        // cast the void* argument to the type for the cpu_alloc lambda function
+        alloc_type* cpualloc = static_cast<alloc_type*>(_cpu_alloc);
+        // call cpu_alloc to actually allocate memory and get the pointer
+        void* ptr = (*cpualloc)(size);
+        return ptr;
+      };
+
+      // retrieve op state object created from CreateOpState
+      CustomStatefulOpWrapper& op = state_ptr.get_state<CustomStatefulOpWrapper>();
+      CustomStatefulOp* state_op_inst = op.get_instance();
+      CHECK(state_op_inst != nullptr)
+      << "Error MXNet cannot load custom stateful operator'" << name_str << "'";
+
+      // call fcompute function
+      CHECK(callFStatefulComp(is_forward, state_op_inst, in_shapes.data(), in_dims.data(),
+                              in_data.data(), in_types.data(), in_data.size(),
+                              out_shapes.data(), out_dims.data(), out_data.data(),
+                              out_types.data(), out_data.size(), cpu_malloc, &cpu_alloc))
+      << "Error calling FStatefulCompute for custom operator '" << name_str << "'";
+    };
+
+    auto fstateful_forward = [=](const OpStatePtr& state_ptr,
+                                 const OpContext& ctx,
+                                 const std::vector<NDArray>& inputs,
+                                 const std::vector<OpReqType>& req,
+                                 const std::vector<NDArray>& outputs) {
+      fstateful_lambda(true, state_ptr, ctx, inputs, req, outputs);
+    };
+
+    auto fstateful_backward = [=](const OpStatePtr& state_ptr,
+                                  const OpContext& ctx,
+                                  const std::vector<NDArray>& inputs,
+                                  const std::vector<OpReqType>& req,
+                                  const std::vector<NDArray>& outputs) {
+      fstateful_lambda(false, state_ptr, ctx, inputs, req, outputs);
+    };
+
+    // check if operator is already registered
+    const nnvm::Op *regOpPtr = dmlc::Registry<nnvm::Op>::Get()->Find(name);
+    nnvm::Op &regOp = dmlc::Registry<nnvm::Op>::Get()->__REGISTER_OR_GET__(name);
+    regOp.set_attr_parser(attr_parser);
+    regOp.set_num_inputs(num_inputs);
+    regOp.set_num_outputs(num_outputs);
+    int plevel = 10;
+    if (regOpPtr != nullptr) {
+      // overwrite registration of existing op with custom op
+      regOp.arguments.clear();
+      // set attribute with higher plevel (11) to allow re-registering once
+      // TODO(samskalicky): enable constant overwriting of registertion multiple times
+      plevel++;
+    }
+    regOp.set_attr<nnvm::FInferType>("FInferType", infer_type, plevel);
+    regOp.set_attr<mxnet::FInferShape>("FInferShape", infer_shape, plevel);
+    regOp.set_attr<FInferStorageType>("FInferStorageType", infer_storage_type, plevel);
+    regOp.set_attr<FResourceRequest>("FResourceRequest", resc_req, plevel);
+    // optionally add stateful forward
+    if (create_opstate_fp != nullptr) {
+      regOp.set_attr<FCreateOpState>("FCreateOpState", create_opstate, plevel);
+      regOp.set_attr<FStatefulComputeEx>("FStatefulComputeEx<cpu>",
+                                        fstateful_forward, plevel);
+    } else {
+      regOp.set_attr<FComputeEx>("FComputeEx<cpu>", forward_lambda, plevel);
+    }
+    // optionally add fmutate inputs if user specified a function
+    if (mutate_fp != nullptr)
+      regOp.set_attr<nnvm::FMutateInputs>("FMutateInputs", mutate_inputs, plevel);
+    // optionally add fgradient if user specified a function
+    if (fgrad_fp != nullptr || create_opstate_fp != nullptr) {
+      regOp.set_attr<nnvm::FGradient>("FGradient", grad_reg, plevel);
+      std::string grad_name = "_backward_" + name_str;
+      nnvm::Op &gradOp = dmlc::Registry<nnvm::Op>::Get()->__REGISTER_OR_GET__(grad_name);
+      gradOp.set_attr<nnvm::TIsBackward>("TIsBackward", true, plevel);
+      gradOp.set_attr_parser(attr_parser);
+      gradOp.set_num_inputs(num_inouts);
+      gradOp.set_num_outputs(num_inputs);
+      gradOp.set_attr<FInferStorageType>("FInferStorageType", infer_storage_type, plevel);
+      gradOp.set_attr<FResourceRequest>("FResourceRequest", resc_req, plevel);
+      if (create_opstate_fp != nullptr) {
+        gradOp.set_attr<bool>("TIsLayerOpBackward", true, plevel);
+        gradOp.set_attr<FStatefulComputeEx>("FStatefulComputeEx<cpu>",
+                                            fstateful_backward, plevel);
+      } else {
+        gradOp.set_attr<FComputeEx>("FComputeEx<cpu>", backward_lambda, plevel);
+      }
+    }
+    regOp.add_argument("data", "NDArray[]", "Source inputs");
+  }
   API_END();
 }
 
diff --git a/tests/python/gpu/test_operator_gpu.py b/tests/python/gpu/test_operator_gpu.py
index 075261300eff..efa55d2c1cde 100644
--- a/tests/python/gpu/test_operator_gpu.py
+++ b/tests/python/gpu/test_operator_gpu.py
@@ -47,7 +47,7 @@
 from test_gluon_gpu import _test_bulking
 from test_contrib_operator import test_multibox_target_op
 from test_tvm_op import *
-from test_library_loading import *
+from test_extensions import *
 from test_contrib_optimizer import test_adamw
 
 set_default_context(mx.gpu(0))
diff --git a/tests/python/unittest/test_library_loading.py b/tests/python/unittest/test_extensions.py
similarity index 50%
rename from tests/python/unittest/test_library_loading.py
rename to tests/python/unittest/test_extensions.py
index 29b99dacdbe1..cc7858dce0fd 100644
--- a/tests/python/unittest/test_library_loading.py
+++ b/tests/python/unittest/test_extensions.py
@@ -21,15 +21,16 @@
 import platform
 import unittest
 import mxnet as mx
+import numpy as np
 from mxnet.base import MXNetError
-from mxnet.test_utils import download, is_cd_run
+from mxnet.test_utils import download, is_cd_run, assert_almost_equal
 
 def check_platform():
     return platform.machine() not in ['x86_64', 'AMD64']
 
 @unittest.skipIf(check_platform(), "not all machine types supported")
 @unittest.skipIf(is_cd_run(), "continuous delivery run - ignoring test")
-def test_library_loading():
+def test_custom_op():
     if (os.name=='posix'):
         lib = 'libsample_lib.so'
         if os.path.exists(lib):
@@ -47,3 +48,39 @@ def test_library_loading():
 
     fname = os.path.abspath(fname)
     mx.library.load(fname)
+
+    # test simple 2D gemm custom op loaded from sample library
+    s = mx.sym.Variable('s')
+    t = mx.sym.Variable('t')
+    c = mx.sym.my_gemm(s,t)
+    d = mx.sym.state_gemm(s,t)
+    base = mx.sym.linalg.gemm2(s,t)  # baseline
+
+    dim_n, dim_k, dim_m = tuple(np.random.randint(1, 5, size=3))
+
+    mat1 = mx.nd.random.uniform(-10, 10, shape=(dim_n, dim_k), ctx=mx.cpu())
+    mat2 = mx.nd.random.uniform(-10, 10, shape=(dim_k, dim_m), ctx=mx.cpu())
+
+    in_grad1 = [mx.nd.empty((dim_n,dim_k),ctx=mx.cpu()),mx.nd.empty((dim_k,dim_m),ctx=mx.cpu())]
+    in_grad2 = [mx.nd.empty((dim_n,dim_k),ctx=mx.cpu()),mx.nd.empty((dim_k,dim_m),ctx=mx.cpu())]
+    in_grad_base = [mx.nd.empty((dim_n,dim_k),ctx=mx.cpu()),mx.nd.empty((dim_k,dim_m),ctx=mx.cpu())]
+
+    exe1 = c.bind(ctx=mx.cpu(),args={'s':mat1,'t':mat2},args_grad=in_grad1)
+    exe2 = d.bind(ctx=mx.cpu(),args={'s':mat1,'t':mat2},args_grad=in_grad2)
+    exe_base = base.bind(ctx=mx.cpu(),args={'s':mat1,'t':mat2},args_grad=in_grad_base)
+
+    out1 = exe1.forward()
+    out2 = exe2.forward()
+    out2 = exe2.forward()  # stateful
+    out_base = exe_base.forward()
+
+    assert_almost_equal(out_base[0].asnumpy(), out1[0].asnumpy(), rtol=1e-3, atol=1e-3)
+    assert_almost_equal(out_base[0].asnumpy(), out2[0].asnumpy(), rtol=1e-3, atol=1e-3)
+
+    out_grad = mx.nd.ones((dim_n, dim_m), ctx=mx.cpu())
+    exe1.backward([out_grad])
+    exe2.backward([out_grad])
+    exe_base.backward([out_grad])
+
+    assert_almost_equal(in_grad_base[0].asnumpy(), in_grad1[0].asnumpy(), rtol=1e-3, atol=1e-3)
+    assert_almost_equal(in_grad_base[0].asnumpy(), in_grad2[0].asnumpy(), rtol=1e-3, atol=1e-3)

From 5823ce64bfec97cdfa0a781253795a6945d469f3 Mon Sep 17 00:00:00 2001
From: "Huang, Guangtai" <guangtai@amazon.com>
Date: Fri, 6 Dec 2019 17:12:18 +0800
Subject: [PATCH 651/813] fix typo and doc (#16921)

---
 python/mxnet/symbol/numpy/_symbol.py | 60 ++++++++++++++--------------
 1 file changed, 30 insertions(+), 30 deletions(-)

diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 8b64bbf86556..683bdb1cb200 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -1028,7 +1028,7 @@ def ones(shape, dtype=_np.float32, order='C', ctx=None):
 
     Returns
     -------
-    out : ndarray
+    out : _Symbol
         Array of ones with the given shape, dtype, and ctx.
     """
     if order != 'C':
@@ -1270,7 +1270,7 @@ def divide(x1, x2, out=None, **kwargs):
                          _npi.rtrue_divide_scalar, out)
 
 
-@set_module('mxnet.ndarray.numpy')
+@set_module('mxnet.symbol.numpy')
 def true_divide(x1, x2, out=None):
     return _ufunc_helper(x1, x2, _npi.true_divide, _np.divide, _npi.true_divide_scalar,
                          _npi.rtrue_divide_scalar, out)
@@ -1302,19 +1302,19 @@ def lcm(x1, x2, out=None, **kwargs):
 
     Parameters
     ----------
-    x1, x2 : ndarrays or scalar values
+    x1, x2 : _Symbols or scalar values
         The arrays for computing lowest common multiple. If x1.shape != x2.shape,
         they must be broadcastable to a common shape (which may be the shape of
         one or the other).
 
-    out : ndarray or None, optional
+    out : _Symbol or None, optional
         A location into which the result is stored. If provided, it must have a shape
         that the inputs broadcast to. If not provided or None, a freshly-allocated array
         is returned.
 
     Returns
     -------
-    y : ndarray or scalar
+    y : _Symbol or scalar
         The lowest common multiple of the absolute value of the inputs
         This is a scalar if both `x1` and `x2` are scalars.
 
@@ -1447,7 +1447,7 @@ def eye(N, M=None, k=0, dtype=_np.float32, **kwargs):
 
     Returns
     -------
-    I : ndarray of shape (N,M)
+    I : _Symbol of shape (N,M)
         An array where all elements are equal to zero,
         except for the k-th diagonal, whose values are equal to one.
     """
@@ -1778,7 +1778,7 @@ def cosh(x, out=None, **kwargs):
     ----------
     x : _Symbol or scalar
         Input array or scalar.
-    out : ndarray or None
+    out : _Symbol or None
         Dummy parameter to keep the consistency with the ndarray counterpart.
 
     Returns
@@ -2290,7 +2290,7 @@ def log2(x, out=None, **kwargs):
     ----------
     x : _Symbol
         Input values.
-    out : ndarray or None
+    out : _Symbol or None
         A location into which the result is stored.
         If provided, it must have the same shape and type as the input.
         If not provided or None, a freshly-allocated array is returned.
@@ -2876,7 +2876,7 @@ def arange(start, stop=None, step=1, dtype=None, ctx=None):
 
     Returns
     -------
-    arange : ndarray
+    arange : _Symbol
         Array of evenly spaced values.
 
         For floating point arguments, the length of the result is
@@ -2907,7 +2907,7 @@ def split(ary, indices_or_sections, axis=0):
 
     Parameters
     ----------
-    ary : ndarray
+    ary : _Symbol
         Array to be divided into sub-arrays.
     indices_or_sections : int or 1-D python tuple, list or set.
         If `indices_or_sections` is an integer, N, the array will be divided
@@ -2926,7 +2926,7 @@ def split(ary, indices_or_sections, axis=0):
 
     Returns
     -------
-    sub-arrays : list of ndarrays
+    sub-arrays : _Symbol
         A list of sub-arrays.
 
     Raises
@@ -2948,7 +2948,7 @@ def split(ary, indices_or_sections, axis=0):
 
 
 # pylint: disable=redefined-outer-name
-@set_module('mxnet.ndarray.numpy')
+@set_module('mxnet.symbol.numpy')
 def hsplit(ary, indices_or_sections):
     """Split an array into multiple sub-arrays horizontally (column-wise).
 
@@ -3110,7 +3110,7 @@ def concatenate(seq, axis=0, out=None):
 
     Parameters
     ----------
-    a1, a2, ... : sequence of array_like
+    a1, a2, ... : sequence of _Symbols
         The arrays must have the same shape, except in the dimension
         corresponding to `axis` (the first, by default).
     axis : int, optional
@@ -3123,7 +3123,7 @@ def concatenate(seq, axis=0, out=None):
 
     Returns
     -------
-    res : ndarray
+    res : _Symbol
         The concatenated array.
 
     Examples
@@ -3152,9 +3152,9 @@ def append(arr, values, axis=None):  # pylint: disable=redefined-outer-name
 
     Parameters
     ----------
-    arr : ndarray
+    arr : _Symbol
         Values are appended to a copy of this array.
-    values : ndarray
+    values : _Symbol
         These values are appended to a copy of `arr`.  It must be of the
         correct shape (the same shape as `arr`, excluding `axis`).  If
         `axis` is not specified, `values` can be any shape and will be
@@ -3165,7 +3165,7 @@ def append(arr, values, axis=None):  # pylint: disable=redefined-outer-name
 
     Returns
     -------
-    append : ndarray
+    append : _Symbol
         A copy of `arr` with `values` appended to `axis`.  Note that
         `append` does not occur in-place: a new array is allocated and
         filled.  If `axis` is None, `out` is a flattened array.
@@ -3192,16 +3192,16 @@ def stack(arrays, axis=0, out=None):
         For example, if `axis=0` it will be the first dimension and if `axis=-1` it will be the last dimension.
     Parameters
     ----------
-    arrays : sequence of array_like
+    arrays : sequence of _Symbols
         Each array must have the same shape.
     axis : int, optional
         The axis in the result array along which the input arrays are stacked.
-    out : ndarray, optional
+    out : _Symbol, optional
         If provided, the destination to place the result. The shape must be correct,
         matching that of what stack would have returned if no out argument were specified.
     Returns
     -------
-    stacked : ndarray
+    stacked : _Symbol
         The stacked array has one more dimension than the input arrays."""
     def get_list(arrays):
         if not hasattr(arrays, '__getitem__') and hasattr(arrays, '__iter__'):
@@ -3744,7 +3744,7 @@ def ravel(x, order='C'):
 
     Parameters
     ----------
-    x : ndarray
+    x : _Symbol
         Input array.  The elements in `x` are read in row-major, C-style order and
         packed as a 1-D array.
     order : `C`, optional
@@ -3752,7 +3752,7 @@ def ravel(x, order='C'):
 
     Returns
     -------
-    y : ndarray
+    y : _Symbol
         y is an array of the same subtype as `x`, with shape ``(x.size,)``.
         Note that matrices are special cased for backward compatibility, if `x`
         is a matrix, then y is a 1-D ndarray.
@@ -3778,7 +3778,7 @@ def unravel_index(indices, shape, order='C'): # pylint: disable=redefined-outer-
 
     Parameters:
     -------------
-    indices : array_like
+    indices : _Symbol
             An integer array whose elements are indices into the flattened version of an array of dimensions shape.
             Before version 1.6.0, this function accepted just one index value.
     shape : tuple of ints
@@ -3786,7 +3786,7 @@ def unravel_index(indices, shape, order='C'): # pylint: disable=redefined-outer-
 
     Returns:
     -------------
-    unraveled_coords : ndarray
+    unraveled_coords : _Symbol
             Each row in the ndarray has the same shape as the indices array.
             Each column in the ndarray represents the unravelled index
 
@@ -4434,16 +4434,16 @@ def outer(a, b):
 
     Parameters
     ----------
-    a : (M,) ndarray
+    a : (M,) _Symbol
         First input vector.  Input is flattened if
         not already 1-dimensional.
-    b : (N,) ndarray
+    b : (N,) _Symbol
         Second input vector.  Input is flattened if
         not already 1-dimensional.
 
     Returns
     -------
-    out : (M, N) ndarray
+    out : (M, N) _Symbol
         ``out[i, j] = a[i] * b[j]``
 
     See also
@@ -4924,18 +4924,18 @@ def diff(a, n=1, axis=-1, prepend=None, append=None):  # pylint: disable=redefin
 
     Parameters
     ----------
-    a : ndarray
+    a : _Symbol
         Input array
     n : int, optional
         The number of times values are differenced. If zero, the input is returned as-is.
     axis : int, optional
         The axis along which the difference is taken, default is the last axis.
-    prepend, append : ndarray, optional
+    prepend, append : _Symbol, optional
         Not supported yet
 
     Returns
     -------
-    diff : ndarray
+    diff : _Symbol
         The n-th differences.
         The shape of the output is the same as a except along axis where the dimension is smaller by n.
         The type of the output is the same as the type of the difference between any two elements of a.

From 61ef4a165740ca6b8166f1940272dbad79a95bab Mon Sep 17 00:00:00 2001
From: Jake Lee <gstu1130@gmail.com>
Date: Fri, 6 Dec 2019 11:46:33 -0800
Subject: [PATCH 652/813] Fix publishing scala gpu with cpu instance (#16987)

---
 ci/publish/Jenkinsfile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ci/publish/Jenkinsfile b/ci/publish/Jenkinsfile
index 4d82b1524d28..b410b8cefc33 100644
--- a/ci/publish/Jenkinsfile
+++ b/ci/publish/Jenkinsfile
@@ -81,7 +81,7 @@ for (x in labels) {
 def toDeploy = [:]
 for (x in labels) {
   def label = x // Required due to language
-  toDeploy["Scala Deploy ${label}"] = wrapStep(nodeMap['cpu'], "deploy-scala-${label}") {
+  toDeploy["Scala Deploy ${label}"] = wrapStep(nodeMap[label], "deploy-scala-${label}") {
     withEnv(["MAVEN_PUBLISH_OS_TYPE=${scalaOSMap[label]}", "mxnet_variant=${scalaVariantMap[label]}"]) {
       utils.unpack_and_init("scala_${label}", mx_scala_pub, false)
       utils.docker_run("publish.ubuntu1604_${label}", 'publish_scala_deploy', label == 'gpu' ? true : false, '500m', 'MAVEN_PUBLISH_OS_TYPE MAVEN_PUBLISH_SECRET_ENDPOINT_URL MAVEN_PUBLISH_SECRET_NAME_CREDENTIALS MAVEN_PUBLISH_SECRET_NAME_GPG DOCKERHUB_SECRET_ENDPOINT_REGION mxnet_variant')

From 8f53e77fa8f7064f65f259f7e32ce0ed3a3531fe Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Fri, 6 Dec 2019 12:57:51 -0800
Subject: [PATCH 653/813] mixed precision for power (#16859)

---
 src/operator/mshadow_op.h                     | 46 +++++++++++++++++++
 .../numpy/np_elemwise_broadcast_op.cc         | 30 ++++++++++--
 .../numpy/np_elemwise_broadcast_op.cu         | 15 +++++-
 src/operator/numpy/np_elemwise_broadcast_op.h | 17 ++++---
 src/operator/operator_tune.cc                 |  2 +-
 src/operator/tensor/broadcast_reduce-inl.cuh  |  7 +--
 src/operator/tensor/broadcast_reduce-inl.h    |  6 ++-
 tests/python/unittest/test_numpy_op.py        | 26 ++++++++++-
 8 files changed, 131 insertions(+), 18 deletions(-)

diff --git a/src/operator/mshadow_op.h b/src/operator/mshadow_op.h
index a0a424b6209b..cf35e8858039 100644
--- a/src/operator/mshadow_op.h
+++ b/src/operator/mshadow_op.h
@@ -299,6 +299,52 @@ struct mixed_mul {
     return static_cast<double>(a) * b;
   }
 };
+
+struct mixed_power {
+  template<typename DType,
+           typename std::enable_if<std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static mshadow::half::half_t Map(DType a, mshadow::half::half_t b) {
+    return static_cast<mshadow::half::half_t>(math::pow(a, b));
+  }
+
+  template<typename DType,
+           typename std::enable_if<std::is_same<DType, mshadow::half::half_t>::value ||
+                                   std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static float Map(DType a, float b) {
+    return static_cast<float>(math::pow(a, b));
+  }
+
+  template<typename DType,
+           typename std::enable_if<std::is_same<DType, mshadow::half::half_t>::value ||
+                                   std::is_same<DType, float>::value ||
+                                   std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static double Map(DType a, double b) {
+    return static_cast<double>(math::pow(a, b));
+  }
+};
+
+struct mixed_rpower {
+  template<typename DType,
+           typename std::enable_if<std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static mshadow::half::half_t Map(DType a, mshadow::half::half_t b) {
+    return static_cast<mshadow::half::half_t>(math::pow(b, a));
+  }
+
+  template<typename DType,
+           typename std::enable_if<std::is_same<DType, mshadow::half::half_t>::value ||
+                                   std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static float Map(DType a, float b) {
+    return static_cast<float>(math::pow(b, a));
+  }
+
+  template<typename DType,
+           typename std::enable_if<std::is_same<DType, mshadow::half::half_t>::value ||
+                                   std::is_same<DType, float>::value ||
+                                   std::is_integral<DType>::value, int>::type = 0>
+  MSHADOW_XINLINE static double Map(DType a, double b) {
+    return static_cast<double>(math::pow(b, a));
+  }
+};
 #endif
 
 MXNET_BINARY_MATH_OP_NC_WITH_BOOL(mul, a * b);
diff --git a/src/operator/numpy/np_elemwise_broadcast_op.cc b/src/operator/numpy/np_elemwise_broadcast_op.cc
index fbe04eeb4eff..6409d4322a27 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op.cc
+++ b/src/operator/numpy/np_elemwise_broadcast_op.cc
@@ -158,9 +158,33 @@ MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_mod)
 .set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, mshadow_op::mod>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_broadcast_mod"});
 
-MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_power)
-.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, mshadow_op::power>)
-.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_broadcast_power"});
+MXNET_OPERATOR_REGISTER_NP_BINARY_MIXED_PRECISION(_npi_power)
+#ifndef _WIN32
+.set_attr<FCompute>(
+  "FCompute<cpu>",
+  NumpyBinaryBroadcastComputeWithBool<cpu, op::mshadow_op::power, op::mshadow_op::mixed_power,
+                                      op::mshadow_op::mixed_rpower>)
+#else
+.set_attr<FCompute>(
+  "FCompute<cpu>",
+  NumpyBinaryBroadcastComputeWithBool<cpu, op::mshadow_op::power>)
+#endif
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_broadcast_power"});
+
+NNVM_REGISTER_OP(_backward_npi_broadcast_power)
+.set_num_inputs(3)
+.set_num_outputs(2)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs){
+    return std::vector<std::pair<int, int> >{{0, 1}};
+  })
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", NumpyBinaryBackwardUseIn<cpu, mshadow_op::power_grad,
+                                                              mshadow_op::power_rgrad>);
 
 MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_add_scalar)
 .set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, op::mshadow_op::plus>)
diff --git a/src/operator/numpy/np_elemwise_broadcast_op.cu b/src/operator/numpy/np_elemwise_broadcast_op.cu
index 59dfc25db963..1e0130494469 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op.cu
+++ b/src/operator/numpy/np_elemwise_broadcast_op.cu
@@ -72,7 +72,20 @@ NNVM_REGISTER_OP(_npi_mod)
 .set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, mshadow_op::mod>);
 
 NNVM_REGISTER_OP(_npi_power)
-.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, mshadow_op::power>);
+#ifndef _WIN32
+.set_attr<FCompute>(
+  "FCompute<gpu>",
+  NumpyBinaryBroadcastComputeWithBool<gpu, op::mshadow_op::power, op::mshadow_op::mixed_power,
+                                      op::mshadow_op::mixed_rpower>);
+#else
+.set_attr<FCompute>(
+  "FCompute<gpu>",
+  NumpyBinaryBroadcastComputeWithBool<gpu, op::mshadow_op::power>);
+#endif
+
+NNVM_REGISTER_OP(_backward_npi_broadcast_power)
+.set_attr<FCompute>("FCompute<gpu>", NumpyBinaryBackwardUseIn<gpu, mshadow_op::power_grad,
+                                                              mshadow_op::power_rgrad>);
 
 NNVM_REGISTER_OP(_npi_add_scalar)
 .set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, op::mshadow_op::plus>);
diff --git a/src/operator/numpy/np_elemwise_broadcast_op.h b/src/operator/numpy/np_elemwise_broadcast_op.h
index a2b7877dc444..057a2ee6b16c 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op.h
+++ b/src/operator/numpy/np_elemwise_broadcast_op.h
@@ -252,6 +252,7 @@ void MixedBinaryBroadcastCompute(const nnvm::NodeAttrs& attrs,
   mxnet::TShape new_lshape, new_rshape, new_oshape;
   int ndim = BinaryBroadcastShapeCompact(lhs.shape_, rhs.shape_, out.shape_,
                                          &new_lshape, &new_rshape, &new_oshape);
+
   if (!ndim) {
     MixedBinaryElemwiseCompute<xpu, LOP, ROP>(attrs, ctx, inputs, req, outputs);
   } else {
@@ -430,12 +431,14 @@ void NumpyBinaryBackwardUseIn(const nnvm::NodeAttrs& attrs,
     Tensor<xpu, 1, char> workspace;
 
     MSHADOW_TYPE_SWITCH(ograd.type_flag_, OType, {
-      BROADCAST_NDIM_SWITCH(new_oshape.ndim(), ndim, {
-        workspace_size_l = ReduceWorkspaceSize<ndim, OType>(
-          s, new_lshape, req[0], new_oshape, new_lshape, new_rshape);
-        workspace_size_r = ReduceWorkspaceSize<ndim, OType>(
-          s, new_rshape, req[1], new_oshape, new_lshape, new_rshape);
-      });
+      if (need_bc) {
+        BROADCAST_NDIM_SWITCH(new_oshape.ndim(), ndim, {
+          workspace_size_l = ReduceWorkspaceSize<ndim, OType>(
+            s, new_lshape, req[0], new_oshape, new_lshape, new_rshape);
+          workspace_size_r = ReduceWorkspaceSize<ndim, OType>(
+            s, new_rshape, req[1], new_oshape, new_lshape, new_rshape);
+        });
+      }
       size_t workspace_size = std::max(workspace_size_l, workspace_size_r);
       size_t cast_tensor_size = tensor_size * sizeof(OType);
       // Allocate the temporary memories now
@@ -462,9 +465,11 @@ void NumpyBinaryBackwardUseIn(const nnvm::NodeAttrs& attrs,
       workspace =
         Tensor<xpu, 1, char>(temp_space.dptr_ + 2 * cast_tensor_size, Shape1(workspace_size), s);
     });
+
     // Cast the input that does not have consistent type to temp_tblob
     CastCompute<xpu>(
       attrs, ctx, {((lgrad.type_flag_ != ograd.type_flag_) ? lhs : rhs)}, {kWriteTo}, {temp_tblob});
+
     if (!need_bc) {
       if (lhs.type_flag_ != ograd.type_flag_) {
         ElemwiseBinaryOp::BackwardUseIn<xpu, LOP, ROP>(
diff --git a/src/operator/operator_tune.cc b/src/operator/operator_tune.cc
index 249da3d049c1..db898f8840f0 100644
--- a/src/operator/operator_tune.cc
+++ b/src/operator/operator_tune.cc
@@ -344,7 +344,7 @@ IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::left);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::left);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::right);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::right);  // NOLINT()
-IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::power);  // NOLINT()
+IMPLEMENT_BINARY_WORKLOAD_FWD_WITH_BOOL(mxnet::op::mshadow_op::power);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::rpower);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::xelu); // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::elu); // NOLINT()
diff --git a/src/operator/tensor/broadcast_reduce-inl.cuh b/src/operator/tensor/broadcast_reduce-inl.cuh
index 360420a1eca7..41940e4b1e07 100644
--- a/src/operator/tensor/broadcast_reduce-inl.cuh
+++ b/src/operator/tensor/broadcast_reduce-inl.cuh
@@ -368,9 +368,10 @@ static inline uint64_t calc_num_load(const int X, const int Y, const int* stride
 }
 
 template<int ndim, typename DType>
-ReduceImplConfig<ndim> ConfigureReduceImpl(const mxnet::TShape& small, const mxnet::TShape& big, const mxnet::TShape* lhs,
-  const mxnet::TShape* rhs) {
-
+ReduceImplConfig<ndim> ConfigureReduceImpl(const mxnet::TShape& small,
+                                           const mxnet::TShape& big,
+                                           const mxnet::TShape* lhs,
+                                           const mxnet::TShape* rhs) {
   ReduceImplConfig<ndim> config;
 
   diff(small.get<ndim>(), big.get<ndim>(), &config.rshape, &config.rstride);
diff --git a/src/operator/tensor/broadcast_reduce-inl.h b/src/operator/tensor/broadcast_reduce-inl.h
index e203da2a127e..0a20e1263fbf 100644
--- a/src/operator/tensor/broadcast_reduce-inl.h
+++ b/src/operator/tensor/broadcast_reduce-inl.h
@@ -90,8 +90,10 @@ MSHADOW_XINLINE index_t ravel(const Shape<ndim>& coord, const Shape<ndim>& shape
 }
 
 template<int ndim>
-MSHADOW_XINLINE int diff(const Shape<ndim>& small, const Shape<ndim>& big, Shape<ndim>* dims,
-  Shape<ndim>* stride) {
+MSHADOW_XINLINE int diff(const Shape<ndim>& small,
+                         const Shape<ndim>& big,
+                         Shape<ndim>* dims,
+                         Shape<ndim>* stride) {
   int mdim = 0;
   #pragma unroll
   for (int i = 0; i < ndim; ++i) {
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 9b7f7036bcda..cbdf5b33654b 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -1667,7 +1667,7 @@ def hybrid_forward(self, F, a, b, *args, **kwargs):
                       [lambda y, x1, x2: -_np.floor(x1 / x2),
                        lambda y, x1, x2: _np.zeros(y.shape)],
                       [[_np.float16, _np.float32, _np.float64], [_np.int32]]),
-        'power': (1.0, 2.0, [lambda y, x1, x2: _np.power(x1, x2 - 1.0) * x2],
+        'power': (1.0, 3.0, [lambda y, x1, x2: _np.power(x1, x2 - 1.0) * x2],
                              [lambda y, x1, x2: _np.power(x1, x2) * _np.log(x1)]),
         'lcm': (-100, 100, [None], None, [[_np.int32]]),
         'bitwise_xor': (-100, 100, [None], None, [[_np.int32]]),
@@ -1728,6 +1728,9 @@ def hybrid_forward(self, F, a, b, *args, **kwargs):
         for hybridize in [True, False]:
             if hybridize:
                 mx_func.hybridize()
+            if lgrad:
+                mx_test_x1.attach_grad()
+                mx_test_x2.attach_grad()
             np_out = np_func(np_test_x1, np_test_x2)
             with mx.autograd.record():
                 y = mx_func(mx_test_x1, mx_test_x2)
@@ -1735,6 +1738,23 @@ def hybrid_forward(self, F, a, b, *args, **kwargs):
             assert_almost_equal(y.asnumpy(), np_out.astype(y.dtype), rtol=rtol, atol=atol,
                                 use_broadcast=False, equal_nan=True)
 
+            if lgrad:
+                y.backward()
+                if ltype not in itypes:
+                    assert_almost_equal(mx_test_x1.grad.asnumpy(),
+                                        collapse_sum_like(lgrad(y.asnumpy(), np_test_x1, np_test_x2), mx_test_x1.shape),
+                                        rtol=1e-1, atol=1e-2, equal_nan=True, use_broadcast=False)
+                if rtype not in itypes:
+                    if rgrad is None:
+                        assert_almost_equal(mx_test_x2.grad.asnumpy(),
+                                            collapse_sum_like(rgrad(y.asnumpy(), np_test_x2, np_test_x1), mx_test_x2.shape),
+                                            rtol=1e-1, atol=1e-2, equal_nan=True, use_broadcast=False)
+                    else:
+                        assert_almost_equal(mx_test_x2.grad.asnumpy(),
+                                            collapse_sum_like(rgrad(y.asnumpy(), np_test_x1, np_test_x2), mx_test_x2.shape),
+                                            rtol=1e-1, atol=1e-2, equal_nan=True, use_broadcast=False)
+
+
         np_out = getattr(_np, func)(np_test_x1, np_test_x2)
         mx_out = getattr(mx.np, func)(mx_test_x1, mx_test_x2)
         assert mx_out.shape == np_out.shape
@@ -1745,7 +1765,9 @@ def hybrid_forward(self, F, a, b, *args, **kwargs):
         'add': (-1.0, 1.0, None, None),
         'subtract': (-1.0, 1.0, None, None),
         'multiply': (-1.0, 1.0, lambda y, x1, x2: _np.broadcast_to(x2, y.shape),
-                                lambda y, x1, x2: _np.broadcast_to(x1, y.shape))
+                                lambda y, x1, x2: _np.broadcast_to(x1, y.shape)),
+        'power': (1.0, 3.0, lambda y, x1, x2: _np.power(x1, x2 - 1.0) * x2,
+                            lambda y, x1, x2: _np.power(x1, x2) * _np.log(x1)),
     }
 
     shape_pairs = [((3, 2), (3, 2)),

From 9c94fdbeb0cc39ea26aa191d33ca1f343cc5b30b Mon Sep 17 00:00:00 2001
From: Jake Lee <gstu1130@gmail.com>
Date: Fri, 6 Dec 2019 13:36:06 -0800
Subject: [PATCH 654/813] Fix NDArrayIter iteration bug when
 last_batch_handle='pad' (#16166)

* fix NDarrayIter pad bug

* retrigger CI
---
 python/mxnet/io/io.py            | 10 +++++++++-
 tests/python/unittest/test_io.py |  8 ++++++++
 2 files changed, 17 insertions(+), 1 deletion(-)

diff --git a/python/mxnet/io/io.py b/python/mxnet/io/io.py
index 2a42840bcf22..dcf964df976a 100644
--- a/python/mxnet/io/io.py
+++ b/python/mxnet/io/io.py
@@ -748,7 +748,15 @@ def _batchify(self, data_source):
             self.cursor + self.batch_size > self.num_data:
             pad = self.batch_size - self.num_data + self.cursor
             first_data = self._getdata(data_source, start=self.cursor)
-            second_data = self._getdata(data_source, end=pad)
+            if pad > self.num_data:
+                while True:
+                    if pad <= self.num_data:
+                        break
+                    second_data = self._getdata(data_source, end=self.num_data)
+                    pad -= self.num_data
+                second_data = self._concat(second_data, self._getdata(data_source, end=pad))
+            else:
+                second_data = self._getdata(data_source, end=pad)
             return self._concat(first_data, second_data)
         # normal case
         else:
diff --git a/tests/python/unittest/test_io.py b/tests/python/unittest/test_io.py
index 144f042ea719..2a806efc9034 100644
--- a/tests/python/unittest/test_io.py
+++ b/tests/python/unittest/test_io.py
@@ -198,6 +198,13 @@ def _test_shuffle(data, labels=None):
         assert np.array_equal(batch.data[0].asnumpy(), batch_list[idx_list[i]])
         i += 1
 
+# fixes the issue https://github.com/apache/incubator-mxnet/issues/15535
+def _test_corner_case():
+    data = np.arange(10)
+    data_iter = mx.io.NDArrayIter(data=data, batch_size=25, shuffle=False, last_batch_handle='pad')
+    expect = np.concatenate((np.tile(data, 2), np.arange(5)))
+    assert np.array_equal(data_iter.next().data[0].asnumpy(), expect)
+
 
 def test_NDArrayIter():
     dtype_list = ['NDArray', 'ndarray']
@@ -220,6 +227,7 @@ def test_NDArrayIter():
             _test_shuffle({'data1': data, 'data2': data})
             _test_shuffle(data, [])
             _test_shuffle(data)
+    _test_corner_case()
 
 
 def test_NDArrayIter_h5py():

From fcc42de7e9b4ad715c0f3fb942e98a8a19ec1122 Mon Sep 17 00:00:00 2001
From: Rohit Kumar Srivastava <srivastava.141@osu.edu>
Date: Fri, 6 Dec 2019 13:46:59 -0800
Subject: [PATCH 655/813] updating MXNet version to 1.6.0 in base.h for C APIs
 (#16905)

---
 include/mxnet/base.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/mxnet/base.h b/include/mxnet/base.h
index 02dd204fc77d..90b36ab2d8a5 100644
--- a/include/mxnet/base.h
+++ b/include/mxnet/base.h
@@ -73,7 +73,7 @@
 /*! \brief major version */
 #define MXNET_MAJOR 1
 /*! \brief minor version */
-#define MXNET_MINOR 5
+#define MXNET_MINOR 6
 /*! \brief patch version */
 #define MXNET_PATCH 0
 /*! \brief mxnet version */

From ca4939f26bac7d1b3c632b81cd1226a4f64b4246 Mon Sep 17 00:00:00 2001
From: Rohit Kumar Srivastava <srivastava.141@osu.edu>
Date: Fri, 6 Dec 2019 16:17:28 -0800
Subject: [PATCH 656/813] [OP] changing data type of 't' to int in
 lamb_update_phase1 (#16903)

* changing data type of 't' to int in lamb_update_phase1

* taking operation beta^t out of kernel call
---
 src/operator/optimizer_op-inl.h | 16 +++++++++-------
 1 file changed, 9 insertions(+), 7 deletions(-)

diff --git a/src/operator/optimizer_op-inl.h b/src/operator/optimizer_op-inl.h
index 698f7977a963..146e411b447c 100644
--- a/src/operator/optimizer_op-inl.h
+++ b/src/operator/optimizer_op-inl.h
@@ -1567,7 +1567,7 @@ struct LambUpdatePhaseOneParam : public dmlc::Parameter<LambUpdatePhaseOneParam>
     float beta1;
     float beta2;
     float epsilon;
-    float t;
+    int t;
     bool bias_correction;
     float wd;
     float rescale_grad;
@@ -1623,8 +1623,8 @@ struct LambUpdatePhaseOneKernel {
   MSHADOW_XINLINE static void Map(int i, DType* out_data,
     DType* mean_data, DType* var_data, const DType* weight_data, const DType* grad_data,
     const DType clip_gradient, const DType rescale_grad,
-    const DType beta1, const DType beta2, const DType wd,
-    const DType epsilon, const DType t,
+    const DType beta1, const DType beta1_t, const DType beta2, const DType beta2_t,
+    const DType wd, const DType epsilon, const int t,
     bool bias_correction, const OpReqType req) {
     using namespace mshadow_op;
 
@@ -1639,8 +1639,8 @@ struct LambUpdatePhaseOneKernel {
     DType g = mean_data[i] / (square_root::Map(var_data[i]) + epsilon) + wd * weight_data[i];
 
     if (bias_correction) {
-      DType mean_hat = mean_data[i] / (1. - power::Map(beta1, t));
-      DType var_hat = var_data[i] / (1 - power::Map(beta2, t));
+      DType mean_hat = mean_data[i] / (1. - beta1_t);
+      DType var_hat = var_data[i] / (1 - beta2_t);
       g = mean_hat / (square_root::Map(var_hat) + epsilon) + wd * weight_data[i];
     }
     KERNEL_ASSIGN(out_data[i], req, g);
@@ -1657,6 +1657,8 @@ inline void LambUpdatePhaseOne(const nnvm::NodeAttrs& attrs,
   const LambUpdatePhaseOneParam& param = nnvm::get<LambUpdatePhaseOneParam>(attrs.parsed);
   Stream<xpu>* s = ctx.get_stream<xpu>();
   MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    DType beta1_t = std::pow(param.beta1, param.t);
+    DType beta2_t = std::pow(param.beta2, param.t);
     Tensor<xpu, 2, DType> weight = inputs[0].FlatTo2D<xpu, DType>(s);
     Tensor<xpu, 2, DType> grad = inputs[1].FlatTo2D<xpu, DType>(s);
     Tensor<xpu, 2, DType> mean = inputs[2].FlatTo2D<xpu, DType>(s);
@@ -1666,9 +1668,9 @@ inline void LambUpdatePhaseOne(const nnvm::NodeAttrs& attrs,
   Kernel<LambUpdatePhaseOneKernel, xpu>::Launch(s, weight.shape_.Size(),
     out.dptr_, mean.dptr_, var.dptr_, weight.dptr_, grad.dptr_,
     static_cast<DType>(param.clip_gradient), static_cast<DType>(param.rescale_grad),
-    static_cast<DType>(param.beta1), static_cast<DType>(param.beta2),
+    static_cast<DType>(param.beta1), beta1_t, static_cast<DType>(param.beta2), beta2_t,
     static_cast<DType>(param.wd), static_cast<DType>(param.epsilon),
-    static_cast<DType>(param.t), static_cast<bool>(param.bias_correction), req[0]);
+    static_cast<int>(param.t), static_cast<bool>(param.bias_correction), req[0]);
   });
 }
 

From f06fdf6f79c07ee839dfc5ed09a85849e4661aab Mon Sep 17 00:00:00 2001
From: liuzh91 <liuzhuanghua1991@gmail.com>
Date: Sat, 7 Dec 2019 13:05:53 +0800
Subject: [PATCH 657/813] [Gluon] Update contrib.Estimator LoggingHandler to
 support logging per batch interval (#16922)

* Update LoggingHandler to support logging per interval

* Fix the constant variable issue in the logging handler

* Remove the constant variable hack in the logging handler.

* 1) replace LOG_PER_BATCH with LOG_PER_INTERVAL 2) add test case

* Improve the test script for LoggingHandler

* small fix on the test script

* logging handler test case bug fix

* remove parameter verbose from LoggingHandler

* move log_interval to the first argument

* resolve unittest mistakes
---
 .../gluon/contrib/estimator/event_handler.py  | 46 ++++++------
 .../unittest/test_gluon_event_handler.py      | 72 +++++++++++++++++--
 2 files changed, 90 insertions(+), 28 deletions(-)

diff --git a/python/mxnet/gluon/contrib/estimator/event_handler.py b/python/mxnet/gluon/contrib/estimator/event_handler.py
index 3cdc407407c1..53ba07dc836a 100644
--- a/python/mxnet/gluon/contrib/estimator/event_handler.py
+++ b/python/mxnet/gluon/contrib/estimator/event_handler.py
@@ -227,29 +227,22 @@ class LoggingHandler(TrainBegin, TrainEnd, EpochBegin, EpochEnd, BatchBegin, Bat
 
     Parameters
     ----------
-    verbose : int, default LOG_PER_EPOCH
-        Limit the granularity of metrics displayed during training process.
-        verbose=LOG_PER_EPOCH: display metrics every epoch
-        verbose=LOG_PER_BATCH: display metrics every batch
+    log_interval: int or str, default 'epoch'
+        Logging interval during training.
+        log_interval='epoch': display metrics every epoch
+        log_interval=integer k: display metrics every interval of k batches
     train_metrics : list of EvalMetrics
         Training metrics to be logged, logged at batch end, epoch end, train end.
     val_metrics : list of EvalMetrics
         Validation metrics to be logged, logged at epoch end, train end.
     """
 
-    LOG_PER_EPOCH = 1
-    LOG_PER_BATCH = 2
-
-    def __init__(self, verbose=LOG_PER_EPOCH,
+    def __init__(self, log_interval='epoch',
                  train_metrics=None,
                  val_metrics=None):
         super(LoggingHandler, self).__init__()
-        if verbose not in [self.LOG_PER_EPOCH, self.LOG_PER_BATCH]:
-            raise ValueError("verbose level must be either LOG_PER_EPOCH or "
-                             "LOG_PER_BATCH, received %s. "
-                             "E.g: LoggingHandler(verbose=LoggingHandler.LOG_PER_EPOCH)"
-                             % verbose)
-        self.verbose = verbose
+        if not isinstance(log_interval, int) and log_interval != 'epoch':
+            raise ValueError("log_interval must be either an integer or string 'epoch'")
         self.train_metrics = _check_metrics(train_metrics)
         self.val_metrics = _check_metrics(val_metrics)
         self.batch_index = 0
@@ -258,6 +251,7 @@ def __init__(self, verbose=LOG_PER_EPOCH,
         # logging handler need to be called at last to make sure all states are updated
         # it will also shut down logging at train end
         self.priority = np.Inf
+        self.log_interval = log_interval
 
     def train_begin(self, estimator, *args, **kwargs):
         self.train_start = time.time()
@@ -275,6 +269,7 @@ def train_begin(self, estimator, *args, **kwargs):
         self.current_epoch = 0
         self.batch_index = 0
         self.processed_samples = 0
+        self.log_interval_time = 0
 
     def train_end(self, estimator, *args, **kwargs):
         train_time = time.time() - self.train_start
@@ -286,31 +281,34 @@ def train_end(self, estimator, *args, **kwargs):
         estimator.logger.info(msg.rstrip(', '))
 
     def batch_begin(self, estimator, *args, **kwargs):
-        if self.verbose == self.LOG_PER_BATCH:
+        if isinstance(self.log_interval, int):
             self.batch_start = time.time()
 
     def batch_end(self, estimator, *args, **kwargs):
-        if self.verbose == self.LOG_PER_BATCH:
+        if isinstance(self.log_interval, int):
             batch_time = time.time() - self.batch_start
             msg = '[Epoch %d][Batch %d]' % (self.current_epoch, self.batch_index)
             self.processed_samples += kwargs['batch'][0].shape[0]
             msg += '[Samples %s] ' % (self.processed_samples)
-            msg += 'time/batch: %.3fs ' % batch_time
-            for metric in self.train_metrics:
-                # only log current training loss & metric after each batch
-                name, value = metric.get()
-                msg += '%s: %.4f, ' % (name, value)
-            estimator.logger.info(msg.rstrip(', '))
+            self.log_interval_time += batch_time
+            if self.batch_index % self.log_interval == 0:
+                msg += 'time/interval: %.3fs ' % self.log_interval_time
+                self.log_interval_time = 0
+                for metric in self.train_metrics:
+                    # only log current training loss & metric after each interval
+                    name, value = metric.get()
+                    msg += '%s: %.4f, ' % (name, value)
+                estimator.logger.info(msg.rstrip(', '))
         self.batch_index += 1
 
     def epoch_begin(self, estimator, *args, **kwargs):
-        if self.verbose >= self.LOG_PER_EPOCH:
+        if isinstance(self.log_interval, int) or self.log_interval == 'epoch':
             self.epoch_start = time.time()
             estimator.logger.info("[Epoch %d] Begin, current learning rate: %.4f",
                                   self.current_epoch, estimator.trainer.learning_rate)
 
     def epoch_end(self, estimator, *args, **kwargs):
-        if self.verbose >= self.LOG_PER_EPOCH:
+        if isinstance(self.log_interval, int) or self.log_interval == 'epoch':
             epoch_time = time.time() - self.epoch_start
             msg = '[Epoch %d] Finished in %.3fs, ' % (self.current_epoch, epoch_time)
             for monitor in self.train_metrics + self.val_metrics:
diff --git a/tests/python/unittest/test_gluon_event_handler.py b/tests/python/unittest/test_gluon_event_handler.py
index 17c75813d516..658fb88f47e5 100644
--- a/tests/python/unittest/test_gluon_event_handler.py
+++ b/tests/python/unittest/test_gluon_event_handler.py
@@ -17,13 +17,19 @@
 
 import os
 import logging
+import sys
+import re
 
 import mxnet as mx
 from common import TemporaryDirectory
 from mxnet import nd
 from mxnet.gluon import nn, loss
 from mxnet.gluon.contrib.estimator import estimator, event_handler
-
+from mxnet.gluon.contrib.estimator.event_handler import LoggingHandler
+try:
+    from StringIO import StringIO
+except ImportError:
+    from io import StringIO
 
 def _get_test_network(net=nn.Sequential()):
     net.add(nn.Dense(128, activation='relu', flatten=False),
@@ -32,9 +38,9 @@ def _get_test_network(net=nn.Sequential()):
     return net
 
 
-def _get_test_data():
-    data = nd.ones((32, 100))
-    label = nd.zeros((32, 1))
+def _get_test_data(in_size=32):
+    data = nd.ones((in_size, 100))
+    label = nd.zeros((in_size, 1))
     data_arr = mx.gluon.data.dataset.ArrayDataset(data, label)
     return mx.gluon.data.DataLoader(data_arr, batch_size=8)
 
@@ -200,3 +206,61 @@ def epoch_end(self, estimator, *args, **kwargs):
     est.fit(test_data, event_handlers=[custom_handler], epochs=10)
     assert custom_handler.num_batch == 5 * 4
     assert custom_handler.num_epoch == 5
+
+def test_logging_interval():
+    ''' test different options for logging handler '''
+    ''' test case #1: log interval is 1 '''
+    batch_size = 8
+    data_size = 100
+    old_stdout = sys.stdout
+    sys.stdout = mystdout = StringIO()
+    log_interval = 1
+    net = _get_test_network()
+    dataloader = _get_test_data(in_size=data_size)
+    num_epochs = 1
+    ce_loss = loss.SoftmaxCrossEntropyLoss()
+    acc = mx.metric.Accuracy()
+    logging = LoggingHandler(train_metrics=[acc], log_interval=log_interval)
+    est = estimator.Estimator(net=net,
+                              loss=ce_loss,
+                              metrics=acc)
+
+    est.fit(train_data=dataloader,
+            epochs=num_epochs,
+            event_handlers=[logging])
+
+    sys.stdout = old_stdout
+    log_info_list = mystdout.getvalue().splitlines()
+    info_len = 0
+    for info in log_info_list:
+        match = re.match(
+            '(\[Epoch \d+\]\[Batch \d+\]\[Samples \d+\] time\/interval: \d+.\d+s' +
+            ' training accuracy: \d+.\d+)', info)
+        if match:
+            info_len += 1
+
+    assert(info_len == int(data_size/batch_size/log_interval) + 1)
+    ''' test case #2: log interval is 5 '''
+    old_stdout = sys.stdout
+    sys.stdout = mystdout = StringIO()
+    acc = mx.metric.Accuracy()
+    log_interval = 5
+    logging = LoggingHandler(train_metrics=[acc], log_interval=log_interval)
+    est = estimator.Estimator(net=net,
+                              loss=ce_loss,
+                              metrics=acc)
+    est.fit(train_data=dataloader,
+            epochs=num_epochs,
+            event_handlers=[logging])
+    sys.stdout = old_stdout
+    log_info_list = mystdout.getvalue().splitlines()
+    info_len = 0
+    for info in log_info_list:
+        match = re.match(
+            '(\[Epoch \d+\]\[Batch \d+\]\[Samples \d+\] time\/interval: \d+.\d+s' +
+            ' training accuracy: \d+.\d+)', info)
+        if match:
+            info_len += 1
+
+    assert(info_len == int(data_size/batch_size/log_interval) + 1)
+

From 82c34ee51052d07d967ed99f573beb22779456cf Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Fri, 6 Dec 2019 22:13:46 -0800
Subject: [PATCH 658/813] boolean_mask_assign with start_axis (#16886)

---
 src/common/utils.h                           | 17 +++--
 src/operator/numpy/np_boolean_mask_assign.cc | 75 ++++++++++++++++----
 src/operator/numpy/np_boolean_mask_assign.cu | 51 +++++++++----
 tests/python/unittest/test_numpy_op.py       | 58 ++++++++++++---
 4 files changed, 159 insertions(+), 42 deletions(-)

diff --git a/src/common/utils.h b/src/common/utils.h
index 0e3e35430652..9a9c686e73c9 100644
--- a/src/common/utils.h
+++ b/src/common/utils.h
@@ -435,6 +435,15 @@ inline std::string dev_type_string(const int dev_type) {
   return "unknown";
 }
 
+inline std::string attr_value_string(const nnvm::NodeAttrs& attrs,
+                                     const std::string& attr_name,
+                                     std::string default_val = "") {
+  if (attrs.dict.find(attr_name) == attrs.dict.end()) {
+    return default_val;
+  }
+  return attrs.dict.at(attr_name);
+}
+
 /*! \brief get string representation of the operator stypes */
 inline std::string operator_stype_string(const nnvm::NodeAttrs& attrs,
                                          const int dev_mask,
@@ -463,10 +472,10 @@ inline std::string operator_stype_string(const nnvm::NodeAttrs& attrs,
 
 /*! \brief get string representation of the operator */
 inline std::string operator_string(const nnvm::NodeAttrs& attrs,
-                                  const OpContext& ctx,
-                                  const std::vector<NDArray>& inputs,
-                                  const std::vector<OpReqType>& req,
-                                  const std::vector<NDArray>& outputs) {
+                                   const OpContext& ctx,
+                                   const std::vector<NDArray>& inputs,
+                                   const std::vector<OpReqType>& req,
+                                   const std::vector<NDArray>& outputs) {
   std::string result = "";
   std::vector<int> in_stypes;
   std::vector<int> out_stypes;
diff --git a/src/operator/numpy/np_boolean_mask_assign.cc b/src/operator/numpy/np_boolean_mask_assign.cc
index 2a5ae116e291..0df3609ecab1 100644
--- a/src/operator/numpy/np_boolean_mask_assign.cc
+++ b/src/operator/numpy/np_boolean_mask_assign.cc
@@ -22,6 +22,7 @@
  * \brief CPU implementation of Boolean Mask Assign
  */
 
+#include "../../common/utils.h"
 #include "../contrib/boolean_mask-inl.h"
 
 namespace mxnet {
@@ -88,6 +89,7 @@ struct BooleanAssignCPUKernel {
                   const size_t idx_size,
                   const size_t leading,
                   const size_t middle,
+                  const size_t valid_num,
                   const size_t trailing,
                   DType* tensor) {
     // binary search for the turning point
@@ -95,7 +97,8 @@ struct BooleanAssignCPUKernel {
     // final answer is in mid
     for (size_t l = 0; l < leading; ++l) {
       for (size_t t = 0; t < trailing; ++t) {
-        data[(l * middle + mid) * trailing + t] = (scalar) ? tensor[0] : tensor[i];
+        data[(l * middle + mid) * trailing + t] =
+          (scalar) ? tensor[0] : tensor[(l * valid_num + i) * trailing + t];
       }
     }
   }
@@ -106,19 +109,47 @@ bool BooleanAssignShape(const nnvm::NodeAttrs& attrs,
                         mxnet::ShapeVector *out_attrs) {
   CHECK(in_attrs->size() == 2U || in_attrs->size() == 3U);
   CHECK_EQ(out_attrs->size(), 1U);
+  CHECK(shape_is_known(in_attrs->at(0)) && shape_is_known(in_attrs->at(1)))
+    << "shape of both input and mask should be known";
   const TShape& dshape = in_attrs->at(0);
+  const TShape& mshape = in_attrs->at(1);
+  const int start_axis = std::stoi(common::attr_value_string(attrs, "start_axis", "0"));
 
-  // mask should have the same shape as the input
-  SHAPE_ASSIGN_CHECK(*in_attrs, 1, dshape);
+  for (int i = 0; i < mshape.ndim(); ++i) {
+    CHECK_EQ(dshape[i + start_axis], mshape[i])
+      << "boolean index did not match indexed array along dimension " << i + start_axis
+      << "; dimension is " << dshape[i + start_axis] << " but corresponding boolean dimension is "
+      << mshape[i];
+  }
 
   // check if output shape is the same as the input data
   SHAPE_ASSIGN_CHECK(*out_attrs, 0, dshape);
 
   // for tensor version, the tensor should have less than 1 dimension
   if (in_attrs->size() == 3U) {
-    CHECK_LE(in_attrs->at(2).ndim(), 1U)
-      << "boolean array indexing assignment requires a 0 or 1-dimensional input, input has "
-      << in_attrs->at(2).ndim() <<" dimensions";
+    if (mshape.ndim() == dshape.ndim()) {
+      CHECK_LE(in_attrs->at(2).ndim(), 1U)
+        << "boolean array indexing assignment requires a 0 or 1-dimensional input, input has "
+        << in_attrs->at(2).ndim() <<" dimensions";
+    } else {
+      const TShape& vshape = in_attrs->at(2);
+      if (vshape.Size() > 1) {
+        for (int i = 0; i < dshape.ndim(); ++i) {
+          if (i < start_axis) {
+            CHECK_EQ(dshape[i], vshape[i])
+              << "shape mismatch of value with input at dimension " << i
+              << "; dimension is " << dshape[i] << " but corresponding value dimension is "
+              << vshape[i];
+          }
+          if (i >= start_axis + mshape.ndim()) {
+            CHECK_EQ(dshape[i], vshape[i - mshape.ndim() + 1])
+              << "shape mismatch of value with input at dimension " << i
+              << "; dimension is " << dshape[i] << " but corresponding value dimension is "
+              << vshape[i - mshape.ndim() + 1];
+          }
+        }
+      }
+    }
   }
 
   return shape_is_known(out_attrs->at(0));
@@ -170,22 +201,26 @@ void NumpyBooleanAssignForwardCPU(const nnvm::NodeAttrs& attrs,
   Stream<cpu>* s = ctx.get_stream<cpu>();
 
   const TBlob& data = inputs[0];
+  const TShape& dshape = data.shape_;
   const TBlob& mask = inputs[1];
+  const TShape& mshape = mask.shape_;
+  const int start_axis = std::stoi(common::attr_value_string(attrs, "start_axis", "0"));
   // Get valid_num
   size_t valid_num = 0;
   size_t mask_size = mask.shape_.Size();
   std::vector<size_t> prefix_sum(mask_size + 1, 0);
-  MSHADOW_TYPE_SWITCH(mask.type_flag_, MType, {
+  MSHADOW_TYPE_SWITCH_WITH_BOOL(mask.type_flag_, MType, {
     valid_num = GetValidNumCPU(mask.dptr<MType>(), prefix_sum.data(), mask_size);
   });
   // If there's no True in mask, return directly
   if (valid_num == 0) return;
 
   if (inputs.size() == 3U) {
+    const TShape& vshape = inputs[2].shape_;
     if (inputs[2].shape_.Size() != 1) {
       // tensor case, check tensor size with the valid_num
-      CHECK_EQ(static_cast<size_t>(valid_num), inputs[2].shape_.Size())
-        << "boolean array indexing assignment cannot assign " << inputs[2].shape_.Size()
+      CHECK_EQ(static_cast<size_t>(valid_num), vshape[start_axis])
+        << "boolean array indexing assignment cannot assign " << vshape
         << " input values to the " << valid_num << " output values where the mask is true"
         << std::endl;
     }
@@ -195,21 +230,29 @@ void NumpyBooleanAssignForwardCPU(const nnvm::NodeAttrs& attrs,
   size_t middle = mask_size;
   size_t trailing = 1U;
 
+  for (int i = 0; i < dshape.ndim(); ++i) {
+    if (i < start_axis) {
+      leading *= dshape[i];
+    }
+    if (i >= start_axis + mshape.ndim()) {
+      trailing *= dshape[i];
+    }
+  }
+
   if (inputs.size() == 3U) {
     MSHADOW_TYPE_SWITCH(data.type_flag_, DType, {
       if (inputs[2].shape_.Size() == 1) {
         Kernel<BooleanAssignCPUKernel<true>, cpu>::Launch(
           s, valid_num, data.dptr<DType>(), prefix_sum.data(), prefix_sum.size(),
-          leading, middle, trailing, inputs[2].dptr<DType>());
+          leading, middle, valid_num, trailing, inputs[2].dptr<DType>());
       } else {
        Kernel<BooleanAssignCPUKernel<false>, cpu>::Launch(
           s, valid_num, data.dptr<DType>(), prefix_sum.data(), prefix_sum.size(),
-          leading, middle, trailing, inputs[2].dptr<DType>());
+          leading, middle, valid_num, trailing, inputs[2].dptr<DType>());
       }
     });
   } else {
-    CHECK(attrs.dict.find("value") != attrs.dict.end())
-      << "value needs be provided";
+    CHECK(attrs.dict.find("value") != attrs.dict.end()) << "value needs be provided";
     MSHADOW_TYPE_SWITCH(data.type_flag_, DType, {
       Kernel<BooleanAssignCPUKernel<true>, cpu>::Launch(
         s, valid_num, data.dptr<DType>(), prefix_sum.data(), prefix_sum.size(),
@@ -240,7 +283,8 @@ NNVM_REGISTER_OP(_npi_boolean_mask_assign_scalar)
 .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
 .add_argument("data", "NDArray-or-Symbol", "input")
 .add_argument("mask", "NDArray-or-Symbol", "mask")
-.add_argument("value", "float", "value to be assigned to masked positions");
+.add_argument("value", "float", "value to be assigned to masked positions")
+.add_argument("start_axis", "int", "starting axis of boolean mask");
 
 NNVM_REGISTER_OP(_npi_boolean_mask_assign_tensor)
 .describe(R"code(Tensor version of boolean assign)code" ADD_FILELINE)
@@ -264,7 +308,8 @@ NNVM_REGISTER_OP(_npi_boolean_mask_assign_tensor)
 .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
 .add_argument("data", "NDArray-or-Symbol", "input")
 .add_argument("mask", "NDArray-or-Symbol", "mask")
-.add_argument("value", "NDArray-or-Symbol", "assignment");
+.add_argument("value", "NDArray-or-Symbol", "assignment")
+.add_argument("start_axis", "int", "starting axis of boolean mask");
 
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/np_boolean_mask_assign.cu b/src/operator/numpy/np_boolean_mask_assign.cu
index 2ccc4ffcd98d..5f3c24c02f33 100644
--- a/src/operator/numpy/np_boolean_mask_assign.cu
+++ b/src/operator/numpy/np_boolean_mask_assign.cu
@@ -23,6 +23,7 @@
  */
 
 #include <cub/cub.cuh>
+#include "../../common/utils.h"
 #include "../contrib/boolean_mask-inl.h"
 
 namespace mxnet {
@@ -70,13 +71,17 @@ struct BooleanAssignGPUKernel {
                              const size_t idx_size,
                              const size_t leading,
                              const size_t middle,
+                             const size_t valid_num,
                              const size_t trailing,
                              const DType val) {
     // binary search for the turning point
-    size_t m = i / trailing % middle;
+    size_t m = i / trailing % valid_num;
+    size_t l = i / trailing / valid_num;
     size_t mid = bin_search(idx, idx_size, m);
     // final answer is in mid
-    data[i + (mid - m) * trailing] = val;
+    // i = l * valid_num * trailing + m * trailing + t
+    // dst = l * middle * trailing + mid * trailing + t
+    data[i + (l * (middle - valid_num) + (mid - m)) * trailing] = val;
   }
 
   template<typename DType>
@@ -86,13 +91,20 @@ struct BooleanAssignGPUKernel {
                              const size_t idx_size,
                              const size_t leading,
                              const size_t middle,
+                             const size_t valid_num,
                              const size_t trailing,
                              DType* tensor) {
     // binary search for the turning point
-    size_t m = i / trailing % middle;
+    size_t m = i / trailing % valid_num;
+    size_t l = i / trailing / valid_num;
     size_t mid = bin_search(idx, idx_size, m);
+    size_t dst = i + (l * (middle - valid_num) + (mid - m)) * trailing;
     // final answer is in mid
-    data[i + (mid - m) * trailing] = (scalar) ? tensor[0] : tensor[m];
+    if (scalar) {
+      data[dst] = tensor[0];
+    } else {
+      data[dst] = tensor[i];
+    }
   }
 };
 
@@ -166,13 +178,17 @@ void NumpyBooleanAssignForwardGPU(const nnvm::NodeAttrs& attrs,
   Stream<gpu>* s = ctx.get_stream<gpu>();
 
   const TBlob& data = inputs[0];
+  const TShape& dshape = data.shape_;
   const TBlob& mask = inputs[1];
+  const TShape& mshape = mask.shape_;
+  const int start_axis = std::stoi(common::attr_value_string(attrs, "start_axis", "0"));
+
   // Get valid_num
   size_t mask_size = mask.shape_.Size();
   size_t valid_num = 0;
   size_t* prefix_sum = nullptr;
   if (mask_size != 0) {
-    MSHADOW_TYPE_SWITCH(mask.type_flag_, MType, {
+    MSHADOW_TYPE_SWITCH_WITH_BOOL(mask.type_flag_, MType, {
       prefix_sum = GetValidNumGPU<MType>(ctx, mask.dptr<MType>(), mask_size);
     });
     cudaStream_t stream = mshadow::Stream<gpu>::GetStream(s);
@@ -180,14 +196,16 @@ void NumpyBooleanAssignForwardGPU(const nnvm::NodeAttrs& attrs,
                               cudaMemcpyDeviceToHost, stream));
     CUDA_CALL(cudaStreamSynchronize(stream));
   }
+
   // If there's no True in mask, return directly
   if (valid_num == 0) return;
 
   if (inputs.size() == 3U) {
+    const TShape& vshape = inputs[2].shape_;
     if (inputs[2].shape_.Size() != 1) {
       // tensor case, check tensor size with the valid_num
-      CHECK_EQ(static_cast<size_t>(valid_num), inputs[2].shape_.Size())
-        << "boolean array indexing assignment cannot assign " << inputs[2].shape_.Size()
+      CHECK_EQ(static_cast<size_t>(valid_num), vshape[start_axis])
+        << "boolean array indexing assignment cannot assign " << vshape
         << " input values to the " << valid_num << " output values where the mask is true"
         << std::endl;
     }
@@ -197,27 +215,36 @@ void NumpyBooleanAssignForwardGPU(const nnvm::NodeAttrs& attrs,
   size_t middle = mask_size;
   size_t trailing = 1U;
 
+  for (int i = 0; i < dshape.ndim(); ++i) {
+    if (i < start_axis) {
+      leading *= dshape[i];
+    }
+    if (i >= start_axis + mshape.ndim()) {
+      trailing *= dshape[i];
+    }
+  }
+
   if (inputs.size() == 3U) {
     if (inputs[2].shape_.Size() == 1) {
       MSHADOW_TYPE_SWITCH(data.type_flag_, DType, {
         Kernel<BooleanAssignGPUKernel<true>, gpu>::Launch(
           s, leading * valid_num * trailing, data.dptr<DType>(), prefix_sum, mask_size + 1,
-          leading, middle, trailing, inputs[2].dptr<DType>());
+          leading, middle, valid_num, trailing, inputs[2].dptr<DType>());
       });
     } else {
       MSHADOW_TYPE_SWITCH(data.type_flag_, DType, {
         Kernel<BooleanAssignGPUKernel<false>, gpu>::Launch(
           s, leading * valid_num * trailing, data.dptr<DType>(), prefix_sum, mask_size + 1,
-          leading, middle, trailing, inputs[2].dptr<DType>());
+          leading, middle, valid_num, trailing, inputs[2].dptr<DType>());
       });
     }
   } else {
-    CHECK(attrs.dict.find("value") != attrs.dict.end())
-      << "value is not provided";
+    CHECK(attrs.dict.find("value") != attrs.dict.end()) << "value is not provided";
+    double value = std::stod(attrs.dict.at("value"));
     MSHADOW_TYPE_SWITCH(data.type_flag_, DType, {
       Kernel<BooleanAssignGPUKernel<true>, gpu>::Launch(
         s, leading * valid_num * trailing, data.dptr<DType>(), prefix_sum, mask_size + 1,
-        leading, middle, trailing, static_cast<DType>(std::stod(attrs.dict.at("value"))));
+        leading, middle, valid_num, trailing, static_cast<DType>(value));
     });
   }
 }
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index cbdf5b33654b..61aec6832d7e 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -1077,34 +1077,70 @@ def gt_grad_batch_dot_numpy(lhs, rhs, ograd, transpose_a, transpose_b, lhs_req,
 @use_np
 def test_npi_boolean_assign():
     class TestBooleanAssignScalar(HybridBlock):
-        def __init__(self, val):
+        def __init__(self, val, start_axis):
             super(TestBooleanAssignScalar, self).__init__()
             self._val = val
+            self._start_axis = start_axis
 
         def hybrid_forward(self, F, a, mask):
-            return F.np._internal.boolean_mask_assign_scalar(a, mask, self._val, out=a)
+            return F.np._internal.boolean_mask_assign_scalar(a, mask, self._val, start_axis=self._start_axis, out=a)
 
     class TestBooleanAssignTensor(HybridBlock):
-        def __init__(self):
+        def __init__(self, start_axis):
             super(TestBooleanAssignTensor, self).__init__()
+            self._start_axis = start_axis
 
         def hybrid_forward(self, F, a, mask, value):
-            return F.np._internal.boolean_mask_assign_tensor(a, mask, value, out=a)
+            return F.np._internal.boolean_mask_assign_tensor(a, mask, value, start_axis=self._start_axis, out=a)
+
+    configs = [
+        ((3, 4), (3, 4), 0),
+        ((3, 0), (3, 0), 0),
+        ((), (), 0),
+        ((2, 3, 4, 5), (2, 3), 0),
+        ((2, 3, 4, 5), (3, 4), 1),
+        ((2, 3, 4, 5), (4, 5), 2),
+    ]
 
-    shapes = [(3, 4), (3, 0), ()]
     for hybridize in [False]:
-        for shape in shapes:
-            test_data = np.random.uniform(size=shape)
-            mx_mask = np.around(np.random.uniform(size=shape))
+        for config in configs:
+            print(config)
+            dshape, mshape, start_axis = config
+            test_data = np.random.uniform(size=dshape)
+            mx_mask = np.around(np.random.uniform(size=mshape))
             valid_num = int(mx_mask.sum())
             np_mask = mx_mask.asnumpy().astype(_np.bool)
-            for val in [42., np.array(42.), np.array([42.]), np.random.uniform(size=(valid_num,))]:
-                test_block = TestBooleanAssignScalar(val) if isinstance(val, float) else TestBooleanAssignTensor()
+            vshape = []
+            for i in range(len(dshape)):
+                if i < start_axis:
+                    vshape.append(dshape[i])
+                elif i == start_axis:
+                    vshape.append(valid_num)
+                elif i >= start_axis + len(mshape):
+                    vshape.append(dshape[i])
+            vshape = tuple(vshape)
+            for val in [42.0, np.array(42.), np.array([42.]), np.random.uniform(size=vshape)]:
+                test_block = TestBooleanAssignScalar(val, start_axis) if isinstance(val, float) else TestBooleanAssignTensor(start_axis)
                 if hybridize:
                     test_block.hybridize()
                 np_data = test_data.asnumpy()
                 mx_data = test_data.copy()
-                np_data[np_mask] = val
+                trailing_axis = len(np_data.shape) - len(np_mask.shape) - start_axis
+                if start_axis == 0:
+                    if trailing_axis == 0:
+                        np_data[np_mask] = val
+                    elif trailing_axis == 1:
+                        np_data[np_mask, :] = val
+                    elif trailing_axis == 2:
+                        np_data[np_mask, :, :] = val
+                elif start_axis == 1:
+                    if trailing_axis == 0:
+                        np_data[:, np_mask] = val
+                    elif trailing_axis == 1:
+                        np_data[:, np_mask, :] = val
+                elif start_axis == 2:
+                    if trailing_axis == 0:
+                        np_data[:, :, np_mask] = val
                 mx_data = test_block(mx_data, mx_mask) if isinstance(val, float) else test_block(mx_data, mx_mask, val)
                 assert_almost_equal(mx_data.asnumpy(), np_data, rtol=1e-3, atol=1e-5, use_broadcast=False)
 

From e577c3d67dde2dd4d280de39505ffc0f7442cacb Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Fri, 6 Dec 2019 22:14:55 -0800
Subject: [PATCH 659/813] support np.argsort (#16949)

---
 python/mxnet/ndarray/numpy/_op.py             | 78 +++++++++++++++++-
 python/mxnet/numpy/multiarray.py              | 79 ++++++++++++++++++-
 python/mxnet/numpy_dispatch_protocol.py       |  1 +
 python/mxnet/symbol/numpy/_symbol.py          | 45 ++++++++++-
 src/operator/tensor/ordering_op-inl.h         |  8 +-
 src/operator/tensor/ordering_op.cc            |  1 +
 .../unittest/test_numpy_interoperability.py   | 12 +++
 tests/python/unittest/test_numpy_op.py        | 35 ++++++++
 8 files changed, 250 insertions(+), 9 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index aed88eaaa56b..2dde199fe5ba 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -32,7 +32,7 @@
            'arctan2', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs',
            'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2',
            'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
-           'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot', 'histogram', 'eye',
+           'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'argsort', 'tensordot', 'histogram', 'eye',
            'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate', 'append',
            'stack', 'vstack', 'column_stack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax',
            'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'unravel_index', 'hanning', 'hamming', 'blackman',
@@ -796,6 +796,82 @@ def power(x1, x2, out=None, **kwargs):
     return _ufunc_helper(x1, x2, _npi.power, _np.power, _npi.power_scalar, _npi.rpower_scalar, out)
 
 
+@set_module('mxnet.ndarray.numpy')
+def argsort(a, axis=-1, kind=None, order=None):
+    """
+    Returns the indices that would sort an array.
+    Perform an indirect sort along the given axis using the algorithm specified
+    by the `kind` keyword. It returns an array of indices of the same shape as
+    `a` that index data along the given axis in sorted order.
+
+    Parameters
+    ----------
+    a : ndarray
+        Array to sort.
+    axis : int or None, optional
+        Axis along which to sort.  The default is -1 (the last axis). If None,
+        the flattened array is used.
+    kind : string, optional
+        This argument can take any string, but it does not have any effect on the
+        final result.
+    order : str or list of str, optional
+        Not supported yet, will raise NotImplementedError if not None.
+
+    Returns
+    -------
+    index_array : ndarray, int
+        Array of indices that sort `a` along the specified `axis`.
+        If `a` is one-dimensional, ``a[index_array]`` yields a sorted `a`.
+        More generally, ``np.take_along_axis(a, index_array, axis=axis)``
+        always yields the sorted `a`, irrespective of dimensionality.
+
+    Notes
+    -----
+    This operator does not support different sorting algorithms.
+
+    Examples
+    --------
+    One dimensional array:
+
+    >>> x = np.array([3, 1, 2])
+    >>> np.argsort(x)
+    array([1, 2, 0])
+
+    Two-dimensional array:
+
+    >>> x = np.array([[0, 3], [2, 2]])
+    >>> x
+    array([[0, 3],
+           [2, 2]])
+    >>> ind = np.argsort(x, axis=0)  # sorts along first axis (down)
+    >>> ind
+    array([[0, 1],
+           [1, 0]])
+    >>> np.take_along_axis(x, ind, axis=0)  # same as np.sort(x, axis=0)
+    array([[0, 2],
+           [2, 3]])
+    >>> ind = np.argsort(x, axis=1)  # sorts along last axis (across)
+    >>> ind
+    array([[0, 1],
+           [0, 1]])
+    >>> np.take_along_axis(x, ind, axis=1)  # same as np.sort(x, axis=1)
+    array([[0, 3],
+           [2, 2]])
+
+    Indices of the sorted elements of a N-dimensional array:
+
+    >>> ind = np.unravel_index(np.argsort(x, axis=None), x.shape)
+    >>> ind
+    (array([0, 1, 1, 0]), array([0, 0, 1, 1]))
+    >>> x[ind]  # same as np.sort(x, axis=None)
+    array([0, 2, 2, 3])
+    """
+    if order is not None:
+        raise NotImplementedError("order not supported here")
+
+    return _npi.argsort(data=a, axis=axis, is_ascend=True, dtype='int64')
+
+
 @set_module('mxnet.ndarray.numpy')
 def tensordot(a, b, axes=2):
     r"""
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 1c6873d342d1..701dc06e043d 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -50,7 +50,7 @@
            'mod', 'remainder', 'power', 'arctan2', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10',
            'sqrt', 'cbrt', 'abs', 'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log',
            'degrees', 'log2', 'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative',
-           'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'append',
+           'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'append', 'argsort',
            'tensordot', 'histogram', 'eye', 'linspace', 'logspace', 'expand_dims', 'tile', 'arange',
            'split', 'vsplit', 'concatenate', 'stack', 'vstack', 'column_stack', 'dstack', 'mean', 'maximum', 'minimum',
            'swapaxes', 'clip', 'argmax', 'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'unravel_index',
@@ -1369,13 +1369,13 @@ def topk(self, *args, **kwargs):
         """
         raise AttributeError('mxnet.numpy.ndarray object has no attribute topk')
 
-    def argsort(self, *args, **kwargs):
+    def argsort(self, axis=-1, kind=None, order=None):  # pylint: disable=arguments-differ
         """Convenience fluent method for :py:func:`argsort`.
 
         The arguments are the same as for :py:func:`argsort`, with
         this array as data.
         """
-        raise NotImplementedError
+        raise argsort(self, axis=axis, kind=kind, order=order)
 
     def argmax_channel(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`argmax_channel`.
@@ -4200,6 +4200,79 @@ def arctanh(x, out=None, **kwargs):
     return _mx_nd_np.arctanh(x, out=out, **kwargs)
 
 
+@set_module('mxnet.numpy')
+def argsort(a, axis=-1, kind=None, order=None):
+    """
+    Returns the indices that would sort an array.
+    Perform an indirect sort along the given axis using the algorithm specified
+    by the `kind` keyword. It returns an array of indices of the same shape as
+    `a` that index data along the given axis in sorted order.
+
+    Parameters
+    ----------
+    a : ndarray
+        Array to sort.
+    axis : int or None, optional
+        Axis along which to sort.  The default is -1 (the last axis). If None,
+        the flattened array is used.
+    kind : string, optional
+        This argument can take any string, but it does not have any effect on the
+        final result.
+    order : str or list of str, optional
+        Not supported yet, will raise NotImplementedError if not None.
+
+    Returns
+    -------
+    index_array : ndarray, int
+        Array of indices that sort `a` along the specified `axis`.
+        If `a` is one-dimensional, ``a[index_array]`` yields a sorted `a`.
+        More generally, ``np.take_along_axis(a, index_array, axis=axis)``
+        always yields the sorted `a`, irrespective of dimensionality.
+
+    Notes
+    -----
+    This operator does not support different sorting algorithms.
+
+    Examples
+    --------
+    One dimensional array:
+
+    >>> x = np.array([3, 1, 2])
+    >>> np.argsort(x)
+    array([1, 2, 0])
+
+    Two-dimensional array:
+
+    >>> x = np.array([[0, 3], [2, 2]])
+    >>> x
+    array([[0, 3],
+           [2, 2]])
+    >>> ind = np.argsort(x, axis=0)  # sorts along first axis (down)
+    >>> ind
+    array([[0, 1],
+           [1, 0]])
+    >>> np.take_along_axis(x, ind, axis=0)  # same as np.sort(x, axis=0)
+    array([[0, 2],
+           [2, 3]])
+    >>> ind = np.argsort(x, axis=1)  # sorts along last axis (across)
+    >>> ind
+    array([[0, 1],
+           [0, 1]])
+    >>> np.take_along_axis(x, ind, axis=1)  # same as np.sort(x, axis=1)
+    array([[0, 3],
+           [2, 2]])
+
+    Indices of the sorted elements of a N-dimensional array:
+
+    >>> ind = np.unravel_index(np.argsort(x, axis=None), x.shape)
+    >>> ind
+    (array([0, 1, 1, 0]), array([0, 0, 1, 1]))
+    >>> x[ind]  # same as np.sort(x, axis=None)
+    array([0, 2, 2, 3])
+    """
+    return _mx_nd_np.argsort(a, axis=axis, kind=kind, order=order)
+
+
 @set_module('mxnet.numpy')
 def tensordot(a, b, axes=2):
     r"""
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index 1f68ca3c522a..0f926102ba8c 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -86,6 +86,7 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'argmin',
     'argmax',
     'around',
+    'argsort',
     'append',
     'broadcast_arrays',
     'broadcast_to',
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 683bdb1cb200..0385ef85ea03 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -40,7 +40,7 @@
            'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs', 'absolute', 'exp',
            'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2', 'log1p',
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
-           'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot', 'histogram', 'eye',
+           'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'argsort', 'tensordot', 'histogram', 'eye',
            'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate', 'append',
            'stack', 'vstack', 'column_stack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax',
            'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'unravel_index', 'hanning', 'hamming', 'blackman',
@@ -484,13 +484,13 @@ def topk(self, *args, **kwargs):
         """
         raise AttributeError('_Symbol object has no attribute topk')
 
-    def argsort(self, *args, **kwargs):
+    def argsort(self, axis=-1, kind=None, order=None):  # pylint: disable=arguments-differ
         """Convenience fluent method for :py:func:`argsort`.
 
         The arguments are the same as for :py:func:`argsort`, with
         this array as data.
         """
-        raise NotImplementedError
+        raise argsort(self, axis=axis, kind=kind, order=order)
 
     def argmax_channel(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`argmax_channel`.
@@ -1325,6 +1325,45 @@ def lcm(x1, x2, out=None, **kwargs):
     return _ufunc_helper(x1, x2, _npi.lcm, _np.lcm, _npi.lcm_scalar, None, out)
 
 
+@set_module('mxnet.symbol.numpy')
+def argsort(a, axis=-1, kind=None, order=None):
+    """
+    Returns the indices that would sort an array.
+    Perform an indirect sort along the given axis using the algorithm specified
+    by the `kind` keyword. It returns an array of indices of the same shape as
+    `a` that index data along the given axis in sorted order.
+
+    Parameters
+    ----------
+    a : _Symbol
+        Array to sort.
+    axis : int or None, optional
+        Axis along which to sort.  The default is -1 (the last axis). If None,
+        the flattened array is used.
+    kind : string, optional
+        This argument can take any string, but it does not have any effect on the
+        final result.
+    order : str or list of str, optional
+        Not supported yet, will raise NotImplementedError if not None.
+
+    Returns
+    -------
+    index_array : _Symbol, int
+        Array of indices that sort `a` along the specified `axis`.
+        If `a` is one-dimensional, ``a[index_array]`` yields a sorted `a`.
+        More generally, ``np.take_along_axis(a, index_array, axis=axis)``
+        always yields the sorted `a`, irrespective of dimensionality.
+
+    Notes
+    -----
+    This operator does not support different sorting algorithms.
+    """
+    if order is not None:
+        raise NotImplementedError("order is not supported yet...")
+
+    return _npi.argsort(data=a, axis=axis, is_ascend=True, dtype='int64')
+
+
 @set_module('mxnet.symbol.numpy')
 def tensordot(a, b, axes=2):
     r"""
diff --git a/src/operator/tensor/ordering_op-inl.h b/src/operator/tensor/ordering_op-inl.h
index 0ccbe410a1e1..8d2663785eb9 100644
--- a/src/operator/tensor/ordering_op-inl.h
+++ b/src/operator/tensor/ordering_op-inl.h
@@ -157,7 +157,9 @@ inline void ParseTopKParam(const TShape& src_shape,
     CHECK(*axis >= 0 && *axis < static_cast<int>(src_shape.ndim()))
                                                   << "Invalid axis! axis should be between 0 and "
                                                   << src_shape.ndim() << ", found axis=" << *axis;
-    *batch_size = src_shape.Size() / src_shape[*axis];
+    if (src_shape[*axis] != 0) {
+      *batch_size = src_shape.Size() / src_shape[*axis];
+    }
     *element_num = src_shape[*axis];
     if (*axis != src_shape.ndim() - 1) {
       *do_transpose = true;
@@ -180,7 +182,7 @@ inline void ParseTopKParam(const TShape& src_shape,
       (*target_shape)[*axis] = *k;
     }
   }
-  CHECK(*k >= 1 && *k <= *element_num) << "k must be smaller than "
+  CHECK(*k >= 0 && *k <= *element_num) << "k must be smaller than "
                                       << *element_num << ", get k = " << *k;
 }
 
@@ -391,6 +393,8 @@ void TopKImpl(const RunContext &ctx,
               const TopKParam& param) {
   using namespace mshadow;
   using namespace mshadow::expr;
+  // 0. If input shape is 0-shape, directly return
+  if (src.Size() == 0) return;
   // 1. Parse and initialize information
   Stream<xpu> *s = ctx.get_stream<xpu>();
   Tensor<xpu, 1, char> workspace;
diff --git a/src/operator/tensor/ordering_op.cc b/src/operator/tensor/ordering_op.cc
index 6c375ce8e3c2..b54986f773cb 100644
--- a/src/operator/tensor/ordering_op.cc
+++ b/src/operator/tensor/ordering_op.cc
@@ -160,6 +160,7 @@ Examples::
 .add_arguments(SortParam::__FIELDS__());
 
 NNVM_REGISTER_OP(argsort)
+.add_alias("_npi_argsort")
 .describe(R"code(Returns the indices that would sort an input array along the given axis.
 
 This function performs sorting along the given axis and returns an array of indices having same shape
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index 930ad5260430..797bb43824af 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -500,6 +500,17 @@ def _add_workload_around():
     OpArgMngr.add_workload('around', np.array([1.56, 72.54, 6.35, 3.25]), decimals=1)
 
 
+def _add_workload_argsort():
+    for dtype in [np.int32, np.float32]:
+        a = np.arange(101, dtype=dtype)
+        OpArgMngr.add_workload('argsort', a)
+    OpArgMngr.add_workload('argsort', np.array([[3, 2], [1, 0]]), 1)
+    OpArgMngr.add_workload('argsort', np.array([[3, 2], [1, 0]]), 0)
+    a = np.ones((3, 2, 1, 0))
+    for axis in range(-a.ndim, a.ndim):
+        OpArgMngr.add_workload('argsort', a, axis)
+
+
 def _add_workload_broadcast_arrays(array_pool):
     OpArgMngr.add_workload('broadcast_arrays', array_pool['4x1'], array_pool['1x2'])
 
@@ -1311,6 +1322,7 @@ def _prepare_workloads():
     _add_workload_argmin()
     _add_workload_argmax()
     _add_workload_around()
+    _add_workload_argsort()
     _add_workload_append()
     _add_workload_broadcast_arrays(array_pool)
     _add_workload_broadcast_to()
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 61aec6832d7e..38633760c400 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -1179,6 +1179,41 @@ def hybrid_forward(self, F, a):
             assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
 
 
+@with_seed()
+@use_np
+def test_np_argsort():
+    class TestArgsort(HybridBlock):
+        def __init__(self, axis):
+            super(TestArgsort, self).__init__()
+            self._axis = axis
+
+        def hybrid_forward(self, F, x):
+            return F.np.argsort(x, axis=self._axis)
+
+    shapes = [
+        (),
+        (2, 3),
+        (1, 0, 2),
+    ]
+
+    for shape in shapes:
+        data = np.random.uniform(size=shape)
+        np_data = data.asnumpy()
+
+        for axis in [None] + [i for i in range(-len(shape), len(shape))]:
+            np_out = _np.argsort(np_data, axis)
+
+            test_argsort = TestArgsort(axis)
+            for hybrid in [False, True]:
+                if hybrid:
+                    test_argsort.hybridize()
+                mx_out = test_argsort(data)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-5, atol=1e-6, use_broadcast=False)
+
+            mx_out = np.argsort(data, axis)
+            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-5, atol=1e-6, use_broadcast=False)
+
+
 @with_seed()
 @use_np
 def test_np_squeeze():

From 083efb86e1ebdbf3d6212f41adb4c8abe4f392f8 Mon Sep 17 00:00:00 2001
From: dmitry-markeshov <58302187+dmitry-markeshov@users.noreply.github.com>
Date: Sat, 7 Dec 2019 12:25:05 +0300
Subject: [PATCH 660/813] Fix crashing on Windows in ObjectPool ~ctor (#16941)

* Fix crashing on Windows in ObjectPool ~ctor

* Remove TAB

* ci

* CI
---
 src/common/object_pool.h | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src/common/object_pool.h b/src/common/object_pool.h
index 362553313b85..b43a0cf25998 100644
--- a/src/common/object_pool.h
+++ b/src/common/object_pool.h
@@ -133,7 +133,11 @@ struct ObjectPoolAllocatable {
 template <typename T>
 ObjectPool<T>::~ObjectPool() {
   for (auto i : allocated_) {
+#ifdef _MSC_VER
+    _aligned_free(i);
+#else
     free(i);
+#endif
   }
 }
 

From 93cf48dfa45c697437e6233577002a5a4e1b3cc0 Mon Sep 17 00:00:00 2001
From: JackieWu <wkcn@live.cn>
Date: Sun, 8 Dec 2019 07:59:28 +0800
Subject: [PATCH 661/813] Fix desired precision for test_ndarray.py:test_reduce
 (#16992)

* fix test_ndarray.py:test_reduce

* ci
---
 tests/python/unittest/test_ndarray.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/python/unittest/test_ndarray.py b/tests/python/unittest/test_ndarray.py
index d097799da286..9a05f4d2c482 100644
--- a/tests/python/unittest/test_ndarray.py
+++ b/tests/python/unittest/test_ndarray.py
@@ -601,7 +601,7 @@ def test_reduce():
     def test_reduce_inner(numpy_reduce_func, nd_reduce_func, multi_axes,
                           allow_almost_equal=False, check_dtype=True):
         dtypes = [(np.float16, 1),
-                  (np.float32, 5),
+                  (np.float32, 4),
                   (np.double, 6)]
         for i in range(sample_num):
             dtype, decimal = random.choice(dtypes)

From 5cbf607cbbddc06687763ea2fa37802ca2033cc9 Mon Sep 17 00:00:00 2001
From: Haibin Lin <linhaibin.eric@gmail.com>
Date: Sat, 7 Dec 2019 17:45:00 -0800
Subject: [PATCH 662/813] minor typo fix (#17008)

---
 .github/PULL_REQUEST_TEMPLATE.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md
index 7895c771ade8..491b9393c198 100644
--- a/.github/PULL_REQUEST_TEMPLATE.md
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -15,7 +15,7 @@ Please feel free to remove inapplicable items for your PR.
 - For new C++ functions in header files, their functionalities and arguments are documented. 
 - For new examples, README.md is added to explain the what the example does, the source of the dataset, expected performance on test set and reference to the original paper if applicable
 - Check the API doc at https://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$PR_ID/$BUILD_ID/index.html
-- [ ] To the my best knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change
+- [ ] To the best of my knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change
 
 ### Changes ###
 - [ ] Feature1, tests, (and when applicable, API doc)

From 251e6f6cb7ec7741434c35a26892fa7450f751c0 Mon Sep 17 00:00:00 2001
From: Jake Lee <gstu1130@gmail.com>
Date: Sun, 8 Dec 2019 16:09:11 -0800
Subject: [PATCH 663/813] Fix NDArrayIter cant pad when size is large (#17001)

* Fix NDArrayIter cant pad when size is large

* ci
---
 python/mxnet/io/io.py            | 44 +++++++++++++++++---------------
 tests/python/unittest/test_io.py |  6 ++---
 2 files changed, 26 insertions(+), 24 deletions(-)

diff --git a/python/mxnet/io/io.py b/python/mxnet/io/io.py
index dcf964df976a..e36665e61c42 100644
--- a/python/mxnet/io/io.py
+++ b/python/mxnet/io/io.py
@@ -36,7 +36,7 @@
 from ..ndarray.sparse import CSRNDArray
 from ..ndarray import _ndarray_cls
 from ..ndarray import array
-from ..ndarray import concat
+from ..ndarray import concat, tile
 
 from .utils import _init_data, _has_instance, _getdata_by_idx
 
@@ -709,23 +709,27 @@ def _getdata(self, data_source, start=None, end=None):
 
     def _concat(self, first_data, second_data):
         """Helper function to concat two NDArrays."""
+        if (not first_data) or (not second_data):
+            return first_data if first_data else second_data
         assert len(first_data) == len(
             second_data), 'data source should contain the same size'
-        if first_data and second_data:
-            return [
-                concat(
-                    first_data[x],
-                    second_data[x],
-                    dim=0
-                ) for x in range(len(first_data))
-            ]
-        elif (not first_data) and (not second_data):
+        return [
+            concat(
+                first_data[i],
+                second_data[i],
+                dim=0
+            ) for i in range(len(first_data))
+        ]
+
+    def _tile(self, data, repeats):
+        if not data:
             return []
-        else:
-            return [
-                first_data[0] if first_data else second_data[0]
-                for x in range(len(first_data))
-            ]
+        res = []
+        for datum in data:
+            reps = [1] * len(datum.shape)
+            reps[0] = repeats
+            res.append(tile(datum, reps))
+        return res
 
     def _batchify(self, data_source):
         """Load data from underlying arrays, internal use only."""
@@ -749,12 +753,10 @@ def _batchify(self, data_source):
             pad = self.batch_size - self.num_data + self.cursor
             first_data = self._getdata(data_source, start=self.cursor)
             if pad > self.num_data:
-                while True:
-                    if pad <= self.num_data:
-                        break
-                    second_data = self._getdata(data_source, end=self.num_data)
-                    pad -= self.num_data
-                second_data = self._concat(second_data, self._getdata(data_source, end=pad))
+                repeats = pad // self.num_data
+                second_data = self._tile(self._getdata(data_source, end=self.num_data), repeats)
+                if pad % self.num_data != 0:
+                    second_data = self._concat(second_data, self._getdata(data_source, end=pad % self.num_data))
             else:
                 second_data = self._getdata(data_source, end=pad)
             return self._concat(first_data, second_data)
diff --git a/tests/python/unittest/test_io.py b/tests/python/unittest/test_io.py
index 2a806efc9034..a13addb0adca 100644
--- a/tests/python/unittest/test_io.py
+++ b/tests/python/unittest/test_io.py
@@ -198,11 +198,11 @@ def _test_shuffle(data, labels=None):
         assert np.array_equal(batch.data[0].asnumpy(), batch_list[idx_list[i]])
         i += 1
 
-# fixes the issue https://github.com/apache/incubator-mxnet/issues/15535
+
 def _test_corner_case():
     data = np.arange(10)
-    data_iter = mx.io.NDArrayIter(data=data, batch_size=25, shuffle=False, last_batch_handle='pad')
-    expect = np.concatenate((np.tile(data, 2), np.arange(5)))
+    data_iter = mx.io.NDArrayIter(data=data, batch_size=205, shuffle=False, last_batch_handle='pad')
+    expect = np.concatenate((np.tile(data, 20), np.arange(5)))
     assert np.array_equal(data_iter.next().data[0].asnumpy(), expect)
 
 

From d58f6cb01803f255f8d42c740bc399d083db4640 Mon Sep 17 00:00:00 2001
From: zburning <798672141@qq.com>
Date: Mon, 9 Dec 2019 11:23:27 +0800
Subject: [PATCH 664/813] Add micro averaging strategy to pearsonr metric
 (#16878)

        Strategy to be used for aggregating across mini-batches.
            "macro": average the pearsonr scores for each batch.
            "micro": compute a single pearsonr score across all batches.
---
 python/mxnet/metric.py               | 79 +++++++++++++++++++++++-----
 tests/python/unittest/test_metric.py | 42 ++++++++++++---
 2 files changed, 102 insertions(+), 19 deletions(-)

diff --git a/python/mxnet/metric.py b/python/mxnet/metric.py
index 6e2d66cb9d15..d1074c923337 100644
--- a/python/mxnet/metric.py
+++ b/python/mxnet/metric.py
@@ -590,8 +590,9 @@ def update(self, labels, preds):
 
 
 class _BinaryClassificationMetrics(object):
-    """Private container class for classification metric statistics. True/false positive and
-     true/false negative counts are sufficient statistics for various classification metrics.
+    """Private container class for classification metric statistics.
+
+    True/false positive and true/false negative counts are sufficient statistics for various classification metrics.
     This class provides the machinery to track those statistics across mini-batches of
     (label, prediction) pairs.
     """
@@ -1430,6 +1431,10 @@ class PearsonCorrelation(EvalMetric):
     label_names : list of str, or None
         Name of labels that should be used when updating with update_dict.
         By default include all labels.
+    average : str, default 'macro'
+        Strategy to be used for aggregating across mini-batches.
+            "macro": average the pearsonr scores for each batch.
+            "micro": compute a single pearsonr score across all batches.
 
     Examples
     --------
@@ -1438,13 +1443,46 @@ class PearsonCorrelation(EvalMetric):
     >>> pr = mx.metric.PearsonCorrelation()
     >>> pr.update(labels, predicts)
     >>> print pr.get()
-    ('pearson-correlation', 0.42163704544016178)
+    ('pearsonr', 0.42163704544016178)
     """
     def __init__(self, name='pearsonr',
-                 output_names=None, label_names=None):
+                 output_names=None, label_names=None, average='macro'):
+        self.average = average
         super(PearsonCorrelation, self).__init__(
             name, output_names=output_names, label_names=label_names,
             has_global_stats=True)
+        if self.average == 'micro':
+            self.reset_micro()
+
+    def reset_micro(self):
+        self._sse_p = 0
+        self._mean_p = 0
+        self._sse_l = 0
+        self._mean_l = 0
+        self._pred_nums = 0
+        self._label_nums = 0
+        self._conv = 0
+
+    def reset(self):
+        self.num_inst = 0
+        self.sum_metric = 0.0
+        self.global_num_inst = 0
+        self.global_sum_metric = 0.0
+        if self.average == 'micro':
+            self.reset_micro()
+
+    def update_variance(self, new_values, *aggregate):
+        #Welford's online algorithm for variance update
+        count, mean, m_2 = aggregate
+        count += len(new_values)
+        delta = new_values - mean
+        mean += numpy.sum(delta / count)
+        delta_2 = new_values - mean
+        m_2 += numpy.sum(delta * delta_2)
+        return count, mean, m_2
+
+    def update_cov(self, label, pred):
+        self._conv = self._conv + numpy.sum((label - self._mean_l) * (pred - self._mean_p))
 
     def update(self, labels, preds):
         """Updates the internal evaluation result.
@@ -1457,17 +1495,34 @@ def update(self, labels, preds):
             Predicted values.
         """
         labels, preds = check_label_shapes(labels, preds, True)
-
         for label, pred in zip(labels, preds):
             check_label_shapes(label, pred, False, True)
-            label = label.asnumpy()
-            pred = pred.asnumpy()
-            pearson_corr = numpy.corrcoef(pred.ravel(), label.ravel())[0, 1]
-            self.sum_metric += pearson_corr
-            self.global_sum_metric += pearson_corr
-            self.num_inst += 1
-            self.global_num_inst += 1
+            label = label.asnumpy().ravel().astype(numpy.float64)
+            pred = pred.asnumpy().ravel().astype(numpy.float64)
+            if self.average == 'macro':
+                pearson_corr = numpy.corrcoef(pred, label)[0, 1]
+                self.sum_metric += pearson_corr
+                self.global_sum_metric += pearson_corr
+                self.num_inst += 1
+                self.global_num_inst += 1
+            else:
+                self.global_num_inst += 1
+                self.num_inst += 1
+                self._label_nums, self._mean_l, self._sse_l = \
+                    self.update_variance(label, self._label_nums, self._mean_l, self._sse_l)
+                self.update_cov(label, pred)
+                self._pred_nums, self._mean_p, self._sse_p = \
+                    self.update_variance(pred, self._pred_nums, self._mean_p, self._sse_p)
 
+    def get(self):
+        if self.num_inst == 0:
+            return (self.name, float('nan'))
+        if self.average == 'macro':
+            return (self.name, self.sum_metric / self.num_inst)
+        else:
+            n = self._label_nums
+            pearsonr = self._conv / ((n-1) * numpy.sqrt(self._sse_p / (n - 1)) * numpy.sqrt(self._sse_l / (n - 1)))
+            return (self.name, pearsonr)
 
 @register
 class PCC(EvalMetric):
diff --git a/tests/python/unittest/test_metric.py b/tests/python/unittest/test_metric.py
index 0ae8aeaa697f..a1e5128d8ac6 100644
--- a/tests/python/unittest/test_metric.py
+++ b/tests/python/unittest/test_metric.py
@@ -17,6 +17,7 @@
 
 import mxnet as mx
 import numpy as np
+import scipy
 import json
 import math
 from common import with_seed
@@ -263,13 +264,40 @@ def test_perplexity():
     assert perplexity == perplexity_expected
 
 def test_pearsonr():
-    pred = mx.nd.array([[0.7, 0.3], [0.1, 0.9], [1., 0]])
-    label = mx.nd.array([[0, 1], [1, 0], [1, 0]])
-    pearsonr_expected = np.corrcoef(pred.asnumpy().ravel(), label.asnumpy().ravel())[0, 1]
-    metric = mx.metric.create('pearsonr')
-    metric.update([label], [pred])
-    _, pearsonr = metric.get()
-    assert pearsonr == pearsonr_expected
+    pred1 = mx.nd.array([[0.3, 0.7], [0, 1.], [0.4, 0.6]])
+    label1 = mx.nd.array([[1, 0], [0, 1], [0, 1]])
+    pearsonr_expected_np = np.corrcoef(pred1.asnumpy().ravel(), label1.asnumpy().ravel())[0, 1]
+    pearsonr_expected_scipy, _ = scipy.stats.pearsonr(pred1.asnumpy().ravel(), label1.asnumpy().ravel())
+    macro_pr = mx.metric.create('pearsonr', average='macro')
+    micro_pr = mx.metric.create('pearsonr', average='micro')
+
+    assert np.isnan(macro_pr.get()[1])
+    assert np.isnan(micro_pr.get()[1])
+
+    macro_pr.update([label1], [pred1])
+    micro_pr.update([label1], [pred1])
+
+    np.testing.assert_almost_equal(macro_pr.get()[1], pearsonr_expected_np)
+    np.testing.assert_almost_equal(macro_pr.get()[1], pearsonr_expected_scipy)
+    np.testing.assert_almost_equal(micro_pr.get()[1], pearsonr_expected_np)
+    np.testing.assert_almost_equal(micro_pr.get()[1], pearsonr_expected_scipy)
+
+    pred2 = mx.nd.array([[1, 2], [3, 2], [4, 6]])
+    label2 = mx.nd.array([[1, 0], [0, 1], [0, 1]])
+    # Note that pred12 = pred1 + pred2; label12 = label1 + label2
+    pred12 = mx.nd.array([[0.3, 0.7], [0, 1.], [0.4, 0.6],[1, 2], [3, 2], [4, 6]])
+    label12 = mx.nd.array([[1, 0], [0, 1], [0, 1], [1, 0], [0, 1], [0, 1]])
+
+    pearsonr_expected_np = np.corrcoef(pred12.asnumpy().ravel(), label12.asnumpy().ravel())[0, 1]
+    pearsonr_expected_scipy, _ = scipy.stats.pearsonr(pred12.asnumpy().ravel(), label12.asnumpy().ravel())
+
+    macro_pr.reset()
+    micro_pr.update([label2], [pred2])
+    macro_pr.update([label12], [pred12])
+    np.testing.assert_almost_equal(macro_pr.get()[1], pearsonr_expected_np)
+    np.testing.assert_almost_equal(macro_pr.get()[1], pearsonr_expected_scipy)
+    np.testing.assert_almost_equal(micro_pr.get()[1], pearsonr_expected_np)
+    np.testing.assert_almost_equal(micro_pr.get()[1], pearsonr_expected_scipy)
 
 def cm_batch(cm):
     # generate a batch yielding a given confusion matrix

From b0098647742663a6937c96b5dea2707388e51da6 Mon Sep 17 00:00:00 2001
From: Rohit Kumar Srivastava <srivastava.141@osu.edu>
Date: Sun, 8 Dec 2019 21:30:06 -0800
Subject: [PATCH 665/813] large tensor faq doc fix (#16953)

---
 .../src/pages/api/faq/large_tensor_support.md       | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/docs/static_site/src/pages/api/faq/large_tensor_support.md b/docs/static_site/src/pages/api/faq/large_tensor_support.md
index 4c77ede24b02..ab251a78fb0b 100644
--- a/docs/static_site/src/pages/api/faq/large_tensor_support.md
+++ b/docs/static_site/src/pages/api/faq/large_tensor_support.md
@@ -69,13 +69,16 @@ The following are the cases for large tensor usage where you must specify `dtype
 
 
 * _randint():_
+
 ```python
 low_large_value = 2*32*
 *high_large_value = 2*34
 # dtype is explicitly specified since default type is int32 for randint
 a = nd.random.randint(low_large_value, high_large_value, dtype=np.int64)
 ```
+
 * _ravel_multi_index()_ and _unravel_index()_:
+
 ```python
 x1, y1 = rand_coord_2d((LARGE_X - 100), LARGE_X, 10, SMALL_Y)
 x2, y2 = rand_coord_2d((LARGE_X - 200), LARGE_X, 9, SMALL_Y)
@@ -87,9 +90,11 @@ idx = mx.nd.ravel_multi_index(mx.nd.array(indices_2d, dtype=np.int64),
 indices_2d = mx.nd.unravel_index(mx.nd.array(idx_numpy, dtype=np.int64),
                                   shape=(LARGE_X, SMALL_Y))
 ```
+
 * _argsort()_ and _topk()_
 
 They both return indices which are specified by `dtype=np.int64`.
+
 ```python
 b = create_2d_tensor(rows=LARGE_X, columns=SMALL_Y)
 # argsort
@@ -97,9 +102,11 @@ s = nd.argsort(b, axis=0, is_ascend=False, dtype=np.int64)
 # topk
 k = nd.topk(b, k=10, axis=0, dtype=np.int64)
 ```
+
 * _index_copy()_
 
 Again whenever we are passing indices as arguments and using large tensor, the `dtype` of indices must be `int64`.
+
 ```python
 x = mx.nd.zeros((LARGE_X, SMALL_Y))
 t = mx.nd.arange(1, SMALL_Y + 1).reshape((1, SMALL_Y))
@@ -107,9 +114,11 @@ t = mx.nd.arange(1, SMALL_Y + 1).reshape((1, SMALL_Y))
 index = mx.nd.array([LARGE_X - 1], dtype="int64")
 x = mx.nd.contrib.index_copy(x, index, t)
 ```
+
 * _one_hot()_
 
 Here again array is used as indices that act as location of bits inside the large vector that need to be activated.
+
 ```python
 # a is the index array here whose dtype should be int64.
 a = nd.array([1, (VLARGE_X - 1)], dtype=np.int64)
@@ -142,6 +151,7 @@ Not supported:
 Randint operator is flaky: https://github.com/apache/incubator-mxnet/issues/16172
 dgemm operations using BLAS libraries currently don’t support int64.
 linspace() is not supported.
+
 ```python
 a = mx.sym.Variable('a')
 b = mx.sym.Variable('b')
@@ -156,7 +166,9 @@ Traceback (most recent call last):
     py_array('i', provided_arg_shape_data)),
 OverflowError: signed integer is greater than maximum}
 ```
+
 Symbolic reshape is not supported. Please see the following example.
+
 ```python
 a = mx.sym.Variable('a')
 b = mx.sym.Variable('b')
@@ -174,6 +186,7 @@ OverflowError: signed integer is greater than maximum
 
 ## Working DGL Example(dgl.ai)
 The following is a sample running code for DGL which works with int64 but not with int32.
+
 ```python
 import mxnet as mx
 from mxnet import gluon

From 3b8fdacd16fd10143e841f202df551cebb89fbf6 Mon Sep 17 00:00:00 2001
From: Xinyu Chen <xinyu1.chen@intel.com>
Date: Mon, 9 Dec 2019 13:57:52 +0800
Subject: [PATCH 666/813] skip quantized conv flaky case (#16866)

* Fix quantized concat when inputs are mixed int8 and uint8

Change-Id: I4da04bf4502425134a466823fb5f73da2d7a419b

* skip flaky test

* trigger ci
---
 tests/python/quantization/test_quantization.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/tests/python/quantization/test_quantization.py b/tests/python/quantization/test_quantization.py
index 723873ac6fb3..6fe33f5ee52b 100644
--- a/tests/python/quantization/test_quantization.py
+++ b/tests/python/quantization/test_quantization.py
@@ -200,8 +200,9 @@ def check_quantized_conv(data_shape, kernel, num_filter, pad, stride, no_bias, q
         if is_test_for_native_cpu():
             print('skipped testing quantized_conv for native cpu since it is not supported yet')
             return
-        elif qdtype == 'int8' and is_test_for_mkldnn():
-            print('skipped testing quantized_conv for mkldnn cpu int8 since it is not supported yet')
+        elif is_test_for_mkldnn():
+            # (TODO)Xinyu: https://github.com/apache/incubator-mxnet/issues/16830
+            print('skipped testing quantized_conv for mkldnn cpu since it is a flaky case')
             return
         elif qdtype == 'uint8' and is_test_for_gpu():
             print('skipped testing quantized_conv for gpu uint8 since it is not supported yet')

From 7736bfdb7843e3cac7ecea4ee1a66e55716142ac Mon Sep 17 00:00:00 2001
From: alicia <32725332+Alicia1529@users.noreply.github.com>
Date: Mon, 9 Dec 2019 15:19:12 +0800
Subject: [PATCH 667/813] [Numpy] add op full_like, c++ impl, fix zeros_like,
 ones_like type inference (#16804)

---
 python/mxnet/_numpy_op_doc.py                 |  71 -------
 python/mxnet/ndarray/numpy/_op.py             | 175 +++++++++++++++++-
 python/mxnet/numpy/multiarray.py              | 173 ++++++++++++++++-
 python/mxnet/numpy_dispatch_protocol.py       |   3 +-
 python/mxnet/symbol/numpy/_symbol.py          | 132 ++++++++++++-
 src/operator/numpy/np_init_op.cc              |  29 +--
 src/operator/numpy/np_init_op.cu              |   7 +-
 src/operator/tensor/init_op.h                 |  46 +++++
 .../unittest/test_numpy_interoperability.py   |  20 +-
 tests/python/unittest/test_numpy_op.py        |  48 +++++
 10 files changed, 596 insertions(+), 108 deletions(-)

diff --git a/python/mxnet/_numpy_op_doc.py b/python/mxnet/_numpy_op_doc.py
index cf991fc8949f..7679d21f0e59 100644
--- a/python/mxnet/_numpy_op_doc.py
+++ b/python/mxnet/_numpy_op_doc.py
@@ -20,77 +20,6 @@
 """Doc placeholder for numpy ops with prefix _np."""
 
 
-def _np_ones_like(a):
-    """
-    Return an array of ones with the same shape and type as a given array.
-
-    Parameters
-    ----------
-    a : ndarray
-        The shape and data-type of `a` define these same attributes of
-        the returned array.
-
-    Returns
-    -------
-    out : ndarray
-        Array of ones with the same shape and type as `a`.
-
-    Examples
-    --------
-    >>> x = np.arange(6)
-    >>> x = x.reshape((2, 3))
-    >>> x
-    array([[0., 1., 2.],
-           [3., 4., 5.]])
-    >>> np.ones_like(x)
-    array([[1., 1., 1.],
-           [1., 1., 1.]])
-
-    >>> y = np.arange(3, dtype=float)
-    >>> y
-    array([0., 1., 2.], dtype=float64)
-    >>>
-    >>> np.ones_like(y)
-    array([1., 1., 1.], dtype=float64)
-    """
-    pass
-
-
-def _np_zeros_like(a):
-    """
-    Return an array of zeros with the same shape and type as a given array.
-
-    Parameters
-    ----------
-    a : ndarray
-        The shape and data-type of `a` define these same attributes of
-        the returned array.
-
-    Returns
-    -------
-    out : ndarray
-        Array of zeros with the same shape and type as `a`.
-
-    Examples
-    --------
-    >>> x = np.arange(6)
-    >>> x = x.reshape((2, 3))
-    >>> x
-    array([[0., 1., 2.],
-           [3., 4., 5.]])
-    >>> np.zeros_like(x)
-    array([[0., 0., 0.],
-           [0., 0., 0.]])
-    >>> y = np.arange(3, dtype=float)
-    >>> y
-    array([0., 1., 2.], dtype=float64)
-    >>>
-    >>> np.zeros_like(y)
-    array([0., 0., 0.], dtype=float64)
-    """
-    pass
-
-
 def _np_cumsum(a, axis=None, dtype=None, out=None):
     """
     Return the cumulative sum of the elements along a given axis.
diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index 2dde199fe5ba..0925f93a7740 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -28,7 +28,8 @@
 from . import _internal as _npi
 from ..ndarray import NDArray
 
-__all__ = ['shape', 'zeros', 'ones', 'full', 'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power',
+__all__ = ['shape', 'zeros', 'zeros_like', 'ones', 'ones_like', 'full', 'full_like',
+           'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power',
            'arctan2', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs',
            'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2',
            'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
@@ -145,7 +146,119 @@ def ones(shape, dtype=_np.float32, order='C', ctx=None):  # pylint: disable=rede
 
 # pylint: disable=too-many-arguments, redefined-outer-name
 @set_module('mxnet.ndarray.numpy')
-def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):
+def zeros_like(a, dtype=None, order='C', ctx=None, out=None):
+    """
+    Return an array of zeros with the same shape and type as a given array.
+
+    Parameters
+    ----------
+    a : ndarray
+        The shape and data-type of `a` define these same attributes of
+        the returned array.
+    dtype : data-type, optional
+        Overrides the data type of the result.
+        Temporarily do not support boolean type.
+    order : {'C'}, optional
+        Whether to store multidimensional data in C- or Fortran-contiguous
+        (row- or column-wise) order in memory. Currently only supports C order.
+    ctx: to specify the device, e.g. the i-th GPU.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+        If provided, it must have the same shape and dtype as input ndarray.
+        If not provided or `None`, a freshly-allocated array is returned.
+
+    Returns
+    -------
+    out : ndarray
+          Array of zeros with the same shape and type as a.
+
+    See Also
+    --------
+    empty_like : Return an empty array with shape and type of input.
+    ones_like : Return an array of ones with shape and type of input.
+    zeros_like : Return an array of zeros with shape and type of input.
+    full : Return a new array of given shape filled with value.
+
+    Examples
+    --------
+    >>> x = np.arange(6)
+    >>> x = x.reshape((2, 3))
+    >>> x
+    array([[0., 1., 2.],
+           [3., 4., 5.]])
+    >>> np.zeros_like(x)
+    array([[0., 0., 0.],
+           [0., 0., 0.]])
+    >>> np.zeros_like(x, int)
+    array([[0, 0, 0],
+           [0, 0, 0]], dtype=int64)
+    >>> y = np.arange(3, dtype=float)
+    >>> y
+    array([0., 1., 2.], dtype=float64)
+    >>> np.zeros_like(y)
+    array([0., 0., 0.], dtype=float64)
+    """
+    return _npi.full_like(a, fill_value=0, dtype=dtype, ctx=None, out=None)
+
+
+@set_module('mxnet.ndarray.numpy')
+def ones_like(a, dtype=None, order='C', ctx=None, out=None):
+    """
+    Return an array of ones with the same shape and type as a given array.
+
+    Parameters
+    ----------
+    a : ndarray
+        The shape and data-type of `a` define these same attributes of
+        the returned array.
+    dtype : data-type, optional
+        Overrides the data type of the result.
+        Temporarily do not support boolean type.
+    order : {'C'}, optional
+        Whether to store multidimensional data in C- or Fortran-contiguous
+        (row- or column-wise) order in memory. Currently only supports C order.
+    ctx: to specify the device, e.g. the i-th GPU.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+        If provided, it must have the same shape and dtype as input ndarray.
+        If not provided or `None`, a freshly-allocated array is returned.
+
+    Returns
+    -------
+    out : ndarray
+        Array of ones with the same shape and type as a.
+
+    See Also
+    --------
+    empty_like : Return an empty array with shape and type of input.
+    zeros_like : Return an array of zeros with shape and type of input.
+    full_like : Return a new array with shape of input filled with value.
+    ones : Return a new array setting values to one.
+
+    Examples
+    --------
+    >>> x = np.arange(6)
+    >>> x = x.reshape((2, 3))
+    >>> x
+    array([[0., 1., 2.],
+           [3., 4., 5.]])
+    >>> np.ones_like(x)
+    array([[1., 1., 1.],
+           [1., 1., 1.]])
+    >>> np.ones_like(x, int)
+    array([[1, 1, 1],
+           [1, 1, 1]], dtype=int64)
+    >>> y = np.arange(3, dtype=float)
+    >>> y
+    array([0., 1., 2.], dtype=float64)
+    >>> np.ones_like(y)
+    array([1., 1., 1.], dtype=float64)
+    """
+    return _npi.full_like(a, fill_value=1, dtype=dtype, ctx=None, out=None)
+
+
+@set_module('mxnet.ndarray.numpy')
+def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):  # pylint: disable=too-many-arguments
     """
     Return a new array of given shape and type, filled with `fill_value`.
     Parameters
@@ -200,6 +313,64 @@ def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):
 # pylint: enable=too-many-arguments, redefined-outer-name
 
 
+@set_module('mxnet.ndarray.numpy')
+def full_like(a, fill_value, dtype=None, order='C', ctx=None, out=None): # pylint: disable=too-many-arguments
+    """
+    Return a full array with the same shape and type as a given array.
+
+    Parameters
+    ----------
+    a : ndarray
+        The shape and data-type of `a` define these same attributes of
+        the returned array.
+    fill_value : scalar
+        Fill value.
+    dtype : data-type, optional
+        Overrides the data type of the result.
+        Temporarily do not support boolean type.
+    order : {'C'}, optional
+        Whether to store multidimensional data in C- or Fortran-contiguous
+        (row- or column-wise) order in memory. Currently only supports C order.
+    ctx: to specify the device, e.g. the i-th GPU.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+        If provided, it must have the same shape and dtype as input ndarray.
+        If not provided or `None`, a freshly-allocated array is returned.
+
+    Returns
+    -------
+    out : ndarray
+        Array of `fill_value` with the same shape and type as `a`.
+
+    See Also
+    --------
+    empty_like : Return an empty array with shape and type of input.
+    ones_like : Return an array of ones with shape and type of input.
+    zeros_like : Return an array of zeros with shape and type of input.
+    full : Return a new array of given shape filled with value.
+
+    Examples
+    --------
+    >>> x = np.arange(6, dtype=int)
+    >>> np.full_like(x, 1)
+    array([1, 1, 1, 1, 1, 1], dtype=int64)
+    >>> np.full_like(x, 0.1)
+    array([0, 0, 0, 0, 0, 0], dtype=int64)
+    >>> np.full_like(x, 0.1, dtype=np.float64)
+    array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1], dtype=float64)
+    >>> np.full_like(x, np.nan, dtype=np.double)
+    array([nan, nan, nan, nan, nan, nan], dtype=float64)
+    >>> y = np.arange(6, dtype=np.float32)
+    >>> np.full_like(y, 0.1)
+    array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1])
+    """
+    if order != 'C':
+        raise NotImplementedError
+    if ctx is None:
+        ctx = current_context()
+    return _npi.full_like(a, fill_value=fill_value, dtype=dtype, ctx=ctx, out=out)
+
+
 @set_module('mxnet.ndarray.numpy')
 def arange(start, stop=None, step=1, dtype=None, ctx=None):
     """Return evenly spaced values within a given interval.
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 701dc06e043d..5aecae4c3997 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -46,8 +46,9 @@
 from ..ndarray.numpy import _internal as _npi
 from ..ndarray.ndarray import _storage_type
 
-__all__ = ['ndarray', 'empty', 'array', 'shape', 'zeros', 'ones', 'full', 'add', 'subtract', 'multiply', 'divide',
-           'mod', 'remainder', 'power', 'arctan2', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10',
+__all__ = ['ndarray', 'empty', 'array', 'shape', 'zeros', 'zeros_like', 'ones', 'ones_like', 'full', 'full_like',
+           'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power',
+           'arctan2', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10',
            'sqrt', 'cbrt', 'abs', 'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log',
            'degrees', 'log2', 'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative',
            'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'append', 'argsort',
@@ -946,7 +947,7 @@ def attach_grad(self, grad_req='write'):  # pylint: disable=arguments-differ
             - 'add': gradient will be added to existing value on every backward.
             - 'null': do not compute gradient for this NDArray.
         """
-        grad = _mx_np_op.zeros_like(self)  # pylint: disable=undefined-variable
+        grad = _mx_nd_np.zeros_like(self)  # pylint: disable=undefined-variable
         grad_req = _GRAD_REQ_MAP[grad_req]
         check_call(_LIB.MXAutogradMarkVariables(
             1, ctypes.pointer(self.handle),
@@ -7425,6 +7426,172 @@ def resize(a, new_shape):
     return _mx_nd_np.resize(a, new_shape)
 
 
+@set_module('mxnet.numpy')
+def full_like(a, fill_value, dtype=None, order='C', ctx=None, out=None): # pylint: disable=too-many-arguments
+    """
+    Return a full array with the same shape and type as a given array.
+
+    Parameters
+    ----------
+    a : ndarray
+        The shape and data-type of `a` define these same attributes of
+        the returned array.
+    fill_value : scalar
+        Fill value.
+    dtype : data-type, optional
+        Overrides the data type of the result.
+        Temporarily do not support boolean type.
+    order : {'C'}, optional
+        Whether to store multidimensional data in C- or Fortran-contiguous
+        (row- or column-wise) order in memory. Currently only supports C order.
+    ctx: to specify the device, e.g. the i-th GPU.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+        If provided, it must have the same shape and dtype as input ndarray.
+        If not provided or `None`, a freshly-allocated array is returned.
+
+    Returns
+    -------
+    out : ndarray
+        Array of `fill_value` with the same shape and type as `a`.
+
+    See Also
+    --------
+    empty_like : Return an empty array with shape and type of input.
+    ones_like : Return an array of ones with shape and type of input.
+    zeros_like : Return an array of zeros with shape and type of input.
+    full : Return a new array of given shape filled with value.
+
+    Examples
+    --------
+    >>> x = np.arange(6, dtype=int)
+    >>> np.full_like(x, 1)
+    array([1, 1, 1, 1, 1, 1], dtype=int64)
+    >>> np.full_like(x, 0.1)
+    array([0, 0, 0, 0, 0, 0], dtype=int64)
+    >>> np.full_like(x, 0.1, dtype=np.float64)
+    array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1], dtype=float64)
+    >>> np.full_like(x, np.nan, dtype=np.float64)
+    array([nan, nan, nan, nan, nan, nan], dtype=float64)
+    >>> y = np.arange(6, dtype=np.float32)
+    >>> np.full_like(y, 0.1)
+    array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1])
+    """
+    return _mx_nd_np.full_like(a, fill_value=fill_value, dtype=dtype, order=order, ctx=None, out=None)
+
+
+@set_module('mxnet.numpy')
+def zeros_like(a, dtype=None, order='C', ctx=None, out=None):
+    """
+    Return an array of zeros with the same shape and type as a given array.
+
+    Parameters
+    ----------
+    a : ndarray
+        The shape and data-type of `a` define these same attributes of
+        the returned array.
+    dtype : data-type, optional
+        Overrides the data type of the result.
+        Temporarily do not support boolean type.
+    order : {'C'}, optional
+        Whether to store multidimensional data in C- or Fortran-contiguous
+        (row- or column-wise) order in memory. Currently only supports C order.
+    ctx: to specify the device, e.g. the i-th GPU.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+        If provided, it must have the same shape and dtype as input ndarray.
+        If not provided or `None`, a freshly-allocated array is returned.
+
+    Returns
+    -------
+    out : ndarray
+          Array of zeros with the same shape and type as a.
+
+    See Also
+    --------
+    empty_like : Return an empty array with shape and type of input.
+    ones_like : Return an array of ones with shape and type of input.
+    zeros_like : Return an array of zeros with shape and type of input.
+    full : Return a new array of given shape filled with value.
+
+    Examples
+    --------
+    >>> x = np.arange(6)
+    >>> x = x.reshape((2, 3))
+    >>> x
+    array([[0., 1., 2.],
+           [3., 4., 5.]])
+    >>> np.zeros_like(x)
+    array([[0., 0., 0.],
+           [0., 0., 0.]])
+    >>> np.zeros_like(x, int)
+    array([[0, 0, 0],
+           [0, 0, 0]], dtype=int64)
+    >>> y = np.arange(3, dtype=float)
+    >>> y
+    array([0., 1., 2.], dtype=float64)
+    >>> np.zeros_like(y)
+    array([0., 0., 0.], dtype=float64)
+    """
+    return _mx_nd_np.full_like(a, fill_value=0, dtype=dtype, order=order, ctx=None, out=None)
+
+
+@set_module('mxnet.numpy')
+def ones_like(a, dtype=None, order='C', ctx=None, out=None):
+    """
+    Return an array of ones with the same shape and type as a given array.
+
+    Parameters
+    ----------
+    a : ndarray
+        The shape and data-type of `a` define these same attributes of
+        the returned array.
+    dtype : data-type, optional
+        Overrides the data type of the result.
+        Temporarily do not support boolean type.
+    order : {'C'}, optional
+        Whether to store multidimensional data in C- or Fortran-contiguous
+        (row- or column-wise) order in memory. Currently only supports C order.
+    ctx: to specify the device, e.g. the i-th GPU.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+        If provided, it must have the same shape and dtype as input ndarray.
+        If not provided or `None`, a freshly-allocated array is returned.
+
+    Returns
+    -------
+    out : ndarray
+        Array of ones with the same shape and type as a.
+
+    See Also
+    --------
+    empty_like : Return an empty array with shape and type of input.
+    zeros_like : Return an array of zeros with shape and type of input.
+    full_like : Return a new array with shape of input filled with value.
+    ones : Return a new array setting values to one.
+
+    Examples
+    --------
+    >>> x = np.arange(6)
+    >>> x = x.reshape((2, 3))
+    >>> x
+    array([[0., 1., 2.],
+           [3., 4., 5.]])
+    >>> np.ones_like(x)
+    array([[1., 1., 1.],
+           [1., 1., 1.]])
+    >>> np.ones_like(x, int)
+    array([[1, 1, 1],
+           [1, 1, 1]], dtype=int64)
+    >>> y = np.arange(3, dtype=float)
+    >>> y
+    array([0., 1., 2.], dtype=float64)
+    >>> np.ones_like(y)
+    array([1., 1., 1.], dtype=float64)
+    """
+    return _mx_nd_np.full_like(a, fill_value=1, dtype=dtype, order=order, ctx=None, out=None)
+
+
 @set_module('mxnet.numpy')
 def nan_to_num(x, copy=True, nan=0.0, posinf=None, neginf=None, **kwargs):
     """
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index 0f926102ba8c..a6bceb51cd01 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -142,6 +142,7 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'diff',
     'resize',
     'where',
+    'full_like',
 ]
 
 
@@ -239,7 +240,7 @@ def _register_array_function():
     'less',
     'less_equal',
     'greater',
-    'greater_equal'
+    'greater_equal',
 ]
 
 
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 0385ef85ea03..8303f3246e87 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -36,7 +36,8 @@
 except ImportError:
     from builtins import slice as py_slice
 
-__all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power', 'arctan2',
+__all__ = ['zeros', 'zeros_like', 'ones', 'ones_like', 'full_like',
+           'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power', 'arctan2',
            'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs', 'absolute', 'exp',
            'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2', 'log1p',
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
@@ -1094,6 +1095,135 @@ def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):  # pylin
     return _npi.full(shape=shape, value=fill_value, ctx=ctx, dtype=dtype, out=out)
 
 
+@set_module('mxnet.symbol.numpy')
+def full_like(a, fill_value, dtype=None, order='C', ctx=None, out=None):  # pylint: disable=too-many-arguments
+    """
+    Return a full array with the same shape and type as a given array.
+
+    Parameters
+    ----------
+    a : _Symbol
+        The shape and data-type of `a` define these same attributes of
+        the returned array.
+    fill_value : scalar
+        Fill value.
+    dtype : data-type, optional
+        Overrides the data type of the result.
+        Temporarily do not support boolean type.
+    order : {'C'}, optional
+        Whether to store multidimensional data in C- or Fortran-contiguous
+        (row- or column-wise) order in memory. Currently only supports C order.
+    ctx: to specify the device, e.g. the i-th GPU.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+        If provided, it must have the same shape and dtype as input ndarray.
+        If not provided or `None`, a freshly-allocated array is returned.
+
+    Returns
+    -------
+    out : _Symbol
+          Array `fill_value` with the same shape and type as `a`.
+
+    See Also
+    --------
+    empty_like : Return an empty array with shape and type of input.
+    ones_like : Return an array of ones with shape and type of input.
+    zeros_like : Return an array of zeros with shape and type of input.
+    full : Return a new array of given shape filled with value.
+    """
+    if order != 'C':
+        raise NotImplementedError
+    if ctx is None:
+        ctx = current_context()
+    return _npi.full_like(a, fill_value=fill_value, ctx=ctx, dtype=dtype, out=out)
+
+
+@set_module('mxnet.symbol.numpy')
+def zeros_like(a, dtype=None, order='C', ctx=None, out=None):  # pylint: disable=too-many-arguments
+    """
+    Return an array of zeros with the same shape and type as a given array.
+
+    Parameters
+    ----------
+    a : _Symbol
+        The shape and data-type of `a` define these same attributes of
+        the returned array.
+    fill_value : scalar
+        Fill value.
+    dtype : data-type, optional
+        Overrides the data type of the result.
+        Temporarily do not support boolean type.
+    order : {'C'}, optional
+        Whether to store multidimensional data in C- or Fortran-contiguous
+        (row- or column-wise) order in memory. Currently only supports C order.
+    ctx: to specify the device, e.g. the i-th GPU.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+        If provided, it must have the same shape and dtype as input ndarray.
+        If not provided or `None`, a freshly-allocated array is returned.
+
+    Returns
+    -------
+    out : _Symbol
+        Array of zeros with the same shape and type as `a`.
+
+    See Also
+    --------
+    empty_like : Return an empty array with shape and type of input.
+    ones_like : Return an array of ones with shape and type of input.
+    zeros_like : Return an array of zeros with shape and type of input.
+    zeros : Return a new array of given shape filled with zeros.
+    """
+    if order != 'C':
+        raise NotImplementedError
+    if ctx is None:
+        ctx = current_context()
+    return _npi.full_like(a, fill_value=0, ctx=ctx, dtype=dtype, out=out)
+
+
+@set_module('mxnet.symbol.numpy')
+def ones_like(a, dtype=None, order='C', ctx=None, out=None):  # pylint: disable=too-many-arguments
+    """
+    Return an array of ones with the same shape and type as a given array.
+
+    Parameters
+    ----------
+    a : _Symbol
+        The shape and data-type of `a` define these same attributes of
+        the returned array.
+    fill_value : scalar
+        Fill value.
+    dtype : data-type, optional
+        Overrides the data type of the result.
+        Temporarily do not support boolean type.
+    order : {'C'}, optional
+        Whether to store multidimensional data in C- or Fortran-contiguous
+        (row- or column-wise) order in memory. Currently only supports C order.
+    ctx: to specify the device, e.g. the i-th GPU.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+        If provided, it must have the same shape and dtype as input ndarray.
+        If not provided or `None`, a freshly-allocated array is returned.
+
+    Returns
+    -------
+    out : _Symbol
+          Array of ones with the same shape and type as `a`.
+
+    See Also
+    --------
+    empty_like : Return an empty array with shape and type of input.
+    ones_like : Return an array of ones with shape and type of input.
+    zeros_like : Return an array of zeros with shape and type of input.
+    zeros : Return a new array of given shape filled with zeros.
+    """
+    if order != 'C':
+        raise NotImplementedError
+    if ctx is None:
+        ctx = current_context()
+    return _npi.full_like(a, fill_value=1, ctx=ctx, dtype=dtype, out=out)
+
+
 @set_module('mxnet.symbol.numpy')
 def identity(n, dtype=None, ctx=None):
     """
diff --git a/src/operator/numpy/np_init_op.cc b/src/operator/numpy/np_init_op.cc
index f2446c85b182..ee52a2939e12 100644
--- a/src/operator/numpy/np_init_op.cc
+++ b/src/operator/numpy/np_init_op.cc
@@ -34,6 +34,7 @@ namespace op {
 DMLC_REGISTER_PARAMETER(NumpyEyeParam);
 DMLC_REGISTER_PARAMETER(IndicesOpParam);
 DMLC_REGISTER_PARAMETER(LogspaceParam);
+DMLC_REGISTER_PARAMETER(FullLikeOpParam);
 
 inline bool NumpyIndicesShape(const nnvm::NodeAttrs& attrs,
                               mxnet::ShapeVector* in_shapes,
@@ -97,11 +98,12 @@ NNVM_REGISTER_OP(_npi_identity)
 .set_attr<FCompute>("FCompute<cpu>", IdentityCompute<cpu>)
 .add_arguments(InitOpParam::__FIELDS__());
 
-NNVM_REGISTER_OP(_np_zeros_like)
+NNVM_REGISTER_OP(_npi_full_like)
 .set_num_inputs(1)
 .set_num_outputs(1)
+.set_attr_parser(ParamParser<FullLikeOpParam>)
 .set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
-.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
+.set_attr<nnvm::FInferType>("FInferType", FullLikeOpType<FullLikeOpParam>)
 .set_attr<nnvm::FIgnoreInputs>("FIgnoreInputs",
   [](const NodeAttrs& attrs) {
     return std::vector<uint32_t>(1, 0);
@@ -110,28 +112,11 @@ NNVM_REGISTER_OP(_np_zeros_like)
   [](const NodeAttrs& attrs) {
     return std::vector<std::string>{"a"};
   })
-.set_attr<FCompute>("FCompute<cpu>", FillCompute<cpu, 0>)
-.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
-.add_argument("a", "NDArray-or-Symbol",
-              "The shape and data-type of a define these same attributes of the returned array.");
-
-NNVM_REGISTER_OP(_np_ones_like)
-.set_num_inputs(1)
-.set_num_outputs(1)
-.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
-.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
-.set_attr<nnvm::FIgnoreInputs>("FIgnoreInputs",
-  [](const NodeAttrs& attrs) {
-    return std::vector<uint32_t>(1, 0);
-  })
-.set_attr<nnvm::FListInputNames>("FListInputNames",
-  [](const NodeAttrs& attrs) {
-    return std::vector<std::string>{"a"};
-  })
-.set_attr<FCompute>("FCompute<cpu>", FillCompute<cpu, 1>)
+.set_attr<FCompute>("FCompute<cpu>", FullLikeOpCompute<cpu>)
 .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
 .add_argument("a", "NDArray-or-Symbol",
-              "The shape and data-type of a define these same attributes of the returned array.");
+              "The shape and data-type of a define these same attributes of the returned array.")
+.add_arguments(FullLikeOpParam::__FIELDS__());
 
 NNVM_REGISTER_OP(_npi_arange)
 .set_num_inputs(0)
diff --git a/src/operator/numpy/np_init_op.cu b/src/operator/numpy/np_init_op.cu
index b6e2f93e8d3c..5095fe60bdef 100644
--- a/src/operator/numpy/np_init_op.cu
+++ b/src/operator/numpy/np_init_op.cu
@@ -38,11 +38,8 @@ NNVM_REGISTER_OP(_npi_ones)
 NNVM_REGISTER_OP(_npi_identity)
 .set_attr<FCompute>("FCompute<gpu>", IdentityCompute<gpu>);
 
-NNVM_REGISTER_OP(_np_zeros_like)
-.set_attr<FCompute>("FCompute<gpu>", FillCompute<gpu, 0>);
-
-NNVM_REGISTER_OP(_np_ones_like)
-.set_attr<FCompute>("FCompute<gpu>", FillCompute<gpu, 1>);
+NNVM_REGISTER_OP(_npi_full_like)
+.set_attr<FCompute>("FCompute<gpu>", FullLikeOpCompute<gpu>);
 
 NNVM_REGISTER_OP(_npi_arange)
 .set_attr<FCompute>("FCompute<gpu>", RangeCompute<gpu, RangeParam>);
diff --git a/src/operator/tensor/init_op.h b/src/operator/tensor/init_op.h
index a0139f7fde2d..c7afdc6baaa3 100644
--- a/src/operator/tensor/init_op.h
+++ b/src/operator/tensor/init_op.h
@@ -80,6 +80,39 @@ struct InitOpWithoutDTypeParam : public dmlc::Parameter<InitOpWithoutDTypeParam>
   }
 };
 
+struct FullLikeOpParam : public dmlc::Parameter<FullLikeOpParam> {
+  double fill_value;
+  std::string ctx;
+  dmlc::optional<int> dtype;
+  DMLC_DECLARE_PARAMETER(FullLikeOpParam) {
+    DMLC_DECLARE_FIELD(fill_value)
+    .describe("Value with which to fill newly created tensor");
+    DMLC_DECLARE_FIELD(ctx)
+    .set_default("")
+    .describe("Context of output, in format [cpu|gpu|cpu_pinned](n)."
+              "Only used for imperative calls.");
+    DMLC_DECLARE_FIELD(dtype).set_default(dmlc::optional<int>())
+    MXNET_ADD_ALL_TYPES
+    .describe("Target data type.");
+  }
+};
+
+/*! \brief Infer type of FullLikeOpCompute*/
+template<typename ParamType>
+inline bool FullLikeOpType(const nnvm::NodeAttrs& attrs,
+                           std::vector<int> *in_attrs,
+                           std::vector<int> *out_attrs) {
+  const ParamType& param = nnvm::get<ParamType>(attrs.parsed);
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  if (param.dtype.has_value()) {
+    TYPE_ASSIGN_CHECK(*out_attrs, 0, param.dtype.value());
+  } else {
+    TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
+  }
+  return out_attrs->at(0) != -1;;
+}
+
 struct EyeParam : public dmlc::Parameter<EyeParam> {
   nnvm::dim_t N;
   nnvm::dim_t M;
@@ -402,6 +435,19 @@ void FillCompute(const nnvm::NodeAttrs& attrs,
   Fill<true>(ctx.get_stream<xpu>(), outputs[0], req[0], value);
 }
 
+/*! \brief Fill output with a scalar integer value */
+template<typename xpu>
+void FullLikeOpCompute(const nnvm::NodeAttrs& attrs,
+                       const OpContext& ctx,
+                       const std::vector<TBlob>& inputs,
+                       const std::vector<OpReqType>& req,
+                       const std::vector<TBlob>& outputs) {
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 1U);
+  const auto& param = nnvm::get<FullLikeOpParam>(attrs.parsed);
+  Fill<false>(ctx.get_stream<xpu>(), outputs[0], req[0], param.fill_value);
+}
+
 /*! \brief Fill output with an arbitrary value */
 template<typename xpu>
 void InitFillWithScalarCompute(const nnvm::NodeAttrs &attrs,
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index 797bb43824af..53bd8f4d9235 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -18,6 +18,8 @@
 # pylint: skip-file
 from __future__ import absolute_import
 from __future__ import division
+from distutils.version import StrictVersion
+import platform
 import itertools
 import numpy as _np
 from mxnet import np
@@ -776,9 +778,17 @@ def _add_workload_var(array_pool):
 
 def _add_workload_zeros_like(array_pool):
     OpArgMngr.add_workload('zeros_like', array_pool['4x1'])
-    OpArgMngr.add_workload('zeros_like', np.random.uniform(size=(3, 3)).astype(np.float64))
-    OpArgMngr.add_workload('zeros_like', np.random.uniform(size=(3, 3)).astype(np.float32))
-    OpArgMngr.add_workload('zeros_like', np.random.randint(2, size = (3, 3)))
+    OpArgMngr.add_workload('zeros_like', np.random.uniform(size=(3, 3)).astype(np.float64), np.int64)
+    OpArgMngr.add_workload('zeros_like', np.random.uniform(size=(3, 3)).astype(np.float32), np.float64)
+    OpArgMngr.add_workload('zeros_like', np.random.randint(2, size = (3, 3)), int)
+
+
+def _add_workload_full_like(array_pool):
+    OpArgMngr.add_workload('full_like', array_pool['4x1'], 1)
+    OpArgMngr.add_workload('full_like', np.random.uniform(low=0, high=100, size=(1,3,4), dtype='float64'), 1)
+    OpArgMngr.add_workload('full_like', np.random.uniform(low=0, high=100, size=(9,3,1)), 2, np.int64)
+    OpArgMngr.add_workload('full_like', np.random.uniform(low=0, high=100, size=(9,3)), np.nan)
+    OpArgMngr.add_workload('full_like', np.random.uniform(low=0, high=100, size=(2,0)), 0, np.float32)
 
 
 def _add_workload_outer():
@@ -1432,6 +1442,7 @@ def _prepare_workloads():
     _add_workload_shape()
     _add_workload_diff()
     _add_workload_resize()
+    _add_workload_full_like(array_pool)
 
 
 _prepare_workloads()
@@ -1478,6 +1489,9 @@ def check_interoperability(op_list):
             continue
         if name in ['shares_memory', 'may_share_memory']:  # skip list
             continue
+        if name in ['full_like', 'zeros_like', 'ones_like'] and \
+                StrictVersion(platform.python_version()) < StrictVersion('3.0.0'):
+            continue
         print('Dispatch test:', name)
         workloads = OpArgMngr.get_workloads(name)
         assert workloads is not None, 'Workloads for operator `{}` has not been ' \
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 38633760c400..5c14f0d6c701 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -17,6 +17,7 @@
 
 # pylint: skip-file
 from __future__ import absolute_import
+from distutils.version import StrictVersion
 import sys
 import unittest
 import itertools
@@ -3661,6 +3662,53 @@ def g(data):
                 assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol)
 
 
+@with_seed()
+@use_np
+def test_np_full_like():
+    class TestFullLike(HybridBlock):
+        def __init__(self, fill_value, dtype, ctx):
+            super(TestFullLike, self).__init__()
+            self._fill_value = fill_value
+            self._dtype = dtype
+            self._ctx = ctx
+
+        def hybrid_forward(self, F, x, *args, **kwargs):
+            return F.np.full_like(x, self._fill_value, dtype=self._dtype, ctx=self._ctx)
+
+    if StrictVersion(platform.python_version()) < StrictVersion('3.0.0'):
+        return
+
+    dtypes = ['float64', 'float32', 'float16', 'int64', 'int32', 'int8']
+    shapes = [
+        (),
+        (1,),
+        (4, 3),
+        (4, 5),
+        (2, 1),
+        (6, 5, 6),
+        (4, 2, 1, 2),
+        (5, 1, 3, 3),
+        (3, 3, 1, 0),
+    ]
+    # numpy.full_like operator in py2 cannot handle shape like (5, 0, 3) properly
+    fill_values = [0, 1, 2, 3, 4, 5, 6]
+    flags = [True, False]
+    for fill_value, dtype, shape, hybridize in itertools.product(
+        fill_values, dtypes, shapes, flags):
+        param_dtype= _np.random.choice(dtypes)
+        a = np.random.uniform(low=0, high=100, size=shape, dtype='float64').astype(dtype)
+        test = TestFullLike(fill_value, param_dtype, npx.current_context())
+        expected_ret = _np.full_like(a.asnumpy(), fill_value=fill_value, dtype=param_dtype)     
+        if hybridize:
+            test.hybridize()
+        ret = test(a)
+        assert_almost_equal(ret.asnumpy(), expected_ret, rtol=1e-3, atol=1e-5)
+
+        # check imperative again
+        ret = np.full_like(a, fill_value, param_dtype)
+        assert_almost_equal(ret.asnumpy(), expected_ret, rtol=1e-3, atol=1e-5)
+
+
 @with_seed()
 @use_np
 def test_np_roll():

From 71b62725a0af8ffa74a76c47589b2473d278db30 Mon Sep 17 00:00:00 2001
From: dw_sjtu <46704444+sjtuWangDing@users.noreply.github.com>
Date: Mon, 9 Dec 2019 15:54:21 +0800
Subject: [PATCH 668/813] use identity_with_cast (#16913)

change the doc
move shape and dtype inference func to .cc file
fix format
fix bug in test
fix bug in MXNET_LAPACK_FSIG_GESV
fix format
fix undefined #gesv
---
 python/mxnet/ndarray/numpy/linalg.py          |  56 +-
 python/mxnet/numpy/linalg.py                  |  56 +-
 python/mxnet/numpy_dispatch_protocol.py       |   1 +
 python/mxnet/symbol/numpy/linalg.py           |  55 +-
 src/operator/c_lapack_api.cc                  |  10 +
 src/operator/c_lapack_api.h                   |  39 +-
 src/operator/numpy/linalg/np_solve-inl.h      | 496 ++++++++++++++++++
 src/operator/numpy/linalg/np_solve.cc         | 116 ++++
 src/operator/numpy/linalg/np_solve.cu         |  43 ++
 .../unittest/test_numpy_interoperability.py   |  22 +
 tests/python/unittest/test_numpy_op.py        |  91 ++++
 11 files changed, 980 insertions(+), 5 deletions(-)
 create mode 100644 src/operator/numpy/linalg/np_solve-inl.h
 create mode 100644 src/operator/numpy/linalg/np_solve.cc
 create mode 100644 src/operator/numpy/linalg/np_solve.cu

diff --git a/python/mxnet/ndarray/numpy/linalg.py b/python/mxnet/ndarray/numpy/linalg.py
index 74ba41f22979..a85c6324f685 100644
--- a/python/mxnet/ndarray/numpy/linalg.py
+++ b/python/mxnet/ndarray/numpy/linalg.py
@@ -21,7 +21,7 @@
 from . import _op as _mx_nd_np
 from . import _internal as _npi
 
-__all__ = ['norm', 'svd', 'cholesky', 'inv', 'det', 'slogdet']
+__all__ = ['norm', 'svd', 'cholesky', 'inv', 'det', 'slogdet', 'solve']
 
 
 def norm(x, ord=None, axis=None, keepdims=False):
@@ -352,3 +352,57 @@ def slogdet(a):
     (1., -1151.2925464970228)
     """
     return _npi.slogdet(a)
+
+
+def solve(a, b):
+    r"""
+    Solve a linear matrix equation, or system of linear scalar equations.
+
+    Computes the "exact" solution, `x`, of the well-determined, i.e., full
+    rank, linear matrix equation `ax = b`.
+
+    Parameters
+    ----------
+    a : (..., M, M) ndarray
+        Coefficient matrix.
+    b : {(..., M,), (..., M, K)}, ndarray
+        Ordinate or "dependent variable" values.
+
+    Returns
+    -------
+    x : {(..., M,), (..., M, K)} ndarray
+        Solution to the system a x = b.  Returned shape is identical to `b`.
+
+    Raises
+    ------
+    MXNetError
+        If `a` is singular or not square.
+
+    Notes
+    -----
+    Broadcasting rules apply, see the `numpy.linalg` documentation for
+    details.
+
+    The solutions are computed using LAPACK routine ``_gesv``.
+
+    `a` must be square and of full-rank, i.e., all rows (or, equivalently,
+    columns) must be linearly independent; if either is not true, use
+    `lstsq` for the least-squares best "solution" of the
+    system/equation.
+
+    Examples
+    --------
+    Solve the system of equations ``3 * x0 + x1 = 9`` and ``x0 + 2 * x1 = 8``:
+
+    >>> a = np.array([[3,1], [1,2]])
+    >>> b = np.array([9,8])
+    >>> x = np.linalg.solve(a, b)
+    >>> x
+    array([2.,  3.])
+
+    Check that the solution is correct:
+
+    >>> np.allclose(np.dot(a, x), b)
+    True
+    """
+    return _npi.solve(a, b)
diff --git a/python/mxnet/numpy/linalg.py b/python/mxnet/numpy/linalg.py
index fbe3631eb6e6..33d636b7044c 100644
--- a/python/mxnet/numpy/linalg.py
+++ b/python/mxnet/numpy/linalg.py
@@ -20,7 +20,7 @@
 from __future__ import absolute_import
 from ..ndarray import numpy as _mx_nd_np
 
-__all__ = ['norm', 'svd', 'cholesky', 'inv', 'det', 'slogdet']
+__all__ = ['norm', 'svd', 'cholesky', 'inv', 'det', 'slogdet', 'solve']
 
 
 def norm(x, ord=None, axis=None, keepdims=False):
@@ -370,3 +370,57 @@ def slogdet(a):
     (1., -1151.2925464970228)
     """
     return _mx_nd_np.linalg.slogdet(a)
+
+
+def solve(a, b):
+    r"""
+    Solve a linear matrix equation, or system of linear scalar equations.
+
+    Computes the "exact" solution, `x`, of the well-determined, i.e., full
+    rank, linear matrix equation `ax = b`.
+
+    Parameters
+    ----------
+    a : (..., M, M) ndarray
+        Coefficient matrix.
+    b : {(..., M,), (..., M, K)}, ndarray
+        Ordinate or "dependent variable" values.
+
+    Returns
+    -------
+    x : {(..., M,), (..., M, K)} ndarray
+        Solution to the system a x = b.  Returned shape is identical to `b`.
+
+    Raises
+    ------
+    MXNetError
+        If `a` is singular or not square.
+
+    Notes
+    -----
+    Broadcasting rules apply, see the `numpy.linalg` documentation for
+    details.
+
+    The solutions are computed using LAPACK routine ``_gesv``.
+
+    `a` must be square and of full-rank, i.e., all rows (or, equivalently,
+    columns) must be linearly independent; if either is not true, use
+    `lstsq` for the least-squares best "solution" of the
+    system/equation.
+
+    Examples
+    --------
+    Solve the system of equations ``3 * x0 + x1 = 9`` and ``x0 + 2 * x1 = 8``:
+
+    >>> a = np.array([[3,1], [1,2]])
+    >>> b = np.array([9,8])
+    >>> x = np.linalg.solve(a, b)
+    >>> x
+    array([2.,  3.])
+
+    Check that the solution is correct:
+
+    >>> np.allclose(np.dot(a, x), b)
+    True
+    """
+    return _mx_nd_np.linalg.solve(a, b)
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index a6bceb51cd01..c8b11d85b000 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -131,6 +131,7 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'linalg.norm',
     'linalg.cholesky',
     'linalg.inv',
+    'linalg.solve',
     'shape',
     'trace',
     'tril',
diff --git a/python/mxnet/symbol/numpy/linalg.py b/python/mxnet/symbol/numpy/linalg.py
index cf33777b2637..1aaf4b990e31 100644
--- a/python/mxnet/symbol/numpy/linalg.py
+++ b/python/mxnet/symbol/numpy/linalg.py
@@ -22,7 +22,7 @@
 from . import _op as _mx_sym_np
 from . import _internal as _npi
 
-__all__ = ['norm', 'svd', 'cholesky', 'inv', 'det', 'slogdet']
+__all__ = ['norm', 'svd', 'cholesky', 'inv', 'det', 'slogdet', 'solve']
 
 
 def norm(x, ord=None, axis=None, keepdims=False):
@@ -339,3 +339,56 @@ def slogdet(a):
     (1., -1151.2925464970228)
     """
     return _npi.slogdet(a)
+
+def solve(a, b):
+    r"""
+    Solve a linear matrix equation, or system of linear scalar equations.
+
+    Computes the "exact" solution, `x`, of the well-determined, i.e., full
+    rank, linear matrix equation `ax = b`.
+
+    Parameters
+    ----------
+    a : (..., M, M) ndarray
+        Coefficient matrix.
+    b : {(..., M,), (..., M, K)}, ndarray
+        Ordinate or "dependent variable" values.
+
+    Returns
+    -------
+    x : {(..., M,), (..., M, K)} ndarray
+        Solution to the system a x = b.  Returned shape is identical to `b`.
+
+    Raises
+    ------
+    MXNetError
+        If `a` is singular or not square.
+
+    Notes
+    -----
+    Broadcasting rules apply, see the `numpy.linalg` documentation for
+    details.
+
+    The solutions are computed using LAPACK routine ``_gesv``.
+
+    `a` must be square and of full-rank, i.e., all rows (or, equivalently,
+    columns) must be linearly independent; if either is not true, use
+    `lstsq` for the least-squares best "solution" of the
+    system/equation.
+
+    Examples
+    --------
+    Solve the system of equations ``3 * x0 + x1 = 9`` and ``x0 + 2 * x1 = 8``:
+
+    >>> a = np.array([[3,1], [1,2]])
+    >>> b = np.array([9,8])
+    >>> x = np.linalg.solve(a, b)
+    >>> x
+    array([2.,  3.])
+
+    Check that the solution is correct:
+
+    >>> np.allclose(np.dot(a, x), b)
+    True
+    """
+    return _npi.solve(a, b)
diff --git a/src/operator/c_lapack_api.cc b/src/operator/c_lapack_api.cc
index e7a97848700d..73b6138df5ea 100644
--- a/src/operator/c_lapack_api.cc
+++ b/src/operator/c_lapack_api.cc
@@ -71,6 +71,13 @@
     return 1; \
   }
 
+  #define MXNET_LAPACK_CWRAPPER7(func, dtype) \
+  int MXNET_LAPACK_##func(int matrix_order, int n, int nrhs, dtype *a, \
+                          int lda, int *ipiv, dtype *b, int ldb) { \
+    LOG(FATAL) << "MXNet build without lapack. Function " << #func << " is not available."; \
+    return 1; \
+  }
+
   #define MXNET_LAPACK_UNAVAILABLE(func) \
   int mxnet_lapack_##func(...) { \
     LOG(FATAL) << "MXNet build without lapack. Function " << #func << " is not available."; \
@@ -101,4 +108,7 @@
   MXNET_LAPACK_CWRAPPER6(sgesvd, float)
   MXNET_LAPACK_CWRAPPER6(dgesvd, double)
 
+  MXNET_LAPACK_CWRAPPER7(sgesv, float)
+  MXNET_LAPACK_CWRAPPER7(dgesv, double)
+
 #endif  // MSHADOW_USE_MKL == 0
diff --git a/src/operator/c_lapack_api.h b/src/operator/c_lapack_api.h
index a47bbd0b5857..8a7cbc067feb 100644
--- a/src/operator/c_lapack_api.h
+++ b/src/operator/c_lapack_api.h
@@ -150,6 +150,19 @@ extern "C" {
 
   MXNET_LAPACK_FSIG_GETRI(sgetri, float)
   MXNET_LAPACK_FSIG_GETRI(dgetri, double)
+
+  #ifdef __ANDROID__
+    #define MXNET_LAPACK_FSIG_GESV(func, dtype) \
+      int func##_(int *n, int *nrhs, dtype *a, int *lda, \
+                   int *ipiv, dtype *b, int *ldb, int *info);
+  #else
+    #define MXNET_LAPACK_FSIG_GESV(func, dtype) \
+      void func##_(int *n, int *nrhs, dtype *a, int *lda, \
+                   int *ipiv, dtype *b, int *ldb, int *info);
+  #endif
+
+  MXNET_LAPACK_FSIG_GESV(sgesv, float)
+  MXNET_LAPACK_FSIG_GESV(dgesv, double)
 }
 
 #endif  // MSHADOW_USE_MKL == 0
@@ -197,6 +210,8 @@ inline void flip(int m, int n, DType *b, int ldb, DType *a, int lda) {
   #define MXNET_LAPACK_dpotri LAPACKE_dpotri
   #define mxnet_lapack_sposv  LAPACKE_sposv
   #define mxnet_lapack_dposv  LAPACKE_dposv
+  #define MXNET_LAPACK_dgesv  LAPACKE_dgesv
+  #define MXNET_LAPACK_sgesv  LAPACKE_sgesv
 
   // The following functions differ in signature from the
   // MXNET_LAPACK-signature and have to be wrapped.
@@ -440,9 +455,23 @@ inline void flip(int m, int n, DType *b, int ldb, DType *a, int lda) {
   MXNET_LAPACK_CWRAP_GETRI(s, float)
   MXNET_LAPACK_CWRAP_GETRI(d, double)
 
-#else
-
+  #define MXNET_LAPACK_CWRAP_GESV(prefix, dtype) \
+  inline int MXNET_LAPACK_##prefix##gesv(int matrix_layout, \
+                                         int n, int nrhs, dtype *a, int lda, \
+                                         int *ipiv, dtype *b, int ldb) { \
+    if (matrix_layout == MXNET_LAPACK_ROW_MAJOR) { \
+      CHECK(false) << "MXNET_LAPACK_" << #prefix << "gesv implemented for col-major layout only"; \
+      return 1; \
+    } else { \
+      int info(0); \
+      prefix##gesv_(&n, &nrhs, a, &lda, ipiv, b, &ldb, &info); \
+      return info; \
+    } \
+  }
+  MXNET_LAPACK_CWRAP_GESV(s, float)
+  MXNET_LAPACK_CWRAP_GESV(d, double)
 
+#else
 
   #define MXNET_LAPACK_ROW_MAJOR 101
   #define MXNET_LAPACK_COL_MAJOR 102
@@ -473,6 +502,9 @@ inline void flip(int m, int n, DType *b, int ldb, DType *a, int lda) {
                           int ldut, dtype* s, dtype* v, int ldv, \
                           dtype* work, int lwork);
 
+  #define MXNET_LAPACK_CWRAPPER7(func, dtype) \
+  int MXNET_LAPACK_##func(int matrix_order, int n, int nrhs, dtype *a, \
+                          int lda, int *ipiv, dtype *b, int ldb); \
 
   #define MXNET_LAPACK_UNAVAILABLE(func) \
   int mxnet_lapack_##func(...);
@@ -501,6 +533,9 @@ inline void flip(int m, int n, DType *b, int ldb, DType *a, int lda) {
   MXNET_LAPACK_CWRAPPER6(sgesvd, float)
   MXNET_LAPACK_CWRAPPER6(dgesvd, double)
 
+  MXNET_LAPACK_CWRAPPER7(sgesv, float)
+  MXNET_LAPACK_CWRAPPER7(dgesv, double)
+
   #undef MXNET_LAPACK_CWRAPPER1
   #undef MXNET_LAPACK_CWRAPPER2
   #undef MXNET_LAPACK_CWRAPPER3
diff --git a/src/operator/numpy/linalg/np_solve-inl.h b/src/operator/numpy/linalg/np_solve-inl.h
new file mode 100644
index 000000000000..03134f8b5688
--- /dev/null
+++ b/src/operator/numpy/linalg/np_solve-inl.h
@@ -0,0 +1,496 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_solve-inl.h
+ * \brief Placeholder for solve linear equation
+ */
+#ifndef MXNET_OPERATOR_NUMPY_LINALG_NP_SOLVE_INL_H_
+#define MXNET_OPERATOR_NUMPY_LINALG_NP_SOLVE_INL_H_
+
+#include <mxnet/operator_util.h>
+#include <vector>
+#include "../../tensor/la_op.h"
+#include "../../tensor/la_op-inl.h"
+#include "../../linalg.h"
+#include "../../operator_common.h"
+#include "../../mshadow_op.h"
+
+namespace mxnet {
+namespace op {
+
+using namespace mshadow;
+
+template<typename xpu, typename DType>
+void linalg_solve(const Tensor<xpu, 2, DType>& A,
+                  const Tensor<xpu, 2, DType>& X,
+                  const Tensor<xpu, 1, int>& ipiv,
+                  Stream<xpu> *s);
+
+template<typename xpu, typename DType>
+void linalg_batch_solve(const Tensor<xpu, 3, DType>& A,
+                        const Tensor<xpu, 3, DType>& X,
+                        const Tensor<xpu, 2, int>& ipiv,
+                        const mxnet::OpContext& ctx);
+
+template<typename xpu, typename DType> inline
+int linalg_dn_getrf_workspace_query(const Tensor<xpu, 2, DType>& A,
+                                    Stream<xpu> *s);
+
+template<typename xpu, typename DType> inline
+void linalg_dn_getrf(const Tensor<xpu, 2, DType>& A,
+                     const Tensor<xpu, 1, int>& ipiv,
+                     Stream<xpu> *s);
+
+template<typename xpu, typename DType> inline
+void linalg_dn_getrs(const Tensor<xpu, 2, DType>& A,
+                     const Tensor<xpu, 2, DType>& X,
+                     const Tensor<xpu, 1, int>& ipiv,
+                     Stream<xpu> *s);
+
+// kernel for transpose
+struct SolveTypeTransposeHelper {
+  template<typename InDType, typename OutDType>
+  MSHADOW_XINLINE static void Map(int i, const InDType *in_data, OutDType *out_data,
+                                  const int ncol1, const int ncol2, const int step) {
+    int idx = i / step, row = (i % step) / ncol1, col = (i % step) % ncol1;
+    out_data[idx * step + row + col * ncol2] = static_cast<OutDType>(in_data[i]);
+  }
+};
+
+template<typename xpu, typename DType>
+inline void check_solve(const Tensor<xpu, 2, DType>& A,
+                        const Tensor<xpu, 2, DType>& B) {
+  CHECK_EQ(A.size(0), A.size(1)) << "A must bu square matrix";
+  CHECK_EQ(A.size(1), B.size(1)) << "A, B have incompatible sizes";
+}
+
+#define LINALG_CPU_SOLVE(fname, DType) \
+template<> inline \
+void linalg_solve<cpu, DType>(const Tensor<cpu, 2, DType>& A, \
+                              const Tensor<cpu, 2, DType>& X, \
+                              const Tensor<cpu, 1, int>& ipiv, \
+                              Stream<cpu> *s) { \
+  check_solve(A, X); \
+  const int N = X.size(1), nrhs = X.size(0); \
+  const int lda = (N == 0 ? 1 : N), ldx = (N == 0 ? 1 : N); \
+  int res(MXNET_LAPACK_##fname(MXNET_LAPACK_COL_MAJOR, N, nrhs, \
+                               A.dptr_, lda, ipiv.dptr_, X.dptr_, ldx)); \
+  CHECK_LE(res, 0) << #fname << ": U(" << res << ", " << res \
+    << ") is exactly zero. The factorization has been completed," \
+    << "but the factor U is exactly singular, so the solution could not be computed."; \
+  CHECK_GE(res, 0) << #fname << ": the " << -res \
+    << "-th argument had an illegal value"; \
+}
+LINALG_CPU_SOLVE(sgesv, float)
+LINALG_CPU_SOLVE(dgesv, double)
+
+#ifdef __CUDACC__
+
+#if CUDA_VERSION >= 8000
+
+#define LINALG_GPU_DN_GETRF_WORKSPACE_QUERY(fname, DType) \
+template<> inline \
+int linalg_dn_getrf_workspace_query<gpu, DType>(const Tensor<gpu, 2, DType>& A, \
+                                                Stream<gpu> *s) { \
+  using namespace mxnet; \
+  using mshadow::gpu; \
+  int lwork(0); \
+  CUSOLVER_CALL(cusolver##fname##_bufferSize(Stream<gpu>::GetSolverHandle(s), \
+                                             A.size(1), A.size(1), A.dptr_, \
+                                             (A.size(1) == 0 ? 1 : A.size(1)), &lwork)); \
+  return lwork; \
+}
+
+#define LINALG_GPU_DN_GETRF(fname, DType) \
+template<> inline \
+void linalg_dn_getrf<gpu, DType>(const Tensor<gpu, 2, DType>& A, \
+                                 const Tensor<gpu, 1, int>& ipiv, \
+                                 Stream<gpu> *s) { \
+  using namespace mxnet; \
+  using mshadow::gpu; \
+  Storage::Handle info = Storage::Get()->Alloc(sizeof(int), Context::GPU()); \
+  const int lwork = linalg_dn_getrf_workspace_query(A, s); \
+  Storage::Handle workspace = Storage::Get()->Alloc(sizeof(DType) * lwork, Context::GPU()); \
+  CUSOLVER_CALL(cusolver##fname(Stream<gpu>::GetSolverHandle(s), \
+                                A.size(1), A.size(1), A.dptr_, (A.size(1) == 0 ? 1 : A.size(1)), \
+                                static_cast<DType*>(workspace.dptr), ipiv.dptr_, \
+                                static_cast<int*>(info.dptr))); \
+  Storage::Get()->Free(info); \
+  Storage::Get()->Free(workspace); \
+}
+
+#define LINALG_GPU_DN_GETRS(fname, DType) \
+template<> inline \
+void linalg_dn_getrs<gpu, DType>(const Tensor<gpu, 2, DType>& A, \
+                                 const Tensor<gpu, 2, DType>& X, \
+                                 const Tensor<gpu, 1, int>& ipiv, \
+                                 Stream<gpu> *s) { \
+  using namespace mxnet; \
+  using mshadow::gpu; \
+  const int N = A.size(0), nrhs = X.size(0); \
+  const int lda = (A.size(1) == 0 ? 1 : A.size(1)), ldx = (X.size(1) == 0 ? 1 : X.size(1)); \
+  Storage::Handle info = Storage::Get()->Alloc(sizeof(int), Context::GPU()); \
+  CUSOLVER_CALL(cusolver##fname(Stream<gpu>::GetSolverHandle(s), \
+                                CUBLAS_OP_N, N, nrhs, \
+                                A.dptr_, lda, ipiv.dptr_, X.dptr_, ldx, \
+                                static_cast<int*>(info.dptr))); \
+  Storage::Get()->Free(info); \
+}
+
+#define LINALG_GPU_SOLVE(DType) \
+template<> inline \
+void linalg_solve<gpu, DType>(const Tensor<gpu, 2, DType>& A, \
+                              const Tensor<gpu, 2, DType>& X, \
+                              const Tensor<gpu, 1, int>& ipiv, \
+                              Stream<gpu> *s) { \
+  using namespace mxnet; \
+  using mshadow::gpu; \
+  CHECK_NOTNULL(s); \
+  check_solve(A, X); \
+  linalg_dn_getrf(A, ipiv, s); \
+  linalg_dn_getrs(A, X, ipiv, s); \
+}
+
+#else  // CUDA_VERSION >= 8000
+
+#define LINALG_GPU_DN_GETRF_WORKSPACE_QUERY(fname, DType) \
+template<> inline \
+int linalg_dn_getrf_workspace_query<gpu, DType>(const Tensor<gpu, 2, DType>& A, \
+                                                Stream<gpu> *s) { \
+  LOG(FATAL) << "Dn_getrf_workspace_query requires CUDA version >= 8.0!"; \
+}
+
+#define LINALG_GPU_DN_GETRF(fname, DType) \
+template<> inline \
+void linalg_dn_getrf<gpu, DType>(const Tensor<gpu, 2, DType>& A, \
+                                 const Tensor<gpu, 1, int>& ipiv, \
+                                 Stream<gpu> *s) { \
+  LOG(FATAL) << "Dn_getrf requires CUDA version >= 8.0!"; \
+}
+
+#define LINALG_GPU_DN_GETRS(fname, DType) \
+template<> inline \
+void linalg_dn_getrs<gpu, DType>(const Tensor<gpu, 2, DType>& A, \
+                                 const Tensor<gpu, 2, DType>& X, \
+                                 const Tensor<gpu, 1, int>& ipiv, \
+                                 Stream<gpu> *s) { \
+  LOG(FATAL) << "Dn_getrs requires CUDA version >= 8.0!"; \
+}
+
+#define LINALG_GPU_SOLVE(DType) \
+template<> inline \
+void linalg_solve<gpu, DType>(const Tensor<gpu, 2, DType>& A, \
+                              const Tensor<gpu, 2, DType>& X, \
+                              const Tensor<gpu, 1, int>& ipiv, \
+                              Stream<gpu> *s) { \
+  LOG(FATAL) << "gpu solve requires CUDA version >= 8.0!"; \
+}
+
+#endif  // CUDA_VERSION >= 8000
+
+LINALG_GPU_DN_GETRF_WORKSPACE_QUERY(DnSgetrf, float)
+LINALG_GPU_DN_GETRF_WORKSPACE_QUERY(DnDgetrf, double)
+
+LINALG_GPU_DN_GETRF(DnSgetrf, float)
+LINALG_GPU_DN_GETRF(DnDgetrf, double)
+
+LINALG_GPU_DN_GETRS(DnSgetrs, float)
+LINALG_GPU_DN_GETRS(DnDgetrs, double)
+
+LINALG_GPU_SOLVE(float)
+LINALG_GPU_SOLVE(double)
+
+#endif  // __CUDACC__
+
+#define LINALG_XPU_BATCH_SOLVE(xpu, DType) \
+template<> inline \
+void linalg_batch_solve<xpu, DType>(const Tensor<xpu, 3, DType>& A, \
+                                    const Tensor<xpu, 3, DType>& X, \
+                                    const Tensor<xpu, 2, int>& ipiv, \
+                                    const mxnet::OpContext& ctx) { \
+  Stream<xpu> *s = ctx.get_stream<xpu>(); \
+  for (index_t i = 0; i < A.size(0); ++i) { \
+    linalg_solve(A[i], X[i], ipiv[i], s); \
+  } \
+}
+LINALG_XPU_BATCH_SOLVE(cpu, float)
+LINALG_XPU_BATCH_SOLVE(cpu, double)
+
+#ifdef __CUDACC__
+
+LINALG_XPU_BATCH_SOLVE(gpu, float)
+LINALG_XPU_BATCH_SOLVE(gpu, double)
+
+#endif  // __CUDACC__
+
+struct solve {
+  template<typename xpu, typename DType>
+  static void op(const Tensor<xpu, 3, DType>& A,
+                 const Tensor<xpu, 3, DType>& X,
+                 const Tensor<xpu, 2, int>& ipiv,
+                 const OpContext& ctx,
+                 const nnvm::NodeAttrs& attrs) {
+    linalg_batch_solve(A, X, ipiv, ctx);  // ipiv for work_space in Lapacke_#gesv
+  }
+};
+
+template<typename xpu, int idim, int odim, int inum, int onum, typename laop>
+void LaOpForwardSolve(const nnvm::NodeAttrs& attrs,
+                      const OpContext& ctx,
+                      const std::vector<TBlob>& inputs,
+                      const std::vector<OpReqType>& req,
+                      const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  CHECK_EQ(inputs.size(), inum);
+  CHECK_EQ(outputs.size(), onum);
+  CHECK_EQ(req.size(), onum);
+  MSHADOW_SGL_DBL_TYPE_SWITCH(outputs[0].type_flag_, OType, {
+    mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+    const mxnet::TBlob& a_tblob = inputs[0];
+    const mxnet::TBlob& b_tblob = inputs[1];
+    const mxnet::TBlob& x_tblob = outputs[0];
+    const mxnet::TShape& a_shape = a_tblob.shape_;
+    mxnet::TShape b_shape(a_shape.ndim(), 1);
+    for (int i = 0; i < a_shape.ndim() - 1; ++i) { b_shape[i] = b_tblob.shape_[i]; }
+    if (b_tblob.shape_.ndim() == a_shape.ndim()) {
+      b_shape[a_shape.ndim() - 1] = b_tblob.shape_[a_shape.ndim() - 1];
+    }
+    const int ndim = a_shape.ndim();
+    mxnet::TShape ipiv_shape(a_shape);
+    ipiv_shape[ndim - 1] = 1;
+    if (0 == a_shape[ndim - 1] || 0 == a_shape[ndim - 2] ||
+        0 == b_shape[ndim - 1] || 0 == b_shape[ndim - 2]) { return; }
+
+    const int work_space_size =
+      sizeof(OType) * (a_shape.Size() + b_shape.Size()) + sizeof(int) * ipiv_shape.Size();
+    Tensor<xpu, 1, char> work_buffer =
+      ctx.requested[0].get_space_typed<xpu, 1, char>(Shape1(work_space_size), s);
+    MSHADOW_TYPE_SWITCH(a_tblob.type_flag_, AType, {
+      // cast type and transpose
+      mxnet_op::Kernel<SolveTypeTransposeHelper, xpu>::Launch(
+        s, a_shape.Size(),
+        a_tblob.dptr<AType>(),
+        reinterpret_cast<OType*>(work_buffer.dptr_),
+        a_shape[ndim - 1], a_shape[ndim - 2], a_shape[ndim - 1] * a_shape[ndim - 2]);
+    });
+    MSHADOW_TYPE_SWITCH(b_tblob.type_flag_, BType, {
+      // cast type and transpose
+      mxnet_op::Kernel<SolveTypeTransposeHelper, xpu>::Launch(
+        s, b_shape.Size(),
+        b_tblob.dptr<BType>(),
+        reinterpret_cast<OType*>(work_buffer.dptr_) + a_shape.Size(),
+        b_shape[ndim - 1], b_shape[ndim - 2], b_shape[ndim - 1] * b_shape[ndim - 2]);
+    });
+    // transpose shape
+    int temp = b_shape[ndim - 1];
+    b_shape[ndim - 1] = b_shape[ndim - 2];
+    b_shape[ndim - 2] = temp;
+    mxnet::TBlob a_transpose_tblob(reinterpret_cast<OType*>(work_buffer.dptr_),
+      a_shape, a_tblob.dev_mask(), a_tblob.dev_id());
+    mxnet::TBlob b_transpose_tblob(reinterpret_cast<OType*>(work_buffer.dptr_) + a_shape.Size(),
+      b_shape, b_tblob.dev_mask(), b_tblob.dev_id());
+    mxnet::TBlob ipiv_tblob(reinterpret_cast<int*>(
+      reinterpret_cast<OType*>(work_buffer.dptr_) + a_shape.Size() + b_shape.Size()),
+      ipiv_shape, b_tblob.dev_mask(), b_tblob.dev_id());
+
+    laop::op(a_transpose_tblob.FlatToKD<xpu, idim + 1, OType>(s),
+             b_transpose_tblob.FlatToKD<xpu, idim + 1, OType>(s),
+             ipiv_tblob.FlatToKD<xpu, idim, int>(s),
+             ctx,
+             attrs);
+    // X = transpose(B)
+    mxnet_op::Kernel<SolveTypeTransposeHelper, xpu>::Launch(
+      s, b_shape.Size(),
+      b_transpose_tblob.dptr<OType>(),
+      x_tblob.dptr<OType>(),
+      b_shape[ndim - 1], b_shape[ndim - 2], b_shape[ndim - 1] * b_shape[ndim - 2]);
+  });
+}
+
+// X = (inv_A) * B
+struct solve_backward {
+  template<typename xpu, typename DType>
+  static void op(const Tensor<xpu, 3, DType>& dX,
+                 const Tensor<xpu, 3, DType>& inv_A,
+                 const Tensor<xpu, 3, DType>& B,
+                 const Tensor<xpu, 3, DType>& X,
+                 const Tensor<xpu, 3, DType>& dA,
+                 const Tensor<xpu, 3, DType>& dB,
+                 const OpContext& ctx,
+                 const nnvm::NodeAttrs& attrs) {
+    // (1) calcualte dB = trans(inv(A)) * dX
+    // (2) calcualte dA = dB * trans(X)
+    Stream<xpu> *s = ctx.get_stream<xpu>();
+    gemm2::op(inv_A, dX, dB, DType(1), true, false, s);
+    gemm2::op(dB, X, dA, DType(-1), false, true, s);
+  }
+};
+
+template<typename xpu, typename DType>
+inline void batch_inverse(const Tensor<xpu, 3, DType>& inv_A,
+                          const Tensor<xpu, 3, DType>& LU,
+                          const Tensor<xpu, 2, int>& pivot,
+                          const mxnet::OpContext& ctx);
+
+#define CPU_BATCH_INVERSE(xpu, DType) \
+template<> inline \
+void batch_inverse<xpu, DType>(const Tensor<xpu, 3, DType>& inv_A, \
+                               const Tensor<xpu, 3, DType>& LU, \
+                               const Tensor<xpu, 2, int>& pivot, \
+                               const mxnet::OpContext& ctx) { \
+  Stream<xpu> *s = ctx.get_stream<xpu>(); \
+  for (index_t i = 0; i < inv_A.size(0); ++i) { \
+    linalg_getrf(inv_A[i], pivot[i], true, s); \
+    const Tensor<xpu, 1, DType> work( \
+      LU[i].dptr_, Shape1(LU.size(1) * LU.size(2))); \
+    linalg_getri(inv_A[i], pivot[i], work, s); \
+  } \
+}
+CPU_BATCH_INVERSE(cpu, float)
+CPU_BATCH_INVERSE(cpu, double)
+
+#ifdef __CUDACC__
+
+// GETRF and GETRI only available with cuda8 or higher.
+#if CUDA_VERSION >= 8000
+
+#define GPU_BATCH_INVERSE(xpu, DType) \
+template<> inline \
+void batch_inverse<xpu, DType>(const Tensor<xpu, 3, DType>& inv_A, \
+                               const Tensor<xpu, 3, DType>& LU, \
+                               const Tensor<xpu, 2, int>& pivot, \
+                               const mxnet::OpContext& ctx) { \
+  Stream<xpu> *s = ctx.get_stream<xpu>(); \
+  if (LU.dptr_ != inv_A.dptr_) Copy(LU, inv_A, s); \
+  linalg_batch_getrf(LU, pivot, true, s); \
+  linalg_batch_getri(inv_A, LU, pivot, s); \
+}
+
+#else  // CUDA_VERSION >= 8000
+
+#define GPU_BATCH_INVERSE(xpu, DType) \
+template<> inline \
+void batch_inverse<xpu, DType>(const Tensor<xpu, 3, DType>& inv_A, \
+                               const Tensor<xpu, 3, DType>& LU, \
+                               const Tensor<xpu, 2, int>& pivot, \
+                               const mxnet::OpContext& ctx) { \
+  LOG(FATAL) << "gpu matrix inverse requires CUDA version >= 8.0!"; \
+}
+
+#endif  // CUDA_VERSION >= 8000
+
+GPU_BATCH_INVERSE(gpu, float)
+GPU_BATCH_INVERSE(gpu, double)
+
+#endif  // __CUDACC__
+
+template<typename xpu, int idim, int odim, int inum, int onum, typename laop>
+void LaOpBackwardSolve(const nnvm::NodeAttrs& attrs,
+                       const OpContext& ctx,
+                       const std::vector<TBlob>& inputs,
+                       const std::vector<OpReqType>& req,
+                       const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  CHECK_EQ(inputs.size(), inum);
+  CHECK_EQ(outputs.size(), onum);
+  CHECK_EQ(req.size(), onum);
+  MSHADOW_SGL_DBL_TYPE_SWITCH(outputs[0].type_flag_, OType, {
+    mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+    const mxnet::TBlob& a_tblob = inputs[1];
+    const mxnet::TBlob& b_tblob = inputs[2];
+    const mxnet::TBlob& x_tblob = inputs[3];
+
+    const mxnet::TShape& a_shape = a_tblob.shape_;
+    mxnet::TShape b_shape(a_shape.ndim(), 1);
+    for (int i = 0; i < a_shape.ndim() - 1; ++i) { b_shape[i] = b_tblob.shape_[i]; }
+    if (b_tblob.shape_.ndim() == a_shape.ndim()) {
+      b_shape[a_shape.ndim() - 1] = b_tblob.shape_[a_shape.ndim() - 1];
+    }
+    const int ndim = a_shape.ndim();
+    const int N = a_shape[ndim - 1];
+    if (0 == a_shape[ndim - 1] || 0 == a_shape[ndim - 2] ||
+        0 == b_shape[ndim - 1] || 0 == b_shape[ndim - 2]) { return; }
+
+    const Tensor<xpu, idim + 1, OType> A = a_tblob.FlatToKD<xpu, idim + 1, OType>(s);
+    int work_space_size = sizeof(OType) * a_shape.Size();  // for inverse(A)
+    work_space_size += sizeof(OType) * a_shape.Size();  // for getri work space
+    work_space_size += 2 * sizeof(OType) * b_shape.Size();  // for B and X
+    work_space_size += sizeof(int) * A.size(0) * N;  // for pivot work space
+    Tensor<xpu, 1, char> work_buffer =
+      ctx.requested[0].get_space_typed<xpu, 1, char>(Shape1(work_space_size), s);
+
+    MSHADOW_TYPE_SWITCH(a_tblob.type_flag_, AType, {
+      mxnet_op::Kernel<mshadow_op::identity_with_cast, xpu>::Launch(
+        s, a_shape.Size(),
+        reinterpret_cast<OType*>(work_buffer.dptr_),
+        a_tblob.dptr<AType>());
+    });
+    mxnet::TBlob a_inverse_tblob(reinterpret_cast<OType*>(work_buffer.dptr_),
+      a_shape, a_tblob.dev_mask(), a_tblob.dev_id());
+    const Tensor<xpu, idim + 1, OType> inv_A = a_inverse_tblob.FlatToKD<xpu, idim + 1, OType>(s);
+
+    mxnet::TBlob lu_tblob(reinterpret_cast<OType*>(work_buffer.dptr_) + a_shape.Size(),
+      inv_A.shape_, a_tblob.dev_mask(), a_tblob.dev_id());
+    const Tensor<xpu, idim + 1, OType> LU = lu_tblob.FlatToKD<xpu, idim + 1, OType>(s);
+
+    MSHADOW_TYPE_SWITCH(b_tblob.type_flag_, BType, {
+      mxnet_op::Kernel<mshadow_op::identity_with_cast, xpu>::Launch(
+        s, b_shape.Size(),
+        reinterpret_cast<OType*>(work_buffer.dptr_) + 2 * a_shape.Size(),
+        b_tblob.dptr<BType>());
+    });
+    mxnet::TBlob b_cp_tblob(reinterpret_cast<OType*>(work_buffer.dptr_) + 2 * a_shape.Size(),
+      b_shape, b_tblob.dev_mask(), b_tblob.dev_id());
+    const Tensor<xpu, idim + 1, OType> B = b_cp_tblob.FlatToKD<xpu, idim + 1, OType>(s);
+
+    MSHADOW_TYPE_SWITCH(x_tblob.type_flag_, XType, {
+      mxnet_op::Kernel<mshadow_op::identity_with_cast, xpu>::Launch(
+        s, b_shape.Size(),
+        reinterpret_cast<OType*>(work_buffer.dptr_) + 2 * a_shape.Size() + b_shape.Size(),
+        x_tblob.dptr<XType>());
+    });
+    mxnet::TBlob x_cp_tblob(
+      reinterpret_cast<OType*>(work_buffer.dptr_) + 2 * a_shape.Size() + b_shape.Size(),
+      b_shape, b_tblob.dev_mask(), b_tblob.dev_id());
+    const Tensor<xpu, idim + 1, OType> X = x_cp_tblob.FlatToKD<xpu, idim + 1, OType>(s);
+
+    mxnet::TBlob pivot_tblob(reinterpret_cast<int*>(
+      reinterpret_cast<OType*>(work_buffer.dptr_) + 2 * a_shape.Size() + 2 * b_shape.Size()),
+      Shape2(A.size(0), N), a_tblob.dev_mask(), a_tblob.dev_id());
+    const Tensor<xpu, idim, int> pivot = pivot_tblob.FlatToKD<xpu, idim, int>(s);
+
+    // calculate inverse(A) on CPU or GPU
+    batch_inverse(inv_A, LU, pivot, ctx);
+    laop::op(inputs[0].FlatToKD<xpu, idim + 1, OType>(s),
+             inv_A,
+             B,
+             X,
+             outputs[0].FlatToKD<xpu, odim + 1, OType>(s),
+             outputs[1].FlatToKD<xpu, odim + 1, OType>(s),
+             ctx,
+             attrs);
+  });
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_NUMPY_LINALG_NP_SOLVE_INL_H_
diff --git a/src/operator/numpy/linalg/np_solve.cc b/src/operator/numpy/linalg/np_solve.cc
new file mode 100644
index 000000000000..55d02f18d4dc
--- /dev/null
+++ b/src/operator/numpy/linalg/np_solve.cc
@@ -0,0 +1,116 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_solve.cc
+ * \brief CPU implementation placeholder of Solve Operator
+ */
+#include <mxnet/operator_util.h>
+#include <vector>
+#include "../../mxnet_op.h"
+#include "../../operator_common.h"
+#include "../../elemwise_op_common.h"
+
+#include "./np_solve-inl.h"
+
+namespace mxnet {
+namespace op {
+
+inline bool SolveOpShape(const nnvm::NodeAttrs &attrs,
+                         std::vector<mxnet::TShape> *in_attrs,
+                         std::vector<mxnet::TShape> *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 2U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  const mxnet::TShape& in_a_shape = (*in_attrs)[0];
+  const mxnet::TShape& in_b_shape = (*in_attrs)[1];
+  if (!ndim_is_known(in_a_shape)) { return false; }
+  int in_a_ndim = in_a_shape.ndim(), in_b_ndim = in_b_shape.ndim();
+
+  CHECK_GE(in_a_ndim, 2)
+    << "Array must be at least two-dimensional";
+  CHECK_EQ(in_a_shape[in_a_ndim - 2], in_a_shape[in_a_ndim - 1])
+    << "Input A's last two dimension must be equal";
+
+  if (in_a_ndim == in_b_ndim + 1) {
+    CHECK_EQ(in_a_shape[in_a_ndim - 1], in_b_shape[in_b_ndim - 1])
+      << "Input A's and B's last dimension must be equal";
+  } else if (in_a_ndim == in_b_ndim) {
+    CHECK_EQ(in_a_shape[in_a_ndim - 1], in_b_shape[in_b_ndim - 2])
+      << "Input A's and B's last second dimension must be equal";
+  } else {
+    dmlc::LogMessageFatal(__FILE__, __LINE__).stream() << "A's and B's dimensions don't match";
+  }
+  for (int i = 0; i < in_a_ndim - 2; ++i) {
+    CHECK_EQ(in_a_shape[i], in_b_shape[i]) << "A's and B's dimensions don't match";
+  }
+
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, in_b_shape);
+  return !mxnet::op::shape_is_none(in_b_shape) && !mxnet::op::shape_is_none(out_attrs->at(0));
+}
+
+inline bool SolveOpType(const nnvm::NodeAttrs& attrs,
+                        std::vector<int>* in_attrs,
+                        std::vector<int>* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 2U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  int a_type = in_attrs->at(0);
+  int b_type = in_attrs->at(1);
+  // unsupport float16
+  CHECK_NE(a_type, mshadow::kFloat16)
+    << "array type float16 is unsupported in linalg";
+  CHECK_NE(b_type, mshadow::kFloat16)
+    << "array type float16 is unsupported in linalg";
+  if (mshadow::kFloat32 == a_type && mshadow::kFloat32 == b_type) {
+    TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(1));
+  } else {
+    TYPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::kFloat64);
+  }
+  return out_attrs->at(0) != -1;
+}
+
+NNVM_REGISTER_OP(_npi_solve)
+.describe(R"code()code" ADD_FILELINE)
+.set_num_inputs(2)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames", [](const NodeAttrs& attrs){
+  return std::vector<std::string>{"A", "B"};
+})
+.set_attr<mxnet::FInferShape>("FInferShape", SolveOpShape)
+.set_attr<nnvm::FInferType>("FInferType", SolveOpType)
+.set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& attrs){
+  return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+})
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
+.set_attr<FCompute>("FCompute<cpu>", LaOpForwardSolve<cpu, 2, 2, 2, 1, solve>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseInOut{"_backward_npi_solve"})
+.add_argument("A", "NDArray-or-Symbol", "Tensor of square matrix")
+.add_argument("B", "NDArray-or-Symbol", "Tensor of right side vector");
+
+NNVM_REGISTER_OP(_backward_npi_solve)
+.set_num_inputs(4)
+.set_num_outputs(2)
+.set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& ){
+  return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+})
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FCompute>("FCompute<cpu>", LaOpBackwardSolve<cpu, 2, 2, 4, 2, solve_backward>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/linalg/np_solve.cu b/src/operator/numpy/linalg/np_solve.cu
new file mode 100644
index 000000000000..b849cf55540e
--- /dev/null
+++ b/src/operator/numpy/linalg/np_solve.cu
@@ -0,0 +1,43 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file np_solve.cu
+ * \brief GPU implementation of the Solve Operator
+ */
+
+#include <mxnet/operator_util.h>
+#include <vector>
+#include "./np_solve-inl.h"
+
+namespace mxnet {
+namespace op {
+
+#if MXNET_USE_CUSOLVER == 1
+
+NNVM_REGISTER_OP(_npi_solve)
+.set_attr<FCompute>("FCompute<gpu>", LaOpForwardSolve<gpu, 2, 2, 2, 1, solve>);
+
+NNVM_REGISTER_OP(_backward_npi_solve)
+.set_attr<FCompute>("FCompute<gpu>", LaOpBackwardSolve<gpu, 2, 2, 4, 2, solve_backward>);
+
+#endif
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index 53bd8f4d9235..6b5efa0c96b0 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -319,6 +319,27 @@ def _add_workload_linalg_inv():
     OpArgMngr.add_workload('linalg.inv', np.array(_np.ones((0, 1, 1)), dtype=np.float64))
 
 
+def _add_workload_linalg_solve():
+    shapes = [(0,0), (1,1), (5,5), (20,20), (3,5,5), (3,0,0), (2,20,20), (0,20,20), (2,3,20,20)]
+    nrhs = (0, 1, 2, 10)
+    dtypes = (np.float32, np.float64)
+    for dtype, shape in itertools.product(dtypes, shapes):
+        a = _np.random.rand(*shape)
+        shape_b = list(shape)
+        shape_b[-1] = 1
+        x = _np.random.rand(*shape_b)
+        b = _np.matmul(a, x)
+        shape_b.pop()
+        b = b.reshape(shape_b)
+        OpArgMngr.add_workload('linalg.solve', np.array(a, dtype=dtype), np.array(b, dtype=dtype))
+        for nrh in nrhs:
+            shape_b = list(shape)
+            shape_b[-1] = nrh
+            x = _np.random.rand(*shape_b)
+            b = _np.matmul(a, x)
+            OpArgMngr.add_workload('linalg.solve', np.array(a, dtype=dtype), np.array(b, dtype=dtype))
+
+
 def _add_workload_linalg_det():
     OpArgMngr.add_workload('linalg.det', np.array(_np.ones((2, 2)), dtype=np.float32))
     OpArgMngr.add_workload('linalg.det', np.array(_np.ones((0, 1, 1)), dtype=np.float64))
@@ -1374,6 +1395,7 @@ def _prepare_workloads():
     _add_workload_linalg_norm()
     _add_workload_linalg_cholesky()
     _add_workload_linalg_inv()
+    _add_workload_linalg_solve()
     _add_workload_linalg_det()
     _add_workload_linalg_slogdet()
     _add_workload_trace()
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 5c14f0d6c701..6b62a4386524 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -3506,6 +3506,97 @@ def check_inv(A_inv, data_np):
         check_inv(A_inv, data_np)
 
 
+@with_seed()
+@use_np
+def test_np_linalg_solve():
+    class TestSolve(HybridBlock):
+        def __init__(self):
+            super(TestSolve, self).__init__()
+        
+        def hybrid_forward(self, F, a, b):
+            return F.np.linalg.solve(a, b)
+
+    def check_solve(x, a_np, b_np):
+        try:
+            x_expected = _np.linalg.solve(a_np, b_np)
+        except Exception as e:
+            print("a:", a_np)
+            print("a shape:", a_np.shape)
+            print("b", b_np)
+            print("b shape:", b_np.shape)
+            print(e)
+        else:
+            assert x.shape == x_expected.shape
+            assert_almost_equal(x.asnumpy(), x_expected, rtol=rtol, atol=atol)
+
+    def get_grad_b(A, X):
+        dX = _np.ones_like(X)
+        A_inv = _np.linalg.inv(A)
+        A_inv_trans = _np.swapaxes(A_inv, -1, -2)
+        return _np.matmul(A_inv_trans, dX)
+
+    shapes = [
+        (0, 0),
+        (1, 1),
+        (3, 3),
+        (20, 20),
+        (3, 20, 20),
+        (1, 0, 0),
+        (0, 1, 1),
+        (0, 5, 3, 3),
+        (5, 0, 0, 0),
+        (2, 3, 10, 10)
+    ]
+    nrhs = (-1, 0, 1, 2, 5)
+    dtypes = ['float32', 'float64']
+    for hybridize, shape, dtype, nrh in itertools.product([False, True], shapes, dtypes, nrhs):
+        rtol = 1e-3 
+        atol = 1e-5
+        test_solve = TestSolve()
+        if hybridize:
+            test_solve.hybridize()
+
+        if 0 in shape:
+            a = _np.ones(shape)
+            b = _np.ones(shape)
+        else:
+            shape_a = shape
+            a = _np.random.rand(*shape_a)
+            shape_b = list(shape_a)
+            if nrh == -1:
+                shape_b[-1] = 1
+                x = _np.random.rand(*shape_b)
+                b = _np.matmul(a, x)
+                shape_b.pop()
+                b = b.reshape(shape_b)
+            else :
+                shape_b[-1] = nrh
+                x = _np.random.rand(*shape_b)
+                b = _np.matmul(a, x)
+        a = np.array(a, dtype=dtype)
+        b = np.array(b, dtype=dtype)
+        a.attach_grad()
+        b.attach_grad()
+        with mx.autograd.record():
+            mx_out = test_solve(a, b)
+        # check solve validity
+        assert mx_out.shape == b.shape
+        check_solve(mx_out, a, b)
+
+        # check backward. backward does not support empty input
+        if 0 not in mx_out.shape:
+            if nrh != -1:
+                mx.autograd.backward(mx_out)
+                b_backward_expected = get_grad_b(a.asnumpy(), mx_out.asnumpy())
+                a_backward_expected = -_np.matmul(b_backward_expected, _np.swapaxes(mx_out, -1, -2).asnumpy())
+                assert_almost_equal(a.grad.asnumpy(), a_backward_expected, rtol=rtol, atol=atol)
+                assert_almost_equal(b.grad.asnumpy(), b_backward_expected, rtol=rtol, atol=atol)
+
+        # check imperative once again
+        mx_out = np.linalg.solve(a, b)
+        check_solve(mx_out, a, b)
+
+
 @with_seed()
 @use_np
 def test_np_linalg_det():

From 44cd63e5a8f6cd9c3d786974a487e5c993cbee59 Mon Sep 17 00:00:00 2001
From: Li Jun <875241499@qq.com>
Date: Mon, 9 Dec 2019 15:56:49 +0800
Subject: [PATCH 669/813] [Numpy] Implement numpy operator 'average' (#16720)

* Complete forward and backward

Add tests

Implement returned

Implement none weights returned

Replace reduce implementation with workspace version

Temp space can be requested only once.

Use FNumVisibleOutputs attribute, always return sum of weights

Fix bug

Fix GPU bug

Fix GPU bug

Formatting

Apply suggestions from code review

two blank lines between python functions

Fix pylint

Fix GPU bug

Add support for null and addTo reqs

Remove TBlob constructors

Refactor

* Retrigger

* Retrigger

* Retrigger

* Retrigger
---
 python/mxnet/ndarray/numpy/_op.py             | 100 ++++-
 python/mxnet/numpy/multiarray.py              | 101 ++++-
 python/mxnet/symbol/numpy/_symbol.py          |  98 ++++-
 src/operator/numpy/np_broadcast_reduce_op.h   | 348 ++++++++++++++++++
 .../numpy/np_broadcast_reduce_op_value.cc     |  71 ++++
 .../numpy/np_broadcast_reduce_op_value.cu     |   6 +
 tests/python/unittest/test_numpy_op.py        | 113 ++++++
 7 files changed, 823 insertions(+), 14 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index 0925f93a7740..538d5202942d 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -35,13 +35,14 @@
            'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'argsort', 'tensordot', 'histogram', 'eye',
            'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate', 'append',
-           'stack', 'vstack', 'column_stack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax',
-           'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'unravel_index', 'hanning', 'hamming', 'blackman',
-           'flip', 'around', 'hypot', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril',
-           'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less',
+           'stack', 'vstack', 'column_stack', 'dstack', 'average', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip',
+           'argmax', 'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'unravel_index', 'hanning', 'hamming',
+           'blackman', 'flip', 'around', 'hypot', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad', 'unique', 'lcm',
+           'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less',
            'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory',
            'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where']
 
+
 @set_module('mxnet.ndarray.numpy')
 def shape(a):
     """
@@ -3706,6 +3707,97 @@ def argmin(a, axis=None, out=None):
     return _npi.argmin(a, axis=axis, keepdims=False, out=out)
 
 
+@set_module('mxnet.ndarray.numpy')
+def average(a, axis=None, weights=None, returned=False, out=None):
+    """
+    Compute the weighted average along the specified axis.
+
+    Parameters
+    --------
+    a : ndarray
+        Array containing data to be averaged.
+    axis : None or int or tuple of ints, optional
+        Axis or axes along which to average a.
+        The default, axis=None, will average over
+        all of the elements of the input array.
+        If axis is negative it counts from the last to the first axis.
+        New in version 1.7.0.
+        If axis is a tuple of ints, averaging is
+        performed on all of the axes specified in the tuple
+        instead of a single axis or all the axes as before.
+    weights : ndarray, optional
+        An array of weights associated with the values in a, must be the same dtype with a.
+        Each value in a contributes to the average according to its associated weight.
+        The weights array can either be 1-D (in which case its length must be
+        the size of a along the given axis) or of the same shape as a.
+        If weights=None, then all data in a are assumed to have a weight equal to one.
+        The 1-D calculation is: avg = sum(a * weights) / sum(weights)
+        The only constraint on weights is that sum(weights) must not be 0.
+    returned : bool, optional
+        Default is False.
+        If True, the tuple (average, sum_of_weights) is returned,
+        otherwise only the average is returned.
+        If weights=None, sum_of_weights is equivalent to
+        the number of elements over which the average is taken.
+    out : ndarray, optional
+        If provided, the calculation is done into this array.
+
+    Returns
+    --------
+    retval, [sum_of_weights] : ndarray
+        Return the average along the specified axis.
+        When returned is True, return a tuple with the average as the first element
+        and the sum of the weights as the second element. sum_of_weights is of the same type as retval.
+        If a is integral, the result dtype will be float32, otherwise it will be the same as dtype of a.
+
+    Raises
+    --------
+        MXNetError
+        - When all weights along axis sum to zero.
+        - When the length of 1D weights is not the same as the shape of a along axis.
+        - When given 1D weights, the axis is not specified or is not int.
+        - When the shape of weights and a differ, but weights are not 1D.
+
+    See also
+    --------
+        mean
+
+    Notes
+    --------
+    This function differs from the original `numpy.average`
+    <https://numpy.org/devdocs/reference/generated/numpy.average.html>`_ in
+    the following way(s):
+
+    - Does not guarantee the same behavior with numpy when given float16 dtype and overflow happens
+    - Does not support complex dtype
+    - The dtypes of a and weights must be the same
+    - Integral a results in float32 returned dtype, not float64
+
+    Examples
+    --------
+    >>> data = np.arange(1, 5)
+    >>> data
+    array([1., 2., 3., 4.])
+    >>> np.average(data)
+    array(2.5)
+    >>> np.average(np.arange(1, 11), weights=np.arange(10, 0, -1))
+    array(4.)
+    >>> data = np.arange(6).reshape((3,2))
+    >>> data
+    array([[0., 1.],
+           [2., 3.],
+           [4., 5.]])
+    >>> weights = np.array([0.25, 0.75])
+    array([0.25, 0.75])
+    >>> np.average(data, axis=1, weights=weights)
+    array([0.75, 2.75, 4.75])
+    """
+    if weights is None:
+        return _npi.average(a, axis=axis, weights=None, returned=returned, weighted=False, out=out)
+    else:
+        return _npi.average(a, axis=axis, weights=weights, returned=returned, out=out)
+
+
 @set_module('mxnet.ndarray.numpy')
 def mean(a, axis=None, dtype=None, out=None, keepdims=False):  # pylint: disable=arguments-differ
     """
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 5aecae4c3997..aa0762bf0e3f 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -53,12 +53,13 @@
            'degrees', 'log2', 'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative',
            'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'append', 'argsort',
            'tensordot', 'histogram', 'eye', 'linspace', 'logspace', 'expand_dims', 'tile', 'arange',
-           'split', 'vsplit', 'concatenate', 'stack', 'vstack', 'column_stack', 'dstack', 'mean', 'maximum', 'minimum',
-           'swapaxes', 'clip', 'argmax', 'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'unravel_index',
-           'hanning', 'hamming', 'blackman', 'flip', 'around', 'arctan2', 'hypot', 'bitwise_xor', 'bitwise_or',
-           'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer',
-           'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum',
-           'true_divide', 'nonzero', 'shares_memory', 'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where']
+           'split', 'vsplit', 'concatenate', 'stack', 'vstack', 'column_stack', 'dstack', 'average', 'mean',
+           'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'argmin', 'std', 'var', 'indices', 'copysign',
+           'ravel', 'unravel_index', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'arctan2', 'hypot',
+           'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
+           'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal',
+           'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory',
+           'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -5476,6 +5477,94 @@ def argmin(a, axis=None, out=None):
     return _mx_nd_np.argmin(a, axis, out)
 
 
+@set_module('mxnet.numpy')
+def average(a, axis=None, weights=None, returned=False, out=None):
+    """
+    Compute the weighted average along the specified axis.
+
+    Parameters
+    --------
+    a : ndarray
+        Array containing data to be averaged.
+    axis : None or int or tuple of ints, optional
+        Axis or axes along which to average a.
+        The default, axis=None, will average over
+        all of the elements of the input array.
+        If axis is negative it counts from the last to the first axis.
+        New in version 1.7.0.
+        If axis is a tuple of ints, averaging is
+        performed on all of the axes specified in the tuple
+        instead of a single axis or all the axes as before.
+    weights : ndarray, optional
+        An array of weights associated with the values in a, must be the same dtype with a.
+        Each value in a contributes to the average according to its associated weight.
+        The weights array can either be 1-D (in which case its length must be
+        the size of a along the given axis) or of the same shape as a.
+        If weights=None, then all data in a are assumed to have a weight equal to one.
+        The 1-D calculation is: avg = sum(a * weights) / sum(weights)
+        The only constraint on weights is that sum(weights) must not be 0.
+    returned : bool, optional
+        Default is False.
+        If True, the tuple (average, sum_of_weights) is returned,
+        otherwise only the average is returned.
+        If weights=None, sum_of_weights is equivalent to
+        the number of elements over which the average is taken.
+    out : ndarray, optional
+        If provided, the calculation is done into this array.
+
+    Returns
+    --------
+    retval, [sum_of_weights] : ndarray
+        Return the average along the specified axis.
+        When returned is True, return a tuple with the average as the first element
+        and the sum of the weights as the second element. sum_of_weights is of the same type as retval.
+        If a is integral, the result dtype will be float32, otherwise it will be the same as dtype of a.
+
+    Raises
+    --------
+        MXNetError
+        - When all weights along axis sum to zero.
+        - When the length of 1D weights is not the same as the shape of a along axis.
+        - When given 1D weights, the axis is not specified or is not int.
+        - When the shape of weights and a differ, but weights are not 1D.
+
+    See also
+    --------
+        mean
+
+    Notes
+    --------
+    This function differs from the original `numpy.average`
+    <https://numpy.org/devdocs/reference/generated/numpy.average.html>`_ in
+    the following way(s):
+
+    - Does not guarantee the same behavior with numpy when given float16 dtype and overflow happens
+    - Does not support complex dtype
+    - The dtypes of a and weights must be the same
+    - Integral a results in float32 returned dtype, not float64
+
+    Examples
+    --------
+    >>> data = np.arange(1, 5)
+    >>> data
+    array([1., 2., 3., 4.])
+    >>> np.average(data)
+    array(2.5)
+    >>> np.average(np.arange(1, 11), weights=np.arange(10, 0, -1))
+    array(4.)
+    >>> data = np.arange(6).reshape((3,2))
+    >>> data
+    array([[0., 1.],
+           [2., 3.],
+           [4., 5.]])
+    >>> weights = np.array([0.25, 0.75])
+    array([0.25, 0.75])
+    >>> np.average(data, axis=1, weights=weights)
+    array([0.75, 2.75, 4.75])
+    """
+    return _mx_nd_np.average(a, axis=axis, weights=weights, returned=returned, out=out)
+
+
 @set_module('mxnet.numpy')
 def mean(a, axis=None, dtype=None, out=None, keepdims=False):  # pylint: disable=arguments-differ
     """
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 8303f3246e87..4b06bbec7cae 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -43,10 +43,10 @@
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'argsort', 'tensordot', 'histogram', 'eye',
            'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate', 'append',
-           'stack', 'vstack', 'column_stack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax',
-           'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'unravel_index', 'hanning', 'hamming', 'blackman',
-           'flip', 'around', 'hypot', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril',
-           'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less',
+           'stack', 'vstack', 'column_stack', 'dstack', 'average', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip',
+           'argmax', 'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'unravel_index', 'hanning', 'hamming',
+           'blackman', 'flip', 'around', 'hypot', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad', 'unique', 'lcm',
+           'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less',
            'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'shares_memory',
            'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where']
 
@@ -3639,6 +3639,96 @@ def argmin(a, axis=None, out=None):
     return _npi.argmin(a, axis=axis, keepdims=False, out=out)
 
 
+def average(a, axis=None, weights=None, returned=False, out=None):
+    """
+    Compute the weighted average along the specified axis.
+
+    Parameters
+    --------
+    a : _Symbol
+        Array containing data to be averaged.
+    axis : None or int or tuple of ints, optional
+        Axis or axes along which to average a.
+        The default, axis=None, will average over
+        all of the elements of the input array.
+        If axis is negative it counts from the last to the first axis.
+        New in version 1.7.0.
+        If axis is a tuple of ints, averaging is
+        performed on all of the axes specified in the tuple
+        instead of a single axis or all the axes as before.
+    weights : _Symbol, optional
+        An array of weights associated with the values in a, must be the same dtype with a.
+        Each value in a contributes to the average according to its associated weight.
+        The weights array can either be 1-D (in which case its length must be
+        the size of a along the given axis) or of the same shape as a.
+        If weights=None, then all data in a are assumed to have a weight equal to one.
+        The 1-D calculation is: avg = sum(a * weights) / sum(weights)
+        The only constraint on weights is that sum(weights) must not be 0.
+    returned : bool, optional
+        Default is False.
+        If True, the tuple (average, sum_of_weights) is returned,
+        otherwise only the average is returned.
+        If weights=None, sum_of_weights is equivalent to
+        the number of elements over which the average is taken.
+    out : _Symbol, optional
+        If provided, the calculation is done into this array.
+
+    Returns
+    --------
+    retval, [sum_of_weights] : _Symbol
+        Return the average along the specified axis.
+        When returned is True, return a tuple with the average as the first element
+        and the sum of the weights as the second element. sum_of_weights is of the same type as retval.
+        If a is integral, the result dtype will be float32, otherwise it will be the same as dtype of a.
+
+    Raises
+    --------
+        MXNetError
+        - When all weights along axis sum to zero.
+        - When the length of 1D weights is not the same as the shape of a along axis.
+        - When given 1D weights, the axis is not specified or is not int.
+        - When the shape of weights and a differ, but weights are not 1D.
+
+    See also
+    --------
+        mean
+
+    Notes
+    --------
+    This function differs from the original `numpy.average`
+    <https://numpy.org/devdocs/reference/generated/numpy.average.html>`_ in
+    the following way(s):
+
+    - Does not guarantee the same behavior with numpy when given float16 dtype and overflow happens
+    - Does not support complex dtype
+    - The dtypes of a and weights must be the same
+    - Integral a results in float32 returned dtype, not float64
+
+    Examples
+    --------
+    >>> data = np.arange(1, 5)
+    >>> data
+    array([1., 2., 3., 4.])
+    >>> np.average(data)
+    array(2.5)
+    >>> np.average(np.arange(1, 11), weights=np.arange(10, 0, -1))
+    array(4.)
+    >>> data = np.arange(6).reshape((3,2))
+    >>> data
+    array([[0., 1.],
+           [2., 3.],
+           [4., 5.]])
+    >>> weights = np.array([0.25, 0.75])
+    array([0.25, 0.75])
+    >>> np.average(data, axis=1, weights=weights)
+    array([0.75, 2.75, 4.75])
+    """
+    if weights is None:
+        return _npi.average(a, axis=axis, weights=None, returned=returned, weighted=False, out=out)
+    else:
+        return _npi.average(a, axis=axis, weights=weights, returned=returned, out=out)
+
+
 @set_module('mxnet.symbol.numpy')
 def mean(a, axis=None, dtype=None, out=None, keepdims=False):  # pylint: disable=arguments-differ
     """
diff --git a/src/operator/numpy/np_broadcast_reduce_op.h b/src/operator/numpy/np_broadcast_reduce_op.h
index 3566323f1eb3..df9a7c932490 100644
--- a/src/operator/numpy/np_broadcast_reduce_op.h
+++ b/src/operator/numpy/np_broadcast_reduce_op.h
@@ -30,6 +30,7 @@
 #include <string>
 #include "../nn/moments-inl.h"
 #include "../tensor/broadcast_reduce_op.h"
+#include "../tensor/elemwise_binary_broadcast_op.h"
 
 namespace mxnet {
 namespace op {
@@ -406,6 +407,353 @@ void ReduceAxesComputeWithWorkspaceImpl(const OpContext& ctx,
   });
 }
 
+struct NumpyWeightedAverageParam : public dmlc::Parameter<NumpyWeightedAverageParam> {
+  dmlc::optional<mxnet::Tuple<int>> axis;
+  bool returned;
+  bool weighted;
+
+  DMLC_DECLARE_PARAMETER(NumpyWeightedAverageParam) {
+    DMLC_DECLARE_FIELD(axis)
+      .set_default(dmlc::optional<mxnet::Tuple<int>>())
+      .describe("Axis or axes along which a average is performed. "
+                "The default, axis=None, will average "
+                "all of the elements of the input array. If axis is negative it counts from the "
+                "last to the first axis.");
+    DMLC_DECLARE_FIELD(returned)
+      .set_default(false)
+      .describe("If True, the tuple (average, sum_of_weights) is returned,"
+                "otherwise only the average is returned."
+                "If weights=None, sum_of_weights is equivalent to"
+                "the number of elements over which the average is taken.");
+    DMLC_DECLARE_FIELD(weighted)
+      .set_default(true)
+      .describe("Auxiliary flag to deal with none weights.");
+  }
+};
+
+inline bool NumpyWeightedAverageShape(const nnvm::NodeAttrs& attrs,
+                                      std::vector<TShape> *in_attrs,
+                                      std::vector<TShape> *out_attrs) {
+  const auto& param = nnvm::get<NumpyWeightedAverageParam>(attrs.parsed);
+  CHECK_EQ(in_attrs->size(), (param.weighted ? 2U : 1U));
+  CHECK_EQ(out_attrs->size(), 2U);
+  if (!shape_is_known(in_attrs->at(0))) {
+    return false;
+  }
+
+  const TShape& a_shape = (*in_attrs)[0];
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0,
+                     NumpyReduceAxesShapeImpl(a_shape, param.axis, false));
+
+  if (param.weighted) {
+    const TShape& w_shape = (*in_attrs)[1];
+    if (w_shape.ndim() != a_shape.ndim()) {
+      CHECK_EQ(w_shape.ndim(), 1U)
+        << "1D weights expected when shapes of a and weights differ.";
+      CHECK_EQ(param.axis.has_value(), true)
+        << "Axis must be specified when shapes of a and weights differ.";
+      mxnet::Tuple<int> axes(param.axis.value());
+      CHECK_EQ(axes.ndim(), 1U) << "Axis must be int when shapes of a and weights differ.";
+      int red_axis = axes[0] < 0 ? axes[0] + a_shape.ndim() : axes[0];
+      CHECK_EQ(a_shape[red_axis], w_shape[0])
+        << "Length of weights not compatible with specified axis.";
+      SHAPE_ASSIGN_CHECK(*out_attrs, 1,
+                         NumpyReduceAxesShapeImpl(
+                           w_shape, dmlc::optional<mxnet::Tuple<int>>(), false));
+    } else {
+      for (int i = 0; i < w_shape.ndim(); i++) {
+        CHECK_EQ(w_shape[i], a_shape[i]);
+      }
+      SHAPE_ASSIGN_CHECK(*out_attrs, 1,
+                         NumpyReduceAxesShapeImpl(w_shape, param.axis, false));
+    }
+  } else {
+    SHAPE_ASSIGN_CHECK(*out_attrs, 1, TShape(0, -1));
+  }
+
+  return shape_is_known(out_attrs->at(0)) && shape_is_known(out_attrs->at(1));
+}
+
+template<int req, int NDim, bool onedim = false>
+struct avg_grad_a_kernel {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int i,
+                                  DType* out,
+                                  const DType* w,
+                                  const DType* scl,
+                                  const DType* ograd,
+                                  mshadow::Shape<NDim> small,
+                                  mshadow::Shape<NDim> big) {
+    // partial a = w / sum(w)
+    size_t big_idx = i;
+    size_t small_idx = i;
+    size_t big_stride = 1;
+    size_t small_stride = 1;
+    size_t red_axis_idx = 0;
+    for (int axis = NDim-1; axis >= 0; --axis) {
+      size_t axis_idx = big_idx % big[axis];
+      small_idx -= axis_idx * big_stride;
+      if (small[axis] != 1) {
+        small_idx += axis_idx * small_stride;
+      } else if (onedim && small[axis] != big[axis]) {
+        red_axis_idx = axis_idx;
+      }
+      big_idx /= big[axis];
+      big_stride *= big[axis];
+      small_stride *= small[axis];
+    }
+    if (onedim) {
+      KERNEL_ASSIGN(out[i], req, (ograd[small_idx] * (w[red_axis_idx] / *scl)));
+    } else {
+      KERNEL_ASSIGN(out[i], req, (ograd[small_idx] * (w[i] / scl[small_idx])));
+    }
+  }
+};
+
+template<int req, int NDim>
+struct avg_grad_w_kernel {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int i,
+                                  DType* out,
+                                  const DType* a,
+                                  const DType* scl,
+                                  const DType* sum_of_wa,
+                                  const DType* ograd,
+                                  mshadow::Shape<NDim> small,
+                                  mshadow::Shape<NDim> big) {
+    // partial w = (a * sum(w) - sum(a*w)) / (sum(w) * sum(w))
+    size_t big_idx = i;
+    size_t small_idx = i;
+    size_t big_stride = 1;
+    size_t small_stride = 1;
+    for (int axis = NDim-1; axis >= 0; --axis) {
+      size_t axis_idx = big_idx % big[axis];
+      small_idx -= axis_idx * big_stride;
+      if (small[axis] != 1) {
+        small_idx += axis_idx * small_stride;
+      }
+      big_idx /= big[axis];
+      big_stride *= big[axis];
+      small_stride *= small[axis];
+    }
+    DType ret = ograd[small_idx] *
+      (((a[i] * scl[small_idx] - sum_of_wa[small_idx]) / scl[small_idx]) / scl[small_idx]);
+    KERNEL_ASSIGN(out[i], req, ret);
+  }
+};
+
+template<int req, int NDim>
+struct avg_grad_w_1D_kernel {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int i,
+                                  DType* out,
+                                  const DType* a,
+                                  const DType* scl,
+                                  const DType* sum_of_wa,
+                                  const DType* ograd,
+                                  mshadow::Shape<NDim> big,
+                                  const int red_axis) {
+    DType scl_val = *scl;
+    size_t tail = 1;
+    size_t head = 1;
+    for (int axis = NDim-1; axis > red_axis; --axis) {
+      tail *= big[axis];
+    }
+    for (int axis = 0; axis < red_axis; ++axis) {
+      head *= big[axis];
+    }
+    DType ret = 0;
+    for (size_t j = 0; j < head; ++j) {
+      for (size_t k = 0; k < tail; ++k) {
+        size_t a_idx = j*(tail*big[red_axis]) + i * tail + k;
+        size_t small_idx = j*tail + k;
+        ret += (ograd[small_idx] *
+          (((a[a_idx] * scl_val - sum_of_wa[small_idx]) / scl_val) / scl_val));
+      }
+    }
+    KERNEL_ASSIGN(out[i], req, ret);
+  }
+};
+
+template<typename xpu, bool back = false>
+void NumpyWeightedAverageComputeImpl(const nnvm::NodeAttrs& attrs,
+                                     const OpContext& ctx,
+                                     const std::vector<TBlob>& inputs,
+                                     const std::vector<OpReqType>& req,
+                                     const std::vector<TBlob>& outputs,
+                                     const dmlc::optional<mxnet::Tuple<int>>& axis) {
+  using namespace mshadow;
+  using namespace mxnet_op;
+  Stream<xpu>* s = ctx.get_stream<xpu>();
+  const TBlob& data = inputs[0];
+  TShape small1 = NumpyReduceAxesShapeImpl(data.shape_, axis, true);
+  // Reshape weights
+  TShape small2 = small1;
+  TBlob weights = inputs[1];
+
+  bool one_dim = weights.shape_.ndim() != data.shape_.ndim();
+
+  int red_axis = -1;
+
+  if (one_dim) {
+    CHECK_EQ(weights.shape_.ndim(), 1U)
+      << "1D weights expected when shapes of a and weights differ.";
+    CHECK_EQ(axis.has_value(), true)
+      << "Axis must be specified when shapes of a and weights differ.";
+    Tuple<int> axes(axis.value());
+    CHECK_EQ(axes.ndim(), 1U)
+      << "Axis must be int when shapes of a and weights differ.";
+    red_axis = axes[0] < 0 ? axes[0] + data.shape_.ndim() : axes[0];
+    CHECK_EQ(weights.shape_[0], data.shape_[red_axis])
+      << "Length of weights not compatible with specified axis.";
+    TShape new_w_shape(data.shape_.ndim(), 1);
+    new_w_shape[red_axis] = weights.shape_[0];
+    weights = weights.reshape(new_w_shape);
+    small2 = TShape(new_w_shape.ndim(), 1);
+  }
+  MSHADOW_TYPE_SWITCH(data.type_flag_, DType, {
+    // Get temp space
+    size_t temp_data_size = data.shape_.Size() * sizeof(DType);
+    size_t temp_sum_size = small1.Size() * sizeof(DType);
+    TShape src_shape, dst_shape;
+    BroadcastReduceShapeCompact(data.shape_, small1, &src_shape, &dst_shape);
+    size_t workspace_size = 0;
+    MXNET_NDIM_SWITCH(dst_shape.ndim(), NDim, {
+      workspace_size = broadcast::ReduceWorkspaceSize<NDim, DType>(
+        s, dst_shape, {kWriteTo}, src_shape);
+    });
+    size_t temp_mem_size = temp_data_size + temp_sum_size + workspace_size;
+    Tensor<xpu, 1, char> temp_mem =
+    ctx.requested[0].get_space_typed<xpu, 1, char>(Shape1(temp_mem_size), s);
+    DType *temp_data_ptr = reinterpret_cast<DType*>(temp_mem.dptr_);
+    DType *temp_sum_ptr = reinterpret_cast<DType*>(temp_mem.dptr_ + temp_data_size);
+    char *workspace_ptr = temp_mem.dptr_ + temp_data_size + temp_sum_size;
+    Tensor<xpu, 1, char> workspace(workspace_ptr, Shape1(workspace_size), s);
+
+    // Compute weighted data
+    TBlob wa = TBlob(temp_data_ptr, data.shape_, xpu::kDevMask);
+    BinaryBroadcastCompute<xpu, mshadow_op::mul>(
+      attrs, ctx, {data, weights}, {kWriteTo}, {wa});
+
+    // Compute sum of weighted data
+    TBlob sum_of_wa = TBlob(temp_sum_ptr, small1, xpu::kDevMask);
+    ReduceAxesComputeWithWorkspaceImpl<xpu, mshadow_op::sum, true>(
+      ctx, {wa}, {kWriteTo}, {sum_of_wa}, workspace, src_shape, dst_shape);
+    if (!back) {
+      const TBlob& avg = outputs[0];
+      const TBlob& sum_of_weights = outputs[1];
+      TShape w_src_shape, w_dst_shape;
+      BroadcastReduceShapeCompact(weights.shape_, small2, &w_src_shape, &w_dst_shape);
+      // Compute sum of weight
+      TBlob scl = sum_of_weights.reshape(small2);
+      ReduceAxesComputeWithWorkspaceImpl<xpu, mshadow_op::sum, true>(
+        ctx, {weights}, {kWriteTo}, {scl}, workspace, w_src_shape, w_dst_shape);
+
+      // Compute avg and assign output
+      BinaryBroadcastCompute<xpu, mshadow_op::div>(
+        attrs, ctx, {sum_of_wa, scl}, req, {avg.reshape(small1)});
+    } else {
+      // Compute and assign the derivatives of a and weights
+      const TBlob& igrad_a = outputs[0];
+      const TBlob& igrad_w = outputs[1];
+      const TBlob& scl = inputs[2];
+      const TBlob& ograd = inputs[3];
+      MXNET_NDIM_SWITCH(igrad_a.shape_.ndim(), NDim, {
+        MXNET_ASSIGN_REQ_SWITCH(req[0], req_a, {
+          if (one_dim) {
+            // 1D weights
+            Kernel<avg_grad_a_kernel<req_a, NDim, true>, xpu>::Launch(
+                s, igrad_a.shape_.Size(), igrad_a.dptr<DType>(),
+                weights.dptr<DType>(), scl.dptr<DType>(), ograd.dptr<DType>(),
+                small1.get<NDim>(),
+                igrad_a.shape_.get<NDim>());
+          } else {
+            Kernel<avg_grad_a_kernel<req_a, NDim, false>, xpu>::Launch(
+                s, igrad_a.shape_.Size(), igrad_a.dptr<DType>(),
+                weights.dptr<DType>(), scl.dptr<DType>(), ograd.dptr<DType>(),
+                small1.get<NDim>(),
+                igrad_a.shape_.get<NDim>());
+          }
+        });
+        MXNET_ASSIGN_REQ_SWITCH(req[1], req_w, {
+          if (one_dim) {
+            Kernel<avg_grad_w_1D_kernel<req_w, NDim>, xpu>::Launch(
+                s, igrad_w.shape_.Size(), igrad_w.dptr<DType>(),
+                data.dptr<DType>(), scl.dptr<DType>(), sum_of_wa.dptr<DType>(), ograd.dptr<DType>(),
+                data.shape_.get<NDim>(),
+                red_axis);
+          } else {
+            Kernel<avg_grad_w_kernel<req_w, NDim>, xpu>::Launch(
+                s, igrad_w.shape_.Size(), igrad_w.dptr<DType>(),
+                data.dptr<DType>(), scl.dptr<DType>(), sum_of_wa.dptr<DType>(), ograd.dptr<DType>(),
+                small1.get<NDim>(),
+                igrad_w.shape_.get<NDim>());
+          }
+        });
+      })
+    }
+  });
+}
+
+template<typename xpu>
+void NumpyWeightedAverageForward(const nnvm::NodeAttrs& attrs,
+                                 const OpContext& ctx,
+                                 const std::vector<TBlob>& inputs,
+                                 const std::vector<OpReqType>& req,
+                                 const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mshadow::expr;
+  if (req[0] == kNullOp) return;
+  CHECK_NE(req[0], kWriteInplace) << "Average does not support write in-place";
+  const auto& param = nnvm::get<NumpyWeightedAverageParam>(attrs.parsed);
+  const TBlob& data = inputs[0];
+  MSHADOW_TYPE_SWITCH(data.type_flag_, DType, {
+    if (!param.weighted) {
+      TShape small = NumpyReduceAxesShapeImpl(data.shape_, param.axis, true);
+      // Compute sum of weights which equals to the product of sizes of reduced axes
+      Stream<xpu>* s = ctx.get_stream<xpu>();
+      auto ret = outputs[1].FlatTo1D<xpu, DType>(s);
+      ret = scalar<DType>(data.shape_.Size()/small.Size());
+      // Compute mean
+      ReduceAxesComputeImpl<xpu, mshadow_op::sum, true, true>(
+        ctx, inputs, req, {outputs[0]}, small);
+    } else {
+      NumpyWeightedAverageComputeImpl<xpu>(
+        attrs, ctx, inputs, req, outputs, param.axis);
+    }
+  });
+}
+
+template<typename xpu>
+void NumpyWeightedAverageBackward(const nnvm::NodeAttrs& attrs,
+                                  const OpContext& ctx,
+                                  const std::vector<TBlob>& inputs,
+                                  const std::vector<OpReqType>& req,
+                                  const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mshadow::expr;
+  const auto& param = nnvm::get<NumpyWeightedAverageParam>(attrs.parsed);
+  if (req[0] == kNullOp && !param.weighted) return;
+  CHECK_EQ(inputs.size(), (param.weighted ? 6U : 5U));
+  CHECK_EQ(outputs.size(), (param.weighted ? 2U : 1U));
+  const TBlob& ograd = inputs[0];
+  const TBlob& data = inputs[2];
+  MSHADOW_TYPE_SWITCH(data.type_flag_, DType, {
+    if (!param.weighted) {
+      TShape small = NumpyReduceAxesShapeImpl(outputs[0].shape_, param.axis, true);
+      Stream<xpu>* s = ctx.get_stream<xpu>();
+      auto ograd_tensor = ograd.FlatTo1D<xpu, DType>(s);
+      ograd_tensor /= scalar<DType>(data.shape_.Size()/small.Size());
+      BroadcastComputeImpl<xpu>(attrs, ctx, {ograd}, req, {outputs[0]}, small);
+    } else {
+      const TBlob& weights = inputs[3];
+      const TBlob& scl = inputs[5];
+      NumpyWeightedAverageComputeImpl<xpu, true>(
+        attrs, ctx, {data, weights, scl, ograd}, req, outputs, param.axis);
+    }
+  });
+}
+
 template<typename xpu, bool sqrt>
 void NumpyMomentsForward(const nnvm::NodeAttrs& attrs,
                          const OpContext& ctx,
diff --git a/src/operator/numpy/np_broadcast_reduce_op_value.cc b/src/operator/numpy/np_broadcast_reduce_op_value.cc
index fb133568a7a5..2a1bc5261701 100644
--- a/src/operator/numpy/np_broadcast_reduce_op_value.cc
+++ b/src/operator/numpy/np_broadcast_reduce_op_value.cc
@@ -35,6 +35,7 @@ namespace op {
 DMLC_REGISTER_PARAMETER(NumpyReduceAxesParam);
 DMLC_REGISTER_PARAMETER(NumpyReduceAxesNoDTypeParam);
 DMLC_REGISTER_PARAMETER(NumpyMomentsParam);
+DMLC_REGISTER_PARAMETER(NumpyWeightedAverageParam);
 
 inline bool NumpySumType(const nnvm::NodeAttrs& attrs,
                          std::vector<int> *in_attrs,
@@ -249,6 +250,76 @@ inline bool IsIntType(const int dtype) {
           dtype == mshadow::kInt64);
 }
 
+inline bool NumpyWeightedAverageType(const nnvm::NodeAttrs& attrs,
+                                     std::vector<int> *in_attrs,
+                                     std::vector<int> *out_attrs) {
+  const auto &param = nnvm::get<NumpyWeightedAverageParam>(attrs.parsed);
+  CHECK_EQ(in_attrs->size(), (param.weighted ? 2U : 1U));
+  CHECK_EQ(out_attrs->size(), 2U);
+
+  TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
+  if (param.weighted) {
+    TYPE_ASSIGN_CHECK(*in_attrs, 1, in_attrs->at(0));
+  }
+  TYPE_ASSIGN_CHECK(*out_attrs, 1, in_attrs->at(0));
+
+  return in_attrs->at(0) != -1 && out_attrs->at(0) != -1 &&
+         (!param.weighted || (in_attrs->at(1) != -1)) &&
+         out_attrs->at(1) != -1;
+}
+
+NNVM_REGISTER_OP(_npi_average)
+.set_num_inputs(
+  [](const NodeAttrs& attrs) {
+    const auto& param = nnvm::get<NumpyWeightedAverageParam>(attrs.parsed);
+    return param.weighted ? 2 : 1;
+  })
+.set_num_outputs(2)
+.set_attr<nnvm::FNumVisibleOutputs>("FNumVisibleOutputs",
+  [](const NodeAttrs& attrs) {
+    const auto& param = nnvm::get<NumpyWeightedAverageParam>(attrs.parsed);
+    return param.returned ? 2 : 1;
+  })
+.set_attr_parser(ParamParser<NumpyWeightedAverageParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyWeightedAverageShape)
+.set_attr<nnvm::FInferType>("FInferType", NumpyWeightedAverageType)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    const auto& param = nnvm::get<NumpyWeightedAverageParam>(attrs.parsed);
+    return param.weighted ?
+    std::vector<std::string>{"a", "weights"} :
+    std::vector<std::string>{"a"};
+  })
+.add_argument("a", "NDArray-or-Symbol", "The input")
+.add_argument("weights", "NDArray-or-Symbol", "The weights to calculate average")
+.add_arguments(NumpyWeightedAverageParam::__FIELDS__())
+.set_attr<FCompute>("FCompute<cpu>", NumpyWeightedAverageForward<cpu>)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseInOut{"_backward_np_average"});
+
+NNVM_REGISTER_OP(_backward_np_average)
+.set_num_outputs(
+  [](const NodeAttrs& attrs) {
+    const auto& param = nnvm::get<NumpyWeightedAverageParam>(attrs.parsed);
+    return param.weighted ? 2 : 1;
+  })
+.set_attr_parser(ParamParser<NumpyWeightedAverageParam>)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_num_inputs(
+  [](const NodeAttrs& attrs) {
+    const auto& param = nnvm::get<NumpyWeightedAverageParam>(attrs.parsed);
+    return param.weighted ? 6 : 5;
+  })
+.set_attr<FCompute>("FCompute<cpu>", NumpyWeightedAverageBackward<cpu>)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+});
+
 inline bool NumpyMeanType(const nnvm::NodeAttrs& attrs,
                           std::vector<int> *in_attrs,
                           std::vector<int> *out_attrs) {
diff --git a/src/operator/numpy/np_broadcast_reduce_op_value.cu b/src/operator/numpy/np_broadcast_reduce_op_value.cu
index 53e78787d47d..56194ff34a7e 100644
--- a/src/operator/numpy/np_broadcast_reduce_op_value.cu
+++ b/src/operator/numpy/np_broadcast_reduce_op_value.cu
@@ -50,6 +50,12 @@ NNVM_REGISTER_OP(_np_prod)
 NNVM_REGISTER_OP(_backward_np_prod)
 .set_attr<FCompute>("FCompute<gpu>", NumpyReduceAxesBackwardUseInOut<gpu, mshadow_op::rdiv>);
 
+NNVM_REGISTER_OP(_npi_average)
+.set_attr<FCompute>("FCompute<gpu>", NumpyWeightedAverageForward<gpu>);
+
+NNVM_REGISTER_OP(_backward_np_average)
+.set_attr<FCompute>("FCompute<gpu>", NumpyWeightedAverageBackward<gpu>);
+
 NNVM_REGISTER_OP(_npi_mean)
 .set_attr<FCompute>("FCompute<gpu>", NumpyReduceAxesCompute<gpu, mshadow_op::sum, true, true>);
 
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 6b62a4386524..549141b92c4c 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -600,6 +600,119 @@ def _test_np_exception(func, shape, dim):
                 _test_np_exception(func, shape, dim)
 
 
+@with_seed()
+@use_np
+def test_np_average():
+    class TestAverage(HybridBlock):
+        def __init__(self, axis=None, returned=False):
+            super(TestAverage, self).__init__()
+            # necessary initializations
+            self._axis = axis
+            self._returned = returned
+             
+        def hybrid_forward(self, F, a, weights):
+            return F.np.average(a, weights=weights, axis=self._axis, returned=self._returned)
+    
+    def avg_backward(a, w, avg, axes, init_a_grad=None, init_w_grad=None):
+        # avg = sum(a * w) / sum(w)
+        if axes is not None and not isinstance(axes, tuple) and axes < 0:
+            axes += a.ndim
+        if w is None:
+            a_grad = _np.ones(shape=a.shape, dtype=a.dtype)/(a.size/avg.size)
+            if init_a_grad is not None:
+                a_grad += init_a_grad.asnumpy()
+            return [a_grad, None]
+        onedim = a.ndim != w.ndim
+        if onedim:
+            new_shape = [a.shape[i] if i == axes else 1 for i in range(a.ndim)]
+            w = w.reshape(new_shape)
+            w = _np.broadcast_to(w, a.shape)
+       
+        # partial a = w / sum(w)
+        # partial w = (a*sum(w) - sum(a*w)) / (sum(w) * sum(w))
+        scl = _np.sum(w, axis=axes, keepdims=True)
+        a_grad = _np.divide(w, scl)
+        w_grad = _np.divide(a*scl-_np.sum(a*w, axis=axes, keepdims=True), scl*scl)
+        
+        if onedim:
+            axis = list(range(a.ndim))
+            axis.remove(axes)
+            w_grad = _np.sum(w_grad, axis=tuple(axis))
+        if init_a_grad is not None:
+            a_grad += init_a_grad.asnumpy()
+        if init_w_grad is not None:
+            w_grad += init_w_grad.asnumpy()
+        return [a_grad, w_grad]
+
+    tensor_shapes = [
+        ((3, 5), (3, 5), None),  # (a_shape, w_shape, axes)
+        ((4, 5, 6), (4, 5, 6), (0, 2)),
+        ((3,), (3,), 0),
+        ((2, 3), (3,), 1),
+        ((2, 3, 4), (2,), 0),
+        ((2, 3, 4), (3,), 1),
+        ((2, 3, 4), (4,), -1),
+        ((2, 3, 4, 5), (5,), 3)
+    ]
+
+    flags = [True, False]
+    dtypes = ['float32', 'float64']
+    reqs = ['null', 'add', 'write']
+    for hybridize, returned, (a_shape, w_shape, axes), dtype, is_weighted, req_a in \
+        itertools.product(flags, flags, tensor_shapes, dtypes, flags, reqs):
+        if req_a == 'null' and not is_weighted:
+            continue
+        rtol, atol = 1e-3, 1e-4
+        test_average = TestAverage(axes, returned)
+        if hybridize:
+            test_average.hybridize()
+        a = np.random.uniform(-1.0, 1.0, size=a_shape, dtype=dtype)
+        a.attach_grad(req_a)
+        init_a_grad = np.random.uniform(-1.0, 1.0, size=a_shape, dtype=dtype) if req_a == 'add' else None
+        init_w_grad = None
+        req_w = req_a
+        w, np_w = None, None
+        if is_weighted:
+            w = np.random.uniform(-1.0, 1.0, size=w_shape, dtype=dtype)
+            if req_a == 'null':
+                req_w = random.choice(['add', 'write'])
+            w.attach_grad(req_w)
+            if req_w == 'add':
+                init_w_grad = np.random.uniform(-1.0, 1.0, size=w_shape, dtype=dtype)
+            np_w = w.asnumpy()
+        np_out = _np.average(a.asnumpy(), axis=axes, weights=np_w, returned=returned)
+        with mx.autograd.record():
+            mx_out = test_average(a, w)
+        if returned:
+            np_out, np_sum_of_weights = np_out
+            mx_out, mx_sum_of_weights = mx_out
+            assert_almost_equal(mx_sum_of_weights.asnumpy(), np_sum_of_weights, rtol=rtol, atol=atol)
+        assert mx_out.shape == np_out.shape
+        assert_almost_equal(mx_out.asnumpy(), np_out.astype(dtype), rtol=rtol, atol=atol)
+        if req_a == 'add':
+            a.grad[:] = init_a_grad
+        if is_weighted and req_w == 'add':
+            w.grad[:] = init_w_grad
+        mx_out.backward()
+        # Code to get reference backward value
+        a_grad, w_grad = avg_backward(a.asnumpy(), np_w, np_out, axes, init_a_grad, init_w_grad)
+        if is_weighted:
+            assert_almost_equal(w.grad.asnumpy(), w_grad, rtol=rtol*10, atol=atol*10)
+        if req_a == 'null':
+            assert a.grad is None
+        else:
+            assert_almost_equal(a.grad.asnumpy(), a_grad, rtol=rtol, atol=atol)
+        
+        # Test imperative once again
+        np_out = _np.average(a.asnumpy(), weights=np_w, axis=axes, returned=returned)
+        mx_out = np.average(a, weights=w, axis=axes, returned=returned)
+        if returned:
+            np_out, np_sum_of_weights = np_out
+            mx_out, mx_sum_of_weights = mx_out
+            assert_almost_equal(mx_sum_of_weights.asnumpy(), np_sum_of_weights, rtol=rtol, atol=atol)
+        assert_almost_equal(mx_out.asnumpy(), np_out.astype(dtype), rtol=rtol, atol=atol)
+
+
 @with_seed()
 @use_np
 def test_np_mean():

From e40cceb271a10e15fbdc6a5e9e42e88694ca43cb Mon Sep 17 00:00:00 2001
From: Leonard Lausen <lausen@amazon.com>
Date: Mon, 9 Dec 2019 08:19:28 +0000
Subject: [PATCH 670/813] Upgrade 3rdparty/openmp to release_90 version
 (#17012)

Fixes https://github.com/apache/incubator-mxnet/issues/10856
---
 3rdparty/openmp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/3rdparty/openmp b/3rdparty/openmp
index 37c72127e903..b76842ed1698 160000
--- a/3rdparty/openmp
+++ b/3rdparty/openmp
@@ -1 +1 @@
-Subproject commit 37c72127e90360a020f351f18d9cccfc30e5145a
+Subproject commit b76842ed16984ae5edcbbc4b00a94fda20419431

From ce97e22813442b55211703c1bfa8fa20d58ef0e8 Mon Sep 17 00:00:00 2001
From: Xi Wang <xidulu@gmail.com>
Date: Mon, 9 Dec 2019 16:53:41 +0800
Subject: [PATCH 671/813] fix axis=-1 bug (#17016)

---
 src/operator/numpy/np_broadcast_reduce_op.h | 2 +-
 tests/python/unittest/test_numpy_op.py      | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/operator/numpy/np_broadcast_reduce_op.h b/src/operator/numpy/np_broadcast_reduce_op.h
index df9a7c932490..7d0025a62ad2 100644
--- a/src/operator/numpy/np_broadcast_reduce_op.h
+++ b/src/operator/numpy/np_broadcast_reduce_op.h
@@ -188,7 +188,7 @@ inline bool NumpyReduceAxesNoDTypeShape(const nnvm::NodeAttrs& attrs,
   if (param.axis.has_value()) {
     const mxnet::Tuple<int>& axes = param.axis.value();
     for (int i = 0; i < axes.ndim(); ++i) {
-      if (ishape[axes[i]] == 0) {
+      if ((axes[i] >= 0) && (ishape[axes[i]] == 0)) {
         is_all_reducded_axes_not_zero = false;
         break;
       }
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 549141b92c4c..0947b6112158 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -532,7 +532,7 @@ def get_grad(axis, func_name):
             elif axis == 2:
                 temp[:,:,index,:] = 1
                 return temp
-            elif axis == 3:
+            elif (axis == 3 or axis == -1):
                 temp[:,:,:,index] = 1
                 return temp
             elif not axis:
@@ -550,7 +550,7 @@ def _test_np_exception(func, shape, dim):
     for func in ['max', 'min']:
         for hybridize in [False, True]:
             for keepdims in [True, False]:
-                for axis in ([i for i in range(in_data_dim)] + [(), None]):
+                for axis in ([i for i in range(in_data_dim)] + [(), None] + [-1]):
                     for itype in ['float16', 'float32', 'float64', 'int']:
                         # test gluon
                         if func == 'max':

From 538b18bb4ea25448334092d0a15af174dcb44663 Mon Sep 17 00:00:00 2001
From: Xinyu Chen <xinyu1.chen@intel.com>
Date: Mon, 9 Dec 2019 20:36:26 +0800
Subject: [PATCH 672/813] [MKL-DNN] Enable and Optimization for s8 eltwise_add
 (#16931)

* optimization for s8 sum

* fix lint

* fix lint

* exclude sum in lstm cell

* remove debug info

* remove todo
---
 src/operator/operator_common.h                |  5 ++
 .../mkldnn/mkldnn_quantized_elemwise_add.cc   | 58 ++++++++++++++++++-
 .../quantization/quantized_elemwise_add.cc    |  4 --
 .../python/quantization/test_quantization.py  |  2 +
 4 files changed, 62 insertions(+), 7 deletions(-)

diff --git a/src/operator/operator_common.h b/src/operator/operator_common.h
index c23a5a852dcb..f6af58bce995 100644
--- a/src/operator/operator_common.h
+++ b/src/operator/operator_common.h
@@ -607,6 +607,11 @@ class OpSignature {
     eles.push_back(val);
   }
 
+  void AddSign(float val) {
+    hash = dmlc::HashCombine(hash, val);
+    eles.push_back(val);
+  }
+
   bool operator==(const OpSignature &sign) const {
     if (hash != sign.hash)
       return false;
diff --git a/src/operator/quantization/mkldnn/mkldnn_quantized_elemwise_add.cc b/src/operator/quantization/mkldnn/mkldnn_quantized_elemwise_add.cc
index 2078ac4fead8..06a0ea37f95b 100644
--- a/src/operator/quantization/mkldnn/mkldnn_quantized_elemwise_add.cc
+++ b/src/operator/quantization/mkldnn/mkldnn_quantized_elemwise_add.cc
@@ -39,6 +39,57 @@ static inline float GetScale(const NDArray& data, float min, float max) {
   return data_range / MaxAbs(min, max);
 }
 
+class MKLDNNQuantizedElemwiseAddFwd {
+ public:
+  mkldnn::sum::primitive_desc fwd_pd;
+
+  MKLDNNQuantizedElemwiseAddFwd(
+               const mkldnn::memory::desc &output_desc,
+               const std::vector<float> &scales,
+               const std::vector<mkldnn::memory::desc> &data_md)
+      : fwd_pd(output_desc, scales, data_md, CpuEngine::Get()->get_engine()) {
+    fwd_ = std::make_shared<mkldnn::sum>(fwd_pd);
+    data_.resize(data_md.size());
+  }
+
+  const mkldnn::sum &GetFwd() const { return *fwd_; }
+
+ private:
+  std::shared_ptr<mkldnn::sum> fwd_;
+  std::vector<std::shared_ptr<mkldnn::memory>> data_;
+  std::shared_ptr<mkldnn::memory> out_;
+};
+
+static MKLDNNQuantizedElemwiseAddFwd &GetQuantizedElemwiseAddForward(
+    const mkldnn::memory::desc &output_desc, const std::vector<float> &scales,
+    const std::vector<NDArray> &in_data, const std::vector<NDArray> &out_data,
+    const std::vector<mkldnn::memory::desc> &data_md) {
+#if DMLC_CXX11_THREAD_LOCAL
+  static thread_local std::unordered_map<OpSignature,
+              MKLDNNQuantizedElemwiseAddFwd, OpHash> fwds;
+#else
+  static MX_THREAD_LOCAL std::unordered_map<OpSignature,
+              MKLDNNQuantizedElemwiseAddFwd, OpHash> fwds;
+#endif
+  OpSignature key;
+  key.AddSign(in_data);
+  key.AddSign(in_data[quantized_elemwise_add_enum::kAMin].data().dptr<float>()[0]);
+  key.AddSign(in_data[quantized_elemwise_add_enum::kAMax].data().dptr<float>()[0]);
+  key.AddSign(in_data[quantized_elemwise_add_enum::kBMin].data().dptr<float>()[0]);
+  key.AddSign(in_data[quantized_elemwise_add_enum::kBMax].data().dptr<float>()[0]);
+  key.AddSign(out_data);
+  key.AddSign(out_data[quantized_elemwise_add_enum::kMin].data().dptr<float>()[0]);
+  key.AddSign(out_data[quantized_elemwise_add_enum::kMax].data().dptr<float>()[0]);
+
+  auto it = fwds.find(key);
+  if (it == fwds.end()) {
+    MKLDNNQuantizedElemwiseAddFwd fwd(output_desc, scales, data_md);
+    it = AddToCache(&fwds, key, fwd);
+  }
+  return it->second;
+}
+
+
 static void MKLDNNQuantizedElemwiseAddForward(const nnvm::NodeAttrs& attrs, const OpContext& ctx,
                                               const std::vector<NDArray>& in_data,
                                               const std::vector<OpReqType>& req,
@@ -166,16 +217,17 @@ static void MKLDNNQuantizedElemwiseAddForward(const nnvm::NodeAttrs& attrs, cons
   auto output_desc = mkldnn::memory::desc(i_dims,
                                           output_data_type,
                                           mkldnn::memory::format_tag::any);
-  mkldnn::sum::primitive_desc pdesc(output_desc, scales, in_desc, engine);
+  MKLDNNQuantizedElemwiseAddFwd &fwd = GetQuantizedElemwiseAddForward(output_desc, scales,
+                                                                      in_data, out_data, in_desc);
   auto mem = CreateMKLDNNMem(out_data[quantized_elemwise_add_enum::kOut],
-                             pdesc.dst_desc(),
+                             fwd.fwd_pd.dst_desc(),
                              req[0],
                              &in_data[0]);
   mkldnn_args_map_t args({{MKLDNN_ARG_MULTIPLE_SRC,  *dataA_mem},
                           {MKLDNN_ARG_MULTIPLE_SRC + 1,  *dataB_mem},
                           {MKLDNN_ARG_DST, *mem.second}});
   MKLDNNStream *stream = MKLDNNStream::Get();
-  stream->RegisterPrimArgs(mkldnn::sum(pdesc), args);
+  stream->RegisterPrimArgs(fwd.GetFwd(), args);
   CommitOutput(out_data[quantized_elemwise_add_enum::kOut], mem);
   stream->Submit();
 
diff --git a/src/operator/quantization/quantized_elemwise_add.cc b/src/operator/quantization/quantized_elemwise_add.cc
index 0e7034e88b8c..f821e6598192 100644
--- a/src/operator/quantization/quantized_elemwise_add.cc
+++ b/src/operator/quantization/quantized_elemwise_add.cc
@@ -125,9 +125,6 @@ and max thresholds representing the threholds for quantizing the float32 output
 .add_argument("rhs_max", "NDArray-or-Symbol", "6th input");
 
 
-// TODO(zhangrong): need extra condition check if there's benefited if it's switched on
-// Since it's not compute-intensive.
-#if 0
 NNVM_REGISTER_OP(elemwise_add)
 .set_attr<FQuantizedOp>("FQuantizedOp", [](const NodeAttrs& attrs) {
   nnvm::NodePtr node = nnvm::Node::Create();
@@ -139,7 +136,6 @@ NNVM_REGISTER_OP(elemwise_add)
   }
   return node;
 });
-#endif
 
 }  // namespace op
 }  // namespace mxnet
diff --git a/tests/python/quantization/test_quantization.py b/tests/python/quantization/test_quantization.py
index 6fe33f5ee52b..a371abddd22e 100644
--- a/tests/python/quantization/test_quantization.py
+++ b/tests/python/quantization/test_quantization.py
@@ -958,6 +958,8 @@ def check_qsym_forward(qsym, qarg_params, qaux_params, data_shape, label_shape=N
                         excluded_sym_names = excluded_names
                     else:
                         excluded_sym_names = excluded_names + optional_names
+            if name == 'sym4':
+                excluded_op_names += ['elemwise_add']
 
             qsym, qarg_params, qaux_params = mx.contrib.quant.quantize_model(sym=s,
                                                                              arg_params=arg_params,

From 2d41f2a83d4205831fccecdd23cf1b547c2f1244 Mon Sep 17 00:00:00 2001
From: Xinyu Chen <xinyu1.chen@intel.com>
Date: Mon, 9 Dec 2019 20:37:29 +0800
Subject: [PATCH 673/813] add inference benchmark script (#16978)

---
 example/quantization/README.md                |  53 ++++++---
 .../quantization/launch_inference_mkldnn.sh   | 111 ++++++++++++++++++
 2 files changed, 149 insertions(+), 15 deletions(-)
 create mode 100644 example/quantization/launch_inference_mkldnn.sh

diff --git a/example/quantization/README.md b/example/quantization/README.md
index 032ca97176df..8cdc1bb7e06f 100644
--- a/example/quantization/README.md
+++ b/example/quantization/README.md
@@ -80,6 +80,29 @@ optional arguments:
                         if calibration mode is enabled
 ```
 
+A new benchmark script `launch_inference_mkldnn.sh` has been designed to launch performance benchmark for float32 or int8 image-classification models with Intel® MKL-DNN.
+```
+usage: bash ./launch_inference_mkldnn.sh [[[-s symbol_file ] [-b batch_size] [-iter iteraton] [-ins instance] [-c cores/instance]] | [-h]]
+
+optional arguments:
+  -h, --help                show this help message and exit
+  -s, --symbol_file         symbol file for benchmark
+  -b, --batch_size          inference batch size
+                            default: 64
+  -iter, --iteration        inference iteration
+                            default: 500
+  -ins, --instance          launch multi-instance inference
+                            default: one instance per socket
+  -c, --core                number of cores per instance
+                            default: divide full physical cores
+
+example: resnet int8 performance benchmark on c5.24xlarge(duo sockets, 24 physical cores per socket).
+
+    bash ./launch_inference_mkldnn.sh -s ./model/resnet50_v1-quantized-5batches-naive-symbol.json
+
+will launch two instances for throughput benchmark and each instance will use 24 physical cores.
+```
+
 Use the following command to install [Gluon-CV](https://gluon-cv.mxnet.io/):
 
 ```
@@ -120,8 +143,8 @@ python imagenet_inference.py --symbol-file=./model/resnet50_v1-symbol.json --par
 python imagenet_inference.py --symbol-file=./model/resnet50_v1-quantized-5batches-naive-symbol.json --param-file=./model/resnet50_v1-quantized-0000.params --rgb-mean=123.68,116.779,103.939 --rgb-std=58.393,57.12,57.375 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu
 
 # Launch dummy data Inference
-python imagenet_inference.py --symbol-file=./model/resnet50_v1-symbol.json --batch-size=64 --num-inference-batches=500 --ctx=cpu --benchmark=True
-python imagenet_inference.py --symbol-file=./model/resnet50_v1-quantized-5batches-naive-symbol.json --batch-size=64 --num-inference-batches=500 --ctx=cpu --benchmark=True
+bash ./launch_inference_mkldnn.sh -s ./model/resnet50_v1-symbol.json
+bash ./launch_inference_mkldnn.sh -s ./model/resnet50_v1-quantized-5batches-naive-symbol.json
 ```
 
 <h3 id='4'>SqueezeNet 1.0</h3>
@@ -142,8 +165,8 @@ python imagenet_inference.py --symbol-file=./model/squeezenet1.0-symbol.json --p
 python imagenet_inference.py --symbol-file=./model/squeezenet1.0-quantized-5batches-naive-symbol.json --param-file=./model/squeezenet1.0-quantized-0000.params --rgb-mean=123.68,116.779,103.939 --rgb-std=58.393,57.12,57.375 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu
 
 # Launch dummy data Inference
-python imagenet_inference.py --symbol-file=./model/squeezenet1.0-symbol.json --batch-size=64 --num-inference-batches=500 --ctx=cpu  --benchmark=True
-python imagenet_inference.py --symbol-file=./model/squeezenet1.0-quantized-5batches-naive-symbol.json --batch-size=64 --num-inference-batches=500 --ctx=cpu --benchmark=True
+bash ./launch_inference_mkldnn.sh -s ./model/squeezenet1.0-symbol.json
+bash ./launch_inference_mkldnn.sh -s ./model/squeezenet1.0-quantized-5batches-naive-symbol.json 
 ```
 
 <h3 id='5'>MobileNet 1.0</h3>
@@ -164,8 +187,8 @@ python imagenet_inference.py --symbol-file=./model/mobilenet1.0-symbol.json --pa
 python imagenet_inference.py --symbol-file=./model/mobilenet1.0-quantized-5batches-naive-symbol.json --param-file=./model/mobilenet1.0-quantized-0000.params --rgb-mean=123.68,116.779,103.939 --rgb-std=58.393,57.12,57.375 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu
 
 # Launch dummy data Inference
-python imagenet_inference.py --symbol-file=./model/mobilenet1.0-symbol.json --batch-size=64 --num-inference-batches=500 --ctx=cpu  --benchmark=True
-python imagenet_inference.py --symbol-file=./model/mobilenet1.0-quantized-5batches-naive-symbol.json --batch-size=64 --num-inference-batches=500 --ctx=cpu --benchmark=True
+bash ./launch_inference_mkldnn.sh -s ./model/mobilenet1.0-symbol.json
+bash ./launch_inference_mkldnn.sh -s ./model/mobilenet1.0-quantized-5batches-naive-symbol.json
 ```
 
 <h3 id='6'>MobileNetV2 1.0</h3>
@@ -186,8 +209,8 @@ python imagenet_inference.py --symbol-file=./model/mobilenetv2_1.0-symbol.json -
 python imagenet_inference.py --symbol-file=./model/mobilenetv2_1.0-quantized-5batches-naive-symbol.json --param-file=./model/mobilenetv2_1.0-quantized-0000.params --rgb-mean=123.68,116.779,103.939 --rgb-std=58.393,57.12,57.375 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu
 
 # Launch dummy data Inference
-python imagenet_inference.py --symbol-file=./model/mobilenetv2_1.0-symbol.json --batch-size=64 --num-inference-batches=500 --ctx=cpu  --benchmark=True
-python imagenet_inference.py --symbol-file=./model/mobilenetv2_1.0-quantized-5batches-naive-symbol.json --batch-size=64 --num-inference-batches=500 --ctx=cpu --benchmark=True
+bash ./launch_inference_mkldnn.sh -s ./model/mobilenetv2_1.0-symbol.json
+bash ./launch_inference_mkldnn.sh -s ./model/mobilenetv2_1.0-quantized-5batches-naive-symbol.json
 ```
 
 <h3 id='7'>Inception-V3</h3>
@@ -208,8 +231,8 @@ python imagenet_inference.py --symbol-file=./model/inceptionv3-symbol.json --par
 python imagenet_inference.py --symbol-file=./model/inceptionv3-quantized-5batches-naive-symbol.json --param-file=./model/inceptionv3-quantized-0000.params --image-shape=3,299,299 --rgb-mean=123.68,116.779,103.939 --rgb-std=58.393,57.12,57.375 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu
 
 # Launch dummy data Inference
-python imagenet_inference.py --symbol-file=./model/inceptionv3-symbol.json --image-shape=3,299,299 --batch-size=64 --num-inference-batches=500 --ctx=cpu  --benchmark=True
-python imagenet_inference.py --symbol-file=./model/inceptionv3-quantized-5batches-naive-symbol.json --image-shape=3,299,299 --batch-size=64 --num-inference-batches=500 --ctx=cpu --benchmark=True
+bash ./launch_inference_mkldnn.sh -s ./model/inceptionv3-symbol.json
+bash ./launch_inference_mkldnn.sh -s ./model/inceptionv3-quantized-5batches-naive-symbol.json
 ```
 
 <h3 id='8'>ResNet152-V2</h3>
@@ -231,8 +254,8 @@ python imagenet_inference.py --symbol-file=./model/imagenet1k-resnet-152-symbol.
 python imagenet_inference.py --symbol-file=./model/imagenet1k-resnet-152-quantized-5batches-naive-symbol.json --param-file=./model/imagenet1k-resnet-152-quantized-0000.params --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu
 
 # Launch dummy data Inference
-python imagenet_inference.py --symbol-file=./model/imagenet1k-resnet-152-symbol.json --batch-size=64 --num-inference-batches=500 --ctx=cpu --benchmark=True
-python imagenet_inference.py --symbol-file=./model/imagenet1k-resnet-152-quantized-5batches-naive-symbol.json --batch-size=64 --num-inference-batches=500 --ctx=cpu --benchmark=True
+bash ./launch_inference_mkldnn.sh -s ./model/imagenet1k-resnet-152-symbol.json
+bash ./launch_inference_mkldnn.sh -s ./model/imagenet1k-resnet-152-quantized-5batches-naive-symbol.json
 ```
 
 <h3 id='9'>Inception-BN</h3>
@@ -254,8 +277,8 @@ python imagenet_inference.py --symbol-file=./model/imagenet1k-inception-bn-symbo
 python imagenet_inference.py --symbol-file=./model/imagenet1k-inception-bn-quantized-5batches-naive-symbol.json --param-file=./model/imagenet1k-inception-bn-quantized-0000.params --rgb-mean=123.68,116.779,103.939 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu
 
 # Launch dummy data Inference
-python imagenet_inference.py --symbol-file=./model/imagenet1k-inception-bn-symbol.json --batch-size=64 --num-inference-batches=500 --ctx=cpu --benchmark=True
-python imagenet_inference.py --symbol-file=./model/imagenet1k-inception-bn-quantized-5batches-naive-symbol.json --batch-size=64 --num-inference-batches=500 --ctx=cpu --benchmark=True
+bash ./launch_inference_mkldnn.sh -s ./model/imagenet1k-inception-bn-symbol.json
+bash ./launch_inference_mkldnn.sh -s ./model/imagenet1k-inception-bn-quantized-5batches-naive-symbol.json
 ```
 
 <h3 id='10'>SSD-VGG16</h3>
@@ -307,7 +330,7 @@ python imagenet_gen_qsym_mkldnn.py --model=custom --num-calib-batches=5 --calib-
 python imagenet_inference.py --symbol-file=./model/*.json --param-file=./model/*.params --rgb-mean=* --rgb-std=* --num-skipped-batches=* --batch-size=* --num-inference-batches=*--dataset=./data/* --ctx=cpu
 
 # Launch dummy data Inference
-python imagenet_inference.py --symbol-file=./model/*.json --batch-size=* --num-inference-batches=500 --ctx=cpu --benchmark=True
+bash ./launch_inference_mkldnn.sh -s ./model/*.json
 ```
 
 <h2 id="2">Model Quantization with CUDNN</h2>
diff --git a/example/quantization/launch_inference_mkldnn.sh b/example/quantization/launch_inference_mkldnn.sh
new file mode 100644
index 000000000000..f67787b41b03
--- /dev/null
+++ b/example/quantization/launch_inference_mkldnn.sh
@@ -0,0 +1,111 @@
+#!/bin/sh
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+usage()
+{
+    echo "usage: bash ./launch_inference_mkldnn.sh [[[-s symbol_file ] [-b batch_size] [-iter iteraton] [-ins instance] [-c cores/instance]] | [-h]]"
+}
+
+while [ $# -gt 0 ]; do
+  case "$1" in
+    --symbol | -s)
+      shift
+      SYMBOL=$1
+      ;;
+    --batch-size | -b)
+      shift
+      BS=$1
+      ;;
+    --iteration | -iter)
+      shift
+      ITERATIONS=$1
+      ;;
+    --instance | -ins)
+      shift
+      INS=$1
+      ;;
+    --core | -c)
+      shift
+      CORES=$1
+      ;;
+    --help | -h)
+      usage
+      exit 1
+      ;;
+    *)
+      usage
+      exit 1
+  esac
+  shift
+done
+
+NUM_SOCKET=`lscpu | grep 'Socket(s)' | awk '{print $NF}'`
+NUM_NUMA_NODE=`lscpu | grep 'NUMA node(s)' | awk '{print $NF}'`
+CORES_PER_SOCKET=`lscpu | grep 'Core(s) per socket' | awk '{print $NF}'`
+NUM_CORES=$((CORES_PER_SOCKET * NUM_SOCKET))
+CORES_PER_NUMA=$((NUM_CORES / NUM_NUMA_NODE))
+echo "target machine has $NUM_CORES physical core(s) on $NUM_NUMA_NODE numa nodes of $NUM_SOCKET socket(s)."
+
+if [ -z $SYMBOL ]; then
+  echo "Error: Need a symbol file as input."
+fi
+if [ -z $INS ]; then
+  echo "Default: launch one instance per socket."
+  INS=$NUM_SOCKET
+fi
+if [ -z $CORES ]; then
+  echo "Default: divide full physical cores."
+  CORES=$((NUM_CORES / $INS))
+fi
+if [ -z $BS ]; then
+  echo "Default: set batch size to 64."
+  BS=64
+fi
+if [ -z $ITERATIONS ]; then
+  echo "Default: set iterations to 500."
+  ITERATIONS=500
+fi
+
+echo "  benchmark configs"
+echo "  cores per instance: $CORES"
+echo "  total instances: $INS"
+echo "  batch size: $BS"
+echo "  iterations: $ITERATIONS"
+echo ""
+
+rm BENCHMARK_*.log  || echo "benchmarking..."
+
+for((i=0;i<$INS;i++));
+do
+  ((a=$i*$CORES))
+  ((b=$a+$CORES-1))
+  memid=$((b/CORES_PER_NUMA))
+  LOG=BENCHMARK_$i.log
+  echo "  $i instance use $a-$b cores and $memid mem with $LOG"
+  KMP_AFFINITY=granularity=fine,noduplicates,compact,1,0 \
+  OMP_NUM_THREADS=$CORES \
+  nohup numactl --physcpubind=$a-$b --membind=$memid python imagenet_inference.py --symbol-file=$SYMBOL --batch-size=$BS --num-inference-batches=$ITERATIONS --ctx=cpu --benchmark=True > $LOG 2>&1 &
+done
+wait
+
+fps=`grep image/sec BENCHMARK_*.log | awk '{ sum += $(NF) }; END { print sum }'`
+latency=$(echo "scale=2; 1000*$INS/$fps" | bc)
+echo "overall throughput: $fps"
+echo "latency per instance: $latency"
+echo "benchmark finish:)"

From e48ff96ef4375f3d7c505e152b73b1f15a8b7afe Mon Sep 17 00:00:00 2001
From: Xinyu Chen <xinyu1.chen@intel.com>
Date: Mon, 9 Dec 2019 20:38:43 +0800
Subject: [PATCH 674/813] Quantized Embedding (#16691)

* add quantized embedding

* add quantized embedding

* add quantized embedding

* imporve lint

* change to ksupport

* fix lint

* add quantized embedding test case

* skip gpu ut
---
 .../quantization/quantized_indexing_op.cc     | 192 ++++++++++++++++++
 src/operator/tensor/indexing_op.h             |   6 +
 .../python/quantization/test_quantization.py  |  46 +++++
 3 files changed, 244 insertions(+)
 create mode 100644 src/operator/quantization/quantized_indexing_op.cc

diff --git a/src/operator/quantization/quantized_indexing_op.cc b/src/operator/quantization/quantized_indexing_op.cc
new file mode 100644
index 000000000000..b4af3ecb704f
--- /dev/null
+++ b/src/operator/quantization/quantized_indexing_op.cc
@@ -0,0 +1,192 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2017 by Contributors
+ * \file quantized_indexing_op.cc
+*/
+#include <mxnet/op_attr_types.h>
+#include "../tensor/indexing_op.h"
+
+namespace mxnet {
+namespace op {
+
+
+inline bool QuantizedEmbeddingOpShape(const nnvm::NodeAttrs& attrs,
+                                      mxnet::ShapeVector *in_attrs,
+                                      mxnet::ShapeVector *out_attrs) {
+  using namespace mshadow;
+  const mxnet::TShape &dshape = (*in_attrs)[quantized_embedding::kData];
+  if (!ndim_is_known(dshape)) return false;
+  const EmbeddingParam& param = nnvm::get<EmbeddingParam>(attrs.parsed);
+  SHAPE_ASSIGN_CHECK(*in_attrs, quantized_embedding::kWeight, Shape2(param.input_dim,
+                                                                     param.output_dim));
+  SHAPE_ASSIGN_CHECK(*in_attrs, quantized_embedding::kWeightMin, mxnet::TShape(1, 1));
+  SHAPE_ASSIGN_CHECK(*in_attrs, quantized_embedding::kWeightMax, mxnet::TShape(1, 1));
+  out_attrs->clear();
+
+  mxnet::TShape oshape(dshape.ndim()+1, -1);
+  for (int i = 0; i < dshape.ndim(); ++i) {
+    oshape[i] = dshape[i];
+  }
+  oshape[dshape.ndim()] = param.output_dim;
+  out_attrs->push_back(oshape);
+  out_attrs->push_back(mxnet::TShape(1, 1));
+  out_attrs->push_back(mxnet::TShape(1, 1));
+  return shape_is_known(oshape);
+}
+
+inline bool QuantizedEmbeddingOpType(const nnvm::NodeAttrs& attrs,
+                                     std::vector<int> *in_type,
+                                     std::vector<int> *out_type) {
+  CHECK_EQ(in_type->size(), 4U);
+  CHECK_GE(out_type->size(), 3U);
+  int itype = (*in_type)[0];
+  CHECK_NE(itype, -1) << "First input must have specified type";
+  TYPE_ASSIGN_CHECK(*in_type, 1, mshadow::kInt8);
+  TYPE_ASSIGN_CHECK(*in_type, 2, mshadow::kFloat32);
+  TYPE_ASSIGN_CHECK(*in_type, 3, mshadow::kFloat32);
+  out_type->clear();
+  out_type->push_back(mshadow::kInt8);
+  int dtype_out_min = 0;
+  int dtype_out_max = 0;
+  out_type->push_back(dtype_out_min);
+  out_type->push_back(dtype_out_max);
+  return true;
+}
+
+// storage type inference function for Embedding
+inline bool QuantizedEmbeddingOpForwardStorageType(const nnvm::NodeAttrs& attrs,
+                                                   const int dev_mask,
+                                                   DispatchMode* dispatch_mode,
+                                                   std::vector<int>* in_attrs,
+                                                   std::vector<int>* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 4U);
+  CHECK_EQ(out_attrs->size(), 3U);
+  const int& data_stype = in_attrs->at(quantized_embedding::kData);
+  const int& weight_stype = in_attrs->at(quantized_embedding::kWeight);
+  const int& weight_min_stype = in_attrs->at(quantized_embedding::kWeightMin);
+  const int& weight_max_stype = in_attrs->at(quantized_embedding::kWeightMax);
+  int& out_stype = out_attrs->at(quantized_embedding::kOut);
+  int& out_stype_min = out_attrs->at(quantized_embedding::kOutMin);
+  int& out_stype_max = out_attrs->at(quantized_embedding::kOutMax);
+  bool dispatched = false;
+  CHECK_EQ(weight_min_stype, kDefaultStorage);
+  CHECK_EQ(weight_max_stype, kDefaultStorage);
+  if (!dispatched && data_stype == kDefaultStorage && weight_stype == kDefaultStorage) {
+    // dns, dns -> dns
+    dispatched = storage_type_assign(&out_stype, kDefaultStorage,
+                                     dispatch_mode, DispatchMode::kFCompute);
+    dispatched = storage_type_assign(&out_stype_min, kDefaultStorage,
+                                     dispatch_mode, DispatchMode::kFCompute);
+    dispatched = storage_type_assign(&out_stype_max, kDefaultStorage,
+                                     dispatch_mode, DispatchMode::kFCompute);
+  }
+  if (!dispatched && data_stype == kDefaultStorage && weight_stype == kRowSparseStorage) {
+    // dns, rsp -> dns
+    dispatched = storage_type_assign(&out_stype, kDefaultStorage,
+                                     dispatch_mode, DispatchMode::kFComputeEx);
+  }
+  return dispatched;
+}
+
+void QuantizedEmbeddingOpForward(const nnvm::NodeAttrs& attrs,
+                                 const OpContext& ctx,
+                                 const std::vector<TBlob>& inputs,
+                                 const std::vector<OpReqType>& req,
+                                 const std::vector<TBlob>& outputs) {
+  CHECK_EQ(req[quantized_embedding::kOut], kWriteTo);
+  CHECK_EQ(inputs.size(), 4U);
+  CHECK_EQ(outputs.size(), 3U);
+  CHECK_EQ(inputs[quantized_embedding::kWeight].ndim(), 2U)
+          << "Embedding layer expects its weight to be two-dimensional. "
+          << inputs[quantized_embedding::kWeight].ndim()
+          << " dimensional input is given instead";
+  mshadow::Stream<cpu> *s = ctx.get_stream<cpu>();
+  EmbeddingOpForwardDnsImpl<cpu>(s, inputs[quantized_embedding::kData],
+                                 inputs[quantized_embedding::kWeight],
+                                 req[quantized_embedding::kOut],
+                                 outputs[quantized_embedding::kOut]);
+  float min_weight = inputs[quantized_embedding::kWeightMin].dptr<float>()[0];
+  float max_weight = inputs[quantized_embedding::kWeightMax].dptr<float>()[0];
+  outputs[quantized_embedding::kOutMin].dptr<float>()[0] = min_weight;
+  outputs[quantized_embedding::kOutMax].dptr<float>()[0] = max_weight;
+}
+
+NNVM_REGISTER_OP(_contrib_quantized_embedding)
+.describe(R"code(Maps integer indices to int8 vector representations (embeddings).
+)code" ADD_FILELINE)
+.set_num_inputs(4)
+.set_num_outputs(3)
+.set_attr_parser(ParamParser<EmbeddingParam>)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"data", "weight", "min_weight", "max_weight"};
+  })
+.set_attr<nnvm::FListOutputNames>("FListOutputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"output", "min_output", "max_output"};
+  })
+.set_attr<mxnet::FInferShape>("FInferShape", QuantizedEmbeddingOpShape)
+.set_attr<nnvm::FInferType>("FInferType", QuantizedEmbeddingOpType)
+.set_attr<FInferStorageType>("FInferStorageType", QuantizedEmbeddingOpForwardStorageType)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", QuantizedEmbeddingOpForward)
+// TODO(Xinyu): a temp solution to enable GluonCV INT8 flow,
+// will be reverted after the improvement of CachedOP is done.
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.add_argument("data", "NDArray-or-Symbol", "The input array to the embedding operator.")
+.add_argument("weight", "NDArray-or-Symbol", "The embedding weight matrix.")
+.add_argument("min_weight", "NDArray-or-Symbol", "Minimum value of data.")
+.add_argument("max_weight", "NDArray-or-Symbol", "Maximum value of data.")
+.add_arguments(EmbeddingParam::__FIELDS__());
+
+NNVM_REGISTER_OP(Embedding)
+.set_attr<FQuantizable>("FQuantizable", [](const NodeAttrs& attrs) {
+    return QuantizeType::kSupport;
+})
+.set_attr<FQuantizedOp>("FQuantizedOp", [](const NodeAttrs& attrs) {
+    EmbeddingParam param;
+    param.Init(attrs.dict);
+    nnvm::NodePtr node = nnvm::Node::Create();
+    if (param.dtype == mshadow::kFloat32) {
+      node->attrs.op = Op::Get("_contrib_quantized_embedding");
+      node->attrs.name = "quantized_" + attrs.name;
+    } else {
+      node->attrs.op = Op::Get("Embedding");
+      node->attrs.name = attrs.name;
+    }
+    node->attrs.dict = attrs.dict;
+    if (node->op()->attr_parser != nullptr) {
+      node->op()->attr_parser(&(node->attrs));
+    }
+    return node;
+  })
+.set_attr<FAvoidQuantizeInput>("FAvoidQuantizeInput", [](const NodeAttrs &attrs, size_t index) {
+  if (index == 0)
+    return true;
+  else
+    return false;
+});
+}  // namespace op
+}  // namespace mxnet
+
diff --git a/src/operator/tensor/indexing_op.h b/src/operator/tensor/indexing_op.h
index 828d761fefd4..f81d831b562b 100644
--- a/src/operator/tensor/indexing_op.h
+++ b/src/operator/tensor/indexing_op.h
@@ -58,6 +58,12 @@ enum EmbeddingOpOutputs {kOut};
 enum EmbeddingOpResource {kTempSpace};
 }  // namespace embedding
 
+namespace quantized_embedding {
+enum QuantizedEmbeddingOpInputs {kData, kWeight, kWeightMin, kWeightMax};
+enum QuantizedEmbeddingOpOutputs {kOut, kOutMin, kOutMax};
+enum QuantizedEmbeddingOpResource {kTempSpace};
+}  // namespace quantized_embedding
+
 
 struct SparseEmbeddingParam: public dmlc::Parameter<SparseEmbeddingParam> {
   int input_dim;
diff --git a/tests/python/quantization/test_quantization.py b/tests/python/quantization/test_quantization.py
index a371abddd22e..527737e03cd7 100644
--- a/tests/python/quantization/test_quantization.py
+++ b/tests/python/quantization/test_quantization.py
@@ -517,6 +517,52 @@ def maxabs(a, b):
         check_quantized_fc((256, 2048, 2, 2), 800, True, qdtype)
         check_quantized_fc((256, 111, 2, 2), 800, True, qdtype)
 
+@with_seed()
+def test_quantized_embedding():
+    def check_quantized_embedding(data_shape, input_dim, output_dim):
+        if is_test_for_gpu():
+            print('skipped testing test_quantized_embedding for gpu since it is not supported yet')
+            return
+
+        def maxabs(a, b):
+            return mx.nd.maximum(mx.nd.abs(a), mx.nd.abs(b))
+
+        data0 = mx.sym.Variable(name='data', shape=data_shape, dtype='int32')
+        embedding_fp32 = mx.sym.Embedding(data=data0, input_dim=input_dim, output_dim=output_dim)
+        arg_shapes, _, _ = embedding_fp32.infer_shape(data=data_shape)
+        arg_names = embedding_fp32.list_arguments()
+        embedding_fp32_exe = embedding_fp32.simple_bind(ctx=mx.current_context(), grad_req='null')
+        int8_range = 127.0
+        data = mx.nd.random.uniform(low=0, high=input_dim,
+                                      shape=arg_shapes[0]).astype('int32')
+        weight = mx.nd.random.uniform(low=-int8_range, high=int8_range,
+                                      shape=arg_shapes[1]).astype('int32')
+        embedding_fp32_exe.arg_dict[arg_names[0]][:] = data
+        embedding_fp32_exe.arg_dict[arg_names[1]][:] = weight
+
+        weight_min = mx.nd.min(weight).astype('float32')
+        weight_max = mx.nd.max(weight).astype('float32')
+        weight_range = maxabs(weight_min, weight_max)
+
+        output = embedding_fp32_exe.forward()[0]
+
+        embedding_int8 = mx.sym.contrib.quantized_embedding(data=data0, input_dim=input_dim, output_dim=output_dim)
+        qarg_names = embedding_int8.list_arguments()
+        type_dict = {qarg_names[1]: 'int8'}
+        embedding_int8_exe = embedding_int8.simple_bind(ctx=mx.current_context(), type_dict=type_dict, grad_req='null')
+        embedding_int8_exe.arg_dict[qarg_names[0]][:] = embedding_fp32_exe.arg_dict[arg_names[0]]
+        embedding_int8_exe.arg_dict[qarg_names[1]][:] = embedding_fp32_exe.arg_dict[arg_names[1]].astype('int8')
+        embedding_int8_exe.arg_dict[qarg_names[2]][:] = -weight_range
+        embedding_int8_exe.arg_dict[qarg_names[3]][:] = weight_range
+        qoutput, min_range, max_range = embedding_int8_exe.forward()
+
+        assert_almost_equal(output.asnumpy(), qoutput.asnumpy())
+
+    check_quantized_embedding((1,), 1000, 256)
+    check_quantized_embedding((1,), 1024, 512)
+    check_quantized_embedding((32,), 1000, 256)
+    check_quantized_embedding((32,), 1024, 512)
+
 @with_seed()
 def test_quantized_flatten():
     def check_quantized_flatten(shape, qdtype):

From c573154e9deb3881ee3a64a9fa0886248622bacf Mon Sep 17 00:00:00 2001
From: Talia <31782251+TEChopra1000@users.noreply.github.com>
Date: Mon, 9 Dec 2019 10:20:03 -0800
Subject: [PATCH 675/813] Link fixes5 (#16986)

* link fixes

* fixing broken links, added data tutorials back into gluon package section

* Added autograd images back in to the tutorial in the python package docs

* created relative link for symbol api reference

* renaming data folder

* incorporating new folder-name on gluon index cards

* fixed gotcha link

* Fixed relative link

* fixing links on performance index page

* took out data files, checked and modified index files for python tutorials

* added license header to multi_devices.md

* link fixes

* fixing broken links, added data tutorials back into gluon package section

* Added autograd images back in to the tutorial in the python package docs

* created relative link for symbol api reference

* renaming data folder

* incorporating new folder-name on gluon index cards

* fixed gotcha link

* fixing links on performance index page

* took out data files, checked and modified index files for python tutorials

* added license header to multi_devices.md

* adding updated submodule
---
 docs/python_docs/python/tutorials/index.rst   |   2 +-
 .../_static/autograd_control_flow.png         | Bin 0 -> 21635 bytes
 .../_static/autograd_control_flow_grad.png    | Bin 0 -> 20585 bytes
 .../_static/autograd_forward_backward.png     | Bin 0 -> 63061 bytes
 .../autograd/_static/autograd_grad_req.mp4    | Bin 0 -> 71224 bytes
 .../autograd/_static/autograd_gradient.png    | Bin 0 -> 39559 bytes
 .../autograd/_static/autograd_graph.mp4       | Bin 0 -> 113606 bytes
 .../autograd/_static/autograd_head_grad.mp4   | Bin 0 -> 59549 bytes
 .../tutorials/packages/autograd/index.md      |  14 +-
 .../tutorials/packages/gluon/image/index.rst  |  10 -
 .../packages/gluon/image/info_gan.md          | 454 ++++++++++++++++++
 .../python/tutorials/packages/gluon/index.rst |  22 +-
 .../python/tutorials/packages/index.rst       |   6 +-
 .../tutorials/performance/backend/index.rst   |   2 +-
 .../python/tutorials/performance/index.rst    |  19 +-
 .../src/pages/api/faq/multi_devices.md        | 217 +++++++++
 16 files changed, 701 insertions(+), 45 deletions(-)
 create mode 100644 docs/python_docs/python/tutorials/packages/autograd/_static/autograd_control_flow.png
 create mode 100644 docs/python_docs/python/tutorials/packages/autograd/_static/autograd_control_flow_grad.png
 create mode 100644 docs/python_docs/python/tutorials/packages/autograd/_static/autograd_forward_backward.png
 create mode 100644 docs/python_docs/python/tutorials/packages/autograd/_static/autograd_grad_req.mp4
 create mode 100644 docs/python_docs/python/tutorials/packages/autograd/_static/autograd_gradient.png
 create mode 100644 docs/python_docs/python/tutorials/packages/autograd/_static/autograd_graph.mp4
 create mode 100644 docs/python_docs/python/tutorials/packages/autograd/_static/autograd_head_grad.mp4
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/image/info_gan.md
 create mode 100644 docs/static_site/src/pages/api/faq/multi_devices.md

diff --git a/docs/python_docs/python/tutorials/index.rst b/docs/python_docs/python/tutorials/index.rst
index c130ce607ea6..8e2362e4ea5f 100644
--- a/docs/python_docs/python/tutorials/index.rst
+++ b/docs/python_docs/python/tutorials/index.rst
@@ -92,7 +92,7 @@ Performance
 
    .. card::
       :title: MKL-DNN
-      :link: performance/backend/mkldnn/mkldnn_quantization
+      :link: performance/backend/mkldnn/index.html
 
       How to get the most from your CPU by using Intel's MKL-DNN.
 
diff --git a/docs/python_docs/python/tutorials/packages/autograd/_static/autograd_control_flow.png b/docs/python_docs/python/tutorials/packages/autograd/_static/autograd_control_flow.png
new file mode 100644
index 0000000000000000000000000000000000000000..804950f8b5bc4cf966cb490d98035ce20791b4db
GIT binary patch
literal 21635
zcmbq*1z42Z`tJbJ-3`)6OE*e~fRuoAgLHREw=~j?ba#W4fRuDgON*3*ca8g;dv5)o
z=icYe-fo0F%=gWT_x;tHh}TLo=qMy85C{ZaPF7MC0)e%IKww@VA%LI2@16~VFHfAs
z<<ybD!w1PU9DI-LAgk>RfuQ0*|G-SI`rU&c3b;sVxv1HjySN)UnL%ugTpVodU2Lq3
zpSqbjIa}G=J>_Hr|FAr@ba8PIWM}`+6WHvXEZ9SHzM(=OPa$%WFV#IVk5;`s)Mq<I
z&)g@ntxq(aK0Xc3g+(aH%~zbFXk7cAHz!eEnd~KRYs)O6+BiG?+j{CPJ6?I^Y@~Xn
z)^T82V=ZAtQ=`XQ{MXo$+~NVW+kO+eSGEt2X=C((SP36KE_&}i2**vBBoJ$jeCS-i
zt!nI2KzRZSD+wN^p0Sdc0pZ{=7}^^Hz6{e7tw6uEdH?_NZQsXzn9uJ=cjSLzL4SEr
zQe*bHSPDKLHF#tgI?#YGb;t<+pWo)I+!tx|!+8(-(PFEx(Cug!fj5wtXMo${WKHxS
zcy?e+tma$c{@VE5;3+ot6t)e2Zk|Ehz5jyjkybbadi8vo-?M(>_x|xcj#yI9Td=XQ
zV+<{F0{vZU$E4@_Hl!sbx!(_6VXl98s^vt3`uDyCgN>1d7l}U7yUHO`piN<+*@26O
z-AF{my1{=MOX3h=O<2)#pke;!$&xN=eW}H5SgbA2TjKFKpjSXwq2xGI&A^C*yXja*
z{+g7K{F$}IcBKVdLqp?JMaAbbjdF~;-|0No4!;jdOnTqL^PhHH)Hv@cU07eQd9yPy
zGplH5grw?wJ=2$<r_RS~aLYk}+4yNpe0vg98<T-Mj8gdGDb|I5WLwqNcA~Phtu5=l
zpBMp~Zuk(TKUuxS1TW>x@^Vx~1q<W__<t0h+t?jveW(kq&J0zpyCfaIf0Xk(Y(U<<
zdskz-%sX7?^SOIs$7$fV4j3-lLK4`W>L*h;1UJjt`aZ(SgDc&LXO96rP4&KMStAil
z={{wGS2VNVf3iDV?2do>^y!7Cr@)FG9|605^wQFjQCvhQF9hic?1#eP_ROWFw=DV{
zL=<s|GbPG0Qd01|_G@HuM!482k)C-{G74ITN}<6&RyLbu308(tzT>Q<r>AdR-;KvT
z_=$nPVtB9gV~Ry5DhVPpzp3a^_WOx@(Q0O9wre7<pdjOOveam;qNf-0<Hx%S?Rt8I
zb$D!Sx^|vB>f|_f$~{h0Bpkxl_>j+ZEfLrR2(mQy^t$e=t_!s$h$k&u7%RTlt6N?t
z(?tsBOV-t!jr02b;Exb;0T^+0RX)Rw9XB|A#Iq32JtG5)ftZS?Y`x>B*9En;Q}->k
zwb*oYbTU5o7NQr#_nVmh968IAlZJkkrKMKi_HeM{MLOn6xBduTN@DgmxEU0}4_nA*
z4U<p1ro!{pCA2(gzP;EhsH(!ayu5sk2T#gt^FmQ^yk_Xz?Ci%se@et2kn!>HApz;>
zBsL2*Fq;lU;CY|Q%loZLvIzbTUi1+L5&xfcDxV+Yuk*uTpE{}b;iAV|O<Ttabu1y&
ze3zc%_}Tq+<WV70sY1V#B#MCbebu2>ohkC}*vFt`W=-R@4$qcf(+cNj{g_+!{&%FA
zN(@fU&f~7{oOj21cTx>vJuh~94_v-?-JOuS$~?oy)?QzAW#Wc$v5u#EPQw})QL>le
z+VHGr<rU)BF=G80Loh(JY{xIY#~i;153jJc@9C-NM$AKB^X*G5?2X^paC_{2e<M>8
zh^XYWWjzzNE}?WQ0}=WS-^BLI$c|t+aB{m|R|pHNzfay`s^gOg5b5`$i0!}8?+a#L
zq>~9e+vH#YQwiN&Dl6Pi(1qu6(MQE2vadW{YT$?Qi<C^;F_f)@%abaCp*8Hg`K%p&
zB#ck7WT^KUM$&Mkjx6DgtAr`vK|C)VmNs$_DK_@NK;VZ!8AKhs6|auv3RB_;Qk-nt
zxv5gv5>o+(w^zf>l2#6d?|Jy@{+5x*sGOrW*u2zX&6?(dW2CRCw<aYn!H8GCa;EpO
zV4l+YduwAnVQ9j2?lnuiTy@qn&(jidcr@CIWQMxi(M^Q{CsHXq)49|4r1^)#7*C)z
zmvQ%GwUXKr5*2BhnLvIpV5O8y&xj?WPKlf_|H_qm)$`XoQfE?an7{X{t~T7mj#zHn
zoaIm>ldkQk(32*J7if`AZPI49wBIQv5GeoVZ$W$R>aJ&MyX=oY`ss_H$4^D-`4*8T
zG0x&B6tl5cl*^SL@cHUsp|vsc<HJk1xRbbFws&jDCq@Pe-$?lMkc5S`VD5kaDrkln
zfm!`Kp2oXXZL;f46DiVe2^h(rc<lUbW^4HvIcOC7LPAW$?$ZDJD@Obd2pqbyc7WWh
zouBk~QB<}J2w^=KgGq9o(KQjINe0iv`FrgM%K6gQ@D4==gB8*J_Iy*5rk`sk2Japa
z9+&KT_QH?o$uXPYsQwn&e7WnXfItQk?&0a-SzZXTfWYQvXZ*Y6y$7Z3SO>NP*@e{9
ze{Ph1HOrOB1x9zr{re5g(Ngqv)wJmGMr)w~X@7CBMD2$3n;vM%PyEpj19yl}Zp$1i
z<`mc9amu7OpPQ!)Cz`!SG?%h%39ar%poQhjxekS)pg{~+oQ<WI;_bQ4S2UP{IZEWZ
z{gOi4W$wA?onJ`ee*XKC=Af%skhr!K23aIf$O$Ved9<$fct60>Q6Q({$94X*E&t9>
z;_g-NyJdf6SL5G|v%F8ctXEFfxwLFEPJ&0D*D?<36`8_ATMqZLrmKA|{}*9HFe^?;
z??|z_dF+Jl(C%9m!zN&Ky;p8Z<XZo|;OPf!+w-l5Gt+(u{XVACw_fq%U2wQYHB;Jd
zXUOjRceBbjxq25C+ud+5>;k)<E#S1?Br&kH9gHDmxN3;{!9GRVMhzC#&Xf*%b(dGc
zSX$>@u#lTMG(wa?t=Y)60Ap=OoqA=dNeCMg+3l~;+HgO<ihuRIspp=x8qjr)*B1DP
zSVlu3^1MoQ0P9A|w_%C*F|<AG`|gu}7F{}r9+Ho}X5o_C=qo<XER!6q=*z0<OGQD9
zV0fi39~}I8Z{7x=4V0?>vrf`$z@Q%O<MMxH*eoB4sV<`%aL_Ep>a&5Vt5|`z!f6Vv
z`}eHk<`cn+-9J^eZ9n!P6#jYB7tgIV%4y7j*jhT%qtl6ivJwxs69>N>_#qj(Tc?7B
z+m!pWb9;`^kPR3~{rMwzs-`U)kIRQzH%KjVg>l}1mIU3UmMaL{fkKDWo0dBfcEh-g
z3Lo3$860&XXJAnhzIACK?{S@p_5IJ#aodSiYk~&KFESLUjIU_3>0hCwbfevg0<#gR
z$`IMvF|OhivE02ML&}$5R)&(CoUGOOZq~@ae<Ki)!+uRT({YfvWjjHk#(rJ&!kU<v
zSgXbmhJZyYU?7Tc$a)xxSd^qT*5asVn<%sPv(NheXk}(j<{=3;iA}?9`_t#aBlSd*
zYL9N;my4q4&DZ+eI(?g)>pQ(R+3XAXR9f15v{*krq^_Z1G*hf7tDw+N6DRy0pZU$k
z2t&>j$c>L^$K&tAb0Q{eZ1Mp;R}?SqjSg7E$1S2Io6p8E%M#uiPh2+o#ECVuaVeK&
z7lxp%5x;pngRdYzA9iPF$Dw^ytHa~@J8SDOk=;z^uP@)eC4p>2bA5ebVqzj3DlN%P
zFOGz?)>_M8xY64gA32v*Kj_vl8~^HR1Q|weqqo;t&OZHl?N+69AJ__Xj>vY3ii&4<
z54%Ht-IU<3pzY!+3kRaIwe;Nc<DulWwUO*)I%DX2Y=&Ux)Vm$ORoB!k06(;ztN7T_
zi>vYlSA0X^l<|`XUAR6^WB;;kJ02s#nP+#j=i<<tsq~{n-0X$wJUy6Q3n2%k$L;ks
z)w^Vd^6*jh1KCs6xCZ*~2PHX!bCo)^j$6_f&h2i;;#OAAu8tOiqS(8Lz~rSpHyNzi
zW7TWJQ%GflhqUM>UMod~?;p^UACk~$6l206>{6N-)|QX23T9hPZ#qJlmHC_Ak#e*q
z!;NFL*t>(B;5@L+y%J;G3vHtKeBJ9}Hb3hB!5Cp8C&`-ny&~5uL6?W~{afd0Iqa;g
z@Q|d`)UBiXi9w;$4&1w!xY#h3=%_x{G7I<o8Wsv6ST|q4%GR%%&FH12xE(dejjU+=
zwyBR5c^UZ83Wtq3R<TBDDm9yho~_sR@%|P-3khFe5j?5hP;}DQYHD0Z&+WS(cvDz)
zLcujnz9c={-D_`a!@`hZOFuq7zM1cS4AHJPA23;vlx#7QAtL|L8>e=;`!Mu|yxNUq
z*d1p5qkup?Nn&HUdU#N~Zt8@C;-``oOD#tl=7>@C(wT$LKW58oEvKmW+hXYyQVhRl
z3TU-D(d~|BgekCh;z5yKPY<N3?FSR2=Ub^-tx-S1(NZJoXbNjk|7L&@QOo%tY1pwe
z9d%@~1*RpakTPv(+ikSm!IH1O&Nc{wLA`vCZ9HxtYLDMa&t&A%ZkIPW;5c)u4t!>}
z@dG(?gX2x<TU$Eo2n7eYfGD;$BtkuxshWvZw`JSiKixOqqi94=$G9dwXV`ZU&(wll
zP^O2i)g+>N-0hq~s?77w5-B37qD5pb*T|x~Cz{(zRf#%0y&@6!jV~T@^1O0R$5v&i
zF|>8E7Da?h*Z9Z~RiZe!Wy8%YI;ZoUs-xOHr1xNog};(mbId2T8`>U73$k9Je^gst
zD%h$%Mwm9+XgjD~O-p%KEuxJJTK^PPw*B1MtHfAtVvlW8OtJ*LM7BR}HD2!9mgQVd
zWLmnMtx=B!VzVZ<@l_Nna=&-BGKaGYpRGvL#*zNTI#XEWkv*5oG-~!@f>SY(zlO~?
z3G^N-PhJdj1Wm2hGx&ksM|M~>y$YIQh6_L4hW{56O<RT8cg+7>A8XV-7)l5{vf&JJ
z{2W9ynF#dkmWzyEAc|_xZf$uct{ZP@DJ!*H)8HcnFe_xG{fK%vjs~ka0Dv*eNz`$Y
zY`f`AXNWaIiEKfk+>wAeqK>zV@KkOu9gJAE@6ouMj1vn30~FWQ8UzK8SSvfxmJ%(E
zv_Y#c#DxiVohXyK(<FtIYbMNqqJXkU+z#yN!D~Du$h<P>^uHEXm|}M<ekZOnsl%|a
z#Xo1J5pCxDH+q0QtGveJ4udaqCU5cky~)Zx=Bo6};li&;d#G3mF2v4o;Jmi92?p0-
zYYvCN!^L15sZPgy(9TC?CG*8!K=rJ;NvdBhhNJ_-P;vI`+4QFoQLNQ6@wOh;AQ_#9
zjVF9{CJk!1X*VOtVgSvV{zY?{_}$A|@7&I5$^nkZPns)2@p)L4eck#Xd5v8HuVz;E
z>rsU1M-XiCN1QHho+gnchB~!o32YA!d()Cb*5~@m1OxB?nY^L~(seb5rz{R{s^CVy
z#F44z<2RdTGl^hvI%w+A;#FpE1v=wo9LJhcGcerS#6|j%x8%MaJP@cAc=afoa8txU
z`u7el#p^>ENWlasUqyTIw+d9Ier`TJJ&g>`my+UqEO>gAXW3!B;-wy#GcWUgr_!;k
z2?miA9``TyEGnF<>2W=k{!0BY#loi~{N?NC{kCS)$`;#$sq@3$D@yl)PY*+RgUbt7
zd7VsN4a3h(YxkMmxkr94C3b>VtMUa~e1r5fbgCGVGfe48c#xOS>fMC}%Xbpq@JH*-
zNu>5&oJ35?civp&dzUl3u+u?@l0(^+e}Nm-uWi!k6V3Yow8Ui#|74q%pu5m6BE{JE
zsAQgg+-i5sdLK<RI`J#smqbu~Bbp5H>$vh*B{Q;tR~;2Vn)1)?f)MN$uotDg*2(ya
zi(-yB(<&^IF)U%%DQy0n_v4x@KW@E7)TxMmR72nI6Xv>{`&cC>#&!8`6qui6gcQi$
zjVoPEpdHX%XmfPTmf%|UYsi&}L>%^_j{Ld(A4kTa4dqKqu^EGt*-5h4=vs66`}rm*
zx@BwSD=0dAwncbJqJI$n3R#qUo~!DMBBxTa-=uw7a>bppQ(Rj)FTr5lU?osBt_v(B
zHf}VRApm>41*@Nt?&t^K+3Gb7%m$&eNZNPe^<)0UVtG*D=e$wWVSH-GBDkEl3`R&4
zi2N+8BYElSnvV1PK5HyPPMGMdfk2U8SV9c@=Rt|+CEU&X^@!2PAu+l%8?~awO2NnJ
zVwz>-6#?e1cJ6{$f>3XbKn+SMruPwG>H(~4gihRa`yLIxy&GxTk7S2+Ib76dBitG!
z-{89%w>CTrw<<-IQVOy%&kQ>wmGE!0s^Ip9@~F<HXnPfu<)B+XVQG$nyxE_qbV>N_
z&daY)+!g6()DOzyFtTO5RvT{YWQpE2@Ysp`=*=n=<L}L?0Znncr9L<&VE-v};@-a0
z+fc9%%R}DyeV~--ji!-d`jKfBm&0LV<JT4(!@#5P^i=c}m#<-5q#Zgx!vuEfR<m^f
zzks9s+jU=q-dMjt+!?z2eWH0sIg<q#D@4BTlBcGS9OmR&4zb$6eOdfw0HsR2sW*TA
z$@-9X=}uF73vM1Nge6Sp=<wWk=m)96X7kSmao;Zdpr48=%CcYcx0*e>BOoVsy8lW;
z9@9E_{DJa^^7W_v*?<caGGxqD2(z6jE5Oi6E^qr`4b}*>eY$^267E`1-i@#8?wQVP
z6MzvEXrbD?8){eip?JUJU)X4J9N9Ag2ubt6?W^g6+CyvQ5xVp4RzOu`kpio2Pz&f-
z9F(%**3+L0SlFin{JEnX)eRWb0(rQNtk#?C7Ib8Zn1=FSYjNto4tqjEI7L%ZZ{Csi
zNbHXMT}P$&PC223I<DLNfkR5?ua>MJyW(D?Z}i-*h|R?s5Sc|B=o}>aUWZjVS|gEo
z)QEC^rOYPk?mrp&63Q+@_jezW0P?YNuWu@NmX|1!_9LT1VMPm5vYCYHN!2<<C{sX@
zT2@M_F-%z4W-?_N5^0Zx{I=Z3wQQET@K#A|=y2fmwe`%}hhcLD(3vT*uzg%PcwrTd
zn6ERQ7(=3e9Gy9l{s|W+_A`}~e=+RoHK~VjH-dKLFWY+R_Rg!MOz3WSwsI91>F$L-
zCYyLRgPa}9K@x_}$Kb*UyS_+V`eKc1IYzj-@tusse9LN+LLkhBf?5_oRsY*&dh3U8
zjI#sB%_dueF`vrH#Ldj;-c-JIU6`lz*nk<!;KQ`YbVOil-NrduZl1VbYI8m0a6i!<
z%Mv0yoU4qQ{}3@uuWvVB>(z{+4OYWI(5t=e^>@cD0-JN!_zlcG3_r<RzSzfrIgsi<
zJJdtSww?p+Ze3{2%X&Hg2yf|`=Kx*HL0jz3XzJGOUd|8{P5M1}9j&xFFPM~-mDO0y
z(02e-({fZdT4Owb0(oI!!En-Xp>)4R`KPD6x+@Zj^Yrxe&WU)j7hi<o2#~`-y8Js(
zEp`XZZIf1c8Ek%hJ74faw?jO;kXIIuixy1S5BFQohl8Xfx3f$YZ@-?q8&P438nqt=
zFiS;6CGh7@r<PSas5sZy*w|0u{8eD@DJv^F#9@%wA{Ss!$Qgin_<PyjY(Ibg{E(3m
z<vbxm$lm!&RX9{pl7Jz&XMPR6;Mw1<v|bvn*EpzTk<1j@!?zX_ylAi4;*dX3Oi>VO
zXE*K$%QtX#nSti!as2FdV5hmYv=rH|d;NX^h-syz2jOx4-y;|tdf^GvCRmx7g?>2C
zSLsCoMGv5Olks$(M1C*ox-yw-YmX{znPM(ZDUwlVzGH3;uXQ{8(@WH`SBb_X=5e!=
zq?64SZ66Nn%xy>C56H_KIcooUDxIdoue$j7AN)S(DB^xFU8L3K@(j|mQ+E;)8BRZR
zB?`ArB8<GzE06hQ#`sS38=G1Y<uB5fhkjZ1h8zvA4UxfuF5_oj&)6?zZw6fs4VqaW
zr`00E6$XGFn4y*fnOs;1Pe@4Ur?~#YT)mO)ebW2;@^JIqV{DI?o15&iRWeBswyj4)
zM*@kshsR9GhYbVu^q52A9a7-o8aj;)L^D;u(J#-{E00_rDKKtFcGwM8Okl)!%wl9P
z!Bs}Ol=1%_rca3d@#(n#IlBMjhq$IDp}ya(S*HKvEp*vvfBQ*}vUW(AmQ0Z`M@jR%
zM4mNsW$tL^Y(fc6ZKa0^m5f>}eQiyD4292RUEFHg`(z+8#K23Q%JJ6LQ7}uZ(~F0I
z_3abDCrw9i#y)Z(GHX_S?(n>DI4DXr9*D#zA|mS1(A0y6qzSq*@!Bi|baV)>IE}Ju
zwKz~gHs*BfrS$dn|1O?i%k=2oOVzzwknHq5DKE6Q{?VT^nPNuQdHv8|!ZmkMeiZEL
zo;GRCwv|Apnw+0U)X=~cNqSy;qMV=_kB9W6!izkeqsoy+UWpQ&h~HNY%a;VKdcH0}
zR=@7U^%aUi32&bJU$o6Tb0)}C4!64;?{r*Qm=bD@x_FlT(ddGq>|%OmhE;5ixjFc(
z-JHBzU^ZwXX{kU^_KGu`@A*BUtYlLz5v*n>zH|<Pu0G2a^U%QcPqUrU=}SbHZ%sdj
zonSH5+gK@s1BU7AqoboGF->04l2+Ikelr^16$M@a5Ki8+iXar<(%%2vzR^so@VYp8
z{RDuJgGIM_8@_u28$+RQ;5NcnVZ$wLDjrCcB%f@=v6A?;F*reP+tfsc<z94G?+;AB
zi{An;7YMBao)|1y?~dh?UNZ)#q!4g(b1yJ;X#4uo6cK<{S=TW5A+Ttf8oy{+IeInz
zjSOyOi?&sni%CXk2?>t}c~3us_eM=GUg_vg-9TGY-Vv9oJp7!WH$WzeTu)Lu@TJ>f
zed=Be#&F>@KkIqNcdD<k(PkU3SNP}j388th&L7{F`9mpf?T?jZwi1gj3rVJ+8V}d`
zodrf}3Zdh9(+a!E8#BxAs+UUHt??bJ*ND+NylsNxOVY7UJ+!2itJ7#i--y@z+Z9da
z{B5Qju4S0WAlZVmLDb5|r`c0AMp}kNGoaxL^Dd0q*=pEKSZ1MtdIt$OI<ny>o2y2n
zko#`nNez9kxt%a@^Bv9(ddrKq5n^p4OsL=5)dQ<Bl;R)Ho)GSuz7;|OrS{~+4vA{x
z;&$WG3s<DjvEGfPSiK)Wu@oQ@<#;nkv*CaGNWE2zV*Ls(-6ENuzjgH{`+adu&qo1_
zl@C2U{w8#zoRLvc*eD@3#7m*_E|v_i(D!WI)v4B-u^R7Z+g(=oRY|TM#vXR)EQ|nM
znOD*+zN+%)i>Bd;T}vNPN*_^!{IHivcoljk4EafQn*pJShdeE`8+;cPu4}~t8tJ)p
z3Srb|0CK1mx{o<+1*2ITbcJ9w2}HS6;ON=X{k<Hu=sw?Ba<8B5^;gHO=*-X6)}&wJ
zHxhBdQj&Jo+SmfCBE#-sBmwN(qxEje)z#GqiZ9RWzb+&G9jbr;Hiv;Ztl!)lTy&gW
zqT^n@wDD?16XcWsut_bGMCtaacNwE*53U|2bS+<ESbDrPWvJx^)>o<wy>ohy4<ZD@
zTv&jLy+p=&c6l5xznxXT(q!3y3W0}*AO7-1K61mn*FQF?589pcD3+_gM<Ae!BA$&2
zvvX$6btupqm#IVXi`rxrWN%mKj0g1%wak`VJFZBu+<+IUcA&^w8@7t7SG~?q-Elnp
zwOG#K;0nv3e!^JkH2+$sf2(qT&XUV^8+E&ErKl8B@CzLsQamZSU$5ztKWo^TQq6VB
z<EW@wqoV58(nZ(6_+0Wcq$Fv3%PU4Y4;(-E!L9qT;{^CUpAX{9xn!U3?Cjh>Jd{vS
zh_aoaAy@qAD`P5iRo0HHqB@BdSiXj0<j*)Q>vkzOm!rxx!4%WKUPJ_I;3E{B3**y!
z`uRJKw{961Cuc4r%JlR!ORJJ&KF%sis3oJfE_yg)sgHeXWVb63<4@U}V;{^F0x@gc
z@Kf~za>}xWB@@Fo<-P}Aq(+rGmV%lZj;a2q^6%!VJHeH;*9$ip+BE>_{^&`O<aO*w
z967IkP^?7mK40np@i@Z6tka#tY)mhv+xcdx-cd3VA#D=JOpi?$a_oyx6~nujbv!t9
zQ88=k&V8Q7bgTjYcz4W!2I13eu{pnZjs}N_LDmxphnQ>iHa|=v>DM6n3IYN`?x7dU
zptz)5g%1ABQ3@7<%f^Kt@p{e6>pyw<8mU@J$!llX?|<j^lxkzFxV>-Wvz9C^L+R(p
zJrp!u{Qc6IdW^>onH*l_vr2VlsFC5r?@N!}j}|qqhi?F+V2phK{ynTNP~&c<Q@esc
za#;+TR7jetg@&sqh>~G_`r@>a*E(lYCzP(W-r+w(B=BNw4+1b1#o;5QXX`w-q5`e0
zYWakRynOq<YPc%)tz-LsepHS3wOxg7D~|WoQEwQfUwEhY_4lGw{XSxg%)nFsKX)F$
zN!@z>hPtj4fZ|WKF~4YZ#MXnj7yX*Whc=Fd2&`LwAfWkdtq$Y;{E@hlmSt6~!BWS<
zpjW>dPkYWOS{GmNW<}rUd;mL+Wn^@;-z-%xbd;@qOgJ$%eE&5&xAjdgA`>m-Q0X#P
zcw9e^_Dd#H*0va51*(r9@S(l7Gv|ubj-Q9DfM!MYgp-Rav1ythd1ak^aa^T3cWTn@
zVw{iiJj<LjQ%XiM(catq(YF8JS%92C_Ac^L+EvGVR6@KZnxts+x+pegw`U&ZeAB=C
z+0=&HstP(g$${{Ogb)-#p|ny1^lpyb=B|T-1E|glrV|43e>gO7JZaq<v@w)4wFhd?
zj(zB4HJoP3mzLJPHTyHN=L?x<#!;bn*Om_Q@$XEV(BJw)0{S;!ynG46$;k=oB#uaC
zTK&tEVm!{>=5Dc@Ud*%Q^+y6ieGe*O3QW)~9q*2thn#G{M3zxG?95?ZJ%Qgf6FM6n
zx%xyKV_wI{z#WRl;=5<a<Mgu1*UeNuA$V-;m8rQoy%(e708pcQ(}KVYUhxTEEttqE
zE6X@Lb5VHhQ)N2zA=i?TVlRGEJbiZK9TsRD7rZ`x-DA-|jue(YW)kN@RkK~m0f_y3
zapF;ng#wb&L&0Dy2f<^p9TAGFKl2JBrekVF*DKDLcJ0T!_dD$Vy=uxLApkRzfMv06
z!p}`zSHN?pes5fj&`eVo^bI4dATGoNhmYaDMQNZsE1ss?Ph%&rpMG{YHWmZ))u&iU
zv|fzJy?^hy`jh|fnVhrQIA1i#Yi;d_y&QiEF<|%(-4p(AetxlHhQpk;1r-+;?gY?l
zYpTsmO$~v$|5v?d*B9RHP7MEkm_shu)v5;r5$2wc;j-m^aOY0caP%(H8#)eZx5)w2
zf;vt_JOit*qNQjll-s*QJc%jx?J0qHbPI1$Vc`>hzzAe~UnK!Mx64NWXzZKUceM_i
z;@`e~GX$FSu1T>{R-_Dt=acJo|35|(*S9_%50~>pztFK&jE~TXZjR=#xM5aapL|ZJ
z+iq)B9wcc7$f0~4r5AFx`jIVD(=f-7Sq=v@zPz$`U*MY=uYhRF|8C&;dldVi1g1#v
z!`(IJT}M76^i>^?8|RHz+guACi8J`@rGdNuWps4Yt-aRc+!z=R7~W$IRx?l@#ZNZ!
zyiz<#{x?scvOPwDUPQ&>@AHan>p`yy-xYO<jj~Fk2Bx0Y+be<TcZ_lgs`*nVc{ZGh
ziqF1(!f%d@MByjH&~+TZx3sg9R#L(c&%<)j8}i)C3OlUsrT~!&aQ*+mA+Ybd-AzLz
zMhSVXr-xoL6G3X<fej3Bk_+bu#-{us?apP3jbImK<*XlpGxS3|F=3v=_fN{%nAbGV
z-loEIYxX6Ucu_6WqCbI!`E_}jmz(?4YJ!@a8JbUsC0Wt;e{dDP*$DEuKOZ{pMWoEF
ztV9>h!4wb>FnLsvezzi&JqP(OEP|m5y>^1`-?P;a$h^KcYgOmf((^yJlH88q(&s)=
zu{I26?K2(2wQ}rr=aKq#^iCIkCjp^%2zQMlu4Ra7HVsvk1qE<rWqpU2)Ivf+Qxft>
z6r9R$oW22b{dR8)H|OTbTOFPAZ)(a$Co8RKTozwj9&x(Y;|2$%FSfvcl9H05Z*Pl=
ziXZ`i{AK!HEqd%_xnrQ(0o!5##2{eKXc6;0wA+;j%2t^&an8CgI@6NlI~2(C`A*My
zHy#{%dQzLEem6dNq>#>fUX{L;ns2=P_3J4!GxJnG6?L>x%(9;QYRC|UHyY@2U0oC)
zY(c0b_TVy>BSxud;Cq?&nDBqfJ)~tX&(4N8@H<Ue+uILyKVGtrfoROD3A+3RPp~g8
zgM?)L^$!MHdEZ)!VehIR@Mq(GgRfe^k*@xW&HcX|&;J?QaRM{dbA_H1H(+{oMkDyf
zq%9Kakqi(Td_EtdwD+s(?xsBLyk1*)#3{io*Q_4Qmx==YEQLk;y@2zs;m()jG(LL@
zHb8J~);fqa^*!m@+S|{E#2&HTR~!+WY!=_^wYwQD)|ol*lX0NI@vV7m)vSJ!#PbKL
zh>x3sDdK@5m#&tV?oZ9uAt7KA_tYJutQ~VX__PJ{@Cu5E&v1jr*wD}*i5U`(K?M&R
zSm=$}TdJDBV1<gR-c9fs_>d)0X2M((qUfe^16y|*y6uX+g!f9qZjpPR;{NRD<Ime`
zZa71#HSRe0Mk!{7#gVxA_-KgGsKvy@%;p=>T_4(wpdt@Y6t@m%JpJ=VO3HquQCi0_
zBV%iegZx7^Ydn|io`ula&9hSMrGOv?hUlHF6M!`Mj>37VuNLSVq;7pghwGw${rdIy
zE{(e32_=)T#F2E{S)=`3i5V~CJx;hWrxP~NrfUUT>K%UVgK7erLCs?LO&O=Ro;jVI
zIM|?db#)7x&XcuAjP&U?_m#By&s!fP@F%zrhCc+Z=bT7I@HMhgnk;INOv8)T(?zG$
zfUS?A!$VCV-ot^>L}+eFQ%C=3jx(k5(DriRn6eCn3C4vnm6{g6t()>MCDmZT8mw0t
zI>!Ybh$1VGUo4ejI|8mtLPCOt5`rUwH@aD+T9@#x{Ws23OUJk3&GKdH!^RV#k(j8G
z1?wO7b!3&uhe4Ml**244I7yW*a(irZCh3D;a1aF-ThZ{oQg*%aq<e;6xQm|_K4@wk
z7dv5xoM9`b>M65osaEu7f6{U0t89y&+RjUjluoZDxrE^9Y1Jn|FK>LhBr%8Th^$6X
zfYp_cJ&TA2@;My8T{oPIIQ_VpT_s2zw)KiGVc<0F&|ebv#Bk}h<bTUP4WpAYUT>n2
zjgOfz#RWQ<OB#mM#x$R}B?jufOcz|-n<Ay<I2vNBEVXJThs6vksNRHnL}=&j<u$ps
z_Vr-u`FG`rik@tH^aw`t_ia5Td|@@#FAVL9;STq_H=CxY@3bOz=k~i4fbYIp$>H`3
zbxc0>EW?vxm=~oPfQ$NiTydnrqN3U*UX;6zvLvYIJzR(yNM|qQ)u?zWf74G)VcPDo
z3>%uMA*khcd!+>L|86G!6gFFIHHs|bA=6Y_T22o6eeY9=Wun^e|3oYx)IzA;jS{^V
ztb$INFOG@zZ`3L@mn}n2n0!sEBN4AG9prj`-|DLuE??|zUwUmqSsL1Cy(Vpm=@4LP
zkN^<3IgjcjS;5#?oasnQw3^_+VV=^SZ1zG21RN3&w%b<Av92#9dv|xY=^{wHKp&t0
zGlJq_Mo%r=WH3;9TK)kd>=-%ZW<*PoPdMuP5=IY-iO&W~ua=?#f(Mmy>f1N&Dv3(9
zte6xwRTPk5VjK`zuh?v(LU$ufJ*(qL$m2Uz#vf_flJpMYg*de80>1Me3zjYQVKB#%
z1>_#iP!yB;9_OmI%9{H)AS-SisOz$u2<-&`b>mC(P19m34ZD1gP&-<rkV>G%!^5k2
z7~)?%O)fSa+>lPnUpty}yxqmZgS+|7&Cum8EU_H#;_lv0dNe@l?8{%(KqR`T-~98%
zQ%KGPq7{dYJTF?7BwjE0&G$gFy_w(>-=7a;Rxn#}PsrfAP}$zzexaeUy}R3ITR%yF
zhA_3Vf)+Ov1XgC(S6rwHEDvT?b*w8lP+<)zPXeBeE$N&&JJLZNBMeSTN-Bb4Fc@@M
z{;aP?Ai-J3j=-0XH%&m$vVDmKOb*IX*Nu<26fGW1ec`|F?(!H>Kv>L{+yfv(kUJ#9
z;Kfe+6eM!5<y7$5#DPX0Tcqs8I4NRW-S3A3{lZ0?C3bxmJsH5<g0ap#gaP<d{`Fx{
z3JFF-1Aqk>NhP_b`P)6shLX>64Wh_M{=BIUB(}oQ7w$J{r%C@Vmtuy8k545ew45^Z
z611D#ygV4d<02^JRm0!tYTJVtCYZSqc-O$uHMGt3)%Xh;KDM*`U>WTgStO&i!egJa
zHX|a4mKBY>G}v5nEET9MYdC{qg6K&ev^K&sQC3!Hwlq`R=#`a~jEsyNB}B(e?Ch8z
zb<y-kw8D5_piwenkeOd4K@~l4q8op3ke3|ZZes$qBh6~2e{w6Yyc{*}H4-^FIUwG_
z`)zh_mLMDriqvdOPnkh{iV^uhd?UTN-vfjlW4UmaJS^|`e_mWr$>D$z1frHBu6S-y
z5qzjoZc7W15(E2jZI7s(Dg6C0y*y_qR*PH4`#&PNG&9Kej)8<4+Pa6_3HwvZ3ADYN
z4!HoB^wOjg>CNX^k;K(SVw1WD<<SDODM=CpcbfEi6VXYgO*cYjI{8Jy@@#3vks6ws
z#8b@bm&`SlN^n7xBE7hFi9jNHz!W}Hh)Nzu9LdKy?7hsD)fs=lp7O=_El)ICnS4eR
z*g(kd-@pIzM=q+is*FGEqnyGN%CItt9Y>@G&xp?}iB)hof_wOynQwLsE$>!+Fdqv7
z@@%<5=o73g&@RE&iJ<t}64GzP&BH^*$49uhxY$%hv;+@)5@^zi*8)u`PMX<vB73(N
zKIDFuA_5B*Txcf3?RW+O0!CLHi7_%^KaW;K+PDr!e>K8p3=0-?!y5A(gi~-uT8ZWG
zV66S4BS`}TN_lyC8?nl?tSt1@(SiQ0t#EHK5G-vPwEbU|e?Hr#;QFtEs2@Lm^Z?8$
z)Yle3*)$<{w%xB;QLcw`10ZRWVvZDOkA`eJk+o}PDTE@WVn{St(`e-8w7)PjJx#Rq
zJpkQfME<)|^1LhgXeztRe}cFmyE&FEvg|Vk0u1NFG;te`cc+wBD__4VtpMb@&|rm2
zD<|&i$^$aEHjCB~mUTqp8;7S%TJ2&uOVGM{(Nb)yo<N%imIW99(H!=ve&S}!YF@>e
z*Q%<0s|AwJnEuPqjrmf874(ge#coRcJ~a3f36<_WJw1oV#|K*XvZJ8EVv2tHI@-{t
zT3m5M@X+e!6$<4A$*CFc;~HEX#;bfOMQHlze+%Re-mie}ULfjzywv!qs!AHXgSiTA
z=c6F{x~Jn<H`7A#le^B4ZVO9N4R?3Gw{PFtct_8^25_*U0S_c*g}+8Jh71(ViK_CQ
z)>e%Y9XhA7=|vVPq{0TUHecbA#~35&KD;5C;60R0rSV-7?VaRhiW`EHOZcneB_X4R
z_3uifXs1w(`iyVISg#Dre^+(9xO=d)vEdf8v$2tslN(8cMsYdv_HgK;a@o!dGHCKx
zmyz$SU?i5cK$F{S*@~5rmX^*B<D?Q15dps_>F(~HA0{qIfStO*>;LEjLb!PVyhi35
z_J^W_v=s4-^M9eLKc{#e@A}IM_Rp;}lE2-I$_9eI;7nA#6Y-KXj_oZrM%ehoVyifQ
zmL9*$G5bKOwxh{N49inCO*p_xX3I5Art+ouFTbfd-tMHD{F8wxYLznrq3SdNXZmd4
zD{U5?CbWya2}H>D&dz3vj$LnG2r889t#}<)4v&sT4b?>8R4L%M>|bI%PFdxCrrF%#
z^5y4Q?s`c(2lnjb>T1mH?rx!0Pfl`jNL?MLDX_h~7$r@yRi9$Xwq0ZCZb^^i@~}9Z
zlTz6n@^FkZY_g+ve3G@;G2@;}A=cK`ZW6(#B0}riajIPn3%NgogLn7&^c}dC4!}1i
z5>HhA8~N8nphkU%MAC9|iM2+F#ZRTzmqY0Be0FG(7pI@&sa(RXfuo~iei*kASjgk!
z;~&h3f&DW#(;<mR3N}AMef<RJ7F0)=$m-_uHy5y!k-|r@VmNy9&=%Ib90SCp<s9k_
zr&j2{=N>Ip<Kq8H1`LZot`=>n0i~l9)?!ES?A}U$yY-k{+E`9QgE7*Wx47}{mvMup
zPJ@@Mqzj|B!A^L>Fp!vaYk8srFuuKtKL;47aHPXCZt(Cjzolhi&bkj2J3OhE0K-B~
zo;Feg51qMoFFBTRq{8~O<1sIMIwU1Y;C!^zj-MPf4dk$XEb%l&*JH0>e28UwDA47B
z<m}dN+nz_gSbHP)rmxve64?zHRltC!ZCKUa>?>g4Y9NouXS96xPTbk~qQ%WfJ9*|n
z($7y6Y%3e`7i#bze+ggFf=w5W1>4+J{hgtm24;J0(Qbt!sM1Dy3k(5}v>o_ugm_RS
z_3!pcc&{w$MZ03CnQKpz<Aw);!`isq=g}q?rUpwPvKUnU<>rt-Vc&CL3jR*5_*L!Y
z_Xo?wjf4aoov;b@&p_gUsV0$hoGV2FJ+V(r2)%H$$9~V#VH59>DOM6G@aoEqDAWjn
zSBQO$P&~J}nXs(P*w7FH>URVMP7Dkm!-wQEyO($}?QjSLo!EU<FoCLZ!F$TrXGrBL
zpUNU1AN?-=tlD|ISH|%oVLX8y29nS(hw$N0fJMG<uj>i2t&@poz>bZ(fIcO{xMhV@
z;MBg#$i(!%f3wEc5?A~w5n8&Cw3)WKWj!(P1|2k$8yN&LUpL>Eje^mjl8|!FkC!Q<
z+13rltVC$yNJa?c?ajlM*47gK{$jwm9MiXzl*AoqzLR`T8{FgDuA$q?gR_*P<of+~
zhG;3477t|;sH2v)wu+H%lrIV=GHK<SDv73hMeU%$RuH7eqlu()=*NJ!zOsS@2wAi`
z%%G*(x=RcO4Q;*j@)FEcDyekW#jw4O|M)SYp@Ex%NLezmuS-*!8r53!2_UI`<yi>A
z7#o@z6h2K!-F7rnuB$7of(@%-dY~OPG&dJ~`ZSD|gJyd*$OD46nM`!)rptg(+NfcH
zC>`UuEc6gYXcG-MB@h!61MZW2u6q1y8#Ig;HZ?n&yq|}qg7bYf*b=96l58;;NPZ<i
zlxv?6X1x6QFcmMG@Z_&*i`Fk2`9<Bxh|0^$3wXI1`+1YJMwEY(*!gl#gL_0MOK%?8
z@L_wBi#RC+7U6PiPJgJdpy~bDacTwq1~zrnY<aLv9O>8fH4P07TmtRO6f;nh=)ANf
zlM2CP#;Uv^y!p|@0rHJhFaR(UcIovhB0*6VD=X{p$Ov?(0)|7MwBv~9wzd-Y8e+9o
z)vwAVd<RSoq`~t|pi$Zh8|1B3Q8;4GvqLqJn+fX2x8!n9v0m_#Ndq$+DDF0tc7R_3
zMG;RbaQ;vcEF?zwGYkptm+VcFbM=>&J?95FkM3QB$seyWPoh%6S_An)a0W&vj8|`d
z{L%wJIuLfPKUQZaCwEUyNIsL7;8tqGy4gfTVOu6cwgn;l`|>4FwqO-!CYP2#au!CC
zNxikTwP`i@Y48gf8N{p8b+L+<91^711E|b)l3SBt-*^f|gFv+dM9VWD6%dS#8plh<
z6mSj(3k?CFJQP$^T#-z<yfP-Ve(+90p~s~h7%?zTn9RmwaN>GnETh+cOgRpuZ~#5^
zr(RE+S|@+^WOj0a$1_Go5CSMUcGs46?~{fDp#|NU6mi<K7-DV2AkrxnB&xGBeRSZp
z<r4LD*dRW-XCux`^NYAKN~vHWJsDd~W=ll{@C%t@_A@Y1XJ&#x(a7F^I$CF2;&rf4
zv-X#!aYx!gjXtCMDuFub!=<fEEs-H~_PcuWK@K#4)p|M5fS?b$CY_h;(5rb@C_mvq
z3l^vPOLZe~0+^|Wp}Zn?W898h(>CT+jJaT7*xt{MsVP7v041TF<<6fu;GW-o-hd4S
zdmX6Q<l;zyK!v_JxdZv7%=Dce`&~~Ct}L~Jupg(VVc^L@Z{6IQDwlnbs3sQ|kpYK^
zjz)uUfGFC(j#r^iRU!G9=@9u_M!bsA7h8>JH*ekC{I+idn8_5FHqcx_s{_{u^ZxXw
z2%ro-n+`R6q}bXxVBB(JY)QKAW8cFW1(GqGStcXqFzr?k#}}5A^dDZPTe#3eFBjZz
zWNK>4(GVXIHkO$D`S?o%i2j1SbY5K@PWhWk!J|WPCI={5Jm933<JF>t$>~}rBmh_v
z)POml60r2VhexfkS*-Iw|EMbYnoz5YCOVz3j~|QR(^uDx@et9_CJE3knwoftLSddx
zziKWl{5)6k6co&qloTWM_oyf+3Fa{r*mOB9D<6T>`EzGfMMnn}NO>Wsg!#qA2!Bs_
zUEFNN(J(L|E;ri`fxN+a=czBIvgj=>#Bea07%I<hJy(~1ZhyTGEtVbg>>Eh|l$jfS
ziui29yy(=m-=?SEJMg1vr;K;mxS`vq{Npcx<_wIz2nq~sJj{;_%n=F$w*TitUY_Rz
zgy$jbJ}{8817-ieQu|_2y73?utk|t=?~p#~X6N7Nl}*{zcOkA-KqWrp<ivpr`40w3
zEY8h^fDH*Csp7BK;PfCZTQ4{~^*;wgK<WTcZgH^jAh`gMS!8?Cv$br%c3dr4(>#(}
z&sRwS_*qt7UTZ#{PUOpwgysemm!IoYD?htXG$0^t`#mN^bZo+0ZI-d0V{&RL5TNf1
zXZ>ozfq?;FAP9u81it_%b!mBde4<sC?bS!l5Oh~7bz#$N5X08oh-b10Q>rC9cn+Sv
z&}dBvcv~&7xS&e(LY>*D(dqAhgICA|W+67Q_y!31y*X0Fnglsx!|@RcT~*VGr#K3c
z`MD(}hzYcS!wNlnA5&}v!WlqE0PqSV<v}0Co@yD`iQT!rn_5_~^jrBSOoC0R_T~)&
zC`8~K9|%%_LJn{+1S(OQkEI1-irr%YU>G+v`AqH`Q1!nwaXc;(Yis#Sl}6`eZ@&!8
zn8{ZG6bfb>XvJVn>Q~Rt<*|AVGGcj3q~_#cmduPyN^kuWum8xDCFU>s<HwKJ1_qc?
zDm5T&4a@{I?=iYm-S>b8!9YNwMF8aZ1wPOs+uGXh##W1XU*&?+Yar_nf+ch?5=g=|
zG6Nl&K{pm9fz)xqmlq;h?q#WYw&7sFL7)mVGCwV6bY!IV^5hGE1kj)K8cyEpzIpRT
zm25ytvXmKw-v5J%Ctb-EQdoL31)OV~cI2VqEGS`t9$aJr8G8`8hw_+e{|9SGk9Bot
z=(2q`IUuw^$G{Xm3urBfvE&Bl#(Z3Lh^k$s%4|T?&v9LjVw<Z5TGWuJKygecCMhao
z0+Ixvi*e=oCVkM>`xg@XN9k^*EQz;48)C~H4LCvu%{FKq3{qd2afOKI8I1z#q4|Nh
zpsh+`hWo;s6Ado#1q2vzP4+tC5#W6@rF55USkGj7<W48<Z@$J+)o>k)+sTSf__Dt2
z>SN+fM2RN37N@JW`+2o~b=V|%98aX|3otzaJ^I^Jb*jw($AKQs1frU@HZiRnfI=J-
zq;!G=M_ba+G_qVCE6dq&7RnsYJ4k@`0S?sM4!ONTr%ANhr+xq%Y`88{eJ0GVnY50j
z|G=e>15GFFHFf?JM@xJqSfb|-BzC?AFH2B7$a>r@+8nmf2r)xtOxtSy>KMZnRRUxf
zWI*w5$`Zwc!moU=bU_>0-Mj+Eft=%{h&Cp)cY;^5?OZD|x%Ea_JBqat<*)7u%+d4$
zg*+?>b9Q|lQ*Rz5C&rjER{Iq}_Xe06jCEaoJxqWGZzQqzpQhJ!0Lv{4U~z(QtA_L2
z2EbiT7VUJknU+OAkTHYp3f4V{e$=<U(b7T+>o+=#K?L*x^t3uM&zOsg3&5@b<odXu
zr-EYiPte?krUF&?b@lhJm>Rzh1D0V}*xff%KC+qes|nRe@c^kD4+Y33X(dq$o%Y(`
zo`Ad`pO|RU1pD*WN3Kyit^#_n7Id@WpTStrv%aTC>aY10I(NmQOjm@KV<Pc#2xeVZ
zD=J_(`qNjvYsuR_QhVJKV5^h?$xGo*Gc{Jnjp?Slm%gojnyx~0Bu1IwXO5k{J-eWw
z;D^M-pp_L}$O|v8w#vJde~@4Qu^Wg2V7=JBskE9Y{<xXMxV8`gj<LCm)fMVJAaq@K
zz2DQJ9?HUR2|WGkvejm#)IxTJMwhrbS<e@O)gN10XKfgjK#L4oQF<}@oj&nWcsMn<
zeN~KLw3WxaNQ2|~T-2UDh+W!dAFw8dofd_X$f6whqk+u{x;oG!A>r!zU%q^?5n?Sn
z6!h};&a+C_)^q*`fKTkz`yv%>_qsiW9tSf;2JzG%t4tIXn%I*th|s8K%kdLJ`Ob<a
z*@WQNj88N);z3!FO8|7wRzgP|RMlWCfcu`ul_>zZ@9Yye6B^YUg98pywAgdhpi7!d
z)qav4Bxh)QF2uzHm+|kK2q+2+R;i@oN6vw!WJandprvD@A*g6+SyI#|r=}vNj>4BD
zrc&3jL7Ca+YywWciLBuvC7KN(;g_37WVX!ge(5q_UtdVT{k>0pZAI3^DxP>QaLq!E
zfK4@e^o-CpTR~FN1O$N`XTgDn#q7<%<NV$pH)#|E01^SBhS$(g0e~0cAehC-!V)~R
zRl8#a0l@d_iW&<Eh;$1!nhB|?sdzK(NnZb=q2IrQ!-VDT<sNk}CHSWo;Rp%9y_~bL
z3^y9^3lK=SD*!LNt4kD#Dz~hx_3Nk|11#D@c7SX-j)n$!bK{>gGEN{6++pbF0wDBE
zQ8<J1U5;pWcxoB#t`)y!2kOTKpHQFbih+|ByOG~eZGd7AJjb%K{E7;jg?M$g)KN5B
zH|pRQ-rhohy#o999mP+IW9Fe5bVgza&1cX9Ac}&%XN5sIrz+=hUu8YV>0k^PoxkgF
z=>mI?Bm*N~WDW?q0kZ6M01xQRhxx84S<hoUWP{pMRtAbOsMRaj?@CT1!iSZJRxGDM
z+&1o5^Bka0@1XeR;*Uxj%tNhb<4zXxjhEo*ch{$3Sbayo*@2B58M)yiXxkd4iWCTp
zV^A`orDgZ}1DMLN{@zCGq=uHkn2OJS?wEC%?q06<`TG6#Bs{?g^VJ%KayLsZgjLU+
zsf9*J_eeqmffBBvG5$<kI|b<dRKWqoR_31~PKp}@sh)rc1h9Ujk4Q_(%7|Bveu{u&
z;BW|h=^sFhtr?N~{#{P!xJc;jEh?&+k4YOYm>`FRn)L-O;!q=se%jy{*4E4c!?5@J
zN|S&YFCU@AAdKZ59zk)z75P*Tuh|zouFu#-C{Qol$f4jlID7J^4Gq`~{lDQzV1tf@
z>$2i6tqn4E`y%AJ{1i1bfXxSifSm=AEVBk}K1|VniLODa#1+O6D0Ai<pcOtR-H|>Y
z19kbcu|q+@5;@1&tAfxHQl1Vmhcl7AMNbSLt1{Ho)Zw#{5zW9US8_NwI0!~l9axpG
zHD>R<xW7qGU^ceJE17*TTSPOJD7OO!G%_hee6mB#-%}$opXgX>y5#z~6iM_?MfvgH
z9>v!bjzw4(3p)CE{Zu!|R|^qGa(?@Ih8u8D&?cz4xMnIOlT%WF)}!6^yU>aR*g3Rv
zobH~|cY|^X+!fd9>!*Btud$|v7chdM=Xzd^Q^EOL&YrKpkh3=*h7UHn5Z|cRMo%OQ
zI_d|arS?Daxl=s}#xlS80TNeWZGs4iin{ufr%s}l($a%QQKto!92JuDt^jKf*V)7>
ztKc;>Hui$^ZM}*>@yEj>yAoM2dz;FLedIq-VA{8l@*M<t?z^=6Pjy`G+ebM{U^8(X
z<np4KACz<jrKH<t)>5nTW{W`V&&Aahh|Keu7{9zaf%@DJ8%-;>>yn>rnT{Z(0_2%-
zS665d3?yXVyh$Pc0*$v!Ph(iWAH_ur9}N4a38=o(inHOkQOJlDYqnaLaz9huc7*H^
z4r{5?ZmA7`Q?<wBbC6u%oIN}|fI7hq7tJ!di>;;tcB5{ZJzZq}3l%)TDKbo=vejQh
z%U8_KUz02B7$w<^7ywFSVm@#L9U(|30XP6`Pitxwhk&!-e>yq7nhW``^Mvgq4)Qy&
zP=^~-%#J57o&(YjTAs?AH|B4?qV<Et51OL611PRx6MLjhsN7S5;~Z$<(ZhjeYGIL)
zV{c~{T5QG0%uG6mYG)~g^VF#UFD{ijGA1YkSIZgd(<lIgVPTOS#DB>%1YPgz`(LPe
z+zb?Rs`bH#DCxdtG%?DCvx}<M=0XG2Gwr1U*u4A>5D16s5A_R2fGvmQq$FOwc|!ow
zPGPQ>?)+qqt6EPHG`VH^jR0i_jZc>?RX~IJZ)6W>-C(S5wSbic3e1BiSgkAPa*|2;
zL@PExz=Q_m6q;k_+tERceX110|5X<%fAxz&22`3qx2#^jlJXhB+Xdc{`6M8TGS=3I
zHHvU`EEzMI63ohguRv#+$?9A<^cfz?OAU>`1OEfq0m{ml0JM)cnt_^GU5zQ4Gl6DU
z#{v|Ltr~rYkdP2ZQZek9Xeh_yXju%#f<^3$f49`652xehVspUznvnlq{`<AOR@z?A
z(1j`VqcD!po*9)8AyF9;A-L#K66Ay44!lG_^21nbPLGd4!a4hPA^z*HBwA$fDHUM1
z_A^=3E*ulDB5}Ah7lVrBYO_yL2Z-ONGL+_J5gQiqa3c+_zar=Re(@rFK5;DU_{M*I
zSP0*`4;Ns6JoCCi8Fp<}3-bU8c7cG?R%P>=xV}C`w$D$wR~ft@vJY|%AVZ%Wz%mLB
z661^BjwlaWFE|oT!-E56z;32QLpnO*JJUDV?zTHV8}cT9p^*Z2QBTg#%78Ohd}r)W
z8((Y%Dz{R}jIJ^PN(eM^9XG^P(bizU-t}jFZFNvu`^1y%&sT|(BIOySI9o$`8|8@J
z?Cky%JVb*}t2~i~Qn63FIE065>u9Z!s552@$E$iju`tnxwuC-;^4T(z#6txq>LvQ~
z_DFb@2!}pe0?U%jqQ<61vuq24Z^VLG#zP)yVP8)#LSOB5Sv($hbBI;lwN;}gj^G_y
z`~s^+hJz@2`GOM<Wk9iJVq&5MjNs;-oEt4GNji1B?8+eqCMF;NChc%Nl4)d=lqLE3
z@B8}1A%jCh#6(1(Yx5ISL`$34_%@7O9D&(FMhJ3jd|UaZ;*IsnYMFVxw!2%TDR%#a
zi5-bU;;Rn{8(<I?+FY5lva%MA-A=mh?0}Qe@^H0mG6Yh#IY3_cnk^DFx|848nbo}R
zm*Kqkb!uv=w^XfU|Maw^s3-(vkmwKRvLYp^qn~U>viGg~#4qIdUr4naq0ewp<XH$O
za8TOT{S^Mh&igYgAtHB9%e=5_fq$#*-KGP7lespxI!GUZZVpVF+84NJ2!JW~nsN>(
zez^7!u0{Sig<aL#`*Lt_5PX<SbR{;hdx^=(V~*{S=QVfY)<%eZ{mW<NX>lS*Rif7Z
zTO-#V&xG2?SER@)cQ0M%E;F)JE;$tqBSJ3cU~Un~v3Lt}&@rY9UX#lZi8v}sPHxj0
z!%|D9B<*!6m#oFmkWCKnZ|D5=UO(@@d$xU^>+ktJ-{1H9H8E*vdiA1L4};olg2meI
z-o^}V=<*Mai!02@QGf}U{{68(2BH{*T+p7kHP2W4_4s>?vYf&u=}j{C^(NZd+Q{_%
z6<X)qlQ-@)G*nFb%Kb_*M4@PCo%UKfy|}g20$6D4B=P{5E@Hdj)2j-I<m)7Fx6-8L
za8%AF_#9kFiXbI}Y8Qx&E8u6K`>njS5WeWYdA@fk80(X+jx3IjPsZ(*Z?`8uQTFRP
zN*+kk=aHV!UImc#o~4V~hmuBi6tT+7QHU7~qq{FJctY&7;zfv`egtMQDOOr-CJUAk
zS2%CfG{s?k2)jTVmB#aqc)i|T8X7?&N}vpTb5Ow)l&c{<RF{{5o&?>G=fBqJ7&QS3
znX-y=$*vuKMOGI%Q;Wj!Y?~mg#`w?0FL_tdWeSg_)#cJm)a3#7^1gW`ofR!wKCwAB
zbu6T!qQaMcArk~V-g6q7n!y19DWC&+H&hpWkRvJZ#4vBcB7t<BNFt}RM#&#e@1u=1
z#$G9k>f8$)*Ec4I7NeX~+qY@vDCKZ578$Gk(S^z1dJ9H^w9%{c=J@V=t*{v<zr;Nl
z6x6dX&~IR#tO(QDwiq4cmMvpn;vm4(k4t3J>~V*<Y`~u$f@1)}JZk7^cc$i#-A(J6
zDK;Hz@}js(3wwhjBdv+n`YY@XgsxPgjq1b?v$ozMN=uUJ1f{vcgt@jX@9|Mn6cb+f
zo<}JRyZnIXq8h<duR&wCweB-j`}|AH38P5gwoiJ>Te{N9zS|9GX7}t8yI*x!A>i{s
z3Yo5?XR~ntN2&79$7*DfkCr#j1=oAeB|IkXVZ+u<Yy5@JP(`}uzTaz+9VtAkk$D`l
zL&f8DkS0NFCu#*kc~F-9y-|KM{@0V{#puzqxdgne_MK^7GIiBLb@cvw7qc+C(8tO5
z8(gV-E^er-yjF8$y&|hUGs|JpH|6bHPoQ*MDqdR`i7|N{RK`+S5oPMGe#=(n$UHSu
z7wz#TcWs+ov{7-3G<7cu+r3t#63l!ad`f5K?5j@u%z?83A=1w{p{qf3|JnXBHIEyd
zh)0U@GQkz6zl*F47y4pyd&n<B@#=oC*eHlYnTp&<bD&9%CwZz~O@l#@=Ya=z*%n2-
z^fcm;ZVAEFmAN9GS6Vh3?r7|D?!*`xUir2&5Y=pllh!M?T2|dZoGFh&J?Ve_gVQwO
z`pY%({^w{fm9XE~ct^+jK><(?yLK)uE}CAP84Ay?sjJI+@#rBp62cKY)PmXZR;A<^
zcR_xl)S8=@v=jMS@)eS~X^3lK^Q33&?Qi6P(oivl|8}})CNZ}ME2!`Dd)we`B&)Pi
z?Vok6C@OHECuaOs)USt&CgU&Lo11AO@746)bcyN-H}LuGE~vkn2A$#ZOnOoVk41=m
zV_D%*QIo%wA46<EoLE>@r7JHZ1;~DEED*=VpZNTC!G(snFJm6p(ct<;J?SPSY--?g
z>ip>5^+6lTT{FN&z@0#N`BgATJh<J6z$ig#nghh{c<h)IfJob|;<lwMFDHN^FO4<I
zj5MDIL#A+P9Jp?srv@e_7OD6)l<3l~W@Okm7PbmIFg={d+0baKed*_7upYC|ca2s8
z+pQi&xhRSIn9tF6XGtqwzxgsWhsbV#TuULx$aL@0oM``4`HCuOX(m&UZAd#}-Q)Bb
zKY7w+U+yxMwyy@GLHve<UKY$KhR7{yo7G@wxIexU`mB=iFaH(@VUJ`yY!_liVdeb$
z_pdWJ&zvXipdX=HeugUl+&#1`J8Ore{tJBLIS`F$Tg}dky_=$TAd$!+sMh-euN!%4
zGM|<pT%Ni=9(@o_I<Wm>K)Z(Zppim9<KK+3C<aDG6F@I8xMzGor!RNwVG)ADIuabL
z1%6WY=Pn(JsABjs5^1NO=M#bFITmXn2Dhg^*V9p4U>`J%B<#JahAI6jw%6`xJU4je
z%o!#>R@m=FxZT_#D;H(9DeY+FaBH3xE-QL@A>7wkjR&*{%h0ve->>K85x-9RfRmRK
zi9{;8^8(qk5CUUK1SGt~DxON&nUK-g*oZw<Y)R#BW`6pl|6|)18F={NH12TFJqw+J
z3-iL~n3xq2@x%>_uHGA#eIDm#?5i-A7p4RH@0>~mHY=o>Db=M3hR*$yS%5(jC!XeL
z9)1rr(Luo^0Dh;JW`+S@*GcR5tNvh~3Q0fUm%r53b_LZxNLY-pu(TY{I+U=W4;b+V
z3AHm3)B(1rR^E81LzNzIg_TuR*Tc({KQ)EN)g02HF%XgO88>QwXmWFP{ad<W41S+=
z{8Vj|gr))e6XD_MSyl0-gz7|qeGfq&0h%x1H4nvJFLIx0oidIzA4HbTPEUt`UJ>pV
zFw<hOSWqjge(=XsLgM;P0JL}da29*KWz;mZfd;%RnT;FWzYjBp4(0BE(E<sg>ejC%
z`A!6&0vv<%8A|0f#uz<V+0AOk@N8)TYq;=;h%xXlt1&j?Y4;UC7BoVdwFYMx=Ci!L
z@r(Ul?tWEHAUp(SMwWFczkwG@%OmBjT00*&A8MkWv}h_^nm?N~GRyd8T<uc!mU2>#
z^QHjqDzleXtWs-|n~&!S{N${oi8?G%Uw^+HV0KHx7bU79So7O=I3>AG1lS2MtFWoi
zpq5ax`qHTcRiR*PM4OJ^zK;$5Pu7{iWkoFD0{=iU{hr>gKeHEc*QlrX9F;H21cy2f
zEIF~ocY?>Oe%M)pbtfFh;lwla**NgOk*EdrTuLW$a64Zi<qn*DxR!*5cmWVN+}O>6
z#zDZ$YmE0-!|*}cG6y?a>H+8x+x#)`L^h~(ALITRA+vv6;Kno<G{2B&S^rx!V|EUo
zhuxq-q+h1;dqAo3t}WioxBwq>!7ZeU;a%%#Oki+;vV16c)e0cyg6DSNV54!jbeJlp
z3qPc8JwMhdivaMUIeK(GsA&262g0CVpj|yIo=`&cmf2(vOnn62h6Aoe*Z}N+k$Bz?
z$cJEjl>pSD+noy>!vf5UxlmjH)05rGauQK6yqCF*pDedfQb8@;0BAhiYvE_k+-z$r
zN!_@$iazawrj<g5WomBT4LoT?A)xm_KMSt|7IGDa`hCyB1aOUbqA%l619Q?<!Z1WA
zu#@8(|KthnaK<DyKOt~{5MTp@lUjd(8=P(3eOXCQef#zhkShQnptMOE@vfPu1&ds9
z_wLp7bQwsR<3YpH=T_a=ruauJYxRI65JlBd9+E`jhvfe|66gE{Xf4;q+1XNa5bEmB
z4K`xEv{5aNmU<Z(oSTqQgT|s12-?EM0m50eEeZ~rf|A`RDj5!lu+PN?FgiO22Zz?@
z3OU<v<8Px-W(P@MjlAl7_t@AF%AIXrJ3{cEP=nJaDUi*>RJeP1bmDgXbbW?ut4%S0
zD`AK!5*^GwysQs$W<@w$6Wp+}k7;WD_7{CnR2G)~=jCyx^FLl9<sWqa*SOcVGE!*|
VzN-lNI~ax{9PCcm)*TJ_`5%KUtqA}C

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/packages/autograd/_static/autograd_control_flow_grad.png b/docs/python_docs/python/tutorials/packages/autograd/_static/autograd_control_flow_grad.png
new file mode 100644
index 0000000000000000000000000000000000000000..550d41f6ee4ec9a269af7469b373a0c3cfbbf2cb
GIT binary patch
literal 20585
zcmagG2RN7g`#%1*$)3sHWR)#Knb~A!WM+>yAxZYij7o_RMfS?b$jXWc*>Af-c9{{s
z>+X3zpYQSi9>3r5_Z-jhJd*o;zh3wCx~}s&&-1z;=;^4Do?$qHLZL|2uPI+gp|Cwr
zD6A`l`0yuq-?zHqKe)FQ)eQ;ZE0EAW2EHdkU%PP|g(9Xx{>2)a4myTEyyT-|>SN&P
z;B&{u`xeUG#s}@@>Eq^X%jS2>`?j;E2b-uc{3FQb<l}>uxp3is{(`Wlx8sGVtS`hU
z6dOui`KqCR#@h5<f5Y7-g{}RxZ+%)k_Y~RE%9ZulB67mi%LIk1T~yxA#A#M{-`Bcb
zabgks<%L<CiJ6pJ(*|A1lo4^QN(Vom@R!$Y8)qu{;_PR6dbJ$B3(n56K4PLGv`^{t
z=DFLPnLoho=<saQqNnek{OmW*!#_b-9PlM7r<p@=MH#-5ZP1+X2O3l)9>}-qVXX-<
zF)@7V7wahDi<<~v3Vxcy)jFpNKYh$akAa`Yq(lg@=vF!nax|X&UZoeY#jSCh@a|sU
zpT5I-(|;Zh6_V$C@IBqL+;djm-qF!+=#~04P0jEQ+t5!A?MIu<tV!(DaPhQ6nxuZB
z&8B@8jE59IKYswJ$8^*0A4UFip#)o#u4Py#9_{B5`!n~*-@SYHCqSoRT7GVu3VyNU
zQ4SyF^aWvq*HsRm6#e}Cl=m&GU1<9iPLJu6ko)61eCCpnKD*h)=-C*Wb+VJ+DWRTn
z(R1c<hX29?(|6wAC|R4mziISkT%s0p?EkQsq`<N||M$v97Nd14KUhx>2!amUS@+)b
zyN{P+;o;%EDJp7jy!-2-*Mf52bBUs{uBT6*<~BAmq4G5z#Kbr)ix_pdj}#i!&4m#M
zSP1a(p-^AHeY2TqynDYi8QEWkLM>{HnVH$v%)QO|wW<Nh(!)Ucx7F3>f@jKYaAM=)
zs3nw<jgE-zHC$<nB(CsUR27$$v~7zZGOhP1sJNGtlY@=g`q_J7QBC1DCB~z6yRN=o
z#=>V%1%)y)@@mC9JGV2eo7D9XZYM^ISGX&b3wsQChWeEkN4(gOXDjaie|yHvt<BFR
zFg0?x+9W;P^N;Pdd{GxDDU;I<Btm~Fkmd03p(hCi9B!FY%W*M$H}!(W$i%Ddy?H|v
zCE$Klt~Hz;JDd?drEJnLwBbGJIfdJKkNZN9AbNB5w95RdoxPeXWd|wpcLuLAC}Hmh
zoz-!lc(L@3xcsZ?gDxbjd>%G58+|Us&i`hH4#ZdB@40+XmJGk2{xKwxU_YYBy;k>e
z!fJ7C^0~i3>3h3N2M>$+U|-+3ulucH7;Z>9bvM)Bz?514x#Iux9xWZJmYH+^Zg7sj
z9TMF?b{d%3-(Z{SIkkU%yQTJuM!3$a@Ud1Xa+icHOC=09-wd&thnOc-@CHaLH`yY;
zgN<I@+lq=%v2&wel-b%Izk#;vNciVhqMG*8#gB7!!l%w)q(fb&x*L%XcQU0`v|iam
zyB1l|P9!fq48V98Vobd`sjcC1IX3Q=O;P*Lee9<~JJgu#YqWoS6H$K9hAh85RTwwb
zuhhN4rZJx6Xx@&2*ux$lc~atTC559~cZ<l94B9SqO$0P0eMn(UBSf~6RP(C#oYZW@
zSk0gBPO=uGoV?=3m~N7P?jyiHD|VV0yQ6@Lj&AR!J5F6^LSH1Z)+pvOj`Bdqy2+Zr
z6q5AUoBo}#jA>YZuP1xweo?kM)^~ezv~lKc%>J#8guaNszw=~M*5`O&V@qdfRyUPh
zrS2$XsqUXAF<9<BMFvlzj%_w}sE^ZR3bnz{eH+OqIW~)N#>@k2CN@t)Zc!hNO~&F$
ziH6tV)~dwO*{qFMJQJ}~Evxm!)KTWDCMe#xaqi>CkL7`f-itN6t7CSPZ=LzhpKq1h
z$51E1sT`#6{Uyath<ch-ZPO@QnRG=mjaTf_@yQaOS-CZqaPzUZp`jtuo;+`+KgY>l
zZS!tjo-+g8g`4r!u48SB+tzz)6VGJt2=nM>GOa@18KkIsd9}ShfOJK2?tAXUwagR)
z3x1D(6G<~`?X6zAcvb}#6&00-x6b`q8t9s=cD1{x2uJv%4?B9XW^0>OD!!J}Br$NF
zA-(TW+B1iQsqdfC^^J|AZV$+vHLvx+Lm3(wwJvVc?{E8Gep6T|vo>4tzp~9Q5o$_(
zZ~vDX?T#ffXI@a1^2@wUzN}vv(`)E#l_<Ao8u;sz)HQAGj@7c}MB)pL6jnip+{zcM
zJQLr#H$d9?_U#)A)$pSEnX)pjj}vzD1WRenw4c^&NeNk}goiy(-kZAD`T3j@60|7v
z_cTXEeHw9b@s`Ey?8?faI;&f^xPSioMJ=HPd0#Pjvc%ddy5);FaZpgisIhWS3r%ZD
zUS8*$()&AuYM*_XFI~FyO#Z%%-}+>v+`T<Enlm=LKSp3jJ;Ky9m6dyPc(ND!E$7?)
zHic4cm|I@DS(4j8@URJ6$<Ya0#3V3E<y2HqQuAoF487KT$v1EHDmM1a@t@7$uB_8z
z$8k4F=@&Ye|LhDu<RX$xClxR*Zn?CSfulW)UU~gAr>;(#MTv(ahkb68`OKt*^~^N^
zou2iha!Zb?N5?^@509x@CE%pAPdN%PXv`9~@MC2Sc+Efi5~?j~dEkW#E*jiA-X`lC
zSTYpoP&K()bRq8<KQ{+QEDt+w=@gy3+kUK94OX;di1zm$i@%4b`|fMwcG(1(%Jr5Z
zKIWR<+y6OL*N9fIrF>Xwj?!~)*-sFdh}k{5D@u)2M~3Sxm|WYZR@N!zZ8;T#=8rnV
z{&T9%k+O%%`b8DF-=Xa-H^;XUwrl;*v5LPXtLUU3%DQCc^*v^p<+Sy`N0G>U%I9*8
zZY18k%T%%%*B*fYJWwPbJl=_*IM^Mf5XU;UjVI-H9z6A5EWIv@gWQBhK-m`;|J<ue
zd8Bpg&z6}^2P1{by0wrs@fG+w&ATw7Fp~P6gao`&&dW%G%Xw9^AdA-^i>oojCCEVi
zC)LnW<RPupzgIImlJUX36TmEP>l!eXtIQvebBH0SH0PCXdkuw*&s<ob!1u4p`v*zk
zUbnIT$sydY?=XlLmySNP#XGrpsh*K4rY{!BASsE3#K$hK6e#qfzpQG5?Y@_vbpoTN
z$K$`{=&=vWOwq!sWN7YnE<yQ^nalGO|5OX^C%>~>HvJ-!PV4ebqlOu#NQo|Y{kwZq
z9IuGq^8ZOv(yWa>O3oB%i>>-)+rS>$?e=%U8w`rrVkZ-!M4m_U2At%c=}ioo$2TJ_
zu{RQI6^1yIWlY{xET}rPAlcVz<RwwYzCLPd67M-}HV^#`uAzvQCst8xC<_Dot`A=h
z0wcEVrT=--v5eY;g2#d}!FPQZ&8{i5;nd%?FN@VR_l@_{Fb(iImg3rC`gaFo(-~_b
zxAqxG&q=bC{aUWp!+r1u4$A2IJSow>cJh2pM;RGi^n__nj0hBIeG5#pd25zm^jLn$
z^9}L-n{lre{HALt%#7xWFAMaaOM6C2B=~RE)$c1Q(BEDynU9@VR6x%?wh;K*ubcfo
zY-)-Wsbh0*QNbBhS)@eM6?3W%e+!JrZkYoqu{^0F%G58O`6<mL3gzv{aXpa&qgdZ!
zWECmPg}L0CnDh9<eYgK^i%3;({$isL7Ou}+<)9=+dcGwOTWzWJ-PJ!^$xa=2&eR;?
z^3-!%q-T>671t%FA%$6AOFRPiyE|DnJ8p)2TP%S_fX;0{BaC8nk9)%(nT_!R6l&vw
z7yZ=>`gKK*AwRUb59X-Q%lZjA^gSC~UBA${g{`5fsc&W$YoK!p&nj>`(rfCK`Xh83
z5pQ;JF$n;*_%8H=7^jEFFC44ae-dkH*3LyPGMAeN;FochctQ2u9%G05&A}GX`J`<T
z9f70${Q=bl_?*@-JX1*UHlD0f9!#Sp=Fy+hr4~CGJwH4#xjC`8H+J(LX}Y9)8-h$%
zUqZoQE2wW7P$113k(@j~|BagI+UmE1L{7X~6Bmg_vwZexe2m16#O;^|DbWCfMO{Y)
z*QOix%WF@!q7}qXEMNgp&iqN@Vq$W%2tx&$&&)qDBfa5g?Os5Q*FWQmYe`%8`Kl`h
z0F%{y=9$pn4R-clS-)>@xAFXf%x%87_r*YW9>APbP%0PEukWAQAMj?zJP|PALE9@U
ze|b7}hu`$D=}bP}!4U~>>@eA>$n81_k6I7%`J<4Q?Q?V%@{+>4csZ|Z3TI>P%-%n%
zE4NEC6ZlJeaWaaOQBO}V1aM<tx)eDzug-_!8&v`Kk3T*U*#1DRi$Vbi*VEUheCh@8
zd~5tBUhSr#HP#l!i>$9t{%8H$nLG`tdlHxH)FO?rS<?919n_CAbxOTB#YD8xM*vN|
zI%rL-KRuV|?&%RJJxfD_LjC;t6M@}s6P4e}TTA|1xa35pIv>PD<vw*WwAeNB5BSwu
zAliR!WWOtJF?%j=89VMhVb=a$5r=GI?=?&mE=QGUhvCZpY;aQ6pEXCo@7#&<znvEA
z2bPq~&C|D+NEs!wp;G9#b8+O{Y#e(vzm$dl66auD#P8Tv%ib#ETffWmp?96@?>A`z
zM|kwUrl40vz90T{{|BGNXm+^Iw?&TEd2uo@GK$!=;R;&T)5KEAr{@Fa?tOYe&A@=V
z^xgO8TiI@sji)C^NH3sr))E(`XGD9}0(~`U*AtSnsa5TY^k(iBRs*e_Tiq?iuc1Y*
zh2n3b$$vC8{pzcU&XakVGAA|_)2H$C=#SD*SzM%Zuw6YOW4<j<XdQfI4wG!$C_Rnk
zs_;AJB*>Alc0&){?k_9<)5X6?zl)oN@x;5BGb(qZ*(&JVhphbq|7_*XoQh+&m*dO)
zPP-lAu1KoCD=vWT=t&k`LtQ24|Gn-cQgZG}$9e}p?L8S>@3ofc%T)=>F<KR!NLp4?
zb2N!4Sx0c<iF`ZJ7%ES)q2Ft2m@@O=UA}22Vf10%wRdn*|H43y(GlADKMu^1Iz+CT
zFS=6+=xV-tWL;7CSUpFTbcr{#?<}{o^xp~*v#dDR{GMd~n};Q(Y6Jd>$dE5nOfU9j
z(}m^1kePVo?6+iCdB((a8%9vn1(_29OiZ@LyD8K9;Jn&$EX9LI-o?){_~$2c*XTDT
zJ(*iEUMW$GNR9h3%<6akVAzA!kH}PRIyAVK;ds7I?X~Gvhx&Paddt9aNO;yWWzx#Z
z_GgmTjL@rOcvK<ziN14PocQ}MthgpJa(f)n&d>fL)b@Z1jgFCNvl}n0F4ld{Tb1D#
z5Ez`CeC~rK{7zL<i55Y{yDpY5Vo#3+F|s?&yp#HrHUdSQ&YqrXd^C!#uB&qd^t^6&
zRUFz71mY7z{#-oc)Qh4JMR#@r{pVoWhveU$ciKbhkMMO4vA<Q9<-OC0*M=IF&vHwf
zBI8Depqh}qdP>BXFJImqTW+`E`1fc@8)+yjM|s|t`R+m1QiQ#f_;w+=V35cizVwVF
z&Tw)u%pg^c5T!@^*=zJW$|Vdt|EuZNnitu)U^uyZ=oQrN?(TSVu!4_eQroq~B7xdU
zq}UZ)=ipGhuQ%7HjTN|b_F1M)>**(S+oh0`+@^{NH}pFTv@}z>sp$na1`^op9GlV`
zlm|WEQ8m@XTI!FsTneb2YnI)a$|v0DC0q=@oV@p6WRMc24%%hsVCaNYb~hd8pwXyn
z(1WP#sZ6q1OWroYu=uBgDlT=5iUtiHf?wJVqj}IXYFKVRf6%X{l$Z8B(C=Rk6kM)P
z{ByLQTT>%>eyrNJ3lP^|*b?<Jn+JW;b+vKoRc;F<nR~9~;L$Zg{BS^|g@u(vsx&GV
zi>k`veQ*BeYFlWpEYA5i^*z|$SPn|M^Ui`#P!Kod6}bn-NW)=hbJj3xV9ahB8z>EO
zZ9<E^yA-Z2684iJV;V1>WzePfoNbsUYmk#+K1+4}ogFW!#C>Cok7ZY(QPEZ#@rAiZ
zy7Hldy@Zj9-Jd>noO8?w?G<hy0Z1FuWeNp}0$^FNRn%7ClPE)h@|rIX?8J)o{~R$g
zFwFTiB&VcMRX0D4AzS|CI-2H~`C#+?5m|4lQ2WMoL&fbS9Vi`VJsY?2q4aI1&mQ9s
zD4Ygf?t}=y;fiP}tBov&c2<~pYluka@hz1LZ?QsVH`UC|8Grov5%r~y1c8PrvzrPF
zZ<})X?x)sszSC_?bHA*t{J>{*k0Pj9Gt?4$G-Ix1g)^u5!wtgF)Kogt6605|U#tAt
z)P1~KgTLK1&A*YswK%zrWD-3Z?wq6D8(yntu8djUVg6vVrzpk+M9tW`Y}?CE{8$65
ztm&WeBc#~v9URCCKI4}!sD7u~jy0^EN;v$r)V}l!3x!mgjDQ~nB8>FVrq29)!7eye
z(1Ja;V#h3f$8bj}ePBuRchw{ZdtKRMEw+W(xhanZ(hKhoIQpd*Nu-&`>ZUy~K}YKq
zOh}j8_M$=YU_BA-bktixW}!#%=QAB4uAaF$ar&pIwzf9*i8%3zB(aw-xff;^YG;EO
zb>$DafJ<_5aoO5xCtrMKm-yh@IJD*=8-FAtUQf|pLmBHl+7{Dw%Tp7gBeuU0HN3)<
z^0GvBbgG+#<3y-owX^}fT2gB}UGM9^@ouTyx)r<eSHG+X69e4Lhx@bV>$cO;PWO%p
zM1yY9H~4l}3brq9v+?rsIt(N`xrypJ3^}4#Cm$l@)jSkbT-4*okCB3CTH|&j>4>Q(
zM<wo}-<oOPGm*vefhzkRD)@Q!+qX1XLB9*Urm6<y;|dJ&ZGU_z@ZXzoJPI;`GxOz7
z1Fc1~5H8B!zA!#A*N%E}{7NRD2!~A9`g}BNCltlm#Q+^0vQ)tvVX?8XF>PPM@QBMD
zK0R-rdG9eTvo={xjP#By{&QFsw?9b$HJ8UDX!V{6!cbV!0~7PD_h~NBYXeK191I@N
z$XNG`!U~-V<9j<l@VqT3jNNQLWl2a#puF#;>-X;x??G#3U13^|o>ye@{h}{@a<Hgx
zVG$4azA$29)d8sL0Uj~k&Y|)@UG<)gr&WWWknp>&RM%1>hK8=cefu`MzJ7AN>G_Kn
z3B|>NNWAzjCP@9>=YJ4`Utmc)KIt&?;t}NVt)jB_m+QeA_J$w$-eO%*QzL++c7Qq0
z5t}J*QX~v@dLMI}oKDab)i9kH6@_1CAq=6Z>dR(iWTdKOi`x3P0pFb|ws=RSdwCld
zl?_FmmX=nZnUj)Kp`OCup*a>J;|ngHWajdRW|en~E!J}Hbz1=!D^0QHch?2FxebhJ
zX=;l7en=L5{yIWFtccb5x?hoAe>=R@O2EpdprF8mvr>W)Rv)q{+t2$K<@obmg!qsC
z#6TC!+}0&T!0-M9>-PP?GEDwl>;+g<rtP^?Pm92rqYNEKi_GYlfFHLDce)f#M0Dg2
z?Z1qUE)?Wvw(({KhFxK!*bVi+%(6DyJbUo2vXbiX@G!bbN0~<82^KT40<m>J0ahR;
zC%1*&CP(%-QeRBp<DO4hSg)+ol;xva76vp8pMel5EGnWT!MEFEa_F#pZO?NbzjpTY
zWZ(a153fzNS%u>O%keyUa0?M{M##!=zS`Rwq3gGo`a6KpAYjLp^qh&`+41?hw6yqk
zSf?Fe<Yx>f{`+?aHy}szU-+4Q$$T2~M)L~w!JmGL*x8kqQ)3sjX-GQH`1Wp(Fw;}Y
zpaw(j%D(l`=`*Ek87xirD&srUI%<Z#?*<GG4vM%*a#5riDf5cb(bCQ%0W_$_>Tcoc
z>DdKXoiivWlK8CcVrPPro7;y7BI<IVmFspkt|}^c{g;1f6`R+RadUHDQ&(^0OuCGX
z>hACFOpuz1nW%EaL3z}#<Z*g<vvK^mz>a%zGj~49oqcVEFghie^;QQfPcNSa5rL%n
z5*6tuT;Hch?`6?nyY6?0md4w>F3~hLrbDsG$=x?Cc3n+u(CE?PYMxc#Iz8{5z(}dG
z0PL0~Bu^c?y2r*TJT@v7IQd`qFd<uU1L6KI&9Q2B{(Y)k8z+(x>CHsJEB6toM(?T2
zX1yZCjyWs|yVW)BH9Y8@6Ro6V_^RQp-7rpMO!rslT5H|O=eO_k`=BQL=I&76I<|dF
zD%tyzmfL)Zd;OvLg5zO7dQ+chzTMZ3nN31l$<<XrR7@<|@=1v-0>c5=<k+YT;;~E=
z*-j)<H1b<N2@&X;+(Iae-e_{((f)L*icW39P-r3Q?mJ4q{d4zuyr$iFXQ8H&5>B8z
ztD#Xx3?--SNuYy7yqG|mURFAFed3ruM7f<jJx7jdK-JCm_+Y=e$&&fcerBb3S$%%<
z@6RW4MoDIycL??0I?ImhG0j5ePKVNEUX9Uo!U>6wCmCPgdy_SlRt(&+*mBjv-b#L!
z?d<#e{=Zl9y|z2fvR0<YFyh5@ulXJNK%$E<acQT1v1q`Bu@qcfyX^YGAVjr8C^2hB
zkr1<!5O%NG02bv4pzb*AK>IOpM>KSFWUJ2C=UNQUC!gCnB-@I!I>uhgyeI6pIg^kk
z=D0`bk;1j>>Uf?~|8jK!cJu7(Io$@@2p_hQGZ^o;%q9s{?KbPPUE86t=7A~c6xd_#
z#PhlhM&n#_?-rR3_K(M-CAEjM3;CJI2twLf{pFdE9{KZ9Lkq2mMW{e&9kz+peTj|c
z(-Z%Azyf+?9tL=*r*xBBv()R|clO>XzA~|7q+1X%DkMf)LC=hG%@*P=S#<v!xG+2J
zkJZ)r{UZ_Yuh;6PH=Zdtidy?{i#<4aF?Vvb*>%<`FccCz>y<j^<1YmUc9Iu4azYPG
z9McPhUbq*26cwuTjE$!jB^a}MyE<5r$#3Kn`V#{1y+-&j_OWk0jzUk|?9Q7{4v6w1
z_-i~ad>iPA@R;TR`JK;7b!(NqdRA6Rzkdf<_)XmMKbViUyClhx6XRQ5@chqJl)225
z;h$U2aTs-d$YcO_l>C|&O{J{`P&UXr|2fqKZ!}eAkztZcUD3ls_zW@eT{;ITq&`q>
z%eGma{5;!uw9%lt64;Z%E&c7W!B(%;iKs(3C&v#+6Lp)ic)<t8p;kCdbaR07>w+4Y
z3u>0Ny*mPKhw0KjJM}_&N-lR-;*@2~Dc=~K!*S#i*M>!ij8;v!T|OV%UCeQ^PNUf3
zBDck3TWvN`mKZ~&N*F33!BDz6n)jozU(!hJHHjPZRPu+P(huoW<}#A753+BlWs)=+
ze{Uln$~|QW;EcGUtxbxu{@R^P;@&ie)o9N{c^V~bT8`p<;X?(4E&<B_+Y=M0ac$2?
z<71Z59PS3R+7*R$5)rullQb>)B{HhA+>JNCe-J6yCUQqXgcuq{0@}T*@feqb%maxE
zhk?ula)o0GMZ(L!mvg1}#_j&@Fbl{awd;iQ6-~+21+hH2J%Kci429S-L6zr2pdW+S
zFBZ6M>qb%-sb*U{rEh9Vs68CeSNIw0Z}}~Ie&E%`9KDz*D{O4X&hg_(4)NuBla9(4
z?BW9RKgLcOUE53Uzb%N(b27o~9r$KcoDbPN#x}Ul4QprG(&xrukGRP1W?b~-Wdff%
zS_FqOZ88d<bS#MZ!{{E`_P2g1-aeh|MQ)+@PYRsyYdt4@Gv1vK`54l&LqdF0$d`jx
z2aAT#Qx#}!MO9V&q2b}hy~dtcBtb$`(*5}Tx|_l7Pqs-z6@#~uyl&er&wyJaWw$o2
z!MpGY%`wd~JGS`iFq`kp<mBYT)YQ&ucV%_1g|cGJr@{%#43vY`Zws)cf~nicCH>~U
zO>W(F=;fW*K4d!kJ>f04puKYL!k%$>6ziz~(_}xrO-Iq|ZE|<i!_3UY#>UI>OqWt#
z3SlF7pUMk8Rc_PHJ~mor#X8bTF}U@bI5hvgiQcge0c2&2`Oc6cV@>8-oA2VcpnbZ8
znpR`XNtszkV-mL0SV$JF>Gu9PvR5AtC43dy&Pzx<Q46NvekmX9%6e~2<T9Y0@uquH
z0N4{&p(i_lx=BCWN;$ZNMyD4JLF~QOU-qYfg75K1*CT1e2t8r~>ILMPc_{g*(kAuT
zp(r34?ix>_&?e{8)U%qK&ffHANANEuV6~*~&)!tRYC%B(l(NK?xfmy>B9i60!*q(1
zTG7VD5~%WrT;~*inJ%)p{YF~rnIv*gcHGp>>u3;TQ0OCNSr2;8q@$fz6xaaUpkZ=y
zcmH-zN}p;~tnQY*JsT${PLlj@zMBCn+(_?HSjbR23u=Q1GaDtT9G3Q6QUmR=%~NbE
zk|{%#M<rRV7*IXdM=1arwqef!PZK1<Bl1UqTfOa#e(vX?p|IlO;)r<mG_M54H1!X=
zKZ%9wQ0LB_lisLXoI^|7UgF_k$XS@q<9}ZK$8?cy2=Ai;`9dT=X(gWjo+&|pukPB;
zs}Yx5uiZnmjE`U22rt}8DJ<k~JpS2R!9+|+YGdVBQxMQlr9JXkoM^wJPTNRa#mwth
z0ruHIst>;dNS!6;JcnOMU%Fa0xVno?IFNzfU!Fq}G=&#hKtP}!PGMw9N{VS>bWGH<
znMuQGzxeoAkqS>Z%AMbF|7`6Ske@mB(L|*;$*6X>OkU?r9Jr?CL>#H;<l=&j0yNqG
zm_+gCvB2wjwuQMp0Ne}E!d(T5jEO9IQ1}**a}`Xil-%cy&ieag#m=r@vq&#=5>3Rp
z)VDtS+b5&xQix?xsFyI&Dc|h{r#lOI!MARx-n_{ou5ITON98+baQYuF0HWSO)^Hsy
ze(+5?#))v^bGA*-*ww%nbCO1b_UjoghW93n>nDol^I7rqSg{keYv-O&Gttv_z0Zw)
z^!)krWp)yx%VZIKTnl$n2XoaH|Lo6pA^n7iUFVe)E`XDr-fqf|?ceXN93%;0AxFAw
z3NKM3c`JCB*}vpV+LxE4(#KehWut`$+s$n+*LNt58+SeXBO)a&$Mm7$QqiaSd8|xx
zkFvP|X(PtpI#U~$nBlP<YkO=jbRf+hcT)J2;k%L+X6p3=+eG4}cnxGPDt+IZ`^{!-
z9mwOIzUL-v#upTPYx9YOV2H{;LmhZE03A>Q@A;@_x7vW9@+c->L*e%_XCPD~)!GQ?
z4T6d|8NU0lZ@ImWHGi+KM@<r3EX4PA9^qrG!wTA*egsH7CuDU+l*ryYk+V2YS=_ry
z)VfZsr^9bKIw}o|g8_jzP*9nMk&-|Pntbiyc&YH;zkdfci4<EkvmkA*xORK+$x`sb
zSo4W*(o|XVNdQv2P$7wlWM9U{a_j3EuGG~_-TFv_nm<1^Qe>ib+z6LDbs0s@h5XOw
zwGfrQMi@{<DhHJOYpx^gD4#l}wVt5LRFM9Xw|!2u$k#2@O;}y{6oMKMK@T{ZgLTi~
zI0@I$x!UGG?#f9c;-qnBFLZ+3IKS<G@!p<W-wWBZk9c(@H*UjSo(m3wJ=vac%sQvg
zU?J=_`S$$KODcU5BRn6>aYEP<e><~b9|q1@eIm{w5HU+7GS`b+esx_!k9L1xDPH|`
zNO30eVOm-bKjYv<if!3`U@#+~h>&?ULvKvs4@gYJdFWLPOT+&Npr{b&>h0DitL4JX
z?*#u@wW{!08M+p&s_gaIJiV|(#Se3_wb8vWr)RWeMpO|Wq421wEG)l>lluDlQYc<Y
zy#=5qBGK`UOnK2`2`P=np!-G{(q0@qIvL}eyak1YVO@(NL3{p}50-P20-848-#;pc
zP9!7nXHRGK6}AvSZ*2}$HVA}R%D5eHf9-z})q|TARQkG#gpGzp^S?{j*K{i3u;di-
zi#8fPThUQsX7c}r%iGDj6<;H4Tts{K?%n0(<trK*8i2cD36AKa!K9!FAdlHDTqsjJ
z_kI;Mb1e~fW!?PayvFY$<C3<!OD_nDc3Y$-)~9N7v$IhRcQ!k9gMLy&*FN_qH&Nzm
z1?aGR7cLM`+<c^_96k)2Lfb)>@th0xq{F4UZRO$ij>y%oNgpGom6=(Am=JIm;qe{C
zn0f*d7OO+7B`7@;6R9~~L?|vU77r>dE>;c-3M%{SXE{0OQrN3A*3(0w`1twTGA`ZT
zI$X-yXTZep$wX<S7q%|{Zac3|#icI#zL6(@IjwXRm}w<HKS}!=I?JXpd?yRnSF`?a
z%IyaG{#1n>LF3H}AXLP=e){wY$^<F|`e)?aQI?f(=A7vJh7#=aJv&Zhg*KdBy@l4!
zecaFcOwgr%`C>(LJG<P9AQHmOaV;MwoVLveLCsl?)g6=r0P$C<7W_{C?6Yp~6_vfO
z-{h^BD7Wo6lX1KM(r0nfr#A!6gW09RZ9<eO_;_3TXsrq>LW(;h_~cM-hN;$bHnFI*
z{tR6lUVW(@Jof16FISXt?m%5NxGh+BCl-fWeP-L5&Co~%D6j|$Ams9X?4~_=f~P(F
z@X3=~v25zf+uVnLPB?=e)In^cP_lP6K6J4LyBU5w*-i?!{@$1N7w0N`#F}G}%IOYV
z9rpTDLvgc|v{qMvU!@KD<qWYQnd0!!+0AGG&BH5NJ9+_HQ`e8D_g{pq)<&XzlS%jK
z(s@4&f2MLa2$vV|sKs~_qpZQ`b1)Z94v5CVqWNozCccO=hsG;OqdD>hnq#lpu!m%I
zB`u$**XruK$?|;<@FK3Tp)AJ|rqdiIU$5*2yj+!$<)G02+7%iITPH`?_OUYl{?T5C
zy`D0SEN~R1RoX_o6i~Ga^!V(3Tp(KV%y!E7&OA>~$BMY~@cBz@X$+Hem(a_0Htf$y
zb9+@;e@JeB?`s9e5}P{J&7i|u7J=Ix2&bkpk>@}f7p<ROcx__c@71eUEw&ZUJklbk
zJ-VQt1Bg{J4=e9hM-WuQd=w)ZnzY4ma(NW$zX%qZkl0K}_1|Q<1heeBI#{M2dKma}
z)w7V5SvIKr`uP*}%#r!V*p0N1cX{s7Vsud0eve$T!PI$yT+(VyvA(|U51y}!Q0hz*
z5@PVrXA>Pe*M`mBsVPViYN>92qdhXu7aR8cIm7t6A7Hv6nx!h=vq1Zqm_+O6s;0S2
z`qhaJG?U~6;no5@LPajOLjm0Vb~x)XDqYG`72G5@@2yvxCMLz;XV*#^DKI;Cyy$4p
z*EX^e*A_b|`;<Dax=rvapb2iLq@)BGUF{w88){%u@hO|b6`(UZSW-BBfE3Z)F{`d<
z)}U@F8V>5;l}26X(y^sa7uo#o5CSER@YhCS86@s6*8F6_P60_<>+B>7atZmw!~{rS
z>N|Rbn2akxkjkdE&tIhL#1Fr!l_5<j>$es&(Gv3}7}|j5Day87=;bPrW^3%Ci-E7S
z<qHZ6;irRR!&=clu;-P6OGdXQ?!t*kOG|seLPeRT_o4OE;&#jGt}O%|a)6T4((aB2
z<to!C6bf%G%hSR&6|1mKx#wa<BryT;@l1F932KDpv&dn!PM)68z2Cw*Y)e1p=I{ZC
z51U$7)zL{T7@g9ujWt(PyGlc+kET|f1Dl$Pg9A^hux0Ec9T_4aAt7n-XxPcgbI63%
zGKgFBN*dkwZkX=w?hct+m$ZZ=(5gM=8=?4;r9u5c3>jSH5f>4NW1(I&-D?w7b~1b%
zGL@idc#PAvX}THsnp%!jm#(OZm7Ud8SHBt*ByWF1@>V(1#<+bZf?;BSB?QrsJJpUo
zM;=-N&1Rx`0!G=b>m~TLv~_kl%!thcqc0g%qjR(DEL0oFd}6-?pC=R+LQ?1)(8+)F
zG9l(S%{D_V7*K4#eSAVM=0F0jIsN|G#=Ex#yVR7Io3uv;<Rmd7&IdIGGAUoa7y>QX
zvhJ5I+UN2->c<Zkr{_^&Vc3}?4&MK^6y{Y9+JaqIm7~2NAyGs3U1q9!V{{9m|DuY1
zyq^)+S8~;oB;D~eQ@F<6=Fb$2d^}B5rg<kHJ|$4r820g_hDFffH&n=_+uyTazQjVI
zyO{4-0~cJu%szOrL((YO)!khMh!_w}A3lG629-zZe#PI7Q!O!qBCJ)2=$~exb^ZO(
zM*Fa?g8MNO3I6s#@CkKLGiw~35K^tq4r5V~N;Op%m|-UJ_}%GiVPU1dZqsA&X$d)e
zPOh#>#>S~$#*CEu!Mo=$iZ3akZ%@bqT2%S#*RQ~16Ub*>$P4FW&<Sug@ZGp3uwk2C
zc+*!wl2cBusf&Hy;h(0NMrD4!UOfis`F@jl+$^i@8UW{K*7j2Ac>@lHjIv1|!9$st
zT88%3@w`_WNZMBT_)Xl*b8>2G5O_n_)WtgTswhcRrxzDIy_J=f2P|Zi+!gZ8omrsv
zgGG~<?A-|*!gFE{eK9@RF;Q`O4_ij8@I>z05zM%JNxnsJBCgVzIw2fY(@vq7{rYvs
zvjFj`4Yg#S7bBhLhrYHW7AR0~HX48FSoqIJ9;{lO#@Z#x(V@10eBz)dS?D-x6@Q3&
z5XAw&y%>@!^_1am9G5zPHLl|zg`uG#D!GjIGx*_%)Z3lRnXo9|=Pyn~$^BG5+#K-x
zOwf@#A`FbzxHF>W`oso4^zddetyWNysuRGTb9aT&LX0cJnG3zrvK(YxHKC>OZQO`<
zC!ndjw^wrq?L^_9i1bTKbuR$ykhm&`Z@sYUA~jbs9iGT5?B<@|u&pWh5m#8KN)Yk*
zF-@ICsWSO_4&dp_r`9ns7jd`79>FmI{|e4<-Wkk0&DQ$}rPbn&=keUSOG3p@y5v}S
zIqs8#5-0;cmQuU;xF|wgY*6?*y;yJDxB-boh4NpUp-7vg5zf-DLm>3ye>|VaR|&<5
zM&*NfjF&gs=nH!Um@ARi-qzZnD$2Dss{Gwwlp`9{4H@9)(VsUZC6Oa8DmFGYLq2qH
zcWyQ+$|7Lvq){=#Vy+F$otuD;I2Y2JC43I$ZIBsdUI1IGUYILpA$%5zzn}tfA2gVV
zh{755Y09-nT8P-^ejUbV^QconTGdt<d9y0Rz`*b@IT<Uu9ScR`u1s?q<iv2ch?d@J
zhSFKOwmU&J1q?ADY`_Ty&XyVW36-+gST#Co{I<51lb$4}y!_(%PA#0jf#G#zf1^1_
zE>Al>3bZ*4P~6d&6L^NmQREX4P=W%I8TeCuvBoj$KAl0nP6tqL+hE!4bnRAF+c8Q{
z6qCZc2xZ{?FHlCGYz3rI7dc9woBYiVZaccTjyGo9alm@M%FE-pa3Kk@&;pMzt(5j~
zx&O9pv1J1tOA<kq<3K0o-EEP(Kfki<A0?e0B(XH<g>KbugBY{ev_BKhrT*;FZJzJ_
zFM8eg9$(aE51j?NXz)J2IF|iH=u~3zww0Y*`Hcgt86aG-LqjU;zRaVM+gGp16rwqE
zE}eo)eWA6!9T)K@e)#sn8}vqcg`mrz53%)%ejmt60&Aq|&bZWUAa#Cb0DomwRq~T3
zkp{YQv>-*Q#;a-h@WH`wN($@QJlOtxlU5Pc%;ef$^T+_*AuX$9jvGPornFSm*H;2Q
z+)M79qB#gNh<^V0^XFUwglO&fHeFd*c!njZ>tF(0hlPbd9v+q4sKS5!`gPsGLR>}g
zY0zRtWMt%hc{-GFsl@|8`nCu-JKpZfJ?3|kzE^bhFVXzGti4G^x2M9o*X=BrhstZ6
z6t1P<LKqZ{JolN;ca<?5+r>?aSV(XM)p$YFXmW~ir`1)lUuIR%0^N!S0gqf#Utd2I
zOg=(6U4G{;Tu{4v_Y!JjBWHQJM0>=tL$v=^dSQ(7b9}&Kvps8TZXh7>Oy70X$)|?f
zN9=+c5p{-`%Pi!u!h|ef6_T^EW*6nb7TLM*6%m3F^=^B-^vBdZizbcxdST0ur{c5U
z9t5LbT+rsBr<qP&XL;Xk4eLt=q1E{4No-F~kW(Y^K7zZKy}G^>l<%6Z$EAY3$DRr#
z2t)e_jItoY*o~E1j#oL7Lz*@&yP5Fr_R?ORuCH(P_7Xfj+l3DgP$9!Qve3S`UAcW*
z44`0zO*?*u-=y<4y(Fm;phmyuu!TD^=%w2EnVhHoZU?{Z&w4y#tAaiBHOd#H(;U(k
zfsbxm`t|btd-;_--v>_1;65(^kA5tz0J{Iu(v@Lxi-48ae8R$=mA87)u#=0(IcO(W
zhzDYY7U~`3692V|E>uLmkq1OhU#1)*3O%eX#V#u=%hH}_69t_RoIi#q=i@rUAn}`)
z^hi}_z)3bTN`@~VyZq$^urN~hZ0?iy1jm7*pIk_`Tz9J>%yAD$AM3kKH^n6+&aIB8
zBn&Kt_kPoewJfDk(A_lAJGo1Utf{x6$9IN}n_NYoqn=fk_j5VzXWWfVH-nLxiM$^s
z(4j(_!r|!^4f%?>J3%XBU|=9(`+?ASq8n{(vhuL_wqA-2I@e|i?Oq6847<kkC#ff3
zFle>@EUhKQ&5Z~3T<2r#9Sw~$&@5%>@G3D<rqP8nk_uQ=ubBSyrSO!95>Nzg6(idb
z%KFWl**tiqJ`oE2o5I+(Hbu^2ZUZN9L5$Dy_j(WcptBGDE*4*kfes4n?v$OqAQV`v
z)fp@CbNQ-Mlap|MLIC5Qqg9HM{E_hG-1IXLrs*+G*9?3^yY>nWrW4U!IK*x4g+ce?
z`w|IoC<t+_tIZM$&47Xr`ATv*Hs=aqD9{$1V8=^ihWotXDdl9^C=pXO)i(P`x4*&~
zlq!SaM;Gj$T}4a;AU=4%yXf+TZ?P`Po+IMoXxXh>w^~<xI6zWs&E=?COi@Fej||@9
zdHj;suU|(#Y-P|M7~Hwt05}HZaUIy|DfE*ud@C301|JTV{-+k<`u<NX^1DnA6AFW)
zqfvc*TDjUI%Y(U(N<ZR92)Ju&YwOYA0f!1O^4iWrz6a$!myt5SEoVu1?D;x2y;)z6
znWVY;=0e4MqPm}dM$cK%K3E|zgl`JZfZ_|F4b}55m6UK_jrrfwJv>Y;$Hb9?Wrxmv
zRRet4WSP?V2T3D+Zgnb<%0O$G_mQEN%fQ+`4Ai6PSe|3+t@OHbQ9pP~ngUs>h6bRg
zdb<>61yEVA27Q?Upi~Mnj#R&WD-N~^?r-&1RP+%C0ty^6>J7q4G0WB@9*+%?4XUTx
zFb8wRt5@`K`;VSXd#Hhi$fa)kBmqAG{KZg2`OXuvZ)uc7t=3l;$T+^Dy;Gq*65^}C
zfcUh*w&U#Vto|+$<hj(;RF;uyAXagcVq^Gz(_^}K-g^vJ*t=3NGPJFZ#~N^B%-33+
zIqMSL+3FQ_Km~;CR6M#Ra-LH){@TuG{Nn0rTG(7D$KZm|`r*Q!Mt<i>3b-_rotc3n
z8m0rxdT0toQ?PlK+{kGD*J0EL;a*rGluJgnrxcW=MkF5gGSE*P$kW#P0vNmt+hGB}
zK%t`l7bqYRXK187iQ@z^q^4$EMdByhitOy{ke3OzVgINB-N>f=2GFMuDib^@I~nn`
zq)xzVd3pHI2STZo-Ux^K7zGAq(a7l_W+^w~`ArEo16ub=K_x<);|mj~r@(JiR#rl^
z&HFXLgJr-+{A1uqqbh{fPD-bI(ZtO_!Wu5e%*-rDmdPv^Aim~zFHB7m4dAQ3lv7BE
z^h%v)A!Lr<6dKB3tl(Kuz$Y+1&wB~=Ns$l)#N5@ZqR}<h7h@(T&7ipC4?8!~MM=h<
zg<%FzRCDWO>$FGWNr;WLlD9qK!`P<t6-tZ+L`6l}BCbtJxDnizW<s1^(;m^EI%@@E
z6@paQ#O52=I!J*)x4L&v7P$W8r%wr<_TnMxw9eKWd|sL}FToQJFh$i@C~e?b?#po4
zGC1gb-@~4_{JBRXID0o+djxPC20}MHkME4QHsW-o#H73!TUCK#3)vp7`4r63$RG<0
zci83?X^-qqDIU}SJd@?^ebxebkBS68Vb>LfAg6}u%$)`SUrQ-7cc4YvZ7a%e(NgMP
zfRE&X&;mR0)y5t;C5!t<`UVEWxh0P}Y_EciCMR0by09<@x)nvjcm|0xDO{}%RqRqy
zQiDpIqW>P5*g`<(AU|EMtc4&^v2!m6K;7&+>p$oH&boFAGECHmyKYzf9i7-HLMX5c
z3wd@oOgM7<t<kw}tkVmZhyETicwB&tpwCU=;=YgLi{Kg*BwQ}_FhG|8x$SR!Pt+F`
zLS*g-Q;NC&-#L-nFncx|tdOX1;{P}`b)mSbzYVy=_^Im#2FP4SWu<6emV&}fr98r>
z>1><Md(3seSJ6(gm%74)sw@w(s0fs%Q0ghTa2QndKZ7M<F+jAatV|66PN002mk>91
zB*ZD7qSxJ8;5PpsB(0eHq<A9?t07hd=ry9uJ>KCBT>eE8vPClpSaU(d@JBSzCJDFI
z>s%}2vl1cmpTN=~`$6=G6}nokHr7jWkl+FCfQAkTT|KJ5j5vS+A@dsR<AZG=0kKiA
z5LVZ*(sfFVj?4<z;s78j=y1+1h=?<Uz~x_?hNh3_x#&S^PpHwcY&ic&uH099|6`?_
zTQ(A<Q|nP9AGP_xUPk$}U<eW^7)ur%le)qS3k%z4P7%)uv`8MuOoX@>Z-3Xy@EKC~
zKm7L8PR3Z~vL)f=TC0x7cVxDrJ;A_Yn?!4_>o?y?B&uYtuw0KY>siCY!|c7HOk`@<
zYP6QH49K1!La9cUxLPvmi_Sx-e*{=6Af4_K#&Gi2fI<>Dws2R+%A`u&XWui!oE@U9
z1^s-KmHp}!t{SuN7xpAeu={5EFA$-wfaL=W$~ik{VgOZi{e>+opp$j_ya7t1Yf=5j
zc?Wh}8Wj;U_jl7CkgVB(wJ5MU3$tt=xa;m-W-&B2W>7FOHs(-JIDL53v^#2E2Zpb5
zkLh}rC(!^-P**_M0Tdbz9=yh}Ipdo*6Mz2nM)gb^PH7e5$fSChZ23ThCzBD~(Ryol
zm3_*P^f8F*rK><W$R+Mx`;N~2>$7=T$ln=%&aQaau85!E43e<8U{rB9U$?8&vH>Bu
zBNW+-Z#FT2!DihnYe4>p#Kh8<7UYzX+)vs7<-%IoPzXW58g#%86Oc4XHsr1_?zQ2m
z#21v01K@|<JY=<Vs$!SxEL~!a*jtrr!{UIFUcP+UTK63-J-6-uFe@wTi%p4aT!(Fh
z1V}eXMPwI%HUxZgv;xL+z{ZB=IoItBX(GO@TfM3C2a8Evpe-aIQ42@$sf9NY%6eyc
z(6lK)qUL|ovT~9VdOk$#<uy9(zUDwQFho#n1vH_^-sWRJ+;N?dk&%Ifjo>Vp?g7Ua
zXtSmz7=10JzeVU;P;>CL6N9gY&ZkiR=J3L=9{f3Bxi4vMX2#b0^Pw{~IZ8r8V(aJ+
zfPyPJIyyks(-<JS)JiH_kZ4=%Q_V;BYNgUlURBXv-c5lLqv2zzW@JPgK>-z6j-i!T
zj2#zLB@pPgBcYumCX41=v0~KcLZ;v%THWaZN+|@FE!3>Di-WdkCf*q}#Z#$m#N}fd
z4WuuSSq$$T9UK(LcG*XWvfsTs2O*3E*>8jB_T-)6CYX3~S`Mt&Yi=CQw%HzKd+$OV
z9DVuxR2Z!R)4^4lu?m$!5R9N8A(L%@8&NyYIJAwQ=UpB1&8>&R9<{f(_ez1{nNv2x
z01$;wU2KO;4g4l_W`e2cI^TwArBdHV;PP~L7s}Ad_p(m{ZWb`p;bwT0kj~#KPD%|;
zhUF~3>GpIeRKdjBMH97&dE2gV`4P~oN=irp23#>SV+h%rtd5V5e?=y)NVr_@;Ul&z
z|L2ln|1+@qY0zV$q`?$bd%gYBY=j;yNwC%NjZoU=EO0b_eY*?{EH(rWE(27<9jf(|
zH5*lZ8c-3jLk6o`U8-#%GL&2rSO%@P5!m!zmfVKbx2E7XVog~&M_0_3>SZgLv@e|k
z9x-t#DNX@_Uel^n;IwL|+_fI@2tcbf23YcC;d~-J2+*#R03l=g;x<1kQ2-I6YvR9h
zM$mFNlE7pv<y{+?*TC(X%@<p76EFt2?O6R9<W_Lb>kBfEXSAd+a<ha_QBZxs-I&d_
zKGeZmXMj;o*Vwf>uSVvTy|1Lq6<<K5?(9^3m7l-wbRZA@OS|XdHwp?}BJ=^i8X3KG
zW>9?jkW%OFD*~9wvq3Xr12~uP{0E+~!(oIPB-o3xX7U=mf5&OetjzxPstjVMI}?eR
zIdh|(pgg%rnqP00%u$|0x*7ahuG(ttt`raVLY{n$x?0oDB(*Jt^;R=;b7+xK;FeA1
zRKq4T$w+Cc=ZUy-kzQQUY2SGOxP1#pUnn$(+rx6|5(3!0ei2(}z&W)noQvKy1=#59
zqR<f+iGYZ_zq~zD<$?T!(iBv>Y-a`}<22TkMr~R&sEYevcl?y_52Q#zbo|bLlBG`m
zFrf!@ZFXIW+LPM&iiD7ee0+S^#Kfqt_?wN*qp#>cW^tXY_ltocn9pr<sme3XpAnJs
zy&JJ^92^o;aK3A5f^k;zd1gM`!#tXPnl$R;)yk2P5onvvpFiIz)7045_-b}n!`|Vq
zIesn`*YC&|QzsPB3=zUG1#MZQ&19YiAr^at2<e+belUUnN=TS<s5S!%o$6BGRY2VU
zUV*RboUcpaVh2K}zI6X!vwua8BWFIm_0xyI!j=~V0Ml(6nd73p(hK+Y{9wG1O+uo&
zYnvH(a&*Hq5zBOoFZ1xAM2Ubp6;OJHM(Y*ZY|xb^w{tP1{sSUEMS$-4o*$D}D2fKS
z^;2E+jAeU%{ZMsli$m49)((d%IJB@ui$Q=%e6t6|N$>4%fW=2HaT8PnV7(;rlwsPM
z9;4+@S6lGoJxj?yup8z!*S%i-rNo5an;0QT95x5?0X9YEsIN2=OgcW#J7<su+x38n
zpVHU1?7HYmz>i-j6nQD~UI1uA9=#!gE*SV*7vOB6BtYwuoS8|+#$*A2gmMzKBYk;n
z4e}m9!3YZYJUoh<Nux6}WNa4Bt1_{#va_vCOHE)J=EMfAS)R?arErf1GdPj=Q8;qS
za9Z4E8OTMMp|)?{=h(Ffp%(|pV}mPoBlVF>IMf9SMS3-FST~&aHjmnULJv?*AKrS`
zm4_WA$?<CevHj!M;$8xIAAqKGKumSPbt78i)llYgD7Xm8?B`cQi2=D2bjat!e;Ul}
zeVg*Y-Vb6r(&@m}P;JBY!cU2H@~gUN&?YgQIbUH=4d(GuQ84>>9N<dmDISruk4%Ap
zWhS4zRnq9+k}xnJ<)`KMqGa^`$6=Rh9&G}gvN7Vau{baaEFMtaNM%<aVwcnX_0Mo?
zEEU~{9<8XG*DqGQ#6ZM_g4GQd8w(7B48tLQ4a_NASB;Do-@{{2uiAPKfE)@R+?-rM
zC`)6NQR=ODMyW)DOl2jC{J-v|g)7w9!87>_wbPTJ)zC9~$f|Y{s{OqGd9|Jl?m~O`
zB2+xo{EzD<Q?IYES>sm0O@a9!UG%e0)_WSo?9x)QR_k)LtuNy1539bw_E-p;BxUA1
z1m1^H@12Wv9pkO;g)o3!ZW|l#ywW2ggdC2kDR%V~5g|TxDtLJSTf`OET9J;lM~`sg
zI))bCkcnmdC|i|@py-sQRk;d&HgMzF*szCext@ts1byqJUN@mL9Ek3|zP0V=Db8k3
zWAKx@LLWjW*l|s?rT`f3t;4{Fw<b)llsPkijRctL0NmQrmeD`eJ_<FQl=X^FVSFXb
z*fSl!*jNCt)#xkVrYK5IRVJ#-kG?Ha!t^T(Jtjx+#-w3&ZEbFDE)F!Ys&mM71DfC%
zs-hp=ItK(l5OH#fIwmo3Pd5fgBczbT1BwIvNEs9ecnbnMFK-vU)tHaNzBUfD06+}F
zoJro9Ol5l=#PilRN5}2pY``4>@0E}nwel^Q=I7(PsHnq6agm|w4jx_p@Pod-J|(io
z^&dnGuCY1Ua37NCpG-OP3;BVxV^^m_vLmLpsfqce9wE4sI$gbi3LoUOgbo8pQL+N>
z$`qId?cwuyVEgCy?lVbu2CxaB2jupdyzgD4{{WVHz3bP*K)A8qoN4NGhp{?f3N$Vv
zkbk?Aaht*WLyf;g-yvH_WEP(OISORDrs4GahvBYEc65_8Dp#RJKMo2`as>y0Dp=E#
zj-nLVp~Ql0<myq&2vnk+-yP6=*VfjsHV@X$Buw9Vh&_LM3Z6)SVH1$w2x>eClwUMs
zqqqVJ5#-ExSJ)^>BhgcSJ$-%R0eAh>b#>LiJCJmH2pj?HU&JZ7&myj(h3j6!|Ieo7
z*VE=>L|HueQB+p$GQy8cz9CIZjr&v-I9(Ru%@_#H3rutz%xJXH#I?I({{_N^P?k>R
z1`{<oLFm}Zxy#nbqd6=NC6<x+h>?+TaAqbkBZG<aF>d%(Wb%C7kMU~Jy(c9{Uu-=*
zHrPvY0G@dns_l0{`9UpD@K!<QEG*=ZlbiNWL|FTp8sz_%eJDuq^^J`1qT2^^7q3Dd
z^s(IB_J<))%T3Ck_BS>}e2F8%r{HY@WH)rXl-$67D;=weo~pb>+?m9hl#|1OYB@gq
zRRN>xEUoK^vs|bagN>5jZ2W~@uO5qPATG9yLk3rXJqLXnp~&GyCSgE0_>(l50OTH&
zW0saNO{#LEIx+buGHH0o;8K2i4-aYF51gHxu0VGWtXMqWPiS=@LzRQwN6I5(`;L^j
zIM7t5voq6?%GDb-s9@E6)h-{VUO$PNUQ5|FF$%}xf;cC(-xLx(@?;UtDe6o)27{s4
zO@z5A^6@Zxxno1)&Y*OGWT>2@o=;35g~|e5oFQ`U!jLHM(xOEjOv2`Vntbf~$y-ig
z*><#1>GU{%b^HF9nP*^Np!HcSpc<f~E3<0G$Bt9jy4y6<;`nF2PikS5_q5N##f6`W
ziV8(?L1J%bC&J?G=5BymNkMZnD<tHACV7g+;{g%k%#OGR4~UM5=mHJGvj&|a2AxQV
zN@QLLZ}}%%w}w8Bx#n_`W$^+>{`ja`bJXk_%c(|IgcWz=+R8nyOPn$<`X8R7f7BC^
zubY*4NwsK%<NSG|rluxPe#*^CLcL%r<c#^5od*AH4w&G;Mezv<sW>~Y44mYnP&b43
zay01Mnr7v;4nv;S8~}~c6pkb*_D}~WC%(1Nk`uIqsQuxl4~hH+xp3<pS(oa`@^4C~
zv)@hwx5PdkSKSK|5fO<=NFbfMASN#_FX+$B!xI%DY3<)BAS^8W_1P)=Th4O{o#l2U
zcFM14dV)6?r`LbZE1deO(Q8u=DmXZAyEbBsNFIG%yCiCXnkN@zc8z~F@cRi&>OfC=
z#i{#n;>07SLm_y0{<a%a(1W;+3s>*O!O$FdogRLZ4{t@i&^cR4p0ATn+@MNKAH)?o
zwn}<qRGgmV^Xe{p7rcfd9(2u!`!lRT;mxYhT0pijyT+C-dP`&6+fGZX?~S?m;ZwzM
z=W4U43mn%Yo4GsZ@>HVx%NjpwmpF8(3{(%LWZc!}p@uGduDX4mkeKj91hOt<+vU|&
zC1^Rcc&O{^>#-MMu*7if)PHVY8dcSn2zV`mnbAFt_q2H7+R5uaYfdHBi^qbMgB~38
z>mEH${)EDm$tm9kzFsJ#VdlD&i!?zLxITV<3sbe8Zwd;oLNC_+4D=C;20v2NKSNmf
z398|6%wc-5<(v1*sw(O*Hfyj0UVp3&ubZJU;yR>NEE>rHq7BL_jN$-+ftZG&i3bkx
zVS0Mp!h$V$sao75+nvN0evA~Mka+=Qs04DGSqU8yKe4jN3wB~&<(;FafA%nLZ}Bb+
zV8FX%2515e7WcOnc;T%%9FfR7Rya7W6Cz`x|G(cpE5GL-bD!m7QXNQg)}Ds3WNYd^
zwpKaRM^yL6A1%{p-=B}4BCqpVNx^V`Cj8$&`{#X0|NW>KAprqdt~-<(4i%<fg+o=X
zb&>157=OClo~fMgw9*?fZJ!AXaLSp#ejaW-S@EAYE1ky;PbUO!#PqEbWuJqXPUhuv
zd+&R{A>|vsH7+&{1vk`3z1Q{1KYBnNgAId*DH-Oa?rhHV=)L}}{3Ff^I>em-xlFlx
zJT|^Rxbk>wJ*L6haTW9{@L0VT(tFLZ`wd1byFY$x1N{bm-)pI#`COsl(-*RLZ2L1V
znKt@wzu7zrl!wtGR)D-<k{ifUcv{zHL*hXR#L(JQEjf5Az(9=ngTP#)*lF?38?UFQ
z2T&d|z^H%orm#Wv54JpB#DD-|2{IQ1)Ld?Hu~weTh|>RQ<Vu5@y29uaArz3BAl4uV
z0$LWG&{(yIMnDK_fHn%uI3Sh4*r}GKl^r6Gj(`F+(3C|4+CWHLXsA#@5YgaJmO&6h
zLE53(vZPqk+8CrG*mK)I{`7z5y?1Z!{qDKvJLm9XsCrbEx#0_{vaP{!a(;BL&X;Zo
z4WI!)ak~DoRjHjPd4j#nO(Ky@APULWQ&<e~1*FN5c!E@^q~i+&-P5=?dDH*=UA?V_
zll4}X{;^nj{iPu3ESmpDP~rMyW_pmF6>@$zX=cR@L9y48vJgReL0V$FFP*CUVllgT
z1DgY@E4Cp;T~eDCsO<d}7ae476g=;FkXzZaSfQs~)m&Z}E2g^`b9DQood%RFtd8~n
z?>3gumY0{QT)Y6uQ3p%;;qgmPXTbMa{k;rmX=!QcB^~Ygxa|?Bp7|$FzEb`-k6@T9
zd9t44<^7<R_slY{M4WAC*rL<xgAO-U7it2P7%go0erN6-3gN{Z=}OOQZe{^D21nPr
z7HI;3@D06tAbdP}b#i24!a1etqNB_WZ5l8(p6{Gm$tJEWbg20ifsS|;XfLB9{vM0P
z6(*gLn!7t53|09_mj(s%b4%M|wASw>)VH-soE%NCe12TGlegL!{stqLLH;iGG_-8i
z`I`yx^k%_o$REi~udKYh(e+?TukKF5HNnF871EEa-ItNSX>Tf-c{aNyQ5@V_v#{j3
z*`U1N^1C7MPq$^4MSZYw>YhEzNxLo)7FX6%JvXuzlU_VehNNr$q=!rJp~iq5jaJ(S
z>JO*$;q6iFbf^#~x-#BPe4ptbdrCMnI~$AGFcRED+<o5k23B}@udWf?3I<_F5uhRx
ziRLvLacih){En&-wR(LzXT0NB7rUJcovVpAePnX7C~>;`YxEm{u<QkBb0`1tU>BuI
z8=O3`d$_&Ck<H2Y>yZ(RLgt-64<Aq6Vxe%OiB@T+Z~Eh82lj?F?&!5C2&`NL>UHNU
zwUU&D@rMsp1ypWrtu47Zb2!nZpN3<dLo$-|awUf%kbq`vEUvD0_3zL%p*Tj}6mG)B
z0>cXk3M$*$o~+nufHeT>6$xQT396UN2_%)v<+<3qXsclFe!%A&T%BAa9WA^R`Rzco
z{`0qGPUJ6tI%8&PiU>Oh2>e1qN`@q1Cu09OaL;0_9AHhVP?(@@wzRakXX>0~p%+}!
zH0!D>yFq)#qLu8PrQc5df@rZCI`i1n_n)X#1#3sArU?EaL1sQaK2$DLS8?(2AQD%h
z8f4zEox59C(HNSL!9_W5phuDDoYyU~uT<o3-T<N+xqsh~az8mw*K4QUKI8f}MEtZT
zuh2Ok46DulY^I&3uoqDa9cIRi{?mJ%=!_zGST|;`ys$DiHxJ<PzU;Jd;JdIS77XqP
zI*DuT_Iovrv0EA9%ft)cE$!^I#xd20U=Q)RbGd-o1JM=?1rsj*Q6`N>Q$o~ivp02(
z*#=hcEs=5ZZ$B_P1|1yM|4O|3zP_fntX}8fcx6oFlzDD9CaW7rGfEXA7LW?$hWa)i
H_%Q!pWmgC)

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/packages/autograd/_static/autograd_forward_backward.png b/docs/python_docs/python/tutorials/packages/autograd/_static/autograd_forward_backward.png
new file mode 100644
index 0000000000000000000000000000000000000000..2e13fdb502c5e498486872a36eef11e1bc01fd95
GIT binary patch
literal 63061
zcmeFZRalg5+cu1dN{Aqch|<ysNOyNPC@CF7O1H#_h;%ooGy~ENf|PU)4bt7+-#O}i
zKkxf*+rRRyd@IkAGIPZlN9_B49OncnDM(_XKSf7ELc)}m5>r7!x^0Psgsg>n7kr{e
zRfYrpx#gfDDS}kkOSlI9K(mv2?SO=Y55B<1xn-6%2yPKVk`@zIbG@~haxYm&)9I#9
z!$NZ~?_?&Q@^x#h_3M&F5ljwyIgI){e!c-XsAsr5P0`^fIH<n;O~sf`jO2<<e(LI$
z7(3<5#+I#XSr5Q(F8I$vXX_Hir^i!LQug`N`rMbxFSf5{s9hVmS5j5qq^9t?*cciZ
z%-uc@6-GkFeusoYigXKA2=QN`6C4`&R#IZ3yQ_$`#ECPsR`maK#XHnTez*SjO-Lv#
z!AK$urOW;^zaR3S=Y9Xu#`@2r|9(n3g!)UltBfE+@PEH}23II&>FPNW(w~9+=W5bn
z7Hf%BtpAJ=d=-9zf^F$`g9;Y=e?Fyy4H>muF6rU<?Z4Cc^OQyYTLD@#g2+h!bEaU>
zAz;u$rUBRgd(du*NExvwVMs{-E(+o)zH-msDgAfY|6T1r=KNnH`L9d<C$RlDlKfx9
z?G9sPv%b`FuZ!c?va+&NE>R8tB%^Rc<W^dnu;t$YI6VqQqu`an&5{0%$z+9fDZZX-
zlPyd!O@PDW*s6BFlYWbT$1N;`L{MN(_vzE8n|(glrS<FTMKZ1mj6|+0yt5Y1IBm*$
zk~p&+UnOx^my9-EA8pwjIHX-9*YAbcTAiGoxz3AX@f-V`xcHbEwTG4ml6Y6?qNcj9
z(|WY?&P41^&CeTdj25N!#54c6KDNDqFZU$H?dm*0q>T<Lka4UM8P3sfp)RudE-#y2
zbZRyjx=cI8?FiG;(UmY8wY>@>=C``JzRJSaxVqZ8xh}f;bx-gEeT(De50dL|x*l8Q
z_F*eHLTH1E-uqF`vtB3T`TAGu(OA}76XnjAr+dzoyl(r8&S^<S)B*?HtO7%j!s@JP
zRCvMbp=CZo?zb(e9^2d(S35qoCsPh#x;|H6erdR5z&wrVK;p~ly?^D0gt8y{z0doY
zRX+#s@#BeX!7B@o*^3#odXJN1ZZ-~%bG;HpuXR;#n#J1LE1Rfxv$1am%E<!PdtsB?
z)3savB$sc7uXQH5+z)f=k6^atP8;wuQSEPUggXOim{R1dZ~AU76_d8Yo^clTIP6~q
zU_RqGMP2Soo3(iamhPml{%kpEi(2qZdf4gQ{!3O?oAc(F0m)g1<mTB*T4iPZmUV^8
zjvhS?%?F-1OauZLmjGGVnpMJw49Pc{GX2}j1TlEKz;SVoJzd>KOp#}%u6-+BgI!Kx
zVTIu39F{VffxE$UgIBGOE(=q$GH2&5X2>3xN}<<5(rnqmN}r_rB;K5jg4zqMvJNVR
z<R{hm`aBXspF5%1R0p0sO?7s2A{!(5n%zBkkNF%|m4>j067yBF70wKPl$!Q@<c=dk
zWWJIhq@Q9X78R@X0D=smq`J<d`FLst$971B5nKE0;rq;yz<J8j`7bt0v*PATN?4Kv
zyxez{hEFCYv?j|TaXTSA_VZ%H#c+=3F)^kdVc+42v+c1=**H(8G`&H}G9F98^MNp{
z-em6M@jh?(j(-F1&_uo31QY}RQXs#l_oEPu^-*PBUf$PQ*VTS9Uaxb<!O`*)zfb{;
z!Yi>WhrW;AN_JF=JFUU^^27_(4l6a&FmJlUVc3urg!r&KE;jaji`yRGrtbNIIynhc
zgAPS?!P?Rio%31gbOglkslmPf4XrvJA<Micjmj=yk80o2Y&?(gT4qh#Q=BX@3C1K~
zp(s(1W%}|X5UVm*HCuwc)M-P*1FZIDplm0v{Xz(5B3xZQfu*(K=4vOYnSLQb@7wHQ
z7UvlnsZG_~-5b?Wx^Zb;-IS0g%*_&^gcdl)!b2Fxvo}|>NiCVp_u^i>o<ETlQH5+r
zGS~0Yqd^0fx)WHh_I!TbCfQBue{z1j^{kQ`O%qD-u@ArTw6$ubWpa&AkOv+s^ugsS
zfz^y$T0cuZ$#kAdqAQkxwMUdswJQIdwl5s6U4Oa6#OVf&<}%&=)pX4a^?biobiH15
zX;h;))|<k+qF>}Zl2sbYhY@tnY0!+~u^1tFnT7W@$4u_02%9Oa*mDb_51F46d2*!F
zcJQgX;f`R6rs~DSe)C%3$MbXdoBeP1%D@7P3Igx39{eip{TT<UXDPrIrhnRu|AI#3
zDyiW#EmQ}$E_DZCRD`!gNQlq`5dP}OdJD?2uIWM99`1FNuU{_gf4B9JZdH1)sk2ii
zYIm?JN3WYwjv~}AwYr_yqg(H!W;8)8IWmf7{KJG^xn)dRj_`&@S?|iT*;9$(E5~Vl
z5H*wd<SbR0X3wwN1+NEeYPUEp2ICqQJ)|9($~WZZm61h=AHu#wtv=zhOXojZjLPD0
zy2(!St{K^kDP-`HNzVzZob|3{8ZJ}Z*fWNq(YigL#4-LE(9G@I-{|8Lg)QBtQKV;W
z>vJ*br5G-<KePLu^OWht5at}msB77HwPkCBqqoX~rayhabhFRo<&2Zg?{Rz>)n%R<
zM#Q^Zxpe-8+a-0xbw(Kmi;~<nVe)uwd`aZ>SZgD#je;zT8{al40a)`z-<J|zx@TD^
zVFe6LOKn+-oLD&C?%rCZ`ZwpJ*9K6u?D_GVnUjW<K+-PJtB;y>%{Rge1Ck_1Hz{6V
zw+`wDaYlqUI@p^w&K>}2Kq~cJ-(uq`?DSJ)WTbV|ZO_2p5dRP-{6uo5B<=bz`#Pkn
ztLrL&K!%%fZzNXw`%l%(f^c^OXR5uda%=S0!`_+Kqg{=1WLh^MAuM~WJc^`Zir=PJ
zjgLc#2^iO&*?)Wyc5{lQN+pv}XW@RPNt1LAJ6yZD-nrQ(8S<haq#UFuXaIp!Jo59Z
z_suTW%^P6&H`bL?Z)L@z&+)0w(w4g7F6M>&G=ene6VBaErd=)qTQpKLkK^k^u=sZ0
z35M=?uJBsxkr$;#YJP}meyNzsw^l?O?b3?&%Km&+QgGdDbNH<D-KxmZBL(WduK{H!
zA$7^~w2Fyhd3=fcPp;<i8$Y-zjL=fXD94JHKevtS9Z6(Jn23zi5Rs=OVWS3{FtMH%
zInx2jvu|8xFWO5J=Hv)+Fj*QtHPO;PEp(~xx|xCd*wi0sXji|@+=;>M63gO92|N=e
zIag}&m>Aew=Z@hTK5~C>cpF+ed$sLy9fm&v$9jCuNe*`68hUvT!8ehSp=igY&wkUm
zK{+9jwaMA*3fj^y-mNY{tYxW^#82H6aT4^}bh!-1eo!#I<0Hhuc0Y3=m$#PI=`f-i
z3NF)=OOYOva+M0dTJgCy6Z^VW)r_??_m*i^m?=mvo^r5E(XZR2aM-CPEp&rYFEw3k
z&WNXyUDeXtZOeFW?{1N*AYN8h<MBr}-5od=ZT?pjDTohFGUJb5agCQI`UM?H6t=;*
z7M7|97iWDwGx#bV$D2V@qV3Tr63sFkmcuj`OH4j%)E)io`^GXYKU+o;;PSL68pML_
zq+C8Y9PW&dY-l5>HdagfHEzmml*r``LTZX|Eo!n~btG+cAB|X3pGWU1o2n+HjtgF`
zDMkoV&O{)^2}My)1hGN+@{IjULqj#ET2wA6$NZ=UXm7p1N-L4$0V}wz^*d&?JQrFu
z;-lO22PWJvk8;>tgfH(VL-F=eO_mZ1)M;&}e#fhbcC!=h_Ne@c2=ANk@8XsCp#y77
z5wv_t%i<bhgIDzTRZ{OKU@#wYBHt*#=QmTxksiCcToF8R*{J!2QOm2@Y@NJ9Hax+J
z7JEz;Yd5-~x&d1hMh~pM)3>(j0(~Nb@^f(j{UglPnkTHY!Rz9Zdx$&jw1xVz0ooT=
zDJ#vS_C<b*NR;|M`#pre8ZB!=9+;qeIa2lI@agU=RL>9Wo63wycXZySQ`Jv)T72DK
z4m(oRQ(q|`F5Rdr+)h1LqxsxBaL_-4tp;;(XjW}j_kks#B-N!gk=*6s(SzQBc%{^J
z?o(~dCg)#e`FNn;#?+Q9M5}D#KrnYw0D{8r&i)2OsJH45uZ9G#qSAy!Gd{Vm!JksV
ze3b{Wp!9cpU`x`Au>Rm$zE?P?`<|;%Jz^v`bBdS7IEwqE9ozLMdN-;}W16~7F*p2|
zByoIV8Ig<;(B|eTZ|Eof(_j8`U6<CGN^!X)M9iFeZry&w&7oy@4Q=JHD=o=vC(|uv
z7*n)eB=%4uOl&zIjpaFyLlB2e%^H>9=D?Oqnn741%BPYyAM2ah)K_(u145d~C^?F1
zw`-)#Qz7)@X2PT8a6z}qMa<|x=^)YhgoEUDx5y{fVchmPW-L12wUDPNizDu-1X&(l
zwv~o{(%f2|jTcc^s*ce-ThA{#7S$}DVTo{BOcb*&CP6rxNQl{Qz2`2jk~1HabY1i6
zq;7@Zdo?Z~i4Vdk`?Y3A&vF5+eD-pg)B9LFPEB-$UK*-rSMDs&RKIV-T@)YMl|%+%
z*sAw%8=c@<5g$l=X{Mz(68qC=<RQemj>v+^H3XtP_jwD?qGra8O55l$AJeOXscBV%
z=)LZg4U}yxpHopEuV1s5J+r4jCC`sGR`jVdvciR_7yW10Qu-tQ&RE}M{}WHf&JOBj
zJHO&yYTtJYW&M;l@GzwFTj|f?Ajs(9^z5tA>8PjS%%jf@H{!Z&Ple$&d4s9`I{F>g
zu_?YkEhoinKnPh<thegFuiw$kux|QRVaIt$?mepOBJNz92(byol@~6~G|I1&^G8J1
zt6gam61AsJl*wG4r{B`LMS^4(?ZfW?=fLLj{gsA&9tOAl>38Bt=8qF|4~y99fN7@7
zW|hO96E}o&`=|7_BQhC*a)vfn23#vvs;5|l-lWjeuTHs8$&EZ?na>huazBUeg~sQO
z7#cefd4msHU6~CXw;e@E(Ih0(pcUsU>&fLDaW<Y!X03t!ojy~16b|y=szXCL$KHDf
z^z#V0eLmWe>>go)sYXfiq=L!1Zhgc0pZm&*(~nUV`HqI|#j9F+y*M?+uKMtMKb9#%
zANA5;G!A#>XWH>W+i9&-47*M444=7YxJXO+iOA1zB%tW>Mg3Arba}1RU$wzToS6`I
zl5Hnae@Z9D5^;}ehJRWh=ppsT<^`t}$K@fnuxKF)0tX}Ik5^*LEcljJQ3rLjNne(w
zHp?1bq;h9uxoL9cwnVUy*VqYaN-BKrgvnzRF>L;hhKms~lsJrGg4eDTd$JxYvWCNy
z!4^C01~x=QJ1&hEx))O`0%p7}+n?BIALjW8;xs)iYN^nU%Z}oCEC1EL0YSf{gaAuG
zx?hX%?XUjtN_b`7{VZdYGk^ri&1u?Tswjz7iIKY}V3~0^>Y=%5HfMbb>3qK`N4t<x
zov5jGu?WYnHmXq|7y2yp*fYZUYS3#TM4YKFFW0G^$mNxiRZYd@0j+R;g+F9r!a>)g
zH^SVB?6dH#iByQL%0sjy*Q}ODGDzeYS=lZQj~1Ua%uwPEBnrR;qi8YfS*eF9Y@#3N
zlfT?daX67aer-2Oxt`|X?N5NM+jw|}fcLY*SSU?q-Lnb51PkM99#HdQ6-tlp7`M|i
z!`%JoEU-Dsm*8D@HsD})?Ix`d=8Fn6QfSaOOaz4KK~eLU%Muyg_pI*c8pT9=e&t>y
z7tT`u!jj<=Yg~msB(Hgyj}3hmJu#p+#bst~^uwSx+N~f7Gc8<dvah@1u!yKivC)oa
zS&r8-kH}SHrB6N=TAMJdm7u!SEt;Svs$}|{{vbJak<Ou6laTC&^4}e8gHA?_%IEGM
ztpr$|Cc94dCS@7*F5vCdxn4|R)S;Bqi){7cN}{QuSyKu<^f22_4GIYFjWSb_R&}gz
z){LQ`aBVm!tLgrbr;c9%D2w%x;@XKxBEDvU?&Lm>WwK2}3u`h=ZKajfMUhhPVNvKS
z$@Y!y>|GTo+CBNnb57D)Kq8+9g56&<{JZ<V-qwlu`ES1e4c`i>N%uZ02~o<~`N))z
zkv%VpDPgJk#)4O%aceD|+m{l{uVkB+znVS~R&IzRrB(E$UjhbG&XRP)SWHn4VaS}r
z%3{-;)JmeVodisi`DRI5nt-D7ks)nS%KBT25UsIEG61$x>b9(H4~Cj==CFKryY5Ko
zbbO&u6*kUH-^r<tJ55(YtITw0*pDeXt@Yn1YhHm?g2`Q~si_5ERo1{hy_Z>FwEYsP
z&;iK_VA^NPE%W1xa?glcVT(WOck@o>WEqgB$kL6s>`%%%NRgB;KZ(B9%U%BgA27>+
zT}vl#cz0gsc&v`A=A@@Gqsd<7q@Q64($8JrsZ6PorIIz?b@$p?b&cT@yP<kO$o0#k
z&!%oq!iPWO{A+mz`uGUzCSOjCrexuO(-TBugaaiW67rxOp|gAqdMA=iVV8_A=_dv8
zpm<-lEUm|ecQV4=i9%3T37~4jufeCZ)ZeG}G=aKu?S#%dl=uvd!foY{<))jp?^Hx?
zrE}pqmQ?@YXZpT`fKajEI*$G8HJ%lMAZo@kXPKi}M=Uh4qu1o}BBFX6^&Pb@BAPDB
zPik==!0JzH9a<<7c&rsjTb^lpoM*rI)QL(H0mBx@GlaV?5vbgiaGRXs5h%~zRYbGj
zv&v6nytMC(rhTmNkSQ#Q)op0%vdZgxv!k?s=GyD#V)i7;dX*ruBZ5=LPw!x-fZbrL
z*FH_*WhF=Z_LNu}5kb+!U>N4Puds-=!MB?epPLm7W;EriXQw_lJ2$O-KZvTSlpDvf
zfRBBDNYjtM6E{IHQ{=(4q~<Jn7Vz89%5as&QgH%k3<8@g5n94YEAJGuTgM{9GhSya
zIcsLO<wv18_!ek1nqBl$5*`)m!&)aw+U^<ZPxQ!#f(r*L>G@QcO3#cFvGA5-UDOt^
z+y`o+MCsC?yIgFu?!Al37$4hr-PWeIyIq&=#F)va3J2M`#Y~oj>I`Wl5dyBV9|bBG
zf-5P?zjK<DkRCP^Q|8rgS?OVr;LTaz7Eps4^?$uO8f6iGM?r{v?{6*RwbZ-*_7}-)
zHTS{v(&^DUg0lnAWbPNcAFbzT(^6~&>tB9uRAjHiworB*`&%vf`U#1+JUZhm8A6t+
z78Tm?h~^R|i|j1SStLilrAm`lqGZVc2kOAc6I<<V>^BsDY6QO9l<$-_^sc9f5HMc?
zN2$pGDo8h7OYaUl+?8>DJ84~cmJvoUZqIWE2vrQX@ZM^gJBf<=>)#cF7%-zQvI5j5
z&?jJopl1CKkUM#D&y|hZK9LO<6V-9u0ii$j9S&LU;|zKVM%xBUF7LxHBq2W?lRP(c
zZuxE^QvPN#r~B_nX^pZdt-34?|B{Kx&FLpJ^H2#UOg+fvI+Cx-{V>K!X-oJ%@|hG!
zBw?k!@rX=<$Sm)K?mk?du63E3p&{JY-Eohbe%r-xyjj}U#vho1-JuR-Kb@4<ZD8!b
zuLjys!1t}nSsP6qd-3KOy4zckl(L(E3WcxVQ1OM`#_pg*+>t2XhWwv9!i9?T>VFHC
zja0C*GPZ40EiJ80Nx}1oo)q5EhJ%!y_();{><$xT3f!Md<0e*rRu(*fv<r1}wsH1)
zkK|^6*nKra!$ZJnU9C~Bqr{|3i$BaCyF&xnb-2W7d6g45`kyy;2qQ1FhgH_@G)!(c
z-gqamn8-9g+@2`!LQpu5p`@9`Q8zhP@_VG=nV`7#n#bPp4=Y35zsTYX_-GP5>#D2q
zGFV+rje>C9A4|X-1D~-B2(AYjK*-tUdmuzQiHdz5kC5$#M1;uj!}#{4w6<I2{~3aL
z%6A}2eI7ia=J9Cx@bDPQ<k_@cwGb+n{fz|qwJ6vEc2#l7A?aT*Td#bQw-LP_Z<+pC
zUlq|e1E0l@%Zt2vmb(+867O2vL7o-@=4|t>m*L-CLqe$ueJ8u(17Mxgw7fY1vaVcu
zRq$lC+dq#%4CQN#5RhayfvA~2?Y5F)QP0HbFGPn%I;<icRq@YQf%VtKd<ROb<;8wn
zqt!xN$Ohco&H>di76~%UBOAWq5cz%JizF;BkB+*%E&cCp<6>`S8@!Hz8roOkak8yZ
zE3aFwv1j)Z0}T{Co29>W=_<Zi?1<3y!QKAX&_s}B%5RQ}e5MhCz-uorujT`wZf)Rq
zc<*@3LRwqvCa6$aeG8s{A4+yol?eQIu0l4rRV%3;n?q^WWkkHrFXn_W(9t)oZ9S3@
zGF`Fn?R$SLPXi%Qr*NMmoB?6kNGNYVeFqx$)&j{*73_6s)<{HzccVHVVfRCDC1HZp
z`&<8BS(5O5Mc{<{V$|nK6JlW@&2=ALi(2f5FChh}hBir?Nd3nolTZs@6*o5kO`T%1
zM7Hu~Ak2Fh#Fd!eb`gh4I{emxg8<_{<|u-!U1=jf1{vJ?{XlB)z&$im*Z(|l2u;VK
zhdpbNwCS7V;Fj5W!9Zgnl@L*RA(h<4zgHn4TjNxn%y?X}TZV*$93y0j8K5;p*)+;q
zCWm(cORnat{e<~@+W%~AhYT|G^*Ds*KMV5v8`3~v0&iX45<LBPkcbc%o&p{+&TV`6
z^nX9Z0I^u9I`8lPyI6>)7*m6|<=p$M{x>H1_p7(@AYunn$7TFe<M^}t<I=$9Qw}7G
z{@v8y35JjgEXLeV6Z8KuO%RcsafEVAR_X>I|Lo&`hyCBx{;!ez>#ZICFI<wQx7TLq
zw@02yre|a{g%a~yW-F#uu$uLbfx3;{#rnnu?Ay0~85x-ZQ165n=?iYmHa21s@eIGe
zPhfcG&YiP+!4Lm-*_0%MFwsRFz777amQSy#nQ-2ovZ!_0sQ{^4+rhy><#w#yGk*6n
z`80uPD!KSK;^N}cx_v+Yi4%@kw{FZdc1FhuW|)d6YqBw>@O}raW)zA}P%ktBDXBa*
zHg<RJfwgQ|Pb&W;DACz2EiDmQ`Un3L9i^~91as$dz(+)xE%Nm6witTN36M=DoV+C!
z=olF*+zyt<KZ^%n+WibIYhwTR-3;Kq(fv%PKlk+y4;$xe7FCo(MyZHq#tNUcaOZ-m
zt@DsP=NVaq{ex1Bzv0x^2IP_riPI@We0_&XL)(Es7VV)#5Zn(Uk7Fpr&_pT9WMpN%
zisGA}$GrMlQ31DAOsxhL5r~zQRjE8wL=Tia3-lpTkEs9Vw?WBw#^h^8Jt>GewP1&H
zU4Zmz?d0Ud+&=Xnh?NqVfYT-!pqQ#@Yj3)}4=oedR~K9T8KP80x_p!04E$5L?3)p#
zk`0jH>r}_?{SD(ni6D#1vf9}oKKTAH{GIzR-+MUj_3kIz)}Ttbe<yKp$E9u2LiM;q
zx7L~RK8%ph_1m=f#TFbaMDNr`$UjkD>K0fkbkac-V7k7>Xowo_Ho#?7dW<9dN!$)u
zH*xEZ=lD!|WuVMTdA~IJmB-bFewZ1<INFlupV%+-5zTR%dg77G``=aa#oM>=3L0*%
zFBU$sLAhr<4|=?xUa1AM701NcjF*@=FU4rKF$8$+Fa8Yh>I$qj`1EJfeC5bMqKQsO
zCjr?-4_m1j3(Bik<49{-cl+-BZ$STE;xO+|k2lJl$v_^%v62}f;c&YERnMSZn~Hzu
z@*K?NseuDMVqo+d1uspE?<cj4hj7lA)5)YTKI5>8XV7opD}1Hu7XfPhnfdfn2;u*C
zp2G&WqM{}t{;K)-j)lo)vVtMY&E&04jiW{7qqJBdof2c|sh1s+#BME$XCy-g&VM(U
zw0{XCW!=#DXNV78`tT>#Sr`oQu#LRj=q)?lF97(NbBaAbFK<8CuAT4=C~N-7x4H^Q
z8SteK)wd8IlmgJyg6XF7?Tzoz)@0@6LyMk?-MJq|=f(C568X8gLF0W7{;W0_UM!Lb
z401NegSZg@p<-axZQ)9Eo_Hm_kF=}oGN2pf>>M14@O*<ma|0+d4)}6?UG*~nAp%j<
z#?Cj#OPQaiaoXxll^ZZqXITWQoG7O7urJ$aS6BsU%aAUM{X4yI762~(;aq!YR<jNK
z%kP@+-+Ms#q_nZtMJ|b>r}=1deSN(HX?z;@$JOu6Al(5VYu~1m;3asY0&-pwD5=lH
zYbzwMP@3vfZPL)wmx6AD7DsW&=F5NPH7E%HhyZ*F4YV?dB1?u5v%?P16IuA$WTzhT
zW%D+Mfxe36<3KEu<I#!M|GAJNU?C}ASv&$;`P~E=hs)p@v!R&WVCXLQbTz+mM|eE>
z7`jhELBVFPjlJz(o7H;=TKq;FSziGHGmWB<%=OTrD?>D}ZL{kuQRgL1@#6|tBeb+1
z2+{tx$K?X+l*N?P@&(3%9rqm+bq$ZPp1dSA)z8}cFxu{#K+g)BJ^X7;;q(Zs#}f7F
zHkb+*vb?;!hPpc5P?3PwdCO*384(^H9<wfw%eGFpz`x7f&yKL7A2Aw;^+VL*4QXg1
zq`0oUx}^!eiH?)}$a1noC?&r~vxXQAmf1ESaEoNV=jZ3~%5l-r;Mpxid*`_-xBq`G
zbj|PsG5)nMxt`#H8)~WHOGlhP<{u74`B?g`&yfK9DI|egWjE&s8a}$zL>a8c=VLyO
zDx$Gxu7<@uWZ_fz*V$GHfyb<z+OvZ}`J(y;<1<a9@_UdY%Uf*!$g=B*jeQ)vQjDH9
znKHT8%)y>|v@tr_48^bpb?)hL{_Vebx5Et>S8|%+T~ttUX7>fX3Hs@|RpnywtZ2`!
z*f}|Cb+1nn6Q3g5A&m8@yo2IkB~?{pJ2y2>8=HCgg(aXWZDKgOhWlq>NU2d<uBVc4
ze;8sTEWal8U7f;YrEMi5X@JfBES4zSq_3s*bST0VGHR%>z6@4twIwqkyJcc+D7&^g
zZi@KtdekB|khk*FePrxaa%8Q>a*q=m?P5bH@J*&pBM!S?LZIQq_h%QCD$n&d5TeQJ
z=ocy$j`lj~i^{+%oO`K<Q~|fts`Hy6;C#Y^@+WF{h=AGtl{`Rt48lb#l_c>R#<!es
zM&x4cGV=&s1kJ$iXls+WQ38EB)3gzNAl*tfn9bu;iYmLB_CIRo27MbopA<ZKik*))
z$A9JisThMdklq2KrhO-c3pUXPS1D(<$^V|0Z<bQ~8woKeS8tl&>|-Xq&7yIhw~HU{
z)GKRs;c-Cm+AtXIr;X}P0)PHcee})Xhg|sxNFrOo%J_q{!ZoHniJyoSyWfoc<zXGj
zh$+NV)F3tt>lWo*pth^$YmCET+6=EG3+n2omU~kmvav6U=Y9lU5Do8HPgaQY@Y+mq
z0jS`(=(_msWC^s{VA+^<y9BAYVgBGS>5T3u6f@kqJUfVRIQ>VG5Ly8Ngr_|$AN1T1
zQj!*pErbc$fS#$3=(mTT0-|U-j>$Ro=8HtgN14)_DThAsI%^XX6AtSMwzS?)1gwTM
zThq0pASf9gyPQ058)`NPqoe%QHh)g7_*sO1$t3D$SC#vbrRo^PKa@#m1u!n2zVOGu
znhDA6)`w|5_J2}e6})eHbc92JLyzd(aO*5Hr8n1d0S!aN9fjkiW=VtNU5+WZY}+WX
zoyc!p6tctAFnI?868|e$PR<8<xZjlD$^k-j`ki8qaX)wuncp}iqaijc8<mEvjkS+&
z!2{~I(f;w{M=OvLD?HEMCb3yWTJT!Q3>EcDnW4Mfe;g2tZv*;pw(S;GTZ>s~9|ksa
zsF(3tY3h49xoR5M+Rip)Eht2PgmSe(sNX)+{~IH)&4DrplNU<{A!Cu{7IYn^-#7z0
z$#QdQ-t5PNCbb!aAe+SFWcf+*MTZ!2J4|0yqQx~MRA!vyIL`ueYDiI#=qEOT73=iu
zlJxz;B8QW>6Qv+AEmo5{v4V1m4@Q5dwFnk2(yR>+Slt6`&;)fnnr1<y^-{M1ZuIkc
z7Ir&`m5E(8@#Y;dF)`lF4_z)l(ieVsXv~w<FPyoJm4suL<Lc;=2=AnwQf+27$H|}X
zCsrju4q{)ikG300WVdhoXF^h`Ad9(~>Ht$TX1*1K1Er;l`oh9<SAP7NEokErlvDmG
zG{KUYHCtjLyI2<ST(V-_E0NPSHJ8+R?09F^wol-~%GE`yA_hh5MKhY1qeIN&CokU!
z)n1SlXF-x*;X<IoZIIgOkhkQ2!-*o8*}mgE0SC%{$UC^g)>!e6B4}2kx$w?FIGN}r
z+eKdBV<rm;a*e#)WfsoP11c;X2-U5Fb<IYel2m%v!sZ7<4DiJxmA!3%z)@=Fed5g3
zHQJ5kMI7gvp%KRP3I!u@IMw8%mA{aCKT*h6!sq;OjbLUXDDLCUB0J7qApS1m+;Xc|
z!<X|pCuVaC&QeUX*+-#-%-q<V&yc&M)R{b&_+*y^nyV%k5bYvWE9Sb%0im+J4^O^6
z+EnXU!d2vZ$eGd*nO3*NV9RXcw3%bkGdWQXk#`z5&g0GQ?>ly-<8@aoL{#k&wt@rV
zI%5x&5lD69DAAUBsjMa%YOebI(YZ>r2mT2J{_T_Jg%u<oo5fO6vjC}j*^j`TnigdA
zKC|oW$z9bRPe6DnNEgcSr+~AG<>Ta-6B<Mbc}{QW;a5tzoK96#lzS_l*c<l1hN7@r
z{@Gmf6kx4R0)$W?Uw(Qg)&#cC>STL5;+q&}z)Zcz1py%*e3U>n{xdx0Cxum6A?*3Q
zQR_!M1OcehRaS|jRn0n`izc$*)%&uKY%^6gppeQ(u=B04ViT@ydsM31#-DMUSDj18
zum_qbQy?8c$4m9E>l=cY)?&p4Vvq5|TWG$Bwod1*3A{bW>TUWdh0kWgIm%SGh6z-s
zS>1jl=L#g2h&c~y9Teos!yu|n79)15PcMz-o7JIJlsf)*s()-raZNi<S_^#HE$+3U
z&tHCz`Hb99-tV<R_=ob|$R+pR5CkZxK!i=zZM*BsM@CxI1G>Es`kiG94nQ!V^)rN`
z@&gaqPfRGfrEVPkazX!3_($Q&N;lZ+C>Msh_l%zP0f}|KGTxtev`+9Ir>=kDq7nX3
zB~@4R)OPN8ssg@V4;p2Hk|G9EhQ<|x#M};&%@Ccc4X+^im%?T{T}OYt*9u7EUGRi2
zvw-l>8bDDd5UQwo&bP8UC7JFyl~&1={w7S5kf~aiBA@aJ^$w%FNF(zXSOTYS)CRUH
zYI1qZaCn{;p@hTd%PBjXt?5)VKe`77+Xwc^wpNIJX-Q<Iq*peTZb^LJO#R2Jej(K7
zxL3PC2kQWHHN`Q<;S`Q?jpT}wk}cm=ifwtLY>rnEo6Q)L^->&)ns@w)>y`}N&X<Zs
z@hG8Ztfq2#NjByDBZZS?7OIPclnN2AWRE<>ZNC5Jh#&^VB9y7MmH}(j#pk!6hZdNL
zaK1b8sg!7;KclE!e%4T<(FBzNvG}wyreok~zTKF?H^r1QJzd=`>R(E_<!Trln$hlU
zp9pBt?rn8&VUY+Bs<SE>B%_mw7dQn_js8acLWSTZOY7Y%jHrv)z5t*I61*?rzAG;x
zjV2OJL!nOgq3L+qg>t?>P42VZ+F+(MUYqk4#YL{+z&>H#H)lwY(1fLC0_~DXG*?u6
zaXyNv_7y6zpyO(|^>_(oi7c{;AuNBRUEHSQpGmz3)mD>JlmUK3F#;`SCCz81C~!Ga
ziHle!j~U4As&|_T+1&P`g~asO6Iyx$&~R(^W9=t3ka2^a1*t<l=`OyWwy;vJhmR^A
zO-Dz(1-dW68cZdn7N%V(@jhKZ-)ThsLpkch-?hwlLIQ{bFbA8g&Zx#T0A9KaS_E3g
zaQiOS<jDooX~4?mb**s%Wzu#cV`#*7YBmbHP{X3z7!|hrC?6|lb*WJ)ty#7KK=IO~
z-VuS7^b1Uep6JZ}DOmwF5-DU)_v{M;!Vgl&(hm=D%sLC6D5x{p2BRbj44GUUZv}r~
zgJR^FYEcDX*6h)18&;Uf5dxmf)P^@htE#*Wxac?llch3tHQaN!e=!>!uq(BQuSBQ;
zXlxqV13%d{T)k`Y^u~;!>@(+i@9F5x>&!KjtQaWk>l+*3gJz%EHRUR@t{q_2H+VC}
z&33Zfjj#!Q^o_|<v#=d9^arm}S)p8c&aZgn(fFgQS`y$*v|q7h`ENlZJ3Vsu@3BCg
z3qeMG{NmNSSF-#zT7T+sFAyv?GU#*CxNv%U#o$M*8Kc%fABcE1=PO}ZBNZZB+AZ4<
z(YRL&W4s0SEnPhyWuj^XCKcda)htMK%95`2G4NDrLJJ11n)+AkaV(5A-(*KFR9GGq
zY@mK9a^<!6++UP#RP${~Z02G9OJy1!z6sH4=;2z}!a;^+b84*}7isSe1oGKCbFYb+
z*-{K>2(A&qUmhty*0^&@drXFpa6~!{bK>5*75ZguhTxcYF2~#WE}uWt;mj0xp;yPn
z0GXe<IyhsEH&7*v#x}XWJo`}Y=}q&57dWslA0l211(}WIk}3UyvK>#IO4AD*+gU#S
zG$bRdoG_y3)i~03d9QSSpUcd6v=B%mf=hMz(#!5e<r;bZS>%J$Yr?rOSUCWV3C+wV
z>ro<1#fLz@(0o!*YVhOzc%9op%~7?T*T37!2)5NRVp*VCJiWR}Catzh#>I08y>4gi
z+w#7$M&)i2oNeA@txC6ZCxhZ)9$Hq}zOZV95lnedpUU`LyVNv<c2gm1w`-%QQQ-MG
zo^dSJY`LDiv`I2(j5CDQ))f?zc!~hx4j|f~I3S>mgHzOIsE^c0DoV0vwqe5BqsQ@`
z?-MZf-e}ChY}ffG3d&|pie3e;!1f|TUQN@M=G``3adszo#NmEnsbyW-$~*CL%ir0e
z*Q~y?ixQTaM0+o0LXQ}S+FPfQqEpTDH6)4x&eGH@9v|~j9~xi0O_nYf-dVRMck~M&
z=ipZkLZ^Q$DVa)$B_cUh?{1tW*jD)KGb5K*!dD3`^0LVxUghKFOaxMC_Cb9ZBo@aw
zVaXG3oL9+8pNGY>>!YU`oSS4iu2dH6c2ni+zO8&ME#A$YP$GIKk+Q$+F@yR32bwx%
zGHBje3~iP%72`(wIRzF8In2XMvb8nljTq#v2_WA`;#yfT(!0FvK7TVbE!8_ohE^d9
zMi;b4L>?`jsjj7XY?Qn1F=1>;*EWjj_00#)PWznUHAAKz-ZmAZxJErk_8A;UNhal-
zQ??8yqVvQ%g(>Xp?B0*tpG$_eI0_?T<<~mC1NcO)^Jow*HRvn%k&R=dTx2%ATWi)1
zD8<&VCjW>eP#~Ky?Mn?YV-N{o`kl7IgAx3UEB_Pi63R!qw2G3LO81Qc!+M>(AXC-Z
z?W_79ve{{dSjy$p&K$EQ&hrUXMwaU?(2=Rbz)!mcx;2hJ!NEFwOB1L2iEw;cvOu_}
z$>*P{r4~b3QlpO<w2b}lE@@=-bo?sW4Nj#FVuxn&fW-3&(dPew{a%3Lp9Ibg3^O@N
z<!&_CpA{Favmcz=!avT&vhbE&k2JgQc7NoL?d13HHa(uI?3o`nfe@CAS;k#eOu`G;
z+1O5{NdP1z`5C?XUchzt4PZ7}64@0+MMWp2eCw)N-{jDcUX-F%O*jiHZdKVYSiuVz
zuPftTKUu~61JWMe>ce{n3OHzJu4sbp0&7s-He8FVtOVT?hEie+DGuhd9#b#sB>n^D
z(yWP`?Z=t|7W}DKVCws_$``fnUy6$dD0-t46leXj%5`zfZ{K@xf4jG1XMdP00$ysS
zh&U`Dam+f)n^_(Lx=@;^uyW)eqh&sz+<6unxc3{c3Ozt1!Czllz79WJt-fB+a5QkS
z^1sa&)-``4XRBLErkSLBLsUvVL-HIhQ-|^#guSnnPK}Yg!1|mDlfT=)qobp{#FoUP
z?Y+zEed(%Jf(!GYV_f!m%<8mF(HVWDY`6u**W0blCBZ1u50d;V6bUgt{1rkGXBt)f
zHCkvOs2yf}(`PuTM%50vVW=nXIT9g@9rc{v%*B#~3e&x>Ua4uUT-Su{lz6W#y)rNZ
z`ZWh0^nn(rc6<6}m7BX;^xjqN$f~pGja~&(QMXL@{CV4oEXtv$uG>;f!z%{@@t9_P
z@xi6m*1vvG4dkX+de)y@Dze|{4X1zL3NUL74_QS}N6TEa5z0(HaD$spTU3nT2H&DD
z;5N*hY_L4aD~=V35*orNR@u_j^eCT2&QcOKu~&QCVD-?v@B$nqVx<&{$dyAPCXl2$
z5cD^c*Ha!p=o8!#^q0}{|68Ve>kHTqzG(#W*JPnR-&$CXrJ_}>Lmj)JS6l10;t4VA
zb-oU66lRIzs7rytdJ24ym7M^Co}S)%r8mVH@Qs{@Ky9D+Djjv%@{CR=#X4(#vgzw1
z8L13rgK<$I;xdGi4q6}O`*P*xswRq_di@oVNGH)AnO-2R3Zkeeq-T!1TiuvimEK-)
zr@dtxGCiy7vFzR4b@+sV5xq|AIc06#W_U^&qTT@5qcvo-Fa*a!tC2FSb$_Yr%CCQ%
zW-$8H7*OelB7n5KgjyMnOA39qYVy0IOqRcY*;)Jk?_^5=8(^3;3%|}z78)oHgngsy
z4W8Kyi^tcF*}rhAYSR1CP1QiQyg#_&vr73aNS}gtd9gnfoUOT-mm`-q>`Bo3K&_Cx
zioxbaU&1}t7D~iR4%q}{kB%p+RNS(HM^-=$5zO08sr#-~`R_XukTSx?`8*EmWl^Iy
z8+j|H$%4Dj&E9BK|Mao0IJxOYhE`#$_i626_>{E`6a^7U`tbu{{BShAdhz}?8|;pX
zfk6-}OtQ-v9EK@ND;^9HJsK<%!0fU?-rr=@sp`Lp5O#Pm2@VD|np%UiRO9?y?ugbN
zBxE;PU{aN;ecu;w%nOR=zchyNv+W%fU*K$7G^iOJ)sDXYacu_s)+a=-f2paVv>*9I
z>20X&Wa_u~x3yvEW!>(x`@JX(<wh=wMGE6NN!<^`PpVIU)o<ZgcFyFy2nCX9Xmvsf
zDV?&Eo10sAvDMVT@bDwN{ha$@=pHR^{ol1rsB7w)K;Q*IuKqDEB?LJ>J>z8Ly!(^p
zx5<+hA+%h}8UibAl#dF96@FPYt3xsu37GX%?)rYP*mIuznw@;O=Uzd%tDn4vl8)ih
zXd~lW@t!Y&N6<v)M_6f3#%Jf4jPwZThwBEBmtp0ley0OHoz6K<e0hOcHRi$yVZQMJ
zf}S)@zrN9(vtxv0ig2p4Vs<eg3nNsn*FJWA{3ppwaIlj}+WsbNf3m;<%6wz%(&aY-
zQj~dI8lJsZo4y9Y^<Rm$F60;HNgB<>MccF*PrQfRdwr^B;hDOS=hpB-j}Ckb{^b{y
z%?<wi863243x4R|?Fq|G$=YpiE@c`)b+FENjz{VG5+-0t_lWS3h5ud562!5Op9G-Q
zb1hSM!HL6RxjPwk$#G%sfD##J4(KrDpk?^#`~K%#Jw3jnV&2}rREHE7t!!tD71u){
zjkNy`%Ew-tP-~}MzcDYg<HO_=7J6cinTtD({W94MB{i5nWwD6|aL2;Ilai6bhS|=?
zvj!#yHq6@RZ748`YHjkj{d}bhiW0gx#cWvd3p=V~X#t&p7|R<nm|tC-oba3DSXP_k
zd_jmD#^z76*BcC~WxofN1LZUcXR+h(KFxg<vig5~`We{L5`)-PqY2bUdLN>;if7B5
zXB&-&Wm}zt@9}w$zWL|307A?BN(A64+9n~AF0Xsm?-jJ;aq2v_*s>{3q0S2<%g}H&
z2-{)i1=K}fzZ1{JnL$HM?G)L1wLil~Q^DI3Gc_`^<Iz*e4kHBxhwk*~0S83m3~165
zlaaHUsT;?K04VT@JMH(8Hh?bwJ=s1j`CZmRUkO(sZ!U9Thd=9w7acM4&!@*WCF|;q
zyL{HT1)kCuQIQl*LtNrj8+2)<K;xBA8K^x5Z5~V1Xv*Z6ZcpBhtL13xiJ1*$EA}NU
z(%8s(Qc?YCN#uhRnqPqPSeGlQVk|+`_9(x>AB#WerC~85UKPKKO=Dj5hPfoOtZ3zZ
z5F=fRoMv!fXrrgQLBx@F>qXrZvG`U^-I5B7U9(=Ty`U%9z3n{%JXP>9<zTd){@N<y
zw9!7zVvUq)#}_`D&Jx;ZFW<b2Q2N|MO}nB%!@zLF4{36W!-2Gik@Qs;vylEl6AS=N
zOeHEx3=trQs26MEpM0E-Ye{WJXYy(&XgYwqjgw54#AZq4Pu4Z}UY)9HR640^XE!?X
zDG17HF`5+(2d@L!(+U*W8kCfMe|f%i8%)L*H`3@ZDhwX-j2{jCG!Rsd&{5)K>ukrq
zHGzu9K*Vy@-M`%FG4k17TkPZLDT)m=HqN#O87aiy#xUxi4zsIkTr7Gq5nC3wb^7lq
z1aIcgmd^CvY^K4M_iAC8z1pfV-SrrT{7!e^#xlvp#y@eAUK;o{3edkTCA|5}Zh<(V
z^6BmRaPG3+;B(%yNKH_E(t*Gqozq9X(r)ywSJ%*3#=CbH<98e!N3>n>JBTaRCJIR>
z*BI#C+6;~7Gr}i155;ps6aOfws~|Dxx|vz7(!}Ak4fSX=EZ^|9HPLKrlWEg0+=+=k
zp;i!lhT%Q|4gp$?<f&IcMj7*qim+xl%o;cD=n!1%toed7A?G6OX7{?kPsj_+HIC{y
z$Q!48#)VKTrdqi?O9rwwg5gWy0}-r7$|3C&b-#)y_Y1nbFGu)?LmtD0$5v`ZmkDu^
zHvvaAXDJ$Nt8aOB?=l@2@zK-JRg~mkH`C4RT5QZ^9x@U0%(V^~YIL{OUhX%XJ21CD
zE$E&d9UEKqh$D7ed<YVO$8b$~eDK>hft}b@hV3}DAg+)tGT&QfR~NN2gcWS=H)5fk
zBaXKUT7F$8F8ljS5>j`-*yv~mC2X*1)~p#aufDVoZn|YyPAc&9501TISG;LUtV1DX
zSDZ3BnoO&hVe$yWi#;ihoWs51cow$JL5UWjE_kk*KYeO;Q+ut-;+z#{{uTFzkm-G?
zdg90<3F;>U7;A{5GlrvdNIJT@MAbfpo&)h}{gdO-w5oC@s4r1b(4@ZpEPeb40y2C#
zaIbrc3kY%u)-GKe&2hfxoAyOd4_QAYO5%lFsP&2GWq9kappck*1vXcr&exJ@Wi|3x
z7Z#_o68aC^UJ&qh6^wNe=Gt`x-i%&cWQ^=goAXmKp>hmV@8BC=xMtW@&1S((@379$
zPHg}};Jxl`(&&K3$usJ^-Ke!N0n}cKMP;K@uDuee?dzEVf4S#MdV1Tz>~3Sm=VYRW
zTipp1B{0*8^uIscvG@qApMpHK|Jn=RtcJK&bv52>xTJO4q&Z*3&i=Na2z~7_6KrMb
z+4F3?=;N{yXwhNivtu_%c3pUfK+#nW-Z23-clMCJLfi44=bU-tYfAVN&<4Q(GCaUf
z_)hs>B*P{^xni4I9n{g3sf`ETqkHg%eZX+*j=LZCnoXWyM4cKQZ7v1}Z3&2^8_;X2
zX0~?!53nb?g|4?U9nJa+j>?8vKVvnk_*s#=cw+P8s_q;tRr5u8q<v|@Hy0oJO=V(%
zO|){&*I{d;=Lv)uU8;m1HJwPQ_)0U=$IcbpphUASEjz)HK{OW&^R2;N%%z~?;zP~=
zS{`j?Bd82b0w`$p>pOlI`l+VbwT~qPVhtJ{B0@W(9zV{E@b~l;diSnGi0=M=f>~-Y
zpk?AMi7aCK_wNYp{psTvR6q*Tnuu`tAxG8EW-M)W!pLXqJaYkQU1X;}231>Ji)|MD
zmQ1a$xqCl+94kL~a$qLg;%4`R2Jm&NrptXm$BITB3eFXq1QPP|(beFR*){0VPwKSp
zys{-NhwQJkEcLF6sf5%4BZ8eiq}Z}Tb4@s=d6iT{r4)S=X9p`ril%>mh=o)hut5vt
z9_tc+A=xuK$kVEq6{YvK!=~fVJk&0>e)%)=TM&BX({9>sPgO^J3e@uWLf+rI$e_fv
zIG8DG4Z1%g4jeD1svWM~iJ;;QyUiF4-yKFxCRRWR>x)=s3EyKpJQFR>HO2S1{fQDm
z<&lwH(6whzpIRNQk9;V<pT=2(Xu=?kL$vI<ITL6HqOyJRG1_#(v5f0&+ur%e&mixe
z&%n@QUFm&_W#3GU0khxSxP+*S$|Yc>&@L)N;C@;7!wCEhT4t@f<C($h+hfd)L7%5L
z0|y$pW>PoN&bA^nFL&`>>E1&7<hxt6Iu|QXmsy!mX++KWieu@~T=ord2q+K&4Iw!Y
zXfl2p)8vx%zdUw^&~D>Rzfw&Z?<Eo2+3<rcCt_&${Niq0YI|N=P+b`F=v+Ro+u2hv
z3qRz!z6GG^<QL_7U70y?{S$R=4mwx~IP~41IVYA$-v)<IxF~vXhTd@>9A_;Dl<{=K
zm7H_6jS1Z?dPOwrS~2M{HGv%GahSFl$*rp@vSJM#PdBv6g93GRb(7gQtIWSG+?f5A
zpuj3FIF$EhCv)@mYknLaEMK*UG<2nY=LZ^&mhz6lX8e6UyP2l^s%hq5Opk0~>n^j$
zs+W~&`iy$hn<K{gle>~k(>KrIgGy$UU$P}n%(AHmL-`-q_f6($AM&fBX0w_Ifu7_>
zTKdlgg@wIX4sy{i#h7wL-`_7THfld#S-UghHdyAIT~ab=E4wkh7=nS$<I?BoNk`$(
zm&zZZ-2+OcWpGq~Au}`@lbdv5|I2Us{1;jaxyHO&Lu>G2r(<HBKgb>1JXpwAVOK?~
zZ+CvbvfPQKoh+OQ1>KS!HtO$Ckj}g~khi=3mf@oK^}y1E>~!10rxhoU&1qAHI9&58
zZ?*@G<CeCkF_WLphtJvK2$bjLUz1XnO|S&9x(%qksBM^`M<RY1#N#%57}tyc>c|M>
zw1{gOW7Qd<`^dQz>5+I^*;E-+QSVgAi75-3{t{GOdEl6uxLH@56iS0#TIj*B)6>q?
zYe-5W^}JOJ=D@{Jb*2E{8u#~SjAGN}ncrG0<EE_P&l}nm_1I0Eq9x_C;*>@-qs$>a
z(a~ZX4Q(OM%<?!h*=-nqe6GWkQ7Q4c@fJi;CT58Hlu^#X>mg?lFXKu+)OFx@2cxE^
zbacGf&TAP5ba)(;eys`Mao=8g4e<Rdt<-~VO3}VQjDiR_>{@y^e@UcJY219nX~#~%
z?qKffb=s^oeDkVVfI`8!W0h64apcuPP+22;tt)BP);R%5X%=%)$oP)4RJgPqEsBak
zuuMyaBGk3KonnmQm}2Y@nZ9Wm9+*pnCg?UpfkHV})A_@Bz6DKHm&;63&&|XbGj+0^
z8bw81sITUFniQ@g9_{=mY1M=AY*DtlfwsByj>Nl66_Hnhm3~SeGE!!uy^EJ&Nu}8W
ztJTeVoo`pE#3}ErKlU2!-f)0W?R~WqcY_<_Xll7UFH-Z6_ftf}rx<(0lPmTd$E#P(
zL&i`30*{=ENheRP*$#A6JPe!3QT}p?Je_Nq=1htm_6}!cRq|u{t0AnruJHK*Lz3Sf
z^7SJizv>hg)YNEYhlEx=CO=?{t2;*R)WsTCe&hY(rh4(laatp{6u(Zd;msV^xsgF=
ziQcexk?e{PUj&A>%&J|Lf!~V|hYDGSXlj#%&%qrV0um!brK(1Ths7p_t7Syq?*0;1
zwRCW!JRE)Pv0c4#sU%zed?1&{ZaSv>YpK!P`6fAOah4A+$Os4g1Lp%mhSsOMa}oC=
z#@Wf;|A4gw(7zPB#wvDGlOp8c!z2yW94a_nR4w+3u;V$7AAiTK8;LpS@%#{7>jrLh
z)L!Qpl7TH(#K|DOq*1!b2_IyNe#lah9l?+Ds`GSJgfu2z^e3ox5)*P$k29e=MUSc0
zqI*S~J7i%?kGvVu;s|1|-*s-GVDx7nQtQ@c>U{#_S|MV?D;l!C#QVjujQhV^xrDqC
zn$$`i>pBG*8>X(|oj3*UfdtHAO}gs#>)Ci(w&F(W_>a6h-21klvD?ejSSDQ*<(wsf
zEWbHNiFF2L$Ep{oP_*6z2X=g9%9&JAzpCt0<02F6JjO9VmsHb64^%t-!WvWwSj*{&
z8kaFk?qN)eK~ExkM7X3Ivmdl>n*TIFNE6r2P(BqDViK>3=3Z}D?w28=1?dffN89!i
z*xy5A3#p<kHtXT*8-2}Wa^2w3!<fRX+O(E>JF-!=G|uLxYNgua7-Blvuw)X?M_d^a
z8`<Ff^g7ExPq*?xBH!)(x1ex*&ahew610VsnY^DZDCk^w<%`AES@tg4M@fn>)!+o<
zSXo1LJEN$=66Ib~7S*iNHTMuI2@EL+^%XRG$J|n(Dk_6}g+5{SQ<^I4yUU4?ox^KU
z(An75bC$MISWAX+X=YAXH>$6{X!JY2{zXwI+Dc)2HNkG`znpZGvKmxrpjU!*lQtzA
z$2Q_YKv*v_yyBdgqO%iJMqOXo&}Ffr&qI5zt=jr1U;C03vdznIE_APAamjtNxSibT
zvl^B)P!oyFbB|;l(+~x%lbq`L@ojWB8Y=n3MT}Zka%JTGwd^$K?4%Mt85Be8RU72<
zCIgTZBW%DKwlUE4S@UASVo|~q(UXPbTSp4=Ua*B}crKNat4-jM=lPa~qqpQm661>_
zmN#my84)v288S~anXRF+ozF%UAWv>g^R<7uO@8z!KloUoICRcDLh0bVRX%#Y)-ml&
zC%7c9I%f?Fa}W>Ez1VM-2ZTv~V;Lu4HMyO{hYv?2H(bVT=q0K#x-SCnWy$LqX5h;k
z%8M5dDJ%9*NBtFv<&gsfmY3^T@))Vot->*JXAKzi3^qTjLSX6D8C$#+Qps-892C7z
z^STq)CmNPpSv{F1oCGW0Tlj34#2=#Qqsx<pYkgV9dFM*(tpB3*z{csgf)10kI8!dd
zQa^z#B~YF$5}m|BTH%ov;*duN505T=L>q>knVIi~s<cqE9E4T($yc~3om||@4mFjm
z-$l1GX^<hjdwIVokoo#e$k0!pG0?kRUucjry+(&s_fgccEj)*FZDZ<cAq#B`KSHK0
zrck}xK3-aH6{l-elV(}CHQOkd_c+OhzDTQoBkMXx|IA88hYTyUrA$Ob{aomxOhTqv
zlUyTJ+T@4YG}A~&)Jq!(<{JT4`H`_7sTMuH&7?=SZ~ra7uc87tE}3JGi8B8%M-W1J
z&u0m*<2n;BMGt@Hlk*mYcHIIX6BEpZ**Ee(Y>(mPP6E}E9yWvz<_2Y#L2jOKu*mL<
zAdRB72B%#j7O9#fC<O|^VfE-?LyU?f%-I-j2cUp9xG3vATk*BBoo!W+HbnGL;8Mwv
zj*ELVIsM`r!JMjbf-K4eE#((A@O)|2axyA~ajP0lGU=4?s~<Ukt!T=t^TK~t7fAuV
z1Fr=*Klaf?i(V#2wPJ41mZquZ;g$h(?x-R{`QXlPLap+D$U4ioD8p{+E27dZ2uKet
zAl+R<cMaVgf^;`Xcb9Yw9n#Vz-7PJhLkhe%&vVZC@_yqt--i2|>)w0qwf_5Ml0+9@
z|Hl87e?Ypiy6WA}4_R*A+5?T#Nafd>{?yyuMYEO+JNeLNqgr-Yj<K9*3S|0^c*=ZD
zu!4NgHQ>&m$O|cRZ~V4DRo`JE9R&VDH<HgME}WnN4iG(DNc02E%l(k0M655W5{F$W
z8(`U9$O4jPXAkU1c_J#)PK4O#gPALb!LL{P70u2mcD}AY>T~Q%{E}nK_A%;<A$1LN
zsoPt#>)3WO?`~>&CDay1LT_r66(;^`8IAP-)QB0@LUDv%>IsKQ<fyBu8EbDy9v+(7
z%7FIPE+rnV^l^IKEvNmu9_51~LIJiEmITnfTCVre^2ls>De#?(m2Zk^kx(ww3((BV
zl10&LR9=aSv|8C94P*e89Fp{b^v)R-b(+&g+huapa*Vb)4VNa(<-g&zRi`|*=tP%u
zArc<*%}iLfrytkXPcqqly@Qedu5)5*ez9f?u>og>4ZZONzSvC`i?m9>=jH(UIp}n?
zouB|T2g4!zv*<S>1E9feHRbMvYD?5)<%h#I8_r<gO1<`_?O9olwwionjqc<OyOtsJ
z@FNRC9lRXB>_sA8y<w#CFPnf{-X%hFS;cuspcxj#??v8VZ4pSpL{C?JqwvY#`HORG
zlIKSJf7P^afGr7niDLO0j$2ZwAy1V;y2yb$Sg5Gy@(08k5<{ut7)dFUeEYVh0SZs>
zqKDvg%n6u#ASN5g%%}6XuB&<vM*z$Q^n3AN%T_A*?<f_MDnvwN#CzB|v6?S}=$Ipy
z63i<Bf>S(cQG?3|B=<+yylX_51yq00=kqO}^=Ij$T7#~DM4Tjh^o)>Q+*a#7+K1f}
zmAP0GH)GGG7}v3qTBC>Em5u9K!}cvAv;P(uAYtH$!he<z7V5xI{Qfmqh%O^nHZO#Z
zZ8gc|d{eyBEd7#74)FHk>2G)rFH?2xPUXFik!h37=Ho7BAtL5=`RxN3!85w$vD13f
zu>4;X<5CBOnnX<%8ojn9=dOn+0`}qmHLg2wuJcL>jUQC%wvt1^CNdvg#qS~p<=U7+
ztqG2ftz(vWm{w{S2qC+ai1<2&*mT1-s;AlNN{}P>zP5_;?At0!n_3lTn9I+yF_*0J
zMso!<Eoh2O0mDNs9@5=nnrKB3%-S|@lk4R@5Cqh6h?5Sl;O^vv;EZ}gP%#mZmZxOQ
zG*a7y6$Br@f!dTBRskdAiWS6t)*xX(B)ik07S%l+v=D+$vSvl7%baYol=%Po=^^I&
zM?01YP0ByI!Z#fBKWutD95?NW*CwAWv^r}4rd-GbIAg!{DHayFosWYB=d(&1c(b`}
z%n#a^q7ZGusK@jKTw+)GJaVJ<Rg(WyEUZRlr`TPVB46)`7TfH^v8tciQrGqc4j-S0
z=U4pbWm-)C%Xa<WIopfn65e0|u*{~~0-9;`C+^Ms*kW<u+&9QH&@yzg@DC@hDjB*R
z%i;VGS;ewBFxT`O#4d~lyGOJf6#mTrJhT3A(#lk(o@p(EVm6k!|6IuPI+!oza1JW-
z(Tuj{`hLEXcsxBL631H_bja;oM1q+(K{?BQLd^Yja;<GQY?iGLPqAmjb78qC<_vy`
zRPeFmP(e~)e1?62-!po#>N*=tvtXunRsB#!Xe{axZ&5%I*$haFgZ6Mw<2o<+m3N*@
zZ%791aUubOEdsWp)mKC$%9e?oHWrUA*+MCcA*a1IBb6fA8cYg_`UhM{4y8g4i|TN_
zz(_+OyEZV%`SO8c4!NdRp9+Bk+LQoxT4WClO!}$QXp;sQ89QX|>GGhBNq0&%-9>k?
zDay|CEdFXgeHUAxumrlZllV>WmvV{hHtSL>YlWP$n$8P8OXsg`I^t`W&vTX(M>$pL
zCe!yf@=`?gEKGckT@xKH342MrUNvLU>iXZ)vWI2lt}RR(R+(G6J#gYoK<_YkU)qNH
z7vDM3Cp2Nwb;QAp{4?GZO|gxTC3OqKGMmSH*b{R3l1eKCc|HzL0}<Ul5Exw?BBQ`&
z-50I`UR)f$S;^N@)rCtcvw%Cm9Ec58?Tho<VD%4oFX-7D-QJrt_DTC<)kG(>>KSP~
ze%gbE_b5vWwe`UOp~5HMLjtxH>SOVDkd@?UQNGFmW}VGrGW(U!5)ybldswu@V9awW
zj=VPQeYyp;R9POr{&{Ya6zuJeWo_N=@OwV$#2PE|zt>lN3~+sUXL2C}V<@0r2xrt8
z_Hqxzk)|=6(c%S0)2i)XX494Pj>l5rQ`_*c@DK9!LUvFS_f;O}y(!l~C@Rd+Cif3#
zJ?IYs%wAFVZC-a5^&y|E>?pB%DL&8DODL^n>(F>(n5||l5C1Cx@yl08;T*1OnD;^&
zQdUICkAs)`Q}g8ZHSE?QJgXAaHE0Y)aV{sR?A0-&pB9$0xjg3Re-JUuqqz^sS!JXj
z_ve`e+EB~Ozql0*5ZH;~0L@Vj?&7MXBfU4OH@bT(9#J9E^^H5$wwx23Fl<Wa)~M|=
zVYRrLAEq53NBIu4FyotcQhEgg{7Gl}>68moN<Knqcp6a1&JEcuiTea-s>SG0|4iV#
zgMLQ&ZCW3>P5LEZ^>xj-VP476Sxh#UOH*5oA;Gd<N)9^=Ge17%AgW8Ewl+HZ{b^#k
z!~KyhVSrvs+xxZx%Ih)4sAE;Hq{lIMy%eU$Z~GLBr2X~wTcl=!hOOQ+OU4W@>`-TU
zS#a#-%f!B@Sc`1hS0CV3#f0?e!tvXGj^H#F<NX^8`fIyXKZK`XRi#m-r<?p%AwbUZ
zfpTHS9JUHY1$rZQ4v_NpY;0(0d2c-Y$>YuXX=sb(@3G$NenR`BIoSw;CccP``1cq8
z!s5E6YGyEFKPJ#jvr@-VC0JnFyU)ef3@E63_EpduGvY577eAI$PkE$(n5KMc_p7~O
zLwmNcxVr1JwFLDOQ`=1Tdh%W3a2J$o5r$&azdPX#tjvb7<C&E;u91fdOHX2F3;*eL
z02m6cUzY~Z^#D%YQiDNtbD+h`H91{tkISHibuOJsPmk10J;_u{eWQyDZ2LHN_2Y9F
z6<KjdK}&XOEkppoN1nLEDFk~K@d(QloP#<BN1T8uLTZDPG#7algoiI$>Ko^w!t5oD
z64kh)V#U5BeE84nR3ylNOwanX%o3Txk|I-&G0WU`!%bB2_0lu{O#mtlea-yXM>|h>
zjb+wsS2@eOy_moAC%fBPhx<@1Z4Gy=uoh${H^cO@ZkL|e%Ez0N6F@EiUMV&&8Q9&$
z{8Iqh<8Oda(EXm$N)?3{KY>fx!wv6(W+PEPCQ2`sa^{<Y`w~t+?RW@lFxYA|8Q>Q=
zWa+XN1DU6LfPz5cOmdvUrF?D-qs5keRBC5RVOQ~<;v1q*8w1c5xch$tv8{ntdpt{)
zSTFdm72{H^{wE%LL4G%Z-TD|E?lD}C#g7R!OCeMbSKqU)|30ZS+WaGJJxs=TSzYjN
z^y(&Ec0c$5U2q*9o8egaG5NUs*k(1lbj^xqTq|rRpt0vZdw(}18NW}l_hNV&<OE(T
zUKwBek^SW{CXidZiFPm(_TQ=;c5atiRh4pNT#|y}88oYNm#2P>IB}T${^%Q(3|}CY
z!5SrpkGaFGDoDA+V?}2JRUEG_)zdLwLT#E~v<>zi&ZiiMO!Nu`C||}DCj0fK$2b0!
zM1LGg?AFh#g@!knDg5hnT193ShgO}%yL-8fEx1I`uAa);wtFANEFb?`BzPDoOgJXG
z>~~xH@(lY$=aINTUxae5a=5<bzJ!(uZRj(8U(ehA{JBc7b6MZA%Oo*MFB{X;x3`??
z{^d&Z?}d*?*Hc)*l?49gBh5VuD(j&<CsP6E=+P{1xhqq3{o$bro+;!#G-{(&W<h-%
zilB;7u8zT?y-&EY1x)xv(zk!UyT3n$I!LavPTMTiTfVeb;38R=0<q)=%K6)x8SGe1
z6)hxpmesoA2?N9}VBXZ<zR^Qnc6=8BV;(rk<fo#ID|%OUm|>&S$?`DFFx?(IupmYf
ztoQunF-Ftsq3YYZ*PO3@TFS^&R-+39K9;G`8uU-NiJhO&Z*m$G<Vb7^gZFQ!I5{{M
zg6~z-SN}qhZ7N}61t}jmc%)2K^`~p{%yUa}dp&TIv~%KLuPt?7dooT>3Z4Mwcp#U-
zXSLds)ha?-jtDjYe;a!EF~JpVOKa+DT?DfQ)Y@VpDup?~BnoK-9S>|1sdC6X(*FA$
zoL@?TMPEi;jAsd*SH3OKt6XAXzQ4?Q{)7nowl^X=n`ZL4GE3Fh%7&mxhU9c<o{t&}
z_2iQPulz6fxoz8IPK~191+{JgPcq92OVMR1bD9Tj!J_v|8AXkqnr)SFuwyNw`4~bA
zQ@yF!IU6DOQeQo(_~3HtD~6`^BYw;sHNe@q=<T^M^L=}6vSF^XF`Av76JmP&n)ln+
zP+bcyTW7i1xe1+9Sb5&^GeNrm8{51)B}$vGCoIWj?t%U~NE=<@XTzSa`4R!|0FWCw
z0s%kUG57p=jKAt|_l5c!(MYI?v?hv%P8U3swG7&gw`96JjKT%611EGmStT)co&7%?
z*uoX4#a3Gw5aq+!O@Dn`{G#m{hm{YcSelC-6)5SH;F9JlBU(sD39+olj1TOf++*7U
ztv+U##o6!OAv=rCMsjO9kDk-_istNkBGKqJe7l96uAYp(J`<Nkl-f+Tp&GgOdQ1i9
z9mfOpsZ&%Qiv&H_5e$+?rGe%)?J1SMJ;-})D6x8vU7&7*k+v_x6nKX=r*}&wMIX@a
zQu3E_<X8?iL7Y~E<u<3m=Ep``i=e8b?CA_`0)A%qVQf3R_6sB=cH3g)I?t97UErU<
zNdBwyA0wPh9mb$DoX1zyV^(usm!mMq*3R`|-Ahxkq`&+E3Q;$~oVjO)sj-i3KO!T^
zAmW9Ozhslpk-ue&L@`;&|9*EfWSlZ|*q-S8<x@Oe%#U5nLkOG)<F+korNVNvleud0
z%&d7EJXf|PorQNh#8LxQ?w_dCna<7Qd^VTo`Wx+|C!zs4=jo2C1>)s>#HMPw>)z!z
zyfMp6O~eiQHr!UlRz{+XJ@Y|L_V}zcZ6m4>A?xFor82^?2Xbf)<(-eK^ZJriXW2&N
z@g=-j0`&^u2m3!sjTv59!T{j#_#88{)8t(MY!nGolo_AkC#eZ8mE=JaYw>35)<YHI
zU-+zMx3IB#dHU2cJva&L34rIkQDr~v7Fcw3Z8&muF7vrp29w18^F~e0syp1hun@61
z^gU1!i`2_2UTbD<&P9a4F57f$LpNKlIY@Zz*R@@LJsmQcEi2e{PHB@kyQj^OT=#xI
z4_;fU4}Zhtxs(V7rNJ$n_&B{+)6}vsTwV+@TlNs(@tV%cDI32p;^B4s+S&v!C;qy2
z8s~@m?dDkOv+d~E<OC4oCdCI}D^el2FW0pa{FMkdkQn?=DCrStT6s{%;W$n23#FL}
z5^d&7PM4qXIUf-Dllj7>H>&W=H6rB5;#ysfFZf9S$?OullBRbO%8C@t`LQ2&B}Zy~
zRuVddoL-A*v`?1ntBu4Cq=cb4za?B}(c!Qr$Fn0Iqny|N2uL~Ge5Y<Z@mNI3;NHG)
zj*vjFE=J3$VHn6i%x;!I&uabDc-v1OPd~6xnG%-CS`q^lAl8*PsGC2@^x<E%q<!|v
zGpp)+`n=-c|3R~g*t`8Gc}HDU^T^h@inhL`OU6-IMc>ppDrKJLo#)F~?>=F!O7g+#
z=xx;wFi?(t(d8F;U9UdD;S2vV6;*8%OY5%RW>!A%EOZC#@`?pHFIne4h>2vfkCJ9?
zZ#sOM2w@U16NoUC7P<I$IsQN{Z@m(LD7E5{6b71R(EE|#5D+IzBHDPvL^GH9M`QAe
z=xu^g2uGp*(Nb+{0vzvfyW$Xcj~-q5*PXMD4n<yv%6>+64lQ(h-L$&l0~wdf$}Tf>
z=-FJcvzOeWYE_J3USo0DVK-(uA){te3LD6V5-8#ot1z?Qv()MNOGArcbjQXGsIx9D
z_imARXFpRA3hnp*MCvXR?EVJAr4#OOHG(6~9dZ!Be|!<?*Zn%{R`CLLRyx4|F@%qf
zJ9N%$Cw^ZXVgv*OOgYTQGQl59`f1P@RT;R<+!w#;7HPLRB9F1A?|Y9IKJ82TmshsX
z#g|ATibc;#hMo@*vX!~+wJEdeT~%1_FL&X!n@4Yv=%a*sZ5>6sPwVjJKlEOIwRfIJ
zY+y}kgQq2<r=w}?YL_s}1S7lDRTQM49<hJ^Ir3at5sOE7W;NgkK$#s=h0Go^6MJ$2
zG>K!HUw4fTLV<`I4tW!h6a+hBLigi288qa!NPvlW4y7_a*^ZLRu0VK0_1pQT;26^>
zmo}f_^2^(;7)fW=)Ml1jUFx?@*xs&Dlsv>MBzCNAt4qyY#$BhjtAc5s^)7ZSHz+yf
zwv>41*ez~-rTn#(%TMOPdqnhdET%rHZ`S%jOom}|rL@>v0##?QMB(;Fm2|$HA~Eu^
zI+xMEPa7lgOT;s*jS|a42P1})QtUHh30vXm9j0&E6$d`w=JjG*^GA601qsu^D{GMg
zetJ^D7m40D8=n%sCDU&TM&BGZXE_vbm{!jyhsf=?dL;tAI=EK}=>?RLJ{0bMSj0{u
zAxB~J3Y3vbSxglV2s4nQXWNwb-8j5Ub;No<Vi2=6mz`Rc(A-2u7pK=+8WOVS54|~M
z!?D&*+*oqX@7-(5mR^ENNjofQ9N1aqKjzMB`I3`#aC5^eM+zQy^gf!nwL^5LWt<YE
zybA&Iv(P*S=~a#O&nY_ii+H~|+u(VQA?yit<|CazO6GhM3cFQ^gV#nyY?pD`6~CU~
z4tH-$ijdP_M$y_Jb}~{1%UNw0_g{e2IFxbSNr=Aq57Ya&C_IqYJA(W)G_raqu*q(f
z$CuywR@1yut(V6}njl^dHYklG(=8g7@sV;D<N~;3xJ5~?i<>j?2>^K$eo9V=>Efc|
zZ98MTr};S3Le`EmARh^ecYZe<*+e8X4OMls;EPc+Kg}6FoE@)5{lqm<+4*!|vRaAp
z)8$=fXj9-oz`Cw+mv?wa=rW4^`zq^W&D*xQF}Yn$>pd!`@w4V7_18ARv)7RcW*tSe
z9KGt^QgcNp2d16twl25bI}T#ATD+Uy&zo}7@wRj)cMaK+95(#O>5mwO;q1`9Y1U*U
z>vQF=LC{~dhoK>E&xDvh9Noqy08eW0e=S#U{Ou@nowX#RKZPIk0C0(aFl|Ex?HMVp
z4zlsIT0OcqCTHT(B635diJE<BbI?-F##$q@+P)F>(!@usO-&EP5}zy?SruH#65R@W
z+3;Ie;s#?iXT6_Wl<OJS*YuLhgWqctQEvYgraTe-eCOG&`5<T5Kqp>pF-Z>KH=L|D
zbe8`UK^Sy_TZCOIH%=ElV*vaREYhxkc`O<hL_Aaf$P`VA<*$Q>1xxBLO6mv(ym&g2
z1e0zH+sRUf4cLjKDrQmfnPEAn)Zw{d?kk{r#gV*vcSTp{Lpk|8uX`FylYN#D8B`^2
z=eg<UC|F%PX?=Q0@49>@z<&fIs>r}S9g}42r$S5Pw#lW%E|WEqlM379_dH)Reo+hk
zb*M2OJFlAsApL;T;T!&IQ=V^RF&7|UPA-UbU;NnW>dCCeZ3O8oJb&RX-F$-=FA-@1
zM0g&-NEu4xcc=t7tXA$|meoxdvzno0n!vT}eonkig?^{N1%;%$d-F@`nWGO|(Qw)K
z$&sk@?8&52jn`InePpc;O3k=1fTwUVwJ=agFH<!7r}=>qxU@o2eu)_?GrOm?jbm9~
zjQuBzdhuc!Ue9^ekM56yp_rOU?*`oke15y0@qpe>Y^f`rSNFIh(|Jn|fBeGZ4o+{(
zDm`oJZoHbpM_K)z0v5N9Vp#-Le8|RU3qOsc;MI_%Um#xBrY4Oma@IbPd^OuJT~|16
z`88;%NTqbBpF0n5mDVhqqc!aZs%WQrUk)t@jFtS|m?xR(B+|eV5e%x(Yv;M>Q8+B%
z0#-5zk-EMZL|pCg@!_XLgf@J-VkHri0XxUTjXJXh9%ITVS*(YaI<6!lTQJW%R`OwM
zs?!a?xU=Ok4y4hrS<#V>hquodq4X#jNXe~u`)ACJDl&g&N>vcW(@(xV{uwc%bDMXj
z<~j8*yO@hT=5d|`&8^iL*i_%i`wFV65n^X+9dRF4UUxH?LV5@kD*CkP7x%{|{xduK
zztj@Opeo$O7w$>2su7^5Ajd~dYLHzP7f(U{eNbo(j8W2f5>tJobm#)N%GAnj00Gej
zNVg4gBZ;cSoxX_N&eP=YMf4M4%FeUhMT(QMY~ji&KgkyaX9s(LG`?7RycL&1`T_cI
zLebvnV!SYux9bCr`p+A$db}AT>+#}jn6JB64DH6io_*%-2W@#<rN5!*Inn$5|DH_e
zM#|TBWy7s1JMOInU&mZqqL%Pk4;OuZaf}a2!ZAHEnFvH@GXs)o7I`injm94cs$4eT
z;<h1DdM)8tDl<i`3^}PL3a=w+PlrX++>1dA4~iD@`k>Dpz|pw$pmN^AHZ}_<(9kKl
zd|YMoa-E%PhCZyEpWG*MGH~g^M2%Is=;s>X<#PmxcBCSxr)B~-Oeq^Du(=H%9{m$G
zbdT4buG1f{6E#-u6Xq3aCz&c$tZ({kGfTEXzW<(&+7Z1zX(U~HLsqShGa9s#O=Q+?
zR%?i;T{5GDMB+IS$q?P~Kc?P=q<CkXjiRp^3h3<`XBe6dz9RV|YS8@(5u>&i^mUVc
zO~bv(W~i3Zy6#$+!TW}`88WN8s@ycA-H@H2UtDglw%T%|A3v**<uqPu$3g@Xs@d!O
z!1apQJ&gTv1C2OZs3MO~`14X@-T25;*?eK+vGQlM7-RC+Sk+vwk=P^Fh0t=~e~7<Q
z_ygcJPJAw6CMa>*u1yi1S*ZXpEqLpmQGZXY9F;f<s245uQ=<$u5FP?G7h9am6H8;|
z-O%5#XYLVkgxzVVs=_aci$z~@QmxEuGErk-3K_Ny0uTsxh4d%Sya+bm6_OlF#)Px)
zSXNN)U6JvQkjGhDa}!Cmx^L(|x3zhh2JPkNhN$OKMr?O>w>dk;(2I^?=~R|y!_wjF
z*^)B9orF3yw=8k2-Ez;zcu!M_N#R*T96l{iuhU9a9VO}}F^yCrGi5MNuC9|2wKEjC
zaKyesj1|g-*OX*XROX9@2mcCu#m98(h&V0!=`kGdNHUdF1mRp9M_~Z)p+nt^_;{i=
z-@DymBqLUZOrDwe0<49l5p5g%7(#18{T58P?<hVJqHwrjq{bJRTTfv796t9c^Hlr}
zw&`xkSDliB-%29b3d#pO7<|c5Dcpa8R63NEm~<;AyT`oC&S2x?GsDH|k?kE7P*<U~
zmYL73Ws*L=p);Z>&(^D--Pfn^$#OvbS9Q}3MLz2)iDYkwo;m{zZ0V5<0+G9goNHVz
zM>(w#)nthz^73k&O!QnCymWB!R=0`$tp_Qqa_$N<%a*Geb_zcX&Ap8QTaNJo>LuxG
zEK+|V1o$T~SL~V_3S#W9L{h&At}RF%kok?)M!;V@z+frKQ2M)XJLx7FB8pG~Kqo!J
z*uvyN;>}ypye2?SgDOU|+W4hcC`EUpyg6FjQo`U}@DX+9;|s^IT6=d&h7~^1E&2)_
zp_*!xW~!PB)tYdi_EWL00MD|{$NS)6kLC%UYe<7i6cJ?p4IQXPmJ;pm=7uqaIyoiM
zxf${Fp>GjVdPuChT<Uri{I>bNXx-IqV;-u#cdeIS&Bte5gT=DKxj?~P`nI;hEq?)9
zV*bij@VS+ce3WXD_HJs$Sxyh?Ch^BGKknsee}Si&3CQa+mhyuK*0H(IbZTxQQ_dB=
zH7YDDBz~KV2^ZcXHTlI228>jGKt)-LfFUUQmRQ&p0L)y0Wl`};xJjP9U0GJFePy|j
zZ+W{J;h=yZnJ-)s@P>g8o^<LD^OWy<MVZeSaYZq^2~JLwqm|0k1U6vX=bXSKIE7@v
z)>1A>pYx8{i-Tt!CP8-x)4}Pp!+Cz^c9m>??PA|=o$G3;ROQ|kHBKZ33xnh1czoNx
zPAO5)!%62N!@W^LCBLzDC6k^d<3sQ7wHQ9JOr9j(RNdA8+ZokYs(NN99Yf*P@a=V@
zX#&0=*gyvk$7+jw^EF5aP6954Ngvlu|C=Z{r!!Xa@#EG5kic2hT0JX7-anzR#GoAf
z6ZS>c<Qp47E$bQ(R)?gL^ZDg90%-xyirMLh&Tsqi>YGB(pN3o$30@(e!jpe|BQkz<
zffV;?TJ<L`OQF0(fj_3m`y5EBeYdleso%e13bi7a_O8nBnkgnl;Ff9Mzj;1KV(T^P
zC*==}VIwmCsOj2aQKrGd&f1EKf5_&}V&58}YLVqzHd{{bIF4}Re<IMr3uCwT<$)pF
zg0*+~L4AvL4aXIUI$D}PB`zz(o!Z(s5M%vd2#O_Ir`cb7K^W|C7~B$u!42A7l~n<+
zAiyxl6K$<~yq%(#@K!n0`D;mVNV<i@DP{)(`KZ7_UQ`34MXhMJM-wm%8)s0BdL%o-
zfc+)6Jd^ed?U<Wzvkg<PNca(){i>~`LoD63e@3YC-+A-2Nn8xvf^W+TNRZo|GL})q
zNA@OHrsL;BMX4PD<JIDwJInZh+W?`Mp<&)1;m{K;(S9wHz{n!a5$PcnQxxSL!5c5R
z*vE6c_Z<{j-cS}^`_j)$9gD`&_(R-Nv-XU)$6ql7U#=!UJmCIiBK-%31O3T)#i!(v
ziVKfAXD8SDJtE?*e%rS|p~;8YWf54k&v+JZF(Ot$@BE=kp08U306i&ikB)6%!Aed^
zDTaDpIx~c^yCHS=b-T%h1<&jIt{UrRcaRJzkkY_#_C0vlsTE$WkbSxOu@6>!Bg}^x
z7cvQ=J6&Mq{2=pP0|9jXm;xcewXB(26Qs#mca|8t)1(fKhf`q4bW@k&<I`-g0(Mm3
z&!vc*9ejpegWE;f)|!rbrg$d`gLxZo^il#Mv6?+6w~EMgtavDg!gudoi{2~>!2yTz
z9o*8vd~vo1o&KZn)6cxD4B#`+AK_Pq0sZ8onw%{7r`Yf^a&l2rnWVCRpvb0ENELaa
z-B=(bOB<Uj3=5iyh|h`O43cOJNpuGRfnR~|W7d-B&0^k*Y)(WsaBqAhtc-xTlwiK!
zUE6iw0ygCkW3G@uED(h>Z?P<I-7_P1sJRQk!NMJodCP-cH>c1UXV{Qju-2yH55`RO
z)yaJKDaLwODkG~LrJ(zMa#{e0CV?3Ng7Z8#pVUnsDGSB;+W#Iou;-0q`?!<0PQWN5
zzSLqubKCyPRl#R1wH9$C7MX_MF&Sp-)KcO3hSQ^2&b6mgIoq<2!f6RjveJ&b>Gx;z
zU)YY~$DTN1R?)P7Y*U2_@D>k<-}=LYNzH(R-S->%M+i*ng7{|ygJpVutpotwNNH&;
z7Q90d_G8j<cBarTI-|txv!kYX`a&9s33mpVry=Y2sWs@~3K%ZoAhLvZwEK+6yhb2w
zy06a_<?YYe=8JF`Fppj#Vt6YOag==ag+Npg)^RR=<Nuy`BEIymtrt4#>wjK7+GU=;
z-xQWi#M1>@b{2;MuE<(5e&dtSFSnR#=tg|bN+z3*$A+nhTu0{=&Z`O^GM(}>jnct7
zn+oad*O4>KhJXA9o$6xWqnvWKb<jwo_q=%@bPCj6J?(lN$|9)qP1IcQhqJM-S-N5$
zF(1%WJ846$8<EB4LoPgl&*9jS8ib_Ixv;|Yfm`g{)1I3Z3jzOi5EZ!z@;fH<Hp?y$
z#yKYjAJG5=yS<t%2%d$JASd#HcfOTKAJ^L2n%)0hOd_)vhH(g&D0xD7NUFO&6HZP|
zKl;}7j+W^2pVvykOg}IhZXF3Kgi8)|Voj$trZBF;mvqtjuVRIQrbT3439d^h5Cm~{
z6P+87CpK~m%B4T(I+pYu(b^spCTcLUgF}(zbYc^V=G5iJ>I!2XWMzu~^*l+tZx{ul
zl;xP(%P8x$J+CQ}H<i@{PmwyDvo23e<T@WR%#qX1^rvfRorq}hUvi|SvFe+djB+40
z*Nhb^5=Nli#QmBf&FvE|VR{oWJI(L(I;*tHuMYGY>myK~2=t4L()dJHw<+ScNeuFo
zP`~|k&%VWzC{!6peOPi>ge>s*$tzAYiZd)m5kaPXU9l4=yaqGuJNC&Ky>;K{A7O3=
zPe|E_w(SpnxZ1+JKtwzH;)g6o!1v7<zs==l-M4c*GSN6|z<Ue(SNj(;8k7_;YxMSE
zG2u>zxddw+!HpsBP&VmJ(vEf2qE=T`27xMU{(8P?_>_)tTPV=nBKsVo6^?juh+63A
zqz4@CYt}gqmLh_~=e%oz+9Wh^Ri-`f_SZ6sE?V)lJZ}tt5cxh`CKg!~Ywa+9CFdgg
zn@WM^xvz`)83jSAX*un4`B5ZbKlsg*SHX~fYuaOx@KkcIHZ1`l#0k^=nF|?_Z^@`O
zO1ClIpa***qMuMt3u8W6#mka1NY_EQ+g_x%xD}xj5Zu-9qPjWbd>5uLv)1jkktV9S
z>h|MzE0Y_6(hQ;Z1n{uejKbI+vxN#age9xfl&M+D%*(ZRE(a>5Vn&X3Sgtv>3-<@^
z8<O19l(Un_gK6axZcjDX7c0MAm%)?iI%<9xS@VRdEhU=Uea^_)NHn*niwh=0a{Y7&
z(R8x>EfMivYDAqOd02kD#WWAs3Ow)}nB`lH3I%yq^0n-$5ci1rThCAAl5TB@ebx(t
zbe3x{Hf1M;j1gEKlqEH;NvQ2rX()bKshmUQAsQO)$)IX3ou*`R4*5NpNfC^Hu#sM-
zB1z$2D(@#^TlNg^@C5qu?sd@czM=b>d|toi&yERCeQO|P>k}a-nkt-&f7onDfME)-
zHPgY!^IQYJ*!Oi2RC<?qwjf1Pt_Q_$+$KpUs@0=$J>b^JQ3W#)TqA?P)8<Ys?Fs_*
z&t;h>AD8pXNMc-zk<`Sz8&p3K>?>!p7A#3p4b<#p*SwFTHa8n;(vVGIlicpGpqRvb
zlAC~5YZi{>>}Z)Y%!nLcz??2JC=^C?kxAyU!WZl-6r50-w_&WEi7L(y4I8;M*Z)(a
zdb_=D*VZb@lkGaUZrxUW)N7vd5!TMfn<%jVl(-2_xv(tz-F2mH;`3ycgb<1zAFwfC
zycPO#R#s%jg5X7@Us(&r3W+rdwd>rUcXfDro?>i<wwOtXeq015_;QF`f3yPrqyqM{
zM!P<@=nqXw6L=d%nuI0PIS8|Sq&OG^jc(_gsc7#}XT9<#(NER^lG{o)TNs<$6lpYt
zErN>IeYf)mq(vItEw_0pUJkd(CoM#_ilm4TLw;(#>fS{7v>Liq<Bz}--y@73svJ()
z=^^Yq`brceO59$n(y$29o`qN+TE^p;w|F@`rhsPCHohKdl2p*lR5q#&N6U03utvR4
z`YGv=F+?b~ycFNMy{mO2o$lm_uS_jSa4iHEM~RWHY7|GrS|tGo!Z3pa_kR6%`}IaG
zR*oT7QS1g0dqD_+B;NZImxB;5;UZ~G2hzL*9E#WL>2^<#8_gUwWo2?8BaJRY=lRGJ
z=OZa0?S^x{L#ziE<h;MgOwEEDv_gi1vx!11dFpJ7y`QjmOZ6(w48=9F2;74Z7yrz9
z>9m8mvva=|Y<4%)mmG`S*t{=)m)X*9T)4sX`cK9^nEr{#Ph&|_!lYu0x)nXo{eMbV
zxdO_itc!`^XG)-`OH#B>#*2B@9GfMHODfEW3LO~b5FvPWxGr|=xU(4Cn_*p`<t^8X
zZBNlpLS;QoVyGj*E#_y#7ZVlGbOFPWQ3>VY8s#2E!*BZk0<e9n?2V+k$&9)Lah7=q
z9DK$pOfjExLp8-v*$~(&x>yMK>}Y;QB|e;64PJzK*t?M>qUrpCBg<i6U^wyI=Jz#e
z47z#8S5t|B2&wppbiiMw`5gHk3B%IEn+JICO!*s9mu70Y2NmA7mU&pJ?ib7S#}^|b
zwvM@dI;X!M_<VB`l3ukn(TEbOz@1fgvo&Ocktfbw-Flal2r-SIMU`|%^J3CZBMf^c
z4i-Ylqdn;jf?4;$E~^Ze#8YAf0;H7B-QDa!-@<TY{Qc@}^}EyKh|V}&HKy;@Zb!}$
z2}H}k6g#OFqV<<)PjzoKAmDA2hwVEM#v#zz7(JqW^#WlS!hsR>0en#hofttcq`(G9
z*g_gbKn8y1=<Jh`A!4s@S#ZnlRr))O-3bG5h?(2IiT*5kn!=7SU?78sW2glh8H^L4
zSzU<%qK17sRB||(Pk)a-9jnc=lJcsqHe^MuI!Zu|d8_mo-gNZLN~?TgVWN;yFY&O|
zzECmksaI`?W^lBg2x!i@CBm;oFPY$d#-|P3A78K0qN`wDjH7#Q-m_-m^i9*T;i1*c
zVCPkULg_jdx7a{9&2s|xr)2~35k3~jmWg}vUK*A4StPFSQhhD2`G#be;*Vy$q@-jM
z#5`*~UhT#EB(2ugRmnkG2n#xN`6I2MI6>a`b&YFO?$>{x%SFi6>0L*?Maqr$PBj}7
z{ALk>PT>*F@gE371P**0?X1+&MG-jB1I7>1Qd3JBXM>$d3+46&xRNXL^|zwIe*=kM
zxoxGDJt+NL`70F&4y0yaVqz+a!zly}KdAjA*K2V`Lz60&CY{QIC+wzTP5d|_tD1H<
z)0l)}%H1}78S3#5fvm%RF?44>^a<9V&KCE-rLc_de@fvEdKH5fgH%GRL@v+kK|$$p
zq2hIVQ_zlulsc}QKK=9*(j)O+yD^y3xYV+pv{;QL*$GvWJ*M!6`L?b2-n7)VDv?@U
zpb*haf1o-FzCvE;Atl9mroxAi`zaOlWr6a#-#dQ7MH*{qTt?*PL!3WaO{tq_4Yvsw
zvu=B>372s+is=}(#+WqzuvXEv_$e*>0cIW}Cdy$tVAE_w{Gf$)?IT1=UU3SL7*9N`
z`UU=wEdjP@v3{dA#wIBPJ~#}^vw{txBF`xiEo+B1W{1u+p{nu`(2PiytXQYjvq6=r
zu3&z!;E(Et9)a|{>gGE6X#G_NYOD1)n5pZ?{e|m(anu8G`H<KcCsC3<(L*!}%H6ex
zDdpbqOAQFeupvS9`z!U~o?m5$3%BZJ@~6_p$|RmMXHCQV&vy~C`f~@1cT3|OSk)}A
zJ!UGrZn&;fDGUxRF(hG5n<ef_N28&=xF)}!s#zu9A#;xWskj;X6X~hiaW@$J$!Iiz
zaq1N=tf5+hE;Gd09KlxX2L`{1Y$q|673mh4$OGwiwH~S-5&>c_((Ba(GaSpoXz4gy
zdRH2jEg_=nySMb-dKEwHaz0J62%(LjBx&{@sLwh=eB6(;1=@AMm5vEm`JAIIBcc`Z
zW@DC8X|?VrZ|D-gCD`y@wqf-a%dO73s*jALJp*fITb@I+*|CW@G_zjuTd8*je)5&Y
z09y!r9#dJz9GIO?<hpF{r&#b8sfUd}R-=5r4ML#~<qSfKICVW*$fK<I#dR)@jsL#K
z6vJZBC`|QXFy6dAF%gF$f})mNuhT~tnRfbyf*y3}v(I)*EC=US?dKr*xQ7S4#_9tC
z0xZxX<~xK*<B1#2bMvb&8yc;WbWG$G;i%^LtY|bJ6W-`2uXWi?C)T_>?kFTl{!t;Y
zqOJA6EC8124n>YnEp2ixdTvs)rmW9d3?o_<eAfCK=?#i-=K4z1LNSmT``7OcOyM{_
zOr?6=%50W+@^4R9!<xWTrM|d(izMLZfEcOg_)fL+unn!PS24J?NZlA_2*NmwqTU<@
znfCVfH$UO$AbDs@6<T#Guy#i$r-pAAEyd`T2V~@AyA~h}hL<A6I!W;|hMWQ73Tmv%
z*apXGH6t5)#%@JcuFukU@;oUBh(m-WO6kPUIE81!2=^R)T&vRDTcyFupP5P>-@joT
z57<>>DBgx4KQr9?e$HLLYz~C4knj^3wQ`Nzib92>BdV+0%-H06)U7E!c1Rzc%|~qP
zE!!(_U3Kkar#NFr^N%ctLgnb_=~qL^4~@8jFlwbla>;t9EWm_<z%uCL9eQmttT3UK
zE+>Bz3b0G9Vb42bycH_{Fw8T`sXtPpn?y@96tBj|&gomX3#7gLyXS`JGRUyZNn1DK
zf1t+avwZ3gZ1>m|EG#`Nv!oMGGrc$~i;0-VY;G~DD`JS1RQFc)aoYBzstJLosB=nY
zaqirgujwj-|9EdP%RBH@GRhBHE}`t=3x*04(*EY#6;duL=jv`CPjeERk`d2bq|jqd
ziq}-F#Z<BkDa@BHfJK6*4QI{JXqm5Lq>bJn3(|Vica+uQMlx!05@Y}75FvIwUfQ{~
zBg@|e0)e6sjOK<0HRP{i{A~HciFvmyNX#)BTgaPs0J8C9T8SmMqT=^s8X*V+5sn7m
zAMjB<RCrEx>!Ju?IYl&Ci_u-3kFt&r)s(k-ZPt~tYlBN_YW}T47}#^(M@@dD&x^sU
z(imh7ed=PHvMde0cxuR*R6fpdk;r}B*Qd{Q|3>AbNiu})Y$)S7SiFY90o^V93_Dkn
z;a{YCFc*33#5W=3rcMQ$$N9%`q)nSSZ}LJqe6U2?^H$qUNq64VH}Lj_HMzzb#(A6)
zvVwZ1c?UJ(d<=MeS3ewN=5d2HW9~L}g&|f^qg7)hJ}5@e@!JTMVD1M#s=giOTG$uK
znSV&Vj*gD-`;AOGKXZwq16B}NW#mv5Xe>yLH<?u;{Fvez0k~`)I0GX1zJJl>s?mbn
zR__TZO}kpBRt2d*jWV%iZ8o<}p+R}oOG2JjfpAUGVH!_Oy?c@cq2I^Io$xJGpZn2x
z8Y52l@@a=8DjnS8_eZPM`Q)+o@!u(be<+XjGSid#N%~EYB(Gqs--nks-FfBL-%4Z#
zRi2{CE&{Y1DI`@IRyBh^V(9#@?RJjSe^ud*(JCVHok8wt<1RZfN>z<)23gTa5?ld6
zVutf1E_>OBspnv%OROS4UsNWvDklO%)C7`Qk$1*4*QVR-*f;Y5E9iHQpsxrVayPK6
z$2y(!jTR}u<|1%B&9VGtL;70$RhlI5{0}#zw1EY{I3GU5jpI;F%%~MZM?A)-IvuF1
zX@=8xsHOxLs6RM+nkwv+UtF9&$4bt74SQ!k<bGV|Qg0GXZ?TrkU*UP)$hFE(<3Uh*
zGgqO;nZs@xxZ-)IriSsLC*=fD_!NqO+8T}xH%Y&ML;Z?7s#N&bTD7FI3*U)$L20^0
z?O8s#D}OQZdAq9-`6yw2nJC3bqNZq0Fk$<RRn9T=L;S_{vtmtbwiLG)@zHEpMTF*I
z@PwqMi%TPJ$X{noMUsuLB_$=XO=}(_psBN9T=qrvN*!*pxDbC*Rjj=piua-tVE&=7
zxnIw5LLU+SlB1$?d<z6en2@<Wws^%bz@dHciT)hwB8a|QZD;Mgk2{(%H?>Nt0g;@C
z8<>;zig|Z<>Z0!MQSPuES+Q9AP2#8myTg0qLH5Q#i2u)u%46AScuf-gXEESH65)&x
zjY9JeYGjozXxFDiJy%-6HjLKxA+No)PmD+S9LZFoRge^BbDxc6n=|>{#52d*o<Fbu
zI`yLEZOQ~Z+<WT*0}O!#M0YVLD+qD1(KbU>O$|{j*KTma?GV~zg?~OZJq_DM>=acb
zhgas*(a>iqXgbe=7ZDf2v-EKLa$g8W{-Aq!!^-cJJkvCfFm)PZ3Z@CTH-a6f4Dfv3
zz1CF>hGw+#i_S9-F7FC%`xtP<3A<i3A|d$)4yaM;kr`1NFVec#N+TNC^SbRg=fKw5
zVz$h&!b}03r^d^btMM<kBBEftYpKJ4KgXqJE#CK)Cw=ni<n){17Spu^>FURFtiav+
zD}+E<p6gk^s<5MQ46YV39A*j%(dUn6yewa`1%35^Jp*#K#~-^foqN(*K$OA^jTIFW
zA=#wxEUZo@w#0k^2>xu}MZHnHsQrg9UasIt21a$VF*Ynkc&uajAyrt<%iO@@{lB2z
zb<(5**_3{Hr@3CuckZge{2t2{mdK_ayRxUdxdNx{SCV(LdA@P_pKhc7-A{B52ZwA4
zXPqAYVK3BvODvxcLva~y?(gZRvP<yWc@e8mlE40n1I2osMHtx&ns;p1o04bvg5nd6
z*YGNFD|B07?*;lGKcv4HRXtAs)VM}-5BeiK*JpsTi9pQlHWB+7K0uE1>cy9_SZ|q-
z`wqf|BBc;X7JG(5FoGPkm44)8h3S{#287>E#CTKr+r~7Fr6^ug_%GwNDOhPU4Y7K5
zGCq{+;ekG$n-*6QMOtm5>KhZ3L)T}i%?U=+qBN51h!5-iu2&oWGLO<1ljtO>Roctn
z@-xt??BZ3hW%>SFVpJg{1pktTpV=fJ0EB8Lxa)hOc(;A6OGXn%)l%OcHLoQvVNv8Z
zSHez;+J|ZKo_sjO^2T^{FZGraey-i0W##1!4Z44o(A|U15PL~4VoBk{MLazCM7?8!
zUWQ5#$G6_7TBP3cS052X+~CM?3_oHYXv+1na!oRywcUbpBiLr}W>CqY^{Pqpxp97Y
z{y=5T@UU^Jntlrm`mC$i%!{P!nROl2_#4Pzr)JKVmX?RM$vb@+ZDY}^F)3<8slrua
z>a^ke${e;z;CE0-VNeOyyWq6EyH$wlw}i}=U0Oz_(kVDi>(Ijq@UQRiw+YsLt^LfQ
zIew7Kq=KNBp`#&k!bHXIW>mBc^xM?3qB4Iw$j1`aSsD&eF*Lx|RfK$&{y~(R=vKD2
z(6-0xWe6NrK3GOxJ{j0mW*sgPt3!rGK01F3!vOPhfA>LImm>WLcZSxJP*k<v+kAUh
z1V#?ooZ)rZt-rh2mh&wSS{SAKH%6=bc&t3_C?Bo2NIRew#U92sqmrRf-Ob`a>d*$|
zbFBe^7TPnYue8wLj4CkAiGZnR!sn$57oF7fv#t9upETE%j-5e*@qW$-6ZvLYo_%o=
zv3~gn8b+sF_8ptwncy`DM^{8GAk}72!yND<FYzPy_><5(TrZIjwJrsF0Lu6C@8MwV
z!W--M{<@HmceeZ)%xQ3AUwN`1%IGtqq8$Aesl;%=@KBz?lai0Lr0$&pVHA7-<sX>>
zz+Ec~M2%KFG7Sd0wxCj^Ozq$ko!iv1LzthA1s@r7HEL>d{>Q95y%OAy&zx-8CE&?Y
z+fIuX)&2VOXR}oKi)qDq^}=IB@r8UJ#IV8mBW}miyTrAE_W4E)X{Fum9(sPBo%dK_
z7_X5U5d3#dKZ^+kOM$ZPE8TxJ(>;05z1h9CXsGa#-iAr?A5<*r@W;DdTkh+htHme*
zDR{84aegamo{ll%=YkH&Zp=2q(G3R+>!sn5k+;i}96>zf+sZdM<jsg8q;Jk~Bs`DW
ze2y?C#33>=5uS<w3}qInu9fhoFx+bluw%g>)`tLd=SwwS=obn$vhRVFuPy)n{6UtE
z8341Cm<y(x3~y4rWXId$2I@aes$UZWnjO5Kv{exn4)s!kqLFv4nVvu4x}+`V&j!pq
zI;mRuFgfYnmWEl^gI(<Ht1@}m?}ISOk+tx>@!OOiZcbBxt@UnzHc^E^=MH4hth0Y=
zVp!ha0t<bPvtwLqaUMT*B=`ve-P&#14X!0X#eb4F<VB2>d8I`<FapnQN3FzTpcwqr
z1mkemU8Pzq`z=IM(UC6!B_tfeisbp0SD!fpr{X%gIxlMrywuAv!k#F;md9>#6{Gm~
z%5<`fHy8wh$Bdjj>$P{ig$gd_hs!x2TcdO9<k3rl!v_10OMd~l6d5@76)?+53Px7b
zdd(!tzyjJWDZi{p3uHG(coazm&E@y~*q2fQNcwrfLgcd8L*ibethfW?%;G>C$sZk9
z7<#T~G!#!M5{KLFiN&N*Y1;~7B7TS?T?CgxdN^3kJ8;a$IiPvp`|hq$T{1$F;#19}
zeeHQ@eueP#y-Pm$9b1t&VfJ2LVVEWAhepz3UYwsABJd-4DUL%!{qEoF{Jbw~KIJ~;
zY`U|=KTuInR<KElPI;1RS-THLL67`SlL=R)VBd$f@3ymEcsc&OU!3wPBVm@LIi6SU
zJ#Sgg{8B7@K}W}0J4rZZBv}j?#qnr?nL#P62`ox?0Z^5Z=JtEbz|+(r?5+{GQI)TS
zTYQW~5FWXJXCwcL95uUsch{_43dkXTKI57O8`Q$bx^4M?@P)?Pb8Q>IG6E`M-_Nvu
zsIae|XOk_~h)0`#EzBm=+bwb|Cz{jIzj$gaz?Cckr%oyqlm6iZYv3a@+;xAuk(2vl
zoTFI;jva~xO3!2a)Om*ekfM163VqkRI&LgV*qr3*o~!)>(>`yr%EJ*2Qe{Y5EB03Z
z@0&24Yn&7`HArq}l?EN11gmQoYjO%_yh%Y-m4Zy0|F_4rG%R41R3-GG7E<QvCQQmv
zQ;BBuF}T_u*w~0}Xw}<)Tq+}UQ-Ci{5G2rTVF|)Of;+n!AJZn^p$i{8iKr?iu=Yh0
zZ<?B2rL-sU<^_4*9Ts^0oUI?h?Y_G9Adka01fLn!r6p)L+XZQ*Q}w;Ws(u9wM3GlO
zXv~iAN3i8lybPa+Fb_I{Fz2UpNh79I$~1+4_WO&M0awKL`Bz!1r?^)9@Aam|AXDF$
zO>+-|xW4bcn>t<?*oNYlW|<*q2;Umd=HV4~3d-@~5i`3XW56NWV<V{_;*-M0$Tl$;
zQ?sB!rEgL7BNY)mfr-P?%9Vrs?+ghzp4Yks?CepMU0vh+a|To3UnKt5*@w#09%O&T
z$S}Boc^`ce9kvM#3i=!mZx7+{Jd;&aoq4h+zT(?zQNurB7jLT-&Aes?cC9m|Xv#$?
zUPoXd94!}%Qx5=G{A&0khJoM3+g0qqIh4|SWc(`2q`@tT)!Xb4NHr7Ey4*Xvl<U6e
zas8yHZ0DX+P|=}nGP334fmn-A&SKv%*q}>Hji$F;yeVWizf^rvR8}U9ZF%p({%8Qg
z72XwFT`p?UJ}0t1BmG^?i2f4ng(5U{Zquq~QVj5Y1?~u7UpP)axM%!nrJ2mlfaToq
zToxJQqhepqy1<RLT=nlEZ7TdBe?h?0QJ|ytYPsmCE?|*zlI^!1j1(gdDRsm|7TpuY
zG|zg=&Ep+U6iI`v$!5XJPYtMhU}Ilb49Q|APV!wKjof!3oe|W@pB+vew{asFC`Cg&
zc}&g@wD#8?;z10pwqga%F-Vi|`u|eSu!Krr1^yBZ!2k#G5-_l`s$g4y%}EqOmh2Gx
zXF4M(;2ke_M^XGEi!=EAn}1<{{X0Px!}0;c(tid!^Vg1>^e3#pghz3<<~j4gYtubl
z65+7oJIe@2(7QjiFPF%r?lukKNBs;NKw3Ww4aIJLH^%M$M||Ho%Hu>wG;nHinLLel
znrf-gC7W?ZMC<Po7wJsZWn;y**C~|`)$0+AT*bQI&`qHpd7;AkCg*uKT?u}E*&Men
z?SaT<7DaclmbEAQ9m@=c(u_9>(wpqDHG*b|Y1if`F(Axy`Lej=<f0G3*}(Fnf}H4d
zMgj`d66{b6aDm?G!NJr)MIIxrtreCZpgp3#U2zsQ{PvwK>)m=F<;2o7$SteGMTmDc
zE|#WbqY#<1?O|Bx<B@Y)jAUsdxPl}kw#<@u@9SMrWs4LtJO7q3S2r>9LeW^<`^0|#
zy~5EiKHD6Kn%`dqrx)7cy(g51YxH+<GWf1ic;CQF?WHAY*i1D8>SEkKeIl$Wy*alj
zDOL&LNjG(K5IWMji;p#v=d?MXa0f|+)$o6HyRxKNs*y+4mz9x`nN3)~mA_4o|M7jS
z%t67VK8Wn7uA~I)=%kh`yOc<t>p2xkj@tPi!-@?HAw+1gUfl6!le(a|Qw5N5w;`Sl
zGmUDco1^RjOU4@7^bIL%<`T+dVGb%kpmj}ZwXucs<jFaDR#;Z-40{dmwAfkQejXo}
zCfwC{`DcPFVa&7j>G?)TGddO^12v$bP;Yno<oXv8#U=IOTO?@f%PB$yCD~;^$?oeN
zsLCSA<6<mQ%n|QX@|8Z<=gQr3BMrj@3>(e$T^cPKOIIK8k%xW<&pGp0??&8FQh0cH
zx{xS1mzlk@%5Jk1H2{Y!_8#wB7)Ov$zjDoq+a^(jlRuc(>$X}l9H~N5bdUn5eaK?7
z;+ak%|6WVU4A1bH-r@JLbUGx>^-aXeV3Q7-A;e&=Eqi1X=2waH5J0wddN7ZFZrU7z
zDrhyFMbY(PpQ#nSA7#G`$_{Y2=qMeogw$W}4?ldgYF$&S3N|NrkW<tmH8;DwSse^o
zo+B5^GlM?#O@<`+eedGb0#H%Y+?)g<ExZNh^Y(UL1j$_?R?L-#tl4{3KR@$7=q1DN
z2#?%PaIz_JR6O0fwm+%t3$`k>thq_BkleSO<BZ#q(1zP?PGJR+VjES=3twi+{uRix
z`y@gdCsf$lsx6<zLurxUC-^$79^l^$0{e|t62#3UaYbCW{zzcJA061g7EAws?7d}M
zm*3VdEQo~Cpmc+jbW3-)beA+pNOvRMNQ0zwOLv!acS(2Gp7>wSTI+s}{Q>ri`#6vn
zAlLPqbB;Ok9Os~ul)EDYSq7zgHO}mY7C|%%mDq%X3RL#6)N00}$I5SU+lhi*$rHHr
zNJTx#L93dWor+sw0@Imda#<0Z=heQqxpz&Zb|pWHyNe>M2p7t5y|2SPD;5q`ynaX!
zGIY2(3--I7$vn|?l<@Dk9l#_R!vi_j)yi2$v*;^WOJo@7unVB?+o(Rm=cv7G<ok8Q
zLa>foMP?4ivgC1R$>DlgBr#d4Kr74O%g0h8-QadpA<7s`8DP*ag`x8ZFkSMK2rOfv
z!lM@s%`trJu-PA(3}qe}XBm(9Z_@W#NHw;3QNsuPibn=FOckP8MkYMFCb@HK_;5bn
z%~c5!IYnKdtY*>SKb5Bx%@delctrTBoW(|Xyu+|Mc13gqxtiyYaFy036orU>UyMx$
z=0bZSp;@<huh>{g;?YICVUMmWO(iRpCxemqmZ`{Sz-_~ttbE39F_ea(?@K?XY1G}&
zD+w4wQZfcGr~Wzeby`Ol`Y|y+Zt#ccku3y(g@hZw3c_YP21B|NEEmhRCWukf(M|WW
zs(L!4cq_qupj#r93@IYRR(@y6u*xHU7v8GDjxx?|gJ;&Xbl9)5ISuoqM7Sh}=~e-4
zjV&M@_BjShE&O9XGin+Avq4_wLgem*LLE1n`My3|@H3T!`$wi?8mA~P(TQj+)t7_a
zU$l#y=`oLruA~oIo@8(ZSkbD?6|{QCyBt`DI4I&;^N*4)h)>Q{FL^ayD!rY<-`<a-
zLg~Y7DM?+LrBXVLY`pP-j{pHa{MRl(4w5^GCCJ)YpbyH~XhoC07nhg)V2(##|3uyd
z7txqpcxH_QY3J*hKrummENnO-F!P9<Jb8|1Y+Z-dJ%-W(S-Z?V;gLBrGj^M1N&+iC
zFWvUMA2E)sQfR4VOEEf{{npWp7!Ro)+VCicH%Ue--z+1S{FV06@B{>o7$G$5moRns
z1t>%!2UvKcK{VG&6-<%j*J@HU*mjY=2ufqF$1F-Kw@67av|rB*qE$blo+h$OcHMHO
zP+gYmeAIc8Eho$A=bN<YbJ?dhxP%*>t~?Sh&oPqRb?PdgAJU5}U{db0d}B4<T&1k6
zEaBR4ib&OwFuS{JcJRs?ZR#K+SDi~kK_Qy2h^QQDzdw#z{avq9ArZ#N)tAP))otvh
zF)+=s9*KsU`iGd^_zE-}lqjJvccZ3?ZS8)EN8ftq4;6?0Xe^u@KC(}6-KYoRr`Po(
zaAv7`Vc6{if_is_I@8g$PFK}+_eB3#Hx$?*>)_4YA&3|+Ysiu!6r0WR+P+4<S%a2a
zdR0f8Z1IOx@wyu#ac$?oJLF_0HHE1r(L1g$CyDR{k-ec$C(h!%iCa9~t}oMShgBZ6
zgq+(L@vqOXDQ|Ix3~>9^izdZ=F$HTnK%o)tn+^_wj@ffjV=~dq^aLJ<0UrZtJ|8DA
z!3r?rrbq8xp5OG0H_(m|wtf|iS6sgpYDzr<h%s-p8RosF_odj;S@)|OBd>NY$@PI8
z?n?y;VKZ7BmNJUw4Gw5gcBxJERdAq$a~Yc<XNX#wEf^0o@<EogYY5XmE}Z?jZ6Y4v
zN^ayOqnjrVHj*SG;ub}X?HQa`m|CP353y3-DcPsOwY4~!JmyQcZ#=hVoS4doS~Tvm
zF$ha`Og|jKQ_4La)JqoByxCg})P1=`(wLN-ya}io<{CU+Fs`r0rKi)0>EM@MbRT!&
zogMA%jcbFce-$7tjQ4VxMhrv%S@RTQAVL<8`rPK)N7RE4;fJ`ydWUjzc4N#9Jzexo
zr^R`Tqt3W52LB-H-mBWS^wa&P0ITf#=~7D-&!?rPm5y6c5KQDeM>RY1J#B$WUWK}(
z)fJ*G2N;0l%^co$dN9*0{RY+a)-)*yM>e~l+6u2fhIPhGm=XUIu{0SL*0kAR`Ax|J
z3)RAy;{vbH49M(n#%oxO`X!e?feCbA9=RhJ;h}YSYta3myzBtNe%Ndtq#`56T<hui
z8!Iru#}sgLL?$JTvLxlF2hyt~TZV3MP9@@tibFE+>#h&F<`5Q7FvlxSUz&^aOK}qa
z{ta@!Eu6Q4ii^yexA7G)q;!_dF2s->t7Kw?7NDfv9uSYG5!G6z9<tT1WsL7Kc{F6H
zXTsmaoP9F#>uG!~5#LKp@Bk3{OOarGaBrR1TJ)k9^AiMvMTOtMF?*C-T3W^dHf(d2
zUc%4E{_NWDScH-i5)0#cu-Q2NIjr_k0nu;j`Li=+(oT%kmU|olAUYVOjGTRAbTd7!
z6RLV@Zs1bsfpSg1*f>Md!*ydhC)3~@B8=xlEV>s=qzL4RfR_M`_95vu3M1sp&4}bX
zAGtG$2Xh`1Tbd@(<;X|7JF<tgmiSIAyT#PZ_Zstb%~UU|c^X|<sGNgUl>7B2^1ff>
z-`$_EGBPq&9k)Eki2~iI@Uvu`A3s(>;pTTim%d^Y?e-;llc5cTd$G9^m#G%=qFp(R
z7-21$cu4&!@<%nIx6ipko%N`wJ0hn3K?Hev>TwcFdg`rdBXAPNZl6LXs#U&+^PR?s
zCb%mTegL5u9n^keuc9n>oOSIyFFsw&8kd+ZBUAumf0y%PkG`U5&?0LQm&!GlRx#5X
z-@3VEpoEX!2v7vVxa2(Dw?!qOS~bCFqfK9^$7O8Dj9gfj@I<*k)%JQBODDHQyBd8N
zhVjKG8=E#jz-dH1R_4pzNn@VC1H{YIo!EZ3ovHG_U%8i4q-Q)f#hjnpI|j5456cwC
z@;4f{gfd=6YT*+je3$$x*q#zctpxWbfOA(Y_x^6>*h}*WhsFt!5RSFMP>N9tfO)<a
z^D*r@XqlJB8;btS@T#QSBiILZ<olA8VqW67u%7C@?cA^_`}42i<(4KhWT_7%!dVv%
zfi&{AF+b3zRI(pgiKE1n1}NRx53jn13(dmG4MnCbTDTTqWOZM9hESPZ^-K165^LpK
z?-~WeiJ!Ky=tK#SQGgAU1){mOV`WR5Nu(=_l6<>vVm$|nnUCym3m`N4>e3E~t|uCv
z-eMJT(`D_p!E2S`xL0e&tfrw5#aCQ%Q&m5Sp_m^;8+#Bd)GpdzEFtis`hAg^z(ov1
zM@NUP*jf#tgGxRW7d+VqNw%RFZ{8GS3uJC(=7~f>>FS!AC^`W#M#!jdH{<#VFU(c|
zn`#|QkW^Zsa&eZJ9oi~1te;@MR<nn48s|B8fyKwn;4123jE(Eb#j&M%VkWz-WG)WY
zw~H$Z`g`ZqFUKa-sF6)`IL;uO`ODu=&>x?gO6F*A<2HV)p4XmY`b|k*au@|_z?6d&
zZ?o5t{I$$mK9nkRk@S=tehL=U%%#PH3qHO6251Y)wX@(5aX!el24LmWQ2^|sy|P#J
z{lNw(9kfo36PlJ5A<ye37$<;|W6h&fXWITO{*LUl64Cwv75u9-9$$oai)7rj4^zJx
zZ={2m+!#d4E&SlH5wf6P`JV`6Ar@k7)wtg|?CfAs3L!=iPaTZv$Fu^Xg{pKxcYAcx
zk8sVdXn($b?YpTF#2-Se?*2((7cZ)jK@h2SZ0!0Z52A}IMaCG>{=(J{=mE)eBBYXQ
z^U?Y0Zk#9AyM?2TFGKScnfLg0&X-~Z@76s$=U-Bpt*Q6?!Ir{Jh>fH=tDL^&B-1~b
zr}Ct+*0~Q`-s9(@QLLVdf4f{%e&U*ujwdKcTTt(z9zL-jxw~h4kDHU0Ock+(M%_Yc
z#1V3j-+@O8%HZeuYBz^A`>Q08iSIk8h8JxPXhzI+Q<G3_7}r@xF2y|BF&zrTHX%li
zBlIo;%Z<-UCv;AKyWAtMv4c^Tl+@H{=;0!Xgiwg^X*GM(r2}9tEHerW5{>{xv3?>>
zJKn{_p_Z0bbaXVanwlCIs1%E}4Iok6LhADJQ8hKutKo`UqJ;Uu0dqeB?9Ww0l+qeh
zsplG-_pP3xsA`|8WHgeMCf(GBS#YB&OhcXlX$(^$o6^2)C8WkBo7`r%ohxbsGJOgC
zpOtVX0u$b}zst-7k`R3s){|YFCw@jt{Iz*J%Hq@u>1WO>;9!CjEpGFDO{2{v$J^cO
zaECMx>7%R0D*c<4UAju`BsNTsaB9fVli<SnzCOR`)>fYg$+aAvZ%qeP#<dAb@m;Y3
zQDg*X77?A~Zxx6ecuge;eI&2wZj0cJq4AZ`_P?O4!H!@F5EEH|%KNK!Xy`WRa_AA9
zc9`wK?>~xfUSDF5vh&c<$)aUqa>8eRHSyK`(FN@d#*vutTHl&P*2S!be?({&^?AW%
z3~SWrxDyB*LmehgGTB4z^IwB>s+6Tl_fj72e{?o8l?Y;Xphr@rt5(P^o}wHdGz(iw
zlggF8F>UwT^|VkUXt8+Y`IgBxB5E(?;`tg99QzS5+p5CVdu(zDy%&8;yjF{Jg!k*R
z>CMyFr_lL3c91(L1U2fRUB}3EBm#))<ApfS5LELP<YbkgOlAW4U7@ai3?X_c#E^cX
zF}DvJBI4daPoT>iAlA0|LPVf(SsZ8Z7iD@?S*f1A8ufK3dg+TnJB5HR7)!)Ah8`s2
zgb+q~Z{U7^!o?E=#@mTZcMx)Y1;nKBGM~{#uBer_re+t34oXBKR;Av57c+N^nJBDQ
zF#o)GIGXoJ#L1$w_3p!Tqo}CTFMI)_yqp3UmwvN(TGLfm)%4F7EM~WE7gx;2@kNx)
ziqWwNMY#tlr%BEM>w7&|%S6M-Q^D01?!MfN>>F4L)?8DrINDXB37#@mX<tiCr8BO|
z@1-D5_<-TWKP28pW`*qNyJ3t~APrX0VbOUBCfzA#fAN(dfA@LYUNjEXG+oZWcI10E
z!+y&9#oCr^0em~DyY|@^ZP1y}NH~5Pj*hkQDS|EUqaz8KbX;8<wo<<tA-xqKWPoY<
z!ng(L9jtCw^#=t2tUd^<pC4;n_KVrupI__Q$ef-3mJ-S5*KD-gL7GdV_B*_(K!3~F
zlK&-4)3PwucXFPTl0oTPMgN+5zPF&yDf954HbtQ^U*}wQbLB_EwyluNmKx1C!o9Q`
zg4iqCd-Fc&)WakCni#$Xlnl9$2916=|D;yAVcWM+45Z=Ri(4f`DZ4cxQW_}n7Lio6
zLT{@)<PDE7gj|xPrU`2DB(A0~4B0vB-xNgc;<L;Em<vlebmkA7Mnz;k)dA@8_@tzt
zN8bt@LlMl6)Ya9iY>3{FHkv7`+hHj3k#i&hO3S{RGb*%Q1i|-XAP^sm$$h!npaXe5
zTVUPz9!oe4F8n>oL`pb%+>Cxh{K=QCs#pEV>zpn`po2(wd|Et0La6#^j9~I(@c>mP
zojjWP$sc*9B`DaLx;kn@t-i(RVtiX-PD9sav5L3rHr`ewHv?-v2)0*^<eQ6ccu1KT
zjm@*@f1f$y!gaocpwUfIBUoQmwnbchA8CL5=4bO<#J#4-WO({nes6n(-*BZra?kox
z(O3okh4YY}5?ikE8C{o>ecs5!sD*{4<z%zrkISUmHc1xiC9dMn$yd!Lo(^@I6$U+s
zS96@OBY;>h@m;g1Ys!g-J1n!)xcv{Yaq(k!E=Z@=Q_%V+rZ3?$s2X=@b8T3`ll6l)
z{V^wg1@b!{f%*-DqsfmRVR!<Wslyny;rqD*tvS_4t6BED#Q>7-rO?fy2!jYWd%CGh
zEwci-QpK?uH4<s6*Ez;zcIPiTl$CAMHgbl4Lt#b7wMuTo%YXCiF#0798ULJjDVXnS
z6G`?uyPhW7*<gD>dTDw$fRF2JG<4ya(!woB)!g!khZ1+IihX&@DG`8H87ro!^WwGD
zrK{A3eZFgwDGa@b1Z^%20*v<eL^F|1bn%Q=Z9f3bQ2rI5{LGni;c{PaL!yJYZ`9RF
zvbwpsNg^Nmd2xI0e9V0hRo0EZ$tp*{fGcnyG4RUy@VLbk%~|(Gr!VE+XkF%KPJ_II
zN^_QxUufR>{kn`>=*u!%Q0*-&mi0|Q+nbO6WQ&r0o(KBloKQ35&NkX)N$F`Jdb*6s
zZdSItwy+DDHm{)K5F}r-{e<K+7pmCGmx`h)(JtOQrB`VY%ljEK{<Are6(7B>Ml#3R
z;)1>N&X?M*`F!cL2;PY1eCcgvI?C%{x|QB{1sEe!Fd=><Kvv_{*47f?%$eSodGXCO
zI#(K1sP1YOUx!O&iFK6XR9I(~;0Y(m_#GLbu!Bi56O)ssf!T)aIlAAs)hqg3CVn#r
zL4T0<-z2++^PE6Dz9kSKJSqE0VIDt?|3t9f_G8P_VL=c8qG3TTkdNX76ZIH?fLzq0
zuiSc5>X-M}BO-Q@8rYeHCt?I?3`oD3lEfGwQwGEIr^hZ;FDtueJKI~vAvukO(XBss
z&G}(r({OO8N$y2dTa7;IBQiV}IK4%Y1BxPX*sMY+N7AFTxGa8Te0;6G7f?117(^*|
zdEeYRUE|L~9b5N<j_yTY%{qtgRqTa}>E$S|Gq*<d5_#qF{jum9$RXZ5Cy-mE*N_cj
zy<RIyaotssD!D0IAZzI{JB{8>Jy!mD%@%k<6}4C2(z2}Oh_EUlV)rqX5n#$_k`0;B
zoHGVn0gL7WQ8E~~KGki6OI<4{=tWG!j7z={a(gwf^h*|XPDWhd&RHB(sIyS{XUgKm
z-csPpNKLmF2QqE-M*f`q)2~J;$U(I)YjhX#O0b=nOLdgskr3^mv8!^^ZJsD%Rahba
z0x^{IlZ5hj0&+8SGc>ZEGciJs=LV&brVXhqp=bYYm}xnt^9?Utoi0v)BDH%6FS)N#
zgntVZANn=X0I^v~rfN<3JDh;EFYHy-ddQZ@)}!=>5gl&=&>vyFg#rdlu!Igkx8W-|
zv`AsJ9HigSt05=+Gwn>leqi%C)Z5>Yt_9_=crERJIg&TjWOJgMF4d8hO+qA<I=Xfo
zkbg*FH`7LUJtML!?zJi5+#ihMzgRK(GIeM8rJI|WYF>{Se#x3kuI&gCz9k*tcnaag
ztUvkBb>+sKu9Xvr^NR$>$|TbP=UCF_(xSN6E+d6v0+Ot29V`>nF1n7IHV28NGg^dE
z3q1>88$KzxpK#Ex79th;k@|LB_4C~rQ2kifVmO;{X-9j#BE$tJ`UjPZnwvY#{E3k7
z*bmo-W~_Rqu?0%t1z_%@%xZ`6?<oI_=yL*KQ@Jc2>#P1>sA*{8ghSq$!vDZiHp8V8
zauF8xZ-664SPe(ssX7LTQ00p*8Fx`!6&=Mj3kp&UZ<D?BJ3TDbV>m43?{~ze$T!G$
z^!FMz12nZ_pFiiG*VkuJO_bpuD4bEws$3WsD84GylX{<}xAFuUYJ3O*W*+JTz67K<
zYT4K6;3mDmnLWp!UoDJDGuInuVk6I3mB^$66eX?w(2C_U!!$bMTJ#L@lCRnnSD5@z
zxJ1ObAiedOo%{*e0cSVbXeYjtLPQxAxA`WR!dksS-d*l*fRkkbFer0LJP&iA2~B7o
z^qU$1|3kjiI8gr&NE3XKg~R7_8lxCn5EH1^QzQ!O3v3|`ub&p2D=n|obNzNscb#}n
zcQYS!k25thlm8I@$3V5hFgP|L!JOa+&BQrFrraf{BldZa%}`9(Q@N2tpu)z2-j9rI
zCtNtG_*TB{-Xx^zOF@vvX^qO9a?jr&mVb?vv~S<;%;N`FQOjSpLnPh3M<hrJg_D*d
z+8+9~>39M^Q;C8O8BtJdUBkL5$KNmDhvzvQLlsEV6pLswPeHhZFgsDnmxdG5z&pB`
z@THjH6`!3ucIDQP=m{2(jWrxLtR<b4lxZPmj^#V&2TGB2Vl1gH3eZf<xt2&u@4Zq}
zV0J-xP+xCaU#?6)x(gVjRwZH^@fdLmY`9`N9}V-tA*DBhqi;6sUviP*T7#;3bz|m^
zHg-PVP5R<!HWcgUae+&w2`d!NZt@OvDUP<kzkibBo2A4r*!?iNxD*=*rOTr~M5~d-
zf@<Wj+Ia!`jv62YkUlmt%rNL$5|;RWx^`T0HWrG>ChVMi2DSenr<vizoH?l}!4{t_
zt{8_#{TAk#a60|X@TUUtP7alz<z<kHg{qOUa7Fgg`>61rQ3}M;flBjY74SMF{EX4D
z2S+j9{^_yJZ*`QAas_k<_GZcrtfJy$V@au%i;h`hBdZbMF9znfJ*bgo?M~yxMJm?N
zGFF!>7pivj6`w|dFp2@_3%Xg`@3nG}%|ju7!4QCfYSLOcIg#B=zeFEI-Y94NM*sEE
zD!tpNdYx^~B@}@G0u~Vw{`N>ZLG(bI`E@^a=`VA>ut`%hv!C5BAz`$+N!7ho;AIb@
zRH4l8e-m)oAs`??%5ROxd8~i|lD`FrR>|P6ZmYqZ<_Pp%5GW;yg#@UK5X3l{%9?y6
zERgsxds}*!j@mg}bSEUC#26XM_|)TkB#&jC1E}^I?~{7t*gn-o@f?vbi~EpD{tGKw
z&~A`<=3yX>?NRf!<LG^-`Y!~QiJDY*ZC9hb4FE#i+*NuHaJ$jzJO+dHW{9qTy3BrU
z4?KcS?JQZ~3*TOOk${GLSg#kmW~;BFpxOdhH0${rS6`hl0xt+oG*{3trsI~mX$qtJ
z1q56<1D0UW_L0XGCw~}+C$vp~Ar%02@cX+8d|G=$(>!zgQ!7sg$=`>_iNNi+t;~H@
zxZKW(-~J_=AE!e0j*!5W)&C6_dKg8&uS*y9`@rXdq%<cTmE-A6lmuCK>~!BeE<yw8
zMVM>$cFpp@7YXmH!wlYaBENEqj~;ZVXJ@{L#uemMm&|b5d8J{b;kFc|dU{(@wO@R_
zBxREpS`cFJNJhZ@EaQPFk*|E@-hda=l|c1kgVipcn^SkpLc)XMl18==78WKN`VCt;
z`P^rvm-q{jOCAlu+t15oCnw?FrHK#`Oj7#^R~4)+XbdTfQy~ie+my8lS@SW^{c#78
z?354vB&b9E5ghG&FQi`8Px}i7H4x`AfIIqh>ZKWX((HggfP$_F?q29sv$A&L=a*0u
zi?&cIJcY~tEZNT)bKfQ)2fd+A^r&|kR@m_7pQ1KLr;kMPr}J4HwPFsowIdrac!{_l
zQpW&xb9$~Yr>0l}`=goR@1EP1(CieH)m))@vbd5KcGqx=l;TbsuWlAI2%j}QQskcJ
zbkX&etr;o(TUdKXM{^$pd>p69P-t0n0b-Dkbo2Pmgpc!wwL;!%xp~o@<{U{@qb%U$
zaf_|R24jaMEkaJxu&jf6Vm0VG+I6KBqD3zr+5E`!=}vG<?S~lC((JJnc|Lq)LFqDC
z4ZuwLBX9kB#Q6s@S+RLvN&Hr}(7t9Xa=y(+Binc{1Wmir0knw?F@>(KT|pv=-%ql@
zSIdDOyllq|$+2ig>8gN<=78ZsDg@8C_-_P9mQ6#s>#XrD$pS+{{s`cb8laRJY(M7@
zOCLw?u2o@d3E)7-dM7c2-!KsrO2M5TY?#mXn*bIK*qx3!iXZ|m@2iV*El&2<r?xkK
z`+i~wh#l(me(J;*?im3W4#=jM>9zctR*UsYl1x0a<t|rYBbdAji3W(e>z2sP#|l}8
zbB3i69-(Q>PK0b$3u3ZKZ0B-r%5c02OZ7UaE@nebMujkN8XiScnL~_6Z@x+%+D0uH
z5vwdS23Px)3{c@#icSVLlKd$oo|wfv{RM{NF$m(r1j1~%=%n_1MYV9~6MM}C7j&`#
zrQySl*W47zT8*R^F05Ah>2pt9h0B&@Mb^f<A>a6t61IHQNc$^CG4Wwd%AxPZ`woK#
zL~*@2mCK6uoEE{+$y^45XTM^AM*FvK1Oy$;>llc7S2+<qGIoLMOr+e)_Sbt=zoFY$
zv1H#)wex4}bk&t3WMT%Dd9%Ad-fS<fYIG+D_>A<nV+L2e)b+;@DjF6ic+*MSt(H60
zDLqkkIjoUQw7`HWPVoNYXX^BX#wVt=e3iA{`CQ6T`#q#M0XA2kMkIvU?JCr2kAwG7
zNxa{?)W<z+HcR4HL+c=P5ck;R73H2dgvFTfNkg9-QVp~QrkjP{s(+*@mg~30Fw3Ih
zV376~y;RJ<@#$hkC4>N;OM=GS5!o+aD4?;4MPK0mc=hI8FP`H<ravjRKfkAEOO43h
z5Zm&^l=%)zq7NQkqn#K`NeH(en*M<bVSu^dj57|Svi@M3(^lgh?B&_{&iLl#y!Dk@
z3p|_%t#%`(MZ(+(vMCr%>fp+Xk|q+xN)!u`YFN5V6rAv>H?x)~W_{=hHEL~qDPc<B
z1Dl6v)mNK<R8{LJx0OnR+$%QRk_UMH<Gm&0CX<E}Olk|dzg10ERlR&%5<vU`{~VKN
zCbwk*IrR&F!DXS!3r*zXhf7IGc{sePCrFGMlp8}XThDE9_!4r4FK7)AEiH-)OkAVt
z9=@D_=5qO!2GTAyMg#HrhJ1c}3m|40$)>h$JPEjaQ*o8};4TuBB%cjfL`+x1McVd{
zG;3tNyd?CapjOu{9o~Lx;D;kKMrT4IX4)8&b&8OZGahn2)yQ?wL5PcsQ#VC)Uk3C^
zS*Na9I-XfamrxCcxl+l0cDNz{iN`k(lqF}erH#M$E)aO3P3L#4w}{cG+r9A;Hb-Eq
z3lNc-7OY26kYWlZlC{Hp6})ve%sn$Q38X(~4Mh@1$)Yq|Q4^%0vKG9i`1S?HO!W*}
zHl<l4l9)!$7L9jU{(`#PF@?>sZ0$|kqzKa$a#Fmr>^F2P=8H|PyO-seL6?{TkY^&Y
zER1zoLNH;^hoAczL{$a0c^tB+iEB`09`Jj;D|PBkF4a9B>0#d#O}YRSIkPqLl*ucz
zfdo3oKtzE*SZMf|cYF<W5%a*`CD#fWa!L)hG8jUddUV2RG!Ua_OykeS<8lI2+r(J?
z97~w%R!_%~f3chDbrSX?F0Bjr#qn{n1yT_*5rrfLVryz~cw-T5dzR;eTN@jGMXinL
zk`>>dKb(Fz@BjxFQ4xc{B}{=TYqkJfS?a%*rP&rquOUsC4XGB&4Fcoan{#ryehqB1
z3Q$Ff=+yrFNmYDZ^N4kX>;t?dHdfR5<L*pd>!O+ZuX|E(^2h#I+7;#S!qs7E%zpm*
z(%8atsuS;9MzpW$N$oZ19jor`%q$a?-Fq78uxZ3-8Mhh6<5Dp^sMZ)U`ntVAnePw=
z3f4fRm&C)-^qJy<J`O}q(m4%7b<rT`M&?t00Q7-<;PULFv<LZuF1sez+QzeC&iAXE
zqrtZ?Wbtmt5Qrk5LGF!X34eTz>EfH+h(DkQt2QEB6yE#W6DPmtGIbH*31ZG(VJm?<
zKa5Bh6eK(uD;>mpoA39Q<A*u<5R6cP(GpCo$`DgiQ*nzhZnY;f0&Mk_oUt5R_FB)#
z+kcK0`}t@cdN_2zl}Cff?$QAhIct>R_o_n!l8jaJKw^njx^ZW(G_*$-hbS_C2U>9M
z5logcoxe`E(XbXu)ca6U%YS0Uot(p?oGp442T@T_Ab!TR3#wJ2#L{{1wyxT&Y&6{`
z-J+g>mR$mp73vzwwt1$c;k(=1bI6}v!E`Q1oNtpD7#Iz_FwLt2cdWSAI}Bg4M@Dax
zl;K0;1fN|bBnVNbdZW_kt<>d)1pb>3i-A~eL{X$uMd=$RQdagaD_$8UOcmn9ZexEC
z1QQ7=BzLPjB3DuL;aR35>xSpIM$(TK(b3Y0YCyrYfJ}`65!QS^a*&@bh}qBX**_w_
zv$9?&{3d$kqCHXYjpI&iVbwYENw&kCd(81M1O8KLq+hJP`0RBCX|~Ih4vxjI4-eZS
z9Zq;mTfughub)z}uLj*BB;0o?Y4LtmdF59-WPKA^H-_6Av$tk$G`UVq3Yt~2F}DUL
zw`$pV{iyhgq5OQ7g?Tq_x@Xf|`<ACU2AER<a2VGOc_}D>n28jQ5DHlhnJAaAB2+jt
z3Jxq~_7CCAai<QQeHi&uK2ZSr1-&01Abe?PaoX(=2oTi?<~)BG_2@}NDKTZH_cG~9
z&~(BAWkce(Y0Lq6Si$~|^V%(oZr(e!`N!?S4{8LvRtcBO?xF#p+!t0E_0_;+z^T;O
z<-xD&1J>?N<m0$&)gEWVJS2vhv$=>_J*9Pq6|AR}2?vqjxN<KTzxWDCNcWmU(1d;}
zPr4JAHE)KLKR`IR6(``pgUl)h_eajhbNBM=CG(Z+h>`EQVdZ=4o19=_8?f}}Hn#Up
zfXast7g);HTg(Z4!7(HQF+zc=GKV(>4=C^QOIO=ZDC!XedovF8nDl+vWH21NFI8u|
zT{w^EdK2G72gqf2b!%1B;xZwTdKTBww8jtfKC0q6=R}`PKM{*d#;z!`e%blOSA>Ni
zKx4XUQva}_<cQ@M(S(5Vy>`w%v}rT{((YAeF3yxhI}W$J?{7&v!|yWIM=Ti0Y<TD|
z5q$fTrzWRAV+WUVd`HxO2|nrq$l8P?YxD&_+mnzNTp|=!?mc*pCfcm-G}t9D5WXxX
z`B4FMNyxg$=JiCg0kv=6y>nsfnlK)0OT-l>z=fjZGOeeW<TlMu5DEF<?RP0Jzus(n
zDRA|<42P0!k=)eLP0k`*NL;=7qI;)<QPs6=BCgP=(_lZbP8IitN&2qG;+Ua(HJ%9{
ziMh<d4iH`#IH2_o?`Mx<2i~3>D^y>GYPCr7vZ^1!j#}*gR9fO2Q8HuQBpI~d5;j+D
zqy<Yr2TDj7;v=0nxJA;C$FW4Xs6Gf^dqRbO-re0*e-VSNsi|Qd?ZTXELg<s!UvvvY
zSxu`K>sW~LNhhXx?WpiCw4<ugcQGUdW-J%64{C%pol!-h4x4I~WMPJCxEA@0xGWVM
zGjhe2*+q3dRn`jup^W21snw#@F!(dxfrOb7UFioO&~-9>@Z4#}W}KC7p<Nfu<)nST
z33NoN7fw$u(_Y{u7`09sWAkg<mb0UZpuYR=Q1Q%;LklPM;hK|H{)@u18bHQ`@O!lu
z?5U>qy~YD!ZzggFB|r#PObD_RUE4(;%3wrH40SL5`_6YE$n}FgoSReFr~npI)dA5E
zE{J))hSOrF?~496v)myPu6QWNqJ9V;Uk@0qoQm79=v=T{J7vg1)8eAKwtW!)ptVs>
zmx0vxIpQ~RJBqd8vd(i$;O>zgzHotEc70_#Gqcl_3ui<1SBpz1QhWv!mQ=K1=%Vzq
zI4+@<_frd-<g%JUdk@E{fNoA5L<L1(TiDeB=m3-0i^Uw-yf-=sJ_^~Q7ZXkpno|c{
z)}CKeGy%Em4Cp#vf3HaUs-UDXe`7|{jC!D-(Q8g4sanmuY_Gr(D4tqZcQ?N3`{EfP
z)telU<`&9i<3#GZ33_Hb8@K{`y2y`-#yO;4D|{9gl&v1AB#MNdJTm)$6P*=n^a14#
z3azX2bb%c>dfw$A+Z8qx==v`LvE!7?KP-%V#OownX0V<Uiqklp^d;6zH9d1REjH~j
zpI=4j7M;NFIJsq8in`p%B?-c@%*eUMw1(sc3SZ)RN}}54O0MDBDIg#Cojh)nSbDgq
zSG=PN{hF~_CO|f!``D?@UV`<L=Kv5n$2F@e;FcfxBTc5vA_2`n0o0%Ty9K~RNa!t&
z@>`(z(*bk#l4yXC!K!wX0j|NTGZq=AS&A|Jl|!=I>#;*@xF&?jKe$v?@@%9?ZQCA7
z21Aqf0NV1HHRg|o9#Aqmaqsd32U@W~=hTCN>JrhE!akP5v=-%qV5ZUAc!)w7CN#V1
z-_m*99GVHbEkLXoWORL=#=5VI*tvi`L=9quFqGB=7SR;_<;$13HL4cJEHZ}9*~00q
zWgR_pd~tpPM%}QpE2v|3o$-vsuv*0zNh-zW+jh}a_LZdXM$)gp-iqvk<Z`5mHquTx
zuMhz;GzYJFacuTbf_-38`fJKimJW4hQos&Xr|`Mpi|Ds1)doRaBsigX&>0@JIaCO8
z^io8SF5E2Zu~DxS=x{A9BC0KQW~tnV$fvrw$+}|_X**=dm_L=9)FKq@XK_<mdYDBu
zm{#C~`+R4Vb5{sZ$H_Kc9#D0nq)f(7)2#gqu&XT~i`ioes$8WU7EdZ^I(B7eU!W_|
zOV}l{CuR#;{e>jF>+Z+FSoi}6jtzKN=m7Kgi1|!V#+L}oH`DsYU){W3ZxQ>OQICuI
zT{xm$`)i%=gcr^y+!X*(vK8Fq+_Eh^PbEk-vHw#9+fUtd63(U|_I~H+HlK7sZ2ri3
zm5`^pkilhEZi}llzSqsqM6!wu4h<YMTd4rJ*<9W;XLm5XFlqeHlwk~#gyiHs%r#TW
z1gko^KeFam%tM>a3WXhG6|Z(niu||w*zlNZrL(%y*w%JzwHhdbD?ec_HCD&`Ip{73
z+hRa8@+S}O-GCXu2TKtntOr*xYam?(<R*gbAf27J>g6vkE*N;tbqofFhQe-aWaA*}
zUt6=tR(q7zS)$ThF04gW0>r|^JP&<vnpMi2JPT@?=^xXXEo7BuXPUfKWGmp(0Ukv%
zzD!mlYi@EGumU(pvDqU_u~mYBsimb7fZFW|LWSz;Vr%||<M@S>3QF@;^3b$p3*lI<
zHc>@e(zv>;sYLcHQ?7LLbP{jU?U&7x6E%$?dqSM&kF5LpIUev~OI3UiJ~J)^ddDTJ
z-`m^QH0M0D>gk0)e@Z2Y5b1kXPfEd56753;D2*Yjltjc{DzzZ>Rm1F2<+x5;SAB>O
z4`G#QmYwh@^yr!x(cO8O%lelN^95WeFl_6xkk9eFUlgB<OTDtn&>bqpH@uJlLvjo{
zZDs_^$9wT?L$-_VfJY(p{TDA-ro@H#Kgl6uQg|*bAo!`L6X9gv1lefFu@$e!<pJy}
zRY5WV&egRSV*ZV9L|G3MR?3A5U)~wv^PNik22LP%O=~*^C>&x5-4lJg3cvn-i3p2G
z6ujS<7`|)>YLsCC`%Z5#ZpF0qxPs<U$m!|#^spSVowcWP7(>%;kVa~3crH|w#Cir~
z&5cN)&4$J9tXoKGvYGpSJ;mtT`4N(570Pa$%S!#g{NND*pNF0}_YeOZDLp`Mh4I4u
zwIH7XdEk<sFKvP(6%|#x+vZCB6<aQc-Bza?hxf{7Y;w^;b+eatyk==qn|8#-+k*!l
z>d-%Bds}l10L02&{iF6Zxwx1z@j=^&gn(JlYx(qt0<kNl@yR<3&$fwf=;y@}ddq`E
z=Z_D+gr7xOkH|~i4CF}ZFWevRolQ-@YbGWYjEgg;a5*b{<m8m*F47q(a(hp~F!sCU
zIOcO{5_~Z3{WsV~bBGz;MX~(7V-O;h)`>C_+*Qml3C6CT)w^KYTw;w=K!+SgXD&?s
zi{TU;DUcW(qc)o7j{u9~4V?I-MzfBNjtb)ud@S5g!SAvxq@|@HGq)^33eiW1xN3&f
z>vvmoj_h}s!1giGW<9H`(dd)bJhDByM|s=Ft!2`$;~klBj-6l2l&zK%gaWudn*-5M
za~!|w8k{j`vOY6ywgMCaxB*ry<PzWpNJE;MnpW8S;BeK}&`1VsM1)C#<5T@$0IsCQ
z4?|p7g;VRr`pKEj158r-vX%9<esx3q*-aH8na9?J!^!B#XxhsnxAIwm>o{GxV!MS0
zaYaZJ&3eyKV<UFEv}ua#sOQj6tTmYr_DSjTFe&}nczHpELMa1^n&xjJI`#JA;dq>%
zxtR6mf#yC1d52`wAl(;ii}o>6$?tq_)lia$(3_CCKC$tVbX=k{c~w%Ci@neva$6%+
zznvFyc%ZA6ISeg8i$(u)wF$wD*Z0qHK5IhwznahwcILpL?x^{$bM;GNNl;mbSV(nq
zGtYB(H82>9%xPI!#It33z6GfW<>I;&u2JVD59G_fzN(zplU~zBH;1IHQ{hxm2Lvc>
zbIf!&4l*%m(j!3>dZ2Ol`P4AeU--{mhdM$1y_TVT+S+LgHa&PefN9qiU^0m-J|hq%
zfUyeo%xrAQ-98)S$g#-r%zMa~DG%A_G$-ZS2#S1|8ycP{pICb{K9@{syf*t~>8b#w
zKQs_<P%zfC>5L^qeKFS&@q82bARl2(JpI#Mb_?7tU~bkQ5%qzi{;co+zyf%h$zEHD
zW}W_S72tdLod!^?l$1OIvr@)T(_T>TDNLF`kv{$F!peawAY6p<iT=zDFt$Vbd*<)%
zE(EvQNa}iWFeZo@-X<mfn5yCsI6s@Tl`wo-4%Yl+1ks2ZsEgbICq7nr(_)l=tbCR@
z11tdeNGl0m2(A-olU+Z%BRCclmdF1|Lp^uNJ@<KYxSZ#YSjic>Jl@|_81|uh2U4R$
zAI*Yk?K$P;qsETM8QjOMFBP=45AAqyiUnJp@AsnZPm>>EEL3<EjgZ0{E4FTVhnLfc
z;`+)T@uK>5LYVh29An|hKT}3sn%lfx*uTO&e7?UBsK?Tv1)mM0=2^@03Bm3!HECQP
zUOWk7tiip?1AN%jv$M1O5vC?4V!OM$8-L-yEr<Y)wb2Gc5E~th>KRzJs1JpUksi0<
zn~srOXuKa~OqHpe&ltT`jMA4x5}no0kPq_;e`^<Gr;#g~F#99}v~=||P4C(SUBReq
zx$QJ<s$uo3|7i;Omy|<3m-1ea;{V;LRZ=;5d7|TzFcnz@0R(X#<d4Az6BC~<+~>~T
zR1a)MhleB5@Qb;-H%&}Uy^anDHR|1WdV09J8x-T1a$dl=gF;o8l{`&D3T`^EU-6ax
z@{OHI$;9wyZzRY+o0DoPOnj_*VVQ9+9=EVD|1+ZM#mI`@KO(aP)Ocg?G1If42zkGF
zEz@$^Pt6nKdX0m)o5GPQQyj@Ul}0rA_?LChPnozb__gQNQrF0d0G~hQ`!sj;XsTvP
z)}Ru4>_!uJ^CvaHJ_i`hz|qrl0F@cD-c$K}rwI+AkaL|te;3UeiI56Af;Ka3)psW2
zfHTH@&H&gM7wk@FK<@6eN=}4fA&6y{V|}u1Shmj)=dp|;Sh*$lS4ljZ{36#Qc3Ys1
z*~xxSPLhF^w~5`K{_16$8yhD6MZm~?IPM+ICG>uLCtmbeOsajGGn0%Yh1psrA;zfc
zRtg`>`@STrVWOV(kxLr$y)2++B!nOUk9FhILwnwUPDt;iC2e3kLQ_6|H5(KnTn~Fo
zc@-52wFZxc*EC%n=E!r^LkFSZm^y>w5Y*(6r{3crg8Fed1k9AM?%1tbolwB)Pa!W!
zc}rDg6Y}d+iQm4+;4gtOFv|WQ!KGSOq4-yu(|7~uY#nG>Q8qx>n+3Qaw<-P(B_>8j
zsLU`>8dOkMr|RnJ()4{dH;=O1gkb;kcdty8cX$-Nh-iRBWodNCj8ZfTXyJdB=FaDM
z5T1oj=y~5W$$l>{)7?tSe(+(w6WfvfM-PDuP{s;Ta$SA-u>J=eNG?5up<8FjKPUAZ
zhIvB$I%Ru2q%{8fkbgwu`Ma(SIK_7@-ncw}-vwUx&llhQb6&Q^wIA!*X#CfgY6@sf
z>#`;cl>I9O;1U0Pfj59s%2ArF$bTOG_c!mz0SM;=r*{=(PX7Br|D8qo@85WJJ+~y@
zL)%{e{~mNA3W{<6yM(`c^}na^A6fceLHf@{`G5I3kCJ@Z=tXf}3nt$HSiso4bdQfN
zFIVIKaS5L-0HBi%_aZs`+zA)Omk7tq!qUfk-@)5E;`wNYg^xc~QsepLQ9gfgS{~v4
zVNk{R>OafogH-eek+|Y2tz9?~5j{IQ#q!hRmC@u+rPIpvhe4XL^WmraZQi})XDrG;
z{=)Njki7s6M+(<Yr=N(9f$uqh(Q+;P+;_N437EU5k9hv&Tm0)Uz(7g4{8xe@Ju)1S
zlarIDj-FSJr>6sS5gd+AvuVI9V~ALl`LD<Sg}n$8yaZsSTy*clL#rDwzL(Z~x&%)#
z8#wfRY|SSK&R{wK86BOq1jCx)fA2)Qs26_qak<gRJF?^UH*_Z2RiJ<DHr0FRa}1Kl
zvG3`X=hGFsi3g<(%zrLIWUCNRozk(mBO#oSptv#ju?ma@h~TIN!yE=`TOKUc>TEX?
zdu+)6>rX)50zh--g!)bze-V9oxcy2qc*6U*&TD95(tFg1ycKj|7Qu6y?;vGoSJ_kU
z^3&r1kJH`q&J0w=4<5S3Z?Hp3YgDmYY}}f+^cRlI=T$U542?{u=$pEsZRU-bXKn9o
z?;MG|?>_i)G`+3Ym}|qiFpZ@0blccI*|JpnXl3|$A#LBo9lx6=Y(D0ISABc$?Az4E
z#oq3bNN)Fm?&Y4{>+^dc0cW<$?pIi{>!33_%?p7^PKO<X{Qdj1s(TA|u+2VRz=bkS
za~5g7tS*J?#Qs||`Ri09L8W=znp%qcu!xSxKex)A=ih9#PqCAiff(zi9iy2L)-n5k
zm^i+oXUkX3*?!`e+6u5nWJtXCWf@*9|9<9QN!5>fF@DywrnQ>bE};vit5i51Y1e_u
zP=^bi14u3W%O+w$c3)UW)e(by$trm(i`lC2Aay6bH}Cx_lDbc1e}BJ?=#KqIG8!6b
zPQ;y?rlFZ!>%AlPsp*<h*7fqyxzZ6D_37!-!WNwGn6*9gr6V0q;Wn;~X9izecuH0A
z(s^hf7JG5$;o#uZv+&kE+-15A!tS0=C&d5kqONoI`rHO={^xGX=ifbE;zUf=UO29E
zbRU=;OmCwW@9lv+ZkSZ)eB1u?HuLD2yVih;2I<`uOI~@2j<z;!U0q%G_%CV5_-6}#
zqtOenTYR9L?DllGoM7#~r!h7=yHnecolSakp2tg*ez&kq%EB^vWV7%$A@uLt8AAq$
zRQgaiZsN&b$j%z`oMCa;ZUnilyd*g0Ob4-;^kA`elX{xxqjO8o{}5*&oAE$44dG7j
ze-X*4s#5S?w;@6GiA;eZ6OP)|)zzm_5pF_<&39HD4*QAA>;H;9{+<sw{Pz-obUACo
zNqYfRweoXV<4HHpX$Qfh9O2SE7HYJ`QllErO`oiUqGC)-$IQRa?C;6(qpe_9XGER&
z6$rgBpK5+Mcxq`n^NP>Thcx+)_6@GmClPs&BLh-|ugeQ_R9Ki{4~0o0l7i~}V84E+
zAEq2U1^7LL-8+)c5gdob9d~&(wZ@N9L(8d;idU&;Pkx6bCG|>+XWC6Fr4dqKcB8#M
z%{;}dehAH2FnL;H&Ftr7`G)HzC{jWQI59p*)JU+F_drqUe>$8Y_8wxL|A6PT;=L2f
zG%VJzUojvCWt{Hr@Nq4OM8wL`_rL!tmnb8X6@li;GQM?6!#Xvb^YE8_I~;koxz?U~
z6E6J}`v3R%=QkBWK_FDeWtd<MA<vlRrH+Ar@rb<l$5l65$)(eWFktid+%Nyf^UdIX
zi4@1D^svaTJgb*s)SD2Ud*Xxoy%GaKlH@?L7yo&_cLA(PzO?^9D%pP)_54-~H^=}U
zE4OZtM0{T7zn9$l35t0lw|#T?fBe)7ij@a^%CM0cCNW(-SfJ9M#Fy`uuEx?I&-46E
zV-TXeptfXJsC8~6ZVw$>8uZ`ZfT5@<&1b1c*`D|71k}p8uPj|40X}DWJfZnK=X^lt
z(RMt2fk{I51$ZCyZ#ZRsNWu55f-UKnRe3q{3tOx^wh0ZI^`l`{u_g97)SJ^ymvhuj
zCt=o7;2BTdtgC}eBbxGL7W;jyC*5>zt8aos)7k3!q{k*pJud89?iT5q{s_}akS42{
zI=8;Wk1TN>GPgQ*eAv_BwQPB~+(C(O61x2(%A52E9#?W+kVkt2OqXN*@Yj-<z1!+i
zcgx*%l|2Z~qeeTcGgq)yqvTfAojuv+bp}FBbH};?tue5|`%contk(w(2bR{!9$=j#
z3!UJH+opkd4!u_tC4B>$Ta2dQb;)%LnQwoy4SRt1(}m?N-*2(mm7Yp0T-7BkK3>+!
z%DSzgZ7<<d6M8!%(|_;f%6FB4D?)`_zMZNa^Tj{U;9`=Fb0%$jTk^&Kyin(X>H%!B
zhS{rcqnt}8;r8BdG<A14m+lX&JqM8BJtqsb<C<@VtUn7{H{mJY1o|R=q@W&UUMI$=
z6SG>5@Hqcb^y!C1%hSV=PC4t3GR>23DpH;|13K|J4q$J2n@;0OrM5q+G<VT8Jax}N
zAi;G%IFGn434NY8oSDJgPI3G$W%gTCO4IGoE*<A)P<S_ZwX3?5g)w;Hz{*jw2iH#O
z<I~LuZ{K0z=k%+lBljI|BsX1`VY{@L%SFfK?|V%Hv<=y^?z@W4`%E^^e!>SlfXLff
zMe@8JG(yVpl3(BVCAinEKXN}xKH_jf+a{xSUv62)J4$T6?xM>jxL<+6;d*DRZF$~^
zI`awYJl~RPEKL-$FtU~V$a%yfo1royZ!3@Y!Sv(9`8ZlP?6=kq{L8W;<W$>!%2~oa
z){~7GSs6O8;<F;wrC-9)j!k=7k|NmOR@97B;{@$j+TrO-2VF{I9rxy!A1|Y$E_2zM
zDAPkC2_NZS^8gEy=YBa$PU5krv79tjqQ$AQK%oq)UHvYUdG2<T&XWVj6a$FfI7^8Q
zLNhj{l%`+QWhRn41x_Vq)kzOaKrL%127Ho11h<2sbD_=4%y8GECdc|V1gprj^K9~=
z1*pcE^qWCrTMqJ6dxb5S5f<~QLCwp^*O)BBiRf(&PuD%X9wn1nJ9j*zkWav`IqjU*
z#M$a2qlbaSHna0Oq$DfDhFw9?W5p)J`LXettzp(>vitbsZvKz>&p(%Mh8?YtA#ZL*
zJb4K0)^V1dnhY>jU*f;m7>)3-R}KY-bha^~VNov38cplmCz<;UsZ(<v!TEe2I2QgT
zjdO8FX2(pWVQ!2-&$jkg7tPpN8fabv<6^cS<l?s|Ll&-n{1QoK;2L&6JmD?X>s=#S
zzilPacx3RPzr-|l1o!fclK1f>BI4u4&pZ6CJFqh*`Uqo^+NaT6CB~MW1$k*n<RZtP
zmQLeq2fJ34y1mp$u~>$WxtjKk=p-ZD_YJn^7V?)R7OzeQNqPu8?=|N)!P=1^*FD9z
z<Vi*-A>iPBTFH4%f;A(6HtOF|NT{Fa{iv5u_~G&q>{j!Vhn{AcvSBRp`|ffl|6}aT
zU(&=;Gf*R)w&ol4o8|GoXRAa-D8pDimz2VOK_bb%21CyM20oi$08j$0Q`?*P<SAFr
zv*cwiPyB?$%y^Dhm6xxCL+P_^mfGosj=P5?Kbw+&?M}SzW@|3z=frY7J_tw<P9~R^
z6a3zE__egbf}JFQz~`z;)@)uJ7H<!x<#w_(_jyO$g{_aZh8S?rD-Pd;GQIb`T(BG2
zm*1NYJQ9O$_jvYP5}Jm6JslNHKg}*48Q0~>P34~`%=1z+4A)zxTr7zaQyvN%{c*j*
zwJH7Ps_V#^o`)qmZ`EK)OWc^_zIl@h^)AjUU@q&j=pec^6p9`exq{2#?O4U1LQTLw
zCC4=-YEeC@?2Oir5pw-~RRk7c<IG&=ZtniOCk4yk<0;9Y3vK%=YnSolt%v@vHj*$Y
zXV5v@Z%dto>w)W+8bhO-SoV&LOA}~5Roe9+lJGzHNyoQ~K!FY4+40PYJ*Wwa*2VOL
z3v#n>WQd0utaR)5)lF<V-(^QqCuvU<%M+-^JH&$r+SuQlp)YRV)a@#j*7I{z4^qN9
zI33h1x$ISq%WxWPx&S!8R4LHrthzlPKZdGh7Z&M-cBGzsIdWa*j7p7YUbE9__ZsoI
ztaQei*SV^n-eer`99=eY(vaqSIu7K`9?-t52=4p<Yw#}Oo#nHIYP6T=rZ<T9$gphm
z?7&%ytPS+3*CZ%EW4>c7@M>+2O>(g166aLK2-ptZ^Zw2F-tgD^l|^!vp7Xd4N6fot
z+f$iGEx_4u7ajWQ(&_Ld|2N%4!ej1Y&cZeim<a862Pcq7vF`8MNvdEc``xf{;u7g=
znnTKJQ{GUh-tJVK>5=;=PYJ~J7rK@Xyu-*}TaF-=?dD0;_lYuQ;mW*wG>6W8#|`_C
zI!1;f_Tsb&N}0*K>l=*f5VzhU89hB@ZT)2NSQnkg$^8#mF$D7=hsCec2xiV;Y1Wj?
z)U2m?t4@P{Bq#xXYyS7ZW(}HC@*I03ZH!<#r67^Klzl0I{SCr=8TRCjo<nN(3!p+>
zQMZ3B9#|!WmB@PDj&qC?zkc<-`?$ZHl?rNPL^1t#;(S-7<)f#6rVZFGdg@TWuRNQS
zvVBWju|48YwDeeH#wj);m!Hb;)+^C#<XbFjX5VXFw`0(<g}KQ#kl3xUle~zK#6&0}
z_>dFNA@%G+7_TRu{vp5JBO$mE`*^osIUbXKzfr^<XdLH${JS+pj;EU+cKN7g-im`{
z3*U7=ss}yO(=Vi*v@y1vw-l^AB%!frQ7UP$-=NCQqG+JNl6+s4DZn-&N(6JKi`JpM
z5gJYK#ZmV<{?#uoN`4AFXhd90SDVB9CtGcyVc$GNH7gx13{4oZ*{kXUp;59}RHU~O
z?CY2jtPjEby)45npGqPklML2onx{!3xEOK%AUXF6R;=k9`r33PsyA4Lk?A#Kc9eZC
zvMJWj*ORhEg4Kg>W8k92{2JOsUvJRTJU!>yZ@ZpT<7w7|6~H?U(am+07dhvuQ-yRy
z3VjSr;67KIKm}^F6oXA?bzp76hH$U#)x(@k=Y>PCq$csh<5kPZ>b1}Z3RDw+uK;yw
zZc9!wdNK$ueoU#WUp?HmiEntjDXb|9-Po)ad-immZtZ~xZQ7qu_>!kNt+^O`w!P7g
zq#sj#>aJn@6ii^ivmQl!@dN>bTW0@3n+ctH>ZtK<@km?Yy}Z?Ncg94|T*g?8VB%Ym
zxzEX#@q5Bl$H>u`$KSCm=4M!m^jpMrPLTDavItj$DdWuhGb|~tSIbkfHQ{l`7D*^7
zjM&MTmdo19{K-C~T@)Ex>WWP<_V9@kHT?FcjRgZBYr$c=$PvJ6HfqA)JL$^2_Rb0r
zQq`$benYYX@PjI8D55CKo=*>F&G#WfOr#zZ{>K8{aNpR!Sx+mr*4>1Ef24OZo5#YJ
z90pN@2P00Sp?asB{=#wZE3TeK&-wYK_$IE2x0{Jjh$xUDqrr}5DfHg_yhMv9^7vOk
ztVo$U%1HRdP2s0&&W5Z#>{!PVxrl<rVci(!<bKQd_o-ql*h%5eyLW}ai~6ycX|++b
ziW#8#X0tn43#1+UksDgmnqTCjY4iJCVIpa26#bv}t~4CV_U)IX8I5hmGBg=uixS4x
z$d(z)jFcHFWF2dW&|^!4rV++i6FrP<rT3AY2O;ZBAqfqsWN9qf31w3MyXQTQ=l}HK
z{qTN&?yvWKoyUFu?&~<u-+BJ7>%0h^MSK;6;w?t?yYNR#`C~!in+XdtW6qH)N$U*(
zIp-##`-^0;c&+Y}cFTI?(&J$t@;wog=MUnJV^Kw_tro6nVE9hMvlhR%^6GMXMg2uN
z>eq-<#PTnUK;z6|612d{seA8!WCfJTkGfKlW@g`)OPwvhE-qX%2Zw;j`W%{(=1M#=
zI$UQ$Jr$HD%L_sc&kW#%UaWwJjwNfgdhp$wO+jVobC0fIrjTa)Ca-%e%AL`B8UKl&
z3J^H%yKl{P%k`vuoR6CxmE*K>ZKTAg!vuZ3RL_jZ;_?akM_IbkDh;x=`-Qwf)SV*f
z&C+ioxI#ymL=@5tG(WgDHL({Lq>`+~fB$LfGR~hgwl+8U3TuHAu)<TCCXbu?zI|Rr
zb{rIhSS!lGZPh)UtO8v%WYP}>B4ARU+5WXT{zdXTFlyOmr)%O<*jWt9JIrG$wn~c3
zqUESx^7iqjFhe_)(9|`E+ikPafZ?Yz*_e8+1vk5|&BocebCIPvu<9WT=POBLhEeeG
z#<VI&i_b`|rEl@;aU0B)+=?sB>SWT@lxgSldi_l!h>%>)xWrOtB{NVpS*u6!9fGWq
z1>Kw*-+tqr6(pJ>rM$A26A`5rYpMWbexyitD8cm>NImlF&*E1+uAo@sW{;)OxM*g8
z7XkgQ3EFeXHpo=B`;d;ss#cfK$psX4by23Q2;7OTU$ZEMP13`E@3~s&;Yx(^iSfBc
zAXeloH8Q7`w+Mzsm1}$4o!3ey(v%jA2$DrxU`~AM17fP13q-u5gIJFKjW}n2!1h{*
zZ_dVL{f;|!@e(yNLC(&k#CdNlZ!bmD^8?I`b+bt%(@*0<r~*VAa#f$I)Koiv{7S@W
z)w_YmnD&o~tCDrx`%Liu{(6tBhI2xpzP9`fCk%|4fEpKL?{h=A#(Y*TeZN~|L<;tK
zi)oLv#H)@!OSK<+UKhI!zaqAvxi)C3B<a84=kfwg8d>%WUz5%7Y|N|bJt^NDrT=@C
zRmb#;i!AHHA3MteoM;YrERx@|>+25lwEvG`x+arE$*CA_Q5$15)daiAeyLIvoF7k_
zYN(bDvRuNrRr3|Wlvn&n<rPpn4}iTUy?auBhUXYQW>w=mQN!Ln?qRaFZn$)~3$^I}
zWmtLq=Uvwg13wQCse?;F$3W@9be@=wBoiz|Uq>WSd%3+%X$T*2mL|ZvuM%>&*M+f>
z!V;D#K?U|dNN~3MaPwhWXzDv&IUcJ%3xd*?(wTTfF#bwapks~K8J+)$n7dNub1aNO
z)n&b^05Q=$-ub_YX*UNB?{CLpFG)_vPMbfLlr00Vh(%G>Zjo=Zvc7lSjH`+>`B8~P
zx#345pM?$8YciaeRg(%Iqd<N|!c1oho2ldc$SVA9Z<Xz`O{<Wnc*O<VwXREryWhy5
z>GO%HgVm66tG8$A?ZWPbl-cE~@q-oe3zCzT8i|IFbRSe8S`Y5e*S_aOZb52%I_c>k
zKZ}BtXEvBAqBC^^vVKMsg-_al0gZ)8-f+Zmv@b-i9DB>UIA{p&T)j5(WPEUjv&^lV
zz6~yI_%qC){q&LTWM1*iQ$N&J;&j+vgi|~<uu~!mDqsbM$DXoDRINHYFOUy<ZM8Eo
z*!g>EP4emqdS?UJxV`O<xL}LhXO!RtpQqZBgnuU`!Y!~$LO}>VBuiHR<bCd!7|e}k
zHrzH`%zmh|3^;NgE=);#oay>)vIJ*Vm*-DOhl(elS&N!N-2ztL0#|$FhsAQVBBi-h
zkq$1<3|cKdOGEG(51|mIU***P1yv?=Q?f#eD(Q@bWSSeUr=7OA2=^_sF#$6-toye{
z>NP((dPUJrY5=b{NBz`oNJBG`8s#aY^jHzj;ok;~56MrF^Et)!d6`KCz>1lD$wrFi
z@EVF(h87h)tZn8;mwe5KHAu^*B<!kdb@)N2b?Wa_OcLb$d_hs7BcYDmD*s`>C4*b*
z%w&oBk<Twxrd2+5hV)#t_Q;BK$ux%p;99=196$%8rf)ZJ4Y_Q3QT7*<O4zUm59b)v
z&V;9&<k00@cLtgr+os7_qapg^I&{vij#b`AkOiPr3w&Py%o^X3Y7N@#z~ht^TMYQc
zdvuM~DW;}6rnCpdnVve|qUciPBh69;bGIJSHZ&J#vY2JHYHZf#;Z12Z{)HFnW_Cug
zRTfIGEu?&M58XUgd@gb7$$FjR5KQ^~8|O|$%04q$jpPFHsFy)-Yl`7=%}c}UmOysi
zXAMpMX1b=<fkb;`gIsxHh}MvXXKv<p2rNhy>Nm6=AM(R@?0Hp{bbMu>-9eaV7WnK=
z^QlD%Nd!8pS9n0F=(>PH_Da^m%k_r!D1W^o2*kDg%BM5gDkG9q9s(I3_X;4#zx5h7
zvThqsjnSjBX%o^N5lzm_(fE;UeWjX&DFY^PW-Lj4W<9%gyKY;wN9u(0sPm9f4Xvis
zN|)i)xNREY5x%rh%z}Fz4k+9I0B8!c{eJrmeV8Vv=$X2l{oG~8is#_jQ#k*2z0nzh
z>qQ>;1$nF6$oE04c;hY{5$%Ybif>+UnmBNmb`4+V<dz4Kt`laks9hID$BTq1xgl)(
zwa^yZ%t(q#M%B!N+>nRSeZpZsvLn0%q%}7)9`w%Y|4hrz?zExh^KZ)^xa>ekh;Hkp
zpw!RC>2bSu8ZOS1Od9lHqWj#XGoE!xFf98Ig|wv}3F2)I1M1~{S*?2W#Tldi`x06^
zh@CO!>XbBmhLzhs#r;IKeUb^@3-7O|YxoJ__rB25nWTbiN;z<Q1s$5Y9G<eo@yk8<
zfYYG5ExFxVpZ8gWVCCi{K09%d_2kS{62NNXF*~g@*+C&fR<h3d*6p*|t5cJ@LMst}
zs?eH&BfAZ69Ny&M9OW|f_4RvH-dJ6=n*1`?+4?h8{0m>7((X-@sD~}vn|I80QRHWL
zX5AeSBQ1hnhs~=(M=M!#dtDr4?-#JGue`kgwmc=%eB@gL6PLsl#N{Lw%fK&l`W8x&
zi^S4Z1_edRdJ$CSfa{UG>NVPF(>->tJP;vyIL$hu&DA|m2s+!3?Ee}cPo>30F4c`#
zn>`QamToJ}crZ*F<BVP6Xe8EI?ygcvse>Tj5udLjnG<-X@Dd|Sm+z8NjmWig4A{-2
zj|V7^+%CtPM2(a=s7XYb6(-}UueJw#=>jQIRQMxyM6Gdn<!UgwY*HIs;eCQqU7rV6
zKs$4xBx1!SbNR-E`t`&x4CBM=^TgiWsi@%^#KS88^bt(-*mwEil+EO_(+m834whQ#
z^eN-TpR-og$VPfFY^jDR_ELFQWWk}=J;G}D5psNMkeKxScdE~lfoJ1SEbFFet^6qg
zZ@vR8{!MFjbG4e;8hz!-NPF3|QZ!6<tgtL;MWJ#IC~k_k#QW#-K9FxqLHTumX`eB8
zIg_|~f+RY5v#%>ZdS_nu94+v<@RsQ-GwfHozsR6TI@sF%F7`Guf#9aol55aCEw20-
ztgX+<v<co_4RpPKhZS;%f@h_qODr74s4)()jmWgZVdAoUT=m&F2!)8wlw=sc-nPwR
z-81ZvTeGv2EBIE)uFyYF;2RFlZfBkDz%_&Sx*vY_${g9^;W(`EkWOACMbD!ZERRDC
zR})7d<RuGAX2>0VhBkGAP}1)N7TUw74i!qH?-Hj-gM_2F4oY6wxULRoWGr-wgX+rI
zRO5U+B}z|&FZ?za)(}5%0<HSB{N+GW)U>wfSJnK{)^~pXdhR^b`mI|hm`%iv^Uv=m
z4kI+nzfaB!uGlxK<$~*Qb{m8_f6%rpQ)XHUl_Nrp#9IpfR<S!ezI`#$%02){o2`~G
zou!;K`Ecc;r-d!rKob<C=jX@IKRSv4O{OM;ws%WMwA|Ik+9;^)Lrf3Ltu7rGg#n(u
z840xmz4as>8)2C)L12r$09J}%lspT1Daw$_41)69Z>Uid((T5BCgTw$caffq{oU90
zr(a%OREr)ZJs<|6%U4Y%wNWL8leYRc0-a#FL=6kF0n~5w%PTLPJo^wfnk3XiFH0?1
zZ(A$=#*Aq%H36HpdmDfz>F`_ObfPslC)WgzMw<0%0+qckm;4D+*<$|vkoHrZ8*%Hy
z+SncXtX_2<d019yB#J+DJiOnip8)rQi(fZ5xUo+VBNK@M+m=*-yo-N4_kDit)K0<W
z8*r4;AMd+VC<*P^-`)EL)JiA?W*x$!T^a-sx8S3Bb;9zPR{hT~NwaNu8FT+KMqRh<
zWKVeCK6g-2M#w$N`@xr<OauA?{{nSN(sKwd=BZn^B(FEy%3c0FKc2<AkKqH6gUB*k
zBw*s6H`oXpb{*GHzm`KGGzyJ?%=F(N|NIcm)o)cVZMcADy7o~{KdQW%;%14;fcJ2|
z`YtQ?Cf^<VUf`@ftwKAe7~E6J*YgM9*16&RPGtfmWx^f8IWmQ67Px?SQ8%)JYD1UC
zuKTk82)zL-Jo{HlG)HpyI9}%uEtQX{j^#N81$k-Y95*GHlCjEIkt44)$i4vIp9d9T
zMY@ieKRd{fAFPm5g2H3<F*&J++3IUCw$10t^2t#bi@&BU27YY|`9YLYpR<#=HM?&G
zY9?f1G~X?P9d5E$d}QP-CF)Dp-5PbTNl&cm4y&!X@|d7nIqMQGeNE@_wwQ$_+$Z-~
zdYomEC3UdYxjjz^t`2{#&dOFw)(XwLrAyU~>c3{3o)IV*t{Hh_wf(^Cc1aLy^ROp{
zVtbq+abBn7bL*C5b%B<KUJ7~q&R8gwm5w&Dm^;ljBVTvJOm`bB)ef!Sd_s7W!iq;r
zGD=CDuEtU4qm4ydD_kG|WZoM2Qd&gsDTp6Nb|S6B3LeuA?KJtxj39<=ue2r^GGTPL
z4`S@4pr@`pd)(<eYkMx2lc1c5_bI|JFgne8me!Uh-#}UHcQ%DW=E-;McOH7>CjVje
zqUDNXL|~F1f~_tj8TP?D|6Wj+iA0WDj^#om#60({Em%b(R{!;r<p+&RO_Cd$5{(4_
zH;0zW#b9rFAF$$I`4gxuZg$X_cX}3&QB6(+?P0CT(c<um71=&WTXus##Uc$XtN`;2
z$RysV#!=nQWV@c$ICxdmdk5xztMZ)aU_p<{5@JM=DMavI?V3^+#9e=fK6KT=Ur+J}
zJlFAt$0L(@RoKqw*NrpTt-<QdRpylLp}VMz4H0ZI3zE{;5XJ4+TnNh;w_)ZztiPed
z#JtViZUvlx4^t$EU_aF3E@xr2f|&`(Xe$&MAUf1a*BL<V0F{yJ8U+O(Z5kJCJ|7-P
z{D#krVVwcwD=HyoLE<H5e4~6j%!WN0+4G>-V7D=w=suO4$^~|Ihs+z#4&D7I0=0Y3
z3yE@kzw5oEFu=Rli)(MwiA!~lf(snUOv|}I(_;6oReGE9K;f#fb}nY4pY^*Vd-Ug1
zj82Rjb>y#&Z7g=PWs%IGAU{7qx~OAM*V_Wp#h&Ma;o=g096KayBi(MA*`$kI2R-&^
zIYiD$<ds)<<nh?AkeTLzO51Zux^9!9A@nIDFQZta%*Wj=AjM!Sdr@XPy)6z3d?#?w
zw>{KOi>J6?=8b*x`ZrC=Y*?Cd`lftYh>g5V>?CZ||Ihcq8shB0Zp}|KB)=8qjr)e*
zb3MMv>u1s0xhqe9e93!%G?ovL4YdcJ>yUVo%wj$&$_LM#wywV9{}V$k9?H=Skt~9^
z#%)34=7*3132Vmf=~t@Zj<b1X-f)vL(*+>xT}1A!clkwIgd+e~3@JZdmik}05l|in
zP6Emz+dO9J?muM67+yYefZhc<x!w9(n*4b?mJPmCT4%^}`ROk<)G<K%cyj@IO#W}>
z_UEu#fFeHL6mjMLB7VlU1M}gK<6S${e`Qi&*!>W|#^aPdZ`FRW0igp`^NFS|ejr==
zhbH<MHbC@?;vJ3t#fEDj@a23X3K#JId=`2s4=CC#$hB>q{>8>kIY1_EP_4rL!!9)S
z)c=W|KwW!Cf>`=5FkL(XaJ3y!<?b&w+9rVCC-5)Se?#>z)c?-vzm_@t>p=c>ApaXW
d{=dF@^#*^jV*=Osch5b*MKC>UQjT?v`wzAPIUoQ4

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/packages/autograd/_static/autograd_grad_req.mp4 b/docs/python_docs/python/tutorials/packages/autograd/_static/autograd_grad_req.mp4
new file mode 100644
index 0000000000000000000000000000000000000000..5f3662d02e50848b1a7115c7bc4cf64d47d915b0
GIT binary patch
literal 71224
zcmeFZ1yCJJ7sq*VcXto&E*EzT?(XgoEI@Dw5Ih7YNN@-a!QCYg+}#Nhg0nX-$@}so
z-`3XF?pAGmRdDazp6=f{J<~na|L!vl002lW+`OEuT^;QK0C2#!Z_q!Rv4<(Ey%Ps3
z004lsa4|Or01jvD&5YeZdCH-o9v_PrCH7jj=cMbC8D>f6D2`7&*|<1J8A(kYUCc>Y
zIoU{E*myWONX>bTIeA&wK_{e{KpB`7lq94WIY>n`#6UUC%uPWj#2lTx?946PNZD9e
z*csVaSh+!oR&H)ie9X+xo;_pour@Pyv@>>Ka&)m|{$_>A%FW&ml*iG@&Dznym5<ca
z*u>Zr$V%#BZUJN`H8VG{b2PODvhuO;v5*=&7~6Tdngdxp+4)#JSy|ah?ahH!=ANXk
z?k1okZc-;#FHlj?r=g1(kd=uAR0#AzYH#gnZf5w6$O<Z9=wj?(X%1xNA~m&gakMu!
z1Qlf^b#pPdv$J*u9r1Ybn3=kP4oscxfh-^wjLp0q9n67jtPHHIq!z}mZiY^-w$@JH
za(o9kI~h7!Sh$+I0U6my-K<<dC0v1Aq;`&uw#HVVABI2ESV>*&tW80_{7hgWb#VE^
zhN-o^vD-I4tR39UUF?iOnLz0#cJ405UWTTQ_D;rbpx>sTOXTKaZ0!I-fHJxmf6HUx
zVr*~j3c6S(hE86fV{0=Y8|c{B%-HE$Eldnetc_j28L@UT|D#^d%&jf0+)O~{9G%P^
z3@sg<Kxe-vI)RGXntOrRKsHX6ABTqa)()Vbq^_ps4(6urZa@x}Z*IC6f4ihE=B`$t
z;x49!e^&R~c^6Zlsfz`vy$R^LeybMf2at`OiG|epTV;SOOkAKtr*GgtzKuPBT)dzJ
zS2uGfAP1?n6X+&^8U*Mbf>_4Rp!@&rZUO=T0H|s6@L&M*<HKe7-4NYIM-mf}wokS4
z*{+PseK^Z=D*yoEk3TfKWn8(KT7=eB004e3MiXBWX$Hj!FlT&6!Z1r1dOC9Vj_pnU
zRQZR;mliepcHR|b8Hme4xx-FIuCDcOF=ygut>v^#<4jjd5tI}=D$gy=$h<;(90FgQ
zCZ71jBE6uVjvhQ*%(ri_o?3tvee}Iu49XK@zPA>kI`R`c5Qu>mc^GepAbzWgN0H%{
z=CZXs*<;K_UQW>|CjK^7T?%UTbsJF8%|e*NR<5uuH}+b@N1Cjg;N>YS6qumvHSC8c
zy4Z)KQ{iTQMMtXyY$*_`@3Zmd{Rv7=+KP8>OzC{)SWPL4o+PLXtSWP>%x;W^VK<;U
zy*{)IoIw{ap7W)Dp2;hce4Mb-4gJurF%5Z~^$NEC)dCKkM)Mg(i{9`CO5$-E0{B;6
zcms_jaMM!1q>s30r)Y5r2sh5nR&aLP35%QD3E^AKn(n;4>(ZV3{SDB)2^d=2lZfwi
zy!Vdm`59!-g+ogzuG!jnu9PNl1GkUd=t<R$*4`N~BSs1GF743qkyMX;s7&*djy)Kb
zXGMT!*=58WSME8eapH_=oc%hzxg`3ESuIh|t7}O|qh%<jZzeos)JH%N(-hu%YF7`^
zAM&jY{gK(u{?}az(U*Z@?R#oEyEHyUI+pY>q1Swcv+rnSF{|+{uImmdlKs>2-LL$3
zi9(vCq;CCbWlx!_p1T6KDJ<D<>0?s5GvD&?b+}6r%UYaXh!(1+lbWB|RD-`iI_Qv&
zH+aH!9Lr|JpV-TC(;C|Oj0l(*d`O!fZRAX;<ddVwLZ^h&_99&zQ&lO{ltsv@z3W6$
zdB<bNM$uyTa)K23xmmFme#Hbtf5LiRQv$*l3fb)<8V#)7!%DY%_9=$`8)$2=`6T-+
zVcI)r^a#mMr<<C*U{cK?{bS1gq9o6t?8T)Omz*9lsf=y?Osi`)Qe;I$(w>8Dk1$j!
zYL?#DG=r_eE>f>D2VdByOcI0X(HJwEG|z~gm$<rxcgyP476>JTR8W^&IJL%s$AoUa
zQNEI)Eo^V-6|M|Amy1G)$)gM|ZqOm}2oqf1nB9E$+!e|QS!hW)5@Yv;H4F|v46d#=
zey#L&pd*|Mqsga!n^=QtZHU(1J{z2&J7KSp^<l(Qw7R59QEY1J)l+1~T{RE6tpI!!
zdyW;SQE58aCL<kNGZwKlGwNu0wbZJBJKLH-zH{2(%2Z!?D5&Jp-H!SVHk(bl;XIpI
z@yFi94BRs~AWhsQ+Hp6!#jFXdhnXB4yj!bEc&B!;I_=&H$rD#PR@Jv>A?<~V<XcAs
zybz&DY}ug-RWmS)5^b>Ywr$hcD(xR+3%R1quX3M`_6>8QzQho_*HacnFCRMh$_g;Y
z)1j*cknaKjUc)8ydtj|HabBp|PC5jW`dY}bBl`<7Ee!MouH^uXNRjQwlB0>^e(XN6
zAVv$mMv76aiQ$Nf7mZipA0?a#UD}L`^PJUeY68@F>XM6z_%UXk!?;$)7<og(O=r$D
zX`KOp(3az~rFNuAJxn1Lf{&G5T}QN-dz`S%{S0(+N0$ZMWd7QLU(xzW4*hSsNhzZs
zbM&P#8hS??A;+MdC6`}J`D1-iMB2H<LhCjx^v^;=&>VkCSbee3mnGHCjy(PRgtYh-
zdt^oF0%n)Rwe^VO8<y!iX%1(`X<w0j0C**5qpSQ*k#&_0)6VAusD(cKLQ}GuCNlmF
z>46+$CMsE{7pUn2d5?R(UM6vpyvko!T-Q6h$|zHO0p2xy(457Nr7Rfuw$Fsq<ia&J
zNN*Qeu`BO_0QTzr==#B(332FMHi+(DcOg44*<xdq^KS<_!bC&{TDi8NeZh7Xbo7#5
zt|YtRY|lYDa)yY+$^?i<1VE_csm4Oo|9lRAe;WUveJYB8#wO?@sg!o2j}K9mKMKPv
zf>KdLGDSUu{eL8ZCwxl+%QSYAdi;?DarZsR)aE})_n;(PkyI<0LJ_Ms^00=#LYF}2
z&_psrCD0fselmVy^MAp9V}LBZ`vv>W5(rxg!rJ{E`^{Gnc2ENApV+-$us^&#_$T)B
zzhS?7n}z%@*eu3hu;0DSV*Up<jpN_2-@VOJ`8TZdFWB$iX1V?o>-lfkAKoVa1sngz
zh0Ln`1^dI>`d_f$l5j=Rn*R;^!`u14V86G7tm|K}KfJyDCwBGUu;0DSB>oHbySJIZ
ze_%6({(()U{5R}(Z!<;zg8jeTZvWB1es~-DZ`l8}TNJRY|Fv7t<Ldu!?H1Pm@6TJX
zFVpECijQCc*|<t??10h6HK$kpU==%WUrzZ;)``uXHqWm}vfIQq2wC!koGz1qRban2
zhDagTnn|19V-BpHU?|*@quc~4Uqs>F(v7)3P%y7LYF7@N9WtS;D|s<xlTghs3C~r_
zU9+bxz<yl%lI7T}TuwV@czV&4{b(dm)TXDqrmTWQ%T?NS*)gR>T0a+m{`$ssL2r!9
zby|s!j0&381njx7tdpT+=_ZbHEN*X?1s|3q{tg~op#BH=6B9Hz&PxPy=+VIfKEuyP
z!&p-#IaIFCGy6gx{EvZ?@mZ*Q@zH`<?zr*?Ql}EYN#)DWBURTTU7w)N7j56dsl3_u
zQ;fu2K1E?sl{O-7^I;jEj_>^>uXM)i+59@g%qX+Zot>*v+jwtN+}<UPT~jNBXjUr7
zcLxg`137B;a6CpNXfwd=&5e-9g?S?xe>e~0;bSwLi#EF9$a5%7_#7A)kAms;ha|-H
zIq^=3O9t-%r*!MgtBPFlc_fiAi^@yt$n{MhpAXczzHS8I&gU0-i{^#LOq9h6?{tC}
z3Es(a+n=$`x|jJXL=5^ccovbvbJ^3_260`vf5~5MVP-Qh7j@+P9HU7+rYxUdh@tDF
zw;uK05v$=rgOV`8=xq^Yeo!)6s0sAdvO|P<TMT$~NOEzGA9At6I+h3$gTRyRjAyr$
z@{jHsEh3mn?+qS&<ZECto{Es5SO{*eh^@=>Xc}Ek^TRXw$(Alq`9T(Wq*Qj!Vi#33
zCEcIl=o$NWJfqnG9Z0^|OhyVo;OBhX-O(TO0#&>!19C+(Z>#33zD>fjAp1pj_TtT~
zi-kkPEY_f+3+uKCxlLqUWXuWNxOLkpNeQ0TCp!Uwb!w#VJSMD=z0T%HGAO*OIz`w;
z3A%4!^gDI4RwYBb4~)w+NX9?vMH$Pg8=VkC8&NN^WVQrL0<%W}t!X(S_wIG}I-g-{
zi<8iAz$i3}ps$LmoQK6MT~k~Zbi6)Hz$x5FvVSnQ>5rVElINNh6+Fu_+8aW|j$F5e
zvpju1Y;g$1{FtSUh9DordiAJ8HIR@SzkTo|nA*O0Q6y#@t6*NUN56Hb&eNU@;Qa*0
zSNymc5<xikT8t17J8@VAZu3c7ElS*o?R{X>ha?6G3gxpW0pJnK9Sbs;ofpRyWw%FI
zCpfSuGY4PZ(p`+N-<qW&LFy?`alT69DzeT?Jzu~0Y}6h>%pgo~_y(-iSrADRX>WKi
zBJc`lZUyaNZprOnKutQjzPEa9e%=M|JoTdhqF2Yg%u)<+`V56Z@uOtqMz*^frz1%N
zE|u%9*A2FD+ENn_bmBnuV?NT-L_hc{j@agtuPfFq*7FDOGdQ){S2yonj>;sl(YYkF
z*Q;m;9S6WQR24mA5%Z{EEy3P~!Ilc*9QGp%jk`V>^z=eDsT&^eT5FY^3fAO9XcB8p
zHVTI7XqFEW9pD!e(N`+F>^SsT+7*f~j?cv(B6BLnxL<}|-3@J5!pmTC7F%Eu+EIkr
zrzVhFSLLkSSYK6!$}r=md=sHzuV&e~1}&3T#0ODG$s}?%bFq%OIDW4&c<|BUX`Pz-
z(g;ijp+a-3gVJ3P(C9iN!+n_Uy^r_H$yedflc-_2GL944&;gf9kxhf$JTwl1<)H+8
zKE{fwzRZ$$WL6(gUm-m;qwsjef$QRNsw((}IpQ+9BT4GKjq$Xo1Oo%U<qFPHx?w=b
zYCvoFW5wVmYF5oV-FHTN-US4HT-Lsn8SsaSffum|`{cIsd2gn;Due(uZ}BzP3A{GT
zWS=4P)}2Tti5TkLtJ~a0q@W?qx8-?QP?*X9`kbSHcUh)TV7@I)oV;~X4EhF{hICqq
zR_|*ZYjp*_DA6`#N->2?qi5pe!HCpw>Be^e*oo%{y9bl++_s#|&bb1{KeD`_7hLza
z!ypC!3J{xv!<FaQUgL}b#{~vf>#nYqGa%D3ZNgyt%B3V_-C01{B?G}Jgg7SMAz<mp
zyOtHrm)DBVZCJeUCBD?1&Aovng>LTOkIB*v3$p@W0HUP%TG1BSU7Dp*6I+ot%gl0t
zuPI&m_fBy7L8iX7(4+fs!B}hFGFH~k$ZevE0nbh6{CW)G(KIUL7_x>4=G2XRGAauq
zvW>yzs$;E}cm|w7ss+@nBdw_l^Y)h!4!;UOZdIVB(znbc4|nnx+ke8&ELX@-{+in)
zk@4DXqkM>g@cgNc6oJ<Z(cpI*<p@+d<bmP|><sBvwiEQ(I$6^8{Zj!PN83$W6s1iu
z<@ksx!t6&r-b*!Jj;YWT6JU7UE(~q9W-wZ?@!Y4Sc9-*b)Xe^aNeb6jB4o5lq$_Yh
zGAK`K;C2PREYrw?!fccL!As`KgpVG9rY4?m4edAEHG<HF9-dWvPAk<!ZG2q$La7On
z51s%qe(jHXihmBa%}db}HY@y*gkvA#6BpEI$<as<sk4CuW1^#d7(0Gwf9QN|+hfvw
zfG&Cf^3eq%zb)>@9-(JXIFPo~<_TK69%KO(&H&}O-Uy4jubUUV&D<w6@=J)TcRiA8
z`f1M-C@PwjF7KdZ#_mU{BTgKx0s}JFa}=K5FigRPzsS*wZB0#VV^YX6Au}vuR$j7*
z+k$Nry+goAoPs_t7^Wl(8bfl3Y$X=xHoZZR>Su^*JSZJFi8pE_w*ZTm48=-J=E7VY
zb59AS$o0hZr1VD;9q3T4TSkc(U8%vpkLjr%M7SO&C3QPad*2!YTtG$ALg#M8)IYfD
zNE#?0dBE8&eLDPwSTm}{u)Z!<vgC&0(JDIu8QB$K0u9FVdMO}no7;)p>SLpSHhcHf
zf@}c^#!Zgqcz`ha#(q=FmS)Ubh!CSu>TL=*hKrzX_f5!jkSwA6kF3jGCx+~et;gFu
zuT&)fAh_<FIP~gBt9?e&{zK$s{$1&jgJphKdJjUf){^k8M*BBGZmR$zfUdv$RlI3d
zfK5B7Ad^VyKg1sk%6uiXBFRo^On-%IfvNk;@yG1f(pSy_pgsiv;E4Pw31L~Ec685}
zFI!m!$aR2Dp@IDnhDb~pLpXexU`5{(ze__Tj*QFaL3v=*phS?|`mH#`6!}vef(*20
zMeqU8L9-p-=-<cLf1`g_h<~I1RwIJwKQ-c?bf@q1?=tak^zTCPZ}i_vMG*a`RQ!|v
z!}srM@o)6+dhu`c-zr8-kv|pVpY-p({}_D#mHtCE{+0fF;RvGt6pnw=zx)1U&f{<N
z@3S6%qko(C_*eZ1qW{#7f2V(!kbk3ppZxe6{kPK}Ao|bgkH6Eu_lv*LzxRy4(f`pA
zKoj~f-?T62uKt__`5XOz?frl4{eSKK|K;a9Y^_MzU!U(_U%q^BT@(u_%yUK7aN!_r
zUB8=t0|=1kQA*iX>)Pg7zCn0`Sjvv#l19EOD0<zAU1?y3=GoHT-5)_SR~PYqg6Fzq
z$oy^Xsv*)GKP;3VIv!Ze7yor~wJY%ldUBF?xt8yU8u~A3WeBRl$`wM#94QQtsUHPy
zhiwM@-FBcc3`=@lQWM_O=TQoF_GkC==ju+TZ`(Ypi)18zQFvY0NSIr%@@9s{hvuNJ
z3ZTu$dg^U8Iw5HQ{w8pYaiGa;F=BEQdPi<LHkvzPAGQ~cq|&geKrm$7btiYURkLQt
zazTc4q{d{=n&*C>HY~`0!}fAeP1PN9;r0})?arNN|FT9G;&$cUq^5p)<aOs_x9X(0
z;^XVlhceu>G6A?4gteMdtl2qIF`1^%sdLqXZi3Ki?lI+;>Ua;8q8{=$Ts+86bM+%o
zkY;k$o_>PoQ1cJvZ-dpwQ@Z8OuRobOTqMWE;#8Jb=v1m>8iX@+w=U%x+41^X<jtLX
zjTTPhh2G4$C87LEvgEPa_LbIW;2TdBWc;8*7g_yKaol6nY4bU{nvxq&*-2wymu{PP
zOtWqQm!`k_E^cX3^;sOe3^jcJG?wl3L_LkJisf82*fYcWrlAnWc3$MQHfv###&j@f
zWWgm|xz-iNCiGd;r(U%eGra{=YQ>$WAr**YqGJm9drH&$?3-hKnkj-E6T0`j!wk4L
z#rx)yDwjN_;h)xDe;JnIA<~G3vUVvind@#?I=Iw5Ol`<O#KRXZ{+QLe0yE(Jg{En}
z@78YLpUwguI)h&O^F5D4Q4U~hctbMK+X!<iETq%}vMe~`Vlq@tj%ABBxz&@4J1X^^
zJbvhsxM+;SqBM$LjyuXyr?e-QL9D1;Bu(e8&6>_AnSGMxC>B#z#mfg^^z_9d<%?&Q
zCOKs`{4UxB)5#6dD3KPP%RFxLQ(vjr>gl;Rw>R#2z^3PG^!z6`7)<0G#Z<E4s9tBB
zs{6J%R#9BmP#CkS8JWAz;BduM4>qe8#x3az%^&6Z6F@#QW986#?><HcB}xqD){eC9
z);iQsZt6NpFCCeK*nRYsW-<9?{fnvR0ujI|9aig)N8(>Jkh5)?EzTt*R2+^>Fq7~q
z@g^J!0Lfux2=9vHDzak}@EAk=);>{R*(qz=y!T2^5_WiYn~$KA-j!QA2%#y87@EW{
zfvp|6=;II_Z?9xs&#I2DNj`*kj|c15Y1zVnYs~Kr){*hZFYBeMyB3nF3I<ve#bH-;
zUmPOPt|{At$*|6TQIftHim#>)eHUhvkX&VM0DaanO|CMcA>H$UgtUvx=g99=8k>mz
zX5Rhksp0j`Xxz0xnH5j7yVUdn2_|AtBA#87iwxns=iGZPm*s*vHgq3eo{vK<y-9^|
zLQ&-T7nxVF$T4M#w#5F-d#uA&S|rW$9<=njcaPd<ru|EGs%<TawtLoRvKr%<)D^eS
zEK1FA?T(u6qm!|<T@K>L_yJ1|!%Xrdci1Ijk;{&Fp9hDkvfevwWid^bsOIRwo}PBK
zA#>i|J1M+Cv!#2bRPdpN5zU*`e0@tzB-~XZT^2}|9sV$&VdiN<kx1$&Cz&z9Ab(`D
zYcciKBGbcnYrBh?a6B(2RBemxX13@0kVas$(oDQs$@wNt#$}9vxsWpab8vlfFmwCH
z?302H&$s^g6{xq}9~Hw}vZ=hgY!L7d%csJgs<1gsT(T5lj2G94(xTAwG$X)@3h(*C
z=wG`a!iu4bMsPRnS+RV4s1C7bipFQb%IB(=!kZ?V>ix{85lr%i$(FpKxj`2SHx)3-
z>>B>nY=2?5w(KnG{wg+CBy>?N?)^vbu}^6&!R&{Tw2DXtD7FZc1Ng5cMo+GE`(o7w
z+6|%wlkY-A?+S_~G{ed5&P0}xM(PVRp(2!6Y<7gMr~QbEssfWx&c|1*xVcWox0mSy
z@(9{7%%oc@HqPK)S~n#YTh{X1u+U3e>50=CqO@O%?%|D-;716FvQGFW)_UM+avHmm
z2!<SGyV0ce5FgyZ+6U^v_n#x>y5wY?3J~Xq<VOY|n5V#GWsTG4bskn8dja*HkNeYv
z_7V3&G7TeIlHzpbDSmky)3V**hlgUgsac|Mm`ctJL#dms!xhH4%;-fx(Ej@IRFTlq
z4rj#IQLbkIt|phi^W{WR`)17>^-IM;wUK%bDW@+QDZbR7O%<>>0kz=??<QXmO`=;b
zsq!cTXd&XL^tKd|4rfpqi16K?ZzepXKCNhfn4EbNX#Tj9R~(~N;OpEtBm7jW%$QR)
zFek~aVZR<XR-x9p9yoY!Ns!Os>~4EORr+~3YG((WS@UVwI!^0LTsAXjl6=Zm<~vUG
zGra|2W2*FSwXeha(V2BJT83xz`gLAIRE<I+uW50MNN!wPifOSNIg)QNEK$R>5-UDS
zb){Oqu(|i3JmQH%!2cM%L<K`Q`>5ug-4f|(n~_2!xJ+XSU(o5MFKt}u=t<s9AK2D{
zrL<F(V$(KcaF7I!TNK;OXv|?Ylv;;qLVOWp1<td@wDRDcv1(YF?0jp9VjncI;%d-`
zy{1%sHmQqql(<YY+S+eGCf~*+Jn(uEQkISca*>x{DSzjch<x-;eZ<|&)!{*IJ0kQ#
zr<WFjeI>uRCURb`Zx||5EWPs_IEsFbCC8NS89}9UKe*X_reRGSwX%?l?a`EvV?P<(
zl(njIdlVCQ<wsPsy3yR?0?`AQqh$5ah5k1t)(vdz%jeUC>YniMFKKZu;-xLCN7i;J
zdQ5rKpQNld81?(L3$JpTVCPjHqhrK3Eq`IzdwWvBxhk8oQv0FqPX5qmC@Cs&_{L5g
z8y-5D9gMCVaP25`gD<d&R9nz5npX&j$Amo4yvN=3TXH_nf<S&EWv<BW-Z0GwESPfp
zNK|o%$wT_q3)rEwQoWDtW(mGg4EXQ@BqSLABR;>&`H1>(&S%k>?POV`A+|cS_8-LF
z??fW9@FW{SL;g;bGJs`$7p3=`xil60?^Ox$&{1LHiR~=swM=h5aiCD30O;=^<9(z7
zb>EDd{>#!Ped~7Ge7e1W=XQW8=@)Oe9(%56HUo6x+}YwcJ*d-rHD?kZ3J!gBp*iiI
zoosx}{Hlk$_UZ~M1cufA1#d(s*&0+3blowA@i??(d)Q-849E=SU!?C~c*3KZfg)4>
z1}zc6=pkxH`iU=qHV2fC2<*qgDM*sto);B4l_09UdT-3je;f}O3;<;y0L%I*i!+IF
zh0)8-v&JysLxgAmlxv{V6e4N=5XiKC+d)+#!VE98@+H20hKvSfBmRq42Dx+3E09pJ
z?H8mxd8P!$49bE3bIIzvBi2q8j+O`dLkL#jOn-Rsr+of5FF<SYzg>X^nf^sc|Hkz9
zFoNHi{;92hWBRu$8)W(ymHivjf9h?}<^QGD->wP4&!__=j=!5;1)ctH5eOjDzew=k
znEq3Y|JC%53;t7;|Hkxh^*Qlh^!dM={*O}stLg8x{t><KyITK#vI=DSXCwn?YT!4f
z|D3k^tLg8x{t@BuTho74I^Saq|Cp5knf_%a>o=zVoYVTN=^wTJIl1-s3;wO2{kMn+
zkm+Bhxqf5%&k3);n*LGi|JKDprvK5!f6m4HKisdW|D|7(Lo|@WYg%9^60J#pCe3Y<
z4!{S+2?0oc*Ud@5&I4G$l0aEN-RYnGocqJUZ~#Qi_tXEaqf>nkBl^p98aV3S7$l^w
z*#@1kR&kUECNv|6^S@sI+phOLQ0X6!+5h!=z&{^2|KIj{D7}C5dYGB%qcLY>@VDM5
zx;EtP05TTwwUtPcE&&vpgha4C1@i}KqKa#^hyDYmqlA=OQu~mW)H9-(H*<tit(<hL
zT?LPq-e#-%bYN`Yj`0aRW`Z`Z_~W8n!z_Lj#<6?0$bo=p`7(zg%>{V>A@cRtdMMpB
ztU#(Lxo2P8FN5Lu-WdmBSD6dg;dFFhCj~Fkdc1gIOBId@<v;@^)bFKa^(yv5_*~5d
z9N_b<$<vEN2)pa9Lf8$>4aKBbYub3G8U6u!jD*^mwb$+;&=8PaXelK!)L%>DGwsoj
zEkA~Nq`vNS#mB7iFMwc!!9;o$pe2d9c}}+Y$R?uHui@4-wN>Bop5;_!Z`;r+)OeqQ
zI6$B#4{law18PQN5_*y2dCsGI^*lkClRh>-r>)wdhhi-XL4T<Fyn%F24vvlJ(2@jL
zO~FD80(-x}We7>u+g1dsXS?(rHdT`y#;qK6Pt!|>8!bGj+E?A6v7(i^GPdL(zsx1V
z&~(*&es9=!>-uq99+!&7AEB%$M<57Uw4tZ6vx%>vZAufK$*syBnR_zRR(Ji#2bnr=
zCufRSEAFE;&I6brhhwt6YG=4N`iG@Rj#M9;&=cGXCgsusAUc*`>?75q+X!xgDY;NB
zjtSLkMRXZy7noD-3+EI*5%dfF`abw1B8)8L(C{*Os7(r?j0P^fU9ng5>O&ju6kgXz
zcvkQbGs=@3<6X%|&2&#*ba`fq!Y*E1VJ)2Fs~6T&e9F*z_dr8KKfV9j<Y^3ToO&Ck
zuZheEMrqLbcIRM;q-jLCx!V?^`s!95w_#~|?30$Ly3MvcU7lxNDV_+U8D3?ScMI`j
zCHgvyMc{kHY#6B|JUX9mhfh2e6*|35Hq7))DJ+-6+3g^~=ZM1dNs!FO<X(TFkIX{?
zzV;f=sJ@4yE2YgNgo=V6D?5waJt>jq;HxyDZFP2ltUSV_9>8oR{RE}wKhN+Ik=Eiv
z2jY_MmxdUFk4&=Ij2j{<qV~J_*O98YR_LPy)AzWs*wIYS^gB{Ep=6!}Y3D$mB;^1~
zs1Dj3`866@G~o(-xk<EwXuQ_A+W_D}ki*$7Sh2JnxaRA39c3Avz&wouljnuotEhv>
z>Y12nvbxduU#8?E8Q+(bXaH}XeBvCH;uHctEex|fM9j{vIg$b<QpXItW`s9PXCnjs
z8fb9!-Ur6s!#?-(Op&OZm+m=xPLeC(g~sBwLttTV)JEM`Mv<*Up}jHOUmb3W86@v<
zmuANER?HiI2jjl39d!eOq2>M2YpG8rP1@!2nTcQ|F5XhID8;Ohl%#9QwGzk~!>;q~
zNy+maf@+$(G8aA4%+Kl6x0MpLtWag!@o9$peeMO;i5$d{1fv^qp5*iAnQ&)0@B3+|
zDGE!{QdquJ$gdo4yG*E`78k#{odpvO8hYLb50zQe(mPFcq>8tC+^;qndcQHkL2~uH
zW2APgTzyjx%{sz4RlFBrYaQ;y$G|0=R`!K`-v>qF=EjxPnRpth7+^H^O%Y1;#RZ0^
zm-b|QxABE<gw1Q%=%J_Vk@nqsh%ZKMd<to*z3xSgyVTX53iu+1R$xyQpxQdKy5z}x
zC?S6|WygPx-&$XqI<uhXC#ScHM9}ygw2WG0l=IH(V96l`eD>4ikd;1cihw!ah4V{v
zzBU)GesG-!V6ptbiz*F~m2{b6u1+aehgqyJJZ|7%V_Cwbj{VWdh7*e6b$m8B_;ia+
zJ=LZQZdY7biEF{x3!WhUIR-6X(Ndv6(NX8Cm+%8*ij%@(OGm4+#PtJgvXn@s5h)no
z*74YXRYXb%m*@F$g_(tGV}u&Q*+ITE0Adn^#4>j3tq#q&W&;HlL1Gis&!A%eT_!Ps
z{V9{OC=9M)XHSeV(ZycJO_d-4pxq}Ex_K`o8#ZfUR#oC>n1WkCJkW~sKa>+lj)shS
zAHN7NlN^M^yLeD!Wq_<+P|QF*4gi%P|NB7er?MoDf@-NG(gEzf0Ocb3>safjuH?Bn
zzS4pK2W0>a#{S!A>qpS`Uq)NM3_AZOtNv9&|4&x^Q$qikp859y7Pd%Q<Bv7+$GhMk
zFA{)F0sw?{=HY<=<j04vif!O&l-D*8@tW|LgLzN18&zz<&I*2H1weoS^Z2T|2u)H(
zza^Dok<^eQhV8(wf>mwZj`pgdbUd+BAW>+H;(5I*<g>F!42;F<i=tgB7R<sRw<=8Y
zLc1;`Q?()4x<mAYjpX;v`D!BhI2#<v@>SA<)h{-G!$p4BjZu1n)s^Jg+o3iNXM#ud
zPn_Vr_wo)+iyZS{F80H(3YJ#XqN*JQp12ze2J7HB%*|hXipx}y1-hlV;r4h9ZMQuH
zQ*+J4q?o&{*S0<m6=`n-y>wb7ovH||X^IS9cRMBe&|Pe?iYx1F;!L*^ZBI>BCzNQ8
zm>7OI(bc0#;PF0}<4lG}2SIz`8gl$;x>3X{&Es}XsB0#E2De&hc@;=_hB1_7-q<<+
z*r^Odud1SLdJzhPGRlry2M8ob)i)>jgC1JBA&WA$d_yLR#S))gzUbsy^TG`;>8Bds
zqY$6Uq;6|)nvc!tpYtIq@yYep0l0KS((@prWBJZWPe(*Bh7Arh<DW>~c8(EE5hm*s
zG6K09O6#hsBD!0b0`>MMyyPC*6X0`Io1X<po1abA)_mk?9>VIu&xW|GpPA*eDB^$e
zUU*f#99|(KuY&h=n{aR~rwcNjY@1y7YR8oCEs$<EyhKZBt0$6Lx-<U7wPfV6Vn}l<
z0t^~W7%brl$pL!LC%P%0kO-3l3egF2g{|^MhsBwWDxOeF4}?Uq_<)$QHqpcl|5?Uh
z;DJ+7iN06}c(IimLptpMn85mJ;n8}gz0_gQafFc77*@$fOZD?l^!jrppIV`h8sjD(
z%okIXTiDQ5ZH(~h27#%CU!xn|zKS9w=<2rvrpGY*yrBqcox`}_`p6hWPIeL%O~T`2
zXzOEYi#@S9H_{!0?syfLgfGJ4_H^Fxvojum{30MHhvs}eCxUyFxYc}he0sune&_0A
zPo;Onp8eNH*S?2MSSSKSah=WQbNQBQD9k}%E8v8%H{`wO72A(SHhW4ZbEgY2cVN+H
z1S}(IGharobMa?KULZA7M@9NEqS75aqdT<+>Y=s;TPrWkJ~nL2I`Uay5pz`bk`3zU
zg+-j-;v-u`+080?jMPO7r>mjMtUeyl8fn>F9?0f;M%NAkPp103rXI8Coh=g0tIsP0
zx6*gl6D`iF*^xq+Q9Sl4*5|2^&p-4DgPAJEv?Bs}0;u3t)L#gA!?aH$zth*W`3lEZ
z_Wb3AKtPx{o1sDnL_A^?0!uO~%D}@Qi(KN+EIt{PZi~P=Xu;y4cw9BxRgD%S+n(WV
zh=K+=t0F9W4H*~_Dw0h-6P`Tvp(_Kxp|e|Y4sQlGlDiQW%mAojV9aHa=7O0pbMg+?
z2P=@nz4X-7b0ssXZI2Nby1+%O!$9^bthl-sBH78#7Ur#Gj*8@qRJnmC5>JkK&25yA
zSi4inaG7sX$+g#s2B4(asJxIwQnJW(VTKGmqHgYpM;6pD?nJLb-S}|%^B+p*ZdUBX
z@i+#JVP~Rtzq&ZZL)$A)GT=v#@3%K6?L(f@%+Tm@e7G}!u{5M)j3MbyTtno0Z|XxH
zmbSz2?!$wzL{!ppV`!_c|H(3Q%9$fB7i25|w{rRpqP}TvGcc)#HKP`6-|g02LAJo*
zsHoMg340UQFLeayom>{^q-jSnB@`G!y#kmL^c?l0M7*nIRn*N&0>aF58uT*5dA|uZ
zCLj4!J0H%Ip5E&DYA3CXn^+)+@ntUzDUI-|4?U&>WKS|9{wKJEMwVE&p~Cae%$hJ!
zOt{wehyFX@9**r^^eS8U-HmT{d0Pw=<|&|Pn<c96v9TqYuMsjgCxp)3XZ*#mwl?<<
zrv*~39K9L!7S<B3HRIYHLIG|cGw`L{W%E~BD#PYQn7`U<hidkq;BBk<aCMy)xhEyc
z>P<tkcsOpq7_Gt~!nOH4Z|XH^gkS$|>vhb_46>u7$93-T%VSHPx-}S>88xZi+DU(0
zLh|y>;+ciX?y|)KU6@!qqmy<h^eFXq{6y<jXRr@Ed>_{6i)#Z~y&muq$|*^9ZuA^z
zM@b%tW(^;(8Sx$8N13xGDk6#FoZ~iro@$4`$mM`us%feE2r-u%fjW?Kp1;;X_-Zsl
zl<K}qurnb7A9LzeejTz0UJ9a^^7G&ptiT+i4G+kSP9g!m6P@0`af)|D&CeQ`gt*iS
zxvp}`(5GIf)R(vbl2k_Mt8iaM>_5&8=`fQ6Sc@$<6f&u{^L@+*%*yO}{mf8f3Q|91
z9qbM<;pgK5>_!DNZM-VJNZQpaW*|L4mLJFsOFS{x8;oiQXDn}f-KeVl(lcJ^g4zoC
zrBm@GWLgMs$4T0KJYE<A5La>XB0BB5PfFuj5XxmcOAVU3KYVdsgUc0=2Pvg5!5jHS
z|1pAgZ`N$k>568uq;+V$>Qzk4F{|2By8Yc_|7D{Wo;rq3+>m>7FaVtG@o^T^FQ&Si
z9?Nrxab|K)m9F!U!3$@(@JW<3@M(h)WoVzH@*%$u@hgNA{IvJk*G~16fM0<)(*4b6
z#dkEf)@)CTlZ&yu(8Jbip*s&H0(%o^=-`i)=Z66Y(-PCHcb9nGxLc6VY7b&_R&2JC
z3D!KPlIh^-leKmM;1bJ)ff!0hCA|_Sbq6+^<xJ67#+PT3qoUER1W0h^ZdvY+q@$nO
zGq5=r++-fJ#~KsXN--h}&+mt8O|gzL<#D#_XVY=zT@g>?{f6jPks8L{;est>E4>YH
z(<Y4GbUfdV;3Bwrrz>j|o6AUcL9t7XmLI1joX1y2{NZh+^ZT1@!L_gY<Q`#dOAU@^
zdQms?(JM^~lmeRYUnbo8X-6g~S>0t{oE5aBrCmC&mB}B8N;Boun_Wl3R1_S3t<ve+
zB9yUd6Ub?}JGrPtJ74Xbh3nf3t&x}N{=h~2X&1Fzj%YQ?rYw>^@|m+kOWX@X(+O*%
zo@?vZMlvz2JJ}ZGIg=jk4R{$nK1z8Q5~1(xjdt+~D%$B4as!(|{cK#FBFbOLgcQ%v
z0I?b(%O<6cTGIQYZ2e(?P7Xn+ywTt(JDAcC6Jm6JkKdQwa;L6LVChR8+&A%pEpS$W
ztqir#&B)Ky_AF#bP$d_-xBZaOG5OQV{R}+b>IkJsu!y7iR57pyh~4(IJuE`r3-?Ce
z`^Py>BF+|LVei`D>!bksOXQnrb!%}p!iuOF%PX(W?pWw_w^TQ)2Zm_gA(mh=S?^U*
znPRFbl$tP2_zUKhB+K5#UvI4e7gl{ZJYXAjSw4+}23ugve0}sl7D!$RuqHH1F%9qB
zjW^ntFYb)(7FMe#J89HupJ$yz(`plEX`s!MkUxk&N1cHJBD~~X%6!jRwWW9UaP;{N
zFwukcZ3Z!%Gi+kH;r%EZp+%=IhJ~(>mi^SLChQ78QAHKS%+g|ewYf28S&lMn?<p5y
z6WNf!#uRD>a*R;J*Exeo%X*+(eTgkUzLm)S4J(0+Y<*A~Tk&~*zG_lH-=msFtIC#)
zKypQB>~)%aCdBIQ0T#ydf^j6xFwl!LU=wK*CL)ZeJmVf;NFA=1#@lAAa9)jRqZH;*
zYj6;}BWOMlBDTxP(pF(Q1V<A$N*5>4Y1qrh%62=Esn&?fnC~X+Ar83#n5A4h<sTtR
zjdz8Nw+)f4DloF}Vx^I(se~%uF+2neeXgm5!5Be)5=T;sP!YcZVVDJ<0;OJ}Purih
zgo~wD4jIy<y{fin`f4s)_$4(<jaG2}?aedF%~gX#q{jSNS#Z1}*)lP`mMxklXzlgI
z7+iA72lSUVx{d)`1<++PdTa^g<k~DvZu+&QtIua16S5h(rukAh$R?CaF6fD-sUfn;
z6W_NAo#TfzGa%ce=zH=Ab5{`C9E)Z-2{3+Ax{`h8GIn$rq9z8X*OA<|-4krr=m3#z
zcwfK}ksdhFM6wi`7_eW!$A;m}$54pOe0FpZWAOg1nr0OJ8yDypZL_>q8e#kE?S`W}
z(ZXxjCdBOC%P&C_sT9x3cKquR(8gUzUXyOUES<&UmK4GMSZ9Gt^vDaCTZtblU4ma1
zB>ce3WE(LE<DN^iH0U29Lw7YZ7PkQrl60SRG8C2OG|(y-t$>%KB=c4G%UwF0_NxsJ
z-VjZEQkf3X@QxMMHoP(?kIGa$%)N<(X19DB=4A*uW_tn}8{--ep>}mxt^A(&gfHYz
zvR*k8aW2q1*|*P2vw8=w^RF&#8t268u<G_ftS(M?+U&^?hb-=<W((%EeHj{t#<<|c
z?DCp`*=c-rq~6`rtcwv}Ef(2LZ~5F;WX!h=W>fNkR#6#x%>dokPRr;Vt(haK`XY#4
ztU`H8Pys%JRFaKR^#q8ONydu3pDwHYxv#iiuFS1*#Zx)<qTXJDia?TILGb+9+OyeY
zt`$o~&T`gR;Wuv>{iQOnF9JqY*-Ltp;t0+MjIV~kU=S=wo<@!^OxTMgIVR?-*_HRD
zLxeMV11jiZ-XkAg0U%y3H#5!b(z=Xt=Ot|87xa0k=R~Bqr`G54mx^gTQ(xb@m`oyO
z#(iKf#U1CgdskB(PRKc(jwHl27OVMDukDa_o-@#A#X}=AH7$l4KD6=kiZTTYWpGXG
z?YU7T^c=l&WOT0}m=M&^(W{!I{>d4YLUy$p1`=Q9<~SA(+)F<oHKu0X16XHqTqr+b
znBAWHH52=WnSzr!^Ucd!Q&o;ndj-3^1ByHvMWpq^A_u}B=m-a}*Qi|s%LI~vY3%OL
z)eqgAmIvhn@NA5B_VonHLS*Uq<T!n-=JzWJB9g?#K16k-Q%JWgk*I~<=__LpJVV=5
z@uQao8%MkmV=Xhsm@}!dN<|SnnzB0#v@dkZUc{V0x8Z>q)FcQPVV?7r*F`r!@X{u;
zgEL#2CTsw70mOx0C!<<!-ckm(IwBcrc{fIvS_$D-hIN+E*G*<|&@eqyQB(1Cm4_Vn
z0f%N52BQ0%DRpp4UVXhVq_lqudouFSw>IX^!Y}Qov{GfZ`+0kBqqfPaskCU2BBS9(
zLY}4vNYhB0=wa$Y@Cpk+@Vs7eeCC8Pc{<8+VRzm-EVMd9z$ESsVWYU6&q!bH#YgwO
zmfkk0o(RG6Y%81h+=N1HkY`0tVK}G>XAR;gb)W88*$j`Xxk9~o1%&uS3S;RYlFlOy
zO$mUU^ZbhbD2>XgUruxHe+Q#&o~yVFo>w^t3E_O$iIu78NSH@p>MJi@)>a195#zY%
z(wh**^d!f@kS$7z8To7WOUcF44}?#)3cIv*`Ql~W=jT4Fg<g~rI0Ob(wB_>&h4y!D
zN(UDvmWV4*LRL0*$^kW<126`;tllYOe45RiGS~+`>3nE&1yjbu_2zz&anT=DNt@Ap
zI7%1e1siUm+Vi%pL#f*>>$Lzf#kJo^3G37C^;%QdL3M-LEr-zeCxiTDi;w34TYbs_
zSq@H3h)ef=EBj8ozU!K+Juv8Z{T`g}@(0kMz^7bA3iYZBN}}sIQj<@_f}E#?8|XTh
zXA5d5$#_jL3^m0`95z*SULxPWRS_KltCi5-`m}oCDu5?^nYr8Fl9;0=|2m#R<axU^
zN$2>UJK|Q4sK1U#rk?>R1YIj#T5h4%Yp$4^39C~0U_aqQ>tN$G)uV~mky(TQuv93F
z!op3&E#s|;F3s}|lWwY4B(6`f#S4H^)gvDK3KN8^1SGe`ZcNTwc~xLmb;>XiWkvyk
zRC`omb{YgR1g0YEl3@1Y2JO`cJ`X8eUeg+n0Xad#15^oXbG39QuPwbN=j|cpKieE;
zI;$&Cyox7$kAfgI*3gvu7G^vm1sNVolWL0?2btrZo5QI)a4gylR-x8kt^S@YSb3uK
z!4mw1P(Ep{H*0JQj6+kk3xBC*y#E5$vso~#kSYWR+A|-bC7oCGUF(eX!@BKMnuu(n
zu&ZzQn-wH1SkF@^@H<u@8ED5P^X=H>H^ApnC1d$UsAMqRcXoj(zWu8L?am3fS<!9@
z3CC$=s`Qv90*dQx3t9=|MDdE5;o*RYg^;a4`#Cr_@=Yz$>)BLVfic)a+TnyEDmBun
z@=2gp$1@LcRN|BKh_r69{e0qBJK0(rWqK0V7&H|x)OqX}a=oY*=8YW6&s;N4myHu0
zaXHa-<NFMZx*B=T6-0Hd!59!2@d&0Xj2%~7ZO{}(hrJsiYiQ!u+rj9~=08i5ONNr5
z>3ITM`-P_0yRrwQz9^0cQ8UHjHkJb>SvfX`0ZzKd7u;m(^B5KuvU>-@r$|a_map94
z=7U%o-n8GLF?TDbmokf4*-YdZLycqBA=t*pr@BW*Y2|P2V6T!KVi6l)!U$p#1@)7r
zWfKyOd@QojXY=O+4?alK=Swtx<*Q#wv2**Dw)+*hR_5~HXg($0LgMCt&RV84A=@gE
zHO2fib}@_f5s4gg#+%^Grf}ZO)mr-emtH-a&>D8W=RGi+YaW-B!9Go~s0;#g3u%FT
zJOumo06N(I>loEUFRbfQy^fShBsw^P>C<<I_2FjynykZtD-0z}@(CNTH{eu{k!hMZ
zFw6$vwFkKU^p`{4x=B~bxS~~R0SxK_eQn*OU$BqBnY9Wx=ajKd1NfTL`cW9n9|b+!
z$;XmUZA%*^T6X+jOe<o5y_m#aZ&~K)<Z<)7GbcdT6`{-^ZVV5M5%58%#GyX6fM@KK
zD}a6}z+C5%$WyrFdO0BoUAN!9OFiwg+oef9*0Lv4FE}WM<$+9Pc<zem`5MInWmU-G
z3nXHM(3MB5`qDEuZ6~KkF6x^M&9BrH9Tkg84Es7H@!>B^kP*ywklC-3!N*^Azyi&N
zc?@IOJ#OrK0hx2h@Al>A-yGm#4Jd7%C+^5|!g5!lZxXP4=!7;Vqh=I~VI6W&Lq#=X
zLT<xono8N~KVx}=L_7=TgV~sTCI9htq2E4y-TKN2;ely}o4x*{eZ@2_lkaqnYG9AH
zTf*uN%d@U8f>1!CH7_4&D3Yr1lkJEPO$`<I&o(i5R#5EZ-D?^-<ousE1463^S*ZpL
z%+oOlgaQ{WRR*XJ&4M&Hw}ED=bX?dYtWJ=dIB9}vhV8-}N$bK;VcZ`v2!Kzj)KC#*
zWX*!wJ$tZj5ogO;(5}9~KELr_B}=tcFEle34^M?)+&Df~=bMnHb03R&&?C`>tQ<V`
zQ4nc)mukX}Z$ZywZ67d=1CdyHI05gSvVkXfom2Y>8t9(aXv86MGG#OzKwjQvXmK2+
zjA->4yeo-xRoC3J+!?}|$YYOsMzL`xTJVgBTX4?dCYojzr#hinCQd6refOT#mr`Q}
zQ56)B7(y<jgo^i+OfhKM$xK$7PTW|kG$gOqp~0pzU8;-|0smecVzewfQ*3Ox2~a_m
zTw-JOIMtX;gx_HmQlz4t-X0Z3l#lizI0~&2zLK&$R78_Ag1X=JK{L$euuNYmq+z6;
z$a9IhveOOk;FId8;u0^aT*_A%zBNG%+8hTx31Zz;N+IIFSNR=sUU36mOe18{HH_BU
z3|1%9F_0fWAYGT0_z>}@d&_=qOO{kqqst>ebIBXU;T@cULsY#)A=*(lrr^adkVAgy
z=vWIy<BAqn<}~@dF}t{x*!X_))NlLB^=l<EPbF9I$E`~z$ZIaC1e5I3ja@QssEApW
zWv(-RwMwR^RH5`%43tmuh`IP>yuN_jDx2pI9veiK#d&;?$E93`y-9KXFiDW|W<u}w
z#R?LHBqarHabPDlsxw82)1E>iA|Z!r@92t1GU*x;tiP*}oN(f>iA8UGU8<IkwPqzn
z+;+`5RiHn-@MUV&@X8GB4cT%Ywv}h_hAaAjuygc{DE?6N4VC)@xX#NwQ)x~N4b3;{
z14(r112cra%zgV+RWoAJDqVc4Wdrw>JKzT-C7t$!q3vM%Yb~FJMrB@tUP(l_X#rsQ
zagF<aa_uD%P-!;z*P9B*3nu(}F|vJG)QD?bc7AHVLM%pj2q%PNsY20D*=vdX#(}L*
z3lr<AYH&$_cJZthXA?W9y|mA-VvUQ|ej?&^4H-c4B*4X07!9w~**vE21?@2MI__Zi
zd{d6WHVV_nw$b&4qbq~m$6-5KYw`+FA%Fab=`bij@n;88;tH_mP^-rZvEvj95+~q=
z47;>&<1Y^vvwR{owqO(Y9p%Bd8+Ni$b_&+p;cGEyboq5w21R;K`kVsVsKPVy3H6(R
zo!w*rpDwRq%$(IT93)Axhp4PdCrA%L_$mlfHteF|wrxdL=3!4sLqkTH1fikWoY$it
z{60lbxok2(B<7&cDg)lc4f&U>=pJl<J%ry<5<<wd1F)w<7Z2278o)3Pi-}eyBjYGj
zr{-t@YEy&k87qERV;=qMs0@WXR4|q+#|`z=dN_@^6$qc39Phadag%STb!g^~RhkOj
zSn(rrko6jspe%KCOlY1H*JrBP4~4;Mi)$y<jPk`_V&#t$W6}@I<WN6#$gg#kby9V7
z<<ESsdHmL3nrJ8obFW9g@@6rWmjB}*MQkrc>?fVtduCDv@Y80z^?Fi64SUM?&g2H0
z_2VEqH1c~Zb^>;RRDi_2^%r>nQ$9v;x?Igk|J%h?mrf!=xlff8Z%>%%*F{rQw<e#2
zMwZoL%N9RZs<$oxqe<;}2CnLKX)U_jDZF_iT6f-+4=~g0BuI;F;)C&hKT-0ESdn%W
zoh`TQHa#%7H0R#wP^)X9K<FsvK^5QsYhqeHAg0@3ng`}ClsI@yF~4o8#1E6g3mrS9
zl`w~eQck%g)aI*}-yZHFe*N+%2z{kt=Jy-Jlo3pP>FteQqu<2J<Yk~~sk5#>=$jPI
zqttBUp_PpEz85Hqbl^T;H>Atp#P3&wKSNX1+Pm5NbZk!Ejm*4vPEQ2uA+C<SjX6S#
zUR%Z;5cagR#O6xKi*Cp8Gjv5o@-hG0;o^w^03_OqqA-!X2Lgg>B5w8&hkHPX8W&`<
zNae>yua{zOf%$;Bj-=kIyYTavy5z_r!5Ia-maXSF3eS8;3nNeJ?+x;qGlvvdZl!$v
zIZ{8~zra7sSOp+F4D{fs%RqSXSeR<P3hK6@4=wLS`&3`q&~M0lg6e>a?iy^(s7*%J
z<8`_Wr%w=QL9hVU85mN$B(U3)B9cgIn!t#F*j5HMa~L<NFUZRG@<>|_N+|C3@?I34
zxmJHO*^Mb$3EB_Zh0Liu<D=E0jGO^vfVJbb4k|@{qLbjN&*nWn5sjdb^=szEfHn`8
zE8W?$dhpYl^eXy?tRT2x`>#?&50vVC8C)ZMo_!>R+Zzj}gC+wwPJDV}`s?p#ZnUR^
zPP=82E6nx7`8T)mMG(6@LCb6e+te;>QpyQ^GVbk}v5kZkP$|xakj=MEkCeJnB#-O+
z2$ExajVkIPsro%LU4cLgY#N->*!K<)q4^rNHUy@ueMqbK4T9=?&xPYQW+G5Kp}(RV
zki?{2e!|qr>tVo)eS(pdxAFlJE=q7D?c&0Q<0%?M3=?citBBxx1ekkOtY=Qss!Q=*
zN0MvoBD0lNy&@Hks0UYy6A|oP^_}oZ+T59nm=&`-7q=`TJO1Y4dMjx2?n4R7e5rh&
zoL07=VpukJM5OuK#K0GVY_0k*Wuh16efz)QKw}KMaQSF!nyJ{?ssO+Qo{5<n3D3Qu
zmnXBf-g|hB-jy${7zV}wfMRcHg<LPYeGhOtlPbeH^Zjgh0L2kIZv=_YrQj#2Qb^=g
zolKkc>JuVCE^7;l^BZJ6<d6pMqNNPvPnu}^aI<}JxZxod05Epq>g>Ef*CGMXRG^i0
zt^aJF22D3V3MNB_6XLd~A|V<0V$9C>vn9FOBF&!E{*W@c28K%DbnW)GL|1*~SO@EU
zB!sqS?r_VDE(2?UieM6J(EzW)TNXBk^o$^H=$w}zHackW|Hme5C@jcuwkFqEpj6DC
zi~SG)TwjEC<l!X@AQ>nfv<=bsHU2-gefwj7wjb;I|JaP}+g@21zi!5cRI#iBfU5mY
z`E^^X?|XawWjnTeP_F+L3=IPR42FgPAWfgN0~m9@gMWQZ{~HD5@%OOj?;igE|2EVa
z<3FL!Kf&35P<|}L|EJ!dZK;0UlI=VA_kqkH@XtWzzkz?<-0Hi>KSGv&_4scuWd0JS
z3<CcQQ~n$H*KOIpU*jJu=YIkJ_Fa1r_-7FE-@w0a%=W|MUpHp^X6wI&7=v!!e>P_O
z%gy`0xy2O-{PV?tpC12c6~AsO_}$~*wc~%pr~KKDfA0SG7mxqfjsc86I>`SY+cA{k
zKZ0iA+K*x3!nAhohF(i<zv`9T@h07uON{q5Rk2r3`Yeqp4mFYh2x>e_wMTk;xHcKq
ztLyuG=U7`qXWNQ3YM(E9GTtvErNA$EOJ@}iDMiue#-tL3bZq)w^E{O(AHSaIGO!Bw
z$(T~c48B|wj1Nr4#}$Hm0r?@lJWioj<8c(q|AW1^jH)YJ+J!gn?ykYzf@^S>;4Z=4
z-QC^Y-Q6Wv2oM~C26qh*^pfsQclw<4H^%+maqs=pf7hJ5YR<K5t$J!z)id3~8Z6e_
z2r1Cy4NDhR%k2!rh#T%iJf6MZF(Lx6(L%QuXEGBdU_e0Y*t{M@lt~s~qcczXDbHjJ
z!HL>Qxde24V)|F#su^N*W-8o9&$LD_RB@6nZ+z%TlNv0=1zn4^Wb6UL^j0`1svDc{
zIHU~02cCjePt<qe#5jG!O7b9E!XW;o)+F_-LMAqe&c%gWM6EM-WC*^1pm0703xHt!
zvCwoBU1K6D!7Wl3AC*uHPXh?9%j(Mo!P)&O+2_c0eZ66e2(a!U@5Uut<<ba<SSu=6
zs#`|taE<kXZh0P*qI`RU5lb@Y30#+SxvFKqv@!)|!W3(*o;&f3j3hnx3_{;hgND}x
zEV8T<q0GSI3FzHlCa04W1Ow!w&z;=A<xYWi7cn5t`dH>X-*MhL9+wRr!9NByrb16r
zq1;73xLgm4Z@GeINH^`Yqc_XWM1N&T4I?jJkCF18U0tPNuDSoPv~lKUoLp6luUoNg
z%A5sSdp>jZKEjI-rXbq5D1AJ`A~r;Og7nT-;{-uewou&bG1M7AQPSATaU|GqH_(wq
zn4$BDSzzg#Ee=^qLhycb|5sx3cE=dY%2+J39gZRUM1u-)BxBu`(a%Xfk?ozcHTlG$
zine=G*eW4s?<znHS@fDUecCTU4GQQ~xN~MVhq3y_6Z@)H^E5&@2t+qj;a$KbSa{C&
zeV6dOr8YF%7B<M5OT2STeBQt@5Q+_VKR`7M^7g1kXuN%DymNf@Hb0LsCFzoj`KVpi
z>Eq~hDhmxmUKykS3^yBthBrYu-Q-y^GLISQJ8G6F{0!kpNuCvV=`6&u1S@^eHQo+(
zmojW|=JG2m*j<6gZc!~(nkOnPlEtz6LMQc8|29MK5oUh^i=jd$1R40Ow#_&6T4N!p
znfQ^)DGn>uL;D*&Y83G#Vab7JOE4mIoALVGj~AXFAWJ&v9i$HSWyt{VodfVd)-ow5
z2&<qd;1Qy6rG^yC`M5pYRQ13F;^2Ys#9Xsgj9mjAyV;FL^NGn+T<TXcc%tXE#CT9g
zc1D)Cv{$=aH3IfOvvj2(Du#c~{3NQP^onh#S8NxFZNKds-9YuL`o^#LXSXXx4wSy@
zZxc)F0=-d?$INxj<n!HYF|00RkTo02Pkjc+N|;5vLGMSMa6?Jz@dWbSbOHs{5W6u1
zg<tDT51(*KuUHF989LF1v`g?|&PN^Xm$wR=l=1<kQkaN*VU%_skKJANYRbYViL3#W
z$Jcd^s*=oXS^<N?;<|Q(ZZXdiX{0kE6~hy8ZhAucG6-*4&;D?Z*Rw#QL#MH20)^+v
zV=EO~Z*5FQyjYFy2C<W(*57r|*krDqV?(CL44yEv;Rr_kjdI{QVD*E#GgdpgrEBtN
z5)D&tn$lOz#c`)ki9wjj!P12-aV-~nbI{H$<tTSnBl$?P{I-?l2Y_5A+SGf6txLVB
zQuyILZmwvxcWYZ8O=Ae?YG}Vce|_$ad-lF~I%+~sOQX_^`aA^vupf3b6Dxn&?L`tR
zlpdd+5`gy9{rU`zV<NS}xZsGHPoaKuTmGD8@6hbjaJUAzG3*1wa%klz8r?Q%i)nF>
z6fH!Qby+-rpIIR@39F(BJs?BnSP_-RFU~Fr6Ru0D(Bdypf$HyY;M}zXUXmE@;%x!S
zwXR~s#s$1*OsO5isX`Xq{>yCAA6<nY0EBH11%xZoqY`<Er*uJ$4(Z*H)s#|ZfAFi9
zd1yZhXJ6)R!GuI>s=$dsa=Z+%{-Qn>hrIa>u(|l@eY?t~w>8EUTnOYIR-Bff&z^O9
z+Gs66jsN9`KlLMhM;GT2(hywM(eTl+MWOKU01Tv;DHwmjW`t!uRQm0aN<Xngpz@a?
z%)gNHAL~I9n>PXgy&UK89qInE<M2E5!27G?@Gq@VYyoJIx&YX3zYG2WLVnj-Rw5OJ
z34nRw%m1$Q2g&iTl>Q^#&x_KZ;q~uZ{ST!-fcD>%n*Ofz2jc#f(tm{Bcz=i9e^+|@
zo8Uh|_}`TNzSBR^_%D?HGc12m`ZFy5rP3e3=5I=W-|3%7{Z~r=v9s_+>Cce;mr8$t
z_rEFqW2e8j75-`1KO53Nvn;<T{Ta^xQt1z1|2L(7?DPl1|J6?av77M4kbZ{xe^>g4
z_5J|;e^dI0_5L7c{)N*2u5$lgP5#Ft^K%&ROQk;og1;&K!+L*2xW7{R?{oU{M*5Sj
z>%ZSf|30UGy?g(Rwtrzr|AXfg<41%4%M0K)CsS4obKDd9y+;G{M{EDD-wpphrvJLr
z-+{yb<S~`|4{k}|_7mgM2_O6shGQccf$LlEmOnmU!fq><CY=Bv7YaW3H$u%X=PL1L
zalgY>^viZ4glpgWhJ}lFf$0O(`C_<qf4bonsTUefw{0#&KYjo#UPA@Id*cWILxw}Z
z9sLbuQkKzI7w}ut(u=A8qhF>5`pqu`=}X?qi!1pHOah?28Y+PGf@=We<(Sii5RZ4+
z5iV+VS^&)Ns15Hw>CRpp3Dm@7DRrcp&zs(t2?W3L*S{e@H<MTZOvuX=Y@k0+LHPZl
zd_jKVmfH~?NDMEdFN^)xXMcye|J9lHuh0H1jQAH@|L)0t4_*Gn)_+BY|IF0!FSh<S
z2BzQsTeSag4E!5^)qkh7e2|rWSkC<LwQK66#GoO6bNc^h!eMBU|Hv4CfK@Wp-rpuD
zfvC*n(jJL>G$~hFcEZGEU78bbQL+1sxu@gK_+Qwi?>sky+%^(}<J;5BtN?R-z~6I4
zyCnlnhBOJ=XKuLiQ|{ISYBWM-LGzi~01!OCht*lP%ZUzHYC4IqAH~+qKd-kMj^oPL
zOz|^!vdirJ0JFV^E@V0YCm4zOu_Odra~qBA)M1W_)^FtO;x4#(YKwtw?Da^GU$sND
zfJyerLCm?M+Dl!ZV<fZzIoT%@p4d_`?&_vsOj%XD5HA-K(-j5gtj#Pz(4aoMq7a}i
zv;=RhW(#8)nzJ&>U3iy6rAD{p)3z!9kFQ^wm2xbICYkLA+yWw_#ETJLlh*k3I3>~x
z)0NQr<A+4Wf7pIk4h!E_@zhpx+rghP>-z2jDAvPboUDqn;}c{Xl!|Lkm}as<cu8^V
zhL$5d@z|F~t(gc5(xV>fVD#DL3VIm@p5XlpvkpO0MPPYgZ|_$&1YJTV*-ggc*+ZN#
zl*YT4Xy9ge<8AeLb{Fg0B-ozzHEZjex$-A;YDrIAMCW`00tOob{Q)G@wsxxT(LKLT
zl0GFO*M0QAKluQE?wGiBCangIt5wA#+ZJ1Apa8(iP8pL`7O>OB(c@o&@>?Gz3>+!W
zdlJ;VFkJP@iZGg_)mIAt2((4(px&tEq!m0IKjTIlmvNuU>hu(=dj}u4ni0mNx8}#Z
zbp~ll(XgCoL`fBMsl3ByR!x(|0iOTbtx4=ngfy?vd&5@3DN_c(>%K3FM~9oeFs`wW
zPeYp!k5ZYbt+;+Rh7E&2g!bXyF_dU{!-vBvK4}f&mC#0O8TBgXl83aKs9$JAaaWQG
z6pCvd^<#H!7xL|4uS%z>E_?J5bKb;vLW5O!HwG2ge2calq)HGtoPb+h32;9o)6~X7
zrsoX|a@)7n8C?CA<|GQ&D{Qz$9#Pd=gOP!Q50%hrzXf+-MlQ(<hGtqND0Aes8;0RI
zSCVt3+X3v^%;KJ=a_3NV9_ae5bu>Ih8UE|c^e4ZT4w6Cp>5)Wk1bx5R^Bhjc0Q=J~
zRGjxSw6viqncYQ>Y2aTl298s(1*qVTYwM$2qd%iCq6^Q_>%6V=6lE8GGX}`D^#8_5
z&>K1fg!+k-888B-<ci!sod<)#^q7K<6S>?anbg?XblL4LX9{qlgmir3c9uLgA9F+&
z1WJjbwpyaYRPa$Yd{SfxbMU}n7~PH=G%R4=8D&~CK^77>h|F6@p+99rgA>1UP6W>k
zbtFMeu<p+TTvf68h+JzTFHoM@)z(jB1y_7oF@P;0+5QwejRV)cNPSDEy@P-Dc(poz
zOr;|gWauL~WNJ~e#2YjV;&_i~k+*;1TfxvHLPsgdp;wy8g(t!bdd}#1<*QIN_~vL+
zif+@5zyx}kUzqtrgrx4ytB#0P2EP8HEJZz5Y$)!p1S9Wg;0^ByrU`FRo^RjV4a9j5
z66v4dU1Wb-_c(}htcOTNTtQLHsK4<)uYbNYd8>crY?IoWeDs!`8UMrM=Zsb<eA^=M
z<Cvo{Dem{*I>X0<NK^9m9@!*vW67K3>caFrml8zi2YV2%Cdvg$r^u@f%=r3ml0KDE
zHXFfC&o2YpPWOBm0#4eQ5_xqA4LTiWESQ3xZ*(ttn{7q<1)SjK>Snr6*CPP_3nfn3
znYi!ki;Ec15bXvFrXbLd-bN7Il^Cki5Kt&k&f^2-MNiUFjw}6nqnNCgJ&Npa!-FMW
zFGyI>t5hRk!qOhsL#Sa#uG(+J?YJ>O^?w|bkz%pNnwEy>v4sJSL@K@<s~Vh!ho3p)
zD6KddF%s&whsZ1%K5pZ2g(If~K^3Iuy34^d7M6}Li)$^jWY!NS-NXVp417}lLfC}6
zG@MEzqt?*RopS_0DmVszuVXMXFl>RG-NK;wWwJ3p(3nZ19s;68r$f<tvy4|xq@jsw
z7KL}YszY}C-furgxt)c6iH~C@LB>KW@?+KdoocO@$;ib{>OM^G8$<$%IvLx^qxHxJ
z&a(m0;j#V0w(Sp)Uxs-35gd8Cws7@K48UMYPSwO>zW{XJ&rGmS#Wthkm-)Y4j7V(g
znU!&Zz$*oG552Vm3J{<fY$fgFsBNNI5}F?R0Hu-9e;9m9%VUcfoglHx11c0f47C3K
zbCxRbW#{MCV^>>fLbK0-3(Ad5Wh$y)PkbIh#Su5wj#g0t4Rk5O3KCtHz2jxxmsb#4
zK}{c<ZV=ed5|&n?KR8T+#KGn5^&_+kS;ki5xIDJq;TN2Dkk10L!voJ~Gu@~PQw17o
ziz_;BpI?IoO6KiaWrjG=+=av?6iR}YN()AnovoH1@Ke1P)c0g&MaTm<*X8Va-bh%5
zw1mP{g0qDx%*v*MMa-{nq|o3xFMWCrAav=H2l(2e*>Mm!mMQ@N3H`?wi<}IL%ZPZh
zlkAkuy1A1?XWCseSOB*9gtu*w^@(QSOQoqjH`9gPs<D0jNROCed~SfD=7iDfp7HfA
zG>}^?Dp=!vbW->7gJW`aJ=gdHdDbp6k8keVOBXYPOiqHh-iL6$$akyek7r+Zw;GZf
z4D9iY_4Hx;j*+JIKhba?CIRUmj5U{f@k%l-d!9)y56KVdxTbj*UAoKVb#Fmo%kYt)
zwikAGHP`Gx<ubs3$;hfW;4}8RD9P~&(uHAWoK4CGm&AQ?k{!1!0SQv-%_NGHH;aj!
z64*cym|CfHW+Ac0&a{ZX0U}jp-!ua9D(yCCKjIYjCrV!XO5oeBAoZ02zMICxeenE>
zlp=U*uXt#ejH2Blm;zd0<6xlHzMCb+bRxXgXI3ZbYRtK$183y(dC)?2f3mTQ0}6C-
z;75hU<j8la7IeGO0oFKIvl&L@OS`IxpP9N9F5mD!NYW@C>JbgK5*MgI>MN~@^3;mU
zb-(w_jH+8|N^UuzL9t!OF5S0kk-?l^*g!gnr=&M#n$#{01(dW{vq#KK_D9L0VIoVJ
ze@G@vh>{=6&+=3Tlp8Y8C;H1{9cJ<$D2{qjgNDf2bsmD-djr!^p%7c3#Kk3DM3cQA
zWd`5Yfa$jOQSy1m`9U#vimQ;Tcl;=O5ibbD20`{!vxs@e%?#Jw;{NR+KBoA`uL*(q
zTUCCM=H%xj@^LXL0y?RAoKaY0$xIdCdsk8=iy%BZtOkl>%RONqCCmuv)`3A|$Pe8T
ze5p4egm-Uxo@pqfER0qMR(Hdf6%g{nTV%08)ga@-;3X^E=WDv^3wAoOtgMfKG_xi2
z9})TTHV)Bg_1lH|W)pQ_uub!rijJo?dLha;vsCH{sBnl3sNu6E!e;C4x{wKU0<Ewm
z)fu366(yT^*QRdd?1M&@oToY|mH6C(9pSN{wu<wG530K=H0hjce7<yN&>t`hhSM`l
z8l#X@`8s^I2XnE?(b`oj>^v)#<J%hIY%Sx!AZvg`N>tiSy_0hV9?hJ@*MP1}GsJE_
zX*z%30<JwA+@$;5l82_`i|_(b+5SwPTncTbM|>T1!UO<#GwAM>#_uus0q|C^ycFBd
zON9!I6n%ya!Q@swvZ**oj4!{$`YB2J5?J#M=~=)L6qW#_dR_*pe$R{o1z!)od>!*!
z2ar$lI+bQ|iSh&J%)T8cDnvk?HJN?h#nxMSnJw~*k2cWY;tQZ3k)&-VAAPRCylOtJ
zf%bF>*Q*J<r!dL|4>Q=@Fc-nrL8;5|91~=yF<Ekf0DX~!^Y`kk^9Tt))rsRW)G!X<
z;+Nr<gzaBR8Ab7w9WSsPH-VP{q@OaUn@?AOE1#E(c)v)Qf)s@FD<4inB7Dd9X#bfv
zl`sSRM$?20l=WA>R6!}19Z2zI`peZnGo_o)h8ti&-#7pNRf71U;O{xo9|{nG{yZxP
zVOf3{i1|g(%O3vBiT<{S-@exW;%uPr-^0FZM*Ghs%XjHEF9V1_&H(!U_5V-le+7O&
zN&n}p=Zo~eXFY$C{?}L4zoh>-Bo^Uk>HnDCe3Ab5^yW{}|4M8ACH>D&qkl^OM-rb`
z7T?c^=lb6!@!_3V;O$z_9RQK-R*h1-UbEZ>ho^-;%8=@zw?7G%AwXS1Vjn8fp-Q^f
z_EpZFCsPKrwI`og5$XFd$?po2F?h>{ba~AurOpZPhQB4Ud}qo>sx7|}PB=+7<C*5)
zPd0%^`dEaN?rbi+$ffxPN!Tf+9a21;69KN6mfi|lO!FCT(UfwEHYKo-8~%cUmwjq4
zI&HO8`k_InFu!3M$XIOXwbkewP2blnEbz4QpK#{{(JC}^BTx+27U=4Rhj~Nn#gcPx
z^P`9z9p7EIC}9VfAExr2pBdPv7HQ6d(ezkoxG{f{DbQn2T{#|`LAcwX+zmc>Jc?EE
zP!JMhJ%QUS0!@FOKyauFksbvQsnZ=oO&r$UEyV%%hzwK$N80BggK1XVR02gs^zVsK
zgk;Yq@<oa-Q9qnB*b9!jaO8zR(6r@n_>adUNEm-fVUr1OOEVG(^H7S5?qbE<$;rGn
z*Urz$)VLa!5TJy$J;v*ft2rE&M4EHzbSb=JcS%dSI|lYU>eo6Iox2dJG^cBA$}m;6
zoh+SaT8p%?0lr5s@HBJK;4QKc+RuD#jB6CF;PE9aEiF{CxF5pFYA^U;FE~Y*pE0fC
zbuGJ)NUSdHiqZ<&Nf?SX0u-i<bx!`yUg@bCjL4>0;1{F%%)qk98b6Y-%MGnKWS+}X
z42#_A>Ph%Ogn<N%;#qE;P7;Q~^_XJJC3HZm*uGtD+9}oALr6~_4_Y72XOuS`kRe~V
zf*q?JXm06gvmSzgP{h<*OceM};1Nku-Q-Xsk&k-?(Zrq9FhnA{gyypMP^)m}OdiLE
zT{X8c{c&{z3zKSB??OYM+PBGdx!9#9hFe24*dz@oZs$g!l8M_u%DgjisKZX?u?BWk
z2_g_eG^@Y*Fjc%|dDW|tMTmAK;G{37K_nwQ=l-cjNQB#*)!ya=lZElT3yFmYK-?LB
zt^<*${2<j(Ta#&q%dtT~msU1&&A|Z9WNg#aH6gTzf@vmI<_07yRF*?71s9yGSmNz=
zk{byv#tytVtMe_TyD&>B+-DwdI082`G`j8WJB;pdgy9yhT6H|g(D2bk7=(h4D)phz
z4`q?;6C`FPc1d~yxI|Q9S)m>z$TRVJw*d@K&uiByj7;$y77K-}Wz|GQ8Fd@*D0I>7
z4jz<1Ah$`)o#B?GcJqU%JOnKZ_uuk{1-5sa=OhrYCbtr*OR;(f>E6CAqFS-^VmDON
zCAS+{!$)PcBK*X)yi4MR4}YndX9kZelD_Y;eaO%8<&i{k{#^F_qC(K%PVCv{+QEl>
zdpRfPgQ0F!?`=;p*_cFF$CY1(OpqUerrR?=gA1j&hsL{0EgX?q-nf$y1sS29j|OjO
z`sdFs^J?`JKJo99(v&6*_e5LNe<C0)5DyNtGnt}E!dww&5H0S`L3%m*z}<7JV0rNs
z_(?1G4du|8g?6ik;n6}Zj7h_6MdJZ>_Tjn&4_-!u8kn@`hh=<iw2l}L(0VAt$xMQv
z>EcNrjcD4{29lK<Cq4V{J6t56=#%1V5-|hCk-7~v<4f_|eoHWm6<wZ4>~MRc?Ww{v
zSZ=p3MOvxp1!dUSuYs;5=qsbpJUYh^tGB9fEQU(@eQE*xZ^9S_bU~V@yve5)uz2TQ
z4=Xi)N+Mc~?A{s^u~RVY3aGiM(!a};n&b##WY|0A=1V}y*Pt%VkrJQ-=4}@jmC=jy
z8ETz$wQF^bGO1NL*!A*y%st!_i`mr{*k2E(m0n}}KrE0Z<rNonW(7|qy`fPK)<-aX
z%&9kptbQnZcp{hQW@?4)rc5|2zry940bjzTP5MeTkQWR8CFGb^RFr4_+*U8MZBsUV
z(|(}wt<U&24@c^8A%i^}3<|ARv_OqdVrOHtMM?j3<OF?UEqg+OXc-_GxF2gppK0NR
z^RD;8KN$BMe)euUF<8;H#C{oTfWt1dI^M@(a{*WzmOor#t8$W}l34<!ysM898>&-X
z_?r#dwQga(@z>46f!NQ>r%I>;A9F6K_NS3Y>@dYQR6CLT7hG(--9^WR)7!%0#?CL8
zBV93W#aTvrtScMZpYn-;Z~{^Y<OJv<#Wrd1;U!g?E|IcNl(m&V^-u8H%a*QcBlI*2
zT<P<6WzXVs`xdcHc)w5K5J5<=lRB`4(^j5jt9Wwy>~f<=A%u4uXX!?9QSt66E&t+c
z5Ej7&AtfnlrgyA_LyTJ<J}V=IlaoZ@b2dJW>@0zdC+LO^Qq4*`Lxo8PFP;GyL&KhL
zl<qumd`>|O*JEH~_Kl!JhlA6)&5cCs5S*V<O)Bzq$MS;Ee5P=H>U=<rri2ycy8_la
zTU(8XmIy@XkXtk?1p_GQNU5B@QQIBxAsT!e1AntN(GPb$K<usceC2DkILFUPX9D&D
zee)x}<&BNFSYLo|dMp^5`4O2-ii8>*!^Y2P2FPutVJ>r+<--mZz_;n-!r)buw~Y3N
zx=^DN=r~89<i_AhZmMr)F(cc<3`@W<2*FIMO_|WTI@RP3kaCu$Z11#UH>st1um`+W
zqa~Df*%fX$KQ>gQ)e#9+SWHTcu_nN#h<EQ53OXUu6A|O`Io|^Nq&X-$)-J7f`U+Cz
zna4^KPjgw0>!QpY2(a}gN^9x1)OwXdLnk!dHVOzsMfKNZ8=#9~!=Jx*hAq#&y*f2O
z&C8QKpVA!)?)xN);*s|%O8qXy0}eid5(j3|#-;}wmydv1FgHq@WqRB^qF~_Ej?g?3
z+N5UTSsNGRG<A!S?_C=WD2Tq4>i4X*KP5p<z%xB2Y{g~AM=~57UT&4EnSC15sJzpa
zHp?vLYromV`)v_Z7!7J*VDTFN;Q`cRw_Z+i47XWYTgB7(m7qlq05PJ_2DcN>W2p}S
z?yW1wEVD{)(<9x|v8jV|_|2D1x3M<}9^(Us3^};LdZ{EOW3LC8SuC9LdsmW0uPwl2
zML_w#AQn1oG`uHLHN@L<U#|i6e>e=~$x44TUJa*z)(oRrdEknbQf0-4p^sc$0T1#4
zH8~MNKd*DLb6^Hb$J|worW`SyUo1DegQ-$&eQPOK?B#;{3i>7KkK<8sEpN<FW&KFz
z@J{-X4%8%kq2%fuyDQ{!>e+YeoU6inA$V4c&7gmJ-TO60uu0@*F!i~(*%*6)BS61d
zGw2RFffzaq2_v7#gzxKFb$6YxpW{Hwn=&4X>p(*vo-eK0aK*UTp%gQ4^U1_>M(bXt
z8w6iDP75IC2tBCEmejMWZ)Jn8aY7^GjD!vJ+$?<h3{r<%kWPsS5TW!m6s9E2_t>c;
zhcK5V$o*W_DQ=ZH*F*HI1t`RkQ3=_<DpWHdJF_L-0JR%qMhPE`%NO~vBGmFM-EDKC
zWz17C?To8`xrUaId}XeF5{oFvnI=#@_{IGB4FCY%W*Z-R>ERm(G(R>ohZL4-s|jQg
zf8>U|GsBs`_P{YxD&QfzI?8Q|3JHm01Z{Kk#{0F|K=22}op}XUzJO4eb4pNs^XtV;
zuecISN}*TGcID)~5Jgk9VTLb7)rE`0YiA(q&N}lHi%c23&@6|m9gAwBL!!=@Nrve3
zDdJN;bUk>1tB7hXF>bW+;g?HK1mlPO$>-GF<uqjwhde?%G3?O!x(gh2SgIyPh>WD!
zp}Mpoy)JQG%D$R!+m}~!ut8F3iMN>5$!~aqo8pPhO3iXrT<(}iXdUk*da78m&2p-?
z@hW=YI}Npk%qR3R1ft|b-L+f6*wx6fr@-DO5<>xi2oDNflJPXC7ParKn(8<Y+#l2R
zWuJ0=cNPKGYM+vzfrf}p<-A9{y}*(@$-K9mLV&frX7{CvhI~bCZX)E4tNR={9{54u
zaXc#$P;N%iF*XQvJIBwsTDHY-)YSIE-}q4KYoVuaC<tGCU0d?e#^PN{aN*2`L~~KV
zsK2AofQ0Zh54XQf8&&32bvl{GtG$R`0%zZR7#JF5q<`mxg5qDVSG+v~|GeGhg&~9M
zw*s6d@<m}53Ks>g2U$b~iz!7xejus*aug@6`BD;yfESk5r}T+he-odr_gydUEHK)L
zK?hQ#T+m6k`+QS(o;(r`@}^Hj0+8K&?_H)V{GH<DIjSb?_?NyM76nc<N*e}|vx%cg
z=xLKLSp<{s9ni!!2|kHI38>8b)ASs;^<kb>6Pmu#xaOyoi|pr_G;&GxDKSb07un8K
zzBpieq0u6w`9Y&)yodsv!H`)<xgiYw2aT4u9#}kE7c_MtS7_|x_zJo+3%tsXu#~6e
zJsEGq(BllO@#z<mK4k`KkIFz^&|yT3q~%nR$Og&Cl5SicaSltz;P=r>TD+8#ZSj?U
z5=WSfOZ{{G3+m@kmZn0oJ|^=9+iScnYsS1TvuWH@!lVcnL~j8AC0u0i@@bzumQ=CQ
z3>7-~@XB>3FB0GfSg9N~H<7AVQpI!ab(XzXbM{ZBE(gy@4xNlPi0iGNyS`x>=)+fx
zZoeacAj{cYfu)?*5D82h_nPJTy0m{YYUzLt8t?g3x-vJ&nbU0A=OV=$=Dbk@xd}~t
z_Rejn``Yjc&w9xZ*#>FoMlg7X_o!;O)amN3*nr@rn4HXi%FxPL0Vq8{oVFN>?hAip
z4=H(21=KA0G0JT`dDMHGV4DVycIW3)9}LYsbFbC}7ui?-&f#qHb}4m41nt<F4;}SW
z?v&A@v)KqAZx;}y)m#&2Bv<4>KhCD)1cT=>%2uXdxLLYOiHsi8(j#xKTk69N8Jw~2
zQ>5X%dT7kYUk4gqf$p@+pz&qF?ZO64fF>xf)O2vD;MBQC8&>=9$tFj2cY=8~V<HT4
zh;DIpblZimW}zX@333e+n<Gf8$rqR=qwiC5(L&3Fgl&Y=G7QY3qC^I2{P@!9j<+Ij
zh!4!?S%qV@+I?L?(#IR`+#3H0rY)jWaOTl^K4W$bzNeU25_V`hWj1GT1t|q=kky3|
zE6-hSM|+Gi(}9jRv=|kE%`v6|NnSV}b7sqGOp!vfq^zh=@srhnviqBwx-tS-M5_Q1
zh{2WJhzcDB`8rQW%RU@9ySfpxu`1LpVXvt}1oy1nn}>lxMNu_V4>_ODmxA-FErIeE
zyAG|wIZoyf`79!*8Y>CWuMTsz_~-;CTb!}I&&_>18}n`ZN0Hd^vuM*#vD(lEJUE=}
zzJx<;P#euEtVGP9k(O`j@QQ}gpsQS~bki|e1}I?NlTN(Lm<pNg)*0zZOu182YW1|u
z15K~FDb#1@0}72x#RE-Jbx^M#@-Q|NOuq0rWR7zN((<#48oGtf#Y5`bVNK3wQB;6w
z06tx}Ncbui<Et~7A?ktC5s6vHn|zA1MQ1O(QC>(|?5|rexqXUJx5hO;aUr**2)Fm7
z#W?nHjHDaJzLV_R;E2DiC^kd1T1Cy6r$MFt+aArm^7Y4w$Z(jFu+e_}EUy?N6URe>
zkHS*Qq<I!>$MCYk#vxK<M6q#6?h6YF$cI{V&Z}rSt4YTt1^FhU%EhZz4|Mm)LkQeD
zZW;u>3VULn)q9tDt1*~Y+FxC!>PUiRK_Reo3BQ;#`m3oGrjIt&FS_mwBP*IV4e7X-
ziYqtg$DFcR0D!xlUwHMDyjU&ID}2Lv)K7sdlv6KnvTjukv$vdhA5FS;$I#{6p7IKK
z8W!tI#v6-0FkmvhxiCZE;66Ee*?<@w-(6DC=7c%1np>b+$izT2tB8qdy>d-OPU(dW
zgzsCwqe<@ts_@5b@z}_ib((uSg6k&uwvXUW5|jv^s>{_l6vLI()bLhjc_#rMNh22S
zfdPDY1qYWO=gGX$)g3NZ0ZfLUx%{Z;)usQ59H%N=8JG!mM2a*F@S&_HBR3JGi_=-B
zwBpzJ2o{gA$)rw4dx$yNpa8<FFva5>^jaZS1Huog>jD~l;<cm#F3elt=qa+}WH(X+
zk5UgxUBnWRm!Ru%cIbX)m@*xtAQwKTpA6UZZs0vA?-o`{VoO$52Bt}%BR}v!9dU6_
zf+Ta8>nwNO9jmQ_0!kE0vOZXW)XOAArz37(7rV33OiRF&2w5ClZmK1lR1SgX6DW<`
z9hw*q0pq@h1+~>rjKd^a*8VU+%F1jdL$868_XcP>eKEwrS_*^Dqed@Vu-$qvylgZh
zPsqn1;B{?&)DhOmw`5JhEcFYv>t_=5Bl`Ofrki^8yAo_^dK;Cm-;Xc=i=E4ppBUKg
zJ+frFgQ7Ct8JY^LCo{$CvLk_Mx;pB7)4y6!!RIeTJ(O7quhR=nj;crrF3PSfy5P<U
zF~rGZq3RHq0|2U8p}%A=f}MZc?C<iv8{7xX>IL^oRtem~g1xfEOycynxfAD#f0F9k
zeoL9<N0%s3S~w=ycTjXk#T5NaU?>v5`DJ5s^fJ;R6W#JQS?~yv;m!+-pmEn4TO>3C
z!j~sV%Z3#<qNc3JEV^JCoKrim4zdQ|246mFmu6Rmq#oC~V`n}YucrS28bVq3jDm*t
z&<NVrTsDdX1cWUs<8u&{vTeI@OQX^C>P~k|7cXx2B@Vg=XoluI46_b{0ZEHJo4x`e
zCFl0C%)XzmKM%t@e=>nRz#-;!p%<*$>ruGHd7CrG`haO72=M%rarRPGV;d+6Y9*=8
zH+0^L9hgOF)Vx%WF0b!G$MF&eWCT!|Fm%A|FrbfXqSaw2w2=yL^Vk;#oXL;%{kQbM
zrnMhy?lWOE0ILO<*8YW-1BT+a!u0O2-%9mnM&Aa@ZLKI9wKx|;;v}Us183PIOcheC
zzZ}g2f$F(zvwV5{>W)tG@TdXTWWc)ebeU){?UbiJn_w_xh$j2CN4;|*Q)&AiLp)0K
zn{~~#w#`?`Jy+RIXb9tZ#{fX=A&BB2Jmxe^U=LW!%G?Z$t1F}9dMwBclU(lE2Sjbb
zkXgI!6VV!tvkvPEm~>BWo^gB`w+7#YtlMDBGANQ8s5~x)<(%todTSVuw@ZnuNwSg@
z)`>uHvzgp|9ZaOFOW++}757uOtn77(-%--0Glwf5PXbxK=atnfk)6@2dtNMMGxLt0
zHuK5nksgBjjOj_&n+-?JnvcAYrYoL@79+;kPGxflT+RA?KCvIqoAwTtkw&2pmGyJl
zT2C-?I>(Kenz{s`uvS^`ruHoSjuo){mXj^h=`q%IUUeJUfoO>q%ZFMP1=lRpbNJK_
zQ@hmlv1*)i$oIJwn+N%bSNrJ$1bZMfbob_(loT!gXjmrsO+#bE9=*$LBL*L$mEGk>
z-f~rZK3sHxpmma-Exy8Q>U-jjeFgjmVPgG&3nw60*vQOF+>^Cf@u*`fLwp?0p%5i@
zC!jPgFu@vI%vlUM7bZ`%(Ug;bQwg1CeH@Czw>vcFz)*i^AA^uqRHCFVv+BcV)=RzK
z36R>{83)vFWHUuHQ#O(zTx+EhtlX$3O7r#LCWApyTnLxTaOly*;esaY4Ji&yuI#1j
z0MA*+lQoc1z_3OhOj1>&0TZ=5hH3ulBbV#b-15xrLQHTb$@tcRQXB?el);u=H;;Ag
zTNwCLb#vox^ROuXV_K2p<j^&kaKcc&OaIYuyxH7GAD#2=75m-|a48H0_H=S|=Q;?Y
zcx3;oR|)Zoo<6h7CEZIQc<X&^!uOtmt0Aq3D6@vwiUP$jZnPZ%N;dOJ1!Oi<dZl_^
zb<DEUf!#_7_*MXkjUZ|2qhdPLXZcTJVYhL>r_spn#7;zsZ$X-7KjaeWC-S+Hrtf?D
z)}m|(%u4CFD*?u>hB3Vp<Y%FWzB)T>>lrv2a}$S_kI=9_ftP2eMg@mmv6=uabZ}MU
zVrud|t(vcvMkx|kJXj%Y@q8Ru>h!4wJ;|IV*0`P>@^g7WP!gG<5WY}@1$9^=qQCEc
zRgA^Gz@f7Z_lz#Lucg6YL|Hjbk=VR*H_R)ajK$E$GEk36$_UoeI=(7f>9`AFN@U}B
zi&O7ACGT->m=dWxgaeGTxavbx&@J~kgEsaNy-2b$%8@)p>sjJ(5@c*23HsuFSlvFw
zy`K-Fz{g3K*I^}C)l(dtbQ53%ObljUlKG{!ta#@jBU81plgUCxi<*0C#e+K^pe*8M
z2v*eYS|{(NaqD~A-?R!U@RCcOg>S1IvL`Ty-pMeEq#Ld-ck3r5zb6dsz%@18(aII$
zZ_6(J%nJG&7Z$PYg53SpbB~Z&MZVndXejbId-u@Fa+&T#-YeBK)3;d;D6lVFSpJ@N
zne`tTv4UF+%vqWSsX?vgDx6FL@0$tBJ(nk!wv6#Chid5DnG~sKr(eN`kWQedB-$^~
z<V?q?y(>PwcJSj(NI2e`vMw)tADtNs*X{XjX2Dk7TlVd=tV~+tTeT>~y|+|4$#I*g
zQyIY-@#kGGG?(M^0*CAb5M-bQ<zLE)OldW$pd1=W@6rMzU6v*&zc!!@yWYyD=ni<%
zUqqtN`2l~<SEWU4Y=G#|o%Gve%-sKt3u_a*sE(>w2!2Usx_aByz<pp>2q{kjVN0v!
z4MFz%WgI+mncc`~#KTN@JFpa{ZRwrcHq$!)kY_$3vl#y<>&qmkvL5}Dmccxr7cQ*B
z-5SiPc6g94*w>uSX;+B`^Y#Yl!pLs<R-2pbn!D6;(Cpp1D>m_3DzsE-=Az@pa;BEL
zK2#ti>g!J#l4LEYdsf;2&~zm@tA+=*#AJmR9xT|RMFQLQTRW43&1WLV;J1Uao&zF?
z^N`OzADZUiTH7;18=GB+uA?3pn_xa^$+ZXY%srFe_gERqM^brNWm~JzCJXYky++r0
zI(sv@O#0~@M6`C#bh*X2&P7h8>v4~?E8Y6;Nmr<|JiPFXMPX?ccC`oT8ed8MiO}S$
zHp$*xmG=T^XuT)%ge-QEi*Ap+2owLuwo-;A5@Sah<D#-%J_sx|;2>@jL-NR^m-t0W
zX9IwA?T*gM?An4n$mTmyZC_uVCQ2%Dot@ylt@`01l6lI9>$9(T;vJIi%@Fy4%4mY9
zGr^*w69e^m-7c|UZN}(c{PK(h5jD(AtfE7a?8eX$Tp;LUkfk~eacEYKXKyn@YAX75
zo*EXgQUg)Ix^=yYYSD5C)kq8Vd@NLT)7GbpZ$p{yec=ir4fWqOicLvvi^#*1YI2GT
znQaumfZ;|_6(fNVyUqp8+$_DkBT8DlK{1oIV0%y7c@CItW+^ZtC+d=B`E)l6DJv)b
zuSOLM0<k!F$hI#eyraEDtnx^Gr2~ANDfMycLU8lwEcb&F&U?Ze)eP(2l2jZ+6mjoF
zmS$J{go=%&Z)n<ZlQ~j56tQMi!A9`7->(&POR^1uc5h$rz3s8h%2dL?gl8pWL(QNW
zR~kc3_y&o*D~xh@XOm5hT3r0F^@L|q1wLT=L?OA_CY-5~1I50+U5sBgcw6tLM=`eI
zZ3e0L9$axIGTuH|73Vc-rJC9n{NWvZ>cNsq%OUl-QQ}T%yoThHvbI5^DO&@G@hlCg
zTX!i_M?IYQ8pDYpfdrxu))q!Iuwr`sCo4l_;k$_HG17c)daiIkf(>U}4X`i?Q9Qar
zK0x6!gSJt{AT}KwY@+WG*l_ow6T<$~Kp!G}+C62fjF%S8eE4ADHk)aD;1HbCyiVJR
zRC+l4?GS9P#j&|JCE6`}+MdE4^?<VknB&6}45IJeiW!J2Rd5^4V#6(LeR5@<4@og=
zoPjx5aa*n|pMJb&ZA={a?#X*L6&6NEjGlqE%wZeN1DAV0DBM?1vKA*Fh}?!Z2yVl1
zaF@E1d*uL3i~)fHJq8m@_&z!L+|1s>Cg<7&dLOTb-;9Mex(R;eo8f9H$_hVydXnA5
zaNcFZ^t@C4LQAZGwQF80hx9<xJXU#dW@)dAqne&>e2|uFZr-M&u^K)+Muik|+trru
z5FMff!O_H#fq$81ad@~A^%Yf#-x6yC+uIvKQ3qXjKK3It3yAs?*#k=ZPD%Q7f2$Y&
zx2sI^E=RvZwqBhz?MrppLX|ov*>5Ym-WW7MExpb++Vi_7>$kpMKJ?*p;EXWB#b*I_
zf$xMwbCpY$zQIvc4D4e**H`X=3rov2MsMzX5KOt~x>U7f<@}b?Qfl`JfWDV?%^n+e
zOSoTfIKhu$vW&BDy;a$`$zX1(scvqvGggG*O;h8M=*szg>0g+rY2~<Q*FIlxovq`P
z<l8uwJ+^UhWUCJ;9X@Ob-Ee(^`l_YZ#nQ#kI^+3Ul1R|@r(TV<G^fRn$6xVS)E9k_
z{4{VOpYodx1pC$X=H!TPeVr`#i-<u$UzK^J9(mVRHujOscf3i_C}#V3xApnS9K1uB
zehDA=T0|spt=2nWiXxFCeB)b2PDXsHnl-m?ja#c+5oLB^HRK?bd>H^Dyz)HUPyu}}
z=!K$ykM=#ztRRr>rY-p+aj;gim=X(3fpA99eH=;arZM618*;?Lj(mc4j;1(K(r<pR
zMg&5p<+1@ri$WX*INYzqL$|_$x^XvflvPYCy9x?s()wD!Ppd2As4pwl4l%KJZDdF3
zVuFVifp9G@_L3rNncwmn>2WFsK>`XvTC+z?=|zlkmJyGrKkY!`PLd897%f<n%F4P+
z)q@~#iw%(ah;TpIKck+GN5eKyfv@4Gl4*VeylcA30_Ob~k_wRpPc%vKQ3;W7Bzi}g
z6S4PG8SNNloUgo-?kjN>mppbHB4i}d+QvN<-4=@1Q`^IDv4Q~Bd09pgJtV>sD%ZR=
zoMh;F(P!9=d?VYkh67dC@2!h^#Ha|eQ{9Zv=GCDWMmh{RiP=%(0v)GsW8;O{f%CTM
zV_gKmk05fbxZ5C$;@C&)IOO~j(2e7#Z{kL?zids63K{$3pdv4oYKAZz?4xi&wL+L~
zBP&sn;S4>-BH6aL^a=Q$XL91&Kxf7%PQM<c^sJCZ3=;*|p?`7)#rzQRrf89j+-_S-
z5fiV*FiOWS)pL;JL5(WKhUeJ-9saAJO1f;+G$_+#OG+n#0+;vR9ViN^cPNs<mF1nf
zZ_*x=OodWA-iumvc5*ex`8O8I=8AxpwB$jF)zrG*7s4+#={TcBhr;kCC2WzGjdD7z
zH>N0Yb6j5oz4`F+#_!Qad52bKmd&apRU^MQg9+IM2Cp2M@|aMfq^j`6M>BowL1H6&
zT)){CV_vtgi3ZKeY1;WDqFcJSe4~_XiHz`rnCCh(P+zMpJZC(!MC6ER0#K=0g>+Vm
zsXPZ5vJ6&=Tsd$x^}wS6U6`jeM8tR<0STW1q;!kVzJYkyD2$$tHhKX!a$qjZJCKT%
z&dZ4CSO-+}HXM#*-MK=x!vUCE8e|G2JF2uBu_XQ#GCoRH=Mp)<Y^0ZqePe9W9B&iH
zOw}$E>*e)ieHj!SKl{E|N-$^G9-j*Uts;zP*=>|z@P@c!MRL86nt($G24?}jxb1@4
zw@*vwGOJ$AINQeP-c79cG#~Ooo~Wv^f>*cbdCrQ9XTbPIUtWv(l-)CMhK^5sv23aa
z%beJjZ^%}r%Ik*DgZArjNMI-#JenSF5u#5}3*O^~;`luj>^^<frmL}p;9(c{+q%r$
z*2ev#jkD9UjFUwd3Ll!Avo*%EyOM7ji7A332do}D#Hj^QY=F{E+{}2HM(`v1<R<@{
z=~^L17eyJ5c7lw=3nSVK@DKNq{c5?oF#478(iF5AW8j>-0@Ti59y$avJyyd*(ib<b
zjNN)F_OZ&u{P?^3(50mOs86?uO+(tS=Ed5hYvwkwan*S}qEx!CIrU)Kb;&<?T2Z31
zAoh(QtH})>@-1(05PzdQFXQj}Oxb+j>ajEDf5~2p>$=?n7Hn>^?64rw@aBz3IC4>@
zA4>VYG$iEb0Uxtb-l(RHci8nL!lGu65!_F7hHpTv2Rx}Cwoz9qadvjDMU`U<_QF$U
zWisKZH92!1$SBtY@sX}sFvi^1)wF$Jk1#XJ>ZZv-rxV%vyJsV7P#MfR;f224ycc?B
z$YqO&QD+0Mp+A^7c?^}aPmS$r7IaY@=LZ|T)iv1b`uvrF&~Kfj1#2L#Fdf+qc!?-K
z-K!E|WA$^Aq=)d99rdYM?|aC3F56sC0FZU%$5VY+`VhcY9_!bMIaaMO>3D)-PhBVi
zavxfmDuVsz-J#dtfKUjidr894*i_5)KaaW(GZCBZ!Xax|O7MIRUq)9`DZ;=+JTd4U
z;-bO@R&lO3_>d)G@>CJGnV|yNCxsW6XA@dQ)Ns;MA}%=)n0+<s`mB*H4l<N~`%E>J
z)Ki)_zoZU2Hebgx-0OlcC7Bl|GLn6IcDTm18{;4p3l64cV@0G?)Q-_a3Z3GSWbu5I
zFnT}HQ`Z9L*dcX@_jV5COqJ7H@q^5{<81gS+NU%Hn>Nk_erVN?IE`b8D&)t0TeGAU
zF*-q!L?TT%b~Z`twnwR()DpS92Gb-x?w(nm`*IUnQGS6wyjgAVXg8O`lju=}^kj|@
zE@~B7T`LbcJvVPBREWW$q96-qnW|%3t`JZvEfS$Dc??5~2(M2?WbPgAc(BK+E9=wg
zHfOwr24=W5kl4XDhC7<R&KX7*vQn;*!aS<&SB_p+cN<_Y`T&5dGfw8;tB^pY9$Jci
zvK4b!<I4f6`T7VbLTP{U=O>{U2T$`Rb96~b2R`U=YQcXwqII9~NG}BM%s}mivqe0O
zJadJeKO@+dq;(xSRmYo*_Q~??DE)Cy7XS<e74a|XL7JSGax>HarXCEF(Y3`an_ck{
zluGisqr+hEFHhV!pL;fQkH!jxnF2iT+H4}2YzYjG;D_V~z|n&uG%SIgi<GQoia$Rq
zY@?Ej(Ow9#mxmhW@=Ow=s#}c3uQ&mkz8Az{y;KPP;35pZGB`>o1p%`8Y><LJ`7-#z
zW%8pw5Yq#w<}FoovZMpPkH+^B!T+EoeEdHB_X5HHd$fe#tM*<9=zgvVJbj@h%=vwJ
zKiE=Ua?HQ6CjM{G68=s=_czXk?}BrG6Z|J5%3p&2tOfjj>Gxal>q@}iSNyk1z@G&F
zr~~{V<%RF?M;YJ`T`wemzbpd$W5qug0sbWTM+x8$DKCP5Q~>_C^dk7bd$|6njQj;H
z;UBl+2Ybm+EB--R@{{0Sk|6xP;=gf~{I%k&9}Fcwq`drjKZr?wT>7Wp=0B|BXFihe
zg8#6J9|rx`ivLDS`1_yt&)h&S=Jw;*%<oJbKdtyjh3k*yy{!00dFu~d|5V%h$BO^|
z+cCkf%35vvIC<<#LSdg+bYJZD<v)ZM{uQ6%|4VZQN%@626M`)JDBUHvZ~)D$%F703
z_g|Qv@(u)k#$9KAM*rFl+COirT6+_L-`!!w7Wg*(eX-7TXzQYB42Tjz!)ueFCZT5E
zmDRo)O_~gTWp*Dvv;t6B_v{0(wv;HauMRgSMLL}gGr1O=HTB~fZU-*5EL!YF;KNz%
z+oFfitLQFNN{f&U>J>oG3T&d0!#c45tfjRy#EE=TZ<f*E+Rv7u(K7c&&7)OTGsQU&
zX~7OpQ!zUZ)96kuJ*7ue1pS0KXa$W4?=lG0T%8<*U0+xB0@=+C3&E-xS%>G%k+=1`
znl@;czh-E-I?;PyNVEd5siEayh4~1iI!^wTKgBj|{m}wc$A&TORq~r<uk@JP_=w7G
zL_WUQnEm*P=aC@_FHr_^u^E4i#PmDr_GIz;cj+~~%~61FT$|J?&wV_yz)UHEWBxNj
zAPNfN6`-0<<4I=;Pu6zsTVOP|k<TKZ;Scd1Kl9Q7MQ{mcPD(%vaUJA4>ahh@%^$>J
z%x-ni#~9S4xq`ocD`z$xZoyQPslLDlga;M!lAIiF?6M{xL_ZA<W+rKT%Ug|oTF4xK
zI|?!Qz$KD)Z4B669Y3rkt3KXi2D(4ZTu+KIn{a!?rgYCuB%}y#`l!Ag*m@~BB)l%4
zTEmvZnZ9J(0Bb6$Qwm&+e**LQ=GDgvpAiR|86}NQXoNsj!};PlO-gxo5FdY@{IxVf
zQ;Fl{ktH*6H)nlA$mve{VPiZhqCtY!>{;zRM-6IG`JgCM$EH>VXAdc1cvW0W9YdJO
zG)pu?+pZFRmGtZ@+TJVY2j=lxbz{rtkZkt;jZRt9@$A+y=*z8`c`~BHMZgUcw<&@Z
z7>6uvsv{i(rOLPv&JOI3)<F@A;`w1;(T-Qa&sE;U(d(mW%Y*Pj)tBa+=~z+bYm$F_
zSMa`;9g5*oj7!-h@@2^z2eY28ai@(n`o3l`%ZGc`bmFCWbUUB&HdB9<wB9wa3P&l?
zrjfv|4lt)<wnBGlIhJvWlzPFjTItgVK_5jAS+xt+F3(0O|BN?x;*zSpcTzNA>fb`2
zGYoxW-|$1^Y<a*KJ9_b%LaW{*T?XUUjwi{0=PWp#e^a^eY|0dBXn`ih*@0%TdxdQW
zE((h%>SvIsdgDDAxj%<hDkxy*8V;8pjX|DFPcT$;AQ&JEhiqFxNWX|!6(=(@f>wxn
zD`c$=>GJZ%c=aDO<rQZhxVxNQgqXM?OI|lAK0$UsHB5i_f&X+cM1MILL>)g6CC%{~
z8+ds$OPQ1g#x|U&7Sxy*1;oE_IoQ=2&Ag0<LWe!gGR#2YFSD@!PNH?Tr4$R*x4VfO
zt3dEFiuM;X51bcZnijerX`NT^W#Gld{p*ST1U~=QJkg7b`z71lzlf^l519X3ugSlN
z>igaJjXLgsgs7+l(*BE45qeeA2Vm)BPe*Ws+XIjst2`dT9Y{TYc_;iEEBtiV|JE?{
z7modtQ|T`p`){o9M^yKVm>2&SO>uw-6vB=+w8aZxneQIzKO%;HQW%y$+~%KMaev+G
z|BM;H)c?!EA?DS+fxc0!<RE0^<1Z~Pfpk;?PVOvZS_xfb);E=zuHTV;<Z;=Psxewg
z?ZCLNQ<c$Bh|CI#YDHk`n||;~f^MFB<uCNSXwem*NmMFNg9os*jin;z*fli{n^&)Z
zz@ink>{oow+Lfwwj<@r6ROTSdU3w<`pgYUIaXL0RbJ9bKf6#ql3>gFzgmvBXexu@Z
zlH1OsDpOP-zvRm7`X?{EQ#Q@xll4mGj<y47ALPTY8939q874{U=S(1IV()lN9}mCz
zg)c*~4e8+u$gWA$y>8*QlCJ|k(7J;=%Dj1GpBHmObfc{d@SiaLcAFj`;3r<M4d0QV
zBd%sFDP)g-ey@*G8Nf#H)y`?)qG7gDujirj+3(Z-WTtvRBh5?k&Z*~}(2|D!2SPfJ
zE)Px(UBY3S{a|+@nvZ(B*-QZ|oN#QA8S-9kxA%N|DjX*$9Q9!NPz!6O*49sO3E(+{
zE2khy$tIaV4*{-cb;xQ-9<~#{+#G`>B%MBkJdUm>cD8lMw($LSRSyKm9q$F_qSt{Y
zNyI7$T8}Z?v_PszKJHo~>ui<rUHB_)U=;Rex~oI_&718@eXbnU$m%8;@s4#IeLTd4
zm*haBEaYE1)a6qju##`ih)+isFS`zscgh$O&~$s9UDyrrF7CJtv4lQ5&a?Ji2SavP
z%<AKzSBU$&3zT&^I;9z%L$<*-FH$U)6@1B*fsMR{xIAf;$sPD!WM$%1>?Y^8%J)xF
z<JtiW?1%lY%Na?wuZkBmSR<`iH3)bqinsP)lGN{{8c*^&G9~<G*4?Nn#A(fuMI(Fi
zOa+kRCxh-a%bX>x=D%IU?m?0I!5goe%&gC0f0AR6iG0W1Uuj?t4blXATsqDvKR5OG
z6(K+C3t`3?UN6L>Tay#tJP^=+V<jI-&S~wN@XiJnw+`x>*mmx$Bup`7OJ~{s6%LdS
zLb5q+q37%2D2Q~}M9$QCdibt`ezK>}d0tF5sHp>^rRH-9_b$FyduvTgwc;vTur=jf
zg|PN%Fw$1Sqw;MO>PfXGl1^T^3zx<8!d5e{kP8p&F6CAUp(nXsMLZrIeX{ekH?33h
zKo4{(wJ?HUJzER)G_3+gIfSGZdY8DQO)VbLK5t@gl&aU<RWJd`Wj;Sx<{zM_LTJRD
zKw{tZV4h?GYvn9GJa_->-?zoazD~F>e_62bs>aD2F;!6OJ+ACLBK1U%ZATQ56q{3g
z-OWl4sQ4AN^H$L{;R55ka*?EaE2YnIB^${om~X@)OCe3MjoKq&c9$%YvXCkf#HXi+
z0<NZf>#b=b-amZ38Tw!CeFaz*-PZphrMp8qq)QqJr9nZYk?w8*B}HkFMj8pFL_oS*
zLP|=I6r?)@B)%CC<@J5<{hsfC@BQNbo#(7GJ7#`+?=@@3th3fWer?1CGm!Qq+!RRd
zRQ1D%E@EGK3X~9j{|0Z!>l<ojaW^!S$Z61fu1TFRn9w|n?JXIKaIJe@!@RB(ixh8<
zzhfBXr}|lZ+p;iIOR}JQ(oC_B5DDj5n$#}NO1L+Lh|<?7=Hx76KG&zZTd*{WzFN_i
zp4``DDhGF`N2prc>={o_DfHh++~zk&qHHdc_B<iCt5#Kfqyw$8E)(Hq&D+<&Yof#8
z9xo|D!VD2<4%#%Syz2-bbKLj<hp~n<=|xbA$JoYTppriCK}j2rZ?n3kYtIdeEhbq5
z<GuikK2ro8-mS@3R1>q3(Nzp_d-oC->Po^|mfLpkVHNVQDY;yoWni-!YC}n2!khJB
zG2uJuRL`1oS&nR%e3zY<t8#;&Q|IlgCBBT(4?&MVU+EC=mwF{2VcganLWbc<`H<H4
z@jNSo7xp#Da1VH|0Nxph^^xL9S0jPuZEfzg#SCk|41Qt4R{?lxL!G+Vg}~s<bV+*%
z9k&(1@-3{8=D;eGaqWfe()?Qy2%H}3w;uK<NKf9vqMW@|e0Ma4#r1Q8oH;DhUicZq
z;!h%AH@b}5HmW&<91F5R*87z4+mRm^HEBSoz*iEXKd+6USCeufe9&XT0=18P3L~gr
zTSv)8gqzp<)g8mJ8QfX9drQ`8I%O_<^okq+KzK=q1N{OAUTjQoDY7L0RgyQ5iFM(n
z3|5Uv0hN5f3rGcCgU<gl1s4CaJp_soNCGr{T+**Z|MagLpcd+~mooGV>`f^wM+h3Y
zg*Sk6uAThZa3JYiJNXCbzp0(P?4tZD^!e-KAE3|mlRu%a{>97U4y+@Ddm&2%!T_=t
zDB#C#%fA+IuBZG%0q1XrKMMfZ@;CLAz#RT#Pvu{rziTQlLx0y){)7fR=l`?|BUtwY
zr(g&?;6VVc5q_vEf8g_;vHiOS{tx))yCwg`=lP382<ohG1>`NAr4bzGn=d$XA{>?k
z<JVT1qgd?>2abaLj2fw}>VX3AFRf0_OAbCz_M)CXFZ*1l`G>O4-z0yQ{r7a5K(l`6
zG=FW@xpw{!(C6;@e}Mj*?~=g7{-MtN0sUQ2nH?ohA&|?^|0aFl@9F6O_e%fY)6@Th
zf)7Cd&E3HP=pUN;UnJ!Fy8E((d|!9}LHPdkW(mHso{y5h<@$#@1JvyNgJV0_FJn6l
z+_Lk0xgXRWhxm>}pPL0<=%G_*+7lLep%S^XlA$z#VrhSj_<yxq3v`3^bSMN;1Wt~?
zT=nY|Kz^<u{EIzVkiU>aAPyqHZU4eKf%(sK0t|%GaAuBxZ!Yd?=z+e-qw)SLW?XC^
z(t?7)ZTo?d`qw$acus|C3dq8|_?;G%Xw*jwii<$vkC)Xy$n@{-0?Z8m$CpCD=GhhB
z7jUOXzGoeSNJ*ob5N=w)j-CPnAx|*!_kp1Omv?M|km#->ZY1lS+z6}WsFx2EpUH8{
zyB}CmQ~7)|b<%)u|Cy(!5%D_Djlo9ryo5K`3nyRTrE*PnFujAC+ivz095*Z9oI|wK
zX5)hry8`p+xmYmfIHv<lCiiNDu}7SqtLaM`)hod)1ZGw%p7YSoHubKIcjt(jD`Ia7
z@Vjm*dJ{zz_aEUhWs=NtNsu@8X&T}76SXVaDyY@Q62uH@j-s|5=@A@m6;OGNqd*oj
z`ME-uqGjtI>Y=~&(ODZUM!QpnsMe1JgQ$WSRoT%&!(stVAi%RNGOvyt8hqWw>iQAV
zK1#yyW@cA@IR<yf=_YaW!v~y+nzoN0FXuyPU0bjc#-vcSSGHJPoEe&y;4zH<U~t6)
zLLa678ZwYB<wbzfJ53$I6h*6~C8uNvjr>YVc*s?_qdighNZ?J~5t9bjqya&P0evEo
zKkFBEhv^*SewI1dbguQv)DIja)IPU3j0G3%HhGe=NZ!+Hp|f-%-KI8Gx_cvLgm1)H
zmmO7n4_P4zy4_)k(cd5{1N%)5?T#9$%H1n=L*EXJp84sy^C=7kn9Fu)4u(>E5((jW
zjUfdcccoN|HBg77cl-kp!Y4R^7Qtns=j4QMs;JFXOK5gmBa}Z_mhy(ZfxOcR>K@p3
z{#x8dV*0jK+P065)rYU}2xU^~8aC0b+0B@!DJWmATOQdK5AxpYWS1HV;NcTHA}o-7
zkux_ID6Z5z*7l{8C)yEf&rN{JstV};Y92irQAz>L$}<!1P<`-5?XDrbAe(ejrfuNh
z+l?KcB6+o>3CH@j1@z2v`7a3gPcpy4*xpWhW_Lg$;Z0ojEVP+@BYyQ+J6_pHXHUGy
z6hp{kUADd3Z~ElO^`-Sb?8;xbVt4WgZ6CH)I>MAzb4Rb_5;<85n4KJ}6fEt^W*gSI
z2AQehwuax!{nSQy(gj%_67L^=_3EB)ZyvP3=<Yj(^%DC|)QxKW)GH8T%>cNUS3*at
z4MAV**9CX5OB*#kw<Nm7LTZUo(c^M+;$M<?57wJmFosWEI}M$gpGyhs5h3L~k^3l5
znl8L<M#jf0&9VH@5^gVNrJ()eaZ1UA$!dQ*ju$^u3ZLp~_V%p=G;FKnOq)E+m}iG<
ziyvA=^YX}*NaefxDlwxBtW29{R=<TOBV!ktMn~qpf?77`@mS<~wyDK9ad!+cAZ0^>
z>q=qPmh7V=mgueOijjoR<Ii(29BD*$cgB<VWFuhmnc9o+qj;_jIdk~jctoHHC3wut
zU@FhLDet464aFGI=0Zgx@1gElV}-Z;kpOp32rmXjC+4{`Y~1pi8@xUPDUYSB_w!G(
zQF|6xSs%yL_~9c~6hL2Wj^gjW*u60eF=xKmi*8xW0ez`luTgsRkla&!u?eX)C-xX@
zg%Aoyzf@ro(S;odMTp(b?Me~URi`n=eHS#+SM$uogyw^-1}D9{MDR)|^w);%_@{Gf
zZDYrgZI+m@+ABdQx)>*$Hm7A!FBRC+ZG0un4}9~tN^$nFQ70z^?2U-H`<VA$9NF$t
z&y@^a9nvv#+<z&ybmwCL;~U*J3v^BNn^ybkxY!m1LB%+=4+geSQzCQ(tLds9p&N%b
z;4_*fFWAh}j-g^Di$GwJ!t#`;E4Awd=3(2>b~oA-F`wss6FFfkwc1c$oW@~@s_i*=
zjFb0Z*+DTw@?D)lMa)(YjQTLL!UW&cfp`j*VF~lBeTM)+UoX0)i?56->nT>{Yfe4p
zfMVv9*WBT4x>|Q2`L!KN!{eXoiy#t=FVhqe+Mu~lTe?@q)dw(=&_abPcF@WZWIpS9
zs}Us#uFTN8)doxS-_4);(s|&Ew?=ha$&Lb!u^_6l8%~C~XaCXrEE`y=oJ^1D+7Al|
z<Kd-oX|ItU4di@f#^CLB-b>l;=9i{{QDdCBPCQPZL8rn+^p=ikiK$65CZ3=tZGq5f
z30q}?xhiF4#fJ>>z0@khW56dHEj@zj2*Ud+73sjp-<%I$WMdThi{PljL5y@?mSOtQ
zBzHp?9hQQI8ce`s{vD~XA-Lgoiu(1QCvwo`yUfK?nH&1lxNcl2<;<(4eN+BT2?G{T
ztXW-zKJB#+sAs3NS5PZ5M`WOev-vCW(`Y${c|<w|HN%@=rcxRetz@k>NQx{6E0>?%
zHI$KgK=o35OsPyqio5V5e2uU<#==roXLRT?ws+64jM^mGfnRxz(rvnzSGX$kKIJ)5
zehTzhP7l*eH_hgtNlBPsa}f@D3bTP)G%Fc*64&QnLxNsUI3x?>;OR`HxWQC1;#KxF
znXzug#--LR2nVSUbDcnyV-}U9Fi%|p^vy1`UGW%7xFh*K=Y9_Fci-%8jaodaGvDm?
zfdlfsVrTELpT61gIKkscGdD=8_;e?)Yrk|#ryoC*bECCodbwBm7`nRc3B;X{m%CQr
zkvma5r`U^Fn^=Dt7p!8kVuo1O*IXjeT#phS*BW?mYD}%_adBMxkWS|^A3vZ|b(+!Q
zrlK&=WV+(%m4ms^9NFTqOG#gI$By*+7^AIK{g@ikiSLZS)cCB4c=%48%Tz7;v}u1!
z<*kno_-S9EXR{tVZj+qSpoz0h7>I6B;FKb2$!R7m@^o{{5eG8gr(SQcnKKop<k^(5
z_vb;hjr^?NxcRJf#Nb0uKT1AYO+A8e6oaEzu2jjL>Kzpq-&(!HYmq~w6}c2FCH?d6
z)3)yytw#f8xPzlL+`p_ht`{=d>EBMY&@v$R=3`yUN>Eo7G^T!`>s~LkR(q6>VHX39
zMqa;hJvv_90$#4MYnAz)<?FUY7=IQwNC87=A#$@}x`$!Y6+3f0JO^<lNGx7J@ZKxb
zP+jy>2WK=HKPs$=;QLOc^7k-c`79e$SiEkk-)tu#AuX!QC@?C05z*4*kl_J~cN~vp
zu}x&NdgL#rk{4se5J>BhQBtnKXDJBZ<cf~B%!D$vZZt03ce-1Y1EXad>O;$k9V&yj
zmMRJJ96k;U$J+sing<FoC7vW&^IIqF93=Cp)kx^U8z~6?hhWq$?y%A>RX1iAl*9LJ
z_tl27!;IKV=nMS5#7t>Tpg+&v`t~7mr<f02XJ*2@q?K*7o7#+9M55=dN>10VS+m=M
z!-Lk5ZB0bEo3x^M*$J~Zt8Bkb&#qWtlN%f@FYHk##X%q`RD(rLM}RT)ZI7<t=b>b%
z8;D`XYMp|r$1Np{?U*?slr1P+@y1FA)r#i2h!64)+LITCoq`8f4O-v&o9{#L1^2(v
zkT^Duvu~0PcIi6bj-aI?+wZ(>l~%MA$$Q6$9y+JCTypm7fl0$_bG>>8xE<LO&DWCt
z%L#R4eP*v>sB3h05#G2fy}t9f6v2|(bYdsWkiiDi+J-ZnRB{*6N^}@fE<P_#j?Ytw
zCqSc5V`X>Lxlb}<0gD$p1eNWy61|-hjKY*+ta}|xhr#ZpSZ>V_z0wWhshj%>DW)#D
z9znjtx0b^7YmO#A@7jAIz3|C^AMxL!Q}ir&qQ(8S1hbDcFoko+tncO_JFW%%)*1yW
zVSr41rdEiE;fXP+bA@)W%RNs0s6O*A+q@H#?hng_m&a9!@nQITg?QB7bOZ;>DK}2^
z-~OnhjYEwMdmK@kr(%eNiSRLb052J;9t+wOG3SJ)?j5)5IM!S7+xLXzuJ@uTcX+5o
z<KeGAu{?dTSITn5#MF;Dv!^f2V)8Xwb240JrnT@s-WMc!Pe><z?``Z<gHza2DYcRt
zbPhK6A2zQtu11(RWm(<Tu}E66Y<!vXoINm?_RVyrV~!s3Cs;H(7M<rNDo>W`<VNo{
zO3)34cF+uU@uhenKJ_pvs+svn5PHk^W(NvKkL!Z;!dH6Z3?`bQdmOQ+N*j;;O^HS1
zzAzs$K9Z`hecZr3oKJKbOIx7`;mCr?9;y${pI>c<M|?7dXS>+utQ`>I4_Bz8?GuN&
zhVzJ)QJZ?du*Z7-S^ax@h0f}9y0woy473F_l#^KmUm0+nikb}?pc<0`%Ag)8K#1f-
zJlNa%XWFkE2xl>;D6KhbWpAa29^Qp}kya%3G3kA??YobGe#|8al%=clALFk@nj<ap
zl6<ll7EO6TuqNgD;XXpeu!n8q%^-$#>Ku>pLGmLcMfiTFrbsO&#FLOp90J#TOF0Gg
zmucL@H&FHoMO`^@tTt!RdzEJ2=NY>`VAp9UP;f)_W23CFEz?XF$yXQSTxUZ~;a4?q
zcxAVn@lAgs-eSdYw1t|KS3@K6j^Wg^(j(3*xR2SwIWR*KkUECm2RMA1bUPVE#>33e
zuZ9oveZjUSfMp@2;04vkp-NlV<QHjCJY8_A#j3}U7qBqxB2UhYWJt~~ufy67gBLEi
zo&}rQWGQ_;be;bBf~uG*t9wCKu~d^7yMnJlHYH4mcV=^asX6A5e4Qvkvv1mQOxuFC
z$-b_BXm9DRw)8f?<7367cB&x56h@V&-}Jk5w@f~pLEcG6j$0P&>cuJS6Et-_HZw#w
z`shMT>$pG1{0U~9pz(7_V6o>{eX+MU#tK|w4)c}+GxD?b3mR!;mMi-0uF8Glz=zZ(
z=szu7?<j;v+zuW?Pi2~r3_(xz(S6}6FbGq*;Qo^NmYPIETQC2~=f}?FYoQ62vMT#*
z;&)_W4pw$_3vPTc#fcr?g8EEN6e~m^jA-M<@!|vdC;g+6nDjfX@>Q*!@6@JP)$1zz
zomm^AxbAny#w!k383v^IP=s`IP$MHXr3l)6-Ty?nvd}!{T_@Bgd4$iHr9wpg5#!zz
zw;z&sKKF<Elbb!-WcnkTb^Z`IXPn!wV;6^1Ca^vbW_yisqHTK5X3~xD*3yq&Wu_+e
zz}LVz<sq(U3~nideOP>Zr-KuFGg`=A5u5A9d*!t&dkYUSmZnx3?CcpyF$W|0C1%B4
zod~LELNHz;6Bna*j0|XbMG%tGoNQcQ!Lv8%pn((44;?N&nh<I%U>HPWoFx=s7;tk4
zdSFm>^_}YT(BvuZKDU-b6$PxmA+&n3n)qyT|DC4i%gzA?>rPtcQABYPP^n9fZ92Uy
zPrEGAUhKw;J}r7|ag2<2wg07k#$Mibhd6X;Hep&Nv98+WL$i5^eQa1ywQ4|_5}k%Y
z-4g}vBqyz^4z}=qnqYjLAqyjz?HaTOQ@4unTZO<okzv)qwAS!&u)Yd|yal;v#nMQy
z!nzaoo8o$jsqFQol&jZVo-Qe&wN!|kkL`@2_d}#!4dc(B8mJ^kj(#c^U)6VvE0EDA
zGca`)UoxlNK2TSJpRPjkAGCUKpTcpR*`v<A#uMEUbrm}>m6-!GrMfb$?YhFj%YEU%
zn90^cVcT8yZ4^jh&CMJ)XA$@u_<^44m)$|RhF4amox_*L$cRbGdc%~r^?l6KS4EfZ
z)7Ye4VNI5`JN%%GOvv<Ae_<5IMz6vr@Gc*}qop-{`A(#W;P_OlY$e7B`s>D2Gzcc%
zw*(Og6!6y_Bv%^hPE;t&J?kj4Wg$iq6xCOe3f$Y@?LUwyt&je!B&kiP!tBLknceNR
z(QAqQ-a`?d@ZHEb%#s$b(!J$Ki_%mkW$zJnBC_#f1{Rperc?@)SsF9bfmdAcwf+N5
z`~{pH^TQi*?;4z+-jF587%v%gt8mfgGWh72D~?Vb4LAGPvA2HJIk8_z(QV0Sq8@NX
zj99>TRGh0;j+F}9NX9^{q1B40`dp?hUcD5F7<ll)dhK<PXIn(|Bf1_ZC=&{VEE-<K
za>$vZdmsg`SZ%!#ng4T*m^bVU5ox`L;}eUw5Bmm#6F>67ed&ps&(7RK67Yi3|6qv3
z^EiYl8t*MN1Yc=aw>wof(EA_;mWBTdnMcY#f^lw?IU%7UC7I@f{cly#ii`dxL0M#X
zguBS`pQ;(T_%+ub7w=pr?WIyh&l5(ERI)o%;hhyL_vNm+Q)_DFh|~EbyRw%M`ekQ~
zr7kS5m`RYFbgu+5J=ZLrdcIVXzACSGdFX5G+d?UF+pR%SNVco-X$r_sY&Kkg#m75s
z=%pQxxLRx;i>KEzaVfHE0v|FmVYj!Z?ndNQlHgOAZLvJ-*C^Avu6LVi2yk?N<mmX2
zLS*gvYU?6fga47v91&G-jFF$j8x0C`BYlyEG{iO&!+Jd`SWD<Ugjw%r%J221w}^Ez
zr$4QG*cBX-#VB}b-xar8GVv}=QI=Dft-tEv%n)5Xwr^K-LpzbA(FgCf`s=loJ9O6C
zv4NOzz0#+Y0_y^1VOEZQnB@<1>Zzn0P*0bquijRH8%ekYna?msy(&bPqFTt5RQ8Ti
z?3r<RrXEbX$YNCYmVOhmZ^#MdjV4dIjM4p^WXdb^1~jCzW|MarI=UCz;H>*%l$~mY
z)Ydo#4(B^z-i;FC1)F+pPfeCd-l7(Ie{IqdV>cUmpeJyR{6PbsQSg2CB&F@iQ`5~k
z+o3vNJ6M50`z0;B8_*x-`Kn^eFeyYmc9`!O9Op{yY?t<{HK^d&`RuV;-OTw4E#p~W
z@oHGr>?=X^eVyZ`2n*FL_qy%aVOA!)$P=XX&R0e5Lfpf&3&CM&I#5Yoxu%jIuw`x-
zsH#`PHp;rdyN#w*K$xnMl?G~9ExC2E50ws{sW;e*rJ;O2ZQm)ur4V;L#lFer?MpvV
z2&>o~9Mk9_(#P|pQ_S^CO-rNf<Eug0OC@+Kfg>UoC%6?ZoJRA;&HUu|SrMA;te;wJ
z7Ls12UJ=<7)}o3rvJ`j96rw8kgjY*4+SJd0d4ayPLO=ZxhD@3kA?uK2$+#911t(F8
zD&>j7;|2_6@~R<*XB+DLtht3k#jyRmETu#ObVx^O7P=EN!Eu_SMHPpN7!3$ou3GH;
zi}cMdcJB++mP|CR)r!>PSI~qh;prf>j^o$m<hDhmNL0c&JCarM2I=4!ppl1aOd|*)
zv?Fu^n?tItxbr)%a~QSB(7c&{BhG;MVxXkSRP3FErezhM>-c&s4pw7MJ@KsYtIo3f
z_hn0;y`UHh%wQYO%fwak;y%a?-YDNUuWjAdRqV}yao7HEokeZvNTT!`6{~gsoP!T!
zEcsJaWZx}EZ{m$v=7eVm?yCZX_d^4fhu=SSjjF(3Thk!=U<R$3+eTT}6D`@LgIk%;
zT5wW<wOy~U|N6;x&(Tych9Yzg??La0JCTb*-3hFyF{uLNuqPWabBbPD+KMpjk-pF~
zrTb<0Q=9QEqw|hxY$%*C)PX8Ye0#Y&1f|KlU!~tYkm4FP+@CyTe=dQdV26oFZ6oBL
z{g!_WG4?G5Ow%A`ilx_dGRclaBxfp7cYu!;UD7H_krX3ZM`d+C#?&&B|CdL*eYTb<
z<iMWRv5&$rY6Kgf!eS~T-sN|zsVX}RMe|yoW*|8xvUEof*%&uvn%|y&nbJjFELe*c
z@<}Z%S+=fiTlr+{>iZ(MWX!s04*^ZoLM%0AX`KE{))!^+9u<?;VYY?%yaO0dgPjs$
zJ`P<iT8CD(lzm^FMkt3YDP1NUC}QYsjkj4{6BzCAjWAi%XCjJOjb)pOWe$~AXz*(_
zU$zYEI{f@<$6)}%fk)!q_f&*3n3TiCGd{SKY&zN3tPU$RIVQevC&3-NHh-*{%9LP@
zH}r7d7FC2FGf;bIdtX0TxE~K8TGX}r^u>sXXk0?7Yst0Ir*+;6F}SeXO%;f>V_n@d
zQPLhaUn+ztQpV8VOpKXkf0O(4^Ou@IGbJ^Gq!1<ldz`N78!+?~>4A{12t+wy?ve?6
zyt~jeEhNU(Se_BLMdNVjVsEnhR>x4A*mMfOPb}C;S~F$ycjz0GUTYOW@Wisi_5A=}
z9gcovWzF1zM!>-v5UHxGfS_mLd40<u3gJFfV3VEKCr9=Gbq!<zF*NLHA`JL2oQZnD
zVR2D5*al^v?183wm2yRR=Ez$9Se$3gM;qhy0%h1br^;wfn2eO&FHCs#J<Q0myHL4S
z>V@*DXit3>s%8V;LA1(LY_GuHE-9kVCZdL<xMKUXDcyX8{$_S0@+!2s3YHjsaDI0W
zs}Qp+FUdIhHY{iKrp+}g=Pm5wE&f#7c+H*etSvoV$dQbH?dT@Lds$^f&EUQYk!4fG
z0ULPX(;IiBiN{bUKW7h5usSXj`={(DeR52^zTc|oxYKMq71LQD+7n4lUNOpVuV|N3
zUdrd6<H|LJ{*cs4_d1?}O)-n`bu#Ny#wtDxZF2*pdd4s#IH$#_xR*)|H%Hpi7r0Cm
z#hz>4A}z_mdIY<|lhak)lYXoZKRi{&TJzvNAskIh8ZoY=K$;7@*Qz&lgwe?M$_6)C
z#!9cDqXr|Ia)r<o?%gdE{1<a=Z8Ud~JqrgU(yHd!$N0^*_MTvQ4dFa&4G>L>$2(23
z;C6g9ksM?q&%P5m8!(RC((GR|B6vsI%xs|Gz^rbJnb<K7x5Vr?sT+=vqVIGsPIB#G
zDhB<XNG15S71p7(ibLWu(^nRo-Iygt&hb?`A+&f01bsnyI|Z-{Z%s1hSM<g1izVUb
zr5>?9_8NW{!v2U)s!=y64421^7;0BWM*tZ!eLXj<#Fp`@V5yOnVwF2i3-XfAm+MsY
ztK!2@Cq#0^=)#GoEdtx=u|8L)N2!Y^XV$N2A+<k2;38R}%!q!?;89QWIgt(u|2XVs
z8VX@J<)_By^tkxO&?vWs!m0V4urm~g^$ksQsXR^1KY8}Tsj)X#_`d4#&u9F?htOg+
zj&NXx#PV4+tKYI&M*sQpr#o}UJE<_R`ILiK+~)`cWJ#qUTiRByv2|W2VSbKcPjOp3
zl4^gE2pOx9oz=z=t%9m(xGUjvEhCg8FGLcBH9dF7@p!f<HvKy1*0YaOt<7Iuo_%`L
zcUXC=yU`^;C!7H~(XXZK+X>H?XKos9PoA9cJ>71>p->j^lJS0*sc-6gmlr0%DVJ!_
zFR&+GaZSgEE;q*j<{5KR>{OPHU8sGQ^B0MrZrzVhuit#uS*E5b8FBh`7Ri$%K=4-B
zzG#;PmvyloN}%@?%U$wXuC%TRWo}#TiKMZ1(b4|6-gM}DT8gk4YO2h~{eG5XtW|++
z%|>LhGWOVmMx?x<yBCiA^*M0UGTI+N$NoA&z_I^MEe`0|FT~NbZ}`3}L4?tGIzm!6
zjHR%RLS*=~Y0vduX^T&O_o($rtK{O_-AC7{@4pWeu_X(JP#?oW4p_*`>0{zmGvPm5
z$|tD!X^AzhGx}@?chM^&sHMgC(Rt#0rht~Os=|0-H!I0<LL6(Nc#kQt12G3>lW58H
z1@t>7v5t-cu8GY1UowvD2Kt2)k>*TD32CTFvpVvfEAG#_J#B0Y*baA)YTau;-nAcd
zk`{|z4eVVa=o=+{a)1<CE%Q`mwmHO$et&Te-g8jL5XHiR<w+@W`I4jM6;VxO%Z+h?
z#3y^ZG$fP6{3YzM`On+ixoXz##(8qF&)ji}kxSZYXw456!h*WzMc;o-JVfycQMY{q
zrRCFzkwMBmT}Bjb<71V2+8f2O{cun2uT^nWlx($2g(?kbD8pw*dvaJvO<Xme7L!L>
zYcI0AXVSjD#_iOVLes8p+a(<u&l^-rLDvfV<amo$BtmRdGD0KQDUU8~Atp5S)#657
zL}IybS1iM334RUKn|E$5!qDC<i`TW;O+Lk)<ZX~IJdZ-Cv7#aCU%U3PC@R;ne`}rI
zcO!l9vp|$JD#xUtw5a^>*49l+bCFeJoSm#dZ}HLgH_!|wH}zIs90DlNk@x%#r<G%q
z1*S*zDr=ilG16_{P-!{w=WY7u$lvt~^IL@EpsCNo;aQ7FUSF}4Pw6Z_pqH2DH5(A#
zs#ad|Rh{WmMP;-jEP7)+;vVrJ($rPV<W%LpBtC-nI^<JlPyzL{rU6Oc9Dl;h7s8a(
zucb=8pGz4nm|Y%eyA8O1Jo-FwAl0>tvgO#?XCojWxTSqBm(iB6OY}5@dVVmL_47<{
zhKls@F8Ol2brwXu?&fY`8Y`YzH1gUiW-}`Eggf_vDYPElR=-|tpx-7^eqrS8eNOhP
z*a}|)w8-EQ76eoC-x;!6Q7?7g!|<}25xH)Jr(nr4<O=LOaGP=>mVG1{mHD~oz+<d=
z<Tb*pjTI(kn~h=<1}KB=xtw{f;gcscEy9s6VPWN%XKJRBV_Ton_`dqQsrWn}eUL>0
zMf#R<s(lU~dU<?ecXU^9?8EnvwMP~%v!~fL=v7bmr`>z+b7ut?m?6TMcRt{&j`9x1
zuld^2f+O@6O`Rw%@I;@vO_NPKuNz;@ADvzBGy1n{Fa6R6IXm_hP;lqS5*QsGj+l*_
z4Ax8gJjqN(e+^Gtj^)cfSGyE;pH(8p`1F(^__V$#DN$v}&ttLyQoOQ|t^9a#h?~Vq
za7F^z44SH57G;^VU?XOa^ATFAaIrC8%9gcJ7Xp2}LAeu}?m7oz8J^>}kGL(>MXBwY
zyjmd}#%%KZ;tH{il&}aK6FW~H94jl5FNIk&Znelaw88H~`x(q7k|jp+Or?4th3jW)
zD!gk9IAu&PiFWY}!G4sCq4<g_lS+tIAtpul=@pZ$+|6e;IGX$%kZZe1F|;`HL4JnC
zEF*q<@5_;^O>JMp?(roadzC?H>*t$=^p$QTraXMrIZk1#)Ze^IzPe&3lP(BF^#-X#
zP&S;pj!BR|p6ULap+@mWsN}sHk*QhPywc)nx3*`bk5Z>J>VzLcVSdSQfMUm)z!yLX
zQ59mipUpvmU5RGMV{%wlX+K4N!q9ei=CfUpb|@v3m*aL`yIU`>?P4CY6XF)4wAhYP
zxPa%Lwb+exiY)=hIysZfBb+-7?W%WM_g$HcYMO~yJ=io??M>+lM)Z~LRS>ju!q~D~
z4WT|l=go?V{vtzIe|MWDf$V^n7k%G+j$sSd9V*4IL^p;1MoP43zoW@1G~ynqO8{*X
z^ArR|&CHOAa@0xr*6d+SK(T_9M)mYPFJ5DazB&`8O^eZqi<Y%QR|%d9mccy1D!->(
z_=6G2w_kZ0H@1c*e8LFAcQGo-WU=4#wQi?gr_|q)C+n@L)@W`y$n=m#Z}oye7Cmgw
zj1*uX5S}!^GDROSeX$ffJqdmTfoB-)hLWbhf8w}!S6ss9c~t`|e$RH{VdOYW`M`ib
z=XB9{xwYlwaQ_EPF2qkf$Pjb*JBQ+@xIGHERtD?>S-!rbQfzB{r2Url8j}1A_Xe1J
z;;usyK$D+KI~$DS3{3`WI3FsXl13yVjR2YW7Zx)x5Q0b73>7D`GzC2BB7u7Tw3`8{
z`@@_6%*^aBH#Wco&CD>Fr~r$@vm2lz&M_{#gM&`xzqo@R0ZD()bP|C5VLAySLr0!t
z{DYesU|uqtyaf9PAMo?q{$Wdl^V<Hk*)VAT1;GCNbEBWN{qcA4AR|GHA11?>dHc6F
zGyq_KZfI}`_OiF>_u5|G&HzmMd!~{A><>fXGbWrrvhNmR=T-Y&+siJb-(mlzkudPc
zei#W~g8jo*2IsZ?!&U}h65zl8Cj()~Pyc<rGZz0e|GJERc|U{mO8=MkGdL69i){fw
z7XN=BzN)7uydoeEfzQ=vXT1Gqi0@y){u9La&gn7WyLeVvh-|}oZU0}z7hg2t<lJ%$
zN6z!R=@<+I>H~lZj8CNIpxyKj`M<_zWn<$EWLa7{n;HV=O9#vX1j4ljfr5aapT{2}
zK=Lnfk)K!oRR|gap*L`}*RudJwHz(Z$^>fj<M@sSl>6)POF4g4F0?67oyM~psf`RB
z9e@ytk)?y<cPIb>#09EwhDA4KWoV`c<dIt${@QjyfJe*_<>s?H*-Z^C?Z0C{oXrf4
zF2dmXrj5N3xx5j8M6Lp4*#Jp&K2~Pd#y|ka+3F1Va@DB~&+|wO?Tx@|zyr)jak950
zKM&K1IXLQD0^uA7M~91t0$vlZ=WM_ZoWbKV=2-%?DbRE&AP5q8{OOAG<V)WKr+_R#
zxB%3fP=>&NUr$4bO@MRWN0CH<Isf`wprF74Ur<wmx~*zZ5O~w&&z+R@z&#kYdJf=3
z3YGy9frH~2yMZ7W2Y7d0JUP$;@G*b~fOMAl4LI!rk6|5{zjY0M+y}#Oo)0{pv+Ll#
zpD*HI6uciS4*dF^f6j;B{5j_XC>Fov<9Gk~JwBZGgWu!hIUjzHkLP><zW@8iM}X-U
zeE%(n=X}55$A9Z{_`Od0y-xhz?|;kRc|ZK!|IYdFd!2O7hu`zlIUoLVd;k(J(tq>m
z@9^pOJo|f|1;hi?KV0+|F#1~`@mnA9d!9Y-2fyc!b3Xi!j1Rx(ad0R;8;t(l<?!s+
zG@!9(?Ii(ze=z_X4jf<tK;UmP;2Gr|kmm+woF8C1LNE}<0|@&PkpN-b-|-8cAUp<m
zLI0f}w7{7IAYy<(9)Se%i{N+sf*%MK0590S^8-Aa00i=n0w6Mg=m4Sw=<4@4$iM%|
zIJkcv)%Uu9^}F`3tq+_ofX@RE*nUu!Kmz6Jf<OOkeh>j7S_$B50R;ASP*%bI4wi%e
zJ^XJNhXcn!7Jxw6BLs*KAn-YZeHUyyNFdKI!v8Mue^vYL0lWfb7332uKp^ixeu8p*
z!NY$?eqOZgKa&q|T)WhMBpD#i4-hFppd5m6aC`w}5u7)`Yr!zsCqV-7!8i^;7xT)0
zB@Xm+8qh|=3Lvmd5bys&zXoOK?~Fq*i9kL$CWCFE0SFu`L74-2^M9dz;B&a#K2R1e
z$A8WLXUD<0^@3Lbzu>fiI5;nZyaR1;L;(Wj<zgKOmVfE`A94v^57IAqf6Bj*>x=6J
zfP26(=_3B0;s0;X=LOmV@(pYcH9)`C7qr*{+Y9z#@S0!q|Is+WU-A?H*BBu1xn1xU
z#Jwo<qTGLF9PGFMO8j5jbJ0#52S)?Y><&Dj00L!M-^tKX4>)UB8J(ShAcW$w86RN!
uXRl{#3tHLzIGD}OZ3Stv9c@5BQbWD7n?X>BoSw7sbzT+@HWqd^w*LSHVcNa`

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/packages/autograd/_static/autograd_gradient.png b/docs/python_docs/python/tutorials/packages/autograd/_static/autograd_gradient.png
new file mode 100644
index 0000000000000000000000000000000000000000..793fef3658dc857d7058d93f96ec7721b323bab9
GIT binary patch
literal 39559
zcmeFZWmwc}*FFp*NVkfJz(}Knf^?07BCUvYr_$Xu;wD5vKsuG~l1`-=x`qa67`mJP
zHG1p5pW}Exy`S$7+s)p`k1JN5>s;$H@VSB%5&kWF3=9k+>1UEk7#LTIFfbstxL3jd
z`CP-R4*rE{uO#&tBftCh3iuB^n`i3w7#Nh~=zlOxllsB0z%ZmGA1S+Fu1(^^D-Vqx
zZ7*3B6k<C?X}fXZD=90l>9tOg8_KSwFVV>lDNoOPzI^zgMa}SQQ4_iOTJl#Pd|Z4P
zYlKoHN8!i3!^?Q}vd#(NLeItSv+gBdVf&>>`+C!f1k-N89<nb`9}EcYfBi8-hMAL@
z$;$NLfo+A<sw^)V3<DF3^zR>E)u5b$g2!7UBO}KzEE@6{{_n55NJ3i}_ETg3dqwmY
zxR2PaA^)`udchuC(k8d`tW4Jbv%e3<BlgYz-tm8*{!aq`lfeHZ@IMLsPXhmw!2cxh
z|62l6l&@bG8{2<eKd1glj9%q89(l}6N?Cl*ULT7T_ipdtAmezMW%wNlKdi}C0<P#}
z$8FlsLqwOjrr5;2uyAPc87qQdC?oh^e520_Q?$6rhyUua-e|G$PRG51XP%z584t{2
zCA+mu5Cwc=+y!!}*)NO?KR$cGQCpN~rFJ*u59E6H+y~0{?nvtvCO>?2)l9v@=8Lk5
zO2=$-(4^CLXW5>BEOUl%&S~A%Z`b|{JXO)dNXM`920u!s#J<6<1}!Zut-I%Ow%w|v
zlW%A%sukRBqcTOvk^Wz(uJ=0T+mZZCTS7=JW1;<$!tvgKqGr$3$O!B1QjfLgMl(*U
zk-$=YtYGo~_eN1zQ(mW*tue>~U9LDjOFN6h4!;yFHv?7GpwEMS)Tju))WknBfeGM|
zHu=3Ye;$>2<LGR+CoMCxZAfpb#?^rPhI^4wt0woIA@yC7hvpBk|HXOmZhX=vCzTK9
zWXsr?;r|>>I!_b1E%x^-J)jDz)zDgMj_jjA4IQLT{oREX8`nT0i!KexCmp0gPWgll
zK367x!yx?Q5v<vWoi8S+RxO$m)vS`z{r6W~z#e|4)so72I&LdWRAL?#^NWiz^<L*j
zGY#;aJZsN;UpI<``3;Ln;@F#T?lEuiqgR;#*L>YsR}P<2<0@lueEQdL*kGj8GUu$7
z2kNE?OmtpHUq0LxEZz?e5tt1aZtc5hFrwpA4-i(8TQms&K4Q+q)fe`n{i2lbC+czW
zwbPU>!oRnlNrqq*;V>EaCz7_cw})oE%qUA--D)JU_PpIiA~&ob?peXp%;9Tm8cZ@&
zCsl34W>P!yy!0ya$)BUt+=G##Cd+oUy2>p^XLIb$%(CU;1^VpwH&^iEEE<_nHl{)C
zdKTPN?s*kmLFYb+Y6ZLu`+tA^F(<gm4s$>-{zw@IB8uy9a&2#AfQgTfPsr*F!}(1y
zQCx1LHXCzzQTyw)v{^nbr#JsRb7a^xs2vO*y#D7JxTP46EHX8rGWqpx`(~ph8Ls-z
z7aPrH($)Rs%YHV5MMvMPke(=uk!xX2DT?YSG?K-KSoO?1o_yK|3w`*ng(|^9ZE;Yc
zyL#lprPto7Z5vZLX__o&rUaYmo1MQY77U>kt=e}*TnX<RG2%N=ifOJ<<Km@s{ClAv
zN>EOA^CxNa3dx=Y`cmOS92(!Abrz21X_m`qX~ln_5mpHnzc?c9%9yP4tY%j)mQ=~t
zVx#7>_>`K;xH4T|Pe>!AY|<6WG}rQx0#W={$ocRnnPr$#&PhMqSfEfYQAAx1!d`Co
z1COPx<s<cZ@y)moEzBj`l}m{zr)O$bJi7)-6D#5#N1oQrKlN8+(*A@S_-k@#z{m7q
z;-9r;drh&l&1-9GSB9GiMK3y3>*yCvb#xNt;`rocWMt;Q_|xuSCr%%=@Xs%aUsg6X
zRgf^ctQED+S5Kek4-O8tML#g~P!Cnj(^%j?J7}S{{ycqgP@ccPys@3nVp-nDQC&#I
z?a}$5^=PAHGCz+*9z)Eqg|OcJK38mK3v*+Ux<8VjnL}b*%Kgb|tc7#{-bLD5%8idg
z^nWY5y^lWb*LwMpi+5kp>-p8h`V4;dC1T<eEz5|vd!MUaJxrMyJD2D(i|0D`k$-->
zm5wsJ?=Zi5BTS0cwdvL7l>6p&isaZYRo1opkp=t*a2(n#n#=ic#WGr+H~p!@t?P6S
zheroXcJ3C%_;G7L;LU^#3ewVe6KjWOZ44owzkPdHZ@C!QZeyWU^SmWpgZuteeR`?J
zKGUr>r9binwznJrtVQbcNQNea&U5btY8&U4%-NWELHnp_5{I#|vEAu<Il{Sam!|$s
zy=lCy-)+ymzB>DuM78pUF07jL=sRU_q|o`^K(o<PKBg_+>8Iz4&LP%<iiJs5?eWFM
z#b?94L{1hS9yPa}Cv1bG<o)J&&4&fb{84^}+zGsz4WA5H@HPKEj}{0^+Ug@C;Sdgq
zz%D`G{iflep>}!x$$4ss#iF0rZrrp(-ebf`F|!TGG;z~n*RNkMc34rGE;~E(s7Gof
zPT026*rD>uPZpcXEX<EbC+9S~?UR#Uy?PZKr6{zLnk<oW#X0f5{@ZKUu3=f-DBl%w
z!Vf31LN(XvZip-L$z{v%{(G7q=mbMZ&D5VgNZyp5o_<xnV-JL6SzFtp&0*_`pMJ@x
zwlZDaVMYrx-Tr|A;|3qhI{6T>lgwPr^89l|U8~8+Mu_*BAu_qBx>cBSvG{vupQWzc
z+bsP?ztU-k-d5&i%T2w-t06ZZ%qF+~gCMq|dQXYs7A6wnihTPg%Rzk_zGLAUkT5r)
zCduzhcy%e=Pmlgmv+i4Pz*4tacj}=S9v)5-ZCQR^INB+<R56&7U)AAE%R}OPur_M&
zcCP#L2Ca9Yd5lmDmOQg;am<|CYE}mA8fhzv*?0RNN}Xj*C)4@1P`kb-lz~)XYZ2TJ
zUNQ1O)3jctw6R3!R9;yP{Cg5o8IT8`uS$I{tv{M2c+lt|kH`0Ugq?q&7%OD7mBaM~
z!7JIftR*4}dIjmDODy8Yf$`oK$90okpXLx;^;-G02V=wq8Dl^vt5j<l>UZ1NDU2YB
zC4(U`OJfn-E?atnOY!#eHM`vuyCbsA-zHyhqMA8bcl))UtNu;k@aO3KC?h75KTaMW
zJ5F??$!qI7PTnmO7t2B=b@kEMQ6%Aa-r6$Y4|(UO)0dpi)1KwgKN0ECxlKH#-PcMw
zyT8jghlr0HyVj~VSn90dbOtS~!~fo~7Zco(7+NHY#MW4BjOD>X;MTbAE)EmiW(}~U
zdsoKjVkUZj=KFP3tvk#Dd=gIb4-jLW3Bu)#qQ#S<V#z`xM5H!xl-19dn)yaMgNTx6
z+5QqVFu=}OVBjjct0Wt{CcH>_D(&P{hH4kS)l`-=J;v=kwEymuKh{XfWHfO=LAa~D
znK9T6ge+o*fNAqd(Wy-%k!_0Tc+N0dRJruQ!vK$#8a3~0iFf`+906b^G>jI@Y`@S@
zwNu`v!^;c>BDb*vdp%#{Rm<0}w}=S%SYr4rHO%$K?D~KFc#JeQ>D-#py*xp3R9Bj?
zgWPm@G)%CSECDGJ;&L+aUdqp<L_O3e_<IMRdW=W8^*B9l&-Knv_6;`aXoZ~CMuvF`
zv?`V4`qLu0-S(Cv{gsrI`cEp(ukf@wBJ;KD<i(G^lZ@5RQ`1}cQC^-ci64$n?HILv
zy2C+Y+@C6)7otfUE!Q$BShpZ@wie%zD~GUcc+2faA^d#g@8zH~EFep_eh*K)mYJPx
zCaP_ilK<taaeEk3oz9@}W=+XHFV6&WFFJPT1XWOOW$jvM(2QR>7cA_d$W=K{F2!Ym
zAQw{<-Typ@M3&zkJ}L2>&E)A8NO5#W_-@cn+tGSCDa_oAB5bBxt{I8s5)*RfJNOG$
zNCR#Ft9bopg>)i=>L4yLKb)>K+EK4-EFB%pJ7ajHtg6?`hHufhEq}3_bVME}#?1^H
zQS$Ao%5$gV(0TMa%V}#Tob5!_e?Hx;uivkT#NpVrlHc4Q;-7$~XCSe9ne{HZti9$m
z4O-bz==de$<>iIY&l9h9_do@@^s>RTPKKZ19T6#KPe1bLJD8Np=x+TB%`hN+cl076
zB53HSsfERbtfH#d%;D7=RS3#wvB`F5=kDS~wsrAOvomK}=*n%z;i<_dXK%I-NBI%c
z-1Vn%iuPDH*A1so6e)6aKgHpyKPwiwIGQWA`T1CPYy5(@gcx<SJ?FV=peXS3-2qpr
z*v7_o9?L2mN3-1O`asG=9gu}Qi34L~NfdAUS=r*j<m_gvhTXc}<)IYgl<?uZf4z$r
zjif7Wm07Sl;|E0D51tjK%EL3pKrD~ev+9g~@QCLY4(-5>$5MAfoogvGDXoA_GM7zo
zP>{LnaWas`*&{BSPc&aS0?wyDmYvC5mU1mE1c=x@gHc+Z(zty1yAkOSgyzzLZb)vz
z!x@X4o4e@@^_q>9m2|h@9uq&mYG-|ZB+&X0>M6>$gnPf6|7807txux*rK+pnziX^R
z9GoRjg?&z?pzk09r-6)D&u-iub}cf`K65vfP<z7p@!xMvAeeM?jk!+BIG9-U8qfzV
zVU4_>h}buyaIAg`{&gP=NEZOBH58;UXey+HJ6<~e#^(VF>i(xcXaC~tn*<=h+0H%Z
z#rpcFfuD<S=4lKiHO7yZ|As&nAVd{>TBr|<6|2)`_Q<DuWZjXTI(y|K(GzzY`9B{*
zCqOoBto<jG4noHH>9%>G^wu@C8~3^RKD}G|vl|BFE{qg04xsR3&ie>sZ05ygzZ&(-
zfA19u&Z|Glu8a>~CKur2(Gw|}65BOML5Vg@{>3^p*P#LHnb%J5_`nMAZjb6RazcYS
za<7jnH`a{kNGvt|*~~`@97uG`MwuG^jQp9tjs+tx%i-Tdx0emVW5JG{9Y7_S4G*k#
z4G_zL>zQ<5+5a4;50q}y92{Mq^rVZBRuilbEW;;tQhu}dt$DNk{~UR*7A!LzXa59?
z)P^iNOSDKRd!)c(lBGDtwaANE<k270|5Y0#_pxcr32}%%{ikfQwQmll#>*caV+;Ql
zRskkxueb8tpn>0~h>lpv?S(8h3%wZmdt}KXu-7$yvGQP81s<pkv>R$hY|NQgeg7Qh
zoif-Y^-SwFCe|21=it7<m8E8@KG!16Xv*=#z}7#4fhhx>KXu%--^G0w1=F~_YtWeU
zB$Mcm*^qhxCHzD(58}czZ-DG_N((e_BKp1SZ~tH&2zK0j>n2)_Gkjc;gw1!XZ`}PO
zH{5q0!8u1BVqXWhXR!I4PnnbQ{`|c<mlMUV!MU8vzxT-j-+e0+Is6EV#>dj;Dr1+s
zCs+C}AUXlpDO0Shya~Z|kepGA+BcZKS1xSFdirm%ELj1Tc*lRg+~0>1JGhJRreZGt
zHmzupj)lzXU-#TYe-sjK_ZX-;-$$K`&qimhOe1uEXs^Wy89Dd2Yf=Ej;#P~DB@Q8D
z{X}lw{=uu8I)#7u&5s*@ZF~$Qy_CG>N?j}wdYgDe&4_ahB`S0{<!=)Owg~{kK_Z9g
zQw+Ej?(HRRt^@7GW+#cy5`VUVw~>Of;)ByXCux8HoWViB)}X;UQ`?c=JUg0_+V*1?
z(O(NpAjLfj7ztz;Mb~Z1PvDFRl<pPiO_x8eKXFv}8xP)rG-+{EPR1(<HNy^ercydn
zpb{;5aq2hI@@H2};4}-CKt=lZY-(ifGO))e%*U~c^~YZ;|E+D8je!B`TwUD5h4wna
zSA^mPm}bhA-}ttfMrez(Nd0v{To+B~%-ol9YrioDAD9tdQ>&VtgWql<DB=Grd6RMg
zGwSQ_pJ0K|UboRT^Z(gIYpIa^_s|ZSK)d7b;(>>_y_^v!cP(0=Cz8i1u3hP_{Bvr!
z4LCLtGV+TKE_8-MfL)uBp_?wH_=S=5KbL&}g+(O&fdCx7@i+(im|`2S%bHYn26+N|
z5gkug2F~!?q?UuhN6vc!H!!hIBxjD;5w;1(X@o3J$%UAIJ_&#T39!C4vLeGQAc&vg
z=5hj*QtVTRdO<nIzSJLI76KLgxcY+wDGaz6<;^HFX{)!F#;Av0+Np5%zupXkR-tVr
zr?yfPOXS|nFtu>Vo#sSK#k;|Oyw%|q5I~OIZGYH1f)OVdq9I4%YsBpd^k>>_>wj%P
z0q*n9y?9}CnD{E8=PsSssj0)fxm-Uhx*ztLl)*pvSvXfh^u5O2Fo|HBufRAI9byuS
z-88x95(1MR>*)XMmeAgNKqIw3I;6?Af?-b_eJA!ORMe}6y?+jQI?C_#t=3j~7I<v_
z(C4-{xcoGw{3whmCEQY7bwGt^K-D60OpqnRk~-vH_oN33<SQPJs+O4Xfjp@;Ur@aq
zV-=P`d7OTvi>0?WCH{Qp3Psu@p382e&M4)LjbW&B(1%Vs7C|b^(FkIfgGBG`ckH2#
z<7YkQi_MAQ5{kL?_&tA?hqnREf1D^==q43Nh8c!)Tpn(A)nN=1(p;Pf`YtR>Q+c&&
zr8NeernR)ESZ{0*@zgCJY1Jc7Puiucbk?qnpDnF+8|ql-@?Swp{88NFyD)={PdoOu
zSdUq@R;8GVU6;h9r9XsT);fJ-@UhGqUGX=SeH_mu3-O~aubGMx4;gYTg4ciJTiu$K
z=e>Yi>JA5*|J4lcULI^_>3dzRkg5c*1d7*#Kff<T?B^j*^^AAvb9BW76|a)WnN89j
zK?NVEke6ogaR{n(3O-gpF~2Wbn^bajS0-Wn-{=k{jREQj9nGJ(4)KDN#-^?WxAJ$)
z?K+iNO{=S*+&$)xuCQ2)z0zU1KJ-yZeSmfP2w|pP^j#cN8HIPc{5?>Ji?6Wr9)YC|
zjT&9z-&l>imk7*$bJWfR>&MfXNkxRwXqAlXWr^Jbdwu!95Y))!1xdZi{J6KgeyR1v
zT2PSFX`=tCxQUL1o~x9*LEH}u?ePYoo&R_dLt)i>E*)1EXs<Rr*z^7R#w!6@kOl4*
zT4>vEuEej5l2Z@d#-bIk6buY+hpnlYq2lJdP0QM5dk#-@f{|ZpqE=&z%C$r~Ef2-U
zohpb~6h7|PYpMGNsPp`%289U_oH^!jNU@hKgwjJsZLC;UZ(Kl(ScotAzWU)hXhM0j
z1Xtr8ij?kau_$(hsfw}p-DweKtk$i$?f%-2tASS*Rde{JQlwfh%3^@YMNZH{IabTr
z1U_%fR7OjejkA60T~9~hLMi#>t!USxfrkxgzq1Lc6~yFQZwPX9Cn~Rt_w>L_b7x<1
zHQr)r`KwKi@ctv{Z8W?odX-555oNz$1${E5p~8$ns&;%R>I$8LY47fr6@2)VbZ+l^
zwo;k5)5Bjb8+)}n?K+WCX+fW=Z|66G%9C#%F^&P3{lbgQOy&n2d*@55KUZn@99ULA
z%_3#a>YMp?4eI&hNv_~D8{(jY7F_K5NQN%4ZgofGQqVZ3`3|Gbb__>>0!2(%_jEoV
z744ng2bg{o&Ygj^q+8z_@LqB=i8857scF#v(|oG2*^E_A;^F+&<JFZR(FmgA(u!fq
z291-$y*-BFg&tkHC0xDojWHH+SC`te6Wuru(Xu=iR3i>cC8M*3GN%&F>{`3$vxeVv
zm<aT_k~LyN@$AY-q?zi<Ve=v7iT9oHom=8$u$cy6w>SqsUt!TdhCUE`$|=mIxVjgT
zg(oIHt(8XQU)uVLhq&i7RXNeiIfMDBS=~^mZ`eXP@<XI~59_DT=Z&Ab?wJIyrHpBw
zPP!x8?uv@t6IBRvTuFHTc{?kTP-d&%E@9Xke)SLC{o|2dO1{3>lA-1e{({#_nf<)j
z^v)kMD2Fi{uVM*78*FAlK}`S&h1zs{uu!kBeLAJ4bXR;haj>CZ(cO$o?2f9!z`4Nz
zi?`WmD<MtYWrSsMPK{-PdWqK>0YOk&>Q^0wA;XiZ=&6xbrz72cOBw?Y{N13l7cRfB
zdhZg1ZnJb@cA;#|>a=7<)nE5R@%(rruHN}`m$xt$5c7rM&H1=4+A}SczQ$?_VIsRz
zoNNO>8`Gm{hr%<A+bg9=u2DpnaWT207QSCvg+DpvxfX|PxyBf@lE*Qas+eS%y>p&p
zUAT}Nb7hQf3>DI)BpkB7Md!C4$?W{~KgKEpj|#FpUX{@_i;XXR)?2YZ6&R@%EsKE~
z1l2PQB>>13*WKw!$sne;1sjm|Rkhy)ceiHX(!r<P=IwRGF6AbiQ*5WJv6uLC59*TQ
z#qUYA5G!o2TbrjhS0`}|{%kWYwi!wkBQXHkgtEFy!)RPg6F=<8X6Cgr->)O=RW*1D
zweFvqTWnvxFFh`%I>|cSmf#R%4zGVvJ4LDmodxc~P#0MF2rC{^R1{f(U0~8v`p=Nv
zrtCk(wo>+x%=iy3(`O?Ma!rvY-m!Uzy_%A*a;wyFsnS=b8}!?{?;TD=VxBl2e151I
zsd|#MYq|chR%F_n`ac<BSsrt@dBYNAS!o`0`pR$lu(?N}$S8i%AsN&uhhXn&>ozLV
zfSZ^+9!F9nc@!U|9lO**?HvNPpR_K$mnK|LX(5sh^*-MsmB!tv8h;>Kxz3pIp894}
zow}JhDZ>9<qQj!rFTngA+iYKU0_A|0u;f&^X|Gn*R?ngt7S}e-?0lz;zSk8XhVD39
zEC|`XPm^3a3c@1eNCNls7;J9Ir5@VMhs$AnPnb%JYriSrUuEp*^<DE$ws%}89Tnm_
z2#Gt#vFt)7s+DAVd{Acz_zjF<T=<{OhA+AD3XSzY5jJHkN9BdwHjvH^_5sR=^`PNp
zJd^2TC_PqwG|#iyy^uUAPLIOklw5_V3H9-+@xi_op*;cJonms$^8H2MnDZy%Hv^(e
z4>V+A7IQQ-?r^8*B5rv726Mf;kT`HLf`?bOHM&}7RPy_?IJ|G0N?YJuBv#S%@PR%X
z=Zx6X`$rcw;BaJ|q;!-6UoeL6M<=R=Z?V1PxgE#eiWq*?#zi;I8o}EY6XG6sS0@T0
z#M$T?Q7#kzC%eHE@z{r$92ACm^?b1l%R|1+E6y3>>bc{`??OqTpeF}tDl%Aub^})C
zZdBBYMv)^oJ(qg7s7dm?5n~$(C(VX%17oSnYJ&F8{8_@zI14|bhm($t8Ln&{74E1#
za=Cg^xE>Tk>5dARs&_gy-vN~gsCk93bVl=Jmpx2mOA+Kk+b0=IiFlD(VXf22%P#yj
zQC|SPPZS}7e9|BR;o=zMAgR?D)R-I6%<;rrcjmfzm#2j{WSqA#giG*_>ab-_B$N5Q
z`77E^OCu86RnnS0{X8TJPU>b$3<+_{`MaOFJ2UX`f0t0Aj9WgOUv@7Y@MxlrUY(#a
zR=roqg<6Z>DUiIezlHUd;C{S71yKRi#|K||-TZUw)YwKzTaUtLgy~Yd?XCFOeM4^m
zc8Z@y&A-u=<1>7zGwf(``t^fMk&$HaWkjcckgM^<`0r@s15?HBe4Ub%ZyvUJ6;BVx
z{B8h$Iz@xiP}CghaxO^xrp+UCa8`<uV)LXyvx0Q#0|yjS<9v;};Ev@wy!K@9krGl5
zaVb*w!s*T1j7aXg^@3j?eqY}ZwH(*f+5DJPqU~llO?cB_f*QFH|H~ahx%47?k5@Zp
z%o`LGC|uqBnC8^4@a;?!PUw$-Au&Jsm(R(te!PS-y%@MOqxu{%Q$F84k#e;oN})4C
zH1^gJ5`e^4yj^T>yKH77zv^loO2_2w8eBodF*@Tj{^l-VEZ7};vJ)jyqdRh+c=fuX
zG7A(+7(;mVgxL@qxDJW{HoJS>{s?O#AVkRT7~h#Ht5yNjrY$eOl^=1Emt>g*M}EXO
ztvc6cKI?#wQ+Q8PE_Gb!F#o_t2;v%E{6&?vFkJQ3!Ha9_)rJ?pif2-3NSq^H!l>-q
z0!;U2XlMqrX4s9TGkV^z+ZB{pKVAT!dt37#p*y4|5M41gRfmTWXVWj^(!P#)AIGDU
z*h^ER_7<8`0Cmb&cZ5EMMc%$QE#@5%z2J88HTubXh*SHWeWBVS&+k2Gz8@YyabI>h
z-ob~-!7E#Kc|!Eg(^-cseqcR&wXkWAA8V(lv+qF-<_nSfu*s#`NySKAhHP8E9D&jm
zOyx8NP^T8UBu!j)@ad+{Mj~Py4L56Sb3b$_z8M{45jQmz6n|Fpy=FwLXo((Z4918T
z4O~yxOxCt`H@`D8>6BSx=lo}Py<m6kC~)dpre1!-bGMv>cuG(g$u;B>#3hpFn1A2+
zEKEB+U$N0}v2FM9po6x~yPc+$xVGl!XN%CJ{KNXT$J%BYkHZzycm@%4ex|={BOh{z
zA{Uw_34>Q8r3V~E5z~nUGdXCjj|9MUK|A*#jSu;_VGe@EZF4N99g~-%cQ?H>Hma$4
z!d5}Hd)WRmk&*cu(Z+oQnMYlk49_)WkTfyx8<%XxO-1q{YC?*BcB=T-vig?eqr6@b
z$5n&f6L8CMm*0dREZOHc&T;%?hAN<oT&PE`sOc;ZOiaR-K{Ak-zB-73sfcGWrOrT?
z+s~=MAz@FxK)pn~O)aEVRjxC2k*W;v++q~&-50HTo||9wxeYDqb*iTY$EZ(jLN8Zz
zk$0Nyady5m>i+ZlsvHm#yabfW=#CbM(cs4nULU3_wm~<=$#Bt((DJy?E$9pz{A^8f
zCFvFmsLtk3Z+&KND&Md3Dz1C%b=t%{T~)l89`?<YkA*>JmD+MtM~S7r4`~%EtftUU
zJ2}Ogv8*IaLV0Y;=;b@FQ2VG$5ERAm08&;ENIA9j5nmTKO=u1uN?mG++Tl_K(A>9x
zL7M;&yJLmH8RCP)>inb^?&>0`Q>=?o{mk9;a;x*kn_eGYfRm#Izm;SdOV3_Fore}>
zrnSaE#{V4HsKmr2+iFsJuljJX-RA;tz0Bd_=k{sTSYHogUfMH%%7Vc(C*}uOxkK-8
zekq%t8h8wWSdp>z$Lf}dV|wq~=ArPB1phqTcgo3m1Wc(zGk|5BVS-CH4lpuF^JuMc
zp;N`?MeQhMg92R~r}NZ9m2MF`m^p^IQ=9<1e5ajA`Zq!;z7W)E-28UAbDO-k1_!|*
z$xFWRYNbvMPb=B=nzHJ9rgJxcVdH1lkJO@i1=XW^@oqFsrOPg#xr)dxOPZb9>rMdx
zwC_c2hlBf0Yo?FC{B$@})b6qPabJ|5tXVZix#vRgNK&PXsd|F-F3YVmFH|D$&Ze{T
zF+`6is;s)1kd2tVpm<OLir0gddFFMOtdU3_kS8Y}MfSSFqI!?L*}qVfZjx9{P(uh=
z4xS3Xkj46;(!gjpR$-XL_?m0EG=nn0SkSjEvO{&<d{6Y@=zH%1(elj#i^O+Ei``x;
zO<IBy?$*_s3PbY>;yO#-ktF-v#zHLu1MFK;zbL~14S2m(=>m?q<r{uf>d@DEue%q>
zFOj!F9?N4EfkDxo%f?dUOiBnX8S8LmZu&gQv(M}a1n$XTa3gN#45vA-x@&N1yx`52
z`C{LJBvqVQdw`MVl2B<vo-|!u7G5G}hxvF5!!JoLlfCW-p(yn`O7x$NLlH*ldG#)s
zh{C1cbC5*Oux~2dzOa-eBEvO@fB3dIQ}TjBh$b_4LYb|n23h6>Z`U}R<06UD<fPib
z)k9Lgk`3o{?Hw|3GrWQ@2a^09(@C#Gcux*jc6#386#AzH)rclw@ZFsvoG@wvIpe9^
zOFIVKcTb`GJdcn;@?m++r3T!j)e%uQt|LIyn~6KTl~eE7oy7Zmqsld2jzo>8Ov9i0
za=-cU@r5U9zBHt|j;}k5bJsv#G544`@fVCD6@^rzECkJCIJnA)Fm|es2lixpE<qaM
z52aNTznKUHoGzf6r(nt?m|VD8o~zKnpi&Rn<(krO${qKpjiQ*X-Ar9$JeCT~=f9}(
zZiff2fXedhr(e-d#HjBXH)Z)mN{m9agXKS)EANRmFl`ZaI46R#QDf40N9!tMP=m#m
z-;HlT+E|vFKy?fdQtp$Rhvu>OFSpwxQ&*?CK)iR6^${$ZGUGA}f~n!lQAKcq(pc_d
zFDY*)iZY%exc{ZWLWiL5d}>yenFoxGeQGI=!Tns_w>kHbd!srwngnQ7?0fbthzT?R
zC`v3`1@+z}s5*^v^Z1h7(>7YT57gVh10g_IRyzE{VSIv}u6f+dDqAP|p&rMI&qAVs
z0LVb+HPdQ<4-S^e@{VB01ow-?=<<PbBL&$A#l%=P)z=ttiL;lemRu6^$l(XliMJf;
zlyp?SYS~x>8wS`ST!l_|_b?-`7F{&@>ki#%uD>}s%2Onc#z8(&Sc#)=5c@e6eW6Xk
zVVNlo7kuBtcJ1->Xap=^0CQa=-4#`~&3=yo`N{HJONXn6^R>d%!-p&{^2O~7<$CWl
zfu<@Ix;*PI)>+${U=%c}u&-~J6WX2Ns2retMIUEoL6z9)AJwf^%mp@_xegb%DqA?%
z=j_Xwf<=0*dOr3i;h}H6mnSVshKrt>nebC3g(ZhW1Y_q{zRuY(=^#Ido@T<ysqgo+
z<+7dj<~-T?p1s(fW#Ty^tJoI7V@mu<jRrCi-psfX9chK=Ut}xG%kX;Te{=eN>OLFE
zFRF?jmi&U^(9^V(!<);lK8CEFzGs<SDCMQwf%V7Jp%HMoS`NS#j{k^9!>|f9-~NXF
zOvJ+3VI#S##}si^7H>}3``dz3xg>F1S58>4Hl_@F6bJ4!%icSy54|iPeo&{6OK0Fl
z7(;>Po$lPov?yG_*M7XzrF8iIcAw(B^g6stVaB<>npBf)3ot#ohhraZF@VhDIG(*Q
z6;;;mx6WJVMzTi7g-m<_H6gaNjvBxTm?-MIyi3sW<X{yi(Q8ao?pMs&Ducbq<Dldy
zUl@Smt@qHf?lefshC!ZQbv@0`5`_Rq-Ju2HP>1Zkd7aq7FtO_^q&qL7Bz9lFi*zn1
zq$fg>=IKP;;2__L+iMi>_m?Ve@x)RG-nHj-39ceclVN9)n(xiVgR_xU(Y%H|&Q3SW
z*PDI^phC){-2O-cULVU#;X2Q>`Y$h~ym-gR5xLV(AAy{Q9>d98oHrrGrGv*-F~?+}
znqo1W&U&wD3z-%Yc1iE#%*6gMbA8iirCyP(bt_JaO7^Kne7q2YW_Q19ah7zE*QxVD
zI~7Rf4Sbgjpqw^J-{b#Dg|-<cIKOPHKdCC8khx{u)${41NG<;8nW3!48(8!b0QGT?
zg;v9$Ygomn&4{54;;@_NSI-!=O)}2;f10=^R3}Z}B@i=L*fP4Cbkq8n{85A|Ut9RD
za|a9R-n^;mhr#tpRD`I+QnOg0Jj069g5!FiWv^?Mt!TVl_9T-_0GbKzm4$dCr&p%c
z4TFfMY(HN+58vLp6B%th%!&&otpxrVI$kzG1-U~R%_BPg!hfg3t#WQ#zSv50_qKXT
zqif04lj3V}`b}v5^--5zjfM6<6UUu5D);+;nnZC%&2c~J|0Yp>UcSFTL{Gr$C8naA
z@~eJ#k!TqHA}T(vhoIxHM)IrfVqIwRkkX~Ult%A63YkRB23SyU5Cd}l(TsKMd^=X*
zgOhR7jTb`0stCa5mFci~!@i!)^y_oq6<5FEU8GvJHikUWP^D#jZH;PI?`F{DbUMbL
zuB84wZ}S7Dm*iBfQ_JmwA*>U1%iH}brp3*N*I&Vs6#->(`Yt92L<};uoS66WdS)FQ
zlq3T`TaN91d?sYOn-#G%#dToGfB8VP*pznk_Do$Zds{4Co;%$coJ#4onoj?|0oyM~
z@{{Yik||)REL@$U+_giHFL<rx%8j0d?K;<<l39R>vwu50wB%}MmBBxUxGm+XZa=82
z-`tMKm_H>K5Cv?_X#RH#+4dfVJyG^i&FSJSgt+2>i-3*eKOAG<Pv11$D%P&U!1+}`
z@4auRGSgH5!F_g7oG#eh!}wEMiO{<WaYVYH#R$OO=ti3z%FY`1T?{NweflZlTi;3^
zFA6|H)z0>CH|^^EI@Pg$T<h^kkq-iCINaAv&H6J9xiz>(Pkg3+cU?ithGpwfB}a*P
znX!Z>K6bs(h0!~vL%D!Zm$1F#$}KGTYji`&Pi2vo43`4_4m$n0Coz)A5GSK;XFnpg
zs6s2-?AF4Hb6o4kk91-E45i`-%V!UE4cwa|!_lT7%CR*_tl6z&BAdlLOTd0on<{d@
zFHG}c03b$T4gj`4HLbQs;=Yrdsc7$-{6d$zLq};6IbHcP7ouC7;m^ynt$0zVQx-Mc
zJ_8T5n=ndP8@#g63GWW~&|oXB^utnApejS}^M&FFG|{z6I&LK3qgZ3A@>>Y})$MF)
zF>|l@6X8mR0@Qqsm#_RiEEC&rfjMYJUAweWW&`=9QNBvWT$U;^iXF5RK5=WaIPG?O
zhJ{^Y;P0YgFA{wy*U5Mm9X4!NT9!n}W>qpn2Aqmi2JUtqXET@0d>nDL$jVr<xGgRg
zeiSVSQRAm8yF)4u{HkwoY~mh7A8Io&>dPef;OWJwe&E^imwh{AiROF(*A1DWx~G-%
z??l=+D7k!#$>-68<+g-l*Sqty7vd8&6@_XyuI|m2)l7vZ+2aU_AJr5h&Ks4ax?9D5
z*`g@J^K?Oq?jgdakaZjJ>Cjk$ElYr^n46E?C(d!p=+VZpoJ@$O40@d*zBtV7c~OLi
z*ahbJIn8H0OaKaXCq>L@GVpQnyif`^)VybWP<fd4c=VfLl2Ll|aWOSZi}QtWMWs;c
zkt(&OBFeq+nH1k<{{wSDJdgW3QNO}lFF%CZQ3H&6S*nA6NP}(PuAL-038ywZ+zl2T
z1xop^9`{p7xKw(3Qk|HB&SgbYxl?Yt+ywdK;0|lqc_VTW(UV~(S5N7zkum+)?3nlU
zUK^w<G8pDo-j8E{b%~Ii18FP%p6XGVwN=m$KAGrykupJo4oB=(@HJ)L=B6Jc=+lc+
zzX@zlfT7ck25@^##v?H9<#|^R(!cv{iC4|l`ALk(*_hMl%c02FenP>EIUY@;Y)&v6
zn{^!arM5VM!+t-^dt%LdkRUegXcZy`J<j<xn!BtGH+3GfJ6|dr<P$6~6w=<jW!irI
z_{_>_72P-!y2K;p0ayskV83@lc1-I`F(+9K4u01lcjWkIbfDOH@tv2!4ovh+BNW{`
zVD`=q)zuP|Iav`ZNnkTY)r^Q+@tqe~E+lY=+9iZU=nq>0mJtMRK!R@ajqU^yUDO9@
za*ipQhM(;|msRMTb+!w_1I&1XZnAyS#aYM#I@k0XLiUN|cRJmx=SQ)d`+G;uzAw&a
z#n?5+KMG6iKge12uH)0#aQ)It*>>^7`(jJBQTUbr$~imRvv0<lmgD+DvFbXb96Jk-
z{#3kxykowy&0mk3*zo3cSlhr$cB&*9jPc!*crth!+3QoIn}UZ;2_UStVu`8i-Z^};
zi6Yjf9{&7AFwhQwNaM%VYfT*GMvIL)_HK1Gm+F=5>LscjcUPHsMC%q7h|fhdDMZdo
zd%clMyt3O-INM*^e;2=igN!C@it;o=ytO;DS5TyMRl1@^zFv7+2-?(RSVBotCF^Rm
zXt*LuulM!^MMLbD#;Bg|@M&G~VUXf0qgP^hclEorW_!w&OD#6GdhG4vzprYh(Ywn>
z{qCP8w_@`tk8`_AZ($63R33FagpAPoFTKN+%)}5aA{#55xB<~2KiZLYRZ58w<phMj
z9XC0##(odKs`Q&D_CGQXBtDY3&0NrQy7^YR#Uymm)Ys)zbp+RBOT6dmMlGw4kf8a&
zWCjS`XVf#wE|}I>WJb?sp*6|%<6~BC($g<nSeRJE;IY6OrmG7XphX@-1&dr);)AgY
zgH+Z-b;Zj1RP$<bv8Z15mN6<yp>Czf@<v;qUqN^~Jo`N;pS*;XIHHf3Fy6qlh$ntu
z>0ZNBf0L$>Tu4-^JBBm>AJfes<!el(G*%7Qa~*XqZzPlR)KF(rdMZ<E2bz17#@m=~
zUcXx)tXGgvrQ~tRcbU9TM5CmYCGmcsljR=TyL@0#*cRFor$}nc<7xSgjOBtP*rjU#
z6U!&~2u(m9F4P7FsOc*^Tl5gzcN)pkEOm87smd3}!%kUK$eQSx4@E0=O^T!Eu@pk<
zta%YQmzs>KiQH|JgJQ`c;%5d0`7CkFx`M8sKHU7gR`D@~NAl|O&uY}T4!8OBSawh&
zTk-rvhuU6sxM!^X#Z~Rg{ZZ`=DPkmH)8#Oh!dx3{jkrk#i&P*J<I&oqz!A`6f6P>9
z>;7@#ROw6Zq{>{*TRe?=Cylz!_{?yP@d)jO{bD1_e=Lym&p~G-RfR_{7SL!JF+r2w
zUCp3DW^ZTVYNJ_AvC?Sj_-wn`b+75<KfWK`z$t&<Cac@~+@;|3f|=#}T-DVzA1tq)
zDuN<lkwWw{7c2P;QpF_b)5=14OF!;3)g<if%ud!3Ha$2|U+QGQX-i{Sd><oDttO{3
zH<+r%cK40Wnd5V(w_4sNa5>s66z}ci_yURdbc8pR&P=53BWJw`*~ENqbD^zV0Ui-i
zruA(i@`n@3rhFRpRV&n;SuO0fSJq23*=GfSv(uFjF#nP4jK%kw1hiNkv<f00V7J@(
z=2M~g7%Pg?A6;y>5WCuEDVR*^&KhtWFT6@&%(-ITN}D3_-u|KNcD@gLv&pmVG$S^v
zI}0q}9yZV6NT+eTeHqQW)RWHV-)&^Q9)`^fX#@5JXawDoc#p2aK!8g9`DSc4mcti^
zu|0l8oqTR5@4QB_V?GoaWMu8Q%aYE<FksX<bItW+QoBxPBCea(qt^}ScZvAhLS4h1
zW?@Hx+Y;|dR$GbDB^_CXX%2_DhPkZ$FvOdJ$82P;j$6KQ8qJCIih^A?j%^=;UBeoX
z3S2g#_WIjm$K!}`qlNUl(<rJpws!kjzVjmzJk3UpX9ltn^TxCud83ixNlE5!NKr{?
zNAl|<90RVUF+y@Nzl!)7&fes}!;9%#nzVQAyeAoZ+&zk!qz;yxv(V)ZEI9|HaqWmX
z0Wci%68^Ny=@=bc-<%_pLrN)LDI|n)L?y&8#gUlbHC$=YU4A~>{W;}fdmHqlYk4`f
zQf|&#8$mO@>Z0?a+*%Nb=8_=|wC7J;n={Hf@7dQcZD+;8)vk=_1LV8$7=TCk25tpc
z;Yb3H9Xr~P8X=&4HeV*i*G94Hd;%E>tupGi&Zpu)zS&`>=oPDmwh0l_WcuTIaP=B7
zz!%=oDE+RoZ162|ix%cXd~pg(N+hS^6);er4Z+0g-e(ZoM48%ML102+iD}eIi6QlW
zq-)td&ew9$3T|2zQ7`dZ3!&54M99QvHNICAkrNkm{GydC@qTkyP<tKWK2}{=G8{P}
z=|(nIjh@Z=syTDct;sn+JJNsE*&pS=+-BXXVG#M?R{>DV1llgv?RGjqb`RiH=cu|^
zUR`-obBNkYxgQxp50iNpzNnVVx*FGR+L~VS)Fv@8gq??Q<H;nV+;9pm+kdqmBz+ph
zgDeD3DGouLPCiSxT2N0-DS<(aY1zdACEWnp`8oA?5CmJJ#iom}WK>z?tA}krUb&x1
z1}BBF4YZ`nqA`{tg_vWhR_|?sQ<{rw67SDN&=X>PY`v+{yIxiK(pDEgBG0GUP9CMA
zb|w`Si{`7o@70}rM~7%PdC&?#z7~9d79Y$^Dc1zr9fgj|k#X|^>qMs4pD8{5z<%OU
zz>be=M<a|gY7<HfxIMbf>!Z)t#XALTe$PpSvOmwli?7RV^xqtt^@?iTQ!S3U6cQdk
z79RYC)G#2E=#FaL9aG@WVx$85TNcOX`-Y6-!hN=-nCBGp?*)B5>Uw9z{`_44R6zvJ
zoAv-15&_0qkg=<B>80?0Mm;2b8-_j0I@Jvj_o^p1ozhL5llU9ScTdC72EUhv5twkI
z6bEKZVC7h06po!5pX^pYu*ckg_{81p3Um3@(P_)wW>Cngr+N3F>OmkRdAfz!)fHs<
zt5^&=1mOi=^Sy8Cts|wxwxm|gutIUENAof^z(GlaA$v`JhB?n!fXcu5sNZeHDnxU=
z1+?R=Nx22fdxD((X-<&Fq0%9G*~1f@vIMaQIB1wdOYUOe&tb|*MB8v-k;>#9cT)lb
z^@T4j8&@(GfcDf<)<a*|X9D)4T&>9byifIYYOE%nqurn1GRlJ2ZM6Dgy)V#l@EK(x
zFMOb9n7`dI$0E!AxHRf((ydF!Or~iX+9OBZRD_K;IHVQYXol$r!CY$<)BVFtIJe#N
z;%iYzgrT+N=3XUdm%W$3X?b^$WiRg=bkPS(#gE2P(Cx=k@E%$7^2d%1o^h0)aTtaz
zZ9Y3>K=UvEH2_133-?i%I9Rm5in>)yU*MrepHr^ln0;djQ0;-3^;EJB3M15M2|`4|
z=<%6EvJoZw87u{!lfX_SmO}Udo}TDHrt&p7E+y_!=8d1u2r#7uQ^x(!CL6gM+)hSA
z#o{ck%L?)|oyvqRGDXzhCp_>@vjPPue|!$HZ$ymS{b()ACEW2$YQMhKp)6}hfu~nH
zJvM;}31tBEl%QohKx;5zIN(x3e^#Zh75N)2_X{VSZY}Voj<7!{lJXz_ElDyLi)v;Y
z4HLu$lq$eG8!w(V{u+aHQNk(BEjF<U9FXq|Fl5)>S&u&lsCU^9=ArfBY}(O~QAOM%
z#)!#1`&O#AS*zDVS2sBoDVG=Bnpr8&AyoqP>Cjs%<@e}8MfiQv*ZtFh<$2``ikcJm
z49Nv1#oNOwJG~uo4DNyDwR{psKJjB-2VYK+-^a{Vw_9o@DLCY)9vJyPFJ~)Ma9}|5
zm{Luy02@6Iw^iJrL-WF}DF5;nyN{*xj3fqXK_z_ySF#S6&wTj<=KwH~n#$ZP)1K(2
zuB*(z#mT-=*lg?@-?$_;cCR@7J*=!TU=6S(`b>p&W0Bzw7bq0d#*ols3V~XC0lfGL
zN3@=QG6ESY<UP^`0kjo6jKXjM3}ARJc^XV901jcjmzQ{mJ>EZE(Ao0?uVDhK1z?s6
zDWi4G5P`QE^0S-@R?}SRxfDwnS-u}*C;p}jz?c|VQ^-9ZjFNgX+Kena2<pqE53by=
za@S$q>k3_OOPh2vOzKTDrIsHvM1!&!GI;&<F}o={%?|4L>wI@mTu;^QrkieR>wLf%
zW@`e2q_|QbPuf^qUj<z&QBE6>5j_GER4ntu{>eFTSU!tdu2IvrZ7r}U7~nPZC2Zau
z4`o4{c>wn6Ly`vmy4PWw1x5P^epBOq!zYRH7VYK!*k@nuH#*QWs?c04p~!5W+-x~x
zw8wQ5q-Hd=qr+ekvs<j-7jJ)sPkD{23W%}=CT*Pz&ts`jjtrt130-#zS}@4zf_Du$
zYB!ou#*V*}Fc~nvh~eaMw;u5$X8}DM>>vqr9?V(<P?Y4mhR|CziM;m7#4I;lHwTJ|
zCkL({cayjjm;nOb=NY=Z`|(20^&gH_?`tAHqMR>{rU+$m^%{%z!Sob*C1@Fg%o^yt
zk}+-VdRu)G7b|%+rER+fb5ba4OUAk!M`o4o91&kyil9gLemt5PO(=^mZi@T?6<o_7
zD$T#-X%5SES)z3K`P_RIc<TRM(x!&lm3!g4YLJm)glu}alQXrsIw+gg`PRIT0*Kju
zJ+=KwuZJQH*UhyK%TXCq8ko<Gh?(|A3dnkib3X0V7(qX*Z9srfq+=zz3I;Z{$pu`^
zBIT%Jn8EDb&Wv{uv0E@gd-#KP4nny`_bJr<xrh@GxDMq=PNM{;x;|o)uUhx6u+Ds1
zR-~3TY-^Ph4TZNrxXeXrd^iEce+X`LsUd}<z~tMWj)l1)cM_NGP`1cuK_mErK4_gt
zyMSFi*$d06T+dZ`RHyTz_w%PkC&E&AaP=lVM!+P})HTrKGO2!H4m>;^reN|`%gHWr
zlbt}EB&@Dvk~iN;dTY<4=-C_5+M@MnG}Y+Iy5$zg!QyEVL`5~L(%sd^^gUgZn=ue$
zxXT};j~Q*u$dPTU@fY^!1skZO2tUPtz!Ee#`yMqBBmDd!*?T;VaZZh#-EO(J8FBvK
zEa!fqe@ck=gax8!c$|FA{9Dq8!ucELzdBg-FMzo`vv{?E2M{<TX-d1i9EPUqG~=a<
zVYNqD5%;n!Ue%_m79bYBag0c{C1=cHqf0UbWC(CX`|9Bnw2VITA=kk>WK4@FxF9Je
zmdG9OA}7$~YZw4uagYKow$RHOUG~EoHP+Hwy;tw4RuQuCH6BedeSdkMy9}5!3BE;k
zk#QMk((W4PVR*324OdO1x2Pe8=zrkcGcv%MeU6+L`?YNbjTj8jPVK?{LaqinTiAN7
zSU`XnwME)xOE4gj=!P>TO@*Vj$ey=^H)9f*xD~IA*k)lB(oh(4p(7m7Y?Nk571%cf
zx2ajfZg<qTnLXiX_QzzgljbLOHn&o9t;kFlX$q0`Tw^uckcU@UfSdR?B$bcYZLpMm
zzN$WaqABqtG0{NU&qON&op<}7AovjFb&AsaG(5t7N}1XQ<|t&sTfTa`);+)FFcqUI
zXzue2%pTvXl1w=}pFwn;&^4iHju|Luwz)6IM#%5XIhIw(J3Jd!v?Y-*o{vf~+>;f6
zjQHB@a055~1i~*CFLOD79+MOmdreyH2$@fBF#{hhwY0o(UXM8LMu}ufyr-7K`UPFx
zV0yb3#|4V(4&tM|jG_FF$MFRAcsi5+prdanJ2-569-<bc#0)I*Im2Ik*`}_DoKm<!
zTHW`&O^K#lsvL$-=N-X9p_C44zv|@W{DyI%%YA`_%ab>sjtg2%-`ChzF!`tBoYAjt
z{38fPOG&rDlhItMTkp+)(@f)nfF><(nKA)*V(*hfqW<~*YK>1uf$IExg%g_36fKg`
zE!$*!i1X@6!4m2WSq$CP?W!8Q6zmMMSgdy<j;tkvLB9g(i6L9Ck_zY*4^z{y4@~wL
zf$`Ux8kceuTY<Cm*0<LoZSV0R>Z<qofbcP~vq8$aUUh-8th&e<zxj#ryvgZOQue}B
z?n5xv7n}p1=o;W)WL!W5+wz@6;H0M5N)D{s`=&RJh)<E@!bYYs!PkjdTa7fh*?9F*
z#LlT}ixjQ_TmaS{RvEXOZ7n-vX*((4*wy7~XK)dPGID`M2GPAy`Iq)7z~ZUkRC6co
zMgAtQ-?C_4WJ?51b`VL#)3GKE8k;yH!K?noW<Kx)1;0jXqUHic`KlY)8A)-_<DJlX
zrs@nu(~Zh#D%ZHgegLdS1}{j6<XT}XDmIWv>pV^Ev~6pyq>r(^h?74%0Zo$Mffs@u
z>oE0$3m~>AvC_1!54MIDmrTx)OVO9B!HpoE1(<<UmTBc>1@uJB*vrMMkS{2zs{q=D
zQm=a~^rVNcHLLQ52pzbucme7H6-L@&&5{Um*&zPsRooDQ$nl-(t_>`<la;m2Q>3@?
zMNTytsOq3qptqe;eO~Df$b*0eaOIA!CBo3bc{Say84Z&KbQaQD4VkmEZlT^d>f;Z#
zT)?a|%mL5iYx{KPj-Yi!)%0PeQK%~qyI|73?j-yd1OyxeyY&@xfVl!O(ODy*Rb!#c
zWj`Dj7Vit&wL9ZGPCPRv*fmJFYw-wdfAPWy5zka+F}k#2(}EFt3>5$Fn~!zNgtI9C
zKTy(AkQiQ!?@b7R#Pd{d9=cB3XLot;iPDSpdY`#2PP{}Fv#>~hSzIGw!_GQf$!wn(
zovr<zxOtRrbk^1G>W6nwici&cj=n_hk=!2pIg!ADPVj^1R>8R@1_m(7fCjdK5~Kbt
zWk7^N(bL(^ZQr~iJJ&YL+YjeC-F~4LXa^P_^5J3Kg<<vS$GCYLFz_;egUEnm*xOMG
zjnUA9b)^4!$kia<;89WYW<yW9S10TkfiNDe!mppK;~f+{+oX|y4Lk$jN~jRF4zlm2
z$pwn#qMlRZL3(OnO-`e~sj0BH_qUBEwDds00?z3BjtI>KR5v8ANmlMPiA)Dwx_U=V
zE0}POM_;R#uVZnxWDao5F2shn-MEifI<u1q*UF>{K%AM<eVaO3@Rgj3wrVdlf{dtK
z1CPouAT-x-)qWQGN)=I~Pn`y_54i<>a1ygSNHyvONzWM0U3tA&Q_9uzCfB{1nB{gi
z&d>q7$Wy2RwiC*rPgna5!egnwzs_BucbcxA&a^$^7^{HnlzFBFXd8(Q-gG${aDrVz
z8A)@<)<&gBJ~zF)>56Xc7d_z-Sm%m*&0=Zw7*VpA#O#kq*lRfff7t^Z_qtk5Yoxl6
zhkzP}@WZUi!w3w%$I}>qcf{R`0Z}vQh5a31|BP_gq|Y7NJ(dyZ5t<XfMTeBYTQgIc
zsFtgdQoLUf!5WveV-23@Tt0Q<2%(~h9>aQ(ck0>MZkBi{^ors;8qb4D2f)m6QSo5#
zW{CBAwBs{BLfU9nFg1#vcFe;^65d#$96uR6gh^3$OrBsyfp{ItQkd~Q5+P<8Mwegu
zgih!^p1OB#jDONFVUzkcRv|Hz4=q`3&=^{W-10~Nylwjcos=A<2}3VEp0iVzBafb+
zyg8UfJp}ItNvtT?@|=&~QCBd4_z6Y^5WJ@q?P6yJMeY$LbNh#l`jHdakJu4Q71U@-
zjdb}v5Q6HUz*X?`bGWU9(gNVno`nZ9*PR~c^^c6)PMvz#6gKZOo&V}1y>}Xa<RKA+
zjA3hwPgCJyB=Td|w65R=D!_}Lz~MNaqGZ|Aapu<>8~|h*T63)uKQ^Lk5LOB_#Tyn*
z#`W=5TjZ<Z(+w}5bh_HR(|Fs+Jo<&b79stSw?6(@cO5&okScub<tpO_>G%~9aSjh#
zyff65K7l%8u@OW?SaLOly=XxDQ80M1lX>G=KYMezu9(tQ6UJv+7UMX>*2JarnCn?@
z=t?$*rbgT!emd`K%VeHgB-_euFa^}!aSVrf&ZPDCPXBt-w<h;{-kfUB)Ukk2g;pH{
z;8A`*Dgu&tG#W^rQ!lTpUZd%Cy!^DtavXWZ{vS<ii!U6^WQ3p=s$tr4K37_0q!GK#
z?voHd9Pb&8F|4^RV+o`l2g1sf1-}`1A*Ij!Tzj0cr>0ysGB4-TrI+$-&g7w=I(hU5
z2Swi)YHE{+3?bkv$_<2cbx-|CykGj0Y?|xHBZ>-Q#I_b4WS^$@UO_eJUjYu)i=OeQ
z_32;(k@W-%B;$oK9s&>ki{&`s)QTE~t0<2O1dbrb#*~4q`l)S8w>J7kiaxZ^!2955
z#+XXqPR^@rVmdFquT0*z=oqBe(<zSDn<&u108Wc$MQoK6K<?|id*A-Cx1gDU_oyn8
z@vtt{eD(G5l#Qu%ZVa`1)UVNfTwVC7<MRJ&@2kJ63fhI`hzgQ|h)7CGD@fN-@<<8N
z-Cfch79b6B=#&uY?odLyJ5>%Kt#td%M)ZB}{R6(Au4}oL;NG)mo_RVONrdazr1ric
za;P`Ei%{PM-F2HQiE==IL<}m(%&1`5e+}{+ZdC0Bdm^MmcX61L8|!4=pf88itNPz0
zz9wybCo<ka0UiH7jQ-_;aSC_G{wR8%uxBRNqvqxDVxW@MSwfq83WVpI$4Qnz^0izy
z%J>t$FMj#V$kDrGQ`nY79|6klrYw>)_iJT$+9cLU{YbYnn*2Si;&vlg*>BaV&H>o!
z`s4jp_s3)4^c*J-%!k-CB?h`@-pWg6drSZVs?S&C1X-jv4p#1w2;N6L*&CJiPNy5=
zmXF!3-M;43@@v!!y~O*T;3Fz@m~6bl3EolWV2-r)L`h#g%zp#y8nfB|LPG7NfzwkO
zI4}aXLg5#CTUtj=jfH1+_qFFiE5!p46=#<_Hjp6vZE^XL6=bgUkHG{it@-u|HNCTb
zvvXfPocnd>l%Nxvl0tOvTZ0Wn0aT${1w?*LNfC%(;QwfVCtfYVIm$V(Ww(vW$fB=y
z3)oJSU&BAl<7j6vUD|-`VTzls^)1La%1MsK?$eCZc`*v1mPnD{paDiSVEU_`i<SO;
zfKOhwy+i<?o}kN#P;0i~AtFlNUUft5u5b0EA&pv(=jt$ZZFME_R|`LLqPwf6R&*dK
z3#=otVx|-oH@)+lj{{kFrz1-pFfNowj8}3X*TQj6iv(0NYc-XyR4=c6BFM(no7K`_
zN~lokV68#P>AAgELd|f%Cg8-$H2tKo+}!w>-oqdh3_O(vP8n3WITBmi`Rw<dwc9}Z
zhul6%0HSm14s-ybkI|y0m%q<Dc)FbZv=ld39L15he|%z>&q`knQKh^5*KgJpWZ4t`
zBKeMu5^^uvxBb<(wiO;2WL*Y1aF7l93~uW2=bxZhvChywqpsvpudsV6?T>5oQxbum
zPMJPU7Q#v*71}6!ZJ$U$Lqg0?(=8jl=N&Y4Cdp6g97@E_vbvVArKntS3R;*UTyn+e
zCnTj<hqD4qdU3_TiT4+m=}jp)i5Ge7ACWmBTpV08Zp|WNYagO<&OYTkumgP&Zi+t3
z(UEZL8lK)fmr1>08J)cRUElR!=3^)yAr07sIGMs@K#qJMP^)n+oOb`se>mizxiChP
z^l7DlKSIRH*(QvM*5;NU1;KcB+$UT0A=)MLt*oS@-$+f8C~6LQ0Mi<JZBB>rKPeTn
zK!og`Cph8<T=ElrlZ~)Zc>(2De<o_R*V$H5ZxlElx)l+^^#{>Gb`>*|6`#sd!>s1*
zlcrq9A7>u%Uat21pDgX@bj}?E&%jj+PQBIuh64I^+LkZvJ@NVD?@VW>=y>*WN6Hya
zZOi25$0+me2JEVWC^k9a{f>c+;Q?$0Z>z2qi+w{5F4A^Ge~qo|Uk@VFMNcobNr-W9
zfT|}=ZG%*k7<77E>8K&xK4cqxgsCp`yM*hBXsbTbj&5Nyz3VzP@+)uQx>!~-rd@Ge
zR@K(^tnptu$`dmHT*=Klrv1<h3*c5_;MN5}1^8;jn{-g!i_ZsxVwh{*ze?Vh$+1Zn
zMxBT))z5sC-WcY-a!9)YX>nrZO;G|?d{3fy>UB4I^&|vOOoHk90CXP(f*8URC;<h%
zj;4t~VAh=30@zG9sKXO)Ggs<x$Xwl#4-W<vV$CHBAARXuxQAnD<*7x;ZrtVRv4%K!
z=H$ZB%t(b`E%QMKo-85ALG6tTm-u-{D-dL~T|%?P>?d5!-%GCm`@9Q#kEQ5|wq{Do
zoN1<zMi%pJ&FS=%UUO7Mpi&Y8t<n#8mb=4`Y0CGx)Gk>*9<{}>?iH4~G{tIx3pJql
z3{m1*{HyEWdde_{f$qPVGD~gC&KwoAA1$pHN)m-y7MSc31Kfh!@4B}1;c5y`hdA>K
z_9RX<{~wJHM<R7YLnB_@cGsKQIZw<MdjrVOsEo!1G(DlXDK%`>I+j?l>S^Y7nzU{&
z>Rdn1Hp@!r<|*KpcliyD0%K9C%u<85S(iw~wZ_vRd+}QqTUmyjm^v?+f1Uf+tkGVm
z*;Z1PgJRq;EHVeFH)O&EDN3NP*ovt*2TO;WsK_v_d~22_@&)|ebtRrj`_*`QKG9M<
z+li&n@HBN{ig=@rmr({8K4fTlwFHn1|F3#%cvCZMEJdA)F<#hdIsSTe$K-rf)Zo>K
z&0^=g=GQm_cZ-Uqk?+g*cMD&C+H?tJODa|K&}HwY`$y3z$YPVzv<EJ`uL^!F+BQp_
z&#od&+!M+8s8rb6pU<}YJUm_rY6ZHB@i;*ajBE1vsJUF$eo|3WroyH&@(!~-a6%>X
zvAoH2nD&L-JW$MAdQHr%*<KT@$?C^9RBDXGW##VLJo)%hVKG|606>};TuLl<M4qdA
z0to%^ZlT`-vHp;hsS;tSpB5VjW~V;iW-*c2@{7Oy+dZz?Ndg*a@&l@|P!V1s#5cU?
z^<d*+s+-YMo7${%a(Q-IE~?}cZ^`dj1>EyvTK76_==Bm(_Rs~zJBXu>h0p<HUHXJS
zrXJ15L&%7Rr#1tbK%QkF3H^Kj%G)OvnPusZR2p<?v-fQswSMxEs{lg-o7uUk=+icA
z`$@u>*J(np7tS88NJ(S+v83A8ZW^*hZKuh-VW1iaD|l)USWx@bJe0ti%7fk;&j*(v
zrj_oPb&pf8dRCgpUuY01NHlWwICgV7g%#sfX%mI^{;mo5n<)Rnlw!2bbc=c2%gZ{R
z&QK9Ur@2}v2eK{ti6POI)_q=sfqT8BMGa`gqO@$Ik^9K#&V2KcP?|2Mf;F66t72=I
z`ZZiqK{~z2X!3@3qz{GO%oxoPfa*7-XVk2!d^u}0M9uDinN{#O@$o>bU-8qgM%aHU
z?qPg8#>Vol5cflbR_^Gg%yyU9#`O2fKn4s~3BB4-+N!l7`cu;`+@#fYmB?Mfw{gcu
zM~>2MS%b$bCM}S<N~Li{>*_~w<}3S9vHYvBro+;!ogk0XzHR5G`G<qYS=m_~J<*0I
zOb1;!6=VsF9PcS9f1c{O5>&uUcAA>SsVnnKjSC$*T`cRvM!K7QC~P~V-r!co{{%Y*
z@RS9fIc}`;1Py%}P|-yrax!Q~!gp^%HTwppY-r80(&NL)CpcHTFJFi0vlZ2J?&TjI
zS!**fsa;AaSZ$ciS#4;@hy&N(T=yu40-PNGG4KQ6e|InktAEvlf!b5g+BMePXj^RO
ztGT33<g%s7^jAPB|9E!S;)#J*6xnGS$m>ThTri)Yrlc_MP{m>?ZWkmYdwb5z&|gs@
zdC;I`+hBYom<^Nbxt5U~Q!ZV@pkszE%V=7RCWcWGhZSadlO|n_RVWBLPO&IbqFDLw
z>!7|Nkb+!v64xVd-=SRLo<D-50Wom*)b~Fb25L~c3f0|)^|2k!&5g*p#gQlRxYgJ!
zAj)B^;1+)GuAM1REV^I<T+fG8Rk=Idx~w0(zw#ow>sp|v;7}x%J>Wtl#pDnSn;84|
zmfH{ST;w*2QE>{%-EmY~+%B|@5(#rqd7>NHB~sdI<dsQ<&*Uc|JMGw~qM5Z^!Y99p
ztrMicKQh(raoY9jc1JLSFAiHPqzznxL7^`jCaZIow+xgEFGE#ZQKUgXI?^8UGKpw9
zM>^UQ_|(fmj|4gLik%8y5=^@_$%+%bd{&}I9C!(?yU1_USYbt|Dw!8!vo?giQ0q#d
zRRi{6(Pgg-0D1%V;K75)ckgH!I}ls>+gGKY7z78iNk!<Ir`D8xMz*2_+9Q<$JXf(a
zH<F}$@o(5(!kcc{;MeT7+mo!MtSTvWoFI#Gi{;YATWiTZ-}5zK0ti*z@PPJzmrzPp
zHX0E{f2i%F>Xrm$(o^u!tA>tb@U`xGvv)N*2L#KPFMwKEqf=wo(I^<sk$9)ql-Nf9
z?%S8FafSsknSlx3LX(kQSATi0A$)p)A%G!P3dG05O5uHALnnh0`;I~pFW35;n>oas
zEeFM-oD1E?6*Swq%Os>}+qiulckO;^(RieO-j=qm7Wc>TQ6Q=-Sxj;<ZfNh5P`}f@
zC5-(S+DSxua_7}?;65?Z;8uBbpt7q^^`*b=6x;m#9aI><*igO}sJsr_yDu`@^Tt}6
za9qo<OBI;hr6PQGmpo$GuI!FtOP@Uyesih9a+nO-EtTCRKnj<?{v?3#Hx%|ozT=&_
z;b5kP?)&NeE$(XSxkyqi0-<$Ff@(EFq5NF>aGWpBJBmq6cdQO4j_Bix$C1R2+TZo)
zM8CM4>gUmIbNMC>^qqD^>wPIXN4!gZp+Kj1R(g91**NzJsz4Sd;z>Y+c*<t9c<x^N
z@8DuonU(1kX~%}O>PgGCK><0^3No!!L`*jn?Rn<ZktkwytH*QQI~Y^7u%Am8)Z-V9
zVOP+$QTsZ@oGbXgaCsob_FhphL5i<CA~h!mVPRpBQBg6vc^#yvw-Y&BMI_>~v*qW-
zsNvk95N?TNH21DOOtzXBBWp*F4bgxiWa;~UE{?Og>Ksd}@$2p}Y;!mBK(@N4P@&6e
zeBosR44vOT9{Xq%l1^NnCu^N0Mu?3oSfrX8zSH;Ht`gd%-6>+c8%r=rjDi2POWKvH
z>}S7D1Nvtyc0Y6isvUz!eC(`woqJbbTBBw?q7zuUN}L-#+DM8comETfCX)n?8~bR=
z1ULm8Ha2lfLN}5;4Eg?$E!A_F%-TC%>0YQ}j8-c(BSY*Ks|Kx`o7-TnY9Z5-h@l?y
z@*J!DWM!v)#ZvsMy>)MD8k5(?vLlJAiwT&nYzM+Dn|s9OlL2DyuhZIo#JIUGTx8VC
zpqXn`@N2QCjT$c&k-)|sW_Im|TmCmtjSgMX8^TnEif>kYoB$>IQ(^JEX0Te5ZJp}v
zL%OY*LG3|dW~$#Cg{7ZH>vr;zt`STSa5g`<;<p0hN1e1C-X$?WTEC5HiqCwzfb69%
z?dks}-Ce>*TQoxFcHWt`zaK_hjKkV}2x&4`ZymUQlT)v94?9_?=PGg0m2zLaiqbuW
zMJ6b0f7ac=Ka5+Bm=UGcBxI1ST)Q8|J!4C6y&}TNQkX{^_-j1(y@R(vg!;?N_CA%V
zsDbw>SuWTkkW#3~4kt)B;--S80(=cem%uY;*4|ENUtC|<np~pCdSL7uN3s_pj25^T
z@V#vPT5`mUc_i_ZqwDYDMT`WCIas_*Uo8|zQ)fOe8ACJaNR+>dj>?>Uv^@Rl+mB_B
zv8N#71aBe*=a@CSmJT7^3TyZc?Dmi)=Xa@xfoGpoHDwF)+%Ap2)6a)(eMA^>zDJSi
z#!^fSlr}4N8_DlFSMI(%7S%sIo-Eqo6e`?j<JxaJ8|PYn$GTPd{xZh^OPqse&^;c-
zwXhP9Rsh|}RYIyj>-!pVP>Ud1`Bdp+di%4$^_IK<YWezFiOzk>A1VT+vTz;l(p!?G
zhllHI5fO7GCvsaFjOD5y@AG`gUBzRG*<O9EKY?%BrmwxZo3?>=*e1BopC!~+pH;C?
z#Sbpq7w&`>Ile+&2Q`jiA*wPn6Igb3B_wgvvNT;Lue@VgiPIkRdVp|(ddS@4ZCsD5
z0;Xt?HhZJjSh>#JBtFv5B!5h|2e0c%DM~Rfy}8d;_%Z635VqwbaD}#OXA4R$1E&E&
zMd>xyeDMJ2XaV>b@ee*vOiV;ZJL!=m1!!~Wm~db3q#1K|F$tQnO?H`9S7ghNCy&i?
zb*%NRvpKVJJFF{ur|hw(5HruWP8#)Nhv-f4=+knSmwR;$^$Mr~B7OBb25+Gq_Q{xq
zpy>%ZD)Lf<*1TmgC>8~Z$_Ti$92>L(2o+mywD<HxfByWLS5j#vvn$DJT!r4p)iU!(
zX^GE$M85Y}x@gdE^U(-$P6ie_B4v^6KJjG3<cQNb8m_pzfjJ)y$7=1jyH^uPTVzIW
zB8@Z4zX;dlx7TZ5?#&x5r|h|LE^jb0bbP<H<k()Oh7M1_)Gt^Wu{NQ3w`(+}(}R;o
zm2ba;_3~Ch0S)PbeC%;|z<1JayP-K&%s|{nnd5Y+r8vK>Wz~)n^qYCuJ_z{<l}ESJ
za_SG|dcB}6a^~V1fRtf#2-){4FI@uqHjf{@I*EPrhfull*~BV^QERUG*EF4}i)e?#
zwCq<*7AMRWd{hIq^hP6RKB}{}Zo~MS%Wz599!f7v*W6>tofq8x7)>&&h#E5N(fEA&
zvR6?ixsVF&ymWx-sjM$;#T^V=$c-q1v9md=^k&zR@wi)~$cLh`ty@Ej>+X64VR)Ym
zwMVe}Z%&Q4B3TrJuK&U+xv~iHx>`l1gx>CK(R4X4tz-gf_mi>ygd&AVXp}~Pr_rc8
zxr@xxs|g}Te_R&$MasUkqoYU~8*pi<xBPPZ+KI_Ew8~1v8g!~k+XmLVmC7jNGZs&r
zxt#e(N8@p)IlBo2<zr2mNp#3&?h$G3;$f-wU?Fr%dsm<3X0~njj2Pb0e}m&=HR*QZ
zn~JTUKei%xj^_A9PpLYeDcl-;5e}Uz<@&3~cNv+P36nCb*f?)Way;xYH+n<l5-w!I
z|NRjt^_*5d<Y%CvVvln&!iw=<xVx=f9~-Jw?P9JbJi)Xz_WpI8W$tCV<x_7?^&#GE
z*KOTy&O+Ud4^!fj6}PH;y1505Na_om2<PuXWR0eYE@fbC&iGyIl3&@)dyI?`;z73%
zpiS@2%E)Re&NQk#PmuQ6;>|AeFY(><PpW5V1?qeHzpuXiT+-aFq*X67#b*CmRo|_u
zRy}LUE;4;Z7-!d8R9)??K@Ro0Tz{gfa4$Eu5z&4n%k&i4n<cbumOa_Z3xg=3NdhfW
zjZ`xv7GlFMZ&odBN*gcND0XyWcXoL7bPFqVs+(XY=X?p&G9Qghs?S|Si=SS}t2E?!
zvmh@SAD9*&PCKH+zr%WTvh~g&s;U61%Oh)F*`wK#S+=|Rkt!LR{m>1xWtrOHwpQ=+
zqjp_!r61k8O6v*TC>zX_!X@?E-eK5iM`+|d?fl<-f%IAY4(q$u=y^ijS32lF-Pti#
z^R|6MxyjX4^OS(5Xke%2EG*7gEvB7hS6gj%LdgR*#r+J$HWZ}eXaJ{JC!zI|m3d}&
z>-=M3Sm7=1b$%F7+O*WGCdMyik>1+dMq|m>-OfZgg}LO+$*|jL=<m8`>%u2_LcPDv
zhPOA<2a=mbPPp(yv`eUd^ja0(&dWX07G$86j8}iN$ytic&YpkjP`};1T`YOUr~#p~
zc^_f5D&yX2sXC8w{?$f|@Rp-Yx*U+tMD;Ytw}3psI5Nq6yP_-aUMZ34?*qpC%Ui8q
zeYb;+cfB@_l}%&^yLx`k-za^|Q66hF>J+%I?T_VSZgRW9SYN6}9!tQR(&|P|{xi+r
zpD0FDP^X444#THU*}Cuo&OZ48e;vfIYcRuGvNVI9>4I}=`Z=?bb{S`vxvcy?Etzq$
zH#3f$CHA<skl%o|98PphQ`&u9*t8F~tGZN_BgBZCY1pOq^Hwe0-DPWJ8{c|Dkzk{W
zk;huZO!ShOse7|6Gyb8z6kKQ3FxlJ&@m(XZ84EfkKvX@0>@q87CSdC=Gue%%au4Qc
z6E0i6xkV{MVovXMgusgM@3tFjj7Kr66n+!iu3Hm}YQeQIalg}pT}?9;fPQ&7&e1$<
zRh7Ql$7kCQGafmjA8TinfX%sWt8HxZVsv&)#$AiX-1WTkTKFPh+2-DtpLzk&QQ=#F
zmynd?)o<C-_EEW?Ry4i|xt1RuBdpHOuy201zIK>3&3f`4mCp~7@LYjc3wz@FaYh8A
zYT{9q(u=@FQPIxM|D!}O@>ZYq2KO_})fQDsTbv-=8?#FO#?Ps7wq*-f`$9$*Og`K%
z6_M=xc?Y7DOMaABqzrt@z7he5f93O=p4MVyVUcmwO7Sk06+U)O=+-ROq-adbF5QvS
zX16!8fw#piafpl)Adh3-7<(hu<O?45b<bLcx|zkb$60^2M;!Ym+R#7QlkQ93tLWvT
zDdkH0Jxp7`+3jAlaP82nh%L+^blF-t;ak2K$6(nK|DJXW#W{YVf4m;vqO$vy9Bd@~
z7Ho-ZPJ+IV5gARo-<d$j(;l6ju+jLErKVslj{dHf$Gl(M+xec4wSBbC^WE{;^WFWE
zDvP?w5%g(7+17B0*o=I{8=>Y9W|_gGmk15h4g6B1rCL@3%1M(v*)m@)mrya$>J65z
z5y6;)EG)Nv`fu<44+)nLRx^pb*8y;TM#E{McxQQQ-cSFUCUCP|yhWlo{@C5Io$m+#
z%65Tax0V7i1^t$MRJU-$Ag$YTcavRM{7!wvqsGsX)+N<<SwC}h%cwl;rY-a8ZZ~1y
z6)RxD=^~3N>z#G`dN^v^eSbOf?MoAT;Z*aLz{TqCSd(!r<w;UgZL|fuQK)C<0(O}V
zNWTr2OQ74GUm4L4o5)cGhO%fEQ!8ZloHmV5U4g%uy=F>!;F36f+(>e3SkL`=)3H|z
zrPk#sh_Qquz0icB9>)<jqK*d$YadR=;paVK?n_rtAG9Kp=_`8+Ots1-Q=^n#8*9h4
zM+#A6$-)}U<kfD)Zf?tLX~aytoYxlJR+8=Q<E|eyEx*CXlIz{@tKgIHpZgjF5|evI
zBN60J0p}@<7*9l|L8D02ojii(xaVE-uB-u#9HVLME7?Y5oYBw2a(%bvof;d)FhT0Q
zRU!JEk)<cQ(x|59%=kU>2y$eoN#qB)Md_F$b8F<8c{F3Ms?HlJB*ME~m1oHFb@!KU
z^F)$&(z1oiHrtu?*#fe7MQz0$!Pc`@^jd>0PUUVUE+K~$mKDY!-}SomP3*IVy6>+J
z_3+nb$D?zHrG368-Tv#3%F;qa6&TXhEC&ujRSrvgD%IK9DQ3#p+xfQisijbJH;4M1
z;n@DWwKn==PUFpnrwgm@$t7Ec+77U-IUyL(#fv;`+%@MOHv2oZ%_{cX$4fV3s_mpr
zIO2N0<#3H)y0eneGmVfF$A-;BG3zqIG9_!LBCCk63S{B~mUahl(X2-_&p?CHBGvz8
z@m$Rbpf9T3TZ=KB(`D9lMs_v-&Q3tY6UGE^aV+sSV`q-cPACp7<TU$#Nx1ukGX9mb
zCAo(gX~y?mL37TbZGnE;e9`!8C|XXwAvovG1bZp#Sk$X9s3rnXloC*WGwdB6fMbKu
zCJFt3R+UMZ6&GUdfW4BpuajFWgP-u%gbDX-X-{`cjbMnd%g3&vA2N-)aU?!Ls}F{3
zMO&7WpLwWb^%y<#>KQBUf!QY_bUflr9WS%IoG18oVGm~j*+id|hy~%qEMa>`Z}@8O
z21uGHeyo#H^yn)2gch!)ImL!mQ+l9YN|uR)!M2k__GO_)=^lr&(r)Y~2bY9hQ`r}_
zO)?c3efivu=K5z`YlC7L{)nd@@%sHjJ+Blc6ACvL>wBC2B=7@0V-vvCJ)1QEq@d$V
z<uo<v^q?|?S&dev*SBK!N87zr9cA4GXJlX#J+z^Qs4p$g!$@A_1?C->56E<ul?$>=
zbw5QJY-dJ2ijE6R*ioN4T~tsf$IO>MSaDfL^l&x%ICQIWZnNu<a*`;KaxyBBMy0Hx
z%kV3_le^u@pO3J$U;`mn=yK};D_xcSat+e8eq)||?`~eG&8#)*Et%mkmMc!29uwS&
z`yOfdhH9UqKK*Fd@bE#$>2uP;Kb8ZWiX7-mLut`DG+?o(l71%DA*jhx{`NtqOpb$l
z+n4Tv(Ab|xf5!TTR$cTSd&>ATYL*q%IkYVvGj}+<)A^^*0rVg{5TEWjTmjH~ALvt8
zn@vnjdD`z3kFY7vuTy?0cz3L;&c^9Iu470^da;K~eoBzc1!V=$1v=sXu^}FV=E)zL
z2LgpMY*kAOf*E~|^^!_Mtql5lC;q*61l}9%3jyJ;ORxrF-t}=6MYV8dfhT)ibD*Wh
z@sZ-Ai_I5_0_8W<nfeJtfXlF&(A7c-r*~~3FplNpbq){AzAyCkH>#*0fC8uv<5LCZ
zKl*%byQ8U6H98@+1ncUz_udy))+8JPFVvsMGGu(|oE0VdUPGW4K$l4Bm#{B=$&v2E
zEC8d2zI1Mzm4lj&1{)nF5R9$Bf>dEv)@U%M;GHrZ&F%1^P-|8b$Ktx*t+(PDKhhc(
z+v#7t2lu5#dYms%1V<nOZxRu8yd`2*q*%zFvx*`X)?kotgsfD5MG8E$^GyR(a0kLH
z%e3|x$OnagO}!#PHl)a3(G%&U*@enSkm4O}dqNAIZ%gVG5INL0`5?tA{3<Li<c0nG
zkI>$t?>>FwEL~$US&#B_K+)Lw%C3Otj#{!l76d=~`d<OOme~}?f@V=c+ihiLL{Z_!
zo?Y3i^Tv1pc-}Xu0(T8_6+wjoJQeK<n?PoLnPmi{dfcN4WsLz})$hZ976sdt1=*%K
z7oBzecwrMEFSoub&t%AM%W#(QQhl=<KcrZqS$q6<&jj>vd8^O&_@JEAaABCW=0oIl
z&J5QzREYgtS67l4x0i*yEOZs%%T&>jRXV&h2|fmAg?-X{T2fj{$IP6Iq={;*wbGRm
zK6B6Q)^5-g=9v2vJ%k?Q0JIQw5gTy74$Yd;*G~gu8-};BarTR+=}GL@C&&}A>b54X
zPSknnHfslS{_C;%`vz6ug39^VKU6mTlH`gZ47FkNkS$!}+$_xBcRTotTvSh00avVf
z@|+9;FI>sbvX5VDOQvvbjjq;cYrj<?Odp~}2oI^k?}10f5cZI=LM3}e17+MfVv6c|
zFq7~^|B+2R(^Koe*NFrk09xtzRp>q8TeoK$qkqI8cQw0c8_W0U)3+XoDF5kzN|lfP
zBTzPXF{=cFN-qI^?`9;J*#oSzQtBNZdYkZs6peejtmFFPb?ASGFzf*un;bI{1AY>7
z+NX+&)Z*geC7{ziv&D>HcEqdC)P144%yb^ic>!@$7tn8F1C~(jWRMy3vpiNX*|0{a
zAhqOMm-QH8K$pvEn4qV?IUcS7Y$-3D34qkXpBZo}{BYfnje8*+u&u*2gSb+wN``sb
zKXx4?x&Ru^R?vWM-SDO;82OY*m$>K@Uvd4>p}TyW)YA)?=@nioC7haa7K7;2Rpsjm
z;S^%IAv{<(E#K6T7K$c87*g~MEY|BxV*xET1S2uw{k4$2DDVJO87AhXI&Ox~FUS68
zm-u?T#pLT^@4zQ~qhXvu&h1|8>`@}89_8;g@zm9?y?{Jq(89J5_sbOufRds<P~&4?
zr4ta)1l0b~s&b*HSSbF9_i9^-yF=WG!wS9TXL?8<fZqIBbP18?PwL+fe!S3z-@SMN
zV}5YQ^y-2Hr)KdjSp!v@X-i`vj6VuA6hwv-tR_`Z5kLosfl0dbMsbE&vJ#m*{53SM
zlCY_-I~w)mj~Moa+e3&w->nQSUv{hNWO#3|Mm_81<|KPU<)*YH*1ri?{`5_2E&hvN
zvd?dFg{q&HS#+bO4329#pk`mfx$+Ye7Yg8??>Zd|q%O?7hEifK!7y+A7;FfPYVv;I
z<A2g8qJrS<F8Yp6=)nKy*}=y^ZDWm(dqVB&n-A$fPo$+Y^YXe!nUn%+%kmy4d-;UO
zBvfhx<zF+}tM1=7R;b|wn#6RW&@UAK3;W*Q6b~QoS}FQ&DdB7<cQnYbYCP4u)uQ8D
zdU0gmO&}J%=310M);Ru8WK&?hX8ZH{_>Sa~a6tj@{K^+JcmBe@4#0jXA>a~p<lmTD
zF3#6FI<9w)VY%>&2A!&kTKs7(b_s?<2Pc?%S_1V*1JsxWXD*%Yjm}<@_Reh^tT8v~
zo*(}=(?f|^FEHuC>xVD(w#ocU1v1S`^T~X7D)3Z&8Wcb%oHu3z7&^`lm(M%3mcoHx
zYI)_MUy@+9T7idwpJ%CAcKI%q-NiPgk^#fs%BzN=3cm20e&lq=nJqgl!rNyW19YyS
zVaGYc@P!Kkj^WC`22r~r%YP71kqptSeU|viIz!T*zGQ@@a(gR#m2~5AB1pG6zfI4(
z0H1<PG6;Z0ruttv*r=lL;*;;R>&A`2do)y(eUtPedE@I-HHwf@2cZJ5Blw~y4+{Hy
z3;y@|bF?c?b~2oM)g9Nz?RY%{Oi}X}yZfq&X2P&%*`)~5<>5^nXtD-lHjGgY{$&wx
zDn0d`S5O5ebdC-Jsz6!f!?ANT2w+Za4)k%*XIP4rZ9UEa?i<Ni3XSvXXXtT!M$qh4
z3P<Oh5oc(nnl0EI?xqlT&i~e;BO=0PmUDZFf3YkD@c8bky~M{oe+~co>x?qc=&7@l
zn&<e)sq%!(JR*jXnR_GJCChfpNjvA9KZ94v0D`Q2O8A8dHce#?qobn(6Mbo!Rq~`J
zxt@8Y<+VMk)>o2b2riD|>4@*~ct%bz_P0$q9>fklJis_tqP~LU^#V8h6<?K-Ic$cr
zscV+{|L~mYO6+(}<O|~S<LeMqZS8nYXxHenkq^vD>)!ySq373|p$4d^6{l(8oI2C7
zP$s7B>xS3UH;tfVUkCv*@-H<94pl7YfV|`jjDQZWmjZCy%Ua^!T!eFoz$YPn!FuS~
z^NXuIzfX9RFyP+DlfeNXyDJ3!#ivvwQ&VXb6_q<9@gE@t?R?>4uwlju91nm4e($jU
zh)Vs=c`A^)e6b2MC^dA?qzkxwfnXQEPgMcvq7lgZmy7)8aoS=XbIk95-v0N$AAr}g
zLWLUlfBpLBTW}y%jTgMo_wW6Op|Eu7a9?l$;7VZtcC5FxTnI?4r1p!9Bw%D>iX>uI
zQM<{N_U|A#3?LZ?2V?=A!}NSNEW`)jgV+;A=vUllUS7qCq!4mf)1mn++2joYW-9#7
ze<1tNw+T%9VHnDmFBIIadOn{4#@}#w9@v==Wyvl$>i|l%2ffo0wQLLi>PO%*0-NHw
zEG@)pEJm2z4i{pKKfl5(aoL=`a}0IkzyYr3sh@3HpjQ4bXde4jSWiiW@RQ+otxc3J
zfC)<Bd=85N4WL2<z6Ei1)1K&yX93(;2yWlkgHN04Iqe<Di<~Oyc<m|&j#^qOC}R?#
z<$}laE!bW_i3@*TkwU^vOtOM0gfw~uHU-TG(#5;GY$nSPv+k>`BqH8*Ym?>7i_g~p
zm|HOJ7e6x~<AboVp9=OzrxG`9&G>_P5~*M|i{jI#RHY_8kzk;UVrN&^`>Q2-pJ3`G
zMuFHAf@)WB$?k>R`IYOebdQ_bE&z6t;uPTP33U}Wle*wIR_)Sukc0Y?A3uhXkr8i4
z&Y4Uyhw<C)6cLe??7hvo&2BS6mklivZY#CvZ|w^5O%g!RUf{^j^rld90kPi^KQ#y3
zmD!KRX2JY8mAP(EL~xEiQW3s;WngxYLY8a-{gIYcSqYe|sE<fC<FFF_+J6Ps+S)4G
zql=^`Kg!{~4C^55Uy_T3-*ubS!n^?13OhaqdWO&MGY+G6{G}TaM=62wK%iZ}Ure|U
zWh?w84xD>Uk%Bk@T{%OFkl0H|dh$<A#GuuL`%}m#@+#b7*GXKHb`V1Mq-)|VZm^JJ
z`ZvRa>Z`J!PQ80af0}3}z$6`<h${RdAVEjpnozf|z<T1jqEzxVl;^BYJ(BXCFuVw0
z;TLKgg>u5r_b^KU`MrVGq4CmUsv<x5_JhxH*dIq<@_|dYAOfyNi2VFen0Y$&8f~HE
znhP)ZkH3NGbRVte{|*4jkb@JB#c`T;0)(TxdA$`o1H|<0zXvirnQQP8XMlQ8!wn!!
zGML8n9}~m>S?w#=|Il_;Fpl5JqC3;+o$o||mp9GR`gg!U&#J@3_=k1yrd8PLXnqtX
zAw4vZZ~o0>&NBvRz+;*D5dFsUHQ>EJ0`H8@g#rLy91G(>OK*PzShbxu<3xPCr{h;n
zh??KvL;><_5eiKLf%hLk>G!t*J)i$^3(tE`wX|4l8xFI;K^RQd9bs*sUS8{J^1Cdq
z3xa2<->w{p<bMmPhW)!e-`B7?FgkM@9ZBcFVx{zW?=|2w?C}v#{@pGHt%e?ef`WWU
zB%gx%cmRA5qCttC*Z=vf6~T|O!W0&*s;5j$Oo>J69AC0VX;ehOiH@qB&hwD|910lk
zxFRQZ)oUePiDKN$%+g0Y%QPT#w<}CZ2|&;Sd<^t$5vBk6^ubfOKkh0qkSBFC_&aj<
z#Qxs-U*v;(0`__!y!IagKZnK7aBvF$J1;L{LI2?M9c-y>Umxn7_YcgFkq1sBG<^Hj
z|5_bi*LNat{<sLzfTZG^z+IuHY#XC-NV%nA*tCs)e*H99X*-vpor3j`2XliXO!hx%
z0nUnsTe193AE%wCtReZy$H%9rrgpDS<agaWB(gKVa+ay_cxIFKw$az%LR%PQE}!qo
z>DJfEbpB`0slbonu70K|UMdn{6zjA_(9}B?-3Be;-zN@n4-DS`ywjraSbIRiW25z*
zy_@78KbHoYAgeX+XP~1WUzv4VBG0~7k)Hk;p;B18!4C!-20kmcnW^b;TS{80^9Crs
zR>;Ej4?qO8K!#9B$w9P97TokvITr<{eYptRe|t^bSw5ztIX^#B>*7-3wLjyM9?163
zTLKTL@~ysf>CR1r*drzQD_UeNg!@u{P4i(Q8z3auR|9sEq(EafT~%Bvbn|-0(eCPd
zie|vg{(PEh23)~Verpc6nhY(tDW&&9v}z(RTf>3D^1yq^;n=r9LCgTy4fR`G?0*r@
z&+(R?^LIxZFQ{IX@rLVG2ZJ%d)ZX&K>c#lj2V(Ta){|v{Ze#+`3nJHFHS5O-c~p2F
zeDm+cURTw2ibgFhsj4VT=F}Z;)N(j1%5=21hu@%)XgAMJQmGc14Fa*Wj#rq3MrS97
ziaI)Ml4Q?cC3+t%+k=^Loh8OysGRKNNK)H61xIgKrI{ck2*52#ad2{q&zpm-)@nNb
z2D8bifVhmYo~l5Afr<;s9vcyPd3l|N9~(c&lWp+rC=k}`P~qDd1eOg*K6RXZ4MfSH
z8=3LJ&u<(S!}s>z_qv?&EQavR+R@*#8c2Vw<v1YDvZ^8;z@u{uBbL<l!y%<lWXwxi
zZuCKMz1@`&4#4o4M%KrR-v^U$W_5DUjx(n=J|uG*Rwyy;qu@LF*_@=Cx<edpwgBcV
zx|lGHo*rREoJE4BgJ|f3kKjIw+FrXO9M8YEbgqEuhC>Kyr)DtjH>t&XW4b!}c(+yC
zWrhw6nv{&Tj<5ygV$=W+QtFOYatA*s(C{tB8fIwMI&+v0(oU3H#y}G(d;1mu!U$lN
zCA0vn8>yZ}bwn}CkL9Ye9|FWm+Lw-Di)JpUdQ3e(u+64klCgErPP`i;+LzW<m=i)@
zWHZ2b(=q8eE7mO*JA;AE2)e<=^~yP<rcJfD^TxEgTfiA9r}v2~35Su?0=EtjBw#j#
zN4aqT78<%I<9)z78T>{%lN@^adUg8^l^wy9-`;}Je9fq)w#NnnFpL8^J)pVRFUFa6
z);O)A7GiXfV2<K+&ig%$m9hOfpUs_ki@te>J|PDF8ZgytjBjzj@pt`?7Po=7^r1A8
zp^m*1+47X8jHTzAhsQdXsT=3|>yZp4LNze0h)~T79Tm*kiytCQdF7nx4Y6xg^#?8`
z*fvBwljCa*{I*fMiA1t$@*iKrCA-avonuSp&LNvsE~y-~C`BrZELT-G98lX0%Z&eE
zX<MS{$aZx-%?s=n^@h|YT$-)ACFd9n_gx@MKOT;J;fn)0^H!uq2Y{?UPny)`?rD2K
z5z?q|eV1DAv2{`&8%J)Un>Tsq4naXXRX8qeG9d@CEOPb>Z$RYx@}S{{AZ!xrur%eg
z8(~H1`ql4AXiox9*GoL%d;BeG9wcB)>S(9ek-_F{eQ*49ysPu_p!lrMZ!c3;n|-Pz
zsm-<0KIcgIj58SYTnqfMiF|{3>YNP8KyvdnzvTKgk}lL1KWvenKQ@+5xVLP%K9aA4
zONT2IBcNH2YhGaUOi}SWZC2yiZs7t?<6(${xwijF{r;>2fk(N=a%N0o@{ciHpP55Z
zUy6+S0h_`C+{`R0y_bN^<K&A410#;q52y~xqYGddt=qz?E4!TFuNK$Z35c&AJb`;~
z?H;P^D`2fhjHGw0+4>x7n%<N)g5@5|@qR-;U3>@B<ddb5u(>lZJ26P0X5nxtr7*Io
zMsnGq*d|cMU{_t;YjiY3(nGZT$8my9O-JAtSv{xPJ?|Amc)XDcyFY<{a&AqC@S6<r
zD7*D5qduzB=%W+*XG66vo1L@fvK!_8DyyhlURDTeu8t9-B<QYai@D1P9sqrc9o_NG
zH!z?1Q;u}DTvFmU`(h?;rxhhrv)GL>3QfrJ3zvi0eE#6%>~W2&L!;Cr<YZO%_s2(R
zgoM?tvK%w}lhVguM9v1>1gysI2Xb}u*fW;4XMm2sBw>5q76XBaju-{F`k^d91f`42
z=f;S&q%8#pw|XtAFklVe5F9{S4XxIQGuRGNEVY*ye!&GKW6h5p_rLe3B7K@E86+ID
z`{R9BTVBjD);qbfZw;Yt_s`iYCDspojY`3-cA9jA!7c&cBZXaxr{u`IzG{oZR(5ZN
z)A!MvGs%-{YgWr(NcY0t3j1_NqGPSF>Bst5C&s`~(ZZa*rW$d>=VQb!mFAJ_XTOwY
zJ~B+ZC!S5AkY=K(GhUvOP0}Ej4V5`c1w!;c%Y6lC<(Spg)NYp+@Zan`%`^nVVIyN=
zh_}_8A>r<QtDm-8w#4zOjtfEhiqj|}W}2XSIM`-rNp~i_&qsp!C~t)!#q;~xAZ9Y?
zb8RLb<`88;?wu!8>%<xu47O8WSc4vc>`fAoE|ZksA<;|1(G8MAF39mOSRM5FIFe9Q
ziV67FhHYZFB7mwYo&SghV4<a7`(5o{Z>+@6(%%!twlJ~R{d}=K9epe<lQ%yOdFaNO
zQtRLI>qj8fF!MHsNP=bvCW1bShR<N`I+lEBU(%VG->og+$qn-~3*<Qxk13vL^l5aM
zzCx_^)w8h6hk)E)=NW(znaF0{cKVwe6P71KDQ6E?9&Bjg#Gll1a!3k=hhVr$fend<
zJOW;lueN9|75e#^1vf}@f3QE(a18WkF0A8N(dWOMEj+*b&7bngu8#i)N<GL2tCt2h
zV%M*uRav^xpDya>k4LGa{)`;$y$E~xkD7vrz9<z6Bb^_4^)`Wt!yPk=-eJK@!_VZM
zc*c%|2A==8vPvM0dLZhLn|~vFkHu$qM74J><pjmRwO5LURtQXQ_Y1zv@Krb}lPHKg
zL%MQRw`E3`-=^VkF}6=5gj`@W?yDFB9wa-x-$BaQfn3w6+lLO*ytF3%yHUqhA9|_D
z4!iRTeyt#B!UPAs)E8<CgjV+?Rze@WysA7wdAJzF-e5OtA$U|JsV$v8-@iIacWbOr
zGK@k2q{t_0RPw~oRutrSJlDb3GhkpZgFOI>y#@xE^SFNW?y$b~K&L345Xx$_90O7z
z?)d*;XHD_iixUL%mwSa}gMs#T(m7vdN@f52xRbmNQe9YX+C|-WA&E6pJ^)0y(oK*T
zn925uP<<L*wo;tFa^t}y;bE8Vnc^KT3luit=&D$+;~K@1x6Ay1<l}g5>lK@VT@SEN
ztQv$>o|+?au{DTTpm^D#Vc@L??hKFX&#z|wML1~k*Fm*Qhsj;w8~*tojw)5Fx%qgP
z0|-7y6D&OhL#^NPr$x!xl~_-4V_wBOIT*!EuK`n=4~cTK(~inrp=32zZMX+JG>-cf
z2`kzNc@3%;$h9Rh2f$n*D<^4e!-wDT$Jd5;<(6YA4bHkJV?L)b+uOrl4ay8*_Ca#D
zG6`v_#XYQcmkN<DlUjKqp8wc&GADOoOj9eaYJRnQpoYjhW+|rswb4k)e=>PmDJ3HP
zs3{0?ixevd0Gra2F*T^G)@OaJadDMHz-9n7tbO!|ICm`B&uk$p!Nwl>wjYsHHVqsM
z*G{{X%C<{zfC~9D-gjy>=eap72ce1D;^Y4o)k~VXng~o|k(I3n{Fa5n4QW2Si4V;q
zfq6F>H;rI0b^q#j4fQ@u*!~THuIH~-x!oB$i-&%ryGl-HFZO3W`X-1;<m>K!>HRcv
z7S+zXGaMoGq1YfWm}j)#l!tLlBzQ%pzXm(;?06dp0RckE<quTXVLCTc8AgNhAIkue
zl13xXa&9G3<#nnBdSjCAV<ek4lf2-sS6Z&$UiWff06A4Jc!-L6zFOGly{y|=Zc07M
zY4BXW337io!Tj23WukJyK;5Ck>KxAc)%NHpIex@WoLo}KL6XaC#9~jv*av0cfLeG@
z!Y^9nE#aYk49uj$#<LnvFo-!_Bw>bPJdluqzqA35jQ8nIaTu|#XYp2|6M~ExuV9ZF
z+)K_XLC|Wpk6`|+==z&%3Ju4qZD{oSNkV#k5x+SVW?pWK3p+%S7Z=0)bKEej11MG-
z*nUIF>aveF>-Tj`Z;t_Mhc!%aD_}^I6tC%YLHzeoL1W^%O(k-@3K7e8obGdJSWMZ+
zX5|<tBVs#lDyKB5dqom^{VJ7JU@~xG-zsP6<o1W{>p%MBWz~RkY_?>V^fDFbh(Mp-
z*n#VKA1-Wx$?U3pg`{EhU0_^oo_Cza90L<>efaSvJWs7GTRrCMKzdk`83B3zhxZ9z
zSNaXs2UJpAyPCuW@TTykD&S4ng}37)A|5bK5HGrepzbrcKi_g*X@vOi$ATu^vF);T
zdw#xq85Zn$a@`qyWo-#k8SYL_)C}Y+PJKfAyqM2-PtWuCtcd&3l~ZU&G8Fx>e=p1X
z*hi|Ix+so0tWTC(Mx&Rq?aov?WCIIq`Y>)PPrdXG<$&dK--4_3ui=BILfo!&q3!*|
zENWj*o>Osha@unif>P>BKA-okhVa2K&jWRMipU@x9&Ux%)+(Eh2$;?M@|5=r<R?+V
z=uQ@zLg;a+kx%A|kNM{J^~o+ZFN(xht@O{Ui4*d=*uPi%LTgZ~`aK{|nq!6BO-P5v
z?gP;Egq0o%LzGRcpa(3Jbf4BS{pW&s31Y5nATb|7Waq!JB5o-A<lV4u71)C76Cf3y
z^*p){>`b_)U$w&$|21F$2}FffNv>uvocI4>hi!1CXrzcPd+i2Y+XZf^k3daP7IKBO
zC|3=5Ok{t$wDgaD16H6+z5_GXJAgKyw0{ES;TCB(tp|=^COF<cZ*vj=B3&WFU%7x-
z`TE=s7*)JCEI2%xad_IB@kCgFl!yjisS=&MFH@mEfn&l3#$O#-fSD`aD4!x+Z$pU!
zoKA`q*!E@S7EGXS&>O#B-)T(8Bfpg5JFvLEb&tCNzQr#s|D&e(eCN(++AEpHUC3zN
zBhHO9e~OZNjTQhK%$p!Gfo@>WgpR8WqSv^}_q~KS*{%!#_sd;3*%asOtP4*7)n@_$
zFwGY@175>AG_HSseCP4sAXf?)UNpXhBeVP0?gXcW3`*eaKFZ<B`RlFz<5BqP0Gy^N
zJoiWb#2^29fKF(E)63gJcEz9`KHnBXCnD1LM948j@Sk5Faz~Ki>p2^2p#QarFW$98
zzb$7a`|QR&0LuSzT3kY~?1Ojy`&YhP;I>8|68Zhdhjjkm;Vt%f|NR_zO$~(8EdMN;
z5!G~5Ql!PrLtAh93iZ+Lu6hMSqTEP!!QZvvGHA&0KtwYx*cg=)Otl-UD@FiU%V$I<
zVxPx<p-oP|I^O}lb?1sA@om+=Km*YjxV+U{Qr|8J$49O(eHNiF+Gy~~qS^ZuO?|lm
zqbq^*GXeLPe;yh?M}RT1UsSSt0mhj71;gQ?ogoz8-*{0svRv;npL=DzCr2>x<4;NH
zIsE_QUQ%QJIX!^397`l{Qt12^JHhzP?LNE?ll833f5$clk+lSR99b^l9B1<rmc7ie
zdf+>)LlsFh&rqIK0IC7_Oia|0HHeT~_#JuL`7&h}$A3!ccf0Y<$dZyL6y$_cMSuz|
zwCZfjrzSYK?H?+N`|3&`qYCKs`Ob2SJS(EbxGQc#Q3B}MCOmK--e%%ZzpjQ`Kc;D^
z3U@I(Jx&cpm~$@$?FVT6^ipF>ZjKaeXt?{~BK%^`hJTNZjpCf4^^kuM1~(jj-!D2-
zpAm;{%~4sdY|gRl3?y{xXfY5J_LUc_zg}vS`y45#yyN=UpXv?LId(;v6|Vv5`L3zm
zR+Pi5mglj>oEpk+1C7KXR;sDn{a;=Wx)m`9_O7!^oq+Sg$<UCfz?aQz7bsW+{JEo@
zCNyT?=dWMU9T61Ty3U5`b&uMwxyXUm9e)S`UGZ-JJtE?T_;ZML5Sk(HZcVYFRX4ns
zG6}!i+7Y$%<N}uc_y8a{X`Or^)RKRN-!#ao!(8o!xC#CD$s;8$?NhggcCQ)Opo*#c
zMdA$roG^5nM>L9{R~CqCa!T?{N8NF7e!Z~Qs*jmK%dJB)EQ5+jMkDDDMsXz*9{&aX
ze+6}Lu2dlX7d~a`A9}D#uihtperJ|U=}#Qc?;(hh$(CNtt^wL_3|ySoYV<Xco2#2z
z#by|}!0P9=3Vp~{hRE(f8o)w#9bpabRT36eNq(DOu#wg~Q=;P5p^p%3kjp=<`B&!n
z-<aNiUqVuogXQO~1o2<lHY@t{VIWyjN8Va?Yo(Ud7t|b|6EUE%$nn^RAnpL-m<)Em
zOU5x-q!%*^w5veZs`KxIDgavUU28Zx_z_No=7sAlFCH!1OS*1r+>sd126`Es--h1~
zfmxLk)nHe|B*X<U3EMHyQAeT5^4ZdhGGw>Iv%{{JELtkxnf?3Wcu+{4V1gs(1PBAy
z?^^9O)a|G$Z`;w=LcMvr{2%kBnrJKkCT_y1X8wc?I5{}zGX>B=#^ePG`~myV1`v*`
zpOX;#ldJWQJ^iEvOwk7FcJ|-TTSCDZb0*mT^4)^G5-@Y)f2><10bn1WMg8=Dtq`fU
z@jspeul?Vm{|D3mL#h8sKL1Bi|Hn}N2cP`^$xr=$<H1QMt%Ce%NiTsv2yr>Fg2#F<
F{vSnI8%_WK

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/packages/autograd/_static/autograd_graph.mp4 b/docs/python_docs/python/tutorials/packages/autograd/_static/autograd_graph.mp4
new file mode 100644
index 0000000000000000000000000000000000000000..0bf34617c39c593fe7205c0d7ef12477bbd6192a
GIT binary patch
literal 113606
zcmeFZ1yEhd7B+ft4H_i41$TFMcXxMpcL@Y{_W;2I1b3G}f)hNryURPd$=o}4W-{}t
z-mAY})w@*z_SwC<zprJj-Tke70ssIAOr1UKEu8FZ002<H_rJg&MgvzP1{-^31^@s6
zZR%)Z0ssjcvoSVs2HI3YLOeg0EsE^6ZOw~+Os1YAm?u6uc4uT|CZHiOvU4;cU|?Y+
zaAag>VJ0x)Fks=JX95<8(*h0XWaLG}XqX8ERE2<+#wJF<0wFtl4{H-sX97lgdL|l1
zdImP&O><{wdrmq!H#aw0R|{hkJ8J`5T02KGy6-5o=FT?OKpQ)IXA3)9Cr$z*149EN
z9tHwO6H^{00%H?HYda$=9tKW&PI>|ZTLWtkClelecP37HcLoL~0vi(^a}#$0Cl^ED
z6&r!QlLxRW@VB0$F%JVRJ+Kn+7lDn1yNR*h_ZAs|HS`<}Y|Tt~7+496%pL7)4D^6i
z83>#mO{}dgoPbyC?(D`!&cF*J2OA!GAO!<sPdi%^9!3Ug1_lCC11D!adnYRk`|lP%
z8gQ`JvokeyGI8diVI*)icLdgO;$bDQwzIP`Fb96n`|TbBfs?g`5s=GoH|Pm$9sh-4
zWMO0A{GEq|t+R=vwE@rwc;C?4#nHe+&&bZk-oP37*$6m9&W;8aw!kKUhK>f`ZA={v
zY)qVhgJq~^?*Y8FFy>(dUK<!2*njVZp`M|Ifzx*o3rCZG^~=q~!pz*+5LjksZ(^%w
zW@is9{c+PCSk=nJ1K2hXBMbe{OFbJ4Ti{0mCnFPE6C)RA9%lOQG#w4T52>SxlR2=u
zqmkafyZgP|(TK;$(Uic(5IC;iy9NBf!^lKSPvG#qGd%RPtiVhA?+yR!uYo%cD+lm~
zle39E4>N&<J#dnM3j#QYz_tt=fb;)-HUa(s0OXEIcrbwO`RS_iVTAHqR}w9*hSvv$
zvmFV?$8dTda{vJB=RZ*K09szJ{j|cV?>C^u)l5$mxo(4};1!N9w%nXy4>v~|E&4B9
zZF;4&+wn2a+uG7y%?$UI2u;mEWWX`Wwb{<W^<bxS@Er^^G7snUq~F$k2xKUZoqF3x
zL!#8mnpbS1=bz;MKq};Vgt%*hv*0;j-WF51Jq5W1FI(Eicwbjd8OL>v7@!{#RFk3+
z&3SS=b-rHkVlQ9E)+IwarD8#9lTNNFXGb+ce1oE#?kxB6#?f{Wz*=87erUvdwvk2m
z$r$W|FSMH0ZGtd&m4FjUZVvpQZt<Bco0=+pv9PYWM+=^>X`t965#E_pyPN(hFIr27
zYU8wZU^d~SElO}o8q%a4s+BAnSrRKeINR6J?UYwHiuVg@i9u5-f@hl&CNS*O5t33g
z(!~kKIx)BO2cu@y(V!o00u(bU6`khx<}>-T)1kx+BTn5Cd>3R#P1rH|RgyeXhc#IH
zR!+L0woxl*gCNTNp~!qB!n`1ZCOo|q%5u-NKJ3o6g4C#{JY+-X&JkzB+R&*9#~oes
zfY0@PWE$+Lj@!Klp{t>jOOBw)<PuC#2=u{lu<4vox*lPoi)KO#641n*Q46*?4}?`T
zXB<c29b*Bj-ArGEa`B~64g3<gSPhq*vG%B1UB{<1Qq4_Fyao=ZgSkf@ZxqXS@!5>W
z+kguy%wV7Xq{uV0Y(OU6MiG0MUQx|CouvYiR-AEX(5}fOt~Qg0WgAbX^G%O_^%8DN
zwL%7^^PKQ)zwT;<yb;;z9Ff1)OGZN`A(2j7F68%pWeaZW1yt^BBW@K)*u5!DgmpTX
z*w-JJHKUornY#SbYc0(qkz6U}@_jT}&baP!3pG<37gv&Iw~Z^5&xnhPWC@AbeE2dO
zA@Ue+wx~!*$_g-1wVrHbJKt*|zfBsfi1_kt=8XbvZ)|bI^a{>;Wg$rdJ&tKdd1>sd
zB@5SzBJ88jaH+5WAw(=KJO9co2YLEfeUQhrfY%%MC`yQf;F6Bv58GsHRvP-dkgu5{
zkoQ&C*!8Yem96i)hie5IK2JV-gOS#(uRuuDv0|{Nh(w%t%F^{8`w-)QVrzL<8YzX7
zmB){%WvH(ex&BShW<oI_caIXsE3aT}E2&1b_BsTbgFj{j6$Yg@19zClJuhZSuv)!Y
zQ7Wo^xxBXO6qTsa2hHQS%sx;h)uTBGV=O?|Cd(f!0`Ham@%jQb<Dj&OMMcGO$hMf3
zT36rjm1ALN{Dh%Z*!l6tNlT9WNc&obmhxM>$(8=kl8b1Z*=RC?mVTZ=O*CB9U3KLk
z!`Jj%N9K-6Db#ydMj`C`Mh{QMC(8*0Vo9x@<)s4p4eTu>>p1n{w3$P+hEUsi!ZPng
zQ)hbSQ*)Odmx%~97_j5XGb!H&RzE6fufN1v>bi$}FVNU^@3K!ILkv+gcQs0$MEF(6
z#!v>km^6Hb!YfSoeJ14^fggk0L3@C<gfu$)GaU2drxy~LL2hgj{BbfhI;Az}L~?Hf
z&I_-d#}Ybcz1I#aTis7Kj-CeqZ<Wcm4?C^JVX=)|u%*=+gt2pCroFX~89Y9-gL*zD
z(2e=ND1|c$<Lw<lKiZ{z0pH*r3AF>610bP~a^5ubf9)N2a;I`D*CJX(A0|aJGb{r~
zp<d(sU_*u`V|3C!mpLCBZ;#mkFZoq*r5iQuwS~5?`*`pQT1|3iH8)o^C>wP92>{?L
z4nyPx9i||8qSf&LW@h?{8?jQ;cVlgrEogV+(QFvfcEvkcVVngc0kb}sY`NR@E_ml`
z=ZsVn$3Q)XRkMd9sCi2rQ9@Kw$*Lz>kdXyvzEDJI7!W&4aTgc+;YCO}x0ysL;RekP
zj$<Msn-J8wp%G8^;>F}JBo(iaf+N#LR*eLbp-THF?NKqp=cc2NO|1R){)W>5vlQsr
zsM}WL^$N}Cmm%d1woH}J+h_*_RPS#?HrSnKr;l=F-?s1gWTFXKt*|NEGZ5WCU^mgC
zBKX~^idu)iRx`&P#9vmxCLL>a92QchXT*^rOyIOmgnExdQS0;iG@dEGw+lW3ypsUl
z&7#^&me=Wpo<Bg)iUX_mtp{9<Z>r9qwnm7eRHmmrW8H>WVm^{!s@)!+S@3}Y7!mlY
zS$KG|7X*F61SoaxxPs|u&bIcJ15;j{ILHQ>;DYSqTWC6wWr#Rrgs_;Gh?CHk?@wa9
zH!6_v7<f4IqGF}L88)C}bGcll0l;|6|MNKd_jBq0-USFEaJPmjkWOMP`1}-A{a@`c
z<O0jk1hNI)gZ=)i05R=*0Z6uiv)J>06}*!FQD9{G#{val0iHm*xkQP8d73n|-k-tp
zfEE}6*`XrH)WrYY>ThsGe}enoDv-y!Kf!(H0fZ|D!dd?{T<#CJVG*=n!R`JD?q~1z
z{}PT+ApPWzaJ7bZ-%j~|3=!D&zYuc}eu<bv^C!d~9n7KoOGG?@4CX%~{(IK{HF+2U
zIf}oAbNCb7j}GQI{R+<g*Kj|kFDLmg;8KAdM+3>J{}bHL-hKQN+>h0g(fVt++#iFv
z@E34D7H7`wpWuG>?*3PBtG|Z(xj6Cu0`5oevUz?1m(BMJxO9?V!~N)8w%}jD{XcTO
zU--hmUGJB^@c(Vs1A6+OT<<@Q@N;GU&+mRavAp`9-~GZ7{^xgpd~W~kBah_Ieg_(I
z7YRVScp36@UjVN2BT)ap=6$ehfAQ2yJ?-GFRZsC}PX(BKD4a^wG`Gr6pg;3zyQC1`
zi!{7L9$<|QmU99q_1jw1JO&h6TEa`~I>8v>490;;b$|A;Z{V(<71HkkxCW}+s`iFn
zj6lhoq!pt$55V||(Y4AvPI&K$#wHFziN`7+1+tlEbJK&(9_NW56yNU?xXErc&fk{v
zL+Ik2s@8ZsGi;v+C%nWL9MH?gJ!nDQ8u?rs49*F?Nfv@P(D0%|{KEY!;VL+Ub6jlk
zlF1jkx8pu>&1Nq>BW2&n#YUpzLN{3WjH7%uz;PvrL<vlgQW(FS%b=fm?N^7G|LEJ5
z)(}(**J&B=JR{HKOX5RX6?I~V21DiL5>1DkuqOg*hU_Rj2M0wW$fW24kLmkrHbj6m
zoam`lm7wl5Y=LDk<raC0`{k!GfK&WISl;J{yUoW;y0;;Pr|&viRf>}`A~*`mZ4A^7
zg(L3+Uw<;MVZG1aV|X=)XjSoHRP~FWx2L`H6u1b$vFq(C3D;~k7|wyCF-Bd_X&v0y
z4i`V9d9v3vY5lS3_>dS!mXBWd0*2?`0GZG#bx;~7Om1+DA?b(Xm)B5S;8&p5wRfl~
zy!@}Tq@F{|XB5j=%WpD!xdjf|yPq&Dd6B*pcPNEbG+omcO<*n}M^xM%(d%-yp~!yv
ziuy|9Q~AQzlS<J>D=>B#aide^B!sW~4vgZ^iJ;|pG%FYL<rerDrF%z-K8saXS+9J{
zv8<vZaqz-spyH;*$!@1a?V51(o@3Xxq~xf5%e~157M}>1MHAnOLch15)CE%ueK&i}
zG8=-w$w5ZP963HemNoR0`$m3OZ-3=|>MK-kC?OfaO7v5wAq8+SzEAu)9X{0vBcHwS
zl}c`|P(D&foLnq%&QM=}puyS6^7?o#4X)T;dJcQ=KBhSp^$V5O$J2yM>4~6jO+WIl
z*0}AC;?l+w8K+-TT^jRoXS?#DTq*n+OyFIvO!QdZvjm8l6#2WHR(J(8luQK$<{EoD
z5Sl+ktK^fK-YwxjYQz&$72GQ>&daVx7QLt)GGPfMdY_q@HH=|nQ@nnhW3kTljD|yG
zdZtab>lSaU$e_J2L+L#bhOp|JIE*K;CsnFLoXn8UGF)<>L$AosJYMzu9=;CpsRQ1M
zG+m$NV2kKQc7x)(Fbs+f%berqAUT9!%wU(JlgP;?Kz84Lc;>i<rJc)ITm%N5o0|BU
zw}HF-BH_0dWbsA256#wji0VCjl8DR1Hy;B-oO!kYHtx^8)ePM`i@ueSGOGRyE|(Qj
zGKVjejAB0W&mP)3pAO){5z=P%m)eacyat1=lBo$xD#)2LeGr+LJ(HPw>UCEuetcPv
zVl~iPNn5PuQ|4G=Z4||U!FG1LC{xMdRc@0UFQq6-D|vI{WAxzYM1JcA<9ad4#@xO2
z5tVPAPg_^})$ICO)1E7%Lj)$nILv3Vw<>seZ107Abc!jpNGo(n1z^5ycyyGfk9dY_
zEJsY!XGBBUtY1Nv7`G!>Wf-p?o%LVoYWAb<#!cT3u2l+IIC$|J5#D{E3}G13*Tr)c
zh=6FyM_ATBt4k>0C~+0kZ9hL(7Uv?4&EG<ey!eVY{MhfTS`jXJhHt<gbIVeB7r|bU
zqVNL#3Y$Go^vF3nZ(eWf@*&=P$`3>9Y`l)!m-4CB@oO=D5gzMs7jC~EPbZp^`isnl
z-XkzENykM&HR56(W`}JUz74`j&xsb+>K9EJAN_D)Lf(yVtW{BtO3_oy-r!EHC+cJY
zg(7Oo7zd*0v}rR87C}X~Dndeu<GQLV_%^=qrHHZ~()hVAw@F-n!3kr$fUDP23>;xZ
ztipjRUOL%IE+m{q0GrR;xIqDT07QJV>P9H(&{e?uB73+IAEI^gUKw}9NUyPm$?G(v
zmT&Kc4%fe;SroonS(Zwu@h02eQ+co6h2wa%rJwQcnNbN+P&GZ&KjoJFW(q+yQ!-8y
zKk)q}N4sqPHxr9z1(zNZ2f;Oh$yK$U=+8BHxc7+a-dC;hcZGD`u&-*bPAlNVY;`eo
zjAQMF*s%iiNBCTpP-f>bFU-!jY%3{+O_86mNe!sPYbs}ROlvZ3*}$Ww$!teCOL(6=
z6dIJAST1_&&mB@=KaziS0IS?t>ZmTENvtf5ZbTtvta;^911Rm>#)MVN9GiDgQ_%R<
zv!fof?^B|X!yb%sLwS0*Vm8P-Vz2iCM&XRHi))y&)&Q>)qU+uMir|T;z_irDVTZQW
zN!_PfVVT8@BFIp?h~D?|QW9RGvifO$ciU^lQZGsfx*L#adm6_@KZfhXtT_ryZuZR$
zzCm>4bDqH_XhCT0|5n;r+Vi>~tGVK~bX1K|n5DVEzN)H!VCzGsQ+vPSk;I!77zuyT
zWQD=jZC(>^`P2#(>Y(d_%|s_7v9O9Lhx_jAcFW5=3V}z;mp%rPh^V)Zo%U>H#5y>c
z?99!!iuI62>}&=;(8j&pa`;)M5*8niogWu1cE-yvvx7ckf`>Bdo`TMF);t`Jg6IvS
z^&%fwNA;#|5FLjEZZe#&U?Ux7RDFERW^)z3G1mffaN}ND_f#`0LaSsfeott$qD{|r
z98#spnU<rY+u=V{NwrV~)2v~2No=I4dj0jelk<578P`11ay#uwUfFq#JP*B2pMkWN
za`Ndzl6w;XZbezM$Rn2b1SQdNZ^fbxP^&=wxZ4TGYBuV)TXXpa9Qz1zjqq+}vF~A@
zE~i&?ZG;uvjL4=st$kt8vy~);)mVO1Qlc$3;N8WFu>Yi%+}t=~AItP6_DM6b(tF~|
z6Dd!5OvuZ-0^<^SDB*J<l_I{6DjZ|2EtzxjMz+G`)qL`TuB(VU^WyK2Q3sN~Dctxq
z7Fms3xIMv7zO(gn?QqzEbEcJl*0qV0s<qXqZs354c`AXaP{LrJt2x8o842E%`{tSh
z$EB)y;7v1^a!SC{(JV%=MCPG4Z4j{3hs^j=1XixrT&$pXHre}p!m~NqBYGa*UNN&1
z+LBm{KBQlmk4zoA9i^v1vkcQH-9;2>tJBbFGvjJg@VX}`coQx^l9s7zkU?9>wU|Ib
zm7|6HrS?VPMvwr>XjieZ?|MOEh<fertL2kvgMnd)S|3=_+DaCi@`CEKP#H@LF1W`>
zeENb+S=&Q1NZKsrSQT8FQ}A8<)XuF)A&ltWwh8>061~dx@aU{=O=^o!^o}f6*VEbt
zviOcq(6zi>HyaV`0E;**whF?vp4xbK`J~1Gz>qpP70Pi=io7sgk==TuWfycXXiXAM
zPbf4ZxOMc$dsudFZ!#(+mt8Y9u)zsse|+#x$6LwT<B>WRmJ*ox2F{f3l-HU4*Fj`7
zk4vLIsqK*xl#2ZyWAPN&&^wTt3{JZXg%u>CICTTnqAl9PJ2bFAm@>DXba~zJ_cAsK
z(wKV9#1m$=r;|^1z>v7H(-E^l^j5nD_%wAQNF@X^aCbOH?4N_Gbz(a+#5mwQ)W{bP
z`?iREeI={JL1{qk+ndaNpOM7}5mH%-x*!>TT0Os(doErgL^;tbVv9B77pB(Tgj>e5
zxx(4A@8ixm!P(MK;r3Q^rhgW2!Jtvln~VH83`7fX&`Y-%!`S8Yt;uFpiYv^3Keyr%
zlUPVtF_N3frPrJ=A+Ox!;+vY|TPBgD0oQRYgJC|*m3qe%M2;-Obn-o54vC%sxMSx0
zy>L@whzF5PU@E>jL`?|`?Fa*6AB<=sTaG(`L?nfE#)(EsfW73dX)u%O4*>9W0c%r&
z<owijK=58Yyvn^qvK*15IEfErJE4o96l%;F?*cj2xxoQ|lme}ZL4Ff_K=61TaL65S
zgK~uRR0BF6GJ-J)zVG9)?SW?O0vUgle?W;(Il8WRi*K>wgz=dG_6uU0Sl8+JSC`=h
zYbdGTfPxnoz(n9XA%WisL-m(v!fpO!ULf$eG=nKIZn5BRqK)5-Odse20C40$YodQ^
zMIg9(9i8^bbE-N$N&tIBG1->{-`m9f479=r$@wW9f#BW|f~YS9k1)Xclyw!Xem4Qh
z`EDZc7ZOsQS2|MecN0)rpb4=+&VMwd1_-WCFYMZ58`30wiJ_T2zFPs%zMqzXU_bRF
z5L~0f$IwLZJjD>*1*q%Q?>4X{KTLiqOTXj;6zu*%V<HyF{*T7=*L1$0$nZga)0_T=
z&Tq=o-_ZFd4GKu-KN{3u)A=zvze&M=N9U&~{C9M|%fr7ontxYufOJCtEs6atou7Ku
zU()%$=79tFQ#AfNI{zRa1L^!nKK@%eKc(cqr1N9#Wd9VE|BlW-Xk5Uh{GU_y-_iL^
zXZ}k%KSt*_#rbdO{FC+!r1Kx``ETj`7OVcovwkYle@Ew!!RcSl`!C6h;m6wfT?qPX
zIzK$?H=X+LM(3Xb575#7LA(B6-1(KG=l*c?|8sGN<wsiTKXFH%Aq_zI;y_@|KhK3+
z@URpsEDz!HNGkxM{HS+OkpBC^f~@~P9gYb8n9X`glB}J`{o5*sr2awZe*x()lZ!tV
z)&Bz0uXCaQ3rPQ82}p1@e+)>#fRp+ls|0!D{CNE&uWIgd)Nbp<3QmC9RA&A(8&A30
z2qyX5$=$IFmOg0LPh;~caPrtI9{-bg!O8SfYOVY;EtAA@D47LnM>B8IoThPXfEg0J
zw9<K5Jfb?2*R$ED>@6~=+EwkI#S$fChW*c@=bW&?h745H+J&zX_vW;sr{KTvA=J*m
ztKhk@5PEA&%@7apM=|BpP!+L{3&hX36Zb&H_+A($f)AB@B=eZ7F`8JsoZPg7qk2tz
zEeX2VtBqnGTWS=89Hz~Q=ywaReU@62TXIJeC{$hl#lFX~`J??IJ%q_ypJZD6)d`dW
z4sdQ;syzd{J!kHVE}L)Z;XG!7V8Tp$!nc(sHpt^+%qucUEh0^!qaZ@jvU3l}r_7m-
z)Wf2#Qd?;@*3qi9t>rpxRbsu|L@P;hO7kGD3^7>~MiNuVZt5f1>lvpeQEx-eXAg&|
zH$^)m@U5?<N_1_+8)t3N*3ybiy0sKi4j|8;ro7_$c!uper7Zz%k>p}obVU+sRH12A
z9K3OAEv`HIt?LXIuuI03+{t>h{NA<8%y=DW{yGEYJP^jV{rnQT#O>Uvn>2h_@a-_I
zCshv=xiiiMbK2KC`7=5J@Pq|$7Wx`3{;!lATvKnngAhv<jnXU}Yj6CDK4PM#ys`Dv
z2H)^09Uvw#7&*<$J*$;bt!58oN7pb5?Lwcz5{wUeVn1&ipYQ@fvQV3GLI6j)ROr3i
zY8`fr@vNzWHvm{xmn*|z9ZP1&DM*m?Qn=77&{dJ2I5RzkIG)mS)<x-sK0&_d@z$dE
zCUAc)1sj^jnc5HO;=7*;?2_}vVNjBqm3SelW2OAi+48jWM&E|xHPWMCHGh$`@2#9(
zsk5%i)$Sqv^tfqJO|H`8R~p>I51xt&SKIBoi?><FNm7QdMIs(mZ>`|tD^}**8%0MV
zd(}4-`m8%mM#mORYOaD?<%7hWX{q24hc%3-6%p_E!oR@oUU|Gq{@{wl;c$VG^!oW8
z;R~{KE<N6Kpao%p=8{?q!|an~o~72Dnjo?=&O{p)WgssZsP2&7*as*v*I|LEmah@-
zG!r7CJ~&Je4WFw8Kcsr3xS*h*`VmEO^?kUu<{KhVVy9@uBsTSZaayZjF<88Ui6LvQ
zMSn^ly}JWi>Z@r(N3JfD7pPyF?!8RWV>B0e<y-^Sh3Lt|wY=v2(QvkONw#=_RmhW?
z7gj23R3(~_R}@~Rf*oAUIPG1zj4{@jb{)#%Rtswt6`cuDL?DS`!|PQzC8pdDp9ScK
zRvF$C`A*+_7NNA;h0Hm*E263%Mu~j^hciXb_bL;exmnHZM(!FvP0$f#XRX^FQS>JM
zs@0sbie2WEcutx&rezq#Xsb>=Z`NTbnf-W=g+&`ap%3ElAa|kxZGoNBq3^i&TiW`Q
z{|&?(8%tz&x*&+bk=UFxHCldz<&Mz!@#jP6(kGAu^4ojw+QSdx8b{@XJ~C{S!=bcf
z&>%JbD16NFVEV(Fbp47A<w_7V4Jut)=gz#JB=mU;r|w<Er^!a=Pj(?`b@U+?j4Uab
zderBM%r%y_bsG$0*hpGh!;N0*%3lw5IfNgJe{!l9e{IN=y=UI?($(Xscm&r){7OZg
z?R6RmgQgg_!+j&F8Vr*GP5(}s+_ix&$=z<&P3vU11tOa0o6p;R+O%;jwy0ea{yuEv
z^rz`Wg+#nn9VYP%#lBdzk04MU*(AJvWMNu{H`wn35g2gi?!GoPM%fcFP2{b5tUvpQ
zL|ImFHZn}^?Rk`uR`mH{u$SHJlA6fdB;-k>28Gy$b2Xun^DBD>95^0YRJbT1NFv9e
zE<nZwEa6f2z>}EcA-?S7kmL?(?G<x2%2FKhd&4)kN84tY*$L7x0UvEV>Q`QGdtFZA
z<!wpmjzffEw%(1bKL|MBBqh=cp?cAXKRj1T71OS{+(-~9eB8uXN^I<_qP=ZV6E{jK
zEwRXev9hM9fc}gS=IF=Lj}nfKYIhvt%n?f($N+!b_<DGHkJ}K|SIhk^pZj+3c>XIb
z<9#NN*CV~0N=Xx`A#+Gy9%;p*2>MQI<d+m#ygxXs)-r#lIHyRZO9U6R-_NK~qsoSK
z-7>exT7ru5ey1;>HAq4IX2qzl$m_%<C2QJ6x$Y|!h_oRy`<I3AH=z#V#M(wCd-Rdx
z7}9jT6AF%2+I-ed%_+vBFirxUZk_w+qV|?=QAM#Hl~03O`4h-i)q7eB`Da#0f*ds>
zV44fuZW(U)5(CC?P?=sz3u?3o3hC9xiVH@xP?jCYya<PNAUdphwss87eG5CS!1}U(
zMPahfki1%%^Mjdc>DF8_s481`k@11gmkuM9ixthQyHMT|>y@6dZ_ZfrV^d+r_!9~j
zDxu1ugI}N@^OHVlj++J8K52SU^@+3cJ~wqQ%;0#Pq}hzQ`s>El)b@?atRL@#y{(Lz
zAz3zUfAxM&25GJ#<NX_HtnN{aQb=Sx>czMYQb+}&=uQ@cti-DEJoG?4f4nyCUdU0z
z>9_Jrj&OqI#>B^|rPI>sPOMm!Z{Es>VW+$x2v`j3hWb)`kY^$i5;>IzA14xPE?VFs
zPWXnra6z?5@cBb<4)|gWdtSUIILwOv07(q9TCBXidh~pdPr<$Cplm9hV{-Cr#DJH1
zji?6B9+Y9b_CQ~l_U!slLS+|s@{;|xcby6Q-X&D6*&xUhj5iuyj?eaLhR*d<Gt_$D
zme)XCJ9G{0N0A;i=<n}85|Oc;TDyD<j&tU%ckmrjtu-NGx%iqL@%9~0Y-RpsU;!LB
zWE!dy%Pu`u`+N2<3Qiw461(vEnHlmrJ^XZXgW$?hP_mxQcW;RprJ1X!M>(O{=06^G
z7grf_u?gf1(7dtkY+TWGO&(2Yt5zSIi1!$rkmu}wVCuZ4fWy<Pwj2NOT7y$>EV3km
zQZ+zw<BDXmf;F+LfGJ7In!JD;%Tm#GFiRTJz=mO?d~jjXbZo3FcNf8qu#68Cqo0f*
zG!r*b<x6Ql3%1{?B5wUosdXNssp}14AOXxkG8o=RV&ZyI93zStLC}OgE(a+~$<pgd
z=P?&rC&l$>RH+{PN*!Vf+Is~n#z=GCB-u-&JYK1%<t3^wC}gJ{r=NoO+H|h-FuK|q
z{lN#xP+pEWMQE3G3kuafU!+SqHe&a~y&;xz1*PPWAANtz0|AFEq$gv?4Rb}*_7r)y
z%mI%2n3=G{X`%&ryV)^dmHe$gf;y%@SYU|pIcHg3=97(`&ZN(9pK$<BO5eAqy)Fsi
z<6aIDG&;XtU8$~BnM3C%LO17JmKZMJYub@6D4VZmYzIs%jXrlKE=AB^Dp?6hwe8)B
zEZ45j((;>sTYtxGE%`1VvHtpcC)k>+Nn}f*cD-yp)A+EwEVeUg>{J)FFz<S+(`gek
zw2l2?JK<%n0-%ik3p&!5Gu0{KV6zv};<IkJ$6gwmDt-Il;Gr8%#!F{TCJd*2=W102
z)wMJ}wFlVuG?ceovWthbSa{*KYbqF}YBW^Df(4lO7AKx6!BASa$WW|Og{D!|d%~#~
z)O2hpdx4nl>NHYUxYy}~$^?e9xb9`S?^gAdq@ya7N8#d3Uio+V*TEVUZZv*?R?u5|
zA?e=WSo%fmTS~kEJO;cQ!2n4npaJX(LK7>AKF<<v<1@HU8TQqne4=H<SNu^5=zOE~
z*5<s50<QhwFRCJP6shQ+XfZvRQtv>ARZG!r*@NT^YeH7)NUXQ9y3P~D<)nPz<Y?)|
zedjuzi=rxJQ>{CR4%v+0vaFg;GD&7;`Why57~`mFsm=2@SkOpVjIA4%jkr`A*QA`<
zx<`ALc%7409%VPmN6env+TLiDOtB(F^ncXd0Dh5a4R~(m`jf6`zazM4a~l`hxRr6y
zBOLtXq8jv42#IF5Ym58<36uRjc4xw~SQ!AY9005i6chhRZ)Evzy-@>RaWW?kacuv%
zM;c@*8X5Ya6tiwKU8DIx4Zq~&HN@_2Z6g2@9N4z#ACLXOvjBh`bJQQnO%?5h98oB9
z8shan4<5R>kX(GBtknXB4!>x!8}S1P(Zpi2gV!a)DzJaKz?Nu!saC?y%nzIA6S3dR
z`6#3DBZvb4VqL%r<RIC9rCZvL$NNjv*};}0feL<CA1eHTw!|R65-<ZHrdDbA0RRYP
zpxHmas7LX4A|_<_Ee3#v9|Z{j5ctlJ1mqV|<~Ku#evqi|?3ljKFEGRL^9|ZR3W$GT
z_fMK8kln8}&A(vx*UIMa*scCx_X~ORkL><I;smn$&+E5;lsJFSt`mp~9J`+{<o-eE
z{2jZWWA_WS^WW@LfX)50;t6E;>rBh<+5Mc^zs|S(j@>E{7C5uNNV@!y-9IE=DE=<-
z@_TljKva@nrC<KU?pHaOKd}3!JPeTCul3O1v->f2zsSb?ncXi^GJj<E4`~@7yI-ee
z{({|K2g~2F`?2nRm7)1JyPpx|AF?z+cE1)y|AO6L#XP9%-?96#?*13={ycX7&N}~h
zCg)FM_wRi39}>5JnAtz&n|~9#!2thC>HII={VwVK@3H$CjsF+#{=#Sf7w`VGgZj%o
z-v8nqaO?PYJE)&~l>h(i?{EY%ntr~V@%#|{pFj*8CpdH95eUpAJP?5J{B$kb0h&Q_
zYY7&w275JJNUG7KXa#as{JRQd5|bO@64L{Y`rmJ|5&>UXg26w5DdC^-4#EgDh{Jpp
z;&=@0jZhAPYp7v$oicR*KyY=Vg^HLKJ^<+?q?N47@F0jsz@&Dw+o*+O+qc2*gm0nR
z+iK<A3p>#zeV~z~TEF(y+f&Rug;^Uv=5oK_RS_8TJt-ThHg;xQHYK3Ro)r_VMN~wL
z8fjwAasC87(mSfYNF75zI%M3^pA&w%*auqKo(iHSUF+?}sj}m?C6_{<4buh_sbEVr
z>Js$+V6C?ohkK6zEtd+`uO5W(4oxEPi-?C>^jV}w<%RS(DdUpILLud_)|I5li<Y+%
zp=hia{kI8BP4GRtuk?v?qcj;bCkT!kP#JC!NRaDZ-N4n}<GVvv!5x6(osZk+`^*_Z
zdn60e9@p@z0vxh7vsch=>9pL>>nEGa+fzlPC}Xh)IG~3#CB0AHoZ1xEfY{x={#ssD
zTI*NnA+{FnUH<tdzKq4s9aR86Da8kg*S(+cW+!CMdjs@@sMH_Y!1*lvW&G8vu21+^
z!3O&@ri)vkh=fCPFDALz45|$j>apJIT(HO2uNqX>c+}0GV!uljymrv4z649!V!`VT
zZ9R{@)LSGpLzO{K1B&;UmidfuW=ktdnlWz%;Gpi%^V6*`zT#Zt5%`10cI#)rQ(knD
zV1kzq!>^7BaQimpR@lfZfcC_8bIB>(uw>N?TVO5}*`(zwx3Uu#gL;hJ75Z9bz3<W!
zvAKMRibrAWg4v>>5wnwf>O*6Q>J+0TVv^H(DwKi07Fg<lpfqFoOy~NQRb{w(p2w0&
z(Z99fTcI5NkO>ugGoyGXDzqu-$#8&GY-hC}YN#!ReXSIP^aYL{Wa=)w3M_fZuv!%E
zhxhArlc-|7#W19rpf#Z4=#0rGp$;U;OCXQA6QHY`9VZK7cM$yDXI4sk94QvVtwQ*X
zJE58_iA@wHfoSIphcPi<#}fMUs3O-6u2|qApwhWp1>wm>Es?15(OU*7z8PV3)d^cL
zf`2=6$sgB2tV38bf5^aPOHM<j3SR48z5j+4sp|}BNDrO}^^S@YSvF*!aw_0UXeSC~
zpau%(?b(hqM-oPe%@->r1W~(Hfg8}ZCWbqa4BF5K`#jUIKxNYHL|^-PLlcDlkx?F!
z!{%>~?PQaihxJMQ^$xc}={YqO`BJb%+@YsKnm4(;oTW>z9OqBi+pnj<@24W)3<1JU
z-5bu5+!p;G6|VTb3OLO|094xHjCr+H9d27lp^g<4ge5I=%+WX>SE+o?adJ7kj5E<`
zBs_(&&?h3NFW2g-=Tr!J7YnlSFZQQ>x;}(~!1-O%6?W6cRl63!LXFgFo0%XJl3eBu
zZFJ#Y6~A=@XMCF`Qq?xh=vvT_(U7CNc53nXJj1dJ+DWXOT98sgb9>TcCfWdJv(zFI
zcLQc~_Kq-swVjZ1m!`porhTu=_Zh@(i*eN7Rl0{sdY`=e0N~i``q*^C0P5U6eThiM
zjnT>wUw?AE4iCUqyi87Afs%)+tCC`|#ZLGX&D6Eoq7s!JsO*|7R5`IL?0H}e`tDm6
zKm9UH;dKnk6=YsL@Jsz^hG6+|1PuX*YOG2mOq{C>ZKn@2H`&S0`(v<karfp2FWC=D
z-ZLMLd>g)52<>~JoWmoB_)S=kNuR%JzDD)ARkY7;Av1kC+M5!y`4#>Wr<P<)du-PU
zy{?QA?DL7erOemZ>kgIekD)@~BTN==G%h+S+MW#%a1?mN;f}01^<>t3D;OTz)<*Bx
zq8FBM07e8c=hp*$AxWvZ?~;WNP9#u@Zec#Aoag9vmeIP#jXDZKn1|^OP;{lBTu&r0
z2$4QSUi%OTJ!IpS=|Q{8BsVo9Y!5*#Pd?{fg69B0`cfWLwvUPP=a=4($ArI@SDnD~
zhpLniK(TmG4OXM)F|D0x82qqwLS&NYG@r|$4E~6xmFQu2U=xHs5LRP^k2iE`55gN3
z0A~cxQ_G+++S7Q=NO&g55+ogf5!Q}yNYVm3j94w_j@M7|veqAmz;lr{3$u}Jxn$fB
z31u@D<tXN?kLy8=QqMQ}gmS|aWIv-N%7C~^c>v|hj5pq1)_e_p8|gdX_rXD;3VIhM
zt=tH$R~=ZlCve}jp`?f8)hRn3gP6L7S)#Dt^jSNcNAeHZ-K>^mpogvqrMwL=ud?sQ
zTq7jWr$`o4pZwe~6>e8LY70_5k_<hlQjf(ct9LrU;3`QrQ;LE!*a^V6;1W}jK#jW<
zZN%L4E6fc>difTXNM)@+)VVZ;phodh5zu3&%?}o;RnIAjk{(2u$H#vKolS(b{$zx*
z>3FXsBv{p+*|kf@Ex29(CUr_)<GdpC%x6+9g_qA!@#1KkVPhj|p)x)@yi^IEBTsW^
zA~qk-iCm_I$mr7lVs-bte$YT{-?T<pVC?k=Iq8?mg??=$@!$k(5ypc0ZF*V`u$%z0
zE;7DW;tgzp(S;*_oxNqO0jmjo*0#g|04U8T)f8g7cV|;K97|h44cZO3Mj)if=jE$Y
zpO(cd1}MrOGt^Uc8XxxVQq-D#%}5j7RgXz|NBv!x6y#LTq(7XxCoWnamRIjALbNCt
z1&foKM78!tx)&oHmQRSXE(tr7wKz~2QIU~_t3vU&gEX*ZiAfG*7DstjaNHU~s4)2I
zUc6l0sJ3t^pIdb-p2<4yOIW@WCh;3`XnbPe2f#`%`(iF$oPGMpn*LDeqD8x^w~PX!
zznAD2X8nPk*6U+3x<8%BE%d-vciwB!ghC3#O0cKEcQ3sYE2#IEH<Q_fAhPHdrUBsm
zTg`dO9_pfWE}pxdy~ag0e$}M;pA+6P277rLZP0LpHrT`a1FYpws>NGthvP4}i*Acd
zA6Ji_3E`B`O_u7^goqOSEqIq@p%JayOWy7JQOCQivxc2elN(nW_;cgBc~?4r<{5Y!
z_;AHs>P{2GM`;U9LbX+!*z{_<_5Sl>Zu{9G_dw#TTK7mf93$9mU{8r(MgP+SflQdv
z5)MJ+U`CZk@*5$k;(SN9Rs+yETc0vdlV|xX84f$z-H@%^3^2HM0scY)A-!~c<fN$T
zAT0Ge#=KY8*&eKb<2SvUy|=TA=;)27GJN12TjZ<O=d*Wd5~|0|V7;IE%j%2ughM@0
zTn)sn2^;*VB^O#z_Hy6fXCS|!0CeQUbd4SGXB6M#Zqna(Pc;wqJAG_NN-`FbnKr6*
zb`G4oHzmpmigrebD|moF>_64gioa)&A&T3Vu);^k5>7(*T0IRaYp-M=^77bP?&<!x
zbbn@Tpp1sNyiHqyUBTLq(|FK-hMr-8A?g$+c*HA)Ak4ztz}9jRer|rJEhn09C(S;C
z|HQCfP0_i2lkKHQ{(umaRND|L%S1#4-er%$kFScEHnzUDc~RnzsAnMEUld@3HB$64
zfnD(-$m|gf`lVd*%U~-kw|GbSvPFIOtroQdSq+TF4t(m8Qjm{#9-}h|W!C0(n21UO
z_EM(Q8y|78W+L8kopCh1+^SXHcT(^2dkH{#=!9I1bdH2=4WIhVh!(aB{q+&wS<O#W
zTyK>5g^MJ_!D2bSOZbhLv-o{hO0#aw{E&a#-KOeMQmVpCAYWiW8N5;f)%ha~>MTER
z)n;GdwC$aj<J7Ac1ryEveDL{MJ(muFI_6cWjA5`9uazw`DrdoFPL!6+k3kb&e2(_>
z5CP5FXz`=^?4ij{pZ2Xi`TWH+*Pvwq&(sT20-wNlVfygRT!GtJ91e;W{nnF%2=T@W
zR!~(~Q(HIuQHA5s;7gs*UUh|%QX%PeTfM6$B3No0U`VfAvMa5vG(a`-&;hxqI#CjB
zAY%iHNf$x*U!dX}$J8a%umENiqZe2xP9pR#!OraNq-4sTErbSEI;6|x^bqIRq{D)T
zQ>J&2S!p)M7va9K$me%-unsCiLeDI<RXFy^9L<09WAv2;L*u!X0pEnT-fG8Dw=l=G
z+D*Bh_BsPQ@ZJ?`WnpM21cC44Wg&kZRZE?FfP6V5t9vc~Wz8LQD2vR6%=yMrC7-Nk
zoE~~aVpN=hPwBcFb5D=5UcEZ{$%*!$yh0b(J*!A1`=fkdk1{)#kI_aX1#z4o1rgR&
z4)W)DK?u`c!@TM(qTBScoi(bk{Vc6lxsFUQhqumGSSSgTF2|`~Jz2!Ok#g!A6X}do
z!H%(Kv9a*Bg?ee!znENwlL(j7E0VG2#<POSlKOT+wq$6KKA^j597~d$iul**JsO^>
zJBeP@@FRRZK{V?zEZ8Y|;TKggvY(Tq1ZX10RW6S^uoIq4HOtKQypQGWu<Rrw_Ijlp
z<54D&D5%KG6w+F<FYG+bwHskSECF>Dom%g~>{X=J+_gCZpIh&lX(V6l^8VE)7wL%9
z5xbZ3gdr}(j^3Xkc*mV7igO(&zs4%#w&#L`bD3E$=Ve3JuQ5(PMJB=0eJto57b`nj
zHGoWE+m|ms+(hg%0e^cTKZRta6`a<?C0bnb-tmn6Q#+&JQ(Cu}WRu?I^Rl^%riSXY
zzGAIW`;fjCb>K`W@q(6Fx{s4&^<J3j=gNUfWK?|<iasWI1`mcY2G5nj`O{o6q!EPv
z*tu%$qv9CkwK4L2rKIt&q1NIps#V?RsG92QbA3$>F)<9PHQ{&JzDgYhQG#ELtPVec
zJ=O9MXxB7%yL!c$HWL*l<t2s$eu>a*?-v~VNM(1xZ98YWQ5BVr9S)YPzdEpJ!#zva
z#(-dc)mK?Dv$T44INwcoO?wR=x%zd{lW8xM7u=$Cs1b|~&T(=U{Uf{$ar82)zm!k<
zE_(el)Dk-fe%`KOF`VVe(LK!)&Z761Gnsw|BT9*#30cIoL8Hsy#uW(u;YF~T^v|Hi
zIneJ*_hwj0;Q6-vGlYp9OwC?~AvG0z5y8<BZ}*1S_aCa5e&moUS)=v3@x>Ni9Ea<o
z;aETmlo<7=n&sPips9tkjciWx!5`mVEv%Ty#L=bMC}X8a#vslWk+_{z@Nx7()2+_y
zqN(^ihVob~D$s{x(`(0k)4PS>e2XkAmY%_bAk_8r>g#eL-Yc45p~f<O+(YI!L4BsQ
zbjF$-N<JYv;PK{JBBl<fYF^CDvWvzzyF#mBa~1IkjQ9CowWpNhxwOtw;|aVcm=%q&
z-s5VktiGBVVg3Fw0;t&({R27c;4V|Kk=Tk;771)4A}F^7p7~{}s}ee&sZc!Q!?RbJ
zmC(^e)<P-b>py$svQBV(YqyoL=@dYE2So&rs16d+DFa8fVCa8WJ&=V;Jxt13phg$!
zB5tvA*$l0FSjK#gKn~Z_nh#?gULgO~>ZP&@V^I7Ik1Qe}7`QQY+&<8x;%!(|r<Ip+
zrtrlG+lVPfmCK(Gre)$mU5#{{DH%2MY~Fez?X}PdpuALnhbJBz(jj8Mb9kY_2`!S`
zW{=%gobwIaJoW%FAXx>p>uY+=b^WEvml0H1n@G7Vx>MTb^yRl!8XJZD%&#wG=zVFj
z1sXkh)FSwBq?oVUTBKa;gAE!Sv-&6RAL~=>Tba<c&?*k;aSwMyW$0WBf;JP)c`ZqB
zZ^Njv3DxgyB05jL(bo6K6MkX5pgw`vVP=9%(3Kc_`%+5YU~E>-M`pka(+=8d{k?g^
zJ4`|T_Dz5NF;Cr4P9GYIcSjCPzV(*){8-5}Z<In(%GJ^G^$gy*LLz%*PKHNywJlWI
z4#6@X*_hBPuJ=i{v?%wt88NPGcw9Av<~nVi$yq??j|wW?^2h8$FP+0W?NMP|jn`iw
zn^6(D^Cku45Ujj}jXiQ@3+4jIpsQ4jlQ%8<YUVkJ_pqL7PWWQgQyd^Ev##1%=iV3~
zE6w$vRfF~*6u&rPR$xtSKQ_t33$-pf<5jIoj;1+0{WLHhs}CE9vgzfq#<7C7uy{vp
z8;QmmQ|bA1yRT0|>O@rKqdjQu$T<lBcwsKgz`P4O$KOoo_r_XK9RTr`?yy}*5D7$I
zd_&2s>kn%BF0<KXuG+uS>Cw+KEe82Y>@jk3zAc$-JDhi{Vl2`iT8WsTap?^0qsa+7
zJjKSmoBe(EMJOqr7M!1t^z&BOu#}nYXC$X61D9TQeBRtV5@8pXt!R?fgbxwnf+_wh
zK_g>M0!N4vt(G_FYT;N;ay3b#e8XO7!;6y^m5=Fm!nao1H@;Hhqwm3sG$ULSK|b=u
zOp*J!%Wm&&z^lv4-D}S>ePF^BTM(q6@s*}XVfP0O3?!e|xXJm@SxFXZhPt`g)@PhI
zF|VXW6)<E}owY;AC!=w_`V5hfMq3TvuTmzxdeu_0Tk&#ixCf3F9ukfrN@_x+teAsb
zep1n@@9OC$F+MV&$qXvX!Tw{!V!#R}<%VXe{s*jNv^-(9nA4pEl#&z;iPrs=*b%Xj
z?TBgZRoA(x*ASEzs;8i^N2_T97)1Wxm8^#ik`pywdk}-xMI<)fPeX>JR^VkO&MBw>
z1ybW{P9~Xa{3=6fFzfM@CV(wmm%T&Mcumy=vNre)>ZrXlHl<syzViNY+Fc1V{<EJ3
zje3^#8r>z7hvYK=I+q$1j2jDKD{=bX{FUDc&IUUZ?2tx|81GR8=S8{3Q_Juzq+V`y
z`gIK?U7bAea8H4<>t{RWfYE0Tq1^PMmo3+vZSC3%a+>O}$1y8?^TFu(@$G@!1lVV+
zA?VF7Evt}ZpP7(+Dg^6xy{W02UK2A~Na^c%v=8&PL147liV-_W&1_Ac5~YLES3Tf}
z^=zd;ot)i~vg9Q9V7$nidpV<&yZ)JbjZBHGmjFf<g3nK0LF&BShHjv57anm_@YPOj
zA7^&AuEJNwK3ml<w{MF2Ix=(9bRK5zQrPraE{j64VEud~P1u5woY(q=J|;uIP<TxK
zB3YK(;%HVUzjC*Ncv*o<ByIsy@<8bs9UQ786ZWvbto+jNVI)lb;Hz54o~SI8`}TNY
zx+*(2s=zzP#WtesWYFr<b#vH`0!U|dg^GJf9TG_Cw!)s1%8f+z8vY2oTyTg-NF||6
zp|gMxiqbCEgS*L<5zfaCvF-MCD>?`5S>E%}iL<Wb!6!jDdHW^_uT3&DST%aAQ4dk*
zAs=1bb2>I&OM;}i5)*M=TYiE@Ut}@1ightpYoUE5fnt264FZQxJ&K`HD^rF)Qr2@<
zt`MHPSVLySrhx6?h><)k#E<RG`YpcR!YMVfL!~$LRdZp@?tnhD!8BYq+h&B&A<H?<
zK8a1{(udG}=gDa6kZDNcXEh^Ey-on2*x)^b=xXy&jrmb_^EXMWB+#TNnRj<RmAW#L
zWz7z5X=$rC3_8wWnH82i$Fw^d9xq7i5=%mGp!exYiaXInKm+-MV>^1Uh8^A(ynQxu
z$Ym8m>3yztMtzcTgC#a#Suouj&OH*`qP9G)AP#^~^AX(-HLn4Y5u&)zqI5M4NynV(
zx?xP3EPIgCEuM~2TegD9k>#&^@y3QylwEyj(9kJIwHHm}4MEcB<ak3ldhwB{y%^D9
z5Psa$gifh%9-kg%!=T4qp`FUNeJ#SU*`zX|eQ{PDw8EH2-jZvB%P;GV#$!mO!CbN-
zifq*dA3kAkNX3ZTTyt@`ESjqyyYqF1;BF22!ZRuhBC<Xk^?d99cr(bHd9w7jEXMQt
zW%`>VRq!obV&T&02?aiMa&EWO!sKp}?PrmM2vr1~t&qF!d6x)9bRio(ZcfO)<`8bw
zz$}06S8vZ)Eg5BUx8=0HesOVH^XfcDrPx@(L39W>yvBAae$Aq5*MO>A3y!NF`!2ID
z)u8IzMo7ywZR**LCwDFY1Ofj@xLeuem7o~rvOc%*d#L`f98eTSEd^8n#LO$w8QbU=
zs#2WWHw_p>>_XTWq?q1lp<v)$+DQ(<;|x3T<X3ICjbGG0zus$00KYSRmZ_0zhOL{h
z&rFs!Wd~6R&&pD^pg(@I>kSA?5uOQ20lr3<v&JEhbc_=laE?qNz%mcf0|&R5Ux_?j
zMX%2JB3^@baWVlge()|NkP*|)Ezv8+F8?{S0L`eDO8a4x1bG}w>{yU&QXi$aF(hVm
z2V&L_vud4t4VT~(Y92GUumkwpjhJsG#2>qAYQ~im8xCS$p%|?&$9$I7)_%I~_n#d|
z9q%kZ-O-*fHXDYBzkKKuaaWCsGK3{^TBRGF0C+B?e|b)ELd1ALfEhn7wwUe4JcGiB
zSr&gW_T}kCp%z-a6bc=OR9etRoSDogJXz1?UQW6C7t7ixZPN-aD)i_d#roLLJqFR%
zk2Ykaaw|Q_9;#kF`IypnyGnnZvZV1n;oDjsGlP=1GOquOLqK)~XqvY?L_0%hN7D@F
z#W0f#;!S-y*-%kLD{fb)7|3IdK`O72-{uhQuJEC}Os0)C5d)wmIrWWBTauShX?raL
zKF6m-l%xvTHlSU3J5Dq5b!$*q8G(_w8i@E1auB_y(4_es12fh&NDw@1*eA);a5VnW
zq9SeO#>?bP$7fXH_e!}=w$;XT`|J+#I24@$dRp5f#`q>v;IzW{TStP1rQSC=tPkAa
zr#7!o0~t`$@K4Rr#N&K~rUeTrzpiMV2wvo6>bu2drOz5jEmL+Tku&5dJ^0@iyr@3~
z?@*?1|ESG?K&#waG=9E9G5>8`G`@~vN#0;F$vAh|i(a2jB`j92W592RuQVTV<I^}F
z*0NGgA%kjtj{)_&!Tjjgb=WKB6RKs%QjEpXcDEOy>r@PE9UlJt0DzOu>qv{t5~|=9
zeQy_S7m<L;L`a;n&8P*fIumv*37)v3sMjpv5%T(H$y>-h$$V`VUT_`D2XrxH-D-{^
za{ILH=A-v?7eoAD2;#(dv!``cZMv-MDh}dtA)!-_5+*n@`FyuTzLNBGz9{xE^+r~D
zD{&gmY{t}_2|`vCaS&#7@DM~fvum&WX5^v`yB4;CLA4Br+5P&;XRi9V-Y4cIy3?ka
zwgUfWNIAdP_KtV1v5OkBP=Qzj>`t_Tk{x;Q0mKwq$Y_}pVQJV%f>WQQQeIYd_K=O}
za6MYeI(B=W!m&}Uldd$#Q`Qm3FLmKuRM60k<+h2SP>I^Nt{H)_hlDdT#HSZENTj@d
z&>vJ6pSUwKge$QNeR;`1pYc|lg;!^k1fot^<&sAdy?@jxFpRcN8oVoarb#sm5oXHe
zG6=3EixqKcZH#Q|!FFLTr1havJeVsV4q`8lB}6<#jW+}45Pr3T0(xpSlVv}%K!pc$
z%BQkrE@M#<v!hs^%gezuUP8UL@0oiF5B<XqY;GN+@02EKEk(lx0nbX^TG9A;lvd?x
z@)*NavpzNN-9nuC2$8t3FCncqvX%D~Smh1i<@lk*-S!ts=w?IKRY=><M^}3{sPatU
zSIY8~)P4(*<S?)3XxZe3E)Lg?7XaW(O3dQo+}KJJ4%Mu5WFd`|;r-hNo8dS?CL++6
zmzxE}q!I7@QG)qcNLPqfx-t}2by2US<fT$UzV_|6+G4o!!4y|8=BzO;);_MJP>AmJ
zGi4qT#io+B-VfhhQ8np{ej~b_CcqczkY2$#s)Tvz=lhji797J&<4d?n$IwWslc2O`
z5l)V<X--*yquMt4NUdKsx-L;rKy0v@4(m2%cYhk#YH!pOsT@#Ad7iQ!dQZ*9E>1MN
zC)X5jCtsM1=f_3b-A<k2!u}wK6DUx~f6_c}6x9+gDsY>18O0RKNq@C(_^jfpv8TNx
z)Hmq!^bo3hhuP5w8z>zw<C_H=4BKoe_RuQyI)ymcK&S1k;?4_qBYt*AUYddy`XO{o
zyiKlcEpRa+P`8)S3Z-9IPEU;EV=xqE$Znh!H)lQN?PHAiGc?&rq56CqDG}k(QU&^Z
zSCtT9hb#7CpLB;1gSgPZdG00z(BFQ#ShY^Efbgu-E|%)5!mQqNfVUf<Gb6#LynUA@
zClYY5+_C<DvG*20c_m%D=!?5cZ~}p#!3i4N-GggzcXxN!1PQ_2A-Dv0x8Uv$!OuIB
zeBUtnXC|j^)jf6U-l=*k6>9hHetPZhwY%5u=UFTCj11S_gwM=wE-<6pdgBA~Q07<g
zG<F?zjb4WG-A*i2*2wJldvJ{#QNE@_OH1v6_ZHA_8^k_Kj7e`fJ`YJ0%#dv31k;1c
zC1T&0AOc2eBL^N-#+mo<Or+EMm|I$ovD<SvoZ+cit-d09Fak*dh>zoe$Fe*CL=MSr
z0%xUfyV0#9$BPn6FLD{u#dcyF(aDVrXy4iG8hEL*v>WR>GQ;jfm(_49S)x9#IHUba
z;{#d%eO)bf*%3HdDM5m1C(#ws^6^zfQ%^FLFC2hVC=kDRLC1yJ@iSPohtfq5P4)%7
zL}Qf=nu}}O0I`i+4=)0oXL`KHs+nYn+}R`i81Xm5{<n9#-sNS89(euW8JUzFZ!gO3
znW0Fb<R~!V#Kb%+9fP?!Bo&Z8bhD9M+rvGO741ccta0L+B|UOe_qCX_3QkO^ji`Lu
z(iByQ#IE+{hypIkK`fdTJ~4SWPSSbwS?0RKv0a&mZy|!aDo;~MGgl5<DYx|4$5xht
zb4rh#p<8M6$X&==0M#lgKI$EaAjQm%)OySS|Fmpfa(eOs`#&;@z%~EVAE+7t0LA`?
zk}eu3Iwcc10PORgEk>C;0rEnL*_Yn!_(c3cS0+G6Z94!Dufh3BjvEQ((nmS*7=!7-
z>R91<PiuRoB9q)W9pec>_U9~)#`ntZ2}R|wC9AYi_4p;Q%hGYJ`72Ezbsy2#g#c$F
zq>6S<;qZO#jKp(3wrv&R3topq@7+_re28T8f{xVCpw&2nljDGw&TlXOL?V33{%Pvi
z)wE?|Xz<*)vxIk+Y5fv2qQJok#ixGv!S8UcLHAT<Bm!b(JfMA%qS*1mwDg^miS%8+
zd<6=6j8aC%WfflH3PVL_Y)-`tOJR8q?h;{f&W1ecL6GEIFQJk2tZzE_VD!N+?Kl%g
z+2d9lF)uSqJ6ydvqvrhRM5aFUt3$hxp=-NBhSb6w3!wKF*`*n?pf2$zA$lebCv4z*
zYGVfx>Xg5zjSfZv?Sdnk3;|;YgTSm{=(@TDt1N>X|4^Y=Kr<0h+*0%PeatQk>}8q~
zpH{|KInxTb$U7I#Xw+!U&>Af01u56GrrxBPQG&whh)Kgj1cu&5U2h?^z?Pl{d2wu*
zJ#cL7f=AZFVp>uhnO4j4eat~5W>xP55*apYk$JM$J+%R{d>?}u2X$xS=!tCKuPV1o
z>cD2e#Z%P;tSd+<ND;5zomPW*R1|*YbkIol`9v%`d$1-P?f#HMkO&>$>@Dp~`^~Y~
zP$cpNzbv>!DUnq5;TuCJz&ahAfE(|u({3qtWxg`TG_2;q))JXCQ%FHMezfqYZYUyK
z1YZ}g5U8p(hj_Vl9a7*Ix>AX8eJ+lcPoWEo!{E=LL1hjZ6#dwC>(d%Vzd8}pA5t2E
zQ7lK$D*<)lHc?me-VBFbC|^@6ml0dLyV9T+$G!=dGAI&0vXaL4w>KqwjU`L`4VEt`
ztORn{G_P;tA>JRX0*s@Hgcwos9?b7%oQHT~^0a0asbHU3XMT|h8G>?5eqGv=!>Ox@
z6|9UN)4qr9X#UQfxl5f*4o+TRGc*){lNN%_9Ug3Q8qJ@jH%Ch1#PjGi^nppC`kX!j
zd+&KwZx$dqlDAhtDnUZ5(~<-DEl;aeaaFpZAiY51K7Ea^*)*VECF|;2+FOx#O>aY3
zG#o$76MR(!p|W_v;8HMifuWnS(am^#a#^^}yGSB0GhsS|%uu=Lewd1cLryVZ=)+gP
z96F&c3%%qbk0yBvx@W$lR`9^Y_ALUuOtNb-oo6--POT!fcl>Tr@*4_-+`)UgbNpk`
z>nRe9QpT@75s6=Y;EZoI;)f)KlzDgQVvMA~vJ@dikFxt|o=ZJ~<bsUr_xY~ZD<ZWW
zV0l?|yC|OB%c9DitX(!LA*}8oopf9-r#(hDCy+Fe*q^<IC#F)%ap93bE_)TPXYTe|
zTyi4g?V%_5f&TesIh!8R#(CFOQQZU5H|B8W%7XGH^f=u3Z;1F23zja-O6lKfCd)eA
zY$9&pmr3=s@);kEX-y=F0D-C*okjuP`RTcOHxtVyMC9HTxk7a5MZDTsbOWRcnFe?s
zwK5!3Z21awBAM}<nC4QVg0o`sZ8CGh(s|AcfH)Gx#@C0><@jUdvewfu5r{hxwk%7S
zLYRj)cC$NiB%y=ZU69d^!8hrj`Lx>%<5zN-MwyLQSdD7CyP_4)ysXOVW(u(VObLe5
z)%yN)dJ``uLTSO7;VnM<=k#s?FId06b5ZY&4<;N;?!hmaCt0tU&Rt&I${u+I=RIP;
z?6q~USD#xQ9yw(0%qz@T+o&rWOi}qj@ns9_T%lBP$*#coa4X2aT#0KWQ84Gqx9aP-
zV4Y$<4rp>uu_BE+<h18_E%`z9(bp&!ymZi#r8_mS*gwaH|C5A?ApzmRbGFNl92ctP
z^=YI;4(o5s`#!v&wI@8crR9OEMZ`?(luK1|chfH=GPAquTWDKp-&$0!*Adl6vY%t2
z2UQ~>=)4Io*I|HA*-eIrv`<c8uZUE;mpy1g%3#RynYPTtZeUr{+M0fpol|SMq4Bie
z){I=w+95KsXrpaG<Zpi-q$3aD_{b0m<=jwH%Nxarm(3nRn}@~koI<^mn%qJ`+JB2}
z9Dk0CoMzdMk<A<MCaoEzNUDNl{p6-&P1qJ`qi$$1y(LsvbY5O-y=H*RZiwwFeWn6>
zJK%#zvy|AU1mfPUotfO}HcCDO(3?uStz{({+7-1Wk*aa(?98_7f%1xET!&>SXpW#2
zB6uS_j8v>hDUce*7hllKDq~ud>jo4QN5rkesUgTH+Z*esix-rQILy6JGpG}A+8m5P
zqggq*bC%nejYKYk?T}2)z05P8Gf2<|ws;1kej`u?CswAm#rew6jn>Oeu#)FdX996c
zNLAhM|LTZW@NuqJ3kNlFz^pKxFz7f7EQFZ5+wMd(S`yvB>$CI#R{C+KO)Ihn@hfb%
z{!VvD>=V+eFjWRrSF=Ysb+Po-1oKE@BXEn|3xF9gtO0oaLu(%LdG-(h@}X5+OtQ7a
zQ`1)QOu6~8zQAxzhK0G)BCX~$Lpw=gXCpgkok$Qc;SIbK^G{;*kc0peAU6e4A9#fU
zB<;Uy^yqHwyi8x$9*b21LB}YAVFxSM<-_8qQlecxD{-hcMbzyPU3!sc0PkY^S*jk*
z%eu-)=C>FA^f;ayoCs)bQ~HULx3UdcWxIM|xK+n&mXF@fGW%~gQ~q`y{&y97ND*L1
zK*IJG#UngX7<4gks>=usl>LxloL||^1pp9|fu)K5%O3izb_gChldmnl^)yH+^@)bB
zEI_^NdA=8VB61JGPnR%U&bwd@$A~DkuZhtl16Bwu^zvt}5ISsjc1XteRSKB67(vDo
z5nNk0uq4^jGw}d0&|S3EZ3$QPH`!_~*)-w2c_V7hF)7-=>+S>L{WuZ-H}H6Y^%4A+
z{_}gGC-B6v0bstrmH3}h!QXNH_V4efxk;ZgfBpi~5OoxQ1=ty2WB+0O{EWz{2k>I~
zdx<BC`A@C&2b^C~eSmQOp!)m*&W~fDe+B0^DgzMk6Gi>Mf%6M)4-n2D+@4>+c_Os`
zbT~b+&JRY<?|}dEVf{+J2ngp7GS4sIJQ3pm8O{&uJW=HT**ZSI+3%OKX{1ltJAVh~
z<L{5+Kf-B$^{|$|mw2Ms|1+E)L-`ld{XjT>&~<(R=SdEYzqZaF?hEK0etKBnt@C?U
zjX&gzo^DEh&jJC2^9MiY7jT{k{r?Q-hjsqO5B)K-ewB3t2<Hz%&M)BnjqCYsD1RS2
z|Doah{ZRf@YJh+J&ff{&{5E0!^*jG;ohNdQzZl9-5`_HC%=*{w{2o#H8|(b*cmBaT
z|KT_Py&L>@z4QIO`133D`y2h=_0B(8=U4RP{~xb+pg?}!bO8;Subabo2IVDN8lVlc
zr}hYI{}#!3*F?$6ve#SmjP2T69gp7DdBdvio_zUK?T(B~ENY@(W*Y(y>dngQ=gh3K
zWAF7tE+p2AhcO6Uy(sBCkI@Q*rR8{JEN=$1v6H|#9L7ytD1$7aWaB`Uy{m_P`1l!?
z>xDj*hh`cq-kN*fp$3iVdRWe#KR2WxT!Jq$+}8IlYo=umZ-hCwm|R<->d&Ho#RQJ^
z(5{XQqO$4x6-GAMOm$IB7!^Bz;(e$LCS|D<jS%ilD@f=U9+MV&ISUh?1~3ju;w?$X
zGs$cJJJ(g~c;8;mz|0ET;slD48D&xlz<qsWYutH3XB?H!NM`JSgSWRWoK(7aMqflC
z-ICaIg*mhoSu<!aFHm?H5iGUE3try3D&a&$vCO_~4#~l{WOQ=5d2Gb9tZ%%tvYRTd
zDBpHc-rj^C)uhx?l)(;pg)T$9Dqd_CuD+prNNpl}pN?h4q{zLc?@mK4t4swh_#Px@
z(Xl3g&Vh~Ec+{S~3BA6AG=z9ugn*N*eJa=eJUmwq_M9^?B7@P=aR*+CrralE_(sGS
z*^_b~9?ZNRfz0_dWROSSAA6QSKcDfqgI#dpu!<H_oHcnI=M~Bq=FsATt8<q#oCI#f
z!xCzeVLw|wGX_1vBP}k1XvL*U^KN^A!d7O##%209D<2f@Lo$8nE}DDBj?#UNZ)2|;
z8HPu;h->RdE#NTe_Y<t_^O{L>Ce^MpFKE?vd)W+MC4YkN4Xss-ppa;e-dRd=h6!vo
z8MGO7kzCU;O=#5}lCAlwxgtj{Y864AxwmvKF~-#qL@SqGU*9US;fHFajqwsn@D(~l
zVieq<8UCpvOwc4r64fO*qv^MPcb6<s<2)PH-H*3|5Zgh@gu`bDl9CMQ&+o}*c9?KU
zI?H^Vn#Z6cq|hM4(^I+bMiAw6;!bJ{-+lg|k(w&A&pHFDZ?I2I$>yV`<V_j>BAb`5
zB9J#pM2R-(LHod^X2_^9Fs|`Xf`GQ05zIw|zogIoM8pCwGWfvz;0>eXTlr(v&BkGk
zuEh9>(!K|MV{e^~kQ3S0afD-2WWsK@y2b`u9L1}p3ifw#j7b}(5`irqS!1y!8hxqd
zMq=x*m1#Y^uqBn_wh|_o3I5?`W0pD=8V3qdB_=ie<xpQi$`nG7A{IN%!SXWKR4uI%
z3kD|J8nV>XorPnL7jayT?$L@x^BPxOkedx)W&G1WFU5oO8%AH1COc=>$_fVEmczHa
z26d0Qy$WP}d|Z4~h(R`6!Li(m(C>t5TW(lCSB>0ljw1iq-9`o4J6bJ7#Tan}3+~c(
zEv=lXnGGacP<`DKhO0av)|CTmkP)tLT_0=4f5C4nBt!PhK)tkuWj!zAGI8X#{8O(}
zH_DJz5ocF?EwtKDn&no==hsyVvgaa0TsSYany&eaomIFpP~L3iY~-xPxa5nBp_>-7
zAtr3Gw{$Jt85iHMf<IGOI#1H|)^cFA!U+&g(;C$3KN~N$Y(WH7jU-SJ6{Xqm(Po|}
zNsklyO49Q7foohuB8Pvm_mi6cf@f2uxB~ARr23NwxeOHy+u^f5j2H2osvIIzab|X%
zxs+E!jR{-3m6h)b?Yz4VbW-8{w=={{-sDi?kYRa#v*Jqq*q5)r1G^>aAHBcySOXHC
zvlabu%Omc6cQAPw?iY_59(NQHvQDIxo2iXSBbjw&0^T_t__8(P6;1?V=IfG=hBHDH
z$ye;QA<+Ubga*yI3c_874_%<1*T@xNd~&%&w@A2?ncP8H3}4#!OUnRIfYj!}*b{O5
zP^%8US(7P_DygyZ2)o^xh|5yqhC8^;j{0m+g#7{1?o$j$bx^DmS8j<x8;_4pyB(`H
z7f}F6D@lv@b1;3rYlcKCeXXHFqtZ<#8Wy*>mj!!~k4qmfQ84}0+AE;@p@?0BCn<W4
zid>v0rPCqkVUInEK1Z;V!hpwU;^YkbDQ+IuYR&1>+#l#&*S*3ceKn|Elgd^Fk5pLZ
zOqo>#=WN`67A4TjfoBr2Nhhsid(-VgQk7mSN=Um?S-wlbAi^&SK^4;wXk5ueEbF))
zQ9&J!PBA5XxMXocXJvj>Z-V>)$q2Te5_R2H*_B)cj*hy5zTlR&>-J$;Qh4~7v_7cv
zvB39*{ppZYCO;nNE?y5ex>#O^q1!f0vL6B-<_UcdNW)ux-*e2Zt<;=Kfk}$h=tv9d
zA}xIf=j<g<DT;$sJxROkV}{NY&nQfbKqIlMNvNU|sMeq~VP|vB6BFI~1d5K&d;;gQ
zJ+V!+18ZqX`_rFqYi0P+pq(x!1XUO2i$EvcNOVhviu;rm7a@6(%<^c)3#wfWp@`Z6
zU@5>|BJcm)((hs!conBsYZS!lgONku`yr-pV18od6;#2M(lRody5Zz8juiszbVLvN
zj{ua~fR!-)Inro2x1aQGio(OsQc+Xzv=!?8oqIyc*U7*VayuxtJ$Ja8sDKe*!UX_;
z;?KcHLUz^b-Gn}Sh#AS4udka;3cP3iB^Z?e04^P1VVb{-LxR?Z^#H7A3bsAqaUzsD
zz?~v3Th(}hcQE|Vxa6ip9~sE8-{Gv%7Cfy%+2PU-70UDHh8B@yalnp1;r$NkPeDpN
zU4sfA@4l>O`n|Ie<n)VdGccj&Cn-GuSZH8jV7`W@@yb8-fa|bKLed-F=+3Io>${!E
z{vrX|_thKxg^Dwq=!o5V*<2a?Zxz%3&miWXu<Iuoo#%%*5nOz{K5Et!0f4Z4hyFC6
z`4qbKl(+q65{{VpC*~*sIts8b*`H&Ze+iujVEx<k1%~l{5#*%#pFz&QfqP;G0EY+G
z`SQOiM!)ZT{tBxy0O7Y3oj{;Zqn^J2`U~}TAkd$~pT7qBG!FVVK!5ul00jCp9{P7c
zTY*J?9T=tgpMlYT0Ez&t^W}fFjec0v@P%qqBAcHqy#Kq_3NV%o0?IPL?}H0i@@d3^
zkX?<ZOMnvKLvBL+(f$)>+8gl6?C*IvfWiM?L`;D=e~y^`8t2o{DW2|M;)MC$qyK&Z
z0)|lk8RtJp9c2LP0^<BRl=^F&Ph+ZogY&n^3bg0b7{@>3{DtTd5a-V^)?ed%8f^U=
zoImXOBm?p<?D>nF6~Kw~N6w1x0knU>`D4aENs|omH#ooB^Cz$R599M!(GeidpT7qE
z8t0$A=s%3lKYbqh$LIckz=QrJ&L8&tvj_brd;Y)gK>rfw_wo6Y1N|qQzmQ4#*MI)*
zm;UviXsiD)-+#O}|Mj1Ln|tG5|M`F43iMwG`hC~*`#19cI~JgVKWF|px>>kiRJz&Y
zEx`1;cr8bo>?)xB+q(SsAJ{hF?c4-Vg&6qvyO)}75Cp&d7yJWoEy?%ab*iK@4_x5~
znjUH=9#FO@02ooR#AbkD2gSRpuK+*~8)<H@&=OBQoUiR$`PcaOB2|eXQ_~}LUKsS>
zSET=&Ygd*ZYggy=$ELIKpb>Xn0F)H)c=<ws;};P2%q(DAV1TRCnnT1gd^WfRc4FjN
z<pBWc3a}C2=HTDO?DtQ5|1M^KjO_0x!hkQ0pU*t~!%O%t!vTNk<NjUD{_Z}0JN5Su
z_WU;(@T+rG{{{no-yHlK4EV=+^usUxZw3SYzZg&bg?8u*cKskf0LT9W?a=2i(2yBW
zNw66xbgwuFS33OXP+R$}#$*n@&HGG7y`0U__%x)6Wc!Iq_hAzEbC6PWQ=L>I$yy#f
zImWTx)Czi-9vD8sPNsaFK2yHia%%uCTW*n#xrl-<bfd2qzdRvKeI4Jn2OU7p+-*~*
z4_zBA%Gcw}vqorU`dQDOnzW)0o+S^3aPf`7NMxoXj#-9HH>>nrwaHe+yIRVkqfZd-
zn)omy`@P~=DejBkrsr*>k2JSJBA?Zr*0E(&`#Q+^x*GcSI%yek51b8To`5S4^fgne
zLNbDq;O0?dmnNCd?7kFzd$@7W)UfEb!Kpa$+!0`(x_w!S%9}@AzY2<uI%Pa26E}fJ
zJiqZdeKR#rnP0_pCnlj^>qY`WfG9<L0tJU5@g7<eBRVHzP2xjwUwt(SB}27M#EANT
z^ym-{-Oj$U&#NB0^!iwF+8Zj#z#^H}r&MAA+EUH)6EF=w)OgK-_wM1nz&#B0&z#4*
zJw^GHgM+p{dJIs~h3~b&Ia8eFG0$n+R;oyQwa=}oZ_ddB&f1e%Yn?WquX>Ol6;3f5
z@D6`^rv{>N%F4WaNlQdOaF5$y4DXE1BWi1xXsLg(*ZBz2I-0>xJEc}S!MxD<rOHKg
z4(4lzDP?){a~hmD-p^D@%52M7W@Z(3g`>xn^g}N?*Zk^-TDe?N9ro7{oWqppX+Bi9
z@@BRVJbasGBf~HLSOo(L_Kl%R-6O6$$vF6-FpkSS%jFddbqPU~2><}kV1IHP<>E7g
zH96qW*>tJ8f_koz+`Qd!WC1Zo!citMH@A=&Oq1*hRCRo<sHSmztm?IB$#XOq!r`{>
z0D-}SBK2M-WCUR4VpY8zAhH*SUIRifLq4f^=!=<Qyl!E)_`=jUGOB75XJ<AD0En-a
z1VlUQC$1`5u!PkT5swU}4TjEBiV4voB(aI5yp#^~n}_9KWJW3C9RIMdPD5G+s@FZZ
zAV;jIu`G0&h7^Wwt5whAgMxojwj1W+)udH*%nN>UN_zyomjM%pC2YbzGY294@qL4M
zH;!c5gJ(HR-Rx_;XYm9Qb0le!Cc5+%d(3FDP0J3j>`sL}v392_<V#*$ORG*Xvm_n$
z5u|ez?Kl|>i!g|~h=@(^r2Ejj-n&kvn!a&>Itq>VnKJ8V@<+E#$$o}8&T)@;)}8pw
zpgy$<eb}t0+8^!3Ckesq_FP1&ag#;m<Gdsrb&#E|?gMX69_wCa^8hVR$`pdea7wBW
z!<X2>;{+I2e3evC@3wa!it&7)OAw1mM`1F;t`u=Q@ealJJDEa+i@_b}*zFCU&F(wq
z`%<KEhMUa4n5HZ6_li3CNToiaz?C*z6BbZaie<A_iQA)U&M2C;xU*jvV9Y2zwISI*
z>V`1O3SM)Z-KVk=gjH;o6iWyB2-11Nc{w1dk7rS+Z($z6G1bukv7!NRmwwhO92%H7
zmI)v-Y@WEeCV>XwK|gNF_;`wt0YY|~_Do&5q&~cD9<*yXv=&<5##7g(_ieu^SDpCB
zVb9zTgIaK?U7@@eJqDOiI;Qmv+OKtsv`9Y|<suQd^J49w=0AVS>I(p1HQPW>LqxOe
zHb!=hGN45=Lw|)WFDvolZ~P%lmw9zef&0asbCO;MgmW}-Fif8nm0P`FVXV>*PDc3H
zQII#pPT#}xBQ9LAK*pU`%)-Tzw1uiKQI%bEP!{cV>!+eLu@zOSjYE%iDp5t8+*gMQ
zSZ+p9s_hnXI`#C!M>N{<jfs{Uu3?N^@GqT?we$4sr8esGhV8)`73%k+S7yLyB9>6L
zq|_Bstkb>0KV>DOtF~iqgQ68t=vXqggRoeE5*(1m#H<(;vz4>rWede$1HM3Nye+cD
znc_)EF!v${pVqy!<gvA$$o{;kcf)h~7CRrjH?Qrzk%QPcP<nw9GwVU}QPj9v3xV9}
z_G%|ScrT*?awqD<S)0Zwc4{v^vg-rBN=R$z(O`}8q)O?MoW@=LqGyeDGrWJKVheGg
z5hWvOcH@D}Y6riUiT+_^^LvH%`j#P2B{mH0_dzZXYHJY=%KgNBh;4-GaT|V}2sF>U
zsFTMX*;%9mvQ@FnIL`oKRUf|i#$Ir{5k+L57HWh-A8j!l_H|9JOb5>?s+@FV?o$FV
z_XQ>0o@azYhjD!@H9f({kBe6_6QaaL)TK?D!d+WfytggXV?q?tdDB9Z*1;#|VxD?B
z=&OwP;HLlL)~bxs+rWLzWi70B5s#B^GW?~Mk&l2_2L!Qg2+Rv@rd7jzfa&Mz7|Uj1
z{j~GUfOpDpf{!D#7=cWd;^=^b7Qm(3Yf7#Vuj$)K<07K|u(Lf*^_1+OVGpB1PBlwM
z<Gn+koBKk*hwyy^xi_2nFJ)mwz71iXekw*8rvS?#ZHrPx4r9=rTpvA**SuwUuS&+-
zQFe{MLW&x+krAU$p)v22(+f|Pj~lxgL~-f*QIZ)fF}jz<W%w%qI!quP)^ow3EY`38
z?G23rdGlf<+8ctx25&zc^`%L^_qB1{7;&g9qx>?w#TE>P@CP_4UB&&|YwX{8q1Pt;
zBI0dY_8uEYcB?AN=v%Yjb-Ty3)~G-f!wXl{Au-)-#_LQXPJYseR40DfW+ESAfTer-
zj$QYnCAxBr$ATyUKb)vs`}3kC!HbR**{NLSpqVvi$$_`6osZahI!9ftRTU9lk_P1X
zTAW3>p2ArESzTj|+Gp3?u{=rTNGFC@#2t2mqE!48a1hz3)$U>b{T52Vw37>EgZ@a^
zOf1Myl(lV<b#u-oT3)(%qX(fHn#B!m8}pTZT0<I#*w5N(u+9x9;pM!boMWvZH8D3T
z^X@IM=7jMZ%>fCw%ww;7ACT&;Y`FQyIwdeCR~9Z+=h*75v)A8;Eq5uzj%Iv#4(88T
ziMr;7CQfCA*kvjJ6Vq^05m~4_Q%3ig0&0$2+?4!54v&8`QWV|Vp7?t8SqYdD8`Sv*
z%_qOlM=RgV(QLVC!K=26lwimhyh_y{-yhfX(AU%KDxQ0Cr-#JUs#;k))yBjs=afob
zdK27kX|O4F&Y-fl`-9ii2s4Rajy~JFlIz;gEnzyAWt(;*6(6$nd@u4W`(BjrWoVDj
zvqO>8RQ37Kf}QAZz_3vA$Ac-dL|mJV(d9G!_3T6Whxhi(e4JJ2tStJ6O4%ULeHIUU
zRn7~!ygrY{ImYu&j%KmdSK+VUSDsFM<zknHz<-Xc8XkAVG4Md(g@-_&x#h#9jHP=#
zF{j>rd<}86Iukn)qi_Y1nN(Im3lEeO&8^KRD4}MI(P}L5wnCn>G5`##YN&2iL04Vm
zP$stICI_<`%7<MDm1x25MvZ^u;eAKEL;p>MUknt}SpanwMOZ4G<GVK97!!?n(A#I5
zh`8faRekL56&2(xtiBMdh*bG<XGdOT{H{%hE@k2e)TT4BKmh3q_~MKGLz^z#_=h%~
zj@od)W*x%f*rWAPm52jWN8U$N$p|kwMQI13@bc;U;!x@0@7i<`7&H1c7`Ncu`6G8~
zd`gcEZCs?r_a_Mx_i{NUM=lA}8yw;1ys?EeD^7Y^MO124+8pyEp)Q&S!cchtNZkVB
z?w$>r4h}fp47=bwYxld5-dp?^HsuSrCZdu@Ej(N+{Ib5PpQ`tql3t{Z?kZGz&YQ2r
z>0z<H{yIn!f`Im1cANMnf?PbIKS>q6zf~R`2AGkH5{xrJvcy|+D#nr93*ibU?$(zC
z^$w#g7|?0!n=B_aA4xyo*UT~}tV4JHzAL{bV>KYL(S*9H@kZYcQKocK&=XoD>fT(?
zzav}Fu=|SUgx`)M^!iaJj5Z;-MtpO^ah;k1lg*@^qofLEyf4O;em%N5k5Vo(7}*XO
z!UJWTO1h`@F#|kXsMpKc9Uu#JkOiNl2V3l*St0$Ra2RE|ID#%~Uv|$&dKxi>+M+sA
z`nZV_5*uK9hYy<nzSoQm!J5cd1(4222~K4lD@%3&&sQbG1eGl1#>MO91z?kIgG+B7
zBIEZH$VmQO=!}lZB4(foKFTG}LK)?8o!}MG?C^^l>)GJ}DBZeQ<lmLf?3Iv5#KO^>
z0vJ0%bGRaw$6fF&A51JWO1s#q?I9{ry$CrR-mqWq;^`2WF9QG-0rT$H6zDESU?)k@
zH(=IQTYVi8)vfN%avElbUn&T*9Iouf>R&ZIyNJ;gqBEh;9cn6RSVP`HGm84=+H*oa
zndbV{Kqyh_0<IPA^A<NHu8Z$!sO9U#wQ;4s^yfBB(xz#db*{w_Gt?rf<p-iY57bCY
zu1Qd(Ak7IS>L2O3gD)xrXbsl|^v>?D70RU_CkrE54tc;a0KQ#`ThgGpt5vFfqhloO
zQP+I!xgTSx^{^`k(>H2D0bwc#mLFr>j&qTNDo3hM4pd5x@J<6%$^@j0{JMGs=i_#9
z$wbUNl8xQXJqbVu(;x)jria&D2}{Q+d1|Wc1W`a>W_Li&s>>hu`T~|NawG7R@4ig8
z?D#9d`3bx<CpBJ>;v+$r9^X-izI`cmA?jcyrp(m{LH|CCScPlAQMX*SRY`=&=d5~q
zlZv9D-QbXSGINE;RG&mNcGlTs(5;eA<IPO3L1Lm}xLDja1-WfBK|7WvW#F617k2rS
zSwk%m;-JJ<?p_%Q#&+Fcdh^!KF(!Si&t_}Ay4Q!xV8D6+4BLLBjn*~~8|>*AUI4%s
zjOvY5AMYKOdDE`ioVs|M)mnZvLXTPIJ8@g7Xt_OoZj|>j{4p<7x6>OzE76}%hKJy&
zP<bS?gO;I8NaxS9T*-d3spWN4*DuVwJQ5Ok9{l=QD%)9`yYGACxs{{n2AEPR>SqyT
z-B*P#Aq^G!#a!5VX-?I<ii5SM2xvIuE19T=n@%UnI`f8ms$Umb$w11MynTz-D9FKp
z%DYh|sTxdi4YSQ!f+?Soj91Es@=%bqReQgm+#d424FyJVlvqSMfpCjS(x6z6$Nhkx
zvIm)t40Mbywk;PCH3XmNo~u|adBEH+Gn*AGX>dX9TfHPdIwAo>cP_Hty{Rh9qUG#b
zg&o$&?VBWYf*ZckPkU~y*mOo-I#l9<rv~U|(8%oL%CLdd;jzge>s0_-=2Mro;}hD5
z>UY~rbH2gcR*nxyM~-F_)adI?!Fp-<V7d!@X;3PNsuN?PRj#{uD_#??=>pDESzqfq
zDO}?Cc0hn9U0mH(C4rdgA2uk_AUfx0E7*z|@(obNpPUzIkz;9aP9Wg50w9?)1KDDA
ziky^d=_TJ%`9jgJmvP<mF-YpvD7_=ULQ5kMxw+qb(NcZPwDj=BoMl2|)0VT4+P5e=
z|D6q-u^wg<s?1vl^>6bzybHb>!5&omUe7Fp+=>Q1e9qX;o-oGvqG6<SyEq(zGD<+_
zTCcm1VMSy<{+Z5M8VMXD1eul2w<{t|<pCj_bjT^2v|wpeziOMvp%Zdw?UZ$cj=4KG
zG&?~2<D3z%o9<ygSnBSjiZ>ItT~_2{0Rw@O53wnN)KX;i=a!({USQ@$AHR|P_?#{N
zgpLzlvlFkEyL&b#NAJ9PVhPSv+^+(V^u#PpZ<d)E`6`6EKicv}bIy36be~~hsN~js
zJJaaVUNB#tKIIyn_dTRCGak<?LU6?W1QP%t&Qa6kc77fn&O$k?J5#NMV-ZDn#A-ir
z*5%RPt#4gc#$`mpxGxhinHt$3Cft_*t6g81C%+lm5KZ{`Mq5R0UY^{wh28<J!eFT@
z)2i0{@%5$2U64stWDm}H+}HDP5X!A2xkqOTozGNx{Jfdn4T<0v)}4GQi}SFym_&Ia
zy!@jPT6p_pO*GqfWp-4Yr7&`njbH2HH$)!63`E4~o!|h2l`%SGI~!rJADmI8%oMT@
zLAqbpcZq}%?s^ft7itU6m>x)9)mGrLc^jc$EVS{8a*e*bsA?_oTQWC07k*xN@#{jr
z>kzLMDR4#SQE5#f3hNZKasMyKdrdH33dy<Nq3t6);(ewT81}K4d+bCqrp<H7lkg*Z
zd?>Bh4NZtCkYlV=J!p5=^C$l1iyLi?;Y?1^RT@6uqxFTk{)4W1uRLlZ_e6TJC4mcr
zEs@l&FXH;Bnx}jsLYPYe0k6kBmN<g1Jy2%zk>B&MFxw+)vBzQ1uOgi|*dq{?RD+7o
zcbtMb0ftc<S_M5bN4qlaJS77FMUpRKon!q5dLn<|wp+knvr5IMu<}U36GO!=YUa87
zM?AJ8-wg2?jfX28=jp+nZ?if{*!E{aM$Mu^VgM`~UYLZI@ND<p-3!}9^k6Jyb77JK
zXmXbqk1=GMps>q)Vj!=JYz_hHw{44?kxVT!?wZa+Np+iEZ52A+BO}G=T+XOsLdJlE
znTiO)6I14qGsA)x&Q>0<E3&0T(E@T>z3O<)saZtAoo&kjLPck$BP=U`edxt`CamN)
zJ81;FqoGz+eWw7H+ie=^bzD$fY$w0*pt;Tpb$sd2n4CQnMzcngQQrmb#OQ-Jb#?}J
z{qjo-57tX&#T3G^-dEE+`MH8}`{IPX3p}6O3K+Y?RWelG7(<=U5jtz`J(!<u_nSdk
zJ;O}I_Wxi7;$EWh=_al{p@6{ks)a^@p>7;jQFjP8+c~Jjz@O5k)Q3GFXgvQ7a{=>+
zDSo8G9R3(k8BeIRvAC>vCYBYaLBcK5az5lhnNFM>-hK65EKKFP&^Vq6f{@Ip!+pxe
zI04{ugwRLO{o611X6XLVY7u*M>q0jmdJhz<Bgce{V(+*WwZg|Q1)llld*{Y@dTj4o
zRBYOHx4*8f2YGc@)7&Hj-q%;wuxds3b&<&3bBJpv3AeBe+ZKi<_T!iSb(Ci65xY_p
z^ZS=fjg;J2B+B8VMEQ(7@JN(*J6Hf&4L63=m=l(M+m5bCyKg-TmtlUf-Oa=00zzyj
zk>f+NW5e#w1E)uHD4-;!J04;*REV1_d}~Ey5R%WT6pVb&PU_SpL_RR9g8<s-S=0e1
zOMUoQSjiU5V|>~v%Q_4+<?*F^`a%Ykb>v*3F3HVo3u+i3kDTUoAE0SrQ$+C}@4bTT
zL$SmTg;b2Nay&6Tpl{q^2jS!4<&93r^>+qKxi6sZ!P(Fp?eH-8d#5+O=-Dy`qxkTl
z&ML1t93OHc3RWHp#>y}je8x?<Uo|_tvD9thFy?Qt4D+1fr9DTaplos{vbhQgzrLgu
zDN@~%rKwhWA5hTXAjdD*w2J18HBBexq>)BHp-Gic_9&L5eM4Oh0a6MdpBH11ub<0l
zVuT$BR)91s_8Ehx#Msbp9{K>NffkD~w)K^F@$t}m40WO)(7?FcNlPw?v3mA7n`>ps
z^jYFI7XBA7>S0BJ4hAsLW=d$+i;u2DTXmwSDk$}b>)K&9YwWw$1w9d?vonT?!ov(&
z0O0=Jl_BMGXm;tP+e^dU!A(oSY$7c>cXOroLrZ5`Vb5*ShTd1s`1zme)NLA@AIokb
z#DyfX&g|2d4KcdVoFy*SbC2#gDL2+S<IUy=%(TzfC8zR+ca8v*rp>FR#&FPhM10o?
zJ~Sh*PTJZmmdB|a?lNCey5jHOCPIC1O3~QZo~kMhAJ@Y~2Q8b*sWw{8ESc$IIDVUM
ztd4fTb$vmcOu4mY<ED06=lXsh8_ZKSuC{85VIzSwxLcq5R9x=M;s#Q9m%waz;eLX{
zvuS(1O=*55M8vS$cb1`}@94Hxu&W_x!q0l}2spDEJZ!;ut1A*M7P4$zOIgaKYcV7?
zytD=^21|AxQ(y=N%qRDf6^9a-K)^FeQ<qpXx;sa7oLo-8@CD;A>5KdY?9%r?wW7uF
z%r>tO^T%5BAn|c3O<nBK%X;7zzF3y9q7g8#7n7(#G0sswcJ`H8$FB<+PSk196Q<{r
zD-*$R#bGkcvKiz6t=rU1ky5QKuN`SA@Jp`=j=v$>_9n8jk_&!*Rn}i)Zr`2?GFPdT
zGu~w<_G0DBXMSF_FXn8mo$rc2m7!A(6uqo99)VacT>vR*Ss1T`AwWNW6<^mVTGUkH
z`?=xGW}Xu1^8Qx>c+<#olH?};ci#7?3=-H%K7k2<JNy)|DQb%kCCsoz1<D`_VqeK@
zw_UX8`Q)E9aIj9ySHoqYGUh<q0l*9mws#_gJ>HEK%8;&Ae0)8^?TUzPs73<=ijJJy
zJ6<XzPH4d^hfRhUhaZtIee?B=Fi6Rc27AG2ldz`D<>TGt>D;Pr9Qxq4i37gA!~{d*
zUQ|i3@RI2BW$UWf8$3&c$E1YC=mGqviM-F_qUA;Mql!oF5lZWBY&vsz$t-HxcjCgl
zqadel**yp+8+`j8m-%7iiQ(NkuFz=ds8(+chKIhgJ+dhlASu$0Y_&I~$(GlJhmmh8
z-;<<M#0X(GZ8(VZcy}YlHFhI#J4DV)Azh10-bmjlo}BkHciHxaz&<R_PN-xS89J4|
z6uQvqvZ8d3{>W44%?8lJAkA*jv7lj5Q%5pJ@EA#k;fz1>X?kuz@g?#bKKWP?ihpVp
zY1$dK*=en1h7Ah?(%NZX$dbf3`1{x%O7e#jHzqgDYDRRQ5kZNw+vMPH(|u@rY&<c8
z?AZQ1m9jLil&Ik&_bsKeV)-o|^vJ3^TtRmLBl#(4JaI=L^-1<GFO}Pnzg<X{!uIZ$
zs`QiU>5S*(QbvEKeYF$YqxS$iAZR`WcfPNtVp0d10$7m8<ejq%twAaocJHj9iC?ee
z;Nz?GPuk<j-Y%-xw7SNjdBxQ}W-RAdc-@-b?JQPqFPf#o-uKP=c}?r+n(<VzhDBN0
zeqhKK<Rf`wkBK@>^5Bk<g21^xR9CGE84-%NPFY?qt7zz#UHD+O*-bLeFOmka`Yosh
z(=F){bEzS|RX$UARy7c3oiACOSKqHa7*5;vG56(y4Y6eRe!ond))lJ2?nSw8x;S7{
zY&u^3GBk(c>*+CnCP7;`gUq9w{ThgZhL9h0PV0p#brHs#9Tx*M1ZKu^abJp~SqnZU
z#>7mHHzFLA%iaggAY45V#oA24-jd>Jf=_EssE#QYqqo)S1x?E&6I6;U&?X4+QrK0I
zkuSvNwA394GcKl`KqKKCqjiSC>dhZ<X64NJ75QSVUvBALLJ!B5j5j_*#-oKZypu(z
zZ?Jt+G=E?(Z0#uyWtw;N4Dl^KS~h00_cm|Z!h|{sD&sr=EnGI4Ab156&#6i`$<xld
zPl?Wh9HLXcoC~d7(A)EI^HZ=|;JoA`lIB|eV~$-xwz@8<(dNDGb3@o@-=G92D7cY)
zHHOlFl5bJW?kQX{E*Hw_-2qv~vPS*h_$8q!Z>$f|EV9Dh*)ue-DH9oeJ5HT3nWsn)
z_;Sswy@{f<H}mfO=s}^`*Keiw<(g?MgOlCw`b#(snfO^>vE@{cLi)K+y78LD67j`x
zswNVtCK@u_-*Cr3ewy_<V&pnJMGm~qut39o0UFi0{FNzd2zK35AM9pM;WR*eO*V#d
zPi&H=Ns3gfKlqGJ4CaW|P|E0m*_it#)oX&lj&spz2J>O*8o#ok>+#Z3Mk9_VdjdJl
z@bTJo45dHg2n|*`#Csv&bOFC~>0Z`Ba2#8Etr_q>QWB{Q&62odOejX^F-zy-{Rc`Y
zlB#y@Iqg+s+nA(BGMQFaH*?+q9@-s8ZzrQqk<yh%*|2^luXwK2?){6S*sqEiOu5fn
zUQ{Q^Gi%NC-?T5Py|9BKq_Mv8wW1?k-Cr%@rBh_B{w$_=D@D?BoJhoT7#!hn_VJBZ
z|9X;{74A&*o6+~$-q011tL{T70q@u@=H*2RxvB68>M|ao1Q@OJM0sVe+e_*tA1kx{
zIJ~*yEe2O$v)V}<MQCN~mvgVqufC3cSqpsV6DDbg>Y=MAZ&>Kw!6@e)MTP6jPxT{G
z5^?lfz)fvCkjBl_2P6xD4bE$-E^^N>xmU>9E+d4D4CP%7;1t?*W7wC<mjyMgzM8th
z9hZ8k?QViFsM`{%dYgvNZWmlb6<n47QFQo3ZW99I5?TkHojq@GK!{n%okI|g49_;1
zWF3u<Y2F&arPhRCB!ydo5`09tIjBcv=R_n!ziXB!J{^hmxPfzJQz(k!yglj&)UiF9
z40)b49%*t^4}B*ctpq2moJK!EH>~L*=M(dml^%5>WieyC+P1@8)5W&e{mk@<na|5k
zMjCY6<ChM1-wg7sG`Q$cYD*^H!4Uy>IOL~Ywb?yIgcQ_r0-_Dx>115t5VvWF_#A|R
z07RZ>?OCo>iMlyT?5zbXwlm=wM#IWSkx11kcQ0G(s#cTOpcGAunc;$Gib}V+p4L&L
zDHoum(Q@dt>FjuhZxdhAE%nQ{B@lk)^dNr4teP{gSz!a?!rB1K99mJw+RP{YE{-px
z-*APVotQM&n`Bv^a9RAA_HH)`#`^k5-~`)?DJZsLec*j*h;X;BM{3`J=M*QL{+e4{
zo&59%d&(M#P2Nv0yP!@iT$Of7tFJC6Qy9WPL3-r<(t#TV5dye+LC^f1Rt0(TgO_3-
z$pT)qAyd%AI7X#Pkt-M5GJx3M!!%@fjbeiP6(MF>@A+XowivohzfnxM4yg87^|gUB
z^b4_@t|G|-CGBkhsrdA!s$Z0E_6>qKkxaooI>G7Gvrz_jXI|1nNsx!;R$o;kIe7lz
zAf<t{=J?3VVSRE&8kXncvuWSnAuRSV2(RbVH=FXjO^(z{SNNve!v#OCmiH1Gvw%PS
zgfD|$9@+E(6vyrpN4N5<j{ye%5%w!u$~<ij%2l3X(68eeGR^+AXjVzsC)C=b0^N@g
zlF)-Xa?~+zBPw#tH(Dhdz%XscuC6p(2uC=%r9_7EVeoPnMsq-Q=;osD6k#Eo+l5dD
zYosne_<qh0aJSN!ewV~;+*~lz4`}9$BM<Z0EkC*|Km&l*G^Lv!B9JzxXf{k;X|2OH
z^Qpfxer5Dd!c}8Va&neGUn|7@)hTrsY|_yKPe5b8O%&GV;6pI|Ca>h3j!k7)eAbO1
z*c$;-j8M|H*N|@~Ew!R#(waT*tOrD*mJj=g^TbUfz3lHXK&A2{2<jbYg?4yJwW_}<
zY&F@VhZuP5CF5AT$&HC+&b&>3L;J~>8^w;Ga}%*m+x#=TY@hes2GL{3<Iy?ZmxLbS
zeZ5EB86qhAQ`m6-7nRq87rR}8k>)A`P|Y5O=n9GRr^v4QSsC|z=zX<5!F;c8`u3<@
zmz=zt1Rc5I;S>ctv&;R0iRy&V0RR=fliPRO>5pdEC98vFqmy4hR4*ws1hA*;2eH43
zqM{0jBfP@kd4nC5x<qo!Z_FEi-*xI+Rw$#e?E2+!H}-91$)}pdf`vyxgwNlKtTY1|
z7dPK^9A1*JvOEk<r3O0&Bn)oIZYpH62o=|*zkkt*PIA##X!hL2o~m(GXy>)xHG3i@
zmMjzAGN`t0zQT0Neo2Dj*a#h%31_lI11_@Ql(9o~Fiw91+v6e}f~JuO>Z?!4_uQi&
zN;s(;GhsvI*s&*4@y6YfOTWDtuD{70NF<;Rq6^GK!r#56I<Fm*&UpdibeBLa>KbTw
zUH_Wd5OqJFmMFeFUqQXUXWGqocudB4hNzLdiQ4%TmQix#gu6r(Qb*+sBo&(VUFkC`
z3xUcbWljBq4SItcQxO?eu@K4d$Uzj*D4m<qW=Bgun10HTk(0>JzNSL0(U1sWt2pa2
zEIDRPJizES2$D`&CfVByGmsfnh}q%8=;I@%&_Tx3NXfFCvqP5x4akFR^ZuHw<=03%
z5&>29DyN<H%;iCG->T-(636MMszU;u0FbD72CX2V3EFhIeH;7Of>3*GjK(v%`<n_|
zRbB4t0@JvTSi(8t?Atja)diF=phS>ECT>1~ABHfMvUOi|Uu%?)wRth6F|a2QRnXQX
zA`G-LAcG76Xh=kW2!Hy43<J;40;LK`%=jK3!peSJg@;&22UN-64%ZiZ`9eyCO}<B|
z7F@n842a-=U$MLrF*M{3rTAGJe-wB#{tv~)?^T2K-;)WC&J+7G;`SRM(`y3@;QS9^
zMInKFX^nDLf^y(hpzPukY6!3rsFO!XNM_)b=RXT5{uew_3Ikro?=pSZ|J6zSaS?ts
zY3Jl>A0+*I)lbeI|5o)OIUO`B@CwGCcyixw#g+wL_~(<vAAu#F{h1aAJO=`7(F?@z
zd#z9S8$W6V3O@eU+>;Y-e{Sv{*liiVH}@xY+n<|z$`Afib5B`oe`@ZZNNV4E_a~Ct
zPt83!fcB(Tj6d;rpWygsw(gJKJw2ZLQ*%#52%ppnZ0-q%?8yaSbN@gY`_a3noZFw8
zdqS@LbMKyTY5&~ZKQO<(_wG;9UVm=xsfgf{T7g652_yE&g?}c%{ur5039vsk_k`*C
zr{4WE`+(Q}GZB8lV+Brxr##l5n)~y_`>oa=6X6Lz^$Cvuv-1uy{$~omHhwY)$!epV
zsOT*^LQ)Sy?%V4G*2WLw58yJV+SXbW&&uQc6DVZNbxsa6={8^S8Q*nX=GHW(sl!TX
zf|-0|Um<NA<oBa9EsHc#L_pFVt6!*5b$RDd3rDNo0ymt!II+OA#R_3O&wsuEZ6OOX
zskflvdJ`j%5e5sX5LBh|<?ZBb@o+S9zU1Mq)SG4D^v_npa-M~%VB<Cs_~>r*1MG03
zkm9n?P5pr;G{Zrn`70J7{Vg~=$#@$zPRV07&mVDfL~!e3+Ye&<04cXU4mX2}*PW|+
zMVpTvZwPNyTwY?oC02QM%8&NNCs>hwM~Tg!sjn^E%Vr)g$whZ=>l5ZnHqE?51AiJZ
zk~czx*eS4Ua^INKPcvqFl#i}4ySgNU@{xVOZ`wZ!+i9f^nY-m84=~DeZqJU;u|kC_
zBL=#|eRz(ZCUPwaZ`D>|pooXb7Q3N73YEhrhApLycS*qI`MM97MQ3WZW#A}^2Sp4y
z;yHFT5le!>E784_!P`7_;i`$}6ecfcK7wc$MF%NE>3%-Gooirg>Opi9^9i7D!$aEM
z=$@Ljsc)c@*taX7Ei4bf>w8rGXq%mA1JB#L!xxi?fY4wLTRmpgyu)NmA|;vrJ{%_z
z7WEw`wY1VC=L^D7OdfM$hG>_QRDfQJ*om|Ji8*<30)wxPs${`S<ag&rnQ&Pzb{5mf
zF<7n?4zltztKPT6#q30&@cLSmGQNJOIz%|J{06~e9xqI{4KS_hw&cs54XsEcm+z?{
zXMeqirn+^^pvN8SwR!NhP~4+!$S2UxH3iII6{+6@oF8m_2WHKbvRg}VLE?Jxvzrn@
z>%REJ?3Hb<m-~EBW9%f`IloTCBR{LkA&Acwz@s`y?=2p(&**E2&AuF+p#s9X66=9H
z^5iM?sz5kE#H>1<0*!h3H}gJ5cWq^MG#=?sEg;Og$O~L+rxt10EE;U~9uA`uYWE`7
z6vNv7vGw&MjPSVi##9DXbU0*M{dbXRttiu3QcdnWgC$Bh^%E)nN&dyS$Xv^;hPQWo
zd?xa3EUzIToAHhnvLe#quN4b-0t8;8tCcssU#CIL;tB}mSzE|_W>}A&wVa!&TVV5i
z(9YoyT@)Q^My!=%Z0L3^?bSV|b}YJI{Rbm13*tmpYx{y^Mi?ith=*b7H{C-nN$J%K
zix8j!!D4&mmi{;<kG{@UhE(Bi-fA{6$ICk&4<f-EZ_D`wpPkTaOTP70C_TWM3X$0N
z^l;Bcq@H<1>$g|PP(l||;-OV2bO53Kbd(P&hAySbXU>|O$De&iSyF2}14HK!3BfF>
zyOHslGa@0bWN%NUH}{Py+?8CiA*1ycksgt(K?&w9Itmrsnuhdq<XVb79y3c~F@yXG
zKvp7gmOx$?@k?O3alb5Zh-Xfx(-miy1q8Y{^~CEOcSshpy=Qg=_y@TU8@p@#%ql3V
z#oVvI+6!npLYPM>p}v}EVW*k#aCsPvM~4cOMZCVlZMB{K2<QCXfuhV+ymLwvD+D=!
zr11Q{@KrEQP`LVbCOdqN#|N$;$&VP|)=FQUvvV!J+HJ=eyi@?!?IJarRfsg*ihT!p
zon(_DqA7rxX4j}k(DzmLh^|ipPcu29?x0oI4oZpox`i(u$*j?=rynmsn>r2xnV_O}
zQE<wzn{Ioue9@|qQ4-Y!85NUREsYa1cv+aO_<Tmk(n}I0*&tObS)OW7ptuSnrloM!
zwYhU8xzj9goP{o-d#<jF2N|zl?s^6ND!5y3x?%`zh(JGSKH;%Ntfc<N<?(8wG<)M-
zo^X;Q>K!*)oHQl$@z=bgyz87dI&Z)`@vmOc^pP%!3JcF8JQIgRtPI+OhdfHBc$tV>
z`Ov&ck9(mWyV4;ykrT#aqw!^$Eaby7&8~0t&b1N35kc-07Bg!=+Ip9srK4RIQWK0M
z#~O~_I|T7+<pR2HNe)H?$wrfVG>_6;YQOnxye(d4*<G;_oH5i7P3mE+E2y+IpU^#2
zb9DPEbv^6nHDn<7^V;iC=@<>ckL*sJ0w)?K-l9`K7jN91KJ}&$neKWJkWg{EKEKo2
z(P2r}^A$UwPsTGrDr*j)5gm}ps+Q#XAfi@(=IN_wn68aGLT`S#%AI`fR;AT(N*HQD
zu^~|$9ceOTkM_}2Hy6C*SkN)}p(7nn^)%yDzraXl`*Q-`4)Y~%)$F?Ft(bXNH=ph6
zJP2}$!@<cJ!cyk<z10yA+})(j12_I<M7)mTT>Ym3Y(}o<U~yN6R__uAL_rVvbyj4s
z^w)j8vB74A;cS!J&nY5I*JC2R8ZgD>$0E4Y)aJ6zK3s=vH_&G|4iQ|*7leLPEH*vR
zLYwf-ncyuSvl2`6)09kPO8pGiph%8Q9<W=(J233k^?}U_^bsNY-n$wm3v(3oEiUXN
z!lO~vi~c=fDz;+vE-^L^Nc%b=<<{P;{3a$zXmILB|AuR1(^1(XM_Uz%J%;4Z%8^Kc
zNRlLS;HYdHkeZN;NBNeP;H3?FjnX0nXyG=RH6qhvC@-uF`st`11*O35j6<Vbi!98m
zVcNT&<zX@wMz-52+b_h7q|4&%ii7T94mS|p^#?26=TeUn7jF3*#z%|F7{v35$f4A>
zzKp5UcuS?=L>%Potr1r|6Sh1|(-R?IN><_ow<nPFc#WW#WrW^td^a72L#Z=ud1P<f
zI-8GW+c@i0iM&b+T~?rK0drQw9oaHh9wD<P-3IZ+*254-YD$jCld}i-8H)J}$z-xv
zhjK~P(t>IMOMee*ne)r$1o(nQN92wpZKPKfY)d}c8pUPO=m=t(>~UcA3ntMkz{8*>
z@Hk-J_JUXFIsRFY@Xzunsw~sEe5v^Daw^kDY*cPIWUXaLOW=8ciO@wa2SsI(T}j9J
zSfsh=E(mKzj>l=<@1qAiAaciv%?>gsN=CTpda=e3-hD-W_Q7GzWtR{c>wmHLR$+N$
zUHj*QyL)hVg1fr~cXxNU;1b*&g1c*g;BLVsxCVE3hVIVy{oj5k-96XL*<5|X0nbC#
z&8odut^KQA>t3mHA);NCzY?5p`H)n(@E{{-Z|oMZDCW>47#v6RJ$^dpIqRnU>4k3*
z%PtISrLsXYpuzGe1<_9Zu;XG>7@Ok+BMQi-NR2Z^0$=3^ixd7!m;;dhsw0yBHLt2g
z1MHfR76rGBuN(iES8q5l$qB$_&jFP`ieJF$aqPJJJYJGkiBR#@dy{GTOAeDkVgN$H
zZ6{oKUvXp!SZ(-FWVyh5{ia=^CsG9LnhMUCtAZ0j$yt#fhNxL}5T3Z!*e3~b@kDIy
zn=??*9A#-cHGXjR*P?o;m@vI(H|a6>TlF6Ol#Tn|*@=|+YyqW`?y#jj>=js)HsnIt
z;smb*dkLK=jTRk8yHF~82ch#ko5JpWLIn4u_^c~YUv+i;$@1ToaMV&7r}u5|$vcqm
zoC~Vb(|5dLjG<bH@D=H?E=|rml^#rWE3BUQ`($11ei;wf`v9qTW|GoQ2Oqe$^i^%J
z67O@ul4XlM?6ZsR$9gbk(#ucJdRm~ez?g6qO3Br}L>$gdeI`1}rN+J><LivZ1S;%+
z_rOkU`4h`&nDutfLF4bY4w$-=<x8E}An(1X+8!;qsg?A13<o_O)m^+~a{^C@cC8p@
zTdE!7@_I#-Ukn#7l{$k#;KW75D896nA&bm&1b*j-`Yf&ZO{UXJm;o(qb2#KjMy%%;
z;@ZtxwG)DVQ6Cen&dx$idz=x<^Yll<PE(oq{&A`I3iCkytUe0<2B2DG#wY?lP-DrO
zLAvQ$2hl7C)wkB9z}2{WEMKw~+({swiLBRD)L~}Zq4yIU9%fFe!eDqN>Y<BW?uq@v
z-Aw&cEXDP%Nv}r=j|><Y6xqedp1R>-6&JaLrW;O0^5tf&AV=jnhLzW+z&RimgxH{E
zV(v_tqbwaaR2Xh#JRuD}NSb`3y<}8l@JziN>M2d=*~Oot6rMcj(|uRgZIL)eduZ_`
z5DgKWQiEzgFMfL<oGrg<3g;kt{XMxZhM|)k3-eoiJjzH*dwrT<2}siGi%PdlKaOI3
zvf1L5cl!F?CS=C9Vg_9}wcwaZ%py8qT^mdXI-(V<ChUt5=JX0W<zXCBEM(`s72@R-
z2|0%l1u^)M%n$i`$k_%auF5c5IkOVluG#0g+kS3Y1U%w#Y7f|ph`y<@4?M+(0c||d
zj+;-ZJ4k*5PH5YBW=2bkG&}hTIG2*k(6q4TosVRysGuWbW%_XH89q`#hR4qm#?x+z
zu5(frF`){>g$M$!a0NV@J&F{f9);eYU?lt<?P#8gR}MQ?(kh5w!shSs!h#(_G7REm
zF`Ao;H0kgi?2*8H-TJs|m%}7Y{Cq+X)*nMS4*Shy(Ps;&r+K%Q<cZ=tbC2l^y1CJF
zCtv0*#DnOur9ltSEFkzbqG*G)O>K-v&|n|ry?8e`4pa`{Rz*}ON;I;g+<D07pc-~N
z;Q`P|R6PCn2xJ7S%^rvbG2!M;^T{mJ&E;)u_Y-b+_XU>*Atu$}%dwQN6a^1?RMLP2
z=i^}PFHN3%u{cjc*RSV{QF6j5spfebZ`+E4df@r|TL=u9Ln?iY0CUz{+>4;6c5wV;
zo04WMOz7XqL-ZzwlsBr4F+)yvOkoGl?!}BFh*=>7z$vs!ZTdXUI0t_efz5!iN|}=i
z$cl$kR0{^|%aGfPe5^2+s|Pog6GZZ;$hwln5XhZRxIhb*Xdc3Te{Il)b_rAI*~Vkt
z{)TeE+UdUC+}syd9J&`Z(1?{w=wqd76gr|h4qECu?>ea<H{ia=1;PFZIknW5--GEQ
z6N+<9lcS6S7)h(2a3H*F{+P{Dc&vB7U7UcgVkl-DVoDBQqnjoGPjKF`J~6EC4!c>z
z{l*OqrQ6F0(tN2-HuIPx|7_2qc1QK)-9)qgCtG@^LGw$LeXfMKlOF=2ev)W5oypa<
zQl6iz8{8w~PnnMEJD?OVqpmB}uY5T#wB2+vAW}&3dB93dL?Oz+-F$fh_B^O>)A)ho
z-f$1!|28wSH%NHq@iWtW;;Yf({{zbbZ|8MXU|BJjzt{Th7xkRZ5MIsIijme2t|kz(
zFu3Re7;lGo<0Jpk-RS2U`?tk6&(9Wd4ZAVpy_u{N!$vZ{9|r(^m9N|&S%}p=-w$x~
z1%M%sC$O(iK3LrmjZA+!tRR`fpa--5?EwGXH4X{b2o)5`bq$hjJLpXjFelAxPgSo-
zd(kRYW7%M8w805G^%}u$WK$;qisS77B>&q2$=+3fT$Lm44FG}wh}u(vieS<aLC@C~
z0nop--@yjT`W-bjL}XjM_kFv_hO(U`$V2Dvd~xjGKF0Y8oZkMCQH;*tPF$x7ZvYlp
zw(bj7|MvMmbEh{UKe^LC3i%yE{X&S{A3}adQGX-kABfbOke@{AABFsmr+y*iXGDI7
zRDUDnA6OO1|8{x%Cn0};t6vEDBO-s`tG^cVcWvB$qgZc3ep0Nz7V_KM_t-zF@-Kw^
z5s^Q@)~|##{60qh>DBBvAwT)nUkmwTuzvm>cKt%g&xrhvy#7YW-}J8d8Ihlj*#26`
z&xrhvpZ{9O?-2TLg!}`GejBVmJFWe-ke}1{_YCxxLjJ(he=X#nDD}U2<Ubzc{O)}8
zZyx!-N95l;^2g2p?dQ)i@;?n7|9blVn@9ewM?}zQh+hGJJw|?}_<!q>|E*d7tw;WQ
z*8G3ZBVZ{1{frJ1;#dtd=(&XLyf6`DHjdMA`>pf`(%9mos3T&s+(c#%)<unwVsEp>
zZ|pO9Vpf=s=9|CiDWjZQQrIlm;+q-FuycE4-X_Se)1PV?^-wtS5GH;+<~ymbWm%bi
z?}OL~6RkKH(xc_KcUcq1syA@Gpv@|8Xgm>F5<sJ0owW_Vn0I+>XTed=g9y9``EkO2
zUPD}ja2ig1Wc}GUYgZqBAQaR1hZ5+`$DVHOZF+dQ{Yubw720K4Hww2}Ab7Viyu&uj
z3*QHF=mFn*U$=qXDBC!@A=D(jt=k41cU5iMU2W0`)u0yE*Pabb2>eH3FC`|or!2QS
z8H)wNX3?q}Zm3&wJ@Fv)SWH6jd1~UkGQpI6d-44Yaus?RiZ!>vD(#V&xLP4^{Q#`t
zN8XFY4z1+sl~O0iqsQmoM8ts*ZdjN8EBMGBrgwC^hzht-PiSeunWAS$D(3kff|9!{
zW5ePJ$0Pm6s4@M<yb=%Cl`u3wRu6$D4FE)E3!_9-^&Q_+sgZcr-sRydh}AFL5z{qv
zEil*w@g_mV1#y@L8s(I7Po8&`hZ&_d%M|>!!LYTqyqQSTIH~@CgW+gqS-pPUlP4P!
z$+|P0@2nD8OLiYbOqwbu6o<lOrDapG1z*YZ6GJharG+VUQZDJL4f7X(2oSn-S#y>>
z=j&_S^_(2^kg$bAQ!u5>Eu&c%#-FF)5LJV7WNhwx>z39+F;uE=iqhGQRC4Rti1gm}
z%EQA{Zhknp_siQb9eDuyNVpT(js4#6A#&94DH=|^b~9?9;rIhW8mL<`Q5@S8Uy>G$
zY3e>U8+PJ5c$e$|DF?fk*kk<RHciDBz0mkWLBd6$CUr=s(55KRwYH79XDH9+ff+yJ
zwa6SljePloc0r-}*QxC#QOjdExl){z0!;xAhAUz~@d-+h!Up!ouOW&Rw|QF{5uq*3
zK`zG}2~;k)HJX{C5;%n;@1#SlyfL{|mOEbXVsfEfi$fULI(&7%2w<Se^&4pgJ6;fD
z07@MB_Werkpjue*(o8YkHl@B;Y1z6-#pENcM(-t;zDwByV*hrJGme>;iAyFwSezkw
zO`rW0iHwi;oEyE|euUZEr_c<UJI3&w>EMXzS*E1M%+7A)^bvz__`~|HgkBmY3*T$Z
zxRRo*xId0dTLi^RRL_8owz|a(`AVD+1CXWHmhBF>W%9vw;lVzfZt$)M!G~rLgfb_d
zo#o~&T|aJ6&-Fb>T-v7n3JvFm&CjF9C-}2tw5nZ1BD|j>pzXpc0u0k_5bUIYUo=a5
zpOlHVUO+>6z=_QQxSnx#kHn7-J#M0yQ#6IvT)H?j=~KiJ@3DGzJ|TFrPOVTjA~{g{
z7oc+1`1KSS7%w+DK`;lY)*QJL@F0A>Nw8{&kk3ty<wgO52y>PJ1hv4%F4jhTovn=~
zwqkRwLA{C$v906>c*g<Fl$i_P#j0(H_EwYJurdx2vBW91JRt*psAm(RD~5}2Ed)S2
zV8&l`>QN)6*rGhDZ-vs9bP<Gb_1Zn*1Xsn)WSvvq`!^rdkh>?OaTneXQ3VM#oc4)9
zt12Q<n1jsi&WPbcF_Q*0JG6pyN(G0}*0iZ}5*|KZN1xy$4ol8Y+4D>?FSegOgfoGP
zK{Atk@C7AVIiXYhBuVay=QEat*I7qjzb~1_|GvVS^N`#lkVH&=fK-W&D<9z8`L=Ln
z|K%c#M(lf1QE3D(1=s5gi<1H#rCQuK?n%NpddGtI@UrBX0FkcW*UW#Qp}$y|vHu@H
z^aob~2{ZLwJa0o7{eUNzb8rESF`@6XMje##sSs#Xn*u7e-)4y#5di$wo&>=A-)!iQ
z+7miSGZ!d7$Zr6@Nh6$tRMGgwrk(A<HH9(E-CBJm!6{iQgZBO2?>kQDe+h?Z)BqsH
zLbg5mdSkut;!-2NI2)hxoR!`T5A@&8O7dTz2m;YfFeri0*R!;0fi9UUpu`sd9RJ7n
zez#Bk-z6tVSP)K)5|JAE$Z|tui=?eD0RFcQ{{(;OFMtGUhm%dP{`MW}U$Ud$bqczo
z@bMRsW%N$n6_fpRiy-=GFYrGx|4)g;rXXqnpxHkJ{+T5Gg}{H_<Mbx)7ewjr<o&6T
z`%8I$=1YGq?;ki5^)ETo-^lwjZu%>E|3#htQr<t&r#E@OpijS)r}uW*i2e&g{iVF$
zw>bXY==;sK;cXMtKkT`BllKcI^-FobO^-Kue?n7#DeupO>KD=bDeoUh)tkIukg8wG
z`x(7I0js~1_h)YP7xL);yIz!mTm}VJQGd%7AOLs%eLObUI3A8U6D4A@hBq*m?E4m=
zx5@GgqV-F`f5h|8`SY&?{}<o-3&C%5nOUOv@4P|Z<o&b^_|MhY-^u$EHvda`|BrIv
zKU4I7GvV9N`o+cl?N0I*y?-;|&pYvd-sb*`%KsvI|7OBB|EK?`WBwGq|3{hd?LP7k
zB>&&M_qQ|mZ#<^5|F~=X;_2bvrR6_8MEt9Z`^UESzbg`cCYFDArucSo|E&Ojky!qS
z-oF*#|16OHlvw_MPyvF+{VMNKXl~sh^jaJPyw=FQc1NG6K(raeJuEl)dP}b!*{?AO
z>U(Pyqb;!*h&jTCx)bgjjv$T%q03TLe;lE}x%Wjuq$iz|LER}};@I5y_^bvP;uIi-
z4FCig4?G*DChKGlW--z*@6~oSYt>o?V}TvOUwWr;gy!@gE4bKY(@X7>-U(H!Z}ZBw
zgBeFnnrkVYX-cFgPzGJvH)QDcL_h=1829@+QBhYb^l*_V+l-+Y1>R-$RNRQc@q3da
zea{?gl!+RRZ>ou+BMh+wYGNOvyp&yCn+EJw9ZYg!<L%_DX{w2Z6XEAz(j?*0&6^lM
z0^?Yd-})yxRbnJ%p%5l-fpQz$BVIJAnsSO5i4<;_It-XHg11%_L-b+r3bWswY4+O!
z9e$4Ncesl=T4i9ksm@)dG52=ePZt#4qfNFrD-H}XPpIzFM|ryT_@Kup$|j8R92lI<
z?<k|)E<+p8`rt8QeIi6&PD7iy>X@wO&uWLR(Q5N$?sZPhcyYlJX)ui11BrPo?0hp$
zdP6d@w(h0O<V)bhlHlYXh&=-_8PE_IekM8kX=qYHLlhagAWp~-?Qkl*&u*pkRg(M0
zJdAZ-TDz8rp`h-&3tntE3`(BJcjE`r;SgwXp?Qs6qGhzBFLSc{3YI{89?a}z32Ja?
zxX$2MzTxc=9^Tn~hvGDX$PM?j1aR7f1s~xT!~^&fD`L!z+`|Fi)1-6xnjgf^XUyA#
zz*U;xgUq4AI^j(ge{Uy_>24Vnl!ET90%MVJ-V_{>_9+ILvri2~+bYi&-nR?_hQtEB
zSkerY9&E_+j#EH1rTe@C?~%RB+mVd|J7^V2enxS|`h~Wv*`;2qzbgF%a!;wbVHOF?
z`Zgteg4HmGXDnkZs4h-K$vU6r8u$hnx@?>Al`Y0(Xi}Bc=k<7YTl+_ggpvP?1vN`r
zfpK3|G|M^37D<=43HRI+6BqJ&2#^Yn544(S)g|qYq$=<rD0yFkLWdJjneIm{CGxeM
z1_JiMLxIlhz(w`erF*TNk&Wck)+|$55tl_uTcGjjPWTU!j1J25acB&Wf|HCSx8L}J
z52{l9Ro!bfs*A@~L1`iS-M^_PY0YXm_>!{GfjXl;R#7h~kM?JE504TK@}4~6S$<1L
zPfNG2$sEIwd$q0<i|48Kj$`uH@@ok^NgFi{Yle|`17;!pL>s>;OmaBim}X+hxrzAQ
zp$H67X2&tp@ouda9$w4=Y{XZ3@~qk)I=d-K8Q5C!NSD}6B4|HHf<g^7Is<Ag4FYoI
zsjTb=t4e)^l<vtOfbXj#a*1A!Z&nC=!S>AEoe9ERVyRlQ_f-n%w%>r_*sCR~+(*37
z&|{`L#zF}ExsEX1+SJ0~bLTTa5k~fb5-?@a{v3fhWwhg2c0h|`%VZSAL^~4}hTHRV
z14*Agn4Z%RLK3(mxqyql)1(+ObXNm9D@>1c@lat%oB~Y7a+D!#jTWjZWOJ*&iWw$s
zqK|a^%p7|?`qYLWgB$nf?KNwbpmoDoTQhb)EZ^fM*01rww5QBOdH2G$)37!kR|64S
z*pE3AB8rHe^EpP7u&S@YP|o=N;c#^GlGIDMFpO^<tHZ>a`&~gvMMZm6gEAzjBrmS$
z+@FD{(FcaLo6VQ6_-qwTm5g?lwND$ERp=H<G|)n7Uy(YT7MSg@tCt`GN+HZQkR}G`
z#vWbOx-`w{$2nNkM_;Vm_o;E!^4Sow)_6H_iq51Q74ZyfibAsn8`jeHzOEBByeCE=
zk7&mgV!IviSrlX38r%k!rn2*IY3>yFb;&DcX<U!=ZG2o!0Y7sEIgdE{Bys6b%|tLQ
zLC(1?#eUcMy|K|f6KcLM_EE%s{(XCyD!>Rxr`Z`Fd+(`yC(Yc-*s;FRl;OfpC5M~R
z)asxDU+_K)Nv0+uruG}cqSAfuLf(GoJ6>-j3RSO@C$?8O=;u#-gtAV0AIWnEG+3GP
zzus#jaJ7fuF7W87151sYSoZHXcf5`}<u}j^cW7IX4T#TC4(%WrHII`Xvubg|N#J0q
zu%o~9e~_&tczS0gpG!kXF;n$yA7<{kNhcj7+NQKNVhrZ)I27e9?f`6$7@M0(PS8oj
zp)!FE0qa6j9+6cR5bu<znikq2eaA5BL8k;ylc(I8=WZ-_wX>q!wbc)Ddym?Efvmp)
z=|o=^%7iw7C8(~pi`dro+ztS!biP%K0>5Ak<pnq@B&5m|GNE&#-s3-9Wc#;)VmDe_
zb99xx*e+nP`?m?@+gg>e@+1iWroWNfH;VYDEj7QcA$k7{aN(xMY7xF6N|`z5^ai`&
zlRyLT$?2kydDij~yWg!p@c<smXJuuJr!cYW-p=v6!Rm)!F$pSNlL+$O)nS-)Yx;Mn
zo7MUQ4?wR{%3}p(t2IwsOyj|8cbF@L>PF{h)L$=i9}j4kTRD~mK*7P@gfsmDVU!bl
zd;k)-hSn(xbrsg)d_g=;DyL{LM$(PeN~#8ct-Sr&o1N`{q73Y$l-Bdk*%mLFb|%c^
z)*FfJ8<fqWKnOhm%5R&!{>Sm)e`@DT+)CUu{&6QWjuNDHH*4f4yJKlR0OD`kvHqEA
z{2$Ah{{vlMuFC69Ui)(BaO_a-39~`1Xey#GZ@B<~0Kd0mlKmO+0{uV>WBZ(%xssE&
zmw1&H$&k!#M*;v!{zK@WFz??8{X544a{z!f&iIo*0RI1l;{o9%6JD8@SjZOx!+cf(
zeZzH^wlU<j&1IizE9Bf{Hs3a{WxfX{x&#q=VHuiFp@nVlmU0L_mum5DpU^;>zC<()
zWUA?hOoI!FUuCen6|nh%HEglnzD!_;_Wk%io-pNE(iJ#E;J(3}yLj(;RcYfM#;F&S
znR%v5!9RM6y4${hCMaZt+j7a+=sjJ0w^^hrXd4#)Y!u*<l=!)u*J$UKeo1bQ;`#K4
z+EZltgV?HUVifP8rqx`KchH5Z(|Qvw+|4Rs!v{Dwp@NcYDfLIi@6i1Wz*Z6AhAa~}
zXU5wfGCw;ZzIV@z9oY>L>KCNzmSeJ+*EM@JGE6n0I<6tBJ`B+(2PosEP!_Eap4+Ts
zH-{fk^Ot@eja#uBZ-a(X{UF;_I3>h@o#BDygzjG8Pf@I67|d`hqx|N)4tIzf43ZRP
z)>#{C<1{E5=xp5UwjY$Xb|{y~^Y&)sXtoPyY=r(WKZStV=|to=m?w;$7les3o>g4F
zF|u;?gVn`mW@s%kvTjf|AF!UM!lCc(M7F-^`mye<ovM^)F%4ex+Z1i-ce6s1XqxN*
zwvb0ym)QngyVP=~GkE*&CG_eGy^q!2a1fg!1#qZX=qMbt3~e>SQ+iWZc%eNxGPRV!
zoGoSjm~6c!5bvp?Vy;`9C%oIiK62UK-@7?ZgNP^;reyegm&6ty?xMY;gA(RBfhUqE
zxh^jTvb|{zc$M#=G|vQ5Vze}<jq*G?DwCQl;au1i*XctEiXA*R34Tmxts`vZm#wmi
zK?qFA7!r&?IE^wT@+FF6e?Tk428a9(MsyegZI%er+NRo0i;1fj9Eq(ZZmzaViL;uZ
z^P?6v{H#;(Ot1Ozg#(@+6S#FKInSgtcR3FLLhr(P!VsGq;EAVt*SFY!`)I=AD%*85
z9_ry3VL{A+bY|}1_ob>2Qgj^6LYZ|R62YXU2GTLyCnYhBjSzcETd<oVlKc2P-zjzL
ztM2D_!uNH-Y@yuWEn~$X7#RW{jUq?#te$hG>?P9x4wo=wCs!O2$563TPaZxu`uSgg
zzAA&AGg%cn7An3PW_sCwW3Y$~P3pX*S5*N%pWPw88Ss=PRmlx4(=@&w31`DlopN~>
zf9Vkx9eHYwIUQvd8r~qMk{9%2)0pqj-wKk|HfqQhU0Ejq&EkxMq;cbb;;TFtpY}C@
zWY#w~bJ&V~%X4Qh#@6Vaap;B%^tx(-g)L<<)U|z17Yg@>?#vyAv9sdu71H<X4Ubx%
zy=w{?^oVzCT^yWuz55d(p7q?rIgajf0iet2G^NcNK!j2nX`FDx$ZpNe@UAjY?9P|)
z8urf9J(GnG400*_XH#%_<rM{_>{zNud3#~*7f;qp42Iy^T^J&YX(nj%oa^OM(+=Ah
zRRZn|Ih3Uabm`IeB~GvLWac7X*u=X*tdWx5J0xo6A`fXnX|lmW3FjwMhL9GxLA<*1
zop;3@!&-f~u+n{PO{o&AtJ+(f7$Z5!FJOET0W8UU+)Az!-(eH{ub_8}WGYaDc%w2C
zYg5&xHQ?kn9M$)`dP(lLBeHNGpn_5(kQ&A_X%E#pJ*rN=Y$90hH>hW#bOfsb;rXo2
z29SVzL1>WhShU{QjVmBs*2SAp4)Wip-n9^A%=En89JWk;X*O1ARJZ&zfXUO*?9nta
z!DBVV&fpTvLlJyrq#L^-Fr_6S{S-L+u>|I}aKOys4v=Alqu)4KOW*D*#fn={lMb{3
z?#krDFFNYH1gv{bYWeI9T_9PoD8eVkoTEBwypk&2lvUs33v%zvZTlX2qEAm7{&lqP
z3Nua;HyhlzCB5Y)_k95YV}9&eb-GZspH8~4cYIl<6%X*5$`kSRv#>!dTuBJ$R-!da
z>2AP&RFM$lHNLhOJ>BWF>65b>t*!ntqRn}W&ei8zgLBTxTHZKBzE*&&b^KzP{T#;i
zZ1NS)XH3;6P+8;9gF>UvByCv^N!UfLJS@V(O?&n2v*y0%O1k+Z=yD&(Br24l2X8lx
zb*$>R>)PDC(;V{%C^&pNLtt1KQ|X_^??K77ba^Sb=Zogs1;JaMOB6=H-{t$$1TaU0
z0gXIR+|qAu?g-|4v>m1Az%D_JEMH;jc!|t~?b1(<(n?)AbNEQUuxUNj?R)|#3*S;`
z6dImyJAGT?n$-2xEWzdsDy)*}68bivk&Cc1Ic8=~$_Km^TBfyluIXf8zWwQbbOv25
z66Iai4l(9eN}R{{)sK?7<)!<XF@bbnaSK^%zvgHx$Z)LX-qIV{+`HwPm(RLig)!87
z;uJI7*3X2yWn<~TcvZ~{0X8bRgSbV{Bv{JS>dp};xKac<jo+iGHwxfYNsSAv>^{xk
z3|6b6ei!$eE`>8x5!VT~Ml5|^7k|gUSiq-l>Yx;wxZPavF>C4ez7fFy^A(#5Z^2>I
z%Q_(f_2GUf<JFcKM@izFGNTE0OB(u_uJOmigLO-!Xu$>uS*wE=!ED|&8(NUhjKDw=
zPG2Kc8w%R;oPOx*PPmtEERuyVzv8|dM&qCsG{oa27;b9A^O|Zc_Zp9e8s59bUlh8Q
z1nFkp!<K|aYjnL*hHkQY(Wl?NrVxG6xuaE60T*lNVXf$-((2=1ZTxYg>Wfi^ypo?l
zJk>~*Xfa@CMyR8GK*AonpFaQ=wGR_vY)H3GJF>qY!HECu?&j%7%I7^Js<4C0@F=qq
z$n4_H^zjAq`#vMs)YR4Z%OUq1+UWNcgF4LgafsReh+(LBhng^Wl&INl3Qa0F8qV8+
zO>S0Brsn{tzBJfPbql@#%{kWjua><akFRUY<>`7lhP<@+3|JV318j>$;EKZEpsACC
zhnmP)FuY|*d*8{Jz(elKw#L6*)=VbC>^QA&&b!AXcy>Wt@n!z`5&q-^<0aSIrDJ2E
z6g14wG@hb1>j-2T&d;x4G!`}Z1AGHXVm4iwE{kM6SE@$hFrGO~>jVe;6h#AJx3e`*
z$$<WZ?wtyGEkg%qrnu!{m9L%<cSaw(@;d2}y?wHbGd9c=BbsqIY!WxYzUhUA@PNa_
zVuDu`na|ii{J5-$gddj3i$#p)lvej?#+Y+S03UCqUIr2Chu*&Dq?xhb7M5Y_QKD^U
z;gVPJZp-ta;k)&&Mv)B?&}f~!{+0rt0qDws@)_WF>I`=b0)yDxMlt-sy&Si$cU4VH
zd8j)b#$v#GNRQIqH1eJZTUdg97+4*`3m8y?>F(L~=qet(OBVS#u@?p%aw7#Yz+*V}
zRh?Qy6KEP%U|xM}(3lm1`7>ye=I*Sh-p-8~3xXeOUtXvJ7amZ^C!AFTt^#w_T54w9
zvaFbd5?eEp{I7|Tugae4K?bP=Gn!HF?^dAqmly(@LeysGtMoWmPGMp*Pp*{tq)OIw
ztNN+XH#53xIKoRT`6Crc3DWJ~S+luzp^`F)6vy4R3sWcU;?va_olM$DO1*XrX1fEY
z%hZ^1ggRycvxTR+NVy2JGe{h+#mEQ%$<uFsQU;Saj1)Kb^LgBkXK;a5AV1DAlU^JH
zy$_}d`g(RYt3%vtYEM7kMHAmq6P*XWa|y!(DD`1PoO-X#BcR<T`V=*zdDNfD(^j7b
z-l>N{O)Hp_1${5r8+uXiq@XG|)pj137TI}7%wOd7V_37x$Eib(t}j3egx%8W!$OL=
z7#j^kRZ0!=9E;N|0<F{Qe$cGcN;?E`PNe#U?+ynOe}ww5XCM;$lC2|Y43ejefM(Ei
zSo5>uW~9L;p||HziOPV>MQAkYIsW_ej%L(ZTh#Wvz7VPFx{A+r?a@DaJZ#f^{T(WF
zhFmG_S(PNPyhNU3GVEMUEwvq324%VpaH-LUeDKXy7(RoQ{s^zq@t3_poDEdrEa0w5
zYilOuZCV-7GlTC+vn1M&?(z`NxjWDFWU32`Bp)PDE{h(y%aZxji`tySmjZges?}`L
z>6gf5K0&U}_ygwfbiLO!<_M;AwssrgYwuWuP3#sGQlb!3W-DU*`w%*Se!T7vh_Z7_
zAK{O{CY3JSzCkm9T%ZW(M_s<9k|M>Y0b?dkZew9aq88eN(rjUu5H>-zgF?O4eP4kj
z5{qQSm}T)r$nOSbgtfl1b{~shU87FW)EVR*tqj?6>e4*=(Px6h>B9%vL0K4pdhY6H
zEh_mU^B3<&6aD>tKQIM6Esfijlcq>o*jdz?`z^1m6X`?(S0b~frye7Rme01(8)J<_
z$6veBo3yTt%J?HzH$v>g$9Q-;XZ3Gq)u;v$<BEkZyX#udr=QV_o?*0XR?&aVF;nzx
zo8%Z6O-*~Eq0JKnE{-vmHVfd6c2^j<v|NeLw-6}2`Cxz{=eG<r=i2+Z1+!c)ZR8WA
zQgb>n<0NIp2i>o0)~^dy>$$}TpY&l^s!9@A!qKR}%WDj4lb;z&X99BL>`MnqZz?N<
z6|~^A^6Ha_S<CXra;Xe_UbDDw;Tu<YbxENK_vR(L0~^bL4k|wo`ody8gJV=1a99I1
zLdDOqi?Z!Pz=(o#+-?(#sSd-8>G%S{++#-zIJZsR^5nYJwCQR1iJF%*`1zTkzC0Tt
zQSD1~Z8+9EGU8X(@2ypX%l50xj$`7l#{<<SQX)2LHNIJz%@j5Q(hv0;7mdT<lDxlF
zaAy1KMdk2uJv7Ab&`N3^8yLe3m+`ktmqa~fRFy||_F|_g<L$4j>`8hJnXU{nUeZ8>
z1JNIsPl&Rv2-_d0Fw>|Pibth7r<{6mS|67$J;m3FY$|-iJA0v3C1Qh|Zq5@OhwBZT
zL#XwlJ$GviL(ts`TH@b!QUd^xOxWVMhYxmcz7Z-GdOy<ISwaYyN05LEe2kqGz%_B5
zIV;&Z#MCml=XG9ONTNU7h63@qEuIh3l^mgOejhe50)EMi2mo@OJ*)>(%xzXp2Is&g
zVAkDN0*VWu0RNWji94O<Od9uobM-7L#0*kAgoHE^B^TSi%a*L1II#0E_~_~KydX23
zXy2~S8T*m07ouS*RSY`*K$vKG08<%=ndUjr8;5|vQiXRvdLbI_qP?wfm#a0u&JT3U
z#VZmN`m^V>0uH65?vrwUaA!$ZcjO+EJ0UcJMBUo>4IvvgS&#pv;ZVAo8IWo(ck;MV
zL9J^ml&B!wQ)jq@dJRQC6hlCOA&VqWszJAu84g+6CSw~!VVo0@@>*<av3i_a964Aw
zM`qvmFFbTa&vP^?9~S6cK7|f{wf8_$;D&0fx4Yyd;wJ@aTZBTJD}nQoIOK&k<>J;g
zRz}6~wwGHRrvsCmaEtj+?Cd`vsGq<!QmjBf4o1u{#=6{2(EV-;^3Bi8giF==oIagk
zhm*MVW{%6T0n91i8^S+~WSHhyxKi=l{tmS*S4#?gWU3!LVlGj>(4+Cf{A0snQVD-m
zv=Xz!fTk6hPDZ$id-n6ZuDnv7UDx+0j;MQPX(&UtAJwsC<r)YX_oqMJ`Vh0XlYj1A
zs)#B`tL~J&GA_=z(3?{;YbcK^Nwv4Tcu?X8Q^GrV8F{^3&ZLfdIj%##*F+dGszwW2
zo}=oP)YcTI#P#|DmIU%GbXZpv<H|~bE`;ahvm=WyKG2tmAzH*?fIzFmW`PN-60@8W
z{M#m*@ac_MWeJ|IFzg<P01!iZ$QSR<C{xTZlj)eEPnspV1jKg(a0Xw;#5Tg)IG0!w
zylO<x@nImfM*Y7y*!gOGjyoQxRJPfr1neR&EF-PYR(S5MWgYB5>*~*1arU9^bQH&r
zSqnL}-rMyZIss9fR_X9N8BXf5RlJ*07A=WTu-CQ)<C*(V!zKy)m0aK`dz!=@QeLA}
z&-v=~2M!eq<ccB>cq!8ge}ct@h^RRUJ(s?%v^^b+tpK`g%;M&S9dp#;PD5)^#Zc*R
z9Bj`8V@94{y_3MU@ubE(1~_$tA$lwvsI=LP)9cZXKyfDD2Vn$NMB03z<?=7o0F;*E
zc#>Nz@pjjkOje##_2cT7Q%`l>O4H6%nXf%hhJ3JqPemq<u_435oDZ%jsa9Xv;GFF|
z6A^t>&%k!OuZ5}?c}fFbX*vjxGj#b&x1m>WNe-UYZk!I{(1>heST+0lH>(~b#_AK}
zj)V#=idKDv7-W56D1tP($0;c@eG6gforJi>E><jFH#fN^SP2}OHzTeLRgKB%`yfns
zSURk9o8H<UoepG!2c=8hWq}q*dS{=J$=e)!Vifu!*$s4eyCz8h%>WiS(u8&FW`I}v
z?NgtS{;0@l-gA5X^XWWiBNP8N9g2~smv%lxJp7Pk=)tMPV-ufzX7HH05h5NED97Vl
zBdcjRlK1}P$I6dT1|P^reQ7I=C%+_jt92x!jA}}?RgLnrG@q_xj}Pe_raROmlk%+>
zmUb~KeMAGQx_#hTftS^l#PsbQUtJ(x<uL|d`P|~(^-MiEsu0Zn2%;nt<_LE!EvWqM
z3lzn0LoTN=rndrqKv?8vlmWGa=<`(BTK<xybQ+LrrX~^{YC}dpC0YyTr7e~>VURPi
z11qxwB^j!%jG^jc)8yCa?|OiWGR5yyU#Wy0eJCwnMe4Jo(<e7gd%w(Q$aZeua!Hc_
zMU|+tfoxuIJC{`I6@?p*O_r2fEca}Zv0)Kj!>ZacZC2g9Ui4zmP!GX;!bwd6fl2Xf
zw=Y{Dcd7GWs&NO_MZ|c<IjuxO(c_TnV}fOHXK>mJv*%~p;-{(p>1MWt_S4Ob9S<S?
zcQ><o1PpBb(~IBS%rds-+cTw3G9<#Nt6lbr4NKXv9tN-c-a43=FPw;!<fR4QZ!?fK
zq;yp@a~mq0k3M_hXC5P*IA!52PJv9r(Hov7EAgatvsyVgWx!XIYOh}$FLb{@pi7y<
zifQ_uLo@ZN>c2BN<7yA&H<K73EsGZKdC@B_*|h0TY`;r%hDK=WEh4X_Hg5c#k!W3s
z?D#w);@FE}eFRo?J=67d>61T*V54<;)&d1>8TZLY!<OYVsVPR}V%g_xszar%=i<~4
zN5{iX_Jaed6=u82c_4X-)Cu10JR+fX3C)|{`{mtfuDfdlG&dq^n9l3VH&BZ`)G0UZ
z(Yx2dLs=Z57!d&AdHfrdA}3@IiE;K5(JeCC6q$7pmhsn-So8M{xO7UP5|_$BGa1iX
zY5`^`{(FqCCYq3d4!vSo{R&ligW~?LQW9bUc@iLvR^g{}*aqFxMO1L49v0)Nbf)%k
z8^(0TOO~8apD7R)E+fYKcx5jd8DXkWPhvMX-g+x!f>F4=hT7TMNGxL#1)rkXKD!lZ
z2MYq6i4{(JwQzXbyi*UzoW+tPlHs?Av?5XjWRr>$;Gq3=RPFQ+3!H{n3_!l5^iDBp
zB3KEolDj%3akPK+b`a1(mU;Bl56Gj={z%6!>}I~#o^2!+s|M?vEgobF{>0~*R6WFJ
z8Moh9MDtMYIqcWhSc!uuXFm!pQ|Qrlxql3m#5L&xY2N!7nIgEwG!ee)zi7eGPC`yG
z2wm9w?AH9)q}e7Ot6#3@mTY~cp7=qQ`ga#INQ*?_Hy5*!%$zv{(HgbK#e2Ret$Qjl
zB5nPaw#KH1d1ZJd?nB2DIT<m(yT%YiSD2CU2oDxVst*V7z0(p@zqy#jHWroM&>gb$
z+orC_uajPnrHw{wajqgH2V{_KpoV=ig}mz?nNuyFjM-axGkf)1R4F6#jYZ_5+v`WZ
z`|-ZCW!2}xo&_qFiUzpL&dyidoB!Q_vz`{#Zsin+NSBX>l5x#5RR=bEtCV#!6@>F`
zF@wGiHn0^V_DRmHgsM2C71_sO8xTVg4o-z89D*%Ri}BibAM-}Jic)|LisaU!d9LQr
zgr5T898?y;7sl|)rMP@wi}wgto*QUfOmc27#=zU`-#0bFh$45DMVmVsM5xel<Q_dm
z$*}q85x&2F_9c|f1SHPqCyxZtW(@|AkIn+s&RJNUtQGsQH=<^J+qgY!Sro$Cdmn1A
zq>pj@Y2|rTg!4u@rED%*Fwq<(lF5rM5;W0R6th}>Xf=)W373x2E>AW7d>hdA!#iGp
zg#t&b<h+-f^2SXJVSm*NV5MtZyDVl<uj31B^0*(EqE_u&X{n7CMj`g1xwu6i`f)EU
zm?IBeNyU`C6>Jvs9@UW{b$sxEF(EC|l5L}M^oQ!QlK<eE@s|6iVta;=dnc<!sl;r&
zLxVh>&jyJWY)~^sVo$kAFnM1f6A)>tBwFqEK9!=t<~4!$l+Q=+LZBq4Om6TSm7!ld
z!NRXcF2&JZ;9W$g$8W~!@tux%zjSnOR5a5{Eh2+*a_NSDagTvCq^~2H$9rO2aFr!<
z1jS2Y{-{pzEcaZ9#W7T&7EqU~{w|_Is+eyAewUEDApd+#i7TM+8_0FdD>b7@N7oiZ
zkSNl{gvG06R|-%P#~4QAeF@8k$+;WKS<-!4(&l7aAUv1EOidr^BqaCb9-In?qV(nN
zB?OO+_BXrOc(haTny{G=Qj-GBZ1ZOx31i-w-8PA5J<WJe7xu=$dWH<U1^7>DDJ7Rh
zgIXRE6^?FGmb++iR&n6<l;z{fvb3Q7>3$CyT5k1D40ehXhz0H9Mu3l**giCF9tW6m
zJ|JTBAUqU-&XlA_j|@^=@Uh^&xaM)a3CGI|8x};;v(}fwPv?Odce%$Wbj|XuJ_FOO
zEPe6y4i^4cYW&el9Hri+?C&~@&cKJN>cQoe5(DT2y9>NxKBiP`Z!XJRCcb+-tZ8UU
zs3rq5JhH)u(11<@)x-H%!z_1F>Y1ra7LIn2h;da2l|Zb{XH9cj-r_{~uzC<$U941r
z7<Vng`dlD7_htWz%QKUfpa6*^R=sI?EGVI4h^qTZByZsz6Uju}dm;O|9(a#dJgp@H
zo8WqDfv;+A&SQIY3Vzq-$i$*nft8OKO_LHX&^s4>Se4x0$O?P`mXH3T-65^=3ejO7
z=WIBw_cuogV>_6+qcQedZTrGKXFWv4RDv(oA<6EabBW-E7jq2|XVs*H3bU;qdo~2^
zMnl8us%Z;rUG{5su^#zoc$th{(Tw^~6Uo!{fHdwuHr|9=qQ0n1=0%n;Wq8l3m|zx}
zS&&HxY}H=6L=_XZ7atT@9UnCgg}7bkpijx;zpiNdn2##zcnK7)7hfGK$pa9;s%JZH
zOK-YctU@^O-aWr|>Jx$Di0tND!*NQ@gkL>?4#v)#?j~$YzQg1)^s$zmP3tUKJXBFX
zM@(z-4pDyzQPGOjEwFSIVwcE;87Z7uBht&L;+-O~y=&>om3H5$7VHEdzKo>PKy8(g
ziV0$7X@;wiN~3J|-c9D!?fJ5BT&Wtf%2$fBn2d|~iOkVk2a)<mH5`sX3L|K-?wby}
zhtxf^Ni-M221fIZk-QlAB6f>?yw%7pU$ReFKhbd$b@28Q7s0LNA9<fr0Hl#ZOv_2a
zR_wPy4EcU>ZA?zyxIxD9TVs(Ni}gA9HD*BjwT>Vpr^C!=5gx2OUGJOjSOqE|OKF7R
zHCj{z84d~PCB6%dqxLD3W-i8f)RPLvX(Q{=FqNes_*#N<?s*Z+Lm?IY;0zn1LNz|P
z=&7oR1z9<>*by$q?3P%FXNfMKOt4u`FKk<|Vz!=rHvHwxC`B^Ja}Yb3aDzUO_aX3p
z7@Lb!{LNkz<)_rb6k7_e9J-OW#VN}bh?v21Dq@YA4Bv0<B{Ej>Rx*%P?-E!<larn|
z{SN0wlU~-shQMQVT)x6?P#ethV|IlcN7*qz!vIuSwd4HAyKWL+xep(8_e?S11FIWG
z><d4DrP`>f%{0RdkD<*l*%IP}XNeL#IDe%ya?f%%vCZwV5Eh?s{(*2Dn};!d#(x2T
zZHZ>**!Cjo_9m{XXOGa_tUCK`>p$(p5$m2mZJxAeL5!Mq_iCxu=NMv03Qi`;>UXj^
z*dyl+i5tOFpS2YNgtUeV*mjB(9NIot(;WxX_Lfsuhl6;@@rsZWyJ#!!LUNqn<O{LU
zh8+82okHQ#@CKZ0R6c+5jdhpQp>o|Hnku88^Dliis~1(t1|t5vq3>Jmlx0-1kcMuq
z`a^)7q2O|x&_}lT;RrU89DSafd2H0%&^>w@slNY~4x#J2En^)V2xez{boB!X0P@qZ
z(=HHx%Z#qv=Ld@vE~EK)<Z2yN7Hs{J;7@qNk`xladBv))DzMGqJ}1fp#_ieh=<el)
z0INQbkdcS0!rnsOTau*7(=tENaYC_zCEt?03|3*Ij>QjBa`NCN%t#YNYbn?!?Nqu{
z^Kw2GYJ>^k+C{U}e8Ed+v}l2J!UTz7-^!q>=S%ZWzgH=`%{e+qcgV^51&SG)RzYIh
zu-|(ZhOuEh+<KeSsl{Dg`i2=1&G;{%-dhJC!xvwddjrvj@aDw%RHl?(I#eE{)rMG)
zix2w(ta3x-nS(b+zy&3sKU4Taz#M|+*HcF0;tMZ}e_Jj0R|Pi+ST$)SFJ$*<?RjPU
z&hy~%P4J|Lw(Oa#F^A;T2i#7B4*;!hr<=}-*<)=W1#1T^&^WcaOu`gpBT1}vs58}l
z<u=Q3W9gS)n`tX3HIz^AEE);d7cB4PKSN@_(TeVOyTP1?ZFRy=19HCvmM#l}QN*pm
zmS22ix|{z!d<T2w2j15AJ04OUQ~1TBJoBco`DQ3;6T-G1A0&LDj`U2zFhWvo{a>pK
zbe!)jeb;K}*nV75N}9kHpY33;W*6CsQ3A!1<`+cXu0!4OwVf3qr}Y`V>S^nwqJ*ns
z9LbW%jMteQx94wlq6+V>%vlV#+bcD7q)Lt=Po<#2>UY!-9BSM%d4@Ohxt)HoH;W}b
zD9`gEjv7Y*>%bZ4d!ZCZo`v6VfiJW*13#37CrG^4!VW`#(Ty?qA@&+0(h)zp=I-c*
zr52HR)fcnr+|mx;X$ti)ducl#NL#~KeiCx>?O+A#TRnN&%&<*yQH#CC6u*#CEzV%!
zJm`k$-O_f`ZKtGXPIjby<kW}eo|lz}2UWUZARV%M?&AD7R5Vv9^eUpeIL$CNsT+1T
z9!%&$r)z5nVGt1n(ZK+#Mi+N3Og|PHYL)37DJ<iBVS4FmE~VU<XeU3pZ@Jrrl~8WU
zg_CE;!q(<yCE#b>w{dm^9dq{=3lMiXs-*~FkZCnv8bl7n1x!yV914`hWMi-mmawKJ
zwe#C^4#HjbGwXP7=bjMTs8FfO7@fj~=z89q|Je6kWq^`IkopTk-*pvuS<Zh5-8b)F
z+-^rl(=XPJ4^eKcu$E8vuXf$)SWWVU8mz-%vE*`76tS7k0KzKCCd}*+g0jpOHAYsd
zd<Iscl86JPPeF}~^S~@TR`{wUW{UrP^n5aUr5*s}P!;Alj;@U|{;6%t>(%ba(Dz(S
zHgN5Hh~u(4+iYDNUKgJrI~g;arQ|#}Iyua`BQ5Jv+cRs#Ba|rki|^h3AHIKQb5a#}
z!Wx^rxatRo(*-y_Qsn*yG%MY|`Tp6fceX(&N$*6xtJZL_G~zeN>;V!ly1qlnwzYV)
z#1GNGjx|qM5{SN2xJq4+52RV2A4_Z@buRampeQgfLO8~Q%IwRub0aL?nRb`6*A}%*
zO9kssTBPK@VR1PKof{8#SXL-ST${IbqVtRTT6v;DwX8|n^-9z;D_szYhc%0l5fQ&q
zoMYFI(Okedb9?Kn`XQ<~sk<LE+R}7pn!N<7nHq0@n9k1pCBKU!VTHVc;3Zw?;G%Zm
zD6<dCh`zl?#^<{{Ov?6h&8|2BI47$bIiOL&`iJFGBzJ)~c6^IVMn(RFr>ydsm~1_T
zBI$VIw|!*M&5I7g9yawkp;jqaj#;O%4pbN+l<+>M`CtPqXlp;Fh^ei8+jo$pW7DFM
zL<nrR8O0?SdwV}n@R$W*Vh1%<%j%6{S$En?HRPDAo{ZN&$|h-!G!an`^^Bxb3!{>B
zyXkzElERl9`dDkdJlGYsI;xAP{$<<b<%Z5uS9!3cHICR#%KE#P2d!Mzd?2!W-AhJ!
z82Q2h??qcLO-jyDe_~!)A|jDQ+75tV`dKPMS#p+gaJxO}Bknl&SG#?yKB<@MG}&jV
z;angj@_XJxEU$;yf=mIz6#T`}wti%1i`tMb^;`F13SV%8Q?74xjqtR$*TP~HDPHKO
zZ)O_tniM#ko|-uOd*4P<nX5S7zZWOlw+^Hr*o&WxQviT8-1bl|P<j<6BFh2WrFVTx
zQGHhmq$%!+s`loq(XBymvdbUK5|c^^Lf($cY_T%c-{oxi;&;`2pq5ZGiaP9AZ=q+7
zD6yF(7Ynq5yzy0eN@KkhiprM}1#~`r`7KRdKRXMAmTzYdFjgtD4RDn?SgG!Y?V$KU
zaWx~epT6#aX-R#otK2qD@*n)bx7<pSgM^h6mlI}<N9sxKXgU5Y@G5XPOC^t$)Ptvf
zdHa!koD|=IjJA5{DRT<nb3obdRoSBI&ZELv33h)HansWi?S4s;sEOO~g33WQ9KE;n
zmgQ5n6-)h#AEwU}1o;sWbWGY%aIrs|`FSCW>1^Id%I7SZ8dVv4M+mqL<pkC{3QYS1
z350XV{K&iHx;;YK_dM!+ADBc#=syotTaav$S%)B#S#ZF>vk*KX-9L?+L?z`~MZ<q?
z>gujqezT7+cIA%<Wcuz)w-BjR=8?~=e1%utc$gj1p@BJg;u#oA8tY)1FM5!Yn2<#B
zZhD?z#X3el>%w<mRFJUx7@4cXI=`?hf@uZ0{|iPhg58JWi(=cCi1a}6VZ|*HN?w>z
zVTDA`q81y>uAM&M_bXl_0(EBeDt$?PtP!{@fVGf!7<-wRI2b6@df8YD8rj>28@2Hk
z=ENbSCj|o(F#Z9_YNRRfmih5{{Uuj+q53Y7(!7Y;@HYsJhxQb<gkQWpK9J3QW$)Vk
zMmsYgp-@QromhXNE}Oe?7n~b}f)F^7tMaAzD9>UO$SxL_84n>dgS*n(753QP=Mi}}
zpqlW+ZBCfv^-fHA5R=>gOXMm#+>P@HXx8{tLclBp*2nYC)_TeuNbrtyU~*V+xC(fi
zX9q`U^b|(jfZhrJ2%%?nwRhyg<U%ap3vfVeevAqzh>A!ZZFWmi)?8y?*E~xQ46@kN
zNte{7I)II@?v1j!=ZfAV2xH29yztMLB*ngJq*i~K#Y1W-;{BG1nT^JfGnaWXQ=PhJ
z6uC9><pr=aKNumdiTWkYJ?MkMYYx=986?Uy%jufK4#Y{SIR`Ym6W120kiSR8dPW(!
z#E8oLT~(S^ZAbt*y}=36&=?~F7H!cCtd6+KN6~4N#8FGCPN8S=Z~jZq%I9ud3hAxd
z)U)~{=XjCLQ%dt~d1v-;E%yPa0Ll$$-cz=o1DIHYcA6Ucg)|Z-%!hb6tpG496fhbe
zx%;531(^`0>d(ggcao9H2Ozjsmx-;$HHAkqc+icI?5M*ir*nmLB}NF^^Q9(wJ1#qX
zW{zJVI1P_ZRcCQ->hoLyda6~uFj5oHl)Sm3_&F7(J_V@ou#+-;8T#JOK~pZl6F0eC
zpt_PCITe$U$Ja+7Rf0<H>v6RYaV0{W?IUh&uT_yu9Y6&Qrn*ZZZGI+^ceY@J5p2&5
zC3@vf0F?&K4GMdJc)ko~p8=}rI1}kZulTK%Oqqjic}D;!@{3^NjT+eDqXlotzG9tP
zf>b>1v^7AQdKj&QDji9{o81D}y$acq2`;8m4)1aj4<fT>RYc@|761!^|9B?UcmfZ5
z^J0m@KB@uxHSx&3v5;MW^o$KiUnM#GO7865+tzRU@-qvC942boa^d*OSP#&Olq#(1
zYS_b<GM0fh;1}p|t8!T`H8b@4{<0b^yqoB?snk@ig)!N91J-*c6!UjU$vFCI9EV+?
zF$m18?EbHmmZ>@@%_UJ7r?am<HI-x_5K!rxh!f@o4lfwU5<O;S7kZM$<Lomtl6}X3
zqye36*oB~w@6yv+8FSi52okh~!R02wh(}HMOjQcc6iPTdf(1V31O({`{2RnVE`3oC
zxL3#AuVX?sRy`A^P*lPoX(>(11VmkF%?wYiN1o)n2!|$pd<oQ&a47cg4wiF+Bn^19
zIqU-zt=e_YV3Y>Oyiakv7ABKE#vPZ?E(jP!!C#0rT3ncYthO|g2IYonaoN0Y1JiyE
zG=ji(PWy4VmKz!o>A^vL=};HA87uVU{lyY$Br19Ua0-obp$NSSBrEbtt`}4)!A?h8
z-Vo?U4TX-Yqru|V;2DbArcsO-NZ7=rV|nY-Q~G0waNl0ei0lbLB^;tK$}>O*Cv%V1
z6X`?E=Swpu!(7ysHgz$i_yDBQ_&sJ3n*Dp-pm?)g6X7&<Lc&qcLY&Y{|9azCEDm>j
z5VDyNCKEa_L~Ac^b;&(O5R9{Lz4Ojk5wIgndNekl>5@gK%^Z6kFiPXXaqH&*kk}Ce
zCMn?1QgY`(1OTyf;QR@&bOy?R^K}sL_G*I?5`?nUA;N~Tvzfdymp(MAFQh2(?*tv=
zmFn<87Db1L+|YXbhW%ZXD!XOc`CcK9Pi9e@B<j-JE%f|Lp$=i0(6#9M8Ok3ACDg0=
zFoEH!s!OJ}G^pCJS=nfI5w`VwSAfNtubR?4rtdH;$$7kJi%UGtxEnuZLHbSTTVTfr
zF4$wEPg*fL9lc<14aOQldgCZdjER1#SbrGfdraCt0-gN7*n6wsx{@SZcZ->snHel*
zW@gDEi<y}%vRD>b43@>r%wRDyGh57b<c67^nV#v0bK^#whmP1U(yCgO1zD>y_g>%c
zADg6uNer4ZgIlyhIJfKUcgo-j(K0Jw2X@blEzcq;uRgJ(zyD&ZyBNfbCCb50C`3q4
z?pfZ@nP+JpUKgw>O4*j1+KlMfV-9I|La>)M_id~?g2cp3e|-bz$+ae%G(e<Lu32_Q
z+2SBHl*5L`V;Xeum?19}c{gi}RH?($<_90u_>{qgZan)Q^9GaYG!o42w*!Eka__BE
z<kpU7{VPdhH=}gGAU|CdOnle!!DOrOwobjb-GKZx%Llnn>IRm*1pS~>*=HevUx^a#
zi@z&6n#I3M{v^He`INb3OSAn0zLJwQE5%eAm`-hK@f*MeUGv31E>yu9fgyl(>z%zn
zRvEq{D9|Vw{e3{|MCiNj%K#u&*z=A)flsJ`DcDt-o5vUuryg^->B2=4y$o+pf}qQ8
zCABo9+?=PpWQtUcw5Hp@>dU7G4>Y=%N_JKP$vlBXrNGXF&Ae7*#B<<K#w5GBK4Wwc
z+3AZTETRA@UXOVcSO<9ZPYZ;?+mrP<BsKN~-i0lwd3uUCgpoVmnJReq(aJJQ-414G
z?LosoRzK7ZTcVR7t4=7t?n9Q~Y+0Lk+^x4N(1v3$ko2M*+vS=hl6(q!DjZ1^ze;jl
z1ezZ)3%`N!k)oNj>m+Eb@r2PM&^n6Mc)zQx`W(Lb$a8T0taZpCJm3EaEsVBm{isc`
z4ThE<D>p^0=jJ4#OCU@I(`j$7?GEH;#4kqVaq)#TfaSyb#+oewM(I1d%TAE9o0Ca2
zO9El^HLokD%MN$@*%(xbYmmMVVDH)DK&GfU4ESKb`4bpTqs$(|1et;aqQVd<i4eG^
zESYa$Sl}yA4W8blX3@Qzp|rviyQ4eFvqcA$zD^wj<zD$j5OBygF3t9TWQ_N?+GPLG
z0mk|q;H-3%OAg74@gr8qb$R!7NT=7%on{%@K|ErpJ~7Zew7)aqbtb*RU4D=Pr9Aoe
z)KQN>(4qKa2ZVgRHZ^=VOq1^j!K1ebwuX(RdUxu_I;|p$6r?F(VH9x6Ltq~ncH1uO
z1Im-&fHfKe7-u)?<K$xNaN0$cVNj^%_2%02Gvz@MnlaS8m*G#a#pJ5AZ`**F489LX
z*O6nm+j?mJ2ealcjByu<@n2a-I!-QiP;4Q0+qfSsxP3<qi?nPVnS8X(A{{dlEjhc%
z0NM>|fgNHQVQRs<b#0uKE6>b(-dZ7|eu|g<<JNrcp1M#$)YzM5sIX*tm-c%mHYzV>
zHR%%?2DQlz=8!zTI(~gQA)7H5nat%R+v!nV;%y_9r-j~7%(vBH5WyOa8mYIqSBTsW
ztD7tP7t?aRE&P1hYnRn-!im1)80ks5fo0b+8;2AfZSVW=DSNcxsMWngLw!*it9AEQ
zu6mz$mcQOpdPWkrv-;+7YK6Cg5o5g!!*)&~_Ing>i|FKb_t8rR1tf?jVO$-okfVRD
zl`B{idLg-D;`BSTd;Zb5JJjy`?S=%i4XH6QV~)1n1=9~*ERZpIFoOY`D<1NOyfQHL
zvs9V(N55xic5BEygBXsg{@TX1CG<Y*rROs+!dFya@zPdfE25rv`Y%UJ?pk-F$<BNy
ztIL7mDa+yKpOq_=O+r`fX7A{G`Qg7;zT+(CCWBDmsvP=uA$l`a@2>zow2SBn$<fsk
zQ#2b|%~c9yl#ouM%@ZR+0Twe~%`~Hw2M1<dklV7p1%#|ovKm}W#=*VNF%itq_Q3>f
zo~$cM8*Fg+=Gfq*H>o+P!eY9nOJlNU_55T$5_j|=t}-1fAfBamKQXZxDk>SeZ+}(t
z2T6M4u+|E20ee)he@?)~u+9BM5G50-NW`^Pq=3V_vzUoFZc?cSeMK&`%cA*y7;=cP
z$$9DMltX<&#|osPcQIgH;SZTbmE<wcwxnL!Z32-c13>3zo%vqMn=S4GW5oI&;qOvh
zEpCiz5_l)SsunNYFt$IT3w+}2Q3Dd_t0T1J$$}=L26u9&@92M;TwIt49vfr5(&Hb!
zhP1FEx#WOyXBfCzDaI6jsXvGB_vU<vQMd6)sE}%_5bn^a(#1^b#?rymVWOz1tC0id
zVuRc9Dz?qG-ULw^NbFPY!ud|G|3lFsoeR{aFcmu*TvB>Hoj9iy0!=JY%1#%n$2w;~
ziFJ?uQ%SLk7oWza5Zd!9B?n*D=Tl5HzoYhqbk*2sU+6aKbZ;s5T+_N_b3TgCIV{`M
zXnMH@wv%R;MuZX2PkK_DepIy2uE6Xqk))v4*y$;!h`L-^n-G{$Vd@mtsLLP~DFYge
zjvkI*GGbwN&zrmBr)V+@g<i!!r*{T##1!t!fdv5dkVS%>IYB(_CU~Azn%8F*GXvvG
zj){bN$Ybe3<e^X7v>u8y7}_|F)Oz~SRk~G{^uJg@tg${Yd?Z6uCs5N(oR&@L1DrBs
zD7PI;zd~SHardrv#nc0`*0=LORlZtFx&K&#(i21DMTaVkBgfwHM6?q$QfQy!*$V&A
z*fM3yh+nX{T21RA_e8)5k-u%akafbN8=$X*?Dv&sBCW$v<|_v>+q!*mczxsRvBd{J
zGizWEv)IFZ6olPzO^QW|bSd<AmRtOUH}3@_+^u8W7zt%yg$LhdA&9HwN<Iy>>y)kn
zNg$nRj)sJE<h^TgI9XC6cGVw1H@^=JBoS!JpFzFGR{tz6cSP_=-*V~oJ;~=KVt&Xd
z)tAjwFkLdS^)vz!P-v1i-E1rnLe0h(ROSwP-K5XcKFuv_a%74DbwHHYa*eSm`w=2V
z*|{SYV%t~HseF6C>+5}xQg>`7JPZYs1pfIdQCOOKYlmZh>)NrWgH??rje<yB!CV*I
z+~5#lq`8~7p)fzgS59&!px4m)3`;3k`l9&dloR0%cpvGl$_M!$9UhZAhkZRihFmJb
z6_gXFiWa<Z(8yZYtK#nY_<86wOevzKX~^)xr?-&gE<3hR<VCMtK_B}?q8ek3j<-WK
zK9?8q6b*gRv6lDwnag%^B%{kv3T#_?T?em24=z;jc9J=-YsG_~*<ix>X5!5SLf&Mm
zSbbbdX`Xexq+J%%!X!pnzC;bnBJoL&gq@>FgT<6xS3tKY%xb-@s2Eq`<u-6aiEqKS
zn>%(b%{*Lp(e{%E+V<-QkJN6nvpfnHef5`*$<zp_jfcq|wXb%=R+KfOH;#y=rMpjy
z3c=Am16hMNpfroa3-faP4M!W^-y-Q%k?QPoNw(S1b+e$4psI1-!_^z~DfabdxxcXa
zuUNU@5NcW+9igbh&vLVULw1}PsTuNdqG`Rj&62>Dg+kGiNF(c4+eeGooBb@$Iy#e=
z;Uh?;F@eA{nr)|_2p^<{KduGmyD7w!h8Il~Tq%}$(Sn|zOm5P~H>~3L6$we({CrTf
z20f5)RPof@zpfn3OA1Af7Q@gydce*(YI@3F?0(*UZ8q?z8Y)qqmwwu0?Z?M(Va&}Z
z!L91Noqe${2n2PFv`A&vrs9si9{soA3t8Qr=w~O<Mx6LV^dIWEHst&iCr%5^8K7)V
zmygUl*bv@T;^3YP?DDiGw0ru2j?>aDFEaKRLoz2OX1GntGi^8NmRs9#y~~KR$X!6v
z(Nf`-{m`)3;-8R(qI?Z;FQYML*WKpk1azMd14vF@&&W9X^cj#okuXUQ0M-!SkU*YP
zjkSPyK6QuNQJJ<jaN&^bRd!Q9@0&b^Jg%vy+H)zw>;2M&_C70bYEW_E`Ne$kesh)5
z8Km&cz<oFb`u!e6HUL<`hyT3hhk@_c5Thds%$3R#f)Nws`(ESAetjvTXkD4Log*Ci
zoP}$KQ({`UF6F)~lam@KPbW4>nX!-6$e$F>{ZyJ+`?4d~<{eee-4Gp(g+H$YU|D`H
z%HKZV&TlWcG5o2LXtB&5Q)s-uNhg+GdABALI@d!b5a%Kx;1`-d=6~vk@~-FupnoDn
z-^4-xpj!w|0I(1O0KkpjuDt0a{;O>0n-zZ)E;LJ)fz<nhqUo<jGG)D0{YAJC=_lj%
zpT>g#5Psqz-tPLJ!i9>zUieSpLWQ>rZ>*KSfMUHh_Zt-JUz+=gxp`~u|9`>IUnU6Q
z*XI628RFO8{fS`cuf6+Uh^nl&E@1rQ{mwt>-LLO({_(<ZDv+7K5UBp;!cRN?`sK{G
z=KfVM^j~`SpMs&k_3l3fLw{@TFHkCP&HaW_`J=7BEtSmw6b$`2W^ei%zp(QDSmU>q
z`BzA&x21yrD}DKom49EEyiVf(x@3R3>)RUoPr=Y%d-qqUlegynRWS6|!Tik;{@{4O
zTJyVt=l}FOtH1EOU)a)r*1Ny`?hm8<<_P}-4fnU-{ktRl?RS6s-M>D~|F52R5Sf4C
zcWDR?ba#{3X@y0q7h*TN)jh*@)^UZ<PoenF<x<pc&UJ^SpAl>JpcG@LWnJbPoemm#
zbTKas@rez&G8Db}uRr}D^(K#y%;#rp`8H!*{k8i^Ytn2xZ6*`k%E6Yp%`_N%wYcO|
zG*z8bI@pz9wN-lKOFbR%^D}BG>M_Vxn;i#`rM29Szjb{)sN}xytJbdlxSJCzyNiLg
zV5{;|Y@Y5j8}_b({q+dWu8Q2r8f994L|8v)VLN7pwaF(#l7P>C<2#?OinDzU(NBeI
zWe4I*kX#7I%TVj`I-s**jBG+iquA4TdOhoxuO07q{39gEQM0zNc}*qGBTI9#r#RRS
zA#_2yoXvuah<!T8te<-K%R6edNP{Wo_-gjQTQ6H~de0B6Ic2${)W5)7B#K!*a}-EM
z1Q!>ItyDEWOxz+qBWRr{Lq&5t;IE5oyC|C8kDW)C_I0Tpz<y8a`dZdjTy{oO!Ljsu
z?nF?rEqFG?54FF!A}wV@)t|_h_syKb@aW-3SgVf~9OGH~uvj%H*{6czFBKJKeg)h|
zt(3_DhlfUkEk!8o#s*R1Ffqjb>4MWOr_%Tc#n~R{HmJ~vdTDJh?+Y7+QOlzF1#a`j
z*REiM{7**$%jA=2PSC`BavXhv^M@gfH0Q^ORL6|~m4*9AOG^$4gf^Pb&K}|)gL07E
z+sado&IGBMBim+jDcgLTC0?U2s*avlp13?+_V^je@}@7<0~rk9pd7YkB_KTE(_0^6
zNJasMcDOZkqfXOvNX6T1Xk-Jyx6Rq)9p9E-k(WRq*=DE?cTUZFjmmDJ+UZvK<)=y+
zYsN-YQ6mB{IC~5hp^P@fNg{-(VH=e4aVcvtZ0hoLf_K<$0=Ut09DFF+%dFPu1{uZ%
z!rH&C(t0MZ40eMT%q`;TD3aJ9*&0&!L|-Y;IV!SsQIJ8WX}=qIz`#@&jl2*R)Xd+8
zKI}6Si{ep#PdtQjHe^wN{1OhV!v50RQ`%}^hx-r*X9bpbvRHwWw$I4C>*$&AS{`c9
zbb&`_Xu0?aDb{G_f%Sy1jhSMGn8|&Y8O@;)MhxvsH%{h;00=BlMMQcv#K+>zkRH;J
zyigE`?EuZ|%<ACeknPySP70}691O5H6!--&<5QGkH=jESAv<7ATpirBzRHuPHO0@h
zdh9Xd#qDq``)3?o<JlIW^Ic>?Q9_U1_k`a~qbNa^LdNN^#8T~tqwf~d$fSrARMnf|
zN1pmFCc#mM&cH2s-Y76(2m#5+dqaTt8DJiE-_gLV;A&?Rj`w5vmcrppj>E}z&73Oy
zWT`F%9QK(s1IxRjEPZ4&bP(g32DDPMeG#v082Ampqj9|Fn5!6$X{#|tG(ds^OI(PN
z&?*uJt;P{M0!Rsn3-w@v`MY7(c5hPadT)lyJtq*nKDGPvVczXddBOI{)OO{u5Q)PD
zl=`wC76rT>KxHxa5^|f7bVhh-JZSt-+mXpQFJco%sGrb*kBNrCkdP*adxC;wBj{vb
zKAkVY7?EdQIaI)kBq<jh&uQ1Obgp~8O3P%#Q`JTCLmvjagI_dOwwCNzL8!`qsfH8d
zWr69dDtDL@Ry7?Rx3}|LpB@g$?{EEZM|Lpy<OC5hHwlhAmqDYc3Y|*?#si3ioVi&&
z1bVoMd~R19Dbu${b{m}6_I5j+tmAA=NsqCQsnF&q{DGHJfPthgb#_yW8P?G=E)HTl
z8Q!Q4UdxCE6<08K;_<v^Rp_jIahH&e0F+)loHyAMgYk7mlxfPHlySj`&1-b(kP!gl
zV?lL_1|iR4eYMqX->N38L`XbT-1SKbn*ZA;qf$(lWZFY7lg9b-<(ZPYl8a@50XFu%
zRC);YXiGyt#t@mGkj2Vy=Smlw3e}La{8vxHCfYq%`DTlj)mX_?mxT2RS^tckG|%y+
zu$Dm_zwL6oR5U!4adAOdo6=Ts)JgLuYGeEMgs6r11NP9$<!b7-vOAjv<5g*#VLXlJ
zFUHJ<wY$=G^yTQj!QeEvl4DDZX=b?)55({v=X;j-Y`gKXesEz$zhkw!M?mJX*k*P~
zh&j1no${c7Xp)-d4zT)Io8AUQV`o7?U;y+wL6P;4td6}7UH?&9lP|Kr>G41h!@G3%
zuyk~L3hi>bTpg3RL`u&)thHyElZRy5w?xnx0_~V$6w~7EtHtJq*E@<SOk@?p<_&7?
z=lU;HIyGX@3xWDW4`NGu`*3{#nVAM_zP{aUPksymyiq%J_8VIFtcCnu!Pp&|ibXjc
zAd&Y@!XBVh?jJ~KUCPnfo%7xo7>s;-L8n&0XHAbGC~jt@0~QLeno{}r4RSU+*@Xv&
zVSJ%S=ER@HEu*D~Q8xt^Vmc~`XX!igVd7)CYZNjzYFxx4tx%2&<#l>w!B|*sPBpGx
z8*wS4wNZ80nKb)*5$!p?*dD@+*O|`vVgJXSgrVAoQ1SVM@_t~-v5tp#!gvcl$0^N=
zDtg0ko%TZRTsjCw?*f7xj}PtxH9AatzdWDy&TN&%#Wu%nVtoBdgyqX*kSo5>e1>2h
z5m}!Ruo?lppZ4?VjvDh8Ua|i%;H)%sJ0i5`d^Jxiy*NF{ZJ-q#_sn?LaN`4@WJcH4
z--*?y^5<g_007p1a(Wnlg9^Q=hJa%>`$87bIUA*)86Uud7N*(EP%2DMb)I!tBQ8$?
z?3o+<03g(Fb?|@F8hopR_CQ?Vo2rqd!uph9@ylsL@(r5=VEhwid<;^_I+(CZE+=Ud
zm$lq(e?snk=6C0}Qz{66ZT+pzn_SmF!`RBe`y6Kh3xV=Zcotmg;J}KCpXGoO-paiR
zA^sNA0-+fU$ciZ?16*6)(jOoI!JkdP8TVUc3xswzWC|&F@_gQRCyHVBvji~zTL}We
z)c*)^q2(8{&VH5vFcQ5LA^BsB`>QQ#`O}xKWQm;-qXIw6fqMPg)^G9dFQ!OPc-qxt
z8e)Cx_*nve|E&bxKLX!htbyPv4xVzvNByNGOTnMF_)8`-@gD==Uu@xTGTURBhj0S5
z`*l=)>QLeW{R)NuIVZo43S8>oXT+Zk{W5BJ{|Jfyt08|C8owFx&(QdP8S-0{{9g_E
zEm;2VhWrKb{mqcyi0^N``(K9q7C--2Lw-wN{fQw0zf8?vrLo=&`DYsIe;M*yM(e*C
z@(aTF=hVHe*gsn%f5k2){s&L(e~-xTxvu|a$nPnyKQZLj5&5eS{LPTx=<I)C$S)+b
z|Grp%Plo-8A-{ReU+{?E4EeX+<j=k4-}jP#_nLp%O#W<Y{&O#R>-n#rUliuv4Eble
z?0>A0-!o_bb!vV~p#Ap|`3tuC-}}h#^YS)3za+i?-bem(M1I$P_zU;_1-$q_+?T=P
zmvq#hEY`pGkw4$!|K3Ob{b2O>dE}oX^8eI%MC{+0;-?R*BD6PdYltfp!36}7RFBzA
zi-00O{;TDmPeR}rU>h`<UyiD8f5D!9nOX-*fCI*VJ7T^2$GJsTzmb{Ud|K>E|7R7j
zbbIR}<BF*o><vB*_T}fZl3k#((GErcFyyZ+REFQ!ukMC7Hg2c58)>LnKkI=UKxWJ^
z&*PUh2-t}#n1Nv$`~Cbk#K>D)Z%6UJccp)e3I0Ca{QG(D_b(3r<jM8x!SnB3>HpDP
z>8}-D{*IFV<h=KHl=S<c{2mkh^XCTtBV7AC+wvdzr@yl;|CW*aJKOT_Yvli9w&gGQ
z@ARg|p@9JI{|o**ArvHd7Q`3mEF?xcZi0>Ophe^k1}sS`Ca|F#-fHz+7S=(u4(^mv
zF?~XY@R%S4jif3x{WP*n4$Y_<uIYSwbw#74x@Z_)!3*Bu_}5tm;4(3N{Kn>nK4h+I
z8GAZ9)6O4-E2$`Oc!9Qn9*YgCtSg4B?uzySHCF4=^1<*jc~Ys<xbpaOvXA0Pb5+XV
zSCdxFPD-?C-!T+F8U^^<hrek0hB_m&>T!1B7+vFIGP3VlI&3>f^5R%a1V5zpt>eHp
z>PG}=D}K%0dBt_aFv{_^h$R~NbOuZ=;)hrMX*ZKT*#R!P>Z*YD{B{^Xyt9juiGT_;
zIAX8S33n6<up4s8mlRS~FiLi9c-I~{+1|{F-wS>Zlr_hPq2&gJ5$Z}^;N!cbC}=ub
z=QwaHtE+SOJRm*QoYo)rnZ=?>%7gfu4143HP~?%dW#_K<aSFn%1>J1JR$?|NoG?)2
zVB~`OnjAMTWlA#dhrkcs6L!@_A2NM}Se^aqPUyG*P{|UDgjKp0&6IF8^Sscr%50b+
zN5kJ9&-lx?LY-ZK@tZ_qfH3;&5ki!qk|z7ejz;dmW9YvPu>kqx_e$MjZMFY!;|^ub
z@v}Q?j247SQ?2w|a3DDbM1ezP4#~tY+v+sR6<?}sUy4SK5(#%Zb6ooPo|VY8zayf7
zW;EM3ut)3?77D{mcc0ZH@bk#T#{b&O_vygj{L9))&mR0PxR_D=1kDH?d9(;{j+d>|
z_dPjA<936l2U*uoNDd;A8v|BdsFN?1H8Dt+7R|nIuVY4XdP<n?Y&pUBpjR%eY{sQW
zq|cPQQlo#k7RmYGwKDf$#I|8aV#r&n&HT)aUFBXFRlv$6$G-&X8}y#7QKSCuv|1tB
z?Z<-v&-OAhYL${M5E&R;I!E^uaoPu{zPdX<z5XRucH@exCI6H{L6&g87rpVx4K(<e
zlg>Kr!eO}PL5L2w!v^aKC9X_%y9+Q+d+PHFV~Uj%DLyh6SY2v7EnmCcq!407uiQ}n
z2iARjv~VpMW!vRhi^s294Ys6?TnwRms5ic6N{)u=W+G9k(z7fD3QIyHJ)Oiqd?G`;
z?F6NQC&TtIzqmw=6F)SYjZJ34oyv-*^-GQu?gH!>Vzdgdo|#vk2I=GF!c;Wvz9hlN
zk;eDA*w;svz=^(#ycGD*n)jX`+WuY9@iEYHb@E5x`KxG1Gd)%K98VFbsZuSPymnb)
zqT|U%do=RgeA!_lXYrP%Z`#uU1qcEfu`+AUNSYg)`OF|Ad$HI%HlhngRddfMr2#hh
z&p?r}sdLZhk~JNkXbv5uq2MLGH#-AQYS>o7^_2zWJ30RLeE6}*1LQhT=nazWv&Wiz
zH33{wH9-!XPt@}5PMUc3A|a@YBLjYb-Ed&co4d91Z0EhN?=Q8M=(XXGuEGshEjixV
zskdOc*#Rf7MN}tI6K>2sOOsqXz|0ZRL*hV)K+Uai+Rmp54f%-Hmu@GUF*!w7NK$;d
znj_<JII-HR%_-(E?+zY}6{!cT{IHBC!4Gafa7;vrxyyXt1UR$<gD73T^?;f8J(re$
z8M=NFgB+hyyW7+-W|0cy&-f~HBj36<PV)X7Wq$E?>9AI$p{!&oTFDidP1o+o05qd4
zp`e7Lm!zKzCo!oHXgr%((00S^`A2i0)Nxchkm#YED*T!XNDKgYh~Ci?q<^lR*2fqV
zCwYG2hE$OUrV%Lm^6o~?YorvRaj~)MyTBW(L(kxcvjgh)mVra(dIC*pzOgL$T?2Qe
z-h!Btm*H^9<%xSE#6cI0hgBqsWi=I~^K;Fzg!G8srR_F=w!KPQepWN@&O0Yxh!4Ru
z+)nNLGH;m9$mLk2_{qjf(<FFW=OgW>DoLz&Rh?s>WZQs>&!|A0M=yp}9D5Y>0kDeZ
zw#JUS4+oYaaQ00z)oIfr3NXOTUlL6ZjT=G)Ji&77mU+GVN5vOU`Pco%Jj5nffz_Jq
z)BLEj&*)c)UY%(dEH&`_ggoj!3YQ#bX65y~&MoXX(@*ey9KO1Vc7J>Mwt^~oK)#_V
za7=k-dw^TJc`Cs1@QOcA9Ajj@kZnk%pDKK--GzAC$CrBB!$A5DHDbWK8#^wvMT=Zf
zP=Q0S+Sf2(xh2wMsr80d=5e-w#2|~lnF{UrM`mzAoU)73-u#x<YDvTs_N3AR_i9un
zf-RHd`7F+m(pgp?ir5-l_~K^{Yngtct0(6?Hs<R+1SFUF?aN>Bs-TQa%1&L<m~PZ+
z8hg(cTLFBC@YN1_4?pp$^<0S#iKAiZQ#9AI8CVymJcqN7$60Cy{)f@NPHH=J-%|Uh
zW}?~pWBj}wJYpVPKhvR_9Pr}b`#~bN1{R4#F-AopfuEv<eyFOt1Nt~ndLUHFB4y4B
zE_|0<7ygC4cTFX<z=xk+|FFC`!V(&dbjQ4oxS`E)$;`ESMxFz<6sI&KZ(5E@>;gU2
z!s1Xrg2t6VRrKHpveLdbTR1s@h(^8>@BUto<*)$5RjDn316L1atf&aibKId$ZG525
zcqiNZ7<eD#@QC1CZ|`6=0TJj7PY!NvJBAPpC{o7Hi8JUy+pJGK$EzZjM5jCEM7JWU
zO*<I1N!Xv*`${IE6BSGXu6=6Mq!PmWQZ%c>twvgx+$7r$7URl;Q_#tNECe*YRiTmP
zic;O05<nrcjX<4Dm7EOWX|a5IX3!fGR%xU4UXX4v-s?wwN1_Fpjy%oh8?SiAK1#Ia
zxImDFH2h_c9km}8j=}tNmsl242KxDDp>N>WqK8Td2;Q!pM1^5wvS&40TzJLS#ZPo-
zkVk7apoZBvbBJa|Z+zA1RKlIFQe&-Hc8Oz!BPcwH$ciG>!u#wAEx^z?fy!$d?edCi
z27zEB1`EWF={>LL@o8fT0|ij~!t=g~&sh^IS}Byh5{m)rhD^9GVu<#P3$&v7=Cv)B
z2q`x_KP;dkZE_t6!){lueqg{6Vkw~}m9z!z#NPDO(qJgoKk5~S!*V1-y;rGAm&%4G
z2NIyruVYhz?fZO<erY#BAYY4FjUSs>-71zWCK#$%k(l@c#Cl$V)X|||wV8!4=V^(4
zl-4=;<is+O+F?3P`s;opaDD3l*Om_6BbhI(HH2X4Zj9y4Q8E&9yik{#*l>H-N~LLj
z!B#1|03@|=lAq>m)`C*He}SXa{=1@xPQ6tfa>7}jV2%Bv?EDfE{R|@vI-Cg>y0|mb
zg-k-3e1umZ2=SqYkkT_Bs`sSx?RE;}dAgtgY;<c-{D%ptDA(@hAO4qoSw`MwDEu{Z
z+BnD9B*(dr$nfK1K}26Q@dP4N*l>IFsM!3BP!*gCwmz`J3!r|cqR;3SQt~h%?;L;E
zbE!=(q7CB=ElidS5hw^BLTeI+C5Wu=l@>N<Fc{0(3mRK4@QD{yU()7tM$A=-@#DF@
zh^FgG^~iNs4bwPVt+)ASjsS<aH83rh%y2DiM|_t$N^*}!UFaQkWv>*;(neZ@_JtAj
zAgHHUHmC<9P40b%cJUjjKG|VAt=#pfWO0R$OS>dM<MfB$aC7b!RXvlEh)uwzAzXwd
zJU+ucbka>%hB5k*b@XOi0597}pzkbO5I|gM4`={kDmyyXS$VQP&JP0v)PSNn8R==j
z$F{V3Pm8+d&+e<*n~Gv}025U)6ZT9d1+T^F8YbcsF9-JQJd0(ixY$_c;e5m&>^~#3
z=6oKN9egc+<rjiG6QAin%pI=4G@V~r+?9kO(sS1c+UjUJB3D4^CL|M)^^31Zo@MOa
z?nC(~jn`t@=UZR0+QJ&#8?dLmjb;v72vInjiY5w1S=aQq3$!2U+{nRKJ}yh3Yt;-y
zv8K34giAXMn?qLn$hX$6Bn!}*DFgPrt#9k7mD1eG^qE1@hOtK0mNkQGuQ+cuNR^Sq
zV&&=-#CNJIC987X<QcV7*goJeWGY>`mXKz~c-^DFg&SN?JI^tXPD5WtdPm?iYp^Nz
zJjGTIYfTarSLP?}opONQ8Wpm_!AX#PNUR29arWbpE))au{@ZqH#CmOf+(cIDq*_zq
zuq5rhHgkVKH0KnTE`_CGE2%K$6^9f5u3swZ8Fguz60Ts)F(F<QRL983tPO}7_a);f
z4)V4_>DurT_Mxi$%&nAvhk_&X_Z!Yv_6_S89cwMwN-iBa)cZhq0-lbgowM8ai;ZLq
zHstpJHZq})!NFuY_9{~+TSN4PaH=<o;}PwAvf=VyxFSO4K06;)k&6tRPd~dRFfFR!
z1x9ERvF;<oILB0L8fN%aAOQ&10EIfODm?jhRv^-fSm<HI10PfuY`!xvl+QSU?*ncy
zDIR$}>F&r~W%IBY{kTv_Egoo|&J)fGbUj!uIiy&JFVjm#Vqy(`09{!5((dAyL0>Kk
zodg`AI+h}I$<b&?e~kQoAWN?KLqMp_YCNlfIPva2f)oe_bA>;Zi%i60->&$z4HFBr
zrCvdkp0oN#&puU1iteFE$>yemVs{MjZ547SeJz?Vw;`g`vLc03ghNe*Xd*ZBn%hzC
z1XI-{gw#_mBq92IsbinQiahGTPz!YfXl}H(Vsg1yy6}vKOle;qaHaW?wBwrUU6lL{
zF^LUJlFp2%qqQ%k^9bI|$%AmLzA#^MMJ&09f?EK>E`P`j1X&r*M&{k~@FKl~kLo5T
z0~5MwtaY3~l(4I?f*-7WVmQRf8|#%A`!3eyNJ00!`5gzuYCes+2Pf>uoffVxl=918
zSg!`dy@{80IITHQ!OgC+0Sv*3HUezE-Tu+7<sF{aG8UEom9#7EQjsrt5vT+M`4Ei*
zT}ZpGEiA0mgcx7MOt9srDUWvhx3WitQazJQLdW;r)Uz)~>Udm>brPuThD&Zzn9yh4
z>%C+FAl*WuPJW)JyjWY3G$=6kUKtPQ1R^5C?xqVQvB5wkfxe235(%yM2O(hTay?y0
zIbb3kWz%8lC?{HwV(s?D?I|XP@`O|$zt;&0UI^n~W|Wc7n@UR48Hg+u&9B5@g?gE0
z&?WAym{3t|a~B+~oscK)vOJ3@+C)yWf7q;knNoh!G!TpXxE-zH%EL1Wp5;kQmVMsz
z0R-d`Ait}VMUkU52I5bVsvM??vTBCf*mSC5zsNj^ClXBg-m<6xTNCBG%#3R)0KZ<i
zz<Rc>=t{bMO|U?E6>EE7mzrJP*hdFxww-JWkqP|Hxc%}2;hi4c#zv|}t7E8yO8+d6
zn~p>W1<G~#`1&9P|Cd@`jZaIojuZT9Ha)mKKF(yW_ztdsJR#QbhXCXS2B{$GumHN4
zUXWG7p6C}G*%(8G`mU=srBx`!VVjN4b`!+4ZyUnX?1agFbE<7H8cby!w_3wcTZ6-A
zgy-~DfpWeTFATIdP4PMO{F)$?s+u^lsiXTG+673No3(R1S_hE_bgnk)$lpsRxwLkG
z=PSQ__`Wb!I%_2W&{@KB0Le|RKSUmN_4f?tkwW?qwD^#{{1xYnurXmnwDV2Bs$Sl(
zgo*t4LE0D+)f##AQl5tyGWZj7q$Jmk_q4tPx_1YvDlEM1a;7Oy{kUKsLv9ew_@ja;
zjLNWh??CZBMg`wRohjyRCPrW_!Y+-5W5ZqmT65#4$<Hj7P^R7-?09dlIpdXesd~1Y
zh;)(*eK&F3<d9xGTA;AyLUug+4yO8%9@%7_CFTU&_+0EU==sGC@<ReFW9pC0gLI=x
zP(3}8+lo^!*Wz)9ZJ*S54iOP*uP!z6!HLSN>eq&AgC7g(J6s2kpbqXYF1m2tlIeZP
ziX*0(d%_{F3Xr6d!l(73FH^^A$#)mIUx}dff=0;{UgA~<Zi!RUVXwZ<e4*L#Ebm0p
z@&WDg9u5~z@BsFI@={pd8Ot|u3jQ)*VyhMTj_>=Z<yzYhgO{K#2j+|QMq^(TuP$*y
z0cw7>%fdO3V@@;(wOGJNF5vySiH_^*YABEu{j4YtPY+q+a}9k&MG}sc<wy|L?e`6v
z{?EoOA8?nI%`yN0#2FjQj)S^h8EV#z(fHY3g@*b_@z4e4rTRc4L3R`@JX>qk1;FB9
z4|r**QH>q{*FD>k5DBa75<32D{O)UT?7B*mSXmrzyXcvFMzhaXqtindl!#4>fgFk_
zv=g6GH*!HDTlIi1zX-(H<jWy@bXS@kmd?tnmPO+O6K`l6H1i31?S3}tjEx1dEXFoL
zch?kHYFip*N-Ve}5?1N|xN<=o@QLMoB2C-#1oLro6)|fAnF>_=fP6yEvO1eB0%=Ff
zSxioj1dW$S8c>QcC?kkpKxQIy6Nb(0wIP9)lXKn}Y{!SfMM;InzxX(GfcOGdps<F<
z#Y!SHEB9Kpi~bo6wzKj0zyz&AT&r$pQ^1b2q{`X!mV1~&g&Lx<*){5+P-k$JvA+|~
ztX*KpE@h3hn0Xeb>Z)vw6^cJwg)^>v$bQO>8G!1+f_9hDb5CmfWOTubtg^mi8vr9q
z3nPdW4r*a#;_TSXyVHz5E#6Lg&Utd7ogT;dQYDy+kzQ6>^eoy3!|niIBE<be=3@hJ
zV^y7~zSP&_p}T5)kSb^GoKZkm#O$Kv^bc-mo@Q<n;x?pdb-F3_uT3jY&okg38|&bY
z8>NWE$Hs7R9IeS?7D_~Sc+K=|>vtKvKw6?Mh`;w6>G{EF6KyRGk$!S~Z8-1YILh4Y
zsW6K9p6D*^Y*x}US4m$aMN6}PM*JvV?7V9?VoaLFkthb8O{XzK?<lS?55Am(JIB<H
zDk)#{nx3&kELhp$3|Ef%+83Yt_1jaE5luJ+T3y6&ww7?QK@&Flp_Iv|$Xv(vc@UR@
zlL(5Fe$aB5+vm@5*dm*44`(YaQJUBnCKu|;C1`lim{}}VwiTbkP|2OG_AtKTlRXR4
zckQ24NS1%f^xzpA=+5HZ@c%NCV#&gZ_*HJ0^%yv^edEf+u-O%wb?hLvEL8a0Y*4$?
zec2mD6T?*O9J^o1&Dbc2niRNF!FPNTvXy5>tH1>cUG0cesjt^rq1wr-N&q7x@M;Z|
z+)bhs%iR9nxFi<#hOTUgCi1x)j|Gp8GCmYD3A;UO(@mfRsGZNwq=-36a`rn<A2Ln3
zSzTq8yet3FS66mEB-!Y-s_)mD5~MES2<3bJrFxBxMy6@c`(2r4;sPARXq*(YRmAxd
z$QUlD0Rj&Q_tjzKZI_6m4)^aXTO$cC`-bVKNTyVC9E$wCd$cD2K>2CJa4j)t>ruY?
z`r3Ynx!C?F+QRdi5tu9{HlViwX;Q3>v0-6|5evDTl(W4d6h<T_bA$EwzIfc2>5m@_
zc1va>wVj4pu6m#Y-iu{5O$G7WA1wpZS}4pxp&Qkp+rY*<e5GR~!Jz@ldCm(J$2Q}9
zEwDm6FNM|9w6_gRFLRRii&&SgaE>OuYuKl^TW7Dj$g4vu7g#kENSqXIuwsK7((dI1
z3rY#_DZv+P>m}ER4r7Xnr21Sn$VCiQ*a;E11-QhIgYoHA!RHC=V(GzDf|lc}d-=Y}
z6hhk-I`4c{DxD8U(2adFrNTaFW717(doGSe(4znvjXPl2gdaqP@zEu@BMV$W-g`y2
znR>^^TG!MDhjRmSAsL0+(jbsr@`4#fSdZ^pf?x4b%lsTYV49fOKWrInccEuvWM4O%
zfM)V?s8l_SI17T*`Uz$9ghenBh2J;oGxeruI&LnwNMc;P=_I&mjaHS;@!stPRv#(P
zv{l_iu`16C`k0_)_V}t?r;hCD+D!ELnJ-Cx5>(*rJC?vP3@f*#txO!f7$!SjuxeJN
z%oZ2THc+F_#635byvGZ^3~gnrf*d2llJvPBJMD6Id83vBdP;YbxC129G+CR4xkc`t
z&52^%g@uPwh0UdXI*ILz_}E=W!4JLlKNq0#@*p~7POZPTXQ$3Iv81w3fw0Md%$8(b
z_ze_9Ug%C+hTSNkO^a%W9tHO2jucc5Un@rG;TS)9mshwR9q1Gg?k7Z7^@<erv#GAo
zYPIN%x0Z(cB<+2`0-jt_TbQZ1&HuU+NL}9j#!YpQ^4y43`pHcNRU1OEI#ef0SWW$!
zq?Zz#Aap}=&10w4dgU2Hz2zidzxz_rx}64Lblv7!C-_2XhY&wekgmtPf`>ZPEQO2W
z)*@DDs=@k^B}=i&g6SGTg&V)TNb2=<=H|WPCoq=c5Q&n{AChJ{l%tZIwh$Ddc#mXv
zH5lX5Jq{`{+vOZ}lDfKbvA*lMAlZJ|@;B*`G(~^}Eg=d9K{#Y&aEWp`*R}2bUR|E>
zL`q3dDwKJ_S6U{8W;dmoilC%8=hvxDB%11gKs(x$>sH`c7VLfF4ghS=&@XIBcnN{5
z>0$e-ovWmoaw@ElaMtRl>VL&^TAZ3T!R57ZRat3PHG$)i9SBMM5%dxSnGYHqGR%=;
zdzt>GY~L6qcFs^*d5>yAe{3u(jDdDU*Rni=T#~99impG*ed(MvKF6;=(%Z4${!q&w
z(AAmBOi@I&!EG;!SyZwV<R!!k?pc;e)4-I|BUR3`hhcZlQAYLb9ichb(TgwAv6&?W
z53LJ4w!eZB1}rN5Xte%lLtCw@NBpHPM=p!gn_JS-uYdZsh#1-}!P&n7WqE|tffH8z
zbqoX%PL^W-?d>_5)7<upIk{J@xHM+`7NL>ByYSFCq>l$c(~W21xCo{~WLXUSy(AK>
zx8Z(zCgYXh!OTkI#NkXD2kp#3fsqJ#u6|ylv1O!gUb20;*qQXZ_(csC)DIco;2%rY
zkUR`y@Cnq$Ep>|JdCQU>dG)(aGx7O7&w#BC@ki0?i0p7!u6flv@q|U(X5K3isgC9I
z>h`!+Z*jc$cxGn#;-h9lvRbHI38vlpg9=t}ippxDp<0r(MAI0nhbTAL+Lboo-quE*
zR)k$yc1GN?bi!7x+{{gB9XtQsdlO&++`gC7FLr}^V6~oSU_wwKkdU3jt>jLD=c_<w
zX9lNg=1K+i_wO~Z+s3biiRtd{bWD_?H&k}uaLoYG_FiaB2j3y4%t3Y94Ua?dO@S1v
zYkv6AFkP7i1ga{6V(N24e<~5B1NO78sURALceJz`Kr&eS*dez`x88O!Ue#q}lk+^x
zX_8Ftyz&E1Fz1NMoiMaj@tPGMkKj?76us{2=rPl17PPNhdJEONZ5a@;VTFC9@HPZP
zxS$A;%G+MPfUd0kD;=cHdq2Qcii72&0!VRI+4I-quKrcC4bhimBZIC^s*ep80+H@t
z<ff^D;5{-aKHB2u>v+<-G<+BApJqih;!&VI4EpgjCvj8vQBR%5KIO0>Sqeg)dKgYw
zzv=u-0;m(`aW977hvY^xk6U!Z2cz#F_F>=YYeh+BGb#o0W60Nmn=)}%I0mtv`Dn{O
zI+$UL(ZdnECt<W7xsNnBB&M3N+1=llk_BhPX7iAo&${Yk;Ik;XlFY-k3}lp2+zv8O
zQ)V3jA6xX&@y}mJ5y7}(aDG+gq*JOD*H;=#JDQs%wE%tTPAZK@?27=5pzz)2lsHH3
zu*&61C{;SoNi+gQUCf6D-mC?kXAz<^yR7DCwjHR(@;nOir(S-s{<Mo$>|8ANeN|C}
zZPi)6Q_yYv(&fh_ASRDHw#R;9wcF1B8^`Oc0FNT#mt1u3!5~TC(-#v+1=`W$@+f}H
z8~kIoS-B#24PPlt0oAV?q_BY|dXgaikf-2nqO9hj3ivw*0_S({T{ohx+Mq*+V-O9B
z@eXC~E&A!JPf96N5fBapWlvDbz7WF9(-m72MPd(Inu|}#GvIivBcP6Y4lpaGC@6Om
zWeHJrusKpXpcum(hYO9cC_gH{#(JgFQJLYdB(6fGCdTn$E2q_(%M(dQR41tTWEMwt
zz<6+Q9f`$$lcQ+PzL4z?*+CV=gY-E5_6W%JzU*!55zX_=m54)XNGv2fjZdg@w6mbV
z?YzIFV8cN)>`7?tajut?*K2L478CLAVM_Z*XyA~B`dE`{G?lV=CLy5;nEj^mzPe;r
z3SuS`#495Je;z?i+vKZ30!Iv1Avc7ZZn2S{U<qAAA=JsJ1}2&(2}r7-?uxfm7UXJm
zHph&4RX@CS1qz?$2C!EUD@9j|2sZakePB3J_P5vP<mx4HOTxJxNu1aF2@?RALI$vi
zg2rjB7zY2D@j`Jw5qg6IN;lv#p+@*vJs%iP+adV2Jn$&8*~m(w{|e{Hz9Aq{TL;0k
zyo**+4ZXBt@^!eKJI=|Qw>82nW6yVw=!2VJ$|!{(jl1QsHLT6G^z>N7+QV6gg(Lup
zeozw4P9jrFU1Pj31+QL1?hWN>#~J~Yz9b@R@`=Is44L_occ#gv!lQX!Kl8Q3@%ppP
zr#Xe$!14nMv4gcFl7^~^yp51NmWn&<j8f0BIK}jY{lpn~AI&JI{H#m%K0>HT$Vb7I
zfmDmaRo@7RFF4Ud=Z`4Jlw_LzQ+!14>Vj&umhKuXo$v{FOA|&hY*V8ljtmN3w>6l!
zIv1yM-aP@|>k)psf_<h-zgbj&ePD%z0$@-~S6@}QM%2+@SlDa}SL=4crEf%Wpg|@H
zjfHp5!P|VWAH80Xi!u1NBgQu07&U#T@u<y|Zjp_gw?e$`7dpAyXlHn?R>Kh7o>jqC
zFw!b#4pFcPsmbOPlSWiny3yk7D9a5g3nLR2*H-(kasNre6ljqrrsrKe$Bp#5&q(`v
z6ir%qTNToeT$kK9t;mX5bz*Gyw1RR}T;N*p$77dv2GBJd!iqLit)LTVM)Dpci*yyM
z-fKmB@6j~7B=y2gU+Z$V2V6rcC-0&diH#fgCsZ2p49J*$QOfYa6JVjE@GlnANan!-
z1HemYREW*b9WVQ54w+0*xr@F$&~X9MGLyLX56C{wUL`TSIys`+)Z+@5n70}bM-;Ev
zrc#!5)Eb7}Tesq9-$(%VFe%`=Ia@L~@J8~83mY6_umDEp9_IXmX@y7E_eB(pht~qz
zMpTZU8Y_Cx8`v%V9_~2f0ef&k<DGmaoeiCee)phQQ}j=`s1zYY4DF^5K}&BUS^O9y
z*D3y(ww%-H9fF)ABDDLG%O`YHmSNi|-$HLibz=0xy=~T2^xkvuBUdL@Dpp&?<m6@E
zYNZ`f4XF|%$dPuj66=IXcloNqRI6bEdXeIZLao0b7#mQPCCcJV(j@ulm_Fx|sDzke
ze~|O`e~yT#ve&Qr0gH45y&uH)TzF34!lRvCLt80LqD`kvA@)V!#ad#S`CjvJ($?C?
z;;x8D+3q|9&yxZVN<9lY4!|C}$d3>CQTGe5dLg_R)38-0dvriVi9Xo?ghQR200(sd
zV(m916CbjaZE%*Pcry@bAmJyFS{eE8ax<i(uB(+K!S)<d&@=>WLOwF<=HJ*i<&szY
zauC4sv1_|w4%wi${R;);7eFw4w^lGXBQ^Uv6g$<!yZw>qKSkT9w!6g2RhF2gn3&;-
z*j!k7ja6$lweW3&o{nxIw}Xd!Oj=vG<LEA-AgQXBC}V6^Y={q(=7xyQnwt%7KJnBJ
zv~zxgM{&K@-@L@>h`J8wZ{NEiA&y*KI<!cG9v*bd3dPC36h<iE5Ldkh01UtAlaYeZ
zmesbz>ealGHe5<9_>9%wMM9fw6Y-y_AvmE)bX|Ao8;i;Y1C@~-Pk0-9gAkZTYPh2-
zk>76Rs$-}2d^kI=ripGOwWEiG<iYkYG9+2_2)w~vdI>l!P4oW<P{lYgPF#Qz<_!m~
zKRkXW+eb&4HWaDRSfaxbbcBX`3GrKxn=GG2$r}0!V}m%@iTjipd27Edu)r6B963Ir
z5FH#z!x)_?7v_}r1l~nI&iG1Y8-K~T&~~S^rfxB3Ux?PUZw_ZP$Gx|~Fy%vVGc8~m
zGX$-lixbp&9QH0*x9_AQxX6r;g);_MY0Jj0tMIntW`N88+bt&NA;8YoY<O57l!3x(
zA(U^)jyS~Zm1nn=-cXNE%_JBeo+kK!QS()?*Fz6j!MpERB&`9r(flg^-lRL~?N=ku
zmoJ4P&a8Rtt^}z2<`pY^>jam{c3-LwPB5+;9|Ukw&%I!^$)O0>^ew$tW-EoIg%b*w
z9Y5f7jWTgdiLQ4*cA0B2coNFmIGk4czdvg##S-8j!VDvM^wr|^W{Do}Ys=VrzsrO5
z4yQ7@o_=T%Yp5II+FFC+*g&9a><Vf1-f1s!FHS5zUWW=I<PBr)+nwaqse9y9H-gEF
zluSRq3!K9CmAzyV;k>g3O|b3x)p_E&M#dV%HWZ5=x)tjSn^!Y~cpE$Rh2k}(WD*a7
z#kh)0SOlfwI0uM8h=e{&08C*?neqlOpb9vxkWH8weN-)pBCOnjE4T*rT_k-uK1{u^
z;#e&^Wj~b+FuZmAR<Zc!@MOyeqEdzw1^l)(UGl7!pyu}J<xr|Nv+H-F5Md}w5T}Sb
zWrz)!diMpJ`FOD+zEmDU@phgckwFyCsOBup;vIudRD?(r!j?S|ft4(0);mjBAr4&P
zTEUo9C`V0zEjDn@m%np-Bk^lWS&UkGB>2M10n^DPH{_R7b3wIeEPX*W`em-LDQiJx
z>B{UZ#*}R?ci|HH4nrXnqilZUC&BfD;fLZRDA7qYi{#%4t{&pH1lG#+o78K*6WqfH
zG6frfcg*oeKyl=8;ypqz9o#M*1WGck4vZ)I4^&{jBs|fes}s6W8C^O<m{A**wRx!w
zuiK|5VC9EcW`eIt>Dju&2HLHmyWKrZ^Dea%ZSUm8XP)-tS7bw4AbqlNyrvhP`o7~~
z%M>v~!Gl=b_&#e$P2WY-1T_|z5>Q?jhnJ?ykc@4i6RBM<YNjv}F%;!=L!nS-D%-$w
z8LKE7LyCb9Hhdjv&H|I(qgg`A;c3-z#g&9-&9-It9o!vA3}kB0#tu;*fi)iYh{FRe
ziC!PhS8(Q_#kFpPx<g1FDCgCis;<zYs|2O}<VJ^G<fXvz&0VgoHO1)NlZ$aY%+_l)
z$Ttgs5dWA%fM(Zn>HJ!a%fo5^rpf-NdR3|*3CJ%l!cY$Ej`WTV_cb(aT368}^5*Mz
z8rUi1nPAyR*LH`8+se2Y9;%fML8b>!OW+SpDJv#EO6FJ~(@mg#lES@&r+zNZH!VP7
z&UJd?5S9v`C%KLVP$(PZ!Xr8jy;&+>Ty|Q|w%xPyWFtq~kZQsaVb}xtkK!`xe<8SD
zEg-4L`+GG<0F3MFuvN+)kr0hwUm5Kl&&<Ogd%yTaJjlFa@>wURky+5Ydb*iDGO|F(
z6s|xnuT{;ks?TPB<Ri(XPY^jYcF`-~CrB)kpN6Ze1;3H*=4F@{m@=x~(@Wf_aA&ON
zswGL7*cbFOR0uY8%O3GD3HD`wAn&iXk#;&%by&tZ^i^y&6J>$*UzoN|oYo6V4<HK}
zsmS2XYu}JxD4d#C$^gcLB*b&I+$`45GzWv!ZZ4=Neq2FS*8(K9>%2P}sn7*VXxkXF
z00-s?(Z=^LBqYYWN3?jb5zTWb_J&_eay%qmOyEJWSq^t9AjZd0jiyZUd%a0z9Jxy7
zveR`n4j2Cv#_whsKK0&mexqX9^CSGmXnuirGqTR}gN*gpZ8nZ|sH6rdm`~`^2IH~C
z7i;>jUMR4KJE{?j@3p6NG!TRbKBePaJAK2@K6$i!%tn!&2};RlSb`1(P;MC2cJmZA
zJ3LCEW;HF^bMmC<*RO@84dle#NF-c!6kJjHjB4m`u?sx5dg%q!zA1Mc7(6VSkKOvp
z-!edMieB^n3Uf7AAv&wWMOA@Dt27{ap%Be5JA~AU#sxb0V#ZF^muzw49l`LT#Pn2V
zkLIVq^tC5`3&cw6_L{`gMFaCSMO({2;X46R)J)H-E*V7ZD~&P{aLQA<*hV!G|B){;
zS#xXk_>&WVd|~Wz$RRXRKoK5dT@TYy@JE-@X=%$hrN4(rMeF3EUK<6<K~3iQ!H+@J
z62o|d^ve0{AR;!1&t{&fPbj%6s#yyt)r02E7l<;cTJh(tjl-2sYeK-^R(?FcG<pF8
zt!*=;rrkobSh5u{ICcRr<6;d+IX;q?JI-uQdS$jlrG#mYwo7R7=T;$zbu#*&d((X4
zYbPT$2z``ft#V;#S~0Mm3X`}{AajzS(n+sTwzG_kDR)9f2d_I5qm>D!Ahqtbr9Z_)
zT!lS3BJcLCY~L5WqDCe5J!_!5WAqE#Y4(ks+hRE^#-@N=15#nc%k!C9<>nSV$ECwl
z3-+D__makBRls~r8x4aOIJN>-M5%aCt-G$9+C)$jK5&k4u-h3fD?v%xWylQ0g>8+@
z%XCj4&*F`WCuN-!<(S-L=zs!q#Ghf8%Q{LbL(YF%8r;?+;IW~ytiYNp&E&%59<z<r
z33YWf1Fmph&UDYhrnCdGN3ZuOp-|5SP5>aVGeW0;5UX1&cX<X&eqSePoFETx01SAG
zl{qYAACJYQ8jRTYbWscqq)Q;kq6*XBTzd$_k*S|p89bZzCn8q{->|wN$UQ%W2cWpH
zw8TJR^SL~rV;l5gP<~9n4Z$wzw-+5LpKPnFtI<iTLTm8+KkZ!$RFg**{!t<*0&egD
z$|FSF)ukW=5NK7NK9HhPL_Af%KtQ4ii6n$7TChcHZN=gP6h$eB0xD|-QR{Anbp=H%
zD0^t3Mc`1{Wh-c@NO|wQ`Tv*@L~D=FJ^vhTX6}FI`OTd>L(V@pjosae<v-n9Q834Q
zNB8xeuKTP!4UYNb)UCA5z5QX=$Bl<GwqCW2jx1asDf!CxbWi5rt139Y+1IK=zW4m5
z?%bzM|M1rT*<|j;|6XE^H7NYV=;H<NMUM&ob}^@Q@q^;noYsuWmYl83+RUJv1&M2u
zZ6<tQ-!r-Og6FpPs`S{5b2UE}q`#Zva#_<rw#h86W|Pdtt0$$WgwrNma%M@tS0z(y
zIA+bNpHhx)Hn(iC(ymL%S-IS(+>={&Z9z!Kmxly7&XFyROySp3Gu^bcjKE37d8OgC
z6S6Bzf81c;*Bn*Z79{stP!_uVeDAl7@2~9V*@^P=%vWXc_1b%mzL9v~8v~<+od#C*
z{Ir6MJFOf1>sRak_Sfl-GJ$V!p>OBR--^DPxA{oPm-pK^g_UjEF8}nL=ryJO&Tgj_
z_TA0p0q%t{_3<CnUI>cb-o@YJuu1f{eyy^)8q4Lzm#XjTFK>*yRhx12*U9<2PaIi#
zINtHQ_m2ou87#YY>uK%BbXRT(Wb~izHCTJxeSyCH=XojrmhR)6Khr_@l{#yCxdkK{
zrM90f(03`RaMCZY;*Axgdc3o?BmIW{*Dmo5i<A16vX54{*(KCmVTuJ!Tm1uSTTbfC
zk0N}`7vATe2^wJKZdx{}NMGlKPwDmtAKc6_eW$C-Ut;S1NtT{ZN1(<1AM|3r3x2CQ
zwz|CRYBJOKKbJQqwzec43-^69{+8dR-CxMKyLQaG*mUT6g<ZnU`9|kXWN9>PmhPWn
z@gPu>eObHy3ft%QgZ-9=c#AH^%+8+V>OVKe*2C{$gaiBdF98R%S{B-;+_)MT@4iQP
zdwuu8^sMC5hW)e5dRVb(Vi7yrC7zr3MdzZLs!Iu>Y+hwi$J=gu&U>AkB5hf}x3r*I
zzt^pzuP!uS%vy8BC$^VxtlfVh?o?WK@U=OydHc3~=y*2o+GjC(d-G3CJe$;zeyFd=
zDV^AmHsL_wgyZeY#@EfP6P0kLHSscTOx5bRlB3(^5UAZ$Jo1zYH`9G@@Xn;}@Vl2a
z{Z5pc2HjfHx?oFHwzIAsk!v_>*I4Tsg76OiHFD3K+pbf+56(#5b==wGtW%wLad)#@
zM48km>xYeZn5LPV<E}CTvlBZR?7n7K4wLBZbnQIzQU7Pt?YSq-a#Kn}JhOLI>dnpH
z*q`)i@s0-KPpdVO+V0^6!a;wG`*54^DV@5Cp~!GfT}OiqJXWgsmK<d^+J+!>rjvr9
zz9pA}0IoZsn~Y(6x_%Bhz?J2;c|(1=l3w7Rtxwl<vN@$QjE8_xPFA1Brz<tOdoa3T
zJou5LQ@N_HY>sr)sknKeoH)uasNxE!n&6f4ElG_|c`<9q=!W`qMa;m->L2mxO3s~%
zlL}>Xl*7P3?$b43px?<=i8a(*w1rd^<s~ci<`g%pl*NYk>59WP0r$$3sw^4k`$bs{
zD%PkXTT}Y28Pb}{vLj`~yL83YjNMY#P^}I0CJbs!hY#|JZilymyV|%x62$<HkzNs$
zG8EX|6pBPLC@K)jBEXN4Y8VMVgggof4Fdm^VGsk4)p%EB&PSz25Co@38pT})!d23B
z@;1T5Jq%QB(5`w=Yv)nzj<hF;5ewx7Ey8$G38aj}1QID#71V(BLKoz9aU?<>p9?aU
zkf%QO*#x0Ej`07NyrgXePY^}bLCE;LFhv?;4^dPY%O?zKVl4-e2p(}J3;B`ZkQgr$
z$}3jwx&@Dv8S|pTFb@WlkByEJu*kG+twb6kfOM`zDp5=d=1JwsLTw?&pjxN=!AN@;
zIu4!$;1N0G*;GFa2f%+ZVGp%>Xz&QuKqE0_4a6sFTq~Jf%|)K5E1K}3(J*^;$qEf(
zRK(9s*V#o-W}KKS5kri<Kr)m_<#0n1dBBeBpaqNorvU>42=n$p1YduRxC=G65CkhC
z^kG>|9WoD0kvuOJ!7>USS>&-A{GjC|4m3U_4yYEie$eZKwh!bwpzR}x18pBk9N_cC
z*+&3ffjg~-B<>14Ug$i~=Op@^NUeKX|HySnuRjt8`kX}KK<`f^4v(}C@KEub#_2IQ
z(fcgD&w_g3_YaEof-L=g1pR#kz0Z>CfZjhy9A1ijp!ab!#d2dr>)}h(L;9RVpOdI{
z_)_d3t$(!sz0Uea@8iFFABUG0<o}Gx_kCk{7a<fMK)?q;_?`mB<CcMJBMQiX&qssb
z55gG8NQKytp7I!e+7skYD*w96rw<<gm}cPT0$Bt71qj}&k*EI*@l=ZNxgw3@f0cP>
zwf<SC@z1&cI_sf5{24jK3;VJN2;T^L0|;@$_N{<$9%%l_c(C9X`LqZ^3w88Opvge9
zfaU<9&f<DT!e1V=q|%5n(hMr5`R9&@76Y)J2jm2V`pp5tdBQZ#6V8nT#bX-h1oJ57
z?QuNjQI-F{@#=W|p7D7BCi5x$-g?dOQ6DezEHIh4#&KPn1DOKhT1WeBLZxw^M?HAl
z{{DjHdIz9C9zfVPH;T}<n*p(aus<~a-0)yb0(|kzf_P)>4g@b5%P~jXQGaFv;dusm
zoR4S8V_VO%zGvOP=Um^YWEel%eZ&{Bhga4KM<9jG$M#k2JPWo3EMo_RXLnrVui(0V
z)$ELVf3z|IH?;exe~7U{=W!h%kLORt`4a6ZKEpX;0nz-^<FN>Ea|S}arSW?*ek?En
z>nOZ%o#PsR8S5P9O7*<J0$yQr#!IB3BH%G-0NDVAMDwIvh~E{4$zw<o=Bn3A;7Upq
qS1iWc9}j~SpS;5|Ek`QC3Qc%id10)Sc$+H=cW|=i*jU@z*!&NljyG%o

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/packages/autograd/_static/autograd_head_grad.mp4 b/docs/python_docs/python/tutorials/packages/autograd/_static/autograd_head_grad.mp4
new file mode 100644
index 0000000000000000000000000000000000000000..0639209428c088e57348be40cfa3e1268c767969
GIT binary patch
literal 59549
zcmeEv1yq*X)9*`{bT>$MBi#+sh?I19cc&mA2uet|G)RjAlG5Fs3erf!eF5tr=R5y<
z*ZS7I>wae~m6vDFGc$Ykp4s!8z4t2s03b4QaJ4bDx3&ZTKmk`jfq$8Go%ES3ZCIHA
z006X+ouMHB(D2pLK-U3CQv?ZddHH5a^mF~nxJ1=U+A*SWlFcm_W_DI0IwE~*J3}HS
zHfAC_W)3!1B12AHHcmzsV1oobkbvQ#yr?)GE0N$MVIZY}p+2xd*xJU`!qCWph?$X*
zg^rn#=>hPeiGza;7XyQ{vopPusez%jg{~F7wVg4;l@xjt2TKbejkS$~skN0o7m>cM
zo~}MG6Oo;v5ibjofuWv-wZ1to6Bi>FBayC^u7#_;Aupo~3m2mc6B7%Or6I40p$n0{
zqaN_n10oxHSKzC_f3@ulc$w%KfiD67A+j`eF*MM=;>ZMiL)%W*%Gi*XiJeH_#Ln7M
zR~z^$6On_Rp@oI1J@6BU3x|Qe1Mq{sttBrbPz7BBH)|_HUS=j*CMF^yU3&*@8+&t8
zn=6VN2DUcZ)<#D5h7P=R%tQ_*cEC66dD)39tgX#;O@O~>|9HkkWN%@r57gzy14be%
zyK5Qxrk1)6S9+LQIT+em=mLp=&-E-E?Q~tW^{p*!bRB@d>jO>XV5e(p1!Msvw9~z!
zF|yOOG_(gAOHbRz75LfIfR`EgS=T_<=BgKZ+IptC_E$no?F_H`<!oqbY~r8?Y_qm8
zw9+=VwgI-@JhTD6YHsKX<ju>>#`yiCwxy{R@K+*xeM2ileMbjgR>mte?R2k9YG-I~
z0({+0U;AfwuiEYOdG+m#h%EJhcD?Er@E2ZY7J5b^+pEs-GSagHKiXU|zW%A}!pqJH
zd|>ZjXv51&WNHH(B;bSqjv<hjt}Sr<uSOH#0{}qz8ioV_7%nfq6`gfbFE+-~<Ey!s
zE9|aG+MS0mdYS+LVAp?;EoQK!BP!tD&jA3iYoLM$p8J&>eSnQDeVU1<O(ZP|xdU!5
z3)2Hl@IKf&c+lwY&Npy#PZB9&<PZAJ$v8U*ZckpG)P}v0nSjkTr%)apTVMAu9(R65
zRx3FXNTEafL5~|HNHhgT9zD*|FZBSGy7#NVz|M;=Z;cazUxJZ^Fpf2gL7x+E5-KH8
znW;{n<dMFj*XS9Bp5O8H@`1B%dKZB$l23rCsi%bHAet$4umAoOeDb2Z?&UcOo@|8n
zGjX}Cmpaq=?bDhc`DPPYlQ>%U7?ykvT%KyO&Y+ITsK3diRVUydPXyFEAzH&kC0F$u
z1VHqKjJIrAw)sTJBlMnkFy<yeG8@xJInKd@&pamHB;$8B;Zkl?W~NAU#)%o3@R}T>
zor!MoeYlFwS)WN0Y*?1<4EZTv7?wUNg0JG0kUEO>Mv8yK8_LG*4Vi~rhl=1>4~4o6
zb2Z=asxgcgV4vVJR@Jn}m~cGe2R#ciUJM~xllaVFq0O?nPp0S<k--!16AYDX1V=dz
zA3mrN{hUxNM^vBFGp?`M=kq4mE6Z2s>zY(F!O&k}o8yQFTu|I0J{LxZ5~l~h1QCNK
z<AJWL0H?LljYo`$v8WkwG$?plx5EgT`E6OknvEfg8z=nj<AYUrhneMgiW*hEq3y=~
zuN!&wLq0O6EEZI7dQv7SB?-sDVb<vFT_-sdnZ6c?`{M{>v3*nu+6gTW_SCVvVS*S;
z^QfMSS2`h4@-W$PP=)#}54>)U?oer#eJax9Oo1hX(POm<c;<@=E~M@%9(LdjrJRj(
z;-*hNdSOG1(H~NN5bp62ZX;MrMVsI$uQ|-hhA;WrV5+&J$ziVFkR*tAZ+LjWUE0~k
zjwEZY`91H|xC5-M(t=j}x4DAfY=cwl1D50Rr%}JI)FQO;!zKl{MY-o3LKiFfJndKX
zmY)9n2q30h3TFrUVkTQVt>J-0Cl9{_*n66OPzg@QA;dTEn$I#`AchiHX$ze}f1OTo
zJ84?@obDMW@VLZk=6qsV*%hEGOu;zfaPSFlk$yl+O&d@DKF)-($C0a!%i07b`&^#G
z<7z0^?O4GvK?>6Q_+|dhmy5}`739P3xOb&NABUC7#Yo3AUW8C_6V96+Ck%@Rrm0R2
z^|6SKn}tewp%2-?G%q_GLBE|e)l3D%PcdyV#5yC%`;79l-{<AmD%qEg#LFGxPi3RT
zB^Q}jib<OPMr$dH$GT|=sl7p1lAoUbK0Lb!{Af+MAC&DdXBxC++O&@sxuz;7;Z>rL
zoNI~sAxF9!j*X3`0)sX~=F><Ashr_ZV{7YNe*u%xb==3SzJgnukxe0o_1^;DksnIS
zT?j<+C*#&6aE@X5=Bn`5%0Qy4_$;xHmLmej$Xop3xKEOW71Wb$@XU16FsTVO*4fNZ
z>4m-^6`OX3JgH|c=zj}7<ADVe%1fVxqP6|5VsVv4B!KkYn9UTnl5b_D+>BijDqqeX
z(pf^P@TEVdTE$7p)Ps3qh5QXx5(zex?A9F=&zDiYn5J-3B=mh&7{q+V4-5z5(*!1M
zd=+U6X_i#m$qG&yP$5E_8d1~tOtqD09=|e*!&H7d-6o9sEWMjFdWoKr9c@57$RNGS
zvTx&9#LLyq^99_u*^;jN*^bgHBH6EGLUk@(Usc=4s8DBv*(^lz*q{ZRuo<ug+Ly8f
zJzNmL<{vbCLX1lxvVra_Hx%sVQGcJxuBiH4a2eiEJ5E)62bcRZC7(iMN5_<;dNW%~
z+^bkPVK?Oj99L5i-x!uSwqs^VS<%|{sG&@|i#(zl<X3AN{zmopl+@Y4PdIGgl;hq=
z;y2IYXXgmn*F8;Ht<>4t6>0|Xw=C9QcJgyuXE;52-GxYpj=mXukUjg*adfV`pws#@
zJaJGWM(CrnIj<~OKL1xw)4e^>-|LEWo0^IBxl!w5X~s4<slZkWw)c;=^*CKF(X;sw
zLsMhtE9ZOlNR0L15Mm;D#m&ZXMM@+zX}nlyOg<JXo3Q&*N4r54S<EpC@O+BINgmM6
zibr;-DaVyVfa*+68gQRY7UIeLxS}g8M-vK`3i&6vQf2Q&&x(AW*?v-8#EL>cOAfB$
zB<Pk;5IHCh8S5}a1<$LZIs?C$Tlsm=Fqu3#Z;kCQN7rFR+;9CHSHZu)*$Uz-Bnw|*
zveJP?w!%$1zo8de<ToLLSnPk;(9hFuv;jUWECvUOUtpC#ApovMMwzf6FSGV(119NB
zZA1We_L{p6tsx^Db+3~=Zd(tI3`pRkaGUB9E+w{)7~aAMLc4Us4OP25i5pP>yN`Hb
zVlRX<OcTJqrtCeM2#_sg*$Eeyc1wBIBmyx20SALpCiu~}$c*B(=huRsEV=^C_cQa2
zw)vDGIvx*$^4opOhB`vqTVE$26nr=!>30qi+M#JSz-)lcsqkPXpT0nG;2rl{Zo6>q
zvM#JI)+1S%#vJkh0fk3<(8Y0)OTtJ`cS1+e0M91(_{YL(+z3n~Ndyx}Erc#Fo|ORT
zKJ+0W>#wO=2L|c9e1AUTQv%YDktGin0VG2dOcrtp@CE?N>tGOlPJMxokU)}k9mFpI
zm=Yr-WyAu)4ZvpzAj$e>z-J)%M^ipeuJ{uOCYVU(3Yx^rKx<!10Tn(z@Il=oy&!G{
zs9aGX2_^@LBGZyw0sxiZX8@F)8$MZAyg~4FXMA2=Ndt1u`Ng>b_yowg0La<mZq8Q}
zK+c~;(f{E5`4?v&AQh1F`X8LP|DN-W&M63ga!#T9#ra0(6o%WJ6IuVB^Nr3aig$3f
z{l)o4=M?)tIJ^8k=kGed{GIdmb>CAeesTV;bJZ`-SML!BCcgW7&fj&O_?`332&NqW
z;{09b(?2-R{XOR!os$WE=X|4cGVg89$^5rDCy@R<=Np}qg?{J!eb!yi=$l!0qx0X)
z=f9bC-*pbUgY&=h`8V%Q`FB2FTjSsPe21s}8=rrh8~+#MGpK}M;$P!41cRyn>f*(w
z09xx*U3&(pA7B$zU5yX$iDRarsJ973@$$(b1)PI(+j0z<5an8K-zg~My|Ww_JI9iZ
zowr5bdInsAAAuDNM>K^ldEr-iQyltD!jf<A<zb)%^Wh7ri!KOQWDC0vBd68k#;&|s
z=f|7o8}X!8D7Ent`Y7Ke(-Wj?%x-z=nUor<cqwV`z1-+G^Hir2nd`7Hc;aJlHJ1~b
zsaZlGKO2H8WTsE*M?b3lLZsC7!9ox?F$-L>M`jwAxCLP|#Rh4Omqucr4PnN%!Po*n
z7j*Cxp&YV_BWb@|N<)AARcvl6@ioOox!mG_%3v&IppN;v0fQ=uS3|~vbxR>en5F7~
z{KJ|m(k_TpdGr7Q1bOp_#(|F0jyw}-qQO$jub@Xh5!@eQf?&%yIfkMN7)icW<8REU
zvQ&i+``fGYpJ*Xe0hAIh!jwo%0`*h9cVlsB1Mc(bbfQ<2aiV%A6b#$Y1sYS>z8X?;
zKQDCv#p8#O^A~?M1U8PNtBJoKl=pCZ^8j+HwsUee=^Io60IOtY8hgWHz9yH;)%$Hz
zxY^<$tw6m3fp#9kGxwzOnt<eljZdQ8c=K6>PnWb6VpK-BDYTVOgIgIFJc*F4aaQAf
zAItDS4vfVT^O(4!tTu8)#U_w^g*ehZAqo_D1|dgJL^^9O1=}*S3<@LA*8|0CP+Qn~
z%pe#6rdsv6mt*wg#U)WfGTAUrza+0Q3HMO?VNtsTOSbXLljnmW<l3;ufU7+7$4U<F
z979^qOieBFEmd_wTvAF6WY-co-5dZR10(|`W49J}Nsybx9SQ@$JwKWYz-;`M&rOlw
zFn~{@crFtkos)l0{}tFYIl5%+5~Q-IwR?@7<vJ$5Gb7K4f663W|Ji(FfL#Yas4psz
zy*Du1{Yzc|LHRLy4#HP81wR-<PHkpTSv2Kcqw2I7S(nzAa??f8Wy8}#sWZCT{HU^)
zlC(tU7E-DzWc2o+9{Hl#HK(K#d<)R8yON0}@7Y$`%}09e)2O*=9`BSTKK_6?Gq)8)
zr^6T|<zbIkxcv<zeh3a&(*m#oGwGY42kw%K6fd=EuCVvk#r<TwS0>Nhcv5vg-=U%b
zMt9BGp{F~}nLAbn?l}PJ%7Xac^KB>;s_w}uZ={~eHBl-H&OQ{$9v={AAOYU@<on7}
zE0cW!bPDf%+`=uO<^X`!tJIG5CKbOUb)i2N+j#lW1*6O@T2>DnUj@4<dg%nTbKB(6
zRYF62m7rgz(bpOml(>6S4Aa<$ZVBGg48ZpRgk3Sm1xdcnaIUml7~4G8Yc*+yoo8|t
z0H9x`R+!(@_g}hIz<q|qv$<yfcX>ShvB_Y84eS_@2=8Yq{}&y!pD<$FgSp{<y=?pv
zujmFEBGquybe-SdAsPrF{vpxF6a10r|DD{Mr2m%OE3;vO++_cE%Kax80F?V32K=qu
z>lEsi+?)R1V1qm4-WN>#8<0T#HIeHTCLJggP;sE_??B;C+0Zu~y9N!nWPk7THF~%u
z+ZOoNKf?&1-0v{rPq{Z^d;=+N$^G8vYix11+`leje*jS6-1-hO{*-&u-<z0pr`%t`
z>Bi-*r}a&LZ-tt_$h}e;DEIeB@WZcupVQZH<ZijwIQ!22UIXqsy!;2?2Fkt5pMRHo
z%ayNv>1KRyQ1~smS8n{zaQt5%zK)x}+4%+r-8sI0>%%}l`WqkqHNOA)FK~Ujw)4OK
zd$kn*>%Tu!qMvC8(2xG16!+h=1~dGv|58CjRd-3C;OIudP`s<d9LHRU1Yln!Pya5+
zzXIp~u|tDF-p>7bK`a1F_25orm-%N<X5otB8C}Dt`?<o9oFrS;<{d1uHn~z+89~F=
z0tNbRR8p?wGKZ>}V@*&9W~(U?A9bjtcIfP+@E+p}+OGsrn%jhy9WodY7wqpUyiS)W
zd0lf5d_vJpDE@g;GXWds9orfOGg?Z9=6NUqSrAp3f?Pi;I7uW@|F_-piEn~m5q&2u
zYxYW+vCHg}n)^d+Z6V?nqh9AAe^J0~*(eon$Yf6la?}w!+7}Fpy*Hwal|LorxC$HU
z;YRwRZw!mK&ovGVcc&$y(^S~1Iw9+m%j1X%vk?>!2GJZ?$r>f3%4!1CmD4nC5sweb
z-8kO{h-5#W_2w|YQM`X7dmhcUNFkQfu0qU<3Mqx>z9;Kgn{*zZec;#~C`>h%k<(5B
zN+C9?gEu<1ME;>nb@^rD1Gl<}vhhb*Y>2g!qcg@$0vNLPYi$(OPl+Q|2sEsH_h?=c
z`jw@dzl#a!GOwt7E#!sJE@C5Y5D-LCZNXsx57V>bYRWSJU7$g3jsxg-(B-8QZhNRq
zC|0mJ@~j0CBb$ub4isqw9R%G&>Un+c0)be%#r)?;-UxgB)YHwH^KF4oWlnrKqgdW!
zbMz(zZ5qRG@gUvUkMTBNR76Nw-P3!&0f$b7z7VB~GDhz=vx_Gj(zGjsM8?zbB2|`U
zPr44dQXal=F7*h8_&h+T7LzyY*?I;3_Gwh<<L3ecubg}+3pKg*Wdz7YWoM&ASi*)(
z$z;uH>6o_9Ph{sU#NME<_z<?8u;F0}JZ+FzwTUPQ_V!e@Ct7<prlnEfW+Uot{>TSj
zTZI0ZpGk6h4iau>bNH$crk_Ipu*k(`lQ0ru3}$39-PnddTt5S9KRas!U0Uv1^XNU(
zJnC}@W_W}~l~;sS0okvkdn%f^Sl$!*tb&nS5#AG*s#`9%vzDBw8q7dVlwro3>`#V%
zjn3=OiY;)@L(OSHN{Zw}&qcN|F3*o{Eqpeqr?6V*0o$4q)*Ot&%l>|cw}AU|z<9#-
zGx0=SrjfR?ZGclK^!{iB`{fCsHJeKFTQJlG6^|V;xW3XL!lf4y^&7V&=!nD_eV;O4
z;M+{!aVR0Q=iA`}NS_5R8EHohe7#lIiuQp;!=u3|zDF%`>(ep-S9L3Yb|3aDk(A%D
zDSSYhxYd|jn7@l?$%aPebF`0z%t(qHhmqo_qEP^V^i`6{b{nVSgqEvVIRH$K6ju^2
z-)`bdK&)DNPHFT6apJa*xMv(*8qoVu0AB>w7``XA&=v^c0TWb#bOJsPp>zA9UzLKE
zzw-70$O{6=u)af8crVN*9Dli&U7=71Eul8o7#i4iz0CxL0b1SEuRImW8GbTc0*Leh
zzDIg{!wIH|L&*4{A2|2U69B~w_hg|D;Z-L5mpnID0Ykm4&%^JuA8U>afClU%00SiX
zJ65Ibb$$v$)-a@9>!T<DY(f3L74<#6zR+&<RRKQ!XPAopJ51FWeW&3U5(0l&@Co$A
zHP_!U>UK25IcWaP<Llk2KZO0$x(F2Z3!B~{>^ER~E$q#Wu<H%0KZX56G7A*;3yl6Q
zEc%A)Z%Fj_p8bwOf0^;0HnM=iej(7`g?%^U^^Vp}y8e65eutjF3Ht}wiTyk5{9V{h
z&wj_8zX|&b-uxx(pRguS*e|U4yRe&{-R$K4(X*S)uRq4@p8zIM*e`&2hp^w!<!@v5
z!||?*SHJb_A2xY`!oF|v0@3>&!hS0Y{cgtJOF}<{J-A&C!U?tj2j$#*zhJ!`j|xH+
zyz~gLTL|H*@4OH`8u6$c?w(XJKcKN3I1_PxcR<aaMGhn!$Ef3n5;`E$z*cmSUmmy^
zSnI_U!rj0?eADpV2SHrhb3meq_0{LVHGvHzx3g2IZfp$f(-I;i<9Q+6qk4Tnu@8{!
zFRrLb5pFW>-jv{(Eq@(nfKtD^B8~X_9Dky{q8T6Ls{n-x@)vaZqx=7iEP=iJ<%~%2
z#iy<j-L)k#PyiKy8@B%&-iR)kSo6IUdwCWB0C=_9V1cv&E5PT5A$|aa%Zo$V51@&p
z$7W#Bs<7WaWs<AaD4K)pX8$<700zRF$yN3dPcN?L>QMoJfWB>5T6F#xWGo@r_(CI@
zlRC4HGlsq^f`XAHSmhb$$D{@2P~+&(+NhZ?;|d8(LK!+<R$V+RjNe93W%SyfQNyXR
zGJ#SJzwkon<`A7S9pLilV%NfYOamt+WtyZn!j$-7xH~9GsDLEW^HB{6!2=bWr>NUa
zdT**%RX%dcZ@bAq*fo7&b;#CECLx^sdTl1U-(449aB+z1qKGR|E>fCEK=W{yk?tH+
zaFKMP+2@#e;(gxEnB94-QtB|<fsBr<0CxI%nuVE*2+I_vvngA)^^9_OlAgYva4lOf
zeA2~NRpI-wf|1XaSf^;&9vch2n`96&y{sgEs9KaAVbC{|<Wn?8`nJ^Wo(hFt7$=(w
z|Kd#eA!;^Cgc0H{u7;=u0q@v68BSQ<8wIQCr<>xhnobr<+lvsoT&t<M(Y6}Vl{Uf4
zH=GZZu>>##3Bxs2jC^+A%LId9EH*nLlyqr=Iw4Z$XMB8jq$)AX#V7NDxMf4_yb>wU
zNVcJThGwB-&26CvmTmmhIwY@We#pEag%$&i8c9HZ5w6xO$d9$u>5@L0W=|((wZc#f
zy&TPxa6I7U8IhWG6of>Bj&}W6;rkxZNn-gz1dS;bgLaD67g(~NRO45m76kWb_28eP
zo*=f5FcZAE=quEA*|Qx^l4RXTrh+CKp5ijC#<zBCiDMwmgQ(cXY*5l5{Xlr^Ryf4P
zCEPWZ$aR@LbgJtb@B1bmk$AxjYdHf(K+MAy>sT5w3M#l|P%2|t*nhAp1$Nj%8ME6(
z<*PVy1Qtl|@O%exN(8;3*11=^#dE6dg2N*4ul`>@#0ODqZ=fh2u5e;(gIOp6>L3Lr
znSE_rdLL%-A3RPq1T$NsIE1&CwHvgM>$B>PQ`-Yvpg$gU1L@DG!9!c`gYCqjZkuM#
zn?Hl`gQB+SaufDOH#m^MZj>Z_6J0-ulLfHdef<a*Qf0&J6+X9jLTV|Lln2C^yv=z4
zk;71NY&xwZRLzLrJ>-{{GGx9_E}%I;2?k3`oprU7?GrV+TODan3NK{UE~Y;_18R0P
zloVGe%0B3h6Ee}<<K{-Hmzn6Fg1|c;EAeUw`I@6OUd}2l(-FfHMIxj>Ea>bg0?z}H
zch82Ru@t<NXn05F)NDL#G=>DdLn!INMpzDCh6n7!AbN8NStU}DZ_SrrZq6U;oi>A5
zAFP{C&MmG$2n(|K=xxwE*^ClDsN3B+80C12sKp&mHi=*vN730~CE+~pq#ndF=9Cd6
zJaZb;j(^#NK>HA<%I8_;%BMc_89~*7RFpRn4lhx2!$Y2Zrm)`7;{hG&MOzu`i`rYW
zjkB0G)w@?$?K$7BdN~-Y^=>5&J-;~ZtdCZy&{Hl*tX&2+xc#K?qqac`@jQh`yd|H_
zfSDtwL~@LZKzTl(vqxBJN02sgVI;l&o@RfHQaILmXzK{{W_snayW9T6{@_4d968vc
z5LW^CF!L0}C!3rmMjpLK;WQmRtF@loBYY9G58Gtkh3Sbk`=aW-M=|RDG&qz<jUTuE
zjQ%0lFxBj3@r<0S=$yLw&_08&^|X0olz=(l^9cDj1d>Y>LBoIo7smM*ty(sr?ck8j
zO@veB1Z95Zz{W;d-_0HgW=Ixusne{iYFSom8nxEmBq7D>JfpX{^(9*09y>o$%SH(D
z`GCB%K3*^SwdcaMVbg1=NSf3c?KyFemV33b!C>#bSy8UScXG%u<1anY`jOjo$3Atn
ze+isUcpImZ!gRn3yP}{0E#8+)%fFHS7PdlsVyzDUq12Re|5yY}KP5@1p)n3gVmOUu
zQqW=MI0Q^#n?yKvlcq*>pDpbqQRy~&T<+5?`PUvToFMZvjXWB@bqtFgIcLQ@Mln{O
z%zM{BSJCz&eca~W%$TanrndCQA}#P|9HSp)M<?+;6*>o9A`VNvcg(UKk^8v^M=cpY
zRYRYc&I#U-M|QR%#FQX1vEoJl!Mkv}CT;^_>Tl?e<^)rc)mjA{T9MQHJfz^=*WKRK
z%vOLA8ZW#aPT;2$TpotKIItePe}oLB6ZEpk7Y!D{x23soIzAJ>oW~G}<b>@p5pyt@
zjH`<JqG8nw1pDsK5{D?0D|M~+6}pD(5Zm0TXJ2fkw@YxobBfm??G-+xVEa(DbK+*K
z3=XFm;L=DFf}f&b3RxNMHd}2=F>{ek=%3qX_2^OVQDL}~!*O)mLqY#@+UPFPN=~KM
zxUZ1&8)Dz7Or16zGHbgUnpb`hb~Y+wBhgt)){W2xeIv;ffw}Cx=uKOfy^O;d?N6uC
zL$mvk7ek2JFWy7&nJSV)W~2^<nm-?=5E|Laykko;wOuvm`WNHDgaAn!3wGhePP=c<
zut{4D;@&0NI@=%yK=<P;(UEst1WFZUI-oY?vsZ5Oc&NTAQ0=vWJgyr{@Z)B4)Wfy5
zfnimE&<kzBwRO3CTqU-qv9E9`V{hV5qhhD7nd`q}$3TDq64oKd>+(Eanip_X?qVi;
zU|$QsbtQ3mLa(EH5x7b?S?HRWO*CYB>A=sB^V<1w#5w+X#DKT_r!5)KwQiBjLdAZT
z2m-tY=kCu`(k`<D{xWn8e8m1rN#$jYk@zdKK1EpqT|tPB=%SmaDE2syHze8`)eC*=
zI7&k5hI3O4I|FMJFzm-ORLvDn*Q2-!Ql9DtgpiHrier|?hwtF+&+Rbj;#ynVHV;y!
zg+;&jS8Ki`O@iW1z@N^~H|Q$ZCxhSm8lcwNQFm{^;;Ge^l{c0*ELMT(*I_o3h(z=!
zh82|e+wLj0!Xc|M43F=^8`eb==065u8`bHqnC-np^X;(p=rvxSO_}uF;2V;05k$}Y
z(%UU^;8#o7)m5%%@e!iaYv5}f)$7&#<@o2dsAkGw>NLIY0e;UJBcG8B<(_2Z5tA=0
zE`>lRibz&b7c(ihA$4xO9SKLL&7$q2_s}_@Kl1KQ*Q(-pb|JVD_@N8b)1=RBp!c)f
zD^%2q*+=M^tf@7#vN}C$kd_f#FG_?~<~e)1xxc_fSVMWCj@rmE&;@2kmG6=3_8zED
z_0`0anA4k*HZb)h6It1UiD)~vwjs}28a%k1uL(gwBv4#Ye0skiTC{1kgvd?DjDBh!
z<>b=8yU24XFRrVB$CDOZ1_A;^;~nqb$9LUw!7qjVpL_OotH8Nh^>uvvAdgEJJF!|B
zm`v2=i9?*-BT){wE7IY{T5bF^Jc)Q{mOG*C;|ROtGxJUGjc%J%z3oA*2k-iQj)$7f
zjSOA7Bt1U1c2shh^gEsv&*{QCV_rPat%d=QV_l$l#RniIj;CS>fXKHcRYqpj&hcAQ
zqSxBZ$;1&`$VyqUi%Fj^VC}2K2zDu8=w)A{CY>aqJX@S**huFOSY5E{4(JpWl2RcP
zIX;FG=;yNWYJX~71lcO`UbRa+McP2v4F;@>9gT-)h>Bgm|I{>*Y;M(#t^NL!!3>+~
zhLkySyM!5rgN6h*PA2|L51#~cs8?U9o3h_Yq32aDi}7Z|J~1J0@2;n}?d&9IMS71j
z%7{j}BW8%R!?>>U3?xeRxeolROKTsD)8?KXYsxA#unc;}Ed9WCYwBIUz7P$}`j>lU
zoPlVU+nSH;OVaI%HD9cJj5ywn|H$h!Uz%mgKbLO2ixI%z>q|!<V7B}9?8_K+tEj4J
zk@cgN0xzpEro-h1Z!V*r#*?zCZqZLTfY{Ds00LOUJ=;@^BnkgV#kw-Wxmq#Ko0KMq
z&IkRfb<xq>MM7I`6y373h6rvp_k{SLBTM=XDgjz7w}g%7F1+*ipBNt!fjt~!fkVx^
zpIJte&twbV{s!5_6?NfqzjMQhFqMc)<Dz0uO=ZMlTd*qM$zPT0g~+5IoD?SEw0lzf
za3qh>M<q|o7ox$6AKn&iGM6Z@E1QwMSNcM6nBO=ipgw7%_Ntpy+J*lkwNeW>^q8J8
zE4+yJZe`H7LD|>xjL9{2L6q8!<zg%9JB<p82T8*Y?X@asCno5lHl_IowpI#H+H_oG
zD&owB#Xqpep}$|Fe}%L1kY)T+kR`rMcxePZJK?DS4;A!5!{B&i8h<*~lrc+?H^xYf
z6XWXZHlGBt`NPG1a<HA#<}f4A<*3GVL)S-=&MnCtV@E}nhL&!cW7O$`>IO)qNEI7Y
zOuiyA@dBLt0U+#O?k^X5Z9ab7bP$qUqL+$SVK9wvHm>{`5;tT$F+Zk@O=mZL7w;6m
zqBSjsF?wpP@!sbt-ImE1d}D~{FP$;>yWcV1Ut)rR^TBph*|XiaFq?fz+Kk#c^qF0n
z#oOx`<h&toi{>FqVNWp53X7x$9oyH`N<Inx1_0qf%xj0JmNaIfTJn-OUP-6S%Q-Ns
zx@RY0XUd(=DQs}^6!Fn)UJ8`8RY_uWblNCz<r0=Y$$Bgzph6~hV1f=U43dIw!A?R!
zvN5)w739DJ*`Kx~;zzu2WFfYOGs{F!KF^=n`D6&w%XTsXV)P55?jtq=PRhEnr)2D(
z$T0vc6mRSJXC06xf>4zm3eyvz+SPjjC|u?c1kQd_g-;UJRiVX3BEn+No)>R_){>3Y
zUW3KkCoPPSrGIWZO^El<6FDYAMcl4kEF;UC5mV#*gi|zZF(nY^OE{vQz|&gRhB68z
zN^`X6H2>gLlz;@doiZ{3^Ctx$W{Lh^?tw1gW`ck`IT_#4eGLv8HO<>2-5?>`GjH@9
z#M+><j2HeBT9}CbjzyfX^vdkMcWwF_B+3MY7MxxxNdE4uw5Bu2UI1=4qA6py?PtZW
zPS&YTGtXr}Gh=IJKSRw1s#(=67Q>zqZY_v}#DbGqM1~1{<4^(JKQlekE)LxDbqaJM
zCxtPOace0Gnf_W0hMOLH&^WQ4!frzCjs37(9rrWLLbKU|-&<M^R!;BUZ!=6ea+CvF
zb=2Jtg9RAm6}GE#40%%#0MvQXF`Q%o7B`fE0o4=ZnA2lA?FZ*W+a)gZ4+4!wk!ty#
z^lCqG?g_!utt@GoQ`(lMr)<f3iv(b2^E=|Sz`BpKTp{82W>LFZrw?HM=sDvLP|KSJ
z#L>FH9m#K`5uhRj#nL-_ps^<JtUvbXIhdC9#PVBJEOWFvjx{eQZ?UX<yh`!a{U#M`
zE6`}$d7FB@mG(*?a8v8UU!m`%rf@q`50Mv>RVB@^wLq<B=s8Uh%>jjw0t-S{2l^3w
z5ZVJ3qfpo6nWLUi1c3RV_)#VGzf9gs0A%_C>BxVa@)xuTq!NrY&gWs}4z6zi$Crgn
zEqgxA+}s+9=mX^lq{RMS;sxySuXTeP^rV7)3z1b-*9mwBY-ITJ8o-uzK}N5j2VtT_
zksKuI9O&qJ)bma@H;bj!l1U%nD+E_nq?>yIwKR(ou}obFJ}9InQq!B6VQE2GKnlX|
zg<QaH3kvMB#ZTV=U<yfgPF-ovzHAU@I6$icfM=qv>$w1B6fxKl0M4`Z7nO!^@8A%l
z!=X8W?PRxWx_~s(mX5iCOn_AnI5W1<diRSt5*;8R*3U8%fcePT2Rsc2rTQVYjc6mV
z4JhQg$^=m6mN+N?uwjo<SbV~C!6_C_9l5e0&iAtJEhXTe!KeXSfj2PzdI;&33STuL
zVu55BKPybX6wvldxMqJ-UAiS67Yn82ruDkS^moE<ReS#w9&;o7$E}2)z5glvy2$jW
z@X{OM*Ja<kg;U?&3cW47aRoL9pnTY8CU~R6kNXRM>Tq3n`csGR2Dq*U->n0%(Dv8c
z4YzgBg!pcNA6Fdy6n<S6{!{pO16)^!{}c|qhI3Wr`)iT-PvJL1|Kott??az>ZQq+R
z@t?wPdVW(a{!{qXyaT%AU(Z6`7T%c08N+ShjhAd7%`TuR0DJ}GXBGJlMXt{({i(=}
z6>ciYe<}jB0tD*q{T>iXf+dPxO~SdAHVZ2x{Kx}vu&fF9^pf7SRhHOn6a(M#6p<}L
z4}^G$OW#HM0vx+6UJ!o%-Loex<iU-7dp2z@g06>a<g)0M3GnPBkw#C{v(vjykwLvY
z@}-GI8+&Y<*0GW|&2hk0KEntmMzYKBihS%a@*FWDR4hh-=e8J5Qvn#^CuPc4jds(!
zux*1~jHw>KsuX`s?YEtEF+~Ei6l%%>9xP{tOd5gn89VKLN&^jfGFeltbvL}Q2a}yk
z2m)p$ejJT&Tcv8|;~29kCXLr{0-Hi>DVuK|X11;=P@oMeIe`quROGai7r4xh!E5j&
z4Ms6U6?sNX5>|DQHotQrIB<9%tz(eUxZAn>^>9?abdtnB<Ls4kHacGpLr@U*J0w&3
z8pmiDM>c%V!vb#@s^zacDb>-d4OEP{8gItpiv@MC?qdu>A2%Lw=~jG6iR*M;RkRdL
zQj|En7yDvH?brvpF++?Z<=AUm7qJFs=RME(7xr($Yi*O2<7x-ajRt9>JIFDpE*y9s
zk{CtlkdlN)J#WbJ4Zs#7RUJL;HCpcn=UFB44|v0>`tXG?7z#@?yw7x0XDU#MZ`SlS
zMXhL|TY!#=*eFUNwbBMuI}*Gd`t<%NeN+>(-GKV7bwfQ%P};UckrI6=c}h%Nu-s(@
zg&6|Od-ApP5YJd!p5CL56uC220%b?-rm54z4pY~R1OKOFnvMheP=zun!sTsdPpdp}
zOVyutDx~u{`?u#b?JL$8HdeNir#9{L+L{QFQuLI|Ls%iz(^I`aOx^7aD^sE<n^4~t
z>zq_XtGBpJ=2k~oKHc9}?-6{}6N!gGw7Kxo%-HX3zN{wHla($?21#<t5z^+xZ=mHJ
z*4x1H4~!`}_MBThWE-kHs92<)YT5JR`;24DwYgUPQ$fVfKU%2>3I_?0TO<@@qPBD$
zKIuk&)#)+MZ46TQUOHuAUjx$S^&t{<FPW4TBq9274<>R7$Hp4{lc(PhC;MKvKxrXo
zSr+a1Thv^(#1n6q70lrUO~$@jlZ+f4t2Uu&yu{EARIapdnL}I?a|?zgT}*%LF85iV
zJ=YKGQP|l?a!v9IRz>)*4S}_}p2vt2Tf&C~GG0e3uf`s}{r9t2wqfbe7_YMPaqejw
zPX*btVoWuoQ_uBT`E)m9%&%RNJ`-=5jb$wH&tkVySWRwxKH)6i(gRjlUy8Rs(g{jZ
zjoOi!ec!9>gkRpFyCtR-*L$0fBZuqw6V1^jw@_~e4dzj{{%N7*>o*^9Gk9{A`?|+j
z((_@z$k^qn$X!Ck^MSo8BO0!$cYG1)x!jIX<);}bOxV|HAX&PNk(WD!+;yP(#-3^w
zCa6LC+@iFLk<Cf!ljel21I#?-dv~j^k!MC$LTI#J0j~h4P@6*cxB+Gbl#N!6yViA<
z?y=GsGl4u%J2b7YQWG5li%p)>`)e?jtL_XO<8?ii(Vtwl_EG+*p}3hQkTgIIljg@y
ztFKB59jq!8pS}{)H-Pa5R)hpvCOwIlnlRq_TLp}8C?wKz*~GT#npL8@=BG6t7mCK2
z6em3IU-Hyz9kf1Mgf|9vOJ_=yIG(Ac_h{z!2>j%NAM1TDe8E*e^L+)8D^4>^YGLDZ
zlFknc%=Dyh#z@`^EYcz^mfXh)5@)YlOP3?+{{WK+qU51fiq}MY_HH8l%f-RvhmA9i
z8580M;vJuAFoD6A`{yniK>KMvS}wzB1E_pBj27ii$8f6thKB1@&Ap012Uw!I0Vq0s
zKOZfgHySR}dahIrI9I#O>_3vDm&LPl3|b|vEK>l%p*b@+D)>Ry<=~)^eAM_Pg$6W>
zQN3#EDJ2%FOxw*=g6F_Dsej~5K^@8@f-@2zAWFWuw3!bcCN25N5s3*sc5L~ipt4Tg
z0RI_Ct@95V)B&5<x7=++a>)|`!A}kL2a%YSOTH-ztmLx=2R3~UFEn1_4G2*bS6Xbz
z1K{HO`}sP2pjIHPCDumKFBkEd$K`=CDE>^oYF@9q02FevkgCLjv(}_wVTm_=wSlCh
zKXS2Pc-7I~VFAF9v1U>^rPf?w^gth5=t;ERV=$T<y6Y`Kz^zWo^r=HlcVIT)hO5(>
z0_mh~XJ^nbZ(#v40gfGt>Eb1r6n+zNymr7uaplVi)aR5p2@l|Db|5ly<Mz0T_~TtF
zrAsL^$rHl!%4~Rvf!yx@nZ(t+UV_@4pl!|WG8jB(fttFa`o3LwYca(mr^xpUUR%Hr
z?`Qy0k$yj4err)hKozNIlyCZkH*MzJN(ZE)yS<foYn|WC7#<#3;yU?&DfL2j4fq0Z
zm+y~c&!Yb{<Km(c*h=_)SMio|xQ@6dpM-VR_ijk;%=&7R@ePTIPOlChQ2bNsXXFu#
zFJXO^UH>`(@GI#sFcCR!kpwp4f!th>x~&a2m8qr$upI{^<-bV&$p0z%GYa=2&WF5F
z;*b2#VpxQ=&<EH^0CICM{k9TUCJ`yu08$Wxq}-VXURi`J@bs-G5s--TpRz$0-guwN
zV#NnQTHK$92Ywj^I7Se??)z7W1Bi^SuhHDr$VEOUFXkmKACQFj`@Nc5(+B=qB`0^G
zLf{t<0m%O;NBn6ZoI6rOi(%mjW9=)X1{8K@dU$0aX{6vI@xdFS>qG6gHF_s&&1QIi
z=Zys|kcjejvUqDYe|6z2Z(}T%t_ITL|B9eLlXi>#ofoU;qToYUW&&QRxju{Vhnf7m
z-%DLhc7h+F@UQR@z7C`Ae$lnkK=k(40}8kGxxo*=&IJ87UItHA;OdpMuKNBr5E7T0
z=)&<Tg9f(WgsWSMBqghm2LjvwLlBAoXZ)$Q0IC6-VJx*72jqi)cj&=&iQ39HyRrz7
z;7&OCj)qPdF6~;A{|He6HTg5hTp8s3jK{J7kPpza?*x{=EOK**;r49#WszIw<o_I)
zD{KYS;}2AM+ah;@%ik?>XNdXTBL4}*1ZwhUh`D8vJCWvZ7P&LXTv-Hozydf&{v)&r
z^qL=c?tgjBk2w;5WwO5uF2Cl;-MI5Ni~Ixhym1<!CO_`z|3#DQIU;yHNA3<QzbtY$
z?)+tuTSuAxoFiA@|IRpa%OZEjk>4zGR~ZWar$uOPhY@JNCNzu&eT^XY;X&=vwuTZ^
zk(e;;XuiO*&y7YuzS-h$Ept~%>fd+*xE<?%&6R)S$#vQCw>ffm((=oD{*5Qs)nxcz
z7WscAp8SVt2{5w#x5g33&fD<mc7%QE@T^pN#IFC616(83Ie?+n#w9rTUeDwwN(1p>
zYec7>N{Y?VYU6GeOX?KNWF%y+()ftdM{etNrZR}K_jKY2Yzd~-4{(ILG&y}m?_J*0
zpdS;M=`ar_cdXOno?exYA!YDL@b~|)#r9ZcKOGj$oSl72bVhnqr=5!9@l0EK;AEZt
zoOg?a6Nnt`#JIJANT^jl$-CCCS<8^AwGiYDNb)>H@|-L3I7wM=HX~l@9j84OKz&Yr
z+_1-76v7=;Ml0b14HYy_I0NrX%l5)UsFzN<&PVYASz6LJ>oP{wLJcw31EpBtVUK#n
z1EFznaFua9rOlm8x}ETj-SE7jfY+yy=3$J+nb}%PAy43!iffv%cb-nXAwgjdi#{!7
z`<VX$R~I$P!`w=PS(pu+bPp3l2{9k;(3gh@pH6?b*4(x#)kv%AT*t{o+NR+_DP>{k
zUNgbPXQSe8DP#eOin<)#)!yKCxxR1DyR63gMz=|;8Jy2XXJ}RuQu2;`U|#RVl|kmj
zfY5P`tqCv1ybW7eR5nYKZ&zdsRJMGxd$0Gx&=fQ1Y&Y+VXnh7VhiTn-5efUCX~)CG
zsF(6D;#*J_rHD~-B@ODl=0mgkM&35UDk>M|MNzv&C$e0U3b|Cfyr+I*913oy5=IAN
zG(EuQ`mAE{1XP>ctpQ=TxIQa?0!qZ#ZGc6RktXFG|FoId)3LpkBPkFqlfL_N9ziOl
zaah=2UeRJX(6k$1VTgQUx`=amYl`}T9DIw?#Dho7O@KqqQ~St2>;r37-p*G0!hWn6
zo7M0lNNGk+b&Tb=o@8sB0v74x?qOb>7wJ@m-zw8~Qw=J+nv5Olsbg^C^(5PE9~PD^
znkm5<<R>;RD^;4Dv@1PC-#v?v?K0Vu9xD@=LItHKSG~{<kNzrajLwQ!%}P*s6c^0t
zE$#8GD0XUz6x&?lAj#+}Er_+nYN)4Zx>fM>c{S@rsjz3B9;w?q8Q_6XWrF&=)hCv%
z^69(GQJ^8(kw}YEuxw|I%29j~P+;=M_m5ajlj4=Q4G03!Qwml_h8}2b=CXApr=~(4
z*0KzGMb2QjMrYSVBo?L^utaFZcWlTfx=|gZsX+EWeo-$igFA}!z>B+@l5&OhvG5~#
zSJD&wLq(YMTHc`sQ`9X`pQe`)RA?~j)Qe5R$tq#I4r%L7QbeFMPeKqt(!+aJU1RM@
zHi)^wU5f?B!B1kM2UMJVvMaeMgah?*f~BAaS)Gk@`_tSQ`Jp61rn-8=i1i~*%0b1R
zw-ZPWE|&vpx7D;YVRfKQmHQ(;4{#Kt#RlvXkuxmpzcZ9X985!%?Xn;{*E5=juF4yo
zfb4@hL6%c|-tT-Mnbp{>+4JqWLbKVSffeaw>HC#LWZ6c%L`c{UPT?NxF#<W8hzudL
z8l>S5%{)Wt1?;xcYP>~nwl}woyI(wlUdOkmBF+?UM_5YNz}4WGV+^Tk@ZC+-7a2@(
zxu^n%n^~2D$v@R{e>0)wimgIUXsgC{_$~FEYp5Y|pNXVf;h;Do3k;+~;UrhgpO57=
zf>}Nvqdaj}ky1SefVb|2K54S^8H7a^We_sO(_CkIkpZsQZr`J%;!f2s>#t&WEml+4
zy0yy)I=u6i-!Y&a3^A@J+LFe0Ns&e)d>9}j9YlPZJ$w-O<w>*XYydO-(MR~hH}uP&
zvKz{3I_{Iz7Jk`gVOT(nTf^>B@|z6bJq>*+1^qec9@`VMBri2Mc$#@*>+$rjE4r8;
z47IvG6uId3a}zZRGj=}dIr>(~O5$r=N$7i7VJXMA)3P?u6$nx;ZZL<9gidttMS;#>
z=z~#DWQxl2;2x-Fpba#P7oQ~qtOj6N9f%d$miSrgLr@<JBj~_Nq;Q?aZFQxLI_qs^
z^gUtM3(5(uJk?!&){>X==p1s|@QD}7R#MP3N_ZtH*+?eXKy_>`O|t_dff)y93={IH
zBr;4p6K~RV6)hNKzC?8u0^9@#gBF;3X&00Mk@(W1fUJ9XHkS(0ei0R>4DlmJI_h~2
zm}ztkbw`SD^`*gU=Nk#RGz}h$RrEnB<k;GI^^Y6_;bb5mu6VHa=k2h^SM*WiY3HDu
zeuhLAG_uiezF+3`_S3js;$A81j%tV;Xc*Hz5e+79@ha?k`0je@hPh~?Y`;1lEGXmf
zwpNU`j$T|F08QtE!VG%;Z1NlZ$R}#e)+nhH)~M!q)Q`<TjZ3877_RX*-5()Eh<Ltq
z#7q?U?(ORUa!<EMA7*589N+s$7n_bgDI1upWoQKE-As4jXNRtH<*@O%hh%u_Gns0a
zrqGWNWVR9ffG64&V}$2`F9FL#*Ix{GJM$FXX&jGjYXDNv{|%@fbA?xeg!8b*qofLR
z8;JuI0f0r8Iyzsy@ef74uP(eg<&bkq^?W)z-JZZ>^#G!~0C)dNPek@>Sp7WG2aG@(
zlE1;+?x-#AhC~~4`&~G$s#(Cw<DK=rdW(Wqj!$z#CL88?zCgNvEb=+m&N+OMxH9aW
zFg&3911S?Z&fFZ30BFQJVYoYL$4gz3?-Y09@Oq0}>aOD}eg5a}^#aqu|9Y?Y$1eAu
zfLq(|`$qGf1w1U2f85^%u-q=$U1M9Fh#EmGiQlnpnL0L%Edq-yrg-IB;AjCio4?;8
z`c*Rfsmz^l`^SNYJCA()I&JZv)a!u4|458~3cnLs|Fp!Nb-MrQM?HY9_t%Slw{gJF
zLoR<*<9?nqxzqoDjQM|6=Kt64e||^mpJu-~-SXez_rRcY_0_MxzKP~<h5zg4KdX2@
zXZxKg!_V1%XAS;Gkok`a@WAtAe-z+<3cs`R{v*QOz4-qW{y)^uX>M27|Gt8Im5Tru
zm_MrQKNpxg_wIg-{oS?oALjqRxX%CU@Bc-Z{r~d_CkWW>bsZ%67#0hJs+xvfr~p{t
z2qB8_uQ{Iq^*VN+ZATL9sj|GlF}`pK3H`ckRoMBSdfy78Yu=J@6Z-WO5h`3Z2?S!}
zXLkuSv?i_$;;52YSBrMZ)VKOOQ*xoEGgvzQ3|meMH4kg^vb|wTCXg0bScfkvr;=fH
z=A6891?8Bw_fo>`OH>?k{X$0k4+-W%)xk@Jb<}-mh>m3^IgM4g6&Bx)+Y?G49n}O0
zma*q#yy&C^IhI6>eN~Jt2g}`|7i`9eZAa$Y1~9~?fNy-45b0?q+?%aCIAlc=sr2nd
zcLODAIsa(i`K)!qN%Yn<j>{rH|J4QWU*FIJ@Y^Nul~N@F6WJ@b8gwry!M_4lC_m7P
z{5+)q<kR3j!3N+{!XJkd22M@OTZRDeVvZe?43IOi(2vuO7&QceB*eFFNs}h0Z~%Pr
z8ip`P%!wQwCVdHk*vb9sKT_fTKe{nJy7d5fV|vQlzIXQN0ZdJUVpM%O1{sE5`%H>Q
zNRMN{a#@$-yL9uzE!iGI=<ue>(e>D8^=^mKFkqCA*GJw(zMza9MWj}>x)<i@_j<O^
z_A*-U4fVZA^*3kcgjJpNYD0N83ss;_w5Sr31&>uBK1gDWbA*wk9;7GC$7GB~K%RW&
zP|{IEPKJvYW;i}pnCR`y=2lk4lYY!=_z`ZdYVTwt$@h^=Aw6}PlkD35PLub%C3#`c
zRLWMEYnC1M3`D{>FW+!Qea6!Z&t@B(58WUa%c9!%UsxVJdil=zG^mJ#NgAYdA-n^j
zn_n*4Rr);Evit+Amr#9o#e2yQ@$4hujjVR%188Ftq4K6m9!eT|GCP@EbN2>JAGW^5
zdMlq{peF-BdNk6<Y{ousjXmI87(Epuiz7&}+Q>#`sOC`mX$EAqL0J}w-~iAzPY;hE
zR+~&v=T<Nk-H=Vm1ByVWk>~i533y%Najcyml18eOCCJ>1bChvzt`4Rz_3tl3k1!rw
zIyoE+$Qbw&#ztzzq>+lQSa<J2gG*xA2+c9Tx6ZzP{v3={hI1H3z0WdCJ&3;QlI+Zd
zwUMT9JJ!AM0XS#0qe>k`gO=XKIr{xts`GJmK|%i&W^edbE@*^-*HiA0Cl9i}Ma+{Y
zMTH`~h2(z!N-Lq`t=oC~HW5ebr-FNou*R85hiu~$Nb%{V?;y?qjK_3S2w~lstwslx
zM01ez&twe*#29Ic-x!o0W*k@!dimqdw15`9Su)8{;9F`4?VMby*h5^T5aJY#JuaPf
zgjkW<AePWn`GQphqN2SwD?wuhb?G?b0#>qlY%Q2}Sva_gDH@=IuP^+`iNDAWQm)b!
zVk1n3)qD+uRDM=^Pb@k;G*rm1r$Rj0Ya%m{$>u?z>zD?+JiqTFc_|@{PucdHGokMN
zQSbqa^Qmto*?odr-1~ExDs*(3N8J-CIIU$}Ctw0KqwAVB_TL{?FXXPGAzDNBrJ6o<
z-jaf1)>qO~*wD#a7;3j8&JZ(E5jkVSi#kd%53bL*%h9#b#Ow)Cc(j!$jLlVID4xdD
zoPzQCxnQXd%$6>f2*9tuI97s1+V)l1qpY!`mjdaUJ27PMh+^q+`-U+tvIxCD`6tGy
zRpKH05K5M$m6VEGl;Z=s2LxQUTkBM<KC9uA?^ZiEenj$n;sJ8nT&rdSzbNa00#=+L
zU=3JL!^@JwqF35g7&0I|ikyvO-rsyLNC4fp3`?*N2#_)4F~Knt{UVHFN*`Nga8iRz
zuGkTNk<iPxdj8fONWtf0iO2wQQWJImNM$c)qXdn;5-|}eT4&*_si+Ef#Aq5i4nT*M
z&;HBOmxh3b+#rhnbzToK$Q;p-9ts;Zu@@O_JwpR4JD^$vLhRY3h*>x>(sc(dV`nZx
zKI8Ao*CHLy+R!;)<XAL)s6j-5ObVi<=ppkR&pax68A-b|lR!;Qp~uQJqduN(UlhgT
zP^%c`3~vf&>Qo*HYV29gWgYc;7Tb;OQ<V#Y8!gOc?Z?u_p>-#-<gPwb+y2}}HYOZ!
zzi49uxMDlD*cDkxa-Dr-WV9V@#zSYylz4JY-VkzI?L_EaK)y7?vK!}VdOnvI{6Qq0
zVkDd|eu@)8luC=@TP-UG=nhgg)lZ`z$@qA-QFK_=@o6EnhU}|qGiB|*q0_;C3y@Tn
zYwYN%TB_UK7nf`{A=i$O`jQa*Vn+$Rbmg%}1rKFIGhR=RlX}~NpS_|Z5`Q4<3bUku
z<nGxC$hhI2oP)#a6RlGP5k64}HKX|?2Sb6g6?d{kIO(OYkbe3Wa1UOW;Rf9Qs>BzO
zj|7Ot-pEQ4c*y{r&F_cM`G~+hmprzZX<Y^h{O~x2<TD>R;)id~5Ujbpd#%<}99v|>
zyfys!o0;p_9CJ<5WSXiz@d<{FrzIdXBE}7`6xk|ViW#X^pc^OkwvknCOu~b%SG=RV
z6lGA1-}@Yvh2zoSP!GH&ZEKlJDX#uXAfbuhXe+PH;$xcNv0H+QWBzQm7-om{S82#4
zr}QI?Qqs7Guy7kN8>;sv-uK#pe>{>!?7^YV*R-hIefq4dNZtn}%q*XaO=!9RRRoi5
z!20yTAr`W`5nsh@$AQIGOt0{!r{$&O!>_NVz$|I4Jz^mV?-w{DXxhS4?X%cKrCJ5Z
zcsMWdnttiHtU&D7j^{X>>^y&|I36&VZpJ&MOq3`!jQ(ye-2Hf}TzOslNzmgQ?V=(k
z?sj-+RDnVDFq!p$eugi+iYlNbeox-N%`Tk&$kzXYX=3NZU<dVsfO|vcdFF!M{=<c*
z$FKI6GX^I8l{GX~an}e)Lh|tY-@v}E%h_Eqa>SXaTyjS3IW4u*jZxv2TE0gxNsaxj
zq?@b;*2r8jEYb-xd)>>e<%1~F<0)A?s<7s1viG6Usr7jybCF!;uMKC7ilaMyGtJ$G
zx5dX-Cp2nb<nZ);9aGDbQh3EyHGAj+VBfVpd)&&nt=!5Q3&x_dY*IZfnPgkE9SUg?
z$p59*MzidpHkq%G2^5jctF6kkrW~D^ST7TE2xK-@im?k_%nIMe7Evl)=TrTaMwrVL
z3QAJ>N@$NpRXJ^rF)VlV{VF#Ak|{C)i{`n5wE5;9_==b;*@4;u_~`5*YUpab+nQZ5
zd*KB{mLYe>=G!n{+lPFOX^TCBR1<2b#M*e!7%T2rmPlKv=Lv5{$ysE-f_n!esanyQ
zQ=#|~#r0MpJ+F7y_+SCk5fj0o$}&ZQ9F_f$1#UW&JWx%<gJV=$A~#s!J&LOwX2xZc
zx*hr6kdX;d(k5?g;S-i;LTsl?m%QSK(~HZ9I$0`XLu$R%E&g#rocT0mksDjlj%a;(
zk6{*VVJWH|qCv<(umuzNhzB03kjK0f7f55aexf5mv4|jN6mEH6af51MC=OwXyS8Oz
ze3Ml@QvSiEY{7U99lGnA$}d^%<@-n^i8+c$4#irkgj`x1Mm5v<=4<krOYXF3;}Iv9
zJCaT)PsWmkOnH;lh9T&<L>@QkdKnItc!B^omF3Y;?PW#+&=Z~QQqIxXtc<6>(u1^+
zJsf$~mda@cwa=Q98MImR%v7f3Qj`LxxVGGWO1i^Fx8=);n?v(*>{h#c8y1T7ULymv
z8b%ta-kUzuxu*gd3um9c_~o+E5k|Ve_2Y`4t)QVctdSZ8mu7V7hU-Zy2Yt%MR1B|U
zAr++dKaXlPiJE>XK|}Nqd<Oo7G{RDrsmyVgTtZN-(t}rm(epf5G=*nA$6Hg(u4i!(
z3(Gjpz%Rc~Hi^7Eb`tF#lH*STc?nB{SGm9~PV=>MIJUFPQ1&fy$7U}I7bsYXhtN#n
z0n-0!?=8Tx>b`wZx<fjoySqE2I|Y<3>5xV`rMm>_Zjep^Dd|SK8w8~5zOU$~-?#t!
z?C0!r_SyH`>v|ZhnZGsXm^I$D#+Y+nP>H^VX|}W}5QohiV@%zhNUBry64Xh+Ew_-Y
zv_>qWN*m=ha~)G9WEu_lgp2Zyrx~4l0nU5IBZu25C|K2^5NCH@d<(ZKuUA2r_i9h8
z^{l5aGkk7glFtsKYu0gEsc5)LpUMoKt4>7Mp4Q`G!n9NbPcMJmAOPWoyyPBaNN9=|
zA=GKpz{Xv5mPG3aFOZC8iZ_9R`KBIv$?b}#P|S+_3>WqFIO{IAD93x9bMMh=BP{}a
z3wr#JA|hg5g0hWGT1g0-{deSjeiVzDqg-&=^EgVnr@ZS+YEIWdJo*lr$o<<fG^AJ6
z{hv-d_xeR^KiZj;D8~AM1PSJ)+_=)pxVsuJu%u;w8UgERU3)G|^m&!PqR7;ZKFbDN
zQhE5|C3>3VO*&7{dBDl4PKWDxSbjx|OZbWo{}`pW1$y9&tIHRcbaE<r!&Rr4!wrKb
zIY@DSZ0J=M&S7fDOq0mD$>GNH)PfgM^_aREIEQtZCcZ4$n$1Xj%?88Rj&@h+XIY;6
zl^FC@G5yuh2WhuCqo5xdL=_0#Q42zOh`7=uVoF5MjcJ+u!}|wIOZ@^9-$d|6Q@B#X
z#%#sA=Gf_o=Ti&S8xy<C=09UE4WevZh$o{>3VO?rOB<N`nW~oK8E40On~!6mN_()v
zv%}EMi#u2Qqx%*nw+`mAwd-NY@Twl+yt;bWip$-R3C2%QDEb_^V>C?t(}`>8Z}Q>p
zqRTr=AO%C$-+)NjvlS}4?=3!oviBoQBh5$MRZG3M3i37~IZgS}&uFK%c;+XOgOtRv
zDd850!l7g;d+C=S2NRcqn?Vl3_zfOI#Zk&042;b<y~%%N_HvfE`X~xxV49jt=T^N0
zCeEG%e4R{`P~>{VMD*i!qUb9q7Vt397HNr^E6zL7vz^Y~Y8t3OK`ABe_o40-pbcNU
z@%NJWM+^J1+`sbO55Q0jeI~MVa@{E!nETXB#J9vMOq5=*nSgC-?SdW$e@$9<k--;8
z0+QFDUV>M>ue8tT>4cKPA6t=pK$tv@lojc<(#2hKG3%EVo+RZ!*UyX@$cOrRUocNI
z*M9<AzM3ZXP9mlbU{1~h1;;p*Ij=|V*ESTJ?2OD8LmQjPTiH;95$@GU1^?x0Ac95}
zC5W?Uq~?MqPhgsY7D3a6pu6i3B}U~-=zHVMa3y42^D&`zib}sH9C9)bkSUGBqiK+7
z;UI@M-}IUd3W$f*b_khk$wd%up&G_+V2lt}$F#}t$i)@#Zop=Vb*L0_Vezfm{8!|V
zPkBE-)#$N{G21Evg+9&Cr5taTE6=z^uCU1BSdx=+BNsx}ShfbY4Q45=zF*D<^)P(7
zF=7u7K4!5d#`=w6(>iuTa~>vdQyfP$PVhddsB|7yReBn41C`Dwo=<*DnZe+skYb^u
z+e&jj&{%Q+Qek(XpTl23qjyjbrPF;OT%L+HZS**%b0H85&RAy2*!EO9sVdQzm}kPS
zE(R7nXHxTo*VB>v3pdFtAqf7WVnH-M?hnj|)|<-%34<a!g@-elQ^|{@Am8Lc3P`sd
z1WG=`S7Bg1z(HpwEud%6-p%ExuCwY9o#kXn`mwf0x9*Ig82ipKz7w26gXFURsAf^G
zR898IF9`1a>rvy>a!H8Zovy~JimYXn^mnjlDG}=WAL6Dz)44i5XAs2P3^6q_6XY@I
zu7|5BXt6{*GU>vX$o0QDTjuV~^^vL%IL9ci#Bq}?VW%$lHKLcEmRIE0e;4Ge_9i>P
z-`E*C>g)T&TP9^iLj~crmk`Qs<;liXM(n7n_ku=7^Jt}ni;8X*@nUa2+z1Z}Te#}T
z!o;TG^Rpe<)-FBB&>q6uJ^6U<o4S&9aTZWFJ)4+ADfZ;sb0fbu$;&TISLdQMmS1{9
ziGF}SOw69_&A#uT7A#c#nn1#aoZixZ)N+YeQF0-3*W*gh$mBq@<&{D%Gl|<sQtGHz
zI4>%9<@D^6ig#g-B(xCKqM08_KVOsJsR|o3ew!yIsF*5}D|`}OI}V)?B^?LKWfkVI
zcO53~jmL7zUFo;E+Ja|Kampi6c#MgLO=ZpF3`9%=KCa!*<JFG(b|IZB>*P)mU>^na
zOTIG}`K;F83k6wTy@j7c9pUyRz>=`8Z6spwl_TE$<dhwY=?5VvgG*ZuI0_AH0a{5F
zm!Y(x(7+uqao3xM?#9pa0ub*_5UW0QllpoYm>E#QY-}jq*Nk?NZ0e_uE{-FFX-B+T
z7%gK<3Bs&WH&MD<<UV8qH_68u_LbzqNXumM6=STOzI~F<;OKKVduI3{YjA|gbjf%o
zm?pjzKY~zk6<5j<>bSKgwKUb4crhhza|fYrNT2tW=1}PYC1pT(yJ*?&GzUya_`?Wd
z`3L(+hKF3yuIH49E%n+4s=V1s)h(l+42cW2P=nghaQqXC>-+~m*Go5u??QXDjlFT#
z0-${BzutS74~%`D*dM5GjK_Re5$j6(ezVkjJp`4yfMU>capE;xkVI4<;w_#tVc+m%
zeNxq{VeFlbQVB0%Fpja6`8;oe<e=g>CKVI^NLOrwa0?6waVE1a-bT5j=&hF+@V$ZW
z<brM|KWFKcgkNAc%~GeP=S6|aij&rxSH%;q>^7#SB;-ik;dx4q-7|q`XmIDDKRn>R
zci|@p>;{=jDYOrKZOPIj6fUS&0txb^{#2}WLHHoetMK{rYXJr_G*n7M-#0d2$?UJF
zEpE~L(}g1_*T>$SEOOtVj)R`M@66B6A8wry4_AK?J}y~maY-K9l8cqcAHR`<Ng2^h
zM;tt<8kUY>qlnYYu8E<JW_5s{)(Jy=3H5bDDGBb?U?T3&FeKL+T@y2`f|s@um(Ylv
zL#S2!2kK<Z{D|GW<U>r6wZ3ZMcx0TnGE81ZRk$#6>zv(oZu4{QX$o10;}S6}BNQCz
zOiz`R1XXOgq(rVb13^%BiSr3|3x+5=%gN26rs1BA+e6xehTrlthjGpZMcFcpV}B`>
z+>6O}V8~kw$^1r&s35GP7OHENk(&4*(p!QwiDR9=;ZYGb097M8L?sF?Hq(EptPsak
zuMgdOG~r1-zV{j3^<(yfq1<-!LZ3lp(o?BXUp685#*Y|Dpq0A#p83<aMW%ej+ic-y
zEKAqunbcyde3vVXxt`3(NaSS9U4#M40a=(XX{(5lzHoX1kwxoV*YJs@fkxs)!8Fn=
zp@uO+dZ|cdqL)-3bA)b$PHiD|Es9^&ANRtBq-Qfe#mDyockXT-kZ()YTGC-*VEZ6N
zhJ_0_E@cid>>(0W`0W-os901>Qn8#V2@_W{;**vaaHSV^BZNA+m1VBscd9tjkE@((
zhuCuU;7x__kZoN7hvig1DIX=Fq_$Bon2&aSCloBu!VmqL89U4w5)*snTFab)<(2t6
z%yPh<(kR@pQkOXt%HwixAVWiHmV;#@(pgO#4i>P4Rpz!Qp+eElks1;j;4rOEsY4)k
zC~+=|W2PR!`o{@C6~_%t+Jg)-N;cN=$7t_AOt^fVE`}Iu8kXqvE=6A!`W%w<?2QE3
zodrcaBuF^uA&Qgod~PahDrX}7(U5;qga&4T#<>Dz@mG}<u8hksIBqB;xb*s8!jCmZ
z$ErhhLSYSP$IbEUo^#2nGF4HL-6$ARYBGbZHWtF>II}AROyWVA^<<-tU!x#d9t1#Y
z$)NRh<V<;?TWK^3nGsE(wKeSbiH@l&a)Otmi+k>7&3m3KfeKA+e|Eqz-J(xJHzo*D
z4zh(#$S3jai!iE<&DY$$7mo!`EUj8FoF}hBWoK_5sbu5(Y!f0O{DB?|y)ds=1e|D9
z)apk4Sq`PG)?z?><U*WY$a{|Bm+h+)wMkle<W+GqxqH6mDsO2tUE4*BC;h~kYl_f<
zMtrIq>BB0u2gZ$5SPugFsvx#&NKpLYElv#ZCSHOx%p0P^Cr*_qm98)0O9r+PZP;tS
zr8G&;`jYk`c3QpbNz=S93+`16_T`D}!EqMChZb)nG>t-O?rF)Qx`>p0PLmPsA?wx(
zM7^3AcgttGJk;~=^^<S1vkw*K$8A*a+lBkt_}{o++T_X>?eLO)Gh0~eNl@umYsluQ
zhL;I$aI!LgeyJMx(1N)fpPR8ab(A%!U;37hpKzIaE`0TkDLfWBOiHHi4QgL(7sX5Z
zC>4=}ch|{A^ZE+oM><?JPiTB*kGQ!bC%3fDSl99{jc(>LEYNgf1+ur!?M1ZY3>w$9
z!Ye@PST*={Uv#}kQB3_dcI%C8qir0gT8Q}JnJw96cR&4ji`u>hA-j?``E8@|gQ1(b
zn|jmmJFB<YN{TJ8MY5s3&q^l>;KDFLkVGnlj{VAfBjB@gqi?Wn=)E}nWr{>qwNqDR
zzaV1mpvLWkkIw|?YPH~SaiNe7&RYju9Vjc0Its%2tIRs~hjUvM_xUjUvnfY=At_au
zw^e~$QDcIx<>63+fSB>5@<6Q_l#|D+PN!oEuDhz5Q5Rn@w{1zUGE^9g-Uf}b+iwmw
zr-p<Hz4S9Fy5EjujOs%vqnkLyWf9{;1be>hPb5rsx;)z-_ifyB(|zOl^Aih{^3WSZ
zb$ruqA!X9T0U=UF?k~X=){VjzlV&uFCPbSGD@FYma9tk!F|AjhUvq|z=`ZPH0$!3k
z_%@81Hav8BP@p+J>+*_4JuAp|)DnN`*rIn_SG}(dqYgs`8Ks{|9gh#Px~sADcBVjT
zA|0`@u6@4Z6dn~{oRLceMElh4cwpWn%d5-gy=GD1Rf)MZewNRwDT1h9f+gh0hrt4*
zH+kyAM53bpF?LWM8L<bkZYXgeJ^eWDJ|lPvYUZX;Bbw&u<4LvMNCDV>>#Cslvj*?i
zu91dJ&T^L44Rd16UeGOA*(;B+j@D8gxs|<N9Z(@s9V)UGW(K3rLw(7iwO&|)qDHXo
z=<sG^HAWgKr`MxvI>`I;R6|Rdd(h?A)>{X^37IpbdK?5|`n3-cEBR&}71!rOR2W<<
za1SSwpN#9T!>pNBYu;yAvCz0NRyTrAqSpu`KV9bvsnpUc!^*vV5r6iiovWrCUGQ^P
z3WTWNGZFzq^`a)EFI(c3et9p9&CiqRS;=3P;I_gAkRY$0t3T12kw038N^gz>A&8TR
z^sNzL+2n6V18Z@9`dWajh2v&5T+set=YIWCGKM~AqITTuMwx-l%^C$;Ip(WFl;_6v
z@|)`WU3v_Ck{nK{H>lTTvT}MMt)<eaB`i99QGQ+F-}c_x#~sn7D3E=WNH99W)KX~C
zUx>R~+MXJ6?^=w7JT_!Qg2-H;d#|y}{h7CyNG{zs-lM4|G_+T`31W!U_zTqB_Lg3@
zlXyO>J=4S>abX#idK7h1G^3;jcyX93QCNW89sE-i?m6}xh~?fRa<yzU8^#wMhe&}W
z%ogB!ww_1%Ss6Sk7ZOg2LX3%;*>=A4);h<@u91t-eU%!$9$IFX_NEzpIom9QEAoex
zQMk!Ue#lr1sXgTaZo5S<A+SS(UMH{xMLvDC@Z7(ayFqqMlQDNYrK?+Ax=};H5}Kbu
zchd9~O3;pdmm<>zY*H1q8Mu>9vi}|%<|tL^&C@swEtd~9S*0ngnrOjhl!EA$Ym+FL
z?*mQwPRpuY7KD{b`_yS=zicEP`J^Y;CJLqv3re>ku(Ml>1a#^<z<)@)ZV1>d8Qz(I
zc~!T9yD%mkm4a&)Ucbnu>|r-|6i?RLgFg)(Si}&OBJV{+Weh_GUFPa@LS4SQbK2@e
zx3c9ZG2%^=<t-ukLghGZC(w2(L=p#V{PvXyRrXGkKLL#-v`#Ei*8zpCQhh%+|KhtS
z>OLA8DGoh5{s$}L)J6Mm)*K(G`^A$}f_WGjUhY4w{5Wv3oYSKtvSf83`&l5cdx3dH
z1Vc6_@>4<fNR62O8LwCzo3%=8F6W`^9>nlwLiI;ixqB?mPUUm0Ym)w_PjND*s;**H
zoMxICld!qioFQ##X?0pJ+or$t_<Ebr6#FE^(+flhGPsrPb-pP=e>NSA`auR8o;6t)
z&POcP?7*zRme(ba4n2fy=g=*&oE*Z)Q@wVt<yxVY8gKZP8{4PFyRz;8i+Ijj&p2_T
zWcoQMvxvWLzn`TeNNYNNDD)M<SE7@zrdZn_tm?@9UNskos;d-6VCBE?F@?Jz^q8JI
z6p`R3%<9E;EaLIXWfUxF>%S|I<_Ym`CuFW0>Y^5~W{bjZU)Dr>1BS4aha{(Ub|RvW
zxxvEVtfN3SSTa_qF8>5!-A^Y!tG^2DIa-g+!{Dg@HE*U_sxKJUjN?wQy?MWpW1(;A
zba`ybvRsYTEoZN{M9y@`TSy;_+W;L56QrV!O-nfqbMt6QQ0f=mLFp9P*4sAEX);hg
zf%_*WITP+g4&i)-dM17Z?u7akT@@~>*&$f%or%_$8JIpH_uyw^A3dQfB;4UO%2|1N
z=B1Y`KCta03b68<_TFoyM8vDm6Xxd+A2X#{pKG1$<^bL``6ui$+h~GVs7zC>olE2=
z>HC%mI%t=Il5|f%J7s(#NG>LY!`%>A8Inf`N75Ek!p^42jY$o>Wr2JxWdQ2Z{lNNi
zk9U#+8Yh^jt0$1FnY{|4&1^_CmvCA=bER6h$Lp47x8sP$aTPyce*x3``<7i?8GvuJ
z9suRaA4h0IQd#kW(7`od-U;v!yly2`iHcOc;^G<dU2NJQhaosxR}U;YTO<_S^r;~x
zitU4mY*`9&vHljJJLB|RNUp1C@qQ^&ICD!B9C!0>du7|>Ru*M*qu+)t%!uAn0w$yh
z5u>pgHN{OD-;;PR8O{|#C>E$o0yv=W`(9q4d31(G&4xYXGD8b!ZTrltI#0{QNvJQ8
zfL8s0?YX{daDrv95RDYF<Rv)W)4jeB1AzwGXJh=_yK6z}?wAs^Q*~|H_xquPgpWF0
zzmD$)tt#eF>6+WV<~VC$>UskN{<Tk6Nc-aQl$dng8*XD(kXm5T0Kku`n)56AR8~BU
z(cS?FycvK9z(J3X7LmklPooS!0+asJ1QM{TH&`1W0`-?Jrm$=+-ozlx<s-nO9wu=K
z??{ICW5}cK<?oRH*jft!+4>#w=dpeuKOnEZ1N>0b{tfbnwexR~|J1JftCWA~Q~e$C
zQJ3mFB#<LWKb5DyL$(7#06U@oLucw&DSv81{vGmB6Y}>`K59Py4*3t0<6ouxX=MC&
z$VbcK-%I&uOZ+?JKlZBntCW9fGW`wm#|{L)mGXz?(qE7OTmHXmCI8r4!?iXacRlo@
zT(Rk8U0x88u`qy1fKKq!7Wl_gesqFIwdCIl{=?+=`%`}OtAD5={wnxS#lycB{ISRW
zUhqF%HOlb+*IgAbIsLt>{x&)N>6eeQ@_*wLO7w50(A0;RV8~QHg~RC?Gi<1V?=#3>
zJ3ahuxc{&#`E9uW&?WzCp87XOm;O@ioEO3ZZnlK_Yww1?WB*6fDyZc@)kYy1O)EH8
zf}lZ6^TN??#`y$PPoZ$3C|ns7cm=)mU%|2;RLG8ZIu{OS*tF<2y4pVDxqAVHt@#=X
z^MnDm%7hvJVq)du+fl7!*M~sFwu;k$*a!?5=qqg{8cR*{6>h^oB4zD7bZ4@7Z-pX?
zlqKEgrC!~8b6P<j0q{JcjdafG)rA-C5A7CydQYWa=TQnqyN!i@ZS2-I2^Q~xABH?d
z3DLR8jnIm$Eg8Al-sox-{pdNd9L`S10`|Iv=c-OVWJk)bqu}2B5}Xu$*6~txOd0a7
z?o10$5#&>))&SLk5HjNKsoeFG5Bnzd;UEWcarKj*d3oC_hD?wmNGbQ<ujSajxl)g7
z%ft#WuX-2hd>yYD_AQEYZdub<Bt@k{#|oVr#5I5AODHLJE$&!yvf{+Y#U@y#k<$~Q
z!WxB^lDQ92Sj}|r(%!N`a=r9X5-U*#2V2tyTW?aP&j7R(>tA}epcRSRg`WtdK#<5q
zApO>7x~#ZKM7tSZ&cUT;GIdRxAEUE2wdpYqd_TXK^v9kT(3lft>1E7f-~~3A=t{j*
zx_%Myd<?|PYGB`OkN{g_2%1YgfIwrlZ&ODLVf)KFD$;??uiLNfllU?K9?$X5c?42r
zeylZ;bGF<f=t8-yJyy70$SrA&SQZ|nrxwoOZLw?H(QqZuev9C*1rW4?j@GbUJ5)W>
z60Y9a?aI`zos|fX;x7rWin(jYV16~fJwR`!EN6sp1rQ04w;ngwAuzq$)&#DoPyda2
zzL2mlp_fR7+jo^k^k3#T!m>42{dG|Gs=(zG0Q9&%B1E9|26zKP5Pphs;2@yJ0RO)$
z$UXU8kgL3;U4G2`D8~KB3;=r+;(mesYZ30rFH<~WSpXQYk_bO7?fwG$A-??u_U{O9
z0I;9R@V|pSif+Ha9<APff&FW-4FL8_Z2MKRN1^Q(*rUky3+!JDYyhxd0^2XJ$9d%!
z*rTxa3+!KuYJdj#sdWBZHGc?de}VlF)BXbccUC?C*sqoES8G1XW52*2d)+Uve=Umv
zz<$YMe^v8GPx}S-D2M$5`-dsl4<YyeTm9_cm`**K&n0`9Q!}VO(cF-?^aU83g8b=q
zz+dFwCm_Ha@~78@RRH<%{Ri}a_v{8P@y~tsz@Bi;LoB3fdNW?~i9WOrUTvXuEKM_I
zdOf@%RXr1dK-RV8#KJaSs0$fC<)<M2nqdF-;mfw7VtbT7822@iJfg$4dd%C|CVm)<
zbbX4?I9TVbA!5z=$TEYB+Ar@aO~zJZ!{Qu>MCR}O?Wpbs^d%3tR@K@E9s*^bUeUaG
z1}Q~NrPQoo31YRFv&-A7I4#r$`HVd{-nrt24D7_b_lt`7)xO1cP%hr3c(n;jeW!2J
z9lxHMF$YSnWUfY($=zJ?U7C22z2K71PUQ_1RedSv9sGWn{=J7phg*&|Rh#R^sOO5Y
zT`pN*v75?z<!7Dtd#c8#v$4f$qZ&(|r9S3fgdof#&j4Sm?RwqNd1x+0U1G^Atg>On
zuWlxGdhc}_QK@1Ebak}@SE3K41;@cnSg_p!VEbaWyF@^1r##_%7Cushe2(f=Zgt;M
zkIN;cXS6LA6dYgT_2k=}Nz7Pu+~5y>HY2$<N1cRtPfenx+hw20*R#69fsbrB#9PE$
z-#2dxf&}yOGkmA9@o@{6WC93HN|=}92@dzv0BU6V!&;&{g{p135Nk=Efb)Wb2{VZt
zv%Eh)Y=C|fZIt$vwd+nIC$q2GtBVU3wu|{Z<k@D@z~f#fO=&Zk`^ot$mO7Ya#zIBs
zn35%lb(bai*ofmGQ?-U3!g71*OkX~}(Pz7c8?}<iUbI+7Xa@6e+19#99{AQO_DES_
zsjA&uU!}fyf!2Y$yy?svR(Gz7@62t$FWcOrLGcJX*m_65r2ed$Dj_i-;agUXJXLGz
zZM}2%7^(?K*fqu=CoM^zeZwR`1wR{wpxu?|GD5}PPiaY3R^KXm>&g*%V{NeDQsUd7
zeL2ZTy}MV<7cZ!0d0Qlks|)hnm)@t0!xg4}(X)+P6eKm-&F-~s@1Eyo?VnzJNfd@M
zLbi-T8|%B!!nkWPY^Cd4NmLeTC^@tv*&C^~0ZSZ2h%IH0+krm3#}rFCzGw`4c$;BX
z>`!`=$t}mk6_15<`woeW(_?Mkvmvtz31LgfAeg#fWNf{}zEC?dp|hOJQW4VuK7_FZ
z!=X0l<wm#K)$V5>))@IGP!e26x$d9H0n_~RU-CdHS1+g;w?IW}_YI?B@g!ZR8uIjw
zuGeWaae=Vs!>AyNt@6+WF|7z&jb9VHihZ6v8Uoh=;>ST$>*bq{+?mr2h02)Q%gw+e
zZl{n!0JPYTZ7e~N5*5J|f@~<2MIA}S!*2&?mndH{rr1*BHD&lB9FfrWcNExPZ5}hi
zkH1|vn`X9~ni>|VdbwXM`Z{CJ2LJ>(U+_^Lx!W9M$v0hq9c2V=$M|pew`>=%1kvd3
z*pM)P`<h0F|E-`dBDH}e7a%R*b<ZzJrP{AY`WZhU6d-#(n#LJCnH%BE0E7Uh`tJhw
z&#52lpYk;bc2Tujhae#7A9>}s1@iB5G2rL(-_Jt-@dMY|sYBXR+`AOmsQ=xI2=F!O
z9}yXYj`RTp@Xq>=hz$Ih{d3g+yKm6{GeR-?KTPhxK|nG91Aw6aKYuRi#B{x<5X;Gt
zeiW(fr@6&~PcwZV10C!(HJqqi;p!VAR2)JyLp~sLN<oHM!3e_ctDhWd(S2CMNi)Aq
z0Z$ka@MBEZQ&5<{dLSb~Zu4s-#@9*^aI;N<s3(+}k8--*o&#0$KPt3i-v%=q=g}2h
zv24yP8!fMqH6#=qiqBS;XYmu&yG*GLx?Bs0yF4W0lD{u~p$J{f|Ay2>;8S|EtKiC#
zunNfwlEicZS4tamKkEX1uGRb!OA!mrvUaTU_OTN%3wfLr1U<eAPkI|C-ul#2oeY>}
zAH{NY%k8KA8E(|oDqM*>`6kYBHfS^NSf{@2Ng3XN<_o1An8@Ji_1}%{tSBr(yY4zY
zKzIn535a<`<TXc>#v)MRRyvFbVs~#yl2&)ie0%EOht@6dOovc1r!x~Dy5O?xp0YWw
z3&GLDfmXw((gk}1C*wkk`eUwK4Wv-|6RCsOM>32JgN!a-?o-5Eu!4tqr1qPxABG!v
z_q6*Uc6Ihfv~9oiqhtii#RtrFeQ8_Xm}Q_AaeoodxNKcEF@mu5YM`*1m%L=u>&~mN
z=DH^j;iJh5KI_g0gC%|LDbZ*XlKy}~`9UXX)vbEY(acLj(&U99Ei-NNfr!9XIsNA{
zT3y$7#5AN|CMfY4yd91sVC9{o+7`lEztoSx^^oZiZhbq4{4jD-Y_d~~#jKX{st;KX
zd{KhCss}7i%ae-5za^_t?Lp}I{lURX8mG>U-72dio$0}(Fn)a}1G}_-VMj6j>ahs)
zyEgA+9h&+QIp^>YCXR@-1yJ<Hi9OH;9_Rp3J;88vO&rNj&!9!G<`%6~H+9`aK`?e$
z!SwouNX!c*gPwpKlIM)?3CD%2?`@K4b9h<5F!J-hl<<A_b;*_8p{uRRD3E98_ACA<
z1rqm}Kayk%4Ju`VG~60J3Dgjk63<L`LxnThw!pwmxMt&3*p3vzHpXHX2fl2)pwa%K
zL3QUeni%8S`<oy|-yJ$EMvqbv{U(<8tt(AmvQGD~53|%S7`EkUg7Zst;0O3&z|fFS
zB^QLH+S%V|fOnk7tvG|W5Ku|0kRDleVIEMGm!G^7VaE}{5uK2t`}Fno8xagFvz;5I
zw?^ofm#je~Jic8?bstUFWrweq?UTRdtam6_#mQ3}IoTGwK^;9@a}avR5UF}l(VV7f
zIHSsUz`U_3QhP_F--d^UoKW!IArqHPqV^$|-O{60QtTG(_yAiwPWT>oTpo{p;=Nxd
zvdykkIdn_>NAU+kV|V-T7c`+B{z2p$ysup!=>4h$422e+FO8#p>r2%k<Q+%A1AO=o
zN0LWywZRWa(UC5Eic9XhJLWDx0D+@WPPL_7TR}+)5g@)VwTnmV(i}Am{V8m&8ozgL
z)-c@ueLdlG<VH6?WrgXmlTi+K!??WKucT8O6f_K=t8|}~1dI_kFOvpT<7@1ci&ern
z#k8|yWHxU^7^FmegoiY(c^4N3?n~f6RdJ8n#jSQEV-jYg&-DgBM?7R;h%-?xh2Sqa
zKy!5F@`6Yrf-(>@C#C5+qp_k<!@->P)=@^>3?{4X?&}Y9pQw=!58^cSk$s^h&9n3T
zXn1;nuu4vnTNa|q_i@;xImK<*ee?DO5+c{y9G+kWue(yAq?V9idq?4-@mrP~m;TL|
z=DH7tc3yo{I$OTT<FGZwL)(M38!Yn7T6YybnbBv38@Nz~-FW^F@*wpjA1wqbAmG?V
zo>n7oe#z!XiJ{m-5y=7=!jVzIc%C4Ojp3v5=r-fiDSv_$onlJRA}lpXQ9S40^M(>h
z_%!m;>X4|YV5nqJWE%y8SzfwbquySIQ?<G-dOzByqnDy}5Sl_25esyBc*~tom8Q(o
z#zI!({T<U(++dP{>?=`a?<CdTHY#om3|0b&+V<4dMiJRHb}1El+o~06s|{2viD--9
z<O6kToLB=3sLc38u<5?B7`L>H1iKz4vCi<By-~D>LYL5`3%jVvcuH(Gicdb2z5dSA
z8x`yH9A!Fw$+n-QiD|f!O_!44PXKd<)8xI&3&VJ>`n;X|jUY;5kU-150e%R>37NL1
z61q5NIk5s$7K&qDGuQOa@(}QP-7+2;&A9!`#ywQ9c?ekjSPq-r>4c<2?d)ALxRg4~
z6{mtPU%FStaH3n6_!w8<+G{q~G48{1z&{J@b$g=>1G3N;>NV3Ksm&$>DXJLLIUbbx
zq_TWC<U=g=xu5&O<~yg?(HaGqCaJ19b))o%gJY@kmc+gif+O=!!Lyggn0Kp7{d!Oj
z(9oXxHygabPj3l*jubKFBn>*oQpGX+!goQD^|@fB6ly@A2g6r=zCiWe`+=#2YkBw~
zVUZ8jR`UaK%Kq^QNqlBHMSE8Ea21mH3_&aLmiYTM7z=|^^0})sr5~!upip1MF&Wzt
zRPlCsIoF4G@+k*gH+_Un+QCnlwY$m1zuLGtXuYvFO;zT~wr$>Z-+3QAIt<U82ti<e
z6<lJ>5gs)jxdtlZ6ze?lO{O8~wkSscgKNAf-6PGHSKN92fLhavJgcvO(xQ&%6#fD{
zxRQukFEYvCgxf4KFGZaJ4=O|5qoZv-i?G%Nx%YJy|Kx^iCVc0}X-~qbz3wa>+sj53
zze8W=`b<9v3$ke>$_&J|$uh<o!|TA0)o0CfPyHun;}6N3u|A?&#Ne=>O<AU=@QGAs
z<Im|H=BL4UjNTSWSP5OTBf49xVuyI;BOQ2i$p=4d(V<bf&%_)-Mp-%*Y>TW&ZKeAT
z>XE~J6>vbbKf#IFDLv9zmroB5+S0LPKGWR5Gx0sPGq_px;w{`GClo#?67R-(=7X0g
zPA#9ZLAEv82q!P*C@XVg%Wy08K+!V`j>pj)iD#gSs_9WD%sEs22?DG)(Q&RECih6V
zl^6rN9r+$0Gc7~cTq*AmC?<S-9>5@8ozIM%A=Go>?vv(mf`uX7+&cNqtaHLWC_Q3I
zwCs(OO4?$Ms(JJ1osI3&d3<&+-f7HdpoRiZUEjWqQ1L+1&QnQ|g^WLViC0=ZbGYFo
z0sFdQRiG+llFy?sFf$-cPTc3+Or#JR;6037Q$@tfv<(55CHRFS*dW%ruDYSxr8~B_
z-)LWIZC!HFl-j^$6efFk^N0r0=lIIv=4=Z@@Qg}Zukq|Wq3#<@K+1k2W-6<kd))=s
zG}Ou}8ih+O*!5oTnNx&ql8j#TtoO8lfO&Vryue}!3ZI7JvbvcP&3q3pMv$p)?^amv
z)TfjsF!Wh56FBaIH*4DFxyXq*EN{a(mcUiIW?wHCZ;->2>9Cm!7?m%V9>$%3pNU^4
z%3cXj;3iem(#Wu?am0kEKpd7h!3DgjQ}H0P<SJWuSaE6@ySht5AXBx{R3mWX8;_F}
z#dAZVC4wm&r^ieqC&S#518m7kxU3nuD$5L_cuT@zJ@O`_MqEP=#UnG8XV~JTAbCwN
z?*R--w;g=KgkP?m$IJ5m)()N7zp1*s!o(CEI~BJ~^UK#~YH;Vz5QkoEdp0g<dQ)z>
z*qPNN5M^zGhz|{^Z$HeEIx^9h_c4hxwqX>VR)uYxs8eZKBZB6&fxO$6);)ShB`5S1
zssU;EV|p}(dT9PTY}8R->Sgd{73!1WRF*5FtA<0X=|0GG>VSTJ*!S6N&A6QA!%1=@
ziKu?ubuLpCm6yY0Sd!2EMZ6id3!-p|4dBwRRjepnVeZ(V(DT-}NujAH*(ynp-AtHw
z0>NfVp$#BsQz^Z`5=8NbTS_Pp7DMM$uf-9ecXs;o!!D4Xe;UF-HodHgD9fiTCoW}{
z4~QWVEE61>MMV*g!t`l;4~4<$ur&{5VKX)Mgbdv$;60Qoz~HN0xTbeN0iJ*h#xu}C
z#ax)i<{BA}XZ#Ma$%}ijW7L@dY?-bv8@hO$qWyA{$a|PZrML+r2C0e$4S%GNWO#XO
zZZ$;sg`M2l{fz(%A?E#}Z~KXW=k(0#y-oJQfUWBKK}`P5M>`l}4pBiq@c0`Kh5^+1
zcRldF^{r@lMqs#8mq^nL#a<*?`&586yp^}9TZxr8iA;$Kf3v7sU)K_A4BV}|=<Bfv
zFJGwB681~xfD15q1mx6N$GuF|;h7uRDPJnZZ;P$uI0tU<^ucMV3@E22{h<9}Z&G#I
zWn>hhOZPTSf+BJAo}S(J=VQSbYwnD_@#ww0phBf2ecoSaV#%vSmB!K6JoK5^jZ$br
z4EaXBa9c2oboUh@{V=S={MMDIHZO*M++j1t83(OyV-I-a*OF0wp_c{f-;xi6E-VJ9
zcMtow8~xc$wvNA6!6<R!O!U6I-{-<2f}>z?=Euh~58b?R^TQ0ht|U@S#V~`++Q8Tz
zbo3XF(!CK=S8?GLmW0$EClF;~h+jq=hH87eFswYGwl%ZmEJXB<Zb?;OopyJbgM~?)
z4ia+{v!C?(6XS^m(L+ZvQ4q~Vu8B^+A~;9xb|3+iS5ytTe#aR$M_Dj(%)8M^5u4LI
zKSYT728&StPI2evV?xn9_c|}EhQ(DnVIp}DpI6Qms|x#vg5CSj>=c-s89Zq%G-ZTN
zS}o}L@81Y426%418#1)e$d^lOoE4;_P?1Y8X{%VbaCe4$GgCX~J#|4m9zjxx3IkJC
zxD@%-8RY{4(rdKOAFzoBvxRNS3g0p`Ym%kLYU~<LTn!4cv|u@FXVAe?LMkw%$Z4Fi
zTQ@Tu<AB?*c8R*?Rf=Sg8XT+`kOp@PHTY4n#>{B0L}GfU8C+B7)tHCsFz}U337@hW
zdF1ysrpU94R_44;7JI)YoT%$gKWHL9vNOp$NJsdJp9bA=AY8~@k=zX*lFS*TscZYW
zPx0sccjQJ5p=%LHcc>QL6e2IzVmpW9NKUJx4X<h)W9VvG&S}&<MRHwzYJDalUMX^p
zV%7{Gmz0VHFy*9>Y@T-5nDE=ns^nQv=d9m->-dtr5{a*3a_aZy;aXF%Ld#M7>>^X?
zR-VWU%d6TDE{)Qf13rc4DT%Y%;3V-7+`zL?#f~^|7QE1<Yt{1tVx)y2GjRisX_qjP
zd{?@=Estp{4~7j*p|c$Jg63kkc%<0M&n7~J)RDD??W$vB;62TS=4o}A_>yP`@k`K_
zf+X<+91<O;#4=-9r>-)SDwRg>9m#|0w6W@FW5de10?uJSg1koDq?^uks6jbnjQOfy
z{#5K!%4>X!!5yE&vPq$b{6tE%#ZQPls3-v>o6wBckS9Y^%oDC9CC&H-sh!0l)T7=s
zHcvLMM;bw3gy7@7NUy(WeRE8Gho1&Zeu9F_maHQeZeC#7f}1c)17CA(=-k~~-JXgO
z)>5vur*OrVN;6VOw1_Zz8%17)@qz97CY&j9erasiv3lBtns-wIieN-_z0a36nwZvl
z_BJr`snmNFHo~fyzNrTSnIp|3T_nvHGfnTp7@{xoD`+72HQ+<tYB)rc!>xqw?>xjs
zH~j1yky>8j6WQ@qpu3p0-dxC@g^G%IWl4RbPHn0+dO|*+H_z`nz;s1`CDDJK$j{|n
z54r8$%~C5WZdyCs6k2p6%fMD{3dVCB&<X;|3ZK}9OmAn2Y3Q8SJ#F$O87|ZStM|jF
z1M}2p&!&V0&?~f&^3aFtU`)*eLF|}9=?Efsqra@nDx3-~+mSdLJ1lAy3&)>C3<lZM
zU7ZYWwrHyK>XFW_TMVjy7T{MlJb{;KCJ<kJ74F0=y!aJ5Fk7&bfw$<)t4DNo4%)lX
zxM0DGm4?|R<Xh>s#{7sp>6*iBPF@WYYhc6Z2z1dv5=s%7B@S0EMUNM{93>9Md!DdS
zCvh#&leM5)?a625V?#-~py#v6U|+jotIAeMM);l|1ihHcF1ELFDV{ZMVOdfPm9{L4
zV#gxHqeFYDMPEa<0h{CF_K_KCIS)Nk(7ZRQYWRrT+xWe48Y~V!hJlP8-FchwJ9sJZ
zh2T_1tr6|n608~+w7DyOoZUCGpj>3Q2MUbIk<;b@${!{lHZRM^=%%oshQf&u8o~`J
zXqTBwn<qXK6^e?X3)Na!WLb$9jfn+QzgT6gXsvu!V|xkq>4Vw3nfeY0By4=?eY}v*
zbsccK)ELg66)O_9-gg3a17RzTfJ@4_A4!^-v~jjz^nxSdbtHhs4yx_Co^48a0e39T
zPwMnFXpeH&w^En^H)8G$(=tUv^Iq1KVNtST0Nqxp9=yh<*$7$FF1z(BR1E~B=%9lk
z$Ia8iVZVH&jyAsTJZgLlE`L^97SSM|*FFi>?mQW6Ox<-oZ&z+_(~lt$B(8}_kCj%o
zF|RTgXJ_jgyE2|liaQs5yOHeJQ|Z*Qep;w)3=RtmZ83J~TM>NrbWcB=LRaC<PG2Q)
zM$!A@QoWQ9M}?Ags?<tfeZ!hIH-g@~#`rsS<=|p@s;jY`J030OK2cc<Ci#przXF>P
zyq?J#___piY>5Fc3C*r}A^j3kO6+Z&Ev}7SZ%N|IDYg}4zl>w#*xTL4256G}L9Iel
zVOM+x0|%t?Z!+H3k=gR^sGXe<MdKuGBriqRwQZN>mcjPv!qrQ&o2^X)nm5o(%dZPs
zqYs>{Bb#kd-*iCHw+4%rCV(4Sc2@!Z%pub>+oR4T*w3`aAq$}m6~_r2ghj8}aRfKp
zEtLZ@FBm4VTo}6@@)J4_;fiVP<smXn;U6w1KS6C~W$~RPx+koEeVfd*6Q0m4u}*Dk
z7Q3Gn!4sUiW(kc>bKR%RicTkou|W8gw-IHzWU55J3_IOCp4MgC$ey!vh3$Ca6CKW&
zZm1GwcU8G#HBs43uP&ZRt5ATZgTn>6hw}lIROO6>{kzJuz45M=u2W$p<tL>~BdJjl
zMx8IoAh+}A2RKRSExP$a%gMh4oRi88hU1|bhMhoJ$QQ|lD!^yN7;(PU-wT!_=c2`Z
zA*GwhCy5}kH8V7|TPLBK5sP6uWE1Pq@7SeJ6E0GK*BB=uOT1z-@!I5}G%l-lqN&zv
zGzq-)y#Kw)nQS>`l(VqAXi63K$K4tgq?O5r2CkR!3aqMG(u}2ZFmE&cCTC5W2~W?|
zDrI@4q4%9wBj2e;Eqtz|<(wp}mOrgqp3{T^*n+Dl5A<lF#<XFhbfd>vgtZvDdMU`F
z>*-6tX^jx^g3B%;1%YuEXUn*eEP2o!d%fbWSX8L&6im}b4kDsUicPI|PORV_hiO7p
zNR}b{{FyfR7PDUHn?N3<F}jg*?wCnB6a_!`OiqW2&GN%OjLE53X6eV*8S+P07^pZT
zMeM?BF7KeD!UjuKyrl4g0?t<}%ys4b_Tw76^R2~%TAt++B}kT;Zy;t)xjG|dBFt)G
ztD|th*sM`gu|ti^6%3@?bQ&&d=T%pK!_JdEuCf=&-vzgcDaJMHD|CVtOc5KnH?<v;
z_966c<7@c3GN4`dHu}TpIX-37Q#sryn|<m=TTafDE4Cb{qTsn&17}fjP$zxlHRb}R
zC9+5}^A{OU9K58D8`QuJ@O<z;o$hjH(PZcQA8E0VaoC2S?rpWs=@7K)E_rBdc5s=g
z*(NwJq2ikxQw<Ho+PBPveio=?fXb|333xZR*U~h5_saU_2^z)_eQE-3Q;YHDL~Ltu
z2<Gvw7>i^LSjaaWO-8Ha(Z;h$zw?x5#7`7=6X&d{Uu;w?L7bay5J^_MU0<v$@B2tn
zsn{lbjU#5SddGT6I9u=W>G^oGD>p}25=>O!r0TNlP@QDNTU0m~zBkWyLvyExlUWd?
z5JyKz!mK{K$i#uL73in3jv-!q%cz%M(@6S&!0d6gAn4CXS3x20S4%ECys(V4OJsa~
zhZv0Z@al`O7zWfsm<_s2L!0Hoh)Z<;I+V+0;CwF&<0kxA+~V<8)C;tCNy6dF^rdxp
zLwZ-A3}RLBzILE1g<OpJ--UkIY`K_2XTjFfgmZZMWjjj`>4@EkA?tdV4#X8o0g`lI
z=o}~YlJI<`_R|W>4r{#p7geP%)z17FamZ9{;R!-#^xmw|hwnbvWx1pJkt5}Zx*>0D
zm+9zV*=0MCBX&e}obFLWl9Y88iVH#wb~Q6_Ah0B3@uJ<q3`6C3Sc&ecmJWAznt58;
z*o?=L3cUhn^OV(R0HeUx=vXh@SaVx;CFzD!dvJ6={|M(ERdvhwWNDh+ov;x{R%B`)
z36n?PMO2UFE|_3>6jv6<PS6&{mf{)j<)$gc3CrRdlF(r<J=@DFwaipVmqH_d9$IA-
zh3kvM8@gaJO`8dpV?*4Ihz|2cQ>BK!+!brmp7mFT-KN38s~I9qyn9q8_G(>P!=8?w
zb0*m6uN;hW&0EbnKD<ySZpcCutA)Eiu}^W9qq%H7#}(_ah*G-i;qp@g!^jIfskZq_
zeW~U8%3%RQ<x<o)s5wPiYF6}PN61Gn_1?ZEck>Jof`qW}yuFppP@7P|sT->m&wCEG
zd|vXDWap{pt6Ut7xJ$o9v21HFsxiqhS&K(LhDR<Ku7pyp>Ct+sXN<E?=%0~mv;3ru
zlmB2j9ecswZiqveenytHyB%dLZR6dhDaffe9vvsbakjsmpOu#pue4;Njk-xc3K^3b
zCSS#>&7>%s(s6_JXM!1q=`N7Ehczbu3k(<BtJSPkNNJ+y2rdlx`$$k=SSh7zpc5}%
zfJoSOxor;d<Czx5%(W+YxzMJ{n7tGkY-Y8u$WWr=j67D#lc)x9l=VUKC+`SAT-NP8
zf|if0&EueqC)6Al?6$RyE@AS;k7Jl!uOOp>3ll$AQxmFUT*R2XJ4VOA6kxo>7(Pxg
z5<CpT=}lo$yFZj-9N|+%)au?9*4AO@NS$&0ED2GwZX~cFhbO4bY!%YOwl3zW*UAS)
z90CdF9w9cDC5BE}7OTMWMP#Jy_46$FQFub7L?sSH_x8$fG`MLI(V|1o^@Q%7W1%aE
zDTR1t-($bg=;UghV}O3ReRmyT>1i1%!Jqsr5O<x|f)+fO0|%zX;k5ILg4}7Jr8WR>
zKTe-1Uy-WS3Mxc`1`jbJR&946TGEQdqS(yChGo%@4Rjg<vTk_s65``96S1cJ?r6o5
zh5u{hR*ua#Ctu(C*7-G+Dc+q$!E!P@n<#bGW%B*jT!(>5-al8OM$Q+Vmte=-!yyl^
zrl1r6N0zEuF#b{JTWl-@eeb*?Ws1O=2@ii5*mmf>WHcJY*MlRw!YC}b;}6bAmzAYy
zXR{ZGQN->Js4ju{Es4*i*=;atB-|2f-0|s$@kaV6E<$OmI;?Oak&KCMH!^oJx?sA5
zmTk|baP~BpK^9BBH=Zb}99~M%F13sefmMh*+9N9Qv}*2oGS1cWDD(T0B6DIDezuoG
z9cS|?<q)B>oasV~U1W6H4R+tI<C2E<6#piok=L9^JF3yU5?fEUrtsYO8gQ}(UNB5h
z`WJJGDEr_Y+hjNgQg;Ryxr?W%6aFpD(GvnQs<Y+d_I<HzOE%@4Igm%;(kOlh_Vkm|
z@iS)in;gMSP%F$;QRI!c)yM(7N-=(<=j>stA%l`Fvj@b6HJbd4mzwgZ)4Wjvh%O0t
zn1UZG-NYOqKJQ>4ebF5Tk>My31?Tfmd6Ubh-TU-CnEtHm3mJ;AtsZkV5g5=my%ZKW
zP~PZ6q@ayWZ&Ex^nPx>rgLf*}OxOKjhTfoG+FG6}rnB|!GMi(><gj-~FcM-7Z=i!(
z_-R$*<*$A~dm}6>g5NO#4<U_P%sRD_jUr0FW?Wz(aP)0URIpz9Wv5To)u249T0_<O
z=T+ORqpQty<aG*-0tgXs@w_3Ly6PKp{{k7`Lrouq4JHFi+bZRNh%Qg^RO)CHs}7l4
z-o2iq^Y)l6aG#a*1dFep1>VN>8^zfN--y;3a&Sw}eZ5Xi&A^l1qN(9%w5E%EULF^J
z{!ocy1B#n1pR?KIArzdoTKLXNi!xyNJTxWE(V0U}+ooCNNEG{;_d!%ij9JrfhP+Nu
z8Gky>$2&?Nr<&pJTlY#Z=?bAP-;*KE=IixNt$muJw>D67{QW!Xq%7i7Uqo9;!%l=G
z%P4Kfg`Py{`E0TDC68`U(u9L}NgqXa!}X95Seom5+wybKJhMxRz%0~wjpw3uVn*Tf
z@g+iw<1U{4*vHpztosXH680${zJ>=AKQk+|o~oNrgsn(hnOg9EE}?Yw5I{BzHl4*z
zoO^swKQC1HaRj@di^tiu+NeU*b6jps5yf72Gz-_4Cu3!-#B*fOz18CbyioXfwGA_0
zt^pla))1C!<U*9O>)Wmwh;kUnM2%X6#%|pJPpY&TVTh1y#<j%5yDGyI5v(qG+Mbyd
zwGG6I;%4>Sj*N_HiLCtO(I*ra0X*mZ;u`dvZmENbV2)|}@atGJ-&}c-2<E5i7NTJC
z3&M4#LsES+j=Gx%u$1T#iIO5nVxtHsK6RRqFY>L{+vS_!;AMO*#3X=tB7KSWnh1;!
zR<slH4oRJ(*&F!990aljpw3|jP?Y&$w1^ur?+r6CHXSDM*(PzHK{9YEGN#_`I6~Xj
z?&&i6P%RwY=q1X4wAkC6*mHp^jnheUTIqmvN57cM2l_Nl^_Hg`nfH>;_Bb%ichyIN
z7<}|zFnZCp?XHnxMCJ2sFZExm-CSh^0d+_S0V=~T{$3z3mhjO~AzO^k=Z&Z?y>#$7
zhINS3x(Js6NuK^NQ$+McPaUBen35{GGsopkTA}TI@)Zz``a`u7bi`Yxx+&=hI~<5h
z_NdDK%SsVEA}q3gleZ5az3v+)xe$r(*YE22fJ15n#ZZ7M6rkij6YL&jTwnXR6pP<;
zBU}PdFn?&agARw(i{oy=mWPgzBw;WEV*WB#RAp7j@bU(P{JT2tzZol9a{9F=oPy^+
z0{l2M0<u*o3kU#!eAILQ4hgIT0P?4%`}2o;E+JrB!6gAA0LCRqVvmX+e}nvastgbk
z2mr7w{IM<kuTuV{*Yj^6f9kvc9r97*UG-Nfe{S{;gnTUJU&e|G4|m7kI~30MmieXi
z{&&b9$2R;{${)wH{07<hcuoMitk<pocSxYd1+YT1-_<-+B?+UbG5`n41OwzWCqRs)
zmD4LD!2P!i9AM<ik^lk<0{r`R{mlai{)b=aSIU3o0s|a@T;*V|ZvlvW>0t5wnSc@W
z*YyJp@Z5i1|9H;7dM?;2Kyk>vXCyN=a(E4J;Tc=LcK87WKmg(bitrtag2u|oOdk+O
zWM%Zvy7K{e1`DFr^*tx^D<ez$9~dA`W=24hjK3~X8+&6SIb#44kvbsC1`t6$YGr0^
z0&t)^S$zloz35~{k8yZL_Qt?8zzZ0U;An42^yns!e(hiYJZrn~wZrS5jRH)QtnXw9
z96iA6cg*hrV6OmGmjpP01ib!DipSvJ{$oD?G=4x>0jQ#&Mj(U_D<HV0fP2ZTP%7m{
zI_MK%#{v9<_CEH*(E|m6dbPLqnWP<%<%zBSYg@n__y{0jzjpY}ZGaQ#2e9sYFbEN#
z0>D-PUJw8Rro92&&H$}&1jr_2_*Xt~;Gy7vctGoXzeE1G|MUaBz<3}8@ZX>KM?U=F
z&m$jz-QrLG_%lBK%ny&_;LrT{$cI1k<0Bsc|Nqa;j{v6s<olm~_{jI4{P;iG4u9mN
zKXT%aasQ|PJ&waa<L{9Vf8?Y`KKxmq9{KPu=LbOG&+tEd`XBJ=&pP{Oodt9c;QNQ4
z;|1vb^FHFw`-ng5?Bh82vwl4C;lE{m`2Wy4{{3SSU{HM@S9pN$3x>dn7H|Or00jK@
z0K6k`0pi?%5sw!z9F7y<rUVcgfY1Si3Lui-+<*N4+xY<|3*ax1=Rn@d0mu(P+yEj5
z5D|d507MQTLI6Ge;U@aw2Ks^R^FQ)a{0H&>QU3qi<5~ab>JJVFNG|{&b^y@<2-p_D
z_QCr>RDe4XfN%kX1t1`Q@PD}fJN&$WXA=Mj=LZ4H3M>!M|Fb;6c%U0dfN*3`z}*-?
zz`D=?2$&woCtzJj0Yn5KY5)Q240HqY195?M2D<;1AGq1z|0w>S`GL>-Ej=W#Y=Qvd
z0T2)i_+gh8K)^BrNghDJz6>OwANZjcSPo!$fdototOpT*fZ;#u1*{Wr`~ce$IEH{}
zfqq~-FdfkSGoBdW1`;q2u)IXS5zzlL9(e!h|C#2e|7V(?{{L(|<B#Y4XY>EBKK~#2
zp@sqF0geeEZ&-d18Q=~~2W$@@|ABP}#{cBsZ|(aZ_8(3_zW<r@z~}zolpffg|AplR
zg$Cdg0SFU7Kl(Vn7r^ZUAYlLgIVS+)e!78c4sf0T<^j6@EBwH5_g{(sUtRux;sg8q
z@AU!p_22xUK)nxOU;a6McmQr-8X(Vs`~}WI*gxC=<&p3F2g+JN_x}xkV7>ldR9<iy
zKzUvOND4r}HRSjH2IMDDM*3eVFR;D;SMme<&wpipKwlyl0zAVJK)|sL>|enC2%M9F
z>l<*+`QJ6JfjH=|9Sm&%ci;sIASM7AI2t+V1MXT@#^3J%CkXNHT44Z9GJAbnTT5WX
n&x_vdQT2?x(7^@>h;O9-Ju?s#L{{I)gq@3lg^7WgiRu3V;op2G

literal 0
HcmV?d00001

diff --git a/docs/python_docs/python/tutorials/packages/autograd/index.md b/docs/python_docs/python/tutorials/packages/autograd/index.md
index 586794a696ba..6623e15c1e2b 100644
--- a/docs/python_docs/python/tutorials/packages/autograd/index.md
+++ b/docs/python_docs/python/tutorials/packages/autograd/index.md
@@ -23,7 +23,7 @@
 
 Gradients are fundamental to the process of training neural networks, and tell us how to change the parameters of the network to improve its performance.
 
-![autograd-gradient](/_static/autograd/autograd_gradient.png)
+![autograd-gradient](_static/autograd_gradient.png)
 
 ### Long Answer:
 
@@ -39,7 +39,7 @@ Assuming we've calculated the gradient of each parameter with respect to the los
 
 We differentiate. [MXNet Gluon](/api/python/docs/tutorials/packages/gluon/index.html) uses Reverse Mode Automatic Differentiation (`autograd`) to backprogate gradients from the loss metric to the network parameters.
 
-![forward-backward](/_static/autograd/autograd_forward_backward.png)
+![forward-backward](_static/autograd_forward_backward.png)
 
 ### Long Answer:
 
@@ -56,7 +56,7 @@ Stage 2. Work backwards through this record and evaluate the partial derivatives
 
 <p style="text-align:center">
     <video width="600" controls playsinline autoplay muted loop>
-        <source src="/_static/autograd/autograd_graph.mp4" type="video/mp4">
+        <source src="_static/autograd_graph.mp4" type="video/mp4">
     </video>
 </p>
 
@@ -173,7 +173,7 @@ net[0].weight.grad_req = 'null'
 
 <p style="text-align:center">
     <video width="600" controls playsinline autoplay muted loop>
-        <source src="/_static/autograd/autograd_grad_req.mp4" type="video/mp4">
+        <source src="_static/autograd_grad_req.mp4" type="video/mp4">
     </video>
 </p>
 
@@ -217,7 +217,7 @@ def f(x):
 
 We can plot the resultant function for $x$ between 0 and 1, and we should recognise certain functions in segments of $x$. Starting with a quadratic curve from 0 to 1/2, we have a cubic curve from 1/2 to 2/3, a quartic from 2/3 to 3/4 and finally a flatline.
 
-![control-flow](/_static/autograd/autograd_control_flow.png)
+![control-flow](_static/autograd_control_flow.png)
 
 Using `autograd`, let's now find the gradient of this arbritrary function. We don't have a vectorized function in this case, because of the control flow, so let's also create a function to calculate the gradient using `autograd`.
 
@@ -234,7 +234,7 @@ grads = [get_grad(f, x).asscalar() for x in xs]
 print(grads)
 ```
 
-![flow-grad](/_static/autograd/autograd_control_flow_grad.png)
+![flow-grad](_static/autograd_control_flow_grad.png)
 
 We can calculate the gradients by hand in this situation (since it's a toy example), and for the four segments discussed before we'd expect $2x$, $3x^2$, $4x^3$ and 0. As a spot check, for $x=0.6$ the hand calculated gradient would be $3x^2=1.08$, which equals `1.08` as computed by `autograd`.
 
@@ -247,7 +247,7 @@ Most of the time `autograd` will be aware of the complete computational graph, a
 
 <p style="text-align:center">
     <video width="600" controls playsinline autoplay muted loop>
-        <source src="/_static/autograd/autograd_head_grad.mp4" type="video/mp4">
+        <source src="_static/autograd_head_grad.mp4" type="video/mp4">
     </video>
 </p>
 
diff --git a/docs/python_docs/python/tutorials/packages/gluon/image/index.rst b/docs/python_docs/python/tutorials/packages/gluon/image/index.rst
index b6bf3dc7834d..27cb4c3182b2 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/image/index.rst
+++ b/docs/python_docs/python/tutorials/packages/gluon/image/index.rst
@@ -39,16 +39,6 @@ Basic Image Tutorials
 
       How to use pretrained models to recognize what is in an image.
 
-Advanced Image Tutorials
----------------------
-
-.. container:: cards
-
-   .. card::
-      :title: Image similarity search with InfoGAN
-      :link: https://mxnet.apache.org/versions/master/tutorials/gluon/info_gan.html
-
-      Implementing an InfoGAN based on the MXNet Gluon.
 
 GluonCV Toolkit Tutorials
 -------------------------
diff --git a/docs/python_docs/python/tutorials/packages/gluon/image/info_gan.md b/docs/python_docs/python/tutorials/packages/gluon/image/info_gan.md
new file mode 100644
index 000000000000..646cf728e5ef
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/image/info_gan.md
@@ -0,0 +1,454 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+
+# Image similarity search with InfoGAN
+
+This notebook shows how to implement an InfoGAN based on Gluon. InfoGAN is an extension of GANs, where the generator input is split in 2 parts: random noise and a latent code (see [InfoGAN Paper](https://arxiv.org/pdf/1606.03657.pdf)). 
+The codes are made meaningful by maximizing the mutual information between code and generator output. InfoGAN learns a disentangled representation in a completely unsupervised manner. It can be used for many applications such as image similarity search. This notebook uses the DCGAN example from the [Straight Dope Book](https://gluon.mxnet.io/chapter14_generative-adversarial-networks/dcgan.html) and extends it to create an InfoGAN. 
+
+
+```python
+from __future__ import print_function
+from datetime import datetime
+import logging
+import multiprocessing
+import os
+import sys
+import tarfile
+import time
+
+import numpy as np
+from matplotlib import pyplot as plt
+from mxboard import SummaryWriter
+import mxnet as mx
+from mxnet import gluon
+from mxnet import ndarray as nd
+from mxnet.gluon import nn, utils
+from mxnet import autograd
+
+```
+
+The latent code vector can contain several variables, which can be categorical and/or continuous. We set `n_continuous` to 2 and `n_categories` to 10.
+
+
+```python
+batch_size   = 64
+z_dim        = 100
+n_continuous = 2
+n_categories = 10
+ctx = mx.gpu() if mx.context.num_gpus() else mx.cpu()
+```
+
+Some functions to load and normalize images.
+
+
+```python
+lfw_url = 'http://vis-www.cs.umass.edu/lfw/lfw-deepfunneled.tgz'
+data_path = 'lfw_dataset'
+if not os.path.exists(data_path):
+    os.makedirs(data_path)
+    data_file = utils.download(lfw_url)
+    with tarfile.open(data_file) as tar:
+        tar.extractall(path=data_path)
+
+```
+
+
+```python
+def transform(data, width=64, height=64):
+    data = mx.image.imresize(data, width, height)
+    data = nd.transpose(data, (2,0,1))
+    data = data.astype(np.float32)/127.5 - 1
+    if data.shape[0] == 1:
+        data = nd.tile(data, (3, 1, 1))
+    return data.reshape((1,) + data.shape)
+```
+
+
+```python
+def get_files(data_dir):
+    images    = []
+    filenames = []
+    for path, _, fnames in os.walk(data_dir):
+        for fname in fnames:
+            if not fname.endswith('.jpg'):
+                continue
+            img = os.path.join(path, fname)
+            img_arr = mx.image.imread(img)
+            img_arr = transform(img_arr)
+            images.append(img_arr)
+            filenames.append(path + "/" + fname)
+    return images, filenames        
+```
+
+Load the dataset `lfw_dataset` which contains images of celebrities.
+
+
+```python
+data_dir = 'lfw_dataset'
+images, filenames = get_files(data_dir)
+split = int(len(images)*0.8)
+test_images = images[split:]
+test_filenames = filenames[split:]
+train_images = images[:split]
+train_filenames = filenames[:split]
+
+train_data = gluon.data.ArrayDataset(nd.concatenate(train_images))
+train_dataloader = gluon.data.DataLoader(train_data, batch_size=batch_size, shuffle=True, last_batch='rollover', num_workers=multiprocessing.cpu_count()-1)
+```
+
+## Generator
+Define the Generator model. Architecture is taken from the DCGAN implementation in [Straight Dope Book](https://gluon.mxnet.io/chapter14_generative-adversarial-networks/dcgan.html). The Generator consist of  4 layers where each layer involves a strided convolution, batch normalization, and rectified nonlinearity. It takes as input random noise and the latent code and produces an `(64,64,3)` output image.
+
+
+```python
+class Generator(gluon.HybridBlock):
+    def __init__(self, **kwargs):
+        super(Generator, self).__init__(**kwargs)
+        with self.name_scope():
+            self.prev = nn.HybridSequential()
+            self.prev.add(nn.Dense(1024, use_bias=False), nn.BatchNorm(), nn.Activation(activation='relu'))
+            self.G = nn.HybridSequential()
+         
+            self.G.add(nn.Conv2DTranspose(64 * 8, 4, 1, 0, use_bias=False))
+            self.G.add(nn.BatchNorm())
+            self.G.add(nn.Activation('relu'))
+            self.G.add(nn.Conv2DTranspose(64 * 4, 4, 2, 1, use_bias=False))
+            self.G.add(nn.BatchNorm())
+            self.G.add(nn.Activation('relu'))
+            self.G.add(nn.Conv2DTranspose(64 * 2, 4, 2, 1, use_bias=False))
+            self.G.add(nn.BatchNorm())
+            self.G.add(nn.Activation('relu'))
+            self.G.add(nn.Conv2DTranspose(64, 4, 2, 1, use_bias=False))
+            self.G.add(nn.BatchNorm())
+            self.G.add(nn.Activation('relu'))
+            self.G.add(nn.Conv2DTranspose(3, 4, 2, 1, use_bias=False))
+            self.G.add(nn.Activation('tanh'))
+
+    def hybrid_forward(self, F, x):
+        x = self.prev(x)
+        x = F.reshape(x, (0, -1, 1, 1))
+        return self.G(x)
+```
+
+## Discriminator
+Define the Discriminator and Q model. The Q model shares many layers with the Discriminator. Its task is to estimate the code `c` for a given fake image.  It is used to maximize the lower bound to the mutual information.
+
+
+```python
+class Discriminator(gluon.HybridBlock):
+    def __init__(self, **kwargs):
+        super(Discriminator, self).__init__(**kwargs)
+        with self.name_scope():
+            self.D = nn.HybridSequential()
+            self.D.add(nn.Conv2D(64, 4, 2, 1, use_bias=False))
+            self.D.add(nn.LeakyReLU(0.2))
+            self.D.add(nn.Conv2D(64 * 2, 4, 2, 1, use_bias=False))
+            self.D.add(nn.BatchNorm())
+            self.D.add(nn.LeakyReLU(0.2))
+            self.D.add(nn.Conv2D(64 * 4, 4, 2, 1, use_bias=False))
+            self.D.add(nn.BatchNorm())
+            self.D.add(nn.LeakyReLU(0.2))
+            self.D.add(nn.Conv2D(64 * 8, 4, 2, 1, use_bias=False))
+            self.D.add(nn.BatchNorm())
+            self.D.add(nn.LeakyReLU(0.2))
+
+            self.D.add(nn.Dense(1024, use_bias=False), nn.BatchNorm(), nn.Activation(activation='relu'))
+       
+            self.prob = nn.Dense(1)
+            self.feat = nn.HybridSequential()
+            self.feat.add(nn.Dense(128, use_bias=False), nn.BatchNorm(), nn.Activation(activation='relu'))
+            self.category_prob = nn.Dense(n_categories)
+            self.continuous_mean = nn.Dense(n_continuous)
+            self.Q = nn.HybridSequential()
+            self.Q.add(self.feat, self.category_prob, self.continuous_mean)
+
+    def hybrid_forward(self, F, x):
+        x               = self.D(x)
+        prob            = self.prob(x)
+        feat            = self.feat(x)
+        category_prob   = self.category_prob(feat)
+        continuous_mean = self.continuous_mean(feat)
+        
+        return prob, category_prob, continuous_mean
+```
+
+The InfoGAN has the following layout.
+<img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/info_gan/InfoGAN.png" style="width:800px;height:250px;">
+
+Discriminator and Generator are the same as in the DCGAN example. On top of the Disciminator is the Q model, which is estimating the code `c` for given fake images. The Generator's input is random noise and the latent code `c`.  
+
+## Training Loop
+Initialize Generator and Discriminator and define correspoing trainer function.
+
+
+```python
+generator = Generator()
+generator.hybridize()
+generator.initialize(mx.init.Normal(0.002), ctx=ctx)
+
+discriminator = Discriminator()
+discriminator.hybridize()
+discriminator.initialize(mx.init.Normal(0.002), ctx=ctx)
+
+lr   = 0.0001
+beta = 0.5
+
+g_trainer = gluon.Trainer(generator.collect_params(), 'adam', {'learning_rate': lr, 'beta1': beta})
+d_trainer = gluon.Trainer(discriminator.collect_params(), 'adam', {'learning_rate': lr, 'beta1': beta})
+q_trainer = gluon.Trainer(discriminator.Q.collect_params(), 'adam', {'learning_rate': lr, 'beta1': beta})
+```
+
+Create vectors with real (=1) and fake labels (=0).
+
+
+```python
+real_label = nd.ones((batch_size,), ctx=ctx)
+fake_label = nd.zeros((batch_size,),ctx=ctx)
+```
+
+Load a pretrained model.
+
+
+```python
+if os.path.isfile('infogan_d_latest.params') and os.path.isfile('infogan_g_latest.params'):
+    discriminator.load_parameters('infogan_d_latest.params', ctx=ctx, allow_missing=True, ignore_extra=True)
+    generator.load_parameters('infogan_g_latest.params', ctx=ctx, allow_missing=True, ignore_extra=True)
+```
+There are 2 differences between InfoGAN and DCGAN: the extra latent code and the Q network to estimate the code.
+The latent code is part of the Generator input and it contains mutliple variables (continuous, categorical) that can represent different distributions. In order to make sure that the Generator uses the latent code, mutual information is introduced into the GAN loss term. Mutual information measures how much X is known given Y or vice versa. It is defined as:
+
+![gif](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/info_gan/entropy.gif) 
+
+The InfoGAN loss is:
+
+![gif](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/info_gan/loss.gif)
+
+where `V(D,G)` is the GAN loss and the mutual information `I(c, G(z, c))` goes in as regularization. The goal is to reach high mutual information, in order to learn meaningful codes for the data. 
+
+
+Define the loss functions. `SoftmaxCrossEntropyLoss` for the categorical code,  `L2Loss` for the continious code and `SigmoidBinaryCrossEntropyLoss` for the normal GAN loss.
+
+
+```python
+loss1 = gluon.loss.SigmoidBinaryCrossEntropyLoss()
+loss2 = gluon.loss.L2Loss()
+loss3 = gluon.loss.SoftmaxCrossEntropyLoss()
+```
+
+This function samples `c`, `z`, and concatenates them to create the generator input.
+
+
+```python
+def create_generator_input():
+    
+    #create random noise
+    z      = nd.random_normal(0, 1, shape=(batch_size, z_dim), ctx=ctx)
+    label  = nd.array(np.random.randint(n_categories, size=batch_size)).as_in_context(ctx)
+    c1     = nd.one_hot(label, depth=n_categories).as_in_context(ctx)
+    c2     = nd.random.uniform(-1, 1, shape=(batch_size, n_continuous)).as_in_context(ctx)
+
+    # concatenate random noise with c which will be the input of the generator
+    return nd.concat(z, c1, c2, dim=1), label, c2
+```
+
+Define the training loop. 
+1. The discriminator receives `real_data` and `loss1` measures how many real images have been identified as real
+2. The discriminator receives `fake_image` from the Generator and `loss1` measures how many fake images have been identified as fake
+3. Update Discriminator. Currently, it is updated every second iteration in order to avoid that the Discriminator becomes too strong. You may want to change that.
+4. The updated discriminator receives `fake_image` and `loss1` measures how many fake images have been been identified as real, `loss2` measures the difference between the sampled continuous latent code `c` and the output of the Q model and `loss3` measures the difference between the sampled categorical latent code `c` and the output of the Q model.
+4. Update Generator and Q
+
+
+```python
+with SummaryWriter(logdir='./logs/') as sw:
+    
+    epochs = 1
+    counter = 0
+    for epoch in range(epochs):
+        print("Epoch", epoch)
+        starttime = time.time()
+        
+        d_error_epoch = nd.zeros((1,), ctx=ctx)
+        g_error_epoch = nd.zeros((1,), ctx=ctx)
+        
+        for idx, data in enumerate(train_dataloader):
+                
+            #get real data and generator input
+            real_data = data.as_in_context(ctx)     
+            g_input, label, c2 = create_generator_input()
+
+            
+            #Update discriminator: Input real data and fake data
+            with autograd.record():
+                output_real,_,_ = discriminator(real_data)
+                d_error_real    = loss1(output_real, real_label)
+                
+                # create fake image and input it to discriminator
+                fake_image      = generator(g_input)
+                output_fake,_,_ = discriminator(fake_image.detach())
+                d_error_fake    = loss1(output_fake, fake_label)
+                
+                # total discriminator error
+                d_error         = d_error_real + d_error_fake
+
+            d_error_epoch += d_error.mean()
+            
+            #Update D every second iteration
+            if (counter+1) % 2 == 0:
+                d_error.backward()
+                d_trainer.step(batch_size)
+
+            #Update generator: Input random noise and latent code vector
+            with autograd.record():
+                fake_image = generator(g_input)
+                output_fake, category_prob, continuous_mean = discriminator(fake_image)
+                g_error = loss1(output_fake, real_label) + loss3(category_prob, label) + loss2(c2, continuous_mean)
+
+            g_error.backward()
+            g_error_epoch += g_error.mean()
+            
+            g_trainer.step(batch_size)
+            q_trainer.step(batch_size)
+
+            # logging
+            if idx % 10 == 0:
+                count = idx + 1
+                logging.info('speed: {} samples/s'.format(batch_size / (time.time() - starttime)))
+                logging.info('discriminator loss = %f, generator loss = %f at iter %d epoch %d'
+                         %(d_error_epoch.asscalar()/count,g_error_epoch.asscalar()/count, count, epoch))
+
+                g_input,_,_ = create_generator_input()
+                
+                # create some fake image for logging in MXBoard
+                fake_image = generator(g_input)
+
+                sw.add_scalar(tag='Loss_D', value={'test':d_error_epoch.asscalar()/count}, global_step=counter)
+                sw.add_scalar(tag='Loss_G', value={'test':d_error_epoch.asscalar()/count}, global_step=counter)
+                sw.add_image(tag='data_image', image=((fake_image[0]+ 1.0) * 127.5).astype(np.uint8)  , global_step=counter)
+                sw.flush()
+        
+        discriminator.save_parameters("infogan_d_latest.params")
+        generator.save_parameters("infogan_g_latest.params")
+```
+
+## Image similarity
+Once the InfoGAN is trained, we can use the Discriminator to do an image similarity search. The idea is that the network learned meaningful features from the images based on the mutual information e.g. pose of people in an image.
+
+Load the trained discriminator and retrieve one of its last layers.
+
+
+```python
+discriminator = Discriminator()
+discriminator.load_parameters("infogan_d_latest.params", ctx=ctx, ignore_extra=True)
+
+discriminator = discriminator.D[:11]
+print (discriminator)
+
+discriminator.hybridize()
+```
+
+Nearest neighbor function, which takes a matrix of features and an input feature vector. It returns the 3 closest features.
+
+
+```python
+def get_knn(features, input_vector, k=3):
+    dist = (nd.square(features - input_vector).sum(axis=1))/features.shape[0]
+    indices = dist.asnumpy().argsort()[:k]
+    return [(index, dist[index].asscalar()) for index in indices]
+```
+
+A helper function to visualize image data.
+
+
+```python
+def visualize(img_array):
+    plt.imshow(((img_array.asnumpy().transpose(1, 2, 0) + 1.0) * 127.5).astype(np.uint8))
+    plt.axis('off')
+```
+
+Take some images from the test data, obtain its feature vector from `discriminator.D[:11]` and plot images of the corresponding closest vectors in the feature space.
+
+
+```python
+feature_size = 8192 
+
+features = nd.zeros((len(test_images), feature_size), ctx=ctx)
+
+for idx, image in enumerate(test_images):
+  
+    feature = discriminator(nd.array(image, ctx=ctx))
+    feature = feature.reshape(feature_size,)
+    features[idx,:] = feature.copyto(ctx)
+
+
+for image in test_images[:100]:
+
+    feature = discriminator(mx.nd.array(image, ctx=ctx))
+    feature = feature.reshape((feature_size,))
+    image   = image.reshape((3,64,64))
+
+
+    indices = get_knn(features, feature, k=10)
+    fig = plt.figure(figsize=(15,12))
+    plt.subplot(1,10,1)
+
+    visualize(image)
+    for i in range(2,9): 
+        if indices[i-1][1] < 1.5:
+            plt.subplot(1,10,i)
+            sim = test_images[indices[i-1][0]].reshape(3,64,64)
+            visualize(sim)
+    plt.show()
+    plt.clf()
+```
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/info_gan/output.png)<!--notebook-skip-line--> 
+
+## How the Generator learns
+We trained the Generator for a couple of epochs and stored a couple of fake images per epoch. Check the video.
+                    ![alt text](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/info_gan/infogan.gif)
+                                                        
+
+The following function computes the TSNE on the feature matrix and stores the result in a json-file. This file can be loaded with [TSNEViewer](https://ml4a.github.io/guides/ImageTSNEViewer/) 
+
+
+```python
+import json
+
+from sklearn.manifold import TSNE
+from scipy.spatial import distance
+
+tsne = TSNE(n_components=2, learning_rate=150, perplexity=30, verbose=2).fit_transform(features.asnumpy())
+
+# save data to json
+data = []
+counter = 0
+for i,f in enumerate(test_filenames):
+    
+    point = [float((tsne[i,k] - np.min(tsne[:,k]))/(np.max(tsne[:,k]) - np.min(tsne[:,k]))) for k in range(2) ]
+    data.append({"path": os.path.abspath(os.path.join(os.getcwd(),f)), "point": point})
+    
+with open("imagetsne.json", 'w') as outfile:
+    json.dump(data, outfile)
+```
+
+Load the file with TSNEViewer. You can now inspect whether similiar looking images are grouped nearby or not. 
+
+<img src="https://raw.githubusercontent.com/NRauschmayr/web-data/master/mxnet/doc/tutorials/info_gan/tsne.png" style="width:800px;height:600px;">
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
\ No newline at end of file
diff --git a/docs/python_docs/python/tutorials/packages/gluon/index.rst b/docs/python_docs/python/tutorials/packages/gluon/index.rst
index e2bdb1856953..d97279465287 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/index.rst
+++ b/docs/python_docs/python/tutorials/packages/gluon/index.rst
@@ -72,21 +72,21 @@ Data
 
    .. card::
       :title: Data Loading
-      :link: data.html
+      :link: data/data.html
 
       How to load data for training.
 
    .. card::
-      :title: Image Augmentation
-      :link: image-augmentation.html
+      :title: Data Augmentation
+      :link: data/data_augmentation.md
 
-      Boost your training dataset with image augmentation.
+      A guide to data augmentation.
 
    .. card::
-      :title: Data Augmentation
+      :title: Image Augmentation
       :link: image/image-augmentation.html
 
-      A guide to data augmentation.
+      Boost your training dataset with image augmentation.
 
    .. card::
       :title: Gluon Datasets and DataLoader
@@ -167,7 +167,7 @@ Training
 
    .. card::
       :title: Autograd API
-      :link: /api/python/docs/tutorials/packages/autograd/index.html
+      :link: ../autograd/index.html
 
       How to use Automatic Differentiation with the Autograd API.
 
@@ -194,19 +194,15 @@ Advanced Topics
 
       Building custom operators with numpy.
 
-..
-<-- tutorial missing -->
    .. card::
       :title: Custom Loss
-      :link: custom-loss/custom-loss.html
+      :link: loss/custom-loss.md
 
       A guide to implementing custom losses.
 
-..
-
    .. card::
       :title: Gotchas using NumPy in Apache MXNet
-      :link: ../ndarray/gotchas_numpy_in_mxnet.html
+      :link: ../ndarray/gotchas_numpy_in_mxnet.md
 
       Common misconceptions when using NumPy in Apache MXNet.
 
diff --git a/docs/python_docs/python/tutorials/packages/index.rst b/docs/python_docs/python/tutorials/packages/index.rst
index c24ebb8c04eb..9fdd75c837f7 100644
--- a/docs/python_docs/python/tutorials/packages/index.rst
+++ b/docs/python_docs/python/tutorials/packages/index.rst
@@ -46,13 +46,13 @@ Shared APIs
 
    .. card::
       :title: Symbol API
-      :link: symbol/index.html
+      :link: ../../api/symbol/
 
-      How to use MXNet's Symbol API.
+      MXNet Symbol API has been depricated. API documentation is still available for reference.
 
    .. card::
       :title: Autograd API
-      :link: autograd/autograd.html
+      :link: autograd/index.html
 
       How to use Automatic Differentiation with the Autograd API.
 
diff --git a/docs/python_docs/python/tutorials/performance/backend/index.rst b/docs/python_docs/python/tutorials/performance/backend/index.rst
index 1113948e30d0..c4cfaf635701 100644
--- a/docs/python_docs/python/tutorials/performance/backend/index.rst
+++ b/docs/python_docs/python/tutorials/performance/backend/index.rst
@@ -47,7 +47,7 @@ The following tutorials will help you learn how to use backend tools to boost pe
 
   .. card::
      :title: MXNet Operator Profiler
-     :link: profilder.html
+     :link: profiler.html
 
      Use the profiler to monitor the performance of individual operators
 ..
diff --git a/docs/python_docs/python/tutorials/performance/index.rst b/docs/python_docs/python/tutorials/performance/index.rst
index 78943a87dc16..e547ecd297ed 100644
--- a/docs/python_docs/python/tutorials/performance/index.rst
+++ b/docs/python_docs/python/tutorials/performance/index.rst
@@ -26,7 +26,7 @@ Essential
 
    .. card::
       :title: Improving Performance
-      :link: https://mxnet.apache.org/api/faq/perf
+      :link: /api/faq/perf
 
       How to get the best performance from MXNet.
 
@@ -38,8 +38,7 @@ Essential
 
    .. card::
       :title: Tuning NumPy Operations
-      :link: https://mxnet.apache.org/versions/master/tutorials/gluon/gotchas_numpy_in_mxnet.html
-
+      :link: ../packages/ndarray/gotchas_numpy_in_mxnet.html
       Gotchas using NumPy in MXNet.
 
 Compression
@@ -49,13 +48,13 @@ Compression
 
    .. card::
       :title: Compression: float16
-      :link: compression/float16.html
+      :link: /api/faq/float16
 
       How to use float16 in your model to boost training speed.
 
    .. card::
       :title: Gradient Compression
-      :link: compression/gradient_compression.html
+      :link: /api/faq/gradient_compression
 
       How to use gradient compression to reduce communication bandwidth and increase speed.
    ..
@@ -74,7 +73,7 @@ Accelerated Backend
 
    .. card::
       :title: TensorRT
-      :link: backend/tensorRt.html
+      :link: backend/tensorrt/tensorrt.html
 
       How to use NVIDIA's TensorRT to boost inference performance.
 
@@ -82,7 +81,7 @@ Accelerated Backend
       TBD Content
       .. card::
          :title: MKL-DNN
-         :link: backend/mkl-dnn.html
+         :link: backend/mkldnn/mkldnn_readme
 
          How to get the most from your CPU by using Intel's MKL-DNN.
 
@@ -100,19 +99,19 @@ Distributed Training
 
    .. card::
       :title: Distributed Training Using the KVStore API
-      :link: https://mxnet.apache.org/versions/master/faq/distributed_training.html
+      :link: /api/faq/distributed_training.html
 
       How to use the KVStore API to use multiple GPUs when training a model.
 
    .. card::
       :title: Training with Multiple GPUs Using Model Parallelism
-      :link: https://mxnet.apache.org/versions/master/faq/model_parallel_lstm.html
+      :link: /api/faq/model_parallel_lstm.html
 
       An overview of using multiple GPUs when training an LSTM.
 
    .. card::
       :title: Data Parallelism in MXNet
-      :link: https://mxnet.apache.org/versions/master/faq/multi_devices.html
+      :link: /api/faq/multi_devices.html
 
       An overview of distributed training strategies.
 
diff --git a/docs/static_site/src/pages/api/faq/multi_devices.md b/docs/static_site/src/pages/api/faq/multi_devices.md
new file mode 100644
index 000000000000..d8bc81cb1106
--- /dev/null
+++ b/docs/static_site/src/pages/api/faq/multi_devices.md
@@ -0,0 +1,217 @@
+---
+layout: page_category
+title: Data Parallelism with Multiple CPU/GPUs on MXNet
+category: faq
+faq_c: Model
+question: How do I run MXNet on Multiple CPU/GPUs with data parallelism?
+permalink: /api/faq/multi_device
+---
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Run MXNet on Multiple CPU/GPUs with Data Parallelism
+
+_MXNet_ supports training with multiple CPUs and GPUs, which may be located on different physical machines.
+
+## Data Parallelism vs Model Parallelism
+
+By default, _MXNet_ uses data parallelism to partition the workload over multiple
+devices.
+Assume there are *n* devices.
+Then each one will receive a copy of the complete model
+and train it on *1/n* of the data.
+The results such as gradients and
+updated model are communicated across these devices.
+
+MXNet also supports model parallelism.
+In this approach, each device holds onto only part of the model.
+This proves useful when the model is too large to fit onto a single device.
+As an example, see the following [tutorial](./model_parallel_lstm.md)
+which shows how to use model parallelism for training a multi-layer LSTM model.
+In this tutorial, we'll focus on data parallelism.
+
+## Multiple GPUs within a Single Machine
+
+### Workload Partitioning
+
+By default, _MXNet_ partitions a data batch evenly among the available GPUs.
+Assume a batch size *b* and assume there are *k* GPUs, then in one iteration
+each GPU will perform forward and backward on *b/k* examples. The
+gradients are then summed over all GPUs before updating the model.
+
+### How to Use
+
+> To use GPUs, we need to compile MXNet with GPU support. For
+> example, set `USE_CUDA=1` in `config.mk` before `make`. (see
+> [MXNet installation guide](/get_started) for more options).
+
+If a machine has one or more GPU cards installed,
+then each card is labeled by a number starting from 0.
+To use a particular GPU, one can either
+specify the context `context` in code
+or pass `--gpus` at the command line.
+For example, to use GPU 0 and 2 in python,
+one can typically create a module with
+```python
+import mxnet as mx
+module = mx.module.Module(context=[mx.gpu(0), mx.gpu(2)], ...)
+```
+while if the program accepts a `--gpus` flag (as seen in
+[example/image-classification](https://github.com/dmlc/mxnet/tree/master/example/image-classification)),
+then we can try
+```bash
+python train_mnist.py --gpus 0,2 ...
+```
+
+### Advanced Usage
+If the available GPUs are not all equally powerful,
+we can partition the workload accordingly.
+For example, if GPU 0 is 3 times faster than GPU 2,
+then we might use the workload option `work_load_list=[3, 1]`,
+see [Module](/api/python/docs/api/module/index.html)
+for more details.
+
+Training with multiple GPUs should yield the same results
+as training on a single GPU if all other hyper-parameters are the same.
+In practice, the results may exhibit small differences,
+owing to the randomness of I/O (random order or other augmentations),
+weight initialization with different seeds, and CUDNN.
+
+We can control on which devices the gradient is aggregated
+and on which device the model is updated via [`KVStore`](/api/python/docs/api/kvstore/index.html),
+the _MXNet_ module that supports data communication.
+One can either use `mx.kvstore.create(type)` to get an instance
+or use the program flag `--kv-store type`.
+
+There are two commonly used types,
+
+- `local`: all gradients are copied to CPU memory and weights are updated there.
+- `device`: both gradient aggregation and weight updates are run on GPUs.
+With this setting, the `KVStore` also attempts to use GPU peer-to-peer communication,
+potentially accelerating the communication.
+Note that this option may result in higher GPU memory usage.
+
+When using a large number of GPUs, e.g. >=4, we suggest using `device` for better performance.
+
+## Distributed Training with Multiple Machines
+
+`KVStore` also supports a number of options for running on multiple machines.
+
+- `dist_sync` behaves similarly to `local` but exhibits one major difference.
+  With `dist_sync`, `batch-size` now means the batch size used on each machine.
+  So if there are *n* machines and we use batch size *b*,
+  then `dist_sync` behaves like `local` with batch size *n\*b*.
+- `dist_device_sync` is similar to `dist_sync`. The difference between them is that
+  `dist_device_sync` aggregates gradients and updates weight on GPUs
+  while `dist_sync` does so on CPU memory.
+- `dist_async`  performs asynchronous updates.
+  The weight is updated whenever gradients are received from any machine.
+  The update is atomic, i.e., no two updates happen on the same weight at the same time.
+  However, the order is not guaranteed.
+
+### How to Launch a Job
+
+> To use distributed training, we need to compile with `USE_DIST_KVSTORE=1`
+> (see [MXNet installation guide](/get_started) for more options).
+
+Launching a distributed job is a bit different from running on a single
+machine. MXNet provides
+[tools/launch.py](https://github.com/dmlc/mxnet/blob/master/tools/launch.py) to
+start a job by using `ssh`, `mpi`, `sge`, or `yarn`.
+
+An easy way to set up a cluster of EC2 instances for distributed deep learning
+is using an [AWS CloudFormation template](https://github.com/awslabs/deeplearning-cfn).
+If you do not have a cluster, you can check the repository before you continue.
+
+Assume we are at the directory `mxnet/example/image-classification`
+and want to train LeNet to classify MNIST images, as demonstrated here:
+[train_mnist.py](https://github.com/dmlc/mxnet/blob/master/example/image-classification/train_mnist.py).
+
+On a single machine, we can run:
+
+```bash
+python train_mnist.py --network lenet
+```
+
+Now, say we are given two ssh-able machines and _MXNet_ is installed on both machines.
+We want to train LeNet on these two machines.
+First, we save the IPs (or hostname) of these two machines in file `hosts`, e.g.
+
+```bash
+$ cat hosts
+172.30.0.172
+172.30.0.171
+```
+
+Next, if the mxnet folder is accessible from both machines, e.g. on a
+[network filesystem](https://help.ubuntu.com/lts/serverguide/network-file-system.html),
+then we can run:
+
+```bash
+python ../../tools/launch.py -n 2 --launcher ssh -H hosts python train_mnist.py --network lenet --kv-store dist_sync
+```
+
+Note that here we
+
+- use `launch.py` to submit the job.
+- provide launcher, `ssh` if all machines are ssh-able, `mpi` if `mpirun` is
+  available, `sge` for Sun Grid Engine, and `yarn` for Apache Yarn.
+- `-n` number of worker nodes to run on
+- `-H` the host file which is required by `ssh` and `mpi`
+- `--kv-store` use either `dist_sync` or `dist_async`
+
+
+### Synchronize Directory
+
+Now consider if the mxnet folder is not accessible.
+We can first copy the `MXNet` library to this folder by
+```bash
+cp -r ../../python/mxnet .
+cp -r ../../lib/libmxnet.so mxnet
+```
+
+then ask `launch.py` to synchronize the current directory to all machines'
+ `/tmp/mxnet` directory with `--sync-dst-dir`
+
+```bash
+python ../../tools/launch.py -n 2 -H hosts --sync-dst-dir /tmp/mxnet \
+   python train_mnist.py --network lenet --kv-store dist_sync
+```
+
+### Use a Particular Network Interface
+
+_MXNet_ often chooses the first available network interface.
+But for machines that have multiple interfaces,
+we can specify which network interface to use for data
+communication by the environment variable `DMLC_INTERFACE`.
+For example, to use the interface `eth0`, we can
+
+```
+export DMLC_INTERFACE=eth0; python ../../tools/launch.py ...
+```
+
+### Debug Connection
+
+Set`PS_VERBOSE=1` to see the debug logging, e.g
+```
+export PS_VERBOSE=1; python ../../tools/launch.py ...
+```
+
+### More
+
+- See more launch options by `python ../../tools/launch.py -h`
+- See more options of [ps-lite](http://ps-lite.readthedocs.org/en/latest/how_to.html)
\ No newline at end of file

From 21e320a9850df210cb054629ffa32b47aea0863e Mon Sep 17 00:00:00 2001
From: Xingjian Shi <xshiab@connect.ust.hk>
Date: Mon, 9 Dec 2019 10:41:28 -0800
Subject: [PATCH 676/813] [Bugfix] [Numpy] Add `kAddTo` and kNullOp to
 Transpose (#16979)

* update

Check for repeated axes

enable addto to transpose

fix

fix

fix

fix

remove unused ndim

Update pseudo2DTranspose_op-inl.cuh

Update pseudo2DTranspose_op-inl.cuh

Update pseudo2DTranspose_op-inl.cuh

fix

Update pseudo2DTranspose_op-inl.cuh

try to fix

Update pseudo2DTranspose_op-inl.cuh

Update pseudo2DTranspose_op-inl.cuh

Update pseudo2DTranspose_op-inl.cuh

fix

Update np_matrix_op.cc

Update test_numpy_op.py

update test case

fix implementation

fix bug

update

fix bug

Update pseudo2DTranspose_op-inl.cuh

fix

fix

Update test_numpy_op.py

* Fix bug

* fix docstring

* try to address comment

* no need to change this line

* Fix bug

* address comments

* address comment
---
 src/operator/numpy/np_matrix_op-inl.h         |  16 +-
 src/operator/numpy/np_matrix_op.cc            |  12 +-
 src/operator/tensor/matrix_op-inl.h           | 106 ++++++++----
 src/operator/tensor/matrix_op.cc              |   5 +-
 .../tensor/pseudo2DTranspose_op-inl.cuh       | 160 +++++++-----------
 tests/python/unittest/test_numpy_op.py        |  84 +++++----
 6 files changed, 206 insertions(+), 177 deletions(-)

diff --git a/src/operator/numpy/np_matrix_op-inl.h b/src/operator/numpy/np_matrix_op-inl.h
index fee534315b77..37af908042c9 100644
--- a/src/operator/numpy/np_matrix_op-inl.h
+++ b/src/operator/numpy/np_matrix_op-inl.h
@@ -124,16 +124,22 @@ void NumpyTranspose(const nnvm::NodeAttrs& attrs,
                     const std::vector<OpReqType>& req,
                     const std::vector<TBlob>& outputs) {
   const NumpyTransposeParam& param = nnvm::get<NumpyTransposeParam>(attrs.parsed);
-  CHECK_EQ(req[0], kWriteTo) << "Transpose does not support inplace";
+  if (req[0] == kNullOp) return;
+  CHECK(req[0] == kWriteTo || req[0] == kAddTo)
+      << "Transpose only supports kWriteTo, kNullOp and kAddTo";
+  mxnet::TShape axes;
   if (ndim_is_known(param.axes)) {
-    mxnet::TShape axes = common::CanonicalizeAxes(param.axes);
-    TransposeImpl<xpu>(ctx.run_ctx, inputs[0], outputs[0], axes);
+    axes = common::CanonicalizeAxes(param.axes);
   } else {
-    mxnet::TShape axes(inputs[0].ndim(), -1);
+    axes = mxnet::TShape(inputs[0].ndim(), -1);
     for (int i = 0; i < axes.ndim(); ++i) {
       axes[i] = axes.ndim() - 1 - i;
     }
-    TransposeImpl<xpu>(ctx.run_ctx, inputs[0], outputs[0], axes);
+  }
+  if (req[0] == kAddTo) {
+    TransposeImpl<xpu, true>(ctx.run_ctx, inputs[0], outputs[0], axes);
+  } else {
+    TransposeImpl<xpu, false>(ctx.run_ctx, inputs[0], outputs[0], axes);
   }
 }
 
diff --git a/src/operator/numpy/np_matrix_op.cc b/src/operator/numpy/np_matrix_op.cc
index e496202a0b41..05b7e948e8a0 100644
--- a/src/operator/numpy/np_matrix_op.cc
+++ b/src/operator/numpy/np_matrix_op.cc
@@ -24,6 +24,7 @@
  */
 
 #include <vector>
+#include <set>
 #include "./np_matrix_op-inl.h"
 #include "../nn/concat-inl.h"
 
@@ -67,8 +68,13 @@ bool NumpyTransposeShape(const nnvm::NodeAttrs& attrs,
   mxnet::TShape ret(ndim, -1);
 
   if (ndim_is_known(param.axes)) {
-    CHECK_EQ(ndim, param.axes.ndim());
+    CHECK_EQ(ndim, param.axes.ndim())
+        << "The number of axes does not match the dimension of the tensor. axes = "
+        << param.axes << ", input tensor shape = " << shp;
     mxnet::TShape axes = common::CanonicalizeAxes(param.axes);
+    std::set<dim_t> axes_set(axes.begin(), axes.end());
+    CHECK_EQ(axes_set.size(), axes.ndim()) << "Repeated axis in transpose. param.axes = "
+                                                << param.axes;
     if (ndim_is_known(shp)) {
       for (int i = 0; i < ndim; ++i) {
         ret[i] = shp[axes[i]];
@@ -117,9 +123,9 @@ NNVM_REGISTER_OP(_np_transpose)
       }
       std::ostringstream os;
       os << axes;
-      return MakeNonlossGradNode("transpose", n, ograds, {}, {{"axes", os.str()}});
+      return MakeNonlossGradNode("_np_transpose", n, ograds, {}, {{"axes", os.str()}});
     } else {
-      return MakeNonlossGradNode("transpose", n, ograds, {},
+      return MakeNonlossGradNode("_np_transpose", n, ograds, {},
                                  std::unordered_map<std::string, std::string>());
     }
   })
diff --git a/src/operator/tensor/matrix_op-inl.h b/src/operator/tensor/matrix_op-inl.h
index 0fee2a26c0ed..4bd059ae81df 100644
--- a/src/operator/tensor/matrix_op-inl.h
+++ b/src/operator/tensor/matrix_op-inl.h
@@ -269,8 +269,10 @@ struct TransposeParam : public dmlc::Parameter<TransposeParam> {
  * \param out output tensor
  * \param row shape of dim 0 of input
  * \param col shape of dim 1 of input
+ * \tparam DType Data type
+ * \tparam is_addto
  */
-template<typename DType>
+template<typename DType, bool is_addto>
 MSHADOW_XINLINE void Transpose2D(const DType *in, DType *out, index_t row, index_t col) {
   // ensure cache line hits and prevent cache miss for any configuration
   // L1 cache size to be utilized = 32kb = 2^15
@@ -282,7 +284,7 @@ MSHADOW_XINLINE void Transpose2D(const DType *in, DType *out, index_t row, index
   // Block-size - 2^5 v 2^5 (32 v 32) with potential 4 pragma for loop unrolled
   // blocksize * blocksize * num_threads = cache_size / dtype_size
   // Instead of explicit unroll, let compiler figure out optimal unroll factor
-  index_t blocksize = 32;
+  const index_t blocksize = 32;
 
   // collapse 2 parallelizes 2 for loops
   // inner 2 for loops aren't parallelized to prevent cache miss
@@ -299,14 +301,25 @@ MSHADOW_XINLINE void Transpose2D(const DType *in, DType *out, index_t row, index
       // transpose the block
       for (index_t a = j; (a < blocksize + j) && (a < col); ++a) {
         for (index_t b = i; (b < blocksize + i) && (b < row); ++b) {
-          out[a * row + b] = in[b * col + a];
+          if (!is_addto) {
+            out[a * row + b] = in[b * col + a];
+          } else {
+            out[a * row + b] += in[b * col + a];
+          }
         }
       }
     }
   }
 }
 
-template<typename xpu>
+inline bool IsIdentityTranspose(const TShape& axes) {
+  for (dim_t i = 0; i < axes.ndim(); i++) {
+    if (axes[i] != i) return false;
+  }
+  return true;
+}
+
+template<typename xpu, bool is_addto = false>
 void TransposeImpl(RunContext ctx,
                    const TBlob& src,
                    const TBlob& ret,
@@ -323,62 +336,79 @@ void TransposeImpl(RunContext ctx,
   // Example: (0, 2, 3, 1) or (0, 3, 1, 2), but not (0, 2, 1, 3).
   if (isPseudo2DTranspose(axes)) {
     MSHADOW_TYPE_SWITCH(ret.type_flag_, DType, {
-      transpose_pseudo2D<DType>(ret, src, axes, s);
+      transpose_pseudo2D<DType, is_addto>(ret, src, axes, s);
     });
     return;
   }
 #endif
+  // Special handle the identity case
+  if (IsIdentityTranspose(axes)) {
+    MSHADOW_TYPE_SWITCH(ret.type_flag_, DType, {
+      Tensor<xpu, 1, DType> in = src.get_with_shape<xpu, 1, DType>(mshadow::Shape1(src.Size()), s);
+      Tensor<xpu, 1, DType> out = ret.get_with_shape<xpu, 1, DType>(mshadow::Shape1(ret.Size()), s);
+      if (!is_addto) {
+        // Use memcpy to accelerate the speed
+        Copy(out, in, s);
+      } else {
+        mxnet_op::Kernel<mxnet_op::op_with_req<mshadow_op::identity, kAddTo>, xpu>::Launch(
+            s, ret.Size(), out.dptr_, in.dptr_);
+      }
+    });
+    return;
+  }
+  // Handle the general transpose case
   MSHADOW_TYPE_SWITCH(ret.type_flag_, DType, {
     switch (axes.ndim()) {
-     case 0: {
-      Tensor<xpu, 1, DType> in = src.get_with_shape<xpu, 1, DType>(mshadow::Shape1(1), s);
-      Tensor<xpu, 1, DType> out = ret.get_with_shape<xpu, 1, DType>(mshadow::Shape1(1), s);
-      Copy(out, in, s);
-      break;
-     }
-     case 1: {
-      Tensor<xpu, 1, DType> in = src.get<xpu, 1, DType>(s);
-      Tensor<xpu, 1, DType> out = ret.get<xpu, 1, DType>(s);
-      Copy(out, in, s);
-      break;
-     }
      case 2: {
-      mshadow::Tensor<xpu, 2, DType> in = src.FlatTo2D<xpu, DType>(s);
-      mshadow::Tensor<xpu, 2, DType> out = ret.FlatTo2D<xpu, DType>(s);
-
-      if (axes[0] == 1 && axes[1] == 0) {
-        if (ctx.get_ctx().dev_mask() == cpu::kDevMask) {
-          Transpose2D<DType>(in.dptr_, out.dptr_, in.shape_[0], in.shape_[1]);
-        } else {
-          out = in.T();
-        }
+      Tensor<xpu, 2, DType> in = src.get<xpu, 2, DType>(s);
+      Tensor<xpu, 2, DType> out = ret.get<xpu, 2, DType>(s);
+      if (ctx.get_ctx().dev_mask() == cpu::kDevMask) {
+        Transpose2D<DType, is_addto>(in.dptr_, out.dptr_, in.shape_[0], in.shape_[1]);
       } else {
-        Copy(out, in, s);
+        LOG(FATAL) << "Not Implemented. We should never reach here because the 2D case "
+                      "in GPU has been covered by transpose_pseudo2D."
+                      " Report an issue in Github.";
       }
       break;
      }
      case 3: {
       Tensor<xpu, 3, DType> in = src.get<xpu, 3, DType>(s);
       Tensor<xpu, 3, DType> out = ret.get<xpu, 3, DType>(s);
-      out = transpose(in, axes.get<3>());
+      if (!is_addto) {
+        out = transpose(in, axes.get<3>());
+      } else {
+        out += transpose(in, axes.get<3>());
+      }
       break;
      }
      case 4: {
       Tensor<xpu, 4, DType> in = src.get<xpu, 4, DType>(s);
       Tensor<xpu, 4, DType> out = ret.get<xpu, 4, DType>(s);
-      out = transpose(in, axes.get<4>());
+      if (!is_addto) {
+        out = transpose(in, axes.get<4>());
+      } else {
+        out += transpose(in, axes.get<4>());
+      }
       break;
      }
      case 5: {
       Tensor<xpu, 5, DType> in = src.get<xpu, 5, DType>(s);
       Tensor<xpu, 5, DType> out = ret.get<xpu, 5, DType>(s);
-      out = transpose(in, axes.get<5>());
+      if (!is_addto) {
+        out = transpose(in, axes.get<5>());
+      } else {
+        out += transpose(in, axes.get<5>());
+      }
       break;
      }
      case 6: {
       Tensor<xpu, 6, DType> in = src.get<xpu, 6, DType>(s);
       Tensor<xpu, 6, DType> out = ret.get<xpu, 6, DType>(s);
-      out = transpose(in, axes.get<6>());
+      if (!is_addto) {
+        out = transpose(in, axes.get<6>());
+      } else {
+        out += transpose(in, axes.get<6>());
+      }
       break;
      }
      default:
@@ -399,15 +429,21 @@ void Transpose(const nnvm::NodeAttrs& attrs,
     return;
   }
   const TransposeParam& param = nnvm::get<TransposeParam>(attrs.parsed);
-  CHECK_EQ(req[0], kWriteTo) << "Transpose does not support kWriteInplace and kAddTo";
+  CHECK(req[0] == kWriteTo || req[0] == kAddTo)
+       << "Transpose only supports kNullOp, kWriteTo and kAddTo";
+  mxnet::TShape axes;
   if (param.axes.ndim() == 0) {
-    mxnet::TShape axes(inputs[0].ndim(), -1);
+    axes = mxnet::TShape(inputs[0].ndim(), -1);
     for (int i = 0; i < axes.ndim(); ++i) {
       axes[i] = axes.ndim() - 1 - i;
     }
-    TransposeImpl<xpu>(ctx.run_ctx, inputs[0], outputs[0], axes);
   } else {
-    TransposeImpl<xpu>(ctx.run_ctx, inputs[0], outputs[0], param.axes);
+    axes = common::CanonicalizeAxes(param.axes);
+  }
+  if (req[0] == kAddTo) {
+    TransposeImpl<xpu, true>(ctx.run_ctx, inputs[0], outputs[0], axes);
+  } else {
+    TransposeImpl<xpu, false>(ctx.run_ctx, inputs[0], outputs[0], axes);
   }
 }
 
diff --git a/src/operator/tensor/matrix_op.cc b/src/operator/tensor/matrix_op.cc
index 5b702fbaa2d6..15b954f11c1d 100644
--- a/src/operator/tensor/matrix_op.cc
+++ b/src/operator/tensor/matrix_op.cc
@@ -283,11 +283,12 @@ static void TransposeComputeExCPU(const nnvm::NodeAttrs& attrs,
     return;
   }
   const TransposeParam& param = nnvm::get<TransposeParam>(attrs.parsed);
-  CHECK_EQ(req[0], kWriteTo) << "Transpose does not support kWriteInplace and kAddTo";
+  CHECK(req[0] == kWriteTo || req[0] == kAddTo) <<
+      "Transpose only supports kNullOp, kWriteTo and kAddTo";
   CHECK_EQ(inputs.size(), 1U);
   CHECK_EQ(outputs.size(), 1U);
 
-  if (SupportMKLDNNTranspose(param, inputs[0])) {
+  if (SupportMKLDNNTranspose(param, inputs[0]) && req[0] == kWriteTo) {
     MKLDNNTransposeForward(attrs, ctx, inputs[0], req[0], outputs[0]);
     return;
   }
diff --git a/src/operator/tensor/pseudo2DTranspose_op-inl.cuh b/src/operator/tensor/pseudo2DTranspose_op-inl.cuh
index 5b7cf04daef4..b3ca9fbfa0c9 100644
--- a/src/operator/tensor/pseudo2DTranspose_op-inl.cuh
+++ b/src/operator/tensor/pseudo2DTranspose_op-inl.cuh
@@ -39,22 +39,31 @@ namespace mxnet {
 namespace op {
 namespace cuda {
 
-
-template <typename DType, typename CType>
+/*!
+ * \brief The `transpose_pseudo2D` based on chosen vectorized types. It transposes an array of
+ *    shape (k, m, n) to (k, n, m)
+ * \param out Pointer to output memory.
+ * \param inp Pointer to input memory.
+ * \param m First of tensor dimensions.
+ * \param n Second of tensor dimensions.
+ * \param nIterY The number of iterations in the y-dim of the thread to cover all rows. (1-->m)
+ * \param nIterZ The number of iterations in the z-dim of the thread to cover all rows. (1-->k)
+ * \tparam DType Data type
+ * \tparam CType The type to load the data.
+ * \tparam is_addto Whether to perform out += transpose(data) or out = transpose(data)
+ */
+template <typename DType, typename CType, bool is_addto>
 __global__ void transpose_pseudo2D(DType* out, DType* inp,
                                    const index_t m, const index_t n,
                                    const index_t nIterY, const index_t nIterZ) {
-  const index_t TSR = sizeof(CType)/sizeof(DType);  // TypeSizeRatio
+  // Calculate the TypeSizeRatio
+  const index_t TSR = sizeof(CType) / sizeof(DType) > 0 ? sizeof(CType) / sizeof(DType) : 1;
   const index_t chunked_n = n/TSR;
   const index_t chunked_m = m/TSR;
 
-  union transp_t {
-    CType valChunk;
-    DType values[TSR];
-  };
-
-  __shared__ DType d_shm[1024*TSR*TSR];
-  CType* c_shm = reinterpret_cast<CType*>(d_shm);
+  extern __shared__ char buf[];
+  DType* d_shm = reinterpret_cast<DType*>(buf);
+  CType* c_shm = reinterpret_cast<CType*>(buf);
 
   CType* cInp = reinterpret_cast<CType*>(inp);
   CType* cOut = reinterpret_cast<CType*>(out);
@@ -78,23 +87,34 @@ __global__ void transpose_pseudo2D(DType* out, DType* inp,
         }
         __syncthreads();
 
-        // read from shared to registers
-        transp_t tmp[TSR];
+        // read from shared to local registers
+        CType tmp[TSR];
         #pragma unroll
         for (index_t i = 0; i < TSR; i++) {
+          DType* tmp_dptr = reinterpret_cast<DType*>(&tmp[i]);
           #pragma unroll
           for (int j = 0; j < TSR; j++) {
             index_t shmIdx = (TSR*threadIdx.y + j)*blockDim.x*TSR + TSR*threadIdx.x + i;
-            tmp[i].values[j] = d_shm[shmIdx];
+            tmp_dptr[j] = d_shm[shmIdx];
           }
         }
         __syncthreads();
 
         // write back to global output
-        offset = blockIdx_z*m*chunked_n + blockIdx.x*blockDim.x*TSR*chunked_m + blockIdx_y*blockDim.y;
+        offset = blockIdx_z*m*chunked_n + blockIdx.x*blockDim.x*TSR*chunked_m
+                                        + blockIdx_y*blockDim.y;
         #pragma unroll
         for (index_t i = 0; i < TSR; i++) {
-            cOut[offset + (TSR*threadIdx.x + i)*chunked_m + threadIdx.y] = tmp[i].valChunk;
+          if (is_addto) {
+            DType* tmp_dptr = reinterpret_cast<DType*>(&tmp[i]);
+            #pragma unroll
+            for (int j = 0; j < TSR; j++) {
+              out[TSR * (offset + (TSR*threadIdx.x + i)*chunked_m + threadIdx.y) + j]
+                += tmp_dptr[j];
+            }
+          } else {
+            cOut[offset + (TSR*threadIdx.x + i)*chunked_m + threadIdx.y] = tmp[i];
+          }
         }
       }
     }
@@ -107,7 +127,6 @@ __global__ void transpose_pseudo2D(DType* out, DType* inp,
 /*!
  * \brief Calls proper version of kernel `transpose_pseudo2D`
  *        basing on chosen type sizes.
- * \param dTypeSize Size of data type.
  * \param cTypeSize Size of type that should be use to copy.
  * \param grid Grid dimensions for the kernel.
  * \param block Block dimensions for the kernel.
@@ -116,92 +135,39 @@ __global__ void transpose_pseudo2D(DType* out, DType* inp,
  * \param inp Pointer to input memory.
  * \param m First of tensor dimensions.
  * \param n Second of tensor dimensions.
+ * \tparam DType Data type
+ * \tparam is_addto Whether to trigger add the transpose result to the output tensor.
  */
-inline void call_transpose_pseudo2D(index_t dTypeSize, index_t cTypeSize,
-                                   dim3 grid, dim3 block, cudaStream_t stream,
-                                   void* out, void* inp, const index_t m, const index_t n,
-                                   const index_t nIterY, const index_t nIterZ) {
-  switch (dTypeSize) {
-   case (1): {
-    uint8_t* d_outPtr = reinterpret_cast<uint8_t*>(out);
-    uint8_t* d_inpPtr = reinterpret_cast<uint8_t*>(inp);
-    switch (cTypeSize) {
-     case (1):
-      cuda::transpose_pseudo2D<uint8_t, uint8_t><<<grid, block, 0, stream>>>
+template <typename DType, bool is_addto>
+inline void call_transpose_pseudo2D(index_t cTypeSize,
+                                    dim3 grid, dim3 block, cudaStream_t stream,
+                                    DType* d_outPtr, DType* d_inpPtr,
+                                    const index_t m, const index_t n,
+                                    const index_t nIterY, const index_t nIterZ) {
+  const int nshared = 1024 * cTypeSize / sizeof(DType) * cTypeSize;
+  switch (cTypeSize) {
+    case (1):
+      cuda::transpose_pseudo2D<DType, uint8_t, is_addto><<<grid, block, nshared, stream>>>
                               (d_outPtr, d_inpPtr, m, n, nIterY, nIterZ);
       break;
-     case (2):
-      cuda::transpose_pseudo2D<uint8_t, uint16_t><<<grid, block, 0, stream>>>
+    case (2):
+      cuda::transpose_pseudo2D<DType, uint16_t, is_addto><<<grid, block, nshared, stream>>>
                               (d_outPtr, d_inpPtr, m, n, nIterY, nIterZ);
       break;
-     case (4):
-      cuda::transpose_pseudo2D<uint8_t, uint32_t><<<grid, block, 0, stream>>>
+    case (4):
+      cuda::transpose_pseudo2D<DType, uint32_t, is_addto><<<grid, block, nshared, stream>>>
                               (d_outPtr, d_inpPtr, m, n, nIterY, nIterZ);
       break;
-     case (8):
-      // case guarded against in function getBestCopyTypeSize
-      LOG(FATAL) << "cuda::transpose_pseudo2D<uint8_t, uint64_t> would take too much shared memory";
-     default:
-      LOG(FATAL) << "Unsupported type combination";
-    }
-    break;
-   }
-   case (2): {
-    uint16_t* d_outPtr = reinterpret_cast<uint16_t*>(out);
-    uint16_t* d_inpPtr = reinterpret_cast<uint16_t*>(inp);
-    switch (cTypeSize) {
-     case (2):
-      cuda::transpose_pseudo2D<uint16_t, uint16_t><<<grid, block, 0, stream>>>
+    case (8):
+      cuda::transpose_pseudo2D<DType, uint64_t, is_addto><<<grid, block, nshared, stream>>>
                               (d_outPtr, d_inpPtr, m, n, nIterY, nIterZ);
       break;
-     case (4):
-      cuda::transpose_pseudo2D<uint16_t, uint32_t><<<grid, block, 0, stream>>>
-                              (d_outPtr, d_inpPtr, m, n, nIterY, nIterZ);
-      break;
-     case (8):
-      cuda::transpose_pseudo2D<uint16_t, uint64_t><<<grid, block, 0, stream>>>
-                              (d_outPtr, d_inpPtr, m, n, nIterY, nIterZ);
-      break;
-     default:
-      LOG(FATAL) << "Unsupported type combination";
-    }
-    break;
-   }
-   case (4): {
-    uint32_t* d_outPtr = reinterpret_cast<uint32_t*>(out);
-    uint32_t* d_inpPtr = reinterpret_cast<uint32_t*>(inp);
-    switch (cTypeSize) {
-     case (4):
-      cuda::transpose_pseudo2D<uint32_t, uint32_t><<<grid, block, 0, stream>>>
-                              (d_outPtr, d_inpPtr, m, n, nIterY, nIterZ);
-      break;
-     case (8):
-      cuda::transpose_pseudo2D<uint32_t, uint64_t><<<grid, block, 0, stream>>>
-                              (d_outPtr, d_inpPtr, m, n, nIterY, nIterZ);
-      break;
-     default:
-      LOG(FATAL) << "Unsupported type combination";
-    }
-    break;
-   }
-   case (8): {
-    uint64_t* d_outPtr = reinterpret_cast<uint64_t*>(out);
-    uint64_t* d_inpPtr = reinterpret_cast<uint64_t*>(inp);
-    switch (cTypeSize) {
-     case (8):
-      cuda::transpose_pseudo2D<uint64_t, uint64_t><<<grid, block, 0, stream>>>
-                              (d_outPtr, d_inpPtr, m, n, nIterY, nIterZ);
-      break;
-     default:
-      LOG(FATAL) << "Unsupported type combination";
-    }
-    break;
-   }
-   default:
-    LOG(FATAL) << "Unsupported type combination";
+    default:
+      LOG(FATAL) << "Unsupported type combination. " << "Copy type size = " << cTypeSize;
   }
   auto cuErr = cudaPeekAtLastError();
-  CHECK_EQ(cuErr, cudaSuccess) << "Transpose kernel failure: " << cudaGetErrorString(cuErr) << ". "
+  CHECK_EQ(cuErr, cudaSuccess) << "TransposePseudo2D kernel failure: "
+                               << cudaGetErrorString(cuErr) << ". "
                                << "block: (" << block.x << "," << block.y << "," << block.z << ")"
                                << " grid: (" << grid.x << "," << grid.y << "," << grid.z << ")";
 }
@@ -225,7 +191,6 @@ inline bool isPseudo2DTranspose(const TShape& params) {
   return n_swpDims == 2;
 }
 
-
 struct pseudo2DSizes {
   index_t leadDimS;
   index_t M;
@@ -306,15 +271,14 @@ inline std::pair<dim3, dim3> calculateKernelParams(pseudo2DSizes sizes, const in
  * \param outBlob Tensor blob to store result.
  * \param inpBlob Tensor blob with input data.
  * \param params Parameters (axes) of the transpose.
+ * \param is_addto Whether to add the transpose result to the outBlob
  * \param s Pointer to GPU stream.
  */
-template <typename DType, typename gpu>
+template <typename DType, bool is_addto>
 void transpose_pseudo2D(const TBlob& outBlob, const TBlob& inpBlob,
                         const TShape& params, mshadow::Stream<gpu>* s) {
   const TShape& shape = inpBlob.shape_;
   CHECK_EQ(shape.ndim(), params.ndim());
-  auto ndim = params.ndim();
-
   auto sizes = getPackedTransposeDimensions(shape, params);
 
   index_t cTypeSize = getBestCopyTypeSize(sizeof(DType), sizes.M, sizes.N);
@@ -337,8 +301,10 @@ void transpose_pseudo2D(const TBlob& outBlob, const TBlob& inpBlob,
   }
 
   cudaStream_t stream = mshadow::Stream<gpu>::GetStream(s);
-  call_transpose_pseudo2D(sizeof(DType), cTypeSize, grid, block, stream,
-                          outBlob.dptr_, inpBlob.dptr_, sizes.M, sizes.N, nIterY, nIterZ);
+  call_transpose_pseudo2D<DType, is_addto>
+      (cTypeSize, grid, block, stream,
+       outBlob.dptr<DType>(), inpBlob.dptr<DType>(),
+       sizes.M, sizes.N, nIterY, nIterZ);
 }
 
 }  // namespace op
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 0947b6112158..07942505464c 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -1489,7 +1489,9 @@ def np_transpose_grad(out_shape, dtype, axes=None):
         if axes is None or axes == ():
             return _np.transpose(ograd, axes)
         np_axes = _np.array(list(axes))
-        return _np.transpose(ograd, tuple(list(_np.argsort(np_axes))))
+        transpose_axes = _np.zeros_like(np_axes)
+        transpose_axes[np_axes] = _np.arange(len(np_axes))
+        return _np.transpose(ograd, tuple(list(transpose_axes)))
 
     class TestTranspose(HybridBlock):
         def __init__(self, axes=None):
@@ -1498,45 +1500,57 @@ def __init__(self, axes=None):
 
         def hybrid_forward(self, F, a):
             return F.np.transpose(a, self.axes)
+    test_workloads = [[(), [(), None]],
+                      [(2,), [(0,), None]],
+                      [(0, 2), [(0, 1), (1, 0)]],
+                      [(5, 10), [(0, 1), (1, 0), None]],
+                      [(8, 2, 3), [(2, 0, 1), (0, 2, 1), (0, 1, 2), (2, 1, 0), (-1, 1, 0), None]],
+                      [(8, 2, 16), [(0, 2, 1), (2, 0, 1), (0, 1, 2), (2, 1, 0), (-1, -2, -3)]],
+                      [(8, 3, 4, 8), [(0, 2, 3, 1), (1, 2, 3, 0), (0, 3, 2, 1)]],
+                      [(8, 3, 2, 3, 8), [(0, 1, 3, 2, 4), (0, 1, 2, 3, 4), (4, 0, 1, 2, 3)]],
+                      [(3, 4, 3, 4, 3, 2), [(0, 1, 3, 2, 4, 5), (2, 3, 4, 1, 0, 5), None]]]
 
     for hybridize in [True, False]:
-        for dtype in [_np.int32, _np.float32]:
-            for ndim in range(7):
-                shape = rand_shape_nd(ndim, dim=5, allow_zero_size=True)
-                axeses = [None]
-                if ndim == 0:
-                    axeses += [()]
-                else:
-                    axes = [i for i in range(ndim)]
-                    axeses.append(tuple(axes))
-                    random.shuffle(axes)
-                    axeses.append(tuple(axes))
-                    axeses.append([i - len(axes) for i in axes])
-                for axes in axeses:
-                    test_trans = TestTranspose(axes)
-                    if hybridize:
-                        test_trans.hybridize()
-                    x = rand_ndarray(shape).as_np_ndarray()
-                    x = x.astype(dtype)
-                    x.attach_grad()
-                    np_out = _np.transpose(x.asnumpy(), axes)
-                    with mx.autograd.record():
-                        mx_out = test_trans(x)
-                    assert mx_out.shape == np_out.shape
-                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
-                    mx_out.backward()
-                    np_backward = np_transpose_grad(np_out.shape, dtype, axes)
-                    assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=1e-3, atol=1e-5, use_broadcast=False)
-
-                    mx_out = x.transpose(axes)
-                    np_out = x.asnumpy().transpose(axes)
-                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
+        for dtype in [_np.float32, _np.float16, _np.int32]:
+            for data_shape, axes_workload in test_workloads:
+                for axes in axes_workload:
+                    for grad_req in ['write', 'add']:
+                        test_trans = TestTranspose(axes)
+                        if hybridize:
+                            test_trans.hybridize()
+                        x = np.random.normal(0, 1, data_shape).astype(dtype)
+                        x = x.astype(dtype)
+                        x.attach_grad(grad_req=grad_req)
+                        if grad_req == 'add':
+                            x.grad[()] = np.random.normal(0, 1, x.grad.shape).astype(x.grad.dtype)
+                            x_grad_np = x.grad.asnumpy()
+                        np_out = _np.transpose(x.asnumpy(), axes)
+                        with mx.autograd.record():
+                            mx_out = test_trans(x)
+                        assert mx_out.shape == np_out.shape
+                        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
+                        mx_out.backward()
+                        np_backward = np_transpose_grad(np_out.shape, dtype, axes)
+                        if grad_req == 'add':
+                            assert_almost_equal(x.grad.asnumpy(), np_backward + x_grad_np,
+                                                rtol=1e-3, atol=1e-5, use_broadcast=False)
+                        else:
+                            assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=1e-3, atol=1e-5, use_broadcast=False)
 
-                    if isinstance(axes, (list, tuple)):
-                        mx_out = x.transpose(*axes)
-                        np_out = x.asnumpy().transpose(*axes)
+                        mx_out = x.transpose(axes)
+                        np_out = x.asnumpy().transpose(axes)
                         assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
 
+                        if isinstance(axes, (list, tuple)):
+                            mx_out = x.transpose(*axes)
+                            np_out = x.asnumpy().transpose(*axes)
+                            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
+    # Test for error raising
+    dat = np.random.normal(0, 1, (3, 4, 5), dtype=np.float32)
+    assert_raises(MXNetError, lambda: dat.transpose((0, 0, 1)))
+    assert_raises(MXNetError, lambda: dat.transpose((0, 1, 3)))
+
+
 
 @with_seed()
 @use_np

From 4b7262be310e8781ffac53d8d4e0da86c7da3617 Mon Sep 17 00:00:00 2001
From: dw_sjtu <46704444+sjtuWangDing@users.noreply.github.com>
Date: Tue, 10 Dec 2019 03:01:11 +0800
Subject: [PATCH 677/813] set rtol = 1e-2 and atol = 1e-4 when dtype ==
 np.float32 in test_numpy_op.py:test_np_linalg_solve (#17025)

---
 python/mxnet/symbol/numpy/linalg.py                 |  1 +
 .../python/unittest/test_numpy_interoperability.py  |  4 ++--
 tests/python/unittest/test_numpy_op.py              | 13 ++++++++-----
 3 files changed, 11 insertions(+), 7 deletions(-)

diff --git a/python/mxnet/symbol/numpy/linalg.py b/python/mxnet/symbol/numpy/linalg.py
index 1aaf4b990e31..6df175ebfc4b 100644
--- a/python/mxnet/symbol/numpy/linalg.py
+++ b/python/mxnet/symbol/numpy/linalg.py
@@ -340,6 +340,7 @@ def slogdet(a):
     """
     return _npi.slogdet(a)
 
+
 def solve(a, b):
     r"""
     Solve a linear matrix equation, or system of linear scalar equations.
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index 6b5efa0c96b0..d1047c80b7b4 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -320,8 +320,8 @@ def _add_workload_linalg_inv():
 
 
 def _add_workload_linalg_solve():
-    shapes = [(0,0), (1,1), (5,5), (20,20), (3,5,5), (3,0,0), (2,20,20), (0,20,20), (2,3,20,20)]
-    nrhs = (0, 1, 2, 10)
+    shapes = [(0,0), (1,1), (5,5), (6,6), (3,5,5), (3,0,0), (2,5,5), (0,5,5), (2,3,4,4)]
+    nrhs = (0, 1, 2, 3)
     dtypes = (np.float32, np.float64)
     for dtype, shape in itertools.product(dtypes, shapes):
         a = _np.random.rand(*shape)
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 07942505464c..20482e8241da 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -3666,19 +3666,22 @@ def get_grad_b(A, X):
         (0, 0),
         (1, 1),
         (3, 3),
-        (20, 20),
-        (3, 20, 20),
+        (4, 4),
+        (3, 2, 2),
         (1, 0, 0),
         (0, 1, 1),
         (0, 5, 3, 3),
         (5, 0, 0, 0),
-        (2, 3, 10, 10)
+        (2, 2, 5, 5)
     ]
-    nrhs = (-1, 0, 1, 2, 5)
+    nrhs = (-1, 0, 1, 2, 3)
     dtypes = ['float32', 'float64']
     for hybridize, shape, dtype, nrh in itertools.product([False, True], shapes, dtypes, nrhs):
-        rtol = 1e-3 
+        rtol = 1e-3
         atol = 1e-5
+        if dtype == 'float32':
+            rtol = 1e-2
+            atol = 1e-4
         test_solve = TestSolve()
         if hybridize:
             test_solve.hybridize()

From 5f5b83f267b2bff2bbfea947deed9c6565e975a7 Mon Sep 17 00:00:00 2001
From: alicia <32725332+Alicia1529@users.noreply.github.com>
Date: Tue, 10 Dec 2019 03:02:17 +0800
Subject: [PATCH 678/813] skip quantized conv flaky case (#16866) (#16774)

* Fix quantized concat when inputs are mixed int8 and uint8

Change-Id: I4da04bf4502425134a466823fb5f73da2d7a419b

* skip flaky test

* trigger ci

[Numpy] add op full_like, c++ impl, fix zeros_like, ones_like type inference (#16804)

From 1e1d774edfe1020e17af59bac43df6a6acb274f3 Mon Sep 17 00:00:00 2001
From: Jake Lee <gstu1130@gmail.com>
Date: Mon, 9 Dec 2019 14:41:33 -0800
Subject: [PATCH 679/813] Revert downgrade cublas version as Nvidia-docker have
 fixed this (#17026)

---
 ci/docker/Dockerfile.build.centos7_gpu        |  4 ---
 ci/docker/Dockerfile.build.ubuntu_base_gpu    |  4 ---
 ci/docker/Dockerfile.build.ubuntu_build_cuda  |  4 ---
 ci/docker/Dockerfile.build.ubuntu_gpu_cu101   |  4 ---
 ci/docker/Dockerfile.build.ubuntu_nightly_gpu |  4 ---
 ci/docker/install/centos7_cublas.sh           | 27 -------------------
 ci/docker/install/ubuntu_cublas.sh            | 26 ------------------
 7 files changed, 73 deletions(-)
 delete mode 100755 ci/docker/install/centos7_cublas.sh
 delete mode 100755 ci/docker/install/ubuntu_cublas.sh

diff --git a/ci/docker/Dockerfile.build.centos7_gpu b/ci/docker/Dockerfile.build.centos7_gpu
index a784e872083d..7e49e88b3a52 100644
--- a/ci/docker/Dockerfile.build.centos7_gpu
+++ b/ci/docker/Dockerfile.build.centos7_gpu
@@ -33,10 +33,6 @@ ENV CUDNN_VERSION=7.6.0.64
 COPY install/centos7_cudnn.sh /work/
 RUN /work/centos7_cudnn.sh
 
-# hotfix nvidia-docker image come with wrong version of libcublas
-COPY install/centos7_cublas.sh /work/
-RUN /work/centos7_cublas.sh
-
 ARG USER_ID=0
 COPY install/centos7_adduser.sh /work/
 RUN /work/centos7_adduser.sh
diff --git a/ci/docker/Dockerfile.build.ubuntu_base_gpu b/ci/docker/Dockerfile.build.ubuntu_base_gpu
index daf6fa342d43..94e6437e578b 100644
--- a/ci/docker/Dockerfile.build.ubuntu_base_gpu
+++ b/ci/docker/Dockerfile.build.ubuntu_base_gpu
@@ -29,10 +29,6 @@ ENV CUDNN_VERSION=7.6.0.64
 COPY install/ubuntu_cudnn.sh /work/
 RUN /work/ubuntu_cudnn.sh
 
-# hotfix nvidia-docker image come with wrong version of libcublas
-COPY install/ubuntu_cublas.sh /work/
-RUN /work/ubuntu_cublas.sh
-
 ARG USER_ID=0
 ARG GROUP_ID=0
 COPY install/ubuntu_adduser.sh /work/
diff --git a/ci/docker/Dockerfile.build.ubuntu_build_cuda b/ci/docker/Dockerfile.build.ubuntu_build_cuda
index ecc2b0f1498a..ce6d0007875e 100644
--- a/ci/docker/Dockerfile.build.ubuntu_build_cuda
+++ b/ci/docker/Dockerfile.build.ubuntu_build_cuda
@@ -54,10 +54,6 @@ RUN /work/ubuntu_cudnn.sh
 COPY install/ubuntu_nvidia.sh /work/
 RUN /work/ubuntu_nvidia.sh
 
-# hotfix nvidia-docker image come with wrong version of libcublas
-COPY install/ubuntu_cublas.sh /work/
-RUN /work/ubuntu_cublas.sh
-
 # Keep this at the end since this command is not cachable
 ARG USER_ID=0
 ARG GROUP_ID=0
diff --git a/ci/docker/Dockerfile.build.ubuntu_gpu_cu101 b/ci/docker/Dockerfile.build.ubuntu_gpu_cu101
index d005312ffe5b..7e0f8d93ed37 100644
--- a/ci/docker/Dockerfile.build.ubuntu_gpu_cu101
+++ b/ci/docker/Dockerfile.build.ubuntu_gpu_cu101
@@ -69,10 +69,6 @@ ENV CUDNN_VERSION=7.5.1.10
 COPY install/ubuntu_cudnn.sh /work/
 RUN /work/ubuntu_cudnn.sh
 
-# hotfix nvidia-docker image come with wrong version of libcublas
-COPY install/ubuntu_cublas.sh /work/
-RUN /work/ubuntu_cublas.sh
-
 # Always last
 ARG USER_ID=0
 ARG GROUP_ID=0
diff --git a/ci/docker/Dockerfile.build.ubuntu_nightly_gpu b/ci/docker/Dockerfile.build.ubuntu_nightly_gpu
index 9a6781685031..5e812c433b43 100644
--- a/ci/docker/Dockerfile.build.ubuntu_nightly_gpu
+++ b/ci/docker/Dockerfile.build.ubuntu_nightly_gpu
@@ -72,10 +72,6 @@ ENV CUDNN_VERSION=7.6.0.64
 COPY install/ubuntu_cudnn.sh /work/
 RUN /work/ubuntu_cudnn.sh
 
-# hotfix nvidia-docker image come with wrong version of libcublas
-COPY install/ubuntu_cublas.sh /work/
-RUN /work/ubuntu_cublas.sh
-
 ARG USER_ID=0
 ARG GROUP_ID=0
 COPY install/ubuntu_adduser.sh /work/
diff --git a/ci/docker/install/centos7_cublas.sh b/ci/docker/install/centos7_cublas.sh
deleted file mode 100755
index 0e0f91fc441f..000000000000
--- a/ci/docker/install/centos7_cublas.sh
+++ /dev/null
@@ -1,27 +0,0 @@
-#!/usr/bin/env bash
-
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-# build and install are separated so changes to build don't invalidate
-# the whole docker cache for the image
-
-set -ex
-
-# fix nvidia docker image come with wrong version of libcublas
-yum -y downgrade libcublas-devel-10.2.1.243-1.x86_64
-yum -y downgrade libcublas10-10.2.1.243-1.x86_64
\ No newline at end of file
diff --git a/ci/docker/install/ubuntu_cublas.sh b/ci/docker/install/ubuntu_cublas.sh
deleted file mode 100755
index eb6c4f145fae..000000000000
--- a/ci/docker/install/ubuntu_cublas.sh
+++ /dev/null
@@ -1,26 +0,0 @@
-#!/usr/bin/env bash
-
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-# build and install are separated so changes to build don't invalidate
-# the whole docker cache for the image
-
-apt update
-# fix nvidia docker image come with wrong version of libcublas
-apt install -y --allow-downgrades libcublas-dev=10.2.1.243-1
-apt install -y --allow-downgrades  libcublas10=10.2.1.243-1

From 986a90217b1dfbbe5213a35a6d6dc6b82001c060 Mon Sep 17 00:00:00 2001
From: liuzh91 <liuzhuanghua1991@gmail.com>
Date: Tue, 10 Dec 2019 07:13:24 +0800
Subject: [PATCH 680/813] introduce  gradient update handler to the  base
 estimator (#16900)

* introduce  gradient update handler to the  base estimator

* Modify the gradient update handler to include the batch size

* Remove unrelated gradient update handler.

* Modify gradient update handler to take the current batch size.

* Remove white space to avoid the sanity check failure

* add small tweak to the handler code

* Modify the documentation of priority parameter of relevant handlers.

* small modification on the documentation.

* Add small modification on the documentation.

* Remove unnecessary list check
---
 .../gluon/contrib/estimator/estimator.py      |  8 +--
 .../gluon/contrib/estimator/event_handler.py  | 54 ++++++++++++++++---
 tests/python/unittest/test_gluon_estimator.py |  7 +--
 3 files changed, 56 insertions(+), 13 deletions(-)

diff --git a/python/mxnet/gluon/contrib/estimator/estimator.py b/python/mxnet/gluon/contrib/estimator/estimator.py
index 54a0b165016e..ab7018f58e1f 100644
--- a/python/mxnet/gluon/contrib/estimator/estimator.py
+++ b/python/mxnet/gluon/contrib/estimator/estimator.py
@@ -24,7 +24,7 @@
 import sys
 import warnings
 
-from .event_handler import MetricHandler, ValidationHandler, LoggingHandler, StoppingHandler
+from .event_handler import MetricHandler, ValidationHandler, LoggingHandler, StoppingHandler, GradientUpdateHandler
 from .event_handler import TrainBegin, EpochBegin, BatchBegin, BatchEnd, EpochEnd, TrainEnd
 from .event_handler import _check_event_handlers
 from .utils import _check_metrics, _suggest_metric_for_loss, _check_handler_metric_ref
@@ -307,8 +307,6 @@ def fit_batch(self, train_batch, batch_axis=0):
         for l in loss:
             l.backward()
 
-        self.trainer.step(batch_size)
-
         return data, label, pred, loss
 
     def fit(self, train_data,
@@ -360,6 +358,7 @@ def fit(self, train_data,
 
         self.max_epoch = epochs
         self.max_batch = batches
+        self.batch_axis = batch_axis
 
         # provide default handlers
         event_handlers = self._prepare_default_handlers(val_data, event_handlers)
@@ -414,6 +413,9 @@ def _prepare_default_handlers(self, val_data, event_handlers):
         # no need to add to default handler check as StoppingHandler does not use metrics
         added_default_handlers.append(StoppingHandler(self.max_epoch, self.max_batch))
 
+        if not any(isinstance(handler, GradientUpdateHandler) for handler in event_handlers):
+            added_default_handlers.append(GradientUpdateHandler())
+
         if not any(isinstance(handler, MetricHandler) for handler in event_handlers):
             added_default_handlers.append(MetricHandler(train_metrics=self.train_metrics))
 
diff --git a/python/mxnet/gluon/contrib/estimator/event_handler.py b/python/mxnet/gluon/contrib/estimator/event_handler.py
index 53ba07dc836a..64777608bef0 100644
--- a/python/mxnet/gluon/contrib/estimator/event_handler.py
+++ b/python/mxnet/gluon/contrib/estimator/event_handler.py
@@ -31,7 +31,7 @@
 
 __all__ = ['TrainBegin', 'TrainEnd', 'EpochBegin', 'EpochEnd', 'BatchBegin', 'BatchEnd',
            'StoppingHandler', 'MetricHandler', 'ValidationHandler',
-           'LoggingHandler', 'CheckpointHandler', 'EarlyStoppingHandler']
+           'LoggingHandler', 'CheckpointHandler', 'EarlyStoppingHandler', 'GradientUpdateHandler']
 
 
 class EventHandler(object):
@@ -130,13 +130,16 @@ class MetricHandler(EpochBegin, BatchEnd):
     ----------
     train_metrics : List of EvalMetrics
         Training metrics to be updated at batch end.
+    priority : scalar
+        Priority level of the MetricHandler. Priority level is sorted in ascending
+        order. The lower the number is, the higher priority level the handler is.
     """
 
-    def __init__(self, train_metrics):
+    def __init__(self, train_metrics, priority=-1000):
         self.train_metrics = _check_metrics(train_metrics)
         # order to be called among all callbacks
         # metrics need to be calculated before other callbacks can access them
-        self.priority = -np.Inf
+        self.priority = priority
 
     def epoch_begin(self, estimator, *args, **kwargs):
         for metric in self.train_metrics:
@@ -176,6 +179,10 @@ class ValidationHandler(TrainBegin, BatchEnd, EpochEnd):
     batch_period : int, default None
         How often to run validation at batch end, by default
         :py:class:`ValidationHandler` does not validate at batch end.
+    priority: scalar, default -1000
+        Priority level of the ValidationHandler. Priority level is sorted in
+        ascending order. The lower the number is, the higher priority level the
+        handler is.
     """
 
     def __init__(self,
@@ -183,7 +190,8 @@ def __init__(self,
                  eval_fn,
                  val_metrics=None,
                  epoch_period=1,
-                 batch_period=None):
+                 batch_period=None,
+                 priority=-1000):
         self.val_data = val_data
         self.eval_fn = eval_fn
         self.epoch_period = epoch_period
@@ -193,7 +201,7 @@ def __init__(self,
         self.current_epoch = 0
         # order to be called among all callbacks
         # validation metrics need to be calculated before other callbacks can access them
-        self.priority = -np.Inf
+        self.priority = priority
 
     def train_begin(self, estimator, *args, **kwargs):
         # reset epoch and batch counter
@@ -235,11 +243,16 @@ class LoggingHandler(TrainBegin, TrainEnd, EpochBegin, EpochEnd, BatchBegin, Bat
         Training metrics to be logged, logged at batch end, epoch end, train end.
     val_metrics : list of EvalMetrics
         Validation metrics to be logged, logged at epoch end, train end.
+    priority : scalar, default np.Inf
+        Priority level of the LoggingHandler. Priority level is sorted in
+        ascending order. The lower the number is, the higher priority level the
+        handler is.
     """
 
     def __init__(self, log_interval='epoch',
                  train_metrics=None,
-                 val_metrics=None):
+                 val_metrics=None,
+                 priority=np.Inf):
         super(LoggingHandler, self).__init__()
         if not isinstance(log_interval, int) and log_interval != 'epoch':
             raise ValueError("log_interval must be either an integer or string 'epoch'")
@@ -250,7 +263,7 @@ def __init__(self, log_interval='epoch',
         self.processed_samples = 0
         # logging handler need to be called at last to make sure all states are updated
         # it will also shut down logging at train end
-        self.priority = np.Inf
+        self.priority = priority
         self.log_interval = log_interval
 
     def train_begin(self, estimator, *args, **kwargs):
@@ -704,3 +717,30 @@ def train_end(self, estimator, *args, **kwargs):
             estimator.logger.info('[Epoch %d] EarlyStoppingHanlder: '
                                   'early stopping due to %s not improving',
                                   self.stopped_epoch, self.monitor.get()[0])
+
+class GradientUpdateHandler(BatchEnd):
+    """Gradient Update Handler that apply gradients on network weights
+
+    :py:class:`GradientUpdateHandler` takes the priority level. It updates weight parameters
+    at the end of each batch
+
+    Parameters
+    ----------
+    priority : scalar, default -2000
+        priority level of the gradient update handler. Priority level is sorted in ascending
+        order. The lower the number is, the higher priority level the handler is.
+    ----------
+    """
+    def __init__(self, priority=-2000):
+        self.priority = priority
+
+    def batch_end(self, estimator, *args, **kwargs):
+        loss = kwargs['loss']
+        batch_size = 0
+        if not isinstance(loss, list):
+            loss = [loss]
+        if isinstance(loss, list):
+            for l in loss:
+                batch_size += l.shape[estimator.batch_axis]
+
+        estimator.trainer.step(batch_size)
diff --git a/tests/python/unittest/test_gluon_estimator.py b/tests/python/unittest/test_gluon_estimator.py
index cf913a6161c0..21f949a0bba6 100644
--- a/tests/python/unittest/test_gluon_estimator.py
+++ b/tests/python/unittest/test_gluon_estimator.py
@@ -367,6 +367,7 @@ def test_default_handlers():
     val_metrics = est.val_metrics
     early_stopping = EarlyStoppingHandler(monitor=val_metrics[0])
     handlers = est._prepare_default_handlers(val_data=None, event_handlers=[early_stopping])
-    assert len(handlers) == 4
-    assert isinstance(handlers[0], MetricHandler)
-    assert isinstance(handlers[3], LoggingHandler)
+    assert len(handlers) == 5
+    assert isinstance(handlers[0], GradientUpdateHandler)
+    assert isinstance(handlers[1], MetricHandler)
+    assert isinstance(handlers[4], LoggingHandler)

From 248acfa655729f40b19476d61f0484a9d72f438e Mon Sep 17 00:00:00 2001
From: Rohit Kumar Srivastava <srivastava.141@osu.edu>
Date: Mon, 9 Dec 2019 16:52:02 -0800
Subject: [PATCH 681/813] Multi Precision Lamb Update operator (#16885)

* multi-precision lamb update operator

* removing multi-tensor code from lamb

* doing operation beta^t outside of kernel call

* removing unecessary functions from PyLAMB
---
 python/mxnet/optimizer/optimizer.py     |  59 ++++++---
 src/operator/optimizer_op-inl.h         | 159 +++++++++++++++++++++++-
 src/operator/optimizer_op.cc            |  90 +++++++++++++-
 src/operator/optimizer_op.cu            |   5 +
 tests/python/gpu/test_operator_gpu.py   |   1 +
 tests/python/unittest/test_optimizer.py |  14 ++-
 6 files changed, 304 insertions(+), 24 deletions(-)

diff --git a/python/mxnet/optimizer/optimizer.py b/python/mxnet/optimizer/optimizer.py
index 00d130bb5534..b2509d8b4d8a 100644
--- a/python/mxnet/optimizer/optimizer.py
+++ b/python/mxnet/optimizer/optimizer.py
@@ -34,7 +34,8 @@
                        multi_sgd_update, multi_sgd_mom_update, multi_mp_sgd_update,
                        multi_mp_sgd_mom_update, preloaded_multi_sgd_update,
                        preloaded_multi_sgd_mom_update, preloaded_multi_mp_sgd_update,
-                       preloaded_multi_mp_sgd_mom_update, lamb_update_phase1, lamb_update_phase2)
+                       preloaded_multi_mp_sgd_mom_update, lamb_update_phase1, lamb_update_phase2,
+                       mp_lamb_update_phase1, mp_lamb_update_phase2)
 from ..ndarray import sparse
 from ..random import normal
 from ..util import is_np_array
@@ -1262,11 +1263,10 @@ def __init__(self, learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-6,
 
     def create_state(self, index, weight):
         stype = weight.stype
-        dtype = weight.dtype
-        return (zeros(weight.shape, weight.context, dtype=dtype, stype=stype),
-                zeros(weight.shape, weight.context, dtype=dtype, stype=stype))
+        return (zeros(weight.shape, weight.context, dtype=numpy.float32, stype=stype),
+                zeros(weight.shape, weight.context, dtype=numpy.float32, stype=stype))
 
-    def update(self, index, weight, grad, state):
+    def _update_impl(self, index, weight, grad, state, multi_precision=False):
         assert(isinstance(weight, NDArray))
         assert(isinstance(grad, NDArray))
         self._update_count(index)
@@ -1277,19 +1277,46 @@ def update(self, index, weight, grad, state):
         kwargs = {'beta1': self.beta1, 'beta2': self.beta2, 'epsilon': self.epsilon,
                   'bias_correction': self.bias_correction, 't': t,
                   'rescale_grad': self.rescale_grad}
-        mean, var = state
+
         if self.clip_gradient:
             kwargs['clip_gradient'] = self.clip_gradient
-        g = lamb_update_phase1(weight, grad, mean, var, wd=wd, **kwargs)
-
-        kwargs = {}
-        if self.lower_bound:
-            kwargs['lower_bound'] = self.lower_bound
-        if self.upper_bound:
-            kwargs['upper_bound'] = self.upper_bound
-        r_1 = weight.norm()
-        r_2 = g.norm()
-        lamb_update_phase2(weight, g, r_1, r_2, lr=lr, out=weight, **kwargs)
+
+        if multi_precision:
+            mean, var = state[1]
+            weight32 = state[0]
+            g = mp_lamb_update_phase1(weight, grad, mean, var, weight32, wd=wd, **kwargs)
+
+            kwargs = {}
+            if self.lower_bound:
+                kwargs['lower_bound'] = self.lower_bound
+            if self.upper_bound:
+                kwargs['upper_bound'] = self.upper_bound
+            r_1 = weight32.norm()
+            r_2 = g.norm()
+            mp_lamb_update_phase2(weight, g, r_1, r_2, weight32, lr=lr, out=weight, **kwargs)
+        else:
+            mean, var = state
+            g = lamb_update_phase1(weight, grad, mean, var, wd=wd, **kwargs)
+
+            kwargs = {}
+            if self.lower_bound:
+                kwargs['lower_bound'] = self.lower_bound
+            if self.upper_bound:
+                kwargs['upper_bound'] = self.upper_bound
+            r_1 = weight.norm()
+            r_2 = g.norm()
+            lamb_update_phase2(weight, g, r_1, r_2, lr=lr, out=weight, **kwargs)
+
+    def update(self, index, weight, grad, state):
+        self._update_impl(index, weight, grad, state, multi_precision=False)
+
+    def update_multi_precision(self, index, weight, grad, state):
+        if not isinstance(index, (tuple, list)):
+            use_multi_precision = self.multi_precision and weight.dtype == numpy.float16
+        else:
+            use_multi_precision = self.multi_precision and weight[0].dtype == numpy.float16
+        self._update_impl(index, weight, grad, state,
+                          multi_precision=use_multi_precision)
 
 
 # pylint: enable=line-too-long
diff --git a/src/operator/optimizer_op-inl.h b/src/operator/optimizer_op-inl.h
index 146e411b447c..2df574c46909 100644
--- a/src/operator/optimizer_op-inl.h
+++ b/src/operator/optimizer_op-inl.h
@@ -1751,6 +1751,164 @@ inline void LambUpdatePhaseTwo(const nnvm::NodeAttrs& attrs,
   });
 }
 
+template<int n_in, int n_out, int total_in>
+inline bool MPLambPhaseOneType(const nnvm::NodeAttrs& attrs,
+                             std::vector<int> *in_attrs,
+                             std::vector<int> *out_attrs) {
+  CHECK_EQ(in_attrs->size(), static_cast<size_t>(total_in)) << " in operator " << attrs.name;
+  CHECK_EQ(out_attrs->size(), static_cast<size_t>(n_out)) << " in operator " << attrs.name;
+  for (int i = 0; i < n_in; ++i) {
+    TYPE_ASSIGN_CHECK(*in_attrs, i, mshadow::kFloat16);
+  }
+  for (int i = n_in; i < total_in; ++i) {
+    TYPE_ASSIGN_CHECK(*in_attrs, i, mshadow::kFloat32);
+  }
+  for (int i = 0; i < n_out; ++i) {
+    TYPE_ASSIGN_CHECK(*out_attrs, i, mshadow::kFloat32);
+  }
+  return true;
+}
+
+struct MPLambUpdatePhaseOneKernel {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int i, float* out_data,
+    float* mean_data, float* var_data, const DType* weight_data,
+    const DType* grad_data, const float* weight32_data,
+    const float clip_gradient, const float rescale_grad,
+    const float beta1_t, const float beta1,
+    const float beta2_t, const float beta2,
+    const float wd, const float epsilon, const int t,
+    bool bias_correction, const OpReqType req) {
+    using namespace mshadow_op;
+
+    float grad_rescaled = grad_data[i] * rescale_grad;
+    if (clip_gradient >= 0.f) {
+      grad_rescaled = clip::Map(grad_rescaled, clip_gradient);
+    }
+
+    mean_data[i] = beta1 * mean_data[i] + (1.f - beta1) * grad_rescaled;
+    var_data[i] = beta2 * var_data[i] + (1.f - beta2) * grad_rescaled * grad_rescaled;
+
+    float g = mean_data[i] / (square_root::Map(var_data[i]) + epsilon) + wd * weight32_data[i];
+
+    if (bias_correction) {
+      float mean_hat = mean_data[i] / (1. - beta1_t);
+      float var_hat = var_data[i] / (1 - beta2_t);
+      g = mean_hat / (square_root::Map(var_hat) + epsilon) + wd * weight32_data[i];
+    }
+    KERNEL_ASSIGN(out_data[i], req, g);
+  }
+};
+
+template<typename xpu>
+inline void MPLambUpdatePhaseOne(const nnvm::NodeAttrs& attrs,
+                       const OpContext &ctx,
+                       const std::vector<TBlob> &inputs,
+                       const std::vector<OpReqType> &req,
+                       const std::vector<TBlob> &outputs) {
+  using namespace mxnet_op;
+  const LambUpdatePhaseOneParam& param = nnvm::get<LambUpdatePhaseOneParam>(attrs.parsed);
+  Stream<xpu>* s = ctx.get_stream<xpu>();
+  MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    float beta1_t = std::pow(param.beta1, param.t);
+    float beta2_t = std::pow(param.beta2, param.t);
+    Tensor<xpu, 2, DType> weight = inputs[0].FlatTo2D<xpu, DType>(s);
+    Tensor<xpu, 2, DType> grad = inputs[1].FlatTo2D<xpu, DType>(s);
+    Tensor<xpu, 2, float> mean = inputs[2].FlatTo2D<xpu, float>(s);
+    Tensor<xpu, 2, float> var = inputs[3].FlatTo2D<xpu, float>(s);
+    Tensor<xpu, 2, float> weight32 = inputs[4].FlatTo2D<xpu, float>(s);
+    Tensor<xpu, 2, float> out = outputs[0].FlatTo2D<xpu, float>(s);
+
+  Kernel<MPLambUpdatePhaseOneKernel, xpu>::Launch(s, weight.shape_.Size(),
+    out.dptr_, mean.dptr_, var.dptr_, weight.dptr_, grad.dptr_, weight32.dptr_,
+    param.clip_gradient, param.rescale_grad, beta1_t, param.beta1, beta2_t, param.beta2,
+    param.wd, param.epsilon, param.t, param.bias_correction, req[0]);
+  });
+}
+
+inline bool MPLambUpdatePhaseTwoShape(const nnvm::NodeAttrs& attrs,
+                            mxnet::ShapeVector* in_attrs,
+                            mxnet::ShapeVector* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 5U);
+  CHECK_EQ(out_attrs->size(), 1U);
+
+  mxnet::TShape expected_out(in_attrs->at(0).ndim(), -1);
+
+  mxnet::TShape& weight_shape = in_attrs->at(0);
+  mxnet::TShape& g_shape = in_attrs->at(1);
+  mxnet::TShape& weight32_shape = in_attrs->at(4);
+  CHECK_EQ(weight_shape.ndim(), g_shape.ndim())
+           << "total no. of dimensions for weights and g must match";
+  CHECK_EQ(weight_shape.ndim(), weight32_shape.ndim())
+           << "total no. of dimensions for weights and g must match";
+  for (int i=0; i < weight_shape.ndim(); ++i) {
+    CHECK_EQ(weight_shape[i], g_shape[i])
+           << "weight and g dimension size mismatch at " << i << "-th index";
+    CHECK_EQ(weight_shape[i], weight32_shape[i])
+           << "weight and g dimension size mismatch at " << i << "-th index";
+  }
+  mxnet::TShape& r1_shape = in_attrs->at(2);
+  mxnet::TShape& r2_shape = in_attrs->at(3);
+  CHECK_EQ(r1_shape[0], 1U) << "r1 shape incorrect";
+  CHECK_EQ(r2_shape[0], 1U) << "r2 shape incorrect";
+  for (int i=0; i < expected_out.ndim(); ++i) {
+    expected_out[i] = weight_shape[i];
+  }
+
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, expected_out);
+  return shape_is_known(expected_out);
+}
+
+struct MPLambUpdatePhaseTwoKernel {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int i, DType* out_data,
+    const DType* weight_data, const float* g,
+    const float* r1, const float* r2, const float* weight32_data,
+    float lr, const float lower_bound,
+    const float upper_bound, const OpReqType req) {
+    using namespace mshadow_op;
+
+    float new_r1 = r1[0];
+    if (lower_bound >= 0) {
+      new_r1 = maximum::Map(new_r1, lower_bound);
+    }
+    if (upper_bound >= 0) {
+      new_r1 = minimum::Map(new_r1, upper_bound);
+    }
+    if (new_r1 == 0.0f || r2[0] == 0.0f) {
+      lr = lr * 1.0f;
+    } else {
+      lr = lr * new_r1 / r2[0];
+    }
+
+    KERNEL_ASSIGN(out_data[i], req, weight32_data[i] - lr * g[i]);
+  }
+};
+
+template<typename xpu>
+inline void MPLambUpdatePhaseTwo(const nnvm::NodeAttrs& attrs,
+                       const OpContext &ctx,
+                       const std::vector<TBlob> &inputs,
+                       const std::vector<OpReqType> &req,
+                       const std::vector<TBlob> &outputs) {
+  using namespace mxnet_op;
+  const LambUpdatePhaseTwoParam& param = nnvm::get<LambUpdatePhaseTwoParam>(attrs.parsed);
+  Stream<xpu>* s = ctx.get_stream<xpu>();
+  MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    Tensor<xpu, 2, DType> weight = inputs[0].FlatTo2D<xpu, DType>(s);
+    Tensor<xpu, 2, float> g = inputs[1].FlatTo2D<xpu, float>(s);
+    Tensor<xpu, 2, float> r1 = inputs[2].FlatTo2D<xpu, float>(s);
+    Tensor<xpu, 2, float> r2 = inputs[3].FlatTo2D<xpu, float>(s);
+    Tensor<xpu, 2, float> weight32 = inputs[4].FlatTo2D<xpu, float>(s);
+    Tensor<xpu, 2, DType> out = outputs[0].FlatTo2D<xpu, DType>(s);
+
+  Kernel<MPLambUpdatePhaseTwoKernel, xpu>::Launch(s, weight.shape_.Size(),
+    out.dptr_, weight.dptr_, g.dptr_, r1.dptr_, r2.dptr_, weight32.dptr_,
+    param.lr, param.lower_bound,
+    param.upper_bound, req[0]);
+  });
+}
+
 // This RMSProp code follows the version in
 // http://arxiv.org/pdf/1308.0850v5.pdf Eq(38) - Eq(45)
 // by Alex Graves, 2013.
@@ -2493,5 +2651,4 @@ inline void AdagradUpdateEx(const nnvm::NodeAttrs& attrs,
 }  // namespace op
 }  // namespace mxnet
 
-
 #endif  // MXNET_OPERATOR_OPTIMIZER_OP_INL_H_
diff --git a/src/operator/optimizer_op.cc b/src/operator/optimizer_op.cc
index 9cf32778b15c..93e1267cc8c7 100644
--- a/src/operator/optimizer_op.cc
+++ b/src/operator/optimizer_op.cc
@@ -947,7 +947,7 @@ Link to paper: https://arxiv.org/pdf/1904.00962.pdf
          var_hat = var / (1 - beta2^t);
          g = mean_hat / (var_hat^(1/2) + epsilon) + wd * weight;
     else
-         g = mean / (var_data^(1/2) + epsilon) + wd * weight_data[i];
+         g = mean / (var_data^(1/2) + epsilon) + wd * weight;
     \end{gather*}
 
 )code" ADD_FILELINE)
@@ -1002,5 +1002,93 @@ Link to paper: https://arxiv.org/pdf/1904.00962.pdf
 .add_argument("r2", "NDArray-or-Symbol", "r2")
 .add_arguments(LambUpdatePhaseTwoParam::__FIELDS__());
 
+NNVM_REGISTER_OP(mp_lamb_update_phase1)
+.describe(R"code(Mixed Precision version of Phase I of lamb update 
+it performs the following operations and returns g:.
+
+          Link to paper: https://arxiv.org/pdf/1904.00962.pdf
+
+          .. math::
+              \begin{gather*}
+              grad32 = grad(float16) * rescale_grad
+              if (grad < -clip_gradient)
+              then
+                   grad = -clip_gradient
+              if (grad > clip_gradient)
+              then
+                   grad = clip_gradient
+
+              mean = beta1 * mean + (1 - beta1) * grad;
+              variance = beta2 * variance + (1. - beta2) * grad ^ 2;
+
+              if (bias_correction)
+              then
+                   mean_hat = mean / (1. - beta1^t);
+                   var_hat = var / (1 - beta2^t);
+                   g = mean_hat / (var_hat^(1/2) + epsilon) + wd * weight32;
+              else
+                   g = mean / (var_data^(1/2) + epsilon) + wd * weight32;
+              \end{gather*}
+
+          )code" ADD_FILELINE)
+.set_num_inputs(5)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<LambUpdatePhaseOneParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<5, 1>)
+.set_attr<nnvm::FInferType>("FInferType", MPLambPhaseOneType<2, 1, 5>)
+.set_attr<FCompute>("FCompute<cpu>", MPLambUpdatePhaseOne<cpu>)
+.set_attr<nnvm::FMutateInputs>("FMutateInputs",
+  [](const nnvm::NodeAttrs& attrs) {
+    return std::vector<uint32_t>{2, 3};
+  })
+.add_argument("weight", "NDArray-or-Symbol", "Weight")
+.add_argument("grad", "NDArray-or-Symbol", "Gradient")
+.add_argument("mean", "NDArray-or-Symbol", "Moving mean")
+.add_argument("var", "NDArray-or-Symbol", "Moving variance")
+.add_argument("weight32", "NDArray-or-Symbol", "Weight32")
+.add_arguments(LambUpdatePhaseOneParam::__FIELDS__());
+
+NNVM_REGISTER_OP(mp_lamb_update_phase2)
+.describe(R"code(Mixed Precision version Phase II of lamb update 
+it performs the following operations and updates grad.
+
+          Link to paper: https://arxiv.org/pdf/1904.00962.pdf
+
+          .. math::
+              \begin{gather*}
+              if (lower_bound >= 0)
+              then
+                   r1 = max(r1, lower_bound)
+              if (upper_bound >= 0)
+              then
+                   r1 = max(r1, upper_bound)
+
+              if (r1 == 0 or r2 == 0)
+              then
+                   lr = lr
+              else
+                   lr = lr * (r1/r2)
+              weight32 = weight32 - lr * g
+              weight(float16) = weight32
+              \end{gather*}
+
+          )code" ADD_FILELINE)
+.set_num_inputs(5)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<LambUpdatePhaseTwoParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", MPLambUpdatePhaseTwoShape)
+.set_attr<nnvm::FInferType>("FInferType", MP_InferType<1, 1, 5>)
+.set_attr<FCompute>("FCompute<cpu>", MPLambUpdatePhaseTwo<cpu>)
+.set_attr<nnvm::FMutateInputs>("FMutateInputs",
+  [](const nnvm::NodeAttrs& attrs) {
+    return std::vector<uint32_t>{4};
+  })
+.add_argument("weight", "NDArray-or-Symbol", "Weight")
+.add_argument("g", "NDArray-or-Symbol", "Output of mp_lamb_update_phase 1")
+.add_argument("r1", "NDArray-or-Symbol", "r1")
+.add_argument("r2", "NDArray-or-Symbol", "r2")
+.add_argument("weight32", "NDArray-or-Symbol", "Weight32")
+.add_arguments(LambUpdatePhaseTwoParam::__FIELDS__());
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/optimizer_op.cu b/src/operator/optimizer_op.cu
index a602b649b63d..6920cb06e482 100644
--- a/src/operator/optimizer_op.cu
+++ b/src/operator/optimizer_op.cu
@@ -283,6 +283,11 @@ NNVM_REGISTER_OP(lamb_update_phase1)
 NNVM_REGISTER_OP(lamb_update_phase2)
 .set_attr<FCompute>("FCompute<gpu>", LambUpdatePhaseTwo<gpu>);
 
+NNVM_REGISTER_OP(mp_lamb_update_phase1)
+.set_attr<FCompute>("FCompute<gpu>", MPLambUpdatePhaseOne<gpu>);
+
+NNVM_REGISTER_OP(mp_lamb_update_phase2)
+.set_attr<FCompute>("FCompute<gpu>", MPLambUpdatePhaseTwo<gpu>);
 
 }  // namespace op
 }  // namespace mxnet
diff --git a/tests/python/gpu/test_operator_gpu.py b/tests/python/gpu/test_operator_gpu.py
index efa55d2c1cde..114fab770efd 100644
--- a/tests/python/gpu/test_operator_gpu.py
+++ b/tests/python/gpu/test_operator_gpu.py
@@ -422,6 +422,7 @@ def test_preloaded_multi_sgd():
                 shapes = [np.random.randint(1, maxdim + 1, size=maxndim) for i in range(nparam)]
                 check_preloaded_multi_sgd(dtype, shapes, momentum, use_master_weights)
 
+
 @with_seed()
 def test_batchnorm_with_type():
   ctx_list_v1_2D = [
diff --git a/tests/python/unittest/test_optimizer.py b/tests/python/unittest/test_optimizer.py
index 4dbf25128c5c..811923000411 100644
--- a/tests/python/unittest/test_optimizer.py
+++ b/tests/python/unittest/test_optimizer.py
@@ -443,20 +443,20 @@ def __init__(self, learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-6,
 
     def create_state(self, index, weight):
         stype = weight.stype
-        return (mx.nd.zeros(weight.shape, weight.context, dtype=weight.dtype, stype=stype),
-                mx.nd.zeros(weight.shape, weight.context, dtype=weight.dtype, stype=stype))
+        return (mx.nd.zeros(weight.shape, weight.context, dtype=np.float32, stype=stype),
+                mx.nd.zeros(weight.shape, weight.context, dtype=np.float32, stype=stype))
+
 
     def update(self, index, weight, grad, state):
         self._update_count(index)
         lr = self._get_lr(index)
         wd = self._get_wd(index)
         t = self._index_update_count[index]
-
+        mean, var = state
         grad *= self.rescale_grad
         if self.clip_gradient is not None:
             grad = mx.nd.clip(grad, -self.clip_gradient, self.clip_gradient)
 
-        mean, var = state
         mean[:] = self.beta1 * mean + (1. - self.beta1) * grad
         var[:] = self.beta2 * var + (1. - self.beta2) * mx.nd.square(grad)
 
@@ -472,6 +472,7 @@ def update(self, index, weight, grad, state):
             var_hat = var / (1. - mx.nd.power(self.beta2, t))
 
         g = mean_hat / (mx.nd.sqrt(var_hat) + self.epsilon) + wd * weight
+
         r2 = g.norm()
         # calculate lamb_trust_ratio
         r = 1. if r1 == 0. or r2 == 0. else r1 / r2
@@ -479,8 +480,6 @@ def update(self, index, weight, grad, state):
         # update weight
         weight[:] -= lr * g
 
-    def update_multi_precision(self, index, weight, grad, state):
-        self.update(index, weight, grad, state)
 
 @with_seed()
 def test_lamb():
@@ -495,7 +494,10 @@ def test_lamb():
     ub_options = [{}, {'upper_bound': None}, {'upper_bound': 10}]
     for params in itertools.product(cg_options, rg_options, wd_options, bc_options, lb_options, ub_options):
         kwarg = {k: v for param in params for k, v in param.items()}
+        kwarg['multi_precision'] = False
         compare_optimizer(opt1(**kwarg), opt2(**kwarg), shape, np.float32)
+        kwarg['multi_precision'] = True
+        compare_optimizer(opt1(**kwarg), opt2(**kwarg), shape, np.float16, rtol=1e-3, atol=1e-3)
 
 
 #SGLD

From 59535dbe7e0a135ad0c9378940d4bb3442df4ace Mon Sep 17 00:00:00 2001
From: Minghao Liu <40382964+Tommliu@users.noreply.github.com>
Date: Tue, 10 Dec 2019 09:52:39 +0800
Subject: [PATCH 682/813] Op_Diagonal [Numpy] (#16989)

* numpy diagonal

* diagonal fix
---
 python/mxnet/_numpy_op_doc.py                 |  54 ++++-
 python/mxnet/numpy_dispatch_protocol.py       |   1 +
 src/operator/numpy/np_matrix_op-inl.h         | 218 ++++++++++++++++++
 src/operator/numpy/np_matrix_op.cc            |  23 ++
 src/operator/numpy/np_matrix_op.cu            |   6 +
 .../unittest/test_numpy_interoperability.py   |  18 ++
 tests/python/unittest/test_numpy_op.py        |  79 +++++++
 7 files changed, 398 insertions(+), 1 deletion(-)

diff --git a/python/mxnet/_numpy_op_doc.py b/python/mxnet/_numpy_op_doc.py
index 7679d21f0e59..0d0e3b64491b 100644
--- a/python/mxnet/_numpy_op_doc.py
+++ b/python/mxnet/_numpy_op_doc.py
@@ -1053,6 +1053,58 @@ def _np_diag(array, k=0):
     pass
 
 
+def _np_diagonal(a, offset=0, axis1=0, axis2=1):
+    """
+    If a is 2-D, returns the diagonal of a with the given offset, i.e., the collection of elements of
+    the form a[i, i+offset]. If a has more than two dimensions, then the axes specified by axis1 and
+    axis2 are used to determine the 2-D sub-array whose diagonal is returned. The shape of the
+    resulting array can be determined by removing axis1 and axis2 and appending an index to the
+    right equal to the size of the resulting diagonals.
+
+    Parameters
+    ----------
+    a : Symbol
+        Input data from which diagonal are taken.
+    offset: int, Optional
+        Offset of the diagonal from the main diagonal
+    axis1: int, Optional
+        Axis to be used as the first axis of the 2-D sub-arrays
+    axis2: int, Optional
+        Axis to be used as the second axis of the 2-D sub-arrays
+
+    Returns
+    -------
+    out : Symbol
+        Output result
+
+    Raises
+    -------
+    ValueError:  If the dimension of a is less than 2.
+
+    Examples
+    --------
+    >>> a = np.arange(4).reshape(2,2)
+    >>> a
+    array([[0, 1],
+        [2, 3]])
+    >>> np.diagonal(a)
+    array([0, 3])
+    >>> np.diagonal(a, 1)
+    array([1])
+
+    >>> a = np.arange(8).reshape(2,2,2)
+    >>>a
+    array([[[0, 1],
+            [2, 3]],
+            [[4, 5],
+            [6, 7]]])
+    >>> np.diagonal(a, 0, 0, 1)
+    array([[0, 6],
+            [1, 7]])
+    """
+    pass
+
+
 def _np_diagflat(array, k=0):
     """
     Create a two-dimensional array with the flattened input as a diagonal.
@@ -1086,4 +1138,4 @@ def _np_diagflat(array, k=0):
            [0, 0, 2],
            [0, 0, 0]])
     """
-    pass
\ No newline at end of file
+    pass
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index c8b11d85b000..1a238ec2c7c7 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -95,6 +95,7 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'copy',
     'cumsum',
     'diag',
+    'diagonal',
     'diagflat',
     'dot',
     'expand_dims',
diff --git a/src/operator/numpy/np_matrix_op-inl.h b/src/operator/numpy/np_matrix_op-inl.h
index 37af908042c9..3abf5627f20f 100644
--- a/src/operator/numpy/np_matrix_op-inl.h
+++ b/src/operator/numpy/np_matrix_op-inl.h
@@ -1156,6 +1156,224 @@ void NumpyDiagOpBackward(const nnvm::NodeAttrs &attrs,
                              in_data.Size(), param.k, s, req[0]);
 }
 
+struct NumpyDiagonalParam : public dmlc::Parameter<NumpyDiagonalParam> {
+  int offset;
+  int32_t axis1;
+  int32_t axis2;
+  DMLC_DECLARE_PARAMETER(NumpyDiagonalParam) {
+    DMLC_DECLARE_FIELD(offset)
+      .set_default(0)
+      .describe("Diagonal in question. The default is 0. "
+                "Use k>0 for diagonals above the main diagonal, "
+                "and k<0 for diagonals below the main diagonal. "
+                "If input has shape (S0 S1) k must be between -S0 and S1");
+    DMLC_DECLARE_FIELD(axis1)
+      .set_default(0)
+      .describe("The first axis of the sub-arrays of interest. "
+                "Ignored when the input is a 1-D array.");
+    DMLC_DECLARE_FIELD(axis2)
+      .set_default(1)
+      .describe("The second axis of the sub-arrays of interest. "
+                "Ignored when the input is a 1-D array.");
+  }
+};
+
+inline mxnet::TShape NumpyDiagonalShapeImpl(const mxnet::TShape& ishape, const int k,
+                                            const int32_t axis1, const int32_t axis2) {
+  int32_t x1 = CheckAxis(axis1, ishape.ndim());
+  int32_t x2 = CheckAxis(axis2, ishape.ndim());
+
+  CHECK_NE(x1, x2) << "axis1 and axis2 cannot refer to the same axis " << x1;
+
+  auto h = ishape[x1];
+  auto w = ishape[x2];
+  if (k > 0) {
+    w -= k;
+  } else if (k < 0) {
+    h += k;
+  }
+  auto s = std::min(h, w);
+  if (s < 0) s = 0;
+  if (x1 > x2) std::swap(x1, x2);
+
+  int32_t n_dim = ishape.ndim() - 1;
+  mxnet::TShape oshape(n_dim, -1);
+
+  // remove axis1 and axis2 and append the new axis to the end
+  uint32_t idx = 0;
+  for (int i = 0; i <= n_dim; ++i) {
+    if (i != x1 && i != x2) {
+      oshape[idx++] = ishape[i];
+    }
+  }
+  oshape[n_dim - 1] = s;
+  return oshape;
+}
+
+inline bool NumpyDiagonalOpShape(const nnvm::NodeAttrs& attrs,
+                                 mxnet::ShapeVector* in_attrs,
+                                 mxnet::ShapeVector* out_attrs) {
+    CHECK_EQ(in_attrs->size(), 1U);
+    CHECK_EQ(out_attrs->size(), 1U);
+
+    const mxnet::TShape& ishape = (*in_attrs)[0];
+    CHECK_GE(ishape.ndim(), 2) << "Input array should be at least 2d";
+    if (!mxnet::ndim_is_known(ishape)) {
+      return false;
+    }
+
+    const NumpyDiagonalParam& param = nnvm::get<NumpyDiagonalParam>(attrs.parsed);
+    mxnet::TShape oshape = NumpyDiagonalShapeImpl(ishape, param.offset, param.axis1,
+                                         param.axis2);
+    if (shape_is_none(oshape)) {
+      LOG(FATAL) << "Diagonal does not exist.";
+    }
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, oshape);
+    return shape_is_known(out_attrs->at(0));
+}
+
+inline bool NumpyDiagonalOpType(const nnvm::NodeAttrs& attrs,
+                                std::vector<int> *in_attrs,
+                                std::vector<int> *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, (*in_attrs)[0]);
+  TYPE_ASSIGN_CHECK(*in_attrs, 0, (*out_attrs)[0]);
+  return (*out_attrs)[0] != -1;
+}
+
+template<int ndim, int req, bool back>
+struct diag_n {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(index_t i, DType* out, const DType* a,
+                                  mshadow::Shape<ndim> oshape,
+                                  mshadow::Shape<ndim> ishape,
+                                  index_t stride, index_t offset,
+                                  index_t base) {
+    using namespace mxnet_op;
+    index_t idx = i / base;
+    index_t j = ravel(unravel(idx, oshape), ishape) + offset + stride * (i - idx * base);
+    if (back) {
+      KERNEL_ASSIGN(out[j], req, a[i]);
+    } else {
+      KERNEL_ASSIGN(out[i], req, a[j]);
+    }
+  }
+};
+
+template<typename xpu, bool back>
+void NumpyDiagonalOpImpl(const TBlob& in_data,
+                         const TBlob& out_data,
+                         const mxnet::TShape& ishape,
+                         const mxnet::TShape& oshape,
+                         index_t dsize,
+                         const NumpyDiagonalParam& param,
+                         mxnet_op::Stream<xpu> *s,
+                         const std::vector<OpReqType>& req) {
+  using namespace mxnet_op;
+  using namespace mshadow;
+  uint32_t x1 = CheckAxis(param.axis1, ishape.ndim());
+  uint32_t x2 = CheckAxis(param.axis2, ishape.ndim());
+  uint32_t idim = ishape.ndim(), odim = oshape.ndim();
+  uint32_t minx = x1, maxx = x2;
+  if (minx > maxx) std::swap(minx, maxx);
+
+  index_t oleading = 1,
+          obody = 1,
+          otrailing = 1;
+  for (uint32_t i = 0; i < minx; ++i) {
+    oleading *= ishape[i];
+  }
+  for (uint32_t i = minx + 1; i < maxx; ++i) {
+    obody *= ishape[i];
+  }
+  for (uint32_t i = maxx + 1; i < idim; ++i) {
+    otrailing *= ishape[i];
+  }
+
+  index_t ileading = oleading,
+      ibody = obody * ishape[minx],
+      itrailing = otrailing * ishape[maxx];
+
+  index_t stride1 = itrailing * obody,
+      stride2 = otrailing;
+  // stride1 + stride2 is the stride for iterating over the diagonal
+
+  if (x1 == maxx) std::swap(stride1, stride2);
+  index_t offset;
+  int k = param.offset;
+  if (k > 0) {
+    offset = stride2 * k;
+  } else if (k < 0) {
+    offset = stride1 * -k;
+  } else {
+    offset = 0;
+  }  // the extra index offset introduced by k
+
+  MSHADOW_TYPE_SWITCH(out_data.type_flag_, DType, {
+    MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
+      if (back && req[0] != kAddTo && req[0] != kNullOp) {
+        out_data.FlatTo1D<xpu, DType>(s) = 0;
+      }
+      if (ileading == 1) {
+        Kernel<diag_n<2, req_type, back>, xpu>::Launch(s, dsize, out_data.dptr<DType>(),
+          in_data.dptr<DType>(), Shape2(obody, otrailing), Shape2(ibody, itrailing),
+          stride1 + stride2, offset, oshape[odim - 1]);
+      } else {
+        Kernel<diag_n<3, req_type, back>, xpu>::Launch(s, dsize, out_data.dptr<DType>(),
+          in_data.dptr<DType>(), Shape3(oleading, obody, otrailing),
+          Shape3(ileading, ibody, itrailing), stride1 + stride2, offset, oshape[odim - 1]);
+      }
+    });
+  });
+}
+
+template<typename xpu>
+void NumpyDiagonalOpForward(const nnvm::NodeAttrs& attrs,
+                            const OpContext& ctx,
+                            const std::vector<TBlob>& inputs,
+                            const std::vector<OpReqType>& req,
+                            const std::vector<TBlob>& outputs) {
+  using namespace mxnet_op;
+  using namespace mshadow;
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 1U);
+  CHECK_EQ(req.size(), 1U);
+  CHECK_EQ(req[0], kWriteTo);
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  const TBlob& in_data = inputs[0];
+  const TBlob& out_data = outputs[0];
+  const mxnet::TShape& ishape = inputs[0].shape_;
+  const mxnet::TShape& oshape = outputs[0].shape_;
+  const NumpyDiagonalParam& param = nnvm::get<NumpyDiagonalParam>(attrs.parsed);
+
+  NumpyDiagonalOpImpl<xpu, false>(in_data, out_data, ishape, oshape,
+                                  out_data.Size(), param, s, req);
+}
+
+template<typename xpu>
+void NumpyDiagonalOpBackward(const nnvm::NodeAttrs& attrs,
+                             const OpContext& ctx,
+                             const std::vector<TBlob>& inputs,
+                             const std::vector<OpReqType>& req,
+                             const std::vector<TBlob>& outputs) {
+  using namespace mxnet_op;
+  using namespace mshadow;
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 1U);
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+
+  const TBlob& in_data = inputs[0];
+  const TBlob& out_data = outputs[0];
+  const mxnet::TShape& ishape = inputs[0].shape_;
+  const mxnet::TShape& oshape = outputs[0].shape_;
+  const NumpyDiagonalParam& param = nnvm::get<NumpyDiagonalParam>(attrs.parsed);
+
+  NumpyDiagonalOpImpl<xpu, true>(in_data, out_data, oshape, ishape,
+                                 in_data.Size(), param, s, req);
+}
+
 struct NumpyDiagflatParam : public dmlc::Parameter<NumpyDiagflatParam> {
   int k;
   DMLC_DECLARE_PARAMETER(NumpyDiagflatParam) {
diff --git a/src/operator/numpy/np_matrix_op.cc b/src/operator/numpy/np_matrix_op.cc
index 05b7e948e8a0..0a85887e2d4f 100644
--- a/src/operator/numpy/np_matrix_op.cc
+++ b/src/operator/numpy/np_matrix_op.cc
@@ -38,6 +38,7 @@ DMLC_REGISTER_PARAMETER(NumpyRot90Param);
 DMLC_REGISTER_PARAMETER(NumpyReshapeParam);
 DMLC_REGISTER_PARAMETER(NumpyXReshapeParam);
 DMLC_REGISTER_PARAMETER(NumpyDiagParam);
+DMLC_REGISTER_PARAMETER(NumpyDiagonalParam);
 DMLC_REGISTER_PARAMETER(NumpyDiagflatParam);
 
 
@@ -1332,6 +1333,28 @@ NNVM_REGISTER_OP(_backward_np_diag)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
 .set_attr<FCompute>("FCompute<cpu>", NumpyDiagOpBackward<cpu>);
 
+NNVM_REGISTER_OP(_np_diagonal)
+.set_attr_parser(ParamParser<NumpyDiagonalParam>)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"data"};
+  })
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyDiagonalOpShape)
+.set_attr<nnvm::FInferType>("FInferType", NumpyDiagonalOpType)
+.set_attr<FCompute>("FCompute<cpu>", NumpyDiagonalOpForward<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_np_diagonal"})
+.add_argument("data", "NDArray-or-Symbol", "Input ndarray")
+.add_arguments(NumpyDiagonalParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_backward_np_diagonal)
+.set_attr_parser(ParamParser<NumpyDiagonalParam>)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FCompute>("FCompute<cpu>", NumpyDiagonalOpBackward<cpu>);
+
 NNVM_REGISTER_OP(_np_diagflat)
 .set_attr_parser(ParamParser<NumpyDiagflatParam>)
 .set_num_inputs(1)
diff --git a/src/operator/numpy/np_matrix_op.cu b/src/operator/numpy/np_matrix_op.cu
index 6f292ab95802..10ff0eac2c29 100644
--- a/src/operator/numpy/np_matrix_op.cu
+++ b/src/operator/numpy/np_matrix_op.cu
@@ -124,6 +124,12 @@ NNVM_REGISTER_OP(_np_diag)
 NNVM_REGISTER_OP(_backward_np_diag)
 .set_attr<FCompute>("FCompute<gpu>", NumpyDiagOpBackward<gpu>);
 
+NNVM_REGISTER_OP(_np_diagonal)
+.set_attr<FCompute>("FCompute<gpu>", NumpyDiagonalOpForward<gpu>);
+
+NNVM_REGISTER_OP(_backward_np_diagonal)
+.set_attr<FCompute>("FCompute<gpu>", NumpyDiagonalOpBackward<gpu>);
+
 NNVM_REGISTER_OP(_np_diagflat)
 .set_attr<FCompute>("FCompute<gpu>", NumpyDiagflatOpForward<gpu>);
 
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index d1047c80b7b4..4c4e8b90eca9 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -97,6 +97,23 @@ def get_mat(n):
     OpArgMngr.add_workload('diag', vals_f, k=-2)
 
 
+def _add_workload_diagonal():
+    A = np.arange(12).reshape((3, 4))
+    B = np.arange(8).reshape((2,2,2))
+
+    OpArgMngr.add_workload('diagonal', A)
+    OpArgMngr.add_workload('diagonal', A, offset=0)
+    OpArgMngr.add_workload('diagonal', A, offset=-1)
+    OpArgMngr.add_workload('diagonal', A, offset=1)
+    OpArgMngr.add_workload('diagonal', B, offset=0)
+    OpArgMngr.add_workload('diagonal', B, offset=1)
+    OpArgMngr.add_workload('diagonal', B, offset=-1)
+    OpArgMngr.add_workload('diagonal', B, 0, 1, 2)
+    OpArgMngr.add_workload('diagonal', B, 0, 0, 1)
+    OpArgMngr.add_workload('diagonal', B, offset=1, axis1=0, axis2=2)
+    OpArgMngr.add_workload('diagonal', B, 0, 2, 1)
+
+
 def _add_workload_concatenate(array_pool):
     OpArgMngr.add_workload('concatenate', [array_pool['4x1'], array_pool['4x1']])
     OpArgMngr.add_workload('concatenate', [array_pool['4x1'], array_pool['4x1']], axis=1)
@@ -1364,6 +1381,7 @@ def _prepare_workloads():
     _add_workload_ravel()
     _add_workload_unravel_index()
     _add_workload_diag()
+    _add_workload_diagonal()
     _add_workload_diagflat()
     _add_workload_dot()
     _add_workload_expand_dims()
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 20482e8241da..078e37fc4146 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -5158,6 +5158,85 @@ def hybrid_forward(self, F, a):
         assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
 
 
+
+@with_seed()
+@use_np
+def test_np_diagonal():
+    class TestDiagonal(HybridBlock):
+        def __init__(self, k=0, axis1=0, axis2=1):
+            super(TestDiagonal, self).__init__()
+            self._k = k
+            self._axis1 = axis1
+            self._axis2 = axis2
+
+        def hybrid_forward(self, F, a):
+            return F.np.diagonal(a, self._k, self._axis1, self._axis2)
+
+    configs = [
+        [(1, 5), (0, 1)], [(2, 2),(0, 1)],
+        [(2, 5), (0, 1)], [(5, 5), (0, 1)],
+        [(2, 2, 2), (0, 1)], [(2, 4, 4), (0, 2)],
+        [(3, 3, 3), (1, 2)], [(4, 8, 8), (1, 2)],
+        [(4, 4, 4, 4), (1, 2)], [(5, 6, 7, 8), (2, 3)],
+        [(6, 7, 8, 9, 10), (3, 4)]
+    ]
+    dtypes = [np.int8, np.uint8, np.int32, np.int64, np.float16, np.float32, np.float64]
+    offsets = [0, 2, 4, 6]
+    combination = itertools.product([False, True], configs, dtypes, offsets)
+    for hybridize, config, dtype, k in combination:
+        rtol = 1e-2 if dtype == np.float16 else 1e-3
+        atol = 1e-4 if dtype == np.float16 else 1e-5
+        shape = config[0]
+        axis = config[1]
+        axis1 = axis[0]
+        axis2 = axis[1]
+        x = np.random.uniform(-5.0, 5.0, size=shape).astype(dtype)
+        x.attach_grad()
+        test_diagonal = TestDiagonal(k, axis1, axis2)
+        if hybridize:
+            test_diagonal.hybridize()
+        np_out = _np.diagonal(x.asnumpy(), offset=k, axis1=axis[0], axis2=axis[1])
+        with mx.autograd.record():
+            mx_out = test_diagonal(x)
+        assert mx_out.shape == np_out.shape
+        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
+
+        # check backward function
+        mx_out.backward()
+        size_out = np_out.size
+        shape_out = np_out.shape
+        ndim = len(shape)
+        h = shape[axis1]
+        w = shape[axis2]
+        np_backward_slice = _np.zeros((h, w))
+        np_backward = _np.zeros(shape)
+        if k > 0:
+            w -= k
+        else:
+            h += k
+        s = min(w, h)
+        if s > 0:
+            if k >= 0:
+                for i in range(s):
+                    np_backward_slice[0+i][k+i] = 1
+            else:
+                for i in range(s):
+                    np_backward_slice[-k+i][0+i] = 1
+            ileading = int(size_out/s)
+            array_temp = _np.array([np_backward_slice for i in range(ileading)])
+            array_temp = array_temp.reshape(shape_out[:-1] + (shape[axis1], shape[axis2]))
+            axis_idx = [i for i in range(ndim-2)]
+            axis_idx[axis1:axis1] = [ndim - 2]
+            axis_idx[axis2:axis2] = [ndim - 1] 
+            np_backward = _np.transpose(array_temp, tuple(axis_idx))
+        assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=rtol, atol=atol)
+
+        # Test imperative once again
+        mx_out = np.diagonal(x, k, axis[0], axis[1])
+        np_out = _np.diagonal(x.asnumpy(), offset=k, axis1=axis[0], axis2=axis[1])
+        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
+
+
 @with_seed()
 @use_np
 def test_np_nan_to_num():

From 8645b9a4a220940bdccba3aeee577eb645a86b34 Mon Sep 17 00:00:00 2001
From: Leonard Lausen <lausen@amazon.com>
Date: Tue, 10 Dec 2019 02:15:55 +0000
Subject: [PATCH 683/813] Fix CUDNN detection for CMake build (#17019)

---
 CMakeLists.txt                 | 10 ++++++----
 cmake/BuildTVM.cmake           | 12 ------------
 cmake/FirstClassLangCuda.cmake | 28 ----------------------------
 cmake/Modules/FindCUDNN.cmake  | 33 +++++++++++++++++++++++++++++++++
 4 files changed, 39 insertions(+), 44 deletions(-)
 create mode 100644 cmake/Modules/FindCUDNN.cmake

diff --git a/CMakeLists.txt b/CMakeLists.txt
index aec5f21131d1..8a8a3d5c512e 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -502,13 +502,15 @@ add_subdirectory(${GTEST_ROOT})
 find_package(GTest REQUIRED)
 
 # cudnn detection
-if(USE_CUDNN AND USE_CUDA)
-  detect_cuDNN()
-  if(HAVE_CUDNN)
+if(USE_CUDNN)
+  find_package(CUDNN)
+  if(CUDNN_FOUND)
     add_definitions(-DUSE_CUDNN)
     include_directories(SYSTEM ${CUDNN_INCLUDE})
     list(APPEND mxnet_LINKER_LIBS ${CUDNN_LIBRARY})
-      add_definitions(-DMSHADOW_USE_CUDNN=1)
+    add_definitions(-DMSHADOW_USE_CUDNN=1)
+  else()
+    set(USE_CUDNN OFF)
   endif()
 endif()
 
diff --git a/cmake/BuildTVM.cmake b/cmake/BuildTVM.cmake
index 7fcf706ffb7c..4bb749552f01 100644
--- a/cmake/BuildTVM.cmake
+++ b/cmake/BuildTVM.cmake
@@ -98,18 +98,6 @@ set(USE_RANDOM OFF)
 # Whether use NNPack
 set(USE_NNPACK OFF)
 
-# Whether use CuDNN
-if(USE_CUDNN AND USE_CUDA)
-    detect_cuDNN()
-    if(HAVE_CUDNN)
-        set(USE_CUDNN ON)
-    else()
-        set(USE_CUDNN OFF)
-    endif()
-else()
-    set(USE_CUDNN OFF)
-endif()
-
 # Whether use cuBLAS
 set(USE_CUBLAS OFF)
 
diff --git a/cmake/FirstClassLangCuda.cmake b/cmake/FirstClassLangCuda.cmake
index 8d79c2b63ad9..0eca1aff78d4 100644
--- a/cmake/FirstClassLangCuda.cmake
+++ b/cmake/FirstClassLangCuda.cmake
@@ -23,34 +23,6 @@ if(USE_CXX14_IF_AVAILABLE)
   check_cxx_compiler_flag("-std=c++14"   SUPPORT_CXX14)
 endif()
 
-################################################################################################
-# Short command for cuDNN detection. Believe it soon will be a part of CUDA toolkit distribution.
-# That's why not FindcuDNN.cmake file, but just the macro
-# Usage:
-#   detect_cuDNN()
-function(detect_cuDNN)
-  set(CUDNN_ROOT "" CACHE PATH "CUDNN root folder")
-
-  find_path(CUDNN_INCLUDE cudnn.h
-            PATHS ${CUDNN_ROOT} $ENV{CUDNN_ROOT}
-            DOC "Path to cuDNN include directory." )
-
-
-  find_library(CUDNN_LIBRARY NAMES libcudnn.so cudnn.lib # libcudnn_static.a
-                             PATHS ${CUDNN_ROOT} $ENV{CUDNN_ROOT} ${CUDNN_INCLUDE}
-                             PATH_SUFFIXES lib lib/x64
-                             DOC "Path to cuDNN library.")
-
-  if(CUDNN_INCLUDE AND CUDNN_LIBRARY)
-    set(HAVE_CUDNN  TRUE PARENT_SCOPE)
-    set(CUDNN_FOUND TRUE PARENT_SCOPE)
-
-    mark_as_advanced(CUDNN_INCLUDE CUDNN_LIBRARY CUDNN_ROOT)
-    message(STATUS "Found cuDNN (include: ${CUDNN_INCLUDE}, library: ${CUDNN_LIBRARY})")
-  endif()
-endfunction()
-
-
 
 ################################################################################################
 # A function for automatic detection of GPUs installed  (if autodetection is enabled)
diff --git a/cmake/Modules/FindCUDNN.cmake b/cmake/Modules/FindCUDNN.cmake
new file mode 100644
index 000000000000..a8fda5c87d9a
--- /dev/null
+++ b/cmake/Modules/FindCUDNN.cmake
@@ -0,0 +1,33 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+include(FindPackageHandleStandardArgs)
+
+set(CUDNN_ROOT "/usr/local/cuda/include" CACHE PATH "cuDNN root folder")
+
+find_path(CUDNN_INCLUDE cudnn.h
+  PATHS ${CUDNN_ROOT} $ENV{CUDNN_ROOT}
+  DOC "Path to cuDNN include directory." )
+
+find_library(CUDNN_LIBRARY NAMES libcudnn.so cudnn.lib # libcudnn_static.a
+  PATHS ${CUDNN_ROOT} $ENV{CUDNN_ROOT} ${CUDNN_INCLUDE}
+  PATH_SUFFIXES lib lib/x64  cuda/lib cuda/lib64 lib/x64
+  DOC "Path to cuDNN library.")
+
+find_package_handle_standard_args(CUDNN DEFAULT_MSG CUDNN_LIBRARY CUDNN_INCLUDE)
+
+mark_as_advanced(CUDNN_ROOT CUDNN_INCLUDE CUDNN_LIBRARY)

From bf3b4dcc3f12aa54246de32af392787647b5f839 Mon Sep 17 00:00:00 2001
From: Zach Kimberg <kimbergz@amazon.com>
Date: Mon, 9 Dec 2019 20:23:47 -0600
Subject: [PATCH 684/813] Merge make/pip and make/maven configurations (#17027)

---
 ci/docker/runtime_functions.sh                |   2 +-
 ci/publish/python/build.sh                    |   2 +-
 ci/publish/scala/build.sh                     |   2 +-
 make/maven/maven_darwin_mkl.mk                | 184 -----------------
 make/maven/maven_linux_cu90mkl.mk             | 187 ------------------
 make/maven/maven_linux_cu92mkl.mk             | 187 ------------------
 make/maven/maven_linux_mkl.mk                 | 183 -----------------
 .../darwin_cpu.mk}                            |   0
 .../darwin_mkl.mk}                            |   0
 .../linux_cpu.mk}                             |   0
 .../linux_cu100.mk}                           |   0
 .../linux_cu100mkl.mk}                        |   0
 .../linux_cu101.mk}                           |   0
 .../linux_cu101mkl.mk}                        |   0
 .../linux_cu75.mk}                            |   0
 .../linux_cu75mkl.mk}                         |   0
 .../linux_cu80.mk}                            |   0
 .../linux_cu80mkl.mk}                         |   0
 .../linux_cu90.mk}                            |   0
 .../linux_cu90mkl.mk}                         |   0
 .../linux_cu91.mk}                            |   0
 .../linux_cu91mkl.mk}                         |   0
 .../linux_cu92.mk}                            |   0
 .../linux_cu92mkl.mk}                         |   0
 .../linux_mkl.mk}                             |   0
 tools/dependencies/README.md                  |   6 +-
 tools/staticbuild/README.md                   |   7 +-
 tools/staticbuild/build.sh                    |   3 +-
 tools/staticbuild/build_lib.sh                |   2 +-
 29 files changed, 11 insertions(+), 754 deletions(-)
 delete mode 100644 make/maven/maven_darwin_mkl.mk
 delete mode 100644 make/maven/maven_linux_cu90mkl.mk
 delete mode 100644 make/maven/maven_linux_cu92mkl.mk
 delete mode 100644 make/maven/maven_linux_mkl.mk
 rename make/{pip/pip_darwin_cpu.mk => staticbuild/darwin_cpu.mk} (100%)
 rename make/{pip/pip_darwin_mkl.mk => staticbuild/darwin_mkl.mk} (100%)
 rename make/{pip/pip_linux_cpu.mk => staticbuild/linux_cpu.mk} (100%)
 rename make/{pip/pip_linux_cu100.mk => staticbuild/linux_cu100.mk} (100%)
 rename make/{pip/pip_linux_cu100mkl.mk => staticbuild/linux_cu100mkl.mk} (100%)
 rename make/{pip/pip_linux_cu101.mk => staticbuild/linux_cu101.mk} (100%)
 rename make/{pip/pip_linux_cu101mkl.mk => staticbuild/linux_cu101mkl.mk} (100%)
 rename make/{pip/pip_linux_cu75.mk => staticbuild/linux_cu75.mk} (100%)
 rename make/{pip/pip_linux_cu75mkl.mk => staticbuild/linux_cu75mkl.mk} (100%)
 rename make/{pip/pip_linux_cu80.mk => staticbuild/linux_cu80.mk} (100%)
 rename make/{pip/pip_linux_cu80mkl.mk => staticbuild/linux_cu80mkl.mk} (100%)
 rename make/{pip/pip_linux_cu90.mk => staticbuild/linux_cu90.mk} (100%)
 rename make/{pip/pip_linux_cu90mkl.mk => staticbuild/linux_cu90mkl.mk} (100%)
 rename make/{pip/pip_linux_cu91.mk => staticbuild/linux_cu91.mk} (100%)
 rename make/{pip/pip_linux_cu91mkl.mk => staticbuild/linux_cu91mkl.mk} (100%)
 rename make/{pip/pip_linux_cu92.mk => staticbuild/linux_cu92.mk} (100%)
 rename make/{pip/pip_linux_cu92mkl.mk => staticbuild/linux_cu92mkl.mk} (100%)
 rename make/{pip/pip_linux_mkl.mk => staticbuild/linux_mkl.mk} (100%)

diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index 78d541e0ad5f..9f56a6708ea4 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -1977,7 +1977,7 @@ build_static_libmxnet() {
     set -ex
     pushd .
     local mxnet_variant=${1:?"This function requires a python command as the first argument"}
-    source tools/staticbuild/build.sh ${mxnet_variant} pip
+    source tools/staticbuild/build.sh ${mxnet_variant}
     popd
 }
 
diff --git a/ci/publish/python/build.sh b/ci/publish/python/build.sh
index 61549896c0b7..976963c93eef 100755
--- a/ci/publish/python/build.sh
+++ b/ci/publish/python/build.sh
@@ -18,7 +18,7 @@
 
 set -ex
 
-source tools/staticbuild/build.sh $mxnet_variant pip
+source tools/staticbuild/build.sh $mxnet_variant
 
 set -ex
 
diff --git a/ci/publish/scala/build.sh b/ci/publish/scala/build.sh
index 30ee20e732f0..77bb34178c5c 100755
--- a/ci/publish/scala/build.sh
+++ b/ci/publish/scala/build.sh
@@ -22,7 +22,7 @@ set -ex
 # MAVEN_PUBLISH_OS_TYPE: linux-x86_64-cpu|linux-x86_64-gpu|osx-x86_64-cpu
 # export MAVEN_PUBLISH_OS_TYPE=linux-x86_64-cpu
 
-source tools/staticbuild/build.sh $mxnet_variant maven
+source tools/staticbuild/build.sh $mxnet_variant
 
 set -ex
 
diff --git a/make/maven/maven_darwin_mkl.mk b/make/maven/maven_darwin_mkl.mk
deleted file mode 100644
index 9bf3fc46ce0b..000000000000
--- a/make/maven/maven_darwin_mkl.mk
+++ /dev/null
@@ -1,184 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-#
-#-------------------------------------------------------------------------------
-#  Template configuration for compiling mxnet for making maven package
-#-------------------------------------------------------------------------------
-
-#---------------------
-# choice of compiler
-#--------------------
-
-export CC = gcc
-export CXX = g++
-export NVCC = nvcc
-
-# whether compile with options for MXNet developer
-DEV = 0
-
-# whether compile with debug
-DEBUG = 0
-
-
-# whether to turn on signal handler (e.g. segfault logger)
-USE_SIGNAL_HANDLER = 1
-
-# the additional link flags you want to add
-ADD_LDFLAGS += -L$(DEPS_PATH)/lib -lpng -ltiff -lz -framework CoreFoundation -framework Security  -Wl,-exported_symbols_list,$(CURDIR)/make/config/libmxnet.sym,-rpath,'$${ORIGIN}',-dead_strip
-
-# the additional compile flags you want to add
-ADD_CFLAGS += -I$(DEPS_PATH)/include -ffunction-sections -fdata-sections
-
-#---------------------------------------------
-# matrix computation libraries for CPU/GPU
-#---------------------------------------------
-
-# choose the version of blas you want to use
-# can be: mkl, blas, atlas, openblas
-# in default use atlas for linux while apple for osx
-USE_BLAS=apple
-
-# whether use opencv during compilation
-# you can disable it, however, you will not able to use
-# imbin iterator
-USE_OPENCV = 1
-# Add OpenCV include path, in which the directory `opencv2` exists
-USE_OPENCV_INC_PATH = NONE
-# Add OpenCV shared library path, in which the shared library exists
-USE_OPENCV_LIB_PATH = NONE
-
-# whether use CUDA during compile
-USE_CUDA = 0
-
-# add the path to CUDA library to link and compile flag
-# if you have already add them to environment variable, leave it as NONE
-# USE_CUDA_PATH = /usr/local/cuda
-USE_CUDA_PATH = NONE
-
-# whether use CuDNN R3 library
-USE_CUDNN = 0
-
-# CUDA architecture setting: going with all of them.
-# For CUDA < 6.0, comment the *_50 lines for compatibility.
-# CUDA_ARCH :=
-
-# whether use cuda runtime compiling for writing kernels in native language (i.e. Python)
-ENABLE_CUDA_RTC = 0
-
-# use openmp for parallelization
-USE_OPENMP = 0
-USE_OPERATOR_TUNING = 1
-USE_LIBJPEG_TURBO = 1
-
-
-# MKL ML Library for Intel CPU/Xeon Phi
-# Please refer to MKL_README.md for details
-
-# whether use MKL-DNN library
-USE_MKLDNN = 1
-
-# MKL ML Library folder, need to be root for /usr/local
-# Change to User Home directory for standard user
-# For USE_BLAS!=mkl only
-MKLML_ROOT=/usr/local
-
-# whether use MKL2017 library
-USE_MKL2017 = 0
-
-# whether use MKL2017 experimental feature for high performance
-# Prerequisite USE_MKL2017=1
-USE_MKL2017_EXPERIMENTAL = 0
-
-# whether use NNPACK library
-USE_NNPACK = 0
-
-# whether use lapack during compilation
-# only effective when compiled with blas versions openblas/apple/atlas/mkl
-USE_LAPACK = 1
-
-# path to lapack library in case of a non-standard installation
-USE_LAPACK_PATH = $(DEPS_PATH)/lib
-
-# add path to intel library, you may need it for MKL, if you did not add the path
-# to environment variable
-USE_INTEL_PATH = NONE
-
-# If use MKL, choose static link automatically to allow python wrapper
-ifeq ($(USE_BLAS), mkl)
-USE_STATIC_MKL = 1
-else
-USE_STATIC_MKL = NONE
-endif
-
-#----------------------------
-# Settings for power and arm arch
-#----------------------------
-ARCH := $(shell uname -a)
-ifneq (,$(filter $(ARCH), armv6l armv7l powerpc64le ppc64le aarch64))
-	USE_SSE=0
-else
-	USE_SSE=1
-endif
-
-#----------------------------
-# distributed computing
-#----------------------------
-
-# whether or not to enable multi-machine supporting
-USE_DIST_KVSTORE = 1
-
-# whether or not allow to read and write HDFS directly. If yes, then hadoop is
-# required
-USE_HDFS = 0
-
-# path to libjvm.so. required if USE_HDFS=1
-LIBJVM=$(JAVA_HOME)/jre/lib/amd64/server
-
-# whether or not allow to read and write AWS S3 directly. If yes, then
-# libcurl4-openssl-dev is required, it can be installed on Ubuntu by
-# sudo apt-get install -y libcurl4-openssl-dev
-USE_S3 = 1
-
-#----------------------------
-# additional operators
-#----------------------------
-
-# path to folders containing projects specific operators that you don't want to put in src/operators
-EXTRA_OPERATORS =
-
-
-#----------------------------
-# plugins
-#----------------------------
-
-# whether to use caffe integration. This requires installing caffe.
-# You also need to add CAFFE_PATH/build/lib to your LD_LIBRARY_PATH
-# CAFFE_PATH = $(HOME)/caffe
-# MXNET_PLUGINS += plugin/caffe/caffe.mk
-
-# whether to use torch integration. This requires installing torch.
-# You also need to add TORCH_PATH/install/lib to your LD_LIBRARY_PATH
-# TORCH_PATH = $(HOME)/torch
-# MXNET_PLUGINS += plugin/torch/torch.mk
-
-# WARPCTC_PATH = $(HOME)/warp-ctc
-# MXNET_PLUGINS += plugin/warpctc/warpctc.mk
-
-# whether to use sframe integration. This requires build sframe
-# git@github.com:dato-code/SFrame.git
-# SFRAME_PATH = $(HOME)/SFrame
-# MXNET_PLUGINS += plugin/sframe/plugin.mk
diff --git a/make/maven/maven_linux_cu90mkl.mk b/make/maven/maven_linux_cu90mkl.mk
deleted file mode 100644
index e8caf73f186e..000000000000
--- a/make/maven/maven_linux_cu90mkl.mk
+++ /dev/null
@@ -1,187 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-#
-#-------------------------------------------------------------------------------
-#  Template configuration for compiling mxnet for making maven package
-#-------------------------------------------------------------------------------
-
-#---------------------
-# choice of compiler
-#--------------------
-
-export CC = gcc
-export CXX = g++
-export NVCC = nvcc
-
-# whether compile with options for MXNet developer
-DEV = 0
-
-# whether compile with debug
-DEBUG = 0
-
-# whether to turn on signal handler (e.g. segfault logger)
-USE_SIGNAL_HANDLER = 1
-
-# the additional link flags you want to add
-ADD_LDFLAGS += -L$(DEPS_PATH)/lib $(DEPS_PATH)/lib/libculibos.a -lpng -ltiff -ljpeg -lz -ldl -lgfortran -Wl,--version-script=$(CURDIR)/make/config/libmxnet.ver,-rpath,'$${ORIGIN}',--gc-sections
-
-# the additional compile flags you want to add
-ADD_CFLAGS += -I$(DEPS_PATH)/include -ffunction-sections -fdata-sections
-
-#---------------------------------------------
-# matrix computation libraries for CPU/GPU
-#---------------------------------------------
-
-# choose the version of blas you want to use
-# can be: mkl, blas, atlas, openblas
-# in default use atlas for linux while apple for osx
-USE_BLAS=openblas
-
-# whether use opencv during compilation
-# you can disable it, however, you will not able to use
-# imbin iterator
-USE_OPENCV = 1
-# Add OpenCV include path, in which the directory `opencv2` exists
-USE_OPENCV_INC_PATH = NONE
-# Add OpenCV shared library path, in which the shared library exists
-USE_OPENCV_LIB_PATH = NONE
-
-# whether use CUDA during compile
-USE_CUDA = 1
-
-# add the path to CUDA library to link and compile flag
-# if you have already add them to environment variable, leave it as NONE
-# USE_CUDA_PATH = /usr/local/cuda
-USE_CUDA_PATH = $(DEPS_PATH)/usr/local/cuda-9.0
-
-# whether to use CuDNN library
-USE_CUDNN = 1
-
-# whether to use NCCL library
-USE_NCCL = 1
-
-# CUDA architecture setting: going with all of them.
-# For CUDA < 6.0, comment the *_50 lines for compatibility.
-# CUDA_ARCH :=
-
-# whether use cuda runtime compiling for writing kernels in native language (i.e. Python)
-USE_NVTX=1
-ENABLE_CUDA_RTC = 1
-
-# use openmp for parallelization
-USE_OPENMP = 1
-USE_OPERATOR_TUNING = 1
-USE_LIBJPEG_TURBO = 1
-
-# whether use MKL-DNN library
-USE_MKLDNN = 1
-
-
-# MKL ML Library for Intel CPU/Xeon Phi
-# Please refer to MKL_README.md for details
-
-# MKL ML Library folder, need to be root for /usr/local
-# Change to User Home directory for standard user
-# For USE_BLAS!=mkl only
-MKLML_ROOT=/usr/local
-
-# whether use MKL2017 library
-USE_MKL2017 = 0
-
-# whether use MKL2017 experimental feature for high performance
-# Prerequisite USE_MKL2017=1
-USE_MKL2017_EXPERIMENTAL = 0
-
-# whether use NNPACK library
-USE_NNPACK = 0
-
-# whether use lapack during compilation
-# only effective when compiled with blas versions openblas/apple/atlas/mkl
-USE_LAPACK = 1
-
-# path to lapack library in case of a non-standard installation
-USE_LAPACK_PATH = $(DEPS_PATH)/lib
-
-# add path to intel library, you may need it for MKL, if you did not add the path
-# to environment variable
-USE_INTEL_PATH = NONE
-
-# If use MKL, choose static link automatically to allow python wrapper
-ifeq ($(USE_BLAS), mkl)
-USE_STATIC_MKL = 1
-else
-USE_STATIC_MKL = NONE
-endif
-
-#----------------------------
-# Settings for power and arm arch
-#----------------------------
-ARCH := $(shell uname -a)
-ifneq (,$(filter $(ARCH), armv6l armv7l powerpc64le ppc64le aarch64))
-	USE_SSE=0
-else
-	USE_SSE=1
-endif
-
-#----------------------------
-# distributed computing
-#----------------------------
-
-# whether or not to enable multi-machine supporting
-USE_DIST_KVSTORE = 1
-
-# whether or not allow to read and write HDFS directly. If yes, then hadoop is
-# required
-USE_HDFS = 0
-
-# path to libjvm.so. required if USE_HDFS=1
-LIBJVM=$(JAVA_HOME)/jre/lib/amd64/server
-
-# whether or not allow to read and write AWS S3 directly. If yes, then
-# libcurl4-openssl-dev is required, it can be installed on Ubuntu by
-# sudo apt-get install -y libcurl4-openssl-dev
-USE_S3 = 1
-
-#----------------------------
-# additional operators
-#----------------------------
-
-# path to folders containing projects specific operators that you don't want to put in src/operators
-EXTRA_OPERATORS =
-
-
-#----------------------------
-# plugins
-#----------------------------
-
-# whether to use caffe integration. This requires installing caffe.
-# You also need to add CAFFE_PATH/build/lib to your LD_LIBRARY_PATH
-# CAFFE_PATH = $(HOME)/caffe
-# MXNET_PLUGINS += plugin/caffe/caffe.mk
-
-# whether to use torch integration. This requires installing torch.
-# You also need to add TORCH_PATH/install/lib to your LD_LIBRARY_PATH
-# TORCH_PATH = $(HOME)/torch
-# MXNET_PLUGINS += plugin/torch/torch.mk
-
-# WARPCTC_PATH = $(HOME)/warp-ctc
-# MXNET_PLUGINS += plugin/warpctc/warpctc.mk
-
-# whether to use sframe integration. This requires build sframe
-# git@github.com:dato-code/SFrame.git
-# SFRAME_PATH = $(HOME)/SFrame
-# MXNET_PLUGINS += plugin/sframe/plugin.mk
diff --git a/make/maven/maven_linux_cu92mkl.mk b/make/maven/maven_linux_cu92mkl.mk
deleted file mode 100644
index 930341e71cb1..000000000000
--- a/make/maven/maven_linux_cu92mkl.mk
+++ /dev/null
@@ -1,187 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-#
-#-------------------------------------------------------------------------------
-#  Template configuration for compiling mxnet for making maven package
-#-------------------------------------------------------------------------------
-
-#---------------------
-# choice of compiler
-#--------------------
-
-export CC = gcc
-export CXX = g++
-export NVCC = nvcc
-
-# whether compile with options for MXNet developer
-DEV = 0
-
-# whether compile with debug
-DEBUG = 0
-
-# whether to turn on signal handler (e.g. segfault logger)
-USE_SIGNAL_HANDLER = 1
-
-# the additional link flags you want to add
-ADD_LDFLAGS += -L$(DEPS_PATH)/lib $(DEPS_PATH)/lib/libculibos.a -lpng -ltiff -ljpeg -lz -ldl -lgfortran -Wl,--version-script=$(CURDIR)/make/config/libmxnet.ver,-rpath,'$${ORIGIN}',--gc-sections
-
-# the additional compile flags you want to add
-ADD_CFLAGS += -I$(DEPS_PATH)/include -ffunction-sections -fdata-sections
-
-#---------------------------------------------
-# matrix computation libraries for CPU/GPU
-#---------------------------------------------
-
-# choose the version of blas you want to use
-# can be: mkl, blas, atlas, openblas
-# in default use atlas for linux while apple for osx
-USE_BLAS=openblas
-
-# whether use opencv during compilation
-# you can disable it, however, you will not able to use
-# imbin iterator
-USE_OPENCV = 1
-# Add OpenCV include path, in which the directory `opencv2` exists
-USE_OPENCV_INC_PATH = NONE
-# Add OpenCV shared library path, in which the shared library exists
-USE_OPENCV_LIB_PATH = NONE
-
-# whether use CUDA during compile
-USE_CUDA = 1
-
-# add the path to CUDA library to link and compile flag
-# if you have already add them to environment variable, leave it as NONE
-# USE_CUDA_PATH = /usr/local/cuda
-USE_CUDA_PATH = $(DEPS_PATH)/usr/local/cuda-9.2
-
-# whether to use CuDNN library
-USE_CUDNN = 1
-
-# whether to use NCCL library
-USE_NCCL = 1
-
-# CUDA architecture setting: going with all of them.
-# For CUDA < 6.0, comment the *_50 lines for compatibility.
-# CUDA_ARCH :=
-
-# whether use cuda runtime compiling for writing kernels in native language (i.e. Python)
-USE_NVTX=1
-ENABLE_CUDA_RTC = 1
-
-# use openmp for parallelization
-USE_OPENMP = 1
-USE_OPERATOR_TUNING = 1
-USE_LIBJPEG_TURBO = 1
-
-# whether use MKL-DNN library
-USE_MKLDNN = 1
-
-
-# MKL ML Library for Intel CPU/Xeon Phi
-# Please refer to MKL_README.md for details
-
-# MKL ML Library folder, need to be root for /usr/local
-# Change to User Home directory for standard user
-# For USE_BLAS!=mkl only
-MKLML_ROOT=/usr/local
-
-# whether use MKL2017 library
-USE_MKL2017 = 0
-
-# whether use MKL2017 experimental feature for high performance
-# Prerequisite USE_MKL2017=1
-USE_MKL2017_EXPERIMENTAL = 0
-
-# whether use NNPACK library
-USE_NNPACK = 0
-
-# whether use lapack during compilation
-# only effective when compiled with blas versions openblas/apple/atlas/mkl
-USE_LAPACK = 1
-
-# path to lapack library in case of a non-standard installation
-USE_LAPACK_PATH = $(DEPS_PATH)/lib
-
-# add path to intel library, you may need it for MKL, if you did not add the path
-# to environment variable
-USE_INTEL_PATH = NONE
-
-# If use MKL, choose static link automatically to allow python wrapper
-ifeq ($(USE_BLAS), mkl)
-USE_STATIC_MKL = 1
-else
-USE_STATIC_MKL = NONE
-endif
-
-#----------------------------
-# Settings for power and arm arch
-#----------------------------
-ARCH := $(shell uname -a)
-ifneq (,$(filter $(ARCH), armv6l armv7l powerpc64le ppc64le aarch64))
-	USE_SSE=0
-else
-	USE_SSE=1
-endif
-
-#----------------------------
-# distributed computing
-#----------------------------
-
-# whether or not to enable multi-machine supporting
-USE_DIST_KVSTORE = 1
-
-# whether or not allow to read and write HDFS directly. If yes, then hadoop is
-# required
-USE_HDFS = 0
-
-# path to libjvm.so. required if USE_HDFS=1
-LIBJVM=$(JAVA_HOME)/jre/lib/amd64/server
-
-# whether or not allow to read and write AWS S3 directly. If yes, then
-# libcurl4-openssl-dev is required, it can be installed on Ubuntu by
-# sudo apt-get install -y libcurl4-openssl-dev
-USE_S3 = 1
-
-#----------------------------
-# additional operators
-#----------------------------
-
-# path to folders containing projects specific operators that you don't want to put in src/operators
-EXTRA_OPERATORS =
-
-
-#----------------------------
-# plugins
-#----------------------------
-
-# whether to use caffe integration. This requires installing caffe.
-# You also need to add CAFFE_PATH/build/lib to your LD_LIBRARY_PATH
-# CAFFE_PATH = $(HOME)/caffe
-# MXNET_PLUGINS += plugin/caffe/caffe.mk
-
-# whether to use torch integration. This requires installing torch.
-# You also need to add TORCH_PATH/install/lib to your LD_LIBRARY_PATH
-# TORCH_PATH = $(HOME)/torch
-# MXNET_PLUGINS += plugin/torch/torch.mk
-
-# WARPCTC_PATH = $(HOME)/warp-ctc
-# MXNET_PLUGINS += plugin/warpctc/warpctc.mk
-
-# whether to use sframe integration. This requires build sframe
-# git@github.com:dato-code/SFrame.git
-# SFRAME_PATH = $(HOME)/SFrame
-# MXNET_PLUGINS += plugin/sframe/plugin.mk
diff --git a/make/maven/maven_linux_mkl.mk b/make/maven/maven_linux_mkl.mk
deleted file mode 100644
index 10aee5f35a46..000000000000
--- a/make/maven/maven_linux_mkl.mk
+++ /dev/null
@@ -1,183 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-#
-#-------------------------------------------------------------------------------
-#  Template configuration for compiling mxnet for making maven package
-#-------------------------------------------------------------------------------
-
-#---------------------
-# choice of compiler
-#--------------------
-
-export CC = gcc
-export CXX = g++
-export NVCC = nvcc
-
-# whether compile with options for MXNet developer
-DEV = 0
-
-# whether compile with debug
-DEBUG = 0
-
-# whether to turn on signal handler (e.g. segfault logger)
-USE_SIGNAL_HANDLER = 1
-
-# the additional link flags you want to add
-ADD_LDFLAGS += -L$(DEPS_PATH)/lib -lpng -ltiff -ljpeg -lz -lgfortran -ldl -Wl,--version-script=$(CURDIR)/make/config/libmxnet.ver,-rpath,'$${ORIGIN}',--gc-sections
-
-# the additional compile flags you want to add
-ADD_CFLAGS += -I$(DEPS_PATH)/include -ffunction-sections -fdata-sections
-
-#---------------------------------------------
-# matrix computation libraries for CPU/GPU
-#---------------------------------------------
-
-# choose the version of blas you want to use
-# can be: mkl, blas, atlas, openblas
-# in default use atlas for linux while apple for osx
-USE_BLAS=openblas
-
-# whether use opencv during compilation
-# you can disable it, however, you will not able to use
-# imbin iterator
-USE_OPENCV = 1
-# Add OpenCV include path, in which the directory `opencv2` exists
-USE_OPENCV_INC_PATH = NONE
-# Add OpenCV shared library path, in which the shared library exists
-USE_OPENCV_LIB_PATH = NONE
-
-# whether use CUDA during compile
-USE_CUDA = 0
-
-# add the path to CUDA library to link and compile flag
-# if you have already add them to environment variable, leave it as NONE
-# USE_CUDA_PATH = /usr/local/cuda
-USE_CUDA_PATH = NONE
-
-# whether use CuDNN R3 library
-USE_CUDNN = 0
-
-# CUDA architecture setting: going with all of them.
-# For CUDA < 6.0, comment the *_50 lines for compatibility.
-# CUDA_ARCH :=
-
-# whether use cuda runtime compiling for writing kernels in native language (i.e. Python)
-ENABLE_CUDA_RTC = 0
-
-# use openmp for parallelization
-USE_OPENMP = 1
-USE_OPERATOR_TUNING = 1
-USE_LIBJPEG_TURBO = 1
-
-# whether use MKL-DNN library
-USE_MKLDNN = 1
-
-
-# MKL ML Library for Intel CPU/Xeon Phi
-# Please refer to MKL_README.md for details
-
-# MKL ML Library folder, need to be root for /usr/local
-# Change to User Home directory for standard user
-# For USE_BLAS!=mkl only
-MKLML_ROOT=/usr/local
-
-# whether use MKL2017 library
-USE_MKL2017 = 0
-
-# whether use MKL2017 experimental feature for high performance
-# Prerequisite USE_MKL2017=1
-USE_MKL2017_EXPERIMENTAL = 0
-
-# whether use NNPACK library
-USE_NNPACK = 0
-
-# whether use lapack during compilation
-# only effective when compiled with blas versions openblas/apple/atlas/mkl
-USE_LAPACK = 1
-
-# path to lapack library in case of a non-standard installation
-USE_LAPACK_PATH = $(DEPS_PATH)/lib
-
-# add path to intel library, you may need it for MKL, if you did not add the path
-# to environment variable
-USE_INTEL_PATH = NONE
-
-# If use MKL, choose static link automatically to allow python wrapper
-ifeq ($(USE_BLAS), mkl)
-USE_STATIC_MKL = 1
-else
-USE_STATIC_MKL = NONE
-endif
-
-#----------------------------
-# Settings for power and arm arch
-#----------------------------
-ARCH := $(shell uname -a)
-ifneq (,$(filter $(ARCH), armv6l armv7l powerpc64le ppc64le aarch64))
-	USE_SSE=0
-else
-	USE_SSE=1
-endif
-
-#----------------------------
-# distributed computing
-#----------------------------
-
-# whether or not to enable multi-machine supporting
-USE_DIST_KVSTORE = 1
-
-# whether or not allow to read and write HDFS directly. If yes, then hadoop is
-# required
-USE_HDFS = 0
-
-# path to libjvm.so. required if USE_HDFS=1
-LIBJVM=$(JAVA_HOME)/jre/lib/amd64/server
-
-# whether or not allow to read and write AWS S3 directly. If yes, then
-# libcurl4-openssl-dev is required, it can be installed on Ubuntu by
-# sudo apt-get install -y libcurl4-openssl-dev
-USE_S3 = 1
-
-#----------------------------
-# additional operators
-#----------------------------
-
-# path to folders containing projects specific operators that you don't want to put in src/operators
-EXTRA_OPERATORS =
-
-
-#----------------------------
-# plugins
-#----------------------------
-
-# whether to use caffe integration. This requires installing caffe.
-# You also need to add CAFFE_PATH/build/lib to your LD_LIBRARY_PATH
-# CAFFE_PATH = $(HOME)/caffe
-# MXNET_PLUGINS += plugin/caffe/caffe.mk
-
-# whether to use torch integration. This requires installing torch.
-# You also need to add TORCH_PATH/install/lib to your LD_LIBRARY_PATH
-# TORCH_PATH = $(HOME)/torch
-# MXNET_PLUGINS += plugin/torch/torch.mk
-
-# WARPCTC_PATH = $(HOME)/warp-ctc
-# MXNET_PLUGINS += plugin/warpctc/warpctc.mk
-
-# whether to use sframe integration. This requires build sframe
-# git@github.com:dato-code/SFrame.git
-# SFRAME_PATH = $(HOME)/SFrame
-# MXNET_PLUGINS += plugin/sframe/plugin.mk
diff --git a/make/pip/pip_darwin_cpu.mk b/make/staticbuild/darwin_cpu.mk
similarity index 100%
rename from make/pip/pip_darwin_cpu.mk
rename to make/staticbuild/darwin_cpu.mk
diff --git a/make/pip/pip_darwin_mkl.mk b/make/staticbuild/darwin_mkl.mk
similarity index 100%
rename from make/pip/pip_darwin_mkl.mk
rename to make/staticbuild/darwin_mkl.mk
diff --git a/make/pip/pip_linux_cpu.mk b/make/staticbuild/linux_cpu.mk
similarity index 100%
rename from make/pip/pip_linux_cpu.mk
rename to make/staticbuild/linux_cpu.mk
diff --git a/make/pip/pip_linux_cu100.mk b/make/staticbuild/linux_cu100.mk
similarity index 100%
rename from make/pip/pip_linux_cu100.mk
rename to make/staticbuild/linux_cu100.mk
diff --git a/make/pip/pip_linux_cu100mkl.mk b/make/staticbuild/linux_cu100mkl.mk
similarity index 100%
rename from make/pip/pip_linux_cu100mkl.mk
rename to make/staticbuild/linux_cu100mkl.mk
diff --git a/make/pip/pip_linux_cu101.mk b/make/staticbuild/linux_cu101.mk
similarity index 100%
rename from make/pip/pip_linux_cu101.mk
rename to make/staticbuild/linux_cu101.mk
diff --git a/make/pip/pip_linux_cu101mkl.mk b/make/staticbuild/linux_cu101mkl.mk
similarity index 100%
rename from make/pip/pip_linux_cu101mkl.mk
rename to make/staticbuild/linux_cu101mkl.mk
diff --git a/make/pip/pip_linux_cu75.mk b/make/staticbuild/linux_cu75.mk
similarity index 100%
rename from make/pip/pip_linux_cu75.mk
rename to make/staticbuild/linux_cu75.mk
diff --git a/make/pip/pip_linux_cu75mkl.mk b/make/staticbuild/linux_cu75mkl.mk
similarity index 100%
rename from make/pip/pip_linux_cu75mkl.mk
rename to make/staticbuild/linux_cu75mkl.mk
diff --git a/make/pip/pip_linux_cu80.mk b/make/staticbuild/linux_cu80.mk
similarity index 100%
rename from make/pip/pip_linux_cu80.mk
rename to make/staticbuild/linux_cu80.mk
diff --git a/make/pip/pip_linux_cu80mkl.mk b/make/staticbuild/linux_cu80mkl.mk
similarity index 100%
rename from make/pip/pip_linux_cu80mkl.mk
rename to make/staticbuild/linux_cu80mkl.mk
diff --git a/make/pip/pip_linux_cu90.mk b/make/staticbuild/linux_cu90.mk
similarity index 100%
rename from make/pip/pip_linux_cu90.mk
rename to make/staticbuild/linux_cu90.mk
diff --git a/make/pip/pip_linux_cu90mkl.mk b/make/staticbuild/linux_cu90mkl.mk
similarity index 100%
rename from make/pip/pip_linux_cu90mkl.mk
rename to make/staticbuild/linux_cu90mkl.mk
diff --git a/make/pip/pip_linux_cu91.mk b/make/staticbuild/linux_cu91.mk
similarity index 100%
rename from make/pip/pip_linux_cu91.mk
rename to make/staticbuild/linux_cu91.mk
diff --git a/make/pip/pip_linux_cu91mkl.mk b/make/staticbuild/linux_cu91mkl.mk
similarity index 100%
rename from make/pip/pip_linux_cu91mkl.mk
rename to make/staticbuild/linux_cu91mkl.mk
diff --git a/make/pip/pip_linux_cu92.mk b/make/staticbuild/linux_cu92.mk
similarity index 100%
rename from make/pip/pip_linux_cu92.mk
rename to make/staticbuild/linux_cu92.mk
diff --git a/make/pip/pip_linux_cu92mkl.mk b/make/staticbuild/linux_cu92mkl.mk
similarity index 100%
rename from make/pip/pip_linux_cu92mkl.mk
rename to make/staticbuild/linux_cu92mkl.mk
diff --git a/make/pip/pip_linux_mkl.mk b/make/staticbuild/linux_mkl.mk
similarity index 100%
rename from make/pip/pip_linux_mkl.mk
rename to make/staticbuild/linux_mkl.mk
diff --git a/tools/dependencies/README.md b/tools/dependencies/README.md
index 024fedf16f73..ec1e80088895 100644
--- a/tools/dependencies/README.md
+++ b/tools/dependencies/README.md
@@ -190,7 +190,7 @@ cd incubator-mxnet
 # http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/
 
 # Build PyPi package
-tools/staticbuild/build.sh cu100mkl pip
+tools/staticbuild/build.sh cu100mkl
 
 # Wait for 10 - 30 mins, you will find libmxnet.so under the incubator-mxnet/lib
 
@@ -279,7 +279,7 @@ sudo apt-get install -y git \
 2. Build PyPi package
 ```
 # Update the dependency under tools/dependencies, then
-tools/staticbuild/build.sh mkl pip
+tools/staticbuild/build.sh mkl
 
 # Wait for 10 - 30 mins, you will find libmxnet.so under the incubator-mxnet/lib
 
@@ -328,7 +328,7 @@ sudo apt-get install -y git \
 2. Build PyPi package
 ```
 # Update the dependency under tools/dependencies, then
-tools/staticbuild/build.sh mkl pip
+tools/staticbuild/build.sh mkl
 
 # Wait for 10 - 30 mins, you will find libmxnet.so under the incubator-mxnet/lib
 
diff --git a/tools/staticbuild/README.md b/tools/staticbuild/README.md
index b861cc3308c6..b48043b1ef87 100644
--- a/tools/staticbuild/README.md
+++ b/tools/staticbuild/README.md
@@ -23,11 +23,11 @@ This folder contains the core script used to build the static library. This READ
 This script is a wrapper around `build_lib.sh. It simplifies the things by automatically identifing the system version, number of cores, and all environment variable settings. Here are examples you can run with this script:
 
 ```
-tools/staticbuild/build.sh cu92 maven
+tools/staticbuild/build.sh cu92
 ```
-This would build the mxnet package based on CUDA9.2 and Maven (Scala) build setttings.
+This would build the mxnet package based on CUDA9.2.
 ```
-tools/staticbuild/build.sh mkl pip
+tools/staticbuild/build.sh mkl
 ```
 This would build the mxnet package based on MKLDNN and and pypi configuration settings.
 
@@ -37,7 +37,6 @@ As the result, users would have a complete static dependencies in `/staticdeps`
 This script clones the most up-to-date master and builds the MXNet backend with a static library. In order to run the static library, you must set the following environment variables:
 
 - `DEPS_PATH` Path to your static dependencies
-- `STATIC_BUILD_TARGET` Either `pip` or `maven` as your publish platform
 - `PLATFORM` linux, darwin
 - `VARIANT` cpu, cu*, cu*mkl, mkl
 
diff --git a/tools/staticbuild/build.sh b/tools/staticbuild/build.sh
index 7207cf062180..2d1ac43e4a1b 100755
--- a/tools/staticbuild/build.sh
+++ b/tools/staticbuild/build.sh
@@ -18,13 +18,12 @@
 # under the License.
 
 if [ $# -lt 1 ]; then
-    >&2 echo "Usage: build.sh <VARIANT> <TARGET>"
+    >&2 echo "Usage: build.sh <VARIANT>"
 fi
 
 export CURDIR=$PWD
 export DEPS_PATH=$PWD/staticdeps
 export VARIANT=$(echo $1 | tr '[:upper:]' '[:lower:]')
-export STATIC_BUILD_TARGET=$(echo $2 | tr '[:upper:]' '[:lower:]')
 export PLATFORM=$(uname | tr '[:upper:]' '[:lower:]')
 
 if [[ $VARIANT == darwin* ]]; then
diff --git a/tools/staticbuild/build_lib.sh b/tools/staticbuild/build_lib.sh
index 4935d67b9316..f4fbf399ce86 100755
--- a/tools/staticbuild/build_lib.sh
+++ b/tools/staticbuild/build_lib.sh
@@ -18,7 +18,7 @@
 # under the License.
 
 # This script builds the libraries of mxnet.
-make_config=make/${STATIC_BUILD_TARGET}/${STATIC_BUILD_TARGET}_${PLATFORM}_${VARIANT}.mk
+make_config=make/staticbuild/${PLATFORM}_${VARIANT}.mk
 if [[ ! -f $make_config ]]; then
     >&2 echo "Couldn't find make config $make_config for the current settings."
     exit 1

From 9f5b8bca2e972d30ba22ab4a7287d55277af865e Mon Sep 17 00:00:00 2001
From: Przemyslaw Tredak <ptredak@nvidia.com>
Date: Mon, 9 Dec 2019 20:21:09 -0800
Subject: [PATCH 685/813] Workaround problem with fusion in CUDA 9 (#17028)

---
 src/operator/fusion/fused_op-inl.h | 232 +++++++++++++++--------------
 src/operator/fusion/fused_op.cu    |   5 +-
 2 files changed, 123 insertions(+), 114 deletions(-)

diff --git a/src/operator/fusion/fused_op-inl.h b/src/operator/fusion/fused_op-inl.h
index e86ce7682ad8..7373cd07400a 100644
--- a/src/operator/fusion/fused_op-inl.h
+++ b/src/operator/fusion/fused_op-inl.h
@@ -256,22 +256,22 @@ struct LoadType<half> {
 };
 
 template <typename DType>
-inline typename LoadType<DType>::Type load(const DType input) {
+__device__ inline typename LoadType<DType>::Type load(const DType input) {
   return input;
 }
 
 template <>
-inline float load(const half input) {
+__device__ inline float load(const half input) {
   return __half2float(input);
 }
 
 template <typename DType1, typename DType2>
-inline DType1 store(const DType2 input, DType1* ref) {
+__device__ inline DType1 store(const DType2 input, DType1* ref) {
   return input;
 }
 
 template <typename DType>
-inline half store(const DType input, half* ref) {
+__device__ inline half store(const DType input, half* ref) {
   return __float2half(input);
 }
 
@@ -297,12 +297,12 @@ struct VectorConfig<32> {
 };
 
 template <typename DType>
-inline DType add_elem(const DType& x, const DType& y) {
+__device__ inline DType add_elem(const DType& x, const DType& y) {
   return x + y;
 }
 
 template <>
-inline half add_elem(const half& x, const half& y) {
+__device__ inline half add_elem(const half& x, const half& y) {
   return __float2half(__half2float(x) + __half2float(y));
 }
 
@@ -310,14 +310,14 @@ template <typename DType, int nvec>
 union VectorType {
     typename VectorConfig<sizeof(DType)*nvec>::IndexType y;
     DType x[nvec];
-    VectorType () {};
-    VectorType (const VectorType<DType, nvec>& y2) {
+    __device__ VectorType () {};
+    __device__ VectorType (const VectorType<DType, nvec>& y2) {
         y = y2.y;
     }
-    VectorType (const decltype(y) &y2) {
+    __device__ VectorType (const decltype(y) &y2) {
         y = y2;
     }
-    inline VectorType<DType, nvec>& operator+=(const VectorType<DType, nvec>& rhs) {
+    __device__ inline VectorType<DType, nvec>& operator+=(const VectorType<DType, nvec>& rhs) {
       #pragma unroll
       for (int i = 0; i < nvec; ++i) {
         x[i] = add_elem(x[i], rhs.x[i]);
@@ -330,13 +330,13 @@ template <int ndim>
 struct Shape {
    int x[ndim];
    size_t size;
-   inline const int& operator [](const int i) const {
+   __device__ inline const int& operator [](const int i) const {
        return x[i];
    }
-   inline int& operator [](const int i) {
+   __device__ inline int& operator [](const int i) {
        return x[i];
    }
-   inline void set(const int def) {
+   __device__ inline void set(const int def) {
        #pragma unroll
        for (int i = 0; i < ndim; i++) {
            x[i] = def;
@@ -350,7 +350,8 @@ struct Shape<0> {
 };
 
 template <int nvec, typename DType, int ndim>
-inline VectorType<DType, nvec> load_index(const DType * input, int i, const Shape<ndim> &shape) {
+__device__ inline VectorType<DType, nvec> load_index(const DType * input, int i,
+                                                     const Shape<ndim> &shape) {
   if (i < shape.size) {
     const auto* vector_input = reinterpret_cast<
                                 const typename VectorConfig<sizeof(DType)*nvec>::IndexType *>(
@@ -364,7 +365,8 @@ inline VectorType<DType, nvec> load_index(const DType * input, int i, const Shap
 }
 
 template <int nvec, typename DType, int ndim>
-inline VectorType<DType, nvec> global_load_index(const DType * input, int i, const Shape<ndim> &shape) {
+__device__ inline VectorType<DType, nvec> global_load_index(const DType * input, int i,
+                                                            const Shape<ndim> &shape) {
   if (i < shape.size) {
     const auto* vector_input = reinterpret_cast<
                                 const typename VectorConfig<sizeof(DType)*nvec>::IndexType *>(
@@ -378,7 +380,9 @@ inline VectorType<DType, nvec> global_load_index(const DType * input, int i, con
 }
 
 template <int nvec, typename DType, int ndim>
-inline VectorType<DType, nvec> load_slice(const DType * input, const Shape<ndim>& shape, Shape<ndim> begin, Shape<ndim> end, int offset) {
+__device__ inline VectorType<DType, nvec> load_slice(const DType * input, const Shape<ndim>& shape,
+                                                     Shape<ndim> begin, Shape<ndim> end,
+                                                     int offset) {
   int idx[nvec];
 
   Shape<ndim> ref_strides;
@@ -417,7 +421,11 @@ inline VectorType<DType, nvec> load_slice(const DType * input, const Shape<ndim>
 }
 
 template <int nvec, typename DType, int ndim>
-inline VectorType<DType, nvec> fast_load_slice(const DType * input, const Shape<ndim>& shape, Shape<ndim> begin, Shape<ndim> end, int offset) {
+__device__ inline VectorType<DType, nvec> fast_load_slice(const DType * input,
+                                                          const Shape<ndim>& shape,
+                                                          Shape<ndim> begin,
+                                                          Shape<ndim> end,
+                                                          int offset) {
   int idx = 0;
 
   Shape<ndim> ref_strides;
@@ -447,7 +455,7 @@ inline VectorType<DType, nvec> fast_load_slice(const DType * input, const Shape<
 }
 
 template <int nvec, typename DType, int ndim>
-inline void store_index(const VectorType<DType, nvec> value, int i,
+__device__ inline void store_index(const VectorType<DType, nvec> value, int i,
                         DType * output, const Shape<ndim>& shape) {
   if (i < (shape.size + nvec - 1) / nvec) {
     auto vector_output = reinterpret_cast<
@@ -457,7 +465,7 @@ inline void store_index(const VectorType<DType, nvec> value, int i,
 }
 
 template <int nvec, typename DType, int ndim>
-inline void store_add_index(const VectorType<DType, nvec> value, int i,
+__device__ inline void store_add_index(const VectorType<DType, nvec> value, int i,
                             DType * output, const Shape<ndim>& shape) {
   if (i < (shape.size + nvec - 1) / nvec) {
     auto vector_output = reinterpret_cast<
@@ -469,116 +477,116 @@ inline void store_add_index(const VectorType<DType, nvec> value, int i,
 }
 
 template <typename DType>
-inline DType identity(const DType val) {
+__device__ inline DType identity(const DType val) {
   return val;
 }
 
 template <typename DType, typename DType2>
-inline DType add(const DType a, const DType2 b) {
+__device__ inline DType add(const DType a, const DType2 b) {
   return a + b;
 }
 
 template <typename DType, typename DType2>
-inline DType sub(const DType a, const DType2 b) {
+__device__ inline DType sub(const DType a, const DType2 b) {
   return a - b;
 }
 
 template <typename DType, typename DType2>
-inline DType mul(const DType a, const DType2 b) {
+__device__ inline DType mul(const DType a, const DType2 b) {
   return a * b;
 }
 
 template <typename DType, typename DType2>
-inline DType div(const DType a, const DType2 b) {
+__device__ inline DType div(const DType a, const DType2 b) {
   return a / b;
 }
 
 template <typename DType, typename DType2>
-inline DType rdiv(const DType a, const DType2 b) {
+__device__ inline DType rdiv(const DType a, const DType2 b) {
   return b / a;
 }
 
 template <typename DType, typename DType2>
-inline DType power(const DType a, const DType2 b) {
+__device__ inline DType power(const DType a, const DType2 b) {
   return powf(a, b);
 }
 
 template <typename DType, typename DType2>
-inline DType rpow(const DType a, const DType2 b) {
+__device__ inline DType rpow(const DType a, const DType2 b) {
   return powf(b, a);
 }
 
 template <typename DType, typename DType2>
-inline DType max(const DType a, const DType2 b) {
+__device__ inline DType max(const DType a, const DType2 b) {
   return a > b ? a : b;
 }
 
 template <typename DType, typename DType2>
-inline DType min(const DType a, const DType2 b) {
+__device__ inline DType min(const DType a, const DType2 b) {
   return a < b ? a : b;
 }
 
 template <typename DType, typename DType2>
-inline DType hypot(const DType a, const DType2 b) {
+__device__ inline DType hypot(const DType a, const DType2 b) {
   return hypotf(a, b);
 }
 
 template <typename OutType, typename DType>
-inline typename LoadType<OutType>::Type cast(const DType val) {
+__device__ inline typename LoadType<OutType>::Type cast(const DType val) {
   return static_cast<typename LoadType<OutType>::Type>(val);
 }
 
 // activations
 
 template <typename DType>
-inline DType relu(const DType val) {
+__device__ inline DType relu(const DType val) {
   return val > 0 ? val : 0;
 }
 
 template <typename DType>
-inline DType sigmoid(const DType val) {
+__device__ inline DType sigmoid(const DType val) {
   return 1.f/(1 + expf(-val));
 }
 
 template <typename DType>
-inline DType softrelu(const DType val) {
+__device__ inline DType softrelu(const DType val) {
   return logf(1 + expf(val));
 }
 
 template <typename DType>
-inline DType softsign(const DType val) {
+__device__ inline DType softsign(const DType val) {
   return val / (1 + fabsf(val));
 }
 
 // exp and log
 
 template <typename DType>
-inline DType exp(const DType val) {
+__device__ inline DType exp(const DType val) {
   return expf(val);
 }
 
 template <typename DType>
-inline DType expm1(const DType val) {
+__device__ inline DType expm1(const DType val) {
   return expm1f(val);
 }
 
 template <typename DType>
-inline DType log(const DType val) {
+__device__ inline DType log(const DType val) {
   return logf(val);
 }
 
 template <typename DType>
-inline DType log10(const DType val) {
+__device__ inline DType log10(const DType val) {
   return log10f(val);
 }
 
 template <typename DType>
-inline DType log2(const DType val) {
+__device__ inline DType log2(const DType val) {
   return log2f(val);
 }
 
 template <typename DType>
-inline DType log1p(const DType val) {
+__device__ inline DType log1p(const DType val) {
   return log1pf(val);
 }
 
@@ -587,197 +595,197 @@ inline DType log1p(const DType val) {
 constexpr double pi = 3.14159265358979323846;
 
 template <typename DType>
-inline DType degrees(const DType val) {
+__device__ inline DType degrees(const DType val) {
   return (val / pi) * 180;
 }
 
 template <typename DType>
-inline DType radians(const DType val) {
+__device__ inline DType radians(const DType val) {
   return (val / 180.0) * pi;
 }
 
 template <typename DType>
-inline DType sin(const DType val) {
+__device__ inline DType sin(const DType val) {
   return sinf(val);
 }
 
 template <typename DType>
-inline DType cos(const DType val) {
+__device__ inline DType cos(const DType val) {
   return cosf(val);
 }
 
 template <typename DType>
-inline DType tan(const DType val) {
+__device__ inline DType tan(const DType val) {
   return tanf(val);
 }
 
 template <typename DType>
-inline DType arcsin(const DType val) {
+__device__ inline DType arcsin(const DType val) {
   return asinf(val);
 }
 
 template <typename DType>
-inline DType arccos(const DType val) {
+__device__ inline DType arccos(const DType val) {
   return acosf(val);
 }
 
 template <typename DType>
-inline DType arctan(const DType val) {
+__device__ inline DType arctan(const DType val) {
   return atanf(val);
 }
 
 template <typename DType>
-inline DType sinh(const DType val) {
+__device__ inline DType sinh(const DType val) {
   return sinhf(val);
 }
 
 template <typename DType>
-inline DType cosh(const DType val) {
+__device__ inline DType cosh(const DType val) {
   return coshf(val);
 }
 
 template <typename DType>
-inline DType tanh(const DType val) {
+__device__ inline DType tanh(const DType val) {
   return tanhf(val);
 }
 
 template <typename DType>
-inline DType arcsinh(const DType val) {
+__device__ inline DType arcsinh(const DType val) {
   return asinhf(val);
 }
 
 template <typename DType>
-inline DType arccosh(const DType val) {
+__device__ inline DType arccosh(const DType val) {
   return acoshf(val);
 }
 
 template <typename DType>
-inline DType arctanh(const DType val) {
+__device__ inline DType arctanh(const DType val) {
   return atanhf(val);
 }
 
 // sqrt
 
 template <typename DType>
-inline DType sqrt(const DType val) {
+__device__ inline DType sqrt(const DType val) {
   return sqrtf(val);
 }
 
 template <typename DType>
-inline DType rsqrt(const DType val) {
+__device__ inline DType rsqrt(const DType val) {
   return rsqrtf(val);
 }
 
 template <typename DType>
-inline DType cbrt(const DType val) {
+__device__ inline DType cbrt(const DType val) {
   return cbrtf(val);
 }
 
 template <typename DType>
-inline DType rcbrt(const DType val) {
+__device__ inline DType rcbrt(const DType val) {
   return rcbrtf(val);
 }
 
 template <typename DType>
-inline DType square(const DType val) {
+__device__ inline DType square(const DType val) {
   return val * val;
 }
 
 template <typename DType>
-inline typename LoadType<DType>::Type zero(const DType val) {
+__device__ inline typename LoadType<DType>::Type zero(const DType val) {
   return 0;
 }
 
 template <typename DType>
-inline typename LoadType<DType>::Type zero() {
+__device__ inline typename LoadType<DType>::Type zero() {
   return 0;
 }
 
 template <typename DType>
-inline typename LoadType<DType>::Type one(const DType val) {
+__device__ inline typename LoadType<DType>::Type one(const DType val) {
   return 1;
 }
 
 template <typename DType>
-inline typename LoadType<DType>::Type one() {
+__device__ inline typename LoadType<DType>::Type one() {
   return 1;
 }
 
 template <typename DType>
-inline DType round(const DType val) {
+__device__ inline DType round(const DType val) {
   return roundf(val);
 }
 
 template <typename DType>
-inline DType rint(const DType val) {
+__device__ inline DType rint(const DType val) {
   return rintf(val);
 }
 
 template <typename DType>
-inline DType fix(const DType val) {
+__device__ inline DType fix(const DType val) {
     const auto floor = floorf(val);
     const auto ceil = ceilf(val);
     return (floor > 0 ? floor : -floor) < (ceil > 0 ? ceil : -ceil) ? floor : ceil;
 }
 
 template <typename DType>
-inline DType floor(const DType val) {
+__device__ inline DType floor(const DType val) {
     return floorf(val);
 }
 
 template <typename DType>
-inline DType ceil(const DType val) {
+__device__ inline DType ceil(const DType val) {
     return ceilf(val);
 }
 
 template <typename DType>
-inline DType trunc(const DType val) {
+__device__ inline DType trunc(const DType val) {
     return truncf(val);
 }
 
 template <typename DType>
-inline DType clip(const DType val, const float a_min, const float a_max) {
+__device__ inline DType clip(const DType val, const float a_min, const float a_max) {
   return max(min(val, a_max), a_min);
 }
 
 template <typename DType>
-inline DType sign(const DType val) {
+__device__ inline DType sign(const DType val) {
   if (val < 0) return -1;
   return val > 0 ? 1 : 0;
 }
 
 template <typename DType>
-inline DType reciprocal(const DType val) {
+__device__ inline DType reciprocal(const DType val) {
   return 1.0f / val;
 }
 
 template <typename DType>
-inline DType abs(const DType val) {
+__device__ inline DType abs(const DType val) {
   return fabsf(val);
 }
 
 template <typename DType>
-inline DType gamma(const DType val) {
+__device__ inline DType gamma(const DType val) {
   return tgammaf(val);
 }
 
 template <typename DType>
-inline DType gammaln(const DType val) {
+__device__ inline DType gammaln(const DType val) {
   return lgammaf(val);
 }
 
 template <typename DType>
-inline DType erf(const DType val) {
+__device__ inline DType erf(const DType val) {
   return erff(val);
 }
 
 template <typename DType>
-inline DType erfinv(const DType val) {
+__device__ inline DType erfinv(const DType val) {
   return erfinvf(val);
 }
 
 template <typename DType1, typename DType2>
-inline DType1 smooth_l1(const DType1 val, const DType2 scalar) {
+__device__ inline DType1 smooth_l1(const DType1 val, const DType2 scalar) {
   const auto bsq = scalar * scalar;
   const auto ibsq = 1.0f / bsq;
   if (val > ibsq) {
@@ -798,147 +806,148 @@ const char backward_function_definitions[] = R"code(
 namespace op {
 
 template <typename DType, typename DTypeGrad>
-inline DTypeGrad backward_relu(const DType val, const DTypeGrad grad) {
+__device__ inline DTypeGrad backward_relu(const DType val, const DTypeGrad grad) {
   return val > 0 ? grad : 0;
 }
 
 template <typename DType, typename DTypeGrad>
-inline DTypeGrad backward_sigmoid(const DType out, const DTypeGrad grad) {
+__device__ inline DTypeGrad backward_sigmoid(const DType out, const DTypeGrad grad) {
   return grad * out * (1 - out);
 }
 
 template <typename DType, typename DTypeGrad>
-inline DTypeGrad backward_softrelu(const DType val, const DTypeGrad grad) {
+__device__ inline DTypeGrad backward_softrelu(const DType val, const DTypeGrad grad) {
   return grad * sigmoid(val);
 }
 
 template <typename DType, typename DTypeGrad>
-inline DTypeGrad backward_softsign(const DType val, const DTypeGrad grad) {
+__device__ inline DTypeGrad backward_softsign(const DType val, const DTypeGrad grad) {
   const DType ap1 = 1 + fabsf(val);
   return grad / (ap1 * ap1);
 }
 
 template <typename DType, typename DTypeGrad>
-inline DTypeGrad backward_exp(const DType val, const DTypeGrad grad) {
+__device__ inline DTypeGrad backward_exp(const DType val, const DTypeGrad grad) {
   return grad * expf(val);
 }
 
 template <typename DType, typename DTypeGrad>
-inline DTypeGrad backward_expm1(const DType val, const DTypeGrad grad) {
+__device__ inline DTypeGrad backward_expm1(const DType val, const DTypeGrad grad) {
   return grad * expf(val);
 }
 
 template <typename DType, typename DTypeGrad>
-inline DTypeGrad backward_log(const DType val, const DTypeGrad grad) {
+__device__ inline DTypeGrad backward_log(const DType val, const DTypeGrad grad) {
   return grad / val;
 }
 
 template <typename DType, typename DTypeGrad>
-inline DTypeGrad backward_log10(const DType val, const DTypeGrad grad) {
+__device__ inline DTypeGrad backward_log10(const DType val, const DTypeGrad grad) {
   return grad / (val * logf(10));
 }
 
 template <typename DType, typename DTypeGrad>
-inline DTypeGrad backward_log2(const DType val, const DTypeGrad grad) {
+__device__ inline DTypeGrad backward_log2(const DType val, const DTypeGrad grad) {
   return grad / (val * logf(2));
 }
 
 template <typename DType, typename DTypeGrad>
-inline DTypeGrad backward_log1p(const DType val, const DTypeGrad grad) {
+__device__ inline DTypeGrad backward_log1p(const DType val, const DTypeGrad grad) {
   return grad / (1 + val);
 }
 
 template <typename DType, typename DTypeGrad>
-inline DTypeGrad backward_sin(const DType val, const DTypeGrad grad) {
+__device__ inline DTypeGrad backward_sin(const DType val, const DTypeGrad grad) {
   return grad * cosf(val);
 }
 
 template <typename DType, typename DTypeGrad>
-inline DTypeGrad backward_cos(const DType val, const DTypeGrad grad) {
+__device__ inline DTypeGrad backward_cos(const DType val, const DTypeGrad grad) {
   return -grad * sinf(val);
 }
 
 // Uses output from tan
 template <typename DType, typename DTypeGrad>
-inline DTypeGrad backward_tan(const DType out, const DTypeGrad grad) {
+__device__ inline DTypeGrad backward_tan(const DType out, const DTypeGrad grad) {
   return grad * (out * out + 1);
 }
 
 template <typename DType, typename DTypeGrad>
-inline DTypeGrad backward_arcsin(const DType val, const DTypeGrad grad) {
+__device__ inline DTypeGrad backward_arcsin(const DType val, const DTypeGrad grad) {
   return grad / sqrtf(1 - val*val);
 }
 
 template <typename DType, typename DTypeGrad>
-inline DTypeGrad backward_arccos(const DType val, const DTypeGrad grad) {
+__device__ inline DTypeGrad backward_arccos(const DType val, const DTypeGrad grad) {
   return -grad / sqrtf(1 - val*val);
 }
 
 template <typename DType, typename DTypeGrad>
-inline DTypeGrad backward_arctan(const DType val, const DTypeGrad grad) {
+__device__ inline DTypeGrad backward_arctan(const DType val, const DTypeGrad grad) {
   return grad / (1 + val*val);
 }
 
 template <typename DType, typename DTypeGrad>
-inline DTypeGrad backward_sinh(const DType val, const DTypeGrad grad) {
+__device__ inline DTypeGrad backward_sinh(const DType val, const DTypeGrad grad) {
   return grad * coshf(val);
 }
 
 template <typename DType, typename DTypeGrad>
-inline DTypeGrad backward_cosh(const DType val, const DTypeGrad grad) {
+__device__ inline DTypeGrad backward_cosh(const DType val, const DTypeGrad grad) {
   return grad * sinhf(val);
 }
 
 // Uses tanh output
 template <typename DType, typename DTypeGrad>
-inline DTypeGrad backward_tanh(const DType out, const DTypeGrad grad) {
+__device__ inline DTypeGrad backward_tanh(const DType out, const DTypeGrad grad) {
   return grad * (1 - out * out);
 }
 
 template <typename DType, typename DTypeGrad>
-inline DTypeGrad backward_arcsinh(const DType val, const DTypeGrad grad) {
+__device__ inline DTypeGrad backward_arcsinh(const DType val, const DTypeGrad grad) {
   return grad / sqrtf(val * val + 1);
 }
 
 template <typename DType, typename DTypeGrad>
-inline DTypeGrad backward_arccosh(const DType val, const DTypeGrad grad) {
+__device__ inline DTypeGrad backward_arccosh(const DType val, const DTypeGrad grad) {
   return grad / sqrtf(val * val - 1);
 }
 
 template <typename DType, typename DTypeGrad>
-inline DTypeGrad backward_arctanh(const DType val, const DTypeGrad grad) {
+__device__ inline DTypeGrad backward_arctanh(const DType val, const DTypeGrad grad) {
   return grad / (1 - val * val);
 }
 
 template <typename DType, typename DTypeGrad>
-inline DTypeGrad backward_sqrt(const DType out, const DTypeGrad grad) {
+__device__ inline DTypeGrad backward_sqrt(const DType out, const DTypeGrad grad) {
   return 0.5 * grad / out;
 }
 
 template <typename DType, typename DTypeGrad>
-inline DTypeGrad backward_rsqrt(const DType val, const DTypeGrad grad) {
+__device__ inline DTypeGrad backward_rsqrt(const DType val, const DTypeGrad grad) {
   const DType inv = 1 / val;
   return -0.5 * grad * sqrtf(inv) * inv;
 }
 
 template <typename DType, typename DTypeGrad>
-inline DTypeGrad backward_cbrt(const DType out, const DTypeGrad grad) {
+__device__ inline DTypeGrad backward_cbrt(const DType out, const DTypeGrad grad) {
   return grad / (3.0f * out * out);
 }
 
 template <typename DType, typename DTypeGrad>
-inline DTypeGrad backward_rcbrt(const DType val, const DTypeGrad grad) {
+__device__ inline DTypeGrad backward_rcbrt(const DType val, const DTypeGrad grad) {
   const DType inv = 1 / val;
   return -1.f/3.f * grad * cbrtf(inv) * inv;
 }
 
 template <typename DType, typename DTypeGrad>
-inline DTypeGrad backward_square(const DType val, const DTypeGrad grad) {
+__device__ inline DTypeGrad backward_square(const DType val, const DTypeGrad grad) {
   return 2 * val * grad;
 }
 
 template <typename DType, typename DTypeGrad>
-inline DTypeGrad backward_clip(const DType val, const DTypeGrad grad, const float a_min, const float a_max) {
+__device__ inline DTypeGrad backward_clip(const DType val, const DTypeGrad grad,
+                                          const float a_min, const float a_max) {
   if (val > a_max || val < a_min) {
     return 0;
   } else {
@@ -947,22 +956,23 @@ inline DTypeGrad backward_clip(const DType val, const DTypeGrad grad, const floa
 }
 
 template <typename DType, typename DTypeGrad>
-inline DTypeGrad backward_reciprocal(const DType val, const DTypeGrad grad) {
+__device__ inline DTypeGrad backward_reciprocal(const DType val, const DTypeGrad grad) {
   return -grad / (val * val);
 }
 
 template <typename DType, typename DTypeGrad>
-inline DTypeGrad backward_erf(const DType val, const DTypeGrad grad) {
+__device__ inline DTypeGrad backward_erf(const DType val, const DTypeGrad grad) {
   return 2.0f / sqrt(pi) * exp(-(val*val)) * grad;
 }
 
 template <typename DType, typename DTypeGrad>
-inline DTypeGrad backward_erfinv(const DType val, const DTypeGrad grad) {
+__device__ inline DTypeGrad backward_erfinv(const DType val, const DTypeGrad grad) {
   return 0.5f * sqrt(pi) * exp(val * val) * grad;
 }
 
 template <typename DType, typename DType2, typename DTypeGrad>
-inline DTypeGrad backward_smooth_l1(const DType val, const DType2 scalar, const DTypeGrad grad) {
+__device__ inline DTypeGrad backward_smooth_l1(const DType val, const DType2 scalar,
+                                               const DTypeGrad grad) {
   auto bsq = scalar * scalar;
   auto ibsq = 1.0f / bsq;
   if (val > ibsq) {
diff --git a/src/operator/fusion/fused_op.cu b/src/operator/fusion/fused_op.cu
index 34360544cd11..c8a888301abd 100644
--- a/src/operator/fusion/fused_op.cu
+++ b/src/operator/fusion/fused_op.cu
@@ -594,13 +594,12 @@ CUfunction FusedOp::CompileCode(const std::string &code,
 
     std::string gpu_arch_arg = "--gpu-architecture=compute_" + std::to_string(sm_arch);
     const char *opts[] = {gpu_arch_arg.c_str(),
-                          "--std=c++11",
-                          "-default-device"};
+                          "--std=c++11"};
     const std::string kernel_name_demangled = "FusedKernel_" + kernel_name;
     NVRTC_CALL(nvrtcAddNameExpression(program, (kernel_name_demangled).c_str()));
 
     nvrtcResult compileResult = nvrtcCompileProgram(program,  // prog
-                                                    3,        // num options
+                                                    2,        // num options
                                                     opts);    // options
     CHECK_EQ(compileResult, NVRTC_SUCCESS)
         << "NVRTC Compilation failed. Please set environment variable MXNET_USE_FUSION to 0.\n"

From e18e4ce553ae7818780078e6f1ecccc91fe3c0df Mon Sep 17 00:00:00 2001
From: liuzh91 <liuzhuanghua1991@gmail.com>
Date: Tue, 10 Dec 2019 14:12:15 +0800
Subject: [PATCH 686/813] Include eval_net the validation model in the gluon
 estimator api (#16957)

* Include eval_net the validation model in the estimator api

* fix small issue
---
 .../gluon/contrib/estimator/estimator.py      | 32 +++++++-
 tests/python/unittest/test_gluon_estimator.py | 75 ++++++++++++++++++-
 2 files changed, 101 insertions(+), 6 deletions(-)

diff --git a/python/mxnet/gluon/contrib/estimator/estimator.py b/python/mxnet/gluon/contrib/estimator/estimator.py
index ab7018f58e1f..ac7c3d3825ab 100644
--- a/python/mxnet/gluon/contrib/estimator/estimator.py
+++ b/python/mxnet/gluon/contrib/estimator/estimator.py
@@ -59,9 +59,29 @@ class Estimator(object):
         Trainer to apply optimizer on network parameters.
     context : Context or list of Context
         Device(s) to run the training on.
-    evaluation_loss: gluon.loss.loss
-        Loss (objective) function to calculate during evaluation. If set evaluation_loss
+    evaluation_loss : gluon.loss.loss
+        Loss (objective) function to calculate during validation. If set evaluation_loss
         None, it will use the same loss function as self.loss
+    eval_net : gluon.Block
+        The model used for validation. The validation model does not necessarily belong to
+        the same model class as the training model. But the two models typically share the
+        same architecture. Therefore the validation model can reuse parameters of the
+        training model.
+
+        The code example of consruction of eval_net sharing the same network parameters as
+        the training net is given below:
+
+        >>> net = _get_train_network()
+        >>> eval_net = _get_test_network(params=net.collect_params())
+        >>> net.initialize(ctx=ctx)
+        >>> est = Estimator(net, loss, eval_net=eval_net)
+
+        Proper namespace match is required for weight sharing between two networks. Most networks
+        inheriting :py:class:`Block` can share their parameters correctly. An exception is
+        Sequential networks that Block scope must be specified for correct weight sharing. For
+        the  naming in mxnet Gluon API, please refer to the site
+        (https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/naming.html)
+        for future information.
 
     """
 
@@ -89,7 +109,8 @@ def __init__(self, net,
                  initializer=None,
                  trainer=None,
                  context=None,
-                 evaluation_loss=None):
+                 evaluation_loss=None,
+                 eval_net=None):
         self.net = net
         self.loss = self._check_loss(loss)
         self._train_metrics = _check_metrics(metrics)
@@ -98,6 +119,9 @@ def __init__(self, net,
         self.evaluation_loss = self.loss
         if evaluation_loss is not None:
             self.evaluation_loss = self._check_loss(evaluation_loss)
+        self.eval_net = self.net
+        if eval_net is not None:
+            self.eval_net = eval_net
 
         self.logger = logging.Logger(name='Estimator', level=logging.INFO)
         self.logger.addHandler(logging.StreamHandler(sys.stdout))
@@ -234,7 +258,7 @@ def evaluate_batch(self,
             Batch axis to split the validation data into devices.
         """
         data, label = self._get_data_and_label(val_batch, self.context, batch_axis)
-        pred = [self.net(x) for x in data]
+        pred = [self.eval_net(x) for x in data]
         loss = [self.evaluation_loss(y_hat, y) for y_hat, y in zip(pred, label)]
         # update metrics
         for metric in val_metrics:
diff --git a/tests/python/unittest/test_gluon_estimator.py b/tests/python/unittest/test_gluon_estimator.py
index 21f949a0bba6..dba3f122a9b6 100644
--- a/tests/python/unittest/test_gluon_estimator.py
+++ b/tests/python/unittest/test_gluon_estimator.py
@@ -29,11 +29,16 @@
 from nose.tools import assert_raises
 
 
-def _get_test_network():
-    net = nn.Sequential()
+def _get_test_network(params=None):
+    net = nn.Sequential(params=params)
     net.add(nn.Dense(4, activation='relu', flatten=False))
     return net
 
+def _get_test_network_with_namescope(params=None):
+    net = nn.Sequential(params=params)
+    with net.name_scope():
+        net.add(nn.Dense(4, activation='relu', flatten=False))
+    return net
 
 def _get_test_data():
     batch_size = 4
@@ -371,3 +376,69 @@ def test_default_handlers():
     assert isinstance(handlers[0], GradientUpdateHandler)
     assert isinstance(handlers[1], MetricHandler)
     assert isinstance(handlers[4], LoggingHandler)
+
+def test_eval_net():
+    ''' test estimator with a different evaluation net '''
+    ''' test weight sharing of sequential networks without namescope '''
+    net = _get_test_network()
+    eval_net = _get_test_network(params=net.collect_params())
+    dataloader, dataiter = _get_test_data()
+    num_epochs = 1
+    ctx = mx.cpu()
+    loss = gluon.loss.L2Loss()
+    evaluation_loss = gluon.loss.L2Loss()
+    acc = mx.metric.Accuracy()
+    net.initialize(ctx=ctx)
+    trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.001})
+    est = Estimator(net=net,
+                    loss=loss,
+                    metrics=acc,
+                    trainer=trainer,
+                    context=ctx,
+                    evaluation_loss=evaluation_loss,
+                    eval_net=eval_net)
+
+    with assert_raises(RuntimeError):
+        est.fit(train_data=dataloader,
+                val_data=dataloader,
+                epochs=num_epochs)
+
+    ''' test weight sharing of sequential networks with namescope '''
+    net = _get_test_network_with_namescope()
+    eval_net = _get_test_network_with_namescope(params=net.collect_params())
+    net.initialize(ctx=ctx)
+    trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.001})
+    est = Estimator(net=net,
+                    loss=loss,
+                    metrics=acc,
+                    trainer=trainer,
+                    context=ctx,
+                    evaluation_loss=evaluation_loss,
+                    eval_net=eval_net)
+
+    est.fit(train_data=dataloader,
+            val_data=dataloader,
+            epochs=num_epochs)
+
+    ''' test weight sharing of two resnets '''
+    net = gluon.model_zoo.vision.resnet18_v1(pretrained=False, ctx=ctx)
+    net.output = gluon.nn.Dense(10)
+    eval_net = gluon.model_zoo.vision.resnet18_v1(pretrained=False, ctx=ctx)
+    eval_net.output = gluon.nn.Dense(10, params=net.collect_params())
+    dataset = gluon.data.ArrayDataset(mx.nd.zeros((10, 3, 224, 224)), mx.nd.zeros((10, 10)))
+    dataloader = gluon.data.DataLoader(dataset=dataset, batch_size=5)
+    net.initialize(ctx=ctx)
+    eval_net.initialize(ctx=ctx)
+    trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.001})
+    est = Estimator(net=net,
+                    loss=loss,
+                    metrics=acc,
+                    trainer=trainer,
+                    context=ctx,
+                    evaluation_loss=evaluation_loss,
+                    eval_net=eval_net)
+
+    est.fit(train_data=dataloader,
+            val_data=dataloader,
+            epochs=num_epochs)
+

From 60f77f5ab56e3a47e483bb270f1c1a4dc275f183 Mon Sep 17 00:00:00 2001
From: perdasilva <perdasilva@gmail.com>
Date: Tue, 10 Dec 2019 16:44:58 +0100
Subject: [PATCH 687/813] [CD] dynamic libmxet pipeline fix + small fixes
 (#16966)

* Adds mx_mkldnn_deps back to dynamic libmxnet pipeline

* Fixes cudnn version for cuda 10.1 Dockefile

* Turns off unnecessary DEBUG flag

* Adds USE_NVTX=1 to cu90 make configuration
---
 cd/mxnet_lib/dynamic/Jenkins_pipeline.groovy | 2 ++
 cd/mxnet_lib/static/Jenkins_pipeline.groovy  | 2 ++
 ci/docker/Dockerfile.build.ubuntu_gpu_cu101  | 2 +-
 ci/docker/runtime_functions.sh               | 2 +-
 make/staticbuild/linux_cu90.mk               | 2 ++
 make/staticbuild/linux_cu90mkl.mk            | 2 ++
 6 files changed, 10 insertions(+), 2 deletions(-)

diff --git a/cd/mxnet_lib/dynamic/Jenkins_pipeline.groovy b/cd/mxnet_lib/dynamic/Jenkins_pipeline.groovy
index 6a51b69cfe50..e3d4b5908d57 100644
--- a/cd/mxnet_lib/dynamic/Jenkins_pipeline.groovy
+++ b/cd/mxnet_lib/dynamic/Jenkins_pipeline.groovy
@@ -22,6 +22,7 @@
 
 // NOTE: ci_utils is loaded by the originating Jenkins job, e.g. jenkins/Jenkinsfile_release_job
 
+// NOTE: the following variables are referenced in the mxnet_lib_pipeline jenkins file imported bellow
 // libmxnet location
 libmxnet = 'lib/libmxnet.so'
 
@@ -30,6 +31,7 @@ licenses = 'licenses/*'
 
 // libmxnet dependencies
 mx_deps = ''
+mx_mkldnn_deps = ''
 
 // library type
 // either static or dynamic - depending on how it links to its dependencies
diff --git a/cd/mxnet_lib/static/Jenkins_pipeline.groovy b/cd/mxnet_lib/static/Jenkins_pipeline.groovy
index 84466dd551e7..c6b6eaeaef90 100644
--- a/cd/mxnet_lib/static/Jenkins_pipeline.groovy
+++ b/cd/mxnet_lib/static/Jenkins_pipeline.groovy
@@ -23,6 +23,8 @@
 // To avoid confusion, please note:
 // ci_utils is loaded by the originating Jenkins job, e.g. jenkins/Jenkinsfile_release_job
 
+// NOTE: the following variables are referenced in the mxnet_lib_pipeline jenkins file imported bellow
+
 // libmxnet location
 libmxnet = 'lib/libmxnet.so'
 
diff --git a/ci/docker/Dockerfile.build.ubuntu_gpu_cu101 b/ci/docker/Dockerfile.build.ubuntu_gpu_cu101
index 7e0f8d93ed37..ff6b89bf8e06 100644
--- a/ci/docker/Dockerfile.build.ubuntu_gpu_cu101
+++ b/ci/docker/Dockerfile.build.ubuntu_gpu_cu101
@@ -65,7 +65,7 @@ RUN /work/ubuntu_docs.sh
 COPY install/ubuntu_tutorials.sh /work/
 RUN /work/ubuntu_tutorials.sh
 
-ENV CUDNN_VERSION=7.5.1.10
+ENV CUDNN_VERSION=7.6.0.64
 COPY install/ubuntu_cudnn.sh /work/
 RUN /work/ubuntu_cudnn.sh
 
diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index 9f56a6708ea4..63fe32879d75 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -1002,7 +1002,7 @@ sanity_check() {
 cd_unittest_ubuntu() {
     set -ex
     export PYTHONPATH=./python/
-    export MXNET_MKLDNN_DEBUG=1  # Ignored if not present
+    export MXNET_MKLDNN_DEBUG=0  # Ignored if not present
     export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
     export MXNET_SUBGRAPH_VERBOSE=0
     export MXNET_ENABLE_CYTHON=0
diff --git a/make/staticbuild/linux_cu90.mk b/make/staticbuild/linux_cu90.mk
index 1bca27b2b80c..310b1fac421b 100644
--- a/make/staticbuild/linux_cu90.mk
+++ b/make/staticbuild/linux_cu90.mk
@@ -81,6 +81,8 @@ USE_NCCL = 1
 # whether use cuda runtime compiling for writing kernels in native language (i.e. Python)
 ENABLE_CUDA_RTC = 1
 
+USE_NVTX=1
+
 # use openmp for parallelization
 USE_OPENMP = 1
 USE_OPERATOR_TUNING = 1
diff --git a/make/staticbuild/linux_cu90mkl.mk b/make/staticbuild/linux_cu90mkl.mk
index 8a8c273f3a09..c46c10f6358b 100644
--- a/make/staticbuild/linux_cu90mkl.mk
+++ b/make/staticbuild/linux_cu90mkl.mk
@@ -81,6 +81,8 @@ USE_NCCL = 1
 # whether use cuda runtime compiling for writing kernels in native language (i.e. Python)
 ENABLE_CUDA_RTC = 1
 
+USE_NVTX=1
+
 # use openmp for parallelization
 USE_OPENMP = 1
 USE_OPERATOR_TUNING = 1

From 0c17ddd61988d05eaca14312a22738fb84bfa2ac Mon Sep 17 00:00:00 2001
From: liuzh91 <liuzhuanghua1991@gmail.com>
Date: Wed, 11 Dec 2019 01:50:22 +0800
Subject: [PATCH 688/813] Extend estimator.evaluate() to support event handlers
 (#16971)

* fix unittest failures for the new api interface

* Add comments in the code for readability

* Remove unused argument val_metrics

* merge changes with the master branch

* fix some regression errors

* fix bugs introduced in the merging phase
---
 .../gluon/contrib/estimator/estimator.py      | 105 ++++++++++++------
 .../gluon/contrib/estimator/event_handler.py  |  59 +++++-----
 tests/python/unittest/test_gluon_estimator.py |  73 ++++++++----
 .../unittest/test_gluon_event_handler.py      |  23 ++--
 4 files changed, 160 insertions(+), 100 deletions(-)

diff --git a/python/mxnet/gluon/contrib/estimator/estimator.py b/python/mxnet/gluon/contrib/estimator/estimator.py
index ac7c3d3825ab..0d9ab9ec0a02 100644
--- a/python/mxnet/gluon/contrib/estimator/estimator.py
+++ b/python/mxnet/gluon/contrib/estimator/estimator.py
@@ -51,8 +51,10 @@ class Estimator(object):
         The model used for training.
     loss : gluon.loss.Loss
         Loss (objective) function to calculate during training.
-    metrics : EvalMetric or list of EvalMetric
-        Metrics for evaluating models.
+    train_metrics : EvalMetric or list of EvalMetric
+        Training metrics for evaluating models on training dataset.
+    val_metrics : EvalMetric or list of EvalMetric
+        Validation metrics for evaluating models on validation dataset.
     initializer : Initializer
         Initializer to initialize the network.
     trainer : Trainer
@@ -105,7 +107,8 @@ class Estimator(object):
 
     def __init__(self, net,
                  loss,
-                 metrics=None,
+                 train_metrics=None,
+                 val_metrics=None,
                  initializer=None,
                  trainer=None,
                  context=None,
@@ -113,7 +116,8 @@ def __init__(self, net,
                  eval_net=None):
         self.net = net
         self.loss = self._check_loss(loss)
-        self._train_metrics = _check_metrics(metrics)
+        self._train_metrics = _check_metrics(train_metrics)
+        self._val_metrics = _check_metrics(val_metrics)
         self._add_default_training_metrics()
         self._add_validation_metrics()
         self.evaluation_loss = self.loss
@@ -226,13 +230,21 @@ def _add_default_training_metrics(self):
             self._train_metrics.append(metric_loss(loss_name))
 
         for metric in self._train_metrics:
-            metric.name = "training " + metric.name
+            # add training prefix to the metric name
+            # it is useful for event handlers to distinguish them from validation metrics
+            metric.name = 'training ' + metric.name
 
     def _add_validation_metrics(self):
-        self._val_metrics = [copy.deepcopy(metric) for metric in self._train_metrics]
+        if not self._val_metrics:
+            self._val_metrics = [copy.deepcopy(metric) for metric in self._train_metrics]
 
         for metric in self._val_metrics:
-            metric.name = "validation " + metric.name
+            # add validation prefix to the metric name
+            # it is useful for event handlers to distinguish them from training metrics
+            if 'training' in metric.name:
+                metric.name = metric.name.replace('training', 'validation')
+            else:
+                metric.name = 'validation ' + metric.name
 
     @property
     def train_metrics(self):
@@ -244,7 +256,6 @@ def val_metrics(self):
 
     def evaluate_batch(self,
                        val_batch,
-                       val_metrics,
                        batch_axis=0):
         """Evaluate model on a batch of validation data.
 
@@ -252,25 +263,19 @@ def evaluate_batch(self,
         ----------
         val_batch : tuple
             Data and label of a batch from the validation data loader.
-        val_metrics : EvalMetric or list of EvalMetrics
-            Metrics to update validation result.
         batch_axis : int, default 0
             Batch axis to split the validation data into devices.
         """
         data, label = self._get_data_and_label(val_batch, self.context, batch_axis)
         pred = [self.eval_net(x) for x in data]
         loss = [self.evaluation_loss(y_hat, y) for y_hat, y in zip(pred, label)]
-        # update metrics
-        for metric in val_metrics:
-            if isinstance(metric, metric_loss):
-                metric.update(0, loss)
-            else:
-                metric.update(label, pred)
+
+        return data, label, pred, loss
 
     def evaluate(self,
                  val_data,
-                 val_metrics,
-                 batch_axis=0):
+                 batch_axis=0,
+                 event_handlers=None):
         """Evaluate model on validation data.
 
         This function calls :py:func:`evaluate_batch` on each of the batches from the
@@ -281,21 +286,42 @@ def evaluate(self,
         ----------
         val_data : DataLoader
             Validation data loader with data and labels.
-        val_metrics : EvalMetric or list of EvalMetrics
-            Metrics to update validation result.
         batch_axis : int, default 0
             Batch axis to split the validation data into devices.
+        event_handlers : EventHandler or list of EventHandler
+            List of :py:class:`EventHandlers` to apply during validation. Besides
+            event handlers specified here, a default MetricHandler and a LoggingHandler
+            will be added if not specified explicitly.
         """
         if not isinstance(val_data, DataLoader):
             raise ValueError("Estimator only support input as Gluon DataLoader. Alternatively, you "
                              "can transform your DataIter or any NDArray into Gluon DataLoader. "
                              "Refer to gluon.data.DataLoader")
 
-        for metric in val_metrics:
+        for metric in self.val_metrics:
             metric.reset()
 
+        event_handlers = self._prepare_default_validation_handlers(event_handlers)
+
+        _, epoch_begin, batch_begin, batch_end, \
+        epoch_end, _ = self._categorize_handlers(event_handlers)
+
+        estimator_ref = self
+
+        for handler in epoch_begin:
+            handler.epoch_begin(estimator_ref)
+
         for _, batch in enumerate(val_data):
-            self.evaluate_batch(batch, val_metrics, batch_axis)
+            for handler in batch_begin:
+                handler.batch_begin(estimator_ref, batch=batch)
+
+            _, label, pred, loss = self.evaluate_batch(batch, batch_axis)
+
+            for handler in batch_end:
+                handler.batch_end(estimator_ref, batch=batch, pred=pred, label=label, loss=loss)
+
+        for handler in epoch_end:
+            handler.epoch_end(estimator_ref)
 
     def fit_batch(self, train_batch, batch_axis=0):
         """Trains the model on a batch of training data.
@@ -441,23 +467,17 @@ def _prepare_default_handlers(self, val_data, event_handlers):
             added_default_handlers.append(GradientUpdateHandler())
 
         if not any(isinstance(handler, MetricHandler) for handler in event_handlers):
-            added_default_handlers.append(MetricHandler(train_metrics=self.train_metrics))
+            added_default_handlers.append(MetricHandler(metrics=self.train_metrics))
 
         if not any(isinstance(handler, ValidationHandler) for handler in event_handlers):
             # no validation handler
             if val_data:
-                val_metrics = self.val_metrics
                 # add default validation handler if validation data found
                 added_default_handlers.append(ValidationHandler(val_data=val_data,
-                                                                eval_fn=self.evaluate,
-                                                                val_metrics=val_metrics))
-            else:
-                # set validation metrics to None if no validation data and no validation handler
-                val_metrics = []
+                                                                eval_fn=self.evaluate))
 
         if not any(isinstance(handler, LoggingHandler) for handler in event_handlers):
-            added_default_handlers.append(LoggingHandler(train_metrics=self.train_metrics,
-                                                         val_metrics=val_metrics))
+            added_default_handlers.append(LoggingHandler(metrics=self.train_metrics))
 
         # if there is a mix of user defined event handlers and default event handlers
         # they should have the same set of metrics
@@ -474,6 +494,29 @@ def _prepare_default_handlers(self, val_data, event_handlers):
         event_handlers.sort(key=lambda handler: getattr(handler, 'priority', 0))
         return event_handlers
 
+    def _prepare_default_validation_handlers(self, event_handlers):
+        event_handlers = _check_event_handlers(event_handlers)
+        added_default_handlers = []
+
+        # add default logging handler and metric handler for validation
+        if not any(isinstance(handler, MetricHandler) for handler in event_handlers):
+            added_default_handlers.append(MetricHandler(metrics=self.val_metrics))
+
+        if not any(isinstance(handler, LoggingHandler) for handler in event_handlers):
+            added_default_handlers.append(LoggingHandler(metrics=self.val_metrics))
+
+        mixing_handlers = event_handlers and added_default_handlers
+        event_handlers.extend(added_default_handlers)
+
+        # check if all handlers refer to well-defined validation metrics
+        if mixing_handlers:
+            known_metrics = set(self.val_metrics)
+            for handler in event_handlers:
+                _check_handler_metric_ref(handler, known_metrics)
+
+        event_handlers.sort(key=lambda handler: getattr(handler, 'priority', 0))
+        return event_handlers
+
     def _categorize_handlers(self, event_handlers):
         """
         categorize handlers into 6 event lists to avoid calling empty methods
diff --git a/python/mxnet/gluon/contrib/estimator/event_handler.py b/python/mxnet/gluon/contrib/estimator/event_handler.py
index 64777608bef0..c7551362fa5b 100644
--- a/python/mxnet/gluon/contrib/estimator/event_handler.py
+++ b/python/mxnet/gluon/contrib/estimator/event_handler.py
@@ -128,28 +128,28 @@ class MetricHandler(EpochBegin, BatchEnd):
 
     Parameters
     ----------
-    train_metrics : List of EvalMetrics
-        Training metrics to be updated at batch end.
+    metrics : List of EvalMetrics
+        Metrics to be updated at batch end.
     priority : scalar
         Priority level of the MetricHandler. Priority level is sorted in ascending
         order. The lower the number is, the higher priority level the handler is.
     """
 
-    def __init__(self, train_metrics, priority=-1000):
-        self.train_metrics = _check_metrics(train_metrics)
+    def __init__(self, metrics, priority=-1000):
+        self.metrics = _check_metrics(metrics)
         # order to be called among all callbacks
         # metrics need to be calculated before other callbacks can access them
         self.priority = priority
 
     def epoch_begin(self, estimator, *args, **kwargs):
-        for metric in self.train_metrics:
+        for metric in self.metrics:
             metric.reset()
 
     def batch_end(self, estimator, *args, **kwargs):
         pred = kwargs['pred']
         label = kwargs['label']
         loss = kwargs['loss']
-        for metric in self.train_metrics:
+        for metric in self.metrics:
             if isinstance(metric, metric_loss):
                 # metric wrapper for loss values
                 metric.update(0, loss)
@@ -171,8 +171,6 @@ class ValidationHandler(TrainBegin, BatchEnd, EpochEnd):
     eval_fn : function
         A function defines how to run evaluation and
         calculate loss and metrics.
-    val_metrics : List of EvalMetrics
-        Validation metrics to be updated.
     epoch_period : int, default 1
         How often to run validation at epoch end, by default
         :py:class:`ValidationHandler` validate every epoch.
@@ -188,7 +186,6 @@ class ValidationHandler(TrainBegin, BatchEnd, EpochEnd):
     def __init__(self,
                  val_data,
                  eval_fn,
-                 val_metrics=None,
                  epoch_period=1,
                  batch_period=None,
                  priority=-1000):
@@ -196,7 +193,6 @@ def __init__(self,
         self.eval_fn = eval_fn
         self.epoch_period = epoch_period
         self.batch_period = batch_period
-        self.val_metrics = _check_metrics(val_metrics)
         self.current_batch = 0
         self.current_epoch = 0
         # order to be called among all callbacks
@@ -211,20 +207,12 @@ def train_begin(self, estimator, *args, **kwargs):
     def batch_end(self, estimator, *args, **kwargs):
         self.current_batch += 1
         if self.batch_period and self.current_batch % self.batch_period == 0:
-            self.eval_fn(val_data=self.val_data,
-                         val_metrics=self.val_metrics)
-            msg = '[Epoch %d] ValidationHandler: %d batches reached, ' \
-                  % (self.current_epoch, self.current_batch)
-            for monitor in self.val_metrics:
-                name, value = monitor.get()
-                msg += '%s: %.4f, ' % (name, value)
-            estimator.logger.info(msg.rstrip(','))
+            self.eval_fn(val_data=self.val_data)
 
     def epoch_end(self, estimator, *args, **kwargs):
         self.current_epoch += 1
         if self.epoch_period and self.current_epoch % self.epoch_period == 0:
-            self.eval_fn(val_data=self.val_data,
-                         val_metrics=self.val_metrics)
+            self.eval_fn(val_data=self.val_data)
 
 
 class LoggingHandler(TrainBegin, TrainEnd, EpochBegin, EpochEnd, BatchBegin, BatchEnd):
@@ -239,10 +227,8 @@ class LoggingHandler(TrainBegin, TrainEnd, EpochBegin, EpochEnd, BatchBegin, Bat
         Logging interval during training.
         log_interval='epoch': display metrics every epoch
         log_interval=integer k: display metrics every interval of k batches
-    train_metrics : list of EvalMetrics
-        Training metrics to be logged, logged at batch end, epoch end, train end.
-    val_metrics : list of EvalMetrics
-        Validation metrics to be logged, logged at epoch end, train end.
+    metrics : list of EvalMetrics
+        Metrics to be logged, logged at batch end, epoch end, train end.
     priority : scalar, default np.Inf
         Priority level of the LoggingHandler. Priority level is sorted in
         ascending order. The lower the number is, the higher priority level the
@@ -250,14 +236,12 @@ class LoggingHandler(TrainBegin, TrainEnd, EpochBegin, EpochEnd, BatchBegin, Bat
     """
 
     def __init__(self, log_interval='epoch',
-                 train_metrics=None,
-                 val_metrics=None,
+                 metrics=None,
                  priority=np.Inf):
         super(LoggingHandler, self).__init__()
         if not isinstance(log_interval, int) and log_interval != 'epoch':
             raise ValueError("log_interval must be either an integer or string 'epoch'")
-        self.train_metrics = _check_metrics(train_metrics)
-        self.val_metrics = _check_metrics(val_metrics)
+        self.metrics = _check_metrics(metrics)
         self.batch_index = 0
         self.current_epoch = 0
         self.processed_samples = 0
@@ -265,6 +249,7 @@ def __init__(self, log_interval='epoch',
         # it will also shut down logging at train end
         self.priority = priority
         self.log_interval = log_interval
+        self.log_interval_time = 0
 
     def train_begin(self, estimator, *args, **kwargs):
         self.train_start = time.time()
@@ -288,7 +273,7 @@ def train_end(self, estimator, *args, **kwargs):
         train_time = time.time() - self.train_start
         msg = 'Train finished using total %ds with %d epochs. ' % (train_time, self.current_epoch)
         # log every result in train stats including train/validation loss & metrics
-        for metric in self.train_metrics + self.val_metrics:
+        for metric in self.metrics:
             name, value = metric.get()
             msg += '%s: %.4f, ' % (name, value)
         estimator.logger.info(msg.rstrip(', '))
@@ -307,7 +292,7 @@ def batch_end(self, estimator, *args, **kwargs):
             if self.batch_index % self.log_interval == 0:
                 msg += 'time/interval: %.3fs ' % self.log_interval_time
                 self.log_interval_time = 0
-                for metric in self.train_metrics:
+                for metric in self.metrics:
                     # only log current training loss & metric after each interval
                     name, value = metric.get()
                     msg += '%s: %.4f, ' % (name, value)
@@ -316,15 +301,23 @@ def batch_end(self, estimator, *args, **kwargs):
 
     def epoch_begin(self, estimator, *args, **kwargs):
         if isinstance(self.log_interval, int) or self.log_interval == 'epoch':
+            is_training = False
+            # use the name hack defined in __init__() of estimator class
+            for metric in self.metrics:
+                if 'training' in metric.name:
+                    is_training = True
             self.epoch_start = time.time()
-            estimator.logger.info("[Epoch %d] Begin, current learning rate: %.4f",
-                                  self.current_epoch, estimator.trainer.learning_rate)
+            if is_training:
+                estimator.logger.info("[Epoch %d] Begin, current learning rate: %.4f",
+                                      self.current_epoch, estimator.trainer.learning_rate)
+            else:
+                estimator.logger.info("Validation Begin")
 
     def epoch_end(self, estimator, *args, **kwargs):
         if isinstance(self.log_interval, int) or self.log_interval == 'epoch':
             epoch_time = time.time() - self.epoch_start
             msg = '[Epoch %d] Finished in %.3fs, ' % (self.current_epoch, epoch_time)
-            for monitor in self.train_metrics + self.val_metrics:
+            for monitor in self.metrics:
                 name, value = monitor.get()
                 msg += '%s: %.4f, ' % (name, value)
             estimator.logger.info(msg.rstrip(', '))
diff --git a/tests/python/unittest/test_gluon_estimator.py b/tests/python/unittest/test_gluon_estimator.py
index dba3f122a9b6..924dd083bef4 100644
--- a/tests/python/unittest/test_gluon_estimator.py
+++ b/tests/python/unittest/test_gluon_estimator.py
@@ -63,7 +63,7 @@ def test_fit():
     trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.001})
     est = Estimator(net=net,
                     loss=loss,
-                    metrics=acc,
+                    train_metrics=acc,
                     trainer=trainer,
                     context=ctx)
 
@@ -93,7 +93,7 @@ def test_validation():
     trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.001})
     est = Estimator(net=net,
                     loss=loss,
-                    metrics=acc,
+                    train_metrics=acc,
                     trainer=trainer,
                     context=ctx,
                     evaluation_loss=evaluation_loss)
@@ -105,8 +105,7 @@ def test_validation():
     # using validation handler
     train_metrics = est.train_metrics
     val_metrics = est.val_metrics
-    validation_handler = ValidationHandler(val_data=dataloader, eval_fn=est.evaluate,
-                                           val_metrics=val_metrics)
+    validation_handler = ValidationHandler(val_data=dataloader, eval_fn=est.evaluate)
 
     with assert_raises(ValueError):
         est.fit(train_data=dataiter,
@@ -132,7 +131,7 @@ def test_initializer():
     # no initializer
     est = Estimator(net=net,
                     loss=loss,
-                    metrics=acc,
+                    train_metrics=acc,
                     context=ctx)
     est.fit(train_data=train_data,
             epochs=num_epochs)
@@ -145,7 +144,7 @@ def test_initializer():
     with warnings.catch_warnings(record=True) as w:
         est = Estimator(net=net,
                         loss=loss,
-                        metrics=acc,
+                        train_metrics=acc,
                         initializer=mx.init.MSRAPrelu(),
                         trainer=trainer,
                         context=ctx)
@@ -153,7 +152,7 @@ def test_initializer():
     # net partially initialized, fine tuning use case
     net = gluon.model_zoo.vision.resnet18_v1(pretrained=True, ctx=ctx)
     net.output = gluon.nn.Dense(10) #last layer not initialized
-    est = Estimator(net, loss=loss, metrics=acc, context=ctx)
+    est = Estimator(net, loss=loss, train_metrics=acc, context=ctx)
     dataset =  gluon.data.ArrayDataset(mx.nd.zeros((10, 3, 224, 224)), mx.nd.zeros((10, 10)))
     train_data = gluon.data.DataLoader(dataset=dataset, batch_size=5)
     est.fit(train_data=train_data,
@@ -175,7 +174,7 @@ def test_trainer():
     with warnings.catch_warnings(record=True) as w:
         est = Estimator(net=net,
                         loss=loss,
-                        metrics=acc,
+                        train_metrics=acc,
                         context=ctx)
         assert 'No trainer specified' in str(w[-1].message)
     est.fit(train_data=train_data,
@@ -186,7 +185,7 @@ def test_trainer():
     with assert_raises(ValueError):
         est = Estimator(net=net,
                         loss=loss,
-                        metrics=acc,
+                        train_metrics=acc,
                         trainer=trainer,
                         context=ctx)
 
@@ -212,7 +211,7 @@ def test_metric():
     metrics = [mx.metric.Accuracy(), mx.metric.Accuracy()]
     est = Estimator(net=net,
                     loss=loss,
-                    metrics=metrics,
+                    train_metrics=metrics,
                     trainer=trainer,
                     context=ctx)
     est.fit(train_data=train_data,
@@ -221,7 +220,7 @@ def test_metric():
     with assert_raises(ValueError):
         est = Estimator(net=net,
                         loss=loss,
-                        metrics='acc',
+                        train_metrics='acc',
                         trainer=trainer,
                         context=ctx)
     # test default metric
@@ -244,7 +243,7 @@ def test_loss():
     with assert_raises(ValueError):
         est = Estimator(net=net,
                         loss='mse',
-                        metrics=acc,
+                        train_metrics=acc,
                         trainer=trainer,
                         context=ctx)
 
@@ -257,26 +256,26 @@ def test_context():
     # input no context
     est = Estimator(net=net,
                     loss=loss,
-                    metrics=metrics)
+                    train_metrics=metrics)
     # input list of context
     gpus = mx.context.num_gpus()
     ctx = [mx.gpu(i) for i in range(gpus)] if gpus > 0 else [mx.cpu()]
     net = _get_test_network()
     est = Estimator(net=net,
                     loss=loss,
-                    metrics=metrics,
+                    train_metrics=metrics,
                     context=ctx)
     # input invalid context
     with assert_raises(ValueError):
         est = Estimator(net=net,
                         loss=loss,
-                        metrics=metrics,
+                        train_metrics=metrics,
                         context='cpu')
 
     with assert_raises(AssertionError):
         est = Estimator(net=net,
                         loss=loss,
-                        metrics=metrics,
+                        train_metrics=metrics,
                         context=[mx.gpu(0), mx.gpu(100)])
 
 
@@ -341,7 +340,7 @@ def test_default_handlers():
 
     est = Estimator(net=net,
                     loss=loss,
-                    metrics=train_acc,
+                    train_metrics=train_acc,
                     trainer=trainer,
                     context=ctx)
     # no handler(all default handlers), no warning
@@ -352,18 +351,18 @@ def test_default_handlers():
     # use mix of default and user defined handlers
     train_metrics = est.train_metrics
     val_metrics = est.val_metrics
-    logging = LoggingHandler(train_metrics=train_metrics, val_metrics=val_metrics)
+    logging = LoggingHandler(metrics=train_metrics)
     est.fit(train_data=train_data, epochs=num_epochs, event_handlers=[logging])
 
     # handler with all user defined metrics
     # use mix of default and user defined handlers
-    metric = MetricHandler(train_metrics=[train_acc])
-    logging = LoggingHandler(train_metrics=[train_acc])
+    metric = MetricHandler(metrics=[train_acc])
+    logging = LoggingHandler(metrics=[train_acc])
     est.fit(train_data=train_data, epochs=num_epochs, event_handlers=[metric, logging])
 
     # handler with mixed metrics, some handler use metrics prepared by estimator
     # some handler use metrics user prepared
-    logging = LoggingHandler(train_metrics=train_metrics, val_metrics=[mx.metric.RMSE("val acc")])
+    logging = LoggingHandler(metrics=[mx.metric.RMSE("val acc")])
     with assert_raises(ValueError):
         est.fit(train_data=train_data, epochs=num_epochs, event_handlers=[logging])
 
@@ -392,7 +391,7 @@ def test_eval_net():
     trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.001})
     est = Estimator(net=net,
                     loss=loss,
-                    metrics=acc,
+                    train_metrics=acc,
                     trainer=trainer,
                     context=ctx,
                     evaluation_loss=evaluation_loss,
@@ -410,7 +409,7 @@ def test_eval_net():
     trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.001})
     est = Estimator(net=net,
                     loss=loss,
-                    metrics=acc,
+                    train_metrics=acc,
                     trainer=trainer,
                     context=ctx,
                     evaluation_loss=evaluation_loss,
@@ -432,7 +431,7 @@ def test_eval_net():
     trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.001})
     est = Estimator(net=net,
                     loss=loss,
-                    metrics=acc,
+                    train_metrics=acc,
                     trainer=trainer,
                     context=ctx,
                     evaluation_loss=evaluation_loss,
@@ -442,3 +441,29 @@ def test_eval_net():
             val_data=dataloader,
             epochs=num_epochs)
 
+def test_val_handlers():
+    net = _get_test_network()
+    train_data, _ = _get_test_data()
+    val_data, _ = _get_test_data()
+
+    num_epochs = 1
+    ctx = mx.cpu()
+    net.initialize(ctx=ctx)
+    trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.001})
+
+    train_acc = mx.metric.RMSE()
+    loss = gluon.loss.L2Loss()
+
+    est = Estimator(net=net,
+                    loss=loss,
+                    train_metrics=train_acc,
+                    trainer=trainer,
+                    context=ctx)
+
+    with warnings.catch_warnings(record=True) as w:
+        est.fit(train_data=train_data, epochs=num_epochs)
+        est.evaluate(val_data=val_data)
+
+    logging = LoggingHandler(log_interval=1, metrics=est.val_metrics)
+    est.evaluate(val_data=val_data, event_handlers=[logging])
+
diff --git a/tests/python/unittest/test_gluon_event_handler.py b/tests/python/unittest/test_gluon_event_handler.py
index 658fb88f47e5..41b790102f62 100644
--- a/tests/python/unittest/test_gluon_event_handler.py
+++ b/tests/python/unittest/test_gluon_event_handler.py
@@ -54,7 +54,7 @@ def test_checkpoint_handler():
         net = _get_test_network()
         ce_loss = loss.SoftmaxCrossEntropyLoss()
         acc = mx.metric.Accuracy()
-        est = estimator.Estimator(net, loss=ce_loss, metrics=acc)
+        est = estimator.Estimator(net, loss=ce_loss, train_metrics=acc)
         checkpoint_handler = event_handler.CheckpointHandler(model_dir=tmpdir,
                                                              model_prefix=model_prefix,
                                                              monitor=acc,
@@ -72,7 +72,7 @@ def test_checkpoint_handler():
         file_path = os.path.join(tmpdir, model_prefix)
         net = _get_test_network(nn.HybridSequential())
         net.hybridize()
-        est = estimator.Estimator(net, loss=ce_loss, metrics=acc)
+        est = estimator.Estimator(net, loss=ce_loss, train_metrics=acc)
         checkpoint_handler = event_handler.CheckpointHandler(model_dir=tmpdir,
                                                              model_prefix=model_prefix,
                                                              epoch_period=None,
@@ -100,7 +100,7 @@ def test_resume_checkpoint():
         net = _get_test_network()
         ce_loss = loss.SoftmaxCrossEntropyLoss()
         acc = mx.metric.Accuracy()
-        est = estimator.Estimator(net, loss=ce_loss, metrics=acc)
+        est = estimator.Estimator(net, loss=ce_loss, train_metrics=acc)
         checkpoint_handler = event_handler.CheckpointHandler(model_dir=tmpdir,
                                                              model_prefix=model_prefix,
                                                              monitor=acc,
@@ -125,7 +125,7 @@ def test_early_stopping():
     net = _get_test_network()
     ce_loss = loss.SoftmaxCrossEntropyLoss()
     acc = mx.metric.Accuracy()
-    est = estimator.Estimator(net, loss=ce_loss, metrics=acc)
+    est = estimator.Estimator(net, loss=ce_loss, train_metrics=acc)
     early_stopping = event_handler.EarlyStoppingHandler(monitor=acc,
                                                         patience=0,
                                                         mode='min')
@@ -149,14 +149,13 @@ def test_logging():
         net = _get_test_network()
         ce_loss = loss.SoftmaxCrossEntropyLoss()
         acc = mx.metric.Accuracy()
-        est = estimator.Estimator(net, loss=ce_loss, metrics=acc)
+        est = estimator.Estimator(net, loss=ce_loss, train_metrics=acc)
 
         est.logger.addHandler(logging.FileHandler(output_dir))
 
         train_metrics = est.train_metrics
         val_metrics = est.val_metrics
-        logging_handler = event_handler.LoggingHandler(train_metrics=train_metrics,
-                                                       val_metrics=val_metrics)
+        logging_handler = event_handler.LoggingHandler(metrics=train_metrics)
         est.fit(test_data, event_handlers=[logging_handler], epochs=3)
         assert logging_handler.batch_index == 0
         assert logging_handler.current_epoch == 3
@@ -197,7 +196,7 @@ def epoch_end(self, estimator, *args, **kwargs):
     net = _get_test_network()
     ce_loss = loss.SoftmaxCrossEntropyLoss()
     acc = mx.metric.Accuracy()
-    est = estimator.Estimator(net, loss=ce_loss, metrics=acc)
+    est = estimator.Estimator(net, loss=ce_loss, train_metrics=acc)
     custom_handler = CustomStopHandler(3, 2)
     est.fit(test_data, event_handlers=[custom_handler], epochs=3)
     assert custom_handler.num_batch == 3
@@ -220,10 +219,10 @@ def test_logging_interval():
     num_epochs = 1
     ce_loss = loss.SoftmaxCrossEntropyLoss()
     acc = mx.metric.Accuracy()
-    logging = LoggingHandler(train_metrics=[acc], log_interval=log_interval)
+    logging = LoggingHandler(metrics=[acc], log_interval=log_interval)
     est = estimator.Estimator(net=net,
                               loss=ce_loss,
-                              metrics=acc)
+                              train_metrics=acc)
 
     est.fit(train_data=dataloader,
             epochs=num_epochs,
@@ -245,10 +244,10 @@ def test_logging_interval():
     sys.stdout = mystdout = StringIO()
     acc = mx.metric.Accuracy()
     log_interval = 5
-    logging = LoggingHandler(train_metrics=[acc], log_interval=log_interval)
+    logging = LoggingHandler(metrics=[acc], log_interval=log_interval)
     est = estimator.Estimator(net=net,
                               loss=ce_loss,
-                              metrics=acc)
+                              train_metrics=acc)
     est.fit(train_data=dataloader,
             epochs=num_epochs,
             event_handlers=[logging])

From 7895f93e67dc3e9da360f7a9c667e3c0f1e76c0f Mon Sep 17 00:00:00 2001
From: Leonard Lausen <leonard@lausen.nl>
Date: Wed, 11 Dec 2019 02:52:20 +0900
Subject: [PATCH 689/813] Replace mxnet_option macro with standard
 CMAKE_DEPENDENT_OPTION (#17018)

---
 CMakeLists.txt              | 73 +++++++++++++++++++++----------------
 cmake/Modules/FindMKL.cmake | 10 ++---
 cmake/Utils.cmake           | 41 ---------------------
 3 files changed, 46 insertions(+), 78 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 8a8a3d5c512e..6a06ac548190 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -18,39 +18,48 @@ endif()
 
 include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/Utils.cmake)
 
+include(CMakeDependentOption)
 #Some things have order. This must be put in front alone
-mxnet_option(USE_CUDA             "Build with CUDA support"   ON)
-mxnet_option(USE_OLDCMAKECUDA     "Build with old cmake cuda" OFF)
-mxnet_option(USE_NCCL             "Use NVidia NCCL with CUDA" OFF)
-mxnet_option(USE_OPENCV           "Build with OpenCV support" ON)
-mxnet_option(USE_OPENMP           "Build with Openmp support" ON)
-mxnet_option(USE_CUDNN            "Build with cudnn support"  ON) # one could set CUDNN_ROOT for search path
-mxnet_option(USE_SSE              "Build with x86 SSE instruction support" ON IF NOT ARM)
-mxnet_option(USE_F16C             "Build with x86 F16C instruction support" ON) # autodetects support if ON
-mxnet_option(USE_LAPACK           "Build with lapack support" ON)
-mxnet_option(USE_MKL_IF_AVAILABLE "Use MKL if found" ON)
-mxnet_option(USE_MKLDNN           "Build with MKL-DNN support" ON IF USE_MKL_IF_AVAILABLE AND (NOT APPLE) AND (NOT MSVC) AND (CMAKE_HOST_SYSTEM_PROCESSOR STREQUAL "x86_64") AND (NOT CMAKE_CROSSCOMPILING))
-mxnet_option(USE_OPERATOR_TUNING  "Enable auto-tuning of operators" ON IF NOT MSVC)
-mxnet_option(USE_GPERFTOOLS       "Build with GPerfTools support" OFF)
-mxnet_option(USE_JEMALLOC         "Build with Jemalloc support"   ON)
-mxnet_option(USE_DIST_KVSTORE     "Build with DIST_KVSTORE support" OFF)
-mxnet_option(USE_PLUGINS_WARPCTC  "Use WARPCTC Plugins" OFF)
-mxnet_option(USE_PLUGIN_CAFFE     "Use Caffe Plugin" OFF)
-mxnet_option(USE_CPP_PACKAGE      "Build C++ Package" OFF)
-mxnet_option(USE_MXNET_LIB_NAMING "Use MXNet library naming conventions." ON)
-mxnet_option(USE_GPROF            "Compile with gprof (profiling) flag" OFF)
-mxnet_option(USE_CXX14_IF_AVAILABLE "Build with C++14 if the compiler supports it" OFF)
-mxnet_option(USE_VTUNE            "Enable use of Intel Amplifier XE (VTune)" OFF) # one could set VTUNE_ROOT for search path
-mxnet_option(USE_TVM_OP           "Enable use of TVM operator build system." OFF)
-mxnet_option(ENABLE_CUDA_RTC      "Build with CUDA runtime compilation support" ON)
-mxnet_option(BUILD_CPP_EXAMPLES   "Build cpp examples" ON)
-mxnet_option(INSTALL_EXAMPLES     "Install the example source files." OFF)
-mxnet_option(USE_SIGNAL_HANDLER   "Print stack traces on segfaults." ON)
-mxnet_option(USE_TENSORRT         "Enable inference optimization with TensorRT." OFF)
-mxnet_option(USE_ASAN             "Enable Clang/GCC ASAN sanitizers." OFF)
-mxnet_option(ENABLE_TESTCOVERAGE  "Enable compilation with test coverage metric output" OFF)
-mxnet_option(USE_INT64_TENSOR_SIZE "Use int64_t to represent the total number of elements in a tensor" OFF)
-mxnet_option(BUILD_CYTHON_MODULES "Build cython modules." OFF)
+option(USE_CUDA "Build with CUDA support"   ON)
+option(USE_OLDCMAKECUDA "Build with old cmake cuda" OFF)
+option(USE_NCCL "Use NVidia NCCL with CUDA" OFF)
+option(USE_OPENCV "Build with OpenCV support" ON)
+option(USE_OPENMP "Build with Openmp support" ON)
+cmake_dependent_option(USE_CUDNN "Build with cudnn support" ON "USE_CUDA" OFF) # one could set CUDNN_ROOT for search path
+cmake_dependent_option(USE_SSE "Build with x86 SSE instruction support" ON "NOT ARM" OFF)
+option(USE_F16C "Build with x86 F16C instruction support" ON) # autodetects support if ON
+option(USE_LAPACK "Build with lapack support" ON)
+option(USE_MKL_IF_AVAILABLE "Use MKL if found" ON)
+if(USE_MKL_IF_AVAILABLE AND (NOT APPLE) AND (NOT MSVC) AND (CMAKE_HOST_SYSTEM_PROCESSOR STREQUAL "x86_64") AND (NOT CMAKE_CROSSCOMPILING))
+  option(USE_MKLDNN "Build with MKL-DNN support" ON)
+else()
+  option(USE_MKLDNN "Build with MKL-DNN support" OFF)
+endif()
+if(NOT MSVC)
+  option(USE_OPERATOR_TUNING  "Enable auto-tuning of operators" ON)
+else()
+  option(USE_OPERATOR_TUNING  "Enable auto-tuning of operators" OFF)
+endif()
+option(USE_GPERFTOOLS "Build with GPerfTools support" OFF)
+option(USE_JEMALLOC "Build with Jemalloc support"   ON)
+option(USE_DIST_KVSTORE "Build with DIST_KVSTORE support" OFF)
+option(USE_PLUGINS_WARPCTC "Use WARPCTC Plugins" OFF)
+option(USE_PLUGIN_CAFFE "Use Caffe Plugin" OFF)
+option(USE_CPP_PACKAGE "Build C++ Package" OFF)
+option(USE_MXNET_LIB_NAMING "Use MXNet library naming conventions." ON)
+option(USE_GPROF "Compile with gprof (profiling) flag" OFF)
+option(USE_CXX14_IF_AVAILABLE "Build with C++14 if the compiler supports it" OFF)
+option(USE_VTUNE "Enable use of Intel Amplifier XE (VTune)" OFF) # one could set VTUNE_ROOT for search path
+option(USE_TVM_OP "Enable use of TVM operator build system." OFF)
+option(ENABLE_CUDA_RTC "Build with CUDA runtime compilation support" ON)
+option(BUILD_CPP_EXAMPLES "Build cpp examples" ON)
+option(INSTALL_EXAMPLES "Install the example source files." OFF)
+option(USE_SIGNAL_HANDLER "Print stack traces on segfaults." ON)
+option(USE_TENSORRT "Enable inference optimization with TensorRT." OFF)
+option(USE_ASAN "Enable Clang/GCC ASAN sanitizers." OFF)
+option(ENABLE_TESTCOVERAGE "Enable compilation with test coverage metric output" OFF)
+option(USE_INT64_TENSOR_SIZE "Use int64_t to represent the total number of elements in a tensor" OFF)
+option(BUILD_CYTHON_MODULES "Build cython modules." OFF)
 
 message(STATUS "CMAKE_CROSSCOMPILING ${CMAKE_CROSSCOMPILING}")
 message(STATUS "CMAKE_HOST_SYSTEM_PROCESSOR ${CMAKE_HOST_SYSTEM_PROCESSOR}")
diff --git a/cmake/Modules/FindMKL.cmake b/cmake/Modules/FindMKL.cmake
index 51fca23c1161..51eff8fe09aa 100644
--- a/cmake/Modules/FindMKL.cmake
+++ b/cmake/Modules/FindMKL.cmake
@@ -45,11 +45,11 @@ set(INTEL_ROOT "/opt/intel" CACHE PATH "Folder contains intel libs")
 
 
   # ---[ Options
-  mxnet_option(MKL_USE_SINGLE_DYNAMIC_LIBRARY "Use single dynamic library interface" ON)
-  mxnet_option(MKL_USE_STATIC_LIBS "Use static libraries" OFF IF NOT MKL_USE_SINGLE_DYNAMIC_LIBRARY)
-  mxnet_option(MKL_MULTI_THREADED  "Use multi-threading"   ON IF NOT MKL_USE_SINGLE_DYNAMIC_LIBRARY)
-  mxnet_option(MKL_USE_ILP64  "Use ilp64 data model" OFF)
-  mxnet_option(MKL_USE_CLUSTER "Use cluster functions" OFF IF CMAKE_SIZEOF_VOID_P EQUAL 4)
+  option(MKL_USE_SINGLE_DYNAMIC_LIBRARY "Use single dynamic library interface" ON)
+  cmake_dependent_option(MKL_USE_STATIC_LIBS "Use static libraries" OFF "NOT MKL_USE_SINGLE_DYNAMIC_LIBRARY" OFF)
+  cmake_dependent_option(MKL_MULTI_THREADED  "Use multi-threading"  ON "NOT MKL_USE_SINGLE_DYNAMIC_LIBRARY" OFF)
+  option(MKL_USE_ILP64 "Use ilp64 data model" OFF)
+  cmake_dependent_option(MKL_USE_CLUSTER "Use cluster functions" OFF "CMAKE_SIZEOF_VOID_P EQUAL 4" OFF)
 
   find_path(MKL_ROOT include/mkl.h PATHS $ENV{MKL_ROOT} ${INTEL_ROOT}/mkl
     DOC "Folder contains MKL")
diff --git a/cmake/Utils.cmake b/cmake/Utils.cmake
index 6b427db85ec9..294e7cf4cc5c 100644
--- a/cmake/Utils.cmake
+++ b/cmake/Utils.cmake
@@ -179,47 +179,6 @@ function(mxnet_parse_header_single_define LIBNAME HDR_PATH VARNAME)
   endif()
 endfunction()
 
-########################################################################################################
-# An option that the user can select. Can accept condition to control when option is available for user.
-# Usage:
-#   mxnet_option(<option_variable> "doc string" <initial value or boolean expression> [IF <condition>])
-function(mxnet_option variable description value)
-  set(__value ${value})
-  set(__condition "")
-  set(__varname "__value")
-  foreach(arg ${ARGN})
-    if(arg STREQUAL "IF" OR arg STREQUAL "if")
-      set(__varname "__condition")
-    else()
-      list(APPEND ${__varname} ${arg})
-    endif()
-  endforeach()
-  unset(__varname)
-  if("${__condition}" STREQUAL "")
-    set(__condition 2 GREATER 1)
-  endif()
-
-  if(${__condition})
-    if("${__value}" MATCHES ";")
-      if(${__value})
-        option(${variable} "${description}" ON)
-      else()
-        option(${variable} "${description}" OFF)
-      endif()
-    elseif(DEFINED ${__value})
-      if(${__value})
-        option(${variable} "${description}" ON)
-      else()
-        option(${variable} "${description}" OFF)
-      endif()
-    else()
-      option(${variable} "${description}" ${__value})
-    endif()
-  else()
-    option(${variable} "${description}" OFF)
-  endif()
-endfunction()
-
 ################################################################################################
 # Utility macro for comparing two lists. Used for CMake debugging purposes
 # Usage:

From c31ee9954f4add59c3c3c38e44d304d52d6ee78d Mon Sep 17 00:00:00 2001
From: Chris Olivier <cjolivier01@gmail.com>
Date: Tue, 10 Dec 2019 20:55:31 -0800
Subject: [PATCH 690/813] Fix omp assert issue (#17039)

---
 CMakeLists.txt       | 17 +++++++++++------
 src/engine/openmp.cc |  7 +++++++
 src/engine/openmp.h  |  7 +++++++
 src/initialize.cc    |  2 ++
 4 files changed, 27 insertions(+), 6 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 6a06ac548190..1be19c577ae3 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -249,7 +249,7 @@ if(USE_TENSORRT)
 endif()
 
 # please note that when you enable this, you might run into an linker not being able to work properly due to large code injection.
-# you can find more information here https://github.com/apache/incubator-mxnet/issues/15971 
+# you can find more information here https://github.com/apache/incubator-mxnet/issues/15971
 if(ENABLE_TESTCOVERAGE)
   message(STATUS "Compiling with test coverage support enabled. This will result in additional files being written to your source directory!")
   find_program( GCOV_PATH gcov )
@@ -445,6 +445,15 @@ endif()
 
 # ---[ OpenMP
 if(USE_OPENMP)
+
+  function(load_omp)
+    # Intel/llvm OpenMP: https://github.com/llvm-mirror/openmp
+    set(OPENMP_STANDALONE_BUILD TRUE)
+    set(LIBOMP_ENABLE_SHARED TRUE)
+    set(CMAKE_BUILD_TYPE Release)
+    add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/3rdparty/openmp)
+  endfunction()
+
   find_package(OpenMP REQUIRED)
   # This should build on Windows, but there's some problem and I don't have a Windows box, so
   # could a Windows user please fix?
@@ -452,11 +461,7 @@ if(USE_OPENMP)
      AND SYSTEM_ARCHITECTURE STREQUAL "x86_64"
      AND NOT MSVC
      AND NOT CMAKE_CROSSCOMPILING)
-
-    # Intel/llvm OpenMP: https://github.com/llvm-mirror/openmp
-    set(OPENMP_STANDALONE_BUILD TRUE)
-    set(LIBOMP_ENABLE_SHARED TRUE)
-    add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/3rdparty/openmp)
+    load_omp()
     list(REMOVE_ITEM mxnet_LINKER_LIBS iomp5)
     list(APPEND mxnet_LINKER_LIBS omp)
     if(UNIX)
diff --git a/src/engine/openmp.cc b/src/engine/openmp.cc
index 8fe3939892d2..98fbc407fce8 100644
--- a/src/engine/openmp.cc
+++ b/src/engine/openmp.cc
@@ -41,6 +41,7 @@ OpenMP *OpenMP::Get() {
 OpenMP::OpenMP()
   : omp_num_threads_set_in_environment_(is_env_set("OMP_NUM_THREADS")) {
 #ifdef _OPENMP
+  initialize_process();
   const int max = dmlc::GetEnv("MXNET_OMP_MAX_THREADS", INT_MIN);
   if (max != INT_MIN) {
     omp_thread_max_ = max;
@@ -61,6 +62,12 @@ OpenMP::OpenMP()
 #endif
 }
 
+void OpenMP:: initialize_process() {
+#ifdef _OPENMP
+  omp_get_num_procs();  // will force OpenMP to be initialized
+#endif
+}
+
 void OpenMP::on_start_worker_thread(bool use_omp) {
 #ifdef _OPENMP
   if (!omp_num_threads_set_in_environment_) {
diff --git a/src/engine/openmp.h b/src/engine/openmp.h
index 800ea2f91b62..94b83e3aa25b 100644
--- a/src/engine/openmp.h
+++ b/src/engine/openmp.h
@@ -74,6 +74,13 @@ class OpenMP {
    */
   void on_start_worker_thread(bool use_omp);
 
+  /*!
+   * \brief Initialize a new process to use omp (after a fork,
+   *        in case you're starting threads in the atfork() that may interfere
+   *        with the initialization. Can serialize the init with this first.
+   */
+  void initialize_process();
+
   /*!
    * \brief Get the OpenMP object's singleton pointer
    * \return Singleton OpenMP object pointer
diff --git a/src/initialize.cc b/src/initialize.cc
index 071e8d32e548..a3dbce22a384 100644
--- a/src/initialize.cc
+++ b/src/initialize.cc
@@ -209,6 +209,7 @@ void LibraryInitializer::atfork_child() {
 #if MXNET_USE_OPENCV && !__APPLE__
   cv::setNumThreads(mp_cv_num_threads_);
 #endif  // MXNET_USE_OPENCV
+  engine::OpenMP::Get()->initialize_process();
   engine::OpenMP::Get()->set_thread_max(1);
   engine::OpenMP::Get()->set_enabled(false);
   Engine::Get()->Start();
@@ -218,6 +219,7 @@ void LibraryInitializer::atfork_child() {
 
 void LibraryInitializer::install_pthread_atfork_handlers() {
 #ifndef _WIN32
+  engine::OpenMP::Get()->initialize_process();  // force omp to set its atfork handler first
   pthread_atfork(pthread_atfork_prepare, pthread_atfork_parent, pthread_atfork_child);
 #endif
 }

From 9634786f96388004f68c223d72e120ad425c2f12 Mon Sep 17 00:00:00 2001
From: Tao Lv <tao.a.lv@intel.com>
Date: Wed, 11 Dec 2019 13:06:59 +0800
Subject: [PATCH 691/813] mshadow: fix vector access (#17021)

---
 3rdparty/mshadow/mshadow/dot_engine-inl.h | 9 +++------
 1 file changed, 3 insertions(+), 6 deletions(-)

diff --git a/3rdparty/mshadow/mshadow/dot_engine-inl.h b/3rdparty/mshadow/mshadow/dot_engine-inl.h
index 21816f209e40..1a02eb9a39d8 100644
--- a/3rdparty/mshadow/mshadow/dot_engine-inl.h
+++ b/3rdparty/mshadow/mshadow/dot_engine-inl.h
@@ -312,12 +312,9 @@ struct BLASEngine<cpu, float> {
   CBLAS_TRANSPOSE p_transa[GROUP_SIZE] = {cblas_a_trans};
   CBLAS_TRANSPOSE p_transb[GROUP_SIZE] = {cblas_b_trans};
 
-  std::vector<const float*> pp_A;
-  std::vector<const float*> pp_B;
-  std::vector<float*> pp_C;
-  pp_A.reserve(batch_count);
-  pp_B.reserve(batch_count);
-  pp_C.reserve(batch_count);
+  std::vector<const float*> pp_A(batch_count, nullptr);
+  std::vector<const float*> pp_B(batch_count, nullptr);
+  std::vector<float*> pp_C(batch_count, nullptr);
 
   auto m_k = m * k;
   auto k_n = k * n;

From 27389b1046770e34aac86bd3b18564e4e6283414 Mon Sep 17 00:00:00 2001
From: liuzh91 <liuzhuanghua1991@gmail.com>
Date: Wed, 11 Dec 2019 13:30:10 +0800
Subject: [PATCH 692/813] Fix Gluon Estimator nightly test (#17042)

---
 tests/nightly/estimator/test_estimator_cnn.py | 4 ++--
 tests/nightly/estimator/test_sentiment_rnn.py | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/tests/nightly/estimator/test_estimator_cnn.py b/tests/nightly/estimator/test_estimator_cnn.py
index 4a3bb2076524..af519536dbed 100644
--- a/tests/nightly/estimator/test_estimator_cnn.py
+++ b/tests/nightly/estimator/test_estimator_cnn.py
@@ -116,7 +116,7 @@ def test_estimator_cpu():
         # Define estimator
         est = estimator.Estimator(net=net,
                                   loss=loss,
-                                  metrics=mx.metric.Accuracy(),
+                                  train_metrics=mx.metric.Accuracy(),
                                   trainer=trainer,
                                   context=context)
         # Call fit()
@@ -145,7 +145,7 @@ def test_estimator_gpu():
     # Define estimator
     est = estimator.Estimator(net=net,
                               loss=loss,
-                              metrics=acc,
+                              train_metrics=acc,
                               trainer=trainer,
                               context=context)
     # Call fit()
diff --git a/tests/nightly/estimator/test_sentiment_rnn.py b/tests/nightly/estimator/test_sentiment_rnn.py
index 233355b7ebfd..ab124ba95db3 100644
--- a/tests/nightly/estimator/test_sentiment_rnn.py
+++ b/tests/nightly/estimator/test_sentiment_rnn.py
@@ -197,7 +197,7 @@ def run(net, train_dataloader, test_dataloader, num_epochs, ctx, lr):
     nested_metrics.add([metrics, mx.metric.Accuracy()])
 
     # Define estimator
-    est = estimator.Estimator(net=net, loss=loss, metrics=nested_metrics,
+    est = estimator.Estimator(net=net, loss=loss, train_metrics=nested_metrics,
                               trainer=trainer, context=ctx)
     # Begin training
     est.fit(train_data=train_dataloader, val_data=test_dataloader,

From c82af38211dbf8356a4f3b35f023632c5bf880ae Mon Sep 17 00:00:00 2001
From: liuzh91 <liuzhuanghua1991@gmail.com>
Date: Wed, 11 Dec 2019 19:14:59 +0800
Subject: [PATCH 693/813] Add support of plug and play fit_batch and
 evaluate_batch (#16982)

* Add support of plug and play fit_batch and evaluate_batch

* Add check for the validity of the estimator model

* Rename estimator model as batch processor

* Remove unused import

* Add documentation of the batch processor class

* refine the documentation of the batch processor

* Fix merge bugs

* fix bugs introduced during merge

* fix sanity check failures

* fix CI bugs
---
 .../mxnet/gluon/contrib/estimator/__init__.py |   2 +
 .../contrib/estimator/batch_processor.py      | 105 ++++++++++++++++
 .../gluon/contrib/estimator/estimator.py      |  84 ++++---------
 .../unittest/test_gluon_batch_processor.py    | 117 ++++++++++++++++++
 4 files changed, 248 insertions(+), 60 deletions(-)
 create mode 100644 python/mxnet/gluon/contrib/estimator/batch_processor.py
 create mode 100644 tests/python/unittest/test_gluon_batch_processor.py

diff --git a/python/mxnet/gluon/contrib/estimator/__init__.py b/python/mxnet/gluon/contrib/estimator/__init__.py
index bb0a0917c363..5ffd603b426c 100644
--- a/python/mxnet/gluon/contrib/estimator/__init__.py
+++ b/python/mxnet/gluon/contrib/estimator/__init__.py
@@ -19,5 +19,7 @@
 """Gluon Estimator Module"""
 from . import estimator
 from . import event_handler
+from . import batch_processor
 from .estimator import *
 from .event_handler import *
+from .batch_processor import *
diff --git a/python/mxnet/gluon/contrib/estimator/batch_processor.py b/python/mxnet/gluon/contrib/estimator/batch_processor.py
new file mode 100644
index 000000000000..4985f8c81bf3
--- /dev/null
+++ b/python/mxnet/gluon/contrib/estimator/batch_processor.py
@@ -0,0 +1,105 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# coding: utf-8
+# pylint: disable=wildcard-import, unused-argument, too-many-ancestors
+"""Gluon Batch Processor for Estimators"""
+
+from ...utils import split_and_load
+from .... import autograd
+
+__all__ = ['BatchProcessor']
+
+class BatchProcessor(object):
+    """BatchProcessor Class for plug and play fit_batch & evaluate_batch
+
+    During training or validation, data are divided into minibatches for processing. This
+    class aims at providing hooks of training or validating on a minibatch of data. Users
+    may provide customized fit_batch() and evaluate_batch() methods by inheriting from
+    this class and overriding class methods.
+
+    :py:class:`BatchProcessor` can be used to replace fit_batch() and evaluate_batch()
+    in the base estimator class
+    """
+
+    def __init__(self):
+        pass
+
+    def _get_data_and_label(self, batch, ctx, batch_axis=0):
+        data = batch[0]
+        label = batch[1]
+        data = split_and_load(data, ctx_list=ctx, batch_axis=batch_axis)
+        label = split_and_load(label, ctx_list=ctx, batch_axis=batch_axis)
+        return data, label
+
+    def evaluate_batch(self, estimator,
+                       val_batch,
+                       batch_axis=0):
+        """Evaluate the estimator model on a batch of validation data.
+
+        Parameters
+        ----------
+        estimator : Estimator
+            Reference to the estimator
+        val_batch : tuple
+            Data and label of a batch from the validation data loader.
+        batch_axis : int, default 0
+            Batch axis to split the validation data into devices.
+        """
+        data, label = self._get_data_and_label(val_batch, estimator.context, batch_axis)
+        pred = [estimator.eval_net(x) for x in data]
+        loss = [estimator.evaluation_loss(y_hat, y) for y_hat, y in zip(pred, label)]
+
+        return data, label, pred, loss
+
+    def fit_batch(self, estimator,
+                  train_batch,
+                  batch_axis=0):
+        """Trains the estimator model on a batch of training data.
+
+        Parameters
+        ----------
+        estimator : Estimator
+            Reference to the estimator
+        train_batch : tuple
+            Data and label of a batch from the training data loader.
+        batch_axis : int, default 0
+            Batch axis to split the training data into devices.
+
+        Returns
+        -------
+        data: List of NDArray
+            Sharded data from the batch. Data is sharded with
+            `gluon.split_and_load`.
+        label: List of NDArray
+            Sharded label from the batch. Labels are sharded with
+            `gluon.split_and_load`.
+        pred: List of NDArray
+            Prediction on each of the sharded inputs.
+        loss: List of NDArray
+            Loss on each of the sharded inputs.
+        """
+        data, label = self._get_data_and_label(train_batch, estimator.context, batch_axis)
+
+        with autograd.record():
+            pred = [estimator.net(x) for x in data]
+            loss = [estimator.loss(y_hat, y) for y_hat, y in zip(pred, label)]
+
+        for l in loss:
+            l.backward()
+
+        return data, label, pred, loss
diff --git a/python/mxnet/gluon/contrib/estimator/estimator.py b/python/mxnet/gluon/contrib/estimator/estimator.py
index 0d9ab9ec0a02..09f43151e235 100644
--- a/python/mxnet/gluon/contrib/estimator/estimator.py
+++ b/python/mxnet/gluon/contrib/estimator/estimator.py
@@ -32,9 +32,9 @@
 from ...loss import Loss as gluon_loss
 from ...trainer import Trainer
 from ...utils import split_and_load
-from .... import autograd
 from ....context import Context, cpu, gpu, num_gpus
 from ....metric import Loss as metric_loss
+from .batch_processor import BatchProcessor
 
 __all__ = ['Estimator']
 
@@ -84,7 +84,8 @@ class Estimator(object):
         the  naming in mxnet Gluon API, please refer to the site
         (https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/naming.html)
         for future information.
-
+    batch_processor: BatchProcessor
+        BatchProcessor provides customized fit_batch() and evaluate_batch() methods
     """
 
     logger = None
@@ -113,7 +114,8 @@ def __init__(self, net,
                  trainer=None,
                  context=None,
                  evaluation_loss=None,
-                 eval_net=None):
+                 eval_net=None,
+                 batch_processor=None):
         self.net = net
         self.loss = self._check_loss(loss)
         self._train_metrics = _check_metrics(train_metrics)
@@ -133,6 +135,7 @@ def __init__(self, net,
         self.context = self._check_context(context)
         self._initialize(initializer)
         self.trainer = self._check_trainer(trainer)
+        self.batch_processor = self._check_batch_processor(batch_processor)
 
     def _check_loss(self, loss):
         if not isinstance(loss, gluon_loss):
@@ -173,6 +176,18 @@ def _check_context(self, context):
                 context = [cpu()]
         return context
 
+    def _check_batch_processor(self, batch_processor):
+        # check whether the batch processor contains fit_batch() and evaluate_batch() methods
+        if batch_processor is not None:
+            model_fit = getattr(batch_processor, 'fit_batch', None)
+            model_evaluate = getattr(batch_processor, 'evaluate_batch', None)
+            if not callable(model_fit) or not callable(model_evaluate):
+                raise ValueError('Customized Batch Processor must contain fit_batch()'
+                                 ' and evaluate_batch() methods')
+        else:
+            batch_processor = BatchProcessor()
+        return batch_processor
+
     def _initialize(self, initializer):
         # initialize the network
         if not self._is_initialized():
@@ -254,24 +269,6 @@ def train_metrics(self):
     def val_metrics(self):
         return self._val_metrics
 
-    def evaluate_batch(self,
-                       val_batch,
-                       batch_axis=0):
-        """Evaluate model on a batch of validation data.
-
-        Parameters
-        ----------
-        val_batch : tuple
-            Data and label of a batch from the validation data loader.
-        batch_axis : int, default 0
-            Batch axis to split the validation data into devices.
-        """
-        data, label = self._get_data_and_label(val_batch, self.context, batch_axis)
-        pred = [self.eval_net(x) for x in data]
-        loss = [self.evaluation_loss(y_hat, y) for y_hat, y in zip(pred, label)]
-
-        return data, label, pred, loss
-
     def evaluate(self,
                  val_data,
                  batch_axis=0,
@@ -300,6 +297,7 @@ def evaluate(self,
 
         for metric in self.val_metrics:
             metric.reset()
+        estimator_ref = self
 
         event_handlers = self._prepare_default_validation_handlers(event_handlers)
 
@@ -315,7 +313,9 @@ def evaluate(self,
             for handler in batch_begin:
                 handler.batch_begin(estimator_ref, batch=batch)
 
-            _, label, pred, loss = self.evaluate_batch(batch, batch_axis)
+            _, label, pred, loss = \
+            self.batch_processor.evaluate_batch(estimator_ref, batch,
+                                                batch_axis)
 
             for handler in batch_end:
                 handler.batch_end(estimator_ref, batch=batch, pred=pred, label=label, loss=loss)
@@ -323,42 +323,6 @@ def evaluate(self,
         for handler in epoch_end:
             handler.epoch_end(estimator_ref)
 
-    def fit_batch(self, train_batch, batch_axis=0):
-        """Trains the model on a batch of training data.
-
-        Parameters
-        ----------
-        train_batch : tuple
-            Data and label of a batch from the training data loader.
-        batch_axis : int, default 0
-            Batch axis to split the training data into devices.
-
-        Returns
-        -------
-        data: List of NDArray
-            Sharded data from the batch. Data is sharded with
-            `gluon.split_and_load`.
-        label: List of NDArray
-            Sharded label from the batch. Labels are sharded with
-            `gluon.split_and_load`.
-        pred: List of NDArray
-            Prediction on each of the sharded inputs.
-        loss: List of NDArray
-            Loss on each of the sharded inputs.
-        """
-        data, label = self._get_data_and_label(train_batch, self.context, batch_axis)
-
-        batch_size = train_batch[0].shape[batch_axis]
-
-        with autograd.record():
-            pred = [self.net(x) for x in data]
-            loss = [self.loss(y_hat, y) for y_hat, y in zip(pred, label)]
-
-        for l in loss:
-            l.backward()
-
-        return data, label, pred, loss
-
     def fit(self, train_data,
             val_data=None,
             epochs=None,
@@ -432,8 +396,8 @@ def fit(self, train_data,
                 for handler in batch_begin:
                     handler.batch_begin(estimator_ref, batch=batch)
 
-                _, label, pred, loss = self.fit_batch(batch, batch_axis)
-
+                _, label, pred, loss = self.batch_processor.fit_batch(estimator_ref,
+                                                                      batch, batch_axis)
                 # batch end
 
                 batch_end_result = []
diff --git a/tests/python/unittest/test_gluon_batch_processor.py b/tests/python/unittest/test_gluon_batch_processor.py
new file mode 100644
index 000000000000..4bd6f769aa44
--- /dev/null
+++ b/tests/python/unittest/test_gluon_batch_processor.py
@@ -0,0 +1,117 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+''' Unit tests for Gluon Batch Processor '''
+
+import sys
+import unittest
+import warnings
+
+import mxnet as mx
+from mxnet import gluon
+from mxnet.gluon import nn
+from mxnet.gluon.contrib.estimator import *
+from mxnet.gluon.contrib.estimator.event_handler import *
+from mxnet.gluon.contrib.estimator.batch_processor import BatchProcessor
+from nose.tools import assert_raises
+
+def _get_test_network():
+    net = nn.Sequential()
+    net.add(nn.Dense(4, activation='relu', flatten=False))
+    return net
+
+
+def _get_test_data():
+    batch_size = 4
+    in_data = mx.nd.random.uniform(shape=(10, 3))
+    out_data = mx.nd.random.uniform(shape=(10, 4))
+    # Input dataloader
+    dataset = gluon.data.dataset.ArrayDataset(in_data, out_data)
+    dataloader = gluon.data.DataLoader(dataset, batch_size=batch_size)
+    dataiter = mx.io.NDArrayIter(data=in_data, label=out_data, batch_size=batch_size)
+    return dataloader, dataiter
+
+def test_batch_processor_fit():
+    ''' test estimator with different train data types '''
+    net = _get_test_network()
+    dataloader, dataiter = _get_test_data()
+    num_epochs = 1
+    ctx = mx.cpu()
+    loss = gluon.loss.L2Loss()
+    acc = mx.metric.Accuracy()
+    net.initialize(ctx=ctx)
+    processor = BatchProcessor()
+    trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.001})
+    est = Estimator(net=net,
+                    loss=loss,
+                    train_metrics=acc,
+                    trainer=trainer,
+                    context=ctx,
+                    batch_processor=processor)
+
+    est.fit(train_data=dataloader,
+            epochs=num_epochs)
+
+    with assert_raises(ValueError):
+        est.fit(train_data=dataiter,
+                epochs=num_epochs)
+
+    # Input NDArray
+    with assert_raises(ValueError):
+        est.fit(train_data=[mx.nd.ones(shape=(10, 3))],
+                epochs=num_epochs)
+
+
+def test_batch_processor_validation():
+    ''' test different validation data types'''
+    net = _get_test_network()
+    dataloader, dataiter = _get_test_data()
+    num_epochs = 1
+    ctx = mx.cpu()
+    loss = gluon.loss.L2Loss()
+    acc = mx.metric.Accuracy()
+    evaluation_loss = gluon.loss.L1Loss()
+    net.initialize(ctx=ctx)
+    processor = BatchProcessor()
+    trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.001})
+    est = Estimator(net=net,
+                    loss=loss,
+                    train_metrics=acc,
+                    trainer=trainer,
+                    context=ctx,
+                    evaluation_loss=evaluation_loss,
+                    batch_processor=processor)
+    # Input dataloader
+    est.fit(train_data=dataloader,
+            val_data=dataloader,
+            epochs=num_epochs)
+
+    # using validation handler
+    train_metrics = est.train_metrics
+    val_metrics = est.val_metrics
+    validation_handler = ValidationHandler(val_data=dataloader, eval_fn=est.evaluate)
+
+    with assert_raises(ValueError):
+        est.fit(train_data=dataiter,
+                val_data=dataiter,
+                epochs=num_epochs)
+    # Input NDArray
+    with assert_raises(ValueError):
+        est.fit(train_data=[mx.nd.ones(shape=(10, 3))],
+                val_data=[mx.nd.ones(shape=(10, 3))],
+                epochs=num_epochs)
+

From 04ebe45aed44433c8527fc9c1fcaf5d2662c5063 Mon Sep 17 00:00:00 2001
From: Pedro Larroy <pedro.larroy.lists@gmail.com>
Date: Wed, 11 Dec 2019 12:30:59 -0800
Subject: [PATCH 694/813] Prevent after-fork number of OMP threads being bigger
 than 1. (#16999)

* Prevent after-fork number of OMP threads being bigger than 1.
This could happen if it was set in the environment. As we are setting engine::OpenMP::Get()->set_enabled(false) in initialize.cc in the child after forking, the behaviour goes back to what it was before #15762 was introduced.

Regions using omp get the threads count from GetRecommendedOMPThreadCount, so if omp is disabled they will get 1 thread and run serially

* add C++ unit test

* Add comment
---
 src/engine/openmp.cc                 | 10 +++---
 tests/cpp/engine/omp_test.cc         | 50 ++++++++++++++++++++++++++++
 tests/python/unittest/test_engine.py | 41 +++++++++++++++++++++++
 3 files changed, 97 insertions(+), 4 deletions(-)
 create mode 100644 tests/cpp/engine/omp_test.cc

diff --git a/src/engine/openmp.cc b/src/engine/openmp.cc
index 98fbc407fce8..0d31f71aa9a3 100644
--- a/src/engine/openmp.cc
+++ b/src/engine/openmp.cc
@@ -90,10 +90,11 @@ void OpenMP::set_reserve_cores(int cores) {
 
 int OpenMP::GetRecommendedOMPThreadCount(bool exclude_reserved) const {
 #ifdef _OPENMP
-  if (omp_num_threads_set_in_environment_) {
-    return omp_get_max_threads();
-  }
   if (enabled_) {
+    // OMP_NUM_THREADS was set in the environment at the time of static initialization
+    if (omp_num_threads_set_in_environment_) {
+      return omp_get_max_threads();
+    }
     int thread_count = omp_get_max_threads();
     if (exclude_reserved) {
       if (reserve_cores_ >= thread_count) {
@@ -107,8 +108,9 @@ int OpenMP::GetRecommendedOMPThreadCount(bool exclude_reserved) const {
       return thread_count;
     }
     return omp_thread_max_;
+  } else {
+    return 1;
   }
-  return 1;
 #else
   return 1;
 #endif
diff --git a/tests/cpp/engine/omp_test.cc b/tests/cpp/engine/omp_test.cc
new file mode 100644
index 000000000000..2be7d9d0307c
--- /dev/null
+++ b/tests/cpp/engine/omp_test.cc
@@ -0,0 +1,50 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+#include <gtest/gtest.h>
+
+#include "../include/test_util.h"
+#include "../../src/engine/openmp.h"
+
+#if defined(unix) || defined(__unix__) || defined(__unix)
+#include <unistd.h>
+#include <sys/types.h>
+#include <dmlc/logging.h>
+
+
+TEST(OMPBehaviour, after_fork) {
+    /* 
+     * Check that after fork, OMP is disabled, and the recommended thread count is 1 to prevent 
+     * process fanout.
+     */
+    using namespace mxnet::engine;
+    auto openmp = OpenMP::Get();
+    pid_t pid = fork();
+    if (pid == 0) {
+        EXPECT_FALSE(openmp->enabled());
+        EXPECT_EQ(openmp->GetRecommendedOMPThreadCount(), 1);
+    } else if (pid > 0) {
+        int status;
+        int ret = waitpid(pid, &status, 0);
+        CHECK_EQ(ret, pid) << "waitpid failed";
+    } else {
+        CHECK(false) << "fork failed";
+    }
+}
+#endif
diff --git a/tests/python/unittest/test_engine.py b/tests/python/unittest/test_engine.py
index 29b7b822b3ef..61d94ddbf4ec 100644
--- a/tests/python/unittest/test_engine.py
+++ b/tests/python/unittest/test_engine.py
@@ -17,6 +17,9 @@
 
 import nose
 import mxnet as mx
+import os
+import unittest
+from mxnet.test_utils import EnvManager
 
 def test_bulk():
     with mx.engine.bulk(10):
@@ -30,6 +33,44 @@ def test_bulk():
             x += 1
     assert (x.asnumpy() == 104).all()
 
+@unittest.skip("OMP platform dependent")
+def test_engine_openmp_after_fork():
+    """
+    Test that the number of max threads in the child is 1. After forking we should not use a bigger
+    OMP thread pool.
+
+    With GOMP the child always has the same number when calling omp_get_max_threads, with LLVM OMP
+    the child respects the number of max threads set in the parent.
+    """
+    with EnvManager('OMP_NUM_THREADS', '42'):
+        r, w = os.pipe()
+        pid = os.fork()
+        if pid:
+            os.close(r)
+            wfd = os.fdopen(w, 'w')
+            wfd.write('a')
+            omp_max_threads = mx.base._LIB.omp_get_max_threads()
+            print("Parent omp max threads: {}".format(omp_max_threads))
+            try:
+                wfd.close()
+            except:
+                pass
+            try:
+                (cpid, status) = os.waitpid(pid, 0)
+                assert cpid == pid
+                exit_status = status >> 8
+                assert exit_status == 0
+            except:
+                pass
+        else:
+            os.close(w)
+            rfd = os.fdopen(r, 'r')
+            rfd.read(1)
+            omp_max_threads = mx.base._LIB.omp_get_max_threads()
+            print("Child omp max threads: {}".format(omp_max_threads))
+            assert omp_max_threads == 1
+
+
 
 if __name__ == '__main__':
     import nose

From 05af5c40bfb82fb534a5be2d6a9fe33ad4836008 Mon Sep 17 00:00:00 2001
From: Haibin Lin <linhaibin.eric@gmail.com>
Date: Wed, 11 Dec 2019 15:18:09 -0800
Subject: [PATCH 695/813] [BUGFIX] Fix race condition in kvstore.pushpull
 (#17007)

* add back gluon test

* fix typo

* change back gpu ctx

* also handle the case there some are pull and some are pushpull

* fix typo
---
 src/kvstore/kvstore_dist_server.h         | 35 ++++++++++++++++-------
 tests/nightly/dist_device_sync_kvstore.py | 35 ++++++++++++-----------
 2 files changed, 43 insertions(+), 27 deletions(-)

diff --git a/src/kvstore/kvstore_dist_server.h b/src/kvstore/kvstore_dist_server.h
index 65ded79743e4..1dc222c0d7da 100644
--- a/src/kvstore/kvstore_dist_server.h
+++ b/src/kvstore/kvstore_dist_server.h
@@ -364,21 +364,34 @@ class KVStoreDistServer {
       if (log_verbose_)  {
         LOG(INFO) << "sent response to " << update_buf->request.size() << " workers";
       }
+      /**
+       * Request can be for either push, pull or pushpull
+       * If pull flag is set, respond immediately with the updated values
+       * Otherwise, only send the notification
+       */
+      bool has_pull = false;
       for (const auto& req : update_buf->request) {
-        /**
-         * Request can be for either push, pull or pushpull
-         * If pull flag is set, respond immediately with the updated values
-         * Otherwise, only send the notification
-         */
-        if (req.pull) {
-          DefaultStorageResponse(type, key, req, req_data, server);
-        } else {
+        has_pull = has_pull || req.pull;
+      }
+      if (has_pull) {
+        // if there is a pull request, perform WaitToRead() once before DefaultStorageResponse
+        if (has_multi_precision_copy(type)) CopyFromTo(stored, store_[key]);
+        stored.WaitToRead();
+        for (const auto& req : update_buf->request) {
+          if (req.pull) {
+            DefaultStorageResponse(type, key, req, req_data, server);
+          }
+        }
+        update_buf->request.clear();
+      } else {
+        // otherwise, send response directly
+        for (const auto& req : update_buf->request) {
           server->Response(req);
         }
+        update_buf->request.clear();
+        if (has_multi_precision_copy(type)) CopyFromTo(stored, store_[key]);
+        stored.WaitToRead();
       }
-      update_buf->request.clear();
-      if (has_multi_precision_copy(type)) CopyFromTo(stored, store_[key]);
-      stored.WaitToRead();
     } else {
       update_buf->merged.WaitToRead();
     }
diff --git a/tests/nightly/dist_device_sync_kvstore.py b/tests/nightly/dist_device_sync_kvstore.py
index dc2c7bc35747..f3fe737f5653 100644
--- a/tests/nightly/dist_device_sync_kvstore.py
+++ b/tests/nightly/dist_device_sync_kvstore.py
@@ -44,7 +44,10 @@ def check_diff_to_scalar(A, x, rank=None):
 def init_kv():
     # init kv dns keys
     kv.init(keys, [mx.nd.ones(shape)] * len(keys))
+    kv.init('9', mx.nd.ones(shape))
+    kv.init('10', mx.nd.ones(shape))
     kv.init('99', mx.nd.ones(big_shape))
+    kv.init('100', mx.nd.ones(big_shape))
     # worker info
     my_rank = kv.rank
     nworker = kv.num_workers
@@ -55,33 +58,30 @@ def init_kv():
 def test_sync_push_pull():
     kv, my_rank, nworker = init_kv()
     num_gpus = 2
-    def check_default_keys(kv, my_rank, nworker, nrepeat=3, offset=0, use_pushpull=False):
+    def check_default_keys(kv, my_rank, nworker, nrepeat=3):
         # checks pull after push in loop, because behavior during
         # consecutive pushes doesn't offer any guarantees
-        for i in range(offset, nrepeat):
+        for i in range(nrepeat):
             scale = my_rank + 1
             num = (nworker + 1) * nworker * rate * num_gpus / 2 * (i + 1) + 1
 
             arr = [mx.nd.ones(shape, ctx=mx.gpu(j)) * scale for j in range(num_gpus)]
             val = mx.nd.zeros(shape)
-            if use_pushpull:
-                kv.pushpull('3', arr, out=val)
-            else:
-                kv.push('3', arr)
-                kv.pull('3', out=val)
+            kv.push('9', arr)
+            kv.pull('9', out=val)
+            check_diff_to_scalar(val, num)
+            kv.pushpull('10', arr, out=val)
             check_diff_to_scalar(val, num)
 
             big_arr = [mx.nd.ones(big_shape, ctx=mx.gpu(j)) * scale for j in range(num_gpus)]
             big_val = mx.nd.zeros(big_shape)
-            if use_pushpull:
-                kv.pushpull('99', big_arr, out=big_val)
-            else:
-                kv.push('99', big_arr)
-                kv.pull('99', out=big_val)
+            kv.push('99', big_arr)
+            kv.pull('99', out=big_val)
+            check_diff_to_scalar(big_val, num)
+            kv.pushpull('100', big_arr, out=big_val)
             check_diff_to_scalar(big_val, num)
 
-    check_default_keys(kv, my_rank, nworker, nrepeat=3, offset=0, use_pushpull=False)
-    check_default_keys(kv, my_rank, nworker, nrepeat=3, offset=3, use_pushpull=True)
+    check_default_keys(kv, my_rank, nworker, nrepeat=3)
     print('worker ' + str(my_rank) + ' is done')
 
 def test_sync_init():
@@ -106,10 +106,12 @@ def check_trainer_kv_update(update_on_kv):
         x = params.get('x', shape=(10,1), lr_mult=1.0)
         params.initialize(ctx=[mx.cpu(0), mx.cpu(1)], init='zeros')
         try:
-            trainer = mx.gluon.Trainer(params, 'sgd', {'learning_rate': 0.1}, kvstore=kv, update_on_kvstore=update_on_kv)
+            trainer = mx.gluon.Trainer(params, 'sgd', {'learning_rate': 0.1},
+                                       kvstore=kv, update_on_kvstore=update_on_kv)
             trainer._init_kvstore()
             assert trainer._kv_initialized
-            assert trainer._update_on_kvstore is True
+            if update_on_kv is not None:
+                assert trainer._update_on_kvstore is update_on_kv
         except ValueError:
             assert update_on_kv is False
 
@@ -122,3 +124,4 @@ def check_trainer_kv_update(update_on_kv):
 if __name__ == "__main__":
     test_sync_init()
     test_sync_push_pull()
+    test_gluon_trainer_type()

From 634f95e2431ec107f0e5182a60db27e3a7dd9545 Mon Sep 17 00:00:00 2001
From: Lanking <lanking520@live.com>
Date: Wed, 11 Dec 2019 16:12:04 -0800
Subject: [PATCH 696/813] swap wget to curl in Scala scripts (#17041)

* swap wget to curl

* fix cifar10
---
 scala-package/core/scripts/get_cifar_data.sh | 2 +-
 scala-package/core/scripts/get_mnist_data.sh | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/scala-package/core/scripts/get_cifar_data.sh b/scala-package/core/scripts/get_cifar_data.sh
index b061c1895e4a..a5cdcda419b7 100755
--- a/scala-package/core/scripts/get_cifar_data.sh
+++ b/scala-package/core/scripts/get_cifar_data.sh
@@ -32,7 +32,7 @@ fi
 
 cifar_data_path="$data_path/cifar10.zip"
 if [ ! -f "$cifar_data_path" ]; then
-  wget http://data.mxnet.io/mxnet/data/cifar10.zip -P $data_path
+  curl -L -o $cifar_data_path http://data.mxnet.io/mxnet/data/cifar10.zip
   cd $data_path
   unzip -u cifar10.zip
 fi
diff --git a/scala-package/core/scripts/get_mnist_data.sh b/scala-package/core/scripts/get_mnist_data.sh
index ded206fbb134..a7be96a9f401 100755
--- a/scala-package/core/scripts/get_mnist_data.sh
+++ b/scala-package/core/scripts/get_mnist_data.sh
@@ -32,7 +32,7 @@ fi
 
 mnist_data_path="$data_path/mnist.zip"
 if [ ! -f "$mnist_data_path" ]; then
-  wget http://data.mxnet.io/mxnet/data/mnist.zip -P $data_path
+  curl -L -o $mnist_data_path http://data.mxnet.io/mxnet/data/mnist.zip
   cd $data_path
   unzip -u mnist.zip
 fi

From 9092f17c909ae9f2af049d7787cc19015ee624d0 Mon Sep 17 00:00:00 2001
From: Minghao Liu <40382964+Tommliu@users.noreply.github.com>
Date: Fri, 13 Dec 2019 05:52:38 +0800
Subject: [PATCH 697/813] array_split pr (#17032)

---
 python/mxnet/ndarray/numpy/_op.py             | 71 ++++++++++++++++++-
 python/mxnet/numpy/multiarray.py              | 56 ++++++++++++++-
 python/mxnet/numpy_dispatch_protocol.py       |  1 +
 python/mxnet/symbol/numpy/_symbol.py          | 54 +++++++++++++-
 src/operator/tensor/matrix_op-inl.h           | 10 ++-
 .../unittest/test_numpy_interoperability.py   | 13 ++++
 tests/python/unittest/test_numpy_op.py        | 57 +++++++++++++++
 7 files changed, 252 insertions(+), 10 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index 538d5202942d..c111a95a707a 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -32,9 +32,9 @@
            'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power',
            'arctan2', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs',
            'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2',
-           'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
-           'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'argsort', 'tensordot', 'histogram', 'eye',
-           'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate', 'append',
+           'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor', 'histogram',
+           'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'argsort', 'tensordot', 'eye', 'linspace',
+           'logspace', 'expand_dims', 'tile', 'arange', 'array_split', 'split', 'vsplit', 'concatenate', 'append',
            'stack', 'vstack', 'column_stack', 'dstack', 'average', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip',
            'argmax', 'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'unravel_index', 'hanning', 'hamming',
            'blackman', 'flip', 'around', 'hypot', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad', 'unique', 'lcm',
@@ -3029,6 +3029,71 @@ def split(ary, indices_or_sections, axis=0):
 # pylint: enable=redefined-outer-name
 
 
+# pylint: disable=redefined-outer-name
+@set_module('mxnet.ndarray.numpy')
+def array_split(ary, indices_or_sections, axis=0):
+    """Split an array into multiple sub-arrays.
+
+    If `indices_or_sections` is an integer, N, the array will be divided
+    into N equal arrays along `axis`.  If such a split is not possible,
+    an array of length l that should be split into n sections, it returns
+    l % n sub-arrays of size l//n + 1 and the rest of size l//n.
+
+    If `indices_or_sections` is a 1-D array of sorted integers, the entries
+        indicate where along `axis` the array is split.  For example,
+        ``[2, 3]`` would, for ``axis=0``, result in
+          - ary[:2]
+          - ary[2:3]
+          - ary[3:]
+    If an index exceeds the dimension of the array along `axis`,
+    an empty sub-array is returned correspondingly.
+
+    Parameters
+    ----------
+    ary : ndarray
+        Array to be divided into sub-arrays.
+    indices_or_sections : int or 1-D Python tuple, list or set.
+        Param used to determine the number and size of the subarray.
+    axis : int, optional
+        The axis along which to split, default is 0.
+
+    Returns
+    -------
+    sub-arrays : list of ndarrays
+        A list of sub-arrays.
+
+    Examples
+    --------
+    >>> x = np.arange(9.0)
+    >>> np.array_split(x, 3)
+    [array([0., 1., 2.]), array([3., 4., 5.]), array([6., 7., 8.])]
+
+    >>> np.array_split(x, [3, 5, 6, 8])
+    [array([0., 1., 2.]), array([3., 4.]), array([5.]), array([6., 7.]), array([])]
+
+    >>> x = np.arange(8.0)
+    >>> np.array_split(x, 3)
+    [array([0.,  1.,  2.]), array([3.,  4.,  5.]), array([6.,  7.])]
+
+    >>> x = np.arange(7.0)
+    >>> np.array_split(x, 3)
+    [array([0.,  1.,  2.]), array([3.,  4.]), array([5.,  6.])]
+    """
+    indices = []
+    sections = 0
+    if isinstance(indices_or_sections, integer_types):
+        sections = indices_or_sections
+    elif isinstance(indices_or_sections, (list, set, tuple)):
+        indices = [0] + list(indices_or_sections)
+    else:
+        raise ValueError('indices_or_sections must be either int, or tuple / list / set of ints')
+    ret = _npi.split(ary, indices, axis, False, sections)
+    if not isinstance(ret, list):
+        return [ret]
+    return ret
+# pylint: enable=redefined-outer-name
+
+
 # pylint: disable=redefined-outer-name
 @set_module('mxnet.ndarray.numpy')
 def hsplit(ary, indices_or_sections):
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index aa0762bf0e3f..5795c62942df 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -50,9 +50,9 @@
            'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power',
            'arctan2', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10',
            'sqrt', 'cbrt', 'abs', 'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log',
-           'degrees', 'log2', 'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative',
+           'degrees', 'log2', 'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative', 'histogram',
            'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'append', 'argsort',
-           'tensordot', 'histogram', 'eye', 'linspace', 'logspace', 'expand_dims', 'tile', 'arange',
+           'tensordot', 'eye', 'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'array_split',
            'split', 'vsplit', 'concatenate', 'stack', 'vstack', 'column_stack', 'dstack', 'average', 'mean',
            'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'argmin', 'std', 'var', 'indices', 'copysign',
            'ravel', 'unravel_index', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'arctan2', 'hypot',
@@ -4841,6 +4841,58 @@ def split(ary, indices_or_sections, axis=0):
     return _mx_nd_np.split(ary, indices_or_sections, axis=axis)
 
 
+@set_module('mxnet.numpy')
+def array_split(ary, indices_or_sections, axis=0):
+    """Split an array into multiple sub-arrays.
+
+    If `indices_or_sections` is an integer, N, the array will be divided
+    into N equal arrays along `axis`.  If such a split is not possible,
+    an array of length l that should be split into n sections, it returns
+    l % n sub-arrays of size l//n + 1 and the rest of size l//n.
+
+    If `indices_or_sections` is a 1-D array of sorted integers, the entries
+        indicate where along `axis` the array is split.  For example,
+        ``[2, 3]`` would, for ``axis=0``, result in
+          - ary[:2]
+          - ary[2:3]
+          - ary[3:]
+    If an index exceeds the dimension of the array along `axis`,
+    an empty sub-array is returned correspondingly.
+
+    Parameters
+    ----------
+    ary : ndarray
+        Array to be divided into sub-arrays.
+    indices_or_sections : int or 1-D Python tuple, list or set.
+        Param used to determine the number and size of the subarray.
+    axis : int, optional
+        The axis along which to split, default is 0.
+
+    Returns
+    -------
+    sub-arrays : list of ndarrays
+        A list of sub-arrays.
+
+    Examples
+    --------
+    >>> x = np.arange(9.0)
+    >>> np.array_split(x, 3)
+    [array([0., 1., 2.]), array([3., 4., 5.]), array([6., 7., 8.])]
+
+    >>> np.array_split(x, [3, 5, 6, 8])
+    [array([0., 1., 2.]), array([3., 4.]), array([5.]), array([6., 7.]), array([])]
+
+    >>> x = np.arange(8.0)
+    >>> np.array_split(x, 3)
+    [array([0.,  1.,  2.]), array([3.,  4.,  5.]), array([6.,  7.])]
+
+    >>> x = np.arange(7.0)
+    >>> np.array_split(x, 3)
+    [array([0.,  1.,  2.]), array([3.,  4.]), array([5.,  6.])]
+    """
+    return _mx_nd_np.array_split(ary, indices_or_sections, axis=axis)
+
+
 @set_module('mxnet.numpy')
 def vsplit(ary, indices_or_sections):
     r"""
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index 1a238ec2c7c7..23593a47e6ba 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -113,6 +113,7 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'reshape',
     'roll',
     'split',
+    'array_split',
     'squeeze',
     'stack',
     'std',
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 4b06bbec7cae..c61d5b2d393d 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -40,9 +40,9 @@
            'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power', 'arctan2',
            'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs', 'absolute', 'exp',
            'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2', 'log1p',
-           'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
-           'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'argsort', 'tensordot', 'histogram', 'eye',
-           'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate', 'append',
+           'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor', 'histogram',
+           'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'argsort', 'tensordot', 'eye', 'linspace',
+           'logspace', 'expand_dims', 'tile', 'arange', 'array_split', 'split', 'vsplit', 'concatenate', 'append',
            'stack', 'vstack', 'column_stack', 'dstack', 'average', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip',
            'argmax', 'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'unravel_index', 'hanning', 'hamming',
            'blackman', 'flip', 'around', 'hypot', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad', 'unique', 'lcm',
@@ -3116,6 +3116,54 @@ def split(ary, indices_or_sections, axis=0):
 # pylint: enable=redefined-outer-name
 
 
+# pylint: disable=redefined-outer-name
+@set_module('mxnet.symbol.numpy')
+def array_split(ary, indices_or_sections, axis=0):
+    """Split an array into multiple sub-arrays.
+
+    If `indices_or_sections` is an integer, N, the array will be divided
+    into N equal arrays along `axis`.  If such a split is not possible,
+    an array of length l that should be split into n sections, it returns
+    l % n sub-arrays of size l//n + 1 and the rest of size l//n.
+
+    If `indices_or_sections` is a 1-D array of sorted integers, the entries
+        indicate where along `axis` the array is split.  For example,
+        ``[2, 3]`` would, for ``axis=0``, result in
+          - ary[:2]
+          - ary[2:3]
+          - ary[3:]
+    If an index exceeds the dimension of the array along `axis`,
+    an empty sub-array is returned correspondingly.
+
+    Parameters
+    ----------
+    ary : _Symbol
+        Array to be divided into sub-arrays.
+    indices_or_sections : int or 1-D Python tuple, list or set.
+        Param used to determine the number and size of the subarray.
+    axis : int, optional
+        The axis along which to split, default is 0.
+
+    Returns
+    -------
+    sub-arrays : list of ndarrays
+        A list of sub-arrays.
+    """
+    indices = []
+    sections = 0
+    if isinstance(indices_or_sections, int):
+        sections = indices_or_sections
+    elif isinstance(indices_or_sections, (list, set, tuple)):
+        indices = [0] + list(indices_or_sections)
+    else:
+        raise ValueError('indices_or_sections must either int or tuple / list / set of ints')
+    ret = _npi.split(ary, indices, axis, False, sections)
+    if not isinstance(ret, list):
+        return [ret]
+    return ret
+# pylint: enable=redefined-outer-name
+
+
 # pylint: disable=redefined-outer-name
 @set_module('mxnet.symbol.numpy')
 def hsplit(ary, indices_or_sections):
diff --git a/src/operator/tensor/matrix_op-inl.h b/src/operator/tensor/matrix_op-inl.h
index 4bd059ae81df..0c501808a6c0 100644
--- a/src/operator/tensor/matrix_op-inl.h
+++ b/src/operator/tensor/matrix_op-inl.h
@@ -2727,9 +2727,15 @@ struct SplitParam : public dmlc::Parameter<SplitParam> {
 inline mxnet::TShape GetSplitIndices(const mxnet::TShape& ishape, int axis, int sections) {
   mxnet::TShape indices(sections+1, -1);
   indices[0] = 0;
-  int64_t section_size = ishape[axis] / sections;
+  int64_t section_size_b = (int64_t) (ishape[axis] / sections);
+  int64_t section_size_a = section_size_b + 1;
+  int section_a = ishape[axis] % sections;
   for (int i = 0; i < sections; ++i) {
-    indices[i+1] = section_size * (i + 1);
+    if ( i < section_a ) {
+      indices[i+1] = section_size_a * (i + 1);
+    } else {
+      indices[i+1] = section_size_b + indices[i];
+    }
   }
   return indices;
 }
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index 4c4e8b90eca9..0e875825a699 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -181,6 +181,18 @@ def _add_workload_split():
     assertRaises(ValueError, np.split, np.arange(10), 3)
 
 
+def _add_workload_array_split():
+    a = np.arange(10)
+    b = np.array([np.arange(10), np.arange(10)])
+
+    for i in range(1, 12):
+        OpArgMngr.add_workload('array_split', a, i)
+    OpArgMngr.add_workload('array_split', b, 3, axis=0)
+    OpArgMngr.add_workload('array_split', b, [0, 1, 2], axis=0)
+    OpArgMngr.add_workload('array_split', b, 3, axis=-1)
+    OpArgMngr.add_workload('array_split', b, 3)
+
+    
 def _add_workload_squeeze():
     OpArgMngr.add_workload('squeeze', np.random.uniform(size=(4, 1)))
     OpArgMngr.add_workload('squeeze', np.random.uniform(size=(20, 10, 10, 1, 1)))
@@ -1398,6 +1410,7 @@ def _prepare_workloads():
     _add_workload_rint(array_pool)
     _add_workload_roll()
     _add_workload_split()
+    _add_workload_array_split()
     _add_workload_squeeze()
     _add_workload_stack(array_pool)
     _add_workload_std()
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 078e37fc4146..54bce0aaae84 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -2237,6 +2237,63 @@ def get_indices(axis_size):
                     assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
 
 
+@with_seed()
+@use_np
+def test_np_array_split():
+    class TestArray_split(HybridBlock):
+        def __init__(self, indices_or_sections, axis=None):
+            super(TestArray_split, self).__init__()
+            self._axis = axis
+            self._indices_or_sections = indices_or_sections
+
+        def hybrid_forward(self, F, a, *args, **kwargs):
+            return F.np.array_split(a, indices_or_sections=self._indices_or_sections,
+                              axis=self._axis)
+
+    def get_indices(axis_size):
+        if axis_size is 0:
+            axis_size = random.randint(3, 6)
+        samples = random.randint(1, axis_size - 1)
+        indices = sorted(random.sample([i for i in range(0, axis_size + 1)], samples))
+        indices = tuple(indices)
+        return indices
+
+    shapes = [(), (5, ), (10, ),
+              (2, 5), (5, 5), (10, 10),
+              (4, 4, 4), (4, 6, 9), (6, 6, 6),
+              (7, 8, 9, 10)]
+    dtypes = [np.int8, np.uint8, np.int32, np.int64, np.float16, np.float32, np.float64]
+
+    combinations = itertools.product([False, True], shapes, dtypes)
+    for hybridize, shape, dtype in combinations:
+        rtol = 1e-2 if dtype == np.float16 else 1e-3
+        atol = 1e-4 if dtype == np.float16 else 1e-5
+        for axis in range(len(shape)):
+            x = np.random.uniform(-5.0, 5.0, size=shape).astype(dtype)
+            indices = get_indices(shape[axis])
+            sections = 7 if x.shape[axis] is 0 else random.randint(1,x.shape[axis])
+            for indices_or_sections in [indices, sections]:
+                # test gluon
+                test_array_split = TestArray_split(axis=axis, indices_or_sections=indices_or_sections)
+                if hybridize:
+                    test_array_split.hybridize()
+                x.attach_grad()
+                expected_ret = _np.array_split(x.asnumpy(), indices_or_sections=indices_or_sections, axis=axis)
+                with mx.autograd.record():
+                    y = test_array_split(x)
+                assert len(y) == len(expected_ret)
+                for mx_out, np_out in zip(y, expected_ret):
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
+                mx.autograd.backward(y)
+                assert_almost_equal(x.grad.asnumpy(), _np.ones(x.shape), rtol=rtol, atol=atol)
+
+                # test imperative
+                mx_outs = np.array_split(x, indices_or_sections=indices_or_sections, axis=axis)
+                np_outs = _np.array_split(x.asnumpy(), indices_or_sections=indices_or_sections, axis=axis)
+                for mx_out, np_out in zip(mx_outs, np_outs):
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
+
+
 @with_seed()
 @use_np
 def test_np_vsplit():

From f701f3f76a2a2f741c08354c622924344d01f13c Mon Sep 17 00:00:00 2001
From: jonatanmil <55560698+jonatanmil@users.noreply.github.com>
Date: Thu, 12 Dec 2019 15:07:18 -0800
Subject: [PATCH 698/813] [MXNET-1431] Multiple channel support in Gluon PReLU
 (#16262)

* Multiple channel support in Gluon PReLU

* Update activations.py
---
 python/mxnet/gluon/nn/activations.py | 11 ++++++++---
 tests/python/unittest/test_gluon.py  |  5 +++++
 2 files changed, 13 insertions(+), 3 deletions(-)

diff --git a/python/mxnet/gluon/nn/activations.py b/python/mxnet/gluon/nn/activations.py
index a3baae004311..1b9ce91dd2aa 100644
--- a/python/mxnet/gluon/nn/activations.py
+++ b/python/mxnet/gluon/nn/activations.py
@@ -120,7 +120,10 @@ class PReLU(HybridBlock):
     ----------
     alpha_initializer : Initializer
         Initializer for the `embeddings` matrix.
-
+    in_channels : int, default 1
+        Number of channels (alpha parameters) to learn. Can either be 1
+        or `n` where `n` is the size of the second dimension of the input
+        tensor.
 
     Inputs:
         - **data**: input tensor with arbitrary shape.
@@ -128,10 +131,12 @@ class PReLU(HybridBlock):
     Outputs:
         - **out**: output tensor with the same shape as `data`.
     """
-    def __init__(self, alpha_initializer=initializer.Constant(0.25), **kwargs):
+    def __init__(self, alpha_initializer=initializer.Constant(0.25),
+                 in_channels=1, **kwargs):
         super(PReLU, self).__init__(**kwargs)
         with self.name_scope():
-            self.alpha = self.params.get('alpha', shape=(1,), init=alpha_initializer)
+            self.alpha = self.params.get('alpha', shape=(in_channels,),
+                                         init=alpha_initializer)
 
     def hybrid_forward(self, F, x, alpha):
         return F.LeakyReLU(x, gamma=alpha, act_type='prelu', name='fwd')
diff --git a/tests/python/unittest/test_gluon.py b/tests/python/unittest/test_gluon.py
index 0163341dc9bb..d27c241f9c99 100644
--- a/tests/python/unittest/test_gluon.py
+++ b/tests/python/unittest/test_gluon.py
@@ -1389,6 +1389,11 @@ def selu(x):
     x = point_to_validate.reshape((1, 3, 2))
     assert_almost_equal(prelu(x).asnumpy(), mx.nd.where(x >= 0, x, 0.25 * x).asnumpy())
 
+    multichannel_init = mx.initializer.Constant(mx.nd.array([0.1, 0.25, 0.5]))
+    prelu_multichannel = mx.gluon.nn.PReLU(alpha_initializer=multichannel_init, in_channels=3)
+    prelu_multichannel.initialize()
+    assert_almost_equal(prelu_multichannel(x).asnumpy(), np.array([[-0.01, 0.1], [-0.025, 0.1], [-0.05, 0.1]]))
+
     gelu = mx.gluon.nn.GELU()
     def gelu_test(x):
         CUBE_CONSTANT = 0.044715

From 61013a8bf9ef8a7b79d684504df1b321b1efb8d8 Mon Sep 17 00:00:00 2001
From: Sheng Zha <szha@users.noreply.github.com>
Date: Thu, 12 Dec 2019 16:25:34 -0800
Subject: [PATCH 699/813] use env var to control stack trace logging (#17038)

* use env var to control stack trace logging

* update issue template
---
 .github/ISSUE_TEMPLATE/bug_report.md          |  2 +-
 3rdparty/dmlc-core                            |  2 +-
 CMakeLists.txt                                |  3 +-
 Makefile                                      |  2 +
 ci/docker/runtime_functions.sh                | 39 +++++++++++++++++++
 docs/static_site/src/pages/api/faq/env_var.md |  6 +++
 6 files changed, 51 insertions(+), 3 deletions(-)

diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
index 4540f8f5f7af..7a0115d36433 100644
--- a/.github/ISSUE_TEMPLATE/bug_report.md
+++ b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -10,7 +10,7 @@ assignees: ''
 (A clear and concise description of what the bug is.)
 
 ### Error Message
-(Paste the complete error message, including stack trace.)
+(Paste the complete error message. Please also include stack trace by setting environment variable `DMLC_LOG_STACK_TRACE_DEPTH=10` before running your script.)
 
 ## To Reproduce
 (If you developed your own code, please provide a short script that reproduces the error. For existing examples, please provide link.)
diff --git a/3rdparty/dmlc-core b/3rdparty/dmlc-core
index ca9f9329654d..b3a4c715bfc3 160000
--- a/3rdparty/dmlc-core
+++ b/3rdparty/dmlc-core
@@ -1 +1 @@
-Subproject commit ca9f9329654dbd42f06988276c6f74eb8bdd5038
+Subproject commit b3a4c715bfc37a08f245844a800933f10e47c1ea
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 1be19c577ae3..fcbe707aa9ef 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -111,7 +111,8 @@ endif()
 
 #Switch off modern thread local for dmlc-core, please see: https://github.com/dmlc/dmlc-core/issues/571#issuecomment-543467484
 add_definitions(-DDMLC_MODERN_THREAD_LOCAL=0)
-
+# disable stack trace in exception by default.
+add_definitions(-DDMLC_LOG_STACK_TRACE_SIZE=0)
 
 if(MSVC)
   add_definitions(-DWIN32_LEAN_AND_MEAN)
diff --git a/Makefile b/Makefile
index 639f259487ab..1b858cc48671 100644
--- a/Makefile
+++ b/Makefile
@@ -94,6 +94,8 @@ WARNFLAGS= -Wall -Wsign-compare
 CFLAGS = -DMSHADOW_FORCE_STREAM $(WARNFLAGS)
 # use old thread local implementation in DMLC-CORE
 CFLAGS += -DDMLC_MODERN_THREAD_LOCAL=0
+# disable stack trace in exception by default.
+CFLAGS += -DDMLC_LOG_STACK_TRACE_SIZE=0
 
 ifeq ($(DEV), 1)
 	CFLAGS += -g -Werror
diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index 63fe32879d75..9198eee32896 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -1007,6 +1007,7 @@ cd_unittest_ubuntu() {
     export MXNET_SUBGRAPH_VERBOSE=0
     export MXNET_ENABLE_CYTHON=0
     export CD_JOB=1 # signal this is a CD run so any unecessary tests can be skipped
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
 
     local mxnet_variant=${1:?"This function requires a mxnet variant as the first argument"}
     local python_cmd=${2:?"This function requires a python command as the first argument"}
@@ -1050,6 +1051,7 @@ unittest_ubuntu_python2_cpu_cython() {
     export MXNET_SUBGRAPH_VERBOSE=0
     export MXNET_ENABLE_CYTHON=1
     export MXNET_ENFORCE_CYTHON=1
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
     check_cython 2
     nosetests-2.7 $NOSE_COVERAGE_ARGUMENTS $NOSE_TIMER_ARGUMENTS --with-xunit --xunit-file nosetests_unittest.xml --verbose tests/python/unittest
     nosetests-2.7 $NOSE_COVERAGE_ARGUMENTS $NOSE_TIMER_ARGUMENTS --with-xunit --xunit-file nosetests_train.xml --verbose tests/python/train
@@ -1063,6 +1065,7 @@ unittest_ubuntu_python2_cpu() {
     export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
     export MXNET_SUBGRAPH_VERBOSE=0
     export MXNET_ENABLE_CYTHON=0
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
     nosetests-2.7 $NOSE_COVERAGE_ARGUMENTS $NOSE_TIMER_ARGUMENTS --with-xunit --xunit-file nosetests_unittest.xml --verbose tests/python/unittest
     nosetests-2.7 $NOSE_COVERAGE_ARGUMENTS $NOSE_TIMER_ARGUMENTS --with-xunit --xunit-file nosetests_train.xml --verbose tests/python/train
     nosetests-2.7 $NOSE_COVERAGE_ARGUMENTS $NOSE_TIMER_ARGUMENTS --with-xunit --xunit-file nosetests_quantization.xml --verbose tests/python/quantization
@@ -1075,6 +1078,7 @@ unittest_ubuntu_python3_cpu() {
     export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
     export MXNET_SUBGRAPH_VERBOSE=0
     export MXNET_ENABLE_CYTHON=0
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
     nosetests-3.4 $NOSE_COVERAGE_ARGUMENTS $NOSE_TIMER_ARGUMENTS --with-xunit --xunit-file nosetests_unittest.xml --verbose tests/python/unittest
     nosetests-3.4 $NOSE_COVERAGE_ARGUMENTS $NOSE_TIMER_ARGUMENTS --with-xunit --xunit-file nosetests_quantization.xml --verbose tests/python/quantization
 }
@@ -1086,6 +1090,7 @@ unittest_ubuntu_python3_cpu_mkldnn() {
     export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
     export MXNET_SUBGRAPH_VERBOSE=0
     export MXNET_ENABLE_CYTHON=0
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
     nosetests-3.4 $NOSE_COVERAGE_ARGUMENTS $NOSE_TIMER_ARGUMENTS --with-xunit --xunit-file nosetests_unittest.xml --verbose tests/python/unittest
     nosetests-3.4 $NOSE_COVERAGE_ARGUMENTS $NOSE_TIMER_ARGUMENTS --with-xunit --xunit-file nosetests_mkl.xml --verbose tests/python/mkl
 }
@@ -1097,6 +1102,7 @@ unittest_ubuntu_python2_gpu() {
     export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
     export MXNET_SUBGRAPH_VERBOSE=0
     export CUDNN_VERSION=${CUDNN_VERSION:-7.0.3}
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
     nosetests-2.7 $NOSE_COVERAGE_ARGUMENTS $NOSE_TIMER_ARGUMENTS --with-xunit --xunit-file nosetests_gpu.xml --verbose tests/python/gpu
 }
 
@@ -1108,6 +1114,7 @@ unittest_ubuntu_python3_gpu() {
     export MXNET_SUBGRAPH_VERBOSE=0
     export CUDNN_VERSION=${CUDNN_VERSION:-7.0.3}
     export MXNET_ENABLE_CYTHON=0
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
     nosetests-3.4 $NOSE_COVERAGE_ARGUMENTS $NOSE_TIMER_ARGUMENTS --with-xunit --xunit-file nosetests_gpu.xml --verbose tests/python/gpu
 }
 
@@ -1120,6 +1127,7 @@ unittest_ubuntu_python3_gpu_cython() {
     export CUDNN_VERSION=${CUDNN_VERSION:-7.0.3}
     export MXNET_ENABLE_CYTHON=1
     export MXNET_ENFORCE_CYTHON=1
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
     check_cython 3
     nosetests-3.4 $NOSE_COVERAGE_ARGUMENTS $NOSE_TIMER_ARGUMENTS --with-xunit --xunit-file nosetests_gpu.xml --verbose tests/python/gpu
 }
@@ -1131,6 +1139,7 @@ unittest_ubuntu_python3_gpu_nocudnn() {
     export MXNET_SUBGRAPH_VERBOSE=0
     export CUDNN_OFF_TEST_ONLY=true
     export MXNET_ENABLE_CYTHON=0
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
     nosetests-3.4 $NOSE_COVERAGE_ARGUMENTS $NOSE_TIMER_ARGUMENTS --with-xunit --xunit-file nosetests_gpu.xml --verbose tests/python/gpu
 }
 
@@ -1142,6 +1151,7 @@ unittest_ubuntu_tensorrt_gpu() {
     export LD_LIBRARY_PATH=/work/mxnet/lib:$LD_LIBRARY_PATH
     export CUDNN_VERSION=${CUDNN_VERSION:-7.0.3}
     export MXNET_ENABLE_CYTHON=0
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
     python tests/python/tensorrt/lenet5_train.py
     nosetests-3.4 $NOSE_COVERAGE_ARGUMENTS $NOSE_TIMER_ARGUMENTS --with-xunit --xunit-file nosetests_trt_gpu.xml --verbose --nocapture tests/python/tensorrt/
 }
@@ -1156,6 +1166,7 @@ unittest_ubuntu_python2_quantization_gpu() {
     export MXNET_SUBGRAPH_VERBOSE=0
     export CUDNN_VERSION=${CUDNN_VERSION:-7.0.3}
     export MXNET_ENABLE_CYTHON=0
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
     nosetests-2.7 $NOSE_COVERAGE_ARGUMENTS $NOSE_TIMER_ARGUMENTS --with-xunit --xunit-file nosetests_quantization_gpu.xml --verbose tests/python/quantization_gpu
 }
 
@@ -1169,6 +1180,7 @@ unittest_ubuntu_python3_quantization_gpu() {
     export MXNET_SUBGRAPH_VERBOSE=0
     export CUDNN_VERSION=${CUDNN_VERSION:-7.0.3}
     export MXNET_ENABLE_CYTHON=0
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
     nosetests-3.4 $NOSE_COVERAGE_ARGUMENTS $NOSE_TIMER_ARGUMENTS --with-xunit --xunit-file nosetests_quantization_gpu.xml --verbose tests/python/quantization_gpu
 }
 
@@ -1313,12 +1325,14 @@ unittest_centos7_gpu() {
     set -ex
     cd /work/mxnet
     export CUDNN_VERSION=${CUDNN_VERSION:-7.0.3}
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
     python3.6 -m "nose" $NOSE_COVERAGE_ARGUMENTS $NOSE_TIMER_ARGUMENTS --with-xunit --xunit-file nosetests_gpu.xml --verbose tests/python/gpu
 }
 
 integrationtest_ubuntu_cpu_onnx() {
 	set -ex
 	export PYTHONPATH=./python/
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
 	python tests/python-pytest/onnx/backend_test.py
 	pytest tests/python-pytest/onnx/mxnet_export_test.py
 	pytest tests/python-pytest/onnx/test_models.py
@@ -1330,18 +1344,21 @@ integrationtest_ubuntu_gpu_python() {
     export PYTHONPATH=./python/
     export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
     export MXNET_SUBGRAPH_VERBOSE=0
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
     python example/image-classification/test_score.py
 }
 
 integrationtest_ubuntu_gpu_caffe() {
     set -ex
     export PYTHONPATH=/work/deps/caffe/python:./python
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
     python tools/caffe_converter/test_converter.py
 }
 
 integrationtest_ubuntu_cpu_asan() {
     set -ex
     export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libasan.so.5
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
 
     cd /work/mxnet/build/cpp-package/example/
     /work/mxnet/cpp-package/example/get_data.sh
@@ -1350,6 +1367,7 @@ integrationtest_ubuntu_cpu_asan() {
 
 integrationtest_ubuntu_gpu_cpp_package() {
     set -ex
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
     cpp-package/tests/ci_test.sh
 }
 
@@ -1360,6 +1378,7 @@ integrationtest_ubuntu_cpu_dist_kvstore() {
     export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
     export MXNET_SUBGRAPH_VERBOSE=0
     export MXNET_USE_OPERATOR_TUNING=0
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
     cd tests/nightly/
     ../../tools/launch.py -n 7 --launcher local python dist_sync_kvstore.py --type=gluon_step_cpu
     ../../tools/launch.py -n 7 --launcher local python dist_sync_kvstore.py --type=gluon_sparse_step_cpu
@@ -1375,6 +1394,7 @@ integrationtest_ubuntu_cpu_dist_kvstore() {
 
 integrationtest_ubuntu_cpu_scala() {
     set -ex
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
     scala_prepare
     cd scala-package
     mvn -B verify -DskipTests=false
@@ -1382,6 +1402,7 @@ integrationtest_ubuntu_cpu_scala() {
 
 integrationtest_ubuntu_gpu_scala() {
     set -ex
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
     scala_prepare
     cd scala-package
     export SCALA_TEST_ON_GPU=1
@@ -1394,6 +1415,7 @@ integrationtest_ubuntu_gpu_dist_kvstore() {
     export PYTHONPATH=./python/
     export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
     export MXNET_SUBGRAPH_VERBOSE=0
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
     cd tests/nightly/
     ../../tools/launch.py -n 4 --launcher local python dist_device_sync_kvstore.py
     ../../tools/launch.py -n 4 --launcher local python dist_sync_kvstore.py --type=init_gpu
@@ -1404,6 +1426,7 @@ test_ubuntu_cpu_python2() {
     set -ex
     pushd .
     export MXNET_LIBRARY_PATH=/work/build/libmxnet.so
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
 
     VENV=mxnet_py2_venv
     virtualenv -p `which python2` $VENV
@@ -1421,6 +1444,7 @@ test_ubuntu_cpu_python3() {
     set -ex
     pushd .
     export MXNET_LIBRARY_PATH=/work/build/libmxnet.so
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
     VENV=mxnet_py3_venv
     virtualenv -p `which python3` $VENV
     source $VENV/bin/activate
@@ -1478,6 +1502,7 @@ nightly_test_installation() {
 # Runs Imagenet inference
 nightly_test_imagenet_inference() {
     set -ex
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
     echo $PWD
     cp /work/mxnet/build/cpp-package/example/imagenet_inference /work/mxnet/cpp-package/example/inference/
     cd /work/mxnet/cpp-package/example/inference/
@@ -1487,6 +1512,7 @@ nightly_test_imagenet_inference() {
 #Runs a simple MNIST training example
 nightly_test_image_classification() {
     set -ex
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
     ./tests/nightly/test_image_classification.sh
 }
 
@@ -1494,6 +1520,7 @@ nightly_test_image_classification() {
 nightly_test_KVStore_singleNode() {
     set -ex
     export PYTHONPATH=./python/
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
     python tests/nightly/test_kvstore.py
 }
 
@@ -1501,12 +1528,14 @@ nightly_test_KVStore_singleNode() {
 nightly_test_large_tensor() {
     set -ex
     export PYTHONPATH=./python/
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
     nosetests-3.4 tests/nightly/test_large_array.py
 }
 
 #Tests Amalgamation Build with 5 different sets of flags
 nightly_test_amalgamation() {
     set -ex
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
     # Amalgamation can not be run with -j nproc
     make -C amalgamation/ clean
     make -C amalgamation/ ${1} ${2}
@@ -1516,6 +1545,7 @@ nightly_test_amalgamation() {
 nightly_test_javascript() {
     set -ex
     export LLVM=/work/deps/emscripten-fastcomp/build/bin
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
     # This part is needed to run emcc correctly
     cd /work/deps/emscripten
     ./emcc
@@ -1527,6 +1557,7 @@ nightly_test_javascript() {
 nightly_model_backwards_compat_test() {
     set -ex
     export PYTHONPATH=/work/mxnet/python/
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
     ./tests/nightly/model_backwards_compatibility_check/model_backward_compat_checker.sh
 }
 
@@ -1534,6 +1565,7 @@ nightly_model_backwards_compat_test() {
 nightly_model_backwards_compat_train() {
     set -ex
     export PYTHONPATH=./python/
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
     ./tests/nightly/model_backwards_compatibility_check/train_mxnet_legacy_models.sh
 }
 
@@ -1543,6 +1575,7 @@ nightly_straight_dope_python2_single_gpu_tests() {
     cd /work/mxnet/tests/nightly/straight_dope
     export PYTHONPATH=/work/mxnet/python/
     export MXNET_TEST_KERNEL=python2
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
     nosetests-2.7 $NOSE_TIMER_ARGUMENTS --with-xunit --xunit-file nosetests_straight_dope_python2_single_gpu.xml \
       test_notebooks_single_gpu.py --nologcapture
 }
@@ -1552,6 +1585,7 @@ nightly_straight_dope_python3_single_gpu_tests() {
     cd /work/mxnet/tests/nightly/straight_dope
     export PYTHONPATH=/work/mxnet/python/
     export MXNET_TEST_KERNEL=python3
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
     nosetests-3.4 $NOSE_TIMER_ARGUMENTS --with-xunit --xunit-file nosetests_straight_dope_python3_single_gpu.xml \
       test_notebooks_single_gpu.py --nologcapture
 }
@@ -1562,6 +1596,7 @@ nightly_straight_dope_python2_multi_gpu_tests() {
     cd /work/mxnet/tests/nightly/straight_dope
     export PYTHONPATH=/work/mxnet/python/
     export MXNET_TEST_KERNEL=python2
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
     nosetests-2.7 $NOSE_TIMER_ARGUMENTS --with-xunit --xunit-file nosetests_straight_dope_python2_multi_gpu.xml \
       test_notebooks_multi_gpu.py --nologcapture
 }
@@ -1571,6 +1606,7 @@ nightly_straight_dope_python3_multi_gpu_tests() {
     cd /work/mxnet/tests/nightly/straight_dope
     export PYTHONPATH=/work/mxnet/python/
     export MXNET_TEST_KERNEL=python3
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
     nosetests-3.4 $NOSE_TIMER_ARGUMENTS --with-xunit --xunit-file nosetests_straight_dope_python3_multi_gpu.xml \
       test_notebooks_multi_gpu.py --nologcapture
 }
@@ -1580,6 +1616,7 @@ nightly_tutorial_test_ubuntu_python3_gpu() {
     cd /work/mxnet/docs
     export BUILD_VER=tutorial
     export MXNET_DOCS_BUILD_MXNET=0
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
     make html
     export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
     export MXNET_SUBGRAPH_VERBOSE=0
@@ -1594,6 +1631,7 @@ nightly_tutorial_test_ubuntu_python2_gpu() {
     cd /work/mxnet/docs
     export BUILD_VER=tutorial
     export MXNET_DOCS_BUILD_MXNET=0
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
     make html
     export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
     export MXNET_SUBGRAPH_VERBOSE=0
@@ -1621,6 +1659,7 @@ nightly_scala_demo_test_cpu() {
 
 nightly_estimator() {
     set -ex
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
     cd /work/mxnet/tests/nightly/estimator
     export PYTHONPATH=/work/mxnet/python/
     nosetests test_estimator_cnn.py
diff --git a/docs/static_site/src/pages/api/faq/env_var.md b/docs/static_site/src/pages/api/faq/env_var.md
index e4fe58a116c5..bc98c39d9570 100644
--- a/docs/static_site/src/pages/api/faq/env_var.md
+++ b/docs/static_site/src/pages/api/faq/env_var.md
@@ -223,6 +223,12 @@ The following environments can be used to profile the application without changi
 If cython modules are used, `mx.nd._internal.NDArrayBase` must be `mxnet._cy3.ndarray.NDArrayBase` for python 3 or `mxnet._cy2.ndarray.NDArrayBase` for python 2.
 If ctypes is used, it must be `mxnet._ctypes.ndarray.NDArrayBase`.
 
+## Logging
+
+* DMLC_LOG_STACK_TRACE_DEPTH
+  - Values: Int ```(default=0)```
+  - The depth of stack trace information to log when exception happens.
+
 ## Other Environment Variables
 
 * MXNET_GPU_WORKER_NSTREAMS

From c99a1108c0ed9d6fc32044ea79d3ae528c9d7a1d Mon Sep 17 00:00:00 2001
From: Przemyslaw Tredak <ptredak@nvidia.com>
Date: Fri, 13 Dec 2019 13:50:51 -0800
Subject: [PATCH 700/813] Add my key to KEYS (#17060)

---
 KEYS | 59 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 59 insertions(+)

diff --git a/KEYS b/KEYS
index 51675754d62c..9f2db10a92e1 100644
--- a/KEYS
+++ b/KEYS
@@ -806,3 +806,62 @@ O4432VBxwfqYDQ4z1Qx4VvM6gUAFPvTpr/9ZqsVUFcK4RW/Tsst6bFWwlqksmxl4
 AccomJAgVJoFPw==
 =hhsZ
 -----END PGP PUBLIC KEY BLOCK-----
+pub   rsa4096 2019-12-13 [SC]
+      ABD3F3423F41245A6E5E065D7273634B104F135F
+uid           [ultimate] Przemysław Krzysztof Trędak (CODE SIGNING KEY) <ptrendx@apache.org>
+sig 3        7273634B104F135F 2019-12-13  Przemysław Krzysztof Trędak (CODE SIGNING KEY) <ptrendx@apache.org>
+sub   rsa4096 2019-12-13 [E]
+sig          7273634B104F135F 2019-12-13  Przemysław Krzysztof Trędak (CODE SIGNING KEY) <ptrendx@apache.org>
+
+-----BEGIN PGP PUBLIC KEY BLOCK-----
+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+=m8Mt
+-----END PGP PUBLIC KEY BLOCK-----

From 3b911cf5edaabc384b360f9768df512cf00f0ce1 Mon Sep 17 00:00:00 2001
From: Talia <31782251+TEChopra1000@users.noreply.github.com>
Date: Fri, 13 Dec 2019 14:38:57 -0800
Subject: [PATCH 701/813] Website edits (#17050)

* adding gluon data tutorials and autogluon images

* moved autogluon images to general python docs image (_static) folder

* making modifications as per @sojiadeshina recommendations

* fixed autogluon images

* fixed link to data tutorials on package index page

* Update docs/python_docs/python/tutorials/packages/autograd/index.md

fixed link path
---
 .../autograd_control_flow.png                 | Bin
 .../autograd_control_flow_grad.png            | Bin
 .../autograd_forward_backward.png             | Bin
 .../autograd_images}/autograd_grad_req.mp4    | Bin
 .../autograd_images}/autograd_gradient.png    | Bin
 .../autograd_images}/autograd_graph.mp4       | Bin
 .../autograd_images}/autograd_head_grad.mp4   | Bin
 .../python/tutorials/deploy/index.rst         |   3 +-
 .../tutorials/packages/autograd/index.md      |  14 +-
 .../packages/gluon/data/data_augmentation.md  | 235 ++++++++++++
 .../tutorials/packages/gluon/data/datasets.md | 333 ++++++++++++++++++
 .../tutorials/packages/gluon/data/index.rst   |  40 +++
 .../python/tutorials/packages/gluon/index.rst |   8 +-
 .../python/tutorials/packages/index.rst       |   2 +-
 .../python/tutorials/performance/index.rst    |   2 +-
 .../_includes/get_started/macos/perl/perl.md  |   2 +-
 .../macos/python/cpu/build-from-source.md     |   2 +-
 17 files changed, 622 insertions(+), 19 deletions(-)
 rename docs/python_docs/{python/tutorials/packages/autograd/_static => _static/autograd_images}/autograd_control_flow.png (100%)
 rename docs/python_docs/{python/tutorials/packages/autograd/_static => _static/autograd_images}/autograd_control_flow_grad.png (100%)
 rename docs/python_docs/{python/tutorials/packages/autograd/_static => _static/autograd_images}/autograd_forward_backward.png (100%)
 rename docs/python_docs/{python/tutorials/packages/autograd/_static => _static/autograd_images}/autograd_grad_req.mp4 (100%)
 rename docs/python_docs/{python/tutorials/packages/autograd/_static => _static/autograd_images}/autograd_gradient.png (100%)
 rename docs/python_docs/{python/tutorials/packages/autograd/_static => _static/autograd_images}/autograd_graph.mp4 (100%)
 rename docs/python_docs/{python/tutorials/packages/autograd/_static => _static/autograd_images}/autograd_head_grad.mp4 (100%)
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/data/data_augmentation.md
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/data/datasets.md
 create mode 100644 docs/python_docs/python/tutorials/packages/gluon/data/index.rst

diff --git a/docs/python_docs/python/tutorials/packages/autograd/_static/autograd_control_flow.png b/docs/python_docs/_static/autograd_images/autograd_control_flow.png
similarity index 100%
rename from docs/python_docs/python/tutorials/packages/autograd/_static/autograd_control_flow.png
rename to docs/python_docs/_static/autograd_images/autograd_control_flow.png
diff --git a/docs/python_docs/python/tutorials/packages/autograd/_static/autograd_control_flow_grad.png b/docs/python_docs/_static/autograd_images/autograd_control_flow_grad.png
similarity index 100%
rename from docs/python_docs/python/tutorials/packages/autograd/_static/autograd_control_flow_grad.png
rename to docs/python_docs/_static/autograd_images/autograd_control_flow_grad.png
diff --git a/docs/python_docs/python/tutorials/packages/autograd/_static/autograd_forward_backward.png b/docs/python_docs/_static/autograd_images/autograd_forward_backward.png
similarity index 100%
rename from docs/python_docs/python/tutorials/packages/autograd/_static/autograd_forward_backward.png
rename to docs/python_docs/_static/autograd_images/autograd_forward_backward.png
diff --git a/docs/python_docs/python/tutorials/packages/autograd/_static/autograd_grad_req.mp4 b/docs/python_docs/_static/autograd_images/autograd_grad_req.mp4
similarity index 100%
rename from docs/python_docs/python/tutorials/packages/autograd/_static/autograd_grad_req.mp4
rename to docs/python_docs/_static/autograd_images/autograd_grad_req.mp4
diff --git a/docs/python_docs/python/tutorials/packages/autograd/_static/autograd_gradient.png b/docs/python_docs/_static/autograd_images/autograd_gradient.png
similarity index 100%
rename from docs/python_docs/python/tutorials/packages/autograd/_static/autograd_gradient.png
rename to docs/python_docs/_static/autograd_images/autograd_gradient.png
diff --git a/docs/python_docs/python/tutorials/packages/autograd/_static/autograd_graph.mp4 b/docs/python_docs/_static/autograd_images/autograd_graph.mp4
similarity index 100%
rename from docs/python_docs/python/tutorials/packages/autograd/_static/autograd_graph.mp4
rename to docs/python_docs/_static/autograd_images/autograd_graph.mp4
diff --git a/docs/python_docs/python/tutorials/packages/autograd/_static/autograd_head_grad.mp4 b/docs/python_docs/_static/autograd_images/autograd_head_grad.mp4
similarity index 100%
rename from docs/python_docs/python/tutorials/packages/autograd/_static/autograd_head_grad.mp4
rename to docs/python_docs/_static/autograd_images/autograd_head_grad.mp4
diff --git a/docs/python_docs/python/tutorials/deploy/index.rst b/docs/python_docs/python/tutorials/deploy/index.rst
index e0ff0db75f17..9c4d60578226 100644
--- a/docs/python_docs/python/tutorials/deploy/index.rst
+++ b/docs/python_docs/python/tutorials/deploy/index.rst
@@ -29,8 +29,9 @@ The following tutorials will help you learn export MXNet models.
 
    .. card::
       :title: Export ONNX Models
+      :link: export/onnx.html
 
-      COMING SOON
+      Export your MXNet model to the Open Neural Exchange Format
 
    .. card::
       :title: Export with GluonCV
diff --git a/docs/python_docs/python/tutorials/packages/autograd/index.md b/docs/python_docs/python/tutorials/packages/autograd/index.md
index 6623e15c1e2b..b1fe274ec41f 100644
--- a/docs/python_docs/python/tutorials/packages/autograd/index.md
+++ b/docs/python_docs/python/tutorials/packages/autograd/index.md
@@ -23,7 +23,7 @@
 
 Gradients are fundamental to the process of training neural networks, and tell us how to change the parameters of the network to improve its performance.
 
-![autograd-gradient](_static/autograd_gradient.png)
+![auto-gradient](//_static/autograd_images/autograd_gradient.png)
 
 ### Long Answer:
 
@@ -39,7 +39,7 @@ Assuming we've calculated the gradient of each parameter with respect to the los
 
 We differentiate. [MXNet Gluon](/api/python/docs/tutorials/packages/gluon/index.html) uses Reverse Mode Automatic Differentiation (`autograd`) to backprogate gradients from the loss metric to the network parameters.
 
-![forward-backward](_static/autograd_forward_backward.png)
+![forward-backward](//_static/autograd_images/autograd_forward_backward.png)
 
 ### Long Answer:
 
@@ -56,7 +56,7 @@ Stage 2. Work backwards through this record and evaluate the partial derivatives
 
 <p style="text-align:center">
     <video width="600" controls playsinline autoplay muted loop>
-        <source src="_static/autograd_graph.mp4" type="video/mp4">
+        <source src="/_static/autograd_images/autograd_graph.mp4" type="video/mp4">
     </video>
 </p>
 
@@ -173,7 +173,7 @@ net[0].weight.grad_req = 'null'
 
 <p style="text-align:center">
     <video width="600" controls playsinline autoplay muted loop>
-        <source src="_static/autograd_grad_req.mp4" type="video/mp4">
+        <source src="../../../../_static/autograd_images/autograd_grad_req.mp4" type="video/mp4">
     </video>
 </p>
 
@@ -217,7 +217,7 @@ def f(x):
 
 We can plot the resultant function for $x$ between 0 and 1, and we should recognise certain functions in segments of $x$. Starting with a quadratic curve from 0 to 1/2, we have a cubic curve from 1/2 to 2/3, a quartic from 2/3 to 3/4 and finally a flatline.
 
-![control-flow](_static/autograd_control_flow.png)
+![control-flow](//_static/autograd_images/autograd_control_flow.png)
 
 Using `autograd`, let's now find the gradient of this arbritrary function. We don't have a vectorized function in this case, because of the control flow, so let's also create a function to calculate the gradient using `autograd`.
 
@@ -234,7 +234,7 @@ grads = [get_grad(f, x).asscalar() for x in xs]
 print(grads)
 ```
 
-![flow-grad](_static/autograd_control_flow_grad.png)
+![flow-grad](//_static/autograd_images/autograd_control_flow_grad.png)
 
 We can calculate the gradients by hand in this situation (since it's a toy example), and for the four segments discussed before we'd expect $2x$, $3x^2$, $4x^3$ and 0. As a spot check, for $x=0.6$ the hand calculated gradient would be $3x^2=1.08$, which equals `1.08` as computed by `autograd`.
 
@@ -247,7 +247,7 @@ Most of the time `autograd` will be aware of the complete computational graph, a
 
 <p style="text-align:center">
     <video width="600" controls playsinline autoplay muted loop>
-        <source src="_static/autograd_head_grad.mp4" type="video/mp4">
+        <source src="../../../../_static/autograd_images/autograd_head_grad.mp4" type="video/mp4">
     </video>
 </p>
 
diff --git a/docs/python_docs/python/tutorials/packages/gluon/data/data_augmentation.md b/docs/python_docs/python/tutorials/packages/gluon/data/data_augmentation.md
new file mode 100644
index 000000000000..0e320fc2890e
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/data/data_augmentation.md
@@ -0,0 +1,235 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Image Augmentation
+
+Augmentation is the process of randomly adjusting the dataset samples used for training. As a result, a greater diversity of samples will be seen by the network and it is therefore less likely to overfit the training dataset. Some of the spurious characteristics of the dataset can be reduced using this technique. One example would be a dataset of images from the same camera having the same color tint: it's unhelpful when you want to apply this model to images from other cameras. You can avoid this by randomly shifting the colours of each image slightly and training your network on these augmented images.
+
+Although this technique can be applied in a variety of domains, it's very common in Computer Vision, and we will focus on image augmentations in this tutorial. Some example image augmentations include random crops and flips, and adjustments to the brightness and contrast.
+
+#### What are the prerequisites?
+
+You should be familiar with the concept of a transform and how to apply it to a dataset before reading this tutorial. Check out the [Data Transforms tutorial]() if this is new to you or you need a quick refresher.
+
+#### Where can I find the augmentation transforms?
+
+You can find them in the `mxnet.gluon.data.vision.transforms` module, alongside the deterministic transforms we've seen previously, such as `ToTensor`, `Normalize`, `CenterCrop` and `Resize`. Augmentations involve an element of randomness and all the augmentation transforms are prefixed with `Random`, such as `RandomResizedCrop` and `RandomBrightness`. We'll start by importing MXNet and the `transforms`.
+
+
+```python
+import matplotlib.pyplot as plt
+import mxnet as mx
+from mxnet.gluon.data.vision import transforms
+```
+
+#### Sample Image
+
+So that we can see the effects of all the vision augmentations, we'll take a sample image of a giraffe and apply various augmentations to it. We can see what it looks like to begin with.
+
+
+```python
+image_url = 'https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/inputs/0.jpg'
+mx.test_utils.download(image_url, "giraffe.jpg")
+example_image = mx.image.imread("giraffe.jpg")
+plt.imshow(example_image.asnumpy())
+```
+
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/transforms/output_5_1.png)
+
+
+Since these augmentations are random, we'll apply the same augmentation a few times and plot all of the outputs. We define a few utility functions to help with this.
+
+
+```python
+def show_images(imgs, num_rows, num_cols, scale=2):
+    # show augmented images in a grid layout 
+    aspect_ratio = imgs[0].shape[0]/imgs[0].shape[1]
+    figsize = (num_cols * scale, num_rows * scale * aspect_ratio)
+    _, axes = plt.subplots(num_rows, num_cols, figsize=figsize)
+    for i in range(num_rows):
+        for j in range(num_cols):
+            axes[i][j].imshow(imgs[i * num_cols + j].asnumpy())
+            axes[i][j].axes.get_xaxis().set_visible(False)
+            axes[i][j].axes.get_yaxis().set_visible(False)
+    plt.subplots_adjust(hspace=0.1, wspace=0)
+    return axes
+
+def apply(img, aug, num_rows=2, num_cols=4, scale=3):
+    # apply augmentation multiple times to obtain different samples
+    Y = [aug(img) for _ in range(num_rows * num_cols)]
+    show_images(Y, num_rows, num_cols, scale)
+```
+
+# Spatial Augmentation
+
+One form of augmentation affects the spatial position of pixel values. Using combinations of slicing, scaling, translating, rotating and flipping the values of the original image can be shifted to create new images. Some operations (like scaling and rotation) require interpolation as pixels in the new image are combinations of pixels in the original image.
+
+### `RandomResizedCrop`
+
+Many Computer Visions tasks, such as image classification and object detection, should be robust to changes in the scale and position of objects in the image. You can incorporate this into the network using pooling layers, but an alternative method is to crop random regions of the original image. 
+
+As an example, we randomly (using a uniform distribution) crop a region of the image with:
+
+* an area of 10% to 100% of the original area
+* a ratio of width to height between 0.5 and 2
+
+And then we resize this cropped region to 200 by 200 pixels.
+
+
+```python
+shape_aug = transforms.RandomResizedCrop(size=(200, 200),
+                                         scale=(0.1, 1),
+                                         ratio=(0.5, 2))
+apply(example_image, shape_aug)
+```
+
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/transforms/output_12_0.png)
+
+
+### `RandomFlipLeftRight`
+
+A simple augmentation technique is flipping. Usually flipping horizontally doesn't change the category of object and results in an image that's still plausible in the real world. Using `RandomFlipLeftRight`, we randomly flip the image horizontally 50% of the time.
+
+
+```python
+apply(example_image, transforms.RandomFlipLeftRight())
+```
+
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/transforms/output_15_0.png)
+
+
+### `RandomFlipTopBottom`
+
+Although it's not as common as flipping left and right, you can flip the image vertically 50% of the time with `RandomFlipTopBottom`. With our giraffe example, we end up with less plausible samples that horizontal flipping, with the ground above the sky in some cases.
+
+
+```python
+apply(example_image, transforms.RandomFlipTopBottom())
+```
+
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/transforms/output_18_0.png)
+
+
+# Color Augmentation
+
+Usually, exact coloring doesn't play a significant role in the classification or detection of objects, so augmenting the colors of images is a good technique to make the network invariant to color shifts. Color properties that can be changed include brightness, contrast, saturation and hue.
+
+### `RandomBrightness`
+
+Use `RandomBrightness` to add a random brightness jitter to images. Use the `brightness` parameter to control the amount of jitter in brightness, with value from 0 (no change) to 1 (potentially large change). `brightness` doesn't specify whether the brightness of the augmented image will be lighter or darker, just the potential strength of the effect. Specifically the augmentation is given by:
+
+```
+alpha = 1.0 + random.uniform(-brightness, brightness)
+image *= alpha
+```
+
+So by setting this to 0.5 we randomly change the brightness of the image to a value between 50% ($1-0.5$) and 150% ($1+0.5$) of the original image.
+
+
+```python
+apply(example_image, transforms.RandomBrightness(0.5))
+```
+
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/transforms/output_23_0.png)
+
+
+### `RandomContrast`
+
+Use `RandomContrast` to add a random contrast jitter to an image. Contrast can be thought of as the degree to which light and dark colors in the image differ. Use the `contrast` parameter to control the amount of jitter in contrast, with value from 0 (no change) to 1 (potentially large change). `contrast` doesn't specify whether the contrast of the augmented image will be higher or lower, just the potential strength of the effect. Specifically, the augmentation is given by:
+
+```
+coef = nd.array([[[0.299, 0.587, 0.114]]])
+alpha = 1.0 + random.uniform(-contrast, contrast)
+gray = image * coef
+gray = (3.0 * (1.0 - alpha) / gray.size) * nd.sum(gray)
+image *= alpha
+image += gray
+```
+
+
+```python
+apply(example_image, transforms.RandomContrast(0.5))
+```
+
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/transforms/output_26_0.png)
+
+
+### `RandomSaturation`
+
+Use `RandomSaturation` to add a random saturation jitter to an image. Saturation can be thought of as the 'amount' of color in an image. Use the `saturation` parameter to control the amount of jitter in saturation, with value from 0 (no change) to 1 (potentially large change). `saturation` doesn't specify whether the saturation of the augmented image will be higher or lower, just the potential strength of the effect. Specifically the augmentation is using the method detailed [here](https://beesbuzz.biz/code/16-hsv-color-transforms).
+
+
+```python
+apply(example_image, transforms.RandomSaturation(0.5))
+```
+
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/transforms/output_29_0.png)
+
+
+### `RandomHue`
+
+Use `RandomHue` to add a random hue jitter to images. Hue can be thought of as the 'shade' of the colors in an image. Use the `hue` parameter to control the amount of jitter in hue, with value from 0 (no change) to 1 (potentially large change). `hue` doesn't specify whether the hue of the augmented image will be shifted one way or the other, just the potential strength of the effect. Specifically the augmentation is using the method detailed [here](https://beesbuzz.biz/code/16-hsv-color-transforms).
+
+
+```python
+apply(example_image, transforms.RandomHue(0.5))
+```
+
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/transforms/output_32_0.png)
+
+
+### `RandomColorJitter`
+
+`RandomColorJitter` is a convenience transform that can be used to perform multiple color augmentations at once. You can set the `brightness`, `contrast`, `saturation` and `hue` jitters, that function the same as above for their individual transforms.
+
+
+```python
+color_aug = transforms.RandomColorJitter(brightness=0.5,
+                                         contrast=0.5,
+                                         saturation=0.5,
+                                         hue=0.5)
+apply(example_image, color_aug)
+```
+
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/transforms/output_35_0.png)
+
+
+### `RandomLighting`
+
+Use `RandomLighting` for an AlexNet-style PCA-based noise augmentation.
+
+
+```python
+apply(example_image, transforms.RandomLighting(alpha=1))
+```
+
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/transforms/output_38_0.png)
+
+# Composed Augmentations
+
+In practice, we apply multiple augmentation techniques to an image to increase the variety of images in the dataset. Using the `Compose` transform that was introduced in the [Data Transforms tutorial](), we can apply 3 of the transforms we previously used above.
+
+
+```python
+augs = transforms.Compose([
+    transforms.RandomFlipLeftRight(), color_aug, shape_aug])
+apply(example_image, augs)
+```
+
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/transforms/output_41_0.png)
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
\ No newline at end of file
diff --git a/docs/python_docs/python/tutorials/packages/gluon/data/datasets.md b/docs/python_docs/python/tutorials/packages/gluon/data/datasets.md
new file mode 100644
index 000000000000..1c952b86a3da
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/data/datasets.md
@@ -0,0 +1,333 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+
+# Gluon `Dataset`s and `DataLoader`
+
+One of the most critical steps for model training and inference is loading the data: without data you can't do Machine Learning! In this tutorial we use the Gluon API to define a [`Dataset`](/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset) and use a [`DataLoader`](/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader) to iterate through the dataset in mini-batches.
+
+## Introduction to `Dataset`s
+
+[`Dataset`](/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset) objects are used to represent collections of data, and include methods to load and parse the data (that is often stored on disk). Gluon has a number of different [`Dataset`](/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset) classes for working with image data straight out-of-the-box, but we'll use the [`ArrayDataset`](/api/python/gluon/data.html) to introduce the idea of a [`Dataset`](/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset).
+
+We first start by generating random data `X` (with 3 variables) and corresponding random labels `y` to simulate a typical supervised learning task. We generate 10 samples and we pass them all to the [`ArrayDataset`](/api/python/gluon/data/data.html).
+
+
+```python
+import mxnet as mx
+import os
+import tarfile
+
+mx.random.seed(42) # Fix the seed for reproducibility
+X = mx.random.uniform(shape=(10, 3))
+y = mx.random.uniform(shape=(10, 1))
+dataset = mx.gluon.data.dataset.ArrayDataset(X, y)
+```
+
+A key feature of a [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset) is the __*ability to retrieve a single sample given an index*__. Our random data and labels were generated in memory, so this [`ArrayDataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=arraydataset#mxnet.gluon.data.ArrayDataset) doesn't have to load anything from disk, but the interface is the same for all [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset)s.
+
+
+```python
+sample_idx = 4
+sample = dataset[sample_idx]
+
+assert len(sample) == 2
+assert sample[0].shape == (3, )
+assert sample[1].shape == (1, )
+print(sample)
+```
+
+(
+[ 0.4375872   0.29753461  0.89177299]
+<NDArray 3 @cpu(0)>,
+[ 0.83261985]
+<NDArray 1 @cpu(0)>)
+
+
+We get a tuple of a data sample and its corresponding label, which makes sense because we passed the data `X` and the labels `y` in that order when we instantiated the [`ArrayDataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=arraydataset#mxnet.gluon.data.ArrayDataset). We don't usually retrieve individual samples from [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset) objects though (unless we're quality checking the output samples). Instead we use a [`DataLoader`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader).
+
+## Introduction to `DataLoader`
+
+A [`DataLoader`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader) is used to create mini-batches of samples from a [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset), and provides a convenient iterator interface for looping these batches. It's typically much more efficient to pass a mini-batch of data through a neural network than a single sample at a time, because the computation can be performed in parallel. A required parameter of [`DataLoader`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader) is the size of the mini-batches you want to create, called `batch_size`.
+
+Another benefit of using [`DataLoader`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader) is the ability to easily load data in parallel using [`multiprocessing`](https://docs.python.org/3.6/library/multiprocessing.html). You can set the `num_workers` parameter to the number of CPUs avalaible on your machine for maximum performance, or limit it to a lower number to spare resources.
+
+
+```python
+from multiprocessing import cpu_count
+CPU_COUNT = cpu_count()
+
+data_loader = mx.gluon.data.DataLoader(dataset, batch_size=5, num_workers=CPU_COUNT)
+
+for X_batch, y_batch in data_loader:
+    print("X_batch has shape {}, and y_batch has shape {}".format(X_batch.shape, y_batch.shape))
+```
+
+`X_batch has shape (5, 3), and y_batch has shape (5, 1)` <!--notebook-skip-line-->
+
+`X_batch has shape (5, 3), and y_batch has shape (5, 1)` <!--notebook-skip-line-->
+
+
+We can see 2 mini-batches of data (and labels), each with 5 samples, which makes sense given we started with a dataset of 10 samples. When comparing the shape of the batches to the samples returned by the [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset), we've gained an extra dimension at the start which is sometimes called the batch axis.
+
+Our `data_loader` loop will stop when every sample of `dataset` has been returned as part of a batch. Sometimes the dataset length isn't divisible by the mini-batch size, leaving a final batch with a smaller number of samples. [`DataLoader`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader)'s default behavior is to return this smaller mini-batch, but this can be changed by setting the `last_batch` parameter to `discard` (which ignores the last batch) or `rollover` (which starts the next epoch with the remaining samples).
+
+## Machine learning with `Dataset`s and `DataLoader`s
+
+You will often use a few different [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset) objects in your Machine Learning project. It's essential to separate your training dataset from testing dataset, and it's also good practice to have validation dataset (a.k.a. development dataset) that can be used for optimising hyperparameters.
+
+Using Gluon [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset) objects, we define the data to be included in each of these separate datasets. Common use cases for loading data are covered already (e.g. [`mxnet.gluon.data.vision.datasets.ImageFolderDataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=imagefolderdataset#mxnet.gluon.data.vision.datasets.ImageFolderDataset)), but it's simple to create your own custom [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset) classes for other types of data. You can even use included [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset) objects for common datasets if you want to experiment quickly; they download and parse the data for you! In this example we use the [Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset from Zalando Research.
+
+Many of the image [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset)s accept a function (via the optional `transform` parameter) which is applied to each sample returned by the [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset). It's useful for performing data augmentation, but can also be used for more simple data type conversion and pixel value scaling as seen below.
+
+
+```python
+def transform(data, label):
+    data = data.astype('float32')/255
+    return data, label
+
+train_dataset = mx.gluon.data.vision.datasets.FashionMNIST(train=True, transform=transform)
+valid_dataset = mx.gluon.data.vision.datasets.FashionMNIST(train=False, transform=transform)
+```
+
+
+```python
+%matplotlib inline
+from matplotlib.pylab import imshow
+
+sample_idx = 234
+sample = train_dataset[sample_idx]
+data = sample[0]
+label = sample[1]
+label_desc = {0:'T-shirt/top', 1:'Trouser', 2:'Pullover', 3:'Dress', 4:'Coat', 5:'Sandal', 6:'Shirt', 7:'Sneaker', 8:'Bag', 9:'Ankle boot'}
+
+imshow(data[:,:,0].asnumpy(), cmap='gray')
+print("Data type: {}".format(data.dtype))
+print("Label: {}".format(label))
+print("Label description: {}".format(label_desc[label]))
+```
+
+`Data type: <class 'numpy.float32'>`<!--notebook-skip-line-->
+
+`Label: 8`<!--notebook-skip-line-->
+
+`Label description: Bag`<!--notebook-skip-line-->
+
+
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/datasets/fashion_mnist_bag.png)<!--notebook-skip-line-->
+
+
+When training machine learning models it is important to shuffle the training samples every time you pass through the dataset (i.e. each epoch). Sometimes the order of your samples will have a spurious relationship with the target variable, and shuffling the samples helps remove this. With [`DataLoader`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader) it's as simple as adding `shuffle=True`. You don't need to shuffle the validation and testing data though.
+
+If you have more complex shuffling requirements (e.g. when handling sequential data), take a look at [`mxnet.gluon.data.BatchSampler`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=batchsampler#mxnet.gluon.data.BatchSampler) and pass this to your [`DataLoader`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader) instead.
+
+
+```python
+batch_size = 32
+train_data_loader = mx.gluon.data.DataLoader(train_dataset, batch_size, shuffle=True, num_workers=CPU_COUNT)
+valid_data_loader = mx.gluon.data.DataLoader(valid_dataset, batch_size, num_workers=CPU_COUNT)
+```
+
+With both `DataLoader`s defined, we can now train a model to classify each image and evaluate the validation loss at each epoch. Our Fashion MNIST dataset has 10 classes including shirt, dress, sneakers, etc. We define a simple fully connected network with a softmax output and use cross entropy as our loss.
+
+
+```python
+from mxnet import gluon, autograd, ndarray
+
+def construct_net():
+    net = gluon.nn.HybridSequential()
+    with net.name_scope():
+        net.add(gluon.nn.Dense(128, activation="relu"))
+        net.add(gluon.nn.Dense(64, activation="relu"))
+        net.add(gluon.nn.Dense(10))
+    return net
+
+# construct and initialize network.
+ctx =  mx.gpu() if mx.context.num_gpus() else mx.cpu()
+
+net = construct_net()
+net.hybridize()
+net.initialize(mx.init.Xavier(), ctx=ctx)
+# define loss and trainer.
+criterion = gluon.loss.SoftmaxCrossEntropyLoss()
+trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.1})
+```
+
+```python
+
+
+epochs = 5
+for epoch in range(epochs):
+    # training loop (with autograd and trainer steps, etc.)
+    cumulative_train_loss = mx.nd.zeros(1, ctx=ctx)
+    training_samples = 0
+    for batch_idx, (data, label) in enumerate(train_data_loader):
+        data = data.as_in_context(ctx).reshape((-1, 784)) # 28*28=784
+        label = label.as_in_context(ctx)
+        with autograd.record():
+            output = net(data)
+            loss = criterion(output, label)
+        loss.backward()
+        trainer.step(data.shape[0])
+        cumulative_train_loss += loss.sum()
+        training_samples += data.shape[0]
+    train_loss = cumulative_train_loss.asscalar()/training_samples
+
+    # validation loop
+    cumulative_valid_loss = mx.nd.zeros(1, ctx)
+    valid_samples = 0
+    for batch_idx, (data, label) in enumerate(valid_data_loader):
+        data = data.as_in_context(ctx).reshape((-1, 784)) # 28*28=784
+        label = label.as_in_context(ctx)
+        output = net(data)
+        loss = criterion(output, label)
+        cumulative_valid_loss += loss.sum()
+        valid_samples += data.shape[0]
+    valid_loss = cumulative_valid_loss.asscalar()/valid_samples
+
+    print("Epoch {}, training loss: {:.2f}, validation loss: {:.2f}".format(epoch, train_loss, valid_loss))
+```
+
+`Epoch 0, training loss: 0.54, validation loss: 0.45`<!--notebook-skip-line-->
+
+`...`<!--notebook-skip-line-->
+
+`Epoch 4, training loss: 0.32, validation loss: 0.33`<!--notebook-skip-line-->
+
+
+# Using own data with included `Dataset`s
+
+Gluon has a number of different [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset) classes for working with your own image data straight out-of-the-box. You can get started quickly using the [`mxnet.gluon.data.vision.datasets.ImageFolderDataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=imagefolderdataset#mxnet.gluon.data.vision.datasets.ImageFolderDataset) which loads images directly from a user-defined folder, and infers the label (i.e. class) from the folders.
+
+We will run through an example for image classification, but a similar process applies for other vision tasks. If you already have your own collection of images to work with you should partition your data into training and test sets, and place all objects of the same class into seperate folders. Similar to:
+```
+    ./images/train/car/abc.jpg
+    ./images/train/car/efg.jpg
+    ./images/train/bus/hij.jpg
+    ./images/train/bus/klm.jpg
+    ./images/test/car/xyz.jpg
+    ./images/test/bus/uvw.jpg
+```
+
+You can download the Caltech 101 dataset if you don't already have images to work with for this example, but please note the download is 126MB.
+
+```python
+
+data_folder = "data"
+dataset_name = "101_ObjectCategories"
+archive_file = "{}.tar.gz".format(dataset_name)
+archive_path = os.path.join(data_folder, archive_file)
+data_url = "https://s3.us-east-2.amazonaws.com/mxnet-public/"
+
+if not os.path.isfile(archive_path):
+    mx.test_utils.download("{}{}".format(data_url, archive_file), dirname = data_folder)
+    print('Extracting {} in {}...'.format(archive_file, data_folder))
+    tar = tarfile.open(archive_path, "r:gz")
+    tar.extractall(data_folder)
+    tar.close()
+    print('Data extracted.')
+```
+
+After downloading and extracting the data archive, we have two folders: `data/101_ObjectCategories` and `data/101_ObjectCategories_test`. We load the data into separate training and testing  [`ImageFolderDataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=imagefolderdataset#mxnet.gluon.data.vision.datasets.ImageFolderDataset)s.
+
+```python
+training_path = os.path.join(data_folder, dataset_name)
+testing_path = os.path.join(data_folder, "{}_test".format(dataset_name))
+```
+
+We instantiate the [`ImageFolderDataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=imagefolderdataset#mxnet.gluon.data.vision.datasets.ImageFolderDataset)s by providing the path to the data, and the folder structure will be traversed to determine which image classes are available and which images correspond to each class. You must take care to ensure the same classes are both the training and testing datasets, otherwise the label encodings can get muddled.
+
+Optionally, you can pass a `transform` parameter to these [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset)s as we've seen before.
+
+
+```python
+train_dataset = mx.gluon.data.vision.datasets.ImageFolderDataset(training_path)
+test_dataset = mx.gluon.data.vision.datasets.ImageFolderDataset(testing_path)
+```
+
+Samples from these datasets are tuples of data and label. Images are loaded from disk, decoded and optionally transformed when the `__getitem__(i)` method is called (equivalent to `train_dataset[i]`).
+
+As with the Fashion MNIST dataset the labels will be integer encoded. You can use the `synsets` property of the [`ImageFolderDataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=imagefolderdataset#mxnet.gluon.data.vision.datasets.ImageFolderDataset)s to retrieve the original descriptions (e.g. `train_dataset.synsets[i]`).
+
+
+```python
+sample_idx = 539
+sample = train_dataset[sample_idx]
+data = sample[0]
+label = sample[1]
+
+imshow(data.asnumpy(), cmap='gray')
+print("Data type: {}".format(data.dtype))
+print("Label: {}".format(label))
+print("Label description: {}".format(train_dataset.synsets[label]))
+assert label == 1
+```
+
+`Data type: <class 'numpy.uint8'>`<!--notebook-skip-line-->
+
+`Label: 1`<!--notebook-skip-line-->
+
+`Label description: Faces_easy` <!--notebook-skip-line-->
+
+
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/datasets/caltech101_face.png)<!--notebook-skip-line-->
+
+
+# Using own data with custom `Dataset`s
+
+Sometimes you have data that doesn't quite fit the format expected by the included [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset)s. You might be able to preprocess your data to fit the expected format, but it is easy to create your own dataset to do this.
+
+All you need to do is create a class that implements a `__getitem__` method, that returns a sample (i.e. a tuple of [`mx.nd.NDArray`](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray)s).
+
+See the [Data Augmentation with Masks](http://mxnet.incubator.apache.org/tutorials/python/data_augmentation_with_masks.html) tutorial for an example of this.
+
+# Appendix: Upgrading from Module `DataIter` to Gluon `DataLoader`
+
+Before Gluon's [`DataLoader`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader), MXNet used [`DataIter`](https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=dataiter#mxnet.io.DataIter) objects for loading data for training and testing. [`DataIter`](https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=dataiter#mxnet.io.DataIter) has a similar interface for iterating through data, but it isn't directly compatible with typical Gluon [`DataLoader`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader) loops. Unlike Gluon [`DataLoader`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader) which often returns a tuple of `(data, label)`, a [`DataIter`](https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=dataiter#mxnet.io.DataIter) returns a [`DataBatch`](https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=databatch#mxnet.io.DataBatch) object that has `data` and `label` properties. Switching to [`DataLoader`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader)s is highly recommended when using Gluon, but you'll need to take care of pre-processing steps such as augmentations in a `transform` function.
+
+So you can get up and running with Gluon quicker if you have already imlemented complex pre-processing steps using [`DataIter`](https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=dataiter#mxnet.io.DataIter), we have provided a simple class to wrap existing [`DataIter`](https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=dataiter#mxnet.io.DataIter) objects so they can be used in a typical Gluon training loop. You can use this class for `DataIter`s such as [`mxnet.image.ImageIter`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=imageiter#mxnet.image.ImageIter) and [`mxnet.io.ImageRecordIter`](https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=imagere#mxnet.io.ImageRecordIter) that have single data and label arrays.
+
+
+```python
+class DataIterLoader():
+    def __init__(self, data_iter):
+        self.data_iter = data_iter
+
+    def __iter__(self):
+        self.data_iter.reset()
+        return self
+
+    def __next__(self):
+        batch = self.data_iter.__next__()
+        assert len(batch.data) == len(batch.label) == 1
+        data = batch.data[0]
+        label = batch.label[0]
+        return data, label
+
+    def next(self):
+        return self.__next__() # for Python 2
+```
+
+
+```python
+data_iter = mx.io.NDArrayIter(data=X, label=y, batch_size=5)
+data_iter_loader = DataIterLoader(data_iter)
+for X_batch, y_batch in data_iter_loader:
+    assert X_batch.shape == (5, 3)
+    assert y_batch.shape == (5, 1)
+```
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/python_docs/python/tutorials/packages/gluon/data/index.rst b/docs/python_docs/python/tutorials/packages/gluon/data/index.rst
new file mode 100644
index 000000000000..9a1a0c263699
--- /dev/null
+++ b/docs/python_docs/python/tutorials/packages/gluon/data/index.rst
@@ -0,0 +1,40 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+
+Data Tutorials
+===============
+
+These tutorials will help you learn how to load and use datasets with the Gluon API.
+
+   .. card::
+      :title: Data Augmentation
+      :link: data_augmentation.html
+
+      A guide to data augmentation.
+
+   .. card::
+      :title: Gluon Datasets and DataLoader
+      :link: datasets.html
+
+      A guide to loading data using the Gluon API.
+
+.. toctree::
+   :hidden:
+   :maxdepth: 1
+   :glob:
+
+   *
diff --git a/docs/python_docs/python/tutorials/packages/gluon/index.rst b/docs/python_docs/python/tutorials/packages/gluon/index.rst
index d97279465287..4435637ee381 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/index.rst
+++ b/docs/python_docs/python/tutorials/packages/gluon/index.rst
@@ -70,15 +70,9 @@ Data
 
 .. container:: cards
 
-   .. card::
-      :title: Data Loading
-      :link: data/data.html
-
-      How to load data for training.
-
    .. card::
       :title: Data Augmentation
-      :link: data/data_augmentation.md
+      :link: data/data_augmentation.html
 
       A guide to data augmentation.
 
diff --git a/docs/python_docs/python/tutorials/packages/index.rst b/docs/python_docs/python/tutorials/packages/index.rst
index 9fdd75c837f7..e43d658a0dbe 100644
--- a/docs/python_docs/python/tutorials/packages/index.rst
+++ b/docs/python_docs/python/tutorials/packages/index.rst
@@ -69,7 +69,7 @@ Shared APIs
 
    .. card::
       :title: Data APIs
-      :link: data/index.html
+      :link: gluon/data/index.html
 
       How to use MXNet's data APIs.
 
diff --git a/docs/python_docs/python/tutorials/performance/index.rst b/docs/python_docs/python/tutorials/performance/index.rst
index e547ecd297ed..b1f5c66c2001 100644
--- a/docs/python_docs/python/tutorials/performance/index.rst
+++ b/docs/python_docs/python/tutorials/performance/index.rst
@@ -111,7 +111,7 @@ Distributed Training
 
    .. card::
       :title: Data Parallelism in MXNet
-      :link: /api/faq/multi_devices.html
+      :link: /api/faq/multi_device
 
       An overview of distributed training strategies.
 
diff --git a/docs/static_site/src/_includes/get_started/macos/perl/perl.md b/docs/static_site/src/_includes/get_started/macos/perl/perl.md
index ab08489fdc56..45d59ddf78a6 100644
--- a/docs/static_site/src/_includes/get_started/macos/perl/perl.md
+++ b/docs/static_site/src/_includes/get_started/macos/perl/perl.md
@@ -1 +1 @@
-Refer to the [Perl section of installation guide](get_started/osx_setup.html#install-the-mxnet-package-for-perl).
\ No newline at end of file
+Refer to the [Perl section of installation guide](/get_started/osx_setup.html#install-the-mxnet-package-for-perl).
\ No newline at end of file
diff --git a/docs/static_site/src/_includes/get_started/macos/python/cpu/build-from-source.md b/docs/static_site/src/_includes/get_started/macos/python/cpu/build-from-source.md
index 01dd35b9f332..ee8e378ec20e 100644
--- a/docs/static_site/src/_includes/get_started/macos/python/cpu/build-from-source.md
+++ b/docs/static_site/src/_includes/get_started/macos/python/cpu/build-from-source.md
@@ -1,2 +1,2 @@
-To build from source, refer to the [MXNet macOS installation guide](get_started/osx_setup.html).
+To build from source, refer to the [MXNet macOS installation guide](/get_started/osx_setup.html).
 MXNet developers should refer to the MXNet wiki's [Developer Setup on Mac](https://cwiki.apache.org/confluence/display/MXNET/MXNet+Developer+Setup+on+Mac).

From f045018f2c22f33c560eb58a23406c457855bb72 Mon Sep 17 00:00:00 2001
From: kshitij12345 <kshitijkalambarkar@gmail.com>
Date: Sat, 14 Dec 2019 10:18:53 +0530
Subject: [PATCH 702/813] [MXNET-978] Higher Order Gradient Support `logp1`,
 `expm1`, `square`. (#15416)

* support logp1, expm1, square for higher order grad

* add relevant tests

* update comments

* update comments and tests

* use NodeOpGen for readability.
---
 .../tensor/elemwise_unary_op_logexp.cc        | 55 ++++++++++++++++++-
 src/operator/tensor/elemwise_unary_op_pow.cc  | 30 +++++++++-
 .../python/unittest/test_higher_order_grad.py | 33 +++++++++++
 3 files changed, 115 insertions(+), 3 deletions(-)

diff --git a/src/operator/tensor/elemwise_unary_op_logexp.cc b/src/operator/tensor/elemwise_unary_op_logexp.cc
index 7ca12e0b248b..7bc742104724 100644
--- a/src/operator/tensor/elemwise_unary_op_logexp.cc
+++ b/src/operator/tensor/elemwise_unary_op_logexp.cc
@@ -200,7 +200,32 @@ The storage type of ``log1p`` output depends upon the input storage type:
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_log1p"});
 
 MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_log1p,
-                                                  unary_bwd<mshadow_op::log1p_grad>);
+                                                  unary_bwd<mshadow_op::log1p_grad>)
+.set_attr<nnvm::FGradient>("FGradient",
+  [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
+    // ograds[0]: head_grad_grads (dL/dxgrad)
+    // inputs[0]: dL/dy
+    // inputs[1]: x (ElemwiseGradUseIn)
+    // f(x) = y = log(1+x)
+    // f'(x) = 1/(1+x)
+    // f''(x) = -1/(1+x)^2
+    auto dldy = n->inputs[0];
+    auto x = n->inputs[1];
+    auto dydx_mul_dldy = nnvm::NodeEntry{n};  // f'(x) * head_grads
+    auto op = mxnet::util::NodeOpGen{n};
+
+    auto dydx = op.div(dydx_mul_dldy, dldy);
+
+    auto d2ydx2_mid = op.mul(dydx_mul_dldy, dydx_mul_dldy);
+    auto d2ydx2_neg_mid = op.negative(d2ydx2_mid);
+    auto d2ydx2 = op.div(d2ydx2_neg_mid, dldy);
+
+    std::vector<nnvm::NodeEntry> ret;
+
+    ret.emplace_back(op.mul(ograds[0], dydx));
+    ret.emplace_back(op.mul(ograds[0], d2ydx2));
+    return ret;
+  });
 
 // expm1
 MXNET_OPERATOR_REGISTER_UNARY_WITH_RSP_CSR(expm1, cpu, mshadow_op::expm1)
@@ -217,7 +242,33 @@ The storage type of ``expm1`` output depends upon the input storage type:
 )code" ADD_FILELINE)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_expm1"});
 
-MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_expm1, unary_bwd<mshadow_op::exp>);
+MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_expm1, unary_bwd<mshadow_op::exp>)
+.set_attr<nnvm::FGradient>("FGradient",
+  [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
+    // ograds[0]: head_grad_grads (dL/dxgrad)
+    // inputs[0]: dL/dy
+    // inputs[1]: x (ElemwiseGradUseIn)
+    // f(x) = y = exp(x) - 1
+    // f'(x) = exp(x)
+    // f''(x) = exp(x)
+    auto dldy = n->inputs[0];
+    auto x = n->inputs[1];
+    auto dydx_mul_dldy = nnvm::NodeEntry{n};  // f'(x) * head_grads
+    auto op = mxnet::util::NodeOpGen{n};
+
+    auto dydx = op.div(dydx_mul_dldy, dldy);
+
+    auto exp_x = MakeNode("exp", n->attrs.name + "_backward_exp_grad",
+                         {n->inputs[1]}, nullptr, &n);
+    auto d2ydx2_mul_dldy = op.mul(nnvm::NodeEntry{exp_x}, dldy);
+
+    std::vector<nnvm::NodeEntry> ret;
+
+
+    ret.emplace_back(op.mul(ograds[0], dydx));
+    ret.emplace_back(op.mul(ograds[0], d2ydx2_mul_dldy));
+    return ret;
+  });
 
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/tensor/elemwise_unary_op_pow.cc b/src/operator/tensor/elemwise_unary_op_pow.cc
index 486fe268b0cf..084772980ed1 100644
--- a/src/operator/tensor/elemwise_unary_op_pow.cc
+++ b/src/operator/tensor/elemwise_unary_op_pow.cc
@@ -25,6 +25,7 @@
 #include "elemwise_unary_op.h"
 #include "./elemwise_binary_op-inl.h"
 #include "../nn/mkldnn/mkldnn_ops-inl.h"
+#include "../../nnvm/node_op_util.h"
 
 namespace mxnet {
 namespace op {
@@ -120,7 +121,34 @@ The storage type of ``square`` output depends upon the input storage type:
 #endif
 
 MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU(_backward_square,
-                                               unary_bwd<mshadow_op::square_grad>);
+                                               unary_bwd<mshadow_op::square_grad>)
+.set_attr<nnvm::FGradient>("FGradient",
+  [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
+    // ograds[0]: head_grad_grads (dL/dxgrad)
+    // inputs[0]: dL/dy
+    // inputs[1]: x (ElemwiseGradUseIn)
+    // f(x) = y = x^2
+    // f'(x) = 2*x
+    // f''(x) = 2
+    auto dldy = n->inputs[0];
+    auto x = n->inputs[1];
+    auto dydx_mul_dldy = nnvm::NodeEntry{n};  // f'(x) * head_grads
+    auto op = mxnet::util::NodeOpGen{n};
+
+    auto dydx = op.div(dydx_mul_dldy, dldy);
+
+    std::unordered_map<std::string, std::string> args = {{"scalar", "2.0"}};
+    auto ones_like = MakeNode("ones_like", n->attrs.name + "_backward_ones_like",
+                         {n->inputs[1]}, nullptr, &n);
+    auto d2ydx2 = op.mul(2.0, nnvm::NodeEntry{ones_like});
+    auto d2ydx2_mul_dldy = op.mul(d2ydx2, dldy);
+
+    std::vector<nnvm::NodeEntry> ret;
+
+    ret.emplace_back(op.mul(ograds[0], dydx));
+    ret.emplace_back(op.mul(ograds[0], d2ydx2_mul_dldy));
+    return ret;
+  });
 
 // sqrt
 MXNET_OPERATOR_REGISTER_UNARY_WITH_RSP_CSR(sqrt, cpu, mshadow_op::square_root)
diff --git a/tests/python/unittest/test_higher_order_grad.py b/tests/python/unittest/test_higher_order_grad.py
index 0b0b00fffac7..527c35d5dd94 100644
--- a/tests/python/unittest/test_higher_order_grad.py
+++ b/tests/python/unittest/test_higher_order_grad.py
@@ -273,6 +273,39 @@ def grad_grad_op(x):
         check_second_order_unary(array, log10, grad_grad_op)
 
 
+@with_seed()
+def test_square():
+    def grad_grad_op(x):
+        return nd.ones_like(x) * 2
+
+    for dim in range(1, 5):
+        shape = rand_shape_nd(dim)
+        array = random_arrays(shape)
+        check_second_order_unary(array, nd.square, grad_grad_op)
+
+
+@with_seed()
+def test_expm1():
+    def grad_grad_op(x):
+        return nd.exp(x)
+
+    for dim in range(1, 5):
+        shape = rand_shape_nd(dim)
+        array = random_arrays(shape)
+        check_second_order_unary(array, nd.expm1, grad_grad_op)
+
+
+@with_seed()
+def test_log1p():
+    def grad_grad_op(x):
+        return -1/((1+x)**2)
+
+    for dim in range(1, 5):
+        shape = rand_shape_nd(dim)
+        array = random_arrays(shape)
+        check_second_order_unary(array, nd.log1p, grad_grad_op)
+
+
 @with_seed()
 def test_reciprocal():
     def reciprocal(x):

From 042682edca9b40e7d80e71f9713162227bd65ac7 Mon Sep 17 00:00:00 2001
From: Haibin Lin <linhaibin.eric@gmail.com>
Date: Sat, 14 Dec 2019 08:28:25 -0800
Subject: [PATCH 703/813] [DOC] Fix tutorial link, and better error msg
 (#17057)

---
 python/mxnet/gluon/parameter.py | 14 ++++++++++----
 1 file changed, 10 insertions(+), 4 deletions(-)

diff --git a/python/mxnet/gluon/parameter.py b/python/mxnet/gluon/parameter.py
index b0f8fef2c0dd..695565e9cf46 100644
--- a/python/mxnet/gluon/parameter.py
+++ b/python/mxnet/gluon/parameter.py
@@ -972,7 +972,7 @@ def save(self, filename, strip_prefix=''):
                     "this may be due to your Block shares parameters from other "
                     "Blocks or you forgot to use 'with name_scope()' when creating "
                     "child blocks. For more info on naming, please see "
-                    "https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/naming.html"%(
+                    "https://mxnet.io/api/python/docs/tutorials/packages/gluon/blocks/naming.html"%(
                         strip_prefix, param.name, strip_prefix))
             arg_dict[param.name[len(strip_prefix):]] = weight
         ndarray.save(filename, arg_dict)
@@ -1006,7 +1006,9 @@ def load(self, filename, ctx=None, allow_missing=False,
             for name in self.keys():
                 assert name.startswith(restore_prefix), \
                     "restore_prefix is '%s' but Parameters name '%s' does not start " \
-                    "with '%s'"%(restore_prefix, name, restore_prefix)
+                    "with '%s'. For more info on naming, please see " \
+                    "https://mxnet.io/api/python/docs/tutorials/packages/gluon/blocks/naming.html"%(
+                        restore_prefix, name, restore_prefix)
         ndarray_load = ndarray.load(filename)
         self.load_dict(ndarray_load, ctx, allow_missing,
                        ignore_extra, restore_prefix, filename, cast_dtype, dtype_source)
@@ -1043,14 +1045,18 @@ def load_dict(self, param_dict, ctx=None, allow_missing=False,
             for name in self.keys():
                 assert name in arg_dict, \
                     "Parameter '%s' is missing in %s, which contains parameters: %s. " \
-                    "Please make sure source and target networks have the same prefix."%(
+                    "Please make sure source and target networks have the same prefix." \
+                    "For more info on naming, please see " \
+                    "https://mxnet.io/api/python/docs/tutorials/packages/gluon/blocks/naming.html"%(
                         name[lprefix:], error_str, _brief_print_list(arg_dict.keys()))
         for name in arg_dict:
             if name not in self._params:
                 assert ignore_extra, \
                     "Parameter '%s' loaded from %s is not present in ParameterDict, " \
                     "choices are: %s. Set ignore_extra to True to ignore. " \
-                    "Please make sure source and target networks have the same prefix."%(
+                    "Please make sure source and target networks have the same prefix." \
+                    "For more info on naming, please see " \
+                    "https://mxnet.io/api/python/docs/tutorials/packages/gluon/blocks/naming.html"%(
                         name[lprefix:], error_str, _brief_print_list(self._params.keys()))
                 continue
             self[name]._load_init(arg_dict[name], ctx, cast_dtype=cast_dtype,

From 696c54738f2ceca89c9eb7eac18060b942e6dfab Mon Sep 17 00:00:00 2001
From: Haibin Lin <linhaibin.eric@gmail.com>
Date: Sat, 14 Dec 2019 08:28:57 -0800
Subject: [PATCH 704/813] [BUGFIX] Fix trainer param order (#17068)

* fix trainer param order

* Update trainer.py

* Update trainer.py

* Update trainer.py
---
 python/mxnet/gluon/trainer.py               |  5 ++++-
 tests/python/unittest/test_gluon_trainer.py | 16 ++++++++++++++++
 2 files changed, 20 insertions(+), 1 deletion(-)

diff --git a/python/mxnet/gluon/trainer.py b/python/mxnet/gluon/trainer.py
index 01f76d637a97..1ab86af2b93f 100644
--- a/python/mxnet/gluon/trainer.py
+++ b/python/mxnet/gluon/trainer.py
@@ -71,8 +71,11 @@ class Trainer(object):
     """
     def __init__(self, params, optimizer, optimizer_params=None, kvstore='device',
                  compression_params=None, update_on_kvstore=None):
+        param_list = []
         if isinstance(params, (dict, ParameterDict)):
-            params = list(params.values())
+            for key in sorted(list(params.keys())):
+                param_list.append(params[key])
+            params = param_list
         if not isinstance(params, (list, tuple)):
             raise ValueError(
                 "First argument must be a list or dict of Parameters, " \
diff --git a/tests/python/unittest/test_gluon_trainer.py b/tests/python/unittest/test_gluon_trainer.py
index 2d5874a8b97b..9f02733d0a25 100644
--- a/tests/python/unittest/test_gluon_trainer.py
+++ b/tests/python/unittest/test_gluon_trainer.py
@@ -291,3 +291,19 @@ def test_trainer_lr_sched():
             assert trainer.learning_rate == lr, (lr, trainer.learning_rate, i)
             lr *= factor
     mx.nd.waitall()
+
+@with_seed()
+def test_gluon_trainer_param_order():
+    net = mx.gluon.nn.Sequential()
+    # layers may be added in a random order for all workers
+    layers = {'ones_': 1, 'zeros_': 0}
+    for name, init in layers.items():
+        net.add(mx.gluon.nn.Dense(10, in_units=10, weight_initializer=mx.init.Constant(init),
+                                  use_bias=False, prefix=name))
+    params = net.collect_params()
+    net.initialize()
+    trainer = gluon.Trainer(params, 'sgd')
+    for name, init in layers.items():
+        expected_idx = 0 if name == 'ones_' else 1
+        expected_name = name + 'weight'
+        assert trainer._params[expected_idx].name == expected_name

From bbdc1c3627ad5254c049c2bb871ecb4527d7dc14 Mon Sep 17 00:00:00 2001
From: MoisesHer <50716238+MoisesHer@users.noreply.github.com>
Date: Sat, 14 Dec 2019 08:32:50 -0800
Subject: [PATCH 705/813] [reproducibility] multi_sum_sq review, AtomicAdd
 removal (#17002)

* Update multi_sum_sq to avoid AtomicAdd

* Add specific test for multi_sum_sq

* Add a determism test and lint issues

* better test for cheching op is deterministic

* Follow MXNet letters case format

* Reduce dimensions of tensors in the test
---
 src/operator/contrib/multi_sum_sq-inl.h |  10 ++-
 src/operator/contrib/multi_sum_sq.cc    |  20 +++--
 src/operator/contrib/multi_sum_sq.cu    | 110 +++++++++++++++---------
 tests/python/gpu/test_operator_gpu.py   |  30 +++++++
 4 files changed, 118 insertions(+), 52 deletions(-)

diff --git a/src/operator/contrib/multi_sum_sq-inl.h b/src/operator/contrib/multi_sum_sq-inl.h
index 876155215d1c..b8609c0f217f 100644
--- a/src/operator/contrib/multi_sum_sq-inl.h
+++ b/src/operator/contrib/multi_sum_sq-inl.h
@@ -21,7 +21,7 @@
  *  Copyright (c) 2019 by Contributors
  * \file multi_l2_norm-inl.h
  * \brief vectorized L2 norm over multiple arrays operators
- * \author Clement Fuji Tsang, Andrei Ivanov
+ * \author Clement Fuji Tsang, Andrei Ivanov, Moises Hernandez
  */
 
 
@@ -32,6 +32,10 @@
 #include <vector>
 #include "../operator_common.h"
 
+namespace multi_sum_sq {
+enum MultiSumSqUpdateResource {kTempSpace};
+}  // namespace multi_sum_sq
+
 namespace mxnet {
 namespace op {
 
@@ -80,7 +84,7 @@ inline bool MultiSumSqType(const NodeAttrs& attrs,
 
 template<typename xpu>
 void MultiSumSqRun(const std::vector<TBlob> &inputs, int nInputs,
-                   float *out_ptr, mshadow::Stream<xpu> *s);
+                   float *out_ptr, const OpContext &ctx);
 
 template<typename xpu>
 void MultiSumSq(const nnvm::NodeAttrs& attrs,
@@ -91,7 +95,7 @@ void MultiSumSq(const nnvm::NodeAttrs& attrs,
   auto s = ctx.get_stream<xpu>();
   const auto& p = dmlc::get<MultiSumSqParam>(attrs.parsed);
   float* out_ptr = outputs[0].FlatTo2D<xpu, float>(s).dptr_;
-  MultiSumSqRun<xpu>(inputs, p.num_arrays, out_ptr, s);
+  MultiSumSqRun<xpu>(inputs, p.num_arrays, out_ptr, ctx);
 }
 
 }  // namespace op
diff --git a/src/operator/contrib/multi_sum_sq.cc b/src/operator/contrib/multi_sum_sq.cc
index cdb5423db23f..16c99d1c9699 100644
--- a/src/operator/contrib/multi_sum_sq.cc
+++ b/src/operator/contrib/multi_sum_sq.cc
@@ -21,7 +21,7 @@
  *  Copyright (c) 2019 by Contributors
  * \file multi_sum_sq.cc
  * \brief vectorized sum or squared over multiple arrays operators
- * \author Clement Fuji Tsang, Andrei Ivanov
+ * \author Clement Fuji Tsang, Andrei Ivanov, Moises Hernandez
  */
 
 #include "./multi_sum_sq-inl.h"
@@ -52,20 +52,24 @@ NNVM_REGISTER_OP(multi_sum_sq)
     return ret;
   })
 .set_attr<FCompute>("FCompute<cpu>", MultiSumSq<cpu>)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
 .add_argument("data", "NDArray-or-Symbol[]", "Arrays")
 .add_arguments(MultiSumSqParam::__FIELDS__());
 
 template<typename DType>
-inline void CalcSumSq(const std::vector<TBlob> &inputs, int nInputs,
+inline void CalcSumSq(const std::vector<TBlob> &inputs, int n_inputs,
                       float *out_ptr, mshadow::Stream<cpu> *s) {
   int i;
   size_t j;
 #pragma omp parallel for private(i, j)
-  for (i = 0; i < nInputs; ++i) {  // array index in inputs
+  for (i = 0; i < n_inputs; ++i) {  // array index in inputs
     float sum = 0;
     const auto address = inputs[i].FlatTo2D<cpu, DType>(s).dptr_;
-    const auto jMax = inputs[i].shape_.Size();
-    for (j = 0; j < jMax; ++j)
+    const auto j_max = inputs[i].shape_.Size();
+    for (j = 0; j < j_max; ++j)
       sum += address[j] * address[j];
 
     out_ptr[i] = sum;
@@ -73,10 +77,10 @@ inline void CalcSumSq(const std::vector<TBlob> &inputs, int nInputs,
 }
 
 template<>
-void MultiSumSqRun<cpu>(const std::vector<TBlob> &inputs, int nInputs,
-                        float *out_ptr, mshadow::Stream<cpu> *s) {
+void MultiSumSqRun<cpu>(const std::vector<TBlob> &inputs, int n_inputs,
+                        float *out_ptr, const OpContext &ctx) {
   MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType,
-    CalcSumSq<DType>(inputs, nInputs, out_ptr, s);
+    CalcSumSq<DType>(inputs, n_inputs, out_ptr, ctx.get_stream<cpu>());
   )
 }
 
diff --git a/src/operator/contrib/multi_sum_sq.cu b/src/operator/contrib/multi_sum_sq.cu
index 6f6fe56bfd81..620c9ca8a073 100644
--- a/src/operator/contrib/multi_sum_sq.cu
+++ b/src/operator/contrib/multi_sum_sq.cu
@@ -21,7 +21,7 @@
  *  Copyright (c) 2019 by Contributors
  * \file multi_sum_sq.cu
  * \brief vectorized sums of squares norm over multiple arrays operators
- * \author Clement Fuji Tsang, Andrei Ivanov
+ * \author Clement Fuji Tsang, Andrei Ivanov, Moises Hernandez
  */
 #include "./multi_sum_sq-inl.h"
 #include <cub/cub.cuh>
@@ -43,96 +43,121 @@ struct MultiSumSqKernelParam {
   int sizes[ARRAY_LIMIT];
   unsigned char block_to_tensor[BLOCK_LIMIT];
   int block_to_chunk[BLOCK_LIMIT];
+  int max_chunks_per_tensor = -1;
 };
 
 template<typename DType>
-__device__ __forceinline__ DType reduce_block_into_lanes(DType* x,
-                                                         DType val,
-                                                         int lanes = 1,
-                                                         bool share_result = false) {
-  int tid = threadIdx.x + threadIdx.y * blockDim.x;
-  int blockSize = blockDim.x * blockDim.y;  // blockSize is intended to be a multiple of 32.
-
-  if (blockSize >= 64) {
+__device__ __forceinline__ DType ReduceBlockIntoLanes(DType* x,
+                                                      DType val) {
+  int tid = threadIdx.x;
+  int block_size = blockDim.x;
+
+  if (block_size >= 64) {
     x[tid] = val;
     __syncthreads();
   }
 
   #pragma unroll
-  for (int i = (blockSize >> 1); i >= 64; i >>= 1) {
+  for (int i = (block_size >> 1); i >= 64; i >>= 1) {
     if (tid < i)
       x[tid] = x[tid] + x[tid+i];
     __syncthreads();
   }
 
   DType final;
-
   if (tid < 32) {
-    if (blockSize >= 64)
+    if (block_size >= 64)
       final = x[tid] + x[tid+32];
     else
       final = val;
-    // __SYNCWARP();
 
     #pragma unroll
-    for (int i = 16; i >= lanes; i >>= 1)
+    for (int i = 16; i >= 1; i >>= 1)
       final = final + __shfl_down_sync(0xffffffff, final, i);
   }
-
-  if (share_result) {
-    if (tid < lanes)
-      x[tid] = final;  // EpilogueOp
-    // Make sure the smem result is visible to all warps.
-    __syncthreads();
-  }
-
   return final;
 }
 
 template<typename DType>
 __global__ void MultiSumSqKernel(int chunk_size,
                                  MultiSumSqKernelParam<DType> param,
-                                 float* output) {
+                                 float* block_reductions,
+                                 int start_tensor_id) {
   const int tensor_loc = param.block_to_tensor[blockIdx.x];
   const int chunk_len = param.block_to_chunk[blockIdx.x] * chunk_size;
   const int n = param.sizes[tensor_loc] - chunk_len;
   const DType* x = param.addresses[tensor_loc] + chunk_len;
-  const auto iMax = n <= chunk_size? n : chunk_size;
+  const auto i_max = n <= chunk_size ? n : chunk_size;
   __shared__ float vals[512];
 
   // Non-divergent exit condition for __syncthreads, not necessary here
   float val = 0;
   for (int i_start = 0;
-       i_start < iMax;
+       i_start < i_max;
        i_start += blockDim.x * ILP) {
     int i = i_start + threadIdx.x;
-    //    #pragma unroll
-    for (int ii = 0; ii < ILP && i < iMax; ++ii, i += blockDim.x) {
+#pragma unroll
+    for (int ii = 0; ii < ILP && i < i_max; ++ii, i += blockDim.x) {
       const auto incoming_val = static_cast<float>(x[i]);
       val += incoming_val * incoming_val;
     }
   }
+  const float final = ReduceBlockIntoLanes(vals, val);
+
+  if (threadIdx.x == 0) {
+    block_reductions[(start_tensor_id + tensor_loc) * param.max_chunks_per_tensor +
+                    param.block_to_chunk[blockIdx.x]] = final;
+  }
+}
+
+template<typename DType>
+__global__ void GlobalReductionKernel(MultiSumSqKernelParam<DType> param,
+                                     float* block_reductions,
+                                     float* output) {
+  __shared__ float vals[512];
+  float* reductions_this_tensor = block_reductions + blockIdx.x * param.max_chunks_per_tensor;
+  float val = 0;
+  for (int i = threadIdx.x; i < param.max_chunks_per_tensor; i += blockDim.x)
+    val += reductions_this_tensor[i];
+
+  float final = ReduceBlockIntoLanes(vals, val);
 
-  const float final = reduce_block_into_lanes(vals, val);
   if (threadIdx.x == 0)
-    atomicAdd(output + tensor_loc, final);
+    output[blockIdx.x] = final;
 }
 
 template<>
-void MultiSumSqRun<gpu>(const std::vector<TBlob> &inputs, int nInputs,
-                        float *out_ptr, mshadow::Stream<gpu> *s) {
+void MultiSumSqRun<gpu>(const std::vector<TBlob> &inputs, int n_inputs,
+                        float *out_ptr, const OpContext &ctx) {
   const int chunk_size = 32768;
   const int block_size = 512;
   using namespace mxnet_op;
+  auto s = ctx.get_stream<gpu>();
   auto stream = mshadow::Stream<gpu>::GetStream(s);
-  CUDA_CALL(cudaMemsetAsync(out_ptr, 0, nInputs * sizeof(float), stream));
 
   MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
     MultiSumSqKernelParam<DType> param;
+    // find max num of chunks in tensors
+    for (int t = 0; t < n_inputs; t++) {
+      int chunks_this_tensor = (inputs[t].shape_.Size() + chunk_size - 1) / chunk_size;
+      if (chunks_this_tensor > param.max_chunks_per_tensor)
+        param.max_chunks_per_tensor = chunks_this_tensor;
+    }
+     // temporary storage for the reduction of each block
+    size_t workspace_size = n_inputs * param.max_chunks_per_tensor * sizeof(float);
+    Tensor<gpu, 1, char> workspace =
+      ctx.requested[multi_sum_sq::kTempSpace].get_space_typed<gpu, 1, char>(
+        Shape1(workspace_size), s);
+    Tensor<gpu, 1, float> block_reductions(reinterpret_cast<float*>(&workspace[0]),
+      Shape1(n_inputs * param.max_chunks_per_tensor), s);
+    CUDA_CALL(cudaMemsetAsync(block_reductions.dptr_, 0,
+                              n_inputs * param.max_chunks_per_tensor* sizeof(float),
+                              stream));
+
     int loc_block_info = 0;   // position in param.block_to_tensor and param.block_to_chunck
     int loc_tensor_info = 0;  // position in param.sizes and param.addresses
-    int output_offset = 0;    // array index of the first block pointed on by param.addresses
-    for (int t = 0; t < nInputs; t++, loc_tensor_info++) {  // array index in inputs
+    int start_tensor_id = 0;
+    for (int t = 0; t < n_inputs; t++, loc_tensor_info++) {  // array index in inputs
       param.sizes[loc_tensor_info] = inputs[t].shape_.Size();
       param.addresses[loc_tensor_info] = inputs[t].FlatTo2D<gpu, DType>(s).dptr_;
       const int chunks_this_tensor = (inputs[t].shape_.Size() - 1) / chunk_size;
@@ -142,27 +167,30 @@ void MultiSumSqRun<gpu>(const std::vector<TBlob> &inputs, int nInputs,
         loc_block_info++;
 
         const bool last_curr_chunk = chunk == chunks_this_tensor;
-        const bool tensors_full = last_curr_chunk && loc_tensor_info == 109;
-        const bool blocks_full = (loc_block_info == 320);
-        const bool last_chunk = last_curr_chunk && t == nInputs - 1;
+        const bool tensors_full = last_curr_chunk && loc_tensor_info == (ARRAY_LIMIT-1);
+        const bool blocks_full = (loc_block_info == BLOCK_LIMIT);
+        const bool last_chunk = last_curr_chunk && t == n_inputs - 1;
         if (!(tensors_full || blocks_full || last_chunk))
           continue;
-
         MultiSumSqKernel<<<loc_block_info, block_size, 0, stream>>>
-          (chunk_size, param, out_ptr + output_offset);
+          (chunk_size, param, block_reductions.dptr_, start_tensor_id);
         MSHADOW_CUDA_POST_KERNEL_CHECK(MultiSumSqKernel);
+
         loc_block_info = 0;
         if (last_curr_chunk) {  // if you start from a new tensor
           loc_tensor_info = -1;
-          output_offset = t + 1;
+          start_tensor_id = t + 1;
         } else {  // if you start from the same tensor
           param.sizes[0] = param.sizes[loc_tensor_info];
           param.addresses[0] = param.addresses[loc_tensor_info];
           loc_tensor_info = 0;
-          output_offset = t;
+          start_tensor_id = t;
         }
       }
     }
+    // Global reduction
+    GlobalReductionKernel<<<n_inputs, block_size, 0, stream>>>
+      (param, block_reductions.dptr_, out_ptr);
   });
 }
 
diff --git a/tests/python/gpu/test_operator_gpu.py b/tests/python/gpu/test_operator_gpu.py
index 114fab770efd..7d23c2ca0aaf 100644
--- a/tests/python/gpu/test_operator_gpu.py
+++ b/tests/python/gpu/test_operator_gpu.py
@@ -271,6 +271,36 @@ def test_fft():
 def _make_ndarrays(input_list, ctx=mx.gpu(0)):
     return [mx.nd.array(arr, dtype=arr.dtype, ctx=ctx) for arr in input_list]
 
+def check_multi_sum_sq(dtype, shapes, ctx, tol1, tol2):
+    values_arr = [np.random.rand(*shape).astype(dtype) * 10. for shape in shapes]
+    mx_vals = _make_ndarrays(values_arr, ctx=ctx)
+    sum_sq = mx.nd.multi_sum_sq(*mx_vals, num_arrays=len(shapes))
+    sum_sq2 = mx.nd.multi_sum_sq(*mx_vals, num_arrays=len(shapes))
+    # checks that operator is deterministic
+    assert np.array_equal(sum_sq.asnumpy(), sum_sq2.asnumpy())
+
+    ref_sum_sq = mx.nd.array([(v.astype('float32') ** 2).sum() for v in values_arr],
+                             dtype='float32', ctx=ctx)
+    assert_almost_equal(ref_sum_sq.asnumpy(), sum_sq.asnumpy(), atol=tol1, rtol=tol1)
+
+@with_seed()
+def test_multi_sum_sq():
+    min_nparam = 100
+    max_nparam = 120
+    min_dim = 50000
+    max_dim = 100000
+    max_ndim = 1
+
+    dtypes = ['float16','float32', 'float64']
+    for ctx in [mx.gpu(0)]:
+        for dtype in dtypes:
+            nparam = np.random.randint(min_nparam + 1, max_nparam + 1)
+            shapes = [np.random.randint(min_dim, max_dim + 1, size=max_ndim) for i in range(nparam)]
+            low_tol = ctx == mx.cpu(0) and ('float16'in [dtype])
+            tol1 = 1e-3 if low_tol else 1e-5
+            tol2 = 1e-6 if low_tol else 1e-7
+            check_multi_sum_sq(dtype, shapes, ctx, tol1, tol2)
+
 def check_fast_lars(w_dtype, g_dtype, shapes, ctx, tol1, tol2):
     weights_arr = [np.random.rand(*shape).astype(w_dtype) * 10. for shape in shapes]
     grads_arr = [np.random.rand(*shape).astype(g_dtype) for shape in shapes]

From 831b548279be5d598660515158469f4e685f1078 Mon Sep 17 00:00:00 2001
From: Sam Skalicky <samskalicky@gmail.com>
Date: Sat, 14 Dec 2019 13:36:14 -0800
Subject: [PATCH 706/813] fix for number of inputs/outputs for backward custom
 ops (#17069)

* fix for number of inputs/outputs for backward custom ops

* retrigger CI
---
 src/c_api/c_api.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/c_api/c_api.cc b/src/c_api/c_api.cc
index 24374cf19cdc..f8db501d46f0 100644
--- a/src/c_api/c_api.cc
+++ b/src/c_api/c_api.cc
@@ -265,7 +265,7 @@ int MXLoadLib(const char *path) {
                            &num_in, &num_out))
       << "Error calling ParseAttrs::num_outputs for custom operator '" << name_str << "'";
 
-      return num_in + num_out;
+      return num_in + 2*num_out;
     };
 
     // lambda function to call infer shape

From 3d38dbde744954854015919d4faf56ac1aea16de Mon Sep 17 00:00:00 2001
From: perdasilva <perdasilva@gmail.com>
Date: Sat, 14 Dec 2019 22:47:42 +0100
Subject: [PATCH 707/813] Adds min cuda version assertion decorator for unit
 tests (#17054)

---
 tests/python/gpu/test_operator_gpu.py |  4 +-
 tests/python/unittest/common.py       | 54 ++++++++++++++++++++++++---
 2 files changed, 52 insertions(+), 6 deletions(-)

diff --git a/tests/python/gpu/test_operator_gpu.py b/tests/python/gpu/test_operator_gpu.py
index 7d23c2ca0aaf..e548217b9369 100644
--- a/tests/python/gpu/test_operator_gpu.py
+++ b/tests/python/gpu/test_operator_gpu.py
@@ -30,7 +30,7 @@
 
 curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
 sys.path.insert(0, os.path.join(curr_path, '../unittest'))
-from common import setup_module, with_seed, teardown, assert_raises_cudnn_not_satisfied
+from common import setup_module, with_seed, teardown, assert_raises_cudnn_not_satisfied, assert_raises_cuda_not_satisfied
 from common import run_in_spawned_process
 from test_operator import *
 from test_numpy_ndarray import *
@@ -2696,6 +2696,7 @@ def convert_bias(F, q_bias, k_bias, v_bias, num_heads):
         assert(grads_orig[k].shape == grads_opti[k].shape)
         assert_allclose(grads_orig[k], grads_opti[k], rtol=1e-2, atol=1e-3)
 
+@assert_raises_cuda_not_satisfied(min_version='9.1')
 def test_multihead_attention_selfatt():
     for dtype in ['float16', 'float32']:
         check_multihead_attention_selfatt(dtype=dtype)
@@ -2859,6 +2860,7 @@ def convert_bias(F, k_bias, v_bias, num_heads):
         assert(grads_orig[k].shape == grads_opti[k].shape)
         assert_allclose(grads_orig[k], grads_opti[k], rtol=1e-2, atol=1e-3)
 
+@assert_raises_cuda_not_satisfied(min_version='9.1')
 def test_multihead_attention_encdec():
     for dtype in ['float16', 'float32']:
         check_multihead_attention_encdec(dtype=dtype)
diff --git a/tests/python/unittest/common.py b/tests/python/unittest/common.py
index 816508fbc3c8..5a4604603a61 100644
--- a/tests/python/unittest/common.py
+++ b/tests/python/unittest/common.py
@@ -99,14 +99,42 @@ def random_seed(seed=None):
         random.seed(next_seed)
 
 
-def assert_raises_cudnn_not_satisfied(min_version):
+def _assert_raise_cuxx_version_not_satisfied(min_version, cfg):
+
+    def less_than(version_left, version_right):
+        """Compares two version strings in the format num(.[num])*"""
+        if not version_left or not version_right:
+            return False
+
+        left = version_left.split(".")
+        right = version_right.split(".")
+
+        # 0 pad shortest version - e.g. 
+        # less_than("9.1", "9.1.9") == less_than("9.1.0", "9.1.9")
+        longest = max(len(left), len(right))
+        left.extend([0] * (longest - len(left)))
+        right.extend([0] * (longest - len(right)))
+
+        # compare each of the version components
+        for l, r in zip(left, right):
+            if int(r) < int(l):
+                return False
+
+            # keep track of how many are the same
+            if int(r) == int(l):
+                longest = longest - 1
+
+        # longest = 0 mean version_left == version_right -> False
+        # longest > 0 version_left < version_right -> True
+        return longest > 0
+
     def test_helper(orig_test):
         @make_decorator(orig_test)
         def test_new(*args, **kwargs):
-            cudnn_off = os.getenv('CUDNN_OFF_TEST_ONLY') == 'true'
-            cudnn_env_version = os.getenv('CUDNN_VERSION', None if cudnn_off else '7.3.1')
-            cudnn_test_disabled = cudnn_off or cudnn_env_version < min_version
-            if not cudnn_test_disabled or mx.context.current_context().device_type == 'cpu':
+            cuxx_off = os.getenv(cfg['TEST_OFF_ENV_VAR']) == 'true'
+            cuxx_env_version = os.getenv(cfg['VERSION_ENV_VAR'], None if cuxx_off else cfg['DEFAULT_VERSION'])
+            cuxx_test_disabled = cuxx_off or less_than(cuxx_env_version, min_version)
+            if not cuxx_test_disabled or mx.context.current_context().device_type == 'cpu':
                 orig_test(*args, **kwargs)
             else:
                 assert_raises((MXNetError, RuntimeError), orig_test, *args, **kwargs)
@@ -114,6 +142,22 @@ def test_new(*args, **kwargs):
     return test_helper
 
 
+def assert_raises_cudnn_not_satisfied(min_version):
+    return _assert_raise_cuxx_version_not_satisfied(min_version, {
+        'TEST_OFF_ENV_VAR': 'CUDNN_OFF_TEST_ONLY',
+        'VERSION_ENV_VAR': 'CUDNN_VERSION',
+        'DEFAULT_VERSION': '7.3.1'
+    })
+
+
+def assert_raises_cuda_not_satisfied(min_version):
+    return _assert_raise_cuxx_version_not_satisfied(min_version, {
+        'TEST_OFF_ENV_VAR': 'CUDA_OFF_TEST_ONLY',
+        'VERSION_ENV_VAR': 'CUDA_VERSION',
+        'DEFAULT_VERSION': '10.1'
+    })
+
+
 def with_seed(seed=None):
     """
     A decorator for nosetests test functions that manages rng seeds.

From 7dbb4b710ce84e0cbfab70e90fa0f7e47eb2e602 Mon Sep 17 00:00:00 2001
From: dw_sjtu <46704444+sjtuWangDing@users.noreply.github.com>
Date: Mon, 16 Dec 2019 14:38:58 +0800
Subject: [PATCH 708/813] fix precision problem in linalg_solve,
 linalg_tensorinv, linalg_cholesky op test (#16981)

fix format
set atol=rtol=1e-1 in test_np_linalg_cholesky
fix test_np_linalg_tensorinv
fix bug in test_np_linalg_tensorinv
commit tensorinv src
---
 python/mxnet/ndarray/numpy/linalg.py          |  57 +++++-
 python/mxnet/numpy/linalg.py                  |  57 +++++-
 python/mxnet/numpy_dispatch_protocol.py       |   1 +
 python/mxnet/symbol/numpy/linalg.py           |  57 +++++-
 src/operator/numpy/linalg/np_tensorinv-inl.h  | 166 ++++++++++++++++++
 src/operator/numpy/linalg/np_tensorinv.cc     | 134 ++++++++++++++
 src/operator/numpy/linalg/np_tensorinv.cu     |  43 +++++
 .../unittest/test_numpy_interoperability.py   |  26 +++
 tests/python/unittest/test_numpy_op.py        | 166 +++++++++++++++---
 9 files changed, 682 insertions(+), 25 deletions(-)
 create mode 100644 src/operator/numpy/linalg/np_tensorinv-inl.h
 create mode 100644 src/operator/numpy/linalg/np_tensorinv.cc
 create mode 100644 src/operator/numpy/linalg/np_tensorinv.cu

diff --git a/python/mxnet/ndarray/numpy/linalg.py b/python/mxnet/ndarray/numpy/linalg.py
index a85c6324f685..4c49c35b4a44 100644
--- a/python/mxnet/ndarray/numpy/linalg.py
+++ b/python/mxnet/ndarray/numpy/linalg.py
@@ -21,7 +21,7 @@
 from . import _op as _mx_nd_np
 from . import _internal as _npi
 
-__all__ = ['norm', 'svd', 'cholesky', 'inv', 'det', 'slogdet', 'solve']
+__all__ = ['norm', 'svd', 'cholesky', 'inv', 'det', 'slogdet', 'solve', 'tensorinv']
 
 
 def norm(x, ord=None, axis=None, keepdims=False):
@@ -406,3 +406,58 @@ def solve(a, b):
     True
     """
     return _npi.solve(a, b)
+
+
+def tensorinv(a, ind=2):
+    r"""
+    Compute the 'inverse' of an N-dimensional array.
+
+    The result is an inverse for `a` relative to the tensordot operation
+    ``tensordot(a, b, ind)``, i. e., up to floating-point accuracy,
+    ``tensordot(tensorinv(a), a, ind)`` is the "identity" tensor for the
+    tensordot operation.
+
+    Parameters
+    ----------
+    a : array_like
+        Tensor to 'invert'. Its shape must be 'square', i. e.,
+        ``prod(a.shape[:ind]) == prod(a.shape[ind:])``.
+    ind : int, optional
+        Number of first indices that are involved in the inverse sum.
+        Must be a positive integer, default is 2.
+
+    Returns
+    -------
+    b : ndarray
+        `a`'s tensordot inverse, shape ``a.shape[ind:] + a.shape[:ind]``.
+
+    Raises
+    ------
+    MXNetError
+        If `a` is singular or not 'square' (in the above sense).
+
+    See Also
+    --------
+    tensordot, tensorsolve
+
+    Examples
+    --------
+    >>> a = np.eye(4*6)
+    >>> a.shape = (4, 6, 8, 3)
+    >>> ainv = np.linalg.tensorinv(a, ind=2)
+    >>> ainv.shape
+    (8, 3, 4, 6)
+    >>> b = np.random.randn(4, 6)
+    >>> np.allclose(np.tensordot(ainv, b), np.linalg.tensorsolve(a, b))
+    True
+
+    >>> a = np.eye(4*6)
+    >>> a.shape = (24, 8, 3)
+    >>> ainv = np.linalg.tensorinv(a, ind=1)
+    >>> ainv.shape
+    (8, 3, 24)
+    >>> b = np.random.randn(24)
+    >>> np.allclose(np.tensordot(ainv, b, 1), np.linalg.tensorsolve(a, b))
+    True
+    """
+    return _npi.tensorinv(a, ind)
diff --git a/python/mxnet/numpy/linalg.py b/python/mxnet/numpy/linalg.py
index 33d636b7044c..2ee2d2670693 100644
--- a/python/mxnet/numpy/linalg.py
+++ b/python/mxnet/numpy/linalg.py
@@ -20,7 +20,7 @@
 from __future__ import absolute_import
 from ..ndarray import numpy as _mx_nd_np
 
-__all__ = ['norm', 'svd', 'cholesky', 'inv', 'det', 'slogdet', 'solve']
+__all__ = ['norm', 'svd', 'cholesky', 'inv', 'det', 'slogdet', 'solve', 'tensorinv']
 
 
 def norm(x, ord=None, axis=None, keepdims=False):
@@ -424,3 +424,58 @@ def solve(a, b):
     True
     """
     return _mx_nd_np.linalg.solve(a, b)
+
+
+def tensorinv(a, ind=2):
+    r"""
+    Compute the 'inverse' of an N-dimensional array.
+
+    The result is an inverse for `a` relative to the tensordot operation
+    ``tensordot(a, b, ind)``, i. e., up to floating-point accuracy,
+    ``tensordot(tensorinv(a), a, ind)`` is the "identity" tensor for the
+    tensordot operation.
+
+    Parameters
+    ----------
+    a : array_like
+        Tensor to 'invert'. Its shape must be 'square', i. e.,
+        ``prod(a.shape[:ind]) == prod(a.shape[ind:])``.
+    ind : int, optional
+        Number of first indices that are involved in the inverse sum.
+        Must be a positive integer, default is 2.
+
+    Returns
+    -------
+    b : ndarray
+        `a`'s tensordot inverse, shape ``a.shape[ind:] + a.shape[:ind]``.
+
+    Raises
+    ------
+    MXNetError
+        If `a` is singular or not 'square' (in the above sense).
+
+    See Also
+    --------
+    tensordot, tensorsolve
+
+    Examples
+    --------
+    >>> a = np.eye(4*6)
+    >>> a.shape = (4, 6, 8, 3)
+    >>> ainv = np.linalg.tensorinv(a, ind=2)
+    >>> ainv.shape
+    (8, 3, 4, 6)
+    >>> b = np.random.randn(4, 6)
+    >>> np.allclose(np.tensordot(ainv, b), np.linalg.tensorsolve(a, b))
+    True
+
+    >>> a = np.eye(4*6)
+    >>> a.shape = (24, 8, 3)
+    >>> ainv = np.linalg.tensorinv(a, ind=1)
+    >>> ainv.shape
+    (8, 3, 24)
+    >>> b = np.random.randn(24)
+    >>> np.allclose(np.tensordot(ainv, b, 1), np.linalg.tensorsolve(a, b))
+    True
+    """
+    return _mx_nd_np.linalg.tensorinv(a, ind)
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index 23593a47e6ba..e93720564774 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -134,6 +134,7 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'linalg.cholesky',
     'linalg.inv',
     'linalg.solve',
+    'linalg.tensorinv',
     'shape',
     'trace',
     'tril',
diff --git a/python/mxnet/symbol/numpy/linalg.py b/python/mxnet/symbol/numpy/linalg.py
index 6df175ebfc4b..a445c79001ec 100644
--- a/python/mxnet/symbol/numpy/linalg.py
+++ b/python/mxnet/symbol/numpy/linalg.py
@@ -22,7 +22,7 @@
 from . import _op as _mx_sym_np
 from . import _internal as _npi
 
-__all__ = ['norm', 'svd', 'cholesky', 'inv', 'det', 'slogdet', 'solve']
+__all__ = ['norm', 'svd', 'cholesky', 'inv', 'det', 'slogdet', 'solve', 'tensorinv']
 
 
 def norm(x, ord=None, axis=None, keepdims=False):
@@ -393,3 +393,58 @@ def solve(a, b):
     True
     """
     return _npi.solve(a, b)
+
+
+def tensorinv(a, ind=2):
+    r"""
+    Compute the 'inverse' of an N-dimensional array.
+
+    The result is an inverse for `a` relative to the tensordot operation
+    ``tensordot(a, b, ind)``, i. e., up to floating-point accuracy,
+    ``tensordot(tensorinv(a), a, ind)`` is the "identity" tensor for the
+    tensordot operation.
+
+    Parameters
+    ----------
+    a : array_like
+        Tensor to 'invert'. Its shape must be 'square', i. e.,
+        ``prod(a.shape[:ind]) == prod(a.shape[ind:])``.
+    ind : int, optional
+        Number of first indices that are involved in the inverse sum.
+        Must be a positive integer, default is 2.
+
+    Returns
+    -------
+    b : ndarray
+        `a`'s tensordot inverse, shape ``a.shape[ind:] + a.shape[:ind]``.
+
+    Raises
+    ------
+    MXNetError
+        If `a` is singular or not 'square' (in the above sense).
+
+    See Also
+    --------
+    tensordot, tensorsolve
+
+    Examples
+    --------
+    >>> a = np.eye(4*6)
+    >>> a.shape = (4, 6, 8, 3)
+    >>> ainv = np.linalg.tensorinv(a, ind=2)
+    >>> ainv.shape
+    (8, 3, 4, 6)
+    >>> b = np.random.randn(4, 6)
+    >>> np.allclose(np.tensordot(ainv, b), np.linalg.tensorsolve(a, b))
+    True
+
+    >>> a = np.eye(4*6)
+    >>> a.shape = (24, 8, 3)
+    >>> ainv = np.linalg.tensorinv(a, ind=1)
+    >>> ainv.shape
+    (8, 3, 24)
+    >>> b = np.random.randn(24)
+    >>> np.allclose(np.tensordot(ainv, b, 1), np.linalg.tensorsolve(a, b))
+    True
+    """
+    return _npi.tensorinv(a, ind)
diff --git a/src/operator/numpy/linalg/np_tensorinv-inl.h b/src/operator/numpy/linalg/np_tensorinv-inl.h
new file mode 100644
index 000000000000..4f92ccf9d125
--- /dev/null
+++ b/src/operator/numpy/linalg/np_tensorinv-inl.h
@@ -0,0 +1,166 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_tensorinv-inl.h
+ * \brief Placeholder for tensor inverse operator
+ */
+#ifndef MXNET_OPERATOR_NUMPY_LINALG_NP_TENSORINV_INL_H_
+#define MXNET_OPERATOR_NUMPY_LINALG_NP_TENSORINV_INL_H_
+
+#include <mxnet/operator_util.h>
+#include <vector>
+#include "../../operator_common.h"
+#include "../../mshadow_op.h"
+#include "../../tensor/la_op.h"
+#include "../../tensor/la_op-inl.h"
+
+namespace mxnet {
+namespace op {
+
+using namespace mshadow;
+
+struct TensorinvParam : public dmlc::Parameter<TensorinvParam> {
+  int ind;
+  DMLC_DECLARE_PARAMETER(TensorinvParam) {
+    DMLC_DECLARE_FIELD(ind)
+      .set_default(2)
+      .describe("Number of first indices that are involved in the inverse sum.");
+  }
+};
+
+template<typename xpu>
+void TensorinvOpForward(const nnvm::NodeAttrs& attrs,
+                        const OpContext& ctx,
+                        const std::vector<TBlob>& inputs,
+                        const std::vector<OpReqType>& req,
+                        const std::vector<TBlob>& outputs) {
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 1U);
+  CHECK_EQ(req.size(), 1U);
+
+  mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+  const mxnet::TBlob& a_tblob = inputs[0];
+  const mxnet::TBlob& inv_a_tblob = outputs[0];
+  const mxnet::TShape& a_shape = a_tblob.shape_;
+  CHECK_EQ(inv_a_tblob.type_flag_, a_tblob.type_flag_)
+      << "Binary function only support input/output with the same type.";
+  MSHADOW_SGL_DBL_TYPE_SWITCH(outputs[0].type_flag_, OType, {
+    const int ind = nnvm::get<TensorinvParam>(attrs.parsed).ind;
+    dim_t prod_front = 1, prod_back = 1;
+    if (ind < a_shape.ndim()) {
+      for (int i = 0; i < ind; ++i) {
+        prod_front *= a_shape[i];
+      }
+      for (int i = ind; i < a_shape.ndim(); ++i) {
+        prod_back *= a_shape[i];
+      }
+    } else {
+      for (int i = 0; i < a_shape.ndim(); ++i) {
+        prod_front *= a_shape[i];
+      }
+    }
+    Tensor<xpu, 3, OType> A =
+      a_tblob.get_with_shape<xpu, 3, OType>(Shape3(1, prod_back, prod_front), s);
+    Tensor<xpu, 3, OType> inv_A =
+      inv_a_tblob.get_with_shape<xpu, 3, OType>(Shape3(1, prod_back, prod_front), s);
+    inverse::op(A, inv_A, ctx, attrs);
+  });
+}
+
+template<typename xpu>
+void TensorinvOpBackward(const nnvm::NodeAttrs& attrs,
+                         const OpContext& ctx,
+                         const std::vector<TBlob>& inputs,
+                         const std::vector<OpReqType>& req,
+                         const std::vector<TBlob>& outputs) {
+  CHECK_EQ(inputs.size(), 2U);
+  CHECK_EQ(outputs.size(), 1U);
+  CHECK_EQ(req.size(), 1U);
+
+  mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+  const TBlob& out_grad = inputs[0];
+  const TBlob& inv_a = inputs[1];
+  const TBlob& grad_a = outputs[0];
+  const TShape& inv_a_shape = inv_a.shape_;
+  MSHADOW_SGL_DBL_TYPE_SWITCH(outputs[0].type_flag_, OType, {
+    const int axes = nnvm::get<TensorinvParam>(attrs.parsed).ind;
+    CHECK_LE(inv_a_shape.ndim(), 6U)
+      << "tensorinv backward only support tensor's dimension <= 6";
+    if (axes < inv_a_shape.ndim()) {
+      const int axes1 = inv_a_shape.ndim() - axes, axes2 = axes;
+      TShape inv_a_transpose_shape(inv_a_shape.ndim(), -1);
+      for (int i = 0; i < axes; ++i) {
+        inv_a_transpose_shape[i] = inv_a_shape[i + inv_a_shape.ndim() - axes];
+      }
+      for (int i = axes; i < inv_a_shape.ndim(); ++i) {
+        inv_a_transpose_shape[i] = inv_a_shape[i - axes];
+      }
+      TShape temp_shape(2 * axes, -1);
+      for (int i = 0; i < axes; ++i) {
+        temp_shape[i] = inv_a_transpose_shape[i];
+        temp_shape[i + axes] = inv_a_transpose_shape[i];
+      }
+      Tensor<xpu, 1, char> workspace =
+        ctx.requested[0].get_space_typed<xpu, 1, char>(Shape1(temp_shape.Size() * sizeof(OType)),
+                                                       ctx.get_stream<xpu>());
+      TBlob temp_tblob =
+        TBlob(reinterpret_cast<OType*>(workspace.dptr_), temp_shape, xpu::kDevMask);
+      dim_t a1 = 1, a2 = 1;
+      for (int i = 0; i < axes2; ++i) {
+        a1 *= inv_a_transpose_shape[i];
+      }
+      for (int i = 0; i < axes1; ++i) {
+        a2 *= inv_a_shape[i];
+      }
+      Tensor<xpu, 3, OType> inv_a_tensor =
+        inv_a.get_with_shape<xpu, 3, OType>(Shape3(1, a2, a1), s);
+      Tensor<xpu, 3, OType> out_grad_tensor =
+        out_grad.get_with_shape<xpu, 3, OType>(Shape3(1, a2, a1), s);
+      Tensor<xpu, 3, OType> temp_tensor =
+        temp_tblob.get_with_shape<xpu, 3, OType>(Shape3(1, a1, a1), s);
+      Tensor<xpu, 3, OType> grad_a_tensor =
+        grad_a.get_with_shape<xpu, 3, OType>(Shape3(1, a1, a2), s);
+      gemm2::op(inv_a_tensor, out_grad_tensor, temp_tensor, OType(1), true, false, s);
+      gemm2::op(temp_tensor, inv_a_tensor, grad_a_tensor, OType(-1), false, true, s);
+    } else {  // axes >= inv_a_shape.ndim()
+      dim_t a = 1;
+      for (int i = 0; i < inv_a_shape.ndim(); ++i) {
+        a *= inv_a_shape[i];
+      }
+      // check again
+      CHECK_EQ(a, 1U)
+        << "a shape must be square, i. e., prod(a.shape[:ind]) == prod(a.shape[ind:]).";
+      Tensor<xpu, 1, OType> inv_a_tensor =
+        inv_a.get_with_shape<xpu, 1, OType>(Shape1(1), s);
+      Tensor<xpu, 1, OType> out_grad_tensor =
+        out_grad.get_with_shape<xpu, 1, OType>(Shape1(1), s);
+      Tensor<xpu, 1, OType> grad_a_tensor =
+        grad_a.get_with_shape<xpu, 1, OType>(Shape1(1), s);
+      ASSIGN_DISPATCH(grad_a_tensor, kWriteTo,
+        OType(-1) * inv_a_tensor * out_grad_tensor * inv_a_tensor);
+    }
+  });
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_NUMPY_LINALG_NP_TENSORINV_INL_H_
diff --git a/src/operator/numpy/linalg/np_tensorinv.cc b/src/operator/numpy/linalg/np_tensorinv.cc
new file mode 100644
index 000000000000..2fee11c846b7
--- /dev/null
+++ b/src/operator/numpy/linalg/np_tensorinv.cc
@@ -0,0 +1,134 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_tensorinv.cc
+ * \brief CPU implementation placeholder of Tensor Inverse Operator
+ */
+#include "./np_tensorinv-inl.h"
+
+namespace mxnet {
+namespace op {
+
+inline bool TensorinvOpShape(const nnvm::NodeAttrs &attrs,
+                             std::vector<mxnet::TShape> *in_attrs,
+                             std::vector<mxnet::TShape> *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+
+  const mxnet::TShape& a_shape = (*in_attrs)[0];
+  const int a_ndim = a_shape.ndim();
+  mxnet::TShape inv_a_shape(a_shape);
+  if (!ndim_is_known(a_shape)) {
+    return false;
+  }
+  // ind > 0, defalut = 2
+  int ind = 2;
+  ind = nnvm::get<TensorinvParam>(attrs.parsed).ind;
+  CHECK_GT(ind, 0) << "Invalid ind argument.";
+
+  if (a_ndim > 0 && ind < a_ndim) {
+    for (int i = 0; i < ind; ++i) {
+      inv_a_shape[a_ndim - ind + i] = a_shape[i];
+    }
+    for (int i = ind; i < a_ndim; ++i) {
+      inv_a_shape[i - ind] = a_shape[i];
+    }
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, inv_a_shape);
+  } else {  // ind >= a_ndim
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, inv_a_shape);
+  }
+  CHECK_NE(inv_a_shape.ndim(), 0)
+    << "can not reshape array";
+
+  dim_t prod_front = 1, prod_back = 1;
+  if (ind < a_ndim) {
+    for (int i = 0; i < ind; ++i) {
+      prod_front *= a_shape[i];
+    }
+    for (int i = ind; i < a_ndim; ++i) {
+      prod_back *= a_shape[i];
+    }
+    CHECK_GT(prod_back, 0)
+      << "can not reshape array of size 0 into shape";
+  } else {
+    for (int i = 0; i < a_ndim; ++i) {
+      prod_front *= a_shape[i];
+    }
+  }
+  // prod_back >= 1 and prod_front == prod_back
+  CHECK_EQ(prod_front, prod_back)
+    << "a shape must be square, i. e., prod(a.shape[:ind]) == prod(a.shape[ind:]).";
+  return !mxnet::op::shape_is_none(out_attrs->at(0));
+}
+
+inline bool TensorinvOpType(const nnvm::NodeAttrs& attrs,
+                            std::vector<int>* in_attrs,
+                            std::vector<int>* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  int a_type = in_attrs->at(0);
+  // unsupport float16
+  CHECK_NE(a_type, mshadow::kFloat16)
+    << "array type float16 is unsupported in linalg";
+  if (mshadow::kFloat32 == a_type) {
+    TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
+  } else {
+    TYPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::kFloat64);
+  }
+  return out_attrs->at(0) != -1;
+}
+
+DMLC_REGISTER_PARAMETER(TensorinvParam);
+
+NNVM_REGISTER_OP(_npi_tensorinv)
+.describe(R"code()code" ADD_FILELINE)
+.set_attr_parser(mxnet::op::ParamParser<TensorinvParam>)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"a"};
+})
+.set_attr<mxnet::FInferShape>("FInferShape", TensorinvOpShape)
+.set_attr<nnvm::FInferType>("FInferType", TensorinvOpType)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>(1, ResourceRequest::kTempSpace);
+})
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
+.set_attr<FCompute>("FCompute<cpu>", TensorinvOpForward<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", mxnet::op::ElemwiseGradUseOut{"_backward_npi_tensorinv"})
+.add_argument("a", "NDArray-or-Symbol", "First input")
+.add_arguments(TensorinvParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_backward_npi_tensorinv)
+.set_attr_parser(mxnet::op::ParamParser<TensorinvParam>)
+.set_num_inputs(2)
+.set_num_outputs(1)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& ){
+    return std::vector<ResourceRequest>{1, ResourceRequest::kTempSpace};
+})
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FCompute>("FCompute<cpu>", TensorinvOpBackward<cpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/linalg/np_tensorinv.cu b/src/operator/numpy/linalg/np_tensorinv.cu
new file mode 100644
index 000000000000..8cad95f40b3a
--- /dev/null
+++ b/src/operator/numpy/linalg/np_tensorinv.cu
@@ -0,0 +1,43 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file np_tensorinv.cu
+ * \brief GPU implementation of the Tensor Inverse Operator
+ */
+
+#include <mxnet/operator_util.h>
+#include <vector>
+#include "./np_tensorinv-inl.h"
+
+namespace mxnet {
+namespace op {
+
+#if MXNET_USE_CUSOLVER == 1
+
+NNVM_REGISTER_OP(_npi_tensorinv)
+.set_attr<FCompute>("FCompute<gpu>", TensorinvOpForward<gpu>);
+
+NNVM_REGISTER_OP(_backward_npi_tensorinv)
+.set_attr<FCompute>("FCompute<gpu>", TensorinvOpBackward<gpu>);
+
+#endif
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index 0e875825a699..a670f794860f 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -374,6 +374,31 @@ def _add_workload_linalg_det():
     OpArgMngr.add_workload('linalg.det', np.array(_np.ones((0, 1, 1)), dtype=np.float64))
 
 
+def _add_workload_linalg_tensorinv():
+    shapes = [
+        (1, 20, 4, 5),
+        (2, 2, 10, 4, 5),
+        (2, 12, 5, 3, 4, 5),
+        (3, 2, 3, 4, 24)
+    ]
+    dtypes = (np.float32, np.float64)
+    for dtype, shape in itertools.product(dtypes, shapes):
+        ind = shape[0]
+        prod_front = 1
+        prod_back = 1
+        for k in shape[1:ind + 1]:
+            prod_front *= k
+        for k in shape[1 + ind:]:
+            prod_back *= k
+        a_shape = (prod_back, prod_front)
+        a = _np.random.randn(*a_shape)
+        if prod_back == prod_front:
+            if _np.allclose(_np.dot(a, _np.linalg.inv(a)), _np.eye(prod_front)):
+                a_shape = shape[1:]
+                a = a.reshape(a_shape)
+                OpArgMngr.add_workload('linalg.tensorinv', np.array(a, dtype=dtype), ind)
+
+
 def _add_workload_linalg_slogdet():
     OpArgMngr.add_workload('linalg.slogdet', np.array(_np.ones((2, 2)), dtype=np.float32))
     OpArgMngr.add_workload('linalg.slogdet', np.array(_np.ones((0, 1, 1)), dtype=np.float64))
@@ -1428,6 +1453,7 @@ def _prepare_workloads():
     _add_workload_linalg_inv()
     _add_workload_linalg_solve()
     _add_workload_linalg_det()
+    _add_workload_linalg_tensorinv()
     _add_workload_linalg_slogdet()
     _add_workload_trace()
     _add_workload_tril()
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 54bce0aaae84..545466bf0814 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -3537,11 +3537,28 @@ def check_cholesky(L, data_np):
             assert L.shape == L_expected.shape
             assert_almost_equal(L.asnumpy(), L_expected, rtol=rtol, atol=atol)
 
+    def newSymmetricPositiveDefineMatrix_2D(shape, ran=(0., 10.), max_cond=4):
+        while 1:
+            D = _np.diag(_np.random.uniform(ran[0], ran[1], shape[-1]))
+            I = _np.eye(shape[-1]).reshape(shape)
+            v = _np.random.uniform(-1., 1., shape[-1]).reshape(shape[:-1] + (1,))
+            v = v / _np.linalg.norm(v, axis=-2, keepdims=True)
+            v_T = _np.swapaxes(v, -1, -2)
+            U = I - 2 * _np.matmul(v, v_T)
+            a = _np.matmul(_np.matmul(U, D), _np.swapaxes(U, -1, -2))
+            if (_np.linalg.cond(a, 2) < max_cond):
+                return a
+
+    def newSymmetricPositiveDefineMatrix_nD(shape, ran=(0., 10.), max_cond=4):
+        n = int(_np.prod(shape[:-2])) if len(shape) > 2 else 1
+        return _np.array([newSymmetricPositiveDefineMatrix_2D(shape[-2:], ran, max_cond) for i in range(n)]).reshape(shape)
+
     shapes = [
         (0, 0),
         (1, 1),
         (5, 5),
         (6, 6),
+        (10, 10),
         (6, 6, 6),
         (1, 0, 0),
         (0, 1, 1),
@@ -3549,7 +3566,11 @@ def check_cholesky(L, data_np):
     ]
     dtypes = ['float32', 'float64']
     for hybridize, dtype, shape in itertools.product([True, False], dtypes, shapes):
-        atol = rtol = 1e-2
+        rtol = 1e-3
+        atol = 1e-5
+        if dtype == 'float32':
+            rtol = 1e-2
+            atol = 1e-4
 
         test_cholesky = TestCholesky()
         if hybridize:
@@ -3571,19 +3592,7 @@ def check_cholesky(L, data_np):
         if 0 in shape:
             data_np = np.ones(shape)
         else:
-            data_np_l = _np.random.uniform(-10., 10., shape)
-            if dtype == 'float32':
-                data_np_l_flat = data_np_l.reshape((-1, shape[-2], shape[-1]))
-            else:
-                data_np_l_flat = _np.tril(data_np_l.reshape((-1, shape[-2], shape[-1])))
-            for i in range(data_np_l_flat.shape[0]):
-                for j in range(data_np_l_flat.shape[-1]):
-                    if data_np_l_flat[i, j, j] < 0:
-                        data_np_l_flat[i, j, j] = -data_np_l_flat[i, j, j]
-                    elif data_np_l_flat[i, j, j] == 0:
-                        data_np_l_flat[i, j, j] = 2
-            data_np = _np.matmul(data_np_l_flat, data_np_l_flat.swapaxes(-1, -2))
-            data_np = data_np.reshape(shape)
+            data_np = newSymmetricPositiveDefineMatrix_nD(shape)
 
         # When dtype is np.FP32, truncation from FP64 to FP32 could also be a source of
         # instability since the ground-truth gradient is computed using FP64 data.
@@ -3696,7 +3705,7 @@ def test_np_linalg_solve():
     class TestSolve(HybridBlock):
         def __init__(self):
             super(TestSolve, self).__init__()
-        
+
         def hybrid_forward(self, F, a, b):
             return F.np.linalg.solve(a, b)
 
@@ -3713,6 +3722,23 @@ def check_solve(x, a_np, b_np):
             assert x.shape == x_expected.shape
             assert_almost_equal(x.asnumpy(), x_expected, rtol=rtol, atol=atol)
 
+    def newInvertibleMatrix_2D(shape, max_cond=4):
+        while 1:
+            # generate well-conditioned matrices with small eigenvalues
+            D = _np.diag(_np.random.uniform(-1.0, 1.0, shape[-1]))
+            I = _np.eye(shape[-1]).reshape(shape)
+            v = _np.random.uniform(-10., 10., shape[-1]).reshape(shape[:-1] + (1,))
+            v = v / _np.linalg.norm(v, axis=-2, keepdims=True)
+            v_T = _np.swapaxes(v, -1, -2)
+            U = I - 2 * _np.matmul(v, v_T)
+            a = _np.matmul(U, D)
+            if (_np.linalg.cond(a, 2) < max_cond):
+                return a
+
+    def newInvertibleMatrix_nD(shape, max_cond=4):
+        n = int(np.prod(np.array(shape[:-2]))) if len(shape) > 2 else 1
+        return _np.array([newInvertibleMatrix_2D(shape[-2:]) for i in range(n)]).reshape(shape)
+
     def get_grad_b(A, X):
         dX = _np.ones_like(X)
         A_inv = _np.linalg.inv(A)
@@ -3748,18 +3774,14 @@ def get_grad_b(A, X):
             b = _np.ones(shape)
         else:
             shape_a = shape
-            a = _np.random.rand(*shape_a)
             shape_b = list(shape_a)
             if nrh == -1:
                 shape_b[-1] = 1
-                x = _np.random.rand(*shape_b)
-                b = _np.matmul(a, x)
-                shape_b.pop()
-                b = b.reshape(shape_b)
             else :
                 shape_b[-1] = nrh
-                x = _np.random.rand(*shape_b)
-                b = _np.matmul(a, x)
+            a = newInvertibleMatrix_nD(shape_a)
+            x = _np.random.randn(*shape_b)
+            b = _np.matmul(a, x)
         a = np.array(a, dtype=dtype)
         b = np.array(b, dtype=dtype)
         a.attach_grad()
@@ -3784,6 +3806,106 @@ def get_grad_b(A, X):
         check_solve(mx_out, a, b)
 
 
+def test_np_linalg_tensorinv():
+    class TestTensorinv(HybridBlock):
+        def __init__(self, ind=2):
+            super(TestTensorinv, self).__init__()
+            self._ind = ind
+        
+        def hybrid_forward(self, F, a):
+            return F.np.linalg.tensorinv(a, ind=self._ind)
+
+    def check_tensorinv(inv_a, a_np, ind):
+        try:
+            inv_a_expected = _np.linalg.tensorinv(a_np, ind=ind)
+        except Exception as e:
+            print(a_np)
+            print(a_np.shape)
+            print(e)
+        else:
+            assert inv_a.shape == inv_a_expected.shape
+            assert_almost_equal(inv_a.asnumpy(), inv_a_expected, rtol=rtol, atol=atol)
+
+    def newInvertibleMatrix_2D(shape, max_cond=4):
+        while 1:
+            # generate well-conditioned matrices with small eigenvalues
+            D = _np.diag(_np.random.uniform(-1.0, 1.0, shape[-1]))
+            I = _np.eye(shape[-1]).reshape(shape)
+            v = _np.random.uniform(-10., 10., shape[-1]).reshape(shape[:-1] + (1,))
+            v = v / _np.linalg.norm(v, axis=-2, keepdims=True)
+            v_T = _np.swapaxes(v, -1, -2)
+            U = I - 2 * _np.matmul(v, v_T)
+            a = _np.matmul(U, D)
+            if (_np.linalg.cond(a, 2) < max_cond):
+                return a
+
+    def get_grad_A(A, ind):
+        inv_A = _np.linalg.tensorinv(A, ind)
+        d_inv_A = _np.ones_like(inv_A)
+        axes1 = len(A.shape) - ind
+        axes2 = ind
+        inv_A_trans_axes = tuple(_np.arange(len(A.shape)))[axes1:] + tuple(_np.arange(len(A.shape)))[:axes1]
+        inv_A_trans = _np.transpose(inv_A, inv_A_trans_axes)
+        temp_tensor = -_np.tensordot(inv_A_trans, d_inv_A, axes = axes1)
+        return _np.tensordot(temp_tensor, inv_A_trans, axes = axes2)
+
+    shapes = [
+        (1, 1, 1),
+        (1, 2, 2),
+        (1, 6, 2, 3),
+        (1, 10, 2, 5),
+        (1, 12, 3, 4),
+        (2, 1, 1),
+        (2, 1, 1, 1),
+        (2, 2, 5, 5, 2),
+        (2, 1, 6, 3, 2),
+        (2, 1, 8, 4, 2),
+        (2, 12, 1, 3, 4, 1),
+        (3, 1, 1, 1),
+        (3, 2, 3, 1, 6),
+        (3, 3, 2, 1, 2, 3, 1)
+    ]
+    dtypes = ['float32', 'float64']
+    for hybridize, shape, dtype, in itertools.product([False, True], shapes, dtypes):
+        rtol = 1e-3
+        atol = 1e-5
+        if dtype == 'float32':
+            rtol = 1e-2
+            atol = 1e-4
+        ind = shape[0]
+        test_tensorinv = TestTensorinv(ind=ind)
+        if hybridize:
+            test_tensorinv.hybridize()
+
+        prod_front = 1
+        prod_back = 1
+        for k in shape[1:ind + 1]:
+            prod_front *= k
+        for k in shape[1 + ind:]:
+            prod_back *= k
+        a_shape = (prod_back, prod_front)
+        a = newInvertibleMatrix_2D(a_shape)
+        a_shape = shape[1:]
+        inv_a_shape = shape[(1 + ind):] + shape[1:(ind + 1)]
+        a = np.array(a.reshape(a_shape), dtype=dtype)
+        a.attach_grad()
+        with mx.autograd.record():
+            mx_out = test_tensorinv(a)
+        # check tensorinv validity
+        assert mx_out.shape == inv_a_shape
+        check_tensorinv(mx_out, a, ind)
+
+        # check tensorinv backward
+        if 0 not in mx_out.shape:
+            mx.autograd.backward(mx_out)
+            grad_A_expected = get_grad_A(a.asnumpy(), ind)
+            assert_almost_equal(a.grad.asnumpy(), grad_A_expected, rtol=rtol, atol=atol)
+
+    # check imperative once again
+    mx_out = np.linalg.tensorinv(a, ind)
+    check_tensorinv(mx_out, a, ind)
+
+
 @with_seed()
 @use_np
 def test_np_linalg_det():

From 843daf589b318e1fdd0dfba47939d2b2a41319d4 Mon Sep 17 00:00:00 2001
From: Rohit Kumar Srivastava <srivastava.141@osu.edu>
Date: Sun, 15 Dec 2019 23:00:49 -0800
Subject: [PATCH 709/813] adding stacktrace in Jenkinsfile_utils.groovy to
 inspect Python2 failure cause in CI (#17065)

---
 ci/Jenkinsfile_utils.groovy | 1 +
 1 file changed, 1 insertion(+)

diff --git a/ci/Jenkinsfile_utils.groovy b/ci/Jenkinsfile_utils.groovy
index bf6c2d740b14..cdc02d99d7b0 100644
--- a/ci/Jenkinsfile_utils.groovy
+++ b/ci/Jenkinsfile_utils.groovy
@@ -278,6 +278,7 @@ def main_wrapper(args) {
     currentBuild.result = "SUCCESS"
     update_github_commit_status('SUCCESS', 'Job succeeded')
   } catch (caughtError) {
+    println(caughtError.getStackTrace());  
     node(NODE_UTILITY) {
       echo "caught ${caughtError}"
       err = caughtError

From 897f4fae075c5261958de1a07cded0b5b3058a7a Mon Sep 17 00:00:00 2001
From: Zixuan Wei <zixuan.wei@intel.com>
Date: Mon, 16 Dec 2019 16:03:12 +0800
Subject: [PATCH 710/813] [MKLDNN] mkldnn RNN operator enhancement (#17075)

* mkldnn rnn operator enhancement

`add` operation support

Rename AddTo

Add MXNET_USE_MKLDNN_RNN env

Add Env var for switching to naive RNN impl and naive add/copy impl

* Re-run CI, op:test_reduce failed on Unix-CPU

* Rerun CI, Python2 CPU on Unix-CPU timeout
---
 docs/static_site/src/pages/api/faq/env_var.md |  12 +-
 src/common/utils.h                            |  20 +-
 src/operator/nn/mkldnn/mkldnn_rnn-inl.h       |  35 +-
 src/operator/nn/mkldnn/mkldnn_rnn.cc          | 311 ++++++++++++------
 src/operator/rnn.cc                           |  11 +-
 tests/python/unittest/test_operator.py        |  21 +-
 6 files changed, 264 insertions(+), 146 deletions(-)

diff --git a/docs/static_site/src/pages/api/faq/env_var.md b/docs/static_site/src/pages/api/faq/env_var.md
index bc98c39d9570..57ab27630a8f 100644
--- a/docs/static_site/src/pages/api/faq/env_var.md
+++ b/docs/static_site/src/pages/api/faq/env_var.md
@@ -289,11 +289,11 @@ If ctypes is used, it must be `mxnet._ctypes.ndarray.NDArrayBase`.
   If no such algorithm exists given other constraints, MXNet will error out. This variable affects the choice
   of CUDNN convolution algorithms. Please see [CUDNN developer guide](https://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html) for more details.
 
-* MXNET_CPU_PARALLEL_COPY_SIZE
+* MXNET_CPU_PARALLEL_SIZE
   - Values: Int ```(default=200000)```
-  - The minimum size to call parallel copy by OpenMP in CPU2CPU mode.
-  - When the array size is bigger than or equal to  this threshold, NDArray::Copy(from, to) is implemented by OpenMP with the Recommended OMP Thread Count.
-  - When the array size is less than this threshold, NDArray::Copy(from , to)) is implemented by memcpy in single thread.
+  - The minimum size to call parallel operations by OpenMP for CPU context.
+  - When the array size is bigger than or equal to this threshold, the operation implemented by OpenMP is executed with the Recommended OMP Thread Count.
+  - When the array size is less than this threshold, the operation is implemented naively in single thread.
 
 * MXNET_OPTIMIZER_AGGREGATION_SIZE
   - Values: Int ```(default=4)```
@@ -349,6 +349,10 @@ If ctypes is used, it must be `mxnet._ctypes.ndarray.NDArrayBase`.
   - Values: 0(false) or 1(true) ```(default=1)```
   - If this variable is set, MXNet will simplify the computation graph, eliminating duplicated operations on the same inputs.
 
+* MXNET_USE_MKLDNN_RNN
+  - Values: 0(false) or 1(true) ```(default=1)```
+  - This variable controls whether to use the MKL-DNN backend in fused RNN operator for CPU context. There are two fusion implementations of RNN operator in MXNet. The MKL-DNN implementation has a better performance than the naive one, but the latter is more stable in the backward operation currently.
+
 Settings for Minimum Memory Usage
 ---------------------------------
 - Make sure ```min(MXNET_EXEC_NUM_TEMP, MXNET_GPU_WORKER_NTHREADS) = 1```
diff --git a/src/common/utils.h b/src/common/utils.h
index 9a9c686e73c9..2187ad053b66 100644
--- a/src/common/utils.h
+++ b/src/common/utils.h
@@ -769,7 +769,7 @@ inline void EmplaceBackZeros(const NDArrayStorageType stype, const mxnet::TShape
  */
 template<typename DType>
 inline void ParallelCopy(DType* dst, const DType* src, index_t size) {
-  static index_t copy_block_size = dmlc::GetEnv("MXNET_CPU_PARALLEL_COPY_SIZE", 200000);
+  static index_t copy_block_size = dmlc::GetEnv("MXNET_CPU_PARALLEL_SIZE", 200000);
   if (size >= copy_block_size) {
     #pragma omp parallel for num_threads(engine::OpenMP::Get()->GetRecommendedOMPThreadCount())
     for (index_t i = 0; i < size; ++i) {
@@ -780,6 +780,24 @@ inline void ParallelCopy(DType* dst, const DType* src, index_t size) {
   }
 }
 
+/*!
+ * \breif parallelize add by OpenMP
+ */
+template<typename DType>
+inline void ParallelAdd(DType* dst, const DType* src, index_t size) {
+  static index_t add_block_size = dmlc::GetEnv("MXNET_CPU_PARALLEL_SIZE", 200000);
+  if (size >= add_block_size) {
+    #pragma omp parallel for num_threads(engine::OpenMP::Get()->GetRecommendedOMPThreadCount())
+    for (index_t i = 0; i < size; ++i) {
+      dst[i] += src[i];
+    }
+  } else {
+    for (index_t i = 0; i < size; ++i) {
+      dst[i] += src[i];
+    }
+  }
+}
+
 /*!
  * \brief If numpy compatibility is turned off (default), the shapes passed in
  * by users follow the legacy shape definition:
diff --git a/src/operator/nn/mkldnn/mkldnn_rnn-inl.h b/src/operator/nn/mkldnn/mkldnn_rnn-inl.h
index ad3f7332a8f4..314106b98eb9 100644
--- a/src/operator/nn/mkldnn/mkldnn_rnn-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_rnn-inl.h
@@ -120,25 +120,24 @@ class RnnPrimitive {
   template<typename rnn_fwd, typename... Args>
   static RnnPrimitive Create(Args&&... args) {
     RnnPrimitive rnn_fwd_prim;
-    rnn_fwd_prim.pd_.reset(
-      new typename rnn_fwd::desc(std::forward<Args>(args)...),
-      [](typename rnn_fwd::desc* pd) {
-        delete reinterpret_cast<typename rnn_fwd::desc*>(pd);
+    auto fwd_desc = typename rnn_fwd::desc(std::forward<Args>(args)...);
+    rnn_fwd_prim.fwd_pd_.reset(
+      new typename rnn_fwd::primitive_desc(fwd_desc, CpuEngine::Get()->get_engine()),
+      [](typename rnn_fwd::primitive_desc* pd) {
+        delete reinterpret_cast<typename rnn_fwd::primitive_desc*>(pd);
       });
-    const typename rnn_fwd::desc& fwd_desc =
-        *(reinterpret_cast<typename rnn_fwd::desc*>(rnn_fwd_prim.pd_.get()));
-    typename rnn_fwd::primitive_desc fwd_pd(fwd_desc, CpuEngine::Get()->get_engine());
-    rnn_fwd_prim.weights_layer_desc_ = fwd_pd.weights_layer_desc();
-    rnn_fwd_prim.weights_iter_desc_  = fwd_pd.weights_iter_desc();
-    rnn_fwd_prim.workspace_desc_ = fwd_pd.workspace_desc();
+    auto fwd_pd = reinterpret_cast<typename rnn_fwd::primitive_desc*>(rnn_fwd_prim.fwd_pd_.get());
+    rnn_fwd_prim.weights_layer_desc_ = fwd_pd->weights_layer_desc();
+    rnn_fwd_prim.weights_iter_desc_  = fwd_pd->weights_iter_desc();
+    rnn_fwd_prim.workspace_desc_ = fwd_pd->workspace_desc();
 
-    rnn_fwd_prim.primitive_ = std::shared_ptr<mkldnn::primitive>(new rnn_fwd(fwd_pd));
+    rnn_fwd_prim.primitive_ = std::shared_ptr<mkldnn::primitive>(new rnn_fwd(*fwd_pd));
 
     return rnn_fwd_prim;
   }
 
   RnnPrimitive() {
-    this->pd_ = nullptr;
+    this->fwd_pd_ = nullptr;
     this->primitive_ = nullptr;
     this->weights_layer_desc_ = mkldnn::memory::desc();
     this->weights_iter_desc_ = mkldnn::memory::desc();
@@ -146,7 +145,7 @@ class RnnPrimitive {
   }
 
   RnnPrimitive(const RnnPrimitive& rnn_fwd_prim) {
-    this->pd_ = rnn_fwd_prim.pd_;
+    this->fwd_pd_ = rnn_fwd_prim.fwd_pd_;
     this->primitive_ = rnn_fwd_prim.primitive_;
     this->weights_layer_desc_ = rnn_fwd_prim.weights_layer_desc_;
     this->weights_iter_desc_ = rnn_fwd_prim.weights_iter_desc_;
@@ -155,7 +154,7 @@ class RnnPrimitive {
 
   RnnPrimitive& operator=(const RnnPrimitive& rnn_fwd_prim) {
     if (this != &rnn_fwd_prim) {
-      this->pd_ = rnn_fwd_prim.pd_;
+      this->fwd_pd_ = rnn_fwd_prim.fwd_pd_;
       this->primitive_ = rnn_fwd_prim.primitive_;
       this->weights_layer_desc_ = rnn_fwd_prim.weights_layer_desc_;
       this->weights_iter_desc_ = rnn_fwd_prim.weights_iter_desc_;
@@ -165,7 +164,7 @@ class RnnPrimitive {
     return *this;
   }
 
-  const void* GetPrimDesc() const { return pd_.get(); }
+  const void* GetPrimDesc() const { return fwd_pd_.get(); }
   const mkldnn::primitive& GetPrim() const { return *primitive_; }
 
   const mkldnn::memory::desc& GetLayerDesc() const {
@@ -181,7 +180,7 @@ class RnnPrimitive {
   }
 
  private:
-  std::shared_ptr<void> pd_;
+  std::shared_ptr<void> fwd_pd_;
   std::shared_ptr<mkldnn::primitive> primitive_;
   mkldnn::memory::desc weights_layer_desc_;
   mkldnn::memory::desc weights_iter_desc_;
@@ -370,7 +369,9 @@ class MKLDNNRnnBackward {
   void SetDataGradsMem(void* diff_src, void* diff_state, void* diff_statecell,
                        void* diff_out, void* diff_state_out, void* diff_statecell_out,
                        const int dtype = mshadow::kFloat32);
-  void CommitWeightsDiff(void* diff_weights, void* diff_bias, const int dtype = mshadow::kFloat32);
+  void CommitWeightsDiff(void* diff_weights, void* diff_bias,
+                         const OpReqType req,
+                         const int dtype = mshadow::kFloat32);
 
   const mkldnn::primitive& GetBwd() const { return *bwd_.primitive_; }
   const mkldnn_args_map_t& GetArgsMap() const { return net_args_; }
diff --git a/src/operator/nn/mkldnn/mkldnn_rnn.cc b/src/operator/nn/mkldnn/mkldnn_rnn.cc
index e797b649d295..6da8f3b8a58a 100644
--- a/src/operator/nn/mkldnn/mkldnn_rnn.cc
+++ b/src/operator/nn/mkldnn/mkldnn_rnn.cc
@@ -213,13 +213,13 @@ RnnBwdPrimitive GetRnnBwdPrim(const MKLDNNRnnForwardTraining &fwd,
   auto dst_state_desc = layer_param.state_outputs ? memory::desc(
       layer_param.state_dims, data_type, tag::ldnc) : memory::desc();
 
-  const void* fwd_desc = fwd.GetPrimDesc();
+  const void* fwd_pd = fwd.GetPrimDesc();
   auto bwd = RnnBwdPrimitive();
   switch (mode) {
     case rnn_enum::kLstm: {
-      const lstm_forward::primitive_desc* desc =
-          reinterpret_cast<const lstm_forward::primitive_desc*>(fwd_desc);
-      bwd = RnnBwdPrimitive::Create<lstm_forward, lstm_backward>(*desc,
+      const lstm_forward::primitive_desc* pd =
+          reinterpret_cast<const lstm_forward::primitive_desc*>(fwd_pd);
+      bwd = RnnBwdPrimitive::Create<lstm_forward, lstm_backward>(*pd,
           prop, mkldnn_rnn_direction,
           // data desc
           src_layer_desc, src_state_desc, src_state_desc, weight_layer_desc,
@@ -231,9 +231,9 @@ RnnBwdPrimitive GetRnnBwdPrim(const MKLDNNRnnForwardTraining &fwd,
           dst_state_desc);
     } break;
     case rnn_enum::kGru: {
-      const lbr_gru_forward::primitive_desc* desc =
-          reinterpret_cast<const lbr_gru_forward::primitive_desc*>(fwd_desc);
-      bwd = RnnBwdPrimitive::Create<lbr_gru_forward, lbr_gru_backward>(*desc,
+      const lbr_gru_forward::primitive_desc* pd =
+          reinterpret_cast<const lbr_gru_forward::primitive_desc*>(fwd_pd);
+      bwd = RnnBwdPrimitive::Create<lbr_gru_forward, lbr_gru_backward>(*pd,
           prop, mkldnn_rnn_direction,
           // data desc
           src_layer_desc, src_state_desc, weight_layer_desc,
@@ -244,10 +244,10 @@ RnnBwdPrimitive GetRnnBwdPrim(const MKLDNNRnnForwardTraining &fwd,
     } break;
     case rnn_enum::kRnnRelu:
     case rnn_enum::kRnnTanh: {
-      const vanilla_rnn_forward::primitive_desc* desc =
-          reinterpret_cast<const vanilla_rnn_forward::primitive_desc*>(fwd_desc);
+      const vanilla_rnn_forward::primitive_desc* pd =
+          reinterpret_cast<const vanilla_rnn_forward::primitive_desc*>(fwd_pd);
       bwd = RnnBwdPrimitive::Create<vanilla_rnn_forward, vanilla_rnn_backward>(
-          *desc, prop,
+          *pd, prop,
           mode == rnn_enum::kRnnTanh ? algorithm::eltwise_tanh : algorithm::eltwise_relu,
           mkldnn_rnn_direction,
           // data desc
@@ -776,16 +776,8 @@ void MKLDNNRnnBackward::SetDataGradsMem(
   }
 }
 
-template <typename DType>
-void HalveWeightsDiff(DType* w, const size_t size) {
-  const int omp_threads = mxnet::engine::OpenMP::Get()->GetRecommendedOMPThreadCount();
-  #pragma omp parallel for num_threads(omp_threads)
-  for (int i = 0; i < static_cast<int>(size); ++i) {
-    w[i] *= 0.5;
-  }
-}
-
-void MKLDNNRnnBackward::CommitWeightsDiff(void* diff_weights, void* diff_bias, int dtype) {
+void MKLDNNRnnBackward::CommitWeightsDiff(void* diff_weights, void* diff_bias,
+                                          const OpReqType req, const int dtype) {
   using tag = mkldnn::memory::format_tag;
   auto& cpu_engine = CpuEngine::Get()->get_engine();
   auto s = mkldnn::stream(cpu_engine);
@@ -795,11 +787,12 @@ void MKLDNNRnnBackward::CommitWeightsDiff(void* diff_weights, void* diff_bias, i
   const int direction = param.bidirectional ? 2 : 1;
   const int ngates = GetRnnGatesNum(param.mode);
   const size_t dtype_bytes = mshadow::mshadow_sizeof(dtype);
+  const size_t wxh_size = param.single_w_size;
+  const size_t wx_size = param.input_size * param.state_size * ngates;
+  const size_t wh_size = param.state_size * param.state_size * ngates;
   const size_t wxh_bytes = param.single_w_size * dtype_bytes;
   const size_t wx_bytes = param.input_size * param.state_size * ngates * dtype_bytes;
   const size_t wh_bytes = param.state_size * param.state_size * ngates * dtype_bytes;
-  char* diff_wx_ptr = static_cast<char *>(diff_weights_layer_->get_data_handle());
-  char* diff_wh_ptr = static_cast<char *>(diff_weights_iter_->get_data_handle());
 
   /* naive weights layout is:
           1st-layer: | wx_lr  | wh_lr  | wx_rl | wh_rl |
@@ -807,68 +800,109 @@ void MKLDNNRnnBackward::CommitWeightsDiff(void* diff_weights, void* diff_bias, i
   size:              |    wxh_bytes    |
                      |wx_bytes|wh_bytes|      
   */
-  char* naive_weights = static_cast<char *>(diff_weights);
-  if (param.mode != rnn_enum::kGru) {
-    for (int shift = 0; shift < num_layer * direction; ++shift) {
-      std::memcpy(naive_weights + shift * wxh_bytes,
-          diff_wx_ptr + shift * wx_bytes, wx_bytes);
-    }
-    // align naive_weights to weights_iter memory
-    naive_weights += wx_bytes;
-    for (int shift = 0; shift < num_layer * direction; ++shift) {
-      std::memcpy(naive_weights + shift * wxh_bytes,
-          diff_wh_ptr + shift * wh_bytes, wh_bytes);
-    }
-  } else {
-    const size_t wx_bytes_per_gate = param.input_size * param.state_size * dtype_bytes;
-    const size_t wh_bytes_per_gate = param.state_size * param.state_size * dtype_bytes;
-    for (int shift = 0; shift < num_layer * direction; ++shift) {
-      std::memcpy(naive_weights + shift * wxh_bytes + wx_bytes_per_gate,
-          diff_wx_ptr + shift * wx_bytes, wx_bytes_per_gate);
-      std::memcpy(naive_weights + shift * wxh_bytes,
-          diff_wx_ptr + shift * wx_bytes + wx_bytes_per_gate, wx_bytes_per_gate);
-      std::memcpy(naive_weights + shift * wxh_bytes + 2 * wx_bytes_per_gate,
-          diff_wx_ptr + shift * wx_bytes + 2 * wx_bytes_per_gate, wx_bytes_per_gate);
+  if (kWriteTo == req) {
+    char* naive_weights = static_cast<char *>(diff_weights);
+    char* diff_wx_ptr = static_cast<char *>(diff_weights_layer_->get_data_handle());
+    char* diff_wh_ptr = static_cast<char *>(diff_weights_iter_->get_data_handle());
+    if (param.mode != rnn_enum::kGru) {
+      for (int shift = 0; shift < num_layer * direction; ++shift) {
+        std::memcpy(naive_weights + shift * wxh_bytes,
+            diff_wx_ptr + shift * wx_bytes, wx_bytes);
+      }
+      // align naive_weights to weights_iter memory
+      naive_weights += wx_bytes;
+      for (int shift = 0; shift < num_layer * direction; ++shift) {
+        std::memcpy(naive_weights + shift * wxh_bytes,
+            diff_wh_ptr + shift * wh_bytes, wh_bytes);
+      }
+    } else {
+      const size_t wx_bytes_per_gate = param.input_size * param.state_size * dtype_bytes;
+      const size_t wh_bytes_per_gate = param.state_size * param.state_size * dtype_bytes;
+      for (int shift = 0; shift < num_layer * direction; ++shift) {
+        std::memcpy(naive_weights + shift * wxh_bytes + wx_bytes_per_gate,
+            diff_wx_ptr + shift * wx_bytes, wx_bytes_per_gate);
+        std::memcpy(naive_weights + shift * wxh_bytes,
+            diff_wx_ptr + shift * wx_bytes + wx_bytes_per_gate, wx_bytes_per_gate);
+        std::memcpy(naive_weights + shift * wxh_bytes + 2 * wx_bytes_per_gate,
+            diff_wx_ptr + shift * wx_bytes + 2 * wx_bytes_per_gate, wx_bytes_per_gate);
+      }
+      // align naive_weights to weights_iter memory
+      naive_weights += wx_bytes;
+      for (int shift = 0; shift < num_layer * direction; ++shift) {
+        std::memcpy(naive_weights + shift * wxh_bytes + wh_bytes_per_gate,
+            diff_wh_ptr + shift * wh_bytes, wh_bytes_per_gate);
+        std::memcpy(naive_weights + shift * wxh_bytes,
+            diff_wh_ptr + shift * wh_bytes + wh_bytes_per_gate, wh_bytes_per_gate);
+        std::memcpy(naive_weights + shift * wxh_bytes + 2 * wh_bytes_per_gate,
+            diff_wh_ptr + shift * wh_bytes + 2 * wh_bytes_per_gate, wh_bytes_per_gate);
+      }
     }
-    // align naive_weights to weights_iter memory
-    naive_weights += wx_bytes;
-    for (int shift = 0; shift < num_layer * direction; ++shift) {
-      std::memcpy(naive_weights + shift * wxh_bytes + wh_bytes_per_gate,
-          diff_wh_ptr + shift * wh_bytes, wh_bytes_per_gate);
-      std::memcpy(naive_weights + shift * wxh_bytes,
-          diff_wh_ptr + shift * wh_bytes + wh_bytes_per_gate, wh_bytes_per_gate);
-      std::memcpy(naive_weights + shift * wxh_bytes + 2 * wh_bytes_per_gate,
-          diff_wh_ptr + shift * wh_bytes + 2 * wh_bytes_per_gate, wh_bytes_per_gate);
+  } else if (kAddTo == req) {
+    if (param.mode != rnn_enum::kGru) {
+      MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
+        DType* naive_weights = static_cast<DType *>(diff_weights);
+        DType* diff_wx_ptr = static_cast<DType *>(diff_weights_layer_->get_data_handle());
+        DType* diff_wh_ptr = static_cast<DType *>(diff_weights_iter_->get_data_handle());
+        for (int shift = 0; shift < num_layer * direction; ++shift) {
+          common::ParallelAdd(naive_weights + shift * wxh_size,
+              diff_wx_ptr + shift * wx_size, wx_size);
+        }
+        // align naive_weights to weights_iter memory
+        naive_weights += wx_size;
+        for (int shift = 0; shift < num_layer * direction; ++shift) {
+          common::ParallelAdd(naive_weights + shift * wxh_size,
+              diff_wh_ptr + shift * wh_size, wh_size);
+        }
+      });
+    } else {
+      const size_t wx_size_per_gate = param.input_size * param.state_size;
+      const size_t wh_size_per_gate = param.state_size * param.state_size;
+      MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
+        DType* naive_weights = static_cast<DType *>(diff_weights);
+        DType* diff_wx_ptr = static_cast<DType *>(diff_weights_layer_->get_data_handle());
+        DType* diff_wh_ptr = static_cast<DType *>(diff_weights_iter_->get_data_handle());
+        for (int shift = 0; shift < num_layer * direction; ++shift) {
+          common::ParallelAdd(naive_weights + shift * wxh_size + wx_size_per_gate,
+              diff_wx_ptr + shift * wx_size, wx_size_per_gate);
+          common::ParallelAdd(naive_weights + shift * wxh_size,
+              diff_wx_ptr + shift * wx_size + wx_size_per_gate, wx_size_per_gate);
+          common::ParallelAdd(naive_weights + shift * wxh_size + 2 * wx_size_per_gate,
+              diff_wx_ptr + shift * wx_size + 2 * wx_size_per_gate, wx_size_per_gate);
+        }
+        // align naive_weights to weights_iter memory
+        naive_weights += wx_size;
+        for (int shift = 0; shift < num_layer * direction; ++shift) {
+          common::ParallelAdd(naive_weights + shift * wxh_size + wh_size_per_gate,
+              diff_wh_ptr + shift * wh_size, wh_size_per_gate);
+          common::ParallelAdd(naive_weights + shift * wxh_size,
+              diff_wh_ptr + shift * wh_size + wh_size_per_gate, wh_size_per_gate);
+          common::ParallelAdd(naive_weights + shift * wxh_size + 2 * wh_size_per_gate,
+              diff_wh_ptr + shift * wh_size + 2 * wh_size_per_gate, wh_size_per_gate);
+        }
+      });
     }
   }
 
-  char* naive_bias = static_cast<char *>(diff_bias);
-  char* diff_bias_ptr = static_cast<char *>(this->diff_bias_->get_data_handle());
-  const size_t bias_bytes = param.single_b_size * dtype_bytes;
-  const size_t naive_bias_bytes = param.naive_single_b_size * dtype_bytes;
-  if (param.mode != rnn_enum::kGru) {
-    MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
-      DType* typed_bias = reinterpret_cast<DType *>(diff_bias_ptr);
-      HalveWeightsDiff(typed_bias, num_layer * direction * param.single_b_size);
-    });
-    for (int shift = 0; shift < num_layer * direction; ++shift) {
-      std::memcpy(naive_bias + shift * naive_bias_bytes,
-          diff_bias_ptr + shift * bias_bytes, bias_bytes);
-      std::memcpy(naive_bias + shift * naive_bias_bytes + bias_bytes,
-          diff_bias_ptr + shift * bias_bytes, bias_bytes);
-    }
-  } else {
-    const size_t bias_bytes_per_gate = param.state_size * dtype_bytes;
-    MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
+  if (kWriteTo == req) {
+    const size_t bias_bytes = param.single_b_size * dtype_bytes;
+    const size_t naive_bias_bytes = param.naive_single_b_size * dtype_bytes;
+    char* naive_bias = static_cast<char *>(diff_bias);
+    char* diff_bias_ptr = static_cast<char *>(this->diff_bias_->get_data_handle());
+    if (param.mode != rnn_enum::kGru) {
+      for (int shift = 0; shift < num_layer * direction; ++shift) {
+        std::memcpy(naive_bias + shift * naive_bias_bytes,
+            diff_bias_ptr + shift * bias_bytes, bias_bytes);
+        std::memcpy(naive_bias + shift * naive_bias_bytes + bias_bytes,
+            diff_bias_ptr + shift * bias_bytes, bias_bytes);
+      }
+    } else {
+      const size_t bias_bytes_per_gate = param.state_size * dtype_bytes;
       for (int shift = 0; shift < num_layer * direction; ++shift) {
         char* naive_reset = naive_bias + shift * naive_bias_bytes;
         char* naive_update = naive_reset + bias_bytes_per_gate;
         char* update = diff_bias_ptr + shift * bias_bytes;
         char* reset = update + bias_bytes_per_gate;
 
-        DType* typed_update = reinterpret_cast<DType *>(update);
-        HalveWeightsDiff(typed_update, param.state_size * 2);
-
         std::memcpy(naive_update, update, bias_bytes_per_gate);
         std::memcpy(naive_reset, reset, bias_bytes_per_gate);
         std::memcpy(naive_update + naive_bias_bytes / 2, update, bias_bytes_per_gate);
@@ -881,7 +915,46 @@ void MKLDNNRnnBackward::CommitWeightsDiff(void* diff_weights, void* diff_bias, i
         std::memcpy(naive_new_bx, new_bx, bias_bytes_per_gate);
         std::memcpy(naive_new_bh, new_bh, bias_bytes_per_gate);
       }
-    });
+    }
+  } else if (kAddTo == req) {
+    const size_t bias_size = param.single_b_size;
+    const size_t naive_bias_size = param.naive_single_b_size;
+    if (param.mode != rnn_enum::kGru) {
+      MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
+        DType* naive_bias = static_cast<DType *>(diff_bias);
+        DType* diff_bias_ptr = static_cast<DType *>(this->diff_bias_->get_data_handle());
+        for (int shift = 0; shift < num_layer * direction; ++shift) {
+          common::ParallelAdd(naive_bias + shift * naive_bias_size,
+              diff_bias_ptr + shift * bias_size, bias_size);
+          common::ParallelAdd(naive_bias + shift * naive_bias_size + bias_size,
+              diff_bias_ptr + shift * bias_size, bias_size);
+        }
+      });
+    } else {
+      const size_t bias_size_per_gate = param.state_size;
+      MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
+        DType* naive_bias = static_cast<DType *>(diff_bias);
+        DType* diff_bias_ptr = static_cast<DType *>(this->diff_bias_->get_data_handle());
+        for (int shift = 0; shift < num_layer * direction; ++shift) {
+          DType* naive_reset = naive_bias + shift * naive_bias_size;
+          DType* naive_update = naive_reset + bias_size_per_gate;
+          DType* update = diff_bias_ptr + shift * bias_size;
+          DType* reset = update + bias_size_per_gate;
+
+          common::ParallelAdd(naive_update, update, bias_size_per_gate);
+          common::ParallelAdd(naive_reset, reset, bias_size_per_gate);
+          common::ParallelAdd(naive_update + naive_bias_size / 2, update, bias_size_per_gate);
+          common::ParallelAdd(naive_reset + naive_bias_size / 2, reset, bias_size_per_gate);
+
+          DType* naive_new_bx = naive_update + bias_size_per_gate;
+          DType* naive_new_bh = naive_new_bx + naive_bias_size / 2;
+          DType* new_bx = reset + bias_size_per_gate;
+          DType* new_bh = new_bx + bias_size_per_gate;
+          common::ParallelAdd(naive_new_bx, new_bx, bias_size_per_gate);
+          common::ParallelAdd(naive_new_bh, new_bh, bias_size_per_gate);
+        }
+      });
+    }
   }
 }
 
@@ -899,19 +972,11 @@ void MKLDNNRnnOp::Forward(const OpContext &ctx,
                           const std::vector<NDArray> &inputs,
                           const std::vector<OpReqType> &req,
                           const std::vector<NDArray> &outputs) {
+  TmpMemMgr::Get()->Init(ctx.requested[0]);
   // In the `autograd.record()` context, RNNOp is required to run into
   // forward_training mode.
   const bool is_training = (ctx.is_train || ctx.need_grad);
-  // check output requests
-  if (kAddTo == req[rnn_enum::kOut])
-    LOG(FATAL) << "Currently, `add` operation is not supported by RNNs.";
   const RNNParam& default_param = full_param_.default_param;
-  if (default_param.state_outputs) {
-    if (kAddTo == req[rnn_enum::kStateOut])
-      LOG(FATAL) << "Currently, `add` operation is not supported by RNNs.";
-    if (default_param.mode == rnn_enum::kLstm && kAddTo == req[rnn_enum::kStateCellOut])
-      LOG(FATAL) << "Currently, `add` operation against lstm-cell output is not supported.";
-  }
 
   // Initialize weights version
   if (!initialized_ && weights_version_ == 0) {
@@ -932,24 +997,40 @@ void MKLDNNRnnOp::Forward(const OpContext &ctx,
 
   // Get data type
   int data_dtype = inputs[rnn_enum::kData].dtype();
+  // Get temporary memory for output, state_out, statecell_out
+  const int num_layers = default_param.num_layers;
+  const int seq_length = default_param.seq_length_;
+  const int batch_size = default_param.batch_size_;
+  const int state_size = default_param.state_size;
+  const int directions = default_param.bidirectional ? 2 : 1;
+  mkldnn::memory::desc dst_desc({seq_length, batch_size, directions * state_size},
+      get_mkldnn_type(data_dtype), mkldnn::memory::format_tag::tnc);
+  mkldnn::memory::desc state_desc({num_layers, directions, batch_size, state_size},
+      get_mkldnn_type(data_dtype), mkldnn::memory::format_tag::ldnc);
+  auto out_mem = CreateMKLDNNMem(outputs[rnn_enum::kOut], dst_desc, req[rnn_enum::kOut]);
+  mkldnn_output_t stateout_mem;
+  mkldnn_output_t statecellout_mem;
 
   // Get input & output NDArray
   char *src = static_cast<char *>(inputs[rnn_enum::kData].data().dptr_);
   char *src_state = static_cast<char *>(inputs[rnn_enum::kState].data().dptr_);
-  char *dst = req[rnn_enum::kOut] == kNullOp ? nullptr
-      : static_cast<char *>(outputs[rnn_enum::kOut].data().dptr_);
+  char *dst = static_cast<char *>(out_mem.second->get_data_handle());
   char *dst_state = nullptr;          // Output state
   char *src_state_cell = nullptr;     // Used in LSTM for cell state
   char *dst_state_cell = nullptr;     // Used in LSTM for cell state
 
   if (default_param.state_outputs && req[rnn_enum::kStateOut] != kNullOp) {
-    dst_state = static_cast<char *>(outputs[rnn_enum::kStateOut].data().dptr_);
+    stateout_mem = CreateMKLDNNMem(
+        outputs[rnn_enum::kStateOut], state_desc, req[rnn_enum::kStateOut]);
+    dst_state = static_cast<char *>(stateout_mem.second->get_data_handle());
   }
 
   if (default_param.mode == rnn_enum::kLstm) {
     src_state_cell = static_cast<char *>(inputs[rnn_enum::kStateCell].data().dptr_);
     if (default_param.state_outputs && req[rnn_enum::kStateCellOut] != kNullOp) {
-      dst_state_cell = static_cast<char *>(outputs[rnn_enum::kStateCellOut].data().dptr_);
+      statecellout_mem = CreateMKLDNNMem(
+          outputs[rnn_enum::kStateCellOut], state_desc, req[rnn_enum::kStateCellOut]);
+      dst_state_cell = static_cast<char *>(statecellout_mem.second->get_data_handle());
     }
   }
 
@@ -1000,6 +1081,12 @@ void MKLDNNRnnOp::Forward(const OpContext &ctx,
   } else {
     for (auto& inf_lyr : fwd_inf_vec_) RegisterMKLDNNRnn(inf_lyr);
   }
+  CommitOutput(outputs[rnn_enum::kOut], out_mem);
+  if (default_param.state_outputs) {
+    CommitOutput(outputs[rnn_enum::kStateOut], stateout_mem);
+    if (default_param.mode == rnn_enum::kLstm)
+      CommitOutput(outputs[rnn_enum::kStateCellOut], statecellout_mem);
+  }
   MKLDNNStream::Get()->Submit();
 }
 
@@ -1008,18 +1095,9 @@ void MKLDNNRnnOp::Backward(const OpContext& ctx,
                            const std::vector<OpReqType>& req,
                            const std::vector<NDArray>& outputs) {
   using tag = mkldnn::memory::format_tag;
+  TmpMemMgr::Get()->Init(ctx.requested[0]);
   const RNNParam& default_param = full_param_.default_param;
-  if (kAddTo == req[rnn_enum::kData] || kAddTo == req[rnn_enum::kParams])
-    LOG(FATAL) << "Currently, `add` operations against gradients of input and weights"
-               << " are not supported by RNNs.";
-  if (default_param.state_outputs) {
-    if (kAddTo == req[rnn_enum::kStateOut])
-      LOG(FATAL) << "Currently, `add` operation against gradients of begining state"
-                 << " is not supported by RNNs.";
-    if (default_param.mode == rnn_enum::kLstm && req[rnn_enum::kStateCell])
-      LOG(FATAL) << "Currently, `add` operation against gradients of begining cell-state"
-                 << " is not supported by LSTM.";
-  }
+
   // Initialize the bwd_vec_
   if (bwd_vec_.size() != fwd_inf_vec_.size()) {
     bwd_vec_.clear();
@@ -1038,21 +1116,38 @@ void MKLDNNRnnOp::Backward(const OpContext& ctx,
   const int data_dtype = inputs[rnn_enum::kData].dtype();
   const int w_dtype = inputs[rnn_enum::kParams].dtype();
   const size_t w_bytes = mshadow::mshadow_sizeof(w_dtype);
+  // Get temporary memory for diff_src, diff_state, diff_statecell
+  const int num_layers = default_param.num_layers;
+  const int seq_length = default_param.seq_length_;
+  const int batch_size = default_param.batch_size_;
+  const int input_size = default_param.input_size_;
+  const int state_size = default_param.state_size;
+  const int directions = default_param.bidirectional ? 2 : 1;
+  mkldnn::memory::desc src_desc({seq_length, batch_size, input_size},
+      get_mkldnn_type(data_dtype), tag::tnc);
+  mkldnn::memory::desc state_desc({num_layers, directions, batch_size, state_size},
+      get_mkldnn_type(data_dtype), tag::ldnc);
+  auto diff_input_mem = CreateMKLDNNMem(outputs[rnn_enum::kData], src_desc, req[rnn_enum::kData]);
+  mkldnn_output_t diff_state_mem;
+  mkldnn_output_t diff_statecell_mem;
   // index description of outputs NDArray
   //   0    1    2     3
   // | dx | dw | dhx | dcx|
-  char* dx = req[rnn_enum::kData] == kNullOp ? nullptr
-      : static_cast<char *>(outputs[rnn_enum::kData].data().dptr_);
+  char* dx = static_cast<char *>(diff_input_mem.second->get_data_handle());
   char* dw = static_cast<char *>(outputs[rnn_enum::kParams].data().dptr_);
   char* db = dw + (inputs[rnn_enum::kParams].data().Size() -
       GetRnnBiasSize(default_param.num_layers, default_param.state_size,
         default_param.bidirectional + 1, default_param.mode)) * w_bytes;
-  char* dhx = req[rnn_enum::kState] == kNullOp ? nullptr
-      : static_cast<char *>(outputs[rnn_enum::kState].data().dptr_);
+  diff_state_mem = CreateMKLDNNMem(
+      outputs[rnn_enum::kState], state_desc, req[rnn_enum::kState]);
+  char* dhx = static_cast<char *>(diff_state_mem.second->get_data_handle());
   char* dcx = nullptr;
   if (full_param_.default_param.mode == rnn_enum::kLstm
-      && req[rnn_enum::kStateCell] != kNullOp)
-    dcx = static_cast<char *>(outputs[rnn_enum::kStateCell].data().dptr_);
+      && req[rnn_enum::kStateCell] != kNullOp) {
+    diff_statecell_mem = CreateMKLDNNMem(
+        outputs[rnn_enum::kStateCell], state_desc, req[rnn_enum::kStateCell]);
+    dcx = static_cast<char *>(diff_statecell_mem.second->get_data_handle());
+  }
 
   // index description of inputs NDArray
   //   0   1   2    3   4    5     6    7    8     9
@@ -1100,12 +1195,16 @@ void MKLDNNRnnOp::Backward(const OpContext& ctx,
       RegisterMKLDNNRnn(*bwd);
     }
   }
+  CommitOutput(outputs[rnn_enum::kData], diff_input_mem);
+  CommitOutput(outputs[rnn_enum::kState], diff_state_mem);
+  if (full_param_.default_param.mode == rnn_enum::kLstm)
+    CommitOutput(outputs[rnn_enum::kStateCell], diff_statecell_mem);
   MKLDNNStream::Get()->Submit();
 
   // Commit weights diff
   if (req[rnn_enum::kParams] != kNullOp) {
     for (size_t lyr = 0; lyr < bwd_vec_.size(); ++lyr) {
-      bwd_vec_.at(lyr).CommitWeightsDiff(dw, db, w_dtype);
+      bwd_vec_.at(lyr).CommitWeightsDiff(dw, db, req[rnn_enum::kParams], w_dtype);
       dw += full_param_.layer_params.at(lyr).single_w_size * w_bytes;
       db += full_param_.layer_params.at(lyr).single_b_size * w_bytes;
     }
diff --git a/src/operator/rnn.cc b/src/operator/rnn.cc
index 6d568c81bc1c..542968ef0a2c 100644
--- a/src/operator/rnn.cc
+++ b/src/operator/rnn.cc
@@ -181,6 +181,10 @@ static std::vector<ResourceRequest> RNNResourceEx(const NodeAttrs& attrs, const
     if (param.p != 0 && 1.0f - param.p > 0) {
       request.emplace_back(ResourceRequest::kCuDNNDropoutDesc);
     }
+#endif
+  } else {
+#if MXNET_USE_MKLDNN == 1
+    request.emplace_back(ResourceRequest::kTempSpace);
 #endif
   }
   return request;
@@ -243,7 +247,8 @@ static OpStatePtr CreateRNNState(const nnvm::NodeAttrs &attrs,
 
 #if MXNET_USE_MKLDNN == 1
   if ((in_types[0] == mshadow::kFloat32 || in_types[0] == mshadow::kFloat16)
-      && in_shapes[0].ndim() == 3 && ctx.dev_type == kCPU) {
+      && in_shapes[0].ndim() == 3 && ctx.dev_type == kCPU
+      && dmlc::GetEnv("MXNET_USE_MKLDNN_RNN", 1)) {
     const mxnet::TShape& data_shape = in_shapes[rnn_enum::kData];
     state = OpStatePtr::Create<MKLDNNRnnOp>(param, data_shape[0],
         data_shape[1], data_shape[2]);
@@ -270,7 +275,7 @@ static void RNNStatefulComputeExCPU(const OpStatePtr& state_ptr,
                                     const std::vector<OpReqType>& req,
                                     const std::vector<NDArray>& outputs) {
   if ((inputs[0].dtype() == mshadow::kFloat32 || inputs[0].dtype() == mshadow::kFloat16) &&
-      inputs[0].shape().ndim() == 3) {
+      inputs[0].shape().ndim() == 3 && dmlc::GetEnv("MXNET_USE_MKLDNN_RNN", 1)) {
     MKLDNNRnnOp& op = state_ptr.get_state<MKLDNNRnnOp>();
     op.Forward(ctx, inputs, req, outputs);
   } else {
@@ -284,7 +289,7 @@ static void RNNStatefulGradComputeExCPU(const OpStatePtr& state_ptr,
                                         const std::vector<OpReqType>& req,
                                         const std::vector<NDArray>& outputs) {
   if ((inputs[0].dtype() == mshadow::kFloat32 || inputs[0].dtype() == mshadow::kFloat16) &&
-      inputs[0].shape().ndim() == 3) {
+      inputs[0].shape().ndim() == 3 && dmlc::GetEnv("MXNET_USE_MKLDNN_RNN", 1)) {
     MKLDNNRnnOp& op = state_ptr.get_state<MKLDNNRnnOp>();
     op.Backward(ctx, inputs, req, outputs);
   } else {
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 7b0404d8abb7..66031d20d65b 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -36,15 +36,6 @@
 import os
 
 def check_rnn_consistency(cell1, cell2, T, N, I, H, grad_req, rtol=1e-2, atol=1e-4):
-    if default_context().device_type == 'cpu':
-    # NOTE(zixuanweeei): Currently, we don't add `add` requests support on fused mkl-dnn rnn operator.
-    # We tracked this issue by https://github.com/apache/incubator-mxnet/issues/16578
-        if isinstance(grad_req, dict) and 'add' in grad_req.values():
-            print("Skip the test when requiring `add` operation against gradients on CPU context.")
-            return
-        if isinstance(grad_req, str) and grad_req == 'add':
-            print("Skip the test when requiring `add` operation against gradients on CPU context.")
-            return
     dshape = (N, T, I)
     data = mx.sym.Variable('data')
 
@@ -182,9 +173,9 @@ def test_gru_sym():
         stack.add(mx.rnn.GRUCell(H, prefix='l1_'))
         stack.add(mx.rnn.GRUCell(H, prefix='l2_'))
 
-        check_rnn_consistency(fused, stack, T, N, I, H, 'write', atol=2e-4)
-        check_rnn_consistency(fused, stack, T, N, I, H, 'add', atol=2e-4)
-        check_rnn_consistency(fused, stack, T, N, I, H, 'null', atol=2e-4)
+        check_rnn_consistency(fused, stack, T, N, I, H, 'write')
+        check_rnn_consistency(fused, stack, T, N, I, H, 'add')
+        check_rnn_consistency(fused, stack, T, N, I, H, 'null')
 
 @with_seed()
 @assert_raises_cudnn_not_satisfied(min_version='5.1.10')
@@ -208,9 +199,9 @@ def test_gru_bidirectional():
                     mx.rnn.GRUCell(H, prefix='r1_'),
                     output_prefix='bi_gru_1_'))
 
-        check_rnn_consistency(fused, stack, T, N, I, H, 'write', atol=2e-4)
-        check_rnn_consistency(fused, stack, T, N, I, H, 'add', atol=2e-4)
-        check_rnn_consistency(fused, stack, T, N, I, H, 'null', atol=2e-4)
+        check_rnn_consistency(fused, stack, T, N, I, H, 'write')
+        check_rnn_consistency(fused, stack, T, N, I, H, 'add')
+        check_rnn_consistency(fused, stack, T, N, I, H, 'null')
 
 @with_seed()
 @assert_raises_cudnn_not_satisfied(min_version='5.1.10')

From 52c9a45abc8d1f8228ec9ed1bad7f137137fd96b Mon Sep 17 00:00:00 2001
From: Zhennan Qin <zhennan.qin@intel.com>
Date: Mon, 16 Dec 2019 16:05:32 +0800
Subject: [PATCH 711/813] [MKLDNN] enable MaxPooling with full pooling
 convention (#16860)

* [MKLDNN] enable MaxPooling for full pooling convention

* Run CI

* Fix UT

* Add comment

* Run CI
---
 src/operator/nn/mkldnn/mkldnn_pooling-inl.h | 20 +++++++++++++++++++-
 src/operator/nn/mkldnn/mkldnn_pooling.cc    |  7 -------
 src/operator/nn/pooling.cc                  |  5 ++---
 3 files changed, 21 insertions(+), 11 deletions(-)

diff --git a/src/operator/nn/mkldnn/mkldnn_pooling-inl.h b/src/operator/nn/mkldnn/mkldnn_pooling-inl.h
index 22e9abd156a3..08d91af6fbb3 100644
--- a/src/operator/nn/mkldnn/mkldnn_pooling-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_pooling-inl.h
@@ -89,6 +89,14 @@ class MKLDNNPoolingBwd {
   const mkldnn::pooling_backward::primitive_desc &GetPd();
 };
 
+inline int GetPaddingSizeFull(dim_t x, int padl, int padr, int k, int s) {
+  if ((x + padl + padr - k) % s != 0) {
+    return (padr + s - ((x + padl + padr - k) % s));
+  } else {
+    return padr;
+  }
+}
+
 inline bool SupportMKLDNNPooling(const PoolingParam &param) {
   return param.kernel.ndim() == 2 &&
          (param.pool_type == pool_enum::kMaxPooling ||
@@ -105,7 +113,17 @@ inline bool SupportMKLDNNPooling(const PoolingParam &param,
   if (param.pooling_convention == pool_enum::kValid) {
     return true;
   } else {
-    // currently, only max-pooling is supported for full convention
+    if (param.pool_type == pool_enum::kAvgPooling) {
+      CHECK_EQ(dshape.ndim(), 4);
+      // mkldnn works differently when padding is asymmetric, so let's skip this case.
+      if (param.pad[0] == GetPaddingSizeFull(dshape[2], param.pad[0], param.pad[0], param.kernel[0],
+                                             param.stride[0]) &&
+          param.pad[1] == GetPaddingSizeFull(dshape[3], param.pad[1], param.pad[1], param.kernel[1],
+                                             param.stride[1])) {
+        return true;
+      }
+      return false;
+    }
     return param.pool_type == pool_enum::kMaxPooling;
   }
 }
diff --git a/src/operator/nn/mkldnn/mkldnn_pooling.cc b/src/operator/nn/mkldnn/mkldnn_pooling.cc
index 6eda2aa33b34..d2f79700051a 100644
--- a/src/operator/nn/mkldnn/mkldnn_pooling.cc
+++ b/src/operator/nn/mkldnn/mkldnn_pooling.cc
@@ -127,13 +127,6 @@ mkldnn::algorithm GetMKLDNNPoolAlgo(const PoolingParam &param) {
   }
 }
 
-static inline int GetPaddingSizeFull(dim_t x, int padl, int padr, int k, int s) {
-  if ((x + padl + padr - k) % s != 0) {
-    return (padr + s - ((x + padl + padr - k) % s));
-  } else {
-    return padr;
-  }
-}
 
 mkldnn::pooling_forward::primitive_desc GetPoolingFwdPdesc(
     const PoolingParam &param, const bool is_train, const mkldnn::memory::desc &data_md,
diff --git a/src/operator/nn/pooling.cc b/src/operator/nn/pooling.cc
index 485fc1345dfd..f998c33d16a8 100644
--- a/src/operator/nn/pooling.cc
+++ b/src/operator/nn/pooling.cc
@@ -278,9 +278,8 @@ void PoolingComputeExCPU(const nnvm::NodeAttrs &attrs, const OpContext &ctx,
     return;
   }
 
-
-  if (SupportMKLDNN(inputs[0]) &&
-      SupportMKLDNNPooling(param, inputs[0].shape())) {
+  if (SupportMKLDNN(inputs[0])
+      && SupportMKLDNNPooling(param, inputs[0].shape())) {
     if (MKLDNNRequireWorkspace(param)) {
       CHECK_GT(outputs.size(), 1U);
       workspace = &outputs[1];

From db943e95e86f7eabfc45e1dd827d7b680ed43803 Mon Sep 17 00:00:00 2001
From: "Joshua Z. Zhang" <cheungchih@gmail.com>
Date: Mon, 16 Dec 2019 11:15:39 -0800
Subject: [PATCH 712/813] [BugFix] fix filter channel calculation in
 ModulatedDeformableConvV2 (#17070)

* fix filter channel

* fix slice during forward
---
 python/mxnet/gluon/contrib/cnn/conv_layers.py | 7 ++++---
 tests/python/unittest/test_gluon_contrib.py   | 4 ++++
 2 files changed, 8 insertions(+), 3 deletions(-)

diff --git a/python/mxnet/gluon/contrib/cnn/conv_layers.py b/python/mxnet/gluon/contrib/cnn/conv_layers.py
index 098463eca968..c4924c130a28 100644
--- a/python/mxnet/gluon/contrib/cnn/conv_layers.py
+++ b/python/mxnet/gluon/contrib/cnn/conv_layers.py
@@ -313,7 +313,8 @@ def __init__(self, channels, kernel_size=(1, 1), strides=(1, 1), padding=(0, 0),
                 dilation = (dilation,) * len(kernel_size)
             self._op_name = op_name
 
-            offset_channels = 27
+            offset_channels = num_deformable_group * 3 * kernel_size[0] * kernel_size[1]
+            self.offset_split_index = num_deformable_group * 2 * kernel_size[0] * kernel_size[1]
             self._kwargs_offset = {
                 'kernel': kernel_size, 'stride': strides, 'dilate': dilation,
                 'pad': padding, 'num_filter': offset_channels, 'num_group': groups,
@@ -377,8 +378,8 @@ def hybrid_forward(self, F, x, offset_weight, deformable_conv_weight, offset_bia
         else:
             offset = F.Convolution(x, offset_weight, offset_bias, cudnn_off=True, **self._kwargs_offset)
 
-        offset_t = F.slice_axis(offset, axis=1, begin=0, end=18)
-        mask = F.slice_axis(offset, axis=1, begin=18, end=None)
+        offset_t = F.slice_axis(offset, axis=1, begin=0, end=self.offset_split_index)
+        mask = F.slice_axis(offset, axis=1, begin=self.offset_split_index, end=None)
         mask = F.sigmoid(mask) * 2
 
         if deformable_conv_bias is None:
diff --git a/tests/python/unittest/test_gluon_contrib.py b/tests/python/unittest/test_gluon_contrib.py
index fdba553c8560..0ed0d4e8a545 100644
--- a/tests/python/unittest/test_gluon_contrib.py
+++ b/tests/python/unittest/test_gluon_contrib.py
@@ -411,6 +411,10 @@ def test_ModulatedDeformableConvolution():
     net = nn.HybridSequential()
     net.add(
         DeformableConvolution(10, kernel_size=(3, 3), strides=1, padding=0),
+        DeformableConvolution(10, kernel_size=(1, 1), strides=1, padding=0),
+        DeformableConvolution(10, kernel_size=(5, 5), strides=1, padding=0),
+        DeformableConvolution(10, kernel_size=(3, 5), strides=1, padding=0),
+        DeformableConvolution(10, kernel_size=(5, 1), strides=1, padding=0, num_deformable_group=2),
         DeformableConvolution(10, kernel_size=(3, 2), strides=1, padding=0, activation='relu',
                                offset_use_bias=False, use_bias=False),
         DeformableConvolution(10, kernel_size=(3, 2), strides=1, padding=0, activation='relu',

From 491246567737d09cbc9e1dfc9b24bfb36201a0af Mon Sep 17 00:00:00 2001
From: Leonard Lausen <lausen@amazon.com>
Date: Tue, 17 Dec 2019 07:32:33 +0900
Subject: [PATCH 713/813] Update pypi_publish.py to disable nighlty build
 upload to Pypi (#17082)

---
 cd/python/pypi/pypi_publish.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/cd/python/pypi/pypi_publish.py b/cd/python/pypi/pypi_publish.py
index 94d6e8703a52..7e09f644c734 100755
--- a/cd/python/pypi/pypi_publish.py
+++ b/cd/python/pypi/pypi_publish.py
@@ -48,6 +48,10 @@ def post_wheel(path):
         print('Would have run: {}'.format(cmd))
         return 0
     else:
+        print('Skipping publishing nightly builds to Pypi.')
+        print('See https://github.com/pypa/pypi-support/issues/50 for details')
+        return 0
+
         # DO NOT PRINT CMD IN THIS BLOCK, includes password
         p = subprocess.run(cmd.split(' '),
                         stdout=subprocess.PIPE)

From 18c9a69e2f75c3f157149ec3344faef8ba12ff7f Mon Sep 17 00:00:00 2001
From: Lukas Adamowicz <lukas.adamowicz95@gmail.com>
Date: Mon, 16 Dec 2019 19:09:58 -0500
Subject: [PATCH 714/813] Install current OpenCV (#17090)

Recommend installing the latest version of OpenCV instead of OpenCV 3 in OS X install guide. OpenCV 3 does not work. We already commonly build with OpenCV 4 on other platforms.
---
 docs/static_site/src/pages/get_started/osx_setup.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/static_site/src/pages/get_started/osx_setup.md b/docs/static_site/src/pages/get_started/osx_setup.md
index b5234126e334..369edb36c148 100644
--- a/docs/static_site/src/pages/get_started/osx_setup.md
+++ b/docs/static_site/src/pages/get_started/osx_setup.md
@@ -88,7 +88,7 @@ Install the dependencies, required for MXNet, with the following commands:
 	brew install graphviz
 	brew install openblas
 	brew tap homebrew/core
-	brew install opencv@3
+	brew install opencv
 
 	# If building with MKLDNN
 	brew install llvm

From 814be5953420846d6b0835acd5591ac09fdff2e5 Mon Sep 17 00:00:00 2001
From: "yuri@FreeBSD" <yurivict@users.noreply.github.com>
Date: Mon, 16 Dec 2019 17:18:08 -0800
Subject: [PATCH 715/813] Add #include <sys/wait.h> needed for waitpid (#17078)

---
 tests/cpp/engine/omp_test.cc | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tests/cpp/engine/omp_test.cc b/tests/cpp/engine/omp_test.cc
index 2be7d9d0307c..f4ef421a8595 100644
--- a/tests/cpp/engine/omp_test.cc
+++ b/tests/cpp/engine/omp_test.cc
@@ -25,6 +25,7 @@
 #if defined(unix) || defined(__unix__) || defined(__unix)
 #include <unistd.h>
 #include <sys/types.h>
+#include <sys/wait.h>
 #include <dmlc/logging.h>
 
 

From f86a8d10074f983b990ef420b96d664e911ee1bf Mon Sep 17 00:00:00 2001
From: Haibin Lin <linhaibin.eric@gmail.com>
Date: Mon, 16 Dec 2019 20:48:31 -0800
Subject: [PATCH 716/813] [API] unified API for custom kvstores (#17010)

* abstract kvstore api

* add test

* reorg folder

* add split kvstore.py to kvstore and base

* fix import

* add horovod class

* add registration

* add unit test for kvstore base

* add nightly test

* fix pushpull

* fix test

* simply API

* Trainer API

* fix a bug

* Fix typo

* update horovod tutorial

* better error message

* fix incorrect usage of name

* + query capacity

* unit test for test kvstore

* add trainer  test

* refactor

* renmae

* remove horovod example

* revert horovod example

* rename test_kvstore_custom.py

* more tests

* more tests for teststore

* fix type name

* fix lint

* fix lint

* fix lint

* Update dist_device_sync_kvstore_custom.py

* address CR

* add optimizer test

* add optimizer test
---
 ci/docker/runtime_functions.sh                |   1 +
 python/mxnet/__init__.py                      |  24 +-
 python/mxnet/gluon/trainer.py                 |  50 +-
 python/mxnet/kvstore/__init__.py              |  24 +
 python/mxnet/kvstore/base.py                  | 455 ++++++++++++++++++
 python/mxnet/{ => kvstore}/kvstore.py         | 183 +++----
 python/mxnet/{ => kvstore}/kvstore_server.py  |   6 +-
 python/mxnet/model.py                         |  35 +-
 src/kvstore/kvstore_local.h                   |   4 +-
 .../dist_device_sync_kvstore_custom.py        |  96 ++++
 tests/python/unittest/test_gluon_trainer.py   |  33 +-
 tests/python/unittest/test_kvstore_custom.py  | 195 ++++++++
 12 files changed, 955 insertions(+), 151 deletions(-)
 create mode 100644 python/mxnet/kvstore/__init__.py
 create mode 100644 python/mxnet/kvstore/base.py
 rename python/mxnet/{ => kvstore}/kvstore.py (85%)
 rename python/mxnet/{ => kvstore}/kvstore_server.py (97%)
 create mode 100644 tests/nightly/dist_device_sync_kvstore_custom.py
 create mode 100644 tests/python/unittest/test_kvstore_custom.py

diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index 9198eee32896..b658f953a78a 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -1418,6 +1418,7 @@ integrationtest_ubuntu_gpu_dist_kvstore() {
     export DMLC_LOG_STACK_TRACE_DEPTH=10
     cd tests/nightly/
     ../../tools/launch.py -n 4 --launcher local python dist_device_sync_kvstore.py
+    ../../tools/launch.py -n 4 --launcher local python dist_device_sync_kvstore_custom.py
     ../../tools/launch.py -n 4 --launcher local python dist_sync_kvstore.py --type=init_gpu
     popd
 }
diff --git a/python/mxnet/__init__.py b/python/mxnet/__init__.py
index fb9680a6db18..ed6e81daaca8 100644
--- a/python/mxnet/__init__.py
+++ b/python/mxnet/__init__.py
@@ -27,6 +27,10 @@
 from .util import is_np_shape, set_np_shape, np_shape, use_np_shape
 from .util import is_np_array, np_array, use_np_array, use_np
 from . import base
+
+# version info
+__version__ = base.__version__
+
 from . import contrib
 from . import ndarray
 from . import ndarray as nd
@@ -59,8 +63,6 @@
 from . import callback
 # from . import misc
 from . import lr_scheduler
-# use mx.kv as short for kvstore
-from . import kvstore as kv
 # Runtime compile module
 from . import rtc
 # Attribute scope to add attributes to symbolic graphs
@@ -84,22 +86,20 @@
 from . import test_utils
 
 from . import rnn
-
 from . import gluon
 
-# Dynamic library module should be done after ndarray and symbol are initialized
-from . import library
-from . import tvmop
-
-__version__ = base.__version__
-
-# Dist kvstore module which launches a separate process when role is set to "server".
-# This should be done after other modules are initialized.
+# With the native kvstore module (such as 'dist_sync_device'), the module launches a separate
+# process when role is set to "server". This should be done after other modules are initialized.
 # Otherwise this may result in errors when unpickling custom LR scheduler/optimizers.
 # For example, the LRScheduler in gluoncv depends on a specific version of MXNet, and
 # checks the __version__ attr of MXNet, which is not set on kvstore server due to the
 # fact that kvstore-server module is imported before the __version__ attr is set.
-from . import kvstore_server
+# use mx.kv as short for kvstore
+from . import kvstore as kv
+
+# Dynamic library module should be done after ndarray and symbol are initialized
+from . import library
+from . import tvmop
 
 from . import numpy_op_signature
 from . import numpy_dispatch_protocol
diff --git a/python/mxnet/gluon/trainer.py b/python/mxnet/gluon/trainer.py
index 1ab86af2b93f..966ed2cc9964 100644
--- a/python/mxnet/gluon/trainer.py
+++ b/python/mxnet/gluon/trainer.py
@@ -23,6 +23,7 @@
 from .. import optimizer as opt
 from ..model import _create_kvstore, _create_sparse_kvstore
 from .parameter import ParameterDict, Parameter
+from ..kvstore import KVStore
 
 class Trainer(object):
     """Applies an `Optimizer` on a set of Parameters. Trainer should
@@ -153,9 +154,10 @@ def _init_params(self):
                 else:
                     param_arrays = param._check_and_get(param._data, list)
                     idx = self._param2idx[param.name]
-                    self._kvstore.init(idx, param_arrays[0])
-                    if param._stype == 'default':
-                        self._kvstore.pull(idx, param_arrays, priority=-idx)
+                    if param._stype != 'default':
+                        self._kvstore.init(idx, param_arrays[0])
+                    else:
+                        self._kvstore.broadcast(idx, param_arrays[0], param_arrays)
 
         self._params_to_init = params_to_init
 
@@ -218,6 +220,10 @@ def _init_kvstore(self):
                     raise ValueError("Cannot set update_on_kvstore=False on dist kvstore "
                                      "when sparse gradients are present.")
                 update_on_kvstore = config['update_on_kvstore']
+            # raise err if a custom kvstore is used for sparse training
+            if not isinstance(kvstore, KVStore):
+                raise ValueError("Cannot use {} for multi-device training with sparse gradients"
+                                 .format(type(kvstore)))
 
         else:
             # Training with dense weight and dense gradients.
@@ -234,6 +240,12 @@ def _init_kvstore(self):
                                      "when training in async mode.")
             if config['update_on_kvstore'] is not None:
                 update_on_kvstore = config['update_on_kvstore']
+            # raise err if update_on_kvstore is set to True with kvstores that do not support optimizers
+            if update_on_kvstore and not type(kvstore).is_capable('optimizer'):
+                if config['update_on_kvstore']:
+                    raise ValueError("Please set update_on_kvstore=False "
+                                     "when training with {}".format(type(kvstore)))
+                update_on_kvstore = False
 
         # set grad compression and optimizers
         if kvstore:
@@ -357,14 +369,30 @@ def allreduce_grads(self):
         self._allreduce_grads()
 
     def _allreduce_grads(self):
-        if self._kvstore:
-            for i, param in enumerate(self._params):
-                if param.grad_req != 'null':
+        # nothing to reduce
+        if not self._kvstore:
+            return
+        for i, param in enumerate(self._params):
+            if param.grad_req != 'null':
 
-                    self._kvstore.push(i, param.list_grad(), priority=-i)
-                    if not self._update_on_kvstore:
-                        self._kvstore.pull(i, param.list_grad(), priority=-i,
+                grad_list = param.list_grad()
+                # sparse gradients, call push and pull separately
+                if grad_list[0].stype != 'default':
+                    self._kvstore.push(i, grad_list, priority=-i)
+                    if param._stype == 'default':
+                        if self._update_on_kvstore:
+                            pull_list = param.list_data()
+                        else:
+                            pull_list = param.list_grad()
+                        self._kvstore.pull(i, pull_list, priority=-i,
                                            ignore_sparse=self._distributed)
+                else:
+                    # allreduce dense gradients if not update_on_kvstore,
+                    # otherwise push dense gradients, pull dense weights
+                    if self._update_on_kvstore:
+                        self._kvstore.pushpull(i, grad_list, out=param.list_data(), priority=-i)
+                    else:
+                        self._kvstore.pushpull(i, grad_list, priority=-i)
 
     def update(self, batch_size, ignore_stale_grad=False):
         """Makes one step of parameter update.
@@ -419,10 +447,6 @@ def _update(self, ignore_stale_grad=False):
                             %(param.name, str(data.context)))
 
             if self._kvstore and self._update_on_kvstore:
-                if param._stype == 'default':
-                    # 'row_sparse' parameters are not pulled immediately - they're pulled
-                    # in `Block.forward`
-                    self._kvstore.pull(i, param.list_data(), priority=-i)
                 continue
 
             for upd, arr, grad in zip(updates, param.list_data(), param.list_grad()):
diff --git a/python/mxnet/kvstore/__init__.py b/python/mxnet/kvstore/__init__.py
new file mode 100644
index 000000000000..ccb58a1c6229
--- /dev/null
+++ b/python/mxnet/kvstore/__init__.py
@@ -0,0 +1,24 @@
+#!/usr/bin/env python
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# coding: utf-8
+"""Key-value store for distributed communication"""
+from .kvstore import *
+from .base import *
+from .kvstore_server import *
diff --git a/python/mxnet/kvstore/base.py b/python/mxnet/kvstore/base.py
new file mode 100644
index 000000000000..50310b6a6899
--- /dev/null
+++ b/python/mxnet/kvstore/base.py
@@ -0,0 +1,455 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# coding: utf-8
+""" Key value store interface of MXNet for parameter synchronization."""
+from __future__ import absolute_import
+
+from array import array
+import ctypes
+import warnings
+from ..ndarray import NDArray
+from ..base import _LIB, c_str_array, c_handle_array, c_array, c_array_buf, c_str
+from ..base import check_call, string_types
+from ..base import KVStoreHandle
+from ..profiler import set_kvstore_handle
+
+__all__ = ['create', 'KVStoreBase']
+
+def _ctype_key_value(keys, vals):
+    """Returns ctype arrays for the key-value args, and the whether string keys are used.
+    For internal use only.
+    """
+    if isinstance(keys, (tuple, list)):
+        assert(len(keys) == len(vals))
+        c_keys = []
+        c_vals = []
+        use_str_keys = None
+        for key, val in zip(keys, vals):
+            c_key_i, c_val_i, str_keys_i = _ctype_key_value(key, val)
+            c_keys += c_key_i
+            c_vals += c_val_i
+            use_str_keys = str_keys_i if use_str_keys is None else use_str_keys
+            assert(use_str_keys == str_keys_i), "inconsistent types of keys detected."
+        c_keys_arr = c_array(ctypes.c_char_p, c_keys) if use_str_keys \
+                     else c_array(ctypes.c_int, c_keys)
+        c_vals_arr = c_array(ctypes.c_void_p, c_vals)
+        return (c_keys_arr, c_vals_arr, use_str_keys)
+
+    assert(isinstance(keys, (int,) + string_types)), \
+           "unexpected type for keys: " + str(type(keys))
+    use_str_keys = isinstance(keys, string_types)
+    if isinstance(vals, NDArray):
+        c_keys = c_str_array([keys]) if use_str_keys \
+                 else c_array_buf(ctypes.c_int, array('i', [keys]))
+        return (c_keys, c_handle_array([vals]), use_str_keys)
+    else:
+        for value in vals:
+            assert(isinstance(value, NDArray))
+        c_keys = c_str_array([keys] * len(vals)) if use_str_keys \
+                 else c_array_buf(ctypes.c_int, array('i', [keys] * len(vals)))
+        return (c_keys, c_handle_array(vals), use_str_keys)
+
+def _ctype_dict(param_dict):
+    """Returns ctype arrays for keys and values(converted to strings) in a dictionary"""
+    assert(isinstance(param_dict, dict)), \
+        "unexpected type for param_dict: " + str(type(param_dict))
+    c_keys = c_array(ctypes.c_char_p, [c_str(k) for k in param_dict.keys()])
+    c_vals = c_array(ctypes.c_char_p, [c_str(str(v)) for v in param_dict.values()])
+    return (c_keys, c_vals)
+
+class KVStoreBase(object):
+    """An abstract key-value store interface for data parallel training."""
+
+    def broadcast(self, key, value, out, priority=0):
+        """ Broadcast the `value` NDArray at rank 0 to all ranks,
+        and store the result in `out`
+
+        Parameters
+        ----------
+        key : str or int
+            The key.
+
+        value : NDArray
+            The value corresponding to the key to broadcast
+
+        out : NDArray, or list of NDArray
+            Values corresponding to the key to store the result
+
+        priority : int, optional
+            The priority of the operation.
+            Higher priority operations are likely to be executed before other actions.
+        """
+        raise NotImplementedError()
+
+    def pushpull(self, key, value, out=None, priority=0):
+        """ Performs push and pull a single value or a sequence of values from the store.
+
+        This function is coalesced form of push and pull operations.
+
+        `value` is pushed to the kvstore server for summation with the specified keys,
+        and the results are pulled from the server to `out`. If `out` is not specified
+        the pulled values are written to `value`.
+
+        Note that for allreduce based approaches such as horovod, there is no notion of
+        server or store. This function performs allreduce.
+
+        Parameters
+        ----------
+        key : str or int
+            The key.
+
+        value : NDArray, or list of NDArray
+            Values corresponding to the keys.
+
+        out: NDArray, or list of NDArray
+            Values corresponding to the key.
+
+        priority : int, optional
+            The priority of the operation.
+            Higher priority operations are likely to be executed before other actions.
+        """
+        raise NotImplementedError()
+
+    def set_optimizer(self, optimizer):
+        """ Registers an optimizer with the kvstore.
+
+        When using a single machine, this function updates the local optimizer.
+        If using multiple machines and this operation is invoked from a worker node,
+        it will serialized the optimizer with pickle and send it to all servers.
+        The function returns after all servers have been updated.
+
+        Parameters
+        ----------
+        optimizer : KVStoreBase
+            The new optimizer for the store
+        """
+        raise NotImplementedError()
+
+    OPTIMIZER = 'optimizer'
+
+    @staticmethod
+    def is_capable(capability):
+        """Queries if the KVStore type supports certain capability, such as optimizer algorithm,
+        gradient compression, sparsity, etc.
+
+        Parameters
+        ----------
+        capability: str
+            The capability to query
+
+        Returns
+        -------
+        result : bool
+            Whether the capability is supported or not.
+        """
+        raise NotImplementedError()
+
+    def save_optimizer_states(self, fname, dump_optimizer=False):
+        """Saves the optimizer (updater) state to a file. This is often used when checkpointing
+        the model during training.
+
+        Parameters
+        ----------
+        fname : str
+            Path to the output states file.
+        dump_optimizer : bool, default False
+            Whether to also save the optimizer itself. This would also save optimizer
+            information such as learning rate and weight decay schedules.
+        """
+        raise NotImplementedError()
+
+    def load_optimizer_states(self, fname):
+        """Loads the optimizer (updater) state from the file.
+
+        Parameters
+        ----------
+        fname : str
+            Path to input states file.
+        """
+        raise NotImplementedError()
+
+    @property
+    def type(self):
+        """ Returns the type of this kvstore backend.
+
+        Returns
+        -------
+        type : str
+            the string type
+        """
+        raise NotImplementedError()
+
+    @property
+    def rank(self):
+        """ Returns the rank of this worker node.
+
+        Returns
+        -------
+        rank : int
+            The rank of this node, which is in range [0, num_workers())
+        """
+        raise NotImplementedError()
+
+    @property
+    def num_workers(self):
+        """Returns the number of worker nodes.
+
+        Returns
+        -------
+        size :int
+            The number of worker nodes.
+        """
+        raise NotImplementedError()
+
+    kv_registry = {}
+
+    @staticmethod
+    def register(klass):
+        """Registers a new KVStore.
+        Once a kvstore is registered, we can create an instance of this
+        kvstore with `create` later.
+
+        Examples
+        --------
+        >>> @mx.kvstore.KVStoreBase.register
+        ... class MyKVStore(mx.kvstore.KVStoreBase):
+        ...     pass
+        >>> kv = mx.kv.create('MyKVStore')
+        >>> print(type(kv))
+        <class '__main__.MyKVStore'>
+        """
+        assert(isinstance(klass, type))
+        name = klass.__name__.lower()
+        if name in KVStoreBase.kv_registry:
+            warnings.warn('WARNING: New kvstore %s.%s is overriding '
+                          'existing kvstore %s.%s' %
+                          (klass.__module__, klass.__name__,
+                           KVStoreBase.kv_registry[name].__module__,
+                           KVStoreBase.kv_registry[name].__name__))
+        KVStoreBase.kv_registry[name] = klass
+        return klass
+
+@KVStoreBase.register
+class TestStore(KVStoreBase):
+    """A key-value store for testing."""
+
+    def broadcast(self, key, value, out, priority=0):
+        """ Broadcast the `value` NDArray at rank 0 to all ranks,
+        and store the result in `out`
+
+        Parameters
+        ----------
+        key : str or int
+            The key.
+
+        value : NDArray
+            The value corresponding to the key to broadcast
+
+        out : NDArray, or list of NDArray
+            Values corresponding to the key to store the result
+
+        priority : int, optional
+            The priority of the operation.
+            Higher priority operations are likely to be executed before other actions.
+        """
+        out = out if isinstance(out, list) else [out]
+        for o in out:
+            o[:] = value
+
+    def pushpull(self, key, value, out=None, priority=0):
+        """ Performs push and pull a single value or a sequence of values from the store.
+
+        This function is coalesced form of push and pull operations.
+
+        `value` is pushed to the kvstore server for summation with the specified keys,
+        and the results are pulled from the server to `out`. If `out` is not specified
+        the pulled values are written to `value`.
+
+        Parameters
+        ----------
+        key : str or int
+            The key.
+
+        value : NDArray, or list of NDArray
+            Values corresponding to the keys.
+
+        out: NDArray, or list of NDArray
+            Values corresponding to the key.
+
+        priority : int, optional
+            The priority of the operation.
+            Higher priority operations are likely to be executed before other actions.
+        """
+        ctx = value[0].context
+        if isinstance(value, NDArray):
+            if out is not None:
+                out = out if isinstance(out, list) else [out]
+                for o in out:
+                    o[:] = value
+        else:
+            reduced_value = sum([val.as_in_context(ctx) for val in value])
+            if out is None:
+                for v in value:
+                    v[:] = reduced_value
+            else:
+                out = out if isinstance(out, list) else [out]
+                for o in out:
+                    o[:] = reduced_value
+
+    @staticmethod
+    def is_capable(capability):
+        """Queries if the KVStore type supports certain capability, such as optimizer algorithm,
+        gradient compression, sparsity, etc.
+
+        Parameters
+        ----------
+        capability: str
+            The capability to query
+
+        Returns
+        -------
+        result : bool
+            Whether the capability is supported or not.
+        """
+        if capability.lower() == KVStoreBase.OPTIMIZER:
+            return False
+        else:
+            raise ValueError('Unknown capability: {}'.format(capability))
+
+    @property
+    def type(self):
+        """ Returns the type of this kvstore.
+
+        Returns
+        -------
+        type : str
+            the string type
+        """
+        return 'teststore'
+
+    @property
+    def rank(self):
+        """ Returns the rank of this worker node.
+
+        Returns
+        -------
+        rank : int
+            The rank of this node, which is in range [0, num_workers())
+        """
+        return 0
+
+    @property
+    def num_workers(self):
+        """Returns the number of worker nodes.
+
+        Returns
+        -------
+        size :int
+            The number of worker nodes.
+        """
+        return 1
+
+    def set_optimizer(self, optimizer):
+        """ Registers an optimizer with the kvstore.
+
+        When using a single machine, this function updates the local optimizer.
+        If using multiple machines and this operation is invoked from a worker node,
+        it will serialized the optimizer with pickle and send it to all servers.
+        The function returns after all servers have been updated.
+
+        Parameters
+        ----------
+        optimizer : KVStoreBase
+            The new optimizer for the store
+        """
+        raise NotImplementedError()
+
+    def save_optimizer_states(self, fname, dump_optimizer=False):
+        """Saves the optimizer (updater) state to a file. This is often used when checkpointing
+        the model during training.
+
+        Parameters
+        ----------
+        fname : str
+            Path to the output states file.
+        dump_optimizer : bool, default False
+            Whether to also save the optimizer itself. This would also save optimizer
+            information such as learning rate and weight decay schedules.
+        """
+        raise NotImplementedError()
+
+    def load_optimizer_states(self, fname):
+        """Loads the optimizer (updater) state from the file.
+
+        Parameters
+        ----------
+        fname : str
+            Path to input states file.
+        """
+        raise NotImplementedError()
+
+def create(name='local'):
+    """Creates a new KVStore.
+
+    For single machine training, there are two commonly used types:
+
+    ``local``: Copies all gradients to CPU memory and updates weights there.
+
+    ``device``: Aggregates gradients and updates weights on GPUs. With this setting,
+    the KVStore also attempts to use GPU peer-to-peer communication,
+    potentially accelerating the communication.
+
+    For distributed training, KVStore also supports a number of types:
+
+    ``dist_sync``: Behaves similarly to ``local`` but with one major difference.
+    With ``dist_sync``, batch-size now means the batch size used on each machine.
+    So if there are ``n`` machines and we use batch size ``b``,
+    then ``dist_sync`` behaves like ``local`` with batch size ``n * b``.
+
+    ``dist_device_sync``: Identical to ``dist_sync`` with the difference similar
+    to ``device`` vs ``local``.
+
+    ``dist_async``: Performs asynchronous updates.
+    The weights are updated whenever gradients are received from any machine.
+    No two updates happen on the same weight at the same time. However, the order is not
+    guaranteed.
+
+    Parameters
+    ----------
+    name : {'local', 'device', 'nccl', 'dist_sync', 'dist_device_sync', 'dist_async', 'horovod'}
+        The type of KVStore.
+    Returns
+    -------
+    kv : KVStoreBase
+        The created KVStore.
+    """
+    if not isinstance(name, string_types):
+        raise TypeError('name must be a string')
+    name = name.lower()
+    # first lookup the registry
+    if name in KVStoreBase.kv_registry:
+        return KVStoreBase.kv_registry[name]()
+    else:
+        # fall back to the native kvstore implementation
+        handle = KVStoreHandle()
+        check_call(_LIB.MXKVStoreCreate(c_str(name),
+                                        ctypes.byref(handle)))
+        from .kvstore import KVStore
+        kv = KVStore(handle)
+        set_kvstore_handle(kv.handle)
+        return kv
diff --git a/python/mxnet/kvstore.py b/python/mxnet/kvstore/kvstore.py
similarity index 85%
rename from python/mxnet/kvstore.py
rename to python/mxnet/kvstore/kvstore.py
index 61c64ec0984f..236ce773e547 100644
--- a/python/mxnet/kvstore.py
+++ b/python/mxnet/kvstore/kvstore.py
@@ -19,58 +19,17 @@
 """ Key value store interface of MXNet for parameter synchronization."""
 from __future__ import absolute_import
 
-from array import array
-import ctypes
 import pickle
-from .ndarray import NDArray
-from .ndarray import _ndarray_cls
-from .base import _LIB, c_str_array, c_handle_array, c_array, c_array_buf, c_str
-from .base import check_call, string_types, mx_uint, py_str
-from .base import NDArrayHandle, KVStoreHandle
-from . import optimizer as opt
-from .profiler import set_kvstore_handle
-
-def _ctype_key_value(keys, vals):
-    """Returns ctype arrays for the key-value args, and the whether string keys are used.
-    For internal use only.
-    """
-    if isinstance(keys, (tuple, list)):
-        assert(len(keys) == len(vals))
-        c_keys = []
-        c_vals = []
-        use_str_keys = None
-        for key, val in zip(keys, vals):
-            c_key_i, c_val_i, str_keys_i = _ctype_key_value(key, val)
-            c_keys += c_key_i
-            c_vals += c_val_i
-            use_str_keys = str_keys_i if use_str_keys is None else use_str_keys
-            assert(use_str_keys == str_keys_i), "inconsistent types of keys detected."
-        c_keys_arr = c_array(ctypes.c_char_p, c_keys) if use_str_keys \
-                     else c_array(ctypes.c_int, c_keys)
-        c_vals_arr = c_array(ctypes.c_void_p, c_vals)
-        return (c_keys_arr, c_vals_arr, use_str_keys)
-
-    assert(isinstance(keys, (int,) + string_types)), \
-           "unexpected type for keys: " + str(type(keys))
-    use_str_keys = isinstance(keys, string_types)
-    if isinstance(vals, NDArray):
-        c_keys = c_str_array([keys]) if use_str_keys \
-                 else c_array_buf(ctypes.c_int, array('i', [keys]))
-        return (c_keys, c_handle_array([vals]), use_str_keys)
-    else:
-        for value in vals:
-            assert(isinstance(value, NDArray))
-        c_keys = c_str_array([keys] * len(vals)) if use_str_keys \
-                 else c_array_buf(ctypes.c_int, array('i', [keys] * len(vals)))
-        return (c_keys, c_handle_array(vals), use_str_keys)
-
-def _ctype_dict(param_dict):
-    """Returns ctype arrays for keys and values(converted to strings) in a dictionary"""
-    assert(isinstance(param_dict, dict)), \
-        "unexpected type for param_dict: " + str(type(param_dict))
-    c_keys = c_array(ctypes.c_char_p, [c_str(k) for k in param_dict.keys()])
-    c_vals = c_array(ctypes.c_char_p, [c_str(str(v)) for v in param_dict.values()])
-    return (c_keys, c_vals)
+import ctypes
+from ..ndarray import NDArray
+from ..ndarray import _ndarray_cls
+from ..base import _LIB, c_str
+from ..base import check_call, mx_uint, py_str
+from ..base import NDArrayHandle, KVStoreHandle
+from .. import optimizer as opt
+from .base import _ctype_key_value, _ctype_dict, KVStoreBase
+
+__all__ = ['KVStore']
 
 def _updater_wrapper(updater):
     """A wrapper for the user-defined handle."""
@@ -91,8 +50,10 @@ def _get_kvstore_server_command_type(command):
     assert (command in command_types), "Unknown command type to send to server"
     return command_types[command]
 
-class KVStore(object):
+
+class KVStore(KVStoreBase):
     """A key-value store for synchronization of values, over multiple devices."""
+
     def __init__(self, handle):
         """Initializes a new KVStore.
 
@@ -110,6 +71,62 @@ def __init__(self, handle):
     def __del__(self):
         check_call(_LIB.MXKVStoreFree(self.handle))
 
+    def broadcast(self, key, value, out, priority=0):
+        """ Broadcast the `value` NDArray at rank 0 to all ranks,
+        and store the result in `out`.
+
+        Note that the native KVStore does not support broadcasting the same key more than once.
+
+        Parameters
+        ----------
+        key : str, or int
+            The key.
+
+        value : NDArray
+            The value corresponding to the key to broadcast
+
+        out : NDArray, list of NDArray
+            Values corresponding to the key to store the result
+
+        priority : int, optional
+            The priority of the operation.
+            Higher priority operations are likely to be executed before other actions.
+
+        Examples
+        --------
+        >>> # broadcast a single key-value pair
+        >>> shape = (2,3)
+        >>> kv = mx.kv.create('local')
+        >>> a = mx.nd.zeros(shape)
+        >>> kv.broadcast('3', mx.nd.ones(shape)*2, out=a)
+        >>> print a.asnumpy()
+        [[ 2.  2.  2.]
+        [ 2.  2.  2.]]
+
+        """
+        self.init(key, value)
+        self.pull(key, out=out, priority=priority)
+
+    @staticmethod
+    def is_capable(capability):
+        """Queries if the KVStore type supports certain capability, such as optimizer algorithm,
+        gradient compression, sparsity, etc.
+
+        Parameters
+        ----------
+        capability: str
+            The capability to query
+
+        Returns
+        -------
+        result : bool
+            Whether the capability is supported or not.
+        """
+        if capability.lower() == KVStoreBase.OPTIMIZER:
+            return True
+        else:
+            raise ValueError('Unknown capability: {}'.format(capability))
+
     def init(self, key, value):
         """ Initializes a single or a sequence of key-value pairs into the store.
 
@@ -327,27 +344,25 @@ def pushpull(self, key, value, out=None, priority=0):
         key : str, int, or sequence of str or int
             Keys.
 
-        value : NDArray, RowSparseNDArray, list of NDArray or RowSparseNDArray,
-                or list of list of NDArray or RowSparseNDArray
+        value : NDArray, list of NDArray, or list of list of NDArray
             Values corresponding to the keys.
 
         out: NDArray or list of NDArray or list of list of NDArray
             Values corresponding to the keys.
 
         priority : int, optional
-            The priority of the pull operation.
-            Higher priority pull operations are likely to be executed before
-            other pull actions.
+            The priority of the operation.
+            Higher priority operations are likely to be executed before other actions.
 
         Examples
         --------
-        >>> # push a single key-value pair
+        >>> # pushpull a single key-value pair
         >>> kv.pushpull('3', mx.nd.ones(shape)*8, out=a)
         >>> print a.asnumpy()
         [[ 8.  8.  8.]
         [ 8.  8.  8.]]
 
-        >>> # aggregate the value and the push
+        >>> # aggregate the value and then pushpull
         >>> gpus = [mx.gpu(i) for i in range(4)]
         >>> b = [mx.nd.ones(shape, gpu) for gpu in gpus]
         >>> kv.pushpull('3', b, out=a)
@@ -355,11 +370,11 @@ def pushpull(self, key, value, out=None, priority=0):
         [[ 4.  4.  4.]
         [ 4.  4.  4.]]
 
-        >>> # push a list of keys.
+        >>> # pushpull a list of keys.
         >>> # single device
         >>> keys = ['4', '5', '6']
         >>> b = [mx.nd.zeros(shape)]*len(keys)
-        >>> kv.push(keys, [mx.nd.ones(shape)]*len(keys), out=b)
+        >>> kv.pushpull(keys, [mx.nd.ones(shape)]*len(keys), out=b)
         >>> print b[1].asnumpy()
         [[ 1.  1.  1.]
         [ 1.  1.  1.]]
@@ -371,8 +386,8 @@ def pushpull(self, key, value, out=None, priority=0):
         >>> print b[1][1].asnumpy()
         [[ 4.  4.  4.]
         [ 4.  4.  4.]]
-        """
 
+        """
         cvkeys, cvals, use_str_keys = _ctype_key_value(key, value)
         if out is not None:
             cokeys, couts, _ = _ctype_key_value(key, out)
@@ -709,47 +724,3 @@ def _send_command_to_servers(self, head, body):
         """
         check_call(_LIB.MXKVStoreSendCommmandToServers(
             self.handle, mx_uint(head), c_str(body)))
-
-def create(name='local'):
-    """Creates a new KVStore.
-
-    For single machine training, there are two commonly used types:
-
-    ``local``: Copies all gradients to CPU memory and updates weights there.
-
-    ``device``: Aggregates gradients and updates weights on GPUs. With this setting,
-    the KVStore also attempts to use GPU peer-to-peer communication,
-    potentially accelerating the communication.
-
-    For distributed training, KVStore also supports a number of types:
-
-    ``dist_sync``: Behaves similarly to ``local`` but with one major difference.
-    With ``dist_sync``, batch-size now means the batch size used on each machine.
-    So if there are ``n`` machines and we use batch size ``b``,
-    then ``dist_sync`` behaves like ``local`` with batch size ``n * b``.
-
-    ``dist_device_sync``: Identical to ``dist_sync`` with the difference similar
-    to ``device`` vs ``local``.
-
-    ``dist_async``: Performs asynchronous updates.
-    The weights are updated whenever gradients are received from any machine.
-    No two updates happen on the same weight at the same time. However, the order is not
-    guaranteed.
-
-    Parameters
-    ----------
-    name : {'local', 'device', 'nccl', 'dist_sync', 'dist_device_sync', 'dist_async'}
-        The type of KVStore.
-    Returns
-    -------
-    kv : KVStore
-        The created KVStore.
-    """
-    if not isinstance(name, string_types):
-        raise TypeError('name must be a string')
-    handle = KVStoreHandle()
-    check_call(_LIB.MXKVStoreCreate(c_str(name),
-                                    ctypes.byref(handle)))
-    kv = KVStore(handle)
-    set_kvstore_handle(kv.handle)
-    return kv
diff --git a/python/mxnet/kvstore_server.py b/python/mxnet/kvstore/kvstore_server.py
similarity index 97%
rename from python/mxnet/kvstore_server.py
rename to python/mxnet/kvstore/kvstore_server.py
index 2504b4674a83..592d2500755f 100644
--- a/python/mxnet/kvstore_server.py
+++ b/python/mxnet/kvstore/kvstore_server.py
@@ -22,8 +22,10 @@
 import sys
 import pickle
 import logging
-from .base import _LIB, check_call
-from .kvstore import create
+from ..base import _LIB, check_call
+from .base import create
+
+__all__ = ['KVStoreServer']
 
 class KVStoreServer(object):
     """The key-value store server."""
diff --git a/python/mxnet/model.py b/python/mxnet/model.py
index f718db080a9c..51ab576a7356 100644
--- a/python/mxnet/model.py
+++ b/python/mxnet/model.py
@@ -98,6 +98,10 @@ def _create_kvstore(kvstore, num_device, arg_params):
     elif isinstance(kvstore, kvs.KVStore):
         kv = kvstore
     elif isinstance(kvstore, str):
+        if kvstore in kvs.KVStoreBase.kv_registry:
+            # we do not assume all custom kvstore supports
+            # updates on kvstore with optimizers
+            return (kvs.create(kvstore), False)
         # create kvstore using the string type
         if num_device == 1 and 'dist' not in kvstore:
             # no need to use kv for single device and single machine
@@ -110,6 +114,10 @@ def _create_kvstore(kvstore, num_device, arg_params):
                                arg_params.values())
                 if max_size > 1024 * 1024 * 16:
                     update_on_kvstore = False
+    elif isinstance(kvstore, kvs.KVStoreBase):
+        # we do not assume all custom kvstore supports
+        # updates on kvstore with optimizers
+        return (kvstore, False)
     else:
         raise TypeError('kvstore must be KVStore, str or None')
 
@@ -122,10 +130,10 @@ def _initialize_kvstore(kvstore, param_arrays, arg_params, param_names, update_o
     """Initialize kvstore"""
     for idx, param_on_devs in enumerate(param_arrays):
         name = param_names[idx]
-        kvstore.init(name, arg_params[name])
-
-        if update_on_kvstore:
-            kvstore.pull(name, param_on_devs, priority=-idx)
+        if not update_on_kvstore or arg_params[name].stype != 'default':
+            kvstore.init(name, arg_params[name])
+        else:
+            kvstore.broadcast(name, arg_params[name], out=param_on_devs)
 
 def _update_params_on_kvstore_nccl(param_arrays, grad_arrays, kvstore, param_names):
     """Perform update of param_arrays from grad_arrays on NCCL kvstore."""
@@ -142,9 +150,9 @@ def _update_params_on_kvstore_nccl(param_arrays, grad_arrays, kvstore, param_nam
     while start < size:
         end = start + batch if start + batch < size else size
         # push gradient, priority is negative index
-        kvstore.push(valid_param_names[start:end], valid_grad_arrays[start:end], priority=-start)
         # pull back the weights
-        kvstore.pull(valid_param_names[start:end], valid_param_arrays[start:end], priority=-start)
+        kvstore.pushpull(valid_param_names[start:end], valid_grad_arrays[start:end],
+                         out=valid_param_arrays[start:end], priority=-start)
         start = end
 
 def _update_params_on_kvstore(param_arrays, grad_arrays, kvstore, param_names):
@@ -155,9 +163,12 @@ def _update_params_on_kvstore(param_arrays, grad_arrays, kvstore, param_names):
             continue
         name = param_names[index]
         # push gradient, priority is negative index
-        kvstore.push(name, grad_list, priority=-index)
         # pull back the weights
-        kvstore.pull(name, arg_list, priority=-index)
+        if grad_list[0].stype == 'default' and arg_list[0].stype == 'default':
+            kvstore.pushpull(name, grad_list, out=arg_list, priority=-index)
+        else:
+            kvstore.push(name, grad_list, priority=-index)
+            kvstore.pull(name, out=arg_list, priority=-index)
 
 def _update_params(param_arrays, grad_arrays, updater, num_device,
                    kvstore=None, param_names=None):
@@ -171,9 +182,11 @@ def _update_params(param_arrays, grad_arrays, updater, num_device,
         if kvstore:
             name = param_names[index]
             # push gradient, priority is negative index
-            kvstore.push(name, grad_list, priority=-index)
-            # pull back the sum gradients, to the same locations.
-            kvstore.pull(name, grad_list, priority=-index)
+            if grad_list[0].stype == 'default' and arg_list[0].stype == 'default':
+                kvstore.pushpull(name, grad_list, priority=-index)
+            else:
+                kvstore.push(name, grad_list, priority=-index)
+                kvstore.pull(name, out=grad_list, priority=-index)
         for k, p in enumerate(zip(arg_list, grad_list)):
             # faked an index here, to make optimizer create diff
             # state for the same index but on diff devs, TODO(mli)
diff --git a/src/kvstore/kvstore_local.h b/src/kvstore/kvstore_local.h
index ad70bc15ea0a..d6d37f0a142b 100644
--- a/src/kvstore/kvstore_local.h
+++ b/src/kvstore/kvstore_local.h
@@ -196,7 +196,9 @@ class KVStoreLocal : public KVStore {
                         const std::vector<NDArray>& values) {
     for (size_t i = 0; i < keys.size(); ++i) {
       CHECK(local_.find(keys[i]) == local_.end())
-          << "duplicate init of key " << keys[i];
+          << "duplicate init of key " << keys[i]
+          << ". Please double check if you called kv.init or kv.broadcast with this key "
+          << "multiple times";
       local_[keys[i]] = values[i].Copy(pinned_ctx_);
       comm_->Init(keys[i], values[i].storage_type(), values[i].shape(), values[i].dtype());
     }
diff --git a/tests/nightly/dist_device_sync_kvstore_custom.py b/tests/nightly/dist_device_sync_kvstore_custom.py
new file mode 100644
index 000000000000..05ba2610ba75
--- /dev/null
+++ b/tests/nightly/dist_device_sync_kvstore_custom.py
@@ -0,0 +1,96 @@
+#!/usr/bin/env python
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import sys
+sys.path.insert(0, "../../python/")
+import mxnet as mx
+import numpy as np
+import numpy.random as rnd
+import time
+import argparse
+
+# parser
+parser = argparse.ArgumentParser(description='kvstore test')
+parser.add_argument('--name', type=str, default='dist_device_sync')
+args = parser.parse_args()
+
+def check_diff_to_scalar(A, x, rank=None):
+    """ assert A == x"""
+    assert(np.sum(np.abs((A - x).asnumpy())) == 0), (rank, A.asnumpy(), x)
+
+# setup
+keys = ['3', '5', '7']
+init_test_keys = [str(i) for i in range(200,300)]
+init_test_keys_big = [str(i) for i in range(300,400)]
+init_test_keys_device = [str(i) for i in range(400,500)]
+init_test_keys_device_big = [str(i) for i in range(500,600)]
+
+shape = (2, 3)
+big_shape = (1200, 1200)        # bigger than MXNET_KVSTORE_BIGARRAY_BOUND
+
+kv = mx.kv.create(args.name)
+my_rank = kv.rank
+my_num_workers = kv.num_workers
+
+def test_pushpull():
+    num_gpus = 2
+    def check_default_keys(nrepeat=3):
+        # init kv dns keys
+        kv.broadcast('3', mx.nd.ones(shape, ctx=mx.gpu()), mx.nd.ones(shape, ctx=mx.gpu()))
+        kv.broadcast('99', mx.nd.ones(big_shape, ctx=mx.gpu()), mx.nd.ones(big_shape, ctx=mx.gpu()))
+        for i in range(nrepeat):
+            scale = my_rank + 1
+            num = (my_num_workers + 1) * my_num_workers * num_gpus / 2
+
+            arrs = [mx.nd.ones(shape, ctx=mx.gpu(j)) * scale for j in range(num_gpus)]
+            # inplace
+            kv.pushpull('3', arrs)
+            for arr in arrs:
+                check_diff_to_scalar(arr, num)
+
+            big_arrs = [mx.nd.ones(big_shape, ctx=mx.gpu(j)) * scale for j in range(num_gpus)]
+            # inplace
+            kv.pushpull('99', big_arrs)
+            for big_arr in big_arrs:
+                check_diff_to_scalar(big_arr, num)
+
+    check_default_keys(nrepeat=3)
+    print('worker ' + str(my_rank) + ' is done')
+
+def test_broadcast():
+    def check_broadcast(kv, cur_keys, cur_shape, device=False):
+        ctx = mx.gpu(0) if device else mx.cpu(0)
+        val = [mx.nd.zeros(cur_shape, ctx) for i in cur_keys]
+        for i in range(len(cur_keys)):
+            expected = i
+            kv.broadcast(cur_keys[i], [mx.nd.ones(cur_shape, ctx) * i], out=val[i])
+            check_diff_to_scalar(val[i], expected, my_rank)
+    check_broadcast(kv, init_test_keys, shape)
+    check_broadcast(kv, init_test_keys_big, big_shape)
+    check_broadcast(kv, init_test_keys_device, shape, device=True)
+    check_broadcast(kv, init_test_keys_device_big, big_shape, device=True)
+    print('worker ' + str(my_rank) + ' is initialized')
+
+def test_type():
+    assert kv.type == args.name
+
+if __name__ == "__main__":
+    test_type()
+    test_broadcast()
+    test_push_pull()
diff --git a/tests/python/unittest/test_gluon_trainer.py b/tests/python/unittest/test_gluon_trainer.py
index 9f02733d0a25..fbd04ee1beec 100644
--- a/tests/python/unittest/test_gluon_trainer.py
+++ b/tests/python/unittest/test_gluon_trainer.py
@@ -26,6 +26,11 @@
 from copy import deepcopy
 from nose.tools import raises, assert_raises
 
+def dict_equ(a, b):
+    assert set(a) == set(b)
+    for k in a:
+        assert (a[k].asnumpy() == b[k].asnumpy()).all()
+
 @with_seed()
 @raises(RuntimeError)
 def test_multi_trainer():
@@ -41,12 +46,27 @@ def test_multi_trainer():
     # multiple trainers for a sparse Parameter is not allowed
     trainer1 = gluon.Trainer([x], 'sgd')
 
+@with_seed()
+def test_trainer_with_teststore():
+    x = gluon.Parameter('x', shape=(10,))
+    x.initialize(ctx=[mx.cpu(0), mx.cpu(1)], init='zeros')
+    kv = mx.kv.create('teststore')
+    trainer = gluon.Trainer([x], 'sgd', {'learning_rate': 1.0, 'momentum': 0.5}, kvstore=kv)
+    with mx.autograd.record():
+        for w in x.list_data():
+            y = w + 1
+            y.backward()
+    trainer.step(1)
+
+    assert trainer._update_on_kvstore == False
+    assert (x.data(mx.cpu(1)).asnumpy() == -2).all()
+    # Expect exceptions if update_on_kvstore is set to True,
+    # because TestStore does not support that
+    invalid_trainer = gluon.Trainer([x], 'sgd', kvstore=kv, update_on_kvstore=True)
+    assert_raises(ValueError, invalid_trainer._init_kvstore)
+
 @with_seed()
 def test_trainer():
-    def dict_equ(a, b):
-        assert set(a) == set(b)
-        for k in a:
-            assert (a[k].asnumpy() == b[k].asnumpy()).all()
     x = gluon.Parameter('x', shape=(10,))
     x.initialize(ctx=[mx.cpu(0), mx.cpu(1)], init='zeros')
     trainer = gluon.Trainer([x], 'sgd', {'learning_rate': 1.0, 'momentum': 0.5})
@@ -120,7 +140,8 @@ def test_trainer_save_load():
 
 @with_seed()
 def test_trainer_sparse_save_load():
-    x = gluon.Parameter('x', shape=(10, 1), lr_mult=1.0, stype='row_sparse')
+    x = gluon.Parameter('x', shape=(10, 1), lr_mult=1.0,
+                        stype='row_sparse', grad_stype='row_sparse')
     x.initialize(ctx=[mx.cpu(0)], init='zeros')
     trainer = gluon.Trainer([x], 'sgd', {'learning_rate': 0.1})
     all_rows = mx.nd.arange(0, 10, ctx=mx.cpu(0))
@@ -236,7 +257,7 @@ def check_trainer_sparse_kv(kv, stype, grad_stype, update_on_kv, expected):
             # the updated parameter should be based on the loaded checkpoint
             mx.nd.waitall()
             updated_w = x.data(mx.cpu(0)) if stype == 'default' else x.row_sparse_data(all_rows)
-            assert (updated_w == -0.2).asnumpy().all()
+            assert (updated_w == -0.2).asnumpy().all(), updated_w
         except Exception as err:
             assert isinstance(err, expected)
 
diff --git a/tests/python/unittest/test_kvstore_custom.py b/tests/python/unittest/test_kvstore_custom.py
new file mode 100644
index 000000000000..4f1f309d24c1
--- /dev/null
+++ b/tests/python/unittest/test_kvstore_custom.py
@@ -0,0 +1,195 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# pylint: skip-file
+import mxnet as mx
+import numpy as np
+import unittest
+from mxnet.test_utils import rand_ndarray, assert_almost_equal
+from common import setup_module, with_seed, assertRaises, teardown
+from mxnet.base import py_str, MXNetError
+
+shape = (4, 4)
+keys = [5, 7, 11]
+str_keys = ['b', 'c', 'd']
+
+def check_diff_to_scalar(A, x):
+    """ assert A == x"""
+    assert(np.sum(np.abs((A - x).asnumpy())) == 0), (A, x)
+
+def init_kv(name='device'):
+    return mx.kv.create(name)
+
+@with_seed()
+def test_broadcast_single_kv_pair():
+    """single key-value pair push & pull"""
+    def check_single_kv_pair(kv, key):
+        # single output
+        ones = mx.nd.ones(shape)
+        out = mx.nd.empty(shape)
+        kv.broadcast(key, ones, out)
+        check_diff_to_scalar(out, 1)
+        # list output
+        out_list = [mx.nd.empty(shape)] * 3
+        key_list = key + key
+        kv.broadcast(key_list, ones, out_list)
+        for o in out_list:
+            check_diff_to_scalar(o, 1)
+
+    for name in ['device', 'teststore']:
+        check_single_kv_pair(init_kv(name), 3)
+        check_single_kv_pair(init_kv(name), 'a')
+
+@with_seed()
+def test_broadcast_list_kv_pair():
+    """list key-value pair push & pull"""
+    def check_list_kv_pair(kv, key):
+        ones = [mx.nd.ones(shape)] * len(key)
+        out = [mx.nd.empty(shape)] * len(key)
+        kv.broadcast(key, ones, out)
+        for o in out:
+            check_diff_to_scalar(o, 1)
+        out_list = [[mx.nd.empty(shape)] * 2 for _ in range(len(key))]
+        key_list = [k + k for k in key]
+        kv.broadcast(key_list, ones, out_list)
+        for o in out_list:
+            for oo in o:
+                check_diff_to_scalar(oo, 1)
+
+    check_list_kv_pair(init_kv(), keys)
+    check_list_kv_pair(init_kv(), str_keys)
+
+@with_seed()
+def test_pushpull_single_kv_pair():
+    """aggregate value on muliple devices"""
+    def check_aggregator(kv, key, key_list=None):
+        kv.broadcast(key, mx.nd.zeros(shape), out=mx.nd.empty(shape))
+        # devices
+        num_devs = 4
+        devs = [mx.Context('cpu', i) for i in range(num_devs)]
+
+        # single
+        vals = [mx.nd.ones(shape, d) for d in devs]
+        outs = [mx.nd.empty(shape, d) for d in devs]
+
+        kv.pushpull(key, vals, out=outs)
+        for out in outs:
+            check_diff_to_scalar(out, num_devs)
+
+        # inplace
+        kv.pushpull(key, vals)
+        for val in vals:
+            check_diff_to_scalar(val, num_devs)
+
+        # list
+        if key_list is None:
+            return
+        num_keys = len(key_list)
+        kv.broadcast(key_list, [mx.nd.zeros(shape)] * num_keys,
+                     out=[mx.nd.empty(shape)] * num_keys)
+        vals = [[mx.nd.ones(shape, d)*2.0 for d in devs]] * num_keys
+        outs = [[mx.nd.empty(shape, d) for d in devs]] * num_keys
+        kv.pushpull(key_list, vals, out=outs)
+        for out in outs:
+            for o in out:
+                check_diff_to_scalar(o, num_devs * 2.0)
+
+        # inplace
+        kv.pushpull(key_list, vals)
+        for val in vals:
+            for v in val:
+                check_diff_to_scalar(v, num_devs * 2.0)
+
+    check_aggregator(init_kv('device'), 3, keys)
+    check_aggregator(init_kv('device'), 'a', str_keys)
+    check_aggregator(init_kv('teststore'), 3)
+    check_aggregator(init_kv('teststore'), 'a')
+
+@with_seed()
+def test_pushpull_list_kv_pair():
+    """aggregate value on muliple devices"""
+    def check_aggregator(kv, key, key_list=None):
+        kv.broadcast(key, mx.nd.zeros(shape), out=mx.nd.empty(shape))
+        # devices
+        num_devs = 4
+        devs = [mx.Context('cpu', i) for i in range(num_devs)]
+
+        # single
+        vals = [mx.nd.ones(shape, d) for d in devs]
+        outs = [mx.nd.empty(shape, d) for d in devs]
+
+        kv.pushpull(key, vals, out=outs)
+        for out in outs:
+            check_diff_to_scalar(out, num_devs)
+
+        # list
+        if key_list is None:
+            return
+        num_keys = len(key_list)
+        kv.broadcast(key_list, [mx.nd.zeros(shape)] * num_keys,
+                     out=[mx.nd.empty(shape)] * num_keys)
+        vals = [[mx.nd.ones(shape, d)*2.0 for d in devs]] * num_keys
+        outs = [[mx.nd.empty(shape, d) for d in devs]] * num_keys
+        kv.pushpull(key_list, vals, out=outs)
+        for out in outs:
+            for o in out:
+                check_diff_to_scalar(o, num_devs * 2.0)
+
+    check_aggregator(init_kv('device'), 3, keys)
+    check_aggregator(init_kv('device'), 'a', str_keys)
+    check_aggregator(init_kv('teststore'), 3)
+    check_aggregator(init_kv('teststore'), 'a')
+
+
+@with_seed()
+def test_custom_store():
+    kv = mx.kv.create('teststore')
+    out = mx.nd.empty((1,))
+    kv.broadcast(1, mx.nd.ones((1,)), out=out)
+    check_diff_to_scalar(out, 1)
+    assert type(kv).is_capable('optimizer') == False
+    kv.broadcast(1, mx.nd.ones((1,)), out=out)
+    check_diff_to_scalar(out, 1)
+    arr_list = [mx.nd.empty((1,))] * 2
+    kv.pushpull(1, [mx.nd.ones((1,))] * 2, out=arr_list)
+    for arr in arr_list:
+        check_diff_to_scalar(arr, 2)
+    kv.pushpull(1, arr_list)
+    for arr in arr_list:
+        check_diff_to_scalar(arr, 4)
+
+@with_seed()
+def test_get_type_device():
+    kvtype = 'teststore'
+    kv = mx.kv.create(kvtype)
+    assert kv.type == kvtype
+
+@with_seed()
+def test_set_optimizer():
+    def check_unsupported_methods(kv):
+        assert not kv.is_capable('optimizer')
+        optimizer = mx.optimizer.create('sgd')
+        assertRaises(NotImplementedError, kv.set_optimizer, optimizer)
+        assertRaises(NotImplementedError, kv.save_optimizer_states, 'test')
+        assertRaises(NotImplementedError, kv.load_optimizer_states, 'test')
+
+    kv = mx.kv.create('teststore')
+    check_unsupported_methods(kv)
+
+if __name__ == '__main__':
+    import nose
+    nose.runmodule()

From a01ded2a04d0736d88dd421b2e2c548bb603a917 Mon Sep 17 00:00:00 2001
From: "yuri@FreeBSD" <yurivict@users.noreply.github.com>
Date: Tue, 17 Dec 2019 13:44:34 -0800
Subject: [PATCH 717/813] FreeBSD comparibility patch (#17077)

---
 3rdparty/mshadow/mshadow/packet-inl.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/3rdparty/mshadow/mshadow/packet-inl.h b/3rdparty/mshadow/mshadow/packet-inl.h
index f5a89bfa8421..58cbc4005aaf 100644
--- a/3rdparty/mshadow/mshadow/packet-inl.h
+++ b/3rdparty/mshadow/mshadow/packet-inl.h
@@ -6,7 +6,7 @@
 #ifndef MSHADOW_PACKET_INL_H_
 #define MSHADOW_PACKET_INL_H_
 
-#ifdef __APPLE__
+#if defined(__APPLE__) || defined(__FreeBSD__)
 #include <stdlib.h>
 #else
 #include <malloc.h>

From faa283228d8e4aa391dd0877b7388996e9a0e223 Mon Sep 17 00:00:00 2001
From: Wang Jiajun <wangjiajun.phy@gmail.com>
Date: Wed, 18 Dec 2019 10:57:35 +0800
Subject: [PATCH 718/813] Add im2col and col2im operator (#16502)

* add im2col

* add col2im

* fix typo

* add docs

* add unittest

* more tests

* fix lint

* fix doc

* fix request

* trigger CI
---
 src/operator/nn/im2col-inl.h           | 259 +++++++++++++++++++++++
 src/operator/nn/im2col.cc              | 272 +++++++++++++++++++++++++
 src/operator/nn/im2col.cu              |  45 ++++
 tests/python/unittest/test_operator.py | 147 +++++++++++++
 4 files changed, 723 insertions(+)
 create mode 100644 src/operator/nn/im2col-inl.h
 create mode 100644 src/operator/nn/im2col.cc
 create mode 100644 src/operator/nn/im2col.cu

diff --git a/src/operator/nn/im2col-inl.h b/src/operator/nn/im2col-inl.h
new file mode 100644
index 000000000000..b5caa035f911
--- /dev/null
+++ b/src/operator/nn/im2col-inl.h
@@ -0,0 +1,259 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2015 by Contributors
+ * \file im2col-inl.h
+ * \brief
+ * \author Jiajun Wang
+*/
+
+#ifndef MXNET_OPERATOR_NN_IM2COL_INL_H_
+#define MXNET_OPERATOR_NN_IM2COL_INL_H_
+#include <vector>
+#include "../mxnet_op.h"
+#include "../mshadow_op.h"
+#include "../elemwise_op_common.h"
+#include "./im2col.h"
+
+namespace mxnet {
+namespace op {
+
+struct Im2colParam : public dmlc::Parameter<Im2colParam> {
+  mxnet::TShape kernel;
+  mxnet::TShape stride;
+  mxnet::TShape dilate;
+  mxnet::TShape pad;
+  DMLC_DECLARE_PARAMETER(Im2colParam) {
+    DMLC_DECLARE_FIELD(kernel).describe("Sliding kernel size: (w,), (h, w) or (d, h, w).");
+    DMLC_DECLARE_FIELD(stride).set_default(mxnet::TShape(0, 0))
+    .describe("The stride between adjacent sliding blocks in spatial dimension: "
+              "(w,), (h, w) or (d, h, w). Defaults to 1 for each dimension.");
+    DMLC_DECLARE_FIELD(dilate).set_default(mxnet::TShape(0, 0))
+    .describe("The spacing between adjacent kernel points: (w,), (h, w) or (d, h, w). "
+              "Defaults to 1 for each dimension.");
+    DMLC_DECLARE_FIELD(pad).set_default(mxnet::TShape(0, 0))
+    .describe("The zero-value padding size on both sides of spatial dimension: "
+              "(w,), (h, w) or (d, h, w). Defaults to no padding.");
+  }
+
+  index_t DilatedKernelSize(int dim) const {
+    return 1 + (kernel[dim] - 1) * dilate[dim];
+  }
+};  // struct Im2colParam
+
+
+template<typename xpu>
+void Im2colCompute(const nnvm::NodeAttrs& attrs,
+                   const OpContext& ctx,
+                   const std::vector<TBlob>& inputs,
+                   const std::vector<OpReqType>& req,
+                   const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  const Im2colParam& param = nnvm::get<Im2colParam>(attrs.parsed);
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  const mxnet::TShape im_shape = inputs[0].shape_;
+  const mxnet::TShape col_shape = outputs[0].shape_;
+  const index_t num = im_shape[0];
+
+  const int spatial_size = param.kernel.ndim();
+  mxnet::TShape col_buffer_shape(1 + spatial_size, 1);
+  col_buffer_shape[0] = col_shape[1];
+  for (int i = 0; i < spatial_size; ++i) {
+    const index_t pad_size = im_shape[i + 2] + 2 * param.pad[i];
+    const index_t output_size = (pad_size - param.DilatedKernelSize(i)) / param.stride[i] + 1;
+    col_buffer_shape[i + 1] = output_size;
+  }
+
+  MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    Tensor<xpu, 4, DType> im = inputs[0].get_with_shape<xpu, 4, DType>(
+        Shape4(im_shape[0], im_shape[1], im_shape[2], im_shape[3]), s);
+    Tensor<xpu, 3, DType> col = outputs[0].get_with_shape<xpu, 3, DType>(
+        Shape3(col_shape[0], col_shape[1], col_shape[2]), s);
+
+    if (req[0] == kNullOp) return;
+    if (req[0] != kAddTo) {
+      for (index_t n = 0; n < num; ++n) {
+        im2col(s, im[n].dptr_, im_shape, col_buffer_shape,
+               param.kernel, param.pad, param.stride, param.dilate, col[n].dptr_);
+      }
+    } else {
+      Tensor<xpu, 2, DType> tcol = ctx.requested[0]
+        .get_space_typed<xpu, 2, DType>(Shape2(col_shape[1], col_shape[2]), s);
+      for (index_t n = 0; n < num; ++n) {
+        im2col(s, im[n].dptr_, im_shape, col_buffer_shape,
+               param.kernel, param.pad, param.stride, param.dilate, tcol.dptr_);
+        Tensor<xpu, 2, DType> ocol = col[n];
+        ocol += tcol;
+      }
+    }
+  });
+}
+
+template<typename xpu>
+void Im2colGradCompute(const nnvm::NodeAttrs& attrs,
+                       const OpContext& ctx,
+                       const std::vector<TBlob>& inputs,
+                       const std::vector<OpReqType>& req,
+                       const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  const Im2colParam& param = nnvm::get<Im2colParam>(attrs.parsed);
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+
+  const mxnet::TShape im_shape = outputs[0].shape_;
+  const mxnet::TShape col_shape = inputs[0].shape_;
+  const index_t num = im_shape[0];
+
+  const int spatial_size = param.kernel.ndim();
+  mxnet::TShape col_buffer_shape(1 + spatial_size, 1);
+  col_buffer_shape[0] = col_shape[1];
+  for (int i = 0; i < spatial_size; ++i) {
+    const index_t pad_size = im_shape[i + 2] + 2 * param.pad[i];
+    const index_t output_size = (pad_size - param.DilatedKernelSize(i)) / param.stride[i] + 1;
+    col_buffer_shape[i + 1] = output_size;
+  }
+
+  MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+    Tensor<xpu, 4, DType> im_grad = outputs[0].get_with_shape<xpu, 4, DType>(
+        Shape4(im_shape[0], im_shape[1], im_shape[2], im_shape[3]), s);
+    Tensor<xpu, 3, DType> col_grad = inputs[0].get_with_shape<xpu, 3, DType>(
+        Shape3(col_shape[0], col_shape[1], col_shape[2]), s);
+
+    for (index_t n = 0; n < num; ++n) {
+      col2im(s, col_grad[n].dptr_, im_shape, col_buffer_shape,
+             param.kernel, param.pad, param.stride, param.dilate,
+             im_grad[n].dptr_, req[0]);
+    }
+  });
+}
+
+struct Col2imParam : public dmlc::Parameter<Col2imParam> {
+  mxnet::TShape output_size;
+  mxnet::TShape kernel;
+  mxnet::TShape stride;
+  mxnet::TShape dilate;
+  mxnet::TShape pad;
+  DMLC_DECLARE_PARAMETER(Col2imParam) {
+    DMLC_DECLARE_FIELD(output_size)
+    .describe("The spatial dimension of image array: (w,), (h, w) or (d, h, w).");
+    DMLC_DECLARE_FIELD(kernel).describe("Sliding kernel size: (w,), (h, w) or (d, h, w).");
+    DMLC_DECLARE_FIELD(stride).set_default(mxnet::TShape(0, 0))
+    .describe("The stride between adjacent sliding blocks in spatial dimension: "
+              "(w,), (h, w) or (d, h, w). Defaults to 1 for each dimension.");
+    DMLC_DECLARE_FIELD(dilate).set_default(mxnet::TShape(0, 0))
+    .describe("The spacing between adjacent kernel points: (w,), (h, w) or (d, h, w). "
+              "Defaults to 1 for each dimension.");
+    DMLC_DECLARE_FIELD(pad).set_default(mxnet::TShape(0, 0))
+    .describe("The zero-value padding size on both sides of spatial dimension: "
+              "(w,), (h, w) or (d, h, w). Defaults to no padding.");
+  }
+
+  index_t DilatedKernelSize(int dim) const {
+    return 1 + (kernel[dim] - 1) * dilate[dim];
+  }
+};  // struct Col2imParam
+
+template<typename xpu>
+void Col2imCompute(const nnvm::NodeAttrs& attrs,
+                   const OpContext& ctx,
+                   const std::vector<TBlob>& inputs,
+                   const std::vector<OpReqType>& req,
+                   const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  const Col2imParam& param = nnvm::get<Col2imParam>(attrs.parsed);
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  const mxnet::TShape im_shape = outputs[0].shape_;
+  const mxnet::TShape col_shape = inputs[0].shape_;
+  const index_t num = im_shape[0];
+
+  const int spatial_size = param.kernel.ndim();
+  mxnet::TShape col_buffer_shape(1 + spatial_size, 1);
+  col_buffer_shape[0] = col_shape[1];
+  for (int i = 0; i < spatial_size; ++i) {
+    const index_t pad_size = im_shape[i + 2] + 2 * param.pad[i];
+    const index_t output_size = (pad_size - param.DilatedKernelSize(i)) / param.stride[i] + 1;
+    col_buffer_shape[i + 1] = output_size;
+  }
+
+  MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+    Tensor<xpu, 4, DType> im = outputs[0].get_with_shape<xpu, 4, DType>(
+        Shape4(im_shape[0], im_shape[1], im_shape[2], im_shape[3]), s);
+    Tensor<xpu, 3, DType> col = inputs[0].get_with_shape<xpu, 3, DType>(
+        Shape3(col_shape[0], col_shape[1], col_shape[2]), s);
+
+    for (index_t n = 0; n < num; ++n) {
+      col2im(s, col[n].dptr_, im_shape, col_buffer_shape,
+             param.kernel, param.pad, param.stride, param.dilate,
+             im[n].dptr_, req[0]);
+    }
+  });
+}
+
+template<typename xpu>
+void Col2imGradCompute(const nnvm::NodeAttrs& attrs,
+                       const OpContext& ctx,
+                       const std::vector<TBlob>& inputs,
+                       const std::vector<OpReqType>& req,
+                       const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  const Col2imParam& param = nnvm::get<Col2imParam>(attrs.parsed);
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+
+  const mxnet::TShape im_shape = inputs[0].shape_;
+  const mxnet::TShape col_shape = outputs[0].shape_;
+  const index_t batch_size = im_shape[0];
+
+  const int spatial_size = param.kernel.ndim();
+  mxnet::TShape col_buffer_shape(1 + spatial_size, 1);
+  col_buffer_shape[0] = im_shape[1];
+  for (int i = 0; i < spatial_size; ++i) {
+    const index_t pad_size = im_shape[i + 2] + 2 * param.pad[i];
+    const index_t output_size = (pad_size - param.DilatedKernelSize(i)) / param.stride[i] + 1;
+    col_buffer_shape[i + 1] = output_size;
+  }
+
+  MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    Tensor<xpu, 4, DType> im_grad = inputs[0].get_with_shape<xpu, 4, DType>(
+        Shape4(im_shape[0], im_shape[1], im_shape[2], im_shape[3]), s);
+    Tensor<xpu, 3, DType> col_grad = outputs[0].get_with_shape<xpu, 3, DType>(
+        Shape3(col_shape[0], col_shape[1], col_shape[2]), s);
+
+    if (req[0] == kNullOp) return;
+    if (req[0] != kAddTo) {
+      for (index_t n = 0; n < batch_size; ++n) {
+        im2col(s, im_grad[n].dptr_, im_shape, col_buffer_shape,
+               param.kernel, param.pad, param.stride, param.dilate, col_grad[n].dptr_);
+      }
+    } else {
+      Tensor<xpu, 2, DType> tgrad = ctx.requested[0]
+        .get_space_typed<xpu, 2, DType>(Shape2(col_shape[1], col_shape[2]), s);
+      for (index_t n = 0; n < batch_size; ++n) {
+        im2col(s, im_grad[n].dptr_, im_shape, col_buffer_shape,
+               param.kernel, param.pad, param.stride, param.dilate, tgrad.dptr_);
+        Tensor<xpu, 2, DType> cgrad = col_grad[n];
+        cgrad += tgrad;
+      }
+    }
+  });
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_NN_IM2COL_INL_H_
diff --git a/src/operator/nn/im2col.cc b/src/operator/nn/im2col.cc
new file mode 100644
index 000000000000..ae493f1bc594
--- /dev/null
+++ b/src/operator/nn/im2col.cc
@@ -0,0 +1,272 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2015 by Contributors
+ * \file im2col.cc
+ * \brief
+ * \author Jiajun Wang
+*/
+
+#include "./im2col-inl.h"
+#include "../operator_common.h"
+#include "mxnet/op_attr_types.h"
+
+namespace mxnet {
+namespace op {
+
+DMLC_REGISTER_PARAMETER(Im2colParam);
+DMLC_REGISTER_PARAMETER(Col2imParam);
+
+template<typename PType>
+void SlidingParser(nnvm::NodeAttrs* attrs) {
+  using namespace mshadow;
+  PType param_;
+  try {
+    param_.Init(attrs->dict);
+  } catch (const dmlc::ParamError& e) {
+    std::ostringstream os;
+    os << e.what();
+    os << ", in operator " << attrs->op->name << "("
+       << "name=\"" << attrs->name << "\"";
+    for (const auto& k : attrs->dict) {
+      os << ", " << k.first << "=\"" << k.second << "\"";
+    }
+    os << ")";
+    throw dmlc::ParamError(os.str());
+  }
+
+  if (param_.kernel.ndim() == 1) {
+    if (param_.stride.ndim() == 0) param_.stride = Shape1(1);
+    if (param_.dilate.ndim() == 0) param_.dilate = Shape1(1);
+    if (param_.pad.ndim() == 0) param_.pad = Shape1(0);
+  } else if (param_.kernel.ndim() == 2) {
+    if (param_.stride.ndim() == 0) param_.stride = Shape2(1, 1);
+    if (param_.dilate.ndim() == 0) param_.dilate = Shape2(1, 1);
+    if (param_.pad.ndim() == 0) param_.pad = Shape2(0, 0);
+  } else {
+    CHECK_EQ(param_.kernel.ndim(), 3U) << param_.kernel.ndim() << "D convolution not supported";
+    if (param_.stride.ndim() == 0) param_.stride = Shape3(1, 1, 1);
+    if (param_.dilate.ndim() == 0) param_.dilate = Shape3(1, 1, 1);
+    if (param_.pad.ndim() == 0) param_.pad = Shape3(0, 0, 0);
+  }
+  CHECK_EQ(param_.kernel.ndim(), param_.stride.ndim())
+    << "Stride must have the same number of dimensions with kernel_size,"
+    << "but kernel_size is set to " << param_.kernel << " while stride is "
+    << param_.stride;
+  CHECK_EQ(param_.kernel.ndim(), param_.dilate.ndim())
+    << "Dilate must have the same number of dimensions with kernel_size,"
+    << "but kernel_size is set to " << param_.kernel << " while dilate is "
+    << param_.dilate;
+  CHECK_EQ(param_.kernel.ndim(), param_.pad.ndim())
+    << "Padding must have the same number of dimensions with kernel_size,"
+    << "but kernel_size is set to " << param_.kernel << " while padding is "
+    << param_.pad;
+  attrs->parsed = std::move(param_);
+}
+
+NNVM_REGISTER_OP(im2col)
+.describe(R"(Extract sliding blocks from input array.
+
+This operator is used in vanilla convolution implementation to transform the sliding
+blocks on image to column matrix, then the convolution operation can be computed
+by matrix multiplication between column and convolution weight. Due to the close
+relation between im2col and convolution, the concept of **kernel**, **stride**,
+**dilate** and **pad** in this operator are inherited from convolution operation.
+
+Given the input data of shape :math:`(N, C, *)`, where :math:`N` is the batch size,
+:math:`C` is the channel size, and :math:`*` is the arbitrary spatial dimension,
+the output column array is always with shape :math:`(N, C \times \prod(\text{kernel}), W)`,
+where :math:`C \times \prod(\text{kernel})` is the block size, and :math:`W` is the
+block number which is the spatial size of the convolution output with same input parameters.
+Only 1-D, 2-D and 3-D of spatial dimension is supported in this operator.
+
+)" ADD_FILELINE)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser(SlidingParser<Im2colParam>)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+    [](const NodeAttrs& attrs) {
+  return std::vector<std::string>{"data"};
+})
+.set_attr<nnvm::FListOutputNames>("FListOutputNames",
+    [](const NodeAttrs& attrs) {
+  return std::vector<std::string>{"output"};
+})
+.set_attr<mxnet::FInferShape>("FInferShape", [](const nnvm::NodeAttrs& attrs,
+      mxnet::ShapeVector *in_shape, mxnet::ShapeVector *out_shape){
+  using namespace mshadow;
+  CHECK_EQ(in_shape->size(), 1U);
+  const Im2colParam& param = nnvm::get<Im2colParam>(attrs.parsed);
+  if (mxnet::op::shape_is_none(in_shape->at(0))) {
+    return false;
+  }
+
+  CHECK_GT(param.kernel.Size(), 0U) \
+    << "incorrect kernel size: " << param.kernel;
+  CHECK_GT(param.stride.Size(), 0U) \
+    << "incorrect stride size: " << param.stride;
+  CHECK_GT(param.dilate.Size(), 0U) \
+    << "incorrect dilate size: " << param.dilate;
+
+  index_t out_dim = 1;
+  mxnet::TShape dshape(in_shape->at(0));
+  for (int i = 0; i < param.kernel.ndim(); ++i) {
+    const index_t pad_size = dshape[i + 2] + 2 * param.pad[i];
+    const index_t dilated_kernel_size = param.DilatedKernelSize(i);
+    CHECK_LE(dilated_kernel_size, pad_size)
+      << "kernel size exceed input";
+    const index_t output_size = (pad_size - dilated_kernel_size) / param.stride[i] + 1;
+    out_dim *= output_size;
+  }
+  SHAPE_ASSIGN_CHECK(*out_shape, 0, Shape3(dshape[0], dshape[1] * param.kernel.Size(), out_dim));
+  return true;
+})
+.set_attr<nnvm::FInferType>("FInferType", [](const nnvm::NodeAttrs& attrs,
+      std::vector<int> *in_type, std::vector<int> *out_type) {
+  CHECK_EQ(in_type->size(), 1U);
+  if (mxnet::op::type_is_none(in_type->at(0))) {
+    return false;
+  }
+
+  int dtype = in_type->at(0);
+  TYPE_ASSIGN_CHECK(*out_type, 0, dtype);
+  return true;
+})
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+})
+.set_attr<FCompute>("FCompute<cpu>", Im2colCompute<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_im2col"})
+.add_argument("data", "NDArray-or-Symbol", "Input array to extract sliding blocks.")
+.add_arguments(Im2colParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_backward_im2col)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser(SlidingParser<Im2colParam>)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FCompute>("FCompute<cpu>", Im2colGradCompute<cpu>);
+
+NNVM_REGISTER_OP(col2im)
+.describe(R"(Combining the output column matrix of im2col back to image array.
+
+Like :class:`~mxnet.ndarray.im2col`, this operator is also used in the vanilla convolution
+implementation. Despite the name, col2im is not the reverse operation of im2col. Since there
+may be overlaps between neighbouring sliding blocks, the column elements cannot be directly
+put back into image. Instead, they are accumulated (i.e., summed) in the input image
+just like the gradient computation, so col2im is the gradient of im2col and vice versa.
+
+Using the notation in im2col, given an input column array of shape
+:math:`(N, C \times  \prod(\text{kernel}), W)`, this operator accumulates the column elements
+into output array of shape :math:`(N, C, \text{output_size}[0], \text{output_size}[1], \dots)`.
+Only 1-D, 2-D and 3-D of spatial dimension is supported in this operator.
+
+)" ADD_FILELINE)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser(SlidingParser<Col2imParam>)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+    [](const NodeAttrs& attrs) {
+  return std::vector<std::string>{"data"};
+})
+.set_attr<nnvm::FListOutputNames>("FListOutputNames",
+    [](const NodeAttrs& attrs) {
+  return std::vector<std::string>{"output"};
+})
+.set_attr<mxnet::FInferShape>("FInferShape", [](const nnvm::NodeAttrs& attrs,
+      mxnet::ShapeVector *in_shape, mxnet::ShapeVector *out_shape){
+  using namespace mshadow;
+  CHECK_EQ(in_shape->size(), 1U);
+  const Col2imParam& param = nnvm::get<Col2imParam>(attrs.parsed);
+  if (mxnet::op::shape_is_none(in_shape->at(0))) {
+    return false;
+  }
+
+  CHECK_EQ(param.kernel.ndim(), param.output_size.ndim())
+    << "Output size must have the same number of dimensions with kernel_size,"
+    << "but kernel_size is set to " << param.kernel << " while output size is "
+    << param.output_size;
+
+  CHECK_GT(param.output_size.Size(), 0U) \
+    << "incorrect output size: " << param.output_size;
+  CHECK_GT(param.kernel.Size(), 0U) \
+    << "incorrect kernel size: " << param.kernel;
+  CHECK_GT(param.stride.Size(), 0U) \
+    << "incorrect stride size: " << param.stride;
+  CHECK_GT(param.dilate.Size(), 0U) \
+    << "incorrect dilate size: " << param.dilate;
+
+  const int spatial_size = param.kernel.ndim();
+  mxnet::TShape dshape(in_shape->at(0));
+
+  index_t out_dim = 1;
+  for (int i = 0; i < spatial_size; ++i) {
+    const index_t pad_size = param.output_size[i] + 2 * param.pad[i];
+    const index_t dilated_kernel_size = param.DilatedKernelSize(i);
+    CHECK_LE(dilated_kernel_size, pad_size)
+      << "kernel size exceed output size";
+    const index_t output_size = (pad_size - dilated_kernel_size) / param.stride[i] + 1;
+    out_dim *= output_size;
+  }
+
+  CHECK_EQ(dshape[2], out_dim)
+    << "output size does not match convolution parameters";
+  CHECK_EQ(dshape[1] % param.kernel.Size(), 0)
+    << "the second dim of input shape should be multiples of kernel size";
+
+  mxnet::TShape oshape(param.kernel.ndim() + 2, 1);
+  oshape[0] = dshape[0];
+  oshape[1] = dshape[1] / param.kernel.Size();
+  for (int i = 0; i < spatial_size; ++i) {
+    oshape[i + 2] = param.output_size[i];
+  }
+  SHAPE_ASSIGN_CHECK(*out_shape, 0, oshape);
+  return true;
+})
+.set_attr<nnvm::FInferType>("FInferType", [](const nnvm::NodeAttrs& attrs,
+      std::vector<int> *in_type, std::vector<int> *out_type) {
+  CHECK_EQ(in_type->size(), 1U);
+  if (mxnet::op::type_is_none(in_type->at(0))) {
+    return false;
+  }
+
+  int dtype = in_type->at(0);
+  TYPE_ASSIGN_CHECK(*out_type, 0, dtype);
+  return true;
+})
+.set_attr<FCompute>("FCompute<cpu>", Col2imCompute<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_col2im"})
+.add_argument("data", "NDArray-or-Symbol", "Input array to combine sliding blocks.")
+.add_arguments(Col2imParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_backward_col2im)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser(SlidingParser<Col2imParam>)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+})
+.set_attr<FCompute>("FCompute<cpu>", Col2imGradCompute<cpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/nn/im2col.cu b/src/operator/nn/im2col.cu
new file mode 100644
index 000000000000..94d5b504611f
--- /dev/null
+++ b/src/operator/nn/im2col.cu
@@ -0,0 +1,45 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2015 by Contributors
+ * \file im2col.cu
+ * \brief
+ * \author Jiajun Wang
+*/
+
+#include "./im2col-inl.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(im2col)
+.set_attr<FCompute>("FCompute<gpu>", Im2colCompute<gpu>);
+
+NNVM_REGISTER_OP(_backward_im2col)
+.set_attr<FCompute>("FCompute<gpu>", Im2colGradCompute<gpu>);
+
+NNVM_REGISTER_OP(col2im)
+.set_attr<FCompute>("FCompute<gpu>", Col2imCompute<gpu>);
+
+NNVM_REGISTER_OP(_backward_col2im)
+.set_attr<FCompute>("FCompute<gpu>", Col2imGradCompute<gpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 66031d20d65b..d59c3063f95a 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -9374,6 +9374,153 @@ def check_random_uniform():
             assertRaises(MXNetError, mx.nd.random_uniform, alpha, beta, shape)
 
 
+@with_seed()
+def test_im2col_col2im():
+    def compute_output_size(spatial, kernel, stride=1, dilate=1, pad=0):
+        pad_size = spatial + 2 * pad
+        dilated_kernel = dilate * (kernel - 1) + 1
+        return (pad_size - dilated_kernel) // stride + 1
+
+    def build_kwargs(kernel, stride=1, dilate=1, pad=0):
+        return {'kernel': (kernel, kernel),
+                'stride': (stride, stride),
+                'dilate': (dilate, dilate),
+                'pad': (pad, pad)}
+
+    # use im2col to compute convolution
+    def test_conv_compute(input_shape, num_filter, kernel, stride=1, dilate=1, pad=0):
+        batch_size = input_shape[0]
+        channel = input_shape[1]
+        kwargs = build_kwargs(kernel, stride, dilate, pad)
+        data = mx.nd.uniform(shape=input_shape)
+        col = mx.nd.im2col(data, **kwargs)
+        w = mx.nd.uniform(shape=(num_filter, channel, kernel, kernel))
+        c1 = mx.nd.dot(col.transpose((0, 2, 1)), w.reshape(num_filter, -1).T).transpose((0, 2, 1))
+        hos = compute_output_size(input_shape[2], kernel, stride, dilate, pad)
+        wos = compute_output_size(input_shape[3], kernel, stride, dilate, pad)
+        c1 = c1.reshape((batch_size, num_filter, hos, wos))
+
+        c2 = mx.nd.Convolution(data, num_filter=num_filter, weight=w, no_bias=True, **kwargs)
+        assert_almost_equal(c1.asnumpy(), c2.asnumpy(), rtol=1e-5, atol=1e-5)
+
+    test_conv_compute(
+        input_shape = (5, 3, 30, 20),
+        num_filter  = 10,
+        kernel      = 3
+    )
+
+    test_conv_compute(
+        input_shape = (5, 3, 30, 20),
+        num_filter  = 10,
+        kernel      = 3,
+        stride      = 2
+    )
+
+    test_conv_compute(
+        input_shape = (5, 3, 30, 20),
+        num_filter  = 10,
+        kernel      = 3,
+        stride      = 2,
+        dilate      = 2
+    )
+
+    test_conv_compute(
+        input_shape = (5, 3, 30, 20),
+        num_filter  = 10,
+        kernel      = 3,
+        stride      = 2,
+        dilate      = 2,
+        pad         = 1
+    )
+
+    # use composite of im2col and col2im to reconstruct image
+    def test_reconstruct(input_shape, kernel, stride=1, dilate=1, pad=0):
+        batch_size = input_shape[0]
+        channel = input_shape[1]
+        kwargs = build_kwargs(kernel, stride, dilate, pad)
+        data = mx.nd.uniform(shape=input_shape)
+        col = mx.nd.im2col(data, **kwargs)
+        im1 = mx.nd.col2im(col, input_shape[2:], **kwargs)
+
+        im2 = mx.nd.col2im(mx.nd.ones_like(col), input_shape[2:], **kwargs) * data
+        assert_almost_equal(im1.asnumpy(), im2.asnumpy(), rtol=1e-5, atol=1e-5)
+
+    test_reconstruct(
+        input_shape = (5, 3, 30, 20),
+        kernel      = 3
+    )
+
+    test_reconstruct(
+        input_shape = (5, 3, 30, 20),
+        kernel      = 3,
+        stride      = 2
+    )
+
+    test_reconstruct(
+        input_shape = (5, 3, 30, 20),
+        kernel      = 3,
+        stride      = 2,
+        dilate      = 2
+    )
+
+    test_reconstruct(
+        input_shape = (5, 3, 30, 20),
+        kernel      = 3,
+        stride      = 2,
+        dilate      = 2,
+        pad         = 1
+    )
+
+    # test gradient
+    # the grad of im2col is col2im, and vice versa
+    def test_grad(input_shape, kernel, stride=1, dilate=1, pad=0):
+        # im2col
+        data = mx.sym.Variable('data')
+        kwargs = build_kwargs(kernel, stride, dilate, pad)
+        sym = mx.sym.im2col(data, **kwargs)
+
+        im = mx.nd.uniform(shape=input_shape)
+        col = mx.nd.im2col(im, **kwargs)
+        col_shape = col.shape
+        expected = mx.nd.col2im(col, input_shape[2:], **kwargs)
+        check_symbolic_backward(sym, [im.asnumpy()], [col.asnumpy()], [expected.asnumpy()])
+
+        # col2im
+        data = mx.sym.Variable('data')
+        sym = mx.sym.col2im(data, input_shape[2:], **kwargs)
+
+        col = mx.nd.uniform(shape=col_shape)
+        im = mx.nd.col2im(col, input_shape[2:], **kwargs)
+        expected = mx.nd.im2col(im, **kwargs)
+        check_symbolic_backward(sym, [col.asnumpy()], [im.asnumpy()], [expected.asnumpy()])
+
+    test_grad(
+        input_shape = (5, 3, 30, 20),
+        kernel      = 3
+    )
+
+    test_grad(
+        input_shape = (5, 3, 30, 20),
+        kernel      = 3,
+        stride      = 2
+    )
+
+    test_grad(
+        input_shape = (5, 3, 30, 20),
+        kernel      = 3,
+        stride      = 2,
+        dilate      = 2
+    )
+
+    test_grad(
+        input_shape = (5, 3, 30, 20),
+        kernel      = 3,
+        stride      = 2,
+        dilate      = 2,
+        pad         = 1
+    )
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From ed0954717048d76e2dc63206c26cb7ab138a69d7 Mon Sep 17 00:00:00 2001
From: kshitij12345 <kshitijkalambarkar@gmail.com>
Date: Thu, 19 Dec 2019 00:20:09 +0530
Subject: [PATCH 719/813] [MXNET-978] Higher Order Gradient Support `arcsin`,
 `arccos`. (#15515)

* support arcsin, arccos for higher order grad

* add relevant tests

* add small note for computation

* update comments

* use NodeOpGen

* retrigger CI

* address comment

* rename grad_x -> x_grad

* retrigger CI

* retrigger CI
---
 src/operator/tensor/elemwise_unary_op_trig.cc | 53 ++++++++++++++++++-
 .../python/unittest/test_higher_order_grad.py | 38 +++++++++++++
 2 files changed, 89 insertions(+), 2 deletions(-)

diff --git a/src/operator/tensor/elemwise_unary_op_trig.cc b/src/operator/tensor/elemwise_unary_op_trig.cc
index a436ebb284a3..e5d662a1b262 100644
--- a/src/operator/tensor/elemwise_unary_op_trig.cc
+++ b/src/operator/tensor/elemwise_unary_op_trig.cc
@@ -188,7 +188,31 @@ The storage type of ``arcsin`` output depends upon the input storage type:
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{ "_backward_arcsin" });
 
 MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_arcsin,
-                                                  unary_bwd<mshadow_op::arcsin_grad>);
+                                                  unary_bwd<mshadow_op::arcsin_grad>)
+.set_attr<nnvm::FGradient>("FGradient",
+    [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
+      // ograds[0]: head_grad_grads (dL/dxgrad)
+      // inputs[0]: dL/dy
+      // inputs[1]: x (ElemwiseGradUseIn)
+      // f(x) = arcsin(x)
+      // n: f'(x) = 1/(1-x^2)^1/2
+      // f''(x) = f'(x) * x/(1-x^2)
+      // Note: x/(1-x^2) = x * f'(x)^2
+      auto dydx = n->inputs[0];
+      auto x = n->inputs[1];
+      auto dydx_mul_grad_x = nnvm::NodeEntry{n};
+      auto op = mxnet::util::NodeOpGen{n};
+
+      auto x_grad = op.div(dydx_mul_grad_x, dydx);
+      auto x_grad_square = op.square(x_grad);
+      auto x_grad_square_mul_x = op.mul(x_grad_square, x);
+      auto x_grad_grad = op.mul(dydx_mul_grad_x, x_grad_square_mul_x);
+
+      std::vector<nnvm::NodeEntry> ret;
+      ret.emplace_back(op.mul(ograds[0], x_grad));
+      ret.emplace_back(op.mul(ograds[0], x_grad_grad));
+      return ret;
+    });
 
 // arccos
 MXNET_OPERATOR_REGISTER_UNARY_WITH_SPARSE_DR(arccos, cpu, mshadow_op::arccos)
@@ -207,7 +231,32 @@ The storage type of ``arccos`` output is always dense
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{ "_backward_arccos" });
 
 MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_arccos,
-                                                  unary_bwd<mshadow_op::arccos_grad>);
+                                                  unary_bwd<mshadow_op::arccos_grad>)
+.set_attr<nnvm::FGradient>("FGradient",
+    [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
+      // ograds[0]: head_grad_grads (dL/dxgrad)
+      // inputs[0]: dL/dy
+      // inputs[1]: x (ElemwiseGradUseIn)
+      // f(x) = arccos(x)
+      // n: f'(x) = -1/(1-x^2)^1/2
+      // f''(x) = f'(x) * x/(1-x^2)
+      // Note: x/(1-x^2) = x * f'(x)^2
+      auto dydx = n->inputs[0];
+      auto x = n->inputs[1];
+      auto dydx_mul_grad_x = nnvm::NodeEntry{n};
+      auto op = mxnet::util::NodeOpGen{n};
+
+      auto x_grad = op.div(dydx_mul_grad_x, dydx);
+      auto x_grad_square = op.square(x_grad);
+      auto x_grad_square_mul_x = op.mul(x_grad_square, x);
+      auto x_grad_grad = op.mul(dydx_mul_grad_x, x_grad_square_mul_x);
+
+      std::vector<nnvm::NodeEntry> ret;
+      ret.emplace_back(op.mul(ograds[0], x_grad));
+      ret.emplace_back(op.mul(ograds[0], x_grad_grad));
+      return ret;
+    });
+
 
 // arctan
 MXNET_OPERATOR_REGISTER_UNARY_WITH_RSP_CSR(arctan, cpu, mshadow_op::arctan)
diff --git a/tests/python/unittest/test_higher_order_grad.py b/tests/python/unittest/test_higher_order_grad.py
index 527c35d5dd94..eeba4226dc36 100644
--- a/tests/python/unittest/test_higher_order_grad.py
+++ b/tests/python/unittest/test_higher_order_grad.py
@@ -133,6 +133,44 @@ def grad_grad_op(x):
             array, tanh, grad_grad_op, rtol=1e-6, atol=1e-6)
 
 
+@with_seed()
+def test_arcsin():
+    def arcsin(x):
+        return nd.arcsin(x)
+
+    def grad_grad_op(x):
+        return x / nd.sqrt((1-x**2)**3)
+
+    for dim in range(1, 5):
+        shape = rand_shape_nd(dim)
+        array = random_arrays(shape)
+        # Hack: Decrease std_dev to make
+        # sure all elements
+        # are in range -1 to 1
+        # i.e. Domain of arcsin
+        array *= 0.2
+        check_second_order_unary(array, arcsin, grad_grad_op)
+
+
+@with_seed()
+def test_arccos():
+    def arccos(x):
+        return nd.arccos(x)
+
+    def grad_grad_op(x):
+        return -x / nd.sqrt((1-x**2)**3)
+
+    for dim in range(1, 5):
+        shape = rand_shape_nd(dim)
+        array = random_arrays(shape)
+        # Hack: Decrease std_dev to make
+        # sure all elements
+        # are in range -1 to 1
+        # i.e. Domain of arccos
+        array *= 0.2
+        check_second_order_unary(array, arccos, grad_grad_op)
+
+
 @with_seed()
 def test_arctan():
     def arctan(x):

From a18250d57ecf34b1499e590b1eea9453d02ab05a Mon Sep 17 00:00:00 2001
From: Zhennan Qin <zhennan.qin@intel.com>
Date: Thu, 19 Dec 2019 07:55:08 +0800
Subject: [PATCH 720/813] Add silent option to quantization script (#17094)

* Add silent option to quantization script

* Refactor code

* Fix lint
---
 .../quantization/imagenet_gen_qsym_mkldnn.py  | 70 +++++++++------
 python/mxnet/contrib/quantization.py          | 90 +++++++++++--------
 .../quantization/quantize_graph_pass.cc       | 26 ++++--
 3 files changed, 117 insertions(+), 69 deletions(-)

diff --git a/example/quantization/imagenet_gen_qsym_mkldnn.py b/example/quantization/imagenet_gen_qsym_mkldnn.py
index a4c1491039b9..130282714e30 100644
--- a/example/quantization/imagenet_gen_qsym_mkldnn.py
+++ b/example/quantization/imagenet_gen_qsym_mkldnn.py
@@ -140,17 +140,23 @@ def save_params(fname, arg_params, aux_params, logger=None):
                         help='If enabled, the quantize op will '
                              'be calibrated offline if calibration mode is '
                              'enabled')
+    parser.add_argument('--quiet', action='store_true', default=False,
+                        help='suppress most of log')
     args = parser.parse_args()
     ctx = mx.cpu(0)
-    logging.basicConfig()
-    logger = logging.getLogger('logger')
-    logger.setLevel(logging.INFO)
+    logger = None
+    if not args.quiet:
+        logging.basicConfig()
+        logger = logging.getLogger('logger')
+        logger.setLevel(logging.INFO)
 
-    logger.info(args)
-    logger.info('shuffle_dataset=%s' % args.shuffle_dataset)
+    if logger:
+        logger.info(args)
+        logger.info('shuffle_dataset=%s' % args.shuffle_dataset)
 
     calib_mode = args.calib_mode
-    logger.info('calibration mode set to %s' % calib_mode)
+    if logger:
+        logger.info('calibration mode set to %s' % calib_mode)
 
     # download calibration dataset
     if calib_mode != 'none':
@@ -158,13 +164,16 @@ def save_params(fname, arg_params, aux_params, logger=None):
 
     # download model
     if not args.no_pretrained:
-        logger.info('Get pre-trained model from MXNet or Gluoncv modelzoo.')
-        logger.info('If you want to use custom model, please set --no-pretrained.')
+        if logger:
+            logger.info('Get pre-trained model from MXNet or Gluoncv modelzoo.')
+            logger.info('If you want to use custom model, please set --no-pretrained.')
         if args.model in ['imagenet1k-resnet-152', 'imagenet1k-inception-bn']:
-            logger.info('model %s is downloaded from MXNet modelzoo' % args.model)
+            if logger:
+                logger.info('model %s is downloaded from MXNet modelzoo' % args.model)
             prefix, epoch = download_model(model_name=args.model, logger=logger)
         else:
-            logger.info('model %s is converted from GluonCV' % args.model)
+            if logger:
+                logger.info('model %s is converted from GluonCV' % args.model)
             prefix = convert_from_gluon(model_name=args.model, image_shape=args.image_shape, classes=1000, logger=logger)
             rgb_mean = '123.68,116.779,103.939'
             rgb_std = '58.393, 57.12, 57.375'
@@ -178,14 +187,16 @@ def save_params(fname, arg_params, aux_params, logger=None):
 
     # get batch size
     batch_size = args.batch_size
-    logger.info('batch size = %d for calibration' % batch_size)
+    if logger:
+        logger.info('batch size = %d for calibration' % batch_size)
 
     # get number of batches for calibration
     num_calib_batches = args.num_calib_batches
-    if calib_mode == 'none':
-        logger.info('skip calibration step as calib_mode is none')
-    else:
-        logger.info('number of batches = %d for calibration' % num_calib_batches)
+    if logger:
+        if calib_mode == 'none':
+            logger.info('skip calibration step as calib_mode is none')
+        else:
+            logger.info('number of batches = %d for calibration' % num_calib_batches)
 
     # get number of threads for decoding the dataset
     data_nthreads = args.data_nthreads
@@ -195,7 +206,8 @@ def save_params(fname, arg_params, aux_params, logger=None):
 
     exclude_first_conv = args.exclude_first_conv
     if args.quantized_dtype == "uint8":
-        logger.info('quantized dtype is set to uint8, will exclude first conv.')
+        if logger:
+            logger.info('quantized dtype is set to uint8, will exclude first conv.')
         exclude_first_conv = True
     excluded_sym_names = []
     if not args.no_pretrained:
@@ -242,42 +254,48 @@ def save_params(fname, arg_params, aux_params, logger=None):
         else:
             raise ValueError('Currently, model %s is not supported in this script' % args.model)
     else:
-        logger.info('Please set proper RGB configs for model %s' % args.model)
+        if logger:
+            logger.info('Please set proper RGB configs for model %s' % args.model)
         # add rgb mean/std of your model.
         rgb_mean = '0,0,0'
         rgb_std = '0,0,0'
         # add layer names you donnot want to quantize.
-        logger.info('Please set proper excluded_sym_names for model %s' % args.model)
+        if logger:
+            logger.info('Please set proper excluded_sym_names for model %s' % args.model)
         excluded_sym_names += ['layers']
         if exclude_first_conv:
             excluded_sym_names += ['layers']
 
-    logger.info('These layers have been excluded %s' % excluded_sym_names)
+    if logger:
+        logger.info('These layers have been excluded %s' % excluded_sym_names)
 
     label_name = args.label_name
-    logger.info('label_name = %s' % label_name)
+    if logger:
+        logger.info('label_name = %s' % label_name)
 
     data_shape = tuple([int(i) for i in image_shape.split(',')])
-    logger.info('Input data shape = %s' % str(data_shape))
-
-    logger.info('rgb_mean = %s' % rgb_mean)
+    if logger:
+        logger.info('Input data shape = %s' % str(data_shape))
+        logger.info('rgb_mean = %s' % rgb_mean)
+        logger.info('rgb_std = %s' % rgb_std)
     rgb_mean = [float(i) for i in rgb_mean.split(',')]
     mean_args = {'mean_r': rgb_mean[0], 'mean_g': rgb_mean[1], 'mean_b': rgb_mean[2]}
-    logger.info('rgb_std = %s' % rgb_std)
     rgb_std = [float(i) for i in rgb_std.split(',')]
     std_args = {'std_r': rgb_std[0], 'std_g': rgb_std[1], 'std_b': rgb_std[2]}
     combine_mean_std = {}
     combine_mean_std.update(mean_args)
     combine_mean_std.update(std_args)
     if calib_mode == 'none':
-        logger.info('Quantizing FP32 model %s' % args.model)
+        if logger:
+            logger.info('Quantizing FP32 model %s' % args.model)
         qsym, qarg_params, aux_params = quantize_model_mkldnn(sym=sym, arg_params=arg_params, aux_params=aux_params,
                                                               ctx=ctx, excluded_sym_names=excluded_sym_names,
                                                               calib_mode=calib_mode, quantized_dtype=args.quantized_dtype,
                                                               logger=logger)
         sym_name = '%s-symbol.json' % (prefix + '-quantized')
     else:
-        logger.info('Creating ImageRecordIter for reading calibration dataset')
+        if logger:
+            logger.info('Creating ImageRecordIter for reading calibration dataset')
         data = mx.io.ImageRecordIter(path_imgrec=args.calib_dataset,
                                      label_width=1,
                                      preprocess_threads=data_nthreads,
diff --git a/python/mxnet/contrib/quantization.py b/python/mxnet/contrib/quantization.py
index b0714037bb42..01051ab7c8e4 100644
--- a/python/mxnet/contrib/quantization.py
+++ b/python/mxnet/contrib/quantization.py
@@ -188,8 +188,8 @@ def collect(self, name, arr):
             return
         handle = ctypes.cast(arr, NDArrayHandle)
         arr = NDArray(handle, writable=False).copyto(cpu()).asnumpy()
-        if self.logger is not None:
-            self.logger.info("Collecting layer %s histogram of shape %s" % (name, arr.shape))
+        if self.logger:
+            self.logger.debug("Collecting layer %s histogram of shape %s" % (name, arr.shape))
         min_range = np.min(arr)
         max_range = np.max(arr)
         th = max(abs(min_range), abs(max_range))
@@ -224,9 +224,9 @@ def collect(self, name, arr):
                                        max(cur_min_max[1], max_range))
         else:
             self.min_max_dict[name] = (min_range, max_range)
-        if self.logger is not None:
-            self.logger.info("Collecting layer %s min_range=%f, max_range=%f"
-                             % (name, min_range, max_range))
+        if self.logger:
+            self.logger.debug("Collecting layer %s min_range=%f, max_range=%f"
+                              % (name, min_range, max_range))
 
 def _calibrate_quantized_sym(qsym, th_dict):
     """Given a dictionary containing the thresholds for quantizing the layers,
@@ -358,18 +358,19 @@ def _get_optimal_thresholds(hist_dict, quantized_dtype, num_quantized_bins=255,
         else:
             th_dict[name] = (-th, th)
         del hist_dict[name]  # release the memory
-        if logger is not None:
-            logger.info('layer=%s, min_val=%f, max_val=%f, th=%f, divergence=%f'
-                        % (name, min_val, max_val, th, divergence))
+        if logger:
+            logger.debug('layer=%s, min_val=%f, max_val=%f, th=%f, divergence=%f'
+                         % (name, min_val, max_val, th, divergence))
     return th_dict
 
 
-def _load_sym(sym, logger=logging):
+def _load_sym(sym, logger=None):
     """Given a str as a path the symbol .json file or a symbol, returns a Symbol object."""
     if isinstance(sym, str):  # sym is a symbol file path
         cur_path = os.path.dirname(os.path.realpath(__file__))
         symbol_file_path = os.path.join(cur_path, sym)
-        logger.info('Loading symbol from file %s' % symbol_file_path)
+        if logger:
+            logger.info('Loading symbol from file %s' % symbol_file_path)
         return sym_load(symbol_file_path)
     elif isinstance(sym, Symbol):
         return sym
@@ -378,14 +379,15 @@ def _load_sym(sym, logger=logging):
                          ' while received type %s' % str(type(sym)))
 
 
-def _load_params(params, logger=logging):
+def _load_params(params, logger=None):
     """Given a str as a path to the .params file or a pair of params,
     returns two dictionaries representing arg_params and aux_params.
     """
     if isinstance(params, str):
         cur_path = os.path.dirname(os.path.realpath(__file__))
         param_file_path = os.path.join(cur_path, params)
-        logger.info('Loading params from file %s' % param_file_path)
+        if logger:
+            logger.info('Loading params from file %s' % param_file_path)
         save_dict = nd_load(param_file_path)
         arg_params = {}
         aux_params = {}
@@ -451,7 +453,7 @@ def quantize_model(sym, arg_params, aux_params,
                    data_names=('data',), label_names=('softmax_label',),
                    ctx=cpu(), excluded_sym_names=None, excluded_op_names=None, calib_mode='entropy',
                    calib_data=None, num_calib_examples=None,
-                   quantized_dtype='int8', quantize_mode='smart', logger=logging):
+                   quantized_dtype='int8', quantize_mode='smart', logger=None):
     """User-level API for generating a quantized model from a FP32 model w/ or w/o calibration.
     The backend quantized operators are only enabled for Linux systems. Please do not run
     inference using the quantized models on Windows for now.
@@ -530,7 +532,9 @@ def quantize_model(sym, arg_params, aux_params,
                          ' the names of the operators that will not be quantized,'
                          ' while received type %s' % str(type(excluded_op_names)))
 
-    logger.info('Quantizing symbol')
+    if logger:
+        os.environ['MXNET_QUANTIZATION_VERBOSE'] = '1'
+        logger.info('Quantizing symbol')
     if quantized_dtype not in ('int8', 'uint8', 'auto'):
         raise ValueError('unknown quantized_dtype %s received,'
                          ' expected `int8`, `uint8` or `auto`' % quantized_dtype)
@@ -561,21 +565,24 @@ def quantize_model(sym, arg_params, aux_params,
                                                                include_layer=calib_layer,
                                                                max_num_examples=num_calib_examples,
                                                                logger=logger)
-            logger.info('Collected layer outputs from FP32 model using %d examples' % num_examples)
-            logger.info('Calculating optimal thresholds for quantization')
+            if logger:
+                logger.info('Collected layer outputs from FP32 model using %d examples' % num_examples)
+                logger.info('Calculating optimal thresholds for quantization')
             th_dict = _get_optimal_thresholds(hist_dict, quantized_dtype, logger=logger)
         elif calib_mode == 'naive':
             th_dict, num_examples = _collect_layer_output_min_max(
                 mod, calib_data, quantized_dtype, include_layer=calib_layer, max_num_examples=num_calib_examples,
                 logger=logger)
-            logger.info('Collected layer output min/max values from FP32 model using %d examples'
-                        % num_examples)
+            if logger:
+                logger.info('Collected layer output min/max values from FP32 model using %d examples'
+                            % num_examples)
         else:
             raise ValueError('unknown calibration mode %s received,'
                              ' expected `none`, `naive`, or `entropy`' % calib_mode)
         qsym = _calibrate_quantized_sym(qsym, th_dict)
 
-    logger.info('Quantizing parameters')
+    if logger:
+        logger.info('Quantizing parameters')
     qarg_params = _quantize_params(qsym, arg_params, th_dict)
 
     return qsym, qarg_params, aux_params
@@ -584,7 +591,7 @@ def quantize_model_mkldnn(sym, arg_params, aux_params,
                           data_names=('data',), label_names=('softmax_label',),
                           ctx=cpu(), excluded_sym_names=None, excluded_op_names=None,
                           calib_mode='entropy', calib_data=None, num_calib_examples=None,
-                          quantized_dtype='int8', quantize_mode='smart', logger=logging):
+                          quantized_dtype='int8', quantize_mode='smart', logger=None):
     """User-level API for generating a fusion + quantized model from a FP32 model
     w/ or w/o calibration with Intel MKL-DNN.
     The backend quantized operators are only enabled for Linux systems. Please do not run
@@ -621,7 +628,7 @@ def quantize_model_mkldnn(sym, arg_params, aux_params,
 
 def quantize_graph(sym, arg_params, aux_params, ctx=cpu(),
                    excluded_sym_names=None, excluded_op_names=None, calib_mode='entropy',
-                   quantized_dtype='int8', quantize_mode='full', logger=logging):
+                   quantized_dtype='int8', quantize_mode='full', logger=None):
     """User-level API for generating a quantized model from a FP32 model w/o calibration
     and a collector for naive or entropy calibration.
     The backend quantized operators are only enabled for Linux systems. Please do not run
@@ -676,7 +683,9 @@ def quantize_graph(sym, arg_params, aux_params, ctx=cpu(),
                          ' while received type %s' % str(type(excluded_sym_names)))
     if not isinstance(ctx, Context):
         raise ValueError('currently only supports single ctx, while received %s' % str(ctx))
-    logger.info('Quantizing graph')
+    if logger:
+        os.environ['MXNET_QUANTIZATION_VERBOSE'] = '1'
+        logger.info('Quantizing graph')
     if quantized_dtype not in ('int8', 'uint8', 'auto'):
         raise ValueError('unknown quantized_dtype %s received,'
                          ' expected `int8`, `uint8` or `auto`' % quantized_dtype)
@@ -693,20 +702,24 @@ def quantize_graph(sym, arg_params, aux_params, ctx=cpu(),
         if calib_mode == 'entropy':
             collector = _LayerHistogramCollector(
                 include_layer=calib_layer, logger=logger)
-            logger.info(
-                'Create a layer output collector for entropy calibration.')
+            if logger:
+                logger.info(
+                    'Create a layer output collector for entropy calibration.')
         elif calib_mode == 'naive':
             collector = _LayerOutputMinMaxCollector(quantized_dtype=quantized_dtype,
                                                     include_layer=calib_layer, logger=logger)
-            logger.info(
-                'Create a layer output minmax collector for naive calibration')
+            if logger:
+                logger.info(
+                    'Create a layer output minmax collector for naive calibration')
         else:
             raise ValueError('unknown calibration mode %s received,'
                              ' expected `none`, `naive`, or `entropy`' % calib_mode)
-        logger.info('Collector created, please use set_monitor_callback'
-                    ' to collect calibration information.')
+        if logger:
+            logger.info('Collector created, please use set_monitor_callback'
+                        ' to collect calibration information.')
 
-    logger.info('Quantizing parameters')
+    if logger:
+        logger.info('Quantizing parameters')
     qarg_params = _quantize_params(qsym, arg_params, th_dict)
 
     return qsym, qarg_params, aux_params, collector
@@ -751,7 +764,8 @@ def calib_graph(qsym, arg_params, aux_params, collector,
     th_dict = {}
     if calib_mode is not None and calib_mode != 'none':
         if calib_mode == 'entropy':
-            logger.info('Calculating optimal thresholds for quantization')
+            if logger:
+                logger.info('Calculating optimal thresholds for quantization')
             th_dict = _get_optimal_thresholds(
                 collector.hist_dict, quantized_dtype, logger=logger)
         elif calib_mode == 'naive':
@@ -763,7 +777,8 @@ def calib_graph(qsym, arg_params, aux_params, collector,
     else:
         raise ValueError('please set calibration mode to naive or entropy.')
 
-    logger.info('Quantizing parameters')
+    if logger:
+        logger.info('Quantizing parameters')
     qarg_params = _quantize_params(qsym, arg_params, th_dict)
 
     return qsym, qarg_params, aux_params
@@ -771,7 +786,7 @@ def calib_graph(qsym, arg_params, aux_params, collector,
 def quantize_net(network, quantized_dtype='auto', quantize_mode='full',
                  exclude_layers=None, exclude_layers_match=None, exclude_operators=None,
                  calib_data=None, data_shapes=None, calib_mode='none',
-                 num_calib_examples=None, ctx=cpu(), logger=logging):
+                 num_calib_examples=None, ctx=cpu(), logger=None):
     """User-level API for Gluon users to generate a quantized SymbolBlock from a FP32 HybridBlock w/ or w/o calibration.
     The backend quantized operators are only enabled for Linux systems. Please do not run
     inference using the quantized models on Windows for now.
@@ -825,7 +840,8 @@ def quantize_net(network, quantized_dtype='auto', quantize_mode='full',
     -------
     """
 
-    logger.info('Export HybridBlock')
+    if logger:
+        logger.info('Export HybridBlock')
     network.hybridize()
     import mxnet as mx
     if calib_data is not None:
@@ -881,7 +897,8 @@ def __exit__(self, exc_type, exc_value, traceback):
         for layers in list(symnet.get_internals()):
             if layers.name.find(name_match) != -1:
                 exclude_layers.append(layers.name)
-    logger.info('These layers have been excluded %s' % exclude_layers)
+    if logger:
+        logger.info('These layers have been excluded %s' % exclude_layers)
 
     if ctx == mx.cpu():
         symnet = symnet.get_backend_symbol('MKLDNN_QUANTIZE')
@@ -906,8 +923,9 @@ def __exit__(self, exc_type, exc_value, traceback):
             mod.set_params(args, auxs, allow_missing=False, force_init=True)
             num_examples = _collect_layer_statistics(mod, calib_data, collector,
                                                      num_calib_examples, logger)
-            logger.info('Collected layer output values from FP32 model using %d examples'
-                        % num_examples)
+            if logger:
+                logger.info('Collected layer output values from FP32 model using %d examples'
+                            % num_examples)
             qsym, qarg_params, aux_params = calib_graph(
                 qsym=qsym, arg_params=args, aux_params=auxs, collector=collector,
                 calib_mode=calib_mode, quantized_dtype=quantized_dtype, logger=logger)
diff --git a/src/operator/quantization/quantize_graph_pass.cc b/src/operator/quantization/quantize_graph_pass.cc
index 182f6339308a..01365067ce93 100644
--- a/src/operator/quantization/quantize_graph_pass.cc
+++ b/src/operator/quantization/quantize_graph_pass.cc
@@ -275,12 +275,15 @@ Graph QuantizeGraph(Graph &&src) {
   std::unordered_map<Node*, NodePtr> mirror_map;
   std::unordered_map<NodePtr, NodePtr> reverse_mirror_map;
   nnvm::NodeEntryMap<NodeEntry> mirror_entry_map;
+  static int verbose = dmlc::GetEnv("MXNET_QUANTIZATION_VERBOSE", 0);
   DFSVisit(src.outputs, [&](const NodePtr& node) {
     NodePtr new_node = Node::Create();
     // If the currently visited node needs quantization, insert a quantize op node before the
     // current node and replace the current node with the quantized version in the new graph.
     if (quantized_node_map.count(node)) {
-      std::cout << node->attrs.name << " is quantized." << std::endl;
+      if (verbose) {
+        LOG(INFO) << node->attrs.name << " is quantized.";
+      }
       new_node = quantized_node_map[node];
 
       // add data into quantized op input
@@ -395,7 +398,8 @@ Graph QuantizeGraph(Graph &&src) {
       // (e.g., a quantized_conv2d node), and insert a dequantize op node in the new graph if there
       // are any. Otherwise, simply add a copy of the current node's entry to the inputs of
       // the new_node.
-      if (!node->is_variable()) std::cout << node->attrs.name << " is NOT quantized." << std::endl;
+      if (verbose && !node->is_variable())
+        LOG(INFO) << node->attrs.name << " is NOT quantized.";
       *new_node = *node;
       new_node->inputs.clear();
       for (const auto& e : node->inputs) {
@@ -516,15 +520,20 @@ static inline void SetCalibTableForEntry(
     out_data_name = out_data_name.substr(prefix.size());
   }
   const auto calib_table_iter = calib_table.find(out_data_name);
+  static int verbose = dmlc::GetEnv("MXNET_QUANTIZATION_VERBOSE", 0);
   if (calib_table_iter != calib_table.end()) {
-    std::cout << "Set calibration result to " << node->attrs.name
-              << " : min=" << calib_table_iter->second.first
-              << " max=" << calib_table_iter->second.second << std::endl;
+    if (verbose) {
+      LOG(INFO) << "Set calibration result to " << node->attrs.name
+                << " : min=" << calib_table_iter->second.first
+                << " max=" << calib_table_iter->second.second;
+    }
     node->attrs.dict["min_calib_range"] = std::to_string(calib_table_iter->second.first);
     node->attrs.dict["max_calib_range"] = std::to_string(calib_table_iter->second.second);
     if (node->op() && node->op()->attr_parser) node->op()->attr_parser(&(node->attrs));
   } else {
-    std::cout << "Can't find calibration result for " << node->attrs.name << std::endl;
+    if (verbose) {
+      LOG(INFO) << "Can't find calibration result for " << node->attrs.name;
+    }
   }
 }
 
@@ -535,7 +544,10 @@ Graph SetCalibTableToQuantizedGraph(Graph&& g) {
       Op::GetAttr<mxnet::FNeedCalibrateInput>("FNeedCalibrateInput");
   static const auto& need_calib_output_map =
       Op::GetAttr<mxnet::FNeedCalibrateOutput>("FNeedCalibrateOutput");
-  std::cout << "Set calibration result to quantized symbol." << std::endl;
+  static int verbose = dmlc::GetEnv("MXNET_QUANTIZATION_VERBOSE", 0);
+  if (verbose) {
+    LOG(INFO) << "Set calibration result to quantized symbol.";
+  }
   DFSVisit(g.outputs, [&](const NodePtr& node) {
     if (need_calib_input_map.count(node->op())) {
       const auto calib_idx = need_calib_input_map[node->op()](node->attrs);

From a7f33eb1e1a0e1b1959c5184363844c2b346536f Mon Sep 17 00:00:00 2001
From: Minghao Liu <40382964+Tommliu@users.noreply.github.com>
Date: Thu, 19 Dec 2019 14:15:37 +0800
Subject: [PATCH 721/813] numpy bincount (#16965)

---
 python/mxnet/ndarray/numpy/_op.py             |  59 ++++++-
 python/mxnet/numpy/multiarray.py              |  53 +++++-
 python/mxnet/numpy_dispatch_protocol.py       |   1 +
 python/mxnet/symbol/numpy/_symbol.py          |  36 +++-
 src/operator/numpy/np_bincount_op-inl.h       | 147 ++++++++++++++++
 src/operator/numpy/np_bincount_op.cc          | 133 +++++++++++++++
 src/operator/numpy/np_bincount_op.cu          | 160 ++++++++++++++++++
 .../unittest/test_numpy_interoperability.py   |  18 ++
 tests/python/unittest/test_numpy_op.py        |  50 ++++++
 9 files changed, 654 insertions(+), 3 deletions(-)
 create mode 100644 src/operator/numpy/np_bincount_op-inl.h
 create mode 100644 src/operator/numpy/np_bincount_op.cc
 create mode 100644 src/operator/numpy/np_bincount_op.cu

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index c111a95a707a..8d56c1f651a3 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -40,7 +40,7 @@
            'blackman', 'flip', 'around', 'hypot', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad', 'unique', 'lcm',
            'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less',
            'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory',
-           'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where']
+           'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where', 'bincount']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -5929,3 +5929,60 @@ def where(condition, x=None, y=None):
         return nonzero(condition)
     else:
         return _npi.where(condition, x, y, out=None)
+
+
+@set_module('mxnet.ndarray.numpy')
+def bincount(x, weights=None, minlength=0):
+    """
+    Count number of occurrences of each value in array of non-negative ints.
+
+    Parameters
+    ----------
+    x : ndarray
+        input array, 1 dimension, nonnegative ints.
+    weights: ndarray
+        input weigths same shape as x. (Optional)
+    minlength: int
+        A minimum number of bins for the output. (Optional)
+
+    Returns
+    --------
+    out : ndarray
+        the result of binning the input array. The length of out is equal to amax(x)+1.
+
+    Raises
+    --------
+    Value Error
+        If the input is not 1-dimensional, or contains elements with negative values,
+        or if minlength is negative
+    TypeError
+        If the type of the input is float or complex.
+
+    Examples
+    --------
+    >>> np.bincount(np.arange(5))
+    array([1, 1, 1, 1, 1])
+    >>> np.bincount(np.array([0, 1, 1, 3, 2, 1, 7]))
+    array([1, 3, 1, 1, 0, 0, 0, 1])
+
+    >>> x = np.array([0, 1, 1, 3, 2, 1, 7, 23])
+    >>> np.bincount(x).size == np.amax(x)+1
+    True
+
+    >>> np.bincount(np.arange(5, dtype=float))
+    Traceback (most recent call last):
+    File "<stdin>", line 1, in <module>
+    TypeError: array cannot be safely cast to required type
+
+    >>> w = np.array([0.3, 0.5, 0.2, 0.7, 1., -0.6]) # weights
+    >>> x = np.array([0, 1, 1, 2, 2, 2])
+    >>> np.bincount(x,  weights=w)
+    array([ 0.3,  0.7,  1.1])
+    """
+    if not isinstance(x, NDArray):
+        raise TypeError("Input data should be NDarray")
+    if minlength < 0:
+        raise ValueError("Minlength value should greater than 0")
+    if weights is None:
+        return _npi.bincount(x, minlength=minlength, has_weights=False)
+    return _npi.bincount(x, weights=weights, minlength=minlength, has_weights=True)
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 5795c62942df..c3c6f4db8ba0 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -59,7 +59,7 @@
            'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
            'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal',
            'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory',
-           'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where']
+           'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where', 'bincount']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -7892,3 +7892,54 @@ def where(condition, x=None, y=None):
            [ 0.,  3., -1.]])
     """
     return _mx_nd_np.where(condition, x, y)
+
+
+@set_module('mxnet.numpy')
+def bincount(x, weights=None, minlength=0):
+    """
+    Count number of occurrences of each value in array of non-negative ints.
+
+    Parameters
+    ----------
+    x : ndarray
+        input array, 1 dimension, nonnegative ints.
+    weights: ndarray
+        input weigths same shape as x. (Optional)
+    minlength: int
+        A minimum number of bins for the output. (Optional)
+
+    Returns
+    --------
+    out : ndarray
+        the result of binning the input array. The length of out is equal to amax(x)+1.
+
+    Raises
+    --------
+    Value Error
+        If the input is not 1-dimensional, or contains elements with negative values,
+        or if minlength is negative
+    TypeError
+        If the type of the input is float or complex.
+
+    Examples
+    --------
+    >>> np.bincount(np.arange(5))
+    array([1, 1, 1, 1, 1])
+    >>> np.bincount(np.array([0, 1, 1, 3, 2, 1, 7]))
+    array([1, 3, 1, 1, 0, 0, 0, 1])
+
+    >>> x = np.array([0, 1, 1, 3, 2, 1, 7, 23])
+    >>> np.bincount(x).size == np.amax(x)+1
+    True
+
+    >>> np.bincount(np.arange(5, dtype=float))
+    Traceback (most recent call last):
+    File "<stdin>", line 1, in <module>
+    TypeError: array cannot be safely cast to required type
+
+    >>> w = np.array([0.3, 0.5, 0.2, 0.7, 1., -0.6]) # weights
+    >>> x = np.array([0, 1, 1, 2, 2, 2])
+    >>> np.bincount(x,  weights=w)
+    array([ 0.3,  0.7,  1.1])
+    """
+    return _mx_nd_np.bincount(x, weights=weights, minlength=minlength)
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index e93720564774..bd5c388a5100 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -147,6 +147,7 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'resize',
     'where',
     'full_like',
+    'bincount'
 ]
 
 
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index c61d5b2d393d..0fb0d538082d 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -48,7 +48,7 @@
            'blackman', 'flip', 'around', 'hypot', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad', 'unique', 'lcm',
            'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less',
            'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'shares_memory',
-           'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where']
+           'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where', 'bincount']
 
 
 @set_module('mxnet.symbol.numpy')
@@ -5468,4 +5468,38 @@ def load_json(json_str):
     return _Symbol(handle)
 
 
+@set_module('mxnet.symbol.numpy')
+def bincount(x, weights=None, minlength=0):
+    """
+    Count number of occurrences of each value in array of non-negative ints.
+
+    Parameters
+    ----------
+    x : _Symbol
+        input data
+    weights: _Symbol
+        input weigths same shape as x. (Optional)
+    minlength: int
+        A minimum number of bins for the output. (Optional)
+
+    Returns
+    --------
+    out : _Symbol
+        the result of binning the input data. The length of out is equal to amax(x)+1.
+
+    Raises:
+    --------
+    Value Error
+        If the input is not 1-dimensional, or contains elements with negative values,
+        or if minlength is negative
+    TypeError
+        If the type of the input is float or complex.
+    """
+    if minlength < 0:
+        raise ValueError("Minlength value should greater than 0")
+    if weights is None:
+        return _npi.bincount(x, minlength=minlength, has_weights=False)
+    return _npi.bincount(x, weights=weights, minlength=minlength, has_weights=True)
+
+
 _set_np_symbol_class(_Symbol)
diff --git a/src/operator/numpy/np_bincount_op-inl.h b/src/operator/numpy/np_bincount_op-inl.h
new file mode 100644
index 000000000000..254ea8fdec22
--- /dev/null
+++ b/src/operator/numpy/np_bincount_op-inl.h
@@ -0,0 +1,147 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file np_bicount_op-inl.h
+ * \brief numpy compatible bincount operator
+ */
+#ifndef MXNET_OPERATOR_NUMPY_NP_BINCOUNT_OP_INL_H_
+#define MXNET_OPERATOR_NUMPY_NP_BINCOUNT_OP_INL_H_
+
+#include <mxnet/operator_util.h>
+#include <utility>
+#include <vector>
+#include "../mshadow_op.h"
+#include "../mxnet_op.h"
+#include "../operator_common.h"
+#include "../elemwise_op_common.h"
+#include "np_broadcast_reduce_op.h"
+
+namespace mxnet {
+namespace op {
+
+struct NumpyBincountParam : public dmlc::Parameter<NumpyBincountParam> {
+  int minlength;
+  bool has_weights;
+  DMLC_DECLARE_PARAMETER(NumpyBincountParam) {
+    DMLC_DECLARE_FIELD(minlength)
+    .set_default(0)
+    .describe("A minimum number of bins for the output array"
+              "If minlength is specified, there will be at least this"
+              "number of bins in the output array");
+    DMLC_DECLARE_FIELD(has_weights)
+    .set_default(false)
+    .describe("Determine whether Bincount has weights.");
+  }
+};
+
+inline bool NumpyBincountType(const nnvm::NodeAttrs& attrs,
+                              std::vector<int> *in_attrs,
+                              std::vector<int> *out_attrs) {
+  const NumpyBincountParam& param = nnvm::get<NumpyBincountParam>(attrs.parsed);
+  if (!param.has_weights) {
+    return ElemwiseType<1, 1>(attrs, in_attrs, out_attrs) && in_attrs->at(0) != -1;
+  } else {
+    CHECK_EQ(out_attrs->size(), 1U);
+    CHECK_EQ(in_attrs->size(), 2U);
+    TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(1));
+    TYPE_ASSIGN_CHECK(*in_attrs, 1, out_attrs->at(0));
+    return out_attrs->at(0) != -1 && in_attrs->at(0) != -1;
+  }
+}
+
+inline bool NumpyBincountStorageType(const nnvm::NodeAttrs& attrs,
+                                     const int dev_mask,
+                                     DispatchMode* dispatch_mode,
+                                     std::vector<int> *in_attrs,
+                                     std::vector<int> *out_attrs) {
+  const NumpyBincountParam& param = nnvm::get<NumpyBincountParam>(attrs.parsed);
+  if (param.has_weights) {
+    CHECK_EQ(in_attrs->size(), 2U);
+  } else {
+    CHECK_EQ(in_attrs->size(), 1U);
+  }
+  CHECK_EQ(out_attrs->size(), 1U);
+  for (int &attr : *in_attrs) {
+    CHECK_EQ(attr, kDefaultStorage) << "Only default storage is supported";
+  }
+  for (int &attr : *out_attrs) {
+    attr = kDefaultStorage;
+  }
+  *dispatch_mode = DispatchMode::kFComputeEx;
+  return true;
+}
+
+template<typename xpu>
+void NumpyBincountForwardImpl(const OpContext &ctx,
+                              const NDArray &data,
+                              const NDArray &weights,
+                              const NDArray &out,
+                              const size_t &data_n,
+                              const int &minlength);
+
+template<typename xpu>
+void NumpyBincountForwardImpl(const OpContext &ctx,
+                              const NDArray &data,
+                              const NDArray &out,
+                              const size_t &data_n,
+                              const int &minlength);
+
+template<typename xpu>
+void NumpyBincountForward(const nnvm::NodeAttrs& attrs,
+                          const OpContext &ctx,
+                          const std::vector<NDArray> &inputs,
+                          const std::vector<OpReqType> &req,
+                          const std::vector<NDArray> &outputs) {
+  CHECK_GE(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 1U);
+  CHECK(req[0] == kWriteTo);
+  const NumpyBincountParam& param = nnvm::get<NumpyBincountParam>(attrs.parsed);
+  const bool has_weights = param.has_weights;
+  const int minlength = param.minlength;
+  const NDArray &data = inputs[0];
+  const NDArray &out = outputs[0];
+  CHECK_LE(data.shape().ndim(), 1U) << "Input only accept 1d array";
+  CHECK(!common::is_float(data.dtype())) <<"Input data should be int type";
+  size_t N = data.shape().Size();
+  if (N == 0) {
+    mshadow::Stream<xpu> *stream = ctx.get_stream<xpu>();
+    mxnet::TShape s(1, minlength);
+    const_cast<NDArray &>(out).Init(s);
+    MSHADOW_TYPE_SWITCH(out.dtype(), OType, {
+      mxnet_op::Kernel<mxnet_op::set_zero, xpu>::Launch(
+        stream, minlength, out.data().dptr<OType>());
+    });
+  } else {
+    if (has_weights) {
+      CHECK_EQ(inputs.size(), 2U);
+      const NDArray &weights = inputs[1];
+      CHECK_EQ(data.shape(), weights.shape()) << "weights should has same size as input";
+      NumpyBincountForwardImpl<xpu>(ctx, data, weights, out, N, minlength);
+    } else {
+      NumpyBincountForwardImpl<xpu>(ctx, data, out, N, minlength);
+    }
+  }
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_NUMPY_NP_BINCOUNT_OP_INL_H_
diff --git a/src/operator/numpy/np_bincount_op.cc b/src/operator/numpy/np_bincount_op.cc
new file mode 100644
index 000000000000..6256db176977
--- /dev/null
+++ b/src/operator/numpy/np_bincount_op.cc
@@ -0,0 +1,133 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file np_bicount_op.cc
+ * \brief numpy compatible bincount operator CPU registration
+ */
+
+#include "./np_bincount_op-inl.h"
+
+namespace mxnet {
+namespace op {
+
+void BinNumberCount(const NDArray& data, const int& minlength,
+                    const NDArray& out, const size_t& N) {
+  int bin = minlength;
+  MSHADOW_TYPE_SWITCH(data.dtype(), DType, {
+    DType* data_ptr = data.data().dptr<DType>();
+    for (size_t i = 0; i < N; i++) {
+      CHECK_GE(data_ptr[i], 0) << "input should be nonnegative number";
+      if (data_ptr[i] + 1 > bin) {
+        bin = data_ptr[i] + 1;
+      }
+    }
+  });  // bin number = max(max(data) + 1, minlength)
+  mxnet::TShape s(1, bin);
+  const_cast<NDArray &>(out).Init(s);  // set the output shape forcefully
+}
+
+template<typename DType, typename OType>
+void BincountCpuWeights(const DType* data, const OType* weights,
+                        OType* out, const size_t& data_n) {
+  for (size_t i = 0; i < data_n; i++) {
+    int target = data[i];
+    out[target] += weights[i];
+  }
+}
+
+template<typename DType, typename OType>
+void BincountCpu(const DType* data, OType* out, const size_t& data_n) {
+  for (size_t i = 0; i < data_n; i++) {
+    int target = data[i];
+    out[target] += 1;
+  }
+}
+
+template<>
+void NumpyBincountForwardImpl<cpu>(const OpContext &ctx,
+                                   const NDArray &data,
+                                   const NDArray &weights,
+                                   const NDArray &out,
+                                   const size_t &data_n,
+                                   const int &minlength) {
+  using namespace mxnet_op;
+  BinNumberCount(data, minlength, out, data_n);
+  mshadow::Stream<cpu> *s = ctx.get_stream<cpu>();
+  MSHADOW_TYPE_SWITCH(data.dtype(), DType, {
+      MSHADOW_TYPE_SWITCH(weights.dtype(), OType, {
+        size_t out_size = out.shape()[0];
+        Kernel<set_zero, cpu>::Launch(s, out_size, out.data().dptr<OType>());
+        BincountCpuWeights(data.data().dptr<DType>(), weights.data().dptr<OType>(),
+                          out.data().dptr<OType>(), data_n);
+      });
+    });
+}
+
+template<>
+void NumpyBincountForwardImpl<cpu>(const OpContext &ctx,
+                                   const NDArray &data,
+                                   const NDArray &out,
+                                   const size_t &data_n,
+                                   const int &minlength) {
+  using namespace mxnet_op;
+  BinNumberCount(data, minlength, out, data_n);
+  mshadow::Stream<cpu> *s = ctx.get_stream<cpu>();
+  MSHADOW_TYPE_SWITCH(data.dtype(), DType, {
+      MSHADOW_TYPE_SWITCH(out.dtype(), OType, {
+        size_t out_size = out.shape()[0];
+        Kernel<set_zero, cpu>::Launch(s, out_size, out.data().dptr<OType>());
+        BincountCpu(data.data().dptr<DType>(), out.data().dptr<OType>(), data_n);
+      });
+    });
+}
+
+DMLC_REGISTER_PARAMETER(NumpyBincountParam);
+
+NNVM_REGISTER_OP(_npi_bincount)
+.set_attr_parser(ParamParser<NumpyBincountParam>)
+.set_num_inputs([](const NodeAttrs& attrs) {
+  const NumpyBincountParam& params =
+    nnvm::get<NumpyBincountParam>(attrs.parsed);
+  return params.has_weights? 2 : 1;
+  })
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    const NumpyBincountParam& params =
+    nnvm::get<NumpyBincountParam>(attrs.parsed);
+    return params.has_weights ?
+           std::vector<std::string>{"data", "weights"} :
+           std::vector<std::string>{"data"};
+  })
+.set_attr<FResourceRequest>("FResourceRequest",
+[](const NodeAttrs& attrs) {
+  return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+})
+.set_attr<nnvm::FInferType>("FInferType", NumpyBincountType)
+.set_attr<FInferStorageType>("FInferStorageType", NumpyBincountStorageType)
+.set_attr<FComputeEx>("FComputeEx<cpu>", NumpyBincountForward<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.add_argument("data", "NDArray-or-Symbol", "Data")
+.add_argument("weights", "NDArray-or-Symbol", "Weights")
+.add_arguments(NumpyBincountParam::__FIELDS__());
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_bincount_op.cu b/src/operator/numpy/np_bincount_op.cu
new file mode 100644
index 000000000000..ed1f90f00c16
--- /dev/null
+++ b/src/operator/numpy/np_bincount_op.cu
@@ -0,0 +1,160 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file np_bicount_op.cu
+ * \brief numpy compatible bincount operator GPU registration
+ */
+
+#include "./np_bincount_op-inl.h"
+#include <thrust/device_ptr.h>
+#include <thrust/extrema.h>
+#include "../tensor/util/tensor_util-inl.cuh"
+#include "../tensor/util/tensor_util-inl.h"
+
+namespace mxnet {
+namespace op {
+
+struct BincountFusedKernel {
+  template<typename DType, typename OType>
+  static MSHADOW_XINLINE void Map(int i, const DType* data, OType* out) {
+    int idx = data[i];
+    atomicAdd(&out[idx], 1);
+  }
+
+  template<typename DType, typename OType>
+  static MSHADOW_XINLINE void Map(int i, const DType* data, const OType* weights,
+                                  OType* out) {
+    int idx = data[i];
+    atomicAdd(&out[idx], weights[i]);
+  }
+};
+
+struct is_valid_check {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int i, char* invalid_ptr, const DType* data) {
+    if (data[i] < 0) *invalid_ptr = 1;
+  }
+};
+
+template<typename DType>
+bool CheckInvalidInput(mshadow::Stream<gpu> *s, const DType *data, const size_t& data_size,
+                       char* is_valid_ptr) {
+  using namespace mxnet_op;
+  int32_t is_valid = 0;
+  Kernel<set_zero, gpu>::Launch(s, 1, is_valid_ptr);
+  Kernel<is_valid_check, gpu>::Launch(s, data_size, is_valid_ptr, data);
+  CUDA_CALL(cudaMemcpyAsync(&is_valid, is_valid_ptr, sizeof(char),
+                            cudaMemcpyDeviceToHost, mshadow::Stream<gpu>::GetStream(s)));
+  CUDA_CALL(cudaStreamSynchronize(mshadow::Stream<gpu>::GetStream(s)));
+  return is_valid == 0;
+}
+
+template<>
+void NumpyBincountForwardImpl<gpu>(const OpContext &ctx,
+                                   const NDArray &data,
+                                   const NDArray &weights,
+                                   const NDArray &out,
+                                   const size_t &data_n,
+                                   const int &minlength) {
+  using namespace mxnet_op;
+  mshadow::Stream<gpu> *s = ctx.get_stream<gpu>();
+
+  MXNET_NO_FLOAT16_TYPE_SWITCH(data.dtype(), DType, {
+    DType* h_ptr;
+    DType* d_ptr;
+    int bin = minlength;
+    d_ptr = data.data().dptr<DType>();
+    Tensor<gpu, 1, char> workspace = ctx.requested[0]
+            .get_space_typed<gpu, 1, char>(Shape1(1), s);
+    char* is_valid_ptr = reinterpret_cast<char*>(workspace.dptr_);
+    bool is_valid = CheckInvalidInput(s, d_ptr, data_n, is_valid_ptr);
+    CHECK(is_valid) << "Input should be nonnegative number";   // check invalid input
+
+    h_ptr = reinterpret_cast<DType*>(malloc(data_n*sizeof(DType)));
+    CUDA_CALL(cudaMemcpyAsync(h_ptr, d_ptr, data_n*sizeof(DType), cudaMemcpyDeviceToHost,
+                              mshadow::Stream<gpu>::GetStream(s)));
+    CUDA_CALL(cudaStreamSynchronize(mshadow::Stream<gpu>::GetStream(s)));
+    for (size_t i = 0; i < data_n; i++) {
+      if (h_ptr[i] + 1 > bin) bin = h_ptr[i] + 1;
+    }
+    free(h_ptr);
+    mxnet::TShape s(1, bin);
+    const_cast<NDArray &>(out).Init(s);  // set the output shape forcefully
+  });
+
+  MSHADOW_TYPE_SWITCH(data.dtype(), DType, {
+      MSHADOW_TYPE_SWITCH(weights.dtype(), OType, {
+        size_t out_size = out.shape().Size();
+        Kernel<set_zero, gpu>::Launch(s, out_size, out.data().dptr<OType>());
+        Kernel<BincountFusedKernel, gpu>::Launch(
+          s, data_n, data.data().dptr<DType>(), weights.data().dptr<OType>(),
+          out.data().dptr<OType>());
+      });
+    });
+}
+
+template<>
+void NumpyBincountForwardImpl<gpu>(const OpContext &ctx,
+                                   const NDArray &data,
+                                   const NDArray &out,
+                                   const size_t &data_n,
+                                   const int &minlength) {
+  using namespace mxnet_op;
+  mshadow::Stream<gpu> *s = ctx.get_stream<gpu>();
+
+  MXNET_NO_FLOAT16_TYPE_SWITCH(data.dtype(), DType, {
+    DType* h_ptr;
+    DType* d_ptr;
+    int bin = minlength;
+    d_ptr = data.data().dptr<DType>();
+    Tensor<gpu, 1, char> workspace = ctx.requested[0]
+            .get_space_typed<gpu, 1, char>(Shape1(1), s);
+    char* is_valid_ptr = reinterpret_cast<char*>(workspace.dptr_);
+    bool is_valid = CheckInvalidInput(s, d_ptr, data_n, is_valid_ptr);
+    CHECK(is_valid) << "Input should be nonnegative number";   // check invalid input
+
+    h_ptr = reinterpret_cast<DType*>(malloc(data_n*sizeof(DType)));
+    CUDA_CALL(cudaMemcpyAsync(h_ptr, d_ptr, data_n*sizeof(DType), cudaMemcpyDeviceToHost,
+                              mshadow::Stream<gpu>::GetStream(s)));
+    CUDA_CALL(cudaStreamSynchronize(mshadow::Stream<gpu>::GetStream(s)));
+    for (size_t i = 0; i < data_n; i++) {
+      if (h_ptr[i] + 1 > bin) bin = h_ptr[i] + 1;
+    }
+    free(h_ptr);
+    mxnet::TShape s(1, bin);
+    const_cast<NDArray &>(out).Init(s);  // set the output shape forcefully
+  });
+
+  MSHADOW_TYPE_SWITCH(data.dtype(), DType, {
+    MSHADOW_TYPE_SWITCH(out.dtype(), OType, {
+      size_t out_size = out.shape().Size();
+      Kernel<set_zero, gpu>::Launch(s, out_size, out.data().dptr<OType>());
+      Kernel<BincountFusedKernel, gpu>::Launch(
+        s, data_n, data.data().dptr<DType>(), out.data().dptr<OType>());
+    });
+  });
+}
+
+NNVM_REGISTER_OP(_npi_bincount)
+.set_attr<FComputeEx>("FComputeEx<gpu>", NumpyBincountForward<gpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index a670f794860f..5b5af8b20e36 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -67,6 +67,23 @@ def _add_workload_unravel_index():
     OpArgMngr.add_workload('unravel_index', np.array([],dtype=_np.int64), (10, 3, 5))
     OpArgMngr.add_workload('unravel_index', np.array([3], dtype=_np.int32), (2,2))
 
+def _add_workload_bincount():
+    y = np.arange(4).astype(int)
+    y1 = np.array([1, 5, 2, 4, 1], dtype=_np.int64)
+    y2 = np.array((), dtype=_np.int8)
+    w = np.array([0.2, 0.3, 0.5, 0.1])
+    w1 = np.array([0.2, 0.3, 0.5, 0.1, 0.2])
+
+    OpArgMngr.add_workload('bincount', y)
+    OpArgMngr.add_workload('bincount', y1)
+    OpArgMngr.add_workload('bincount', y, w)
+    OpArgMngr.add_workload('bincount', y1, w1)
+    OpArgMngr.add_workload('bincount', y1, w1, 8)
+    OpArgMngr.add_workload('bincount', y, minlength=3)
+    OpArgMngr.add_workload('bincount', y, minlength=8)
+    OpArgMngr.add_workload('bincount', y2, minlength=0)
+    OpArgMngr.add_workload('bincount', y2, minlength=5)
+    
 
 def _add_workload_diag():
     def get_mat(n):
@@ -1409,6 +1426,7 @@ def _prepare_workloads():
     _add_workload_around()
     _add_workload_argsort()
     _add_workload_append()
+    _add_workload_bincount()
     _add_workload_broadcast_arrays(array_pool)
     _add_workload_broadcast_to()
     _add_workload_clip()
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 545466bf0814..b39703b8ebda 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -5670,6 +5670,56 @@ def hybrid_forward(self, F, a):
             assert_almost_equal(elem_mx.asnumpy(), elem_np, rtol=rtol, atol=atol)
 
 
+@with_seed()
+@use_np
+def test_np_bincount():
+    class TestBincount(HybridBlock):
+        def __init__(self, minlength=0):
+            super(TestBincount, self).__init__()
+            self._minlength = minlength
+
+        def hybrid_forward(self, F, a):
+            return F.np.bincount(a, None, self._minlength)
+    
+    class TestBincountWeights(HybridBlock):
+        def __init__(self, minlength=0):
+            super(TestBincountWeights, self).__init__()
+            self._minlength = minlength
+
+        def hybrid_forward(self, F, a, weights):
+            return F.np.bincount(a, weights, self._minlength)
+
+    dtypes = [np.int8, np.uint8, np.int32, np.int64]
+    weight_types = [np.int32, np.int64, np.float16, np.float32, np.float64]
+    shapes = [(), (5,), (10,), (15,), (20,), (30,), (50,)]
+    min_lengths = [0, 5, 20, 50]
+    has_weights = [True, False] 
+    combinations = itertools.product([True, False], shapes, dtypes, weight_types, has_weights, min_lengths)
+    for hybridize, shape, dtype, weight_type, has_weight, minlength in combinations: 
+        rtol = 1e-2 if weight_type == np.float16 else 1e-3
+        atol = 1e-4 if weight_type == np.float16 else 1e-5
+        if shape != ():
+            data = np.random.uniform(0, 10, size=shape).astype(dtype) 
+            weights = np.random.uniform(0, 10, size=shape).astype(weight_type) if has_weight else None
+        else:
+            data = np.array(()).astype(dtype)
+            weights = np.array(()).astype(weight_type) if has_weight else None
+        weights_np = weights.asnumpy() if has_weight else None
+        test_bincount = TestBincountWeights(minlength) if has_weight else TestBincount(minlength)
+        if hybridize:
+            test_bincount.hybridize()
+        mx_out = test_bincount(data, weights) if has_weight else test_bincount(data)
+        np_out = _np.bincount(data.asnumpy(), weights_np, minlength)
+        assert mx_out.shape == np_out.shape
+        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
+        # No backward operation for operator bincount at this moment
+
+        # Test imperative once again
+        mx_out = np.bincount(data, weights, minlength)
+        np_out = _np.bincount(data.asnumpy(), weights_np, minlength)
+        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
+
+        
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 521c477ad32864d887481abf6c53acae3b717cf6 Mon Sep 17 00:00:00 2001
From: Yiyan66 <57363390+Yiyan66@users.noreply.github.com>
Date: Thu, 19 Dec 2019 14:42:09 +0800
Subject: [PATCH 722/813] [numpy] add op bitwise_not (#16947)

* all

* test op not change

* all

* delete describe

* have output

* add test

* only

* solve conflict
---
 python/mxnet/ndarray/numpy/_op.py             | 110 +++++++++++++++++-
 python/mxnet/numpy/multiarray.py              | 108 ++++++++++++++++-
 python/mxnet/numpy_dispatch_protocol.py       |   2 +
 python/mxnet/symbol/numpy/_symbol.py          |  94 ++++++++++++++-
 src/operator/mshadow_op.h                     |  12 ++
 .../numpy/np_elemwise_unary_op_basic.cc       |  14 +++
 .../numpy/np_elemwise_unary_op_basic.cu       |   3 +
 src/operator/operator_tune.cc                 |   1 +
 src/operator/tensor/elemwise_unary_op.h       |  17 +++
 .../unittest/test_numpy_interoperability.py   |  20 ++++
 tests/python/unittest/test_numpy_op.py        |  66 ++++++++++-
 11 files changed, 437 insertions(+), 10 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index 8d56c1f651a3..02e42145fb18 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -28,8 +28,8 @@
 from . import _internal as _npi
 from ..ndarray import NDArray
 
-__all__ = ['shape', 'zeros', 'zeros_like', 'ones', 'ones_like', 'full', 'full_like',
-           'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power',
+__all__ = ['shape', 'zeros', 'zeros_like', 'ones', 'ones_like', 'full', 'full_like', 'invert',
+           'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power', 'bitwise_not',
            'arctan2', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs',
            'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2',
            'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor', 'histogram',
@@ -2690,6 +2690,112 @@ def floor(x, out=None, **kwargs):
     return _unary_func_helper(x, _npi.floor, _np.floor, out=out, **kwargs)
 
 
+@set_module('mxnet.ndarray.numpy')
+@wrap_np_unary_func
+def bitwise_not(x, out=None, **kwargs):
+    r"""
+    Compute bit-wise inversion, or bit-wise NOT, element-wise.
+    Computes the bit-wise NOT of the underlying binary representation of
+    the integers in the input arrays. This ufunc implements the C/Python
+    operator ``~``.
+
+    Parameters
+    ----------
+    x : array_like
+        Only integer and boolean types are handled.
+    out : ndarray, None, or tuple of ndarray and None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned. A tuple (possible only as a
+        keyword argument) must have length equal to the number of outputs.
+
+    Returns
+    -------
+    out : ndarray or scalar
+        Result.
+        This is a scalar if `x` is a scalar.
+
+    See Also
+    --------
+    bitwise_and, bitwise_or, bitwise_xor
+    logical_not
+    binary_repr :
+        Return the binary representation of the input number as a string.
+
+    Examples
+    --------
+    We've seen that 13 is represented by ``00001101``.
+    The invert or bit-wise NOT of 13 is then:
+
+    >>> x = np.invert(np.array(13, dtype=np.uint8))
+    >>> x
+    242
+    >>> np.binary_repr(x, width=8)
+    '11110010'
+
+    Notes
+    -----
+    `bitwise_not` is an alias for `invert`:
+
+    >>> np.bitwise_not is np.invert
+    True
+    """
+    return _unary_func_helper(x, _npi.bitwise_not, _np.bitwise_not, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+@wrap_np_unary_func
+def invert(x, out=None, **kwargs):
+    r"""
+    Compute bit-wise inversion, or bit-wise NOT, element-wise.
+    Computes the bit-wise NOT of the underlying binary representation of
+    the integers in the input arrays. This ufunc implements the C/Python
+    operator ``~``.
+
+    Parameters
+    ----------
+    x : array_like
+        Only integer and boolean types are handled.
+    out : ndarray, None, or tuple of ndarray and None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned. A tuple (possible only as a
+        keyword argument) must have length equal to the number of outputs.
+
+    Returns
+    -------
+    out : ndarray or scalar
+        Result.
+        This is a scalar if `x` is a scalar.
+
+    See Also
+    --------
+    bitwise_and, bitwise_or, bitwise_xor
+    logical_not
+    binary_repr :
+        Return the binary representation of the input number as a string.
+
+    Examples
+    --------
+    We've seen that 13 is represented by ``00001101``.
+    The invert or bit-wise NOT of 13 is then:
+
+    >>> x = np.invert(np.array(13, dtype=np.uint8))
+    >>> x
+    242
+    >>> np.binary_repr(x, width=8)
+    '11110010'
+
+    Notes
+    -----
+    `bitwise_not` is an alias for `invert`:
+
+    >>> np.bitwise_not is np.invert
+    True
+    """
+    return _unary_func_helper(x, _npi.bitwise_not, _np.bitwise_not, out=out, **kwargs)
+
+
 @set_module('mxnet.ndarray.numpy')
 @wrap_np_unary_func
 def trunc(x, out=None, **kwargs):
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index c3c6f4db8ba0..4910b4d6b925 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -47,8 +47,8 @@
 from ..ndarray.ndarray import _storage_type
 
 __all__ = ['ndarray', 'empty', 'array', 'shape', 'zeros', 'zeros_like', 'ones', 'ones_like', 'full', 'full_like',
-           'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power',
-           'arctan2', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10',
+           'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power', 'bitwise_not',
+           'arctan2', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'invert',
            'sqrt', 'cbrt', 'abs', 'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log',
            'degrees', 'log2', 'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative', 'histogram',
            'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'append', 'argsort',
@@ -3981,6 +3981,110 @@ def floor(x, out=None, **kwargs):
     """
     return _mx_nd_np.floor(x, out=out, **kwargs)
 
+@set_module('mxnet.numpy')
+@wrap_np_unary_func
+def invert(x, out=None, **kwargs):
+    r"""
+    Compute bit-wise inversion, or bit-wise NOT, element-wise.
+    Computes the bit-wise NOT of the underlying binary representation of
+    the integers in the input arrays. This ufunc implements the C/Python
+    operator ``~``.
+
+    Parameters
+    ----------
+    x : array_like
+        Only integer and boolean types are handled.
+    out : ndarray, None, or tuple of ndarray and None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned. A tuple (possible only as a
+        keyword argument) must have length equal to the number of outputs.
+
+    Returns
+    -------
+    out : ndarray or scalar
+        Result.
+        This is a scalar if `x` is a scalar.
+
+    See Also
+    --------
+    bitwise_and, bitwise_or, bitwise_xor
+    logical_not
+    binary_repr :
+        Return the binary representation of the input number as a string.
+
+    Examples
+    --------
+    We've seen that 13 is represented by ``00001101``.
+    The invert or bit-wise NOT of 13 is then:
+
+    >>> x = np.invert(np.array(13, dtype=np.uint8))
+    >>> x
+    242
+    >>> np.binary_repr(x, width=8)
+    '11110010'
+
+    Notes
+    -----
+    `bitwise_not` is an alias for `invert`:
+
+    >>> np.bitwise_not is np.invert
+    True
+    """
+    return _mx_nd_np.bitwise_not(x, out=out, **kwargs)
+
+@set_module('mxnet.numpy')
+@wrap_np_unary_func
+def bitwise_not(x, out=None, **kwargs):
+    r"""
+    Compute bit-wise inversion, or bit-wise NOT, element-wise.
+    Computes the bit-wise NOT of the underlying binary representation of
+    the integers in the input arrays. This ufunc implements the C/Python
+    operator ``~``.
+
+    Parameters
+    ----------
+    x : array_like
+        Only integer and boolean types are handled.
+    out : ndarray, None, or tuple of ndarray and None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned. A tuple (possible only as a
+        keyword argument) must have length equal to the number of outputs.
+
+    Returns
+    -------
+    out : ndarray or scalar
+        Result.
+        This is a scalar if `x` is a scalar.
+
+    See Also
+    --------
+    bitwise_and, bitwise_or, bitwise_xor
+    logical_not
+    binary_repr :
+        Return the binary representation of the input number as a string.
+
+    Examples
+    --------
+    We've seen that 13 is represented by ``00001101``.
+    The invert or bit-wise NOT of 13 is then:
+
+    >>> x = np.invert(np.array(13, dtype=np.uint8))
+    >>> x
+    242
+    >>> np.binary_repr(x, width=8)
+    '11110010'
+
+    Notes
+    -----
+    `bitwise_not` is an alias for `invert`:
+
+    >>> np.bitwise_not is np.invert
+    True
+    """
+    return _mx_nd_np.bitwise_not(x, out=out, **kwargs)
+
 
 @set_module('mxnet.numpy')
 @wrap_np_unary_func
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index bd5c388a5100..c7e9dd1398eb 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -219,6 +219,8 @@ def _register_array_function():
     'square',
     'cbrt',
     'reciprocal',
+    'invert',
+    'bitwise_not',
     'remainder',
     'sin',
     'cos',
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 0fb0d538082d..6efc333cc16c 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -36,7 +36,7 @@
 except ImportError:
     from builtins import slice as py_slice
 
-__all__ = ['zeros', 'zeros_like', 'ones', 'ones_like', 'full_like',
+__all__ = ['zeros', 'zeros_like', 'ones', 'ones_like', 'full_like', 'bitwise_not', 'invert',
            'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power', 'arctan2',
            'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs', 'absolute', 'exp',
            'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2', 'log1p',
@@ -1040,6 +1040,98 @@ def ones(shape, dtype=_np.float32, order='C', ctx=None):
     return _npi.ones(shape=shape, ctx=ctx, dtype=dtype)
 
 
+@set_module('mxnet.symbol.numpy')
+@wrap_np_unary_func
+def invert(x, out=None, **kwargs):
+    r"""
+    Compute bit-wise inversion, or bit-wise NOT, element-wise.
+    Computes the bit-wise NOT of the underlying binary representation of
+    the integers in the input arrays. This ufunc implements the C/Python
+    operator ``~``.
+    Parameters
+    ----------
+    x : array_like
+        Only integer and boolean types are handled.
+    out : ndarray, None, or tuple of ndarray and None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned. A tuple (possible only as a
+        keyword argument) must have length equal to the number of outputs.
+    Returns
+    -------
+    out : ndarray or scalar
+        Result.
+        This is a scalar if `x` is a scalar.
+    See Also
+    --------
+    bitwise_and, bitwise_or, bitwise_xor
+    logical_not
+    binary_repr :
+        Return the binary representation of the input number as a string.
+    Examples
+    --------
+    We've seen that 13 is represented by ``00001101``.
+    The invert or bit-wise NOT of 13 is then:
+    >>> x = np.invert(np.array(13, dtype=np.uint8))
+    >>> x
+    242
+    >>> np.binary_repr(x, width=8)
+    '11110010'
+    Notes
+    -----
+    `bitwise_not` is an alias for `invert`:
+    >>> np.bitwise_not is np.invert
+    True
+    """
+    return _unary_func_helper(x, _npi.bitwise_not, _np.bitwise_not, out=out, **kwargs)
+
+
+@set_module('mxnet.symbol.numpy')
+@wrap_np_unary_func
+def bitwise_not(x, out=None, **kwargs):
+    r"""
+    Compute bit-wise inversion, or bit-wise NOT, element-wise.
+    Computes the bit-wise NOT of the underlying binary representation of
+    the integers in the input arrays. This ufunc implements the C/Python
+    operator ``~``.
+    Parameters
+    ----------
+    x : array_like
+        Only integer and boolean types are handled.
+    out : ndarray, None, or tuple of ndarray and None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned. A tuple (possible only as a
+        keyword argument) must have length equal to the number of outputs.
+    Returns
+    -------
+    out : ndarray or scalar
+        Result.
+        This is a scalar if `x` is a scalar.
+    See Also
+    --------
+    bitwise_and, bitwise_or, bitwise_xor
+    logical_not
+    binary_repr :
+        Return the binary representation of the input number as a string.
+    Examples
+    --------
+    We've seen that 13 is represented by ``00001101``.
+    The invert or bit-wise NOT of 13 is then:
+    >>> x = np.invert(np.array(13, dtype=np.uint8))
+    >>> x
+    242
+    >>> np.binary_repr(x, width=8)
+    '11110010'
+    Notes
+    -----
+    `bitwise_not` is an alias for `invert`:
+    >>> np.bitwise_not is np.invert
+    True
+    """
+    return _unary_func_helper(x, _npi.bitwise_not, _np.bitwise_not, out=out, **kwargs)
+
+
 @set_module('mxnet.symbol.numpy')
 def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):  # pylint: disable=too-many-arguments
     """
diff --git a/src/operator/mshadow_op.h b/src/operator/mshadow_op.h
index cf35e8858039..e3a3c0443428 100644
--- a/src/operator/mshadow_op.h
+++ b/src/operator/mshadow_op.h
@@ -359,6 +359,18 @@ MXNET_UNARY_MATH_OP(negation, -a);
 
 MXNET_UNARY_MATH_OP(reciprocal, 1.0f / math::id(a));
 
+struct bitwise_not : public mxnet_op::tunable {
+  template<typename DType,
+           typename std::enable_if<!std::is_same<DType, bool>::value, int>::type = 0>
+  MSHADOW_XINLINE static DType Map(DType a) {
+    return ~static_cast<int64_t>(a);
+  }
+
+  MSHADOW_XINLINE static bool Map(bool a) {
+    return !a;
+  }
+};
+
 MXNET_UNARY_MATH_OP(reciprocal_grad, -1.0f / math::sqr(a));
 
 MXNET_UNARY_MATH_OP(sigmoid, 1.0f / (1.0f + math::exp(-a)));
diff --git a/src/operator/numpy/np_elemwise_unary_op_basic.cc b/src/operator/numpy/np_elemwise_unary_op_basic.cc
index cad736aab65b..5e15d7ad4e67 100644
--- a/src/operator/numpy/np_elemwise_unary_op_basic.cc
+++ b/src/operator/numpy/np_elemwise_unary_op_basic.cc
@@ -169,6 +169,20 @@ Example::
 )code" ADD_FILELINE)
 .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes);
 
+// bitwise_not
+NNVM_REGISTER_OP(_npi_bitwise_not)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+     return std::vector<std::string>{"x"};
+})
+.set_attr<FCompute>("FCompute<cpu>", UnaryOp::ComputeInt<cpu, mshadow_op::bitwise_not>)
+.add_argument("x", "NDArray-or-Symbol", "The input array.")
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes);
+
 // trunc
 MXNET_OPERATOR_REGISTER_NUMPY_UNARY(_npi_trunc, "x", mshadow_op::trunc)
 .describe(R"code(Return the truncated value of the input, element-wise.
diff --git a/src/operator/numpy/np_elemwise_unary_op_basic.cu b/src/operator/numpy/np_elemwise_unary_op_basic.cu
index af8834f01664..517ef9c2b52a 100644
--- a/src/operator/numpy/np_elemwise_unary_op_basic.cu
+++ b/src/operator/numpy/np_elemwise_unary_op_basic.cu
@@ -53,6 +53,9 @@ MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_ceil, mshadow_op::ceil);
 
 MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_floor, mshadow_op::floor);
 
+NNVM_REGISTER_OP(_npi_bitwise_not)
+.set_attr<FCompute>("FCompute<gpu>", UnaryOp::ComputeInt<gpu, mshadow_op::bitwise_not>);
+
 MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_trunc, mshadow_op::trunc);
 
 MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_fix, mshadow_op::fix);
diff --git a/src/operator/operator_tune.cc b/src/operator/operator_tune.cc
index db898f8840f0..7ca594d21e59 100644
--- a/src/operator/operator_tune.cc
+++ b/src/operator/operator_tune.cc
@@ -311,6 +311,7 @@ IMPLEMENT_UNARY_WORKLOAD_FWD(mxnet::op::mshadow_op::radians);  // NOLINT()
 IMPLEMENT_UNARY_WORKLOAD_BWD(mxnet::op::mshadow_op::radians_grad);  // NOLINT()
 IMPLEMENT_UNARY_WORKLOAD_FWD(mxnet::op::mshadow_op::nt);  // NOLINT()
 IMPLEMENT_UNARY_WORKLOAD_FWD_WITH_BOOL(mxnet::op::mshadow_op::np_logical_not);  // NOLINT()
+IMPLEMENT_UNARY_WORKLOAD_FWD_WITH_BOOL(mxnet::op::mshadow_op::bitwise_not);  // NOLINT()
 IMPLEMENT_UNARY_WORKLOAD_BWD(mxnet::op::mshadow_op::nt);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::clip);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::clip);  // NOLINT()
diff --git a/src/operator/tensor/elemwise_unary_op.h b/src/operator/tensor/elemwise_unary_op.h
index 05e10ffa4e16..4486b0dcd712 100644
--- a/src/operator/tensor/elemwise_unary_op.h
+++ b/src/operator/tensor/elemwise_unary_op.h
@@ -252,6 +252,23 @@ class UnaryOp : public OpBase {
     });
   }
 
+  template<typename xpu, typename OP>
+  static void ComputeInt(const nnvm::NodeAttrs& attrs,
+                      const OpContext& ctx,
+                      const std::vector<TBlob>& inputs,
+                      const std::vector<OpReqType>& req,
+                      const std::vector<TBlob>& outputs) {
+    mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+    MXNET_INT_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+      MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
+        if (inputs[0].Size() != 0) {
+          mxnet_op::Kernel<mxnet_op::op_with_req<OP, Req>, xpu>::Launch(
+            s, inputs[0].Size(), outputs[0].dptr<DType>(), inputs[0].dptr<DType>());
+        }
+      });
+    });
+  }
+
   template<typename xpu, typename OP>
   static void ComputeLogic(const nnvm::NodeAttrs& attrs,
                            const OpContext& ctx,
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index 5b5af8b20e36..fcdf547bfbec 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -1242,6 +1242,24 @@ def _add_workload_logical_not(array_pool):
     OpArgMngr.add_workload('logical_not', np.array([True, False, True, False], dtype=np.bool))
 
 
+def _add_workload_bitwise_not():
+    OpArgMngr.add_workload('bitwise_not', np.array([True, False, True, False], dtype=np.bool))
+    for dtype in [np.int8, np.int32, np.int64]:
+        zeros = np.array([0], dtype=dtype)
+        ones = np.array([-1], dtype=dtype)
+        OpArgMngr.add_workload('bitwise_not', zeros)
+        OpArgMngr.add_workload('bitwise_not', ones)
+
+
+def _add_workload_invert():
+    OpArgMngr.add_workload('invert', np.array([True, False, True, False], dtype=np.bool))
+    for dtype in [np.int8, np.int32, np.int64]:
+        zeros = np.array([0], dtype=dtype)
+        ones = np.array([-1], dtype=dtype)
+        OpArgMngr.add_workload('invert', zeros)
+        OpArgMngr.add_workload('invert', ones)
+
+
 def _add_workload_vdot():
     OpArgMngr.add_workload('vdot', np.random.normal(size=(2, 4)), np.random.normal(size=(4, 2)))
     OpArgMngr.add_workload('vdot', np.random.normal(size=(2, 4)).astype(np.float64), np.random.normal(size=(2, 4)).astype(np.float64))
@@ -1526,6 +1544,8 @@ def _prepare_workloads():
     _add_workload_turnc(array_pool)
     _add_workload_floor(array_pool)
     _add_workload_logical_not(array_pool)
+    _add_workload_bitwise_not()
+    _add_workload_invert()
     _add_workload_vdot()
     _add_workload_vstack(array_pool)
     _add_workload_column_stack()
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index b39703b8ebda..af9228d45991 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -1778,6 +1778,66 @@ def hybrid_forward(self, F, a, *args, **kwargs):
             check_unary_func(func, ref_grad, shape, low, high)
 
 
+@with_seed()
+@use_np
+def test_np_bitwise_not():
+    def check_unary_func(func, ref_grad, shape, low, high):
+        class TestUnary(HybridBlock):
+            def __init__(self, func):
+                super(TestUnary, self).__init__()
+                self._func = func
+
+            def hybrid_forward(self, F, a, *args, **kwargs):
+                return getattr(F.np, self._func)(a)
+
+        np_func = getattr(_np, func)
+        mx_func = TestUnary(func)
+        np_test_data = _np.random.uniform(low, high, shape).astype(_np.int32)
+        mx_test_data = mx.numpy.array(np_test_data)
+        for hybridize in [True, False]:
+            if hybridize:
+                mx_func.hybridize()
+            if ref_grad:
+                mx_test_data.attach_grad()
+            np_out = np_func(np_test_data)
+            with mx.autograd.record():
+                y = mx_func(mx_test_data)
+            assert y.shape == np_out.shape
+            assert_almost_equal(y.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+            if np_out.dtype == np.bool_:
+                assert y.dtype == np.bool_
+
+            if ref_grad:
+                y.backward()
+                assert_almost_equal(mx_test_data.grad.asnumpy(), ref_grad(np_test_data), rtol=1e-1, atol=1e-2, equal_nan=True)
+
+        np_out = getattr(_np, func)(np_test_data)
+        mx_out = getattr(mx.np, func)(mx_test_data)
+        assert mx_out.shape == np_out.shape
+        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+
+
+        assertRaises(NotImplementedError, getattr(np, func), mx_test_data, where=False)
+        assertRaises(NotImplementedError, getattr(np, func), mx_test_data,  subok=False)
+        assertRaises(NotImplementedError, getattr(np, func), mx_test_data,  dtype=_np.int8)
+        assertRaises(TypeError, getattr(np, func), mx_test_data,  dtype="abcdefg")
+        assertRaises(NotImplementedError, getattr(np, func), mx_test_data,  casting='safe')
+        assertRaises(TypeError, getattr(np, func), mx_test_data,  casting='mxnet')
+        assertRaises(NotImplementedError, getattr(np, func), mx_test_data,  order='C')
+        assertRaises(NotImplementedError, getattr(np, func), mx_test_data,  order='mxnet')
+
+    funcs = {
+        'bitwise_not' : (None, -5, 5),
+        'invert' : (None, -5, 5),
+    }
+    ndim = random.choice([2, 3, 4])
+    shape = random.choice([rand_shape_nd(ndim, dim=3), (1, 0, 2)])
+    for shape in [rand_shape_nd(ndim, dim=3), (1, 0, 2)]:
+        for func, func_data in funcs.items():
+            ref_grad, low, high = func_data
+            check_unary_func(func, ref_grad, shape, low, high)
+
+
 @with_seed()
 @use_np
 def test_np_binary_funcs():
@@ -3760,11 +3820,7 @@ def get_grad_b(A, X):
     nrhs = (-1, 0, 1, 2, 3)
     dtypes = ['float32', 'float64']
     for hybridize, shape, dtype, nrh in itertools.product([False, True], shapes, dtypes, nrhs):
-        rtol = 1e-3
-        atol = 1e-5
-        if dtype == 'float32':
-            rtol = 1e-2
-            atol = 1e-4
+        rtol, atol =1e-2, 1e-4
         test_solve = TestSolve()
         if hybridize:
             test_solve.hybridize()

From 8a3519934f3ee5e9ac9406c2a4edb377af5e8cc7 Mon Sep 17 00:00:00 2001
From: Przemyslaw Tredak <ptredak@nvidia.com>
Date: Fri, 20 Dec 2019 08:34:36 -0800
Subject: [PATCH 723/813] Improve the speed of the pointwise fusion graph pass
 (#17114)

* Debug the long startup time

* Optimize backward fusion

* Figure out why the fusion pass is called twice

* Cleaning

* Small optimization
---
 src/executor/simple_partition_pass.h | 98 ++++++++++++++++++----------
 src/imperative/cached_op.cc          | 22 ++++---
 2 files changed, 76 insertions(+), 44 deletions(-)

diff --git a/src/executor/simple_partition_pass.h b/src/executor/simple_partition_pass.h
index 5b26a4523c13..ea1dcf39b8ba 100644
--- a/src/executor/simple_partition_pass.h
+++ b/src/executor/simple_partition_pass.h
@@ -102,8 +102,7 @@ class BidirectionalGraph {
   std::vector<std::unordered_set<Node*>> get_subsets(FCompatible is_compatible) {
     std::vector<std::unordered_set<Node*>> subgraphs;
     std::unordered_set<Node*> incomp_set;
-    std::unordered_set<Node*> all_set(nodes.size());
-    std::vector<PairSet> separation_sets;
+    std::vector<std::pair<bool, PairSet>> separation_sets;
     // Check each node for compatibility
     // and, if it is incompatible, mark nodes
     // on each side of it as not possible to be
@@ -111,48 +110,79 @@ class BidirectionalGraph {
     for (Node& node : nodes) {
       if (!is_compatible(node.nnvmptr)) {
         incomp_set.insert(&node);
-        std::unordered_set<Node*> in_graph;
-        std::unordered_set<Node*> out_graph;
-        std::vector<Node*> dummy_head;
-        dummy_head.emplace_back(&node);
-        DFS(dummy_head, false, [&out_graph, &is_compatible](Node* node) {
-          if (is_compatible(node->nnvmptr))
-            out_graph.insert(node);
-        });
-        DFS(dummy_head, true, [&in_graph, is_compatible](Node* node) {
-          if (is_compatible(node->nnvmptr))
-            in_graph.insert(node);
-        });
-        if (!(in_graph.empty() || out_graph.empty()))
-          separation_sets.push_back(std::make_pair(in_graph, out_graph));
       }
-      all_set.emplace(&node);
     }
-    IncompMap incomp_map;
-    std::unordered_set<Node*> comp_set;
-    comp_set.insert(all_set.begin(), all_set.end());
-    for (Node* n : incomp_set) {
-      comp_set.erase(n);
+    for (Node& node : nodes) {
+      if (incomp_set.count(&node) != 0) {
+        // Check if all your inputs are incompatible too.
+        // If so, then your separation set does not matter,
+        // because it will covered by the sets of your inputs
+        bool inside_node = true;
+        for (Node* input : node.inputs) {
+          if (incomp_set.count(input) == 0) {
+            inside_node = false;
+          }
+        }
+        if (!inside_node) {
+          std::unordered_set<Node*> in_graph;
+          std::unordered_set<Node*> out_graph;
+          std::vector<Node*> dummy_head;
+          dummy_head.emplace_back(&node);
+          DFS(dummy_head, false, [&out_graph](Node* node) {
+              out_graph.insert(node);
+          });
+          DFS(dummy_head, true, [&in_graph](Node* node) {
+              in_graph.insert(node);
+          });
+            separation_sets.push_back(std::make_pair(true,
+                                                     std::make_pair(in_graph, out_graph)));
+        } else {
+          separation_sets.push_back(std::make_pair(false, PairSet()));
+        }
+      } else {
+        separation_sets.push_back(std::make_pair(false, PairSet()));
+      }
     }
+    IncompMap incomp_map;
     // For each node construct the map of nodes that cannot be in
     // the same subset
-    for (Node* n : comp_set) {
-      for (PairSet p : separation_sets) {
-        if (p.first.count(n)) {
-          incomp_map[n].insert(p.second.begin(), p.second.end());
-        } else if (p.second.count(n)) {
-          incomp_map[n].insert(p.first.begin(), p.first.end());
+    index_t num_nodes = nodes.size();
+    for (index_t i = 0; i < num_nodes; ++i) {
+      const auto n = &(nodes[i]);
+      if (incomp_set.count(n) == 0) {
+        for (index_t j = i + 1; j < num_nodes; ++j) {
+          const auto& sep_set_pair = separation_sets[j];
+          if (sep_set_pair.first && incomp_map[n].count(&nodes[j]) == 0) {
+            const auto& p = sep_set_pair.second;
+            if (p.first.count(n)) {
+              incomp_map[n].insert(p.second.begin(), p.second.end());
+            } else if (p.second.count(n)) {
+              incomp_map[n].insert(p.first.begin(), p.first.end());
+            }
+          }
+        }
+        for (index_t j = i - 1; j >= 0; --j) {
+          const auto& sep_set_pair = separation_sets[j];
+          if (sep_set_pair.first && incomp_map[n].count(&nodes[j]) == 0) {
+            const auto& p = sep_set_pair.second;
+            if (p.first.count(n)) {
+              incomp_map[n].insert(p.second.begin(), p.second.end());
+            } else if (p.second.count(n)) {
+              incomp_map[n].insert(p.first.begin(), p.first.end());
+            }
+          }
+        }
+        for (Node* incomp_n : incomp_set) {
+          incomp_map[n].erase(incomp_n);
         }
-      }
-      for (Node* incomp_n : incomp_set) {
-        incomp_map[n].erase(incomp_n);
       }
     }
     std::unordered_set<Node*> unused_set;
-    unused_set.reserve(comp_set.size());
 
-    for (auto& n : comp_set) {
-      unused_set.insert(n);
+    for (auto& n : nodes) {
+      if (incomp_set.count(&n) == 0) {
+        unused_set.insert(&n);
+      }
     }
     std::unordered_set<Node*> visited;
     std::deque<Node*> stack(outputs.begin(), outputs.end());
diff --git a/src/imperative/cached_op.cc b/src/imperative/cached_op.cc
index ec5a79a2e675..1edd9897ec82 100644
--- a/src/imperative/cached_op.cc
+++ b/src/imperative/cached_op.cc
@@ -1032,17 +1032,19 @@ OpStatePtr CachedOp::Forward(
   CHECK_EQ(inputs.size(), num_inputs());
 
   Context default_ctx = inputs[0]->ctx();
-  auto state_ptr = GetCachedOpState(default_ctx);
-  auto& state = state_ptr.get_state<CachedOpState>();
+  {
+    auto state_ptr = GetCachedOpState(default_ctx);
+    auto& state = state_ptr.get_state<CachedOpState>();
 
-  const auto& idx = state.info.fwd_graph.indexed_graph();
-  for (size_t i = 0; i < inputs.size(); ++i) {
-    CHECK_EQ(inputs[i]->ctx(), default_ctx)
-        << "CachedOp requires all inputs to live on the same context. But "
-        << idx[idx.input_nodes()[0]].source->attrs.name
-        << " is on " << default_ctx << " while "
-        << idx[idx.input_nodes()[i]].source->attrs.name
-        << " is on " << inputs[i]->ctx();
+    const auto& idx = state.info.fwd_graph.indexed_graph();
+    for (size_t i = 0; i < inputs.size(); ++i) {
+      CHECK_EQ(inputs[i]->ctx(), default_ctx)
+          << "CachedOp requires all inputs to live on the same context. But "
+          << idx[idx.input_nodes()[0]].source->attrs.name
+          << " is on " << default_ctx << " while "
+          << idx[idx.input_nodes()[i]].source->attrs.name
+          << " is on " << inputs[i]->ctx();
+    }
   }
 
   int prev_bulk_size = Engine::Get()->set_bulk_size(config_.forward_bulk_size);

From 615f609da748a771901478cf37e37064763b0d8c Mon Sep 17 00:00:00 2001
From: liuzh91 <liuzhuanghua1991@gmail.com>
Date: Sat, 21 Dec 2019 08:31:40 +0800
Subject: [PATCH 724/813] fix parameter names in the estimator api (#17051)

---
 .../contrib/estimator/batch_processor.py      |  4 +--
 .../gluon/contrib/estimator/estimator.py      | 30 ++++++++---------
 .../unittest/test_gluon_batch_processor.py    |  4 +--
 tests/python/unittest/test_gluon_estimator.py | 32 +++++++++----------
 4 files changed, 35 insertions(+), 35 deletions(-)

diff --git a/python/mxnet/gluon/contrib/estimator/batch_processor.py b/python/mxnet/gluon/contrib/estimator/batch_processor.py
index 4985f8c81bf3..aa5adbfdea5f 100644
--- a/python/mxnet/gluon/contrib/estimator/batch_processor.py
+++ b/python/mxnet/gluon/contrib/estimator/batch_processor.py
@@ -61,8 +61,8 @@ def evaluate_batch(self, estimator,
             Batch axis to split the validation data into devices.
         """
         data, label = self._get_data_and_label(val_batch, estimator.context, batch_axis)
-        pred = [estimator.eval_net(x) for x in data]
-        loss = [estimator.evaluation_loss(y_hat, y) for y_hat, y in zip(pred, label)]
+        pred = [estimator.val_net(x) for x in data]
+        loss = [estimator.val_loss(y_hat, y) for y_hat, y in zip(pred, label)]
 
         return data, label, pred, loss
 
diff --git a/python/mxnet/gluon/contrib/estimator/estimator.py b/python/mxnet/gluon/contrib/estimator/estimator.py
index 09f43151e235..ed8a53d7c3a6 100644
--- a/python/mxnet/gluon/contrib/estimator/estimator.py
+++ b/python/mxnet/gluon/contrib/estimator/estimator.py
@@ -61,22 +61,19 @@ class Estimator(object):
         Trainer to apply optimizer on network parameters.
     context : Context or list of Context
         Device(s) to run the training on.
-    evaluation_loss : gluon.loss.loss
-        Loss (objective) function to calculate during validation. If set evaluation_loss
-        None, it will use the same loss function as self.loss
-    eval_net : gluon.Block
+    val_net : gluon.Block
         The model used for validation. The validation model does not necessarily belong to
         the same model class as the training model. But the two models typically share the
         same architecture. Therefore the validation model can reuse parameters of the
         training model.
 
-        The code example of consruction of eval_net sharing the same network parameters as
+        The code example of consruction of val_net sharing the same network parameters as
         the training net is given below:
 
         >>> net = _get_train_network()
-        >>> eval_net = _get_test_network(params=net.collect_params())
+        >>> val_net = _get_test_network(params=net.collect_params())
         >>> net.initialize(ctx=ctx)
-        >>> est = Estimator(net, loss, eval_net=eval_net)
+        >>> est = Estimator(net, loss, val_net=val_net)
 
         Proper namespace match is required for weight sharing between two networks. Most networks
         inheriting :py:class:`Block` can share their parameters correctly. An exception is
@@ -84,6 +81,9 @@ class Estimator(object):
         the  naming in mxnet Gluon API, please refer to the site
         (https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/naming.html)
         for future information.
+    val_loss : gluon.loss.loss
+        Loss (objective) function to calculate during validation. If set val_loss
+        None, it will use the same loss function as self.loss
     batch_processor: BatchProcessor
         BatchProcessor provides customized fit_batch() and evaluate_batch() methods
     """
@@ -113,8 +113,8 @@ def __init__(self, net,
                  initializer=None,
                  trainer=None,
                  context=None,
-                 evaluation_loss=None,
-                 eval_net=None,
+                 val_net=None,
+                 val_loss=None,
                  batch_processor=None):
         self.net = net
         self.loss = self._check_loss(loss)
@@ -122,12 +122,12 @@ def __init__(self, net,
         self._val_metrics = _check_metrics(val_metrics)
         self._add_default_training_metrics()
         self._add_validation_metrics()
-        self.evaluation_loss = self.loss
-        if evaluation_loss is not None:
-            self.evaluation_loss = self._check_loss(evaluation_loss)
-        self.eval_net = self.net
-        if eval_net is not None:
-            self.eval_net = eval_net
+        self.val_loss = self.loss
+        if val_loss is not None:
+            self.val_loss = self._check_loss(val_loss)
+        self.val_net = self.net
+        if val_net is not None:
+            self.val_net = val_net
 
         self.logger = logging.Logger(name='Estimator', level=logging.INFO)
         self.logger.addHandler(logging.StreamHandler(sys.stdout))
diff --git a/tests/python/unittest/test_gluon_batch_processor.py b/tests/python/unittest/test_gluon_batch_processor.py
index 4bd6f769aa44..8604713fc129 100644
--- a/tests/python/unittest/test_gluon_batch_processor.py
+++ b/tests/python/unittest/test_gluon_batch_processor.py
@@ -84,7 +84,7 @@ def test_batch_processor_validation():
     ctx = mx.cpu()
     loss = gluon.loss.L2Loss()
     acc = mx.metric.Accuracy()
-    evaluation_loss = gluon.loss.L1Loss()
+    val_loss = gluon.loss.L1Loss()
     net.initialize(ctx=ctx)
     processor = BatchProcessor()
     trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.001})
@@ -93,7 +93,7 @@ def test_batch_processor_validation():
                     train_metrics=acc,
                     trainer=trainer,
                     context=ctx,
-                    evaluation_loss=evaluation_loss,
+                    val_loss=val_loss,
                     batch_processor=processor)
     # Input dataloader
     est.fit(train_data=dataloader,
diff --git a/tests/python/unittest/test_gluon_estimator.py b/tests/python/unittest/test_gluon_estimator.py
index 924dd083bef4..ca61e4b40caa 100644
--- a/tests/python/unittest/test_gluon_estimator.py
+++ b/tests/python/unittest/test_gluon_estimator.py
@@ -88,7 +88,7 @@ def test_validation():
     ctx = mx.cpu()
     loss = gluon.loss.L2Loss()
     acc = mx.metric.Accuracy()
-    evaluation_loss = gluon.loss.L1Loss()
+    val_loss = gluon.loss.L1Loss()
     net.initialize(ctx=ctx)
     trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.001})
     est = Estimator(net=net,
@@ -96,7 +96,7 @@ def test_validation():
                     train_metrics=acc,
                     trainer=trainer,
                     context=ctx,
-                    evaluation_loss=evaluation_loss)
+                    val_loss=val_loss)
     # Input dataloader
     est.fit(train_data=dataloader,
             val_data=dataloader,
@@ -376,16 +376,16 @@ def test_default_handlers():
     assert isinstance(handlers[1], MetricHandler)
     assert isinstance(handlers[4], LoggingHandler)
 
-def test_eval_net():
-    ''' test estimator with a different evaluation net '''
+def test_val_net():
+    ''' test estimator with different training and validation networks '''
     ''' test weight sharing of sequential networks without namescope '''
     net = _get_test_network()
-    eval_net = _get_test_network(params=net.collect_params())
+    val_net = _get_test_network(params=net.collect_params())
     dataloader, dataiter = _get_test_data()
     num_epochs = 1
     ctx = mx.cpu()
     loss = gluon.loss.L2Loss()
-    evaluation_loss = gluon.loss.L2Loss()
+    val_loss = gluon.loss.L2Loss()
     acc = mx.metric.Accuracy()
     net.initialize(ctx=ctx)
     trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.001})
@@ -394,8 +394,8 @@ def test_eval_net():
                     train_metrics=acc,
                     trainer=trainer,
                     context=ctx,
-                    evaluation_loss=evaluation_loss,
-                    eval_net=eval_net)
+                    val_loss=val_loss,
+                    val_net=val_net)
 
     with assert_raises(RuntimeError):
         est.fit(train_data=dataloader,
@@ -404,7 +404,7 @@ def test_eval_net():
 
     ''' test weight sharing of sequential networks with namescope '''
     net = _get_test_network_with_namescope()
-    eval_net = _get_test_network_with_namescope(params=net.collect_params())
+    val_net = _get_test_network_with_namescope(params=net.collect_params())
     net.initialize(ctx=ctx)
     trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.001})
     est = Estimator(net=net,
@@ -412,8 +412,8 @@ def test_eval_net():
                     train_metrics=acc,
                     trainer=trainer,
                     context=ctx,
-                    evaluation_loss=evaluation_loss,
-                    eval_net=eval_net)
+                    val_loss=val_loss,
+                    val_net=val_net)
 
     est.fit(train_data=dataloader,
             val_data=dataloader,
@@ -422,20 +422,20 @@ def test_eval_net():
     ''' test weight sharing of two resnets '''
     net = gluon.model_zoo.vision.resnet18_v1(pretrained=False, ctx=ctx)
     net.output = gluon.nn.Dense(10)
-    eval_net = gluon.model_zoo.vision.resnet18_v1(pretrained=False, ctx=ctx)
-    eval_net.output = gluon.nn.Dense(10, params=net.collect_params())
+    val_net = gluon.model_zoo.vision.resnet18_v1(pretrained=False, ctx=ctx)
+    val_net.output = gluon.nn.Dense(10, params=net.collect_params())
     dataset = gluon.data.ArrayDataset(mx.nd.zeros((10, 3, 224, 224)), mx.nd.zeros((10, 10)))
     dataloader = gluon.data.DataLoader(dataset=dataset, batch_size=5)
     net.initialize(ctx=ctx)
-    eval_net.initialize(ctx=ctx)
+    val_net.initialize(ctx=ctx)
     trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.001})
     est = Estimator(net=net,
                     loss=loss,
                     train_metrics=acc,
                     trainer=trainer,
                     context=ctx,
-                    evaluation_loss=evaluation_loss,
-                    eval_net=eval_net)
+                    val_loss=val_loss,
+                    val_net=val_net)
 
     est.fit(train_data=dataloader,
             val_data=dataloader,

From d000c3baa32171964f1b8ed3780472af0e05be1a Mon Sep 17 00:00:00 2001
From: Xi Wang <xidulu@gmail.com>
Date: Sat, 21 Dec 2019 10:31:27 +0800
Subject: [PATCH 725/813] [Numpy ]Modify np.random.shuffle to enable inplace by
 default (#17133)

* shuffle done

* fix dodstring
---
 python/mxnet/ndarray/numpy/random.py | 38 +++++++++++++++++++++++++++-
 python/mxnet/numpy/random.py         | 38 +++++++++++++++++++++++++++-
 python/mxnet/symbol/numpy/random.py  | 38 +++++++++++++++++++++++++++-
 src/operator/random/shuffle_op.cc    |  2 +-
 4 files changed, 112 insertions(+), 4 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/random.py b/python/mxnet/ndarray/numpy/random.py
index 9d1a6f9119ee..e67c766c6bdf 100644
--- a/python/mxnet/ndarray/numpy/random.py
+++ b/python/mxnet/ndarray/numpy/random.py
@@ -23,7 +23,7 @@
 from ..ndarray import NDArray
 
 
-__all__ = ['randint', 'uniform', 'normal', "choice", "rand", "multinomial"]
+__all__ = ['randint', 'uniform', 'normal', "choice", "rand", "multinomial", "shuffle"]
 
 
 def randint(low, high=None, size=None, dtype=None, ctx=None, out=None):
@@ -344,3 +344,39 @@ def rand(*size, **kwargs):
     for s in size:
         output_shape += (s,)
     return uniform(0, 1, size=output_shape, **kwargs)
+
+
+def shuffle(x):
+    """
+    Modify a sequence in-place by shuffling its contents.
+
+    This function only shuffles the array along the first axis of a
+    multi-dimensional array. The order of sub-arrays is changed but
+    their contents remain the same.
+
+    Parameters
+    ----------
+    x: ndarray
+        The array or list to be shuffled.
+
+    Returns
+    -------
+    None
+
+    Examples
+    --------
+    >>> arr = np.arange(10)
+    >>> np.random.shuffle(arr)
+    >>> arr
+    array([5., 1., 0., 6., 7., 3., 9., 8., 4., 2.])  # random
+
+    Multi-dimensional arrays are only shuffled along the first axis:
+
+    >>> arr = np.arange(9).reshape((3, 3))
+    >>> np.random.shuffle(arr)
+    >>> arr
+    array([[6., 7., 8.], # random
+           [3., 4., 5.],
+           [0., 1., 2.]])
+    """
+    _npi.shuffle(x, out=x)
diff --git a/python/mxnet/numpy/random.py b/python/mxnet/numpy/random.py
index 1cad4a55c466..ebc24de63282 100644
--- a/python/mxnet/numpy/random.py
+++ b/python/mxnet/numpy/random.py
@@ -20,7 +20,7 @@
 from __future__ import absolute_import
 from ..ndarray import numpy as _mx_nd_np
 
-__all__ = ["randint", "uniform", "normal", "choice", "rand", "multinomial"]
+__all__ = ["randint", "uniform", "normal", "choice", "rand", "multinomial", "shuffle"]
 
 
 def randint(low, high=None, size=None, dtype=None, ctx=None, out=None):
@@ -321,3 +321,39 @@ def rand(*size, **kwargs):
     for s in size:
         output_shape += (s,)
     return _mx_nd_np.random.uniform(0, 1, size=output_shape, **kwargs)
+
+
+def shuffle(x):
+    """
+    Modify a sequence in-place by shuffling its contents.
+
+    This function only shuffles the array along the first axis of a
+    multi-dimensional array. The order of sub-arrays is changed but
+    their contents remain the same.
+
+    Parameters
+    ----------
+    x: ndarray
+        The array or list to be shuffled.
+
+    Returns
+    -------
+    None
+
+    Examples
+    --------
+    >>> arr = np.arange(10)
+    >>> np.random.shuffle(arr)
+    >>> arr
+    array([5., 1., 0., 6., 7., 3., 9., 8., 4., 2.])  # random
+
+    Multi-dimensional arrays are only shuffled along the first axis:
+
+    >>> arr = np.arange(9).reshape((3, 3))
+    >>> np.random.shuffle(arr)
+    >>> arr
+    array([[6., 7., 8.], # random
+           [3., 4., 5.],
+           [0., 1., 2.]])
+    """
+    _mx_nd_np.random.shuffle(x)
diff --git a/python/mxnet/symbol/numpy/random.py b/python/mxnet/symbol/numpy/random.py
index 48bccb64a2b4..94c29f407acc 100644
--- a/python/mxnet/symbol/numpy/random.py
+++ b/python/mxnet/symbol/numpy/random.py
@@ -21,7 +21,7 @@
 from ...context import current_context
 from . import _internal as _npi
 
-__all__ = ['randint', 'uniform', 'normal', 'rand']
+__all__ = ['randint', 'uniform', 'normal', 'rand', 'shuffle']
 
 
 def randint(low, high=None, size=None, dtype=None, ctx=None, out=None):
@@ -288,3 +288,39 @@ def choice(a, size=None, replace=True, p=None, ctx=None, out=None):
             return _npi.choice(a=a, size=size, replace=replace, ctx=ctx, weighted=False, out=out)
         else:
             return _npi.choice(p, a=a, size=size, replace=replace, ctx=ctx, weighted=True, out=out)
+
+
+def shuffle(x):
+    """
+    Modify a sequence in-place by shuffling its contents.
+
+    This function only shuffles the array along the first axis of a
+    multi-dimensional array. The order of sub-arrays is changed but
+    their contents remain the same.
+
+    Parameters
+    ----------
+    x: _Symbol
+        The array or list to be shuffled.
+
+    Returns
+    -------
+    None
+
+    Examples
+    --------
+    >>> arr = np.arange(10)
+    >>> np.random.shuffle(arr)
+    >>> arr
+    array([5., 1., 0., 6., 7., 3., 9., 8., 4., 2.])  # random
+
+    Multi-dimensional arrays are only shuffled along the first axis:
+
+    >>> arr = np.arange(9).reshape((3, 3))
+    >>> np.random.shuffle(arr)
+    >>> arr
+    array([[6., 7., 8.], # random
+           [3., 4., 5.],
+           [0., 1., 2.]])
+    """
+    _npi.shuffle(x, out=x)
diff --git a/src/operator/random/shuffle_op.cc b/src/operator/random/shuffle_op.cc
index 86797c136bab..0f64fbc51449 100644
--- a/src/operator/random/shuffle_op.cc
+++ b/src/operator/random/shuffle_op.cc
@@ -122,7 +122,7 @@ void ShuffleForwardCPU(const nnvm::NodeAttrs& attrs,
 
 NNVM_REGISTER_OP(_shuffle)
 .add_alias("shuffle")
-.add_alias("_np__random_shuffle")
+.add_alias("_npi_shuffle")
 .describe(R"code(Randomly shuffle the elements.
 
 This shuffles the array along the first axis.

From 5aa3a7a5251f7ed9276fa898158957da0538df4f Mon Sep 17 00:00:00 2001
From: Leonard Lausen <lausen@amazon.com>
Date: Mon, 23 Dec 2019 12:31:13 +0800
Subject: [PATCH 726/813] Disable OpenMP offloading support for 3rdparty/openmp
 (#17098)

* Disable OpenMP offloading support for 3rdparty/openmp

OpenMP offloading was introduced some time during the past two years and is
enabled by default. With upgrading 3rdparty/openmp in
https://github.com/apache/incubator-mxnet/pull/17012
it was made part of the MXNet CMake build.

But we don't use OpenMP offloading and the Cuda target in the llvm OpenMP
Offloading build is broken in our setting.

* Update CMake on CI
---
 CMakeLists.txt                           |  3 +-
 ci/build_windows.py                      | 48 +++++++++++++++---------
 ci/docker/Dockerfile.build.android_armv7 |  2 +-
 ci/docker/Dockerfile.build.android_armv8 |  4 +-
 ci/docker/Dockerfile.build.armv6         |  2 +-
 ci/docker/Dockerfile.build.armv7         |  2 +-
 ci/docker/Dockerfile.build.armv8         |  2 +-
 ci/docker/Dockerfile.build.jetson        |  2 +-
 ci/docker/install/ubuntu_core.sh         |  9 ++---
 ci/util.py                               | 33 +++++++++++++++-
 10 files changed, 75 insertions(+), 32 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index fcbe707aa9ef..f0779abd06d4 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -1,4 +1,4 @@
-cmake_minimum_required(VERSION 3.0.2)
+cmake_minimum_required(VERSION 3.13)
 
 # workaround to store CMAKE_CROSSCOMPILING because is getting reset by the project command
 if(CMAKE_CROSSCOMPILING)
@@ -452,6 +452,7 @@ if(USE_OPENMP)
     set(OPENMP_STANDALONE_BUILD TRUE)
     set(LIBOMP_ENABLE_SHARED TRUE)
     set(CMAKE_BUILD_TYPE Release)
+    set(OPENMP_ENABLE_LIBOMPTARGET OFF CACHE BOOL "LLVM OpenMP offloading support")  # Requires CMP0077 CMake 3.13
     add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/3rdparty/openmp)
   endfunction()
 
diff --git a/ci/build_windows.py b/ci/build_windows.py
index ce77c316ab20..5839e8d793d1 100755
--- a/ci/build_windows.py
+++ b/ci/build_windows.py
@@ -1,4 +1,4 @@
-#!/usr/bin/env python
+#!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
 # Licensed to the Apache Software Foundation (ASF) under one
@@ -28,7 +28,9 @@
 import platform
 import shutil
 import sys
+import tempfile
 import time
+import zipfile
 from distutils.dir_util import copy_tree
 from enum import Enum
 from subprocess import check_call
@@ -147,22 +149,33 @@ def windows_build(args):
     mxnet_root = get_mxnet_root()
     logging.info("Found MXNet root: {}".format(mxnet_root))
 
-    with remember_cwd():
-        os.chdir(path)
-        cmd = "\"{}\" && cmake -G \"NMake Makefiles JOM\" {} {}".format(args.vcvars,
-                                                                        CMAKE_FLAGS[args.flavour],
-                                                                        mxnet_root)
-        logging.info("Generating project with CMake:\n{}".format(cmd))
-        check_call(cmd, shell=True)
-
-        cmd = "\"{}\" && jom".format(args.vcvars)
-        logging.info("Building with jom:\n{}".format(cmd))
-
-        t0 = int(time.time())
-        check_call(cmd, shell=True)
-
-        logging.info("Build flavour: {} complete in directory: \"{}\"".format(args.flavour, os.path.abspath(path)))
-        logging.info("Build took {}".format(datetime.timedelta(seconds=int(time.time() - t0))))
+    url = 'https://github.com/Kitware/CMake/releases/download/v3.16.1/cmake-3.16.1-win64-x64.zip'
+    with tempfile.TemporaryDirectory() as tmpdir:
+        cmake_file_path = download_file(url, tmpdir)
+        with zipfile.ZipFile(cmake_file_path, 'r') as zip_ref:
+            # Create $tmpdir\cmake-3.16.1-win64-x64\bin\cmake.exe
+            zip_ref.extractall(tmpdir)
+
+        with remember_cwd():
+            os.chdir(path)
+            cmd = "\"{}\" && {} -G \"NMake Makefiles JOM\" {} {}".format(
+                args.vcvars,
+                os.path.join(tmpdir, 'cmake-3.16.1-win64-x64', 'bin', 'cmake.exe'),
+                CMAKE_FLAGS[args.flavour], mxnet_root)
+            logging.info("Generating project with CMake:\n{}".format(cmd))
+            check_call(cmd, shell=True)
+
+            cmd = "\"{}\" && jom".format(args.vcvars)
+            logging.info("Building with jom:\n{}".format(cmd))
+
+            t0 = int(time.time())
+            check_call(cmd, shell=True)
+
+            logging.info(
+                "Build flavour: {} complete in directory: \"{}\"".format(
+                    args.flavour, os.path.abspath(path)))
+            logging.info("Build took {}".format(
+                datetime.timedelta(seconds=int(time.time() - t0))))
     windows_package(args)
 
 
@@ -262,4 +275,3 @@ def main():
 
 if __name__ == '__main__':
     sys.exit(main())
-
diff --git a/ci/docker/Dockerfile.build.android_armv7 b/ci/docker/Dockerfile.build.android_armv7
index a2e98cd2efe1..2c923a015b63 100644
--- a/ci/docker/Dockerfile.build.android_armv7
+++ b/ci/docker/Dockerfile.build.android_armv7
@@ -18,7 +18,7 @@
 #
 # Dockerfile to build MXNet for Android ARMv7
 
-FROM mxnetcipinned/dockcross-base:11262018
+FROM dockcross/base
 MAINTAINER Pedro Larroy "pllarroy@amazon.com"
 
 # The cross-compiling emulator
diff --git a/ci/docker/Dockerfile.build.android_armv8 b/ci/docker/Dockerfile.build.android_armv8
index f7de86763457..ca62288129bb 100644
--- a/ci/docker/Dockerfile.build.android_armv8
+++ b/ci/docker/Dockerfile.build.android_armv8
@@ -18,7 +18,7 @@
 #
 # Dockerfile to build MXNet for Android ARM64/ARMv8
 
-FROM mxnetcipinned/dockcross-base:11262018
+FROM dockcross/base
 MAINTAINER Pedro Larroy "pllarroy@amazon.com"
 
 RUN apt-get update && apt-get install -y \
@@ -82,4 +82,4 @@ RUN /work/ubuntu_adduser.sh
 
 COPY runtime_functions.sh /work/
 
-WORKDIR /work/build
\ No newline at end of file
+WORKDIR /work/build
diff --git a/ci/docker/Dockerfile.build.armv6 b/ci/docker/Dockerfile.build.armv6
index 60e223b7a60f..e6a7ffe758b9 100644
--- a/ci/docker/Dockerfile.build.armv6
+++ b/ci/docker/Dockerfile.build.armv6
@@ -18,7 +18,7 @@
 #
 # Dockerfile to build MXNet for ARMv6
 
-FROM mxnetcipinned/dockcross-linux-armv6:11262018
+FROM dockcross/linux-armv6
 
 ENV ARCH armv6l
 ENV HOSTCC gcc
diff --git a/ci/docker/Dockerfile.build.armv7 b/ci/docker/Dockerfile.build.armv7
index 0b557d5839e9..bad9ab214050 100644
--- a/ci/docker/Dockerfile.build.armv7
+++ b/ci/docker/Dockerfile.build.armv7
@@ -18,7 +18,7 @@
 #
 # Dockerfile to build MXNet for Android ARMv7
 
-FROM mxnetcipinned/dockcross-linux-armv7:11262018
+FROM dockcross/linux-armv7
 
 ENV ARCH armv7l
 ENV HOSTCC gcc
diff --git a/ci/docker/Dockerfile.build.armv8 b/ci/docker/Dockerfile.build.armv8
index ef9c95865590..bd2373180f0b 100644
--- a/ci/docker/Dockerfile.build.armv8
+++ b/ci/docker/Dockerfile.build.armv8
@@ -18,7 +18,7 @@
 #
 # Dockerfile to build MXNet for ARM64/ARMv8
 
-FROM mxnetcipinned/dockcross-linux-arm64:11262018
+FROM dockcross/linux-arm64
 
 ENV ARCH aarch64
 ENV HOSTCC gcc
diff --git a/ci/docker/Dockerfile.build.jetson b/ci/docker/Dockerfile.build.jetson
index 07097887f87d..e31ee43a93d8 100644
--- a/ci/docker/Dockerfile.build.jetson
+++ b/ci/docker/Dockerfile.build.jetson
@@ -22,7 +22,7 @@
 
 FROM nvidia/cuda:9.0-cudnn7-devel as cudabuilder
 
-FROM mxnetcipinned/dockcross-linux-arm64:11262018
+FROM dockcross/linux-arm64
 
 ENV ARCH aarch64
 ENV HOSTCC gcc
diff --git a/ci/docker/install/ubuntu_core.sh b/ci/docker/install/ubuntu_core.sh
index 8426b539f4c4..bd5d1f6fdf6f 100755
--- a/ci/docker/install/ubuntu_core.sh
+++ b/ci/docker/install/ubuntu_core.sh
@@ -57,11 +57,10 @@ apt-get install -y \
 ln -s /usr/lib/x86_64-linux-gnu/libturbojpeg.so.0.1.0 /usr/lib/x86_64-linux-gnu/libturbojpeg.so
 
 
-# Note: we specify an exact cmake version to work around a cmake 3.10 CUDA 10 issue.
-# Reference: https://github.com/clab/dynet/issues/1457
+# CMake 3.13.2+ is required
 mkdir /opt/cmake && cd /opt/cmake
-wget -nv https://cmake.org/files/v3.12/cmake-3.12.4-Linux-x86_64.sh
-sh cmake-3.12.4-Linux-x86_64.sh --prefix=/opt/cmake --skip-license
+wget -nv https://cmake.org/files/v3.13/cmake-3.13.5-Linux-x86_64.sh
+sh cmake-3.13.5-Linux-x86_64.sh --prefix=/opt/cmake --skip-license
 ln -s /opt/cmake/bin/cmake /usr/local/bin/cmake
-rm cmake-3.12.4-Linux-x86_64.sh
+rm cmake-3.13.5-Linux-x86_64.sh
 cmake --version
diff --git a/ci/util.py b/ci/util.py
index 4b3a399184f9..cd5665d04df8 100644
--- a/ci/util.py
+++ b/ci/util.py
@@ -15,12 +15,15 @@
 # specific language governing permissions and limitations
 # under the License.
 
-import os
 import contextlib
 import logging
 import logging.config
+import os
+import subprocess
 import sys
 
+import requests
+
 
 def get_mxnet_root() -> str:
     curpath = os.path.abspath(os.path.dirname(__file__))
@@ -130,3 +133,31 @@ def config_logging():
     # or sensitive information
     logging.getLogger("botocore").setLevel(logging.WARNING)
     logging.getLogger("requests").setLevel(logging.WARNING)
+
+
+# Takes url and downloads it to the dest_path directory on Windows.
+def download_file(url, dest_path):
+    file_name = url.split('/')[-1]
+    full_path = "{}\\{}".format(dest_path, file_name)
+    logging.info("Downloading: {}".format(full_path))
+    r = requests.get(url, stream=True)
+    if r.status_code == 404:
+        return r.status_code
+    elif r.status_code != 200:
+        logging.error("{} returned status code {}".format(url, r.status_code))
+    with open(full_path, 'wb') as f:
+        for chunk in r.iter_content(chunk_size=1024):
+            if chunk: # filter out keep-alive new chunks
+                f.write(chunk)
+    return full_path
+
+
+# Takes arguments and runs command on host.  Shell is disabled by default.
+def run_command(args, shell=False):
+    try:
+        logging.info("Issuing command: {}".format(args))
+        res = subprocess.check_output(args, shell=shell, timeout=1800).decode("utf-8").replace("\r\n", "")
+        logging.info("Output: {}".format(res))
+    except subprocess.CalledProcessError as e:
+        raise RuntimeError("command '{}' return with error (code {}): {}".format(e.cmd, e.returncode, e.output))
+    return res

From efc4ad838a5193856d79ab3f70537af89e4ebffb Mon Sep 17 00:00:00 2001
From: Iblis Lin <iblis@hs.ntnu.edu.tw>
Date: Tue, 24 Dec 2019 09:14:39 +0800
Subject: [PATCH 727/813] [MXNET-1440] julia: porting `current_context`
 (#17142)

* julia: porting `current_context`

- And introduce new macros for changing default context
  `@context`, `@gpu`, `@cpu`
---
 julia/src/MXNet.jl             |   6 +-
 julia/src/context.jl           | 104 ++++++++++++++++++++++++++++++++-
 julia/src/ndarray/array.jl     |  18 +++---
 julia/src/ndarray/type.jl      |   2 +-
 julia/test/unittest/context.jl |  77 ++++++++++++++++++++++++
 julia/test/unittest/ndarray.jl |   2 +-
 6 files changed, 197 insertions(+), 12 deletions(-)

diff --git a/julia/src/MXNet.jl b/julia/src/MXNet.jl
index 86d7d06806fc..4c50d7321158 100644
--- a/julia/src/MXNet.jl
+++ b/julia/src/MXNet.jl
@@ -80,7 +80,11 @@ export Context,
        cpu,
        gpu,
        num_gpus,
-       gpu_memory_info
+       gpu_memory_info,
+       current_context,
+       @context,
+       @cpu,
+       @gpu
 
 # model.jl
 export AbstractModel,
diff --git a/julia/src/context.jl b/julia/src/context.jl
index 68e69138e10e..bb80e0728724 100644
--- a/julia/src/context.jl
+++ b/julia/src/context.jl
@@ -31,11 +31,89 @@ struct Context
   Context(dev_type::CONTEXT_TYPE, dev_id::Integer = 0) = new(dev_type, dev_id)
 end
 
+const _default_ctx = Ref{Context}(Context(CPU, 0))
+
 Context(dev_type::Integer, dev_id::Integer = 0) =
   Context(convert(CONTEXT_TYPE, dev_type), dev_id)
 
 Base.show(io::IO, ctx::Context) =
-  print(io, "$(ctx.device_type)$(ctx.device_id)")
+  print(io, lowercase("$(ctx.device_type)$(ctx.device_id)"))
+
+function _with_context(dev_type::Union{Symbol,Expr}, dev_id, e::Expr)
+  global _default_ctx
+  quote
+    ctx = current_context()
+    ctx′ = Context($(esc(dev_type)), $(esc(dev_id)))
+    $_default_ctx[] = ctx′
+    try
+      return $(esc(e))
+    finally
+      $_default_ctx[] = ctx
+    end
+  end
+end
+
+"""
+    @context device_type [device_id] expr
+
+Change the default context in the following expression.
+
+# Examples
+```jl-repl
+julia> mx.@context mx.GPU begin
+         mx.zeros(2, 3)
+       end
+2×3 NDArray{Float32,2} @ gpu0:
+ 0.0f0  0.0f0  0.0f0
+ 0.0f0  0.0f0  0.0f0
+
+julia> @context mx.GPU mx.zeros(3, 2)
+3×2 NDArray{Float32,2} @ gpu0:
+ 0.0f0  0.0f0
+ 0.0f0  0.0f0
+ 0.0f0  0.0f0
+```
+"""
+macro context(dev_type, e::Expr)
+  _with_context(dev_type, 0, e)
+end
+
+macro context(dev_type, dev_id, e::Expr)
+  _with_context(dev_type, dev_id, e)
+end
+
+for dev ∈ [:cpu, :gpu]
+  ctx = QuoteNode(Symbol(uppercase(string(dev))))
+  docstring = """
+        @$dev [device_id] expr
+
+    A shorthand for `@context mx.GPU`.
+
+    # Examples
+    ```jl-repl
+    julia> mx.@with_gpu mx.zeros(2, 3)
+    2×3 NDArray{Float32,2} @ gpu0:
+     0.0f0  0.0f0  0.0f0
+     0.0f0  0.0f0  0.0f0
+    ```
+    """
+  @eval begin
+    @doc $docstring ->
+    macro $dev(e::Expr)
+      ctx = $ctx
+      quote
+        @context $ctx $(esc(e))
+      end
+    end
+
+    macro $dev(dev_id, e::Expr)
+      ctx = $ctx
+      quote
+        @context $ctx $(esc(dev_id)) $(esc(e))
+      end
+    end
+  end
+end  # for dev ∈ [:cpu, :gpu]
 
 """
     cpu(dev_id)
@@ -86,3 +164,27 @@ function gpu_memory_info(dev_id = 0)
   @mxcall :MXGetGPUMemoryInformation64 (Cint, Ref{UInt64}, Ref{UInt64}) dev_id free n
   free[], n[]
 end
+
+"""
+    current_context()
+
+Return the current context.
+
+By default, `mx.cpu()` is used for all the computations
+and it can be overridden by using the `@context` macro.
+
+# Examples
+```jl-repl
+julia> mx.current_context()
+cpu0
+
+julia> mx.@context mx.GPU 1 begin  # Context changed in the following code block
+         mx.current_context()
+       end
+gpu1
+
+julia> mx.current_context()
+cpu0
+```
+"""
+current_context() = _default_ctx[]
diff --git a/julia/src/ndarray/array.jl b/julia/src/ndarray/array.jl
index b71e5ddf9397..2cd9c2e24f9c 100644
--- a/julia/src/ndarray/array.jl
+++ b/julia/src/ndarray/array.jl
@@ -28,13 +28,14 @@ Base.similar(x::NDArray{T,N}; writable = x.writable, ctx = context(x)) where {T,
   NDArray{T,N}(undef, size(x)...; writable = writable, ctx = ctx)
 
 """
-    zeros([DType], dims, [ctx::Context = cpu()])
+    zeros([DType], dims, ctx::Context = current_context())
     zeros([DType], dims...)
     zeros(x::NDArray)
 
 Create zero-ed `NDArray` with specific shape and type.
 """
-function zeros(::Type{T}, dims::NTuple{N,Int}, ctx::Context = cpu()) where {N,T<:DType}
+function zeros(::Type{T}, dims::NTuple{N,Int},
+               ctx::Context = current_context()) where {N,T<:DType}
   x = NDArray{T}(undef, dims..., ctx = ctx)
   x[:] = zero(T)
   x
@@ -42,7 +43,7 @@ end
 
 zeros(::Type{T}, dims::Int...) where {T<:DType} = zeros(T, dims)
 
-zeros(dims::NTuple{N,Int}, ctx::Context = cpu()) where N =
+zeros(dims::NTuple{N,Int}, ctx::Context = current_context()) where N =
   zeros(MX_float, dims, ctx)
 zeros(dims::Int...) = zeros(dims)
 
@@ -50,13 +51,14 @@ zeros(x::NDArray)::typeof(x)      = zeros_like(x)
 Base.zeros(x::NDArray)::typeof(x) = zeros_like(x)
 
 """
-    ones([DType], dims, [ctx::Context = cpu()])
+    ones([DType], dims, ctx::Context = current_context())
     ones([DType], dims...)
     ones(x::NDArray)
 
 Create an `NDArray` with specific shape & type, and initialize with 1.
 """
-function ones(::Type{T}, dims::NTuple{N,Int}, ctx::Context = cpu()) where {N,T<:DType}
+function ones(::Type{T}, dims::NTuple{N,Int},
+              ctx::Context = current_context()) where {N,T<:DType}
   arr = NDArray{T}(undef, dims..., ctx = ctx)
   arr[:] = one(T)
   arr
@@ -64,7 +66,7 @@ end
 
 ones(::Type{T}, dims::Int...) where T<:DType = ones(T, dims)
 
-ones(dims::NTuple{N,Int}, ctx::Context = cpu()) where N =
+ones(dims::NTuple{N,Int}, ctx::Context = current_context()) where N =
   ones(MX_float, dims, ctx)
 ones(dims::Int...) = ones(dims)
 
@@ -458,12 +460,12 @@ function Base.fill!(arr::NDArray, x)
 end
 
 """
-    fill(x, dims, ctx=cpu())
+    fill(x, dims, ctx = current_context())
     fill(x, dims...)
 
 Create an `NDArray` filled with the value `x`, like `Base.fill`.
 """
-function fill(x::T, dims::NTuple{N,Integer}, ctx::Context = cpu()) where {T,N}
+function fill(x::T, dims::NTuple{N,Integer}, ctx::Context = current_context()) where {T,N}
   arr = NDArray{T}(undef, dims, ctx = ctx)
   arr[:] = x
   arr
diff --git a/julia/src/ndarray/type.jl b/julia/src/ndarray/type.jl
index 8d90d63f0360..e24c89291dcb 100644
--- a/julia/src/ndarray/type.jl
+++ b/julia/src/ndarray/type.jl
@@ -116,7 +116,7 @@ end
 
 # UndefInitializer constructors
 NDArray{T,N}(::UndefInitializer, dims::NTuple{N,Integer};
-             writable = true, ctx::Context = cpu()) where {T,N} =
+             writable = true, ctx::Context = current_context()) where {T,N} =
   NDArray{T,N}(_ndarray_alloc(T, dims, ctx, false), writable)
 NDArray{T,N}(::UndefInitializer, dims::Vararg{Integer,N}; kw...) where {T,N} =
   NDArray{T,N}(undef, dims; kw...)
diff --git a/julia/test/unittest/context.jl b/julia/test/unittest/context.jl
index 0a8f086a194a..e903f9212930 100644
--- a/julia/test/unittest/context.jl
+++ b/julia/test/unittest/context.jl
@@ -26,8 +26,85 @@ function test_num_gpus()
   @test num_gpus() >= 0
 end
 
+function test_context_macro()
+  @info "Context::@context"
+
+  @context mx.CPU 42 begin
+    ctx = mx.current_context()
+    @test ctx.device_type == mx.CPU
+    @test ctx.device_id   == 42
+
+    @context mx.GPU 24 begin
+      ctx = mx.current_context()
+      @test ctx.device_type == mx.GPU
+      @test ctx.device_id   == 24
+    end
+
+    ctx = mx.current_context()
+    @test ctx.device_type == mx.CPU
+    @test ctx.device_id   == 42
+  end
+
+  function f()
+    ctx = mx.current_context()
+    @test ctx.device_type == mx.GPU
+    @test ctx.device_id   == 123
+  end
+
+  @context mx.GPU 123 begin
+    f()
+  end
+
+  @context mx.GPU begin
+    ctx = mx.current_context()
+    @test ctx.device_type == mx.GPU
+    @test ctx.device_id   == 0
+  end
+
+  @context mx.CPU begin
+    ctx = mx.current_context()
+    @test ctx.device_type == mx.CPU
+    @test ctx.device_id   == 0
+  end
+
+  @info "Context::@gpu"
+  @gpu 123 f()
+  @gpu begin
+    ctx = mx.current_context()
+    @test ctx.device_type == mx.GPU
+    @test ctx.device_id   == 0
+  end
+  let n = 321
+    @gpu n begin
+      ctx = mx.current_context()
+      @test ctx.device_type == mx.GPU
+      @test ctx.device_id   == 321
+    end
+  end
+
+  @info "Context::@cpu"
+  @cpu 123 begin
+    ctx = mx.current_context()
+    @test ctx.device_type == mx.CPU
+    @test ctx.device_id   == 123
+  end
+  @cpu begin
+    ctx = mx.current_context()
+    @test ctx.device_type == mx.CPU
+    @test ctx.device_id   == 0
+  end
+  let n = 321
+    @cpu n begin
+      ctx = mx.current_context()
+      @test ctx.device_type == mx.CPU
+      @test ctx.device_id   == 321
+    end
+  end
+end
+
 @testset "Context Test" begin
   test_num_gpus()
+  test_context_macro()
 end
 
 
diff --git a/julia/test/unittest/ndarray.jl b/julia/test/unittest/ndarray.jl
index 599b0a65bfc4..fb59b71edd60 100644
--- a/julia/test/unittest/ndarray.jl
+++ b/julia/test/unittest/ndarray.jl
@@ -1294,7 +1294,7 @@ function test_show()
     @test occursin("1×4", str)
     @test occursin("NDArray", str)
     @test occursin("Int64", str)
-    @test occursin("CPU", str)
+    @test occursin("cpu", str)
     @test match(r"1\s+2\s+3\s+4", str) != nothing
   end
 

From 318d9c7f8f5434bdff37ddae6bfd7b03b7e1dede Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Wed, 25 Dec 2019 16:40:11 +0800
Subject: [PATCH 728/813] Fix reshape interoperability test (#17155)

* fix reshape interoperability test

* fix for scipy import
---
 ci/docker/install/requirements                       | 4 ++--
 tests/python/unittest/test_metric.py                 | 5 +++--
 tests/python/unittest/test_numpy_interoperability.py | 2 +-
 3 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/ci/docker/install/requirements b/ci/docker/install/requirements
index cbfc521e2c08..fd716f5fa815 100644
--- a/ci/docker/install/requirements
+++ b/ci/docker/install/requirements
@@ -26,8 +26,8 @@ h5py==2.8.0rc1
 mock==2.0.0
 nose==1.3.7
 nose-timer==0.7.3
-numpy>1.16.0,<2.0.0
+numpy>1.16.0,<1.18.0
 pylint==2.3.1; python_version >= '3.0'
 requests<2.19.0,>=2.18.4
-scipy==1.0.1
+scipy==1.2.1
 six==1.11.0
diff --git a/tests/python/unittest/test_metric.py b/tests/python/unittest/test_metric.py
index a1e5128d8ac6..e7273fba35d5 100644
--- a/tests/python/unittest/test_metric.py
+++ b/tests/python/unittest/test_metric.py
@@ -18,6 +18,7 @@
 import mxnet as mx
 import numpy as np
 import scipy
+from scipy.stats import pearsonr
 import json
 import math
 from common import with_seed
@@ -267,7 +268,7 @@ def test_pearsonr():
     pred1 = mx.nd.array([[0.3, 0.7], [0, 1.], [0.4, 0.6]])
     label1 = mx.nd.array([[1, 0], [0, 1], [0, 1]])
     pearsonr_expected_np = np.corrcoef(pred1.asnumpy().ravel(), label1.asnumpy().ravel())[0, 1]
-    pearsonr_expected_scipy, _ = scipy.stats.pearsonr(pred1.asnumpy().ravel(), label1.asnumpy().ravel())
+    pearsonr_expected_scipy, _ = pearsonr(pred1.asnumpy().ravel(), label1.asnumpy().ravel())
     macro_pr = mx.metric.create('pearsonr', average='macro')
     micro_pr = mx.metric.create('pearsonr', average='micro')
 
@@ -289,7 +290,7 @@ def test_pearsonr():
     label12 = mx.nd.array([[1, 0], [0, 1], [0, 1], [1, 0], [0, 1], [0, 1]])
 
     pearsonr_expected_np = np.corrcoef(pred12.asnumpy().ravel(), label12.asnumpy().ravel())[0, 1]
-    pearsonr_expected_scipy, _ = scipy.stats.pearsonr(pred12.asnumpy().ravel(), label12.asnumpy().ravel())
+    pearsonr_expected_scipy, _ = pearsonr(pred12.asnumpy().ravel(), label12.asnumpy().ravel())
 
     macro_pr.reset()
     micro_pr.update([label2], [pred2])
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index fcdf547bfbec..9b445044a3c1 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -758,7 +758,7 @@ def _add_workload_reshape():
     # OpArgMngr.add_workload('reshape', b, (2, 2), order='F')  # Items are not equal with order='F'
 
     a = np.array(_np.ones((0, 2)))
-    OpArgMngr.add_workload('reshape', a, -1, 2)
+    OpArgMngr.add_workload('reshape', a, (-1, 2))
 
 
 def _add_workload_rint(array_pool):

From 410165b0a5f903edf500786d4c1b973e389c7b57 Mon Sep 17 00:00:00 2001
From: Sheng Zha <szha@users.noreply.github.com>
Date: Wed, 25 Dec 2019 13:18:32 -0800
Subject: [PATCH 729/813] [CD] enable s3 publish for nightly builds in cd
 (#17112)

* enable s3 publish for nightly builds in cd

* pass credential through env

* confine credential variables to subprocess
---
 cd/python/pypi/Jenkins_pipeline.groovy |  3 ++-
 cd/python/pypi/pypi_publish.py         | 21 ++++++++++-----------
 ci/docker/runtime_functions.sh         |  9 +++++++++
 3 files changed, 21 insertions(+), 12 deletions(-)

diff --git a/cd/python/pypi/Jenkins_pipeline.groovy b/cd/python/pypi/Jenkins_pipeline.groovy
index e9f172a570fe..fa9300db3ca0 100644
--- a/cd/python/pypi/Jenkins_pipeline.groovy
+++ b/cd/python/pypi/Jenkins_pipeline.groovy
@@ -27,7 +27,7 @@
 // This is a temporary solution until we are confident with the packages generated by CI
 // This should be removed in the not too distant future.
 // We only skip the publish step so we can still QA the other variants.
-pypi_releases = ["cu92", "cu92mkl"]
+pypi_releases = []
 
 def get_pipeline(mxnet_variant) {
   def node_type = mxnet_variant.startsWith('cu') ? NODE_LINUX_GPU : NODE_LINUX_CPU
@@ -72,6 +72,7 @@ def push(mxnet_variant) {
     } else {
       echo "Temporarily skipping publishing PyPI package for '${mxnet_variant}'."
     }
+    sh "./ci/docker/runtime_functions.sh cd_s3_publish"
   }
 }
 
diff --git a/cd/python/pypi/pypi_publish.py b/cd/python/pypi/pypi_publish.py
index 7e09f644c734..2729068dd503 100755
--- a/cd/python/pypi/pypi_publish.py
+++ b/cd/python/pypi/pypi_publish.py
@@ -35,10 +35,8 @@ def post_wheel(path):
     logging.info('Posting {} to PyPI'.format(path))
     pypi_credentials = get_secret()
 
-    cmd = 'python3 -m twine upload --username {} --password {} {}'.format(
-        pypi_credentials['username'],
-        pypi_credentials['password'],
-        path)
+    cmd = 'python3 -m twine upload {}'.format(path)
+    version = os.path.basename(path).split('-')[1]
 
     # The PyPI credentials for DEV has username set to 'skipPublish'
     # This way we do not attempt to publish the PyPI package
@@ -47,14 +45,15 @@ def post_wheel(path):
         print('In DEV account, skipping publish')
         print('Would have run: {}'.format(cmd))
         return 0
-    else:
+    elif any(test_version_mark in version for test_version_mark in ['a', 'b', 'dev']):
         print('Skipping publishing nightly builds to Pypi.')
         print('See https://github.com/pypa/pypi-support/issues/50 for details')
         return 0
-
-        # DO NOT PRINT CMD IN THIS BLOCK, includes password
-        p = subprocess.run(cmd.split(' '),
-                        stdout=subprocess.PIPE)
+    else:
+        env = os.environ.copy()
+        env['TWINE_USERNAME'] = pypi_credentials['username']
+        env['TWINE_PASSWORD'] = pypi_credentials['password']
+        p = subprocess.run(cmd.split(' '), stdout=subprocess.PIPE, env=env)
         logging.info(p.stdout)
         return p.returncode
 
@@ -85,7 +84,7 @@ def get_secret():
             raise e
     else:
         return json.loads(get_secret_value_response['SecretString'])
-        
-            
+
+
 if __name__ == '__main__':
     sys.exit(post_wheel(sys.argv[1]))
diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index b658f953a78a..e078b2a8f89c 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -2065,6 +2065,15 @@ cd_pypi_publish() {
     ./cd/python/pypi/pypi_publish.py `readlink -f wheel_build/dist/*.whl`
 }
 
+cd_s3_publish() {
+    set -ex
+    pip3 install --user awscli
+    filepath=$(readlink -f wheel_build/dist/*.whl)
+    filename=$(basename $file_path)
+    variant=$(echo $filename | cut -d'-' -f1 | cut -d'_' -f2 -s)
+    aws s3 cp --grants read=uri=http://acs.amazonaws.com/groups/global/AllUsers,full=id=43f628fab72838a4f0b929d7f1993b14411f4b0294b011261bc6bd3e950a6822 s3://apache-mxnet/dist/${variant}/${filename}
+}
+
 build_static_scala_mkl() {
     set -ex
     pushd .

From 2551a9d8c8a4f5fd73c98e56ff79ab5410053d0e Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Thu, 26 Dec 2019 07:07:30 +0800
Subject: [PATCH 730/813] fix norm sparse fallback (#17149)

---
 src/operator/tensor/broadcast_reduce_norm_value.cc | 2 +-
 src/operator/tensor/broadcast_reduce_norm_value.cu | 2 +-
 src/operator/tensor/broadcast_reduce_op.h          | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/operator/tensor/broadcast_reduce_norm_value.cc b/src/operator/tensor/broadcast_reduce_norm_value.cc
index 4cd92d44997e..9acc157f8eca 100644
--- a/src/operator/tensor/broadcast_reduce_norm_value.cc
+++ b/src/operator/tensor/broadcast_reduce_norm_value.cc
@@ -40,7 +40,7 @@ void L2NormComputeEx<cpu>(const nnvm::NodeAttrs& attrs,
   const NormParam& param = nnvm::get<NormParam>(attrs.parsed);
   mshadow::Stream<cpu>* s = ctx.get_stream<cpu>();
   const NDArrayStorageType istype = inputs[0].storage_type();
-  const mxnet::TShape axis = param.axis.has_value() ? param.axis.value() : mxnet::TShape();
+  const mxnet::TShape axis = param.axis.has_value() ? param.axis.value() : mxnet::TShape(0, -1);
   if ((istype == kRowSparseStorage || istype == kCSRStorage) && axis.ndim() == 0 &&
        param.ord == 2) {
     // l2 norm on the entire array
diff --git a/src/operator/tensor/broadcast_reduce_norm_value.cu b/src/operator/tensor/broadcast_reduce_norm_value.cu
index 188c93e61221..735c3d7faec9 100644
--- a/src/operator/tensor/broadcast_reduce_norm_value.cu
+++ b/src/operator/tensor/broadcast_reduce_norm_value.cu
@@ -39,7 +39,7 @@ void L2NormComputeEx<gpu>(const nnvm::NodeAttrs& attrs,
   const NormParam& param = nnvm::get<NormParam>(attrs.parsed);
   mshadow::Stream<gpu>* s = ctx.get_stream<gpu>();
   const NDArrayStorageType istype = inputs[0].storage_type();
-  const mxnet::TShape axis = param.axis.has_value() ? param.axis.value() : mxnet::TShape();
+  const mxnet::TShape axis = param.axis.has_value() ? param.axis.value() : mxnet::TShape(0, -1);
   if ((istype == kRowSparseStorage || istype == kCSRStorage) && axis.ndim() == 0 &&
        param.ord == 2) {
     // l2 norm on the entire array
diff --git a/src/operator/tensor/broadcast_reduce_op.h b/src/operator/tensor/broadcast_reduce_op.h
index 27e22491ca35..799f86544160 100644
--- a/src/operator/tensor/broadcast_reduce_op.h
+++ b/src/operator/tensor/broadcast_reduce_op.h
@@ -1152,7 +1152,7 @@ inline bool LpNormStorageType(const nnvm::NodeAttrs& attrs,
                                      DispatchMode::kFCompute);
   }
   if (param.ord == 2) {
-    const mxnet::TShape axis = param.axis.has_value() ? param.axis.value() : mxnet::TShape();
+    const mxnet::TShape axis = param.axis.has_value() ? param.axis.value() : mxnet::TShape(0, -1);
     if (!dispatched && (in_stype == kRowSparseStorage || in_stype == kCSRStorage) &&
         axis.ndim() == 0 && param.ord == 2) {
       // l2 norm: rsp/csr, axis = () -> dns

From 872b533c45a627c79e8be9800bdcadebd77b28af Mon Sep 17 00:00:00 2001
From: Xi Wang <xidulu@gmail.com>
Date: Thu, 26 Dec 2019 10:56:21 +0800
Subject: [PATCH 731/813] randn implemented (#17141)

---
 python/mxnet/numpy/random.py           | 43 +++++++++++++++++++++++++-
 tests/python/unittest/test_numpy_op.py | 22 +++++++++++++
 2 files changed, 64 insertions(+), 1 deletion(-)

diff --git a/python/mxnet/numpy/random.py b/python/mxnet/numpy/random.py
index ebc24de63282..95719a005cec 100644
--- a/python/mxnet/numpy/random.py
+++ b/python/mxnet/numpy/random.py
@@ -20,7 +20,7 @@
 from __future__ import absolute_import
 from ..ndarray import numpy as _mx_nd_np
 
-__all__ = ["randint", "uniform", "normal", "choice", "rand", "multinomial", "shuffle"]
+__all__ = ["randint", "uniform", "normal", "choice", "rand", "multinomial", "shuffle", "randn"]
 
 
 def randint(low, high=None, size=None, dtype=None, ctx=None, out=None):
@@ -357,3 +357,44 @@ def shuffle(x):
            [0., 1., 2.]])
     """
     _mx_nd_np.random.shuffle(x)
+
+
+def randn(*size, **kwargs):
+    r"""Return a sample (or samples) from the "standard normal" distribution.
+    If positive, int_like or int-convertible arguments are provided,
+    `randn` generates an array of shape ``(d0, d1, ..., dn)``, filled
+    with random floats sampled from a univariate "normal" (Gaussian)
+    distribution of mean 0 and variance 1 (if any of the :math:`d_i` are
+    floats, they are first converted to integers by truncation). A single
+    float randomly sampled from the distribution is returned if no
+    argument is provided.
+    This is a convenience function.  If you want an interface that takes a
+    tuple as the first argument, use `numpy.random.standard_normal` instead.
+    Parameters
+    ----------
+    d0, d1, ..., dn : int, optional
+        The dimensions of the returned array, should be all positive.
+        If no argument is given a single Python float is returned.
+    Returns
+    -------
+    Z : ndarray
+        A ``(d0, d1, ..., dn)``-shaped array of floating-point samples from
+        the standard normal distribution, or a single such float if
+        no parameters were supplied.
+    Notes
+    -----
+    For random samples from :math:`N(\mu, \sigma^2)`, use:
+    ``sigma * np.random.randn(...) + mu``
+    Examples
+    --------
+    >>> np.random.randn()
+    2.1923875335537315 #random
+    Two-by-four array of samples from N(3, 6.25):
+    >>> 2.5 * np.random.randn(2, 4) + 3
+    array([[-4.49401501,  4.00950034, -1.81814867,  7.29718677],  #random
+        [ 0.39924804,  4.68456316,  4.99394529,  4.84057254]]) #random
+    """
+    output_shape = ()
+    for s in size:
+        output_shape += (s,)
+    return _mx_nd_np.random.normal(0, 1, size=output_shape, **kwargs)
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index af9228d45991..4bbf9b8040e2 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -3089,6 +3089,28 @@ def hybrid_forward(self, F, x):
                 assert out.shape == expected_shape
 
 
+@with_seed()
+@use_np
+def test_np_randn():
+    # Test shapes.
+    shapes = [
+        (3, 3),
+        (3, 4),
+        (0, 0),
+        (3, 3, 3),
+        (0, 0, 0),
+        (2, 2, 4, 3),
+        (2, 2, 4, 3),
+        (2, 0, 3, 0),
+        (2, 0, 2, 3)
+    ]
+    dtypes = ['float16', 'float32', 'float64']
+    for dtype in dtypes:
+        for shape in shapes:
+            data_mx = np.random.randn(*shape, dtype=dtype)
+            assert data_mx.shape == shape
+
+
 @with_seed()
 @use_np
 def test_random_seed():

From 8f9ae1c883acec8c9e12b149fa44d10737a39de1 Mon Sep 17 00:00:00 2001
From: Tao Lv <tao.a.lv@intel.com>
Date: Thu, 26 Dec 2019 13:20:00 +0800
Subject: [PATCH 732/813] update mkldnn to v1.1.2 (#17165)

---
 3rdparty/mkldnn | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/3rdparty/mkldnn b/3rdparty/mkldnn
index 52c3052df8ec..cb2cc7ac17ff 160000
--- a/3rdparty/mkldnn
+++ b/3rdparty/mkldnn
@@ -1 +1 @@
-Subproject commit 52c3052df8ec1d5b8b45cb6c350a952840eabd42
+Subproject commit cb2cc7ac17ff4e2ef50805c7048d33256d82be4d

From e15c778258f78eacb75865aff9b7fe0b75d6291f Mon Sep 17 00:00:00 2001
From: Yiyan66 <57363390+Yiyan66@users.noreply.github.com>
Date: Thu, 26 Dec 2019 14:59:37 +0800
Subject: [PATCH 733/813] [numpy] fix argsort typo (#17150)

* return

* fix symbol
---
 python/mxnet/numpy/multiarray.py     | 2 +-
 python/mxnet/symbol/numpy/_symbol.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 4910b4d6b925..a1b0e016445a 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -1377,7 +1377,7 @@ def argsort(self, axis=-1, kind=None, order=None):  # pylint: disable=arguments-
         The arguments are the same as for :py:func:`argsort`, with
         this array as data.
         """
-        raise argsort(self, axis=axis, kind=kind, order=order)
+        return argsort(self, axis=axis, kind=kind, order=order)
 
     def argmax_channel(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`argmax_channel`.
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 6efc333cc16c..3ee385660715 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -491,7 +491,7 @@ def argsort(self, axis=-1, kind=None, order=None):  # pylint: disable=arguments-
         The arguments are the same as for :py:func:`argsort`, with
         this array as data.
         """
-        raise argsort(self, axis=axis, kind=kind, order=order)
+        return argsort(self, axis=axis, kind=kind, order=order)
 
     def argmax_channel(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`argmax_channel`.

From 07913f9beb07cb130900567bfe86e8305b84fd3c Mon Sep 17 00:00:00 2001
From: Xinyu Chen <xinyu1.chen@intel.com>
Date: Thu, 26 Dec 2019 15:30:35 +0800
Subject: [PATCH 734/813] fix broken link (#17130)

---
 example/quantization/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/example/quantization/README.md b/example/quantization/README.md
index 8cdc1bb7e06f..b934a811f31d 100644
--- a/example/quantization/README.md
+++ b/example/quantization/README.md
@@ -9,7 +9,7 @@ This folder contains examples of quantizing a FP32 model with Intel® MKL-DNN or
 
 <h2 id="1">Model Quantization with Intel® MKL-DNN</h2>
 
-Intel® MKL-DNN supports quantization with subgraph features on Intel® CPU Platform and can bring performance improvements on the [Intel® Xeon® Scalable Platform](https://www.intel.com/content/www/us/en/processors/xeon/scalable/xeon-scalable-platform.html). A new quantization script `imagenet_gen_qsym_mkldnn.py` has been designed to launch quantization for image-classification models with Intel® MKL-DNN. This script integrates with [Gluon-CV modelzoo](https://gluon-cv.mxnet.io/model_zoo/classification.html), so that more pre-trained models can be downloaded from Gluon-CV and then converted for quantization. To apply quantization flow to your project directly, please refer [Quantize custom models with MKL-DNN backend](https://mxnet.apache.org/tutorials/mkldnn/mkldnn_quantization.html).
+Intel® MKL-DNN supports quantization with subgraph features on Intel® CPU Platform and can bring performance improvements on the [Intel® Xeon® Scalable Platform](https://www.intel.com/content/www/us/en/processors/xeon/scalable/xeon-scalable-platform.html). A new quantization script `imagenet_gen_qsym_mkldnn.py` has been designed to launch quantization for image-classification models with Intel® MKL-DNN. This script integrates with [Gluon-CV modelzoo](https://gluon-cv.mxnet.io/model_zoo/classification.html), so that more pre-trained models can be downloaded from Gluon-CV and then converted for quantization. To apply quantization flow to your project directly, please refer [Quantize custom models with MKL-DNN backend](https://mxnet.apache.org/api/python/docs/tutorials/performance/backend/mkldnn/mkldnn_quantization.html).
 
 ```
 usage: imagenet_gen_qsym_mkldnn.py [-h] [--model MODEL] [--epoch EPOCH]

From d26dd15a1b074b0fef3ef43e660679c0b696887a Mon Sep 17 00:00:00 2001
From: Yiyan66 <57363390+Yiyan66@users.noreply.github.com>
Date: Thu, 26 Dec 2019 17:14:01 +0800
Subject: [PATCH 735/813] [numpy] add op round (#17175)

* add round

* sanity

* space
---
 python/mxnet/ndarray/numpy/_op.py             | 27 +++++++++++++---
 python/mxnet/numpy/multiarray.py              | 23 ++++++++++---
 python/mxnet/numpy_dispatch_protocol.py       |  1 +
 python/mxnet/symbol/numpy/_symbol.py          | 30 +++++++++++++----
 .../unittest/test_numpy_interoperability.py   |  5 +++
 tests/python/unittest/test_numpy_op.py        | 32 +++++++++++++++++++
 6 files changed, 103 insertions(+), 15 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index 02e42145fb18..e380b4937168 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -37,10 +37,10 @@
            'logspace', 'expand_dims', 'tile', 'arange', 'array_split', 'split', 'vsplit', 'concatenate', 'append',
            'stack', 'vstack', 'column_stack', 'dstack', 'average', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip',
            'argmax', 'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'unravel_index', 'hanning', 'hamming',
-           'blackman', 'flip', 'around', 'hypot', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad', 'unique', 'lcm',
-           'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less',
-           'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory',
-           'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where', 'bincount']
+           'blackman', 'flip', 'around', 'round', 'hypot', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad',
+           'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal',
+           'greater', 'less', 'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero',
+           'shares_memory', 'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where', 'bincount']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -4737,6 +4737,25 @@ def around(x, decimals=0, out=None, **kwargs):
         raise TypeError('type {} not supported'.format(str(type(x))))
 
 
+@set_module('mxnet.ndarray.numpy')
+def round(x, decimals=0, out=None, **kwargs):
+    r"""
+    round_(a, decimals=0, out=None)
+    Round an array to the given number of decimals.
+
+    See Also
+    --------
+    around : equivalent function; see for details.
+    """
+    from ...numpy import ndarray
+    if isinstance(x, numeric_types):
+        return _np.around(x, decimals, **kwargs)
+    elif isinstance(x, ndarray):
+        return _npi.around(x, decimals, out=out, **kwargs)
+    else:
+        raise TypeError('type {} not supported'.format(str(type(x))))
+
+
 @set_module('mxnet.ndarray.numpy')
 @wrap_np_binary_func
 def arctan2(x1, x2, out=None, **kwargs):
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index a1b0e016445a..22094a1621d2 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -55,9 +55,9 @@
            'tensordot', 'eye', 'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'array_split',
            'split', 'vsplit', 'concatenate', 'stack', 'vstack', 'column_stack', 'dstack', 'average', 'mean',
            'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'argmin', 'std', 'var', 'indices', 'copysign',
-           'ravel', 'unravel_index', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'arctan2', 'hypot',
-           'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
-           'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal',
+           'ravel', 'unravel_index', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'round', 'arctan2',
+           'hypot', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity',
+           'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal',
            'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory',
            'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where', 'bincount']
 
@@ -1558,13 +1558,13 @@ def norm(self, *args, **kwargs):
         """
         raise AttributeError('mxnet.numpy.ndarray object has no attribute norm')
 
-    def round(self, *args, **kwargs):
+    def round(self, decimals=0, out=None, **kwargs): # pylint: disable=arguments-differ
         """Convenience fluent method for :py:func:`round`.
 
         The arguments are the same as for :py:func:`round`, with
         this array as data.
         """
-        raise NotImplementedError
+        return round(self, decimals=decimals, out=out, **kwargs)
 
     def rint(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`rint`.
@@ -6456,6 +6456,19 @@ def around(x, decimals=0, out=None, **kwargs):
     return _mx_nd_np.around(x, decimals, out=out, **kwargs)
 
 
+@set_module('mxnet.numpy')
+def round(x, decimals=0, out=None, **kwargs):
+    r"""
+    round_(a, decimals=0, out=None)
+    Round an array to the given number of decimals.
+
+    See Also
+    --------
+    around : equivalent function; see for details.
+    """
+    return _mx_nd_np.around(x, decimals, out=out, **kwargs)
+
+
 @set_module('mxnet.numpy')
 @wrap_np_binary_func
 def arctan2(x1, x2, out=None, **kwargs):
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index c7e9dd1398eb..9aa755fb436e 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -86,6 +86,7 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'argmin',
     'argmax',
     'around',
+    'round',
     'argsort',
     'append',
     'broadcast_arrays',
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 3ee385660715..0b341b804758 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -45,10 +45,10 @@
            'logspace', 'expand_dims', 'tile', 'arange', 'array_split', 'split', 'vsplit', 'concatenate', 'append',
            'stack', 'vstack', 'column_stack', 'dstack', 'average', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip',
            'argmax', 'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'unravel_index', 'hanning', 'hamming',
-           'blackman', 'flip', 'around', 'hypot', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad', 'unique', 'lcm',
-           'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less',
-           'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'shares_memory',
-           'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where', 'bincount']
+           'blackman', 'flip', 'around', 'round', 'hypot', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad',
+           'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal',
+           'greater', 'less', 'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide',
+           'shares_memory', 'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where', 'bincount']
 
 
 @set_module('mxnet.symbol.numpy')
@@ -665,13 +665,13 @@ def norm(self, *args, **kwargs):
         """
         raise AttributeError('_Symbol object has no attribute norm')
 
-    def round(self, *args, **kwargs):
+    def round(self, decimals=0, out=None, **kwargs): # pylint: disable=arguments-differ
         """Convenience fluent method for :py:func:`round`.
 
         The arguments are the same as for :py:func:`round`, with
         this array as data.
         """
-        raise NotImplementedError
+        return round(self, decimals=decimals, out=out, **kwargs)
 
     def rint(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`rint`.
@@ -4524,6 +4524,24 @@ def around(x, decimals=0, out=None, **kwargs):
         raise TypeError('type {} not supported'.format(str(type(x))))
 
 
+@set_module('mxnet.symbol.numpy')
+def round(x, decimals=0, out=None, **kwargs):
+    r"""
+    round_(a, decimals=0, out=None)
+    Round an array to the given number of decimals.
+
+    See Also
+    --------
+    around : equivalent function; see for details.
+    """
+    if isinstance(x, numeric_types):
+        return _np.around(x, decimals, **kwargs)
+    elif isinstance(x, _Symbol):
+        return _npi.around(x, decimals, out=out, **kwargs)
+    else:
+        raise TypeError('type {} not supported'.format(str(type(x))))
+
+
 @set_module('mxnet.symbol.numpy')
 @wrap_np_binary_func
 def arctan2(x1, x2, out=None, **kwargs):
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index 9b445044a3c1..3d26ee28b22e 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -594,6 +594,10 @@ def _add_workload_around():
     OpArgMngr.add_workload('around', np.array([1.56, 72.54, 6.35, 3.25]), decimals=1)
 
 
+def _add_workload_round():
+    OpArgMngr.add_workload('round', np.array([1.56, 72.54, 6.35, 3.25]), decimals=1)
+
+
 def _add_workload_argsort():
     for dtype in [np.int32, np.float32]:
         a = np.arange(101, dtype=dtype)
@@ -1442,6 +1446,7 @@ def _prepare_workloads():
     _add_workload_argmin()
     _add_workload_argmax()
     _add_workload_around()
+    _add_workload_round()
     _add_workload_argsort()
     _add_workload_append()
     _add_workload_bincount()
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 4bbf9b8040e2..3f9f1d6677cc 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -4435,6 +4435,38 @@ def hybrid_forward(self, F, x):
                     assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
 
 
+@with_seed()
+@use_np
+def test_np_round():
+    class TestRound(HybridBlock):
+        def __init__(self, decimals):
+            super(TestRound, self).__init__()
+            self.decimals = decimals
+
+        def hybrid_forward(self, F, x):
+            return F.np.round(x, self.decimals)
+
+    shapes = [(), (1, 2, 3), (1, 0)]
+    types = ['int32', 'int64', 'float32', 'float64']
+    for hybridize in [True, False]:
+        for oneType in types:
+            rtol, atol = 1e-3, 1e-5
+            for shape in shapes:
+                for d in range(-5, 6):
+                    test_round = TestRound(d)
+                    if hybridize:
+                        test_round.hybridize()
+                    x = rand_ndarray(shape, dtype=oneType).as_np_ndarray()
+                    np_out = _np.round(x.asnumpy(), d)
+                    mx_out = test_round(x)
+                    assert mx_out.shape == np_out.shape
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
+
+                    mx_out = np.round(x, d)
+                    np_out = _np.round(x.asnumpy(), d)
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
+
+
 @with_seed()
 @use_np
 def test_np_nonzero():

From cf81887a99cea3abebbf1c9728e926a0da9f0f1b Mon Sep 17 00:00:00 2001
From: Xinyu Chen <xinyu1.chen@intel.com>
Date: Thu, 26 Dec 2019 18:53:19 +0800
Subject: [PATCH 736/813] Quantized Elemwise Mul Operator (#17147)

* add elt-wise mul xinyu

* fuse mul dequantize

* change to use subgraph

* address comments and add tests

* fix ut

* improve ut

* skip pragma omp simd for msvc

* fix lint

* fix clang error
---
 .../quantization/quantized_elemwise_mul-inl.h |  64 +++++
 .../quantization/quantized_elemwise_mul.cc    | 267 ++++++++++++++++++
 ...kldnn_elemwisemul_post_quantize_property.h | 222 +++++++++++++++
 .../mkldnn/mkldnn_subgraph_property.cc        |   2 +
 .../python/quantization/test_quantization.py  |  62 +++-
 5 files changed, 616 insertions(+), 1 deletion(-)
 create mode 100644 src/operator/quantization/quantized_elemwise_mul-inl.h
 create mode 100644 src/operator/quantization/quantized_elemwise_mul.cc
 create mode 100644 src/operator/subgraph/mkldnn/mkldnn_elemwisemul_post_quantize_property.h

diff --git a/src/operator/quantization/quantized_elemwise_mul-inl.h b/src/operator/quantization/quantized_elemwise_mul-inl.h
new file mode 100644
index 000000000000..f58db8a45eea
--- /dev/null
+++ b/src/operator/quantization/quantized_elemwise_mul-inl.h
@@ -0,0 +1,64 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file quantized_elemwise_mul.cc
+ * \brief CPU Implementation of basic elementwise binary mul operators
+ */
+#ifndef MXNET_OPERATOR_QUANTIZATION_QUANTIZED_ELEMWISE_MUL_INL_H_
+#define MXNET_OPERATOR_QUANTIZATION_QUANTIZED_ELEMWISE_MUL_INL_H_
+
+#include "../tensor/elemwise_binary_op-inl.h"
+
+namespace mxnet {
+namespace op {
+/* These structure is used for requantization only when fusion */
+struct QuantizeElemwiseMulParam : public dmlc::Parameter<QuantizeElemwiseMulParam> {
+  dmlc::optional<float> min_calib_range;
+  dmlc::optional<float> max_calib_range;
+  bool enable_float_output;
+  DMLC_DECLARE_PARAMETER(QuantizeElemwiseMulParam) {
+    DMLC_DECLARE_FIELD(min_calib_range)
+    .set_default(dmlc::optional<float>())
+    .describe("The minimum scalar value in the form of float32 obtained "
+              "through calibration. If present, it will be used to requantize the "
+              "int8 output data.");
+    DMLC_DECLARE_FIELD(max_calib_range)
+    .set_default(dmlc::optional<float>())
+    .describe("The maximum scalar value in the form of float32 obtained "
+              "through calibration. If present, it will be used to requantize the "
+              "int8 output data.");
+    DMLC_DECLARE_FIELD(enable_float_output).set_default(false)
+    .describe("Whether to enable float32 output");
+  }
+};
+
+namespace quantized_elemwise_mul {
+enum QuantizedElemwiseMulOpInputs {kLhs, kRhs, kLhsMin, kLhsMax, kRhsMin, kRhsMax};
+enum QuantizedElemwiseMulOpOutputs {kOut, kOutMin, kOutMax};
+enum QuantizedElemwiseMulOpResource {kTempSpace};
+}  // namespace quantized_elemwise_mul
+
+
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_QUANTIZATION_QUANTIZED_ELEMWISE_MUL_INL_H_
diff --git a/src/operator/quantization/quantized_elemwise_mul.cc b/src/operator/quantization/quantized_elemwise_mul.cc
new file mode 100644
index 000000000000..a752c14837a6
--- /dev/null
+++ b/src/operator/quantization/quantized_elemwise_mul.cc
@@ -0,0 +1,267 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file quantized_elemwise_mul.cc
+ * \brief CPU Implementation of basic elementwise binary mul operators
+ */
+#include <mxnet/op_attr_types.h>
+#include "../tensor/elemwise_binary_op-inl.h"
+#include "./quantized_elemwise_mul-inl.h"
+#include "./quantization_utils.h"
+
+namespace mxnet {
+namespace op {
+
+DMLC_REGISTER_PARAMETER(QuantizeElemwiseMulParam);
+
+static std::vector<std::string> QuantizedElemwiseMulOutputNames(const NodeAttrs &attrs) {
+  const QuantizeElemwiseMulParam& params = nnvm::get<QuantizeElemwiseMulParam>(attrs.parsed);
+  if (params.enable_float_output)
+    return std::vector<std::string>{"output"};
+  else
+    return std::vector<std::string>{"output", "min_output", "max_output"};
+}
+
+inline bool QuantizedElemwiseMulOpShape(const nnvm::NodeAttrs& attrs,
+                                        mxnet::ShapeVector *in_attrs,
+                                        mxnet::ShapeVector *out_attrs) {
+  using namespace mshadow;
+  const QuantizeElemwiseMulParam& params = nnvm::get<QuantizeElemwiseMulParam>(attrs.parsed);
+  const mxnet::TShape &lshape = (*in_attrs)[quantized_elemwise_mul::kLhs];
+  const mxnet::TShape &rshape = (*in_attrs)[quantized_elemwise_mul::kRhs];
+  if (!ndim_is_known(lshape) || !ndim_is_known(rshape)) return false;
+  CHECK_EQ(lshape.ndim(), rshape.ndim())
+    << "Currently, quantized elemwise multiply doesn't support broadcast.";
+  for (int i = 0; i < lshape.ndim(); ++i) {
+    CHECK_EQ(lshape[i], rshape[i]);
+  }
+  SHAPE_ASSIGN_CHECK(*in_attrs, quantized_elemwise_mul::kLhsMin, mxnet::TShape(1, 1));
+  SHAPE_ASSIGN_CHECK(*in_attrs, quantized_elemwise_mul::kLhsMax, mxnet::TShape(1, 1));
+  SHAPE_ASSIGN_CHECK(*in_attrs, quantized_elemwise_mul::kRhsMin, mxnet::TShape(1, 1));
+  SHAPE_ASSIGN_CHECK(*in_attrs, quantized_elemwise_mul::kRhsMax, mxnet::TShape(1, 1));
+
+  out_attrs->clear();
+  SHAPE_ASSIGN_CHECK(*out_attrs, quantized_elemwise_mul::kOut, lshape);
+  if (!params.enable_float_output) {
+    SHAPE_ASSIGN_CHECK(*out_attrs, quantized_elemwise_mul::kOutMin, mxnet::TShape(1, 1));
+    SHAPE_ASSIGN_CHECK(*out_attrs, quantized_elemwise_mul::kOutMax, mxnet::TShape(1, 1));
+  }
+  return true;
+}
+
+inline bool QuantizedElemwiseMulOpType(const nnvm::NodeAttrs& attrs,
+                                       std::vector<int> *in_type,
+                                       std::vector<int> *out_type) {
+  const QuantizeElemwiseMulParam& params = nnvm::get<QuantizeElemwiseMulParam>(attrs.parsed);
+  for (int i = 0; i < 2; ++i) {
+    if (in_type->at(i) == mshadow::kInt8) {
+      TYPE_ASSIGN_CHECK(*in_type, i, mshadow::kInt8);
+    } else {
+      LOG(ERROR) << "currently, quantized elemwise mul only support int8 inputs.";
+    }
+  }
+  TYPE_ASSIGN_CHECK(*in_type, quantized_elemwise_mul::kLhsMin, mshadow::kFloat32);
+  TYPE_ASSIGN_CHECK(*in_type, quantized_elemwise_mul::kLhsMax, mshadow::kFloat32);
+  TYPE_ASSIGN_CHECK(*in_type, quantized_elemwise_mul::kRhsMin, mshadow::kFloat32);
+  TYPE_ASSIGN_CHECK(*in_type, quantized_elemwise_mul::kRhsMax, mshadow::kFloat32);
+
+  int dtype = mshadow::kInt32;
+  if (params.max_calib_range.has_value() && params.min_calib_range.has_value()) {
+    dtype = mshadow::kInt8;
+  }
+  if (!params.enable_float_output) {
+    TYPE_ASSIGN_CHECK(*out_type, quantized_elemwise_mul::kOut, dtype);
+    TYPE_ASSIGN_CHECK(*out_type, quantized_elemwise_mul::kOutMin, mshadow::kFloat32);
+    TYPE_ASSIGN_CHECK(*out_type, quantized_elemwise_mul::kOutMax, mshadow::kFloat32);
+  } else {
+    TYPE_ASSIGN_CHECK(*out_type, quantized_elemwise_mul::kOut, mshadow::kFloat32);
+  }
+  return true;
+}
+
+inline bool QuantizedElemwiseMulOpStorageType(const nnvm::NodeAttrs& attrs,
+                                              int dev_mask,
+                                              DispatchMode* dispatch_mode,
+                                              std::vector<int> *in_attrs,
+                                              std::vector<int> *out_attrs) {
+  using namespace common;
+  *dispatch_mode = DispatchMode::kFCompute;
+
+  for (auto &v : *out_attrs) {
+    v = kDefaultStorage;
+    if (common::stype_string(v).compare("unknown") == 0) {
+      return false;
+    }
+  }
+
+  for (auto &v : *in_attrs) {
+    v = kDefaultStorage;
+    if (common::stype_string(v).compare("unknown") == 0) {
+      return false;
+    }
+  }
+  return true;
+}
+
+void QuantizedElemwiseMulOpForward(const nnvm::NodeAttrs &attrs,
+                                   const OpContext &ctx,
+                                   const std::vector<TBlob> &inputs,
+                                   const std::vector<OpReqType> &req,
+                                   const std::vector<TBlob> &outputs) {
+  const QuantizeElemwiseMulParam& params = nnvm::get<QuantizeElemwiseMulParam>(attrs.parsed);
+  using namespace mxnet_op;
+
+  float lhs_min = inputs[quantized_elemwise_mul::kLhsMin].dptr<float>()[0];
+  float lhs_max = inputs[quantized_elemwise_mul::kLhsMax].dptr<float>()[0];
+  float rhs_min = inputs[quantized_elemwise_mul::kRhsMin].dptr<float>()[0];
+  float rhs_max = inputs[quantized_elemwise_mul::kRhsMax].dptr<float>()[0];
+
+  float cached_output_min_ = 0.f;
+  float cached_output_max_ = 0.f;
+  float out_data_scale = 1.f;
+  float out_scale = 1.f;
+  if (!params.enable_float_output) {
+    float output_data_range = kInt32Range;
+    // dataA && dataB are int8
+    if (outputs[quantized_elemwise_mul::kOut].type_flag_ == mshadow::kInt8) {
+      output_data_range = kInt8Range;
+    } else {
+      output_data_range = kInt32Range;
+    }
+    if (params.max_calib_range.has_value() && params.min_calib_range.has_value()) {
+      cached_output_min_ = params.min_calib_range.value();
+      cached_output_max_ = params.max_calib_range.value();
+      out_data_scale = output_data_range / MaxAbs(cached_output_min_, cached_output_max_);
+      auto lhs_scale = kInt8Range / MaxAbs(lhs_min, lhs_max);
+      auto rhs_scale = kInt8Range / MaxAbs(rhs_min, rhs_max);
+      out_scale = out_data_scale / lhs_scale / rhs_scale;
+    } else {
+      Stream<cpu> *s = ctx.get_stream<cpu>();
+      if (inputs[quantized_elemwise_mul::kLhs].type_flag_ == mshadow::kInt8 &&
+          inputs[quantized_elemwise_mul::kRhs].type_flag_ == mshadow::kInt8) {
+        mxnet_op::Kernel<QuantizationRangeForS8S8MultiplicationStruct, cpu>::Launch(
+            s, 1, &cached_output_min_, &cached_output_max_, &lhs_min, &lhs_max, &rhs_min, &rhs_max);
+      } else {
+        LOG(ERROR) << "lhs and rhs only support iny8 dtype.";
+      }
+    }
+  } else {
+    auto lhs_scale = kInt8Range / MaxAbs(lhs_min, lhs_max);
+    auto rhs_scale = kInt8Range / MaxAbs(rhs_min, rhs_max);
+    out_scale = 1.0 / lhs_scale / rhs_scale;
+  }
+
+  size_t out_size = outputs[quantized_elemwise_mul::kOut].Size();
+  auto *input_l = inputs[quantized_elemwise_mul::kLhs].dptr<int8_t>();
+  auto *input_r = inputs[quantized_elemwise_mul::kRhs].dptr<int8_t>();
+  // TODO(Xinyu): a temp solution to enable Elemwise INT8 computation,
+  // will be refactored after the DNNL primitive is done.
+  if (!params.enable_float_output) {
+    if (params.max_calib_range.has_value() && params.min_calib_range.has_value()) {
+      typedef int8_t out_type;
+      auto *out_data = outputs[quantized_elemwise_mul::kOut].dptr<out_type>();
+#if !defined(_MSC_VER)
+#pragma omp simd
+#endif
+      for (size_t i = 0; i < out_size; ++i) {
+        const int8_t a = input_l[i];
+        const int8_t b = input_r[i];
+        out_data[i] = static_cast<out_type>(a * b * out_scale);
+      }
+    } else {
+      typedef int32_t out_type;
+      auto *out_data = outputs[quantized_elemwise_mul::kOut].dptr<out_type>();
+#if !defined(_MSC_VER)
+#pragma omp simd
+#endif
+      for (size_t i = 0; i < out_size; ++i) {
+        const int8_t a = input_l[i];
+        const int8_t b = input_r[i];
+        out_data[i] = static_cast<out_type>(a * b * out_scale);
+      }
+    }
+  } else {
+    typedef float_t out_type;
+    auto *out_data = outputs[quantized_elemwise_mul::kOut].dptr<out_type>();
+#if !defined(_MSC_VER)
+#pragma omp simd
+#endif
+    for (size_t i = 0; i < out_size; ++i) {
+      const int8_t a = input_l[i];
+      const int8_t b = input_r[i];
+      out_data[i] = static_cast<out_type>(a * b * out_scale);
+    }
+  }
+
+  if (!params.enable_float_output) {
+    outputs[quantized_elemwise_mul::kOutMin].dptr<float>()[0] = cached_output_min_;
+    outputs[quantized_elemwise_mul::kOutMax].dptr<float>()[0] = cached_output_max_;
+  }
+}
+
+NNVM_REGISTER_OP(_contrib_quantized_elemwise_mul)
+.describe(R"code(Multiplies arguments int8 element-wise.
+)code" ADD_FILELINE)
+.set_num_inputs(6)
+.set_num_outputs([](const NodeAttrs& attrs) {
+  const QuantizeElemwiseMulParam& params = nnvm::get<QuantizeElemwiseMulParam>(attrs.parsed);
+  return (!params.enable_float_output) ? 3 : 1;
+})
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"lhs", "rhs", "lhs_min", "lhs_max", "rhs_min", "rhs_max"};
+  })
+.set_attr<nnvm::FListOutputNames>("FListOutputNames", QuantizedElemwiseMulOutputNames)
+.set_attr<mxnet::FInferShape>("FInferShape", QuantizedElemwiseMulOpShape)
+.set_attr<nnvm::FInferType>("FInferType", QuantizedElemwiseMulOpType)
+.set_attr<FInferStorageType>("FInferStorageType", QuantizedElemwiseMulOpStorageType)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", QuantizedElemwiseMulOpForward)
+// TODO(Xinyu): a temp solution to enable GluonCV INT8 flow,
+// will be reverted after the improvement of CachedOP is done.
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.set_attr<FNeedRequantize>("FNeedRequantize", [](const NodeAttrs& attrs) { return true; })
+.add_argument("lhs", "NDArray-or-Symbol", "first input")
+.add_argument("rhs", "NDArray-or-Symbol", "second input")
+.add_argument("lhs_min", "NDArray-or-Symbol", "Minimum value of first input.")
+.add_argument("lhs_max", "NDArray-or-Symbol", "Maximum value of first input.")
+.add_argument("rhs_min", "NDArray-or-Symbol", "Minimum value of second input.")
+.add_argument("rhs_max", "NDArray-or-Symbol", "Maximum value of second input.")
+.set_attr_parser(ParamParser<QuantizeElemwiseMulParam>)
+.add_arguments(QuantizeElemwiseMulParam::__FIELDS__());
+
+NNVM_REGISTER_OP(elemwise_mul)
+.set_attr<FQuantizedOp>("FQuantizedOp", [](const NodeAttrs& attrs) {
+  nnvm::NodePtr node = nnvm::Node::Create();
+  node->attrs.op = Op::Get("_contrib_quantized_elemwise_mul");
+  node->attrs.name = "quantized_" + attrs.name;
+  node->attrs.dict = attrs.dict;
+  if (node->op()->attr_parser != nullptr) {
+    node->op()->attr_parser(&(node->attrs));
+  }
+  return node;
+});
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/subgraph/mkldnn/mkldnn_elemwisemul_post_quantize_property.h b/src/operator/subgraph/mkldnn/mkldnn_elemwisemul_post_quantize_property.h
new file mode 100644
index 000000000000..1469395ec169
--- /dev/null
+++ b/src/operator/subgraph/mkldnn/mkldnn_elemwisemul_post_quantize_property.h
@@ -0,0 +1,222 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file mkldnn_elemwisemul_post_quantize_property.cc
+ * \brief Partition gragph property for MKLDNN Quantized ElemwiseMul operator
+ * \author Xinyu Chen
+*/
+
+#ifndef MXNET_OPERATOR_SUBGRAPH_MKLDNN_MKLDNN_ELEMWISEMUL_POST_QUANTIZE_PROPERTY_H_
+#define MXNET_OPERATOR_SUBGRAPH_MKLDNN_MKLDNN_ELEMWISEMUL_POST_QUANTIZE_PROPERTY_H_
+#if MXNET_USE_MKLDNN == 1
+
+#include <string>
+#include <vector>
+#include "../../tensor/elemwise_binary_op-inl.h"
+#include "../../quantization/requantize-inl.h"
+#include "../common.h"
+#include "mkldnn_subgraph_base-inl.h"
+
+namespace mxnet {
+namespace op {
+
+#define QUANTIZED_ElemwiseMul_NAME "_contrib_quantized_elemwise_mul"
+
+class ElemwiseMulPostQuantizeSelector : public SubgraphSelector {
+ public:
+  /*! \brief pattern match status */
+  enum SelectStatus {
+    kFail = 0,
+    kStart,
+    kRequantize,
+    kSuccess,
+  };
+
+ private:
+  bool disable_all;
+  bool disable_float_output;
+  SelectStatus status;
+  std::vector<const nnvm::Node *> matched_list;
+
+ public:
+  explicit ElemwiseMulPostQuantizeSelector(const bool dis_all,
+                                           const bool dis_float_output)
+      : disable_all(dis_all),
+        disable_float_output(dis_float_output) {}
+
+  bool Select(const nnvm::Node &n) override {
+    if ((!disable_all) && n.op() == Op::Get(QUANTIZED_ElemwiseMul_NAME)) {
+      status = disable_all ? kSuccess : kStart;
+      matched_list.clear();
+      matched_list.push_back(&n);
+      return true;
+    }
+    return false;
+  }
+
+  bool SelectInput(const nnvm::Node &n, const nnvm::Node &new_node) override {
+    return false;
+  }
+
+  bool SelectOutput(const nnvm::Node &n, const nnvm::Node &new_node) override {
+    if (status == kFail || status == kSuccess || new_node.is_variable())
+      return false;
+    // If n isn't the last matched node, then we encoutered a internal
+    // branch, we should pop out the node behind n and stop fusion.
+    if (matched_list.back() != &n) {
+      if (std::find(matched_list.begin(), matched_list.end(), &n) !=
+        matched_list.end()) {
+        while (matched_list.back() != &n) {
+          matched_list.pop_back();
+        }
+      }
+
+      status = kSuccess;
+      return false;
+    }
+
+    switch (status) {
+      case kStart:
+        if (new_node.op() == Op::Get("_contrib_requantize")) {
+          auto const &param = nnvm::get<RequantizeParam>(new_node.attrs.parsed);
+          if (param.min_calib_range.has_value() &&
+              param.max_calib_range.has_value()) {
+            matched_list.push_back(&new_node);
+            status = kRequantize;
+            return true;
+          }
+        }
+      case kRequantize:
+        if ((!disable_float_output) && (new_node.op() == Op::Get("_contrib_dequantize"))) {
+            matched_list.push_back(&new_node);
+            status = kSuccess;
+            return true;
+        }
+      default:
+        status = kSuccess;
+        return false;
+    }
+  }
+
+  std::vector<nnvm::Node *> Filter(
+      const std::vector<nnvm::Node *> &candidates) override {
+    if ((status != kSuccess) || (matched_list.size() <= 1)) {
+      return std::vector<nnvm::Node *>(0);
+    } else {
+      std::vector<nnvm::Node *> ret;
+      for (auto i : matched_list) {
+        auto non_const_i = const_cast<nnvm::Node *>(i);
+        if (std::find(candidates.begin(), candidates.end(), non_const_i) !=
+            candidates.end()) {
+          ret.push_back(non_const_i);
+        }
+      }
+      return ret;
+    }
+  }
+
+  void Reset() override {
+    CHECK_GE(matched_list.size(), 1);
+    auto new_selector = ElemwiseMulPostQuantizeSelector(disable_all, disable_float_output);
+    new_selector.Select(*matched_list[0]);
+    *this = new_selector;
+  }
+};
+
+class ElemwiseMulPostQuantizeProperty : public SubgraphProperty {
+ public:
+  ElemwiseMulPostQuantizeProperty() {
+    disable_fuse_all = dmlc::GetEnv("MXNET_DISABLE_MKLDNN_QEM_FUSE_ALL", false);
+    disable_float_output = dmlc::GetEnv("MXNET_DISABLE_MKLDNN_QEM_FLOAT_OUTPUT", false);
+  }
+
+  static SubgraphPropertyPtr Create() {
+    static const std::string &name = "MKLDNN EltwiseMul post-quantization optimization pass";
+    auto property = std::make_shared<ElemwiseMulPostQuantizeProperty>();
+    property->SetAttr<std::string>("property_name", name);
+    property->SetAttr<bool>("inference_only", true);
+    return property;
+  }
+
+  nnvm::NodePtr CreateSubgraphNode(const nnvm::Symbol &sym,
+                                   const int subgraph_id = 0) const override {
+    nnvm::NodePtr em_node = nullptr;
+    nnvm::NodePtr requantize_node = nullptr;
+    nnvm::NodePtr dequantize_node = nullptr;
+
+    DFSVisit(sym.outputs, [&](const nnvm::NodePtr &node) {
+      if (node->is_variable()) return;
+      if (node->op() == Op::Get(QUANTIZED_ElemwiseMul_NAME)) {
+        em_node = node;
+      } else if (node->op() == Op::Get("_contrib_requantize")) {
+        requantize_node = node;
+      } else if (node->op() == Op::Get("_contrib_dequantize")) {
+        dequantize_node = node;
+      }
+    });
+
+    CHECK_NOTNULL(em_node);
+    CHECK_NOTNULL(requantize_node);
+    auto const &requantize_param =
+        nnvm::get<RequantizeParam>(requantize_node->attrs.parsed);
+    CHECK(requantize_param.min_calib_range.has_value());
+    CHECK(requantize_param.max_calib_range.has_value());
+
+    // When only fused quantized_elemwise_mul and requantize, set min/max_cablib_range,
+    // When fused quantized_elemwise_mul + requantize + dequantize, set dequantize flag to true.
+    if (dequantize_node != nullptr) {
+      em_node->attrs.dict["enable_float_output"] = "True";
+    } else {
+      em_node->attrs.dict["min_calib_range"] =
+          std::to_string(requantize_param.min_calib_range.value());
+      em_node->attrs.dict["max_calib_range"] =
+          std::to_string(requantize_param.max_calib_range.value());
+    }
+    em_node->op()->attr_parser(&(em_node->attrs));
+    return em_node;
+  }
+
+  SubgraphSelectorPtr CreateSubgraphSelector() const override {
+    auto selector =
+        std::make_shared<ElemwiseMulPostQuantizeSelector>(disable_fuse_all,
+                                                          disable_float_output);
+    return selector;
+  }
+
+  void ConnectSubgraphOutputs(
+      const nnvm::NodePtr n,
+      std::vector<nnvm::NodeEntry *> *output_entries) const override {
+    for (size_t i = 0; i < output_entries->size(); ++i) {
+      auto entry_ptr = output_entries->at(i);
+      *entry_ptr = nnvm::NodeEntry{n, entry_ptr->index, 0};
+    }
+  }
+
+ private:
+  bool disable_fuse_all;
+  bool disable_float_output;
+};
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // if MXNET_USE_MKLDNN == 1
+#endif  // MXNET_OPERATOR_SUBGRAPH_MKLDNN_MKLDNN_ELEMWISEMUL_POST_QUANTIZE_PROPERTY_H_
diff --git a/src/operator/subgraph/mkldnn/mkldnn_subgraph_property.cc b/src/operator/subgraph/mkldnn/mkldnn_subgraph_property.cc
index 269017ea6a03..18cd3031ef18 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_subgraph_property.cc
+++ b/src/operator/subgraph/mkldnn/mkldnn_subgraph_property.cc
@@ -23,6 +23,7 @@
 #include "mkldnn_fc_property.h"
 #include "mkldnn_post_quantize_property.h"
 #include "mkldnn_fc_post_quantize_property.h"
+#include "mkldnn_elemwisemul_post_quantize_property.h"
 #include "mkldnn_post_quantize_align_scale_property.h"
 
 namespace mxnet {
@@ -57,6 +58,7 @@ MXNET_REGISTER_SUBGRAPH_PROPERTY(MKLDNN_QUANTIZE, SgMKLDNNPostQuantizeProperty);
 
 #if MXNET_USE_MKLDNN == 1
 MXNET_REGISTER_SUBGRAPH_PROPERTY(MKLDNN_QUANTIZE, SgMKLDNNFCPostQuantizeProperty);
+MXNET_REGISTER_SUBGRAPH_PROPERTY(MKLDNN_QUANTIZE, ElemwiseMulPostQuantizeProperty);
 
 MXNET_REGISTER_SUBGRAPH_PROPERTY(MKLDNN_QUANTIZE, SgMKLDNNPostQuantizeAlignScaleProperty);
 #endif  // MXNET_USE_MKLDNN == 1
diff --git a/tests/python/quantization/test_quantization.py b/tests/python/quantization/test_quantization.py
index 527737e03cd7..0c40f32d1666 100644
--- a/tests/python/quantization/test_quantization.py
+++ b/tests/python/quantization/test_quantization.py
@@ -341,6 +341,66 @@ def check_quantized_elemwise_add(data_shape, qtype):
         check_quantized_elemwise_add((3, 4, 56, 56), qtype)
         check_quantized_elemwise_add((32, 56, 64, 11), qtype)
 
+@with_seed()
+def test_quantized_elemwise_mul():
+    def check_quantized_elemwise_mul(data_shape, qtype):
+        if is_test_for_native_cpu():
+            print('skipped testing quantized_elemwise_mul for native cpu since it is not supported yet')
+            return
+        elif qtype != 'int8':
+            print('skipped testing quantized_elemwise_mul for not supported data type')
+            return
+        elif is_test_for_gpu():
+            print('skipped testing quantized_elemwise_mul for gpu since it is not supported yet')
+            return
+
+        dataA = mx.sym.Variable(name='dataA', shape=data_shape, dtype='float32')
+        dataB = mx.sym.Variable(name='dataB', shape=data_shape, dtype='float32')
+        elemwise_mul_fp32 = mx.sym.elemwise_mul(dataA, dataB)
+        arg_names = elemwise_mul_fp32.list_arguments()
+        elemwise_mul_fp32_exe = elemwise_mul_fp32.simple_bind(ctx=mx.current_context(), grad_req='null')
+        if qtype == 'uint8':
+            data_low = 0.0
+            data_high = 255.0
+        else:
+            data_low = -127.0
+            data_high = 127.0
+
+        dataA_val = mx.nd.random.uniform(low=data_low, high=data_high, shape=data_shape).astype('int32')
+        dataB_val = mx.nd.random.uniform(low=data_low, high=data_high, shape=data_shape).astype('int32')
+        elemwise_mul_fp32_exe.arg_dict[arg_names[0]][:] = dataA_val
+
+        elemwise_mul_fp32_exe.arg_dict[arg_names[1]][:] = dataB_val
+
+        output = elemwise_mul_fp32_exe.forward()[0]
+
+        qdataA = mx.sym.Variable(name='qdataA', shape=data_shape, dtype=qtype)
+        qdataB = mx.sym.Variable(name='qdataB', shape=data_shape, dtype=qtype)
+        min_dataA = mx.sym.Variable(name='min_dataA')
+        max_dataA = mx.sym.Variable(name='max_dataA')
+        min_dataB = mx.sym.Variable(name='min_dataB')
+        max_dataB = mx.sym.Variable(name='max_dataB')
+        quantized_elemwise_mul = mx.sym.contrib.quantized_elemwise_mul(qdataA, qdataB, min_dataA, max_dataA, min_dataB, max_dataB)
+        elemwise_mul_int8_exe = quantized_elemwise_mul.simple_bind(ctx=mx.current_context(), grad_req='null')
+        qarg_names = quantized_elemwise_mul.list_arguments()
+        elemwise_mul_int8_exe.arg_dict[qarg_names[0]][:] = elemwise_mul_fp32_exe.arg_dict[arg_names[0]].astype(qtype)
+        elemwise_mul_int8_exe.arg_dict[qarg_names[1]][:] = elemwise_mul_fp32_exe.arg_dict[arg_names[1]].astype(qtype)
+        quantized_range = 127.0
+        elemwise_mul_int8_exe.arg_dict[qarg_names[2]][:] = data_low
+        elemwise_mul_int8_exe.arg_dict[qarg_names[3]][:] = data_high
+        elemwise_mul_int8_exe.arg_dict[qarg_names[4]][:] = data_low
+        elemwise_mul_int8_exe.arg_dict[qarg_names[5]][:] = data_high
+        qoutput, min_range, max_range = elemwise_mul_int8_exe.forward()
+
+        fp32_rslt = output.asnumpy()
+        int8_rslt = qoutput.astype(output.dtype)
+        assert_almost_equal(fp32_rslt, int8_rslt, atol = 1e-4)
+
+    for qtype in ['int8', 'uint8']:
+        check_quantized_elemwise_mul((4, 6), qtype)
+        check_quantized_elemwise_mul((13, 74, 52), qtype)
+        check_quantized_elemwise_mul((3, 4, 56, 56), qtype)
+        check_quantized_elemwise_mul((32, 56, 64, 11), qtype)
 
 @with_seed()
 def test_quantized_pooling():
@@ -1005,7 +1065,7 @@ def check_qsym_forward(qsym, qarg_params, qaux_params, data_shape, label_shape=N
                     else:
                         excluded_sym_names = excluded_names + optional_names
             if name == 'sym4':
-                excluded_op_names += ['elemwise_add']
+                excluded_op_names += ['elemwise_add', 'elemwise_mul']
 
             qsym, qarg_params, qaux_params = mx.contrib.quant.quantize_model(sym=s,
                                                                              arg_params=arg_params,

From dfa3d07a74e59297394c624a3373a335f94a64eb Mon Sep 17 00:00:00 2001
From: Xinyu Chen <xinyu1.chen@intel.com>
Date: Fri, 27 Dec 2019 08:37:48 +0800
Subject: [PATCH 737/813] Further optimization for NCF model (#17148)

* further optimization for ncf model

* fix benchmark script

* enhance model optimizer to support general configurations of mlp and neurf models

* fix minor typo
---
 .../neural_collaborative_filtering/README.md  |  36 ++++--
 .../benchmark.sh                              | 114 ++++++++++++++++++
 .../neural_collaborative_filtering/convert.py |   2 +-
 .../core/model.py                             |  60 ++++++---
 .../model_optimizer.py                        |  81 +++++++++++++
 example/neural_collaborative_filtering/ncf.py |  24 ++--
 .../neural_collaborative_filtering/train.py   |   2 +-
 7 files changed, 273 insertions(+), 46 deletions(-)
 create mode 100644 example/neural_collaborative_filtering/benchmark.sh
 create mode 100644 example/neural_collaborative_filtering/model_optimizer.py

diff --git a/example/neural_collaborative_filtering/README.md b/example/neural_collaborative_filtering/README.md
index 819f4d94dff9..00d3ed12295b 100644
--- a/example/neural_collaborative_filtering/README.md
+++ b/example/neural_collaborative_filtering/README.md
@@ -29,15 +29,6 @@ Author: Dr. Xiangnan He (http://www.comp.nus.edu.sg/~xiangnan/)
 
 Code Reference: https://github.com/hexiangnan/neural_collaborative_filtering
 
-## Environment Settings
-We use MXnet with MKL-DNN as the backend. 
-- MXNet version:  '1.5.1'
-
-## Install
-```
-pip install -r requirements.txt
-```
-
 ## Dataset
 
 We provide the processed datasets on [Google Drive](https://drive.google.com/drive/folders/1qACR_Zhc2O2W0RrazzcepM2vJeh0MMdO?usp=sharing): MovieLens 20 Million (ml-20m), you can download directly or 
@@ -66,7 +57,9 @@ We provide the pretrained ml-20m model on [Google Drive](https://drive.google.co
 |dtype|HR@10|NDCG@10|
 |:---:|:--:|:--:|
 |float32|0.6393|0.3849|
-|int8|0.6366|0.3824|
+|float32 opt|0.6393|0.3849|
+|int8|0.6395|0.3852|
+|int8 opt|0.6396|0.3852|
 
 ## Training
 
@@ -75,11 +68,20 @@ We provide the pretrained ml-20m model on [Google Drive](https://drive.google.co
 python train.py # --gpu=0
 ```
 
+## Model Optimizer
+
+```
+# optimize model
+python model_optimizer.py
+```
+
 ## Calibration
 
 ```
 # neumf calibration on ml-20m dataset
 python ncf.py --prefix=./model/ml-20m/neumf --calibration
+# optimized neumf calibration on ml-20m dataset
+python ncf.py --prefix=./model/ml-20m/neumf-opt --calibration
 ```
 
 ## Evaluation
@@ -87,15 +89,25 @@ python ncf.py --prefix=./model/ml-20m/neumf --calibration
 ```
 # neumf float32 inference on ml-20m dataset
 python ncf.py --batch-size=1000 --prefix=./model/ml-20m/neumf
+# optimized neumf float32 inference on ml-20m dataset
+python ncf.py --batch-size=1000 --prefix=./model/ml-20m/neumf-opt
 # neumf int8 inference on ml-20m dataset
 python ncf.py --batch-size=1000 --prefix=./model/ml-20m/neumf-quantized
+# optimized neumf int8 inference on ml-20m dataset
+python ncf.py --batch-size=1000 --prefix=./model/ml-20m/neumf-opt-quantized
 ```
 
 ## Benchmark
 
 ```
+usage: bash ./benchmark.sh [[[-p prefix ] [-e epoch] [-d dataset] [-b batch_size] [-i instance] [-c cores/instance]] | [-h]]
+
 # neumf float32 benchmark on ml-20m dataset
-python ncf.py --batch-size=1000 --prefix=./model/ml-20m/neumf --benchmark
+sh benchmark.sh -p model/ml-20m/neumf
+# optimized neumf float32 benchmark on ml-20m dataset
+sh benchmark.sh -p model/ml-20m/neumf-opt
 # neumf int8 benchmark on ml-20m dataset
-python ncf.py --batch-size=1000 --prefix=./model/ml-20m/neumf-quantized --benchmark
+sh benchmark.sh -p model/ml-20m/neumf-quantized
+# optimized neumf int8 benchmark on ml-20m dataset
+sh benchmark.sh -p model/ml-20m/neumf-opt-quantized
 ```
diff --git a/example/neural_collaborative_filtering/benchmark.sh b/example/neural_collaborative_filtering/benchmark.sh
new file mode 100644
index 000000000000..60fec746cd20
--- /dev/null
+++ b/example/neural_collaborative_filtering/benchmark.sh
@@ -0,0 +1,114 @@
+#!/bin/bash
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+usage()
+{
+    echo "usage: bash ./benchmark.sh [[[-p prefix ] [-e epoch] [-d dataset] [-b batch_size] [-i instance] [-c cores/instance]] | [-h]]"
+}
+
+while [ $# -gt 0 ]; do
+  case "$1" in
+    --prefix | -p)
+      shift
+      PREFIX=$1
+      ;;
+    --epoch | -e)
+      shift
+      EPOCH=$1
+      ;;
+    --dataset | -d)
+      shift
+      DATASET=$1
+      ;;
+    --batch-size | -b)
+      shift
+      BS=$1
+      ;;
+    --instance | -i)
+      shift
+      INS=$1
+      ;;
+    --core | -c)
+      shift
+      CORES=$1
+      ;;
+    --help | -h)
+      usage
+      exit 1
+      ;;
+    *)
+      usage
+      exit 1
+  esac
+  shift
+done
+
+NUM_SOCKET=`lscpu | grep 'Socket(s)' | awk '{print $NF}'`
+NUM_NUMA_NODE=`lscpu | grep 'NUMA node(s)' | awk '{print $NF}'`
+CORES_PER_SOCKET=`lscpu | grep 'Core(s) per socket' | awk '{print $NF}'`
+NUM_CORES=$((CORES_PER_SOCKET * NUM_SOCKET))
+CORES_PER_NUMA=$((NUM_CORES / NUM_NUMA_NODE))
+echo "target machine has $NUM_CORES physical core(s) on $NUM_NUMA_NODE numa nodes of $NUM_SOCKET socket(s)."
+
+if [ -z $PREFIX ]; then
+  echo "Error: Need a model prefix."
+  exit
+fi
+if [ -z $EPOCH ]; then
+  echo "Default: set epoch of model parameters to 7."
+  EPOCH=7
+fi
+if [ -z $DATASET ]; then
+  echo "Default: set dataset to ml-20m."
+  DATASET='ml-20m'
+fi
+if [ -z $INS ]; then
+  echo "Default: launch one instance per physical core."
+  INS=$NUM_CORES
+fi
+if [ -z $CORES ]; then
+  echo "Default: divide full physical cores."
+  CORES=$((NUM_CORES / $INS))
+fi
+if [ -z $BS ]; then
+  echo "Default: set batch size to 700."
+  BS=700
+fi
+
+echo "  cores/instance: $CORES"
+echo "  total instances: $INS"
+echo "  batch size: $BS"
+echo ""
+
+rm NCF_*.log
+
+for((i=0;i<$INS;i++));
+do
+  ((a=$i*$CORES))
+  ((b=$a+$CORES-1))
+  memid=$((b/CORES_PER_NUMA))
+  LOG=NCF_$i.log
+  echo "  $i instance use $a-$b cores with $LOG"
+  KMP_AFFINITY=granularity=fine,noduplicates,compact,1,0 \
+  OMP_NUM_THREADS=$CORES \
+  numactl --physcpubind=$a-$b --membind=$memid python ncf.py --batch-size=$BS --dataset=$DATASET --epoch=$EPOCH --benchmark --prefix=$PREFIX 2>&1 | tee $LOG &
+done
+wait
+
+grep speed NCF_*.log | awk '{ sum += $(NF-1) }; END { print "Total Performance is " sum " samples/sec"}'
diff --git a/example/neural_collaborative_filtering/convert.py b/example/neural_collaborative_filtering/convert.py
index 4c64d2cdedab..7fb7f1ede9e4 100644
--- a/example/neural_collaborative_filtering/convert.py
+++ b/example/neural_collaborative_filtering/convert.py
@@ -38,7 +38,7 @@ def parse_args():
     parser = ArgumentParser()
     parser.add_argument('--dataset', nargs='?', default='ml-20m', choices=['ml-1m', 'ml-20m'],
                         help='The dataset name, temporary support ml-1m and ml-20m.')
-    parser.add_argument('path', type=str, default = './data/',
+    parser.add_argument('--path', type=str, default = './data/',
                         help='Path to reviews CSV file from MovieLens')
     parser.add_argument('-n', '--negatives', type=int, default=999,
                         help='Number of negative samples for each positive'
diff --git a/example/neural_collaborative_filtering/core/model.py b/example/neural_collaborative_filtering/core/model.py
index b516e5039fed..6c03bb01a357 100644
--- a/example/neural_collaborative_filtering/core/model.py
+++ b/example/neural_collaborative_filtering/core/model.py
@@ -37,6 +37,27 @@ def _init_weight(self, _, arr):
         limit = np.sqrt(3. / self._fan_in)
         mx.random.uniform(-limit, limit, out=arr)
 
+# only for inference model optimize
+def mlp_opt(user, item, factor_size, model_layers, max_user, max_item):
+    user_weight = mx.sym.Variable('fused_mlp_user_weight', init=mx.init.Normal(0.01))
+    item_weight = mx.sym.Variable('fused_mlp_item_weight', init=mx.init.Normal(0.01))
+    embed_user = mx.sym.Embedding(data=user, weight=user_weight, input_dim=max_user,
+                                  output_dim=factor_size * 2, name='fused_embed_user'+str(factor_size))
+    embed_item = mx.sym.Embedding(data=item, weight=item_weight, input_dim=max_item,
+                                  output_dim=factor_size * 2, name='fused_embed_item'+str(factor_size))
+    pre_gemm_concat = embed_user + embed_item
+
+    for i in range(1, len(model_layers)):
+        if i==1:
+            pre_gemm_concat = mx.sym.Activation(data=pre_gemm_concat, act_type='relu', name='act_'+str(i-1))
+            continue
+        else:
+            mlp_weight_init = golorot_uniform(model_layers[i-1], model_layers[i])
+        mlp_weight = mx.sym.Variable('fc_{}_weight'.format(i-1), init=mlp_weight_init)
+        pre_gemm_concat = mx.sym.FullyConnected(data=pre_gemm_concat, weight=mlp_weight, num_hidden=model_layers[i], name='fc_'+str(i-1))
+        pre_gemm_concat = mx.sym.Activation(data=pre_gemm_concat, act_type='relu', name='act_'+str(i-1))
+
+    return pre_gemm_concat
 
 def mlp(user, item, factor_size, model_layers, max_user, max_item):
     user_weight = mx.sym.Variable('mlp_user_weight', init=mx.init.Normal(0.01))
@@ -47,14 +68,11 @@ def mlp(user, item, factor_size, model_layers, max_user, max_item):
                                   output_dim=factor_size, name='embed_item'+str(factor_size))
     pre_gemm_concat = mx.sym.concat(embed_user, embed_item, dim=1, name='pre_gemm_concat')
 
-    for i, layer in enumerate(model_layers):
-        if i==0:
-            mlp_weight_init = golorot_uniform(2 * factor_size, model_layers[i])
-        else:
-            mlp_weight_init = golorot_uniform(model_layers[i-1], model_layers[i])
-        mlp_weight = mx.sym.Variable('fc_{}_weight'.format(i), init=mlp_weight_init)
-        pre_gemm_concat = mx.sym.FullyConnected(data=pre_gemm_concat, weight=mlp_weight, num_hidden=layer, name='fc_'+str(i))
-        pre_gemm_concat = mx.sym.Activation(data=pre_gemm_concat, act_type='relu', name='act_'+str(i))
+    for i in range(1, len(model_layers)):
+        mlp_weight_init = golorot_uniform(model_layers[i-1], model_layers[i])
+        mlp_weight = mx.sym.Variable('fc_{}_weight'.format(i-1), init=mlp_weight_init)
+        pre_gemm_concat = mx.sym.FullyConnected(data=pre_gemm_concat, weight=mlp_weight, num_hidden=model_layers[i], name='fc_'+str(i-1))
+        pre_gemm_concat = mx.sym.Activation(data=pre_gemm_concat, act_type='relu', name='act_'+str(i-1))
 
     return pre_gemm_concat
 
@@ -70,24 +88,34 @@ def gmf(user, item, factor_size, max_user, max_item):
     return pred
 
 def get_model(model_type='neumf', factor_size_mlp=128, factor_size_gmf=64,
-              model_layers=[256, 128, 64], num_hidden=1, 
-              max_user=138493, max_item=26744):
+              model_layers=[256, 256, 128, 64], num_hidden=1, 
+              max_user=138493, max_item=26744, opt=False):
     # input
     user = mx.sym.Variable('user')
     item = mx.sym.Variable('item')
 
     if model_type == 'mlp':
-        net = mlp(user=user, item=item,
-                  factor_size=factor_size_mlp, model_layers=model_layers,
-                  max_user=max_user, max_item=max_item)
+        if opt:
+            net = mlp_opt(user=user, item=item,
+                         factor_size=factor_size_mlp, model_layers=model_layers,
+                         max_user=max_user, max_item=max_item)
+        else:
+            net = mlp(user=user, item=item,
+                      factor_size=factor_size_mlp, model_layers=model_layers,
+                      max_user=max_user, max_item=max_item)
     elif model_type == 'gmf':
         net = gmf(user=user, item=item,
                   factor_size=factor_size_gmf,
                   max_user=max_user, max_item=max_item)
     elif model_type == 'neumf':
-        net_mlp = mlp(user=user, item=item,
-                      factor_size=factor_size_mlp, model_layers=model_layers,
-                      max_user=max_user, max_item=max_item)
+        if opt:
+            net_mlp = mlp_opt(user=user, item=item,
+                              factor_size=factor_size_mlp, model_layers=model_layers,
+                              max_user=max_user, max_item=max_item)
+        else:
+            net_mlp = mlp(user=user, item=item,
+                          factor_size=factor_size_mlp, model_layers=model_layers,
+                          max_user=max_user, max_item=max_item)
         net_gmf = gmf(user=user, item=item,
                       factor_size=factor_size_gmf,
                       max_user=max_user, max_item=max_item)
diff --git a/example/neural_collaborative_filtering/model_optimizer.py b/example/neural_collaborative_filtering/model_optimizer.py
new file mode 100644
index 000000000000..2866ae7e7e05
--- /dev/null
+++ b/example/neural_collaborative_filtering/model_optimizer.py
@@ -0,0 +1,81 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# 
+import os
+import time
+import argparse
+import logging
+import math
+import random
+import numpy as np
+import mxnet as mx
+from core.model import get_model
+from core.dataset import NCFTrainData
+
+logging.basicConfig(level=logging.DEBUG)
+
+parser = argparse.ArgumentParser(description="Run model optimizer.",
+                                 formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+parser.add_argument('--path', nargs='?', default='./data/',
+                    help='Input data path.')
+parser.add_argument('--dataset', nargs='?', default='ml-20m',
+                    help='The dataset name.')
+parser.add_argument('--model-prefix', type=str, default='./model/ml-20m/neumf')
+parser.add_argument('--epoch', type=int, default=7, help='parameters epoch')
+parser.add_argument('--model-type', type=str, default='neumf', choices=['neumf', 'gmf', 'mlp'],
+                    help="mdoel type")
+parser.add_argument('--layers', default='[256, 256, 128, 64]',
+                    help="list of number hiddens of fc layers in mlp model.")
+parser.add_argument('--factor-size-gmf', type=int, default=64,
+                    help="outdim of gmf embedding layers.")
+parser.add_argument('--num-hidden', type=int, default=1,
+                    help="num-hidden of neumf fc layer")
+
+head = '%(asctime)-15s %(message)s'
+logging.basicConfig(level=logging.INFO, format=head)
+
+# arg parser
+args = parser.parse_args()
+logging.info(args)
+
+model_prefix = args.model_prefix
+model_type = args.model_type
+model_layers = eval(args.layers)
+factor_size_gmf = args.factor_size_gmf
+factor_size_mlp = int(model_layers[0]/2)
+num_hidden = args.num_hidden
+train_dataset = NCFTrainData((args.path + args.dataset + '/train-ratings.csv'), nb_neg=4)
+net = get_model(model_type, factor_size_mlp, factor_size_gmf, 
+                model_layers, num_hidden, train_dataset.nb_users, train_dataset.nb_items, opt=True)
+
+raw_params, _ = mx.model.load_params(model_prefix, args.epoch)
+fc_0_weight_split = mx.nd.split(raw_params['fc_0_weight'], axis=1, num_outputs=2)
+fc_0_left = fc_0_weight_split[0]
+fc_0_right = fc_0_weight_split[1]
+
+user_weight_fusion = mx.nd.FullyConnected(data = raw_params['mlp_user_weight'], weight=fc_0_left, bias=raw_params['fc_0_bias'], no_bias=False, num_hidden=model_layers[0])
+item_weight_fusion = mx.nd.FullyConnected(data = raw_params['mlp_item_weight'], weight=fc_0_right, no_bias=True, num_hidden=model_layers[0])
+
+opt_params = raw_params
+del opt_params['mlp_user_weight']
+del opt_params['mlp_item_weight']
+del opt_params['fc_0_bias']
+opt_params['fused_mlp_user_weight'] = user_weight_fusion
+opt_params['fused_mlp_item_weight'] = item_weight_fusion
+
+mx.model.save_checkpoint(model_prefix + '-opt', args.epoch, net, opt_params, {})
+
diff --git a/example/neural_collaborative_filtering/ncf.py b/example/neural_collaborative_filtering/ncf.py
index 0fd9f733a1bd..b01be01bc8d9 100644
--- a/example/neural_collaborative_filtering/ncf.py
+++ b/example/neural_collaborative_filtering/ncf.py
@@ -42,20 +42,12 @@
                     help='max number of item index.')
 parser.add_argument('--batch-size', type=int, default=256,
                     help='number of examples per batch')
-parser.add_argument('--model-type', type=str, default='neumf', choices=['neumf', 'gmf', 'mlp'],
-                    help="mdoel type")
-parser.add_argument('--layers', default='[256, 128, 64]',
-                    help="list of number hiddens of fc layers in mlp model.")
-parser.add_argument('--factor-size-gmf', type=int, default=64,
-                    help="outdim of gmf embedding layers.")
-parser.add_argument('--num-hidden', type=int, default=1,
-                    help="num-hidden of neumf fc layer")
 parser.add_argument('--topk', type=int, default=10,
                     help="topk for accuracy evaluation.")
 parser.add_argument('--gpu', type=int, default=None,
                     help="index of gpu to run, e.g. 0 or 1. None means using cpu().")
 parser.add_argument('--benchmark', action='store_true',  help="whether to benchmark performance only")
-parser.add_argument('--epoch', type=int, default=0, help='model checkpoint index for inference')
+parser.add_argument('--epoch', type=int, default=7, help='model checkpoint index for inference')
 parser.add_argument('--prefix', default='./model/ml-20m/neumf', help="model checkpoint prefix")
 parser.add_argument('--calibration', action='store_true', help="whether to calibrate model")
 parser.add_argument('--calib-mode', type=str, choices=['naive', 'entropy'], default='naive',
@@ -85,11 +77,6 @@
     max_user = args.max_user
     max_item = args.max_item
     batch_size = args.batch_size
-    model_type = args.model_type
-    model_layers = eval(args.layers)
-    factor_size_gmf = args.factor_size_gmf
-    factor_size_mlp = int(model_layers[0]/2)
-    num_hidden = args.num_hidden
     benchmark = args.benchmark
     calibration = args.calibration
     calib_mode = args.calib_mode
@@ -129,7 +116,7 @@
         cqsym, cqarg_params, aux_params, collector = quantize_graph(sym=net, arg_params=arg_params, aux_params=aux_params,
                                                                     excluded_sym_names=excluded_sym_names,
                                                                     calib_mode=calib_mode,
-                                                                    quantized_dtype=args.quantized_dtype, logger=logging)
+                                                                    quantized_dtype=quantized_dtype, logger=logging)
         max_num_examples = num_calib_batches * batch_size
         mod._exec_group.execs[0].set_monitor_callback(collector.collect, monitor_all=True)
         num_batches = 0
@@ -144,12 +131,17 @@
                     % (num_batches, batch_size))
         cqsym, cqarg_params, aux_params = calib_graph(qsym=cqsym, arg_params=arg_params, aux_params=aux_params,
                                                       collector=collector, calib_mode=calib_mode,
-                                                      quantized_dtype=args.quantized_dtype, logger=logging)                                                       
+                                                      quantized_dtype=quantized_dtype, logger=logging)                                                       
         sym_name = '%s-symbol.json' % (args.prefix + '-quantized')
         cqsym = cqsym.get_backend_symbol('MKLDNN_QUANTIZE')
         mx.model.save_checkpoint(args.prefix + '-quantized', args.epoch, cqsym, cqarg_params, aux_params)
     elif benchmark:
         logging.info('Benchmarking...')
+        data = [mx.random.randint(0, 1000, shape=shape, ctx=ctx) for _, shape in mod.data_shapes]
+        batch = mx.io.DataBatch(data, []) # empty label
+        for i in range(2000):
+            mod.forward(batch, is_train=False)
+        logging.info('Benchmarking...')
         num_samples = 0
         for ib, batch in enumerate(val_iter):
             if ib == 5:
diff --git a/example/neural_collaborative_filtering/train.py b/example/neural_collaborative_filtering/train.py
index 0b0cfad1ef39..c68f271a6f0d 100644
--- a/example/neural_collaborative_filtering/train.py
+++ b/example/neural_collaborative_filtering/train.py
@@ -45,7 +45,7 @@
                     help="mdoel type")
 parser.add_argument('--num-negative', type=int, default=4,
                     help="number of negative samples per positive sample while training.")
-parser.add_argument('--layers', default='[256, 128, 64]',
+parser.add_argument('--layers', default='[256, 256, 128, 64]',
                     help="list of number hiddens of fc layers in mlp model.")
 parser.add_argument('--factor-size-gmf', type=int, default=64,
                     help="outdim of gmf embedding layers.")

From 8a14a6acb9c31b0bfc1395a0bc8e9019c73846fd Mon Sep 17 00:00:00 2001
From: Xinyu Chen <xinyu1.chen@intel.com>
Date: Fri, 27 Dec 2019 08:44:26 +0800
Subject: [PATCH 738/813] [MKL-DNN] Enhance Quantization Method (#17161)

* support bert quantization

* support bert quantization

* fix lint
---
 python/mxnet/contrib/quantization.py | 55 +++++++++++++++++++++++-----
 1 file changed, 45 insertions(+), 10 deletions(-)

diff --git a/python/mxnet/contrib/quantization.py b/python/mxnet/contrib/quantization.py
index 01051ab7c8e4..a56f8531aa49 100644
--- a/python/mxnet/contrib/quantization.py
+++ b/python/mxnet/contrib/quantization.py
@@ -27,6 +27,7 @@
 import logging
 import os
 import shutil
+import warnings
 import numpy as np
 from ..base import _LIB, check_call, py_str
 from ..base import c_array, c_str, mx_uint, c_str_array
@@ -419,6 +420,7 @@ def __init__(self, calib_data):
         else:
             data_example = [data_example]
         # suppose there must be one label in data_example
+        # TODO(xinyu-intel): little tricky here, need to refactor.
         num_data = len(data_example)
         assert num_data > 0
         # here reshape is to handle the 5D/6D input data
@@ -426,6 +428,10 @@ def __init__(self, calib_data):
             data_example[0] = data_example[0].reshape((-1,) + data_example[0].shape[2:])
         self.provide_data = [DataDesc(name='data', shape=(data_example[0].shape))]
         self.provide_data += [DataDesc(name='data{}'.format(i), shape=x.shape) for i, x in enumerate(data_example[1:])]
+        # data0, data1, ..., label
+        if num_data >= 3:
+            self.provide_data = [DataDesc(name='data{}'.format(i), shape=x.shape)
+                                 for i, x in enumerate(data_example[0:])]
         self.batch_size = data_example[0].shape[0]
         self.reset()
 
@@ -627,8 +633,9 @@ def quantize_model_mkldnn(sym, arg_params, aux_params,
     return qsym, qarg_params, aux_params
 
 def quantize_graph(sym, arg_params, aux_params, ctx=cpu(),
-                   excluded_sym_names=None, excluded_op_names=None, calib_mode='entropy',
-                   quantized_dtype='int8', quantize_mode='full', logger=None):
+                   excluded_sym_names=None, excluded_op_names=None,
+                   calib_mode='entropy', quantized_dtype='int8', quantize_mode='full',
+                   LayerOutputCollector=None, logger=None):
     """User-level API for generating a quantized model from a FP32 model w/o calibration
     and a collector for naive or entropy calibration.
     The backend quantized operators are only enabled for Linux systems. Please do not run
@@ -667,6 +674,8 @@ def quantize_graph(sym, arg_params, aux_params, ctx=cpu(),
         The mode that quantization pass to apply. Support 'full' and 'smart'.
         'full' means quantize all operator if possible.
         'smart' means quantization pass will smartly choice which operator should be quantized.
+    LayerOutputCollector : class
+        For customize calibration method usage.
     logger : Object
         A logging object for printing information during the process of quantization.
     Returns
@@ -711,9 +720,14 @@ def quantize_graph(sym, arg_params, aux_params, ctx=cpu(),
             if logger:
                 logger.info(
                     'Create a layer output minmax collector for naive calibration')
+        elif calib_mode == 'customize' and LayerOutputCollector is not None:
+            collector = LayerOutputCollector
+            if logger:
+                logger.info(
+                    'Create a customize layer output minmax collector for calibration')
         else:
             raise ValueError('unknown calibration mode %s received,'
-                             ' expected `none`, `naive`, or `entropy`' % calib_mode)
+                             ' expected `none`, `naive`, `entropy` or `customize`' % calib_mode)
         if logger:
             logger.info('Collector created, please use set_monitor_callback'
                         ' to collect calibration information.')
@@ -770,9 +784,11 @@ def calib_graph(qsym, arg_params, aux_params, collector,
                 collector.hist_dict, quantized_dtype, logger=logger)
         elif calib_mode == 'naive':
             th_dict = collector.min_max_dict
+        elif calib_mode == 'customize':
+            th_dict = collector.min_max_dict
         else:
             raise ValueError('unknown calibration mode %s received,'
-                             ' expected `none`, `naive`, or `entropy`' % calib_mode)
+                             ' expected `none`, `naive`, `entropy` or `customize`' % calib_mode)
         qsym = _calibrate_quantized_sym(qsym, th_dict)
     else:
         raise ValueError('please set calibration mode to naive or entropy.')
@@ -783,10 +799,10 @@ def calib_graph(qsym, arg_params, aux_params, collector,
 
     return qsym, qarg_params, aux_params
 
-def quantize_net(network, quantized_dtype='auto', quantize_mode='full',
-                 exclude_layers=None, exclude_layers_match=None, exclude_operators=None,
-                 calib_data=None, data_shapes=None, calib_mode='none',
-                 num_calib_examples=None, ctx=cpu(), logger=None):
+def quantize_net_v2(network, quantized_dtype='auto', quantize_mode='full',
+                    exclude_layers=None, exclude_layers_match=None, exclude_operators=None,
+                    calib_data=None, data_shapes=None, calib_mode='none',
+                    num_calib_examples=None, ctx=cpu(), LayerOutputCollector=None, logger=None):
     """User-level API for Gluon users to generate a quantized SymbolBlock from a FP32 HybridBlock w/ or w/o calibration.
     The backend quantized operators are only enabled for Linux systems. Please do not run
     inference using the quantized models on Windows for now.
@@ -830,6 +846,8 @@ def quantize_net(network, quantized_dtype='auto', quantize_mode='full',
     ctx : Context
         Defines the device that users want to run forward propagation on the calibration
         dataset for collecting layer output statistics. Currently, only supports single context.
+    LayerOutputCollector : class
+        For customize calibration method usage.
     logger : Object
         A logging object for printing information during the process of quantization.
 
@@ -906,7 +924,8 @@ def __exit__(self, exc_type, exc_value, traceback):
     qsym, qarg_params, aux_params, collector = quantize_graph(
         sym=symnet, arg_params=args, aux_params=auxs, ctx=ctx,
         excluded_sym_names=exclude_layers, excluded_op_names=exclude_operators,
-        calib_mode=calib_mode, quantized_dtype=quantized_dtype, quantize_mode=quantize_mode, logger=logger)
+        calib_mode=calib_mode, quantized_dtype=quantized_dtype, quantize_mode=quantize_mode,
+        LayerOutputCollector=LayerOutputCollector, logger=logger)
 
     if calib_mode is not None and calib_mode != 'none':
         if not isinstance(ctx, Context):
@@ -915,7 +934,7 @@ def __exit__(self, exc_type, exc_value, traceback):
         if calib_data is None:
             raise ValueError(
                 'calib_data must be provided when calib_mode=%s' % calib_mode)
-        if calib_mode in ['naive', 'entropy']:
+        if calib_mode in ['naive', 'entropy', 'customize']:
             data_names = [pair[0] for pair in calib_data.provide_data]
             mod = Module(symbol=symnet, context=ctx,
                          data_names=data_names, label_names=None)
@@ -956,3 +975,19 @@ def __exit__(self, exc_type, exc_value, traceback):
         net.collect_params().load(param_name, cast_dtype=True, dtype_source='saved')
         net.collect_params().reset_ctx(ctx)
     return net
+
+def quantize_net(network, quantized_dtype='auto', quantize_mode='full',
+                 exclude_layers=None, exclude_layers_match=None, exclude_operators=None,
+                 calib_data=None, data_shapes=None, calib_mode='none',
+                 num_calib_examples=None, ctx=cpu(), logger=None):
+    """User-level API for Gluon users to generate a quantized SymbolBlock from a FP32 HybridBlock w/ or w/o calibration.
+       Will be deprecated after MXNet 2.0, please use quantize_net_v2.
+    """
+    warnings.warn('WARNING: This will be deprecated after MXNet 2.0, please use quantize_net_v2.')
+    return quantize_net_v2(network=network, quantized_dtype=quantized_dtype,
+                           quantize_mode=quantize_mode, exclude_layers=exclude_layers,
+                           exclude_layers_match=exclude_layers_match,
+                           exclude_operators=exclude_operators,
+                           calib_data=calib_data, data_shapes=data_shapes,
+                           calib_mode=calib_mode, num_calib_examples=num_calib_examples,
+                           ctx=ctx, LayerOutputCollector=None, logger=logger)

From 4fda46bd8c0e9f68376a0b6c69f8027ab227f650 Mon Sep 17 00:00:00 2001
From: Xinyu Chen <xinyu1.chen@intel.com>
Date: Fri, 27 Dec 2019 08:45:19 +0800
Subject: [PATCH 739/813] fix py27 quantization (#17153)

---
 python/mxnet/contrib/quantization.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/python/mxnet/contrib/quantization.py b/python/mxnet/contrib/quantization.py
index a56f8531aa49..ce22fb753ace 100644
--- a/python/mxnet/contrib/quantization.py
+++ b/python/mxnet/contrib/quantization.py
@@ -613,7 +613,9 @@ def quantize_model_mkldnn(sym, arg_params, aux_params,
         A tuple of quantized symbol, quantized arg_params, and aux_params.
     -------
     """
-    if ctx != cpu():
+    if not isinstance(ctx, Context):
+        raise ValueError('currently only supports single ctx, while received %s' % str(ctx))
+    if ctx.device_type != 'cpu':
         raise ValueError(
             'quantize_model_mkldnn only support Intel cpu platform with MKL-DNN Backend')
 

From 2ad3ce408b08456bff0b92a82aa3c484adde29f2 Mon Sep 17 00:00:00 2001
From: Tao Lv <tao.a.lv@intel.com>
Date: Fri, 27 Dec 2019 10:23:41 +0800
Subject: [PATCH 740/813] broadcast_axis optimization (#17091)

* broadcast_aixs opt

* fix lint
---
 .../tensor/broadcast_reduce_op_value.cc       | 37 ++++++++++++++++++-
 1 file changed, 36 insertions(+), 1 deletion(-)

diff --git a/src/operator/tensor/broadcast_reduce_op_value.cc b/src/operator/tensor/broadcast_reduce_op_value.cc
index 43bfc729329a..31e0dd054afd 100644
--- a/src/operator/tensor/broadcast_reduce_op_value.cc
+++ b/src/operator/tensor/broadcast_reduce_op_value.cc
@@ -32,6 +32,41 @@ DMLC_REGISTER_PARAMETER(BroadcastAxesParam);
 DMLC_REGISTER_PARAMETER(BroadcastToParam);
 DMLC_REGISTER_PARAMETER(BroadcastLikeParam);
 
+template<typename DType>
+void BroadcastAxisKer(DType* src,
+                      DType* dst,
+                      index_t outer,
+                      index_t inner,
+                      index_t size) {
+#pragma omp parallel for num_threads(engine::OpenMP::Get()->GetRecommendedOMPThreadCount())
+  for (index_t i = 0; i < outer * size; i++) {
+    const index_t m = i / size;
+    const index_t n = i % size;
+    void* offset = reinterpret_cast<void*>(dst + m * size * inner + n * inner);
+    memcpy(offset, reinterpret_cast<void*>(src + m * inner), inner * sizeof (DType));
+  }
+}
+
+inline void BroadcastAxisComputeCPU(const nnvm::NodeAttrs& attrs,
+                                    const OpContext& ctx,
+                                    const std::vector<TBlob>& inputs,
+                                    const std::vector<OpReqType>& req,
+                                    const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  const BroadcastAxesParam& param = nnvm::get<BroadcastAxesParam>(attrs.parsed);
+  if (param.axis.ndim() == 1 && inputs[0].shape_[param.axis[0]] == 1 && req[0] == kWriteTo) {
+    MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+      auto dst = outputs[0].dptr<DType>();
+      auto src = inputs[0].dptr<DType>();
+      index_t outer = inputs[0].shape_.ProdShape(0, param.axis[0]);
+      index_t inner = inputs[0].shape_.ProdShape(param.axis[0], inputs[0].shape_.ndim());
+      BroadcastAxisKer(src, dst, outer, inner, param.size[0]);
+    });
+  } else {
+    BroadcastComputeImpl<cpu>(attrs, ctx, inputs, req, outputs, inputs[0].shape_);
+  }
+}
+
 MXNET_OPERATOR_REGISTER_BROADCAST(broadcast_axis)
 .add_alias("broadcast_axes")
 .describe(R"code(Broadcasts the input array over particular axes.
@@ -59,7 +94,7 @@ Example::
 .set_attr_parser(ParamParser<BroadcastAxesParam>)
 .add_arguments(BroadcastAxesParam::__FIELDS__())
 .set_attr<mxnet::FInferShape>("FInferShape", BroadcastAxesShape)
-.set_attr<FCompute>("FCompute<cpu>", BroadcastCompute<cpu>);
+.set_attr<FCompute>("FCompute<cpu>", BroadcastAxisComputeCPU);
 
 MXNET_OPERATOR_REGISTER_BROADCAST(broadcast_to)
 .describe(R"code(Broadcasts the input array to a new shape.

From 37197b6750a09cf0dbad250bad36ab545f462fbb Mon Sep 17 00:00:00 2001
From: Minghao Liu <40382964+Tommliu@users.noreply.github.com>
Date: Fri, 27 Dec 2019 11:12:55 +0800
Subject: [PATCH 741/813] any/all (#17087)

---
 python/mxnet/_numpy_op_doc.py                 | 101 ++++++++++++++++++
 python/mxnet/numpy_dispatch_protocol.py       |   2 +
 src/operator/mshadow_op.h                     |   8 ++
 src/operator/mxnet_op.h                       |  23 ++++
 src/operator/numpy/np_broadcast_reduce_op.h   |  53 +++++++++
 .../numpy/np_broadcast_reduce_op_boolean.cc   |  85 +++++++++++++++
 .../numpy/np_broadcast_reduce_op_boolean.cu   |  40 +++++++
 src/operator/operator_tune.cc                 |   2 +
 src/operator/tensor/broadcast_reduce-inl.cuh  |  10 ++
 src/operator/tensor/broadcast_reduce-inl.h    |  12 +++
 src/operator/tensor/broadcast_reduce_op.h     |  33 ++++++
 src/operator/tensor/matrix_op-inl.h           |   2 +-
 .../unittest/test_numpy_interoperability.py   |  28 +++++
 tests/python/unittest/test_numpy_op.py        |  78 ++++++++++++++
 14 files changed, 476 insertions(+), 1 deletion(-)
 create mode 100644 src/operator/numpy/np_broadcast_reduce_op_boolean.cc
 create mode 100644 src/operator/numpy/np_broadcast_reduce_op_boolean.cu

diff --git a/python/mxnet/_numpy_op_doc.py b/python/mxnet/_numpy_op_doc.py
index 0d0e3b64491b..d1d67be06b05 100644
--- a/python/mxnet/_numpy_op_doc.py
+++ b/python/mxnet/_numpy_op_doc.py
@@ -20,6 +20,107 @@
 """Doc placeholder for numpy ops with prefix _np."""
 
 
+def _np_all(a, axis=None, keepdims=False, out=None):
+    """
+    Test whether all array elements along a given axis evaluate to True.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array or object that can be converted to an array.
+    axis : None or int or tuple of ints, optional
+        Axis or axes along which a logical AND reduction is performed.
+        The default (axis = None) is to perform a logical AND over
+        all the dimensions of the input array.
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left in
+        the result as dimensions with size one. With this option,
+        the result will broadcast correctly against the input array.
+    out : ndarray, optional
+        Alternate output array in which to place the result. It must have
+        the same shape as the expected output and its type is preserved
+
+    Returns
+    --------
+    all : ndarray, bool 
+        A new boolean or array is returned unless out is specified,
+        in which case a reference to out is returned.
+
+    Examples:
+    ---------
+    >>> np.all([[True,False],[True,True]])
+    False
+
+    >>> np.all([[True,False],[True,True]], axis=0)
+    array([ True, False])
+
+    >>> np.all([-1, 4, 5])
+    True
+
+    >>> np.all([1.0, np.nan])
+    True
+
+    >>> o=np.array(False)
+    >>> z=np.all([-1, 4, 5], out=o)
+    >>> id(z), id(o), z
+    (28293632, 28293632, array(True)) # may vary  
+    """
+    pass
+
+def _np_any(a, axis=None, keepdims=False, out=None):
+    """
+    Test whether any array element along a given axis evaluates to True.
+    Returns single boolean unless axis is not None 
+
+    Parameters
+    ----------
+    a : array_like
+        Input array or object that can be converted to an array.
+    axis : None or int or tuple of ints, optional
+        Axis or axes along which a logical AND reduction is performed.
+        The default (axis = None) is to perform a logical AND over
+        all the dimensions of the input array.
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left in
+        the result as dimensions with size one. With this option,
+        the result will broadcast correctly against the input array.
+    out : ndarray, optional
+        Alternate output array in which to place the result. It must have
+        the same shape as the expected output and its type is preserved
+
+    Returns
+    --------
+    any : bool or ndarray
+        A new boolean or ndarray is returned unless out is specified,
+        in which case a reference to out is returned.
+
+    Examples:
+    ---------
+    >>> np.any([[True, False], [True, True]])
+    True
+
+    >>> np.any([[True, False], [False, False]], axis=0)
+    array([ True, False])
+
+    >>> np.any([-1, 0, 5])
+    True
+
+    >>> np.any(np.nan)
+    True
+
+    >>> o=np.array(False)
+    >>> z=np.any([-1, 4, 5], out=o)
+    >>> z, o
+    (array(True), array(True))
+    >>> # Check now that z is a reference to o
+    >>> z is o
+    True
+    >>> id(z), id(o) # identity of z and o              # doctest: +SKIP
+    (191614240, 191614240)
+    """
+    pass
+
+
 def _np_cumsum(a, axis=None, dtype=None, out=None):
     """
     Return the cumulative sum of the elements along a given axis.
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index 9aa755fb436e..80b3d66c5e5d 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -83,6 +83,8 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
 
 
 _NUMPY_ARRAY_FUNCTION_LIST = [
+    'all',
+    'any',
     'argmin',
     'argmax',
     'around',
diff --git a/src/operator/mshadow_op.h b/src/operator/mshadow_op.h
index e3a3c0443428..4176d3a68792 100644
--- a/src/operator/mshadow_op.h
+++ b/src/operator/mshadow_op.h
@@ -1101,6 +1101,14 @@ struct minimum : public mxnet_op::tunable {
   }
 };
 
+/*! \brief boolean any/all kernel that determines whether elem is NonZero */
+struct NonZero {
+  template<typename DType>
+  MSHADOW_XINLINE static bool Map(DType a) {
+    return (a != DType(0));
+  }
+};
+
 /*! \brief sum reducer that ignores NaN values in the input */
 struct nansum {
   /*! \brief do reduction into dst */
diff --git a/src/operator/mxnet_op.h b/src/operator/mxnet_op.h
index b15117f9f83b..d7752c4759db 100644
--- a/src/operator/mxnet_op.h
+++ b/src/operator/mxnet_op.h
@@ -1148,6 +1148,29 @@ struct set_to_int : public tunable {
  */
 using set_zero = set_to_int<0>;
 using set_one  = set_to_int<1>;
+
+/*!
+ * \brief Set to immediate scalar value kernel
+ * \tparam val Scalar immediate
+ */
+template<bool val>
+struct set_to_bool : public tunable {
+  // mxnet_op version (when used directly with Kernel<>::Launch()) */
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(index_t i, DType *out) {
+    out[i] = DType(val);
+  }
+  // mshadow_op version (when used with op_with_req<>)
+  MSHADOW_XINLINE static int Map() {
+    return val;
+  }
+};
+
+/*!
+ * \brief Special-case kernel shortcut for setting to true and false
+ */
+using set_true = set_to_bool<true>;
+using set_false = set_to_bool<false>;
 }  // namespace mxnet_op
 
 }  // namespace op
diff --git a/src/operator/numpy/np_broadcast_reduce_op.h b/src/operator/numpy/np_broadcast_reduce_op.h
index 7d0025a62ad2..0efe2c2aa3df 100644
--- a/src/operator/numpy/np_broadcast_reduce_op.h
+++ b/src/operator/numpy/np_broadcast_reduce_op.h
@@ -86,6 +86,21 @@ struct NumpyReduceAxesNoDTypeParam : public dmlc::Parameter<NumpyReduceAxesNoDTy
   }
 };
 
+struct NumpyReduceAxesBoolParam : public dmlc::Parameter<NumpyReduceAxesBoolParam> {
+  dmlc::optional<mxnet::Tuple<int>> axis;
+  bool keepdims;
+  DMLC_DECLARE_PARAMETER(NumpyReduceAxesBoolParam) {
+    DMLC_DECLARE_FIELD(axis)
+      .set_default(dmlc::optional<mxnet::Tuple<int>>())
+      .describe("Axis or axes along which a sum is performed. The default, axis=None, will sum "
+                "all of the elements of the input array. If axis is negative it counts from the "
+                "last to the first axis.");
+    DMLC_DECLARE_FIELD(keepdims).set_default(false)
+      .describe("If this is set to `True`, the reduced axes are left "
+                "in the result as dimension with size one.");
+  }
+};
+
 inline TShape NumpyReduceAxesShapeImpl(const TShape& ishape,
                                        const dmlc::optional<mxnet::Tuple<int>>& axis,
                                        bool keepdims) {
@@ -173,6 +188,20 @@ inline bool NumpyReduceAxesShape(const nnvm::NodeAttrs& attrs,
   return shape_is_known(out_attrs->at(0));
 }
 
+inline bool NumpyReduceAxesBoolShape(const nnvm::NodeAttrs& attrs,
+                                     std::vector<TShape> *in_attrs,
+                                     std::vector<TShape> *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  if (!shape_is_known(in_attrs->at(0))) {
+    return false;
+  }
+  const NumpyReduceAxesBoolParam& param = nnvm::get<NumpyReduceAxesBoolParam>(attrs.parsed);
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0,
+                     NumpyReduceAxesShapeImpl((*in_attrs)[0], param.axis, param.keepdims));
+  return shape_is_known(out_attrs->at(0));
+}
+
 inline bool NumpyReduceAxesNoDTypeShape(const nnvm::NodeAttrs& attrs,
                                         std::vector<TShape> *in_attrs,
                                         std::vector<TShape> *out_attrs) {
@@ -298,6 +327,30 @@ void NumpyReduceAxesNoDTypeCompute(const nnvm::NodeAttrs& attrs,
   ReduceAxesComputeImpl<xpu, reducer, false, false, OP>(ctx, inputs, req, outputs, small);
 }
 
+template<typename xpu, typename reducer, typename OP = op::mshadow_op::NonZero>
+void NumpyReduceAxesBoolCompute(const nnvm::NodeAttrs& attrs,
+                                const OpContext& ctx,
+                                const std::vector<TBlob>& inputs,
+                                const std::vector<OpReqType>& req,
+                                const std::vector<TBlob>& outputs) {
+  const NumpyReduceAxesBoolParam& param = nnvm::get<NumpyReduceAxesBoolParam>(attrs.parsed);
+  mshadow::Stream<xpu>* s = ctx.get_stream<xpu>();
+  if (inputs[0].shape_.Size() == 0 && outputs[0].shape_.Size() != 0) {
+    using namespace mxnet_op;
+    Kernel<set_false, xpu>::Launch(s, outputs[0].shape_.Size(), outputs[0].dptr<bool>());
+    return;
+  }
+  if (param.axis.has_value() && param.axis.value().ndim() == 0) {
+    UnaryOp::IdentityCompute<xpu>(attrs, ctx, inputs, req, outputs);
+  }
+  TShape small;
+  if (param.keepdims) {
+    small = outputs[0].shape_;
+  } else {
+    small = NumpyReduceAxesShapeImpl(inputs[0].shape_, param.axis, true);
+  }
+  ReduceAxesComputeBoolImpl<xpu, reducer, false, false, OP>(ctx, inputs, req, outputs, small);
+}
 
 template<typename xpu, bool normalize = false>
 inline void NumpyReduceAxesBackwardUseNone(const nnvm::NodeAttrs& attrs,
diff --git a/src/operator/numpy/np_broadcast_reduce_op_boolean.cc b/src/operator/numpy/np_broadcast_reduce_op_boolean.cc
new file mode 100644
index 000000000000..7529c0d4e1d3
--- /dev/null
+++ b/src/operator/numpy/np_broadcast_reduce_op_boolean.cc
@@ -0,0 +1,85 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file np_broadcast_reduce_op_boolean.cc
+ * \brief CPU Implementation of broadcast and reduce functions based on boolean.
+ */
+
+#include "./np_broadcast_reduce_op.h"
+
+namespace mxnet {
+namespace op {
+
+inline bool NumpyReduceAxesBoolType(const nnvm::NodeAttrs& attrs,
+                                    std::vector<int> *in_attrs,
+                                    std::vector<int> *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::kBool);
+  return out_attrs->at(0) != -1 && in_attrs->at(0) != -1;
+}
+
+DMLC_REGISTER_PARAMETER(NumpyReduceAxesBoolParam);
+
+NNVM_REGISTER_OP(_np_any)
+.set_attr_parser(ParamParser<NumpyReduceAxesBoolParam>)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"data"};
+  })
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyReduceAxesBoolShape)
+.set_attr<nnvm::FInferType>("FInferType", NumpyReduceAxesBoolType)
+.set_attr<FCompute>("FCompute<cpu>", NumpyReduceAxesBoolCompute<cpu,
+  mshadow_op::sum, mshadow_op::NonZero>)
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.add_argument("data", "NDArray-or-Symbol", "Input ndarray")
+.add_arguments(NumpyReduceAxesBoolParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_np_all)
+.set_attr_parser(ParamParser<NumpyReduceAxesBoolParam>)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"data"};
+  })
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyReduceAxesBoolShape)
+.set_attr<nnvm::FInferType>("FInferType", NumpyReduceAxesBoolType)
+.set_attr<FCompute>("FCompute<cpu>", NumpyReduceAxesBoolCompute<cpu,
+  mshadow_op::product, mshadow_op::NonZero>)
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.add_argument("data", "NDArray-or-Symbol", "Input ndarray")
+.add_arguments(NumpyReduceAxesBoolParam::__FIELDS__());
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_broadcast_reduce_op_boolean.cu b/src/operator/numpy/np_broadcast_reduce_op_boolean.cu
new file mode 100644
index 000000000000..2c206bf88b2f
--- /dev/null
+++ b/src/operator/numpy/np_broadcast_reduce_op_boolean.cu
@@ -0,0 +1,40 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file np_broadcast_reduce_op_boolean.cu
+ * \brief GPU Implementation of broadcast and reduce functions based on boolean.
+ */
+
+#include "./np_broadcast_reduce_op.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(_np_any)
+.set_attr<FCompute>("FCompute<gpu>", NumpyReduceAxesBoolCompute<gpu,
+  mshadow_op::sum, mshadow_op::NonZero>);
+
+NNVM_REGISTER_OP(_np_all)
+.set_attr<FCompute>("FCompute<gpu>", NumpyReduceAxesBoolCompute<gpu,
+  mshadow_op::product, mshadow_op::NonZero>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/operator_tune.cc b/src/operator/operator_tune.cc
index 7ca594d21e59..9ca291391c5c 100644
--- a/src/operator/operator_tune.cc
+++ b/src/operator/operator_tune.cc
@@ -404,6 +404,8 @@ IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::smooth_l1_gradient);  // NO
 IMPLEMENT_BINARY_WORKLOAD_FWD_WITH_BOOL(mxnet::op::mshadow_op::lcm);  // NOLINT()
 IMPLEMENT_BLANK_WORKLOAD_FWD_WITH_BOOL(mxnet::op::mxnet_op::set_to_int<0>);  // NOLINT()
 IMPLEMENT_BLANK_WORKLOAD_FWD_WITH_BOOL(mxnet::op::mxnet_op::set_to_int<1>);  // NOLINT()
+IMPLEMENT_BLANK_WORKLOAD_FWD_WITH_BOOL(mxnet::op::mxnet_op::set_to_bool<false>);  // NOLINT()
+IMPLEMENT_BLANK_WORKLOAD_FWD_WITH_BOOL(mxnet::op::mxnet_op::set_to_bool<true>);  // NOLINT()
 IMPLEMENT_BLANK_WORKLOAD_FWD(mxnet::op::PopulateFullIdxRspKernel);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::ldexp);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::rldexp);  // NOLINT()
diff --git a/src/operator/tensor/broadcast_reduce-inl.cuh b/src/operator/tensor/broadcast_reduce-inl.cuh
index 41940e4b1e07..6cd7dd50657a 100644
--- a/src/operator/tensor/broadcast_reduce-inl.cuh
+++ b/src/operator/tensor/broadcast_reduce-inl.cuh
@@ -634,6 +634,16 @@ void Reduce(Stream<gpu> *s, const TBlob& small, const OpReqType req,
   }
 }
 
+template<typename Reducer, int ndim, typename DType, typename OP, bool safe_acc = false>
+void ReduceBool(Stream<gpu> *s, const TBlob& small, const OpReqType req,
+                const Tensor<gpu, 1, char>& workspace, const TBlob& big) {
+  if (req == kNullOp) return;
+  cudaStream_t stream = Stream<gpu>::GetStream(s);
+  ReduceImplConfig<ndim> config =
+    ConfigureReduceImpl<ndim, DType>(small.shape_, big.shape_, NULL, NULL);
+  ReduceImpl<Reducer, ndim, bool, DType, bool, OP>(stream, small, req, big, workspace, config);
+}
+
 template <typename Reducer, int ndim, typename DType, typename OP>
 void ReduceWithExtraMem(Stream<gpu>* s, const TBlob& small, const OpReqType req,
                         const Tensor<gpu, 1, char>& workspace, const TBlob& big) {};
diff --git a/src/operator/tensor/broadcast_reduce-inl.h b/src/operator/tensor/broadcast_reduce-inl.h
index 0a20e1263fbf..841fbcd28a68 100644
--- a/src/operator/tensor/broadcast_reduce-inl.h
+++ b/src/operator/tensor/broadcast_reduce-inl.h
@@ -255,6 +255,18 @@ void Reduce(Stream<cpu>* s, const TBlob& small, const OpReqType req,
   }
 }
 
+template <typename Reducer, int ndim, typename DType, typename OP>
+void ReduceBool(Stream<cpu>* s, const TBlob& small, const OpReqType req,
+                const Tensor<cpu, 1, char>& workspace, const TBlob& big) {
+  if (req == kNullOp) return;
+  Shape<ndim> rshape, rstride;
+  diff(small.shape_.get<ndim>(), big.shape_.get<ndim>(), &rshape, &rstride);
+  size_t N = small.shape_.Size(), M = rshape.Size();
+  seq_reduce_compute<Reducer, ndim, bool, DType, bool, OP>(
+    N, M, req == kAddTo, big.dptr<DType>(), small.dptr<bool>(),
+    big.shape_.get<ndim>(), small.shape_.get<ndim>(), rshape, rstride);
+}
+
 template <typename Reducer, int ndim, typename DType, typename OP>
 void ReduceWithExtraMem(Stream<cpu>* s, const TBlob& small, const OpReqType req,
                         const Tensor<cpu, 1, char>& workspace, const TBlob& big) {
diff --git a/src/operator/tensor/broadcast_reduce_op.h b/src/operator/tensor/broadcast_reduce_op.h
index 799f86544160..608e44dcfe76 100644
--- a/src/operator/tensor/broadcast_reduce_op.h
+++ b/src/operator/tensor/broadcast_reduce_op.h
@@ -636,6 +636,39 @@ void ReduceAxesComputeImpl(const OpContext& ctx,
   });
 }
 
+template<typename xpu, typename reducer, bool safe_acc, bool normalize = false,
+         typename OP = op::mshadow_op::NonZero>
+void ReduceAxesComputeBoolImpl(const OpContext& ctx,
+                               const std::vector<TBlob>& inputs,
+                               const std::vector<OpReqType>& req,
+                               const std::vector<TBlob>& outputs,
+                               const mxnet::TShape& small) {
+  using namespace mshadow;
+  using namespace mshadow::expr;
+
+  mxnet::TShape src_shape, dst_shape;
+  BroadcastReduceShapeCompact(inputs[0].shape_, small, &src_shape, &dst_shape);
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  MSHADOW_TYPE_SWITCH_WITH_BOOL(inputs[0].type_flag_, DType, {
+    MSHADOW_TYPE_SWITCH_WITH_BOOL(outputs[0].type_flag_, OType, {
+      const TBlob in_data = inputs[0].reshape(src_shape);
+      const TBlob out_data = outputs[0].reshape(dst_shape);
+      BROADCAST_NDIM_SWITCH(dst_shape.ndim(), NDim, {
+        size_t workspace_size = broadcast::ReduceWorkspaceSize<NDim, DType>(
+            s, out_data.shape_, req[0], in_data.shape_);
+        Tensor<xpu, 1, char> workspace =
+            ctx.requested[0].get_space_typed<xpu, 1, char>(Shape1(workspace_size), s);
+        broadcast::ReduceBool<reducer, NDim, DType, OP>(
+            s, out_data, req[0], workspace, in_data);
+        if (normalize) {
+          auto out = out_data.FlatTo2D<xpu, OType>(s);
+          out /= scalar<OType>(src_shape.Size()/dst_shape.Size());
+        }
+      });
+    });
+  });
+}
+
 template<typename xpu, typename reducer, bool normalize = false,
          typename OP = op::mshadow_op::identity>
 void ReduceAxesCompute(const nnvm::NodeAttrs& attrs,
diff --git a/src/operator/tensor/matrix_op-inl.h b/src/operator/tensor/matrix_op-inl.h
index 0c501808a6c0..cd0bd8deeac3 100644
--- a/src/operator/tensor/matrix_op-inl.h
+++ b/src/operator/tensor/matrix_op-inl.h
@@ -1244,7 +1244,7 @@ void SliceAssignScalarOpForward(const nnvm::NodeAttrs& attrs,
       const int b = begin[i], e = end[i], s = step[i];
       SetSliceOpOutputDimSize(data.shape_, i, b, e, s, &vshape);
     }
-    MSHADOW_TYPE_SWITCH(out.type_flag_, DType, {
+    MSHADOW_TYPE_SWITCH_WITH_BOOL(out.type_flag_, DType, {
       mxnet_op::Kernel<slice_assign_scalar<ndim>, xpu>::Launch(s, vshape.FlatTo2D()[0],
           out.dptr<DType>(), static_cast<DType>(param.scalar), req[0],
           out.shape_.get<ndim>(), vshape.get<ndim>(), begin, step);
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index 3d26ee28b22e..53f912967cf1 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -59,6 +59,32 @@ def get_workloads(name):
         return OpArgMngr._args.get(name, None)
 
 
+def _add_workload_all():
+    # check bad element in all positions
+    for i in range(256-7):
+        e = np.array([True] * 256, dtype=bool)[7::]
+        e[i] = False
+        OpArgMngr.add_workload('all', e)
+    # big array test for blocked libc loops
+    for i in list(range(9, 6000, 507)) + [7764, 90021, -10]:
+        e = np.array([True] * 100043, dtype=bool)
+        e[i] = False
+        OpArgMngr.add_workload('all', e)
+
+
+def _add_workload_any():
+    # check bad element in all positions
+    for i in range(256-7):
+        d = np.array([False] * 256, dtype=bool)[7::]
+        d[i] = True
+        OpArgMngr.add_workload('any', d)
+    # big array test for blocked libc loops
+    for i in list(range(9, 6000, 507)) + [7764, 90021, -10]:
+        d = np.array([False] * 100043, dtype=bool)
+        d[i] = True
+        OpArgMngr.add_workload('any', d)
+
+
 def _add_workload_unravel_index():
     OpArgMngr.add_workload('unravel_index', indices=np.array([2],dtype=_np.int64), shape=(2, 2))
     OpArgMngr.add_workload('unravel_index', np.array([(2*3 + 1)*6 + 4], dtype=_np.int64), (4, 3, 6))
@@ -1443,6 +1469,8 @@ def _prepare_workloads():
         '1x1x0': np.array([[[]]])
     }
 
+    _add_workload_all()
+    _add_workload_any()
     _add_workload_argmin()
     _add_workload_argmax()
     _add_workload_around()
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 3f9f1d6677cc..42407adddf7c 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -493,6 +493,84 @@ def is_int(dtype):
                         assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
 
 
+@with_seed()
+@use_np
+def test_np_any():
+    class TestAny(HybridBlock):
+        def __init__(self, axis=None, keepdims=False) :
+            super(TestAny, self).__init__()
+            self._axis = axis
+            self._keepdims = keepdims
+             
+        def hybrid_forward(self, F, a):
+            return F.np.any(a, axis=self._axis, keepdims=self._keepdims)
+    
+    keepdims = [True, False]
+    axes = [True, False]
+    shapes = [(), (5, ), (10, ),
+              (2, 5), (5, 5), (10, 10),
+              (4, 4, 4), (4, 6, 9), (6, 6, 6),
+              (7, 8, 9, 10), (7, 9, 11, 13)]
+    dtypes = [np.int8, np.uint8, np.int32, np.int64, np.float16, np.float32, np.float64, np.bool]
+    
+    combinations = itertools.product([False, True], shapes, dtypes, axes, keepdims)
+    for hybridize, shape, dtype, axis, keepdim in combinations:
+        ndim = len(shape)
+        samples = random.randint(0, ndim)
+        axis = None if not axis else tuple(random.sample([i for i in range(0, ndim)], samples))
+        x = np.random.normal(0, 1.0, size=shape).astype(dtype)
+        test_any = TestAny(axis=axis, keepdims=keepdim)
+        if hybridize:
+            test_any.hybridize()
+        y = test_any(x)
+        expected_ret = _np.any(x.asnumpy(), axis=axis, keepdims=keepdim)
+        assert_almost_equal(y.asnumpy(), expected_ret)
+
+        # test imperative
+        mx_outs = np.any(x, axis=axis, keepdims=keepdim)
+        np_outs = _np.any(x.asnumpy(), axis=axis, keepdims=keepdim)
+        assert_almost_equal(mx_outs.asnumpy(), np_outs)
+
+
+@with_seed()
+@use_np
+def test_np_all():
+    class TestAll(HybridBlock):
+        def __init__(self, axis=None, keepdims=False) :
+            super(TestAll, self).__init__()
+            self._axis = axis
+            self._keepdims = keepdims
+             
+        def hybrid_forward(self, F, a):
+            return F.np.all(a, axis=self._axis, keepdims=self._keepdims)
+    
+    keepdims = [True, False]
+    axes = [True, False]
+    shapes = [(), (5, ), (10, ),
+              (2, 5), (5, 5), (10, 10),
+              (4, 4, 4), (4, 6, 9), (6, 6, 6),
+              (7, 8, 9, 10), (7, 9, 11, 13)]
+    dtypes = [np.int8, np.uint8, np.int32, np.int64, np.float16, np.float32, np.float64, np.bool]
+    
+    combinations = itertools.product([False, True], shapes, dtypes, axes, keepdims)
+    for hybridize, shape, dtype, axis, keepdim in combinations:
+        ndim = len(shape)
+        samples = random.randint(0, ndim)
+        axis = None if not axis else tuple(random.sample([i for i in range(0, ndim)], samples))
+        x = np.random.normal(0, 1.0, size=shape).astype(dtype)
+        test_all = TestAll(axis=axis, keepdims=keepdim)
+        if hybridize:
+            test_all.hybridize()
+        y = test_all(x)
+        expected_ret = _np.all(x.asnumpy(), axis=axis, keepdims=keepdim)
+        assert_almost_equal(y.asnumpy(), expected_ret)
+
+        # test imperative
+        mx_outs = np.all(x, axis=axis, keepdims=keepdim)
+        np_outs = _np.all(x.asnumpy(), axis=axis, keepdims=keepdim)
+        assert_almost_equal(mx_outs.asnumpy(), np_outs)
+
+
 @with_seed()
 @use_np
 def test_np_max_min():

From 38388ff939511ed136ad9675d06bac7858ff4238 Mon Sep 17 00:00:00 2001
From: dw_sjtu <46704444+sjtuWangDing@users.noreply.github.com>
Date: Fri, 27 Dec 2019 11:18:12 +0800
Subject: [PATCH 742/813] fix format (#17100)

commit source file
---
 python/mxnet/ndarray/numpy/linalg.py          |  50 +-
 python/mxnet/numpy/linalg.py                  |  50 +-
 python/mxnet/numpy_dispatch_protocol.py       |   1 +
 python/mxnet/symbol/numpy/linalg.py           |  50 +-
 .../numpy/linalg/np_tensorsolve-inl.h         | 557 ++++++++++++++++++
 src/operator/numpy/linalg/np_tensorsolve.cc   | 145 +++++
 src/operator/numpy/linalg/np_tensorsolve.cu   |  42 ++
 .../unittest/test_numpy_interoperability.py   |  61 ++
 tests/python/unittest/test_numpy_op.py        | 154 +++++
 9 files changed, 1107 insertions(+), 3 deletions(-)
 create mode 100644 src/operator/numpy/linalg/np_tensorsolve-inl.h
 create mode 100644 src/operator/numpy/linalg/np_tensorsolve.cc
 create mode 100644 src/operator/numpy/linalg/np_tensorsolve.cu

diff --git a/python/mxnet/ndarray/numpy/linalg.py b/python/mxnet/ndarray/numpy/linalg.py
index 4c49c35b4a44..e4fee158bea4 100644
--- a/python/mxnet/ndarray/numpy/linalg.py
+++ b/python/mxnet/ndarray/numpy/linalg.py
@@ -21,7 +21,7 @@
 from . import _op as _mx_nd_np
 from . import _internal as _npi
 
-__all__ = ['norm', 'svd', 'cholesky', 'inv', 'det', 'slogdet', 'solve', 'tensorinv']
+__all__ = ['norm', 'svd', 'cholesky', 'inv', 'det', 'slogdet', 'solve', 'tensorinv', 'tensorsolve']
 
 
 def norm(x, ord=None, axis=None, keepdims=False):
@@ -461,3 +461,51 @@ def tensorinv(a, ind=2):
     True
     """
     return _npi.tensorinv(a, ind)
+
+
+def tensorsolve(a, b, axes=None):
+    r"""
+    Solve the tensor equation ``a x = b`` for x.
+    It is assumed that all indices of `x` are summed over in the product,
+    together with the rightmost indices of `a`, as is done in, for example,
+    ``tensordot(a, x, axes=b.ndim)``.
+
+    Parameters
+    ----------
+    a : ndarray
+        Coefficient tensor, of shape ``b.shape + Q``. `Q`, a tuple, equals
+        the shape of that sub-tensor of `a` consisting of the appropriate
+        number of its rightmost indices, and must be such that
+        ``prod(Q) == prod(b.shape)`` (in which sense `a` is said to be
+        'square').
+    b : ndarray
+        Right-hand tensor, which can be of any shape.
+    axes : tuple of ints, optional
+        Axes in `a` to reorder to the right, before inversion.
+        If None (default), no reordering is done.
+
+    Returns
+    -------
+    x : ndarray, shape Q
+
+    Raises
+    ------
+    MXNetError
+        If `a` is singular or not 'square' (in the above sense).
+
+    See Also
+    --------
+    numpy.tensordot, tensorinv, numpy.einsum
+
+    Examples
+    --------
+    >>> a = np.eye(2*3*4)
+    >>> a.shape = (2*3, 4, 2, 3, 4)
+    >>> b = np.random.randn(2*3, 4)
+    >>> x = np.linalg.tensorsolve(a, b)
+    >>> x.shape
+    (2, 3, 4)
+    >>> np.allclose(np.tensordot(a, x, axes=3), b)
+    True
+    """
+    return _npi.tensorsolve(a, b, axes)
diff --git a/python/mxnet/numpy/linalg.py b/python/mxnet/numpy/linalg.py
index 2ee2d2670693..96fe1d311028 100644
--- a/python/mxnet/numpy/linalg.py
+++ b/python/mxnet/numpy/linalg.py
@@ -20,7 +20,7 @@
 from __future__ import absolute_import
 from ..ndarray import numpy as _mx_nd_np
 
-__all__ = ['norm', 'svd', 'cholesky', 'inv', 'det', 'slogdet', 'solve', 'tensorinv']
+__all__ = ['norm', 'svd', 'cholesky', 'inv', 'det', 'slogdet', 'solve', 'tensorinv', 'tensorsolve']
 
 
 def norm(x, ord=None, axis=None, keepdims=False):
@@ -479,3 +479,51 @@ def tensorinv(a, ind=2):
     True
     """
     return _mx_nd_np.linalg.tensorinv(a, ind)
+
+
+def tensorsolve(a, b, axes=None):
+    r"""
+    Solve the tensor equation ``a x = b`` for x.
+    It is assumed that all indices of `x` are summed over in the product,
+    together with the rightmost indices of `a`, as is done in, for example,
+    ``tensordot(a, x, axes=b.ndim)``.
+
+    Parameters
+    ----------
+    a : ndarray
+        Coefficient tensor, of shape ``b.shape + Q``. `Q`, a tuple, equals
+        the shape of that sub-tensor of `a` consisting of the appropriate
+        number of its rightmost indices, and must be such that
+        ``prod(Q) == prod(b.shape)`` (in which sense `a` is said to be
+        'square').
+    b : ndarray
+        Right-hand tensor, which can be of any shape.
+    axes : tuple of ints, optional
+        Axes in `a` to reorder to the right, before inversion.
+        If None (default), no reordering is done.
+
+    Returns
+    -------
+    x : ndarray, shape Q
+
+    Raises
+    ------
+    MXNetError
+        If `a` is singular or not 'square' (in the above sense).
+
+    See Also
+    --------
+    numpy.tensordot, tensorinv, numpy.einsum
+
+    Examples
+    --------
+    >>> a = np.eye(2*3*4)
+    >>> a.shape = (2*3, 4, 2, 3, 4)
+    >>> b = np.random.randn(2*3, 4)
+    >>> x = np.linalg.tensorsolve(a, b)
+    >>> x.shape
+    (2, 3, 4)
+    >>> np.allclose(np.tensordot(a, x, axes=3), b)
+    True
+    """
+    return _mx_nd_np.linalg.tensorsolve(a, b, axes)
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index 80b3d66c5e5d..603b118855a5 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -138,6 +138,7 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'linalg.inv',
     'linalg.solve',
     'linalg.tensorinv',
+    'linalg.tensorsolve',
     'shape',
     'trace',
     'tril',
diff --git a/python/mxnet/symbol/numpy/linalg.py b/python/mxnet/symbol/numpy/linalg.py
index a445c79001ec..0bfbb6ee540f 100644
--- a/python/mxnet/symbol/numpy/linalg.py
+++ b/python/mxnet/symbol/numpy/linalg.py
@@ -22,7 +22,7 @@
 from . import _op as _mx_sym_np
 from . import _internal as _npi
 
-__all__ = ['norm', 'svd', 'cholesky', 'inv', 'det', 'slogdet', 'solve', 'tensorinv']
+__all__ = ['norm', 'svd', 'cholesky', 'inv', 'det', 'slogdet', 'solve', 'tensorinv', 'tensorsolve']
 
 
 def norm(x, ord=None, axis=None, keepdims=False):
@@ -448,3 +448,51 @@ def tensorinv(a, ind=2):
     True
     """
     return _npi.tensorinv(a, ind)
+
+
+def tensorsolve(a, b, axes=None):
+    r"""
+    Solve the tensor equation ``a x = b`` for x.
+    It is assumed that all indices of `x` are summed over in the product,
+    together with the rightmost indices of `a`, as is done in, for example,
+    ``tensordot(a, x, axes=b.ndim)``.
+
+    Parameters
+    ----------
+    a : ndarray
+        Coefficient tensor, of shape ``b.shape + Q``. `Q`, a tuple, equals
+        the shape of that sub-tensor of `a` consisting of the appropriate
+        number of its rightmost indices, and must be such that
+        ``prod(Q) == prod(b.shape)`` (in which sense `a` is said to be
+        'square').
+    b : ndarray
+        Right-hand tensor, which can be of any shape.
+    axes : tuple of ints, optional
+        Axes in `a` to reorder to the right, before inversion.
+        If None (default), no reordering is done.
+
+    Returns
+    -------
+    x : ndarray, shape Q
+
+    Raises
+    ------
+    MXNetError
+        If `a` is singular or not 'square' (in the above sense).
+
+    See Also
+    --------
+    numpy.tensordot, tensorinv, numpy.einsum
+
+    Examples
+    --------
+    >>> a = np.eye(2*3*4)
+    >>> a.shape = (2*3, 4, 2, 3, 4)
+    >>> b = np.random.randn(2*3, 4)
+    >>> x = np.linalg.tensorsolve(a, b)
+    >>> x.shape
+    (2, 3, 4)
+    >>> np.allclose(np.tensordot(a, x, axes=3), b)
+    True
+    """
+    return _npi.tensorsolve(a, b, axes)
diff --git a/src/operator/numpy/linalg/np_tensorsolve-inl.h b/src/operator/numpy/linalg/np_tensorsolve-inl.h
new file mode 100644
index 000000000000..829a119b64a2
--- /dev/null
+++ b/src/operator/numpy/linalg/np_tensorsolve-inl.h
@@ -0,0 +1,557 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_tensorsolve-inl.h
+ * \brief Placeholder for tensor solve
+ */
+#ifndef MXNET_OPERATOR_NUMPY_LINALG_NP_TENSORSOLVE_INL_H_
+#define MXNET_OPERATOR_NUMPY_LINALG_NP_TENSORSOLVE_INL_H_
+
+#include <mxnet/operator_util.h>
+#include <vector>
+#include "../../operator_common.h"
+#include "../../mshadow_op.h"
+#include "../../tensor/la_op.h"
+#include "../../tensor/la_op-inl.h"
+#include "../np_tensordot_op-inl.h"
+#include "./np_solve-inl.h"
+
+namespace mxnet {
+namespace op {
+
+using namespace mshadow;
+
+struct TensorsolveParam : public dmlc::Parameter<TensorsolveParam> {
+  mxnet::Tuple<int> a_axes;
+  DMLC_DECLARE_PARAMETER(TensorsolveParam) {
+    DMLC_DECLARE_FIELD(a_axes)
+    .set_default(mxnet::Tuple<int>())
+    .describe("Tuple of ints, optional. Axes in a to reorder to the right, before inversion.");
+  }
+};
+
+// Fix negative axes.
+inline void FixNegativeAxes(mxnet::Tuple<int> *a_axes_param,
+                            const mxnet::TShape& a_shape) {
+  if (-1 == a_axes_param->ndim()) { return; }
+  const int a_ndim = a_shape.ndim();
+  for (auto& i : *a_axes_param) {
+    i = (i + a_ndim) % a_ndim;
+  }
+}
+
+// Get remained axes and axes of a.
+inline void GetReorderedAxes(const mxnet::Tuple<int>& a_axes_param,
+                             mxnet::Tuple<int> *a_axes_remained,
+                             mxnet::Tuple<int> *a_axes,
+                             const mxnet::TShape& a_shape) {
+  std::vector<int> a_axes_vec;
+  for (int i = 0; i < a_shape.ndim(); ++i) {
+    a_axes_vec.push_back(i);
+  }
+  // Get remained axes and axes.
+  if (-1 == a_axes_param.ndim()) {
+    *a_axes_remained = mxnet::Tuple<int>(a_axes_vec);
+    *a_axes = mxnet::Tuple<int>(a_axes_vec);
+    return;
+  }
+  for (const auto& i : a_axes_param) {
+    a_axes_vec.erase(std::find(a_axes_vec.begin(), a_axes_vec.end(), i));
+  }
+  *a_axes_remained = mxnet::Tuple<int>(a_axes_vec);
+
+  a_axes_vec.clear();
+  for (const auto& i : *a_axes_remained) {
+    a_axes_vec.push_back(i);
+  }
+  for (const auto& i : a_axes_param) {
+    a_axes_vec.push_back(i);
+  }
+  *a_axes = mxnet::Tuple<int>(a_axes_vec);
+}
+
+// Calculate output shape if a and b is tensor
+inline mxnet::TShape GetOutShape(const mxnet::TShape& a_shape,
+                                 const mxnet::TShape& b_shape) {
+  const int a_ndim = a_shape.ndim(), b_ndim = b_shape.ndim();
+  const int temp = a_ndim > b_ndim ? b_ndim : b_ndim - a_ndim;
+  mxnet::TShape out_shape(a_ndim - temp, -1);
+  for (int i = temp; i < a_ndim; ++i) {
+    out_shape[i - temp] = a_shape[i];
+  }
+  return out_shape;
+}
+
+// Calculates workspace size of tensorsolve forward.
+template<typename xpu>
+size_t TensorsolveForwardWorkspaceSize(const Tuple<int>& a_axes_param,
+                                       const TBlob& a,
+                                       const TBlob& b,
+                                       const TBlob& out,
+                                       const std::vector<OpReqType>& req) {
+  if (kNullOp == req[0]) { return 0U; }
+
+  // Zero-size output, no need to launch kernel
+  if (0U == out.shape_.Size()) { return 0U; }
+
+  const mxnet::TShape& a_shape = a.shape_;
+  const mxnet::TShape& b_shape = b.shape_;
+  MSHADOW_SGL_DBL_TYPE_SWITCH(out.type_flag_, DType, {
+    if (0U == a_shape.Size() || 0U == b_shape.Size()) {
+      // 0-size input
+      return 0U;
+    } else if (0 == a_shape.ndim() || 0 == b_shape.ndim()) {
+      // At least 1 scalar.
+      return (a.Size() + b.Size()) * sizeof(DType) + b.Size() * sizeof(int);
+    } else {
+      // Two tensors of at least 1 dimensions.
+      return (2 * a.Size() + b.Size()) * sizeof(DType) + b.Size() * sizeof(int);
+    }
+  });
+  LOG(FATAL) << "InternalError: cannot reach here";
+  return 0U;
+}
+
+template<int req>
+struct assign_helper {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int i, const DType *in_data, DType *out_data) {
+    KERNEL_ASSIGN(out_data[i], req, in_data[i]);
+  }
+};
+
+struct tensorsolve {
+  template<typename xpu, typename DType>
+  static void op(const Tensor<xpu, 2, DType>& A,
+                 const Tensor<xpu, 2, DType>& X,
+                 const Tensor<xpu, 1, int>& ipiv,
+                 const OpContext& ctx) {
+    mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+    linalg_solve(A, X, ipiv, s);  // ipiv for work_space in Lapacke_#gesv
+  }
+};
+
+template<typename xpu, typename laop>
+void TensorsolveOpForward(const nnvm::NodeAttrs& attrs,
+                          const OpContext& ctx,
+                          const std::vector<TBlob>& inputs,
+                          const std::vector<OpReqType>& req,
+                          const std::vector<TBlob>& outputs) {
+  CHECK_EQ(inputs.size(), 2U);
+  CHECK_EQ(outputs.size(), 1U);
+  CHECK_EQ(req.size(), 1U);
+
+  mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+  const TBlob& a = inputs[0];
+  const TBlob& b = inputs[1];
+  const TBlob& out = outputs[0];
+  const mxnet::TShape a_shape = a.shape_;
+  const mxnet::TShape b_shape = b.shape_;
+  const mxnet::TShape out_shape = out.shape_;
+  const TensorsolveParam& param = nnvm::get<TensorsolveParam>(attrs.parsed);
+  mxnet::Tuple<int> a_axes_param = param.a_axes;
+  FixNegativeAxes(&a_axes_param, a_shape);
+
+  size_t workspace_size = TensorsolveForwardWorkspaceSize<xpu>(a_axes_param, a, b, out, req);
+  Tensor<xpu, 1, char> workspace = ctx.requested[0].get_space_typed<xpu, 1, char>(
+    Shape1(workspace_size), ctx.get_stream<xpu>());
+
+  if (kNullOp == req[0]) { return; }
+
+  // Zero-size output, no need to launch kernel
+  if (0U == out.shape_.Size()) { return; }
+
+  MSHADOW_SGL_DBL_TYPE_SWITCH(out.type_flag_, DType, {
+    if (0U == a_shape.Size() || 0U == b_shape.Size()) {  // 0-size input
+      if (req[0] != kAddTo) {
+        Tensor<xpu, 1, DType> out_tensor =
+          out.get_with_shape<xpu, 1, DType>(Shape1(out.shape_.Size()), s);
+        out_tensor = static_cast<DType>(0);
+      }
+    } else if (0U == a_shape.ndim() || 0U ==  b_shape.ndim()) {  // At least 1 scalar.
+      // Check again
+      CHECK_EQ(a_shape.Size(), 1U)
+        << "a's and b's dimensions don't match";
+      CHECK_EQ(b_shape.Size(), 1U)
+        << "a's and b's dimensions don't match";
+
+      DType* a_ptr =
+        reinterpret_cast<DType*>(workspace.dptr_);
+      DType* b_ptr =
+        reinterpret_cast<DType*>(workspace.dptr_+ a.Size() * sizeof(DType));
+      int* ipiv_ptr =
+        reinterpret_cast<int*>(workspace.dptr_ + (a.Size() + b.Size()) * sizeof(DType));
+
+      // Cast type
+      MSHADOW_TYPE_SWITCH(a.type_flag_, AType, {
+        mxnet_op::Kernel<mshadow_op::identity_with_cast, xpu>::Launch(
+          s, a_shape.Size(), a_ptr, a.dptr<AType>());
+      });
+      MSHADOW_TYPE_SWITCH(b.type_flag_, BType, {
+        mxnet_op::Kernel<mshadow_op::identity_with_cast, xpu>::Launch(
+          s, b_shape.Size(), b_ptr, b.dptr<BType>());
+      });
+
+      mxnet::TBlob a_tblob(a_ptr, Shape2(1, 1), a.dev_mask(), a.dev_id());
+      mxnet::TBlob b_tblob(b_ptr, Shape2(1, 1), b.dev_mask(), b.dev_id());
+      mxnet::TBlob ipiv_tblob(ipiv_ptr, Shape1(1), out.dev_mask(), out.dev_id());
+      Tensor<xpu, 2, DType> a_tensor = a_tblob.get<xpu, 2, DType>(s);
+      Tensor<xpu, 2, DType> b_tensor = b_tblob.get<xpu, 2, DType>(s);
+      Tensor<xpu, 1, int> ipiv_tensor = ipiv_tblob.get<xpu, 1, int>(s);
+
+      // Solve linear equation
+      laop::op(a_tensor, b_tensor, ipiv_tensor, ctx);
+      MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
+          mxnet_op::Kernel<assign_helper<req_type>, xpu>::Launch(
+            s, out_shape.Size(), b_tensor.dptr_, out.dptr<DType>());
+      });
+    } else {
+      // Two tensors of at least 1 dimensions.
+      Tuple<int> a_axes_remained;
+      Tuple<int> a_axes;
+      GetReorderedAxes(a_axes_param, &a_axes_remained, &a_axes, a_shape);
+      mxnet::TShape a_transpose_shape = GetReorderedShape(a_shape, a_axes);
+      const int N = b_shape.Size();
+
+      DType* a_ptr =
+        reinterpret_cast<DType*>(workspace.dptr_);
+      DType* a_trans_ptr =
+        reinterpret_cast<DType*>(workspace.dptr_ + a.Size() * sizeof(DType));
+      DType* b_ptr =
+        reinterpret_cast<DType*>(workspace.dptr_ + 2 * a.Size() * sizeof(DType));
+      int* ipiv_ptr =
+        reinterpret_cast<int*>(workspace.dptr_ + (2 * a.Size() + b.Size()) * sizeof(DType));
+
+      // Cast type
+      MSHADOW_TYPE_SWITCH(a.type_flag_, AType, {
+        mxnet_op::Kernel<mshadow_op::identity_with_cast, xpu>::Launch(
+          s, a_shape.Size(), a_ptr, a.dptr<AType>());
+      });
+      // Cast type
+      MSHADOW_TYPE_SWITCH(b.type_flag_, BType, {
+        mxnet_op::Kernel<mshadow_op::identity_with_cast, xpu>::Launch(
+          s, b_shape.Size(), b_ptr, b.dptr<BType>());
+      });
+
+      mxnet::TBlob a_tblob =
+        TBlob(a_ptr, a_shape, a.dev_mask(), a.dev_id());
+      mxnet::TBlob a_transpose_tblob =
+        TBlob(a_trans_ptr, a_transpose_shape, a.dev_mask(), a.dev_id());
+      mxnet::TBlob b_tblob =
+        TBlob(b_ptr, b_shape, b.dev_mask(), b.dev_id());
+      mxnet::TBlob ipiv_tblob =
+        TBlob(ipiv_ptr, b_shape, out.dev_mask(), out.dev_id());
+      mxnet::op::TransposeImpl<xpu>(ctx.run_ctx,
+                                    a_tblob,            // src
+                                    a_transpose_tblob,  // res
+                                    mxnet::TShape(a_axes.begin(), a_axes.end()));
+
+      Tensor<xpu, 2, DType> a_tensor =
+        a_tblob.get_with_shape<xpu, 2, DType>(Shape2(N, N), s);
+      Tensor<xpu, 1, int> ipiv_tensor =
+        ipiv_tblob.get_with_shape<xpu, 1, int>(Shape1(N), s);
+      Tensor<xpu, 2, DType> b_tensor =
+        b_tblob.get_with_shape<xpu, 2, DType>(Shape2(1, N), s);
+      Tensor<xpu, 2, DType> out_tensor =
+        out.get_with_shape<xpu, 2, DType>(Shape2(1, N), s);
+
+      a_tblob = a_tblob.reshape(Shape2(N, N));
+      a_transpose_tblob = a_transpose_tblob.reshape(Shape2(N, N));
+      Tuple<int> a_axes_2D(std::vector<int>{1, 0});
+      mxnet::op::TransposeImpl<xpu>(ctx.run_ctx,
+                                    a_transpose_tblob,  // src
+                                    a_tblob,            // res
+                                    mxnet::TShape(a_axes_2D.begin(), a_axes_2D.end()));
+      // Solve linear equation
+      laop::op(a_tensor, b_tensor, ipiv_tensor, ctx);
+      MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
+        mxnet_op::Kernel<assign_helper<req_type>, xpu>::Launch(
+          s, out_shape.Size(), b_tensor.dptr_, out_tensor.dptr_);
+      });
+    }
+  });
+}
+
+// Calculates workspace size of tensordot backward.
+template<typename xpu>
+size_t TensorsolveBackwardWorkspaceSize(const TBlob& out_grad,
+                                        const TBlob& a,
+                                        const TBlob& b,
+                                        const TBlob& x) {
+  const mxnet::TShape& a_shape = a.shape_;
+  const mxnet::TShape& b_shape = b.shape_;
+  const mxnet::TShape& x_shape = x.shape_;
+
+  // Zero-size output, no need to launch kernel
+  if (0U == a_shape.Size() || 0U == b_shape.Size()) { return 0U; }
+
+  MSHADOW_SGL_DBL_TYPE_SWITCH(out_grad.type_flag_, DType, {
+    int work_space_size = 0;
+    if (0U == a_shape.ndim() || 0U == b_shape.ndim()) {
+      // At least 1 scalar.
+      work_space_size += sizeof(DType) * a_shape.Size();  // for tensorinv(a)
+      work_space_size += sizeof(DType) * a_shape.Size();  // for getri work space lu
+      work_space_size += sizeof(int) * b_shape.Size();    // for getri work space pivot
+    } else {
+      // Two tensors of at least 1 dimensions.
+      work_space_size += sizeof(DType) * a_shape.Size();  // for tensorinv(a)
+      work_space_size += sizeof(DType) * a_shape.Size();  // for getri work space lu
+      work_space_size += sizeof(DType) * b_shape.Size();  // for b
+      work_space_size += sizeof(DType) * x_shape.Size();  // for x
+      work_space_size += sizeof(DType) * a_shape.Size();  // for grad_a
+      work_space_size += sizeof(DType) * b_shape.Size();  // for grad_b
+      work_space_size += sizeof(int) * b_shape.Size();    // for getri work space pivot
+    }
+    return work_space_size;
+  });
+  LOG(FATAL) << "InternalError: cannot reach here";
+  return 0U;
+}
+
+// Get original axes for tensor a.
+inline void GetOriginAxes(const mxnet::TShape& a_shape,
+                          const mxnet::Tuple<int>& a_axes,
+                          mxnet::Tuple<int> *a_origin_axes) {
+  std::vector<int> a_origin_axes_vec(a_shape.ndim(), -1);
+  for (int i = 0; i < a_shape.ndim(); ++i) {
+    a_origin_axes_vec[a_axes[i]] = i;
+  }
+  *a_origin_axes = mxnet::Tuple<int>(a_origin_axes_vec);
+}
+
+struct tensorsolve_backward {
+  template<typename xpu, typename DType>
+  static void op(const Tensor<xpu, 3, DType>& dX,
+                 const Tensor<xpu, 3, DType>& inv_A,
+                 const Tensor<xpu, 3, DType>& B,
+                 const Tensor<xpu, 3, DType>& X,
+                 const Tensor<xpu, 3, DType>& dA,
+                 const Tensor<xpu, 3, DType>& dB,
+                 const OpContext& ctx) {
+    // (1) calcualte dB = trans(tensorinv(A)) * dX
+    // (2) calcualte dA = dB * trans(X)
+    Stream<xpu> *s = ctx.get_stream<xpu>();
+    gemm2::op(inv_A, dX, dB, DType(1), true, false, s);
+    gemm2::op(dB, X, dA, DType(-1), false, true, s);
+  }
+};
+
+template<typename xpu, typename laop>
+void TensorsolveBackwardImpl(const Tuple<int>& a_axes_param,
+                             const TBlob& out_grad,
+                             const TBlob& a,
+                             const TBlob& b,
+                             const TBlob& x,
+                             const TBlob& grad_a,
+                             const TBlob& grad_b,
+                             const OpContext& ctx,
+                             const std::vector<OpReqType>& req,
+                             const Tensor<xpu, 1, char>& workspace) {
+  mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+  const mxnet::TShape& a_shape = a.shape_;
+  const mxnet::TShape& b_shape = b.shape_;
+  const mxnet::TShape& x_shape = x.shape_;
+
+  if (kNullOp == req[0] && kNullOp == req[1]) { return; }
+
+  // Zero-size output, no need to launch kernel
+  if (0U == a_shape.Size() || 0U == b_shape.Size()) { return; }
+
+  MSHADOW_SGL_DBL_TYPE_SWITCH(out_grad.type_flag_, DType, {
+    if (0 == a_shape.ndim() || 0 == b_shape.ndim()) {
+      // At least 1 scalar.
+      CHECK_EQ(a_shape.Size(), 1U)
+        << "a's and b's dimensions don't match";
+      CHECK_EQ(b_shape.Size(), 1U)
+        << "a's and b's dimensions don't match";
+
+      // Allocate workspace.
+      DType *tensorinv_a_ptr = reinterpret_cast<DType*>(workspace.dptr_);
+      DType *lu_ptr = reinterpret_cast<DType*>(workspace.dptr_ + a_shape.Size() * sizeof(DType));
+      int *ipiv_ptr = reinterpret_cast<int*>(workspace.dptr_ + 2 * a_shape.Size() * sizeof(DType));
+      TBlob tensorinv_a(tensorinv_a_ptr, a_shape, xpu::kDevMask);
+      TBlob lu(lu_ptr, a_shape, xpu::kDevMask);
+      TBlob ipiv(ipiv_ptr, b_shape, xpu::kDevMask);
+
+      MSHADOW_TYPE_SWITCH(a.type_flag_, AType, {
+        mxnet_op::Kernel<mshadow_op::identity_with_cast, xpu>::Launch(
+          s, a_shape.Size(),
+          tensorinv_a_ptr,
+          a.dptr<AType>());
+      });
+      // Calculate tensorinv(a)
+      Tensor<xpu, 3, DType> tensorinv_a_tensor =
+        tensorinv_a.get_with_shape<xpu, 3, DType>(Shape3(1, 1, 1), s);
+      Tensor<xpu, 3, DType> lu_tensor =
+        lu.get_with_shape<xpu, 3, DType>(Shape3(1, 1, 1), s);
+      Tensor<xpu, 2, int> ipiv_tensor =
+        ipiv.get_with_shape<xpu, 2, int>(Shape2(1, 1), s);
+      batch_inverse(tensorinv_a_tensor, lu_tensor, ipiv_tensor, ctx);
+
+      MSHADOW_TYPE_SWITCH(x.type_flag_, XType, {
+        DType temp1 = (*(tensorinv_a_tensor.dptr_)) * (*(out_grad.dptr<DType>()));
+        DType temp2 = -temp1 * static_cast<DType>(*x.dptr<XType>());
+        ASSIGN_DISPATCH(*grad_b.dptr<DType>(), req[1], temp1);
+        ASSIGN_DISPATCH(*grad_a.dptr<DType>(), req[0], temp2);
+      });
+    } else {
+      // Two tensors of at least 1 dimensions.
+      const int N = b_shape.Size();
+      Tuple<int> a_axes_remained;
+      Tuple<int> a_axes;
+      Tuple<int> a_origin_axes;
+      // Use a_axes to transpose (a_shape) --> (a_reordered_shape).
+      GetReorderedAxes(a_axes_param, &a_axes_remained, &a_axes, a_shape);
+      // Use a_origin_axes to transpose (a_reordered_shape) --> (a_shape).
+      GetOriginAxes(a_shape, a_axes, &a_origin_axes);
+      mxnet::TShape reordered_a_shape = GetReorderedShape(a_shape, a_axes);
+
+      // Allocate workspace.
+      DType *tensorinv_a_ptr = reinterpret_cast<DType*>(
+        workspace.dptr_);
+      DType *lu_ptr = reinterpret_cast<DType*>(
+        workspace.dptr_ + a_shape.Size() * sizeof(DType));
+      DType *b_ptr = reinterpret_cast<DType*>(
+        workspace.dptr_ + 2 * a_shape.Size() * sizeof(DType));
+      DType *x_ptr = reinterpret_cast<DType*>(
+        workspace.dptr_ + (2 * a_shape.Size() + b_shape.Size()) * sizeof(DType));
+      DType *grad_a_ptr = reinterpret_cast<DType*>(
+        workspace.dptr_ + 2 * (a_shape.Size() + b_shape.Size()) * sizeof(DType));
+      DType *grad_b_ptr = reinterpret_cast<DType*>(
+        workspace.dptr_ + (3 * a_shape.Size() + 2 * b_shape.Size()) * sizeof(DType));
+      int *ipiv_ptr = reinterpret_cast<int*>(
+        workspace.dptr_ + 3 * (a_shape.Size() + b_shape.Size()) * sizeof(DType));
+
+      TBlob tensorinv_a_data(tensorinv_a_ptr, a_shape, xpu::kDevMask);
+      TBlob lu_data(lu_ptr, a_shape, xpu::kDevMask);
+      TBlob b_data(b_ptr, b_shape, xpu::kDevMask);
+      TBlob x_data(x_ptr, x_shape, xpu::kDevMask);
+      TBlob grad_a_data(grad_a_ptr, reordered_a_shape, xpu::kDevMask);
+      TBlob grad_b_data(grad_b_ptr, b_shape, xpu::kDevMask);
+      TBlob ipiv_data(ipiv_ptr, b_shape, xpu::kDevMask);
+      MSHADOW_TYPE_SWITCH(a.type_flag_, AType, {
+        mxnet_op::Kernel<mshadow_op::identity_with_cast, xpu>::Launch(
+          s, a_shape.Size(),
+          lu_ptr,
+          a.dptr<AType>());
+      });
+      MSHADOW_TYPE_SWITCH(b.type_flag_, BType, {
+        mxnet_op::Kernel<mshadow_op::identity_with_cast, xpu>::Launch(
+          s, b_shape.Size(),
+          b_ptr,
+          b.dptr<BType>());
+      });
+      MSHADOW_TYPE_SWITCH(x.type_flag_, XType, {
+        mxnet_op::Kernel<mshadow_op::identity_with_cast, xpu>::Launch(
+          s, x_shape.Size(),
+          x_ptr,
+          x.dptr<XType>());
+      });
+      // Eg: lu_data(2, 3, 2, 15, 4, 5) -> tensorinv_a_data(3, 4, 5, 15, 2, 2)
+      tensorinv_a_data = tensorinv_a_data.reshape(reordered_a_shape);
+      mxnet::op::TransposeImpl<xpu>(ctx.run_ctx,
+                                    lu_data,           // src
+                                    tensorinv_a_data,  // res
+                                    mxnet::TShape(a_axes.begin(), a_axes.end()));
+
+      Tensor<xpu, 3, DType> tensorinv_a_tensor =
+        tensorinv_a_data.get_with_shape<xpu, 3, DType>(Shape3(1, N, N), s);
+      Tensor<xpu, 3, DType> lu_tensor =
+        lu_data.get_with_shape<xpu, 3, DType>(Shape3(1, N, N), s);
+      Tensor<xpu, 3, DType> b_tensor =
+        b_data.get_with_shape<xpu, 3, DType>(Shape3(1, N, 1), s);
+      Tensor<xpu, 3, DType> x_tensor =
+        x_data.get_with_shape<xpu, 3, DType>(Shape3(1, N, 1), s);
+      Tensor<xpu, 3, DType> grad_a_tensor =
+        grad_a_data.get_with_shape<xpu, 3, DType>(Shape3(1, N, N), s);
+      Tensor<xpu, 3, DType> grad_b_tensor =
+        grad_b_data.get_with_shape<xpu, 3, DType>(Shape3(1, N, 1), s);
+      Tensor<xpu, 2, int> ipiv_tensor =
+        ipiv_data.get_with_shape<xpu, 2, int>(Shape2(1, N), s);
+
+      // Calculate tensorinv(a).
+      batch_inverse(tensorinv_a_tensor, lu_tensor, ipiv_tensor, ctx);
+      // No need to transpose tensorinv_a
+      // because transpose(tensorinv_a).shape == reordered_a_shape.
+      laop::op(out_grad.get_with_shape<xpu, 3, DType>(x_tensor.shape_, s),
+               tensorinv_a_tensor,
+               b_tensor,
+               x_tensor,
+               grad_a_tensor,
+               grad_b_tensor,
+               ctx);
+      // Eg: grad_a_src(3, 4, 5, 15, 2, 2) --> lu_data(2, 3, 2, 15, 4, 5)
+      mxnet::op::TransposeImpl<xpu>(ctx.run_ctx,
+                                    grad_a_data,  // src
+                                    lu_data,      // res
+                                    mxnet::TShape(a_origin_axes.begin(), a_origin_axes.end()));
+
+      MXNET_ASSIGN_REQ_SWITCH(req[1], req_type, {
+        mxnet_op::Kernel<assign_helper<req_type>, xpu>::Launch(
+          s, b_shape.Size(), grad_b_tensor.dptr_, grad_b.dptr<DType>());
+      });
+      MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
+        mxnet_op::Kernel<assign_helper<req_type>, xpu>::Launch(
+          s, a_shape.Size(), lu_tensor.dptr_, grad_a.dptr<DType>());
+      });
+    }
+  });
+}
+
+template<typename xpu, typename laop>
+void TensorsolveOpBackward(const nnvm::NodeAttrs& attrs,
+                           const OpContext& ctx,
+                           const std::vector<TBlob>& inputs,
+                           const std::vector<OpReqType>& req,
+                           const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  CHECK_EQ(inputs.size(), 4U);
+  CHECK_EQ(outputs.size(), 2U);
+  CHECK_EQ(req.size(), 2U);
+
+  const TBlob& out_grad = inputs[0];
+  const TBlob& a = inputs[1];
+  const TBlob& b = inputs[2];
+  const TBlob& x = inputs[3];
+  const TBlob& grad_a = outputs[0];
+  const TBlob& grad_b = outputs[1];
+  const mxnet::TShape a_shape = a.shape_;
+  const mxnet::TShape b_shape = b.shape_;
+  const TensorsolveParam& param = nnvm::get<TensorsolveParam>(attrs.parsed);
+  mxnet::Tuple<int> a_axes_param = param.a_axes;
+  FixNegativeAxes(&a_axes_param, a_shape);
+
+  size_t workspace_size = TensorsolveBackwardWorkspaceSize<xpu>(out_grad, a, b, x);
+  Tensor<xpu, 1, char> workspace =
+    ctx.requested[0].get_space_typed<xpu, 1, char>(Shape1(workspace_size),
+                                                   ctx.get_stream<xpu>());
+  TensorsolveBackwardImpl<xpu, laop>(a_axes_param,
+                                     out_grad,
+                                     a, b, x,
+                                     grad_a, grad_b,
+                                     ctx, req,
+                                     workspace);
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_NUMPY_LINALG_NP_TENSORSOLVE_INL_H_
diff --git a/src/operator/numpy/linalg/np_tensorsolve.cc b/src/operator/numpy/linalg/np_tensorsolve.cc
new file mode 100644
index 000000000000..1dabcdd0eac4
--- /dev/null
+++ b/src/operator/numpy/linalg/np_tensorsolve.cc
@@ -0,0 +1,145 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_tensorsolve.cc
+ * \brief CPU implementation placeholder of Tensor Solve Operator
+ */
+#include "./np_tensorsolve-inl.h"
+
+namespace mxnet {
+namespace op {
+
+bool TensorsolveOpShape(const nnvm::NodeAttrs& attrs,
+                        mxnet::ShapeVector *in_attrs,
+                        mxnet::ShapeVector *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 2U);
+  CHECK_EQ(out_attrs->size(), 1U);
+
+  const mxnet::TShape& a_shape = in_attrs->at(0);
+  const mxnet::TShape& b_shape = in_attrs->at(1);
+  const int a_ndim = a_shape.ndim();
+  const int b_ndim = b_shape.ndim();
+
+  if (!ndim_is_known(a_shape) || !ndim_is_known(b_shape)) {
+    return false;
+  }
+
+  if (0 == a_ndim && 0 == b_ndim) {
+    // a and b is scalar
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, b_shape);
+  } else if (0 == a_ndim && 0 != b_ndim) {
+    // a is scalar, b is tensor
+    CHECK_EQ(b_shape.Size(), 1U)
+      << "a's and b's dimensions don't match";
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, a_shape);
+  } else if (0 != a_ndim && 0 == b_ndim) {
+    // a is tensor, a is scalar
+    CHECK_EQ(a_shape.Size(), 1U)
+      << "a's and b's dimensions don't match";
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, a_shape);
+  } else {
+    // a and b of at least 1 dimensions.
+    const TensorsolveParam& param = nnvm::get<TensorsolveParam>(attrs.parsed);
+    mxnet::Tuple<int> a_axes_param = param.a_axes;
+    FixNegativeAxes(&a_axes_param, a_shape);
+
+    mxnet::Tuple<int> a_axes_remained;
+    mxnet::Tuple<int> a_axes;
+    GetReorderedAxes(a_axes_param, &a_axes_remained, &a_axes, a_shape);
+    mxnet::TShape a_transpose_shape = GetReorderedShape(a_shape, a_axes);
+
+    // Calculate output shape
+    const int temp = a_ndim > b_ndim ? b_ndim : b_ndim - a_ndim;
+    int prod_front = 1, prod_back = 1;
+    mxnet::TShape out_shape(a_ndim - temp > 0 ? a_ndim - temp : 0, -1);
+    for (int i = 0; i < a_ndim; ++i) {
+      if (i < temp) {
+        prod_front *= a_transpose_shape[i];
+      } else {
+        prod_back *= a_transpose_shape[i];
+        out_shape[i - temp] = a_transpose_shape[i];
+      }
+    }
+    CHECK_EQ(prod_front, prod_back) << "a shape must be square.";
+    CHECK_EQ(prod_back, b_shape.Size()) << "a's and b's dimensions don't match";
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, out_shape);
+  }
+
+  return shape_is_known(*in_attrs) && shape_is_known(*out_attrs);
+}
+
+inline bool TensorsolveOpType(const nnvm::NodeAttrs& attrs,
+                              std::vector<int>* in_attrs,
+                              std::vector<int>* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 2U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  int a_type = in_attrs->at(0);
+  int b_type = in_attrs->at(1);
+  // unsupport float16
+  CHECK_NE(a_type, mshadow::kFloat16)
+    << "array type float16 is unsupported in linalg";
+  CHECK_NE(b_type, mshadow::kFloat16)
+    << "array type float16 is unsupported in linalg";
+  if (mshadow::kFloat32 == a_type && mshadow::kFloat32 == b_type) {
+    TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(1));
+  } else {
+    TYPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::kFloat64);
+  }
+  return out_attrs->at(0) != -1;
+}
+
+DMLC_REGISTER_PARAMETER(TensorsolveParam);
+
+NNVM_REGISTER_OP(_npi_tensorsolve)
+.set_attr_parser(mxnet::op::ParamParser<TensorsolveParam>)
+.set_num_inputs(2)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"a", "b"};
+  })
+.set_attr<mxnet::FInferShape>("FInferShape", TensorsolveOpShape)
+.set_attr<nnvm::FInferType>("FInferType", TensorsolveOpType)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>(1, ResourceRequest::kTempSpace);
+  })
+.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
+.set_attr<FCompute>("FCompute<cpu>", TensorsolveOpForward<cpu, tensorsolve>)
+.set_attr<nnvm::FGradient>("FGradient",
+  mxnet::op::ElemwiseGradUseInOut{"_backward_npi_tensorsolve"})
+.add_argument("a", "NDArray-or-Symbol", "First input")
+.add_argument("b", "NDArray-or-Symbol", "Second input")
+.add_arguments(TensorsolveParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_backward_npi_tensorsolve)
+.set_attr_parser(mxnet::op::ParamParser<TensorsolveParam>)
+.set_num_inputs(4)
+.set_num_outputs(2)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& ){
+    return std::vector<ResourceRequest>{1, ResourceRequest::kTempSpace};
+  })
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FCompute>("FCompute<cpu>", TensorsolveOpBackward<cpu, tensorsolve_backward>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/linalg/np_tensorsolve.cu b/src/operator/numpy/linalg/np_tensorsolve.cu
new file mode 100644
index 000000000000..07e2121750d5
--- /dev/null
+++ b/src/operator/numpy/linalg/np_tensorsolve.cu
@@ -0,0 +1,42 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file np_tensorsolve.cu
+ * \brief GPU implementation placeholder of Tensor Solve Operator
+ */
+
+#include <mxnet/operator_util.h>
+#include "./np_tensorsolve-inl.h"
+
+namespace mxnet {
+namespace op {
+
+#if MXNET_USE_CUSOLVER == 1
+
+NNVM_REGISTER_OP(_npi_tensorsolve)
+.set_attr<FCompute>("FCompute<gpu>", TensorsolveOpForward<gpu, tensorsolve>);
+
+NNVM_REGISTER_OP(_backward_npi_tensorsolve)
+.set_attr<FCompute>("FCompute<gpu>", TensorsolveOpBackward<gpu, tensorsolve_backward>);
+
+#endif
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index 53f912967cf1..6beb26cdf40a 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -442,6 +442,66 @@ def _add_workload_linalg_tensorinv():
                 OpArgMngr.add_workload('linalg.tensorinv', np.array(a, dtype=dtype), ind)
 
 
+def _add_workload_linalg_tensorsolve():
+    shapes = [
+        # a_shape.ndim <= 6
+        # (a_shape, b_shape, axes)
+        ((1, 1), (1,), None),
+        ((1, 1), (1, 1, 1, 1, 1), None),
+        ((4, 4), (4,), None),
+        ((2, 3, 3, 4, 2), (3, 4), (0, 2, 4)),
+        ((1, 3, 3, 4, 4), (1, 3, 4), (1, 3)),
+        ((1, 4, 1, 12, 3), (1, 2, 1, 2, 1, 3, 1), (1, 2, 4)),
+    ]
+    dtypes = (np.float32, np.float64)
+    for dtype in dtypes:
+        for a_shape, b_shape, axes in shapes:
+            a_ndim = len(a_shape)
+            b_ndim = len(b_shape)
+            a_trans_shape = list(a_shape)
+            a_axes = list(range(0, a_ndim))
+            if axes is not None:
+                for k in axes:
+                    a_axes.remove(k)
+                    a_axes.insert(a_ndim, k)
+                for k in range(a_ndim):
+                    a_trans_shape[k] = a_shape[a_axes[k]]
+            x_shape = a_trans_shape[-(a_ndim - b_ndim):]
+            prod = 1
+            for k in x_shape:
+                prod *= k
+            if prod * prod != _np.prod(a_shape):
+                raise ValueError("a is not square")
+            if prod != _np.prod(b_shape):
+                raise ValueError("a's shape and b's shape dismatch")
+            mat_shape = (prod, prod)
+            a_trans_shape = tuple(a_trans_shape)
+            x_shape = tuple(x_shape)
+
+            a_np = _np.eye(prod)
+            shape = mat_shape
+            while 1:
+                # generate well-conditioned matrices with small eigenvalues
+                D = _np.diag(_np.random.uniform(-1.0, 1.0, shape[-1]))
+                I = _np.eye(shape[-1]).reshape(shape)
+                v = _np.random.uniform(-1., 1., shape[-1]).reshape(shape[:-1] + (1,))
+                v = v / _np.linalg.norm(v, axis=-2, keepdims=True)
+                v_T = _np.swapaxes(v, -1, -2)
+                U = I - 2 * _np.matmul(v, v_T)
+                a = _np.matmul(U, D)
+                if (_np.linalg.cond(a, 2) < 4):
+                    a_np = a.reshape(a_trans_shape)
+                    break
+            x_np = _np.random.randn(*x_shape)
+            b_np = _np.tensordot(a_np, x_np, axes=len(x_shape))
+            a_origin_axes = list(range(a_np.ndim))
+            if axes is not None:
+                for k in range(a_np.ndim):
+                    a_origin_axes[a_axes[k]] = k
+            a_np = a_np.transpose(a_origin_axes)
+            OpArgMngr.add_workload('linalg.tensorsolve', np.array(a_np, dtype=dtype), np.array(b_np, dtype=dtype), axes)
+
+
 def _add_workload_linalg_slogdet():
     OpArgMngr.add_workload('linalg.slogdet', np.array(_np.ones((2, 2)), dtype=np.float32))
     OpArgMngr.add_workload('linalg.slogdet', np.array(_np.ones((0, 1, 1)), dtype=np.float64))
@@ -1523,6 +1583,7 @@ def _prepare_workloads():
     _add_workload_linalg_solve()
     _add_workload_linalg_det()
     _add_workload_linalg_tensorinv()
+    _add_workload_linalg_tensorsolve()
     _add_workload_linalg_slogdet()
     _add_workload_trace()
     _add_workload_tril()
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 42407adddf7c..37cdaae328ee 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -4062,6 +4062,160 @@ def get_grad_A(A, ind):
     check_tensorinv(mx_out, a, ind)
 
 
+@with_seed()
+@use_np
+def test_np_linalg_tensorsolve():
+    class TestTensorsolve(HybridBlock):
+        def __init__(self, axes):
+            super(TestTensorsolve, self).__init__()
+            self._axes = axes
+
+        def hybrid_forward(self, F, a, b):
+            return F.np.linalg.tensorsolve(a, b, axes=self._axes)
+
+    def get_tensorsolve_backward(a_np, b_np, mx_out_np, a_axes, a_origin_axes, a_trans_shape):
+        if (a_np.ndim == 0 or b_np.ndim == 0) or (a_np.ndim == b_np.ndim):
+            a_shape = a_np.shape
+            b_shape = b_np.shape
+            a_np = a_np.reshape((1, 1))
+            b_np = b_np.reshape((1,))
+            mx_out_np = mx_out_np.reshape((1,))
+            dx = _np.ones_like(mx_out_np)
+            inv_a_temp_np = _np.linalg.inv(a_np)
+            grad_b = inv_a_temp_np[0][0] * dx[0]
+            grad_a = -grad_b * mx_out_np[0]
+            return grad_a.reshape(a_shape), grad_b.reshape(b_shape)
+        else:
+            dx = _np.ones_like(mx_out_np)
+            a_np = a_np.transpose(a_axes)
+            ind = a_np.ndim - mx_out_np.ndim
+            tensorinv_a_np = _np.linalg.tensorinv(a_np, ind=ind)
+            a_trans_axes = list(range(a_np.ndim))[a_np.ndim - ind:] + list(range(a_np.ndim))[:a_np.ndim - ind]
+            trans_tensorinv_a_np = tensorinv_a_np.transpose(a_trans_axes)
+            grad_b = _np.tensordot(trans_tensorinv_a_np, dx, axes=dx.ndim)
+            grad_a = _np.tensordot(grad_b, mx_out_np, axes=0)
+            grad_a = grad_a.transpose(a_origin_axes)
+            return -grad_a, grad_b.reshape(b_np.shape)
+
+    def check_tensorsolve(x, a_np, b_np, axes):
+        try:
+            x_expected = _np.linalg.tensorsolve(a_np, b_np, axes=axes)
+        except Exception as e:
+            print("a:", a_np)
+            print("a shape:", a_np.shape)
+            print("b", b_np)
+            print("b shape:", b_np.shape)
+            print(e)
+        else:
+            assert x.shape == x_expected.shape
+            assert_almost_equal(x.asnumpy(), x_expected, rtol=rtol, atol=atol)
+
+    def shapeInfer(a_shape, b_shape, axes=None):
+        # b_shape - Right-hand tensor shape, which can be of any shape.
+        a_ndim = len(a_shape)
+        b_ndim = len(b_shape)
+        a_trans_shape = list(a_shape)
+        a_axes = list(range(0, a_ndim))
+        if axes is not None:
+            for k in axes:
+                a_axes.remove(k)
+                a_axes.insert(a_ndim, k)
+            for k in range(a_ndim):
+                a_trans_shape[k] = a_shape[a_axes[k]]
+        x_shape = a_trans_shape[-(a_ndim - b_ndim):]
+        prod = 1
+        for k in x_shape:
+            prod *= k
+        if prod * prod != _np.prod(a_shape):
+            raise ValueError("a is not square")
+        if prod != _np.prod(b_shape):
+            raise ValueError("a's shape and b's shape dismatch")
+        return a_axes, (prod, prod), tuple(a_trans_shape), tuple(x_shape)
+
+    def newInvertibleMatrix_2D(shape, max_cond=4):
+        while 1:
+            # generate well-conditioned matrices with small eigenvalues
+            D = _np.diag(_np.random.uniform(-1.0, 1.0, shape[-1]))
+            I = _np.eye(shape[-1]).reshape(shape)
+            v = _np.random.uniform(-1., 1., shape[-1]).reshape(shape[:-1] + (1,))
+            v = v / _np.linalg.norm(v, axis=-2, keepdims=True)
+            v_T = _np.swapaxes(v, -1, -2)
+            U = I - 2 * _np.matmul(v, v_T)
+            a = _np.matmul(U, D)
+            if (_np.linalg.cond(a, 2) < max_cond):
+                return a
+
+    shapes = [
+        # a_shape.ndim <= 6,
+        # (a_shape, b_shape, axes)
+        ((), (), None),                     # a.ndim == 0, b.ndim == 0, with axes must be None
+        ((), (1, 1, 1), None),              # a.ndim == 0, b.ndim != 0, with axes must be None
+        ((1, 1, 1), (), None),              # a.ndim != 0, b.ndim == 0, with axes == None
+        ((1, 1, 1), (), (0, 1, 2)),         # a.ndim != 0, b.ndim == 0, with axes != None
+        ((1, 1, 1), (1, 1, 1), None),       # a.ndim != 0, b.ndim != 0, a.ndim == b.ndim with axes == None
+        ((1, 1, 1), (1, 1, 1), (2, 0, 1)),  # a.ndim != 0, b.ndim != 0, a.ndim == b.ndim with axes != None
+        ((1, 1), (1,), None),               # a.ndim != 0, b.ndim != 0, a.ndim > b.ndim
+        ((1, 1), (1, 1, 1, 1, 1), None),    # a.ndim != 0, b.ndim != 0, a.ndim < b.ndim - a.ndim
+        ((4, 4), (4,), None),
+        ((6, 2, 3), (6,), None),
+        ((2, 3, 6), (6,), (0, 1)),
+        ((3, 4, 2, 3, 2), (3, 4), None),
+        ((2, 1, 4, 2, 4), (2, 4), (0, 1, 2)),
+        ((2, 3, 3, 4, 2), (3, 4), (0, 2, 4)),
+        ((1, 3, 3, 4, 4), (1, 3, 4), (1, 3)),
+        ((1, 12, 4, 1, 3), (1, 2, 1, 2, 1, 3, 1), None),
+        ((1, 4, 1, 12, 3), (1, 2, 1, 2, 1, 3, 1), (1, 2, 4)),
+    ]
+    dtypes = ['float32', 'float64']
+    for hybridize in [True, False]:
+        for dtype in dtypes:
+            for a_shape, b_shape, axes in shapes:
+                rtol = 1e-2 if dtype == 'float32' else 1e-3
+                atol = 1e-4 if dtype == 'float32' else 1e-5
+                test_tensorsolve = TestTensorsolve(axes)
+                if hybridize:
+                    test_tensorsolve.hybridize()
+
+                a_axes, mat_shape, a_trans_shape, x_shape = shapeInfer(a_shape, b_shape, axes)
+                # generate coefficient tensor a and right side tensor b
+                if (len(a_shape) == 0 or len(b_shape) == 0) or (len(a_shape) == len(b_shape)):
+                    a_np = _np.asarray(1).astype(dtype).reshape(a_shape)
+                    b_np = _np.asarray(2).astype(dtype).reshape(b_shape)
+                else:
+                    a_np = newInvertibleMatrix_2D(mat_shape, max_cond=3).reshape(a_trans_shape)
+                    x_np = _np.random.randn(*x_shape)
+                    b_np = _np.tensordot(a_np, x_np, axes=len(x_shape))
+
+                # resume original shape of tensor a
+                a_origin_axes = list(range(a_np.ndim))
+                if axes is not None:
+                    for k in range(a_np.ndim):
+                        a_origin_axes[a_axes[k]] = k
+                a_np = a_np.transpose(a_origin_axes)
+                a = np.array(a_np, dtype=dtype).reshape(a_shape)
+                b = np.array(b_np, dtype=dtype).reshape(b_shape)
+                a.attach_grad()
+                b.attach_grad()
+
+                with mx.autograd.record():
+                    mx_out = test_tensorsolve(a, b)
+                # check tensorsolve validity
+                assert mx_out.shape == x_shape
+                check_tensorsolve(mx_out, a.asnumpy(), b.asnumpy(), axes)
+
+                # check backward
+                if len(a_shape) != 0 and len(b_shape) != 0:
+                    mx.autograd.backward(mx_out)
+                    grad_a_expected, grad_b_expected = get_tensorsolve_backward(
+                        a.asnumpy(), b.asnumpy(), mx_out.asnumpy(), a_axes, a_origin_axes, a_trans_shape)
+                    assert_almost_equal(a.grad.asnumpy(), grad_a_expected, rtol=rtol, atol=atol)
+                    assert_almost_equal(b.grad.asnumpy(), grad_b_expected, rtol=rtol, atol=atol)
+
+                # check imperative once again
+                mx_out = test_tensorsolve(a, b)
+                check_tensorsolve(mx_out, a.asnumpy(), b.asnumpy(), axes)
+
+
 @with_seed()
 @use_np
 def test_np_linalg_det():

From a6ed69e060da3661972f4cd4041baf0a689773a3 Mon Sep 17 00:00:00 2001
From: Minghao Liu <40382964+Tommliu@users.noreply.github.com>
Date: Fri, 27 Dec 2019 11:18:37 +0800
Subject: [PATCH 743/813] amax (#17176)

---
 python/mxnet/_numpy_op_doc.py                     | 11 +++++++++--
 python/mxnet/numpy_dispatch_protocol.py           |  1 +
 .../numpy/np_broadcast_reduce_op_value.cc         |  1 +
 .../unittest/test_numpy_interoperability.py       | 15 +++++++++++++++
 4 files changed, 26 insertions(+), 2 deletions(-)

diff --git a/python/mxnet/_numpy_op_doc.py b/python/mxnet/_numpy_op_doc.py
index d1d67be06b05..6abb96c62c09 100644
--- a/python/mxnet/_numpy_op_doc.py
+++ b/python/mxnet/_numpy_op_doc.py
@@ -731,7 +731,7 @@ def _np_squeeze(a, axis=None, out=None):
     pass
 
 
-def _np_max(a, axis=None, out=None, keepdims=False):
+def _np_max(a, axis=None, keepdims=False, out=None):
     """
     Return the maximum of an array or maximum along an axis.
     
@@ -795,7 +795,14 @@ def _np_max(a, axis=None, out=None, keepdims=False):
     pass
 
 
-def _np_min(a, axis=None, out=None, keepdims=False):
+def _np_amax(a, axis=None, keepdims=False, out=None):
+    """
+    Refer to _np_max
+    """
+    pass
+
+
+def _np_min(a, axis=None, keepdims=False, out=None):
     """
     Return the minimum of an array or minimum along an axis.
     
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index 603b118855a5..96354684241c 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -106,6 +106,7 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'flip',
     'inner',
     'max',
+    'amax',
     'mean',
     'min',
     'nonzero',
diff --git a/src/operator/numpy/np_broadcast_reduce_op_value.cc b/src/operator/numpy/np_broadcast_reduce_op_value.cc
index 2a1bc5261701..cf92da52d1f8 100644
--- a/src/operator/numpy/np_broadcast_reduce_op_value.cc
+++ b/src/operator/numpy/np_broadcast_reduce_op_value.cc
@@ -161,6 +161,7 @@ inline bool NumpyReduceAxesNoDTypeType(const nnvm::NodeAttrs& attrs,
 }
 
 NNVM_REGISTER_OP(_np_max)
+.add_alias("_np_amax")
 .describe(R"code()code" ADD_FILELINE)
 .set_num_inputs(1)
 .set_num_outputs(1)
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index 6beb26cdf40a..1a31a0d3e4c2 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -791,6 +791,20 @@ def _add_workload_max(array_pool):
     OpArgMngr.add_workload('max', array_pool['4x1'])
 
 
+def _add_workload_amax(array_pool):
+    a = np.array([3, 4, 5, 10, -3, -5, 6.0])
+    b = np.array([[3, 6.0, 9.0],
+                  [4, 10.0, 5.0],
+                  [8, 3.0, 2.0]])
+    c = np.array(1)
+    OpArgMngr.add_workload('amax', array_pool['4x1'])
+    OpArgMngr.add_workload('amax', a)
+    OpArgMngr.add_workload('amax', b, axis=0)
+    OpArgMngr.add_workload('amax', b, axis=1)
+    OpArgMngr.add_workload('amax', c)
+    OpArgMngr.add_workload('amax', c, axis=None)
+
+
 def _add_workload_min(array_pool):
     OpArgMngr.add_workload('min', array_pool['4x1'])
 
@@ -1554,6 +1568,7 @@ def _prepare_workloads():
     _add_workload_fix()
     _add_workload_flip()
     _add_workload_max(array_pool)
+    _add_workload_amax(array_pool)
     _add_workload_min(array_pool)
     _add_workload_mean(array_pool)
     _add_workload_nonzero()

From 1cfaf3cbfceaa474d4d706a5f63be62b9f06c0a4 Mon Sep 17 00:00:00 2001
From: JiangZhaoh <54654391+JiangZhaoh@users.noreply.github.com>
Date: Fri, 27 Dec 2019 11:42:10 +0800
Subject: [PATCH 744/813] [numpy]Add op delete (#17023)

* add_op_delete

* modify code

* reuse existing function / add licenses / modify alignment

* add comment / abstarct basic function

* fix windows build error - remove '#' from MSHADOW_TYPE_SWITCH
---
 python/mxnet/ndarray/numpy/_op.py             |  63 +++-
 python/mxnet/numpy/multiarray.py              |  51 ++-
 python/mxnet/numpy_dispatch_protocol.py       |   1 +
 python/mxnet/symbol/numpy/_symbol.py          |  41 ++-
 src/operator/numpy/np_delete_op-inl.h         | 347 ++++++++++++++++++
 src/operator/numpy/np_delete_op.cc            |  98 +++++
 src/operator/numpy/np_delete_op.cu            |  35 ++
 .../unittest/test_numpy_interoperability.py   |  22 ++
 tests/python/unittest/test_numpy_op.py        |  75 ++++
 9 files changed, 730 insertions(+), 3 deletions(-)
 create mode 100644 src/operator/numpy/np_delete_op-inl.h
 create mode 100644 src/operator/numpy/np_delete_op.cc
 create mode 100644 src/operator/numpy/np_delete_op.cu

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index e380b4937168..c9ffa2328872 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -28,7 +28,7 @@
 from . import _internal as _npi
 from ..ndarray import NDArray
 
-__all__ = ['shape', 'zeros', 'zeros_like', 'ones', 'ones_like', 'full', 'full_like', 'invert',
+__all__ = ['shape', 'zeros', 'zeros_like', 'ones', 'ones_like', 'full', 'full_like', 'invert', 'delete',
            'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power', 'bitwise_not',
            'arctan2', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs',
            'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2',
@@ -913,6 +913,67 @@ def mod(x1, x2, out=None, **kwargs):
     return _ufunc_helper(x1, x2, _npi.mod, _np.mod, _npi.mod_scalar, _npi.rmod_scalar, out)
 
 
+@set_module('mxnet.ndarray.numpy')
+def delete(arr, obj, axis=None):
+    """
+    Return a new array with sub-arrays along an axis deleted. For a one
+    dimensional array, this returns those entries not returned by
+    `arr[obj]`.
+
+    Parameters
+    ----------
+    arr : ndarray
+      Input array.
+    obj : slice, int or ndarray of ints
+      Indicate indices of sub-arrays to remove along the specified axis.
+    axis : int, optional
+      The axis along which to delete the subarray defined by `obj`.
+      If `axis` is None, `obj` is applied to the flattened array.
+
+    Returns
+    -------
+    out : ndarray
+        A copy of `arr` with the elements specified by `obj` removed. Note
+        that `delete` does not occur in-place. If `axis` is None, `out` is
+        a flattened array.
+
+    Examples
+    --------
+    >>> arr = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
+    >>> arr
+    array([[ 1.,  2.,  3.,  4.],
+           [ 5.,  6.,  7.,  8.],
+           [ 9., 10., 11., 12.]])
+
+    >>> np.delete(arr, 1, 0)
+    array([[ 1.,  2.,  3.,  4.],
+           [ 9., 10., 11., 12.]])
+
+    >>> np.delete(arr, slice(None, None, 2), 1)
+    array([[ 2.,  4.],
+           [ 6.,  8.],
+           [10., 12.]])
+
+    >>> np.delete(arr, np.array([1,3,5]), None)
+    array([ 1.,  3.,  5.,  7.,  8.,  9., 10., 11., 12.])
+    >>> np.delete(arr, np.array([1,1,5]), None)
+    array([ 1.,  3.,  4.,  5.,  7.,  8.,  9., 10., 11., 12.])
+    """
+    if not isinstance(arr, NDArray):
+        raise TypeError("'arr' can not support type {}".format(str(type(arr))))
+    if isinstance(obj, slice):
+        start = obj.start
+        stop = obj.stop
+        step = 1 if obj.step is None else obj.step
+        return _npi.delete(arr, start=start, stop=stop, step=step, axis=axis)
+    elif isinstance(obj, integer_types):
+        return _npi.delete(arr, int_ind=obj, axis=axis)
+    elif isinstance(obj, NDArray):
+        return _npi.delete(arr, obj, axis=axis)
+    else:
+        raise TypeError("'obj' can not support type {}".format(str(type(obj))))
+
+
 @set_module('mxnet.ndarray.numpy')
 @wrap_np_binary_func
 def remainder(x1, x2, out=None):
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 22094a1621d2..a870b9a7632e 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -47,7 +47,7 @@
 from ..ndarray.ndarray import _storage_type
 
 __all__ = ['ndarray', 'empty', 'array', 'shape', 'zeros', 'zeros_like', 'ones', 'ones_like', 'full', 'full_like',
-           'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power', 'bitwise_not',
+           'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power', 'bitwise_not', 'delete',
            'arctan2', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'invert',
            'sqrt', 'cbrt', 'abs', 'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log',
            'degrees', 'log2', 'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative', 'histogram',
@@ -5848,6 +5848,55 @@ def std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):  # pylint:
     return _npi.std(a, axis=axis, dtype=dtype, ddof=ddof, keepdims=keepdims, out=out)
 
 
+@set_module('mxnet.numpy')
+def delete(arr, obj, axis=None):
+    """
+    Return a new array with sub-arrays along an axis deleted. For a one
+    dimensional array, this returns those entries not returned by
+    `arr[obj]`.
+
+    Parameters
+    ----------
+    arr : ndarray
+      Input array.
+    obj : slice, int or ndarray of ints
+      Indicate indices of sub-arrays to remove along the specified axis.
+    axis : int, optional
+      The axis along which to delete the subarray defined by `obj`.
+      If `axis` is None, `obj` is applied to the flattened array.
+
+    Returns
+    -------
+    out : ndarray
+        A copy of `arr` with the elements specified by `obj` removed. Note
+        that `delete` does not occur in-place. If `axis` is None, `out` is
+        a flattened array.
+
+    Examples
+    --------
+    >>> arr = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
+    >>> arr
+    array([[ 1.,  2.,  3.,  4.],
+           [ 5.,  6.,  7.,  8.],
+           [ 9., 10., 11., 12.]])
+
+    >>> np.delete(arr, 1, 0)
+    array([[ 1.,  2.,  3.,  4.],
+           [ 9., 10., 11., 12.]])
+
+    >>> np.delete(arr, slice(None, None, 2), 1)
+    array([[ 2.,  4.],
+           [ 6.,  8.],
+           [10., 12.]])
+
+    >>> np.delete(arr, np.array([1,3,5]), None)
+    array([ 1.,  3.,  5.,  7.,  8.,  9., 10., 11., 12.])
+    >>> np.delete(arr, np.array([1,1,5]), None)
+    array([ 1.,  3.,  4.,  5.,  7.,  8.,  9., 10., 11., 12.])
+    """
+    return _mx_nd_np.delete(arr, obj, axis=axis)
+
+
 @set_module('mxnet.numpy')
 def var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):  # pylint: disable=too-many-arguments
     """
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index 96354684241c..65486e6e5f37 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -129,6 +129,7 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'transpose',
     'unique',
     'unravel_index',
+    'delete',
     'var',
     'vdot',
     'vstack',
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 0b341b804758..10751cb0d289 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -36,7 +36,7 @@
 except ImportError:
     from builtins import slice as py_slice
 
-__all__ = ['zeros', 'zeros_like', 'ones', 'ones_like', 'full_like', 'bitwise_not', 'invert',
+__all__ = ['zeros', 'zeros_like', 'ones', 'ones_like', 'full_like', 'bitwise_not', 'invert', 'delete',
            'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power', 'arctan2',
            'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs', 'absolute', 'exp',
            'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2', 'log1p',
@@ -3161,6 +3161,45 @@ def arange(start, stop=None, step=1, dtype=None, ctx=None):
     return _npi.arange(start=start, stop=stop, step=step, dtype=dtype, ctx=ctx)
 
 
+@set_module('mxnet.symbol.numpy')
+def delete(arr, obj, axis=None):
+    """
+    Return a new array with sub-arrays along an axis deleted. For a one
+    dimensional array, this returns those entries not returned by
+    `arr[obj]`.
+
+    Parameters
+    ----------
+    arr : _Symbol
+      Input array.
+    obj : slice, scaler or _Symbol of ints
+      Indicate indices of sub-arrays to remove along the specified axis.
+    axis : scaler, optional
+      The axis along which to delete the subarray defined by `obj`.
+      If `axis` is None, `obj` is applied to the flattened array.
+
+    Returns
+    -------
+    out : _Symbol
+        A copy of `arr` with the elements specified by `obj` removed. Note
+        that `delete` does not occur in-place. If `axis` is None, `out` is
+        a flattened array.
+    """
+    if not isinstance(arr, Symbol):
+        raise TypeError("'arr' can not support type {}".format(str(type(arr))))
+    if isinstance(obj, slice):
+        start = obj.start
+        stop = obj.stop
+        step = 1 if obj.step is None else obj.step
+        return _npi.delete(arr, start=start, stop=stop, step=step, axis=axis)
+    elif isinstance(obj, integer_types):
+        return _npi.delete(arr, int_ind=obj, axis=axis)
+    elif isinstance(obj, Symbol):
+        return _npi.delete(arr, obj, axis=axis)
+    else:
+        raise TypeError("'obj' can not support type {}".format(str(type(obj))))
+
+
 # pylint: disable=redefined-outer-name
 @set_module('mxnet.symbol.numpy')
 def split(ary, indices_or_sections, axis=0):
diff --git a/src/operator/numpy/np_delete_op-inl.h b/src/operator/numpy/np_delete_op-inl.h
new file mode 100644
index 000000000000..a144833f3294
--- /dev/null
+++ b/src/operator/numpy/np_delete_op-inl.h
@@ -0,0 +1,347 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file np_delete_op-inl.h
+ * \brief Function definition of delete operators
+ */
+#ifndef MXNET_OPERATOR_NUMPY_NP_DELETE_OP_INL_H_
+#define MXNET_OPERATOR_NUMPY_NP_DELETE_OP_INL_H_
+
+#include <vector>
+#include <memory>
+#include <algorithm>
+#include "../../common/utils.h"
+#include "../tensor/sort_op.h"
+#include "../tensor/init_op.h"
+#include "../operator_common.h"
+#include "../mxnet_op.h"
+#include "../tensor/broadcast_reduce_op.h"
+#ifdef __CUDACC__
+#include <thrust/device_ptr.h>
+#include <thrust/device_vector.h>
+#include <thrust/functional.h>
+#include <thrust/scan.h>
+#include <thrust/sequence.h>
+#include <thrust/sort.h>
+#endif
+
+namespace mxnet {
+namespace op {
+
+struct NumpyDeleteParam : public dmlc::Parameter<NumpyDeleteParam> {
+  dmlc::optional<int> start;
+  dmlc::optional<int> stop;
+  dmlc::optional<int> step;
+  dmlc::optional<int> int_ind;
+  dmlc::optional<int> axis;
+  DMLC_DECLARE_PARAMETER(NumpyDeleteParam) {
+    DMLC_DECLARE_FIELD(start)
+    .set_default(dmlc::optional<int>())
+    .describe("If 'obj' is slice, 'start' is one of it's arguments.");
+    DMLC_DECLARE_FIELD(stop)
+    .set_default(dmlc::optional<int>())
+    .describe("If 'obj' is slice, 'stop' is one of it's arguments.");
+    DMLC_DECLARE_FIELD(step)
+    .set_default(dmlc::optional<int>())
+    .describe("If 'obj' is slice, 'step' is one of it's arguments.");
+    DMLC_DECLARE_FIELD(int_ind)
+    .set_default(dmlc::optional<int>())
+    .describe("If 'obj' is int, 'int_ind' is the index before which"
+              "'values' is inserted");
+    DMLC_DECLARE_FIELD(axis)
+    .set_default(dmlc::optional<int>())
+    .describe("Axis along which to insert `values`.");
+  }
+};
+
+namespace delete_ {
+
+enum DeleteOpInputs {kArr, kObj};
+enum DeleteOpOutputs {kOut};
+}  // namespace delete_
+
+struct SliceToIndices {
+  /*!
+   * \brief transfer slice to indices array
+   */
+  template<typename IType>
+  MSHADOW_XINLINE static void Map(int i, IType* indices, int start, int step) {
+    indices[i] = start + i * step;
+  }
+};
+
+struct IsDeleteCal {
+  /*!
+   * \brief indicate which indices need to be deleted in input
+   * \param N used to check indices legality 
+   * \param is_delete if is_delete[i] == False, index i needn't to be deleted from output
+   *                  if is_delete[i] == True, index i need to be deleted from output
+   * \param indices the indices need to be deleted
+   */
+  template<typename IType>
+  MSHADOW_XINLINE static void Map(int i, int N, bool* is_delete, const IType* indices) {
+    if ((indices[i] >= 0) && (indices[i] < N)) {
+      is_delete[static_cast<int>(indices[i])] = true;
+    }
+  }
+};
+
+struct OutPosCal {
+  /*!
+   * \brief map the index from input to output. e.g.
+   * \example original_position 0 1 2 3 4
+   *          is_delete         F T T F F
+   *          out_position      0 - - 1 2
+   */
+  MSHADOW_XINLINE static void Map(int i, int64_t* out_pos, const bool* is_delete) {
+    if (!is_delete[i]) {
+      int cnt = 0;
+      for (int j = 0; j < i; ++j) {
+        if (!is_delete[j]) {
+          cnt++;
+        }
+      }
+      out_pos[i] = cnt;
+    }
+  }
+};
+
+template<int req, int ndim>
+struct DeleteKernel {
+  /*!
+   * \brief delete a sub-array from input along an axis according to 'is_delete'.
+   * \param out_data - output: a new array with sub-arrays along an axis deleted.
+   * \param in_arr - input: 'arr', original array.
+   * \param is_delete - mark where will be deleted or be reminded in 'arr'
+   * \param out_pos - if is_delete[i] is 'false', out_pos[i] indicates its.
+   * \param arrshape - the shape of 'arr'.
+   * \param out_stride - the stride of 'out_data'.
+   * \param axis - delete sub-array along this axis
+   */
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int i, DType* out_data,
+                                  const DType* in_arr,
+                                  const bool* is_delete,
+                                  const int64_t* out_pos,
+                                  const mshadow::Shape<ndim> arrshape,
+                                  const mshadow::Shape<ndim> out_stride,
+                                  const int axis) {
+    // i -> position in in_arr's shape
+    mshadow::Shape<ndim> arr_idx = mxnet_op::unravel(i, arrshape);
+    if (!is_delete[arr_idx[axis]]) {
+      arr_idx[axis] = out_pos[arr_idx[axis]];
+      int64_t dest_idx = mxnet_op::dot(arr_idx, out_stride);
+      KERNEL_ASSIGN(out_data[dest_idx], req, in_arr[i]);
+    }
+  }
+};
+
+/*!
+ * /brief equals to numpy's slice.indices(range)
+ * /param pstart - slice.start
+ * /param pstep - slice.step
+ * /param pstop - slice.stop
+ * /return start - slice.indices(range).start
+ * /return stop - slice.indices(range).stop
+ * /return step - slice.indices(range).step
+ * /return tot - total number of slice.indices(range)
+ */
+inline void SliceIndices(const dmlc::optional<int>& pstart,
+                         const dmlc::optional<int>& pstop,
+                         const dmlc::optional<int>& pstep,
+                         const int range,
+                         int* start, int* stop, int* step,
+                         size_t* tot) {
+  *step = pstep.has_value() ? pstep.value() : 1;
+  CHECK_NE(*step, 0) << "'step' can not equal to 0.";
+  if (pstop.has_value()) {
+    *stop = pstop.value();
+    *stop += (*stop < 0) ? range : 0;
+    *stop = (*stop < 0) ? ((*step < 0) ? -1 : 0) : *stop;
+    *stop = (*stop >= range) ? ((*step < 0) ? range - 1 : range) : *stop;
+  } else {
+    *stop = (*step > 0) ? range : -1;
+  }
+  if (pstart.has_value()) {
+    *start = pstart.value();
+    *start += (*start < 0) ? range : 0;
+    *start = (*start < 0) ? ((*step < 0) ? -1 : 0) : *start;
+    *start = (*start >= range) ? ((*step < 0) ? range - 1 : range) : *start;
+  } else {
+    *start = (*step > 0) ? 0 : range - 1;
+  }
+  if (*step > 0 && *stop >= *start) {
+    *tot = static_cast<size_t>((*stop - *start + *step - 1) / *step);
+  } else if (*step < 0 && *stop <= *start) {
+    *tot = static_cast<size_t>((*stop - *start + *step + 1) / *step);
+  }
+}
+
+template<typename xpu>
+void NumpyDeleteCompute(const nnvm::NodeAttrs& attrs,
+                        const OpContext &ctx,
+                        const std::vector<NDArray> &inputs,
+                        const std::vector<OpReqType> &req,
+                        const std::vector<NDArray> &outputs) {
+  using namespace mshadow;
+  using namespace mxnet_op;
+
+  const NumpyDeleteParam& param = nnvm::get<NumpyDeleteParam>(attrs.parsed);
+  CHECK_EQ(inputs.size(),
+          (param.step.has_value() || param.int_ind.has_value()) ? 1U : 2U);
+  CHECK_EQ(outputs.size(), 1U);
+  CHECK_EQ(req.size(), 1U);
+  mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+
+  int ndim = inputs[delete_::kArr].shape().ndim();
+  int axis = param.axis.has_value() ? param.axis.value() : -1;
+  NDArray arr;  // original array
+
+  if (!param.axis.has_value()) {
+    arr = inputs[delete_::kArr].Reshape(Shape1(inputs[delete_::kArr].shape().Size()));
+    ndim = 1;
+    axis = -1;
+  } else {
+    arr = inputs[delete_::kArr];
+  }
+
+  if (ndim == 0) {
+    const_cast<NDArray &>(outputs[delete_::kOut]).Init(arr.shape());
+    mxnet_op::copy(s, outputs[delete_::kOut].data(), inputs[delete_::kArr].data());
+    return;
+  }
+
+  axis = CheckAxis(axis, ndim);
+  int N = (arr.shape())[axis];
+  mxnet::TShape outshape(arr.shape());
+  // if obj is slice, they're obj's arguments
+  int start = 0, stop = 0, step = 0;
+  // total number to be deleted
+  size_t numtodel = 0;
+  // if obj is scaler, index is it's value
+  int index = 0;
+
+  if (param.step.has_value()) {  // obj is slice
+    SliceIndices(param.start, param.stop, param.step,
+                 N, &start, &stop, &step, &numtodel);
+    if (numtodel == 0) {
+      const_cast<NDArray &>(outputs[delete_::kOut]).Init(arr.shape());
+      mxnet_op::copy(s, outputs[delete_::kOut].data(), inputs[delete_::kArr].data());
+      return;
+    }
+    outshape[axis] -= numtodel;
+    const_cast<NDArray &>(outputs[delete_::kOut]).Init(outshape);
+  } else if (param.int_ind.has_value()) {  // obj is scaler
+    index = param.int_ind.value();
+    CHECK((index >= -1 * N) && (index < N))
+      << "index " << index
+      << " is out of bounds for axis " << axis
+      << " with size " << N << "\n";
+    index += ((index < 0) ? N : 0);
+    numtodel = static_cast<size_t>(1);
+    outshape[axis] -= 1;
+    const_cast<NDArray &>(outputs[delete_::kOut]).Init(outshape);
+  } else {  // obj is tensor
+    numtodel = inputs[delete_::kObj].shape().Size();
+  }
+
+  char* out_pos_ptr = NULL;
+  char* indices_ptr = NULL;
+  char* is_delete_ptr = NULL;
+  MSHADOW_TYPE_SWITCH(((inputs.size() == 2U) ?  // obj is tensor
+                      inputs[delete_::kObj].dtype() :
+                      mshadow::DataType<int64_t>::kFlag), IType, {
+    size_t temp_mem_size = sizeof(int64_t) * arr.shape()[axis] +
+                           sizeof(IType) * numtodel +
+                           sizeof(bool) * arr.shape()[axis];
+    Tensor<xpu, 1, char> temp_mem =
+      ctx.requested[0].get_space_typed<xpu, 1, char>(Shape1(temp_mem_size), s);
+    out_pos_ptr = temp_mem.dptr_;
+    indices_ptr = out_pos_ptr + sizeof(int64_t) * arr.shape()[axis];
+    is_delete_ptr = indices_ptr + sizeof(IType) * numtodel;
+    if (param.step.has_value()) {  // obj is slice, transfer slice to tensor
+      Kernel<SliceToIndices, xpu>::Launch(
+        s, numtodel, reinterpret_cast<IType*>(indices_ptr), start, step);
+    } else if (param.int_ind.has_value()) {  // obj is scaler, copy it to tensor
+      Fill(s, TBlob(reinterpret_cast<IType*>(indices_ptr),
+           Shape1(numtodel), xpu::kDevMask), kWriteTo, index);
+    } else {  // obj is tensor, copy it to a unified tensor
+      mxnet_op::copy(s,
+        TBlob(reinterpret_cast<IType*>(indices_ptr), inputs[delete_::kObj].shape(),
+              inputs[delete_::kObj].data().dev_mask()),
+        inputs[delete_::kObj].data());
+    }
+    mxnet_op::Kernel<mxnet_op::set_zero, xpu>::Launch(
+      s, arr.shape()[axis], reinterpret_cast<bool*>(is_delete_ptr));
+    // mark which position need to be deleted from input arr
+    Kernel<IsDeleteCal, xpu>::Launch(
+      s, numtodel, N, reinterpret_cast<bool*>(is_delete_ptr),
+      reinterpret_cast<IType*>(indices_ptr));
+    // calculate output data's original position in input arr
+    Kernel<OutPosCal, xpu>::Launch(
+      s, arr.shape()[axis], reinterpret_cast<int64_t*>(out_pos_ptr),
+      reinterpret_cast<bool*>(is_delete_ptr));
+  });
+
+  if (inputs.size() == 2U) {  // obj is tensor
+    // get total number of nonredundant indices
+    #ifdef __CUDACC__
+      thrust::device_ptr<bool>is_delete_dev(reinterpret_cast<bool*>(is_delete_ptr));
+      thrust::device_vector<bool>vec_is_delete(is_delete_dev, is_delete_dev + arr.shape()[axis]);
+    #else
+      std::vector<bool>vec_is_delete(reinterpret_cast<bool*>(is_delete_ptr),
+                                     reinterpret_cast<bool*>(is_delete_ptr) + arr.shape()[axis]);
+    #endif
+    numtodel = 0;
+    for (int i = 0; i < arr.shape()[axis]; ++i) {
+      if (vec_is_delete[i]) {
+        numtodel++;
+      }
+    }
+    outshape[axis] -= numtodel;
+    const_cast<NDArray &>(outputs[delete_::kOut]).Init(outshape);
+  }
+
+  MSHADOW_TYPE_SWITCH(((inputs.size() == 2U) ?  // obj is tensor
+                      inputs[delete_::kObj].dtype() :
+                      mshadow::DataType<int64_t>::kFlag), IType, {
+    MXNET_NDIM_SWITCH(outshape.ndim(), ndim, {
+      mshadow::Shape<ndim> out_strides = mxnet_op::calc_stride(outshape.get<ndim>());
+      MSHADOW_TYPE_SWITCH(outputs[delete_::kOut].dtype(), DType, {
+        MXNET_ASSIGN_REQ_SWITCH(req[delete_::kOut], req_type, {
+          Kernel<DeleteKernel<req_type, ndim>, xpu>::Launch(
+            s, arr.shape().Size(),
+            outputs[delete_::kOut].data().dptr<DType>(),
+            arr.data().dptr<DType>(),
+            reinterpret_cast<bool*>(is_delete_ptr),
+            reinterpret_cast<int64_t*>(out_pos_ptr),
+            arr.shape().get<ndim>(),
+            out_strides, axis);
+        });
+      });
+    });
+  });
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_NUMPY_NP_DELETE_OP_INL_H_
diff --git a/src/operator/numpy/np_delete_op.cc b/src/operator/numpy/np_delete_op.cc
new file mode 100644
index 000000000000..48840bf9d230
--- /dev/null
+++ b/src/operator/numpy/np_delete_op.cc
@@ -0,0 +1,98 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file np_delete_op.cc
+ * \brief CPU Implementation of numpy insert operations
+ */
+
+#include <vector>
+#include "./np_delete_op-inl.h"
+
+namespace mxnet {
+namespace op {
+
+DMLC_REGISTER_PARAMETER(NumpyDeleteParam);
+
+bool NumpyDeleteType(const nnvm::NodeAttrs& attrs,
+                     std::vector<int> *in_type,
+                     std::vector<int> *out_type) {
+  const NumpyDeleteParam& param = nnvm::get<NumpyDeleteParam>(attrs.parsed);
+  int insize = (param.step.has_value() || param.int_ind.has_value()) ? 1 : 2;
+  CHECK_EQ(in_type->size(), insize);
+  CHECK_EQ(out_type->size(), 1U);
+  if (insize == 3) {
+    CHECK_NE((*in_type)[1], -1) << "Index type must be set for insert operator\n";
+    CHECK(((*in_type)[1] == mshadow::DataType<int64_t>::kFlag) ||
+          ((*in_type)[1] == mshadow::DataType<int32_t>::kFlag))
+      << "Index type only support int32 or int64.\n";
+  }
+  TYPE_ASSIGN_CHECK(*out_type, 0, (*in_type)[0]);
+  TYPE_ASSIGN_CHECK(*in_type, 0, (*out_type)[0]);
+  return (*in_type)[0] != -1;
+}
+
+inline bool NumpyDeleteStorageType(const nnvm::NodeAttrs& attrs,
+                                   const int dev_mask,
+                                   DispatchMode* dispatch_mode,
+                                   std::vector<int> *in_attrs,
+                                   std::vector<int> *out_attrs) {
+  const NumpyDeleteParam& param = nnvm::get<NumpyDeleteParam>(attrs.parsed);
+  unsigned int insize = (param.step.has_value() || param.int_ind.has_value()) ? 1U : 2U;
+  CHECK_EQ(in_attrs->size(), insize);
+  CHECK_EQ(out_attrs->size(), 1U);
+  for (int &attr : *in_attrs) {
+    CHECK_EQ(attr, kDefaultStorage) << "Only default storage is supported";
+  }
+  for (int &attr : *out_attrs) {
+    attr = kDefaultStorage;
+  }
+  *dispatch_mode = DispatchMode::kFComputeEx;
+  return true;
+}
+
+NNVM_REGISTER_OP(_npi_delete)
+.describe(R"code(Delete values along the given axis before the given indices.)code" ADD_FILELINE)
+.set_attr_parser(ParamParser<NumpyDeleteParam>)
+.set_num_inputs([](const NodeAttrs& attrs) {
+  const NumpyDeleteParam& params = nnvm::get<NumpyDeleteParam>(attrs.parsed);
+  return (params.step.has_value() || params.int_ind.has_value()) ? 1U : 2U;
+})
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    const NumpyDeleteParam& params = nnvm::get<NumpyDeleteParam>(attrs.parsed);
+    return (params.step.has_value() || params.int_ind.has_value()) ?
+            std::vector<std::string>{"arr"} :
+            std::vector<std::string>{"arr", "obj"};
+})
+.set_attr<nnvm::FInferType>("FInferType", NumpyDeleteType)
+.set_attr<mxnet::FComputeEx>("FComputeEx<cpu>", NumpyDeleteCompute<cpu>)
+.set_attr<FInferStorageType>("FInferStorageType", NumpyDeleteStorageType)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.add_argument("arr", "NDArray-or-Symbol", "Input ndarray")
+.add_argument("obj", "NDArray-or-Symbol", "Input ndarray")
+.add_arguments(NumpyDeleteParam::__FIELDS__());
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_delete_op.cu b/src/operator/numpy/np_delete_op.cu
new file mode 100644
index 000000000000..599d01788138
--- /dev/null
+++ b/src/operator/numpy/np_delete_op.cu
@@ -0,0 +1,35 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.ø
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file np_delete_op.cu
+ * \brief GPU Implementation of numpy delete operations
+ */
+
+#include "./np_delete_op-inl.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(_npi_delete)
+.set_attr<FComputeEx>("FComputeEx<gpu>", NumpyDeleteCompute<gpu>);
+
+}
+}
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index 1a31a0d3e4c2..bb45fead7cd9 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -959,6 +959,27 @@ def _add_workload_unique():
     # OpArgMngr.add_workload('unique', np.arange(10, dtype=np.uint8).reshape(-1, 2).astype(bool), axis=1)
 
 
+def _add_workload_delete():
+    a = np.arange(5)
+    nd_a = np.arange(5).repeat(2).reshape(1, 5, 2)
+    lims = [-6, -2, 0, 1, 2, 4, 5]
+    steps = [-3, -1, 1, 3]
+    for start in lims:
+        for stop in lims:
+            for step in steps:
+                s = slice(start, stop, step)
+                OpArgMngr.add_workload('delete', a, s)
+                OpArgMngr.add_workload('delete', nd_a, s, axis=1)
+    OpArgMngr.add_workload('delete', a, np.array([]), axis=0)
+    OpArgMngr.add_workload('delete', a, 0)
+    OpArgMngr.add_workload('delete', a, np.array([]))
+    OpArgMngr.add_workload('delete', a, np.array([0, 1]))
+    OpArgMngr.add_workload('delete', a, slice(1, 2))
+    OpArgMngr.add_workload('delete', a, slice(1, -2))
+    k = np.arange(10).reshape(2, 5)
+    OpArgMngr.add_workload('delete', k, slice(60, None), axis=1)
+
+
 def _add_workload_var(array_pool):
     OpArgMngr.add_workload('var', array_pool['4x1'])
     OpArgMngr.add_workload('var', np.array([np.float16(1.)]))
@@ -1590,6 +1611,7 @@ def _prepare_workloads():
     _add_workload_tile()
     _add_workload_transpose()
     _add_workload_unique()
+    _add_workload_delete()
     _add_workload_var(array_pool)
     _add_workload_zeros_like(array_pool)
     _add_workload_linalg_norm()
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 37cdaae328ee..9f746d4dd47c 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -2832,6 +2832,81 @@ def hybrid_forward(self, F, x):
             assert same(ret_mx.asnumpy(), ret_np)
 
 
+@with_seed()
+@use_np
+def test_np_delete():
+    class TestDelete(HybridBlock):
+        def __init__(self, obj, axis=None):
+            super(TestDelete, self).__init__()
+            self._obj = obj
+            self._axis = axis
+
+        def hybrid_forward(self, F, a):
+            return F.np.delete(a, self._obj, axis=self._axis)
+    
+    def GetSize(shp):
+        if len(shp) == 0:
+            return 0
+        else:
+            res = 1
+            shp_list = list(shp)
+            for x in shp:
+                res *= x
+            return res
+
+    def GetDimSize(shp, axis):
+        if axis is None:
+            return GetSize(shp)
+        shp_list = list(shp)
+        return shp_list[axis]
+
+    shape = [(), (0, ), (1, ), (2, 3), (2, 1, 4, 5)]
+    config = []
+    for shp in shape:
+        for ax in range(-1 * len(shp), len(shp), 2):
+            #test slice
+            for st in [-5, -2, 0, 2, 5, None]:
+                for ed in [-5, -2, 0, 2, 5, None]:
+                    for stp in [-5, -2, 2, 5, None]:
+                        config.append(tuple([shp, slice(st, ed, stp), None]))
+                        config.append(tuple([shp, slice(st, ed, stp), ax]))
+            #test iteger
+            for idx in range(-1 * GetDimSize(shp, ax), GetDimSize(shp, ax)):
+                config.append(tuple([shp, idx, ax]))
+            #test ndarray indices
+            idx =  _np.random.randint(-1 * shp[ax], shp[ax] + 1, size = (4)).tolist()
+            config.append(tuple([shp, idx, ax]))
+
+    for arr_shape, obj, axis in config:
+        for objtype in ['int32', 'int64']:
+            if type(obj) == list:
+                obj_mxnp = np.array(obj, dtype=objtype)
+                obj_onp = _np.array(obj, dtype=objtype)
+            elif type(obj) == slice:
+                obj_mxnp = obj
+                obj_onp = obj
+            else:
+                obj_mxnp = (_np.int32(obj) if objtype == 'int32' else _np.int64(obj)) 
+                obj_onp = (_np.int32(obj) if objtype == 'int32' else _np.int64(obj)) 
+            test_delete = TestDelete(obj=obj_mxnp, axis=axis)
+
+            a = mx.nd.random.uniform(-1.0, 1.0, shape=arr_shape).as_np_ndarray()
+            a.attach_grad()
+            expected_ret = _np.delete(a.asnumpy(), obj_onp, axis=axis)
+
+            with mx.autograd.record():
+                y = test_delete(a)
+
+            assert y.shape == expected_ret.shape
+            assert_almost_equal(y.asnumpy(), expected_ret, rtol=1e-3, atol=1e-5)
+
+            #test imperative
+            mx_out = np.delete(a, obj_mxnp, axis=axis)
+            np_out = _np.delete(a.asnumpy(), obj_onp, axis=axis)
+
+            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+
+
 @with_seed()
 @use_np
 def test_np_argmin_argmax():

From 6f95702613dd95aa4a1b467b938d79c4d13bee96 Mon Sep 17 00:00:00 2001
From: Iblis Lin <iblis@hs.ntnu.edu.tw>
Date: Sat, 28 Dec 2019 17:10:46 +0800
Subject: [PATCH 745/813] julia: porting `context.empty_cache` (#17172)

MXNET-1441 #close
---
 julia/src/MXNet.jl   |  3 ++-
 julia/src/context.jl | 13 +++++++++++++
 2 files changed, 15 insertions(+), 1 deletion(-)

diff --git a/julia/src/MXNet.jl b/julia/src/MXNet.jl
index 4c50d7321158..a322da2fc024 100644
--- a/julia/src/MXNet.jl
+++ b/julia/src/MXNet.jl
@@ -84,7 +84,8 @@ export Context,
        current_context,
        @context,
        @cpu,
-       @gpu
+       @gpu,
+       empty_cache
 
 # model.jl
 export AbstractModel,
diff --git a/julia/src/context.jl b/julia/src/context.jl
index bb80e0728724..aac2ab2ce233 100644
--- a/julia/src/context.jl
+++ b/julia/src/context.jl
@@ -147,6 +147,19 @@ function num_gpus()
   n[]
 end
 
+"""
+    empty_cache(ctx::Context = current_context())
+
+Empties the memory cache for the current contexts device.
+MXNet utilizes a memory pool to avoid excessive allocations.
+Calling empty_cache will empty the memory pool of the contexts
+device. This will only free the memory of the unreferenced data.
+"""
+function empty_cache(ctx::Context = current_context())
+  @mxcall :MXStorageEmptyCache (Cint, Cint) ctx.device_type ctx.device_id
+  ctx
+end
+
 """
     gpu_memory_info(dev_id = 0)::Tuple{UInt64,UInt64}
 

From ffeebe46ab5efa8f713161ed466c47f9609fa8d6 Mon Sep 17 00:00:00 2001
From: Xinyu Chen <xinyu1.chen@intel.com>
Date: Mon, 30 Dec 2019 08:57:46 +0800
Subject: [PATCH 746/813] fix int8 add ut (#17166)

---
 tests/python/quantization/test_quantization.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/tests/python/quantization/test_quantization.py b/tests/python/quantization/test_quantization.py
index 0c40f32d1666..8f433524b325 100644
--- a/tests/python/quantization/test_quantization.py
+++ b/tests/python/quantization/test_quantization.py
@@ -328,12 +328,11 @@ def check_quantized_elemwise_add(data_shape, qtype):
         elemwise_add_int8_exe.arg_dict[qarg_names[4]][:] = data_low
         elemwise_add_int8_exe.arg_dict[qarg_names[5]][:] = data_high
         qoutput, min_range, max_range = elemwise_add_int8_exe.forward()
-        min_val = min_range.asnumpy().tolist()[0]
-        max_val = max_range.asnumpy().tolist()[0]
 
-        fp32_rslt = output.asnumpy()
-        int8_rslt = qoutput.asnumpy()*max_val/0x7fffffff
-        assert_almost_equal(int8_rslt, int8_rslt, atol = 1e-4)
+        int8_rslt = qoutput.astype(output.dtype)*max_range/0x7fffffff
+        diff = mx.nd.abs(output - int8_rslt)
+        cond = mx.nd.lesser(2, diff).sum().asscalar()
+        assert cond == 0
 
     for qtype in ['int8', 'uint8']:
         check_quantized_elemwise_add((4, 6), qtype)

From 230ceee5d9e0e02e58be69dad1c4ffdadbaa1bd9 Mon Sep 17 00:00:00 2001
From: Leonard Lausen <lausen@amazon.com>
Date: Mon, 30 Dec 2019 09:37:43 +0000
Subject: [PATCH 747/813] Switch to modern CMake CUDA handling (#17031)

Introduce unified MXNET_CUDA_ARCH option to specify cuda architectures.
Previously cuda architecture setting was partially broken and different options
were applied to different parts of the build (CUDA_ARCH_NAME CUDA_ARCH_BIN
CUDA_ARCH_PTX and CUDA_ARCH_LIST).

Include FindCUDAToolkit from CMake 3.17, which replaces the deprecated FindCUDA
functionality for finding the cuda toolkit include directories and libraries.
---
 3rdparty/mshadow/cmake/Cuda.cmake         | 324 ---------
 3rdparty/mshadow/cmake/Utils.cmake        | 398 -----------
 3rdparty/mshadow/cmake/mshadow.cmake      |  91 ---
 3rdparty/mshadow/cmake/mshadowUtils.cmake |   2 -
 CMakeLists.txt                            | 218 +++---
 ci/build_windows.py                       |   8 +-
 ci/docker/install/ubuntu_core.sh          |   2 +-
 ci/docker/runtime_functions.sh            |  17 +-
 cmake/BuildTVM.cmake                      |  15 +
 cmake/FirstClassLangCuda.cmake            | 249 -------
 cmake/Modules/FindCUDAToolkit.cmake       | 833 ++++++++++++++++++++++
 contrib/tvmop/compile.py                  |   5 +
 12 files changed, 943 insertions(+), 1219 deletions(-)
 delete mode 100644 3rdparty/mshadow/cmake/Cuda.cmake
 delete mode 100644 3rdparty/mshadow/cmake/Utils.cmake
 delete mode 100644 3rdparty/mshadow/cmake/mshadow.cmake
 delete mode 100644 3rdparty/mshadow/cmake/mshadowUtils.cmake
 delete mode 100644 cmake/FirstClassLangCuda.cmake
 create mode 100644 cmake/Modules/FindCUDAToolkit.cmake

diff --git a/3rdparty/mshadow/cmake/Cuda.cmake b/3rdparty/mshadow/cmake/Cuda.cmake
deleted file mode 100644
index bc09a3905076..000000000000
--- a/3rdparty/mshadow/cmake/Cuda.cmake
+++ /dev/null
@@ -1,324 +0,0 @@
-if(NOT USE_CUDA)
-  return()
-endif()
-
-include(CheckCXXCompilerFlag)
-check_cxx_compiler_flag("-std=c++11"   SUPPORT_CXX11)
-
-################################################################################################
-# A function for automatic detection of GPUs installed  (if autodetection is enabled)
-# Usage:
-#   mshadow_detect_installed_gpus(out_variable)
-function(mshadow_detect_installed_gpus out_variable)
-set(CUDA_gpu_detect_output "")
-  if(NOT CUDA_gpu_detect_output)
-    message(STATUS "Running GPU architecture autodetection")
-    set(__cufile ${PROJECT_BINARY_DIR}/detect_cuda_archs.cu)
-
-    file(WRITE ${__cufile} ""
-      "#include <cstdio>\n"
-      "#include <iostream>\n"
-      "using namespace std;\n"
-      "int main()\n"
-      "{\n"
-      "  int count = 0;\n"
-      "  if (cudaSuccess != cudaGetDeviceCount(&count)) { return -1; }\n"
-      "  if (count == 0) { cerr << \"No cuda devices detected\" << endl; return -1; }\n"
-      "  for (int device = 0; device < count; ++device)\n"
-      "  {\n"
-      "    cudaDeviceProp prop;\n"
-      "    if (cudaSuccess == cudaGetDeviceProperties(&prop, device))\n"
-      "      std::printf(\"%d.%d \", prop.major, prop.minor);\n"
-      "  }\n"
-      "  return 0;\n"
-      "}\n")
-    if(MSVC)
-      #find vcvarsall.bat and run it building msvc environment
-      get_filename_component(MY_COMPILER_DIR ${CMAKE_CXX_COMPILER} DIRECTORY)
-      find_file(MY_VCVARSALL_BAT vcvarsall.bat "${MY_COMPILER_DIR}/.." "${MY_COMPILER_DIR}/../..")
-      execute_process(COMMAND ${MY_VCVARSALL_BAT} && ${CUDA_NVCC_EXECUTABLE} -arch sm_30 --run  ${__cufile}
-                      WORKING_DIRECTORY "${PROJECT_BINARY_DIR}/CMakeFiles/"
-                      RESULT_VARIABLE __nvcc_res OUTPUT_VARIABLE __nvcc_out
-                      OUTPUT_STRIP_TRAILING_WHITESPACE)
-    else()
-      if(CUDA_LIBRARY_PATH)
-        set(CUDA_LINK_LIBRARY_PATH "-L${CUDA_LIBRARY_PATH}")
-      endif()
-      execute_process(COMMAND ${CUDA_NVCC_EXECUTABLE} -arch sm_30 --run ${__cufile} ${CUDA_LINK_LIBRARY_PATH}
-                      WORKING_DIRECTORY "${PROJECT_BINARY_DIR}/CMakeFiles/"
-                      RESULT_VARIABLE __nvcc_res OUTPUT_VARIABLE __nvcc_out
-                      OUTPUT_STRIP_TRAILING_WHITESPACE)
-    endif()
-    if(__nvcc_res EQUAL 0)
-      # nvcc outputs text containing line breaks when building with MSVC.
-      # The line below prevents CMake from inserting a variable with line
-      # breaks in the cache
-      message(STATUS "Found CUDA arch ${__nvcc_out}")
-      string(REGEX MATCH "([1-9].[0-9])" __nvcc_out "${__nvcc_out}")
-      string(REPLACE "2.1" "2.1(2.0)" __nvcc_out "${__nvcc_out}")
-      set(CUDA_gpu_detect_output ${__nvcc_out} CACHE INTERNAL "Returned GPU architetures from mshadow_detect_gpus tool" FORCE)
-    else()
-      message(WARNING "Running GPU detection script with nvcc failed: ${__nvcc_out}")
-    endif()
-  endif()
-
-  if(NOT CUDA_gpu_detect_output)
-    message(WARNING "Automatic GPU detection failed. Building for all known architectures (${mshadow_known_gpu_archs}).")
-    set(${out_variable} ${mshadow_known_gpu_archs} PARENT_SCOPE)
-  else()
-    set(${out_variable} ${CUDA_gpu_detect_output} PARENT_SCOPE)
-  endif()
-endfunction()
-
-
-################################################################################################
-# Function for selecting GPU arch flags for nvcc based on CUDA_ARCH_NAME
-# Usage:
-#   mshadow_select_nvcc_arch_flags(out_variable)
-function(mshadow_select_nvcc_arch_flags out_variable)
-  # List of arch names
-  set(__archs_names "Fermi" "Kepler" "Maxwell" "Pascal" "Volta" "All" "Manual")
-  set(__archs_name_default "All")
-  if(NOT CMAKE_CROSSCOMPILING)
-    list(APPEND __archs_names "Auto")
-    set(__archs_name_default "Auto")
-  endif()
-
-  # set CUDA_ARCH_NAME strings (so it will be seen as dropbox in CMake-Gui)
-  set(CUDA_ARCH_NAME ${__archs_name_default} CACHE STRING "Select target NVIDIA GPU achitecture.")
-  set_property( CACHE CUDA_ARCH_NAME PROPERTY STRINGS "" ${__archs_names} )
-  mark_as_advanced(CUDA_ARCH_NAME)
-
-  # verify CUDA_ARCH_NAME value
-  if(NOT ";${__archs_names};" MATCHES ";${CUDA_ARCH_NAME};")
-    string(REPLACE ";" ", " __archs_names "${__archs_names}")
-    message(FATAL_ERROR "Only ${__archs_names} architeture names are supported.")
-  endif()
-
-  if(${CUDA_ARCH_NAME} STREQUAL "Manual")
-    set(CUDA_ARCH_BIN ${mshadow_known_gpu_archs} CACHE STRING "Specify 'real' GPU architectures to build binaries for, BIN(PTX) format is supported")
-    set(CUDA_ARCH_PTX "50"                     CACHE STRING "Specify 'virtual' PTX architectures to build PTX intermediate code for")
-    mark_as_advanced(CUDA_ARCH_BIN CUDA_ARCH_PTX)
-  else()
-    unset(CUDA_ARCH_BIN CACHE)
-    unset(CUDA_ARCH_PTX CACHE)
-  endif()
-
-  if(${CUDA_ARCH_NAME} STREQUAL "Fermi")
-    set(__cuda_arch_bin "20 21(20)")
-  elseif(${CUDA_ARCH_NAME} STREQUAL "Kepler")
-    set(__cuda_arch_bin "30 35")
-  elseif(${CUDA_ARCH_NAME} STREQUAL "Maxwell")
-    set(__cuda_arch_bin "50")
-  elseif(${CUDA_ARCH_NAME} STREQUAL "Pascal")
-    set(__cuda_arch_bin "60 61")
-  elseif(${CUDA_ARCH_NAME} STREQUAL "Volta")
-    set(__cuda_arch_bin "70")
-  elseif(${CUDA_ARCH_NAME} STREQUAL "All")
-    set(__cuda_arch_bin ${mshadow_known_gpu_archs})
-  elseif(${CUDA_ARCH_NAME} STREQUAL "Auto")
-    mshadow_detect_installed_gpus(__cuda_arch_bin)
-  else()  # (${CUDA_ARCH_NAME} STREQUAL "Manual")
-    set(__cuda_arch_bin ${CUDA_ARCH_BIN})
-  endif()
-
-  # remove dots and convert to lists
-  string(REGEX REPLACE "\\." "" __cuda_arch_bin "${__cuda_arch_bin}")
-  string(REGEX REPLACE "\\." "" __cuda_arch_ptx "${CUDA_ARCH_PTX}")
-  string(REGEX MATCHALL "[0-9()]+" __cuda_arch_bin "${__cuda_arch_bin}")
-  string(REGEX MATCHALL "[0-9]+"   __cuda_arch_ptx "${__cuda_arch_ptx}")
-  mshadow_list_unique(__cuda_arch_bin __cuda_arch_ptx)
-
-  set(__nvcc_flags "")
-  set(__nvcc_archs_readable "")
-
-  # Tell NVCC to add binaries for the specified GPUs
-  foreach(__arch ${__cuda_arch_bin})
-    if(__arch MATCHES "([0-9]+)\\(([0-9]+)\\)")
-      # User explicitly specified PTX for the concrete BIN
-      list(APPEND __nvcc_flags -gencode arch=compute_${CMAKE_MATCH_2},code=sm_${CMAKE_MATCH_1})
-      list(APPEND __nvcc_archs_readable sm_${CMAKE_MATCH_1})
-    else()
-      # User didn't explicitly specify PTX for the concrete BIN, we assume PTX=BIN
-      list(APPEND __nvcc_flags -gencode arch=compute_${__arch},code=sm_${__arch})
-      list(APPEND __nvcc_archs_readable sm_${__arch})
-    endif()
-  endforeach()
-
-  # Tell NVCC to add PTX intermediate code for the specified architectures
-  foreach(__arch ${__cuda_arch_ptx})
-    list(APPEND __nvcc_flags -gencode arch=compute_${__arch},code=compute_${__arch})
-    list(APPEND __nvcc_archs_readable compute_${__arch})
-  endforeach()
-
-  string(REPLACE ";" " " __nvcc_archs_readable "${__nvcc_archs_readable}")
-  set(${out_variable}          ${__nvcc_flags}          PARENT_SCOPE)
-  set(${out_variable}_readable ${__nvcc_archs_readable} PARENT_SCOPE)
-endfunction()
-
-################################################################################################
-# Short command for cuda comnpilation
-# Usage:
-#   mshadow_cuda_compile(<objlist_variable> <cuda_files>)
-macro(mshadow_cuda_compile objlist_variable)
-  foreach(var CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_RELEASE CMAKE_CXX_FLAGS_DEBUG)
-    set(${var}_backup_in_cuda_compile_ "${${var}}")
-
-    # we remove /EHa as it generates warnings under windows
-    string(REPLACE "/EHa" "" ${var} "${${var}}")
-
-  endforeach()
-  if(UNIX OR APPLE)
-    list(APPEND CUDA_NVCC_FLAGS -Xcompiler -fPIC)
-  endif()
-
-  if(APPLE)
-    list(APPEND CUDA_NVCC_FLAGS -Xcompiler -Wno-unused-function)
-  endif()
-
-  set(CUDA_NVCC_FLAGS_DEBUG "${CUDA_NVCC_FLAGS_DEBUG} -G")
-
-  if(MSVC)
-    # disable noisy warnings:
-    # 4819: The file contains a character that cannot be represented in the current code page (number).
-    list(APPEND CUDA_NVCC_FLAGS -Xcompiler "/wd4819")
-    foreach(flag_var
-        CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_DEBUG CMAKE_CXX_FLAGS_RELEASE
-        CMAKE_CXX_FLAGS_MINSIZEREL CMAKE_CXX_FLAGS_RELWITHDEBINFO)
-      if(${flag_var} MATCHES "/MD")
-        string(REGEX REPLACE "/MD" "/MT" ${flag_var} "${${flag_var}}")
-      endif(${flag_var} MATCHES "/MD")
-    endforeach(flag_var)
-  endif()
-
-  # If the build system is a container, make sure the nvcc intermediate files
-  # go into the build output area rather than in /tmp, which may run out of space
-  if(IS_CONTAINER_BUILD)
-    set(CUDA_NVCC_INTERMEDIATE_DIR "${CMAKE_CURRENT_BINARY_DIR}")
-    message(STATUS "Container build enabled, so nvcc intermediate files in: ${CUDA_NVCC_INTERMEDIATE_DIR}")
-    list(APPEND CUDA_NVCC_FLAGS "--keep --keep-dir ${CUDA_NVCC_INTERMEDIATE_DIR}")
-  endif()
-
-  cuda_compile(cuda_objcs ${ARGN})
-
-  foreach(var CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_RELEASE CMAKE_CXX_FLAGS_DEBUG)
-    set(${var} "${${var}_backup_in_cuda_compile_}")
-    unset(${var}_backup_in_cuda_compile_)
-  endforeach()
-
-  set(${objlist_variable} ${cuda_objcs})
-endmacro()
-
-################################################################################################
-# Short command for cuDNN detection. Believe it soon will be a part of CUDA toolkit distribution.
-# That's why not FindcuDNN.cmake file, but just the macro
-# Usage:
-#   detect_cuDNN()
-function(detect_cuDNN)
-  set(CUDNN_ROOT "" CACHE PATH "CUDNN root folder")
-
-  find_path(CUDNN_INCLUDE cudnn.h
-            PATHS ${CUDNN_ROOT} $ENV{CUDNN_ROOT} ${CUDA_TOOLKIT_INCLUDE}
-            DOC "Path to cuDNN include directory." )
-
-  get_filename_component(__libpath_hist ${CUDA_CUDART_LIBRARY} PATH)
-  find_library(CUDNN_LIBRARY NAMES libcudnn.so cudnn.lib # libcudnn_static.a
-                             PATHS ${CUDNN_ROOT} $ENV{CUDNN_ROOT} ${CUDNN_INCLUDE} ${__libpath_hist}
-                             DOC "Path to cuDNN library.")
-
-  if(CUDNN_INCLUDE AND CUDNN_LIBRARY)
-    set(HAVE_CUDNN  TRUE PARENT_SCOPE)
-    set(CUDNN_FOUND TRUE PARENT_SCOPE)
-
-    mark_as_advanced(CUDNN_INCLUDE CUDNN_LIBRARY CUDNN_ROOT)
-    message(STATUS "Found cuDNN (include: ${CUDNN_INCLUDE}, library: ${CUDNN_LIBRARY})")
-  endif()
-endfunction()
-
-
-################################################################################################
-###  Non macro section
-################################################################################################
-
-# Try to prime CUDA_TOOLKIT_ROOT_DIR by looking for libcudart.so
-if(NOT CUDA_TOOLKIT_ROOT_DIR)
-  find_library(CUDA_LIBRARY_PATH libcudart.so PATHS ENV LD_LIBRARY_PATH PATH_SUFFIXES lib lib64)
-  if(CUDA_LIBRARY_PATH)
-    get_filename_component(CUDA_LIBRARY_PATH ${CUDA_LIBRARY_PATH} DIRECTORY)
-    set(CUDA_TOOLKIT_ROOT_DIR "${CUDA_LIBRARY_PATH}/..")
-  endif()
-endif()
-
-find_package(CUDA 5.5 QUIET REQUIRED)
-find_cuda_helper_libs(curand)  # cmake 2.8.7 compartibility which doesn't search for curand
-
-if(NOT CUDA_FOUND)
-  return()
-endif()
-
-set(HAVE_CUDA TRUE)
-message(STATUS "CUDA detected: " ${CUDA_VERSION})
-include_directories(SYSTEM ${CUDA_INCLUDE_DIRS})
-list(APPEND mshadow_LINKER_LIBS ${CUDA_CUDART_LIBRARY}
-                              ${CUDA_curand_LIBRARY} ${CUDA_CUBLAS_LIBRARIES})
-
-# Known NVIDIA GPU achitectures mshadow can be compiled for.
-# This list will be used for CUDA_ARCH_NAME = All option
-if(CUDA_ARCH_ALL)
-  set(mshadow_known_gpu_archs "${CUDA_ARCH_ALL}")
-else()
-  if(${CUDA_VERSION} EQUAL 9.0 OR ${CUDA_VERSION} GREATER 9.0)
-    set(mshadow_known_gpu_archs "30 35 50 52 60 61 70")
-  elseif(${CUDA_VERSION} EQUAL 8.0 OR ${CUDA_VERSION} GREATER 8.0)
-    set(mshadow_known_gpu_archs "30 35 50 52 60 61")
-  else()
-    set(mshadow_known_gpu_archs "30 35 50 52")
-  endif()
-endif()
-
-# cudnn detection
-if(USE_CUDNN)
-  detect_cuDNN()
-  if(HAVE_CUDNN)
-    add_definitions(-DUSE_CUDNN)
-    include_directories(SYSTEM ${CUDNN_INCLUDE})
-    list(APPEND mshadow_LINKER_LIBS ${CUDNN_LIBRARY})
-  endif()
-endif()
-
-# setting nvcc arch flags
-mshadow_select_nvcc_arch_flags(NVCC_FLAGS_EXTRA)
-list(APPEND CUDA_NVCC_FLAGS ${NVCC_FLAGS_EXTRA})
-message(STATUS "Added CUDA NVCC flags for: ${NVCC_FLAGS_EXTRA_readable}")
-
-# Boost 1.55 workaround, see https://svn.boost.org/trac/boost/ticket/9392 or
-# https://github.com/ComputationalRadiationPhysics/picongpu/blob/master/src/picongpu/CMakeLists.txt
-if(Boost_VERSION EQUAL 105500)
-  message(STATUS "Cuda + Boost 1.55: Applying noinline work around")
-  # avoid warning for CMake >= 2.8.12
-  set(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} \"-DBOOST_NOINLINE=__attribute__((noinline))\" ")
-endif()
-
-# disable some nvcc diagnostic that apears in boost, glog, glags, opencv, etc.
-foreach(diag cc_clobber_ignored integer_sign_change useless_using_declaration set_but_not_used)
-  list(APPEND CUDA_NVCC_FLAGS -Xcudafe --diag_suppress=${diag})
-endforeach()
-
-# setting default testing device
-if(NOT CUDA_TEST_DEVICE)
-  set(CUDA_TEST_DEVICE -1)
-endif()
-
-mark_as_advanced(CUDA_BUILD_CUBIN CUDA_BUILD_EMULATION CUDA_VERBOSE_BUILD)
-mark_as_advanced(CUDA_SDK_ROOT_DIR CUDA_SEPARABLE_COMPILATION)
-
-# Handle clang/libc++ issue
-if(APPLE)
-  mshadow_detect_darwin_version(OSX_VERSION)
-
-  # OSX 10.9 and higher uses clang/libc++ by default which is incompartible with old CUDA toolkits
-  if(OSX_VERSION VERSION_GREATER 10.8)
-    # enabled by default if and only if CUDA version is less than 7.0
-    mshadow_option(USE_libstdcpp "Use libstdc++ instead of libc++" (CUDA_VERSION VERSION_LESS 7.0))
-  endif()
-endif()
diff --git a/3rdparty/mshadow/cmake/Utils.cmake b/3rdparty/mshadow/cmake/Utils.cmake
deleted file mode 100644
index dc464f0092f5..000000000000
--- a/3rdparty/mshadow/cmake/Utils.cmake
+++ /dev/null
@@ -1,398 +0,0 @@
-################################################################################################
-# Command alias for debugging messages
-# Usage:
-#   dmsg(<message>)
-function(dmsg)
-  message(STATUS ${ARGN})
-endfunction()
-
-################################################################################################
-# Removes duplicates from list(s)
-# Usage:
-#   mshadow_list_unique(<list_variable> [<list_variable>] [...])
-macro(mshadow_list_unique)
-  foreach(__lst ${ARGN})
-    if(${__lst})
-      list(REMOVE_DUPLICATES ${__lst})
-    endif()
-  endforeach()
-endmacro()
-
-################################################################################################
-# Clears variables from list
-# Usage:
-#   mshadow_clear_vars(<variables_list>)
-macro(mshadow_clear_vars)
-  foreach(_var ${ARGN})
-    unset(${_var})
-  endforeach()
-endmacro()
-
-################################################################################################
-# Removes duplicates from string
-# Usage:
-#   mshadow_string_unique(<string_variable>)
-function(mshadow_string_unique __string)
-  if(${__string})
-    set(__list ${${__string}})
-    separate_arguments(__list)
-    list(REMOVE_DUPLICATES __list)
-    foreach(__e ${__list})
-      set(__str "${__str} ${__e}")
-    endforeach()
-    set(${__string} ${__str} PARENT_SCOPE)
-  endif()
-endfunction()
-
-################################################################################################
-# Prints list element per line
-# Usage:
-#   mshadow_print_list(<list>)
-function(mshadow_print_list)
-  foreach(e ${ARGN})
-    message(STATUS ${e})
-  endforeach()
-endfunction()
-
-################################################################################################
-# Function merging lists of compiler flags to single string.
-# Usage:
-#   mshadow_merge_flag_lists(out_variable <list1> [<list2>] [<list3>] ...)
-function(mshadow_merge_flag_lists out_var)
-  set(__result "")
-  foreach(__list ${ARGN})
-    foreach(__flag ${${__list}})
-      string(STRIP ${__flag} __flag)
-      set(__result "${__result} ${__flag}")
-    endforeach()
-  endforeach()
-  string(STRIP ${__result} __result)
-  set(${out_var} ${__result} PARENT_SCOPE)
-endfunction()
-
-################################################################################################
-# Converts all paths in list to absolute
-# Usage:
-#   mshadow_convert_absolute_paths(<list_variable>)
-function(mshadow_convert_absolute_paths variable)
-  set(__dlist "")
-  foreach(__s ${${variable}})
-    get_filename_component(__abspath ${__s} ABSOLUTE)
-    list(APPEND __list ${__abspath})
-  endforeach()
-  set(${variable} ${__list} PARENT_SCOPE)
-endfunction()
-
-################################################################################################
-# Reads set of version defines from the header file
-# Usage:
-#   mshadow_parse_header(<file> <define1> <define2> <define3> ..)
-macro(mshadow_parse_header FILENAME FILE_VAR)
-  set(vars_regex "")
-  set(__parnet_scope OFF)
-  set(__add_cache OFF)
-  foreach(name ${ARGN})
-    if("${name}" STREQUAL "PARENT_SCOPE")
-      set(__parnet_scope ON)
-    elseif("${name}" STREQUAL "CACHE")
-      set(__add_cache ON)
-    elseif(vars_regex)
-      set(vars_regex "${vars_regex}|${name}")
-    else()
-      set(vars_regex "${name}")
-    endif()
-  endforeach()
-  if(EXISTS "${FILENAME}")
-    file(STRINGS "${FILENAME}" ${FILE_VAR} REGEX "#define[ \t]+(${vars_regex})[ \t]+[0-9]+" )
-  else()
-    unset(${FILE_VAR})
-  endif()
-  foreach(name ${ARGN})
-    if(NOT "${name}" STREQUAL "PARENT_SCOPE" AND NOT "${name}" STREQUAL "CACHE")
-      if(${FILE_VAR})
-        if(${FILE_VAR} MATCHES ".+[ \t]${name}[ \t]+([0-9]+).*")
-          string(REGEX REPLACE ".+[ \t]${name}[ \t]+([0-9]+).*" "\\1" ${name} "${${FILE_VAR}}")
-        else()
-          set(${name} "")
-        endif()
-        if(__add_cache)
-          set(${name} ${${name}} CACHE INTERNAL "${name} parsed from ${FILENAME}" FORCE)
-        elseif(__parnet_scope)
-          set(${name} "${${name}}" PARENT_SCOPE)
-        endif()
-      else()
-        unset(${name} CACHE)
-      endif()
-    endif()
-  endforeach()
-endmacro()
-
-################################################################################################
-# Reads single version define from the header file and parses it
-# Usage:
-#   mshadow_parse_header_single_define(<library_name> <file> <define_name>)
-function(mshadow_parse_header_single_define LIBNAME HDR_PATH VARNAME)
-  set(${LIBNAME}_H "")
-  if(EXISTS "${HDR_PATH}")
-    file(STRINGS "${HDR_PATH}" ${LIBNAME}_H REGEX "^#define[ \t]+${VARNAME}[ \t]+\"[^\"]*\".*$" LIMIT_COUNT 1)
-  endif()
-
-  if(${LIBNAME}_H)
-    string(REGEX REPLACE "^.*[ \t]${VARNAME}[ \t]+\"([0-9]+).*$" "\\1" ${LIBNAME}_VERSION_MAJOR "${${LIBNAME}_H}")
-    string(REGEX REPLACE "^.*[ \t]${VARNAME}[ \t]+\"[0-9]+\\.([0-9]+).*$" "\\1" ${LIBNAME}_VERSION_MINOR  "${${LIBNAME}_H}")
-    string(REGEX REPLACE "^.*[ \t]${VARNAME}[ \t]+\"[0-9]+\\.[0-9]+\\.([0-9]+).*$" "\\1" ${LIBNAME}_VERSION_PATCH "${${LIBNAME}_H}")
-    set(${LIBNAME}_VERSION_MAJOR ${${LIBNAME}_VERSION_MAJOR} ${ARGN} PARENT_SCOPE)
-    set(${LIBNAME}_VERSION_MINOR ${${LIBNAME}_VERSION_MINOR} ${ARGN} PARENT_SCOPE)
-    set(${LIBNAME}_VERSION_PATCH ${${LIBNAME}_VERSION_PATCH} ${ARGN} PARENT_SCOPE)
-    set(${LIBNAME}_VERSION_STRING "${${LIBNAME}_VERSION_MAJOR}.${${LIBNAME}_VERSION_MINOR}.${${LIBNAME}_VERSION_PATCH}" PARENT_SCOPE)
-
-    # append a TWEAK version if it exists:
-    set(${LIBNAME}_VERSION_TWEAK "")
-    if("${${LIBNAME}_H}" MATCHES "^.*[ \t]${VARNAME}[ \t]+\"[0-9]+\\.[0-9]+\\.[0-9]+\\.([0-9]+).*$")
-      set(${LIBNAME}_VERSION_TWEAK "${CMAKE_MATCH_1}" ${ARGN} PARENT_SCOPE)
-    endif()
-    if(${LIBNAME}_VERSION_TWEAK)
-      set(${LIBNAME}_VERSION_STRING "${${LIBNAME}_VERSION_STRING}.${${LIBNAME}_VERSION_TWEAK}" ${ARGN} PARENT_SCOPE)
-    else()
-      set(${LIBNAME}_VERSION_STRING "${${LIBNAME}_VERSION_STRING}" ${ARGN} PARENT_SCOPE)
-    endif()
-  endif()
-endfunction()
-
-########################################################################################################
-# An option that the user can select. Can accept condition to control when option is available for user.
-# Usage:
-#   mshadow_option(<option_variable> "doc string" <initial value or boolean expression> [IF <condition>])
-function(mshadow_option variable description value)
-  set(__value ${value})
-  set(__condition "")
-  set(__varname "__value")
-  foreach(arg ${ARGN})
-    if(arg STREQUAL "IF" OR arg STREQUAL "if")
-      set(__varname "__condition")
-    else()
-      list(APPEND ${__varname} ${arg})
-    endif()
-  endforeach()
-  unset(__varname)
-  if("${__condition}" STREQUAL "")
-    set(__condition 2 GREATER 1)
-  endif()
-
-  if(${__condition})
-    if("${__value}" MATCHES ";")
-      if(${__value})
-        option(${variable} "${description}" ON)
-      else()
-        option(${variable} "${description}" OFF)
-      endif()
-    elseif(DEFINED ${__value})
-      if(${__value})
-        option(${variable} "${description}" ON)
-      else()
-        option(${variable} "${description}" OFF)
-      endif()
-    else()
-      option(${variable} "${description}" ${__value})
-    endif()
-  else()
-    unset(${variable} CACHE)
-  endif()
-endfunction()
-
-################################################################################################
-# Utility macro for comparing two lists. Used for CMake debugging purposes
-# Usage:
-#   mshadow_compare_lists(<list_variable> <list2_variable> [description])
-function(mshadow_compare_lists list1 list2 desc)
-  set(__list1 ${${list1}})
-  set(__list2 ${${list2}})
-  list(SORT __list1)
-  list(SORT __list2)
-  list(LENGTH __list1 __len1)
-  list(LENGTH __list2 __len2)
-
-  if(NOT ${__len1} EQUAL ${__len2})
-    message(FATAL_ERROR "Lists are not equal. ${__len1} != ${__len2}. ${desc}")
-  endif()
-
-  foreach(__i RANGE 1 ${__len1})
-    math(EXPR __index "${__i}- 1")
-    list(GET __list1 ${__index} __item1)
-    list(GET __list2 ${__index} __item2)
-    if(NOT ${__item1} STREQUAL ${__item2})
-      message(FATAL_ERROR "Lists are not equal. Differ at element ${__index}. ${desc}")
-    endif()
-  endforeach()
-endfunction()
-
-################################################################################################
-# Command for disabling warnings for different platforms (see below for gcc and VisualStudio)
-# Usage:
-#   mshadow_warnings_disable(<CMAKE_[C|CXX]_FLAGS[_CONFIGURATION]> -Wshadow /wd4996 ..,)
-macro(mshadow_warnings_disable)
-  set(_flag_vars "")
-  set(_msvc_warnings "")
-  set(_gxx_warnings "")
-
-  foreach(arg ${ARGN})
-    if(arg MATCHES "^CMAKE_")
-      list(APPEND _flag_vars ${arg})
-    elseif(arg MATCHES "^/wd")
-      list(APPEND _msvc_warnings ${arg})
-    elseif(arg MATCHES "^-W")
-      list(APPEND _gxx_warnings ${arg})
-    endif()
-  endforeach()
-
-  if(NOT _flag_vars)
-    set(_flag_vars CMAKE_C_FLAGS CMAKE_CXX_FLAGS)
-  endif()
-
-  if(MSVC AND _msvc_warnings)
-    foreach(var ${_flag_vars})
-      foreach(warning ${_msvc_warnings})
-        set(${var} "${${var}} ${warning}")
-      endforeach()
-    endforeach()
-  elseif((CMAKE_COMPILER_IS_GNUCXX OR CMAKE_COMPILER_IS_CLANGXX) AND _gxx_warnings)
-    foreach(var ${_flag_vars})
-      foreach(warning ${_gxx_warnings})
-        if(NOT warning MATCHES "^-Wno-")
-          string(REPLACE "${warning}" "" ${var} "${${var}}")
-          string(REPLACE "-W" "-Wno-" warning "${warning}")
-        endif()
-        set(${var} "${${var}} ${warning}")
-      endforeach()
-    endforeach()
-  endif()
-  mshadow_clear_vars(_flag_vars _msvc_warnings _gxx_warnings)
-endmacro()
-
-################################################################################################
-# Helper function get current definitions
-# Usage:
-#   mshadow_get_current_definitions(<definitions_variable>)
-function(mshadow_get_current_definitions definitions_var)
-  get_property(current_definitions DIRECTORY PROPERTY COMPILE_DEFINITIONS)
-  set(result "")
-
-  foreach(d ${current_definitions})
-    list(APPEND result -D${d})
-  endforeach()
-
-  mshadow_list_unique(result)
-  set(${definitions_var} ${result} PARENT_SCOPE)
-endfunction()
-
-################################################################################################
-# Helper function get current includes/definitions
-# Usage:
-#   mshadow_get_current_cflags(<cflagslist_variable>)
-function(mshadow_get_current_cflags cflags_var)
-  get_property(current_includes DIRECTORY PROPERTY INCLUDE_DIRECTORIES)
-  mshadow_convert_absolute_paths(current_includes)
-  mshadow_get_current_definitions(cflags)
-
-  foreach(i ${current_includes})
-    list(APPEND cflags "-I${i}")
-  endforeach()
-
-  mshadow_list_unique(cflags)
-  set(${cflags_var} ${cflags} PARENT_SCOPE)
-endfunction()
-
-################################################################################################
-# Helper function to parse current linker libs into link directories, libflags and osx frameworks
-# Usage:
-#   mshadow_parse_linker_libs(<mshadow_LINKER_LIBS_var> <directories_var> <libflags_var> <frameworks_var>)
-function(mshadow_parse_linker_libs mshadow_LINKER_LIBS_variable folders_var flags_var frameworks_var)
-
-  set(__unspec "")
-  set(__debug "")
-  set(__optimized "")
-  set(__framework "")
-  set(__varname "__unspec")
-
-  # split libs into debug, optimized, unspecified and frameworks
-  foreach(list_elem ${${mshadow_LINKER_LIBS_variable}})
-    if(list_elem STREQUAL "debug")
-      set(__varname "__debug")
-    elseif(list_elem STREQUAL "optimized")
-      set(__varname "__optimized")
-    elseif(list_elem MATCHES "^-framework[ \t]+([^ \t].*)")
-      list(APPEND __framework -framework ${CMAKE_MATCH_1})
-    else()
-      list(APPEND ${__varname} ${list_elem})
-      set(__varname "__unspec")
-    endif()
-  endforeach()
-
-  # attach debug or optimized libs to unspecified according to current configuration
-  if(CMAKE_BUILD_TYPE MATCHES "Debug")
-    set(__libs ${__unspec} ${__debug})
-  else()
-    set(__libs ${__unspec} ${__optimized})
-  endif()
-
-  set(libflags "")
-  set(folders "")
-
-  # convert linker libraries list to link flags
-  foreach(lib ${__libs})
-    if(TARGET ${lib})
-      list(APPEND folders $<TARGET_LINKER_FILE_DIR:${lib}>)
-      list(APPEND libflags -l${lib})
-    elseif(lib MATCHES "^-l.*")
-      list(APPEND libflags ${lib})
-    elseif(IS_ABSOLUTE ${lib})
-      get_filename_component(name_we ${lib} NAME_WE)
-      get_filename_component(folder  ${lib} PATH)
-
-      string(REGEX MATCH "^lib(.*)" __match ${name_we})
-      list(APPEND libflags -l${CMAKE_MATCH_1})
-      list(APPEND folders    ${folder})
-    else()
-      message(FATAL_ERROR "Logic error. Need to update cmake script")
-    endif()
-  endforeach()
-
-  mshadow_list_unique(libflags folders)
-
-  set(${folders_var} ${folders} PARENT_SCOPE)
-  set(${flags_var} ${libflags} PARENT_SCOPE)
-  set(${frameworks_var} ${__framework} PARENT_SCOPE)
-endfunction()
-
-################################################################################################
-# Helper function to detect Darwin version, i.e. 10.8, 10.9, 10.10, ....
-# Usage:
-#   mshadow_detect_darwin_version(<version_variable>)
-function(mshadow_detect_darwin_version output_var)
-  if(APPLE)
-    execute_process(COMMAND /usr/bin/sw_vers -productVersion
-                    RESULT_VARIABLE __sw_vers OUTPUT_VARIABLE __sw_vers_out
-                    ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
-
-    set(${output_var} ${__sw_vers_out} PARENT_SCOPE)
-  else()
-    set(${output_var} "" PARENT_SCOPE)
-  endif()
-endfunction()
-
-################################################################################################
-# Convenient command to setup source group for IDEs that support this feature (VS, XCode)
-# Usage:
-#   caffe_source_group(<group> GLOB[_RECURSE] <globbing_expression>)
-function(mshadow_source_group group)
-  cmake_parse_arguments(CAFFE_SOURCE_GROUP "" "" "GLOB;GLOB_RECURSE" ${ARGN})
-  if(CAFFE_SOURCE_GROUP_GLOB)
-    file(GLOB srcs1 ${CAFFE_SOURCE_GROUP_GLOB})
-    source_group(${group} FILES ${srcs1})
-  endif()
-
-  if(CAFFE_SOURCE_GROUP_GLOB_RECURSE)
-    file(GLOB_RECURSE srcs2 ${CAFFE_SOURCE_GROUP_GLOB_RECURSE})
-    source_group(${group} FILES ${srcs2})
-  endif()
-endfunction()
\ No newline at end of file
diff --git a/3rdparty/mshadow/cmake/mshadow.cmake b/3rdparty/mshadow/cmake/mshadow.cmake
deleted file mode 100644
index 1ef76988d8d0..000000000000
--- a/3rdparty/mshadow/cmake/mshadow.cmake
+++ /dev/null
@@ -1,91 +0,0 @@
-set(mshadow_LINKER_LIBS "")
-
-set(BLAS "Open" CACHE STRING "Selected BLAS library")
-set_property(CACHE BLAS PROPERTY STRINGS "Atlas;Open;MKL")
-
-if(DEFINED USE_BLAS)
-  set(BLAS "${USE_BLAS}") 
-else()
-  if(USE_MKL_IF_AVAILABLE)
-    if(NOT MKL_FOUND)
-      find_package(MKL)
-    endif()
-    if(MKL_FOUND)
-      set(BLAS "MKL")
-    endif()
-  endif()
-endif()
-
-if(BLAS STREQUAL "Atlas" OR BLAS STREQUAL "atlas")
-  find_package(Atlas REQUIRED)
-  include_directories(SYSTEM ${Atlas_INCLUDE_DIR})
-  list(APPEND mshadow_LINKER_LIBS ${Atlas_LIBRARIES})
-  add_definitions(-DMSHADOW_USE_CBLAS=1)
-  add_definitions(-DMSHADOW_USE_MKL=0)
-elseif(BLAS STREQUAL "Open" OR BLAS STREQUAL "open")
-  find_package(OpenBLAS REQUIRED)
-  include_directories(SYSTEM ${OpenBLAS_INCLUDE_DIR})
-  list(APPEND mshadow_LINKER_LIBS ${OpenBLAS_LIB})
-  add_definitions(-DMSHADOW_USE_CBLAS=1)
-  add_definitions(-DMSHADOW_USE_MKL=0)
-elseif(BLAS STREQUAL "MKL" OR BLAS STREQUAL "mkl")
-  find_package(MKL REQUIRED)
-  include_directories(SYSTEM ${MKL_INCLUDE_DIR})
-  list(APPEND mshadow_LINKER_LIBS ${MKL_LIBRARIES})
-  add_definitions(-DMSHADOW_USE_CBLAS=0)
-  add_definitions(-DMSHADOW_USE_MKL=1)
-elseif(BLAS STREQUAL "apple")
-  find_package(Accelerate REQUIRED)
-  include_directories(SYSTEM ${Accelerate_INCLUDE_DIR})
-  list(APPEND mshadow_LINKER_LIBS ${Accelerate_LIBRARIES})
-  add_definitions(-DMSHADOW_USE_MKL=0)
-  add_definitions(-DMSHADOW_USE_CBLAS=1)
-endif()
-
-if(SUPPORT_MSSE2)
-	add_definitions(-DMSHADOW_USE_SSE=1)
-else()
-	add_definitions(-DMSHADOW_USE_SSE=0)
-endif()
-
-if(NOT DEFINED SUPPORT_F16C AND NOT MSVC)
-    check_cxx_compiler_flag("-mf16c"     COMPILER_SUPPORT_MF16C)
-    if(CMAKE_SYSTEM_NAME STREQUAL "Linux")
-        execute_process(COMMAND cat /proc/cpuinfo
-                COMMAND grep flags
-                COMMAND grep f16c
-                OUTPUT_VARIABLE CPU_SUPPORT_F16C)
-    elseif(CMAKE_SYSTEM_NAME STREQUAL "Darwin")
-        execute_process(COMMAND sysctl -a
-                COMMAND grep machdep.cpu.features
-                COMMAND grep F16C
-                OUTPUT_VARIABLE CPU_SUPPORT_F16C)
-    endif()
-    if(NOT CPU_SUPPORT_F16C)
-        message("CPU does not support F16C instructions")
-    endif()
-    if(CPU_SUPPORT_F16C AND COMPILER_SUPPORT_MF16C)
-        set(SUPPORT_F16C TRUE)
-    endif()
-endif()
-
-if(SUPPORT_F16C)
-    set(CMAKE_CXX_FLAGS  "${CMAKE_CXX_FLAGS} -mf16c")
-else()
-    add_definitions(-DMSHADOW_USE_F16C=0)
-endif()
-
-if(USE_CUDA)
-	find_package(CUDA 5.5 QUIET)
-	find_cuda_helper_libs(curand)
-	if(NOT CUDA_FOUND)
-		message(FATAL_ERROR "-- CUDA is disabled.")
-	endif()
-	add_definitions(-DMSHADOW_USE_CUDA=1)
-	add_definitions(-DMSHADOW_FORCE_STREAM)
-	include_directories(SYSTEM ${CUDA_INCLUDE_DIRS})
-    list(APPEND mshadow_LINKER_LIBS ${CUDA_CUDART_LIBRARY}
-                              ${CUDA_curand_LIBRARY} ${CUDA_CUBLAS_LIBRARIES})
-else()
-  add_definitions(-DMSHADOW_USE_CUDA=0)
-endif()
diff --git a/3rdparty/mshadow/cmake/mshadowUtils.cmake b/3rdparty/mshadow/cmake/mshadowUtils.cmake
deleted file mode 100644
index d4b8bfc89b7a..000000000000
--- a/3rdparty/mshadow/cmake/mshadowUtils.cmake
+++ /dev/null
@@ -1,2 +0,0 @@
-include("${CMAKE_CURRENT_LIST_DIR}/Utils.cmake")
-
diff --git a/CMakeLists.txt b/CMakeLists.txt
index f0779abd06d4..6f3031502668 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -21,7 +21,14 @@ include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/Utils.cmake)
 include(CMakeDependentOption)
 #Some things have order. This must be put in front alone
 option(USE_CUDA "Build with CUDA support"   ON)
-option(USE_OLDCMAKECUDA "Build with old cmake cuda" OFF)
+set(MXNET_CUDA_ARCH "Auto" CACHE STRING "Target NVIDIA GPU achitecture.
+Format: Auto | Common | All | LIST(ARCH_AND_PTX ...)
+- \"Auto\" detects local machine GPU compute arch at runtime.
+- \"Common\" and \"All\" cover common and entire subsets of architectures
+- ARCH_AND_PTX : NAME | NUM.NUM | NUM.NUM(NUM.NUM) | NUM.NUM+PTX
+- NAME: Fermi Kepler Maxwell Kepler+Tegra Kepler+Tesla Maxwell+Tegra Pascal Volta Turing
+- NUM: Any number. Only those pairs are currently accepted by NVCC though:
+       2.0 2.1 3.0 3.2 3.5 3.7 5.0 5.2 5.3 6.0 6.2 7.0 7.2 7.5")
 option(USE_NCCL "Use NVidia NCCL with CUDA" OFF)
 option(USE_OPENCV "Build with OpenCV support" ON)
 option(USE_OPENMP "Build with Openmp support" ON)
@@ -71,28 +78,25 @@ if(USE_TVM_OP)
   add_definitions(-DMXNET_USE_TVM_OP=1)
 endif()
 
-if(USE_CUDA AND NOT USE_OLDCMAKECUDA)
-  message(STATUS "CMake version '${CMAKE_VERSION}' using generator '${CMAKE_GENERATOR}'")
-  if(
-      (
-        (${CMAKE_GENERATOR} MATCHES "Visual Studio.*")
-        OR (${CMAKE_GENERATOR} MATCHES "Xcode.*")
-        OR (${CMAKE_GENERATOR} STREQUAL "Unix Makefiles")
-      ) AND (
-        (${CMAKE_VERSION} VERSION_GREATER "3.9.0") OR (${CMAKE_VERSION} VERSION_EQUAL "3.9.0")
-      )
-    )
-    set(FIRST_CUDA TRUE)
-    project(mxnet C CXX CUDA)
-  else()
-    set(FIRST_CUDA FALSE)
-    set(USE_OLDCMAKECUDA TRUE)
-    project(mxnet C CXX)
+message(STATUS "CMake version '${CMAKE_VERSION}' using generator '${CMAKE_GENERATOR}'")
+project(mxnet C CXX)
+if(USE_CUDA)
+  cmake_minimum_required(VERSION 3.13.2)  # CUDA 10 (Turing) detection available starting 3.13.2
+  enable_language(CUDA)
+  set(CMAKE_CUDA_STANDARD 11)
+  include(CheckCXXCompilerFlag)
+  if(USE_CXX14_IF_AVAILABLE)
+    check_cxx_compiler_flag("-std=c++14" SUPPORT_CXX14)
+    if (SUPPORT_CXX14)
+      set(CMAKE_CUDA_STANDARD 14)
+    endif()
   endif()
-else()
-  project(mxnet C CXX)
+  set(CMAKE_CUDA_STANDARD_REQUIRED ON)
 endif()
 
+if(UNIX)
+  set(CMAKE_POSITION_INDEPENDENT_CODE ON)
+endif()
 
 if(MSVC)
   set(SYSTEM_ARCHITECTURE x86_64)
@@ -129,7 +133,7 @@ if(MSVC)
   endif()
   set(CMAKE_C_FLAGS "/MP")
   set(CMAKE_CXX_FLAGS "${CMAKE_C_FLAGS} /bigobj")
-else(MSVC)
+else()
   include(CheckCXXCompilerFlag)
   if(USE_CXX14_IF_AVAILABLE)
     check_cxx_compiler_flag("-std=c++14" SUPPORT_CXX14)
@@ -142,6 +146,7 @@ else(MSVC)
     check_cxx_compiler_flag("-msse3"     SUPPORT_MSSE3)
     check_cxx_compiler_flag("-msse2"     SUPPORT_MSSE2)
   else()
+    set(SUPPORT_MSSE3 FALSE)
     set(SUPPORT_MSSE2 FALSE)
   endif()
   # For cross complication, turn off flag if target device does not support it
@@ -158,7 +163,6 @@ else(MSVC)
   else()
     add_definitions(-DMSHADOW_USE_F16C=0)
   endif()
-  set(CMAKE_POSITION_INDEPENDENT_CODE ON)
   set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Wall -Wno-unknown-pragmas -Wno-sign-compare")
   if ("${CMAKE_CXX_COMPILER_ID}" MATCHES ".*Clang$")
     set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Wno-braced-scalar-init")
@@ -176,8 +180,12 @@ else(MSVC)
   endif()
   if(SUPPORT_MSSE3)
     set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -msse3")
+    add_definitions(-DMSHADOW_USE_SSE=1)
   elseif(SUPPORT_MSSE2)
     set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -msse2")
+    add_definitions(-DMSHADOW_USE_SSE=1)
+  else()
+    add_definitions(-DMSHADOW_USE_SSE=0)
   endif()
   set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${CMAKE_C_FLAGS}")
   if(SUPPORT_CXX14)
@@ -290,45 +298,6 @@ endif()
 include_directories(${CMAKE_CURRENT_SOURCE_DIR}/include)
 include_directories(${CMAKE_CURRENT_SOURCE_DIR}/src)
 
-if(USE_CUDA)
-  find_package(CUDA REQUIRED)
-  add_definitions(-DMSHADOW_USE_CUDA=1)
-  if(FIRST_CUDA AND (NOT USE_OLDCMAKECUDA))
-    if(NOT CUDA_TOOLSET)
-      set(CUDA_TOOLSET "${CUDA_VERSION_STRING}")
-    endif()
-  else()
-    set(FIRST_CUDA FALSE)
-  endif()
-  if(USE_NCCL)
-    find_package(NCCL)
-    if(NCCL_FOUND)
-      include_directories(${NCCL_INCLUDE_DIRS})
-      list(APPEND mxnet_LINKER_LIBS ${NCCL_LIBRARIES})
-    else()
-      message(WARNING "Could not find NCCL libraries")
-    endif()
-  endif()
-  if(UNIX)
-    find_package(NVTX)
-    if(NVTX_FOUND)
-      include_directories(${NVTX_INCLUDE_DIRS})
-      list(APPEND mxnet_LINKER_LIBS ${NVTX_LIBRARIES})
-      add_definitions(-DMXNET_USE_NVTX=1)
-    else()
-      message(WARNING "Could not find NVTX libraries")
-    endif()
-  endif()
-else()
-  add_definitions(-DMSHADOW_USE_CUDA=0)
-endif()
-
-if(NCCL_FOUND)
-  add_definitions(-DMXNET_USE_NCCL=1)
-else()
-  add_definitions(-DMXNET_USE_NCCL=0)
-endif()
-
 if (USE_INT64_TENSOR_SIZE)
   message(STATUS "Using 64-bit integer for tensor size")
   add_definitions(-DMSHADOW_INT64_TENSOR_SIZE=1)
@@ -337,21 +306,6 @@ else()
 endif()
 
 include(cmake/ChooseBlas.cmake)
-if(USE_CUDA AND FIRST_CUDA)
-  include(3rdparty/mshadow/cmake/Utils.cmake)
-  include(cmake/FirstClassLangCuda.cmake)
-  include_directories(${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES})
-else()
-  if(EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/3rdparty/mshadow/cmake)
-    include(3rdparty/mshadow/cmake/mshadow.cmake)
-    include(3rdparty/mshadow/cmake/Utils.cmake)
-    include(3rdparty/mshadow/cmake/Cuda.cmake)
-  else()
-    include(mshadowUtils)
-    include(Cuda)
-    include(mshadow)
-  endif()
-endif()
 
 if(USE_ASAN)
   set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fno-omit-frame-pointer -fsanitize=address")
@@ -534,9 +488,7 @@ if(EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/3rdparty/dmlc-core/cmake)
   add_subdirectory("3rdparty/dmlc-core")
 endif()
 
-if(EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/3rdparty/mshadow/cmake)
-  add_subdirectory("3rdparty/mshadow")
-endif()
+add_subdirectory("3rdparty/mshadow")
 FILE(GLOB_RECURSE SOURCE "src/*.cc" "src/*.h" "include/*.h")
 FILE(GLOB_RECURSE CUDA "src/*.cu" "src/*.cuh")
 
@@ -636,61 +588,55 @@ if(MSVC)
 endif()
 
 if(USE_CUDA)
-  if(FIRST_CUDA)
-    mshadow_select_nvcc_arch_flags(NVCC_FLAGS_ARCH)
-    string(REPLACE ";" " " NVCC_FLAGS_ARCH "${NVCC_FLAGS_ARCH}")
-    set(CMAKE_CUDA_FLAGS "${NVCC_FLAGS_ARCH}")
-    list(APPEND mxnet_LINKER_LIBS cublas cufft cusolver curand)
-    if(ENABLE_CUDA_RTC)
-        list(APPEND mxnet_LINKER_LIBS nvrtc cuda)
-        add_definitions(-DMXNET_ENABLE_CUDA_RTC=1)
-    endif()
-    list(APPEND SOURCE ${CUDA})
-    add_definitions(-DMXNET_USE_CUDA=1)
-    link_directories(${CUDA_TOOLKIT_ROOT_DIR}/lib64)
-  else()
-    list(APPEND CUDA_INCLUDE_DIRS ${INCLUDE_DIRECTORIES})
-    # define preprocessor macro so that we will not include the generated forcelink header
-    if(ENABLE_CUDA_RTC)
+  # CUDA_SELECT_NVCC_ARCH_FLAGS is not deprecated, though part of deprecated
+  # FindCUDA https://gitlab.kitware.com/cmake/cmake/issues/19199
+  include(${CMAKE_ROOT}/Modules/FindCUDA/select_compute_arch.cmake)
+  CUDA_SELECT_NVCC_ARCH_FLAGS(CUDA_ARCH_FLAGS ${MXNET_CUDA_ARCH})
+  message("-- CUDA: Using the following NVCC architecture flags ${CUDA_ARCH_FLAGS}")
+  string(REPLACE ";" " " CUDA_ARCH_FLAGS_SPACES "${CUDA_ARCH_FLAGS}")
+  string(APPEND CMAKE_CUDA_FLAGS " ${CUDA_ARCH_FLAGS_SPACES}")
+
+  find_package(CUDAToolkit REQUIRED cublas cufft cusolver curand
+    OPTIONAL_COMPONENTS nvToolsExt nvrtc)
+
+  list(APPEND mxnet_LINKER_LIBS CUDA::cudart CUDA::cublas CUDA::cufft CUDA::cusolver CUDA::curand)
+  if(ENABLE_CUDA_RTC)
+    if(CUDA_nvrtc_LIBRARY)
+      list(APPEND mxnet_LINKER_LIBS CUDA::nvrtc cuda)
       add_definitions(-DMXNET_ENABLE_CUDA_RTC=1)
+    else()
+      message(FATAL_ERROR "ENABLE_CUDA_RTC=ON, but failed to find NVRTC. CMake will exit." )
     endif()
-    # Create '.cmake' files for cuda compiles given definitions added thus far
-    mshadow_cuda_compile(cuda_objs ${CUDA})
-    if(MSVC)
-        if(ENABLE_CUDA_RTC)
-            FIND_LIBRARY(CUDA_nvrtc_LIBRARY nvrtc "${CUDA_TOOLKIT_ROOT_DIR}/lib/x64"  "${CUDA_TOOLKIT_ROOT_DIR}/lib/win32")
-            list(APPEND mxnet_LINKER_LIBS ${CUDA_nvrtc_LIBRARY})
-            set(CUDA_cuda_LIBRARY "${CUDA_nvrtc_LIBRARY}/../cuda.lib")
-            list(APPEND mxnet_LINKER_LIBS ${CUDA_cuda_LIBRARY})
-        endif()
-        FIND_LIBRARY(CUDA_cufft_LIBRARY nvrtc "${CUDA_TOOLKIT_ROOT_DIR}/lib/x64"  "${CUDA_TOOLKIT_ROOT_DIR}/lib/win32")
-        list(APPEND mxnet_LINKER_LIBS "${CUDA_cufft_LIBRARY}/../cufft.lib") # For fft operator
-        FIND_LIBRARY(CUDA_cusolver_LIBRARY nvrtc "${CUDA_TOOLKIT_ROOT_DIR}/lib/x64"  "${CUDA_TOOLKIT_ROOT_DIR}/lib/win32")
-        list(APPEND mxnet_LINKER_LIBS "${CUDA_cusolver_LIBRARY}/../cusolver.lib") # For cusolver
-        link_directories(${CUDA_TOOLKIT_ROOT_DIR}/lib/win32)
-        link_directories(${CUDA_TOOLKIT_ROOT_DIR}/lib/x64)
-    else(MSVC)
-        list(APPEND mxnet_LINKER_LIBS cufft cusolver)
-        if(ENABLE_CUDA_RTC)
-            list(APPEND mxnet_LINKER_LIBS nvrtc cuda)
-        endif()
-        link_directories("${CUDA_TOOLKIT_ROOT_DIR}/lib64")
+  endif()
+  list(APPEND SOURCE ${CUDA})
+  add_definitions(-DMXNET_USE_CUDA=1)
+  add_definitions(-DMSHADOW_USE_CUDA=1)
+	add_definitions(-DMSHADOW_FORCE_STREAM)
+
+  if(USE_NCCL)
+    find_package(NCCL)
+    if(NCCL_FOUND)
+      include_directories(${NCCL_INCLUDE_DIRS})
+      list(APPEND mxnet_LINKER_LIBS ${NCCL_LIBRARIES})
+      add_definitions(-DMXNET_USE_NCCL=1)
+    else()
+      add_definitions(-DMXNET_USE_NCCL=0)
+      message(WARNING "Could not find NCCL libraries")
     endif()
-    list(APPEND SOURCE ${cuda_objs} ${CUDA})
-    add_definitions(-DMXNET_USE_CUDA=1)
-    if(CUDA_LIBRARY_PATH)
-        if(IS_CONTAINER_BUILD)
-        # In case of building on a production-like build container which may not have Cuda installed
-        if(NOT CMAKE_SYSTEM_HAS_CUDA)
-            # Assuming building in a container that doesn't have CUDA installed (ie CPU-only build machine)
-            # so use the stub cuda driver shared library
-            if(EXISTS ${CUDA_LIBRARY_PATH}/stubs/libcuda.so)
-            link_directories(${CUDA_LIBRARY_PATH}/stubs)
-            endif()
-        endif()
-        endif()
+  endif()
+  if(UNIX)
+    if(CUDA_nvToolsExt_LIBRARY)
+      list(APPEND mxnet_LINKER_LIBS CUDA::nvToolsExt)
+      add_definitions(-DMXNET_USE_NVTX=1)
+    else()
+      message("Building without NVTX support.")
     endif()
- endif()
+  endif()
+
+  include_directories(${CUDAToolkit_INCLUDE_DIRS})
+  link_directories(${CUDAToolkit_LIBRARY_DIR})
+else()
+  add_definitions(-DMSHADOW_USE_CUDA=0)
 endif()
 
 # unsupported: if caffe is a subdirectory of mxnet, load its CMakeLists.txt as well
@@ -738,11 +684,9 @@ else()
   set_target_properties(sample_lib PROPERTIES PREFIX "lib")
 endif()
 
-if(USE_CUDA)
-  if(FIRST_CUDA AND MSVC)
-    target_compile_options(mxnet PUBLIC "$<$<CONFIG:DEBUG>:-Xcompiler=-MTd -Gy>")
-    target_compile_options(mxnet PUBLIC "$<$<CONFIG:RELEASE>:-Xcompiler=-MT -Gy>")
-  endif()
+if(USE_CUDA AND MSVC)
+  target_compile_options(mxnet PUBLIC "$<$<CONFIG:DEBUG>:-Xcompiler=-MTd -Gy>")
+  target_compile_options(mxnet PUBLIC "$<$<CONFIG:RELEASE>:-Xcompiler=-MT -Gy>")
 endif()
 if(USE_DIST_KVSTORE)
   if(EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/3rdparty/ps-lite/CMakeLists.txt)
@@ -780,8 +724,8 @@ if(USE_TVM_OP)
   endif()
 
   set(TVM_OP_COMPILE_OPTIONS "-o${CMAKE_CURRENT_BINARY_DIR}/libtvmop.so" "--config" "${CMAKE_CURRENT_BINARY_DIR}/tvmop.conf")
-  if(CUDA_ARCH_BIN)
-    set(TVM_OP_COMPILE_OPTIONS "${TVM_OP_COMPILE_OPTIONS}" "--cuda-arch" "${CUDA_ARCH_BIN}")
+  if(USE_CUDA)
+    set(TVM_OP_COMPILE_OPTIONS "${TVM_OP_COMPILE_OPTIONS}" "--cuda-arch" "\"${CUDA_ARCH_FLAGS}\"")
   endif()
   add_custom_command(TARGET mxnet POST_BUILD
     COMMAND ${CMAKE_COMMAND} -E env
diff --git a/ci/build_windows.py b/ci/build_windows.py
index 5839e8d793d1..b334b68fef2c 100755
--- a/ci/build_windows.py
+++ b/ci/build_windows.py
@@ -114,9 +114,7 @@ class BuildFlavour(Enum):
         '-DUSE_BLAS=open '
         '-DUSE_LAPACK=ON '
         '-DUSE_DIST_KVSTORE=OFF '
-        '-DCUDA_ARCH_NAME=Manual '
-        '-DCUDA_ARCH_BIN=52 '
-        '-DCUDA_ARCH_PTX=52 '
+        '-DMXNET_CUDA_ARCH="5.2" '
         '-DCMAKE_CXX_FLAGS="/FS /MD /O2 /Ob2" '
         '-DUSE_MKL_IF_AVAILABLE=OFF '
         '-DCMAKE_BUILD_TYPE=Release')
@@ -130,9 +128,7 @@ class BuildFlavour(Enum):
         '-DUSE_BLAS=open '
         '-DUSE_LAPACK=ON '
         '-DUSE_DIST_KVSTORE=OFF '
-        '-DCUDA_ARCH_NAME=Manual '
-        '-DCUDA_ARCH_BIN=52 '
-        '-DCUDA_ARCH_PTX=52 '
+        '-DMXNET_CUDA_ARCH="5.2" '
         '-DUSE_MKLDNN=ON '
         '-DCMAKE_CXX_FLAGS="/FS /MD /O2 /Ob2" '
         '-DCMAKE_BUILD_TYPE=Release')
diff --git a/ci/docker/install/ubuntu_core.sh b/ci/docker/install/ubuntu_core.sh
index bd5d1f6fdf6f..70bc285d163b 100755
--- a/ci/docker/install/ubuntu_core.sh
+++ b/ci/docker/install/ubuntu_core.sh
@@ -53,7 +53,7 @@ apt-get install -y \
     wget
 
 # Use libturbojpeg package as it is correctly compiled with -fPIC flag
-# https://github.com/HaxeFoundation/hashlink/issues/147 
+# https://github.com/HaxeFoundation/hashlink/issues/147
 ln -s /usr/lib/x86_64-linux-gnu/libturbojpeg.so.0.1.0 /usr/lib/x86_64-linux-gnu/libturbojpeg.so
 
 
diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index e078b2a8f89c..328a008c07a5 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -25,7 +25,7 @@ set -ex
 NOSE_COVERAGE_ARGUMENTS="--with-coverage --cover-inclusive --cover-xml --cover-branches --cover-package=mxnet"
 NOSE_TIMER_ARGUMENTS="--with-timer --timer-ok 1 --timer-warning 15 --timer-filter warning,error"
 CI_CUDA_COMPUTE_CAPABILITIES="-gencode=arch=compute_52,code=sm_52 -gencode=arch=compute_70,code=sm_70"
-CI_CMAKE_CUDA_ARCH_BIN="52,70"
+CI_CMAKE_CUDA_ARCH="5.2 7.0"
 
 clean_repo() {
     set -ex
@@ -753,8 +753,7 @@ build_ubuntu_gpu_tensorrt() {
           -DUSE_OPENMP=0                          \
           -DUSE_MKLDNN=0                          \
           -DUSE_MKL_IF_AVAILABLE=OFF              \
-          -DCUDA_ARCH_NAME=Manual                 \
-          -DCUDA_ARCH_BIN=$CI_CMAKE_CUDA_ARCH_BIN \
+          -DMXNET_CUDA_ARCH="$CI_CMAKE_CUDA_ARCH" \
           -G Ninja                                \
           /work/mxnet
 
@@ -872,8 +871,7 @@ build_ubuntu_gpu_cmake_mkldnn() {
         -DPython3_EXECUTABLE=/usr/bin/python3   \
         -DUSE_MKLML_MKL=1                       \
         -DCMAKE_BUILD_TYPE=Release              \
-        -DCUDA_ARCH_NAME=Manual                 \
-        -DCUDA_ARCH_BIN=$CI_CMAKE_CUDA_ARCH_BIN \
+        -DMXNET_CUDA_ARCH="$CI_CMAKE_CUDA_ARCH" \
         -G Ninja                                \
         /work/mxnet
 
@@ -898,8 +896,7 @@ build_ubuntu_gpu_cmake() {
         -DUSE_MKLDNN=OFF                        \
         -DUSE_DIST_KVSTORE=ON                   \
         -DCMAKE_BUILD_TYPE=Release              \
-        -DCUDA_ARCH_NAME=Manual                 \
-        -DCUDA_ARCH_BIN=$CI_CMAKE_CUDA_ARCH_BIN \
+        -DMXNET_CUDA_ARCH="$CI_CMAKE_CUDA_ARCH" \
         -DBUILD_CYTHON_MODULES=1                \
         -G Ninja                                \
         /work/mxnet
@@ -925,8 +922,7 @@ build_ubuntu_gpu_cmake_no_tvm_op() {
         -DUSE_MKLDNN=OFF                        \
         -DUSE_DIST_KVSTORE=ON                   \
         -DCMAKE_BUILD_TYPE=Release              \
-        -DCUDA_ARCH_NAME=Manual                 \
-        -DCUDA_ARCH_BIN=$CI_CMAKE_CUDA_ARCH_BIN \
+        -DMXNET_CUDA_ARCH="$CI_CMAKE_CUDA_ARCH" \
         -DBUILD_CYTHON_MODULES=1                \
         -G Ninja                                \
         /work/mxnet
@@ -972,8 +968,7 @@ build_ubuntu_gpu_large_tensor() {
         -DUSE_MKLDNN=OFF                        \
         -DUSE_DIST_KVSTORE=ON                   \
         -DCMAKE_BUILD_TYPE=Release              \
-        -DCUDA_ARCH_NAME=Manual                 \
-        -DCUDA_ARCH_BIN=$CI_CMAKE_CUDA_ARCH_BIN \
+        -DMXNET_CUDA_ARCH="$CI_CMAKE_CUDA_ARCH" \
         -DUSE_INT64_TENSOR_SIZE=ON              \
         -G Ninja                                \
         /work/mxnet
diff --git a/cmake/BuildTVM.cmake b/cmake/BuildTVM.cmake
index 4bb749552f01..2c2f573cddbd 100644
--- a/cmake/BuildTVM.cmake
+++ b/cmake/BuildTVM.cmake
@@ -98,6 +98,21 @@ set(USE_RANDOM OFF)
 # Whether use NNPack
 set(USE_NNPACK OFF)
 
+# First-class Cuda in modern CMake provides us with CMAKE_CUDA_COMPILER But TVM
+# uses the deprecated findCUDA functionality which requires
+# CUDA_TOOLKIT_ROOT_DIR We follow the FindCUDAToolkit.cmake logic to compute
+# CUDA_TOOLKIT_ROOT_DIR for TVM https://gitlab.kitware.com/cmake/cmake/merge_requests/4093/
+if(USE_CUDA)
+  get_filename_component(cuda_dir "${CMAKE_CUDA_COMPILER}" DIRECTORY)
+  set(CUDA_BIN_DIR "${cuda_dir}" CACHE PATH "" FORCE)
+  unset(cuda_dir)
+  get_filename_component(CUDA_TOOLKIT_ROOT_DIR ${CUDA_BIN_DIR} DIRECTORY ABSOLUTE)
+
+  message("CMAKE_CUDA_COMPILER: ${CMAKE_CUDA_COMPILER}")
+  message("Inferred CUDA_TOOLKIT_ROOT_DIR for TVM as: ${CUDA_TOOLKIT_ROOT_DIR}")
+  set(USE_CUDA ${CUDA_TOOLKIT_ROOT_DIR})
+endif()
+
 # Whether use cuBLAS
 set(USE_CUBLAS OFF)
 
diff --git a/cmake/FirstClassLangCuda.cmake b/cmake/FirstClassLangCuda.cmake
deleted file mode 100644
index 0eca1aff78d4..000000000000
--- a/cmake/FirstClassLangCuda.cmake
+++ /dev/null
@@ -1,249 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-#this file is CUDA help function with CMAKE first class CUDA
-
-include(CheckCXXCompilerFlag)
-check_cxx_compiler_flag("-std=c++11"   SUPPORT_CXX11)
-if(USE_CXX14_IF_AVAILABLE)
-  check_cxx_compiler_flag("-std=c++14"   SUPPORT_CXX14)
-endif()
-
-
-################################################################################################
-# A function for automatic detection of GPUs installed  (if autodetection is enabled)
-# Usage:
-#   mshadow_detect_installed_gpus(out_variable)
-function(mshadow_detect_installed_gpus out_variable)
-  if(NOT CUDA_gpu_detect_output)
-    set(__cufile ${PROJECT_BINARY_DIR}/detect_cuda_archs.cu)
-
-    file(WRITE ${__cufile} ""
-      "#include <cstdio>\n"
-      "int main()\n"
-      "{\n"
-      "  int count = 0;\n"
-      "  if (cudaSuccess != cudaGetDeviceCount(&count)) return -1;\n"
-      "  if (count == 0) return -1;\n"
-      "  for (int device = 0; device < count; ++device)\n"
-      "  {\n"
-      "    cudaDeviceProp prop;\n"
-      "    if (cudaSuccess == cudaGetDeviceProperties(&prop, device))\n"
-      "      std::printf(\"%d.%d \", prop.major, prop.minor);\n"
-      "  }\n"
-      "  return 0;\n"
-      "}\n")
-    enable_language(CUDA)
-
-    try_run(__nvcc_res __compile_result ${PROJECT_BINARY_DIR} ${__cufile}
-      COMPILE_OUTPUT_VARIABLE __compile_out
-      RUN_OUTPUT_VARIABLE __nvcc_out)
-
-    if(__nvcc_res EQUAL 0 AND __compile_result)
-      # nvcc outputs text containing line breaks when building with MSVC.
-      # The line below prevents CMake from inserting a variable with line
-      # breaks in the cache
-      string(REGEX MATCH "([1-9].[0-9])" __nvcc_out "${__nvcc_out}")
-      string(REPLACE "2.1" "2.1(2.0)" __nvcc_out "${__nvcc_out}")
-      set(CUDA_gpu_detect_output ${__nvcc_out})
-    else()
-      message(WARNING "Running GPU detection script with nvcc failed: ${__nvcc_out} ${__compile_out}")
-    endif()
-  endif()
-
-  if(NOT CUDA_gpu_detect_output)
-    message(WARNING "Automatic GPU detection failed. Building for all known architectures (${mxnet_known_gpu_archs}).")
-    set(${out_variable} ${mxnet_known_gpu_archs} PARENT_SCOPE)
-  else()
-    set(${out_variable} ${CUDA_gpu_detect_output} PARENT_SCOPE)
-  endif()
-endfunction()
-
-
-# This list will be used for CUDA_ARCH_NAME = All option
-set(CUDA_KNOWN_GPU_ARCHITECTURES "Kepler" "Maxwell")
-
-# This list will be used for CUDA_ARCH_NAME = Common option (enabled by default)
-set(CUDA_COMMON_GPU_ARCHITECTURES "3.0" "3.5" "5.0")
-
-if (CUDA_TOOLSET VERSION_GREATER "6.5")
-  list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Kepler+Tegra" "Kepler+Tesla" "Maxwell+Tegra")
-  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "5.2" "3.7")
-endif ()
-
-if (CUDA_TOOLSET VERSION_GREATER "7.5")
-  list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Pascal")
-  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "6.0" "6.1" "6.1+PTX")
-else()
-  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "5.2+PTX")
-endif ()
-
-if (CUDA_TOOLSET VERSION_GREATER "9.0")
-  list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Volta")
-  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "7.0")
-endif()
-
-if (CUDA_TOOLSET VERSION_GREATER "10.0")
-  list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Turing")
-  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "7.5")
-endif()
-
-################################################################################################
-# Function for selecting GPU arch flags for nvcc based on CUDA_ARCH_NAME
-# Usage:
-#   mshadow_select_nvcc_arch_flags(out_variable)
-function(mshadow_select_nvcc_arch_flags out_variable)
-
-  set(CUDA_ARCH_LIST "Auto" CACHE STRING "Select target NVIDIA GPU achitecture.")
-  set_property( CACHE CUDA_ARCH_LIST PROPERTY STRINGS "" "Auto" "All" "Common" ${CUDA_KNOWN_GPU_ARCHITECTURES} )
-  mark_as_advanced(CUDA_ARCH_NAME)
-
-
-  if("X${CUDA_ARCH_LIST}" STREQUAL "X" )
-    set(CUDA_ARCH_LIST "All")
-  endif()
-
-  set(cuda_arch_bin)
-  set(cuda_arch_ptx)
-
-  message(STATUS " CUDA_ARCH_LIST: ${CUDA_ARCH_LIST}")
-  if("${CUDA_ARCH_LIST}" STREQUAL "All")
-    set(CUDA_ARCH_LIST ${CUDA_KNOWN_GPU_ARCHITECTURES})
-  elseif("${CUDA_ARCH_LIST}" STREQUAL "Common")
-    set(CUDA_ARCH_LIST ${CUDA_COMMON_GPU_ARCHITECTURES})
-  elseif("${CUDA_ARCH_LIST}" STREQUAL "Auto" OR "${CUDA_ARCH_LIST}" STREQUAL "")
-    set(mxnet_known_gpu_archs ${CUDA_COMMON_GPU_ARCHITECTURES})
-    mshadow_detect_installed_gpus(CUDA_ARCH_LIST)
-    message(STATUS "Autodetected CUDA architecture(s): ${CUDA_ARCH_LIST}")
-  endif()
-
-  # Now process the list and look for names
-  string(REGEX REPLACE "[ \t]+" ";" CUDA_ARCH_LIST "${CUDA_ARCH_LIST}")
-  list(REMOVE_DUPLICATES CUDA_ARCH_LIST)
-  foreach(arch_name ${CUDA_ARCH_LIST})
-    set(arch_bin)
-    set(arch_ptx)
-    set(add_ptx FALSE)
-    # Check to see if we are compiling PTX
-    if(arch_name MATCHES "(.*)\\+PTX$")
-      set(add_ptx TRUE)
-      set(arch_name ${CMAKE_MATCH_1})
-    endif()
-    if(arch_name MATCHES "^([0-9]\\.[0-9](\\([0-9]\\.[0-9]\\))?)$")
-      set(arch_bin ${CMAKE_MATCH_1})
-      set(arch_ptx ${arch_bin})
-    else()
-      # Look for it in our list of known architectures
-      if(${arch_name} STREQUAL "Fermi")
-        if (CUDA_TOOLSET VERSION_LESS "8.0")
-          set(arch_bin 2.0 "2.1(2.0)")
-        endif()
-      elseif(${arch_name} STREQUAL "Kepler+Tegra")
-        set(arch_bin 3.2)
-      elseif(${arch_name} STREQUAL "Kepler+Tesla")
-        set(arch_bin 3.7)
-      elseif(${arch_name} STREQUAL "Kepler")
-        set(arch_bin 3.0 3.5)
-        set(arch_ptx 3.5)
-      elseif(${arch_name} STREQUAL "Maxwell+Tegra")
-        set(arch_bin 5.3)
-      elseif(${arch_name} STREQUAL "Maxwell")
-        set(arch_bin 5.0 5.2)
-        set(arch_ptx 5.2)
-      elseif(${arch_name} STREQUAL "Pascal")
-        set(arch_bin 6.0 6.1)
-        set(arch_ptx 6.1)
-      elseif(${arch_name} STREQUAL "Volta")
-        set(arch_bin 7.0)
-        set(arch_ptx 7.0)
-      elseif(${arch_name} STREQUAL "Turing")
-        set(arch_bin 7.5)
-        set(arch_ptx 7.5)
-      else()
-        message(SEND_ERROR "Unknown CUDA Architecture Name ${arch_name} in CUDA_SELECT_NVCC_ARCH_FLAGS")
-      endif()
-    endif()
-    list(APPEND cuda_arch_bin ${arch_bin})
-    if(add_ptx)
-      if (NOT arch_ptx)
-        set(arch_ptx ${arch_bin})
-      endif()
-      list(APPEND cuda_arch_ptx ${arch_ptx})
-    endif()
-  endforeach()
-
-  # remove dots and convert to lists
-  string(REGEX REPLACE "\\." "" cuda_arch_bin "${cuda_arch_bin}")
-  string(REGEX REPLACE "\\." "" cuda_arch_ptx "${cuda_arch_ptx}")
-  string(REGEX MATCHALL "[0-9()]+" cuda_arch_bin "${cuda_arch_bin}")
-  string(REGEX MATCHALL "[0-9]+"   cuda_arch_ptx "${cuda_arch_ptx}")
-
-  if(cuda_arch_bin)
-    list(REMOVE_DUPLICATES cuda_arch_bin)
-  endif()
-  if(cuda_arch_ptx)
-    list(REMOVE_DUPLICATES cuda_arch_ptx)
-  endif()
-
-  message(STATUS "cuda arch bin: ${cuda_arch_bin}")
-  message(STATUS "cuda arch ptx: ${cuda_arch_ptx}")
-  set(nvcc_flags "")
-  set(nvcc_archs_readable "")
-
-  # Tell NVCC to add binaries for the specified GPUs
-  foreach(arch ${cuda_arch_bin})
-    if(arch MATCHES "([0-9]+)\\(([0-9]+)\\)")
-      # User explicitly specified ARCH for the concrete CODE
-      list(APPEND nvcc_flags -gencode arch=compute_${CMAKE_MATCH_2},code=sm_${CMAKE_MATCH_1})
-      list(APPEND nvcc_archs_readable sm_${CMAKE_MATCH_1})
-    else()
-      # User didn't explicitly specify ARCH for the concrete CODE, we assume ARCH=CODE
-      list(APPEND nvcc_flags -gencode arch=compute_${arch},code=sm_${arch})
-      list(APPEND nvcc_archs_readable sm_${arch})
-    endif()
-  endforeach()
-
-  # Tell NVCC to add PTX intermediate code for the specified architectures
-  foreach(arch ${cuda_arch_ptx})
-    list(APPEND nvcc_flags -gencode arch=compute_${arch},code=compute_${arch})
-    list(APPEND nvcc_archs_readable compute_${arch})
-  endforeach()
-
-  if(NOT MSVC)
-    if(SUPPORT_CXX14)
-      list(APPEND nvcc_flags "-std=c++14")
-    elseif(SUPPORT_CXX11)
-      list(APPEND nvcc_flags "-std=c++11")
-    endif()
-  endif()
-
-  string (REPLACE " " ";" CMAKE_CXX_FLAGS_STR "${CMAKE_CXX_FLAGS}")
-  foreach(_flag ${CMAKE_CXX_FLAGS_STR})
-    # Remove -std=c++XX flags
-    if(NOT "${_flag}" MATCHES "-std=.+")
-      # Remove link flags
-      if(NOT "${_flag}" MATCHES "-Wl,.+")
-        list(APPEND nvcc_flags "-Xcompiler ${_flag}")
-      endif()
-    endif()
-  endforeach()
-
-  string(REPLACE ";" " " nvcc_archs_readable "${nvcc_archs_readable}")
-  set(${out_variable}          ${nvcc_flags}          PARENT_SCOPE)
-  set(${out_variable}_readable ${nvcc_archs_readable} PARENT_SCOPE)
-endfunction()
-
diff --git a/cmake/Modules/FindCUDAToolkit.cmake b/cmake/Modules/FindCUDAToolkit.cmake
new file mode 100644
index 000000000000..1d9af2f548d0
--- /dev/null
+++ b/cmake/Modules/FindCUDAToolkit.cmake
@@ -0,0 +1,833 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# Original license notice, prior to modification by MXNet Contributors:
+#
+# Copyright 2000-2019 Kitware, Inc. and Contributors
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#
+# * Redistributions of source code must retain the above copyright
+#   notice, this list of conditions and the following disclaimer.
+#
+# * Redistributions in binary form must reproduce the above copyright
+#   notice, this list of conditions and the following disclaimer in the
+#   documentation and/or other materials provided with the distribution.
+#
+# * Neither the name of Kitware, Inc. nor the names of Contributors
+#   may be used to endorse or promote products derived from this
+#   software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+# HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+#[=======================================================================[.rst:
+FindCUDAToolkit
+---------------
+
+This script locates the NVIDIA CUDA toolkit and the associated libraries, but
+does not require the ``CUDA`` language be enabled for a given project. This
+module does not search for the NVIDIA CUDA Samples.
+
+Search Behavior
+^^^^^^^^^^^^^^^
+
+Finding the CUDA Toolkit requires finding the ``nvcc`` executable, which is
+searched for in the following order:
+
+1. If the ``CUDA`` language has been enabled we will use the directory
+   containing the compiler as the first search location for ``nvcc``.
+
+2. If the ``CUDAToolkit_ROOT`` cmake configuration variable (e.g.,
+   ``-DCUDAToolkit_ROOT=/some/path``) *or* environment variable is defined, it
+   will be searched.  If both an environment variable **and** a
+   configuration variable are specified, the *configuration* variable takes
+   precedence.
+
+   The directory specified here must be such that the executable ``nvcc`` can be
+   found underneath the directory specified by ``CUDAToolkit_ROOT``.  If
+   ``CUDAToolkit_ROOT`` is specified, but no ``nvcc`` is found underneath, this
+   package is marked as **not** found.  No subsequent search attempts are
+   performed.
+
+3. If the CUDA_PATH environment variable is defined, it will be searched.
+
+4. The user's path is searched for ``nvcc`` using :command:`find_program`.  If
+   this is found, no subsequent search attempts are performed.  Users are
+   responsible for ensuring that the first ``nvcc`` to show up in the path is
+   the desired path in the event that multiple CUDA Toolkits are installed.
+
+5. On Unix systems, if the symbolic link ``/usr/local/cuda`` exists, this is
+   used.  No subsequent search attempts are performed.  No default symbolic link
+   location exists for the Windows platform.
+
+6. The platform specific default install locations are searched.  If exactly one
+   candidate is found, this is used.  The default CUDA Toolkit install locations
+   searched are:
+
+   +-------------+-------------------------------------------------------------+
+   | Platform    | Search Pattern                                              |
+   +=============+=============================================================+
+   | macOS       | ``/Developer/NVIDIA/CUDA-X.Y``                              |
+   +-------------+-------------------------------------------------------------+
+   | Other Unix  | ``/usr/local/cuda-X.Y``                                     |
+   +-------------+-------------------------------------------------------------+
+   | Windows     | ``C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vX.Y`` |
+   +-------------+-------------------------------------------------------------+
+
+   Where ``X.Y`` would be a specific version of the CUDA Toolkit, such as
+   ``/usr/local/cuda-9.0`` or
+   ``C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0``
+
+   .. note::
+
+       When multiple CUDA Toolkits are installed in the default location of a
+       system (e.g., both ``/usr/local/cuda-9.0`` and ``/usr/local/cuda-10.0``
+       exist but the ``/usr/local/cuda`` symbolic link does **not** exist), this
+       package is marked as **not** found.
+
+       There are too many factors involved in making an automatic decision in
+       the presence of multiple CUDA Toolkits being installed.  In this
+       situation, users are encouraged to either (1) set ``CUDAToolkit_ROOT`` or
+       (2) ensure that the correct ``nvcc`` executable shows up in ``$PATH`` for
+       :command:`find_program` to find.
+
+Options
+^^^^^^^
+
+``VERSION``
+    If specified, describes the version of the CUDA Toolkit to search for.
+
+``REQUIRED``
+    If specified, configuration will error if a suitable CUDA Toolkit is not
+    found.
+
+``QUIET``
+    If specified, the search for a suitable CUDA Toolkit will not produce any
+    messages.
+
+``EXACT``
+    If specified, the CUDA Toolkit is considered found only if the exact
+    ``VERSION`` specified is recovered.
+
+Imported targets
+^^^^^^^^^^^^^^^^
+
+An :ref:`imported target <Imported targets>` named ``CUDA::toolkit`` is provided.
+
+This module defines :prop_tgt:`IMPORTED` targets for each
+of the following libraries that are part of the CUDAToolkit:
+
+- :ref:`CUDA Runtime Library<cuda_toolkit_rt_lib>`
+- :ref:`CUDA Driver Library<cuda_toolkit_driver_lib>`
+- :ref:`cuBLAS<cuda_toolkit_cuBLAS>`
+- :ref:`cuFFT<cuda_toolkit_cuFFT>`
+- :ref:`cuRAND<cuda_toolkit_cuRAND>`
+- :ref:`cuSOLVER<cuda_toolkit_cuSOLVER>`
+- :ref:`cuSPARSE<cuda_toolkit_cuSPARSE>`
+- :ref:`NPP<cuda_toolkit_NPP>`
+- :ref:`nvBLAS<cuda_toolkit_nvBLAS>`
+- :ref:`nvGRAPH<cuda_toolkit_nvGRAPH>`
+- :ref:`nvJPEG<cuda_toolkit_nvJPEG>`
+- :ref:`nvidia-ML<cuda_toolkit_nvML>`
+- :ref:`nvRTC<cuda_toolkit_nvRTC>`
+- :ref:`nvToolsExt<cuda_toolkit_nvToolsExt>`
+- :ref:`OpenCL<cuda_toolkit_opencl>`
+- :ref:`cuLIBOS<cuda_toolkit_cuLIBOS>`
+
+.. _`cuda_toolkit_rt_lib`:
+
+CUDA Runtime Library
+""""""""""""""""""""
+
+The CUDA Runtime library (cudart) are what most applications will typically
+need to link against to make any calls such as `cudaMalloc`, and `cudaFree`.
+They are an explicit dependency of almost every library.
+
+Targets Created:
+
+- ``CUDA::cudart``
+- ``CUDA::cudart_static``
+
+.. _`cuda_toolkit_driver_lib`:
+
+CUDA Driver Library
+""""""""""""""""""""
+
+The CUDA Driver library (cuda) are used by applications that use calls
+such as `cuMemAlloc`, and `cuMemFree`. This is generally used by advanced
+
+
+Targets Created:
+
+- ``CUDA::cuda_driver``
+- ``CUDA::cuda_driver``
+
+.. _`cuda_toolkit_cuBLAS`:
+
+cuBLAS
+""""""
+
+The `cuBLAS <https://docs.nvidia.com/cuda/cublas/index.html>`_ library.
+
+Targets Created:
+
+- ``CUDA::cublas``
+- ``CUDA::cublas_static``
+
+.. _`cuda_toolkit_cuFFT`:
+
+cuFFT
+"""""
+
+The `cuFFT <https://docs.nvidia.com/cuda/cufft/index.html>`_ library.
+
+Targets Created:
+
+- ``CUDA::cufft``
+- ``CUDA::cufftw``
+- ``CUDA::cufft_static``
+- ``CUDA::cufftw_static``
+
+cuRAND
+""""""
+
+The `cuRAND <https://docs.nvidia.com/cuda/curand/index.html>`_ library.
+
+Targets Created:
+
+- ``CUDA::curand``
+- ``CUDA::curand_static``
+
+.. _`cuda_toolkit_cuSOLVER`:
+
+cuSOLVER
+""""""""
+
+The `cuSOLVER <https://docs.nvidia.com/cuda/cusolver/index.html>`_ library.
+
+Targets Created:
+
+- ``CUDA::cusolver``
+- ``CUDA::cusolver_static``
+
+.. _`cuda_toolkit_cuSPARSE`:
+
+cuSPARSE
+""""""""
+
+The `cuSPARSE <https://docs.nvidia.com/cuda/cusparse/index.html>`_ library.
+
+Targets Created:
+
+- ``CUDA::cusparse``
+- ``CUDA::cusparse_static``
+
+.. _`cuda_toolkit_NPP`:
+
+NPP
+"""
+
+The `NPP <https://docs.nvidia.com/cuda/npp/index.html>`_ libraries.
+
+Targets Created:
+
+- `nppc`:
+
+  - ``CUDA::nppc``
+  - ``CUDA::nppc_static``
+
+- `nppial`: Arithmetic and logical operation functions in `nppi_arithmetic_and_logical_operations.h`
+
+  - ``CUDA::nppial``
+  - ``CUDA::nppial_static``
+
+- `nppicc`: Color conversion and sampling functions in `nppi_color_conversion.h`
+
+  - ``CUDA::nppicc``
+  - ``CUDA::nppicc_static``
+
+- `nppicom`: JPEG compression and decompression functions in `nppi_compression_functions.h`
+
+  - ``CUDA::nppicom``
+  - ``CUDA::nppicom_static``
+
+- `nppidei`: Data exchange and initialization functions in `nppi_data_exchange_and_initialization.h`
+
+  - ``CUDA::nppidei``
+  - ``CUDA::nppidei_static``
+
+- `nppif`: Filtering and computer vision functions in `nppi_filter_functions.h`
+
+  - ``CUDA::nppif``
+  - ``CUDA::nppif_static``
+
+- `nppig`: Geometry transformation functions found in `nppi_geometry_transforms.h`
+
+  - ``CUDA::nppig``
+  - ``CUDA::nppig_static``
+
+- `nppim`: Morphological operation functions found in `nppi_morphological_operations.h`
+
+  - ``CUDA::nppim``
+  - ``CUDA::nppim_static``
+
+- `nppist`: Statistics and linear transform in `nppi_statistics_functions.h` and `nppi_linear_transforms.h`
+
+  - ``CUDA::nppist``
+  - ``CUDA::nppist_static``
+
+- `nppisu`: Memory support functions in `nppi_support_functions.h`
+
+  - ``CUDA::nppisu``
+  - ``CUDA::nppisu_static``
+
+- `nppitc`: Threshold and compare operation functions in `nppi_threshold_and_compare_operations.h`
+
+  - ``CUDA::nppitc``
+  - ``CUDA::nppitc_static``
+
+- `npps`:
+
+  - ``CUDA::npps``
+  - ``CUDA::npps_static``
+
+.. _`cuda_toolkit_nvBLAS`:
+
+nvBLAS
+""""""
+
+The `nvBLAS <https://docs.nvidia.com/cuda/nvblas/index.html>`_ libraries.
+This is a shared library only.
+
+Targets Created:
+
+- ``CUDA::nvblas``
+
+.. _`cuda_toolkit_nvGRAPH`:
+
+nvGRAPH
+"""""""
+
+The `nvGRAPH <https://docs.nvidia.com/cuda/nvgraph/index.html>`_ library.
+
+Targets Created:
+
+- ``CUDA::nvgraph``
+- ``CUDA::nvgraph_static``
+
+
+.. _`cuda_toolkit_nvJPEG`:
+
+nvJPEG
+""""""
+
+The `nvJPEG <https://docs.nvidia.com/cuda/nvjpeg/index.html>`_ library.
+Introduced in CUDA 10.
+
+Targets Created:
+
+- ``CUDA::nvjpeg``
+- ``CUDA::nvjpeg_static``
+
+.. _`cuda_toolkit_nvRTC`:
+
+nvRTC
+"""""
+
+The `nvRTC <https://docs.nvidia.com/cuda/nvrtc/index.html>`_ (Runtime Compilation) library.
+This is a shared library only.
+
+Targets Created:
+
+- ``CUDA::nvrtc``
+
+.. _`cuda_toolkit_nvml`:
+
+nvidia-ML
+"""""""""
+
+The `NVIDIA Management Library <https://developer.nvidia.com/nvidia-management-library-nvml>`_.
+This is a shared library only.
+
+Targets Created:
+
+- ``CUDA::nvml``
+
+.. _`cuda_toolkit_opencl`:
+
+.. _`cuda_toolkit_nvToolsExt`:
+
+nvToolsExt
+""""""""""
+
+The `NVIDIA Tools Extension <https://docs.nvidia.com/gameworks/content/gameworkslibrary/nvtx/nvidia_tools_extension_library_nvtx.htm>`_.
+This is a shared library only.
+
+Targets Created:
+
+- ``CUDA::nvToolsExt``
+
+OpenCL
+""""""
+
+The `NVIDIA OpenCL Library <https://developer.nvidia.com/opencl>`_.
+This is a shared library only.
+
+Targets Created:
+
+- ``CUDA::OpenCL``
+
+.. _`cuda_toolkit_cuLIBOS`:
+
+cuLIBOS
+"""""""
+
+The cuLIBOS library is a backend thread abstraction layer library which is
+static only.  The ``CUDA::cublas_static``, ``CUDA::cusparse_static``,
+``CUDA::cufft_static``, ``CUDA::curand_static``, and (when implemented) NPP
+libraries all automatically have this dependency linked.
+
+Target Created:
+
+- ``CUDA::culibos``
+
+**Note**: direct usage of this target by consumers should not be necessary.
+
+.. _`cuda_toolkit_cuRAND`:
+
+
+
+Result variables
+^^^^^^^^^^^^^^^^
+
+``CUDAToolkit_FOUND``
+    A boolean specifying whether or not the CUDA Toolkit was found.
+
+``CUDAToolkit_VERSION``
+    The exact version of the CUDA Toolkit found (as reported by
+    ``nvcc --version``).
+
+``CUDAToolkit_VERSION_MAJOR``
+    The major version of the CUDA Toolkit.
+
+``CUDAToolkit_VERSION_MAJOR``
+    The minor version of the CUDA Toolkit.
+
+``CUDAToolkit_VERSION_PATCH``
+    The patch version of the CUDA Toolkit.
+
+``CUDAToolkit_BIN_DIR``
+    The path to the CUDA Toolkit library directory that contains the CUDA
+    executable ``nvcc``.
+
+``CUDAToolkit_INCLUDE_DIRS``
+    The path to the CUDA Toolkit ``include`` folder containing the header files
+    required to compile a project linking against CUDA.
+
+``CUDAToolkit_LIBRARY_DIR``
+    The path to the CUDA Toolkit library directory that contains the CUDA
+    Runtime library ``cudart``.
+
+``CUDAToolkit_NVCC_EXECUTABLE``
+    The path to the NVIDIA CUDA compiler ``nvcc``.  Note that this path may
+    **not** be the same as
+    :variable:`CMAKE_CUDA_COMPILER <CMAKE_<LANG>_COMPILER>`.  ``nvcc`` must be
+    found to determine the CUDA Toolkit version as well as determining other
+    features of the Toolkit.  This variable is set for the convenience of
+    modules that depend on this one.
+
+
+#]=======================================================================]
+
+# NOTE: much of this was simply extracted from FindCUDA.cmake.
+
+#   James Bigler, NVIDIA Corp (nvidia.com - jbigler)
+#   Abe Stephens, SCI Institute -- http://www.sci.utah.edu/~abe/FindCuda.html
+#
+#   Copyright (c) 2008 - 2009 NVIDIA Corporation.  All rights reserved.
+#
+#   Copyright (c) 2007-2009
+#   Scientific Computing and Imaging Institute, University of Utah
+#
+#   This code is licensed under the MIT License.  See the FindCUDA.cmake script
+#   for the text of the license.
+
+# The MIT License
+#
+# License for the specific language governing rights and limitations under
+# Permission is hereby granted, free of charge, to any person obtaining a
+# copy of this software and associated documentation files (the "Software"),
+# to deal in the Software without restriction, including without limitation
+# the rights to use, copy, modify, merge, publish, distribute, sublicense,
+# and/or sell copies of the Software, and to permit persons to whom the
+# Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included
+# in all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
+# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+# DEALINGS IN THE SOFTWARE.
+#
+###############################################################################
+
+if(CMAKE_CUDA_COMPILER_LOADED AND NOT CUDAToolkit_BIN_DIR)
+  get_filename_component(cuda_dir "${CMAKE_CUDA_COMPILER}" DIRECTORY)
+  # use the already detected cuda compiler
+  set(CUDAToolkit_BIN_DIR "${cuda_dir}" CACHE PATH "")
+  unset(cuda_dir)
+endif()
+
+# Try language- or user-provided path first.
+if(CUDAToolkit_BIN_DIR)
+  find_program(CUDAToolkit_NVCC_EXECUTABLE
+    NAMES nvcc nvcc.exe
+    PATHS ${CUDAToolkit_BIN_DIR}
+    NO_DEFAULT_PATH
+    )
+endif()
+
+# Search using CUDAToolkit_ROOT
+find_program(CUDAToolkit_NVCC_EXECUTABLE
+  NAMES nvcc nvcc.exe
+  PATHS ENV CUDA_PATH
+  PATH_SUFFIXES bin
+)
+
+# If the user specified CUDAToolkit_ROOT but nvcc could not be found, this is an error.
+if (NOT CUDAToolkit_NVCC_EXECUTABLE AND (DEFINED CUDAToolkit_ROOT OR DEFINED ENV{CUDAToolkit_ROOT}))
+  # Declare error messages now, print later depending on find_package args.
+  set(fail_base "Could not find nvcc executable in path specified by")
+  set(cuda_root_fail "${fail_base} CUDAToolkit_ROOT=${CUDAToolkit_ROOT}")
+  set(env_cuda_root_fail "${fail_base} environment variable CUDAToolkit_ROOT=$ENV{CUDAToolkit_ROOT}")
+
+  if (CUDAToolkit_FIND_REQUIRED)
+    if (DEFINED CUDAToolkit_ROOT)
+      message(FATAL_ERROR ${cuda_root_fail})
+    elseif (DEFINED ENV{CUDAToolkit_ROOT})
+      message(FATAL_ERROR ${env_cuda_root_fail})
+    endif()
+  else()
+    if (NOT CUDAToolkit_FIND_QUIETLY)
+      if (DEFINED CUDAToolkit_ROOT)
+        message(STATUS ${cuda_root_fail})
+      elseif (DEFINED ENV{CUDAToolkit_ROOT})
+        message(STATUS ${env_cuda_root_fail})
+      endif()
+    endif()
+    set(CUDAToolkit_FOUND FALSE)
+    unset(fail_base)
+    unset(cuda_root_fail)
+    unset(env_cuda_root_fail)
+    return()
+  endif()
+endif()
+
+# CUDAToolkit_ROOT cmake / env variable not specified, try platform defaults.
+#
+# - Linux: /usr/local/cuda-X.Y
+# - macOS: /Developer/NVIDIA/CUDA-X.Y
+# - Windows: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vX.Y
+#
+# We will also search the default symlink location /usr/local/cuda first since
+# if CUDAToolkit_ROOT is not specified, it is assumed that the symlinked
+# directory is the desired location.
+if (NOT CUDAToolkit_NVCC_EXECUTABLE)
+  if (UNIX)
+    if (NOT APPLE)
+      set(platform_base "/usr/local/cuda-")
+    else()
+      set(platform_base "/Developer/NVIDIA/CUDA-")
+    endif()
+  else()
+    set(platform_base "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v")
+  endif()
+
+  # Build out a descending list of possible cuda installations, e.g.
+  file(GLOB possible_paths "${platform_base}*")
+  # Iterate the glob results and create a descending list.
+  set(possible_versions)
+  foreach (p ${possible_paths})
+    # Extract version number from end of string
+    string(REGEX MATCH "[0-9][0-9]?\\.[0-9]$" p_version ${p})
+    if (IS_DIRECTORY ${p} AND p_version)
+      list(APPEND possible_versions ${p_version})
+    endif()
+  endforeach()
+
+  # Cannot use list(SORT) because that is alphabetical, we need numerical.
+  # NOTE: this is not an efficient sorting strategy.  But even if a user had
+  # every possible version of CUDA installed, this wouldn't create any
+  # significant overhead.
+  set(versions)
+  foreach (v ${possible_versions})
+    list(LENGTH versions num_versions)
+    # First version, nothing to compare with so just append.
+    if (num_versions EQUAL 0)
+      list(APPEND versions ${v})
+    else()
+      # Loop through list.  Insert at an index when comparison is
+      # VERSION_GREATER since we want a descending list.  Duplicates will not
+      # happen since this came from a glob list of directories.
+      set(i 0)
+      set(early_terminate FALSE)
+      while (i LESS num_versions)
+        list(GET versions ${i} curr)
+        if (v VERSION_GREATER curr)
+          list(INSERT versions ${i} ${v})
+          set(early_terminate TRUE)
+          break()
+        endif()
+        math(EXPR i "${i} + 1")
+      endwhile()
+      # If it did not get inserted, place it at the end.
+      if (NOT early_terminate)
+        list(APPEND versions ${v})
+      endif()
+    endif()
+  endforeach()
+
+  # With a descending list of versions, populate possible paths to search.
+  set(search_paths)
+  foreach (v ${versions})
+    list(APPEND search_paths "${platform_base}${v}")
+  endforeach()
+
+  # Force the global default /usr/local/cuda to the front on Unix.
+  if (UNIX)
+    list(INSERT search_paths 0 "/usr/local/cuda")
+  endif()
+
+  # Now search for nvcc again using the platform default search paths.
+  find_program(CUDAToolkit_NVCC_EXECUTABLE
+    NAMES nvcc nvcc.exe
+    PATHS ${search_paths}
+    PATH_SUFFIXES bin
+  )
+
+  # We are done with these variables now, cleanup for caller.
+  unset(platform_base)
+  unset(possible_paths)
+  unset(possible_versions)
+  unset(versions)
+  unset(i)
+  unset(early_terminate)
+  unset(search_paths)
+
+  if (NOT CUDAToolkit_NVCC_EXECUTABLE)
+    if (CUDAToolkit_FIND_REQUIRED)
+      message(FATAL_ERROR "Could not find nvcc, please set CUDAToolkit_ROOT.")
+    elseif(NOT CUDAToolkit_FIND_QUIETLY)
+      message(STATUS "Could not find nvcc, please set CUDAToolkit_ROOT.")
+    endif()
+
+    set(CUDAToolkit_FOUND FALSE)
+    return()
+  endif()
+endif()
+
+if(NOT CUDAToolkit_BIN_DIR AND CUDAToolkit_NVCC_EXECUTABLE)
+  get_filename_component(cuda_dir "${CUDAToolkit_NVCC_EXECUTABLE}" DIRECTORY)
+  set(CUDAToolkit_BIN_DIR "${cuda_dir}" CACHE PATH "" FORCE)
+  unset(cuda_dir)
+endif()
+
+if(CUDAToolkit_NVCC_EXECUTABLE AND
+   CUDAToolkit_NVCC_EXECUTABLE STREQUAL CMAKE_CUDA_COMPILER)
+  # Need to set these based off the already computed CMAKE_CUDA_COMPILER_VERSION value
+  # This if statement will always match, but is used to provide variables for MATCH 1,2,3...
+  if(CMAKE_CUDA_COMPILER_VERSION MATCHES [=[([0-9]+)\.([0-9]+)\.([0-9]+)]=])
+    set(CUDAToolkit_VERSION_MAJOR "${CMAKE_MATCH_1}")
+    set(CUDAToolkit_VERSION_MINOR "${CMAKE_MATCH_2}")
+    set(CUDAToolkit_VERSION_PATCH "${CMAKE_MATCH_3}")
+    set(CUDAToolkit_VERSION "${CMAKE_CUDA_COMPILER_VERSION}")
+  endif()
+else()
+  # Compute the version by invoking nvcc
+  execute_process (COMMAND ${CUDAToolkit_NVCC_EXECUTABLE} "--version" OUTPUT_VARIABLE NVCC_OUT)
+  if(NVCC_OUT MATCHES [=[ V([0-9]+)\.([0-9]+)\.([0-9]+)]=])
+    set(CUDAToolkit_VERSION_MAJOR "${CMAKE_MATCH_1}")
+    set(CUDAToolkit_VERSION_MINOR "${CMAKE_MATCH_2}")
+    set(CUDAToolkit_VERSION_PATCH "${CMAKE_MATCH_3}")
+    set(CUDAToolkit_VERSION  "${CMAKE_MATCH_1}.${CMAKE_MATCH_2}.${CMAKE_MATCH_3}")
+  endif()
+  unset(NVCC_OUT)
+endif()
+
+
+get_filename_component(CUDAToolkit_ROOT_DIR ${CUDAToolkit_BIN_DIR} DIRECTORY ABSOLUTE)
+
+# Now that we have the real ROOT_DIR, find components inside it.
+list(APPEND CMAKE_PREFIX_PATH ${CUDAToolkit_ROOT_DIR})
+
+# Find the include/ directory
+find_path(CUDAToolkit_INCLUDE_DIR
+  NAMES cuda_runtime.h
+)
+
+# And find the CUDA Runtime Library libcudart
+find_library(CUDA_CUDART
+  NAMES cudart
+  PATH_SUFFIXES lib64 lib/x64
+)
+if (NOT CUDA_CUDART AND NOT CUDAToolkit_FIND_QUIETLY)
+  message(STATUS "Unable to find cudart library.")
+endif()
+
+unset(CUDAToolkit_ROOT_DIR)
+list(REMOVE_AT CMAKE_PREFIX_PATH -1)
+
+#-----------------------------------------------------------------------------
+# Perform version comparison and validate all required variables are set.
+# MXNET NOTE: This differs from CMake source by ${CMAKE_CURRENT_LIST_DIR}
+# replaced with ${CMAKE_ROOT}/Modules
+include(${CMAKE_ROOT}/Modules/FindPackageHandleStandardArgs.cmake)
+find_package_handle_standard_args(CUDAToolkit
+  REQUIRED_VARS
+    CUDAToolkit_INCLUDE_DIR
+    CUDA_CUDART
+    CUDAToolkit_NVCC_EXECUTABLE
+  VERSION_VAR
+    CUDAToolkit_VERSION
+)
+
+#-----------------------------------------------------------------------------
+# Construct result variables
+if(CUDAToolkit_FOUND)
+ set(CUDAToolkit_INCLUDE_DIRS ${CUDAToolkit_INCLUDE_DIR})
+ get_filename_component(CUDAToolkit_LIBRARY_DIR ${CUDA_CUDART} DIRECTORY ABSOLUTE)
+endif()
+
+#-----------------------------------------------------------------------------
+# Construct import targets
+if(CUDAToolkit_FOUND)
+
+  function(find_and_add_cuda_import_lib lib_name)
+
+    if(ARGC GREATER 1)
+      set(search_names ${ARGN})
+    else()
+      set(search_names ${lib_name})
+    endif()
+
+    find_library(CUDA_${lib_name}_LIBRARY
+      NAMES ${search_names}
+      PATHS ${CUDAToolkit_LIBRARY_DIR}
+            ENV CUDA_PATH
+      PATH_SUFFIXES nvidia/current lib64 lib/x64 lib
+    )
+
+    if (NOT CUDA::${lib_name} AND CUDA_${lib_name}_LIBRARY)
+      add_library(CUDA::${lib_name} IMPORTED INTERFACE)
+      target_include_directories(CUDA::${lib_name} SYSTEM INTERFACE "${CUDAToolkit_INCLUDE_DIRS}")
+      target_link_libraries(CUDA::${lib_name} INTERFACE "${CUDA_${lib_name}_LIBRARY}")
+    endif()
+  endfunction()
+
+  function(add_cuda_link_dependency lib_name)
+    foreach(dependency IN LISTS ${ARGN})
+      target_link_libraries(CUDA::${lib_name} INTERFACE CUDA::${dependency})
+    endforeach()
+  endfunction()
+
+  add_library(CUDA::toolkit IMPORTED INTERFACE)
+  target_include_directories(CUDA::toolkit SYSTEM INTERFACE "${CUDAToolkit_INCLUDE_DIRS}")
+  target_link_directories(CUDA::toolkit INTERFACE "${CUDAToolkit_LIBRARY_DIR}")
+
+
+  find_and_add_cuda_import_lib(cuda_driver cuda)
+
+  find_and_add_cuda_import_lib(cudart)
+  find_and_add_cuda_import_lib(cudart_static)
+
+  foreach (cuda_lib cublas cufft cufftw curand cusolver cusparse nvgraph nvjpeg)
+    find_and_add_cuda_import_lib(${cuda_lib})
+    add_cuda_link_dependency(${cuda_lib} cudart)
+
+    find_and_add_cuda_import_lib(${cuda_lib}_static)
+    add_cuda_link_dependency(${cuda_lib}_static cudart_static)
+  endforeach()
+
+  # cuSOLVER depends on cuBLAS, and cuSPARSE
+  add_cuda_link_dependency(cusolver cublas cusparse)
+  add_cuda_link_dependency(cusolver_static cublas_static cusparse)
+
+  # nvGRAPH depends on cuRAND, and cuSOLVER.
+  add_cuda_link_dependency(nvgraph curand cusolver)
+  add_cuda_link_dependency(nvgraph_static curand_static cusolver_static)
+
+  find_and_add_cuda_import_lib(nppc)
+  find_and_add_cuda_import_lib(nppc_static)
+
+  add_cuda_link_dependency(nppc cudart)
+  add_cuda_link_dependency(nppc_static cudart_static culibos)
+
+  # Process the majority of the NPP libraries.
+  foreach (cuda_lib nppial nppicc nppidei nppif nppig nppim nppist nppitc npps nppicom nppisu)
+    find_and_add_cuda_import_lib(${cuda_lib})
+    find_and_add_cuda_import_lib(${cuda_lib}_static)
+    add_cuda_link_dependency(${cuda_lib} nppc)
+    add_cuda_link_dependency(${cuda_lib}_static nppc_static)
+  endforeach()
+
+  find_and_add_cuda_import_lib(nvrtc)
+  add_cuda_link_dependency(nvrtc cuda_driver)
+
+  find_and_add_cuda_import_lib(nvml nvidia-ml nvml)
+
+  if(WIN32)
+    # nvtools can be installed outside the CUDA toolkit directory
+    # so prefer the NVTOOLSEXT_PATH windows only environment variable
+    # In addition on windows the most common name is nvToolsExt64_1
+    find_library(CUDA_nvToolsExt_LIBRARY
+      NAMES nvToolsExt64_1 nvToolsExt64 nvToolsExt
+      PATHS ENV NVTOOLSEXT_PATH
+            ENV CUDA_PATH
+      PATH_SUFFIXES lib/x64 lib
+    )
+  endif()
+  find_and_add_cuda_import_lib(nvToolsExt nvToolsExt nvToolsExt64)
+
+  add_cuda_link_dependency(nvToolsExt cudart)
+
+  find_and_add_cuda_import_lib(OpenCL)
+
+  find_and_add_cuda_import_lib(culibos)
+  if(TARGET CUDA::culibos)
+    foreach (cuda_lib cublas cufft cusparse curand nvjpeg)
+      add_cuda_link_dependency(${cuda_lib}_static culibos)
+    endforeach()
+  endif()
+
+endif()
diff --git a/contrib/tvmop/compile.py b/contrib/tvmop/compile.py
index b0254218077a..43657f274348 100644
--- a/contrib/tvmop/compile.py
+++ b/contrib/tvmop/compile.py
@@ -50,6 +50,11 @@ def get_cuda_arch(arch):
     if len(arch) == 0:
         return None
 
+    # the arch string is of format '-gencode;arch=compute_XX,code=sm_XX'
+    # this format is computed by CMake CUDA_SELECT_NVCC_ARCH_FLAGS
+    if arch.startswith('-gencode;'):
+        return arch.split(';')
+
     # the arch string contains '-arch=sm_xx'
     flags = arch.split()
     for flag in flags:

From 44784c2554f0330b94d8bde411394da89405c397 Mon Sep 17 00:00:00 2001
From: alicia <32725332+Alicia1529@users.noreply.github.com>
Date: Tue, 31 Dec 2019 04:34:47 +0800
Subject: [PATCH 748/813] add op empty_like, add nan_to_num to dispatch
 (#17169)

---
 python/mxnet/ndarray/numpy/_op.py             | 74 ++++++++++++++++++-
 python/mxnet/numpy/multiarray.py              | 62 +++++++++++++++-
 python/mxnet/numpy_dispatch_protocol.py       |  4 +-
 python/mxnet/numpy_op_fallback.py             | 44 +++++++++++
 python/mxnet/symbol/numpy/_symbol.py          | 63 +++++++++++++++-
 .../unittest/test_numpy_interoperability.py   | 22 +++++-
 tests/python/unittest/test_numpy_op.py        | 60 ++++++++++++++-
 7 files changed, 323 insertions(+), 6 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index c9ffa2328872..0fcfe8a6e54d 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -28,7 +28,7 @@
 from . import _internal as _npi
 from ..ndarray import NDArray
 
-__all__ = ['shape', 'zeros', 'zeros_like', 'ones', 'ones_like', 'full', 'full_like', 'invert', 'delete',
+__all__ = ['shape', 'zeros', 'zeros_like', 'ones', 'ones_like', 'full', 'full_like', 'empty_like', 'invert', 'delete',
            'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power', 'bitwise_not',
            'arctan2', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs',
            'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2',
@@ -372,6 +372,78 @@ def full_like(a, fill_value, dtype=None, order='C', ctx=None, out=None): # pylin
     return _npi.full_like(a, fill_value=fill_value, dtype=dtype, ctx=ctx, out=out)
 
 
+@set_module('mxnet.ndarray.numpy')
+def empty_like(prototype, dtype=None, order='C', subok=False, shape=None): # pylint: disable=W0621
+    """
+    Return a new array with the same shape and type as a given array.
+
+    Parameters
+    ----------
+    prototype : ndarray
+        The shape and data-type of `prototype` define these same attributes
+        of the returned array.
+    dtype : data-type, optional
+        Overrides the data type of the result.
+    order : {'C'}, optional
+        Whether to store multidimensional data in C- or Fortran-contiguous
+        (row- or column-wise) order in memory. Currently only supports C order.
+    subok : {False}, optional
+        If True, then the newly created array will use the sub-class
+        type of 'a', otherwise it will be a base-class array. Defaults
+        to False.
+        (Only support False at this moment)
+    shape : int or sequence of ints, optional.
+        Overrides the shape of the result. If order='K' and the number of
+        dimensions is unchanged, will try to keep order, otherwise,
+        order='C' is implied.
+        (Not supported at this moment)
+
+    Returns
+    -------
+    out : ndarray
+        Array of uninitialized (arbitrary) data with the same
+        shape and type as `prototype`.
+
+    See Also
+    --------
+    ones_like : Return an array of ones with shape and type of input.
+    zeros_like : Return an array of zeros with shape and type of input.
+    full_like : Return a new array with shape of input filled with value.
+    empty : Return a new uninitialized array.
+
+    Notes
+    -----
+    This function does *not* initialize the returned array; to do that use
+    `zeros_like` or `ones_like` instead.  It may be marginally faster than
+    the functions that do set the array values.
+
+    Examples
+    --------
+    >>> a = np.array([[1,2,3], [4,5,6]])
+    >>> np.empty_like(a)
+    array([[-5764607523034234880, -2305834244544065442,           4563075075], # uninitialized
+           [          4567052944, -5764607523034234880,      844424930131968]])
+    >>> a = np.array([[1., 2., 3.],[4.,5.,6.]])
+    >>> np.empty_like(a)
+    array([[4.9e-324, 9.9e-324, 1.5e-323], # uninitialized
+           [2.0e-323, 2.5e-323, 3.0e-323]])
+    """
+    dtype_list = {None:'None', _np.int8:'int8', _np.uint8:'uint8', _np.int32:'int32',
+                  _np.int64:'int64', _np.float16:'float16', _np.float32:'float32',
+                  _np.float64:'float64', _np.bool_:'bool'}
+    if order != 'C':
+        raise NotImplementedError("Only support C-order at this moment")
+    if subok:
+        raise NotImplementedError("Creating array by using sub-class is not supported at this moment")
+    if shape is not None:
+        raise NotImplementedError("Assigning new shape is not supported at this moment")
+    try:
+        dtype = dtype if isinstance(dtype, str) else dtype_list[dtype]
+    except:
+        raise NotImplementedError("Do not support this dtype at this moment")
+    return _npi.empty_like_fallback(prototype, dtype=dtype, order=order, subok=subok, shape=shape)
+
+
 @set_module('mxnet.ndarray.numpy')
 def arange(start, stop=None, step=1, dtype=None, ctx=None):
     """Return evenly spaced values within a given interval.
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index a870b9a7632e..8993b03eab08 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -46,7 +46,8 @@
 from ..ndarray.numpy import _internal as _npi
 from ..ndarray.ndarray import _storage_type
 
-__all__ = ['ndarray', 'empty', 'array', 'shape', 'zeros', 'zeros_like', 'ones', 'ones_like', 'full', 'full_like',
+__all__ = ['ndarray', 'empty', 'empty_like', 'array', 'shape',
+           'zeros', 'zeros_like', 'ones', 'ones_like', 'full', 'full_like',
            'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power', 'bitwise_not', 'delete',
            'arctan2', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'invert',
            'sqrt', 'cbrt', 'abs', 'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log',
@@ -2202,6 +2203,65 @@ def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):
 # pylint: enable=too-many-arguments, redefined-outer-name
 
 
+@set_module('mxnet.numpy')
+def empty_like(prototype, dtype=None, order='C', subok=False, shape=None): # pylint: disable=W0621
+    """
+    Return a new array with the same shape and type as a given array.
+
+    Parameters
+    ----------
+    prototype : ndarray
+        The shape and data-type of `prototype` define these same attributes
+        of the returned array.
+    dtype : data-type, optional
+        Overrides the data type of the result.
+    order : {'C'}, optional
+        Whether to store multidimensional data in C- or Fortran-contiguous
+        (row- or column-wise) order in memory. Currently only supports C order.
+    subok : {False}, optional
+        If True, then the newly created array will use the sub-class
+        type of 'a', otherwise it will be a base-class array. Defaults
+        to False.
+        (Only support False at this moment)
+    shape : int or sequence of ints, optional.
+        Overrides the shape of the result. If order='K' and the number of
+        dimensions is unchanged, will try to keep order, otherwise,
+        order='C' is implied.
+        (Not supported at this moment)
+
+    Returns
+    -------
+    out : ndarray
+        Array of uninitialized (arbitrary) data with the same
+        shape and type as `prototype`.
+
+    See Also
+    --------
+    ones_like : Return an array of ones with shape and type of input.
+    zeros_like : Return an array of zeros with shape and type of input.
+    full_like : Return a new array with shape of input filled with value.
+    empty : Return a new uninitialized array.
+
+    Notes
+    -----
+    This function does *not* initialize the returned array; to do that use
+    `zeros_like` or `ones_like` instead.  It may be marginally faster than
+    the functions that do set the array values.
+
+    Examples
+    --------
+    >>> a = np.array([[1,2,3], [4,5,6]])
+    >>> np.empty_like(a)
+    array([[-5764607523034234880, -2305834244544065442,           4563075075], # uninitialized
+           [          4567052944, -5764607523034234880,      844424930131968]])
+    >>> a = np.array([[1., 2., 3.],[4.,5.,6.]])
+    >>> np.empty_like(a)
+    array([[4.9e-324, 9.9e-324, 1.5e-323], # uninitialized
+           [2.0e-323, 2.5e-323, 3.0e-323]])
+    """
+    return _mx_nd_np.empty_like(prototype, dtype=dtype, order=order, subok=subok, shape=shape)
+
+
 @set_module('mxnet.numpy')
 def identity(n, dtype=None, ctx=None):
     """
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index 65486e6e5f37..db1e2684d7a4 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -153,7 +153,9 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'resize',
     'where',
     'full_like',
-    'bincount'
+    'bincount',
+    'empty_like',
+    'nan_to_num',
 ]
 
 
diff --git a/python/mxnet/numpy_op_fallback.py b/python/mxnet/numpy_op_fallback.py
index f3fd2bf6ae6c..301f510b7db3 100644
--- a/python/mxnet/numpy_op_fallback.py
+++ b/python/mxnet/numpy_op_fallback.py
@@ -18,6 +18,7 @@
 """Fallback-to-NumPy operator implementation."""
 
 from __future__ import absolute_import
+from distutils.version import StrictVersion
 import functools
 import ast
 import numpy as np
@@ -49,6 +50,49 @@ def _register_helper(prop_cls):
     return _register_helper
 
 
+@use_np  # enforce np shape and array semantics for all the methods in this class
+class EmptyLike(operator.CustomOp):
+    """Fallback to NumPy empty_like operator."""
+    def __init__(self, dtype, order, subok, shape):
+        super(EmptyLike, self).__init__()
+        self._dtype = dtype
+        self._order = order
+        self._subok = subok
+        self._shape = shape
+
+    def forward(self, is_train, req, in_data, out_data, aux):
+        np_version = np.version.version
+        if StrictVersion(np_version) >= StrictVersion('1.6.0'):
+            out = np.empty_like(in_data[0].asnumpy(), dtype=self._dtype, order=self._order,
+                                subok=self._subok)
+        else:
+            out = np.empty_like(in_data[0].asnumpy())
+        self.assign(out_data[0], req[0], _mx_np.array(out, dtype=out.dtype, ctx=out_data[0].ctx))
+
+    def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
+        raise NotImplementedError('Operator empty_like does not support gradient computation')
+
+
+@register('empty_like_fallback')
+class EmptyLikeProp(operator.CustomOpProp):
+    """Fallback empty_like operator properties."""
+    def __init__(self, dtype, order, subok, shape):
+        super(EmptyLikeProp, self).__init__(need_top_grad=True)
+        self._dtype = None if dtype == 'None' else dtype
+        self._order = order
+        self._subok = ast.literal_eval(subok)
+        self._shape = ast.literal_eval(shape)
+
+    def list_arguments(self):
+        return ['prototype']
+
+    def infer_shape(self, in_shape):
+        return (in_shape[0],), (in_shape[0],), ()
+
+    def create_operator(self, ctx, in_shapes, in_dtypes):
+        return EmptyLike(self._dtype, self._order, self._subok, self._shape)
+
+
 @use_np  # enforce np shape and array semantics for all the methods in this class
 class Resize(operator.CustomOp):
     """Fallback to NumPy resize operator."""
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 10751cb0d289..0340a5d2220f 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -36,7 +36,7 @@
 except ImportError:
     from builtins import slice as py_slice
 
-__all__ = ['zeros', 'zeros_like', 'ones', 'ones_like', 'full_like', 'bitwise_not', 'invert', 'delete',
+__all__ = ['zeros', 'zeros_like', 'ones', 'ones_like', 'full_like', 'empty_like', 'bitwise_not', 'invert', 'delete',
            'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power', 'arctan2',
            'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs', 'absolute', 'exp',
            'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2', 'log1p',
@@ -1719,6 +1719,67 @@ def eye(N, M=None, k=0, dtype=_np.float32, **kwargs):
     return _npi.eye(N, M, k, ctx, dtype)
 
 
+@set_module('mxnet.symbol.numpy')
+def empty_like(prototype, dtype=None, order='C', subok=False, shape=None): # pylint: disable=W0621
+    """
+    Return a new array with the same shape and type as a given array.
+
+    Parameters
+    ----------
+    prototype : _Symbol
+        The shape and data-type of `prototype` define these same attributes
+        of the returned array.
+    dtype : data-type, optional
+        Overrides the data type of the result.
+    order : {'C'}, optional
+        Whether to store multidimensional data in C- or Fortran-contiguous
+        (row- or column-wise) order in memory. Currently only supports C order.
+    subok : bool, optional.
+        If True, then the newly created array will use the sub-class
+        type of 'a', otherwise it will be a base-class array. Defaults
+        to False.
+        (Only support False at this moment)
+    shape : int or sequence of ints, optional.
+        Overrides the shape of the result. If order='K' and the number of
+        dimensions is unchanged, will try to keep order, otherwise,
+        order='C' is implied.
+        (This parameter is not supported at this moment)
+
+    Returns
+    -------
+    out : _Symbol
+        Array of uninitialized (arbitrary) data with the same
+        shape and type as `prototype`.
+
+    See Also
+    --------
+    ones_like : Return an array of ones with shape and type of input.
+    zeros_like : Return an array of zeros with shape and type of input.
+    full_like : Return a new array with shape of input filled with value.
+    empty : Return a new uninitialized array.
+
+    Notes
+    -----
+    This function does *not* initialize the returned array; to do that use
+    `zeros_like` or `ones_like` instead.  It may be marginally faster than
+    the functions that do set the array values.
+    """
+    dtype_list = {None:'None', _np.int8:'int8', _np.uint8:'uint8', _np.int32:'int32',
+                  _np.int64:'int64', _np.float16:'float16', _np.float32:'float32',
+                  _np.float64:'float64', _np.bool_:'bool'}
+    if order != 'C':
+        raise NotImplementedError("Only support C order at this moment")
+    if subok:
+        raise NotImplementedError("Creating array by using sub-class is not supported at this moment")
+    if shape is not None:
+        raise NotImplementedError("Parameter 'shape' is not supported at this moment")
+    try:
+        dtype = dtype if isinstance(dtype, str) else dtype_list[dtype]
+    except:
+        raise NotImplementedError("Do not support this dtype at this moment")
+    return _npi.empty_like_fallback(prototype, dtype=dtype, order=order, subok=subok, shape=shape)
+
+
 @set_module('mxnet.symbol.numpy')
 def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0, ctx=None): # pylint: disable=too-many-arguments
     r"""
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index bb45fead7cd9..d2064789c761 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -1555,6 +1555,24 @@ def _add_workload_resize():
     OpArgMngr.add_workload('resize', np.zeros((10, 0)), (0, 10))
     OpArgMngr.add_workload('resize', np.zeros((10, 0)), (0, 100))
 
+def _add_workload_empty_like():
+    OpArgMngr.add_workload('empty_like', np.random.uniform(low=0, high=100, size=(1,3,4), dtype='float64'))
+    OpArgMngr.add_workload('empty_like', np.random.uniform(low=0, high=100, size=(9,3,1)), np.int32)
+    OpArgMngr.add_workload('empty_like', np.random.uniform(low=0, high=100, size=(9,3)), 'float32')
+    OpArgMngr.add_workload('empty_like', np.random.uniform(low=0, high=100, size=(9,3,1)), np.bool_)
+    OpArgMngr.add_workload('empty_like', np.random.uniform(low=0, high=100, size=(0,3)), np.float32)
+
+
+def _add_workload_nan_to_num():
+    array1 = np.array([[-433, 0, 456, _np.inf], [-1, -_np.inf, 0, 1]])
+    array2 = np.array([_np.nan, _np.inf, -_np.inf, -574, 0, 23425, 24234,-5])
+    array3 = np.array(-_np.inf)
+    OpArgMngr.add_workload('nan_to_num', array1, True, 0, 100, -100)
+    OpArgMngr.add_workload('nan_to_num', array1, True, 0.00)
+    OpArgMngr.add_workload('nan_to_num', array2, True)
+    OpArgMngr.add_workload('nan_to_num', array2, True, -2000, 10000, -10000)
+    OpArgMngr.add_workload('nan_to_num', array3, True)
+
 
 @use_np
 def _prepare_workloads():
@@ -1691,6 +1709,8 @@ def _prepare_workloads():
     _add_workload_diff()
     _add_workload_resize()
     _add_workload_full_like(array_pool)
+    _add_workload_empty_like()
+    _add_workload_nan_to_num()
 
 
 _prepare_workloads()
@@ -1735,7 +1755,7 @@ def check_interoperability(op_list):
     for name in op_list:
         if name in _TVM_OPS and not is_op_runnable():
             continue
-        if name in ['shares_memory', 'may_share_memory']:  # skip list
+        if name in ['shares_memory', 'may_share_memory', 'empty_like']:  # skip list
             continue
         if name in ['full_like', 'zeros_like', 'ones_like'] and \
                 StrictVersion(platform.python_version()) < StrictVersion('3.0.0'):
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 9f746d4dd47c..7e8c34456723 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -6136,7 +6136,65 @@ def hybrid_forward(self, F, a, weights):
         np_out = _np.bincount(data.asnumpy(), weights_np, minlength)
         assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
 
-        
+
+@with_seed()
+@use_np
+def test_np_empty_like():
+    class TestEmptyLike(HybridBlock):
+        def __init__(self, dtype, order, subok):
+            super(TestEmptyLike, self).__init__()
+            self._dtype = dtype
+            self._order = order 
+            self._subok = subok
+
+        def hybrid_forward(self, F, x, *args, **kwargs):
+            return F.np.empty_like(x, self._dtype, self._order, self._subok)
+
+    if StrictVersion(platform.python_version()) < StrictVersion('3.0.0'):
+        return
+
+    dtypes = [None, 'float16', 'float32', np.int8, np.uint8, np.int32, np.int64,
+              np.float16, np.float32, np.float64, np.bool_]
+    shapes = [
+        (),
+        (1,),
+        (5,),
+        (4, 3),
+        (3, 5),
+        (4, 4),
+        (4, 5),
+        (5, 5),
+        (5, 6),
+        (6, 6),
+        (0, 1),
+        (6, 5, 6),
+        (2, 3, 3, 4),
+        (4, 2, 1, 2),
+        (0, 5, 3, 3),
+        (5, 0, 3, 3),
+        (3, 3, 0, 0),
+    ]
+    orders = ["C"]
+    subok_list = [False]
+    flags = [False]
+    _np_version = _np.version.version
+    for dtype, shape, hybridize, order, subok in itertools.product(dtypes, shapes, flags, orders, subok_list):
+        prototype = np.random.uniform(low=0, high=100, size=shape, dtype='float64').astype(dtype)
+        test = TestEmptyLike(dtype, order, subok)
+        if StrictVersion(_np_version) >= StrictVersion('1.6.0'):
+            expected_ret = _np.empty_like(prototype, dtype=dtype, order=order, subok=subok)
+        else:
+            expected_ret = _np.empty_like(prototype)
+        if hybridize:
+            test.hybridize()
+        ret = test(prototype)
+        assert ret.asnumpy().shape == expected_ret.shape
+
+        # check imperative again
+        ret = np.empty_like(prototype, dtype, order, subok)
+        assert ret.asnumpy().shape == expected_ret.shape
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From 498fd650aeb0b152718ff2cf82e23f6f2ac79653 Mon Sep 17 00:00:00 2001
From: Yiyan66 <57363390+Yiyan66@users.noreply.github.com>
Date: Tue, 31 Dec 2019 15:12:27 +0800
Subject: [PATCH 749/813] [numpy] add op flipud, fliplr (#17192)

* add op

* change something

* change py

* raise error

* change error

* change error info
---
 python/mxnet/ndarray/numpy/_op.py             | 111 ++++++++++++++++-
 python/mxnet/numpy/multiarray.py              | 112 +++++++++++++++++-
 python/mxnet/numpy_dispatch_protocol.py       |   2 +
 python/mxnet/symbol/numpy/_symbol.py          |  56 ++++++++-
 src/operator/numpy/np_matrix_op-inl.h         |   3 +-
 .../unittest/test_numpy_interoperability.py   |  14 +++
 tests/python/unittest/test_numpy_op.py        |  54 +++++++++
 7 files changed, 338 insertions(+), 14 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index 0fcfe8a6e54d..e226c30ff20b 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -37,10 +37,11 @@
            'logspace', 'expand_dims', 'tile', 'arange', 'array_split', 'split', 'vsplit', 'concatenate', 'append',
            'stack', 'vstack', 'column_stack', 'dstack', 'average', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip',
            'argmax', 'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'unravel_index', 'hanning', 'hamming',
-           'blackman', 'flip', 'around', 'round', 'hypot', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad',
-           'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal',
-           'greater', 'less', 'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero',
-           'shares_memory', 'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where', 'bincount']
+           'blackman', 'flip', 'flipud', 'fliplr', 'around', 'round', 'hypot', 'bitwise_xor', 'bitwise_or',
+           'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer',
+           'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum',
+           'true_divide', 'nonzero', 'shares_memory', 'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where',
+           'bincount']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -4813,6 +4814,108 @@ def flip(m, axis=None, out=None):
         raise TypeError('type {} not supported'.format(str(type(m))))
 
 
+@set_module('mxnet.ndarray.numpy')
+def flipud(m):
+    r"""
+    flipud(*args, **kwargs)
+
+    Flip array in the up/down direction.
+
+    Flip the entries in each column in the up/down direction.
+    Rows are preserved, but appear in a different order than before.
+
+    Parameters
+    ----------
+    m : array_like
+        Input array.
+
+    Returns
+    -------
+    out : array_like
+        A view of `m` with the rows reversed.  Since a view is
+        returned, this operation is :math:`\mathcal O(1)`.
+
+    See Also
+    --------
+    fliplr : Flip array in the left/right direction.
+    rot90 : Rotate array counterclockwise.
+
+    Notes
+    -----
+    Equivalent to ``m[::-1,...]``.
+    Does not require the array to be two-dimensional.
+
+    Examples
+    --------
+    >>> A = np.diag(np.array([1.0, 2, 3]))
+    >>> A
+    array([[1.,  0.,  0.],
+           [0.,  2.,  0.],
+           [0.,  0.,  3.]])
+    >>> np.flipud(A)
+    array([[0.,  0.,  3.],
+           [0.,  2.,  0.],
+           [1.,  0.,  0.]])
+
+    >>> A = np.random.randn(2,3,5)
+    >>> np.all(np.flipud(A) == A[::-1,...])
+    array(True)
+
+    >>> np.flipud(np.array([1,2]))
+    array([2., 1.])
+    """
+    return flip(m, 0)
+
+
+@set_module('mxnet.ndarray.numpy')
+def fliplr(m):
+    r"""
+    fliplr(*args, **kwargs)
+
+    Flip array in the left/right direction.
+
+    Flip the entries in each row in the left/right direction.
+    Columns are preserved, but appear in a different order than before.
+
+    Parameters
+    ----------
+    m : array_like
+        Input array, must be at least 2-D.
+
+    Returns
+    -------
+    f : ndarray
+        A view of `m` with the columns reversed.  Since a view
+        is returned, this operation is :math:`\mathcal O(1)`.
+
+    See Also
+    --------
+    flipud : Flip array in the up/down direction.
+    rot90 : Rotate array counterclockwise.
+
+    Notes
+    -----
+    Equivalent to m[:,::-1]. Requires the array to be at least 2-D.
+
+    Examples
+    --------
+    >>> A = np.diag(np.array([1.,2.,3.]))
+    >>> A
+    array([[1.,  0.,  0.],
+           [0.,  2.,  0.],
+           [0.,  0.,  3.]])
+    >>> np.fliplr(A)
+    array([[0.,  0.,  1.],
+           [0.,  2.,  0.],
+           [3.,  0.,  0.]])
+
+    >>> A = np.random.randn(2,3,5)
+    >>> np.all(np.fliplr(A) == A[:,::-1,...])
+    array(True)
+    """
+    return flip(m, 1)
+
+
 @set_module('mxnet.ndarray.numpy')
 def around(x, decimals=0, out=None, **kwargs):
     r"""
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 8993b03eab08..847f106575a8 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -56,11 +56,11 @@
            'tensordot', 'eye', 'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'array_split',
            'split', 'vsplit', 'concatenate', 'stack', 'vstack', 'column_stack', 'dstack', 'average', 'mean',
            'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'argmin', 'std', 'var', 'indices', 'copysign',
-           'ravel', 'unravel_index', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'round', 'arctan2',
-           'hypot', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity',
-           'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal',
-           'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory',
-           'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where', 'bincount']
+           'ravel', 'unravel_index', 'hanning', 'hamming', 'blackman', 'flip', 'flipud', 'fliplr', 'around',
+           'round', 'arctan2', 'hypot', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad', 'unique', 'lcm',
+           'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater',
+           'less', 'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero',
+           'shares_memory', 'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where', 'bincount']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -6513,6 +6513,108 @@ def flip(m, axis=None, out=None):
     return _mx_nd_np.flip(m, axis, out=out)
 
 
+@set_module('mxnet.numpy')
+def flipud(m):
+    r"""
+    flipud(*args, **kwargs)
+
+    Flip array in the up/down direction.
+
+    Flip the entries in each column in the up/down direction.
+    Rows are preserved, but appear in a different order than before.
+
+    Parameters
+    ----------
+    m : array_like
+        Input array.
+
+    Returns
+    -------
+    out : array_like
+        A view of `m` with the rows reversed.  Since a view is
+        returned, this operation is :math:`\mathcal O(1)`.
+
+    See Also
+    --------
+    fliplr : Flip array in the left/right direction.
+    rot90 : Rotate array counterclockwise.
+
+    Notes
+    -----
+    Equivalent to ``m[::-1,...]``.
+    Does not require the array to be two-dimensional.
+
+    Examples
+    --------
+    >>> A = np.diag(np.array([1.0, 2, 3]))
+    >>> A
+    array([[1.,  0.,  0.],
+           [0.,  2.,  0.],
+           [0.,  0.,  3.]])
+    >>> np.flipud(A)
+    array([[0.,  0.,  3.],
+           [0.,  2.,  0.],
+           [1.,  0.,  0.]])
+
+    >>> A = np.random.randn(2,3,5)
+    >>> np.all(np.flipud(A) == A[::-1,...])
+    array(True)
+
+    >>> np.flipud(np.array([1,2]))
+    array([2., 1.])
+    """
+    return flip(m, 0)
+
+
+@set_module('mxnet.numpy')
+def fliplr(m):
+    r"""
+    fliplr(*args, **kwargs)
+
+    Flip array in the left/right direction.
+
+    Flip the entries in each row in the left/right direction.
+    Columns are preserved, but appear in a different order than before.
+
+    Parameters
+    ----------
+    m : array_like
+        Input array, must be at least 2-D.
+
+    Returns
+    -------
+    f : ndarray
+        A view of `m` with the columns reversed.  Since a view
+        is returned, this operation is :math:`\mathcal O(1)`.
+
+    See Also
+    --------
+    flipud : Flip array in the up/down direction.
+    rot90 : Rotate array counterclockwise.
+
+    Notes
+    -----
+    Equivalent to m[:,::-1]. Requires the array to be at least 2-D.
+
+    Examples
+    --------
+    >>> A = np.diag([1.,2.,3.])
+    >>> A
+    array([[1.,  0.,  0.],
+        [0.,  2.,  0.],
+        [0.,  0.,  3.]])
+    >>> np.fliplr(A)
+    array([[0.,  0.,  1.],
+        [0.,  2.,  0.],
+        [3.,  0.,  0.]])
+
+    >>> A = np.random.randn(2,3,5)
+    >>> np.all(np.fliplr(A) == A[:,::-1,...])
+    array(True)
+    """
+    return flip(m, 1)
+
+
 @set_module('mxnet.numpy')
 def around(x, decimals=0, out=None, **kwargs):
     r"""
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index db1e2684d7a4..56a5dd5ac813 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -104,6 +104,8 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'expand_dims',
     'fix',
     'flip',
+    'flipud',
+    'fliplr',
     'inner',
     'max',
     'amax',
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 0340a5d2220f..d3ed84ae0e49 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -45,10 +45,10 @@
            'logspace', 'expand_dims', 'tile', 'arange', 'array_split', 'split', 'vsplit', 'concatenate', 'append',
            'stack', 'vstack', 'column_stack', 'dstack', 'average', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip',
            'argmax', 'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'unravel_index', 'hanning', 'hamming',
-           'blackman', 'flip', 'around', 'round', 'hypot', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad',
-           'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal',
-           'greater', 'less', 'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide',
-           'shares_memory', 'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where', 'bincount']
+           'blackman', 'flip', 'flipud', 'fliplr', 'around', 'round', 'hypot', 'bitwise_xor', 'bitwise_or',
+           'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer',
+           'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum',
+           'true_divide', 'shares_memory', 'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where', 'bincount']
 
 
 @set_module('mxnet.symbol.numpy')
@@ -4581,6 +4581,54 @@ def flip(m, axis=None, out=None):
         raise TypeError('type {} not supported'.format(str(type(m))))
 
 
+@set_module('mxnet.symbol.numpy')
+def flipud(m):
+    r"""
+    flipud(*args, **kwargs)
+
+    Flip array in the up/down direction.
+
+    Flip the entries in each column in the up/down direction.
+    Rows are preserved, but appear in a different order than before.
+
+    Parameters
+    ----------
+    m : array_like
+        Input array.
+
+    Returns
+    -------
+    out : array_like
+        A view of `m` with the rows reversed.  Since a view is
+        returned, this operation is :math:`\mathcal O(1)`.
+    """
+    return flip(m, 0)
+
+
+@set_module('mxnet.symbol.numpy')
+def fliplr(m):
+    r"""
+    fliplr(*args, **kwargs)
+
+    Flip array in the left/right direction.
+
+    Flip the entries in each row in the left/right direction.
+    Columns are preserved, but appear in a different order than before.
+
+    Parameters
+    ----------
+    m : array_like
+        Input array, must be at least 2-D.
+
+    Returns
+    -------
+    f : ndarray
+        A view of `m` with the columns reversed.  Since a view
+        is returned, this operation is :math:`\mathcal O(1)`.
+    """
+    return flip(m, 1)
+
+
 @set_module('mxnet.symbol.numpy')
 def around(x, decimals=0, out=None, **kwargs):
     r"""
diff --git a/src/operator/numpy/np_matrix_op-inl.h b/src/operator/numpy/np_matrix_op-inl.h
index 3abf5627f20f..2007a49ca662 100644
--- a/src/operator/numpy/np_matrix_op-inl.h
+++ b/src/operator/numpy/np_matrix_op-inl.h
@@ -498,7 +498,8 @@ void NumpyFlipForward(const nnvm::NodeAttrs& attrs,
   std::vector<index_t>  trailing_(axistemp.ndim());
   index_t flip_index = 0;
   for (int axis : axistemp) {
-    CHECK_LT(axis, ishape.ndim());
+    CHECK_LT(axis, ishape.ndim()) << "axis " << axis
+      << " is out of bounds for array of dimension " << ishape.ndim() << std::endl;
     stride_[flip_index] = ishape[axis];
     trailing_[flip_index] = 1;
     for (int i2 = axis + 1; i2 < ishape.ndim(); ++i2) {
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index d2064789c761..d940422408f4 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -787,6 +787,18 @@ def _add_workload_flip():
     OpArgMngr.add_workload('flip', np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]), (1, 2))
 
 
+def _add_workload_flipud():
+    OpArgMngr.add_workload('flipud', np.random.normal(size=(4, 4)))
+    OpArgMngr.add_workload('flipud', np.array([[0, 1, 2], [3, 4, 5]]))
+    OpArgMngr.add_workload('flipud', np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]))
+
+
+def _add_workload_fliplr():
+    OpArgMngr.add_workload('fliplr', np.random.normal(size=(4, 4)))
+    OpArgMngr.add_workload('fliplr', np.array([[0, 1, 2], [3, 4, 5]]))
+    OpArgMngr.add_workload('fliplr', np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]))
+
+
 def _add_workload_max(array_pool):
     OpArgMngr.add_workload('max', array_pool['4x1'])
 
@@ -1606,6 +1618,8 @@ def _prepare_workloads():
     _add_workload_expand_dims()
     _add_workload_fix()
     _add_workload_flip()
+    _add_workload_flipud()
+    _add_workload_fliplr()
     _add_workload_max(array_pool)
     _add_workload_amax(array_pool)
     _add_workload_min(array_pool)
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 7e8c34456723..fe32352b90be 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -4710,6 +4710,60 @@ def hybrid_forward(self, F, x):
                 assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
 
 
+@with_seed()
+@use_np
+def test_np_flipud_fliplr():
+    class TestFlipud(HybridBlock):
+        def __init__(self):
+            super(TestFlipud, self).__init__()
+
+        def hybrid_forward(self, F, x):
+            return F.np.flipud(x)
+    
+    class TestFliplr(HybridBlock):
+        def __init__(self):
+            super(TestFliplr, self).__init__()
+
+        def hybrid_forward(self, F, x):
+            return F.np.fliplr(x)
+
+    shapes = [(1, 2, 3), (1, 0)]
+    types = ['int32', 'int64', 'float16', 'float32', 'float64']
+    for func in ['flipud', 'fliplr']:
+        for hybridize in [True, False]:
+            for oneType in types:
+                rtol, atol=1e-3, 1e-5
+                for shape in shapes:
+                    if func == 'flipud':
+                        test_flip = TestFlipud()
+                    else:
+                        test_flip = TestFliplr()
+                    if hybridize:
+                        test_flip.hybridize()
+                    x = rand_ndarray(shape, dtype=oneType).as_np_ndarray()
+                    x.attach_grad()
+                    if func == 'flipud':
+                        np_out = _np.flipud(x.asnumpy())
+                    else:
+                        np_out = _np.fliplr(x.asnumpy())
+                    with mx.autograd.record():
+                        mx_out = test_flip(x)
+                    assert mx_out.shape == np_out.shape
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
+                    mx_out.backward()
+                    np_backward = _np.ones(np_out.shape)
+                    assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=rtol, atol=atol)
+
+                    # Test imperative once again
+                    if func == 'flipud':
+                        mx_out = np.flipud(x)
+                        np_out = _np.flipud(x.asnumpy())
+                    else:
+                        mx_out = np.fliplr(x)
+                        np_out = _np.fliplr(x.asnumpy())
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
+
+
 @with_seed()
 @use_np
 def test_np_around():

From e9c482e1817ff8ae4e5433c9a0025201ebca6825 Mon Sep 17 00:00:00 2001
From: Sheng Zha <szha@users.noreply.github.com>
Date: Tue, 31 Dec 2019 16:51:31 +0800
Subject: [PATCH 750/813] Static Build and CD for mxnet-cu102/mxnet-cu102mkl
 (#17074)

* add cu102 packages

* cd job

* update dockers

* update docker image tag

* fix cudnn script

* update ar on static build dockers
---
 cd/Jenkinsfile_cd_pipeline                  |   2 +-
 cd/Jenkinsfile_release_job                  |   2 +-
 cd/README.md                                |   4 +-
 cd/utils/mxnet_base_image.sh                |   5 +-
 ci/docker/Dockerfile.build.ubuntu_gpu_cu100 |   2 +-
 ci/docker/Dockerfile.build.ubuntu_gpu_cu101 |   2 +-
 ci/docker/Dockerfile.build.ubuntu_gpu_cu102 |  81 +++++++++
 ci/docker/Dockerfile.build.ubuntu_gpu_cu90  |   2 +-
 ci/docker/Dockerfile.build.ubuntu_gpu_cu92  |   2 +-
 ci/docker/Dockerfile.publish.ubuntu1404_cpu |   3 +
 ci/docker/Dockerfile.publish.ubuntu1404_gpu |   3 +
 ci/docker/install/ubuntu_cudnn.sh           |   4 +
 make/staticbuild/linux_cu102.mk             | 188 ++++++++++++++++++++
 make/staticbuild/linux_cu102mkl.mk          | 173 ++++++++++++++++++
 tools/pip/doc/CU101MKL_ADDITIONAL.md        |   2 +-
 tools/pip/doc/CU101_ADDITIONAL.md           |   2 +-
 tools/pip/doc/CU102MKL_ADDITIONAL.md        |  46 +++++
 tools/pip/doc/CU102_ADDITIONAL.md           |  46 +++++
 tools/pip/doc/CU75MKL_ADDITIONAL.md         |   2 +
 tools/pip/doc/CU75_ADDITIONAL.md            |   2 +
 tools/pip/doc/CU80MKL_ADDITIONAL.md         |   4 +
 tools/pip/doc/CU80_ADDITIONAL.md            |   4 +
 tools/pip/doc/CU90MKL_ADDITIONAL.md         |   2 +
 tools/pip/doc/CU90_ADDITIONAL.md            |   2 +
 tools/pip/doc/CU92MKL_ADDITIONAL.md         |   2 +
 tools/pip/doc/CU92_ADDITIONAL.md            |   2 +
 tools/pip/setup.py                          |   6 +-
 tools/setup_gpu_build_tools.sh              |  51 ++++--
 28 files changed, 625 insertions(+), 21 deletions(-)
 create mode 100644 ci/docker/Dockerfile.build.ubuntu_gpu_cu102
 create mode 100644 make/staticbuild/linux_cu102.mk
 create mode 100644 make/staticbuild/linux_cu102mkl.mk
 create mode 100644 tools/pip/doc/CU102MKL_ADDITIONAL.md
 create mode 100644 tools/pip/doc/CU102_ADDITIONAL.md

diff --git a/cd/Jenkinsfile_cd_pipeline b/cd/Jenkinsfile_cd_pipeline
index afb7b9b6d27f..79b1df0bfcd4 100644
--- a/cd/Jenkinsfile_cd_pipeline
+++ b/cd/Jenkinsfile_cd_pipeline
@@ -36,7 +36,7 @@ pipeline {
 
   parameters {
     // Release parameters
-    string(defaultValue: "cpu,mkl,cu90,cu90mkl,cu92,cu92mkl,cu100,cu100mkl,cu101,cu101mkl", description: "Comma separated list of variants", name: "MXNET_VARIANTS")
+    string(defaultValue: "cpu,mkl,cu90,cu90mkl,cu92,cu92mkl,cu100,cu100mkl,cu101,cu101mkl,cu102,cu102mkl", description: "Comma separated list of variants", name: "MXNET_VARIANTS")
     booleanParam(defaultValue: false, description: 'Whether this is a release build or not', name: "RELEASE_BUILD")
   }
 
diff --git a/cd/Jenkinsfile_release_job b/cd/Jenkinsfile_release_job
index 4d6f3b5e9012..38620756af41 100644
--- a/cd/Jenkinsfile_release_job
+++ b/cd/Jenkinsfile_release_job
@@ -43,7 +43,7 @@ pipeline {
     // any disruption caused by different COMMIT_ID values chaning the job parameter configuration on 
     // Jenkins.
     string(defaultValue: "mxnet_lib/static", description: "Pipeline to build", name: "RELEASE_JOB_TYPE")
-    string(defaultValue: "cpu,mkl,cu90,cu90mkl,cu92,cu92mkl,cu100,cu100mkl,cu101,cu101mkl", description: "Comma separated list of variants", name: "MXNET_VARIANTS")
+    string(defaultValue: "cpu,mkl,cu90,cu90mkl,cu92,cu92mkl,cu100,cu100mkl,cu101,cu101mkl,cu102,cu102mkl", description: "Comma separated list of variants", name: "MXNET_VARIANTS")
     booleanParam(defaultValue: false, description: 'Whether this is a release build or not', name: "RELEASE_BUILD")
   }
 
diff --git a/cd/README.md b/cd/README.md
index a3dc0387a4b3..af71fa626683 100644
--- a/cd/README.md
+++ b/cd/README.md
@@ -37,8 +37,10 @@ Currently, 10 variants are supported:
 * *cu92mkl*: CUDA 9.2 w/ MKL-DNN
 * *cu100*: CUDA 10
 * *cu100mkl*: CUDA 10 w/ MKL-DNN
-* *cu101*: CUDA 10
+* *cu101*: CUDA 10.1
 * *cu101mkl*: CUDA 10.1 w/ MKL-DNN
+* *cu102*: CUDA 10.2
+* *cu102mkl*: CUDA 10.2 w/ MKL-DNN
 
 *For more on variants, see [here](https://github.com/apache/incubator-mxnet/issues/8671)*
 
diff --git a/cd/utils/mxnet_base_image.sh b/cd/utils/mxnet_base_image.sh
index dcfe7216dcb4..c39746797ac9 100755
--- a/cd/utils/mxnet_base_image.sh
+++ b/cd/utils/mxnet_base_image.sh
@@ -20,7 +20,7 @@
 
 mxnet_variant=${1:?"Please specify the mxnet variant as the first parameter"}
 
-case ${mxnet_variant} in 
+case ${mxnet_variant} in
     cu80*)
     echo "nvidia/cuda:8.0-cudnn7-runtime-ubuntu16.04"
     ;;
@@ -36,6 +36,9 @@ case ${mxnet_variant} in
     cu101*)
     echo "nvidia/cuda:10.1-cudnn7-runtime-ubuntu16.04"
     ;;
+    cu102*)
+    echo "nvidia/cuda:10.2-cudnn7-runtime-ubuntu16.04"
+    ;;
     cpu)
     echo "ubuntu:16.04"
     ;;
diff --git a/ci/docker/Dockerfile.build.ubuntu_gpu_cu100 b/ci/docker/Dockerfile.build.ubuntu_gpu_cu100
index 514f6bb1495b..ccf69404ee3a 100644
--- a/ci/docker/Dockerfile.build.ubuntu_gpu_cu100
+++ b/ci/docker/Dockerfile.build.ubuntu_gpu_cu100
@@ -65,7 +65,7 @@ RUN /work/ubuntu_docs.sh
 COPY install/ubuntu_tutorials.sh /work/
 RUN /work/ubuntu_tutorials.sh
 
-ENV CUDNN_VERSION=7.6.0.64
+ENV CUDNN_VERSION=7.6.5.32
 COPY install/ubuntu_cudnn.sh /work/
 RUN /work/ubuntu_cudnn.sh
 
diff --git a/ci/docker/Dockerfile.build.ubuntu_gpu_cu101 b/ci/docker/Dockerfile.build.ubuntu_gpu_cu101
index ff6b89bf8e06..a6e59f3435d5 100644
--- a/ci/docker/Dockerfile.build.ubuntu_gpu_cu101
+++ b/ci/docker/Dockerfile.build.ubuntu_gpu_cu101
@@ -65,7 +65,7 @@ RUN /work/ubuntu_docs.sh
 COPY install/ubuntu_tutorials.sh /work/
 RUN /work/ubuntu_tutorials.sh
 
-ENV CUDNN_VERSION=7.6.0.64
+ENV CUDNN_VERSION=7.6.5.32
 COPY install/ubuntu_cudnn.sh /work/
 RUN /work/ubuntu_cudnn.sh
 
diff --git a/ci/docker/Dockerfile.build.ubuntu_gpu_cu102 b/ci/docker/Dockerfile.build.ubuntu_gpu_cu102
new file mode 100644
index 000000000000..3a6e15bb0f6d
--- /dev/null
+++ b/ci/docker/Dockerfile.build.ubuntu_gpu_cu102
@@ -0,0 +1,81 @@
+# -*- mode: dockerfile -*-
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+# Dockerfile to run MXNet on Ubuntu 16.04 for GPU
+
+FROM nvidia/cuda:10.2-devel-ubuntu16.04
+
+WORKDIR /work/deps
+
+COPY install/ubuntu_core.sh /work/
+RUN /work/ubuntu_core.sh
+
+COPY install/deb_ubuntu_ccache.sh /work/
+RUN /work/deb_ubuntu_ccache.sh
+
+COPY install/ubuntu_python.sh /work/
+COPY install/requirements /work/
+RUN /work/ubuntu_python.sh
+
+COPY install/ubuntu_scala.sh /work/
+COPY install/sbt.gpg /work/
+RUN /work/ubuntu_scala.sh
+
+COPY install/ubuntu_r.sh /work/
+COPY install/r.gpg /work/
+RUN /work/ubuntu_r.sh
+
+COPY install/ubuntu_perl.sh /work/
+RUN /work/ubuntu_perl.sh
+
+COPY install/ubuntu_clang.sh /work/
+RUN /work/ubuntu_clang.sh
+
+COPY install/ubuntu_tvm.sh /work/
+RUN /work/ubuntu_tvm.sh
+
+COPY install/ubuntu_llvm.sh /work/
+RUN /work/ubuntu_llvm.sh
+
+COPY install/ubuntu_caffe.sh /work/
+RUN /work/ubuntu_caffe.sh
+
+COPY install/ubuntu_onnx.sh /work/
+RUN /work/ubuntu_onnx.sh
+
+COPY install/ubuntu_docs.sh /work/
+COPY install/requirements /work/
+RUN /work/ubuntu_docs.sh
+
+COPY install/ubuntu_tutorials.sh /work/
+RUN /work/ubuntu_tutorials.sh
+
+ENV CUDNN_VERSION=7.6.5.32
+COPY install/ubuntu_cudnn.sh /work/
+RUN /work/ubuntu_cudnn.sh
+
+# Always last
+ARG USER_ID=0
+ARG GROUP_ID=0
+COPY install/ubuntu_adduser.sh /work/
+RUN /work/ubuntu_adduser.sh
+
+COPY runtime_functions.sh /work/
+
+WORKDIR /work/mxnet
+ENV LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda/compat
diff --git a/ci/docker/Dockerfile.build.ubuntu_gpu_cu90 b/ci/docker/Dockerfile.build.ubuntu_gpu_cu90
index 579ad7bffddf..e1fe585d75bf 100644
--- a/ci/docker/Dockerfile.build.ubuntu_gpu_cu90
+++ b/ci/docker/Dockerfile.build.ubuntu_gpu_cu90
@@ -65,7 +65,7 @@ RUN /work/ubuntu_docs.sh
 COPY install/ubuntu_tutorials.sh /work/
 RUN /work/ubuntu_tutorials.sh
 
-ENV CUDNN_VERSION=7.6.0.64
+ENV CUDNN_VERSION=7.6.5.32
 COPY install/ubuntu_cudnn.sh /work/
 RUN /work/ubuntu_cudnn.sh
 
diff --git a/ci/docker/Dockerfile.build.ubuntu_gpu_cu92 b/ci/docker/Dockerfile.build.ubuntu_gpu_cu92
index dc125aee371d..0de9d87c6516 100644
--- a/ci/docker/Dockerfile.build.ubuntu_gpu_cu92
+++ b/ci/docker/Dockerfile.build.ubuntu_gpu_cu92
@@ -65,7 +65,7 @@ RUN /work/ubuntu_docs.sh
 COPY install/ubuntu_tutorials.sh /work/
 RUN /work/ubuntu_tutorials.sh
 
-ENV CUDNN_VERSION=7.6.0.64
+ENV CUDNN_VERSION=7.6.5.32
 COPY install/ubuntu_cudnn.sh /work/
 RUN /work/ubuntu_cudnn.sh
 
diff --git a/ci/docker/Dockerfile.publish.ubuntu1404_cpu b/ci/docker/Dockerfile.publish.ubuntu1404_cpu
index 04ce94f95eae..993d9d873075 100644
--- a/ci/docker/Dockerfile.publish.ubuntu1404_cpu
+++ b/ci/docker/Dockerfile.publish.ubuntu1404_cpu
@@ -25,6 +25,9 @@ WORKDIR /work/deps
 COPY install/ubuntu_publish.sh /work/
 RUN /work/ubuntu_publish.sh
 
+COPY install/ubuntu_ar.sh /work/
+RUN /work/ubuntu_ar.sh
+
 ARG USER_ID=0
 ARG GROUP_ID=0
 COPY install/ubuntu_adduser.sh /work/
diff --git a/ci/docker/Dockerfile.publish.ubuntu1404_gpu b/ci/docker/Dockerfile.publish.ubuntu1404_gpu
index 4d9fa819a39e..eb214a620009 100644
--- a/ci/docker/Dockerfile.publish.ubuntu1404_gpu
+++ b/ci/docker/Dockerfile.publish.ubuntu1404_gpu
@@ -25,6 +25,9 @@ WORKDIR /work/deps
 COPY install/ubuntu_publish.sh /work/
 RUN /work/ubuntu_publish.sh
 
+COPY install/ubuntu_ar.sh /work/
+RUN /work/ubuntu_ar.sh
+
 ARG USER_ID=0
 ARG GROUP_ID=0
 COPY install/ubuntu_adduser.sh /work/
diff --git a/ci/docker/install/ubuntu_cudnn.sh b/ci/docker/install/ubuntu_cudnn.sh
index b773fea0f82f..eaf50447305c 100755
--- a/ci/docker/install/ubuntu_cudnn.sh
+++ b/ci/docker/install/ubuntu_cudnn.sh
@@ -32,6 +32,10 @@ fi
 apt-get update || true
 
 case ${CUDA_VERSION} in
+	10\.2*)
+		export libcudnn7_version="${CUDNN_VERSION}-1+cuda10.2"
+		export libcudnn7_dev_version="${CUDNN_VERSION}-1+cuda10.2"
+		;;
 	10\.1*)
 		export libcudnn7_version="${CUDNN_VERSION}-1+cuda10.1"
 		export libcudnn7_dev_version="${CUDNN_VERSION}-1+cuda10.1"
diff --git a/make/staticbuild/linux_cu102.mk b/make/staticbuild/linux_cu102.mk
new file mode 100644
index 000000000000..7e2a46e3b411
--- /dev/null
+++ b/make/staticbuild/linux_cu102.mk
@@ -0,0 +1,188 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+#-------------------------------------------------------------------------------
+#  Template configuration for compiling mxnet for making python wheel
+#-------------------------------------------------------------------------------
+
+#---------------------
+# choice of compiler
+#--------------------
+
+export CC = gcc
+export CXX = g++
+export NVCC = nvcc
+
+# whether compile with options for MXNet developer
+DEV = 0
+
+# whether compile with debug
+DEBUG = 0
+
+# whether to turn on signal handler (e.g. segfault logger)
+USE_SIGNAL_HANDLER = 1
+
+# the additional link flags you want to add
+ADD_LDFLAGS += -L$(DEPS_PATH)/lib $(DEPS_PATH)/lib/libculibos.a -lpng -ltiff -ljpeg -lz -ldl -lgfortran -Wl,--version-script=$(CURDIR)/make/config/libmxnet.ver,-rpath,'$${ORIGIN}',--gc-sections
+
+# the additional compile flags you want to add
+ADD_CFLAGS += -I$(DEPS_PATH)/include -ffunction-sections -fdata-sections
+
+#---------------------------------------------
+# matrix computation libraries for CPU/GPU
+#---------------------------------------------
+
+# choose the version of blas you want to use
+# can be: mkl, blas, atlas, openblas
+# in default use atlas for linux while apple for osx
+USE_BLAS=openblas
+
+# whether use opencv during compilation
+# you can disable it, however, you will not able to use
+# imbin iterator
+USE_OPENCV = 1
+# Add OpenCV include path, in which the directory `opencv2` exists
+USE_OPENCV_INC_PATH = NONE
+# Add OpenCV shared library path, in which the shared library exists
+USE_OPENCV_LIB_PATH = NONE
+
+# whether use CUDA during compile
+USE_CUDA = 1
+
+# add the path to CUDA library to link and compile flag
+# if you have already add them to environment variable, leave it as NONE
+# USE_CUDA_PATH = /usr/local/cuda
+USE_CUDA_PATH = $(DEPS_PATH)/usr/local/cuda-10.2
+
+# whether to use CuDNN library
+USE_CUDNN = 1
+
+# whether to use NCCL library
+USE_NCCL = 1
+
+# CUDA architecture setting: going with all of them.
+# For CUDA < 6.0, comment the *_50 lines for compatibility.
+# CUDA_ARCH :=
+
+# whether use cuda runtime compiling for writing kernels in native language (i.e. Python)
+ENABLE_CUDA_RTC = 1
+
+USE_NVTX=1
+
+# use openmp for parallelization
+USE_OPENMP = 1
+USE_OPERATOR_TUNING = 1
+USE_LIBJPEG_TURBO = 1
+
+# whether use MKL-DNN library
+USE_MKLDNN = 0
+
+
+# MKL ML Library for Intel CPU/Xeon Phi
+# Please refer to MKL_README.md for details
+
+# MKL ML Library folder, need to be root for /usr/local
+# Change to User Home directory for standard user
+# For USE_BLAS!=mkl only
+MKLML_ROOT=/usr/local
+
+# whether use MKL2017 library
+USE_MKL2017 = 0
+
+# whether use MKL2017 experimental feature for high performance
+# Prerequisite USE_MKL2017=1
+USE_MKL2017_EXPERIMENTAL = 0
+
+# whether use NNPACK library
+USE_NNPACK = 0
+
+# whether use lapack during compilation
+# only effective when compiled with blas versions openblas/apple/atlas/mkl
+USE_LAPACK = 1
+
+# path to lapack library in case of a non-standard installation
+USE_LAPACK_PATH = $(DEPS_PATH)/lib
+
+# add path to intel library, you may need it for MKL, if you did not add the path
+# to environment variable
+USE_INTEL_PATH = NONE
+
+# If use MKL, choose static link automatically to allow python wrapper
+ifeq ($(USE_BLAS), mkl)
+USE_STATIC_MKL = 1
+else
+USE_STATIC_MKL = NONE
+endif
+
+#----------------------------
+# Settings for power and arm arch
+#----------------------------
+ARCH := $(shell uname -a)
+ifneq (,$(filter $(ARCH), armv6l armv7l powerpc64le ppc64le aarch64))
+	USE_SSE=0
+else
+	USE_SSE=1
+endif
+
+#----------------------------
+# distributed computing
+#----------------------------
+
+# whether or not to enable multi-machine supporting
+USE_DIST_KVSTORE = 1
+
+# whether or not allow to read and write HDFS directly. If yes, then hadoop is
+# required
+USE_HDFS = 0
+
+# path to libjvm.so. required if USE_HDFS=1
+LIBJVM=$(JAVA_HOME)/jre/lib/amd64/server
+
+# whether or not allow to read and write AWS S3 directly. If yes, then
+# libcurl4-openssl-dev is required, it can be installed on Ubuntu by
+# sudo apt-get install -y libcurl4-openssl-dev
+USE_S3 = 1
+
+#----------------------------
+# additional operators
+#----------------------------
+
+# path to folders containing projects specific operators that you don't want to put in src/operators
+EXTRA_OPERATORS =
+
+
+#----------------------------
+# plugins
+#----------------------------
+
+# whether to use caffe integration. This requires installing caffe.
+# You also need to add CAFFE_PATH/build/lib to your LD_LIBRARY_PATH
+# CAFFE_PATH = $(HOME)/caffe
+# MXNET_PLUGINS += plugin/caffe/caffe.mk
+
+# whether to use torch integration. This requires installing torch.
+# You also need to add TORCH_PATH/install/lib to your LD_LIBRARY_PATH
+# TORCH_PATH = $(HOME)/torch
+# MXNET_PLUGINS += plugin/torch/torch.mk
+
+# WARPCTC_PATH = $(HOME)/warp-ctc
+# MXNET_PLUGINS += plugin/warpctc/warpctc.mk
+
+# whether to use sframe integration. This requires build sframe
+# git@github.com:dato-code/SFrame.git
+# SFRAME_PATH = $(HOME)/SFrame
+# MXNET_PLUGINS += plugin/sframe/plugin.mk
diff --git a/make/staticbuild/linux_cu102mkl.mk b/make/staticbuild/linux_cu102mkl.mk
new file mode 100644
index 000000000000..4bc649fb5423
--- /dev/null
+++ b/make/staticbuild/linux_cu102mkl.mk
@@ -0,0 +1,173 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+#-------------------------------------------------------------------------------
+#  Template configuration for compiling mxnet for making python wheel
+#-------------------------------------------------------------------------------
+
+#---------------------
+# choice of compiler
+#--------------------
+
+export CC = gcc
+export CXX = g++
+export NVCC = nvcc
+
+# whether compile with options for MXNet developer
+DEV = 0
+
+# whether compile with debug
+DEBUG = 0
+
+# whether to turn on signal handler (e.g. segfault logger)
+USE_SIGNAL_HANDLER = 1
+
+# the additional link flags you want to add
+ADD_LDFLAGS += -L$(DEPS_PATH)/lib $(DEPS_PATH)/lib/libculibos.a -lpng -ltiff -ljpeg -lz -ldl -lgfortran -Wl,--version-script=$(CURDIR)/make/config/libmxnet.ver,-rpath,'$${ORIGIN}',--gc-sections
+
+# the additional compile flags you want to add
+ADD_CFLAGS += -I$(DEPS_PATH)/include -ffunction-sections -fdata-sections
+
+#---------------------------------------------
+# matrix computation libraries for CPU/GPU
+#---------------------------------------------
+
+# choose the version of blas you want to use
+# can be: mkl, blas, atlas, openblas
+# in default use atlas for linux while apple for osx
+USE_BLAS=openblas
+
+# whether use opencv during compilation
+# you can disable it, however, you will not able to use
+# imbin iterator
+USE_OPENCV = 1
+# Add OpenCV include path, in which the directory `opencv2` exists
+USE_OPENCV_INC_PATH = NONE
+# Add OpenCV shared library path, in which the shared library exists
+USE_OPENCV_LIB_PATH = NONE
+
+# whether use CUDA during compile
+USE_CUDA = 1
+
+# add the path to CUDA library to link and compile flag
+# if you have already add them to environment variable, leave it as NONE
+# USE_CUDA_PATH = /usr/local/cuda
+USE_CUDA_PATH = $(DEPS_PATH)/usr/local/cuda-10.2
+
+# whether to use CuDNN library
+USE_CUDNN = 1
+
+# whether to use NCCL library
+USE_NCCL = 1
+
+# CUDA architecture setting: going with all of them.
+# For CUDA < 6.0, comment the *_50 lines for compatibility.
+# CUDA_ARCH :=
+
+# whether use cuda runtime compiling for writing kernels in native language (i.e. Python)
+ENABLE_CUDA_RTC = 1
+
+USE_NVTX=1
+
+# use openmp for parallelization
+USE_OPENMP = 1
+USE_OPERATOR_TUNING = 1
+USE_LIBJPEG_TURBO = 1
+
+# whether use MKL-DNN library
+USE_MKLDNN = 1
+
+# whether use NNPACK library
+USE_NNPACK = 0
+
+# whether use lapack during compilation
+# only effective when compiled with blas versions openblas/apple/atlas/mkl
+USE_LAPACK = 1
+
+# path to lapack library in case of a non-standard installation
+USE_LAPACK_PATH = $(DEPS_PATH)/lib
+
+# add path to intel library, you may need it for MKL, if you did not add the path
+# to environment variable
+USE_INTEL_PATH = NONE
+
+# If use MKL, choose static link automatically to allow python wrapper
+ifeq ($(USE_BLAS), mkl)
+USE_STATIC_MKL = 1
+else
+USE_STATIC_MKL = NONE
+endif
+
+#----------------------------
+# Settings for power and arm arch
+#----------------------------
+ARCH := $(shell uname -a)
+ifneq (,$(filter $(ARCH), armv6l armv7l powerpc64le ppc64le aarch64))
+	USE_SSE=0
+else
+	USE_SSE=1
+endif
+
+#----------------------------
+# distributed computing
+#----------------------------
+
+# whether or not to enable multi-machine supporting
+USE_DIST_KVSTORE = 1
+
+# whether or not allow to read and write HDFS directly. If yes, then hadoop is
+# required
+USE_HDFS = 0
+
+# path to libjvm.so. required if USE_HDFS=1
+LIBJVM=$(JAVA_HOME)/jre/lib/amd64/server
+
+# whether or not allow to read and write AWS S3 directly. If yes, then
+# libcurl4-openssl-dev is required, it can be installed on Ubuntu by
+# sudo apt-get install -y libcurl4-openssl-dev
+USE_S3 = 1
+
+#----------------------------
+# additional operators
+#----------------------------
+
+# path to folders containing projects specific operators that you don't want to put in src/operators
+EXTRA_OPERATORS =
+
+
+#----------------------------
+# plugins
+#----------------------------
+
+# whether to use caffe integration. This requires installing caffe.
+# You also need to add CAFFE_PATH/build/lib to your LD_LIBRARY_PATH
+# CAFFE_PATH = $(HOME)/caffe
+# MXNET_PLUGINS += plugin/caffe/caffe.mk
+
+# whether to use torch integration. This requires installing torch.
+# You also need to add TORCH_PATH/install/lib to your LD_LIBRARY_PATH
+# TORCH_PATH = $(HOME)/torch
+# MXNET_PLUGINS += plugin/torch/torch.mk
+
+# WARPCTC_PATH = $(HOME)/warp-ctc
+# MXNET_PLUGINS += plugin/warpctc/warpctc.mk
+
+# whether to use sframe integration. This requires build sframe
+# git@github.com:dato-code/SFrame.git
+# SFRAME_PATH = $(HOME)/SFrame
+# MXNET_PLUGINS += plugin/sframe/plugin.mk
+
diff --git a/tools/pip/doc/CU101MKL_ADDITIONAL.md b/tools/pip/doc/CU101MKL_ADDITIONAL.md
index 9cdebe3dddbb..028cc2556425 100644
--- a/tools/pip/doc/CU101MKL_ADDITIONAL.md
+++ b/tools/pip/doc/CU101MKL_ADDITIONAL.md
@@ -40,5 +40,5 @@ Installation
 ------------
 To install:
 ```bash
-pip install mxnet-cu100mkl
+pip install mxnet-cu101mkl
 ```
diff --git a/tools/pip/doc/CU101_ADDITIONAL.md b/tools/pip/doc/CU101_ADDITIONAL.md
index f58413c09e72..7d9ae5bcfc12 100644
--- a/tools/pip/doc/CU101_ADDITIONAL.md
+++ b/tools/pip/doc/CU101_ADDITIONAL.md
@@ -40,5 +40,5 @@ Installation
 ------------
 To install:
 ```bash
-pip install mxnet-cu100
+pip install mxnet-cu101
 ```
diff --git a/tools/pip/doc/CU102MKL_ADDITIONAL.md b/tools/pip/doc/CU102MKL_ADDITIONAL.md
new file mode 100644
index 000000000000..07be16b2f462
--- /dev/null
+++ b/tools/pip/doc/CU102MKL_ADDITIONAL.md
@@ -0,0 +1,46 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+Prerequisites
+-------------
+This package supports Linux and Windows platforms. You may also want to check:
+- [mxnet-cu102](https://pypi.python.org/pypi/mxnet-cu102/) with CUDA-10.2 support.
+- [mxnet-cu101](https://pypi.python.org/pypi/mxnet-cu101/) with CUDA-10.1 support.
+- [mxnet-cu101mkl](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 and MKLDNN support.
+- [mxnet-cu100](https://pypi.python.org/pypi/mxnet-cu100/) with CUDA-10.0 support.
+- [mxnet-cu100mkl](https://pypi.python.org/pypi/mxnet-cu100mkl/) with CUDA-10.0 support and MKLDNN support.
+- [mxnet-cu92](https://pypi.python.org/pypi/mxnet-cu92/) with CUDA-9.2 support.
+- [mxnet-cu92mkl](https://pypi.python.org/pypi/mxnet-cu92mkl/) with CUDA-9.2 support and MKLDNN support.
+- [mxnet-cu90](https://pypi.python.org/pypi/mxnet-cu90/) with CUDA-9.0 support.
+- [mxnet-cu90mkl](https://pypi.python.org/pypi/mxnet-cu90mkl/) with CUDA-9.0 support and MKLDNN support.
+- [mxnet-cu80](https://pypi.python.org/pypi/mxnet-cu80/) with CUDA-8.0 support.
+- [mxnet-cu80mkl](https://pypi.python.org/pypi/mxnet-cu80mkl/) with CUDA-8.0 support and MKLDNN support.
+- [mxnet-cu75](https://pypi.python.org/pypi/mxnet-cu75/) with CUDA-7.5 support.
+- [mxnet-cu75mkl](https://pypi.python.org/pypi/mxnet-cu75mkl/) with CUDA-7.5 support and MKLDNN support.
+- [mxnet-mkl](https://pypi.python.org/pypi/mxnet-mkl/) with MKLDNN support.
+- [mxnet](https://pypi.python.org/pypi/mxnet/).
+
+To download CUDA, check [CUDA download](https://developer.nvidia.com/cuda-downloads). For more instructions, check [CUDA Toolkit online documentation](http://docs.nvidia.com/cuda/index.html).
+
+To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions of CUDA, check [Installing MXNet](https://mxnet.incubator.apache.org/versions/master/install/index.html) for instructions on building from source.
+
+Installation
+------------
+To install:
+```bash
+pip install mxnet-cu102mkl
+```
diff --git a/tools/pip/doc/CU102_ADDITIONAL.md b/tools/pip/doc/CU102_ADDITIONAL.md
new file mode 100644
index 000000000000..c9efd4b64118
--- /dev/null
+++ b/tools/pip/doc/CU102_ADDITIONAL.md
@@ -0,0 +1,46 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+Prerequisites
+-------------
+This package supports Linux and Windows platforms. You may also want to check:
+- [mxnet-cu102mkl](https://pypi.python.org/pypi/mxnet-cu102/) with CUDA-10.2 support and MKLDNN support.
+- [mxnet-cu101](https://pypi.python.org/pypi/mxnet-cu101/) with CUDA-10.1 support.
+- [mxnet-cu101mkl](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support and MKLDNN support.
+- [mxnet-cu100](https://pypi.python.org/pypi/mxnet-cu100mkl/) with CUDA-10.0 support.
+- [mxnet-cu100mkl](https://pypi.python.org/pypi/mxnet-cu100mkl/) with CUDA-10.0 support and MKLDNN support.
+- [mxnet-cu92](https://pypi.python.org/pypi/mxnet-cu92/) with CUDA-9.2 support.
+- [mxnet-cu92mkl](https://pypi.python.org/pypi/mxnet-cu92mkl/) with CUDA-9.2 support and MKLDNN support.
+- [mxnet-cu90](https://pypi.python.org/pypi/mxnet-cu90/) with CUDA-9.0 support.
+- [mxnet-cu90mkl](https://pypi.python.org/pypi/mxnet-cu90mkl/) with CUDA-9.0 support and MKLDNN support.
+- [mxnet-cu80](https://pypi.python.org/pypi/mxnet-cu80/) with CUDA-8.0 support.
+- [mxnet-cu80mkl](https://pypi.python.org/pypi/mxnet-cu80mkl/) with CUDA-8.0 support and MKLDNN support.
+- [mxnet-cu75](https://pypi.python.org/pypi/mxnet-cu75/) with CUDA-7.5 support.
+- [mxnet-cu75mkl](https://pypi.python.org/pypi/mxnet-cu75mkl/) with CUDA-7.5 support and MKLDNN support.
+- [mxnet-mkl](https://pypi.python.org/pypi/mxnet-mkl/) with MKLDNN support.
+- [mxnet](https://pypi.python.org/pypi/mxnet/).
+
+To download CUDA, check [CUDA download](https://developer.nvidia.com/cuda-downloads). For more instructions, check [CUDA Toolkit online documentation](http://docs.nvidia.com/cuda/index.html).
+
+To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions, check [Installing MXNet](https://mxnet.incubator.apache.org/versions/master/install/index.html) for instructions on building from source.
+
+Installation
+------------
+To install:
+```bash
+pip install mxnet-cu102
+```
diff --git a/tools/pip/doc/CU75MKL_ADDITIONAL.md b/tools/pip/doc/CU75MKL_ADDITIONAL.md
index dc485d687f1e..cb74a01f11d3 100644
--- a/tools/pip/doc/CU75MKL_ADDITIONAL.md
+++ b/tools/pip/doc/CU75MKL_ADDITIONAL.md
@@ -15,6 +15,8 @@
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
 
+**CUDA 7.5 package for MXNet is no longer maintained for new releases.**
+
 Prerequisites
 -------------
 This package supports Linux only, up to 1.2.1. You may also want to check:
diff --git a/tools/pip/doc/CU75_ADDITIONAL.md b/tools/pip/doc/CU75_ADDITIONAL.md
index a5257913adc5..53665ce33d42 100644
--- a/tools/pip/doc/CU75_ADDITIONAL.md
+++ b/tools/pip/doc/CU75_ADDITIONAL.md
@@ -15,6 +15,8 @@
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
 
+**CUDA 7.5 package for MXNet is no longer maintained for new releases.**
+
 Prerequisites
 -------------
 This package supports Linux only, up to 1.2.1. You may also want to check:
diff --git a/tools/pip/doc/CU80MKL_ADDITIONAL.md b/tools/pip/doc/CU80MKL_ADDITIONAL.md
index 24cec2b93668..e4f73f94850f 100644
--- a/tools/pip/doc/CU80MKL_ADDITIONAL.md
+++ b/tools/pip/doc/CU80MKL_ADDITIONAL.md
@@ -15,9 +15,13 @@
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
 
+**CUDA 8.0 package for MXNet is no longer maintained for new releases.**
+
 Prerequisites
 -------------
 This package supports Linux and Windows platforms. You may also want to check:
+- [mxnet-cu102](https://pypi.python.org/pypi/mxnet-cu102/) with CUDA-10.2 support.
+- [mxnet-cu102mkl](https://pypi.python.org/pypi/mxnet-cu102mkl/) with CUDA-10.2 support and MKLDNN support.
 - [mxnet-cu101](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support.
 - [mxnet-cu101mkl](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support and MKLDNN support.
 - [mxnet-cu100](https://pypi.python.org/pypi/mxnet-cu100mkl/) with CUDA-10.0 support.
diff --git a/tools/pip/doc/CU80_ADDITIONAL.md b/tools/pip/doc/CU80_ADDITIONAL.md
index 767f034e120e..23e5c2ea56d0 100644
--- a/tools/pip/doc/CU80_ADDITIONAL.md
+++ b/tools/pip/doc/CU80_ADDITIONAL.md
@@ -15,9 +15,13 @@
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
 
+**CUDA 8.0 package for MXNet is no longer maintained for new releases.**
+
 Prerequisites
 -------------
 This package supports Linux and Windows platforms. You may also want to check:
+- [mxnet-cu102](https://pypi.python.org/pypi/mxnet-cu102/) with CUDA-10.2 support.
+- [mxnet-cu102mkl](https://pypi.python.org/pypi/mxnet-cu102mkl/) with CUDA-10.2 support and MKLDNN support.
 - [mxnet-cu101](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support.
 - [mxnet-cu101mkl](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support and MKLDNN support.
 - [mxnet-cu100](https://pypi.python.org/pypi/mxnet-cu100mkl/) with CUDA-10.0 support.
diff --git a/tools/pip/doc/CU90MKL_ADDITIONAL.md b/tools/pip/doc/CU90MKL_ADDITIONAL.md
index 66f18d35ef73..27cd5c3d8619 100644
--- a/tools/pip/doc/CU90MKL_ADDITIONAL.md
+++ b/tools/pip/doc/CU90MKL_ADDITIONAL.md
@@ -18,6 +18,8 @@
 Prerequisites
 -------------
 This package supports Linux and Windows platforms. You may want to check:
+- [mxnet-cu102](https://pypi.python.org/pypi/mxnet-cu102/) with CUDA-10.2 support.
+- [mxnet-cu102mkl](https://pypi.python.org/pypi/mxnet-cu102mkl/) with CUDA-10.2 support and MKLDNN support.
 - [mxnet-cu101](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support.
 - [mxnet-cu101mkl](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support and MKLDNN support.
 - [mxnet-cu100](https://pypi.python.org/pypi/mxnet-cu100mkl/) with CUDA-10.0 support.
diff --git a/tools/pip/doc/CU90_ADDITIONAL.md b/tools/pip/doc/CU90_ADDITIONAL.md
index f21b77019d12..8fb007eac867 100644
--- a/tools/pip/doc/CU90_ADDITIONAL.md
+++ b/tools/pip/doc/CU90_ADDITIONAL.md
@@ -18,6 +18,8 @@
 Prerequisites
 -------------
 This package supports Linux and Windows platforms. You may also want to check:
+- [mxnet-cu102](https://pypi.python.org/pypi/mxnet-cu102/) with CUDA-10.2 support.
+- [mxnet-cu102mkl](https://pypi.python.org/pypi/mxnet-cu102mkl/) with CUDA-10.2 support and MKLDNN support.
 - [mxnet-cu101](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support.
 - [mxnet-cu101mkl](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support and MKLDNN support.
 - [mxnet-cu100](https://pypi.python.org/pypi/mxnet-cu100mkl/) with CUDA-10.0 support.
diff --git a/tools/pip/doc/CU92MKL_ADDITIONAL.md b/tools/pip/doc/CU92MKL_ADDITIONAL.md
index a4e64f55bde9..840159053db7 100644
--- a/tools/pip/doc/CU92MKL_ADDITIONAL.md
+++ b/tools/pip/doc/CU92MKL_ADDITIONAL.md
@@ -18,6 +18,8 @@
 Prerequisites
 -------------
 This package supports Linux and Windows platforms. You may also want to check:
+- [mxnet-cu102](https://pypi.python.org/pypi/mxnet-cu102/) with CUDA-10.2 support.
+- [mxnet-cu102mkl](https://pypi.python.org/pypi/mxnet-cu102mkl/) with CUDA-10.2 support and MKLDNN support.
 - [mxnet-cu101](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support.
 - [mxnet-cu101mkl](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support and MKLDNN support.
 - [mxnet-cu100](https://pypi.python.org/pypi/mxnet-cu100mkl/) with CUDA-10.0 support.
diff --git a/tools/pip/doc/CU92_ADDITIONAL.md b/tools/pip/doc/CU92_ADDITIONAL.md
index 849245de72c9..0485e3075c6f 100644
--- a/tools/pip/doc/CU92_ADDITIONAL.md
+++ b/tools/pip/doc/CU92_ADDITIONAL.md
@@ -18,6 +18,8 @@
 Prerequisites
 -------------
 This package supports Linux and Windows platforms. You may also want to check:
+- [mxnet-cu102](https://pypi.python.org/pypi/mxnet-cu102/) with CUDA-10.2 support.
+- [mxnet-cu102mkl](https://pypi.python.org/pypi/mxnet-cu102mkl/) with CUDA-10.2 support and MKLDNN support.
 - [mxnet-cu101](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support.
 - [mxnet-cu101mkl](https://pypi.python.org/pypi/mxnet-cu101mkl/) with CUDA-10.1 support and MKLDNN support.
 - [mxnet-cu100](https://pypi.python.org/pypi/mxnet-cu100mkl/) with CUDA-10.0 support.
diff --git a/tools/pip/setup.py b/tools/pip/setup.py
index 4a8b49eb01eb..36934a80761b 100644
--- a/tools/pip/setup.py
+++ b/tools/pip/setup.py
@@ -126,7 +126,11 @@ def has_ext_modules(self):
 if variant == 'CPU':
     libraries.append('openblas')
 else:
-    if variant.startswith('CU100'):
+    if variant.startswith('CU102'):
+        libraries.append('CUDA-10.2')
+    elif variant.startswith('CU101'):
+        libraries.append('CUDA-10.1')
+    elif variant.startswith('CU100'):
         libraries.append('CUDA-10.0')
     elif variant.startswith('CU92'):
         libraries.append('CUDA-9.2')
diff --git a/tools/setup_gpu_build_tools.sh b/tools/setup_gpu_build_tools.sh
index fa5596fc66b8..bba37108b98b 100755
--- a/tools/setup_gpu_build_tools.sh
+++ b/tools/setup_gpu_build_tools.sh
@@ -27,24 +27,30 @@ VARIANT=$1
 DEPS_PATH=$2
 
 >&2 echo "Setting CUDA versions for $VARIANT"
-if [[ $VARIANT == cu101* ]]; then
+if [[ $VARIANT == cu102* ]]; then
+    CUDA_VERSION='10.2.89-1'
+    CUDA_PATCH_VERSION='10.2.2.89-1'
+    LIBCUDA_VERSION='440.33.01-0ubuntu1'
+    LIBCUDNN_VERSION='7.6.5.32-1+cuda10.2'
+    LIBNCCL_VERSION='2.5.6-1+cuda10.2'
+elif [[ $VARIANT == cu101* ]]; then
     CUDA_VERSION='10.1.105-1'
     CUDA_PATCH_VERSION='10.1.0.105-1'
     LIBCUDA_VERSION='418.39-0ubuntu1'
-    LIBCUDNN_VERSION='7.6.0.64-1+cuda10.1'
-    LIBNCCL_VERSION='2.4.7-1+cuda10.1'
+    LIBCUDNN_VERSION='7.6.5.32-1+cuda10.1'
+    LIBNCCL_VERSION='2.5.6-1+cuda10.1'
 elif [[ $VARIANT == cu100* ]]; then
     CUDA_VERSION='10.0.130-1'
     CUDA_PATCH_VERSION='10.0.130-1'
     LIBCUDA_VERSION='410.48-0ubuntu1'
-    LIBCUDNN_VERSION='7.6.0.64-1+cuda10.0'
-    LIBNCCL_VERSION='2.4.7-1+cuda10.0'
+    LIBCUDNN_VERSION='7.6.5.32-1+cuda10.0'
+    LIBNCCL_VERSION='2.5.6-1+cuda10.0'
 elif [[ $VARIANT == cu92* ]]; then
     CUDA_VERSION='9.2.148-1'
     CUDA_PATCH_VERSION='9.2.148.1-1'
     LIBCUDA_VERSION='396.44-0ubuntu1'
-    LIBCUDNN_VERSION='7.6.0.64-1+cuda9.2'
-    LIBNCCL_VERSION='2.4.7-1+cuda9.2'
+    LIBCUDNN_VERSION='7.6.5.32-1+cuda9.2'
+    LIBNCCL_VERSION='2.4.8-1+cuda9.2'
 elif [[ $VARIANT == cu91* ]]; then
     CUDA_VERSION='9.1.85-1'
     CUDA_PATCH_VERSION='9.1.85.3-1'
@@ -55,8 +61,8 @@ elif [[ $VARIANT == cu90* ]]; then
     CUDA_VERSION='9.0.176-1'
     CUDA_PATCH_VERSION='9.0.176.3-1'
     LIBCUDA_VERSION='384.145-0ubuntu1'
-    LIBCUDNN_VERSION='7.6.0.64-1+cuda9.0'
-    LIBNCCL_VERSION='2.4.7-1+cuda9.0'
+    LIBCUDNN_VERSION='7.6.5.32-1+cuda9.0'
+    LIBNCCL_VERSION='2.5.6-1+cuda9.0'
 elif [[ $VARIANT == cu80* ]]; then
     CUDA_VERSION='8.0.61-1'
     CUDA_PATCH_VERSION='8.0.61.2-1'
@@ -92,7 +98,32 @@ if [[ $VARIANT == cu* ]]; then
 fi
 
 # list of debs to download from nvidia
-if [[ $VARIANT == cu101* ]]; then
+if [[ $VARIANT == cu102* ]]; then
+    cuda_files=( \
+      "cuda-core-${CUDA_MAJOR_DASH}_${CUDA_VERSION}_amd64.deb" \
+      "libcublas10_${CUDA_PATCH_VERSION}_amd64.deb" \
+      "libcublas-dev_${CUDA_PATCH_VERSION}_amd64.deb" \
+      "cuda-cudart-${CUDA_MAJOR_DASH}_${CUDA_VERSION}_amd64.deb" \
+      "cuda-cudart-dev-${CUDA_MAJOR_DASH}_${CUDA_VERSION}_amd64.deb" \
+      "cuda-curand-${CUDA_MAJOR_DASH}_${CUDA_VERSION}_amd64.deb" \
+      "cuda-curand-dev-${CUDA_MAJOR_DASH}_${CUDA_VERSION}_amd64.deb" \
+      "cuda-cufft-${CUDA_MAJOR_DASH}_${CUDA_VERSION}_amd64.deb" \
+      "cuda-cufft-dev-${CUDA_MAJOR_DASH}_${CUDA_VERSION}_amd64.deb" \
+      "cuda-nvrtc-${CUDA_MAJOR_DASH}_${CUDA_VERSION}_amd64.deb" \
+      "cuda-nvrtc-dev-${CUDA_MAJOR_DASH}_${CUDA_VERSION}_amd64.deb" \
+      "cuda-cusolver-${CUDA_MAJOR_DASH}_${CUDA_VERSION}_amd64.deb" \
+      "cuda-cusolver-dev-${CUDA_MAJOR_DASH}_${CUDA_VERSION}_amd64.deb" \
+      "cuda-misc-headers-${CUDA_MAJOR_DASH}_${CUDA_VERSION}_amd64.deb" \
+      "cuda-nvcc-${CUDA_MAJOR_DASH}_${CUDA_VERSION}_amd64.deb" \
+      "cuda-nvtx-${CUDA_MAJOR_DASH}_${CUDA_VERSION}_amd64.deb" \
+      "libcuda1-${LIBCUDA_MAJOR}_${LIBCUDA_VERSION}_amd64.deb" \
+      "nvidia-${LIBCUDA_MAJOR}_${LIBCUDA_VERSION}_amd64.deb" \
+    )
+    ml_files=( \
+      "libcudnn${LIBCUDNN_MAJOR}-dev_${LIBCUDNN_VERSION}_amd64.deb" \
+      "libnccl-dev_${LIBNCCL_VERSION}_amd64.deb" \
+    )
+elif [[ $VARIANT == cu101* ]]; then
     cuda_files=( \
       "cuda-core-${CUDA_MAJOR_DASH}_${CUDA_VERSION}_amd64.deb" \
       "libcublas10_${CUDA_PATCH_VERSION}_amd64.deb" \

From c020f378bde8fe80cc15d8e0fabaa54027f63dd5 Mon Sep 17 00:00:00 2001
From: Sheng Zha <szha@users.noreply.github.com>
Date: Tue, 31 Dec 2019 16:59:44 +0800
Subject: [PATCH 751/813] [DEBUG] enable custom error type (#17128)

* enable custom error type

* fix test

* fix lint

* fix error for cython

* handle windows path in error message normalize
---
 include/mxnet/c_api_error.h            |  32 ++++-
 python/mxnet/__init__.py               |   2 +-
 python/mxnet/base.py                   | 162 +++++++++++++++++++++---
 python/mxnet/cython/base.pyi           |   4 +-
 python/mxnet/error.py                  |  57 +++++++++
 src/c_api/c_api_error.cc               | 167 +++++++++++++++++++++++++
 src/operator/numpy/np_matrix_op.cc     |   5 +-
 tests/python/unittest/test_numpy_op.py |  64 +++++-----
 8 files changed, 437 insertions(+), 56 deletions(-)
 create mode 100644 python/mxnet/error.py

diff --git a/include/mxnet/c_api_error.h b/include/mxnet/c_api_error.h
index e76a2c99f8d3..5c6176b465ae 100644
--- a/include/mxnet/c_api_error.h
+++ b/include/mxnet/c_api_error.h
@@ -22,6 +22,8 @@
  * \file c_api_error.h
  * \brief Error handling for C API.
  */
+#include <string>
+
 #ifndef MXNET_C_API_ERROR_H_
 #define MXNET_C_API_ERROR_H_
 
@@ -51,6 +53,34 @@
   }                                                                            \
   on_exit_api();                                                               \
   return 0; // NOLINT(*)
+
+//--------------------------------------------------------
+// Error handling mechanism
+// -------------------------------------------------------
+// Standard error message format, {} means optional
+//--------------------------------------------------------
+// {error_type:} {message0}
+// {message1}
+// {message2}
+// {Stack trace:}    // stack traces follow by this line
+//   {trace 0}       // two spaces in the begining.
+//   {trace 1}
+//   {trace 2}
+//--------------------------------------------------------
+/*!
+ * \brief Normalize error message
+ *
+ *  Parse them header generated by by LOG(FATAL) and CHECK
+ *  and reformat the message into the standard format.
+ *
+ *  This function will also merge all the stack traces into
+ *  one trace and trim them.
+ *
+ * \param err_msg The error message.
+ * \return normalized message.
+ */
+std::string NormalizeError(std::string err_msg);
+
 /*!
  * \brief Set the last error message needed by C API
  * \param msg The error message to set.
@@ -62,7 +92,7 @@ void MXAPISetLastError(const char* msg);
  * \return the return value of API after exception is handled
  */
 inline int MXAPIHandleException(const std::exception &e) {
-  MXAPISetLastError(e.what());
+  MXAPISetLastError(NormalizeError(e.what()).c_str());
   return -1;
 }
 
diff --git a/python/mxnet/__init__.py b/python/mxnet/__init__.py
index ed6e81daaca8..5bd6963a4b9e 100644
--- a/python/mxnet/__init__.py
+++ b/python/mxnet/__init__.py
@@ -22,7 +22,7 @@
 from __future__ import absolute_import
 
 from .context import Context, current_context, cpu, gpu, cpu_pinned
-from . import engine
+from . import engine, error
 from .base import MXNetError
 from .util import is_np_shape, set_np_shape, np_shape, use_np_shape
 from .util import is_np_array, np_array, use_np_array, use_np
diff --git a/python/mxnet/base.py b/python/mxnet/base.py
index 6478b00e5047..a71f3e98e421 100644
--- a/python/mxnet/base.py
+++ b/python/mxnet/base.py
@@ -49,6 +49,7 @@
 integer_types = (int, long, _np.int32, _np.int64)
 numeric_types = (float, int, long, _np.generic)
 string_types = basestring,
+error_types = {}
 
 if sys.version_info[0] > 2:
     # this function is needed for python3
@@ -109,11 +110,121 @@ def __repr__(self):
 _Null = _NullType()
 
 
-class MXNetError(Exception):
-    """Error that will be thrown by all mxnet functions."""
-    pass
+class MXNetError(RuntimeError):
+    """Default error thrown by MXNet functions.
 
+    MXNetError will be raised if you do not give any error type specification,
+    """
+
+def register_error(func_name=None, cls=None):
+    """Register an error class so it can be recognized by the ffi error handler.
+
+    Parameters
+    ----------
+    func_name : str or function or class
+        The name of the error function.
+
+    cls : function
+        The function to create the class
+
+    Returns
+    -------
+    fregister : function
+        Register function if f is not specified.
+
+    Examples
+    --------
+    .. code-block:: python
+
+      @mxnet.error.register_error
+      class MyError(RuntimeError):
+          pass
+
+      err_inst = mxnet.error.create_ffi_error("MyError: xyz")
+      assert isinstance(err_inst, MyError)
+    """
+    if callable(func_name):
+        cls = func_name
+        func_name = cls.__name__
+
+    def register(mycls):
+        """internal register function"""
+        err_name = func_name if isinstance(func_name, str) else mycls.__name__
+        error_types[err_name] = mycls
+        return mycls
+    if cls is None:
+        return register
+    return register(cls)
+
+
+def _valid_error_name(name):
+    """Check whether name is a valid error name."""
+    return all(x.isalnum() or x in "_." for x in name)
 
+
+def _find_error_type(line):
+    """Find the error name given the first line of the error message.
+
+    Parameters
+    ----------
+    line : str
+        The first line of error message.
+
+    Returns
+    -------
+    name : str The error name
+    """
+    end_pos = line.find(":")
+    if end_pos == -1:
+        return None
+    err_name = line[:end_pos]
+    if _valid_error_name(err_name):
+        return err_name
+    return None
+
+
+def c2pyerror(err_msg):
+    """Translate C API error message to python style.
+
+    Parameters
+    ----------
+    err_msg : str
+        The error message.
+
+    Returns
+    -------
+    new_msg : str
+        Translated message.
+
+    err_type : str
+        Detected error type.
+    """
+    arr = err_msg.split("\n")
+    if arr[-1] == "":
+        arr.pop()
+    err_type = _find_error_type(arr[0])
+    trace_mode = False
+    stack_trace = []
+    message = []
+    for line in arr:
+        if trace_mode:
+            if line.startswith("  "):
+                stack_trace.append(line)
+            else:
+                trace_mode = False
+        if not trace_mode:
+            if line.startswith("Stack trace"):
+                trace_mode = True
+            else:
+                message.append(line)
+    out_msg = ""
+    if stack_trace:
+        out_msg += "Traceback (most recent call last):\n"
+        out_msg += "\n".join(reversed(stack_trace)) + "\n"
+    out_msg += "\n".join(message)
+    return out_msg, err_type
+
+@register_error
 class NotImplementedForSymbol(MXNetError):
     """Error: Not implemented for symbol"""
     def __init__(self, function, alias, *args):
@@ -132,6 +243,36 @@ def __str__(self):
         return msg
 
 
+def get_last_ffi_error():
+    """Create error object given result of MXGetLastError.
+
+    Returns
+    -------
+    err : object
+        The error object based on the err_msg
+    """
+    c_err_msg = py_str(_LIB.MXGetLastError())
+    py_err_msg, err_type = c2pyerror(c_err_msg)
+    if err_type is not None and err_type.startswith("mxnet.error."):
+        err_type = err_type[10:]
+    return error_types.get(err_type, MXNetError)(py_err_msg)
+
+
+def check_call(ret):
+    """Check the return value of C API call.
+
+    This function will raise an exception when an error occurs.
+    Wrap every API call with this function.
+
+    Parameters
+    ----------
+    ret : int
+        return value from API calls.
+    """
+    if ret != 0:
+        raise get_last_ffi_error()
+
+
 class NotSupportedForSparseNDArray(MXNetError):
     """Error: Not supported for SparseNDArray"""
     def __init__(self, function, alias, *args):
@@ -263,21 +404,6 @@ def _load_lib():
 #----------------------------
 # helper function definition
 #----------------------------
-def check_call(ret):
-    """Check the return value of C API call.
-
-    This function will raise an exception when an error occurs.
-    Wrap every API call with this function.
-
-    Parameters
-    ----------
-    ret : int
-        return value from API calls.
-    """
-    if ret != 0:
-        raise MXNetError(py_str(_LIB.MXGetLastError()))
-
-
 if sys.version_info[0] < 3:
     def c_str(string):
         """Create ctypes char * from a Python string.
diff --git a/python/mxnet/cython/base.pyi b/python/mxnet/cython/base.pyi
index c7cdea7a0bef..ed4bbf76a25e 100644
--- a/python/mxnet/cython/base.pyi
+++ b/python/mxnet/cython/base.pyi
@@ -1,4 +1,4 @@
-from ..base import MXNetError
+from ..base import get_last_ffi_error
 
 from libcpp.vector cimport vector
 from libcpp.string cimport string
@@ -38,7 +38,7 @@ cdef c_str(pystr):
 
 cdef CALL(int ret):
     if ret != 0:
-        raise MXNetError(NNGetLastError())
+        raise get_last_ffi_error()
 
 
 cdef const char** CBeginPtr(vector[const char*]& vec):
diff --git a/python/mxnet/error.py b/python/mxnet/error.py
new file mode 100644
index 000000000000..66b60db87483
--- /dev/null
+++ b/python/mxnet/error.py
@@ -0,0 +1,57 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Structured error classes in MXNet.
+
+Each error class takes an error message as its input.
+See the example sections for for suggested message conventions.
+To make the code more readable, we recommended developers to
+copy the examples and raise errors with the same message convention.
+"""
+from .base import MXNetError, register_error
+
+__all__ = ['MXNetError', 'register']
+
+register = register_error
+
+@register_error
+class InternalError(MXNetError):
+    """Internal error in the system.
+
+    Examples
+    --------
+    .. code :: c++
+
+        // Example code C++
+        LOG(FATAL) << "InternalError: internal error detail.";
+
+    .. code :: python
+
+        # Example code in python
+        raise InternalError("internal error detail")
+    """
+    def __init__(self, msg):
+        # Patch up additional hint message.
+        if "MXNet hint:" not in msg:
+            msg += ("\nMXNet hint: You hit an internal error. Please open an issue in "
+                    "https://github.com/apache/incubator-mxnet/issues/new/choose"
+                    " to report it.")
+        super(InternalError, self).__init__(msg)
+
+
+register_error("ValueError", ValueError)
+register_error("TypeError", TypeError)
+register_error("AttributeError", AttributeError)
diff --git a/src/c_api/c_api_error.cc b/src/c_api/c_api_error.cc
index 6dd4719281b6..537e95a23b76 100644
--- a/src/c_api/c_api_error.cc
+++ b/src/c_api/c_api_error.cc
@@ -25,6 +25,173 @@
 #include <nnvm/c_api.h>
 #include "./c_api_common.h"
 
+#ifndef _LIBCPP_SGX_NO_IOSTREAMS
+//--------------------------------------------------------
+// Error handling mechanism
+// -------------------------------------------------------
+// Standard error message format, {} means optional
+//--------------------------------------------------------
+// {error_type:} {message0}
+// {message1}
+// {message2}
+// {Stack trace:}    // stack traces follow by this line
+//   {trace 0}       // two spaces in the begining.
+//   {trace 1}
+//   {trace 2}
+//--------------------------------------------------------
+/*!
+ * \brief Normalize error message
+ *
+ *  Parse them header generated by by LOG(FATAL) and CHECK
+ *  and reformat the message into the standard format.
+ *
+ *  This function will also merge all the stack traces into
+ *  one trace and trim them.
+ *
+ * \param err_msg The error message.
+ * \return normalized message.
+ */
+std::string NormalizeError(std::string err_msg) {
+  // ------------------------------------------------------------------------
+  // log with header, {} indicates optional
+  //-------------------------------------------------------------------------
+  // [timestamp] file_name:line_number: {check_msg:} {error_type:} {message0}
+  // {message1}
+  // Stack trace:
+  //   {stack trace 0}
+  //   {stack trace 1}
+  //-------------------------------------------------------------------------
+  // Normalzied version
+  //-------------------------------------------------------------------------
+  // error_type: check_msg message0
+  // {message1}
+  // Stack trace:
+  //   File file_name, line lineno
+  //   {stack trace 0}
+  //   {stack trace 1}
+  //-------------------------------------------------------------------------
+  int line_number = 0;
+  std::istringstream is(err_msg);
+  std::string line, file_name, error_type, check_msg;
+
+  // Parse log header and set the fields,
+  // Return true if it the log is in correct format,
+  // return false if something is wrong.
+  auto parse_log_header = [&]() {
+    // skip timestamp
+    if (is.peek() != '[') {
+      getline(is, line);
+      return true;
+    }
+    if (!(is >> line)) return false;
+    // get filename
+    while (is.peek() == ' ') is.get();
+    if (!getline(is, file_name, ':')) {
+      return false;
+    } else {
+      if (is.peek() == '\\') {
+        // windows path
+        if (!getline(is, line, ':')) return false;
+        file_name = file_name + ':' + line;
+      }
+    }
+    // get line number
+    if (!(is >> line_number)) return false;
+    // get rest of the message.
+    while (is.peek() == ' ' || is.peek() == ':') is.get();
+    if (!getline(is, line)) return false;
+    // detect check message, rewrite to remote extra :
+    if (line.compare(0, 13, "Check failed:") == 0) {
+      size_t end_pos = line.find(':', 13);
+      if (end_pos == std::string::npos) return false;
+      check_msg = line.substr(0, end_pos + 1) + ' ';
+      line = line.substr(end_pos + 1);
+    }
+    return true;
+  };
+  // if not in correct format, do not do any rewrite.
+  if (!parse_log_header()) return err_msg;
+  // Parse error type.
+  {
+    size_t start_pos = 0, end_pos;
+    for (; start_pos < line.length() && line[start_pos] == ' '; ++start_pos) {}
+    for (end_pos = start_pos; end_pos < line.length(); ++end_pos) {
+      char ch = line[end_pos];
+      if (ch == ':') {
+        error_type = line.substr(start_pos, end_pos - start_pos);
+        break;
+      }
+      // [A-Z0-9a-z_.]
+      if (!std::isalpha(ch) && !std::isdigit(ch) && ch != '_' && ch != '.') break;
+    }
+    if (error_type.length() != 0) {
+      // if we successfully detected error_type: trim the following space.
+      for (start_pos = end_pos + 1;
+           start_pos < line.length() && line[start_pos] == ' '; ++start_pos) {}
+      line = line.substr(start_pos);
+    } else {
+      // did not detect error_type, use default value.
+      line = line.substr(start_pos);
+      error_type = "MXNetError";
+    }
+  }
+  // Seperate out stack trace.
+  std::ostringstream os;
+  os << error_type << ": " << check_msg << line << '\n';
+
+  bool trace_mode = true;
+  std::vector<std::string> stack_trace;
+  while (getline(is, line)) {
+    if (trace_mode) {
+      if (line.compare(0, 2, "  ") == 0) {
+        stack_trace.push_back(line);
+      } else {
+        trace_mode = false;
+        // remove EOL trailing stacktrace.
+        if (line.length() == 0) continue;
+      }
+    }
+    if (!trace_mode) {
+      if (line.compare(0, 11, "Stack trace") == 0) {
+        trace_mode = true;
+      } else {
+        os << line << '\n';
+      }
+    }
+  }
+  if (stack_trace.size() != 0 || file_name.length() != 0) {
+    os << "Stack trace:\n";
+    if (file_name.length() != 0) {
+      os << "  File \"" << file_name << "\", line " << line_number << "\n";
+    }
+    // Print out stack traces, optionally trim the c++ traces
+    // about the frontends (as they will be provided by the frontends).
+    bool ffi_boundary = false;
+    for (const auto& line : stack_trace) {
+      // Heuristic to detect python ffi.
+      if (line.find("libffi.so") != std::string::npos ||
+          line.find("core.cpython") != std::string::npos) {
+        ffi_boundary = true;
+      }
+      // If the backtrace is not c++ backtrace with the prefix "  [bt]",
+      // then we can stop trimming.
+      if (ffi_boundary && line.compare(0, 6, "  [bt]") != 0) {
+        ffi_boundary = false;
+      }
+      if (!ffi_boundary) {
+        os << line << '\n';
+      }
+    }
+  }
+  return os.str();
+}
+
+#else
+std::string NormalizeError(std::string err_msg) {
+  return err_msg;
+}
+#endif
+
 const char *MXGetLastError() {
   return NNGetLastError();
 }
diff --git a/src/operator/numpy/np_matrix_op.cc b/src/operator/numpy/np_matrix_op.cc
index 0a85887e2d4f..50f476d849b9 100644
--- a/src/operator/numpy/np_matrix_op.cc
+++ b/src/operator/numpy/np_matrix_op.cc
@@ -74,8 +74,9 @@ bool NumpyTransposeShape(const nnvm::NodeAttrs& attrs,
         << param.axes << ", input tensor shape = " << shp;
     mxnet::TShape axes = common::CanonicalizeAxes(param.axes);
     std::set<dim_t> axes_set(axes.begin(), axes.end());
-    CHECK_EQ(axes_set.size(), axes.ndim()) << "Repeated axis in transpose. param.axes = "
-                                                << param.axes;
+    CHECK_EQ(axes_set.size(), axes.ndim()) << "ValueError: Repeated axis in transpose."
+                                           << " param.axes = "
+                                           << param.axes;
     if (ndim_is_known(shp)) {
       for (int i = 0; i < ndim; ++i) {
         ret[i] = shp[axes[i]];
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index fe32352b90be..759677a3e9d2 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -501,10 +501,10 @@ def __init__(self, axis=None, keepdims=False) :
             super(TestAny, self).__init__()
             self._axis = axis
             self._keepdims = keepdims
-             
+
         def hybrid_forward(self, F, a):
             return F.np.any(a, axis=self._axis, keepdims=self._keepdims)
-    
+
     keepdims = [True, False]
     axes = [True, False]
     shapes = [(), (5, ), (10, ),
@@ -512,7 +512,7 @@ def hybrid_forward(self, F, a):
               (4, 4, 4), (4, 6, 9), (6, 6, 6),
               (7, 8, 9, 10), (7, 9, 11, 13)]
     dtypes = [np.int8, np.uint8, np.int32, np.int64, np.float16, np.float32, np.float64, np.bool]
-    
+
     combinations = itertools.product([False, True], shapes, dtypes, axes, keepdims)
     for hybridize, shape, dtype, axis, keepdim in combinations:
         ndim = len(shape)
@@ -540,10 +540,10 @@ def __init__(self, axis=None, keepdims=False) :
             super(TestAll, self).__init__()
             self._axis = axis
             self._keepdims = keepdims
-             
+
         def hybrid_forward(self, F, a):
             return F.np.all(a, axis=self._axis, keepdims=self._keepdims)
-    
+
     keepdims = [True, False]
     axes = [True, False]
     shapes = [(), (5, ), (10, ),
@@ -551,7 +551,7 @@ def hybrid_forward(self, F, a):
               (4, 4, 4), (4, 6, 9), (6, 6, 6),
               (7, 8, 9, 10), (7, 9, 11, 13)]
     dtypes = [np.int8, np.uint8, np.int32, np.int64, np.float16, np.float32, np.float64, np.bool]
-    
+
     combinations = itertools.product([False, True], shapes, dtypes, axes, keepdims)
     for hybridize, shape, dtype, axis, keepdim in combinations:
         ndim = len(shape)
@@ -687,10 +687,10 @@ def __init__(self, axis=None, returned=False):
             # necessary initializations
             self._axis = axis
             self._returned = returned
-             
+
         def hybrid_forward(self, F, a, weights):
             return F.np.average(a, weights=weights, axis=self._axis, returned=self._returned)
-    
+
     def avg_backward(a, w, avg, axes, init_a_grad=None, init_w_grad=None):
         # avg = sum(a * w) / sum(w)
         if axes is not None and not isinstance(axes, tuple) and axes < 0:
@@ -705,13 +705,13 @@ def avg_backward(a, w, avg, axes, init_a_grad=None, init_w_grad=None):
             new_shape = [a.shape[i] if i == axes else 1 for i in range(a.ndim)]
             w = w.reshape(new_shape)
             w = _np.broadcast_to(w, a.shape)
-       
+
         # partial a = w / sum(w)
         # partial w = (a*sum(w) - sum(a*w)) / (sum(w) * sum(w))
         scl = _np.sum(w, axis=axes, keepdims=True)
         a_grad = _np.divide(w, scl)
         w_grad = _np.divide(a*scl-_np.sum(a*w, axis=axes, keepdims=True), scl*scl)
-        
+
         if onedim:
             axis = list(range(a.ndim))
             axis.remove(axes)
@@ -780,7 +780,7 @@ def avg_backward(a, w, avg, axes, init_a_grad=None, init_w_grad=None):
             assert a.grad is None
         else:
             assert_almost_equal(a.grad.asnumpy(), a_grad, rtol=rtol, atol=atol)
-        
+
         # Test imperative once again
         np_out = _np.average(a.asnumpy(), weights=np_w, axis=axes, returned=returned)
         mx_out = np.average(a, weights=w, axis=axes, returned=returned)
@@ -1625,7 +1625,7 @@ def hybrid_forward(self, F, a):
                             assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
     # Test for error raising
     dat = np.random.normal(0, 1, (3, 4, 5), dtype=np.float32)
-    assert_raises(MXNetError, lambda: dat.transpose((0, 0, 1)))
+    assert_raises(ValueError, lambda: dat.transpose((0, 0, 1)))
     assert_raises(MXNetError, lambda: dat.transpose((0, 1, 3)))
 
 
@@ -2843,7 +2843,7 @@ def __init__(self, obj, axis=None):
 
         def hybrid_forward(self, F, a):
             return F.np.delete(a, self._obj, axis=self._axis)
-    
+
     def GetSize(shp):
         if len(shp) == 0:
             return 0
@@ -2886,8 +2886,8 @@ def GetDimSize(shp, axis):
                 obj_mxnp = obj
                 obj_onp = obj
             else:
-                obj_mxnp = (_np.int32(obj) if objtype == 'int32' else _np.int64(obj)) 
-                obj_onp = (_np.int32(obj) if objtype == 'int32' else _np.int64(obj)) 
+                obj_mxnp = (_np.int32(obj) if objtype == 'int32' else _np.int64(obj))
+                obj_onp = (_np.int32(obj) if objtype == 'int32' else _np.int64(obj))
             test_delete = TestDelete(obj=obj_mxnp, axis=axis)
 
             a = mx.nd.random.uniform(-1.0, 1.0, shape=arr_shape).as_np_ndarray()
@@ -3057,15 +3057,15 @@ def _test_bernoulli_exception(prob, logit):
         assertRaises(ValueError, _test_bernoulli_exception, prob, logit)
         if prob.size > 0:
             # larger than 1
-            assertRaises(MXNetError, _test_bernoulli_exception, prob + 2.0, None)
+            assertRaises(ValueError, _test_bernoulli_exception, prob + 2.0, None)
             # smaller than 0
-            assertRaises(MXNetError, _test_bernoulli_exception, prob - 2.0, None)
+            assertRaises(ValueError, _test_bernoulli_exception, prob - 2.0, None)
             # mixed case
             low, high = (-1.0, 2.0)
             # uniform(-1, 2)
             scaled_prob = low + (high - low) * prob
             if not ((scaled_prob.asnumpy() >= 0).all() and (scaled_prob.asnumpy() <= 1).all()):
-                assertRaises(MXNetError, _test_bernoulli_exception, scaled_prob, None)
+                assertRaises(ValueError, _test_bernoulli_exception, scaled_prob, None)
 
 
 @with_seed()
@@ -3188,7 +3188,7 @@ def hybrid_forward(self, F, param1, param2):
     event_shapes = [(), (2,), (2,2)]
     dtypes = ['float16', 'float32', 'float64']
     op_names = ['uniform_n', 'normal_n']
-    
+
     for bshape, eshape, dtype, op in itertools.product(batch_shapes, event_shapes, dtypes, op_names):
         for hybridize in [True, False]:
             net = TestSampleN(bshape, op)
@@ -4042,7 +4042,7 @@ class TestTensorinv(HybridBlock):
         def __init__(self, ind=2):
             super(TestTensorinv, self).__init__()
             self._ind = ind
-        
+
         def hybrid_forward(self, F, a):
             return F.np.linalg.tensorinv(a, ind=self._ind)
 
@@ -4483,7 +4483,7 @@ def hybrid_forward(self, F, x, *args, **kwargs):
         param_dtype= _np.random.choice(dtypes)
         a = np.random.uniform(low=0, high=100, size=shape, dtype='float64').astype(dtype)
         test = TestFullLike(fill_value, param_dtype, npx.current_context())
-        expected_ret = _np.full_like(a.asnumpy(), fill_value=fill_value, dtype=param_dtype)     
+        expected_ret = _np.full_like(a.asnumpy(), fill_value=fill_value, dtype=param_dtype)
         if hybridize:
             test.hybridize()
         ret = test(a)
@@ -5356,7 +5356,7 @@ def hybrid_forward(self,F,a):
             # Test imperative once again
             mx_out = np.diagflat(x, k)
             np_out = _np.diagflat(x.asnumpy(), k)
-            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol) 
+            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
 
 
 @with_seed()
@@ -5877,7 +5877,7 @@ def hybrid_forward(self, F, a):
             array_temp = array_temp.reshape(shape_out[:-1] + (shape[axis1], shape[axis2]))
             axis_idx = [i for i in range(ndim-2)]
             axis_idx[axis1:axis1] = [ndim - 2]
-            axis_idx[axis2:axis2] = [ndim - 1] 
+            axis_idx[axis2:axis2] = [ndim - 1]
             np_backward = _np.transpose(array_temp, tuple(axis_idx))
         assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=rtol, atol=atol)
 
@@ -6097,10 +6097,10 @@ def __init__(self, shape, order='C') :
             super(TestUnravel_index, self).__init__()
             self._shape = shape
             self._order = order
-             
+
         def hybrid_forward(self, F, a):
             return F.np.unravel_index(a, self._shape, self._order)
-     
+
     in_shapes = [
         2, 5,
         (), (1,), (4,),
@@ -6112,7 +6112,7 @@ def hybrid_forward(self, F, a):
         (3, 4), (4, 5),
         (2,3,4)
     ]
-    dtypes = [np.uint8, np.int8, np.int32, np.int64] 
+    dtypes = [np.uint8, np.int8, np.int32, np.int64]
     for hybridize, ishape, dtype, rshape in itertools.product([False, True], in_shapes, dtypes, unravel_shapes):
         rtol = 1e-2 if dtype == np.float16 else 1e-3
         atol = 1e-4 if dtype == np.float16 else 1e-5
@@ -6131,7 +6131,7 @@ def hybrid_forward(self, F, a):
             assert elem_mx.asnumpy().shape == elem_np.shape
             assert_almost_equal(elem_mx.asnumpy(), elem_np, rtol=rtol, atol=atol)
         # no backward function for unravel_index operator
- 
+
         # Test imperative once again
         mx_out = np.unravel_index(x, rshape)
         np_out = _np.unravel_index(x.asnumpy(), rshape)
@@ -6151,7 +6151,7 @@ def __init__(self, minlength=0):
 
         def hybrid_forward(self, F, a):
             return F.np.bincount(a, None, self._minlength)
-    
+
     class TestBincountWeights(HybridBlock):
         def __init__(self, minlength=0):
             super(TestBincountWeights, self).__init__()
@@ -6164,13 +6164,13 @@ def hybrid_forward(self, F, a, weights):
     weight_types = [np.int32, np.int64, np.float16, np.float32, np.float64]
     shapes = [(), (5,), (10,), (15,), (20,), (30,), (50,)]
     min_lengths = [0, 5, 20, 50]
-    has_weights = [True, False] 
+    has_weights = [True, False]
     combinations = itertools.product([True, False], shapes, dtypes, weight_types, has_weights, min_lengths)
-    for hybridize, shape, dtype, weight_type, has_weight, minlength in combinations: 
+    for hybridize, shape, dtype, weight_type, has_weight, minlength in combinations:
         rtol = 1e-2 if weight_type == np.float16 else 1e-3
         atol = 1e-4 if weight_type == np.float16 else 1e-5
         if shape != ():
-            data = np.random.uniform(0, 10, size=shape).astype(dtype) 
+            data = np.random.uniform(0, 10, size=shape).astype(dtype)
             weights = np.random.uniform(0, 10, size=shape).astype(weight_type) if has_weight else None
         else:
             data = np.array(()).astype(dtype)
@@ -6198,7 +6198,7 @@ class TestEmptyLike(HybridBlock):
         def __init__(self, dtype, order, subok):
             super(TestEmptyLike, self).__init__()
             self._dtype = dtype
-            self._order = order 
+            self._order = order
             self._subok = subok
 
         def hybrid_forward(self, F, x, *args, **kwargs):

From 8dee5b7bf0307450a8663b3ae4bad53564a08402 Mon Sep 17 00:00:00 2001
From: Leonard Lausen <lausen@amazon.com>
Date: Wed, 1 Jan 2020 13:21:35 +0000
Subject: [PATCH 752/813] Fix gluon.Trainer regression if no kvstore is used
 with sparse gradients (#17199)

---
 python/mxnet/gluon/trainer.py               |  2 +-
 tests/python/unittest/test_gluon_trainer.py | 15 +++++++++++++++
 2 files changed, 16 insertions(+), 1 deletion(-)

diff --git a/python/mxnet/gluon/trainer.py b/python/mxnet/gluon/trainer.py
index 966ed2cc9964..a27c951c01b9 100644
--- a/python/mxnet/gluon/trainer.py
+++ b/python/mxnet/gluon/trainer.py
@@ -221,7 +221,7 @@ def _init_kvstore(self):
                                      "when sparse gradients are present.")
                 update_on_kvstore = config['update_on_kvstore']
             # raise err if a custom kvstore is used for sparse training
-            if not isinstance(kvstore, KVStore):
+            if kvstore is not None and not isinstance(kvstore, KVStore):
                 raise ValueError("Cannot use {} for multi-device training with sparse gradients"
                                  .format(type(kvstore)))
 
diff --git a/tests/python/unittest/test_gluon_trainer.py b/tests/python/unittest/test_gluon_trainer.py
index fbd04ee1beec..350700cc129f 100644
--- a/tests/python/unittest/test_gluon_trainer.py
+++ b/tests/python/unittest/test_gluon_trainer.py
@@ -46,6 +46,21 @@ def test_multi_trainer():
     # multiple trainers for a sparse Parameter is not allowed
     trainer1 = gluon.Trainer([x], 'sgd')
 
+@with_seed()
+def test_trainer_with_sparse_grad_on_single_context():
+    x = gluon.Parameter('x', shape=(10,), grad_stype='row_sparse')
+    x.initialize(ctx=[mx.cpu(0)], init='zeros')
+    trainer = gluon.Trainer([x], 'sgd', {'learning_rate': 1.0, 'momentum': 0.5})
+    with mx.autograd.record():
+        for w in x.list_data():
+            y = w + 1
+            y.backward()
+    trainer.step(1)
+
+    assert trainer._update_on_kvstore is None
+    assert trainer._kvstore is None  # No kvstore created for single-device training
+    assert (x.data(mx.cpu(0)).asnumpy() == -1).all()
+
 @with_seed()
 def test_trainer_with_teststore():
     x = gluon.Parameter('x', shape=(10,))

From 5d9cbdbe1424ecd32350bd0c5e7ac5dd794e70ca Mon Sep 17 00:00:00 2001
From: Wuxun Zhang <wuxun.zhang@intel.com>
Date: Thu, 2 Jan 2020 09:37:03 +0800
Subject: [PATCH 753/813] improve mkldnn doc (#17198)

---
 .../backend/mkldnn/mkldnn_readme.md           | 14 +++++++----
 .../tutorials/performance/backend/profiler.md | 24 +++++++++++++++++++
 2 files changed, 33 insertions(+), 5 deletions(-)

diff --git a/docs/python_docs/python/tutorials/performance/backend/mkldnn/mkldnn_readme.md b/docs/python_docs/python/tutorials/performance/backend/mkldnn/mkldnn_readme.md
index c66fd2e8e642..086839a7bb97 100644
--- a/docs/python_docs/python/tutorials/performance/backend/mkldnn/mkldnn_readme.md
+++ b/docs/python_docs/python/tutorials/performance/backend/mkldnn/mkldnn_readme.md
@@ -214,13 +214,17 @@ export MKLDNN_VERBOSE=1
 ```
 For example, by running above code snippet, the following debugging logs providing more insights on MKL-DNN primitives `convolution` and `reorder`. That includes: Memory layout, infer shape and the time cost of primitive execution.
 ```
-mkldnn_verbose,exec,reorder,jit:uni,undef,in:f32_nchw out:f32_nChw16c,num:1,32x32x256x256,6.47681
-mkldnn_verbose,exec,reorder,jit:uni,undef,in:f32_oihw out:f32_OIhw16i16o,num:1,32x32x3x3,0.0429688
-mkldnn_verbose,exec,convolution,jit:avx512_common,forward_inference,fsrc:nChw16c fwei:OIhw16i16o fbia:undef fdst:nChw16c,alg:convolution_direct,mb32_g1ic32oc32_ih256oh256kh3sh1dh0ph1_iw256ow256kw3sw1dw0pw1,9.98193
-mkldnn_verbose,exec,reorder,jit:uni,undef,in:f32_oihw out:f32_OIhw16i16o,num:1,32x32x3x3,0.0510254
-mkldnn_verbose,exec,reorder,jit:uni,undef,in:f32_nChw16c out:f32_nchw,num:1,32x32x256x256,20.4819
+dnnl_verbose,info,DNNL v1.1.2 (commit cb2cc7ac17ff4e2ef50805c7048d33256d82be4d)
+dnnl_verbose,info,Detected ISA is Intel AVX-512 with Intel DL Boost
+dnnl_verbose,exec,cpu,reorder,jit:uni,undef,src_f32::blocked:abcd:f0 dst_f32::blocked:aBcd16b:f0,,,32x32x256x256,7.43701
+dnnl_verbose,exec,cpu,reorder,jit:uni,undef,src_f32::blocked:abcd:f0 dst_f32::blocked:ABcd16b16a:f0,,,32x32x3x3,0.202148
+dnnl_verbose,exec,cpu,convolution,jit:avx512_common,forward_inference,src_f32::blocked:aBcd16b:f0 wei_f32::blocked:ABcd16b16a:f0 bia_undef::undef::f0 dst_f32::blocked:aBcd16b:f0,,alg:convolution_direct,mb32_ic32oc32_ih256oh256kh3sh1dh0ph1_iw256ow256kw3sw1dw0pw1,20.7539
+dnnl_verbose,exec,cpu,reorder,jit:uni,undef,src_f32::blocked:abcd:f0 dst_f32::blocked:ABcd16b16a:f0,,,32x32x3x3,1.86694
+dnnl_verbose,exec,cpu,reorder,jit:uni,undef,src_f32::blocked:aBcd16b:f0 dst_f32::blocked:abcd:f0,,,32x32x256x256,35.9771
 ```
 
+You can find step-by-step guidance to do profiling for MKLDNN primitives in [Profiling MKLDNN Operators](https://mxnet.apache.org/api/python/docs/tutorials/performance/backend/profiler.html#Profiling-MKLDNN-Operators).
+
 <h2 id="5">Enable MKL BLAS</h2>
 
 With MKL BLAS, the performace is expected to furtherly improved with variable range depending on the computation load of the models.
diff --git a/docs/python_docs/python/tutorials/performance/backend/profiler.md b/docs/python_docs/python/tutorials/performance/backend/profiler.md
index f90d5ba9559e..3a2ef8ee629d 100644
--- a/docs/python_docs/python/tutorials/performance/backend/profiler.md
+++ b/docs/python_docs/python/tutorials/performance/backend/profiler.md
@@ -211,6 +211,30 @@ Let's zoom in to check the time taken by operators
 
 The above picture visualizes the sequence in which the operators were executed and the time taken by each operator.
 
+### Profiling MKLDNN Operators
+Reagrding MKLDNN operators, the library has already provided the internal profiling tool. Firstly, you need set `MKLDNN_VERBOSE=1` to enable internal profiler.
+
+`$ MKLDNN_VERBOSE=1 python my_script.py > mkldnn_verbose.log`
+
+Now, the detailed profiling insights of each mkldnn prmitive are saved into `mkldnn_verbose.log` (like below).
+
+```
+dnnl_verbose,info,DNNL v1.1.2 (commit cb2cc7ac17ff4e2ef50805c7048d33256d82be4d)
+dnnl_verbose,info,Detected ISA is Intel AVX-512 with Intel DL Boost
+dnnl_verbose,exec,cpu,convolution,jit:avx512_common,forward_inference,src_f32::blocked:aBcd16b:f0 wei_f32::blocked:ABcd16b16a:f0 bia_undef::undef::f0 dst_f32::blocked:aBcd16b:f0,,alg:convolution_direct,mb32_ic32oc32_ih256oh256kh3sh1dh0ph1_iw256ow256kw3sw1dw0pw1,20.7539
+```
+
+For example, if you want to calculate the total executing time of `convolution` primitive, you can just run:
+
+`$ cat mkldnn_verbose.log | grep "exec,cpu,convolution" | awk 'BEGIN{FS=","} {SUM+=$11} END {print SUM}'`
+
+Moreover, you can set `MKLDNN_VERBOSE=2` to collect both creating and executing time of each primitive.
+
+`$ cat mkldnn_verbose.log | grep "create,cpu,convolution" | awk 'BEGIN{FS=","} {SUM+=$11} END {print SUM}'`
+
+`$ cat mkldnn_verbose.log | grep "exec,cpu,convolution" | awk 'BEGIN{FS=","} {SUM+=$11} END {print SUM}'`
+
+
 ### Profiling Custom Operators
 Should the existing NDArray operators fail to meet all your model's needs, MXNet supports [Custom Operators](/api/python/docs/tutorials/extend/customop.html) that you can define in Python. In `forward()` and `backward()` of a custom operator, there are two kinds of code: "pure Python" code (NumPy operators included) and "sub-operators" (NDArray operators called within `forward()` and `backward()`). With that said, MXNet can profile the execution time of both kinds without additional setup. Specifically, the MXNet profiler will break a single custom operator call into a pure Python event and several sub-operator events if there are any. Furthermore, all of those events will have a prefix in their names, which is, conveniently, the name of the custom operator you called.
 

From e65fc4bee5b8602b38ba2e419a4ea2e5ce5b7e9a Mon Sep 17 00:00:00 2001
From: Wuxun Zhang <wuxun.zhang@intel.com>
Date: Thu, 2 Jan 2020 09:37:42 +0800
Subject: [PATCH 754/813] [MKLDNN] Fix _copyto  (#17173)

* fix_copyto

* only exclude _copyto

* trigger CI
---
 src/imperative/imperative_utils.h             |  4 ++--
 .../python/quantization/test_quantization.py  | 20 ++++++++++---------
 2 files changed, 13 insertions(+), 11 deletions(-)

diff --git a/src/imperative/imperative_utils.h b/src/imperative/imperative_utils.h
index edc2f2adf4c2..c6d9b8b7b969 100644
--- a/src/imperative/imperative_utils.h
+++ b/src/imperative/imperative_utils.h
@@ -479,7 +479,7 @@ inline void PushFComputeEx(const FComputeEx& fn,
       }
       // add for mkldnn OP + no mkldnn OP
       const auto is_mkldnn = Op::GetAttr<bool>("TIsMKLDNN");
-      if (!is_mkldnn.get(attrs.op, false)) {
+      if (!is_mkldnn.get(attrs.op, false) && exec_type != ExecType::kCrossDeviceCopy) {
         std::vector<NDArray> inputs_fallback;
         CreateDefaultInputs(inputs, &inputs_fallback);
         fn(attrs, opctx, inputs_fallback, req, outputs);
@@ -543,7 +543,7 @@ inline void PushOperator(const OpStatePtr& state,
       }
       // add for mkldnn OP + no mkldnn OP
       const auto is_mkldnn = Op::GetAttr<bool>("TIsMKLDNN");
-      if (!is_mkldnn.get(attrs.op, false)) {
+      if (!is_mkldnn.get(attrs.op, false) && exec_type != ExecType::kCrossDeviceCopy) {
         std::vector<NDArray> inputs_fallback;
         CreateDefaultInputs(inputs, &inputs_fallback);
         fcompute_ex(state, opctx, inputs_fallback, req, outputs);
diff --git a/tests/python/quantization/test_quantization.py b/tests/python/quantization/test_quantization.py
index 8f433524b325..ec6ddfdf67f4 100644
--- a/tests/python/quantization/test_quantization.py
+++ b/tests/python/quantization/test_quantization.py
@@ -1137,15 +1137,17 @@ def check_quantize_net(qdtype):
         quantized_resnet18_v1.hybridize(static_alloc=True, static_shape=True)
         quantized_resnet18_v1(random_data)
 
-        quantized_resnet18_v1 = mx.contrib.quant.quantize_net(resnet18_v1, quantized_dtype=qdtype,
-                                                              exclude_layers=None,
-                                                              exclude_layers_match=excluded_names_match,
-                                                              calib_data=calib_data,
-                                                              calib_mode='naive',
-                                                              num_calib_examples=num_calib_examples,
-                                                              ctx=mx.current_context())
-        quantized_resnet18_v1.hybridize(static_alloc=True, static_shape=True)
-        quantized_resnet18_v1(random_data)
+        for mode in ['naive', 'entropy']:
+            qdtype = qdtype if mode is 'naive' else 'auto'
+            quantized_resnet18_v1 = mx.contrib.quant.quantize_net(resnet18_v1, quantized_dtype=qdtype,
+                                                                  exclude_layers=None,
+                                                                  exclude_layers_match=excluded_names_match,
+                                                                  calib_data=calib_data,
+                                                                  calib_mode=mode,
+                                                                  num_calib_examples=num_calib_examples,
+                                                                  ctx=mx.current_context())
+            quantized_resnet18_v1.hybridize(static_alloc=True, static_shape=True)
+            quantized_resnet18_v1(random_data)
 
     for qdtype in ['int8', 'uint8']:
         check_quantize_net(qdtype)

From ffeacfa4f0e16023ee965bb19d50fcee0b782ce8 Mon Sep 17 00:00:00 2001
From: Hu Shiwen <yajiedesign@gmail.com>
Date: Thu, 2 Jan 2020 16:56:27 +0800
Subject: [PATCH 755/813] Workaround for DLL size limitation on Windows
 (#16980)

* change windows build system.
add gen_warp cpp version
add add_custom_command to run warp_gen
add download cmake
add option
change option
add dynamic read mxnet dll
---
 CMakeLists.txt                  | 102 ++++++++++++----
 tools/windowsbuild/README.md    |  19 +++
 tools/windowsbuild/gen_warp.cpp | 209 ++++++++++++++++++++++++++++++++
 tools/windowsbuild/warp_dll.cpp | 151 +++++++++++++++++++++++
 4 files changed, 459 insertions(+), 22 deletions(-)
 create mode 100644 tools/windowsbuild/README.md
 create mode 100644 tools/windowsbuild/gen_warp.cpp
 create mode 100644 tools/windowsbuild/warp_dll.cpp

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 6f3031502668..9e4ef25e5af4 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -67,6 +67,8 @@ option(USE_ASAN "Enable Clang/GCC ASAN sanitizers." OFF)
 option(ENABLE_TESTCOVERAGE "Enable compilation with test coverage metric output" OFF)
 option(USE_INT64_TENSOR_SIZE "Use int64_t to represent the total number of elements in a tensor" OFF)
 option(BUILD_CYTHON_MODULES "Build cython modules." OFF)
+cmake_dependent_option(USE_SPLIT_ARCH_DLL "Build a separate DLL for each Cuda arch (Windows only)." ON "MSVC" OFF)
+
 
 message(STATUS "CMAKE_CROSSCOMPILING ${CMAKE_CROSSCOMPILING}")
 message(STATUS "CMAKE_HOST_SYSTEM_PROCESSOR ${CMAKE_HOST_SYSTEM_PROCESSOR}")
@@ -100,6 +102,7 @@ endif()
 
 if(MSVC)
   set(SYSTEM_ARCHITECTURE x86_64)
+  enable_language(ASM_MASM)
 else()
   execute_process(COMMAND uname -m COMMAND tr -d '\n' OUTPUT_VARIABLE SYSTEM_ARCHITECTURE)
 endif()
@@ -593,8 +596,16 @@ if(USE_CUDA)
   include(${CMAKE_ROOT}/Modules/FindCUDA/select_compute_arch.cmake)
   CUDA_SELECT_NVCC_ARCH_FLAGS(CUDA_ARCH_FLAGS ${MXNET_CUDA_ARCH})
   message("-- CUDA: Using the following NVCC architecture flags ${CUDA_ARCH_FLAGS}")
+  set(arch_code_list)
+  foreach(arch_str ${CUDA_ARCH_FLAGS})
+    if((arch_str MATCHES ".*sm_[0-9]+")) 
+      string( REGEX REPLACE  ".*sm_([0-9]+)" "\\1" arch_code ${arch_str} )
+      list(APPEND arch_code_list ${arch_code})
+    endif()
+  endforeach()
+
   string(REPLACE ";" " " CUDA_ARCH_FLAGS_SPACES "${CUDA_ARCH_FLAGS}")
-  string(APPEND CMAKE_CUDA_FLAGS " ${CUDA_ARCH_FLAGS_SPACES}")
+
 
   find_package(CUDAToolkit REQUIRED cublas cufft cusolver curand
     OPTIONAL_COMPONENTS nvToolsExt nvrtc)
@@ -667,6 +678,7 @@ add_library(sample_lib SHARED ${CMAKE_CURRENT_SOURCE_DIR}/example/extensions/lib
 target_include_directories(sample_lib PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/include/mxnet)
 set(MXNET_INSTALL_TARGETS mxnet)
 if(UNIX)
+  string(APPEND CMAKE_CUDA_FLAGS "${CUDA_ARCH_FLAGS_SPACES}")
   # Create dummy file since we want an empty shared library before linking
   set(DUMMY_SOURCE ${CMAKE_BINARY_DIR}/dummy.c)
   file(WRITE ${DUMMY_SOURCE} "")
@@ -678,30 +690,66 @@ if(UNIX)
   target_link_libraries(mxnet_static PUBLIC ${CMAKE_DL_LIBS})
   target_compile_options(sample_lib PUBLIC -shared)
   set_target_properties(mxnet_static PROPERTIES OUTPUT_NAME mxnet)
-else()
-  add_library(mxnet SHARED ${SOURCE})
+elseif(MSVC)
   target_compile_options(sample_lib PUBLIC /LD)
   set_target_properties(sample_lib PROPERTIES PREFIX "lib")
-endif()
 
-if(USE_CUDA AND MSVC)
-  target_compile_options(mxnet PUBLIC "$<$<CONFIG:DEBUG>:-Xcompiler=-MTd -Gy>")
-  target_compile_options(mxnet PUBLIC "$<$<CONFIG:RELEASE>:-Xcompiler=-MT -Gy>")
+  if(USE_CUDA)
+    if(MSVC)
+      if(USE_SPLIT_ARCH_DLL)
+        add_executable(gen_warp tools/windowsbuild/gen_warp.cpp)
+        add_library(mxnet SHARED tools/windowsbuild/warp_dll.cpp ${CMAKE_BINARY_DIR}/warp_gen_cpp.cpp
+                    ${CMAKE_BINARY_DIR}/warp_gen.asm)
+        target_link_libraries(mxnet PRIVATE cudart Shlwapi)
+        list(GET arch_code_list 0 mxnet_first_arch)
+        foreach(arch ${arch_code_list})
+          add_library(mxnet_${arch} SHARED ${SOURCE})
+          target_compile_options(
+            mxnet_${arch}
+            PRIVATE
+            "$<$<COMPILE_LANGUAGE:CUDA>:--gpu-architecture=compute_${arch}>"
+          )
+          target_compile_options(
+            mxnet_${arch}
+            PRIVATE
+            "$<$<COMPILE_LANGUAGE:CUDA>:--gpu-code=sm_${arch},compute_${arch}>"
+          )
+          target_compile_options(
+            mxnet_${arch} 
+            PRIVATE "$<$<AND:$<CONFIG:DEBUG>,$<COMPILE_LANGUAGE:CUDA>>:-Xcompiler=-MTd -Gy /bigobj>")
+          target_compile_options(
+            mxnet_${arch}
+            PRIVATE "$<$<AND:$<CONFIG:RELEASE>,$<COMPILE_LANGUAGE:CUDA>>:-Xcompiler=-MT -Gy /bigobj>")
+        endforeach()
+
+        add_custom_command(
+          OUTPUT ${CMAKE_BINARY_DIR}/warp_gen_cpp.cpp ${CMAKE_BINARY_DIR}/warp_gen.asm
+          COMMAND gen_warp $<TARGET_FILE:mxnet_${mxnet_first_arch}> WORKING_DIRECTORY ${CMAKE_BINARY_DIR}/ DEPENDS $<TARGET_FILE:mxnet_${mxnet_first_arch}>)
+      else(USE_SPLIT_ARCH_DLL)
+        string(REPLACE ";" " " NVCC_FLAGS_ARCH "${NVCC_FLAGS_ARCH}")
+        set(CMAKE_CUDA_FLAGS "${CUDA_ARCH_FLAGS_SPACES}")
+        add_library(mxnet SHARED ${SOURCE})
+        target_compile_options(
+            mxnet 
+            PRIVATE "$<$<AND:$<CONFIG:DEBUG>,$<COMPILE_LANGUAGE:CUDA>>:-Xcompiler=-MTd -Gy /bigobj>")
+        target_compile_options(
+            mxnet
+            PRIVATE "$<$<AND:$<CONFIG:RELEASE>,$<COMPILE_LANGUAGE:CUDA>>:-Xcompiler=-MT -Gy /bigobj>")
+
+      endif(USE_SPLIT_ARCH_DLL)
+    else()
+      add_library(mxnet SHARED ${SOURCE})
+    endif()
+  else()
+    add_library(mxnet SHARED ${SOURCE})
+  endif()
+
 endif()
+
 if(USE_DIST_KVSTORE)
   if(EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/3rdparty/ps-lite/CMakeLists.txt)
     add_subdirectory("3rdparty/ps-lite")
     list(APPEND pslite_LINKER_LIBS pslite protobuf)
-    target_link_libraries(mxnet PUBLIC debug ${pslite_LINKER_LIBS_DEBUG})
-    target_link_libraries(mxnet PUBLIC optimized ${pslite_LINKER_LIBS_RELEASE})
-    if(CMAKE_BUILD_TYPE STREQUAL "Debug")
-      list(APPEND mxnet_LINKER_LIBS ${pslite_LINKER_LIBS_DEBUG})
-    else()
-      list(APPEND mxnet_LINKER_LIBS ${pslite_LINKER_LIBS_RELEASE})
-    endif()
-    target_link_libraries(mxnet PUBLIC debug ${pslite_LINKER_LIBS_DEBUG})
-    target_link_libraries(mxnet PUBLIC optimized ${pslite_LINKER_LIBS_RELEASE})
-
   else()
     set(pslite_LINKER_LIBS protobuf zmq-static)
   endif()
@@ -735,13 +783,24 @@ if(USE_TVM_OP)
     )
 endif()
 
-target_link_libraries(mxnet PUBLIC ${mxnet_LINKER_LIBS})
-
 if(USE_PLUGINS_WARPCTC)
-  target_link_libraries(mxnet PUBLIC debug ${WARPCTC_LIB_DEBUG})
-  target_link_libraries(mxnet PUBLIC optimized ${WARPCTC_LIB_RELEASE})
+  list(APPEND mxnet_LINKER_LIBS ${WARPCTC_LIB})
 endif()
 
+if(MSVC)
+  if(USE_SPLIT_ARCH_DLL AND USE_CUDA)
+    foreach(arch ${arch_code_list})
+      target_link_libraries(mxnet_${arch} PUBLIC ${mxnet_LINKER_LIBS})
+      target_link_libraries(mxnet_${arch} PUBLIC dmlc)
+    endforeach()
+  else()
+    target_link_libraries(mxnet PUBLIC ${mxnet_LINKER_LIBS})
+    target_link_libraries(mxnet PUBLIC dmlc)
+  endif()
+else()
+  target_link_libraries(mxnet PUBLIC ${mxnet_LINKER_LIBS})
+  target_link_libraries(mxnet PUBLIC dmlc)
+endif()
 
 if(USE_OPENCV AND OpenCV_VERSION_MAJOR GREATER 2)
   add_executable(im2rec "tools/im2rec.cc")
@@ -761,7 +820,6 @@ else()
     is required for im2rec, im2rec will not be available")
 endif()
 
-target_link_libraries(mxnet PUBLIC dmlc)
 
 if(MSVC AND USE_MXNET_LIB_NAMING)
   set_target_properties(mxnet PROPERTIES OUTPUT_NAME "libmxnet")
diff --git a/tools/windowsbuild/README.md b/tools/windowsbuild/README.md
new file mode 100644
index 000000000000..7d8e7cf331cf
--- /dev/null
+++ b/tools/windowsbuild/README.md
@@ -0,0 +1,19 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+Due to dll size limitation under windows. Split dll into different dlls according to arch
+Reference https://github.com/apache/incubator-mxnet/pull/16980
\ No newline at end of file
diff --git a/tools/windowsbuild/gen_warp.cpp b/tools/windowsbuild/gen_warp.cpp
new file mode 100644
index 000000000000..2d90eaf364f3
--- /dev/null
+++ b/tools/windowsbuild/gen_warp.cpp
@@ -0,0 +1,209 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+ 
+#include <iostream>  
+#include <io.h>  
+#include <Windows.h>  
+#include <cstdint>
+#include <memory>
+#include <vector>
+#include <string>
+#include <iostream>
+#include <fstream>
+
+#define  IMAGE_SIZEOF_SIGNATURE 4
+
+
+DWORD rva_to_foa(IN DWORD RVA, IN PIMAGE_SECTION_HEADER section_header) 
+{
+
+	size_t count = 0;
+	for (count = 1; RVA > (section_header->VirtualAddress + section_header->Misc.VirtualSize); count++, section_header++);
+
+	DWORD FOA = RVA - section_header->VirtualAddress + section_header->PointerToRawData;
+
+	return FOA;
+} 
+
+std::string format(const char* format, ...)
+{
+	va_list args;
+	va_start(args, format);
+#ifndef _MSC_VER
+	size_t size = std::snprintf(nullptr, 0, format, args) + 1; // Extra space for '\0'
+	std::unique_ptr<char[]> buf(new char[size]);
+	std::vsnprintf(buf.get(), size, format, args);
+	return std::string(buf.get(), buf.get() + size - 1); // We don't want the '\0' inside
+#else
+	int size = _vscprintf(format, args) +1;
+	std::unique_ptr<char[]> buf(new char[size]);
+	vsnprintf_s(buf.get(), size, _TRUNCATE, format, args);
+	return std::string(buf.get());
+#endif
+	va_end(args);
+}
+
+int main(int argc, char* argv[])
+{
+
+	if (argc != 2)
+	{
+		return 0;
+	}
+
+	//open file
+	const HANDLE h_file = CreateFile(
+		argv[1],   
+		GENERIC_READ , 
+		FILE_SHARE_READ ,
+		nullptr,
+		OPEN_EXISTING,  
+		FILE_ATTRIBUTE_NORMAL,
+		nullptr);
+
+
+	DWORD size_high;
+	const DWORD size_low = GetFileSize(h_file, &size_high);
+
+	uint64_t dll_size = ((uint64_t(size_high)) << 32) + (uint64_t)size_low;
+
+	// Create File Mapping
+	const HANDLE h_map_file = CreateFileMapping(
+		h_file,
+		nullptr,
+		PAGE_READONLY, 
+		size_high,
+		size_low,   
+		nullptr);
+	if (h_map_file == INVALID_HANDLE_VALUE || h_map_file == nullptr)
+	{
+		std::cout << "error";
+		CloseHandle(h_file);
+		return 0;
+	}
+
+	//Map File to memory
+	void* pv_file = MapViewOfFile(
+		h_map_file,
+		FILE_MAP_READ,
+		0,
+		0,
+		0);
+
+	if (pv_file == nullptr)
+	{
+		std::cout << "error";
+		CloseHandle(h_file);
+		return 0;
+	}
+
+	uint8_t* p = static_cast<uint8_t*>(pv_file);
+
+
+	PIMAGE_DOS_HEADER dos_header = reinterpret_cast<PIMAGE_DOS_HEADER>(p);
+
+	const PIMAGE_NT_HEADERS nt_headers = reinterpret_cast<const PIMAGE_NT_HEADERS>(p + dos_header->e_lfanew);
+
+	const PIMAGE_FILE_HEADER file_header = &nt_headers->FileHeader;
+
+	PIMAGE_OPTIONAL_HEADER	optional_header = (PIMAGE_OPTIONAL_HEADER)(&nt_headers->OptionalHeader);
+
+	const DWORD file_alignment = optional_header->FileAlignment;
+
+
+	PIMAGE_SECTION_HEADER section_table =
+		reinterpret_cast<PIMAGE_SECTION_HEADER>(p + dos_header->e_lfanew +
+			IMAGE_SIZEOF_SIGNATURE +
+			IMAGE_SIZEOF_FILE_HEADER +
+			file_header->SizeOfOptionalHeader);
+
+	DWORD	export_foa = rva_to_foa(optional_header->DataDirectory[0].VirtualAddress, section_table);
+
+	PIMAGE_EXPORT_DIRECTORY export_directory = (PIMAGE_EXPORT_DIRECTORY)(p + export_foa);
+
+
+	DWORD name_list_foa = rva_to_foa(export_directory->AddressOfNames, section_table);
+
+	PDWORD name_list = (PDWORD)(p + name_list_foa);
+
+
+
+
+	std::vector<std::string> func_list;
+
+	for (size_t i = 0; i < export_directory->NumberOfNames; i++, name_list++)
+	{
+
+		DWORD name_foa = rva_to_foa(* name_list, section_table);
+		char* name = (char*)(p + name_foa);
+		func_list.emplace_back(name);
+
+	}
+
+
+	UnmapViewOfFile(pv_file); 
+	CloseHandle(h_map_file);
+	CloseHandle(h_file); 
+
+
+	std::ofstream gen_cpp_obj;
+	gen_cpp_obj.open("warp_gen_cpp.cpp", std::ios::out | std::ios::trunc);
+	gen_cpp_obj << "#include <Windows.h>\n";
+	gen_cpp_obj << "extern \"C\" \n{\n";
+
+
+	for (size_t i = 0; i < func_list.size(); ++i)
+	{
+		auto fun = func_list[i];
+		gen_cpp_obj << format("void * warp_point_%d;\n", i);
+		gen_cpp_obj << format("#pragma comment(linker, \"/export:%s=warp_func_%d\")\n", fun.c_str(), i);
+		gen_cpp_obj << format("void warp_func_%d();\n", i);
+		gen_cpp_obj << ("\n");
+	}
+	gen_cpp_obj << ("}\n");
+
+
+	gen_cpp_obj << ("void load_function(HMODULE hm)\n{\n");
+	for (size_t i = 0; i < func_list.size(); ++i)
+	{
+		auto fun = func_list[i];
+		gen_cpp_obj << format("warp_point_%d = (void*)GetProcAddress(hm, \"%s\");\n", i, fun.c_str());
+	}
+	gen_cpp_obj << ("}\n");
+
+	gen_cpp_obj.close();
+
+
+
+	std::ofstream gen_asm_obj;
+	gen_asm_obj.open("warp_gen.asm", std::ios::out | std::ios::trunc);
+	for (size_t i = 0; i < func_list.size(); ++i)
+	{
+		auto fun = func_list[i];
+		gen_asm_obj << format("EXTERN warp_point_%d:QWORD;\n", i);
+	}
+	gen_asm_obj << ".CODE\n";
+	for (size_t i = 0; i < func_list.size(); ++i)
+	{
+		auto fun = func_list[i];
+		gen_asm_obj << format("warp_func_%d PROC\njmp warp_point_%d;\nwarp_func_%d ENDP\n", i,i,i);
+	}
+	gen_asm_obj << "END\n";
+	gen_asm_obj.close();
+}
diff --git a/tools/windowsbuild/warp_dll.cpp b/tools/windowsbuild/warp_dll.cpp
new file mode 100644
index 000000000000..6a89a4e189de
--- /dev/null
+++ b/tools/windowsbuild/warp_dll.cpp
@@ -0,0 +1,151 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+ 
+#include <cstdio>
+#include <iostream>
+#include <cuda_runtime.h>
+#include <algorithm>
+#include <Windows.h>
+#include <io.h>
+#include <vector>
+#include <regex>
+#include <shlwapi.h>
+
+
+extern "C" IMAGE_DOS_HEADER __ImageBase;
+
+
+std::vector<int> find_mxnet_dll()
+{
+	std::vector<int> version;
+	intptr_t handle;
+
+	_wfinddata_t findData{};
+	std::wregex reg(L".*?mxnet_([0-9]+)\\.dll");
+
+	HMODULE hModule = reinterpret_cast<HMODULE>(&__ImageBase);
+	WCHAR szPathBuffer[MAX_PATH] = { 0 };
+	GetModuleFileNameW(hModule, szPathBuffer, MAX_PATH);
+
+	PathRemoveFileSpecW(szPathBuffer);
+	wcscat_s(szPathBuffer, L"\\mxnet_*.dll");
+
+	handle = _wfindfirst(szPathBuffer, &findData);
+	if (handle == -1)
+	{
+		return version;
+	}
+
+	do
+	{
+		if (!(findData.attrib & _A_SUBDIR) || wcscmp(findData.name, L".") != 0 || wcscmp(findData.name, L"..") != 0)
+		{
+			std::wstring str(findData.name);
+			std::wsmatch base_match;
+			if(std::regex_match(str, base_match, reg))
+			{
+				if (base_match.size() == 2) {
+					std::wssub_match base_sub_match = base_match[1];
+					std::wstring base = base_sub_match.str();
+					version.push_back(std::stoi(base)) ;
+				}
+			}
+		}
+	} while (_wfindnext(handle, &findData) == 0);    
+
+	_findclose(handle);
+	std::sort(version.begin(), version.end());
+	return version;
+}
+
+int find_version()
+{
+	std::vector<int> known_sm = find_mxnet_dll();
+	int count = 0;
+	int version = 75;
+	if (cudaSuccess != cudaGetDeviceCount(&count))
+	{
+		return 30;
+	}
+	if (count == 0)
+	{
+		return 30;
+	}
+
+
+	for (int device = 0; device < count; ++device)
+	{
+		cudaDeviceProp prop{};
+		if (cudaSuccess == cudaGetDeviceProperties(&prop, device))
+		{
+			version = std::min(version, prop.major * 10 + prop.minor);
+		}
+	}
+
+	for (int i = known_sm.size() -1 ; i >=0; --i)
+	{
+		if(known_sm[i]<= version)
+		{
+			return known_sm[i];
+		}
+	}
+
+	return version;
+}
+
+void load_function(HMODULE hm);
+
+void mxnet_init()
+{
+	int version = find_version();
+	WCHAR dll_name[MAX_PATH];
+	wsprintfW(dll_name, L"mxnet_%d.dll", version);
+	HMODULE hm = LoadLibraryW(dll_name);
+	load_function(hm);
+}
+
+
+extern "C" BOOL WINAPI DllMain(
+	HINSTANCE const instance,  // handle to DLL module
+	DWORD     const reason,    // reason for calling function
+	LPVOID    const reserved)  // reserved
+{
+	// Perform actions based on the reason for calling.
+	switch (reason)
+	{
+	case DLL_PROCESS_ATTACH:
+		mxnet_init();
+		// Initialize once for each new process.
+		// Return FALSE to fail DLL load.
+		break;
+
+	case DLL_THREAD_ATTACH:
+		// Do thread-specific initialization.
+		break;
+
+	case DLL_THREAD_DETACH:
+		// Do thread-specific cleanup.
+		break;
+
+	case DLL_PROCESS_DETACH:
+		// Perform any necessary cleanup.
+		break;
+	}
+	return TRUE;  // Successful DLL_PROCESS_ATTACH.
+}
\ No newline at end of file

From 06aec8aada4c79991a854d27907e16f8815d95ad Mon Sep 17 00:00:00 2001
From: reminisce <wujun.nju@gmail.com>
Date: Thu, 2 Jan 2020 00:58:58 -0800
Subject: [PATCH 756/813] [CI] Re-enable testing with numpy 1.18 (#17200)

---
 ci/docker/install/requirements | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ci/docker/install/requirements b/ci/docker/install/requirements
index fd716f5fa815..6fd89bc65663 100644
--- a/ci/docker/install/requirements
+++ b/ci/docker/install/requirements
@@ -26,7 +26,7 @@ h5py==2.8.0rc1
 mock==2.0.0
 nose==1.3.7
 nose-timer==0.7.3
-numpy>1.16.0,<1.18.0
+numpy>1.16.0,<2.0.0
 pylint==2.3.1; python_version >= '3.0'
 requests<2.19.0,>=2.18.4
 scipy==1.2.1

From b9bce35aa07159607397293e8e73c7db0e70ce07 Mon Sep 17 00:00:00 2001
From: alicia <32725332+Alicia1529@users.noreply.github.com>
Date: Fri, 3 Jan 2020 05:25:55 +0800
Subject: [PATCH 757/813] boolean indexing (#17009)

fix bugs

reorganize boolean indexing
---
 python/mxnet/ndarray/ndarray.py             |  33 ++--
 python/mxnet/numpy/multiarray.py            | 170 ++++++++++++++++++--
 src/operator/tensor/matrix_op-inl.h         |   6 +-
 tests/python/unittest/test_numpy_ndarray.py |  81 +++++++++-
 4 files changed, 260 insertions(+), 30 deletions(-)

diff --git a/python/mxnet/ndarray/ndarray.py b/python/mxnet/ndarray/ndarray.py
index 260d9d3bfd11..255d85222e00 100644
--- a/python/mxnet/ndarray/ndarray.py
+++ b/python/mxnet/ndarray/ndarray.py
@@ -110,6 +110,8 @@
 _NDARRAY_UNSUPPORTED_INDEXING = -1
 _NDARRAY_BASIC_INDEXING = 0
 _NDARRAY_ADVANCED_INDEXING = 1
+_NDARRAY_EMPTY_TUPLE_INDEXING = 2
+_NDARRAY_BOOLEAN_INDEXING = 3
 
 # Caching whether MXNet was built with INT64 support or not
 _INT64_TENSOR_SIZE_ENABLED = None
@@ -2951,7 +2953,6 @@ def indexing_key_expand_implicit_axes(key, shape):
     """
     if not isinstance(key, tuple):
         key = (key,)
-
     # We need to loop explicitly since tuple functions like `index()` or
     # `count()` use `==` internally, which doesn't play well with fancy
     # indexing.
@@ -2968,7 +2969,12 @@ def indexing_key_expand_implicit_axes(key, shape):
         else:
             if idx is None:
                 num_none += 1
-            nonell_key.append(idx)
+            if isinstance(idx, NDArrayBase) and idx.ndim == 0 and idx.dtype != np.bool_:
+                # This handles ndarray of zero dim. e.g array(1)
+                # while advoid converting zero dim boolean array
+                nonell_key.append(idx.item())
+            else:
+                nonell_key.append(idx)
 
     nonell_key = tuple(nonell_key)
 
@@ -3040,23 +3046,32 @@ def _is_advanced_index(idx):
 def get_indexing_dispatch_code(key):
     """Returns a dispatch code for calling basic or advanced indexing functions."""
     assert isinstance(key, tuple)
+    num_bools = 0
+    basic_indexing = True
 
     for idx in key:
         if isinstance(idx, (NDArray, np.ndarray, list, tuple)):
+            if isinstance(idx, tuple) and len(idx) == 0:
+                return _NDARRAY_EMPTY_TUPLE_INDEXING
             if getattr(idx, 'dtype', None) == np.bool_:
-                raise TypeError('ndarray indexing does not support boolean ndarray'
-                                ' in a tuple of indices. Only single boolean ndarray'
-                                ' as an index is supported.')
-            return _NDARRAY_ADVANCED_INDEXING
+                num_bools += 1
+            basic_indexing = False
         elif sys.version_info[0] > 2 and isinstance(idx, range):
-            return _NDARRAY_ADVANCED_INDEXING
+            basic_indexing = False
         elif not (isinstance(idx, (py_slice, integer_types)) or idx is None):
             raise ValueError(
                 'NDArray does not support slicing with key {} of type {}.'
                 ''.format(idx, type(idx))
             )
-
-    return _NDARRAY_BASIC_INDEXING
+    if basic_indexing and num_bools == 0:
+        return _NDARRAY_BASIC_INDEXING
+    elif not basic_indexing and num_bools == 0:
+        return _NDARRAY_ADVANCED_INDEXING
+    elif num_bools == 1:
+        return _NDARRAY_BOOLEAN_INDEXING
+    else:
+        raise TypeError('ndarray indexing does not more than one boolean ndarray'
+                        ' in a tuple of complex indices.')
 
 
 def _get_index_range(start, stop, length, step=1):
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 847f106575a8..3918d0bd085e 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -66,6 +66,10 @@
 _NDARRAY_UNSUPPORTED_INDEXING = -1
 _NDARRAY_BASIC_INDEXING = 0
 _NDARRAY_ADVANCED_INDEXING = 1
+_NDARRAY_EMPTY_TUPLE_INDEXING = 2
+_NDARRAY_BOOLEAN_INDEXING = 3
+_NDARRAY_INT_BOOLEAN_INDEXING = 4
+_NDARRAY_SLICE_BOOLEAN_INDEXING = 5
 
 
 # This function is copied from ndarray.py since pylint
@@ -300,6 +304,19 @@ def _get_np_basic_indexing(self, key):
         else:
             return sliced.reshape_view(tuple(final_shape))
 
+    def _get_np_empty_tuple_indexing(self, key):
+        new_shape = []
+        num_none = 0
+        for i, idx in enumerate(key):
+            if idx is None:
+                new_shape.append(1) # expand dimension
+                num_none += 1
+            elif idx == ():
+                new_shape.append(0) # 0 shape
+            elif idx == slice(None, None, None):
+                new_shape.append(self.shape[i - num_none])
+        return empty(new_shape, dtype=self.dtype)
+
     def _get_np_advanced_indexing(self, key):
         idcs, new_axes = self._get_index_nd(key)
         if type(idcs) == NDArray:  # pylint: disable=unidiomatic-typecheck
@@ -327,6 +344,123 @@ def _set_np_advanced_indexing(self, key, value):
         value_nd = self._prepare_value_nd(value, bcast_shape=vshape, squeeze_axes=new_axes)
         self._scatter_set_nd(value_nd, idcs)
 
+    def _check_boolean_indexing_type(self, key):
+        """Check boolean indexing type arr[bool, :, :], arr[1, bool, 4], or arr[:, bool, :]
+           return bool_type, bool_position"""
+
+        dim = len(key)
+        rest_int = True
+        rest_full_slice = True
+        pos = None
+        for idx in range(dim):
+            if isinstance(key[idx], _np.ndarray) and key[idx].dtype == _np.bool_:
+                key[idx] = array(key[idx], dtype='bool', ctx=self.ctx)
+            if isinstance(key[idx], ndarray) and key[idx].dtype == _np.bool_:
+                pos = idx
+            elif isinstance(key[idx], integer_types):
+                rest_full_slice = False
+            elif isinstance(key[idx], py_slice) and key[idx] == slice(None, None, None):
+                rest_int = False
+            # not arr[:, bool, :] format slicing or not arr[3,bool,4]
+            else:
+                raise TypeError('ndarray boolean indexing does not support slicing '
+                                'with key {} of type {}'.format(idx, type(idx))
+                                )
+
+        if rest_int:
+            return _NDARRAY_INT_BOOLEAN_INDEXING, pos
+        elif rest_full_slice:
+            return _NDARRAY_SLICE_BOOLEAN_INDEXING, pos
+        raise NotImplementedError("Do not support {} as key for boolean indexing".format(key))
+
+    @staticmethod
+    def _calculate_new_idx(key, shape, mask_pos, mask_ndim): # pylint: disable=redefined-outer-name
+        new_idx = 0
+        step = 1
+        for idx in range(len(key)-1, mask_pos, -1):
+            new_idx += key[idx]*step
+            step *= shape[idx+mask_ndim-1]
+        return new_idx
+
+    def _get_np_boolean_indexing(self, key):
+        if not isinstance(key, tuple):
+            key = (key,)
+        bool_type, pos = self._check_boolean_indexing_type(key)
+
+        from functools import reduce
+        mask_shape = key[pos].shape
+        mask_ndim = len(mask_shape)
+        ndim = len(self.shape)
+        for i in range(mask_ndim):
+            if key[pos].shape[i] != self.shape[pos + i]:
+                raise IndexError('boolean index did not match indexed array along axis {};'
+                                 ' size is {} but corresponding boolean size is {}'
+                                 .format(pos + i, self.shape[pos + i], key[pos].shape[i]))
+        remaining_idces = pos + mask_ndim
+        remaining_shapes = self.shape[remaining_idces:]
+        mask = _reshape_view(key[pos], -1)
+
+        if bool_type == _NDARRAY_SLICE_BOOLEAN_INDEXING:
+            data = _reshape_view(self, -1, *remaining_shapes)
+            # if mask is at the begining, then the scale is one
+            scale = reduce(lambda x, y: x * y, self.shape[:pos], 1)
+            keys = mask if scale == 1 else _reshape_view(_npi.stack(*[mask for i in range(scale)]), -1)
+            all_shapes = self.shape[:pos] + remaining_shapes
+            return _reshape_view(_npi.boolean_mask(data, keys), -1, *all_shapes)
+
+        elif bool_type == _NDARRAY_INT_BOOLEAN_INDEXING:
+            out = self
+            for idx in range(pos):
+                out = out[key[idx]]
+            data = _reshape_view(out, -1, *remaining_shapes)
+            after_mask = _reshape_view(_npi.boolean_mask(data, mask), -1, *remaining_shapes)
+            if pos == len(key) - 1:
+                return after_mask
+            # check boundary
+            for idx in range(pos+1, len(key)):
+                if key[idx] >= self.shape[idx+mask_ndim-1]:
+                    raise IndexError('index {} on a dimension of' # pylint: disable=too-many-format-args
+                                     .format(key[idx], self.shape[idx+mask_ndim-1]))
+            implicit_idces = len(key)+mask_ndim-1 # idces not explictly shown in the key
+            implicit_shape = self.shape[implicit_idces:]
+            new_dim = reduce(lambda x, y: x * y, self.shape[pos+mask_ndim:implicit_idces], 1)
+            new_idx = self._calculate_new_idx(key, self.shape, pos, mask_ndim)
+            after_reshape = _reshape_view(after_mask, -1, new_dim, *implicit_shape)
+            return _reshape_view(_npi.take(after_reshape, array([new_idx]), axis=1), -1, *implicit_shape)
+
+        raise NotImplementedError("This boolean indexing type is not supported.")
+
+    def _set_np_boolean_indexing(self, key, value):
+        if not isinstance(key, tuple):
+            key = (key,)
+        bool_type, pos = self._check_boolean_indexing_type(key)
+
+        mask = key[pos]
+        mask_shape = mask.shape
+        mask_ndim = len(mask_shape)
+        for i in range(mask_ndim):
+            if mask_shape[i] != self.shape[pos + i]:
+                raise IndexError('boolean index did not match indexed array along axis {};'
+                                 ' size is {} but corresponding boolean size is {}'
+                                 .format(pos + i, self.shape[pos + i], mask_shape[i]))
+
+        data = self # when bool_type == _NDARRAY_SLICE_BOOLEAN_INDEXING
+        if bool_type == _NDARRAY_INT_BOOLEAN_INDEXING:
+            if pos != len(key) - 1:
+                raise NotImplementedError('only support boolean array at the end of the idces '
+                                          'when it is mixed with integers')
+            for idx in range(pos):
+                data = data[key[idx]]
+                pos -= 1
+
+        if isinstance(value, numeric_types):
+            _npi.boolean_mask_assign_scalar(data=data, mask=mask, value=value, start_axis=pos, out=data)
+        elif isinstance(value, ndarray):
+            _npi.boolean_mask_assign_tensor(data=data, mask=mask, value=value, start_axis=pos, out=data)
+        else:
+            raise NotImplementedError('type %s is not supported.'%(type(value)))
+
+
     # pylint: disable=too-many-return-statements
     def __getitem__(self, key):
         """Return self[key].
@@ -476,21 +610,6 @@ def __getitem__(self, key):
                 raise TypeError('{}'.format(str(err)))
         if isinstance(key, _np.ndarray) and key.dtype == _np.bool_:
             key = array(key, dtype='bool', ctx=self.ctx)
-        if isinstance(key, ndarray) and key.dtype == _np.bool_:  # boolean indexing
-            key_shape = key.shape
-            key_ndim = len(key_shape)
-            if ndim < key_ndim:
-                raise IndexError('too many indices, whose ndim = {}, for array with ndim = {}'
-                                 .format(key_ndim, ndim))
-            for i in range(key_ndim):
-                if key_shape[i] != shape[i]:
-                    raise IndexError('boolean index did not match indexed array along dimension {};'
-                                     ' dimension is {} but corresponding boolean dimension is {}'
-                                     .format(i, shape[i], key_shape[i]))
-            remaining_dims = shape[key_ndim:]
-            data = _reshape_view(self, -1, *remaining_dims)
-            key = _reshape_view(key, -1)
-            return _reshape_view(_npi.boolean_mask(data, key), -1, *remaining_dims)
 
         if ndim == 0:
             if key != ():
@@ -519,15 +638,21 @@ def __getitem__(self, key):
             elif key.step == 0:
                 raise ValueError("slice step cannot be zero")
 
+        key_before_expaned = key
         key = indexing_key_expand_implicit_axes(key, self.shape)
         indexing_dispatch_code = get_indexing_dispatch_code(key)
         if indexing_dispatch_code == _NDARRAY_BASIC_INDEXING:
             return self._get_np_basic_indexing(key)
+        elif indexing_dispatch_code == _NDARRAY_EMPTY_TUPLE_INDEXING:
+            return self._get_np_empty_tuple_indexing(key)
         elif indexing_dispatch_code == _NDARRAY_ADVANCED_INDEXING:
             return self._get_np_advanced_indexing(key)
+        elif indexing_dispatch_code == _NDARRAY_BOOLEAN_INDEXING:
+            return self._get_np_boolean_indexing(key_before_expaned)
         else:
             raise RuntimeError
 
+    # pylint: disable=inconsistent-return-statements
     def __setitem__(self, key, value):
         """Sets ``self[key]`` to ``value``.
 
@@ -577,7 +702,6 @@ def __setitem__(self, key, value):
         """
         if isinstance(value, NDArray) and not isinstance(value, ndarray):
             raise TypeError('Cannot assign mx.nd.NDArray to mxnet.numpy.ndarray')
-
         # handle basic and advanced indexing
         if self.ndim == 0:
             if not isinstance(key, tuple) or len(key) != 0:
@@ -595,6 +719,7 @@ def __setitem__(self, key, value):
             else:
                 raise ValueError('setting an array element with a sequence.')
         else:
+            key_before_expaned = key
             key = indexing_key_expand_implicit_axes(key, self.shape)
             slc_key = tuple(idx for idx in key if idx is not None)
             if len(slc_key) < self.ndim:
@@ -611,8 +736,12 @@ def __setitem__(self, key, value):
             indexing_dispatch_code = get_indexing_dispatch_code(slc_key)
             if indexing_dispatch_code == _NDARRAY_BASIC_INDEXING:
                 self._set_nd_basic_indexing(key, value)  # function is inheritated from NDArray class
+            elif indexing_dispatch_code == _NDARRAY_EMPTY_TUPLE_INDEXING:
+                pass # no action needed
             elif indexing_dispatch_code == _NDARRAY_ADVANCED_INDEXING:
                 self._set_np_advanced_indexing(key, value)
+            elif indexing_dispatch_code == _NDARRAY_BOOLEAN_INDEXING:
+                return self._set_np_boolean_indexing(key_before_expaned, value)
             else:
                 raise ValueError(
                     'Indexing NDArray with index {} of type {} is not supported'
@@ -937,6 +1066,15 @@ def __str__(self):
             return array_str
         return '{array} @{ctx}'.format(array=array_str, ctx=context)
 
+    def __format__(self, fmt):
+        """Return value.__format__(format_spec). Overwrite to include 0-d array"""
+        if self.ndim == 0:
+            return self.item().__format__(fmt)
+        elif len(fmt) == 0:
+            return self.__str__().__format__(fmt)
+        else:
+            raise TypeError("Cannot format mxnet.numpy.ndarray with format_spec")
+
     def attach_grad(self, grad_req='write'):  # pylint: disable=arguments-differ
         """Attach a gradient buffer to this ndarray, so that `backward`
         can compute gradient with respect to it.
diff --git a/src/operator/tensor/matrix_op-inl.h b/src/operator/tensor/matrix_op-inl.h
index cd0bd8deeac3..fa7b8a10b212 100644
--- a/src/operator/tensor/matrix_op-inl.h
+++ b/src/operator/tensor/matrix_op-inl.h
@@ -968,7 +968,7 @@ void SliceOpForward(const nnvm::NodeAttrs& attrs,
   MXNET_NDIM_SWITCH(data.ndim(), ndim, {
     common::StaticArray<index_t, ndim> begin, end, step;
     GetIndexRange(data.shape_, param.begin, param.end, param.step, &begin, &end, &step);
-    MSHADOW_TYPE_SWITCH(out.type_flag_, DType, {
+    MSHADOW_TYPE_SWITCH_WITH_BOOL(out.type_flag_, DType, {
       MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
         size_t num_threads = out.shape_.FlatTo2D()[0];
         if (std::is_same<xpu, gpu>::value) {
@@ -2252,7 +2252,7 @@ void StackOpForward(const nnvm::NodeAttrs& attrs,
   int axis = CheckAxis(param.axis, outputs[0].ndim());
 
   Stream<xpu> *s = ctx.get_stream<xpu>();
-  MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+  MSHADOW_TYPE_SWITCH_WITH_BOOL(outputs[0].type_flag_, DType, {
     std::vector<Tensor<xpu, 3, DType> > data(inputs.size());
     Tensor<xpu, 3, DType> out;
     size_t leading = 1, trailing = 1;
@@ -2286,7 +2286,7 @@ void StackOpBackward(const nnvm::NodeAttrs& attrs,
   int axis = CheckAxis(param.axis, inputs[0].ndim());
 
   Stream<xpu> *s = ctx.get_stream<xpu>();
-  MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+  MSHADOW_TYPE_SWITCH_WITH_BOOL(inputs[0].type_flag_, DType, {
     std::vector<Tensor<xpu, 3, DType> > grad_in(outputs.size());
     Tensor<xpu, 3, DType> grad;
     size_t leading = 1, trailing = 1;
diff --git a/tests/python/unittest/test_numpy_ndarray.py b/tests/python/unittest/test_numpy_ndarray.py
index d7b32c4d382e..a994413ceac1 100644
--- a/tests/python/unittest/test_numpy_ndarray.py
+++ b/tests/python/unittest/test_numpy_ndarray.py
@@ -601,6 +601,34 @@ def test_np_ndarray_copy():
     assert same(mx_ret.asnumpy(), np_ret)
 
 
+@with_seed()
+def test_formatting():
+    def test_0d():
+        a = np.array(np.pi)
+        _a = a.asnumpy()
+        assert '{:0.3g}'.format(a) == '{:0.3g}'.format(_a)
+        assert '{:0.3g}'.format(a[()]) == '{:0.3g}'.format(_a[()])
+
+    def test_nd_format():
+        a = np.array([np.pi])
+        assert_exception('{:30}'.format, TypeError, a)
+
+    def test_nd_no_format():
+        a = np.array([np.pi])
+        _a = a.asnumpy()
+        assert '{}'.format(a) == '{}'.format(_a)
+        b = np.arange(8).reshape(2,2,2)
+        assert '{}'.format(a) == '{}'.format(_a)
+
+    context = mx.context.current_context()
+    if str(context)[:3] != 'gpu':
+        test_0d()
+        test_nd_format()
+        test_nd_no_format() 
+    # if the program is running in GPU, the formatted string would be appended with context notation
+    # for exmpale, if a = np.array([np.pi]), the return value of '{}'.format(a) is '[3.1415927] @gpu(0)'
+
+
 @with_seed()
 @use_np
 def test_np_ndarray_indexing():
@@ -1095,8 +1123,6 @@ def test_boolean_catch_exception():
         index = np.zeros((4, 4), dtype=bool)
         assert_exception(arr.__getitem__, IndexError, index)
 
-        assert_exception(arr.__getitem__, TypeError, (slice(None), index))
-
     def test_boolean_indexing_onedim():
         # adapted from numpy's test_indexing.py
         # Indexing a 2-dimensional array with
@@ -1127,6 +1153,55 @@ def test_boolean_indexing_list():
         assert same(a[b].asnumpy(), _np.array([1, 3], dtype=a.dtype))
         (a[None, b], [[1, 3]])
 
+    def test_boolean_indexing_tuple():
+        # case arr[:, mask, :] and arr[1, mask, 0]
+        # when a boolean array is in a tuple
+        a = np.array([[[0, 1],
+                       [2, 3]],
+                      [[4, 5],
+                       [6, 7]]], dtype=np.int32)
+        b = np.array([[False,True],
+                      [True,False]],dtype=np.bool)
+        _np_a = a.asnumpy()
+        _np_b = b.asnumpy()
+        assert same(a[:, b].asnumpy(), _np_a[:, _np_b])
+        assert same(a[b, :].asnumpy(), _np_a[_np_b, :])
+        assert same(a[0, b].asnumpy(), _np_a[0, _np_b])
+        assert same(a[b, 1].asnumpy(), _np_a[_np_b, 1])
+
+    def test_boolean_indexing_assign():
+        # test boolean indexing assign
+        shape = (3, 2, 3)
+        mx_data = np.random.uniform(size=shape)
+        mx_mask = np.array([[False,True], [True,False], [True,False]],dtype=np.bool)
+        np_data = mx_data.asnumpy()
+        np_mask = mx_mask.asnumpy()
+
+        np_data[np_data>0.5] = 0
+        mx_data[mx_data>0.5] = 0
+        assert_almost_equal(mx_data.asnumpy(), np_data, rtol=1e-3, atol=1e-5, use_broadcast=False)
+        np_data[np_mask] = 1
+        mx_data[mx_mask] = 1
+        assert_almost_equal(mx_data.asnumpy(), np_data, rtol=1e-3, atol=1e-5, use_broadcast=False)
+        # not supported at this moment
+        # only support boolean array at the end of the idces when it is mixed with integers
+        # np_data[np_mask, 1] = 2
+        # mx_data[mx_mask, 1] = 2
+        # assert_almost_equal(mx_data.asnumpy(), np_data, rtol=1e-3, atol=1e-5, use_broadcast=False)
+        np_data[np_mask, :] = 3
+        mx_data[mx_mask, :] = 3
+        assert_almost_equal(mx_data.asnumpy(), np_data, rtol=1e-3, atol=1e-5, use_broadcast=False)
+
+        mx_mask = np.array([[False,True, True],[False, True,False]],dtype=np.bool)
+        np_mask = mx_mask.asnumpy()
+        
+        np_data[0, np_mask] = 5
+        mx_data[0, mx_mask] = 5
+        assert_almost_equal(mx_data.asnumpy(), np_data, rtol=1e-3, atol=1e-5, use_broadcast=False)
+        np_data[:, np_mask] = 6
+        mx_data[:, mx_mask] = 6
+        assert_almost_equal(mx_data.asnumpy(), np_data, rtol=1e-3, atol=1e-5, use_broadcast=False)
+
     def test_boolean_indexing_autograd():
         a = np.random.uniform(size=(3, 4, 5))
         a.attach_grad()
@@ -1147,6 +1222,8 @@ def test_boolean_indexing_autograd():
     test_boolean_indexing_onedim()
     test_boolean_indexing_twodim()
     test_boolean_indexing_list()
+    test_boolean_indexing_tuple()
+    test_boolean_indexing_assign()
     test_boolean_indexing_autograd()
 
 

From b38816d26a7d4eaaee7e028de8ecb92b2f9b7cad Mon Sep 17 00:00:00 2001
From: Vandana Kannan <vandanavk@users.noreply.github.com>
Date: Thu, 2 Jan 2020 14:32:06 -0800
Subject: [PATCH 758/813] ONNX export: Gather (#15995)

---
 .../contrib/onnx/mx2onnx/_op_translations.py   | 18 ++++++++++++++++++
 tests/python-pytest/onnx/test_cases.py         |  6 +++---
 2 files changed, 21 insertions(+), 3 deletions(-)

diff --git a/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py b/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
index 8fdc5944fb91..b92797563b1d 100644
--- a/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
+++ b/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
@@ -2080,3 +2080,21 @@ def convert_topk(node, **kwargs):
     )
 
     return [topk_node]
+
+
+@mx_op.register("take")
+def convert_take(node, **kwargs):
+    """Map MXNet's Take operator attributes to onnx's Gather operator.
+    """
+    name, input_nodes, attrs = get_inputs(node, kwargs)
+
+    axis = int(attrs.get('axis', 0))
+
+    node = onnx.helper.make_node(
+        "Gather",
+        input_nodes,
+        [name],
+        axis=axis,
+        name=name,
+    )
+    return [node]
diff --git a/tests/python-pytest/onnx/test_cases.py b/tests/python-pytest/onnx/test_cases.py
index eb6b71896e28..0f822d3b240f 100644
--- a/tests/python-pytest/onnx/test_cases.py
+++ b/tests/python-pytest/onnx/test_cases.py
@@ -79,10 +79,10 @@
              'test_softplus',
              'test_reduce_',
              'test_split_equal',
-             'test_top_k'
+             'test_top_k',
+             'test_gather'
              ],
-    'import': ['test_gather',
-               'test_softsign',
+    'import': ['test_softsign',
                'test_mean',
                'test_averagepool_1d',
                'test_averagepool_2d_pads_count_include_pad',

From 77c7c3a9346220c9a13f23ae559696d4f4f916aa Mon Sep 17 00:00:00 2001
From: Sam Skalicky <samskalicky@gmail.com>
Date: Thu, 2 Jan 2020 18:13:51 -0500
Subject: [PATCH 759/813] Enhancements for custom subgraph op (#17194)

* initial commit

* added flag on user library side in example

* fixed whitespace

* simplified API
---
 .../extensions/lib_custom_op/subgraph_lib.cc  | 17 +------
 include/mxnet/lib_api.h                       | 13 +++--
 src/c_api/c_api.cc                            | 51 ++++++++++++++-----
 3 files changed, 48 insertions(+), 33 deletions(-)

diff --git a/example/extensions/lib_custom_op/subgraph_lib.cc b/example/extensions/lib_custom_op/subgraph_lib.cc
index 8e7e8833745a..27da0fd9a324 100644
--- a/example/extensions/lib_custom_op/subgraph_lib.cc
+++ b/example/extensions/lib_custom_op/subgraph_lib.cc
@@ -46,20 +46,6 @@ MXReturnValue parseAttrs(std::map<std::string, std::string> attrs,
   return MX_SUCCESS;
 }
 
-MXReturnValue inferType(std::map<std::string, std::string> attrs,
-                        std::vector<int> &intypes,
-                        std::vector<int> &outtypes) {
-  outtypes[0] = intypes[0];
-  return MX_SUCCESS;
-}
-
-MXReturnValue inferShape(std::map<std::string, std::string> attrs,
-                         std::vector<std::vector<unsigned int>> &inshapes,
-                         std::vector<std::vector<unsigned int>> &outshapes) {
-  outshapes[0] = inshapes[0];
-  return MX_SUCCESS;
-}
-
 class MyStatefulOp : public CustomStatefulOp {
  public:
   explicit MyStatefulOp(std::string sym) : subgraph_sym(sym) {}
@@ -98,8 +84,7 @@ MXReturnValue createOpState(std::map<std::string, std::string> attrs,
 
 REGISTER_OP(_custom_subgraph_op)
 .setParseAttrs(parseAttrs)
-.setInferType(inferType)
-.setInferShape(inferShape)
+.setIsSubgraphOp()
 .setCreateOpState(createOpState);
 
 MXReturnValue initialize(int version) {
diff --git a/include/mxnet/lib_api.h b/include/mxnet/lib_api.h
index 290a63518373..f21e484216ea 100644
--- a/include/mxnet/lib_api.h
+++ b/include/mxnet/lib_api.h
@@ -541,7 +541,8 @@ class CustomOp {
  public:
   explicit CustomOp(const char* op_name) : name(op_name),
     forward(NULL), backward(NULL), parse_attrs(NULL), infer_type(NULL),
-    infer_shape(NULL), mutate_inputs(NULL), create_opstate(NULL) {}
+    infer_shape(NULL), mutate_inputs(NULL), create_opstate(NULL),
+    isSGop(false) {}
   ~CustomOp() {}
   CustomOp& setForward(fcomp_t fcomp) {
     forward = fcomp;
@@ -571,6 +572,10 @@ class CustomOp {
     create_opstate = func;
     return *this;
   }
+  CustomOp& setIsSubgraphOp() {
+    isSGop = true;
+    return *this;
+  }
 
   /*! \brief operator name */
   const char* name;
@@ -582,6 +587,7 @@ class CustomOp {
   inferShape_t infer_shape;
   mutateInputs_t mutate_inputs;
   createOpState_t create_opstate;
+  bool isSGop;
 };
 
 /*!
@@ -658,7 +664,7 @@ typedef int (*opRegSize_t)(void);
 typedef int (*opRegGet_t)(int, const char**, fcomp_t*, fcomp_t*,
                           parseAttrs_t*, inferType_t*,
                           inferShape_t*, mutateInputs_t*,
-                          createOpState_t*);
+                          createOpState_t*, bool*);
 
 #define MXLIB_OPCALLFREE_STR "_opCallFree"
 typedef int (*opCallFree_t)(void*);
@@ -737,7 +743,7 @@ extern "C" {
   _opRegGet(int idx, const char** name, fcomp_t* fcomp, fcomp_t* fgrad,
             parseAttrs_t* parse, inferType_t* type,
             inferShape_t* shape, mutateInputs_t* mutate,
-            createOpState_t* create_op) {
+            createOpState_t* create_op, bool *isSGop) {
     CustomOp op = Registry<CustomOp>::get()->get(idx);
     *name = op.name;
     *fcomp = op.forward;
@@ -747,6 +753,7 @@ extern "C" {
     *shape = op.infer_shape;
     *mutate = op.mutate_inputs;
     *create_op = op.create_opstate;
+    *isSGop = op.isSGop;
   }
 
   /*! \brief calls free from the external library for library allocated arrays */
diff --git a/src/c_api/c_api.cc b/src/c_api/c_api.cc
index f8db501d46f0..ca39ef22b20a 100644
--- a/src/c_api/c_api.cc
+++ b/src/c_api/c_api.cc
@@ -51,6 +51,7 @@
 #include "./c_api_common.h"
 #include "../operator/custom/custom-inl.h"
 #include "../operator/operator_common.h"
+#include "../operator/subgraph/common.h"
 #include "../operator/tensor/matrix_op-inl.h"
 #include "../operator/tvmop/op_module.h"
 #include "../common/utils.h"
@@ -162,20 +163,27 @@ int MXLoadLib(const char *path) {
     fcomp_t fgrad_fp = nullptr;
     mutateInputs_t mutate_fp = nullptr;
     createOpState_t create_opstate_fp = nullptr;
+    bool isSubgraphOp = false;
 
     // get custom operator implemenation from the dynamic library
     opRegGet(i, &name, &fcomp_fp, &fgrad_fp, &parse_fp, &type_fp, &shape_fp,
-                &mutate_fp, &create_opstate_fp);
+             &mutate_fp, &create_opstate_fp, &isSubgraphOp);
 
-    // validate custom operator functions from the dynamic library
-    CHECK(fcomp_fp != nullptr || create_opstate_fp != nullptr) << "Error loading '" << name
-                            << "' custom op, Forward or CreateOpState function was not set.";
     CHECK(parse_fp != nullptr) << "Error loading '" << name
-                            << "' custom op, ParseAttrs function was not set.";
-    CHECK(type_fp  != nullptr) << "Error loading '" << name
+                               << "' custom op, ParseAttrs function was not set.";
+    if (!isSubgraphOp) {
+      // validate custom operator functions from the dynamic library
+      CHECK(fcomp_fp != nullptr || create_opstate_fp != nullptr) << "Error loading '" << name
+                            << "' custom op, Forward or CreateOpState function was not set.";
+      CHECK(type_fp  != nullptr) << "Error loading '" << name
                             << "' custom op, InferType function was not set.";
-    CHECK(shape_fp != nullptr) << "Error loading '" << name
+      CHECK(shape_fp != nullptr) << "Error loading '" << name
                             << "' custom op, InferShape function was not set.";
+    } else {
+      // validate custom operator functions from the dynamic library
+      CHECK(create_opstate_fp != nullptr) << "Error loading '" << name
+                            << "' custom subgraph op, CreateOpState function was not set.";
+    }
 
     LOG(INFO) << "\tOp[" << i << "] " << name;
     std::string name_str(name);
@@ -646,10 +654,28 @@ int MXLoadLib(const char *path) {
       // TODO(samskalicky): enable constant overwriting of registertion multiple times
       plevel++;
     }
-    regOp.set_attr<nnvm::FInferType>("FInferType", infer_type, plevel);
-    regOp.set_attr<mxnet::FInferShape>("FInferShape", infer_shape, plevel);
-    regOp.set_attr<FInferStorageType>("FInferStorageType", infer_storage_type, plevel);
-    regOp.set_attr<FResourceRequest>("FResourceRequest", resc_req, plevel);
+    if (!isSubgraphOp) {
+      regOp.set_attr<nnvm::FInferType>("FInferType", infer_type, plevel);
+      regOp.set_attr<mxnet::FInferShape>("FInferShape", infer_shape, plevel);
+      regOp.set_attr<FInferStorageType>("FInferStorageType", infer_storage_type, plevel);
+      regOp.set_attr<FResourceRequest>("FResourceRequest", resc_req, plevel);
+      // optionally add fmutate inputs if user specified a function
+      if (mutate_fp != nullptr)
+        regOp.set_attr<nnvm::FMutateInputs>("FMutateInputs", mutate_inputs, plevel);
+    } else {
+      using namespace mxnet::op;
+      regOp.set_attr<nnvm::FInferType>("FInferType",
+                                       DefaultSubgraphOpType, plevel);
+      regOp.set_attr<mxnet::FInferShape>("FInferShape",
+                                         DefaultSubgraphOpShape, plevel);
+      regOp.set_attr<FInferStorageType>("FInferStorageType",
+                                        DefaultSubgraphOpStorageType, plevel);
+      regOp.set_attr<FResourceRequest>("FResourceRequest",
+                                       DefaultSubgraphOpResourceRequest, plevel);
+      regOp.set_attr<nnvm::FMutateInputs>("FMutateInputs",
+                                          DefaultSubgraphOpMutableInputs, plevel);
+    }
+
     // optionally add stateful forward
     if (create_opstate_fp != nullptr) {
       regOp.set_attr<FCreateOpState>("FCreateOpState", create_opstate, plevel);
@@ -658,9 +684,6 @@ int MXLoadLib(const char *path) {
     } else {
       regOp.set_attr<FComputeEx>("FComputeEx<cpu>", forward_lambda, plevel);
     }
-    // optionally add fmutate inputs if user specified a function
-    if (mutate_fp != nullptr)
-      regOp.set_attr<nnvm::FMutateInputs>("FMutateInputs", mutate_inputs, plevel);
     // optionally add fgradient if user specified a function
     if (fgrad_fp != nullptr || create_opstate_fp != nullptr) {
       regOp.set_attr<nnvm::FGradient>("FGradient", grad_reg, plevel);

From 2a9ec0e4f5ed474c03af0d761b1d8dc6172ed87a Mon Sep 17 00:00:00 2001
From: Tao Lv <tao.a.lv@intel.com>
Date: Fri, 3 Jan 2020 08:59:19 +0800
Subject: [PATCH 760/813] Softmax primitive cache and in-place computation
 (#17152)

* softmax primitive cache

* retrigger ci
---
 src/operator/nn/mkldnn/mkldnn_softmax.cc | 63 ++++++++++++++++++++----
 src/operator/nn/softmax-inl.h            | 21 ++++++++
 2 files changed, 75 insertions(+), 9 deletions(-)

diff --git a/src/operator/nn/mkldnn/mkldnn_softmax.cc b/src/operator/nn/mkldnn/mkldnn_softmax.cc
index a3558847fba1..1235f3c121fc 100644
--- a/src/operator/nn/mkldnn/mkldnn_softmax.cc
+++ b/src/operator/nn/mkldnn/mkldnn_softmax.cc
@@ -31,9 +31,9 @@
 namespace mxnet {
 namespace op {
 
-static mkldnn::softmax_forward::primitive_desc GetSoftmaxFwdPd(
-                                bool is_train, const int axis,
-                                const mkldnn::memory &input_mem) {
+static mkldnn::softmax_forward::primitive_desc GetSoftmaxFwdPd(bool is_train,
+                                                               const int axis,
+                                                               const mkldnn::memory &input_mem) {
   mkldnn::memory::desc data_md = input_mem.get_desc();
   auto cpu_engine = CpuEngine::Get()->get_engine();
   auto prop = is_train ? mkldnn::prop_kind::forward_training
@@ -66,6 +66,51 @@ bool SupportMKLDNNSoftmax(const SoftmaxParam &param,
   return (ndim >= 1 && ndim <= 4);
 }
 
+class MKLDNNSoftmaxFwd {
+ public:
+  mkldnn::softmax_forward::primitive_desc pd;
+
+  MKLDNNSoftmaxFwd(const bool is_train,
+                   const int axis,
+                   const mkldnn::memory &input) : pd(GetSoftmaxFwdPd(is_train, axis, input)) {
+    fwd_ = std::make_shared<mkldnn::softmax_forward>(pd);
+  }
+
+  const mkldnn::softmax_forward &GetFwd() const {
+    return *fwd_;
+  }
+
+ private:
+  std::shared_ptr<mkldnn::softmax_forward> fwd_;
+};
+
+typedef ParamOpSign<SoftmaxParam> MKLDNNSoftmaxSignature;
+
+static MKLDNNSoftmaxFwd &GetSoftmaxFwd(const SoftmaxParam &param,
+                                       const int real_axis,
+                                       const bool is_train,
+                                       const NDArray &data,
+                                       const NDArray &output) {
+#if DMLC_CXX11_THREAD_LOCAL
+  static thread_local std::unordered_map<MKLDNNSoftmaxSignature, MKLDNNSoftmaxFwd, OpHash> fwds;
+#else
+  static MX_THREAD_LOCAL std::unordered_map<MKLDNNSoftmaxSignature, MKLDNNSoftmaxFwd, OpHash> fwds;
+#endif
+
+  MKLDNNSoftmaxSignature key(param);
+  key.AddSign(real_axis);
+  key.AddSign(is_train);
+  key.AddSign(data);
+  key.AddSign(output);
+
+  auto it = fwds.find(key);
+  if (it == fwds.end()) {
+    MKLDNNSoftmaxFwd fwd(is_train, real_axis, *(data.GetMKLDNNData()));
+    it = AddToCache(&fwds, key, fwd);
+  }
+  return it->second;
+}
+
 void MKLDNNSoftmaxForward(const nnvm::NodeAttrs& attrs,
                           const OpContext &ctx,
                           const NDArray &in_data,
@@ -74,15 +119,15 @@ void MKLDNNSoftmaxForward(const nnvm::NodeAttrs& attrs,
   if (req == kNullOp) return;
   // same as the FCompute path, softmax only supports kWriteTo and kWriteInplace for now.
   CHECK_NE(req, kAddTo);
+
   const SoftmaxParam& param = nnvm::get<SoftmaxParam>(attrs.parsed);
   int axis = CheckAxis(param.axis, in_data.shape().ndim());
-  auto data_mem = in_data.GetMKLDNNData();
-  auto pd = GetSoftmaxFwdPd(ctx.is_train, axis, *data_mem);
-  auto out_mem = CreateMKLDNNMem(out_data, pd.dst_desc(), req);
+  auto fwd = GetSoftmaxFwd(param, axis, ctx.is_train, in_data, out_data);
+
+  auto in_mem = in_data.GetMKLDNNData();
+  auto out_mem = out_data.GetMKLDNNData(fwd.pd.dst_desc());
   MKLDNNStream *stream = MKLDNNStream::Get();
-  stream->RegisterPrimArgs(pd,
-                           {{MKLDNN_ARG_SRC, *data_mem}, {MKLDNN_ARG_DST, *out_mem.second}});
-  CommitOutput(out_data, out_mem);
+  stream->RegisterPrimArgs(fwd.GetFwd(), {{MKLDNN_ARG_SRC, *in_mem}, {MKLDNN_ARG_DST, *out_mem}});
   stream->Submit();
 }
 
diff --git a/src/operator/nn/softmax-inl.h b/src/operator/nn/softmax-inl.h
index 89da570c133b..2dbdbe170537 100644
--- a/src/operator/nn/softmax-inl.h
+++ b/src/operator/nn/softmax-inl.h
@@ -613,6 +613,13 @@ struct SoftmaxParam : public dmlc::Parameter<SoftmaxParam> {
     .set_default(dmlc::optional<bool>(false))
     .describe("Whether to use the length input as a mask over the data input.");
   }
+
+  bool operator==(const SoftmaxParam& other) const {
+    return this->axis == other.axis &&
+           this->temperature == other.temperature &&
+           this->dtype == other.dtype &&
+           this->use_length == other.use_length;
+  }
 };
 
 static inline bool softmax_has_dtype_override(const nnvm::NodeAttrs& attrs) {
@@ -899,4 +906,18 @@ void SoftmaxGradCompute(const nnvm::NodeAttrs& attrs,
 }  // namespace op
 }  // namespace mxnet
 
+namespace std {
+template<>
+struct hash<mxnet::op::SoftmaxParam> {
+  size_t operator()(const mxnet::op::SoftmaxParam& val) {
+    size_t ret = 0;
+    ret = dmlc::HashCombine(ret, val.axis);
+    ret = dmlc::HashCombine(ret, val.temperature);
+    ret = dmlc::HashCombine(ret, val.dtype);
+    ret = dmlc::HashCombine(ret, val.use_length);
+    return ret;
+  }
+};
+}  // namespace std
+
 #endif  // MXNET_OPERATOR_NN_SOFTMAX_INL_H_

From 89fe1f6aa1178b04e2106be575b53f2d192f1b22 Mon Sep 17 00:00:00 2001
From: ciyong <ciyong.chen@intel.com>
Date: Fri, 3 Jan 2020 12:26:52 +0800
Subject: [PATCH 761/813] [MKLDNN] Support channel wise quantization for
 FullyConnected (#17187)

* Add channel wise quantization option for fullyconnected

* fix lint

* retrigger CI

* retrigger CI

* Add channel-wise option support for more quantization user-level API

* Only update quatnize_net_v2 API and keep quantize_net API unchange

* retrigger CI

* fix pylint

* Add check for  option
---
 include/mxnet/c_api.h                         |   5 +-
 include/mxnet/op_attr_types.h                 |   4 +-
 python/mxnet/contrib/quantization.py          |  52 ++-
 src/c_api/c_api_symbolic.cc                   |   3 +
 .../nn/mkldnn/mkldnn_fully_connected-inl.h    |   7 +-
 .../nn/mkldnn/mkldnn_fully_connected.cc       |  25 +-
 .../quantization/quantize_graph_pass.cc       |  22 +-
 .../quantization/quantized_batch_norm.cc      |   8 +-
 .../quantization/quantized_indexing_op.cc     |   8 +-
 src/operator/subgraph/mkldnn/mkldnn_common.h  | 138 +++++++
 src/operator/subgraph/mkldnn/mkldnn_conv.cc   | 110 +-----
 src/operator/subgraph/mkldnn/mkldnn_fc.cc     | 358 +++++++++++++-----
 tests/python/mkl/test_subgraph.py             |  69 ++--
 13 files changed, 536 insertions(+), 273 deletions(-)
 create mode 100644 src/operator/subgraph/mkldnn/mkldnn_common.h

diff --git a/include/mxnet/c_api.h b/include/mxnet/c_api.h
index fcd5f3edeabe..27b420f9f243 100644
--- a/include/mxnet/c_api.h
+++ b/include/mxnet/c_api.h
@@ -1932,6 +1932,7 @@ MXNET_DLL int MXSymbolInferTypePartial(SymbolHandle sym,
  * \param quantized_dtype the quantized destination type for input data
  * \param calib_quantize **Deprecated**. quantize op will always be calibrated if could
  * \param quantize_mode quantize mode to be used in quantize pass
+ * \param quantize_granularity quantize granularity, tensor-wise or channel-wise
  * \param out_num_calib_names return the number of nodes to be calibrated
  * \param out_calib_names return the node names to be calibrated
  */
@@ -1944,8 +1945,8 @@ MXNET_DLL int MXQuantizeSymbol(SymbolHandle sym_handle,
                                const char **excluded_op_names,
                                const uint32_t num_offline, const char **offline_params,
                                const char *quantized_dtype, const bool calib_quantize,
-                               const char *quantize_mode, uint32_t* out_num_calib_names,
-                               const char ***out_calib_names);
+                               const char *quantize_mode, const char *quantize_granularity,
+                               uint32_t* out_num_calib_names, const char ***out_calib_names);
 
 /*!
  * \brief Convert a symbol into a mixed precision symbol with cast operators for target dtype casting
diff --git a/include/mxnet/op_attr_types.h b/include/mxnet/op_attr_types.h
index 7c0ea77dc986..237c595ad086 100644
--- a/include/mxnet/op_attr_types.h
+++ b/include/mxnet/op_attr_types.h
@@ -31,6 +31,7 @@
 
 #include <vector>
 #include <functional>
+#include <string>
 
 #include "./base.h"
 #include "./ndarray.h"
@@ -344,7 +345,8 @@ using FNeedRequantize = std::function<bool (const NodeAttrs& attrs)>;
  * which can handle fp32 inputs directly.
  */
 using FAvoidQuantizeInput = std::function<bool (const NodeAttrs& attrs,
-                                                size_t index)>;
+                                                const size_t index,
+                                                const std::string quantize_granularity)>;
 
 /*!
  * \brief Register a function to determine if the input of a quantized operator
diff --git a/python/mxnet/contrib/quantization.py b/python/mxnet/contrib/quantization.py
index ce22fb753ace..3ceccbf42c6e 100644
--- a/python/mxnet/contrib/quantization.py
+++ b/python/mxnet/contrib/quantization.py
@@ -85,7 +85,8 @@ def _quantize_params(qsym, params, th_dict):
     return quantized_params
 
 def _quantize_symbol(sym, ctx, excluded_symbols=None, excluded_operators=None,
-                     offline_params=None, quantized_dtype='int8', quantize_mode='smart'):
+                     offline_params=None, quantized_dtype='int8', quantize_mode='smart',
+                     quantize_granularity='tensor-wise'):
     """Given a symbol object representing a neural network of data type FP32,
     quantize it into a INT8 network.
 
@@ -109,6 +110,9 @@ def _quantize_symbol(sym, ctx, excluded_symbols=None, excluded_operators=None,
         The quantized destination type for input data.
     quantize_mode: str
         The mode that quantization pass to apply.
+    quantize_granularity: str
+        The granularity of quantization, currently supports 'tensor-wise' and 'channel-wise'
+        quantization. The default value is 'tensor-wise'.
 
     """
     num_excluded_symbols = 0
@@ -147,6 +151,7 @@ def _quantize_symbol(sym, ctx, excluded_symbols=None, excluded_operators=None,
                                      c_str(quantized_dtype),
                                      ctypes.c_bool(True),
                                      c_str(quantize_mode),
+                                     c_str(quantize_granularity),
                                      ctypes.byref(size),
                                      ctypes.byref(calib_str)))
     calib_layer = []
@@ -459,7 +464,8 @@ def quantize_model(sym, arg_params, aux_params,
                    data_names=('data',), label_names=('softmax_label',),
                    ctx=cpu(), excluded_sym_names=None, excluded_op_names=None, calib_mode='entropy',
                    calib_data=None, num_calib_examples=None,
-                   quantized_dtype='int8', quantize_mode='smart', logger=None):
+                   quantized_dtype='int8', quantize_mode='smart',
+                   quantize_granularity='tensor-wise', logger=None):
     """User-level API for generating a quantized model from a FP32 model w/ or w/o calibration.
     The backend quantized operators are only enabled for Linux systems. Please do not run
     inference using the quantized models on Windows for now.
@@ -515,6 +521,9 @@ def quantize_model(sym, arg_params, aux_params,
         The mode that quantization pass to apply. Support 'full' and 'smart'.
         'full' means quantize all operator if possible.
         'smart' means quantization pass will smartly choice which operator should be quantized.
+    quantize_granularity: str
+        The granularity of quantization, currently supports 'tensor-wise' and 'channel-wise'
+        quantization. The default value is 'tensor-wise'.
     logger : Object
         A logging object for printing information during the process of quantization.
 
@@ -544,11 +553,15 @@ def quantize_model(sym, arg_params, aux_params,
     if quantized_dtype not in ('int8', 'uint8', 'auto'):
         raise ValueError('unknown quantized_dtype %s received,'
                          ' expected `int8`, `uint8` or `auto`' % quantized_dtype)
+    if quantize_granularity not in ('tensor-wise', 'channel-wise'):
+        raise ValueError('unkonwn quantize_granularity %s received,'
+                         ' expected `tensor-wise` or `channel-wise`.' % quantize_granularity)
     qsym, calib_layer = _quantize_symbol(sym, ctx, excluded_symbols=excluded_sym_names,
                                          excluded_operators=excluded_op_names,
-                                         offline_params=list(
-                                             arg_params.keys()),
-                                         quantized_dtype=quantized_dtype, quantize_mode=quantize_mode)
+                                         offline_params=list(arg_params.keys()),
+                                         quantized_dtype=quantized_dtype,
+                                         quantize_mode=quantize_mode,
+                                         quantize_granularity=quantize_granularity)
     th_dict = {}
     if calib_mode is not None and calib_mode != 'none':
         if not isinstance(ctx, Context):
@@ -597,7 +610,8 @@ def quantize_model_mkldnn(sym, arg_params, aux_params,
                           data_names=('data',), label_names=('softmax_label',),
                           ctx=cpu(), excluded_sym_names=None, excluded_op_names=None,
                           calib_mode='entropy', calib_data=None, num_calib_examples=None,
-                          quantized_dtype='int8', quantize_mode='smart', logger=None):
+                          quantized_dtype='int8', quantize_mode='smart',
+                          quantize_granularity='tensor-wise', logger=None):
     """User-level API for generating a fusion + quantized model from a FP32 model
     w/ or w/o calibration with Intel MKL-DNN.
     The backend quantized operators are only enabled for Linux systems. Please do not run
@@ -628,7 +642,7 @@ def quantize_model_mkldnn(sym, arg_params, aux_params,
                                                    calib_mode=calib_mode, calib_data=calib_data,
                                                    num_calib_examples=num_calib_examples,
                                                    quantized_dtype=quantized_dtype, quantize_mode=quantize_mode,
-                                                   logger=logger)
+                                                   quantize_granularity=quantize_granularity, logger=logger)
 
     qsym = qsym.get_backend_symbol('MKLDNN_QUANTIZE')
 
@@ -636,7 +650,8 @@ def quantize_model_mkldnn(sym, arg_params, aux_params,
 
 def quantize_graph(sym, arg_params, aux_params, ctx=cpu(),
                    excluded_sym_names=None, excluded_op_names=None,
-                   calib_mode='entropy', quantized_dtype='int8', quantize_mode='full',
+                   calib_mode='entropy', quantized_dtype='int8',
+                   quantize_mode='full', quantize_granularity='tensor-wise',
                    LayerOutputCollector=None, logger=None):
     """User-level API for generating a quantized model from a FP32 model w/o calibration
     and a collector for naive or entropy calibration.
@@ -676,6 +691,9 @@ def quantize_graph(sym, arg_params, aux_params, ctx=cpu(),
         The mode that quantization pass to apply. Support 'full' and 'smart'.
         'full' means quantize all operator if possible.
         'smart' means quantization pass will smartly choice which operator should be quantized.
+    quantize_granularity: str
+        The granularity of quantization, currently supports 'tensor-wise' and 'channel-wise'
+        quantization. The default value is 'tensor-wise'.
     LayerOutputCollector : class
         For customize calibration method usage.
     logger : Object
@@ -700,12 +718,16 @@ def quantize_graph(sym, arg_params, aux_params, ctx=cpu(),
     if quantized_dtype not in ('int8', 'uint8', 'auto'):
         raise ValueError('unknown quantized_dtype %s received,'
                          ' expected `int8`, `uint8` or `auto`' % quantized_dtype)
+    if quantize_granularity not in ('tensor-wise', 'channel-wise'):
+        raise ValueError('unkonwn quantize_granularity %s received,'
+                         ' expected `tensor-wise` or `channel-wise`.' % quantize_granularity)
     qsym, calib_layer = _quantize_symbol(sym, ctx, excluded_symbols=excluded_sym_names,
                                          excluded_operators=excluded_op_names,
                                          offline_params=list(
                                              arg_params.keys()),
                                          quantized_dtype=quantized_dtype,
-                                         quantize_mode=quantize_mode)
+                                         quantize_mode=quantize_mode,
+                                         quantize_granularity=quantize_granularity)
 
     th_dict = {}
     collector = None
@@ -801,7 +823,7 @@ def calib_graph(qsym, arg_params, aux_params, collector,
 
     return qsym, qarg_params, aux_params
 
-def quantize_net_v2(network, quantized_dtype='auto', quantize_mode='full',
+def quantize_net_v2(network, quantized_dtype='auto', quantize_mode='full', quantize_granularity='tensor-wise',
                     exclude_layers=None, exclude_layers_match=None, exclude_operators=None,
                     calib_data=None, data_shapes=None, calib_mode='none',
                     num_calib_examples=None, ctx=cpu(), LayerOutputCollector=None, logger=None):
@@ -821,6 +843,9 @@ def quantize_net_v2(network, quantized_dtype='auto', quantize_mode='full',
         The mode that quantization pass to apply. Support 'full' and 'smart'.
         'full' means quantize all operator if possible.
         'smart' means quantization pass will smartly choice which operator should be quantized.
+    quantize_granularity: str
+        The granularity of quantization, currently supports 'tensor-wise' and 'channel-wise'
+        quantization. The default value is 'tensor-wise'.
     exclude_layers : list of strings
         A list of strings representing the names of the symbols that users want to excluding
     exclude_layers_match : list of strings
@@ -927,7 +952,8 @@ def __exit__(self, exc_type, exc_value, traceback):
         sym=symnet, arg_params=args, aux_params=auxs, ctx=ctx,
         excluded_sym_names=exclude_layers, excluded_op_names=exclude_operators,
         calib_mode=calib_mode, quantized_dtype=quantized_dtype, quantize_mode=quantize_mode,
-        LayerOutputCollector=LayerOutputCollector, logger=logger)
+        quantize_granularity=quantize_granularity, LayerOutputCollector=LayerOutputCollector,
+        logger=logger)
 
     if calib_mode is not None and calib_mode != 'none':
         if not isinstance(ctx, Context):
@@ -987,7 +1013,9 @@ def quantize_net(network, quantized_dtype='auto', quantize_mode='full',
     """
     warnings.warn('WARNING: This will be deprecated after MXNet 2.0, please use quantize_net_v2.')
     return quantize_net_v2(network=network, quantized_dtype=quantized_dtype,
-                           quantize_mode=quantize_mode, exclude_layers=exclude_layers,
+                           quantize_mode=quantize_mode,
+                           quantize_granularity='tensor-wise',
+                           exclude_layers=exclude_layers,
                            exclude_layers_match=exclude_layers_match,
                            exclude_operators=exclude_operators,
                            calib_data=calib_data, data_shapes=data_shapes,
diff --git a/src/c_api/c_api_symbolic.cc b/src/c_api/c_api_symbolic.cc
index 06d4b3e93ba0..6b923a80ff0b 100644
--- a/src/c_api/c_api_symbolic.cc
+++ b/src/c_api/c_api_symbolic.cc
@@ -925,6 +925,7 @@ int MXQuantizeSymbol(SymbolHandle sym_handle,
                      const char *quantized_dtype,
                      const bool calib_quantize,
                      const char *quantize_mode,
+                     const char *quantize_granularity,
                      mx_uint* out_num_calib_names,
                      const char ***out_calib_names) {
   nnvm::Symbol *s = new nnvm::Symbol();
@@ -946,12 +947,14 @@ int MXQuantizeSymbol(SymbolHandle sym_handle,
   }
   std::string quantized_type(quantized_dtype);
   std::string quantized_mode(quantize_mode);
+  std::string quantized_granularity(quantize_granularity);
   g.attrs["excluded_nodes"] = std::make_shared<nnvm::any>(std::move(excluded_node_names));
   g.attrs["excluded_ops"] = std::make_shared<nnvm::any>(std::move(excluded_op));
   g.attrs["offline_params"] = std::make_shared<nnvm::any>(std::move(offline));
   g.attrs["quantized_dtype"] = std::make_shared<nnvm::any>(std::move(quantized_type));
   g.attrs["target_ctx"] = std::make_shared<nnvm::any>(target_dev);
   g.attrs["quantize_mode"] = std::make_shared<nnvm::any>(std::move(quantized_mode));
+  g.attrs["quantize_granularity"] = std::make_shared<nnvm::any>(std::move(quantized_granularity));
   g = ApplyPass(std::move(g), "QuantizeGraph");
   const auto& calib_nodes = g.GetAttr<std::vector<std::string>>("calib_nodes");
   MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
diff --git a/src/operator/nn/mkldnn/mkldnn_fully_connected-inl.h b/src/operator/nn/mkldnn/mkldnn_fully_connected-inl.h
index 7d64cf5a92a7..1c9396e890f3 100644
--- a/src/operator/nn/mkldnn/mkldnn_fully_connected-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_fully_connected-inl.h
@@ -43,6 +43,7 @@ struct MKLDNNFCParam: public dmlc::Parameter<MKLDNNFCParam> {
   bool with_eltwise;
   dmlc::optional<float> min_calib_range;  // min float value calculated from calibration dataset
   dmlc::optional<float> max_calib_range;  // max float value calculated from calibration dataset
+  dmlc::optional<bool> channel_wise_quantize;
 
   DMLC_DECLARE_PARAMETER(MKLDNNFCParam) {
     DMLC_DECLARE_FIELD(quantized).set_default(false)
@@ -61,6 +62,9 @@ struct MKLDNNFCParam: public dmlc::Parameter<MKLDNNFCParam> {
     .describe("The maximum scalar value in the form of float32 obtained "
               "through calibration. If present, it will be used to by "
               "quantized fullyconnected op to calculate primitive scale");
+    DMLC_DECLARE_FIELD(channel_wise_quantize)
+    .set_default(dmlc::optional<bool>())
+    .describe("Whether support channel-wise-quantize for weight.");
   }
 };
 
@@ -68,8 +72,7 @@ struct MKLDNNFCFullParam {
   FullyConnectedParam default_param;
   MKLDNNFCParam mkldnn_param;
   MKLDNNPostEltwiseParam eltwise_param;
-  std::vector<float> output_scales = {0.0};
-  std::vector<float> requantize_scales = {0.0};
+  std::vector<float> output_scales = {0.0f};
 };
 
 mkldnn::inner_product_forward::primitive_desc GetFCFwdImpl(
diff --git a/src/operator/nn/mkldnn/mkldnn_fully_connected.cc b/src/operator/nn/mkldnn/mkldnn_fully_connected.cc
index 8c401a879f15..85260940b772 100644
--- a/src/operator/nn/mkldnn/mkldnn_fully_connected.cc
+++ b/src/operator/nn/mkldnn/mkldnn_fully_connected.cc
@@ -37,7 +37,8 @@ mkldnn::inner_product_forward::primitive_desc GetFCFwdImpl(
     const NDArray &data, const NDArray &weight, const NDArray *bias,
     const mkldnn::memory::desc &out_md) {
   auto data_md = GetMemDesc(data);
-  auto weight_md = GetFCWeightDesc(weight);
+  auto weight_md = full_param.mkldnn_param.quantized ?
+    GetFCWeightDesc(weight, mshadow::kInt8) : GetFCWeightDesc(weight);
   auto engine = CpuEngine::Get()->get_engine();
   auto propagation =
     is_train ? mkldnn::prop_kind::forward_training : mkldnn::prop_kind::forward_scoring;
@@ -52,22 +53,9 @@ mkldnn::inner_product_forward::primitive_desc GetFCFwdImpl(
   }
   attr.set_post_ops(ops);
 
-  if (full_param.mkldnn_param.quantized) {
-    if ((full_param.mkldnn_param.min_calib_range.has_value() &&
-         full_param.mkldnn_param.max_calib_range.has_value()) ||
-        full_param.mkldnn_param.enable_float_output) {
-      int mask = 0;
-      std::vector<float> scales = {0.0};
-      if (full_param.requantize_scales.size()) {
-        scales[0] = full_param.requantize_scales[0];
-      } else if (full_param.output_scales.size()) {
-        scales[0] = full_param.output_scales[0];
-      } else {
-        LOG(FATAL) << "Must specified either output_scales or requantize_scales!";
-      }
-
-      attr.set_output_scales(mask, scales);
-    }
+  if (full_param.mkldnn_param.quantized && full_param.output_scales.size()) {
+    int mask = (full_param.output_scales.size() == 1) ? 0 : (1 << 1);
+    attr.set_output_scales(mask, full_param.output_scales);
   }
 
   auto GetFCFwdPd = [&full_param, &attr,
@@ -88,7 +76,8 @@ mkldnn::inner_product_forward::primitive_desc GetFCFwdImpl(
   if (bias) {
     if ((*bias).shape().ndim() != 1)
       LOG(FATAL) << "Unexpected shape for bias " << (*bias).shape();
-    auto bias_md = GetMemDesc(*bias);
+    auto bias_md =
+       full_param.mkldnn_param.quantized ? GetMemDesc(*bias, mshadow::kInt32) : GetMemDesc(*bias);
     mkldnn::inner_product_forward::desc desc(propagation,
         data_md, weight_md, bias_md, out_md);
     return GetFCFwdPd(desc);
diff --git a/src/operator/quantization/quantize_graph_pass.cc b/src/operator/quantization/quantize_graph_pass.cc
index 01365067ce93..229793fad6a5 100644
--- a/src/operator/quantization/quantize_graph_pass.cc
+++ b/src/operator/quantization/quantize_graph_pass.cc
@@ -125,7 +125,8 @@ inline QuantizeType NeedQuantize(NodePtr node,
                                  const std::unordered_set<std::string>& excluded_nodes,
                                  const std::unordered_set<std::string>& excluded_ops,
                                  const int& dev_type,
-                                 std::unordered_map<NodePtr, NodePtr>* quantized_node_map) {
+                                 std::unordered_map<NodePtr, NodePtr>* quantized_node_map,
+                                 const std::string quantize_granularity) {
   std::unordered_map<NodePtr, NodePtr> quantized_node;
   static auto& quantizable_map = Op::GetAttr<mxnet::FQuantizable>("FQuantizable");
   static auto& quantized_op_map = Op::GetAttr<mxnet::FQuantizedOp>("FQuantizedOp");
@@ -165,6 +166,10 @@ inline QuantizeType NeedQuantize(NodePtr node,
       auto quantized_node = quantized_op_map[op](node->attrs);
       if (!quantized_node->op()) need = false;
       if (need) {
+        if ((quantize_granularity == "channel-wise") &&
+            (node->op() == Op::Get("_sg_mkldnn_fully_connected"))) {
+          quantized_node->attrs.dict["channel_wise_quantize"] = "True";
+        }
         quantized_node_map->insert(std::make_pair(node, quantized_node));
       }
       if (quantizable_map.count(op)) {
@@ -189,6 +194,7 @@ static void MarkQuantizedNodes(const Graph& src,
   const auto excluded_ops = src.GetAttr<std::unordered_set<std::string>>("excluded_ops");
   const auto quantize_mode = src.GetAttr<std::string>("quantize_mode");
   const auto dev_type = src.GetAttr<int>("target_ctx");
+  const auto quantize_granularity = src.GetAttr<std::string>("quantize_granularity");
 
   std::unordered_map<NodePtr, std::vector<NodePtr>> node_output_map;
   std::unordered_set<NodePtr> must_quantize_nodes;
@@ -196,7 +202,8 @@ static void MarkQuantizedNodes(const Graph& src,
   // Build node_output_map, must_quantize_nodes and support_quantize_nodes;
   DFSVisit(src.outputs, [&](const NodePtr& node) {
     auto quantize_type =
-        NeedQuantize(node, excluded_nodes, excluded_ops, dev_type, quantized_node_map);
+        NeedQuantize(node, excluded_nodes, excluded_ops, dev_type,
+                     quantized_node_map, quantize_granularity);
     if (quantize_type == QuantizeType::kMust) {
       must_quantize_nodes.insert(node);
     } else if (quantize_type == QuantizeType::kSupport) {
@@ -265,6 +272,13 @@ Graph QuantizeGraph(Graph &&src) {
       Op::GetAttr<mxnet::FAvoidQuantizeInput>("FAvoidQuantizeInput");
   const auto offline_params = src.GetAttr<std::unordered_set<std::string>>("offline_params");
   const auto quantized_dtype = src.GetAttr<std::string>("quantized_dtype");
+  const auto quantize_granularity = src.GetAttr<std::string>("quantize_granularity");
+  const auto dev_type = src.GetAttr<int>("target_ctx");
+
+  if (dev_type == Context::kGPU && quantize_granularity == "channel-wise") {
+    LOG(FATAL) << "`channel-wise` quantization option is not supported yet by GPU,"
+               << " please set quantize_granularity to `tensor-wise` when quantizing model.";
+  }
 
   std::unordered_map<NodePtr, NodePtr> quantized_node_map;
   MarkQuantizedNodes(src, &quantized_node_map);
@@ -298,7 +312,7 @@ Graph QuantizeGraph(Graph &&src) {
         // e's source node and the newly created quantize op so that the quantize op can be
         // reused next time when the same entry is visited again.
         if (avoid_quantize_input_map.count(node->op()) &&
-            avoid_quantize_input_map[node->op()](node->attrs, i)) {
+            avoid_quantize_input_map[node->op()](node->attrs, i, quantize_granularity)) {
           new_node->inputs.emplace_back(mirror_entry);
         } else if (!quantized_node_map.count(e.node)) {
           if (mirror_entry_map.count(e)) {
@@ -349,7 +363,7 @@ Graph QuantizeGraph(Graph &&src) {
         uint32_t min_index = 1;
         uint32_t max_index = 2;
         if (avoid_quantize_input_map.count(node->op()) &&
-            avoid_quantize_input_map[node->op()](node->attrs, i)) {
+            avoid_quantize_input_map[node->op()](node->attrs, i, quantize_granularity)) {
           // skip non-quantized input
           continue;
         }
diff --git a/src/operator/quantization/quantized_batch_norm.cc b/src/operator/quantization/quantized_batch_norm.cc
index 285c34d3a67f..91baf4303971 100644
--- a/src/operator/quantization/quantized_batch_norm.cc
+++ b/src/operator/quantization/quantized_batch_norm.cc
@@ -135,11 +135,9 @@ NNVM_REGISTER_OP(BatchNorm)
     }
     return node;
   })
-.set_attr<FAvoidQuantizeInput>("FAvoidQuantizeInput", [](const NodeAttrs &attrs, size_t index) {
-  if (index == 0)
-    return false;
-  else
-    return true;
+.set_attr<FAvoidQuantizeInput>("FAvoidQuantizeInput", [](
+  const NodeAttrs &attrs, const size_t index, const std::string quantize_granularity) {
+  return (index != 0);
 });
 
 }  // namespace op
diff --git a/src/operator/quantization/quantized_indexing_op.cc b/src/operator/quantization/quantized_indexing_op.cc
index b4af3ecb704f..66f6936d79fd 100644
--- a/src/operator/quantization/quantized_indexing_op.cc
+++ b/src/operator/quantization/quantized_indexing_op.cc
@@ -181,11 +181,9 @@ NNVM_REGISTER_OP(Embedding)
     }
     return node;
   })
-.set_attr<FAvoidQuantizeInput>("FAvoidQuantizeInput", [](const NodeAttrs &attrs, size_t index) {
-  if (index == 0)
-    return true;
-  else
-    return false;
+.set_attr<FAvoidQuantizeInput>("FAvoidQuantizeInput", [](
+  const NodeAttrs &attrs, const size_t index, const std::string quantize_granularity) {
+  return (index == 0);
 });
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/subgraph/mkldnn/mkldnn_common.h b/src/operator/subgraph/mkldnn/mkldnn_common.h
new file mode 100644
index 000000000000..87ddc438d846
--- /dev/null
+++ b/src/operator/subgraph/mkldnn/mkldnn_common.h
@@ -0,0 +1,138 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file mkldnn_common.h
+ * \brief Common header file for MKLDNN backend subgraph
+ * \author Ciyong Chen
+*/
+
+#ifndef MXNET_OPERATOR_SUBGRAPH_MKLDNN_MKLDNN_COMMON_H_
+#define MXNET_OPERATOR_SUBGRAPH_MKLDNN_MKLDNN_COMMON_H_
+#if MXNET_USE_MKLDNN == 1
+#include <vector>
+
+namespace mxnet {
+namespace op {
+
+template <typename DType>
+static std::vector<float> GetWeightScales(const NDArray &weight, const NDArray *bias,
+                                          const float data_scale, bool weight_channelwise_scale) {
+  auto nthreads = engine::OpenMP::Get()->GetRecommendedOMPThreadCount();
+  std::vector<float> weight_scales;
+  const DType *weight_ptr = weight.data().dptr<DType>();
+  const DType *bias_ptr = bias? bias->data().dptr<DType>() : nullptr;
+  const auto wshape = weight.shape();
+  size_t channel = wshape[0];
+
+  size_t offset = wshape.ProdShape(1, wshape.ndim());
+  std::vector<DType> weight_c_min(channel, MaxValue<DType>());
+  std::vector<DType> weight_c_max(channel, MinValue<DType>());
+  for (int c = 0; c < static_cast<int>(channel); ++c) {
+    const DType *p1 = weight_ptr + c * offset;
+    for (size_t k = 0; k < offset; ++k) {
+      if (weight_c_min[c] > p1[k])
+        weight_c_min[c] = p1[k];
+      if (weight_c_max[c] < p1[k])
+        weight_c_max[c] = p1[k];
+    }
+  }
+
+  if (weight_channelwise_scale) {
+    weight_scales.resize(channel);
+    #pragma omp parallel for num_threads(nthreads)
+    for (int c = 0; c < static_cast<int>(channel); ++c) {
+      float scale = GetQuantizeScale(mshadow::kInt8, weight_c_min[c], weight_c_max[c]);
+      if (bias_ptr && bias_ptr[c]) {
+        // avoid overflow on bias
+        // TODO(zhennan): mkldnn has bug to handle INT_MAX in bias, so set the maximum value of bias
+        // to INT_MAX / 2.
+        float scale_max =
+            static_cast<float>(bias_ptr[c] > 0 ? MaxValue<int32_t>() : MinValue<int32_t>()) / 2 /
+            bias_ptr[c] / data_scale;
+        scale = Min(scale, scale_max);
+      }
+      weight_scales[c] = scale;
+    }
+  } else {
+    DType total_min = weight_c_min[0];
+    DType total_max = weight_c_max[0];
+    for (size_t c = 0; c < channel; ++c) {
+      if (total_min > weight_c_min[c]) total_min = weight_c_min[c];
+      if (total_max < weight_c_max[c]) total_max = weight_c_max[c];
+    }
+    weight_scales.resize(3);
+    weight_scales[0] = GetQuantizeScale(mshadow::kInt8, total_min, total_max);
+    weight_scales[1] = total_min;
+    weight_scales[2] = total_max;
+  }
+  return weight_scales;
+}
+
+static void ConvertWeightBias2MKLDNN(NDArray *weight, NDArray *bias, bool has_bias,
+                                     const mkldnn::memory::desc weight_md,
+                                     const mkldnn::memory::desc *bias_md,
+                                     const int num_group, float data_scale,
+                                     const std::vector<float> &weight_scales,
+                                     const bool submit = true) {
+  MKLDNNStream *stream = MKLDNNStream::Get();
+  const auto new_weight = NDArray(weight_md);
+  const auto conv_weights_memory = new_weight.GetMKLDNNData();
+  mkldnn::primitive_attr weight_attr;
+  if (weight_scales.size()) {
+    const int weight_mask = (weight_scales.size()) == 1 ? 0 : 1;
+    weight_attr.set_output_scales(weight_mask, weight_scales);
+  }
+  auto default_weights_memory = GetWeights(*weight, num_group);
+  if (default_weights_memory == nullptr) default_weights_memory = weight->GetMKLDNNData();
+  const auto weight_reorder_pd =
+      mkldnn::reorder::primitive_desc(*default_weights_memory, *conv_weights_memory, weight_attr);
+  MKLDNNStream::Get()->RegisterPrimArgs(
+      mkldnn::reorder(weight_reorder_pd),
+      {{MKLDNN_ARG_FROM, *default_weights_memory}, {MKLDNN_ARG_TO, *conv_weights_memory}});
+  NDArray new_bias;
+  if (has_bias && data_scale) {
+    std::vector<float> bias_scales(weight_scales.size());
+    for (size_t c = 0; c < weight_scales.size(); ++c) {
+      bias_scales[c] = weight_scales[c] * data_scale;
+    }
+    new_bias = NDArray(*bias_md);
+    const auto conv_bias_memory = new_bias.GetMKLDNNData();
+    const int bias_mask = (bias_scales.size()) == 1 ? 0 : 1;
+    mkldnn::primitive_attr bias_attr;
+    bias_attr.set_output_scales(bias_mask, bias_scales);
+    auto bias_weights_memory = bias->GetMKLDNNData();
+    const auto bias_reorder_pd =
+        mkldnn::reorder::primitive_desc(*bias_weights_memory, *conv_bias_memory, bias_attr);
+    MKLDNNStream::Get()->RegisterPrimArgs(
+        mkldnn::reorder(bias_reorder_pd),
+        {{MKLDNN_ARG_FROM, *bias_weights_memory}, {MKLDNN_ARG_TO, *conv_bias_memory}});
+  }
+  if (submit)
+    stream->Submit();
+  *weight = new_weight;
+  if (has_bias && data_scale) *bias = new_bias;
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // if MXNET_USE_MKLDNN == 1
+#endif  // MXNET_OPERATOR_SUBGRAPH_MKLDNN_MKLDNN_COMMON_H_
diff --git a/src/operator/subgraph/mkldnn/mkldnn_conv.cc b/src/operator/subgraph/mkldnn/mkldnn_conv.cc
index f3a7d2c4e914..df440222cf04 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_conv.cc
+++ b/src/operator/subgraph/mkldnn/mkldnn_conv.cc
@@ -29,6 +29,7 @@
 #include "mkldnn_conv-inl.h"
 #include "../../nn/mkldnn/mkldnn_act-inl.h"
 #include "../../tensor/matrix_op-inl.h"
+#include "mkldnn_common.h"
 
 namespace mxnet {
 namespace op {
@@ -81,101 +82,6 @@ static inline size_t GetInSumIndex(const MKLDNNConvFusionParam &param) {
          (param.full_conv_param.mkldnn_param.with_bn ? 4 : 0);
 }
 
-template <typename DType>
-static std::vector<float> GetWeightScales(const NDArray &weight, const NDArray *bias,
-                                          const float data_scale, bool weight_channelwise_scale) {
-  std::vector<float> weight_scales;
-  const DType *weight_ptr = weight.data().dptr<DType>();
-  const DType *bias_ptr = bias? bias->data().dptr<DType>() : nullptr;
-  size_t channel = weight.shape()[0];
-
-  // TODO(Zhennan): Handle the case weight is not in dims 4.
-  size_t offset = weight.shape()[1] * weight.shape()[2] * weight.shape()[3];
-  std::vector<DType> weight_c_min(channel, MaxValue<DType>());
-  std::vector<DType> weight_c_max(channel, MinValue<DType>());
-  for (int c = 0; c < static_cast<int>(channel); ++c) {
-    const DType *p1 = weight_ptr + c * offset;
-    for (size_t k = 0; k < offset; ++k) {
-      if (weight_c_min[c] > p1[k])
-        weight_c_min[c] = p1[k];
-      if (weight_c_max[c] < p1[k])
-        weight_c_max[c] = p1[k];
-    }
-  }
-
-  if (weight_channelwise_scale) {
-    weight_scales.resize(channel);
-#pragma omp parallel for num_threads(engine::OpenMP::Get()->GetRecommendedOMPThreadCount())
-    for (int c = 0; c < static_cast<int>(channel); ++c) {
-      float scale = GetQuantizeScale(mshadow::kInt8, weight_c_min[c], weight_c_max[c]);
-      if (bias_ptr) {
-        // avoid overflow on bias
-        // TODO(zhennan): mkldnn has bug to handle INT_MAX in bias, so set the maximum value of bias
-        // to INT_MAX / 2.
-        float scale_max =
-            static_cast<float>(bias_ptr[c] > 0 ? MaxValue<int32_t>() : MinValue<int32_t>()) / 2 /
-            bias_ptr[c] / data_scale;
-        scale = Min(scale, scale_max);
-      }
-      weight_scales[c] = scale;
-    }
-  } else {
-    DType total_min = weight_c_min[0];
-    DType total_max = weight_c_max[0];
-    for (size_t c = 0; c < channel; ++c) {
-      if (total_min > weight_c_min[c]) total_min = weight_c_min[c];
-      if (total_max < weight_c_max[c]) total_max = weight_c_max[c];
-    }
-    weight_scales.resize(3);
-    weight_scales[0] = GetQuantizeScale(mshadow::kInt8, total_min, total_max);
-    weight_scales[1] = total_min;
-    weight_scales[2] = total_max;
-  }
-  return weight_scales;
-}
-
-static void ConvertWeightBias2MKLDNN(const MKLDNNConvFullParam &param,
-                                     mkldnn::convolution_forward::primitive_desc fwd_pd,
-                                     NDArray *weight, NDArray *bias, bool has_bias,
-                                     float data_scale, const std::vector<float> &weight_scales) {
-  MKLDNNStream *stream = MKLDNNStream::Get();
-  const auto new_weight = NDArray(fwd_pd.weights_desc());
-  const auto conv_weights_memory = new_weight.GetMKLDNNData();
-  mkldnn::primitive_attr weight_attr;
-  if (weight_scales.size()) {
-    const int weight_mask = (weight_scales.size()) == 1 ? 0 : 1;
-    weight_attr.set_output_scales(weight_mask, weight_scales);
-  }
-  auto default_weights_memory = GetWeights(*weight, param.conv_param.num_group);
-  if (default_weights_memory == nullptr) default_weights_memory = weight->GetMKLDNNData();
-  const auto weight_reorder_pd =
-      mkldnn::reorder::primitive_desc(*default_weights_memory, *conv_weights_memory, weight_attr);
-  MKLDNNStream::Get()->RegisterPrimArgs(
-      mkldnn::reorder(weight_reorder_pd),
-      {{MKLDNN_ARG_FROM, *default_weights_memory}, {MKLDNN_ARG_TO, *conv_weights_memory}});
-  NDArray new_bias;
-  if (has_bias && data_scale) {
-    std::vector<float> bias_scales(weight_scales.size());
-    for (size_t c = 0; c < weight_scales.size(); ++c) {
-      bias_scales[c] = weight_scales[c] * data_scale;
-    }
-    new_bias = NDArray(fwd_pd.bias_desc());
-    const auto conv_bias_memory = new_bias.GetMKLDNNData();
-    const int bias_mask = (bias_scales.size()) == 1 ? 0 : 1;
-    mkldnn::primitive_attr bias_attr;
-    bias_attr.set_output_scales(bias_mask, bias_scales);
-    auto bias_weights_memory = bias->GetMKLDNNData();
-    const auto bias_reorder_pd =
-        mkldnn::reorder::primitive_desc(*bias_weights_memory, *conv_bias_memory, bias_attr);
-    MKLDNNStream::Get()->RegisterPrimArgs(
-        mkldnn::reorder(bias_reorder_pd),
-        {{MKLDNN_ARG_FROM, *bias_weights_memory}, {MKLDNN_ARG_TO, *conv_bias_memory}});
-  }
-  stream->Submit();
-  *weight = new_weight;
-  if (has_bias && data_scale) *bias = new_bias;
-}
-
 class SgMKLDNNConvOperator {
  public:
   explicit SgMKLDNNConvOperator(const nnvm::NodeAttrs &attrs)
@@ -396,8 +302,13 @@ void SgMKLDNNConvOperator::Forward(const OpContext &ctx,
     fwd_.reset(new MKLDNNConvForward(
         full_conv_param, ctx.is_train, data, cached_weight_,
         has_bias ? &cached_bias_ : nullptr, output));
-    ConvertWeightBias2MKLDNN(full_conv_param, fwd_->GetPd(), &cached_weight_, &cached_bias_,
-                             has_bias, data_scale_, weight_scales_);
+    mkldnn::memory::desc bias_md;
+    if (has_bias) bias_md = fwd_->GetPd().bias_desc();
+    ConvertWeightBias2MKLDNN(&cached_weight_, &cached_bias_, has_bias,
+                             fwd_->GetPd().weights_desc(),
+                             has_bias ? & bias_md : nullptr,
+                             full_conv_param.conv_param.num_group,
+                             data_scale_, weight_scales_);
     args_[MKLDNN_ARG_SRC] = *data.GetMKLDNNData();
     args_[MKLDNN_ARG_WEIGHTS] = *cached_weight_.GetMKLDNNData();
     if (has_bias) args_[MKLDNN_ARG_BIAS] = *cached_bias_.GetMKLDNNData();
@@ -751,7 +662,8 @@ nnvm::NodePtr SgMKLDNNConvQuantizedOp(const NodeAttrs& attrs) {
   return node;
 }
 
-bool SgMKLDNNAvoidQuantizeInput(const NodeAttrs &attrs, size_t index) {
+bool SgMKLDNNAvoidConvQuantizeInput(const NodeAttrs &attrs, const size_t index,
+                                    const std::string quantize_granularity) {
   auto const &param = nnvm::get<MKLDNNConvFusionParam>(attrs.parsed);
   std::unordered_set<size_t> avoid_indice;
   size_t idx = 0;
@@ -800,7 +712,7 @@ NNVM_REGISTER_OP(_sg_mkldnn_conv)
 })
 .set_attr<FQuantizedOp>("FQuantizedOp", SgMKLDNNConvQuantizedOp)
 .set_attr<FNeedRequantize>("FNeedRequantize", [](const NodeAttrs& attrs) { return true; })
-.set_attr<FAvoidQuantizeInput>("FAvoidQuantizeInput", SgMKLDNNAvoidQuantizeInput);
+.set_attr<FAvoidQuantizeInput>("FAvoidQuantizeInput", SgMKLDNNAvoidConvQuantizeInput);
 
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/subgraph/mkldnn/mkldnn_fc.cc b/src/operator/subgraph/mkldnn/mkldnn_fc.cc
index 349d18f4a3a2..4d5233d3881f 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_fc.cc
+++ b/src/operator/subgraph/mkldnn/mkldnn_fc.cc
@@ -37,6 +37,7 @@
 #include "../../tensor/matrix_op-inl.h"
 #include "../../quantization/quantization_utils.h"
 #include "mkldnn_fc-inl.h"
+#include "mkldnn_common.h"
 
 namespace mxnet {
 namespace op {
@@ -44,8 +45,7 @@ namespace op {
 class SgMKLDNNFCOp {
  public:
   explicit SgMKLDNNFCOp(const nnvm::NodeAttrs &attrs)
-    : initialized_(false),
-      subgraph_sym_(*attrs.subgraphs[0]),
+    : subgraph_sym_(*attrs.subgraphs[0]),
       full_param_(nnvm::get<MKLDNNFCFullParam>(attrs.parsed)) {}
 
   void Forward(const OpContext &ctx,
@@ -62,11 +62,13 @@ class SgMKLDNNFCOp {
   }
 
  private:
-  bool initialized_;
+  bool initialized_{false};
   nnvm::Symbol subgraph_sym_;
   MKLDNNFCFullParam full_param_;
+  mkldnn_args_map_t args_;
   std::shared_ptr<MKLDNNFullyConnectedForward> fwd_;
-  std::shared_ptr<NDArray> cached_weight_;
+  std::shared_ptr<mkldnn::memory> cached_out_mem_;
+  NDArray cached_weight_;
   NDArray cached_bias_;
   float cached_min_data_;
   float cached_max_data_;
@@ -74,10 +76,34 @@ class SgMKLDNNFCOp {
   float cached_max_weight_;
   float cached_min_bias_;
   float cached_max_bias_;
+  size_t weight_ver_;
+  size_t bias_ver_;
   float cached_min_output_;
   float cached_max_output_;
+  float data_scale_{0.0f};
+  std::vector<float> weight_scales_;
 };
 
+static inline void MKLDNNFCFlattenData(const FullyConnectedParam &param,
+                                       NDArray *in_data) {
+  const mxnet::TShape ishape = in_data->shape();
+
+  // If the input data is a view of an MKLDNN array, we should create a new
+  // NDArray with reordered data.
+  if (in_data->IsMKLDNNData() && in_data->IsView())
+    *in_data = in_data->Reorder2Default();
+
+  auto data_ndim = ishape.ndim();
+  if (data_ndim != 2) {
+    if (!param.flatten) {
+      *in_data = in_data->MKLDNNDataReshape(
+          Shape2(ishape.ProdShape(0, data_ndim - 1), ishape[data_ndim - 1]));
+    } else {
+      *in_data = in_data->MKLDNNDataReshape(Shape2(ishape[0], ishape.ProdShape(1, data_ndim)));
+    }
+  }
+}
+
 void SgMKLDNNFCOp::Forward(const OpContext &ctx,
                            const std::vector<NDArray> &in_data,
                            const std::vector<OpReqType> &req,
@@ -90,23 +116,33 @@ void SgMKLDNNFCOp::Forward(const OpContext &ctx,
   size_t base_num_outputs = 1;
   size_t total_num_outputs = base_num_outputs;
 
-  float min_data = 0.0;
-  float max_data = 0.0;
-  float min_weight = 0.0;
-  float max_weight = 0.0;
-  float min_bias = 0.0;
-  float max_bias = 0.0;
+  float min_data = 0.0f;
+  float max_data = 0.0f;
+  float min_weight = 0.0f;
+  float max_weight = 0.0f;
+  float min_bias = 0.0f;
+  float max_bias = 0.0f;
+
+  bool channel_wise = false;
+  if (mkldnn_param.channel_wise_quantize.has_value() &&
+      mkldnn_param.channel_wise_quantize) {
+    channel_wise = true;
+  }
 
   if (mkldnn_param.quantized) {
-    total_num_inputs = base_num_inputs * 3;
+    if (channel_wise) {
+      total_num_inputs = base_num_inputs + 2;
+    } else {
+      total_num_inputs = base_num_inputs * 3;
+      min_weight = in_data[base_num_inputs + quantized_fullc::kWeightMin].data().dptr<float>()[0];
+      max_weight = in_data[base_num_inputs + quantized_fullc::kWeightMax].data().dptr<float>()[0];
+      if (has_bias) {
+        min_bias = in_data[base_num_inputs + quantized_fullc::kBiasMin].data().dptr<float>()[0];
+        max_bias = in_data[base_num_inputs + quantized_fullc::kBiasMax].data().dptr<float>()[0];
+      }
+    }
     min_data = in_data[base_num_inputs + quantized_fullc::kDataMin].data().dptr<float>()[0];
     max_data = in_data[base_num_inputs + quantized_fullc::kDataMax].data().dptr<float>()[0];
-    min_weight = in_data[base_num_inputs + quantized_fullc::kWeightMin].data().dptr<float>()[0];
-    max_weight = in_data[base_num_inputs + quantized_fullc::kWeightMax].data().dptr<float>()[0];
-    if (has_bias) {
-      min_bias = in_data[base_num_inputs + quantized_fullc::kBiasMin].data().dptr<float>()[0];
-      max_bias = in_data[base_num_inputs + quantized_fullc::kBiasMax].data().dptr<float>()[0];
-    }
     if (!mkldnn_param.enable_float_output) {
       total_num_outputs = base_num_outputs * 3;
     }
@@ -115,84 +151,157 @@ void SgMKLDNNFCOp::Forward(const OpContext &ctx,
   CHECK_EQ(out_data.size(), total_num_outputs);
 
   NDArray data = in_data[fullc::kData];
-  NDArray weight = cached_weight_ ? *cached_weight_ : in_data[fullc::kWeight];
+  NDArray weight = in_data[fullc::kWeight];
   NDArray output = out_data[fullc::kOut];
-
-  mkldnn::memory::desc out_md = GetMemDesc(output);
-  MKLDNNFCFlattenData(default_param, out_data[fullc::kOut], &data, &out_md);
+  MKLDNNFCFlattenData(default_param, &data);
 
   if (initialized_ && mkldnn_param.quantized) {
-    if (cached_min_data_ != min_data || cached_max_data_ != max_data ||
-        cached_min_weight_ != min_weight || cached_max_weight_ != max_weight ||
-        (has_bias && (cached_min_bias_ != min_bias || cached_max_bias_ != max_bias))) {
-          initialized_ = false;
-        }
+    if (channel_wise) {
+      if (cached_min_data_ != min_data || cached_max_data_ != max_data ||
+          weight_ver_ != weight.version() ||
+          (has_bias && (bias_ver_ != in_data[fullc::kBias].version()))) {
+        initialized_ = false;
+      }
+    } else {
+      if (cached_min_data_ != min_data || cached_max_data_ != max_data ||
+          cached_min_weight_ != min_weight || cached_max_weight_ != max_weight ||
+          (has_bias && (cached_min_bias_ != min_bias || cached_max_bias_ != max_bias))) {
+        initialized_ = false;
+      }
+    }
   }
 
   if (!initialized_) {
+    const auto nthreads = engine::OpenMP::Get()->GetRecommendedOMPThreadCount();
     cached_min_data_ = min_data;
     cached_max_data_ = max_data;
     cached_min_weight_ = min_weight;
     cached_max_weight_ = max_weight;
+    weight_ver_ = weight.version();
+    cached_weight_ = weight;
     if (has_bias) {
-      cached_bias_ = in_data[fullc::kBias];
       cached_min_bias_ = min_bias;
       cached_max_bias_ = max_bias;
+      bias_ver_ = in_data[fullc::kBias].version();
+      cached_bias_ = in_data[fullc::kBias];
     } else {
       cached_bias_ = NDArray();
     }
 
+    // create cached out_md
+    const mxnet::TShape ishape = data.shape();
+    const mxnet::TShape oshape = output.shape();
+    mkldnn::memory::dims out_dims(2);
+    if (oshape.ndim() == 2) {
+      out_dims[0] = static_cast<int>(oshape[0]);
+      out_dims[1] = static_cast<int>(oshape[1]);
+    } else {
+      if (!default_param.flatten) {
+        out_dims[0] = static_cast<int>(oshape.ProdShape(0, oshape.ndim()-1));
+        out_dims[1] = static_cast<int>(oshape[oshape.ndim()-1]);
+      } else {
+        out_dims[0] = static_cast<int>(static_cast<int>(oshape[0]));
+        out_dims[1] = static_cast<int>(oshape.ProdShape(1, oshape.ndim()));
+      }
+    }
+    mkldnn::memory::desc out_md = mkldnn::memory::desc(out_dims, get_mkldnn_type(output.dtype()),
+      static_cast<mkldnn::memory::format_tag>(GetDefaultFormat(2)));
+    cached_out_mem_ = std::make_shared<mkldnn::memory>(out_md, CpuEngine::Get()->get_engine());
+
+    bool support_channelwise_scale = false;
     if (mkldnn_param.quantized) {
       CHECK(data.dtype() == mshadow::kInt8 || data.dtype() == mshadow::kUint8);
-      float data_scale  = GetQuantizeScale(data.dtype(), cached_min_data_, cached_max_data_);
-      float weight_scale = GetQuantizeScale(mshadow::kInt8, cached_min_weight_, cached_max_weight_);
-      if (has_bias) {
-        NDArray bias = in_data[fullc::kBias];
-        float bias_scale = GetQuantizeScale(mshadow::kInt8, cached_min_bias_, cached_max_bias_);
-        float bias_int32_rescale = data_scale * weight_scale / bias_scale;
-        // TODO(zhennan): mkldnn has bug to handle INT_MAX in bias, so set the maximum value of bias
-        // to INT_MAX / 2.
-        float bias_max_rescale =
-            MaxValue<int32_t>() / 2 / MaxAbs(cached_min_bias_, cached_max_bias_) / bias_scale;
-        if (bias_int32_rescale > bias_max_rescale) {
-          // avoid overflow on bias
-          bias_int32_rescale = bias_max_rescale;
-          float weight_rescale = bias_int32_rescale * bias_scale / data_scale / weight_scale;
-          cached_weight_.reset(new NDArray(weight.storage_type(), weight.shape(), weight.ctx(),
-                                           true, mshadow::kInt8));
-          int8_t *weight_ptr = weight.data().dptr<int8_t>();
-          int8_t *quantized_weight_ptr = cached_weight_->data().dptr<int8_t>();
-          size_t weight_size = weight.shape().Size();
-#pragma omp parallel for num_threads(engine::OpenMP::Get()->GetRecommendedOMPThreadCount())
-          for (index_t i = 0; i < static_cast<index_t>(weight_size); ++i) {
-            quantized_weight_ptr[i] = std::round(weight_ptr[i] * weight_rescale);
+      data_scale_ = GetQuantizeScale(data.dtype(), cached_min_data_, cached_max_data_);
+
+      bool fuse_requantize = false;
+      // Channelwise scaling is only supported when fusion is enabled (requantize or dequantize).
+      if (mkldnn_param.min_calib_range.has_value() &&
+          mkldnn_param.max_calib_range.has_value()) {
+        cached_min_output_ = mkldnn_param.min_calib_range.value();
+        cached_max_output_ = mkldnn_param.max_calib_range.value();
+        support_channelwise_scale = true;
+        fuse_requantize = true;
+      }
+      if (mkldnn_param.enable_float_output) {
+        support_channelwise_scale = true;
+      }
+      // channel_wise  support_channelwise_scale  result
+      // True          True                       True
+      // True          False                      Error
+      // False         True/False                 False
+      if (channel_wise && !support_channelwise_scale) {
+        LOG(FATAL)
+          << "Currently, channel-wise quantization requires fuse requantize or dequantize."
+          << " Please make sure the `min_calib_range` and `max_calib_range` are set when only"
+          << " fuse requantize (outputs of FullyConnected are collected during calibration phase),"
+          << " or the env var of `MXNET_DISABLE_MKLDNN_QFC_FLOAT_OUTPUT` and "
+          << " `MXNET_DISABLE_MKLDNN_QFC_FUSE_ALL` are not set to true (default is false)";
+      }
+      support_channelwise_scale = support_channelwise_scale && channel_wise;
+
+      if (support_channelwise_scale) {
+        MSHADOW_REAL_TYPE_SWITCH(cached_weight_.dtype(), DType, {
+          weight_scales_ =
+            GetWeightScales<DType>(cached_weight_, has_bias ? &cached_bias_ : nullptr,
+                                   data_scale_, support_channelwise_scale);
+        });
+      } else {
+        weight_scales_.resize(1);
+        weight_scales_[0] =
+          GetQuantizeScale(cached_weight_.dtype(), cached_min_weight_, cached_max_weight_);
+        if (has_bias) {
+          float bias_scale = GetQuantizeScale(mshadow::kInt8, cached_min_bias_, cached_max_bias_);
+          float bias_int32_rescale = data_scale_ * weight_scales_[0] / bias_scale;
+          // TODO(zhennan): mkldnn has bug to handle INT_MAX in bias, so set the maximum value
+          // of bias to INT_MAX / 2.
+          float bias_max_rescale =
+              MaxValue<int32_t>() / 2 / MaxAbs(cached_min_bias_, cached_max_bias_) / bias_scale;
+          if (bias_int32_rescale > bias_max_rescale) {
+            // avoid overflow on bias
+            bias_int32_rescale = bias_max_rescale;
+            float weight_rescale =
+              bias_int32_rescale * bias_scale / data_scale_ / weight_scales_[0];
+            int8_t *weight_ptr = weight.data().dptr<int8_t>();
+            size_t weight_size = weight.shape().Size();
+            #pragma omp parallel for num_threads(nthreads)
+            for (index_t i = 0; i < static_cast<index_t>(weight_size); ++i) {
+              weight_ptr[i] = std::round(weight_ptr[i] * weight_rescale);
+            }
+            weight_scales_[0] *= weight_rescale;
+          }
+          NDArray bias = in_data[fullc::kBias];
+          cached_bias_ =
+              NDArray(bias.storage_type(), bias.shape(), bias.ctx(), true, mshadow::kInt32);
+          int8_t *bias_ptr = bias.data().dptr<int8_t>();
+          int32_t *quantized_bias_ptr = cached_bias_.data().dptr<int32_t>();
+          size_t bias_size = bias.shape().Size();
+          #pragma omp parallel for num_threads(nthreads)
+          for (index_t i = 0; i < static_cast<index_t>(bias_size); ++i) {
+            quantized_bias_ptr[i] = std::round(bias_ptr[i] * bias_int32_rescale);
           }
-          weight_scale *= weight_rescale;
-          weight = *cached_weight_;
-        }
-        cached_bias_ =
-            NDArray(bias.storage_type(), bias.shape(), bias.ctx(), true, mshadow::kInt32);
-        int8_t *bias_ptr = bias.data().dptr<int8_t>();
-        int32_t *quantized_bias_ptr = cached_bias_.data().dptr<int32_t>();
-        size_t bias_size = bias.shape().Size();
-        #pragma omp parallel for num_threads(engine::OpenMP::Get()->GetRecommendedOMPThreadCount())
-        for (index_t i = 0; i < static_cast<index_t>(bias_size); ++i) {
-          quantized_bias_ptr[i] = std::round(bias_ptr[i] * bias_int32_rescale);
         }
       }
 
-      if (mkldnn_param.enable_float_output) {
-        full_param_.output_scales[0] = 1.0 / data_scale / weight_scale;
-        full_param_.requantize_scales.resize(0);
-      } else if (mkldnn_param.min_calib_range.has_value() &&
-                 mkldnn_param.max_calib_range.has_value()) {
-        full_param_.output_scales.resize(0);
-        cached_min_output_ = mkldnn_param.min_calib_range.value();
-        cached_max_output_ = mkldnn_param.max_calib_range.value();
-        float out_scale =
-            GetQuantizeScale(IsOutputUint8(full_param_) ? mshadow::kUint8 : mshadow::kInt8,
-                             cached_min_output_, cached_max_output_);
-        full_param_.requantize_scales[0] = out_scale / data_scale / weight_scale;
+      size_t num_channel = cached_weight_.shape()[0];
+      if (fuse_requantize || mkldnn_param.enable_float_output) {
+        float tmp_scale_ = 1.0f;
+        if (fuse_requantize) {
+          tmp_scale_ =
+            GetQuantizeScale(output.dtype(), cached_min_output_, cached_max_output_) / data_scale_;
+        } else {
+          tmp_scale_ = 1.0 / data_scale_;
+        }
+
+        if (support_channelwise_scale) {
+          full_param_.output_scales.resize(num_channel);
+          #pragma omp parallel for num_threads(nthreads)
+          for (index_t i = 0; i < static_cast<index_t>(num_channel); ++i) {
+            full_param_.output_scales[i] = tmp_scale_ / weight_scales_[i];
+          }
+        } else {
+          full_param_.output_scales.resize(1);
+          full_param_.output_scales[0] = tmp_scale_ / weight_scales_[0];
+        }
       } else {
         Stream<cpu> *s = ctx.get_stream<cpu>();
         if (data.dtype() == mshadow::kInt8) {
@@ -204,21 +313,48 @@ void SgMKLDNNFCOp::Forward(const OpContext &ctx,
               s, 1, &cached_min_output_, &cached_max_output_, &min_data, &max_data, &min_weight,
               &max_weight);
         }
+        full_param_.output_scales.resize(0);
       }
     }
 
-    fwd_.reset(new MKLDNNFullyConnectedForward(full_param_, ctx.is_train, data, weight,
+    fwd_.reset(new MKLDNNFullyConnectedForward(full_param_, ctx.is_train, data, cached_weight_,
       (has_bias ? &cached_bias_ : nullptr), out_md));
+
+    // convert weight and bias to the format that MKL-DNN requires
+    if (!mkldnn_param.quantized || support_channelwise_scale) {
+      mkldnn::memory::desc bias_md;
+      if (has_bias) bias_md = fwd_->fwd_pd.bias_desc();
+      ConvertWeightBias2MKLDNN(&cached_weight_, &cached_bias_, has_bias,
+                              fwd_->fwd_pd.weights_desc(),
+                              has_bias ? &bias_md : nullptr,
+                              1, data_scale_, weight_scales_, false);
+    } else {
+      cached_weight_ = NDArray(fwd_->fwd_pd.weights_desc());
+      auto cached_weight_mem = cached_weight_.GetMKLDNNData();
+      auto def_weight_mem = weight.GetMKLDNNData();
+      std::unordered_map<int, mkldnn::memory> args(
+        {{MKLDNN_ARG_FROM, *def_weight_mem},
+        {MKLDNN_ARG_TO, *cached_weight_mem}});
+      MKLDNNStream::Get()->RegisterPrimArgs(
+        mkldnn::reorder(*def_weight_mem, *cached_weight_mem), args);
+    }
+
+    args_[MKLDNN_ARG_SRC] = *data.GetMKLDNNData();
+    args_[MKLDNN_ARG_WEIGHTS] = *cached_weight_.GetMKLDNNData();
+    if (has_bias)
+      args_[MKLDNN_ARG_BIAS] = *cached_bias_.GetMKLDNNData();
+    args_[MKLDNN_ARG_DST] = *cached_out_mem_;
     initialized_ = true;
   }
-  std::vector<NDArray> new_inputs;
-  if (has_bias) {
-    new_inputs = {data, weight, cached_bias_};
-  } else {
-    new_inputs = {data, weight};
-  }
 
-  MKLDNNFCForwardFullFeature(full_param_, ctx, fwd_.get(), new_inputs, req, out_data);
+  auto data_mem = data.GetMKLDNNDataReorder(fwd_->fwd_pd.src_desc());
+  MSHADOW_TYPE_SWITCH(output.dtype(), DType, {
+    cached_out_mem_->set_data_handle(reinterpret_cast<void *>(output.data().dptr<DType>()));
+  });
+  args_[MKLDNN_ARG_SRC] = *data_mem;
+  args_[MKLDNN_ARG_DST] = *cached_out_mem_;
+  MKLDNNStream::Get()->RegisterPrimArgs(fwd_->GetFwd(), args_);
+  MKLDNNStream::Get()->Submit();
 
   if (mkldnn_param.quantized && !mkldnn_param.enable_float_output) {
     float *min_output_ptr = out_data[quantized_fullc::kOutMin].data().dptr<float>();
@@ -277,13 +413,20 @@ static std::vector<std::string> SgMKLDNNFCListInputNames(const NodeAttrs &attrs)
   auto const &full_param = nnvm::get<MKLDNNFCFullParam>(attrs.parsed);
   std::vector<std::string> input_names = DefaultSubgraphOpListInputs(attrs);
   if (full_param.mkldnn_param.quantized) {
+    bool channel_wise = false;
+    if (full_param.mkldnn_param.channel_wise_quantize.has_value() &&
+        full_param.mkldnn_param.channel_wise_quantize) {
+      channel_wise = true;
+    }
     input_names.emplace_back("min_data");
     input_names.emplace_back("max_data");
-    input_names.emplace_back("min_weight");
-    input_names.emplace_back("max_weight");
-    if (!full_param.default_param.no_bias) {
-      input_names.emplace_back("min_bias");
-      input_names.emplace_back("max_bias");
+    if (!channel_wise) {
+      input_names.emplace_back("min_weight");
+      input_names.emplace_back("max_weight");
+      if (!full_param.default_param.no_bias) {
+        input_names.emplace_back("min_bias");
+        input_names.emplace_back("max_bias");
+      }
     }
   }
   return input_names;
@@ -349,17 +492,25 @@ static bool SgMKLDNNFCInferType(const nnvm::NodeAttrs &attrs,
                                 std::vector<int> *out_types) {
   auto const &full_param = nnvm::get<MKLDNNFCFullParam>(attrs.parsed);
   if (full_param.mkldnn_param.quantized) {
+    bool channel_wise = false;
+    if (full_param.mkldnn_param.channel_wise_quantize.has_value() &&
+        full_param.mkldnn_param.channel_wise_quantize) {
+      channel_wise = true;
+    }
     size_t base_num_inputs = full_param.default_param.no_bias ? 2 : 3;
-
     CHECK(in_types->at(0) == mshadow::kInt8 ||
           in_types->at(0) == mshadow::kUint8)
         << "QuantizedFullyConnected only supports int8/uint8 input, while "
         << in_types->at(0) << " is given.";
     for (size_t i = 1; i < in_types->size(); ++i) {
-      if (i < base_num_inputs) {
-        TYPE_ASSIGN_CHECK(*in_types, i, mshadow::kInt8);
-      } else {
+      if (channel_wise) {
         TYPE_ASSIGN_CHECK(*in_types, i, mshadow::kFloat32);
+      } else {
+        if (i < base_num_inputs) {
+          TYPE_ASSIGN_CHECK(*in_types, i, mshadow::kInt8);
+        } else {
+          TYPE_ASSIGN_CHECK(*in_types, i, mshadow::kFloat32);
+        }
       }
     }
 
@@ -448,15 +599,35 @@ nnvm::NodePtr SgMKLDNNFCQuantizedOp(const NodeAttrs& attrs) {
   return node;
 }
 
+static bool SgMKLDNNAvoidFCQuantizeInput(const NodeAttrs& attrs, const size_t index_to_check,
+                                         const std::string quantize_granularity) {
+  auto const &full_param = nnvm::get<MKLDNNFCFullParam>(attrs.parsed);
+  std::unordered_set<size_t> avoid_indexes;
+  if (quantize_granularity == "channel-wise") {
+    avoid_indexes.insert(fullc::kWeight);   // weight
+    if (!full_param.default_param.no_bias) {
+      avoid_indexes.insert(fullc::kBias);   // bias
+    }
+  }
+
+  return avoid_indexes.count(index_to_check);
+}
+
 NNVM_REGISTER_OP(_sg_mkldnn_fully_connected)
 .describe(R"code(_sg_mkldnn_fully_connected)code" ADD_FILELINE)
 .set_num_inputs([](const NodeAttrs& attrs) {
   auto const &full_param = nnvm::get<MKLDNNFCFullParam>(attrs.parsed);
   auto num_inputs = full_param.default_param.no_bias ? 2 : 3;
-  if (full_param.mkldnn_param.quantized)
-    return num_inputs * 3;
-  else
+  if (full_param.mkldnn_param.quantized) {
+    if (full_param.mkldnn_param.channel_wise_quantize.has_value() &&
+        full_param.mkldnn_param.channel_wise_quantize) {
+      return num_inputs + 2;  // min_data, max_data
+    } else {
+      return num_inputs * 3;
+    }
+  } else {
     return num_inputs;
+  }
 })
 .set_num_outputs([](const NodeAttrs& attrs) {
   auto const &full_param = nnvm::get<MKLDNNFCFullParam>(attrs.parsed);
@@ -485,7 +656,8 @@ NNVM_REGISTER_OP(_sg_mkldnn_fully_connected)
     return QuantizeType::kMust;
 })
 .set_attr<FQuantizedOp>("FQuantizedOp", SgMKLDNNFCQuantizedOp)
-.set_attr<FNeedRequantize>("FNeedRequantize", [](const NodeAttrs& attrs) { return true; });
+.set_attr<FNeedRequantize>("FNeedRequantize", [](const NodeAttrs& attrs) { return true; })
+.set_attr<FAvoidQuantizeInput>("FAvoidQuantizeInput", SgMKLDNNAvoidFCQuantizeInput);
 
 }  // namespace op
 }  // namespace mxnet
diff --git a/tests/python/mkl/test_subgraph.py b/tests/python/mkl/test_subgraph.py
index 4a2aedf58281..65b73e438ea6 100644
--- a/tests/python/mkl/test_subgraph.py
+++ b/tests/python/mkl/test_subgraph.py
@@ -131,6 +131,10 @@ def __iter__(self):
 
 def check_quantize(sym, data_shape, out_type, name='conv',
                    check_calibration=True, gluon_forward=False, check_scale_align=False):
+  quantize_granularity_list = ['tensor-wise']
+  if name == 'fc':
+    quantize_granularity_list += ['channel-wise']
+
   if name in config:
     name = config[name][OP_NAME]
   sym_sg = sym.get_backend_symbol(QUANTIZE_SG_PASS_NAME)
@@ -158,33 +162,35 @@ def check_quantize(sym, data_shape, out_type, name='conv',
 
   calib_data = CalibIter(batch, data_shape, 1)
 
-  qsym, qarg_params, qaux_params = mx.contrib.quant.quantize_model(sym=sym_sg,
-                                                                   arg_params=arg_params,
-                                                                   aux_params=aux_params,
-                                                                   ctx=mx.current_context(),
-                                                                   excluded_sym_names=excluded_sym_names,
-                                                                   excluded_op_names=excluded_op_names,
-                                                                   quantized_dtype=out_type,
-                                                                   calib_mode='naive',
-                                                                   calib_data=calib_data,
-                                                                   label_names=None,
-                                                                   num_calib_examples=1,
-                                                                   quantize_mode='full')
-  qsym = qsym.get_backend_symbol(QUANTIZE_SG_PASS_NAME)
-  if check_calibration:
-    check_qsym_calibrated(qsym, out_type, name=name)
-  if check_scale_align:
-    check_qsym_scale_align(qsym)
-  if gluon_forward == True:
-    check_qsym_gluon_forward(qsym, qarg_params, qaux_params, data_shape)
-  else:
-    quantized_out = check_qsym_forward(qsym, qarg_params, qaux_params, batch, data_shape)
-    for i in range(len(ref_out)):
-      min_range = mx.nd.min(ref_out[i]).asscalar()
-      max_range = mx.nd.max(ref_out[i]).asscalar()
-      atol = 0.1 * max(abs(min_range), abs(max_range))
-      assert_almost_equal_with_err(quantized_out[i].asnumpy(), ref_out[i].asnumpy(), rtol=0.1, atol=atol, etol=0.2)
-    check_qsym_dummy_forward(qsym, batch, data_shape)
+  for quantize_granularity in quantize_granularity_list:
+    qsym, qarg_params, qaux_params = mx.contrib.quant.quantize_model(sym=sym_sg,
+                                                                    arg_params=arg_params,
+                                                                    aux_params=aux_params,
+                                                                    ctx=mx.current_context(),
+                                                                    excluded_sym_names=excluded_sym_names,
+                                                                    excluded_op_names=excluded_op_names,
+                                                                    quantized_dtype=out_type,
+                                                                    calib_mode='naive',
+                                                                    calib_data=calib_data,
+                                                                    label_names=None,
+                                                                    num_calib_examples=1,
+                                                                    quantize_mode='full',
+                                                                    quantize_granularity=quantize_granularity)
+    qsym = qsym.get_backend_symbol(QUANTIZE_SG_PASS_NAME)
+    if check_calibration:
+      check_qsym_calibrated(qsym, out_type, name=name)
+    if check_scale_align:
+      check_qsym_scale_align(qsym)
+    if gluon_forward == True:
+      check_qsym_gluon_forward(qsym, qarg_params, qaux_params, data_shape)
+    else:
+      quantized_out = check_qsym_forward(qsym, qarg_params, qaux_params, batch, data_shape)
+      for i in range(len(ref_out)):
+        min_range = mx.nd.min(ref_out[i]).asscalar()
+        max_range = mx.nd.max(ref_out[i]).asscalar()
+        atol = 0.1 * max(abs(min_range), abs(max_range))
+        assert_almost_equal_with_err(quantized_out[i].asnumpy(), ref_out[i].asnumpy(), rtol=0.1, atol=atol, etol=0.2)
+      check_qsym_dummy_forward(qsym, batch, data_shape)
 
 @with_seed()
 def check_quantize_whole_model_with_forward():
@@ -240,7 +246,7 @@ def check_fusion(sym, data_shape, attrs_dict, check_fp32_fusion=True, check_quan
   if check_fp32_fusion:
     data_min = -1.0
     data_max = 1.0
-    if ''.join(sym.get_internals().list_outputs()).find('sqrt'):
+    if ''.join(sym.get_internals().list_outputs()).find('sqrt') != -1:
       check_quantization = False
       data_min = 0
 
@@ -274,12 +280,12 @@ def check_fusion(sym, data_shape, attrs_dict, check_fp32_fusion=True, check_quan
   if check_quantization:
     # fp32 to int8
     for out_type in out_types:
-      check_quantize(sym, data_shape, out_type, name=op_name)
+      check_quantize(sym, data_shape, out_type, name=name)
       # TODO(ciyong), since quantized fc save its params in int8, while gluon treat the default
       # variable from symbol file as fp32 which results in mismatch dtype of params.
       # Skip quantized fc in gluon pass.
       if name != 'fc':
-        check_quantize(sym, data_shape, out_type, name=op_name, gluon_forward=True)
+        check_quantize(sym, data_shape, out_type, name=name, gluon_forward=True)
 
 def check_neg_fusion(syms, attrs_name=None, excluded_attrs=None,
                      date_shape=(4,4,10,10), name='conv'):
@@ -767,7 +773,7 @@ def test_pos_conv_bn_sum_act():
               "softrelu": True,
               "relu6": False,
               "leakyrelu": True,
-              "gelu": True}
+              "gelu": False}
   for data_shape in DATA_SHAPE:
     for (alg, quantize) in act_list.items():
       net, attrs = conv_bn_sum_act(False, data_shape, alg)
@@ -847,7 +853,6 @@ def test_single_fc():
     else:
       check_fusion(syms, dshape, attrs, check_quantization=False)
 
-
 @with_seed()
 def test_fc_eltwise():
   for dshape, no_bias, flatten, alg in itertools.product(DATA_SHAPE,

From 4f9890f35c5df58316ac2b8bf8d25e31dc18c338 Mon Sep 17 00:00:00 2001
From: Yizhi Liu <liuyizhi@apache.org>
Date: Thu, 2 Jan 2020 22:53:28 -0800
Subject: [PATCH 762/813] [tvmop] link libtvmop with libtvm_runtime (#17203)

---
 CMakeLists.txt           |  2 +-
 Makefile                 |  2 +-
 contrib/tvmop/compile.py | 59 +++++++++++++++++++++++++++++++++++++++-
 3 files changed, 60 insertions(+), 3 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 9e4ef25e5af4..26b20f33f46e 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -771,7 +771,7 @@ if(USE_TVM_OP)
     endif()
   endif()
 
-  set(TVM_OP_COMPILE_OPTIONS "-o${CMAKE_CURRENT_BINARY_DIR}/libtvmop.so" "--config" "${CMAKE_CURRENT_BINARY_DIR}/tvmop.conf")
+  set(TVM_OP_COMPILE_OPTIONS "-o${CMAKE_CURRENT_BINARY_DIR}" "--config" "${CMAKE_CURRENT_BINARY_DIR}/tvmop.conf" "-L" "${CMAKE_CURRENT_BINARY_DIR}/3rdparty/tvm")
   if(USE_CUDA)
     set(TVM_OP_COMPILE_OPTIONS "${TVM_OP_COMPILE_OPTIONS}" "--cuda-arch" "\"${CUDA_ARCH_FLAGS}\"")
   endif()
diff --git a/Makefile b/Makefile
index 1b858cc48671..7e65e5a7f1b7 100644
--- a/Makefile
+++ b/Makefile
@@ -621,7 +621,7 @@ lib/libtvm_runtime.so:
 	ls $(ROOTDIR)/lib; \
 	cd $(ROOTDIR)
 
-TVM_OP_COMPILE_OPTIONS = -o $(ROOTDIR)/lib/libtvmop.so --config $(ROOTDIR)/lib/tvmop.conf
+TVM_OP_COMPILE_OPTIONS = -o $(ROOTDIR)/lib --config $(ROOTDIR)/lib/tvmop.conf
 ifneq ($(CUDA_ARCH),)
 	TVM_OP_COMPILE_OPTIONS += --cuda-arch "$(CUDA_ARCH)"
 endif
diff --git a/contrib/tvmop/compile.py b/contrib/tvmop/compile.py
index 43657f274348..ef929b472ad5 100644
--- a/contrib/tvmop/compile.py
+++ b/contrib/tvmop/compile.py
@@ -25,6 +25,8 @@
 import re
 import json
 import logging
+import sys
+import subprocess
 from tvmop.opdef import __OP_DEF__
 from tvmop.space import ConfigSpaces, ConfigSpace
 from tvm.autotvm.measure.measure_methods import set_cuda_target_arch
@@ -32,6 +34,53 @@
 logging.basicConfig(level=logging.INFO)
 
 
+def create_shared(output,
+                  objects,
+                  options=None,
+                  cc="g++"):
+    """Create shared library.
+    Parameters
+    ----------
+    output : str
+        The target shared library.
+    objects : List[str]
+        List of object files.
+    options : List[str]
+        The list of additional options string.
+    cc : Optional[str]
+        The compiler command.
+    """
+    if sys.platform == "darwin" or sys.platform.startswith("linux"):
+        _linux_compile(output, objects, options, cc)
+    # TODO(yzhliu): elif sys.platform == "win32":
+    else:
+        raise ValueError("Unsupported platform")
+
+
+def _linux_compile(output, objects, options, compile_cmd="g++"):
+    cmd = [compile_cmd]
+    if output.endswith(".so") or output.endswith(".dylib"):
+        cmd += ["-shared", "-fPIC"]
+        if sys.platform == "darwin":
+            cmd += ["-undefined", "dynamic_lookup"]
+    elif output.endswith(".obj"):
+        cmd += ["-c"]
+    cmd += ["-o", output]
+    if isinstance(objects, str):
+        cmd += [objects]
+    else:
+        cmd += objects
+    if options:
+        cmd += options
+    proc = subprocess.Popen(
+        cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
+    (out, _) = proc.communicate()
+    if proc.returncode != 0:
+        msg = "Compilation error:\n"
+        msg += str(out)
+        raise RuntimeError(msg)
+
+
 def get_target(device):
     if device == "cpu":
         return "llvm"
@@ -76,6 +125,8 @@ def get_cuda_arch(arch):
     parser = argparse.ArgumentParser(description="Generate tvm operators")
     parser.add_argument("-o", action="store", required=True, dest="target_path",
                         help="Target path which stores compiled library")
+    parser.add_argument("-L", action="store", default=None, dest="ld_path",
+                        help="library link path")
     parser.add_argument('--cuda-arch', type=str, default=None, dest='cuda_arch',
                         help='The cuda arch for compiling kernels for')
     parser.add_argument("--config", action="store", required=True, dest="config_path",
@@ -106,7 +157,13 @@ def get_cuda_arch(arch):
             logging.info('Cuda arch {} set for compiling TVM operator kernels.'.format(cuda_arch))
             set_cuda_target_arch(cuda_arch)
     func_binary = tvm.build(lowered_funcs, name="tvmop")
-    func_binary.export_library(arguments.target_path)
+    # we create libtvmop.o first, which gives us chance to link tvm_runtime together with the libtvmop
+    # to allow mxnet find external helper functions in libtvm_runtime
+    func_binary.save(arguments.target_path + "/libtvmop.o")
+    ld_path = arguments.target_path if arguments.ld_path is None else arguments.ld_path
+    create_shared(arguments.target_path + "/libtvmop.so",
+                  arguments.target_path + "/libtvmop.o",
+                  options=["-L", ld_path, "-ltvm_runtime"])
 
     config_spaces = ConfigSpaces()
     for operator_def in __OP_DEF__:

From 8e946c9027be21cba2a7187f18e348e44602c139 Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Fri, 3 Jan 2020 15:04:40 +0800
Subject: [PATCH 763/813] Implement atleast_1d/2d/3d (#17099)

---
 python/mxnet/_numpy_op_doc.py                 | 107 ++++++++++++++++++
 python/mxnet/numpy_dispatch_protocol.py       |   3 +
 src/operator/numpy/np_init_op.cc              |  71 ++++++++++++
 src/operator/numpy/np_init_op.cu              |   9 ++
 src/operator/numpy/np_init_op.h               |  23 ++++
 .../unittest/test_numpy_interoperability.py   |  25 ++++
 tests/python/unittest/test_numpy_op.py        |  54 +++++++++
 7 files changed, 292 insertions(+)

diff --git a/python/mxnet/_numpy_op_doc.py b/python/mxnet/_numpy_op_doc.py
index 6abb96c62c09..27d4140a532f 100644
--- a/python/mxnet/_numpy_op_doc.py
+++ b/python/mxnet/_numpy_op_doc.py
@@ -509,6 +509,113 @@ def _np_copy(a, out=None):
     pass
 
 
+def _np_atleast_1d(*arys):
+    """
+    Convert inputs to arrays with at least one dimension.
+
+    Scalar inputs are converted to 1-dimensional arrays, whilst higher-dimensional inputs are preserved.
+
+    Parameters
+    ----------
+    arys1, arys2, ... : ndarray
+        One or more input arrays.
+
+    Returns
+    -------
+    ret : ndarray
+        An array, or list of arrays, each with a.ndim >= 1. Copies are made only if necessary.
+
+    See also
+    --------
+    atleast_2d, atleast_3d
+
+    Examples
+    --------
+    >>> np.atleast_1d(1.0)
+    array([1.])
+    >>> x = np.arange(9.0).reshape(3,3)
+    >>> np.atleast_1d(x)
+    array([[0., 1., 2.],
+           [3., 4., 5.],
+           [6., 7., 8.]])
+    >>> np.atleast_1d(np.array(1), np.array([3, 4]))
+    [array([1.]), array([3., 4.])]
+    """
+    pass
+
+
+def _np_atleast_2d(*arys):
+    """
+    Convert inputs to arrays with at least two dimensions.
+
+    Parameters
+    ----------
+    arys1, arys2, ... : ndarray
+        One or more input arrays.
+
+    Returns
+    -------
+    ret : ndarray
+        An array, or list of arrays, each with a.ndim >= 2. Copies are made only if necessary.
+
+    See also
+    --------
+    atleast_1d, atleast_3d
+
+    Examples
+    --------
+    >>> np.atleast_2d(3.0)
+    array([[3.]])
+    >>> x = np.arange(3.0)
+    >>> np.atleast_2d(x)
+    array([[0., 1., 2.]])
+    >>> np.atleast_2d(np.array(1), np.array([1, 2]), np.array([[1, 2]]))
+    [array([[1.]]), array([[1., 2.]]), array([[1., 2.]])]
+    """
+    pass
+
+def _np_atleast_3d(*arys):
+    """
+    Convert inputs to arrays with at least three dimension.
+
+    Parameters
+    ----------
+    arys1, arys2, ... : ndarray
+        One or more input arrays.
+
+    Returns
+    -------
+    ret : ndarray
+        An array, or list of arrays, each with a.ndim >= 3.
+        For example, a 1-D array of shape (N,) becomes a view of shape (1, N, 1),
+        and a 2-D array of shape (M, N) becomes a view of shape (M, N, 1).
+
+    See also
+    --------
+    atleast_1d, atleast_2d
+
+    Examples
+    --------
+    >>> np.atleast_3d(3.0)
+    array([[[3.]]])
+    >>> x = np.arange(3.0)
+    >>> np.atleast_3d(x).shape
+    (1, 3, 1)
+    >>> x = np.arange(12.0).reshape(4,3)
+    >>> np.atleast_3d(x).shape
+    (4, 3, 1)
+    >>> for arr in np.atleast_3d(np.array([1, 2]), np.array([[1, 2]]), np.array([[[1, 2]]])):
+    ...     print(arr, arr.shape)
+    ...
+    [[[1.]
+      [2.]]] (1, 2, 1)
+    [[[1.]
+      [2.]]] (1, 2, 1)
+    [[[1. 2.]]] (1, 1, 2)
+    """
+    pass
+
+
 def _np_reshape(a, newshape, order='C', out=None):
     """
     Gives a new shape to an array without changing its data.
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index 56a5dd5ac813..2ae1be7cb081 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -113,6 +113,9 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'min',
     'nonzero',
     'ones_like',
+    'atleast_1d',
+    'atleast_2d',
+    'atleast_3d',
     'prod',
     'ravel',
     'repeat',
diff --git a/src/operator/numpy/np_init_op.cc b/src/operator/numpy/np_init_op.cc
index ee52a2939e12..e6073bd2a22d 100644
--- a/src/operator/numpy/np_init_op.cc
+++ b/src/operator/numpy/np_init_op.cc
@@ -35,6 +35,7 @@ DMLC_REGISTER_PARAMETER(NumpyEyeParam);
 DMLC_REGISTER_PARAMETER(IndicesOpParam);
 DMLC_REGISTER_PARAMETER(LogspaceParam);
 DMLC_REGISTER_PARAMETER(FullLikeOpParam);
+DMLC_REGISTER_PARAMETER(AtleastNDParam);
 
 inline bool NumpyIndicesShape(const nnvm::NodeAttrs& attrs,
                               mxnet::ShapeVector* in_shapes,
@@ -98,6 +99,76 @@ NNVM_REGISTER_OP(_npi_identity)
 .set_attr<FCompute>("FCompute<cpu>", IdentityCompute<cpu>)
 .add_arguments(InitOpParam::__FIELDS__());
 
+template<int NDim>
+inline bool AtleastNDShape(const nnvm::NodeAttrs& attrs,
+                           std::vector<mxnet::TShape> *in_attrs,
+                           std::vector<mxnet::TShape> *out_attrs) {
+  auto &param = nnvm::get<AtleastNDParam>(attrs.parsed);
+
+  CHECK_EQ(in_attrs->size(), param.num_args);
+  CHECK_EQ(out_attrs->size(), param.num_args);
+
+  for (int i = 0; i < param.num_args; ++i) {
+    auto &shape = in_attrs->at(i);
+    if (shape.ndim() < NDim) {
+      mxnet::TShape new_shape(NDim, 1);
+      if (NDim == 2) {
+        if (shape.ndim() == 1) {
+          new_shape[1] = shape[0];
+        }
+      } else if (NDim == 3) {
+        if (shape.ndim() == 1) {
+          new_shape[1] = shape[0];
+        } else if (shape.ndim() == 2) {
+          new_shape[0] = shape[0];
+          new_shape[1] = shape[1];
+        }
+      }
+      SHAPE_ASSIGN_CHECK(*out_attrs, i, new_shape);
+    } else {
+      SHAPE_ASSIGN_CHECK(*out_attrs, i, shape);
+    }
+  }
+
+  return shape_is_known(*in_attrs) && shape_is_known(*out_attrs);
+}
+
+#define NNVM_REGISTER_ATLEAST_ND(N)                                       \
+NNVM_REGISTER_OP(_np_atleast_##N##d)                                      \
+.set_attr_parser(ParamParser<AtleastNDParam>)                             \
+.set_num_inputs(                                                          \
+[](const NodeAttrs& attrs) {                                              \
+  auto &param = nnvm::get<AtleastNDParam>(attrs.parsed);                  \
+  return param.num_args;                                                  \
+})                                                                        \
+.set_num_outputs(                                                         \
+[](const NodeAttrs& attrs) {                                              \
+  auto &param = nnvm::get<AtleastNDParam>(attrs.parsed);                  \
+  return param.num_args;                                                  \
+})                                                                        \
+.set_attr<std::string>("key_var_num_args", "num_args")                    \
+.set_attr<nnvm::FListInputNames>("FListInputNames",                       \
+[](const nnvm::NodeAttrs& attrs) {                                        \
+  int num_args = nnvm::get<AtleastNDParam>(attrs.parsed).num_args;        \
+  std::vector<std::string> ret;                                           \
+  for (int i = 0; i < num_args; i++) {                                    \
+    ret.push_back(std::string("ary") + std::to_string(i));                \
+  }                                                                       \
+  return ret;                                                             \
+})                                                                        \
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<-1, -1>)           \
+.set_attr<mxnet::FInferShape>("FInferShape", AtleastNDShape<N>)           \
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)                \
+.set_attr<FCompute>("FCompute<cpu>", AtleastNDCompute<cpu>)               \
+.add_argument("arys", "NDArray-or-Symbol[]", "List of input arrays")      \
+.add_arguments(AtleastNDParam::__FIELDS__())                              \
+
+NNVM_REGISTER_ATLEAST_ND(1);
+
+NNVM_REGISTER_ATLEAST_ND(2);
+
+NNVM_REGISTER_ATLEAST_ND(3);
+
 NNVM_REGISTER_OP(_npi_full_like)
 .set_num_inputs(1)
 .set_num_outputs(1)
diff --git a/src/operator/numpy/np_init_op.cu b/src/operator/numpy/np_init_op.cu
index 5095fe60bdef..886bed61ec66 100644
--- a/src/operator/numpy/np_init_op.cu
+++ b/src/operator/numpy/np_init_op.cu
@@ -41,6 +41,15 @@ NNVM_REGISTER_OP(_npi_identity)
 NNVM_REGISTER_OP(_npi_full_like)
 .set_attr<FCompute>("FCompute<gpu>", FullLikeOpCompute<gpu>);
 
+NNVM_REGISTER_OP(_np_atleast_1d)
+.set_attr<FCompute>("FCompute<gpu>", AtleastNDCompute<gpu>);
+
+NNVM_REGISTER_OP(_np_atleast_2d)
+.set_attr<FCompute>("FCompute<gpu>", AtleastNDCompute<gpu>);
+
+NNVM_REGISTER_OP(_np_atleast_3d)
+.set_attr<FCompute>("FCompute<gpu>", AtleastNDCompute<gpu>);
+
 NNVM_REGISTER_OP(_npi_arange)
 .set_attr<FCompute>("FCompute<gpu>", RangeCompute<gpu, RangeParam>);
 
diff --git a/src/operator/numpy/np_init_op.h b/src/operator/numpy/np_init_op.h
index df30d611aa02..cfc2941ecd28 100644
--- a/src/operator/numpy/np_init_op.h
+++ b/src/operator/numpy/np_init_op.h
@@ -272,6 +272,29 @@ void LogspaceCompute(const nnvm::NodeAttrs& attrs,
   });
 }
 
+struct AtleastNDParam : dmlc::Parameter<AtleastNDParam> {
+  int num_args;
+  DMLC_DECLARE_PARAMETER(AtleastNDParam) {
+    DMLC_DECLARE_FIELD(num_args)
+    .set_lower_bound(1)
+    .describe("Number of input arrays.");
+  }
+};
+
+template<typename xpu>
+void AtleastNDCompute(const nnvm::NodeAttrs& attrs,
+                      const OpContext& ctx,
+                      const std::vector<TBlob>& inputs,
+                      const std::vector<OpReqType>& req,
+                      const std::vector<TBlob>& outputs) {
+  auto &param = nnvm::get<AtleastNDParam>(attrs.parsed);
+  CHECK_EQ(inputs.size(), param.num_args);
+  CHECK_EQ(outputs.size(), param.num_args);
+  for (int i = 0; i < param.num_args; ++i) {
+    UnaryOp::IdentityCompute<xpu>(attrs, ctx, {inputs[i]}, {req[i]}, {outputs[i]});
+  }
+}
+
 }  // namespace op
 }  // namespace mxnet
 
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index d940422408f4..1e85adf3b72f 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -833,6 +833,30 @@ def _add_workload_ones_like(array_pool):
     OpArgMngr.add_workload('ones_like', array_pool['4x1'])
 
 
+def _add_workload_atleast_nd():
+    a_0 = np.array(1)
+    b_0 = np.array(2)
+    a_1 = np.array([1, 2])
+    b_1 = np.array([2, 3])
+    a_2 = np.array([[1, 2], [1, 2]])
+    b_2 = np.array([[2, 3], [2, 3]])
+    a_3 = [a_2, a_2]
+    b_3 = [b_2, b_2]
+
+    OpArgMngr.add_workload('atleast_1d', a_0, b_0)
+    OpArgMngr.add_workload('atleast_1d', a_1, b_1)
+    OpArgMngr.add_workload('atleast_1d', a_2, b_2)
+    OpArgMngr.add_workload('atleast_1d', a_3, b_3)
+    OpArgMngr.add_workload('atleast_2d', a_0, b_0)
+    OpArgMngr.add_workload('atleast_2d', a_1, b_1)
+    OpArgMngr.add_workload('atleast_2d', a_2, b_2)
+    OpArgMngr.add_workload('atleast_2d', a_3, b_3)
+    OpArgMngr.add_workload('atleast_3d', a_0, b_0)
+    OpArgMngr.add_workload('atleast_3d', a_1, b_1)
+    OpArgMngr.add_workload('atleast_3d', a_2, b_2)
+    OpArgMngr.add_workload('atleast_3d', a_3, b_3)
+
+
 def _add_workload_prod(array_pool):
     OpArgMngr.add_workload('prod', array_pool['4x1'])
 
@@ -1626,6 +1650,7 @@ def _prepare_workloads():
     _add_workload_mean(array_pool)
     _add_workload_nonzero()
     _add_workload_ones_like(array_pool)
+    _add_workload_atleast_nd()
     _add_workload_prod(array_pool)
     _add_workload_repeat(array_pool)
     _add_workload_reshape()
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 759677a3e9d2..55a64c68aae9 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -2259,6 +2259,60 @@ def hybrid_forward(self, F, a):
             assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
 
 
+@with_seed()
+@use_np
+def test_np_atleast_nd():
+    class TestAtleastND(HybridBlock):
+        def __init__(self, n):
+            super(TestAtleastND, self).__init__()
+            self._n = n
+
+        def hybrid_forward(self, F, *arys):
+            if self._n == 1:
+                return F.np.atleast_1d(*arys)
+            elif self._n == 2:
+                return F.np.atleast_2d(*arys)
+            elif self._n == 3:
+                return F.np.atleast_3d(*arys)
+
+    tensor_shapes = [
+        ((), (2,), (3, 4, 5)),
+        ((2, 3, 4, 5), (), (2, 3))
+    ]
+    flags = [True, False]
+    ns = [1, 2, 3]
+    dtypes = ['int32', 'int64', 'float16', 'float32', 'float64']
+    funcs = {
+        "numpy": {1: lambda *ts: _np.atleast_1d(*ts),
+                  2: lambda *ts: _np.atleast_2d(*ts),
+                  3: lambda *ts: _np.atleast_3d(*ts)},
+        "mxnet": {1: lambda *ts: np.atleast_1d(*ts),
+                  2: lambda *ts: np.atleast_2d(*ts),
+                  3: lambda *ts: np.atleast_3d(*ts)}
+    }
+    for hybridize, n, tensor_shape, dtype in \
+        itertools.product(flags, ns, tensor_shapes, dtypes):
+        test_atleast_nd = TestAtleastND(n)
+        if hybridize:
+            test_atleast_nd.hybridize()
+        if dtype in ['int32', 'int64']:
+            tensors = list(map(lambda s: np.random.randint(-1, 1, size=s, dtype=dtype), tensor_shape))
+        else:
+            tensors = list(map(lambda s: np.random.uniform(-1.0, 1.0, size=s, dtype=dtype), tensor_shape))
+        tensors_np = [t.asnumpy() for t in tensors]
+        mx_out = test_atleast_nd(*tensors)
+        np_out = funcs["numpy"][n](*tensors_np)
+        for i in range(len(tensors)):
+            assert mx_out[i].shape == np_out[i].shape
+            same(mx_out[i].asnumpy(), np_out[i])
+
+        mx_out = funcs["mxnet"][n](*tensors)
+        np_out = funcs["numpy"][n](*tensors_np)
+        for i in range(len(tensors)):
+            assert mx_out[i].shape == np_out[i].shape
+            same(mx_out[i].asnumpy(), np_out[i])
+
+
 @with_seed()
 @use_np
 def test_np_arange():

From 55e222b8c97f99193f832f785cf210f876632add Mon Sep 17 00:00:00 2001
From: Tao Lv <tao.a.lv@intel.com>
Date: Sat, 4 Jan 2020 04:49:54 +0800
Subject: [PATCH 764/813] Interleaved MHA for CPU path (#17138)

* init qk attention

* qk: fake backward

* cpu selfatt and encdec; move tests to test_operator.py

* coding style

* fix lint

* use random seed in tests

* remove ut in test_operator_gpu.py

* coding style

* retrigger ci
---
 src/operator/contrib/transformer.cc    | 549 ++++++++++++++++++++++++-
 tests/python/gpu/test_operator_gpu.py  | 317 --------------
 tests/python/unittest/test_operator.py | 324 +++++++++++++++
 3 files changed, 861 insertions(+), 329 deletions(-)

diff --git a/src/operator/contrib/transformer.cc b/src/operator/contrib/transformer.cc
index 2ca6f8c71093..58826a2d96a8 100644
--- a/src/operator/contrib/transformer.cc
+++ b/src/operator/contrib/transformer.cc
@@ -122,6 +122,531 @@ static bool InterleavedMatMulEncDecValAttShape(const NodeAttrs& attrs,
   return true;
 }
 
+void strided_batch_sgemm(bool transA, bool transB,
+                         index_t m, index_t n, index_t k,
+                         float alpha, const float *a, index_t lda,
+                         index_t strideA, const float *b, index_t ldb,
+                         index_t strideB, float beta, float *c, index_t ldc,
+                         index_t strideC, int32_t batchCount) {
+  std::vector<const float*> pp_A(batchCount, nullptr);
+  std::vector<const float*> pp_B(batchCount, nullptr);
+  std::vector<float*> pp_C(batchCount, nullptr);
+
+  for (int i = 0; i < batchCount; i++) {
+    pp_A[i] = a + i * strideA;
+    pp_B[i] = b + i * strideB;
+    pp_C[i] = c + i * strideC;
+  }
+
+#if (MSHADOW_USE_MKL && INTEL_MKL_VERSION >= 20160000)
+  const int GROUP_SIZE = 1;
+  MKL_INT p_m[GROUP_SIZE] = {m};
+  MKL_INT p_n[GROUP_SIZE] = {n};
+  MKL_INT p_k[GROUP_SIZE] = {k};
+  MKL_INT p_lda[GROUP_SIZE] = {lda};
+  MKL_INT p_ldb[GROUP_SIZE] = {ldb};
+  MKL_INT p_ldc[GROUP_SIZE] = {ldc};
+
+  float p_alpha[GROUP_SIZE] = {alpha};
+  float p_beta[GROUP_SIZE] = {beta};
+
+  CBLAS_TRANSPOSE cblas_a_trans = transA ? CblasTrans : CblasNoTrans;
+  CBLAS_TRANSPOSE cblas_b_trans = transB ? CblasTrans : CblasNoTrans;
+
+  MKL_INT p_group_sizeb[GROUP_SIZE] = {batchCount};
+  CBLAS_TRANSPOSE p_transa[GROUP_SIZE] = {cblas_a_trans};
+  CBLAS_TRANSPOSE p_transb[GROUP_SIZE] = {cblas_b_trans};
+
+  cblas_sgemm_batch(CblasColMajor, p_transa, p_transb,
+                    p_m, p_n, p_k, p_alpha, pp_A.data(), p_lda, pp_B.data(),
+                    p_ldb, p_beta, pp_C.data(), p_ldc, GROUP_SIZE, p_group_sizeb);
+#else
+  for (int i = 0; i < batchCount; ++i) {
+    cblas_sgemm(CblasColMajor,
+                transA ? CblasTrans : CblasNoTrans,
+                transB ? CblasTrans : CblasNoTrans,
+                m, n, k,
+                alpha, pp_A[i], lda,
+                pp_B[i], ldb, beta, pp_C[i], ldc);
+  }
+#endif
+}
+
+void InterleavedMatMulSelfAttQKCPU(const nnvm::NodeAttrs& attrs,
+                                   const OpContext &ctx,
+                                   const std::vector<TBlob> &inputs,
+                                   const std::vector<OpReqType> &req,
+                                   const std::vector<TBlob> &outputs) {
+  const auto& params = nnvm::get<InterleavedMatMulParam>(attrs.parsed);
+
+  if (req[0] == kNullOp)
+    return;
+
+  CHECK_EQ(inputs[0].type_flag_, mshadow::kFloat32)
+    << "Only FP32 is supported on CPU at the moment";
+
+  mshadow::Stream<cpu>* s = ctx.get_stream<cpu>();
+  const float* queries_keys_values = inputs[0].FlatTo2D<cpu, float>(s).dptr_;
+  float* output = outputs[0].FlatTo2D<cpu, float>(s).dptr_;
+
+  const index_t qkv_seq_len    = inputs[0].shape_[0];
+  const index_t sequences      = inputs[0].shape_[1];
+  const index_t output_lin_dim = inputs[0].shape_[2];
+  const index_t embed_dim      = output_lin_dim / 3;
+  const index_t head_dim       = embed_dim / params.heads;
+  const index_t attn_batches   = params.heads * sequences;
+  const index_t lead_dim       = attn_batches * 3 * head_dim;
+  const index_t batch_stride   = 3 * head_dim;
+  const float beta             = req[0] == kAddTo ? 1.f : 0.f;
+  const float scale            = 1.0 / sqrt(static_cast<float>(head_dim));
+
+  strided_batch_sgemm(true,
+                      false,
+                      qkv_seq_len,
+                      qkv_seq_len,
+                      head_dim,
+                      scale,
+                      queries_keys_values + head_dim,
+                      lead_dim,
+                      batch_stride,
+                      queries_keys_values,
+                      lead_dim,
+                      batch_stride,
+                      beta,
+                      output,
+                      qkv_seq_len,
+                      qkv_seq_len * qkv_seq_len,
+                      attn_batches);
+}
+
+void BackwardInterleavedMatMulSelfAttQKCPU(const nnvm::NodeAttrs& attrs,
+                                           const OpContext &ctx,
+                                           const std::vector<TBlob> &inputs,
+                                           const std::vector<OpReqType> &req,
+                                           const std::vector<TBlob> &outputs) {
+  const auto& params = nnvm::get<InterleavedMatMulParam>(attrs.parsed);
+  if (req[0] == kNullOp)
+    return;
+
+  mshadow::Stream<cpu>* s = ctx.get_stream<cpu>();
+  CHECK_EQ(inputs[0].type_flag_, mshadow::kFloat32)
+    << "Only FP32 is supported on CPU at the moment";
+
+  const float* output_grads        = inputs[0].FlatTo2D<cpu, float>(s).dptr_;
+  const float* queries_keys_values = inputs[1].FlatTo2D<cpu, float>(s).dptr_;
+  float* queries_keys_values_grads = outputs[0].FlatTo2D<cpu, float>(s).dptr_;
+  const index_t qkv_seq_len    = inputs[1].shape_[0];
+  const index_t sequences      = inputs[1].shape_[1];
+  const index_t output_lin_dim = inputs[1].shape_[2];
+  const index_t embed_dim      = output_lin_dim / 3;
+  const index_t head_dim       = embed_dim / params.heads;
+  const index_t attn_batches   = params.heads * sequences;
+  const index_t lead_dim       = attn_batches * 3 * head_dim;
+  const index_t batch_stride   = 3 * head_dim;
+  const float scale            = 1.0 / sqrt(static_cast<float>(head_dim));
+  const float beta = req[0] == kAddTo ? 1.f : 0.f;
+
+  if (req[0] == kWriteTo) {
+    memset(queries_keys_values_grads, 0, outputs[0].shape_.Size() * sizeof (float));
+  }
+
+  strided_batch_sgemm(false,
+                      false,
+                      head_dim,
+                      qkv_seq_len,
+                      qkv_seq_len,
+                      scale,
+                      queries_keys_values + head_dim,
+                      lead_dim,
+                      batch_stride,
+                      output_grads,
+                      qkv_seq_len,
+                      qkv_seq_len * qkv_seq_len,
+                      beta,
+                      queries_keys_values_grads,
+                      lead_dim,
+                      batch_stride,
+                      attn_batches);
+
+  strided_batch_sgemm(false,
+                      true,
+                      head_dim,
+                      qkv_seq_len,
+                      qkv_seq_len,
+                      scale,
+                      queries_keys_values,
+                      lead_dim,
+                      batch_stride,
+                      output_grads,
+                      qkv_seq_len,
+                      qkv_seq_len * qkv_seq_len,
+                      beta,
+                      queries_keys_values_grads + head_dim,
+                      lead_dim,
+                      batch_stride,
+                      attn_batches);
+}
+
+void InterleavedMatMulSelfAttValAttCPU(const nnvm::NodeAttrs& attrs,
+                                       const OpContext &ctx,
+                                       const std::vector<TBlob> &inputs,
+                                       const std::vector<OpReqType> &req,
+                                       const std::vector<TBlob> &outputs) {
+  const auto& params = nnvm::get<InterleavedMatMulParam>(attrs.parsed);
+  if (req[0] == kNullOp)
+    return;
+
+  CHECK_EQ(inputs[0].type_flag_, mshadow::kFloat32)
+    << "Only FP32 is supported on CPU at the moment";
+
+  mshadow::Stream<cpu>* s = ctx.get_stream<cpu>();
+  const float* queries_keys_values = inputs[0].FlatTo2D<cpu, float>(s).dptr_;
+  const float* attention_maps      = inputs[1].FlatTo2D<cpu, float>(s).dptr_;
+  float* output                    = outputs[0].FlatTo2D<cpu, float>(s).dptr_;
+  const index_t qkv_seq_len    = inputs[0].shape_[0];
+  const index_t sequences      = inputs[0].shape_[1];
+  const index_t output_lin_dim = inputs[0].shape_[2];
+  const index_t embed_dim      = output_lin_dim / 3;
+  const index_t head_dim       = embed_dim / params.heads;
+  const index_t attn_batches   = params.heads * sequences;
+  const index_t lead_dim       = attn_batches * 3 * head_dim;
+  const index_t batch_stride   = 3 * head_dim;
+  const float alpha             = 1.f;
+  const float beta              = req[0] == kAddTo ? 1.f : 0.f;
+
+  strided_batch_sgemm(false,
+                      false,
+                      head_dim,
+                      qkv_seq_len,
+                      qkv_seq_len,
+                      alpha,
+                      queries_keys_values + 2 * head_dim,
+                      lead_dim,
+                      batch_stride,
+                      attention_maps,
+                      qkv_seq_len,
+                      qkv_seq_len * qkv_seq_len,
+                      beta,
+                      output,
+                      head_dim * attn_batches,
+                      head_dim,
+                      attn_batches);
+}
+
+void BackwardInterleavedMatMulSelfAttValAttCPU(const nnvm::NodeAttrs& attrs,
+                                               const OpContext &ctx,
+                                               const std::vector<TBlob> &inputs,
+                                               const std::vector<OpReqType> &req,
+                                               const std::vector<TBlob> &outputs) {
+  const auto& params = nnvm::get<InterleavedMatMulParam>(attrs.parsed);
+  if (req[0] == kNullOp)
+    return;
+
+  CHECK_EQ(inputs[0].type_flag_, mshadow::kFloat32)
+    << "Only FP32 is supported on CPU at the moment";
+
+  mshadow::Stream<cpu>* s = ctx.get_stream<cpu>();
+  const float* output_grads              = inputs[0].FlatTo2D<cpu, float>(s).dptr_;
+  const float* queries_keys_values       = inputs[1].FlatTo2D<cpu, float>(s).dptr_;
+  const float* attention_maps            = inputs[2].FlatTo2D<cpu, float>(s).dptr_;
+  float* queries_keys_values_grads       = outputs[0].FlatTo2D<cpu, float>(s).dptr_;
+  float* attention_maps_grads            = outputs[1].FlatTo2D<cpu, float>(s).dptr_;
+  const index_t qkv_seq_len    = inputs[1].shape_[0];
+  const index_t sequences      = inputs[1].shape_[1];
+  const index_t output_lin_dim = inputs[1].shape_[2];
+  const index_t embed_dim      = output_lin_dim / 3;
+  const index_t head_dim       = embed_dim / params.heads;
+  const index_t attn_batches   = params.heads * sequences;
+  const index_t lead_dim       = attn_batches * 3 * head_dim;
+  const index_t batch_stride   = 3 * head_dim;
+  const float alpha            = 1.f;
+  if (req[0] != kNullOp) {
+    if (req[0] == kWriteTo) {
+      memset(queries_keys_values_grads, 0, outputs[0].shape_.Size() * sizeof (float));
+    }
+
+    const float beta = req[0] == kAddTo ? 1.f : 0.f;
+    strided_batch_sgemm(false,
+                        true,
+                        head_dim,
+                        qkv_seq_len,
+                        qkv_seq_len,
+                        alpha,
+                        output_grads,
+                        head_dim * attn_batches,
+                        head_dim,
+                        attention_maps,
+                        qkv_seq_len,
+                        qkv_seq_len * qkv_seq_len,
+                        beta,
+                        queries_keys_values_grads + 2 * head_dim,
+                        lead_dim,
+                        batch_stride,
+                        attn_batches);
+  }
+  if (req[1] != kNullOp) {
+    const float beta = req[1] == kAddTo ? 1.f : 0.f;
+    strided_batch_sgemm(true,
+                        false,
+                        qkv_seq_len,
+                        qkv_seq_len,
+                        head_dim,
+                        alpha,
+                        queries_keys_values + 2 * head_dim,
+                        lead_dim,
+                        batch_stride,
+                        output_grads,
+                        head_dim * attn_batches,
+                        head_dim,
+                        beta,
+                        attention_maps_grads,
+                        qkv_seq_len,
+                        qkv_seq_len * qkv_seq_len,
+                        attn_batches);
+  }
+}
+
+void InterleavedMatMulEncDecQKCPU(const nnvm::NodeAttrs& attrs,
+                                  const OpContext &ctx,
+                                  const std::vector<TBlob> &inputs,
+                                  const std::vector<OpReqType> &req,
+                                  const std::vector<TBlob> &outputs) {
+  const auto& params = nnvm::get<InterleavedMatMulParam>(attrs.parsed);
+  if (req[0] == kNullOp)
+    return;
+
+  CHECK_EQ(inputs[0].type_flag_, mshadow::kFloat32)
+    << "Only FP32 is supported on CPU at the moment";
+
+  mshadow::Stream<cpu>* s = ctx.get_stream<cpu>();
+  const float* queries     = inputs[0].FlatTo2D<cpu, float>(s).dptr_;
+  const float* keys_values = inputs[1].FlatTo2D<cpu, float>(s).dptr_;
+  float* output            = outputs[0].FlatTo2D<cpu, float>(s).dptr_;
+  const index_t q_seq_len         = inputs[0].shape_[0];
+  const index_t sequences         = inputs[0].shape_[1];
+  const index_t output_lin_q_dim  = inputs[0].shape_[2];
+  const index_t kv_seq_len        = inputs[1].shape_[0];
+  const index_t embed_dim         = output_lin_q_dim;
+  const index_t head_dim          = embed_dim / params.heads;
+  const index_t attn_batches      = params.heads * sequences;
+  const index_t lead_dim_q        = attn_batches * head_dim;
+  const index_t lead_dim_kv       = attn_batches * 2 * head_dim;
+  const index_t batch_stride_q    = head_dim;
+  const index_t batch_stride_kv   = head_dim * 2;
+  const float beta                = req[0] == kAddTo ? 1.f : 0.f;
+  const float scale               = 1.f / sqrt(static_cast<float>(head_dim));
+
+  strided_batch_sgemm(true,
+                      false,
+                      kv_seq_len,
+                      q_seq_len,
+                      head_dim,
+                      scale,
+                      keys_values,
+                      lead_dim_kv,
+                      batch_stride_kv,
+                      queries,
+                      lead_dim_q,
+                      batch_stride_q,
+                      beta,
+                      output,
+                      kv_seq_len,
+                      kv_seq_len * q_seq_len,
+                      attn_batches);
+}
+
+void BackwardInterleavedMatMulEncDecQKCPU(const nnvm::NodeAttrs& attrs,
+                                          const OpContext &ctx,
+                                          const std::vector<TBlob> &inputs,
+                                          const std::vector<OpReqType> &req,
+                                          const std::vector<TBlob> &outputs) {
+  const auto& params = nnvm::get<InterleavedMatMulParam>(attrs.parsed);
+  if (req[0] == kNullOp)
+    return;
+
+  CHECK_EQ(inputs[0].type_flag_, mshadow::kFloat32)
+    << "Only FP32 is supported on CPU at the moment";
+
+  mshadow::Stream<cpu>* s = ctx.get_stream<cpu>();
+  const float* output_grads = inputs[0].FlatTo2D<cpu, float>(s).dptr_;
+  const float* queries       = inputs[1].FlatTo2D<cpu, float>(s).dptr_;
+  const float* keys_values   = inputs[2].FlatTo2D<cpu, float>(s).dptr_;
+  float* queries_grads       = outputs[0].FlatTo2D<cpu, float>(s).dptr_;
+  float* keys_values_grads   = outputs[1].FlatTo2D<cpu, float>(s).dptr_;
+  const index_t q_seq_len         = inputs[1].shape_[0];
+  const index_t sequences         = inputs[1].shape_[1];
+  const index_t output_lin_q_dim  = inputs[1].shape_[2];
+  const index_t kv_seq_len        = inputs[2].shape_[0];
+  const index_t embed_dim         = output_lin_q_dim;
+  const index_t head_dim          = embed_dim / params.heads;
+  const index_t attn_batches      = params.heads * sequences;
+  const index_t lead_dim_q        = attn_batches * head_dim;
+  const index_t lead_dim_kv       = attn_batches * 2 * head_dim;
+  const index_t batch_stride_q    = head_dim;
+  const index_t batch_stride_kv   = head_dim * 2;
+  const float scale               = 1.f / sqrt(static_cast<float>(head_dim));
+
+  if (req[0] != kNullOp) {
+    const float beta = req[0] == kAddTo ? 1.f : 0.f;
+    strided_batch_sgemm(false,
+                        false,
+                        head_dim,
+                        q_seq_len,
+                        kv_seq_len,
+                        scale,
+                        keys_values,
+                        lead_dim_kv,
+                        batch_stride_kv,
+                        output_grads,
+                        kv_seq_len,
+                        kv_seq_len * q_seq_len,
+                        beta,
+                        queries_grads,
+                        lead_dim_q,
+                        batch_stride_q,
+                        attn_batches);
+  }
+  if (req[1] != kNullOp) {
+    if (req[1] == kWriteTo) {
+      memset(keys_values_grads, 0, outputs[1].shape_.Size() * sizeof (float));
+    }
+    const float beta = req[1] == kAddTo ? 1.f : 0.f;
+    strided_batch_sgemm(false,
+                        true,
+                        head_dim,
+                        kv_seq_len,
+                        q_seq_len,
+                        scale,
+                        queries,
+                        lead_dim_q,
+                        batch_stride_q,
+                        output_grads,
+                        kv_seq_len,
+                        kv_seq_len * q_seq_len,
+                        beta,
+                        keys_values_grads,
+                        lead_dim_kv,
+                        batch_stride_kv,
+                        attn_batches);
+  }
+}
+
+void InterleavedMatMulEncDecValAttCPU(const nnvm::NodeAttrs& attrs,
+                                      const OpContext &ctx,
+                                      const std::vector<TBlob> &inputs,
+                                      const std::vector<OpReqType> &req,
+                                      const std::vector<TBlob> &outputs) {
+  const auto& params = nnvm::get<InterleavedMatMulParam>(attrs.parsed);
+  if (req[0] == kNullOp)
+    return;
+
+  CHECK_EQ(inputs[0].type_flag_, mshadow::kFloat32)
+    << "Only FP32 is supported on CPU at the moment";
+
+  mshadow::Stream<cpu>* s = ctx.get_stream<cpu>();
+  const float* keys_values    = inputs[0].FlatTo2D<cpu, float>(s).dptr_;
+  const float* attention_maps = inputs[1].FlatTo2D<cpu, float>(s).dptr_;
+  float* output               = outputs[0].FlatTo2D<cpu, float>(s).dptr_;
+  const index_t kv_seq_len        = inputs[0].shape_[0];
+  const index_t output_lin_kv_dim = inputs[0].shape_[2];
+  const index_t attn_batches      = inputs[1].shape_[0];
+  const index_t q_seq_len         = inputs[1].shape_[1];
+  const index_t embed_dim         = output_lin_kv_dim / 2;
+  const index_t head_dim          = embed_dim / params.heads;
+  const index_t lead_dim_kv       = attn_batches * head_dim * 2;
+  const index_t batch_stride_kv   = 2 * head_dim;
+  const float alpha               = 1.f;
+  const float beta                = req[0] == kAddTo ? 1.f : 0.f;
+
+  strided_batch_sgemm(false,
+                      false,
+                      head_dim,
+                      q_seq_len,
+                      kv_seq_len,
+                      alpha,
+                      keys_values + head_dim,
+                      lead_dim_kv,
+                      batch_stride_kv,
+                      attention_maps,
+                      kv_seq_len,
+                      kv_seq_len * q_seq_len,
+                      beta,
+                      output,
+                      head_dim * attn_batches,
+                      head_dim,
+                      attn_batches);
+}
+
+void BackwardInterleavedMatMulEncDecValAttCPU(const nnvm::NodeAttrs& attrs,
+                                              const OpContext &ctx,
+                                              const std::vector<TBlob> &inputs,
+                                              const std::vector<OpReqType> &req,
+                                              const std::vector<TBlob> &outputs) {
+  const auto& params = nnvm::get<InterleavedMatMulParam>(attrs.parsed);
+  CHECK_EQ(inputs[0].type_flag_, mshadow::kFloat32)
+    << "Only FP32 is supported on CPU at the moment";
+
+  mshadow::Stream<cpu>* s = ctx.get_stream<cpu>();
+  const float* output_grads   = inputs[0].FlatTo2D<cpu, float>(s).dptr_;
+  const float* keys_values    = inputs[1].FlatTo2D<cpu, float>(s).dptr_;
+  const float* attention_maps = inputs[2].FlatTo2D<cpu, float>(s).dptr_;
+  float* keys_values_grads    = outputs[0].FlatTo2D<cpu, float>(s).dptr_;
+  float* attention_maps_grads = outputs[1].FlatTo2D<cpu, float>(s).dptr_;
+  const index_t kv_seq_len        = inputs[1].shape_[0];
+  const index_t output_lin_kv_dim = inputs[1].shape_[2];
+  const index_t attn_batches      = inputs[2].shape_[0];
+  const index_t q_seq_len         = inputs[2].shape_[1];
+  const index_t embed_dim         = output_lin_kv_dim / 2;
+  const index_t head_dim          = embed_dim / params.heads;
+  const index_t lead_dim_kv       = attn_batches * head_dim * 2;
+  const index_t batch_stride_kv   = 2 * head_dim;
+  const float alpha               = 1.f;
+
+  if (req[0] != kNullOp) {
+    if (req[0] == kWriteTo) {
+      memset(keys_values_grads, 0, outputs[0].shape_.Size() * sizeof (float));
+    }
+    const float beta = req[0] == kAddTo ? 1.f : 0.f;
+    strided_batch_sgemm(false,
+                        true,
+                        head_dim,
+                        kv_seq_len,
+                        q_seq_len,
+                        alpha,
+                        output_grads,
+                        head_dim * attn_batches,
+                        head_dim,
+                        attention_maps,
+                        kv_seq_len,
+                        kv_seq_len * q_seq_len,
+                        beta,
+                        keys_values_grads + head_dim,
+                        lead_dim_kv,
+                        batch_stride_kv,
+                        attn_batches);
+  }
+  if (req[1] != kNullOp) {
+    const float beta = req[1] == kAddTo ? 1.f : 0.f;
+    strided_batch_sgemm(true,
+                        false,
+                        kv_seq_len,
+                        q_seq_len,
+                        head_dim,
+                        alpha,
+                        keys_values + head_dim,
+                        lead_dim_kv,
+                        batch_stride_kv,
+                        output_grads,
+                        head_dim * attn_batches,
+                        head_dim,
+                        beta,
+                        attention_maps_grads,
+                        kv_seq_len,
+                        kv_seq_len * q_seq_len,
+                        attn_batches);
+  }
+}
+
 NNVM_REGISTER_OP(_contrib_interleaved_matmul_selfatt_qk)
 .describe(R"code(Compute the matrix multiplication between the projections of
 queries and keys in multihead attention use as self attention.
@@ -138,8 +663,6 @@ q_proj = mx.nd.contrib.div_sqrt_dim(q_proj)
 k_proj = mx.nd.transpose(tmp[:,:,:,1,:], axes=(1, 2, 0, 3))
 k_proj = mx.nd.reshap(k_proj, shape=(-1, 0, 0), reverse=True)
 output = mx.nd.batch_dot(q_proj, k_proj, transpose_b=True)
-
-This Op is GPU only
 )code" ADD_FILELINE)
 .set_num_inputs(1)
 .set_num_outputs(1)
@@ -152,6 +675,7 @@ This Op is GPU only
 })
 .set_attr<mxnet::FInferShape>("FInferShape", InterleavedMatMulSelfAttQKShape)
 .set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
+.set_attr<FCompute>("FCompute<cpu>", InterleavedMatMulSelfAttQKCPU)
 .set_attr<nnvm::FGradient>("FGradient",
   ElemwiseGradUseIn{"_backward_interleaved_matmul_selfatt_qk"})
 .add_argument("queries_keys_values", "NDArray-or-Symbol", "Interleaved queries, keys and values")
@@ -161,7 +685,8 @@ NNVM_REGISTER_OP(_backward_interleaved_matmul_selfatt_qk)
 .set_num_inputs(2)
 .set_num_outputs(1)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
-.set_attr_parser(ParamParser<InterleavedMatMulParam>);
+.set_attr_parser(ParamParser<InterleavedMatMulParam>)
+.set_attr<FCompute>("FCompute<cpu>", BackwardInterleavedMatMulSelfAttQKCPU);
 
 NNVM_REGISTER_OP(_contrib_interleaved_matmul_selfatt_valatt)
 .describe(R"code(Compute the matrix multiplication between the projections of
@@ -182,8 +707,6 @@ output = mx.nd.batch_dot(attention, v_proj, transpose_b=True)
 output = mx.nd.reshape(output, shape=(-1, num_heads, 0, 0), reverse=True)
 output = mx.nd.transpose(output, axes=(0, 2, 1, 3))
 output = mx.nd.reshape(output, shape=(0, 0, -1))
-
-This Op is GPU only
 )code" ADD_FILELINE)
 .set_num_inputs(2)
 .set_num_outputs(1)
@@ -196,6 +719,7 @@ This Op is GPU only
 })
 .set_attr<mxnet::FInferShape>("FInferShape", InterleavedMatMulSelfAttValAttShape)
 .set_attr<nnvm::FInferType>("FInferType", ElemwiseType<2, 1>)
+.set_attr<FCompute>("FCompute<cpu>", InterleavedMatMulSelfAttValAttCPU)
 .set_attr<nnvm::FGradient>("FGradient",
   ElemwiseGradUseIn{"_backward_interleaved_matmul_selfatt_valatt"})
 .add_argument("queries_keys_values", "NDArray-or-Symbol", "Queries, keys and values interleaved")
@@ -206,7 +730,8 @@ NNVM_REGISTER_OP(_backward_interleaved_matmul_selfatt_valatt)
 .set_num_inputs(3)
 .set_num_outputs(2)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
-.set_attr_parser(ParamParser<InterleavedMatMulParam>);
+.set_attr_parser(ParamParser<InterleavedMatMulParam>)
+.set_attr<FCompute>("FCompute<cpu>", BackwardInterleavedMatMulSelfAttValAttCPU);
 
 NNVM_REGISTER_OP(_contrib_interleaved_matmul_encdec_qk)
 .describe(R"code(Compute the matrix multiplication between the projections of
@@ -226,8 +751,6 @@ tmp = mx.nd.reshape(keys_values, shape=(0, 0, num_heads, 2, -1))
 k_proj = mx.nd.transpose(tmp[:,:,:,0,:], axes=(1, 2, 0, 3))
 k_proj = mx.nd.reshap(k_proj, shape=(-1, 0, 0), reverse=True)
 output = mx.nd.batch_dot(q_proj, k_proj, transpose_b=True)
-
-This Op is GPU only
 )code" ADD_FILELINE)
 .set_num_inputs(2)
 .set_num_outputs(1)
@@ -240,6 +763,7 @@ This Op is GPU only
 })
 .set_attr<mxnet::FInferShape>("FInferShape", InterleavedMatMulEncDecQKShape)
 .set_attr<nnvm::FInferType>("FInferType", ElemwiseType<2, 1>)
+.set_attr<FCompute>("FCompute<cpu>", InterleavedMatMulEncDecQKCPU)
 .set_attr<nnvm::FGradient>("FGradient",
     ElemwiseGradUseIn{"_backward_interleaved_matmul_encdec_qk"})
 .add_argument("queries", "NDArray-or-Symbol", "Queries")
@@ -250,7 +774,8 @@ NNVM_REGISTER_OP(_backward_interleaved_matmul_encdec_qk)
 .set_num_inputs(3)
 .set_num_outputs(2)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
-.set_attr_parser(ParamParser<InterleavedMatMulParam>);
+.set_attr_parser(ParamParser<InterleavedMatMulParam>)
+.set_attr<FCompute>("FCompute<cpu>", BackwardInterleavedMatMulEncDecQKCPU);
 
 NNVM_REGISTER_OP(_contrib_interleaved_matmul_encdec_valatt)
 .describe(R"code(Compute the matrix multiplication between the projections of
@@ -272,8 +797,6 @@ output = mx.nd.batch_dot(attention, v_proj, transpose_b=True)
 output = mx.nd.reshape(output, shape=(-1, num_heads, 0, 0), reverse=True)
 output = mx.nd.transpose(output, axes=(0, 2, 1, 3))
 output = mx.nd.reshape(output, shape=(0, 0, -1))
-
-This Op is GPU only
 )code" ADD_FILELINE)
 .set_num_inputs(2)
 .set_num_outputs(1)
@@ -286,6 +809,7 @@ This Op is GPU only
 })
 .set_attr<mxnet::FInferShape>("FInferShape", InterleavedMatMulEncDecValAttShape)
 .set_attr<nnvm::FInferType>("FInferType", ElemwiseType<2, 1>)
+.set_attr<FCompute>("FCompute<cpu>", InterleavedMatMulEncDecValAttCPU)
 .set_attr<nnvm::FGradient>("FGradient",
     ElemwiseGradUseIn{"_backward_interleaved_matmul_encdec_valatt"})
 .add_argument("keys_values", "NDArray-or-Symbol", "Keys and values interleaved")
@@ -296,7 +820,8 @@ NNVM_REGISTER_OP(_backward_interleaved_matmul_encdec_valatt)
 .set_num_inputs(3)
 .set_num_outputs(2)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
-.set_attr_parser(ParamParser<InterleavedMatMulParam>);
+.set_attr_parser(ParamParser<InterleavedMatMulParam>)
+.set_attr<FCompute>("FCompute<cpu>", BackwardInterleavedMatMulEncDecValAttCPU);
 
 
 // relu
diff --git a/tests/python/gpu/test_operator_gpu.py b/tests/python/gpu/test_operator_gpu.py
index e548217b9369..721eaaebab31 100644
--- a/tests/python/gpu/test_operator_gpu.py
+++ b/tests/python/gpu/test_operator_gpu.py
@@ -2548,323 +2548,6 @@ def test_arange_like_dtype():
         for v in out:
             assert v.dtype == t
 
-@with_seed()
-def check_multihead_attention_selfatt(dtype):
-    def convert_weight(F, q_weight, k_weight, v_weight, num_heads):
-        q_weight = F.reshape(q_weight, shape=(num_heads, -1, 0), reverse=True)
-        k_weight = F.reshape(k_weight, shape=(num_heads, -1, 0), reverse=True)
-        v_weight = F.reshape(v_weight, shape=(num_heads, -1, 0), reverse=True)
-        all_weights = F.concat(q_weight, k_weight, v_weight, dim=-2)
-        all_weights = F.reshape(all_weights, shape=(-1, 0), reverse=True)
-        return all_weights
-
-    def convert_bias(F, q_bias, k_bias, v_bias, num_heads):
-        q_bias = F.reshape(q_bias, shape=(num_heads, -1))
-        k_bias = F.reshape(k_bias, shape=(num_heads, -1))
-        v_bias = F.reshape(v_bias, shape=(num_heads, -1))
-        all_bias = F.stack(q_bias, k_bias, v_bias, axis=1)
-        all_bias = F.reshape(all_bias, shape=(-1,))
-        return all_bias
-
-    batch_size = 2
-    qkv_length = 7  # length of a sequence
-    qkv_dim = 9     # dimension of encoding
-    num_heads = 3   # number of attention head
-    head_dim = 5    # head size
-    out_dim = 13 * num_heads
-    qkv_units = num_heads * head_dim
-
-    arg_params = {
-        'qkv': mx.nd.array(np.random.rand(*(batch_size, qkv_length, qkv_dim)).astype(dtype) * 0.1, dtype=dtype),
-        'q_weight': mx.nd.array(np.random.rand(*(qkv_units, qkv_dim)).astype(dtype) * 0.1, dtype=dtype),
-        'k_weight': mx.nd.array(np.random.rand(*(qkv_units, qkv_dim)).astype(dtype) * 0.1, dtype=dtype),
-        'v_weight': mx.nd.array(np.random.rand(*(qkv_units, qkv_dim)).astype(dtype) * 0.1, dtype=dtype),
-        'q_bias': mx.nd.array(np.random.rand(*(qkv_units,)).astype(dtype) * 0.1, dtype=dtype),
-        'k_bias': mx.nd.array(np.random.rand(*(qkv_units,)).astype(dtype) * 0.1, dtype=dtype),
-        'v_bias': mx.nd.array(np.random.rand(*(qkv_units,)).astype(dtype) * 0.1, dtype=dtype),
-        'out_weight': mx.nd.array(np.random.rand(*(out_dim, qkv_units)).astype(dtype) * 0.1, dtype=dtype),
-        'out_bias': mx.nd.array(np.random.rand(*(out_dim,)).astype(dtype) * 0.1, dtype=dtype),
-        }
-
-    qkv = mx.sym.Variable('qkv')
-    sonde = mx.sym.Variable('sonde')
-    q_weight = mx.sym.Variable('q_weight')
-    k_weight = mx.sym.Variable('k_weight')
-    v_weight = mx.sym.Variable('v_weight')
-    q_bias = mx.sym.Variable('q_bias')
-    k_bias = mx.sym.Variable('k_bias')
-    v_bias = mx.sym.Variable('v_bias')
-    out_weight = mx.sym.Variable('out_weight')
-    out_bias = mx.sym.Variable('out_bias')
-    qkv_weight = convert_weight(mx.sym, q_weight, k_weight, v_weight, num_heads)
-    qkv_bias = convert_bias(mx.sym, q_bias, k_bias, v_bias, num_heads)
-    qkv = mx.sym.transpose(qkv, axes=(1, 0, 2))
-    qkv_proj = mx.sym.FullyConnected(qkv, weight=qkv_weight, bias=qkv_bias, flatten=False,
-                                     num_hidden=qkv_units * 3, no_bias=False)
-    att_score = mx.sym.contrib.interleaved_matmul_selfatt_qk(
-            qkv_proj, heads=num_heads)
-    att_score = att_score + sonde
-    weighted_value = mx.sym.contrib.interleaved_matmul_selfatt_valatt(
-            qkv_proj, att_score, heads=num_heads)
-    output = mx.sym.FullyConnected(weighted_value, weight=out_weight, bias=out_bias, flatten=False,
-                                   num_hidden=out_dim, no_bias=False)
-    output = mx.sym.transpose(output, axes=(1, 0, 2))
-    output = mx.sym.Group([output, att_score])
-    executor = output.simple_bind(ctx=mx.gpu(0),
-                                  qkv=(batch_size, qkv_length, qkv_dim),
-                                  q_weight=(qkv_units, qkv_dim),
-                                  q_bias=(qkv_units,),
-                                  k_weight=(qkv_units, qkv_dim),
-                                  k_bias=(qkv_units,),
-                                  v_weight=(qkv_units, qkv_dim),
-                                  v_bias=(qkv_units,),
-                                  type_dict={'qkv': dtype,
-                                             'q_weight': dtype,
-                                             'k_weight': dtype,
-                                             'v_weight': dtype,
-                                             'q_bias': dtype,
-                                             'k_bias': dtype,
-                                             'v_bias': dtype,
-                                             'sonde': dtype},
-                                  grad_req='write', force_rebind=True)
-    output_shape = executor.outputs[0].shape
-    output_grads = np.random.rand(*output_shape).astype(dtype) * 0.1
-    executor.copy_params_from(arg_params, {})
-    executor.arg_dict['sonde'][:] = 0.
-    executor.arg_dict['sonde'].wait_to_read()
-    executor.forward(is_train=True)
-    output_opti = executor.outputs[0].asnumpy()
-    att_score_opti = executor.outputs[1].asnumpy()
-    executor.backward([mx.nd.array(output_grads, dtype=dtype),
-                       mx.nd.zeros(att_score_opti.shape, dtype=dtype)])
-    grads_opti = {k: v.asnumpy() for k, v in executor.grad_dict.items()}
-    qkv = mx.sym.Variable('qkv')
-    sonde = mx.sym.Variable('sonde')
-    q_weight = mx.sym.Variable('q_weight')
-    k_weight = mx.sym.Variable('k_weight')
-    v_weight = mx.sym.Variable('v_weight')
-    q_bias = mx.sym.Variable('q_bias')
-    k_bias = mx.sym.Variable('k_bias')
-    v_bias = mx.sym.Variable('v_bias')
-    out_weight = mx.sym.Variable('out_weight')
-    out_bias = mx.sym.Variable('out_bias')
-
-    q = mx.sym.FullyConnected(qkv, weight=q_weight, bias=q_bias, flatten=False,
-                              num_hidden=qkv_units, no_bias=False)
-    k = mx.sym.FullyConnected(qkv, weight=k_weight, bias=k_bias, flatten=False,
-                              num_hidden=qkv_units, no_bias=False)
-    v = mx.sym.FullyConnected(qkv, weight=v_weight, bias=v_bias, flatten=False,
-                              num_hidden=qkv_units, no_bias=False)
-    q = mx.sym.reshape(q, shape=(0, 0, num_heads, -1))
-    q = mx.sym.transpose(q, axes=(0, 2, 1, 3))
-    q = mx.sym.reshape(q, shape=(-1, 0, 0), reverse=True)
-    k = mx.sym.reshape(k, shape=(0, 0, num_heads, -1))
-    k = mx.sym.transpose(k, axes=(0, 2, 1, 3))
-    k = mx.sym.reshape(k, shape=(-1, 0, 0), reverse=True)
-    q = mx.sym.contrib.div_sqrt_dim(q)
-    att_score = mx.sym.batch_dot(q, k, transpose_b=True)
-    att_score = att_score + sonde
-    v = mx.sym.reshape(v, shape=(0, 0, num_heads, -1))
-    v = mx.sym.transpose(v, axes=(0, 2, 1, 3))
-    v = mx.sym.reshape(v, shape=(-1, 0, 0), reverse=True)
-    weighted_value = mx.sym.batch_dot(att_score, v)
-    weighted_value = mx.sym.reshape(weighted_value, shape=(-1, num_heads, 0, 0),
-                                    reverse=True)
-    weighted_value = mx.sym.transpose(weighted_value, axes=(0, 2, 1, 3))
-    weighted_value = mx.sym.reshape(weighted_value, shape=(0, 0, -1))
-    output = mx.sym.FullyConnected(weighted_value, weight=out_weight, bias=out_bias, flatten=False,
-                                   num_hidden=out_dim, no_bias=False)
-    output = mx.sym.Group([output, att_score])
-    executor = output.simple_bind(ctx=mx.gpu(0),
-                                  qkv=(batch_size, qkv_length, qkv_dim),
-                                  type_dict={'qkv': dtype},
-                                  grad_req='write', force_rebind=True)
-    executor.copy_params_from(arg_params, {})
-    executor.arg_dict['sonde'][:] = 0.
-    executor.arg_dict['sonde'].wait_to_read()
-    executor.forward(is_train=True)
-    output_orig = executor.outputs[0].asnumpy()
-    att_score_orig = executor.outputs[1].asnumpy()
-    executor.backward([mx.nd.array(output_grads, dtype=dtype),
-                       mx.nd.zeros(att_score_orig.shape, dtype=dtype)])
-    grads_orig = {k : v.asnumpy() for k, v in executor.grad_dict.items()}
-    assert_allclose(att_score_orig, att_score_opti, rtol=1e-2, atol=1e-3)
-    assert_allclose(output_orig, output_opti, rtol=1e-2, atol=1e-3)
-
-    for k in grads_opti.keys():
-        assert(grads_orig[k].dtype == grads_opti[k].dtype)
-        assert(grads_orig[k].shape == grads_opti[k].shape)
-        assert_allclose(grads_orig[k], grads_opti[k], rtol=1e-2, atol=1e-3)
-
-@assert_raises_cuda_not_satisfied(min_version='9.1')
-def test_multihead_attention_selfatt():
-    for dtype in ['float16', 'float32']:
-        check_multihead_attention_selfatt(dtype=dtype)
-
-def check_multihead_attention_encdec(dtype):
-    def convert_weight(F, k_weight, v_weight, num_heads):
-        k_weight = F.reshape(k_weight, shape=(num_heads, -1, 0), reverse=True)
-        v_weight = F.reshape(v_weight, shape=(num_heads, -1, 0), reverse=True)
-        all_weights = F.concat(k_weight, v_weight, dim=-2)
-        all_weights = F.reshape(all_weights, shape=(-1, 0), reverse=True)
-        return all_weights
-
-    def convert_bias(F, k_bias, v_bias, num_heads):
-        k_bias = F.reshape(k_bias, shape=(num_heads, -1))
-        v_bias = F.reshape(v_bias, shape=(num_heads, -1))
-        all_bias = F.stack(k_bias, v_bias, axis=1)
-        all_bias = F.reshape(all_bias, shape=(-1,))
-        return all_bias
-
-    batch_size = 2
-    qkv_length = 7  # length of a sequence
-    qkv_dim = 9     # dimension of encoding
-    num_heads = 3   # number of attention head
-    head_dim = 5    # head size
-    out_dim = 13 * num_heads
-    qkv_units = num_heads * head_dim
-
-    arg_params = {
-        'q': mx.nd.array(np.random.rand(*(batch_size, qkv_length, qkv_dim)).astype(dtype) * 0.1, dtype=dtype),
-        'kv': mx.nd.array(np.random.rand(*(batch_size, qkv_length, qkv_dim)).astype(dtype) * 0.1, dtype=dtype),
-        'q_weight': mx.nd.array(np.random.rand(*(qkv_units, qkv_dim)).astype(dtype) * 0.1, dtype=dtype),
-        'k_weight': mx.nd.array(np.random.rand(*(qkv_units, qkv_dim)).astype(dtype) * 0.1, dtype=dtype),
-        'v_weight': mx.nd.array(np.random.rand(*(qkv_units, qkv_dim)).astype(dtype) * 0.1, dtype=dtype),
-        'q_bias': mx.nd.array(np.random.rand(*(qkv_units,)).astype(dtype) * 0.1, dtype=dtype),
-        'k_bias': mx.nd.array(np.random.rand(*(qkv_units,)).astype(dtype) * 0.1, dtype=dtype),
-        'v_bias': mx.nd.array(np.random.rand(*(qkv_units,)).astype(dtype) * 0.1, dtype=dtype),
-        'out_weight': mx.nd.array(np.random.rand(*(out_dim, qkv_units)).astype(dtype) * 0.1, dtype=dtype),
-        'out_bias': mx.nd.array(np.random.rand(*(out_dim,)).astype(dtype) * 0.1, dtype=dtype),
-        }
-
-    q = mx.sym.Variable('q')
-    kv = mx.sym.Variable('kv')
-    sonde = mx.sym.Variable('sonde')
-    q_weight = mx.sym.Variable('q_weight')
-    k_weight = mx.sym.Variable('k_weight')
-    v_weight = mx.sym.Variable('v_weight')
-    q_bias = mx.sym.Variable('q_bias')
-    k_bias = mx.sym.Variable('k_bias')
-    v_bias = mx.sym.Variable('v_bias')
-    out_weight = mx.sym.Variable('out_weight')
-    out_bias = mx.sym.Variable('out_bias')
-    kv_weight = convert_weight(mx.sym, k_weight, v_weight, num_heads)
-    kv_bias = convert_bias(mx.sym, k_bias, v_bias, num_heads)
-    kv = mx.sym.transpose(kv, axes=(1, 0, 2))
-    kv_proj = mx.sym.FullyConnected(kv, weight=kv_weight, bias=kv_bias, flatten=False,
-                                    num_hidden=qkv_units * 2, no_bias=False)
-    q = mx.sym.transpose(q, axes=(1, 0, 2))
-    q_proj = mx.sym.FullyConnected(q, weight=q_weight, bias=q_bias, flatten=False,
-                                   num_hidden=qkv_units, no_bias=False)
-    att_score = mx.sym.contrib.interleaved_matmul_encdec_qk(
-            q_proj, kv_proj, heads=num_heads) 
-    att_score = att_score + sonde
-    weighted_value = mx.sym.contrib.interleaved_matmul_encdec_valatt(
-            kv_proj, att_score, heads=num_heads)
-    output = mx.sym.FullyConnected(weighted_value, weight=out_weight, bias=out_bias, flatten=False,
-                                   num_hidden=out_dim, no_bias=False)
-    output = mx.sym.transpose(output, axes=(1, 0, 2))
-    output = mx.sym.Group([output, att_score])
-    executor = output.simple_bind(ctx=mx.gpu(0),
-                                  q=(batch_size, qkv_length, qkv_dim),
-                                  kv=(batch_size, qkv_length, qkv_dim),
-                                  q_weight=(qkv_units, qkv_dim),
-                                  q_bias=(qkv_units,),
-                                  k_weight=(qkv_units, qkv_dim),
-                                  k_bias=(qkv_units,),
-                                  v_weight=(qkv_units, qkv_dim),
-                                  v_bias=(qkv_units,),
-                                  out_weight=(out_dim, qkv_units),
-                                  out_bias=(out_dim,),
-                                  type_dict={'q': dtype,
-                                             'kv': dtype,
-                                             'q_weight': dtype,
-                                             'q_bias': dtype,
-                                             'k_weight': dtype,
-                                             'k_bias': dtype,
-                                             'v_weight': dtype,
-                                             'v_bias': dtype,
-                                             'out_weight': dtype,
-                                             'out_bias': dtype,
-                                              },
-                                  grad_req='write', force_rebind=True)
-    output_shape = executor.outputs[0].shape
-    output_grads = np.random.rand(*output_shape).astype(dtype) * 0.1
-    executor.copy_params_from(arg_params, {})
-    executor.arg_dict['sonde'][:] = 0.
-    executor.arg_dict['sonde'].wait_to_read()
-    executor.forward(is_train=True)
-    output_opti = executor.outputs[0].asnumpy()
-    att_score_opti = executor.outputs[1].asnumpy()
-    executor.backward([mx.nd.array(output_grads, dtype=dtype), mx.nd.zeros(att_score_opti.shape, dtype=dtype)])
-
-    grads_opti = {k: v.asnumpy() for k, v in executor.grad_dict.items()}
-
-    q = mx.sym.Variable('q')
-    kv = mx.sym.Variable('kv')
-    sonde = mx.sym.Variable('sonde')
-    q_weight = mx.sym.Variable('q_weight')
-    k_weight = mx.sym.Variable('k_weight')
-    v_weight = mx.sym.Variable('v_weight')
-    q_bias = mx.sym.Variable('q_bias')
-    k_bias = mx.sym.Variable('k_bias')
-    v_bias = mx.sym.Variable('v_bias')
-    out_weight = mx.sym.Variable('out_weight')
-    out_bias = mx.sym.Variable('out_bias')
-
-    q = mx.sym.FullyConnected(q, weight=q_weight, bias=q_bias, flatten=False,
-                              num_hidden=qkv_units, no_bias=False)
-    k = mx.sym.FullyConnected(kv, weight=k_weight, bias=k_bias, flatten=False,
-                              num_hidden=qkv_units, no_bias=False)
-    v = mx.sym.FullyConnected(kv, weight=v_weight, bias=v_bias, flatten=False,
-                              num_hidden=qkv_units, no_bias=False)
-    q = mx.sym.reshape(q, shape=(0, 0, num_heads, -1))
-    q = mx.sym.transpose(q, axes=(0, 2, 1, 3))
-    q = mx.sym.reshape(q, shape=(-1, 0, 0), reverse=True)
-    k = mx.sym.reshape(k, shape=(0, 0, num_heads, -1))
-    k = mx.sym.transpose(k, axes=(0, 2, 1, 3))
-    k = mx.sym.reshape(k, shape=(-1, 0, 0), reverse=True)
-    q = mx.sym.contrib.div_sqrt_dim(q)
-    att_score = mx.sym.batch_dot(q, k, transpose_b=True)
-    att_score = att_score + sonde
-    v = mx.sym.reshape(v, shape=(0, 0, num_heads, -1))
-    v = mx.sym.transpose(v, axes=(0, 2, 1, 3))
-    v = mx.sym.reshape(v, shape=(-1, 0, 0), reverse=True)
-    weighted_value = mx.sym.batch_dot(att_score, v)
-    weighted_value = mx.sym.reshape(weighted_value, shape=(-1, num_heads, 0, 0),
-                                    reverse=True)
-    weighted_value = mx.sym.transpose(weighted_value, axes=(0, 2, 1, 3))
-    weighted_value = mx.sym.reshape(weighted_value, shape=(0, 0, -1))
-    output = mx.sym.FullyConnected(weighted_value, weight=out_weight, bias=out_bias, flatten=False,
-                                   num_hidden=out_dim, no_bias=False)
-    output = mx.sym.Group([output, att_score])
-    executor = output.simple_bind(ctx=mx.gpu(0),
-                                  q=(batch_size, qkv_length, qkv_dim),
-                                  kv=(batch_size, qkv_length, qkv_dim),
-                                  type_dict={'q': dtype,
-                                             'kv': dtype},
-                                  grad_req='write', force_rebind=True)
-    executor.copy_params_from(arg_params, {})
-    executor.arg_dict['sonde'][:] = 0.
-    executor.arg_dict['sonde'].wait_to_read()
-    executor.forward(is_train=True)
-    output_orig = executor.outputs[0].asnumpy()
-    att_score_orig = executor.outputs[1].asnumpy()
-    executor.backward([mx.nd.array(output_grads, dtype=dtype), mx.nd.zeros(att_score_orig.shape, dtype=dtype)])
-    grads_orig = {k : v.asnumpy() for k, v in executor.grad_dict.items()}
-    assert_allclose(att_score_orig, att_score_opti, rtol=1e-2, atol=1e-3)
-    assert_allclose(output_orig, output_opti, rtol=1e-2, atol=1e-3)
-
-    for k in grads_opti.keys():
-        assert(grads_orig[k].dtype == grads_opti[k].dtype)
-        assert(grads_orig[k].shape == grads_opti[k].shape)
-        assert_allclose(grads_orig[k], grads_opti[k], rtol=1e-2, atol=1e-3)
-
-@assert_raises_cuda_not_satisfied(min_version='9.1')
-def test_multihead_attention_encdec():
-    for dtype in ['float16', 'float32']:
-        check_multihead_attention_encdec(dtype=dtype)
-
 if __name__ == '__main__':
     import nose
     nose.runmodule()
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index d59c3063f95a..72350de4d85f 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -9373,6 +9373,330 @@ def check_random_uniform():
             hight = 1
             assertRaises(MXNetError, mx.nd.random_uniform, alpha, beta, shape)
 
+def check_multihead_attention_selfatt(dtype):
+    def convert_weight(F, q_weight, k_weight, v_weight, num_heads):
+        q_weight = F.reshape(q_weight, shape=(num_heads, -1, 0), reverse=True)
+        k_weight = F.reshape(k_weight, shape=(num_heads, -1, 0), reverse=True)
+        v_weight = F.reshape(v_weight, shape=(num_heads, -1, 0), reverse=True)
+        all_weights = F.concat(q_weight, k_weight, v_weight, dim=-2)
+        all_weights = F.reshape(all_weights, shape=(-1, 0), reverse=True)
+        return all_weights
+
+    def convert_bias(F, q_bias, k_bias, v_bias, num_heads):
+        q_bias = F.reshape(q_bias, shape=(num_heads, -1))
+        k_bias = F.reshape(k_bias, shape=(num_heads, -1))
+        v_bias = F.reshape(v_bias, shape=(num_heads, -1))
+        all_bias = F.stack(q_bias, k_bias, v_bias, axis=1)
+        all_bias = F.reshape(all_bias, shape=(-1,))
+        return all_bias
+
+    batch_size = 2
+    qkv_length = 7  # length of a sequence
+    qkv_dim = 9     # dimension of encoding
+    num_heads = 3   # number of attention head
+    head_dim = 5    # head size
+    out_dim = 13 * num_heads
+    qkv_units = num_heads * head_dim
+
+    arg_params = {
+        'qkv': mx.nd.array(np.random.rand(*(batch_size, qkv_length, qkv_dim)).astype(dtype) * 0.1, dtype=dtype),
+        'q_weight': mx.nd.array(np.random.rand(*(qkv_units, qkv_dim)).astype(dtype) * 0.1, dtype=dtype),
+        'k_weight': mx.nd.array(np.random.rand(*(qkv_units, qkv_dim)).astype(dtype) * 0.1, dtype=dtype),
+        'v_weight': mx.nd.array(np.random.rand(*(qkv_units, qkv_dim)).astype(dtype) * 0.1, dtype=dtype),
+        'q_bias': mx.nd.array(np.random.rand(*(qkv_units,)).astype(dtype) * 0.1, dtype=dtype),
+        'k_bias': mx.nd.array(np.random.rand(*(qkv_units,)).astype(dtype) * 0.1, dtype=dtype),
+        'v_bias': mx.nd.array(np.random.rand(*(qkv_units,)).astype(dtype) * 0.1, dtype=dtype),
+        'out_weight': mx.nd.array(np.random.rand(*(out_dim, qkv_units)).astype(dtype) * 0.1, dtype=dtype),
+        'out_bias': mx.nd.array(np.random.rand(*(out_dim,)).astype(dtype) * 0.1, dtype=dtype),
+        }
+
+    qkv = mx.sym.Variable('qkv')
+    sonde = mx.sym.Variable('sonde')
+    q_weight = mx.sym.Variable('q_weight')
+    k_weight = mx.sym.Variable('k_weight')
+    v_weight = mx.sym.Variable('v_weight')
+    q_bias = mx.sym.Variable('q_bias')
+    k_bias = mx.sym.Variable('k_bias')
+    v_bias = mx.sym.Variable('v_bias')
+    out_weight = mx.sym.Variable('out_weight')
+    out_bias = mx.sym.Variable('out_bias')
+    qkv_weight = convert_weight(mx.sym, q_weight, k_weight, v_weight, num_heads)
+    qkv_bias = convert_bias(mx.sym, q_bias, k_bias, v_bias, num_heads)
+    qkv = mx.sym.transpose(qkv, axes=(1, 0, 2))
+    qkv_proj = mx.sym.FullyConnected(qkv, weight=qkv_weight, bias=qkv_bias, flatten=False,
+                                     num_hidden=qkv_units * 3, no_bias=False)
+    att_score = mx.sym.contrib.interleaved_matmul_selfatt_qk(
+            qkv_proj, heads=num_heads)
+    att_score = att_score + sonde
+    weighted_value = mx.sym.contrib.interleaved_matmul_selfatt_valatt(
+            qkv_proj, att_score, heads=num_heads)
+    output = mx.sym.FullyConnected(weighted_value, weight=out_weight, bias=out_bias, flatten=False,
+                                   num_hidden=out_dim, no_bias=False)
+    output = mx.sym.transpose(output, axes=(1, 0, 2))
+    output = mx.sym.Group([output, att_score])
+    executor = output.simple_bind(ctx=default_context(),
+                                  qkv=(batch_size, qkv_length, qkv_dim),
+                                  q_weight=(qkv_units, qkv_dim),
+                                  q_bias=(qkv_units,),
+                                  k_weight=(qkv_units, qkv_dim),
+                                  k_bias=(qkv_units,),
+                                  v_weight=(qkv_units, qkv_dim),
+                                  v_bias=(qkv_units,),
+                                  type_dict={'qkv': dtype,
+                                             'q_weight': dtype,
+                                             'k_weight': dtype,
+                                             'v_weight': dtype,
+                                             'q_bias': dtype,
+                                             'k_bias': dtype,
+                                             'v_bias': dtype,
+                                             'sonde': dtype},
+                                  grad_req='write', force_rebind=True)
+    output_shape = executor.outputs[0].shape
+    output_grads = np.random.rand(*output_shape).astype(dtype) * 0.1
+    executor.copy_params_from(arg_params, {})
+    executor.arg_dict['sonde'][:] = 0.
+    executor.arg_dict['sonde'].wait_to_read()
+    executor.forward(is_train=True)
+    output_opti = executor.outputs[0].asnumpy()
+    att_score_opti = executor.outputs[1].asnumpy()
+    executor.backward([mx.nd.array(output_grads, dtype=dtype),
+                       mx.nd.zeros(att_score_opti.shape, dtype=dtype)])
+    grads_opti = {k: v.asnumpy() for k, v in executor.grad_dict.items()}
+    qkv = mx.sym.Variable('qkv')
+    sonde = mx.sym.Variable('sonde')
+    q_weight = mx.sym.Variable('q_weight')
+    k_weight = mx.sym.Variable('k_weight')
+    v_weight = mx.sym.Variable('v_weight')
+    q_bias = mx.sym.Variable('q_bias')
+    k_bias = mx.sym.Variable('k_bias')
+    v_bias = mx.sym.Variable('v_bias')
+    out_weight = mx.sym.Variable('out_weight')
+    out_bias = mx.sym.Variable('out_bias')
+
+    q = mx.sym.FullyConnected(qkv, weight=q_weight, bias=q_bias, flatten=False,
+                              num_hidden=qkv_units, no_bias=False)
+    k = mx.sym.FullyConnected(qkv, weight=k_weight, bias=k_bias, flatten=False,
+                              num_hidden=qkv_units, no_bias=False)
+    v = mx.sym.FullyConnected(qkv, weight=v_weight, bias=v_bias, flatten=False,
+                              num_hidden=qkv_units, no_bias=False)
+    q = mx.sym.reshape(q, shape=(0, 0, num_heads, -1))
+    q = mx.sym.transpose(q, axes=(0, 2, 1, 3))
+    q = mx.sym.reshape(q, shape=(-1, 0, 0), reverse=True)
+    k = mx.sym.reshape(k, shape=(0, 0, num_heads, -1))
+    k = mx.sym.transpose(k, axes=(0, 2, 1, 3))
+    k = mx.sym.reshape(k, shape=(-1, 0, 0), reverse=True)
+    q = mx.sym.contrib.div_sqrt_dim(q)
+    att_score = mx.sym.batch_dot(q, k, transpose_b=True)
+    att_score = att_score + sonde
+    v = mx.sym.reshape(v, shape=(0, 0, num_heads, -1))
+    v = mx.sym.transpose(v, axes=(0, 2, 1, 3))
+    v = mx.sym.reshape(v, shape=(-1, 0, 0), reverse=True)
+    weighted_value = mx.sym.batch_dot(att_score, v)
+    weighted_value = mx.sym.reshape(weighted_value, shape=(-1, num_heads, 0, 0),
+                                    reverse=True)
+    weighted_value = mx.sym.transpose(weighted_value, axes=(0, 2, 1, 3))
+    weighted_value = mx.sym.reshape(weighted_value, shape=(0, 0, -1))
+    output = mx.sym.FullyConnected(weighted_value, weight=out_weight, bias=out_bias, flatten=False,
+                                   num_hidden=out_dim, no_bias=False)
+    output = mx.sym.Group([output, att_score])
+    executor = output.simple_bind(ctx=default_context(),
+                                  qkv=(batch_size, qkv_length, qkv_dim),
+                                  type_dict={'qkv': dtype},
+                                  grad_req='write', force_rebind=True)
+    executor.copy_params_from(arg_params, {})
+    executor.arg_dict['sonde'][:] = 0.
+    executor.arg_dict['sonde'].wait_to_read()
+    executor.forward(is_train=True)
+    output_orig = executor.outputs[0].asnumpy()
+    att_score_orig = executor.outputs[1].asnumpy()
+    executor.backward([mx.nd.array(output_grads, dtype=dtype),
+                       mx.nd.zeros(att_score_orig.shape, dtype=dtype)])
+    grads_orig = {k : v.asnumpy() for k, v in executor.grad_dict.items()}
+    assert_allclose(att_score_orig, att_score_opti, rtol=1e-2, atol=1e-3)
+    assert_allclose(output_orig, output_opti, rtol=1e-2, atol=1e-3)
+
+    for k in grads_opti.keys():
+        assert(grads_orig[k].dtype == grads_opti[k].dtype)
+        assert(grads_orig[k].shape == grads_opti[k].shape)
+        assert_allclose(grads_orig[k], grads_opti[k], rtol=1e-2, atol=1e-3)
+
+
+@with_seed()
+def test_multihead_attention_selfatt():
+    dtypes = ['float32']
+    if default_context().device_type == 'gpu':
+        dtypes += ['float16']
+
+    for dtype in dtypes:
+        check_multihead_attention_selfatt(dtype=dtype)
+
+def check_multihead_attention_encdec(dtype):
+    def convert_weight(F, k_weight, v_weight, num_heads):
+        k_weight = F.reshape(k_weight, shape=(num_heads, -1, 0), reverse=True)
+        v_weight = F.reshape(v_weight, shape=(num_heads, -1, 0), reverse=True)
+        all_weights = F.concat(k_weight, v_weight, dim=-2)
+        all_weights = F.reshape(all_weights, shape=(-1, 0), reverse=True)
+        return all_weights
+
+    def convert_bias(F, k_bias, v_bias, num_heads):
+        k_bias = F.reshape(k_bias, shape=(num_heads, -1))
+        v_bias = F.reshape(v_bias, shape=(num_heads, -1))
+        all_bias = F.stack(k_bias, v_bias, axis=1)
+        all_bias = F.reshape(all_bias, shape=(-1,))
+        return all_bias
+
+    batch_size = 2
+    qkv_length = 7  # length of a sequence
+    qkv_dim = 9     # dimension of encoding
+    num_heads = 3   # number of attention head
+    head_dim = 5    # head size
+    out_dim = 13 * num_heads
+    qkv_units = num_heads * head_dim
+
+    arg_params = {
+        'q': mx.nd.array(np.random.rand(*(batch_size, qkv_length, qkv_dim)).astype(dtype) * 0.1, dtype=dtype),
+        'kv': mx.nd.array(np.random.rand(*(batch_size, qkv_length, qkv_dim)).astype(dtype) * 0.1, dtype=dtype),
+        'q_weight': mx.nd.array(np.random.rand(*(qkv_units, qkv_dim)).astype(dtype) * 0.1, dtype=dtype),
+        'k_weight': mx.nd.array(np.random.rand(*(qkv_units, qkv_dim)).astype(dtype) * 0.1, dtype=dtype),
+        'v_weight': mx.nd.array(np.random.rand(*(qkv_units, qkv_dim)).astype(dtype) * 0.1, dtype=dtype),
+        'q_bias': mx.nd.array(np.random.rand(*(qkv_units,)).astype(dtype) * 0.1, dtype=dtype),
+        'k_bias': mx.nd.array(np.random.rand(*(qkv_units,)).astype(dtype) * 0.1, dtype=dtype),
+        'v_bias': mx.nd.array(np.random.rand(*(qkv_units,)).astype(dtype) * 0.1, dtype=dtype),
+        'out_weight': mx.nd.array(np.random.rand(*(out_dim, qkv_units)).astype(dtype) * 0.1, dtype=dtype),
+        'out_bias': mx.nd.array(np.random.rand(*(out_dim,)).astype(dtype) * 0.1, dtype=dtype),
+        }
+
+    q = mx.sym.Variable('q')
+    kv = mx.sym.Variable('kv')
+    sonde = mx.sym.Variable('sonde')
+    q_weight = mx.sym.Variable('q_weight')
+    k_weight = mx.sym.Variable('k_weight')
+    v_weight = mx.sym.Variable('v_weight')
+    q_bias = mx.sym.Variable('q_bias')
+    k_bias = mx.sym.Variable('k_bias')
+    v_bias = mx.sym.Variable('v_bias')
+    out_weight = mx.sym.Variable('out_weight')
+    out_bias = mx.sym.Variable('out_bias')
+    kv_weight = convert_weight(mx.sym, k_weight, v_weight, num_heads)
+    kv_bias = convert_bias(mx.sym, k_bias, v_bias, num_heads)
+    kv = mx.sym.transpose(kv, axes=(1, 0, 2))
+    kv_proj = mx.sym.FullyConnected(kv, weight=kv_weight, bias=kv_bias, flatten=False,
+                                    num_hidden=qkv_units * 2, no_bias=False)
+    q = mx.sym.transpose(q, axes=(1, 0, 2))
+    q_proj = mx.sym.FullyConnected(q, weight=q_weight, bias=q_bias, flatten=False,
+                                   num_hidden=qkv_units, no_bias=False)
+    att_score = mx.sym.contrib.interleaved_matmul_encdec_qk(
+            q_proj, kv_proj, heads=num_heads) 
+    att_score = att_score + sonde
+    weighted_value = mx.sym.contrib.interleaved_matmul_encdec_valatt(
+            kv_proj, att_score, heads=num_heads)
+    output = mx.sym.FullyConnected(weighted_value, weight=out_weight, bias=out_bias, flatten=False,
+                                   num_hidden=out_dim, no_bias=False)
+    output = mx.sym.transpose(output, axes=(1, 0, 2))
+    output = mx.sym.Group([output, att_score])
+    executor = output.simple_bind(ctx=default_context(),
+                                  q=(batch_size, qkv_length, qkv_dim),
+                                  kv=(batch_size, qkv_length, qkv_dim),
+                                  q_weight=(qkv_units, qkv_dim),
+                                  q_bias=(qkv_units,),
+                                  k_weight=(qkv_units, qkv_dim),
+                                  k_bias=(qkv_units,),
+                                  v_weight=(qkv_units, qkv_dim),
+                                  v_bias=(qkv_units,),
+                                  out_weight=(out_dim, qkv_units),
+                                  out_bias=(out_dim,),
+                                  type_dict={'q': dtype,
+                                             'kv': dtype,
+                                             'q_weight': dtype,
+                                             'q_bias': dtype,
+                                             'k_weight': dtype,
+                                             'k_bias': dtype,
+                                             'v_weight': dtype,
+                                             'v_bias': dtype,
+                                             'out_weight': dtype,
+                                             'out_bias': dtype,
+                                              },
+                                  grad_req='write', force_rebind=True)
+    output_shape = executor.outputs[0].shape
+    output_grads = np.random.rand(*output_shape).astype(dtype) * 0.1
+    executor.copy_params_from(arg_params, {})
+    executor.arg_dict['sonde'][:] = 0.
+    executor.arg_dict['sonde'].wait_to_read()
+    executor.forward(is_train=True)
+    output_opti = executor.outputs[0].asnumpy()
+    att_score_opti = executor.outputs[1].asnumpy()
+    executor.backward([mx.nd.array(output_grads, dtype=dtype), mx.nd.zeros(att_score_opti.shape, dtype=dtype)])
+
+    grads_opti = {k: v.asnumpy() for k, v in executor.grad_dict.items()}
+
+    q = mx.sym.Variable('q')
+    kv = mx.sym.Variable('kv')
+    sonde = mx.sym.Variable('sonde')
+    q_weight = mx.sym.Variable('q_weight')
+    k_weight = mx.sym.Variable('k_weight')
+    v_weight = mx.sym.Variable('v_weight')
+    q_bias = mx.sym.Variable('q_bias')
+    k_bias = mx.sym.Variable('k_bias')
+    v_bias = mx.sym.Variable('v_bias')
+    out_weight = mx.sym.Variable('out_weight')
+    out_bias = mx.sym.Variable('out_bias')
+
+    q = mx.sym.FullyConnected(q, weight=q_weight, bias=q_bias, flatten=False,
+                              num_hidden=qkv_units, no_bias=False)
+    k = mx.sym.FullyConnected(kv, weight=k_weight, bias=k_bias, flatten=False,
+                              num_hidden=qkv_units, no_bias=False)
+    v = mx.sym.FullyConnected(kv, weight=v_weight, bias=v_bias, flatten=False,
+                              num_hidden=qkv_units, no_bias=False)
+    q = mx.sym.reshape(q, shape=(0, 0, num_heads, -1))
+    q = mx.sym.transpose(q, axes=(0, 2, 1, 3))
+    q = mx.sym.reshape(q, shape=(-1, 0, 0), reverse=True)
+    k = mx.sym.reshape(k, shape=(0, 0, num_heads, -1))
+    k = mx.sym.transpose(k, axes=(0, 2, 1, 3))
+    k = mx.sym.reshape(k, shape=(-1, 0, 0), reverse=True)
+    q = mx.sym.contrib.div_sqrt_dim(q)
+    att_score = mx.sym.batch_dot(q, k, transpose_b=True)
+    att_score = att_score + sonde
+    v = mx.sym.reshape(v, shape=(0, 0, num_heads, -1))
+    v = mx.sym.transpose(v, axes=(0, 2, 1, 3))
+    v = mx.sym.reshape(v, shape=(-1, 0, 0), reverse=True)
+    weighted_value = mx.sym.batch_dot(att_score, v)
+    weighted_value = mx.sym.reshape(weighted_value, shape=(-1, num_heads, 0, 0),
+                                    reverse=True)
+    weighted_value = mx.sym.transpose(weighted_value, axes=(0, 2, 1, 3))
+    weighted_value = mx.sym.reshape(weighted_value, shape=(0, 0, -1))
+    output = mx.sym.FullyConnected(weighted_value, weight=out_weight, bias=out_bias, flatten=False,
+                                   num_hidden=out_dim, no_bias=False)
+    output = mx.sym.Group([output, att_score])
+    executor = output.simple_bind(ctx=default_context(),
+                                  q=(batch_size, qkv_length, qkv_dim),
+                                  kv=(batch_size, qkv_length, qkv_dim),
+                                  type_dict={'q': dtype,
+                                             'kv': dtype},
+                                  grad_req='write', force_rebind=True)
+    executor.copy_params_from(arg_params, {})
+    executor.arg_dict['sonde'][:] = 0.
+    executor.arg_dict['sonde'].wait_to_read()
+    executor.forward(is_train=True)
+    output_orig = executor.outputs[0].asnumpy()
+    att_score_orig = executor.outputs[1].asnumpy()
+    executor.backward([mx.nd.array(output_grads, dtype=dtype), mx.nd.zeros(att_score_orig.shape, dtype=dtype)])
+    grads_orig = {k : v.asnumpy() for k, v in executor.grad_dict.items()}
+    assert_allclose(att_score_orig, att_score_opti, rtol=1e-2, atol=1e-3)
+    assert_allclose(output_orig, output_opti, rtol=1e-2, atol=1e-3)
+
+    for k in grads_opti.keys():
+        assert(grads_orig[k].dtype == grads_opti[k].dtype)
+        assert(grads_orig[k].shape == grads_opti[k].shape)
+        assert_allclose(grads_orig[k], grads_opti[k], rtol=1e-2, atol=1e-3)
+
+@with_seed()
+def test_multihead_attention_encdec():
+    dtypes = ['float32']
+    if default_context().device_type == 'gpu':
+        dtypes += ['float16']
+
+    for dtype in dtypes:
+        check_multihead_attention_encdec(dtype=dtype)
 
 @with_seed()
 def test_im2col_col2im():

From 4ea9aefcd6a414a26a3a20397563fc3a35d69c35 Mon Sep 17 00:00:00 2001
From: Serge Panev <spanev@nvidia.com>
Date: Sat, 4 Jan 2020 09:54:36 +0100
Subject: [PATCH 765/813] Fix BoxNMS GPU workspace size (#17190)

* Fix BoxNMS GPU workspace size

Signed-off-by: Serge Panev <spanev@nvidia.com>

* Fix workspace.scores type

Signed-off-by: Serge Panev <spanev@nvidia.com>
---
 src/operator/contrib/bounding_box.cu | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/operator/contrib/bounding_box.cu b/src/operator/contrib/bounding_box.cu
index ffc48d7d5a44..a3d91841646c 100644
--- a/src/operator/contrib/bounding_box.cu
+++ b/src/operator/contrib/bounding_box.cu
@@ -535,11 +535,11 @@ TempWorkspace<DType> GetWorkspace(const index_t num_batch,
                                  workspace.indices_temp_spaces;
 
   // Obtain the memory for workspace
-  Tensor<gpu, 1, DType> scratch_memory = ctx.requested[box_nms_enum::kTempSpace]
-    .get_space_typed<gpu, 1, DType>(mshadow::Shape1(workspace_size), s);
+  Tensor<gpu, 1, uint8_t> scratch_memory = ctx.requested[box_nms_enum::kTempSpace]
+    .get_space_typed<gpu, 1, uint8_t>(mshadow::Shape1(workspace_size), s);
 
   // Populate workspace pointers
-  workspace.scores = scratch_memory.dptr_;
+  workspace.scores = reinterpret_cast<DType*>(scratch_memory.dptr_);
   workspace.scratch = reinterpret_cast<uint8_t*>(workspace.scores) +
                                                  workspace.scores_temp_space;
   workspace.buffer = reinterpret_cast<DType*>(workspace.scratch +

From 3e638b4fc4de959c7afc614facc336a796347c9d Mon Sep 17 00:00:00 2001
From: Xinyu Chen <xinyu1.chen@intel.com>
Date: Sun, 5 Jan 2020 08:11:54 +0800
Subject: [PATCH 766/813] minor fix for ncf readme (#17191)

---
 example/neural_collaborative_filtering/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/example/neural_collaborative_filtering/README.md b/example/neural_collaborative_filtering/README.md
index 00d3ed12295b..4b16d20a8217 100644
--- a/example/neural_collaborative_filtering/README.md
+++ b/example/neural_collaborative_filtering/README.md
@@ -34,7 +34,7 @@ Code Reference: https://github.com/hexiangnan/neural_collaborative_filtering
 We provide the processed datasets on [Google Drive](https://drive.google.com/drive/folders/1qACR_Zhc2O2W0RrazzcepM2vJeh0MMdO?usp=sharing): MovieLens 20 Million (ml-20m), you can download directly or 
 run the script to prepare the datasets:
 ```
-python convert.py ./data/
+python convert.py
 ```
 
 train-ratings.csv

From 83a23b0988b55c1483f5ecec941207357492bcff Mon Sep 17 00:00:00 2001
From: Zixuan Wei <zixuan.wei@intel.com>
Date: Mon, 6 Jan 2020 21:20:20 +0800
Subject: [PATCH 767/813] MKL-DNN RNN backward path enhancement (#17183)

* Flush memory before RNN backward primitive

* Add gluon rnn unit test for gradients check

* Cache reorder

* Re-write rnn supporting check

* Update OpSignature.AddSign to avoid potential hash collision for
rnn-packed memory

Get the data type from mkldnn memory descriptor when setting grad handle
---
 src/operator/nn/mkldnn/mkldnn_base-inl.h |   9 +-
 src/operator/nn/mkldnn/mkldnn_rnn-inl.h  |   9 +-
 src/operator/nn/mkldnn/mkldnn_rnn.cc     | 349 +++++++++++------------
 src/operator/operator_common.h           |  18 ++
 src/operator/rnn.cc                      |   6 +-
 tests/python/unittest/test_gluon_rnn.py  | 124 ++++++++
 6 files changed, 322 insertions(+), 193 deletions(-)

diff --git a/src/operator/nn/mkldnn/mkldnn_base-inl.h b/src/operator/nn/mkldnn/mkldnn_base-inl.h
index da9b8166a849..ad935556065b 100644
--- a/src/operator/nn/mkldnn/mkldnn_base-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_base-inl.h
@@ -132,9 +132,12 @@ static inline bool SupportMKLDNN(int dtype, const mxnet::TShape &shape) {
   return dtype == mshadow::kFloat32 && (ndim == 1 || ndim == 2 || ndim == 4);
 }
 
-static inline bool SupportMKLDNNRNN(const NDArray &input) {
-  int ndim = input.shape().ndim();
-  return (input.dtype() == mshadow::kFloat32) && (ndim == 3);
+static inline bool SupportMKLDNNRnn(const NDArray &input) {
+  if (input.dtype() == mshadow::kFloat32 && input.shape().ndim() == 3
+      && dmlc::GetEnv("MXNET_USE_MKLDNN_RNN", 1)) {
+    return true;
+  }
+  return false;
 }
 
 static inline bool SupportMKLDNNQuantize(int dtype) {
diff --git a/src/operator/nn/mkldnn/mkldnn_rnn-inl.h b/src/operator/nn/mkldnn/mkldnn_rnn-inl.h
index 314106b98eb9..a4104bf1a437 100644
--- a/src/operator/nn/mkldnn/mkldnn_rnn-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_rnn-inl.h
@@ -369,9 +369,10 @@ class MKLDNNRnnBackward {
   void SetDataGradsMem(void* diff_src, void* diff_state, void* diff_statecell,
                        void* diff_out, void* diff_state_out, void* diff_statecell_out,
                        const int dtype = mshadow::kFloat32);
-  void CommitWeightsDiff(void* diff_weights, void* diff_bias,
-                         const OpReqType req,
-                         const int dtype = mshadow::kFloat32);
+  void SetNativeWeightsGrads() const;
+  void CommitWeightsGrads(void* diff_weights, void* diff_bias,
+                          const OpReqType req,
+                          const int dtype = mshadow::kFloat32);
 
   const mkldnn::primitive& GetBwd() const { return *bwd_.primitive_; }
   const mkldnn_args_map_t& GetArgsMap() const { return net_args_; }
@@ -386,6 +387,8 @@ class MKLDNNRnnBackward {
 
   mkldnn_shared_mem_t diff_weights_layer_;
   mkldnn_shared_mem_t diff_weights_iter_;
+  mkldnn_shared_mem_t diff_weights_layer_r_;
+  mkldnn_shared_mem_t diff_weights_iter_r_;
   mkldnn_shared_mem_t diff_bias_;
 
   mkldnn_args_map_t net_args_;
diff --git a/src/operator/nn/mkldnn/mkldnn_rnn.cc b/src/operator/nn/mkldnn/mkldnn_rnn.cc
index 6da8f3b8a58a..8af0e997483e 100644
--- a/src/operator/nn/mkldnn/mkldnn_rnn.cc
+++ b/src/operator/nn/mkldnn/mkldnn_rnn.cc
@@ -364,18 +364,38 @@ void MKLDNNRnnForward::SetNewDataMem(void* x, void* hx, void* cx,
   }
 }
 
+inline void MKLDNNMemoryReorder(const mkldnn::memory& src,
+                                const mkldnn::memory& dst) {
+#if DMLC_CXX11_THREAD_LOCAL
+  static thread_local std::unordered_map<OpSignature,
+      mkldnn::reorder, OpHash> reorderPrimitives;
+#else
+  static MX_THREAD_LOCAL std::unordered_map<OpSignature,
+      mkldnn::reorder, OpHash> reorderPrimitives;
+#endif
+  OpSignature key{};
+  key.AddSign(src);
+  key.AddSign(dst);
+
+  auto it = reorderPrimitives.find(key);
+  if (it == reorderPrimitives.end()) {
+    auto reorder = mkldnn::reorder(src, dst);
+    it = AddToCache(&reorderPrimitives, key, reorder);
+  }
+
+  mkldnn_args_map_t net_args;
+  net_args.emplace(MKLDNN_ARG_SRC, src);
+  net_args.emplace(MKLDNN_ARG_DST, dst);
+  MKLDNNStream::Get()->RegisterPrimArgs(it->second, net_args);
+}
+
 /*
  * Reorder the concatenated weights memory to a efficient memory block
  * with primitive-prefered format.
  */
 void MKLDNNRnnForward::ReorderWeights() {
-  auto& cpu_engine = CpuEngine::Get()->get_engine();
-  mkldnn::stream s(cpu_engine);
-  mkldnn::reorder(*weights_layer_r_, *weights_layer_)
-      .execute(s, *weights_layer_r_, *weights_layer_);
-  mkldnn::reorder(*weights_iter_r_, *weights_iter_)
-      .execute(s, *weights_iter_r_, *weights_iter_);
-  s.wait();
+  MKLDNNMemoryReorder(*weights_layer_r_, *weights_layer_);
+  MKLDNNMemoryReorder(*weights_iter_r_, *weights_iter_);
 }
 
 void AdjustGruGateOrder(char* weight,
@@ -394,7 +414,7 @@ void AdjustGruGateOrder(char* weight,
  * Fuse uni-directional bias among single layer.
  */
 template <typename DType>
-void FuseBias(DType* fuse_bias, DType* naive_bias,
+void FuseBias(DType* fuse_bias, DType* native_bias,
               const int mode, const size_t state_size) {
   const size_t ngates = GetRnnGatesNum(mode);
   const int omp_threads = mxnet::engine::OpenMP::Get()->GetRecommendedOMPThreadCount();
@@ -403,8 +423,8 @@ void FuseBias(DType* fuse_bias, DType* naive_bias,
   // OpenMP 'for' statement.
   const int state_size_ = static_cast<int>(state_size);
   const int single_b_sz = static_cast<int>(nbias * state_size);
-  DType* bx = naive_bias;
-  DType* bh = naive_bias + state_size * ngates;
+  DType* bx = native_bias;
+  DType* bh = native_bias + state_size * ngates;
   if (mode == rnn_enum::kGru) {
     // While mxnet gru gate order is reset, update and new gates,
     // mkldnn gru gate order is update, reset and new gates. So
@@ -528,12 +548,6 @@ void MKLDNNRnnForward::SetWeightsMem(MKLDNNRnnMemMgr* mgr, void *w_ptr, void *b_
       }
     }
   }
-  // Reorder after adjustment only when is_train == false. When is_train == true, i.e.
-  // in forward training path, we use plain memory (ldxxx) as the space for weights and
-  // their gradients. Then, forward training primitives could fetch them from the scope
-  // of forward inference. And from there, we don't need to reorder the plain memory to
-  // the optimal rnn-packed memory for forward inference.
-  ReorderWeights();
 
   // Process bias
   MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
@@ -553,7 +567,15 @@ void MKLDNNRnnForward::SetWeightsMem(MKLDNNRnnMemMgr* mgr, void *w_ptr, void *b_
   EmplaceNetArgs(&this->net_args_, MKLDNN_ARG_WEIGHTS_ITER,  this->weights_iter_);
   EmplaceNetArgs(&this->net_args_, MKLDNN_ARG_BIAS,          this->bias_);
 
-  initialized_ = true;
+  if (!is_train) {
+    // Reorder after adjustment only when is_train == false. When is_train == true, i.e.
+    // in forward training path, we use plain memory (ldxxx) as the space for weights and
+    // their gradients. Then, forward training primitives could fetch them from the scope
+    // of forward inference. And from there, we don't need to reorder the plain memory to
+    // the optimal rnn-packed memory for forward inference.
+    ReorderWeights();
+    initialized_ = true;
+  }
 }
 
 void MKLDNNRnnForwardTraining::SetTrnMem(const MKLDNNRnnForward& fwd) {
@@ -572,17 +594,14 @@ void MKLDNNRnnForwardTraining::SetTrnMem(const MKLDNNRnnForward& fwd) {
   if (fwd.weights_layer_r_->get_desc() == fwd_trn_.GetLayerDesc()) {
     weights_layer_->set_data_handle(fwd.weights_layer_r_->get_data_handle());
   } else {
-    mkldnn::reorder(*fwd.weights_layer_r_, *weights_layer_)
-        .execute(s, *fwd.weights_layer_r_, *weights_layer_);
+    MKLDNNMemoryReorder(*fwd.weights_layer_r_, *weights_layer_);
   }
 
   if (fwd.weights_iter_r_->get_desc() == fwd_trn_.GetIterDesc()) {
     weights_iter_->set_data_handle(fwd.weights_iter_r_->get_data_handle());
   } else {
-    mkldnn::reorder(*fwd.weights_iter_r_, *weights_iter_)
-        .execute(s, *fwd.weights_iter_r_, *weights_iter_);
+    MKLDNNMemoryReorder(*fwd.weights_iter_r_, *weights_iter_);
   }
-  s.wait();
 
   // bias are always in format_tag::ldgo
   this->bias_ = fwd.bias_;
@@ -687,18 +706,17 @@ void MKLDNNRnnOp::Init(const OpContext &ctx,
         {fwd->GetParam().dst_dims, get_mkldnn_type(data_dtype), format_tag::tnc}));
   }
 
-  initialized_ = true;
+  if (!is_training) initialized_ = true;
 }
 
 void MKLDNNRnnBackward::FetchDataWeightsMem(const MKLDNNRnnForwardTraining& fwd) {
   using memory = mkldnn::memory;
   auto& cpu_engine = CpuEngine::Get()->get_engine();
-  auto s = mkldnn::stream(cpu_engine);
 
-  if (this->weights_layer_ == nullptr)
+  if (this->weights_layer_ == nullptr || this-> weights_iter_ == nullptr) {
     this->weights_layer_ = mkldnn_shared_mem_t(new memory(bwd_.weights_layer_desc_, cpu_engine));
-  if (this->weights_iter_ == nullptr)
     this->weights_iter_ = mkldnn_shared_mem_t(new memory(bwd_.weights_iter_desc_, cpu_engine));
+  }
 
   for (auto& kv : fwd.net_args_) {
     const mkldnn::memory* valid_mem;
@@ -707,17 +725,15 @@ void MKLDNNRnnBackward::FetchDataWeightsMem(const MKLDNNRnnForwardTraining& fwd)
         if (bwd_.weights_layer_desc_ == fwd.fwd_trn_.GetLayerDesc()) {
           this->weights_layer_->set_data_handle(kv.second.get_data_handle());
         } else {
-          mkldnn::reorder(*fwd.weights_layer_, *this->weights_layer_)
-              .execute(s, *fwd.weights_layer_, *this->weights_layer_);
+          MKLDNNMemoryReorder(*fwd.weights_layer_, *this->weights_layer_);
         }
         valid_mem = this->weights_layer_.get();
       } break;
       case MKLDNN_ARG_WEIGHTS_ITER: {
-        if (bwd_.weights_iter_desc_ == fwd.fwd_trn_.GetLayerDesc()) {
+        if (bwd_.weights_iter_desc_ == fwd.fwd_trn_.GetIterDesc()) {
           this->weights_iter_->set_data_handle(kv.second.get_data_handle());
         } else {
-          mkldnn::reorder(*fwd.weights_iter_, *this->weights_iter_)
-              .execute(s, *fwd.weights_iter_, *this->weights_iter_);
+          MKLDNNMemoryReorder(*fwd.weights_iter_, *this->weights_iter_);
         }
         valid_mem = this->weights_iter_.get();
       } break;
@@ -727,20 +743,50 @@ void MKLDNNRnnBackward::FetchDataWeightsMem(const MKLDNNRnnForwardTraining& fwd)
     }
     EmplaceNetArgs(&this->net_args_, kv.first, valid_mem);
   }
-  s.wait();
 }
 
 void MKLDNNRnnBackward::SetWeightsGradsMem() {
-  auto& cpu_engine = CpuEngine::Get()->get_engine();
-  if (this->diff_weights_layer_ == nullptr)
-    this->diff_weights_layer_ = std::make_shared<mkldnn::memory>(
-        bwd_.diff_weights_layer_desc_, cpu_engine);
-  if (this->diff_weights_iter_ == nullptr)
-    this->diff_weights_iter_ = std::make_shared<mkldnn::memory>(
-        bwd_.diff_weights_iter_desc_, cpu_engine);
-  if (this->diff_bias_ == nullptr)
+  using tag = mkldnn::memory::format_tag;
+
+  if (this->diff_weights_layer_ == nullptr
+      || this->diff_weights_iter_ == nullptr
+      || this->diff_bias_ == nullptr) {
+    const auto& cpu_engine = CpuEngine::Get()->get_engine();
+    const MKLDNNRnnLayerParam& param = fwd_ptr_->GetParam();
+    const auto mkldnn_type = static_cast<mkldnn::memory::data_type>(
+        bwd_.diff_weights_layer_desc_.data.data_type);
+
+    auto native_layer_desc = mkldnn::memory::desc(param.weight_layer_dims, mkldnn_type, tag::ldgoi);
+    auto native_iter_desc = mkldnn::memory::desc(param.weight_iter_dims, mkldnn_type, tag::ldgoi);
+
+    this->diff_weights_layer_r_ = std::make_shared<mkldnn::memory>(
+        native_layer_desc, cpu_engine);
+    this->diff_weights_iter_r_ = std::make_shared<mkldnn::memory>(
+        native_iter_desc, cpu_engine);
+
+    if (native_layer_desc == bwd_.diff_weights_layer_desc_) {
+      this->diff_weights_layer_ = std::make_shared<mkldnn::memory>(
+          bwd_.diff_weights_layer_desc_, cpu_engine, diff_weights_layer_r_->get_data_handle());
+    } else {
+      this->diff_weights_layer_ = std::make_shared<mkldnn::memory>(
+          bwd_.diff_weights_layer_desc_, cpu_engine);
+    }
+    if (native_iter_desc == bwd_.diff_weights_iter_desc_) {
+      this->diff_weights_iter_ = std::make_shared<mkldnn::memory>(
+          bwd_.diff_weights_iter_desc_, cpu_engine, diff_weights_iter_r_->get_data_handle());
+    } else {
+      this->diff_weights_iter_ = std::make_shared<mkldnn::memory>(
+          bwd_.diff_weights_iter_desc_, cpu_engine);
+    }
     this->diff_bias_ = std::make_shared<mkldnn::memory>(
         bwd_.diff_bias_desc_, cpu_engine);
+  }
+  std::memset(this->diff_weights_layer_->get_data_handle(), 0,
+      bwd_.diff_weights_layer_desc_.get_size());
+  std::memset(this->diff_weights_iter_->get_data_handle(), 0,
+      bwd_.diff_weights_iter_desc_.get_size());
+  std::memset(this->diff_bias_->get_data_handle(), 0,
+      bwd_.diff_bias_desc_.get_size());
   EmplaceNetArgs(&this->net_args_, MKLDNN_ARG_DIFF_WEIGHTS_LAYER,
       this->diff_weights_layer_.get());
   EmplaceNetArgs(&this->net_args_, MKLDNN_ARG_DIFF_WEIGHTS_ITER,
@@ -776,23 +822,40 @@ void MKLDNNRnnBackward::SetDataGradsMem(
   }
 }
 
-void MKLDNNRnnBackward::CommitWeightsDiff(void* diff_weights, void* diff_bias,
-                                          const OpReqType req, const int dtype) {
-  using tag = mkldnn::memory::format_tag;
-  auto& cpu_engine = CpuEngine::Get()->get_engine();
-  auto s = mkldnn::stream(cpu_engine);
+void MKLDNNRnnBackward::SetNativeWeightsGrads() const {
+  if (this->diff_weights_layer_->get_desc() != this->diff_weights_layer_r_->get_desc()) {
+    MKLDNNMemoryReorder(*this->diff_weights_layer_, *this->diff_weights_layer_r_);
+  }
+  if (this->diff_weights_iter_->get_desc() != this->diff_weights_iter_r_->get_desc()) {
+    MKLDNNMemoryReorder(*this->diff_weights_iter_, *this->diff_weights_iter_r_);
+  }
+}
 
+#define OPREQTYPE_SWITCH(ReqType, DType, FWrapper, ...)                     \
+std::function<void(DType*, DType*, size_t)> FWrapper = nullptr;             \
+if (kWriteTo == ReqType || kWriteInplace == ReqType)                        \
+  FWrapper = common::ParallelCopy<DType>;                                   \
+else                                                                        \
+  FWrapper = common::ParallelAdd<DType>;                                    \
+{__VA_ARGS__}
+
+void MKLDNNRnnBackward::CommitWeightsGrads(void* diff_weights, void* diff_bias,
+                                           const OpReqType req, const int dtype) {
   const MKLDNNRnnLayerParam& param = fwd_ptr_->GetParam();
+
+  void* diff_weights_layer_ptr = this->diff_weights_layer_->get_data_handle();
+  void* diff_weights_iter_ptr = this->diff_weights_iter_->get_data_handle();
+  if (this->diff_weights_layer_->get_desc() != this->diff_weights_layer_r_->get_desc())
+    diff_weights_layer_ptr = this->diff_weights_layer_r_->get_data_handle();
+  if (this->diff_weights_iter_->get_desc() != this->diff_weights_iter_r_->get_desc())
+    diff_weights_iter_ptr = this->diff_weights_iter_r_->get_data_handle();
+
   const int num_layer = param.num_layer;
   const int direction = param.bidirectional ? 2 : 1;
   const int ngates = GetRnnGatesNum(param.mode);
-  const size_t dtype_bytes = mshadow::mshadow_sizeof(dtype);
   const size_t wxh_size = param.single_w_size;
   const size_t wx_size = param.input_size * param.state_size * ngates;
   const size_t wh_size = param.state_size * param.state_size * ngates;
-  const size_t wxh_bytes = param.single_w_size * dtype_bytes;
-  const size_t wx_bytes = param.input_size * param.state_size * ngates * dtype_bytes;
-  const size_t wh_bytes = param.state_size * param.state_size * ngates * dtype_bytes;
 
   /* naive weights layout is:
           1st-layer: | wx_lr  | wh_lr  | wx_rl | wh_rl |
@@ -800,162 +863,81 @@ void MKLDNNRnnBackward::CommitWeightsDiff(void* diff_weights, void* diff_bias,
   size:              |    wxh_bytes    |
                      |wx_bytes|wh_bytes|      
   */
-  if (kWriteTo == req) {
-    char* naive_weights = static_cast<char *>(diff_weights);
-    char* diff_wx_ptr = static_cast<char *>(diff_weights_layer_->get_data_handle());
-    char* diff_wh_ptr = static_cast<char *>(diff_weights_iter_->get_data_handle());
-    if (param.mode != rnn_enum::kGru) {
-      for (int shift = 0; shift < num_layer * direction; ++shift) {
-        std::memcpy(naive_weights + shift * wxh_bytes,
-            diff_wx_ptr + shift * wx_bytes, wx_bytes);
-      }
-      // align naive_weights to weights_iter memory
-      naive_weights += wx_bytes;
-      for (int shift = 0; shift < num_layer * direction; ++shift) {
-        std::memcpy(naive_weights + shift * wxh_bytes,
-            diff_wh_ptr + shift * wh_bytes, wh_bytes);
-      }
-    } else {
-      const size_t wx_bytes_per_gate = param.input_size * param.state_size * dtype_bytes;
-      const size_t wh_bytes_per_gate = param.state_size * param.state_size * dtype_bytes;
-      for (int shift = 0; shift < num_layer * direction; ++shift) {
-        std::memcpy(naive_weights + shift * wxh_bytes + wx_bytes_per_gate,
-            diff_wx_ptr + shift * wx_bytes, wx_bytes_per_gate);
-        std::memcpy(naive_weights + shift * wxh_bytes,
-            diff_wx_ptr + shift * wx_bytes + wx_bytes_per_gate, wx_bytes_per_gate);
-        std::memcpy(naive_weights + shift * wxh_bytes + 2 * wx_bytes_per_gate,
-            diff_wx_ptr + shift * wx_bytes + 2 * wx_bytes_per_gate, wx_bytes_per_gate);
-      }
-      // align naive_weights to weights_iter memory
-      naive_weights += wx_bytes;
-      for (int shift = 0; shift < num_layer * direction; ++shift) {
-        std::memcpy(naive_weights + shift * wxh_bytes + wh_bytes_per_gate,
-            diff_wh_ptr + shift * wh_bytes, wh_bytes_per_gate);
-        std::memcpy(naive_weights + shift * wxh_bytes,
-            diff_wh_ptr + shift * wh_bytes + wh_bytes_per_gate, wh_bytes_per_gate);
-        std::memcpy(naive_weights + shift * wxh_bytes + 2 * wh_bytes_per_gate,
-            diff_wh_ptr + shift * wh_bytes + 2 * wh_bytes_per_gate, wh_bytes_per_gate);
-      }
-    }
-  } else if (kAddTo == req) {
-    if (param.mode != rnn_enum::kGru) {
-      MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
-        DType* naive_weights = static_cast<DType *>(diff_weights);
-        DType* diff_wx_ptr = static_cast<DType *>(diff_weights_layer_->get_data_handle());
-        DType* diff_wh_ptr = static_cast<DType *>(diff_weights_iter_->get_data_handle());
+  MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
+    DType* native_weights = static_cast<DType *>(diff_weights);
+    DType* diff_wx_ptr = static_cast<DType *>(diff_weights_layer_ptr);
+    DType* diff_wh_ptr = static_cast<DType *>(diff_weights_iter_ptr);
+    OPREQTYPE_SWITCH(req, DType, FAccGrad, {
+      if (param.mode != rnn_enum::kGru) {
         for (int shift = 0; shift < num_layer * direction; ++shift) {
-          common::ParallelAdd(naive_weights + shift * wxh_size,
-              diff_wx_ptr + shift * wx_size, wx_size);
+          FAccGrad(native_weights + shift * wxh_size, diff_wx_ptr + shift * wx_size, wx_size);
         }
-        // align naive_weights to weights_iter memory
-        naive_weights += wx_size;
+        // align native_weights to weights_iter memory
+        native_weights += wx_size;
         for (int shift = 0; shift < num_layer * direction; ++shift) {
-          common::ParallelAdd(naive_weights + shift * wxh_size,
-              diff_wh_ptr + shift * wh_size, wh_size);
+          FAccGrad(native_weights + shift * wxh_size, diff_wh_ptr + shift * wh_size, wh_size);
         }
-      });
-    } else {
-      const size_t wx_size_per_gate = param.input_size * param.state_size;
-      const size_t wh_size_per_gate = param.state_size * param.state_size;
-      MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
-        DType* naive_weights = static_cast<DType *>(diff_weights);
-        DType* diff_wx_ptr = static_cast<DType *>(diff_weights_layer_->get_data_handle());
-        DType* diff_wh_ptr = static_cast<DType *>(diff_weights_iter_->get_data_handle());
+      } else {
+        const size_t wx_size_per_gate = param.input_size * param.state_size;
+        const size_t wh_size_per_gate = param.state_size * param.state_size;
         for (int shift = 0; shift < num_layer * direction; ++shift) {
-          common::ParallelAdd(naive_weights + shift * wxh_size + wx_size_per_gate,
+          FAccGrad(native_weights + shift * wxh_size + wx_size_per_gate,
               diff_wx_ptr + shift * wx_size, wx_size_per_gate);
-          common::ParallelAdd(naive_weights + shift * wxh_size,
+          FAccGrad(native_weights + shift * wxh_size,
               diff_wx_ptr + shift * wx_size + wx_size_per_gate, wx_size_per_gate);
-          common::ParallelAdd(naive_weights + shift * wxh_size + 2 * wx_size_per_gate,
+          FAccGrad(native_weights + shift * wxh_size + 2 * wx_size_per_gate,
               diff_wx_ptr + shift * wx_size + 2 * wx_size_per_gate, wx_size_per_gate);
         }
-        // align naive_weights to weights_iter memory
-        naive_weights += wx_size;
+        // align native_weights to weights_iter memory
+        native_weights += wx_size;
         for (int shift = 0; shift < num_layer * direction; ++shift) {
-          common::ParallelAdd(naive_weights + shift * wxh_size + wh_size_per_gate,
+          FAccGrad(native_weights + shift * wxh_size + wh_size_per_gate,
               diff_wh_ptr + shift * wh_size, wh_size_per_gate);
-          common::ParallelAdd(naive_weights + shift * wxh_size,
+          FAccGrad(native_weights + shift * wxh_size,
               diff_wh_ptr + shift * wh_size + wh_size_per_gate, wh_size_per_gate);
-          common::ParallelAdd(naive_weights + shift * wxh_size + 2 * wh_size_per_gate,
+          FAccGrad(native_weights + shift * wxh_size + 2 * wh_size_per_gate,
               diff_wh_ptr + shift * wh_size + 2 * wh_size_per_gate, wh_size_per_gate);
         }
-      });
-    }
-  }
-
-  if (kWriteTo == req) {
-    const size_t bias_bytes = param.single_b_size * dtype_bytes;
-    const size_t naive_bias_bytes = param.naive_single_b_size * dtype_bytes;
-    char* naive_bias = static_cast<char *>(diff_bias);
-    char* diff_bias_ptr = static_cast<char *>(this->diff_bias_->get_data_handle());
-    if (param.mode != rnn_enum::kGru) {
-      for (int shift = 0; shift < num_layer * direction; ++shift) {
-        std::memcpy(naive_bias + shift * naive_bias_bytes,
-            diff_bias_ptr + shift * bias_bytes, bias_bytes);
-        std::memcpy(naive_bias + shift * naive_bias_bytes + bias_bytes,
-            diff_bias_ptr + shift * bias_bytes, bias_bytes);
-      }
-    } else {
-      const size_t bias_bytes_per_gate = param.state_size * dtype_bytes;
-      for (int shift = 0; shift < num_layer * direction; ++shift) {
-        char* naive_reset = naive_bias + shift * naive_bias_bytes;
-        char* naive_update = naive_reset + bias_bytes_per_gate;
-        char* update = diff_bias_ptr + shift * bias_bytes;
-        char* reset = update + bias_bytes_per_gate;
-
-        std::memcpy(naive_update, update, bias_bytes_per_gate);
-        std::memcpy(naive_reset, reset, bias_bytes_per_gate);
-        std::memcpy(naive_update + naive_bias_bytes / 2, update, bias_bytes_per_gate);
-        std::memcpy(naive_reset + naive_bias_bytes / 2, reset, bias_bytes_per_gate);
-
-        char* naive_new_bx = naive_update + bias_bytes_per_gate;
-        char* naive_new_bh = naive_new_bx + naive_bias_bytes / 2;
-        char* new_bx = reset + bias_bytes_per_gate;
-        char* new_bh = new_bx + bias_bytes_per_gate;
-        std::memcpy(naive_new_bx, new_bx, bias_bytes_per_gate);
-        std::memcpy(naive_new_bh, new_bh, bias_bytes_per_gate);
       }
-    }
-  } else if (kAddTo == req) {
-    const size_t bias_size = param.single_b_size;
-    const size_t naive_bias_size = param.naive_single_b_size;
-    if (param.mode != rnn_enum::kGru) {
-      MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
-        DType* naive_bias = static_cast<DType *>(diff_bias);
-        DType* diff_bias_ptr = static_cast<DType *>(this->diff_bias_->get_data_handle());
+    });
+  });
+
+  const size_t bias_size = param.single_b_size;
+  const size_t naive_bias_size = param.naive_single_b_size;
+  MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
+    DType* native_bias = static_cast<DType *>(diff_bias);
+    DType* diff_bias_ptr = static_cast<DType *>(this->diff_bias_->get_data_handle());
+    OPREQTYPE_SWITCH(req, DType, FAccGrad, {
+      if (param.mode != rnn_enum::kGru) {
         for (int shift = 0; shift < num_layer * direction; ++shift) {
-          common::ParallelAdd(naive_bias + shift * naive_bias_size,
+          FAccGrad(native_bias + shift * naive_bias_size,
               diff_bias_ptr + shift * bias_size, bias_size);
-          common::ParallelAdd(naive_bias + shift * naive_bias_size + bias_size,
+          FAccGrad(native_bias + shift * naive_bias_size + bias_size,
               diff_bias_ptr + shift * bias_size, bias_size);
         }
-      });
-    } else {
-      const size_t bias_size_per_gate = param.state_size;
-      MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
-        DType* naive_bias = static_cast<DType *>(diff_bias);
-        DType* diff_bias_ptr = static_cast<DType *>(this->diff_bias_->get_data_handle());
+      } else {
+        const size_t bias_size_per_gate = param.state_size;
         for (int shift = 0; shift < num_layer * direction; ++shift) {
-          DType* naive_reset = naive_bias + shift * naive_bias_size;
-          DType* naive_update = naive_reset + bias_size_per_gate;
+          DType* native_reset = native_bias + shift * naive_bias_size;
+          DType* native_update = native_reset + bias_size_per_gate;
           DType* update = diff_bias_ptr + shift * bias_size;
           DType* reset = update + bias_size_per_gate;
 
-          common::ParallelAdd(naive_update, update, bias_size_per_gate);
-          common::ParallelAdd(naive_reset, reset, bias_size_per_gate);
-          common::ParallelAdd(naive_update + naive_bias_size / 2, update, bias_size_per_gate);
-          common::ParallelAdd(naive_reset + naive_bias_size / 2, reset, bias_size_per_gate);
+          FAccGrad(native_update, update, bias_size_per_gate);
+          FAccGrad(native_reset, reset, bias_size_per_gate);
+          FAccGrad(native_update + naive_bias_size / 2, update, bias_size_per_gate);
+          FAccGrad(native_reset + naive_bias_size / 2, reset, bias_size_per_gate);
 
-          DType* naive_new_bx = naive_update + bias_size_per_gate;
-          DType* naive_new_bh = naive_new_bx + naive_bias_size / 2;
+          DType* native_new_bx = native_update + bias_size_per_gate;
+          DType* native_new_bh = native_new_bx + naive_bias_size / 2;
           DType* new_bx = reset + bias_size_per_gate;
           DType* new_bh = new_bx + bias_size_per_gate;
-          common::ParallelAdd(naive_new_bx, new_bx, bias_size_per_gate);
-          common::ParallelAdd(naive_new_bh, new_bh, bias_size_per_gate);
+          FAccGrad(native_new_bx, new_bx, bias_size_per_gate);
+          FAccGrad(native_new_bh, new_bh, bias_size_per_gate);
         }
-      });
-    }
-  }
+      }
+    });
+  });
 }
 
 template <typename MKLDNNRnnX>
@@ -966,6 +948,7 @@ inline void RegisterMKLDNNRnn(MKLDNNRnnX const& rnn) {
 template <>
 inline void RegisterMKLDNNRnn(MKLDNNRnnBackward const& rnn) {
   MKLDNNStream::Get()->RegisterPrimArgs(rnn.GetBwd(), rnn.GetArgsMap());
+  rnn.SetNativeWeightsGrads();
 }
 
 void MKLDNNRnnOp::Forward(const OpContext &ctx,
@@ -984,8 +967,8 @@ void MKLDNNRnnOp::Forward(const OpContext &ctx,
   }
 
   // Check if weights NDArray was changed. If so, reset initialized_
-  if (weights_version_ != inputs[rnn_enum::kParams].version() &&
-      fwd_inf_vec_.size() > 0) {
+  if (!is_training && fwd_inf_vec_.size() > 0
+      && weights_version_ != inputs[rnn_enum::kParams].version()) {
     initialized_ = false;
     for (auto& fwd : fwd_inf_vec_) fwd.Reset();
     weights_version_ = inputs[rnn_enum::kParams].version();
@@ -1097,6 +1080,8 @@ void MKLDNNRnnOp::Backward(const OpContext& ctx,
   using tag = mkldnn::memory::format_tag;
   TmpMemMgr::Get()->Init(ctx.requested[0]);
   const RNNParam& default_param = full_param_.default_param;
+  const int data_dtype = inputs[rnn_enum::kData].dtype();
+  const int w_dtype = inputs[rnn_enum::kParams].dtype();
 
   // Initialize the bwd_vec_
   if (bwd_vec_.size() != fwd_inf_vec_.size()) {
@@ -1113,8 +1098,6 @@ void MKLDNNRnnOp::Backward(const OpContext& ctx,
     bwd_vec_.at(lyr).SetWeightsGradsMem();
   }
 
-  const int data_dtype = inputs[rnn_enum::kData].dtype();
-  const int w_dtype = inputs[rnn_enum::kParams].dtype();
   const size_t w_bytes = mshadow::mshadow_sizeof(w_dtype);
   // Get temporary memory for diff_src, diff_state, diff_statecell
   const int num_layers = default_param.num_layers;
@@ -1204,7 +1187,7 @@ void MKLDNNRnnOp::Backward(const OpContext& ctx,
   // Commit weights diff
   if (req[rnn_enum::kParams] != kNullOp) {
     for (size_t lyr = 0; lyr < bwd_vec_.size(); ++lyr) {
-      bwd_vec_.at(lyr).CommitWeightsDiff(dw, db, req[rnn_enum::kParams], w_dtype);
+      bwd_vec_.at(lyr).CommitWeightsGrads(dw, db, req[rnn_enum::kParams], w_dtype);
       dw += full_param_.layer_params.at(lyr).single_w_size * w_bytes;
       db += full_param_.layer_params.at(lyr).single_b_size * w_bytes;
     }
diff --git a/src/operator/operator_common.h b/src/operator/operator_common.h
index f6af58bce995..a715bfc2f0cc 100644
--- a/src/operator/operator_common.h
+++ b/src/operator/operator_common.h
@@ -561,6 +561,24 @@ class OpSignature {
       case mkldnn_format_kind_rnn_packed:
         hash = hash * 2 + desc.data.format_desc.rnn_packed_desc.format;
         eles.push_back(desc.data.format_desc.rnn_packed_desc.format);
+        hash = hash * 2 + desc.data.format_desc.rnn_packed_desc.n_parts;
+        eles.push_back(desc.data.format_desc.rnn_packed_desc.n_parts);
+        for (int i = 0; i < desc.data.format_desc.rnn_packed_desc.n_parts; ++i) {
+          hash = hash * 2 + desc.data.format_desc.rnn_packed_desc.parts[i];
+          hash = hash * 2 + desc.data.format_desc.rnn_packed_desc.part_pack_size[i];
+          hash = hash * 2 + desc.data.format_desc.rnn_packed_desc.pack_part[i];
+          eles.push_back(desc.data.format_desc.rnn_packed_desc.parts[i]);
+          eles.push_back(desc.data.format_desc.rnn_packed_desc.part_pack_size[i]);
+          eles.push_back(desc.data.format_desc.rnn_packed_desc.pack_part[i]);
+        }
+        hash = hash * 2 + desc.data.format_desc.rnn_packed_desc.n;
+        hash = hash * 2 + desc.data.format_desc.rnn_packed_desc.ldb;
+        hash = hash * 2 + desc.data.format_desc.rnn_packed_desc.offset_compensation;
+        hash = hash * 2 + desc.data.format_desc.rnn_packed_desc.size;
+        eles.push_back(desc.data.format_desc.rnn_packed_desc.n);
+        eles.push_back(desc.data.format_desc.rnn_packed_desc.ldb);
+        eles.push_back(desc.data.format_desc.rnn_packed_desc.offset_compensation);
+        eles.push_back(desc.data.format_desc.rnn_packed_desc.size);
         break;
       default:
       // nothing need to add
diff --git a/src/operator/rnn.cc b/src/operator/rnn.cc
index 542968ef0a2c..a8e1b128f773 100644
--- a/src/operator/rnn.cc
+++ b/src/operator/rnn.cc
@@ -274,8 +274,7 @@ static void RNNStatefulComputeExCPU(const OpStatePtr& state_ptr,
                                     const std::vector<NDArray>& inputs,
                                     const std::vector<OpReqType>& req,
                                     const std::vector<NDArray>& outputs) {
-  if ((inputs[0].dtype() == mshadow::kFloat32 || inputs[0].dtype() == mshadow::kFloat16) &&
-      inputs[0].shape().ndim() == 3 && dmlc::GetEnv("MXNET_USE_MKLDNN_RNN", 1)) {
+  if (SupportMKLDNNRnn(inputs[0])) {
     MKLDNNRnnOp& op = state_ptr.get_state<MKLDNNRnnOp>();
     op.Forward(ctx, inputs, req, outputs);
   } else {
@@ -288,8 +287,7 @@ static void RNNStatefulGradComputeExCPU(const OpStatePtr& state_ptr,
                                         const std::vector<NDArray>& inputs,
                                         const std::vector<OpReqType>& req,
                                         const std::vector<NDArray>& outputs) {
-  if ((inputs[0].dtype() == mshadow::kFloat32 || inputs[0].dtype() == mshadow::kFloat16) &&
-      inputs[0].shape().ndim() == 3 && dmlc::GetEnv("MXNET_USE_MKLDNN_RNN", 1)) {
+  if (SupportMKLDNNRnn(inputs[0])) {
     MKLDNNRnnOp& op = state_ptr.get_state<MKLDNNRnnOp>();
     op.Backward(ctx, inputs, req, outputs);
   } else {
diff --git a/tests/python/unittest/test_gluon_rnn.py b/tests/python/unittest/test_gluon_rnn.py
index 309756b122e7..0f27f53f83a8 100644
--- a/tests/python/unittest/test_gluon_rnn.py
+++ b/tests/python/unittest/test_gluon_rnn.py
@@ -19,6 +19,8 @@
 from mxnet import gluon, nd
 import numpy as np
 import copy
+from itertools import product
+from functools import partial
 from numpy.testing import assert_allclose
 import unittest
 from mxnet.test_utils import almost_equal, assert_almost_equal
@@ -545,6 +547,128 @@ def test_rnn_layers_fp16():
     run_rnn_layers('float16', 'float32', mx.gpu())
 
 
+def check_rnn_consistency(fused_layer, stack_layer, loss, input_size, hidden_size, bidirectional=False, rtol=1e-2, atol=1e-4):
+    fused_begin_state = fused_layer.begin_state(1)
+    stack_state = stack_layer.begin_state(batch_size=1)
+    x = nd.random.normal(shape=(1, 5, input_size))
+    x.attach_grad()
+    y = nd.random.normal(shape=(1, 5, hidden_size * 2 if bidirectional else hidden_size))
+
+    with mx.autograd.record():
+        fused_out, fused_state = fused_layer(x, fused_begin_state)
+        l = loss(fused_out, y).mean()
+    l.backward()
+    fused_grads = dict([(name, p.grad()) for name, p in fused_layer.collect_params().items()])
+    fused_input_grad = x.grad.asnumpy()
+
+    with mx.autograd.record():
+        stack_out, stack_state = stack_layer.unroll(5, x, stack_state, merge_outputs=True)
+        l = loss(stack_out, y).mean()
+    l.backward()
+    stack_grads = dict([(name, p.grad()) for name, p in stack_layer.collect_params().items()])
+    stack_input_grad = x.grad.asnumpy()
+
+    assert_allclose(fused_out.asnumpy(), stack_out.asnumpy(), rtol=rtol, atol=atol)
+    assert_allclose(fused_input_grad, stack_input_grad, rtol=rtol, atol=atol)
+    for key, value in fused_grads.items():
+        assert_allclose(value.asnumpy(), stack_grads[key].asnumpy(), rtol=rtol, atol=atol)
+
+
+def create_op_by_mode(mode):
+    if mode == 'lstm':
+        fused_op = gluon.rnn.LSTM
+        stack_op = gluon.rnn.LSTMCell
+        recurrent_block_prefix = 'lstm0_'
+    elif mode == 'gru':
+        fused_op = gluon.rnn.GRU
+        stack_op = gluon.rnn.GRUCell
+        recurrent_block_prefix = 'gru0_'
+    elif mode == 'rnn_relu':
+        fused_op = partial(gluon.rnn.RNN, activation='relu')
+        stack_op = partial(gluon.rnn.RNNCell, activation='relu')
+        recurrent_block_prefix = 'rnn0_'
+    elif mode == 'rnn_tanh':
+        fused_op = partial(gluon.rnn.RNN, activation='tanh')
+        stack_op = partial(gluon.rnn.RNNCell, activation='tanh')
+        recurrent_block_prefix = 'rnn0_'
+
+    return fused_op, stack_op, recurrent_block_prefix
+
+
+def check_rnn_unidir_layer_gradients(mode, input_size, hidden_size, loss):
+    fused_op, stack_op, recurrent_block_prefix = create_op_by_mode(mode)
+    # ==== Single layer ====
+    fused_layer = fused_op(hidden_size, num_layers=1, layout='NTC', bidirectional=False)
+    fused_layer.collect_params().initialize()
+
+    params = fused_layer.collect_params()
+    stack_layer = mx.gluon.rnn.HybridSequentialRNNCell(prefix=recurrent_block_prefix, params=params)
+    with stack_layer.name_scope():
+        stack_layer.add(stack_op(hidden_size, prefix='l0_'))
+    stack_layer.initialize()
+
+    check_rnn_consistency(fused_layer, stack_layer, loss, input_size, hidden_size)
+
+    # ==== Multiple layer ====
+    fused_layer = fused_op(hidden_size, num_layers=3, layout='NTC', bidirectional=False)
+    fused_layer.collect_params().initialize()
+
+    params = fused_layer.collect_params()
+    stack_layer = mx.gluon.rnn.HybridSequentialRNNCell(prefix=recurrent_block_prefix, params=params)
+    with stack_layer.name_scope():
+        stack_layer.add(stack_op(hidden_size, prefix='l0_'))
+        stack_layer.add(stack_op(hidden_size, prefix='l1_'))
+        stack_layer.add(stack_op(hidden_size, prefix='l2_'))
+    stack_layer.initialize()
+
+    check_rnn_consistency(fused_layer, stack_layer, loss, input_size, hidden_size)
+
+
+def check_rnn_bidir_layer_gradients(mode, input_size, hidden_size, loss):
+    fused_op, stack_op, recurrent_block_prefix = create_op_by_mode(mode)
+    # ==== Single layer ====
+    fused_layer = fused_op(hidden_size, num_layers=1, layout='NTC', bidirectional=True)
+    fused_layer.collect_params().initialize()
+
+    params = fused_layer.collect_params()
+    stack_layer = mx.gluon.rnn.HybridSequentialRNNCell(prefix=recurrent_block_prefix, params=params)
+    with stack_layer.name_scope():
+        stack_layer.add(gluon.rnn.BidirectionalCell(stack_op(hidden_size, prefix='l0_'),
+                                                    stack_op(hidden_size, prefix='r0_')))
+    stack_layer.initialize()
+
+    check_rnn_consistency(fused_layer, stack_layer, loss, input_size, hidden_size, bidirectional=True)
+
+    # ==== Multiple layer ====
+    fused_layer = fused_op(hidden_size, num_layers=3, layout='NTC', bidirectional=True)
+    fused_layer.collect_params().initialize()
+
+    params = fused_layer.collect_params()
+    stack_layer = mx.gluon.rnn.HybridSequentialRNNCell(prefix=recurrent_block_prefix, params=params)
+    with stack_layer.name_scope():
+        stack_layer.add(gluon.rnn.BidirectionalCell(stack_op(hidden_size, prefix='l0_'),
+                                                    stack_op(hidden_size, prefix='r0_')))
+        stack_layer.add(gluon.rnn.BidirectionalCell(stack_op(hidden_size, prefix='l1_'),
+                                                    stack_op(hidden_size, prefix='r1_')))
+        stack_layer.add(gluon.rnn.BidirectionalCell(stack_op(hidden_size, prefix='l2_'),
+                                                    stack_op(hidden_size, prefix='r2_')))
+    stack_layer.initialize()
+
+    check_rnn_consistency(fused_layer, stack_layer, loss, input_size, hidden_size, bidirectional=True)
+
+
+@assert_raises_cudnn_not_satisfied(min_version='5.1.10')
+def test_fused_rnn_layer():
+    input_sizes = [128]
+    hidden_sizes = [128, 256]
+    modes = ['lstm', 'gru', 'rnn_relu', 'rnn_tanh']
+    # single layer
+    for mode, input_size, hidden_size in product(modes, input_sizes, hidden_sizes):
+        loss = mx.gluon.loss.L2Loss()
+        check_rnn_unidir_layer_gradients(mode, input_size, hidden_size, loss)
+        check_rnn_bidir_layer_gradients(mode, input_size, hidden_size, loss)
+
+
 def test_rnn_unroll_variant_length():
     # Test for imperative usage
     cell_list = []

From 1612533f8cfcb0017a7fd662237bf32d50614ec9 Mon Sep 17 00:00:00 2001
From: zburning <798672141@qq.com>
Date: Tue, 7 Jan 2020 04:04:20 +0800
Subject: [PATCH 768/813] refactor gluon.utils.split_data() following
 np.array_split() (#17123)

* fix

* fix & add test

* add mx.numpy test

* fix name

* fix mis input

* fix test
---
 python/mxnet/gluon/utils.py         | 34 ++++++++++-------------------
 tests/python/unittest/test_gluon.py | 32 +++++++++++++++++++++++----
 2 files changed, 39 insertions(+), 27 deletions(-)

diff --git a/python/mxnet/gluon/utils.py b/python/mxnet/gluon/utils.py
index 81a8dbaa486b..83ed15aed450 100644
--- a/python/mxnet/gluon/utils.py
+++ b/python/mxnet/gluon/utils.py
@@ -70,30 +70,18 @@ def split_data(data, num_slice, batch_axis=0, even_split=True):
             "uneven partitioning of data."%(
                 str(data.shape), num_slice, batch_axis, num_slice))
 
-    step = size // num_slice
-
-    # If size < num_slice, make fewer slices
-    if not even_split and size < num_slice:
-        step = 1
-        num_slice = size
-
-    if batch_axis == 0:
-        slices = [data[i*step:(i+1)*step] if i < num_slice - 1 else data[i*step:size]
-                  for i in range(num_slice)]
-    elif even_split:
-        if is_np_array():
-            slices = _mx_np.split(data, indices_or_sections=num_slice, axis=batch_axis)
-        else:
-            slices = ndarray.split(data, num_outputs=num_slice, axis=batch_axis)
+    n_each_section, extras = divmod(size, num_slice)
+    section_sizes = [0] + (extras * [n_each_section + 1] +
+                           (num_slice - extras) * [n_each_section])
+    div_points = np.array(section_sizes).cumsum()
+    if is_np_array():
+        slices = _mx_np.split(data, indices_or_sections=list(div_points[1: -1]), axis=batch_axis)
     else:
-        if is_np_array():
-            indices = [step * i for i in range(1, num_slice)]
-            slices = _mx_np.split(data, indices_or_sections=indices, axis=batch_axis)
-        else:
-            slices = [ndarray.slice_axis(data, batch_axis, i*step, (i+1)*step)
-                      if i < num_slice - 1 else
-                      ndarray.slice_axis(data, batch_axis, i*step, size)
-                      for i in range(num_slice)]
+        slices = []
+        for i in range(num_slice):
+            st = div_points[i]
+            end = div_points[i + 1]
+            slices.append(ndarray.slice_axis(data, axis=batch_axis, begin=st, end=end))
     return slices
 
 
diff --git a/tests/python/unittest/test_gluon.py b/tests/python/unittest/test_gluon.py
index d27c241f9c99..37b9cd7a0697 100644
--- a/tests/python/unittest/test_gluon.py
+++ b/tests/python/unittest/test_gluon.py
@@ -23,7 +23,10 @@
 from mxnet.gluon import nn
 from mxnet.base import py_str
 from mxnet.test_utils import assert_almost_equal
+from mxnet.util import is_np_array
 from mxnet.ndarray.ndarray import _STORAGE_TYPE_STR_TO_ID
+from mxnet.test_utils import use_np
+import mxnet.numpy as _mx_np
 from common import (setup_module, with_seed, assertRaises, teardown,
                     assert_raises_cudnn_not_satisfied)
 import numpy as np
@@ -952,17 +955,39 @@ def test_deferred_init():
     layer(x)
 
 
+
 def check_split_data(x, num_slice, batch_axis, **kwargs):
     res = gluon.utils.split_data(x, num_slice, batch_axis, **kwargs)
     assert len(res) == num_slice
-    mx.test_utils.assert_almost_equal(mx.nd.concat(*res, dim=batch_axis).asnumpy(),
-                                      x.asnumpy())
+    if not is_np_array():
+        mx.test_utils.assert_almost_equal(mx.nd.concat(*res, dim=batch_axis).asnumpy(),
+                                          x.asnumpy())
+    else:
+        mx.test_utils.assert_almost_equal(_mx_np.concatenate(res, axis=batch_axis).asnumpy(),
+                                          x.asnumpy())
+    np_res = np.array_split(x.asnumpy(), num_slice, axis=batch_axis)
+    res_asnp = [s.asnumpy() for s in res]
+    for r1, r2 in zip(np_res, res_asnp):
+        assert all(r1.reshape(-1) == r2.reshape(-1))
 
 
+@with_seed()
+@use_np
+def test_split_data_np():
+    x = _mx_np.random.uniform(size=(128, 33, 64))
+    check_split_data(x, 8, 0)
+    check_split_data(x, 3, 1)
+    check_split_data(x, 4, 1, even_split=False)
+    check_split_data(x, 15, 1, even_split=False)
+    try:
+        check_split_data(x, 4, 1)
+    except ValueError:
+        return
+    assert False, "Should have failed"
+
 @with_seed()
 def test_split_data():
     x = mx.nd.random.uniform(shape=(128, 33, 64))
-
     check_split_data(x, 8, 0)
     check_split_data(x, 3, 1)
     check_split_data(x, 4, 1, even_split=False)
@@ -973,7 +998,6 @@ def test_split_data():
         return
     assert False, "Should have failed"
 
-
 @with_seed()
 def test_flatten():
     flatten = nn.Flatten()

From bd7eedf3a43279380750dfc54f081e32891620af Mon Sep 17 00:00:00 2001
From: "Joshua Z. Zhang" <cheungchih@gmail.com>
Date: Mon, 6 Jan 2020 13:33:50 -0800
Subject: [PATCH 769/813] Fix #17164 symbolblock with BatchNorm inside during
 cast to fp16 (#17212)

* fix symbolblock with bn+fp16

* add unittest

* fix

* remove unused

* fix lint
---
 python/mxnet/gluon/block.py        | 25 ++++++++++++++++++++++++-
 tests/python/gpu/test_gluon_gpu.py | 18 ++++++++++++++++++
 2 files changed, 42 insertions(+), 1 deletion(-)

diff --git a/python/mxnet/gluon/block.py b/python/mxnet/gluon/block.py
index de523567f3eb..e7f0676bd6e9 100644
--- a/python/mxnet/gluon/block.py
+++ b/python/mxnet/gluon/block.py
@@ -16,7 +16,7 @@
 # under the License.
 
 # coding: utf-8
-# pylint: disable= arguments-differ, too-many-lines
+# pylint: disable= arguments-differ, too-many-lines, reimported
 """Base container class for all neural network models."""
 __all__ = ['Block', 'HybridBlock', 'SymbolBlock']
 
@@ -25,6 +25,7 @@
 import warnings
 import re
 from collections import OrderedDict, defaultdict
+import numpy as np
 
 from ..base import mx_real_t, MXNetError
 from .. import symbol, ndarray, initializer, np_symbol
@@ -1353,6 +1354,28 @@ def _clear_cached_op(self):
     def cast(self, dtype):
         self._clear_cached_op()
         super(SymbolBlock, self).cast(dtype)
+        if np.dtype(dtype).name == 'float16':
+            # correct BatchNorm types back to float32 due to its special requirement
+            out = self._cached_graph[1]
+            params_list = out.get_internals().list_inputs()
+            for node in params_list:
+                if node.endswith('running_var'):
+                    prefix = node[:-11]
+                    sibs = [prefix + t for t in ('running_mean', 'gamma', 'beta')]
+                    is_bn = all(p in params_list for p in sibs)
+                    if is_bn:
+                        self.params.get(node).cast('float32')
+                        for sib in sibs:
+                            self.params.get(sib).cast('float32')
+                if node.endswith('moving_var'):
+                    # another convention used
+                    prefix = node[:-10]
+                    sibs = [prefix + t for t in ('moving_mean', 'gamma', 'beta')]
+                    is_bn = all(p in params_list for p in sibs)
+                    if is_bn:
+                        self.params.get(node).cast('float32')
+                        for sib in sibs:
+                            self.params.get(sib).cast('float32')
 
     def hybrid_forward(self, F, x, *args, **kwargs):
         raise NotImplementedError
diff --git a/tests/python/gpu/test_gluon_gpu.py b/tests/python/gpu/test_gluon_gpu.py
index b938b5783415..6aa766351b62 100644
--- a/tests/python/gpu/test_gluon_gpu.py
+++ b/tests/python/gpu/test_gluon_gpu.py
@@ -594,6 +594,24 @@ def hybrid_forward(self, F, a, b):
     assert_raises(ValueError, lambda: foo_hybrid(mx.nd.ones((10,), ctx=mx.gpu()),
                                                  mx.nd.ones((10,), ctx=mx.cpu())))
 
+@with_seed()
+def test_symbol_block_symbolic_bn_fp16_cast():
+    with mx.gpu(0):
+        net = mx.gluon.nn.HybridSequential()
+        sym = mx.sym.var('data')
+        conv = mx.sym.Convolution(sym, kernel=(3, 3), num_filter=16)
+        bn = mx.sym.BatchNorm(conv, name='bn_test')
+        internals = bn.get_internals()
+        net.add(mx.gluon.nn.SymbolBlock([internals['bn_test_output']], [mx.sym.var('data')]))
+        net.add(mx.gluon.nn.Conv2D(10, kernel_size=1))
+        net.initialize()
+        x = mx.nd.zeros((1, 3, 32, 32), dtype='float32')
+        y = net(x)
+        assert np.dtype(y.dtype).name == 'float32'
+        net.cast('float16')
+        x = x.astype('float16')
+        y1 = net(x)
+        assert np.dtype(y1.dtype).name == 'float16'
 
 if __name__ == '__main__':
     import nose

From 0f04b0dcf609fcf08c1aec91533f98149d02afa5 Mon Sep 17 00:00:00 2001
From: Yizhi Liu <liuyizhi@apache.org>
Date: Tue, 7 Jan 2020 00:25:30 -0800
Subject: [PATCH 770/813] [tvmop] support cuda multi-arch compilation (#17214)

* [tvmop] support cuda multi-arch compilation

* fix use_mkldnn in tvm build

* use function to scope tvm build variables in cmake

* remove unnecessary -gencode parse
---
 3rdparty/tvm             |  2 +-
 CMakeLists.txt           | 18 ++++++++++++------
 cmake/BuildTVM.cmake     |  3 +++
 contrib/tvmop/compile.py | 24 ++++++++----------------
 4 files changed, 24 insertions(+), 23 deletions(-)

diff --git a/3rdparty/tvm b/3rdparty/tvm
index 5d66e7a695d1..f8f4ceb253a6 160000
--- a/3rdparty/tvm
+++ b/3rdparty/tvm
@@ -1 +1 @@
-Subproject commit 5d66e7a695d1d40e08cf2da802b5e2b7015b758a
+Subproject commit f8f4ceb253a6c39ac2a3e282ef89e9d558aa1c73
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 26b20f33f46e..8a1da1e92f6c 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -295,6 +295,7 @@ if(USE_MKLDNN)
   include_directories(${PROJECT_BINARY_DIR}/3rdparty/mkldnn/include)
   add_definitions(-DMXNET_USE_MKLDNN=1)
   list(APPEND mxnet_LINKER_LIBS dnnl)
+  set(INSTALL_MKLDNN ON)
 endif()
 
 # Allow Cuda compiles outside of src tree to find things in 'src' and 'include'
@@ -758,11 +759,15 @@ if(USE_DIST_KVSTORE)
   list(APPEND mxnet_LINKER_LIBS ${pslite_LINKER_LIBS})
 endif()
 
-if(USE_TVM_OP)
-  list(APPEND mxnet_LINKER_LIBS ${CMAKE_CURRENT_BINARY_DIR}/3rdparty/tvm/libtvm_runtime.so)
+function(BuildTVMOP)
+  # scope the variables in BuildTVM.cmake to avoid conflict
   include(cmake/BuildTVM.cmake)
   add_subdirectory("3rdparty/tvm")
+endfunction()
 
+if(USE_TVM_OP)
+  list(APPEND mxnet_LINKER_LIBS ${CMAKE_CURRENT_BINARY_DIR}/3rdparty/tvm/libtvm_runtime.so)
+  BuildTVMOP()
   if(NOT Python3_EXECUTABLE)
     find_package(PythonInterp 3 REQUIRED)
     set(Python3_EXECUTABLE ${PYTHON_EXECUTABLE} CACHE FILEPATH "Path to the python3 executable")
@@ -775,12 +780,13 @@ if(USE_TVM_OP)
   if(USE_CUDA)
     set(TVM_OP_COMPILE_OPTIONS "${TVM_OP_COMPILE_OPTIONS}" "--cuda-arch" "\"${CUDA_ARCH_FLAGS}\"")
   endif()
+
   add_custom_command(TARGET mxnet POST_BUILD
     COMMAND ${CMAKE_COMMAND} -E env
-          PYTHONPATH="${CMAKE_CURRENT_SOURCE_DIR}/3rdparty/tvm/python:${CMAKE_CURRENT_SOURCE_DIR}/3rdparty/tvm/topi/python:${CMAKE_CURRENT_SOURCE_DIR}/contrib"
-          LD_LIBRARY_PATH=${CMAKE_CURRENT_BINARY_DIR}:${CMAKE_CURRENT_BINARY_DIR}/3rdparty/tvm:$ENV{LD_LIBRARY_PATH}
-          ${Python3_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/contrib/tvmop/compile.py ${TVM_OP_COMPILE_OPTIONS}
-    )
+    PYTHONPATH="${CMAKE_CURRENT_SOURCE_DIR}/3rdparty/tvm/python:${CMAKE_CURRENT_SOURCE_DIR}/3rdparty/tvm/topi/python:${CMAKE_CURRENT_SOURCE_DIR}/contrib"
+    LD_LIBRARY_PATH=${CMAKE_CURRENT_BINARY_DIR}:${CMAKE_CURRENT_BINARY_DIR}/3rdparty/tvm:$ENV{LD_LIBRARY_PATH}
+    ${Python3_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/contrib/tvmop/compile.py ${TVM_OP_COMPILE_OPTIONS}
+  )
 endif()
 
 if(USE_PLUGINS_WARPCTC)
diff --git a/cmake/BuildTVM.cmake b/cmake/BuildTVM.cmake
index 2c2f573cddbd..47c1615a2d9f 100644
--- a/cmake/BuildTVM.cmake
+++ b/cmake/BuildTVM.cmake
@@ -139,3 +139,6 @@ set(USE_RELAY_DEBUG OFF)
 
 # Use OPENMP thread pool to be compatible with MXNet
 set(USE_OPENMP ON)
+
+# Disable USE_MKLDNN for TVM
+set(USE_MKLDNN OFF)
diff --git a/contrib/tvmop/compile.py b/contrib/tvmop/compile.py
index ef929b472ad5..6341e70fe766 100644
--- a/contrib/tvmop/compile.py
+++ b/contrib/tvmop/compile.py
@@ -99,24 +99,16 @@ def get_cuda_arch(arch):
     if len(arch) == 0:
         return None
 
-    # the arch string is of format '-gencode;arch=compute_XX,code=sm_XX'
-    # this format is computed by CMake CUDA_SELECT_NVCC_ARCH_FLAGS
-    if arch.startswith('-gencode;'):
-        return arch.split(';')
-
-    # the arch string contains '-arch=sm_xx'
-    flags = arch.split()
+    # an example of arch string,
+    # -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35
+    # -gencode;arch=compute_75,code=[sm_75,compute_75] --fatbin-options -compress-all
+    archs = []
+    flags = arch.replace("-gencode;", "-gencode ").split()
     for flag in flags:
-        if flag.startswith('-arch='):
-            return flag[len('-arch='):]
-
-    # find the highest compute capability
-    comp_caps = re.findall(r'\d+', arch)
-    if len(comp_caps) == 0:
-        return None
+        if flag.startswith('-gencode') or flag.startswith('arch='):
+            archs.append(flag)
 
-    comp_caps = [int(c) for c in comp_caps]
-    return 'sm_' + str(max(comp_caps))
+    return archs
 
 
 if __name__ == "__main__":

From 413f748d31aa507feadbde612911c90815f842f5 Mon Sep 17 00:00:00 2001
From: Talia <31782251+TEChopra1000@users.noreply.github.com>
Date: Tue, 7 Jan 2020 10:26:16 -0800
Subject: [PATCH 771/813] autograd video and image link fixes and removing
 symbol tutorials (#17227)

---
 docs/python_docs/python/api/symbol/index.rst       | 10 ----------
 .../python/tutorials/packages/autograd/index.md    | 14 +++++++-------
 2 files changed, 7 insertions(+), 17 deletions(-)

diff --git a/docs/python_docs/python/api/symbol/index.rst b/docs/python_docs/python/api/symbol/index.rst
index 81340881b1a6..8a80b83c6eb5 100644
--- a/docs/python_docs/python/api/symbol/index.rst
+++ b/docs/python_docs/python/api/symbol/index.rst
@@ -37,16 +37,6 @@ The following example shows how you might build a simple expression with the Sym
 	(a, b, c)
 
 
-Tutorials
----------
-.. container:: cards
-
-   .. card::
-      :title: Symbol Guide
-      :link: ../../tutorials/packages/symbol/
-
-      The Symbol guide. Start here!
-
 Symbol Package
 --------------
 
diff --git a/docs/python_docs/python/tutorials/packages/autograd/index.md b/docs/python_docs/python/tutorials/packages/autograd/index.md
index b1fe274ec41f..fa321f72b839 100644
--- a/docs/python_docs/python/tutorials/packages/autograd/index.md
+++ b/docs/python_docs/python/tutorials/packages/autograd/index.md
@@ -23,7 +23,7 @@
 
 Gradients are fundamental to the process of training neural networks, and tell us how to change the parameters of the network to improve its performance.
 
-![auto-gradient](//_static/autograd_images/autograd_gradient.png)
+![auto-gradient](/api/python/docs/_static/autograd_images/autograd_gradient.png)
 
 ### Long Answer:
 
@@ -39,7 +39,7 @@ Assuming we've calculated the gradient of each parameter with respect to the los
 
 We differentiate. [MXNet Gluon](/api/python/docs/tutorials/packages/gluon/index.html) uses Reverse Mode Automatic Differentiation (`autograd`) to backprogate gradients from the loss metric to the network parameters.
 
-![forward-backward](//_static/autograd_images/autograd_forward_backward.png)
+![forward-backward](/api/python/docs/_static/autograd_images/autograd_forward_backward.png)
 
 ### Long Answer:
 
@@ -56,7 +56,7 @@ Stage 2. Work backwards through this record and evaluate the partial derivatives
 
 <p style="text-align:center">
     <video width="600" controls playsinline autoplay muted loop>
-        <source src="/_static/autograd_images/autograd_graph.mp4" type="video/mp4">
+        <source src="/api/python/docs/_static/autograd_images/autograd_graph.mp4" type="video/mp4">
     </video>
 </p>
 
@@ -173,7 +173,7 @@ net[0].weight.grad_req = 'null'
 
 <p style="text-align:center">
     <video width="600" controls playsinline autoplay muted loop>
-        <source src="../../../../_static/autograd_images/autograd_grad_req.mp4" type="video/mp4">
+        <source src="/api/python/docs/_static/autograd_images/autograd_grad_req.mp4" type="video/mp4">
     </video>
 </p>
 
@@ -217,7 +217,7 @@ def f(x):
 
 We can plot the resultant function for $x$ between 0 and 1, and we should recognise certain functions in segments of $x$. Starting with a quadratic curve from 0 to 1/2, we have a cubic curve from 1/2 to 2/3, a quartic from 2/3 to 3/4 and finally a flatline.
 
-![control-flow](//_static/autograd_images/autograd_control_flow.png)
+![control-flow](/api/python/docs/_static/autograd_images/autograd_control_flow.png)
 
 Using `autograd`, let's now find the gradient of this arbritrary function. We don't have a vectorized function in this case, because of the control flow, so let's also create a function to calculate the gradient using `autograd`.
 
@@ -234,7 +234,7 @@ grads = [get_grad(f, x).asscalar() for x in xs]
 print(grads)
 ```
 
-![flow-grad](//_static/autograd_images/autograd_control_flow_grad.png)
+![flow-grad](/api/python/docs/_static/autograd_images/autograd_control_flow_grad.png)
 
 We can calculate the gradients by hand in this situation (since it's a toy example), and for the four segments discussed before we'd expect $2x$, $3x^2$, $4x^3$ and 0. As a spot check, for $x=0.6$ the hand calculated gradient would be $3x^2=1.08$, which equals `1.08` as computed by `autograd`.
 
@@ -247,7 +247,7 @@ Most of the time `autograd` will be aware of the complete computational graph, a
 
 <p style="text-align:center">
     <video width="600" controls playsinline autoplay muted loop>
-        <source src="../../../../_static/autograd_images/autograd_head_grad.mp4" type="video/mp4">
+        <source src="/api/python/docs/_static/autograd_images/autograd_head_grad.mp4" type="video/mp4">
     </video>
 </p>
 

From ba376af2d62638037c01923a2d5153823cb39ecf Mon Sep 17 00:00:00 2001
From: Leonard Lausen <lausen@amazon.com>
Date: Tue, 7 Jan 2020 20:24:24 +0100
Subject: [PATCH 772/813] Fix and clean up Ubuntu build from source
 instructions (#17229)

* Fix and clean up Ubuntu build from source instructions

* Add ~/.local/bin prefix
---
 .../src/pages/get_started/ubuntu_setup.md     | 193 ++++--------------
 1 file changed, 45 insertions(+), 148 deletions(-)

diff --git a/docs/static_site/src/pages/get_started/ubuntu_setup.md b/docs/static_site/src/pages/get_started/ubuntu_setup.md
index e7879eaa1b9e..effedc02f756 100644
--- a/docs/static_site/src/pages/get_started/ubuntu_setup.md
+++ b/docs/static_site/src/pages/get_started/ubuntu_setup.md
@@ -22,19 +22,26 @@ permalink: /get_started/ubuntu_setup
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
 
-# Installing MXNet on Ubuntu
+# Installing MXNet from source on Ubuntu
 
-The following installation instructions are for installing MXNet on computers running **Ubuntu 16.04**. Support for later versions of Ubuntu is [not yet available](#contributions).
-<hr>
+The following installation instructions are for building MXNet from source on
+computers running **Ubuntu 16.04** or higher. For instructions to build MXNet
+from source on other platforms, see the general [Build From Source
+guide](build_from_source).
+
+Instead of building from source, you can install a binary version of MXNet. For
+that, please follow the information at [Get Started](get_started).
+
+Building MXNet from source is a two-step process:
+
+1. Build the shared library from the MXNet C++ source code.
+2. (optional) Install the supported language-specific packages for MXNet.
 
 ## Contents
 
 * [CUDA Dependencies](#cuda-dependencies)
-* [Quick Installation](#quick-installation)
-    * [Python](#install-mxnet-for-python)
-    * [pip Packages](#pip-package-availability)
-* [Build from Source](#build-mxnet-from-source)
-* [Installing Language Packages](#installing-language-packages-for-mxnet)
+* [Build the MXNet shared library from source](#build-mxnet-from-source)
+* [Install Language Packages](#installing-language-packages-for-mxnet)
     * [R](#install-the-mxnet-package-for-r)
     * [Julia](#install-the-mxnet-package-for-julia)
     * [Scala](#install-the-mxnet-package-for-scala)
@@ -65,80 +72,7 @@ Unzip the file and change to the cuDNN root directory. Move the header and libra
 
 <hr>
 
-
-## Quick Installation
-
-### Install MXNet for Python
-
-#### Dependencies
-
-The following scripts will install Ubuntu 16.04 dependencies for MXNet Python development.
-
-```bash
-wget https://raw.githubusercontent.com/apache/incubator-mxnet/master/ci/docker/install/ubuntu_core.sh
-wget https://raw.githubusercontent.com/apache/incubator-mxnet/master/ci/docker/install/ubuntu_python.sh
-sudo ./ubuntu_core.sh
-sudo ./ubuntu_python.sh
-```
-
-Using the latest MXNet with CUDA 9.2 package is recommended for the fastest training speeds with MXNet.
-
-**Recommended for training:**
-```bash
-pip install mxnet-cu92
-```
-
-**Recommended for inference:**
-```bash
-pip install mxnet-cu92mkl
-```
-
-Alternatively, you can use the table below to select the package that suits your purpose.
-
-| MXNet Version | Basic | CUDA | MKL-DNN | CUDA/MKL-DNN |
-|-|-|-|-|-|
-| Latest | mxnet | mxnet-cu92 | mxnet-mkl | mxnet-cu92mkl |
-
-
-#### pip Package Availability
-
-The following table presents the pip packages that are recommended for each version of MXNet.
-
-![pip package table](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/install/pip-packages-1.4.0.png)
-
-To install an older version of MXNet with one of the packages in the previous table add `==` with the version you require. For example for version 1.1.0 of MXNet with CUDA 8, you would use `pip install mxnet-cu80==1.1.0`.
-
-<hr>
-
-
-## Build MXNet from Source
-
-You can build MXNet from source, and then you have the option of installing language-specific bindings, such as Scala, Java, Julia, R or Perl. This is a two-step process:
-
-1. Build the shared library from the MXNet C++ source code.
-2. (optional) Install the supported language-specific packages for MXNet. Be sure to check that section first, as some scripts may be available to handle all of the dependencies, MXNet build, and language bindings for you. Here they are again for quick access:
-
-* [R](#install-the-mxnet-package-for-r)
-* [Julia](#install-the-mxnet-package-for-julia)
-* [Scala](#install-the-mxnet-package-for-scala)
-* [Java](#install-the-mxnet-package-for-java)
-* [Perl](#install-the-mxnet-package-for-perl)
-
-**Note:** To change the compilation options for your build, edit the ```make/config.mk``` file prior to building MXNet. More information on this is mentioned in the different language package instructions.
-
-### Build the Shared Library
-
-#### Quick MXNet Build
-You can quickly build MXNet from source with the following script found in the `/docs/install` folder:
-
-```bash
-cd docs/install
-./install_mxnet_ubuntu_python.sh
-```
-
-Or you can go through a manual process described next.
-
-#### Manual MXNet Installation
+## Build the MXNet shared library from source
 
 It is recommended that you review the general [build from source](build_from_source) instructions before continuing.
 
@@ -147,67 +81,35 @@ On Ubuntu versions 16.04 or later, you need the following dependencies:
 **Step 1:** Install prerequisite packages.
 ```bash
     sudo apt-get update
-    sudo apt-get install -y build-essential git ninja-build ccache
-```
-
-**For Ubuntu 18.04 and CUDA builds you need to update CMake**
-
-```bash
-#!/usr/bin/env bash
-set -exuo pipefail
-sudo apt remove --purge --auto-remove cmake
-
-# Update CMAKE for correct cuda autotedetection: https://github.com/clab/dynet/issues/1457
-version=3.14
-build=0
-mkdir -p ~/tmp
-cd ~/tmp
-wget https://cmake.org/files/v$version/cmake-$version.$build.tar.gz
-tar -xzvf cmake-$version.$build.tar.gz
-cd cmake-$version.$build/
-./bootstrap
-make -j$(nproc)
-sudo make install
+    sudo apt-get install -y build-essential git ninja-build ccache python3-pip libopenblas-dev libopencv-dev
+    pip3 install --user --upgrade "cmake>=3.13.2"  # Instead of using pip, you could also manually install cmake from https://cmake.org
 ```
 
+Instead of `libopenblas-dev` you may also choose a different math library.
+Further information is provided in the source guide's [Math Library
+Selection](build_from_source#math-library-selection) section.
 
-**Step 2:** Install a Math Library.
+`libopencv-dev` is an optional dependency. You can delete it from above `apt-get
+install` line and build MXNet without OpenCV support by passing
+`-DUSE_OPENCV=OFF` to the `cmake` command below.
 
-Details on the different math libraries are found in the build from source guide's [Math Library Selection](build_from_source#math-library-selection) section.
-
-For OpenBLAS use:
-
-```bash
-    sudo apt-get install -y libopenblas-dev
-```
-
-For other libraries, visit the [Math Library Selection](build_from_source#math-library-selection) section.
-
-**Step 3:** Install OpenCV.
-
-*MXNet* uses [OpenCV](http://opencv.org/) for efficient image loading and augmentation operations.
-
-```bash
-    sudo apt-get install -y libopencv-dev
-```
-
-**Step 4:** Download MXNet sources and build MXNet core shared library.
-
-If building on CPU and using OpenBLAS:
+**Step 2:** Download MXNet sources
 
 Clone the repository:
 
 ```bash
-    git clone --recursive https://github.com/apache/incubator-mxnet.git
-    cd incubator-mxnet
+    git clone --recursive https://github.com/apache/incubator-mxnet.git mxnet
+    cd mxnet
 ```
 
-Build with CMake and ninja, without GPU and without MKL.
+**Step 3:** Build MXNet core shared library.
+
+For a CPU-only build with OpenBLAS math library run:
 
 ```bash
     rm -rf build
     mkdir -p build && cd build
-    cmake -GNinja \
+    ~/.local/bin/cmake -GNinja \
         -DUSE_CUDA=OFF \
         -DUSE_MKL_IF_AVAILABLE=OFF \
         -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache \
@@ -218,12 +120,16 @@ Build with CMake and ninja, without GPU and without MKL.
     ninja
 ```
 
-If building on CPU and using MKL and MKL-DNN (make sure MKL is installed according to [Math Library Selection](build_from_source#math-library-selection) and [MKL-DNN README](https://mxnet.apache.org/api/python/docs/tutorials/performance/backend/mkldnn/mkldnn_readme.html)):
+For a CPU-only build with MKL math library and MKL-DNN you need to make sure MKL
+is installed according to
+[Math Library Selection](build_from_source#math-library-selection) and
+[MKL-DNN README](https://mxnet.apache.org/api/python/docs/tutorials/performance/backend/mkldnn/mkldnn_readme.html)
+respectively. Then run:
 
 ```bash
     rm -rf build
     mkdir -p build && cd build
-    cmake -GNinja \
+    ~/.local/bin/cmake -GNinja \
         -DUSE_CUDA=OFF \
         -DUSE_MKL_IF_AVAILABLE=ON \
         -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache \
@@ -234,13 +140,13 @@ If building on CPU and using MKL and MKL-DNN (make sure MKL is installed accordi
     ninja
 ```
 
-If building on GPU (make sure you have installed the [CUDA dependencies first](#cuda-dependencies)):
-Cuda 10.1 in Ubuntu 18.04 builds fine but is not currently tested in CI.
+For a GPU-enabled build make sure you have installed the
+[CUDA dependencies first](#cuda-dependencies)) and run:
 
 ```bash
     rm -rf build
     mkdir -p build && cd build
-    cmake -GNinja \
+    ~/.local/bin/cmake -GNinja \
         -DUSE_CUDA=ON \
         -DUSE_MKL_IF_AVAILABLE=OFF \
         -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache \
@@ -251,17 +157,9 @@ Cuda 10.1 in Ubuntu 18.04 builds fine but is not currently tested in CI.
     ninja
 ```
 
-*Note* - You can explore and use more compilation options as they are delcared in the top of `CMakeLists.txt` and also review common [usage examples](build_from_source#usage-examples).
-Optionally, you can also use a higher level, scripted version of the above with an editable CMake options file by doing the
-following:
-
-```bash
-cp cmake/cmake_options.yml .
-# Edit cmake_options.yml in the MXNet root to your taste
-$EDITOR cmake_options.yml
-# Launch a local CMake build
-./dev_menu.py build
-```
+*Note* - You can explore and use more compilation options as they are delcared
+in the top of `CMakeLists.txt` and also review common
+[usage examples](build_from_source#usage-examples).
 
 Building from source creates a library called ```libmxnet.so``` in the `build` folder in your MXNet project root.
 
@@ -290,7 +188,7 @@ To install the MXNet Python binding navigate to the root of the MXNet folder the
 
 ```bash
 $ cd python
-$ pip install -e .
+$ pip install --user -e .
 ```
 
 Note that the `-e` flag is optional. It is equivalent to `--editable` and means that if you edit the source files, these changes will be reflected in the package installed.
@@ -300,8 +198,7 @@ Note that the `-e` flag is optional. It is equivalent to `--editable` and means
 You may optionally install ```graphviz``` library that is used for visualizing network graphs you build on MXNet. You may also install [Jupyter Notebook](http://jupyter.readthedocs.io/) which is used for running MXNet tutorials and examples.
 
 ```bash
-sudo pip install graphviz==0.8.4 \
-                 jupyter
+pip install --user graphviz==0.8.4 jupyter
 ```
 <hr>
 

From f17d19ba663df8694c10dfa56863a7e2a19cadcb Mon Sep 17 00:00:00 2001
From: Xi Wang <xidulu@gmail.com>
Date: Wed, 8 Jan 2020 08:40:13 +0800
Subject: [PATCH 773/813] [Numpy] Add broadcast_to scalar case (#17233)

* fix broadcast_to scalar case

* change broadcast_to namespace prefix

* remove unused import
---
 python/mxnet/_numpy_op_doc.py                 | 35 -------------------
 python/mxnet/ndarray/numpy/_op.py             | 32 ++++++++++++++++-
 python/mxnet/numpy/multiarray.py              | 32 +++++++++++++++--
 python/mxnet/numpy/stride_tricks.py           |  4 +--
 python/mxnet/symbol/numpy/_symbol.py          | 32 ++++++++++++++++-
 .../numpy/np_broadcast_reduce_op_value.cc     |  2 +-
 .../numpy/np_broadcast_reduce_op_value.cu     |  2 +-
 tests/python/unittest/test_numpy_op.py        | 20 +++++++++++
 8 files changed, 116 insertions(+), 43 deletions(-)

diff --git a/python/mxnet/_numpy_op_doc.py b/python/mxnet/_numpy_op_doc.py
index 27d4140a532f..190150eb5a0a 100644
--- a/python/mxnet/_numpy_op_doc.py
+++ b/python/mxnet/_numpy_op_doc.py
@@ -1132,41 +1132,6 @@ def _np__random_shuffle(x):
     pass
 
 
-def _np_broadcast_to(array, shape, out=None):
-    """
-    Broadcast an array to a new shape.
-
-    Parameters
-    ----------
-    array : ndarray
-        The array to broadcast.
-    shape : tuple, optional, default=[]
-        The shape of the desired array.
-    out : ndarray, optional
-        The output ndarray to hold the result.
-
-    Returns
-    -------
-    out : ndarray or list of ndarrays
-
-    Raises
-    ------
-    MXNetError
-        - If the array is not compatible with the new shape according to NumPy's
-        broadcasting rules.
-        - If the shape of the output array is not consistent with the desired shape.
-
-    Examples
-    --------
-    >>> x = np.array([1, 2, 3])
-    >>> np.broadcast_to(x, (3, 3))
-    array([[1., 2., 3.],
-           [1., 2., 3.],
-           [1., 2., 3.]])
-    """
-    pass
-
-
 def _npx_reshape(a, newshape, reverse=False, order='C'):
     """
     Gives a new shape to an array without changing its data.
diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index e226c30ff20b..b39ee18b9db7 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -29,7 +29,7 @@
 from ..ndarray import NDArray
 
 __all__ = ['shape', 'zeros', 'zeros_like', 'ones', 'ones_like', 'full', 'full_like', 'empty_like', 'invert', 'delete',
-           'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power', 'bitwise_not',
+           'add', 'broadcast_to', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power', 'bitwise_not',
            'arctan2', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs',
            'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2',
            'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor', 'histogram',
@@ -259,6 +259,36 @@ def ones_like(a, dtype=None, order='C', ctx=None, out=None):
     return _npi.full_like(a, fill_value=1, dtype=dtype, ctx=None, out=None)
 
 
+@set_module('mxnet.ndarray.numpy')
+def broadcast_to(array, shape):
+    """
+    Broadcast an array to a new shape.
+
+    Parameters
+    ----------
+    array : ndarray or scalar
+        The array to broadcast.
+    shape : tuple
+        The shape of the desired array.
+
+    Returns
+    -------
+    broadcast : array
+        A readonly view on the original array with the given shape. It is
+        typically not contiguous. Furthermore, more than one element of a
+        broadcasted array may refer to a single memory location.
+
+    Raises
+    ------
+    MXNetError
+        If the array is not compatible with the new shape according to NumPy's
+        broadcasting rules.
+    """
+    if _np.isscalar(array):
+        return full(shape, array)
+    return _npi.broadcast_to(array, shape)
+
+
 @set_module('mxnet.ndarray.numpy')
 def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):  # pylint: disable=too-many-arguments
     """
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 3918d0bd085e..9994f7eda4a8 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -47,7 +47,7 @@
 from ..ndarray.ndarray import _storage_type
 
 __all__ = ['ndarray', 'empty', 'empty_like', 'array', 'shape',
-           'zeros', 'zeros_like', 'ones', 'ones_like', 'full', 'full_like',
+           'zeros', 'zeros_like', 'ones', 'ones_like', 'full', 'full_like', 'broadcast_to',
            'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power', 'bitwise_not', 'delete',
            'arctan2', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'invert',
            'sqrt', 'cbrt', 'abs', 'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log',
@@ -1998,7 +1998,7 @@ def squeeze(self, axis=None):  # pylint: disable=arguments-differ
         return _mx_np_op.squeeze(self, axis=axis)
 
     def broadcast_to(self, shape):  # pylint: disable=redefined-outer-name
-        return _mx_np_op.broadcast_to(self, shape)
+        return _mx_nd_np.broadcast_to(self, shape)
 
     def broadcast_like(self, other):
         raise AttributeError('mxnet.numpy.ndarray object has no attribute broadcast_like')
@@ -2283,6 +2283,34 @@ def ones(shape, dtype=_np.float32, order='C', ctx=None):  # pylint: disable=rede
     return _mx_nd_np.ones(shape, dtype, order, ctx)
 
 
+@set_module('mxnet.numpy')
+def broadcast_to(array, shape):  # pylint: disable=redefined-outer-name
+    """
+    Broadcast an array to a new shape.
+
+    Parameters
+    ----------
+    array : ndarray or scalar
+        The array to broadcast.
+    shape : tuple
+        The shape of the desired array.
+
+    Returns
+    -------
+    broadcast : array
+        A readonly view on the original array with the given shape. It is
+        typically not contiguous. Furthermore, more than one element of a
+        broadcasted array may refer to a single memory location.
+
+    Raises
+    ------
+    MXNetError
+        If the array is not compatible with the new shape according to NumPy's
+        broadcasting rules.
+    """
+    return _mx_nd_np.broadcast_to(array, shape)
+
+
 # pylint: disable=too-many-arguments, redefined-outer-name
 @set_module('mxnet.numpy')
 def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):
diff --git a/python/mxnet/numpy/stride_tricks.py b/python/mxnet/numpy/stride_tricks.py
index b4a4d0a7b44a..494404ed47f9 100644
--- a/python/mxnet/numpy/stride_tricks.py
+++ b/python/mxnet/numpy/stride_tricks.py
@@ -18,7 +18,7 @@
 """Util functions with broadcast."""
 
 from ..ndarray.ndarray import _get_broadcast_shape
-from . import _op as _mx_np_op
+from ..ndarray import numpy as _mx_nd_np
 
 
 __all__ = ['broadcast_arrays']
@@ -62,4 +62,4 @@ def broadcast_arrays(*args):
         # Common case where nothing needs to be broadcasted.
         return list(args)
 
-    return [_mx_np_op.broadcast_to(array, shape) for array in args]
+    return [_mx_nd_np.broadcast_to(array, shape) for array in args]
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index d3ed84ae0e49..80a74e789f77 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -37,7 +37,7 @@
     from builtins import slice as py_slice
 
 __all__ = ['zeros', 'zeros_like', 'ones', 'ones_like', 'full_like', 'empty_like', 'bitwise_not', 'invert', 'delete',
-           'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power', 'arctan2',
+           'add', 'broadcast_to', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power', 'arctan2',
            'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs', 'absolute', 'exp',
            'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2', 'log1p',
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor', 'histogram',
@@ -1132,6 +1132,36 @@ def bitwise_not(x, out=None, **kwargs):
     return _unary_func_helper(x, _npi.bitwise_not, _np.bitwise_not, out=out, **kwargs)
 
 
+@set_module('mxnet.symbol.numpy')
+def broadcast_to(array, shape):
+    """
+    Broadcast an array to a new shape.
+
+    Parameters
+    ----------
+    array : _Symbol or scalar
+        The array to broadcast.
+    shape : tuple
+        The shape of the desired array.
+
+    Returns
+    -------
+    broadcast : array
+        A readonly view on the original array with the given shape. It is
+        typically not contiguous. Furthermore, more than one element of a
+        broadcasted array may refer to a single memory location.
+
+    Raises
+    ------
+    MXNetError
+        If the array is not compatible with the new shape according to NumPy's
+        broadcasting rules.
+    """
+    if _np.isscalar(array):
+        return full(shape, array)
+    return _npi.broadcast_to(array, shape)
+
+
 @set_module('mxnet.symbol.numpy')
 def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):  # pylint: disable=too-many-arguments
     """
diff --git a/src/operator/numpy/np_broadcast_reduce_op_value.cc b/src/operator/numpy/np_broadcast_reduce_op_value.cc
index cf92da52d1f8..ad6ac9504d52 100644
--- a/src/operator/numpy/np_broadcast_reduce_op_value.cc
+++ b/src/operator/numpy/np_broadcast_reduce_op_value.cc
@@ -481,7 +481,7 @@ bool NumpyBroadcastToShape(const nnvm::NodeAttrs& attrs,
   return true;
 }
 
-NNVM_REGISTER_OP(_np_broadcast_to)
+NNVM_REGISTER_OP(_npi_broadcast_to)
 .set_num_inputs(1)
 .set_num_outputs(1)
 .set_attr<nnvm::FListInputNames>("FListInputNames",
diff --git a/src/operator/numpy/np_broadcast_reduce_op_value.cu b/src/operator/numpy/np_broadcast_reduce_op_value.cu
index 56194ff34a7e..684348fcaa37 100644
--- a/src/operator/numpy/np_broadcast_reduce_op_value.cu
+++ b/src/operator/numpy/np_broadcast_reduce_op_value.cu
@@ -68,7 +68,7 @@ NNVM_REGISTER_OP(_npi_std)
 NNVM_REGISTER_OP(_npi_var)
 .set_attr<FCompute>("FCompute<gpu>", NumpyMomentsForward<gpu, false>);
 
-NNVM_REGISTER_OP(_np_broadcast_to)
+NNVM_REGISTER_OP(_npi_broadcast_to)
 .set_attr<FCompute>("FCompute<gpu>", NumpyBroadcastToForward<gpu>);
 
 NNVM_REGISTER_OP(_backward_np_broadcast_to)
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 55a64c68aae9..d60b86a57d35 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -1533,6 +1533,15 @@ def __init__(self, dst_shape):
         def hybrid_forward(self, F, x):
             return F.np.broadcast_to(x, self._dst_shape)
 
+    class TestScalarBroadcastTo(HybridBlock):
+        def __init__(self, scalar, dst_shape):
+            super(TestScalarBroadcastTo, self).__init__()
+            self._scalar = scalar
+            self._dst_shape = dst_shape
+
+        def hybrid_forward(self, F, x):
+            return F.np.broadcast_to(self._scalar, self._dst_shape)
+
     shapes = [
         ((), (1, 2, 4, 5)),
         ((1,), (4, 5, 6)),
@@ -1558,6 +1567,17 @@ def hybrid_forward(self, F, x):
             expected_grad = collapse_sum_like(_np.ones_like(expected_ret), src_shape)
             assert_almost_equal(a_mx.grad.asnumpy(), expected_grad, rtol=1e-5, atol=1e-6, use_broadcast=False)
 
+    # Test scalar case
+    scalar = 1.0
+    for _, dst_shape in shapes:
+        for hybridize in [True, False]:
+            test_scalar_broadcast_to = TestScalarBroadcastTo(scalar, dst_shape)
+            expected_ret = _np.broadcast_to(scalar, dst_shape)
+            with mx.autograd.record():
+                # `np.empty(())` serves as a dummpy input
+                ret = test_scalar_broadcast_to(np.empty(()))
+            assert_almost_equal(ret.asnumpy(), expected_ret, rtol=1e-5, atol=1e-6, use_broadcast=False)
+
 
 @with_seed()
 @use_np

From c1f6d64dda023bbd48a28136fd633f3d15adf2ff Mon Sep 17 00:00:00 2001
From: Sam Skalicky <samskalicky@gmail.com>
Date: Wed, 8 Jan 2020 02:13:43 -0800
Subject: [PATCH 774/813] Enhancements for MXTensor for custom operators
 (#17204)

* initial commit

* added version initialization in c_api

* added isSame helper function

* fixed whitespace and isSame function

* changed version to verID

* changed update to setTensor

* changed isSame to operator==

* changed operator== back to isSame

* retrigger CI

* retrigger CI

* retrigger ci
---
 include/mxnet/lib_api.h | 90 ++++++++++++++++++++---------------------
 src/c_api/c_api.cc      | 17 +++++---
 2 files changed, 57 insertions(+), 50 deletions(-)

diff --git a/include/mxnet/lib_api.h b/include/mxnet/lib_api.h
index f21e484216ea..c7887aad378f 100644
--- a/include/mxnet/lib_api.h
+++ b/include/mxnet/lib_api.h
@@ -37,7 +37,7 @@
 #include <utility>
 #include <stdexcept>
 
-#define MX_LIBRARY_VERSION 1
+#define MX_LIBRARY_VERSION 2
 
 /*
  * Import from DLPack https://github.com/dmlc/dlpack/blob/master/include/dlpack/dlpack.h
@@ -198,6 +198,7 @@ enum MXDType {
   kInt32 = 4,
   kInt8  = 5,
   kInt64 = 6,
+  kUNSET = 100,
 };
 
 enum MXReturnValue {
@@ -209,10 +210,22 @@ enum MXReturnValue {
  * \brief Tensor data structure used by custom operator
  */
 struct MXTensor {
-  MXTensor() : data_ptr(NULL) {}
-
-  MXTensor(void *data_ptr, const std::vector<int64_t> &shape, MXDType dtype)
-  : data_ptr(data_ptr), shape(shape), dtype(dtype) {}
+  MXTensor() : data_ptr(NULL), dtype(kUNSET), verID(0) {}
+
+  MXTensor(void *data_ptr, const std::vector<int64_t> &shape, MXDType dtype,
+           size_t vID)
+  : data_ptr(data_ptr), shape(shape), dtype(dtype), verID(vID) {}
+
+  /*! \brief populate internal tensor fields */
+  void setTensor(void *dptr, MXDType type, const int64_t* dims,
+                 int ndims, size_t vID) {
+    data_ptr = dptr; dtype = type; verID = vID;
+    shape.clear();
+    for (int j = 0; j < ndims; j++) {
+      shape.push_back(dims[j]);
+    }
+    setDLTensor();
+  }
 
   /*! \brief populate DLTensor fields */
   void setDLTensor() {
@@ -277,6 +290,14 @@ struct MXTensor {
     return size;
   }
 
+  /*! \brief helper function to compare two MXTensors */
+  inline bool isSame(const MXTensor &oth) const {
+    return data_ptr == oth.data_ptr &&
+      dtype == oth.dtype &&
+      verID == oth.verID &&
+      shape == oth.shape;
+  }
+
   // data is flatten 1D repr of tensor, elements are in continuous memory
   // user can access each element using the shape of tensor
   void *data_ptr;
@@ -287,6 +308,9 @@ struct MXTensor {
   // type can only be MXDType enum types
   MXDType dtype;
 
+  // version number updated if the tensor has changed since the last use by custom op
+  size_t verID;
+
   // corresponding DLTensor repr of MXTensor
   // easy way to reuse functions taking DLTensor
   DLTensor dltensor;
@@ -684,15 +708,9 @@ typedef int (*opCallInferType_t)(inferType_t, const char* const*, const char* co
 
 #define MXLIB_OPCALLFCOMP_STR "_opCallFCompute"
 typedef int (*opCallFComp_t)(fcomp_t, const char* const*, const char* const*, int,
-                           const int64_t**, int*, void**, int*, int,
-                           const int64_t**, int*, void**, int*, int,
-                           xpu_malloc_t, void*);
-
-#define MXLIB_OPCALLBKWD_STR "_opCallBackward"
-typedef int (*opCallBkwd_t)(fcomp_t, const char* const*, const char* const*, int,
-                            const int64_t**, int*, void**, int*, int,
-                            const int64_t**, int*, void**, int*, int,
-                            xpu_malloc_t, void*);
+                             const int64_t**, int*, void**, int*, size_t*, int,
+                             const int64_t**, int*, void**, int*, size_t*, int,
+                             xpu_malloc_t, void*);
 
 #define MXLIB_OPCALLMUTATEINPUTS_STR "_opCallMutateInputs"
 typedef int (*opCallMutateInputs_t)(mutateInputs_t, const char* const*, const char* const*, int,
@@ -703,9 +721,9 @@ typedef int (*opCallCreateOpState_t)(createOpState_t, const char* const*, const
                                      void**);
 
 #define MXLIB_OPCALLFSTATEFULCOMP_STR "_opCallFStatefulCompute"
-typedef int (*opCallFStatefulComp_t)(bool, void*, const int64_t**, int*, void**, int*, int,
-                                     const int64_t**, int*, void**, int*, int,
-                                     xpu_malloc_t, void*);
+typedef int (*opCallFStatefulComp_t)(bool, void*, const int64_t**, int*, void**, int*, size_t*,
+                                     int, const int64_t**, int*, void**, int*, size_t*,
+                                     int, xpu_malloc_t, void*);
 
 #define MXLIB_INITIALIZE_STR "initialize"
 typedef int (*initialize_t)(int);
@@ -876,9 +894,9 @@ extern "C" {
   _opCallFCompute(fcomp_t fcomp, const char* const* keys,
                   const char* const* vals, int num,
                   const int64_t** inshapes, int* indims,
-                  void** indata, int* intypes, int num_in,
+                  void** indata, int* intypes, size_t* inIDs, int num_in,
                   const int64_t** outshapes, int* outdims,
-                  void** outdata, int* outtypes, int num_out,
+                  void** outdata, int* outtypes, size_t* outIDs, int num_out,
                   xpu_malloc_t cpu_malloc, void* cpu_alloc) {
     // create map of attributes from list
     std::map<std::string, std::string> attrs;
@@ -889,23 +907,14 @@ extern "C" {
     // create a vector of tensors for inputs
     std::vector<MXTensor> inputs(num_in);
     for (int i = 0; i < num_in; i++) {
-      inputs[i].data_ptr = indata[i];
-      inputs[i].dtype = (MXDType)intypes[i];
-      for (int j = 0; j < indims[i]; j++) {
-        inputs[i].shape.push_back(inshapes[i][j]);
-      }
-      inputs[i].setDLTensor();
+      inputs[i].setTensor(indata[i], (MXDType)intypes[i], inshapes[i], indims[i], inIDs[i]);
     }
 
     // create a vector of tensors for outputs
     std::vector<MXTensor> outputs(num_out);
     for (int i = 0; i < num_out; i++) {
-      outputs[i].data_ptr = outdata[i];
-      outputs[i].dtype = (MXDType) outtypes[i];
-      for (int j = 0; j < outdims[i]; j++) {
-        outputs[i].shape.push_back(outshapes[i][j]);
-      }
-      outputs[i].setDLTensor();
+      outputs[i].setTensor(outdata[i], (MXDType)outtypes[i], outshapes[i], outdims[i],
+                           outIDs[i]);
     }
 
     OpResource res(cpu_malloc, cpu_alloc);
@@ -973,30 +982,21 @@ extern "C" {
 #endif
   _opCallFStatefulCompute(bool is_forward, void* state_op,
                           const int64_t** inshapes, int* indims,
-                          void** indata, int* intypes, int num_in,
+                          void** indata, int* intypes, size_t* inIDs, int num_in,
                           const int64_t** outshapes, int* outdims,
-                          void** outdata, int* outtypes, int num_out,
+                          void** outdata, int* outtypes, size_t* outIDs, int num_out,
                           xpu_malloc_t cpu_malloc, void* cpu_alloc) {
     // create a vector of tensors for inputs
     std::vector<MXTensor> inputs(num_in);
     for (int i = 0; i < num_in; i++) {
-      inputs[i].data_ptr = indata[i];
-      inputs[i].dtype = (MXDType)intypes[i];
-      for (int j = 0; j < indims[i]; j++) {
-        inputs[i].shape.push_back(inshapes[i][j]);
-      }
-      inputs[i].setDLTensor();
+      inputs[i].setTensor(indata[i], (MXDType)intypes[i], inshapes[i], indims[i], inIDs[i]);
     }
 
     // create a vector of tensors for outputs
     std::vector<MXTensor> outputs(num_out);
     for (int i = 0; i < num_out; i++) {
-      outputs[i].data_ptr = outdata[i];
-      outputs[i].dtype = (MXDType) outtypes[i];
-      for (int j = 0; j < outdims[i]; j++) {
-        outputs[i].shape.push_back(outshapes[i][j]);
-      }
-      outputs[i].setDLTensor();
+      outputs[i].setTensor(outdata[i], (MXDType)outtypes[i], outshapes[i], outdims[i],
+                           outIDs[i]);
     }
     OpResource res(cpu_malloc, cpu_alloc);
     CustomStatefulOp* op_ptr = reinterpret_cast<CustomStatefulOp*>(state_op);
diff --git a/src/c_api/c_api.cc b/src/c_api/c_api.cc
index ca39ef22b20a..bb98d96f733a 100644
--- a/src/c_api/c_api.cc
+++ b/src/c_api/c_api.cc
@@ -395,6 +395,7 @@ int MXLoadLib(const char *path) {
       std::vector<const int64_t *> in_shapes, out_shapes;
       std::vector<int> in_dims, out_dims;
       std::vector<int> in_types, out_types;
+      std::vector<size_t> in_verIDs, out_verIDs;
 
       // convert input tensors to constituent parts
       for (size_t i = 0; i < inputs.size(); i++) {
@@ -402,6 +403,7 @@ int MXLoadLib(const char *path) {
         in_shapes.push_back(inputs[i].shape().data());
         in_dims.push_back(inputs[i].shape().ndim());
         in_types.push_back(inputs[i].dtype());
+        in_verIDs.push_back(inputs[i].version());
       }
 
       // convert output tensors to constituent parts
@@ -410,6 +412,7 @@ int MXLoadLib(const char *path) {
         out_shapes.push_back(outputs[i].shape().data());
         out_dims.push_back(outputs[i].shape().ndim());
         out_types.push_back(outputs[i].dtype());
+        out_verIDs.push_back(outputs[i].version());
       }
 
       // get memory resource
@@ -438,9 +441,10 @@ int MXLoadLib(const char *path) {
       // call fcompute function
       CHECK(callFComp(fcomp_fp, attr_keys.data(), attr_vals.data(), attr_keys.size(),
                       in_shapes.data(), in_dims.data(), in_data.data(),
-                      in_types.data(), in_data.size(),
+                      in_types.data(), in_verIDs.data(), in_data.size(),
                       out_shapes.data(), out_dims.data(), out_data.data(),
-                      out_types.data(), out_data.size(), cpu_malloc, &cpu_alloc))
+                      out_types.data(), out_verIDs.data(), out_data.size(),
+                      cpu_malloc, &cpu_alloc))
       << "Error calling FCompute for custom operator '" << name_str << "'";
 
       // return type void
@@ -570,6 +574,7 @@ int MXLoadLib(const char *path) {
       std::vector<const int64_t *> in_shapes, out_shapes;
       std::vector<int> in_dims, out_dims;
       std::vector<int> in_types, out_types;
+      std::vector<size_t> in_verIDs, out_verIDs;
 
       // convert input tensors to constituent parts
       for (size_t i = 0; i < inputs.size(); i++) {
@@ -577,6 +582,7 @@ int MXLoadLib(const char *path) {
         in_shapes.push_back(inputs[i].shape().data());
         in_dims.push_back(inputs[i].shape().ndim());
         in_types.push_back(inputs[i].dtype());
+        in_verIDs.push_back(inputs[i].version());
       }
 
       // convert output tensors to constituent parts
@@ -585,6 +591,7 @@ int MXLoadLib(const char *path) {
         out_shapes.push_back(outputs[i].shape().data());
         out_dims.push_back(outputs[i].shape().ndim());
         out_types.push_back(outputs[i].dtype());
+        out_verIDs.push_back(outputs[i].version());
       }
 
       // get memory resource
@@ -618,9 +625,9 @@ int MXLoadLib(const char *path) {
 
       // call fcompute function
       CHECK(callFStatefulComp(is_forward, state_op_inst, in_shapes.data(), in_dims.data(),
-                              in_data.data(), in_types.data(), in_data.size(),
-                              out_shapes.data(), out_dims.data(), out_data.data(),
-                              out_types.data(), out_data.size(), cpu_malloc, &cpu_alloc))
+                              in_data.data(), in_types.data(), in_verIDs.data(), in_data.size(),
+                              out_shapes.data(), out_dims.data(), out_data.data(), out_types.data(),
+                              out_verIDs.data(), out_data.size(), cpu_malloc, &cpu_alloc))
       << "Error calling FStatefulCompute for custom operator '" << name_str << "'";
     };
 

From ddeac2e66fd06e4e220e5cc6f3bd2dd4555ddd94 Mon Sep 17 00:00:00 2001
From: Sam Skalicky <samskalicky@gmail.com>
Date: Wed, 8 Jan 2020 16:01:45 -0800
Subject: [PATCH 775/813] Dynamic subgraph property (#17034)

* initial commit

* added subgraphCreate API function to partition the model/graph
added partitioner registration to c_api

* cleaned up API, added API to specify subgraphOp

* fixed whitespace

* added custom subgraph property

* fixed whitespace

* fixed subgraph property

* fixed whitespace

* added registration of custom subgraph property in c_api

* added support for calling supportOps from customSubgraphProperty

* sending symbol json to supportedOps API
example is printing

* working partitioning example

* fixed whitespace

* fixed whitespace

* fixed style

* fixed style

* Annotate the symbol json string with shape/type info from the graph object

* Support multiple supportOps functions grouped together. Similar to how subgraphProperties are grouped into subgraphBackends

* fixed whitespace

* changed vector to unordered_set for performance

* fixed clearing supportedNodes set after multiple calls
Added example executor in library and implementations for ops

* changed types to int64_t for tensor sizes, fixed indent

* added support for passing options to supportedOps

* new API to to review subgraphs after partitioning

* added function to revert subgraphing if rejected by subgraph prop

* added infrastructure to test subgraph libs in the CI

* fixed whitespace

* changed default context to CPU since GPU is not supported yet

* fixed

* added include

* fixed return types

* added memory free in error condition

* added clearer function comment

* added file comment

* added more prints in example test for clarity

* enhancements from review

* retrigger CI

* stopped passing bool types across ABI boundary (changed to int)

* removed subgraph op from custom_op example

* changed subgraph example to use isSubgraphOp

* added functionality to set "isArg" attribute for subgraph inputs

* added comment

* added functionality to set aux attribute for inputs to subgraph

* fixed whitespace and cmakelists

* changed acceptSubgraph API to let library set attributes on subgraph node

* fixed whitespace

* fixed seconds

* retrigger ci

* removed unused code

* fixed variable naming
---
 CMakeLists.txt                                |   5 +
 Makefile                                      |   8 +-
 ci/jenkins/Jenkins_steps.groovy               |  14 +-
 example/extensions/lib_custom_op/Makefile     |   7 +-
 .../extensions/lib_custom_op/subgraph_lib.cc  |  98 -------
 example/extensions/lib_subgraph/Makefile      |  24 ++
 .../extensions/lib_subgraph/subgraph_lib.cc   | 250 ++++++++++++++++++
 .../test_subgraph.py                          |  46 ++--
 include/mxnet/lib_api.h                       | 185 ++++++++++++-
 src/c_api/c_api.cc                            |  77 +++++-
 src/operator/subgraph/build_subgraph.cc       |  20 ++
 .../partitioner/custom_subgraph_property.h    | 235 ++++++++++++++++
 src/operator/subgraph/subgraph_property.h     |   9 +
 tests/python/unittest/test_extensions.py      |  64 +++++
 14 files changed, 903 insertions(+), 139 deletions(-)
 delete mode 100644 example/extensions/lib_custom_op/subgraph_lib.cc
 create mode 100644 example/extensions/lib_subgraph/Makefile
 create mode 100644 example/extensions/lib_subgraph/subgraph_lib.cc
 rename example/extensions/{lib_custom_op => lib_subgraph}/test_subgraph.py (52%)
 create mode 100644 src/operator/subgraph/partitioner/custom_subgraph_property.h

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 8a1da1e92f6c..2612854ffdce 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -676,7 +676,9 @@ else()
 endif()
 
 add_library(sample_lib SHARED ${CMAKE_CURRENT_SOURCE_DIR}/example/extensions/lib_custom_op/gemm_lib.cc)
+add_library(subgraph_lib SHARED ${CMAKE_CURRENT_SOURCE_DIR}/example/extensions/lib_subgraph/subgraph_lib.cc)
 target_include_directories(sample_lib PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/include/mxnet)
+target_include_directories(subgraph_lib PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/include/mxnet)
 set(MXNET_INSTALL_TARGETS mxnet)
 if(UNIX)
   string(APPEND CMAKE_CUDA_FLAGS "${CUDA_ARCH_FLAGS_SPACES}")
@@ -690,10 +692,13 @@ if(UNIX)
   target_link_libraries(mxnet PRIVATE mxnet_static)
   target_link_libraries(mxnet_static PUBLIC ${CMAKE_DL_LIBS})
   target_compile_options(sample_lib PUBLIC -shared)
+  target_compile_options(subgraph_lib PUBLIC -shared)
   set_target_properties(mxnet_static PROPERTIES OUTPUT_NAME mxnet)
 elseif(MSVC)
   target_compile_options(sample_lib PUBLIC /LD)
+  target_compile_options(subgraph_lib PUBLIC /LD)
   set_target_properties(sample_lib PROPERTIES PREFIX "lib")
+  set_target_properties(subgraph_lib PROPERTIES PREFIX "lib")
 
   if(USE_CUDA)
     if(MSVC)
diff --git a/Makefile b/Makefile
index 7e65e5a7f1b7..44c719f6d4d9 100644
--- a/Makefile
+++ b/Makefile
@@ -457,7 +457,7 @@ endif
 .PHONY: clean all extra-packages test lint clean_all rcpplint rcppexport roxygen\
 	cython2 cython3 cython cyclean
 
-all: lib/libmxnet.a lib/libmxnet.so $(BIN) extra-packages sample_lib
+all: lib/libmxnet.a lib/libmxnet.so $(BIN) extra-packages sample_lib subgraph_lib
 
 SRC = $(wildcard src/*/*/*/*.cc src/*/*/*.cc src/*/*.cc src/*.cc)
 OBJ = $(patsubst %.cc, build/%.o, $(SRC))
@@ -667,6 +667,8 @@ pylint:
 # sample lib for MXNet extension dynamically loading custom operator
 sample_lib:
 	$(CXX) -shared -fPIC -std=c++11 example/extensions/lib_custom_op/gemm_lib.cc -o libsample_lib.so -I include/mxnet
+subgraph_lib:
+	$(CXX) -shared -fPIC -std=c++11 example/extensions/lib_subgraph/subgraph_lib.cc -o libsubgraph_lib.so -I include/mxnet
 
 # Cython build
 cython:
@@ -762,7 +764,7 @@ clean: rclean cyclean $(EXTRA_PACKAGES_CLEAN)
 	cd $(NNVM_PATH); $(MAKE) clean; cd -
 	cd $(TVM_PATH); $(MAKE) clean; cd -
 	cd $(AMALGAMATION_PATH); $(MAKE) clean; cd -
-	$(RM) libsample_lib.so
+	$(RM) libsample_lib.so libsubgraph_lib.so
 	$(RM) -r  $(patsubst %, %/*.d, $(EXTRA_OPERATORS)) $(patsubst %, %/*/*.d, $(EXTRA_OPERATORS))
 	$(RM) -r  $(patsubst %, %/*.o, $(EXTRA_OPERATORS)) $(patsubst %, %/*/*.o, $(EXTRA_OPERATORS))
 else
@@ -774,7 +776,7 @@ clean: rclean mkldnn_clean cyclean testclean $(EXTRA_PACKAGES_CLEAN)
 	cd $(NNVM_PATH); $(MAKE) clean; cd -
 	cd $(TVM_PATH); $(MAKE) clean; cd -
 	cd $(AMALGAMATION_PATH); $(MAKE) clean; cd -
-	$(RM) libsample_lib.so
+	$(RM) libsample_lib.so libsubgraph_lib.so
 endif
 
 clean_all: clean
diff --git a/ci/jenkins/Jenkins_steps.groovy b/ci/jenkins/Jenkins_steps.groovy
index 81479328f054..c697e1e58788 100644
--- a/ci/jenkins/Jenkins_steps.groovy
+++ b/ci/jenkins/Jenkins_steps.groovy
@@ -23,23 +23,23 @@
 utils = load('ci/Jenkinsfile_utils.groovy')
 
 // mxnet libraries
-mx_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, lib/tvmop.conf, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a'
-mx_lib_cython = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, lib/tvmop.conf, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
+mx_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, lib/tvmop.conf, libsample_lib.so, libsubgraph_lib.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a'
+mx_lib_cython = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, lib/tvmop.conf, libsample_lib.so, libsubgraph_lib.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
 
 // Python wheels
 mx_pip = 'build/*.whl'
 
 // mxnet cmake libraries, in cmake builds we do not produce a libnvvm static library by default.
 mx_cmake_lib = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/tvmop.conf, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so'
-mx_cmake_lib_no_tvm_op = 'build/libmxnet.so, build/libmxnet.a, build/libsample_lib.so, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so'
+mx_cmake_lib_no_tvm_op = 'build/libmxnet.so, build/libmxnet.a, build/libsample_lib.so, build/libsubgraph_lib.so, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so'
 mx_cmake_lib_cython = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/tvmop.conf, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
 // mxnet cmake libraries, in cmake builds we do not produce a libnvvm static library by default.
-mx_cmake_lib_debug = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/tvmop.conf, build/libsample_lib.so, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests'
+mx_cmake_lib_debug = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/tvmop.conf, build/libsample_lib.so, build/libsubgraph_lib.so, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests'
 mx_cmake_mkldnn_lib = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/tvmop.conf, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so'
-mx_mkldnn_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, lib/tvmop.conf, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a'
+mx_mkldnn_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, lib/tvmop.conf, libsample_lib.so, libsubgraph_lib.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a'
 mx_tensorrt_lib = 'build/libmxnet.so, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/tvmop.conf, lib/libnvonnxparser_runtime.so.0, lib/libnvonnxparser.so.0, lib/libonnx_proto.so, lib/libonnx.so'
-mx_lib_cpp_examples = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, lib/tvmop.conf, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, 3rdparty/ps-lite/build/libps.a, deps/lib/libprotobuf-lite.a, deps/lib/libzmq.a, build/cpp-package/example/*, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
-mx_lib_cpp_examples_no_tvm_op = 'lib/libmxnet.so, lib/libmxnet.a, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, 3rdparty/ps-lite/build/libps.a, deps/lib/libprotobuf-lite.a, deps/lib/libzmq.a, build/cpp-package/example/*, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
+mx_lib_cpp_examples = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, lib/tvmop.conf, libsample_lib.so, libsubgraph_lib.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, 3rdparty/ps-lite/build/libps.a, deps/lib/libprotobuf-lite.a, deps/lib/libzmq.a, build/cpp-package/example/*, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
+mx_lib_cpp_examples_no_tvm_op = 'lib/libmxnet.so, lib/libmxnet.a, libsample_lib.so, libsubgraph_lib.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, 3rdparty/ps-lite/build/libps.a, deps/lib/libprotobuf-lite.a, deps/lib/libzmq.a, build/cpp-package/example/*, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
 mx_lib_cpp_examples_cpu = 'build/libmxnet.so, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/tvmop.conf, build/cpp-package/example/*'
 
 // Python unittest for CPU
diff --git a/example/extensions/lib_custom_op/Makefile b/example/extensions/lib_custom_op/Makefile
index 66079a16a338..090d17d98a22 100644
--- a/example/extensions/lib_custom_op/Makefile
+++ b/example/extensions/lib_custom_op/Makefile
@@ -15,13 +15,10 @@
 # specific language governing permissions and limitations
 # under the License.
 
-all: subgraph_lib gemm_lib
+all: gemm_lib
 
 gemm_lib:
 	g++ -shared -fPIC -std=c++11 gemm_lib.cc -o libgemm_lib.so -I ../../../include/mxnet
 
-subgraph_lib:
-	g++ -shared -fPIC -std=c++11 subgraph_lib.cc -o libsubgraph_lib.so -I ../../../include/mxnet
-
 clean:
-	rm -rf libsubgraph_lib.so libgemm_lib.so
+	rm -rf libgemm_lib.so
diff --git a/example/extensions/lib_custom_op/subgraph_lib.cc b/example/extensions/lib_custom_op/subgraph_lib.cc
deleted file mode 100644
index 27da0fd9a324..000000000000
--- a/example/extensions/lib_custom_op/subgraph_lib.cc
+++ /dev/null
@@ -1,98 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- */
-
-/*!
- * Copyright (c) 2019 by Contributors
- * \file subgraph_lib.cc
- * \brief subgraph operator implementation library file
- */
-
-#include <iostream>
-#include "lib_api.h"
-
-MXReturnValue parseAttrs(std::map<std::string, std::string> attrs,
-                         int* num_in, int* num_out) {
-  *num_in = 1;
-  *num_out = 1;
-  if (attrs.count(SUBGRAPH_SYM_JSON)) {
-    // example of subgraph json parsing
-    JsonParser jp;
-    JsonVal val = jp.parse_to_json(attrs[SUBGRAPH_SYM_JSON]);
-    int input = 0;
-    for (auto &item : val.map[JsonVal("nodes")].list) {
-      if (item.map[JsonVal("op")].str == "null")
-        input++;
-    }
-    int output = val.map[JsonVal("heads")].list.size();
-    *num_in = input;
-    *num_out = output;
-  }
-  return MX_SUCCESS;
-}
-
-class MyStatefulOp : public CustomStatefulOp {
- public:
-  explicit MyStatefulOp(std::string sym) : subgraph_sym(sym) {}
-
-  MXReturnValue Forward(std::vector<MXTensor> inputs,
-                        std::vector<MXTensor> outputs,
-                        OpResource op_res) {
-    std::cout << "Info: subgraph symbol is: " << std::endl;
-    std::cout << subgraph_sym << std::endl;
-    float* in_data = inputs[0].data<float>();
-    float* out_data = outputs[0].data<float>();
-    std::cout << "Info: output is: " << std::endl;
-    for (int i = 0; i < inputs[0].size(); i++) {
-      out_data[i] = in_data[i];
-    }
-    return MX_SUCCESS;
-  }
-
- private:
-  std::string subgraph_sym;
-};
-
-MXReturnValue createOpState(std::map<std::string, std::string> attrs,
-                            CustomStatefulOp** op_inst) {
-  std::string serialized_subgraph = "[empty]";
-  // MXNet subgraph is stored as Symbol in operator node attrs subgraphs field
-  // custom subgraph is stored as json string in custom operator attrs map entry
-  if (attrs.count(SUBGRAPH_SYM_JSON)) {
-    // user can now parse json and run other custom ops inside subgraph
-    serialized_subgraph = attrs[SUBGRAPH_SYM_JSON];
-  }
-  *op_inst = new MyStatefulOp(serialized_subgraph);
-  std::cout << "Info: stateful operator created" << std::endl;
-  return MX_SUCCESS;
-}
-
-REGISTER_OP(_custom_subgraph_op)
-.setParseAttrs(parseAttrs)
-.setIsSubgraphOp()
-.setCreateOpState(createOpState);
-
-MXReturnValue initialize(int version) {
-  if (version >= 10400) {
-    std::cout << "MXNet version " << version << " supported" << std::endl;
-    return MX_SUCCESS;
-  } else {
-    std::cout << "MXNet version " << version << " not supported" << std::endl;
-    return MX_FAIL;
-  }
-}
diff --git a/example/extensions/lib_subgraph/Makefile b/example/extensions/lib_subgraph/Makefile
new file mode 100644
index 000000000000..c45100b69ef7
--- /dev/null
+++ b/example/extensions/lib_subgraph/Makefile
@@ -0,0 +1,24 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+all: subgraph_lib
+
+subgraph_lib:
+	g++ -shared -fPIC -std=c++11 subgraph_lib.cc -o libsubgraph_lib.so -I ../../../include/mxnet
+
+clean:
+	rm -rf libsubgraph_lib.so
diff --git a/example/extensions/lib_subgraph/subgraph_lib.cc b/example/extensions/lib_subgraph/subgraph_lib.cc
new file mode 100644
index 000000000000..3ebdfc138a79
--- /dev/null
+++ b/example/extensions/lib_subgraph/subgraph_lib.cc
@@ -0,0 +1,250 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file subgraph_lib.cc
+ * \brief subgraph operator implementation library file
+ */
+
+#include <math.h>
+#include <iostream>
+#include <algorithm>
+#include "lib_api.h"
+
+/* function to execute log operator on floats */
+void myLog(MXTensor &in, MXTensor &out) {
+  float* inp = in.data<float>();
+  float* outp = out.data<float>();
+  for (int64_t i = 0; i < in.size(); i++) {
+    outp[i] = logf(inp[i]);
+  }
+}
+/* function to execute exp operator on floats */
+void myExp(MXTensor &in, MXTensor &out) {
+  float* inp = in.data<float>();
+  float* outp =out.data<float>();
+  for (int64_t i = 0; i < in.size(); i++) {
+    outp[i] = expf(inp[i]);
+  }
+}
+
+/* function to execute ops in subgraph
+ * In MXNet, subgraphs are sorted in topological order
+ * so all we need to do is go through the ops in order
+ * and execute each op. 
+ */
+MXReturnValue myExecutor(std::vector<MXTensor> inputs,
+                         std::vector<MXTensor> outputs,
+                         std::string subgraph_sym) {
+  std::cout << "Info: subgraph symbol is: " << std::endl;
+  std::cout << subgraph_sym << std::endl;
+
+  // convert json string to json object
+  JsonParser parser;
+  JsonVal json_val = parser.parse_to_json(subgraph_sym);
+  // get nodes list
+  JsonVal nodes = json_val.map[JsonVal("nodes")];
+  //counter for inputs
+  int input_cnt = 0;
+  // temporary tensor storage
+  std::vector<MXTensor> data;
+  // track memory allocations to free later
+  std::vector<void*> to_free;
+
+  // loop over nodes
+  for(int i=0; i<nodes.list.size(); i++) {
+    JsonVal node = nodes.list[i];
+    // get the op name
+    std::string op = node.map[JsonVal("op")].str;
+    // get node ID inputs to op
+    JsonVal node_inputs = node.map[JsonVal("inputs")];
+    
+    // handle each op type
+    if (op.compare("null") == 0) {
+      // null is an input data to the subgraph, add to data storage
+      data.push_back(inputs[input_cnt++]);
+    } else if (op.compare("log") == 0) {
+      // get input tensor based on node ID inputs from data storage
+      MXTensor &input = data[node_inputs.list[0].list[0].num];
+      // create temporary storage
+      MXTensor tmp(malloc(input.size()*4), input.shape, input.dtype, 0);
+      // save allocated ptr to free later
+      to_free.push_back(tmp.data_ptr);
+      // execute log operator
+      myLog(input,tmp);
+      // add output tensor to data storage
+      data.push_back(tmp);
+    } else if (op.compare("exp") == 0) {
+      // get input tensor based on node ID inputs from data storage
+      MXTensor &input = data[node_inputs.list[0].list[0].num];
+      // create temporary storage
+      MXTensor tmp(malloc(input.size()*4), input.shape, input.dtype, 0);
+      // save allocated ptr to free later
+      to_free.push_back(tmp.data_ptr);
+      // execute exp operator 
+      myExp(input,tmp);
+      // add output tensor to data storage
+      data.push_back(tmp);
+    } else {
+      std::cout << "Error! Unsupported op '" << op << "' found in myExecutor";
+      // free allocated temporary storage
+      for (void* ptr : to_free)
+        free(ptr);
+      return MX_FAIL;
+    }
+  }
+  
+  // get list of outputs from subgraph
+  JsonVal heads = json_val.map[JsonVal("heads")];
+  // copy all operator results to outputs of subgraph
+  for (int j = 0; j < heads.list.size(); j++) {
+    // get computed result
+    MXTensor &result = data[heads.list[0].list[0].num];
+    // get output tensor to pass to MX
+    MXTensor &out = outputs[j];
+    float *out_data = out.data<float>();
+    float *res_data = result.data<float>();
+    // loop and copy data
+    for (int64_t i = 0; i < result.size(); i++) {
+      out_data[i] = res_data[i];
+    }
+  }
+
+  // free allocated temporary storage
+  for (void* ptr : to_free) {
+    free(ptr);
+  }
+  
+  return MX_SUCCESS;
+}
+
+class MyStatefulOp : public CustomStatefulOp {
+ public:
+  explicit MyStatefulOp(std::string sym, std::map<std::string, std::string> attrs)
+    : subgraph_sym(sym), attrs_(attrs) {
+    for (auto kv : attrs) {
+      std::cout << "subgraphOp attributes: " << kv.first << " ==> " << kv.second << std::endl;
+    }
+  }
+
+  MXReturnValue Forward(std::vector<MXTensor> inputs,
+                        std::vector<MXTensor> outputs,
+                        OpResource op_res) {
+    return myExecutor(inputs, outputs, subgraph_sym);
+  }
+
+ private:
+  std::string subgraph_sym;
+  std::map<std::string, std::string> attrs_;
+};
+
+MXReturnValue createOpState(std::map<std::string, std::string> attrs,
+                            CustomStatefulOp** op_inst) {
+  std::string serialized_subgraph = "[empty]";
+  // MXNet subgraph is stored as Symbol in operator node attrs subgraphs field
+  // custom subgraph is stored as json string in custom operator attrs map entry
+  if (attrs.count(SUBGRAPH_SYM_JSON)) {
+    // user can now parse json and run other custom ops inside subgraph
+    serialized_subgraph = attrs[SUBGRAPH_SYM_JSON];
+  }
+  attrs.erase(SUBGRAPH_SYM_JSON);
+  *op_inst = new MyStatefulOp(serialized_subgraph, attrs);
+  std::cout << "Info: stateful operator created" << std::endl;
+  return MX_SUCCESS;
+}
+
+REGISTER_OP(_custom_subgraph_op)
+.setIsSubgraphOp()
+.setCreateOpState(createOpState);
+
+const std::vector<std::string> op_names({"exp","log"});
+
+MXReturnValue mySupportedOps(std::string json,
+                             const int num_ids,
+                             int *ids,
+                             std::unordered_map<std::string, std::string>& options) {
+  for (auto kv : options) {
+    std::cout << "option: " << kv.first << " ==> " << kv.second << std::endl;
+  }
+  //convert json string to json object
+  JsonParser parser;
+  JsonVal json_val = parser.parse_to_json(json);
+  //get nodes list
+  JsonVal nodes = json_val.map[JsonVal("nodes")];
+
+  //loop over nodes
+  for(int i=0; i<nodes.list.size(); i++) {
+    JsonVal node = nodes.list[i];
+    JsonVal op = node.map[JsonVal("op")];
+
+    //get shape/type if available
+    std::string shape;
+    int dtype = -1;
+    if(node.map.find(JsonVal("attrs")) != node.map.end()) {
+      JsonVal attrs = node.map[JsonVal("attrs")];
+      if(attrs.map.find(JsonVal("shape")) != attrs.map.end()) 
+        shape = attrs.map[JsonVal("shape")].str;
+      if(attrs.map.find(JsonVal("dtype")) != attrs.map.end())
+        dtype = std::stoi(attrs.map[JsonVal("dtype")].str);
+    }
+
+    //check if op dtype is float
+    if(dtype == kFloat32) {
+      //check if op is in whitelist
+      if(std::find(op_names.begin(),op_names.end(),op.str.c_str()) != op_names.end()) {
+        // found op in whitelist, set value to 1 to include op in subgraph
+        ids[i]=1;
+      }
+    }
+  }
+  return MX_SUCCESS;
+}
+
+MXReturnValue myAcceptSubgraph(std::string json, int subraph_id, bool* accept,
+                               std::unordered_map<std::string, std::string>& options,
+                               std::unordered_map<std::string, std::string>& attrs) {
+  for (auto kv : options) {
+    std::cout << "option: " << kv.first << " ==> " << kv.second << std::endl;
+  }
+  if(options.find("reject") != options.end() &&
+     options["reject"].compare("True") == 0) {
+    *accept = false;
+    std::cout << "rejecting subgraph" << std::endl;
+  } else {
+    *accept = true;
+    std::cout << "accepting subgraph" << std::endl;
+    attrs["myKey"] = "myVal";
+  }
+  return MX_SUCCESS;
+}
+
+REGISTER_PARTITIONER(myProp)
+.addStrategy("strategy1", mySupportedOps, "_custom_subgraph_op")
+.setAcceptSubgraph("strategy1", myAcceptSubgraph);
+
+MXReturnValue initialize(int version) {
+  if (version >= 10400) {
+    std::cout << "MXNet version " << version << " supported" << std::endl;
+    return MX_SUCCESS;
+  } else {
+    std::cout << "MXNet version " << version << " not supported" << std::endl;
+    return MX_FAIL;
+  }
+}
diff --git a/example/extensions/lib_custom_op/test_subgraph.py b/example/extensions/lib_subgraph/test_subgraph.py
similarity index 52%
rename from example/extensions/lib_custom_op/test_subgraph.py
rename to example/extensions/lib_subgraph/test_subgraph.py
index 2625b13f6794..8169261d4d42 100644
--- a/example/extensions/lib_custom_op/test_subgraph.py
+++ b/example/extensions/lib_subgraph/test_subgraph.py
@@ -39,22 +39,38 @@
 b = mx.sym.var('b')
 c = a + b
 d = mx.sym.exp(c)
-ret = mx.sym.log(d)
+sym = mx.sym.log(d)
 
-op_names = ['exp','log']
-out = SymbolHandle()
+#execute in MXNet
+print('-------------------------------')
+print('Testing regular MXNet execution')
+exe = sym.bind(ctx=mx.cpu(), args={'a':mx.nd.ones((3,2)), 'b':mx.nd.ones((3,2))})
+out = exe.forward()
+print(out)
 
-check_call(_LIB.MXBuildSubgraphByOpNames(ret.handle,
-                                         c_str('default'),
-                                         mx_uint(len(op_names)),
-                                         c_str_array(op_names),
-                                         ctypes.byref(out)))
-partitioned_sym = mx.sym.Symbol(out)
-json_sym = partitioned_sym.tojson()
+# with propogating shapes/types
+print('-------------------------------')
+print('Testing partitioning with shapes/types')
+arg_array = [mx.nd.ones((3,2),dtype='float32'), mx.nd.ones((3,2),dtype='float32')]
+mysym2 = sym.optimize_for("myProp",arg_array)
+print(mysym2.tojson())
+exe2 = mysym2.bind(ctx=mx.cpu(), args={'a':mx.nd.ones((3,2)), 'b':mx.nd.ones((3,2))})
+out2 = exe2.forward()
+print(out2)
 
-mystr = json_sym.replace("_CachedOp","_custom_subgraph_op")
-mysym = mx.sym.load_json(mystr)
+# with propogating shapes/types, rejecting subgraph
+print('-------------------------------')
+print('Testing partitioning with shapes/types - rejecting subgraph')
+arg_array = [mx.nd.ones((3,2),dtype='float32'), mx.nd.ones((3,2),dtype='float32')]
+mysym2 = sym.optimize_for("myProp", arg_array, reject=True)
+exe2 = mysym2.bind(ctx=mx.cpu(), args={'a':mx.nd.ones((3,2)), 'b':mx.nd.ones((3,2))})
+out2 = exe2.forward()
+print(out2)
 
-exe = mysym.bind(ctx=mx.cpu(), args={'a':mx.nd.ones((3,2)), 'b':mx.nd.ones((3,2))})
-out = exe.forward()
-print(out)
+# without propogating shapes/types
+print('-------------------------------')
+print('Testing partitioning without shapes/types')
+mysym3 = sym.optimize_for("myProp", myOpt='yello')
+exe3 = mysym3.bind(ctx=mx.cpu(), args={'a':mx.nd.ones((3,2)), 'b':mx.nd.ones((3,2))})
+out3 = exe3.forward()
+print(out3)
diff --git a/include/mxnet/lib_api.h b/include/mxnet/lib_api.h
index c7887aad378f..cc0ec0f938af 100644
--- a/include/mxnet/lib_api.h
+++ b/include/mxnet/lib_api.h
@@ -30,8 +30,10 @@
 
 #include <stdint.h>
 #include <stdlib.h>
+#include <string.h>
 #include <vector>
 #include <map>
+#include <unordered_map>
 #include <string>
 #include <iostream>
 #include <utility>
@@ -554,7 +556,7 @@ typedef MXReturnValue (*inferShape_t)(std::map<std::string, std::string>,
                                       std::vector<std::vector<unsigned int> >&,
                                       std::vector<std::vector<unsigned int> >&);
 typedef MXReturnValue (*mutateInputs_t)(std::map<std::string, std::string>,
-                                      std::vector<int>&);
+                                        std::vector<int>&);
 typedef MXReturnValue (*createOpState_t)(std::map<std::string, std::string>,
                                       CustomStatefulOp**);
 
@@ -614,6 +616,52 @@ class CustomOp {
   bool isSGop;
 };
 
+/*! \brief Custom Subgraph Create function template */
+typedef MXReturnValue (*supportedOps_t)(std::string, int, int*,
+                                        std::unordered_map<std::string, std::string>&);
+typedef MXReturnValue (*acceptSubgraph_t)(std::string, int, bool*,
+                                          std::unordered_map<std::string, std::string>&,
+                                          std::unordered_map<std::string, std::string>&);
+
+/*!
+ * \brief An abstract class for subgraph property
+ */
+class CustomPartitioner {
+ public:
+  CustomPartitioner() : name("ERROR") {}
+  explicit CustomPartitioner(const char* backend_name) :
+    name(backend_name) {}
+  CustomPartitioner& addStrategy(const char* prop_name,
+                                 supportedOps_t fn,
+                                 const char* sg_name) {
+    strategies.push_back(prop_name);
+    supportedOps.push_back(fn);
+    op_names.push_back(sg_name);
+    return *this;
+  }
+  CustomPartitioner& setAcceptSubgraph(const char* prop_name, acceptSubgraph_t fn) {
+    accept_map[std::string(prop_name)] = fn;
+    return *this;
+  }
+  acceptSubgraph_t getAcceptSubgraph(int stg_id) {
+    std::string prop(strategies[stg_id]);
+    if (accept_map.find(prop) != accept_map.end())
+      return accept_map[prop];
+    else
+      return nullptr;
+  }
+
+  /*! \brief partitioner  name */
+  const char* name;
+  std::map<std::string, acceptSubgraph_t> accept_map;
+  /*! \brief strategy names */
+  std::vector<const char*> strategies;
+  /*! \brief supported ops function */
+  std::vector<supportedOps_t> supportedOps;
+  /*! \brief subgraph operator name */
+  std::vector<const char*> op_names;
+};
+
 /*!
  * \brief Registry class to registers things (ops, properties)
  *        Singleton class
@@ -670,10 +718,17 @@ class Registry {
 #define MX_REGISTER_NAME_(Name) MXNet ## _CustomOp ## _
 #define MX_REGISTER_DEF_(Name) CustomOp MX_REGISTER_NAME_(Name)
 
+#define MX_REGISTER_PROP_NAME_(Name) MXNet ## _CustomSubProp ## _
+#define MX_REGISTER_PROP_DEF_(Name) CustomPartitioner MX_REGISTER_PROP_NAME_(Name)
+
 /*! \brief assign a var to a value */
 #define REGISTER_OP(Name) MX_STR_CONCAT(MX_REGISTER_DEF_(Name), __COUNTER__) = \
     Registry<CustomOp>::get()->add(MX_TOSTRING(Name))
 
+#define REGISTER_PARTITIONER(Name) \
+  MX_STR_CONCAT(MX_REGISTER_PROP_DEF_(Name), __COUNTER__) = \
+    Registry<CustomPartitioner>::get()->add(MX_TOSTRING(Name))
+
 /* -------------- BELOW ARE CTYPE FUNCTIONS PROTOTYPES --------------- */
 
 /*!
@@ -688,7 +743,7 @@ typedef int (*opRegSize_t)(void);
 typedef int (*opRegGet_t)(int, const char**, fcomp_t*, fcomp_t*,
                           parseAttrs_t*, inferType_t*,
                           inferShape_t*, mutateInputs_t*,
-                          createOpState_t*, bool*);
+                          createOpState_t*, int*);
 
 #define MXLIB_OPCALLFREE_STR "_opCallFree"
 typedef int (*opCallFree_t)(void*);
@@ -721,10 +776,30 @@ typedef int (*opCallCreateOpState_t)(createOpState_t, const char* const*, const
                                      void**);
 
 #define MXLIB_OPCALLFSTATEFULCOMP_STR "_opCallFStatefulCompute"
-typedef int (*opCallFStatefulComp_t)(bool, void*, const int64_t**, int*, void**, int*, size_t*,
+typedef int (*opCallFStatefulComp_t)(int, void*, const int64_t**, int*, void**, int*, size_t*,
                                      int, const int64_t**, int*, void**, int*, size_t*,
                                      int, xpu_malloc_t, void*);
 
+#define MXLIB_PARTREGSIZE_STR "_partRegSize"
+typedef int (*partRegSize_t)(void);
+
+#define MXLIB_PARTREGGETCOUNT_STR "_partRegGetCount"
+typedef int (*partRegGetCount_t)(int, const char**);
+
+#define MXLIB_PARTREGGET_STR "_partRegGet"
+typedef void (*partRegGet_t)(int, int, const char**, supportedOps_t*,
+                            acceptSubgraph_t*, const char**);
+
+#define MXLIB_PARTCALLSUPPORTEDOPS_STR "_partCallSupportedOps"
+typedef int (*partCallSupportedOps_t)(supportedOps_t, const char*, int, int *,
+                                      const char* const*, const char* const*, int);
+#define MXLIB_PARTCALLACCEPTSUBGRAPH_STR "_partCallAcceptSubgraph"
+typedef int (*partCallAcceptSubgraph_t)(acceptSubgraph_t acceptSubgraph,
+                                        const char *json, int subgraph_id,
+                                        int *accept, const char* const*,
+                                        const char* const*, int,
+                                        char***, char***, int*);
+
 #define MXLIB_INITIALIZE_STR "initialize"
 typedef int (*initialize_t)(int);
 
@@ -761,7 +836,7 @@ extern "C" {
   _opRegGet(int idx, const char** name, fcomp_t* fcomp, fcomp_t* fgrad,
             parseAttrs_t* parse, inferType_t* type,
             inferShape_t* shape, mutateInputs_t* mutate,
-            createOpState_t* create_op, bool *isSGop) {
+            createOpState_t* create_op, int *isSGop) {
     CustomOp op = Registry<CustomOp>::get()->get(idx);
     *name = op.name;
     *fcomp = op.forward;
@@ -980,7 +1055,7 @@ extern "C" {
 #else
   int
 #endif
-  _opCallFStatefulCompute(bool is_forward, void* state_op,
+  _opCallFStatefulCompute(int is_forward, void* state_op,
                           const int64_t** inshapes, int* indims,
                           void** indata, int* intypes, size_t* inIDs, int num_in,
                           const int64_t** outshapes, int* outdims,
@@ -1006,6 +1081,106 @@ extern "C" {
     return op_ptr->Backward(inputs, outputs, res);
   }
 
+  /*! \brief returns number of partitioners registered in this library */
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+  __declspec(dllexport) int __cdecl
+#else
+  int
+#endif
+  _partRegSize() {
+    return Registry<CustomPartitioner>::get()->size();
+  }
+
+  /* returns number of strategies registered for partitioner 
+   * at specified index */
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+  __declspec(dllexport) int __cdecl
+#else
+  int
+#endif
+  _partRegGetCount(int idx, const char** name) {
+    CustomPartitioner part = Registry<CustomPartitioner>::get()->get(idx);
+    *name = part.name;
+    return part.strategies.size();
+  }
+
+  /*! \brief returns partitioner registration at specified index */
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+  __declspec(dllexport) void __cdecl
+#else
+  void
+#endif
+  _partRegGet(int part_idx, int stg_idx, const char** strategy, supportedOps_t* supportedOps,
+              acceptSubgraph_t* acceptSubgraph, const char** op_name) {
+    CustomPartitioner part = Registry<CustomPartitioner>::get()->get(part_idx);
+    *strategy = part.strategies[stg_idx];
+    *supportedOps = part.supportedOps[stg_idx];
+    *op_name = part.op_names[stg_idx];
+    *acceptSubgraph = part.getAcceptSubgraph(stg_idx);
+  }
+
+  /*! \brief returns status of calling parse attributes function for operator from library */
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+  __declspec(dllexport) int __cdecl
+#else
+  int
+#endif
+  _partCallSupportedOps(supportedOps_t supportedOps, const char *json,
+                        int num_ids, int *ids, const char* const* opt_keys,
+                        const char* const* opt_vals, int num_opts) {
+    std::string subgraph_json(json);
+    // create map of attributes from list
+    std::unordered_map<std::string, std::string> opts;
+    for (int i = 0; i < num_opts; i++) {
+      opts[std::string(opt_keys[i])] = std::string(opt_vals[i]);
+    }
+    return supportedOps(subgraph_json, num_ids, ids, opts);
+  }
+
+    /*! \brief returns status of calling parse attributes function for operator from library */
+#if defined(_WIN32) || defined(_WIN64) || defined(__WINDOWS__)
+  __declspec(dllexport) int __cdecl
+#else
+  int
+#endif
+  _partCallAcceptSubgraph(acceptSubgraph_t acceptSubgraph, const char *json,
+                          int subgraph_id, int *accept, const char* const* opt_keys,
+                          const char* const* opt_vals, int num_opts,
+                          char*** attr_keys, char*** attr_vals, int *num_attrs) {
+    std::string subgraph_json(json);
+    bool accept_bool = false;
+    // create map of attributes from list
+    std::unordered_map<std::string, std::string> opts;
+    for (int i = 0; i < num_opts; i++) {
+      opts[std::string(opt_keys[i])] = std::string(opt_vals[i]);
+    }
+
+    // attributes to set on subgraph node
+    std::unordered_map<std::string, std::string> attrs;
+
+    MXReturnValue retval = acceptSubgraph(subgraph_json, subgraph_id, &accept_bool, opts, attrs);
+    *accept = accept_bool;
+
+    if (attrs.size() > 0) {
+      *num_attrs = attrs.size();
+      // allocate space for attributes
+      *attr_keys = static_cast<char**>(malloc (attrs.size() * sizeof(char*)));
+      *attr_vals = static_cast<char**>(malloc (attrs.size() * sizeof(char*)));
+
+      // copy attributes
+      int i = 0;
+      for (auto kv : attrs) {
+        (*attr_keys)[i] = static_cast<char*>(malloc ((kv.first.size()+1) * sizeof(char)));
+        (*attr_vals)[i] = static_cast<char*>(malloc ((kv.second.size()+1) * sizeof(char)));
+        snprintf((*attr_keys)[i], kv.first.size()+1, "%s", kv.first.c_str());
+        snprintf((*attr_vals)[i], kv.second.size()+1, "%s", kv.second.c_str());
+        i++;
+      }
+    }
+
+    return retval;
+  }
+
   /*!
    * \brief Checks if the MXNet version is supported by the library.
    * If supported, initializes the library.
diff --git a/src/c_api/c_api.cc b/src/c_api/c_api.cc
index bb98d96f733a..11b1ddcce5a7 100644
--- a/src/c_api/c_api.cc
+++ b/src/c_api/c_api.cc
@@ -54,6 +54,8 @@
 #include "../operator/subgraph/common.h"
 #include "../operator/tensor/matrix_op-inl.h"
 #include "../operator/tvmop/op_module.h"
+#include "../operator/subgraph/partitioner/custom_subgraph_property.h"
+#include "../operator/subgraph/subgraph_property.h"
 #include "../common/utils.h"
 #include "nnvm/pass_functions.h"
 
@@ -142,6 +144,13 @@ int MXLoadLib(const char *path) {
   opCallFStatefulComp_t callFStatefulComp =
     get_func<opCallFStatefulComp_t>(lib, const_cast<char*>(MXLIB_OPCALLFSTATEFULCOMP_STR));
 
+  partCallSupportedOps_t callSupportedOps =
+    get_func<partCallSupportedOps_t>(lib, const_cast<char*>(MXLIB_PARTCALLSUPPORTEDOPS_STR));
+
+
+  partCallAcceptSubgraph_t callAcceptSubgraph =
+    get_func<partCallAcceptSubgraph_t>(lib, const_cast<char*>(MXLIB_PARTCALLACCEPTSUBGRAPH_STR));
+
   // get number of operators registered in the library
   opRegSize_t opRegSize = get_func<opRegSize_t>(lib, const_cast<char*>(MXLIB_OPREGSIZE_STR));
   int numOps = opRegSize();
@@ -164,15 +173,18 @@ int MXLoadLib(const char *path) {
     mutateInputs_t mutate_fp = nullptr;
     createOpState_t create_opstate_fp = nullptr;
     bool isSubgraphOp = false;
+    int _isSubgraphOp = 0;
 
     // get custom operator implemenation from the dynamic library
     opRegGet(i, &name, &fcomp_fp, &fgrad_fp, &parse_fp, &type_fp, &shape_fp,
-             &mutate_fp, &create_opstate_fp, &isSubgraphOp);
+             &mutate_fp, &create_opstate_fp, &_isSubgraphOp);
+    // set bool, dont pass bool across ABI boundary
+    isSubgraphOp = _isSubgraphOp;
 
-    CHECK(parse_fp != nullptr) << "Error loading '" << name
-                               << "' custom op, ParseAttrs function was not set.";
     if (!isSubgraphOp) {
       // validate custom operator functions from the dynamic library
+      CHECK(parse_fp != nullptr) << "Error loading '" << name
+                                 << "' custom op, ParseAttrs function was not set.";
       CHECK(fcomp_fp != nullptr || create_opstate_fp != nullptr) << "Error loading '" << name
                             << "' custom op, Forward or CreateOpState function was not set.";
       CHECK(type_fp  != nullptr) << "Error loading '" << name
@@ -650,9 +662,6 @@ int MXLoadLib(const char *path) {
     // check if operator is already registered
     const nnvm::Op *regOpPtr = dmlc::Registry<nnvm::Op>::Get()->Find(name);
     nnvm::Op &regOp = dmlc::Registry<nnvm::Op>::Get()->__REGISTER_OR_GET__(name);
-    regOp.set_attr_parser(attr_parser);
-    regOp.set_num_inputs(num_inputs);
-    regOp.set_num_outputs(num_outputs);
     int plevel = 10;
     if (regOpPtr != nullptr) {
       // overwrite registration of existing op with custom op
@@ -662,6 +671,9 @@ int MXLoadLib(const char *path) {
       plevel++;
     }
     if (!isSubgraphOp) {
+      regOp.set_attr_parser(attr_parser);
+      regOp.set_num_inputs(num_inputs);
+      regOp.set_num_outputs(num_outputs);
       regOp.set_attr<nnvm::FInferType>("FInferType", infer_type, plevel);
       regOp.set_attr<mxnet::FInferShape>("FInferShape", infer_shape, plevel);
       regOp.set_attr<FInferStorageType>("FInferStorageType", infer_storage_type, plevel);
@@ -671,6 +683,8 @@ int MXLoadLib(const char *path) {
         regOp.set_attr<nnvm::FMutateInputs>("FMutateInputs", mutate_inputs, plevel);
     } else {
       using namespace mxnet::op;
+      regOp.set_num_inputs(DefaultSubgraphOpNumInputs);
+      regOp.set_num_outputs(DefaultSubgraphOpNumOutputs);
       regOp.set_attr<nnvm::FInferType>("FInferType",
                                        DefaultSubgraphOpType, plevel);
       regOp.set_attr<mxnet::FInferShape>("FInferShape",
@@ -712,6 +726,57 @@ int MXLoadLib(const char *path) {
     }
     regOp.add_argument("data", "NDArray[]", "Source inputs");
   }
+
+  // get number of partitioners registered in the library
+  partRegSize_t partRegSize = get_func<partRegSize_t>(lib,
+                                                      const_cast<char*>(MXLIB_PARTREGSIZE_STR));
+  int numParts = partRegSize();
+  LOG(INFO) << "Found " << numParts << " partitioners in library";
+
+  /*
+   * Get all custom partitioners implementation from custom library
+   * loop and register each partitioner in the library to NNVM
+   */
+  partRegGetCount_t partRegGetCount = get_func<partRegGetCount_t>(lib,
+                                                  const_cast<char*>(MXLIB_PARTREGGETCOUNT_STR));
+  partRegGet_t partRegGet = get_func<partRegGet_t>(lib, const_cast<char*>(MXLIB_PARTREGGET_STR));
+  for (int i = 0; i < numParts; i++) {
+    const char* name;
+    // get custom partitioner strategy count from the dynamic library
+    int count = partRegGetCount(i, &name);
+    CHECK(count > 0) << "Error loading '" << name
+                     << "' custom partitioner, no strategies defined";
+    std::string name_str(name);
+    LOG(INFO) << "\tPartitioner[" << i << "] " << name;
+
+    mxnet::op::SubgraphBackendRegistry::Get()->__REGISTER_BACKEND__(name);
+
+    for (int j = 0; j < count; j++) {
+      const char* strategy;
+      // function pointers holding implementation from custom library
+      supportedOps_t supportedOps_fp = nullptr;
+      acceptSubgraph_t acceptSubgraph_fp = nullptr;
+      // name of subgraph op
+      const char* op_name = nullptr;
+
+    // get custom partitioner strategy from the dynamic library
+      partRegGet(i, j, &strategy, &supportedOps_fp, &acceptSubgraph_fp, &op_name);
+      // validate custom partitioner functions from the dynamic library
+      CHECK(supportedOps_fp != nullptr) << "Error loading '" << name
+                                        << "' custom partitioner strategy '" << strategy
+                                        << "', supportedOps function was not set.";
+      std::string strategy_str(strategy);
+      std::string op_name_str(op_name);
+      LOG(INFO) << "\t\tStrategy[" << j << "] " << strategy_str
+                << " subgraphOp: '" << op_name_str << "'";
+
+      // MXNET_REGISTER_SUBGRAPH_PROPERTY(customBackend, CustomSubgraphProperty);
+      mxnet::op::SubgraphBackendRegistry::Get()->__REGISTER_CUSTOM_PROPERTY__(name_str,
+                            std::make_shared<mxnet::op::CustomSubgraphProperty>(
+                           strategy_str, callSupportedOps, supportedOps_fp,
+                           callAcceptSubgraph, acceptSubgraph_fp, callFree, op_name_str));
+    }
+  }
   API_END();
 }
 
diff --git a/src/operator/subgraph/build_subgraph.cc b/src/operator/subgraph/build_subgraph.cc
index 8e7617d57c44..b5380b702f6d 100644
--- a/src/operator/subgraph/build_subgraph.cc
+++ b/src/operator/subgraph/build_subgraph.cc
@@ -559,10 +559,28 @@ void CutGraphInputs(const std::vector<nnvm::NodeEntry*> &input_entries,
       ++(it->second);
     }
     nnvm::NodePtr n = nnvm::CreateVariableNode(var_name + std::to_string(name_count_map[var_name]));
+    // set attribute for subgraph input to indicate if it is from an arg/param to model
+    if (e->node->is_variable())
+      n->attrs.dict["isArg"] = "True";
+    else
+      n->attrs.dict["isArg"] = "False";
     *e = nnvm::NodeEntry{n, 0, 0};
   }
 }
 
+/*!
+ * \brief This function reattaches the original input nodes that were cut
+ * by CutGraphInputs. This function is used when subgraphs are rejected, it
+ * reattaches the subgraph back to the main graph where it was cut earlier.
+ */
+void ReattachGraphInputs(const std::vector<nnvm::NodeEntry*> &input_entries,
+                         std::vector<nnvm::NodeEntry> *orig_entries) {
+  for (size_t i = 0; i < input_entries.size(); ++i) {
+    nnvm::NodeEntry *e = input_entries[i];
+    *e = orig_entries->at(i);
+  }
+}
+
 /*!
  * \brief Replace a set of nodes belonging to the same subgraph with a subgrpah node
  * and keep the subgraph in the subgraph node.
@@ -620,6 +638,8 @@ void CreateSubgraphNode(nnvm::Graph* g,
         sn->outputs[n.get()].push_back(i);
       }
     }
+  } else {
+    ReattachGraphInputs(input_entries, &orig_input_entries);
   }
 #if DEBUG_SUBGRAPH
   if (n)
diff --git a/src/operator/subgraph/partitioner/custom_subgraph_property.h b/src/operator/subgraph/partitioner/custom_subgraph_property.h
new file mode 100644
index 000000000000..b4ea1a087d71
--- /dev/null
+++ b/src/operator/subgraph/partitioner/custom_subgraph_property.h
@@ -0,0 +1,235 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*
+ * This file contains an implementation of a subgraph property
+ * that interfaces between MXNet and custom subgraph properties
+ * created by users in external libraries. It does not implement
+ * any custom subgraphing logic itself, rather it calls APIs
+ * in the user's custom library to enable control of partitioning
+ */
+
+#ifndef MXNET_OPERATOR_SUBGRAPH_PARTITIONER_CUSTOM_SUBGRAPH_PROPERTY_H_
+#define MXNET_OPERATOR_SUBGRAPH_PARTITIONER_CUSTOM_SUBGRAPH_PROPERTY_H_
+
+#include <nnvm/pass_functions.h>
+#include <nnvm/symbolic.h>
+#include <string>
+#include <utility>
+#include <vector>
+#include "../common.h"
+#include "../subgraph_property.h"
+#include "../../include/mxnet/lib_api.h"
+namespace mxnet {
+namespace op {
+
+/*
+ * This selects nodes for a subgraph based on node name as supplied
+ * by the supportedOps from an external library. It visits nodes via
+ * both input and output links.
+ */
+class CustomContainOpSelector: public SubgraphSelector {
+ public:
+  explicit CustomContainOpSelector(std::unordered_set<std::string> supported_nodes) :
+    supported_nodes_(supported_nodes) {}
+  virtual bool Select(const nnvm::Node &n) {
+    return supported_nodes_.count(n.attrs.name) > 0;
+  }
+  virtual bool SelectInput(const nnvm::Node &n, const nnvm::Node &new_node) {
+    return supported_nodes_.count(new_node.attrs.name) > 0;
+  }
+  virtual bool SelectOutput(const nnvm::Node &n, const nnvm::Node &new_node) {
+    return supported_nodes_.count(new_node.attrs.name) > 0;
+  }
+  std::unordered_set<std::string> supported_nodes_;
+};
+
+/*
+ * This subgraph property finds a subgraph that only contains
+ * nodes as specified by the supportedOps from an external library.
+ * The operators in the subgraph will be executed by the operator
+ * specified by the external library too.
+ */
+class  CustomSubgraphProperty: public SubgraphProperty {
+ public:
+  CustomSubgraphProperty() :
+    subgraph_prop("error"),
+    call_supported_ops_(nullptr),
+    supported_ops_(nullptr),
+    call_accept_subgraph_(nullptr),
+    accept_subgraph_(nullptr),
+    subgraph_op_name("error") {}
+  CustomSubgraphProperty(std::string subgraph_prop_name,
+                         partCallSupportedOps_t call_supported_ops,
+                         supportedOps_t supported_ops,
+                         partCallAcceptSubgraph_t call_accept_subgraph,
+                         acceptSubgraph_t accept_subgraph,
+                         opCallFree_t call_free,
+                         std::string op_name) :
+      subgraph_prop(subgraph_prop_name),
+      call_supported_ops_(call_supported_ops),
+      supported_ops_(supported_ops),
+      call_accept_subgraph_(call_accept_subgraph),
+      accept_subgraph_(accept_subgraph),
+      call_free_(call_free),
+      subgraph_op_name(op_name) {}
+
+  // create custom subgraph property
+  static SubgraphPropertyPtr Create() {
+    return std::make_shared<CustomSubgraphProperty>();
+  }
+
+  void PrePartition(const nnvm::Graph& g,
+    const std::vector<std::pair<std::string, std::string>>& options_map) {
+    // clear supported_nodes to remove state from previous calls
+    supported_nodes.clear();
+
+    // remove all graph attrs, some cannot be saved to json
+    nnvm::Graph graph = std::move(g);
+    graph.attrs.clear();
+    const nnvm::IndexedGraph& indexed_graph = graph.indexed_graph();
+
+    // set shape attrs for each node in the graph
+    if (g.HasAttr("shape")) {
+      mxnet::ShapeVector shapes = g.GetAttr<mxnet::ShapeVector>("shape");
+      for (unsigned i = 0; i < indexed_graph.num_nodes(); i++) {
+        nnvm::Node* node = const_cast<nnvm::Node*>(indexed_graph[i].source);
+        mxnet::TShape shape = shapes[i];
+        std::stringstream ss;
+        ss << shape;
+        node->attrs.dict["shape"] = ss.str();
+      }
+    }
+    // set dtype attrs for each node in the graph
+    if (g.HasAttr("dtype")) {
+      std::vector<int> dtypes = g.GetAttr<std::vector<int> >("dtype");
+      for (unsigned i = 0; i < indexed_graph.num_nodes(); i++) {
+        nnvm::Node* node = const_cast<nnvm::Node*>(indexed_graph[i].source);
+        int dtype = dtypes[i];
+        std::stringstream ss;
+        ss << dtype;
+        node->attrs.dict["dtype"] = ss.str();
+      }
+    }
+
+    CHECK(supported_ops_ != nullptr)
+      << "supported_ops_ is null for " << subgraph_prop << std::endl;
+    CHECK(call_supported_ops_ != nullptr)
+      << "call_supported_ops_ is null for " << subgraph_prop << std::endl;
+
+    std::string subgraph_json = nnvm::pass::SaveJSON(graph);
+    std::vector<int> supported_node_IDs(indexed_graph.num_nodes(), 0);
+    const char* json = subgraph_json.c_str();
+    int *ids = supported_node_IDs.data();
+
+    // clear options from previous call
+    opt_keys_.clear();
+    opt_vals_.clear();
+    options_map_.clear();
+    for (auto kv : options_map) {
+      options_map_.push_back(kv);
+      opt_keys_.push_back(options_map_.back().first.c_str());
+      opt_vals_.push_back(options_map_.back().second.c_str());
+    }
+
+    CHECK(call_supported_ops_(supported_ops_, json, supported_node_IDs.size(), ids,
+                            opt_keys_.data(), opt_vals_.data(), opt_keys_.size()))
+      << "Error calling supported_ops for '" << subgraph_prop << "'";
+
+    const auto& idx = g.indexed_graph();
+    // loop and add node names for each supported node ID
+    for (unsigned i = 0; i < supported_node_IDs.size(); i++) {
+      if (supported_node_IDs[i]) {
+        supported_nodes.insert(idx[i].source->attrs.name);
+      }
+    }
+  }
+  // override CreateSubgraphNode
+  virtual nnvm::NodePtr CreateSubgraphNode(const nnvm::Symbol &sym,
+                                           const int subgraph_id = 0) const {
+    int accept = 1;
+    int num_attr = 0;
+    char** attr_keys = nullptr;
+    char** attr_vals = nullptr;
+    if (accept_subgraph_) {
+      nnvm::Graph g;
+      g.outputs = sym.outputs;
+      const auto& idx = g.indexed_graph();
+
+      // set isArg/isAux for each null op/param in the graph
+      const std::vector<std::string> aux_names = sym.ListInputNames(nnvm::Symbol::kAuxiliaryStates);
+      std::unordered_set<std::string> aux_set(aux_names.begin(), aux_names.end());
+      for (unsigned i = 0; i < idx.num_nodes(); i++) {
+        nnvm::Node* node = const_cast<nnvm::Node*>(idx[i].source);
+        // check if this node is input to subgraph
+        if (node->is_variable()) {
+          // check if this node is an aux param
+          if (aux_set.count(node->attrs.name))
+            node->attrs.dict["isAux"] = "True";
+          else
+            node->attrs.dict["isAux"] = "False";
+        }
+      }
+
+      std::string subgraph_json = nnvm::pass::SaveJSON(g);
+      CHECK(call_accept_subgraph_(accept_subgraph_, subgraph_json.c_str(),
+                                subgraph_id, &accept, opt_keys_.data(),
+                                opt_vals_.data(), opt_keys_.size(),
+                                &attr_keys, &attr_vals, &num_attr))
+        << "Error calling accept_subgraph for '" << subgraph_prop << "'";
+    }
+    if (accept) {
+      nnvm::NodePtr n = nnvm::Node::Create();
+      n->attrs.op = Op::Get(subgraph_op_name);
+      n->attrs.name = "_op" + std::to_string(subgraph_id);
+      n->attrs.subgraphs.push_back(std::make_shared<nnvm::Symbol>(sym));
+      // set user specified attributes
+      for (int i=0; i < num_attr; i++) {
+        n->attrs.dict[attr_keys[i]] = attr_vals[i];
+        call_free_(attr_vals[i]);
+        call_free_(attr_keys[i]);
+      }
+      // free memory used by custom op to allocate attributes
+      call_free_(attr_vals);
+      call_free_(attr_keys);
+      return n;
+    } else {
+      return NULL;
+    }
+  }
+  // override CreateSubgraphSelector
+  virtual SubgraphSelectorPtr CreateSubgraphSelector() const {
+    return std::make_shared<CustomContainOpSelector>(supported_nodes);
+  }
+
+  std::string subgraph_prop;
+  partCallSupportedOps_t call_supported_ops_;
+  supportedOps_t supported_ops_;
+  partCallAcceptSubgraph_t call_accept_subgraph_;
+  acceptSubgraph_t accept_subgraph_;
+  opCallFree_t call_free_;
+  std::unordered_set<std::string> supported_nodes;
+  std::string subgraph_op_name;
+  std::vector<std::pair<std::string, std::string>> options_map_;
+  std::vector<const char*> opt_keys_, opt_vals_;
+};
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_SUBGRAPH_PARTITIONER_CUSTOM_SUBGRAPH_PROPERTY_H_
diff --git a/src/operator/subgraph/subgraph_property.h b/src/operator/subgraph/subgraph_property.h
index bbc8f4076899..643c02a82b13 100644
--- a/src/operator/subgraph/subgraph_property.h
+++ b/src/operator/subgraph/subgraph_property.h
@@ -520,6 +520,15 @@ class SubgraphBackendRegistry {
     return SubgraphPropertyEntry(prop);
   }
 
+  SubgraphPropertyEntry __REGISTER_CUSTOM_PROPERTY__(const std::string& name,
+                                                     SubgraphPropertyPtr cprop) {
+    auto it = backend_map_.find(name);
+    CHECK(it != backend_map_.end())
+        << "Subgraph backend " << name << " is not found in SubgraphBackendRegistry";
+    auto prop = it->second->RegisterSubgraphProperty(cprop);
+    return SubgraphPropertyEntry(prop);
+  }
+
   SubgraphBackendRegistry() = default;
   SubgraphBackendRegistry(const SubgraphBackendRegistry&) = delete;
   SubgraphBackendRegistry(SubgraphBackendRegistry&&) = delete;
diff --git a/tests/python/unittest/test_extensions.py b/tests/python/unittest/test_extensions.py
index cc7858dce0fd..63b54d8516b4 100644
--- a/tests/python/unittest/test_extensions.py
+++ b/tests/python/unittest/test_extensions.py
@@ -84,3 +84,67 @@ def test_custom_op():
 
     assert_almost_equal(in_grad_base[0].asnumpy(), in_grad1[0].asnumpy(), rtol=1e-3, atol=1e-3)
     assert_almost_equal(in_grad_base[0].asnumpy(), in_grad2[0].asnumpy(), rtol=1e-3, atol=1e-3)
+
+@unittest.skipIf(check_platform(), "not all machine types supported")
+@unittest.skipIf(is_cd_run(), "continuous delivery run - ignoring test")
+def test_subgraph():
+    # possible places to find library file
+    if (os.name=='posix'):
+        lib = 'libsubgraph_lib.so'
+        if os.path.exists(lib):
+            # plain make build, when run in the CI
+            fname = lib
+        elif os.path.exists('build/'+lib):
+            # plain cmake build when run in the CI
+            fname = 'build/'+lib
+        else:
+            raise MXNetError("library %s not found " % lib)
+    elif (os.name=='nt'):
+        lib = 'libsubgraph_lib.dll'
+        if os.path.exists('windows_package\\lib\\'+lib):
+            # plain make build, when run in the CI
+            fname = 'windows_package\\lib\\'+lib
+        else:
+            # plain cmake build when run in the CI
+            raise MXNetError("library %s not found " % lib)
+
+    fname = os.path.abspath(fname)
+    mx.library.load(fname)
+
+    # test simple graph with add, exp and log operators, library supports exp/log
+    a = mx.sym.var('a')
+    b = mx.sym.var('b')
+    c = a + b
+    d = mx.sym.exp(c)
+    sym = mx.sym.log(d)
+
+    args = {'a':mx.nd.ones((3,2),ctx=mx.cpu()), 'b':mx.nd.ones((3,2),ctx=mx.cpu())}
+    arg_array = [mx.nd.ones((3,2),dtype='float32',ctx=mx.cpu()),
+                 mx.nd.ones((3,2),dtype='float32',ctx=mx.cpu())]
+
+    # baseline - regular execution in MXNet
+    exe = sym.bind(ctx=mx.cpu(), args=args)
+    out = exe.forward()
+
+    # without propogating shapes/types, passing a custom option to subgraph prop "myOpt"
+    # should not create subgraph since subgraph prop requires type info
+    mysym1 = sym.optimize_for("myProp", myOpt='yello')
+    exe1 = mysym1.bind(ctx=mx.cpu(), args=args)
+    out1 = exe1.forward()
+    # check that result matches one executed by MXNet
+    assert_almost_equal(out[0].asnumpy(), out1[0].asnumpy(), rtol=1e-3, atol=1e-3)
+
+    # with propogating shapes/types, rejecting subgraph
+    # this tests creating the subgraph and having the subgraph prop reject it
+    mysym2 = sym.optimize_for("myProp", arg_array, reject=True)
+    exe2 = mysym2.bind(ctx=mx.cpu(), args=args)
+    out2 = exe2.forward()
+    # check that result matches one executed by MXNet
+    assert_almost_equal(out[0].asnumpy(), out2[0].asnumpy(), rtol=1e-3, atol=1e-3)
+
+    # with propogating shapes/types
+    mysym3 = sym.optimize_for("myProp",arg_array)
+    exe3 = mysym3.bind(ctx=mx.cpu(), args=args)
+    out3 = exe3.forward()
+    # check that result matches one executed by MXNet
+    assert_almost_equal(out[0].asnumpy(), out3[0].asnumpy(), rtol=1e-3, atol=1e-3)

From 4ff1c67b53121fb93f2a6c34468c17a9fbc19610 Mon Sep 17 00:00:00 2001
From: ciyong <ciyong.chen@intel.com>
Date: Thu, 9 Jan 2020 10:09:23 +0800
Subject: [PATCH 776/813] fix latency calculation and print issue (#17217)

* fix latency calculation and print issue

* retrigger CI

* minor change for script message

* update benchmark script for ncf

* retrigger CI

* retrigger CI
---
 example/neural_collaborative_filtering/benchmark.sh | 10 +++++++---
 example/quantization/launch_inference_mkldnn.sh     | 10 +++++-----
 2 files changed, 12 insertions(+), 8 deletions(-)
 mode change 100644 => 100755 example/neural_collaborative_filtering/benchmark.sh
 mode change 100644 => 100755 example/quantization/launch_inference_mkldnn.sh

diff --git a/example/neural_collaborative_filtering/benchmark.sh b/example/neural_collaborative_filtering/benchmark.sh
old mode 100644
new mode 100755
index 60fec746cd20..703a087d9a50
--- a/example/neural_collaborative_filtering/benchmark.sh
+++ b/example/neural_collaborative_filtering/benchmark.sh
@@ -102,13 +102,17 @@ for((i=0;i<$INS;i++));
 do
   ((a=$i*$CORES))
   ((b=$a+$CORES-1))
-  memid=$((b/CORES_PER_NUMA))
+  memid=$((b/CORES_PER_NUMA % NUM_NUMA_NODE))
   LOG=NCF_$i.log
-  echo "  $i instance use $a-$b cores with $LOG"
+  echo "  Instance $i use $a-$b cores with $LOG"
   KMP_AFFINITY=granularity=fine,noduplicates,compact,1,0 \
   OMP_NUM_THREADS=$CORES \
   numactl --physcpubind=$a-$b --membind=$memid python ncf.py --batch-size=$BS --dataset=$DATASET --epoch=$EPOCH --benchmark --prefix=$PREFIX 2>&1 | tee $LOG &
 done
 wait
 
-grep speed NCF_*.log | awk '{ sum += $(NF-1) }; END { print "Total Performance is " sum " samples/sec"}'
+sps=`grep speed NCF_*.log | awk '{ sum += $(NF-1) }; END { print sum }'`
+latency=$(awk "BEGIN {printf \"%.2f\", 1000*${BS}*${INS}/${sps}}")
+echo "overall throughput (samples/sec): $sps"
+echo "latency per batch per instance (ms): $latency"
+echo "benchmark finish:)"
diff --git a/example/quantization/launch_inference_mkldnn.sh b/example/quantization/launch_inference_mkldnn.sh
old mode 100644
new mode 100755
index f67787b41b03..8491506fc8f6
--- a/example/quantization/launch_inference_mkldnn.sh
+++ b/example/quantization/launch_inference_mkldnn.sh
@@ -95,9 +95,9 @@ for((i=0;i<$INS;i++));
 do
   ((a=$i*$CORES))
   ((b=$a+$CORES-1))
-  memid=$((b/CORES_PER_NUMA))
+  memid=$((b/CORES_PER_NUMA % NUM_NUMA_NODE))
   LOG=BENCHMARK_$i.log
-  echo "  $i instance use $a-$b cores and $memid mem with $LOG"
+  echo "  Instance $i use $a-$b cores and mem $memid with $LOG"
   KMP_AFFINITY=granularity=fine,noduplicates,compact,1,0 \
   OMP_NUM_THREADS=$CORES \
   nohup numactl --physcpubind=$a-$b --membind=$memid python imagenet_inference.py --symbol-file=$SYMBOL --batch-size=$BS --num-inference-batches=$ITERATIONS --ctx=cpu --benchmark=True > $LOG 2>&1 &
@@ -105,7 +105,7 @@ done
 wait
 
 fps=`grep image/sec BENCHMARK_*.log | awk '{ sum += $(NF) }; END { print sum }'`
-latency=$(echo "scale=2; 1000*$INS/$fps" | bc)
-echo "overall throughput: $fps"
-echo "latency per instance: $latency"
+latency=$(awk "BEGIN {printf \"%.2f\", 1000*${BS}*${INS}/${fps}}")
+echo "overall throughput (image/sec): $fps"
+echo "latency per batch per instance (ms): $latency"
 echo "benchmark finish:)"

From 83578b9a61d5ddb5ed54576fac17e24f97f35e52 Mon Sep 17 00:00:00 2001
From: Jake Lee <gstu1130@gmail.com>
Date: Thu, 9 Jan 2020 03:06:39 -0800
Subject: [PATCH 777/813] remove the straight dope from nightly test (#17253)

---
 tests/nightly/JenkinsfileForBinaries          |  34 +--
 tests/nightly/straight_dope/README.md         |  24 --
 .../straight_dope/straight_dope_test_utils.py | 159 -----------
 .../straight_dope/test_notebooks_multi_gpu.py |  48 ----
 .../test_notebooks_single_gpu.py              | 260 ------------------
 5 files changed, 1 insertion(+), 524 deletions(-)
 delete mode 100644 tests/nightly/straight_dope/README.md
 delete mode 100644 tests/nightly/straight_dope/straight_dope_test_utils.py
 delete mode 100644 tests/nightly/straight_dope/test_notebooks_multi_gpu.py
 delete mode 100644 tests/nightly/straight_dope/test_notebooks_single_gpu.py

diff --git a/tests/nightly/JenkinsfileForBinaries b/tests/nightly/JenkinsfileForBinaries
index 06ccb0a03bd4..e8e406a836b0 100755
--- a/tests/nightly/JenkinsfileForBinaries
+++ b/tests/nightly/JenkinsfileForBinaries
@@ -103,39 +103,7 @@ core_logic: {
             utils.docker_run('ubuntu_nightly_gpu', 'nightly_test_large_tensor', true)
         }
       }
-    },*/
-    'StraightDope: Python2 Single-GPU': {
-      node(NODE_LINUX_GPU_P3) {
-        ws('workspace/straight_dope-single_gpu') {
-          utils.unpack_and_init('gpu', mx_lib)
-          utils.docker_run('ubuntu_nightly_gpu', 'nightly_straight_dope_python2_single_gpu_tests', true)
-        }
-      }
-    },
-    'StraightDope: Python2 Multi-GPU': {
-      node(NODE_LINUX_GPU) {
-        ws('workspace/straight_dope-multi_gpu') {
-          utils.unpack_and_init('gpu', mx_lib)
-          utils.docker_run('ubuntu_nightly_gpu', 'nightly_straight_dope_python2_multi_gpu_tests', true)
-        }
-      }
-    },
-    'StraightDope: Python3 Single-GPU': {
-      node(NODE_LINUX_GPU_P3) {
-        ws('workspace/straight_dope-single_gpu') {
-          utils.unpack_and_init('gpu', mx_lib)
-          utils.docker_run('ubuntu_nightly_gpu', 'nightly_straight_dope_python3_single_gpu_tests', true)
-        }
-      }
-    },
-    'StraightDope: Python3 Multi-GPU': {
-      node(NODE_LINUX_GPU) {
-        ws('workspace/straight_dope-multi_gpu') {
-          utils.unpack_and_init('gpu', mx_lib)
-          utils.docker_run('ubuntu_nightly_gpu', 'nightly_straight_dope_python3_multi_gpu_tests', true)
-        }
-      }
-    },
+    }*/,
     'Gluon estimator: GPU': {
       node(NODE_LINUX_GPU) {
         ws('workspace/estimator-test-gpu') {
diff --git a/tests/nightly/straight_dope/README.md b/tests/nightly/straight_dope/README.md
deleted file mode 100644
index 88153aebbc36..000000000000
--- a/tests/nightly/straight_dope/README.md
+++ /dev/null
@@ -1,24 +0,0 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you under the Apache License, Version 2.0 (the -->
-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Nightly Tests for MXNet: The Straight Dope
-
-These are some longer running tests that are scheduled to run every night. 
-
-### Description
-These tests verify that the straight dope tutorials run without error. They are
-run on both single and multi-gpu configurations.
diff --git a/tests/nightly/straight_dope/straight_dope_test_utils.py b/tests/nightly/straight_dope/straight_dope_test_utils.py
deleted file mode 100644
index 976c234fa997..000000000000
--- a/tests/nightly/straight_dope/straight_dope_test_utils.py
+++ /dev/null
@@ -1,159 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-#pylint: disable=no-member, too-many-locals, too-many-branches, no-self-use, broad-except, lost-exception, too-many-nested-blocks, too-few-public-methods, invalid-name
-"""
-    This file tests and ensures that all straight dope notebooks run
-    without warning or exception.
-
-    env variable MXNET_TEST_KERNEL controls which kernel to use when running
-    the notebook. e.g: `export MXNET_TEST_KERNEL=python2`
-"""
-import io
-import logging
-import os
-import re
-import shutil
-import subprocess
-import sys
-from time import sleep
-
-#TODO(vishaalk): Find a cleaner way to import this notebook.
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', '..', 'utils'))
-from notebook_test import run_notebook
-
-EPOCHS_REGEX = r'epochs\s+=\s+[0-9]+'  # Regular expression that matches 'epochs = #'
-GIT_PATH = '/usr/bin/git'
-GIT_REPO = 'https://github.com/zackchase/mxnet-the-straight-dope'
-KERNEL = os.getenv('MXNET_TEST_KERNEL', None)
-NOTEBOOKS_DIR = os.path.join(os.path.dirname(__file__), 'tmp_notebook')
-RELATIVE_PATH_REGEX = r'\.\.(?=\/(data|img)\/)'  # Regular expression to match the relative data path.
-
-def _test_notebook(notebook, override_epochs=True):
-    """Run Jupyter notebook to catch any execution error.
-
-    Args:
-        notebook : string
-            notebook name in folder/notebook format
-        override_epochs : boolean
-            whether or not to override the number of epochs to 1
-
-    Returns:
-        True if the notebook runs without warning or error.
-    """
-    # Some notebooks will fail to run without error if we do not override
-    # relative paths to the data and image directories.
-    _override_relative_paths(notebook)
-
-    if override_epochs:
-        _override_epochs(notebook)
-
-    return run_notebook(notebook, NOTEBOOKS_DIR, kernel=KERNEL, temp_dir=NOTEBOOKS_DIR)
-
-
-def _override_epochs(notebook):
-    """Overrides the number of epochs in the notebook to 1 epoch. Note this operation is idempotent.
-
-    Args:
-        notebook : string
-            notebook name in folder/notebook format
-    """
-    notebook_path = os.path.join(*([NOTEBOOKS_DIR] + notebook.split('/'))) + ".ipynb"
-
-    # Read the notebook and set epochs to num_epochs.
-    with io.open(notebook_path, 'r', encoding='utf-8') as f:
-        notebook = f.read()
-
-    # Set number of epochs to 1.
-    modified_notebook = re.sub(EPOCHS_REGEX, 'epochs = 1', notebook)
-
-    # Replace the original notebook with the modified one.
-    with io.open(notebook_path, 'w', encoding='utf-8') as f:
-        f.write(modified_notebook)
-
-
-def _override_relative_paths(notebook):
-    """Overrides the relative path for the data and image directories to point
-    to the right places. This is required as we run the notebooks in a different
-    directory hierarchy more suitable for testing.
-
-    Args:
-        notebook : string
-            notebook name in folder/notebook format
-    """
-    notebook_path = os.path.join(*([NOTEBOOKS_DIR] + notebook.split('/'))) + ".ipynb"
-
-    # Read the notebook and set epochs to num_epochs.
-    with io.open(notebook_path, 'r', encoding='utf-8') as f:
-        notebook = f.read()
-
-    # Update the location for the data directory.
-    modified_notebook = re.sub(RELATIVE_PATH_REGEX, NOTEBOOKS_DIR, notebook)
-
-    # Replace the original notebook with the modified one.
-    with io.open(notebook_path, 'w', encoding='utf-8') as f:
-        f.write(modified_notebook)
-
-def _download_straight_dope_notebooks():
-    """Downloads the Straight Dope Notebooks.
-
-    Returns:
-        True if it succeeds in downloading the notebooks without error.
-    """
-    logging.info('Cleaning and setting up notebooks directory "{}"'.format(NOTEBOOKS_DIR))
-    shutil.rmtree(NOTEBOOKS_DIR, ignore_errors=True)
-
-    cmd = [GIT_PATH,
-           'clone',
-           GIT_REPO,
-           NOTEBOOKS_DIR]
-
-    proc, msg = _run_command(cmd)
-
-    if proc.returncode != 0:
-        err_msg = 'Error downloading Straight Dope notebooks.\n'
-        err_msg += msg
-        logging.error(err_msg)
-        return False
-    return True
-
-def _run_command(cmd, timeout_secs=300):
-    """ Runs a command with a specified timeout.
-
-    Args:
-        cmd : list of string
-            The command with arguments to run.
-        timeout_secs: integer
-            The timeout in seconds
-
-    Returns:
-        Returns the process and the output as a pair.
-    """
-    proc = subprocess.Popen(
-        cmd,
-        stdout=subprocess.PIPE,
-        stderr=subprocess.STDOUT)
-
-    for i in range(timeout_secs):
-        sleep(1)
-        if proc.poll() is not None:
-            (out, _) = proc.communicate()
-            return proc, out.decode('utf-8')
-
-    proc.kill()
-    return proc, "Timeout of %s secs exceeded." % timeout_secs
-
diff --git a/tests/nightly/straight_dope/test_notebooks_multi_gpu.py b/tests/nightly/straight_dope/test_notebooks_multi_gpu.py
deleted file mode 100644
index c5dffa455ce1..000000000000
--- a/tests/nightly/straight_dope/test_notebooks_multi_gpu.py
+++ /dev/null
@@ -1,48 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-#pylint: disable=no-member, too-many-locals, too-many-branches, no-self-use, broad-except, lost-exception, too-many-nested-blocks, too-few-public-methods, invalid-name, missing-docstring
-"""
-    This file tests that the notebooks requiring multi GPUs run without
-    warning or exception.
-"""
-import logging
-import unittest
-from straight_dope_test_utils import _test_notebook
-from straight_dope_test_utils import _download_straight_dope_notebooks
-
-class StraightDopeMultiGpuTests(unittest.TestCase):
-    @classmethod
-    def setUpClass(self):
-        logging.basicConfig(level=logging.INFO)
-        assert _download_straight_dope_notebooks()
-
-    # Chapter 7
-
-    def test_multiple_gpus_scratch(self):
-        assert _test_notebook('chapter07_distributed-learning/multiple-gpus-scratch')
-
-    def test_multiple_gpus_gluon(self):
-        assert _test_notebook('chapter07_distributed-learning/multiple-gpus-gluon')
-
-    def test_training_with_multiple_machines(self):
-       assert _test_notebook('chapter07_distributed-learning/training-with-multiple-machines')
-
-    # Chapter 8
-
-    def test_fine_tuning(self):
-       assert _test_notebook('chapter08_computer-vision/fine-tuning')
diff --git a/tests/nightly/straight_dope/test_notebooks_single_gpu.py b/tests/nightly/straight_dope/test_notebooks_single_gpu.py
deleted file mode 100644
index fc3a4f240ddc..000000000000
--- a/tests/nightly/straight_dope/test_notebooks_single_gpu.py
+++ /dev/null
@@ -1,260 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-#pylint: disable=no-member, too-many-locals, too-many-branches, no-self-use, broad-except, lost-exception, too-many-nested-blocks, too-few-public-methods, invalid-name, missing-docstring
-"""
-    This file tests that the notebooks requiring a single GPU run without
-    warning or exception.
-"""
-import glob
-import logging
-import re
-import os
-import unittest
-from straight_dope_test_utils import _test_notebook
-from straight_dope_test_utils import _download_straight_dope_notebooks
-
-NOTEBOOKS_WHITELIST = [
-    'chapter01_crashcourse/preface',
-    'chapter01_crashcourse/introduction',
-    'chapter01_crashcourse/chapter-one-problem-set',
-    'chapter02_supervised-learning/environment',
-    'chapter03_deep-neural-networks/kaggle-gluon-kfold',
-    'chapter04_convolutional-neural-networks/deep-cnns-alexnet',  # > 10 mins.
-    'chapter06_optimization/gd-sgd-scratch',  # Overflow warning is intended.
-    'chapter06_optimization/gd-sgd-gluon',  # Overflow warning is intended.
-    'chapter07_distributed-learning/multiple-gpus-scratch',
-    'chapter07_distributed-learning/multiple-gpus-gluon',
-    'chapter07_distributed-learning/training-with-multiple-machines',
-    'chapter11_recommender-systems/intro-recommender-systems',  # Early draft, non-working.
-    'chapter12_time-series/intro-forecasting-gluon',
-    'chapter12_time-series/intro-forecasting-2-gluon',
-    'chapter13_unsupervised-learning/vae-gluon',
-    'chapter18_variational-methods-and-uncertainty/bayes-by-backprop-rnn',
-    'chapter17_deep-reinforcement-learning/DQN',
-    'chapter17_deep-reinforcement-learning/DDQN',
-    'chapter19_graph-neural-networks/Graph-Neural-Networks',
-    'chapter16_tensor_methods/tensor_basics',
-    'chapter18_variational-methods-and-uncertainty/bayes-by-backprop',  # > 10 mins.
-    'chapter18_variational-methods-and-uncertainty/bayes-by-backprop-gluon',  # > 10 mins.
-    'cheatsheets/kaggle-gluon-kfold'
-]
-
-
-class StraightDopeSingleGpuTests(unittest.TestCase):
-    @classmethod
-    def setUpClass(self):
-        logging.basicConfig(level=logging.INFO)
-        assert _download_straight_dope_notebooks()
-
-    def test_completeness(self):
-        """
-        Make sure that every tutorial that isn't in the whitelist is considered for testing by this
-        file. Exceptions should be added to the whitelist.
-        N.B. If the test is commented out, then that will be viewed as an intentional disabling of the
-        test.
-        """
-        # Open up this test file.
-        with open(__file__, 'r') as f:
-            notebook_test_text = '\n'.join(f.readlines())
-
-        notebooks_path = os.path.join(os.path.dirname(__file__), 'straight_dope_book')
-        notebooks = glob.glob(os.path.join(notebooks_path, '**', '*.ipynb'))
-
-        # Compile a list of notebooks that are tested
-        tested_notebooks = set(re.findall(r"assert _test_notebook\('(.*)'\)", notebook_test_text))
-
-       # Ensure each notebook in the straight dope book directory is on the whitelist or is tested.
-        for notebook in notebooks:
-            friendly_name = '/'.join(notebook.split('/')[-2:]).split('.')[0]
-            if friendly_name not in tested_notebooks and friendly_name not in NOTEBOOKS_WHITELIST:
-                assert False, friendly_name + " has not been added to the nightly/tests/straight_" + \
-                              "dope/test_notebooks_single_gpu.py test_suite. Consider also adding " + \
-                              "it to nightly/tests/straight_dope/test_notebooks_multi_gpu.py as " + \
-                              "well if the notebooks makes use of multiple GPUs."
-
-    def test_ndarray(self):
-        assert _test_notebook('chapter01_crashcourse/ndarray')
-
-    def test_linear_algebra(self):
-        assert _test_notebook('chapter01_crashcourse/linear-algebra')
-
-    def test_probability(self):
-        assert _test_notebook('chapter01_crashcourse/probability')
-
-    def test_autograd(self):
-        assert _test_notebook('chapter01_crashcourse/autograd')
-
-    # Chapter 2
-
-    def test_linear_regression_scratch(self):
-        assert _test_notebook('chapter02_supervised-learning/linear-regression-scratch')
-
-    def test_linear_regression_gluon(self):
-        assert _test_notebook('chapter02_supervised-learning/linear-regression-gluon')
-
-    def test_logistic_regression_gluon(self):
-        assert _test_notebook('chapter02_supervised-learning/logistic-regression-gluon')
-
-    def test_softmax_regression_scratch(self):
-        assert _test_notebook('chapter02_supervised-learning/softmax-regression-scratch')
-
-    def test_softmax_regression_gluon(self):
-        assert _test_notebook('chapter02_supervised-learning/softmax-regression-gluon')
-
-    def test_regularization_scratch(self):
-        assert _test_notebook('chapter02_supervised-learning/regularization-scratch')
-
-    def test_regularization_gluon(self):
-        assert _test_notebook('chapter02_supervised-learning/regularization-gluon')
-
-    def test_perceptron(self):
-        assert _test_notebook('chapter02_supervised-learning/perceptron')
-
-    # Chapter 3
-
-    def test_mlp_scratch(self):
-        assert _test_notebook('chapter03_deep-neural-networks/mlp-scratch')
-
-    def test_mlp_gluon(self):
-        assert _test_notebook('chapter03_deep-neural-networks/mlp-gluon')
-
-    def test_mlp_dropout_scratch(self):
-        assert _test_notebook('chapter03_deep-neural-networks/mlp-dropout-scratch')
-
-    def test_mlp_dropout_gluon(self):
-        assert _test_notebook('chapter03_deep-neural-networks/mlp-dropout-gluon')
-
-    def test_plumbing(self):
-        assert _test_notebook('chapter03_deep-neural-networks/plumbing')
-
-    def test_custom_layer(self):
-        assert _test_notebook('chapter03_deep-neural-networks/custom-layer')
-
-    def test_serialization(self):
-        assert _test_notebook('chapter03_deep-neural-networks/serialization')
-
-    # Chapter 4
-
-    def test_cnn_scratch(self):
-        assert _test_notebook('chapter04_convolutional-neural-networks/cnn-scratch')
-
-    def test_cnn_gluon(self):
-        assert _test_notebook('chapter04_convolutional-neural-networks/cnn-gluon')
-
-    def test_very_deep_nets_vgg(self):
-        assert _test_notebook('chapter04_convolutional-neural-networks/very-deep-nets-vgg')
-
-    def test_cnn_batch_norm_scratch(self):
-        assert _test_notebook('chapter04_convolutional-neural-networks/cnn-batch-norm-scratch')
-
-    def test_cnn_batch_norm_gluon(self):
-        assert _test_notebook('chapter04_convolutional-neural-networks/cnn-batch-norm-gluon')
-
-    # Chapter 5
-
-    def test_simple_rnn(self):
-        assert _test_notebook('chapter05_recurrent-neural-networks/simple-rnn')
-
-    def test_lstm_scratch(self):
-        assert _test_notebook('chapter05_recurrent-neural-networks/lstm-scratch')
-
-    def test_gru_scratch(self):
-        assert _test_notebook('chapter05_recurrent-neural-networks/gru-scratch')
-
-    def test_rnn_gluon(self):
-        assert _test_notebook('chapter05_recurrent-neural-networks/rnns-gluon')
- 
-    # Chapter 6
-
-    def test_optimization_intro(self):
-        assert _test_notebook('chapter06_optimization/optimization-intro')
-
-    def test_momentum_scratch(self):
-        assert _test_notebook('chapter06_optimization/momentum-scratch')
-
-    def test_momentum_gluon(self):
-        assert _test_notebook('chapter06_optimization/momentum-gluon')
-
-    def test_adagrad_scratch(self):
-        assert _test_notebook('chapter06_optimization/adagrad-scratch')
-
-    def test_adagrad_gluon(self):
-        assert _test_notebook('chapter06_optimization/adagrad-gluon')
-
-    def test_rmsprop_scratch(self):
-        assert _test_notebook('chapter06_optimization/rmsprop-scratch')
-
-    def test_rmsprop_gluon(self):
-        assert _test_notebook('chapter06_optimization/rmsprop-gluon')
-
-    def test_adadelta_scratch(self):
-        assert _test_notebook('chapter06_optimization/adadelta-scratch')
-
-    def test_adadelta_gluon(self):
-        assert _test_notebook('chapter06_optimization/adadelta-gluon')
-
-    def test_adam_scratch(self):
-        assert _test_notebook('chapter06_optimization/adam-scratch')
-
-    def test_adam_gluon(self):
-        assert _test_notebook('chapter06_optimization/adam-gluon')
-
-    # Chapter 7
-
-    def test_hybridize(self):
-        assert _test_notebook('chapter07_distributed-learning/hybridize')
-
-
-    # Chapter 8
-
-    def test_object_detection(self):
-        assert _test_notebook('chapter08_computer-vision/object-detection')
-
-    def test_fine_tuning(self):
-        assert _test_notebook('chapter08_computer-vision/fine-tuning')
-
-    def test_visual_qa(self):
-        assert _test_notebook('chapter08_computer-vision/visual-question-answer')
-
-
-    # Chapter 9
-
-    def test_tree_lstm(self):
-        assert _test_notebook('chapter09_natural-language-processing/tree-lstm')
-
-    # Chapter 12
-
-    def test_lds_scratch(self):
-        assert _test_notebook('chapter12_time-series/lds-scratch')
-
-    def test_issm_scratch(self):
-        assert _test_notebook('chapter12_time-series/issm-scratch')
-
-    # Chapter 14
-
-    def test_igan_intro(self):
-        assert _test_notebook('chapter14_generative-adversarial-networks/gan-intro')
-
-    def test_dcgan(self):
-        assert _test_notebook('chapter14_generative-adversarial-networks/dcgan')
-
-    def test_generative_adversarial_networks(self):
-        assert _test_notebook('chapter14_generative-adversarial-networks/conditional')
-
-    def test_pixel2pixel(self):
-        assert _test_notebook('chapter14_generative-adversarial-networks/pixel2pixel')

From 6ba9aad6336982572c4a1a776bda08b39e0f76d2 Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Thu, 9 Jan 2020 11:04:07 -0800
Subject: [PATCH 778/813] Enabling large tensor support for binary broadcast
 operators (#16755)

* using dim_t as type for dimensions in BinaryBroadcastShape

* add arctan2 and hypot test

* large vector for both

* Revert "using dim_t as type for dimensions in BinaryBroadcastShape"

This reverts commit 3d12ed2fd4c79e3e558f77021d4ce99c28fa3732.
---
 tests/nightly/test_large_array.py  | 11 +++++++++++
 tests/nightly/test_large_vector.py | 11 +++++++++++
 2 files changed, 22 insertions(+)

diff --git a/tests/nightly/test_large_array.py b/tests/nightly/test_large_array.py
index 2780b0d45d62..2c0841bf1ff8 100644
--- a/tests/nightly/test_large_array.py
+++ b/tests/nightly/test_large_array.py
@@ -1678,6 +1678,17 @@ def test_gather():
     assert np.sum(arr[idx[0]] == 2) == SMALL_Y
 
 
+def test_binary_broadcast():
+    def check_correctness(mxnet_op, numpy_op, atol=1e-3):
+        a = mx.nd.ones((LARGE_X, SMALL_Y)).as_np_ndarray()
+        b = 2*mx.nd.ones((LARGE_X, SMALL_Y)).as_np_ndarray()
+        res = mxnet_op(a, b)
+        np_res = numpy_op(1, 2)
+        assert np.abs(res[-1][-1] - np_res) < atol
+    check_correctness(mx.np.arctan2, np.arctan2)
+    check_correctness(mx.np.hypot, np.hypot)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()
diff --git a/tests/nightly/test_large_vector.py b/tests/nightly/test_large_vector.py
index c6a99a5d0826..8f01372fcf19 100644
--- a/tests/nightly/test_large_vector.py
+++ b/tests/nightly/test_large_vector.py
@@ -1065,6 +1065,17 @@ def test_infer_shape():
     assert out_shapes == [(LARGE_X,)]
 
 
+def test_binary_broadcast():
+    def check_correctness(mxnet_op, numpy_op, atol=1e-3):
+        a = mx.nd.ones(LARGE_X).as_np_ndarray()
+        b = 2*mx.nd.ones(LARGE_X).as_np_ndarray()
+        res = mxnet_op(a, b)
+        np_res = numpy_op(1, 2)
+        assert np.abs(res[-1] - np_res) < atol
+    check_correctness(mx.np.arctan2, np.arctan2)
+    check_correctness(mx.np.hypot, np.hypot)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

From ac88f1e87aa7da2c33f0cb89524d444ddc3a2ae8 Mon Sep 17 00:00:00 2001
From: Haibin Lin <linhaibin.eric@gmail.com>
Date: Thu, 9 Jan 2020 11:36:14 -0800
Subject: [PATCH 779/813] [DOC] Add a few tips for running horovod (#17235)

* Update perf.md

* Update README.md

* Update README.md

* Update perf.md
---
 docs/static_site/src/pages/api/faq/perf.md    | 23 +++++++++++--------
 .../distributed_training-horovod/README.md    |  8 +++++++
 2 files changed, 21 insertions(+), 10 deletions(-)

diff --git a/docs/static_site/src/pages/api/faq/perf.md b/docs/static_site/src/pages/api/faq/perf.md
index 202a099b324f..527346d912b3 100644
--- a/docs/static_site/src/pages/api/faq/perf.md
+++ b/docs/static_site/src/pages/api/faq/perf.md
@@ -268,6 +268,8 @@ To reduce the communication cost, we can consider:
 - Exploring different `--kv-store` options.
 - Increasing the batch size to improve the computation to communication ratio.
 
+Finally, MXNet is integrated with other distributed training frameworks, including [horovod](https://github.com/apache/incubator-mxnet/tree/master/example/distributed_training-horovod) and [BytePS](https://github.com/bytedance/byteps#use-byteps-in-your-code).
+
 ## Input Data
 
 To make sure you're handling input data in a reasonable way consider the following:
@@ -284,30 +286,31 @@ For example, the safe batch size for CIFAR 10 is approximately 200, while for Im
 
 ## Profiler
 
-As of v0.9.1 (with the NNVM merge), _MXNet_ has a built-in profiler
-that gives detailed information about execution time at the symbol level.
+_MXNet_ has a built-in profiler
+that gives detailed information about execution time at the operator level.
 This feature complements general profiling tools like _nvprof_ and _gprof_
 by summarizing at the operator level, instead of a function, kernel, or instruction level.
 
 The profiler can be turned on with an [environment variable]({{'/api/faq/env_var#control-the-profiler' | relative_url}})
-for an entire program run, or programmatically for just part of a run.
+for an entire program run, or programmatically for just part of a run. Note that by default the profiler hides the details of each individual operator, and you can reveal the details by setting environment variables `MXNET_EXEC_BULK_EXEC_INFERENCE`, `MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN` and `MXNET_EXEC_BULK_EXEC_TRAIN` to 0.
 See [example/profiler](https://github.com/dmlc/mxnet/tree/master/example/profiler)
-for complete examples of how to use the profiler in code, but briefly, the Python code looks like:
+for complete examples of how to use the profiler in code, or [this tutorial](https://mxnet.apache.org/api/python/docs/tutorials/performance/backend/profiler.html) on how to profile MXNet performance.
+
+Briefly, the Python code looks like:
 
 ```python
-    mx.profiler.set_config(profile_all=True, filename='profile_output.json')
+    # wait for previous operations to complete
+    mx.nd.waitall() 
+    mx.profiler.set_config(profile_all=True, aggregate_stats=True, filename='profile_output.json')
     mx.profiler.set_state('run')
 
     # Code to be profiled goes here...
 
+    # wait for previous operations to complete
+    mx.nd.waitall() 
     mx.profiler.set_state('stop')
 ```
 
-The `mode` parameter can be set to
-
-* `symbolic` to only include symbolic operations
-* `all` to include all operations
-
 After the program finishes, navigate to your browser's tracing (Example - chrome://tracing in a Chrome browser) and load the `profile_output.json` file output by the profiler to inspect the results.
 
 ![MLP Profile](https://cloud.githubusercontent.com/assets/17693755/18035938/0a43484a-6d93-11e6-80d4-241c6ca552ea.png)
diff --git a/example/distributed_training-horovod/README.md b/example/distributed_training-horovod/README.md
index 8c939eadfe55..961f7f639655 100644
--- a/example/distributed_training-horovod/README.md
+++ b/example/distributed_training-horovod/README.md
@@ -199,3 +199,11 @@ $ mpirun -np 8 \
     -mca pml ob1 -mca btl ^openib \
     python train.py
 ```
+
+## Tuning Horovod Performance
+
+1. To analyse horovod performance, [horovod timeline](https://github.com/horovod/horovod/blob/master/docs/timeline.rst) is a handy tool to trace and visualize the time spent on horovod operations. 
+
+2. A few tuning knobs affect horovod runtime performance (explained [here](https://github.com/horovod/horovod/blob/master/docs/tensor-fusion.rst)). Apart from `HOROVOD_FUSION_THRESHOLD`, sometimes we find increasing `HOROVOD_CYCLE_TIME` (up to 100 ms), changing [`NCCL_ALGO`](https://docs.nvidia.com/deeplearning/sdk/nccl-developer-guide/docs/env.html#nccl-algo), and [`NCCL_MIN_NCHANNELS`](https://docs.nvidia.com/deeplearning/sdk/nccl-developer-guide/docs/env.html#nccl-min-nchannels) improves performance.
+
+3. If you are running horovod on AWS, you can potentially leverage [EFA](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/efa.html) if your instance supports 100 Gb/s networking. To use EFA, you can refer to the [official documentation](https://docs.aws.amazon.com/eu_us/AWSEC2/latest/UserGuide/efa-start-nccl-dlami.html) for the setup instructions, and the environment variables (`-x FI_PROVIDER`, `-x FI_EFA_TX_MIN_CREDITS`) to set. Besides, you need to make sure EFA library is included in the shared library path (`-x LD_LIBRARY_PATH`).

From 16819a6f34921aaf6759441bcf14e78094ac1e8b Mon Sep 17 00:00:00 2001
From: Xi Wang <xidulu@gmail.com>
Date: Fri, 10 Jan 2020 12:09:50 +0800
Subject: [PATCH 780/813] [Numpy] Random.gamma() implemented (#16152)

* gamma done

* gamma finished

* retrigger CI

* simplify gamma kernel

* fix lint erropr

* fix style
---
 python/mxnet/ndarray/numpy/random.py     |  59 +++-
 python/mxnet/numpy/random.py             |  37 ++-
 python/mxnet/symbol/numpy/random.py      |  59 +++-
 src/operator/numpy/random/np_gamma_op.cc |  84 ++++++
 src/operator/numpy/random/np_gamma_op.cu |  36 +++
 src/operator/numpy/random/np_gamma_op.h  | 347 +++++++++++++++++++++++
 tests/nightly/test_np_random.py          |  27 ++
 tests/python/unittest/test_numpy_op.py   |  20 +-
 8 files changed, 661 insertions(+), 8 deletions(-)
 create mode 100644 src/operator/numpy/random/np_gamma_op.cc
 create mode 100644 src/operator/numpy/random/np_gamma_op.cu
 create mode 100644 src/operator/numpy/random/np_gamma_op.h

diff --git a/python/mxnet/ndarray/numpy/random.py b/python/mxnet/ndarray/numpy/random.py
index e67c766c6bdf..913ceaaff097 100644
--- a/python/mxnet/ndarray/numpy/random.py
+++ b/python/mxnet/ndarray/numpy/random.py
@@ -23,7 +23,7 @@
 from ..ndarray import NDArray
 
 
-__all__ = ['randint', 'uniform', 'normal', "choice", "rand", "multinomial", "shuffle"]
+__all__ = ['randint', 'uniform', 'normal', "choice", "rand", "multinomial", "shuffle", 'gamma']
 
 
 def randint(low, high=None, size=None, dtype=None, ctx=None, out=None):
@@ -319,6 +319,63 @@ def choice(a, size=None, replace=True, p=None, ctx=None, out=None):
             return _npi.choice(p, a=a, size=size, replace=replace, ctx=ctx, weighted=True, out=out)
 
 
+def gamma(shape, scale=1.0, size=None, dtype=None, ctx=None, out=None):
+    """Draw samples from a Gamma distribution.
+
+    Samples are drawn from a Gamma distribution with specified parameters,
+    `shape` (sometimes designated "k") and `scale` (sometimes designated
+    "theta"), where both parameters are > 0.
+
+    Parameters
+    ----------
+    shape : float or array_like of floats
+        The shape of the gamma distribution. Should be greater than zero.
+    scale : float or array_like of floats, optional
+        The scale of the gamma distribution. Should be greater than zero.
+        Default is equal to 1.
+    size : int or tuple of ints, optional
+        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k`` samples are drawn.  If size is ``None`` (default),
+        a single value is returned if ``shape`` and ``scale`` are both scalars.
+        Otherwise, ``np.broadcast(shape, scale).size`` samples are drawn.
+    ctx : Context, optional
+        Device context of output. Default is current context.
+
+    Returns
+    -------
+    out : ndarray or scalar
+        Drawn samples from the parameterized gamma distribution.
+
+    The Gamma distribution is often used to model the times to failure of
+    electronic components, and arises naturally in processes for which the
+    waiting times between Poisson distributed events are relevant.
+    """
+    from ...numpy import ndarray as np_ndarray
+    input_type = (isinstance(shape, np_ndarray), isinstance(scale, np_ndarray))
+    if dtype is None:
+        dtype = 'float32'
+    if ctx is None:
+        ctx = current_context()
+    if out is not None:
+        size = out.shape
+    if size == ():
+        size = None
+    if input_type == (True, True):
+        return _npi.gamma(shape, scale, shape=None, scale=None, size=size,
+                          ctx=ctx, dtype=dtype, out=out)
+    elif input_type == (False, True):
+        return _npi.gamma(scale, shape=shape, scale=None, size=size,
+                          ctx=ctx, dtype=dtype, out=out)
+    elif input_type == (True, False):
+        return _npi.gamma(shape, shape=None, scale=scale, size=size,
+                          ctx=ctx, dtype=dtype, out=out)
+    else:
+        return _npi.gamma(shape=shape, scale=scale, size=size,
+                          ctx=ctx, dtype=dtype, out=out)
+
+    raise ValueError("Distribution parameters must be either mxnet.numpy.ndarray or numbers")
+
+
 def rand(*size, **kwargs):
     r"""Random values in a given shape.
 
diff --git a/python/mxnet/numpy/random.py b/python/mxnet/numpy/random.py
index 95719a005cec..198f2fcb4389 100644
--- a/python/mxnet/numpy/random.py
+++ b/python/mxnet/numpy/random.py
@@ -20,7 +20,8 @@
 from __future__ import absolute_import
 from ..ndarray import numpy as _mx_nd_np
 
-__all__ = ["randint", "uniform", "normal", "choice", "rand", "multinomial", "shuffle", "randn"]
+__all__ = ["randint", "uniform", "normal", "choice", "rand", "multinomial", "shuffle", "randn",
+           "gamma"]
 
 
 def randint(low, high=None, size=None, dtype=None, ctx=None, out=None):
@@ -359,6 +360,40 @@ def shuffle(x):
     _mx_nd_np.random.shuffle(x)
 
 
+def gamma(shape, scale=1.0, size=None, dtype=None, ctx=None, out=None):
+    """Draw samples from a Gamma distribution.
+
+    Samples are drawn from a Gamma distribution with specified parameters,
+    `shape` (sometimes designated "k") and `scale` (sometimes designated
+    "theta"), where both parameters are > 0.
+
+    Parameters
+    ----------
+    shape : float or array_like of floats
+        The shape of the gamma distribution. Should be greater than zero.
+    scale : float or array_like of floats, optional
+        The scale of the gamma distribution. Should be greater than zero.
+        Default is equal to 1.
+    size : int or tuple of ints, optional
+        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k`` samples are drawn.  If size is ``None`` (default),
+        a single value is returned if ``shape`` and ``scale`` are both scalars.
+        Otherwise, ``np.broadcast(shape, scale).size`` samples are drawn.
+    ctx : Context, optional
+        Device context of output. Default is current context.
+
+    Returns
+    -------
+    out : ndarray or scalar
+        Drawn samples from the parameterized gamma distribution.
+
+    The Gamma distribution is often used to model the times to failure of
+    electronic components, and arises naturally in processes for which the
+    waiting times between Poisson distributed events are relevant.
+    """
+    return _mx_nd_np.random.gamma(shape, scale, size, dtype, ctx, out)
+
+
 def randn(*size, **kwargs):
     r"""Return a sample (or samples) from the "standard normal" distribution.
     If positive, int_like or int-convertible arguments are provided,
diff --git a/python/mxnet/symbol/numpy/random.py b/python/mxnet/symbol/numpy/random.py
index 94c29f407acc..c6b23b507d87 100644
--- a/python/mxnet/symbol/numpy/random.py
+++ b/python/mxnet/symbol/numpy/random.py
@@ -21,7 +21,7 @@
 from ...context import current_context
 from . import _internal as _npi
 
-__all__ = ['randint', 'uniform', 'normal', 'rand', 'shuffle']
+__all__ = ['randint', 'uniform', 'normal', 'rand', 'shuffle', 'gamma']
 
 
 def randint(low, high=None, size=None, dtype=None, ctx=None, out=None):
@@ -290,6 +290,63 @@ def choice(a, size=None, replace=True, p=None, ctx=None, out=None):
             return _npi.choice(p, a=a, size=size, replace=replace, ctx=ctx, weighted=True, out=out)
 
 
+def gamma(shape, scale=1.0, size=None, dtype=None, ctx=None, out=None):
+    """Draw samples from a Gamma distribution.
+
+    Samples are drawn from a Gamma distribution with specified parameters,
+    `shape` (sometimes designated "k") and `scale` (sometimes designated
+    "theta"), where both parameters are > 0.
+
+    Parameters
+    ----------
+    shape : float or array_like of floats
+        The shape of the gamma distribution. Should be greater than zero.
+    scale : float or array_like of floats, optional
+        The scale of the gamma distribution. Should be greater than zero.
+        Default is equal to 1.
+    size : int or tuple of ints, optional
+        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k`` samples are drawn.  If size is ``None`` (default),
+        a single value is returned if ``shape`` and ``scale`` are both scalars.
+        Otherwise, ``np.broadcast(shape, scale).size`` samples are drawn.
+    ctx : Context, optional
+        Device context of output. Default is current context.
+
+    Returns
+    -------
+    out : _Symbol
+        Drawn samples from the parameterized gamma distribution.
+
+    The Gamma distribution is often used to model the times to failure of
+    electronic components, and arises naturally in processes for which the
+    waiting times between Poisson distributed events are relevant.
+    """
+    from ._symbol import _Symbol as np_symbol
+    input_type = (isinstance(shape, np_symbol), isinstance(scale, np_symbol))
+    if dtype is None:
+        dtype = 'float32'
+    if ctx is None:
+        ctx = current_context()
+    if out is not None:
+        size = out.shape
+    if size == ():
+        size = None
+    if input_type == (True, True):
+        return _npi.gamma(shape, scale, shape=None, scale=None, size=size,
+                          ctx=ctx, dtype=dtype, out=out)
+    elif input_type == (False, True):
+        return _npi.gamma(scale, shape=shape, scale=None, size=size,
+                          ctx=ctx, dtype=dtype, out=out)
+    elif input_type == (True, False):
+        return _npi.gamma(shape, shape=None, scale=scale, size=size,
+                          ctx=ctx, dtype=dtype, out=out)
+    else:
+        return _npi.gamma(shape=shape, scale=scale, size=size,
+                          ctx=ctx, dtype=dtype, out=out)
+
+    raise ValueError("Distribution parameters must be either _Symbol or numbers")
+
+
 def shuffle(x):
     """
     Modify a sequence in-place by shuffling its contents.
diff --git a/src/operator/numpy/random/np_gamma_op.cc b/src/operator/numpy/random/np_gamma_op.cc
new file mode 100644
index 000000000000..72e337b1642b
--- /dev/null
+++ b/src/operator/numpy/random/np_gamma_op.cc
@@ -0,0 +1,84 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_gamma_op.cc
+ * \brief Operator for random sampling from gamma distribution
+ */
+
+#include "./np_gamma_op.h"
+
+namespace mxnet {
+namespace op {
+
+DMLC_REGISTER_PARAMETER(NumpyGammaParam);
+
+inline bool NumpyGammaOpType(const nnvm::NodeAttrs& attrs,
+                             std::vector<int>* in_attrs,
+                             std::vector<int>* out_attrs) {
+  const NumpyGammaParam& param = nnvm::get<NumpyGammaParam>(attrs.parsed);
+  int otype = param.dtype;
+  if (otype != -1) {
+    (*out_attrs)[0] = otype;
+  } else {
+    (*out_attrs)[0] = mshadow::kFloat32;
+  }
+  return true;
+}
+
+NNVM_REGISTER_OP(_npi_gamma)
+.describe("Numpy behavior gamma")
+.set_num_inputs(
+  [](const nnvm::NodeAttrs& attrs) {
+    const NumpyGammaParam& param = nnvm::get<NumpyGammaParam>(attrs.parsed);
+    int num_inputs = 2;
+    if (param.shape.has_value()) num_inputs -= 1;
+    if (param.scale.has_value()) num_inputs -= 1;
+    return num_inputs;
+  }
+)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    const NumpyGammaParam& param = nnvm::get<NumpyGammaParam>(attrs.parsed);
+    int num_inputs = 2;
+    if (param.scale.has_value()) num_inputs -= 1;
+    if (param.shape.has_value()) num_inputs -= 1;
+    if (num_inputs == 0) return std::vector<std::string>();
+    if (num_inputs == 1) return std::vector<std::string>{"input1"};
+    return std::vector<std::string>{"input1", "input2"};
+  })
+.set_attr_parser(ParamParser<NumpyGammaParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", TwoparamsDistOpShape<NumpyGammaParam>)
+.set_attr<nnvm::FInferType>("FInferType", NumpyGammaOpType)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const nnvm::NodeAttrs& attrs) {
+      return std::vector<ResourceRequest>{
+        ResourceRequest::kRandom,
+        ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", NumpyGammaForward<cpu, double>)
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.add_argument("input1", "NDArray-or-Symbol", "Source input")
+.add_argument("input2", "NDArray-or-Symbol", "Source input")
+.add_arguments(NumpyGammaParam::__FIELDS__());
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/random/np_gamma_op.cu b/src/operator/numpy/random/np_gamma_op.cu
new file mode 100644
index 000000000000..5be15c7b9d13
--- /dev/null
+++ b/src/operator/numpy/random/np_gamma_op.cu
@@ -0,0 +1,36 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_gamma_op.cu
+ * \brief Operator for random sampling from gamma distribution
+ */
+
+#include "./np_gamma_op.h"
+#include <iostream>
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(_npi_gamma)
+.set_attr<FCompute>("FCompute<gpu>", NumpyGammaForward<gpu, double>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/random/np_gamma_op.h b/src/operator/numpy/random/np_gamma_op.h
new file mode 100644
index 000000000000..83e8f1f5242b
--- /dev/null
+++ b/src/operator/numpy/random/np_gamma_op.h
@@ -0,0 +1,347 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_gamma_op.h
+ * \brief Operator for random sampling from gamma distribution
+ */
+
+#ifndef MXNET_OPERATOR_NUMPY_RANDOM_NP_GAMMA_OP_H_
+#define MXNET_OPERATOR_NUMPY_RANDOM_NP_GAMMA_OP_H_
+
+#include <mxnet/operator_util.h>
+#include <mshadow/base.h>
+#include <vector>
+#include <string>
+#include <algorithm>
+#include "./dist_common.h"
+#include "../../elemwise_op_common.h"
+#include "../../tensor/elemwise_binary_broadcast_op.h"
+#include "../../mshadow_op.h"
+#include "../../mxnet_op.h"
+#include "../../operator_common.h"
+
+#define M 2
+
+namespace mxnet {
+namespace op {
+
+struct NumpyGammaParam : public dmlc::Parameter<NumpyGammaParam> {
+  dmlc::optional<float> shape;
+  dmlc::optional<float> scale;
+  std::string ctx;
+  int dtype;
+  dmlc::optional<mxnet::Tuple<int>> size;
+  DMLC_DECLARE_PARAMETER(NumpyGammaParam) {
+    DMLC_DECLARE_FIELD(shape);
+    DMLC_DECLARE_FIELD(scale);
+    DMLC_DECLARE_FIELD(size)
+      .set_default(dmlc::optional<mxnet::Tuple<int>>())
+      .describe("Output shape. If the given shape is, "
+                "e.g., (m, n, k), then m * n * k samples are drawn. "
+                "Default is None, in which case a single value is returned.");
+    DMLC_DECLARE_FIELD(ctx)
+      .set_default("cpu")
+      .describe("Context of output, in format [xpu|xpu|xpu_pinned](n)."
+                " Only used for imperative calls.");
+    DMLC_DECLARE_FIELD(dtype)
+      .add_enum("float32", mshadow::kFloat32)
+      .add_enum("float64", mshadow::kFloat64)
+      .add_enum("float16", mshadow::kFloat16)
+      .set_default(mshadow::kFloat32)
+      .describe("DType of the output in case this can't be inferred. "
+                "Defaults to float32 if not defined (dtype=None).");
+  }
+};
+
+
+namespace mxnet_op {
+
+template <typename IType, typename FType>
+MSHADOW_XINLINE void GammaTransform(IType a, IType b,
+                                    FType* uniforms, FType* normals) {
+  FType d = a < 1 ? a + 2.0 / 3.0 : a - 1.0 / 3.0;
+  FType k = sqrt(9.0 * d);
+  FType c = 1.0 / k;
+  for (size_t i = 0; i < M - 1; i++) {
+    FType u = uniforms[i];
+    FType n = normals[i];
+    uniforms[i] = FType(-1);
+    FType ocn = 1+c*n;
+    FType v = ocn*ocn*ocn;
+    if (v > 0) {
+      if (u <= (1 - 0.0331 * (n * n) * (n * n))) {
+        // rejection sample. The second operation should be
+        // performed with low probability. This is the "squeeze"
+        uniforms[i] = FType(d * v * b);
+      }
+      if (logf(u) < 0.5 * (n * n) + d * (1 - v + logf(v))) {
+        // rejection sample. The second operation should be
+        // performed with low probability. This is the "squeeze"
+        uniforms[i] = FType(d * v * b);
+      }
+    }
+  }
+}
+
+
+template <typename IType, typename FType>
+MSHADOW_XINLINE FType GammaReduce(IType a, FType* uniforms) {
+  FType u2 = uniforms[M - 1];
+  for (size_t i = 0; i < M - 1; i++) {
+    FType sample = uniforms[i];
+    if (sample > 0) {
+      return a < 1 ? sample * powf(u2, FType(1.0 / a)) : sample;
+    }
+  }
+  return -1;
+}
+
+template<typename OType, typename FType>
+struct CheckSuccessKernel {
+  MSHADOW_XINLINE static void Map(int i, OType* out, FType* flag) {
+    if (out[i] < 0) {
+      flag[0] = -1.0;
+    }
+  }
+};
+
+template <int ndim, typename IType, typename OType, typename FType>
+struct gamma_kernel {
+  MSHADOW_XINLINE static void Map(index_t i, const Shape<ndim> &lstride,
+                                  const Shape<ndim> &hstride,
+                                  const Shape<ndim> &oshape, IType *shape,
+                                  IType *scale, FType *uniforms, FType *normals,
+                                  OType *out, FType *flag = nullptr) {
+    // We know the sampling procedure is in its first stage, if `flag` is
+    // nullptr, i.e. there is no need for reseting the indicator
+    // variable(flag[0] = 1) nor checking whether a specific element is sampled
+    // successfully (out[i] < 0).
+    bool in_first_stage = (flag == nullptr);
+    if (!in_first_stage) {
+      flag[0] = 1;
+    }
+    Shape<ndim> coord = unravel(i, oshape);
+    auto lidx = static_cast<index_t>(dot(coord, lstride));
+    auto hidx = static_cast<index_t>(dot(coord, hstride));
+    IType shape_value = shape[lidx];
+    IType scale_value = scale[hidx];
+    if (in_first_stage || out[i] < 0) {
+      // map phase
+      GammaTransform<IType, FType>(shape_value, scale_value, uniforms + i * M,
+                                   normals + i * M);
+      // reduce phase
+      OType sample =
+          (OType)GammaReduce<IType, FType>(shape_value, uniforms + i * M);
+      out[i] = sample;
+    }
+  }
+};
+
+template <int ndim, typename IType, typename OType, typename FType>
+struct gamma_one_scalar_kernel {
+  MSHADOW_XINLINE static void Map(index_t i, int scalar_pos,
+                                  const Shape<ndim> &stride,
+                                  const Shape<ndim> &oshape, IType *array,
+                                  float scalar, FType *uniforms, FType *normals,
+                                  OType *out, FType *flag = nullptr) {
+    // We know the sampling procedure is in its first stage, if `flag` is
+    // nullptr, i.e. there is no need for reseting the indicator
+    // variable(flag[0] = 1) nor checking whether a specific element is sampled
+    // successfully (out[i] < 0).
+    bool in_first_stage = (flag == nullptr);
+    if (!in_first_stage) {
+      flag[0] = 1;
+    }
+    Shape<ndim> coord = unravel(i, oshape);
+    auto idx = static_cast<index_t>(dot(coord, stride));
+    IType shape_value;
+    IType scale_value;
+    if (scalar_pos == 0) {
+      shape_value = scalar;
+      scale_value = array[idx];
+    } else {
+      shape_value = array[idx];
+      scale_value = scalar;
+    }
+    if (in_first_stage || out[i] < 0) {
+      // map phase
+      GammaTransform<IType, FType>(shape_value, scale_value, uniforms + i * M,
+                                   normals + i * M);
+      // reduce phase
+      OType sample =
+          (OType)GammaReduce<IType, FType>(shape_value, uniforms + i * M);
+      out[i] = sample;
+    }
+  }
+};
+
+template <typename OType, typename FType>
+struct gamma_two_scalar_kernel {
+  MSHADOW_XINLINE static void Map(index_t i, float shape_value,
+                                  float scale_value, FType *uniforms_origin,
+                                  FType *normals_origin, OType *out,
+                                  FType *flag = nullptr) {
+    // We know the sampling procedure is in its first stage, if `flag` is
+    // nullptr, i.e. there is no need for reseting the indicator
+    // variable(flag[0] = 1) nor checking whether a specific element is sampled
+    // successfully (out[i] < 0).
+    bool in_first_stage = (flag == nullptr);
+    if (!in_first_stage) {
+      flag[0] = 1;
+    }
+    FType *uniforms = uniforms_origin + i * M;
+    FType *normals = normals_origin + i * M;
+    if (in_first_stage || out[i] < 0) {
+      // map phase
+      GammaTransform<float, FType>(shape_value, scale_value, uniforms, normals);
+      // reduce phase
+      OType sample = (OType)GammaReduce<float, FType>(shape_value, uniforms);
+      out[i] = sample;
+    }
+  }
+};
+}  // namespace mxnet_op
+
+template <typename xpu, typename FType>
+void NumpyGammaForward(const nnvm::NodeAttrs &attrs, const OpContext &ctx,
+                       const std::vector<TBlob> &inputs,
+                       const std::vector<OpReqType> &req,
+                       const std::vector<TBlob> &outputs) {
+  using namespace mshadow;
+  using namespace mxnet_op;
+  const NumpyGammaParam &param = nnvm::get<NumpyGammaParam>(attrs.parsed);
+  CHECK_EQ(outputs.size(), 1);
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  // Generate base random number.
+  Random<xpu, FType> *prnd = ctx.requested[0].get_random<xpu, FType>(s);
+  index_t output_len = outputs[0].Size();
+  Tensor<xpu, 1, FType> random_tensor =
+      ctx.requested[1].get_space_typed<xpu, 1, FType>(Shape1(output_len * 2 * M + 1), s);
+  Tensor<xpu, 1, FType> uniform_tensor = random_tensor.Slice(0, output_len * M);
+  Tensor<xpu, 1, FType> normal_tensor = random_tensor.Slice(output_len * M, output_len * 2 * M);
+  prnd->SampleUniform(&uniform_tensor, 0, 1);
+  prnd->SampleGaussian(&normal_tensor, 0, 1);
+  mxnet::TShape new_lshape, new_hshape, new_oshape;
+  FType failure_indicator = 1.0;
+  Tensor<xpu, 1, FType> failure_indic_workspace =
+      random_tensor.Slice(output_len * 2 * M, output_len * 2 * M + 1);
+  FType *failure_indicator_device = failure_indic_workspace.dptr_;
+  // [scalar scalar] case
+  if (inputs.size() == 0U) {
+    MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, OType, {
+      bool in_resample_stage = false;
+      do {
+        if (in_resample_stage) {
+          prnd->SampleUniform(&uniform_tensor, 0, 1);
+          prnd->SampleGaussian(&normal_tensor, 0, 1);
+        }
+        Kernel<gamma_two_scalar_kernel<OType, FType>, xpu>::Launch(
+          s, outputs[0].Size(), param.shape.value(), param.scale.value(),
+          uniform_tensor.dptr_, normal_tensor.dptr_, outputs[0].dptr<OType>(),
+          in_resample_stage ? failure_indicator_device : nullptr);
+        failure_indicator = 1.0;
+        Kernel<CheckSuccessKernel<OType, FType>, xpu>::Launch(
+          s, outputs[0].Size(), outputs[0].dptr<OType>(),
+          failure_indicator_device);
+        _copy<xpu>(s, &failure_indicator, failure_indicator_device);
+        in_resample_stage = true;
+      } while (failure_indicator < 0);
+    });
+  } else if (inputs.size() == 1U) {
+    // [scalar tensor], [tensor scalar] case
+    int ndim = FillShape(inputs[0].shape_, inputs[0].shape_, outputs[0].shape_,
+                         &new_lshape, &new_lshape, &new_oshape);
+    int scalar_pos;
+    float scalar_value;
+    if (param.shape.has_value()) {
+      scalar_pos = 0;
+      scalar_value = param.shape.value();
+    } else {
+      scalar_pos = 1;
+      scalar_value = param.scale.value();
+    }
+    MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, IType, {
+      MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, OType, {
+        BROADCAST_NDIM_SWITCH(ndim, NDim, {
+          mshadow::Shape<NDim> oshape = new_oshape.get<NDim>();
+          mshadow::Shape<NDim> stride = calc_stride(new_lshape.get<NDim>());
+          bool in_resample_stage = false;
+          do {
+            if (in_resample_stage) {
+              prnd->SampleUniform(&uniform_tensor, 0, 1);
+              prnd->SampleGaussian(&normal_tensor, 0, 1);
+            }
+            Kernel<gamma_one_scalar_kernel<NDim, IType, OType, FType>, xpu>::Launch(
+              s, outputs[0].Size(), scalar_pos, stride, oshape,
+              inputs[0].dptr<IType>(), scalar_value,
+              uniform_tensor.dptr_, normal_tensor.dptr_,
+              outputs[0].dptr<OType>(),
+              in_resample_stage ? failure_indicator_device : nullptr);
+            failure_indicator = 1.0;
+            Kernel<CheckSuccessKernel<OType, FType>, xpu>::Launch(
+              s, outputs[0].Size(), outputs[0].dptr<OType>(),
+              failure_indicator_device);
+            _copy<xpu>(s, &failure_indicator, failure_indicator_device);
+            in_resample_stage = true;
+          } while (failure_indicator < 0);
+        });
+      });
+    });
+  } else if (inputs.size() == 2U) {
+    // [tensor tensor] case
+    int ndim = FillShape(inputs[0].shape_, inputs[1].shape_, outputs[0].shape_,
+                         &new_lshape, &new_hshape, &new_oshape);
+    MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, IType, {
+      MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, OType, {
+        BROADCAST_NDIM_SWITCH(ndim, NDim, {
+        mshadow::Shape<NDim> oshape = new_oshape.get<NDim>();
+        mshadow::Shape<NDim> lstride = calc_stride(new_lshape.get<NDim>());
+        mshadow::Shape<NDim> hstride = calc_stride(new_hshape.get<NDim>());
+        bool in_resample_stage = false;
+        do {
+          if (in_resample_stage) {
+            prnd->SampleUniform(&uniform_tensor, 0, 1);
+            prnd->SampleGaussian(&normal_tensor, 0, 1);
+          }
+          prnd->SampleUniform(&uniform_tensor, 0, 1);
+          prnd->SampleGaussian(&normal_tensor, 0, 1);
+          Kernel<gamma_kernel<NDim, IType, OType, FType>, xpu>::Launch(
+            s, outputs[0].Size(), lstride, hstride, oshape,
+            inputs[0].dptr<IType>(), inputs[1].dptr<IType>(),
+            uniform_tensor.dptr_, normal_tensor.dptr_,
+            outputs[0].dptr<OType>(), in_resample_stage ? failure_indicator_device : nullptr);
+          failure_indicator = 1.0;
+          Kernel<CheckSuccessKernel<OType, FType>, xpu>::Launch(
+            s, outputs[0].Size(), outputs[0].dptr<OType>(),
+            failure_indicator_device);
+          _copy<xpu>(s, &failure_indicator, failure_indicator_device);
+          in_resample_stage = true;
+          } while (failure_indicator < 0);
+        });
+      });
+    });
+  }
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_NUMPY_RANDOM_NP_GAMMA_OP_H_
diff --git a/tests/nightly/test_np_random.py b/tests/nightly/test_np_random.py
index 345fb86b1222..d086ac4b08d6 100644
--- a/tests/nightly/test_np_random.py
+++ b/tests/nightly/test_np_random.py
@@ -78,6 +78,33 @@ def test_np_normal():
             verify_generator(generator=generator_mx_np, buckets=buckets, probs=probs, nsamples=samples, nrepeat=trials)
 
 
+@retry(5)
+@with_seed()
+@use_np
+def test_np_gamma():
+    types = [None, "float32", "float64"]
+    ctx = mx.context.current_context()
+    samples = 1000000
+    # Generation test
+    trials = 8
+    num_buckets = 5
+    for dtype in types:
+        for alpha, beta in [(2.0, 3.0), (0.5, 1.0)]:
+            buckets, probs = gen_buckets_probs_with_ppf(
+                lambda x: ss.gamma.ppf(x, a=alpha, loc=0, scale=beta), num_buckets)
+            buckets = np.array(buckets).tolist()
+            def generator_mx(x): return np.random.gamma(
+                alpha, beta, size=samples, ctx=ctx).asnumpy()
+            verify_generator(generator=generator_mx, buckets=buckets, probs=probs,
+                             nsamples=samples, nrepeat=trials)
+            generator_mx_same_seed =\
+                lambda x: _np.concatenate(
+                    [np.random.gamma(alpha, beta, size=(x // 10), ctx=ctx).asnumpy()
+                        for _ in range(10)])
+            verify_generator(generator=generator_mx_same_seed, buckets=buckets, probs=probs,
+                             nsamples=samples, nrepeat=trials)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index d60b86a57d35..b25c69385e1e 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -3279,34 +3279,44 @@ def hybrid_forward(self, F, param1, param2):
 def test_np_random():
     shapes = [(), (1,), (2, 3), (4, 0, 5), 6, (7, 8), None]
     dtypes = ['float16', 'float32', 'float64']
-    op_names = ['uniform', 'normal']
+    op_names = ['uniform', 'normal', 'gamma']
     for shape in shapes:
         for dtype in dtypes:
             for op_name in op_names:
                 op = getattr(np.random, op_name, None)
                 assert op is not None
-                out = op(size=shape, dtype=dtype)
+                if op_name == 'gamma':
+                    out = op(1, size=shape, dtype=dtype)
+                else:
+                    out = op(size=shape, dtype=dtype)
                 expected_shape = shape
                 if not isinstance(shape, tuple):
                     expected_shape = () if shape is None else (shape,)
                 assert out.shape == expected_shape
 
     class TestRandom(HybridBlock):
-        def __init__(self, shape, op_name):
+        def __init__(self, shape, op_name, param=None):
             super(TestRandom, self).__init__()
             self._shape = shape
             self._op_name = op_name
+            # In case parameters are not optional
+            self._param = param
 
         def hybrid_forward(self, F, x):
             op = getattr(F.np.random, self._op_name, None)
             assert op is not None
-            return x + op(size=shape)
+            if self._param is not None:
+                return x + op(self._param, size=self._shape)
+            return x + op(size=self._shape)
 
     x = np.ones(())
     for op_name in op_names:
         for shape in shapes:
             for hybridize in [False, True]:
-                net = TestRandom(shape, op_name)
+                if op_name == "gamma":
+                    net = TestRandom(shape, op_name, 1)
+                else:
+                    net = TestRandom(shape, op_name)
                 if hybridize:
                     net.hybridize()
                 out = net(x)

From 56292052b0bb6fa947bae5f2bfa10223fd26e05b Mon Sep 17 00:00:00 2001
From: hanke580 <38852697+hanke580@users.noreply.github.com>
Date: Fri, 10 Jan 2020 14:58:04 +0800
Subject: [PATCH 781/813] [Numpy] add row_stack (=vstack) (#17171)

* Fix sanity and 3rd party version

* Reset 3rd party again

* Add round back

* Delete other files

* Modify additional param
---
 python/mxnet/ndarray/numpy/_op.py    | 59 ++++++++++++++++++++++++----
 python/mxnet/numpy/multiarray.py     | 51 +++++++++++++++++++++---
 python/mxnet/symbol/numpy/_symbol.py | 42 +++++++++++++++++---
 3 files changed, 133 insertions(+), 19 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index b39ee18b9db7..f04f94fa38b1 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -35,13 +35,13 @@
            'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor', 'histogram',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'argsort', 'tensordot', 'eye', 'linspace',
            'logspace', 'expand_dims', 'tile', 'arange', 'array_split', 'split', 'vsplit', 'concatenate', 'append',
-           'stack', 'vstack', 'column_stack', 'dstack', 'average', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip',
-           'argmax', 'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'unravel_index', 'hanning', 'hamming',
-           'blackman', 'flip', 'flipud', 'fliplr', 'around', 'round', 'hypot', 'bitwise_xor', 'bitwise_or',
-           'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer',
-           'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum',
-           'true_divide', 'nonzero', 'shares_memory', 'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where',
-           'bincount']
+           'stack', 'vstack', 'row_stack', 'column_stack', 'dstack', 'average', 'mean', 'maximum', 'minimum',
+           'swapaxes', 'clip', 'argmax', 'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'unravel_index',
+           'hanning', 'hamming', 'blackman', 'flip', 'flipud', 'fliplr', 'around', 'round', 'hypot', 'bitwise_xor',
+           'bitwise_or', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot',
+           'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal', 'hsplit',
+           'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory', 'may_share_memory', 'diff', 'resize',
+           'nan_to_num', 'where', 'bincount']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -3715,6 +3715,51 @@ def get_list(arrays):
     return _npi.vstack(*arrays)
 
 
+@set_module('mxnet.ndarray.numpy')
+def row_stack(arrays):
+    r"""Stack arrays in sequence vertically (row wise).
+    This is equivalent to concatenation along the first axis after 1-D arrays
+    of shape `(N,)` have been reshaped to `(1,N)`. Rebuilds arrays divided by
+    `vsplit`.
+    This function makes most sense for arrays with up to 3 dimensions. For
+    instance, for pixel-data with a height (first axis), width (second axis),
+    and r/g/b channels (third axis). The functions `concatenate` and `stack`
+    provide more general stacking and concatenation operations.
+    Parameters
+    ----------
+    tup : sequence of ndarrays
+        The arrays must have the same shape along all but the first axis.
+        1-D arrays must have the same length.
+    Returns
+    -------
+    stacked : ndarray
+        The array formed by stacking the given arrays, will be at least 2-D.
+    Examples
+    --------
+    >>> a = np.array([1, 2, 3])
+    >>> b = np.array([2, 3, 4])
+    >>> np.vstack((a, b))
+    array([[1., 2., 3.],
+            [2., 3., 4.]])
+    >>> a = np.array([[1], [2], [3]])
+    >>> b = np.array([[2], [3], [4]])
+    >>> np.vstack((a, b))
+    array([[1.],
+            [2.],
+            [3.],
+            [2.],
+            [3.],
+            [4.]])
+    """
+    def get_list(arrays):
+        if not hasattr(arrays, '__getitem__') and hasattr(arrays, '__iter__'):
+            raise ValueError("expected iterable for arrays but got {}".format(type(arrays)))
+        return [arr for arr in arrays]
+
+    arrays = get_list(arrays)
+    return _npi.vstack(*arrays)
+
+
 @set_module('mxnet.ndarray.numpy')
 def column_stack(tup):
     """
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 9994f7eda4a8..7fd8c1b4c86f 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -54,12 +54,12 @@
            'degrees', 'log2', 'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative', 'histogram',
            'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'append', 'argsort',
            'tensordot', 'eye', 'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'array_split',
-           'split', 'vsplit', 'concatenate', 'stack', 'vstack', 'column_stack', 'dstack', 'average', 'mean',
-           'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'argmin', 'std', 'var', 'indices', 'copysign',
-           'ravel', 'unravel_index', 'hanning', 'hamming', 'blackman', 'flip', 'flipud', 'fliplr', 'around',
-           'round', 'arctan2', 'hypot', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad', 'unique', 'lcm',
-           'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater',
-           'less', 'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero',
+           'split', 'vsplit', 'concatenate', 'stack', 'vstack', 'row_stack', 'column_stack', 'dstack',
+           'average', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'argmin', 'std', 'var',
+           'indices', 'copysign', 'ravel', 'unravel_index', 'hanning', 'hamming', 'blackman', 'flip', 'flipud',
+           'fliplr', 'around', 'round', 'arctan2', 'hypot', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad',
+           'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal',
+           'greater', 'less', 'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero',
            'shares_memory', 'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where', 'bincount']
 
 # Return code for dispatching indexing function call
@@ -5488,6 +5488,45 @@ def vstack(arrays, out=None):
     return _mx_nd_np.vstack(arrays)
 
 
+@set_module('mxnet.numpy')
+def row_stack(arrays):
+    r"""Stack arrays in sequence vertically (row wise).
+    This is equivalent to concatenation along the first axis after 1-D arrays
+    of shape `(N,)` have been reshaped to `(1,N)`. Rebuilds arrays divided by
+    `vsplit`.
+    This function makes most sense for arrays with up to 3 dimensions. For
+    instance, for pixel-data with a height (first axis), width (second axis),
+    and r/g/b channels (third axis). The functions `concatenate` and `stack`
+    provide more general stacking and concatenation operations.
+    Parameters
+    ----------
+    tup : sequence of ndarrays
+        The arrays must have the same shape along all but the first axis.
+        1-D arrays must have the same length.
+    Returns
+    -------
+    stacked : ndarray
+        The array formed by stacking the given arrays, will be at least 2-D.
+    Examples
+    --------
+    >>> a = np.array([1, 2, 3])
+    >>> b = np.array([2, 3, 4])
+    >>> np.vstack((a, b))
+    array([[1., 2., 3.],
+           [2., 3., 4.]])
+    >>> a = np.array([[1], [2], [3]])
+    >>> b = np.array([[2], [3], [4]])
+    >>> np.vstack((a, b))
+    array([[1.],
+           [2.],
+           [3.],
+           [2.],
+           [3.],
+           [4.]])
+    """
+    return _mx_nd_np.row_stack(arrays)
+
+
 @set_module('mxnet.numpy')
 def column_stack(tup):
     """
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 80a74e789f77..147fba2eaa30 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -43,12 +43,13 @@
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor', 'histogram',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'argsort', 'tensordot', 'eye', 'linspace',
            'logspace', 'expand_dims', 'tile', 'arange', 'array_split', 'split', 'vsplit', 'concatenate', 'append',
-           'stack', 'vstack', 'column_stack', 'dstack', 'average', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip',
-           'argmax', 'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'unravel_index', 'hanning', 'hamming',
-           'blackman', 'flip', 'flipud', 'fliplr', 'around', 'round', 'hypot', 'bitwise_xor', 'bitwise_or',
-           'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer',
-           'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum',
-           'true_divide', 'shares_memory', 'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where', 'bincount']
+           'stack', 'vstack', 'row_stack', 'column_stack', 'dstack', 'average', 'mean', 'maximum', 'minimum',
+           'swapaxes', 'clip', 'argmax', 'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'unravel_index',
+           'hanning', 'hamming', 'blackman', 'flip', 'flipud', 'fliplr', 'around', 'round', 'hypot', 'bitwise_xor',
+           'bitwise_or', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot',
+           'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal', 'hsplit',
+           'rot90', 'einsum', 'true_divide', 'shares_memory', 'may_share_memory', 'diff', 'resize', 'nan_to_num',
+           'where', 'bincount']
 
 
 @set_module('mxnet.symbol.numpy')
@@ -3684,6 +3685,35 @@ def get_list(arrays):
     return _npi.vstack(*arrays)
 
 
+@set_module('mxnet.symbol.numpy')
+def row_stack(arrays):
+    r"""Stack arrays in sequence vertically (row wise).
+    This is equivalent to concatenation along the first axis after 1-D arrays
+    of shape `(N,)` have been reshaped to `(1,N)`. Rebuilds arrays divided by
+    `vsplit`.
+    This function makes most sense for arrays with up to 3 dimensions. For
+    instance, for pixel-data with a height (first axis), width (second axis),
+    and r/g/b channels (third axis). The functions `concatenate` and `stack`
+    provide more general stacking and concatenation operations.
+    Parameters
+    ----------
+    tup : sequence of _Symbol
+        The arrays must have the same shape along all but the first axis.
+        1-D arrays must have the same length.
+    Returns
+    -------
+    stacked : _Symbol
+        The array formed by stacking the given arrays, will be at least 2-D.
+    """
+    def get_list(arrays):
+        if not hasattr(arrays, '__getitem__') and hasattr(arrays, '__iter__'):
+            raise ValueError("expected iterable for arrays but got {}".format(type(arrays)))
+        return [arr for arr in arrays]
+
+    arrays = get_list(arrays)
+    return _npi.vstack(*arrays)
+
+
 @set_module('mxnet.symbol.numpy')
 def column_stack(tup):
     """

From 651eb9d5610464dac2603e64cd110d2bdf63bd8a Mon Sep 17 00:00:00 2001
From: taoli <litaotju@live.cn>
Date: Fri, 10 Jan 2020 23:45:17 +0800
Subject: [PATCH 782/813] Minor fix, use RAII for TensorRT builder and network
 object (#17189)

---
 src/operator/subgraph/tensorrt/onnx_to_tensorrt.cc | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/src/operator/subgraph/tensorrt/onnx_to_tensorrt.cc b/src/operator/subgraph/tensorrt/onnx_to_tensorrt.cc
index 4e7ff660cca0..b02d1094183f 100644
--- a/src/operator/subgraph/tensorrt/onnx_to_tensorrt.cc
+++ b/src/operator/subgraph/tensorrt/onnx_to_tensorrt.cc
@@ -77,8 +77,8 @@ std::tuple<unique_ptr<nvinfer1::ICudaEngine>,
   GOOGLE_PROTOBUF_VERIFY_VERSION;
 
   auto trt_logger = std::unique_ptr<TRT_Logger>(new TRT_Logger(verbosity));
-  auto trt_builder = nvinfer1::createInferBuilder(*trt_logger);
-  auto trt_network = trt_builder->createNetwork();
+  auto trt_builder = InferObject(nvinfer1::createInferBuilder(*trt_logger));
+  auto trt_network = InferObject(trt_builder->createNetwork());
   auto trt_parser  = InferObject(nvonnxparser::createParser(*trt_network, *trt_logger));
   ::ONNX_NAMESPACE::ModelProto parsed_model;
   // We check for a valid parse, but the main effect is the side effect
@@ -125,8 +125,6 @@ std::tuple<unique_ptr<nvinfer1::ICudaEngine>,
   trt_builder->setMaxWorkspaceSize(max_workspace_size);
   trt_builder->setDebugSync(debug_builder);
   auto trt_engine = InferObject(trt_builder->buildCudaEngine(*trt_network));
-  trt_builder->destroy();
-  trt_network->destroy();
   return std::make_tuple(std::move(trt_engine), std::move(trt_parser), std::move(trt_logger));
 }
 

From 4ed14e2b749743a014121f57b265675fa7b4c06d Mon Sep 17 00:00:00 2001
From: alicia <32725332+Alicia1529@users.noreply.github.com>
Date: Sat, 11 Jan 2020 01:25:19 +0800
Subject: [PATCH 783/813] gather_nd: check bound and wrap negative indices
 (#17208)

* gather_nd: check bound and wrap negative indices

gather_nd: increase the interpretability of the error msg

gather_nd: update test

* address comment

* fix NormalizeError for windows path, move out of header

Co-authored-by: Sheng Zha <szha@users.noreply.github.com>
---
 include/mxnet/c_api_error.h            | 32 +---------
 python/mxnet/error.py                  |  1 +
 src/c_api/c_api_error.cc               |  7 ++-
 src/operator/tensor/indexing_op.cc     | 65 +++++++++++++++++++-
 src/operator/tensor/indexing_op.cu     | 68 ++++++++++++++++++++-
 src/operator/tensor/indexing_op.h      | 83 ++++++++++++--------------
 tests/python/unittest/test_operator.py | 34 ++++++++++-
 7 files changed, 207 insertions(+), 83 deletions(-)

diff --git a/include/mxnet/c_api_error.h b/include/mxnet/c_api_error.h
index 5c6176b465ae..48cabc39c056 100644
--- a/include/mxnet/c_api_error.h
+++ b/include/mxnet/c_api_error.h
@@ -54,33 +54,6 @@
   on_exit_api();                                                               \
   return 0; // NOLINT(*)
 
-//--------------------------------------------------------
-// Error handling mechanism
-// -------------------------------------------------------
-// Standard error message format, {} means optional
-//--------------------------------------------------------
-// {error_type:} {message0}
-// {message1}
-// {message2}
-// {Stack trace:}    // stack traces follow by this line
-//   {trace 0}       // two spaces in the begining.
-//   {trace 1}
-//   {trace 2}
-//--------------------------------------------------------
-/*!
- * \brief Normalize error message
- *
- *  Parse them header generated by by LOG(FATAL) and CHECK
- *  and reformat the message into the standard format.
- *
- *  This function will also merge all the stack traces into
- *  one trace and trim them.
- *
- * \param err_msg The error message.
- * \return normalized message.
- */
-std::string NormalizeError(std::string err_msg);
-
 /*!
  * \brief Set the last error message needed by C API
  * \param msg The error message to set.
@@ -91,10 +64,7 @@ void MXAPISetLastError(const char* msg);
  * \param e the exception
  * \return the return value of API after exception is handled
  */
-inline int MXAPIHandleException(const std::exception &e) {
-  MXAPISetLastError(NormalizeError(e.what()).c_str());
-  return -1;
-}
+int MXAPIHandleException(const std::exception &e);
 
 namespace mxnet {
 extern void on_enter_api(const char *function);
diff --git a/python/mxnet/error.py b/python/mxnet/error.py
index 66b60db87483..ca0ab86fe477 100644
--- a/python/mxnet/error.py
+++ b/python/mxnet/error.py
@@ -55,3 +55,4 @@ def __init__(self, msg):
 register_error("ValueError", ValueError)
 register_error("TypeError", TypeError)
 register_error("AttributeError", AttributeError)
+register_error("IndexError", IndexError)
diff --git a/src/c_api/c_api_error.cc b/src/c_api/c_api_error.cc
index 537e95a23b76..b7df6a5e51fe 100644
--- a/src/c_api/c_api_error.cc
+++ b/src/c_api/c_api_error.cc
@@ -89,7 +89,7 @@ std::string NormalizeError(std::string err_msg) {
     if (!getline(is, file_name, ':')) {
       return false;
     } else {
-      if (is.peek() == '\\') {
+      if (is.peek() == '\\' || is.peek() == '/') {
         // windows path
         if (!getline(is, line, ':')) return false;
         file_name = file_name + ':' + line;
@@ -192,6 +192,11 @@ std::string NormalizeError(std::string err_msg) {
 }
 #endif
 
+int MXAPIHandleException(const std::exception &e) {
+  MXAPISetLastError(NormalizeError(e.what()).c_str());
+  return -1;
+}
+
 const char *MXGetLastError() {
   return NNGetLastError();
 }
diff --git a/src/operator/tensor/indexing_op.cc b/src/operator/tensor/indexing_op.cc
index 4bba683f0f28..b802bf24e141 100644
--- a/src/operator/tensor/indexing_op.cc
+++ b/src/operator/tensor/indexing_op.cc
@@ -435,6 +435,65 @@ inline void SparseEmbeddingOpBackwardRspImpl<cpu>(const bool deterministic,
   });
 }
 
+/*
+ * \brief check if any of the indices is out of bound
+ * \param s the stream
+ * \param idx_ptr the indices on the stream
+ * \param N the number of indices in an axis
+ * \param M the number of axises to exmaine
+ * \param mshape the array that stores shape for each dimension
+ * \param is_valid_dim_ptr the temparary workspace that contains out-of-bound indices
+ */
+template<typename DType>
+void GatherNDCheckBoundCPU(mshadow::Stream<cpu> *s, const DType* idx_ptr, index_t N,
+                        index_t M, const mshadow::Shape<10> mshape, DType* is_valid_dim_ptr) {
+  using namespace mxnet_op;
+  Kernel<set_zero, cpu>::Launch(s, M, is_valid_dim_ptr);
+  Kernel<is_valid_check_gather_nd, cpu>::Launch(s, M, is_valid_dim_ptr, idx_ptr, N, mshape);
+  for (int m = 0; m < M; m++) {
+    if (is_valid_dim_ptr[m] > mshape[m] - 1 || is_valid_dim_ptr[m] < - mshape[m]) {
+      LOG(FATAL)<< "IndexError: index " << is_valid_dim_ptr[m] << " is out of bounds for axis "
+        << m << " with size " << mshape[m];
+    }
+  }
+}
+
+void GatherNDForwardCPU(const nnvm::NodeAttrs& attrs,
+                     const OpContext& ctx,
+                     const std::vector<TBlob>& inputs,
+                     const std::vector<OpReqType>& req,
+                     const std::vector<TBlob>& outputs) {
+  using namespace mxnet_op;
+  using namespace mshadow;
+  CHECK_EQ(inputs.size(), 2U);
+  CHECK_EQ(outputs.size(), 1U);
+  if (req[0] == kNullOp) return;
+  mshadow::Stream<cpu> *s = ctx.get_stream<cpu>();
+  const mxnet::TShape& dshape = inputs[0].shape_;
+  const mxnet::TShape& ishape = inputs[1].shape_;
+  int M = ishape[0];
+  int N = ishape.Size() / M;
+  int K = dshape.ProdShape(M, dshape.ndim());
+  mshadow::Shape<10> strides;
+  mshadow::Shape<10> mshape;
+  for (int i = M-1, stride = K; i >= 0; stride *= dshape[i], --i) {
+    strides[i] = stride;
+    mshape[i] = dshape[i];
+  }
+  MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, DType, {  // output data type switch
+    MSHADOW_TYPE_SWITCH(inputs[1].type_flag_, IType, {  // indices data type switch
+      // check whether indices are out of bound
+      IType* idx_ptr = inputs[1].dptr<IType>();
+      Tensor<cpu, 1, IType> workspace =
+        ctx.requested[0].get_space_typed<cpu, 1, IType>(Shape1(M), s);
+      IType* is_valid_dim_ptr = reinterpret_cast<IType*>(workspace.dptr_);
+      GatherNDCheckBoundCPU(s, idx_ptr, N, M, mshape, is_valid_dim_ptr);
+      Kernel<gather_nd, cpu>::Launch(
+        s, N, req[0], N, M, K, strides, mshape, outputs[0].dptr<DType>(),
+        inputs[0].dptr<DType>(), inputs[1].dptr<IType>());
+    });
+  });
+}
 
 template<typename DType, typename IType>
 inline typename std::enable_if<(!std::is_same<DType, mshadow::half::half_t>::value), void>::type
@@ -872,7 +931,11 @@ Examples::
   })
 .set_attr<mxnet::FInferShape>("FInferShape", GatherNDShape)
 .set_attr<nnvm::FInferType>("FInferType", GatherNDType)
-.set_attr<FCompute>("FCompute<cpu>", GatherNDForward<cpu>)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", GatherNDForwardCPU)
 .set_attr<nnvm::FGradient>("FGradient",
   [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
     auto p = nnvm::Node::Create();
diff --git a/src/operator/tensor/indexing_op.cu b/src/operator/tensor/indexing_op.cu
index 8250efb5ca4e..2bace367893a 100644
--- a/src/operator/tensor/indexing_op.cu
+++ b/src/operator/tensor/indexing_op.cu
@@ -33,7 +33,6 @@ namespace op {
 
 /*! \brief If there are out-of-bound indices, out will be assigned to 1.
  */
-
 struct is_valid_check {
   template<typename DType>
   MSHADOW_XINLINE static void Map(int i, char* out, const DType* data,
@@ -437,6 +436,71 @@ inline void SparseEmbeddingOpBackwardRspImpl<gpu>(const bool deterministic,
   });
 }
 
+/*
+ * \brief check if any of the indices is out of bound
+ * \param s the stream
+ * \param idx_ptr the indices on the stream
+ * \param N the number of indices in an axis
+ * \param M the number of axises to exmaine
+ * \param mshape the array that stores shape for each dimension
+ * \param is_valid_dim_ptr the temparary workspace that contains out-of-bound indices
+ */
+template<typename DType>
+void GatherNDCheckBoundGPU(mshadow::Stream<gpu> *s, const DType* idx_ptr, index_t N,
+                          index_t M, const mshadow::Shape<10> mshape, DType* is_valid_dim_ptr) {
+  using namespace mxnet_op;
+  Kernel<set_zero, gpu>::Launch(s, M, is_valid_dim_ptr);
+  Kernel<is_valid_check_gather_nd, gpu>::Launch(s, M, is_valid_dim_ptr, idx_ptr, N, mshape);
+
+  std::vector<DType> is_valid_dim(M);
+  CUDA_CALL(cudaMemcpyAsync(is_valid_dim.data(), is_valid_dim_ptr, sizeof(DType)*M,
+                            cudaMemcpyDeviceToHost, mshadow::Stream<gpu>::GetStream(s)));
+  CUDA_CALL(cudaStreamSynchronize(mshadow::Stream<gpu>::GetStream(s)));
+  for (int m = 0; m < M; m++) {
+    if (is_valid_dim[m] > mshape[m] - 1 || is_valid_dim[m] < - mshape[m]) {
+      LOG(FATAL)<< "IndexError: index " << is_valid_dim[m] << " is out of bounds for axis "
+        << m << " with size " << mshape[m];
+    }
+  }
+}
+
+void GatherNDForwardGPU(const nnvm::NodeAttrs& attrs,
+                        const OpContext& ctx,
+                        const std::vector<TBlob>& inputs,
+                        const std::vector<OpReqType>& req,
+                        const std::vector<TBlob>& outputs) {
+  using namespace mxnet_op;
+  using namespace mshadow;
+  CHECK_EQ(inputs.size(), 2U);
+  CHECK_EQ(outputs.size(), 1U);
+  if (req[0] == kNullOp) return;
+  mshadow::Stream<gpu> *s = ctx.get_stream<gpu>();
+  const mxnet::TShape& dshape = inputs[0].shape_;
+  const mxnet::TShape& ishape = inputs[1].shape_;
+  int M = ishape[0];
+  int N = ishape.Size() / M;
+  int K = dshape.ProdShape(M, dshape.ndim());
+  mshadow::Shape<10> strides;
+  mshadow::Shape<10> mshape;
+  for (int i = M-1, stride = K; i >= 0; stride *= dshape[i], --i) {
+    strides[i] = stride;
+    mshape[i] = dshape[i];
+  }
+  MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, DType, {  // output data type switch
+    MSHADOW_TYPE_SWITCH(inputs[1].type_flag_, IType, {  // indices data type switch
+      // check whether indices are out of bound
+      IType* idx_ptr = inputs[1].dptr<IType>();
+      Tensor<gpu, 1, IType> workspace =
+        ctx.requested[0].get_space_typed<gpu, 1, IType>(Shape1(M), s);
+      IType* is_valid_dim_ptr = reinterpret_cast<IType*>(workspace.dptr_);
+      GatherNDCheckBoundGPU(s, idx_ptr, N, M, mshape, is_valid_dim_ptr);
+      Kernel<gather_nd, gpu>::Launch(
+        s, N, req[0], N, M, K, strides, mshape, outputs[0].dptr<DType>(),
+        inputs[0].dptr<DType>(), inputs[1].dptr<IType>());
+    });
+  });
+}
+
 struct backward_gather_nd_gpu {
   template<typename DType, typename IType>
   MSHADOW_XINLINE static void Map(index_t i, index_t N, index_t M, index_t K,
@@ -813,7 +877,7 @@ NNVM_REGISTER_OP(one_hot)
 .set_attr<FCompute>("FCompute<gpu>", OneHotOpForward<gpu>);
 
 NNVM_REGISTER_OP(gather_nd)
-.set_attr<FCompute>("FCompute<gpu>", GatherNDForward<gpu>);
+.set_attr<FCompute>("FCompute<gpu>", GatherNDForwardGPU);
 
 NNVM_REGISTER_OP(scatter_nd)
 .set_attr<FCompute>("FCompute<gpu>", ScatterNDForward<gpu>);
diff --git a/src/operator/tensor/indexing_op.h b/src/operator/tensor/indexing_op.h
index f81d831b562b..2378a743549b 100644
--- a/src/operator/tensor/indexing_op.h
+++ b/src/operator/tensor/indexing_op.h
@@ -303,7 +303,7 @@ inline bool SparseEmbeddingOpBackwardStorageType(const nnvm::NodeAttrs& attrs,
 }
 
 /*! \brief name the struct TakeNonzeroAxis for general take when
- * axis is not zero, use TakeZeroAxisGPU or TakeZeroAxisCPU for axis zero
+ *         axis is not zero, use TakeZeroAxisGPU or TakeZeroAxisCPU for axis zero
  */
 template<bool clip = true>
 struct TakeNonzeroAxis {
@@ -1272,6 +1272,42 @@ void OneHotOpForward(const nnvm::NodeAttrs& attrs,
   });
 }
 
+struct gather_nd {
+  template<typename DType, typename IType>
+  MSHADOW_XINLINE static void Map(index_t i, OpReqType req, index_t N, index_t M, index_t K,
+                                  const mshadow::Shape<10> strides,
+                                  const mshadow::Shape<10> mshape,
+                                  DType* out, const DType* data,
+                                  const IType* indices) {
+    index_t offset = 0;
+    for (index_t j = 0; j < M; ++j) {
+      offset += strides[j] * (static_cast<index_t>(indices[j*N + i] + mshape[j])%mshape[j]);
+    }
+    for (index_t j = 0; j < K; ++j) {
+      KERNEL_ASSIGN(out[i*K + j], req, data[offset+j]);
+    }
+  }
+};
+
+/*!
+ * \brief If any index in a dimension is out of bound, 
+          then the value in this dimension will be set to be the out-of-bound index
+ */
+struct is_valid_check_gather_nd {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int i, DType* is_valid_dim_ptr, const DType* idx_ptr,
+                                  const index_t N, const mshadow::Shape<10> mshape) {
+    index_t n = N - 1;
+    while (n >= 0) {
+      if (idx_ptr[i*N + n] < -mshape[i] || idx_ptr[i*N + n] > mshape[i] - 1) {
+        is_valid_dim_ptr[i] = idx_ptr[i*N + n];
+        break;
+      }
+      n--;
+    }
+  }
+};
+
 inline bool GatherNDShape(const nnvm::NodeAttrs& attrs,
                           mxnet::ShapeVector *in_attrs,
                           mxnet::ShapeVector *out_attrs) {
@@ -1315,51 +1351,6 @@ inline bool GatherNDType(const nnvm::NodeAttrs& attrs,
   return true;
 }
 
-struct gather_nd {
-  template<typename DType, typename IType>
-  MSHADOW_XINLINE static void Map(index_t i, OpReqType req, index_t N, index_t M, index_t K,
-                                  const mshadow::Shape<10> strides,
-                                  DType* out, const DType* data,
-                                  const IType* indices) {
-    index_t offset = 0;
-    for (index_t j = 0; j < M; ++j) {
-      offset += strides[j] * static_cast<index_t>(indices[j*N + i]);
-    }
-    for (index_t j = 0; j < K; ++j) {
-      KERNEL_ASSIGN(out[i*K + j], req, data[offset+j]);
-    }
-  }
-};
-
-template<typename xpu>
-void GatherNDForward(const nnvm::NodeAttrs& attrs,
-                     const OpContext& ctx,
-                     const std::vector<TBlob>& inputs,
-                     const std::vector<OpReqType>& req,
-                     const std::vector<TBlob>& outputs) {
-  using namespace mxnet_op;
-  using namespace mshadow;
-  CHECK_EQ(inputs.size(), 2U);
-  CHECK_EQ(outputs.size(), 1U);
-  if (req[0] == kNullOp) return;
-  mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
-  const mxnet::TShape& dshape = inputs[0].shape_;
-  const mxnet::TShape& ishape = inputs[1].shape_;
-  int M = ishape[0];
-  int N = ishape.Size() / M;
-  int K = dshape.ProdShape(M, dshape.ndim());
-  mshadow::Shape<10> strides;
-  for (int i = M-1, stride = K; i >= 0; stride *= dshape[i], --i) strides[i] = stride;
-  MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, DType, {  // output data type switch
-    MSHADOW_TYPE_SWITCH(inputs[1].type_flag_, IType, {  // indices data type switch
-      Kernel<gather_nd, xpu>::Launch(
-          s, N, req[0], N, M, K, strides, outputs[0].dptr<DType>(),
-          inputs[0].dptr<DType>(), inputs[1].dptr<IType>());
-    });
-  });
-}
-
-
 struct ScatterNDParam : public dmlc::Parameter<ScatterNDParam> {
   mxnet::TShape shape;
   DMLC_DECLARE_PARAMETER(ScatterNDParam) {
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 72350de4d85f..0729cb3cad47 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -7166,6 +7166,36 @@ def check(data, idx):
             idx = mx.nd.array([[0, 0, 0, 0]], dtype='int32')
             assert (mx.nd._internal._backward_gather_nd(data, idx, shape=(1,)).asscalar() == data.asnumpy().sum())
 
+@with_seed()
+def test_gather_nd_check_bound():
+    # check if indices is out of bound
+    data = mx.nd.array([[0, 1, 2], [3, 4, 5]])
+    indices1 = mx.nd.array([[0, 1, 0], [0, 1, 3]])
+    indices2 = mx.nd.array([[0, 1, 0], [0, 1, -5]])
+    try:
+        mx.nd.gather_nd(data, indices1)
+        mx.nd.waitall()
+    except IndexError:
+            # skip errors since the test is supposed to raise error
+            # IndexError: index 3 is out of bounds for axis 1 with size 3
+            pass
+
+    try:
+        mx.nd.gather_nd(data, indices2)
+        mx.nd.waitall()
+    except IndexError:
+            # skip errors since the test is supposed to raise error
+            # IndexError: index -5 is out of bounds for axis 1 with size 3
+            pass
+
+    # check if the negative indices are wrapped correctly
+    indices1 = mx.nd.array([[0, 1, -1], [0, 1, -2]])
+    indices2 = mx.nd.array([[0, 1, 1], [0, 1, 1]])
+    data1 = mx.nd.gather_nd(data, indices1)
+    data2 = mx.nd.gather_nd(data, indices2)
+    assert_almost_equal(data1, data2, rtol=1e-5, atol=1e-5)
+
+
 def compare_forw_backw_unary_op(
         name, forward_mxnet_call, forward_numpy_call,
         backward_numpy_call, shape, input_low, input_high, rtol, atol,
@@ -7821,7 +7851,7 @@ def check_bilinear_resize_align_corners_op():
 
         x = np.array(data, dtype=np.float32).reshape(img_shape)
         x_nd = mx.nd.array(x)
-        
+
         y0 = np.array(expected_data[0]).reshape((1, 1, target_height, target_width))
         y0_nd = mx.nd.contrib.BilinearResize2D(x_nd, height=target_height, width=target_width, mode='size', align_corners=False)
         assert_almost_equal(y0, y0_nd.asnumpy(), atol=1e-3)
@@ -9586,7 +9616,7 @@ def convert_bias(F, k_bias, v_bias, num_heads):
     q_proj = mx.sym.FullyConnected(q, weight=q_weight, bias=q_bias, flatten=False,
                                    num_hidden=qkv_units, no_bias=False)
     att_score = mx.sym.contrib.interleaved_matmul_encdec_qk(
-            q_proj, kv_proj, heads=num_heads) 
+            q_proj, kv_proj, heads=num_heads)
     att_score = att_score + sonde
     weighted_value = mx.sym.contrib.interleaved_matmul_encdec_valatt(
             kv_proj, att_score, heads=num_heads)

From f88b1edd896a04ef7d26cb6462cef2c1233dc192 Mon Sep 17 00:00:00 2001
From: guanxinq <58794120+guanxinq@users.noreply.github.com>
Date: Fri, 10 Jan 2020 14:46:01 -0800
Subject: [PATCH 784/813] Add contributors (#17268)

---
 CONTRIBUTORS.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md
index 8eb540d8b759..31019e6355ab 100644
--- a/CONTRIBUTORS.md
+++ b/CONTRIBUTORS.md
@@ -246,6 +246,7 @@ List of Contributors
 * [Caner Turkmen](https://github.com/canerturkmen)
 * [Disi A](https://github.com/adis300)
 * [Vandana Kannan](https://github.com/vandanavk)
+* [Guanxin Qiao](https://github.com/guanxinq)
 
 Label Bot
 ---------

From c3b0baaa27e2215eae7ed7676009ea5f4bf49013 Mon Sep 17 00:00:00 2001
From: Sheng Zha <szha@users.noreply.github.com>
Date: Sat, 11 Jan 2020 15:08:57 +0800
Subject: [PATCH 785/813] fix lstm layer with projection save params (#17266)

---
 python/mxnet/gluon/rnn/rnn_layer.py | 2 +-
 tests/python/gpu/test_gluon_gpu.py  | 2 ++
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/python/mxnet/gluon/rnn/rnn_layer.py b/python/mxnet/gluon/rnn/rnn_layer.py
index 9807c5e33108..f4489b7656d6 100644
--- a/python/mxnet/gluon/rnn/rnn_layer.py
+++ b/python/mxnet/gluon/rnn/rnn_layer.py
@@ -126,7 +126,7 @@ def __repr__(self):
     def _collect_params_with_prefix(self, prefix=''):
         if prefix:
             prefix += '.'
-        pattern = re.compile(r'(l|r)(\d)_(i2h|h2h)_(weight|bias)\Z')
+        pattern = re.compile(r'(l|r)(\d)_(i2h|h2h|h2r)_(weight|bias)\Z')
         def convert_key(m, bidirectional): # for compatibility with old parameter format
             d, l, g, t = [m.group(i) for i in range(1, 5)]
             if bidirectional:
diff --git a/tests/python/gpu/test_gluon_gpu.py b/tests/python/gpu/test_gluon_gpu.py
index 6aa766351b62..aa56eee33dc4 100644
--- a/tests/python/gpu/test_gluon_gpu.py
+++ b/tests/python/gpu/test_gluon_gpu.py
@@ -126,6 +126,8 @@ def test_lstmp():
     check_rnn_layer_forward(gluon.rnn.LSTM(10, 2, bidirectional=True, dropout=0.5, projection_size=5),
                             mx.nd.ones((8, 3, 20)),
                             [mx.nd.ones((4, 3, 5)), mx.nd.ones((4, 3, 10))], run_only=True, ctx=ctx)
+    lstm_layer.save_parameters('gpu_tmp.params')
+    lstm_layer.load_parameters('gpu_tmp.params')
 
 
 @with_seed()

From 0b269149762b9f04d25f7b0be3680eae1fcfaefe Mon Sep 17 00:00:00 2001
From: Leonard Lausen <lausen@amazon.com>
Date: Sat, 11 Jan 2020 21:27:26 +0100
Subject: [PATCH 786/813] Improve ubuntu_setup.md cmake instructions (#17273)

---
 .../src/pages/get_started/ubuntu_setup.md       | 17 ++++++++++++-----
 1 file changed, 12 insertions(+), 5 deletions(-)

diff --git a/docs/static_site/src/pages/get_started/ubuntu_setup.md b/docs/static_site/src/pages/get_started/ubuntu_setup.md
index effedc02f756..35c5f009da4b 100644
--- a/docs/static_site/src/pages/get_started/ubuntu_setup.md
+++ b/docs/static_site/src/pages/get_started/ubuntu_setup.md
@@ -81,8 +81,7 @@ On Ubuntu versions 16.04 or later, you need the following dependencies:
 **Step 1:** Install prerequisite packages.
 ```bash
     sudo apt-get update
-    sudo apt-get install -y build-essential git ninja-build ccache python3-pip libopenblas-dev libopencv-dev
-    pip3 install --user --upgrade "cmake>=3.13.2"  # Instead of using pip, you could also manually install cmake from https://cmake.org
+    sudo apt-get install -y build-essential git ninja-build ccache libopenblas-dev libopencv-dev cmake
 ```
 
 Instead of `libopenblas-dev` you may also choose a different math library.
@@ -93,6 +92,14 @@ Selection](build_from_source#math-library-selection) section.
 install` line and build MXNet without OpenCV support by passing
 `-DUSE_OPENCV=OFF` to the `cmake` command below.
 
+Note: CMake 3.13 or higher is required. If you are running an older version of
+CMake, you will see an error message like `CMake 3.13 or higher is required. You
+are running version 3.10.2` during Step 3. In that case please update CMake on
+your system. You can download and install latest CMake from https://cmake.org or
+via `pip3` with `pip3 install --user --upgrade "cmake>=3.13.2"`. If `pip3` is
+not available on your system, you can install it via `sudo apt-get install -y
+python3-pip`.
+
 **Step 2:** Download MXNet sources
 
 Clone the repository:
@@ -109,7 +116,7 @@ For a CPU-only build with OpenBLAS math library run:
 ```bash
     rm -rf build
     mkdir -p build && cd build
-    ~/.local/bin/cmake -GNinja \
+    cmake -GNinja \
         -DUSE_CUDA=OFF \
         -DUSE_MKL_IF_AVAILABLE=OFF \
         -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache \
@@ -129,7 +136,7 @@ respectively. Then run:
 ```bash
     rm -rf build
     mkdir -p build && cd build
-    ~/.local/bin/cmake -GNinja \
+    cmake -GNinja \
         -DUSE_CUDA=OFF \
         -DUSE_MKL_IF_AVAILABLE=ON \
         -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache \
@@ -146,7 +153,7 @@ For a GPU-enabled build make sure you have installed the
 ```bash
     rm -rf build
     mkdir -p build && cd build
-    ~/.local/bin/cmake -GNinja \
+    cmake -GNinja \
         -DUSE_CUDA=ON \
         -DUSE_MKL_IF_AVAILABLE=OFF \
         -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache \

From 25dc90963473c9490a726953180b4ceeb6866677 Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Sat, 11 Jan 2020 17:02:10 -0800
Subject: [PATCH 787/813] bump atol in backward test for softmax with length
 only for fp16 (#17276)

---
 tests/python/unittest/test_operator.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 0729cb3cad47..6c604e3ed914 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -5419,7 +5419,8 @@ def np_softmax_with_length(data, length):
         atol = 1e-4 if dtype == np.float16 else 1e-5
         check_symbolic_forward(mx_sym, location, [np_out], rtol=rtol, atol=atol, dtype="asnumpy")
         check_symbolic_backward(mx_sym, location, [np.ones(shape, dtype=dtype)],
-                                [np.zeros(shape), np.zeros(len_shape, dtype=np.int32)], rtol=1e-2, atol=1e-3, dtype="asnumpy")
+                                [np.zeros(shape), np.zeros(len_shape, dtype=np.int32)],
+                                rtol=1e-2, atol=2e-3 if dtype == np.float16 else 1e-3, dtype="asnumpy")
 
 
 @with_seed()

From 2002d6065d5668f9de4d4050c8cf750dd6cc5ca8 Mon Sep 17 00:00:00 2001
From: Chaitanya Prakash Bapat <chai.bapat@gmail.com>
Date: Sat, 11 Jan 2020 21:25:07 -0800
Subject: [PATCH 788/813] fix typo (#17277)

---
 cpp-package/include/mxnet-cpp/ndarray.h | 8 ++++----
 include/mxnet/c_api.h                   | 4 ++--
 include/mxnet/ndarray.h                 | 4 ++--
 perl-package/AI-MXNetCAPI/mxnet.i       | 4 ++--
 src/ndarray/ndarray_function.cu         | 2 +-
 5 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/cpp-package/include/mxnet-cpp/ndarray.h b/cpp-package/include/mxnet-cpp/ndarray.h
index c4d51c59a532..0a9a41234758 100644
--- a/cpp-package/include/mxnet-cpp/ndarray.h
+++ b/cpp-package/include/mxnet-cpp/ndarray.h
@@ -251,7 +251,7 @@ class NDArray {
   NDArray &operator%=(const NDArray &src);
   NDArray ArgmaxChannel();
   /*!
-  * \brief Do a synchronize copy from a continugous CPU memory region.
+  * \brief Do a synchronize copy from a contiguous CPU memory region.
   *
   *  This function will call WaitToWrite before the copy is performed.
   *  This is useful to copy data from existing memory region that are
@@ -262,7 +262,7 @@ class NDArray {
   */
   void SyncCopyFromCPU(const mx_float *data, size_t size);
   /*!
-  * \brief Do a synchronize copy from a continugous CPU memory region.
+  * \brief Do a synchronize copy from a contiguous CPU memory region.
   *
   *  This function will call WaitToWrite before the copy is performed.
   *  This is useful to copy data from existing memory region that are
@@ -272,7 +272,7 @@ class NDArray {
   */
   void SyncCopyFromCPU(const std::vector<mx_float> &data);
   /*!
-  * \brief Do a synchronize copy to a continugous CPU memory region.
+  * \brief Do a synchronize copy to a contiguous CPU memory region.
   *
   *  This function will call WaitToRead before the copy is performed.
   *  This is useful to copy data from existing memory region that are
@@ -283,7 +283,7 @@ class NDArray {
   */
   void SyncCopyToCPU(mx_float *data, size_t size = 0);
   /*!
-  * \brief Do a synchronize copy to a continugous CPU memory region.
+  * \brief Do a synchronize copy to a contiguous CPU memory region.
   *
   *  This function will call WaitToRead before the copy is performed.
   *  This is useful to copy data from existing memory region that are
diff --git a/include/mxnet/c_api.h b/include/mxnet/c_api.h
index 27b420f9f243..33d79bd9f5c4 100644
--- a/include/mxnet/c_api.h
+++ b/include/mxnet/c_api.h
@@ -723,7 +723,7 @@ MXNET_DLL int MXNDArrayLoadFromBuffer(const void *ndarray_buffer,
                                       const char*** out_names);
 
 /*!
- * \brief Perform a synchronize copy from a continugous CPU memory region.
+ * \brief Perform a synchronize copy from a contiguous CPU memory region.
  *
  *  This function will call WaitToWrite before the copy is performed.
  *  This is useful to copy data from existing memory region that are
@@ -737,7 +737,7 @@ MXNET_DLL int MXNDArraySyncCopyFromCPU(NDArrayHandle handle,
                                        const void *data,
                                        size_t size);
 /*!
- * \brief Perform a synchronize copyto a continugous CPU memory region.
+ * \brief Perform a synchronize copyto a contiguous CPU memory region.
  *
  *  This function will call WaitToRead before the copy is performed.
  *  This is useful to copy data from existing memory region that are
diff --git a/include/mxnet/ndarray.h b/include/mxnet/ndarray.h
index 1b0b119a02ac..3e780a14d601 100644
--- a/include/mxnet/ndarray.h
+++ b/include/mxnet/ndarray.h
@@ -483,7 +483,7 @@ class NDArray {
    */
   NDArray Copy(Context ctx) const;
   /*!
-   * \brief Do a synchronize copy from a continugous CPU memory region.
+   * \brief Do a synchronize copy from a contiguous CPU memory region.
    *
    *  This function will call WaitToWrite before the copy is performed.
    *  This is useful to copy data from existing memory region that are
@@ -500,7 +500,7 @@ class NDArray {
   void SyncCopyFromNDArray(const NDArray &src, int i = -1, int j = -1);
 
   /*!
-   * \brief Do a synchronize copy to a continugous CPU memory region.
+   * \brief Do a synchronize copy to a contiguous CPU memory region.
    *
    *  This function will call WaitToRead before the copy is performed.
    *  This is useful to copy data from existing memory region that are
diff --git a/perl-package/AI-MXNetCAPI/mxnet.i b/perl-package/AI-MXNetCAPI/mxnet.i
index e38402c56100..f35f620bfca9 100644
--- a/perl-package/AI-MXNetCAPI/mxnet.i
+++ b/perl-package/AI-MXNetCAPI/mxnet.i
@@ -510,7 +510,7 @@ int MXNDArrayLoadFromBuffer(const void *in,
                             const char*** out_array);
 
 /*!
- * \brief Perform a synchronize copy from a continugous CPU memory region.
+ * \brief Perform a synchronize copy from a contiguous CPU memory region.
  *
  *  This function will call WaitToWrite before the copy is performed.
  *  This is useful to copy data from existing memory region that are
@@ -524,7 +524,7 @@ int MXNDArraySyncCopyFromCPU(NDArrayHandle handle,
                                        const void *in,
                                        size_t size);
 /*!
- * \brief Perform a synchronize copy to a continugous CPU memory region.
+ * \brief Perform a synchronize copy to a contiguous CPU memory region.
  *
  *  This function will call WaitToRead before the copy is performed.
  *  This is useful to copy data from existing memory region that are
diff --git a/src/ndarray/ndarray_function.cu b/src/ndarray/ndarray_function.cu
index 79bc3453cbc9..9f83ff6812af 100644
--- a/src/ndarray/ndarray_function.cu
+++ b/src/ndarray/ndarray_function.cu
@@ -84,7 +84,7 @@ void Copy<gpu, gpu>(const TBlob &from, TBlob *to,
     })
   } else {
     CHECK(from.CheckContiguous() && to->CheckContiguous())
-      << "copy across only support continugous memory";
+      << "copy across only support contiguous memory";
     CHECK_EQ(to->type_flag_, from.type_flag_)
       << "Source and target must have the same data type when copying across devices.";
     mshadow::Stream<gpu> *s = ctx.get_stream<gpu>();

From 8accaca27899998782d6498c0e13e59547857326 Mon Sep 17 00:00:00 2001
From: Serge Panev <spanev@nvidia.com>
Date: Sun, 12 Jan 2020 00:31:09 -0800
Subject: [PATCH 789/813] Add TypeFlag=>string macro (#16881)

* Add TypeFlag=>string macro

Signed-off-by: Serge Panev <spanev@nvidia.com>

* Add type_flag value check

Signed-off-by: Serge Panev <spanev@nvidia.com>

* Use already existing dtype_string function

Signed-off-by: Serge Panev <spanev@nvidia.com>

* CI

* ci

* ci

Co-authored-by: JackieWu <wkcn@live.cn>
---
 3rdparty/mshadow/mshadow/base.h               | 25 +++++++++++++++++++
 include/mxnet/tensor_blob.h                   |  6 +++--
 src/common/utils.h                            | 24 ------------------
 src/operator/numpy/np_elemwise_broadcast_op.h |  2 +-
 4 files changed, 30 insertions(+), 27 deletions(-)

diff --git a/3rdparty/mshadow/mshadow/base.h b/3rdparty/mshadow/mshadow/base.h
index 5f477e52801d..ec5197317221 100755
--- a/3rdparty/mshadow/mshadow/base.h
+++ b/3rdparty/mshadow/mshadow/base.h
@@ -1287,5 +1287,30 @@ inline size_t mshadow_sizeof(int type) {
   return size;
 }
 
+/*/ \brief get string with the type name from type enum */
+inline std::string dtype_string(const int dtype) {
+  switch (dtype) {
+    case mshadow::kFloat32:
+      return "float";
+    case mshadow::kFloat64:
+      return "double";
+    case mshadow::kFloat16:
+      return "half";
+    case mshadow::kUint8:
+      return "unsigned char";
+    case mshadow::kInt8:
+      return "char";
+    case mshadow::kInt32:
+      return "int";
+    case mshadow::kInt64:
+      return "long long";
+    case mshadow::kBool:
+      return "bool";
+    default:
+      LOG(FATAL) << "Unknown type enum " << dtype;
+  }
+  return "unknown";
+}
+
 }  // namespace mshadow
 #endif  // MSHADOW_BASE_H_
diff --git a/include/mxnet/tensor_blob.h b/include/mxnet/tensor_blob.h
index f64703092121..970fffe3821d 100755
--- a/include/mxnet/tensor_blob.h
+++ b/include/mxnet/tensor_blob.h
@@ -214,7 +214,8 @@ class TBlob {
       << "TBlob.get: device type do not match specified type";
     CHECK(mshadow::DataType<DType>::kFlag == type_flag_)
       << "TBlob.get_with_shape: data type do not match specified type."
-      << "Expected: " << type_flag_ << " v.s. given " << mshadow::DataType<DType>::kFlag;
+      << "Expected: " << mshadow::dtype_string(type_flag_)
+      << " v.s. given " << mshadow::dtype_string(mshadow::DataType<DType>::kFlag);
     return mshadow::Tensor<Device, 2, DType>(static_cast<DType*>(dptr_),
                                              shape_.FlatTo2D(),
                                              stream);
@@ -254,7 +255,8 @@ class TBlob {
   inline DType* dptr() const {
     CHECK(mshadow::DataType<DType>::kFlag == type_flag_)
       << "TBlob.get_with_shape: data type do not match specified type."
-      << "Expected: " << type_flag_ << " v.s. given " << mshadow::DataType<DType>::kFlag;
+      << "Expected: " << mshadow::dtype_string(type_flag_)
+      << " v.s. given " << mshadow::dtype_string(mshadow::DataType<DType>::kFlag);
     return static_cast<DType*>(dptr_);
   }
   /*! \brief device mask of the corresponding device */
diff --git a/src/common/utils.h b/src/common/utils.h
index 2187ad053b66..9673bffdd9c9 100644
--- a/src/common/utils.h
+++ b/src/common/utils.h
@@ -365,30 +365,6 @@ inline bool ContainsStorageType(const std::vector<int>& ndstypes,
   return false;
 }
 
-inline std::string dtype_string(const int dtype) {
-  switch (dtype) {
-    case mshadow::kFloat32:
-      return "float";
-    case mshadow::kFloat64:
-      return "double";
-    case mshadow::kFloat16:
-      return "half";
-    case mshadow::kUint8:
-      return "unsigned char";
-    case mshadow::kInt8:
-      return "char";
-    case mshadow::kInt32:
-      return "int";
-    case mshadow::kInt64:
-      return "long long";
-    case mshadow::kBool:
-      return "bool";
-    default:
-      LOG(FATAL) << "Unknown type enum " << dtype;
-  }
-  return "unknown";
-}
-
 /*! \brief get string representation of dispatch_mode */
 inline std::string dispatch_mode_string(const DispatchMode x) {
   switch (x) {
diff --git a/src/operator/numpy/np_elemwise_broadcast_op.h b/src/operator/numpy/np_elemwise_broadcast_op.h
index 057a2ee6b16c..d58245a798e5 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op.h
+++ b/src/operator/numpy/np_elemwise_broadcast_op.h
@@ -47,7 +47,7 @@ inline bool NumpyBinaryScalarType(const nnvm::NodeAttrs& attrs,
 
 inline void PrintErrorMessage(const std::string& op_name, const int dtype1, const int dtype2) {
   LOG(FATAL) << "Operator " << op_name << " does not support combination of "
-             << common::dtype_string(dtype1) << " with " << common::dtype_string(dtype2)
+             << mshadow::dtype_string(dtype1) << " with " << mshadow::dtype_string(dtype2)
              << " yet...";
 }
 

From 28e053edb4f2079743458bf087557bcac7e58c62 Mon Sep 17 00:00:00 2001
From: Xi Wang <xidulu@gmail.com>
Date: Mon, 13 Jan 2020 12:40:32 +0800
Subject: [PATCH 790/813] [Numpy] Add infra for performing constraint check
 (#17272)

* exception not working

* code polished

* add zero-grad node

* backend polished

* add tests

* add doc, modify tests to pass CI with tvm OP

* fix doc and style
---
 python/mxnet/_numpy_op_doc.py             | 39 +++++++++
 src/operator/numpy/np_constraint_check.cc | 99 +++++++++++++++++++++++
 src/operator/numpy/np_constraint_check.cu | 45 +++++++++++
 src/operator/numpy/np_constraint_check.h  | 71 ++++++++++++++++
 tests/python/unittest/test_numpy_op.py    | 41 ++++++++++
 5 files changed, 295 insertions(+)
 create mode 100644 src/operator/numpy/np_constraint_check.cc
 create mode 100644 src/operator/numpy/np_constraint_check.cu
 create mode 100644 src/operator/numpy/np_constraint_check.h

diff --git a/python/mxnet/_numpy_op_doc.py b/python/mxnet/_numpy_op_doc.py
index 190150eb5a0a..65f1f2f9407c 100644
--- a/python/mxnet/_numpy_op_doc.py
+++ b/python/mxnet/_numpy_op_doc.py
@@ -1132,6 +1132,45 @@ def _np__random_shuffle(x):
     pass
 
 
+def _npx_constraint_check(x, msg):
+    """
+    This operator will check if all the elements in a boolean tensor is true.
+    If not, ValueError exception will be raised in the backend with given error message.
+    In order to evaluate this operator, one should multiply the origin tensor by the return value
+    of this operator to force this operator become part of the computation graph,
+    otherwise the check would not be working under symoblic mode.
+    
+    Parameters
+    ----------
+    x : ndarray
+        A boolean tensor.
+    msg : string
+        The error message in the exception.
+
+    Returns
+    -------
+    out : ndarray
+        If all the elements in the input tensor are true,
+        array(True) will be returned, otherwise ValueError exception would
+        be raised before anything got returned.
+
+    Examples
+    --------
+    >>> loc = np.zeros((2,2))
+    >>> scale = np.array(#some_value)
+    >>> constraint = (scale > 0)
+    >>> np.random.normal(loc,
+                     scale * npx.constraint_check(constraint, 'Scale should be larger than zero'))
+
+    If elements in the scale tensor are all bigger than zero, npx.constraint_check would return
+    `np.array(True)`, which will not change the value of `scale` when multiplied by.
+    If some of the elements in the scale tensor violate the constraint,
+    i.e. there exists `False` in the boolean tensor `constraint`,
+    a `ValueError` exception with given message 'Scale should be larger than zero' would be raised.
+    """
+    pass
+
+
 def _npx_reshape(a, newshape, reverse=False, order='C'):
     """
     Gives a new shape to an array without changing its data.
diff --git a/src/operator/numpy/np_constraint_check.cc b/src/operator/numpy/np_constraint_check.cc
new file mode 100644
index 000000000000..450f4ddb392b
--- /dev/null
+++ b/src/operator/numpy/np_constraint_check.cc
@@ -0,0 +1,99 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file np_constraint_check.cc
+ * \brief helper function for constraint check
+ */
+
+#include "./np_constraint_check.h"
+
+namespace mxnet {
+namespace op {
+
+template<>
+void GetReduceOutput<cpu>(mshadow::Stream<cpu> *s, const TBlob &output_blob, bool *red_output) {
+  *red_output = static_cast<bool>(*output_blob.dptr<bool>());
+}
+
+inline bool ConstraintCheckShape(const nnvm::NodeAttrs& attrs,
+                                 std::vector<TShape>* in_attrs,
+                                 std::vector<TShape>* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  if (!shape_is_known(in_attrs->at(0))) {
+    return false;
+  }
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, TShape(0, -1))
+  return true;
+}
+
+inline bool ConstraintCheckType(const nnvm::NodeAttrs& attrs,
+                                std::vector<int>* in_attrs,
+                                std::vector<int>* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  CHECK(in_attrs->at(0) == mshadow::kBool);
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::kBool);
+  return out_attrs->at(0) != -1 && in_attrs->at(0) != -1;
+}
+
+DMLC_REGISTER_PARAMETER(ConstraintCheckParam);
+
+NNVM_REGISTER_OP(_npx_constraint_check)
+.describe(R"code(This operator will check if all the elements in a boolean tensor is true.
+If not, ValueError exception will be raised in the backend with given error message.
+In order to evaluate this operator, one should multiply the origin tensor by the return value
+of this operator to force this operator become part of the computation graph, otherwise the check
+would not be working under symoblic mode.
+
+Example:
+
+loc = np.zeros((2,2))
+scale = np.array(#some_value)
+constraint = (scale > 0)
+np.random.normal(loc, scale * npx.constraint_check(constraint, 'Scale should be larger than zero'))
+
+If elements in the scale tensor are all bigger than zero, npx.constraint_check would return
+`np.array(True)`, which will not change the value of `scale` when multiplied by.
+If some of the elements in the scale tensor violate the constraint, i.e. there exists `False` in
+the boolean tensor `constraint`, a `ValueError` exception with given message
+'Scale should be larger than zero' would be raised.
+
+)code" ADD_FILELINE)
+.set_attr_parser(ParamParser<ConstraintCheckParam>)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"input"};
+  })
+.set_attr<mxnet::FInferShape>("FInferShape", ConstraintCheckShape)
+.set_attr<nnvm::FInferType>("FInferType", ConstraintCheckType)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", ConstraintCheckForward<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.add_argument("input", "NDArray-or-Symbol", "Input boolean array")
+.add_arguments(ConstraintCheckParam::__FIELDS__());
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_constraint_check.cu b/src/operator/numpy/np_constraint_check.cu
new file mode 100644
index 000000000000..f83fca0e5c33
--- /dev/null
+++ b/src/operator/numpy/np_constraint_check.cu
@@ -0,0 +1,45 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file np_constraint_check.cu
+ * \brief helper function for constraint check
+ */
+
+#include "./np_constraint_check.h"
+
+namespace mxnet {
+namespace op {
+
+template<>
+void GetReduceOutput<gpu>(mshadow::Stream<gpu> *s, const TBlob &output_blob, bool *red_output) {
+  bool tmp = true;
+  cudaStream_t stream = mshadow::Stream<gpu>::GetStream(s);
+  CUDA_CALL(cudaMemcpyAsync(&tmp, output_blob.dptr<bool>(),
+                            sizeof(bool), cudaMemcpyDeviceToHost,
+                            stream));
+  CUDA_CALL(cudaStreamSynchronize(stream));
+  *red_output = static_cast<bool>(tmp);
+}
+
+NNVM_REGISTER_OP(_npx_constraint_check)
+.set_attr<FCompute>("FCompute<gpu>", ConstraintCheckForward<gpu>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/numpy/np_constraint_check.h b/src/operator/numpy/np_constraint_check.h
new file mode 100644
index 000000000000..80beaa3a0bf5
--- /dev/null
+++ b/src/operator/numpy/np_constraint_check.h
@@ -0,0 +1,71 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file np_constraint_check.h
+ * \brief helper function for constraint check
+ */
+
+#ifndef MXNET_OPERATOR_NUMPY_NP_CONSTRAINT_CHECK_H_
+#define MXNET_OPERATOR_NUMPY_NP_CONSTRAINT_CHECK_H_
+
+#include <algorithm>
+#include <string>
+#include <vector>
+#include "./np_broadcast_reduce_op.h"
+
+namespace mxnet {
+namespace op {
+
+template<typename xpu>
+void GetReduceOutput(mshadow::Stream<xpu> *s, const TBlob &output_blob, bool *red_output);
+
+struct ConstraintCheckParam : public dmlc::Parameter<ConstraintCheckParam> {
+  std::string msg;
+  DMLC_DECLARE_PARAMETER(ConstraintCheckParam) {
+    DMLC_DECLARE_FIELD(msg)
+    .set_default("Constraint violated.")
+    .describe("Error message raised when constraint violated");
+  }
+};
+
+template <typename xpu>
+void ConstraintCheckForward(const nnvm::NodeAttrs& attrs, const OpContext& ctx,
+                            const std::vector<TBlob>& inputs,
+                            const std::vector<OpReqType>& req,
+                            const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mxnet_op;
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 1U);
+  const ConstraintCheckParam& param =
+      nnvm::get<ConstraintCheckParam>(attrs.parsed);
+  ReduceAxesComputeImpl<xpu, mshadow_op::product, false, false,
+                        op::mshadow_op::identity>(ctx, inputs, req, outputs,
+                                                  outputs[0].shape_);
+  std::string msg = param.msg;
+  bool red_output = true;
+  GetReduceOutput(ctx.get_stream<xpu>(), outputs[0], &red_output);
+  CHECK_EQ(red_output, true) << "ValueError: " << msg;
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_NUMPY_NP_CONSTRAINT_CHECK_H_
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index b25c69385e1e..274f37124262 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -3142,6 +3142,47 @@ def _test_bernoulli_exception(prob, logit):
                 assertRaises(ValueError, _test_bernoulli_exception, scaled_prob, None)
 
 
+@with_seed()
+@use_np
+def test_npx_constraint_check():
+    msg = "condition violated"
+    class TestConstraintViolatedCheck(HybridBlock):
+        def __init__(self):
+            super(TestConstraintViolatedCheck, self).__init__()
+        
+        def hybrid_forward(self, F, boolean_tensor):
+            return F.npx.constraint_check(boolean_tensor, msg)
+
+    class TestConstraintNotViolatedCheck(HybridBlock):
+        def __init__(self):
+            super(TestConstraintNotViolatedCheck, self).__init__()
+        
+        def hybrid_forward(self, F, input, boolean_tensor):
+            return input * F.npx.constraint_check(boolean_tensor, msg)
+
+    def raiseFunc(block):
+        def executor(boolean_tensor):
+            out = block(boolean_tensor).asnumpy()
+        return executor
+
+    shapes = [(1,), (2, 3), 6, (7, 8)]
+    
+    expect_success_output = np.array(True)
+    for shape, hybridize in itertools.product(shapes, [True, False]):
+        test_constraint = TestConstraintViolatedCheck()
+        if hybridize:
+            test_constraint.hybridize()
+        assertRaises(ValueError, raiseFunc(test_constraint), np.zeros(shape, dtype='bool'))
+
+    for shape, hybridize in itertools.product(shapes, [True, False]):
+        test_constraint = TestConstraintNotViolatedCheck()
+        if hybridize:
+            test_constraint.hybridize()
+        input_tensor = np.random.normal(size=shape)
+        out = test_constraint(input_tensor, np.ones(shape, dtype='bool'))
+        assert (input_tensor.asnumpy() == out.asnumpy()).all()
+
+
 @with_seed()
 @use_np
 def test_npx_special_unary_func():

From 461f16704cfdd7af13eec79b63cee67b427841ac Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Mon, 13 Jan 2020 12:50:28 -0800
Subject: [PATCH 791/813] porting numpy-compatible hstack to master and add
 dstack for interoperability (#17030)

---
 python/mxnet/ndarray/numpy/_op.py             | 42 ++++++++-
 python/mxnet/numpy/multiarray.py              | 41 ++++++++-
 python/mxnet/numpy_dispatch_protocol.py       |  2 +
 python/mxnet/symbol/numpy/_symbol.py          | 42 ++++++++-
 src/operator/nn/concat-inl.h                  | 44 +++++++++
 src/operator/numpy/np_matrix_op.cc            | 91 +++++++++++++++++++
 src/operator/numpy/np_matrix_op.cu            |  6 ++
 .../unittest/test_numpy_interoperability.py   | 14 +++
 tests/python/unittest/test_numpy_op.py        | 64 +++++++++++++
 9 files changed, 343 insertions(+), 3 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index f04f94fa38b1..eb01a6b0e400 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -35,7 +35,8 @@
            'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor', 'histogram',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'argsort', 'tensordot', 'eye', 'linspace',
            'logspace', 'expand_dims', 'tile', 'arange', 'array_split', 'split', 'vsplit', 'concatenate', 'append',
-           'stack', 'vstack', 'row_stack', 'column_stack', 'dstack', 'average', 'mean', 'maximum', 'minimum',
+           'stack', 'vstack', 'row_stack', 'column_stack', 'hstack', 'dstack',
+           'average', 'mean', 'maximum', 'minimum',
            'swapaxes', 'clip', 'argmax', 'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'unravel_index',
            'hanning', 'hamming', 'blackman', 'flip', 'flipud', 'fliplr', 'around', 'round', 'hypot', 'bitwise_xor',
            'bitwise_or', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot',
@@ -3790,6 +3791,45 @@ def column_stack(tup):
     return _npi.column_stack(*tup)
 
 
+@set_module('mxnet.ndarray.numpy')
+def hstack(arrays):
+    """
+    Stack arrays in sequence horizontally (column wise).
+    This is equivalent to concatenation along the second axis,
+    except for 1-D arrays where it concatenates along the first axis.
+    Rebuilds arrays divided by hsplit.
+    This function makes most sense for arrays with up to 3 dimensions.
+    For instance, for pixel-data with a height (first axis), width (second axis),
+    and r/g/b channels (third axis). The functions concatenate,
+    stack and block provide more general stacking and concatenation operations.
+
+    Parameters
+    ----------
+    tup : sequence of ndarrays
+        The arrays must have the same shape along all but the second axis, except 1-D arrays which can be any length.
+
+    Returns
+    -------
+    stacked : ndarray
+        The array formed by stacking the given arrays.
+
+    Examples
+    --------
+    >>> from mxnet import np,npx
+    >>> a = np.array((1,2,3))
+    >>> b = np.array((2,3,4))
+    >>> np.hstack((a,b))
+    array([1., 2., 3., 2., 3., 4.])
+    >>> a = np.array([[1],[2],[3]])
+    >>> b = np.array([[2],[3],[4]])
+    >>> np.hstack((a,b))
+    array([[1., 2.],
+           [2., 3.],
+           [3., 4.]])
+    """
+    return _npi.hstack(*arrays)
+
+
 @set_module('mxnet.ndarray.numpy')
 def dstack(arrays):
     """
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 7fd8c1b4c86f..280cf896fad6 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -54,7 +54,7 @@
            'degrees', 'log2', 'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative', 'histogram',
            'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'append', 'argsort',
            'tensordot', 'eye', 'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'array_split',
-           'split', 'vsplit', 'concatenate', 'stack', 'vstack', 'row_stack', 'column_stack', 'dstack',
+           'split', 'vsplit', 'concatenate', 'stack', 'vstack', 'row_stack', 'column_stack', 'hstack', 'dstack',
            'average', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'argmin', 'std', 'var',
            'indices', 'copysign', 'ravel', 'unravel_index', 'hanning', 'hamming', 'blackman', 'flip', 'flipud',
            'fliplr', 'around', 'round', 'arctan2', 'hypot', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad',
@@ -5563,6 +5563,45 @@ def column_stack(tup):
     return _mx_nd_np.column_stack(tup)
 
 
+@set_module('mxnet.numpy')
+def hstack(arrays):
+    """
+    Stack arrays in sequence horizontally (column wise).
+    This is equivalent to concatenation along the second axis,
+    except for 1-D arrays where it concatenates along the first axis.
+    Rebuilds arrays divided by hsplit.
+    This function makes most sense for arrays with up to 3 dimensions.
+    For instance, for pixel-data with a height (first axis), width (second axis),
+    and r/g/b channels (third axis). The functions concatenate,
+    stack and block provide more general stacking and concatenation operations.
+
+    Parameters
+    ----------
+    tup : sequence of ndarrays
+        The arrays must have the same shape along all but the second axis, except 1-D arrays which can be any length.
+
+    Returns
+    -------
+    stacked : ndarray
+        The array formed by stacking the given arrays.
+
+    Examples
+    --------
+    >>> from mxnet import np,npx
+    >>> a = np.array((1,2,3))
+    >>> b = np.array((2,3,4))
+    >>> np.hstack((a,b))
+    array([1., 2., 3., 2., 3., 4.])
+    >>> a = np.array([[1],[2],[3]])
+    >>> b = np.array([[2],[3],[4]])
+    >>> np.hstack((a,b))
+    array([[1., 2.],
+           [2., 3.],
+           [3., 4.]])
+    """
+    return _mx_nd_np.hstack(arrays)
+
+
 @set_module('mxnet.numpy')
 def dstack(arrays):
     """
diff --git a/python/mxnet/numpy_dispatch_protocol.py b/python/mxnet/numpy_dispatch_protocol.py
index 2ae1be7cb081..1156003045da 100644
--- a/python/mxnet/numpy_dispatch_protocol.py
+++ b/python/mxnet/numpy_dispatch_protocol.py
@@ -139,6 +139,8 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'vdot',
     'vstack',
     'column_stack',
+    'hstack',
+    'dstack',
     'zeros_like',
     'linalg.norm',
     'linalg.cholesky',
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 147fba2eaa30..7c857b0fd083 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -43,7 +43,8 @@
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor', 'histogram',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'argsort', 'tensordot', 'eye', 'linspace',
            'logspace', 'expand_dims', 'tile', 'arange', 'array_split', 'split', 'vsplit', 'concatenate', 'append',
-           'stack', 'vstack', 'row_stack', 'column_stack', 'dstack', 'average', 'mean', 'maximum', 'minimum',
+           'stack', 'vstack', 'row_stack', 'column_stack', 'hstack', 'dstack',
+           'average', 'mean', 'maximum', 'minimum',
            'swapaxes', 'clip', 'argmax', 'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'unravel_index',
            'hanning', 'hamming', 'blackman', 'flip', 'flipud', 'fliplr', 'around', 'round', 'hypot', 'bitwise_xor',
            'bitwise_or', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot',
@@ -3750,6 +3751,45 @@ def column_stack(tup):
     return _npi.column_stack(*tup)
 
 
+@set_module('mxnet.symbol.numpy')
+def hstack(arrays):
+    """
+    Stack arrays in sequence horizontally (column wise).
+    This is equivalent to concatenation along the second axis,
+    except for 1-D arrays where it concatenates along the first axis.
+    Rebuilds arrays divided by hsplit.
+    This function makes most sense for arrays with up to 3 dimensions.
+    For instance, for pixel-data with a height (first axis), width (second axis),
+    and r/g/b channels (third axis). The functions concatenate,
+    stack and block provide more general stacking and concatenation operations.
+
+    Parameters
+    ----------
+    tup : _Symbol
+        The arrays must have the same shape along all but the second axis, except 1-D arrays which can be any length.
+
+    Returns
+    -------
+    stacked : _Symbol
+        The array formed by stacking the given arrays.
+
+    Examples
+    --------
+    >>> from mxnet import np,npx
+    >>> a = np.array((1,2,3))
+    >>> b = np.array((2,3,4))
+    >>> np.hstack((a,b))
+    array([1., 2., 3., 2., 3., 4.])
+    >>> a = np.array([[1],[2],[3]])
+    >>> b = np.array([[2],[3],[4]])
+    >>> np.hstack((a,b))
+    array([[1., 2.],
+           [2., 3.],
+           [3., 4.]])
+    """
+    return _npi.hstack(*arrays)
+
+
 @set_module('mxnet.symbol.numpy')
 def dstack(arrays):
     """
diff --git a/src/operator/nn/concat-inl.h b/src/operator/nn/concat-inl.h
index 1fb20ac65473..ffedba46c1ac 100644
--- a/src/operator/nn/concat-inl.h
+++ b/src/operator/nn/concat-inl.h
@@ -141,6 +141,28 @@ void ConcatCompute(const nnvm::NodeAttrs& attrs, const OpContext& ctx,
   });
 }
 
+template<typename xpu>
+void HStackCompute(const nnvm::NodeAttrs& attrs, const OpContext& ctx,
+                   const std::vector<TBlob>& inputs,
+                   const std::vector<OpReqType>& req,
+                   const std::vector<TBlob>& outputs) {
+  ConcatParam param = nnvm::get<ConcatParam>(attrs.parsed);
+  param.dim = inputs[0].shape_.ndim() > 1 ? 1 : 0;
+  std::vector<TBlob> modified_inputs(inputs.size());
+  for (int i = 0; i < param.num_args; ++i) {
+    if (inputs[i].shape_.ndim() == 0) {
+      modified_inputs[i] = inputs[i].reshape(TShape(1, 1));
+    } else {
+      modified_inputs[i] = inputs[i];
+    }
+  }
+  MSHADOW_TYPE_SWITCH(inputs[concat_enum::kData0].type_flag_, DType, {
+    ConcatOp<xpu, DType> op;
+    op.Init(param);
+    op.Forward(ctx, modified_inputs, req, outputs);
+  });
+}
+
 template<typename xpu>
 void DStackCompute(const nnvm::NodeAttrs& attrs, const OpContext& ctx,
                    const std::vector<TBlob>& inputs,
@@ -185,6 +207,28 @@ void ConcatGradCompute(const nnvm::NodeAttrs& attrs, const OpContext& ctx,
   });
 }
 
+template<typename xpu>
+void HStackGradCompute(const nnvm::NodeAttrs& attrs, const OpContext& ctx,
+                       const std::vector<TBlob>& inputs,
+                       const std::vector<OpReqType>& req,
+                       const std::vector<TBlob>& outputs) {
+  ConcatParam param = nnvm::get<ConcatParam>(attrs.parsed);
+  param.dim = inputs[0].shape_.ndim() > 1 ? 1 : 0;
+  std::vector<TBlob> modified_outputs(outputs.size());
+  for (int i = 0; i < param.num_args; ++i) {
+    if (outputs[i].shape_.ndim() == 0) {
+      modified_outputs[i] = outputs[i].reshape(TShape(1, 1));
+    } else {
+      modified_outputs[i] = outputs[i];
+    }
+  }
+  MSHADOW_TYPE_SWITCH(inputs[concat_enum::kOut].type_flag_, DType, {
+    ConcatOp<xpu, DType> op;
+    op.Init(param);
+    op.Backward(ctx, inputs[concat_enum::kOut], req, modified_outputs);
+  });
+}
+
 template<typename xpu>
 void DStackGradCompute(const nnvm::NodeAttrs& attrs, const OpContext& ctx,
                        const std::vector<TBlob>& inputs,
diff --git a/src/operator/numpy/np_matrix_op.cc b/src/operator/numpy/np_matrix_op.cc
index 50f476d849b9..3dcd41067d44 100644
--- a/src/operator/numpy/np_matrix_op.cc
+++ b/src/operator/numpy/np_matrix_op.cc
@@ -466,6 +466,59 @@ NNVM_REGISTER_OP(_np_squeeze)
 .add_argument("a", "NDArray-or-Symbol", "data to squeeze")
 .add_arguments(SqueezeParam::__FIELDS__());
 
+bool HStackShape(const nnvm::NodeAttrs& attrs,
+                 mxnet::ShapeVector *in_shape,
+                 mxnet::ShapeVector *out_shape) {
+  using namespace mshadow;
+  ConcatParam param_ = nnvm::get<ConcatParam>(attrs.parsed);
+  CHECK_EQ(in_shape->size(), static_cast<size_t>(param_.num_args));
+  mxnet::TShape dshape;
+  dim_t size = 0;
+  bool has_unknown_dim_size = false;
+  int axis = (*in_shape)[0].ndim() > 1 ? 1 : 0;
+  param_.dim = axis;
+  for (int i = 0; i < param_.num_args; ++i) {
+    // scalor tensor is treated as one dimensional vector
+    if ((*in_shape)[i].ndim() == 0) {
+      (*in_shape)[i] = mxnet::TShape(1, 1);
+    }
+    mxnet::TShape &tmp = (*in_shape)[i];
+    if (tmp.ndim() > 0) {
+      CheckAxis(axis, tmp.ndim());
+      if (!mxnet::dim_size_is_known(tmp, axis)) {
+        has_unknown_dim_size = true;
+      } else {
+        size += tmp[axis];
+      }
+      tmp[axis] = -1;
+      shape_assign(&dshape, tmp);
+    }
+  }
+
+  mxnet::TShape tmp = (*out_shape)[0];
+  if (tmp.ndim() > 0) {
+    axis = CheckAxis(param_.dim, tmp.ndim());
+    tmp[axis] = -1;
+    shape_assign(&dshape, tmp);
+  }
+
+  if (dshape.ndim() == -1) return false;
+  CHECK_NE(dshape.ndim(), 0) << "zero-dimensional arrays cannot be concatenated";
+
+  for (int i = 0; i < param_.num_args; ++i) {
+    CHECK(shape_assign(&(*in_shape)[i], dshape))
+        << "Incompatible input shape: expected " << dshape << ", got " << (*in_shape)[i];
+  }
+
+  if (!has_unknown_dim_size) {
+    dshape[axis] = size;
+  }
+  CHECK(shape_assign(&(*out_shape)[0], dshape))
+      << "Incompatible output shape: expected " << dshape << ", got " << (*out_shape)[0];
+
+  return shape_is_known(dshape);
+}
+
 bool DStackShape(const nnvm::NodeAttrs& attrs,
                  mxnet::ShapeVector *in_shape,
                  mxnet::ShapeVector *out_shape) {
@@ -986,6 +1039,44 @@ NNVM_REGISTER_OP(_backward_np_vstack)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
 .set_attr<FCompute>("FCompute<cpu>", NumpyVstackBackward<cpu>);
 
+NNVM_REGISTER_OP(_npi_hstack)
+.describe(R"code(Stack tensors horizontally (in second dimension))code" ADD_FILELINE)
+.set_num_inputs([](const NodeAttrs& attrs) {
+  const ConcatParam& params = nnvm::get<ConcatParam>(attrs.parsed);
+  return params.num_args;
+})
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<ConcatParam>)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    const ConcatParam& params = nnvm::get<ConcatParam>(attrs.parsed);
+    std::vector<std::string> ret;
+    for (int i = 0; i < params.num_args; ++i) {
+      ret.push_back(std::string("data") + std::to_string(i));
+    }
+    return ret;
+})
+.set_attr<nnvm::FListOutputNames>("FListOutputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"out"};
+})
+.set_attr<std::string>("key_var_num_args", "num_args")
+.set_attr<nnvm::FInferType>("FInferType", ConcatType)
+.set_attr<mxnet::FInferShape>("FInferShape", HStackShape)
+.set_attr<FCompute>("FCompute<cpu>", HStackCompute<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", NumpyConcatGrad{"_backward_np_hstack"})
+.add_argument("data", "NDArray-or-Symbol[]", "List of arrays to concatenate")
+.add_arguments(ConcatParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_backward_np_hstack)
+.set_num_outputs([](const NodeAttrs& attrs) {
+  const ConcatParam& params = nnvm::get<ConcatParam>(attrs.parsed);
+  return params.num_args;
+})
+.set_attr_parser(ParamParser<ConcatParam>)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FCompute>("FCompute<cpu>", HStackGradCompute<cpu>);
+
 NNVM_REGISTER_OP(_npi_dstack)
 .describe(R"code(Stack tensors in sequence depthwise (in third dimension))code" ADD_FILELINE)
 .set_num_inputs([](const NodeAttrs& attrs) {
diff --git a/src/operator/numpy/np_matrix_op.cu b/src/operator/numpy/np_matrix_op.cu
index 10ff0eac2c29..be161287887e 100644
--- a/src/operator/numpy/np_matrix_op.cu
+++ b/src/operator/numpy/np_matrix_op.cu
@@ -53,6 +53,12 @@ NNVM_REGISTER_OP(_npi_vstack)
 NNVM_REGISTER_OP(_backward_np_vstack)
 .set_attr<FCompute>("FCompute<gpu>", NumpyVstackBackward<gpu>);
 
+NNVM_REGISTER_OP(_npi_hstack)
+.set_attr<FCompute>("FCompute<gpu>", HStackCompute<gpu>);
+
+NNVM_REGISTER_OP(_backward_np_hstack)
+.set_attr<FCompute>("FCompute<gpu>", HStackGradCompute<gpu>);
+
 NNVM_REGISTER_OP(_npi_dstack)
 .set_attr<FCompute>("FCompute<gpu>", DStackCompute<gpu>);
 
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index 1e85adf3b72f..c8c086ac2cbf 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -1437,6 +1437,18 @@ def _add_workload_column_stack():
     OpArgMngr.add_workload('column_stack', [np.array(_np.arange(3)) for _ in range(2)])
 
 
+def _add_workload_hstack(array_pool):
+    OpArgMngr.add_workload('hstack', (np.random.uniform(size=(1, 4)), np.random.uniform(size=(1, 4))))
+    OpArgMngr.add_workload('hstack', array_pool['4x1'])
+    OpArgMngr.add_workload('hstack', array_pool['1x1x0'])
+
+
+def _add_workload_dstack(array_pool):
+    OpArgMngr.add_workload('dstack', (np.random.uniform(size=(5, 1, 2)), np.random.uniform(size=(5, 1, 3))))
+    OpArgMngr.add_workload('dstack', array_pool['4x1'])
+    OpArgMngr.add_workload('dstack', array_pool['1x1x0'])
+
+
 def _add_workload_equal(array_pool):
     # TODO(junwu): fp16 does not work yet with TVM generated ops
     # OpArgMngr.add_workload('equal', np.array([0, 1, 2, 4, 2], dtype=np.float16), np.array([-2, 5, 1, 4, 3], dtype=np.float16))
@@ -1737,6 +1749,8 @@ def _prepare_workloads():
     _add_workload_vdot()
     _add_workload_vstack(array_pool)
     _add_workload_column_stack()
+    _add_workload_hstack(array_pool)
+    _add_workload_dstack(array_pool)
     _add_workload_equal(array_pool)
     _add_workload_not_equal(array_pool)
     _add_workload_greater(array_pool)
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 274f37124262..4fefb1b4eec3 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -2732,6 +2732,70 @@ def hybrid_forward(self, F, a, *args):
                 assert same(mx_out.asnumpy(), np_out)
 
 
+@with_seed()
+@use_np
+def test_np_hstack():
+    class TestHStack(HybridBlock):
+        def __init__(self):
+            super(TestHStack, self).__init__()
+
+        def hybrid_forward(self, F, a, *args):
+            return F.np.hstack([a] + list(args))
+
+    def get_new_shape(shape):
+        if len(shape) == 0:
+            l = random.randint(0,3)
+            if l == 0:
+                return shape
+            else:
+                return (l,)
+        shape_lst = list(shape)
+        axis = 1 if len(shape) > 1 else 0
+        shape_lst[axis] = random.randint(0, 5)
+        return tuple(shape_lst)
+
+    shapes = [
+        (),
+        (1,),
+        (2,1),
+        (2,2,4),
+        (2,0,0),
+        (0,1,3),
+        (2,0,3),
+        (2,3,4,5)
+    ]
+    for hybridize in [True, False]:
+        for shape in shapes:
+            test_hstack = TestHStack()
+            if hybridize:
+                test_hstack.hybridize()
+            # test symbolic forward
+            a = np.random.uniform(size=get_new_shape(shape))
+            a.attach_grad()
+            b = np.random.uniform(size=get_new_shape(shape))
+            b.attach_grad()
+            c = np.random.uniform(size=get_new_shape(shape))
+            c.attach_grad()
+            d = np.random.uniform(size=get_new_shape(shape))
+            d.attach_grad()
+            with mx.autograd.record():
+                mx_out = test_hstack(a, b, c, d)
+            np_out = _np.hstack((a.asnumpy(), b.asnumpy(), c.asnumpy(), d.asnumpy()))
+            assert mx_out.shape == np_out.shape
+            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+
+            # test symbolic backward
+            mx_out.backward()
+            assert_almost_equal(a.grad.asnumpy(), _np.ones(a.shape), rtol=1e-3, atol=1e-5)
+            assert_almost_equal(b.grad.asnumpy(), _np.ones(b.shape), rtol=1e-3, atol=1e-5)
+            assert_almost_equal(c.grad.asnumpy(), _np.ones(c.shape), rtol=1e-3, atol=1e-5)
+            assert_almost_equal(d.grad.asnumpy(), _np.ones(d.shape), rtol=1e-3, atol=1e-5)
+
+            mx_out = np.hstack((a, b, c, d))
+            np_out = _np.hstack((a.asnumpy(),b.asnumpy(), c.asnumpy(), d.asnumpy()))
+            assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+
+
 @with_seed()
 @use_np
 def test_np_dstack():

From 75b93bb067078b2bec7cce2ff14755ad31439e05 Mon Sep 17 00:00:00 2001
From: Sheng Zha <szha@users.noreply.github.com>
Date: Tue, 14 Jan 2020 11:33:45 +0800
Subject: [PATCH 792/813] [CD] fix CD pipeline (#17259)

* fix CD pipeline

* fix s3 grants

* specify cuda version
---
 cd/Jenkinsfile_release_job                             | 10 +++++-----
 cd/python/pypi/Jenkins_pipeline.groovy                 |  4 ++--
 ci/docker/Dockerfile.build.ubuntu_build_cuda           |  6 +++---
 ci/docker/Dockerfile.build.ubuntu_gpu_cu100            |  1 +
 ci/docker/Dockerfile.build.ubuntu_gpu_cu101            |  1 +
 ci/docker/Dockerfile.build.ubuntu_gpu_cu102            |  1 +
 ci/docker/Dockerfile.build.ubuntu_gpu_cu90             |  1 +
 ci/docker/Dockerfile.build.ubuntu_gpu_cu92             |  1 +
 ci/docker/Dockerfile.publish.ubuntu1404_cpu            |  4 ++--
 ci/docker/Dockerfile.publish.ubuntu1404_gpu            |  4 ++--
 ci/docker/install/{ubuntu_ar.sh => ubuntu_binutils.sh} |  2 +-
 ci/docker/runtime_functions.sh                         |  7 +++++--
 tests/python/mkl/test_mkldnn.py                        |  4 ++--
 tests/python/unittest/test_operator.py                 |  4 +++-
 14 files changed, 30 insertions(+), 20 deletions(-)
 rename ci/docker/install/{ubuntu_ar.sh => ubuntu_binutils.sh} (93%)

diff --git a/cd/Jenkinsfile_release_job b/cd/Jenkinsfile_release_job
index 38620756af41..494e29140700 100644
--- a/cd/Jenkinsfile_release_job
+++ b/cd/Jenkinsfile_release_job
@@ -23,7 +23,7 @@
 // This job executes a "generic" (CD) release job.
 // The pipeline to be executed is defined by the "RELEASE_JOB_TYPE" pipeline parameter.
 // This is the path (relative to the CD directory) to a directory containing the following file groovy script
-// `Jenkins_pipeline.groovy`. 
+// `Jenkins_pipeline.groovy`.
 // See `cd` directory README for more details.
 
 // timeout of each step in minutes
@@ -40,7 +40,7 @@ pipeline {
     string(defaultValue: "master", description: "Git Commit to Build", name: "COMMIT_ID")
 
     // Using string instead of choice parameter to keep the changes to the parameters minimal to avoid
-    // any disruption caused by different COMMIT_ID values chaning the job parameter configuration on 
+    // any disruption caused by different COMMIT_ID values chaning the job parameter configuration on
     // Jenkins.
     string(defaultValue: "mxnet_lib/static", description: "Pipeline to build", name: "RELEASE_JOB_TYPE")
     string(defaultValue: "cpu,mkl,cu90,cu90mkl,cu92,cu92mkl,cu100,cu100mkl,cu101,cu101mkl,cu102,cu102mkl", description: "Comma separated list of variants", name: "MXNET_VARIANTS")
@@ -91,11 +91,11 @@ pipeline {
           // Add new job types here
           def valid_job_types = [
             "mxnet_lib/static",
-            "mxnet_lib/dynamic", 
+            "mxnet_lib/dynamic",
             "python/pypi",
             "python/docker"
           ]
-          
+
           // Convert mxnet variants to a list
           def mxnet_variants = params.MXNET_VARIANTS.trim().split(',').inject([]) { list, item ->
             list << item.trim()
@@ -116,7 +116,7 @@ pipeline {
 
           // Extract the pipelines for the variants
           def pipelines = [:]
-          
+
           for (variant in mxnet_variants) {
             pipelines << ["${variant}": custom_steps.get_pipeline(variant)]
           }
diff --git a/cd/python/pypi/Jenkins_pipeline.groovy b/cd/python/pypi/Jenkins_pipeline.groovy
index fa9300db3ca0..486413f2ec0e 100644
--- a/cd/python/pypi/Jenkins_pipeline.groovy
+++ b/cd/python/pypi/Jenkins_pipeline.groovy
@@ -20,7 +20,7 @@
 // Jenkins pipeline
 // See documents at https://jenkins.io/doc/book/pipeline/jenkinsfile/
 
-// NOTE: 
+// NOTE:
 // ci_utils and cd_utils are loaded by the originating Jenkins job, e.g. jenkins/Jenkinsfile_release_job
 
 // Only post the following variants to PyPI.
@@ -46,7 +46,7 @@ def build(mxnet_variant) {
   ws("workspace/python_pypi/${mxnet_variant}/${env.BUILD_NUMBER}") {
     ci_utils.init_git()
     cd_utils.restore_static_libmxnet(mxnet_variant)
-    
+
     // create wheel file
     def environment = get_environment(mxnet_variant)
     def nvidia_docker = mxnet_variant.startsWith('cu')
diff --git a/ci/docker/Dockerfile.build.ubuntu_build_cuda b/ci/docker/Dockerfile.build.ubuntu_build_cuda
index ce6d0007875e..07f67d178c2c 100644
--- a/ci/docker/Dockerfile.build.ubuntu_build_cuda
+++ b/ci/docker/Dockerfile.build.ubuntu_build_cuda
@@ -42,10 +42,10 @@ COPY install/ubuntu_perl.sh /work/
 RUN /work/ubuntu_perl.sh
 COPY install/ubuntu_clang.sh /work/
 RUN /work/ubuntu_clang.sh
-COPY install/ubuntu_ar.sh /work/
-RUN /work/ubuntu_ar.sh
+COPY install/ubuntu_binutils.sh /work/
+RUN /work/ubuntu_binutils.sh
 
-ENV CUDNN_VERSION=7.6.0.64
+ENV CUDNN_VERSION=7.6.5.32
 COPY install/ubuntu_cudnn.sh /work/
 RUN /work/ubuntu_cudnn.sh
 
diff --git a/ci/docker/Dockerfile.build.ubuntu_gpu_cu100 b/ci/docker/Dockerfile.build.ubuntu_gpu_cu100
index ccf69404ee3a..9a18e94bd40c 100644
--- a/ci/docker/Dockerfile.build.ubuntu_gpu_cu100
+++ b/ci/docker/Dockerfile.build.ubuntu_gpu_cu100
@@ -65,6 +65,7 @@ RUN /work/ubuntu_docs.sh
 COPY install/ubuntu_tutorials.sh /work/
 RUN /work/ubuntu_tutorials.sh
 
+ENV CUDA_VERSION=10.0.130
 ENV CUDNN_VERSION=7.6.5.32
 COPY install/ubuntu_cudnn.sh /work/
 RUN /work/ubuntu_cudnn.sh
diff --git a/ci/docker/Dockerfile.build.ubuntu_gpu_cu101 b/ci/docker/Dockerfile.build.ubuntu_gpu_cu101
index a6e59f3435d5..717a5aa47f0e 100644
--- a/ci/docker/Dockerfile.build.ubuntu_gpu_cu101
+++ b/ci/docker/Dockerfile.build.ubuntu_gpu_cu101
@@ -65,6 +65,7 @@ RUN /work/ubuntu_docs.sh
 COPY install/ubuntu_tutorials.sh /work/
 RUN /work/ubuntu_tutorials.sh
 
+ENV CUDA_VERSION=10.1.105
 ENV CUDNN_VERSION=7.6.5.32
 COPY install/ubuntu_cudnn.sh /work/
 RUN /work/ubuntu_cudnn.sh
diff --git a/ci/docker/Dockerfile.build.ubuntu_gpu_cu102 b/ci/docker/Dockerfile.build.ubuntu_gpu_cu102
index 3a6e15bb0f6d..7a21e74a3b8b 100644
--- a/ci/docker/Dockerfile.build.ubuntu_gpu_cu102
+++ b/ci/docker/Dockerfile.build.ubuntu_gpu_cu102
@@ -65,6 +65,7 @@ RUN /work/ubuntu_docs.sh
 COPY install/ubuntu_tutorials.sh /work/
 RUN /work/ubuntu_tutorials.sh
 
+ENV CUDA_VERSION=10.2.89
 ENV CUDNN_VERSION=7.6.5.32
 COPY install/ubuntu_cudnn.sh /work/
 RUN /work/ubuntu_cudnn.sh
diff --git a/ci/docker/Dockerfile.build.ubuntu_gpu_cu90 b/ci/docker/Dockerfile.build.ubuntu_gpu_cu90
index e1fe585d75bf..f43dc590e2cc 100644
--- a/ci/docker/Dockerfile.build.ubuntu_gpu_cu90
+++ b/ci/docker/Dockerfile.build.ubuntu_gpu_cu90
@@ -65,6 +65,7 @@ RUN /work/ubuntu_docs.sh
 COPY install/ubuntu_tutorials.sh /work/
 RUN /work/ubuntu_tutorials.sh
 
+ENV CUDA_VERSION=9.0.176
 ENV CUDNN_VERSION=7.6.5.32
 COPY install/ubuntu_cudnn.sh /work/
 RUN /work/ubuntu_cudnn.sh
diff --git a/ci/docker/Dockerfile.build.ubuntu_gpu_cu92 b/ci/docker/Dockerfile.build.ubuntu_gpu_cu92
index 0de9d87c6516..3b1ea6c781ad 100644
--- a/ci/docker/Dockerfile.build.ubuntu_gpu_cu92
+++ b/ci/docker/Dockerfile.build.ubuntu_gpu_cu92
@@ -65,6 +65,7 @@ RUN /work/ubuntu_docs.sh
 COPY install/ubuntu_tutorials.sh /work/
 RUN /work/ubuntu_tutorials.sh
 
+ENV CUDA_VERSION=9.2.148
 ENV CUDNN_VERSION=7.6.5.32
 COPY install/ubuntu_cudnn.sh /work/
 RUN /work/ubuntu_cudnn.sh
diff --git a/ci/docker/Dockerfile.publish.ubuntu1404_cpu b/ci/docker/Dockerfile.publish.ubuntu1404_cpu
index 993d9d873075..8ccc41b2143c 100644
--- a/ci/docker/Dockerfile.publish.ubuntu1404_cpu
+++ b/ci/docker/Dockerfile.publish.ubuntu1404_cpu
@@ -25,8 +25,8 @@ WORKDIR /work/deps
 COPY install/ubuntu_publish.sh /work/
 RUN /work/ubuntu_publish.sh
 
-COPY install/ubuntu_ar.sh /work/
-RUN /work/ubuntu_ar.sh
+COPY install/ubuntu_binutils.sh /work/
+RUN /work/ubuntu_binutils.sh
 
 ARG USER_ID=0
 ARG GROUP_ID=0
diff --git a/ci/docker/Dockerfile.publish.ubuntu1404_gpu b/ci/docker/Dockerfile.publish.ubuntu1404_gpu
index eb214a620009..3a005cadecea 100644
--- a/ci/docker/Dockerfile.publish.ubuntu1404_gpu
+++ b/ci/docker/Dockerfile.publish.ubuntu1404_gpu
@@ -25,8 +25,8 @@ WORKDIR /work/deps
 COPY install/ubuntu_publish.sh /work/
 RUN /work/ubuntu_publish.sh
 
-COPY install/ubuntu_ar.sh /work/
-RUN /work/ubuntu_ar.sh
+COPY install/ubuntu_binutils.sh /work/
+RUN /work/ubuntu_binutils.sh
 
 ARG USER_ID=0
 ARG GROUP_ID=0
diff --git a/ci/docker/install/ubuntu_ar.sh b/ci/docker/install/ubuntu_binutils.sh
similarity index 93%
rename from ci/docker/install/ubuntu_ar.sh
rename to ci/docker/install/ubuntu_binutils.sh
index dbf6b2f3c119..51e075723c65 100755
--- a/ci/docker/install/ubuntu_ar.sh
+++ b/ci/docker/install/ubuntu_binutils.sh
@@ -35,4 +35,4 @@ tar -xvf binutils-2.27.tar.gz && cd binutils-2.27
 ./configure --prefix=/opt/binutils_other --exec-prefix=/opt/binutils_install
 make -j$(nproc)
 make install
-ln -s /opt/binutils_install/bin/ar /usr/local/bin/ar
+ls /opt/binutils_install/bin/ | xargs -I{} ln -s /opt/binutils_install/bin/{} /usr/local/bin/{}
diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index 328a008c07a5..b4b0baac2600 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -2064,9 +2064,12 @@ cd_s3_publish() {
     set -ex
     pip3 install --user awscli
     filepath=$(readlink -f wheel_build/dist/*.whl)
-    filename=$(basename $file_path)
+    filename=$(basename $filepath)
     variant=$(echo $filename | cut -d'-' -f1 | cut -d'_' -f2 -s)
-    aws s3 cp --grants read=uri=http://acs.amazonaws.com/groups/global/AllUsers,full=id=43f628fab72838a4f0b929d7f1993b14411f4b0294b011261bc6bd3e950a6822 s3://apache-mxnet/dist/${variant}/${filename}
+    if [ -z "${variant}" ]; then
+        variant="cpu"
+    fi
+    aws s3 cp ${filepath} s3://apache-mxnet/dist/${variant}/${filename} --grants read=uri=http://acs.amazonaws.com/groups/global/AllUsers full=id=43f628fab72838a4f0b929d7f1993b14411f4b0294b011261bc6bd3e950a6822
 }
 
 build_static_scala_mkl() {
diff --git a/tests/python/mkl/test_mkldnn.py b/tests/python/mkl/test_mkldnn.py
index 6424990d254d..869ee6cf216c 100644
--- a/tests/python/mkl/test_mkldnn.py
+++ b/tests/python/mkl/test_mkldnn.py
@@ -71,7 +71,7 @@ def test_mkldnn_ndarray_slice():
     y = net(x)
 
     # trigger computation on ndarray slice
-    assert_almost_equal(y[0].asnumpy()[0, 0, 0], 0.3376348)
+    assert_almost_equal(y[0].asnumpy()[0, 0, 0], np.array(0.3376348))
 
 def test_mkldnn_engine_threading():
     net = gluon.nn.HybridSequential()
@@ -95,7 +95,7 @@ def __getitem__(self, key):
     for _ in loader:
         y = net(mx.nd.array(np.ones(X))).asnumpy()
         # output should be 016711406 (non-mkldnn mode output)
-        assert_almost_equal(y[0, 0, 0, 0], 0.016711406)
+        assert_almost_equal(y[0, 0, 0, 0], np.array(0.016711406))
         break
 
 @with_seed()
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 6c604e3ed914..932925c27f84 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -29,7 +29,7 @@
 from mxnet.test_utils import *
 from mxnet.operator import *
 from mxnet.base import py_str, MXNetError, _as_list
-from common import setup_module, with_seed, teardown, assert_raises_cudnn_not_satisfied, assertRaises
+from common import setup_module, with_seed, teardown, assert_raises_cudnn_not_satisfied, assert_raises_cuda_not_satisfied, assertRaises
 from common import run_in_spawned_process
 from nose.tools import assert_raises, ok_
 import unittest
@@ -9553,6 +9553,7 @@ def convert_bias(F, q_bias, k_bias, v_bias, num_heads):
 
 
 @with_seed()
+@assert_raises_cuda_not_satisfied(min_version='9.1')
 def test_multihead_attention_selfatt():
     dtypes = ['float32']
     if default_context().device_type == 'gpu':
@@ -9721,6 +9722,7 @@ def convert_bias(F, k_bias, v_bias, num_heads):
         assert_allclose(grads_orig[k], grads_opti[k], rtol=1e-2, atol=1e-3)
 
 @with_seed()
+@assert_raises_cuda_not_satisfied(min_version='9.1')
 def test_multihead_attention_encdec():
     dtypes = ['float32']
     if default_context().device_type == 'gpu':

From 1a61a8677a403f41fb50cddd1d57739782ab6c21 Mon Sep 17 00:00:00 2001
From: Minghao Liu <40382964+Tommliu@users.noreply.github.com>
Date: Tue, 14 Jan 2020 14:14:11 +0800
Subject: [PATCH 793/813] HMM Model (#17120)

---
 python/mxnet/numpy/utils.py               | 3 ++-
 src/operator/tensor/broadcast_reduce_op.h | 2 +-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/python/mxnet/numpy/utils.py b/python/mxnet/numpy/utils.py
index c34650a61f31..7f9d7bec5030 100644
--- a/python/mxnet/numpy/utils.py
+++ b/python/mxnet/numpy/utils.py
@@ -23,7 +23,7 @@
 import numpy as onp
 
 __all__ = ['float16', 'float32', 'float64', 'uint8', 'int32', 'int8', 'int64',
-           'bool', 'bool_', 'pi', 'inf', 'nan', 'PZERO', 'NZERO', 'newaxis']
+           'bool', 'bool_', 'pi', 'inf', 'nan', 'PZERO', 'NZERO', 'newaxis', 'finfo']
 
 float16 = onp.float16
 float32 = onp.float32
@@ -42,3 +42,4 @@
 NZERO = onp.NZERO
 
 newaxis = None
+finfo = onp.finfo
diff --git a/src/operator/tensor/broadcast_reduce_op.h b/src/operator/tensor/broadcast_reduce_op.h
index 608e44dcfe76..00e113d5d422 100644
--- a/src/operator/tensor/broadcast_reduce_op.h
+++ b/src/operator/tensor/broadcast_reduce_op.h
@@ -593,7 +593,7 @@ void SearchAxisCompute(const nnvm::NodeAttrs& attrs,
   }
   if (input.shape_.Size() == 0U) return;  // zero-size tensor
   mxnet::TShape shape = AxisShapeCompact(input.shape_, &axis, false);
-  MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+  MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, DType, {
     Tensor<xpu, 2, DType> out = outputs[0].get_with_shape<xpu, 2, DType>(
       Shape2(shape[0], shape[2]), s);
     Tensor<xpu, 3, DType> in = input.get_with_shape<xpu, 3, DType>(

From 2938684d4e23621175fa98c0c45256dbb3de5cf3 Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Mon, 13 Jan 2020 22:15:14 -0800
Subject: [PATCH 794/813] handle array_like fill_value for np.full; add unit
 test coverage (#17245)

---
 python/mxnet/ndarray/numpy/_op.py      | 16 ++++++++-
 python/mxnet/numpy/multiarray.py       |  4 ++-
 python/mxnet/symbol/numpy/_symbol.py   | 12 +++++--
 tests/python/unittest/test_numpy_op.py | 49 ++++++++++++++++++++++++++
 4 files changed, 76 insertions(+), 5 deletions(-)

diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index eb01a6b0e400..0e4237fbdbeb 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -294,11 +294,12 @@ def broadcast_to(array, shape):
 def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):  # pylint: disable=too-many-arguments
     """
     Return a new array of given shape and type, filled with `fill_value`.
+
     Parameters
     ----------
     shape : int or sequence of ints
         Shape of the new array, e.g., ``(2, 3)`` or ``2``.
-    fill_value : scalar
+    fill_value : scalar or ndarray
         Fill value.
     dtype : data-type, optional
         The desired data-type for the array. The default, `None`, means
@@ -311,10 +312,14 @@ def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):  # pylin
         A location into which the result is stored.
         If provided, it must have the same shape and dtype as input ndarray.
         If not provided or `None`, a freshly-allocated array is returned.
+
     Returns
     -------
     out : ndarray
         Array of `fill_value` with the given shape, dtype, and order.
+        If `fill_value` is an ndarray, out will have the same context as `fill_value`
+        regardless of the provided `ctx`.
+
     Notes
     -----
     This function differs from the original `numpy.full
@@ -323,11 +328,13 @@ def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):  # pylin
     - Have an additional `ctx` argument to specify the device
     - Have an additional `out` argument
     - Currently does not support `order` selection
+
     See Also
     --------
     empty : Return a new uninitialized array.
     ones : Return a new array setting values to one.
     zeros : Return a new array setting values to zero.
+
     Examples
     --------
     >>> np.full((2, 2), 10)
@@ -336,11 +343,18 @@ def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):  # pylin
     >>> np.full((2, 2), 2, dtype=np.int32, ctx=mx.cpu(0))
     array([[2, 2],
            [2, 2]], dtype=int32)
+
     """
     if order != 'C':
         raise NotImplementedError
     if ctx is None:
         ctx = current_context()
+    if isinstance(fill_value, NDArray):
+        if dtype is None:
+            ret = broadcast_to(fill_value, shape)
+        else:
+            ret = broadcast_to(fill_value, shape).astype(dtype)
+        return ret
     dtype = _np.float32 if dtype is None else dtype
     return _npi.full(shape=shape, value=fill_value, ctx=ctx, dtype=dtype, out=out)
 # pylint: enable=too-many-arguments, redefined-outer-name
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 280cf896fad6..c92489fa6e9a 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -2321,7 +2321,7 @@ def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):
     ----------
     shape : int or sequence of ints
         Shape of the new array, e.g., ``(2, 3)`` or ``2``.
-    fill_value : scalar
+    fill_value : scalar or ndarray
         Fill value.
     dtype : data-type, optional
         The desired data-type for the array. The default, `None`, means
@@ -2339,6 +2339,8 @@ def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):
     -------
     out : ndarray
         Array of `fill_value` with the given shape, dtype, and order.
+        If `fill_value` is an ndarray, out will have the same context as `fill_value`
+        regardless of the provided `ctx`.
 
     Notes
     -----
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index 7c857b0fd083..71bbcae06b6e 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -36,8 +36,8 @@
 except ImportError:
     from builtins import slice as py_slice
 
-__all__ = ['zeros', 'zeros_like', 'ones', 'ones_like', 'full_like', 'empty_like', 'bitwise_not', 'invert', 'delete',
-           'add', 'broadcast_to', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power', 'arctan2',
+__all__ = ['zeros', 'zeros_like', 'ones', 'ones_like', 'full', 'full_like', 'empty_like', 'bitwise_not', 'invert',
+           'delete', 'add', 'broadcast_to', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power', 'arctan2',
            'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs', 'absolute', 'exp',
            'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2', 'log1p',
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor', 'histogram',
@@ -1172,7 +1172,7 @@ def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):  # pylin
     ----------
     shape : int or sequence of ints
         Shape of the new array, e.g., ``(2, 3)`` or ``2``.
-    fill_value : scalar
+    fill_value : scalar or _Symbol
         Fill value.
     dtype : data-type, optional
         The desired data-type for the array. The default, `None`, means
@@ -1215,6 +1215,12 @@ def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):  # pylin
         raise NotImplementedError
     if ctx is None:
         ctx = current_context()
+    if isinstance(fill_value, Symbol):
+        if dtype is None:
+            ret = broadcast_to(fill_value, shape)
+        else:
+            ret = broadcast_to(fill_value, shape).astype(dtype)
+        return ret
     dtype = _np.float32 if dtype is None else dtype
     return _npi.full(shape=shape, value=fill_value, ctx=ctx, dtype=dtype, out=out)
 
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 4fefb1b4eec3..d952bf7f33dc 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -4636,6 +4636,55 @@ def g(data):
                 assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol)
 
 
+@with_seed()
+@use_np
+def test_np_full():
+    class TestFull(HybridBlock):
+        def __init__(self, shape, dtype=None):
+            super(TestFull, self).__init__()
+            self._shape = shape
+            self._dtype = dtype
+
+        def hybrid_forward(self, F, a):
+            return F.np.full(self._shape, a, dtype=self._dtype)
+
+    configs = [
+        ((3, 4), 2.0),
+        ((0, 3), 2.0),
+        ((3, 4), np.array(2.0)),
+        ((0, 3), np.array(2.0)),
+        ((2, 3), np.array([1, 2, 3], dtype=np.float32)),
+        ((2, 3), np.array([1, 2, 3], dtype=np.int64)),
+        ((0, 3), np.array([1, 2, 3], dtype=np.float32)),
+        ((0, 3), np.array([1, 2, 3], dtype=np.int64)),
+    ]
+
+    rtol, atol = 1e-3, 1e-5
+    dtypes = ['float16', 'float32', 'float64', 'int8', 'int32', 'int64']
+    for shape, fill_value in configs:
+        for hybridize in [True, False]:
+            for dtype in dtypes:
+                if isinstance(fill_value, np.ndarray):
+                    test_full = TestFull(shape, dtype=dtype)
+                    if hybridize:
+                        test_full.hybridize()
+                    mx_out = test_full(fill_value)
+                    expected_np = _np.full(shape, fill_value.asnumpy(), dtype=dtype)
+                    assert mx_out.shape == expected_np.shape
+                    assert mx_out.dtype == expected_np.dtype
+                    assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol)
+
+                # Test imperative once again
+                mx_out = np.full(shape, fill_value, dtype=dtype)
+                if isinstance(fill_value, np.ndarray):
+                    expected_np = _np.full(shape, fill_value.asnumpy(), dtype=dtype)
+                else:
+                    expected_np = _np.full(shape, fill_value, dtype=dtype)
+                assert mx_out.shape == expected_np.shape
+                assert mx_out.dtype == expected_np.dtype
+                assert_almost_equal(mx_out.asnumpy(), expected_np, rtol=rtol, atol=atol)
+
+
 @with_seed()
 @use_np
 def test_np_full_like():

From a8452aaac1321b36063d4b6124e9eeb653b60100 Mon Sep 17 00:00:00 2001
From: Leonard Lausen <lausen@amazon.com>
Date: Tue, 14 Jan 2020 07:21:21 +0100
Subject: [PATCH 795/813] Fix language selection in get_started options.js
 (#17284)

Previously, selecting 'R' will highlight 'Clojure' and 'Perl', as both of them
contain 'r'.
---
 docs/static_site/src/assets/js/options.js | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/docs/static_site/src/assets/js/options.js b/docs/static_site/src/assets/js/options.js
index ae955e469ec1..f1a1ef835536 100644
--- a/docs/static_site/src/assets/js/options.js
+++ b/docs/static_site/src/assets/js/options.js
@@ -62,7 +62,11 @@ $(document).ready(function () {
         $('li.versions').each(function(){is_a_match($(this), versionSelect)});
         $('button.opt').removeClass('active');
         $('button.opt').each(function(){is_a_match($(this), platformSelect)});
-        $('button.opt').each(function(){is_a_match($(this), languageSelect)});
+        $('button.opt').each(function(){
+            if (label($(this).text()) === label(languageSelect)) {
+                $(this).addClass(('active'))
+            }
+        });
         $('button.opt').each(function(){is_a_match($(this), processorSelect)});
         $('button.opt').each(function(){is_a_match($(this), environSelect)});
 

From 58cbd65cfe98e50da36303c5f2adc65358d3a0b8 Mon Sep 17 00:00:00 2001
From: kshitij12345 <kshitijkalambarkar@gmail.com>
Date: Tue, 14 Jan 2020 12:40:42 +0530
Subject: [PATCH 796/813] [MXNET-978] Higher Order Gradient Support `rsqrt`,
 `rcbrt`. (#15476)

* support rsqrt, rcbrt for higher order grad

* add relevant tests

* update comments
---
 src/operator/tensor/elemwise_unary_op_pow.cc  | 56 ++++++++++++++++++-
 .../python/unittest/test_higher_order_grad.py | 40 +++++++++++++
 2 files changed, 94 insertions(+), 2 deletions(-)

diff --git a/src/operator/tensor/elemwise_unary_op_pow.cc b/src/operator/tensor/elemwise_unary_op_pow.cc
index 084772980ed1..6702625fcc43 100644
--- a/src/operator/tensor/elemwise_unary_op_pow.cc
+++ b/src/operator/tensor/elemwise_unary_op_pow.cc
@@ -222,7 +222,33 @@ The storage type of ``rsqrt`` output is always dense
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_rsqrt"});
 
 MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(
-  _backward_rsqrt, unary_bwd<mshadow_op::reciprocal_square_root_grad>);
+  _backward_rsqrt, unary_bwd<mshadow_op::reciprocal_square_root_grad>)
+.set_attr<nnvm::FGradient>("FGradient",
+  [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
+      // NodeEntry{n} : y_grad * f'(x)
+      // n->inputs[0] : y_grad
+      // n->inputs[1] : x
+      // ograds[0] : head_grad_grads (dL/dxgrad)
+      // f(x) = 1/(x^1/2)
+      // f'(x) = -1/(2*x^3/2)
+      // f''(x) = f'(x) * -3/(2*x) = 3/(4 * x^5/2)
+      auto dydx = n->inputs[0];
+      auto x = n->inputs[1];
+      auto dydx_mul_grad_x = nnvm::NodeEntry{n};
+      auto op = mxnet::util::NodeOpGen{n};
+
+      auto two_x = op.mul(2.0, x);
+      auto r_two_x = op.reciprocal(two_x);
+      auto neg_r_two_x = op.negative(r_two_x);
+      auto three_by_two_neg_r_x = op.mul(3.0, neg_r_two_x);
+      auto x_grad_grad = op.mul(three_by_two_neg_r_x, dydx_mul_grad_x);
+      auto x_grad = op.div(dydx_mul_grad_x, dydx);
+
+      std::vector<nnvm::NodeEntry> ret;
+      ret.emplace_back(op.mul(ograds[0], x_grad));
+      ret.emplace_back(op.mul(ograds[0], x_grad_grad));
+      return ret;
+  });
 
 // cbrt
 MXNET_OPERATOR_REGISTER_UNARY_WITH_RSP_CSR(cbrt, cpu, mshadow_op::cube_root)
@@ -301,7 +327,33 @@ Example::
 MXNET_OPERATOR_REGISTER_BINARY(_backward_rcbrt)
 .set_attr<FCompute>("FCompute<cpu>",
                     ElemwiseBinaryOp::Compute<cpu,
-                      unary_bwd<mshadow_op::reciprocal_cube_root_grad>>);
+                      unary_bwd<mshadow_op::reciprocal_cube_root_grad>>)
+.set_attr<nnvm::FGradient>("FGradient",
+  [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
+      // NodeEntry{n} : y_grad * f'(x)
+      // n->inputs[0] : y_grad
+      // n->inputs[1] : x
+      // ograds[0] : head_grad_grads (dL/dxgrad)
+      // f(x) = 1/(x^1/3)
+      // f'(x) = -1/(3*x^4/3)
+      // f''(x) = f'(x) * -4/(3*x) = 4/(9 * x^7/3)
+      auto dydx = n->inputs[0];
+      auto x = n->inputs[1];
+      auto dydx_mul_grad_x = nnvm::NodeEntry{n};
+      auto op = mxnet::util::NodeOpGen{n};
+
+      auto three_x = op.mul(3.0, x);
+      auto r_three_x = op.reciprocal(three_x);
+      auto neg_r_three_x = op.negative(r_three_x);
+      auto four_by_three_neg_r_x = op.mul(4.0, neg_r_three_x);
+      auto x_grad_grad = op.mul(four_by_three_neg_r_x, dydx_mul_grad_x);
+      auto x_grad = op.div(dydx_mul_grad_x, dydx);
+
+      std::vector<nnvm::NodeEntry> ret;
+      ret.emplace_back(op.mul(ograds[0], x_grad));
+      ret.emplace_back(op.mul(ograds[0], x_grad_grad));
+      return ret;
+  });
 
 }  // namespace op
 }  // namespace mxnet
diff --git a/tests/python/unittest/test_higher_order_grad.py b/tests/python/unittest/test_higher_order_grad.py
index eeba4226dc36..4a906aa696a2 100644
--- a/tests/python/unittest/test_higher_order_grad.py
+++ b/tests/python/unittest/test_higher_order_grad.py
@@ -462,6 +462,46 @@ def grad_grad_op(x):
         check_second_order_unary(array, cbrt, grad_grad_op)
 
 
+@with_seed()
+def test_rsqrt():
+    def rsqrt(x):
+        return nd.rsqrt(x)
+
+    def grad_grad_op(x):
+        return 3/(4 * nd.sqrt(x**5))
+
+    sigma = random.randint(25, 100)
+    mu = random.randint(500, 1000)
+
+    for dim in range(1, 5):
+        shape = rand_shape_nd(dim)
+        array = random_arrays(shape)
+        array = sigma * array + mu
+        # Only positive numbers
+        assert((array > 0).all())
+        check_second_order_unary(array, rsqrt, grad_grad_op)
+
+
+@with_seed()
+def test_rcbrt():
+    def rcbrt(x):
+        return nd.rcbrt(x)
+
+    def grad_grad_op(x):
+        return 4/(9 * nd.cbrt(x**7))
+
+    sigma = random.randint(25, 100)
+    mu = random.randint(500, 1000)
+
+    for dim in range(1, 5):
+        shape = rand_shape_nd(dim)
+        array = random_arrays(shape)
+        array = sigma * array + mu
+        # Only positive numbers
+        assert((array > 0).all())
+        check_second_order_unary(array, rcbrt, grad_grad_op)
+
+
 def check_second_order_unary(x, op, grad_grad_op, rtol=None, atol=None):
     check_nth_order_unary(x, op, grad_grad_op, 2, rtol, atol)
 

From 0efc0ca4286aeb98c0b2245e0fe70b296434c75e Mon Sep 17 00:00:00 2001
From: Lin Yuan <apeforest@gmail.com>
Date: Tue, 14 Jan 2020 07:10:59 -0800
Subject: [PATCH 797/813] Fix rendering of ubuntu_setup.md codeblocks (#17294)

---
 .../src/pages/get_started/ubuntu_setup.md     | 200 ++++++++++--------
 1 file changed, 116 insertions(+), 84 deletions(-)

diff --git a/docs/static_site/src/pages/get_started/ubuntu_setup.md b/docs/static_site/src/pages/get_started/ubuntu_setup.md
index 35c5f009da4b..65e8a625e496 100644
--- a/docs/static_site/src/pages/get_started/ubuntu_setup.md
+++ b/docs/static_site/src/pages/get_started/ubuntu_setup.md
@@ -63,11 +63,11 @@ Then download [cuDNN 7.1.4](https://developer.nvidia.com/cudnn).
 Unzip the file and change to the cuDNN root directory. Move the header and libraries to your local CUDA Toolkit folder:
 
 ```bash
-    tar xvzf cudnn-9.2-linux-x64-v7.1
-    sudo cp -P cuda/include/cudnn.h /usr/local/cuda/include
-    sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda/lib64
-    sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
-    sudo ldconfig
+tar xvzf cudnn-9.2-linux-x64-v7.1
+sudo cp -P cuda/include/cudnn.h /usr/local/cuda/include
+sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda/lib64
+sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
+sudo ldconfig
 ```
 
 <hr>
@@ -79,9 +79,10 @@ It is recommended that you review the general [build from source](build_from_sou
 On Ubuntu versions 16.04 or later, you need the following dependencies:
 
 **Step 1:** Install prerequisite packages.
+
 ```bash
-    sudo apt-get update
-    sudo apt-get install -y build-essential git ninja-build ccache libopenblas-dev libopencv-dev cmake
+sudo apt-get update
+sudo apt-get install -y build-essential git ninja-build ccache libopenblas-dev libopencv-dev cmake
 ```
 
 Instead of `libopenblas-dev` you may also choose a different math library.
@@ -105,8 +106,8 @@ python3-pip`.
 Clone the repository:
 
 ```bash
-    git clone --recursive https://github.com/apache/incubator-mxnet.git mxnet
-    cd mxnet
+git clone --recursive https://github.com/apache/incubator-mxnet.git mxnet
+cd mxnet
 ```
 
 **Step 3:** Build MXNet core shared library.
@@ -114,17 +115,17 @@ Clone the repository:
 For a CPU-only build with OpenBLAS math library run:
 
 ```bash
-    rm -rf build
-    mkdir -p build && cd build
-    cmake -GNinja \
-        -DUSE_CUDA=OFF \
-        -DUSE_MKL_IF_AVAILABLE=OFF \
-        -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache \
-        -DCMAKE_C_COMPILER_LAUNCHER=ccache \
-        -DCMAKE_CXX_COMPILER_LAUNCHER=ccache \
-        -DCMAKE_BUILD_TYPE=Release \
-    ..
-    ninja
+rm -rf build
+mkdir -p build && cd build
+cmake -GNinja \
+    -DUSE_CUDA=OFF \
+    -DUSE_MKL_IF_AVAILABLE=OFF \
+    -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache \
+    -DCMAKE_C_COMPILER_LAUNCHER=ccache \
+    -DCMAKE_CXX_COMPILER_LAUNCHER=ccache \
+    -DCMAKE_BUILD_TYPE=Release \
+..
+ninja
 ```
 
 For a CPU-only build with MKL math library and MKL-DNN you need to make sure MKL
@@ -134,34 +135,34 @@ is installed according to
 respectively. Then run:
 
 ```bash
-    rm -rf build
-    mkdir -p build && cd build
-    cmake -GNinja \
-        -DUSE_CUDA=OFF \
-        -DUSE_MKL_IF_AVAILABLE=ON \
-        -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache \
-        -DCMAKE_C_COMPILER_LAUNCHER=ccache \
-        -DCMAKE_CXX_COMPILER_LAUNCHER=ccache \
-        -DCMAKE_BUILD_TYPE=Release \
-    ..
-    ninja
+rm -rf build
+mkdir -p build && cd build
+cmake -GNinja \
+    -DUSE_CUDA=OFF \
+    -DUSE_MKL_IF_AVAILABLE=ON \
+    -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache \
+    -DCMAKE_C_COMPILER_LAUNCHER=ccache \
+    -DCMAKE_CXX_COMPILER_LAUNCHER=ccache \
+    -DCMAKE_BUILD_TYPE=Release \
+..
+ninja
 ```
 
 For a GPU-enabled build make sure you have installed the
 [CUDA dependencies first](#cuda-dependencies)) and run:
 
 ```bash
-    rm -rf build
-    mkdir -p build && cd build
-    cmake -GNinja \
-        -DUSE_CUDA=ON \
-        -DUSE_MKL_IF_AVAILABLE=OFF \
-        -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache \
-        -DCMAKE_C_COMPILER_LAUNCHER=ccache \
-        -DCMAKE_CXX_COMPILER_LAUNCHER=ccache \
-        -DCMAKE_BUILD_TYPE=Release \
-    ..
-    ninja
+rm -rf build
+mkdir -p build && cd build
+cmake -GNinja \
+    -DUSE_CUDA=ON \
+    -DUSE_MKL_IF_AVAILABLE=OFF \
+    -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache \
+    -DCMAKE_C_COMPILER_LAUNCHER=ccache \
+    -DCMAKE_CXX_COMPILER_LAUNCHER=ccache \
+    -DCMAKE_BUILD_TYPE=Release \
+..
+ninja
 ```
 
 *Note* - You can explore and use more compilation options as they are delcared
@@ -177,7 +178,8 @@ After building the MXNet library, you may install language bindings.
 
 ## Installing Language Packages for MXNet
 
-After you have installed the MXNet core library. You may install MXNet interface packages for the programming language of your choice:
+After you have installed the MXNet core library. You may install MXNet interface packages for the programming language
+of your choice:
 - [Python](#install-mxnet-for-python)
 - [C++](#install-the-mxnet-package-for-c&plus;&plus;)
 - [Clojure](#install-the-mxnet-package-for-clojure)
@@ -194,15 +196,18 @@ After you have installed the MXNet core library. You may install MXNet interface
 To install the MXNet Python binding navigate to the root of the MXNet folder then run the following:
 
 ```bash
-$ cd python
-$ pip install --user -e .
+cd python
+pip install --user -e .
 ```
 
-Note that the `-e` flag is optional. It is equivalent to `--editable` and means that if you edit the source files, these changes will be reflected in the package installed.
+Note that the `-e` flag is optional. It is equivalent to `--editable` and means that if you edit the source files, these
+changes will be reflected in the package installed.
 
 #### Optional Python Packages
 
-You may optionally install ```graphviz``` library that is used for visualizing network graphs you build on MXNet. You may also install [Jupyter Notebook](http://jupyter.readthedocs.io/) which is used for running MXNet tutorials and examples.
+You may optionally install ```graphviz``` library that is used for visualizing network graphs you build on MXNet. You
+may also install [Jupyter Notebook](http://jupyter.readthedocs.io/) which is used for running MXNet tutorials and
+examples.
 
 ```bash
 pip install --user graphviz==0.8.4 jupyter
@@ -242,47 +247,58 @@ tar xvf julia-10.tar.gz
 ```
 
 Test Julia.
+
 ```bash
 cd julia-1.0.3/bin
 julia -e 'using InteractiveUtils; versioninfo()'
 ```
 
 If you're still getting the old version, remove it.
+
 ```bash
 sudo apt remove julia
 ```
 
 Update your PATH to have Julia's new location. Add this to your `.zshrc`, `.bashrc`, `.profile` or `.bash_profile`.
+
 ```bash
 export PATH=~/julia/julia-1.0.3/bin:$PATH
 ```
 
 Validate your PATH.
+
 ```bash
 echo $PATH
 ```
 
 Validate Julia works and is the expected version.
+
 ```bash
 julia -e 'using InteractiveUtils; versioninfo()'
 ```
 
 #### Setup Your MXNet-Julia Environment
 
-**For each of the following environment variables, add the commands to your `.zshrc`, `.bashrc`, `.profile` or `.bash_profile` to make them persist.**
+**For each of the following environment variables, add the commands to your `.zshrc`, `.bashrc`, `.profile`
+or `.bash_profile` to make them persist.**
 
 Create a `julia-depot` folder and environment variable.
+
 ```bash
 mkdir julia-depot
 export JULIA_DEPOT_PATH=$HOME/julia/julia-depot
 ```
 
-To use the Julia binding with an existing `libmxnet` installation, set the `MXNET_HOME` environment variable to the MXNet source root. For example:
+To use the Julia binding with an existing `libmxnet` installation, set the `MXNET_HOME` environment variable to the
+MXNet source root. For example:
+
 ```bash
 export MXNET_HOME=$HOME/incubator-mxnet
 ```
 
-Now set the `LD_LIBRARY_PATH` environment variable to where `libmxnet.so` is found. If you can't find it, you might have skipped the building MXNet step. Go back and [build MXNet](#build-the-shared-library) first. For example:
+Now set the `LD_LIBRARY_PATH` environment variable to where `libmxnet.so` is found. If you can't find it, you might
+have skipped the building MXNet step. Go back and [build MXNet](#build-the-shared-library) first. For example:
+
 ```bash
 export LD_LIBRARY_PATH=$HOME/incubator-mxnet/lib:$LD_LIBRARY_PATH
 ```
@@ -292,7 +308,8 @@ Verify the location of `libjemalloc.so` and set the `LD_PRELOAD` environment var
 export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libjemalloc.so:$LD_PRELOAD
 ```
 
-With all of these updates, here's an example of what you might want to have in your `.zshrc`, `.bashrc`, `.profile` or `.bash_profile`.
+With all of these updates, here's an example of what you might want to have in your `.zshrc`, `.bashrc`, `.profile`
+or `.bash_profile`.
 
 ```
 export PATH=$HOME/bin:$HOME/.local/bin:$HOME/julia/julia-1.0.3/bin:$PATH
@@ -310,33 +327,36 @@ julia --color=yes --project=./ -e \
 	   Pkg.develop(PackageSpec(name="MXNet", path = joinpath(ENV["MXNET_HOME"], "julia")))'
 ```
 
-For more details about installing and using MXNet with Julia, see the [MXNet Julia documentation]({{'/api/julia'|relative_url}}).
-<hr>
+For more details about installing and using MXNet with Julia, see the
+[MXNet Julia documentation]({{'/api/julia'|relative_url}}).
 
+<hr>
 
 ### Install the MXNet Package for Perl
 
-Before you build MXNet for Perl from source code, you must complete [building the shared library](#build-the-shared-library). After you build the shared library, run the following command from the MXNet source root directory to build the MXNet Perl package:
+Before you build MXNet for Perl from source code, you must complete [building the shared library](#build-the-shared-library).
+After you build the shared library, run the following command from the MXNet source root directory to build the MXNet
+Perl package:
 
 ```bash
-    sudo apt-get install libmouse-perl pdl cpanminus swig libgraphviz-perl
-    cpanm -q -L "${HOME}/perl5" Function::Parameters Hash::Ordered PDL::CCS
+sudo apt-get install libmouse-perl pdl cpanminus swig libgraphviz-perl
+cpanm -q -L "${HOME}/perl5" Function::Parameters Hash::Ordered PDL::CCS
 
-    MXNET_HOME=${PWD}
-    export LD_LIBRARY_PATH=${MXNET_HOME}/lib
-    export PERL5LIB=${HOME}/perl5/lib/perl5
+MXNET_HOME=${PWD}
+export LD_LIBRARY_PATH=${MXNET_HOME}/lib
+export PERL5LIB=${HOME}/perl5/lib/perl5
 
-    cd ${MXNET_HOME}/perl-package/AI-MXNetCAPI/
-    perl Makefile.PL INSTALL_BASE=${HOME}/perl5
-    make install
+cd ${MXNET_HOME}/perl-package/AI-MXNetCAPI/
+perl Makefile.PL INSTALL_BASE=${HOME}/perl5
+make install
 
-    cd ${MXNET_HOME}/perl-package/AI-NNVMCAPI/
-    perl Makefile.PL INSTALL_BASE=${HOME}/perl5
-    make install
+cd ${MXNET_HOME}/perl-package/AI-NNVMCAPI/
+perl Makefile.PL INSTALL_BASE=${HOME}/perl5
+make install
 
-    cd ${MXNET_HOME}/perl-package/AI-MXNet/
-    perl Makefile.PL INSTALL_BASE=${HOME}/perl5
-    make install
+cd ${MXNET_HOME}/perl-package/AI-MXNet/
+perl Makefile.PL INSTALL_BASE=${HOME}/perl5
+make install
 ```
 <hr>
 
@@ -370,43 +390,52 @@ Or you can go through a manual process described next.
 **Build the MXNet core shared library**
 
 **Step 1** Install build tools and git.
+
 ```bash
-$ sudo apt-get update
-$ sudo apt-get install -y build-essential git
+sudo apt-get update
+sudo apt-get install -y build-essential git
 ```
 
 **Step 2** Install OpenBLAS.
 
-*MXNet* uses [BLAS](https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) and [LAPACK](https://en.wikipedia.org/wiki/LAPACK) libraries for accelerated numerical computations on CPU machine. There are several flavors of BLAS/LAPACK libraries - [OpenBLAS](https://www.openblas.net/), [ATLAS](http://math-atlas.sourceforge.net/) and [MKL](https://software.intel.com/en-us/intel-mkl). In this step we install OpenBLAS. You can choose to install ATLAS or MKL.
+*MXNet* uses [BLAS](https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms)
+and [LAPACK](https://en.wikipedia.org/wiki/LAPACK) libraries for accelerated numerical computations
+on CPU machine. There are several flavors of BLAS/LAPACK libraries - [OpenBLAS](https://www.openblas.net/),
+[ATLAS](http://math-atlas.sourceforge.net/) and [MKL](https://software.intel.com/en-us/intel-mkl). In this step we
+install OpenBLAS. You can choose to install ATLAS or MKL.
+
 ```bash
-$ sudo apt-get install -y libopenblas-dev liblapack-dev
+sudo apt-get install -y libopenblas-dev liblapack-dev
 ```
 
 **Step 3** Install OpenCV.
 
 *MXNet* uses [OpenCV](https://opencv.org/) for efficient image loading and augmentation operations.
+
 ```bash
-$ sudo apt-get install -y libopencv-dev
+sudo apt-get install -y libopencv-dev
 ```
 
-**Step 4** Download MXNet sources and build MXNet core shared library. You can clone the repository as described in the following code block, or you may try the [download links](download) for your desired MXNet version.
+**Step 4** Download MXNet sources and build MXNet core shared library. You can clone the repository as described in the
+following code block, or you may try the [download links](download) for your desired MXNet version.
 
 ```bash
-$ git clone --recursive https://github.com/apache/incubator-mxnet
-$ cd incubator-mxnet
-$ echo "USE_OPENCV = 1" >> ./config.mk
-$ echo "USE_BLAS = openblas" >> ./config.mk
-$ make -j $(nproc)
+git clone --recursive https://github.com/apache/incubator-mxnet mxnet
+cd mxnet
+echo "USE_OPENCV = 1" >> ./config.mk
+echo "USE_BLAS = openblas" >> ./config.mk
+make -j $(nproc)
 ```
 
-*Note* - USE_OPENCV and USE_BLAS are make file flags to set compilation options to use OpenCV and BLAS library. You can explore and use more compilation options in `make/config.mk`.
+*Note* - USE_OPENCV and USE_BLAS are make file flags to set compilation options to use OpenCV and BLAS library. You can
+explore and use more compilation options in `make/config.mk`.
 
 <br/>
 
 **Step 5** Make and install the MXNet-R bindings.
 
 ```bash
-$ make rpkg
+make rpkg
 ```
 #### Verify MXNet-R Installation
 
@@ -442,7 +471,8 @@ To use the MXNet-Scala package, you can acquire the Maven package as a dependenc
 
 Further information is in the [MXNet-Scala Setup Instructions](scala_setup).
 
-If you use IntelliJ or a similar IDE, you may want to follow the [MXNet-Scala on IntelliJ tutorial]({{'/api/scala/docs/tutorials/mxnet_scala_on_intellij'|relative_url}}) instead.
+If you use IntelliJ or a similar IDE, you may want to follow the
+[MXNet-Scala on IntelliJ tutorial]({{'/api/scala/docs/tutorials/mxnet_scala_on_intellij'|relative_url}}) instead.
 <hr>
 
 ### Install the MXNet Package for Java
@@ -451,12 +481,14 @@ To use the MXNet-Java package, you can acquire the Maven package as a dependency
 
 Further information is in the [MXNet-Java Setup Instructions](java_setup).
 
-If you use IntelliJ or a similar IDE, you may want to follow the [MXNet-Java on IntelliJ tutorial]({{'/api/java/docs/tutorials/mxnet_java_on_intellij'|relative_url}}) instead.
+If you use IntelliJ or a similar IDE, you may want to follow the
+[MXNet-Java on IntelliJ tutorial]({{'/api/java/docs/tutorials/mxnet_java_on_intellij'|relative_url}}) instead.
 <hr>
 
 ## Contributions
 
-You are more than welcome to contribute easy installation scripts for other operating systems and programming languages. See the [community contributions page]({{'/community/contribute'|relative_url}}) for further information.
+You are more than welcome to contribute easy installation scripts for other operating systems and programming languages.
+See the [community contributions page]({{'/community/contribute'|relative_url}}) for further information.
 
 ## Next Steps
 

From 058de55d0c1fe4e44dd1cc6c91b6eff5b069bfb9 Mon Sep 17 00:00:00 2001
From: Talia <31782251+TEChopra1000@users.noreply.github.com>
Date: Tue, 14 Jan 2020 07:23:45 -0800
Subject: [PATCH 798/813] Fix image display in python autograd tutorial
 (#17243)

When being build, auto-grad links are not behaving as expected. Adding full links to fix images on this page.
---
 .../python/tutorials/packages/autograd/index.md           | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/docs/python_docs/python/tutorials/packages/autograd/index.md b/docs/python_docs/python/tutorials/packages/autograd/index.md
index fa321f72b839..155fb4ff4118 100644
--- a/docs/python_docs/python/tutorials/packages/autograd/index.md
+++ b/docs/python_docs/python/tutorials/packages/autograd/index.md
@@ -23,7 +23,7 @@
 
 Gradients are fundamental to the process of training neural networks, and tell us how to change the parameters of the network to improve its performance.
 
-![auto-gradient](/api/python/docs/_static/autograd_images/autograd_gradient.png)
+![auto-gradient](http://mxnet.incubator.apache.org/api/python/docs/_static/autograd_images/autograd_gradient.png)
 
 ### Long Answer:
 
@@ -39,7 +39,7 @@ Assuming we've calculated the gradient of each parameter with respect to the los
 
 We differentiate. [MXNet Gluon](/api/python/docs/tutorials/packages/gluon/index.html) uses Reverse Mode Automatic Differentiation (`autograd`) to backprogate gradients from the loss metric to the network parameters.
 
-![forward-backward](/api/python/docs/_static/autograd_images/autograd_forward_backward.png)
+![forward-backward](http://mxnet.incubator.apache.org/api/python/docs/_static/autograd_images/autograd_forward_backward.png)
 
 ### Long Answer:
 
@@ -217,7 +217,7 @@ def f(x):
 
 We can plot the resultant function for $x$ between 0 and 1, and we should recognise certain functions in segments of $x$. Starting with a quadratic curve from 0 to 1/2, we have a cubic curve from 1/2 to 2/3, a quartic from 2/3 to 3/4 and finally a flatline.
 
-![control-flow](/api/python/docs/_static/autograd_images/autograd_control_flow.png)
+![control-flow](https://mxnet.incubator.apache.org/api/python/docs/_static/autograd_images/autograd_control_flow.png)
 
 Using `autograd`, let's now find the gradient of this arbritrary function. We don't have a vectorized function in this case, because of the control flow, so let's also create a function to calculate the gradient using `autograd`.
 
@@ -234,7 +234,7 @@ grads = [get_grad(f, x).asscalar() for x in xs]
 print(grads)
 ```
 
-![flow-grad](/api/python/docs/_static/autograd_images/autograd_control_flow_grad.png)
+![flow-grad](https://mxnet.incubator.apache.org/api/python/docs/_static/autograd_images/autograd_control_flow_grad.png)
 
 We can calculate the gradients by hand in this situation (since it's a toy example), and for the four segments discussed before we'd expect $2x$, $3x^2$, $4x^3$ and 0. As a spot check, for $x=0.6$ the hand calculated gradient would be $3x^2=1.08$, which equals `1.08` as computed by `autograd`.
 

From 397193801381f6ba3a04f6c296485c07237b649d Mon Sep 17 00:00:00 2001
From: taoli <litaotju@live.cn>
Date: Wed, 15 Jan 2020 01:57:26 +0800
Subject: [PATCH 799/813] Fix #17267, add expected and got datatype for concat
 error msgs (#17271)

* Fix #17267, add expected and got datatype when non-uniform dtype fount in concat

* Add op name and input index to concat type error msg

* Fix lint error
---
 src/operator/nn/concat.cc | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/src/operator/nn/concat.cc b/src/operator/nn/concat.cc
index 1eeef7db5cb5..a9e744cc58d1 100644
--- a/src/operator/nn/concat.cc
+++ b/src/operator/nn/concat.cc
@@ -145,13 +145,15 @@ bool ConcatType(const nnvm::NodeAttrs& attrs,
   int dtype = -1;
 
   // checks uniformity of input
-  for (int i : *in_type) {
+  for (size_t i =0; i < in_type->size(); ++i) {
     if (dtype == -1) {
-      dtype = i;
+      dtype = in_type->at(i);
     } else {
-      CHECK(i == dtype ||
-          i == -1) <<
-          "Non-uniform data type in Concat";
+      CHECK(in_type->at(i) == dtype || in_type->at(i) == -1)
+          << "Non-uniform data type in "  << attrs.op->name
+          << ", expected data type " << mxnet::op::type_string(dtype)
+          << ", got data type " << mxnet::op::type_string(in_type->at(i))
+          << " for input " << i;
     }
   }
 

From 9f2e73f39b405ec24cab254076f964452382c224 Mon Sep 17 00:00:00 2001
From: Leonard Lausen <lausen@amazon.com>
Date: Tue, 14 Jan 2020 18:18:11 +0000
Subject: [PATCH 800/813] Support R-package with cmake build and fix
 installation instructions (#17228)

* Support R-package with cmake build and fix installation instructions

* Fix typo

* Fix callback.R

* Clarify creation of personal R library

* Fix generation of R-package documentation

* Remove unused USE_BLAS variable on CI for make rpkg

* Add cmake build command for get_started/linux/r/*.md pages

* Fix zzz.R swallowing error messages

* Fix R-package/src/Makevars for OpenCV dependency

See https://github.com/apache/incubator-mxnet/issues/17282
---
 Makefile                                      | 30 --------
 R-package/Makefile                            | 33 +++++++++
 R-package/R/callback.R                        |  1 +
 R-package/R/zzz.R                             | 14 +++-
 R-package/src/Makevars                        |  5 +-
 R-package/src/Makevars.win                    |  1 -
 ci/docker/runtime_functions.sh                | 13 ++--
 .../src/_includes/get_started/linux/r/cpu.md  |  5 +-
 .../src/_includes/get_started/linux/r/gpu.md  |  5 +-
 .../src/pages/get_started/osx_setup.md        |  2 +-
 .../src/pages/get_started/ubuntu_setup.md     | 71 ++++++-------------
 setup-utils/install-mxnet-ubuntu-r.sh         |  2 +-
 12 files changed, 82 insertions(+), 100 deletions(-)
 create mode 100644 R-package/Makefile

diff --git a/Makefile b/Makefile
index 44c719f6d4d9..16d7d2393736 100644
--- a/Makefile
+++ b/Makefile
@@ -683,36 +683,6 @@ cython3:
 cyclean:
 	rm -rf python/mxnet/*/*.so python/mxnet/*/*.cpp
 
-# R related shortcuts
-rcpplint:
-	3rdparty/dmlc-core/scripts/lint.py mxnet-rcpp ${LINT_LANG} R-package/src
-
-rpkg:
-	mkdir -p R-package/inst/libs
-	cp src/io/image_recordio.h R-package/src
-	cp -rf lib/libmxnet.so R-package/inst/libs
-
-	if [ -e "lib/libtvm_runtime.so" ]; then \
-		cp -rf lib/libtvm_runtime.so R-package/inst/libs; \
-	fi
-
-	mkdir -p R-package/inst/include
-	cp -rl include/* R-package/inst/include
-	Rscript -e "if(!require(devtools)){install.packages('devtools', repo = 'https://cloud.r-project.org/')}"
-	Rscript -e "if(!require(roxygen2)||packageVersion('roxygen2') < '6.1.1'){install.packages('roxygen2', repo = 'https://cloud.r-project.org/')}"
-	Rscript -e "library(devtools); library(methods); options(repos=c(CRAN='https://cloud.r-project.org/')); install_deps(pkg='R-package', dependencies = TRUE)"
-	cp R-package/dummy.NAMESPACE R-package/NAMESPACE
-	echo "import(Rcpp)" >> R-package/NAMESPACE
-	R CMD INSTALL R-package
-	Rscript -e "require(mxnet); mxnet:::mxnet.export('R-package'); warnings()"
-	rm R-package/NAMESPACE
-	Rscript -e "devtools::document('R-package');warnings()"
-	R CMD INSTALL R-package
-
-rpkgtest:
-	Rscript -e 'require(testthat);res<-test_dir("R-package/tests/testthat");if(!testthat:::all_passed(res)){stop("Test failures", call. = FALSE)}'
-	Rscript -e 'res<-covr:::package_coverage("R-package");fileConn<-file(paste("r-package_coverage_",toString(runif(1)),".json"));writeLines(covr:::to_codecov(res), fileConn);close(fileConn)'
-
 scalaclean:
 	(cd $(ROOTDIR)/scala-package && mvn clean)
 
diff --git a/R-package/Makefile b/R-package/Makefile
new file mode 100644
index 000000000000..68249033f595
--- /dev/null
+++ b/R-package/Makefile
@@ -0,0 +1,33 @@
+rcpplint:
+	3rdparty/dmlc-core/scripts/lint.py mxnet-rcpp ${LINT_LANG} R-package/src
+
+rpkg:
+	mkdir -p R-package/inst/libs
+	cp src/io/image_recordio.h R-package/src
+	if [ -d "lib" ]; then \
+		cp -rf lib/libmxnet.so R-package/inst/libs; \
+		if [ -e "lib/libtvm_runtime.so" ]; then \
+			cp -rf lib/libtvm_runtime.so R-package/inst/libs; \
+		fi; \
+	else \
+		cp -rf build/libmxnet.so R-package/inst/libs; \
+		if [ -e "build/libtvm_runtime.so" ]; then \
+			cp -rf build/libtvm_runtime.so R-package/inst/libs; \
+		fi; \
+	fi
+
+	mkdir -p R-package/inst/include
+	cp -rl include/* R-package/inst/include
+	Rscript -e "if(!require(devtools)){install.packages('devtools', repo = 'https://cloud.r-project.org/')}"
+	Rscript -e "if(!require(roxygen2)||packageVersion('roxygen2') < '6.1.1'){install.packages('roxygen2', repo = 'https://cloud.r-project.org/')}"
+	Rscript -e "library(devtools); library(methods); options(repos=c(CRAN='https://cloud.r-project.org/')); install_deps(pkg='R-package', dependencies = TRUE)"
+	cp R-package/dummy.NAMESPACE R-package/NAMESPACE  # NAMESPACE will be replaced by devtools::document later
+	echo "import(Rcpp)" >> R-package/NAMESPACE
+	R CMD INSTALL R-package
+	Rscript -e "require(mxnet); mxnet:::mxnet.export('R-package'); warnings()"
+	Rscript -e "devtools::document('R-package');warnings()"
+	R CMD INSTALL R-package
+
+rpkgtest:
+	Rscript -e 'require(testthat);res<-test_dir("R-package/tests/testthat");if(!testthat:::all_passed(res)){stop("Test failures", call. = FALSE)}'
+	Rscript -e 'res<-covr:::package_coverage("R-package");fileConn<-file(paste("r-package_coverage_",toString(runif(1)),".json"));writeLines(covr:::to_codecov(res), fileConn);close(fileConn)'
diff --git a/R-package/R/callback.R b/R-package/R/callback.R
index 014f926bd9f5..04f31b727ed2 100644
--- a/R-package/R/callback.R
+++ b/R-package/R/callback.R
@@ -16,6 +16,7 @@
 # under the License.
 
 #' @export mx.metric.logger
+library(methods)
 mx.metric.logger <- setRefClass("mx.metric.logger", fields = list(train = "numeric", eval="numeric"))
 
 #' Log training metric each period
diff --git a/R-package/R/zzz.R b/R-package/R/zzz.R
index a4f03ace0cc1..1b185978b8e2 100644
--- a/R-package/R/zzz.R
+++ b/R-package/R/zzz.R
@@ -34,8 +34,18 @@ NULL
 .onLoad <- function(libname, pkgname) {
   # Require methods for older versions of R
   require(methods)
-  tryCatch(library.dynam("libmxnet", pkgname, libname, local=FALSE), error = function(e) { print('Loading local: inst/libs/libmxnet.so'); dyn.load("R-package/inst/libs/libmxnet.so", local=FALSE) })
-  tryCatch(library.dynam("mxnet", pkgname, libname), error = function(e) { print('Loading local: src/mxnet.so'); dyn.load("R-package/src/mxnet.so") })
+  tryCatch(library.dynam("libmxnet", pkgname, libname, local=FALSE), error = function(e) {
+    print('Loading libmxnet.so failed. Error:')
+    print(e)
+    print('Falling back to loading local: inst/libs/libmxnet.so')
+    dyn.load("R-package/inst/libs/libmxnet.so", local=FALSE)
+  })
+  tryCatch(library.dynam("mxnet", pkgname, libname), error = function(e) {
+    print('Loading mxnet.so failed. Error:')
+    print(e)
+    print('Falling back to loading local: src/mxnet.so')
+    dyn.load("R-package/src/mxnet.so")
+  })
   loadModule("mxnet", TRUE)
   init.symbol.methods()
   init.context.default()
diff --git a/R-package/src/Makevars b/R-package/src/Makevars
index c089c093389b..13df4d1db8eb 100644
--- a/R-package/src/Makevars
+++ b/R-package/src/Makevars
@@ -1,3 +1,4 @@
 CXX_STD = CXX11
-PKG_CPPFLAGS = -I../inst/include
-PKG_LIBS =  $(LAPACK_LIBS) $(BLAS_LIBS)
+PKG_LIBS = $(LAPACK_LIBS) $(BLAS_LIBS) `pkg-config --libs opencv`
+PKG_CFLAGS = `pkg-config --cflags opencv`
+PKG_CPPFLAGS = -I../inst/include `pkg-config --cflags opencv` `Rscript -e 'Rcpp:::CxxFlags()'`
diff --git a/R-package/src/Makevars.win b/R-package/src/Makevars.win
index c75a7583d1da..7be3d8f935b1 100644
--- a/R-package/src/Makevars.win
+++ b/R-package/src/Makevars.win
@@ -1,3 +1,2 @@
-
 PKG_CPPFLAGS = -I../inst/include
 PKG_LIBS =  $(LAPACK_LIBS) $(BLAS_LIBS) -L../inst/libs/x64/ -llibmxnet
diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index b4b0baac2600..381cb567fbc0 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -1223,12 +1223,11 @@ unittest_ubuntu_cpu_R() {
     build_ccache_wrappers
     echo  "MAKEFLAGS = -j"$(nproc) > ~/.R/Makevars
     # make -j not supported
-    make rpkg                           \
-        USE_BLAS=openblas               \
+    make -f R-package/Makefile rpkg \
         R_LIBS=/tmp/r-site-library
 
     R CMD INSTALL --library=/tmp/r-site-library R-package
-    make rpkgtest R_LIBS=/tmp/r-site-library
+    make -f R-package/Makefile rpkgtest R_LIBS=/tmp/r-site-library
 }
 
 unittest_ubuntu_minimal_R() {
@@ -1239,8 +1238,7 @@ unittest_ubuntu_minimal_R() {
     build_ccache_wrappers
     echo  "MAKEFLAGS = -j"$(nproc) > ~/.R/Makevars
     # make -j not supported
-    make rpkg                           \
-        USE_BLAS=openblas               \
+    make -f R-package/Makefile rpkg \
         R_LIBS=/tmp/r-site-library
 
     R CMD INSTALL --library=/tmp/r-site-library R-package
@@ -1269,11 +1267,10 @@ unittest_ubuntu_gpu_R() {
     build_ccache_wrappers
     echo  "MAKEFLAGS = -j"$(nproc) > ~/.R/Makevars
     # make -j not supported
-    make rpkg                           \
-        USE_BLAS=openblas               \
+    make -f R-package/Makefile rpkg \
         R_LIBS=/tmp/r-site-library
     R CMD INSTALL --library=/tmp/r-site-library R-package
-    make rpkgtest R_LIBS=/tmp/r-site-library R_GPU_ENABLE=1
+    make -f R-package/Makefile rpkgtest R_LIBS=/tmp/r-site-library R_GPU_ENABLE=1
 }
 
 unittest_ubuntu_cpu_julia() {
diff --git a/docs/static_site/src/_includes/get_started/linux/r/cpu.md b/docs/static_site/src/_includes/get_started/linux/r/cpu.md
index 88ca5dd39933..1077362c9a65 100644
--- a/docs/static_site/src/_includes/get_started/linux/r/cpu.md
+++ b/docs/static_site/src/_includes/get_started/linux/r/cpu.md
@@ -5,5 +5,6 @@ After you have setup R v3.4.4+ and MXNet, you can build and install the MXNet R
 
 {% highlight bash %}
 $ cd incubator-mxnet
-$ make rpkg
-{% endhighlight %}
\ No newline at end of file
+$ mkdir build; cd build; cmake -DUSE_CUDA=OFF ..; make -j $(nproc); cd ..
+$ make -f R-package/Makefile rpkg
+{% endhighlight %}
diff --git a/docs/static_site/src/_includes/get_started/linux/r/gpu.md b/docs/static_site/src/_includes/get_started/linux/r/gpu.md
index 16fbfd09d4d4..1ef16df25da5 100644
--- a/docs/static_site/src/_includes/get_started/linux/r/gpu.md
+++ b/docs/static_site/src/_includes/get_started/linux/r/gpu.md
@@ -10,7 +10,8 @@ MXNet as follows:
 
 {% highlight bash %}
 $ cd incubator-mxnet
-$ make rpkg
+$ mkdir build; cd build; cmake ..; make -j $(nproc); cd ..
+$ make -f R-package/Makefile rpkg
 {% endhighlight %}
 
-{% include /get_started/gpu_snippet.md %}
\ No newline at end of file
+{% include /get_started/gpu_snippet.md %}
diff --git a/docs/static_site/src/pages/get_started/osx_setup.md b/docs/static_site/src/pages/get_started/osx_setup.md
index 369edb36c148..3ba30300fb3e 100644
--- a/docs/static_site/src/pages/get_started/osx_setup.md
+++ b/docs/static_site/src/pages/get_started/osx_setup.md
@@ -202,7 +202,7 @@ Run the following commands to install the MXNet dependencies and build the MXNet
     cd R-package
     Rscript -e "library(devtools); library(methods); options(repos=c(CRAN='https://cran.rstudio.com')); install_deps(dependencies = TRUE)"
     cd ..
-    make rpkg
+    make -f R-package/Makefile rpkg
 ```
 
 ## Install the MXNet Package for Julia
diff --git a/docs/static_site/src/pages/get_started/ubuntu_setup.md b/docs/static_site/src/pages/get_started/ubuntu_setup.md
index 65e8a625e496..de45e80feb56 100644
--- a/docs/static_site/src/pages/get_started/ubuntu_setup.md
+++ b/docs/static_site/src/pages/get_started/ubuntu_setup.md
@@ -363,23 +363,9 @@ make install
 
 ### Install the MXNet Package for R
 
-Building *MXNet* from source is a 2 step process.
-1. Build the *MXNet* core shared library, `libmxnet.so`, from source.
-2. Build the R bindings.
-
-#### Quick MXNet-R Installation
-You can quickly build MXNet-R with the following two scripts found in the `/docs/install` folder:
-
-```bash
-git clone --recursive https://github.com/apache/incubator-mxnet.git mxnet
-cd mxnet/docs/install
-./install_mxnet_ubuntu_python.sh
-./install_mxnet_ubuntu_r.sh
-```
-
-Or you can go through a manual process described next.
-
-#### Manual MXNet-R Installation
+Before you build MXNet for R from source code, you must complete
+[building the shared library](#build-the-shared-library).
+source root directory to build the MXNet Perl package:
 
 **Minimum Requirements**
 1. [GCC 4.8](https://gcc.gnu.org/gcc-4.8/) or later to compile C++ 11.
@@ -389,60 +375,43 @@ Or you can go through a manual process described next.
 
 **Build the MXNet core shared library**
 
-**Step 1** Install build tools and git.
+**Step 1** Install R, cran-devtools and dependencies
+
+To install R and the devtools, run
 
 ```bash
 sudo apt-get update
-sudo apt-get install -y build-essential git
+sudo apt-get install -y r-base-core r-cran-devtools libcairo2-dev libxml2-dev
 ```
 
-**Step 2** Install OpenBLAS.
+`libxml2-dev` is required for the `roxygen2` dependency and `libcairo2-dev` is
+required for the suggested `imager` dependency.
 
-*MXNet* uses [BLAS](https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms)
-and [LAPACK](https://en.wikipedia.org/wiki/LAPACK) libraries for accelerated numerical computations
-on CPU machine. There are several flavors of BLAS/LAPACK libraries - [OpenBLAS](https://www.openblas.net/),
-[ATLAS](http://math-atlas.sourceforge.net/) and [MKL](https://software.intel.com/en-us/intel-mkl). In this step we
-install OpenBLAS. You can choose to install ATLAS or MKL.
+To generate documentation, it is also required to install `roxygen2`.
 
 ```bash
-sudo apt-get install -y libopenblas-dev liblapack-dev
+R
+> install.packages("roxygen2")
+> Would you like to use a personal library instead?  (y/n) y
+> Would you like to create a personal library ... to install packages into?  (y/n) y
 ```
 
-**Step 3** Install OpenCV.
+Note: To successfully complete the next step, you need a personal R library. If
+you were able to run `install.packages("roxygen2")` above, you either had
+already, or you have successfully created a personal library just now.
 
-*MXNet* uses [OpenCV](https://opencv.org/) for efficient image loading and augmentation operations.
+**Step 2** Make and install the MXNet-R bindings.
 
 ```bash
-sudo apt-get install -y libopencv-dev
+make -f R-package/Makefile rpkg
 ```
 
-**Step 4** Download MXNet sources and build MXNet core shared library. You can clone the repository as described in the
-following code block, or you may try the [download links](download) for your desired MXNet version.
-
-```bash
-git clone --recursive https://github.com/apache/incubator-mxnet mxnet
-cd mxnet
-echo "USE_OPENCV = 1" >> ./config.mk
-echo "USE_BLAS = openblas" >> ./config.mk
-make -j $(nproc)
-```
-
-*Note* - USE_OPENCV and USE_BLAS are make file flags to set compilation options to use OpenCV and BLAS library. You can
-explore and use more compilation options in `make/config.mk`.
-
-<br/>
-
-**Step 5** Make and install the MXNet-R bindings.
-
-```bash
-make rpkg
-```
 #### Verify MXNet-R Installation
 
 You can verify your MXNet-R installation as follows:
 
 ```bash
-sudo -i R
+R
 ```
 
 At the R prompt enter the following:
diff --git a/setup-utils/install-mxnet-ubuntu-r.sh b/setup-utils/install-mxnet-ubuntu-r.sh
index ca46d7b37016..a07d9d8ef10e 100644
--- a/setup-utils/install-mxnet-ubuntu-r.sh
+++ b/setup-utils/install-mxnet-ubuntu-r.sh
@@ -59,7 +59,7 @@ sudo Rscript -e "library(devtools); library(methods); options(repos=c(CRAN='http
 cd ..
 
 echo "Compiling R package. This can take few minutes..."
-sudo make rpkg
+sudo make -f R-package/Makefile rpkg
 
 echo "Installing R package..."
 sudo R CMD INSTALL mxnet_current_r.tar.gz

From 71e70f22a4d00f7028cac01c6ec347698c5c3939 Mon Sep 17 00:00:00 2001
From: Rohit Kumar Srivastava <srivastava.141@osu.edu>
Date: Tue, 14 Jan 2020 13:11:21 -0800
Subject: [PATCH 801/813] adding asnumpy() to output of gather(implicitly
 called) to fix gather test in large vector and tensor tests (#17290)

---
 tests/nightly/test_large_array.py  | 4 ++--
 tests/nightly/test_large_vector.py | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/tests/nightly/test_large_array.py b/tests/nightly/test_large_array.py
index 2c0841bf1ff8..a0d893d601b3 100644
--- a/tests/nightly/test_large_array.py
+++ b/tests/nightly/test_large_array.py
@@ -1672,10 +1672,10 @@ def test_gather():
     idx = mx.nd.random.randint(0, LARGE_X, SMALL_X)
     # Calls gather_nd internally
     tmp = arr[idx]
-    assert np.sum(tmp[0] == 1) == SMALL_Y
+    assert np.sum(tmp[0].asnumpy() == 1) == SMALL_Y
     # Calls gather_nd internally
     arr[idx] += 1
-    assert np.sum(arr[idx[0]] == 2) == SMALL_Y
+    assert np.sum(arr[idx[0]].asnumpy() == 2) == SMALL_Y
 
 
 def test_binary_broadcast():
diff --git a/tests/nightly/test_large_vector.py b/tests/nightly/test_large_vector.py
index 8f01372fcf19..bc87fec33e79 100644
--- a/tests/nightly/test_large_vector.py
+++ b/tests/nightly/test_large_vector.py
@@ -1049,10 +1049,10 @@ def test_gather():
     idx = mx.nd.random.randint(0, LARGE_X, 10, dtype=np.int64)
     # Calls gather_nd internally
     tmp = arr[idx]
-    assert np.sum(tmp == 1) == 10
+    assert np.sum(tmp.asnumpy() == 1) == 10
     # Calls gather_nd internally
     arr[idx] += 1
-    assert np.sum(arr[idx] == 2) == 10
+    assert np.sum(arr[idx].asnumpy() == 2) == 10
 
 
 def test_infer_shape():

From 05a0e5b15300198133ea3cb4058219f57c929db1 Mon Sep 17 00:00:00 2001
From: dithyrambe <dithyrambe@outlook.fr>
Date: Wed, 15 Jan 2020 03:21:58 +0100
Subject: [PATCH 802/813] Fix CosineEmbeddingLoss in when symbol API is used
 (#17308)

* Fix CosineEmbeddingLoss in when symbol API is used

Fixes #17275

* Update CONTRIBUTORS.md
---
 CONTRIBUTORS.md            | 1 +
 python/mxnet/gluon/loss.py | 6 +++---
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md
index 31019e6355ab..31400aed651c 100644
--- a/CONTRIBUTORS.md
+++ b/CONTRIBUTORS.md
@@ -247,6 +247,7 @@ List of Contributors
 * [Disi A](https://github.com/adis300)
 * [Vandana Kannan](https://github.com/vandanavk)
 * [Guanxin Qiao](https://github.com/guanxinq)
+* [dithyrambe](https://github.com/dithyrambe)
 
 Label Bot
 ---------
diff --git a/python/mxnet/gluon/loss.py b/python/mxnet/gluon/loss.py
index 40fdaa51573e..dac9a3215aa7 100644
--- a/python/mxnet/gluon/loss.py
+++ b/python/mxnet/gluon/loss.py
@@ -922,9 +922,9 @@ def hybrid_forward(self, F, input1, input2, label, sample_weight=None):
 
     def _cosine_similarity(self, F, x, y, axis=-1):
         # Calculates the cosine similarity between 2 vectors
-        x_norm = F.norm(x, axis=axis).reshape(-1, 1)
-        y_norm = F.norm(y, axis=axis).reshape(-1, 1)
-        x_dot_y = F.sum(x * y, axis=axis).reshape(-1, 1)
+        x_norm = F.norm(x, axis=axis).reshape((-1, 1))
+        y_norm = F.norm(y, axis=axis).reshape((-1, 1))
+        x_dot_y = F.sum(x * y, axis=axis).reshape((-1, 1))
         if F is ndarray:
             eps_arr = F.array([1e-12])
         else:

From e58623ce34bee79d4cde1c83ea2140c313f6a93a Mon Sep 17 00:00:00 2001
From: Minghao Liu <40382964+Tommliu@users.noreply.github.com>
Date: Wed, 15 Jan 2020 17:11:39 +0800
Subject: [PATCH 803/813] all/any_fix (#17314)

---
 python/mxnet/numpy/multiarray.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index c92489fa6e9a..05ae9b297a44 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -986,10 +986,10 @@ def T(self):
     # pylint: enable= invalid-name, undefined-variable
 
     def all(self, axis=None, out=None, keepdims=False):
-        raise NotImplementedError
+        return _mx_nd_np.all(self, axis=axis, keepdims=keepdims, out=out)
 
     def any(self, axis=None, out=None, keepdims=False):
-        raise NotImplementedError
+        return _mx_nd_np.any(self, axis=axis, keepdims=keepdims, out=out)
 
     def as_nd_ndarray(self):
         """Convert mxnet.numpy.ndarray to mxnet.ndarray.NDArray to use its fluent methods."""

From be9e17e02c3d213ab211aaee28197c2a23f7534d Mon Sep 17 00:00:00 2001
From: kshitij12345 <kshitijkalambarkar@gmail.com>
Date: Thu, 16 Jan 2020 03:44:36 +0530
Subject: [PATCH 804/813] Fixes #17304 Flaky Test ->
 test_higher_order_grad.test_tanh (#17321)

* use failed seed and verify first order

* replace grad_op with equivalent expression

* remove fixed seed for tanh

* add relax tolerance for tanh first order
---
 tests/python/unittest/test_higher_order_grad.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tests/python/unittest/test_higher_order_grad.py b/tests/python/unittest/test_higher_order_grad.py
index 4a906aa696a2..1f0cd0741977 100644
--- a/tests/python/unittest/test_higher_order_grad.py
+++ b/tests/python/unittest/test_higher_order_grad.py
@@ -121,7 +121,7 @@ def tanh(x):
         return nd.tanh(x)
 
     def grad_op(x):
-        return 1 / nd.cosh(x)**2
+        return 1 - tanh(x)**2
 
     def grad_grad_op(x):
         return -2 * tanh(x) * grad_op(x)
@@ -129,6 +129,7 @@ def grad_grad_op(x):
     for dim in range(1, 5):
         shape = rand_shape_nd(dim)
         array = random_arrays(shape)
+        check_nth_order_unary(array, tanh, grad_op, 1, rtol=1e-6, atol=1e-6)
         check_second_order_unary(
             array, tanh, grad_grad_op, rtol=1e-6, atol=1e-6)
 

From bd67723da96e6d36e72c9a42535a4fe68f234a71 Mon Sep 17 00:00:00 2001
From: Przemyslaw Tredak <ptredak@nvidia.com>
Date: Wed, 15 Jan 2020 15:20:14 -0800
Subject: [PATCH 805/813] Fix operators lying about their number of inputs
 (#17049)

* Add a check for number of inputs

* Fix num inputs for backward_Deconvolution

* Fix number of inputs to backward ROIAlign

* Fix number of inputs to backward_SoftmaxOutput

* Fix more operators lying about their number of inputs

* Fix input number of backward NMS

* Fixes

* Fix dropout, RNN and upsampling backward number of inputs

* Fix LeakyRelu number of inputs

* Actually fix LeakyRelu

* Fix pooling and concat

* Fix Concat (attempt 2)

* Fix from review

* Incorporate Dick's changes

* Add guard to MakeNonlossGradNode

* Fix

* Fix backward of SoftmaxActivation

* Fix backward of np_prod and norm
---
 src/operator/contrib/bilinear_resize-inl.h    |  9 ----
 src/operator/contrib/bilinear_resize.cc       |  2 +-
 src/operator/contrib/bounding_box.cc          |  2 +-
 src/operator/contrib/roi_align.cc             |  1 +
 src/operator/custom/custom.cc                 |  2 +-
 src/operator/image/image_random.cc            |  2 +-
 src/operator/leaky_relu.cc                    | 13 +++++
 src/operator/nn/batch_norm.cc                 |  1 +
 src/operator/nn/concat.cc                     |  8 +++
 src/operator/nn/convolution.cc                |  4 ++
 src/operator/nn/ctc_loss.cc                   |  2 +-
 src/operator/nn/deconvolution.cc              |  4 ++
 src/operator/nn/lrn.cc                        |  1 +
 src/operator/nn/pooling.cc                    |  5 ++
 src/operator/nn/softmax_activation.cc         |  1 +
 src/operator/nn/upsampling.cc                 |  8 +++
 .../numpy/np_broadcast_reduce_op_value.cc     |  2 +-
 src/operator/operator_common.h                | 52 +++++++++++++------
 src/operator/rnn.cc                           | 14 +++++
 src/operator/softmax_output.cc                |  1 +
 .../tensor/broadcast_reduce_norm_value.cc     |  1 +
 21 files changed, 105 insertions(+), 30 deletions(-)

diff --git a/src/operator/contrib/bilinear_resize-inl.h b/src/operator/contrib/bilinear_resize-inl.h
index 2167f2558a05..0db9494748a0 100644
--- a/src/operator/contrib/bilinear_resize-inl.h
+++ b/src/operator/contrib/bilinear_resize-inl.h
@@ -328,15 +328,6 @@ inline uint16_t BilinearSampleOpNumInputs(const NodeAttrs& attrs) {
   }
 }
 
-inline uint16_t BilinearSampleOpNumBackwardInputs(const NodeAttrs& attrs) {
-  auto& param = nnvm::get<BilinearSampleParam>(attrs.parsed);
-  if (param.mode == bilinear_resize::like) {
-    return 3;
-  } else {
-    return 1;
-  }
-}
-
 inline uint16_t BilinearSampleOpNumBackwardOutputs(const NodeAttrs& attrs) {
   auto& param = nnvm::get<BilinearSampleParam>(attrs.parsed);
   if (param.mode == bilinear_resize::like) {
diff --git a/src/operator/contrib/bilinear_resize.cc b/src/operator/contrib/bilinear_resize.cc
index 0351e479290d..399a5a79bd56 100644
--- a/src/operator/contrib/bilinear_resize.cc
+++ b/src/operator/contrib/bilinear_resize.cc
@@ -232,7 +232,7 @@ for more details.
 
 NNVM_REGISTER_OP(_backward_contrib_BilinearResize2D)
 .set_attr_parser(ParamParser<BilinearSampleParam>)
-.set_num_inputs(BilinearSampleOpNumBackwardInputs)
+.set_num_inputs(1)
 .set_num_outputs(BilinearSampleOpNumBackwardOutputs)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
 .set_attr<FCompute>("FCompute<cpu>", BilinearSampleOpBackward<cpu>);
diff --git a/src/operator/contrib/bounding_box.cc b/src/operator/contrib/bounding_box.cc
index 8b1d53506c47..906daa480786 100644
--- a/src/operator/contrib/bounding_box.cc
+++ b/src/operator/contrib/bounding_box.cc
@@ -110,7 +110,7 @@ Examples::
 .add_arguments(BoxNMSParam::__FIELDS__());
 
 NNVM_REGISTER_OP(_backward_contrib_box_nms)
-.set_num_inputs(3)
+.set_num_inputs(4)
 .set_num_outputs(1)
 .set_attr_parser(ParamParser<BoxNMSParam>)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
diff --git a/src/operator/contrib/roi_align.cc b/src/operator/contrib/roi_align.cc
index eeb9f622c2be..38b889b587c1 100644
--- a/src/operator/contrib/roi_align.cc
+++ b/src/operator/contrib/roi_align.cc
@@ -621,6 +621,7 @@ He, Kaiming, et al. "Mask R-CNN." ICCV, 2017
 
 
 NNVM_REGISTER_OP(_backward_ROIAlign)
+.set_num_inputs(2)
 .set_num_outputs(2)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
 .set_attr_parser(ParamParser<ROIAlignParam>)
diff --git a/src/operator/custom/custom.cc b/src/operator/custom/custom.cc
index 9130830b900c..3c4843c33395 100644
--- a/src/operator/custom/custom.cc
+++ b/src/operator/custom/custom.cc
@@ -586,7 +586,7 @@ Please check the tutorial here: https://mxnet.incubator.apache.org/api/faq/new_o
 NNVM_REGISTER_OP(_backward_Custom)
 .set_num_inputs([](const NodeAttrs& attrs){
     const CustomParam& params = nnvm::get<CustomParam>(attrs.parsed);
-    return params.bwd_idx.size();
+    return params.bwd_idx.size() + params.num_auxs;
   })
 .set_num_outputs([](const NodeAttrs& attrs){
     const CustomParam& params = nnvm::get<CustomParam>(attrs.parsed);
diff --git a/src/operator/image/image_random.cc b/src/operator/image/image_random.cc
index 0c4603ecc475..aa387e683bfd 100644
--- a/src/operator/image/image_random.cc
+++ b/src/operator/image/image_random.cc
@@ -185,7 +185,7 @@ NNVM_REGISTER_OP(_image_normalize)
 
 NNVM_REGISTER_OP(_backward_image_normalize)
 .set_attr_parser(ParamParser<NormalizeParam>)
-.set_num_inputs(1)
+.set_num_inputs(2)
 .set_num_outputs(1)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
 .set_attr<FCompute>("FCompute<cpu>", NormalizeOpBackward<cpu>);
diff --git a/src/operator/leaky_relu.cc b/src/operator/leaky_relu.cc
index 49ba95d306f4..c2414ad74600 100644
--- a/src/operator/leaky_relu.cc
+++ b/src/operator/leaky_relu.cc
@@ -206,6 +206,19 @@ The following modified ReLU Activation functions are supported:
     });
 
 NNVM_REGISTER_OP(_backward_LeakyReLU)
+.set_num_inputs([](const NodeAttrs& attrs) {
+  const LeakyReLUParam& param = nnvm::get<LeakyReLUParam>(attrs.parsed);
+  if (param.act_type == leakyrelu::kPReLU) {
+    // forward has 2 inputs and 1 output
+    return 2 + 2 * 1;
+  } else if (param.act_type == leakyrelu::kRReLU) {
+    // forward has 1 input and 2 outputs
+    return 1 + 2 * 2;
+  } else {
+    // forward has 1 input and 1 output
+    return 1 + 2 * 1;
+  }
+})
 .set_num_outputs([](const NodeAttrs& attrs) {
   const LeakyReLUParam& param = nnvm::get<LeakyReLUParam>(attrs.parsed);
   return param.act_type == leakyrelu::kPReLU ? 2 : 1;
diff --git a/src/operator/nn/batch_norm.cc b/src/operator/nn/batch_norm.cc
index 04e45d4acfed..ea1c76965a9b 100644
--- a/src/operator/nn/batch_norm.cc
+++ b/src/operator/nn/batch_norm.cc
@@ -593,6 +593,7 @@ then set ``gamma`` to 1 and its gradient to 0.
   });
 
 NNVM_REGISTER_OP(_backward_BatchNorm)
+.set_num_inputs(8)
 .set_num_outputs(3)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
 .set_attr<FInferStorageType>("FInferStorageType", BatchNormStorageType)
diff --git a/src/operator/nn/concat.cc b/src/operator/nn/concat.cc
index a9e744cc58d1..081ffde308ba 100644
--- a/src/operator/nn/concat.cc
+++ b/src/operator/nn/concat.cc
@@ -396,6 +396,14 @@ CONCAT_FORWARD_ATTRS
 .add_arguments(ConcatParam::__FIELDS__());
 
 NNVM_REGISTER_OP(_backward_Concat)
+.set_num_inputs([](const NodeAttrs& attrs) {
+#if MXNET_USE_MKLDNN == 1
+  const ConcatParam& params = nnvm::get<ConcatParam>(attrs.parsed);
+  return 1 + params.num_args;
+#else
+  return 1;
+#endif
+})
 .set_num_outputs([](const NodeAttrs& attrs) {
   const ConcatParam& params = nnvm::get<ConcatParam>(attrs.parsed);
   return params.num_args;
diff --git a/src/operator/nn/convolution.cc b/src/operator/nn/convolution.cc
index 6d9f84ffc510..36ee4e0c50d3 100644
--- a/src/operator/nn/convolution.cc
+++ b/src/operator/nn/convolution.cc
@@ -510,6 +510,10 @@ There are other options to tune the performance.
 .add_arguments(ConvolutionParam::__FIELDS__());
 
 NNVM_REGISTER_OP(_backward_Convolution)
+.set_num_inputs([](const NodeAttrs& attrs) {
+  const ConvolutionParam& params = nnvm::get<ConvolutionParam>(attrs.parsed);
+  return params.no_bias ? 3 : 4;
+})
 .set_num_outputs([](const NodeAttrs& attrs) {
   const ConvolutionParam& params = nnvm::get<ConvolutionParam>(attrs.parsed);
   return params.no_bias ? 2 : 3;
diff --git a/src/operator/nn/ctc_loss.cc b/src/operator/nn/ctc_loss.cc
index aba76fb0c452..096ef8c0d7b4 100644
--- a/src/operator/nn/ctc_loss.cc
+++ b/src/operator/nn/ctc_loss.cc
@@ -130,7 +130,7 @@ information on the definition and the algorithm.
 
 NNVM_REGISTER_OP(_backward_ctc_loss)
 .set_attr_parser(ParamParser<CTCLossOpParam>)
-.set_num_inputs(1)
+.set_num_inputs(4)
 .set_num_outputs(CTCLossOpNumInputs)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
 .set_attr<FCompute>("FCompute<cpu>", CTCLossOpBackward<cpu>);
diff --git a/src/operator/nn/deconvolution.cc b/src/operator/nn/deconvolution.cc
index bbcec53e933d..f0a6f8841419 100644
--- a/src/operator/nn/deconvolution.cc
+++ b/src/operator/nn/deconvolution.cc
@@ -445,6 +445,10 @@ NNVM_REGISTER_OP(Deconvolution)
 .add_arguments(DeconvolutionParam::__FIELDS__());
 
 NNVM_REGISTER_OP(_backward_Deconvolution)
+.set_num_inputs([](const NodeAttrs& attrs) {
+  const DeconvolutionParam& params = nnvm::get<DeconvolutionParam>(attrs.parsed);
+  return params.no_bias ? 3 : 4;
+})
 .set_num_outputs([](const NodeAttrs& attrs) {
   const DeconvolutionParam& params = nnvm::get<DeconvolutionParam>(attrs.parsed);
   return params.no_bias ? 2 : 3;
diff --git a/src/operator/nn/lrn.cc b/src/operator/nn/lrn.cc
index 41337352df63..14967912e3c9 100644
--- a/src/operator/nn/lrn.cc
+++ b/src/operator/nn/lrn.cc
@@ -184,6 +184,7 @@ number of kernels in the layer.
 .add_arguments(LRNParam::__FIELDS__());
 
 NNVM_REGISTER_OP(_backward_LRN)
+.set_num_inputs(3)
 .set_num_outputs(1)
 .set_attr_parser(ParamParser<LRNParam>)
 #if MXNET_USE_MKLDNN == 1
diff --git a/src/operator/nn/pooling.cc b/src/operator/nn/pooling.cc
index f998c33d16a8..75c410270591 100644
--- a/src/operator/nn/pooling.cc
+++ b/src/operator/nn/pooling.cc
@@ -453,6 +453,11 @@ For each window ``X``, the mathematical expression for Lp pooling is:
 .add_arguments(PoolingParam::__FIELDS__());
 
 NNVM_REGISTER_OP(_backward_Pooling)
+.set_num_inputs([](const NodeAttrs& attrs) {
+  const PoolingParam &param = nnvm::get<PoolingParam>(attrs.parsed);
+  // 1 input to fwd op and 2 * outputs from fwd op (fwd outputs and gradient inputs)
+  return 1 + 2 * GetNumOutputs(param);
+})
 .set_num_outputs(1)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
 .set_attr<nnvm::FInplaceOption>(
diff --git a/src/operator/nn/softmax_activation.cc b/src/operator/nn/softmax_activation.cc
index 9e5a3ab8f6a2..4779b7732688 100644
--- a/src/operator/nn/softmax_activation.cc
+++ b/src/operator/nn/softmax_activation.cc
@@ -67,6 +67,7 @@ Example::
 .add_arguments(SoftmaxActivationParam::__FIELDS__());
 
 NNVM_REGISTER_OP(_backward_SoftmaxActivation)
+.set_num_inputs(2)
 .set_num_outputs(1)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
 .set_attr<nnvm::FInplaceOption>("FInplaceOption", [](const NodeAttrs& attrs){
diff --git a/src/operator/nn/upsampling.cc b/src/operator/nn/upsampling.cc
index d36b2598ce82..8000106fd6c4 100644
--- a/src/operator/nn/upsampling.cc
+++ b/src/operator/nn/upsampling.cc
@@ -211,6 +211,14 @@ Example::
     });
 
 NNVM_REGISTER_OP(_backward_UpSampling)
+.set_num_inputs([](const NodeAttrs& attrs) {
+  const UpSamplingParam& param_ = nnvm::get<UpSamplingParam>(attrs.parsed);
+  if (param_.sample_type != up_enum::kNearest) {
+    return 3;
+  } else {
+    return 1;
+  }
+})
 .set_num_outputs([](const NodeAttrs& attrs) {
   const UpSamplingParam& params = nnvm::get<UpSamplingParam>(attrs.parsed);
   return params.sample_type == up_enum::kNearest ? params.num_args : 2;
diff --git a/src/operator/numpy/np_broadcast_reduce_op_value.cc b/src/operator/numpy/np_broadcast_reduce_op_value.cc
index ad6ac9504d52..4bc8e6737bcf 100644
--- a/src/operator/numpy/np_broadcast_reduce_op_value.cc
+++ b/src/operator/numpy/np_broadcast_reduce_op_value.cc
@@ -238,7 +238,7 @@ NNVM_REGISTER_OP(_np_prod)
 .set_attr<nnvm::FGradient>("FGradient", ReduceGrad{"_backward_np_prod"});
 
 NNVM_REGISTER_OP(_backward_np_prod)
-.set_num_inputs(1)
+.set_num_inputs(3)
 .set_num_outputs(1)
 .set_attr_parser(ParamParser<NumpyReduceAxesParam>)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
diff --git a/src/operator/operator_common.h b/src/operator/operator_common.h
index a715bfc2f0cc..929182630857 100644
--- a/src/operator/operator_common.h
+++ b/src/operator/operator_common.h
@@ -359,12 +359,36 @@ inline bool dispatch_fallback(StorageTypeVector* stypes, DispatchMode* dispatch)
   return true;
 }
 
+inline std::vector<nnvm::NodeEntry>CreateNodeEntries(
+  nnvm::NodePtr pNode,
+  const std::vector<nnvm::NodeEntry>* pOgrads = nullptr,
+  const std::vector<nnvm::NodeEntry>* pInputs = nullptr) {
+  if (pOgrads)
+    pNode->inputs.insert(pNode->inputs.end(), pOgrads->begin(), pOgrads->end());
+
+  if (pInputs)
+    pNode->inputs.insert(pNode->inputs.end(), pInputs->begin(), pInputs->end());
+
+  if (!pNode->is_variable()) {
+    CHECK_EQ(pNode->num_inputs(), pNode->inputs.size())
+      << "Number of inputs to operator " << pNode->op()->name << " (" << pNode->num_inputs()
+      << ") does not match the actual number of inputs provided to operator "
+      << pNode->attrs.name << " (" << pNode->inputs.size() << ").";
+  }
+
+  std::vector<nnvm::NodeEntry> ret;
+  for (uint32_t i = 0; i < pNode->num_outputs(); ++i)
+    ret.emplace_back(nnvm::NodeEntry{pNode, i, 0});
+
+  return ret;
+}
+
 // make a new node with operator op_name. Inputs are not filled.
 inline nnvm::NodePtr MakeNode(
     const char* op_name, const std::string& name,
-    std::vector<nnvm::NodeEntry> const * inputs,
-    std::unordered_map<std::string, std::string> const * dict,
-    nnvm::NodePtr const * fwd_node) {
+    std::vector<nnvm::NodeEntry> const * inputs = nullptr,
+    std::unordered_map<std::string, std::string> const * dict = nullptr,
+    nnvm::NodePtr const * fwd_node = nullptr) {
   auto p = nnvm::Node::Create();
   p->attrs.op = nnvm::Op::Get(op_name);
   p->attrs.name = name;
@@ -376,6 +400,12 @@ inline nnvm::NodePtr MakeNode(
   if (p->op()->attr_parser != nullptr) {
     p->op()->attr_parser(&(p->attrs));
   }
+  if (inputs != nullptr) {
+    CHECK_EQ(p->num_inputs(), p->inputs.size())
+      << "Number of inputs to operator " << op_name << " (" << p->num_inputs()
+      << ") does not match the actual number of inputs provided to operator "
+      << name << " (" << p->inputs.size() << ").";
+  }
   return p;
 }
 
@@ -395,11 +425,8 @@ inline std::vector<nnvm::NodeEntry> MakeGradNode(
     const std::unordered_map<std::string, std::string>& dict) {
   auto p = MakeNode(op_name, n->attrs.name + "_backward",
                     &inputs, &dict, &n);
-  std::vector<nnvm::NodeEntry> ret;
-  for (uint32_t i = 0; i < p->num_outputs(); ++i) {
-    ret.emplace_back(p, i, 0);
-  }
-  return ret;
+
+  return CreateNodeEntries(p);
 }
 
 // quick helper to make gradient nodes that simply pass back zero. could be used in output ops.
@@ -446,13 +473,8 @@ inline std::vector<nnvm::NodeEntry> MakeNonlossGradNode(
     return MakeZeroGradNodes(n, ograds);
   auto p = MakeNode(op_name, n->attrs.name + "_backward",
                     nullptr, &dict, &n);
-  p->inputs.insert(p->inputs.end(), ograds.begin(), ograds.end());
-  p->inputs.insert(p->inputs.end(), inputs.begin(), inputs.end());
-  std::vector<nnvm::NodeEntry> ret;
-  for (uint32_t i = 0; i < p->num_outputs(); ++i) {
-    ret.emplace_back(p, i, 0);
-  }
-  return ret;
+
+  return CreateNodeEntries(p, &ograds, &inputs);
 }
 
 /*! \brief Parse keyword arguments as PType arguments and save to parsed */
diff --git a/src/operator/rnn.cc b/src/operator/rnn.cc
index a8e1b128f773..204e792f9167 100644
--- a/src/operator/rnn.cc
+++ b/src/operator/rnn.cc
@@ -406,6 +406,20 @@ The definition of GRU here is slightly different from paper but compatible with
 .add_arguments(RNNParam::__FIELDS__());
 
 NNVM_REGISTER_OP(_backward_RNN)
+.set_num_inputs([](const NodeAttrs& attrs) {
+    const RNNParam& params = nnvm::get<RNNParam>(attrs.parsed);
+    int ret = 5;
+    if (params.state_outputs) {
+      ret += 2;
+    }
+    if (params.mode == rnn_enum::kLstm) {
+      ++ret;
+      if (params.state_outputs) {
+      ret += 2;
+      }
+    }
+    return ret;
+})
 .set_num_outputs([](const NodeAttrs& attrs) {
   const RNNParam& params = nnvm::get<RNNParam>(attrs.parsed);
   return GetNumInputArguments(params);
diff --git a/src/operator/softmax_output.cc b/src/operator/softmax_output.cc
index 0bf6e2a014a6..194930f7864a 100644
--- a/src/operator/softmax_output.cc
+++ b/src/operator/softmax_output.cc
@@ -258,6 +258,7 @@ NNVM_REGISTER_OP(SoftmaxOutput)
 NNVM_REGISTER_OP(SoftmaxOutput).add_alias("Softmax");
 
 NNVM_REGISTER_OP(_backward_SoftmaxOutput)
+.set_num_inputs(2)
 .set_num_outputs(2)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
 .set_attr<nnvm::FInplaceOption>("FInplaceOption", [](const NodeAttrs& attrs){
diff --git a/src/operator/tensor/broadcast_reduce_norm_value.cc b/src/operator/tensor/broadcast_reduce_norm_value.cc
index 9acc157f8eca..557c4d9e7746 100644
--- a/src/operator/tensor/broadcast_reduce_norm_value.cc
+++ b/src/operator/tensor/broadcast_reduce_norm_value.cc
@@ -105,6 +105,7 @@ Examples::
 .add_arguments(NormParam::__FIELDS__());
 
 NNVM_REGISTER_OP(_backward_norm)
+.set_num_inputs(3)
 .set_num_outputs(1)
 .set_attr_parser(ParamParser<NormParam>)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)

From 09cc72e9ef301a0c9680700344437dcdd69a2ded Mon Sep 17 00:00:00 2001
From: Lai Wei <royweilai@gmail.com>
Date: Wed, 15 Jan 2020 15:39:05 -0800
Subject: [PATCH 806/813] Update NOTICE to fix copyright years (#17330)

---
 NOTICE | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/NOTICE b/NOTICE
index ecc58f6a2922..6cd2917c2d6f 100644
--- a/NOTICE
+++ b/NOTICE
@@ -1,5 +1,5 @@
     Apache MXNET (incubating)
-    Copyright 2017 and onwards The Apache Software Foundation
+    Copyright 2017-2020 The Apache Software Foundation
 
     This product includes software developed at
     The Apache Software Foundation (http://www.apache.org/).

From 7b349dde52f17bb92d923a62a6fe4bd196770e62 Mon Sep 17 00:00:00 2001
From: Rohit Kumar Srivastava <srivastava.141@osu.edu>
Date: Wed, 15 Jan 2020 16:08:21 -0800
Subject: [PATCH 807/813] grouping large array tests based on type and updating
 nightly CI function (#17305)

---
 ci/docker/runtime_functions.sh    |    4 +-
 tests/nightly/test_large_array.py | 3269 ++++++++++++++---------------
 2 files changed, 1622 insertions(+), 1651 deletions(-)

diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index 381cb567fbc0..9a196f401962 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -1522,7 +1522,9 @@ nightly_test_large_tensor() {
     set -ex
     export PYTHONPATH=./python/
     export DMLC_LOG_STACK_TRACE_DEPTH=10
-    nosetests-3.4 tests/nightly/test_large_array.py
+    nosetests-3.4 tests/nightly/test_large_array.py:test_tensor
+    nosetests-3.4 tests/nightly/test_large_array.py:test_nn
+    nosetests-3.4 tests/nightly/test_large_array.py:test_basic
 }
 
 #Tests Amalgamation Build with 5 different sets of flags
diff --git a/tests/nightly/test_large_array.py b/tests/nightly/test_large_array.py
index a0d893d601b3..10af23ea76f6 100644
--- a/tests/nightly/test_large_array.py
+++ b/tests/nightly/test_large_array.py
@@ -35,1658 +35,1627 @@
 LARGE_SIZE = LARGE_X * SMALL_Y
 
 
-def test_gluon_embedding():
-    m = gluon.nn.Embedding(SMALL_Y, MEDIUM_X)
-    m.initialize()
-    a = nd.zeros((MEDIUM_X, SMALL_Y))
-    b = m(a)
-    assert b.shape == (MEDIUM_X, SMALL_Y, MEDIUM_X)
-    assert b.asnumpy().size == LARGE_SIZE
-
-
-def test_ndarray_zeros():
-    a = nd.zeros(shape=(LARGE_X, SMALL_Y))
-    assert a[-1][0] == 0
-    assert a.shape == (LARGE_X, SMALL_Y)
-    assert a.size == LARGE_SIZE
-
-
-def test_ndarray_ones():
-    a = nd.ones(shape=(LARGE_X, SMALL_Y))
-    assert a[-1][0] == 1
-    assert nd.sum(a).asnumpy() == LARGE_SIZE
-
-
-def test_ndarray_convert():
-    a = nd.zeros(shape=(LARGE_X, SMALL_Y))
-    b = a.astype(np.int32)
-    assert b.dtype == np.int32
-    b = a.tostype('row_sparse')
-    assert isinstance(b, mx.nd.sparse.RowSparseNDArray)
-
-
-@with_seed()
-def test_ndarray_random_uniform():
-    a = nd.random.uniform(shape=(LARGE_X, SMALL_Y))
-    assert a[-1][0] != 0
-
-
-@with_seed()
-def test_ndarray_random_randint():
-    a = nd.random.randint(100, 10000, shape=(LARGE_X, SMALL_Y))
-    assert a.shape == (LARGE_X, SMALL_Y)
-    # check if randint can generate value greater than 2**32 (large)
-    low_large_value = 2**32
-    high_large_value = 2**34
-    a = nd.random.randint(low_large_value, high_large_value, dtype=np.int64)
-    low = mx.nd.array([low_large_value], dtype='int64')
-    high = mx.nd.array([high_large_value], dtype='int64')
-    assert a >= low and a < high
-    assert a[-1][0].dtype == np.int64
-
-
-@with_seed()
-def test_ndarray_random_exponential():
-    scale_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_X))
-    a = nd.random.exponential(scale=scale_array, shape=(SMALL_X, SMALL_Y))
-    assert a[-1][0][0][0] >= 0
-    assert a.shape == (MEDIUM_X, SMALL_X, SMALL_X, SMALL_Y)
-
-
-@with_seed()
-def test_ndarray_random_gamma():
-    alpha_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_X))
-    beta_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_X))
-    a = nd.random.gamma(alpha=alpha_array, beta=beta_array,
-                        shape=(SMALL_X, SMALL_Y))
-    assert a[-1][0][0][0] >= 0
-    assert a.shape == (MEDIUM_X, SMALL_X, SMALL_X, SMALL_Y)
-
-
-@with_seed()
-def test_ndarray_random_multinomial():
-    # test 1 shape dimension
-    probs = nd.random.uniform(shape=(LARGE_X, SMALL_Y))
-    a = nd.random.multinomial(probs)
-    assert a[-1] >= 0
-    assert a.shape == (LARGE_X,)
-    # test for NDArray multi-dimension shape
-    a = nd.random.multinomial(probs, shape=(2, SMALL_Y))
-    assert a[-1][0][0] >= 0
-    assert a.shape == (LARGE_X, 2, SMALL_Y)
-    # test log_likelihood output shape
-    a = nd.random.multinomial(probs, shape=(2, SMALL_Y), get_prob=True)
-    assert a[0][0][0][0] >= 0
-    assert a[0].shape == (LARGE_X, 2, SMALL_Y) and a[0].shape == a[1].shape
-
-
-@with_seed()
-def test_ndarray_random_generalized_negative_binomial():
-    alpha_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_X))
-    mu_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_X))
-    a = nd.random.generalized_negative_binomial(mu=mu_array, alpha=alpha_array,
-                                                shape=(SMALL_X, SMALL_Y))
-    assert a[-1][0][0][0] >= 0
-    assert a.shape == (MEDIUM_X, SMALL_X, SMALL_X, SMALL_Y)
-
-
-@with_seed()
-def test_ndarray_random_negative_binomial():
-    k_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_X))
-    p_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_X))
-    a = nd.random.negative_binomial(k=k_array, p=p_array,
-                                    shape=(SMALL_X, SMALL_Y))
-    assert a[-1][0][0][0] >= 0
-    assert a.shape == (MEDIUM_X, SMALL_X, SMALL_X, SMALL_Y)
-
-
-@with_seed()
-def test_ndarray_random_normal():
-    scale_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_X))
-    loc_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_X))
-    a = nd.random.normal(loc=loc_array, scale=scale_array,
-                         shape=(SMALL_X, SMALL_Y))
-    assert a.shape == (MEDIUM_X, SMALL_X, SMALL_X, SMALL_Y)
-
-
-@with_seed()
-def test_ndarray_random_poisson():
-    lambda_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_X))
-    a = nd.random.poisson(lam=lambda_array, shape=(SMALL_X, SMALL_Y))
-    assert a[-1][0][0][0] >= 0
-    assert a.shape == (MEDIUM_X, SMALL_X, SMALL_X, SMALL_Y)
-
-
-@with_seed()
-def test_ndarray_random_randn():
-    a = nd.random.randn(LARGE_X, SMALL_Y)
-    assert a.shape == (LARGE_X, SMALL_Y)
-    # TODO: Once PR #15772 for randn ndarray dtype for loc,scale param merged
-    # Add check for (x,y,m,n) where x,y shape of loc,scale and m,n input shape
-
-
-@with_seed()
-def test_ndarray_random_shuffle():
-    a = nd.ones(shape=(LARGE_X, SMALL_Y))
-    a[-1] = 3  # assign 3 to entire last row
-    a = nd.random.shuffle(a)
-    # slice first column from shuffled array
-    # pass LARGE_X values to numpy instead of LARGE_X*SMALL_Y
-    # could have assigned to last column (so as to pass SMALL_Y)
-    # but shuffle operation is performed along first axis
-    unique_a = np.unique(a[:, 0].asnumpy())
-    assert len(unique_a) == 2  # only 2 unique values
-    assert unique_a[0] == 1  # first unique value is 1
-    assert unique_a[1] == 3  # second unique value is 3
-    assert a.shape == (LARGE_X, SMALL_Y)
-
-
-def test_ndarray_empty():
-    a = nd.empty((LARGE_X, SMALL_Y))
-    assert a.shape == (LARGE_X, SMALL_Y)
-
-
-def test_elementwise():
-    a = nd.ones(shape=(LARGE_X, SMALL_Y))
-    b = nd.ones(shape=(LARGE_X, SMALL_Y))
-    res = a + b
-    assert np.sum(res[-1].asnumpy() == 2) == a.shape[1]
-    res = a + 1
-    assert np.sum(res[-1].asnumpy() == 2) == a.shape[1]
-    res = nd.sqrt(a + 3)
-    assert np.sum(res[-1].asnumpy() == 2) == a.shape[1]
-
-
-def test_reduce():
-    a = nd.ones(shape=(LARGE_X, SMALL_Y))
-    assert nd.sum(a).asnumpy() == a.shape[0] * a.shape[1]
-
-
-def test_dot():
-    a = nd.ones(shape=(LARGE_X, SMALL_Y))
-    b = nd.ones(shape=(SMALL_Y, SMALL_Y))
-    res = nd.dot(a, b)
-    assert np.sum(res[-1].asnumpy() == SMALL_Y) == b.shape[1]
-
-
-def test_FullyConnected():
-    a = nd.ones(shape=(LARGE_X, SMALL_Y))
-    b = nd.ones(shape=(SMALL_Y, SMALL_Y))
-    c = nd.ones(shape=(b.shape[0],))
-
-    # w/o bias
-    res = nd.FullyConnected(a, b, num_hidden=b.shape[0], no_bias=True)
-    assert np.sum(res[-1].asnumpy() == a.shape[1]) == b.shape[0]
-
-    # w/ bias
-    res = nd.FullyConnected(a, b, c, num_hidden=b.shape[0], no_bias=False)
-    assert np.sum(res[-1].asnumpy() == a.shape[1] + 1) == b.shape[0]
-
-
-def test_broadcast():
-    a = nd.ones(shape=(LARGE_X, SMALL_Y))
-    b = nd.arange(0, LARGE_X).reshape(LARGE_X, 1)
-    res = nd.broadcast_to(b, shape=(b.shape[0], SMALL_Y))
-    assert np.sum(res[-1].asnumpy() == LARGE_X) == res.shape[1]
-    res = mx.nd.broadcast_like(b, a)
-    assert np.sum(res[-1].asnumpy() == LARGE_X) == a.shape[1]
-
-
-def test_clip():
-    a = nd.arange(0, LARGE_X * SMALL_Y).reshape(LARGE_X, SMALL_Y)
-    res = nd.clip(a, a_min=100, a_max=1000)
-    assert np.sum(res[-1].asnumpy() == 1000) == a.shape[1]
-
-
-def test_split():
-    a = nd.arange(0, LARGE_X * SMALL_Y).reshape(LARGE_X, SMALL_Y)
-    outs = nd.split(a, num_outputs=SMALL_Y, axis=1)
-    result = sum(1 for i, v in enumerate(outs) if i == v[0].asnumpy())
-    assert result == a.shape[1]
-
-
-def test_argmin():
-    a = nd.arange(0, LARGE_X * SMALL_Y).reshape(LARGE_X, SMALL_Y)
-    idx = mx.nd.argmin(a, axis=0)
-    assert idx.shape[0] == SMALL_Y
-
-
-def test_tile():
-    a = nd.arange(0, LARGE_X).reshape(LARGE_X, 1)
-    b = nd.tile(a, reps=(1, SMALL_Y))
-    assert np.sum(b[-1].asnumpy() == LARGE_X) == b.shape[1]
-
-
-def test_take():
-    a = nd.ones(shape=(LARGE_X, SMALL_Y))
-    idx = nd.arange(LARGE_X - 1000, LARGE_X)
-    res = nd.take(a, idx)
-    assert np.sum(res[-1].asnumpy() == 1) == res.shape[1]
-
-
-def test_slice():
-    a = nd.ones(shape=(LARGE_X, SMALL_Y))
-    res = nd.slice(a, begin=(LARGE_X-1000, 1), end=(LARGE_X, SMALL_Y))
-    assert np.sum(res[-1].asnumpy() == 1) == res.shape[1]
-
-
-def test_slice_assign():
-    a = nd.ones(shape=(LARGE_X, SMALL_Y))
-    a[LARGE_X-1:LARGE_X] = 1000
-    assert np.sum(a[-1].asnumpy() == 1000) == a.shape[1]
-
-
-def test_expand_dims():
-    a = nd.ones(shape=(LARGE_X, SMALL_Y))
-    res = nd.expand_dims(a, axis=1)
-    res.wait_to_read()
-    assert a[0][0][0] == 1
-    assert res.shape == (a.shape[0], 1, a.shape[1])
-
-
-def test_squeeze():
-    a = nd.ones(shape=(LARGE_X, SMALL_Y))
-    data = nd.expand_dims(a, axis=1)
-    res = nd.squeeze(data)
-    assert res.shape == a.shape
-
-
-def test_broadcast_div():
-    a = nd.ones(shape=(LARGE_X, SMALL_Y))
-    b = nd.ones(shape=(LARGE_X, 1)) * 2
-    res = a / b
-    assert np.sum(res[-1].asnumpy() == 0.5) == a.shape[1]
-
-
-def test_Dense(ctx=mx.cpu(0)):
-    data = mx.nd.ones(shape=(50*1000*1000, 100))
-    linear = gluon.nn.Dense(100)
-    linear.initialize(ctx=ctx)
-    res = linear(data)
-    assert res.shape == (50000000, 100)
-
-
-def test_where():
-    a = nd.ones(shape=(LARGE_X, SMALL_Y))
-    b = nd.arange(0, LARGE_X * SMALL_Y).reshape(LARGE_X, SMALL_Y)
-    res = nd.where(b > 100, a, b)
-    assert np.sum(res[-1].asnumpy() == 1) == b.shape[1]
-    csr_cond = nd.sparse.cast_storage(b < 10, 'csr')
-    res = nd.sparse.where(csr_cond, a, b)
-    assert np.sum(res[0].asnumpy() == 1) == 10
-
-
-def test_pick():
-    a = mx.nd.ones(shape=(256 * 35, 1024 * 1024))
-    b = mx.nd.ones(shape=(256 * 35, ))
-    res = mx.nd.pick(a, b)
-    assert res.shape == b.shape
-
-
-def test_depthtospace():
-    def numpy_depth_to_space(x, blocksize):
-        b, c, h, w = x.shape[0], x.shape[1], x.shape[2], x.shape[3]
-        tmp = np.reshape(x, [b, blocksize, blocksize, c // (blocksize**2), h,
-                         w])
-        tmp = np.transpose(tmp, [0, 3, 4, 1, 5, 2])
-        y = np.reshape(tmp, [b, c // (blocksize**2), h * blocksize,
-                       w * blocksize])
-        return y
-
-    shape_inp = (LARGE_X, 8, 4, 2)
-    data = rand_ndarray(shape_inp, 'default')
-    data_np = data.asnumpy()
-    expected = numpy_depth_to_space(data_np, 2)
-    output = mx.nd.depth_to_space(data, 2)
-    assert_almost_equal(output.asnumpy(), expected, atol=1e-3, rtol=1e-3)
-
-
-def test_spacetodepth():
-    def numpy_space_to_depth(x, blocksize):
-        b, c, h, w = x.shape[0], x.shape[1], x.shape[2], x.shape[3]
-        tmp = np.reshape(x, [b, c, h // blocksize, blocksize, w // blocksize,
-                         blocksize])
-        tmp = np.transpose(tmp, [0, 3, 5, 1, 2, 4])
-        y = np.reshape(tmp, [b, c * (blocksize**2), h // blocksize,
-                       w // blocksize])
-        return y
-
-    shape_inp = (LARGE_X, 2, 8, 4)
-    data = rand_ndarray(shape_inp, 'default')
-    data_np = data.asnumpy()
-    expected = numpy_space_to_depth(data_np, 2)
-    output = mx.nd.space_to_depth(data, 2)
-    assert_almost_equal(output.asnumpy(), expected, atol=1e-3, rtol=1e-3)
-
-
-@with_seed()
-def test_diag():
-    a_np = np.random.random((LARGE_X, SMALL_Y)).astype(np.float32)
-    a = mx.nd.array(a_np)
-
-    # k == 0
-    r = mx.nd.diag(a)
-    assert_almost_equal(r.asnumpy(), np.diag(a_np))
-
-    # k == 1
-    k = 1
-    r = mx.nd.diag(a, k=k)
-    assert_almost_equal(r.asnumpy(), np.diag(a_np, k=k))
-
-    # k == -1
-    k = -1
-    r = mx.nd.diag(a, k=k)
-    assert_almost_equal(r.asnumpy(), np.diag(a_np, k=k))
-
-    # random k
-    k = np.random.randint(-min(LARGE_X, SMALL_Y) + 1, min(LARGE_X, SMALL_Y))
-    r = mx.nd.diag(a, k=k)
-    assert_almost_equal(r.asnumpy(), np.diag(a_np, k=k))
-
-
-@with_seed()
-def test_ravel_multi_index():
-    x1, y1 = rand_coord_2d((LARGE_X - 100), LARGE_X, 10, SMALL_Y)
-    x2, y2 = rand_coord_2d((LARGE_X - 200), LARGE_X, 9, SMALL_Y)
-    x3, y3 = rand_coord_2d((LARGE_X - 300), LARGE_X, 8, SMALL_Y)
-    indices_2d = [[x1, x2, x3], [y1, y2, y3]]
-    idx = mx.nd.ravel_multi_index(mx.nd.array(indices_2d, dtype=np.int64),
-                                  shape=(LARGE_X, SMALL_Y))
-    idx_numpy = np.ravel_multi_index(indices_2d, (LARGE_X, SMALL_Y))
-    assert np.sum(1 for i in range(idx.size) if idx[i] == idx_numpy[i]) == 3
-
-
-@with_seed()
-def test_unravel_index():
-    x1, y1 = rand_coord_2d((LARGE_X - 100), LARGE_X, 10, SMALL_Y)
-    x2, y2 = rand_coord_2d((LARGE_X - 200), LARGE_X, 9, SMALL_Y)
-    x3, y3 = rand_coord_2d((LARGE_X - 300), LARGE_X, 8, SMALL_Y)
-    original_2d_indices = [[x1, x2, x3], [y1, y2, y3]]
-    idx_numpy = np.ravel_multi_index(original_2d_indices, (LARGE_X, SMALL_Y))
-    indices_2d = mx.nd.unravel_index(mx.nd.array(idx_numpy, dtype=np.int64),
-                                     shape=(LARGE_X, SMALL_Y))
-    assert (indices_2d.asnumpy() == np.array(original_2d_indices)).all()
-
-
-def test_transpose():
-    test_dtypes = [np.float32, np.int64]
-    for dtype in test_dtypes:
-        b = create_2d_tensor(rows=LARGE_X, columns=SMALL_Y, dtype=dtype)
-        t = b.T
+def test_nn():
+    def check_gluon_embedding():
+        m = gluon.nn.Embedding(SMALL_Y, MEDIUM_X)
+        m.initialize()
+        a = nd.zeros((MEDIUM_X, SMALL_Y))
+        b = m(a)
+        assert b.shape == (MEDIUM_X, SMALL_Y, MEDIUM_X)
+        assert b.asnumpy().size == LARGE_SIZE
+
+    def check_fully_connected():
+        a = nd.ones(shape=(LARGE_X, SMALL_Y))
+        b = nd.ones(shape=(SMALL_Y, SMALL_Y))
+        c = nd.ones(shape=(b.shape[0],))
+
+        # w/o bias
+        res = nd.FullyConnected(a, b, num_hidden=b.shape[0], no_bias=True)
+        assert np.sum(res[-1].asnumpy() == a.shape[1]) == b.shape[0]
+
+        # w/ bias
+        res = nd.FullyConnected(a, b, c, num_hidden=b.shape[0], no_bias=False)
+        assert np.sum(res[-1].asnumpy() == a.shape[1] + 1) == b.shape[0]
+
+    def check_dense():
+        data = mx.nd.ones(shape=(50*1000*1000, 100))
+        linear = gluon.nn.Dense(100)
+        linear.initialize()
+        res = linear(data)
+        assert res.shape == (50000000, 100)
+
+    def check_softmax():
+        input_data = mx.nd.ones((SMALL_Y, LARGE_X))
+        for axis in [0, 1]:
+            true_output = np.full((SMALL_Y, LARGE_X), (1 / input_data.shape[axis]))
+            output = nd.softmax(input_data, axis=axis)
+            assert_almost_equal(output.asnumpy(), true_output, rtol=1e-5, atol=1e-5)
+
+    def check_softmax_cross_entropy():
+        # dtype of input data, mxnet cross entropy set explicitly to float64
+        # numpy implicitly takes care of double precision
+        batch_size = SMALL_Y
+        num_labels = LARGE_X
+        input_data = mx.nd.ones((batch_size, num_labels), dtype="float64")
+        input_label = mx.nd.zeros((batch_size,), dtype="float64")
+        true_softmax = np.full((batch_size, num_labels), (1 / num_labels))
+        # use 1/batch_size when softmax axis=0
+        # here 1/num_labels since softmax_cross_entropy uses default axis
+        # by default axis=1
+        np_one_hot_label = np.zeros((batch_size, num_labels))
+        np_one_hot_label[:, 0] = 1
+        true_softmax_cross_entropy = np.sum(-np.log(true_softmax) *
+                                            np_one_hot_label)
+        mx_softmax_cross_entropy = mx.nd.softmax_cross_entropy(input_data,
+                                                               input_label,
+                                                               dtype="float64")
+        assert_almost_equal(mx_softmax_cross_entropy.asnumpy(),
+                            true_softmax_cross_entropy, rtol=1e-3, atol=1e-5)
+
+    def check_softmax_output():
+        x = mx.sym.Variable('x')
+        label = mx.sym.Variable('label')
+        x_nd = mx.nd.ones((LARGE_X, SMALL_Y))
+        grad_x = mx.nd.zeros((LARGE_X, SMALL_Y))
+        label_nd = mx.nd.ones((LARGE_X))
+        sym = mx.sym.SoftmaxOutput(data=x, label=label, ignore_label=0,
+                                   use_ignore=False)
+
+        ex = sym.bind(ctx=default_context(), args={'x': x_nd, 'label': label_nd},
+                      args_grad=None)
+        ex.forward(is_train=False)
+        softmax_out = ex.outputs[0][0].asnumpy()
+        expected_softmax_out = (1 / SMALL_Y) * mx.nd.ones((SMALL_Y)).asnumpy()
+        assert np.isclose(softmax_out, expected_softmax_out).all()
+
+        ex = sym.bind(ctx=default_context(), args={'x': x_nd, 'label': label_nd},
+                      args_grad={'x': grad_x})
+        ex.forward(is_train=True)
+        softmax_out = ex.outputs[0][0].asnumpy()
+        expected_softmax_out = (1 / SMALL_Y) * mx.nd.ones((SMALL_Y)).asnumpy()
+        assert np.isclose(softmax_out, expected_softmax_out).all()
+
+        ex.backward(is_train=True)
+        grad_out = ex.grad_arrays[0][0].asnumpy()
+        k = int(label_nd[0].asscalar())
+        expected_grad_out = np.zeros((SMALL_Y,))
+        expected_grad_out[k] = -1
+        assert np.isclose(grad_out - softmax_out, expected_grad_out).all()
+
+    def np_softmax(x, axis=-1, temperature=1.0):
+        x = x - np.max(x, axis=axis, keepdims=True)
+        x = np.exp(x/temperature)
+        x /= np.sum(x, axis=axis, keepdims=True)
+        return x
+
+    def check_log_softmax():
+        ndim = 2
+        shape = (SMALL_Y, LARGE_X)
+        axis = np.random.randint(0, ndim)
+        data = np.random.uniform(-2, 2, size=shape)
+        sym = mx.sym.log_softmax(axis=axis-ndim)
+        check_symbolic_forward(sym, [data], [np.log(np_softmax(data, axis=axis)+1e-20)])
+
+    # TODO: correctness of prelu (currently flaky)
+    def check_leaky_relu():
+        a = -1*mx.nd.ones((LARGE_X, SMALL_Y))
+
+        def check_leaky():
+            res = mx.nd.LeakyReLU(a, act_type="leaky", slope=0.3)
+            assert_almost_equal(res[-1][-1].asnumpy(), 0.3*a[-1][-1].asnumpy(), atol=1e-3, rtol=1e-3)
+
+        def check_elu():
+            res = mx.nd.LeakyReLU(a, act_type="elu", slope=0.3)
+            assert_almost_equal(res[-1][-1].asnumpy(), 0.3*(np.exp(a[-1][-1].asnumpy())-1), atol=1e-3, rtol=1e-3)
+
+        def check_selu():
+            lam = 1.0507009873554804934193349852946
+            alpha = 1.6732632423543772848170429916717
+            res = mx.nd.LeakyReLU(a, act_type="selu")
+            assert_almost_equal(res[-1][-1].asnumpy(), (lam * alpha * (np.exp(a[-1][-1].asnumpy())-1)), atol=1e-3, rtol=1e-3)
+
+        def check_rrelu():
+            lower = 0.125
+            upper = 0.333999991
+            res = mx.nd.LeakyReLU(a, act_type="rrelu")
+            assert_almost_equal(res[0][-1][-1].asnumpy(), (lower + upper) / 2 * a[-1][-1].asnumpy(), atol=1e-3, rtol=1e-3)
+
+        check_leaky()
+        check_elu()
+        check_selu()
+        check_rrelu()
+
+    def check_pooling():
+        a = mx.nd.ones((MEDIUM_X, 200, SMALL_Y, SMALL_Y))
+
+        def check_avg_pooling():
+            res = mx.nd.Pooling(a, kernel=(5, 5), pool_type='avg')
+            assert_almost_equal(res[-1][-1][-1][-1].asnumpy(), 1.0000001, atol=1e-3, rtol=1e-3)
+            assert res.shape[-1] == SMALL_Y - 5 + 1
+
+        def check_max_pooling():
+            res = mx.nd.Pooling(a, kernel=(5, 5), pool_type='max')
+            assert_almost_equal(res[-1][-1][-1][-1].asnumpy(), 1., atol=1e-3, rtol=1e-3)
+            assert res.shape[-1] == SMALL_Y - 5 + 1
+
+        def check_sum_pooling():
+            res = mx.nd.Pooling(a, kernel=(5, 5), pool_type='sum')
+            assert_almost_equal(res[-1][-1][-1][-1].asnumpy(), 25, atol=1e-3, rtol=1e-3)
+            assert res.shape[-1] == SMALL_Y - 5 + 1
+
+        def check_lp_pooling():
+            res = mx.nd.Pooling(a, kernel=(5, 5), pool_type='lp', p_value=2)
+            assert_almost_equal(res[-1][-1][-1][-1].asnumpy(), 5., atol=1e-3, rtol=1e-3)
+            assert res.shape[-1] == SMALL_Y - 5 + 1
+
+            res = mx.nd.Pooling(a, kernel=(5, 5), pool_type='lp', p_value=1)
+            assert_almost_equal(res[-1][-1][-1][-1].asnumpy(), 25., atol=1e-3, rtol=1e-3)
+            assert res.shape[-1] == SMALL_Y - 5 + 1
+
+        check_avg_pooling()
+        check_max_pooling()
+        check_sum_pooling()
+        check_lp_pooling()
+
+    def check_layer_norm():
+        dtype = np.float32
+        forward_check_eps = 1E-3
+        axis = 1
+        eps = 1E-5
+        in_shape = (LARGE_X, SMALL_Y)
+        ctx = mx.cpu()
+
+        def npy_layer_norm(data, gamma, beta, axis=1, eps=1E-5):
+            if axis < 0:
+                axis += data.ndim
+            broadcast_shape = [1 for _ in range(data.ndim)]
+            broadcast_shape[axis] = data.shape[axis]
+            mean = data.mean(axis=axis, keepdims=True).astype(dtype)
+            var = data.var(axis=axis, keepdims=True).astype(dtype)
+            std = np.sqrt(var + dtype(eps)).astype(dtype)
+            out = np.reshape(gamma, broadcast_shape) * (data - mean) / std + \
+                  np.reshape(beta, broadcast_shape)
+            return out
+        data = np.random.normal(0, 1, in_shape).astype(dtype)
+        gamma = np.random.normal(0, 1, (in_shape[axis],)).astype(dtype)
+        beta = np.random.normal(0, 1, (in_shape[axis],)).astype(dtype)
+        data_s = mx.symbol.Variable('data')
+        gamma_s = mx.symbol.Variable('gamma')
+        beta_s = mx.symbol.Variable('beta')
+        out_s = mx.symbol.LayerNorm(data=data_s, gamma=gamma_s, beta=beta_s,
+                                    axis=axis, eps=eps)
+        exe = out_s.simple_bind(ctx, data=in_shape)
+        exe.arg_dict['data'][:] = data
+        exe.arg_dict['gamma'][:] = gamma
+        exe.arg_dict['beta'][:] = beta
+        out_nd = exe.forward()[0]
+        out = npy_layer_norm(data, gamma, beta, axis, eps)
+        assert_almost_equal(out, out_nd.asnumpy(), forward_check_eps,
+                            forward_check_eps)
+
+    # TODO: correctness of dropout
+    # currently only test for dropout to work
+    # since testing for correctness involves flakiness issue #14288
+    def check_dropout():
+        shape = (LARGE_X, SMALL_Y)
+        x = mx.sym.var('data')
+        y = mx.sym.Dropout(x, p=1, cudnn_off=True)
+        exe = y.simple_bind(ctx=default_context(), data=shape)
+        exe.arg_arrays[0][:] = 1
+        out = exe.forward(is_train=True)
+        nd.waitall()
+        assert out[0].shape == shape
+
+    def check_activation():
+        x = mx.nd.ones((LARGE_X, SMALL_Y))
+        check_x = -2
+        x[-1, -1] = check_x
+        # Hyperbolic tangent (tanh)
+        # y = (exp(x)-exp(-x))/(exp(x)+exp(-x))
+        y = mx.nd.Activation(x, act_type="tanh")
+        tanh_x = ((np.exp(check_x)-np.exp(-check_x))/(np.exp(check_x)+np.exp(-check_x)))
+        assert y[-1][-1] == np.float32(tanh_x)
+        # Recitified Linear Unit (relu)
+        # y = max(x,0)
+        y = mx.nd.Activation(x, act_type="relu")
+        assert y[-1][-1] == 0
+        # Sigmoid
+        # y = x/(1+abs(x))
+        y = mx.nd.Activation(x, act_type="sigmoid")
+        sigmoid_x = (1/(1+math.exp(-check_x)))
+        assert_almost_equal(y[-1][-1].asnumpy(), np.float32(sigmoid_x), atol=1e-3, rtol=1e-3)
+        # Soft Sign
+        # y = 1/(1+exp(-x))
+        y = mx.nd.Activation(x, act_type="softsign")
+        softsign_x = (check_x/(1+abs(check_x)))
+        assert y[-1][-1] == np.float32(softsign_x)
+
+
+    # TODO: correctness of batchnorm
+    # in future, we could test if mean, var of output
+    # matches target output's mean, var
+    def check_batchnorm():
+        def get_np_mean_var(data, running_mean, running_var, eps, use_global_status=True):
+            if not use_global_status:
+                # train mode, calculate the real mean and var
+                mean = np.mean(data, axis=(0, 2, 3))
+                mean_broad = np.expand_dims(mean, axis=0)
+                mean_broad = np.expand_dims(mean_broad, axis=2)
+                mean_broad = np.expand_dims(mean_broad, axis=3)
+                mean_broad = np.broadcast_to(mean_broad, data.shape)
+                var = np.square(data - mean_broad)
+                var = np.mean(var, axis=(0, 2, 3))
+            else:
+                # inference mode, use running_mean and running_var instead
+                mean = np.full((data.shape[1],), running_mean)
+                var = np.full((data.shape[1],), running_var)
+            # calculate the inverse of standard variance
+            invstdvar = 1. / np.sqrt(var + eps)
+            return mean, invstdvar
+        # Here use 4D input to cover mkldnn BN and non-mkldnn BN
+        shape = (1, 2, LARGE_X, SMALL_Y)
+        axis = 1  # default
+        eps = 1e-3
+        nch = shape[axis]
+        data = mx.nd.ones(shape=shape)
+        bn_gamma = mx.nd.random.uniform(shape=(nch,))
+        bn_beta = mx.nd.random.uniform(shape=(nch,))
+        bn_running_mean = mx.nd.zeros(nch)
+        bn_running_var = mx.nd.ones(nch)
+        output = mx.nd.BatchNorm(data, bn_gamma, bn_beta,
+                                 bn_running_mean, bn_running_var, output_mean_var=True)
+        assert output[0].shape == shape
+        mean, invstdvar = output[1], output[2]
+        np_mean, np_invstdvar = get_np_mean_var(data.asnumpy(), bn_running_mean.asnumpy(), bn_running_var.asnumpy(),
+                                                eps, use_global_status=True)
+        assert_almost_equal(mean.asnumpy(), np_mean)
+        assert_almost_equal(invstdvar.asnumpy(), np_invstdvar)
+
+    def check_relu():
+        def frelu(x):
+            return np.maximum(x, 0.0)
+
+        def frelu_grad(x):
+            return 1.0 * (x > 0.0)
+        shape = (SMALL_Y, LARGE_X)
+        x = mx.symbol.Variable("x")
+        y = mx.sym.relu(x)
+        xa = np.random.uniform(low=-1.0, high=1.0, size=shape)
+        eps = 1e-4
+        xa[abs(xa) < eps] = 1.0
+        ya = frelu(xa)
+        ga = frelu_grad(xa)
+        check_symbolic_forward(y, [xa], [ya])
+
+    def check_sigmoid():
+        def fsigmoid(a):
+            return np.divide(1.0, (1.0 + np.exp(-a)))
+        shape = (SMALL_Y, LARGE_X)
+        x = mx.symbol.Variable("x")
+        y = mx.sym.sigmoid(x)
+        xa = np.random.uniform(low=-1.0, high=1.0, size=shape)
+        ya = fsigmoid(xa)
+        check_symbolic_forward(y, [xa], [ya])
+
+    def check_linear_and_logistic_regression():
+        shape = (LARGE_X, SMALL_Y)
+
+        def check_regression(symbol, forward, backward, shape):
+            # init executor
+            data_s = mx.symbol.Variable('data')
+            label_s = mx.symbol.Variable('label')
+            out_s = symbol(data=data_s, label=label_s)
+            grad_req = {'data': 'write', 'label': 'null'}
+            exe = out_s.simple_bind(ctx=default_context(), data=shape, label=shape, grad_req=grad_req)
+            arg_map = dict(zip(out_s.list_arguments(), exe.arg_arrays))
+            grad_map = dict(zip(out_s.list_arguments(), exe.grad_arrays))
+            # init data
+            data = mx.random.uniform(-1, -1, shape)
+            arg_map["data"][:] = data
+            atol = 1e-5
+            density = 0.5
+            stype = 'default'
+            label = arg_map["label"]
+            label[:] = rand_ndarray(shape, stype, density=density)
+            exe.forward(is_train=True)
+            exe.backward()
+            np_out = forward(data.asnumpy())
+            out_grad = backward(np_out, label.asnumpy().reshape(np_out.shape)) / shape[1]
+            assert_almost_equal(exe.outputs[0].asnumpy(), np_out, atol=atol)
+            assert_almost_equal(grad_map["data"].asnumpy(), out_grad, atol=atol)
+
+        check_regression(mx.symbol.LogisticRegressionOutput,
+                         lambda x: 1.0 / (1.0 + np.exp(-x)),
+                         lambda x, y: x - y,
+                         shape)
+        check_regression(mx.symbol.LinearRegressionOutput,
+                         lambda x: x,
+                         lambda x, y: x - y,
+                         shape)
+
+    def check_l2_normalization():
+        x = nd.ones((2, LARGE_X*2))
+        x[0] = 3
+        x[1] = 4
+        # Channel Mode
+        z = x.reshape(1, 2, LARGE_X*2)
+        y = nd.L2Normalization(z, mode='channel')
+        assert y[0][0][0] == 0.6
+        assert y[0][0][-1] == 0.6
+        assert y[0][1][0] == 0.8
+        assert y[0][1][-1] == 0.8
+        # Instance Mode
+        z = x.T
+        y = nd.L2Normalization(z, mode='instance')
+        assert y[0][0] == 0.6
+        assert y[0][1] == 0.8
+        assert y[-1][0] == 0.6
+        assert y[-1][1] == 0.8
+        # Spatial Mode
+        z = z.reshape(1, 200000000, 2)
+        y = nd.L2Normalization(z, mode='spatial')
+        assert y[0][0][0] == 0.6
+        assert y[0][0][1] == 0.8
+        assert y[0][-1][0] == 0.6
+        assert y[0][-1][1] == 0.8
+
+    def check_instance_norm():
+        dtype = np.float32
+        forward_check_eps = 1E-3
+        axis = -1
+        eps = 1E-5
+        in_shape = (LARGE_X, 1, SMALL_Y)
+        ctx = mx.cpu()
+
+        # Implementation of instance normalization using numpy
+        def npy_instance_norm(data, gamma, beta, axis, eps=1E-5):
+            if axis < 0:
+                axis += data.ndim
+            broadcast_shape = [1 for _ in range(data.ndim)]
+            broadcast_shape[axis] = data.shape[axis]
+            mean = data.mean(axis=axis, keepdims=True).astype(dtype)
+            var = data.var(axis=axis, keepdims=True).astype(dtype)
+            std = np.sqrt(var + dtype(eps)).astype(dtype)
+            out = gamma * (data - mean) / std + \
+                  beta
+            return out
+        data = np.random.normal(0, 1, in_shape).astype(dtype)
+        gamma = np.random.normal(0, 1, (1,)).astype(dtype)
+        beta = np.random.normal(0, 1, (1,)).astype(dtype)
+        data_s = mx.symbol.Variable('data')
+        gamma_s = mx.symbol.Variable('gamma')
+        beta_s = mx.symbol.Variable('beta')
+        out_s = mx.symbol.InstanceNorm(data=data_s, gamma=gamma_s, beta=beta_s,
+                                       eps=eps)
+        exe = out_s.simple_bind(ctx, data=in_shape)
+        exe.arg_dict['data'][:] = data
+        exe.arg_dict['gamma'][:] = gamma
+        exe.arg_dict['beta'][:] = beta
+        out_nd = exe.forward()[0]
+        # Calls implementation of instance norm in numpy and compares the output
+        out = npy_instance_norm(data, gamma, beta, axis, eps)
+        assert_almost_equal(out, out_nd.asnumpy(), forward_check_eps,
+                            forward_check_eps)
+
+    check_gluon_embedding()
+    check_fully_connected()
+    check_dense()
+    check_softmax()
+    check_softmax_cross_entropy()
+    check_softmax_output()
+    check_log_softmax()
+    check_leaky_relu()
+    check_pooling()
+    check_layer_norm()
+    check_dropout()
+    check_activation()
+    check_batchnorm()
+    check_relu()
+    check_sigmoid()
+    check_linear_and_logistic_regression()
+    check_l2_normalization()
+    check_instance_norm()
+
+
+def test_tensor():
+    def check_ndarray_zeros():
+        a = nd.zeros(shape=(LARGE_X, SMALL_Y))
+        assert a[-1][0] == 0
+        assert a.shape == (LARGE_X, SMALL_Y)
+        assert a.size == LARGE_SIZE
+
+    def check_ndarray_ones():
+        a = nd.ones(shape=(LARGE_X, SMALL_Y))
+        assert a[-1][0] == 1
+        assert nd.sum(a).asnumpy() == LARGE_SIZE
+
+    @with_seed()
+    def check_ndarray_random_uniform():
+        a = nd.random.uniform(shape=(LARGE_X, SMALL_Y))
+        assert a[-1][0] != 0
+
+    @with_seed()
+    def check_ndarray_random_randint():
+        a = nd.random.randint(100, 10000, shape=(LARGE_X, SMALL_Y))
+        assert a.shape == (LARGE_X, SMALL_Y)
+        # check if randint can generate value greater than 2**32 (large)
+        low_large_value = 2**32
+        high_large_value = 2**34
+        a = nd.random.randint(low_large_value, high_large_value, dtype=np.int64)
+        low = mx.nd.array([low_large_value], dtype='int64')
+        high = mx.nd.array([high_large_value], dtype='int64')
+        assert a >= low and a < high
+        assert a[-1][0].dtype == np.int64
+
+    @with_seed()
+    def check_ndarray_random_exponential():
+        scale_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_X))
+        a = nd.random.exponential(scale=scale_array, shape=(SMALL_X, SMALL_Y))
+        assert a[-1][0][0][0] >= 0
+        assert a.shape == (MEDIUM_X, SMALL_X, SMALL_X, SMALL_Y)
+
+    @with_seed()
+    def check_ndarray_random_gamma():
+        alpha_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_X))
+        beta_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_X))
+        a = nd.random.gamma(alpha=alpha_array, beta=beta_array,
+                            shape=(SMALL_X, SMALL_Y))
+        assert a[-1][0][0][0] >= 0
+        assert a.shape == (MEDIUM_X, SMALL_X, SMALL_X, SMALL_Y)
+
+    @with_seed()
+    def check_ndarray_random_multinomial():
+        # test 1 shape dimension
+        probs = nd.random.uniform(shape=(LARGE_X, SMALL_Y))
+        a = nd.random.multinomial(probs)
+        assert a[-1] >= 0
+        assert a.shape == (LARGE_X,)
+        # test for NDArray multi-dimension shape
+        a = nd.random.multinomial(probs, shape=(2, SMALL_Y))
+        assert a[-1][0][0] >= 0
+        assert a.shape == (LARGE_X, 2, SMALL_Y)
+        # test log_likelihood output shape
+        a = nd.random.multinomial(probs, shape=(2, SMALL_Y), get_prob=True)
+        assert a[0][0][0][0] >= 0
+        assert a[0].shape == (LARGE_X, 2, SMALL_Y) and a[0].shape == a[1].shape
+
+    @with_seed()
+    def check_ndarray_random_generalized_negative_binomial():
+        alpha_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_X))
+        mu_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_X))
+        a = nd.random.generalized_negative_binomial(mu=mu_array, alpha=alpha_array,
+                                                    shape=(SMALL_X, SMALL_Y))
+        assert a[-1][0][0][0] >= 0
+        assert a.shape == (MEDIUM_X, SMALL_X, SMALL_X, SMALL_Y)
+
+    @with_seed()
+    def check_ndarray_random_negative_binomial():
+        k_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_X))
+        p_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_X))
+        a = nd.random.negative_binomial(k=k_array, p=p_array,
+                                        shape=(SMALL_X, SMALL_Y))
+        assert a[-1][0][0][0] >= 0
+        assert a.shape == (MEDIUM_X, SMALL_X, SMALL_X, SMALL_Y)
+
+    @with_seed()
+    def check_ndarray_random_normal():
+        scale_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_X))
+        loc_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_X))
+        a = nd.random.normal(loc=loc_array, scale=scale_array,
+                             shape=(SMALL_X, SMALL_Y))
+        assert a.shape == (MEDIUM_X, SMALL_X, SMALL_X, SMALL_Y)
+
+    @with_seed()
+    def check_ndarray_random_poisson():
+        lambda_array = nd.random.uniform(shape=(MEDIUM_X, SMALL_X))
+        a = nd.random.poisson(lam=lambda_array, shape=(SMALL_X, SMALL_Y))
+        assert a[-1][0][0][0] >= 0
+        assert a.shape == (MEDIUM_X, SMALL_X, SMALL_X, SMALL_Y)
+
+    @with_seed()
+    def check_ndarray_random_randn():
+        a = nd.random.randn(LARGE_X, SMALL_Y)
+        assert a.shape == (LARGE_X, SMALL_Y)
+        # TODO: Once PR #15772 for randn ndarray dtype for loc,scale param merged
+        # Add check for (x,y,m,n) where x,y shape of loc,scale and m,n input shape
+
+    @with_seed()
+    def check_ndarray_random_shuffle():
+        a = nd.ones(shape=(LARGE_X, SMALL_Y))
+        a[-1] = 3  # assign 3 to entire last row
+        a = nd.random.shuffle(a)
+        # slice first column from shuffled array
+        # pass LARGE_X values to numpy instead of LARGE_X*SMALL_Y
+        # could have assigned to last column (so as to pass SMALL_Y)
+        # but shuffle operation is performed along first axis
+        unique_a = np.unique(a[:, 0].asnumpy())
+        assert len(unique_a) == 2  # only 2 unique values
+        assert unique_a[0] == 1  # first unique value is 1
+        assert unique_a[1] == 3  # second unique value is 3
+        assert a.shape == (LARGE_X, SMALL_Y)
+
+    def check_ndarray_empty():
+        a = nd.empty((LARGE_X, SMALL_Y))
+        assert a.shape == (LARGE_X, SMALL_Y)
+
+    def check_zeros_like():
+        a = nd.array(np.ones((SMALL_Y, LARGE_X)))
+        b = nd.zeros_like(a)
+        assert b[-1][-1] == 0
+        assert b.shape == a.shape
+
+    def check_ones_like():
+        a = nd.array(np.zeros((SMALL_Y, LARGE_X)))
+        b = nd.ones_like(a)
+        assert b[-1][-1] == 1
+        assert b.shape == a.shape
+
+    def check_broadcast():
+        a = nd.ones(shape=(LARGE_X, SMALL_Y))
+        b = nd.arange(0, LARGE_X).reshape(LARGE_X, 1)
+        res = nd.broadcast_to(b, shape=(b.shape[0], SMALL_Y))
+        assert np.sum(res[-1].asnumpy() == LARGE_X) == res.shape[1]
+        res = mx.nd.broadcast_like(b, a)
+        assert np.sum(res[-1].asnumpy() == LARGE_X) == a.shape[1]
+
+    def check_clip():
+        a = nd.arange(0, LARGE_X * SMALL_Y).reshape(LARGE_X, SMALL_Y)
+        res = nd.clip(a, a_min=100, a_max=1000)
+        assert np.sum(res[-1].asnumpy() == 1000) == a.shape[1]
+
+    def check_split():
+        a = nd.arange(0, LARGE_X * SMALL_Y).reshape(LARGE_X, SMALL_Y)
+        outs = nd.split(a, num_outputs=SMALL_Y, axis=1)
+        result = sum(1 for i, v in enumerate(outs) if i == v[0].asnumpy())
+        assert result == a.shape[1]
+
+    def check_tile():
+        a = nd.arange(0, LARGE_X).reshape(LARGE_X, 1)
+        b = nd.tile(a, reps=(1, SMALL_Y))
+        assert np.sum(b[-1].asnumpy() == LARGE_X) == b.shape[1]
+
+    def check_take():
+        a = nd.ones(shape=(LARGE_X, SMALL_Y))
+        idx = nd.arange(LARGE_X - 1000, LARGE_X)
+        res = nd.take(a, idx)
+        assert np.sum(res[-1].asnumpy() == 1) == res.shape[1]
+
+    def check_slice():
+        a = nd.ones(shape=(LARGE_X, SMALL_Y))
+        res = nd.slice(a, begin=(LARGE_X-1000, 1), end=(LARGE_X, SMALL_Y))
+        assert np.sum(res[-1].asnumpy() == 1) == res.shape[1]
+
+    def check_slice_assign():
+        a = nd.ones(shape=(LARGE_X, SMALL_Y))
+        a[LARGE_X-1:LARGE_X] = 1000
+        assert np.sum(a[-1].asnumpy() == 1000) == a.shape[1]
+
+    def check_slice_like():
+        a = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
+        b = nd.array(np.ones((SMALL_Y//2, LARGE_X//2)))
+        c = nd.slice_like(a, b)
+        d = nd.slice_like(a, b, axes=(0))
+        e = nd.slice_like(a, b, axes=(-1))
+        assert c.shape == b.shape
+        assert d.shape[0] == b.shape[0]
+        assert e.shape[-1] == b.shape[-1]
+        assert c[0][-1] == 0
+        assert d[-1][0] == (SMALL_Y//2-1)
+        assert e[-1][-1] == (SMALL_Y-1)
+
+    def check_slice_axis():
+        a = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
+        c = nd.slice_axis(a, axis=0, begin=0, end=SMALL_Y//2)
+        d = nd.slice_axis(a, axis=1, begin=0, end=LARGE_X//2)
+        assert c.shape[0] == a.shape[0]//2
+        assert d.shape[1] == a.shape[1]//2
+        assert c[-1][0] == (SMALL_Y//2-1)
+        assert d[-1][-1] == (SMALL_Y-1)
+
+    def check_expand_dims():
+        a = nd.ones(shape=(LARGE_X, SMALL_Y))
+        res = nd.expand_dims(a, axis=1)
+        res.wait_to_read()
+        assert a[0][0][0] == 1
+        assert res.shape == (a.shape[0], 1, a.shape[1])
+
+    def check_squeeze():
+        a = nd.ones(shape=(LARGE_X, SMALL_Y))
+        data = nd.expand_dims(a, axis=1)
+        res = nd.squeeze(data)
+        assert res.shape == a.shape
+
+    def check_broadcast_div():
+        a = nd.ones(shape=(LARGE_X, SMALL_Y))
+        b = nd.ones(shape=(LARGE_X, 1)) * 2
+        res = a / b
+        assert np.sum(res[-1].asnumpy() == 0.5) == a.shape[1]
+
+    def check_where():
+        a = nd.ones(shape=(LARGE_X, SMALL_Y))
+        b = nd.arange(0, LARGE_X * SMALL_Y).reshape(LARGE_X, SMALL_Y)
+        res = nd.where(b > 100, a, b)
+        assert np.sum(res[-1].asnumpy() == 1) == b.shape[1]
+        csr_cond = nd.sparse.cast_storage(b < 10, 'csr')
+        res = nd.sparse.where(csr_cond, a, b)
+        assert np.sum(res[0].asnumpy() == 1) == 10
+
+    def check_pick():
+        a = mx.nd.ones(shape=(256 * 35, 1024 * 1024))
+        b = mx.nd.ones(shape=(256 * 35, ))
+        res = mx.nd.pick(a, b)
+        assert res.shape == b.shape
+
+    def check_depthtospace():
+        def numpy_depth_to_space(x, blocksize):
+            b, c, h, w = x.shape[0], x.shape[1], x.shape[2], x.shape[3]
+            tmp = np.reshape(x, [b, blocksize, blocksize, c // (blocksize**2), h,
+                             w])
+            tmp = np.transpose(tmp, [0, 3, 4, 1, 5, 2])
+            y = np.reshape(tmp, [b, c // (blocksize**2), h * blocksize,
+                           w * blocksize])
+            return y
+
+        shape_inp = (LARGE_X, 8, 4, 2)
+        data = rand_ndarray(shape_inp, 'default')
+        data_np = data.asnumpy()
+        expected = numpy_depth_to_space(data_np, 2)
+        output = mx.nd.depth_to_space(data, 2)
+        assert_almost_equal(output.asnumpy(), expected, atol=1e-3, rtol=1e-3)
+
+    def check_spacetodepth():
+        def numpy_space_to_depth(x, blocksize):
+            b, c, h, w = x.shape[0], x.shape[1], x.shape[2], x.shape[3]
+            tmp = np.reshape(x, [b, c, h // blocksize, blocksize, w // blocksize,
+                             blocksize])
+            tmp = np.transpose(tmp, [0, 3, 5, 1, 2, 4])
+            y = np.reshape(tmp, [b, c * (blocksize**2), h // blocksize,
+                           w // blocksize])
+            return y
+
+        shape_inp = (LARGE_X, 2, 8, 4)
+        data = rand_ndarray(shape_inp, 'default')
+        data_np = data.asnumpy()
+        expected = numpy_space_to_depth(data_np, 2)
+        output = mx.nd.space_to_depth(data, 2)
+        assert_almost_equal(output.asnumpy(), expected, atol=1e-3, rtol=1e-3)
+
+    @with_seed()
+    def check_diag():
+        a_np = np.random.random((LARGE_X, SMALL_Y)).astype(np.float32)
+        a = mx.nd.array(a_np)
+
+        # k == 0
+        r = mx.nd.diag(a)
+        assert_almost_equal(r.asnumpy(), np.diag(a_np))
+
+        # k == 1
+        k = 1
+        r = mx.nd.diag(a, k=k)
+        assert_almost_equal(r.asnumpy(), np.diag(a_np, k=k))
+
+        # k == -1
+        k = -1
+        r = mx.nd.diag(a, k=k)
+        assert_almost_equal(r.asnumpy(), np.diag(a_np, k=k))
+
+        # random k
+        k = np.random.randint(-min(LARGE_X, SMALL_Y) + 1, min(LARGE_X, SMALL_Y))
+        r = mx.nd.diag(a, k=k)
+        assert_almost_equal(r.asnumpy(), np.diag(a_np, k=k))
+
+    @with_seed()
+    def check_ravel_multi_index():
+        x1, y1 = rand_coord_2d((LARGE_X - 100), LARGE_X, 10, SMALL_Y)
+        x2, y2 = rand_coord_2d((LARGE_X - 200), LARGE_X, 9, SMALL_Y)
+        x3, y3 = rand_coord_2d((LARGE_X - 300), LARGE_X, 8, SMALL_Y)
+        indices_2d = [[x1, x2, x3], [y1, y2, y3]]
+        idx = mx.nd.ravel_multi_index(mx.nd.array(indices_2d, dtype=np.int64),
+                                      shape=(LARGE_X, SMALL_Y))
+        idx_numpy = np.ravel_multi_index(indices_2d, (LARGE_X, SMALL_Y))
+        assert np.sum(1 for i in range(idx.size) if idx[i] == idx_numpy[i]) == 3
+
+    @with_seed()
+    def check_unravel_index():
+        x1, y1 = rand_coord_2d((LARGE_X - 100), LARGE_X, 10, SMALL_Y)
+        x2, y2 = rand_coord_2d((LARGE_X - 200), LARGE_X, 9, SMALL_Y)
+        x3, y3 = rand_coord_2d((LARGE_X - 300), LARGE_X, 8, SMALL_Y)
+        original_2d_indices = [[x1, x2, x3], [y1, y2, y3]]
+        idx_numpy = np.ravel_multi_index(original_2d_indices, (LARGE_X, SMALL_Y))
+        indices_2d = mx.nd.unravel_index(mx.nd.array(idx_numpy, dtype=np.int64),
+                                         shape=(LARGE_X, SMALL_Y))
+        assert (indices_2d.asnumpy() == np.array(original_2d_indices)).all()
+
+    def check_transpose():
+        check_dtypes = [np.float32, np.int64]
+        for dtype in check_dtypes:
+            b = create_2d_tensor(rows=LARGE_X, columns=SMALL_Y, dtype=dtype)
+            t = b.T
+            assert t.shape == (SMALL_Y, LARGE_X)
+            ref_out = np.transpose(b.asnumpy())
+            assert_almost_equal(t.asnumpy(), ref_out, rtol=1e-10)
+
+    def check_swapaxes():
+        b = create_2d_tensor(rows=LARGE_X, columns=SMALL_Y)
+        t = nd.swapaxes(b, dim1=0, dim2=1)
+        assert np.sum(t[:, -1].asnumpy() == (LARGE_X - 1)) == b.shape[1]
         assert t.shape == (SMALL_Y, LARGE_X)
-        ref_out = np.transpose(b.asnumpy())
-        assert_almost_equal(t.asnumpy(), ref_out, rtol=1e-10)
-        
-
-
-def test_swapaxes():
-    b = create_2d_tensor(rows=LARGE_X, columns=SMALL_Y)
-    t = nd.swapaxes(b, dim1=0, dim2=1)
-    assert np.sum(t[:, -1].asnumpy() == (LARGE_X - 1)) == b.shape[1]
-    assert t.shape == (SMALL_Y, LARGE_X)
-
-
-def test_flip():
-    b = create_2d_tensor(rows=LARGE_X, columns=SMALL_Y)
-    t = nd.flip(b, axis=0)
-    assert np.sum(t[-1, :].asnumpy() == 0) == b.shape[1]
-    assert t.shape == (LARGE_X, SMALL_Y)
-
-
-def test_softmax():
-    input_data = mx.nd.ones((SMALL_Y, LARGE_X))
-    for axis in [0, 1]:
-        true_output = np.full((SMALL_Y, LARGE_X), (1 / input_data.shape[axis]))
-        output = nd.softmax(input_data, axis=axis)
-        assert_almost_equal(output.asnumpy(), true_output, rtol=1e-5, atol=1e-5)
-
-
-def test_argsort():
-    b = create_2d_tensor(rows=LARGE_X, columns=SMALL_Y)
-    s = nd.argsort(b, axis=0, is_ascend=False, dtype=np.int64)
-    mx.nd.waitall()
-    assert (s[0].asnumpy() == (LARGE_X - 1)).all()
-
-
-def test_sort():
-    b = create_2d_tensor(rows=LARGE_X, columns=SMALL_Y)
-    s = nd.sort(b, axis=0, is_ascend=False)
-    assert np.sum(s[-1][SMALL_Y//2:SMALL_Y].asnumpy() == 0).all()
-    s = nd.sort(b, is_ascend=False)
-    assert np.sum(s[0].asnumpy() == 0).all()
-
-
-def test_topk():
-    b = create_2d_tensor(rows=LARGE_X, columns=SMALL_Y)
-    k = nd.topk(b, k=10, axis=0, dtype=np.int64)
-    assert np.sum(k.asnumpy() == (LARGE_X - 1)) == SMALL_Y
-    ind, val = mx.nd.topk(b, k=3, axis=0, dtype=np.int64, ret_typ="both",
-                          is_ascend=False)
-    assert np.all(ind == val)
-    b = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
-    l = nd.topk(b, k=1, axis=-1, dtype=np.int64, ret_typ="value")
-    assert l.sum() == np.sum(np.arange(0, SMALL_Y))
-
-
-def test_exponent_logarithm_operators():
-    a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
-    # exponent
-    result = nd.exp(a)
-    assert result[0][-1] == 7.389056
-    assert result.shape == a.shape
-
-    # exponent minus 1
-    result = nd.expm1(a)
-    assert result[0][-1] == 6.389056
-    assert result.shape == a.shape
-
-    # log2
-    result = nd.log2(a)
-    assert result[0][-1] == 1
-    assert result.shape == a.shape
-
-    # log10
-    result = nd.log10(a)
-    assert result[0][-1] == 0.30103
-    assert result.shape == a.shape
-
-    # log1p
-    result = nd.log1p(a)
-    assert result[0][-1] == 1.0986123
-    assert result.shape == a.shape
-
-    # log
-    result = nd.log(a)
-    assert result[0][-1] == 0.6931472
-    assert result.shape == a.shape
-
-
-def test_power_operators():
-    a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
-    # sqrt
-    result = nd.sqrt(a)
-    assert result[0][-1] == 1.4142135
-    assert result.shape == a.shape
-
-    # rsqrt
-    result = nd.rsqrt(a)
-    assert result[0][-1] == 0.70710677
-    assert result.shape == a.shape
-
-    # cbrt
-    result = nd.cbrt(a)
-    assert result[0][-1] == 1.2599211
-    assert result.shape == a.shape
-
-    # rcbrt
-    result = nd.rcbrt(a)
-    assert result[0][-1] == 0.7937005
-    assert result.shape == a.shape
-
-    # square
-    result = nd.square(a)
-    assert result[0][-1] == 4
-    assert result.shape == a.shape
-
-    # reciprocal
-    result = nd.reciprocal(a)
-    assert result[0][-1] == 0.5
-    assert result.shape == a.shape
-
-
-def test_sequence_mask():
-    # Sequence Mask input [max_sequence_length, batch_size, other_feature_dims]
-    # test with input batch_size = 2
-    a = nd.arange(0, LARGE_X * SMALL_Y * 2).reshape(LARGE_X, 2, SMALL_Y)
-
-    # test as identity operator
-    b = nd.SequenceMask(a)
-    assert b[-1][0][1] == a[-1][0][1]
-    assert b.shape == a.shape
-
-    # test with default mask
-    b = nd.SequenceMask(a, sequence_length=nd.array([1, 1]),
-                        use_sequence_length=True)
-    assert b[0][1][-1] == a[0][1][-1]  # first sequence of each batch kept
-    assert b[-1][-1][-1] != a[-1][-1][-1]  # rest sequences masked
-    assert b[-1][-1][-1] == 0
-
-    # test with mask value
-    b = nd.SequenceMask(a, sequence_length=nd.array([1, 1]),
-                        use_sequence_length=True, value=-1)
-    assert b[-1][-1][-1] == -1
-
-
-def test_sequence_reverse():
-    a = nd.arange(0, LARGE_X * SMALL_Y * 2).reshape(LARGE_X, 2, SMALL_Y)
-    # test as reverse operator
-    b = nd.SequenceReverse(a)
-    assert b[-1][0][0] == a[0][0][0]
-    assert b.shape == a.shape
-
-    # test with sequence length
-    # 2 rows of batch 1 and 3 rows of batch 2 reversed
-    b = nd.SequenceReverse(a, sequence_length=nd.array([2, 3]),
-                           use_sequence_length=True)
-    assert b[1][0][0] == a[0][0][0]  # check if reversed
-    assert b[-1][0][0] == a[-1][0][0]  # check if intact
-    assert b.shape == a.shape
-
-
-def test_sequence_last():
-    a = nd.arange(0, LARGE_X * SMALL_Y * 2).reshape(LARGE_X, 2, SMALL_Y)
-
-    # test if returns last sequence
-    b = nd.SequenceLast(a)
-    assert_almost_equal(b.asnumpy(), a[-1].asnumpy())  # only checks for (2, SMALL_Y) tensor
-    assert b.shape == (2, SMALL_Y)
-
-    # test with sequence length
-    # parameter sequence_length - NDArray with shape (batch_size)
-    # (2,3) indicates 2nd sequence from batch 1 and 3rd sequence from batch 2
-    b = nd.SequenceLast(a, sequence_length=mx.nd.array([2, 3]),
-                        use_sequence_length=True)
-    # check if it takes 2nd sequence from the first batch
-    assert b[0][-1] == a[1][0][-1]
-
-
-def test_softmax_cross_entropy():
-    # dtype of input data, mxnet cross entropy set explicitly to float64
-    # numpy implicitly takes care of double precision
-    batch_size = SMALL_Y
-    num_labels = LARGE_X
-    input_data = mx.nd.ones((batch_size, num_labels), dtype="float64")
-    input_label = mx.nd.zeros((batch_size,), dtype="float64")
-
-    true_softmax = np.full((batch_size, num_labels), (1 / num_labels))
-    # use 1/batch_size when softmax axis=0
-    # here 1/num_labels since softmax_cross_entropy uses default axis
-    # by default axis=1
-    np_one_hot_label = np.zeros((batch_size, num_labels))
-    np_one_hot_label[:, 0] = 1
-
-    true_softmax_cross_entropy = np.sum(-np.log(true_softmax) *
-                                        np_one_hot_label)
-    mx_softmax_cross_entropy = mx.nd.softmax_cross_entropy(input_data,
-                                                           input_label,
-                                                           dtype="float64")
-    assert_almost_equal(mx_softmax_cross_entropy.asnumpy(),
-                        true_softmax_cross_entropy, rtol=1e-3, atol=1e-5)
-
-
-def test_index_copy():
-    x = mx.nd.zeros((LARGE_X, SMALL_Y))
-    t = mx.nd.arange(1, SMALL_Y + 1).reshape((1, SMALL_Y))
-    index = mx.nd.array([LARGE_X - 1], dtype="int64")
-
-    x = mx.nd.contrib.index_copy(x, index, t)
-    assert x[-1][-1] == t[0][-1]
-
-
-def testSoftmaxOutput():
-    x = mx.sym.Variable('x')
-    label = mx.sym.Variable('label')
-    x_nd = mx.nd.ones((LARGE_X, SMALL_Y))
-    grad_x = mx.nd.zeros((LARGE_X, SMALL_Y))
-    label_nd = mx.nd.ones((LARGE_X))
-
-    sym = mx.sym.SoftmaxOutput(data=x, label=label, ignore_label=0,
-                               use_ignore=False)
-
-    ex = sym.bind(ctx=default_context(), args={'x': x_nd, 'label': label_nd},
-                  args_grad=None)
-    ex.forward(is_train=False)
-    softmax_out = ex.outputs[0][0].asnumpy()
-    expected_softmax_out = (1 / SMALL_Y) * mx.nd.ones((SMALL_Y)).asnumpy()
-    assert np.isclose(softmax_out, expected_softmax_out).all()
-
-    ex = sym.bind(ctx=default_context(), args={'x': x_nd, 'label': label_nd},
-                  args_grad={'x': grad_x})
-    ex.forward(is_train=True)
-    softmax_out = ex.outputs[0][0].asnumpy()
-    expected_softmax_out = (1 / SMALL_Y) * mx.nd.ones((SMALL_Y)).asnumpy()
-    assert np.isclose(softmax_out, expected_softmax_out).all()
-
-    ex.backward(is_train=True)
-    grad_out = ex.grad_arrays[0][0].asnumpy()
-    k = int(label_nd[0].asscalar())
-    expected_grad_out = np.zeros((SMALL_Y,))
-    expected_grad_out[k] = -1
-    assert np.isclose(grad_out - softmax_out, expected_grad_out).all()
-
-
-# TODO: correctness of prelu (currently flaky)
-def test_leaky_relu():
-    a = -1*mx.nd.ones((LARGE_X, SMALL_Y))
-
-    def test_leaky():
-        res = mx.nd.LeakyReLU(a, act_type="leaky", slope=0.3)
-        assert_almost_equal(res[-1][-1].asnumpy(), 0.3*a[-1][-1].asnumpy(), atol=1e-3, rtol=1e-3)
-
-    def test_elu():
-        res = mx.nd.LeakyReLU(a, act_type="elu", slope=0.3)
-        assert_almost_equal(res[-1][-1].asnumpy(), 0.3*(np.exp(a[-1][-1].asnumpy())-1), atol=1e-3, rtol=1e-3)
-
-    def test_selu():
-        lam = 1.0507009873554804934193349852946
-        alpha = 1.6732632423543772848170429916717
-        res = mx.nd.LeakyReLU(a, act_type="selu")
-        assert_almost_equal(res[-1][-1].asnumpy(), (lam * alpha * (np.exp(a[-1][-1].asnumpy())-1)), atol=1e-3, rtol=1e-3)
-
-    def test_rrelu():
-        lower = 0.125
-        upper = 0.333999991
-        res = mx.nd.LeakyReLU(a, act_type="rrelu")
-        assert_almost_equal(res[0][-1][-1].asnumpy(), (lower + upper) / 2 * a[-1][-1].asnumpy(), atol=1e-3, rtol=1e-3)
-
-    test_leaky()
-    test_elu()
-    test_selu()
-    test_rrelu()
-
-
-def test_pooling():
-    a = mx.nd.ones((MEDIUM_X, 200, SMALL_Y, SMALL_Y))
-
-    def test_avg_pooling():
-        res = mx.nd.Pooling(a, kernel=(5, 5), pool_type='avg')
-        assert_almost_equal(res[-1][-1][-1][-1].asnumpy(), 1.0000001, atol=1e-3, rtol=1e-3)
-        assert res.shape[-1] == SMALL_Y - 5 + 1
-
-    def test_max_pooling():
-        res = mx.nd.Pooling(a, kernel=(5, 5), pool_type='max')
-        assert_almost_equal(res[-1][-1][-1][-1].asnumpy(), 1., atol=1e-3, rtol=1e-3)
-        assert res.shape[-1] == SMALL_Y - 5 + 1
-
-    def test_sum_pooling():
-        res = mx.nd.Pooling(a, kernel=(5, 5), pool_type='sum')
-        assert_almost_equal(res[-1][-1][-1][-1].asnumpy(), 25, atol=1e-3, rtol=1e-3)
-        assert res.shape[-1] == SMALL_Y - 5 + 1
-
-    def test_lp_pooling():
-        res = mx.nd.Pooling(a, kernel=(5, 5), pool_type='lp', p_value=2)
-        assert_almost_equal(res[-1][-1][-1][-1].asnumpy(), 5., atol=1e-3, rtol=1e-3)
-        assert res.shape[-1] == SMALL_Y - 5 + 1
-
-        res = mx.nd.Pooling(a, kernel=(5, 5), pool_type='lp', p_value=1)
-        assert_almost_equal(res[-1][-1][-1][-1].asnumpy(), 25., atol=1e-3, rtol=1e-3)
-        assert res.shape[-1] == SMALL_Y - 5 + 1
-
-    test_avg_pooling()
-    test_max_pooling()
-    test_sum_pooling()
-    test_lp_pooling()
-
-
-def test_layer_norm():
-    dtype = np.float32
-    forward_check_eps = 1E-3
-    axis = 1
-    eps = 1E-5
-    in_shape = (LARGE_X, SMALL_Y)
-    ctx = mx.cpu()
-
-    def npy_layer_norm(data, gamma, beta, axis=1, eps=1E-5):
-        if axis < 0:
-            axis += data.ndim
-        broadcast_shape = [1 for _ in range(data.ndim)]
-        broadcast_shape[axis] = data.shape[axis]
-        mean = data.mean(axis=axis, keepdims=True).astype(dtype)
-        var = data.var(axis=axis, keepdims=True).astype(dtype)
-        std = np.sqrt(var + dtype(eps)).astype(dtype)
-        out = np.reshape(gamma, broadcast_shape) * (data - mean) / std + \
-              np.reshape(beta, broadcast_shape)
-        return out
-    data = np.random.normal(0, 1, in_shape).astype(dtype)
-    gamma = np.random.normal(0, 1, (in_shape[axis],)).astype(dtype)
-    beta = np.random.normal(0, 1, (in_shape[axis],)).astype(dtype)
-    data_s = mx.symbol.Variable('data')
-    gamma_s = mx.symbol.Variable('gamma')
-    beta_s = mx.symbol.Variable('beta')
-    out_s = mx.symbol.LayerNorm(data=data_s, gamma=gamma_s, beta=beta_s,
-                                axis=axis, eps=eps)
-    exe = out_s.simple_bind(ctx, data=in_shape)
-    exe.arg_dict['data'][:] = data
-    exe.arg_dict['gamma'][:] = gamma
-    exe.arg_dict['beta'][:] = beta
-    out_nd = exe.forward()[0]
-    out = npy_layer_norm(data, gamma, beta, axis, eps)
-    assert_almost_equal(out, out_nd.asnumpy(), forward_check_eps,
-                        forward_check_eps)
-
-
-# TODO: correctness of dropout
-# currently only test for dropout to work
-# since testing for correctness involves flakiness issue #14288
-def test_dropout():
-    shape = (LARGE_X, SMALL_Y)
-    x = mx.sym.var('data')
-    y = mx.sym.Dropout(x, p=1, cudnn_off=True)
-    exe = y.simple_bind(ctx=default_context(), data=shape)
-    exe.arg_arrays[0][:] = 1
-    out = exe.forward(is_train=True)
-    nd.waitall()
-    assert out[0].shape == shape
-
-
-def test_activation():
-    x = mx.nd.ones((LARGE_X, SMALL_Y))
-    test_x = -2
-    x[-1, -1] = test_x
-
-    # Hyperbolic tangent (tanh)
-    # y = (exp(x)-exp(-x))/(exp(x)+exp(-x))
-    y = mx.nd.Activation(x, act_type="tanh")
-    tanh_x = ((np.exp(test_x)-np.exp(-test_x))/(np.exp(test_x)+np.exp(-test_x)))
-    assert y[-1][-1] == np.float32(tanh_x)
-
-    # Recitified Linear Unit (relu)
-    # y = max(x,0)
-    y = mx.nd.Activation(x, act_type="relu")
-    assert y[-1][-1] == 0
-
-    # Sigmoid
-    # y = x/(1+abs(x))
-    y = mx.nd.Activation(x, act_type="sigmoid")
-    sigmoid_x = (1/(1+math.exp(-test_x)))
-    assert_almost_equal(y[-1][-1].asnumpy(), np.float32(sigmoid_x), atol=1e-3, rtol=1e-3)
-
-    # Soft Sign
-    # y = 1/(1+exp(-x))
-    y = mx.nd.Activation(x, act_type="softsign")
-    softsign_x = (test_x/(1+abs(test_x)))
-    assert y[-1][-1] == np.float32(softsign_x)
-
-
-# TODO: correctness of batchnorm
-# in future, we could test if mean, var of output
-# matches target output's mean, var
-def test_batchnorm():
-    def get_np_mean_var(data, running_mean, running_var, eps, use_global_status=True):
-        if not use_global_status:
-            # train mode, calculate the real mean and var
-            mean = np.mean(data, axis=(0, 2, 3))
-            mean_broad = np.expand_dims(mean, axis=0)
-            mean_broad = np.expand_dims(mean_broad, axis=2)
-            mean_broad = np.expand_dims(mean_broad, axis=3)
-            mean_broad = np.broadcast_to(mean_broad, data.shape)
-            var = np.square(data - mean_broad)
-            var = np.mean(var, axis=(0, 2, 3))
-        else:
-            # inference mode, use running_mean and running_var instead
-            mean = np.full((data.shape[1],), running_mean)
-            var = np.full((data.shape[1],), running_var)
-
-        # calculate the inverse of standard variance
-        invstdvar = 1. / np.sqrt(var + eps)
-        return mean, invstdvar
-
-    # Here use 4D input to cover mkldnn BN and non-mkldnn BN
-    shape = (1, 2, LARGE_X, SMALL_Y)
-    axis = 1  # default
-    eps = 1e-3
-
-    nch = shape[axis]
-    data = mx.nd.ones(shape=shape)
-    bn_gamma = mx.nd.random.uniform(shape=(nch,))
-    bn_beta = mx.nd.random.uniform(shape=(nch,))
-    bn_running_mean = mx.nd.zeros(nch)
-    bn_running_var = mx.nd.ones(nch)
-
-    output = mx.nd.BatchNorm(data, bn_gamma, bn_beta,
-                             bn_running_mean, bn_running_var, output_mean_var=True)
-    assert output[0].shape == shape
-    mean, invstdvar = output[1], output[2]
-
-    np_mean, np_invstdvar = get_np_mean_var(data.asnumpy(), bn_running_mean.asnumpy(), bn_running_var.asnumpy(),
-                                            eps, use_global_status=True)
-    assert_almost_equal(mean.asnumpy(), np_mean)
-    assert_almost_equal(invstdvar.asnumpy(), np_invstdvar)
-
-
-def test_elemwise_add():
-    a = nd.ones(shape=(LARGE_X, SMALL_Y))
-    b = nd.ones(shape=(LARGE_X, SMALL_Y))
-    res = nd.elemwise_add(a, b)
-    assert np.sum(res[-1].asnumpy() == 2) == a.shape[1]
-
-
-def test_add():
-    a = nd.ones(shape=(LARGE_X, SMALL_Y))
-    b = nd.ones(shape=(LARGE_X, SMALL_Y))
-    c = b.__add__(a)
-    assert c[0][-1] == 2
-    assert c.shape == a.shape
-
-
-def test_sub():
-    a = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
-    b = nd.ones(shape=(LARGE_X, SMALL_Y))
-    c = b.__sub__(a)
-    assert c[0][-1] == -2
-    assert c.shape == a.shape
-
-
-def test_rsub():
-    a = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
-    b = nd.ones(shape=(LARGE_X, SMALL_Y))
-    c = b.__rsub__(a)
-    assert c[0][-1] == 2
-    assert c.shape == a.shape
-
-
-def test_neg():
-    a = nd.ones(shape=(LARGE_X, SMALL_Y))
-    c = a.__neg__()
-    assert c[0][-1] == -1
-    assert c.shape == a.shape
-
-
-def test_mul():
-    a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
-    b = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
-    c = b.__mul__(a)
-    assert c[0][-1] == 6
-    assert c.shape == a.shape
-
-
-def test_div():
-    a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
-    b = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
-    c = b.__div__(a)
-    mx_divide = nd.divide(b, a)
-    assert c[0][-1] == 3/2
-    assert mx_divide[0][-1] == c[0][-1]
-    assert c.shape == a.shape
-
-
-def test_rdiv():
-    a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
-    b = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
-    c = b.__rdiv__(a)
-    assert c[0][-1] == 2/3
-    assert c.shape == a.shape
-
-
-def test_mod():
-    a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
-    b = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
-    c = b.__mod__(a)
-    assert c[0][-1] == 1
-    assert c.shape == a.shape
-
-
-def test_rmod():
-    a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
-    b = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
-    c = b.__rmod__(a)
-    assert c[0][-1] == 2
-    assert c.shape == a.shape
-
-
-def test_imod():
-    a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
-    b = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
-    c = b.__imod__(a)
-    assert c[0][-1] == 1
-    assert c.shape == a.shape
-
-
-def test_pow():
-    a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
-    b = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
-    c = b.__pow__(a)
-    assert c[0][-1] == 9
-    assert c.shape == a.shape
-
-
-def test_rpow():
-    a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
-    b = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
-    c = b.__rpow__(a)
-    assert c[0][-1] == 8
-    assert c.shape == a.shape
-
-
-def test_shape():
-    b = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
-    mx.nd.waitall()
-    assert b.shape == (SMALL_Y, LARGE_X)
-
-
-def test_size():
-    b = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
-    mx.nd.waitall()
-    assert b.size == LARGE_SIZE
-
-
-def test_copy():
-    a = nd.ones((SMALL_Y, LARGE_X))
-    b = a.copy()
-    nd.waitall()
-    assert b.shape == a.shape
-    assert b.size == LARGE_SIZE
-
-
-def test_copy_to():
-    a = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
-    b = nd.array(np.zeros((SMALL_Y, LARGE_X)))
-    c = a.copyto(b)
-    assert c is b
-    assert b[-1][-1] == SMALL_Y-1
-
-
-def test_zeros_like():
-    a = nd.array(np.ones((SMALL_Y, LARGE_X)))
-    b = nd.zeros_like(a)
-    assert b[-1][-1] == 0
-    assert b.shape == a.shape
 
-
-def test_ones_like():
-    a = nd.array(np.zeros((SMALL_Y, LARGE_X)))
-    b = nd.ones_like(a)
-    assert b[-1][-1] == 1
-    assert b.shape == a.shape
-
-
-def test_reshape_like():
-    a = nd.array(np.zeros((SMALL_Y, LARGE_X)))
-    b = nd.array(np.zeros((SMALL_Y//2, LARGE_X*2)))
-    c = nd.reshape_like(a, b)
-    assert c.shape == (SMALL_Y//2, LARGE_X*2)
-
-
-def test_flatten():
-    test_dtypes = [np.float32, np.int64]
-    for dtype in test_dtypes:
-        a = create_2d_tensor(rows=LARGE_X, columns=SMALL_Y, dtype=dtype).reshape((LARGE_X//2, 2, SMALL_Y))
-        b = nd.flatten(a)
-        # Here we removed the value asserts due to different precision of `int64` and `float32`.
-        # For `float32`, it will lose some precision when `LARGE_X` is too large, that is `LARGE_X-1`
-        # and `LARGE_X-2` can not represent the accurate value in the current situation.
-        assert b.shape == (LARGE_X//2, SMALL_Y*2)
-        assert_almost_equal(b[-1,-1].asnumpy(), a[-1,-1,-1].asnumpy(), rtol=1e-8)
-
-
-def test_concat():
-    a = nd.array(np.ones((SMALL_Y, LARGE_X)))
-    b = nd.array(np.zeros((SMALL_Y, LARGE_X)))
-    for axis in [0, 1]:
-        c = nd.concat(a, b, dim=axis)
-        c.wait_to_read()
-        assert c.shape[axis] == b.shape[axis] * 2
-        assert c.shape[1-axis] == b.shape[1-axis]
-
-def test_stack():
-    a = nd.array(np.ones((SMALL_Y, LARGE_X)))
-    b = nd.array(np.zeros((SMALL_Y, LARGE_X)))
-    c = nd.stack(a, b, axis=1)
-    assert c.shape == (b.shape[0], 2, LARGE_X)
-
-
-def test_broadcast_axes():
-    a = create_2d_tensor(rows=1, columns=LARGE_X)
-    b = nd.broadcast_axis(a, axis=[0], size=2)
-    assert b.shape == (a.shape[0]*2, a.shape[1])
-
-
-def test_sum():
-    a = nd.array(np.ones((SMALL_Y, LARGE_X)))
-    b = nd.sum(a, axis=1)
-    assert b.shape[0] == SMALL_Y
-
-
-def test_prod():
-    a = nd.array(np.ones((SMALL_Y, LARGE_X)))
-    b = nd.prod(a, axis=1)
-    assert b.shape[0] == SMALL_Y
-
-
-def test_mean():
-    a = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
-    b = nd.mean(a, axis=0)
-    assert b[0] == (SMALL_Y/2-1)
-
-
-def test_min():
-    a = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
-    b = nd.min(a, axis=0)
-    assert b[0] == 0
-    assert b[-1] == 0
-
-
-def test_max():
-    a = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
-    b = nd.max(a, axis=0)
-    assert b[0] == (SMALL_Y-1)
-    assert b[-1] == (SMALL_Y-1)
-
-
-def test_norm():
-    a = np.array(np.full((1, LARGE_X), 3))
-    b = np.array(np.full((1, LARGE_X), 4))
-    c = nd.array(np.concatenate((a, b), axis=0))
-    d = nd.norm(c, ord=2, axis=0)
-    e = nd.norm(c, ord=1, axis=0)
-    assert d.shape[0] == LARGE_X
-    assert e.shape[0] == LARGE_X
-    assert d[-1] == 5
-    assert e[-1] == 7
-
-
-def test_argmax():
-    a = np.ones((SMALL_Y, LARGE_X))
-    b = np.zeros((SMALL_Y, LARGE_X))
-    c = nd.array(np.concatenate((a, b), axis=0))
-    d = nd.argmax(c, axis=0)
-    assert d.shape[0] == LARGE_X
-    assert d[-1] == d[0] == 0
-
-
-def test_relu():
-    def frelu(x):
-        return np.maximum(x, 0.0)
-
-    def frelu_grad(x):
-        return 1.0 * (x > 0.0)
-    shape = (SMALL_Y, LARGE_X)
-    x = mx.symbol.Variable("x")
-    y = mx.sym.relu(x)
-    xa = np.random.uniform(low=-1.0, high=1.0, size=shape)
-    eps = 1e-4
-    xa[abs(xa) < eps] = 1.0
-    ya = frelu(xa)
-    ga = frelu_grad(xa)
-    check_symbolic_forward(y, [xa], [ya])
-
-
-def test_sigmoid():
-    def fsigmoid(a):
-        return np.divide(1.0, (1.0 + np.exp(-a)))
-    shape = (SMALL_Y, LARGE_X)
-    x = mx.symbol.Variable("x")
-    y = mx.sym.sigmoid(x)
-    xa = np.random.uniform(low=-1.0, high=1.0, size=shape)
-    ya = fsigmoid(xa)
-    check_symbolic_forward(y, [xa], [ya])
-
-
-def np_softmax(x, axis=-1, temperature=1.0):
-    x = x - np.max(x, axis=axis, keepdims=True)
-    x = np.exp(x/temperature)
-    x /= np.sum(x, axis=axis, keepdims=True)
-    return x
-
-
-def test_log_softmax():
-    ndim = 2
-    shape = (SMALL_Y, LARGE_X)
-    axis = np.random.randint(0, ndim)
-    data = np.random.uniform(-2, 2, size=shape)
-    sym = mx.sym.log_softmax(axis=axis-ndim)
-    check_symbolic_forward(sym, [data], [np.log(np_softmax(data, axis=axis)+1e-20)])
-
-
-def test_iadd():
-    a = nd.array(np.ones((SMALL_Y, LARGE_X)))
-    b = nd.array(np.ones((SMALL_Y, LARGE_X)))
-    c = b + a
-    assert c.shape == a.shape
-    assert c[0][-1] == 2
-
-
-def test_isub():
-    a = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
-    b = nd.array(np.ones((SMALL_Y, LARGE_X)))
-    c = a - b
-    assert c.shape == a.shape
-    assert c[0][-1] == 2
-
-
-def test_imul():
-    a = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
-    b = nd.array(np.ones((SMALL_Y, LARGE_X)))
-    c = b * a
-    assert c.shape == a.shape
-    assert c[0][-1] == 3
-
-
-def test_idiv():
-    a = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 4)))
-    b = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 2)))
-    c = a / b
-    assert c.shape == a.shape
-    assert c[0][-1] == 2
-
-
-def test_eq():
-    a = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
-    b = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
-    c = (a == b)
-    assert np.sum(c[0].asnumpy() == 1).all()
-
-
-def test_neq():
-    a = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 2)))
-    b = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
-    c = (a != b)
-    assert np.sum(c[0].asnumpy() == 1).all()
-
-
-def test_lt():
-    a = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 2)))
-    b = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
-    d = (a <= b)
-    assert np.sum(d[0].asnumpy() == 1).all()
-
-
-def test_lte():
-    a = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 2)))
-    b = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
-    c = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 2)))
-    d = (a <= b)
-    e = (a <= c)
-    assert np.sum(d[0].asnumpy() == 1).all()
-    assert np.sum(e[0].asnumpy() == 1).all()
-
-
-def test_gt():
-    a = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
-    b = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 2)))
-    d = (a >= b)
-    assert np.sum(d[0].asnumpy() == 1).all()
-
-
-def test_gte():
-    a = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
-    b = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 2)))
-    c = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
-    d = (a >= b)
-    e = (a >= c)
-    assert np.sum(d[0].asnumpy() == 1).all()
-    assert np.sum(e[0].asnumpy() == 1).all()
-
-
-def test_slice_like():
-    a = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
-    b = nd.array(np.ones((SMALL_Y//2, LARGE_X//2)))
-    c = nd.slice_like(a, b)
-    d = nd.slice_like(a, b, axes=(0))
-    e = nd.slice_like(a, b, axes=(-1))
-    assert c.shape == b.shape
-    assert d.shape[0] == b.shape[0]
-    assert e.shape[-1] == b.shape[-1]
-    assert c[0][-1] == 0
-    assert d[-1][0] == (SMALL_Y//2-1)
-    assert e[-1][-1] == (SMALL_Y-1)
-
-
-def test_slice_axis():
-    a = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
-    c = nd.slice_axis(a, axis=0, begin=0, end=SMALL_Y//2)
-    d = nd.slice_axis(a, axis=1, begin=0, end=LARGE_X//2)
-    assert c.shape[0] == a.shape[0]//2
-    assert d.shape[1] == a.shape[1]//2
-    assert c[-1][0] == (SMALL_Y//2-1)
-    assert d[-1][-1] == (SMALL_Y-1)
-
-
-def test_one_hot():
-    # default dtype of ndarray is float32 which cannot index elements over 2^32
-    a = nd.array([1, (VLARGE_X - 1)], dtype=np.int64)
-    b = nd.one_hot(a, VLARGE_X)
-    b[0][1] == 1
-    b[1][-1] == 1
-
-
-def test_full():
-    a = nd.full((SMALL_Y, LARGE_X), 3)
-    assert a.shape == (SMALL_Y, LARGE_X)
-    assert a[SMALL_Y//2][LARGE_X//2] == 3
-    assert a[-1][-1] == 3
-
-
-def test_sign():
-    a = mx.nd.random.normal(-1,1, shape=(LARGE_X, SMALL_Y))
-    mx_res = mx.nd.sign(a)
-    assert_almost_equal(mx_res[-1][-1].asnumpy(), np.sign(a[-1][-1].asnumpy()))
-
-
-def test_logical():
-    def test_logical_and(a, b):
-        mx_res = mx.nd.logical_and(a, b)
-        assert_almost_equal(mx_res[-1][-1].asnumpy(), np.logical_and(a[-1][-1].asnumpy(), b[-1][-1].asnumpy()))
-
-    def test_logical_or(a, b):
-        mx_res = mx.nd.logical_or(a, b)
-        assert_almost_equal(mx_res[-1][-1].asnumpy(), np.logical_or(a[-1][-1].asnumpy(), b[-1][-1].asnumpy()))
-
-    def test_logical_not(a, b):
-        mx_res = mx.nd.logical_not(a, b)
-        assert_almost_equal(mx_res[-1][-1].asnumpy(), np.logical_not(a[-1][-1].asnumpy(), b[-1][-1].asnumpy()))
-
-    def test_logical_xor(a, b):
-        mx_res = mx.nd.logical_xor(a, b)
-        assert_almost_equal(mx_res[-1][-1].asnumpy(), np.logical_xor(a[-1][-1].asnumpy(), b[-1][-1].asnumpy()))
-
-    a = mx.nd.ones((LARGE_X, SMALL_Y))
-    b = mx.nd.zeros((LARGE_X, SMALL_Y))
-    test_logical_and(a, b)
-    test_logical_or(a, b)
-    test_logical_not(a, b)
-    test_logical_xor(a, b)
-
-
-def test_regression():
-    shape = (LARGE_X, SMALL_Y)
-
-    def check_regression(symbol, forward, backward, shape):
-        # init executor
-        data_s = mx.symbol.Variable('data')
-        label_s = mx.symbol.Variable('label')
-        out_s = symbol(data=data_s, label=label_s)
-        grad_req = {'data': 'write', 'label': 'null'}
-        exe = out_s.simple_bind(ctx=default_context(), data=shape, label=shape, grad_req=grad_req)
-
-        arg_map = dict(zip(out_s.list_arguments(), exe.arg_arrays))
-        grad_map = dict(zip(out_s.list_arguments(), exe.grad_arrays))
-
-        # init data
-        data = mx.random.uniform(-1, -1, shape)
-        arg_map["data"][:] = data
-        atol = 1e-5
-        density = 0.5
-        stype = 'default'
-        label = arg_map["label"]
-        label[:] = rand_ndarray(shape, stype, density=density)
-        exe.forward(is_train=True)
-        exe.backward()
-        np_out = forward(data.asnumpy())
-        out_grad = backward(np_out, label.asnumpy().reshape(np_out.shape)) / shape[1]
-        assert_almost_equal(exe.outputs[0].asnumpy(), np_out, atol=atol)
-        assert_almost_equal(grad_map["data"].asnumpy(), out_grad, atol=atol)
-
-    check_regression(mx.symbol.LogisticRegressionOutput,
-                     lambda x: 1.0 / (1.0 + np.exp(-x)),
-                     lambda x, y: x - y,
-                     shape)
-    check_regression(mx.symbol.LinearRegressionOutput,
-                     lambda x: x,
-                     lambda x, y: x - y,
-                     shape)
-
-
-def test_astype():
-    x = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
-    y = x.astype('int32')
-    assert y.dtype == np.int32
-    assert y[-1][-1] == SMALL_Y-1
-
-
-def test_cast():
-    x = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
-    y = nd.cast(x, np.int32)
-    assert y.dtype == np.int32
-    assert y[-1][-1] == SMALL_Y-1
-
-
-def test_repeat():
-    x = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X//2)
-    y = nd.repeat(x, repeats=2, axis = 1)
-    assert y.shape == (SMALL_Y, LARGE_X)
-    assert y[0][1] == 0
-    assert y[-1][-1] == SMALL_Y-1
-    x = create_2d_tensor(rows=SMALL_Y//2, columns=LARGE_X)
-    y = nd.repeat(x, repeats=2, axis = 0)
-    assert y.shape == (SMALL_Y, LARGE_X)
-    assert y[0][1] == 0
-    assert y[-1][0] == SMALL_Y//2-1
-
-
-def create_input_for_rounding_ops():
-    # Creates an vector with values (-LARGE_X/2 .... -2, -1, 0, 1, 2, .... , LARGE_X/2-1)
-    # then divides each element by 2 i.e (-LARGE_X/4 .... -1, -0.5, 0, 0.5, 1, .... , LARGE_X/4-1)
-    # and finally broadcasts to 
-    inp = nd.arange(-LARGE_X//2, LARGE_X//2, dtype=np.float64).reshape(1, LARGE_X)
-    inp = inp/2
-    inp = nd.broadcast_to(inp, (SMALL_Y, LARGE_X))
-    return inp
-
-
-def assert_correctness_of_rounding_ops(output, mid, expected_vals):
-    # checks verifies 5 values at the middle positions of the input vector
-    # i.e mid-2, mid-1, mid, mid+1, mid+2
-    output_idx_to_inspect = [mid-2, mid-1, mid, mid+1, mid+2]
-    for i in range(len(output_idx_to_inspect)):
-        assert output[1][output_idx_to_inspect[i]] == expected_vals[i]
-
-
-# TODO(access2rohit): merge similar tests in large vector and array into one file. 
-def test_rounding_ops():
-    x = create_input_for_rounding_ops()
-
-    def check_ceil():
-        y = nd.ceil(x)
-        # expected ouput for middle 5 values after applying ceil()
-        expected_output = [-1, 0, 0, 1, 1]
-        assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
-
-    def check_fix():
-        y = nd.fix(x)
-        # expected ouput for middle 5 values after applying fix()
-        expected_output = [-1, 0, 0, 0, 1]
-        assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
-
-    def check_floor():
-        y = nd.floor(x)
-        # expected ouput for middle 5 values after applying floor()
-        expected_output = [-1, -1, 0, 0, 1]
-        assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
-
-    def check_rint():
-        y = nd.rint(x)
-        # expected ouput for middle 5 values after applying rint()
-        expected_output = [-1, -1, 0, 0, 1]
-        assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
-
-    def check_round():
-        y = nd.round(x)
-        # expected ouput for middle 5 values after applying round()
-        expected_output = [-1, -1, 0, 1, 1]
-        assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
-
-    def check_trunc():
-        y = nd.trunc(x)
-        # expected ouput for middle 5 values after applying trunc()
-        expected_output = [-1, 0, 0, 0, 1]
-        assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
-
-    check_ceil()
-    check_fix()
-    check_floor()
-    check_rint()
-    check_round()
-    check_trunc()
-
-
-def create_input_for_trigonometric_ops(vals):
-    # Creates large vector input of size(LARGE_X*10, SMALL_Y/10) from vals using broadcast_to operator
-    inp = nd.array(vals).reshape(1, 5)
-    inp = nd.broadcast_to(inp, (LARGE_X*10, SMALL_Y//10))
-    return inp
-
-
-def assert_correctness_of_trigonometric_ops(output, expected_vals, atol=1e-3):
-    # checks verifies 5 values at positions(0, 1, -3, -2, -1) of the input vector
-    output_idx_to_inspect = [0, 1, -3, -2, -1]
-    for i in range(len(output_idx_to_inspect)):
-        assert np.abs(output[1][output_idx_to_inspect[i]].asnumpy()-expected_vals[i]) <= atol
-
-
-def test_trigonometric_ops():
-    def check_arcsin():
-        x = create_input_for_trigonometric_ops([-1, -.707, 0, .707, 1])
-        y = nd.arcsin(x)
-        # expected ouput for indices=(0, 1, -3, -2, -1) after applying arcsin()
-        expected_output = [-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2]
-        assert_correctness_of_trigonometric_ops(y, expected_output)
-
-    def check_arccos():
-        x = create_input_for_trigonometric_ops([-1, -.707, 0, .707, 1])
-        y = nd.arccos(x)
-        # expected ouput for indices=(0, 1, -3, -2, -1) after applying arccos()
-        expected_output = [np.pi, 3*np.pi/4, np.pi/2, np.pi/4, 0]
-        assert_correctness_of_trigonometric_ops(y, expected_output)
-
-    def check_arctan():
-        x = create_input_for_trigonometric_ops([-np.Inf, -1, 0, 1, np.Inf])
-        y = nd.arctan(x)
-        # expected ouput for indices=(0, 1, -3, -2, -1) after applying arctan()
-        expected_output = [-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2]
-        assert_correctness_of_trigonometric_ops(y, expected_output)
-
-    def check_sin():
-        x = create_input_for_trigonometric_ops([-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2])
-        y = nd.sin(x)
-        # expected ouput for indices=(0, 1, -3, -2, -1) after applying sin()
-        expected_output = [-1, -.707, 0, .707, 1]
-        assert_correctness_of_trigonometric_ops(y, expected_output)
-
-    def check_cos():
-        x = create_input_for_trigonometric_ops([0, np.pi/4, np.pi/2, 3*np.pi/4, np.pi])
-        y = nd.cos(x)
-        # expected ouput for indices=(0, 1, -3, -2, -1) after applying cos()
-        expected_output = [1, .707, 0, -.707, -1]
-        assert_correctness_of_trigonometric_ops(y, expected_output)
-
-    def check_tan():
-        x = create_input_for_trigonometric_ops([-np.pi/6, -np.pi/4, 0, np.pi/4, np.pi/6])
-        y = nd.tan(x)
-        # expected ouput for indices=(0, 1, -3, -2, -1) after applying tan()
-        expected_output = [-.577, -1, 0, 1, .577]
-        assert_correctness_of_trigonometric_ops(y, expected_output)
-
-    def check_arcsinh():
-        x = create_input_for_trigonometric_ops([-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2])
-        y = nd.arcsinh(x)
-        # expected ouput for indices=(0, 1, -3, -2, -1) after applying arcsinh()
-        expected_output = [np.arcsinh(-np.pi/2), np.arcsinh(-np.pi/4), 0, np.arcsinh(np.pi/4), np.arcsinh(np.pi/2)]
-        assert_correctness_of_trigonometric_ops(y, expected_output)
-
-    def check_arccosh():
-        x = create_input_for_trigonometric_ops([1, np.pi/2, 3*np.pi/4, np.pi, 5*np.pi/4])
-        y = nd.arccosh(x)
-        # expected ouput for indices=(0, 1, -3, -2, -1) after applying arccosh()
-        expected_output = [0, np.arccosh(np.pi/2), np.arccosh(3*np.pi/4), np.arccosh(np.pi), np.arccosh(5*np.pi/4)]
-        assert_correctness_of_trigonometric_ops(y, expected_output)
-
-    def check_arctanh():
-        x = create_input_for_trigonometric_ops([-1/4, -1/2, 0, 1/4, 1/2])
-        y = nd.arctanh(x)
-        # expected ouput for indices=(0, 1, -3, -2, -1) after applying arctanh()
-        expected_output = [np.arctanh(-1/4), np.arctanh(-1/2), 0, np.arctanh(1/4), np.arctanh(1/2)]
-        assert_correctness_of_trigonometric_ops(y, expected_output)
-
-    def check_sinh():
-        x = create_input_for_trigonometric_ops([-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2])
-        y = nd.sinh(x)
-        # expected ouput for indices=(0, 1, -3, -2, -1) after applying sinh()
-        expected_output = [np.sinh(-np.pi/2), np.sinh(-np.pi/4), 0, np.sinh(np.pi/4), np.sinh(np.pi/2)]
-        assert_correctness_of_trigonometric_ops(y, expected_output)
-
-    def check_cosh():
-        x = create_input_for_trigonometric_ops([0, 1, np.pi/2, 3*np.pi/4, np.pi])
-        y = nd.cosh(x)
-        # expected ouput for indices=(0, 1, -3, -2, -1) after applying cosh()
-        expected_output = [1, np.cosh(1), np.cosh(np.pi/2), np.cosh(3*np.pi/4), np.cosh(np.pi)]
-        assert_correctness_of_trigonometric_ops(y, expected_output)
-
-    def check_tanh():
-        x = create_input_for_trigonometric_ops([-1/4, -1/2, 0, 1/4, 1/2])
-        y = nd.tanh(x)
-        # expected ouput for indices=(0, 1, -3, -2, -1) after applying tanh()
-        expected_output = [np.tanh(-1/4), np.tanh(-1/2), 0, np.tanh(1/4), np.tanh(1/2)]
-        assert_correctness_of_trigonometric_ops(y, expected_output)
-
-    def check_radians():
-        x = create_input_for_trigonometric_ops([0, 90, 180, 270, 360])
-        y = nd.radians(x)
-        # expected ouput for indices=(0, 1, -3, -2, -1) after applying radians()
-        expected_output = [0, np.pi/2, np.pi, 3*np.pi/2, 2*np.pi]
-        assert_correctness_of_trigonometric_ops(y, expected_output)
-
-    def check_degrees():
-        x = create_input_for_trigonometric_ops([0, np.pi/2, np.pi, 3*np.pi/2, 2*np.pi])
-        y = nd.degrees(x)
-        # expected ouput for indices=(0, 1, -3, -2, -1) after applying degrees()
-        expected_output = [0, 90, 180, 270, 360]
-        assert_correctness_of_trigonometric_ops(y, expected_output)
-
-    check_arcsin()
-    check_arccos()
-    check_arctan()
-    check_sin()
-    check_cos()
-    check_tan()
-    check_arcsinh()
-    check_arccosh()
-    check_arctanh()
-    check_sinh()
-    check_cosh()
-    check_tanh()
-    check_radians()
-    check_degrees()
-
-
-def test_L2Normalization():
-    x = nd.ones((2, LARGE_X*2))
-    x[0] = 3
-    x[1] = 4
-    # Channel Mode
-    z = x.reshape(1, 2, LARGE_X*2)
-    y = nd.L2Normalization(z, mode='channel')
-    assert y[0][0][0] == 0.6
-    assert y[0][0][-1] == 0.6
-    assert y[0][1][0] == 0.8
-    assert y[0][1][-1] == 0.8
-    # Instance Mode
-    z = x.T
-    y = nd.L2Normalization(z, mode='instance')
-    assert y[0][0] == 0.6
-    assert y[0][1] == 0.8
-    assert y[-1][0] == 0.6
-    assert y[-1][1] == 0.8
-    # Spatial Mode
-    z = z.reshape(1, 200000000, 2)
-    y = nd.L2Normalization(z, mode='spatial')
-    assert y[0][0][0] == 0.6
-    assert y[0][0][1] == 0.8
-    assert y[0][-1][0] == 0.6
-    assert y[0][-1][1] == 0.8
-
-
-def test_instance_norm():
-    dtype = np.float32
-    forward_check_eps = 1E-3
-    axis = -1
-    eps = 1E-5
-    in_shape = (LARGE_X, 1, SMALL_Y)
-    ctx = mx.cpu()
-
-    # Implementation of instance normalization using numpy
-    def npy_instance_norm(data, gamma, beta, axis, eps=1E-5):
-        if axis < 0:
-            axis += data.ndim
-        broadcast_shape = [1 for _ in range(data.ndim)]
-        broadcast_shape[axis] = data.shape[axis]
-        mean = data.mean(axis=axis, keepdims=True).astype(dtype)
-        var = data.var(axis=axis, keepdims=True).astype(dtype)
-        std = np.sqrt(var + dtype(eps)).astype(dtype)
-        out = gamma * (data - mean) / std + \
-              beta
-        return out
-    data = np.random.normal(0, 1, in_shape).astype(dtype)
-    gamma = np.random.normal(0, 1, (1,)).astype(dtype)
-    beta = np.random.normal(0, 1, (1,)).astype(dtype)
-    data_s = mx.symbol.Variable('data')
-    gamma_s = mx.symbol.Variable('gamma')
-    beta_s = mx.symbol.Variable('beta')
-    out_s = mx.symbol.InstanceNorm(data=data_s, gamma=gamma_s, beta=beta_s,
-                                   eps=eps)
-    exe = out_s.simple_bind(ctx, data=in_shape)
-    exe.arg_dict['data'][:] = data
-    exe.arg_dict['gamma'][:] = gamma
-    exe.arg_dict['beta'][:] = beta
-    out_nd = exe.forward()[0]
-    # Calls implementation of instance norm in numpy and compares the output
-    out = npy_instance_norm(data, gamma, beta, axis, eps)
-    assert_almost_equal(out, out_nd.asnumpy(), forward_check_eps,
-                        forward_check_eps)
-
-
-def test_load_save():
-    x = create_2d_tensor(SMALL_Y, LARGE_X)
-    tmp = tempfile.mkdtemp()
-    tmpfile = os.path.join(tmp, 'large_tensor')
-    nd.save(tmpfile, [x])
-    y = nd.load(tmpfile)
-    y = y[0]
-    assert x[0][0] == y[0][0]
-    assert x[-1][-1]== y[-1][-1]
-
-
-def test_add_n():
-    x = [nd.ones(LARGE_X) for j in range(SMALL_Y)]
-    y = nd.add_n(*x)
-    assert y[0] == SMALL_Y
-    assert y[-1] == SMALL_Y
-
-
-def test_modulo():
-    x = mx.nd.ones((SMALL_Y, LARGE_X))*6
-    y = mx.nd.ones(LARGE_X)*4
-    z = (x%y)
-    assert z[0][0] == 2
-    assert z[-1][-1] == 2
-    x = mx.nd.ones((SMALL_Y, LARGE_X))*5
-    z = nd.modulo(x,y)
-    assert z[0][0] == 1
-    assert z[-1][-1] == 1
-
-
-def test_maximum():
-    x = mx.nd.ones((SMALL_Y, LARGE_X))*3
-    y = mx.nd.ones(LARGE_X)*4
-    z = nd.maximum(x, y)
-    assert z[0][0] == 4
-    assert z[-1][-1] == 4
-    z = nd.maximum(x, 5)
-    assert z[0][0] == 5
-    assert z[-1][-1] == 5
-
-
-def test_minimum():
-    x = mx.nd.ones((SMALL_Y, LARGE_X))*3
-    y = mx.nd.ones(LARGE_X)*2
-    z = nd.minimum(x, y)
-    assert z[0][0] == 2
-    assert z[-1][-1] == 2
-    z = nd.minimum(x, 5)
-    assert z[0][0] == 3
-    assert z[-1][-1] == 3
-
-
-def test_pad():
-    x = create_2d_tensor(rows=SMALL_Y-2, columns=LARGE_X//2-2, dtype=np.float32).reshape(1 , 1, SMALL_Y-2, LARGE_X//2-2)
-    y = nd.pad(x, mode="edge", pad_width=(0, 0, 0, 0, 1, 1, 1, 1))
-    assert y[0][0][1][0] == 0
-    assert y[0][0][1][-1] == 0
-    assert y[0][0][-1][0] == SMALL_Y-3
-    assert y[0][0][-1][-1] == SMALL_Y-3
-    assert y.shape == (1, 1, SMALL_Y, LARGE_X//2)
-
-
-def test_gather():
-    arr = mx.nd.ones((LARGE_X, SMALL_Y))
-    idx = mx.nd.random.randint(0, LARGE_X, SMALL_X)
-    # Calls gather_nd internally
-    tmp = arr[idx]
-    assert np.sum(tmp[0].asnumpy() == 1) == SMALL_Y
-    # Calls gather_nd internally
-    arr[idx] += 1
-    assert np.sum(arr[idx[0]].asnumpy() == 2) == SMALL_Y
-
-
-def test_binary_broadcast():
-    def check_correctness(mxnet_op, numpy_op, atol=1e-3):
-        a = mx.nd.ones((LARGE_X, SMALL_Y)).as_np_ndarray()
-        b = 2*mx.nd.ones((LARGE_X, SMALL_Y)).as_np_ndarray()
-        res = mxnet_op(a, b)
-        np_res = numpy_op(1, 2)
-        assert np.abs(res[-1][-1] - np_res) < atol
-    check_correctness(mx.np.arctan2, np.arctan2)
-    check_correctness(mx.np.hypot, np.hypot)
+    def check_flip():
+        b = create_2d_tensor(rows=LARGE_X, columns=SMALL_Y)
+        t = nd.flip(b, axis=0)
+        assert np.sum(t[-1, :].asnumpy() == 0) == b.shape[1]
+        assert t.shape == (LARGE_X, SMALL_Y)
+
+    def check_sequence_mask():
+        # Sequence Mask input [max_sequence_length, batch_size, other_feature_dims]
+        # test with input batch_size = 2
+        a = nd.arange(0, LARGE_X * SMALL_Y * 2).reshape(LARGE_X, 2, SMALL_Y)
+        # test as identity operator
+        b = nd.SequenceMask(a)
+        assert b[-1][0][1] == a[-1][0][1]
+        assert b.shape == a.shape
+        # test with default mask
+        b = nd.SequenceMask(a, sequence_length=nd.array([1, 1]),
+                            use_sequence_length=True)
+        assert b[0][1][-1] == a[0][1][-1]  # first sequence of each batch kept
+        assert b[-1][-1][-1] != a[-1][-1][-1]  # rest sequences masked
+        assert b[-1][-1][-1] == 0
+
+        # test with mask value
+        b = nd.SequenceMask(a, sequence_length=nd.array([1, 1]),
+                            use_sequence_length=True, value=-1)
+        assert b[-1][-1][-1] == -1
+
+    def check_sequence_reverse():
+        a = nd.arange(0, LARGE_X * SMALL_Y * 2).reshape(LARGE_X, 2, SMALL_Y)
+        # test as reverse operator
+        b = nd.SequenceReverse(a)
+        assert b[-1][0][0] == a[0][0][0]
+        assert b.shape == a.shape
+        # test with sequence length
+        # 2 rows of batch 1 and 3 rows of batch 2 reversed
+        b = nd.SequenceReverse(a, sequence_length=nd.array([2, 3]),
+                               use_sequence_length=True)
+        assert b[1][0][0] == a[0][0][0]  # check if reversed
+        assert b[-1][0][0] == a[-1][0][0]  # check if intact
+        assert b.shape == a.shape
+
+    def check_sequence_last():
+        a = nd.arange(0, LARGE_X * SMALL_Y * 2).reshape(LARGE_X, 2, SMALL_Y)
+        # test if returns last sequence
+        b = nd.SequenceLast(a)
+        assert_almost_equal(b.asnumpy(), a[-1].asnumpy())  # only checks for (2, SMALL_Y) tensor
+        assert b.shape == (2, SMALL_Y)
+        # test with sequence length
+        # parameter sequence_length - NDArray with shape (batch_size)
+        # (2,3) indicates 2nd sequence from batch 1 and 3rd sequence from batch 2
+        b = nd.SequenceLast(a, sequence_length=mx.nd.array([2, 3]),
+                            use_sequence_length=True)
+        # check if it takes 2nd sequence from the first batch
+        assert b[0][-1] == a[1][0][-1]
+
+    def check_index_copy():
+        x = mx.nd.zeros((LARGE_X, SMALL_Y))
+        t = mx.nd.arange(1, SMALL_Y + 1).reshape((1, SMALL_Y))
+        index = mx.nd.array([LARGE_X - 1], dtype="int64")
+
+        x = mx.nd.contrib.index_copy(x, index, t)
+        assert x[-1][-1] == t[0][-1]
+
+    def check_one_hot():
+        # default dtype of ndarray is float32 which cannot index elements over 2^32
+        a = nd.array([1, (VLARGE_X - 1)], dtype=np.int64)
+        b = nd.one_hot(a, VLARGE_X)
+        b[0][1] == 1
+        b[1][-1] == 1
+
+    def check_full():
+        a = nd.full((SMALL_Y, LARGE_X), 3)
+        assert a.shape == (SMALL_Y, LARGE_X)
+        assert a[SMALL_Y//2][LARGE_X//2] == 3
+        assert a[-1][-1] == 3
+
+    def check_shape():
+        b = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
+        mx.nd.waitall()
+        assert b.shape == (SMALL_Y, LARGE_X)
+
+    def check_size():
+        b = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
+        mx.nd.waitall()
+        assert b.size == LARGE_SIZE
+
+    def check_copy():
+        a = nd.ones((SMALL_Y, LARGE_X))
+        b = a.copy()
+        nd.waitall()
+        assert b.shape == a.shape
+        assert b.size == LARGE_SIZE
+
+    def check_copy_to():
+        a = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
+        b = nd.array(np.zeros((SMALL_Y, LARGE_X)))
+        c = a.copyto(b)
+        assert c is b
+        assert b[-1][-1] == SMALL_Y-1
+
+    def check_reshape_like():
+        a = nd.array(np.zeros((SMALL_Y, LARGE_X)))
+        b = nd.array(np.zeros((SMALL_Y//2, LARGE_X*2)))
+        c = nd.reshape_like(a, b)
+        assert c.shape == (SMALL_Y//2, LARGE_X*2)
+
+    def check_flatten():
+        check_dtypes = [np.float32, np.int64]
+        for dtype in check_dtypes:
+            a = create_2d_tensor(rows=LARGE_X, columns=SMALL_Y, dtype=dtype).reshape((LARGE_X//2, 2, SMALL_Y))
+            b = nd.flatten(a)
+            # Here we removed the value asserts due to different precision of `int64` and `float32`.
+            # For `float32`, it will lose some precision when `LARGE_X` is too large, that is `LARGE_X-1`
+            # and `LARGE_X-2` can not represent the accurate value in the current situation.
+            assert b.shape == (LARGE_X//2, SMALL_Y*2)
+            assert_almost_equal(b[-1,-1].asnumpy(), a[-1,-1,-1].asnumpy(), rtol=1e-8)
+
+    def check_concat():
+        a = nd.array(np.ones((SMALL_Y, LARGE_X)))
+        b = nd.array(np.zeros((SMALL_Y, LARGE_X)))
+        for axis in [0, 1]:
+            c = nd.concat(a, b, dim=axis)
+            c.wait_to_read()
+            assert c.shape[axis] == b.shape[axis] * 2
+            assert c.shape[1-axis] == b.shape[1-axis]
+
+    def check_stack():
+        a = nd.array(np.ones((SMALL_Y, LARGE_X)))
+        b = nd.array(np.zeros((SMALL_Y, LARGE_X)))
+        c = nd.stack(a, b, axis=1)
+        assert c.shape == (b.shape[0], 2, LARGE_X)
+
+    def check_broadcast_axes():
+        a = create_2d_tensor(rows=1, columns=LARGE_X)
+        b = nd.broadcast_axis(a, axis=[0], size=2)
+        assert b.shape == (a.shape[0]*2, a.shape[1])
+
+    def check_astype():
+        x = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
+        y = x.astype('int32')
+        assert y.dtype == np.int32
+        assert y[-1][-1] == SMALL_Y-1
+
+    def check_cast():
+        x = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
+        y = nd.cast(x, np.int32)
+        assert y.dtype == np.int32
+        assert y[-1][-1] == SMALL_Y-1
+
+    def check_repeat():
+        x = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X//2)
+        y = nd.repeat(x, repeats=2, axis = 1)
+        assert y.shape == (SMALL_Y, LARGE_X)
+        assert y[0][1] == 0
+        assert y[-1][-1] == SMALL_Y-1
+        x = create_2d_tensor(rows=SMALL_Y//2, columns=LARGE_X)
+        y = nd.repeat(x, repeats=2, axis = 0)
+        assert y.shape == (SMALL_Y, LARGE_X)
+        assert y[0][1] == 0
+        assert y[-1][0] == SMALL_Y//2-1
+
+    def check_ndarray_convert():
+        a = nd.zeros(shape=(LARGE_X, SMALL_Y))
+        b = a.astype(np.int32)
+        assert b.dtype == np.int32
+        b = a.tostype('row_sparse')
+        assert isinstance(b, mx.nd.sparse.RowSparseNDArray)
+
+    def check_load_save():
+        x = create_2d_tensor(SMALL_Y, LARGE_X)
+        tmp = tempfile.mkdtemp()
+        tmpfile = os.path.join(tmp, 'large_tensor')
+        nd.save(tmpfile, [x])
+        y = nd.load(tmpfile)
+        y = y[0]
+        assert x[0][0] == y[0][0]
+        assert x[-1][-1]== y[-1][-1]
+
+    def check_pad():
+        x = create_2d_tensor(rows=SMALL_Y-2, columns=LARGE_X//2-2, dtype=np.float32).reshape(1 , 1, SMALL_Y-2, LARGE_X//2-2)
+        y = nd.pad(x, mode="edge", pad_width=(0, 0, 0, 0, 1, 1, 1, 1))
+        assert y[0][0][1][0] == 0
+        assert y[0][0][1][-1] == 0
+        assert y[0][0][-1][0] == SMALL_Y-3
+        assert y[0][0][-1][-1] == SMALL_Y-3
+        assert y.shape == (1, 1, SMALL_Y, LARGE_X//2)
+
+    def check_gather():
+        arr = mx.nd.ones((LARGE_X, SMALL_Y))
+        idx = mx.nd.random.randint(0, LARGE_X, SMALL_X)
+        # Calls gather_nd internally
+        tmp = arr[idx]
+        assert np.sum(tmp[0].asnumpy() == 1) == SMALL_Y
+        # Calls gather_nd internally
+        arr[idx] += 1
+        assert np.sum(arr[idx[0]].asnumpy() == 2) == SMALL_Y
+
+    def check_binary_broadcast():
+        def check_correctness(mxnet_op, numpy_op, atol=1e-3):
+            a = mx.nd.ones((LARGE_X, SMALL_Y)).as_np_ndarray()
+            b = 2*mx.nd.ones((LARGE_X, SMALL_Y)).as_np_ndarray()
+            res = mxnet_op(a, b)
+            np_res = numpy_op(1, 2)
+            assert np.abs(res[-1][-1] - np_res) < atol
+        check_correctness(mx.np.arctan2, np.arctan2)
+        check_correctness(mx.np.hypot, np.hypot)
+
+    check_ndarray_zeros()
+    check_ndarray_ones()
+    check_ndarray_random_uniform()
+    check_ndarray_random_randint()
+    check_ndarray_random_exponential()
+    check_ndarray_random_gamma()
+    check_ndarray_random_multinomial()
+    check_ndarray_random_generalized_negative_binomial()
+    check_ndarray_random_negative_binomial()
+    check_ndarray_random_normal()
+    check_ndarray_random_poisson()
+    check_ndarray_random_randn()
+    check_ndarray_random_shuffle()
+    check_ndarray_empty()
+    check_zeros_like()
+    check_ones_like()
+    check_broadcast()
+    check_clip()
+    check_split()
+    check_tile()
+    check_take()
+    check_slice()
+    check_slice_assign()
+    check_slice_like()
+    check_slice_axis()
+    check_expand_dims()
+    check_squeeze()
+    check_broadcast_div()
+    check_where()
+    check_pick()
+    check_depthtospace()
+    check_spacetodepth()
+    check_diag()
+    check_ravel_multi_index()
+    check_unravel_index()
+    check_transpose()
+    check_swapaxes()
+    check_flip()
+    check_sequence_mask()
+    check_sequence_reverse()
+    check_sequence_last()
+    check_index_copy()
+    check_one_hot()
+    check_full()
+    check_shape()
+    check_size()
+    check_copy()
+    check_copy_to()
+    check_reshape_like()
+    check_flatten()
+    check_concat()
+    check_stack()
+    check_broadcast_axes()
+    check_astype()
+    check_cast()
+    check_repeat()
+    check_ndarray_convert()
+    check_load_save()
+    check_pad()
+    check_gather()
+    check_binary_broadcast()
+
+
+def test_basic():
+    def check_elementwise():
+        a = nd.ones(shape=(LARGE_X, SMALL_Y))
+        b = nd.ones(shape=(LARGE_X, SMALL_Y))
+        res = a + b
+        assert np.sum(res[-1].asnumpy() == 2) == a.shape[1]
+        res = a + 1
+        assert np.sum(res[-1].asnumpy() == 2) == a.shape[1]
+        res = nd.sqrt(a + 3)
+        assert np.sum(res[-1].asnumpy() == 2) == a.shape[1]
+
+    def check_reduce():
+        a = nd.ones(shape=(LARGE_X, SMALL_Y))
+        assert nd.sum(a).asnumpy() == a.shape[0] * a.shape[1]
+
+    def check_dot():
+        a = nd.ones(shape=(LARGE_X, SMALL_Y))
+        b = nd.ones(shape=(SMALL_Y, SMALL_Y))
+        res = nd.dot(a, b)
+        assert np.sum(res[-1].asnumpy() == SMALL_Y) == b.shape[1]
+
+    def check_argmin():
+        a = nd.arange(0, LARGE_X * SMALL_Y).reshape(LARGE_X, SMALL_Y)
+        idx = mx.nd.argmin(a, axis=0)
+        assert idx.shape[0] == SMALL_Y
+
+    def check_argsort():
+        b = create_2d_tensor(rows=LARGE_X, columns=SMALL_Y)
+        s = nd.argsort(b, axis=0, is_ascend=False, dtype=np.int64)
+        mx.nd.waitall()
+        assert (s[0].asnumpy() == (LARGE_X - 1)).all()
+
+    def check_sort():
+        b = create_2d_tensor(rows=LARGE_X, columns=SMALL_Y)
+        s = nd.sort(b, axis=0, is_ascend=False)
+        assert np.sum(s[-1][SMALL_Y//2:SMALL_Y].asnumpy() == 0).all()
+        s = nd.sort(b, is_ascend=False)
+        assert np.sum(s[0].asnumpy() == 0).all()
+
+    def check_topk():
+        b = create_2d_tensor(rows=LARGE_X, columns=SMALL_Y)
+        k = nd.topk(b, k=10, axis=0, dtype=np.int64)
+        assert np.sum(k.asnumpy() == (LARGE_X - 1)) == SMALL_Y
+        ind, val = mx.nd.topk(b, k=3, axis=0, dtype=np.int64, ret_typ="both",
+                              is_ascend=False)
+        assert np.all(ind == val)
+        b = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
+        l = nd.topk(b, k=1, axis=-1, dtype=np.int64, ret_typ="value")
+        assert l.sum() == np.sum(np.arange(0, SMALL_Y))
+
+    def check_exponent_logarithm_operators():
+        a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
+        # exponent
+        result = nd.exp(a)
+        assert result[0][-1] == 7.389056
+        assert result.shape == a.shape
+        # exponent minus 1
+        result = nd.expm1(a)
+        assert result[0][-1] == 6.389056
+        assert result.shape == a.shape
+        # log2
+        result = nd.log2(a)
+        assert result[0][-1] == 1
+        assert result.shape == a.shape
+        # log10
+        result = nd.log10(a)
+        assert result[0][-1] == 0.30103
+        assert result.shape == a.shape
+        # log1p
+        result = nd.log1p(a)
+        assert result[0][-1] == 1.0986123
+        assert result.shape == a.shape
+        # log
+        result = nd.log(a)
+        assert result[0][-1] == 0.6931472
+        assert result.shape == a.shape
+
+    def check_power_operators():
+        a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
+        # sqrt
+        result = nd.sqrt(a)
+        assert result[0][-1] == 1.4142135
+        assert result.shape == a.shape
+        # rsqrt
+        result = nd.rsqrt(a)
+        assert result[0][-1] == 0.70710677
+        assert result.shape == a.shape
+        # cbrt
+        result = nd.cbrt(a)
+        assert result[0][-1] == 1.2599211
+        assert result.shape == a.shape
+        # rcbrt
+        result = nd.rcbrt(a)
+        assert result[0][-1] == 0.7937005
+        assert result.shape == a.shape
+        # square
+        result = nd.square(a)
+        assert result[0][-1] == 4
+        assert result.shape == a.shape
+        # reciprocal
+        result = nd.reciprocal(a)
+        assert result[0][-1] == 0.5
+        assert result.shape == a.shape
+
+    def check_elemwise_add():
+        a = nd.ones(shape=(LARGE_X, SMALL_Y))
+        b = nd.ones(shape=(LARGE_X, SMALL_Y))
+        res = nd.elemwise_add(a, b)
+        assert np.sum(res[-1].asnumpy() == 2) == a.shape[1]
+
+    def check_add():
+        a = nd.ones(shape=(LARGE_X, SMALL_Y))
+        b = nd.ones(shape=(LARGE_X, SMALL_Y))
+        c = b.__add__(a)
+        assert c[0][-1] == 2
+        assert c.shape == a.shape
+
+    def check_sub():
+        a = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
+        b = nd.ones(shape=(LARGE_X, SMALL_Y))
+        c = b.__sub__(a)
+        assert c[0][-1] == -2
+        assert c.shape == a.shape
+
+    def check_rsub():
+        a = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
+        b = nd.ones(shape=(LARGE_X, SMALL_Y))
+        c = b.__rsub__(a)
+        assert c[0][-1] == 2
+        assert c.shape == a.shape
+
+    def check_neg():
+        a = nd.ones(shape=(LARGE_X, SMALL_Y))
+        c = a.__neg__()
+        assert c[0][-1] == -1
+        assert c.shape == a.shape
+
+    def check_mul():
+        a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
+        b = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
+        c = b.__mul__(a)
+        assert c[0][-1] == 6
+        assert c.shape == a.shape
+
+    def check_div():
+        a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
+        b = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
+        c = b.__div__(a)
+        mx_divide = nd.divide(b, a)
+        assert c[0][-1] == 3/2
+        assert mx_divide[0][-1] == c[0][-1]
+        assert c.shape == a.shape
+
+    def check_rdiv():
+        a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
+        b = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
+        c = b.__rdiv__(a)
+        assert c[0][-1] == 2/3
+        assert c.shape == a.shape
+
+    def check_mod():
+        a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
+        b = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
+        c = b.__mod__(a)
+        assert c[0][-1] == 1
+        assert c.shape == a.shape
+
+    def check_rmod():
+        a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
+        b = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
+        c = b.__rmod__(a)
+        assert c[0][-1] == 2
+        assert c.shape == a.shape
+
+    def check_imod():
+        a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
+        b = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
+        c = b.__imod__(a)
+        assert c[0][-1] == 1
+        assert c.shape == a.shape
+
+    def check_pow():
+        a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
+        b = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
+        c = b.__pow__(a)
+        assert c[0][-1] == 9
+        assert c.shape == a.shape
+
+    def check_rpow():
+        a = 2*nd.ones(shape=(LARGE_X, SMALL_Y))
+        b = 3*nd.ones(shape=(LARGE_X, SMALL_Y))
+        c = b.__rpow__(a)
+        assert c[0][-1] == 8
+        assert c.shape == a.shape
+
+    def check_sum():
+        a = nd.array(np.ones((SMALL_Y, LARGE_X)))
+        b = nd.sum(a, axis=1)
+        assert b.shape[0] == SMALL_Y
+
+    def check_prod():
+        a = nd.array(np.ones((SMALL_Y, LARGE_X)))
+        b = nd.prod(a, axis=1)
+        assert b.shape[0] == SMALL_Y
+
+    def check_mean():
+        a = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
+        b = nd.mean(a, axis=0)
+        assert b[0] == (SMALL_Y/2-1)
+
+    def check_min():
+        a = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
+        b = nd.min(a, axis=0)
+        assert b[0] == 0
+        assert b[-1] == 0
+
+    def check_max():
+        a = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
+        b = nd.max(a, axis=0)
+        assert b[0] == (SMALL_Y-1)
+        assert b[-1] == (SMALL_Y-1)
+
+    def check_norm():
+        a = np.array(np.full((1, LARGE_X), 3))
+        b = np.array(np.full((1, LARGE_X), 4))
+        c = nd.array(np.concatenate((a, b), axis=0))
+        d = nd.norm(c, ord=2, axis=0)
+        e = nd.norm(c, ord=1, axis=0)
+        assert d.shape[0] == LARGE_X
+        assert e.shape[0] == LARGE_X
+        assert d[-1] == 5
+        assert e[-1] == 7
+
+    def check_argmax():
+        a = np.ones((SMALL_Y, LARGE_X))
+        b = np.zeros((SMALL_Y, LARGE_X))
+        c = nd.array(np.concatenate((a, b), axis=0))
+        d = nd.argmax(c, axis=0)
+        assert d.shape[0] == LARGE_X
+        assert d[-1] == d[0] == 0
+
+    def check_iadd():
+        a = nd.array(np.ones((SMALL_Y, LARGE_X)))
+        b = nd.array(np.ones((SMALL_Y, LARGE_X)))
+        c = b + a
+        assert c.shape == a.shape
+        assert c[0][-1] == 2
+
+    def check_isub():
+        a = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
+        b = nd.array(np.ones((SMALL_Y, LARGE_X)))
+        c = a - b
+        assert c.shape == a.shape
+        assert c[0][-1] == 2
+
+    def check_imul():
+        a = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
+        b = nd.array(np.ones((SMALL_Y, LARGE_X)))
+        c = b * a
+        assert c.shape == a.shape
+        assert c[0][-1] == 3
+
+    def check_idiv():
+        a = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 4)))
+        b = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 2)))
+        c = a / b
+        assert c.shape == a.shape
+        assert c[0][-1] == 2
+
+    def check_eq():
+        a = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
+        b = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
+        c = (a == b)
+        assert np.sum(c[0].asnumpy() == 1).all()
+
+    def check_neq():
+        a = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 2)))
+        b = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
+        c = (a != b)
+        assert np.sum(c[0].asnumpy() == 1).all()
+
+    def check_lt():
+        a = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 2)))
+        b = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
+        d = (a <= b)
+        assert np.sum(d[0].asnumpy() == 1).all()
+
+    def check_lte():
+        a = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 2)))
+        b = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
+        c = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 2)))
+        d = (a <= b)
+        e = (a <= c)
+        assert np.sum(d[0].asnumpy() == 1).all()
+        assert np.sum(e[0].asnumpy() == 1).all()
+
+    def check_gt():
+        a = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
+        b = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 2)))
+        d = (a >= b)
+        assert np.sum(d[0].asnumpy() == 1).all()
+
+    def check_gte():
+        a = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
+        b = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 2)))
+        c = nd.array(np.array(np.full((SMALL_Y, LARGE_X), 3)))
+        d = (a >= b)
+        e = (a >= c)
+        assert np.sum(d[0].asnumpy() == 1).all()
+        assert np.sum(e[0].asnumpy() == 1).all()
+
+    def check_sign():
+        a = mx.nd.random.normal(-1,1, shape=(LARGE_X, SMALL_Y))
+        mx_res = mx.nd.sign(a)
+        assert_almost_equal(mx_res[-1][-1].asnumpy(), np.sign(a[-1][-1].asnumpy()))
+
+    def check_logical():
+        def check_logical_and(a, b):
+            mx_res = mx.nd.logical_and(a, b)
+            assert_almost_equal(mx_res[-1][-1].asnumpy(), np.logical_and(a[-1][-1].asnumpy(), b[-1][-1].asnumpy()))
+
+        def check_logical_or(a, b):
+            mx_res = mx.nd.logical_or(a, b)
+            assert_almost_equal(mx_res[-1][-1].asnumpy(), np.logical_or(a[-1][-1].asnumpy(), b[-1][-1].asnumpy()))
+
+        def check_logical_not(a, b):
+            mx_res = mx.nd.logical_not(a, b)
+            assert_almost_equal(mx_res[-1][-1].asnumpy(), np.logical_not(a[-1][-1].asnumpy(), b[-1][-1].asnumpy()))
+
+        def check_logical_xor(a, b):
+            mx_res = mx.nd.logical_xor(a, b)
+            assert_almost_equal(mx_res[-1][-1].asnumpy(), np.logical_xor(a[-1][-1].asnumpy(), b[-1][-1].asnumpy()))
+
+        a = mx.nd.ones((LARGE_X, SMALL_Y))
+        b = mx.nd.zeros((LARGE_X, SMALL_Y))
+        check_logical_and(a, b)
+        check_logical_or(a, b)
+        check_logical_not(a, b)
+        check_logical_xor(a, b)
+
+    def create_input_for_rounding_ops():
+        # Creates an vector with values (-LARGE_X/2 .... -2, -1, 0, 1, 2, .... , LARGE_X/2-1)
+        # then divides each element by 2 i.e (-LARGE_X/4 .... -1, -0.5, 0, 0.5, 1, .... , LARGE_X/4-1)
+        # and finally broadcasts to 
+        inp = nd.arange(-LARGE_X//2, LARGE_X//2, dtype=np.float64).reshape(1, LARGE_X)
+        inp = inp/2
+        inp = nd.broadcast_to(inp, (SMALL_Y, LARGE_X))
+        return inp
+
+    def assert_correctness_of_rounding_ops(output, mid, expected_vals):
+        # checks verifies 5 values at the middle positions of the input vector
+        # i.e mid-2, mid-1, mid, mid+1, mid+2
+        output_idx_to_inspect = [mid-2, mid-1, mid, mid+1, mid+2]
+        for i in range(len(output_idx_to_inspect)):
+            assert output[1][output_idx_to_inspect[i]] == expected_vals[i]
+
+    # TODO(access2rohit): merge similar tests in large vector and array into one file. 
+    def check_rounding_ops():
+        x = create_input_for_rounding_ops()
+        def check_ceil():
+            y = nd.ceil(x)
+            # expected ouput for middle 5 values after applying ceil()
+            expected_output = [-1, 0, 0, 1, 1]
+            assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
+        def check_fix():
+            y = nd.fix(x)
+            # expected ouput for middle 5 values after applying fix()
+            expected_output = [-1, 0, 0, 0, 1]
+            assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
+        def check_floor():
+            y = nd.floor(x)
+            # expected ouput for middle 5 values after applying floor()
+            expected_output = [-1, -1, 0, 0, 1]
+            assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
+        def check_rint():
+            y = nd.rint(x)
+            # expected ouput for middle 5 values after applying rint()
+            expected_output = [-1, -1, 0, 0, 1]
+            assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
+        def check_round():
+            y = nd.round(x)
+            # expected ouput for middle 5 values after applying round()
+            expected_output = [-1, -1, 0, 1, 1]
+            assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
+        def check_trunc():
+            y = nd.trunc(x)
+            # expected ouput for middle 5 values after applying trunc()
+            expected_output = [-1, 0, 0, 0, 1]
+            assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
+        check_ceil()
+        check_fix()
+        check_floor()
+        check_rint()
+        check_round()
+        check_trunc()
+
+    def create_input_for_trigonometric_ops(vals):
+        # Creates large vector input of size(LARGE_X*10, SMALL_Y/10) from vals using broadcast_to operator
+        inp = nd.array(vals).reshape(1, 5)
+        inp = nd.broadcast_to(inp, (LARGE_X*10, SMALL_Y//10))
+        return inp
+
+    def assert_correctness_of_trigonometric_ops(output, expected_vals, atol=1e-3):
+        # checks verifies 5 values at positions(0, 1, -3, -2, -1) of the input vector
+        output_idx_to_inspect = [0, 1, -3, -2, -1]
+        for i in range(len(output_idx_to_inspect)):
+            assert np.abs(output[1][output_idx_to_inspect[i]].asnumpy()-expected_vals[i]) <= atol
+
+    def check_trigonometric_ops():
+        def check_arcsin():
+            x = create_input_for_trigonometric_ops([-1, -.707, 0, .707, 1])
+            y = nd.arcsin(x)
+            # expected ouput for indices=(0, 1, -3, -2, -1) after applying arcsin()
+            expected_output = [-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2]
+            assert_correctness_of_trigonometric_ops(y, expected_output)
+
+        def check_arccos():
+            x = create_input_for_trigonometric_ops([-1, -.707, 0, .707, 1])
+            y = nd.arccos(x)
+            # expected ouput for indices=(0, 1, -3, -2, -1) after applying arccos()
+            expected_output = [np.pi, 3*np.pi/4, np.pi/2, np.pi/4, 0]
+            assert_correctness_of_trigonometric_ops(y, expected_output)
+
+        def check_arctan():
+            x = create_input_for_trigonometric_ops([-np.Inf, -1, 0, 1, np.Inf])
+            y = nd.arctan(x)
+            # expected ouput for indices=(0, 1, -3, -2, -1) after applying arctan()
+            expected_output = [-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2]
+            assert_correctness_of_trigonometric_ops(y, expected_output)
+
+        def check_sin():
+            x = create_input_for_trigonometric_ops([-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2])
+            y = nd.sin(x)
+            # expected ouput for indices=(0, 1, -3, -2, -1) after applying sin()
+            expected_output = [-1, -.707, 0, .707, 1]
+            assert_correctness_of_trigonometric_ops(y, expected_output)
+
+        def check_cos():
+            x = create_input_for_trigonometric_ops([0, np.pi/4, np.pi/2, 3*np.pi/4, np.pi])
+            y = nd.cos(x)
+            # expected ouput for indices=(0, 1, -3, -2, -1) after applying cos()
+            expected_output = [1, .707, 0, -.707, -1]
+            assert_correctness_of_trigonometric_ops(y, expected_output)
+
+        def check_tan():
+            x = create_input_for_trigonometric_ops([-np.pi/6, -np.pi/4, 0, np.pi/4, np.pi/6])
+            y = nd.tan(x)
+            # expected ouput for indices=(0, 1, -3, -2, -1) after applying tan()
+            expected_output = [-.577, -1, 0, 1, .577]
+            assert_correctness_of_trigonometric_ops(y, expected_output)
+
+        def check_arcsinh():
+            x = create_input_for_trigonometric_ops([-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2])
+            y = nd.arcsinh(x)
+            # expected ouput for indices=(0, 1, -3, -2, -1) after applying arcsinh()
+            expected_output = [np.arcsinh(-np.pi/2), np.arcsinh(-np.pi/4), 0, np.arcsinh(np.pi/4), np.arcsinh(np.pi/2)]
+            assert_correctness_of_trigonometric_ops(y, expected_output)
+
+        def check_arccosh():
+            x = create_input_for_trigonometric_ops([1, np.pi/2, 3*np.pi/4, np.pi, 5*np.pi/4])
+            y = nd.arccosh(x)
+            # expected ouput for indices=(0, 1, -3, -2, -1) after applying arccosh()
+            expected_output = [0, np.arccosh(np.pi/2), np.arccosh(3*np.pi/4), np.arccosh(np.pi), np.arccosh(5*np.pi/4)]
+            assert_correctness_of_trigonometric_ops(y, expected_output)
+
+        def check_arctanh():
+            x = create_input_for_trigonometric_ops([-1/4, -1/2, 0, 1/4, 1/2])
+            y = nd.arctanh(x)
+            # expected ouput for indices=(0, 1, -3, -2, -1) after applying arctanh()
+            expected_output = [np.arctanh(-1/4), np.arctanh(-1/2), 0, np.arctanh(1/4), np.arctanh(1/2)]
+            assert_correctness_of_trigonometric_ops(y, expected_output)
+
+        def check_sinh():
+            x = create_input_for_trigonometric_ops([-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2])
+            y = nd.sinh(x)
+            # expected ouput for indices=(0, 1, -3, -2, -1) after applying sinh()
+            expected_output = [np.sinh(-np.pi/2), np.sinh(-np.pi/4), 0, np.sinh(np.pi/4), np.sinh(np.pi/2)]
+            assert_correctness_of_trigonometric_ops(y, expected_output)
+
+        def check_cosh():
+            x = create_input_for_trigonometric_ops([0, 1, np.pi/2, 3*np.pi/4, np.pi])
+            y = nd.cosh(x)
+            # expected ouput for indices=(0, 1, -3, -2, -1) after applying cosh()
+            expected_output = [1, np.cosh(1), np.cosh(np.pi/2), np.cosh(3*np.pi/4), np.cosh(np.pi)]
+            assert_correctness_of_trigonometric_ops(y, expected_output)
+
+        def check_tanh():
+            x = create_input_for_trigonometric_ops([-1/4, -1/2, 0, 1/4, 1/2])
+            y = nd.tanh(x)
+            # expected ouput for indices=(0, 1, -3, -2, -1) after applying tanh()
+            expected_output = [np.tanh(-1/4), np.tanh(-1/2), 0, np.tanh(1/4), np.tanh(1/2)]
+            assert_correctness_of_trigonometric_ops(y, expected_output)
+
+        def check_radians():
+            x = create_input_for_trigonometric_ops([0, 90, 180, 270, 360])
+            y = nd.radians(x)
+            # expected ouput for indices=(0, 1, -3, -2, -1) after applying radians()
+            expected_output = [0, np.pi/2, np.pi, 3*np.pi/2, 2*np.pi]
+            assert_correctness_of_trigonometric_ops(y, expected_output)
+
+        def check_degrees():
+            x = create_input_for_trigonometric_ops([0, np.pi/2, np.pi, 3*np.pi/2, 2*np.pi])
+            y = nd.degrees(x)
+            # expected ouput for indices=(0, 1, -3, -2, -1) after applying degrees()
+            expected_output = [0, 90, 180, 270, 360]
+            assert_correctness_of_trigonometric_ops(y, expected_output)
+
+        check_arcsin()
+        check_arccos()
+        check_arctan()
+        check_sin()
+        check_cos()
+        check_tan()
+        check_arcsinh()
+        check_arccosh()
+        check_arctanh()
+        check_sinh()
+        check_cosh()
+        check_tanh()
+        check_radians()
+        check_degrees()
+
+    def check_add_n():
+        x = [nd.ones(LARGE_X) for j in range(SMALL_Y)]
+        y = nd.add_n(*x)
+        assert y[0] == SMALL_Y
+        assert y[-1] == SMALL_Y
+
+    def check_modulo():
+        x = mx.nd.ones((SMALL_Y, LARGE_X))*6
+        y = mx.nd.ones(LARGE_X)*4
+        z = (x%y)
+        assert z[0][0] == 2
+        assert z[-1][-1] == 2
+        x = mx.nd.ones((SMALL_Y, LARGE_X))*5
+        z = nd.modulo(x,y)
+        assert z[0][0] == 1
+        assert z[-1][-1] == 1
+
+    def check_maximum():
+        x = mx.nd.ones((SMALL_Y, LARGE_X))*3
+        y = mx.nd.ones(LARGE_X)*4
+        z = nd.maximum(x, y)
+        assert z[0][0] == 4
+        assert z[-1][-1] == 4
+        z = nd.maximum(x, 5)
+        assert z[0][0] == 5
+        assert z[-1][-1] == 5
+
+    def check_minimum():
+        x = mx.nd.ones((SMALL_Y, LARGE_X))*3
+        y = mx.nd.ones(LARGE_X)*2
+        z = nd.minimum(x, y)
+        assert z[0][0] == 2
+        assert z[-1][-1] == 2
+        z = nd.minimum(x, 5)
+        assert z[0][0] == 3
+        assert z[-1][-1] == 3
+
+    check_elementwise()
+    check_reduce()
+    check_dot()
+    check_argmin()
+    check_argsort()
+    check_sort()
+    check_topk()
+    check_exponent_logarithm_operators()
+    check_power_operators()
+    check_elemwise_add()
+    check_add()
+    check_sub()
+    check_rsub()
+    check_neg()
+    check_mul()
+    check_div()
+    check_rdiv()
+    check_mod()
+    check_rmod()
+    check_imod()
+    check_pow()
+    check_rpow()
+    check_sum()
+    check_prod()
+    check_mean()
+    check_min()
+    check_max()
+    check_norm()
+    check_argmax()
+    check_iadd()
+    check_isub()
+    check_imul()
+    check_idiv()
+    check_eq()
+    check_neq()
+    check_lt()
+    check_lte()
+    check_gt()
+    check_gte()
+    check_sign()
+    check_logical()
+    check_rounding_ops()
+    check_trigonometric_ops()
+    check_add_n()
+    check_modulo()
+    check_maximum()
+    check_minimum()
 
 
 if __name__ == '__main__':

From 6b9a1dac77607544c3017036ff0d03d21eee70b6 Mon Sep 17 00:00:00 2001
From: MoisesHer <50716238+MoisesHer@users.noreply.github.com>
Date: Wed, 15 Jan 2020 18:16:18 -0800
Subject: [PATCH 808/813] Multi-tensor LAMB (#16893)

* Add multi-tensor lamb Op

* Fix compilation issue

* Optimize GPU kernels

* Stable version (included optimizer tests)

* fix lint errors

* fix lint errors

* fix pylint errors

* fix pylint errors

* Remove extra state for temporal_g (now using requested workspace)

* change default value of bounds and bias

* Fix bugs related to removal extra state

* Reuse existing LAMB optimizer

* Fix pylint errors

* Fix pylint erros

* Fix pylint erros

* Remove large tensors from test (memory issues when checking)

* Fix bug: needs to allocate memory for MultiSumSq

* Fix index bug and allows different lrs/wds for each tensor

* Template data type for lrs/wds

* Match single-tensor LAMB, and allows to pass a list (AGGREATION=1) to single-tensor LAMB

* Follow Mxnet case/format

* Clean-up code and follow Mxnet case/format

* fix lint issues

* Fix linking problem

* pylint issue
---
 python/mxnet/ndarray/contrib.py         |  76 +++++
 python/mxnet/optimizer/optimizer.py     | 121 +++++---
 python/mxnet/test_utils.py              |  77 +++--
 src/operator/contrib/multi_lamb-inl.h   | 359 ++++++++++++++++++++++++
 src/operator/contrib/multi_lamb.cc      | 251 +++++++++++++++++
 src/operator/contrib/multi_lamb.cu      | 261 +++++++++++++++++
 src/operator/contrib/multi_sum_sq-inl.h |   4 +
 src/operator/contrib/multi_sum_sq.cc    |   6 +
 src/operator/contrib/multi_sum_sq.cu    |  29 +-
 tests/python/unittest/test_optimizer.py |  46 ++-
 10 files changed, 1157 insertions(+), 73 deletions(-)
 mode change 100644 => 100755 python/mxnet/optimizer/optimizer.py
 mode change 100644 => 100755 python/mxnet/test_utils.py
 create mode 100644 src/operator/contrib/multi_lamb-inl.h
 create mode 100644 src/operator/contrib/multi_lamb.cc
 create mode 100644 src/operator/contrib/multi_lamb.cu
 mode change 100644 => 100755 tests/python/unittest/test_optimizer.py

diff --git a/python/mxnet/ndarray/contrib.py b/python/mxnet/ndarray/contrib.py
index 0297a1dbe575..4bfa97846084 100644
--- a/python/mxnet/ndarray/contrib.py
+++ b/python/mxnet/ndarray/contrib.py
@@ -605,3 +605,79 @@ def multi_mp_adamw_update(weights, grads, mean, var, weights32, rescale_grad, lr
                                                     etas=etas,
                                                     name=name,
                                                     **kwargs)
+
+def multi_lamb_update(weights, grads, mean, var, step_count,
+                      lrs, wds, out=None, num_tensors=0, **kwargs):
+    """Given a list of gradients, update weights, mean and variance of multiple tensors
+    following LAMB Optimizer implementation.
+
+    Parameters
+    ----------
+    weights : List of NDArrays containing the input weights of multiple tensors
+
+    grads : List of NDArrays containing input gradients
+
+    mean : List of NDArrays containing mean of multiple tensors to be updated
+
+    var : List of NDArrays containing variance of multiple tensors to be updated
+
+    step_count : List of scalars with the number of update step for each tensor
+
+    lrs : List of learning rates (one for each tensor)
+
+    wds : List of weight decays (one for each tensor)
+
+    out: List of NDArrays where the updated weights will be stored
+
+    num_tensors : Number of NDArrays/tensors in the list
+    """
+
+    if not num_tensors:
+        num_tensors = len(weights)
+    temp_list = _flatten_list(zip(weights, grads, mean, var))
+    return ndarray._internal._multi_lamb_update(*temp_list,
+                                                out=out,
+                                                num_tensors=num_tensors,
+                                                step_count=step_count,
+                                                learning_rates=lrs,
+                                                wds=wds,
+                                                **kwargs)
+
+def multi_mp_lamb_update(weights, grads, mean, var, weights32, step_count,
+                         lrs, wds, out=None, num_tensors=0, **kwargs):
+    """Given a list of gradients, update weights, mean and variance of multiple tensors
+    following LAMB Optimizer implementation, and using Mixed-Precision.
+
+    Parameters
+    ----------
+    weights : List of NDArrays containing the input weights of multiple tensors
+
+    grads : List of NDArrays containing input gradients
+
+    mean : List of NDArrays containing mean of multiple tensors to be updated
+
+    var : List of NDArrays containing variance of multiple tensors to be updated
+
+    weights32 : Master copy of weights in FP32
+
+    step_count : List of scalars with the number of update step for each tensor
+
+    lrs : List of learning rates (one for each tensor)
+
+    wds : List of weight decays (one for each tensor)
+
+    out: List of NDArrays where the updated weights will be stored
+
+    num_tensors : Number of NDArrays/tensors in the list
+    """
+
+    if not num_tensors:
+        num_tensors = len(weights)
+    temp_list = _flatten_list(zip(weights, grads, mean, var, weights32))
+    return ndarray._internal._multi_mp_lamb_update(*temp_list,
+                                                   out=out,
+                                                   num_tensors=num_tensors,
+                                                   step_count=step_count,
+                                                   learning_rates=lrs,
+                                                   wds=wds,
+                                                   **kwargs)
diff --git a/python/mxnet/optimizer/optimizer.py b/python/mxnet/optimizer/optimizer.py
old mode 100644
new mode 100755
index b2509d8b4d8a..09e881ebfca6
--- a/python/mxnet/optimizer/optimizer.py
+++ b/python/mxnet/optimizer/optimizer.py
@@ -36,6 +36,7 @@
                        preloaded_multi_sgd_mom_update, preloaded_multi_mp_sgd_update,
                        preloaded_multi_mp_sgd_mom_update, lamb_update_phase1, lamb_update_phase2,
                        mp_lamb_update_phase1, mp_lamb_update_phase2)
+from ..ndarray.contrib import (multi_lamb_update, multi_mp_lamb_update)
 from ..ndarray import sparse
 from ..random import normal
 from ..util import is_np_array
@@ -1259,53 +1260,106 @@ def __init__(self, learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-6,
         self.lower_bound = lower_bound
         self.upper_bound = upper_bound
         self.bias_correction = bias_correction
-
+        self.aggregate_num = max(1, min(45, int(os.getenv('MXNET_OPTIMIZER_AGGREGATION_SIZE', "45"))))
 
     def create_state(self, index, weight):
         stype = weight.stype
-        return (zeros(weight.shape, weight.context, dtype=numpy.float32, stype=stype),
-                zeros(weight.shape, weight.context, dtype=numpy.float32, stype=stype))
+        dtype = weight.dtype
+        return (zeros(weight.shape, weight.context, dtype=dtype, stype=stype),
+                zeros(weight.shape, weight.context, dtype=dtype, stype=stype))
 
     def _update_impl(self, index, weight, grad, state, multi_precision=False):
-        assert(isinstance(weight, NDArray))
-        assert(isinstance(grad, NDArray))
-        self._update_count(index)
-        lr = self._get_lr(index)
-        wd = self._get_wd(index)
-        t = self._index_update_count[index]
-
         kwargs = {'beta1': self.beta1, 'beta2': self.beta2, 'epsilon': self.epsilon,
-                  'bias_correction': self.bias_correction, 't': t,
+                  'bias_correction': self.bias_correction,
                   'rescale_grad': self.rescale_grad}
 
-        if self.clip_gradient:
-            kwargs['clip_gradient'] = self.clip_gradient
-
-        if multi_precision:
-            mean, var = state[1]
-            weight32 = state[0]
-            g = mp_lamb_update_phase1(weight, grad, mean, var, weight32, wd=wd, **kwargs)
+        if self.aggregate_num <= 1 or not isinstance(index, (tuple, list)):
+            if isinstance(index, (tuple, list)):
+                assert(len(index) == self.aggregate_num)
+                index, weight, grad, state = index[0], weight[0], grad[0], state[0]
+            assert(isinstance(weight, NDArray))
+            assert(isinstance(grad, NDArray))
+            self._update_count(index)
+            lr = self._get_lr(index)
+            wd = self._get_wd(index)
+            t = self._index_update_count[index]
+            weight_ptr = weight
+            grad_ptr = grad
+            if multi_precision:
+                mean, var = state[1]
+                weight32 = state[0]
+            else:
+                mean, var = state
+            kwargs['t'] = t
+            if self.clip_gradient:
+                kwargs['clip_gradient'] = self.clip_gradient
 
-            kwargs = {}
-            if self.lower_bound:
-                kwargs['lower_bound'] = self.lower_bound
-            if self.upper_bound:
-                kwargs['upper_bound'] = self.upper_bound
-            r_1 = weight32.norm()
-            r_2 = g.norm()
-            mp_lamb_update_phase2(weight, g, r_1, r_2, weight32, lr=lr, out=weight, **kwargs)
+            if multi_precision:
+                g = mp_lamb_update_phase1(weight_ptr, grad_ptr, mean, var, weight32, wd=wd, **kwargs)
+                kwargs = {}
+                if self.lower_bound:
+                    kwargs['lower_bound'] = self.lower_bound
+                if self.upper_bound:
+                    kwargs['upper_bound'] = self.upper_bound
+                r_1 = weight32.norm()
+                r_2 = g.norm()
+                mp_lamb_update_phase2(weight_ptr, g, r_1, r_2, weight32, lr=lr, out=weight_ptr, **kwargs)
+            else:
+                g = lamb_update_phase1(weight_ptr, grad_ptr, mean, var, wd=wd, **kwargs)
+                kwargs = {}
+                if self.lower_bound:
+                    kwargs['lower_bound'] = self.lower_bound
+                if self.upper_bound:
+                    kwargs['upper_bound'] = self.upper_bound
+                r_1 = weight_ptr.norm()
+                r_2 = g.norm()
+                lamb_update_phase2(weight_ptr, g, r_1, r_2, lr=lr, out=weight_ptr, **kwargs)
         else:
-            mean, var = state
-            g = lamb_update_phase1(weight, grad, mean, var, wd=wd, **kwargs)
-
-            kwargs = {}
+            if self.clip_gradient:
+                kwargs['clip_gradient'] = self.clip_gradient
             if self.lower_bound:
                 kwargs['lower_bound'] = self.lower_bound
             if self.upper_bound:
                 kwargs['upper_bound'] = self.upper_bound
-            r_1 = weight.norm()
-            r_2 = g.norm()
-            lamb_update_phase2(weight, g, r_1, r_2, lr=lr, out=weight, **kwargs)
+
+            step_count, lrs, wds = [], [], []
+            for i, w_i, g_i in zip(index, weight, grad):
+                assert(isinstance(w_i, NDArray))
+                assert(isinstance(g_i, NDArray))
+                self._update_count(i)
+                step_count.append(self._index_update_count[i])
+                lrs.append(self._get_lr(i))
+                wds.append(self._get_wd(i))
+
+            updated_tensors = 0
+            while updated_tensors < len(weight):
+                sidx = updated_tensors
+                eidx = min(updated_tensors + self.aggregate_num, len(weight))
+                if not multi_precision:
+                    mean, var = list(zip(*state[sidx:eidx]))
+                    multi_lamb_update(weight[sidx:eidx],
+                                      grad[sidx:eidx],
+                                      mean, var,
+                                      out=weight[sidx:eidx],
+                                      step_count=step_count[sidx:eidx],
+                                      lrs=lrs[sidx:eidx],
+                                      wds=wds[sidx:eidx],
+                                      **kwargs)
+                else:
+                    mean_var = list(zip(*state[sidx:eidx]))[1]
+                    temp = list(zip(*mean_var))
+                    mean = temp[0]
+                    var = temp[1]
+                    multi_mp_lamb_update(weight[sidx:eidx],
+                                         grad[sidx:eidx],
+                                         mean, var,
+                                         list(zip(*state[sidx:eidx]))[0],
+                                         out=weight[sidx:eidx],
+                                         step_count=step_count[sidx:eidx],
+                                         lrs=lrs[sidx:eidx],
+                                         wds=wds[sidx:eidx],
+                                         **kwargs)
+                updated_tensors += self.aggregate_num
 
     def update(self, index, weight, grad, state):
         self._update_impl(index, weight, grad, state, multi_precision=False)
@@ -1318,7 +1372,6 @@ def update_multi_precision(self, index, weight, grad, state):
         self._update_impl(index, weight, grad, state,
                           multi_precision=use_multi_precision)
 
-
 # pylint: enable=line-too-long
 @register
 class DCASGD(Optimizer):
diff --git a/python/mxnet/test_utils.py b/python/mxnet/test_utils.py
old mode 100644
new mode 100755
index 063d65659687..c60e5bc22201
--- a/python/mxnet/test_utils.py
+++ b/python/mxnet/test_utils.py
@@ -2257,36 +2257,57 @@ def compare_ndarray_tuple(t1, t2, rtol=None, atol=None):
 
 
 def compare_optimizer(opt1, opt2, shape, dtype, w_stype='default', g_stype='default',
-                      rtol=1e-4, atol=1e-5, compare_states=True):
+                      rtol=1e-4, atol=1e-5, compare_states=True, ntensors=1):
     """Compare opt1 and opt2."""
-    if w_stype == 'default':
-        w2 = mx.random.uniform(shape=shape, ctx=default_context(), dtype=dtype)
-        w1 = w2.copyto(default_context())
-    elif w_stype in ('row_sparse', 'csr'):
-        w2 = rand_ndarray(shape, w_stype, density=1, dtype=dtype)
-        w1 = w2.copyto(default_context()).tostype('default')
-    else:
-        raise Exception("type not supported yet")
-    if g_stype == 'default':
-        g2 = mx.random.uniform(shape=shape, ctx=default_context(), dtype=dtype)
-        g1 = g2.copyto(default_context())
-    elif g_stype in ('row_sparse', 'csr'):
-        g2 = rand_ndarray(shape, g_stype, dtype=dtype)
-        g1 = g2.copyto(default_context()).tostype('default')
+    if not isinstance(shape, list):
+        assert(ntensors == 1)
+        if w_stype == 'default':
+            w2 = mx.random.uniform(shape=shape, ctx=default_context(), dtype=dtype)
+            w1 = w2.copyto(default_context())
+        elif w_stype in ('row_sparse', 'csr'):
+            w2 = rand_ndarray(shape, w_stype, density=1, dtype=dtype)
+            w1 = w2.copyto(default_context()).tostype('default')
+        else:
+            raise Exception("type not supported yet")
+        if g_stype == 'default':
+            g2 = mx.random.uniform(shape=shape, ctx=default_context(), dtype=dtype)
+            g1 = g2.copyto(default_context())
+        elif g_stype in ('row_sparse', 'csr'):
+            g2 = rand_ndarray(shape, g_stype, dtype=dtype)
+            g1 = g2.copyto(default_context()).tostype('default')
+        else:
+            raise Exception("type not supported yet")
+
+        state1 = opt1.create_state_multi_precision(0, w1)
+        state2 = opt2.create_state_multi_precision(0, w2)
+        if compare_states:
+            compare_ndarray_tuple(state1, state2)
+
+        opt1.update_multi_precision(0, w1, g1, state1)
+        opt2.update_multi_precision(0, w2, g2, state2)
+        if compare_states:
+            compare_ndarray_tuple(state1, state2, rtol=rtol, atol=atol)
+        assert_almost_equal(w1, w2, rtol=rtol, atol=atol)
     else:
-        raise Exception("type not supported yet")
-
-    state1 = opt1.create_state_multi_precision(0, w1)
-    state2 = opt2.create_state_multi_precision(0, w2)
-    if compare_states:
-        compare_ndarray_tuple(state1, state2)
-
-    opt1.update_multi_precision(0, w1, g1, state1)
-    opt2.update_multi_precision(0, w2, g2, state2)
-    if compare_states:
-        compare_ndarray_tuple(state1, state2, rtol=rtol, atol=atol)
-    assert_almost_equal(w1, w2, rtol=rtol, atol=atol)
-
+        # test multi-tensor: Opt1 single-tensor reference, Opt2 multi-tensor
+        from copy import deepcopy
+        w1, g1 = [], []
+        for s in shape:
+            w1.append(mx.random.uniform(shape=s, ctx=default_context(), dtype=dtype))
+            g1.append(mx.random.uniform(shape=s, ctx=default_context(), dtype=dtype))
+        w1 = tuple(w1)
+        w2 = deepcopy(w1)
+        g1 = tuple(g1)
+        g2 = deepcopy(g1)
+        state2 = [opt2.create_state_multi_precision(0, w2[i]) for i in range(ntensors)]
+
+        opt2.update_multi_precision(list(range(ntensors)), w2, g2, state2)
+        for i in range(ntensors):
+            state1 = opt1.create_state_multi_precision(i, w1[i])
+            opt1.update_multi_precision(i, w1[i], g1[i], state1)
+            if compare_states:
+                compare_ndarray_tuple(state1, state2[i], rtol, atol)
+            compare_ndarray_tuple(w1[i], w2[i], rtol, atol)
 
 def same_symbol_structure(sym1, sym2):
     """Compare two symbols to check if they have the same computation graph structure.
diff --git a/src/operator/contrib/multi_lamb-inl.h b/src/operator/contrib/multi_lamb-inl.h
new file mode 100644
index 000000000000..7fb186f91bfc
--- /dev/null
+++ b/src/operator/contrib/multi_lamb-inl.h
@@ -0,0 +1,359 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file multi_lamb-inl.h
+ * \brief multi-tensor LAMB optimizer
+ * \author Moises Hernandez
+ */
+#ifndef MXNET_OPERATOR_CONTRIB_MULTI_LAMB_INL_H_
+#define MXNET_OPERATOR_CONTRIB_MULTI_LAMB_INL_H_
+
+#include <dmlc/parameter.h>
+#include <mxnet/operator.h>
+#include <mxnet/operator_util.h>
+#include <mxnet/op_attr_types.h>
+#include <mshadow/base.h>
+#include <nnvm/op.h>
+#include <nnvm/op_attr_types.h>
+#include <vector>
+#include "../operator_common.h"
+#include "../mshadow_op.h"
+#include "../mxnet_op.h"
+#include "../tensor/init_op.h"
+#include "../tensor/util/tensor_util-inl.h"
+#include "multi_sum_sq-inl.h"
+
+namespace mxnet {
+namespace op {
+
+namespace multilamb {
+enum MultiLambUpdateResource {kTempSpace};
+}  // namespace multilamb
+
+struct MultiLAMBParam : public dmlc::Parameter<MultiLAMBParam> {
+  mxnet::Tuple<float> learning_rates;
+  mxnet::Tuple<float> wds;
+  float beta1;
+  float beta2;
+  float epsilon;
+  float rescale_grad;
+  float lower_bound;
+  float upper_bound;
+  float clip_gradient;
+  bool bias_correction;
+  int num_tensors;
+  mxnet::Tuple<int> step_count;
+
+  DMLC_DECLARE_PARAMETER(MultiLAMBParam) {
+    DMLC_DECLARE_FIELD(learning_rates)
+    .describe("List of learning rates");
+    DMLC_DECLARE_FIELD(beta1)
+    .set_default(0.9f)
+    .describe("Exponential decay rate for the first moment estimates.");
+    DMLC_DECLARE_FIELD(beta2)
+    .set_default(0.999f)
+    .describe("Exponential decay rate for the second moment estimates.");
+    DMLC_DECLARE_FIELD(epsilon)
+    .set_default(1e-6f)
+    .describe("Small value to avoid division by 0.");
+    DMLC_DECLARE_FIELD(wds)
+    .describe("List of Weight decays."
+              "Weight decay augments the objective function with a "
+              "regularization term that penalizes large weights. "
+               "The penalty scales with the square of the magnitude of each weight.");
+    DMLC_DECLARE_FIELD(rescale_grad)
+    .set_default(1.0f)
+    .describe("Gradient rescaling factor");
+    DMLC_DECLARE_FIELD(lower_bound)
+    .set_default(-1.0f)
+    .describe("Lower limit of norm of weight. If lower_bound <= 0, Lower limit is not set");
+    DMLC_DECLARE_FIELD(upper_bound)
+    .set_default(-1.0f)
+    .describe("Upper limit of norm of weight. If upper_bound <= 0, Upper limit is not set");
+    DMLC_DECLARE_FIELD(clip_gradient)
+    .set_default(-1.0f)
+    .describe("Clip gradient to the range of [-clip_gradient, clip_gradient] "
+              "If clip_gradient <= 0, gradient clipping is turned off. "
+              "grad = max(min(grad, clip_gradient), -clip_gradient).");
+    DMLC_DECLARE_FIELD(bias_correction)
+    .set_default(true)
+    .describe("Whether to use bias correction.");
+    DMLC_DECLARE_FIELD(step_count)
+    .describe("Step count for each tensor");
+    DMLC_DECLARE_FIELD(num_tensors)
+    .set_default(1)
+    .describe("Number of tensors");
+  }
+};
+
+template<typename ParamType, int input_stride>
+inline bool MultiLAMBInferShape(const nnvm::NodeAttrs& attrs,
+                                mxnet::ShapeVector *in_attrs,
+                                mxnet::ShapeVector *out_attrs) {
+  const ParamType& param = dmlc::get<ParamType>(attrs.parsed);
+  CHECK_EQ(in_attrs->size(), input_stride * param.num_tensors);
+  CHECK_EQ(out_attrs->size(), param.num_tensors);
+
+  bool all_inferred = true;
+  auto& input_shapes = *in_attrs;
+  auto& output_shapes = *out_attrs;
+
+  CHECK_LE(param.num_tensors, 45)
+    << "Invalid number of tensors, the maximum value is 45, and got "
+    << param.num_tensors;
+  CHECK_EQ(param.learning_rates.ndim(), param.num_tensors)
+    << "Number of learning rates is inconsistent with num_tensors "
+    << "parameter passed. Expected number of learning rates: "
+    << param.num_tensors << ", and got " << param.learning_rates.ndim();
+  CHECK_EQ(param.wds.ndim(), param.num_tensors)
+    << "Number of weight decays is inconsistent with num_tensors "
+    << "parameter passed. Expected number of weight decays: "
+    << param.num_tensors << ", and got " << param.wds.ndim();
+  CHECK_EQ(param.step_count.ndim(), param.num_tensors)
+    << "Number of step counts is inconsistent with num_tensors."
+    << "Expected number of step counts: "
+    << param.num_tensors << ", and got " << param.step_count.ndim();
+
+  // Weights, gradients, mean and variance
+  for (int i = 0; i < param.num_tensors; ++i) {
+    mxnet::ShapeVector input_vec;
+    mxnet::ShapeVector output_vec({output_shapes[i]});
+    for (int j = 0; j < input_stride; ++j) {
+      input_vec.push_back(input_shapes[i * input_stride + j]);
+    }
+    all_inferred = all_inferred && ElemwiseShape<input_stride, 1>(attrs, &input_vec, &output_vec);
+  }
+  return all_inferred;
+}
+
+template <typename ParamType, int input_stride>
+inline bool MPMultiLAMBInferType(const nnvm::NodeAttrs& attrs,
+                                 std::vector<int> *in_attrs,
+                                 std::vector<int> *out_attrs) {
+  const ParamType& param = dmlc::get<ParamType>(attrs.parsed);
+  CHECK_EQ(in_attrs->size(), input_stride * param.num_tensors);
+  CHECK_EQ(out_attrs->size(), param.num_tensors);
+
+  bool all_inferred = true;
+  auto& input_types = *in_attrs;
+  auto& output_types = *out_attrs;
+
+  // weights, gradients
+  for (int i = 0; i < param.num_tensors; ++i) {
+    std::vector<int> input_vec;
+    std::vector<int> output_vec({output_types[i]});
+    for (int j = 0; j < 2; ++j) {
+      input_vec.push_back(input_types[i * input_stride + j]);
+    }
+    all_inferred = all_inferred &&
+            ElemwiseType<2, 1>(attrs, &input_vec, &output_vec);
+  }
+
+  // mean, var, weights32 (master copies of weights)
+  for (int i = 0; i < param.num_tensors; ++i) {
+    TYPE_ASSIGN_CHECK(input_types, input_stride * i + 2, mshadow::kFloat32);
+    TYPE_ASSIGN_CHECK(input_types, input_stride * i + 3, mshadow::kFloat32);
+    TYPE_ASSIGN_CHECK(input_types, input_stride * i + input_stride - 1, mshadow::kFloat32);
+  }
+  return all_inferred;
+}
+
+template<typename T>
+class LAMBTypeIdentity {
+ public:
+  using type = T;
+};
+
+template<typename T>
+class LAMBSinglePrecision {
+ public:
+  using type = float;
+};
+
+template<typename DType, typename MPDType>
+struct MultiLAMBKernelParam {
+  static const int N = 45;
+  size_t ntensors;
+  size_t max_size;
+  size_t total_size;
+  size_t sizes[N];
+  size_t tensor2temp_g[N];
+  DType* weights[N];
+  DType* grads[N];
+  MPDType* mean[N];
+  MPDType* var[N];
+  MPDType* weights32[N];
+  DType* out_data[N];
+  int step_count[N];
+  MPDType learning_rates[N];
+  MPDType wds[N];
+
+  // gpu
+  int chunk_size = 65536;
+  int nchunks;
+};
+
+template<typename xpu,
+         typename DType,
+         typename MPDType,
+         typename ParamType = MultiLAMBParam,
+         int input_stride = 5>
+void FillMultiLAMBKernelParam(const nnvm::NodeAttrs& attrs,
+                              const OpContext &ctx,
+                              const std::vector<TBlob> &inputs,
+                              const std::vector<TBlob> &outputs,
+                              MultiLAMBKernelParam<DType, MPDType> *multi_param) {
+  const ParamType& p = nnvm::get<ParamType>(attrs.parsed);
+  mxnet_op::Stream<xpu>* s = ctx.get_stream<xpu>();
+
+  multi_param->ntensors = p.num_tensors;
+  multi_param->total_size = 0;
+  multi_param->max_size = 0;
+  multi_param->nchunks = 0;
+
+  constexpr bool is_same = std::is_same<DType, MPDType>::value;
+  for (size_t i = 0; i < multi_param->ntensors; ++i) {
+    const auto idx = i * input_stride;
+    multi_param->sizes[i] = inputs[idx].shape_.Size();
+    multi_param->tensor2temp_g[i] = multi_param->total_size;
+    multi_param->total_size += multi_param->sizes[i];
+    if (multi_param->max_size < multi_param->sizes[i])
+      multi_param->max_size = multi_param->sizes[i];
+
+    multi_param->weights[i] = inputs[idx].FlatTo2D<xpu, DType>(s).dptr_;
+    multi_param->grads[i] = inputs[idx + 1].FlatTo2D<xpu, DType>(s).dptr_;
+    multi_param->mean[i] = inputs[idx + 2].FlatTo2D<xpu, MPDType>(s).dptr_;
+    multi_param->var[i]  = inputs[idx + 3].FlatTo2D<xpu, MPDType>(s).dptr_;
+
+    // if mixed precision, then the last input in a set
+    // is 32-bit master copy of the weights
+    if (!is_same)
+      multi_param->weights32[i] = inputs[idx + input_stride - 1].FlatTo2D<xpu, MPDType>(s).dptr_;
+    multi_param->out_data[i] = outputs[i].FlatTo2D<xpu, DType>(s).dptr_;
+    multi_param->nchunks += (multi_param->sizes[i] + multi_param->chunk_size - 1)
+                            / multi_param->chunk_size;
+    multi_param->learning_rates[i] = static_cast<MPDType>(p.learning_rates[i]);
+    multi_param->wds[i] = static_cast<MPDType>(p.wds[i]);
+  }
+  memcpy(multi_param->step_count, p.step_count.begin(), multi_param->ntensors * sizeof(int));
+}
+
+using namespace mxnet_op;
+template<typename MPDType, typename DType>
+void CallKernel1(Stream<cpu>* s);
+template<typename MPDType, typename DType>
+void CallKernel1(Stream<gpu>* s);
+
+template<typename MPDType, typename DType>
+void CallKernel2(Stream<cpu>* s);
+template<typename MPDType, typename DType>
+void CallKernel2(Stream<gpu>* s);
+
+template<typename xpu, template<typename> class MPTypeChooser, int input_stride>
+inline void MultiLAMB(const nnvm::NodeAttrs& attrs,
+                      const OpContext &ctx,
+                      const std::vector<TBlob> &inputs,
+                      const std::vector<OpReqType> &req,
+                      const std::vector<TBlob> &outputs) {
+  auto param = nnvm::get<MultiLAMBParam>(attrs.parsed);
+  Stream<xpu>* s = ctx.get_stream<xpu>();
+
+  MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    using MPDType = typename MPTypeChooser<DType>::type;
+    MultiLAMBKernelParam<DType, MPDType> kernel_params;
+    FillMultiLAMBKernelParam<xpu, DType, MPDType, MultiLAMBParam, input_stride>
+            (attrs, ctx, inputs, outputs, &kernel_params);
+
+    // create vector of TBlob with all the weights contiguous
+    std::vector<TBlob> weights;
+    for (size_t index = 0; index < kernel_params.ntensors; ++index) {
+        weights.emplace_back(inputs[index*input_stride]);
+    }
+
+    // Calculate amount of temporary storage (temp_g, r1, r2, block_to_tensor, block_to_chunk)
+    size_t workspace_size = kernel_params.total_size * sizeof(float) +
+        2 * kernel_params.ntensors * sizeof(float) +
+        2 * kernel_params.nchunks * sizeof(int);
+    // take into account the required storage required within MultiSumSqRun
+    size_t required_storage_multi_sum_sq = 0;
+    required_storage_multi_sum_sq = GetRequiredStorageMultiSumSq<xpu>(inputs);
+    workspace_size += required_storage_multi_sum_sq;
+
+    // Request temporary storage
+    Tensor<xpu, 1, char> workspace =
+    ctx.requested[multilamb::kTempSpace].get_space_typed<xpu, 1, char>(
+      Shape1(workspace_size), s);
+
+    // Create tensors
+    size_t pos_wspace = required_storage_multi_sum_sq;
+    Tensor<xpu, 1, float> temp_g(reinterpret_cast<float*>(&workspace[pos_wspace]),
+      Shape1(kernel_params.total_size), s);
+    // create vector of TBlob with all the temp_g contiguous
+    std::vector<TBlob> temp_g_tblobs;
+    for (size_t index = 0; index < kernel_params.ntensors; ++index) {
+      Tensor<xpu, 1, float> aux(reinterpret_cast<float*>(&workspace[pos_wspace]),
+        Shape1(kernel_params.sizes[index]), s);
+      TBlob newtblob(aux);
+      temp_g_tblobs.emplace_back(newtblob);
+      pos_wspace += kernel_params.sizes[index] * sizeof(float);
+    }
+    Tensor<xpu, 1, float> r1(reinterpret_cast<float*>(&workspace[pos_wspace]),
+      Shape1(kernel_params.ntensors), s);
+    pos_wspace += kernel_params.ntensors * sizeof(float);
+    Tensor<xpu, 1, float> r2(reinterpret_cast<float*>(&workspace[pos_wspace]),
+      Shape1(kernel_params.ntensors), s);
+    pos_wspace += kernel_params.ntensors * sizeof(float);
+    Tensor<xpu, 1, int> block_to_tensor(reinterpret_cast<int*>(&workspace[pos_wspace]),
+      Shape1(kernel_params.nchunks), s);
+    pos_wspace += kernel_params.nchunks * sizeof(int);
+    Tensor<xpu, 1, int> block_to_chunk(reinterpret_cast<int*>(&workspace[pos_wspace]),
+      Shape1(kernel_params.nchunks), s);
+
+    MultiSumSqRun<xpu>(weights, kernel_params.ntensors, r1.dptr_, ctx);
+    CallKernel1<MPDType, DType>(s, kernel_params, param, temp_g.dptr_,
+                                block_to_tensor.dptr_,
+                                block_to_chunk.dptr_);
+    MultiSumSqRun<xpu>(temp_g_tblobs, kernel_params.ntensors, r2.dptr_, ctx);
+    CallKernel2<MPDType, DType>(s, kernel_params, param, r1.dptr_, r2.dptr_,
+                                temp_g.dptr_,
+                                block_to_tensor.dptr_, block_to_chunk.dptr_,
+                                req[0]);
+  });
+}
+
+template<typename xpu, bool MP>
+inline void MultiLAMBUpdate(const nnvm::NodeAttrs& attrs,
+                            const OpContext &ctx,
+                            const std::vector<TBlob> &inputs,
+                            const std::vector<OpReqType> &req,
+                            const std::vector<TBlob> &outputs) {
+  if (!MP) {
+    MultiLAMB<xpu, LAMBTypeIdentity, 4>
+      (attrs, ctx, inputs, req, outputs);
+  } else {
+    MultiLAMB<xpu, LAMBSinglePrecision, 5>
+      (attrs, ctx, inputs, req, outputs);
+  }
+}
+
+}  // namespace op
+}  // namespace mxnet
+#endif  // MXNET_OPERATOR_CONTRIB_MULTI_LAMB_INL_H_
diff --git a/src/operator/contrib/multi_lamb.cc b/src/operator/contrib/multi_lamb.cc
new file mode 100644
index 000000000000..6ddc4dfd6559
--- /dev/null
+++ b/src/operator/contrib/multi_lamb.cc
@@ -0,0 +1,251 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file multi_lamb.cc
+ * \brief multi-tensor LAMB optimizer
+ * \author Moises Hernandez
+ */
+
+#include "./multi_lamb-inl.h"
+#include "../elemwise_op_common.h"
+
+namespace mxnet {
+namespace op {
+
+template<typename MPDType, bool has_mixed_precision>
+struct MultiLAMBKernelStep1 {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int i,
+                                  const MultiLAMBKernelParam<DType, MPDType>& kernel_params,
+                                  const float beta1, const float beta2,
+                                  const float epsilon,
+                                  const float clip_gradient,
+                                  const bool bias_correction,
+                                  const float rescale_grad,
+                                  float* temp_g) {
+    using namespace mshadow_op;
+    for (size_t index = 0; index < kernel_params.ntensors; ++index) {
+      if ((size_t)i < kernel_params.sizes[index]) {
+        MPDType w = has_mixed_precision ? kernel_params.weights32[index][i]:
+                                          MPDType(kernel_params.weights[index][i]);
+        MPDType scaled_grad = static_cast<MPDType>(kernel_params.grads[index][i])*rescale_grad;
+        if (clip_gradient >= 0.0f)
+            scaled_grad = mshadow_op::clip::Map(scaled_grad, static_cast<MPDType>(clip_gradient));
+        MPDType mean = static_cast<MPDType>(beta1) * kernel_params.mean[index][i] +
+          (static_cast<MPDType>(1.0f) - static_cast<MPDType>(beta1)) * scaled_grad;
+        MPDType var = static_cast<MPDType>(beta2) * kernel_params.var[index][i] +
+          (static_cast<MPDType>(1.0f) - static_cast<MPDType>(beta2)) * scaled_grad * scaled_grad;
+        kernel_params.mean[index][i] = mean;
+        kernel_params.var[index][i] = var;
+
+        MPDType g;
+        if (bias_correction) {
+          MPDType mean_hat = mean / (static_cast<MPDType>(1.0f) -
+                                    power::Map(static_cast<MPDType>(beta1),
+                                    static_cast<MPDType>(kernel_params.step_count[index])));
+          MPDType var_hat = var / (static_cast<MPDType>(1.0f) -
+                                  power::Map(static_cast<MPDType>(beta2),
+                                  static_cast<MPDType>(kernel_params.step_count[index])));
+          g = mean_hat / (sqrt(var_hat) + static_cast<MPDType>(epsilon)) +
+                         kernel_params.wds[index] * w;
+        } else {
+          g = mean / (sqrt(var) + static_cast<MPDType>(epsilon)) +
+                      kernel_params.wds[index] * w;
+        }
+        temp_g[kernel_params.tensor2temp_g[index]+i] = g;
+      }
+    }
+  }
+};
+
+template<typename MPDType, bool has_mixed_precision>
+struct MultiLAMBKernelStep2 {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int i,
+                                  const MultiLAMBKernelParam<DType, MPDType>& kernel_params,
+                                  const float* sum_sq_weigths,
+                                  const float* sum_sq_temp_g,
+                                  const float* temp_g,
+                                  const float lower_bound,
+                                  const float upper_bound,
+                                  const OpReqType req) {
+    for (size_t index = 0; index < kernel_params.ntensors; ++index) {
+      if ((size_t)i < kernel_params.sizes[index]) {
+        MPDType w = has_mixed_precision ? kernel_params.weights32[index][i]:
+                                          MPDType(kernel_params.weights[index][i]);
+        float r1 = sqrt(sum_sq_weigths[index]);
+        float r2 = sqrt(sum_sq_temp_g[index]);
+        if (lower_bound >= 0)
+          r1 = std::max(r1, lower_bound);
+        if (upper_bound >= 0)
+          r1 = std::min(r1, upper_bound);
+
+        // calculate lamb_trust_ratio
+        MPDType r;
+        if (r1 == 0.0f || r2 == 0.0f)
+          r = 1.0f;
+        else
+          r = r1/r2;
+
+        MPDType lr_adjusted = kernel_params.learning_rates[index] * r;
+        w -= lr_adjusted * temp_g[kernel_params.tensor2temp_g[index]+i];
+
+        // update weights
+        if (has_mixed_precision)
+          kernel_params.weights32[index][i] = w;
+        KERNEL_ASSIGN(kernel_params.out_data[index][i], req, w);
+      }
+    }
+  }
+};
+
+template<typename MPDType, typename DType>
+void CallKernel1(Stream<cpu>* s,
+                  const MultiLAMBKernelParam<DType, MPDType>& kernel_params,
+                  const MultiLAMBParam &param,
+                  float* temp_g,
+                  int* block_to_tensor,
+                  int* block_to_chunk) {
+  Kernel<MultiLAMBKernelStep1<MPDType, !std::is_same<DType, MPDType>::value>, cpu>::
+                               Launch(s, kernel_params.max_size,
+                               kernel_params,
+                               param.beta1, param.beta2,
+                               param.epsilon,
+                               param.clip_gradient,
+                               param.bias_correction,
+                               param.rescale_grad,
+                               temp_g);
+}
+
+template<typename MPDType, typename DType>
+void CallKernel2(Stream<cpu>* s,
+                  const MultiLAMBKernelParam<DType, MPDType>& kernel_params,
+                  const MultiLAMBParam &param,
+                  float* r1, float* r2,
+                  float* temp_g,
+                  int* block_to_tensor,
+                  int* block_to_chunk,
+                  const OpReqType req) {
+  Kernel<MultiLAMBKernelStep2<MPDType, !std::is_same<DType, MPDType>::value>, cpu>::
+                               Launch(s, kernel_params.max_size,
+                               kernel_params,
+                               r1, r2,
+                               temp_g,
+                               param.lower_bound, param.upper_bound,
+                               req);
+}
+
+DMLC_REGISTER_PARAMETER(MultiLAMBParam);
+
+std::vector<std::string> LAMBParamToVector(uint32_t num_tensors,
+                                           const char *p_names[],
+                                           size_t n_params) {
+  std::vector<std::string> ret;
+  for (uint32_t i = 0; i < num_tensors; ++i) {
+    const auto idx = std::to_string(i);
+    for (size_t j = 0; j < n_params; ++j)
+      ret.push_back(std::string(p_names[i]) + idx);
+  }
+  return ret;
+}
+
+inline uint32_t NumTensors(const nnvm::NodeAttrs& attrs) {
+  return static_cast<uint32_t>(dmlc::get<MultiLAMBParam>(attrs.parsed).num_tensors);
+}
+
+NNVM_REGISTER_OP(_multi_lamb_update)
+.describe(R"code(Compute the LAMB coefficients of multiple weights and grads"
+)code" ADD_FILELINE)
+.set_num_inputs([](const nnvm::NodeAttrs& attrs) {
+    return NumTensors(attrs) * 4;
+  })
+.set_num_outputs([](const nnvm::NodeAttrs& attrs) {
+    return NumTensors(attrs);
+  })
+.set_attr_parser(ParamParser<MultiLAMBParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", MultiLAMBInferShape<MultiLAMBParam, 4>)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<-1, -1>)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    const char *param_names[] = {"weight_", "grad_", "mean_", "var_"};
+    return LAMBParamToVector(NumTensors(attrs), param_names,
+                             sizeof(param_names)/sizeof(param_names[0]));
+  })
+// mutable: mean, var
+.set_attr<nnvm::FMutateInputs>("FMutateInputs",
+  [](const nnvm::NodeAttrs& attrs) {
+    std::vector<uint32_t> ret;
+    const auto i_max = NumTensors(attrs);
+    for (size_t i = 0; i < i_max; ++i) {
+      ret.push_back(i * 4 + 2);
+      ret.push_back(i * 4 + 3);
+    }
+    return ret;
+  })
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", MultiLAMBUpdate<cpu, false>)
+.add_argument("data", "NDArray-or-Symbol[]", "data")
+.add_arguments(MultiLAMBParam::__FIELDS__());
+
+
+NNVM_REGISTER_OP(_multi_mp_lamb_update)
+.describe(R"code(Compute the LAMB coefficients of multiple weights and grads with Mix Precision"
+)code" ADD_FILELINE)
+.set_num_inputs([](const nnvm::NodeAttrs& attrs) {
+    return NumTensors(attrs) * 5;
+  })
+.set_num_outputs([](const nnvm::NodeAttrs& attrs) {
+    return NumTensors(attrs);
+  })
+.set_attr_parser(ParamParser<MultiLAMBParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", MultiLAMBInferShape<MultiLAMBParam, 5>)
+.set_attr<nnvm::FInferType>("FInferType", MPMultiLAMBInferType<MultiLAMBParam, 5>)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    const char *param_names[] = {"weight_", "grad_", "mean_", "var_", "weight32_"};
+    return LAMBParamToVector(NumTensors(attrs), param_names,
+                             sizeof(param_names)/sizeof(param_names[0]));
+  })
+// mutable: mean, var, weights32
+.set_attr<nnvm::FMutateInputs>("FMutateInputs",
+  [](const nnvm::NodeAttrs& attrs) {
+    std::vector<uint32_t> ret;
+    const auto i_max = NumTensors(attrs);
+    for (size_t i = 0; i < i_max; ++i) {
+      ret.push_back(i * 5 + 2);
+      ret.push_back(i * 5 + 3);
+      ret.push_back(i * 5 + 4);
+    }
+    return ret;
+  })
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", MultiLAMBUpdate<cpu, true>)
+.add_argument("data", "NDArray-or-Symbol[]", "data")
+.add_arguments(MultiLAMBParam::__FIELDS__());
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/contrib/multi_lamb.cu b/src/operator/contrib/multi_lamb.cu
new file mode 100644
index 000000000000..6415bfbda015
--- /dev/null
+++ b/src/operator/contrib/multi_lamb.cu
@@ -0,0 +1,261 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file multi_lamb.cu
+ * \brief multi-tensor LAMB optimizer
+ * \author Moises Hernandez
+ */
+
+#include "./multi_lamb-inl.h"
+
+namespace mxnet {
+namespace op {
+
+#define BLOCK_SIZE_LAMB 512
+#define ILP_LAMB 4
+
+template<bool has_mixed_precision, typename MPDType, typename DType>
+__global__ void KernelStep1(const MultiLAMBKernelParam<DType, MPDType> kernel_params,
+                            const float beta1, const float beta2,
+                            const MPDType beta3, const MPDType beta4,
+                            const float epsilon,
+                            const float clip_gradient,
+                            const bool bias_correction,
+                            const float rescale_grad,
+                            float* temp_g,
+                            int* block_to_tensor,
+                            int* block_to_chunk) {
+  const int tensor_id = block_to_tensor[blockIdx.x];
+  const int chunck_id = block_to_chunk[blockIdx.x];
+  const int start_pos = chunck_id * kernel_params.chunk_size + threadIdx.x;
+  const int stop_pos = chunck_id * kernel_params.chunk_size + kernel_params.chunk_size;
+
+  MPDType biascorrection1, biascorrection2;
+  if (bias_correction) {
+    biascorrection1 = 1.0 -
+                      static_cast<MPDType>(std::pow(beta1, kernel_params.step_count[tensor_id]));
+    biascorrection2 = 1.0 -
+                      static_cast<MPDType>(std::pow(beta2, kernel_params.step_count[tensor_id]));
+  } else {
+    biascorrection1 = static_cast<MPDType>(1.0);
+    biascorrection2 = static_cast<MPDType>(1.0);
+  }
+
+  MPDType r_weight[ILP_LAMB];
+  MPDType r_grad[ILP_LAMB];
+  MPDType r_mean[ILP_LAMB];
+  MPDType r_var[ILP_LAMB];
+  MPDType r_g[ILP_LAMB];
+
+  for (size_t i = start_pos; i < stop_pos && i < kernel_params.sizes[tensor_id];
+                                                    i+= blockDim.x*ILP_LAMB) {
+#pragma unroll
+    for (int ii = 0; ii < ILP_LAMB; ii++) {
+      int load_pos = i + ii*blockDim.x;
+      if (load_pos < stop_pos && load_pos < kernel_params.sizes[tensor_id]) {
+        r_weight[ii] = has_mixed_precision ? kernel_params.weights32[tensor_id][load_pos]:
+                       static_cast<MPDType>(kernel_params.weights[tensor_id][load_pos]);
+        r_grad[ii] = static_cast<MPDType>(kernel_params.grads[tensor_id][load_pos]);
+        r_mean[ii] = kernel_params.mean[tensor_id][load_pos];
+        r_var[ii] = kernel_params.var[tensor_id][load_pos];
+      } else {
+        r_weight[ii] = static_cast<MPDType>(0);
+        r_grad[ii] = static_cast<MPDType>(0);
+        r_mean[ii] = static_cast<MPDType>(0);
+        r_var[ii] = static_cast<MPDType>(0);
+      }
+    }
+#pragma unroll
+    for (int ii = 0; ii < ILP_LAMB; ii++) {
+      r_grad[ii] = r_grad[ii] * static_cast<MPDType>(rescale_grad);
+      if (clip_gradient >= 0.0f)
+        r_grad[ii] = max(min(r_grad[ii], clip_gradient), -clip_gradient);
+      r_mean[ii] = static_cast<MPDType>(beta1) * r_mean[ii] + beta3 * r_grad[ii];
+      r_var[ii] = static_cast<MPDType>(beta2) * r_var[ii] + beta4 * r_grad[ii] * r_grad[ii];
+      r_g[ii] = (r_mean[ii] / biascorrection1) /
+                (sqrt(r_var[ii] / biascorrection2) + static_cast<MPDType>(epsilon));
+      r_g[ii] = __fmaf_rn(kernel_params.wds[tensor_id], r_weight[ii], r_g[ii]);
+    }
+#pragma unroll
+    for (int ii = 0; ii < ILP_LAMB; ii++) {
+      int store_pos = i + ii*blockDim.x;
+      if (store_pos < stop_pos && store_pos < kernel_params.sizes[tensor_id]) {
+        kernel_params.mean[tensor_id][store_pos] = r_mean[ii];
+        kernel_params.var[tensor_id][store_pos] = r_var[ii];
+        temp_g[kernel_params.tensor2temp_g[tensor_id]+store_pos] = r_g[ii];
+      }
+    }
+  }
+}
+
+template<bool has_mixed_precision, typename MPDType, typename DType>
+__global__ void KernelStep2(const MultiLAMBKernelParam<DType, MPDType> kernel_params,
+                            const float* sum_sq_weigths,
+                            const float* sum_sq_temp_g,
+                            const float* temp_g,
+                            const float lower_bound,
+                            const float upper_bound,
+                            int* block_to_tensor,
+                            int* block_to_chunk,
+                            const OpReqType req) {
+  const int tensor_id = block_to_tensor[blockIdx.x];
+  const int chunck_id = block_to_chunk[blockIdx.x];
+  const int start_pos = chunck_id * kernel_params.chunk_size + threadIdx.x;
+  const int stop_pos = chunck_id * kernel_params.chunk_size + kernel_params.chunk_size;
+
+  MPDType r1 = sqrtf(sum_sq_weigths[tensor_id]);
+  MPDType r2 = sqrtf(sum_sq_temp_g[tensor_id]);
+  if (lower_bound >= 0)
+    r1 = max(r1, lower_bound);
+  if (upper_bound >= 0)
+    r1 = min(r1, upper_bound);
+
+  MPDType lr_adjusted;
+  if (r1 == 0.0f || r2 == 0.0f)
+    lr_adjusted = kernel_params.learning_rates[tensor_id];
+  else
+    lr_adjusted = kernel_params.learning_rates[tensor_id] * r1/r2;
+
+  MPDType r_weight[ILP_LAMB];
+  MPDType r_g[ILP_LAMB];
+
+  for (size_t i=start_pos; i < stop_pos && i < kernel_params.sizes[tensor_id];
+                                                  i+= blockDim.x*ILP_LAMB) {
+#pragma unroll
+    for (int ii = 0; ii < ILP_LAMB; ii++) {
+      int load_pos = i + ii*blockDim.x;
+      if (load_pos < stop_pos && load_pos < kernel_params.sizes[tensor_id]) {
+        r_weight[ii] = has_mixed_precision ? kernel_params.weights32[tensor_id][load_pos]:
+                       static_cast<MPDType>(kernel_params.weights[tensor_id][load_pos]);
+        r_g[ii] = temp_g[kernel_params.tensor2temp_g[tensor_id]+load_pos];
+      }
+    }
+#pragma unroll
+    for (int ii = 0; ii < ILP_LAMB; ii++) {
+      r_weight[ii] -= lr_adjusted * r_g[ii];
+    }
+#pragma unroll
+    for (int ii = 0; ii < ILP_LAMB; ii++) {
+      int store_pos = i + ii*blockDim.x;
+      if (store_pos < stop_pos && store_pos < kernel_params.sizes[tensor_id]) {
+        if (has_mixed_precision)
+          kernel_params.weights32[tensor_id][store_pos] = r_weight[ii];
+        KERNEL_ASSIGN(kernel_params.out_data[tensor_id][store_pos], req, r_weight[ii]);
+      }
+    }
+  }
+}
+
+template<typename MPDType, typename DType>
+void CallKernel1(Stream<gpu>* s,
+                 const MultiLAMBKernelParam<DType, MPDType>& kernel_params,
+                 const MultiLAMBParam &param,
+                 float* temp_g,
+                 int* block_to_tensor,
+                 int* block_to_chunk) {
+  int nblocks = kernel_params.nchunks;
+  int* host_block2tensor = reinterpret_cast<int*>(malloc(kernel_params.nchunks*sizeof(int)));
+  int* host_block2chunk = reinterpret_cast<int*>(malloc(kernel_params.nchunks*sizeof(int)));
+  int chunk_id = 0;
+  for (size_t index = 0; index < kernel_params.ntensors; ++index) {
+    int current_chunk = 0;
+    for (size_t j = 0; j < kernel_params.sizes[index]; j+=kernel_params.chunk_size) {
+      host_block2tensor[chunk_id] = index;
+      host_block2chunk[chunk_id] = current_chunk;
+      current_chunk++;
+      chunk_id++;
+    }
+  }
+  cudaMemcpyAsync(block_to_tensor, host_block2tensor, kernel_params.nchunks*sizeof(int),
+                  cudaMemcpyHostToDevice, Stream<gpu>::GetStream(s));
+  cudaMemcpyAsync(block_to_chunk, host_block2chunk, kernel_params.nchunks*sizeof(int),
+                  cudaMemcpyHostToDevice, Stream<gpu>::GetStream(s));
+
+  bool has_mixed_precision = !std::is_same<DType, MPDType>::value;
+  MPDType beta3 = 1.0 - param.beta1;
+  MPDType beta4 = 1.0 - param.beta2;
+
+  if (has_mixed_precision)
+    KernelStep1<true><<<nblocks, BLOCK_SIZE_LAMB, 0, Stream<gpu>::GetStream(s)>>>(
+                     kernel_params,
+                     param.beta1, param.beta2,
+                     beta3, beta4,
+                     param.epsilon,
+                     param.clip_gradient,
+                     param.bias_correction,
+                     param.rescale_grad,
+                     temp_g,
+                     block_to_tensor,
+                     block_to_chunk);
+  else
+    KernelStep1<false><<<nblocks, BLOCK_SIZE_LAMB, 0, Stream<gpu>::GetStream(s)>>>(
+                      kernel_params,
+                      param.beta1, param.beta2,
+                      beta3, beta4,
+                      param.epsilon,
+                      param.clip_gradient,
+                      param.bias_correction,
+                      param.rescale_grad,
+                      temp_g,
+                      block_to_tensor,
+                      block_to_chunk);
+  }
+
+template<typename MPDType, typename DType>
+void CallKernel2(Stream<gpu>* s,
+                 const MultiLAMBKernelParam<DType, MPDType>& kernel_params,
+                 const MultiLAMBParam &param,
+                 float* r1, float* r2,
+                 float* temp_g,
+                 int* block_to_tensor,
+                 int* block_to_chunk,
+                 const OpReqType req) {
+  size_t nblocks = kernel_params.nchunks;
+  bool has_mixed_precision = !std::is_same<DType, MPDType>::value;
+  if (has_mixed_precision)
+    KernelStep2<true><<<nblocks, BLOCK_SIZE_LAMB, 0, Stream<gpu>::GetStream(s)>>>(
+                     kernel_params,
+                     r1, r2,
+                     temp_g,
+                     param.lower_bound, param.upper_bound,
+                     block_to_tensor,
+                     block_to_chunk,
+                     req);
+  else
+    KernelStep2<false><<<nblocks, BLOCK_SIZE_LAMB, 0, Stream<gpu>::GetStream(s)>>>(
+                      kernel_params,
+                      r1, r2,
+                      temp_g,
+                      param.lower_bound, param.upper_bound,
+                      block_to_tensor,
+                      block_to_chunk,
+                      req);
+}
+
+
+NNVM_REGISTER_OP(_multi_lamb_update)
+.set_attr<FCompute>("FCompute<gpu>",  MultiLAMBUpdate<gpu, false>);
+
+NNVM_REGISTER_OP(_multi_mp_lamb_update)
+.set_attr<FCompute>("FCompute<gpu>",  MultiLAMBUpdate<gpu, true>);
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/src/operator/contrib/multi_sum_sq-inl.h b/src/operator/contrib/multi_sum_sq-inl.h
index b8609c0f217f..051c573af69a 100644
--- a/src/operator/contrib/multi_sum_sq-inl.h
+++ b/src/operator/contrib/multi_sum_sq-inl.h
@@ -82,6 +82,10 @@ inline bool MultiSumSqType(const NodeAttrs& attrs,
   return true;
 }
 
+template<typename xpu>
+size_t GetRequiredStorageMultiSumSq(const std::vector<TBlob> &inputs,
+                                    int* param_max_chunks_per_tensor = NULL);
+
 template<typename xpu>
 void MultiSumSqRun(const std::vector<TBlob> &inputs, int nInputs,
                    float *out_ptr, const OpContext &ctx);
diff --git a/src/operator/contrib/multi_sum_sq.cc b/src/operator/contrib/multi_sum_sq.cc
index 16c99d1c9699..9d15bf65a9e1 100644
--- a/src/operator/contrib/multi_sum_sq.cc
+++ b/src/operator/contrib/multi_sum_sq.cc
@@ -59,6 +59,12 @@ NNVM_REGISTER_OP(multi_sum_sq)
 .add_argument("data", "NDArray-or-Symbol[]", "Arrays")
 .add_arguments(MultiSumSqParam::__FIELDS__());
 
+template<>
+size_t GetRequiredStorageMultiSumSq<cpu>(const std::vector<TBlob> &inputs,
+                                         int* param_max_chunks_per_tensor) {
+  return 0;
+}
+
 template<typename DType>
 inline void CalcSumSq(const std::vector<TBlob> &inputs, int n_inputs,
                       float *out_ptr, mshadow::Stream<cpu> *s) {
diff --git a/src/operator/contrib/multi_sum_sq.cu b/src/operator/contrib/multi_sum_sq.cu
index 620c9ca8a073..ec2c76484932 100644
--- a/src/operator/contrib/multi_sum_sq.cu
+++ b/src/operator/contrib/multi_sum_sq.cu
@@ -37,6 +37,9 @@ namespace op {
 // https://github.com/NVIDIA/apex/blob/master/csrc/multi_tensor_apply.cuh
 // https://github.com/NVIDIA/apex/blob/master/csrc/multi_tensor_l2norm_kernel.cu
 // https://github.com/NVIDIA/apex/blob/master/csrc/type_shim.h
+
+const int chunk_size = 32768;
+
 template <typename DType>
 struct MultiSumSqKernelParam {
   DType* addresses[ARRAY_LIMIT];
@@ -126,10 +129,24 @@ __global__ void GlobalReductionKernel(MultiSumSqKernelParam<DType> param,
     output[blockIdx.x] = final;
 }
 
+template<>
+size_t GetRequiredStorageMultiSumSq<gpu>(const std::vector<TBlob> &inputs,
+                                         int* param_max_chunks_per_tensor) {
+  // find max num of chunks in tensors
+  int max_chunks_per_tensor = -1;
+  for (size_t t = 0; t < inputs.size(); t++) {
+    int chunks_this_tensor = (inputs[t].shape_.Size() + chunk_size - 1) / chunk_size;
+    if (chunks_this_tensor > max_chunks_per_tensor)
+      max_chunks_per_tensor = chunks_this_tensor;
+  }
+  if (param_max_chunks_per_tensor != NULL)
+    *param_max_chunks_per_tensor = max_chunks_per_tensor;
+  return inputs.size() * max_chunks_per_tensor * sizeof(float);
+}
+
 template<>
 void MultiSumSqRun<gpu>(const std::vector<TBlob> &inputs, int n_inputs,
                         float *out_ptr, const OpContext &ctx) {
-  const int chunk_size = 32768;
   const int block_size = 512;
   using namespace mxnet_op;
   auto s = ctx.get_stream<gpu>();
@@ -137,14 +154,8 @@ void MultiSumSqRun<gpu>(const std::vector<TBlob> &inputs, int n_inputs,
 
   MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
     MultiSumSqKernelParam<DType> param;
-    // find max num of chunks in tensors
-    for (int t = 0; t < n_inputs; t++) {
-      int chunks_this_tensor = (inputs[t].shape_.Size() + chunk_size - 1) / chunk_size;
-      if (chunks_this_tensor > param.max_chunks_per_tensor)
-        param.max_chunks_per_tensor = chunks_this_tensor;
-    }
-     // temporary storage for the reduction of each block
-    size_t workspace_size = n_inputs * param.max_chunks_per_tensor * sizeof(float);
+    size_t workspace_size = GetRequiredStorageMultiSumSq<gpu>(inputs,
+                                                              &param.max_chunks_per_tensor);
     Tensor<gpu, 1, char> workspace =
       ctx.requested[multi_sum_sq::kTempSpace].get_space_typed<gpu, 1, char>(
         Shape1(workspace_size), s);
diff --git a/tests/python/unittest/test_optimizer.py b/tests/python/unittest/test_optimizer.py
old mode 100644
new mode 100755
index 811923000411..6d7cf40f29f7
--- a/tests/python/unittest/test_optimizer.py
+++ b/tests/python/unittest/test_optimizer.py
@@ -443,8 +443,8 @@ def __init__(self, learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-6,
 
     def create_state(self, index, weight):
         stype = weight.stype
-        return (mx.nd.zeros(weight.shape, weight.context, dtype=np.float32, stype=stype),
-                mx.nd.zeros(weight.shape, weight.context, dtype=np.float32, stype=stype))
+        return (mx.nd.zeros(weight.shape, weight.context, dtype=weight.dtype, stype=stype),
+                mx.nd.zeros(weight.shape, weight.context, dtype=weight.dtype, stype=stype))
 
 
     def update(self, index, weight, grad, state):
@@ -499,6 +499,47 @@ def test_lamb():
         kwarg['multi_precision'] = True
         compare_optimizer(opt1(**kwarg), opt2(**kwarg), shape, np.float16, rtol=1e-3, atol=1e-3)
 
+@with_seed()
+def test_multilamb():
+    opt1 = PyLAMB
+    opt2 = mx.optimizer.LAMB
+
+    # shapes as Bert-large
+    dims_x = [1024, 4096, 1024, 1024]
+    dims_y = [1, 1, 1024, 4096]
+    dims_occurrences = [9, 1, 4, 2]
+    nlayers = 4 # 24
+    # extra_dims_x=[30522, 512, 30522]
+    # extra_dims_y=[1, 1024, 1024]
+    shapes=[]
+    for l in range(nlayers):
+        for i, (dx,dy) in enumerate(zip(dims_x, dims_y)):
+            for j in range(dims_occurrences[i]):
+                shapes.append((dx,dy))
+    # for dx,dy in zip(extra_dims_x, extra_dims_y):
+    #    shapes.append((dx,dy))
+
+    cg_options = [{}, {'clip_gradient': 0.4}, {'clip_gradient': 0.5}]
+    rg_options = [{}, {'rescale_grad': 0.14}, {'rescale_grad': 0.8}]
+    wd_options = [{}, {'wd': 0.03}, {'wd': 0.05}, {'wd': 0.07}]
+    bias_options = [{'bias_correction': False}, {'bias_correction': True}]
+
+    for dtype in [np.float16, np.float32, np.float64]:
+        for cg_option in cg_options:
+            for rg_option in rg_options:
+                for wd_option in wd_options:
+                    for bias_option in bias_options:
+                        kwarg = {}
+                        kwarg.update(cg_option)
+                        kwarg.update(rg_option)
+                        kwarg.update(wd_option)
+                        kwarg.update(bias_option)
+                        if (dtype == np.float16):
+                            kwarg.update({'multi_precision': True})
+                        atol = 1e-3
+                        rtol = 1e-6
+                        compare_optimizer(opt1(**kwarg), opt2(**kwarg), shapes, dtype,
+                                          rtol=rtol, atol=atol, ntensors=len(shapes))
 
 #SGLD
 class PySGLD(mx.optimizer.Optimizer):
@@ -764,6 +805,7 @@ def test_adam():
                                           dtype, w_stype='default', g_stype='row_sparse',
                                           rtol=1e-4, atol=2e-5)
 
+
 # AdaMax
 class PyAdamax(mx.optimizer.Optimizer):
     """The python reference of AdaMax optimizer.

From 04c3eec3e8ea793614244e18a89c6b3a13f12d6a Mon Sep 17 00:00:00 2001
From: Rohit Kumar Srivastava <srivastava.141@osu.edu>
Date: Wed, 15 Jan 2020 22:25:37 -0800
Subject: [PATCH 809/813] grouping large vector tests based on their type and
 adding their nightly test function (#17306)

---
 ci/docker/runtime_functions.sh     |   10 +
 tests/nightly/test_large_vector.py | 2061 ++++++++++++++--------------
 2 files changed, 1028 insertions(+), 1043 deletions(-)

diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index 9a196f401962..7fd24575a462 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -1527,6 +1527,16 @@ nightly_test_large_tensor() {
     nosetests-3.4 tests/nightly/test_large_array.py:test_basic
 }
 
+#Test Large Vectors
+nightly_test_large_vector() {
+    set -ex
+    export PYTHONPATH=./python/
+    export DMLC_LOG_STACK_TRACE_DEPTH=10
+    nosetests-3.4 tests/nightly/test_large_vector.py:test_tensor
+    nosetests-3.4 tests/nightly/test_large_vector.py:test_nn
+    nosetests-3.4 tests/nightly/test_large_vector.py:test_basic
+}
+
 #Tests Amalgamation Build with 5 different sets of flags
 nightly_test_amalgamation() {
     set -ex
diff --git a/tests/nightly/test_large_vector.py b/tests/nightly/test_large_vector.py
index bc87fec33e79..ceedab0ad5a3 100644
--- a/tests/nightly/test_large_vector.py
+++ b/tests/nightly/test_large_vector.py
@@ -31,1049 +31,1024 @@
 MEDIUM_X = 1000000000
 
 
-def test_slice():
-    a = nd.ones(LARGE_X)
-    res = nd.slice(a, begin=(LARGE_X - MEDIUM_X), end=LARGE_X)
-    assert res.shape[0] == MEDIUM_X
-    assert res[0] == 1
-
-
-def test_ndarray_zeros():
-    a = nd.zeros(shape=LARGE_X)
-    assert a[-1] == 0
-    assert a.shape == (LARGE_X,)
-    assert a.size == LARGE_X
-
-
-def test_ndarray_ones():
-    a = nd.ones(shape=LARGE_X)
-    assert a[-1] == 1
-    assert nd.sum(a) == LARGE_X
-
-
-@with_seed()
-def test_ndarray_random_uniform():
-    a = nd.random.uniform(shape=LARGE_X)
-    assert a[-1] != 0
-
-
-@with_seed()
-def test_ndarray_random_randint():
-    # check if randint can generate value greater than 2**32 (large)
-    low = 2**32
-    high = 2**34
-    a = nd.random.randint(low, high, dtype=np.int64, shape=LARGE_X).asnumpy()
-    assert a.shape == (LARGE_X,)
-    assert (a >= low).all() and (a < high).all()
-
-
-def test_ndarray_empty():
-    a = nd.empty(LARGE_X)
-    assert a.shape == (LARGE_X,)
-
-
-def test_elementwise():
-    a = nd.ones(shape=LARGE_X)
-    b = nd.ones(shape=LARGE_X)
-    res = a + b
-    assert res[-1].asnumpy() == 2
-    res = a + 1
-    assert res[-1].asnumpy() == 2
-    res = nd.sqrt(a + 8)
-    assert res[-1].asnumpy() == 3
-
-
-def test_clip():
-    a = create_vector(LARGE_X)
-    res = nd.clip(a, a_min=100, a_max=1000)
-    assert res[-1] == 1000
-
-
-def test_argmin():
-    a = create_vector(LARGE_X, dtype=np.float32)
-    assert a[0] == 0
-    idx = mx.nd.argmin(a, axis=0)
-    assert idx[0] == 0
-    assert idx.shape[0] == 1
-
-
-def test_take():
-    a = nd.ones(shape=LARGE_X)
-    idx = nd.arange(LARGE_X - 1000, LARGE_X)
-    res = nd.take(a, idx)
-    assert np.sum(res.asnumpy() == 1) == res.shape[0]
-
-
-def test_slice_assign():
-    a = nd.ones(shape=LARGE_X)
-    a[LARGE_X-1:LARGE_X] = 1000
-    assert np.sum(a[-1].asnumpy() == 1000) == 1
-
-
-def test_expand_dims():
-    a = nd.ones(shape=LARGE_X)
-    res = nd.expand_dims(a, axis=0)
-    assert res[0][0] == 1
-    assert res.shape == (1, a.shape[0])
-
-
-def test_squeeze():
-    a = nd.ones(shape=LARGE_X)
-    data = nd.expand_dims(a, axis=0)
-    res = nd.squeeze(data)
-    assert a[0] == res[0]
-    assert res.shape == a.shape
-
-
-def test_broadcast_div():
-    a = nd.ones(shape=LARGE_X)
-    b = nd.ones(shape=LARGE_X) * 2
-    res = a / b
-    assert np.sum(res.asnumpy() == 0.5) == a.shape[0]
-
-
-def test_Dense(ctx=mx.cpu(0)):
-    data = mx.nd.ones(shape=LARGE_X)
-    linear = gluon.nn.Dense(2)
-    linear.initialize(ctx=ctx)
-    res = linear(data)
-    assert res.shape == (LARGE_X, 2)
-
-
-def test_argsort():
-    a = create_vector(size=LARGE_X)
-    s = nd.argsort(a, axis=0, is_ascend=False, dtype=np.int64)
-    assert s[0] == (LARGE_X - 1)
-
-
-def test_sort():
-    a = create_vector(size=LARGE_X)
-
-    def test_descend(x):
-        s = nd.sort(x, axis=0, is_ascend=False)
-        assert s[-1] == 0
-
-    def test_ascend(x):
-        s = nd.sort(x, is_ascend=True)
-        assert s[0] == 0
-
-    test_descend(a)
-    test_ascend(a)
-
-
-def test_topk():
-    a = create_vector(size=LARGE_X)
-    ind = nd.topk(a, k=10, axis=0, dtype=np.int64)
-    for i in range(10):
-        assert ind[i] == (LARGE_X - i - 1)
-    ind, val = mx.nd.topk(a, k=3, axis=0, dtype=np.int64, ret_typ="both", is_ascend=False)
-    assert np.all(ind == val)
-    val = nd.topk(a, k=1, axis=0, dtype=np.int64, ret_typ="value")
-    assert val == (LARGE_X - 1)
-
-
-def test_mean():
-    a = nd.arange(-LARGE_X // 2, LARGE_X // 2 + 1, dtype=np.int64)
-    b = nd.mean(a, axis=0)
-    assert b == 0
-
-
-@with_seed()
-def test_ndarray_random_exponential():
-    a = nd.random.exponential(shape=LARGE_X)
-    assert a[-1] >= 0.
-    assert a.shape[0] == LARGE_X
-
-
-@with_seed()
-def test_ndarray_random_gamma():
-    a = nd.random.gamma(shape=LARGE_X)
-    assert a[-1] >= 0.
-    assert a.shape[0] == LARGE_X
-
-
-@with_seed()
-def test_ndarray_random_generalized_negative_binomial():
-    a = nd.random.generalized_negative_binomial(shape=LARGE_X)
-    assert a[-1] >= 0.
-    assert a.shape[0] == LARGE_X
-
-
-@with_seed()
-def test_ndarray_random_multinomial():
-    a = nd.random.multinomial(nd.random.uniform(shape=LARGE_X))
-    assert a[-1] >= 0.
-    assert a.shape[0] == 1
-
-
-@with_seed()
-def test_ndarray_random_negative_binomial():
-    a = nd.random.negative_binomial(shape=LARGE_X)
-    assert a[-1] >= 0.
-    assert a.shape[0] == LARGE_X
-
-
-@with_seed()
-def test_ndarray_random_normal():
-    a = nd.random.normal(shape=LARGE_X)
-    assert a.shape[0] == LARGE_X
-
-
-@with_seed()
-def test_ndarray_random_poisson():
-    a = nd.random.poisson(shape=LARGE_X)
-    assert a[-1] >= 0.
-    assert a.shape[0] == LARGE_X
-
-
-@with_seed()
-def test_ndarray_random_randn():
-    a = nd.random.randn(LARGE_X)
-    assert a.shape[0] == LARGE_X
-
-
-@with_seed()
-def test_ndarray_random_shuffle():
-    a = nd.ones(shape=LARGE_X)
-    a[-1] = 3
-    a = nd.random.shuffle(a)
-    unique_a = np.unique(a.asnumpy())
-    assert len(unique_a) == 2  # only 2 unique values
-    assert unique_a[0] == 1  # first unique value is 1
-    assert unique_a[1] == 3  # second unique value is 3
-    assert a.shape[0] == LARGE_X
-
-
-def test_exponent_logarithm_operators():
-    a = 2*nd.ones(shape=LARGE_X)
-    # exponent
-    result = nd.exp(a)
-    assert result[-1] == 7.389056
-    assert result.shape == a.shape
-
-    # exponent minus 1
-    result = nd.expm1(a)
-    assert result[-1] == 6.389056
-    assert result.shape == a.shape
-
-    # log2
-    result = nd.log2(a)
-    assert result[-1] == 1
-    assert result.shape == a.shape
-
-    # log10
-    result = nd.log10(a)
-    assert result[-1] == 0.30103
-    assert result.shape == a.shape
-
-    # log1p
-    result = nd.log1p(a)
-    assert result[-1] == 1.0986123
-    assert result.shape == a.shape
-
-    # log
-    result = nd.log(a)
-    assert result[-1] == 0.6931472
-    assert result.shape == a.shape
-
-
-def test_power_operators():
-    a = 2*nd.ones(shape=LARGE_X)
-    # sqrt
-    result = nd.sqrt(a)
-    assert result[-1] == 1.4142135
-    assert result.shape == a.shape
-
-    # rsqrt
-    result = nd.rsqrt(a)
-    assert result[-1] == 0.70710677
-    assert result.shape == a.shape
-
-    # cbrt
-    result = nd.cbrt(a)
-    assert result[-1] == 1.2599211
-    assert result.shape == a.shape
-
-    # rcbrt
-    result = nd.rcbrt(a)
-    assert result[-1] == 0.7937005
-    assert result.shape == a.shape
-
-    # square
-    result = nd.square(a)
-    assert result[-1] == 4
-    assert result.shape == a.shape
-
-    # reciprocal
-    result = nd.reciprocal(a)
-    assert result[-1] == 0.5
-    assert result.shape == a.shape
-
-
-def test_sequence_mask():
-    # Sequence Mask input [max_sequence_length, batch_size]
-    # test with input batch_size = 2
-    a = nd.arange(0, LARGE_X * 2).reshape(LARGE_X, 2)
-
-    # test as identity operator
-    b = nd.SequenceMask(a)
-    assert b[-1][0] == a[-1][0]
-    assert b.shape == a.shape
-
-    # test with default mask
-    b = nd.SequenceMask(a, sequence_length=nd.array([1, 1]),
-                        use_sequence_length=True)
-    assert b[0][1] == a[0][1]  # first sequence of each batch kept
-    assert b[-1][-1] != a[-1][-1]  # rest sequences masked
-    assert b[-1][-1] == 0
-
-    # test with mask value
-    b = nd.SequenceMask(a, sequence_length=nd.array([1, 1]),
-                        use_sequence_length=True, value=-1)
-    assert b[-1][-1] == -1
-
-
-def test_sequence_reverse():
-    a = nd.arange(0, LARGE_X * 2).reshape(LARGE_X, 2)
-    # test as reverse operator
-    b = nd.SequenceReverse(a)
-    assert b[-1][0] == a[0][0]
-    assert b.shape == a.shape
-
-    # test with sequence length
-    b = nd.SequenceReverse(a, sequence_length=nd.array([2, 3]),
-                           use_sequence_length=True)
-    assert b[1][0] == a[0][0]  # check if reversed
-    assert b[-1][0] == a[-1][0]  # check if intact
-    assert b.shape == a.shape
-
-
-def test_sequence_last():
-    a = nd.arange(0, LARGE_X * 2).reshape(LARGE_X, 2)
-
-    # test if returns last sequence
-    b = nd.SequenceLast(a)
-    assert_almost_equal(b.asnumpy(), a[-1].asnumpy())
-    assert b.shape == (2,)
-
-    # test with sequence length
-    # parameter sequence_length - NDArray with shape (batch_size)
-    # (2,3) indicates 2nd sequence from batch 1 and 3rd sequence from batch 2
-    # need to mention dtype = int64 for sequence_length ndarray to support large indices
-    # else it defaults to float32 and errors
-    b = nd.SequenceLast(a, sequence_length=mx.nd.array([2, 3], dtype="int64"),
-                        use_sequence_length=True)
-    # check if it takes 2nd sequence from the first batch
-    assert b[0] == a[1][0]
-
-
-# TODO: correctness of layernorm
-# numpy implementation for large vector is flaky
-def test_layer_norm():
-    axis = 0
-    eps = 1E-5
-    in_shape = LARGE_X
-
-    data = nd.random.normal(0, 1, in_shape)
-    gamma = nd.random.normal(0, 1, in_shape)
-    beta = nd.random.normal(0, 1, in_shape)
-    mx_out = nd.LayerNorm(data, gamma, beta, axis, eps)
-    assert mx_out.shape == (in_shape,)
-
-
-# TODO: correctness of batchnorm
-# in future, we could test if mean, var of output
-# matches target output's mean, var
-def test_batchnorm():
-    shape = LARGE_X
-    axis = 0  # since vector
-
-    data = mx.nd.ones(shape=shape)
-    bn_gamma = mx.nd.random.uniform(shape=shape)
-    bn_beta = mx.nd.random.uniform(shape=shape)
-    bn_running_mean = mx.nd.zeros(shape)
-    bn_running_var = mx.nd.ones(shape)
-
-    output = mx.nd.BatchNorm(data, bn_gamma, bn_beta,
-                             bn_running_mean, bn_running_var, axis=axis)
-    assert output.shape == (shape,)
-
-
-def test_add():
-    a = nd.ones(shape=LARGE_X)
-    b = nd.ones(shape=LARGE_X)
-    c = b
-    c = c.__add__(a)
-    assert c[-1] == 2
-    assert c.shape == a.shape
-
-
-def test_sub():
-    a = 3*nd.ones(shape=LARGE_X)
-    b = nd.ones(shape=LARGE_X)
-    c = b
-    c = c.__sub__(a)
-    assert c[-1] == -2
-    assert c.shape == a.shape
-
-
-def test_rsub():
-    a = 3*nd.ones(shape=LARGE_X)
-    b = nd.ones(shape=LARGE_X)
-    c = b
-    c = c.__rsub__(a)
-    assert c[-1] == 2
-    assert c.shape == a.shape
-
-
-def test_neg():
-    a = nd.ones(shape=LARGE_X)
-    c = a
-    c = c.__neg__()
-    assert c[-1] == -1
-    assert c.shape == a.shape
-
-
-def test_mul():
-    a = 2*nd.ones(shape=LARGE_X)
-    b = 3*nd.ones(shape=LARGE_X)
-    c = b
-    c = c.__mul__(a)
-    assert c[-1] == 6
-    assert c.shape == a.shape
-
-
-def test_div():
-    a = 2*nd.ones(shape=LARGE_X)
-    b = 3*nd.ones(shape=LARGE_X)
-    c = b
-    c = c.__div__(a)
-    assert c[-1] == 3/2
-    assert c.shape == a.shape
-
-
-def test_rdiv():
-    a = 2*nd.ones(shape=LARGE_X)
-    b = 3*nd.ones(shape=LARGE_X)
-    c = b
-    c = c.__rdiv__(a)
-    assert c[-1] == 2/3
-    assert c.shape == a.shape
-
-
-def test_mod():
-    a = 2*nd.ones(shape=LARGE_X)
-    b = 3*nd.ones(shape=LARGE_X)
-    c = b
-    c = c.__mod__(a)
-    assert c[-1] == 1
-    assert c.shape == a.shape
-
-
-def test_rmod():
-    a = 2*nd.ones(shape=LARGE_X)
-    b = 3*nd.ones(shape=LARGE_X)
-    c = b
-    c = c.__rmod__(a)
-    assert c[-1] == 2
-    assert c.shape == a.shape
-
-
-def test_imod():
-    a = 2*nd.ones(shape=LARGE_X)
-    b = 3*nd.ones(shape=LARGE_X)
-    c = b
-    c = c.__imod__(a)
-    assert c[-1] == 1
-    assert c.shape == a.shape
-
-
-def test_pow():
-    a = 2*nd.ones(shape=LARGE_X)
-    b = 3*nd.ones(shape=LARGE_X)
-    c = b
-    c = c.__pow__(a)
-    assert c[-1] == 9
-    assert c.shape == a.shape
-
-
-def test_rpow():
-    a = 2*nd.ones(shape=LARGE_X)
-    b = 3*nd.ones(shape=LARGE_X)
-    c = b
-    c = c.__rpow__(a)
-    assert c[-1] == 8
-    assert c.shape == a.shape
-
-
-def test_shape():
-    b = create_vector(size=LARGE_X)
-    # explicit wait_to_read()
-    assert b[0] == 0
-    assert b.shape[0] == LARGE_X
-
-
-def test_size():
-    b = create_vector(size=LARGE_X)
-    # explicit wait_to_read()
-    assert b[0] == 0
-    assert b.size == LARGE_X
-
-
-def test_copy():
-    a = nd.ones(LARGE_X)
-    b = a.copy()
-    assert a[0] == b[0]
-    assert b.shape == a.shape
-    assert b.size == LARGE_X
-
-
-def test_copy_to():
-    a = create_vector(size=LARGE_X)
-    # keeping dtype same as input uses parallel copy which is much faster
-    b = nd.zeros(LARGE_X, dtype=np.int64)
-    c = a.copyto(b)
-    assert c is b
-    assert b[-1] == LARGE_X-1
-    assert b[0] == 0
-
-
-def test_zeros_like():
-    a = nd.ones(LARGE_X)
-    b = nd.zeros_like(a)
-    assert b[-1] == 0
-    assert b.shape == a.shape
-
-
-def test_ones_like():
-    a = nd.zeros(LARGE_X)
-    b = nd.ones_like(a)
-    assert b[-1] == 1
-    assert b.shape == a.shape
-
-
-def test_concat():
-    a = nd.ones(LARGE_X)
-    b = nd.zeros(LARGE_X)
-    c = nd.concat(a, b, dim=0)
-    assert c[0] == 1
-    assert c[-1] == 0
-    assert c.shape[0] == (2 * LARGE_X)
-
-
-def test_sum():
-    a = nd.ones(LARGE_X)
-    b = nd.sum(a, axis=0)
-    assert b[0] == LARGE_X
-
-
-def test_prod():
-    a = nd.ones(LARGE_X)
-    b = nd.prod(a, axis=0)
-    assert b[0] == 1
-
-
-def test_min():
-    a = create_vector(size=LARGE_X)
-    b = nd.min(a, axis=0)
-    assert b[0] == 0
-    assert b[-1] == 0
-
-
-def test_max():
-    a = create_vector(size=LARGE_X)
-    b = nd.max(a, axis=0)
-    assert b[0] == (LARGE_X - 1)
-
-
-def test_argmax():
-    a = nd.ones(LARGE_X)
-    b = nd.zeros(LARGE_X)
-    c = nd.concat(a, b, dim=0)
-    d = nd.argmax(c, axis=0)
-    assert c.shape[0] == (2 * LARGE_X)
-    assert d == 0
-
-
-def np_softmax(x, axis=-1, temperature=1.0):
-    x = x - np.max(x, axis=axis, keepdims=True)
-    x = np.exp(x/temperature)
-    x /= np.sum(x, axis=axis, keepdims=True)
-    return x
-
-
-def test_iadd():
-    a = nd.ones(LARGE_X)
-    b = nd.ones(LARGE_X)
-    c = b
-    c += a
-    assert c.shape == a.shape
-    assert c[-1] == 2
-
-
-def test_isub():
-    a = nd.full(LARGE_X, 3)
-    b = nd.ones(LARGE_X)
-    c = a
-    c -= b
-    assert c.shape == a.shape
-    assert c[-1] == 2
-
-
-def test_imul():
-    a = nd.full(LARGE_X, 3)
-    b = nd.ones(LARGE_X)
-    c = b
-    c *= a
-    assert c.shape == a.shape
-    assert c[-1] == 3
-
-
-def test_idiv():
-    a = nd.full(LARGE_X, 4)
-    b = nd.full(LARGE_X, 2)
-    c = a
-    c /= b
-    assert c.shape == a.shape
-    assert c[-1] == 2
-
-
-def test_eq():
-    a = nd.full(LARGE_X, 3)
-    b = nd.full(LARGE_X, 3)
-    c = (a == b)
-    assert (c.asnumpy() == 1).all()
-
-
-def test_neq():
-    a = nd.full(LARGE_X, 2)
-    b = nd.full(LARGE_X, 3)
-    c = (a != b)
-    assert (c.asnumpy() == 1).all()
-
-
-def test_lt():
-    a = nd.full(LARGE_X, 2)
-    b = nd.full(LARGE_X, 3)
-    d = (a <= b)
-    assert (d.asnumpy() == 1).all()
-
-
-def test_lte():
-    a = nd.full(LARGE_X, 2)
-    b = nd.full(LARGE_X, 3)
-    c = nd.full(LARGE_X, 2)
-    d = (a <= b)
-    assert (d.asnumpy() == 1).all()
-    d = (a <= c)
-    assert (d.asnumpy() == 1).all()
-
-
-def test_gt():
-    a = nd.full(LARGE_X, 3)
-    b = nd.full(LARGE_X, 2)
-    d = (a > b)
-    assert (d.asnumpy() == 1).all()
-
-
-def test_gte():
-    a = nd.full(LARGE_X, 3)
-    b = nd.full(LARGE_X, 2)
-    c = nd.full(LARGE_X, 3)
-    d = (a >= b)
-    assert (d.asnumpy() == 1).all()
-    d = (a >= c)
-    assert (d.asnumpy() == 1).all()
-
-
-def test_slice_like():
-    a = create_vector(size=LARGE_X)
-    b = nd.ones(LARGE_X//2)
-    c = nd.slice_like(a, b)
-    assert c.shape == b.shape
-    assert c[0] == 0
-    assert c[-1] == (LARGE_X // 2 - 1)
-
-
-def test_slice_axis():
-    a = create_vector(size=LARGE_X)
-    med = LARGE_X // 2
-    c = nd.slice_axis(a, axis=0, begin=0, end=med)
-    assert c.shape[0] == a.shape[0] // 2
-    assert c[-1][0] == (med - 1)
-
-
-def test_full():
-    a = nd.full(LARGE_X, 3)
-    assert a.shape[0] == LARGE_X
-    assert a[LARGE_X // 2] == 3
-    assert a[-1] == 3
-
-
-def test_regression():
-    shape = (LARGE_X, )
-
-    def check_regression(symbol, forward, shape):
-        # init executor
-        data_s = mx.symbol.Variable('data')
-        label_s = mx.symbol.Variable('label')
-        out_s = symbol(data=data_s, label=label_s)
-        exe = out_s.simple_bind(ctx=mx.cpu(0), data=shape, label=shape)
-
-        arg_map = dict(zip(out_s.list_arguments(), exe.arg_arrays))
-
-        # init data
-        data = mx.random.uniform(-1, -1, shape)
-        arg_map["data"][:] = data
-        atol = 1e-5
-        density = 0.5
-        stype = 'default'
-        label = arg_map["label"]
-        label[:] = rand_ndarray(shape, stype, density=density)
-        exe.forward(is_train=True)
-        exe.backward()
-        np_out = forward(data.asnumpy())
-        assert_almost_equal(exe.outputs[0].asnumpy(), np_out, atol=atol)
-
-    check_regression(mx.symbol.LogisticRegressionOutput,
-                     lambda x: 1.0 / (1.0 + np.exp(-x)),
-                     shape)
-    check_regression(mx.symbol.LinearRegressionOutput,
-                     lambda x: x,
-                     shape)
-
-
-def test_sign():
-    a = mx.nd.random.normal(-1, 1, shape=LARGE_X)
-    mx_res = mx.nd.sign(a)
-    assert_almost_equal(mx_res[-1].asnumpy(), np.sign(a[-1].asnumpy()))
-
-
-def test_logical():
-    def check_logical_and(a, b):
-        mx_res = mx.nd.logical_and(a, b)
-        assert_almost_equal(mx_res[-1].asnumpy(), np.logical_and(a[-1].asnumpy(), b[-1].asnumpy()))
-
-    def check_logical_or(a, b):
-        mx_res = mx.nd.logical_or(a, b)
-        assert_almost_equal(mx_res[-1].asnumpy(), np.logical_or(a[-1].asnumpy(), b[-1].asnumpy()))
-
-    def check_logical_not(a, b):
-        mx_res = mx.nd.logical_not(a, b)
-        assert_almost_equal(mx_res[-1].asnumpy(), np.logical_not(a[-1].asnumpy(), b[-1].asnumpy()))
-
-    def check_logical_xor(a, b):
-        mx_res = mx.nd.logical_xor(a, b)
-        assert_almost_equal(mx_res[-1].asnumpy(), np.logical_xor(a[-1].asnumpy(), b[-1].asnumpy()))
-
-    a = mx.nd.ones(LARGE_X)
-    b = mx.nd.zeros(LARGE_X)
-    check_logical_and(a, b)
-    check_logical_or(a, b)
-    check_logical_not(a, b)
-    check_logical_xor(a, b)
-
-
-def test_astype():
-    x = create_vector(size=LARGE_X//4)
-    x = nd.tile(x, 4)
-    y = x.astype('int32')
-    assert y.dtype == np.int32
-    assert y[-1] == LARGE_X//4-1
-
-
-def test_cast():
-    x = create_vector(size=LARGE_X//4)
-    x = nd.tile(x, 4)
-    y = nd.cast(x, np.int32)
-    assert y.dtype == np.int32
-    assert y[-1] == LARGE_X//4-1
-
-
-def test_repeat():
-    x = create_vector(size=LARGE_X//2)
-    y = nd.repeat(x, repeats=2, axis = 0)
-    assert y.shape[0] == LARGE_X
-    assert y[1] == 0
-    assert y[LARGE_X-1] == LARGE_X//2-1
-
-
-def create_input_for_rounding_ops():
-    # Creates an vector with values (-LARGE/2 .... -2, -1, 0, 1, 2, .... , LARGE/2-1)
-    # then divides each element by 2 i.e (-LARGE/4 .... -1, -0.5, 0, 0.5, 1, .... , LARGE/4-1)
-    inp = nd.arange(-LARGE_X//2, LARGE_X//2, dtype=np.float64)
-    inp = inp/2
-    return inp
-
-
-def assert_correctness_of_rounding_ops(output, mid, expected_vals):
-    # checks verifies 5 values at the middle positions of the input vector
-    # i.e mid-2, mid-1, mid, mid+1, mid+2
-    output_idx_to_inspect = [mid-2, mid-1, mid, mid+1, mid+2]
-    for i in range(len(output_idx_to_inspect)):
-        assert output[output_idx_to_inspect[i]] == expected_vals[i]
-
-
-def test_rounding_ops():
-    x = create_input_for_rounding_ops()
-
-    def check_ceil():
-        y = nd.ceil(x)
-        # expected ouput for middle 5 values after applying ceil()
-        expected_output = [-1, 0, 0, 1, 1]
-        assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
-
-    def check_fix():
-        y = nd.fix(x)
-        # expected ouput for middle 5 values after applying fix()
-        expected_output = [-1, 0, 0, 0, 1]
-        assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
-
-    def check_floor():
-        y = nd.floor(x)
-        # expected ouput for middle 5 values after applying floor()
-        expected_output = [-1, -1, 0, 0, 1]
-        assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
-
-    def check_rint():
-        y = nd.rint(x)
-        # expected ouput for middle 5 values after applying rint()
-        expected_output = [-1, -1, 0, 0, 1]
-        assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
-
-    def check_round():
-        y = nd.round(x)
-        # expected ouput for middle 5 values after applying round()
-        expected_output = [-1, -1, 0, 1, 1]
-        assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
-
-    def check_trunc():
-        y = nd.trunc(x)
-        # expected ouput for middle 5 values after applying trunc()
-        expected_output = [-1, 0, 0, 0, 1]
-        assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
-
-    check_ceil()
-    check_fix()
-    check_floor()
-    check_rint()
-    check_round()
-    check_trunc()
-
-
-def create_input_for_trigonometric_ops(vals):
-    # Creates large vector input of size(LARGE_X) from vals using tile operator
-    inp = nd.array(vals)
-    inp = nd.tile(inp, LARGE_X//len(vals))
-    return inp
-
-
-def assert_correctness_of_trigonometric_ops(output, expected_vals):
-    # checks verifies 5 values at positions(0, 1, -3, -2, -1) of the input vector
-    output_idx_to_inspect = [0, 1, -3, -2, -1]
-    for i in range(len(output_idx_to_inspect)):
-        assert np.abs(output[output_idx_to_inspect[i]].asnumpy()-expected_vals[i]) <= 1e-3
-
-
-def test_trigonometric_ops():
-    def check_arcsin():
-        x = create_input_for_trigonometric_ops([-1, -.707, 0, .707, 1])
-        y = nd.arcsin(x)
-        # expected ouput for indices=(0, 1, -3, -2, -1) after applying arcsin()
-        expected_output = [-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2]
-        assert_correctness_of_trigonometric_ops(y, expected_output)
-
-    def check_arccos():
-        x = create_input_for_trigonometric_ops([-1, -.707, 0, .707, 1])
-        y = nd.arccos(x)
-        # expected ouput for indices=(0, 1, -3, -2, -1) after applying arccos()
-        expected_output = [np.pi, 3*np.pi/4, np.pi/2, np.pi/4, 0]
-        assert_correctness_of_trigonometric_ops(y, expected_output)
-
-    def check_arctan():
-        x = create_input_for_trigonometric_ops([-np.Inf, -1, 0, 1, np.Inf])
-        y = nd.arctan(x)
-        # expected ouput for indices=(0, 1, -3, -2, -1) after applying arctan()
-        expected_output = [-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2]
-        assert_correctness_of_trigonometric_ops(y, expected_output)
-
-    def check_sin():
-        x = create_input_for_trigonometric_ops([-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2])
-        y = nd.sin(x)
-        # expected ouput for indices=(0, 1, -3, -2, -1) after applying sin()
-        expected_output = [-1, -.707, 0, .707, 1]
-        assert_correctness_of_trigonometric_ops(y, expected_output)
-
-    def check_cos():
-        x = create_input_for_trigonometric_ops([0, np.pi/4, np.pi/2, 3*np.pi/4, np.pi])
-        y = nd.cos(x)
-        # expected ouput for indices=(0, 1, -3, -2, -1) after applying cos()
-        expected_output = [1, .707, 0, -.707, -1]
-        assert_correctness_of_trigonometric_ops(y, expected_output)
-
-    def check_tan():
-        x = create_input_for_trigonometric_ops([-np.pi/6, -np.pi/4, 0, np.pi/4, np.pi/6])
-        y = nd.tan(x)
-        # expected ouput for indices=(0, 1, -3, -2, -1) after applying tan()
-        expected_output = [-.577, -1, 0, 1, .577]
-        assert_correctness_of_trigonometric_ops(y, expected_output)
-
-    def check_arcsinh():
-        x = create_input_for_trigonometric_ops([-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2])
-        y = nd.arcsinh(x)
-        # expected ouput for indices=(0, 1, -3, -2, -1) after applying arcsinh()
-        expected_output = [np.arcsinh(-np.pi/2), np.arcsinh(-np.pi/4), 0, np.arcsinh(np.pi/4), np.arcsinh(np.pi/2)]
-        assert_correctness_of_trigonometric_ops(y, expected_output)
-
-    def check_arccosh():
-        x = create_input_for_trigonometric_ops([1, np.pi/2, 3*np.pi/4, np.pi, 5*np.pi/4])
-        y = nd.arccosh(x)
-        # expected ouput for indices=(0, 1, -3, -2, -1) after applying arccosh()
-        expected_output = [0, np.arccosh(np.pi/2), np.arccosh(3*np.pi/4), np.arccosh(np.pi), np.arccosh(5*np.pi/4)]
-        assert_correctness_of_trigonometric_ops(y, expected_output)
-
-    def check_arctanh():
-        x = create_input_for_trigonometric_ops([-1/4, -1/2, 0, 1/4, 1/2])
-        y = nd.arctanh(x)
-        # expected ouput for indices=(0, 1, -3, -2, -1) after applying arctanh()
-        expected_output = [np.arctanh(-1/4), np.arctanh(-1/2), 0, np.arctanh(1/4), np.arctanh(1/2)]
-        assert_correctness_of_trigonometric_ops(y, expected_output)
-
-    def check_sinh():
-        x = create_input_for_trigonometric_ops([-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2])
-        y = nd.sinh(x)
-        # expected ouput for indices=(0, 1, -3, -2, -1) after applying sinh()
-        expected_output = [np.sinh(-np.pi/2), np.sinh(-np.pi/4), 0, np.sinh(np.pi/4), np.sinh(np.pi/2)]
-        assert_correctness_of_trigonometric_ops(y, expected_output)
-
-    def check_cosh():
-        x = create_input_for_trigonometric_ops([0, 1, np.pi/2, 3*np.pi/4, np.pi])
-        y = nd.cosh(x)
-        # expected ouput for indices=(0, 1, -3, -2, -1) after applying cosh()
-        expected_output = [1, np.cosh(1), np.cosh(np.pi/2), np.cosh(3*np.pi/4), np.cosh(np.pi)]
-        assert_correctness_of_trigonometric_ops(y, expected_output)
-
-    def check_tanh():
-        x = create_input_for_trigonometric_ops([-1/4, -1/2, 0, 1/4, 1/2])
-        y = nd.tanh(x)
-        # expected ouput for indices=(0, 1, -3, -2, -1) after applying tanh()
-        expected_output = [np.tanh(-1/4), np.tanh(-1/2), 0, np.tanh(1/4), np.tanh(1/2)]
-        assert_correctness_of_trigonometric_ops(y, expected_output)
-
-    def check_radians():
-        x = create_input_for_trigonometric_ops([0, 90, 180, 270, 360])
-        y = nd.radians(x)
-        # expected ouput for indices=(0, 1, -3, -2, -1) after applying radians()
-        expected_output = [0, np.pi/2, np.pi, 3*np.pi/2, 2*np.pi]
-        assert_correctness_of_trigonometric_ops(y, expected_output)
-
-    def check_degrees():
-        x = create_input_for_trigonometric_ops([0, np.pi/2, np.pi, 3*np.pi/2, 2*np.pi])
-        y = nd.degrees(x)
-        # expected ouput for indices=(0, 1, -3, -2, -1) after applying degrees()
-        expected_output = [0, 90, 180, 270, 360]
-        assert_correctness_of_trigonometric_ops(y, expected_output)
-
-    check_arcsin()
-    check_arccos()
-    check_arctan()
-    check_sin()
-    check_cos()
-    check_tan()
-    check_arcsinh()
-    check_arccosh()
-    check_arctanh()
-    check_sinh()
-    check_cosh()
-    check_tanh()
-    check_radians()
-    check_degrees()
-
-
-def test_load_save():
-    x = create_vector(size=LARGE_X)
-    tmp = tempfile.mkdtemp()
-    tmpfile = os.path.join(tmp, 'large_vector')
-    nd.save(tmpfile, [x])
-    y = nd.load(tmpfile)
-    y = y[0]
-    assert x[0] == y[0]
-    assert x[-1] == y[-1]
-
-
-def test_add_n():
-    x = [nd.ones(LARGE_X)]
-    y = nd.add_n(*x)
-    assert y[0] == 1
-    assert y[-1] == 1
-
-
-def test_modulo():
-    x = mx.nd.ones(LARGE_X)*6
-    y = mx.nd.ones(LARGE_X)*4
-    z = (x % y)
-    assert z[0] == 2
-    assert z[-1] == 2
-    x = mx.nd.ones(LARGE_X)*5
-    z = nd.modulo(x, y)
-    assert z[0] == 1
-    assert z[-1] == 1
-
-
-def test_maximum():
-    x = mx.nd.ones(LARGE_X)*3
-    y = mx.nd.ones(LARGE_X)*4
-    z = nd.maximum(x, y)
-    assert z[0] == 4
-    assert z[-1] == 4
-    z = nd.maximum(x, 5)
-    assert z[0] == 5
-    assert z[-1] == 5
-
-
-def test_minimum():
-    x = mx.nd.ones(LARGE_X)*3
-    y = mx.nd.ones(LARGE_X)*2
-    z = nd.minimum(x, y)
-    assert z[0] == 2
-    assert z[-1] == 2
-    z = nd.minimum(x, 5)
-    assert z[0] == 3
-    assert z[-1] == 3
-
-
-def test_gather():
-    arr = mx.nd.ones(LARGE_X)
-    # Passing dtype=np.int64 since randomly generated indices are 
-    # very large that exceeds int32 limits.
-    idx = mx.nd.random.randint(0, LARGE_X, 10, dtype=np.int64)
-    # Calls gather_nd internally
-    tmp = arr[idx]
-    assert np.sum(tmp.asnumpy() == 1) == 10
-    # Calls gather_nd internally
-    arr[idx] += 1
-    assert np.sum(arr[idx].asnumpy() == 2) == 10
-
-
-def test_infer_shape():
-    data_1 = mx.symbol.Variable('data_1')
-    data_2 = mx.symbol.Variable('data_2')
-    add = data_1+data_2
-    # > add.infer_shape(data_1=(LARGE_X,), data_2=(LARGE_X,))
-    # OUTPUT - arg_shapes, out_shapes, aux_shapes
-    _, out_shapes, _ = add.infer_shape(data_1=(LARGE_X,), data_2=(LARGE_X,))
-    assert out_shapes == [(LARGE_X,)]
-
-
-def test_binary_broadcast():
-    def check_correctness(mxnet_op, numpy_op, atol=1e-3):
-        a = mx.nd.ones(LARGE_X).as_np_ndarray()
-        b = 2*mx.nd.ones(LARGE_X).as_np_ndarray()
-        res = mxnet_op(a, b)
-        np_res = numpy_op(1, 2)
-        assert np.abs(res[-1] - np_res) < atol
-    check_correctness(mx.np.arctan2, np.arctan2)
-    check_correctness(mx.np.hypot, np.hypot)
+def test_nn():
+    def check_dense():
+        data = mx.nd.ones(shape=LARGE_X)
+        linear = gluon.nn.Dense(2)
+        linear.initialize()
+        res = linear(data)
+        assert res.shape == (LARGE_X, 2)
+
+    def check_regression():
+        shape = (LARGE_X, )
+        def check_regression(symbol, forward, shape):
+            # init executor
+            data_s = mx.symbol.Variable('data')
+            label_s = mx.symbol.Variable('label')
+            out_s = symbol(data=data_s, label=label_s)
+            exe = out_s.simple_bind(ctx=mx.cpu(0), data=shape, label=shape)
+            arg_map = dict(zip(out_s.list_arguments(), exe.arg_arrays))
+            # init data
+            data = mx.random.uniform(-1, -1, shape)
+            arg_map["data"][:] = data
+            atol = 1e-5
+            density = 0.5
+            stype = 'default'
+            label = arg_map["label"]
+            label[:] = rand_ndarray(shape, stype, density=density)
+            exe.forward(is_train=True)
+            exe.backward()
+            np_out = forward(data.asnumpy())
+            assert_almost_equal(exe.outputs[0].asnumpy(), np_out, atol=atol)
+        check_regression(mx.symbol.LogisticRegressionOutput,
+                         lambda x: 1.0 / (1.0 + np.exp(-x)),
+                         shape)
+        check_regression(mx.symbol.LinearRegressionOutput,
+                         lambda x: x,
+                         shape)
+
+    def check_sign():
+        a = mx.nd.random.normal(-1, 1, shape=LARGE_X)
+        mx_res = mx.nd.sign(a)
+        assert_almost_equal(mx_res[-1].asnumpy(), np.sign(a[-1].asnumpy()))
+
+    # TODO: correctness of layernorm
+    # numpy implementation for large vector is flaky
+    def check_layer_norm():
+        axis = 0
+        eps = 1E-5
+        in_shape = LARGE_X
+        data = nd.random.normal(0, 1, in_shape)
+        gamma = nd.random.normal(0, 1, in_shape)
+        beta = nd.random.normal(0, 1, in_shape)
+        mx_out = nd.LayerNorm(data, gamma, beta, axis, eps)
+        assert mx_out.shape == (in_shape,)
+
+    # TODO: correctness of batchnorm
+    # in future, we could test if mean, var of output
+    # matches target output's mean, var
+    def check_batchnorm():
+        shape = LARGE_X
+        axis = 0  # since vector
+        data = mx.nd.ones(shape=shape)
+        bn_gamma = mx.nd.random.uniform(shape=shape)
+        bn_beta = mx.nd.random.uniform(shape=shape)
+        bn_running_mean = mx.nd.zeros(shape)
+        bn_running_var = mx.nd.ones(shape)
+        output = mx.nd.BatchNorm(data, bn_gamma, bn_beta,
+                                 bn_running_mean, bn_running_var, axis=axis)
+        assert output.shape == (shape,)
+
+    def check_sequence_mask():
+        # Sequence Mask input [max_sequence_length, batch_size]
+        # test with input batch_size = 2
+        a = nd.arange(0, LARGE_X * 2).reshape(LARGE_X, 2)
+        # test as identity operator
+        b = nd.SequenceMask(a)
+        assert b[-1][0] == a[-1][0]
+        assert b.shape == a.shape
+        # test with default mask
+        b = nd.SequenceMask(a, sequence_length=nd.array([1, 1]),
+                            use_sequence_length=True)
+        assert b[0][1] == a[0][1]  # first sequence of each batch kept
+        assert b[-1][-1] != a[-1][-1]  # rest sequences masked
+        assert b[-1][-1] == 0
+        # test with mask value
+        b = nd.SequenceMask(a, sequence_length=nd.array([1, 1]),
+                            use_sequence_length=True, value=-1)
+        assert b[-1][-1] == -1
+
+    def check_sequence_reverse():
+        a = nd.arange(0, LARGE_X * 2).reshape(LARGE_X, 2)
+        # test as reverse operator
+        b = nd.SequenceReverse(a)
+        assert b[-1][0] == a[0][0]
+        assert b.shape == a.shape
+        # test with sequence length
+        b = nd.SequenceReverse(a, sequence_length=nd.array([2, 3]),
+                               use_sequence_length=True)
+        assert b[1][0] == a[0][0]  # check if reversed
+        assert b[-1][0] == a[-1][0]  # check if intact
+        assert b.shape == a.shape
+
+    def check_sequence_last():
+        a = nd.arange(0, LARGE_X * 2).reshape(LARGE_X, 2)
+        # test if returns last sequence
+        b = nd.SequenceLast(a)
+        assert_almost_equal(b.asnumpy(), a[-1].asnumpy())
+        assert b.shape == (2,)
+        # test with sequence length
+        # parameter sequence_length - NDArray with shape (batch_size)
+        # (2,3) indicates 2nd sequence from batch 1 and 3rd sequence from batch 2
+        # need to mention dtype = int64 for sequence_length ndarray to support large indices
+        # else it defaults to float32 and errors
+        b = nd.SequenceLast(a, sequence_length=mx.nd.array([2, 3], dtype="int64"),
+                            use_sequence_length=True)
+        # check if it takes 2nd sequence from the first batch
+        assert b[0] == a[1][0]
+
+    check_dense()
+    check_regression()
+    check_sign()
+    check_layer_norm()
+    check_batchnorm()
+    check_sequence_mask()
+    check_sequence_reverse()
+    check_sequence_last()
+
+
+def test_tensor():
+    def check_ndarray_zeros():
+        a = nd.zeros(shape=LARGE_X)
+        assert a[-1] == 0
+        assert a.shape == (LARGE_X,)
+        assert a.size == LARGE_X
+
+    def check_ndarray_ones():
+        a = nd.ones(shape=LARGE_X)
+        assert a[-1] == 1
+        assert nd.sum(a) == LARGE_X
+
+    def check_ndarray_empty():
+        a = nd.empty(LARGE_X)
+        assert a.shape == (LARGE_X,)
+
+    @with_seed()
+    def check_ndarray_random_uniform():
+        a = nd.random.uniform(shape=LARGE_X)
+        assert a[-1] != 0
+
+    @with_seed()
+    def check_ndarray_random_randint():
+        # check if randint can generate value greater than 2**32 (large)
+        low = 2**32
+        high = 2**34
+        a = nd.random.randint(low, high, dtype=np.int64, shape=LARGE_X).asnumpy()
+        assert a.shape == (LARGE_X,)
+        assert (a >= low).all() and (a < high).all()
+
+    @with_seed()
+    def check_ndarray_random_exponential():
+        a = nd.random.exponential(shape=LARGE_X)
+        assert a[-1] >= 0.
+        assert a.shape[0] == LARGE_X
+
+    @with_seed()
+    def check_ndarray_random_gamma():
+        a = nd.random.gamma(shape=LARGE_X)
+        assert a[-1] >= 0.
+        assert a.shape[0] == LARGE_X
+
+    @with_seed()
+    def check_ndarray_random_generalized_negative_binomial():
+        a = nd.random.generalized_negative_binomial(shape=LARGE_X)
+        assert a[-1] >= 0.
+        assert a.shape[0] == LARGE_X
+
+    @with_seed()
+    def check_ndarray_random_multinomial():
+        a = nd.random.multinomial(nd.random.uniform(shape=LARGE_X))
+        assert a[-1] >= 0.
+        assert a.shape[0] == 1
+
+    @with_seed()
+    def check_ndarray_random_negative_binomial():
+        a = nd.random.negative_binomial(shape=LARGE_X)
+        assert a[-1] >= 0.
+        assert a.shape[0] == LARGE_X
+
+    @with_seed()
+    def check_ndarray_random_normal():
+        a = nd.random.normal(shape=LARGE_X)
+        assert a.shape[0] == LARGE_X
+
+    @with_seed()
+    def check_ndarray_random_poisson():
+        a = nd.random.poisson(shape=LARGE_X)
+        assert a[-1] >= 0.
+        assert a.shape[0] == LARGE_X
+
+    @with_seed()
+    def check_ndarray_random_randn():
+        a = nd.random.randn(LARGE_X)
+        assert a.shape[0] == LARGE_X
+
+    @with_seed()
+    def check_ndarray_random_shuffle():
+        a = nd.ones(shape=LARGE_X)
+        a[-1] = 3
+        a = nd.random.shuffle(a)
+        unique_a = np.unique(a.asnumpy())
+        assert len(unique_a) == 2  # only 2 unique values
+        assert unique_a[0] == 1  # first unique value is 1
+        assert unique_a[1] == 3  # second unique value is 3
+        assert a.shape[0] == LARGE_X
+
+    def check_full():
+        a = nd.full(LARGE_X, 3)
+        assert a.shape[0] == LARGE_X
+        assert a[LARGE_X // 2] == 3
+        assert a[-1] == 3
+
+    def check_repeat():
+        x = create_vector(size=LARGE_X//2)
+        y = nd.repeat(x, repeats=2, axis = 0)
+        assert y.shape[0] == LARGE_X
+        assert y[1] == 0
+        assert y[LARGE_X-1] == LARGE_X//2-1
+
+    def check_clip():
+        a = create_vector(LARGE_X)
+        res = nd.clip(a, a_min=100, a_max=1000)
+        assert res[-1] == 1000
+
+    def check_slice():
+        a = nd.ones(LARGE_X)
+        res = nd.slice(a, begin=(LARGE_X - MEDIUM_X), end=LARGE_X)
+        assert res.shape[0] == MEDIUM_X
+        assert res[0] == 1
+
+    def check_slice_assign():
+        a = nd.ones(shape=LARGE_X)
+        a[LARGE_X-1:LARGE_X] = 1000
+        assert np.sum(a[-1].asnumpy() == 1000) == 1
+
+    def check_take():
+        a = nd.ones(shape=LARGE_X)
+        idx = nd.arange(LARGE_X - 1000, LARGE_X)
+        res = nd.take(a, idx)
+        assert np.sum(res.asnumpy() == 1) == res.shape[0]
+
+    def check_expand_dims():
+        a = nd.ones(shape=LARGE_X)
+        res = nd.expand_dims(a, axis=0)
+        assert res[0][0] == 1
+        assert res.shape == (1, a.shape[0])
+
+    def check_squeeze():
+        a = nd.ones(shape=LARGE_X)
+        data = nd.expand_dims(a, axis=0)
+        res = nd.squeeze(data)
+        assert a[0] == res[0]
+        assert res.shape == a.shape
+
+    def check_broadcast_div():
+        a = nd.ones(shape=LARGE_X)
+        b = nd.ones(shape=LARGE_X) * 2
+        res = a / b
+        assert np.sum(res.asnumpy() == 0.5) == a.shape[0]
+
+    def check_size():
+        b = create_vector(size=LARGE_X)
+        # explicit wait_to_read()
+        assert b[0] == 0
+        assert b.size == LARGE_X
+
+    def check_copy():
+        a = nd.ones(LARGE_X)
+        b = a.copy()
+        assert a[0] == b[0]
+        assert b.shape == a.shape
+        assert b.size == LARGE_X
+
+    def check_copy_to():
+        a = create_vector(size=LARGE_X)
+        # keeping dtype same as input uses parallel copy which is much faster
+        b = nd.zeros(LARGE_X, dtype=np.int64)
+        c = a.copyto(b)
+        assert c is b
+        assert b[-1] == LARGE_X-1
+        assert b[0] == 0
+
+    def check_zeros_like():
+        a = nd.ones(LARGE_X)
+        b = nd.zeros_like(a)
+        assert b[-1] == 0
+        assert b.shape == a.shape
+
+    def check_ones_like():
+        a = nd.zeros(LARGE_X)
+        b = nd.ones_like(a)
+        assert b[-1] == 1
+        assert b.shape == a.shape
+
+    def check_shape():
+        b = create_vector(size=LARGE_X)
+        # explicit wait_to_read()
+        assert b[0] == 0
+        assert b.shape[0] == LARGE_X
+
+    def check_concat():
+        a = nd.ones(LARGE_X)
+        b = nd.zeros(LARGE_X)
+        c = nd.concat(a, b, dim=0)
+        assert c[0] == 1
+        assert c[-1] == 0
+        assert c.shape[0] == (2 * LARGE_X)
+
+    def check_slice_like():
+        a = create_vector(size=LARGE_X)
+        b = nd.ones(LARGE_X//2)
+        c = nd.slice_like(a, b)
+        assert c.shape == b.shape
+        assert c[0] == 0
+        assert c[-1] == (LARGE_X // 2 - 1)
+
+    def check_slice_axis():
+        a = create_vector(size=LARGE_X)
+        med = LARGE_X // 2
+        c = nd.slice_axis(a, axis=0, begin=0, end=med)
+        assert c.shape[0] == a.shape[0] // 2
+        assert c[-1][0] == (med - 1)
+
+    def check_gather():
+        arr = mx.nd.ones(LARGE_X)
+        # Passing dtype=np.int64 since randomly generated indices are 
+        # very large that exceeds int32 limits.
+        idx = mx.nd.random.randint(0, LARGE_X, 10, dtype=np.int64)
+        # Calls gather_nd internally
+        tmp = arr[idx]
+        assert np.sum(tmp.asnumpy() == 1) == 10
+        # Calls gather_nd internally
+        arr[idx] += 1
+        assert np.sum(arr[idx].asnumpy() == 2) == 10
+
+    def check_infer_shape():
+        data_1 = mx.symbol.Variable('data_1')
+        data_2 = mx.symbol.Variable('data_2')
+        add = data_1+data_2
+        # > add.infer_shape(data_1=(LARGE_X,), data_2=(LARGE_X,))
+        # OUTPUT - arg_shapes, out_shapes, aux_shapes
+        _, out_shapes, _ = add.infer_shape(data_1=(LARGE_X,), data_2=(LARGE_X,))
+        assert out_shapes == [(LARGE_X,)]
+
+    def check_astype():
+        x = create_vector(size=LARGE_X//4)
+        x = nd.tile(x, 4)
+        y = x.astype('int32')
+        assert y.dtype == np.int32
+        assert y[-1] == LARGE_X//4-1
+
+    def check_cast():
+        x = create_vector(size=LARGE_X//4)
+        x = nd.tile(x, 4)
+        y = nd.cast(x, np.int32)
+        assert y.dtype == np.int32
+        assert y[-1] == LARGE_X//4-1
+
+    def check_load_save():
+        x = create_vector(size=LARGE_X)
+        tmp = tempfile.mkdtemp()
+        tmpfile = os.path.join(tmp, 'large_vector')
+        nd.save(tmpfile, [x])
+        y = nd.load(tmpfile)
+        y = y[0]
+        assert x[0] == y[0]
+        assert x[-1] == y[-1]
+
+    def check_binary_broadcast():
+        def check_correctness(mxnet_op, numpy_op, atol=1e-3):
+            a = mx.nd.ones(LARGE_X).as_np_ndarray()
+            b = 2*mx.nd.ones(LARGE_X).as_np_ndarray()
+            res = mxnet_op(a, b)
+            np_res = numpy_op(1, 2)
+            assert np.abs(res[-1] - np_res) < atol
+        check_correctness(mx.np.arctan2, np.arctan2)
+        check_correctness(mx.np.hypot, np.hypot)
+
+    check_ndarray_zeros()
+    check_ndarray_ones()
+    check_ndarray_empty()
+    check_ndarray_random_uniform()
+    check_ndarray_random_randint()
+    check_ndarray_random_exponential()
+    check_ndarray_random_gamma()
+    check_ndarray_random_generalized_negative_binomial()
+    check_ndarray_random_multinomial()
+    check_ndarray_random_negative_binomial()
+    check_ndarray_random_normal()
+    check_ndarray_random_poisson()
+    check_ndarray_random_randn()
+    check_ndarray_random_shuffle()
+    check_full()
+    check_repeat()
+    check_clip()
+    check_slice()
+    check_slice_assign()
+    check_take()
+    check_expand_dims()
+    check_squeeze()
+    check_broadcast_div()
+    check_size()
+    check_copy()
+    check_copy_to()
+    check_zeros_like()
+    check_ones_like()
+    check_shape()
+    check_concat()
+    check_slice_like()
+    check_slice_axis()
+    check_gather()
+    check_infer_shape()
+    check_astype()
+    check_cast()
+    check_load_save()
+    check_binary_broadcast()
+
+
+def test_basic():
+    def check_elementwise():
+        a = nd.ones(shape=LARGE_X)
+        b = nd.ones(shape=LARGE_X)
+        res = a + b
+        assert res[-1].asnumpy() == 2
+        res = a + 1
+        assert res[-1].asnumpy() == 2
+        res = nd.sqrt(a + 8)
+        assert res[-1].asnumpy() == 3
+
+    def check_argmin():
+        a = create_vector(LARGE_X, dtype=np.float32)
+        assert a[0] == 0
+        idx = mx.nd.argmin(a, axis=0)
+        assert idx[0] == 0
+        assert idx.shape[0] == 1
+
+    def check_argsort():
+        a = create_vector(size=LARGE_X)
+        s = nd.argsort(a, axis=0, is_ascend=False, dtype=np.int64)
+        assert s[0] == (LARGE_X - 1)
+
+    def check_sort():
+        a = create_vector(size=LARGE_X)
+
+        def check_descend(x):
+            s = nd.sort(x, axis=0, is_ascend=False)
+            assert s[-1] == 0
+
+        def check_ascend(x):
+            s = nd.sort(x, is_ascend=True)
+            assert s[0] == 0
+
+        check_descend(a)
+        check_ascend(a)
+
+    def check_topk():
+        a = create_vector(size=LARGE_X)
+        ind = nd.topk(a, k=10, axis=0, dtype=np.int64)
+        for i in range(10):
+            assert ind[i] == (LARGE_X - i - 1)
+        ind, val = mx.nd.topk(a, k=3, axis=0, dtype=np.int64, ret_typ="both", is_ascend=False)
+        assert np.all(ind == val)
+        val = nd.topk(a, k=1, axis=0, dtype=np.int64, ret_typ="value")
+        assert val == (LARGE_X - 1)
+
+    def check_mean():
+        a = nd.arange(-LARGE_X // 2, LARGE_X // 2 + 1, dtype=np.int64)
+        b = nd.mean(a, axis=0)
+        assert b == 0
+
+    def check_exponent_logarithm_operators():
+        a = 2*nd.ones(shape=LARGE_X)
+        # exponent
+        result = nd.exp(a)
+        assert result[-1] == 7.389056
+        assert result.shape == a.shape
+        # exponent minus 1
+        result = nd.expm1(a)
+        assert result[-1] == 6.389056
+        assert result.shape == a.shape
+        # log2
+        result = nd.log2(a)
+        assert result[-1] == 1
+        assert result.shape == a.shape
+        # log10
+        result = nd.log10(a)
+        assert result[-1] == 0.30103
+        assert result.shape == a.shape
+        # log1p
+        result = nd.log1p(a)
+        assert result[-1] == 1.0986123
+        assert result.shape == a.shape
+        # log
+        result = nd.log(a)
+        assert result[-1] == 0.6931472
+        assert result.shape == a.shape
+
+    def check_power_operators():
+        a = 2*nd.ones(shape=LARGE_X)
+        # sqrt
+        result = nd.sqrt(a)
+        assert result[-1] == 1.4142135
+        assert result.shape == a.shape
+        # rsqrt
+        result = nd.rsqrt(a)
+        assert result[-1] == 0.70710677
+        assert result.shape == a.shape
+        # cbrt
+        result = nd.cbrt(a)
+        assert result[-1] == 1.2599211
+        assert result.shape == a.shape
+        # rcbrt
+        result = nd.rcbrt(a)
+        assert result[-1] == 0.7937005
+        assert result.shape == a.shape
+        # square
+        result = nd.square(a)
+        assert result[-1] == 4
+        assert result.shape == a.shape
+        # reciprocal
+        result = nd.reciprocal(a)
+        assert result[-1] == 0.5
+        assert result.shape == a.shape
+
+    def check_add():
+        a = nd.ones(shape=LARGE_X)
+        b = nd.ones(shape=LARGE_X)
+        c = b
+        c = c.__add__(a)
+        assert c[-1] == 2
+        assert c.shape == a.shape
+
+    def check_sub():
+        a = 3*nd.ones(shape=LARGE_X)
+        b = nd.ones(shape=LARGE_X)
+        c = b
+        c = c.__sub__(a)
+        assert c[-1] == -2
+        assert c.shape == a.shape
+
+    def check_rsub():
+        a = 3*nd.ones(shape=LARGE_X)
+        b = nd.ones(shape=LARGE_X)
+        c = b
+        c = c.__rsub__(a)
+        assert c[-1] == 2
+        assert c.shape == a.shape
+
+    def check_neg():
+        a = nd.ones(shape=LARGE_X)
+        c = a
+        c = c.__neg__()
+        assert c[-1] == -1
+        assert c.shape == a.shape
+
+    def check_mul():
+        a = 2*nd.ones(shape=LARGE_X)
+        b = 3*nd.ones(shape=LARGE_X)
+        c = b
+        c = c.__mul__(a)
+        assert c[-1] == 6
+        assert c.shape == a.shape
+
+    def check_div():
+        a = 2*nd.ones(shape=LARGE_X)
+        b = 3*nd.ones(shape=LARGE_X)
+        c = b
+        c = c.__div__(a)
+        assert c[-1] == 3/2
+        assert c.shape == a.shape
+
+    def check_rdiv():
+        a = 2*nd.ones(shape=LARGE_X)
+        b = 3*nd.ones(shape=LARGE_X)
+        c = b
+        c = c.__rdiv__(a)
+        assert c[-1] == 2/3
+        assert c.shape == a.shape
+
+    def check_mod():
+        a = 2*nd.ones(shape=LARGE_X)
+        b = 3*nd.ones(shape=LARGE_X)
+        c = b
+        c = c.__mod__(a)
+        assert c[-1] == 1
+        assert c.shape == a.shape
+
+    def check_rmod():
+        a = 2*nd.ones(shape=LARGE_X)
+        b = 3*nd.ones(shape=LARGE_X)
+        c = b
+        c = c.__rmod__(a)
+        assert c[-1] == 2
+        assert c.shape == a.shape
+
+    def check_imod():
+        a = 2*nd.ones(shape=LARGE_X)
+        b = 3*nd.ones(shape=LARGE_X)
+        c = b
+        c = c.__imod__(a)
+        assert c[-1] == 1
+        assert c.shape == a.shape
+
+    def check_pow():
+        a = 2*nd.ones(shape=LARGE_X)
+        b = 3*nd.ones(shape=LARGE_X)
+        c = b
+        c = c.__pow__(a)
+        assert c[-1] == 9
+        assert c.shape == a.shape
+
+    def check_rpow():
+        a = 2*nd.ones(shape=LARGE_X)
+        b = 3*nd.ones(shape=LARGE_X)
+        c = b
+        c = c.__rpow__(a)
+        assert c[-1] == 8
+        assert c.shape == a.shape
+
+    def check_sum():
+        a = nd.ones(LARGE_X)
+        b = nd.sum(a, axis=0)
+        assert b[0] == LARGE_X
+
+    def check_prod():
+        a = nd.ones(LARGE_X)
+        b = nd.prod(a, axis=0)
+        assert b[0] == 1
+
+    def check_min():
+        a = create_vector(size=LARGE_X)
+        b = nd.min(a, axis=0)
+        assert b[0] == 0
+        assert b[-1] == 0
+
+    def check_max():
+        a = create_vector(size=LARGE_X)
+        b = nd.max(a, axis=0)
+        assert b[0] == (LARGE_X - 1)
+
+    def check_argmax():
+        a = nd.ones(LARGE_X)
+        b = nd.zeros(LARGE_X)
+        c = nd.concat(a, b, dim=0)
+        d = nd.argmax(c, axis=0)
+        assert c.shape[0] == (2 * LARGE_X)
+        assert d == 0
+
+    def check_iadd():
+        a = nd.ones(LARGE_X)
+        b = nd.ones(LARGE_X)
+        c = b
+        c += a
+        assert c.shape == a.shape
+        assert c[-1] == 2
+
+    def check_isub():
+        a = nd.full(LARGE_X, 3)
+        b = nd.ones(LARGE_X)
+        c = a
+        c -= b
+        assert c.shape == a.shape
+        assert c[-1] == 2
+
+    def check_imul():
+        a = nd.full(LARGE_X, 3)
+        b = nd.ones(LARGE_X)
+        c = b
+        c *= a
+        assert c.shape == a.shape
+        assert c[-1] == 3
+
+    def check_idiv():
+        a = nd.full(LARGE_X, 4)
+        b = nd.full(LARGE_X, 2)
+        c = a
+        c /= b
+        assert c.shape == a.shape
+        assert c[-1] == 2
+
+    def check_eq():
+        a = nd.full(LARGE_X, 3)
+        b = nd.full(LARGE_X, 3)
+        c = (a == b)
+        assert (c.asnumpy() == 1).all()
+
+    def check_neq():
+        a = nd.full(LARGE_X, 2)
+        b = nd.full(LARGE_X, 3)
+        c = (a != b)
+        assert (c.asnumpy() == 1).all()
+
+    def check_lt():
+        a = nd.full(LARGE_X, 2)
+        b = nd.full(LARGE_X, 3)
+        d = (a <= b)
+        assert (d.asnumpy() == 1).all()
+
+    def check_lte():
+        a = nd.full(LARGE_X, 2)
+        b = nd.full(LARGE_X, 3)
+        c = nd.full(LARGE_X, 2)
+        d = (a <= b)
+        assert (d.asnumpy() == 1).all()
+        d = (a <= c)
+        assert (d.asnumpy() == 1).all()
+
+    def check_gt():
+        a = nd.full(LARGE_X, 3)
+        b = nd.full(LARGE_X, 2)
+        d = (a > b)
+        assert (d.asnumpy() == 1).all()
+
+    def check_gte():
+        a = nd.full(LARGE_X, 3)
+        b = nd.full(LARGE_X, 2)
+        c = nd.full(LARGE_X, 3)
+        d = (a >= b)
+        assert (d.asnumpy() == 1).all()
+        d = (a >= c)
+        assert (d.asnumpy() == 1).all()
+
+    def check_logical():
+        def check_logical_and(a, b):
+            mx_res = mx.nd.logical_and(a, b)
+            assert_almost_equal(mx_res[-1].asnumpy(), np.logical_and(a[-1].asnumpy(), b[-1].asnumpy()))
+
+        def check_logical_or(a, b):
+            mx_res = mx.nd.logical_or(a, b)
+            assert_almost_equal(mx_res[-1].asnumpy(), np.logical_or(a[-1].asnumpy(), b[-1].asnumpy()))
+
+        def check_logical_not(a, b):
+            mx_res = mx.nd.logical_not(a, b)
+            assert_almost_equal(mx_res[-1].asnumpy(), np.logical_not(a[-1].asnumpy(), b[-1].asnumpy()))
+
+        def check_logical_xor(a, b):
+            mx_res = mx.nd.logical_xor(a, b)
+            assert_almost_equal(mx_res[-1].asnumpy(), np.logical_xor(a[-1].asnumpy(), b[-1].asnumpy()))
+
+        a = mx.nd.ones(LARGE_X)
+        b = mx.nd.zeros(LARGE_X)
+        check_logical_and(a, b)
+        check_logical_or(a, b)
+        check_logical_not(a, b)
+        check_logical_xor(a, b)
+
+    def create_input_for_rounding_ops():
+        # Creates an vector with values (-LARGE/2 .... -2, -1, 0, 1, 2, .... , LARGE/2-1)
+        # then divides each element by 2 i.e (-LARGE/4 .... -1, -0.5, 0, 0.5, 1, .... , LARGE/4-1)
+        inp = nd.arange(-LARGE_X//2, LARGE_X//2, dtype=np.float64)
+        inp = inp/2
+        return inp
+
+    def assert_correctness_of_rounding_ops(output, mid, expected_vals):
+        # checks verifies 5 values at the middle positions of the input vector
+        # i.e mid-2, mid-1, mid, mid+1, mid+2
+        output_idx_to_inspect = [mid-2, mid-1, mid, mid+1, mid+2]
+        for i in range(len(output_idx_to_inspect)):
+            assert output[output_idx_to_inspect[i]] == expected_vals[i]
+
+    def check_rounding_ops():
+        x = create_input_for_rounding_ops()
+
+        def check_ceil():
+            y = nd.ceil(x)
+            # expected ouput for middle 5 values after applying ceil()
+            expected_output = [-1, 0, 0, 1, 1]
+            assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
+
+        def check_fix():
+            y = nd.fix(x)
+            # expected ouput for middle 5 values after applying fix()
+            expected_output = [-1, 0, 0, 0, 1]
+            assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
+
+        def check_floor():
+            y = nd.floor(x)
+            # expected ouput for middle 5 values after applying floor()
+            expected_output = [-1, -1, 0, 0, 1]
+            assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
+
+        def check_rint():
+            y = nd.rint(x)
+            # expected ouput for middle 5 values after applying rint()
+            expected_output = [-1, -1, 0, 0, 1]
+            assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
+
+        def check_round():
+            y = nd.round(x)
+            # expected ouput for middle 5 values after applying round()
+            expected_output = [-1, -1, 0, 1, 1]
+            assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
+
+        def check_trunc():
+            y = nd.trunc(x)
+            # expected ouput for middle 5 values after applying trunc()
+            expected_output = [-1, 0, 0, 0, 1]
+            assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
+
+        check_ceil()
+        check_fix()
+        check_floor()
+        check_rint()
+        check_round()
+        check_trunc()
+
+    def create_input_for_trigonometric_ops(vals):
+        # Creates large vector input of size(LARGE_X) from vals using tile operator
+        inp = nd.array(vals)
+        inp = nd.tile(inp, LARGE_X//len(vals))
+        return inp
+
+    def assert_correctness_of_trigonometric_ops(output, expected_vals):
+        # checks verifies 5 values at positions(0, 1, -3, -2, -1) of the input vector
+        output_idx_to_inspect = [0, 1, -3, -2, -1]
+        for i in range(len(output_idx_to_inspect)):
+            assert np.abs(output[output_idx_to_inspect[i]].asnumpy()-expected_vals[i]) <= 1e-3
+
+    def check_trigonometric_ops():
+        def check_arcsin():
+            x = create_input_for_trigonometric_ops([-1, -.707, 0, .707, 1])
+            y = nd.arcsin(x)
+            # expected ouput for indices=(0, 1, -3, -2, -1) after applying arcsin()
+            expected_output = [-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2]
+            assert_correctness_of_trigonometric_ops(y, expected_output)
+
+        def check_arccos():
+            x = create_input_for_trigonometric_ops([-1, -.707, 0, .707, 1])
+            y = nd.arccos(x)
+            # expected ouput for indices=(0, 1, -3, -2, -1) after applying arccos()
+            expected_output = [np.pi, 3*np.pi/4, np.pi/2, np.pi/4, 0]
+            assert_correctness_of_trigonometric_ops(y, expected_output)
+
+        def check_arctan():
+            x = create_input_for_trigonometric_ops([-np.Inf, -1, 0, 1, np.Inf])
+            y = nd.arctan(x)
+            # expected ouput for indices=(0, 1, -3, -2, -1) after applying arctan()
+            expected_output = [-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2]
+            assert_correctness_of_trigonometric_ops(y, expected_output)
+
+        def check_sin():
+            x = create_input_for_trigonometric_ops([-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2])
+            y = nd.sin(x)
+            # expected ouput for indices=(0, 1, -3, -2, -1) after applying sin()
+            expected_output = [-1, -.707, 0, .707, 1]
+            assert_correctness_of_trigonometric_ops(y, expected_output)
+
+        def check_cos():
+            x = create_input_for_trigonometric_ops([0, np.pi/4, np.pi/2, 3*np.pi/4, np.pi])
+            y = nd.cos(x)
+            # expected ouput for indices=(0, 1, -3, -2, -1) after applying cos()
+            expected_output = [1, .707, 0, -.707, -1]
+            assert_correctness_of_trigonometric_ops(y, expected_output)
+
+        def check_tan():
+            x = create_input_for_trigonometric_ops([-np.pi/6, -np.pi/4, 0, np.pi/4, np.pi/6])
+            y = nd.tan(x)
+            # expected ouput for indices=(0, 1, -3, -2, -1) after applying tan()
+            expected_output = [-.577, -1, 0, 1, .577]
+            assert_correctness_of_trigonometric_ops(y, expected_output)
+
+        def check_arcsinh():
+            x = create_input_for_trigonometric_ops([-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2])
+            y = nd.arcsinh(x)
+            # expected ouput for indices=(0, 1, -3, -2, -1) after applying arcsinh()
+            expected_output = [np.arcsinh(-np.pi/2), np.arcsinh(-np.pi/4), 0, np.arcsinh(np.pi/4), np.arcsinh(np.pi/2)]
+            assert_correctness_of_trigonometric_ops(y, expected_output)
+
+        def check_arccosh():
+            x = create_input_for_trigonometric_ops([1, np.pi/2, 3*np.pi/4, np.pi, 5*np.pi/4])
+            y = nd.arccosh(x)
+            # expected ouput for indices=(0, 1, -3, -2, -1) after applying arccosh()
+            expected_output = [0, np.arccosh(np.pi/2), np.arccosh(3*np.pi/4), np.arccosh(np.pi), np.arccosh(5*np.pi/4)]
+            assert_correctness_of_trigonometric_ops(y, expected_output)
+
+        def check_arctanh():
+            x = create_input_for_trigonometric_ops([-1/4, -1/2, 0, 1/4, 1/2])
+            y = nd.arctanh(x)
+            # expected ouput for indices=(0, 1, -3, -2, -1) after applying arctanh()
+            expected_output = [np.arctanh(-1/4), np.arctanh(-1/2), 0, np.arctanh(1/4), np.arctanh(1/2)]
+            assert_correctness_of_trigonometric_ops(y, expected_output)
+
+        def check_sinh():
+            x = create_input_for_trigonometric_ops([-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2])
+            y = nd.sinh(x)
+            # expected ouput for indices=(0, 1, -3, -2, -1) after applying sinh()
+            expected_output = [np.sinh(-np.pi/2), np.sinh(-np.pi/4), 0, np.sinh(np.pi/4), np.sinh(np.pi/2)]
+            assert_correctness_of_trigonometric_ops(y, expected_output)
+
+        def check_cosh():
+            x = create_input_for_trigonometric_ops([0, 1, np.pi/2, 3*np.pi/4, np.pi])
+            y = nd.cosh(x)
+            # expected ouput for indices=(0, 1, -3, -2, -1) after applying cosh()
+            expected_output = [1, np.cosh(1), np.cosh(np.pi/2), np.cosh(3*np.pi/4), np.cosh(np.pi)]
+            assert_correctness_of_trigonometric_ops(y, expected_output)
+
+        def check_tanh():
+            x = create_input_for_trigonometric_ops([-1/4, -1/2, 0, 1/4, 1/2])
+            y = nd.tanh(x)
+            # expected ouput for indices=(0, 1, -3, -2, -1) after applying tanh()
+            expected_output = [np.tanh(-1/4), np.tanh(-1/2), 0, np.tanh(1/4), np.tanh(1/2)]
+            assert_correctness_of_trigonometric_ops(y, expected_output)
+
+        def check_radians():
+            x = create_input_for_trigonometric_ops([0, 90, 180, 270, 360])
+            y = nd.radians(x)
+            # expected ouput for indices=(0, 1, -3, -2, -1) after applying radians()
+            expected_output = [0, np.pi/2, np.pi, 3*np.pi/2, 2*np.pi]
+            assert_correctness_of_trigonometric_ops(y, expected_output)
+
+        def check_degrees():
+            x = create_input_for_trigonometric_ops([0, np.pi/2, np.pi, 3*np.pi/2, 2*np.pi])
+            y = nd.degrees(x)
+            # expected ouput for indices=(0, 1, -3, -2, -1) after applying degrees()
+            expected_output = [0, 90, 180, 270, 360]
+            assert_correctness_of_trigonometric_ops(y, expected_output)
+
+        check_arcsin()
+        check_arccos()
+        check_arctan()
+        check_sin()
+        check_cos()
+        check_tan()
+        check_arcsinh()
+        check_arccosh()
+        check_arctanh()
+        check_sinh()
+        check_cosh()
+        check_tanh()
+        check_radians()
+        check_degrees()
+
+    def check_add_n():
+        x = [nd.ones(LARGE_X)]
+        y = nd.add_n(*x)
+        assert y[0] == 1
+        assert y[-1] == 1
+
+    def check_modulo():
+        x = mx.nd.ones(LARGE_X)*6
+        y = mx.nd.ones(LARGE_X)*4
+        z = (x % y)
+        assert z[0] == 2
+        assert z[-1] == 2
+        x = mx.nd.ones(LARGE_X)*5
+        z = nd.modulo(x, y)
+        assert z[0] == 1
+        assert z[-1] == 1
+
+    def check_maximum():
+        x = mx.nd.ones(LARGE_X)*3
+        y = mx.nd.ones(LARGE_X)*4
+        z = nd.maximum(x, y)
+        assert z[0] == 4
+        assert z[-1] == 4
+        z = nd.maximum(x, 5)
+        assert z[0] == 5
+        assert z[-1] == 5
+
+    def check_minimum():
+        x = mx.nd.ones(LARGE_X)*3
+        y = mx.nd.ones(LARGE_X)*2
+        z = nd.minimum(x, y)
+        assert z[0] == 2
+        assert z[-1] == 2
+        z = nd.minimum(x, 5)
+        assert z[0] == 3
+        assert z[-1] == 3
+
+    check_elementwise()
+    check_argmin()
+    check_argsort()
+    check_sort()
+    check_topk()
+    check_mean()
+    check_exponent_logarithm_operators()
+    check_power_operators()
+    check_add()
+    check_sub()
+    check_rsub()
+    check_neg()
+    check_mul()
+    check_div()
+    check_rdiv()
+    check_mod()
+    check_rmod()
+    check_imod()
+    check_pow()
+    check_rpow()
+    check_sum()
+    check_prod()
+    check_min()
+    check_max()
+    check_argmax()
+    check_iadd()
+    check_isub()
+    check_imul()
+    check_idiv()
+    check_eq()
+    check_neq()
+    check_lt()
+    check_lte()
+    check_gt()
+    check_gte()
+    check_logical()
+    check_rounding_ops()
+    check_trigonometric_ops()
+    check_add_n()
+    check_modulo()
+    check_maximum()
+    check_minimum()
 
 
 if __name__ == '__main__':

From b9ab7ad60f46de289f5b6af6f885925b58156855 Mon Sep 17 00:00:00 2001
From: Leonard Lausen <lausen@amazon.com>
Date: Thu, 16 Jan 2020 15:52:34 +0000
Subject: [PATCH 810/813] Fix typo in "Install the MXNet Package for R"
 (#17340)

---
 docs/static_site/src/pages/get_started/ubuntu_setup.md | 1 -
 1 file changed, 1 deletion(-)

diff --git a/docs/static_site/src/pages/get_started/ubuntu_setup.md b/docs/static_site/src/pages/get_started/ubuntu_setup.md
index de45e80feb56..89601d2a517d 100644
--- a/docs/static_site/src/pages/get_started/ubuntu_setup.md
+++ b/docs/static_site/src/pages/get_started/ubuntu_setup.md
@@ -365,7 +365,6 @@ make install
 
 Before you build MXNet for R from source code, you must complete
 [building the shared library](#build-the-shared-library).
-source root directory to build the MXNet Perl package:
 
 **Minimum Requirements**
 1. [GCC 4.8](https://gcc.gnu.org/gcc-4.8/) or later to compile C++ 11.

From eb27161f281a7fa6b3b05255341242ec78619a66 Mon Sep 17 00:00:00 2001
From: IHateMint <59442700+IHateMint@users.noreply.github.com>
Date: Fri, 17 Jan 2020 01:30:18 +0900
Subject: [PATCH 811/813] Typo Change (#17339)

Co-authored-by: PiljaeChae <neolunar7@gmail.com>
---
 CONTRIBUTORS.md                                      | 1 +
 docs/python_docs/python/tutorials/index.rst          | 2 +-
 docs/python_docs/python/tutorials/packages/index.rst | 2 +-
 3 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md
index 31400aed651c..aa024e3a5378 100644
--- a/CONTRIBUTORS.md
+++ b/CONTRIBUTORS.md
@@ -248,6 +248,7 @@ List of Contributors
 * [Vandana Kannan](https://github.com/vandanavk)
 * [Guanxin Qiao](https://github.com/guanxinq)
 * [dithyrambe](https://github.com/dithyrambe)
+* [Piljae Chae](https://github.com/IHateMint)
 
 Label Bot
 ---------
diff --git a/docs/python_docs/python/tutorials/index.rst b/docs/python_docs/python/tutorials/index.rst
index 8e2362e4ea5f..70f60c3c61a5 100644
--- a/docs/python_docs/python/tutorials/index.rst
+++ b/docs/python_docs/python/tutorials/index.rst
@@ -58,7 +58,7 @@ Packages & Modules
       :title: Symbol API
       :link: /api/python/docs/api/symbol/index.html
 
-      MXNet Symbol API has been depricated. API documentation is still available for reference.
+      MXNet Symbol API has been deprecated. API documentation is still available for reference.
 
    .. card::
       :title: Autograd API
diff --git a/docs/python_docs/python/tutorials/packages/index.rst b/docs/python_docs/python/tutorials/packages/index.rst
index e43d658a0dbe..370028a919d1 100644
--- a/docs/python_docs/python/tutorials/packages/index.rst
+++ b/docs/python_docs/python/tutorials/packages/index.rst
@@ -48,7 +48,7 @@ Shared APIs
       :title: Symbol API
       :link: ../../api/symbol/
 
-      MXNet Symbol API has been depricated. API documentation is still available for reference.
+      MXNet Symbol API has been deprecated. API documentation is still available for reference.
 
    .. card::
       :title: Autograd API

From a296dad87438624bc6388e4659db4cb039a7908a Mon Sep 17 00:00:00 2001
From: Dick Carter <dcarter@nvidia.com>
Date: Thu, 16 Jan 2020 10:28:35 -0800
Subject: [PATCH 812/813] Fix flakey test_ndarray.py:test_reduce (#17312)

---
 tests/python/unittest/test_ndarray.py | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/tests/python/unittest/test_ndarray.py b/tests/python/unittest/test_ndarray.py
index 9a05f4d2c482..3a9bd9e93126 100644
--- a/tests/python/unittest/test_ndarray.py
+++ b/tests/python/unittest/test_ndarray.py
@@ -665,13 +665,9 @@ def test_reduce_inner(numpy_reduce_func, nd_reduce_func, multi_axes,
                       mx.nd.max, True)
     test_reduce_inner(lambda data, axis, keepdims:np_reduce(data, axis, keepdims, np.min),
                       mx.nd.min, True)
-    # argmax and argmin are sensitive to the precision of the calculation (repro seed 1985162693).
-    # Force numpy to match mxnet's float32.
-    test_reduce_inner(lambda data, axis,
-                             keepdims:np_reduce(np.float32(data), axis, keepdims, np.argmax),
+    test_reduce_inner(lambda data, axis, keepdims:np_reduce(data, axis, keepdims, np.argmax),
                       mx.nd.argmax, False, check_dtype=False)
-    test_reduce_inner(lambda data, axis,
-                             keepdims:np_reduce(np.float32(data), axis, keepdims, np.argmin),
+    test_reduce_inner(lambda data, axis, keepdims:np_reduce(data, axis, keepdims, np.argmin),
                       mx.nd.argmin, False, check_dtype=False)
 
 

From c4597f7559c7d4f583e718e1d070fd8e1c9ba158 Mon Sep 17 00:00:00 2001
From: Hao Jin <hjjn.amzn@gmail.com>
Date: Wed, 25 Dec 2019 16:40:11 +0800
Subject: [PATCH 813/813] Fix reshape interoperability test (#17155)

* fix reshape interoperability test

* fix for scipy import
---
 ci/docker/install/requirements | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ci/docker/install/requirements b/ci/docker/install/requirements
index 6fd89bc65663..fd716f5fa815 100644
--- a/ci/docker/install/requirements
+++ b/ci/docker/install/requirements
@@ -26,7 +26,7 @@ h5py==2.8.0rc1
 mock==2.0.0
 nose==1.3.7
 nose-timer==0.7.3
-numpy>1.16.0,<2.0.0
+numpy>1.16.0,<1.18.0
 pylint==2.3.1; python_version >= '3.0'
 requests<2.19.0,>=2.18.4
 scipy==1.2.1